Datasets:

codeparrot

/

github-jupyter

like 5

Follow

CodeParrot 120

hanleilei/note

training/submit/PythonExercises1stAnd2nd.ipynb

2

9436

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python入门 第一周和第二周的练习\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 练习\n", "回答下列粗体文字所描述的问题，如果需要，使用任何合适的方法，以掌握技能，完成自己想要的程序为目标，不用太在意实现的过程。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 7 的四次方是多少？ **" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2401" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 分割以下字符串 **\n", "\n", " s = \"Hi there Sam!\"\n", " \n", "** 到一个列表中 **" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Hi', 'there', 'Sam!']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Hi', 'there', 'dad!']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 提供了一下两个变量 **\n", "\n", " planet = \"Earth\"\n", " diameter = 12742\n", "\n", "** 使用format()函数输出一下字符串 **\n", "\n", " The diameter of Earth is 12742 kilometers." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "planet = \"Earth\"\n", "diameter = 12742" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The diameter of Earth is 12742 kilometers.\n" ] } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 提供了以下嵌套列表，使用索引的方法获取单词‘hello' **" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lst = [1,2,[3,4],[5,[100,200,['hello']],23,11],1,7]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 提供以下嵌套字典，从中抓去单词 “hello” **" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = {'k1':[1,2,3,{'tricky':['oh','man','inception',{'target':[1,2,3,'hello']}]}]}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 字典和列表之间的差别是什么？？ **" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Just answer with text, no code necessary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 编写一个函数，该函数能够获取类似于以下email地址的域名部分 **\n", "\n", " [email protected]\n", " \n", "** 因此，对于这个示例，传入 \"[email protected]\" 将返回: domain.com **" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'domain.com'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'domain.com'" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 创建一个函数，如果输入的字符串中包含‘dog’，（请忽略corn case）统计一下'dog'的个数 **" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 创建一个函数，判断'dog' 是否包含在输入的字符串中（请同样忽略corn case）**" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 如果你驾驶的过快，交警就会拦下你。编写一个函数来返回以下三种可能的情况之一：\"No ticket\", \"Small ticket\", 或者 \"Big Ticket\". \n", " 如果速度小于等于60， 结果为\"No Ticket\". 如果速度在61和80之间， 结果为\"Small Ticket\". 如果速度大于81，结果为\"Big Ticket\". 除非这是你的生日（传入一个boolean值），如果是生日当天，就允许超速5公里／小时。（同样，请忽略corn case）。 **" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Small Ticket'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Big Ticket'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** 计算斐波那契数列，使用生成器实现 **" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fib_dyn(n):\n", " a,b = 1,1\n", " for i in range(n-1):\n", " a,b = b,a+b\n", " return a" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "55" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fib_dyn(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Great job!" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }

cc0-1.0

SN-Isotropy/Isotropy

doc/Maddi/Hubble+Diagram.ipynb

1

294832

{ "cells": [ { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from collections import OrderedDict as odict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the SN light curve fits that were generated in [this notebook](https://github.com/djreiss/AnalyzeSN/blob/master/notebooks/LC%20fitting%202.ipynb).\n", "\n", "This is an array of \"SN fit\" objects. An example is printed at the end of this cell:" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1622\n", "1549\n" ] }, { "data": { "text/plain": [ "z 1.09799\n", "t0 51298.8\n", "x0 5.04702e-07\n", "x1 4.01727\n", "c 0.240365\n", "hostebv 0\n", "hostr_v 3.1\n", "mwebv 0\n", "mwr_v 3.1\n", "mu 15.5573\n", "mu_var 0.691992\n", "inputParams {'t0': 51295.2, 'x0': 5.88266e-07, 'x1': 0.304...\n", "dtype: object" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", "import gzip, pickle\n", "\n", "if sys.version.startswith('2'):\n", " snFits = pickle.load(gzip.GzipFile('snFits.p.gz'))\n", "else:\n", " snFits = pickle.load(gzip.GzipFile('snFits.p.gz'),\n", " encoding='latin1')\n", "print(len(snFits))\n", "snf = [s for s in snFits.values() if s is not None]\n", "print(len(snf))\n", "snf[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use list comprehension to extract the redshift (`z`) and fitted distance modulus (`mu`) from each of the supernova light curve fits. Each of those becomes its own array of numbers.\n", "\n", "Also extract `mu_var`. Need to do this in a loop since for some light curve fits `mu_var` doesn't exist (for a reason that I am not sure of right now)." ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [], "source": [ "z = np.array([s.z for s in snf])\n", "mu = np.array([s.mu for s in snf])\n", "\n", "mu_var = mu.copy() #np.array([s.mu_var for s in snf])\n", "for i,s in enumerate(snf):\n", " try:\n", " mu_var[i] = s.mu_var\n", " except:\n", " mu_var[i] = 999\n", "\n", "# Here, we didn't fit z so zz and z will be identical.\n", "zz = z.copy() #np.array([s['inputParams'].get('z') for s in snf]) \n", "for i,s in enumerate(snf):\n", " try:\n", " zz[i] = s['inputParams'].get('z')\n", " except:\n", " zz[i] = 999" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filter the data to remove points that are probably way wrong (e.g. too high or too low `mu`).\n", "\n", "TODO: we need to look at some of these bad points and understand why their fits gave incorrect results." ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\maddi\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:4: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 1549 but corresponding boolean dimension is 1439\n" ] } ], "source": [ "z = z[(mu > 0) & (mu < 19)]\n", "zz = zz[(mu > 0) & (mu < 19)]\n", "mu = mu[(mu > 0) & (mu < 19)]\n", "mu_var = mu_var[(mu > 0) & (mu < 19)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the Hubble diagram." ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhEAAAF/CAYAAAD3iJulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIAhCIMSIEARBEAQhEP8fOHsi0DMydCQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f4189c7898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plt.scatter(z, mu)\n", "plt.errorbar(z, mu, yerr=np.sqrt(mu_var), fmt='o', ecolor='r', elinewidth=1, ms=2)\n", "plt.xlabel('z')\n", "plt.xscale('log')\n", "plt.ylabel('mu + const')\n", "plt.xlim((0.2,1.4))\n", "plt.ylim((8,18));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `mu`s computed above are offset by some constant that we do not know yet. Let's estimate that offset by computing a Hubble diagram for a model cosmology." ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from astropy.cosmology import Planck15 as cosmo\n", "mu = np.array([s.mu for s in snf])\n", "x0 = np.array([s.x0 for s in snf])\n", "x0 = x0[(mu > 0) & (mu < 19)]\n", "x1 = np.array([s.x1 for s in snf])\n", "x1 = x1[(mu > 0) & (mu < 19)]\n", "c = np.array([s.c for s in snf])\n", "c = c[(mu > 0) & (mu < 19)]\n", "mu = mu[(mu > 0) & (mu < 19)]\n", "muz = np.array(cosmo.distmod(z))\n", "\n", "alpha = 0.14\n", "beta = -3.11\n", "M = -2.5 * np.log10(x0) + alpha * x1 + beta * c - muz\n", "# plt.scatter(z, muz)\n", "# plt.xlabel('z')\n", "# plt.xscale('log')\n", "# plt.xlim((0.2,1.4))\n", "# plt.ylabel('mu')\n", "# plt.ylim((39,45));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the constant offset as the median of the differences between the cosmological `mu` and the fitted SN `mu`." ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "29.7558162588\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f419be1dd8>]" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAAFkCAYAAAC0KZhSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f418bb8278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "const = np.median(muz-mu)\n", "print(const)\n", "plt.scatter(z,muz-mu)\n", "plt.plot([0,1.4],[const,const],'r')" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhEAAAF/CAYAAAD3iJulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0uL/B+Hg0MPTixwRAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f41b636358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.errorbar(z, mu+const, yerr=np.sqrt(mu_var), fmt='o', ecolor='r', elinewidth=1, ms=2)\n", "plt.xlabel('z')\n", "plt.xscale('log')\n", "plt.ylabel('mu + const')\n", "plt.xlim((0.2,1.4))\n", "plt.xticks(np.arange(0.2,1.4,0.2))\n", "plt.ylim((39,48));" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f417933668>]" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhkAAAFkCAYAAACNTikJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f418c67208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(z, np.sqrt(mu_var), 'o')\n" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df= pd.DataFrame(snFits)" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>10005</th>\n", " <th>10018</th>\n", " <th>10024</th>\n", " <th>10034</th>\n", " <th>10059</th>\n", " <th>10087</th>\n", " <th>10092</th>\n", " <th>10135</th>\n", " <th>10136</th>\n", " <th>1014</th>\n", " <th>...</th>\n", " <th>9875</th>\n", " <th>9893</th>\n", " <th>9906</th>\n", " <th>9919</th>\n", " <th>9940</th>\n", " <th>9956</th>\n", " <th>9966</th>\n", " <th>9996</th>\n", " <th>9997</th>\n", " <th>9998</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>c</th>\n", " <td>-0.121929</td>\n", " <td>None</td>\n", " <td>0.136068</td>\n", " <td>0.0450403</td>\n", " <td>-0.313476</td>\n", " <td>0.0462698</td>\n", " <td>0.0988343</td>\n", " <td>-0.0298518</td>\n", " <td>0.0776311</td>\n", " <td>-0.00395545</td>\n", " <td>...</td>\n", " <td>0.151322</td>\n", " <td>0.0332876</td>\n", " <td>0.0601968</td>\n", " <td>-0.064084</td>\n", " <td>-0.0992085</td>\n", " <td>0.168553</td>\n", " <td>0.224081</td>\n", " <td>-0.0279898</td>\n", " <td>-0.0690699</td>\n", " <td>0.554351</td>\n", " </tr>\n", " <tr>\n", " <th>hostebv</th>\n", " <td>0</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>hostr_v</th>\n", " <td>3.1</td>\n", " <td>None</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>...</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " <td>3.1</td>\n", " </tr>\n", " <tr>\n", " <th>inputParams</th>\n", " <td>{'t0': 51366.4, 'x0': 2.33246e-06, 'x1': 0.316...</td>\n", " <td>None</td>\n", " <td>{'t0': 49526.4, 'x0': 1.89643e-06, 'x1': -0.00...</td>\n", " <td>{'t0': 49797.0, 'x0': 3.91948e-06, 'x1': 1.333...</td>\n", " <td>{'t0': 52299.5, 'x0': 7.96518e-07, 'x1': -2.15...</td>\n", " <td>{'t0': 51916.5, 'x0': 1.34099e-06, 'x1': -1.35...</td>\n", " <td>{'t0': 51660.8, 'x0': 2.02691e-05, 'x1': 0.810...</td>\n", " <td>{'t0': 52956.3, 'x0': 1.50645e-06, 'x1': -0.17...</td>\n", " <td>{'t0': 51869.1, 'x0': 2.08861e-06, 'x1': -0.03...</td>\n", " <td>{'t0': 52625.4, 'x0': 1.57086e-05, 'x1': -0.05...</td>\n", " <td>...</td>\n", " <td>{'t0': 50537.5, 'x0': 1.49797e-06, 'x1': 0.749...</td>\n", " <td>{'t0': 50317.4, 'x0': 5.31459e-06, 'x1': 0.939...</td>\n", " <td>{'t0': 50247.6, 'x0': 8.27902e-06, 'x1': 1.859...</td>\n", " <td>{'t0': 52795.5, 'x0': 5.27805e-06, 'x1': -0.88...</td>\n", " <td>{'t0': 52282.4, 'x0': 1.59866e-05, 'x1': 0.300...</td>\n", " <td>{'t0': 51434.1, 'x0': 1.5377e-06, 'x1': -2.071...</td>\n", " <td>{'t0': 49728.8, 'x0': 3.29583e-06, 'x1': 1.077...</td>\n", " <td>{'t0': 51182.1, 'x0': 6.8868e-06, 'x1': 1.9237...</td>\n", " <td>{'t0': 51692.7, 'x0': 2.44391e-06, 'x1': -0.47...</td>\n", " <td>{'t0': 52220.0, 'x0': 3.92665e-05, 'x1': -0.11...</td>\n", " </tr>\n", " <tr>\n", " <th>mu</th>\n", " <td>14.4485</td>\n", " <td>None</td>\n", " <td>14.6638</td>\n", " <td>14.293</td>\n", " <td>15.3082</td>\n", " <td>14.3719</td>\n", " <td>18.5476</td>\n", " <td>14.7065</td>\n", " <td>13.6749</td>\n", " <td>11.9806</td>\n", " <td>...</td>\n", " <td>17.3679</td>\n", " <td>13.2705</td>\n", " <td>12.6044</td>\n", " <td>13.1048</td>\n", " <td>12.4316</td>\n", " <td>13.7975</td>\n", " <td>13.3523</td>\n", " <td>13.3476</td>\n", " <td>14.0323</td>\n", " <td>2.41581</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 1622 columns</p>\n", "</div>" ], "text/plain": [ " 10005 10018 \\\n", "c -0.121929 None \n", "hostebv 0 None \n", "hostr_v 3.1 None \n", "inputParams {'t0': 51366.4, 'x0': 2.33246e-06, 'x1': 0.316... None \n", "mu 14.4485 None \n", "\n", " 10024 \\\n", "c 0.136068 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 49526.4, 'x0': 1.89643e-06, 'x1': -0.00... \n", "mu 14.6638 \n", "\n", " 10034 \\\n", "c 0.0450403 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 49797.0, 'x0': 3.91948e-06, 'x1': 1.333... \n", "mu 14.293 \n", "\n", " 10059 \\\n", "c -0.313476 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 52299.5, 'x0': 7.96518e-07, 'x1': -2.15... \n", "mu 15.3082 \n", "\n", " 10087 \\\n", "c 0.0462698 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 51916.5, 'x0': 1.34099e-06, 'x1': -1.35... \n", "mu 14.3719 \n", "\n", " 10092 \\\n", "c 0.0988343 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 51660.8, 'x0': 2.02691e-05, 'x1': 0.810... \n", "mu 18.5476 \n", "\n", " 10135 \\\n", "c -0.0298518 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 52956.3, 'x0': 1.50645e-06, 'x1': -0.17... \n", "mu 14.7065 \n", "\n", " 10136 \\\n", "c 0.0776311 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 51869.1, 'x0': 2.08861e-06, 'x1': -0.03... \n", "mu 13.6749 \n", "\n", " 1014 \\\n", "c -0.00395545 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 52625.4, 'x0': 1.57086e-05, 'x1': -0.05... \n", "mu 11.9806 \n", "\n", " ... \\\n", "c ... \n", "hostebv ... \n", "hostr_v ... \n", "inputParams ... \n", "mu ... \n", "\n", " 9875 \\\n", "c 0.151322 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 50537.5, 'x0': 1.49797e-06, 'x1': 0.749... \n", "mu 17.3679 \n", "\n", " 9893 \\\n", "c 0.0332876 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 50317.4, 'x0': 5.31459e-06, 'x1': 0.939... \n", "mu 13.2705 \n", "\n", " 9906 \\\n", "c 0.0601968 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 50247.6, 'x0': 8.27902e-06, 'x1': 1.859... \n", "mu 12.6044 \n", "\n", " 9919 \\\n", "c -0.064084 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 52795.5, 'x0': 5.27805e-06, 'x1': -0.88... \n", "mu 13.1048 \n", "\n", " 9940 \\\n", "c -0.0992085 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 52282.4, 'x0': 1.59866e-05, 'x1': 0.300... \n", "mu 12.4316 \n", "\n", " 9956 \\\n", "c 0.168553 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 51434.1, 'x0': 1.5377e-06, 'x1': -2.071... \n", "mu 13.7975 \n", "\n", " 9966 \\\n", "c 0.224081 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 49728.8, 'x0': 3.29583e-06, 'x1': 1.077... \n", "mu 13.3523 \n", "\n", " 9996 \\\n", "c -0.0279898 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 51182.1, 'x0': 6.8868e-06, 'x1': 1.9237... \n", "mu 13.3476 \n", "\n", " 9997 \\\n", "c -0.0690699 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 51692.7, 'x0': 2.44391e-06, 'x1': -0.47... \n", "mu 14.0323 \n", "\n", " 9998 \n", "c 0.554351 \n", "hostebv 0 \n", "hostr_v 3.1 \n", "inputParams {'t0': 52220.0, 'x0': 3.92665e-05, 'x1': -0.11... \n", "mu 2.41581 \n", "\n", "[5 rows x 1622 columns]" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.transpose()\n", "df=df.transpose()" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>z</th>\n", " <th>mu</th>\n", " <th>mu_var</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>10005</th>\n", " <td>0.968958</td>\n", " <td>14.4485</td>\n", " <td>0.253021</td>\n", " </tr>\n", " <tr>\n", " <th>10018</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>10024</th>\n", " <td>0.861278</td>\n", " <td>14.6638</td>\n", " <td>0.276984</td>\n", " </tr>\n", " <tr>\n", " <th>10034</th>\n", " <td>0.820012</td>\n", " <td>14.293</td>\n", " <td>0.301233</td>\n", " </tr>\n", " <tr>\n", " <th>10059</th>\n", " <td>1.041</td>\n", " <td>15.3082</td>\n", " <td>0.537081</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " z mu mu_var\n", "10005 0.968958 14.4485 0.253021\n", "10018 None None None\n", "10024 0.861278 14.6638 0.276984\n", "10034 0.820012 14.293 0.301233\n", "10059 1.041 15.3082 0.537081" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results= df[['z','mu','mu_var']]\n", "results.head()" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lowz= results.query('z<.4')" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLAv16jvhtQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f4177a5b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plt.hist(lowz['mu_var'], bins=np.arange(0., 15, 0.5), histtype='step', normed=1)" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": true }, "outputs": [], "source": [ "highz= results.query('z>.7')" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLSJ9AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f418b1ebe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plt.hist(highz['mu_var'], bins=np.arange(0., 15, 0.5), histtype='step', normed=1)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\maddi\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n" ] } ], "source": [ "results['bindex']= results['z']//0.1" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>z</th>\n", " <th>mu</th>\n", " <th>mu_var</th>\n", " <th>bindex</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>10005</th>\n", " <td>0.968958</td>\n", " <td>14.4485</td>\n", " <td>0.253021</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>10018</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>10024</th>\n", " <td>0.861278</td>\n", " <td>14.6638</td>\n", " <td>0.276984</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>10034</th>\n", " <td>0.820012</td>\n", " <td>14.293</td>\n", " <td>0.301233</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>10059</th>\n", " <td>1.041</td>\n", " <td>15.3082</td>\n", " <td>0.537081</td>\n", " <td>10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " z mu mu_var bindex\n", "10005 0.968958 14.4485 0.253021 9\n", "10018 None None None NaN\n", "10024 0.861278 14.6638 0.276984 8\n", "10034 0.820012 14.293 0.301233 8\n", "10059 1.041 15.3082 0.537081 10" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results.head()" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\maddi\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:2378: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self[k1] = value[k2]\n" ] } ], "source": [ "results[['z', 'mu_var', 'mu']] = results[['z', 'mu_var', 'mu']].astype(np.float)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dic = dict(mu_var=np.mean, )" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mu_var</th>\n", " </tr>\n", " <tr>\n", " <th>bindex</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0.0</th>\n", " <td>0.139478</td>\n", " </tr>\n", " <tr>\n", " <th>1.0</th>\n", " <td>2.432930</td>\n", " </tr>\n", " <tr>\n", " <th>2.0</th>\n", " <td>5.754754</td>\n", " </tr>\n", " <tr>\n", " <th>3.0</th>\n", " <td>0.904804</td>\n", " </tr>\n", " <tr>\n", " <th>4.0</th>\n", " <td>1697.436214</td>\n", " </tr>\n", " <tr>\n", " <th>5.0</th>\n", " <td>92.723792</td>\n", " </tr>\n", " <tr>\n", " <th>6.0</th>\n", " <td>3.099264</td>\n", " </tr>\n", " <tr>\n", " <th>7.0</th>\n", " <td>2015.048042</td>\n", " </tr>\n", " <tr>\n", " <th>8.0</th>\n", " <td>7480.100546</td>\n", " </tr>\n", " <tr>\n", " <th>9.0</th>\n", " <td>35.741047</td>\n", " </tr>\n", " <tr>\n", " <th>10.0</th>\n", " <td>221.750473</td>\n", " </tr>\n", " <tr>\n", " <th>11.0</th>\n", " <td>5773.061635</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mu_var\n", "bindex \n", "0.0 0.139478\n", "1.0 2.432930\n", "2.0 5.754754\n", "3.0 0.904804\n", "4.0 1697.436214\n", "5.0 92.723792\n", "6.0 3.099264\n", "7.0 2015.048042\n", "8.0 7480.100546\n", "9.0 35.741047\n", "10.0 221.750473\n", "11.0 5773.061635" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean=results.groupby('bindex').agg(dic)\n", "mean" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#sd= dict(mu_var=np.std, )" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'sd' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-133-baeb17e9c7d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'bindex'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'sd' is not defined" ] } ], "source": [ "results.groupby('bindex').agg(sd)" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\maddi\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n" ] } ], "source": [ "results['mu_err']=np.sqrt(results['mu_var'])" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>z</th>\n", " <th>mu</th>\n", " <th>mu_var</th>\n", " <th>bindex</th>\n", " <th>mu_err</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>10005</th>\n", " <td>0.968958</td>\n", " <td>14.448511</td>\n", " <td>0.253021</td>\n", " <td>9</td>\n", " <td>0.503012</td>\n", " </tr>\n", " <tr>\n", " <th>10018</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>10024</th>\n", " <td>0.861278</td>\n", " <td>14.663764</td>\n", " <td>0.276984</td>\n", " <td>8</td>\n", " <td>0.526293</td>\n", " </tr>\n", " <tr>\n", " <th>10034</th>\n", " <td>0.820012</td>\n", " <td>14.292957</td>\n", " <td>0.301233</td>\n", " <td>8</td>\n", " <td>0.548847</td>\n", " </tr>\n", " <tr>\n", " <th>10059</th>\n", " <td>1.040996</td>\n", " <td>15.308183</td>\n", " <td>0.537081</td>\n", " <td>10</td>\n", " <td>0.732858</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " z mu mu_var bindex mu_err\n", "10005 0.968958 14.448511 0.253021 9 0.503012\n", "10018 NaN NaN NaN NaN NaN\n", "10024 0.861278 14.663764 0.276984 8 0.526293\n", "10034 0.820012 14.292957 0.301233 8 0.548847\n", "10059 1.040996 15.308183 0.537081 10 0.732858" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results.head()" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def clipped_mean(mu_err, outlier_threshold):\n", " return np.mean(mu_err['mu_err<outlier_threshold'])" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grouped=results.groupby('bindex')" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"3\" halign=\"left\">mu_err</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>count</th>\n", " <th>mean</th>\n", " <th>std</th>\n", " </tr>\n", " <tr>\n", " <th>bindex</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0.0</th>\n", " <td>1</td>\n", " <td>0.373468</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1.0</th>\n", " <td>6</td>\n", " <td>1.103441</td>\n", " <td>1.207650</td>\n", " </tr>\n", " <tr>\n", " <th>2.0</th>\n", " <td>14</td>\n", " <td>1.307009</td>\n", " <td>2.087522</td>\n", " </tr>\n", " <tr>\n", " <th>3.0</th>\n", " <td>55</td>\n", " <td>0.570498</td>\n", " <td>0.768157</td>\n", " </tr>\n", " <tr>\n", " <th>4.0</th>\n", " <td>89</td>\n", " <td>6.898620</td>\n", " <td>40.848421</td>\n", " </tr>\n", " <tr>\n", " <th>5.0</th>\n", " <td>108</td>\n", " <td>2.104664</td>\n", " <td>9.440305</td>\n", " </tr>\n", " <tr>\n", " <th>6.0</th>\n", " <td>152</td>\n", " <td>0.719500</td>\n", " <td>1.612042</td>\n", " </tr>\n", " <tr>\n", " <th>7.0</th>\n", " <td>206</td>\n", " <td>5.152529</td>\n", " <td>44.701225</td>\n", " </tr>\n", " <tr>\n", " <th>8.0</th>\n", " <td>228</td>\n", " <td>10.158995</td>\n", " <td>86.077829</td>\n", " </tr>\n", " <tr>\n", " <th>9.0</th>\n", " <td>236</td>\n", " <td>1.476631</td>\n", " <td>5.805465</td>\n", " </tr>\n", " <tr>\n", " <th>10.0</th>\n", " <td>257</td>\n", " <td>2.408266</td>\n", " <td>14.723936</td>\n", " </tr>\n", " <tr>\n", " <th>11.0</th>\n", " <td>195</td>\n", " <td>7.374892</td>\n", " <td>75.816557</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mu_err \n", " count mean std\n", "bindex \n", "0.0 1 0.373468 NaN\n", "1.0 6 1.103441 1.207650\n", "2.0 14 1.307009 2.087522\n", "3.0 55 0.570498 0.768157\n", "4.0 89 6.898620 40.848421\n", "5.0 108 2.104664 9.440305\n", "6.0 152 0.719500 1.612042\n", "7.0 206 5.152529 44.701225\n", "8.0 228 10.158995 86.077829\n", "9.0 236 1.476631 5.805465\n", "10.0 257 2.408266 14.723936\n", "11.0 195 7.374892 75.816557" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu_err_table=grouped.agg(dict(mu_err=['count', np.mean, np.std]))\n", "mu_err_table" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mu_err_table['frequencies'] = mu_err_table.mu_err['count'] / mu_err_table.mu_err['count'].sum()" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"3\" halign=\"left\">mu_err</th>\n", " <th>frequencies</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>count</th>\n", " <th>mean</th>\n", " <th>std</th>\n", " <th></th>\n", " </tr>\n", " <tr>\n", " <th>bindex</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1.0</th>\n", " <td>6</td>\n", " <td>1.103441</td>\n", " <td>1.207650</td>\n", " <td>0.003878</td>\n", " </tr>\n", " <tr>\n", " <th>2.0</th>\n", " <td>14</td>\n", " <td>1.307009</td>\n", " <td>2.087522</td>\n", " <td>0.009050</td>\n", " </tr>\n", " <tr>\n", " <th>3.0</th>\n", " <td>55</td>\n", " <td>0.570498</td>\n", " <td>0.768157</td>\n", " <td>0.035553</td>\n", " </tr>\n", " <tr>\n", " <th>4.0</th>\n", " <td>89</td>\n", " <td>6.898620</td>\n", " <td>40.848421</td>\n", " <td>0.057531</td>\n", " </tr>\n", " <tr>\n", " <th>5.0</th>\n", " <td>108</td>\n", " <td>2.104664</td>\n", " <td>9.440305</td>\n", " <td>0.069813</td>\n", " </tr>\n", " <tr>\n", " <th>6.0</th>\n", " <td>152</td>\n", " <td>0.719500</td>\n", " <td>1.612042</td>\n", " <td>0.098255</td>\n", " </tr>\n", " <tr>\n", " <th>7.0</th>\n", " <td>206</td>\n", " <td>5.152529</td>\n", " <td>44.701225</td>\n", " <td>0.133161</td>\n", " </tr>\n", " <tr>\n", " <th>8.0</th>\n", " <td>228</td>\n", " <td>10.158995</td>\n", " <td>86.077829</td>\n", " <td>0.147382</td>\n", " </tr>\n", " <tr>\n", " <th>9.0</th>\n", " <td>236</td>\n", " <td>1.476631</td>\n", " <td>5.805465</td>\n", " <td>0.152553</td>\n", " </tr>\n", " <tr>\n", " <th>10.0</th>\n", " <td>257</td>\n", " <td>2.408266</td>\n", " <td>14.723936</td>\n", " <td>0.166128</td>\n", " </tr>\n", " <tr>\n", " <th>11.0</th>\n", " <td>195</td>\n", " <td>7.374892</td>\n", " <td>75.816557</td>\n", " <td>0.126050</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mu_err frequencies\n", " count mean std \n", "bindex \n", "1.0 6 1.103441 1.207650 0.003878\n", "2.0 14 1.307009 2.087522 0.009050\n", "3.0 55 0.570498 0.768157 0.035553\n", "4.0 89 6.898620 40.848421 0.057531\n", "5.0 108 2.104664 9.440305 0.069813\n", "6.0 152 0.719500 1.612042 0.098255\n", "7.0 206 5.152529 44.701225 0.133161\n", "8.0 228 10.158995 86.077829 0.147382\n", "9.0 236 1.476631 5.805465 0.152553\n", "10.0 257 2.408266 14.723936 0.166128\n", "11.0 195 7.374892 75.816557 0.126050" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu_err_table= mu_err_table.ix[1:]\n", "mu_err_table" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bindex\n", "1.0 194\n", "2.0 452\n", "3.0 1778\n", "4.0 2877\n", "5.0 3491\n", "6.0 4913\n", "7.0 6658\n", "8.0 7369\n", "9.0 7628\n", "10.0 8306\n", "11.0 6303\n", "Name: frequencies, dtype: int32\n" ] } ], "source": [ "NumSN= 50000\n", "numObjectsPerBin = np.round(mu_err_table.frequencies * NumSN).astype(np.int)\n", "print(numObjectsPerBin)" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "194" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numObjectsPerBin.ix[1]" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mean 1.103441\n", "std 1.207650\n", "Name: 1.0, dtype: float64" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu_err_table['mu_err'].ix[1, ['mean', 'std']]" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m, s = mu_err_table['mu_err'].ix[1, ['mean', 'std']] " ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.5334237 1.3493424 -1.69873861 0.93661208 0.99768825 2.22468547\n", " 0.54867936 1.18915741 1.4774152 1.54948038 1.58814469 0.81223206\n", " 0.54985637 0.06875585 0.58313104 0.38222378 1.43616498 1.34559921\n", " 0.3696869 0.23511607 0.82197816 1.64402702 1.32913123 1.06764934\n", " 0.96426017 1.02816028 -0.10498685 2.28792507 1.26540069 0.33624556\n", " -0.02761993 -0.19063766 2.10273731 0.45537791 -0.81496024 1.6017042\n", " 0.59255976 0.11172064 0.43301718 1.45772463 2.30180312 -1.08045571\n", " -0.4046269 2.52969342 1.43837123 2.81494578 1.28342557 1.38159851\n", " 0.39667092 0.30478431 3.86966603 1.04361267 -0.58364276 1.99043019\n", " 1.98270918 1.46226079 0.1155377 -0.46010312 1.13311274 2.4539944\n", " -0.40669837 2.17729209 -0.58485982 0.16685962 1.32003441 0.88057741\n", " 1.29386324 0.4737815 3.20907866 0.31037256 1.17972947 0.58078931\n", " 1.7013402 4.14828473 1.10076075 2.13448463 1.9802178 1.17320723\n", " 1.32387357 1.42310522 3.59375562 2.74417223 0.18052938 0.942508\n", " 1.72272046 2.49126291 0.15479239 2.72312321 0.65639656 0.78905351\n", " 0.11451371 0.02306702 1.7188247 3.40861578 -1.0244297 0.10411213\n", " 1.56397645 -0.5741007 0.02296463 2.52930241 1.14697867 1.69569813\n", " -0.4080724 1.37642887 -0.80493095 0.47257154 0.44583831 1.61984531\n", " 1.45287052 2.69811269 0.68711084 -1.35691395 2.36036645 1.9708433\n", " 1.2160483 -0.53092332 0.51192033 -0.96064054 0.55932467 2.01074771\n", " 0.63234868 1.97788808 3.1157351 1.74685975 1.70662811 1.24206343\n", " 1.02654724 -0.05328409 -0.57810538 -0.3396421 3.01450887 2.09672787\n", " 2.33972293 1.2650419 2.5511988 -0.09318392 1.21695878 0.52895011\n", " 0.27582962 1.88815778 1.6047043 -0.67150422 1.65786758 0.67494147\n", " 1.46280054 1.95513494 0.40770159 -1.0714984 0.66899041 2.7275578\n", " 0.9059153 -0.17822747 0.99067286 0.79734838 0.71284781 0.48524701\n", " 2.13659903 -0.50711863 0.16585025 1.99546948 0.20037654 0.78652472\n", " 0.52041152 0.73564866 1.1257642 0.90136372 1.53957386 1.90968217\n", " 2.25760501 0.72474267 2.18310619 0.28956714 2.01053849 2.04535833\n", " 1.03084506 0.76297113 -0.10740381 2.68361958 -0.04009865 1.89902876\n", " 3.15445149 -0.93297016 1.83447992 1.85535646 1.10208692 0.96521538\n", " 1.43142541 0.54710899 2.70630604 1.37382276 0.71933377 2.77721944\n", " 2.15752599 0.68837864]\n" ] } ], "source": [ "X = np.random.normal(m, s, size=numObjectsPerBin.ix[1] )\n", "print(X)" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [], "source": [ "XVals = []\n", "for i in range(1,len(mu_err_table)):\n", " m, s = mu_err_table['mu_err'].ix[1, ['mean', 'std']] \n", " # We will convert the numpy array to list, but that may not be necessary\n", " X = np.random.normal(m, s, size=numObjectsPerBin.ix[i]).tolist()\n", " XVals.append(X)" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "43666" ] }, "execution_count": 217, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(map(len, XVals))" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x=np.array(list(map(len, XVals))) \n", "y=np.float(sum(list(map(len, XVals))))" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.00444282, 0.0103513 , 0.04071818, 0.0658865 , 0.07994779,\n", " 0.11251317, 0.15247561, 0.1687583 , 0.17468969, 0.19021664])" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x/y" ] }, { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": false }, "outputs": [], "source": [ "z[:numObjectsPerBin.values[0]]\n", "for i in range(0,len(numObjectsPerBin)):\n", " z[:numObjectsPerBin.values[i]]+= .1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 41694., 678., 259., 711., 355., 1390., 1422.,\n", " 614., 1099., 1326., 258.]),\n", " array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ,\n", " 1.1]),\n", " <a list of 11 Patch objects>)" ] }, "execution_count": 185, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiEAAAFkCAYAAAD2auvFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3X+0X3V95/vnC2LiQA0RKQlcYdUOU0ytVTj8CMtr1MYL\nWriOHTtzOcjlV7u8IrK4mestrSOFgXVbpavAYNDhAhV/wJnxhrpsJRIELVeBkltCNWrA0cIExQQj\n4ZDGQkjyvn/sfeyXr/nB93By9jfJ87HWd52cz+f93fuzd06yX+ezf3xTVUiSJE23/boegCRJ2jcZ\nQiRJUicMIZIkqROGEEmS1AlDiCRJ6oQhRJIkdcIQIkmSOmEIkSRJnTCESJKkThhCJElSJ15SCEny\nh0m2Jbmqp+1TbVvva1nf+2YluS7J+iQbkyxNcmhfzSuT3JJkPMmGJDcmObCv5ogktyfZlGRtkiuT\nGKwkSdoDTPqAneR44H3AN7fT/WVgLjCvfY329V8DnAq8B1gIHA7c1ldzKzAfWNTWLgSu71n/fsAy\nYAawADgbOAe4fLLbJEmSpk8m8wF2SX4JeBA4H7gEeKiq/n3b9yngoKr6Nzt472zgJ8DpVfWFtu1o\nYDWwoKpWJJkPfAcYqaqH2ppTgNuBV1fV2iTvBP4KOKyq1rc1/xvwUeCXq2rLwBsmSZKmzWRnQq4D\n/rqqvrqD/rcmWZfk4SSfSHJwT98IzezF3RMNVfUIsAY4qW1aAGyYCCCtu4ACTuypWTURQFrLgYOA\n101yuyRJ0jSZMegbkpwOvBE4bgclX6Y5tfIo8C+BPwWWJTmpmmmXecDmqnqm733r2j7ar0/2dlbV\n1iRP9dWs284yJvp+4TRRklcBpwCPAc/ueCslSVKflwO/Aiyvqp9OxQIHCiFJXk1zPcfbq+r57dVU\n1ed7vv1OklXAD4C3Al+b5DinyinALR2PQZKkPdl7aa7bfMkGnQkZAX4ZWJkkbdv+wMIkHwRmVd9F\nJlX1aJL1wFE0IWQtMDPJ7L7ZkLltH+3X/rtl9gcO7qs5vm98c3v6tucxgM997nPMnz9/F5uqCYsX\nL+bqq6/uehh7HPfb4Nxnk+N+G5z7bHCrV6/mzDPPhPZYOhUGDSF3Aa/va7uZ5qLSj/YHEPj57Mmr\ngB+3TQ8CW2jueum9MPVI4P625n5gTpJjeq4LWQQEeKCn5sNJDum5LuRkYBz47g7G/yzA/PnzOfbY\nY1/M9go46KCD3F+T4H4bnPtsctxvg3OfvSRTdjnDQCGkqjbRd4BPsgn4aVWtbp/jcSnNNSFraWY/\nPgZ8j+aiUarqmSQ3AVcl2QBsBK4F7q2qFW3Nw0mWAzckOR+YCXwcGKuqiVmOO9uxfDbJxcBhwBXA\nkh2dKpIkScNj4AtTt6N39mMr8JvAWcAc4Ama8PHHfcFgcVu7FJgF3AFc0LfcM4AlNLMv29rai36+\n0qptSU4DPgncB2yimZW5dAq2SZIk7WYvOYRU1W/1/PlZ4B0v4j3PARe2rx3VPA2cuYvlPA6c9qIH\nK0mShoaPONcujY72P/BWL4b7bXDus8lxvw3OfTYcJvXE1D1VkmOBBx988EEvSJIkaQArV65kZGQE\nmqeZr5yKZToTIkmSOmEIkSRJnTCESJKkThhCJElSJwwhkiSpE4YQSZLUCUOIJEnqhCFEkiR1whAi\nSZI6YQiRJEmdMIRIkqROGEIkSVInDCGSJKkThhBJktSJGV0PoAs33XQTd9xxR9fDeIFXvOIVnH/+\n+cyYsU/+lUiS9kH75BHv+utvJXlZ18PoUWzZsp5XvepVnHHGGV0PRpKkabFPhpCtW+8Gju16GD22\nAC9j8+bNXQ9EkqRp4zUhkiSpE4YQSZLUCUOIJEnqhCFEkiR14iWFkCR/mGRbkqv62i9P8kSSnyX5\nSpKj+vpnJbkuyfokG5MsTXJoX80rk9ySZDzJhiQ3Jjmwr+aIJLcn2ZRkbZIrkxisJEnaA0z6gJ3k\neOB9wDf72i8GPtj2nQBsApYnmdlTdg1wKvAeYCFwOHBb3ypuBeYDi9rahcD1PevZD1hGc4fPAuBs\n4Bzg8slukyRJmj6TCiFJfgn4HPD7wNN93RcBV1TVl6rq28BZNCHj3e17ZwPnAYur6p6qegg4F3hT\nkhPamvnAKcDvVdXfVdV9wIXA6Unmtes5BXgt8N6qWlVVy4FLgAuS7JO3HkuStCeZ7EzIdcBfV9VX\nexuTvAaYB9w90VZVzwAPACe1TcfRzF701jwCrOmpWQBsaAPKhLuAAk7sqVlVVet7apYDBwGvm+R2\nSZKkaTLwjEGS04E30oSJfvNogsK6vvZ1bR/AXGBzG052VDMPeLK3s6q2Jnmqr2Z765no+yaSJGlo\nDRRCkrya5nqOt1fV87tnSJIkaV8w6EzICPDLwMokadv2BxYm+SDNNRqhme3onaWYC0ycWlkLzEwy\nu282ZG7bN1HTf7fM/sDBfTXH941vbk/fTiymOWvTa7R9SZK0bxsbG2NsbOwFbePj41O+nkFDyF3A\n6/vabgZWAx+tqn9IspbmjpZvwc8vRD2R5joSgAdpPixlEfCFtuZo4Ejg/rbmfmBOkmN6rgtZRBNw\nHuip+XCSQ3quCzkZGAe+u/PNuJrh+uwYSZKGx+joKKOjL/zFfOXKlYyMjEzpegYKIVW1ib4DfJJN\nwE+ranXbdA3wkSTfBx4DrgB+CHyxXcYzSW4CrkqyAdgIXAvcW1Ur2pqHkywHbkhyPjAT+DgwVlUT\nsxx3tmP5bHtb8GHtupZ4qkiSpOE3Fbey1gu+qboyyQE0z/SYA3wdeGdV9X5E7GJgK7AUmAXcAVzQ\nt9wzgCU0sy/b2tqLetazLclpwCeB+2ieR3IzcOkUbJMkSdrNXnIIqarf2k7bZcBlO3nPczTP/bhw\nJzVPA2fuYt2PA6e9yKFKkqQh4iPOJUlSJwwhkiSpE4YQSZLUCUOIJEnqhCFEkiR1whAiSZI6YQiR\nJEmdMIRIkqROGEIkSVInDCGSJKkThhBJktQJQ4gkSeqEIUSSJHXCECJJkjphCJEkSZ0whEiSpE4Y\nQiRJUicMIZIkqROGEEmS1AlDiCRJ6oQhRJIkdcIQIkmSOmEIkSRJnTCESJKkTgwUQpK8P8k3k4y3\nr/uSvKOn/1NJtvW9lvUtY1aS65KsT7IxydIkh/bVvDLJLe06NiS5McmBfTVHJLk9yaYka5NcmcRQ\nJUnSHmLQg/bjwMXAscAI8FXgi0nm99R8GZgLzGtfo33LuAY4FXgPsBA4HLitr+ZWYD6wqK1dCFw/\n0dmGjWXADGABcDZwDnD5gNsjSZI6MmOQ4qq6va/pI0nOpwkCq9u256rqJ9t7f5LZwHnA6VV1T9t2\nLrA6yQlVtaINNKcAI1X1UFtzIXB7kg9V1dq2/7XA26pqPbAqySXAR5NcVlVbBtkuSZI0/SZ9+iLJ\nfklOBw4A7uvpemuSdUkeTvKJJAf39I3QBJ+7Jxqq6hFgDXBS27QA2DARQFp3AQWc2FOzqg0gE5YD\nBwGvm+w2SZKk6TPQTAhAkt8A7gdeDmwEfqcNEtCcirkNeBT4l8CfAsuSnFRVRXN6ZnNVPdO32HVt\nH+3XJ3s7q2prkqf6atZtZxkTfd8cdLskSdL0GjiEAA8Db6CZdfhd4DNJFlbVw1X1+Z667yRZBfwA\neCvwtZc62KmzmGb4vUb5xctXJEna94yNjTE2NvaCtvHx8Slfz8AhpL3e4h/abx9KcgJwEXD+dmof\nTbIeOIomhKwFZiaZ3TcbMrfto/3af7fM/sDBfTXH961ubk/fLlxNc22tJEnqNzo6yujoC38xX7ly\nJSMjI1O6nqm4pXU/YNb2OpK8GngV8OO26UFgC81dLxM1RwNH0pziof06J8kxPYtaBAR4oKfm9UkO\n6ak5GRgHvvtSNkaSJE2PgWZCkvwJzXUfa4BXAO8F3gKc3D7H41Kaa0LW0sx+fAz4Hs1Fo1TVM0lu\nAq5KsoHmmpJrgXurakVb83CS5cAN7Z03M4GPA2PtnTEAd9KEjc8muRg4DLgCWFJVz09qT0iSpGk1\n6OmYQ4FP0xz0x4FvASdX1VeTvBz4TeAsYA7wBE34+OO+YLAY2AospZlBuQO4oG89ZwBLaO6K2dbW\nXjTRWVXbkpwGfJLmzpxNwM00IUiSJO0BBn1OyO/vpO9Z4B076u+pew64sH3tqOZp4MxdLOdx4LRd\nrU+SJA0nH3MuSZI6YQiRJEmdMIRIkqROGEIkSVInDCGSJKkThhBJktQJQ4gkSeqEIUSSJHXCECJJ\nkjphCJEkSZ0whEiSpE4YQiRJUicMIZIkqROGEEmS1AlDiCRJ6oQhRJIkdcIQIkmSOmEIkSRJnTCE\nSJKkThhCJElSJwwhkiSpE4YQSZLUCUOIJEnqxEAhJMn7k3wzyXj7ui/JO/pqLk/yRJKfJflKkqP6\n+mcluS7J+iQbkyxNcmhfzSuT3NKuY0OSG5Mc2FdzRJLbk2xKsjbJlUkMVZIk7SEGPWg/DlwMHAuM\nAF8FvphkPkCSi4EPAu8DTgA2AcuTzOxZxjXAqcB7gIXA4cBtfeu5FZgPLGprFwLXT3S2YWMZMANY\nAJwNnANcPuD2SJKkjgwUQqrq9qq6o6p+UFXfr6qPAP9IEwQALgKuqKovVdW3gbNoQsa7AZLMBs4D\nFlfVPVX1EHAu8KYkJ7Q184FTgN+rqr+rqvuAC4HTk8xr13MK8FrgvVW1qqqWA5cAFySZMdmdIUmS\nps+kT18k2S/J6cABwH1JXgPMA+6eqKmqZ4AHgJPapuNoZi96ax4B1vTULAA2tAFlwl1AASf21Kyq\nqvU9NcuBg4DXTXabJEnS9Bk4hCT5jSQbgeeATwC/0waJeTRBYV3fW9a1fQBzgc1tONlRzTzgyd7O\nqtoKPNVXs7310FMjSZKG2GROXTwMvIFm1uF3gc8kWTilo5IkSXu9gUNIVW0B/qH99qH2Wo6LgCuB\n0Mx29M5SzAUmTq2sBWYmmd03GzK37Zuo6b9bZn/g4L6a4/uGNrenbxcW02SoXqPtS5KkfdvY2Bhj\nY2MvaBsfH5/y9UzFRZz7AbOq6tEka2nuaPkW/PxC1BOB69raB4Etbc0X2pqjgSOB+9ua+4E5SY7p\nuS5kEU3AeaCn5sNJDum5LuRkYBz47q6HfDXNDT6SJKnf6Ogoo6Mv/MV85cqVjIyMTOl6BgohSf4E\n+DLNhaSvAN4LvIUmAEBz++1HknwfeAy4Avgh8EVoLlRNchNwVZINwEbgWuDeqlrR1jycZDlwQ5Lz\ngZnAx4GxqpqY5biTJmx8tr0t+LB2XUuq6vmB94IkSZp2g86EHAp8muagP04z43FyVX0VoKquTHIA\nzTM95gBfB95ZVZt7lrEY2AosBWYBdwAX9K3nDGAJzV0x29raiyY6q2pbktOATwL30TyP5Gbg0gG3\nR5IkdWSgEFJVv/8iai4DLttJ/3M0z/24cCc1TwNn7mI9jwOn7Wo8kiRpOPmYc0mS1AlDiCRJ6oQh\nRJIkdcIQIkmSOmEIkSRJnTCESJKkThhCJElSJwwhkiSpE4YQSZLUCUOIJEnqhCFEkiR1whAiSZI6\nYQiRJEmdMIRIkqROGEIkSVInDCGSJKkThhBJktQJQ4gkSeqEIUSSJHXCECJJkjphCJEkSZ0whEiS\npE4YQiRJUicMIZIkqRMDhZAkf5RkRZJnkqxL8oUkv9ZX86kk2/pey/pqZiW5Lsn6JBuTLE1yaF/N\nK5PckmQ8yYYkNyY5sK/miCS3J9mUZG2SK5MYrCRJ2gMMesB+M/Bx4ETg7cDLgDuT/Iu+ui8Dc4F5\n7Wu0r/8a4FTgPcBC4HDgtr6aW4H5wKK2diFw/URnGzaWATOABcDZwDnA5QNukyRJ6sCMQYqr6rd7\nv09yDvAkMAJ8o6fruar6yfaWkWQ2cB5welXd07adC6xOckJVrUgyHzgFGKmqh9qaC4Hbk3yoqta2\n/a8F3lZV64FVSS4BPprksqraMsi2SZKk6fVST13MAQp4qq/9re3pmoeTfCLJwT19IzTh5+6Jhqp6\nBFgDnNQ2LQA2TASQ1l3tuk7sqVnVBpAJy4GDgNe9tM2SJEm726RDSJLQnFb5RlV9t6fry8BZwG8B\nfwC8BVjW1kNzemZzVT3Tt8h1bd9EzZO9nVW1lSbs9Nas284y6KmRJElDaqDTMX0+Afw68Kbexqr6\nfM+330myCvgB8Fbgay9hfVNoMc2ESa9RfvHSFUmS9j1jY2OMjY29oG18fHzK1zOpEJJkCfDbwJur\n6sc7q62qR5OsB46iCSFrgZlJZvfNhsxt+2i/9t8tsz9wcF/N8X2rm9vTtxNXA8fuvESSpH3U6Ogo\no6Mv/MV85cqVjIyMTOl6Bj4d0waQf01zQeiaF1H/auBVwERYeRDYQnPXy0TN0cCRwP1t0/3AnCTH\n9CxqERDggZ6a1yc5pKfmZGAc6D09JEmShtBAMyFJPkFzzuJdwKYkEzMP41X1bPscj0tpbrddSzP7\n8THgezQXjVJVzyS5CbgqyQZgI3AtcG9VrWhrHk6yHLghyfnATJpbg8faO2MA7qQJG59NcjFwGHAF\nsKSqnp/EvpAkSdNo0NMx76e5Q+Vv+trPBT4DbAV+k+bC1DnAEzTh44/7gsHitnYpMAu4A7igb5ln\nAEto7orZ1tZeNNFZVduSnAZ8ErgP2ATcTBOCJEnSkBv0OSE7PX1TVc8C73gRy3kOuLB97ajmaeDM\nXSznceC0Xa1PkiQNHx9xLkmSOmEIkSRJnTCESJKkThhCJElSJwwhkiSpE4YQSZLUCUOIJEnqhCFE\nkiR1whAiSZI6YQiRJEmdMIRIkqROGEIkSVInDCGSJKkThhBJktQJQ4gkSeqEIUSSJHXCECJJkjph\nCJEkSZ0whEiSpE4YQiRJUicMIZIkqROGEEmS1AlDiCRJ6sRAISTJHyVZkeSZJOuSfCHJr22n7vIk\nTyT5WZKvJDmqr39WkuuSrE+yMcnSJIf21bwyyS1JxpNsSHJjkgP7ao5IcnuSTUnWJrkyicFKkqQ9\nwKAH7DcDHwdOBN4OvAy4M8m/mChIcjHwQeB9wAnAJmB5kpk9y7kGOBV4D7AQOBy4rW9dtwLzgUVt\n7ULg+p717AcsA2YAC4CzgXOAywfcJkmS1IEZgxRX1W/3fp/kHOBJYAT4Rtt8EXBFVX2prTkLWAe8\nG/h8ktnAecDpVXVPW3MusDrJCVW1Isl84BRgpKoeamsuBG5P8qGqWtv2vxZ4W1WtB1YluQT4aJLL\nqmrLoDtDkiRNn5d66mIOUMBTAEleA8wD7p4oqKpngAeAk9qm42jCT2/NI8CanpoFwIaJANK6q13X\niT01q9oAMmE5cBDwupe4XZIkaTebdAhJEprTKt+oqu+2zfNogsK6vvJ1bR/AXGBzG052VDOPZobl\n56pqK03Y6a3Z3nroqZEkSUNqoNMxfT4B/DrwpikaiyRJ2odMKoQkWQL8NvDmqvpxT9daIDSzHb2z\nFHOBh3pqZiaZ3TcbMrftm6jpv1tmf+Dgvprj+4Y2t6dvJxbTnLXpNdq+JEnat42NjTE2NvaCtvHx\n8Slfz8AhpA0g/xp4S1Wt6e2rqkeTrKW5o+Vbbf1smus4rmvLHgS2tDVfaGuOBo4E7m9r7gfmJDmm\n57qQRTQB54Gemg8nOaTnupCTgXFg4vTQDlwNHDvQdkuStK8YHR1ldPSFv5ivXLmSkZGRKV3PQCEk\nySdopgveBWxKMjHzMF5Vz7Z/vgb4SJLvA48BVwA/BL4IzYWqSW4CrkqyAdgIXAvcW1Ur2pqHkywH\nbkhyPjCT5tbgsfbOGIA7acLGZ9vbgg9r17Wkqp4fcD9IkqRpNuhMyPtpLjz9m772c4HPAFTVlUkO\noHmmxxzg68A7q2pzT/1iYCuwFJgF3AFc0LfMM4AlNHfFbGtrL5rorKptSU4DPgncR/M8kpuBSwfc\nJkmS1IFBnxPyou6mqarLgMt20v8ccGH72lHN08CZu1jP48BpL2ZMkiRpuPiIc0mS1AlDiCRJ6oQh\nRJIkdcIQIkmSOmEIkSRJnTCESJKkThhCJElSJwwhkiSpE4YQSZLUCUOIJEnqhCFEkiR1whAiSZI6\nYQiRJEmdMIRIkqROGEIkSVInDCGSJKkThhBJktQJQ4gkSeqEIUSSJHXCECJJkjphCJEkSZ0whEiS\npE4YQiRJUicMIZIkqRMDh5Akb07yV0l+lGRbknf19X+qbe99LeurmZXkuiTrk2xMsjTJoX01r0xy\nS5LxJBuS3JjkwL6aI5LcnmRTkrVJrkxisJIkaQ8wmQP2gcDfAx8Aagc1XwbmAvPa12hf/zXAqcB7\ngIXA4cBtfTW3AvOBRW3tQuD6ic42bCwDZgALgLOBc4DLJ7FNkiRpms0Y9A1VdQdwB0CS7KDsuar6\nyfY6kswGzgNOr6p72rZzgdVJTqiqFUnmA6cAI1X1UFtzIXB7kg9V1dq2/7XA26pqPbAqySXAR5Nc\nVlVbBt02SZI0fXbXqYu3JlmX5OEkn0hycE/fCE34uXuioaoeAdYAJ7VNC4ANEwGkdRfNzMuJPTWr\n2gAyYTlwEPC6Kd0aSZI05XZHCPkycBbwW8AfAG8BlvXMmswDNlfVM33vW9f2TdQ82dtZVVuBp/pq\n1m1nGfTUSJKkITXw6ZhdqarP93z7nSSrgB8AbwW+NtXrm5zFNBMmvUb5xUtXJEna94yNjTE2NvaC\ntvHx8Slfz5SHkH5V9WiS9cBRNCFkLTAzyey+2ZC5bR/t1/67ZfYHDu6rOb5vdXN7+nbiauDYgbZD\nkqR9xejoKKOjL/zFfOXKlYyMjEzpenb77axJXg28Cvhx2/QgsIXmrpeJmqOBI4H726b7gTlJjulZ\n1CIgwAM9Na9PckhPzcnAOPDdKd4MSZI0xQaeCWmf1XEUTSAA+NUkb6C5XuMp4FKa223XtnUfA75H\nc9EoVfVMkpuAq5JsADYC1wL3VtWKtubhJMuBG5KcD8wEPg6MtXfGANxJEzY+m+Ri4DDgCmBJVT0/\n6HZJkqTpNZnTMcfRnFap9vXnbfunaZ4d8ps0F6bOAZ6gCR9/3BcMFgNbgaXALJpbfi/oW88ZwBKa\nu2K2tbUXTXRW1bYkpwGfBO4DNgE304QgSZI05CbznJB72PlpnHe8iGU8B1zYvnZU8zRw5i6W8zhw\n2q7WJ0mSho+POJckSZ0whEiSpE4YQiRJUicMIZIkqROGEEmS1AlDiCRJ6oQhRJIkdcIQIkmSOmEI\nkSRJnTCESJKkThhCJElSJwwhkiSpE4YQSZLUCUOIJEnqhCFEkiR1whAiSZI6YQiRJEmdMIRIkqRO\nGEIkSVInDCGSJKkThhBJktQJQ4gkSeqEIUSSJHVi4BCS5M1J/irJj5JsS/Ku7dRcnuSJJD9L8pUk\nR/X1z0pyXZL1STYmWZrk0L6aVya5Jcl4kg1JbkxyYF/NEUluT7IpydokVyYxWEmStAeYzAH7QODv\ngQ8A1d+Z5GLgg8D7gBOATcDyJDN7yq4BTgXeAywEDgdu61vUrcB8YFFbuxC4vmc9+wHLgBnAAuBs\n4Bzg8klskyRJmmYzBn1DVd0B3AGQJNspuQi4oqq+1NacBawD3g18Psls4Dzg9Kq6p605F1id5ISq\nWpFkPnAKMFJVD7U1FwK3J/lQVa1t+18LvK2q1gOrklwCfDTJZVW1ZdBtkyRJ02dKT10keQ0wD7h7\noq2qngEeAE5qm46jCT+9NY8Aa3pqFgAbJgJI6y6amZcTe2pWtQFkwnLgIOB1U7RJkiRpN5nq6yfm\n0QSFdX3t69o+gLnA5jac7KhmHvBkb2dVbQWe6qvZ3nroqZEkSUPKizglSVInBr4mZBfWAqGZ7eid\npZgLPNRTMzPJ7L7ZkLlt30RN/90y+wMH99Uc37f+uT19O7GY5qxNr9H2JUnSvm1sbIyxsbEXtI2P\nj0/5eqY0hFTVo0nW0tzR8i2A9kLUE4Hr2rIHgS1tzRfamqOBI4H725r7gTlJjum5LmQRTcB5oKfm\nw0kO6bku5GRgHPjuzkd6NXDspLdTkqS92ejoKKOjL/zFfOXKlYyMjEzpegYOIe2zOo6iCQQAv5rk\nDcBTVfU4ze23H0nyfeAx4Argh8AXoblQNclNwFVJNgAbgWuBe6tqRVvzcJLlwA1JzgdmAh8Hxto7\nYwDupAkbn21vCz6sXdeSqnp+0O2SJEnTazIzIccBX6O5ALWAP2/bPw2cV1VXJjmA5pkec4CvA++s\nqs09y1gMbAWWArNobvm9oG89ZwBLaO6K2dbWXjTRWVXbkpwGfBK4j+Z5JDcDl05imyRJ0jSbzHNC\n7mEXF7RW1WXAZTvpfw64sH3tqOZp4MxdrOdx4LSd1UiSpOHk3TGSJKkThhBJktQJQ4gkSeqEIUSS\nJHXCECJJkjphCJEkSZ0whEiSpE4YQiRJUicMIZIkqROGEEmS1AlDiCRJ6oQhRJIkdcIQIkmSOmEI\nkSRJnTCESJKkThhCJElSJwwhkiSpE4YQSZLUCUOIJEnqhCFEkiR1whAiSZI6YQiRJEmdMIRIkqRO\nGEIkSVInpjyEJLk0yba+13f7ai5P8kSSnyX5SpKj+vpnJbkuyfokG5MsTXJoX80rk9ySZDzJhiQ3\nJjlwqrdHkiTtHrtrJuTbwFxgXvv6Hyc6klwMfBB4H3ACsAlYnmRmz/uvAU4F3gMsBA4Hbutbx63A\nfGBRW7sQuH43bIskSdoNZuym5W6pqp/soO8i4Iqq+hJAkrOAdcC7gc8nmQ2cB5xeVfe0NecCq5Oc\nUFUrkswHTgFGquqhtuZC4PYkH6qqtbtpuyRJ0hTZXTMh/yrJj5L8IMnnkhwBkOQ1NDMjd08UVtUz\nwAPASW3TcTThqLfmEWBNT80CYMNEAGndBRRw4u7ZJEmSNJV2Rwj5W+AcmpmK9wOvAf7f9nqNeTRB\nYV3fe9a1fdCcxtnchpMd1cwDnuztrKqtwFM9NZIkaYhN+emYqlre8+23k6wA/jvw74CHp3p9k7MY\nOKivbbR9SZK0bxsbG2NsbOwFbePj41O+nt11TcjPVdV4ku8BRwF/A4RmtqN3NmQuMHFqZS0wM8ns\nvtmQuW2V/OsDAAAJWUlEQVTfRE3/3TL7Awf31OzE1cCxA26JJEn7htHRUUZHX/iL+cqVKxkZGZnS\n9ez254Qk+SWaAPJEVT1KExIW9fTPprmO47626UFgS1/N0cCRwP1t0/3AnCTH9KxqEU3AeWD3bIkk\nSZpKUz4TkuTPgL+mOQXzPwD/EXge+C9tyTXAR5J8H3gMuAL4IfBFaC5UTXITcFWSDcBG4Frg3qpa\n0dY8nGQ5cEOS84GZwMeBMe+MkSRpz7A7Tse8muYZHq8CfgJ8A1hQVT8FqKorkxxA80yPOcDXgXdW\n1eaeZSwGtgJLgVnAHcAFfes5A1hCc1fMtrb2ot2wPZIkaTfYHRem7vLqzqq6DLhsJ/3PARe2rx3V\nPA2cOfgIJUnSMPCzYyRJUicMIZIkqROGEEmS1AlDiCRJ6oQhRJIkdcIQIkmSOmEIkSRJnTCESJKk\nThhCJElSJwwhkiSpE4YQSZLUCUOIJEnqhCFEkiR1whAiSZI6YQiRJEmdMIRIkqROGEIkSVInDCGS\nJKkTM7oegKTpt2bNGtavX9/1MH7Bc889x6xZs7oexnYdcsghHHnkkV0PY48zrD9r/n0OB0OItI9Z\ns2YNRx89n2ef/VnXQ9mO/YGtXQ9iu17+8gN45JHVHrgGMMw/a/59DgdDiLSPWb9+fXtQ+Bwwv+vh\n9FgGXMLwjQtgNc8+eybr168fyoPWsM42rF69ekh/1ob773NfYgiR9lnzgWO7HkSP1e3XYRvXcBvm\n2YZ/5t+pts8Qol0aGxtjdHS062Hscdxvmg7DO7MF/zy7JW3fHh9CklwAfAiYB3wTuLCq/r9uRzU5\nP/3pT1m5cmXXw/gFf/EXfzG0B9NhnYaG4d5vmpzVq1fvumia/fOYhnG2Yfj2l4bLHh1CkvwvwJ8D\n7wNWAIuB5Ul+raqG88i0E3/0R/+B559/ruth/IL99tufNWvWDN2502Gfhh7W/abJ+DGwH2eeeWbX\nA5H2Knt0CKEJHddX1WcAkrwfOBU4D7iyy4FNRhNAhm1KdTXbtg3nBVzDPQ3d7Levf/3rzJ8/XGMb\nxt/mh9/TwDaG82fNUx7ac+2xISTJy4AR4E8m2qqqktwFnNTZwF6yYZxSHc4D13BPQ/8YwN+c9zrD\n+LM2fP829xTD+P8a7FvPMNljQwhwCM1DBdb1ta8Djt7Be17efPlL4O9217gmYVvPn5cxXP+pPAQM\n+8F02PYZwL3t198DDutyINuxCvgiw7ffJvbZsI0LHNtkDevYhvv/tZkzX85f/uVSDjtsuP7v6Alt\nL5+qZaaqpmpZ0yrJYcCPgJOq6oGe9o8BC6vqF2ZDkpwB3DJ9o5Qkaa/z3qq6dSoWtCfPhKynebTi\n3L72ucDaHbxnOfBe4DHg2d02MkmS9j4vB36F5lg6JfbYmRCAJH8LPFBVF7XfB1gDXFtVf9bp4CRJ\n0k7tyTMhAFcBNyd5kH++RfcA4OYuByVJknZtjw4hVfX5JIcAl9Ochvl74JSq+km3I5MkSbuyR5+O\nkSRJe679uh6AJEnaNxlCJElSJ/a6EJLkgiSPJvmnJH+b5Phd1L81yYNJnk3yvSRnT9dYh8Ug+yzJ\n7yS5M8mTScaT3Jfk5Okc77AY9Get531vSvJ8kuH7tMLdbBL/Pmcm+b+SPNb+G/2HJOdM03CHxiT2\n23uT/H2STUmeSHJTkoOna7xdS/LmJH+V5EdJtiV514t4zz59LBh0n03VsWCvCiE9H2h3KXAMzafq\nLm8vXt1e/a8AXwLuBt4A/CfgxiT/03SMdxgMus+AhcCdwDtpnl/9NeCvk7xhGoY7NCax3ybedxDw\naeCu3T7IITPJffb/AG8DzgV+DRgFHtnNQx0qk/h/7U00P2M3AL8O/C5wAvB/T8uAh8OBNDcqfADY\n5YWPHguAAfcZU3UsqKq95gX8LfCfer4P8EPgD3ZQ/zHgW31tY8CyrrdlWPfZDpbxbeAjXW/LnrDf\n2p+v/0hzQFnZ9XYM8z4D3gE8Bczpeux72H77P4D/1tf2QWBN19vS0f7bBrxrFzX7/LFg0H22g/cN\nfCzYa2ZCej7Q7u6Jtmr2ys4+0G4Bv/gb6fKd1O9VJrnP+pcR4BU0B4t9wmT3W5JzgdfQhJB9yiT3\n2f9M8yFPFyf5YZJHkvxZkin73IphN8n9dj9wRJJ3tsuYC/xb4PbdO9o92j59LJgKkz0W7DUhhJ1/\noN28Hbxn3g7qZyeZNbXDG0qT2Wf9/k+aabzPT+G4ht3A+y3Jv6L5xOf3VtW27dXs5Sbzs/arwJuB\n1wHvBi6iObVw3W4a4zAaeL9V1X3AmcB/TbKZ5iOdN9DMhmj79vVjwVSY1LFgbwohmmbtBwJeAvzb\nqlrf9XiGVZL9aD448dKq+sFEc4dD2lPsRzMtfEZV/V1V3QH8e+BsDww7luTXaa5puIzmXP0pNDNw\n13c4LO3FXsqxYI9+YmqfyXyg3dod1D9TVc9N7fCG0mT2GQBJTqe50O13q+pru2d4Q2vQ/fYK4Djg\njUkmfovfj2YGczNwclX9zW4a67CYzM/aj4EfVdU/9rStpglwrwZ+sN137V0ms9/+ELi3qq5qv/92\nkg8AX0/yH6qq/zd+eSyYtJd6LNhrZkKq6nngQWDRRFt7jmoRcN8O3nZ/b33r5LZ9rzfJfUaSUeAm\n4PT2t9N9yiT22zPAbwBvpLny/g3AfwYebv/8wG4ecucm+bN2L3B4kgN62o6mmR354W4a6lCZ5H47\nANjS17aN5o4HZ+C2b58+FkzWlBwLur4Kd4qv6P13wM+As4DX0kw//hT45bb/T4FP99T/CrCR5sro\no2luTdoMvL3rbRnifXZGu4/eT/ObwsRrdtfbMsz7bTvv3xfvjhn0Z+1A4L8D/xWYT3NL4CPAf+56\nW4Z8v50NPNf+G30N8CaaD/i8r+ttmcZ9diBNwH8jTQD739vvj9jBPvNYMPg+m5JjQecbvht25AeA\nx4B/okmxx/X0fQr4al/9QprfNP4J+G/A/9r1NgzzPqO5F3zrdl5/0fV2DPN+285797kQMpl9RvNs\nkOXAP7aB5EpgVtfbsQfstwuAVe1++yHNc0MO63o7pnF/vaU9kG73/ymPBS99n03VscAPsJMkSZ3Y\na64JkSRJexZDiCRJ6oQhRJIkdcIQIkmSOmEIkSRJnTCESJKkThhCJElSJwwhkiSpE4YQSZLUCUOI\nJEnqhCFEkiR14v8HxNxt1jgP7SwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f418a3cb70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(z, bins=np.arange(0,1.2,.1))" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from astropy.cosmology import Planck15" ] }, { "cell_type": "code", "execution_count": 203, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 44.54255957, 44.63146526, 44.51691485, ..., 34.85176079,\n", " 37.34013353, 35.35874726])" ] }, "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Planck15.distmod(z).value" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mu_err_or_something=np.concatenate(XVals)" ] }, { "cell_type": "code", "execution_count": 214, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "43666" ] }, "execution_count": 214, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(mu_err_or_something)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.36700896e+00, 1.34614813e+00, 1.31632452e+00, ...,\n", " 8.30270479e-02, 7.58307966e-04, 3.25432115e-02])" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "50000" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z= np.random.uniform(0,.1, NumSN)\n", "len(z)" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 5044., 5004., 4883., 5037., 4954., 5064., 5058., 4988.,\n", " 4977., 4991.]),\n", " array([ 2.70892361e-06, 1.00023040e-02, 2.00018991e-02,\n", " 3.00014942e-02, 4.00010893e-02, 5.00006844e-02,\n", " 6.00002795e-02, 6.99998745e-02, 7.99994696e-02,\n", " 8.99990647e-02, 9.99986598e-02]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAFkCAYAAACEpYlzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1f418a42e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(z)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bindex\n", "0.0 1\n", "1.0 6\n", "2.0 14\n", "3.0 55\n", "4.0 89\n", "5.0 108\n", "6.0 152\n", "7.0 206\n", "8.0 228\n", "9.0 236\n", "10.0 257\n", "11.0 195\n", "Name: count, dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu_err_table.mu_err['count']" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def numinbins(tab,NumSN):\n", " x=(tab.mu_err['count']/tab.mu_err['count'].sum())\n", " return round(x*NumSN).astype(np.int)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bindex\n", "0.0 1\n", "1.0 8\n", "2.0 18\n", "3.0 71\n", "4.0 115\n", "5.0 140\n", "6.0 197\n", "7.0 266\n", "8.0 295\n", "9.0 305\n", "10.0 332\n", "11.0 252\n", "Name: count, dtype: int32" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numinbins(mu_err_table,2000)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ nan, 1.7257748 , 1.8562955 , 1.17330455,\n", " -23.74221733, -1.5677139 , 1.19254091, -54.84906901,\n", " -8.34174425, 0.85861875, -10.34986013, -139.01322727])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "help (np.random.normal)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "help (np.histogram)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hist, edges= np.histogram(z, bins=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "edges" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hist" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (len(edges))\n", "print (len(hist))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "binz=[]\n", "for i in range(0,10):\n", " stuff = edges[i]+edges[i+1]\n", " point = stuff/2\n", " binz.append (point)\n", "print (binz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(binz,hist)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "help (np.random.choice)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wut=[]\n", "for i in range(0,10):\n", " k = hist[i]/hist.sum()\n", " wut.append (k)\n", "print (wut)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(len(binz))\n", "print(len(wut))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "supernovastuff = np.random.choice(binz,100,p=wut)\n", "print (supernovastuff)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ " hist, edges= np.histogram(supernovastuff, bins=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "binzz=[]\n", "for i in range(0,10):\n", " stufff = edges[i]+edges[i+1]\n", " pointt = stufff/2\n", " binzz.append (pointt)\n", "print (binzz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(binzz,hist)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "supernovastuff = np.random.choice(binz,100,p=wut)\n", "print (supernovastuff)\n", "hist, edges= np.histogram(supernovastuff, bins=10)\n", "binzz=[]\n", "for i in range(0,10):\n", " stufff = edges[i]+edges[i+1]\n", " pointt = stufff/2\n", " binzz.append (pointt)\n", "plt.plot(binzz,hist)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

philmui/datascience2016fall

lecture02.ingestion/ch02.ipynb

2

188206

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introductory examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.usa.gov data from bit.ly" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'/Users/pmui/OneDrive/scu/data.science/lecture01.intro'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%pwd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "open(path).readline()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'\n", "records = [json.loads(line) for line in open(path)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{u'a': u'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n", " u'al': u'en-US,en;q=0.8',\n", " u'c': u'US',\n", " u'cy': u'Danvers',\n", " u'g': u'A6qOVH',\n", " u'gr': u'MA',\n", " u'h': u'wfLQtf',\n", " u'hc': 1331822918,\n", " u'hh': u'1.usa.gov',\n", " u'l': u'orofrog',\n", " u'll': [42.576698, -70.954903],\n", " u'nk': 1,\n", " u'r': u'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n", " u't': 1331923247,\n", " u'tz': u'America/New_York',\n", " u'u': u'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "records[0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'America/New_York'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "records[0]['tz']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "America/New_York\n" ] } ], "source": [ "print(records[0]['tz'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Counting time zones in pure Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Expect error" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyError", "evalue": "'tz'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-8-db4fbd348da9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_zones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tz'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyError\u001b[0m: 'tz'" ] } ], "source": [ "time_zones = [rec['tz'] for rec in records]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "time_zones = [rec['tz'] for rec in records if 'tz' in rec]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'America/New_York',\n", " u'America/Denver',\n", " u'America/New_York',\n", " u'America/Sao_Paulo',\n", " u'America/New_York',\n", " u'America/New_York',\n", " u'Europe/Warsaw',\n", " u'',\n", " u'',\n", " u'']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time_zones[:10]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_counts(sequence):\n", " counts = {}\n", " for x in sequence:\n", " if x in counts:\n", " counts[x] += 1\n", " else:\n", " counts[x] = 1\n", " return counts" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import defaultdict\n", "\n", "def get_counts2(sequence):\n", " counts = defaultdict(int) # values will initialize to 0\n", " for x in sequence:\n", " counts[x] += 1\n", " return counts" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "counts = get_counts(time_zones)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1251" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts['America/New_York']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3440" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(time_zones)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def top_counts(count_dict, n=10):\n", " value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n", " value_key_pairs.sort()\n", " return value_key_pairs[-n:]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(33, u'America/Sao_Paulo'),\n", " (35, u'Europe/Madrid'),\n", " (36, u'Pacific/Honolulu'),\n", " (37, u'Asia/Tokyo'),\n", " (74, u'Europe/London'),\n", " (191, u'America/Denver'),\n", " (382, u'America/Los_Angeles'),\n", " (400, u'America/Chicago'),\n", " (521, u''),\n", " (1251, u'America/New_York')]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_counts(counts)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "counts = Counter(time_zones)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(u'America/New_York', 1251),\n", " (u'', 521),\n", " (u'America/Chicago', 400),\n", " (u'America/Los_Angeles', 382),\n", " (u'America/Denver', 191),\n", " (u'Europe/London', 74),\n", " (u'Asia/Tokyo', 37),\n", " (u'Pacific/Honolulu', 36),\n", " (u'Europe/Madrid', 35),\n", " (u'America/Sao_Paulo', 33)]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts.most_common(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Counting time zones with pandas" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "from numpy.random import randn\n", "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "plt.rc('figure', figsize=(10, 6))\n", "np.set_printoptions(precision=4)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'\n", "lines = open(path).readlines()\n", "records = [json.loads(line) for line in lines]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_heartbeat_</th>\n", " <th>a</th>\n", " <th>al</th>\n", " <th>c</th>\n", " <th>cy</th>\n", " <th>g</th>\n", " <th>gr</th>\n", " <th>h</th>\n", " <th>hc</th>\n", " <th>hh</th>\n", " <th>kw</th>\n", " <th>l</th>\n", " <th>ll</th>\n", " <th>nk</th>\n", " <th>r</th>\n", " <th>t</th>\n", " <th>tz</th>\n", " <th>u</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n", " <td>en-US,en;q=0.8</td>\n", " <td>US</td>\n", " <td>Danvers</td>\n", " <td>A6qOVH</td>\n", " <td>MA</td>\n", " <td>wfLQtf</td>\n", " <td>1.331823e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>orofrog</td>\n", " <td>[42.576698, -70.954903]</td>\n", " <td>1.0</td>\n", " <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NaN</td>\n", " <td>GoogleMaps/RochesterNY</td>\n", " <td>NaN</td>\n", " <td>US</td>\n", " <td>Provo</td>\n", " <td>mwszkS</td>\n", " <td>UT</td>\n", " <td>mwszkS</td>\n", " <td>1.308262e+09</td>\n", " <td>j.mp</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[40.218102, -111.613297]</td>\n", " <td>0.0</td>\n", " <td>http://www.AwareMap.com/</td>\n", " <td>1.331923e+09</td>\n", " <td>America/Denver</td>\n", " <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>NaN</td>\n", " <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n", " <td>en-US</td>\n", " <td>US</td>\n", " <td>Washington</td>\n", " <td>xxr3Qb</td>\n", " <td>DC</td>\n", " <td>xxr3Qb</td>\n", " <td>1.331920e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[38.9007, -77.043098]</td>\n", " <td>1.0</td>\n", " <td>http://t.co/03elZC4Q</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n", " <td>pt-br</td>\n", " <td>BR</td>\n", " <td>Braz</td>\n", " <td>zCaLwp</td>\n", " <td>27</td>\n", " <td>zUtuOu</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>alelex88</td>\n", " <td>[-23.549999, -46.616699]</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331923e+09</td>\n", " <td>America/Sao_Paulo</td>\n", " <td>http://apod.nasa.gov/apod/ap120312.html</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n", " <td>en-US,en;q=0.8</td>\n", " <td>US</td>\n", " <td>Shrewsbury</td>\n", " <td>9b6kNl</td>\n", " <td>MA</td>\n", " <td>9b6kNl</td>\n", " <td>1.273672e+09</td>\n", " <td>bit.ly</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[42.286499, -71.714699]</td>\n", " <td>0.0</td>\n", " <td>http://www.shrewsbury-ma.gov/selco/</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n", " <td>en-US,en;q=0.8</td>\n", " <td>US</td>\n", " <td>Shrewsbury</td>\n", " <td>axNK8c</td>\n", " <td>MA</td>\n", " <td>axNK8c</td>\n", " <td>1.273673e+09</td>\n", " <td>bit.ly</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[42.286499, -71.714699]</td>\n", " <td>0.0</td>\n", " <td>http://www.shrewsbury-ma.gov/selco/</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...</td>\n", " <td>pl-PL,pl;q=0.8,en-US;q=0.6,en;q=0.4</td>\n", " <td>PL</td>\n", " <td>Luban</td>\n", " <td>wcndER</td>\n", " <td>77</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>[51.116699, 15.2833]</td>\n", " <td>0.0</td>\n", " <td>http://plus.url.google.com/url?sa=z&amp;n=13319232...</td>\n", " <td>1.331923e+09</td>\n", " <td>Europe/Warsaw</td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/2...</td>\n", " <td>bg,en-us;q=0.7,en;q=0.3</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>wcndER</td>\n", " <td>NaN</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/</td>\n", " <td>1.331923e+09</td>\n", " <td></td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>NaN</td>\n", " <td>Opera/9.80 (X11; Linux zbov; U; en) Presto/2.1...</td>\n", " <td>en-US, en</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>wcndER</td>\n", " <td>NaN</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n", " <td>1.331923e+09</td>\n", " <td></td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n", " <td>pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>zCaLwp</td>\n", " <td>NaN</td>\n", " <td>zUtuOu</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>alelex88</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>http://t.co/o1Pd0WeV</td>\n", " <td>1.331923e+09</td>\n", " <td></td>\n", " <td>http://apod.nasa.gov/apod/ap120312.html</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Seattle</td>\n", " <td>vNJS4H</td>\n", " <td>WA</td>\n", " <td>u0uD9q</td>\n", " <td>1.319564e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>o_4us71ccioa</td>\n", " <td>[47.5951, -122.332603]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331923e+09</td>\n", " <td>America/Los_Angeles</td>\n", " <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Washington</td>\n", " <td>wG7OIH</td>\n", " <td>DC</td>\n", " <td>A0nRz4</td>\n", " <td>1.331816e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>darrellissa</td>\n", " <td>[38.937599, -77.092796]</td>\n", " <td>0.0</td>\n", " <td>http://t.co/ND7SoPyo</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://oversight.house.gov/wp-content/uploads/...</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Alexandria</td>\n", " <td>vNJS4H</td>\n", " <td>VA</td>\n", " <td>u0uD9q</td>\n", " <td>1.319564e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>o_4us71ccioa</td>\n", " <td>[38.790901, -77.094704]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1.331923e+09</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Marietta</td>\n", " <td>2rOUYc</td>\n", " <td>GA</td>\n", " <td>2rOUYc</td>\n", " <td>1.255770e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[33.953201, -84.5177]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://toxtown.nlm.nih.gov/index.php</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n", " <td>zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4</td>\n", " <td>HK</td>\n", " <td>Central District</td>\n", " <td>nQvgJp</td>\n", " <td>00</td>\n", " <td>rtrrth</td>\n", " <td>1.317318e+09</td>\n", " <td>j.mp</td>\n", " <td>NaN</td>\n", " <td>walkeryuen</td>\n", " <td>[22.2833, 114.150002]</td>\n", " <td>1.0</td>\n", " <td>http://forum2.hkgolden.com/view.aspx?type=BW&amp;m...</td>\n", " <td>1.331923e+09</td>\n", " <td>Asia/Hong_Kong</td>\n", " <td>http://www.ssd.noaa.gov/PS/TROP/TCFP/data/curr...</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n", " <td>zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4</td>\n", " <td>HK</td>\n", " <td>Central District</td>\n", " <td>XdUNr</td>\n", " <td>00</td>\n", " <td>qWkgbq</td>\n", " <td>1.317318e+09</td>\n", " <td>j.mp</td>\n", " <td>NaN</td>\n", " <td>walkeryuen</td>\n", " <td>[22.2833, 114.150002]</td>\n", " <td>1.0</td>\n", " <td>http://forum2.hkgolden.com/view.aspx?type=BW&amp;m...</td>\n", " <td>1.331923e+09</td>\n", " <td>Asia/Hong_Kong</td>\n", " <td>http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc...</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10.5; r...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Buckfield</td>\n", " <td>zH1BFf</td>\n", " <td>ME</td>\n", " <td>x3jOIv</td>\n", " <td>1.331840e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>andyzieminski</td>\n", " <td>[44.299702, -70.369797]</td>\n", " <td>0.0</td>\n", " <td>http://t.co/6Cx4ROLs</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.usda.gov/wps/portal/usda/usdahome?c...</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>NaN</td>\n", " <td>GoogleMaps/RochesterNY</td>\n", " <td>NaN</td>\n", " <td>US</td>\n", " <td>Provo</td>\n", " <td>mwszkS</td>\n", " <td>UT</td>\n", " <td>mwszkS</td>\n", " <td>1.308262e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[40.218102, -111.613297]</td>\n", " <td>0.0</td>\n", " <td>http://www.AwareMap.com/</td>\n", " <td>1.331923e+09</td>\n", " <td>America/Denver</td>\n", " <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n", " <td>it-IT,it;q=0.8,en-US;q=0.6,en;q=0.4</td>\n", " <td>IT</td>\n", " <td>Venice</td>\n", " <td>wcndER</td>\n", " <td>20</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>[45.438599, 12.3267]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/</td>\n", " <td>1.331923e+09</td>\n", " <td>Europe/Rome</td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n", " <td>es-ES</td>\n", " <td>ES</td>\n", " <td>Alcal</td>\n", " <td>zQ95Hi</td>\n", " <td>51</td>\n", " <td>ytZYWR</td>\n", " <td>1.331671e+09</td>\n", " <td>bitly.com</td>\n", " <td>NaN</td>\n", " <td>jplnews</td>\n", " <td>[37.516701, -5.9833]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/</td>\n", " <td>1.331923e+09</td>\n", " <td>Africa/Ceuta</td>\n", " <td>http://voyager.jpl.nasa.gov/imagesvideo/uranus...</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.6...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Davidsonville</td>\n", " <td>wcndER</td>\n", " <td>MD</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>[38.939201, -76.635002]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>NaN</td>\n", " <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Hockessin</td>\n", " <td>y3ZImz</td>\n", " <td>DE</td>\n", " <td>y3ZImz</td>\n", " <td>1.331064e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[39.785, -75.682297]</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://portal.hud.gov/hudportal/documents/hudd...</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3)...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Lititz</td>\n", " <td>wWiOiD</td>\n", " <td>PA</td>\n", " <td>wWiOiD</td>\n", " <td>1.330218e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[40.174999, -76.3078]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n", " <td>1.331923e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.tricare.mil/mybenefit/ProfileFilter...</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES...</td>\n", " <td>es-es,es;q=0.8,en-us;q=0.5,en;q=0.3</td>\n", " <td>ES</td>\n", " <td>Bilbao</td>\n", " <td>wcndER</td>\n", " <td>59</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>[43.25, -2.9667]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/</td>\n", " <td>1.331923e+09</td>\n", " <td>Europe/Madrid</td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n", " <td>en-GB,en;q=0.8,en-US;q=0.6,en-AU;q=0.4</td>\n", " <td>MY</td>\n", " <td>Kuala Lumpur</td>\n", " <td>wcndER</td>\n", " <td>14</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>[3.1667, 101.699997]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/</td>\n", " <td>1.331923e+09</td>\n", " <td>Asia/Kuala_Lumpur</td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n", " <td>ro-RO,ro;q=0.8,en-US;q=0.6,en;q=0.4</td>\n", " <td>CY</td>\n", " <td>Nicosia</td>\n", " <td>wcndER</td>\n", " <td>04</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>[35.166698, 33.366699]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/?ref=tn_tnmn</td>\n", " <td>1.331923e+09</td>\n", " <td>Asia/Nicosia</td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n", " <td>en-US,en;q=0.8</td>\n", " <td>BR</td>\n", " <td>SPaulo</td>\n", " <td>zCaLwp</td>\n", " <td>27</td>\n", " <td>zUtuOu</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>alelex88</td>\n", " <td>[-23.5333, -46.616699]</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331923e+09</td>\n", " <td>America/Sao_Paulo</td>\n", " <td>http://apod.nasa.gov/apod/ap120312.html</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (iPad; CPU OS 5_0_1 like Mac OS X)...</td>\n", " <td>en-us</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>vNJS4H</td>\n", " <td>NaN</td>\n", " <td>u0uD9q</td>\n", " <td>1.319564e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>o_4us71ccioa</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331923e+09</td>\n", " <td></td>\n", " <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X...</td>\n", " <td>en-us</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>FPX0IM</td>\n", " <td>NaN</td>\n", " <td>FPX0IL</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>twittershare</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>http://t.co/5xlp0B34</td>\n", " <td>1.331923e+09</td>\n", " <td></td>\n", " <td>http://www.ed.gov/news/media-advisories/us-dep...</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>3530</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.0) AppleWebKit/535.1...</td>\n", " <td>en-US,en;q=0.8</td>\n", " <td>US</td>\n", " <td>San Francisco</td>\n", " <td>xVZg4P</td>\n", " <td>CA</td>\n", " <td>wqUkTo</td>\n", " <td>1.331908e+09</td>\n", " <td>go.nasa.gov</td>\n", " <td>NaN</td>\n", " <td>nasatwitter</td>\n", " <td>[37.7645, -122.429398]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/l.php?u=http%3A%2F%2Fg...</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Los_Angeles</td>\n", " <td>http://www.nasa.gov/multimedia/imagegallery/im...</td>\n", " </tr>\n", " <tr>\n", " <th>3531</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6...</td>\n", " <td>en-US</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>wcndER</td>\n", " <td>NaN</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td></td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>3532</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Washington</td>\n", " <td>Au3aUS</td>\n", " <td>DC</td>\n", " <td>A9ct6C</td>\n", " <td>1.331926e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>ncsha</td>\n", " <td>[38.904202, -77.031998]</td>\n", " <td>1.0</td>\n", " <td>http://www.ncsha.org/</td>\n", " <td>1.331927e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://portal.hud.gov/hudportal/HUD?src=/press...</td>\n", " </tr>\n", " <tr>\n", " <th>3533</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (iPad; CPU OS 5_1 like Mac OS X) A...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Jacksonville</td>\n", " <td>b2UtUJ</td>\n", " <td>FL</td>\n", " <td>ieCdgH</td>\n", " <td>1.301393e+09</td>\n", " <td>go.nasa.gov</td>\n", " <td>NaN</td>\n", " <td>nasatwitter</td>\n", " <td>[30.279301, -81.585098]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://apod.nasa.gov/apod/</td>\n", " </tr>\n", " <tr>\n", " <th>3534</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Frisco</td>\n", " <td>vNJS4H</td>\n", " <td>TX</td>\n", " <td>u0uD9q</td>\n", " <td>1.319564e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>o_4us71ccioa</td>\n", " <td>[33.149899, -96.855499]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Chicago</td>\n", " <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n", " </tr>\n", " <tr>\n", " <th>3535</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Houston</td>\n", " <td>zIgLx8</td>\n", " <td>TX</td>\n", " <td>yrPaLt</td>\n", " <td>1.331903e+09</td>\n", " <td>aash.to</td>\n", " <td>NaN</td>\n", " <td>aashto</td>\n", " <td>[29.775499, -95.415199]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Chicago</td>\n", " <td>http://ntl.bts.gov/lib/44000/44300/44374/FHWA-...</td>\n", " </tr>\n", " <tr>\n", " <th>3536</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (BlackBerry; U; BlackBerry 9800; e...</td>\n", " <td>en-US,en;q=0.5</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>xIcyim</td>\n", " <td>NaN</td>\n", " <td>yG1TTf</td>\n", " <td>1.331728e+09</td>\n", " <td>go.nasa.gov</td>\n", " <td>NaN</td>\n", " <td>nasatwitter</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>http://t.co/g1VKE8zS</td>\n", " <td>1.331927e+09</td>\n", " <td></td>\n", " <td>http://www.nasa.gov/mission_pages/hurricanes/a...</td>\n", " </tr>\n", " <tr>\n", " <th>3537</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n", " <td>es-es,es;q=0.8,en-us;q=0.5,en;q=0.3</td>\n", " <td>HN</td>\n", " <td>Tegucigalpa</td>\n", " <td>zCaLwp</td>\n", " <td>08</td>\n", " <td>w63FZW</td>\n", " <td>1.331547e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bufferapp</td>\n", " <td>[14.1, -87.216698]</td>\n", " <td>0.0</td>\n", " <td>http://t.co/A8TJyibE</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Tegucigalpa</td>\n", " <td>http://apod.nasa.gov/apod/ap120312.html</td>\n", " </tr>\n", " <tr>\n", " <th>3538</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Los Angeles</td>\n", " <td>qMac9k</td>\n", " <td>CA</td>\n", " <td>qds1Ge</td>\n", " <td>1.310474e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>healthypeople</td>\n", " <td>[34.041599, -118.298798]</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Los_Angeles</td>\n", " <td>http://healthypeople.gov/2020/connect/webinars...</td>\n", " </tr>\n", " <tr>\n", " <th>3539</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (compatible; Fedora Core 3) FC3 KDE</td>\n", " <td>NaN</td>\n", " <td>US</td>\n", " <td>Bellevue</td>\n", " <td>zu2M5o</td>\n", " <td>WA</td>\n", " <td>zDhdro</td>\n", " <td>1.331586e+09</td>\n", " <td>bit.ly</td>\n", " <td>NaN</td>\n", " <td>glimtwin</td>\n", " <td>[47.615398, -122.210297]</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Los_Angeles</td>\n", " <td>http://www.federalreserve.gov/newsevents/press...</td>\n", " </tr>\n", " <tr>\n", " <th>3540</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n", " <td>en-US,en;q=0.8</td>\n", " <td>US</td>\n", " <td>Payson</td>\n", " <td>wcndER</td>\n", " <td>UT</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>[40.014198, -111.738899]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Denver</td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>3541</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (X11; U; OpenVMS AlphaServer_ES40;...</td>\n", " <td>NaN</td>\n", " <td>US</td>\n", " <td>Bellevue</td>\n", " <td>zu2M5o</td>\n", " <td>WA</td>\n", " <td>zDhdro</td>\n", " <td>1.331586e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>glimtwin</td>\n", " <td>[47.615398, -122.210297]</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Los_Angeles</td>\n", " <td>http://www.federalreserve.gov/newsevents/press...</td>\n", " </tr>\n", " <tr>\n", " <th>3542</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Pittsburg</td>\n", " <td>y3reI1</td>\n", " <td>CA</td>\n", " <td>y3reI1</td>\n", " <td>1.331926e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[38.0051, -121.838699]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Los_Angeles</td>\n", " <td>http://www.sba.gov/community/blogs/community-b...</td>\n", " </tr>\n", " <tr>\n", " <th>3543</th>\n", " <td>1.331927e+09</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3544</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:5.0.1) ...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Wentzville</td>\n", " <td>vNJS4H</td>\n", " <td>MO</td>\n", " <td>u0uD9q</td>\n", " <td>1.319564e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>o_4us71ccioa</td>\n", " <td>[38.790001, -90.854897]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Chicago</td>\n", " <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n", " </tr>\n", " <tr>\n", " <th>3545</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n", " <td>en-us,en;q=0.5</td>\n", " <td>US</td>\n", " <td>Saint Charles</td>\n", " <td>vNJS4H</td>\n", " <td>IL</td>\n", " <td>u0uD9q</td>\n", " <td>1.319564e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>o_4us71ccioa</td>\n", " <td>[41.9352, -88.290901]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Chicago</td>\n", " <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n", " </tr>\n", " <tr>\n", " <th>3546</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Los Angeles</td>\n", " <td>qMac9k</td>\n", " <td>CA</td>\n", " <td>qds1Ge</td>\n", " <td>1.310474e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>healthypeople</td>\n", " <td>[34.041599, -118.298798]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Los_Angeles</td>\n", " <td>http://healthypeople.gov/2020/connect/webinars...</td>\n", " </tr>\n", " <tr>\n", " <th>3547</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Silver Spring</td>\n", " <td>y0jYkg</td>\n", " <td>MD</td>\n", " <td>y0jYkg</td>\n", " <td>1.331852e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[39.052101, -77.014999]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.epa.gov/otaq/regs/fuels/additive/e1...</td>\n", " </tr>\n", " <tr>\n", " <th>3548</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Mcgehee</td>\n", " <td>y5rMac</td>\n", " <td>AR</td>\n", " <td>xANY6O</td>\n", " <td>1.331916e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>twitterfeed</td>\n", " <td>[33.628399, -91.356903]</td>\n", " <td>1.0</td>\n", " <td>https://twitter.com/fdarecalls/status/18069759...</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Chicago</td>\n", " <td>http://www.fda.gov/Safety/Recalls/ucm296326.htm</td>\n", " </tr>\n", " <tr>\n", " <th>3549</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n", " <td>sv-SE,sv;q=0.8,en-US;q=0.6,en;q=0.4</td>\n", " <td>SE</td>\n", " <td>Sollefte</td>\n", " <td>eH8wu</td>\n", " <td>24</td>\n", " <td>7dtjei</td>\n", " <td>1.260316e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>tweetdeckapi</td>\n", " <td>[63.166698, 17.266701]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>Europe/Stockholm</td>\n", " <td>http://www.nasa.gov/mission_pages/WISE/main/in...</td>\n", " </tr>\n", " <tr>\n", " <th>3550</th>\n", " <td>NaN</td>\n", " <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Conshohocken</td>\n", " <td>A00b72</td>\n", " <td>PA</td>\n", " <td>yGSwzn</td>\n", " <td>1.331918e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>addthis</td>\n", " <td>[40.0798, -75.2855]</td>\n", " <td>0.0</td>\n", " <td>http://www.linkedin.com/home?trk=hb_tab_home_top</td>\n", " <td>1.331927e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.nlm.nih.gov/medlineplus/news/fullst...</td>\n", " </tr>\n", " <tr>\n", " <th>3551</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n", " <td>en-US,en;q=0.8</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>wcndER</td>\n", " <td>NaN</td>\n", " <td>zkpJBR</td>\n", " <td>1.331923e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bnjacobs</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>http://plus.url.google.com/url?sa=z&amp;n=13319268...</td>\n", " <td>1.331927e+09</td>\n", " <td></td>\n", " <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n", " </tr>\n", " <tr>\n", " <th>3552</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...</td>\n", " <td>NaN</td>\n", " <td>US</td>\n", " <td>Decatur</td>\n", " <td>rqgJuE</td>\n", " <td>AL</td>\n", " <td>xcz8vt</td>\n", " <td>1.331227e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bootsnall</td>\n", " <td>[34.572701, -86.940598]</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Chicago</td>\n", " <td>http://travel.state.gov/passport/passport_5535...</td>\n", " </tr>\n", " <tr>\n", " <th>3553</th>\n", " <td>NaN</td>\n", " <td>Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Shrewsbury</td>\n", " <td>9b6kNl</td>\n", " <td>MA</td>\n", " <td>9b6kNl</td>\n", " <td>1.273672e+09</td>\n", " <td>bit.ly</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[42.286499, -71.714699]</td>\n", " <td>0.0</td>\n", " <td>http://www.shrewsbury-ma.gov/selco/</td>\n", " <td>1.331927e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n", " </tr>\n", " <tr>\n", " <th>3554</th>\n", " <td>NaN</td>\n", " <td>Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...</td>\n", " <td>en-us</td>\n", " <td>US</td>\n", " <td>Shrewsbury</td>\n", " <td>axNK8c</td>\n", " <td>MA</td>\n", " <td>axNK8c</td>\n", " <td>1.273673e+09</td>\n", " <td>bit.ly</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[42.286499, -71.714699]</td>\n", " <td>0.0</td>\n", " <td>http://www.shrewsbury-ma.gov/selco/</td>\n", " <td>1.331927e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n", " </tr>\n", " <tr>\n", " <th>3555</th>\n", " <td>NaN</td>\n", " <td>Mozilla/4.0 (compatible; MSIE 9.0; Windows NT ...</td>\n", " <td>en</td>\n", " <td>US</td>\n", " <td>Paramus</td>\n", " <td>e5SvKE</td>\n", " <td>NJ</td>\n", " <td>fqPSr9</td>\n", " <td>1.301298e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>tweetdeckapi</td>\n", " <td>[40.9445, -74.07]</td>\n", " <td>1.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://www.fda.gov/AdvisoryCommittees/Committe...</td>\n", " </tr>\n", " <tr>\n", " <th>3556</th>\n", " <td>NaN</td>\n", " <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...</td>\n", " <td>en-US,en;q=0.8</td>\n", " <td>US</td>\n", " <td>Oklahoma City</td>\n", " <td>jQLtP4</td>\n", " <td>OK</td>\n", " <td>jQLtP4</td>\n", " <td>1.307530e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[35.4715, -97.518997]</td>\n", " <td>0.0</td>\n", " <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Chicago</td>\n", " <td>http://www.okc.gov/PublicNotificationSystem/Fo...</td>\n", " </tr>\n", " <tr>\n", " <th>3557</th>\n", " <td>NaN</td>\n", " <td>GoogleMaps/RochesterNY</td>\n", " <td>NaN</td>\n", " <td>US</td>\n", " <td>Provo</td>\n", " <td>mwszkS</td>\n", " <td>UT</td>\n", " <td>mwszkS</td>\n", " <td>1.308262e+09</td>\n", " <td>j.mp</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[40.218102, -111.613297]</td>\n", " <td>0.0</td>\n", " <td>http://www.AwareMap.com/</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Denver</td>\n", " <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n", " </tr>\n", " <tr>\n", " <th>3558</th>\n", " <td>NaN</td>\n", " <td>GoogleProducer</td>\n", " <td>NaN</td>\n", " <td>US</td>\n", " <td>Mountain View</td>\n", " <td>zjtI4X</td>\n", " <td>CA</td>\n", " <td>zjtI4X</td>\n", " <td>1.327529e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[37.419201, -122.057404]</td>\n", " <td>0.0</td>\n", " <td>direct</td>\n", " <td>1.331927e+09</td>\n", " <td>America/Los_Angeles</td>\n", " <td>http://www.ahrq.gov/qual/qitoolkit/</td>\n", " </tr>\n", " <tr>\n", " <th>3559</th>\n", " <td>NaN</td>\n", " <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n", " <td>en-US</td>\n", " <td>US</td>\n", " <td>Mc Lean</td>\n", " <td>qxKrTK</td>\n", " <td>VA</td>\n", " <td>qxKrTK</td>\n", " <td>1.312898e+09</td>\n", " <td>1.usa.gov</td>\n", " <td>NaN</td>\n", " <td>bitly</td>\n", " <td>[38.935799, -77.162102]</td>\n", " <td>0.0</td>\n", " <td>http://t.co/OEEEvwjU</td>\n", " <td>1.331927e+09</td>\n", " <td>America/New_York</td>\n", " <td>http://herndon-va.gov/Content/public_safety/Pu...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3560 rows × 18 columns</p>\n", "</div>" ], "text/plain": [ " _heartbeat_ a \\\n", "0 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n", "1 NaN GoogleMaps/RochesterNY \n", "2 NaN Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ... \n", "3 NaN Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)... \n", "4 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n", "5 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n", "6 NaN Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1... \n", "7 NaN Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/2... \n", "8 NaN Opera/9.80 (X11; Linux zbov; U; en) Presto/2.1... \n", "9 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n", "10 NaN Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)... \n", "11 NaN Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4... \n", "12 NaN Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)... \n", "13 1.331923e+09 NaN \n", "14 NaN Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US... \n", "15 NaN Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1... \n", "16 NaN Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1... \n", "17 NaN Mozilla/5.0 (Macintosh; Intel Mac OS X 10.5; r... \n", "18 NaN GoogleMaps/RochesterNY \n", "19 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n", "20 NaN Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ... \n", "21 NaN Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.6... \n", "22 NaN Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ... \n", "23 NaN Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3)... \n", "24 NaN Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES... \n", "25 NaN Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1... \n", "26 NaN Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1... \n", "27 NaN Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)... \n", "28 NaN Mozilla/5.0 (iPad; CPU OS 5_0_1 like Mac OS X)... \n", "29 NaN Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X... \n", "... ... ... \n", "3530 NaN Mozilla/5.0 (Windows NT 6.0) AppleWebKit/535.1... \n", "3531 NaN Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6... \n", "3532 NaN Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)... \n", "3533 NaN Mozilla/5.0 (iPad; CPU OS 5_1 like Mac OS X) A... \n", "3534 NaN Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)... \n", "3535 NaN Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/... \n", "3536 NaN Mozilla/5.0 (BlackBerry; U; BlackBerry 9800; e... \n", "3537 NaN Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)... \n", "3538 NaN Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma... \n", "3539 NaN Mozilla/5.0 (compatible; Fedora Core 3) FC3 KDE \n", "3540 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n", "3541 NaN Mozilla/5.0 (X11; U; OpenVMS AlphaServer_ES40;... \n", "3542 NaN Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ... \n", "3543 1.331927e+09 NaN \n", "3544 NaN Mozilla/5.0 (Windows NT 6.1; WOW64; rv:5.0.1) ... \n", "3545 NaN Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)... \n", "3546 NaN Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma... \n", "3547 NaN Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)... \n", "3548 NaN Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma... \n", "3549 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n", "3550 NaN Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ... \n", "3551 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n", "3552 NaN Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US... \n", "3553 NaN Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ... \n", "3554 NaN Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ... \n", "3555 NaN Mozilla/4.0 (compatible; MSIE 9.0; Windows NT ... \n", "3556 NaN Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1... \n", "3557 NaN GoogleMaps/RochesterNY \n", "3558 NaN GoogleProducer \n", "3559 NaN Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ... \n", "\n", " al c cy g \\\n", "0 en-US,en;q=0.8 US Danvers A6qOVH \n", "1 NaN US Provo mwszkS \n", "2 en-US US Washington xxr3Qb \n", "3 pt-br BR Braz zCaLwp \n", "4 en-US,en;q=0.8 US Shrewsbury 9b6kNl \n", "5 en-US,en;q=0.8 US Shrewsbury axNK8c \n", "6 pl-PL,pl;q=0.8,en-US;q=0.6,en;q=0.4 PL Luban wcndER \n", "7 bg,en-us;q=0.7,en;q=0.3 None NaN wcndER \n", "8 en-US, en None NaN wcndER \n", "9 pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4 None NaN zCaLwp \n", "10 en-us,en;q=0.5 US Seattle vNJS4H \n", "11 en-us,en;q=0.5 US Washington wG7OIH \n", "12 en-us,en;q=0.5 US Alexandria vNJS4H \n", "13 NaN NaN NaN NaN \n", "14 en-us,en;q=0.5 US Marietta 2rOUYc \n", "15 zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4 HK Central District nQvgJp \n", "16 zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4 HK Central District XdUNr \n", "17 en-us,en;q=0.5 US Buckfield zH1BFf \n", "18 NaN US Provo mwszkS \n", "19 it-IT,it;q=0.8,en-US;q=0.6,en;q=0.4 IT Venice wcndER \n", "20 es-ES ES Alcal zQ95Hi \n", "21 en-us,en;q=0.5 US Davidsonville wcndER \n", "22 en-us US Hockessin y3ZImz \n", "23 en-us US Lititz wWiOiD \n", "24 es-es,es;q=0.8,en-us;q=0.5,en;q=0.3 ES Bilbao wcndER \n", "25 en-GB,en;q=0.8,en-US;q=0.6,en-AU;q=0.4 MY Kuala Lumpur wcndER \n", "26 ro-RO,ro;q=0.8,en-US;q=0.6,en;q=0.4 CY Nicosia wcndER \n", "27 en-US,en;q=0.8 BR SPaulo zCaLwp \n", "28 en-us None NaN vNJS4H \n", "29 en-us None NaN FPX0IM \n", "... ... ... ... ... \n", "3530 en-US,en;q=0.8 US San Francisco xVZg4P \n", "3531 en-US None NaN wcndER \n", "3532 en-us,en;q=0.5 US Washington Au3aUS \n", "3533 en-us US Jacksonville b2UtUJ \n", "3534 en-us US Frisco vNJS4H \n", "3535 en-us US Houston zIgLx8 \n", "3536 en-US,en;q=0.5 None NaN xIcyim \n", "3537 es-es,es;q=0.8,en-us;q=0.5,en;q=0.3 HN Tegucigalpa zCaLwp \n", "3538 en-us US Los Angeles qMac9k \n", "3539 NaN US Bellevue zu2M5o \n", "3540 en-US,en;q=0.8 US Payson wcndER \n", "3541 NaN US Bellevue zu2M5o \n", "3542 en-us US Pittsburg y3reI1 \n", "3543 NaN NaN NaN NaN \n", "3544 en-us,en;q=0.5 US Wentzville vNJS4H \n", "3545 en-us,en;q=0.5 US Saint Charles vNJS4H \n", "3546 en-us US Los Angeles qMac9k \n", "3547 en-us US Silver Spring y0jYkg \n", "3548 en-us US Mcgehee y5rMac \n", "3549 sv-SE,sv;q=0.8,en-US;q=0.6,en;q=0.4 SE Sollefte eH8wu \n", "3550 en-us US Conshohocken A00b72 \n", "3551 en-US,en;q=0.8 None NaN wcndER \n", "3552 NaN US Decatur rqgJuE \n", "3553 en-us US Shrewsbury 9b6kNl \n", "3554 en-us US Shrewsbury axNK8c \n", "3555 en US Paramus e5SvKE \n", "3556 en-US,en;q=0.8 US Oklahoma City jQLtP4 \n", "3557 NaN US Provo mwszkS \n", "3558 NaN US Mountain View zjtI4X \n", "3559 en-US US Mc Lean qxKrTK \n", "\n", " gr h hc hh kw l \\\n", "0 MA wfLQtf 1.331823e+09 1.usa.gov NaN orofrog \n", "1 UT mwszkS 1.308262e+09 j.mp NaN bitly \n", "2 DC xxr3Qb 1.331920e+09 1.usa.gov NaN bitly \n", "3 27 zUtuOu 1.331923e+09 1.usa.gov NaN alelex88 \n", "4 MA 9b6kNl 1.273672e+09 bit.ly NaN bitly \n", "5 MA axNK8c 1.273673e+09 bit.ly NaN bitly \n", "6 77 zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "7 NaN zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "8 NaN zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "9 NaN zUtuOu 1.331923e+09 1.usa.gov NaN alelex88 \n", "10 WA u0uD9q 1.319564e+09 1.usa.gov NaN o_4us71ccioa \n", "11 DC A0nRz4 1.331816e+09 1.usa.gov NaN darrellissa \n", "12 VA u0uD9q 1.319564e+09 1.usa.gov NaN o_4us71ccioa \n", "13 NaN NaN NaN NaN NaN NaN \n", "14 GA 2rOUYc 1.255770e+09 1.usa.gov NaN bitly \n", "15 00 rtrrth 1.317318e+09 j.mp NaN walkeryuen \n", "16 00 qWkgbq 1.317318e+09 j.mp NaN walkeryuen \n", "17 ME x3jOIv 1.331840e+09 1.usa.gov NaN andyzieminski \n", "18 UT mwszkS 1.308262e+09 1.usa.gov NaN bitly \n", "19 20 zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "20 51 ytZYWR 1.331671e+09 bitly.com NaN jplnews \n", "21 MD zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "22 DE y3ZImz 1.331064e+09 1.usa.gov NaN bitly \n", "23 PA wWiOiD 1.330218e+09 1.usa.gov NaN bitly \n", "24 59 zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "25 14 zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "26 04 zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "27 27 zUtuOu 1.331923e+09 1.usa.gov NaN alelex88 \n", "28 NaN u0uD9q 1.319564e+09 1.usa.gov NaN o_4us71ccioa \n", "29 NaN FPX0IL 1.331923e+09 1.usa.gov NaN twittershare \n", "... ... ... ... ... ... ... \n", "3530 CA wqUkTo 1.331908e+09 go.nasa.gov NaN nasatwitter \n", "3531 NaN zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "3532 DC A9ct6C 1.331926e+09 1.usa.gov NaN ncsha \n", "3533 FL ieCdgH 1.301393e+09 go.nasa.gov NaN nasatwitter \n", "3534 TX u0uD9q 1.319564e+09 1.usa.gov NaN o_4us71ccioa \n", "3535 TX yrPaLt 1.331903e+09 aash.to NaN aashto \n", "3536 NaN yG1TTf 1.331728e+09 go.nasa.gov NaN nasatwitter \n", "3537 08 w63FZW 1.331547e+09 1.usa.gov NaN bufferapp \n", "3538 CA qds1Ge 1.310474e+09 1.usa.gov NaN healthypeople \n", "3539 WA zDhdro 1.331586e+09 bit.ly NaN glimtwin \n", "3540 UT zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "3541 WA zDhdro 1.331586e+09 1.usa.gov NaN glimtwin \n", "3542 CA y3reI1 1.331926e+09 1.usa.gov NaN bitly \n", "3543 NaN NaN NaN NaN NaN NaN \n", "3544 MO u0uD9q 1.319564e+09 1.usa.gov NaN o_4us71ccioa \n", "3545 IL u0uD9q 1.319564e+09 1.usa.gov NaN o_4us71ccioa \n", "3546 CA qds1Ge 1.310474e+09 1.usa.gov NaN healthypeople \n", "3547 MD y0jYkg 1.331852e+09 1.usa.gov NaN bitly \n", "3548 AR xANY6O 1.331916e+09 1.usa.gov NaN twitterfeed \n", "3549 24 7dtjei 1.260316e+09 1.usa.gov NaN tweetdeckapi \n", "3550 PA yGSwzn 1.331918e+09 1.usa.gov NaN addthis \n", "3551 NaN zkpJBR 1.331923e+09 1.usa.gov NaN bnjacobs \n", "3552 AL xcz8vt 1.331227e+09 1.usa.gov NaN bootsnall \n", "3553 MA 9b6kNl 1.273672e+09 bit.ly NaN bitly \n", "3554 MA axNK8c 1.273673e+09 bit.ly NaN bitly \n", "3555 NJ fqPSr9 1.301298e+09 1.usa.gov NaN tweetdeckapi \n", "3556 OK jQLtP4 1.307530e+09 1.usa.gov NaN bitly \n", "3557 UT mwszkS 1.308262e+09 j.mp NaN bitly \n", "3558 CA zjtI4X 1.327529e+09 1.usa.gov NaN bitly \n", "3559 VA qxKrTK 1.312898e+09 1.usa.gov NaN bitly \n", "\n", " ll nk \\\n", "0 [42.576698, -70.954903] 1.0 \n", "1 [40.218102, -111.613297] 0.0 \n", "2 [38.9007, -77.043098] 1.0 \n", "3 [-23.549999, -46.616699] 0.0 \n", "4 [42.286499, -71.714699] 0.0 \n", "5 [42.286499, -71.714699] 0.0 \n", "6 [51.116699, 15.2833] 0.0 \n", "7 NaN 0.0 \n", "8 NaN 0.0 \n", "9 NaN 0.0 \n", "10 [47.5951, -122.332603] 1.0 \n", "11 [38.937599, -77.092796] 0.0 \n", "12 [38.790901, -77.094704] 1.0 \n", "13 NaN NaN \n", "14 [33.953201, -84.5177] 1.0 \n", "15 [22.2833, 114.150002] 1.0 \n", "16 [22.2833, 114.150002] 1.0 \n", "17 [44.299702, -70.369797] 0.0 \n", "18 [40.218102, -111.613297] 0.0 \n", "19 [45.438599, 12.3267] 0.0 \n", "20 [37.516701, -5.9833] 0.0 \n", "21 [38.939201, -76.635002] 0.0 \n", "22 [39.785, -75.682297] 0.0 \n", "23 [40.174999, -76.3078] 0.0 \n", "24 [43.25, -2.9667] 0.0 \n", "25 [3.1667, 101.699997] 0.0 \n", "26 [35.166698, 33.366699] 0.0 \n", "27 [-23.5333, -46.616699] 0.0 \n", "28 NaN 0.0 \n", "29 NaN 1.0 \n", "... ... ... \n", "3530 [37.7645, -122.429398] 0.0 \n", "3531 NaN 0.0 \n", "3532 [38.904202, -77.031998] 1.0 \n", "3533 [30.279301, -81.585098] 1.0 \n", "3534 [33.149899, -96.855499] 1.0 \n", "3535 [29.775499, -95.415199] 1.0 \n", "3536 NaN 0.0 \n", "3537 [14.1, -87.216698] 0.0 \n", "3538 [34.041599, -118.298798] 0.0 \n", "3539 [47.615398, -122.210297] 0.0 \n", "3540 [40.014198, -111.738899] 0.0 \n", "3541 [47.615398, -122.210297] 0.0 \n", "3542 [38.0051, -121.838699] 0.0 \n", "3543 NaN NaN \n", "3544 [38.790001, -90.854897] 1.0 \n", "3545 [41.9352, -88.290901] 1.0 \n", "3546 [34.041599, -118.298798] 1.0 \n", "3547 [39.052101, -77.014999] 1.0 \n", "3548 [33.628399, -91.356903] 1.0 \n", "3549 [63.166698, 17.266701] 1.0 \n", "3550 [40.0798, -75.2855] 0.0 \n", "3551 NaN 0.0 \n", "3552 [34.572701, -86.940598] 0.0 \n", "3553 [42.286499, -71.714699] 0.0 \n", "3554 [42.286499, -71.714699] 0.0 \n", "3555 [40.9445, -74.07] 1.0 \n", "3556 [35.4715, -97.518997] 0.0 \n", "3557 [40.218102, -111.613297] 0.0 \n", "3558 [37.419201, -122.057404] 0.0 \n", "3559 [38.935799, -77.162102] 0.0 \n", "\n", " r t \\\n", "0 http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/... 1.331923e+09 \n", "1 http://www.AwareMap.com/ 1.331923e+09 \n", "2 http://t.co/03elZC4Q 1.331923e+09 \n", "3 direct 1.331923e+09 \n", "4 http://www.shrewsbury-ma.gov/selco/ 1.331923e+09 \n", "5 http://www.shrewsbury-ma.gov/selco/ 1.331923e+09 \n", "6 http://plus.url.google.com/url?sa=z&n=13319232... 1.331923e+09 \n", "7 http://www.facebook.com/ 1.331923e+09 \n", "8 http://www.facebook.com/l.php?u=http%3A%2F%2F1... 1.331923e+09 \n", "9 http://t.co/o1Pd0WeV 1.331923e+09 \n", "10 direct 1.331923e+09 \n", "11 http://t.co/ND7SoPyo 1.331923e+09 \n", "12 direct 1.331923e+09 \n", "13 NaN NaN \n", "14 direct 1.331923e+09 \n", "15 http://forum2.hkgolden.com/view.aspx?type=BW&m... 1.331923e+09 \n", "16 http://forum2.hkgolden.com/view.aspx?type=BW&m... 1.331923e+09 \n", "17 http://t.co/6Cx4ROLs 1.331923e+09 \n", "18 http://www.AwareMap.com/ 1.331923e+09 \n", "19 http://www.facebook.com/ 1.331923e+09 \n", "20 http://www.facebook.com/ 1.331923e+09 \n", "21 http://www.facebook.com/ 1.331923e+09 \n", "22 direct 1.331923e+09 \n", "23 http://www.facebook.com/l.php?u=http%3A%2F%2F1... 1.331923e+09 \n", "24 http://www.facebook.com/ 1.331923e+09 \n", "25 http://www.facebook.com/ 1.331923e+09 \n", "26 http://www.facebook.com/?ref=tn_tnmn 1.331923e+09 \n", "27 direct 1.331923e+09 \n", "28 direct 1.331923e+09 \n", "29 http://t.co/5xlp0B34 1.331923e+09 \n", "... ... ... \n", "3530 http://www.facebook.com/l.php?u=http%3A%2F%2Fg... 1.331927e+09 \n", "3531 direct 1.331927e+09 \n", "3532 http://www.ncsha.org/ 1.331927e+09 \n", "3533 direct 1.331927e+09 \n", "3534 direct 1.331927e+09 \n", "3535 direct 1.331927e+09 \n", "3536 http://t.co/g1VKE8zS 1.331927e+09 \n", "3537 http://t.co/A8TJyibE 1.331927e+09 \n", "3538 direct 1.331927e+09 \n", "3539 direct 1.331927e+09 \n", "3540 http://www.facebook.com/l.php?u=http%3A%2F%2F1... 1.331927e+09 \n", "3541 direct 1.331927e+09 \n", "3542 http://www.facebook.com/l.php?u=http%3A%2F%2F1... 1.331927e+09 \n", "3543 NaN NaN \n", "3544 direct 1.331927e+09 \n", "3545 direct 1.331927e+09 \n", "3546 direct 1.331927e+09 \n", "3547 direct 1.331927e+09 \n", "3548 https://twitter.com/fdarecalls/status/18069759... 1.331927e+09 \n", "3549 direct 1.331927e+09 \n", "3550 http://www.linkedin.com/home?trk=hb_tab_home_top 1.331927e+09 \n", "3551 http://plus.url.google.com/url?sa=z&n=13319268... 1.331927e+09 \n", "3552 direct 1.331927e+09 \n", "3553 http://www.shrewsbury-ma.gov/selco/ 1.331927e+09 \n", "3554 http://www.shrewsbury-ma.gov/selco/ 1.331927e+09 \n", "3555 direct 1.331927e+09 \n", "3556 http://www.facebook.com/l.php?u=http%3A%2F%2F1... 1.331927e+09 \n", "3557 http://www.AwareMap.com/ 1.331927e+09 \n", "3558 direct 1.331927e+09 \n", "3559 http://t.co/OEEEvwjU 1.331927e+09 \n", "\n", " tz u \n", "0 America/New_York http://www.ncbi.nlm.nih.gov/pubmed/22415991 \n", "1 America/Denver http://www.monroecounty.gov/etc/911/rss.php \n", "2 America/New_York http://boxer.senate.gov/en/press/releases/0316... \n", "3 America/Sao_Paulo http://apod.nasa.gov/apod/ap120312.html \n", "4 America/New_York http://www.shrewsbury-ma.gov/egov/gallery/1341... \n", "5 America/New_York http://www.shrewsbury-ma.gov/egov/gallery/1341... \n", "6 Europe/Warsaw http://www.nasa.gov/mission_pages/nustar/main/... \n", "7 http://www.nasa.gov/mission_pages/nustar/main/... \n", "8 http://www.nasa.gov/mission_pages/nustar/main/... \n", "9 http://apod.nasa.gov/apod/ap120312.html \n", "10 America/Los_Angeles https://www.nysdot.gov/rexdesign/design/commun... \n", "11 America/New_York http://oversight.house.gov/wp-content/uploads/... \n", "12 America/New_York https://www.nysdot.gov/rexdesign/design/commun... \n", "13 NaN NaN \n", "14 America/New_York http://toxtown.nlm.nih.gov/index.php \n", "15 Asia/Hong_Kong http://www.ssd.noaa.gov/PS/TROP/TCFP/data/curr... \n", "16 Asia/Hong_Kong http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc... \n", "17 America/New_York http://www.usda.gov/wps/portal/usda/usdahome?c... \n", "18 America/Denver http://www.monroecounty.gov/etc/911/rss.php \n", "19 Europe/Rome http://www.nasa.gov/mission_pages/nustar/main/... \n", "20 Africa/Ceuta http://voyager.jpl.nasa.gov/imagesvideo/uranus... \n", "21 America/New_York http://www.nasa.gov/mission_pages/nustar/main/... \n", "22 America/New_York http://portal.hud.gov/hudportal/documents/hudd... \n", "23 America/New_York http://www.tricare.mil/mybenefit/ProfileFilter... \n", "24 Europe/Madrid http://www.nasa.gov/mission_pages/nustar/main/... \n", "25 Asia/Kuala_Lumpur http://www.nasa.gov/mission_pages/nustar/main/... \n", "26 Asia/Nicosia http://www.nasa.gov/mission_pages/nustar/main/... \n", "27 America/Sao_Paulo http://apod.nasa.gov/apod/ap120312.html \n", "28 https://www.nysdot.gov/rexdesign/design/commun... \n", "29 http://www.ed.gov/news/media-advisories/us-dep... \n", "... ... ... \n", "3530 America/Los_Angeles http://www.nasa.gov/multimedia/imagegallery/im... \n", "3531 http://www.nasa.gov/mission_pages/nustar/main/... \n", "3532 America/New_York http://portal.hud.gov/hudportal/HUD?src=/press... \n", "3533 America/New_York http://apod.nasa.gov/apod/ \n", "3534 America/Chicago https://www.nysdot.gov/rexdesign/design/commun... \n", "3535 America/Chicago http://ntl.bts.gov/lib/44000/44300/44374/FHWA-... \n", "3536 http://www.nasa.gov/mission_pages/hurricanes/a... \n", "3537 America/Tegucigalpa http://apod.nasa.gov/apod/ap120312.html \n", "3538 America/Los_Angeles http://healthypeople.gov/2020/connect/webinars... \n", "3539 America/Los_Angeles http://www.federalreserve.gov/newsevents/press... \n", "3540 America/Denver http://www.nasa.gov/mission_pages/nustar/main/... \n", "3541 America/Los_Angeles http://www.federalreserve.gov/newsevents/press... \n", "3542 America/Los_Angeles http://www.sba.gov/community/blogs/community-b... \n", "3543 NaN NaN \n", "3544 America/Chicago https://www.nysdot.gov/rexdesign/design/commun... \n", "3545 America/Chicago https://www.nysdot.gov/rexdesign/design/commun... \n", "3546 America/Los_Angeles http://healthypeople.gov/2020/connect/webinars... \n", "3547 America/New_York http://www.epa.gov/otaq/regs/fuels/additive/e1... \n", "3548 America/Chicago http://www.fda.gov/Safety/Recalls/ucm296326.htm \n", "3549 Europe/Stockholm http://www.nasa.gov/mission_pages/WISE/main/in... \n", "3550 America/New_York http://www.nlm.nih.gov/medlineplus/news/fullst... \n", "3551 http://www.nasa.gov/mission_pages/nustar/main/... \n", "3552 America/Chicago http://travel.state.gov/passport/passport_5535... \n", "3553 America/New_York http://www.shrewsbury-ma.gov/egov/gallery/1341... \n", "3554 America/New_York http://www.shrewsbury-ma.gov/egov/gallery/1341... \n", "3555 America/New_York http://www.fda.gov/AdvisoryCommittees/Committe... \n", "3556 America/Chicago http://www.okc.gov/PublicNotificationSystem/Fo... \n", "3557 America/Denver http://www.monroecounty.gov/etc/911/rss.php \n", "3558 America/Los_Angeles http://www.ahrq.gov/qual/qitoolkit/ \n", "3559 America/New_York http://herndon-va.gov/Content/public_safety/Pu... \n", "\n", "[3560 rows x 18 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pandas import DataFrame, Series\n", "import pandas as pd\n", "\n", "frame = DataFrame(records)\n", "frame" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 America/New_York\n", "1 America/Denver\n", "2 America/New_York\n", "3 America/Sao_Paulo\n", "4 America/New_York\n", "5 America/New_York\n", "6 Europe/Warsaw\n", "7 \n", "8 \n", "9 \n", "Name: tz, dtype: object" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame['tz'][:10]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "America/New_York 1251\n", " 521\n", "America/Chicago 400\n", "America/Los_Angeles 382\n", "America/Denver 191\n", "Europe/London 74\n", "Asia/Tokyo 37\n", "Pacific/Honolulu 36\n", "Europe/Madrid 35\n", "America/Sao_Paulo 33\n", "Name: tz, dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tz_counts = frame['tz'].value_counts()\n", "tz_counts[:10]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "America/New_York 1251\n", "Unknown 521\n", "America/Chicago 400\n", "America/Los_Angeles 382\n", "America/Denver 191\n", "Missing 120\n", "Europe/London 74\n", "Asia/Tokyo 37\n", "Pacific/Honolulu 36\n", "Europe/Madrid 35\n", "Name: tz, dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clean_tz = frame['tz'].fillna('Missing')\n", "clean_tz[clean_tz == ''] = 'Unknown'\n", "tz_counts = clean_tz.value_counts()\n", "tz_counts[:10]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x103a42bd0>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x103a42bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 4))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10ee1da90>" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArcAAAFrCAYAAADLiaG+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ca57150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tz_counts[:10].plot(kind='barh', rot=0)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'GoogleMaps/RochesterNY'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame['a'][1]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame['a'][50]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P925/V10e Build/FRG83G) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1'" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame['a'][51]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 Mozilla/5.0\n", "1 GoogleMaps/RochesterNY\n", "2 Mozilla/4.0\n", "3 Mozilla/5.0\n", "4 Mozilla/5.0\n", "dtype: object" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = Series([x.split()[0] for x in frame.a.dropna()])\n", "results[:5]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Mozilla/5.0 2594\n", "Mozilla/4.0 601\n", "GoogleMaps/RochesterNY 121\n", "Opera/9.80 34\n", "TEST_INTERNET_AGENT 24\n", "GoogleProducer 21\n", "Mozilla/6.0 5\n", "BlackBerry8520/5.0.0.681 4\n", "dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results.value_counts()[:8]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cframe = frame[frame.a.notnull()]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Windows', 'Not Windows', 'Windows', 'Not Windows', 'Windows'], \n", " dtype='|S11')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "operating_system = np.where(cframe['a'].str.contains('Windows'),\n", " 'Windows', 'Not Windows')\n", "operating_system[:5]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "by_tz_os = cframe.groupby(['tz', operating_system])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Not Windows</th>\n", " <th>Windows</th>\n", " </tr>\n", " <tr>\n", " <th>tz</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>245.0</td>\n", " <td>276.0</td>\n", " </tr>\n", " <tr>\n", " <th>Africa/Cairo</th>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>Africa/Casablanca</th>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>Africa/Ceuta</th>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>Africa/Johannesburg</th>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>Africa/Lusaka</th>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>America/Anchorage</th>\n", " <td>4.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>America/Argentina/Buenos_Aires</th>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>America/Argentina/Cordoba</th>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>America/Argentina/Mendoza</th>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Not Windows Windows\n", "tz \n", " 245.0 276.0\n", "Africa/Cairo 0.0 3.0\n", "Africa/Casablanca 0.0 1.0\n", "Africa/Ceuta 0.0 2.0\n", "Africa/Johannesburg 0.0 1.0\n", "Africa/Lusaka 0.0 1.0\n", "America/Anchorage 4.0 1.0\n", "America/Argentina/Buenos_Aires 1.0 0.0\n", "America/Argentina/Cordoba 0.0 1.0\n", "America/Argentina/Mendoza 0.0 1.0" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agg_counts = by_tz_os.size().unstack().fillna(0)\n", "agg_counts[:10]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "tz\n", " 24\n", "Africa/Cairo 20\n", "Africa/Casablanca 21\n", "Africa/Ceuta 92\n", "Africa/Johannesburg 87\n", "Africa/Lusaka 53\n", "America/Anchorage 54\n", "America/Argentina/Buenos_Aires 57\n", "America/Argentina/Cordoba 26\n", "America/Argentina/Mendoza 55\n", "dtype: int64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use to sort in ascending order\n", "indexer = agg_counts.sum(1).argsort()\n", "indexer[:10]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Not Windows</th>\n", " <th>Windows</th>\n", " </tr>\n", " <tr>\n", " <th>tz</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>America/Sao_Paulo</th>\n", " <td>13.0</td>\n", " <td>20.0</td>\n", " </tr>\n", " <tr>\n", " <th>Europe/Madrid</th>\n", " <td>16.0</td>\n", " <td>19.0</td>\n", " </tr>\n", " <tr>\n", " <th>Pacific/Honolulu</th>\n", " <td>0.0</td>\n", " <td>36.0</td>\n", " </tr>\n", " <tr>\n", " <th>Asia/Tokyo</th>\n", " <td>2.0</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>Europe/London</th>\n", " <td>43.0</td>\n", " <td>31.0</td>\n", " </tr>\n", " <tr>\n", " <th>America/Denver</th>\n", " <td>132.0</td>\n", " <td>59.0</td>\n", " </tr>\n", " <tr>\n", " <th>America/Los_Angeles</th>\n", " <td>130.0</td>\n", " <td>252.0</td>\n", " </tr>\n", " <tr>\n", " <th>America/Chicago</th>\n", " <td>115.0</td>\n", " <td>285.0</td>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <td>245.0</td>\n", " <td>276.0</td>\n", " </tr>\n", " <tr>\n", " <th>America/New_York</th>\n", " <td>339.0</td>\n", " <td>912.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Not Windows Windows\n", "tz \n", "America/Sao_Paulo 13.0 20.0\n", "Europe/Madrid 16.0 19.0\n", "Pacific/Honolulu 0.0 36.0\n", "Asia/Tokyo 2.0 35.0\n", "Europe/London 43.0 31.0\n", "America/Denver 132.0 59.0\n", "America/Los_Angeles 130.0 252.0\n", "America/Chicago 115.0 285.0\n", " 245.0 276.0\n", "America/New_York 339.0 912.0" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_subset = agg_counts.take(indexer)[-10:]\n", "count_subset" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x103f923d0>" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x103f923d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x103fea950>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAFrCAYAAADSAk8YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1045807d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "count_subset.plot(kind='barh', stacked=True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x104371fd0>" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x104371fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10413ee90>" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAAFrCAYAAAAw3lRhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103f6b6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "normed_subset = count_subset.div(count_subset.sum(1), axis=0)\n", "normed_subset.plot(kind='barh', stacked=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MovieLens 1M data set" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pmui/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel/__main__.py:13: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.\n", "/Users/pmui/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel/__main__.py:14: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.\n", "/Users/pmui/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel/__main__.py:15: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "encoding = 'latin1'\n", "\n", "upath = os.path.expanduser('ch02/movielens/users.dat')\n", "rpath = os.path.expanduser('ch02/movielens/ratings.dat')\n", "mpath = os.path.expanduser('ch02/movielens/movies.dat')\n", "\n", "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n", "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "mnames = ['movie_id', 'title', 'genres']\n", "\n", "users = pd.read_csv(upath, sep='::', header=None, names=unames, encoding=encoding)\n", "ratings = pd.read_csv(rpath, sep='::', header=None, names=rnames, encoding=encoding)\n", "movies = pd.read_csv(mpath, sep='::', header=None, names=mnames, encoding=encoding)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>user_id</th>\n", " <th>gender</th>\n", " <th>age</th>\n", " <th>occupation</th>\n", " <th>zip</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>F</td>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>48067</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>M</td>\n", " <td>56</td>\n", " <td>16</td>\n", " <td>70072</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>M</td>\n", " <td>25</td>\n", " <td>15</td>\n", " <td>55117</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>M</td>\n", " <td>45</td>\n", " <td>7</td>\n", " <td>02460</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>M</td>\n", " <td>25</td>\n", " <td>20</td>\n", " <td>55455</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " user_id gender age occupation zip\n", "0 1 F 1 10 48067\n", "1 2 M 56 16 70072\n", "2 3 M 25 15 55117\n", "3 4 M 45 7 02460\n", "4 5 M 25 20 55455" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "users[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ratings[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "movies[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ratings" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = pd.merge(pd.merge(ratings, users), movies)\n", "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.ix[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mean_ratings = data.pivot_table('rating', index='title',\n", " columns='gender', aggfunc='mean')\n", "mean_ratings[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ratings_by_title = data.groupby('title').size()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ratings_by_title[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "active_titles = ratings_by_title.index[ratings_by_title >= 250]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "active_titles[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mean_ratings = mean_ratings.ix[active_titles]\n", "mean_ratings" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mean_ratings = mean_ratings.rename(index={'Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)':\n", " 'Seven Samurai (Shichinin no samurai) (1954)'})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "top_female_ratings = mean_ratings.sort_index(by='F', ascending=False)\n", "top_female_ratings[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Measuring rating disagreement" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sorted_by_diff = mean_ratings.sort_index(by='diff')\n", "sorted_by_diff[:15]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Reverse order of rows, take first 15 rows\n", "sorted_by_diff[::-1][:15]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Standard deviation of rating grouped by title\n", "rating_std_by_title = data.groupby('title')['rating'].std()\n", "# Filter down to active_titles\n", "rating_std_by_title = rating_std_by_title.ix[active_titles]\n", "# Order Series by value in descending order\n", "rating_std_by_title.order(ascending=False)[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### US Baby Names 1880-2010" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "from numpy.random import randn\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "plt.rc('figure', figsize=(12, 5))\n", "np.set_printoptions(precision=4)\n", "%pwd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://www.ssa.gov/oact/babynames/limits.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!head -n 10 ch02/names/yob1880.txt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "names1880 = pd.read_csv('ch02/names/yob1880.txt', names=['name', 'sex', 'births'])\n", "names1880" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names1880.groupby('sex').births.sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 2010 is the last available year right now\n", "years = range(1880, 2011)\n", "\n", "pieces = []\n", "columns = ['name', 'sex', 'births']\n", "\n", "for year in years:\n", " path = 'ch02/names/yob%d.txt' % year\n", " frame = pd.read_csv(path, names=columns)\n", "\n", " frame['year'] = year\n", " pieces.append(frame)\n", "\n", "# Concatenate everything into a single DataFrame\n", "names = pd.concat(pieces, ignore_index=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "total_births = names.pivot_table('births', index='year',\n", " columns='sex', aggfunc=sum)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "total_births.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "total_births.plot(title='Total births by sex and year')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def add_prop(group):\n", " # Integer division floors\n", " births = group.births.astype(float)\n", "\n", " group['prop'] = births / births.sum()\n", " return group\n", "names = names.groupby(['year', 'sex']).apply(add_prop)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.allclose(names.groupby(['year', 'sex']).prop.sum(), 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_top1000(group):\n", " return group.sort_index(by='births', ascending=False)[:1000]\n", "grouped = names.groupby(['year', 'sex'])\n", "top1000 = grouped.apply(get_top1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pieces = []\n", "for year, group in names.groupby(['year', 'sex']):\n", " pieces.append(group.sort_index(by='births', ascending=False)[:1000])\n", "top1000 = pd.concat(pieces, ignore_index=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "top1000.index = np.arange(len(top1000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "top1000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyzing naming trends" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "boys = top1000[top1000.sex == 'M']\n", "girls = top1000[top1000.sex == 'F']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "total_births = top1000.pivot_table('births', index='year', columns='name',\n", " aggfunc=sum)\n", "total_births" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]\n", "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n", " title=\"Number of births per year\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Measuring the increase in naming diversity" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "table = top1000.pivot_table('prop', index='year',\n", " columns='sex', aggfunc=sum)\n", "table.plot(title='Sum of table1000.prop by year and sex',\n", " yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = boys[boys.year == 2010]\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prop_cumsum = df.sort_index(by='prop', ascending=False).prop.cumsum()\n", "prop_cumsum[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prop_cumsum.values.searchsorted(0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = boys[boys.year == 1900]\n", "in1900 = df.sort_index(by='prop', ascending=False).prop.cumsum()\n", "in1900.values.searchsorted(0.5) + 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_quantile_count(group, q=0.5):\n", " group = group.sort_index(by='prop', ascending=False)\n", " return group.prop.cumsum().values.searchsorted(q) + 1\n", "\n", "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n", "diversity = diversity.unstack('sex')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_quantile_count(group, q=0.5):\n", " group = group.sort_index(by='prop', ascending=False)\n", " return group.prop.cumsum().values.searchsorted(q) + 1\n", "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n", "diversity = diversity.unstack('sex')\n", "diversity.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "diversity.plot(title=\"Number of popular names in top 50%\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The \"Last letter\" Revolution" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# extract last letter from name column\n", "get_last_letter = lambda x: x[-1]\n", "last_letters = names.name.map(get_last_letter)\n", "last_letters.name = 'last_letter'\n", "\n", "table = names.pivot_table('births', index=last_letters,\n", " columns=['sex', 'year'], aggfunc=sum)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n", "subtable.head()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "subtable.sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "letter_prop = subtable / subtable.sum().astype(float)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n", "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n", "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female',\n", " legend=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.subplots_adjust(hspace=0.25)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "letter_prop = table / table.sum().astype(float)\n", "\n", "dny_ts = letter_prop.ix[['d', 'n', 'y'], 'M'].T\n", "dny_ts.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.close('all')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dny_ts.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Boy names that became girl names (and vice versa)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "all_names = top1000.name.unique()\n", "mask = np.array(['lesl' in x.lower() for x in all_names])\n", "lesley_like = all_names[mask]\n", "lesley_like" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filtered = top1000[top1000.name.isin(lesley_like)]\n", "filtered.groupby('name').births.sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "table = filtered.pivot_table('births', index='year',\n", " columns='sex', aggfunc='sum')\n", "table = table.div(table.sum(1), axis=0)\n", "table.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.close('all')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "table.plot(style={'M': 'k-', 'F': 'k--'})" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

kdmurray91/kwip-experiments

writeups/chlamy-subsetting/chlamy.ipynb

1

9344

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "library(plyr, warn.conflicts=F)\n", "library(dplyr, warn.conflicts=F)\n", "library(tidyr, warn.conflicts=F)\n", "library(ggplot2)\n", "library(caTools)\n", "library(gtable)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "metadata = read.delim(\"chlamy_meta.tab\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "read.distmat = function (filename) {\n", " dm = as.matrix(read.delim(filename, header=T, row.names=1))\n", " idxs = match(metadata$Run, row.names(dm))\n", " return(dm[idxs, idxs])\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "full = read.distmat(\"kwip/full_wip.dist\")\n", "metadata = metadata[match(row.names(full), metadata$Run),]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "c = cmdscale(as.dist(full))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# From https://gist.github.com/baptiste/6652815\n", "gtable_arrange <- function(..., grobs=list(), as.table=TRUE,\n", " top = NULL, bottom = NULL, \n", " left = NULL, right = NULL, draw=TRUE){\n", " require(gtable)\n", " # alias\n", " gtable_add_grobs <- gtable_add_grob\n", " \n", " dots <- list(...)\n", " params <- c(\"nrow\", \"ncol\", \"widths\", \"heights\",\n", " \"respect\", \"just\", \"z\") # TODO currently ignored\n", " \n", " layout.call <- intersect(names(dots), params)\n", " params.layout <- dots[layout.call]\n", " \n", " if(is.null(names(dots)))\n", " not.grobnames <- FALSE else\n", " not.grobnames <- names(dots) %in% layout.call\n", " \n", " if(!length(grobs))\n", " grobs <- dots[! not.grobnames ]\n", " \n", " ## figure out the layout\n", " n <- length(grobs)\n", " nm <- n2mfrow(n)\n", " \n", " if(is.null(params.layout$nrow) & is.null(params.layout$ncol)) \n", " {\n", " params.layout$nrow = nm[1]\n", " params.layout$ncol = nm[2]\n", " }\n", " if(is.null(params.layout$nrow))\n", " params.layout$nrow = ceiling(n/params.layout$ncol)\n", " if(is.null(params.layout$ncol))\n", " params.layout$ncol = ceiling(n/params.layout$nrow)\n", " \n", " if(is.null(params.layout$widths))\n", " params.layout$widths <- unit(rep(1, params.layout$ncol), \"null\")\n", " if(is.null(params.layout$heights))\n", " params.layout$heights <- unit(rep(1,params.layout$nrow), \"null\")\n", "\n", " positions <- expand.grid(row = seq_len(params.layout$nrow), \n", " col = seq_len(params.layout$ncol))\n", " if(as.table) # fill table by rows\n", " positions <- positions[order(positions$row),]\n", " \n", " positions <- positions[seq_along(grobs), ] # n might be < ncol*nrow\n", " \n", " ## build the gtable, similar steps to gtable_matrix\n", " \n", " gt <- gtable(name=\"table\")\n", " gt <- gtable_add_cols(gt, params.layout$widths)\n", " gt <- gtable_add_rows(gt, params.layout$heights)\n", " gt <- gtable_add_grobs(gt, grobs, t = positions$row, \n", " l = positions$col)\n", " \n", " ## titles given as strings are converted to text grobs\n", " if (is.character(top)) \n", " top <- textGrob(top)\n", " if (is.character(bottom)) \n", " bottom <- textGrob(bottom)\n", " if (is.character(right)) \n", " right <- textGrob(right, rot = -90)\n", " if (is.character(left)) \n", " left <- textGrob(left, rot = 90)\n", " \n", " if(!is.null(top)){\n", " gt <- gtable_add_rows(gt, heights=grobHeight(top), 0)\n", " gt <- gtable_add_grobs(gt, top, t=1, l=1, r=ncol(gt))\n", " }\n", " if(!is.null(bottom)){\n", " gt <- gtable_add_rows(gt, heights=grobHeight(bottom), -1)\n", " gt <- gtable_add_grobs(gt, bottom, t=nrow(gt), l=1, r=ncol(gt))\n", " }\n", " if(!is.null(left)){\n", " gt <- gtable_add_cols(gt, widths=grobWidth(left), 0)\n", " gt <- gtable_add_grobs(gt, left, t=1, b=nrow(gt), l=1, r=1)\n", " }\n", " if(!is.null(right)){\n", " gt <- gtable_add_cols(gt, widths=grobWidth(right), -1)\n", " gt <- gtable_add_grobs(gt, right, t=1, b=nrow(gt), l=ncol(gt), r=ncol(gt))\n", " }\n", " \n", " if(draw){\n", " grid.newpage()\n", " grid.draw(gt)\n", " }\n", " invisible(gt)\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "coverages = c(\"0.01x\", \"0.1x\", \"0.5x\", \"1x\", \"2x\", \"4x\", \"8x\", \"12x\", \"15x\", \"25x\",\n", " \"50x\", \"75x\", \"100x\", \"150x\", \"200x\", \"full\")\n", "\n", "plot_covs = c(\"0.1x\",\"1x\", \"2x\",\n", " \"4x\", \"8x\", \"15x\",\n", " \"50x\", \"150x\", \"full\")\n", "plots = list()\n", "pdf(\"all-pcoas.pdf\")\n", "for (coverage in coverages) {\n", " fname = paste0(\"kwip/\", coverage, \"_wip.dist\")\n", " mat = read.distmat(fname)\n", " mds = cmdscale(mat, k=2, eig=T, x.ret=T)\n", " eigs = mds$eig\n", " pct.contrib = round(eigs / sum(eigs) * 100)\n", " \n", " # Invert axes to match the paper (Flowers et al.) figure.\n", " # The sample here is one of the two red ones in the top right corner.\n", " if (mds$points[\"SRR1734600\", 1] < 0) {\n", " mds$points[,1] = mds$points[,1] * -1\n", " }\n", " if (mds$points[\"SRR1734600\", 2] < 0) {\n", " mds$points[,2] = mds$points[,2] * -1\n", " }\n", " \n", " pts.df = as.data.frame(mds$points)\n", " pts.df$Group = metadata$origin\n", " \n", " cols = c(\"light blue\", \"blue\", \"dark green\", \"red\" )\n", " p = ggplot(pts.df, aes(x=V1, y=V2, colour=Group)) + \n", " geom_point(size=2) + \n", " scale_color_manual(values = cols) +\n", " xlab(paste0(\"PC 1 (\", pct.contrib[1], \"%)\")) +\n", " ylab(paste0(\"PC 2 (\", pct.contrib[2], \"%)\")) +\n", " ggtitle(paste(coverage, \"fold subset\")) + \n", " theme_classic() +\n", " theme(panel.border=element_rect(colour = \"black\", fill=NA),\n", " legend.position=\"bottom\")\n", " print(p)\n", " if (coverage %in% plot_covs) {\n", " p = ggplot(pts.df, aes(x=V1, y=V2, colour=Group)) + \n", " geom_point(size=2) + \n", " scale_color_manual(values = cols) +\n", " ggtitle(coverage) + \n", " theme_classic() +\n", " theme(panel.border=element_rect(colour = \"black\", fill=NA),\n", " legend.position=\"none\",\n", " axis.title.x=element_blank(),\n", " axis.title.y=element_blank(),\n", " axis.ticks=element_blank(),\n", " axis.text.x = element_blank(),\n", " axis.text.y = element_blank()\n", " )\n", " plots = c(plots, list(p))\n", " }\n", "}\n", "dev.off()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "pdf(\"subset-pcoa-matrix.pdf\", height = 5, width = 5)\n", "gtable_arrange(ncol=3, grobs = lapply(plots, ggplotGrob), left=\"PC1\", bottom=\"PC2\")\n", "dev.off()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.3.3" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 0 }

mit

martin-hunt/hublib

examples/UI_Demo-Copy1.ipynb

1

6602

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b0e3510750764baeaff7f5cade6f7878", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(Button(button_style='danger', description='correct', layout=Layout(width='auto'), style=ButtonSt…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ipywidgets import Layout, Button, Box\n", "\n", "items_layout = Layout( width='auto') # override the default width of the button to 'auto' to let the button grow\n", "\n", "box_layout = Layout(display='flex',\n", " flex_flow='column',\n", " align_items='stretch',\n", " justify_content='space-between',\n", " border='solid',\n", " width='100%',\n", " height='200px')\n", "\n", "words = ['correct', 'horse', 'battery', 'staple']\n", "items = [Button(description=word, layout=items_layout, button_style='danger') for word in words]\n", "box = Box(children=items, layout=box_layout)\n", "box" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "saved_height = 0\n", "def resize_cb(height):\n", " global saved_height, fig\n", " if height != saved_height:\n", " box.layout.height = '%spx' % int(height)\n", " saved_height = height\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "function doSomething() {\n", "var h1 = document.getElementById('header').offsetHeight;\n", "var h2 = window.innerHeight - h1;\n", "var kernel = IPython.notebook.kernel;\n", "var command = \"resize_cb(\" + h2 +\")\";\n", "console.log(command)\n", "kernel.execute(command);\n", "};\n", "\n", "var resizeTimer;\n", "$(window).resize(function() {\n", " clearTimeout(resizeTimer);\n", " resizeTimer = setTimeout(doSomething, 100);\n", "});\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "function doSomething() {\n", "var h1 = document.getElementById('header').offsetHeight;\n", "var h2 = window.innerHeight - h1;\n", "var kernel = IPython.notebook.kernel;\n", "var command = \"resize_cb(\" + h2 +\")\";\n", "console.log(command)\n", "kernel.execute(command);\n", "};\n", "\n", "var resizeTimer;\n", "$(window).resize(function() {\n", " clearTimeout(resizeTimer);\n", " resizeTimer = setTimeout(doSomething, 100);\n", "});" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "extensions": { "jupyter_dashboards": { "activeView": "grid_default", "version": 1, "views": { "grid_default": { "cellMargin": 10, "defaultCellHeight": 20, "maxColumns": 12, "name": "grid", "type": "grid" }, "report_default": { "name": "report", "type": "report" } } } }, "kernelspec": { "display_name": "Python3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" }, "widgets": { "state": { "0d31088cb6fd42b592f0c1c9292ef3d2": { "views": [ { "cell_index": 23 } ] }, "15c2dbe1dd594108b0aa9f5659ea67ac": { "views": [ { "cell_index": 11 } ] }, "210972ff394f4a6a8456bc7468ba01e6": { "views": [ { "cell_index": 24 } ] }, "25d3735758334bc3a96a2327172b8adb": { "views": [ { "cell_index": 27 } ] }, "3545792f4c1b428c8736c9cc9866ae7b": { "views": [ { "cell_index": 40 } ] }, "4bf7e7d68ebc4a859a74a3453d44da79": { "views": [ { "cell_index": 22 } ] }, "50bcc61671cf454faf7e409aa1f41a5c": { "views": [ { "cell_index": 36 } ] }, "6149a6f5cd914bfabfe361bce5dd564f": { "views": [ { "cell_index": 20 } ] }, "6155358fb0004fa4bd31cf2db196616c": { "views": [ { "cell_index": 5 } ] }, "65013a8730734fe4995ca4769a67d60a": { "views": [ { "cell_index": 43 } ] }, "650c5153128b4b4ab5d98c4c2e322c38": { "views": [ { "cell_index": 19 } ] }, "6c05daec25a94ed5b908c13c2405da9b": { "views": [ { "cell_index": 38 } ] }, "a031fa03f2094ac8a90f0d7115758d7a": { "views": [ { "cell_index": 44 } ] }, "afbad63223c0407695d618c21cc683a5": { "views": [ { "cell_index": 42 } ] }, "b566c6edc92248e4962cbcba085a0e69": { "views": [ { "cell_index": 17 } ] }, "b7d795373e0249baa46dcf505bb72314": { "views": [ { "cell_index": 9 } ] }, "bf7e9b1b86e1465ab147d5f87ee21d77": { "views": [ { "cell_index": 26 } ] }, "d7a506c1f2f2412a8d039af98a531f3e": { "views": [ { "cell_index": 25 } ] }, "d8f4322158c145e0831e4ed6367eaf0e": { "views": [ { "cell_index": 14 } ] }, "de0ecd1c5cba4341a24072101fb0a372": { "views": [ { "cell_index": 16 } ] }, "e352613f58414fefbcfa32749a27a9ab": { "views": [ { "cell_index": 3 } ] }, "e6f7e5449c244f1fb9eeb0c18c193744": { "views": [ { "cell_index": 28 } ] }, "f376c4671b164f81b8d819c05d0c7b86": { "views": [ { "cell_index": 18 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

nansencenter/nansat-lectures

notebooks/examples/colorbars.ipynb

1

9509

{ "metadata": { "name": "", "signature": "sha256:3c305ab78eb4a36f2b2953130622bcf7ab568e232a1f9c4652a76edf8cacf9c0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from nansat import *\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib import pyplot\n", "import matplotlib as mpl\n", "\n", "# Make a figure and axes with dimensions as desired.\n", "fig = pyplot.figure(figsize=(8,3))\n", "ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])\n", "ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])\n", "\n", "\n", "# ColorbarBase derives from ScalarMappable and puts a colorbar\n", "# in a specified axes, so it has everything needed for a\n", "# standalone colorbar. There are many more kwargs, but the\n", "# following gives a basic continuous colorbar with ticks\n", "# and labels.\n", "cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=mpl.cm.jet,\n", " norm=mpl.colors.Normalize(vmin=-5, vmax=13),\n", " orientation='horizontal')\n", "cb1.set_label('SST, K')\n", "\n", "\n", "# ColorbarBase derives from ScalarMappable and puts a colorbar\n", "# in a specified axes, so it has everything needed for a\n", "# standalone colorbar. There are many more kwargs, but the\n", "# following gives a basic continuous colorbar with ticks\n", "# and labels.\n", "cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=mpl.cm.bone,\n", " norm=mpl.colors.Normalize(vmin=0, vmax=100),\n", " orientation='horizontal')\n", "cb2.set_label('ice concentration, %')\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAACSCAYAAADl7Kj+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x7f983ddfab90>" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-3.0

rishuatgithub/MLPy

nlp/3. Word Vectors + PCA + Cosine Similarity.ipynb

1

34623

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import pickle\n", "import matplotlib.pyplot as plt\n", "import nltk\n", "\n", "from gensim.models import KeyedVectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading a google embedding" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "## load the word embeddings from the google news vectors. Load it once.\n", "\n", "#embeddings = KeyedVectors.load_word2vec_format('../../Data/GoogleNews-vectors-negative300.bin', binary=True)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "## Building a word embeddings for the small subset that is required in here\n", "\n", "f = open('../../Data/word_vectors/capitals.txt', 'r').read()\n", "set_words = set(nltk.word_tokenize(f))\n", "select_words = words = ['king', 'queen', 'oil', 'gas', 'happy', 'sad', 'city', 'town', \n", " 'village', 'country', 'continent', 'petroleum', 'joyful']\n", "for w in select_words:\n", " set_words.add(w)\n", "\n", "\n", "def get_word_embeddings(embeddings):\n", " '''\n", " Get the word embeddings\n", " '''\n", " word_embeddings = {}\n", " for word in embeddings.vocab:\n", " if word in set_words:\n", " word_embeddings[word] = embeddings[word]\n", " \n", " return word_embeddings" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "243\n" ] } ], "source": [ "word_embeddings = get_word_embeddings(embeddings)\n", "print(len(word_embeddings))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "pickle.dump(word_embeddings, open( \"word_embeddings_subset.p\", \"wb\" ) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load Word embeddings from pickle file" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "243" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word_embeddings = pickle.load(open('word_embeddings_subset.p','rb'))\n", "len(word_embeddings)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "300" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word_embeddings['Spain'].size ## The size of the word embeddings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predict relationships between words" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cosine similarity function is:\n", "$$\\cos (\\theta)=\\frac{\\mathbf{A} \\cdot \\mathbf{B}}{\\|\\mathbf{A}\\|\\|\\mathbf{B}\\|}=\\frac{\\sum_{i=1}^{n} A_{i} B_{i}}{\\sqrt{\\sum_{i=1}^{n} A_{i}^{2}} \\sqrt{\\sum_{i=1}^{n} B_{i}^{2}}}\\tag{1}$$\n", "$A$ and $B$ represent the word vectors and $A_i$ or $B_i$ represent index i of that vector. & Note that if A and B are identical, you will get $cos(\\theta) = 1$.\n", "\n", "- Otherwise, if they are the total opposite, meaning, $A= -B$, then you would get $cos(\\theta) = -1$.\n", "- If you get $cos(\\theta) =0$, that means that they are orthogonal (or perpendicular).\n", "- Numbers between 0 and 1 indicate a similarity score.\n", "- Numbers between -1-0 indicate a dissimilarity score." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def cosine_similarity(A,B):\n", " '''\n", " Returns the cosine similarity between vectors A and B\n", " '''\n", " \n", " d = np.dot(A,B)\n", " norm_a = np.sqrt(np.dot(A,A))\n", " norm_b = np.sqrt(np.dot(B,B))\n", " \n", " cos = d / (norm_a * norm_b)\n", " \n", " return cos" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6510957" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "king = word_embeddings['king']\n", "queen = word_embeddings['queen']\n", "\n", "cosine_similarity(king,queen) ## between 0 and 1 is similar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will now implement a function that computes the similarity between two vectors using the Euclidean distance. Euclidean distance is defined as:\n", "$$ \\begin{aligned} d(\\mathbf{A}, \\mathbf{B})=d(\\mathbf{B}, \\mathbf{A}) =\\sqrt{\\left(A_{1}-B_{1}\\right)^{2}+\\left(A_{2}-B_{2}\\right)^{2}+\\cdots+\\left(A_{n}-B_{n}\\right)^{2}} \\\\ =\\sqrt{\\sum_{i=1}^{n}\\left(A_{i}-B_{i}\\right)^{2}} \\end{aligned}$$\n", "\n", "- $n$ is the number of elements in the vector\n", "- $A$ and $B$ are the corresponding word vectors.\n", "- The more similar the words, the more likely the Euclidean distance will be close to 0." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def euclidean_distance(A,B):\n", " '''\n", " Calculate the euclidean distance between two vectors\n", " '''\n", " \n", " d = np.linalg.norm(A - B)\n", " \n", " return d" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.4796925" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "king = word_embeddings['king']\n", "queen = word_embeddings['queen']\n", "\n", "euclidean_distance(king,queen) ## somewhat similar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finding the capital of the country" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def get_country(city1, country1, city2, embeddings):\n", " '''\n", " Find the most likely country (country2) for a given set of inputs\n", " '''\n", " \n", " city1_embed = embeddings[city1]\n", " country1_embed = embeddings[country1]\n", " city2_embed = embeddings[city2]\n", " \n", " # get embedding of country 2 (it's a combination of the embeddings of country 1, city 1 and city 2)\n", " # Remember: King - Man + Woman = Queen\n", " \n", " vec = country1_embed - city1_embed + city2_embed\n", " \n", " similarity = -1\n", " \n", " country = ''\n", " \n", " group = set((city1, country1, city2))\n", " \n", " ## iterate through all words in the embedding\n", " for word in embeddings.keys():\n", " \n", " if word not in group:\n", " \n", " word_embedd = embeddings[word]\n", " \n", " cos_similarity = cosine_similarity(vec, word_embedd) ## find cos similarity\n", " \n", " if cos_similarity > similarity:\n", " \n", " similarity = cos_similarity\n", " \n", " country = (word, similarity)\n", " \n", " return country" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('Egypt', 0.7626821)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_country('Athens', 'Greece', 'Cairo', word_embeddings)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('Russia', 0.6954342)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_country('London', 'England', 'Moscow', word_embeddings)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Accuracy" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>city1</th>\n", " <th>country1</th>\n", " <th>city2</th>\n", " <th>country2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Athens</td>\n", " <td>Greece</td>\n", " <td>Bangkok</td>\n", " <td>Thailand</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Athens</td>\n", " <td>Greece</td>\n", " <td>Beijing</td>\n", " <td>China</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Athens</td>\n", " <td>Greece</td>\n", " <td>Berlin</td>\n", " <td>Germany</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Athens</td>\n", " <td>Greece</td>\n", " <td>Bern</td>\n", " <td>Switzerland</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Athens</td>\n", " <td>Greece</td>\n", " <td>Cairo</td>\n", " <td>Egypt</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city1 country1 city2 country2\n", "0 Athens Greece Bangkok Thailand\n", "1 Athens Greece Beijing China\n", "2 Athens Greece Berlin Germany\n", "3 Athens Greece Bern Switzerland\n", "4 Athens Greece Cairo Egypt" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## load a sample country data set\n", "\n", "data = pd.read_csv('../../Data/word_vectors/capitals.txt', sep=' ')\n", "data.columns = ['city1', 'country1', 'city2', 'country2']\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "def get_accuracy(data, embeddings):\n", " '''\n", " get the overall accuracy of the word embedding model\n", " '''\n", " \n", " correct = 0\n", " \n", " for i, row in data.iterrows():\n", " \n", " city1 = row['city1']\n", " country1 = row['country1']\n", " city2 = row['city2']\n", " country2 = row['country2']\n", " \n", " predict_country, predict_similarity = get_country(city1, country1, city2, embeddings)\n", " \n", " if predict_country == country2:\n", " correct += 1\n", " \n", " total_data = len(data)\n", " \n", " accuracy = correct / total_data\n", " \n", " return accuracy" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Accuracy : 91.92\n" ] } ], "source": [ "print(f'Model Accuracy : {get_accuracy(data, word_embeddings)*100:10.2f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PCA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you will explore the distance between word vectors after reducing their dimension. The technique we will employ is known as principal component analysis (PCA). As we saw, we are working in a 300-dimensional space in this case. Although from a computational perspective we were able to perform a good job, it is impossible to visualize results in such high dimensional spaces.\n", "\n", "You can think of PCA as a method that projects our vectors in a space of reduced dimension, while keeping the maximum information about the original vectors in their reduced counterparts. In this case, by maximum infomation we mean that the Euclidean distance between the original vectors and their projected siblings is minimal. Hence vectors that were originally close in the embeddings dictionary, will produce lower dimensional vectors that are still close to each other.\n", "\n", "You will see that when you map out the words, similar words will be clustered next to each other. For example, the words 'sad', 'happy', 'joyful' all describe emotion and are supposed to be near each other when plotted. The words: 'oil', 'gas', and 'petroleum' all describe natural resources. Words like 'city', 'village', 'town' could be seen as synonyms and describe a similar thing.\n", "\n", "Before plotting the words, you need to first be able to reduce each word vector with PCA into 2 dimensions and then plot it. The steps to compute PCA are as follows:\n", "- Mean normalize the data\n", "- Compute the covariance matrix of your data ($\\Sigma$).\n", "- Compute the eigenvectors and the eigenvalues of your covariance matrix\n", "- Multiply the first K eigenvectors by your normalized data. The transformation should look something as follows:\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def compute_pca(X, n_components=2):\n", " '''\n", " Compute the PCA\n", " \n", " Input:\n", " X: of dimension (m,n) where each row corresponds to a word vector\n", " n_components: Number of components you want to keep.\n", " Output:\n", " X_reduced: data transformed in 2 dims/columns + regenerated original data\n", " '''\n", " \n", " # mean center the data\n", " X_demeaned = X - np.mean(X,axis=0)\n", " print('X_demeaned.shape: ',X_demeaned.shape)\n", " \n", " # calculate the covariance matrix\n", " covariance_matrix = np.cov(X_demeaned, rowvar=False)\n", " \n", " # calculate eigenvectors & eigenvalues of the covariance matrix\n", " eigen_vals, eigen_vecs = np.linalg.eigh(covariance_matrix, UPLO='L')\n", " \n", " # sort eigenvalue in increasing order (get the indices from the sort)\n", " idx_sorted = np.argsort(eigen_vals)\n", " \n", " # reverse the order so that it's from highest to lowest.\n", " idx_sorted_decreasing = idx_sorted[::-1]\n", " \n", " # sort the eigen values by idx_sorted_decreasing\n", " eigen_vals_sorted = eigen_vals[idx_sorted_decreasing]\n", " \n", " # sort eigenvectors using the idx_sorted_decreasing indices\n", " eigen_vecs_sorted = eigen_vecs[:,idx_sorted_decreasing]\n", " \n", " # select the first n eigenvectors (n is desired dimension\n", " # of rescaled data array, or dims_rescaled_data)\n", " eigen_vecs_subset = eigen_vecs_sorted[:,0:n_components]\n", " \n", " X_reduced = np.dot(eigen_vecs_subset.transpose(),X_demeaned.transpose()).transpose()\n", "\n", " return X_reduced" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[4.17022005e-01 7.20324493e-01 1.14374817e-04 3.02332573e-01\n", " 1.46755891e-01 9.23385948e-02 1.86260211e-01 3.45560727e-01\n", " 3.96767474e-01 5.38816734e-01]\n", " [4.19194514e-01 6.85219500e-01 2.04452250e-01 8.78117436e-01\n", " 2.73875932e-02 6.70467510e-01 4.17304802e-01 5.58689828e-01\n", " 1.40386939e-01 1.98101489e-01]\n", " [8.00744569e-01 9.68261576e-01 3.13424178e-01 6.92322616e-01\n", " 8.76389152e-01 8.94606664e-01 8.50442114e-02 3.90547832e-02\n", " 1.69830420e-01 8.78142503e-01]]\n", "X_demeaned.shape: (3, 10)\n", "Your original matrix was (3, 10) and it became:\n", "[[ 0.43437323 0.49820384]\n", " [ 0.42077249 -0.50351448]\n", " [-0.85514571 0.00531064]]\n" ] } ], "source": [ "np.random.seed(1)\n", "X = np.random.rand(3, 10)\n", "print(X)\n", "X_reduced = compute_pca(X, n_components=2)\n", "print(\"Your original matrix was \" + str(X.shape) + \" and it became:\")\n", "print(X_reduced)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "def get_vectors(embeddings, words):\n", " \"\"\"\n", " Input:\n", " embeddings: a word \n", " fr_embeddings:\n", " words: a list of words\n", " Output: \n", " X: a matrix where the rows are the embeddings corresponding to the rows on the list\n", " \n", " \"\"\"\n", " m = len(words)\n", " X = np.zeros((1, 300))\n", " for word in words:\n", " english = word\n", " eng_emb = embeddings[english]\n", " X = np.row_stack((X, eng_emb))\n", " X = X[1:,:]\n", " return X" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You have 11 words each of 300 dimensions thus X.shape is: (11, 300)\n" ] } ], "source": [ "words = ['oil', 'gas', 'happy', 'sad', 'city', 'town',\n", " 'village', 'country', 'continent', 'petroleum', 'joyful']\n", "\n", "# given a list of words and the embeddings, it returns a matrix with all the embeddings\n", "X = get_vectors(word_embeddings, words)\n", "\n", "print('You have 11 words each of 300 dimensions thus X.shape is:', X.shape)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_demeaned.shape: (11, 300)\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAD7CAYAAAC8GzkWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deXxV1bn/8c9DQIhCiQoiQSUOiAwJBMIgo0oBtSiDaFGkRFSkrXJ7+zO3eGkrIl690lblp9WrVUDJlSFCxDrggFQQrCQSZCoVLSgEIYLhBxIEw/P7I4cYYiY45+ScJN/363Ve2cPaez17E/Kcvfbaa5u7IyIiEk71Ih2AiIjUfko2IiISdko2IiISdko2IiISdko2IiISdko2IiISdko2gJklmNn6SMchIlJbKdmIiEjYWTQ/1NmsWTNPSEiocvnCwkI+++wzjhw5grvTsmVLDh06xL59+zh69CiNGzfmvPPOw8z45ptv2LZtG/Xq1aNx48bs27ePDh06hO9gRESqQXZ29lfu3jzScZRWP9IBVCQhIYGsrKwql3/ppZd44403eOaZZwDYt28fhYWFnHHGGQCMGTOGG264gWuuuYakpCRef/11+vfvT1paGq+//voJ1SUiEo3MbFukYyhLrWpGS0xM5O233+Y3v/kNy5cvp2nTprz77rv06NGDxMREli5dyoYNG9i3bx/5+fn0798fKEpCIiISPlF9ZXOiLr74YrKzs3nttde45557GDRoEE888QRZWVmce+65TJkyhUOHDuHumFmkwxURqTNq1ZVNbm4up556KjfffDN33303H330EQDNmjXjwIEDZGRkABAXF0fTpk1ZsWIFAOnp6RGLWUSkLqhVVzbr1q0jLS2NevXq0aBBA5588kkyMzNJTEwkISGBbt26FZedOXMm48aN49RTT2Xw4MERjFpEpPaL6t5oKSkprpv2IiJVZ2bZ7p4S6ThKq1XNaCIiEp1qVTPaMZlrdjB9yWZy8wuIj4slbXBbhiW3inRYIiJ1Vq1LNplrdnDPwnUUHCkEYEd+AfcsXAeghCMiEiG1rhlt+pLNxYnmmIIjhUxfsjlCEYmISK1LNrn5BSe0XEREwq/WJZv4uNgTWi4iIuFX65JN2uC2xDaIOW5ZbIMY0ga3jVBEIiJS6zoIHOsEoN5oIiLRo9YlGyhKOEouIiLRo9Y1o4mISPRRshERkbBTshERkbCrs8mmV69eJ7XdjTfeSFJSEo888ki5ZZYtW8aQIUNONjQRkVqnVnYQqIqVK1ee8DZffvklK1euZNu2qHzrqohI1KqzVzaNGzfG3UlLS6Njx44kJiYyb948oOg10S+//HJx2dGjR7N48WIGDRrE7t276dy5M8uXL+eyyy7j2CsQvvrqKxISEiJxKCIiUa/OXtkALFy4kJycHNauXctXX31Ft27d6NevH7fddhuPPPIIQ4cOZd++faxcuZLZs2eTlJTEkCFDyMnJiXToIiI1SkiubMzsOTPbbWbry1l/mZntM7OcwOf3oag3WCtWrODGG28kJiaGFi1a0L9/f1avXk3//v3ZsmULu3fv5sUXX+S6666jfv06nZdFRIISqr+gs4DHgecrKLPc3aPqrnlFbykdM2YM6enpzJ07l+eee67MMvXr1+fo0aMAHDp0KCwxiojUBiG5snH394C9odhXderXrx/z5s2jsLCQvLw83nvvPbp37w5Aamoqjz76KAAdOnQoc/uEhASys7MByMjIqJ6gRURqoOrsIHCpma01s9fNrOy/3oCZjTezLDPLysvLC1swZsbw4cNJSkqiU6dOXHHFFTz88MOcffbZALRo0YJ27dpxyy23lLuPu+++myeffJJevXrx1VdfhS1WEZGazipqSjqhHZklAH91945lrPsRcNTdD5jZ1cBj7t6msn2mpKT4sd5eobRnzx66dOlS3IV569atDBkyhPXrv7/ldPDgQRITE/noo49o2rRpyGMQEQkHM8t295RIx1FatVzZuPv/c/cDgenXgAZm1qw66i4tNzeXSy+9lLvvvrvcMm+//TaXXHIJd911lxKNiEgIVEsXKzM7G9jl7m5m3SlKcnuqo+7S4uPj+ec//wlA5podTF+ymW3btrJ3934GjriJ3M1radWqFZs3b2bOnDl069aNw4cPc9FFF/HCCy9w6qmnkpqaSqNGjdiwYQO7du3iT3/6E0OGDGHWrFksWrSIb7/9ln/961/cdNNN3Hvvvfzud7+jWbNm/Nu//RsAkydPpkWLFkycODESp0BEpNqFquvzi8AqoK2ZbTezW81sgplNCBQZCaw3s7XADGCUh6r97iRlrtnBPQvXsSPwuuiCr7az5czePDDnTeLi4njppZcYMWIEq1evZu3atbRr145nn322ePutW7fyt7/9jVdffZUJEyYU90b78MMPSU9PJycnhwULFpCVlcWtt97K7NmzATh69Chz585l9OjR1X/QIiIREpIrG3e/sZL1j1PUNTpqTF+ymYIjhcXz9eNa4GcmMH3JZq7t2pWtW7eyfv16fvvb35Kfn8+BAwcYPHhwcfkbbriBevXq0aZNGy644AL+8Y9/ADBw4EDOPPNMAEaMGMGKFSv41a9+xZlnnsmaNWvYtWsXycnJxWVEROqCOvukYm7giuYYi2lQvDymWQwFBQWkpqaSmZlJp06dmDVrFsuWLfu+vNnx2wfmy1t+2223MWvWLL788kvGjRsX6sMREYlqdXZstPi42EqX79+/n5YtW3LkyBHS09OPK7dgwQKOHj3Kp59+ymeffUbbtm0BeOutt9i7dy8FBQVkZmbSu3dvAIYPH84bb7zB6tWrj7tCEhGpC+rslU3a4Lbcs3DdcU1psQ1iSBvcli3vfATA/fffT48ePWjdujWJiYns37+/uGzbtm3p378/u3bt4qmnnqJRo0YA9OnThzFjxrBlyxZuuukmUlKKeiCecsopXH755cTFxRETE1ONRyoiEnl1NtkMS24FFN27yaUF3f7PTNIGty1anvx9t+if//znZW7fu3fvMt9pc9ZZZ/H44z+8PXX06FE++OADFixYEKIjEBGpOepssoGihHMs6YTTxo0bGTJkCMOHD6dNm0qfZRURqXVCNoJAOIRrBIGyHHvmJje/gPi42O+vckREapBoHUGgTl/ZHHPsmZtj92925Bdwz8J1AEo4IiIhUGd7o5VU+pkbgIIjhUxfsjlCEYmI1C5KNvzwmZvKlouIyIlRsqFqz9yIiMjJU7Kh6Jmb2AbHP/ty7JkbEREJnjoIUOqZG/VGExEJOSWbgOp65kZEpC5SM5qIiISdko2IiISdko2IiISdko2IiISdko2IiISdko2IiISdko2IiIRdSJKNmT1nZrvNbH05683MZpjZFjP72My6hKJeERGpGUJ1ZTMLuLKC9VcBbQKf8cCTIapXRERqgJAkG3d/D9hbQZGhwPNe5AMgzsxahqJuERGJftV1z6YV8EWJ+e2BZT9gZuPNLMvMsvLy8qolOBERCa/qSjZWxrIy30ft7k+7e4q7pzRv3jzMYYmISHWormSzHTi3xPw5QG411S0iIhFWXclmMfCzQK+0nsA+d99ZTXWLiEiEheQVA2b2InAZ0MzMtgP3Ag0A3P0p4DXgamALcBC4JRT1iohIzRCSZOPuN1ay3oFfhqIuERGpeTSCgIiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhJ2SjYiIhF1Iko2ZXWlmm81si5lNKmN9qpnlmVlO4HNbKOoVEZGaoX6wOzCzGOAJYCCwHVhtZovdfWOpovPc/c5g6xMRkZonFFc23YEt7v6Zux8G5gJDQ7BfEREpw6xZs8jNzT3h7cxslpmNDENIlQpFsmkFfFFifntgWWnXmdnHZpZhZueWtzMzG29mWWaWlZeXF4LwRERql5NNNpEUimRjZSzzUvOvAAnungS8Dcwub2fu/rS7p7h7SvPmzUMQnohIdNu6dSuXXHIJY8eOJSkpiZEjR3Lw4EGys7Pp378/Xbt2ZfDgwezcuZOMjAyysrIYPXo0nTt3pqCggISEBKZOnUqfPn0ATjezzmb2QeAL/iIzO710nWbW1cz+ZmbZZrbEzFoGli8zs5TAdDMz2xqYTjWzTDN7xcz+ZWZ3mtmvzWxNoK4zKjrGUCSb7UDJK5VzgONSrrvvcfdvA7PPAF1DUK+ISK2xefNmxo8fz8cff8yPfvQjnnjiCe666y4yMjLIzs5m3LhxTJ48mZEjR5KSkkJ6ejo5OTnExsYC0KhRI1asWAHwNfA88JvAF/x1wL0l6zKzBsD/BUa6e1fgOeCBKoTZEbiJotsnDwAH3T0ZWAX8rKINg+4gAKwG2pjZ+cAOYFQgmGJm1tLddwZmrwU2haBeEZGo0atXL1auXMnWrVsZMmQI69evP6Htzz33XHr37g3AzTffzH/913+xfv16Bg4cCEBhYSEtW7Ysd/uf/vSnxyZjgDh3/1tgfjawoFTxthQljrfM7Ng2O6ncu+6+H9hvZvsoarWCooSWVNGGQScbd//OzO4EllAU8HPuvsHMpgJZ7r4YmGhm1wLfAXuB1GDrFRGJJitXrgxq+8Af/WJNmjShQ4cOrFq1qkrbn3baaSdUHbDB3S8tY913fN/q1ajUum9LTB8tMX+USvJJSJ6zcffX3P1id7/Q3R8ILPt9INHg7ve4ewd37+Tul7v7P0JRr4hIJPzpT3+iY8eOdOzYkUcffRSAxo0bB7XPzz//vDixvPjii/Ts2ZO8vLziZUeOHGHDhg1AUSLav39/ebsqBL42s76B+THA30qV2Qw0N7NLoahZzcw6BNZt5ftbHSHruRaKZjQRkTojOzubmTNn8ve//x13p0ePHvTv3z/o/bZr147Zs2dzxx130KZNG+666y4GDx7MxIkT2bdvH9999x2/+tWv6NChA6mpqUyYMIHY2NjyrnzGAk+Z2anAZ8AtJVe6++FAF+gZZtaUolzwKLAB+AMw38zGAEuDPrAAcy/dcSx6pKSkeFZWVqTDEBEp9thjj7Fnzx6mTp0KwO9+9zuaN2/Of/7nf3LgwIGTumdzsvd5ymJm2e6eEvSOQkxjo4mInIBo/oIezZRsREROQL9+/cjMzOTgwYN88803LFq0iL59+1a+YQUSEhKY9sISej+0lPMnvUrvh5aSuWZHiCKODrpnIyJyArp06UJqairdu3cH4LbbbiM5OTmofWau2cE9C9dRcKQQgB35BdyzcB0Aw5LLGpCl5tE9GxGRCOv90FJ25Bf8YHmruFjen3TFCe1L92xERKRMuWUkmoqW10RqRhMROQmZa3YwfclmcvMLiI+LJW1w25Nu8oqPiy3zyiY+LjbYMKOGrmxERE7QsXssO/ILcL6/x3KyN/XTBrcltkHMcctiG8SQNrhtCKKNDko2IiInaPqSzcU3848pOFLI9CWbT2p/w5Jb8eCIRFrFxWIU3at5cERirekcAGpGE5E64P777yc9PZ1zzz2XZs2a0bVrV5o2bcrTTz/N4cOHueiii3jhhRc49dRTWbBgAffddx8xMTE0bdqU99577wf7C8c9lmHJrWpVcilNVzYiUqtlZWXx0ksvsWbNGhYuXMixHq4jRoxg9erVrF27lnbt2vHss88CMHXqVJYsWcLatWtZvHhxmfss715KbbrHEmpKNiJSq61YsYKhQ4cSGxtLkyZNuOaaawBYv349ffv2JTExkfT09OJBLnv37k1qairPPPMMhYWFZe6zLtxjCTUlGxGp1cp7ljA1NZXHH3+cdevWce+993Lo0CEAnnrqKaZNm8YXX3xB586d2bNnzw+2rQv3WEJNyUZEarU+ffrwyiuvcOjQIQ4cOMCrr74KwP79+2nZsiVHjhwhPT29uPynn35Kjx49mDp1Ks2aNeOLL74oc7/Dklvx/qQr+NdDP+H9SVco0VRCHQREpFbr1q0b1157LZ06daJ169akpKTQtGlT7r//fnr06EHr1q1JTEwsfj9MWloan3zyCe7OgAED6NSpU4SPoHbQcDUiUusdOHCAxo0bc/DgQfr168fTTz9Nly5dIh1WWETrcDW6shGRWm/8+PFs3LiRQ4cOMXbs2FqbaKKZrmxEpE4I5fAy0UxXNiIiEVIXhvCPdiHpjWZmV5rZZjPbYmaTyljf0MzmBdb/3cwSQlGviEhVhHp4GTlxQScbM4sBngCuAtoDN5pZ+1LFbgW+dveLgEeA/w62XhGRqqoLQ/hHu1Bc2XQHtrj7Z+5+GJgLDC1VZigwOzCdAQwwMwtB3SIildLwMpEXimTTCij51NP2wLIyy7j7d8A+4MyydmZm480sy8yy8vLyQhCeiNR1Gl4m8kKRbMq6Qindxa0qZYoWuj/t7inuntK8efOggxMR0fAykReK3mjbgXNLzJ8D5JZTZruZ1QeaAntDULeISJXU9iH8o10ormxWA23M7HwzOwUYBZQel3sxMDYwPRJY6tH8gI+IiIRU0Fc27v6dmd0JLAFigOfcfYOZTQWy3H0x8CzwgpltoeiKZlSw9YqISM0Rkoc63f014LVSy35fYvoQcH0o6hIRkZpHrxgQEZGwU7IREZGwU7IREZGwU7IREZGwU7IREZGwU7IREZGwU7KRkHv00Uc5ePBgpMMQkSiiZCMhV1GyKSwsLHO5iNRuSjZ11PPPP09SUhKdOnVizJgxbNu2jQEDBpCUlMSAAQP4/PPPAUhNTSUjI6N4u8aNGwOwbNkyLrvsMkaOHMkll1zC6NGjcXdmzJhBbm4ul19+OZdffnnxNr///e/p0aMH06ZNY/jw4cX7e+uttxgxYkQ1HrmIRIS7R+2na9euLqG3fv16v/jiiz0vL8/d3ffs2eNDhgzxWbNmubv7s88+60OHDnV397Fjx/qCBQuKtz3ttNPc3f3dd9/1H/3oR/7FF194YWGh9+zZ05cvX+7u7q1bty7et7s74PPmzXN396NHj3rbtm199+7d7u5+4403+uLFi8N8xCJ1B0XDhEX873fpj65s6qClS5cycuRImjVrBsAZZ5zBqlWruOmmmwAYM2YMK1asqHQ/3bt355xzzqFevXp07tyZrVu3llkuJiaG6667DgAzY8yYMcyZM4f8/HxWrVrFVVddFZoDE5GoFZKx0aRmcXcqe1HqsfX169fn6NGjxdsdPny4uEzDhg2Lp2NiYvjuu+/K3FejRo2Iifn+xVW33HIL11xzDY0aNeL666+nfn39GorUdrqyqYMGDBjA/Pnz2bNnDwB79+6lV69ezJ07F4D09HT69OkDQEJCAtnZ2QC8/PLLHDlypNL9N2nShP3795e7Pj4+nvj4eKZNm0ZqamqQRyMiNYG+UtZBHTp0YPLkyfTv35+YmBiSk5OZMWMG48aNY/r06TRv3pyZM2cCcPvttzN06FC6d+/OgAEDOO200yrd//jx47nqqqto2bIl7777bpllRo8eTV5eHu3btw/psYlIdDKP4neYpaSkeFZWVqTDkDC48847SU5O5tZbb410KCK1ipllu3tKpOMoTc1oEjZbt27lf//3f4vns7KymDhxIl27duXjjz/m5ptvDkk9mZmZbNy4MST7EpHwUDNaHZa5ZgfTl2wmN7+A+LhY0ga3Dek72o8lm2O93FJSUkhJCf0XrszMTIYMGaImOZEopiubOipzzQ7uWbiOHfkFOLAjv4B7Fq4jc82O4jIn8uDnxIkT6dWrFxdccEHxQ6CTJk1i+fLldO7cmUceeYRly5YxZMgQAKZMmcK4ceO47LLLuOCCC5gxY0ZxvXPmzKF79+507tyZO+64o3jUgcaNGzN58mQ6depEz5492bVrFytXrmTx4sWkpaXRuXNnPv3002o6gyJyIpRs6qjpSzZTcOT4oWMKjhQyfclmADZs2MADDzzA0qVLWbt2LY899hh33nknP/vZz/j4448ZPXo0EydOLN52586drFixgr/+9a9MmjQJgIceeoi+ffuSk5PDv//7v/8ghn/84x8sWbKEDz/8kPvuu48jR46wadMm5s2bx/vvv09OTg4xMTGkp6cD8M0339CzZ0/Wrl1Lv379eOaZZ+jVqxfXXnst06dPJycnhwsvvDBcp0xEgqBmtDoqN7+gwuXlPfi5cOFCoOjBz//4j/8o3m7YsGHUq1eP9u3bs2vXrirF8JOf/ISGDRvSsGFDzjrrLHbt2sU777xDdnY23bp1A6CgoICzzjoLgFNOOaX4yqhr16689dZbJ3HkIhIJQSUbMzsDmAckAFuBG9z96zLKFQLrArOfu/u1wdQrwYuPi2VHGQknPi4WOLEHP+H4Bzyr2sOxrIdC3Z2xY8fy4IMP/qB8gwYNiuus6CFSEYk+wTajTQLecfc2wDuB+bIUuHvnwEeJJgqkDW5LbIOY45bFNoghbXBb4MQe/CxPZQ93lmXAgAFkZGSwe/fu4nq3bdsW8npEpHoFm2yGArMD07OBYUHuT6rJsORWPDgikVZxsRjQKi6WB0ckFvdGK/ngZ6dOnfj1r3/NjBkzmDlzJklJSbzwwgs89thjFdaRlJRE/fr16dSpE4888kiV4mrfvj3Tpk1j0KBBJCUlMXDgQHbu3FnhNqNGjWL69OkkJyerg4BIlArqoU4zy3f3uBLzX7v76WWU+w7IAb4DHnL3zAr2OR4YD3Deeed1rexbrYiIfC9aH+qs9J6Nmb0NnF3GqsknUM957p5rZhcAS81snbuX+RXU3Z8GnoaiEQROoA4REYlSlSYbd/9xeevMbJeZtXT3nWbWEthdzj5yAz8/M7NlQDKg9o4aINwPfopI3RDsPZvFwNjA9Fjg5dIFzOx0M2sYmG4G9AY0tkgNUJUHP0VEqiLYZPMQMNDMPgEGBuYxsxQz+0ugTDsgy8zWAu9SdM9GyaYGqOzBTxGRqgrqORt33wMMKGN5FnBbYHolkBhMPRIZlT34KSJSVRquRsp17AHPqi4XESmPko2Uq7IHP0VEqkpjo0m5jvU6U280EQmWko1UaFhyKyUXEQmamtFERCTslGxERCTslGxERCTslGxERCTslGxERCTslGxERCTslGxERCTslGxERCTslGxERCTslGxERCTslGxERCTslGxERCTslGxqiKeeeornn38egFmzZpGbmxvhiEREqk6jPtcQEyZMKJ6eNWsWHTt2JD4+PoIRiYhUnZJNlHr++ef5wx/+gJmRlJTEhRdeSOPGjUlISCArK4vRo0cTGxvLAw88wF/+8hcWLVoEwFtvvcWTTz7JwoULI3wEIiLfUzNaFNqwYQMPPPAAS5cuZe3atTz22GPF60aOHElKSgrp6enk5ORw9dVXs2nTJvLy8gCYOXMmt9xyS6RCFxEpU1DJxsyuN7MNZnbUzFIqKHelmW02sy1mNimYOuuCpUuXMnLkSJo1awbAGWecUW5ZM2PMmDHMmTOH/Px8Vq1axVVXXVVdoYqIVEmwzWjrgRHA/5RXwMxigCeAgcB2YLWZLXb3jUHWXWu5O2ZW5fK33HIL11xzDY0aNeL666+nfn21jopIdAnqysbdN7n75kqKdQe2uPtn7n4YmAsMDabe2m7AgAHMnz+fPXv2ALB3797j1jdp0oT9+/cXz8fHxxMfH8+0adNITU2tzlBFRKqkOr4CtwK+KDG/HehRDfXWWB06dGDy5Mn079+fmJgYkpOTSUhIKF6fmprKhAkTiI2NZdWqVcTGxjJ69Gjy8vJo37595AIXESlHpcnGzN4Gzi5j1WR3f7kKdZTVHuQV1DceGA9w3nnnVWH3tdPYsWMZO3Zsmeuuu+46rrvuuuOWrVixgttvv706QhMROWGVJht3/3GQdWwHzi0xfw5Q7hOJ7v408DRASkpKuUlJvte1a1dOO+00/vjHP0Y6FBGRMlVHM9pqoI2ZnQ/sAEYBN1VDvTVa5podTF+ymdz8AuLjYkkb3JZhya3KLJudnV3N0YmInJhguz4PN7PtwKXAq2a2JLA83sxeA3D374A7gSXAJmC+u28ILuzaLXPNDu5ZuI4d+QU4sCO/gHsWriNzzY5IhyYiclKC7Y22yN3PcfeG7t7C3QcHlue6+9Ulyr3m7he7+4Xu/kCwQUeD/Px8/vznP4dl39OXbKbgSOFxywqOFDJ9SWUd/0REopNGEDhJ4Uw2ufkFJ7RcRCTaKdmcpEmTJvHpp5/SuXNn0tLSSEtLo2PHjiQmJjJv3jwAfvGLX7B48WIAhg8fzrhx4wB49tln+e1vf8vWrVtp164dt99+Ox06dGDQoEEUFBTdoylLectFRKKdks1Jeuihh7jwwgvJycmhZ8+e5OTksHbtWt5++23S0tLYuXMn/fr1Y/ny5QDs2LGDjRuLBk1YsWIFffv2BeCTTz7hl7/8JRs2bCAuLo6XXnqJtMFtiW0Qc1x9sQ1iSBvctnoPUkQkRJRsQmDFihXceOONxMTE0KJFC/r378/q1avp27cvy5cvZ+PGjbRv354WLVqwc+dOVq1aRa9evQA4//zz6dy5M1DUhXnr1q0MS27FgyMSaRUXiwGt4mJ5cERiub3RRESinQbRCgH3sh8HatWqFV9//TVvvPEG/fr1Y+/evcyfP5/GjRvTpEkT9uzZQ8OGDYvLx8TEUFBQdF9mWHIrJRcRqTV0ZXOSSo5P1q9fP+bNm0dhYSF5eXm89957dO/eHYBLL72URx99lH79+tG3b1/+8Ic/FDehiYjUFbqyOUlnnnkmvXv3pmPHjlx11VUkJSXRqVMnzIyHH36Ys88uGuGnb9++vPnmm1x00UW0bt2avXv3KtmISJ1j5TUBRYOUlBTPysqKdBgiIjWGmWW7e7nvF4sUNaOJiEjYqRktSCcyhpmISF2lZBOEY2OYHRta5tgYZoASjohICWpGC4LGMBMRqRolmyBoDDMRkapRsgmCxjATEakaJZsgaAwzEZGqUQeBIBzrBKDeaCIiFVOyCZLGMBMRqZya0SqRm5vLyJEjAVi2bBlDhgwBYNasWdx5552RDE1EpMZQsqlEfHw8GRkZkQ5DRGmLmiwAAAgCSURBVKRGU7Ip4Te/+c1xr3qeMmUKf/zjH+nYsWOF273yyiv06NGD5ORkfvzjH7Nr1y4A8vLyGDhwIF26dOGOO+6gdevWfPXVVwDMmTOH7t2707lzZ+644w4KCwsrqkJEpEZTsilh1KhRxa90Bpg/fz7dunWrdLs+ffrwwQcfsGbNGkaNGsXDDz8MwH333ccVV1zBRx99xPDhw/n8888B2LRpE/PmzeP9998nJyeHmJgY0tPTw3NQIiJRIKgOAmZ2PTAFaAd0d/cyh2g2s63AfqAQ+C4aRyQFSE5OZvfu3eTm5pKXl8fpp5/OeeedV+l227dv56c//Sk7d+7k8OHDnH/++UDRGzwXLVoEwJVXXsnpp58OwDvvvEN2dnZxIisoKOCss84K01GJiEResL3R1gMjgP+pQtnL3f2rIOsLu5EjR5KRkcGXX37JqFGjqrTNXXfdxa9//WuuvfZali1bxpQpU4Dy3+Dp7owdO5YHH3wwVGGLiES1oJrR3H2Tu9eqgcBGjRrF3LlzycjIKO6FVpl9+/bRqlVR9+fZs2cXL+/Tpw/z588H4M033+Trr78GYMCAAWRkZLB7924A9u7dy7Zt20J5GCIiUaW67tk48KaZZZvZ+IoKmtl4M8sys6y8vLxqCu97HTp0YP/+/bRq1YqWLVtWaZspU6Zw/fXX07dvX5o1a1a8/N577+XNN9+kS5cuvP7667Rs2ZImTZrQvn17pk2bxqBBg0hKSmLgwIHs3LkzXIckIhJxlb6p08zeBs4uY9Vkd385UGYZcHcF92zi3T3XzM4C3gLucvf3Kguupr+p89tvvyUmJob69euzatUqfv7zn5OTkxPpsESkFovWN3VWes/G3X8cbCXunhv4udvMFgHdgUqTTU33+eefc8MNN3D06FFOOeUUnnnmmUiHJCISEWEfrsbMTgPqufv+wPQgYGq46w1GqN6+2aZNG9asWROGCEVEapag7tmY2XAz2w5cCrxqZksCy+PN7LVAsRbACjNbC3wIvOrubwRTbzgde/vmjvwCnO/fvpm5ZkekQxMRqbGCurJx90XAojKW5wJXB6Y/AzoFU091qujtmxpwU0Tk5GgEgVL09k0RkdBTsilFb98UEQk9JZtS9PZNEZHQ08vTStHbN0VEQk/Jpgx6+6aISGipGU1ERMJOyUZERMJOyUZERMJOyUZERMJOyUZERMJOyUZERMKu0vfZRJKZ5QGReoVlMyDqX2MdUJNihZoVr2INn5oUb02KtbW7N490EKVFdbKJJDPLisYXEJWlJsUKNStexRo+NSnemhRrtFIzmoiIhJ2SjYiIhJ2STfmejnQAJ6AmxQo1K17FGj41Kd6aFGtU0j0bEREJO13ZiIhI2CnZiIhI2CnZBJjZ9Wa2wcyOmlm5XRzN7Eoz22xmW8xsUnXGWCKGM8zsLTP7JPDz9HLKFZpZTuCzuJpjrPA8mVlDM5sXWP93M0uozvjKiKeyeFPNLK/E+bwtEnEGYnnOzHab2fpy1puZzQgcy8dm1qW6YywRS2WxXmZm+0qc199Xd4wlYjnXzN41s02BvwX/VkaZqDm3NY6761N036od0BZYBqSUUyYG+BS4ADgFWAu0j0CsDwOTAtOTgP8up9yBCJ3LSs8T8AvgqcD0KGBeBP/tqxJvKvB4pGIsFUs/oAuwvpz1VwOvAwb0BP4exbFeBvw10uc0EEtLoEtgugnwzzJ+D6Lm3Na0j65sAtx9k7tvrqRYd2CLu3/m7oeBucDQ8Ef3A0OB2YHp2cCwCMRQkaqcp5LHkAEMMDOrxhhLipZ/1ypx9/eAvRUUGQo870U+AOLMrGX1RHe8KsQaNdx9p7t/FJjeD2wCSr9FMWrObU2jZHNiWgFflJjfzg9/GatDC3ffCUX/QYCzyinXyMyyzOwDM6vOhFSV81Rcxt2/A/YBZ1ZLdD9U1X/X6wJNJxlmdm71hHZSouX3tKouNbO1Zva6mXWIdDAAgWbdZODvpVbVtHMbNerUa6HN7G3g7DJWTXb3l6uyizKWhaXveEWxnsBuznP3XDO7AFhqZuvc/dPQRFihqpynajuXVVCVWF4BXnT3b81sAkVXZVeEPbKTE03ntjIfUTSW1wEzuxrIBNpEMiAzawy8BPzK3f9f6dVlbBKt5zaq1Klk4+4/DnIX24GS32jPAXKD3GeZKorVzHaZWUt33xm4hN9dzj5yAz8/M7NlFH1Tq45kU5XzdKzMdjOrDzQlcs0tlcbr7ntKzD4D/Hc1xHWyqu33NFgl/5i7+2tm9mcza+buERn00swaUJRo0t19YRlFasy5jTZqRjsxq4E2Zna+mZ1C0Y3tau3lFbAYGBuYHgv84KrMzE43s4aB6WZAb2BjNcVXlfNU8hhGAks9cAc2AiqNt1S7/LUUtedHq8XAzwI9p3oC+441u0YbMzv72L06M+tO0d+kPRVvFbZYDHgW2OTufyqnWI05t1En0j0UouUDDKfoW8u3wC5gSWB5PPBaiXJXU9RL5VOKmt8iEeuZwDvAJ4GfZwSWpwB/CUz3AtZR1LNqHXBrNcf4g/METAWuDUw3AhYAW4APgQsi/O9fWbwPAhsC5/Nd4JIIxvoisBM4EvidvRWYAEwIrDfgicCxrKOc3pVREuudJc7rB0CvCMbah6ImsY+BnMDn6mg9tzXto+FqREQk7NSMJiIiYadkIyIiYadkIyIiYadkIyIiYadkIyIiYadkIyIiYadkIyIiYff/AUoxqOc/spsSAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We have done the plotting for you. Just run this cell.\n", "result = compute_pca(X, 2)\n", "plt.scatter(result[:, 0], result[:, 1])\n", "for i, word in enumerate(words):\n", " plt.annotate(word, xy=(result[i, 0] - 0.05, result[i, 1] + 0.1))\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 4 }

apache-2.0

daniestevez/jupyter_notebooks

Tianwen/orbit/Tianwen-1 Zhurong passes.ipynb

1

323011

{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "21a3ed62", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from astropy.time import Time \n", "plt.rcParams['figure.figsize'] = (12, 6)\n", "plt.rcParams['figure.facecolor'] = 'w'" ] }, { "cell_type": "code", "execution_count": 2, "id": "a39665c2", "metadata": {}, "outputs": [], "source": [ "mjd_unixtimestamp_offset = 10587.5\n", "seconds_in_day = 3600 * 24\n", "\n", "def mjd2unixtimestamp(m):\n", " return (m - mjd_unixtimestamp_offset) * seconds_in_day" ] }, { "cell_type": "code", "execution_count": 4, "id": "97d347ba", "metadata": {}, "outputs": [], "source": [ "def process_report(path, near_start=None, near_end=None):\n", " report = np.fromfile(path, sep=' ').reshape((-1, 4))\n", " t = Time(mjd2unixtimestamp(report[:, 0]), format='unix')\n", " elev = np.rad2deg(np.arctan(report[:, 3] / np.sqrt(report[:, 1]**2 + report[:, 2]**2)))\n", " az_rad = np.arctan2(report[:,2], -report[:,1])\n", " dist = np.sqrt(np.sum(report[:, 1:4]**2, axis=1))\n", " visible = elev >= 0\n", " elev_masked = elev.copy()\n", " elev_masked[~visible] = np.nan\n", " az_rad_masked = az_rad.copy()\n", " az_rad_masked[~visible] = np.nan\n", " dist_masked = dist.copy()\n", " dist_masked[~visible] = np.nan\n", " sel_near = np.ones(t.size, dtype='bool')\n", " if near_start is not None:\n", " sel_near = sel_near & (t.datetime >= near_start)\n", " if near_end is not None:\n", " sel_near = sel_near & (t.datetime <= near_end)\n", " \n", " for near in [False, True]:\n", " plt.figure()\n", " sel = sel_near if near else np.ones(t.size, dtype='bool')\n", " plt.plot(t.datetime[sel], elev_masked[sel])\n", " if not near:\n", " plt.xlim((t.datetime[0], t.datetime[-1]))\n", " plt.ylabel('Elevation (deg)', color='C0')\n", " plt.xlabel('UTC time')\n", " ax2 = plt.gca().twinx()\n", " ax2.plot(t.datetime[sel], dist_masked[sel], color='C1')\n", " ax2.set_ylabel('Distance (km)', color='C1')\n", " title = (\n", " 'Tianwen-1 elevation and distance from Zhurong on periapsis pass'\n", " if near\n", " else 'Tianwen-1 elevation and distance from Zhurong over a Mars sidereal day')\n", " plt.title(title)\n", " \n", " plt.figure()\n", " ax = plt.subplot(1, 1, 1, polar=True)\n", " ax.plot(-az_rad_masked[~sel_near] + np.pi/2, 90 - elev_masked[~sel_near],\n", " label='Apoapsis passes')\n", " ax.plot(-az_rad_masked[sel_near] + np.pi/2, 90 - elev_masked[sel_near],\n", " label='Periapsis pass')\n", " ax.set_xticks(np.arange(0, 2*np.pi, np.pi/4))\n", " ax.set_xticklabels([f'{i}°' for i in np.arange(90, -360 + 90, -45) % 360])\n", " ax.set_yticks(range(0, 90, 15))\n", " ax.set_yticklabels([f'{i}°' for i in range(90, 0, -15)]);\n", " ax.set_ylim([0,90])\n", " plt.legend()\n", " plt.title('Skyplot of Tianwen-1 as viewed from Zhurong')" ] }, { "cell_type": "code", "execution_count": 5, "id": "c89fc4ba", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x432 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x432 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "process_report('ZhurongTopoReportJune.txt',\n", " near_end=np.datetime64('2021-06-03T12:00'))" ] }, { "cell_type": "code", "execution_count": 6, "id": "342c45a8", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x432 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x432 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "process_report('ZhurongTopoReportSeptember.txt',\n", " near_start=np.datetime64('2021-09-07T21:00'),\n", " near_end=np.datetime64('2021-09-07T22:00'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 5 }

gpl-3.0

ml6973/Course

assignment/Suresh-Rengan/assign3.ipynb

2

492307

{ "cells": [ { "cell_type": "code", "execution_count": 370, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import random\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 371, "metadata": { "collapsed": false }, "outputs": [], "source": [ "random.seed(123)\n", "# Display plots inline \n", "%matplotlib inline\n", "# Define plot's default figure size\n", "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)" ] }, { "cell_type": "code", "execution_count": 372, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Feature1 Feature2 Target\n", "0 2.067788 0.258133 1\n", "1 0.993994 -0.609145 1\n", "2 -0.690315 0.749921 0\n", "3 1.023582 0.529003 0\n", "4 0.700747 -0.496724 1\n", "\n", "[5 rows x 3 columns]\n", "(2, 500) (500,)\n" ] }, { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f1118aab438>" ] }, "execution_count": 372, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/matplotlib/collections.py:549: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == 'face':\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0MAAAKaCAYAAAD8hoK8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwson7/hPAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f111995de80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#read the datasets\n", "train = pd.read_csv(\"intro_to_ann.csv\")\n", "print (train.head())\n", "Xd, Yd = np.array(train.ix[:,0:2]), np.array(train.ix[:,2])\n", "Xbar=Xd.transpose()\n", "Ybar=Yd.transpose()\n", "print(Xbar.shape, Ybar.shape)\n", "#print(X,Y)\n", "plt.scatter(Xd[:,0], Xd[:,1], s=40, c=Yd, cmap=plt.cm.BuGn)" ] }, { "cell_type": "code", "execution_count": 378, "metadata": { "collapsed": false }, "outputs": [], "source": [ "onehot = (np.arange(2) == Yd[:, None]).astype(np.float32)\n", "#print(Yd[:, None])\n", "#print(np.arange(2)==Y_data[:,None])\n", "#print(onehot)\n", "#print(onehot.shape)\n", "n_hidden = 4.0 # 1st layer num features\n", "n_input = 2.0 \n", "x1=tf.placeholder(\"float\",[None,2])\n", "#x2=tf.placeholder(\"float\",[None,1])\n", "Y=tf.placeholder(\"float\",[None,2])\n", "b1=tf.Variable(tf.zeros([4]))\n", "b2=tf.Variable(tf.zeros([2]))\n", "#W1=tf.Variable(rng.randn(([n_input,n_hidden]))\n", "W1=tf.Variable(tf.zeros([n_input,n_hidden]))\n", "W2=tf.Variable(tf.zeros([n_hidden,2]))\n", "#teta_=tf.placeholder(tf.float32,[None,4])\n", "teta=tf.nn.sigmoid(tf.matmul(x1,W1)+b1)\n", "#output_Teta=tf.placeholder(tf.float32,[None,1])\n", "OutputTeta=tf.nn.sigmoid(tf.matmul(teta,W2)+b2)" ] }, { "cell_type": "code", "execution_count": 379, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cost=tf.reduce_mean(-tf.reduce_sum(Y*tf.log(OutputTeta),reduction_indices=[1]))\n", "optimizer = tf.train.GradientDescentOptimizer(0.1).minimize(cost)" ] }, { "cell_type": "code", "execution_count": 380, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0.]] \n", " [[ 0.01249999 0.01249999]\n", " [ 0.01249999 0.01249999]\n", " [ 0.01249999 0.01249999]\n", " [ 0.01249999 0.01249999]]\n", "Error: 0.5\n", "[[ 7.46728547e-05 7.46728547e-05 7.46728547e-05 7.46728547e-05]\n", " [ 3.76246935e-05 3.76246935e-05 3.76246935e-05 3.76246935e-05]] \n", " [[ 0.02468756 0.02468756]\n", " [ 0.02468756 0.02468756]\n", " [ 0.02468756 0.02468756]\n", " [ 0.02468756 0.02468756]]\n", "Error: 0.5\n", "[[ 0.00021847 0.00021847 0.00021847 0.00021847]\n", " [ 0.00011008 0.00011008 0.00011008 0.00011008]] \n", " [[ 0.03657189 0.03657215]\n", " [ 0.03657189 0.03657215]\n", " [ 0.03657189 0.03657215]\n", " [ 0.03657189 0.03657215]]\n", "Error: 0.5\n", "[[ 0.00042618 0.00042618 0.00042618 0.00042618]\n", " [ 0.00021474 0.00021474 0.00021474 0.00021474]] \n", " [[ 0.0481621 0.04816308]\n", " [ 0.0481621 0.04816308]\n", " [ 0.0481621 0.04816308]\n", " [ 0.0481621 0.04816308]]\n", "Error: 0.5\n", "[[ 0.00069293 0.00069293 0.00069293 0.00069293]\n", " [ 0.00034914 0.00034914 0.00034914 0.00034914]] \n", " [[ 0.05946719 0.0594695 ]\n", " [ 0.05946719 0.0594695 ]\n", " [ 0.05946719 0.0594695 ]\n", " [ 0.05946719 0.0594695 ]]\n", "Error: 0.5\n", "[[ 0.00101414 0.00101414 0.00101414 0.00101414]\n", " [ 0.00051098 0.00051098 0.00051098 0.00051098]] \n", " [[ 0.07049602 0.0705004 ]\n", " [ 0.07049602 0.0705004 ]\n", " [ 0.07049602 0.0705004 ]\n", " [ 0.07049602 0.0705004 ]]\n", "Error: 0.5\n", "[[ 0.00138553 0.00138553 0.00138553 0.00138553]\n", " [ 0.00069811 0.00069811 0.00069811 0.00069811]] \n", " [[ 0.08125725 0.08126456]\n", " [ 0.08125725 0.08126456]\n", " [ 0.08125725 0.08126456]\n", " [ 0.08125725 0.08126456]]\n", "Error: 0.5\n", "[[ 0.00180312 0.00180312 0.00180312 0.00180312]\n", " [ 0.00090852 0.00090852 0.00090852 0.00090852]] \n", " [[ 0.09175943 0.09177053]\n", " [ 0.09175943 0.09177053]\n", " [ 0.09175943 0.09177053]\n", " [ 0.09175943 0.09177053]]\n", "Error: 0.5\n", "[[ 0.00226318 0.00226318 0.00226318 0.00226318]\n", " [ 0.00114033 0.00114033 0.00114033 0.00114033]] \n", " [[ 0.10201085 0.10202668]\n", " [ 0.10201085 0.10202668]\n", " [ 0.10201085 0.10202668]\n", " [ 0.10201085 0.10202668]]\n", "Error: 0.5\n", "[[ 0.00276224 0.00276224 0.00276224 0.00276224]\n", " [ 0.00139181 0.00139181 0.00139181 0.00139181]] \n", " [[ 0.11201961 0.11204112]\n", " [ 0.11201961 0.11204112]\n", " [ 0.11201961 0.11204112]\n", " [ 0.11201961 0.11204112]]\n", "Error: 0.5\n", "[[ 0.00329706 0.00329706 0.00329706 0.00329706]\n", " [ 0.00166131 0.00166131 0.00166131 0.00166131]] \n", " [[ 0.12179362 0.12182176]\n", " [ 0.12179362 0.12182176]\n", " [ 0.12179362 0.12182176]\n", " [ 0.12179362 0.12182176]]\n", "Error: 0.5\n", "[[ 0.00386464 0.00386464 0.00386464 0.00386464]\n", " [ 0.00194734 0.00194734 0.00194734 0.00194734]] \n", " [[ 0.13134053 0.13137624]\n", " [ 0.13134053 0.13137624]\n", " [ 0.13134053 0.13137624]\n", " [ 0.13134053 0.13137624]]\n", "Error: 0.5\n", "[[ 0.00446215 0.00446215 0.00446215 0.00446215]\n", " [ 0.00224848 0.00224848 0.00224848 0.00224848]] \n", " [[ 0.1406678 0.14071198]\n", " [ 0.1406678 0.14071198]\n", " [ 0.1406678 0.14071198]\n", " [ 0.1406678 0.14071198]]\n", "Error: 0.5\n", "[[ 0.00508701 0.00508701 0.00508701 0.00508701]\n", " [ 0.00256343 0.00256343 0.00256343 0.00256343]] \n", " [[ 0.14978261 0.14983617]\n", " [ 0.14978261 0.14983617]\n", " [ 0.14978261 0.14983617]\n", " [ 0.14978261 0.14983617]]\n", "Error: 0.5\n", "[[ 0.00573679 0.00573679 0.00573679 0.00573679]\n", " [ 0.00289099 0.00289099 0.00289099 0.00289099]] \n", " [[ 0.15869197 0.15875573]\n", " [ 0.15869197 0.15875573]\n", " [ 0.15869197 0.15875573]\n", " [ 0.15869197 0.15875573]]\n", "Error: 0.5\n", "[[ 0.00640923 0.00640923 0.00640923 0.00640923]\n", " [ 0.00323002 0.00323002 0.00323002 0.00323002]] \n", " [[ 0.16740263 0.1674774 ]\n", " [ 0.16740263 0.1674774 ]\n", " [ 0.16740263 0.1674774 ]\n", " [ 0.16740263 0.1674774 ]]\n", "Error: 0.5\n", "[[ 0.00710225 0.00710225 0.00710225 0.00710225]\n", " [ 0.00357948 0.00357948 0.00357948 0.00357948]] \n", " [[ 0.1759211 0.17600764]\n", " [ 0.1759211 0.17600764]\n", " [ 0.1759211 0.17600764]\n", " [ 0.1759211 0.17600764]]\n", "Error: 0.5\n", "[[ 0.00781391 0.00781391 0.00781391 0.00781391]\n", " [ 0.0039384 0.0039384 0.0039384 0.0039384 ]] \n", " [[ 0.18425369 0.18435271]\n", " [ 0.18425369 0.18435271]\n", " [ 0.18425369 0.18435271]\n", " [ 0.18425369 0.18435271]]\n", "Error: 0.5\n", "[[ 0.00854241 0.00854241 0.00854241 0.00854241]\n", " [ 0.0043059 0.0043059 0.0043059 0.0043059 ]] \n", " [[ 0.19240649 0.19251862]\n", " [ 0.19240649 0.19251862]\n", " [ 0.19240649 0.19251862]\n", " [ 0.19240649 0.19251862]]\n", "Error: 0.5\n", "[[ 0.00928608 0.00928608 0.00928608 0.00928608]\n", " [ 0.00468113 0.00468113 0.00468113 0.00468113]] \n", " [[ 0.20038536 0.2005112 ]\n", " [ 0.20038536 0.2005112 ]\n", " [ 0.20038536 0.2005112 ]\n", " [ 0.20038536 0.2005112 ]]\n", "Error: 0.5\n", "[[ 0.01004337 0.01004337 0.01004337 0.01004337]\n", " [ 0.00506333 0.00506333 0.00506333 0.00506333]] \n", " [[ 0.20819595 0.20833606]\n", " [ 0.20819595 0.20833606]\n", " [ 0.20819595 0.20833606]\n", " [ 0.20819595 0.20833606]]\n", "Error: 0.5\n", "[[ 0.01081285 0.01081285 0.01081285 0.01081285]\n", " [ 0.00545179 0.00545179 0.00545179 0.00545179]] \n", " [[ 0.21584371 0.21599856]\n", " [ 0.21584371 0.21599856]\n", " [ 0.21584371 0.21599856]\n", " [ 0.21584371 0.21599856]]\n", "Error: 0.5\n", "[[ 0.01159318 0.01159318 0.01159318 0.01159318]\n", " [ 0.00584584 0.00584584 0.00584584 0.00584584]] \n", " [[ 0.22333387 0.22350392]\n", " [ 0.22333387 0.22350392]\n", " [ 0.22333387 0.22350392]\n", " [ 0.22333387 0.22350392]]\n", "Error: 0.5\n", "[[ 0.01238313 0.01238313 0.01238313 0.01238313]\n", " [ 0.00624488 0.00624488 0.00624488 0.00624488]] \n", " [[ 0.2306715 0.23085713]\n", " [ 0.2306715 0.23085713]\n", " [ 0.2306715 0.23085713]\n", " [ 0.2306715 0.23085713]]\n", "Error: 0.5\n", "[[ 0.01318156 0.01318156 0.01318156 0.01318156]\n", " [ 0.00664835 0.00664835 0.00664835 0.00664835]] \n", " [[ 0.23786144 0.23806302]\n", " [ 0.23786144 0.23806302]\n", " [ 0.23786144 0.23806302]\n", " [ 0.23786144 0.23806302]]\n", "Error: 0.5\n", "[[ 0.01398741 0.01398741 0.01398741 0.01398741]\n", " [ 0.0070557 0.0070557 0.0070557 0.0070557 ]] \n", " [[ 0.24490839 0.24512622]\n", " [ 0.24490839 0.24512622]\n", " [ 0.24490839 0.24512622]\n", " [ 0.24490839 0.24512622]]\n", "Error: 0.5\n", "[[ 0.0147997 0.0147997 0.0147997 0.0147997 ]\n", " [ 0.00746648 0.00746648 0.00746648 0.00746648]] \n", " [[ 0.25181684 0.25205117]\n", " [ 0.25181684 0.25205117]\n", " [ 0.25181684 0.25205117]\n", " [ 0.25181684 0.25205117]]\n", "Error: 0.5\n", "[[ 0.01561752 0.01561752 0.01561752 0.01561752]\n", " [ 0.00788022 0.00788022 0.00788022 0.00788022]] \n", " [[ 0.25859115 0.25884217]\n", " [ 0.25859115 0.25884217]\n", " [ 0.25859115 0.25884217]\n", " [ 0.25859115 0.25884217]]\n", "Error: 0.5\n", "[[ 0.01644003 0.01644003 0.01644003 0.01644003]\n", " [ 0.00829651 0.00829651 0.00829651 0.00829651]] \n", " [[ 0.26523545 0.26550335]\n", " [ 0.26523545 0.26550335]\n", " [ 0.26523545 0.26550335]\n", " [ 0.26523545 0.26550335]]\n", "Error: 0.5\n", "[[ 0.01726647 0.01726647 0.01726647 0.01726647]\n", " [ 0.00871497 0.00871497 0.00871497 0.00871497]] \n", " [[ 0.27175379 0.2720387 ]\n", " [ 0.27175379 0.2720387 ]\n", " [ 0.27175379 0.2720387 ]\n", " [ 0.27175379 0.2720387 ]]\n", "Error: 0.5\n", "[[ 0.0180961 0.0180961 0.0180961 0.0180961 ]\n", " [ 0.00913524 0.00913524 0.00913524 0.00913524]] \n", " [[ 0.27814999 0.27845201]\n", " [ 0.27814999 0.27845201]\n", " [ 0.27814999 0.27845201]\n", " [ 0.27814999 0.27845201]]\n", "Error: 0.5\n", "[[ 0.01892826 0.01892826 0.01892826 0.01892826]\n", " [ 0.00955701 0.00955701 0.00955701 0.00955701]] \n", " [[ 0.28442776 0.28474697]\n", " [ 0.28442776 0.28474697]\n", " [ 0.28442776 0.28474697]\n", " [ 0.28442776 0.28474697]]\n", "Error: 0.5\n", "[[ 0.01976235 0.01976235 0.01976235 0.01976235]\n", " [ 0.00997996 0.00997996 0.00997996 0.00997996]] \n", " [[ 0.2905907 0.29092714]\n", " [ 0.2905907 0.29092714]\n", " [ 0.2905907 0.29092714]\n", " [ 0.2905907 0.29092714]]\n", "Error: 0.5\n", "[[ 0.02059778 0.02059778 0.02059778 0.02059778]\n", " [ 0.01040381 0.01040381 0.01040381 0.01040381]] \n", " [[ 0.29664224 0.29699591]\n", " [ 0.29664224 0.29699591]\n", " [ 0.29664224 0.29699591]\n", " [ 0.29664224 0.29699591]]\n", "Error: 0.5\n", "[[ 0.02143404 0.02143404 0.02143404 0.02143404]\n", " [ 0.01082832 0.01082832 0.01082832 0.01082832]] \n", " [[ 0.30258569 0.30295655]\n", " [ 0.30258569 0.30295655]\n", " [ 0.30258569 0.30295655]\n", " [ 0.30258569 0.30295655]]\n", "Error: 0.5\n", "[[ 0.02227064 0.02227064 0.02227064 0.02227064]\n", " [ 0.01125323 0.01125323 0.01125323 0.01125323]] \n", " [[ 0.30842423 0.30881226]\n", " [ 0.30842423 0.30881226]\n", " [ 0.30842423 0.30881226]\n", " [ 0.30842423 0.30881226]]\n", "Error: 0.5\n", "[[ 0.02310714 0.02310714 0.02310714 0.02310714]\n", " [ 0.01167834 0.01167834 0.01167834 0.01167834]] \n", " [[ 0.31416091 0.31456605]\n", " [ 0.31416091 0.31456605]\n", " [ 0.31416091 0.31456605]\n", " [ 0.31416091 0.31456605]]\n", "Error: 0.5\n", "[[ 0.02394312 0.02394312 0.02394312 0.02394312]\n", " [ 0.01210344 0.01210344 0.01210344 0.01210344]] \n", " [[ 0.31979871 0.32022083]\n", " [ 0.31979871 0.32022083]\n", " [ 0.31979871 0.32022083]\n", " [ 0.31979871 0.32022083]]\n", "Error: 0.5\n", "[[ 0.02477821 0.02477821 0.02477821 0.02477821]\n", " [ 0.01252834 0.01252834 0.01252834 0.01252834]] \n", " [[ 0.32534045 0.32577944]\n", " [ 0.32534045 0.32577944]\n", " [ 0.32534045 0.32577944]\n", " [ 0.32534045 0.32577944]]\n", "Error: 0.5\n", "[[ 0.02561205 0.02561205 0.02561205 0.02561205]\n", " [ 0.01295286 0.01295286 0.01295286 0.01295286]] \n", " [[ 0.33078885 0.33124459]\n", " [ 0.33078885 0.33124459]\n", " [ 0.33078885 0.33124459]\n", " [ 0.33078885 0.33124459]]\n", "Error: 0.5\n", "[[ 0.02644432 0.02644432 0.02644432 0.02644432]\n", " [ 0.01337686 0.01337686 0.01337686 0.01337686]] \n", " [[ 0.33614656 0.3366189 ]\n", " [ 0.33614656 0.3366189 ]\n", " [ 0.33614656 0.3366189 ]\n", " [ 0.33614656 0.3366189 ]]\n", "Error: 0.5\n", "[[ 0.02727472 0.02727472 0.02727472 0.02727472]\n", " [ 0.01380019 0.01380019 0.01380019 0.01380019]] \n", " [[ 0.34141612 0.34190488]\n", " [ 0.34141612 0.34190488]\n", " [ 0.34141612 0.34190488]\n", " [ 0.34141612 0.34190488]]\n", "Error: 0.5\n", "[[ 0.02810298 0.02810298 0.02810298 0.02810298]\n", " [ 0.0142227 0.0142227 0.0142227 0.0142227 ]] \n", " [[ 0.34659997 0.34710497]\n", " [ 0.34659997 0.34710497]\n", " [ 0.34659997 0.34710497]\n", " [ 0.34659997 0.34710497]]\n", "Error: 0.5\n", "[[ 0.02892886 0.02892886 0.02892886 0.02892886]\n", " [ 0.01464428 0.01464428 0.01464428 0.01464428]] \n", " [[ 0.35170045 0.35222152]\n", " [ 0.35170045 0.35222152]\n", " [ 0.35170045 0.35222152]\n", " [ 0.35170045 0.35222152]]\n", "Error: 0.5\n", "[[ 0.02975211 0.02975211 0.02975211 0.02975211]\n", " [ 0.01506481 0.01506481 0.01506481 0.01506481]] \n", " [[ 0.35671988 0.35725677]\n", " [ 0.35671988 0.35725677]\n", " [ 0.35671988 0.35725677]\n", " [ 0.35671988 0.35725677]]\n", "Error: 0.5\n", "[[ 0.03057254 0.03057254 0.03057254 0.03057254]\n", " [ 0.01548419 0.01548419 0.01548419 0.01548419]] \n", " [[ 0.36166039 0.3622129 ]\n", " [ 0.36166039 0.3622129 ]\n", " [ 0.36166039 0.3622129 ]\n", " [ 0.36166039 0.3622129 ]]\n", "Error: 0.5\n", "[[ 0.03138994 0.03138994 0.03138994 0.03138994]\n", " [ 0.01590233 0.01590233 0.01590233 0.01590233]] \n", " [[ 0.36652413 0.36709201]\n", " [ 0.36652413 0.36709201]\n", " [ 0.36652413 0.36709201]\n", " [ 0.36652413 0.36709201]]\n", "Error: 0.5\n", "[[ 0.03220415 0.03220415 0.03220415 0.03220415]\n", " [ 0.01631913 0.01631913 0.01631913 0.01631913]] \n", " [[ 0.37131312 0.37189615]\n", " [ 0.37131312 0.37189615]\n", " [ 0.37131312 0.37189615]\n", " [ 0.37131312 0.37189615]]\n", "Error: 0.5\n", "[[ 0.033015 0.033015 0.033015 0.033015 ]\n", " [ 0.01673452 0.01673452 0.01673452 0.01673452]] \n", " [[ 0.37602934 0.37662727]\n", " [ 0.37602934 0.37662727]\n", " [ 0.37602934 0.37662727]\n", " [ 0.37602934 0.37662727]]\n", "Error: 0.5\n", "[[ 0.03382235 0.03382235 0.03382235 0.03382235]\n", " [ 0.01714842 0.01714842 0.01714842 0.01714842]] \n", " [[ 0.38067466 0.38128725]\n", " [ 0.38067466 0.38128725]\n", " [ 0.38067466 0.38128725]\n", " [ 0.38067466 0.38128725]]\n", "Error: 0.5\n", "[[ 0.03462606 0.03462606 0.03462606 0.03462606]\n", " [ 0.01756077 0.01756077 0.01756077 0.01756077]] \n", " [[ 0.38525093 0.38587791]\n", " [ 0.38525093 0.38587791]\n", " [ 0.38525093 0.38587791]\n", " [ 0.38525093 0.38587791]]\n", "Error: 0.5\n", "[[ 0.03542601 0.03542601 0.03542601 0.03542601]\n", " [ 0.0179715 0.0179715 0.0179715 0.0179715 ]] \n", " [[ 0.38975993 0.39040104]\n", " [ 0.38975993 0.39040104]\n", " [ 0.38975993 0.39040104]\n", " [ 0.38975993 0.39040104]]\n", "Error: 0.5\n", "[[ 0.03622209 0.03622209 0.03622209 0.03622209]\n", " [ 0.01838057 0.01838057 0.01838057 0.01838057]] \n", " [[ 0.39420334 0.3948583 ]\n", " [ 0.39420334 0.3948583 ]\n", " [ 0.39420334 0.3948583 ]\n", " [ 0.39420334 0.3948583 ]]\n", "Error: 0.5\n", "[[ 0.03701421 0.03701421 0.03701421 0.03701421]\n", " [ 0.01878792 0.01878792 0.01878792 0.01878792]] \n", " [[ 0.39858282 0.39925137]\n", " [ 0.39858282 0.39925137]\n", " [ 0.39858282 0.39925137]\n", " [ 0.39858282 0.39925137]]\n", "Error: 0.495999991894\n", "[[ 0.03780228 0.03780228 0.03780228 0.03780228]\n", " [ 0.0191935 0.0191935 0.0191935 0.0191935 ]] \n", " [[ 0.40289995 0.40358183]\n", " [ 0.40289995 0.40358183]\n", " [ 0.40289995 0.40358183]\n", " [ 0.40289995 0.40358183]]\n", "Error: 0.361999988556\n", "[[ 0.03858621 0.03858621 0.03858621 0.03858621]\n", " [ 0.01959728 0.01959728 0.01959728 0.01959728]] \n", " [[ 0.40715629 0.40785119]\n", " [ 0.40715629 0.40785119]\n", " [ 0.40715629 0.40785119]\n", " [ 0.40715629 0.40785119]]\n", "Error: 0.367999970913\n", "[[ 0.03936594 0.03936594 0.03936594 0.03936594]\n", " [ 0.01999922 0.01999922 0.01999922 0.01999922]] \n", " [[ 0.41135332 0.41206095]\n", " [ 0.41135332 0.41206095]\n", " [ 0.41135332 0.41206095]\n", " [ 0.41135332 0.41206095]]\n", "Error: 0.494000017643\n", "[[ 0.0401414 0.0401414 0.0401414 0.0401414 ]\n", " [ 0.02039928 0.02039928 0.02039928 0.02039928]] \n", " [[ 0.41549248 0.41621256]\n", " [ 0.41549248 0.41621256]\n", " [ 0.41549248 0.41621256]\n", " [ 0.41549248 0.41621256]]\n", "Error: 0.5\n", "[[ 0.04091255 0.04091255 0.04091255 0.04091255]\n", " [ 0.02079744 0.02079744 0.02079744 0.02079744]] \n", " [[ 0.41957515 0.4203074 ]\n", " [ 0.41957515 0.4203074 ]\n", " [ 0.41957515 0.4203074 ]\n", " [ 0.41957515 0.4203074 ]]\n", "Error: 0.5\n", "[[ 0.04167933 0.04167933 0.04167933 0.04167933]\n", " [ 0.02119368 0.02119368 0.02119368 0.02119368]] \n", " [[ 0.4236027 0.4243468]\n", " [ 0.4236027 0.4243468]\n", " [ 0.4236027 0.4243468]\n", " [ 0.4236027 0.4243468]]\n", "Error: 0.5\n", "[[ 0.04244169 0.04244169 0.04244169 0.04244169]\n", " [ 0.02158796 0.02158796 0.02158796 0.02158796]] \n", " [[ 0.42757639 0.42833209]\n", " [ 0.42757639 0.42833209]\n", " [ 0.42757639 0.42833209]\n", " [ 0.42757639 0.42833209]]\n", "Error: 0.5\n", "[[ 0.04319961 0.04319961 0.04319961 0.04319961]\n", " [ 0.02198027 0.02198027 0.02198027 0.02198027]] \n", " [[ 0.43149751 0.43226454]\n", " [ 0.43149751 0.43226454]\n", " [ 0.43149751 0.43226454]\n", " [ 0.43149751 0.43226454]]\n", "Error: 0.5\n", "[[ 0.04395306 0.04395306 0.04395306 0.04395306]\n", " [ 0.0223706 0.0223706 0.0223706 0.0223706 ]] \n", " [[ 0.43536729 0.43614534]\n", " [ 0.43536729 0.43614534]\n", " [ 0.43536729 0.43614534]\n", " [ 0.43536729 0.43614534]]\n", "Error: 0.5\n", "[[ 0.044702 0.044702 0.044702 0.044702 ]\n", " [ 0.02275892 0.02275892 0.02275892 0.02275892]] \n", " [[ 0.4391869 0.43997568]\n", " [ 0.4391869 0.43997568]\n", " [ 0.4391869 0.43997568]\n", " [ 0.4391869 0.43997568]]\n", "Error: 0.5\n", "[[ 0.04544642 0.04544642 0.04544642 0.04544642]\n", " [ 0.02314522 0.02314522 0.02314522 0.02314522]] \n", " [[ 0.44295746 0.4437567 ]\n", " [ 0.44295746 0.4437567 ]\n", " [ 0.44295746 0.4437567 ]\n", " [ 0.44295746 0.4437567 ]]\n", "Error: 0.5\n", "[[ 0.04618631 0.04618631 0.04618631 0.04618631]\n", " [ 0.02352951 0.02352951 0.02352951 0.02352951]] \n", " [[ 0.44668013 0.4474895 ]\n", " [ 0.44668013 0.4474895 ]\n", " [ 0.44668013 0.4474895 ]\n", " [ 0.44668013 0.4474895 ]]\n", "Error: 0.5\n", "[[ 0.04692164 0.04692164 0.04692164 0.04692164]\n", " [ 0.02391176 0.02391176 0.02391176 0.02391176]] \n", " [[ 0.45035595 0.45117518]\n", " [ 0.45035595 0.45117518]\n", " [ 0.45035595 0.45117518]\n", " [ 0.45035595 0.45117518]]\n", "Error: 0.5\n", "[[ 0.04765241 0.04765241 0.04765241 0.04765241]\n", " [ 0.02429196 0.02429196 0.02429196 0.02429196]] \n", " [[ 0.45398596 0.45481476]\n", " [ 0.45398596 0.45481476]\n", " [ 0.45398596 0.45481476]\n", " [ 0.45398596 0.45481476]]\n", "Error: 0.5\n", "[[ 0.04837862 0.04837862 0.04837862 0.04837862]\n", " [ 0.02467013 0.02467013 0.02467013 0.02467013]] \n", " [[ 0.45757118 0.45840928]\n", " [ 0.45757118 0.45840928]\n", " [ 0.45757118 0.45840928]\n", " [ 0.45757118 0.45840928]]\n", "Error: 0.5\n", "[[ 0.04910027 0.04910027 0.04910027 0.04910027]\n", " [ 0.02504624 0.02504624 0.02504624 0.02504624]] \n", " [[ 0.46111256 0.46195969]\n", " [ 0.46111256 0.46195969]\n", " [ 0.46111256 0.46195969]\n", " [ 0.46111256 0.46195969]]\n", "Error: 0.5\n", "[[ 0.04981736 0.04981736 0.04981736 0.04981736]\n", " [ 0.02542031 0.02542031 0.02542031 0.02542031]] \n", " [[ 0.46461108 0.46546695]\n", " [ 0.46461108 0.46546695]\n", " [ 0.46461108 0.46546695]\n", " [ 0.46461108 0.46546695]]\n", "Error: 0.5\n", "[[ 0.05052989 0.05052989 0.05052989 0.05052989]\n", " [ 0.02579233 0.02579233 0.02579233 0.02579233]] \n", " [[ 0.46806765 0.46893197]\n", " [ 0.46806765 0.46893197]\n", " [ 0.46806765 0.46893197]\n", " [ 0.46806765 0.46893197]]\n", "Error: 0.5\n", "[[ 0.05123786 0.05123786 0.05123786 0.05123786]\n", " [ 0.0261623 0.0261623 0.0261623 0.0261623 ]] \n", " [[ 0.47148317 0.47235569]\n", " [ 0.47148317 0.47235569]\n", " [ 0.47148317 0.47235569]\n", " [ 0.47148317 0.47235569]]\n", "Error: 0.5\n", "[[ 0.0519413 0.0519413 0.0519413 0.0519413 ]\n", " [ 0.02653022 0.02653022 0.02653022 0.02653022]] \n", " [[ 0.47485849 0.47573894]\n", " [ 0.47485849 0.47573894]\n", " [ 0.47485849 0.47573894]\n", " [ 0.47485849 0.47573894]]\n", "Error: 0.5\n", "[[ 0.0526402 0.0526402 0.0526402 0.0526402]\n", " [ 0.0268961 0.0268961 0.0268961 0.0268961]] \n", " [[ 0.47819448 0.47908258]\n", " [ 0.47819448 0.47908258]\n", " [ 0.47819448 0.47908258]\n", " [ 0.47819448 0.47908258]]\n", "Error: 0.5\n", "[[ 0.05333459 0.05333459 0.05333459 0.05333459]\n", " [ 0.02725994 0.02725994 0.02725994 0.02725994]] \n", " [[ 0.48149192 0.48238742]\n", " [ 0.48149192 0.48238742]\n", " [ 0.48149192 0.48238742]\n", " [ 0.48149192 0.48238742]]\n", "Error: 0.5\n", "[[ 0.05402448 0.05402448 0.05402448 0.05402448]\n", " [ 0.02762175 0.02762175 0.02762175 0.02762175]] \n", " [[ 0.48475164 0.48565426]\n", " [ 0.48475164 0.48565426]\n", " [ 0.48475164 0.48565426]\n", " [ 0.48475164 0.48565426]]\n", "Error: 0.5\n", "[[ 0.05470988 0.05470988 0.05470988 0.05470988]\n", " [ 0.02798152 0.02798152 0.02798152 0.02798152]] \n", " [[ 0.48797441 0.48888388]\n", " [ 0.48797441 0.48888388]\n", " [ 0.48797441 0.48888388]\n", " [ 0.48797441 0.48888388]]\n", "Error: 0.5\n", "[[ 0.05539082 0.05539082 0.05539082 0.05539082]\n", " [ 0.02833927 0.02833927 0.02833927 0.02833927]] \n", " [[ 0.49116093 0.49207702]\n", " [ 0.49116093 0.49207702]\n", " [ 0.49116093 0.49207702]\n", " [ 0.49116093 0.49207702]]\n", "Error: 0.5\n", "[[ 0.05606731 0.05606731 0.05606731 0.05606731]\n", " [ 0.02869501 0.02869501 0.02869501 0.02869501]] \n", " [[ 0.49431199 0.49523443]\n", " [ 0.49431199 0.49523443]\n", " [ 0.49431199 0.49523443]\n", " [ 0.49431199 0.49523443]]\n", "Error: 0.5\n", "[[ 0.05673938 0.05673938 0.05673938 0.05673938]\n", " [ 0.02904874 0.02904874 0.02904874 0.02904874]] \n", " [[ 0.49742827 0.49835679]\n", " [ 0.49742827 0.49835679]\n", " [ 0.49742827 0.49835679]\n", " [ 0.49742827 0.49835679]]\n", "Error: 0.5\n", "[[ 0.05740705 0.05740705 0.05740705 0.05740705]\n", " [ 0.02940048 0.02940048 0.02940048 0.02940048]] \n", " [[ 0.50051045 0.50144482]\n", " [ 0.50051045 0.50144482]\n", " [ 0.50051045 0.50144482]\n", " [ 0.50051045 0.50144482]]\n", "Error: 0.5\n", "[[ 0.05807034 0.05807034 0.05807034 0.05807034]\n", " [ 0.02975022 0.02975022 0.02975022 0.02975022]] \n", " [[ 0.50355923 0.5044992 ]\n", " [ 0.50355923 0.5044992 ]\n", " [ 0.50355923 0.5044992 ]\n", " [ 0.50355923 0.5044992 ]]\n", "Error: 0.5\n", "[[ 0.05872928 0.05872928 0.05872928 0.05872928]\n", " [ 0.03009799 0.03009799 0.03009799 0.03009799]] \n", " [[ 0.50657523 0.50752056]\n", " [ 0.50657523 0.50752056]\n", " [ 0.50657523 0.50752056]\n", " [ 0.50657523 0.50752056]]\n", "Error: 0.5\n", "[[ 0.05938389 0.05938389 0.05938389 0.05938389]\n", " [ 0.03044378 0.03044378 0.03044378 0.03044378]] \n", " [[ 0.50955909 0.51050955]\n", " [ 0.50955909 0.51050955]\n", " [ 0.50955909 0.51050955]\n", " [ 0.50955909 0.51050955]]\n", "Error: 0.5\n", "[[ 0.0600342 0.0600342 0.0600342 0.0600342 ]\n", " [ 0.03078762 0.03078762 0.03078762 0.03078762]] \n", " [[ 0.51251143 0.51346678]\n", " [ 0.51251143 0.51346678]\n", " [ 0.51251143 0.51346678]\n", " [ 0.51251143 0.51346678]]\n", "Error: 0.5\n", "[[ 0.06068024 0.06068024 0.06068024 0.06068024]\n", " [ 0.03112951 0.03112951 0.03112951 0.03112951]] \n", " [[ 0.51543283 0.51639283]\n", " [ 0.51543283 0.51639283]\n", " [ 0.51543283 0.51639283]\n", " [ 0.51543283 0.51639283]]\n", "Error: 0.5\n", "[[ 0.06132204 0.06132204 0.06132204 0.06132204]\n", " [ 0.03146946 0.03146946 0.03146946 0.03146946]] \n", " [[ 0.5183239 0.5192883]\n", " [ 0.5183239 0.5192883]\n", " [ 0.5183239 0.5192883]\n", " [ 0.5183239 0.5192883]]\n", "Error: 0.5\n", "[[ 0.06195962 0.06195962 0.06195962 0.06195962]\n", " [ 0.03180749 0.03180749 0.03180749 0.03180749]] \n", " [[ 0.52118516 0.52215379]\n", " [ 0.52118516 0.52215379]\n", " [ 0.52118516 0.52215379]\n", " [ 0.52118516 0.52215379]]\n", "Error: 0.5\n", "[[ 0.06259301 0.06259301 0.06259301 0.06259301]\n", " [ 0.0321436 0.0321436 0.0321436 0.0321436 ]] \n", " [[ 0.52401721 0.52498984]\n", " [ 0.52401721 0.52498984]\n", " [ 0.52401721 0.52498984]\n", " [ 0.52401721 0.52498984]]\n", "Error: 0.5\n", "[[ 0.06322225 0.06322225 0.06322225 0.06322225]\n", " [ 0.03247782 0.03247782 0.03247782 0.03247782]] \n", " [[ 0.5268206 0.52779698]\n", " [ 0.5268206 0.52779698]\n", " [ 0.5268206 0.52779698]\n", " [ 0.5268206 0.52779698]]\n", "Error: 0.5\n", "[[ 0.06384736 0.06384736 0.06384736 0.06384736]\n", " [ 0.03281014 0.03281014 0.03281014 0.03281014]] \n", " [[ 0.52959579 0.53057575]\n", " [ 0.52959579 0.53057575]\n", " [ 0.52959579 0.53057575]\n", " [ 0.52959579 0.53057575]]\n", "Error: 0.5\n", "[[ 0.06446838 0.06446838 0.06446838 0.06446838]\n", " [ 0.03314059 0.03314059 0.03314059 0.03314059]] \n", " [[ 0.53234339 0.53332663]\n", " [ 0.53234339 0.53332663]\n", " [ 0.53234339 0.53332663]\n", " [ 0.53234339 0.53332663]]\n", "Error: 0.5\n", "[[ 0.06508532 0.06508532 0.06508532 0.06508532]\n", " [ 0.03346918 0.03346918 0.03346918 0.03346918]] \n", " [[ 0.5350638 0.53605014]\n", " [ 0.5350638 0.53605014]\n", " [ 0.5350638 0.53605014]\n", " [ 0.5350638 0.53605014]]\n", "Error: 0.5\n", "[[ 0.06569824 0.06569824 0.06569824 0.06569824]\n", " [ 0.03379591 0.03379591 0.03379591 0.03379591]] \n", " [[ 0.53775758 0.53874683]\n", " [ 0.53775758 0.53874683]\n", " [ 0.53775758 0.53874683]\n", " [ 0.53775758 0.53874683]]\n", "Error: 0.5\n", "[[ 0.06630715 0.06630715 0.06630715 0.06630715]\n", " [ 0.03412081 0.03412081 0.03412081 0.03412081]] \n", " [[ 0.54042512 0.54141712]\n", " [ 0.54042512 0.54141712]\n", " [ 0.54042512 0.54141712]\n", " [ 0.54042512 0.54141712]]\n", "Error: 0.5\n", "[[ 0.06691209 0.06691209 0.06691209 0.06691209]\n", " [ 0.03444388 0.03444388 0.03444388 0.03444388]] \n", " [[ 0.54306698 0.54406148]\n", " [ 0.54306698 0.54406148]\n", " [ 0.54306698 0.54406148]\n", " [ 0.54306698 0.54406148]]\n", "Error: 0.5\n", "[[ 0.06751309 0.06751309 0.06751309 0.06751309]\n", " [ 0.03476514 0.03476514 0.03476514 0.03476514]] \n", " [[ 0.54568356 0.54668033]\n", " [ 0.54568356 0.54668033]\n", " [ 0.54568356 0.54668033]\n", " [ 0.54568356 0.54668033]]\n", "Error: 0.5\n", "[[ 0.06811018 0.06811018 0.06811018 0.06811018]\n", " [ 0.03508461 0.03508461 0.03508461 0.03508461]] \n", " [[ 0.54827529 0.54927415]\n", " [ 0.54827529 0.54927415]\n", " [ 0.54827529 0.54927415]\n", " [ 0.54827529 0.54927415]]\n", "Error: 0.5\n", "[[ 0.0687034 0.0687034 0.0687034 0.0687034 ]\n", " [ 0.03540229 0.03540229 0.03540229 0.03540229]] \n", " [[ 0.55084258 0.55184335]\n", " [ 0.55084258 0.55184335]\n", " [ 0.55084258 0.55184335]\n", " [ 0.55084258 0.55184335]]\n", "Error: 0.5\n", "[[ 0.06929277 0.06929277 0.06929277 0.06929277]\n", " [ 0.0357182 0.0357182 0.0357182 0.0357182 ]] \n", " [[ 0.55338591 0.5543884 ]\n", " [ 0.55338591 0.5543884 ]\n", " [ 0.55338591 0.5543884 ]\n", " [ 0.55338591 0.5543884 ]]\n", "Error: 0.5\n", "[[ 0.06987833 0.06987833 0.06987833 0.06987833]\n", " [ 0.03603235 0.03603235 0.03603235 0.03603235]] \n", " [[ 0.55590564 0.55690968]\n", " [ 0.55590564 0.55690968]\n", " [ 0.55590564 0.55690968]\n", " [ 0.55590564 0.55690968]]\n", "Error: 0.5\n", "[[ 0.07046011 0.07046011 0.07046011 0.07046011]\n", " [ 0.03634476 0.03634476 0.03634476 0.03634476]] \n", " [[ 0.55840218 0.55940759]\n", " [ 0.55840218 0.55940759]\n", " [ 0.55840218 0.55940759]\n", " [ 0.55840218 0.55940759]]\n", "Error: 0.5\n", "[[ 0.07103814 0.07103814 0.07103814 0.07103814]\n", " [ 0.03665543 0.03665543 0.03665543 0.03665543]] \n", " [[ 0.56087595 0.5618825 ]\n", " [ 0.56087595 0.5618825 ]\n", " [ 0.56087595 0.5618825 ]\n", " [ 0.56087595 0.5618825 ]]\n", "Error: 0.5\n", "[[ 0.07161246 0.07161246 0.07161246 0.07161246]\n", " [ 0.03696439 0.03696439 0.03696439 0.03696439]] \n", " [[ 0.56332725 0.56433481]\n", " [ 0.56332725 0.56433481]\n", " [ 0.56332725 0.56433481]\n", " [ 0.56332725 0.56433481]]\n", "Error: 0.5\n", "[[ 0.07218309 0.07218309 0.07218309 0.07218309]\n", " [ 0.03727165 0.03727165 0.03727165 0.03727165]] \n", " [[ 0.56575656 0.56676489]\n", " [ 0.56575656 0.56676489]\n", " [ 0.56575656 0.56676489]\n", " [ 0.56575656 0.56676489]]\n", "Error: 0.5\n", "[[ 0.07275008 0.07275008 0.07275008 0.07275008]\n", " [ 0.03757722 0.03757722 0.03757722 0.03757722]] \n", " [[ 0.56816417 0.56917316]\n", " [ 0.56816417 0.56917316]\n", " [ 0.56816417 0.56917316]\n", " [ 0.56816417 0.56917316]]\n", "Error: 0.5\n", "[[ 0.07331344 0.07331344 0.07331344 0.07331344]\n", " [ 0.03788112 0.03788112 0.03788112 0.03788112]] \n", " [[ 0.57055044 0.57155991]\n", " [ 0.57055044 0.57155991]\n", " [ 0.57055044 0.57155991]\n", " [ 0.57055044 0.57155991]]\n", "Error: 0.5\n", "[[ 0.07387321 0.07387321 0.07387321 0.07387321]\n", " [ 0.03818335 0.03818335 0.03818335 0.03818335]] \n", " [[ 0.57291573 0.5739255 ]\n", " [ 0.57291573 0.5739255 ]\n", " [ 0.57291573 0.5739255 ]\n", " [ 0.57291573 0.5739255 ]]\n", "Error: 0.5\n", "[[ 0.07442943 0.07442943 0.07442943 0.07442943]\n", " [ 0.03848393 0.03848393 0.03848393 0.03848393]] \n", " [[ 0.57526034 0.57627028]\n", " [ 0.57526034 0.57627028]\n", " [ 0.57526034 0.57627028]\n", " [ 0.57526034 0.57627028]]\n", "Error: 0.5\n", "[[ 0.07498212 0.07498212 0.07498212 0.07498212]\n", " [ 0.03878288 0.03878288 0.03878288 0.03878288]] \n", " [[ 0.57758462 0.57859457]\n", " [ 0.57758462 0.57859457]\n", " [ 0.57758462 0.57859457]\n", " [ 0.57758462 0.57859457]]\n", "Error: 0.5\n", "[[ 0.07553132 0.07553132 0.07553132 0.07553132]\n", " [ 0.03908021 0.03908021 0.03908021 0.03908021]] \n", " [[ 0.57988894 0.5808987 ]\n", " [ 0.57988894 0.5808987 ]\n", " [ 0.57988894 0.5808987 ]\n", " [ 0.57988894 0.5808987 ]]\n", "Error: 0.5\n", "[[ 0.07607705 0.07607705 0.07607705 0.07607705]\n", " [ 0.03937592 0.03937592 0.03937592 0.03937592]] \n", " [[ 0.58217353 0.58318299]\n", " [ 0.58217353 0.58318299]\n", " [ 0.58217353 0.58318299]\n", " [ 0.58217353 0.58318299]]\n", "Error: 0.5\n", "[[ 0.07661936 0.07661936 0.07661936 0.07661936]\n", " [ 0.03967005 0.03967005 0.03967005 0.03967005]] \n", " [[ 0.58443874 0.58544773]\n", " [ 0.58443874 0.58544773]\n", " [ 0.58443874 0.58544773]\n", " [ 0.58443874 0.58544773]]\n", "Error: 0.5\n", "[[ 0.07715826 0.07715826 0.07715826 0.07715826]\n", " [ 0.03996259 0.03996259 0.03996259 0.03996259]] \n", " [[ 0.58668488 0.58769321]\n", " [ 0.58668488 0.58769321]\n", " [ 0.58668488 0.58769321]\n", " [ 0.58668488 0.58769321]]\n", "Error: 0.5\n", "[[ 0.07769379 0.07769379 0.07769379 0.07769379]\n", " [ 0.04025356 0.04025356 0.04025356 0.04025356]] \n", " [[ 0.58891225 0.58991981]\n", " [ 0.58891225 0.58991981]\n", " [ 0.58891225 0.58991981]\n", " [ 0.58891225 0.58991981]]\n", "Error: 0.5\n", "[[ 0.07822599 0.07822599 0.07822599 0.07822599]\n", " [ 0.04054297 0.04054297 0.04054297 0.04054297]] \n", " [[ 0.59112114 0.59212774]\n", " [ 0.59112114 0.59212774]\n", " [ 0.59112114 0.59212774]\n", " [ 0.59112114 0.59212774]]\n", "Error: 0.5\n", "[[ 0.07875487 0.07875487 0.07875487 0.07875487]\n", " [ 0.04083084 0.04083084 0.04083084 0.04083084]] \n", " [[ 0.59331179 0.59431732]\n", " [ 0.59331179 0.59431732]\n", " [ 0.59331179 0.59431732]\n", " [ 0.59331179 0.59431732]]\n", "Error: 0.5\n", "[[ 0.07928047 0.07928047 0.07928047 0.07928047]\n", " [ 0.04111719 0.04111719 0.04111719 0.04111719]] \n", " [[ 0.5954845 0.59648883]\n", " [ 0.5954845 0.59648883]\n", " [ 0.5954845 0.59648883]\n", " [ 0.5954845 0.59648883]]\n", "Error: 0.5\n", "[[ 0.07980283 0.07980283 0.07980283 0.07980283]\n", " [ 0.04140201 0.04140201 0.04140201 0.04140201]] \n", " [[ 0.59763956 0.59864253]\n", " [ 0.59763956 0.59864253]\n", " [ 0.59763956 0.59864253]\n", " [ 0.59763956 0.59864253]]\n", "Error: 0.5\n", "[[ 0.08032196 0.08032196 0.08032196 0.08032196]\n", " [ 0.04168534 0.04168534 0.04168534 0.04168534]] \n", " [[ 0.59977722 0.6007787 ]\n", " [ 0.59977722 0.6007787 ]\n", " [ 0.59977722 0.6007787 ]\n", " [ 0.59977722 0.6007787 ]]\n", "Error: 0.5\n", "[[ 0.08083791 0.08083791 0.08083791 0.08083791]\n", " [ 0.04196716 0.04196716 0.04196716 0.04196716]] \n", " [[ 0.60189772 0.60289758]\n", " [ 0.60189772 0.60289758]\n", " [ 0.60189772 0.60289758]\n", " [ 0.60189772 0.60289758]]\n", "Error: 0.5\n", "[[ 0.08135068 0.08135068 0.08135068 0.08135068]\n", " [ 0.04224752 0.04224752 0.04224752 0.04224752]] \n", " [[ 0.60400134 0.60499942]\n", " [ 0.60400134 0.60499942]\n", " [ 0.60400134 0.60499942]\n", " [ 0.60400134 0.60499942]]\n", "Error: 0.5\n", "[[ 0.08186033 0.08186033 0.08186033 0.08186033]\n", " [ 0.0425264 0.0425264 0.0425264 0.0425264 ]] \n", " [[ 0.60608828 0.60708451]\n", " [ 0.60608828 0.60708451]\n", " [ 0.60608828 0.60708451]\n", " [ 0.60608828 0.60708451]]\n", "Error: 0.5\n", "[[ 0.08236688 0.08236688 0.08236688 0.08236688]\n", " [ 0.04280384 0.04280384 0.04280384 0.04280384]] \n", " [[ 0.60815883 0.60915303]\n", " [ 0.60815883 0.60915303]\n", " [ 0.60815883 0.60915303]\n", " [ 0.60815883 0.60915303]]\n", "Error: 0.5\n", "[[ 0.08287034 0.08287034 0.08287034 0.08287034]\n", " [ 0.04307983 0.04307983 0.04307983 0.04307983]] \n", " [[ 0.61021322 0.61120528]\n", " [ 0.61021322 0.61120528]\n", " [ 0.61021322 0.61120528]\n", " [ 0.61021322 0.61120528]]\n", "Error: 0.5\n", "[[ 0.08337076 0.08337076 0.08337076 0.08337076]\n", " [ 0.04335439 0.04335439 0.04335439 0.04335439]] \n", " [[ 0.61225164 0.61324143]\n", " [ 0.61225164 0.61324143]\n", " [ 0.61225164 0.61324143]\n", " [ 0.61225164 0.61324143]]\n", "Error: 0.5\n", "[[ 0.08386816 0.08386816 0.08386816 0.08386816]\n", " [ 0.04362753 0.04362753 0.04362753 0.04362753]] \n", " [[ 0.61427438 0.61526179]\n", " [ 0.61427438 0.61526179]\n", " [ 0.61427438 0.61526179]\n", " [ 0.61427438 0.61526179]]\n", "Error: 0.5\n", "[[ 0.08436257 0.08436257 0.08436257 0.08436257]\n", " [ 0.04389927 0.04389927 0.04389927 0.04389927]] \n", " [[ 0.61628163 0.61726654]\n", " [ 0.61628163 0.61726654]\n", " [ 0.61628163 0.61726654]\n", " [ 0.61628163 0.61726654]]\n", "Error: 0.5\n", "[[ 0.08485401 0.08485401 0.08485401 0.08485401]\n", " [ 0.04416962 0.04416962 0.04416962 0.04416962]] \n", " [[ 0.61827362 0.6192559 ]\n", " [ 0.61827362 0.6192559 ]\n", " [ 0.61827362 0.6192559 ]\n", " [ 0.61827362 0.6192559 ]]\n", "Error: 0.5\n", "[[ 0.08534251 0.08534251 0.08534251 0.08534251]\n", " [ 0.04443859 0.04443859 0.04443859 0.04443859]] \n", " [[ 0.62025052 0.62123013]\n", " [ 0.62025052 0.62123013]\n", " [ 0.62025052 0.62123013]\n", " [ 0.62025052 0.62123013]]\n", "Error: 0.5\n", "[[ 0.0858281 0.0858281 0.0858281 0.0858281 ]\n", " [ 0.04470618 0.04470618 0.04470618 0.04470618]] \n", " [[ 0.62221259 0.62318939]\n", " [ 0.62221259 0.62318939]\n", " [ 0.62221259 0.62318939]\n", " [ 0.62221259 0.62318939]]\n", "Error: 0.5\n", "[[ 0.08631081 0.08631081 0.08631081 0.08631081]\n", " [ 0.04497242 0.04497242 0.04497242 0.04497242]] \n", " [[ 0.62416005 0.62513387]\n", " [ 0.62416005 0.62513387]\n", " [ 0.62416005 0.62513387]\n", " [ 0.62416005 0.62513387]]\n", "Error: 0.5\n", "[[ 0.08679067 0.08679067 0.08679067 0.08679067]\n", " [ 0.04523731 0.04523731 0.04523731 0.04523731]] \n", " [[ 0.62609309 0.62706381]\n", " [ 0.62609309 0.62706381]\n", " [ 0.62609309 0.62706381]\n", " [ 0.62609309 0.62706381]]\n", "Error: 0.5\n", "[[ 0.08726769 0.08726769 0.08726769 0.08726769]\n", " [ 0.04550087 0.04550087 0.04550087 0.04550087]] \n", " [[ 0.62801188 0.62897938]\n", " [ 0.62801188 0.62897938]\n", " [ 0.62801188 0.62897938]\n", " [ 0.62801188 0.62897938]]\n", "Error: 0.5\n", "[[ 0.0877419 0.0877419 0.0877419 0.0877419]\n", " [ 0.0457631 0.0457631 0.0457631 0.0457631]] \n", " [[ 0.62991661 0.63088083]\n", " [ 0.62991661 0.63088083]\n", " [ 0.62991661 0.63088083]\n", " [ 0.62991661 0.63088083]]\n", "Error: 0.5\n", "[[ 0.08821334 0.08821334 0.08821334 0.08821334]\n", " [ 0.04602402 0.04602402 0.04602402 0.04602402]] \n", " [[ 0.63180751 0.63276833]\n", " [ 0.63180751 0.63276833]\n", " [ 0.63180751 0.63276833]\n", " [ 0.63180751 0.63276833]]\n", "Error: 0.5\n", "[[ 0.08868202 0.08868202 0.08868202 0.08868202]\n", " [ 0.04628364 0.04628364 0.04628364 0.04628364]] \n", " [[ 0.63368469 0.63464206]\n", " [ 0.63368469 0.63464206]\n", " [ 0.63368469 0.63464206]\n", " [ 0.63368469 0.63464206]]\n", "Error: 0.5\n", "[[ 0.08914797 0.08914797 0.08914797 0.08914797]\n", " [ 0.04654197 0.04654197 0.04654197 0.04654197]] \n", " [[ 0.63554841 0.63650221]\n", " [ 0.63554841 0.63650221]\n", " [ 0.63554841 0.63650221]\n", " [ 0.63554841 0.63650221]]\n", "Error: 0.5\n", "[[ 0.08961122 0.08961122 0.08961122 0.08961122]\n", " [ 0.04679902 0.04679902 0.04679902 0.04679902]] \n", " [[ 0.63739884 0.63834894]\n", " [ 0.63739884 0.63834894]\n", " [ 0.63739884 0.63834894]\n", " [ 0.63739884 0.63834894]]\n", "Error: 0.5\n", "[[ 0.09007178 0.09007178 0.09007178 0.09007178]\n", " [ 0.0470548 0.0470548 0.0470548 0.0470548 ]] \n", " [[ 0.63923609 0.64018244]\n", " [ 0.63923609 0.64018244]\n", " [ 0.63923609 0.64018244]\n", " [ 0.63923609 0.64018244]]\n", "Error: 0.5\n", "[[ 0.0905297 0.0905297 0.0905297 0.0905297 ]\n", " [ 0.04730932 0.04730932 0.04730932 0.04730932]] \n", " [[ 0.64106041 0.64200288]\n", " [ 0.64106041 0.64200288]\n", " [ 0.64106041 0.64200288]\n", " [ 0.64106041 0.64200288]]\n", "Error: 0.5\n", "[[ 0.09098497 0.09098497 0.09098497 0.09098497]\n", " [ 0.0475626 0.0475626 0.0475626 0.0475626 ]] \n", " [[ 0.64287192 0.64381045]\n", " [ 0.64287192 0.64381045]\n", " [ 0.64287192 0.64381045]\n", " [ 0.64287192 0.64381045]]\n", "Error: 0.5\n", "[[ 0.09143764 0.09143764 0.09143764 0.09143764]\n", " [ 0.04781464 0.04781464 0.04781464 0.04781464]] \n", " [[ 0.64467084 0.64560533]\n", " [ 0.64467084 0.64560533]\n", " [ 0.64467084 0.64560533]\n", " [ 0.64467084 0.64560533]]\n", "Error: 0.5\n", "[[ 0.09188773 0.09188773 0.09188773 0.09188773]\n", " [ 0.04806545 0.04806545 0.04806545 0.04806545]] \n", " [[ 0.64645731 0.64738762]\n", " [ 0.64645731 0.64738762]\n", " [ 0.64645731 0.64738762]\n", " [ 0.64645731 0.64738762]]\n", "Error: 0.5\n", "[[ 0.09233525 0.09233525 0.09233525 0.09233525]\n", " [ 0.04831505 0.04831505 0.04831505 0.04831505]] \n", " [[ 0.64823145 0.64915752]\n", " [ 0.64823145 0.64915752]\n", " [ 0.64823145 0.64915752]\n", " [ 0.64823145 0.64915752]]\n", "Error: 0.5\n", "[[ 0.09278024 0.09278024 0.09278024 0.09278024]\n", " [ 0.04856344 0.04856344 0.04856344 0.04856344]] \n", " [[ 0.64999342 0.65091521]\n", " [ 0.64999342 0.65091521]\n", " [ 0.64999342 0.65091521]\n", " [ 0.64999342 0.65091521]]\n", "Error: 0.5\n", "[[ 0.09322271 0.09322271 0.09322271 0.09322271]\n", " [ 0.04881063 0.04881063 0.04881063 0.04881063]] \n", " [[ 0.65174341 0.65266079]\n", " [ 0.65174341 0.65266079]\n", " [ 0.65174341 0.65266079]\n", " [ 0.65174341 0.65266079]]\n", "Error: 0.5\n", "[[ 0.09366269 0.09366269 0.09366269 0.09366269]\n", " [ 0.04905665 0.04905665 0.04905665 0.04905665]] \n", " [[ 0.6534816 0.65439445]\n", " [ 0.6534816 0.65439445]\n", " [ 0.6534816 0.65439445]\n", " [ 0.6534816 0.65439445]]\n", "Error: 0.5\n", "[[ 0.09410021 0.09410021 0.09410021 0.09410021]\n", " [ 0.04930148 0.04930148 0.04930148 0.04930148]] \n", " [[ 0.65520805 0.65611637]\n", " [ 0.65520805 0.65611637]\n", " [ 0.65520805 0.65611637]\n", " [ 0.65520805 0.65611637]]\n", "Error: 0.5\n", "[[ 0.09453527 0.09453527 0.09453527 0.09453527]\n", " [ 0.04954515 0.04954515 0.04954515 0.04954515]] \n", " [[ 0.656923 0.65782666]\n", " [ 0.656923 0.65782666]\n", " [ 0.656923 0.65782666]\n", " [ 0.656923 0.65782666]]\n", "Error: 0.5\n", "[[ 0.09496791 0.09496791 0.09496791 0.09496791]\n", " [ 0.04978766 0.04978766 0.04978766 0.04978766]] \n", " [[ 0.6586265 0.65952545]\n", " [ 0.6586265 0.65952545]\n", " [ 0.6586265 0.65952545]\n", " [ 0.6586265 0.65952545]]\n", "Error: 0.5\n", "[[ 0.09539814 0.09539814 0.09539814 0.09539814]\n", " [ 0.05002902 0.05002902 0.05002902 0.05002902]] \n", " [[ 0.66031879 0.66121292]\n", " [ 0.66031879 0.66121292]\n", " [ 0.66031879 0.66121292]\n", " [ 0.66031879 0.66121292]]\n", "Error: 0.5\n", "[[ 0.09582599 0.09582599 0.09582599 0.09582599]\n", " [ 0.05026925 0.05026925 0.05026925 0.05026925]] \n", " [[ 0.66199994 0.66288918]\n", " [ 0.66199994 0.66288918]\n", " [ 0.66199994 0.66288918]\n", " [ 0.66199994 0.66288918]]\n", "Error: 0.5\n", "[[ 0.09625148 0.09625148 0.09625148 0.09625148]\n", " [ 0.05050835 0.05050835 0.05050835 0.05050835]] \n", " [[ 0.66367012 0.66455442]\n", " [ 0.66367012 0.66455442]\n", " [ 0.66367012 0.66455442]\n", " [ 0.66367012 0.66455442]]\n", "Error: 0.5\n", "[[ 0.09667463 0.09667463 0.09667463 0.09667463]\n", " [ 0.05074634 0.05074634 0.05074634 0.05074634]] \n", " [[ 0.66532946 0.66620868]\n", " [ 0.66532946 0.66620868]\n", " [ 0.66532946 0.66620868]\n", " [ 0.66532946 0.66620868]]\n", "Error: 0.5\n", "[[ 0.09709546 0.09709546 0.09709546 0.09709546]\n", " [ 0.05098321 0.05098321 0.05098321 0.05098321]] \n", " [[ 0.66697806 0.66785216]\n", " [ 0.66697806 0.66785216]\n", " [ 0.66697806 0.66785216]\n", " [ 0.66697806 0.66785216]]\n", "Error: 0.5\n", "[[ 0.09751399 0.09751399 0.09751399 0.09751399]\n", " [ 0.05121898 0.05121898 0.05121898 0.05121898]] \n", " [[ 0.66861606 0.66948497]\n", " [ 0.66861606 0.66948497]\n", " [ 0.66861606 0.66948497]\n", " [ 0.66861606 0.66948497]]\n", "Error: 0.5\n", "[[ 0.09793025 0.09793025 0.09793025 0.09793025]\n", " [ 0.05145366 0.05145366 0.05145366 0.05145366]] \n", " [[ 0.67024356 0.67110723]\n", " [ 0.67024356 0.67110723]\n", " [ 0.67024356 0.67110723]\n", " [ 0.67024356 0.67110723]]\n", "Error: 0.5\n", "[[ 0.09834424 0.09834424 0.09834424 0.09834424]\n", " [ 0.05168726 0.05168726 0.05168726 0.05168726]] \n", " [[ 0.67186075 0.67271912]\n", " [ 0.67186075 0.67271912]\n", " [ 0.67186075 0.67271912]\n", " [ 0.67186075 0.67271912]]\n", "Error: 0.5\n", "[[ 0.09875599 0.09875599 0.09875599 0.09875599]\n", " [ 0.05191979 0.05191979 0.05191979 0.05191979]] \n", " [[ 0.6734677 0.6743207]\n", " [ 0.6734677 0.6743207]\n", " [ 0.6734677 0.6743207]\n", " [ 0.6734677 0.6743207]]\n", "Error: 0.5\n", "[[ 0.09916553 0.09916553 0.09916553 0.09916553]\n", " [ 0.05215125 0.05215125 0.05215125 0.05215125]] \n", " [[ 0.67506456 0.67591214]\n", " [ 0.67506456 0.67591214]\n", " [ 0.67506456 0.67591214]\n", " [ 0.67506456 0.67591214]]\n", "Error: 0.5\n", "[[ 0.09957287 0.09957287 0.09957287 0.09957287]\n", " [ 0.05238165 0.05238165 0.05238165 0.05238165]] \n", " [[ 0.67665142 0.67749351]\n", " [ 0.67665142 0.67749351]\n", " [ 0.67665142 0.67749351]\n", " [ 0.67665142 0.67749351]]\n", "Error: 0.5\n", "[[ 0.09997802 0.09997802 0.09997802 0.09997802]\n", " [ 0.05261101 0.05261101 0.05261101 0.05261101]] \n", " [[ 0.67822844 0.67906493]\n", " [ 0.67822844 0.67906493]\n", " [ 0.67822844 0.67906493]\n", " [ 0.67822844 0.67906493]]\n", "Error: 0.5\n", "[[ 0.10038102 0.10038102 0.10038102 0.10038102]\n", " [ 0.05283933 0.05283933 0.05283933 0.05283933]] \n", " [[ 0.67979568 0.68062657]\n", " [ 0.67979568 0.68062657]\n", " [ 0.67979568 0.68062657]\n", " [ 0.67979568 0.68062657]]\n", "Error: 0.5\n", "[[ 0.10078187 0.10078187 0.10078187 0.10078187]\n", " [ 0.05306662 0.05306662 0.05306662 0.05306662]] \n", " [[ 0.68135327 0.6821785 ]\n", " [ 0.68135327 0.6821785 ]\n", " [ 0.68135327 0.6821785 ]\n", " [ 0.68135327 0.6821785 ]]\n", "Error: 0.5\n", "[[ 0.10118061 0.10118061 0.10118061 0.10118061]\n", " [ 0.05329289 0.05329289 0.05329289 0.05329289]] \n", " [[ 0.68290138 0.68372083]\n", " [ 0.68290138 0.68372083]\n", " [ 0.68290138 0.68372083]\n", " [ 0.68290138 0.68372083]]\n", "Error: 0.5\n", "[[ 0.10157723 0.10157723 0.10157723 0.10157723]\n", " [ 0.05351814 0.05351814 0.05351814 0.05351814]] \n", " [[ 0.68444008 0.68525368]\n", " [ 0.68444008 0.68525368]\n", " [ 0.68444008 0.68525368]\n", " [ 0.68444008 0.68525368]]\n", "Error: 0.5\n", "[[ 0.10197177 0.10197177 0.10197177 0.10197177]\n", " [ 0.05374238 0.05374238 0.05374238 0.05374238]] \n", " [[ 0.68596941 0.68677717]\n", " [ 0.68596941 0.68677717]\n", " [ 0.68596941 0.68677717]\n", " [ 0.68596941 0.68677717]]\n", "Error: 0.5\n", "[[ 0.10236423 0.10236423 0.10236423 0.10236423]\n", " [ 0.05396562 0.05396562 0.05396562 0.05396562]] \n", " [[ 0.68748957 0.68829137]\n", " [ 0.68748957 0.68829137]\n", " [ 0.68748957 0.68829137]\n", " [ 0.68748957 0.68829137]]\n", "Error: 0.5\n", "[[ 0.10275465 0.10275465 0.10275465 0.10275465]\n", " [ 0.05418788 0.05418788 0.05418788 0.05418788]] \n", " [[ 0.68900061 0.68979645]\n", " [ 0.68900061 0.68979645]\n", " [ 0.68900061 0.68979645]\n", " [ 0.68900061 0.68979645]]\n", "Error: 0.5\n", "[[ 0.10314304 0.10314304 0.10314304 0.10314304]\n", " [ 0.05440916 0.05440916 0.05440916 0.05440916]] \n", " [[ 0.69050264 0.69129246]\n", " [ 0.69050264 0.69129246]\n", " [ 0.69050264 0.69129246]\n", " [ 0.69050264 0.69129246]]\n", "Error: 0.5\n", "[[ 0.1035294 0.1035294 0.1035294 0.1035294 ]\n", " [ 0.05462946 0.05462946 0.05462946 0.05462946]] \n", " [[ 0.6919958 0.69277948]\n", " [ 0.6919958 0.69277948]\n", " [ 0.6919958 0.69277948]\n", " [ 0.6919958 0.69277948]]\n", "Error: 0.5\n", "[[ 0.10391377 0.10391377 0.10391377 0.10391377]\n", " [ 0.05484879 0.05484879 0.05484879 0.05484879]] \n", " [[ 0.69348013 0.69425768]\n", " [ 0.69348013 0.69425768]\n", " [ 0.69348013 0.69425768]\n", " [ 0.69348013 0.69425768]]\n", "Error: 0.5\n", "[[ 0.10429616 0.10429616 0.10429616 0.10429616]\n", " [ 0.05506717 0.05506717 0.05506717 0.05506717]] \n", " [[ 0.69495577 0.69572711]\n", " [ 0.69495577 0.69572711]\n", " [ 0.69495577 0.69572711]\n", " [ 0.69495577 0.69572711]]\n", "Error: 0.5\n", "[[ 0.10467657 0.10467657 0.10467657 0.10467657]\n", " [ 0.05528459 0.05528459 0.05528459 0.05528459]] \n", " [[ 0.69642276 0.6971879 ]\n", " [ 0.69642276 0.6971879 ]\n", " [ 0.69642276 0.6971879 ]\n", " [ 0.69642276 0.6971879 ]]\n", "Error: 0.5\n", "[[ 0.10505504 0.10505504 0.10505504 0.10505504]\n", " [ 0.05550107 0.05550107 0.05550107 0.05550107]] \n", " [[ 0.69788122 0.69864011]\n", " [ 0.69788122 0.69864011]\n", " [ 0.69788122 0.69864011]\n", " [ 0.69788122 0.69864011]]\n", "Error: 0.5\n", "[[ 0.10543158 0.10543158 0.10543158 0.10543158]\n", " [ 0.05571661 0.05571661 0.05571661 0.05571661]] \n", " [[ 0.69933128 0.70008385]\n", " [ 0.69933128 0.70008385]\n", " [ 0.69933128 0.70008385]\n", " [ 0.69933128 0.70008385]]\n", "Error: 0.5\n", "[[ 0.1058062 0.1058062 0.1058062 0.1058062 ]\n", " [ 0.05593123 0.05593123 0.05593123 0.05593123]] \n", " [[ 0.700773 0.70151919]\n", " [ 0.700773 0.70151919]\n", " [ 0.700773 0.70151919]\n", " [ 0.700773 0.70151919]]\n", "Error: 0.5\n", "[[ 0.10617892 0.10617892 0.10617892 0.10617892]\n", " [ 0.05614492 0.05614492 0.05614492 0.05614492]] \n", " [[ 0.70220649 0.70294625]\n", " [ 0.70220649 0.70294625]\n", " [ 0.70220649 0.70294625]\n", " [ 0.70220649 0.70294625]]\n", "Error: 0.5\n", "[[ 0.10654976 0.10654976 0.10654976 0.10654976]\n", " [ 0.05635769 0.05635769 0.05635769 0.05635769]] \n", " [[ 0.70363182 0.70436507]\n", " [ 0.70363182 0.70436507]\n", " [ 0.70363182 0.70436507]\n", " [ 0.70363182 0.70436507]]\n", "Error: 0.5\n", "[[ 0.10691873 0.10691873 0.10691873 0.10691873]\n", " [ 0.05656956 0.05656956 0.05656956 0.05656956]] \n", " [[ 0.70504904 0.7057758 ]\n", " [ 0.70504904 0.7057758 ]\n", " [ 0.70504904 0.7057758 ]\n", " [ 0.70504904 0.7057758 ]]\n", "Error: 0.5\n", "[[ 0.10728585 0.10728585 0.10728585 0.10728585]\n", " [ 0.05678053 0.05678053 0.05678053 0.05678053]] \n", " [[ 0.70645827 0.70717847]\n", " [ 0.70645827 0.70717847]\n", " [ 0.70645827 0.70717847]\n", " [ 0.70645827 0.70717847]]\n", "Error: 0.5\n", "[[ 0.10765113 0.10765113 0.10765113 0.10765113]\n", " [ 0.0569906 0.0569906 0.0569906 0.0569906 ]] \n", " [[ 0.70785964 0.70857322]\n", " [ 0.70785964 0.70857322]\n", " [ 0.70785964 0.70857322]\n", " [ 0.70785964 0.70857322]]\n", "Error: 0.5\n", "[[ 0.10801459 0.10801459 0.10801459 0.10801459]\n", " [ 0.05719979 0.05719979 0.05719979 0.05719979]] \n", " [[ 0.70925319 0.7099601 ]\n", " [ 0.70925319 0.7099601 ]\n", " [ 0.70925319 0.7099601 ]\n", " [ 0.70925319 0.7099601 ]]\n", "Error: 0.5\n", "[[ 0.10837624 0.10837624 0.10837624 0.10837624]\n", " [ 0.05740809 0.05740809 0.05740809 0.05740809]] \n", " [[ 0.710639 0.71133924]\n", " [ 0.710639 0.71133924]\n", " [ 0.710639 0.71133924]\n", " [ 0.710639 0.71133924]]\n", "Error: 0.5\n", "[[ 0.10873611 0.10873611 0.10873611 0.10873611]\n", " [ 0.05761553 0.05761553 0.05761553 0.05761553]] \n", " [[ 0.71201712 0.71271062]\n", " [ 0.71201712 0.71271062]\n", " [ 0.71201712 0.71271062]\n", " [ 0.71201712 0.71271062]]\n", "Error: 0.5\n", "[[ 0.10909419 0.10909419 0.10909419 0.10909419]\n", " [ 0.0578221 0.0578221 0.0578221 0.0578221 ]] \n", " [[ 0.71338767 0.71407443]\n", " [ 0.71338767 0.71407443]\n", " [ 0.71338767 0.71407443]\n", " [ 0.71338767 0.71407443]]\n", "Error: 0.5\n", "[[ 0.10945051 0.10945051 0.10945051 0.10945051]\n", " [ 0.0580278 0.0580278 0.0580278 0.0580278 ]] \n", " [[ 0.71475071 0.71543068]\n", " [ 0.71475071 0.71543068]\n", " [ 0.71475071 0.71543068]\n", " [ 0.71475071 0.71543068]]\n", "Error: 0.5\n", "[[ 0.10980508 0.10980508 0.10980508 0.10980508]\n", " [ 0.05823266 0.05823266 0.05823266 0.05823266]] \n", " [[ 0.71610636 0.71677947]\n", " [ 0.71610636 0.71677947]\n", " [ 0.71610636 0.71677947]\n", " [ 0.71610636 0.71677947]]\n", "Error: 0.5\n", "[[ 0.11015793 0.11015793 0.11015793 0.11015793]\n", " [ 0.05843666 0.05843666 0.05843666 0.05843666]] \n", " [[ 0.71745467 0.71812087]\n", " [ 0.71745467 0.71812087]\n", " [ 0.71745467 0.71812087]\n", " [ 0.71745467 0.71812087]]\n", "Error: 0.5\n", "[[ 0.11050905 0.11050905 0.11050905 0.11050905]\n", " [ 0.05863983 0.05863983 0.05863983 0.05863983]] \n", " [[ 0.71879572 0.71945494]\n", " [ 0.71879572 0.71945494]\n", " [ 0.71879572 0.71945494]\n", " [ 0.71879572 0.71945494]]\n", "Error: 0.5\n", "[[ 0.11085847 0.11085847 0.11085847 0.11085847]\n", " [ 0.05884216 0.05884216 0.05884216 0.05884216]] \n", " [[ 0.72012955 0.7207818 ]\n", " [ 0.72012955 0.7207818 ]\n", " [ 0.72012955 0.7207818 ]\n", " [ 0.72012955 0.7207818 ]]\n", "Error: 0.5\n", "[[ 0.1112062 0.1112062 0.1112062 0.1112062 ]\n", " [ 0.05904366 0.05904366 0.05904366 0.05904366]] \n", " [[ 0.72145623 0.72210151]\n", " [ 0.72145623 0.72210151]\n", " [ 0.72145623 0.72210151]\n", " [ 0.72145623 0.72210151]]\n", "Error: 0.5\n", "[[ 0.11155225 0.11155225 0.11155225 0.11155225]\n", " [ 0.05924434 0.05924434 0.05924434 0.05924434]] \n", " [[ 0.72277588 0.72341412]\n", " [ 0.72277588 0.72341412]\n", " [ 0.72277588 0.72341412]\n", " [ 0.72277588 0.72341412]]\n", "Error: 0.5\n", "[[ 0.11189663 0.11189663 0.11189663 0.11189663]\n", " [ 0.05944421 0.05944421 0.05944421 0.05944421]] \n", " [[ 0.72408855 0.7247197 ]\n", " [ 0.72408855 0.7247197 ]\n", " [ 0.72408855 0.7247197 ]\n", " [ 0.72408855 0.7247197 ]]\n", "Error: 0.5\n", "[[ 0.11223936 0.11223936 0.11223936 0.11223936]\n", " [ 0.05964326 0.05964326 0.05964326 0.05964326]] \n", " [[ 0.72539431 0.72601831]\n", " [ 0.72539431 0.72601831]\n", " [ 0.72539431 0.72601831]\n", " [ 0.72539431 0.72601831]]\n", "Error: 0.5\n", "[[ 0.11258046 0.11258046 0.11258046 0.11258046]\n", " [ 0.05984151 0.05984151 0.05984151 0.05984151]] \n", " [[ 0.72669321 0.72731006]\n", " [ 0.72669321 0.72731006]\n", " [ 0.72669321 0.72731006]\n", " [ 0.72669321 0.72731006]]\n", "Error: 0.5\n", "[[ 0.11291993 0.11291993 0.11291993 0.11291993]\n", " [ 0.06003897 0.06003897 0.06003897 0.06003897]] \n", " [[ 0.72798532 0.72859502]\n", " [ 0.72798532 0.72859502]\n", " [ 0.72798532 0.72859502]\n", " [ 0.72798532 0.72859502]]\n", "Error: 0.5\n", "[[ 0.11325779 0.11325779 0.11325779 0.11325779]\n", " [ 0.06023563 0.06023563 0.06023563 0.06023563]] \n", " [[ 0.7292707 0.72987318]\n", " [ 0.7292707 0.72987318]\n", " [ 0.7292707 0.72987318]\n", " [ 0.7292707 0.72987318]]\n", "Error: 0.5\n", "[[ 0.11359405 0.11359405 0.11359405 0.11359405]\n", " [ 0.0604315 0.0604315 0.0604315 0.0604315 ]] \n", " [[ 0.73054945 0.73114467]\n", " [ 0.73054945 0.73114467]\n", " [ 0.73054945 0.73114467]\n", " [ 0.73054945 0.73114467]]\n", "Error: 0.5\n", "[[ 0.11392872 0.11392872 0.11392872 0.11392872]\n", " [ 0.0606266 0.0606266 0.0606266 0.0606266 ]] \n", " [[ 0.7318216 0.73240954]\n", " [ 0.7318216 0.73240954]\n", " [ 0.7318216 0.73240954]\n", " [ 0.7318216 0.73240954]]\n", "Error: 0.5\n", "[[ 0.11426182 0.11426182 0.11426182 0.11426182]\n", " [ 0.06082091 0.06082091 0.06082091 0.06082091]] \n", " [[ 0.73308724 0.73366791]\n", " [ 0.73308724 0.73366791]\n", " [ 0.73308724 0.73366791]\n", " [ 0.73308724 0.73366791]]\n", "Error: 0.5\n", "[[ 0.11459336 0.11459336 0.11459336 0.11459336]\n", " [ 0.06101447 0.06101447 0.06101447 0.06101447]] \n", " [[ 0.73434645 0.73491979]\n", " [ 0.73434645 0.73491979]\n", " [ 0.73434645 0.73491979]\n", " [ 0.73434645 0.73491979]]\n", "Error: 0.5\n", "[[ 0.11492335 0.11492335 0.11492335 0.11492335]\n", " [ 0.06120725 0.06120725 0.06120725 0.06120725]] \n", " [[ 0.73559922 0.73616523]\n", " [ 0.73559922 0.73616523]\n", " [ 0.73559922 0.73616523]\n", " [ 0.73559922 0.73616523]]\n", "Error: 0.5\n", "[[ 0.1152518 0.1152518 0.1152518 0.1152518 ]\n", " [ 0.06139928 0.06139928 0.06139928 0.06139928]] \n", " [[ 0.73684567 0.73740429]\n", " [ 0.73684567 0.73740429]\n", " [ 0.73684567 0.73740429]\n", " [ 0.73684567 0.73740429]]\n", "Error: 0.5\n", "[[ 0.11557873 0.11557873 0.11557873 0.11557873]\n", " [ 0.06159056 0.06159056 0.06159056 0.06159056]] \n", " [[ 0.73808587 0.73863703]\n", " [ 0.73808587 0.73863703]\n", " [ 0.73808587 0.73863703]\n", " [ 0.73808587 0.73863703]]\n", "Error: 0.5\n", "[[ 0.11590415 0.11590415 0.11590415 0.11590415]\n", " [ 0.06178109 0.06178109 0.06178109 0.06178109]] \n", " [[ 0.7393198 0.73986357]\n", " [ 0.7393198 0.73986357]\n", " [ 0.7393198 0.73986357]\n", " [ 0.7393198 0.73986357]]\n", "Error: 0.5\n", "[[ 0.11622807 0.11622807 0.11622807 0.11622807]\n", " [ 0.06197088 0.06197088 0.06197088 0.06197088]] \n", " [[ 0.7405476 0.74108392]\n", " [ 0.7405476 0.74108392]\n", " [ 0.7405476 0.74108392]\n", " [ 0.7405476 0.74108392]]\n", "Error: 0.5\n", "[[ 0.11655049 0.11655049 0.11655049 0.11655049]\n", " [ 0.06215994 0.06215994 0.06215994 0.06215994]] \n", " [[ 0.74176931 0.74229813]\n", " [ 0.74176931 0.74229813]\n", " [ 0.74176931 0.74229813]\n", " [ 0.74176931 0.74229813]]\n", "Error: 0.5\n", "[[ 0.11687144 0.11687144 0.11687144 0.11687144]\n", " [ 0.06234826 0.06234826 0.06234826 0.06234826]] \n", " [[ 0.74298495 0.74350625]\n", " [ 0.74298495 0.74350625]\n", " [ 0.74298495 0.74350625]\n", " [ 0.74298495 0.74350625]]\n", "Error: 0.5\n", "[[ 0.11719092 0.11719092 0.11719092 0.11719092]\n", " [ 0.06253585 0.06253585 0.06253585 0.06253585]] \n", " [[ 0.74419463 0.74470842]\n", " [ 0.74419463 0.74470842]\n", " [ 0.74419463 0.74470842]\n", " [ 0.74419463 0.74470842]]\n", "Error: 0.5\n", "[[ 0.11750895 0.11750895 0.11750895 0.11750895]\n", " [ 0.06272273 0.06272273 0.06272273 0.06272273]] \n", " [[ 0.7453984 0.74590462]\n", " [ 0.7453984 0.74590462]\n", " [ 0.7453984 0.74590462]\n", " [ 0.7453984 0.74590462]]\n", "Error: 0.5\n", "[[ 0.11782553 0.11782553 0.11782553 0.11782553]\n", " [ 0.06290889 0.06290889 0.06290889 0.06290889]] \n", " [[ 0.74659628 0.74709493]\n", " [ 0.74659628 0.74709493]\n", " [ 0.74659628 0.74709493]\n", " [ 0.74659628 0.74709493]]\n", "Error: 0.5\n", "[[ 0.11814068 0.11814068 0.11814068 0.11814068]\n", " [ 0.06309434 0.06309434 0.06309434 0.06309434]] \n", " [[ 0.74778831 0.74827939]\n", " [ 0.74778831 0.74827939]\n", " [ 0.74778831 0.74827939]\n", " [ 0.74778831 0.74827939]]\n", "Error: 0.5\n", "[[ 0.11845441 0.11845441 0.11845441 0.11845441]\n", " [ 0.06327908 0.06327908 0.06327908 0.06327908]] \n", " [[ 0.74897456 0.74945801]\n", " [ 0.74897456 0.74945801]\n", " [ 0.74897456 0.74945801]\n", " [ 0.74897456 0.74945801]]\n", "Error: 0.5\n", "[[ 0.11876673 0.11876673 0.11876673 0.11876673]\n", " [ 0.06346313 0.06346313 0.06346313 0.06346313]] \n", " [[ 0.75015515 0.75063092]\n", " [ 0.75015515 0.75063092]\n", " [ 0.75015515 0.75063092]\n", " [ 0.75015515 0.75063092]]\n", "Error: 0.5\n", "[[ 0.11907765 0.11907765 0.11907765 0.11907765]\n", " [ 0.06364648 0.06364648 0.06364648 0.06364648]] \n", " [[ 0.75133002 0.75179815]\n", " [ 0.75133002 0.75179815]\n", " [ 0.75133002 0.75179815]\n", " [ 0.75133002 0.75179815]]\n", "Error: 0.5\n", "[[ 0.11938718 0.11938718 0.11938718 0.11938718]\n", " [ 0.06382914 0.06382914 0.06382914 0.06382914]] \n", " [[ 0.75249928 0.75295973]\n", " [ 0.75249928 0.75295973]\n", " [ 0.75249928 0.75295973]\n", " [ 0.75249928 0.75295973]]\n", "Error: 0.5\n", "[[ 0.11969533 0.11969533 0.11969533 0.11969533]\n", " [ 0.06401111 0.06401111 0.06401111 0.06401111]] \n", " [[ 0.753663 0.7541157]\n", " [ 0.753663 0.7541157]\n", " [ 0.753663 0.7541157]\n", " [ 0.753663 0.7541157]]\n", "Error: 0.5\n", "[[ 0.12000211 0.12000211 0.12000211 0.12000211]\n", " [ 0.0641924 0.0641924 0.0641924 0.0641924 ]] \n", " [[ 0.75482118 0.75526619]\n", " [ 0.75482118 0.75526619]\n", " [ 0.75482118 0.75526619]\n", " [ 0.75482118 0.75526619]]\n", "Error: 0.5\n", "[[ 0.12030753 0.12030753 0.12030753 0.12030753]\n", " [ 0.06437302 0.06437302 0.06437302 0.06437302]] \n", " [[ 0.75597394 0.75641119]\n", " [ 0.75597394 0.75641119]\n", " [ 0.75597394 0.75641119]\n", " [ 0.75597394 0.75641119]]\n", "Error: 0.5\n", "[[ 0.12061162 0.12061162 0.12061162 0.12061162]\n", " [ 0.06455296 0.06455296 0.06455296 0.06455296]] \n", " [[ 0.75712126 0.75755072]\n", " [ 0.75712126 0.75755072]\n", " [ 0.75712126 0.75755072]\n", " [ 0.75712126 0.75755072]]\n", "Error: 0.5\n", "[[ 0.12091435 0.12091435 0.12091435 0.12091435]\n", " [ 0.06473224 0.06473224 0.06473224 0.06473224]] \n", " [[ 0.75826323 0.75868487]\n", " [ 0.75826323 0.75868487]\n", " [ 0.75826323 0.75868487]\n", " [ 0.75826323 0.75868487]]\n", "Error: 0.5\n", "[[ 0.12121577 0.12121577 0.12121577 0.12121577]\n", " [ 0.06491085 0.06491085 0.06491085 0.06491085]] \n", " [[ 0.75939989 0.75981367]\n", " [ 0.75939989 0.75981367]\n", " [ 0.75939989 0.75981367]\n", " [ 0.75939989 0.75981367]]\n", "Error: 0.5\n", "[[ 0.12151586 0.12151586 0.12151586 0.12151586]\n", " [ 0.06508881 0.06508881 0.06508881 0.06508881]] \n", " [[ 0.76053125 0.76093721]\n", " [ 0.76053125 0.76093721]\n", " [ 0.76053125 0.76093721]\n", " [ 0.76053125 0.76093721]]\n", "Error: 0.5\n", "[[ 0.12181465 0.12181465 0.12181465 0.12181465]\n", " [ 0.06526611 0.06526611 0.06526611 0.06526611]] \n", " [[ 0.76165736 0.76205552]\n", " [ 0.76165736 0.76205552]\n", " [ 0.76165736 0.76205552]\n", " [ 0.76165736 0.76205552]]\n", "Error: 0.5\n", "[[ 0.12211213 0.12211213 0.12211213 0.12211213]\n", " [ 0.06544276 0.06544276 0.06544276 0.06544276]] \n", " [[ 0.76277828 0.76316857]\n", " [ 0.76277828 0.76316857]\n", " [ 0.76277828 0.76316857]\n", " [ 0.76277828 0.76316857]]\n", "Error: 0.5\n", "[[ 0.12240833 0.12240833 0.12240833 0.12240833]\n", " [ 0.06561877 0.06561877 0.06561877 0.06561877]] \n", " [[ 0.76389408 0.7642765 ]\n", " [ 0.76389408 0.7642765 ]\n", " [ 0.76389408 0.7642765 ]\n", " [ 0.76389408 0.7642765 ]]\n", "Error: 0.5\n", "[[ 0.12270325 0.12270325 0.12270325 0.12270325]\n", " [ 0.06579414 0.06579414 0.06579414 0.06579414]] \n", " [[ 0.76500481 0.76537931]\n", " [ 0.76500481 0.76537931]\n", " [ 0.76500481 0.76537931]\n", " [ 0.76500481 0.76537931]]\n", "Error: 0.5\n", "[[ 0.1229969 0.1229969 0.1229969 0.1229969 ]\n", " [ 0.06596887 0.06596887 0.06596887 0.06596887]] \n", " [[ 0.76611048 0.76647705]\n", " [ 0.76611048 0.76647705]\n", " [ 0.76611048 0.76647705]\n", " [ 0.76611048 0.76647705]]\n", "Error: 0.5\n", "[[ 0.12328928 0.12328928 0.12328928 0.12328928]\n", " [ 0.06614297 0.06614297 0.06614297 0.06614297]] \n", " [[ 0.76721114 0.76756978]\n", " [ 0.76721114 0.76756978]\n", " [ 0.76721114 0.76756978]\n", " [ 0.76721114 0.76756978]]\n", "Error: 0.5\n", "[[ 0.12358042 0.12358042 0.12358042 0.12358042]\n", " [ 0.06631644 0.06631644 0.06631644 0.06631644]] \n", " [[ 0.76830685 0.76865751]\n", " [ 0.76830685 0.76865751]\n", " [ 0.76830685 0.76865751]\n", " [ 0.76830685 0.76865751]]\n", "Error: 0.5\n", "[[ 0.12387031 0.12387031 0.12387031 0.12387031]\n", " [ 0.06648929 0.06648929 0.06648929 0.06648929]] \n", " [[ 0.76939762 0.76974034]\n", " [ 0.76939762 0.76974034]\n", " [ 0.76939762 0.76974034]\n", " [ 0.76939762 0.76974034]]\n", "Error: 0.5\n", "[[ 0.12415897 0.12415897 0.12415897 0.12415897]\n", " [ 0.06666153 0.06666153 0.06666153 0.06666153]] \n", " [[ 0.77048349 0.77081829]\n", " [ 0.77048349 0.77081829]\n", " [ 0.77048349 0.77081829]\n", " [ 0.77048349 0.77081829]]\n", "Error: 0.5\n", "[[ 0.12444641 0.12444641 0.12444641 0.12444641]\n", " [ 0.06683315 0.06683315 0.06683315 0.06683315]] \n", " [[ 0.77156454 0.77189136]\n", " [ 0.77156454 0.77189136]\n", " [ 0.77156454 0.77189136]\n", " [ 0.77156454 0.77189136]]\n", "Error: 0.5\n", "[[ 0.12473263 0.12473263 0.12473263 0.12473263]\n", " [ 0.06700415 0.06700415 0.06700415 0.06700415]] \n", " [[ 0.77264082 0.77295959]\n", " [ 0.77264082 0.77295959]\n", " [ 0.77264082 0.77295959]\n", " [ 0.77264082 0.77295959]]\n", "Error: 0.5\n", "[[ 0.12501764 0.12501764 0.12501764 0.12501764]\n", " [ 0.06717455 0.06717455 0.06717455 0.06717455]] \n", " [[ 0.77371234 0.77402306]\n", " [ 0.77371234 0.77402306]\n", " [ 0.77371234 0.77402306]\n", " [ 0.77371234 0.77402306]]\n", "Error: 0.5\n", "[[ 0.12530145 0.12530145 0.12530145 0.12530145]\n", " [ 0.06734435 0.06734435 0.06734435 0.06734435]] \n", " [[ 0.77477914 0.77508181]\n", " [ 0.77477914 0.77508181]\n", " [ 0.77477914 0.77508181]\n", " [ 0.77477914 0.77508181]]\n", "Error: 0.5\n", "[[ 0.12558408 0.12558408 0.12558408 0.12558408]\n", " [ 0.06751355 0.06751355 0.06751355 0.06751355]] \n", " [[ 0.77584124 0.77613586]\n", " [ 0.77584124 0.77613586]\n", " [ 0.77584124 0.77613586]\n", " [ 0.77584124 0.77613586]]\n", "Error: 0.5\n", "[[ 0.12586552 0.12586552 0.12586552 0.12586552]\n", " [ 0.06768215 0.06768215 0.06768215 0.06768215]] \n", " [[ 0.77689868 0.77718526]\n", " [ 0.77689868 0.77718526]\n", " [ 0.77689868 0.77718526]\n", " [ 0.77689868 0.77718526]]\n", "Error: 0.5\n", "[[ 0.12614579 0.12614579 0.12614579 0.12614579]\n", " [ 0.06785017 0.06785017 0.06785017 0.06785017]] \n", " [[ 0.77795154 0.77823007]\n", " [ 0.77795154 0.77823007]\n", " [ 0.77795154 0.77823007]\n", " [ 0.77795154 0.77823007]]\n", "Error: 0.5\n", "[[ 0.12642489 0.12642489 0.12642489 0.12642489]\n", " [ 0.06801759 0.06801759 0.06801759 0.06801759]] \n", " [[ 0.77899987 0.77927029]\n", " [ 0.77899987 0.77927029]\n", " [ 0.77899987 0.77927029]\n", " [ 0.77899987 0.77927029]]\n", "Error: 0.5\n", "[[ 0.12670285 0.12670285 0.12670285 0.12670285]\n", " [ 0.06818443 0.06818443 0.06818443 0.06818443]] \n", " [[ 0.78004366 0.78030598]\n", " [ 0.78004366 0.78030598]\n", " [ 0.78004366 0.78030598]\n", " [ 0.78004366 0.78030598]]\n", "Error: 0.5\n", "[[ 0.12697965 0.12697965 0.12697965 0.12697965]\n", " [ 0.06835069 0.06835069 0.06835069 0.06835069]] \n", " [[ 0.78108293 0.78133714]\n", " [ 0.78108293 0.78133714]\n", " [ 0.78108293 0.78133714]\n", " [ 0.78108293 0.78133714]]\n", "Error: 0.5\n", "[[ 0.12725531 0.12725531 0.12725531 0.12725531]\n", " [ 0.06851637 0.06851637 0.06851637 0.06851637]] \n", " [[ 0.78211778 0.78236383]\n", " [ 0.78211778 0.78236383]\n", " [ 0.78211778 0.78236383]\n", " [ 0.78211778 0.78236383]]\n", "Error: 0.5\n", "[[ 0.12752983 0.12752983 0.12752983 0.12752983]\n", " [ 0.06868149 0.06868149 0.06868149 0.06868149]] \n", " [[ 0.78314817 0.78338611]\n", " [ 0.78314817 0.78338611]\n", " [ 0.78314817 0.78338611]\n", " [ 0.78314817 0.78338611]]\n", "Error: 0.5\n", "[[ 0.12780324 0.12780324 0.12780324 0.12780324]\n", " [ 0.06884603 0.06884603 0.06884603 0.06884603]] \n", " [[ 0.7841742 0.78440398]\n", " [ 0.7841742 0.78440398]\n", " [ 0.7841742 0.78440398]\n", " [ 0.7841742 0.78440398]]\n", "Error: 0.5\n", "[[ 0.12807553 0.12807553 0.12807553 0.12807553]\n", " [ 0.06901001 0.06901001 0.06901001 0.06901001]] \n", " [[ 0.78519589 0.7854175 ]\n", " [ 0.78519589 0.7854175 ]\n", " [ 0.78519589 0.7854175 ]\n", " [ 0.78519589 0.7854175 ]]\n", "Error: 0.5\n", "[[ 0.12834671 0.12834671 0.12834671 0.12834671]\n", " [ 0.06917343 0.06917343 0.06917343 0.06917343]] \n", " [[ 0.78621328 0.78642672]\n", " [ 0.78621328 0.78642672]\n", " [ 0.78621328 0.78642672]\n", " [ 0.78621328 0.78642672]]\n", "Error: 0.5\n", "[[ 0.12861679 0.12861679 0.12861679 0.12861679]\n", " [ 0.0693363 0.0693363 0.0693363 0.0693363 ]] \n", " [[ 0.78722638 0.78743166]\n", " [ 0.78722638 0.78743166]\n", " [ 0.78722638 0.78743166]\n", " [ 0.78722638 0.78743166]]\n", "Error: 0.5\n", "[[ 0.12888579 0.12888579 0.12888579 0.12888579]\n", " [ 0.06949861 0.06949861 0.06949861 0.06949861]] \n", " [[ 0.78823525 0.78843236]\n", " [ 0.78823525 0.78843236]\n", " [ 0.78823525 0.78843236]\n", " [ 0.78823525 0.78843236]]\n", "Error: 0.5\n", "[[ 0.1291537 0.1291537 0.1291537 0.1291537 ]\n", " [ 0.06966037 0.06966037 0.06966037 0.06966037]] \n", " [[ 0.78923988 0.78942883]\n", " [ 0.78923988 0.78942883]\n", " [ 0.78923988 0.78942883]\n", " [ 0.78923988 0.78942883]]\n", "Error: 0.5\n", "[[ 0.12942052 0.12942052 0.12942052 0.12942052]\n", " [ 0.06982158 0.06982158 0.06982158 0.06982158]] \n", " [[ 0.79024035 0.79042113]\n", " [ 0.79024035 0.79042113]\n", " [ 0.79024035 0.79042113]\n", " [ 0.79024035 0.79042113]]\n", "Error: 0.5\n", "[[ 0.12968627 0.12968627 0.12968627 0.12968627]\n", " [ 0.06998225 0.06998225 0.06998225 0.06998225]] \n", " [[ 0.79123664 0.79140925]\n", " [ 0.79123664 0.79140925]\n", " [ 0.79123664 0.79140925]\n", " [ 0.79123664 0.79140925]]\n", "Error: 0.5\n", "[[ 0.12995096 0.12995096 0.12995096 0.12995096]\n", " [ 0.07014238 0.07014238 0.07014238 0.07014238]] \n", " [[ 0.79222882 0.79239327]\n", " [ 0.79222882 0.79239327]\n", " [ 0.79222882 0.79239327]\n", " [ 0.79222882 0.79239327]]\n", "Error: 0.5\n", "[[ 0.13021459 0.13021459 0.13021459 0.13021459]\n", " [ 0.07030197 0.07030197 0.07030197 0.07030197]] \n", " [[ 0.79321694 0.79337317]\n", " [ 0.79321694 0.79337317]\n", " [ 0.79321694 0.79337317]\n", " [ 0.79321694 0.79337317]]\n", "Error: 0.5\n", "[[ 0.13047718 0.13047718 0.13047718 0.13047718]\n", " [ 0.07046103 0.07046103 0.07046103 0.07046103]] \n", " [[ 0.79420102 0.79434901]\n", " [ 0.79420102 0.79434901]\n", " [ 0.79420102 0.79434901]\n", " [ 0.79420102 0.79434901]]\n", "Error: 0.5\n", "[[ 0.13073872 0.13073872 0.13073872 0.13073872]\n", " [ 0.07061957 0.07061957 0.07061957 0.07061957]] \n", " [[ 0.7951811 0.79532087]\n", " [ 0.7951811 0.79532087]\n", " [ 0.7951811 0.79532087]\n", " [ 0.7951811 0.79532087]]\n", "Error: 0.5\n", "[[ 0.13099924 0.13099924 0.13099924 0.13099924]\n", " [ 0.07077757 0.07077757 0.07077757 0.07077757]] \n", " [[ 0.79615718 0.79628873]\n", " [ 0.79615718 0.79628873]\n", " [ 0.79615718 0.79628873]\n", " [ 0.79615718 0.79628873]]\n", "Error: 0.5\n", "[[ 0.13125873 0.13125873 0.13125873 0.13125873]\n", " [ 0.07093506 0.07093506 0.07093506 0.07093506]] \n", " [[ 0.79712933 0.7972526 ]\n", " [ 0.79712933 0.7972526 ]\n", " [ 0.79712933 0.7972526 ]\n", " [ 0.79712933 0.7972526 ]]\n", "Error: 0.5\n", "[[ 0.13151719 0.13151719 0.13151719 0.13151719]\n", " [ 0.07109202 0.07109202 0.07109202 0.07109202]] \n", " [[ 0.79809755 0.79821253]\n", " [ 0.79809755 0.79821253]\n", " [ 0.79809755 0.79821253]\n", " [ 0.79809755 0.79821253]]\n", "Error: 0.5\n", "[[ 0.13177463 0.13177463 0.13177463 0.13177463]\n", " [ 0.07124846 0.07124846 0.07124846 0.07124846]] \n", " [[ 0.79906183 0.79916859]\n", " [ 0.79906183 0.79916859]\n", " [ 0.79906183 0.79916859]\n", " [ 0.79906183 0.79916859]]\n", "Error: 0.5\n", "[[ 0.13203108 0.13203108 0.13203108 0.13203108]\n", " [ 0.0714044 0.0714044 0.0714044 0.0714044 ]] \n", " [[ 0.80002224 0.80012077]\n", " [ 0.80002224 0.80012077]\n", " [ 0.80002224 0.80012077]\n", " [ 0.80002224 0.80012077]]\n", "Error: 0.5\n", "[[ 0.13228653 0.13228653 0.13228653 0.13228653]\n", " [ 0.07155982 0.07155982 0.07155982 0.07155982]] \n", " [[ 0.80097884 0.80106908]\n", " [ 0.80097884 0.80106908]\n", " [ 0.80097884 0.80106908]\n", " [ 0.80097884 0.80106908]]\n", "Error: 0.5\n", "[[ 0.13254099 0.13254099 0.13254099 0.13254099]\n", " [ 0.07171473 0.07171473 0.07171473 0.07171473]] \n", " [[ 0.80193162 0.80201364]\n", " [ 0.80193162 0.80201364]\n", " [ 0.80193162 0.80201364]\n", " [ 0.80193162 0.80201364]]\n", "Error: 0.5\n", "[[ 0.13279445 0.13279445 0.13279445 0.13279445]\n", " [ 0.07186914 0.07186914 0.07186914 0.07186914]] \n", " [[ 0.80288064 0.80295438]\n", " [ 0.80288064 0.80295438]\n", " [ 0.80288064 0.80295438]\n", " [ 0.80288064 0.80295438]]\n", "Error: 0.5\n", "[[ 0.13304694 0.13304694 0.13304694 0.13304694]\n", " [ 0.07202305 0.07202305 0.07202305 0.07202305]] \n", " [[ 0.80382591 0.80389136]\n", " [ 0.80382591 0.80389136]\n", " [ 0.80382591 0.80389136]\n", " [ 0.80382591 0.80389136]]\n", "Error: 0.5\n", "[[ 0.13329846 0.13329846 0.13329846 0.13329846]\n", " [ 0.07217646 0.07217646 0.07217646 0.07217646]] \n", " [[ 0.80476749 0.80482465]\n", " [ 0.80476749 0.80482465]\n", " [ 0.80476749 0.80482465]\n", " [ 0.80476749 0.80482465]]\n", "Error: 0.5\n", "[[ 0.133549 0.133549 0.133549 0.133549 ]\n", " [ 0.07232938 0.07232938 0.07232938 0.07232938]] \n", " [[ 0.80570537 0.80575418]\n", " [ 0.80570537 0.80575418]\n", " [ 0.80570537 0.80575418]\n", " [ 0.80570537 0.80575418]]\n", "Error: 0.5\n", "[[ 0.13379858 0.13379858 0.13379858 0.13379858]\n", " [ 0.0724818 0.0724818 0.0724818 0.0724818 ]] \n", " [[ 0.80663955 0.80668008]\n", " [ 0.80663955 0.80668008]\n", " [ 0.80663955 0.80668008]\n", " [ 0.80663955 0.80668008]]\n", "Error: 0.5\n", "[[ 0.13404721 0.13404721 0.13404721 0.13404721]\n", " [ 0.07263374 0.07263374 0.07263374 0.07263374]] \n", " [[ 0.8075701 0.80760235]\n", " [ 0.8075701 0.80760235]\n", " [ 0.8075701 0.80760235]\n", " [ 0.8075701 0.80760235]]\n", "Error: 0.5\n", "[[ 0.13429488 0.13429488 0.13429488 0.13429488]\n", " [ 0.07278518 0.07278518 0.07278518 0.07278518]] \n", " [[ 0.80849701 0.80852097]\n", " [ 0.80849701 0.80852097]\n", " [ 0.80849701 0.80852097]\n", " [ 0.80849701 0.80852097]]\n", "Error: 0.5\n", "[[ 0.13454162 0.13454162 0.13454162 0.13454162]\n", " [ 0.07293615 0.07293615 0.07293615 0.07293615]] \n", " [[ 0.80942035 0.80943602]\n", " [ 0.80942035 0.80943602]\n", " [ 0.80942035 0.80943602]\n", " [ 0.80942035 0.80943602]]\n", "Error: 0.5\n", "[[ 0.13478741 0.13478741 0.13478741 0.13478741]\n", " [ 0.07308663 0.07308663 0.07308663 0.07308663]] \n", " [[ 0.81034011 0.8103475 ]\n", " [ 0.81034011 0.8103475 ]\n", " [ 0.81034011 0.8103475 ]\n", " [ 0.81034011 0.8103475 ]]\n", "Error: 0.5\n", "[[ 0.13503228 0.13503228 0.13503228 0.13503228]\n", " [ 0.07323664 0.07323664 0.07323664 0.07323664]] \n", " [[ 0.81125635 0.81125546]\n", " [ 0.81125635 0.81125546]\n", " [ 0.81125635 0.81125546]\n", " [ 0.81125635 0.81125546]]\n", "Error: 0.5\n", "[[ 0.13527623 0.13527623 0.13527623 0.13527623]\n", " [ 0.07338618 0.07338618 0.07338618 0.07338618]] \n", " [[ 0.81216908 0.8121599 ]\n", " [ 0.81216908 0.8121599 ]\n", " [ 0.81216908 0.8121599 ]\n", " [ 0.81216908 0.8121599 ]]\n", "Error: 0.5\n", "[[ 0.13551927 0.13551927 0.13551927 0.13551927]\n", " [ 0.07353524 0.07353524 0.07353524 0.07353524]] \n", " [[ 0.81307834 0.81306082]\n", " [ 0.81307834 0.81306082]\n", " [ 0.81307834 0.81306082]\n", " [ 0.81307834 0.81306082]]\n", "Error: 0.5\n", "[[ 0.13576138 0.13576138 0.13576138 0.13576138]\n", " [ 0.07368384 0.07368384 0.07368384 0.07368384]] \n", " [[ 0.81398416 0.81395829]\n", " [ 0.81398416 0.81395829]\n", " [ 0.81398416 0.81395829]\n", " [ 0.81398416 0.81395829]]\n", "Error: 0.5\n", "[[ 0.1360026 0.1360026 0.1360026 0.1360026 ]\n", " [ 0.07383196 0.07383196 0.07383196 0.07383196]] \n", " [[ 0.81488651 0.81485236]\n", " [ 0.81488651 0.81485236]\n", " [ 0.81488651 0.81485236]\n", " [ 0.81488651 0.81485236]]\n", "Error: 0.5\n", "[[ 0.13624291 0.13624291 0.13624291 0.13624291]\n", " [ 0.07397962 0.07397962 0.07397962 0.07397962]] \n", " [[ 0.81578547 0.81574297]\n", " [ 0.81578547 0.81574297]\n", " [ 0.81578547 0.81574297]\n", " [ 0.81578547 0.81574297]]\n", "Error: 0.5\n", "[[ 0.13648233 0.13648233 0.13648233 0.13648233]\n", " [ 0.07412682 0.07412682 0.07412682 0.07412682]] \n", " [[ 0.81668103 0.81663024]\n", " [ 0.81668103 0.81663024]\n", " [ 0.81668103 0.81663024]\n", " [ 0.81668103 0.81663024]]\n", "Error: 0.5\n", "[[ 0.13672085 0.13672085 0.13672085 0.13672085]\n", " [ 0.07427357 0.07427357 0.07427357 0.07427357]] \n", " [[ 0.81757325 0.81751412]\n", " [ 0.81757325 0.81751412]\n", " [ 0.81757325 0.81751412]\n", " [ 0.81757325 0.81751412]]\n", "Error: 0.5\n", "[[ 0.13695849 0.13695849 0.13695849 0.13695849]\n", " [ 0.07441986 0.07441986 0.07441986 0.07441986]] \n", " [[ 0.81846213 0.81839466]\n", " [ 0.81846213 0.81839466]\n", " [ 0.81846213 0.81839466]\n", " [ 0.81846213 0.81839466]]\n", "Error: 0.5\n", "[[ 0.13719526 0.13719526 0.13719526 0.13719526]\n", " [ 0.0745657 0.0745657 0.0745657 0.0745657 ]] \n", " [[ 0.81934768 0.81927192]\n", " [ 0.81934768 0.81927192]\n", " [ 0.81934768 0.81927192]\n", " [ 0.81934768 0.81927192]]\n", "Error: 0.5\n", "[[ 0.13743116 0.13743116 0.13743116 0.13743116]\n", " [ 0.07471109 0.07471109 0.07471109 0.07471109]] \n", " [[ 0.82022995 0.82014585]\n", " [ 0.82022995 0.82014585]\n", " [ 0.82022995 0.82014585]\n", " [ 0.82022995 0.82014585]]\n", "Error: 0.5\n", "[[ 0.1376662 0.1376662 0.1376662 0.1376662 ]\n", " [ 0.07485604 0.07485604 0.07485604 0.07485604]] \n", " [[ 0.82110894 0.82101655]\n", " [ 0.82110894 0.82101655]\n", " [ 0.82110894 0.82101655]\n", " [ 0.82110894 0.82101655]]\n", "Error: 0.5\n", "[[ 0.13790037 0.13790037 0.13790037 0.13790037]\n", " [ 0.07500053 0.07500053 0.07500053 0.07500053]] \n", " [[ 0.82198471 0.82188398]\n", " [ 0.82198471 0.82188398]\n", " [ 0.82198471 0.82188398]\n", " [ 0.82198471 0.82188398]]\n", "Error: 0.5\n", "[[ 0.13813369 0.13813369 0.13813369 0.13813369]\n", " [ 0.07514459 0.07514459 0.07514459 0.07514459]] \n", " [[ 0.82285726 0.82274818]\n", " [ 0.82285726 0.82274818]\n", " [ 0.82285726 0.82274818]\n", " [ 0.82285726 0.82274818]]\n", "Error: 0.5\n", "[[ 0.13836616 0.13836616 0.13836616 0.13836616]\n", " [ 0.07528821 0.07528821 0.07528821 0.07528821]] \n", " [[ 0.82372659 0.82360917]\n", " [ 0.82372659 0.82360917]\n", " [ 0.82372659 0.82360917]\n", " [ 0.82372659 0.82360917]]\n", "Error: 0.5\n", "[[ 0.13859779 0.13859779 0.13859779 0.13859779]\n", " [ 0.07543138 0.07543138 0.07543138 0.07543138]] \n", " [[ 0.82459277 0.824467 ]\n", " [ 0.82459277 0.824467 ]\n", " [ 0.82459277 0.824467 ]\n", " [ 0.82459277 0.824467 ]]\n", "Error: 0.5\n", "[[ 0.13882858 0.13882858 0.13882858 0.13882858]\n", " [ 0.07557413 0.07557413 0.07557413 0.07557413]] \n", " [[ 0.82545578 0.82532167]\n", " [ 0.82545578 0.82532167]\n", " [ 0.82545578 0.82532167]\n", " [ 0.82545578 0.82532167]]\n", "Error: 0.5\n", "[[ 0.13905853 0.13905853 0.13905853 0.13905853]\n", " [ 0.07571644 0.07571644 0.07571644 0.07571644]] \n", " [[ 0.8263157 0.82617325]\n", " [ 0.8263157 0.82617325]\n", " [ 0.8263157 0.82617325]\n", " [ 0.8263157 0.82617325]]\n", "Error: 0.5\n", "[[ 0.13928765 0.13928765 0.13928765 0.13928765]\n", " [ 0.07585832 0.07585832 0.07585832 0.07585832]] \n", " [[ 0.82717252 0.82702172]\n", " [ 0.82717252 0.82702172]\n", " [ 0.82717252 0.82702172]\n", " [ 0.82717252 0.82702172]]\n", "Error: 0.5\n", "[[ 0.13951595 0.13951595 0.13951595 0.13951595]\n", " [ 0.07599978 0.07599978 0.07599978 0.07599978]] \n", " [[ 0.82802624 0.82786709]\n", " [ 0.82802624 0.82786709]\n", " [ 0.82802624 0.82786709]\n", " [ 0.82802624 0.82786709]]\n", "Error: 0.5\n", "[[ 0.13974343 0.13974343 0.13974343 0.13974343]\n", " [ 0.07614081 0.07614081 0.07614081 0.07614081]] \n", " [[ 0.82887685 0.82870936]\n", " [ 0.82887685 0.82870936]\n", " [ 0.82887685 0.82870936]\n", " [ 0.82887685 0.82870936]]\n", "Error: 0.5\n", "[[ 0.13997009 0.13997009 0.13997009 0.13997009]\n", " [ 0.07628142 0.07628142 0.07628142 0.07628142]] \n", " [[ 0.82972443 0.8295486 ]\n", " [ 0.82972443 0.8295486 ]\n", " [ 0.82972443 0.8295486 ]\n", " [ 0.82972443 0.8295486 ]]\n", "Error: 0.5\n", "[[ 0.14019595 0.14019595 0.14019595 0.14019595]\n", " [ 0.07642161 0.07642161 0.07642161 0.07642161]] \n", " [[ 0.83056897 0.83038479]\n", " [ 0.83056897 0.83038479]\n", " [ 0.83056897 0.83038479]\n", " [ 0.83056897 0.83038479]]\n", "Error: 0.5\n", "[[ 0.140421 0.140421 0.140421 0.140421 ]\n", " [ 0.07656138 0.07656138 0.07656138 0.07656138]] \n", " [[ 0.83141053 0.831218 ]\n", " [ 0.83141053 0.831218 ]\n", " [ 0.83141053 0.831218 ]\n", " [ 0.83141053 0.831218 ]]\n", "Error: 0.5\n", "[[ 0.14064525 0.14064525 0.14064525 0.14064525]\n", " [ 0.07670074 0.07670074 0.07670074 0.07670074]] \n", " [[ 0.8322491 0.83204824]\n", " [ 0.8322491 0.83204824]\n", " [ 0.8322491 0.83204824]\n", " [ 0.8322491 0.83204824]]\n", "Error: 0.5\n", "[[ 0.14086871 0.14086871 0.14086871 0.14086871]\n", " [ 0.07683968 0.07683968 0.07683968 0.07683968]] \n", " [[ 0.8330847 0.83287549]\n", " [ 0.8330847 0.83287549]\n", " [ 0.8330847 0.83287549]\n", " [ 0.8330847 0.83287549]]\n", "Error: 0.5\n", "[[ 0.14109138 0.14109138 0.14109138 0.14109138]\n", " [ 0.07697821 0.07697821 0.07697821 0.07697821]] \n", " [[ 0.83391738 0.83369982]\n", " [ 0.83391738 0.83369982]\n", " [ 0.83391738 0.83369982]\n", " [ 0.83391738 0.83369982]]\n", "Error: 0.5\n", "[[ 0.14131327 0.14131327 0.14131327 0.14131327]\n", " [ 0.07711633 0.07711633 0.07711633 0.07711633]] \n", " [[ 0.83474714 0.83452123]\n", " [ 0.83474714 0.83452123]\n", " [ 0.83474714 0.83452123]\n", " [ 0.83474714 0.83452123]]\n", "Error: 0.5\n", "[[ 0.14153437 0.14153437 0.14153437 0.14153437]\n", " [ 0.07725405 0.07725405 0.07725405 0.07725405]] \n", " [[ 0.83557397 0.83533973]\n", " [ 0.83557397 0.83533973]\n", " [ 0.83557397 0.83533973]\n", " [ 0.83557397 0.83533973]]\n", "Error: 0.5\n", "[[ 0.1417547 0.1417547 0.1417547 0.1417547 ]\n", " [ 0.07739136 0.07739136 0.07739136 0.07739136]] \n", " [[ 0.83639789 0.83615535]\n", " [ 0.83639789 0.83615535]\n", " [ 0.83639789 0.83615535]\n", " [ 0.83639789 0.83615535]]\n", "Error: 0.5\n", "[[ 0.14197427 0.14197427 0.14197427 0.14197427]\n", " [ 0.07752828 0.07752828 0.07752828 0.07752828]] \n", " [[ 0.83721894 0.83696806]\n", " [ 0.83721894 0.83696806]\n", " [ 0.83721894 0.83696806]\n", " [ 0.83721894 0.83696806]]\n", "Error: 0.5\n", "[[ 0.14219306 0.14219306 0.14219306 0.14219306]\n", " [ 0.07766479 0.07766479 0.07766479 0.07766479]] \n", " [[ 0.83803719 0.83777797]\n", " [ 0.83803719 0.83777797]\n", " [ 0.83803719 0.83777797]\n", " [ 0.83803719 0.83777797]]\n", "Error: 0.5\n", "[[ 0.1424111 0.1424111 0.1424111 0.1424111]\n", " [ 0.0778009 0.0778009 0.0778009 0.0778009]] \n", " [[ 0.83885258 0.83858502]\n", " [ 0.83885258 0.83858502]\n", " [ 0.83885258 0.83858502]\n", " [ 0.83885258 0.83858502]]\n", "Error: 0.5\n", "[[ 0.14262839 0.14262839 0.14262839 0.14262839]\n", " [ 0.07793662 0.07793662 0.07793662 0.07793662]] \n", " [[ 0.83966517 0.83938926]\n", " [ 0.83966517 0.83938926]\n", " [ 0.83966517 0.83938926]\n", " [ 0.83966517 0.83938926]]\n", "Error: 0.5\n", "[[ 0.14284492 0.14284492 0.14284492 0.14284492]\n", " [ 0.07807194 0.07807194 0.07807194 0.07807194]] \n", " [[ 0.84047496 0.84019071]\n", " [ 0.84047496 0.84019071]\n", " [ 0.84047496 0.84019071]\n", " [ 0.84047496 0.84019071]]\n", "Error: 0.5\n", "[[ 0.1430607 0.1430607 0.1430607 0.1430607 ]\n", " [ 0.07820688 0.07820688 0.07820688 0.07820688]] \n", " [[ 0.84128195 0.84098941]\n", " [ 0.84128195 0.84098941]\n", " [ 0.84128195 0.84098941]\n", " [ 0.84128195 0.84098941]]\n", "Error: 0.5\n", "[[ 0.14327574 0.14327574 0.14327574 0.14327574]\n", " [ 0.07834143 0.07834143 0.07834143 0.07834143]] \n", " [[ 0.8420862 0.84178537]\n", " [ 0.8420862 0.84178537]\n", " [ 0.8420862 0.84178537]\n", " [ 0.8420862 0.84178537]]\n", "Error: 0.5\n", "[[ 0.14349005 0.14349005 0.14349005 0.14349005]\n", " [ 0.07847559 0.07847559 0.07847559 0.07847559]] \n", " [[ 0.8428877 0.84257859]\n", " [ 0.8428877 0.84257859]\n", " [ 0.8428877 0.84257859]\n", " [ 0.8428877 0.84257859]]\n", "Error: 0.5\n", "[[ 0.14370361 0.14370361 0.14370361 0.14370361]\n", " [ 0.07860937 0.07860937 0.07860937 0.07860937]] \n", " [[ 0.84368652 0.84336907]\n", " [ 0.84368652 0.84336907]\n", " [ 0.84368652 0.84336907]\n", " [ 0.84368652 0.84336907]]\n", "Error: 0.5\n", "[[ 0.14391644 0.14391644 0.14391644 0.14391644]\n", " [ 0.07874276 0.07874276 0.07874276 0.07874276]] \n", " [[ 0.8444826 0.84415686]\n", " [ 0.8444826 0.84415686]\n", " [ 0.8444826 0.84415686]\n", " [ 0.8444826 0.84415686]]\n", "Error: 0.5\n", "[[ 0.14412856 0.14412856 0.14412856 0.14412856]\n", " [ 0.07887578 0.07887578 0.07887578 0.07887578]] \n", " [[ 0.845276 0.84494197]\n", " [ 0.845276 0.84494197]\n", " [ 0.845276 0.84494197]\n", " [ 0.845276 0.84494197]]\n", "Error: 0.5\n", "[[ 0.14433995 0.14433995 0.14433995 0.14433995]\n", " [ 0.07900842 0.07900842 0.07900842 0.07900842]] \n", " [[ 0.84606671 0.8457244 ]\n", " [ 0.84606671 0.8457244 ]\n", " [ 0.84606671 0.8457244 ]\n", " [ 0.84606671 0.8457244 ]]\n", "Error: 0.5\n", "[[ 0.14455062 0.14455062 0.14455062 0.14455062]\n", " [ 0.07914068 0.07914068 0.07914068 0.07914068]] \n", " [[ 0.84685481 0.84650415]\n", " [ 0.84685481 0.84650415]\n", " [ 0.84685481 0.84650415]\n", " [ 0.84685481 0.84650415]]\n", "Error: 0.5\n", "[[ 0.14476059 0.14476059 0.14476059 0.14476059]\n", " [ 0.07927257 0.07927257 0.07927257 0.07927257]] \n", " [[ 0.84764028 0.84728128]\n", " [ 0.84764028 0.84728128]\n", " [ 0.84764028 0.84728128]\n", " [ 0.84764028 0.84728128]]\n", "Error: 0.5\n", "[[ 0.14496985 0.14496985 0.14496985 0.14496985]\n", " [ 0.07940409 0.07940409 0.07940409 0.07940409]] \n", " [[ 0.84842312 0.84805578]\n", " [ 0.84842312 0.84805578]\n", " [ 0.84842312 0.84805578]\n", " [ 0.84842312 0.84805578]]\n", "Error: 0.5\n", "[[ 0.14517841 0.14517841 0.14517841 0.14517841]\n", " [ 0.07953523 0.07953523 0.07953523 0.07953523]] \n", " [[ 0.84920335 0.84882772]\n", " [ 0.84920335 0.84882772]\n", " [ 0.84920335 0.84882772]\n", " [ 0.84920335 0.84882772]]\n", "Error: 0.5\n", "[[ 0.14538626 0.14538626 0.14538626 0.14538626]\n", " [ 0.07966601 0.07966601 0.07966601 0.07966601]] \n", " [[ 0.84998101 0.8495971 ]\n", " [ 0.84998101 0.8495971 ]\n", " [ 0.84998101 0.8495971 ]\n", " [ 0.84998101 0.8495971 ]]\n", "Error: 0.5\n", "[[ 0.14559342 0.14559342 0.14559342 0.14559342]\n", " [ 0.07979643 0.07979643 0.07979643 0.07979643]] \n", " [[ 0.85075611 0.85036385]\n", " [ 0.85075611 0.85036385]\n", " [ 0.85075611 0.85036385]\n", " [ 0.85075611 0.85036385]]\n", "Error: 0.5\n", "[[ 0.14579989 0.14579989 0.14579989 0.14579989]\n", " [ 0.07992648 0.07992648 0.07992648 0.07992648]] \n", " [[ 0.85152864 0.8511281 ]\n", " [ 0.85152864 0.8511281 ]\n", " [ 0.85152864 0.8511281 ]\n", " [ 0.85152864 0.8511281 ]]\n", "Error: 0.5\n", "[[ 0.14600566 0.14600566 0.14600566 0.14600566]\n", " [ 0.08005616 0.08005616 0.08005616 0.08005616]] \n", " [[ 0.85229862 0.85188979]\n", " [ 0.85229862 0.85188979]\n", " [ 0.85229862 0.85188979]\n", " [ 0.85229862 0.85188979]]\n", "Error: 0.5\n", "[[ 0.14621076 0.14621076 0.14621076 0.14621076]\n", " [ 0.08018549 0.08018549 0.08018549 0.08018549]] \n", " [[ 0.85306609 0.85264897]\n", " [ 0.85306609 0.85264897]\n", " [ 0.85306609 0.85264897]\n", " [ 0.85306609 0.85264897]]\n", "Error: 0.5\n", "[[ 0.14641517 0.14641517 0.14641517 0.14641517]\n", " [ 0.08031446 0.08031446 0.08031446 0.08031446]] \n", " [[ 0.85383105 0.85340565]\n", " [ 0.85383105 0.85340565]\n", " [ 0.85383105 0.85340565]\n", " [ 0.85383105 0.85340565]]\n", "Error: 0.5\n", "[[ 0.14661892 0.14661892 0.14661892 0.14661892]\n", " [ 0.08044307 0.08044307 0.08044307 0.08044307]] \n", " [[ 0.85459352 0.85415983]\n", " [ 0.85459352 0.85415983]\n", " [ 0.85459352 0.85415983]\n", " [ 0.85459352 0.85415983]]\n", "Error: 0.5\n", "[[ 0.14682198 0.14682198 0.14682198 0.14682198]\n", " [ 0.08057132 0.08057132 0.08057132 0.08057132]] \n", " [[ 0.85535353 0.85491157]\n", " [ 0.85535353 0.85491157]\n", " [ 0.85535353 0.85491157]\n", " [ 0.85535353 0.85491157]]\n", "Error: 0.5\n", "[[ 0.14702438 0.14702438 0.14702438 0.14702438]\n", " [ 0.08069923 0.08069923 0.08069923 0.08069923]] \n", " [[ 0.85611105 0.85566086]\n", " [ 0.85611105 0.85566086]\n", " [ 0.85611105 0.85566086]\n", " [ 0.85611105 0.85566086]]\n", "Error: 0.5\n", "[[ 0.14722611 0.14722611 0.14722611 0.14722611]\n", " [ 0.08082678 0.08082678 0.08082678 0.08082678]] \n", " [[ 0.85686612 0.8564077 ]\n", " [ 0.85686612 0.8564077 ]\n", " [ 0.85686612 0.8564077 ]\n", " [ 0.85686612 0.8564077 ]]\n", "Error: 0.5\n", "[[ 0.14742719 0.14742719 0.14742719 0.14742719]\n", " [ 0.08095399 0.08095399 0.08095399 0.08095399]] \n", " [[ 0.85761881 0.8571521 ]\n", " [ 0.85761881 0.8571521 ]\n", " [ 0.85761881 0.8571521 ]\n", " [ 0.85761881 0.8571521 ]]\n", "Error: 0.5\n", "[[ 0.14762761 0.14762761 0.14762761 0.14762761]\n", " [ 0.08108084 0.08108084 0.08108084 0.08108084]] \n", " [[ 0.85836905 0.85789412]\n", " [ 0.85836905 0.85789412]\n", " [ 0.85836905 0.85789412]\n", " [ 0.85836905 0.85789412]]\n", "Error: 0.5\n", "[[ 0.14782737 0.14782737 0.14782737 0.14782737]\n", " [ 0.08120735 0.08120735 0.08120735 0.08120735]] \n", " [[ 0.85911691 0.8586337 ]\n", " [ 0.85911691 0.8586337 ]\n", " [ 0.85911691 0.8586337 ]\n", " [ 0.85911691 0.8586337 ]]\n", "Error: 0.5\n", "[[ 0.14802648 0.14802648 0.14802648 0.14802648]\n", " [ 0.08133352 0.08133352 0.08133352 0.08133352]] \n", " [[ 0.85986239 0.85937089]\n", " [ 0.85986239 0.85937089]\n", " [ 0.85986239 0.85937089]\n", " [ 0.85986239 0.85937089]]\n", "Error: 0.5\n", "[[ 0.14822495 0.14822495 0.14822495 0.14822495]\n", " [ 0.08145934 0.08145934 0.08145934 0.08145934]] \n", " [[ 0.86060548 0.86010575]\n", " [ 0.86060548 0.86010575]\n", " [ 0.86060548 0.86010575]\n", " [ 0.86060548 0.86010575]]\n", "Error: 0.5\n", "[[ 0.14842278 0.14842278 0.14842278 0.14842278]\n", " [ 0.08158483 0.08158483 0.08158483 0.08158483]] \n", " [[ 0.86134624 0.86083823]\n", " [ 0.86134624 0.86083823]\n", " [ 0.86134624 0.86083823]\n", " [ 0.86134624 0.86083823]]\n", "Error: 0.5\n", "[[ 0.14861996 0.14861996 0.14861996 0.14861996]\n", " [ 0.08170997 0.08170997 0.08170997 0.08170997]] \n", " [[ 0.86208463 0.86156839]\n", " [ 0.86208463 0.86156839]\n", " [ 0.86208463 0.86156839]\n", " [ 0.86208463 0.86156839]]\n", "Error: 0.5\n", "[[ 0.14881651 0.14881651 0.14881651 0.14881651]\n", " [ 0.08183479 0.08183479 0.08183479 0.08183479]] \n", " [[ 0.86282068 0.86229622]\n", " [ 0.86282068 0.86229622]\n", " [ 0.86282068 0.86229622]\n", " [ 0.86282068 0.86229622]]\n", "Error: 0.5\n", "[[ 0.14901243 0.14901243 0.14901243 0.14901243]\n", " [ 0.08195926 0.08195926 0.08195926 0.08195926]] \n", " [[ 0.86355448 0.86302173]\n", " [ 0.86355448 0.86302173]\n", " [ 0.86355448 0.86302173]\n", " [ 0.86355448 0.86302173]]\n", "Error: 0.5\n", "[[ 0.14920773 0.14920773 0.14920773 0.14920773]\n", " [ 0.0820834 0.0820834 0.0820834 0.0820834 ]] \n", " [[ 0.86428595 0.86374497]\n", " [ 0.86428595 0.86374497]\n", " [ 0.86428595 0.86374497]\n", " [ 0.86428595 0.86374497]]\n", "Error: 0.5\n", "[[ 0.14940239 0.14940239 0.14940239 0.14940239]\n", " [ 0.08220721 0.08220721 0.08220721 0.08220721]] \n", " [[ 0.86501515 0.86446595]\n", " [ 0.86501515 0.86446595]\n", " [ 0.86501515 0.86446595]\n", " [ 0.86501515 0.86446595]]\n", "Error: 0.5\n", "[[ 0.14959644 0.14959644 0.14959644 0.14959644]\n", " [ 0.08233069 0.08233069 0.08233069 0.08233069]] \n", " [[ 0.86574203 0.86518461]\n", " [ 0.86574203 0.86518461]\n", " [ 0.86574203 0.86518461]\n", " [ 0.86574203 0.86518461]]\n", "Error: 0.5\n", "[[ 0.14978987 0.14978987 0.14978987 0.14978987]\n", " [ 0.08245384 0.08245384 0.08245384 0.08245384]] \n", " [[ 0.8664667 0.86590105]\n", " [ 0.8664667 0.86590105]\n", " [ 0.8664667 0.86590105]\n", " [ 0.8664667 0.86590105]]\n", "Error: 0.5\n", "[[ 0.14998268 0.14998268 0.14998268 0.14998268]\n", " [ 0.08257666 0.08257666 0.08257666 0.08257666]] \n", " [[ 0.86718911 0.86661524]\n", " [ 0.86718911 0.86661524]\n", " [ 0.86718911 0.86661524]\n", " [ 0.86718911 0.86661524]]\n", "Error: 0.5\n", "[[ 0.15017487 0.15017487 0.15017487 0.15017487]\n", " [ 0.08269916 0.08269916 0.08269916 0.08269916]] \n", " [[ 0.86790925 0.86732721]\n", " [ 0.86790925 0.86732721]\n", " [ 0.86790925 0.86732721]\n", " [ 0.86790925 0.86732721]]\n", "Error: 0.5\n", "[[ 0.15036647 0.15036647 0.15036647 0.15036647]\n", " [ 0.08282134 0.08282134 0.08282134 0.08282134]] \n", " [[ 0.86862719 0.86803699]\n", " [ 0.86862719 0.86803699]\n", " [ 0.86862719 0.86803699]\n", " [ 0.86862719 0.86803699]]\n", "Error: 0.5\n", "[[ 0.15055746 0.15055746 0.15055746 0.15055746]\n", " [ 0.08294319 0.08294319 0.08294319 0.08294319]] \n", " [[ 0.86934292 0.86874455]\n", " [ 0.86934292 0.86874455]\n", " [ 0.86934292 0.86874455]\n", " [ 0.86934292 0.86874455]]\n", "Error: 0.5\n", "[[ 0.15074785 0.15074785 0.15074785 0.15074785]\n", " [ 0.08306473 0.08306473 0.08306473 0.08306473]] \n", " [[ 0.87005645 0.86944991]\n", " [ 0.87005645 0.86944991]\n", " [ 0.87005645 0.86944991]\n", " [ 0.87005645 0.86944991]]\n", "Error: 0.5\n", "[[ 0.15093765 0.15093765 0.15093765 0.15093765]\n", " [ 0.08318594 0.08318594 0.08318594 0.08318594]] \n", " [[ 0.87076783 0.87015307]\n", " [ 0.87076783 0.87015307]\n", " [ 0.87076783 0.87015307]\n", " [ 0.87076783 0.87015307]]\n", "Error: 0.5\n", "[[ 0.15112685 0.15112685 0.15112685 0.15112685]\n", " [ 0.08330684 0.08330684 0.08330684 0.08330684]] \n", " [[ 0.87147701 0.87085408]\n", " [ 0.87147701 0.87085408]\n", " [ 0.87147701 0.87085408]\n", " [ 0.87147701 0.87085408]]\n", "Error: 0.5\n", "[[ 0.15131545 0.15131545 0.15131545 0.15131545]\n", " [ 0.08342742 0.08342742 0.08342742 0.08342742]] \n", " [[ 0.87218404 0.87155294]\n", " [ 0.87218404 0.87155294]\n", " [ 0.87218404 0.87155294]\n", " [ 0.87218404 0.87155294]]\n", "Error: 0.5\n", "[[ 0.15150347 0.15150347 0.15150347 0.15150347]\n", " [ 0.08354769 0.08354769 0.08354769 0.08354769]] \n", " [[ 0.87288892 0.87224966]\n", " [ 0.87288892 0.87224966]\n", " [ 0.87288892 0.87224966]\n", " [ 0.87288892 0.87224966]]\n", "Error: 0.5\n", "[[ 0.1516909 0.1516909 0.1516909 0.1516909 ]\n", " [ 0.08366764 0.08366764 0.08366764 0.08366764]] \n", " [[ 0.87359172 0.8729443 ]\n", " [ 0.87359172 0.8729443 ]\n", " [ 0.87359172 0.8729443 ]\n", " [ 0.87359172 0.8729443 ]]\n", "Error: 0.5\n", "[[ 0.15187775 0.15187775 0.15187775 0.15187775]\n", " [ 0.08378729 0.08378729 0.08378729 0.08378729]] \n", " [[ 0.87429237 0.87363678]\n", " [ 0.87429237 0.87363678]\n", " [ 0.87429237 0.87363678]\n", " [ 0.87429237 0.87363678]]\n", "Error: 0.5\n", "[[ 0.15206403 0.15206403 0.15206403 0.15206403]\n", " [ 0.08390663 0.08390663 0.08390663 0.08390663]] \n", " [[ 0.87499094 0.87432718]\n", " [ 0.87499094 0.87432718]\n", " [ 0.87499094 0.87432718]\n", " [ 0.87499094 0.87432718]]\n", "Error: 0.5\n", "[[ 0.15224972 0.15224972 0.15224972 0.15224972]\n", " [ 0.08402566 0.08402566 0.08402566 0.08402566]] \n", " [[ 0.87568742 0.8750155 ]\n", " [ 0.87568742 0.8750155 ]\n", " [ 0.87568742 0.8750155 ]\n", " [ 0.87568742 0.8750155 ]]\n", "Error: 0.5\n", "[[ 0.15243486 0.15243486 0.15243486 0.15243486]\n", " [ 0.08414438 0.08414438 0.08414438 0.08414438]] \n", " [[ 0.87638181 0.87570173]\n", " [ 0.87638181 0.87570173]\n", " [ 0.87638181 0.87570173]\n", " [ 0.87638181 0.87570173]]\n", "Error: 0.5\n", "[[ 0.15261941 0.15261941 0.15261941 0.15261941]\n", " [ 0.0842628 0.0842628 0.0842628 0.0842628 ]] \n", " [[ 0.87707412 0.87638587]\n", " [ 0.87707412 0.87638587]\n", " [ 0.87707412 0.87638587]\n", " [ 0.87707412 0.87638587]]\n", "Error: 0.5\n", "[[ 0.15280339 0.15280339 0.15280339 0.15280339]\n", " [ 0.08438092 0.08438092 0.08438092 0.08438092]] \n", " [[ 0.87776434 0.87706798]\n", " [ 0.87776434 0.87706798]\n", " [ 0.87776434 0.87706798]\n", " [ 0.87776434 0.87706798]]\n", "Error: 0.5\n", "[[ 0.15298682 0.15298682 0.15298682 0.15298682]\n", " [ 0.08449873 0.08449873 0.08449873 0.08449873]] \n", " [[ 0.87845254 0.87774807]\n", " [ 0.87845254 0.87774807]\n", " [ 0.87845254 0.87774807]\n", " [ 0.87845254 0.87774807]]\n", "Error: 0.5\n", "[[ 0.15316969 0.15316969 0.15316969 0.15316969]\n", " [ 0.08461624 0.08461624 0.08461624 0.08461624]] \n", " [[ 0.87913871 0.87842613]\n", " [ 0.87913871 0.87842613]\n", " [ 0.87913871 0.87842613]\n", " [ 0.87913871 0.87842613]]\n", "Error: 0.5\n", "[[ 0.15335201 0.15335201 0.15335201 0.15335201]\n", " [ 0.08473346 0.08473346 0.08473346 0.08473346]] \n", " [[ 0.87982285 0.87910217]\n", " [ 0.87982285 0.87910217]\n", " [ 0.87982285 0.87910217]\n", " [ 0.87982285 0.87910217]]\n", "Error: 0.5\n", "[[ 0.15353376 0.15353376 0.15353376 0.15353376]\n", " [ 0.08485037 0.08485037 0.08485037 0.08485037]] \n", " [[ 0.88050497 0.87977618]\n", " [ 0.88050497 0.87977618]\n", " [ 0.88050497 0.87977618]\n", " [ 0.88050497 0.87977618]]\n", "Error: 0.5\n", "[[ 0.15371495 0.15371495 0.15371495 0.15371495]\n", " [ 0.08496699 0.08496699 0.08496699 0.08496699]] \n", " [[ 0.88118511 0.88044822]\n", " [ 0.88118511 0.88044822]\n", " [ 0.88118511 0.88044822]\n", " [ 0.88118511 0.88044822]]\n", "Error: 0.5\n", "[[ 0.15389562 0.15389562 0.15389562 0.15389562]\n", " [ 0.08508332 0.08508332 0.08508332 0.08508332]] \n", " [[ 0.88186324 0.88111824]\n", " [ 0.88186324 0.88111824]\n", " [ 0.88186324 0.88111824]\n", " [ 0.88186324 0.88111824]]\n", "Error: 0.5\n", "[[ 0.15407573 0.15407573 0.15407573 0.15407573]\n", " [ 0.08519936 0.08519936 0.08519936 0.08519936]] \n", " [[ 0.88253939 0.88178629]\n", " [ 0.88253939 0.88178629]\n", " [ 0.88253939 0.88178629]\n", " [ 0.88253939 0.88178629]]\n", "Error: 0.5\n", "[[ 0.15425529 0.15425529 0.15425529 0.15425529]\n", " [ 0.0853151 0.0853151 0.0853151 0.0853151 ]] \n", " [[ 0.88321358 0.88245237]\n", " [ 0.88321358 0.88245237]\n", " [ 0.88321358 0.88245237]\n", " [ 0.88321358 0.88245237]]\n", "Error: 0.5\n", "[[ 0.15443431 0.15443431 0.15443431 0.15443431]\n", " [ 0.08543056 0.08543056 0.08543056 0.08543056]] \n", " [[ 0.8838858 0.88311654]\n", " [ 0.8838858 0.88311654]\n", " [ 0.8838858 0.88311654]\n", " [ 0.8838858 0.88311654]]\n", "Error: 0.5\n", "[[ 0.15461279 0.15461279 0.15461279 0.15461279]\n", " [ 0.08554572 0.08554572 0.08554572 0.08554572]] \n", " [[ 0.88455611 0.88377875]\n", " [ 0.88455611 0.88377875]\n", " [ 0.88455611 0.88377875]\n", " [ 0.88455611 0.88377875]]\n", "Error: 0.5\n", "[[ 0.15479074 0.15479074 0.15479074 0.15479074]\n", " [ 0.08566059 0.08566059 0.08566059 0.08566059]] \n", " [[ 0.88522446 0.88443905]\n", " [ 0.88522446 0.88443905]\n", " [ 0.88522446 0.88443905]\n", " [ 0.88522446 0.88443905]]\n", "Error: 0.5\n", "[[ 0.15496817 0.15496817 0.15496817 0.15496817]\n", " [ 0.08577518 0.08577518 0.08577518 0.08577518]] \n", " [[ 0.8858909 0.88509738]\n", " [ 0.8858909 0.88509738]\n", " [ 0.8858909 0.88509738]\n", " [ 0.8858909 0.88509738]]\n", "Error: 0.5\n", "[[ 0.15514506 0.15514506 0.15514506 0.15514506]\n", " [ 0.08588949 0.08588949 0.08588949 0.08588949]] \n", " [[ 0.88655543 0.88575381]\n", " [ 0.88655543 0.88575381]\n", " [ 0.88655543 0.88575381]\n", " [ 0.88655543 0.88575381]]\n", "Error: 0.5\n", "[[ 0.15532143 0.15532143 0.15532143 0.15532143]\n", " [ 0.08600351 0.08600351 0.08600351 0.08600351]] \n", " [[ 0.88721806 0.88640839]\n", " [ 0.88721806 0.88640839]\n", " [ 0.88721806 0.88640839]\n", " [ 0.88721806 0.88640839]]\n", "Error: 0.5\n", "[[ 0.15549728 0.15549728 0.15549728 0.15549728]\n", " [ 0.08611725 0.08611725 0.08611725 0.08611725]] \n", " [[ 0.88787878 0.88706106]\n", " [ 0.88787878 0.88706106]\n", " [ 0.88787878 0.88706106]\n", " [ 0.88787878 0.88706106]]\n", "Error: 0.5\n", "[[ 0.15567261 0.15567261 0.15567261 0.15567261]\n", " [ 0.08623071 0.08623071 0.08623071 0.08623071]] \n", " [[ 0.88853759 0.88771182]\n", " [ 0.88853759 0.88771182]\n", " [ 0.88853759 0.88771182]\n", " [ 0.88853759 0.88771182]]\n", "Error: 0.5\n", "[[ 0.15584742 0.15584742 0.15584742 0.15584742]\n", " [ 0.08634389 0.08634389 0.08634389 0.08634389]] \n", " [[ 0.88919455 0.88836074]\n", " [ 0.88919455 0.88836074]\n", " [ 0.88919455 0.88836074]\n", " [ 0.88919455 0.88836074]]\n", "Error: 0.5\n", "[[ 0.15602171 0.15602171 0.15602171 0.15602171]\n", " [ 0.08645679 0.08645679 0.08645679 0.08645679]] \n", " [[ 0.88984966 0.88900781]\n", " [ 0.88984966 0.88900781]\n", " [ 0.88984966 0.88900781]\n", " [ 0.88984966 0.88900781]]\n", "Error: 0.5\n", "[[ 0.15619549 0.15619549 0.15619549 0.15619549]\n", " [ 0.08656941 0.08656941 0.08656941 0.08656941]] \n", " [[ 0.89050293 0.88965303]\n", " [ 0.89050293 0.88965303]\n", " [ 0.89050293 0.88965303]\n", " [ 0.89050293 0.88965303]]\n", "Error: 0.5\n", "[[ 0.15636876 0.15636876 0.15636876 0.15636876]\n", " [ 0.08668176 0.08668176 0.08668176 0.08668176]] \n", " [[ 0.89115435 0.8902964 ]\n", " [ 0.89115435 0.8902964 ]\n", " [ 0.89115435 0.8902964 ]\n", " [ 0.89115435 0.8902964 ]]\n", "Error: 0.5\n", "[[ 0.15654153 0.15654153 0.15654153 0.15654153]\n", " [ 0.08679383 0.08679383 0.08679383 0.08679383]] \n", " [[ 0.89180392 0.89093798]\n", " [ 0.89180392 0.89093798]\n", " [ 0.89180392 0.89093798]\n", " [ 0.89180392 0.89093798]]\n", "Error: 0.5\n", "[[ 0.15671378 0.15671378 0.15671378 0.15671378]\n", " [ 0.08690564 0.08690564 0.08690564 0.08690564]] \n", " [[ 0.89245164 0.89157772]\n", " [ 0.89245164 0.89157772]\n", " [ 0.89245164 0.89157772]\n", " [ 0.89245164 0.89157772]]\n", "Error: 0.5\n", "[[ 0.15688553 0.15688553 0.15688553 0.15688553]\n", " [ 0.08701716 0.08701716 0.08701716 0.08701716]] \n", " [[ 0.89309758 0.89221567]\n", " [ 0.89309758 0.89221567]\n", " [ 0.89309758 0.89221567]\n", " [ 0.89309758 0.89221567]]\n", "Error: 0.5\n", "[[ 0.15705679 0.15705679 0.15705679 0.15705679]\n", " [ 0.08712842 0.08712842 0.08712842 0.08712842]] \n", " [[ 0.89374173 0.89285183]\n", " [ 0.89374173 0.89285183]\n", " [ 0.89374173 0.89285183]\n", " [ 0.89374173 0.89285183]]\n", "Error: 0.5\n", "[[ 0.15722755 0.15722755 0.15722755 0.15722755]\n", " [ 0.08723941 0.08723941 0.08723941 0.08723941]] \n", " [[ 0.89438409 0.8934862 ]\n", " [ 0.89438409 0.8934862 ]\n", " [ 0.89438409 0.8934862 ]\n", " [ 0.89438409 0.8934862 ]]\n", "Error: 0.5\n", "[[ 0.15739781 0.15739781 0.15739781 0.15739781]\n", " [ 0.08735014 0.08735014 0.08735014 0.08735014]] \n", " [[ 0.89502466 0.89411879]\n", " [ 0.89502466 0.89411879]\n", " [ 0.89502466 0.89411879]\n", " [ 0.89502466 0.89411879]]\n", "Error: 0.5\n", "[[ 0.15756758 0.15756758 0.15756758 0.15756758]\n", " [ 0.08746059 0.08746059 0.08746059 0.08746059]] \n", " [[ 0.89566344 0.89474958]\n", " [ 0.89566344 0.89474958]\n", " [ 0.89566344 0.89474958]\n", " [ 0.89566344 0.89474958]]\n", "Error: 0.5\n", "[[ 0.15773685 0.15773685 0.15773685 0.15773685]\n", " [ 0.08757078 0.08757078 0.08757078 0.08757078]] \n", " [[ 0.89630044 0.89537865]\n", " [ 0.89630044 0.89537865]\n", " [ 0.89630044 0.89537865]\n", " [ 0.89630044 0.89537865]]\n", "Error: 0.5\n", "[[ 0.15790565 0.15790565 0.15790565 0.15790565]\n", " [ 0.0876807 0.0876807 0.0876807 0.0876807 ]] \n", " [[ 0.8969357 0.89600593]\n", " [ 0.8969357 0.89600593]\n", " [ 0.8969357 0.89600593]\n", " [ 0.8969357 0.89600593]]\n", "Error: 0.5\n", "[[ 0.15807396 0.15807396 0.15807396 0.15807396]\n", " [ 0.08779036 0.08779036 0.08779036 0.08779036]] \n", " [[ 0.89756924 0.89663148]\n", " [ 0.89756924 0.89663148]\n", " [ 0.89756924 0.89663148]\n", " [ 0.89756924 0.89663148]]\n", "Error: 0.5\n", "[[ 0.15824179 0.15824179 0.15824179 0.15824179]\n", " [ 0.08789976 0.08789976 0.08789976 0.08789976]] \n", " [[ 0.89820099 0.8972553 ]\n", " [ 0.89820099 0.8972553 ]\n", " [ 0.89820099 0.8972553 ]\n", " [ 0.89820099 0.8972553 ]]\n", "Error: 0.5\n", "[[ 0.15840915 0.15840915 0.15840915 0.15840915]\n", " [ 0.0880089 0.0880089 0.0880089 0.0880089 ]] \n", " [[ 0.89883101 0.8978774 ]\n", " [ 0.89883101 0.8978774 ]\n", " [ 0.89883101 0.8978774 ]\n", " [ 0.89883101 0.8978774 ]]\n", "Error: 0.5\n", "[[ 0.15857603 0.15857603 0.15857603 0.15857603]\n", " [ 0.08811777 0.08811777 0.08811777 0.08811777]] \n", " [[ 0.89945936 0.89849782]\n", " [ 0.89945936 0.89849782]\n", " [ 0.89945936 0.89849782]\n", " [ 0.89945936 0.89849782]]\n", "Error: 0.5\n", "[[ 0.15874243 0.15874243 0.15874243 0.15874243]\n", " [ 0.08822639 0.08822639 0.08822639 0.08822639]] \n", " [[ 0.90008599 0.89911652]\n", " [ 0.90008599 0.89911652]\n", " [ 0.90008599 0.89911652]\n", " [ 0.90008599 0.89911652]]\n", "Error: 0.5\n", "[[ 0.15890835 0.15890835 0.15890835 0.15890835]\n", " [ 0.08833475 0.08833475 0.08833475 0.08833475]] \n", " [[ 0.90071088 0.89973354]\n", " [ 0.90071088 0.89973354]\n", " [ 0.90071088 0.89973354]\n", " [ 0.90071088 0.89973354]]\n", "Error: 0.5\n", "[[ 0.1590738 0.1590738 0.1590738 0.1590738 ]\n", " [ 0.08844286 0.08844286 0.08844286 0.08844286]] \n", " [[ 0.90133411 0.90034884]\n", " [ 0.90133411 0.90034884]\n", " [ 0.90133411 0.90034884]\n", " [ 0.90133411 0.90034884]]\n", "Error: 0.5\n", "[[ 0.15923879 0.15923879 0.15923879 0.15923879]\n", " [ 0.08855072 0.08855072 0.08855072 0.08855072]] \n", " [[ 0.90195566 0.90096247]\n", " [ 0.90195566 0.90096247]\n", " [ 0.90195566 0.90096247]\n", " [ 0.90195566 0.90096247]]\n", "Error: 0.5\n", "[[ 0.15940331 0.15940331 0.15940331 0.15940331]\n", " [ 0.08865832 0.08865832 0.08865832 0.08865832]] \n", " [[ 0.90257555 0.90157443]\n", " [ 0.90257555 0.90157443]\n", " [ 0.90257555 0.90157443]\n", " [ 0.90257555 0.90157443]]\n", "Error: 0.5\n", "[[ 0.15956737 0.15956737 0.15956737 0.15956737]\n", " [ 0.08876567 0.08876567 0.08876567 0.08876567]] \n", " [[ 0.90319377 0.90218472]\n", " [ 0.90319377 0.90218472]\n", " [ 0.90319377 0.90218472]\n", " [ 0.90319377 0.90218472]]\n", "Error: 0.5\n", "[[ 0.15973097 0.15973097 0.15973097 0.15973097]\n", " [ 0.08887276 0.08887276 0.08887276 0.08887276]] \n", " [[ 0.90381032 0.90279341]\n", " [ 0.90381032 0.90279341]\n", " [ 0.90381032 0.90279341]\n", " [ 0.90381032 0.90279341]]\n", "Error: 0.5\n", "[[ 0.15989411 0.15989411 0.15989411 0.15989411]\n", " [ 0.0889796 0.0889796 0.0889796 0.0889796 ]] \n", " [[ 0.9044252 0.90340042]\n", " [ 0.9044252 0.90340042]\n", " [ 0.9044252 0.90340042]\n", " [ 0.9044252 0.90340042]]\n", "Error: 0.5\n", "[[ 0.16005678 0.16005678 0.16005678 0.16005678]\n", " [ 0.0890862 0.0890862 0.0890862 0.0890862 ]] \n", " [[ 0.90503848 0.90400583]\n", " [ 0.90503848 0.90400583]\n", " [ 0.90503848 0.90400583]\n", " [ 0.90503848 0.90400583]]\n", "Error: 0.5\n", "[[ 0.16021901 0.16021901 0.16021901 0.16021901]\n", " [ 0.08919255 0.08919255 0.08919255 0.08919255]] \n", " [[ 0.90565008 0.90460956]\n", " [ 0.90565008 0.90460956]\n", " [ 0.90565008 0.90460956]\n", " [ 0.90565008 0.90460956]]\n", "Error: 0.5\n", "[[ 0.16038078 0.16038078 0.16038078 0.16038078]\n", " [ 0.08929864 0.08929864 0.08929864 0.08929864]] \n", " [[ 0.90626007 0.90521169]\n", " [ 0.90626007 0.90521169]\n", " [ 0.90626007 0.90521169]\n", " [ 0.90626007 0.90521169]]\n", "Error: 0.5\n", "[[ 0.1605421 0.1605421 0.1605421 0.1605421 ]\n", " [ 0.08940449 0.08940449 0.08940449 0.08940449]] \n", " [[ 0.90686846 0.9058122 ]\n", " [ 0.90686846 0.9058122 ]\n", " [ 0.90686846 0.9058122 ]\n", " [ 0.90686846 0.9058122 ]]\n", "Error: 0.5\n", "[[ 0.16070297 0.16070297 0.16070297 0.16070297]\n", " [ 0.08951011 0.08951011 0.08951011 0.08951011]] \n", " [[ 0.90747523 0.90641111]\n", " [ 0.90747523 0.90641111]\n", " [ 0.90747523 0.90641111]\n", " [ 0.90747523 0.90641111]]\n", "Error: 0.5\n", "[[ 0.1608634 0.1608634 0.1608634 0.1608634 ]\n", " [ 0.08961547 0.08961547 0.08961547 0.08961547]] \n", " [[ 0.9080804 0.90700847]\n", " [ 0.9080804 0.90700847]\n", " [ 0.9080804 0.90700847]\n", " [ 0.9080804 0.90700847]]\n", "Error: 0.5\n", "[[ 0.16102338 0.16102338 0.16102338 0.16102338]\n", " [ 0.08972059 0.08972059 0.08972059 0.08972059]] \n", " [[ 0.90868396 0.90760422]\n", " [ 0.90868396 0.90760422]\n", " [ 0.90868396 0.90760422]\n", " [ 0.90868396 0.90760422]]\n", "Error: 0.5\n", "[[ 0.16118293 0.16118293 0.16118293 0.16118293]\n", " [ 0.08982547 0.08982547 0.08982547 0.08982547]] \n", " [[ 0.90928596 0.90819842]\n", " [ 0.90928596 0.90819842]\n", " [ 0.90928596 0.90819842]\n", " [ 0.90928596 0.90819842]]\n", "Error: 0.5\n", "[[ 0.16134202 0.16134202 0.16134202 0.16134202]\n", " [ 0.08993012 0.08993012 0.08993012 0.08993012]] \n", " [[ 0.90988636 0.90879101]\n", " [ 0.90988636 0.90879101]\n", " [ 0.90988636 0.90879101]\n", " [ 0.90988636 0.90879101]]\n", "Error: 0.5\n", "[[ 0.16150069 0.16150069 0.16150069 0.16150069]\n", " [ 0.09003451 0.09003451 0.09003451 0.09003451]] \n", " [[ 0.91048521 0.90938205]\n", " [ 0.91048521 0.90938205]\n", " [ 0.91048521 0.90938205]\n", " [ 0.91048521 0.90938205]]\n", "Error: 0.5\n", "[[ 0.16165893 0.16165893 0.16165893 0.16165893]\n", " [ 0.09013867 0.09013867 0.09013867 0.09013867]] \n", " [[ 0.91108251 0.90997154]\n", " [ 0.91108251 0.90997154]\n", " [ 0.91108251 0.90997154]\n", " [ 0.91108251 0.90997154]]\n", "Error: 0.5\n", "[[ 0.16181673 0.16181673 0.16181673 0.16181673]\n", " [ 0.09024259 0.09024259 0.09024259 0.09024259]] \n", " [[ 0.91167825 0.91055948]\n", " [ 0.91167825 0.91055948]\n", " [ 0.91167825 0.91055948]\n", " [ 0.91167825 0.91055948]]\n", "Error: 0.5\n", "[[ 0.1619741 0.1619741 0.1619741 0.1619741 ]\n", " [ 0.09034628 0.09034628 0.09034628 0.09034628]] \n", " [[ 0.91227245 0.91114587]\n", " [ 0.91227245 0.91114587]\n", " [ 0.91227245 0.91114587]\n", " [ 0.91227245 0.91114587]]\n", "Error: 0.5\n", "[[ 0.16213104 0.16213104 0.16213104 0.16213104]\n", " [ 0.09044974 0.09044974 0.09044974 0.09044974]] \n", " [[ 0.9128651 0.91173077]\n", " [ 0.9128651 0.91173077]\n", " [ 0.9128651 0.91173077]\n", " [ 0.9128651 0.91173077]]\n", "Error: 0.5\n", "[[ 0.16228755 0.16228755 0.16228755 0.16228755]\n", " [ 0.09055296 0.09055296 0.09055296 0.09055296]] \n", " [[ 0.9134562 0.91231412]\n", " [ 0.9134562 0.91231412]\n", " [ 0.9134562 0.91231412]\n", " [ 0.9134562 0.91231412]]\n", "Error: 0.5\n", "[[ 0.16244364 0.16244364 0.16244364 0.16244364]\n", " [ 0.09065594 0.09065594 0.09065594 0.09065594]] \n", " [[ 0.91404581 0.91289598]\n", " [ 0.91404581 0.91289598]\n", " [ 0.91404581 0.91289598]\n", " [ 0.91404581 0.91289598]]\n", "Error: 0.5\n", "[[ 0.1625993 0.1625993 0.1625993 0.1625993 ]\n", " [ 0.09075869 0.09075869 0.09075869 0.09075869]] \n", " [[ 0.91463387 0.91347629]\n", " [ 0.91463387 0.91347629]\n", " [ 0.91463387 0.91347629]\n", " [ 0.91463387 0.91347629]]\n", "Error: 0.5\n", "[[ 0.16275454 0.16275454 0.16275454 0.16275454]\n", " [ 0.09086121 0.09086121 0.09086121 0.09086121]] \n", " [[ 0.91522044 0.91405511]\n", " [ 0.91522044 0.91405511]\n", " [ 0.91522044 0.91405511]\n", " [ 0.91522044 0.91405511]]\n", "Error: 0.5\n", "[[ 0.16290936 0.16290936 0.16290936 0.16290936]\n", " [ 0.0909635 0.0909635 0.0909635 0.0909635 ]] \n", " [[ 0.91580552 0.91463244]\n", " [ 0.91580552 0.91463244]\n", " [ 0.91580552 0.91463244]\n", " [ 0.91580552 0.91463244]]\n", "Error: 0.5\n", "[[ 0.16306376 0.16306376 0.16306376 0.16306376]\n", " [ 0.09106556 0.09106556 0.09106556 0.09106556]] \n", " [[ 0.91638911 0.91520828]\n", " [ 0.91638911 0.91520828]\n", " [ 0.91638911 0.91520828]\n", " [ 0.91638911 0.91520828]]\n", "Error: 0.5\n", "[[ 0.16321775 0.16321775 0.16321775 0.16321775]\n", " [ 0.09116739 0.09116739 0.09116739 0.09116739]] \n", " [[ 0.91697121 0.91578263]\n", " [ 0.91697121 0.91578263]\n", " [ 0.91697121 0.91578263]\n", " [ 0.91697121 0.91578263]]\n", "Error: 0.5\n", "[[ 0.16337132 0.16337132 0.16337132 0.16337132]\n", " [ 0.09126899 0.09126899 0.09126899 0.09126899]] \n", " [[ 0.91755182 0.91635555]\n", " [ 0.91755182 0.91635555]\n", " [ 0.91755182 0.91635555]\n", " [ 0.91755182 0.91635555]]\n", "Error: 0.5\n", "[[ 0.16352449 0.16352449 0.16352449 0.16352449]\n", " [ 0.09137037 0.09137037 0.09137037 0.09137037]] \n", " [[ 0.91813099 0.91692698]\n", " [ 0.91813099 0.91692698]\n", " [ 0.91813099 0.91692698]\n", " [ 0.91813099 0.91692698]]\n", "Error: 0.5\n", "[[ 0.16367725 0.16367725 0.16367725 0.16367725]\n", " [ 0.09147153 0.09147153 0.09147153 0.09147153]] \n", " [[ 0.91870868 0.91749698]\n", " [ 0.91870868 0.91749698]\n", " [ 0.91870868 0.91749698]\n", " [ 0.91870868 0.91749698]]\n", "Error: 0.5\n", "[[ 0.16382959 0.16382959 0.16382959 0.16382959]\n", " [ 0.09157246 0.09157246 0.09157246 0.09157246]] \n", " [[ 0.91928494 0.91806555]\n", " [ 0.91928494 0.91806555]\n", " [ 0.91928494 0.91806555]\n", " [ 0.91928494 0.91806555]]\n", "Error: 0.5\n", "[[ 0.16398154 0.16398154 0.16398154 0.16398154]\n", " [ 0.09167316 0.09167316 0.09167316 0.09167316]] \n", " [[ 0.91985971 0.91863263]\n", " [ 0.91985971 0.91863263]\n", " [ 0.91985971 0.91863263]\n", " [ 0.91985971 0.91863263]]\n", "Error: 0.5\n", "[[ 0.16413309 0.16413309 0.16413309 0.16413309]\n", " [ 0.09177364 0.09177364 0.09177364 0.09177364]] \n", " [[ 0.92043304 0.91919827]\n", " [ 0.92043304 0.91919827]\n", " [ 0.92043304 0.91919827]\n", " [ 0.92043304 0.91919827]]\n", "Error: 0.5\n", "[[ 0.16428423 0.16428423 0.16428423 0.16428423]\n", " [ 0.09187391 0.09187391 0.09187391 0.09187391]] \n", " [[ 0.92100495 0.91976249]\n", " [ 0.92100495 0.91976249]\n", " [ 0.92100495 0.91976249]\n", " [ 0.92100495 0.91976249]]\n", "Error: 0.5\n", "[[ 0.16443497 0.16443497 0.16443497 0.16443497]\n", " [ 0.09197395 0.09197395 0.09197395 0.09197395]] \n", " [[ 0.92157543 0.92032528]\n", " [ 0.92157543 0.92032528]\n", " [ 0.92157543 0.92032528]\n", " [ 0.92157543 0.92032528]]\n", "Error: 0.5\n", "[[ 0.16458531 0.16458531 0.16458531 0.16458531]\n", " [ 0.09207377 0.09207377 0.09207377 0.09207377]] \n", " [[ 0.92214447 0.9208867 ]\n", " [ 0.92214447 0.9208867 ]\n", " [ 0.92214447 0.9208867 ]\n", " [ 0.92214447 0.9208867 ]]\n", "Error: 0.5\n", "[[ 0.16473526 0.16473526 0.16473526 0.16473526]\n", " [ 0.09217337 0.09217337 0.09217337 0.09217337]] \n", " [[ 0.92271209 0.92144668]\n", " [ 0.92271209 0.92144668]\n", " [ 0.92271209 0.92144668]\n", " [ 0.92271209 0.92144668]]\n", "Error: 0.5\n", "[[ 0.16488481 0.16488481 0.16488481 0.16488481]\n", " [ 0.09227275 0.09227275 0.09227275 0.09227275]] \n", " [[ 0.92327833 0.9220053 ]\n", " [ 0.92327833 0.9220053 ]\n", " [ 0.92327833 0.9220053 ]\n", " [ 0.92327833 0.9220053 ]]\n", "Error: 0.5\n", "[[ 0.16503397 0.16503397 0.16503397 0.16503397]\n", " [ 0.09237192 0.09237192 0.09237192 0.09237192]] \n", " [[ 0.92384315 0.92256248]\n", " [ 0.92384315 0.92256248]\n", " [ 0.92384315 0.92256248]\n", " [ 0.92384315 0.92256248]]\n", "Error: 0.5\n", "[[ 0.16518274 0.16518274 0.16518274 0.16518274]\n", " [ 0.09247087 0.09247087 0.09247087 0.09247087]] \n", " [[ 0.92440659 0.92311829]\n", " [ 0.92440659 0.92311829]\n", " [ 0.92440659 0.92311829]\n", " [ 0.92440659 0.92311829]]\n", "Error: 0.5\n", "[[ 0.16533111 0.16533111 0.16533111 0.16533111]\n", " [ 0.0925696 0.0925696 0.0925696 0.0925696 ]] \n", " [[ 0.92496866 0.92367274]\n", " [ 0.92496866 0.92367274]\n", " [ 0.92496866 0.92367274]\n", " [ 0.92496866 0.92367274]]\n", "Error: 0.5\n", "[[ 0.16547909 0.16547909 0.16547909 0.16547909]\n", " [ 0.09266812 0.09266812 0.09266812 0.09266812]] \n", " [[ 0.9255293 0.92422581]\n", " [ 0.9255293 0.92422581]\n", " [ 0.9255293 0.92422581]\n", " [ 0.9255293 0.92422581]]\n", "Error: 0.5\n", "[[ 0.1656267 0.1656267 0.1656267 0.1656267 ]\n", " [ 0.09276643 0.09276643 0.09276643 0.09276643]] \n", " [[ 0.92608857 0.92477751]\n", " [ 0.92608857 0.92477751]\n", " [ 0.92608857 0.92477751]\n", " [ 0.92608857 0.92477751]]\n", "Error: 0.5\n", "[[ 0.16577393 0.16577393 0.16577393 0.16577393]\n", " [ 0.09286452 0.09286452 0.09286452 0.09286452]] \n", " [[ 0.92664647 0.92532784]\n", " [ 0.92664647 0.92532784]\n", " [ 0.92664647 0.92532784]\n", " [ 0.92664647 0.92532784]]\n", "Error: 0.5\n", "[[ 0.16592076 0.16592076 0.16592076 0.16592076]\n", " [ 0.0929624 0.0929624 0.0929624 0.0929624 ]] \n", " [[ 0.92720306 0.9258768 ]\n", " [ 0.92720306 0.9258768 ]\n", " [ 0.92720306 0.9258768 ]\n", " [ 0.92720306 0.9258768 ]]\n", "Error: 0.5\n", "[[ 0.16606723 0.16606723 0.16606723 0.16606723]\n", " [ 0.09306007 0.09306007 0.09306007 0.09306007]] \n", " [[ 0.92775828 0.92642444]\n", " [ 0.92775828 0.92642444]\n", " [ 0.92775828 0.92642444]\n", " [ 0.92775828 0.92642444]]\n", "Error: 0.5\n", "[[ 0.1662133 0.1662133 0.1662133 0.1662133 ]\n", " [ 0.09315753 0.09315753 0.09315753 0.09315753]] \n", " [[ 0.92831212 0.92697072]\n", " [ 0.92831212 0.92697072]\n", " [ 0.92831212 0.92697072]\n", " [ 0.92831212 0.92697072]]\n", "Error: 0.5\n", "[[ 0.16635901 0.16635901 0.16635901 0.16635901]\n", " [ 0.09325478 0.09325478 0.09325478 0.09325478]] \n", " [[ 0.92886466 0.92751569]\n", " [ 0.92886466 0.92751569]\n", " [ 0.92886466 0.92751569]\n", " [ 0.92886466 0.92751569]]\n", "Error: 0.5\n", "[[ 0.16650434 0.16650434 0.16650434 0.16650434]\n", " [ 0.09335183 0.09335183 0.09335183 0.09335183]] \n", " [[ 0.92941582 0.92805928]\n", " [ 0.92941582 0.92805928]\n", " [ 0.92941582 0.92805928]\n", " [ 0.92941582 0.92805928]]\n", "Error: 0.5\n", "[[ 0.1666493 0.1666493 0.1666493 0.1666493 ]\n", " [ 0.09344866 0.09344866 0.09344866 0.09344866]] \n", " [[ 0.92996567 0.92860156]\n", " [ 0.92996567 0.92860156]\n", " [ 0.92996567 0.92860156]\n", " [ 0.92996567 0.92860156]]\n", "Error: 0.5\n", "[[ 0.16679388 0.16679388 0.16679388 0.16679388]\n", " [ 0.09354529 0.09354529 0.09354529 0.09354529]] \n", " [[ 0.93051422 0.92914253]\n", " [ 0.93051422 0.92914253]\n", " [ 0.93051422 0.92914253]\n", " [ 0.93051422 0.92914253]]\n", "Error: 0.5\n", "[[ 0.1669381 0.1669381 0.1669381 0.1669381 ]\n", " [ 0.09364171 0.09364171 0.09364171 0.09364171]] \n", " [[ 0.93106139 0.9296822 ]\n", " [ 0.93106139 0.9296822 ]\n", " [ 0.93106139 0.9296822 ]\n", " [ 0.93106139 0.9296822 ]]\n", "Error: 0.5\n", "[[ 0.16708194 0.16708194 0.16708194 0.16708194]\n", " [ 0.09373793 0.09373793 0.09373793 0.09373793]] \n", " [[ 0.93160725 0.93022054]\n", " [ 0.93160725 0.93022054]\n", " [ 0.93160725 0.93022054]\n", " [ 0.93160725 0.93022054]]\n", "Error: 0.5\n", "[[ 0.16722542 0.16722542 0.16722542 0.16722542]\n", " [ 0.09383395 0.09383395 0.09383395 0.09383395]] \n", " [[ 0.93215185 0.93075758]\n", " [ 0.93215185 0.93075758]\n", " [ 0.93215185 0.93075758]\n", " [ 0.93215185 0.93075758]]\n", "Error: 0.5\n", "[[ 0.16736853 0.16736853 0.16736853 0.16736853]\n", " [ 0.09392976 0.09392976 0.09392976 0.09392976]] \n", " [[ 0.93269515 0.93129337]\n", " [ 0.93269515 0.93129337]\n", " [ 0.93269515 0.93129337]\n", " [ 0.93269515 0.93129337]]\n", "Error: 0.5\n", "[[ 0.16751128 0.16751128 0.16751128 0.16751128]\n", " [ 0.09402537 0.09402537 0.09402537 0.09402537]] \n", " [[ 0.93323714 0.93182784]\n", " [ 0.93323714 0.93182784]\n", " [ 0.93323714 0.93182784]\n", " [ 0.93323714 0.93182784]]\n", "Error: 0.5\n", "[[ 0.16765368 0.16765368 0.16765368 0.16765368]\n", " [ 0.09412077 0.09412077 0.09412077 0.09412077]] \n", " [[ 0.93377781 0.93236107]\n", " [ 0.93377781 0.93236107]\n", " [ 0.93377781 0.93236107]\n", " [ 0.93377781 0.93236107]]\n", "Error: 0.5\n", "[[ 0.1677957 0.1677957 0.1677957 0.1677957 ]\n", " [ 0.09421597 0.09421597 0.09421597 0.09421597]] \n", " [[ 0.93431723 0.93289298]\n", " [ 0.93431723 0.93289298]\n", " [ 0.93431723 0.93289298]\n", " [ 0.93431723 0.93289298]]\n", "Error: 0.5\n", "[[ 0.16793737 0.16793737 0.16793737 0.16793737]\n", " [ 0.09431098 0.09431098 0.09431098 0.09431098]] \n", " [[ 0.93485534 0.93342364]\n", " [ 0.93485534 0.93342364]\n", " [ 0.93485534 0.93342364]\n", " [ 0.93485534 0.93342364]]\n", "Error: 0.5\n", "[[ 0.16807868 0.16807868 0.16807868 0.16807868]\n", " [ 0.09440579 0.09440579 0.09440579 0.09440579]] \n", " [[ 0.9353922 0.93395305]\n", " [ 0.9353922 0.93395305]\n", " [ 0.9353922 0.93395305]\n", " [ 0.9353922 0.93395305]]\n", "Error: 0.5\n", "[[ 0.16821963 0.16821963 0.16821963 0.16821963]\n", " [ 0.0945004 0.0945004 0.0945004 0.0945004 ]] \n", " [[ 0.93592781 0.9344812 ]\n", " [ 0.93592781 0.9344812 ]\n", " [ 0.93592781 0.9344812 ]\n", " [ 0.93592781 0.9344812 ]]\n", "Error: 0.5\n", "[[ 0.16836023 0.16836023 0.16836023 0.16836023]\n", " [ 0.09459481 0.09459481 0.09459481 0.09459481]] \n", " [[ 0.93646216 0.93500811]\n", " [ 0.93646216 0.93500811]\n", " [ 0.93646216 0.93500811]\n", " [ 0.93646216 0.93500811]]\n", "Error: 0.5\n", "[[ 0.16850048 0.16850048 0.16850048 0.16850048]\n", " [ 0.09468903 0.09468903 0.09468903 0.09468903]] \n", " [[ 0.93699527 0.93553376]\n", " [ 0.93699527 0.93553376]\n", " [ 0.93699527 0.93553376]\n", " [ 0.93699527 0.93553376]]\n", "Error: 0.5\n", "[[ 0.16864038 0.16864038 0.16864038 0.16864038]\n", " [ 0.09478305 0.09478305 0.09478305 0.09478305]] \n", " [[ 0.93752712 0.93605816]\n", " [ 0.93752712 0.93605816]\n", " [ 0.93752712 0.93605816]\n", " [ 0.93752712 0.93605816]]\n", "Error: 0.5\n", "[[ 0.16877992 0.16877992 0.16877992 0.16877992]\n", " [ 0.09487687 0.09487687 0.09487687 0.09487687]] \n", " [[ 0.93805772 0.93658131]\n", " [ 0.93805772 0.93658131]\n", " [ 0.93805772 0.93658131]\n", " [ 0.93805772 0.93658131]]\n", "Error: 0.5\n", "[[ 0.16891913 0.16891913 0.16891913 0.16891913]\n", " [ 0.0949705 0.0949705 0.0949705 0.0949705 ]] \n", " [[ 0.93858707 0.93710321]\n", " [ 0.93858707 0.93710321]\n", " [ 0.93858707 0.93710321]\n", " [ 0.93858707 0.93710321]]\n", "Error: 0.5\n", "[[ 0.16905798 0.16905798 0.16905798 0.16905798]\n", " [ 0.09506394 0.09506394 0.09506394 0.09506394]] \n", " [[ 0.93911517 0.93762392]\n", " [ 0.93911517 0.93762392]\n", " [ 0.93911517 0.93762392]\n", " [ 0.93911517 0.93762392]]\n", "Error: 0.5\n", "[[ 0.16919649 0.16919649 0.16919649 0.16919649]\n", " [ 0.09515718 0.09515718 0.09515718 0.09515718]] \n", " [[ 0.93964207 0.93814337]\n", " [ 0.93964207 0.93814337]\n", " [ 0.93964207 0.93814337]\n", " [ 0.93964207 0.93814337]]\n", "Error: 0.5\n", "[[ 0.16933465 0.16933465 0.16933465 0.16933465]\n", " [ 0.09525023 0.09525023 0.09525023 0.09525023]] \n", " [[ 0.94016773 0.93866163]\n", " [ 0.94016773 0.93866163]\n", " [ 0.94016773 0.93866163]\n", " [ 0.94016773 0.93866163]]\n", "Error: 0.5\n", "[[ 0.16947247 0.16947247 0.16947247 0.16947247]\n", " [ 0.09534309 0.09534309 0.09534309 0.09534309]] \n", " [[ 0.94069219 0.93917871]\n", " [ 0.94069219 0.93917871]\n", " [ 0.94069219 0.93917871]\n", " [ 0.94069219 0.93917871]]\n", "Error: 0.5\n", "[[ 0.16960995 0.16960995 0.16960995 0.16960995]\n", " [ 0.09543576 0.09543576 0.09543576 0.09543576]] \n", " [[ 0.94121546 0.93969458]\n", " [ 0.94121546 0.93969458]\n", " [ 0.94121546 0.93969458]\n", " [ 0.94121546 0.93969458]]\n", "Error: 0.5\n", "[[ 0.1697471 0.1697471 0.1697471 0.1697471 ]\n", " [ 0.09552824 0.09552824 0.09552824 0.09552824]] \n", " [[ 0.94173747 0.94020927]\n", " [ 0.94173747 0.94020927]\n", " [ 0.94173747 0.94020927]\n", " [ 0.94173747 0.94020927]]\n", "Error: 0.5\n", "[[ 0.16988391 0.16988391 0.16988391 0.16988391]\n", " [ 0.09562053 0.09562053 0.09562053 0.09562053]] \n", " [[ 0.9422583 0.94072276]\n", " [ 0.9422583 0.94072276]\n", " [ 0.9422583 0.94072276]\n", " [ 0.9422583 0.94072276]]\n", "Error: 0.5\n", "[[ 0.17002039 0.17002039 0.17002039 0.17002039]\n", " [ 0.09571262 0.09571262 0.09571262 0.09571262]] \n", " [[ 0.94277793 0.94123507]\n", " [ 0.94277793 0.94123507]\n", " [ 0.94277793 0.94123507]\n", " [ 0.94277793 0.94123507]]\n", "Error: 0.5\n", "[[ 0.17015652 0.17015652 0.17015652 0.17015652]\n", " [ 0.09580453 0.09580453 0.09580453 0.09580453]] \n", " [[ 0.94329637 0.94174618]\n", " [ 0.94329637 0.94174618]\n", " [ 0.94329637 0.94174618]\n", " [ 0.94329637 0.94174618]]\n", "Error: 0.5\n", "[[ 0.17029233 0.17029233 0.17029233 0.17029233]\n", " [ 0.09589626 0.09589626 0.09589626 0.09589626]] \n", " [[ 0.94381362 0.94225609]\n", " [ 0.94381362 0.94225609]\n", " [ 0.94381362 0.94225609]\n", " [ 0.94381362 0.94225609]]\n", "Error: 0.5\n", "[[ 0.1704278 0.1704278 0.1704278 0.1704278]\n", " [ 0.0959878 0.0959878 0.0959878 0.0959878]] \n", " [[ 0.94432974 0.94276482]\n", " [ 0.94432974 0.94276482]\n", " [ 0.94432974 0.94276482]\n", " [ 0.94432974 0.94276482]]\n", "Error: 0.5\n", "[[ 0.17056294 0.17056294 0.17056294 0.17056294]\n", " [ 0.09607915 0.09607915 0.09607915 0.09607915]] \n", " [[ 0.94484466 0.94327241]\n", " [ 0.94484466 0.94327241]\n", " [ 0.94484466 0.94327241]\n", " [ 0.94484466 0.94327241]]\n", "Error: 0.5\n", "[[ 0.17069775 0.17069775 0.17069775 0.17069775]\n", " [ 0.09617031 0.09617031 0.09617031 0.09617031]] \n", " [[ 0.9453584 0.94377881]\n", " [ 0.9453584 0.94377881]\n", " [ 0.9453584 0.94377881]\n", " [ 0.9453584 0.94377881]]\n", "Error: 0.5\n", "[[ 0.17083223 0.17083223 0.17083223 0.17083223]\n", " [ 0.0962613 0.0962613 0.0962613 0.0962613 ]] \n", " [[ 0.945871 0.94428408]\n", " [ 0.945871 0.94428408]\n", " [ 0.945871 0.94428408]\n", " [ 0.945871 0.94428408]]\n", "Error: 0.5\n", "[[ 0.17096639 0.17096639 0.17096639 0.17096639]\n", " [ 0.0963521 0.0963521 0.0963521 0.0963521 ]] \n", " [[ 0.9463824 0.94478822]\n", " [ 0.9463824 0.94478822]\n", " [ 0.9463824 0.94478822]\n", " [ 0.9463824 0.94478822]]\n", "Error: 0.5\n", "[[ 0.17110023 0.17110023 0.17110023 0.17110023]\n", " [ 0.09644272 0.09644272 0.09644272 0.09644272]] \n", " [[ 0.94689268 0.94529122]\n", " [ 0.94689268 0.94529122]\n", " [ 0.94689268 0.94529122]\n", " [ 0.94689268 0.94529122]]\n", "Error: 0.5\n", "[[ 0.17123374 0.17123374 0.17123374 0.17123374]\n", " [ 0.09653316 0.09653316 0.09653316 0.09653316]] \n", " [[ 0.94740182 0.94579303]\n", " [ 0.94740182 0.94579303]\n", " [ 0.94740182 0.94579303]\n", " [ 0.94740182 0.94579303]]\n", "Error: 0.5\n", "[[ 0.17136693 0.17136693 0.17136693 0.17136693]\n", " [ 0.09662341 0.09662341 0.09662341 0.09662341]] \n", " [[ 0.94790983 0.94629371]\n", " [ 0.94790983 0.94629371]\n", " [ 0.94790983 0.94629371]\n", " [ 0.94790983 0.94629371]]\n", "Error: 0.5\n", "[[ 0.1714998 0.1714998 0.1714998 0.1714998 ]\n", " [ 0.09671348 0.09671348 0.09671348 0.09671348]] \n", " [[ 0.94841671 0.94679326]\n", " [ 0.94841671 0.94679326]\n", " [ 0.94841671 0.94679326]\n", " [ 0.94841671 0.94679326]]\n", "Error: 0.5\n", "[[ 0.17163235 0.17163235 0.17163235 0.17163235]\n", " [ 0.09680337 0.09680337 0.09680337 0.09680337]] \n", " [[ 0.94892246 0.94729173]\n", " [ 0.94892246 0.94729173]\n", " [ 0.94892246 0.94729173]\n", " [ 0.94892246 0.94729173]]\n", "Error: 0.5\n", "[[ 0.17176458 0.17176458 0.17176458 0.17176458]\n", " [ 0.09689309 0.09689309 0.09689309 0.09689309]] \n", " [[ 0.94942707 0.94778907]\n", " [ 0.94942707 0.94778907]\n", " [ 0.94942707 0.94778907]\n", " [ 0.94942707 0.94778907]]\n", "Error: 0.5\n", "[[ 0.1718965 0.1718965 0.1718965 0.1718965 ]\n", " [ 0.09698262 0.09698262 0.09698262 0.09698262]] \n", " [[ 0.94993055 0.94828528]\n", " [ 0.94993055 0.94828528]\n", " [ 0.94993055 0.94828528]\n", " [ 0.94993055 0.94828528]]\n", "Error: 0.5\n", "[[ 0.17202811 0.17202811 0.17202811 0.17202811]\n", " [ 0.09707198 0.09707198 0.09707198 0.09707198]] \n", " [[ 0.9504329 0.94878036]\n", " [ 0.9504329 0.94878036]\n", " [ 0.9504329 0.94878036]\n", " [ 0.9504329 0.94878036]]\n", "Error: 0.5\n", "[[ 0.1721594 0.1721594 0.1721594 0.1721594 ]\n", " [ 0.09716116 0.09716116 0.09716116 0.09716116]] \n", " [[ 0.95093411 0.94927436]\n", " [ 0.95093411 0.94927436]\n", " [ 0.95093411 0.94927436]\n", " [ 0.95093411 0.94927436]]\n", "Error: 0.5\n", "[[ 0.17229038 0.17229038 0.17229038 0.17229038]\n", " [ 0.09725016 0.09725016 0.09725016 0.09725016]] \n", " [[ 0.95143425 0.94976723]\n", " [ 0.95143425 0.94976723]\n", " [ 0.95143425 0.94976723]\n", " [ 0.95143425 0.94976723]]\n", "Error: 0.5\n", "[[ 0.17242105 0.17242105 0.17242105 0.17242105]\n", " [ 0.09733899 0.09733899 0.09733899 0.09733899]] \n", " [[ 0.95193326 0.95025903]\n", " [ 0.95193326 0.95025903]\n", " [ 0.95193326 0.95025903]\n", " [ 0.95193326 0.95025903]]\n", "Error: 0.5\n", "[[ 0.17255141 0.17255141 0.17255141 0.17255141]\n", " [ 0.09742764 0.09742764 0.09742764 0.09742764]] \n", " [[ 0.9524312 0.95074975]\n", " [ 0.9524312 0.95074975]\n", " [ 0.9524312 0.95074975]\n", " [ 0.9524312 0.95074975]]\n", "Error: 0.5\n", "[[ 0.17268145 0.17268145 0.17268145 0.17268145]\n", " [ 0.09751612 0.09751612 0.09751612 0.09751612]] \n", " [[ 0.95292801 0.95123935]\n", " [ 0.95292801 0.95123935]\n", " [ 0.95292801 0.95123935]\n", " [ 0.95292801 0.95123935]]\n", "Error: 0.5\n", "[[ 0.17281118 0.17281118 0.17281118 0.17281118]\n", " [ 0.09760442 0.09760442 0.09760442 0.09760442]] \n", " [[ 0.95342374 0.95172787]\n", " [ 0.95342374 0.95172787]\n", " [ 0.95342374 0.95172787]\n", " [ 0.95342374 0.95172787]]\n", "Error: 0.5\n", "[[ 0.17294061 0.17294061 0.17294061 0.17294061]\n", " [ 0.09769256 0.09769256 0.09769256 0.09769256]] \n", " [[ 0.9539184 0.95221531]\n", " [ 0.9539184 0.95221531]\n", " [ 0.9539184 0.95221531]\n", " [ 0.9539184 0.95221531]]\n", "Error: 0.5\n", "[[ 0.17306973 0.17306973 0.17306973 0.17306973]\n", " [ 0.09778052 0.09778052 0.09778052 0.09778052]] \n", " [[ 0.95441198 0.95270169]\n", " [ 0.95441198 0.95270169]\n", " [ 0.95441198 0.95270169]\n", " [ 0.95441198 0.95270169]]\n", "Error: 0.5\n", "[[ 0.17319855 0.17319855 0.17319855 0.17319855]\n", " [ 0.09786831 0.09786831 0.09786831 0.09786831]] \n", " [[ 0.9549045 0.95318699]\n", " [ 0.9549045 0.95318699]\n", " [ 0.9549045 0.95318699]\n", " [ 0.9549045 0.95318699]]\n", "Error: 0.5\n", "[[ 0.17332707 0.17332707 0.17332707 0.17332707]\n", " [ 0.09795593 0.09795593 0.09795593 0.09795593]] \n", " [[ 0.95539594 0.95367122]\n", " [ 0.95539594 0.95367122]\n", " [ 0.95539594 0.95367122]\n", " [ 0.95539594 0.95367122]]\n", "Error: 0.5\n", "[[ 0.1734553 0.1734553 0.1734553 0.1734553 ]\n", " [ 0.09804337 0.09804337 0.09804337 0.09804337]] \n", " [[ 0.9558863 0.95415437]\n", " [ 0.9558863 0.95415437]\n", " [ 0.9558863 0.95415437]\n", " [ 0.9558863 0.95415437]]\n", "Error: 0.5\n", "[[ 0.17358321 0.17358321 0.17358321 0.17358321]\n", " [ 0.09813065 0.09813065 0.09813065 0.09813065]] \n", " [[ 0.9563756 0.95463651]\n", " [ 0.9563756 0.95463651]\n", " [ 0.9563756 0.95463651]\n", " [ 0.9563756 0.95463651]]\n", "Error: 0.5\n", "[[ 0.17371082 0.17371082 0.17371082 0.17371082]\n", " [ 0.09821776 0.09821776 0.09821776 0.09821776]] \n", " [[ 0.95686382 0.95511758]\n", " [ 0.95686382 0.95511758]\n", " [ 0.95686382 0.95511758]\n", " [ 0.95686382 0.95511758]]\n", "Error: 0.5\n", "[[ 0.17383814 0.17383814 0.17383814 0.17383814]\n", " [ 0.09830469 0.09830469 0.09830469 0.09830469]] \n", " [[ 0.95735097 0.95559758]\n", " [ 0.95735097 0.95559758]\n", " [ 0.95735097 0.95559758]\n", " [ 0.95735097 0.95559758]]\n", "Error: 0.5\n", "[[ 0.17396517 0.17396517 0.17396517 0.17396517]\n", " [ 0.09839146 0.09839146 0.09839146 0.09839146]] \n", " [[ 0.9578371 0.95607656]\n", " [ 0.9578371 0.95607656]\n", " [ 0.9578371 0.95607656]\n", " [ 0.9578371 0.95607656]]\n", "Error: 0.5\n", "[[ 0.17409191 0.17409191 0.17409191 0.17409191]\n", " [ 0.09847806 0.09847806 0.09847806 0.09847806]] \n", " [[ 0.95832217 0.95655453]\n", " [ 0.95832217 0.95655453]\n", " [ 0.95832217 0.95655453]\n", " [ 0.95832217 0.95655453]]\n", "Error: 0.5\n", "[[ 0.17421834 0.17421834 0.17421834 0.17421834]\n", " [ 0.09856449 0.09856449 0.09856449 0.09856449]] \n", " [[ 0.95880622 0.95703143]\n", " [ 0.95880622 0.95703143]\n", " [ 0.95880622 0.95703143]\n", " [ 0.95880622 0.95703143]]\n", "Error: 0.5\n", "[[ 0.17434448 0.17434448 0.17434448 0.17434448]\n", " [ 0.09865076 0.09865076 0.09865076 0.09865076]] \n", " [[ 0.95928919 0.95750731]\n", " [ 0.95928919 0.95750731]\n", " [ 0.95928919 0.95750731]\n", " [ 0.95928919 0.95750731]]\n", "Error: 0.5\n", "[[ 0.17447034 0.17447034 0.17447034 0.17447034]\n", " [ 0.09873686 0.09873686 0.09873686 0.09873686]] \n", " [[ 0.95977116 0.95798218]\n", " [ 0.95977116 0.95798218]\n", " [ 0.95977116 0.95798218]\n", " [ 0.95977116 0.95798218]]\n", "Error: 0.5\n", "[[ 0.17459589 0.17459589 0.17459589 0.17459589]\n", " [ 0.09882279 0.09882279 0.09882279 0.09882279]] \n", " [[ 0.96025211 0.95845604]\n", " [ 0.96025211 0.95845604]\n", " [ 0.96025211 0.95845604]\n", " [ 0.96025211 0.95845604]]\n", "Error: 0.5\n", "[[ 0.17472117 0.17472117 0.17472117 0.17472117]\n", " [ 0.09890857 0.09890857 0.09890857 0.09890857]] \n", " [[ 0.96073198 0.95892888]\n", " [ 0.96073198 0.95892888]\n", " [ 0.96073198 0.95892888]\n", " [ 0.96073198 0.95892888]]\n", "Error: 0.5\n", "[[ 0.17484616 0.17484616 0.17484616 0.17484616]\n", " [ 0.09899417 0.09899417 0.09899417 0.09899417]] \n", " [[ 0.96121085 0.95940071]\n", " [ 0.96121085 0.95940071]\n", " [ 0.96121085 0.95940071]\n", " [ 0.96121085 0.95940071]]\n", "Error: 0.5\n", "[[ 0.17497085 0.17497085 0.17497085 0.17497085]\n", " [ 0.09907962 0.09907962 0.09907962 0.09907962]] \n", " [[ 0.9616887 0.95987153]\n", " [ 0.9616887 0.95987153]\n", " [ 0.9616887 0.95987153]\n", " [ 0.9616887 0.95987153]]\n", "Error: 0.5\n", "[[ 0.17509526 0.17509526 0.17509526 0.17509526]\n", " [ 0.0991649 0.0991649 0.0991649 0.0991649 ]] \n", " [[ 0.96216553 0.96034133]\n", " [ 0.96216553 0.96034133]\n", " [ 0.96216553 0.96034133]\n", " [ 0.96216553 0.96034133]]\n", "Error: 0.5\n", "[[ 0.17521939 0.17521939 0.17521939 0.17521939]\n", " [ 0.09925002 0.09925002 0.09925002 0.09925002]] \n", " [[ 0.96264136 0.96081012]\n", " [ 0.96264136 0.96081012]\n", " [ 0.96264136 0.96081012]\n", " [ 0.96264136 0.96081012]]\n", "Error: 0.5\n", "[[ 0.17534323 0.17534323 0.17534323 0.17534323]\n", " [ 0.09933498 0.09933498 0.09933498 0.09933498]] \n", " [[ 0.96311623 0.9612779 ]\n", " [ 0.96311623 0.9612779 ]\n", " [ 0.96311623 0.9612779 ]\n", " [ 0.96311623 0.9612779 ]]\n", "Error: 0.5\n", "[[ 0.17546679 0.17546679 0.17546679 0.17546679]\n", " [ 0.09941977 0.09941977 0.09941977 0.09941977]] \n", " [[ 0.96359009 0.96174473]\n", " [ 0.96359009 0.96174473]\n", " [ 0.96359009 0.96174473]\n", " [ 0.96359009 0.96174473]]\n", "Error: 0.5\n", "[[ 0.17559007 0.17559007 0.17559007 0.17559007]\n", " [ 0.09950441 0.09950441 0.09950441 0.09950441]] \n", " [[ 0.96406293 0.96221054]\n", " [ 0.96406293 0.96221054]\n", " [ 0.96406293 0.96221054]\n", " [ 0.96406293 0.96221054]]\n", "Error: 0.5\n", "[[ 0.17571306 0.17571306 0.17571306 0.17571306]\n", " [ 0.09958889 0.09958889 0.09958889 0.09958889]] \n", " [[ 0.96453476 0.96267539]\n", " [ 0.96453476 0.96267539]\n", " [ 0.96453476 0.96267539]\n", " [ 0.96453476 0.96267539]]\n", "Error: 0.5\n", "[[ 0.17583579 0.17583579 0.17583579 0.17583579]\n", " [ 0.0996732 0.0996732 0.0996732 0.0996732 ]] \n", " [[ 0.96500564 0.96313924]\n", " [ 0.96500564 0.96313924]\n", " [ 0.96500564 0.96313924]\n", " [ 0.96500564 0.96313924]]\n", "Error: 0.5\n", "[[ 0.17595823 0.17595823 0.17595823 0.17595823]\n", " [ 0.09975737 0.09975737 0.09975737 0.09975737]] \n", " [[ 0.9654755 0.96360213]\n", " [ 0.9654755 0.96360213]\n", " [ 0.9654755 0.96360213]\n", " [ 0.9654755 0.96360213]]\n", "Error: 0.5\n", "[[ 0.17608039 0.17608039 0.17608039 0.17608039]\n", " [ 0.09984137 0.09984137 0.09984137 0.09984137]] \n", " [[ 0.96594441 0.964064 ]\n", " [ 0.96594441 0.964064 ]\n", " [ 0.96594441 0.964064 ]\n", " [ 0.96594441 0.964064 ]]\n", "Error: 0.5\n", "[[ 0.17620228 0.17620228 0.17620228 0.17620228]\n", " [ 0.09992521 0.09992521 0.09992521 0.09992521]] \n", " [[ 0.96641237 0.96452492]\n", " [ 0.96641237 0.96452492]\n", " [ 0.96641237 0.96452492]\n", " [ 0.96641237 0.96452492]]\n", "Error: 0.5\n", "[[ 0.17632391 0.17632391 0.17632391 0.17632391]\n", " [ 0.1000089 0.1000089 0.1000089 0.1000089 ]] \n", " [[ 0.96687931 0.96498489]\n", " [ 0.96687931 0.96498489]\n", " [ 0.96687931 0.96498489]\n", " [ 0.96687931 0.96498489]]\n", "Error: 0.5\n", "[[ 0.17644525 0.17644525 0.17644525 0.17644525]\n", " [ 0.10009243 0.10009243 0.10009243 0.10009243]] \n", " [[ 0.9673453 0.96544391]\n", " [ 0.9673453 0.96544391]\n", " [ 0.9673453 0.96544391]\n", " [ 0.9673453 0.96544391]]\n", "Error: 0.5\n", "[[ 0.17656632 0.17656632 0.17656632 0.17656632]\n", " [ 0.10017581 0.10017581 0.10017581 0.10017581]] \n", " [[ 0.96781033 0.96590197]\n", " [ 0.96781033 0.96590197]\n", " [ 0.96781033 0.96590197]\n", " [ 0.96781033 0.96590197]]\n", "Error: 0.5\n", "[[ 0.17668712 0.17668712 0.17668712 0.17668712]\n", " [ 0.10025903 0.10025903 0.10025903 0.10025903]] \n", " [[ 0.96827441 0.96635908]\n", " [ 0.96827441 0.96635908]\n", " [ 0.96827441 0.96635908]\n", " [ 0.96827441 0.96635908]]\n", "Error: 0.5\n", "[[ 0.17680766 0.17680766 0.17680766 0.17680766]\n", " [ 0.10034209 0.10034209 0.10034209 0.10034209]] \n", " [[ 0.96873754 0.96681523]\n", " [ 0.96873754 0.96681523]\n", " [ 0.96873754 0.96681523]\n", " [ 0.96873754 0.96681523]]\n", "Error: 0.5\n", "[[ 0.17692791 0.17692791 0.17692791 0.17692791]\n", " [ 0.100425 0.100425 0.100425 0.100425 ]] \n", " [[ 0.96919972 0.96727043]\n", " [ 0.96919972 0.96727043]\n", " [ 0.96919972 0.96727043]\n", " [ 0.96919972 0.96727043]]\n", "Error: 0.5\n", "[[ 0.17704789 0.17704789 0.17704789 0.17704789]\n", " [ 0.10050777 0.10050777 0.10050777 0.10050777]] \n", " [[ 0.96966094 0.96772468]\n", " [ 0.96966094 0.96772468]\n", " [ 0.96966094 0.96772468]\n", " [ 0.96966094 0.96772468]]\n", "Error: 0.5\n", "[[ 0.17716762 0.17716762 0.17716762 0.17716762]\n", " [ 0.10059037 0.10059037 0.10059037 0.10059037]] \n", " [[ 0.9701212 0.96817797]\n", " [ 0.9701212 0.96817797]\n", " [ 0.9701212 0.96817797]\n", " [ 0.9701212 0.96817797]]\n", "Error: 0.5\n", "[[ 0.17728709 0.17728709 0.17728709 0.17728709]\n", " [ 0.10067283 0.10067283 0.10067283 0.10067283]] \n", " [[ 0.97058052 0.96863037]\n", " [ 0.97058052 0.96863037]\n", " [ 0.97058052 0.96863037]\n", " [ 0.97058052 0.96863037]]\n", "Error: 0.5\n", "[[ 0.17740628 0.17740628 0.17740628 0.17740628]\n", " [ 0.10075513 0.10075513 0.10075513 0.10075513]] \n", " [[ 0.97103888 0.96908182]\n", " [ 0.97103888 0.96908182]\n", " [ 0.97103888 0.96908182]\n", " [ 0.97103888 0.96908182]]\n", "Error: 0.5\n", "[[ 0.17752521 0.17752521 0.17752521 0.17752521]\n", " [ 0.10083728 0.10083728 0.10083728 0.10083728]] \n", " [[ 0.97149634 0.96953237]\n", " [ 0.97149634 0.96953237]\n", " [ 0.97149634 0.96953237]\n", " [ 0.97149634 0.96953237]]\n", "Error: 0.5\n", "[[ 0.17764388 0.17764388 0.17764388 0.17764388]\n", " [ 0.10091928 0.10091928 0.10091928 0.10091928]] \n", " [[ 0.97195286 0.96998197]\n", " [ 0.97195286 0.96998197]\n", " [ 0.97195286 0.96998197]\n", " [ 0.97195286 0.96998197]]\n", "Error: 0.5\n", "[[ 0.17776228 0.17776228 0.17776228 0.17776228]\n", " [ 0.10100112 0.10100112 0.10100112 0.10100112]] \n", " [[ 0.97240841 0.97043067]\n", " [ 0.97240841 0.97043067]\n", " [ 0.97240841 0.97043067]\n", " [ 0.97240841 0.97043067]]\n", "Error: 0.5\n", "[[ 0.17788042 0.17788042 0.17788042 0.17788042]\n", " [ 0.10108282 0.10108282 0.10108282 0.10108282]] \n", " [[ 0.97286308 0.97087842]\n", " [ 0.97286308 0.97087842]\n", " [ 0.97286308 0.97087842]\n", " [ 0.97286308 0.97087842]]\n", "Error: 0.5\n", "[[ 0.1779983 0.1779983 0.1779983 0.1779983 ]\n", " [ 0.10116436 0.10116436 0.10116436 0.10116436]] \n", " [[ 0.97331679 0.97132528]\n", " [ 0.97331679 0.97132528]\n", " [ 0.97331679 0.97132528]\n", " [ 0.97331679 0.97132528]]\n", "Error: 0.5\n", "[[ 0.17811593 0.17811593 0.17811593 0.17811593]\n", " [ 0.10124576 0.10124576 0.10124576 0.10124576]] \n", " [[ 0.97376961 0.97177124]\n", " [ 0.97376961 0.97177124]\n", " [ 0.97376961 0.97177124]\n", " [ 0.97376961 0.97177124]]\n", "Error: 0.5\n", "[[ 0.1782333 0.1782333 0.1782333 0.1782333 ]\n", " [ 0.10132701 0.10132701 0.10132701 0.10132701]] \n", " [[ 0.97422153 0.97221631]\n", " [ 0.97422153 0.97221631]\n", " [ 0.97422153 0.97221631]\n", " [ 0.97422153 0.97221631]]\n", "Error: 0.5\n", "[[ 0.1783504 0.1783504 0.1783504 0.1783504 ]\n", " [ 0.10140812 0.10140812 0.10140812 0.10140812]] \n", " [[ 0.97467256 0.97266042]\n", " [ 0.97467256 0.97266042]\n", " [ 0.97467256 0.97266042]\n", " [ 0.97467256 0.97266042]]\n", "Error: 0.5\n", "[[ 0.17846726 0.17846726 0.17846726 0.17846726]\n", " [ 0.10148907 0.10148907 0.10148907 0.10148907]] \n", " [[ 0.97512263 0.97310364]\n", " [ 0.97512263 0.97310364]\n", " [ 0.97512263 0.97310364]\n", " [ 0.97512263 0.97310364]]\n", "Error: 0.5\n", "[[ 0.17858386 0.17858386 0.17858386 0.17858386]\n", " [ 0.10156988 0.10156988 0.10156988 0.10156988]] \n", " [[ 0.97557181 0.97354597]\n", " [ 0.97557181 0.97354597]\n", " [ 0.97557181 0.97354597]\n", " [ 0.97557181 0.97354597]]\n", "Error: 0.5\n", "[[ 0.17870021 0.17870021 0.17870021 0.17870021]\n", " [ 0.10165055 0.10165055 0.10165055 0.10165055]] \n", " [[ 0.9760201 0.9739874]\n", " [ 0.9760201 0.9739874]\n", " [ 0.9760201 0.9739874]\n", " [ 0.9760201 0.9739874]]\n", "Error: 0.5\n", "[[ 0.1788163 0.1788163 0.1788163 0.1788163 ]\n", " [ 0.10173107 0.10173107 0.10173107 0.10173107]] \n", " [[ 0.97646749 0.97442794]\n", " [ 0.97646749 0.97442794]\n", " [ 0.97646749 0.97442794]\n", " [ 0.97646749 0.97442794]]\n", "Error: 0.5\n", "[[ 0.17893215 0.17893215 0.17893215 0.17893215]\n", " [ 0.10181145 0.10181145 0.10181145 0.10181145]] \n", " [[ 0.97691399 0.97486764]\n", " [ 0.97691399 0.97486764]\n", " [ 0.97691399 0.97486764]\n", " [ 0.97691399 0.97486764]]\n", "Error: 0.5\n", "[[ 0.17904773 0.17904773 0.17904773 0.17904773]\n", " [ 0.10189167 0.10189167 0.10189167 0.10189167]] \n", " [[ 0.97735959 0.97530645]\n", " [ 0.97735959 0.97530645]\n", " [ 0.97735959 0.97530645]\n", " [ 0.97735959 0.97530645]]\n", "Error: 0.5\n", "[[ 0.17916307 0.17916307 0.17916307 0.17916307]\n", " [ 0.10197176 0.10197176 0.10197176 0.10197176]] \n", " [[ 0.9778043 0.97574437]\n", " [ 0.9778043 0.97574437]\n", " [ 0.9778043 0.97574437]\n", " [ 0.9778043 0.97574437]]\n", "Error: 0.5\n", "[[ 0.17927815 0.17927815 0.17927815 0.17927815]\n", " [ 0.1020517 0.1020517 0.1020517 0.1020517 ]] \n", " [[ 0.97824818 0.97618139]\n", " [ 0.97824818 0.97618139]\n", " [ 0.97824818 0.97618139]\n", " [ 0.97824818 0.97618139]]\n", "Error: 0.5\n", "[[ 0.17939299 0.17939299 0.17939299 0.17939299]\n", " [ 0.10213149 0.10213149 0.10213149 0.10213149]] \n", " [[ 0.97869116 0.97661757]\n", " [ 0.97869116 0.97661757]\n", " [ 0.97869116 0.97661757]\n", " [ 0.97869116 0.97661757]]\n", "Error: 0.5\n", "[[ 0.17950758 0.17950758 0.17950758 0.17950758]\n", " [ 0.10221115 0.10221115 0.10221115 0.10221115]] \n", " [[ 0.97913325 0.97705287]\n", " [ 0.97913325 0.97705287]\n", " [ 0.97913325 0.97705287]\n", " [ 0.97913325 0.97705287]]\n", "Error: 0.5\n", "[[ 0.17962193 0.17962193 0.17962193 0.17962193]\n", " [ 0.10229066 0.10229066 0.10229066 0.10229066]] \n", " [[ 0.9795745 0.97748733]\n", " [ 0.9795745 0.97748733]\n", " [ 0.9795745 0.97748733]\n", " [ 0.9795745 0.97748733]]\n", "Error: 0.5\n", "[[ 0.17973603 0.17973603 0.17973603 0.17973603]\n", " [ 0.10237003 0.10237003 0.10237003 0.10237003]] \n", " [[ 0.98001486 0.97792089]\n", " [ 0.98001486 0.97792089]\n", " [ 0.98001486 0.97792089]\n", " [ 0.98001486 0.97792089]]\n", "Error: 0.5\n", "[[ 0.17984989 0.17984989 0.17984989 0.17984989]\n", " [ 0.10244926 0.10244926 0.10244926 0.10244926]] \n", " [[ 0.98045433 0.97835362]\n", " [ 0.98045433 0.97835362]\n", " [ 0.98045433 0.97835362]\n", " [ 0.98045433 0.97835362]]\n", "Error: 0.5\n", "[[ 0.1799635 0.1799635 0.1799635 0.1799635 ]\n", " [ 0.10252835 0.10252835 0.10252835 0.10252835]] \n", " [[ 0.98089296 0.97878551]\n", " [ 0.98089296 0.97878551]\n", " [ 0.98089296 0.97878551]\n", " [ 0.98089296 0.97878551]]\n", "Error: 0.5\n", "[[ 0.18007687 0.18007687 0.18007687 0.18007687]\n", " [ 0.10260729 0.10260729 0.10260729 0.10260729]] \n", " [[ 0.98133075 0.97921652]\n", " [ 0.98133075 0.97921652]\n", " [ 0.98133075 0.97921652]\n", " [ 0.98133075 0.97921652]]\n", "Error: 0.5\n", "[[ 0.18019 0.18019 0.18019 0.18019 ]\n", " [ 0.1026861 0.1026861 0.1026861 0.1026861]] \n", " [[ 0.98176765 0.97964668]\n", " [ 0.98176765 0.97964668]\n", " [ 0.98176765 0.97964668]\n", " [ 0.98176765 0.97964668]]\n", "Error: 0.5\n", "[[ 0.18030289 0.18030289 0.18030289 0.18030289]\n", " [ 0.10276477 0.10276477 0.10276477 0.10276477]] \n", " [[ 0.98220372 0.98007601]\n", " [ 0.98220372 0.98007601]\n", " [ 0.98220372 0.98007601]\n", " [ 0.98220372 0.98007601]]\n", "Error: 0.5\n", "[[ 0.18041554 0.18041554 0.18041554 0.18041554]\n", " [ 0.1028433 0.1028433 0.1028433 0.1028433 ]] \n", " [[ 0.98263896 0.98050451]\n", " [ 0.98263896 0.98050451]\n", " [ 0.98263896 0.98050451]\n", " [ 0.98263896 0.98050451]]\n", "Error: 0.5\n", "[[ 0.18052794 0.18052794 0.18052794 0.18052794]\n", " [ 0.10292169 0.10292169 0.10292169 0.10292169]] \n", " [[ 0.98307329 0.98093218]\n", " [ 0.98307329 0.98093218]\n", " [ 0.98307329 0.98093218]\n", " [ 0.98307329 0.98093218]]\n", "Error: 0.5\n", "[[ 0.1806401 0.1806401 0.1806401 0.1806401 ]\n", " [ 0.10299995 0.10299995 0.10299995 0.10299995]] \n", " [[ 0.9835068 0.981359 ]\n", " [ 0.9835068 0.981359 ]\n", " [ 0.9835068 0.981359 ]\n", " [ 0.9835068 0.981359 ]]\n", "Error: 0.5\n", "[[ 0.18075204 0.18075204 0.18075204 0.18075204]\n", " [ 0.10307807 0.10307807 0.10307807 0.10307807]] \n", " [[ 0.98393947 0.981785 ]\n", " [ 0.98393947 0.981785 ]\n", " [ 0.98393947 0.981785 ]\n", " [ 0.98393947 0.981785 ]]\n", "Error: 0.5\n", "[[ 0.18086374 0.18086374 0.18086374 0.18086374]\n", " [ 0.10315605 0.10315605 0.10315605 0.10315605]] \n", " [[ 0.9843713 0.98221016]\n", " [ 0.9843713 0.98221016]\n", " [ 0.9843713 0.98221016]\n", " [ 0.9843713 0.98221016]]\n", "Error: 0.5\n", "[[ 0.1809752 0.1809752 0.1809752 0.1809752 ]\n", " [ 0.10323389 0.10323389 0.10323389 0.10323389]] \n", " [[ 0.98480231 0.98263448]\n", " [ 0.98480231 0.98263448]\n", " [ 0.98480231 0.98263448]\n", " [ 0.98480231 0.98263448]]\n", "Error: 0.5\n", "[[ 0.18108642 0.18108642 0.18108642 0.18108642]\n", " [ 0.1033116 0.1033116 0.1033116 0.1033116 ]] \n", " [[ 0.98523247 0.98305798]\n", " [ 0.98523247 0.98305798]\n", " [ 0.98523247 0.98305798]\n", " [ 0.98523247 0.98305798]]\n", "Error: 0.5\n", "[[ 0.1811974 0.1811974 0.1811974 0.1811974 ]\n", " [ 0.10338917 0.10338917 0.10338917 0.10338917]] \n", " [[ 0.9856618 0.98348063]\n", " [ 0.9856618 0.98348063]\n", " [ 0.9856618 0.98348063]\n", " [ 0.9856618 0.98348063]]\n", "Error: 0.5\n", "[[ 0.18130817 0.18130817 0.18130817 0.18130817]\n", " [ 0.1034666 0.1034666 0.1034666 0.1034666 ]] \n", " [[ 0.98609036 0.98390251]\n", " [ 0.98609036 0.98390251]\n", " [ 0.98609036 0.98390251]\n", " [ 0.98609036 0.98390251]]\n", "Error: 0.5\n", "[[ 0.18141869 0.18141869 0.18141869 0.18141869]\n", " [ 0.1035439 0.1035439 0.1035439 0.1035439 ]] \n", " [[ 0.98651809 0.98432356]\n", " [ 0.98651809 0.98432356]\n", " [ 0.98651809 0.98432356]\n", " [ 0.98651809 0.98432356]]\n", "Error: 0.5\n", "[[ 0.18152899 0.18152899 0.18152899 0.18152899]\n", " [ 0.10362107 0.10362107 0.10362107 0.10362107]] \n", " [[ 0.98694497 0.98474383]\n", " [ 0.98694497 0.98474383]\n", " [ 0.98694497 0.98474383]\n", " [ 0.98694497 0.98474383]]\n", "Error: 0.5\n", "[[ 0.18163905 0.18163905 0.18163905 0.18163905]\n", " [ 0.1036981 0.1036981 0.1036981 0.1036981 ]] \n", " [[ 0.98737103 0.98516327]\n", " [ 0.98737103 0.98516327]\n", " [ 0.98737103 0.98516327]\n", " [ 0.98737103 0.98516327]]\n", "Error: 0.5\n", "[[ 0.18174888 0.18174888 0.18174888 0.18174888]\n", " [ 0.10377499 0.10377499 0.10377499 0.10377499]] \n", " [[ 0.98779631 0.98558187]\n", " [ 0.98779631 0.98558187]\n", " [ 0.98779631 0.98558187]\n", " [ 0.98779631 0.98558187]]\n", "Error: 0.5\n", "[[ 0.18185848 0.18185848 0.18185848 0.18185848]\n", " [ 0.10385176 0.10385176 0.10385176 0.10385176]] \n", " [[ 0.98822075 0.9859997 ]\n", " [ 0.98822075 0.9859997 ]\n", " [ 0.98822075 0.9859997 ]\n", " [ 0.98822075 0.9859997 ]]\n", "Error: 0.5\n", "[[ 0.18196785 0.18196785 0.18196785 0.18196785]\n", " [ 0.10392839 0.10392839 0.10392839 0.10392839]] \n", " [[ 0.98864442 0.98641676]\n", " [ 0.98864442 0.98641676]\n", " [ 0.98864442 0.98641676]\n", " [ 0.98864442 0.98641676]]\n", "Error: 0.5\n", "[[ 0.18207701 0.18207701 0.18207701 0.18207701]\n", " [ 0.1040049 0.1040049 0.1040049 0.1040049 ]] \n", " [[ 0.98906726 0.98683298]\n", " [ 0.98906726 0.98683298]\n", " [ 0.98906726 0.98683298]\n", " [ 0.98906726 0.98683298]]\n", "Error: 0.5\n", "[[ 0.18218593 0.18218593 0.18218593 0.18218593]\n", " [ 0.10408127 0.10408127 0.10408127 0.10408127]] \n", " [[ 0.98948932 0.98724842]\n", " [ 0.98948932 0.98724842]\n", " [ 0.98948932 0.98724842]\n", " [ 0.98948932 0.98724842]]\n", "Error: 0.5\n", "[[ 0.18229464 0.18229464 0.18229464 0.18229464]\n", " [ 0.10415751 0.10415751 0.10415751 0.10415751]] \n", " [[ 0.98991054 0.98766309]\n", " [ 0.98991054 0.98766309]\n", " [ 0.98991054 0.98766309]\n", " [ 0.98991054 0.98766309]]\n", "Error: 0.5\n", "[[ 0.18240312 0.18240312 0.18240312 0.18240312]\n", " [ 0.10423362 0.10423362 0.10423362 0.10423362]] \n", " [[ 0.99033099 0.98807698]\n", " [ 0.99033099 0.98807698]\n", " [ 0.99033099 0.98807698]\n", " [ 0.99033099 0.98807698]]\n", "Error: 0.5\n", "[[ 0.18251136 0.18251136 0.18251136 0.18251136]\n", " [ 0.1043096 0.1043096 0.1043096 0.1043096 ]] \n", " [[ 0.99075067 0.98849005]\n", " [ 0.99075067 0.98849005]\n", " [ 0.99075067 0.98849005]\n", " [ 0.99075067 0.98849005]]\n", "Error: 0.5\n", "[[ 0.18261938 0.18261938 0.18261938 0.18261938]\n", " [ 0.10438544 0.10438544 0.10438544 0.10438544]] \n", " [[ 0.99116957 0.98890233]\n", " [ 0.99116957 0.98890233]\n", " [ 0.99116957 0.98890233]\n", " [ 0.99116957 0.98890233]]\n", "Error: 0.5\n", "[[ 0.18272719 0.18272719 0.18272719 0.18272719]\n", " [ 0.10446116 0.10446116 0.10446116 0.10446116]] \n", " [[ 0.99158764 0.98931384]\n", " [ 0.99158764 0.98931384]\n", " [ 0.99158764 0.98931384]\n", " [ 0.99158764 0.98931384]]\n", "Error: 0.5\n", "[[ 0.18283477 0.18283477 0.18283477 0.18283477]\n", " [ 0.10453675 0.10453675 0.10453675 0.10453675]] \n", " [[ 0.99200493 0.98972458]\n", " [ 0.99200493 0.98972458]\n", " [ 0.99200493 0.98972458]\n", " [ 0.99200493 0.98972458]]\n", "Error: 0.5\n", "[[ 0.18294214 0.18294214 0.18294214 0.18294214]\n", " [ 0.10461221 0.10461221 0.10461221 0.10461221]] \n", " [[ 0.99242145 0.99013454]\n", " [ 0.99242145 0.99013454]\n", " [ 0.99242145 0.99013454]\n", " [ 0.99242145 0.99013454]]\n", "Error: 0.5\n", "[[ 0.18304928 0.18304928 0.18304928 0.18304928]\n", " [ 0.10468754 0.10468754 0.10468754 0.10468754]] \n", " [[ 0.99283719 0.99054372]\n", " [ 0.99283719 0.99054372]\n", " [ 0.99283719 0.99054372]\n", " [ 0.99283719 0.99054372]]\n", "Error: 0.5\n", "[[ 0.18315619 0.18315619 0.18315619 0.18315619]\n", " [ 0.10476275 0.10476275 0.10476275 0.10476275]] \n", " [[ 0.99325216 0.99095213]\n", " [ 0.99325216 0.99095213]\n", " [ 0.99325216 0.99095213]\n", " [ 0.99325216 0.99095213]]\n", "Error: 0.5\n", "[[ 0.1832629 0.1832629 0.1832629 0.1832629 ]\n", " [ 0.10483783 0.10483783 0.10483783 0.10483783]] \n", " [[ 0.99366635 0.99135983]\n", " [ 0.99366635 0.99135983]\n", " [ 0.99366635 0.99135983]\n", " [ 0.99366635 0.99135983]]\n", "Error: 0.5\n", "[[ 0.18336938 0.18336938 0.18336938 0.18336938]\n", " [ 0.10491278 0.10491278 0.10491278 0.10491278]] \n", " [[ 0.99407977 0.99176675]\n", " [ 0.99407977 0.99176675]\n", " [ 0.99407977 0.99176675]\n", " [ 0.99407977 0.99176675]]\n", "Error: 0.5\n", "[[ 0.18347566 0.18347566 0.18347566 0.18347566]\n", " [ 0.10498761 0.10498761 0.10498761 0.10498761]] \n", " [[ 0.99449241 0.9921729 ]\n", " [ 0.99449241 0.9921729 ]\n", " [ 0.99449241 0.9921729 ]\n", " [ 0.99449241 0.9921729 ]]\n", "Error: 0.5\n", "[[ 0.18358171 0.18358171 0.18358171 0.18358171]\n", " [ 0.10506231 0.10506231 0.10506231 0.10506231]] \n", " [[ 0.99490434 0.99257827]\n", " [ 0.99490434 0.99257827]\n", " [ 0.99490434 0.99257827]\n", " [ 0.99490434 0.99257827]]\n", "Error: 0.5\n", "[[ 0.18368755 0.18368755 0.18368755 0.18368755]\n", " [ 0.10513688 0.10513688 0.10513688 0.10513688]] \n", " [[ 0.99531549 0.99298292]\n", " [ 0.99531549 0.99298292]\n", " [ 0.99531549 0.99298292]\n", " [ 0.99531549 0.99298292]]\n", "Error: 0.5\n", "[[ 0.18379317 0.18379317 0.18379317 0.18379317]\n", " [ 0.10521132 0.10521132 0.10521132 0.10521132]] \n", " [[ 0.99572587 0.99338681]\n", " [ 0.99572587 0.99338681]\n", " [ 0.99572587 0.99338681]\n", " [ 0.99572587 0.99338681]]\n", "Error: 0.5\n", "[[ 0.18389858 0.18389858 0.18389858 0.18389858]\n", " [ 0.10528564 0.10528564 0.10528564 0.10528564]] \n", " [[ 0.99613547 0.99378997]\n", " [ 0.99613547 0.99378997]\n", " [ 0.99613547 0.99378997]\n", " [ 0.99613547 0.99378997]]\n", "Error: 0.5\n", "[[ 0.18400379 0.18400379 0.18400379 0.18400379]\n", " [ 0.10535984 0.10535984 0.10535984 0.10535984]] \n", " [[ 0.99654436 0.99419236]\n", " [ 0.99654436 0.99419236]\n", " [ 0.99654436 0.99419236]\n", " [ 0.99654436 0.99419236]]\n", "Error: 0.5\n", "[[ 0.18410876 0.18410876 0.18410876 0.18410876]\n", " [ 0.10543391 0.10543391 0.10543391 0.10543391]] \n", " [[ 0.99695247 0.99459404]\n", " [ 0.99695247 0.99459404]\n", " [ 0.99695247 0.99459404]\n", " [ 0.99695247 0.99459404]]\n", "Error: 0.5\n", "[[ 0.18421353 0.18421353 0.18421353 0.18421353]\n", " [ 0.10550786 0.10550786 0.10550786 0.10550786]] \n", " [[ 0.99735987 0.99499494]\n", " [ 0.99735987 0.99499494]\n", " [ 0.99735987 0.99499494]\n", " [ 0.99735987 0.99499494]]\n", "Error: 0.5\n", "[[ 0.1843181 0.1843181 0.1843181 0.1843181 ]\n", " [ 0.10558169 0.10558169 0.10558169 0.10558169]] \n", " [[ 0.99776649 0.99539512]\n", " [ 0.99776649 0.99539512]\n", " [ 0.99776649 0.99539512]\n", " [ 0.99776649 0.99539512]]\n", "Error: 0.5\n", "[[ 0.18442245 0.18442245 0.18442245 0.18442245]\n", " [ 0.10565539 0.10565539 0.10565539 0.10565539]] \n", " [[ 0.9981724 0.99579453]\n", " [ 0.9981724 0.99579453]\n", " [ 0.9981724 0.99579453]\n", " [ 0.9981724 0.99579453]]\n", "Error: 0.5\n", "[[ 0.18452659 0.18452659 0.18452659 0.18452659]\n", " [ 0.10572897 0.10572897 0.10572897 0.10572897]] \n", " [[ 0.99857754 0.99619323]\n", " [ 0.99857754 0.99619323]\n", " [ 0.99857754 0.99619323]\n", " [ 0.99857754 0.99619323]]\n", "Error: 0.5\n", "[[ 0.18463053 0.18463053 0.18463053 0.18463053]\n", " [ 0.10580242 0.10580242 0.10580242 0.10580242]] \n", " [[ 0.99898195 0.99659121]\n", " [ 0.99898195 0.99659121]\n", " [ 0.99898195 0.99659121]\n", " [ 0.99898195 0.99659121]]\n", "Error: 0.5\n", "[[ 0.18473426 0.18473426 0.18473426 0.18473426]\n", " [ 0.10587576 0.10587576 0.10587576 0.10587576]] \n", " [[ 0.99938565 0.99698848]\n", " [ 0.99938565 0.99698848]\n", " [ 0.99938565 0.99698848]\n", " [ 0.99938565 0.99698848]]\n", "Error: 0.5\n", "[[ 0.18483777 0.18483777 0.18483777 0.18483777]\n", " [ 0.10594898 0.10594898 0.10594898 0.10594898]] \n", " [[ 0.99978858 0.99738503]\n", " [ 0.99978858 0.99738503]\n", " [ 0.99978858 0.99738503]\n", " [ 0.99978858 0.99738503]]\n", "Error: 0.5\n", "[[ 0.18494108 0.18494108 0.18494108 0.18494108]\n", " [ 0.10602207 0.10602207 0.10602207 0.10602207]] \n", " [[ 1.00019085 0.9977808 ]\n", " [ 1.00019085 0.9977808 ]\n", " [ 1.00019085 0.9977808 ]\n", " [ 1.00019085 0.9977808 ]]\n", "Error: 0.5\n", "[[ 0.1850442 0.1850442 0.1850442 0.1850442 ]\n", " [ 0.10609504 0.10609504 0.10609504 0.10609504]] \n", " [[ 1.00059235 0.99817586]\n", " [ 1.00059235 0.99817586]\n", " [ 1.00059235 0.99817586]\n", " [ 1.00059235 0.99817586]]\n", "Error: 0.5\n", "[[ 0.18514711 0.18514711 0.18514711 0.18514711]\n", " [ 0.10616789 0.10616789 0.10616789 0.10616789]] \n", " [[ 1.00099313 0.9985702 ]\n", " [ 1.00099313 0.9985702 ]\n", " [ 1.00099313 0.9985702 ]\n", " [ 1.00099313 0.9985702 ]]\n", "Error: 0.5\n", "[[ 0.18524981 0.18524981 0.18524981 0.18524981]\n", " [ 0.10624062 0.10624062 0.10624062 0.10624062]] \n", " [[ 1.0013932 0.99896383]\n", " [ 1.0013932 0.99896383]\n", " [ 1.0013932 0.99896383]\n", " [ 1.0013932 0.99896383]]\n", "Error: 0.5\n", "[[ 0.1853523 0.1853523 0.1853523 0.1853523 ]\n", " [ 0.10631324 0.10631324 0.10631324 0.10631324]] \n", " [[ 1.00179255 0.99935675]\n", " [ 1.00179255 0.99935675]\n", " [ 1.00179255 0.99935675]\n", " [ 1.00179255 0.99935675]]\n", "Error: 0.5\n", "[[ 0.18545459 0.18545459 0.18545459 0.18545459]\n", " [ 0.10638573 0.10638573 0.10638573 0.10638573]] \n", " [[ 1.00219119 0.99974895]\n", " [ 1.00219119 0.99974895]\n", " [ 1.00219119 0.99974895]\n", " [ 1.00219119 0.99974895]]\n", "Error: 0.5\n", "[[ 0.18555668 0.18555668 0.18555668 0.18555668]\n", " [ 0.10645811 0.10645811 0.10645811 0.10645811]] \n", " [[ 1.00258911 1.00014043]\n", " [ 1.00258911 1.00014043]\n", " [ 1.00258911 1.00014043]\n", " [ 1.00258911 1.00014043]]\n", "Error: 0.5\n", "[[ 0.18565857 0.18565857 0.18565857 0.18565857]\n", " [ 0.10653036 0.10653036 0.10653036 0.10653036]] \n", " [[ 1.00298631 1.0005312 ]\n", " [ 1.00298631 1.0005312 ]\n", " [ 1.00298631 1.0005312 ]\n", " [ 1.00298631 1.0005312 ]]\n", "Error: 0.5\n", "[[ 0.18576026 0.18576026 0.18576026 0.18576026]\n", " [ 0.1066025 0.1066025 0.1066025 0.1066025 ]] \n", " [[ 1.0033828 1.00092137]\n", " [ 1.0033828 1.00092137]\n", " [ 1.0033828 1.00092137]\n", " [ 1.0033828 1.00092137]]\n", "Error: 0.5\n", "[[ 0.18586175 0.18586175 0.18586175 0.18586175]\n", " [ 0.10667451 0.10667451 0.10667451 0.10667451]] \n", " [[ 1.00377858 1.00131083]\n", " [ 1.00377858 1.00131083]\n", " [ 1.00377858 1.00131083]\n", " [ 1.00377858 1.00131083]]\n", "Error: 0.5\n", "[[ 0.18596305 0.18596305 0.18596305 0.18596305]\n", " [ 0.10674642 0.10674642 0.10674642 0.10674642]] \n", " [[ 1.00417364 1.00169957]\n", " [ 1.00417364 1.00169957]\n", " [ 1.00417364 1.00169957]\n", " [ 1.00417364 1.00169957]]\n", "Error: 0.5\n", "[[ 0.18606414 0.18606414 0.18606414 0.18606414]\n", " [ 0.10681821 0.10681821 0.10681821 0.10681821]] \n", " [[ 1.00456798 1.00208759]\n", " [ 1.00456798 1.00208759]\n", " [ 1.00456798 1.00208759]\n", " [ 1.00456798 1.00208759]]\n", "Error: 0.5\n", "[[ 0.18616503 0.18616503 0.18616503 0.18616503]\n", " [ 0.10688987 0.10688987 0.10688987 0.10688987]] \n", " [[ 1.00496161 1.0024749 ]\n", " [ 1.00496161 1.0024749 ]\n", " [ 1.00496161 1.0024749 ]\n", " [ 1.00496161 1.0024749 ]]\n", "Error: 0.5\n", "[[ 0.18626574 0.18626574 0.18626574 0.18626574]\n", " [ 0.10696143 0.10696143 0.10696143 0.10696143]] \n", " [[ 1.00535452 1.0028615 ]\n", " [ 1.00535452 1.0028615 ]\n", " [ 1.00535452 1.0028615 ]\n", " [ 1.00535452 1.0028615 ]]\n", "Error: 0.5\n", "[[ 0.18636625 0.18636625 0.18636625 0.18636625]\n", " [ 0.10703287 0.10703287 0.10703287 0.10703287]] \n", " [[ 1.00574684 1.0032475 ]\n", " [ 1.00574684 1.0032475 ]\n", " [ 1.00574684 1.0032475 ]\n", " [ 1.00574684 1.0032475 ]]\n", "Error: 0.5\n", "[[ 0.18646654 0.18646654 0.18646654 0.18646654]\n", " [ 0.10710419 0.10710419 0.10710419 0.10710419]] \n", " [[ 1.00613844 1.00363278]\n", " [ 1.00613844 1.00363278]\n", " [ 1.00613844 1.00363278]\n", " [ 1.00613844 1.00363278]]\n", "Error: 0.5\n", "[[ 0.18656665 0.18656665 0.18656665 0.18656665]\n", " [ 0.10717539 0.10717539 0.10717539 0.10717539]] \n", " [[ 1.00652933 1.00401735]\n", " [ 1.00652933 1.00401735]\n", " [ 1.00652933 1.00401735]\n", " [ 1.00652933 1.00401735]]\n", "Error: 0.5\n", "[[ 0.18666656 0.18666656 0.18666656 0.18666656]\n", " [ 0.10724649 0.10724649 0.10724649 0.10724649]] \n", " [[ 1.0069195 1.00440121]\n", " [ 1.0069195 1.00440121]\n", " [ 1.0069195 1.00440121]\n", " [ 1.0069195 1.00440121]]\n", "Error: 0.5\n", "[[ 0.18676628 0.18676628 0.18676628 0.18676628]\n", " [ 0.10731747 0.10731747 0.10731747 0.10731747]] \n", " [[ 1.00730896 1.00478446]\n", " [ 1.00730896 1.00478446]\n", " [ 1.00730896 1.00478446]\n", " [ 1.00730896 1.00478446]]\n", "Error: 0.5\n", "[[ 0.18686581 0.18686581 0.18686581 0.18686581]\n", " [ 0.10738833 0.10738833 0.10738833 0.10738833]] \n", " [[ 1.00769782 1.00516701]\n", " [ 1.00769782 1.00516701]\n", " [ 1.00769782 1.00516701]\n", " [ 1.00769782 1.00516701]]\n", "Error: 0.5\n", "[[ 0.18696514 0.18696514 0.18696514 0.18696514]\n", " [ 0.10745908 0.10745908 0.10745908 0.10745908]] \n", " [[ 1.00808597 1.00554883]\n", " [ 1.00808597 1.00554883]\n", " [ 1.00808597 1.00554883]\n", " [ 1.00808597 1.00554883]]\n", "Error: 0.5\n", "[[ 0.18706428 0.18706428 0.18706428 0.18706428]\n", " [ 0.10752972 0.10752972 0.10752972 0.10752972]] \n", " [[ 1.0084734 1.00593007]\n", " [ 1.0084734 1.00593007]\n", " [ 1.0084734 1.00593007]\n", " [ 1.0084734 1.00593007]]\n", "Error: 0.5\n", "[[ 0.18716323 0.18716323 0.18716323 0.18716323]\n", " [ 0.10760025 0.10760025 0.10760025 0.10760025]] \n", " [[ 1.00886023 1.00631058]\n", " [ 1.00886023 1.00631058]\n", " [ 1.00886023 1.00631058]\n", " [ 1.00886023 1.00631058]]\n", "Error: 0.5\n", "[[ 0.187262 0.187262 0.187262 0.187262 ]\n", " [ 0.10767066 0.10767066 0.10767066 0.10767066]] \n", " [[ 1.00924635 1.0066905 ]\n", " [ 1.00924635 1.0066905 ]\n", " [ 1.00924635 1.0066905 ]\n", " [ 1.00924635 1.0066905 ]]\n", "Error: 0.5\n", "[[ 0.18736057 0.18736057 0.18736057 0.18736057]\n", " [ 0.10774095 0.10774095 0.10774095 0.10774095]] \n", " [[ 1.00963175 1.00706971]\n", " [ 1.00963175 1.00706971]\n", " [ 1.00963175 1.00706971]\n", " [ 1.00963175 1.00706971]]\n", "Error: 0.5\n", "[[ 0.18745895 0.18745895 0.18745895 0.18745895]\n", " [ 0.10781114 0.10781114 0.10781114 0.10781114]] \n", " [[ 1.01001656 1.0074482 ]\n", " [ 1.01001656 1.0074482 ]\n", " [ 1.01001656 1.0074482 ]\n", " [ 1.01001656 1.0074482 ]]\n", "Error: 0.5\n", "[[ 0.18755715 0.18755715 0.18755715 0.18755715]\n", " [ 0.10788121 0.10788121 0.10788121 0.10788121]] \n", " [[ 1.01040065 1.00782609]\n", " [ 1.01040065 1.00782609]\n", " [ 1.01040065 1.00782609]\n", " [ 1.01040065 1.00782609]]\n", "Error: 0.5\n", "[[ 0.18765515 0.18765515 0.18765515 0.18765515]\n", " [ 0.10795117 0.10795117 0.10795117 0.10795117]] \n", " [[ 1.01078415 1.00820327]\n", " [ 1.01078415 1.00820327]\n", " [ 1.01078415 1.00820327]\n", " [ 1.01078415 1.00820327]]\n", "Error: 0.5\n", "[[ 0.18775298 0.18775298 0.18775298 0.18775298]\n", " [ 0.10802102 0.10802102 0.10802102 0.10802102]] \n", " [[ 1.01116693 1.00857985]\n", " [ 1.01116693 1.00857985]\n", " [ 1.01116693 1.00857985]\n", " [ 1.01116693 1.00857985]]\n", "Error: 0.5\n", "[[ 0.18785061 0.18785061 0.18785061 0.18785061]\n", " [ 0.10809077 0.10809077 0.10809077 0.10809077]] \n", " [[ 1.011549 1.00895572]\n", " [ 1.011549 1.00895572]\n", " [ 1.011549 1.00895572]\n", " [ 1.011549 1.00895572]]\n", "Error: 0.5\n", "[[ 0.18794805 0.18794805 0.18794805 0.18794805]\n", " [ 0.1081604 0.1081604 0.1081604 0.1081604 ]] \n", " [[ 1.01193047 1.00933099]\n", " [ 1.01193047 1.00933099]\n", " [ 1.01193047 1.00933099]\n", " [ 1.01193047 1.00933099]]\n", "Error: 0.5\n", "[[ 0.18804531 0.18804531 0.18804531 0.18804531]\n", " [ 0.10822992 0.10822992 0.10822992 0.10822992]] \n", " [[ 1.01231122 1.00970554]\n", " [ 1.01231122 1.00970554]\n", " [ 1.01231122 1.00970554]\n", " [ 1.01231122 1.00970554]]\n", "Error: 0.5\n", "[[ 0.18814239 0.18814239 0.18814239 0.18814239]\n", " [ 0.10829933 0.10829933 0.10829933 0.10829933]] \n", " [[ 1.01269138 1.0100795 ]\n", " [ 1.01269138 1.0100795 ]\n", " [ 1.01269138 1.0100795 ]\n", " [ 1.01269138 1.0100795 ]]\n", "Error: 0.5\n", "[[ 0.18823928 0.18823928 0.18823928 0.18823928]\n", " [ 0.10836864 0.10836864 0.10836864 0.10836864]] \n", " [[ 1.01307094 1.01045287]\n", " [ 1.01307094 1.01045287]\n", " [ 1.01307094 1.01045287]\n", " [ 1.01307094 1.01045287]]\n", "Error: 0.5\n", "[[ 0.18833598 0.18833598 0.18833598 0.18833598]\n", " [ 0.10843783 0.10843783 0.10843783 0.10843783]] \n", " [[ 1.01344979 1.01082551]\n", " [ 1.01344979 1.01082551]\n", " [ 1.01344979 1.01082551]\n", " [ 1.01344979 1.01082551]]\n", "Error: 0.5\n", "[[ 0.18843251 0.18843251 0.18843251 0.18843251]\n", " [ 0.10850691 0.10850691 0.10850691 0.10850691]] \n", " [[ 1.01382804 1.01119757]\n", " [ 1.01382804 1.01119757]\n", " [ 1.01382804 1.01119757]\n", " [ 1.01382804 1.01119757]]\n", "Error: 0.5\n", "[[ 0.18852885 0.18852885 0.18852885 0.18852885]\n", " [ 0.10857589 0.10857589 0.10857589 0.10857589]] \n", " [[ 1.01420557 1.0115689 ]\n", " [ 1.01420557 1.0115689 ]\n", " [ 1.01420557 1.0115689 ]\n", " [ 1.01420557 1.0115689 ]]\n", "Error: 0.5\n", "[[ 0.18862501 0.18862501 0.18862501 0.18862501]\n", " [ 0.10864475 0.10864475 0.10864475 0.10864475]] \n", " [[ 1.01458251 1.01193964]\n", " [ 1.01458251 1.01193964]\n", " [ 1.01458251 1.01193964]\n", " [ 1.01458251 1.01193964]]\n", "Error: 0.5\n", "[[ 0.18872099 0.18872099 0.18872099 0.18872099]\n", " [ 0.10871352 0.10871352 0.10871352 0.10871352]] \n", " [[ 1.01495874 1.01230979]\n", " [ 1.01495874 1.01230979]\n", " [ 1.01495874 1.01230979]\n", " [ 1.01495874 1.01230979]]\n", "Error: 0.5\n", "[[ 0.18881679 0.18881679 0.18881679 0.18881679]\n", " [ 0.10878217 0.10878217 0.10878217 0.10878217]] \n", " [[ 1.01533437 1.01267922]\n", " [ 1.01533437 1.01267922]\n", " [ 1.01533437 1.01267922]\n", " [ 1.01533437 1.01267922]]\n", "Error: 0.5\n", "[[ 0.18891241 0.18891241 0.18891241 0.18891241]\n", " [ 0.10885072 0.10885072 0.10885072 0.10885072]] \n", " [[ 1.0157094 1.01304805]\n", " [ 1.0157094 1.01304805]\n", " [ 1.0157094 1.01304805]\n", " [ 1.0157094 1.01304805]]\n", "Error: 0.5\n", "[[ 0.18900785 0.18900785 0.18900785 0.18900785]\n", " [ 0.10891916 0.10891916 0.10891916 0.10891916]] \n", " [[ 1.01608372 1.01341629]\n", " [ 1.01608372 1.01341629]\n", " [ 1.01608372 1.01341629]\n", " [ 1.01608372 1.01341629]]\n", "Error: 0.5\n", "[[ 0.18910311 0.18910311 0.18910311 0.18910311]\n", " [ 0.1089875 0.1089875 0.1089875 0.1089875 ]] \n", " [[ 1.01645744 1.01378393]\n", " [ 1.01645744 1.01378393]\n", " [ 1.01645744 1.01378393]\n", " [ 1.01645744 1.01378393]]\n", "Error: 0.5\n", "[[ 0.1891982 0.1891982 0.1891982 0.1891982 ]\n", " [ 0.10905572 0.10905572 0.10905572 0.10905572]] \n", " [[ 1.01683056 1.01415086]\n", " [ 1.01683056 1.01415086]\n", " [ 1.01683056 1.01415086]\n", " [ 1.01683056 1.01415086]]\n", "Error: 0.5\n", "[[ 0.1892931 0.1892931 0.1892931 0.1892931 ]\n", " [ 0.10912384 0.10912384 0.10912384 0.10912384]] \n", " [[ 1.01720309 1.01451719]\n", " [ 1.01720309 1.01451719]\n", " [ 1.01720309 1.01451719]\n", " [ 1.01720309 1.01451719]]\n", "Error: 0.5\n", "[[ 0.18938783 0.18938783 0.18938783 0.18938783]\n", " [ 0.10919186 0.10919186 0.10919186 0.10919186]] \n", " [[ 1.01757491 1.01488292]\n", " [ 1.01757491 1.01488292]\n", " [ 1.01757491 1.01488292]\n", " [ 1.01757491 1.01488292]]\n", "Error: 0.5\n", "[[ 0.18948238 0.18948238 0.18948238 0.18948238]\n", " [ 0.10925977 0.10925977 0.10925977 0.10925977]] \n", " [[ 1.01794612 1.01524806]\n", " [ 1.01794612 1.01524806]\n", " [ 1.01794612 1.01524806]\n", " [ 1.01794612 1.01524806]]\n", "Error: 0.5\n", "[[ 0.18957675 0.18957675 0.18957675 0.18957675]\n", " [ 0.10932758 0.10932758 0.10932758 0.10932758]] \n", " [[ 1.01831675 1.0156126 ]\n", " [ 1.01831675 1.0156126 ]\n", " [ 1.01831675 1.0156126 ]\n", " [ 1.01831675 1.0156126 ]]\n", "Error: 0.5\n", "[[ 0.18967095 0.18967095 0.18967095 0.18967095]\n", " [ 0.10939528 0.10939528 0.10939528 0.10939528]] \n", " [[ 1.01868677 1.01597643]\n", " [ 1.01868677 1.01597643]\n", " [ 1.01868677 1.01597643]\n", " [ 1.01868677 1.01597643]]\n", "Error: 0.5\n", "[[ 0.18976498 0.18976498 0.18976498 0.18976498]\n", " [ 0.10946288 0.10946288 0.10946288 0.10946288]] \n", " [[ 1.01905608 1.01633966]\n", " [ 1.01905608 1.01633966]\n", " [ 1.01905608 1.01633966]\n", " [ 1.01905608 1.01633966]]\n", "Error: 0.5\n", "[[ 0.18985882 0.18985882 0.18985882 0.18985882]\n", " [ 0.10953037 0.10953037 0.10953037 0.10953037]] \n", " [[ 1.0194248 1.01670229]\n", " [ 1.0194248 1.01670229]\n", " [ 1.0194248 1.01670229]\n", " [ 1.0194248 1.01670229]]\n", "Error: 0.5\n", "[[ 0.18995249 0.18995249 0.18995249 0.18995249]\n", " [ 0.10959776 0.10959776 0.10959776 0.10959776]] \n", " [[ 1.01979291 1.01706433]\n", " [ 1.01979291 1.01706433]\n", " [ 1.01979291 1.01706433]\n", " [ 1.01979291 1.01706433]]\n", "Error: 0.5\n", "[[ 0.190046 0.190046 0.190046 0.190046 ]\n", " [ 0.10966505 0.10966505 0.10966505 0.10966505]] \n", " [[ 1.02016044 1.01742578]\n", " [ 1.02016044 1.01742578]\n", " [ 1.02016044 1.01742578]\n", " [ 1.02016044 1.01742578]]\n", "Error: 0.5\n", "[[ 0.19013932 0.19013932 0.19013932 0.19013932]\n", " [ 0.10973223 0.10973223 0.10973223 0.10973223]] \n", " [[ 1.02052736 1.01778662]\n", " [ 1.02052736 1.01778662]\n", " [ 1.02052736 1.01778662]\n", " [ 1.02052736 1.01778662]]\n", "Error: 0.5\n", "[[ 0.19023249 0.19023249 0.19023249 0.19023249]\n", " [ 0.10979932 0.10979932 0.10979932 0.10979932]] \n", " [[ 1.02089369 1.01814687]\n", " [ 1.02089369 1.01814687]\n", " [ 1.02089369 1.01814687]\n", " [ 1.02089369 1.01814687]]\n", "Error: 0.5\n", "[[ 0.19032547 0.19032547 0.19032547 0.19032547]\n", " [ 0.1098663 0.1098663 0.1098663 0.1098663 ]] \n", " [[ 1.02125943 1.01850653]\n", " [ 1.02125943 1.01850653]\n", " [ 1.02125943 1.01850653]\n", " [ 1.02125943 1.01850653]]\n", "Error: 0.5\n", "[[ 0.19041829 0.19041829 0.19041829 0.19041829]\n", " [ 0.10993318 0.10993318 0.10993318 0.10993318]] \n", " [[ 1.02162457 1.01886559]\n", " [ 1.02162457 1.01886559]\n", " [ 1.02162457 1.01886559]\n", " [ 1.02162457 1.01886559]]\n", "Error: 0.5\n", "[[ 0.19051093 0.19051093 0.19051093 0.19051093]\n", " [ 0.10999995 0.10999995 0.10999995 0.10999995]] \n", " [[ 1.02198899 1.01922405]\n", " [ 1.02198899 1.01922405]\n", " [ 1.02198899 1.01922405]\n", " [ 1.02198899 1.01922405]]\n", "Error: 0.5\n", "[[ 0.19060341 0.19060341 0.19060341 0.19060341]\n", " [ 0.11006662 0.11006662 0.11006662 0.11006662]] \n", " [[ 1.02235281 1.01958191]\n", " [ 1.02235281 1.01958191]\n", " [ 1.02235281 1.01958191]\n", " [ 1.02235281 1.01958191]]\n", "Error: 0.5\n", "[[ 0.19069572 0.19069572 0.19069572 0.19069572]\n", " [ 0.11013319 0.11013319 0.11013319 0.11013319]] \n", " [[ 1.02271605 1.01993918]\n", " [ 1.02271605 1.01993918]\n", " [ 1.02271605 1.01993918]\n", " [ 1.02271605 1.01993918]]\n", "Error: 0.5\n", "[[ 0.19078785 0.19078785 0.19078785 0.19078785]\n", " [ 0.11019967 0.11019967 0.11019967 0.11019967]] \n", " [[ 1.02307868 1.02029586]\n", " [ 1.02307868 1.02029586]\n", " [ 1.02307868 1.02029586]\n", " [ 1.02307868 1.02029586]]\n", "Error: 0.5\n", "[[ 0.19087982 0.19087982 0.19087982 0.19087982]\n", " [ 0.11026604 0.11026604 0.11026604 0.11026604]] \n", " [[ 1.02344072 1.02065194]\n", " [ 1.02344072 1.02065194]\n", " [ 1.02344072 1.02065194]\n", " [ 1.02344072 1.02065194]]\n", "Error: 0.5\n", "[[ 0.19097163 0.19097163 0.19097163 0.19097163]\n", " [ 0.11033231 0.11033231 0.11033231 0.11033231]] \n", " [[ 1.02380216 1.02100742]\n", " [ 1.02380216 1.02100742]\n", " [ 1.02380216 1.02100742]\n", " [ 1.02380216 1.02100742]]\n", "Error: 0.5\n", "[[ 0.19106326 0.19106326 0.19106326 0.19106326]\n", " [ 0.11039848 0.11039848 0.11039848 0.11039848]] \n", " [[ 1.02416301 1.0213623 ]\n", " [ 1.02416301 1.0213623 ]\n", " [ 1.02416301 1.0213623 ]\n", " [ 1.02416301 1.0213623 ]]\n", "Error: 0.5\n", "[[ 0.19115472 0.19115472 0.19115472 0.19115472]\n", " [ 0.11046455 0.11046455 0.11046455 0.11046455]] \n", " [[ 1.02452326 1.02171659]\n", " [ 1.02452326 1.02171659]\n", " [ 1.02452326 1.02171659]\n", " [ 1.02452326 1.02171659]]\n", "Error: 0.5\n", "[[ 0.19124602 0.19124602 0.19124602 0.19124602]\n", " [ 0.11053053 0.11053053 0.11053053 0.11053053]] \n", " [[ 1.02488303 1.02207029]\n", " [ 1.02488303 1.02207029]\n", " [ 1.02488303 1.02207029]\n", " [ 1.02488303 1.02207029]]\n", "Error: 0.5\n", "[[ 0.19133715 0.19133715 0.19133715 0.19133715]\n", " [ 0.1105964 0.1105964 0.1105964 0.1105964 ]] \n", " [[ 1.02524221 1.02242351]\n", " [ 1.02524221 1.02242351]\n", " [ 1.02524221 1.02242351]\n", " [ 1.02524221 1.02242351]]\n", "Error: 0.5\n", "[[ 0.19142812 0.19142812 0.19142812 0.19142812]\n", " [ 0.11066217 0.11066217 0.11066217 0.11066217]] \n", " [[ 1.02560079 1.02277613]\n", " [ 1.02560079 1.02277613]\n", " [ 1.02560079 1.02277613]\n", " [ 1.02560079 1.02277613]]\n", "Error: 0.5\n", "[[ 0.19151893 0.19151893 0.19151893 0.19151893]\n", " [ 0.11072785 0.11072785 0.11072785 0.11072785]] \n", " [[ 1.02595878 1.02312815]\n", " [ 1.02595878 1.02312815]\n", " [ 1.02595878 1.02312815]\n", " [ 1.02595878 1.02312815]]\n", "Error: 0.5\n", "[[ 0.19160958 0.19160958 0.19160958 0.19160958]\n", " [ 0.11079343 0.11079343 0.11079343 0.11079343]] \n", " [[ 1.02631617 1.02347958]\n", " [ 1.02631617 1.02347958]\n", " [ 1.02631617 1.02347958]\n", " [ 1.02631617 1.02347958]]\n", "Error: 0.5\n", "[[ 0.19170006 0.19170006 0.19170006 0.19170006]\n", " [ 0.1108589 0.1108589 0.1108589 0.1108589 ]] \n", " [[ 1.02667296 1.02383041]\n", " [ 1.02667296 1.02383041]\n", " [ 1.02667296 1.02383041]\n", " [ 1.02667296 1.02383041]]\n", "Error: 0.5\n", "[[ 0.19179037 0.19179037 0.19179037 0.19179037]\n", " [ 0.11092428 0.11092428 0.11092428 0.11092428]] \n", " [[ 1.02702916 1.02418065]\n", " [ 1.02702916 1.02418065]\n", " [ 1.02702916 1.02418065]\n", " [ 1.02702916 1.02418065]]\n", "Error: 0.5\n", "[[ 0.19188052 0.19188052 0.19188052 0.19188052]\n", " [ 0.11098956 0.11098956 0.11098956 0.11098956]] \n", " [[ 1.02738476 1.02453041]\n", " [ 1.02738476 1.02453041]\n", " [ 1.02738476 1.02453041]\n", " [ 1.02738476 1.02453041]]\n", "Error: 0.5\n", "[[ 0.19197051 0.19197051 0.19197051 0.19197051]\n", " [ 0.11105475 0.11105475 0.11105475 0.11105475]] \n", " [[ 1.02773988 1.02487957]\n", " [ 1.02773988 1.02487957]\n", " [ 1.02773988 1.02487957]\n", " [ 1.02773988 1.02487957]]\n", "Error: 0.5\n", "[[ 0.19206034 0.19206034 0.19206034 0.19206034]\n", " [ 0.11111984 0.11111984 0.11111984 0.11111984]] \n", " [[ 1.02809441 1.02522814]\n", " [ 1.02809441 1.02522814]\n", " [ 1.02809441 1.02522814]\n", " [ 1.02809441 1.02522814]]\n", "Error: 0.5\n", "[[ 0.19215001 0.19215001 0.19215001 0.19215001]\n", " [ 0.11118483 0.11118483 0.11118483 0.11118483]] \n", " [[ 1.02844834 1.02557611]\n", " [ 1.02844834 1.02557611]\n", " [ 1.02844834 1.02557611]\n", " [ 1.02844834 1.02557611]]\n", "Error: 0.5\n", "[[ 0.19223952 0.19223952 0.19223952 0.19223952]\n", " [ 0.11124972 0.11124972 0.11124972 0.11124972]] \n", " [[ 1.02880168 1.02592361]\n", " [ 1.02880168 1.02592361]\n", " [ 1.02880168 1.02592361]\n", " [ 1.02880168 1.02592361]]\n", "Error: 0.5\n", "[[ 0.19232887 0.19232887 0.19232887 0.19232887]\n", " [ 0.11131452 0.11131452 0.11131452 0.11131452]] \n", " [[ 1.02915454 1.02627051]\n", " [ 1.02915454 1.02627051]\n", " [ 1.02915454 1.02627051]\n", " [ 1.02915454 1.02627051]]\n", "Error: 0.5\n", "[[ 0.19241805 0.19241805 0.19241805 0.19241805]\n", " [ 0.11137923 0.11137923 0.11137923 0.11137923]] \n", " [[ 1.0295068 1.02661681]\n", " [ 1.0295068 1.02661681]\n", " [ 1.0295068 1.02661681]\n", " [ 1.0295068 1.02661681]]\n", "Error: 0.5\n", "[[ 0.19250709 0.19250709 0.19250709 0.19250709]\n", " [ 0.11144384 0.11144384 0.11144384 0.11144384]] \n", " [[ 1.02985847 1.02696264]\n", " [ 1.02985847 1.02696264]\n", " [ 1.02985847 1.02696264]\n", " [ 1.02985847 1.02696264]]\n", "Error: 0.5\n", "[[ 0.19259596 0.19259596 0.19259596 0.19259596]\n", " [ 0.11150835 0.11150835 0.11150835 0.11150835]] \n", " [[ 1.03020954 1.02730787]\n", " [ 1.03020954 1.02730787]\n", " [ 1.03020954 1.02730787]\n", " [ 1.03020954 1.02730787]]\n", "Error: 0.5\n", "[[ 0.19268468 0.19268468 0.19268468 0.19268468]\n", " [ 0.11157277 0.11157277 0.11157277 0.11157277]] \n", " [[ 1.03056014 1.0276525 ]\n", " [ 1.03056014 1.0276525 ]\n", " [ 1.03056014 1.0276525 ]\n", " [ 1.03056014 1.0276525 ]]\n", "Error: 0.5\n", "[[ 0.19277324 0.19277324 0.19277324 0.19277324]\n", " [ 0.11163709 0.11163709 0.11163709 0.11163709]] \n", " [[ 1.03091013 1.02799666]\n", " [ 1.03091013 1.02799666]\n", " [ 1.03091013 1.02799666]\n", " [ 1.03091013 1.02799666]]\n", "Error: 0.5\n", "[[ 0.19286165 0.19286165 0.19286165 0.19286165]\n", " [ 0.11170132 0.11170132 0.11170132 0.11170132]] \n", " [[ 1.03125954 1.02834022]\n", " [ 1.03125954 1.02834022]\n", " [ 1.03125954 1.02834022]\n", " [ 1.03125954 1.02834022]]\n", "Error: 0.5\n", "[[ 0.19294989 0.19294989 0.19294989 0.19294989]\n", " [ 0.11176546 0.11176546 0.11176546 0.11176546]] \n", " [[ 1.03160846 1.0286833 ]\n", " [ 1.03160846 1.0286833 ]\n", " [ 1.03160846 1.0286833 ]\n", " [ 1.03160846 1.0286833 ]]\n", "Error: 0.5\n", "[[ 0.19303799 0.19303799 0.19303799 0.19303799]\n", " [ 0.1118295 0.1118295 0.1118295 0.1118295 ]] \n", " [[ 1.03195679 1.02902579]\n", " [ 1.03195679 1.02902579]\n", " [ 1.03195679 1.02902579]\n", " [ 1.03195679 1.02902579]]\n", "Error: 0.5\n", "[[ 0.19312592 0.19312592 0.19312592 0.19312592]\n", " [ 0.11189345 0.11189345 0.11189345 0.11189345]] \n", " [[ 1.03230453 1.02936769]\n", " [ 1.03230453 1.02936769]\n", " [ 1.03230453 1.02936769]\n", " [ 1.03230453 1.02936769]]\n", "Error: 0.5\n", "[[ 0.1932137 0.1932137 0.1932137 0.1932137]\n", " [ 0.1119573 0.1119573 0.1119573 0.1119573]] \n", " [[ 1.03265178 1.0297091 ]\n", " [ 1.03265178 1.0297091 ]\n", " [ 1.03265178 1.0297091 ]\n", " [ 1.03265178 1.0297091 ]]\n", "Error: 0.5\n", "[[ 0.19330132 0.19330132 0.19330132 0.19330132]\n", " [ 0.11202107 0.11202107 0.11202107 0.11202107]] \n", " [[ 1.03299844 1.03004992]\n", " [ 1.03299844 1.03004992]\n", " [ 1.03299844 1.03004992]\n", " [ 1.03299844 1.03004992]]\n", "Error: 0.5\n", "[[ 0.19338879 0.19338879 0.19338879 0.19338879]\n", " [ 0.11208473 0.11208473 0.11208473 0.11208473]] \n", " [[ 1.03334463 1.03039026]\n", " [ 1.03334463 1.03039026]\n", " [ 1.03334463 1.03039026]\n", " [ 1.03334463 1.03039026]]\n", "Error: 0.5\n", "[[ 0.19347611 0.19347611 0.19347611 0.19347611]\n", " [ 0.11214831 0.11214831 0.11214831 0.11214831]] \n", " [[ 1.03369021 1.03073001]\n", " [ 1.03369021 1.03073001]\n", " [ 1.03369021 1.03073001]\n", " [ 1.03369021 1.03073001]]\n", "Error: 0.5\n", "[[ 0.19356327 0.19356327 0.19356327 0.19356327]\n", " [ 0.11221179 0.11221179 0.11221179 0.11221179]] \n", " [[ 1.03403533 1.03106928]\n", " [ 1.03403533 1.03106928]\n", " [ 1.03403533 1.03106928]\n", " [ 1.03403533 1.03106928]]\n", "Error: 0.5\n", "[[ 0.19365028 0.19365028 0.19365028 0.19365028]\n", " [ 0.11227518 0.11227518 0.11227518 0.11227518]] \n", " [[ 1.03437984 1.03140795]\n", " [ 1.03437984 1.03140795]\n", " [ 1.03437984 1.03140795]\n", " [ 1.03437984 1.03140795]]\n", "Error: 0.5\n", "[[ 0.19373713 0.19373713 0.19373713 0.19373713]\n", " [ 0.11233848 0.11233848 0.11233848 0.11233848]] \n", " [[ 1.03472376 1.03174615]\n", " [ 1.03472376 1.03174615]\n", " [ 1.03472376 1.03174615]\n", " [ 1.03472376 1.03174615]]\n", "Error: 0.5\n", "[[ 0.19382384 0.19382384 0.19382384 0.19382384]\n", " [ 0.11240169 0.11240169 0.11240169 0.11240169]] \n", " [[ 1.0350672 1.03208375]\n", " [ 1.0350672 1.03208375]\n", " [ 1.0350672 1.03208375]\n", " [ 1.0350672 1.03208375]]\n", "Error: 0.5\n", "[[ 0.19391041 0.19391041 0.19391041 0.19391041]\n", " [ 0.11246481 0.11246481 0.11246481 0.11246481]] \n", " [[ 1.03541005 1.03242087]\n", " [ 1.03541005 1.03242087]\n", " [ 1.03541005 1.03242087]\n", " [ 1.03541005 1.03242087]]\n", "Error: 0.5\n", "[[ 0.19399682 0.19399682 0.19399682 0.19399682]\n", " [ 0.11252783 0.11252783 0.11252783 0.11252783]] \n", " [[ 1.03575242 1.03275752]\n", " [ 1.03575242 1.03275752]\n", " [ 1.03575242 1.03275752]\n", " [ 1.03575242 1.03275752]]\n", "Error: 0.5\n", "[[ 0.19408306 0.19408306 0.19408306 0.19408306]\n", " [ 0.11259077 0.11259077 0.11259077 0.11259077]] \n", " [[ 1.03609431 1.03309357]\n", " [ 1.03609431 1.03309357]\n", " [ 1.03609431 1.03309357]\n", " [ 1.03609431 1.03309357]]\n", "Error: 0.5\n", "[[ 0.19416916 0.19416916 0.19416916 0.19416916]\n", " [ 0.11265361 0.11265361 0.11265361 0.11265361]] \n", " [[ 1.0364356 1.03342915]\n", " [ 1.0364356 1.03342915]\n", " [ 1.0364356 1.03342915]\n", " [ 1.0364356 1.03342915]]\n", "Error: 0.5\n", "[[ 0.19425511 0.19425511 0.19425511 0.19425511]\n", " [ 0.11271637 0.11271637 0.11271637 0.11271637]] \n", " [[ 1.03677642 1.03376412]\n", " [ 1.03677642 1.03376412]\n", " [ 1.03677642 1.03376412]\n", " [ 1.03677642 1.03376412]]\n", "Error: 0.5\n", "[[ 0.19434091 0.19434091 0.19434091 0.19434091]\n", " [ 0.11277904 0.11277904 0.11277904 0.11277904]] \n", " [[ 1.03711665 1.03409863]\n", " [ 1.03711665 1.03409863]\n", " [ 1.03711665 1.03409863]\n", " [ 1.03711665 1.03409863]]\n", "Error: 0.5\n", "[[ 0.19442657 0.19442657 0.19442657 0.19442657]\n", " [ 0.11284161 0.11284161 0.11284161 0.11284161]] \n", " [[ 1.03745639 1.03443265]\n", " [ 1.03745639 1.03443265]\n", " [ 1.03745639 1.03443265]\n", " [ 1.03745639 1.03443265]]\n", "Error: 0.5\n", "[[ 0.19451208 0.19451208 0.19451208 0.19451208]\n", " [ 0.11290409 0.11290409 0.11290409 0.11290409]] \n", " [[ 1.03779554 1.03476608]\n", " [ 1.03779554 1.03476608]\n", " [ 1.03779554 1.03476608]\n", " [ 1.03779554 1.03476608]]\n", "Error: 0.5\n", "[[ 0.19459745 0.19459745 0.19459745 0.19459745]\n", " [ 0.11296648 0.11296648 0.11296648 0.11296648]] \n", " [[ 1.03813422 1.03509903]\n", " [ 1.03813422 1.03509903]\n", " [ 1.03813422 1.03509903]\n", " [ 1.03813422 1.03509903]]\n", "Error: 0.5\n", "[[ 0.19468267 0.19468267 0.19468267 0.19468267]\n", " [ 0.11302879 0.11302879 0.11302879 0.11302879]] \n", " [[ 1.03847241 1.0354315 ]\n", " [ 1.03847241 1.0354315 ]\n", " [ 1.03847241 1.0354315 ]\n", " [ 1.03847241 1.0354315 ]]\n", "Error: 0.5\n", "[[ 0.19476774 0.19476774 0.19476774 0.19476774]\n", " [ 0.11309101 0.11309101 0.11309101 0.11309101]] \n", " [[ 1.03881001 1.03576338]\n", " [ 1.03881001 1.03576338]\n", " [ 1.03881001 1.03576338]\n", " [ 1.03881001 1.03576338]]\n", "Error: 0.5\n", "[[ 0.19485267 0.19485267 0.19485267 0.19485267]\n", " [ 0.11315314 0.11315314 0.11315314 0.11315314]] \n", " [[ 1.03914714 1.03609478]\n", " [ 1.03914714 1.03609478]\n", " [ 1.03914714 1.03609478]\n", " [ 1.03914714 1.03609478]]\n", "Error: 0.5\n", "[[ 0.19493744 0.19493744 0.19493744 0.19493744]\n", " [ 0.11321518 0.11321518 0.11321518 0.11321518]] \n", " [[ 1.03948379 1.03642571]\n", " [ 1.03948379 1.03642571]\n", " [ 1.03948379 1.03642571]\n", " [ 1.03948379 1.03642571]]\n", "Error: 0.5\n", "[[ 0.19502208 0.19502208 0.19502208 0.19502208]\n", " [ 0.11327713 0.11327713 0.11327713 0.11327713]] \n", " [[ 1.03981984 1.03675604]\n", " [ 1.03981984 1.03675604]\n", " [ 1.03981984 1.03675604]\n", " [ 1.03981984 1.03675604]]\n", "Error: 0.5\n", "[[ 0.19510657 0.19510657 0.19510657 0.19510657]\n", " [ 0.11333899 0.11333899 0.11333899 0.11333899]] \n", " [[ 1.04015541 1.03708589]\n", " [ 1.04015541 1.03708589]\n", " [ 1.04015541 1.03708589]\n", " [ 1.04015541 1.03708589]]\n", "Error: 0.5\n", "[[ 0.19519091 0.19519091 0.19519091 0.19519091]\n", " [ 0.11340076 0.11340076 0.11340076 0.11340076]] \n", " [[ 1.04049051 1.03741527]\n", " [ 1.04049051 1.03741527]\n", " [ 1.04049051 1.03741527]\n", " [ 1.04049051 1.03741527]]\n", "Error: 0.5\n", "[[ 0.1952751 0.1952751 0.1952751 0.1952751 ]\n", " [ 0.11346246 0.11346246 0.11346246 0.11346246]] \n", " [[ 1.04082501 1.03774416]\n", " [ 1.04082501 1.03774416]\n", " [ 1.04082501 1.03774416]\n", " [ 1.04082501 1.03774416]]\n", "Error: 0.5\n", "[[ 0.19535916 0.19535916 0.19535916 0.19535916]\n", " [ 0.11352406 0.11352406 0.11352406 0.11352406]] \n", " [[ 1.04115903 1.03807247]\n", " [ 1.04115903 1.03807247]\n", " [ 1.04115903 1.03807247]\n", " [ 1.04115903 1.03807247]]\n", "Error: 0.5\n", "[[ 0.19544306 0.19544306 0.19544306 0.19544306]\n", " [ 0.11358557 0.11358557 0.11358557 0.11358557]] \n", " [[ 1.04149258 1.03840029]\n", " [ 1.04149258 1.03840029]\n", " [ 1.04149258 1.03840029]\n", " [ 1.04149258 1.03840029]]\n", "Error: 0.5\n", "[[ 0.19552684 0.19552684 0.19552684 0.19552684]\n", " [ 0.113647 0.113647 0.113647 0.113647 ]] \n", " [[ 1.04182565 1.03872764]\n", " [ 1.04182565 1.03872764]\n", " [ 1.04182565 1.03872764]\n", " [ 1.04182565 1.03872764]]\n", "Error: 0.5\n", "[[ 0.19561046 0.19561046 0.19561046 0.19561046]\n", " [ 0.11370834 0.11370834 0.11370834 0.11370834]] \n", " [[ 1.04215813 1.03905451]\n", " [ 1.04215813 1.03905451]\n", " [ 1.04215813 1.03905451]\n", " [ 1.04215813 1.03905451]]\n", "Error: 0.5\n", "[[ 0.19569395 0.19569395 0.19569395 0.19569395]\n", " [ 0.11376959 0.11376959 0.11376959 0.11376959]] \n", " [[ 1.04249012 1.03938091]\n", " [ 1.04249012 1.03938091]\n", " [ 1.04249012 1.03938091]\n", " [ 1.04249012 1.03938091]]\n", "Error: 0.5\n", "[[ 0.1957773 0.1957773 0.1957773 0.1957773 ]\n", " [ 0.11383076 0.11383076 0.11383076 0.11383076]] \n", " [[ 1.04282165 1.03970671]\n", " [ 1.04282165 1.03970671]\n", " [ 1.04282165 1.03970671]\n", " [ 1.04282165 1.03970671]]\n", "Error: 0.5\n", "[[ 0.19586051 0.19586051 0.19586051 0.19586051]\n", " [ 0.11389184 0.11389184 0.11389184 0.11389184]] \n", " [[ 1.04315269 1.04003203]\n", " [ 1.04315269 1.04003203]\n", " [ 1.04315269 1.04003203]\n", " [ 1.04315269 1.04003203]]\n", "Error: 0.5\n", "[[ 0.19594356 0.19594356 0.19594356 0.19594356]\n", " [ 0.11395284 0.11395284 0.11395284 0.11395284]] \n", " [[ 1.04348314 1.04035687]\n", " [ 1.04348314 1.04035687]\n", " [ 1.04348314 1.04035687]\n", " [ 1.04348314 1.04035687]]\n", "Error: 0.5\n", "[[ 0.19602649 0.19602649 0.19602649 0.19602649]\n", " [ 0.11401375 0.11401375 0.11401375 0.11401375]] \n", " [[ 1.04381311 1.04068124]\n", " [ 1.04381311 1.04068124]\n", " [ 1.04381311 1.04068124]\n", " [ 1.04381311 1.04068124]]\n", "Error: 0.5\n", "[[ 0.19610927 0.19610927 0.19610927 0.19610927]\n", " [ 0.11407457 0.11407457 0.11407457 0.11407457]] \n", " [[ 1.0441426 1.04100513]\n", " [ 1.0441426 1.04100513]\n", " [ 1.0441426 1.04100513]\n", " [ 1.0441426 1.04100513]]\n", "Error: 0.5\n", "[[ 0.19619191 0.19619191 0.19619191 0.19619191]\n", " [ 0.11413532 0.11413532 0.11413532 0.11413532]] \n", " [[ 1.04447162 1.04132855]\n", " [ 1.04447162 1.04132855]\n", " [ 1.04447162 1.04132855]\n", " [ 1.04447162 1.04132855]]\n", "Error: 0.5\n", "[[ 0.19627441 0.19627441 0.19627441 0.19627441]\n", " [ 0.11419597 0.11419597 0.11419597 0.11419597]] \n", " [[ 1.04480016 1.04165149]\n", " [ 1.04480016 1.04165149]\n", " [ 1.04480016 1.04165149]\n", " [ 1.04480016 1.04165149]]\n", "Error: 0.5\n", "[[ 0.19635677 0.19635677 0.19635677 0.19635677]\n", " [ 0.11425655 0.11425655 0.11425655 0.11425655]] \n", " [[ 1.04512823 1.04197395]\n", " [ 1.04512823 1.04197395]\n", " [ 1.04512823 1.04197395]\n", " [ 1.04512823 1.04197395]]\n", "Error: 0.5\n", "[[ 0.196439 0.196439 0.196439 0.196439 ]\n", " [ 0.11431704 0.11431704 0.11431704 0.11431704]] \n", " [[ 1.04545581 1.04229593]\n", " [ 1.04545581 1.04229593]\n", " [ 1.04545581 1.04229593]\n", " [ 1.04545581 1.04229593]]\n", "Error: 0.5\n", "[[ 0.19652109 0.19652109 0.19652109 0.19652109]\n", " [ 0.11437744 0.11437744 0.11437744 0.11437744]] \n", " [[ 1.04578292 1.04261744]\n", " [ 1.04578292 1.04261744]\n", " [ 1.04578292 1.04261744]\n", " [ 1.04578292 1.04261744]]\n", "Error: 0.5\n", "[[ 0.19660304 0.19660304 0.19660304 0.19660304]\n", " [ 0.11443776 0.11443776 0.11443776 0.11443776]] \n", " [[ 1.04610944 1.04293847]\n", " [ 1.04610944 1.04293847]\n", " [ 1.04610944 1.04293847]\n", " [ 1.04610944 1.04293847]]\n", "Error: 0.5\n", "[[ 0.19668485 0.19668485 0.19668485 0.19668485]\n", " [ 0.114498 0.114498 0.114498 0.114498 ]] \n", " [[ 1.04643548 1.04325891]\n", " [ 1.04643548 1.04325891]\n", " [ 1.04643548 1.04325891]\n", " [ 1.04643548 1.04325891]]\n", "Error: 0.5\n", "[[ 0.19676653 0.19676653 0.19676653 0.19676653]\n", " [ 0.11455815 0.11455815 0.11455815 0.11455815]] \n", " [[ 1.04676104 1.04357886]\n", " [ 1.04676104 1.04357886]\n", " [ 1.04676104 1.04357886]\n", " [ 1.04676104 1.04357886]]\n", "Error: 0.5\n", "[[ 0.19684806 0.19684806 0.19684806 0.19684806]\n", " [ 0.11461823 0.11461823 0.11461823 0.11461823]] \n", " [[ 1.04708612 1.04389834]\n", " [ 1.04708612 1.04389834]\n", " [ 1.04708612 1.04389834]\n", " [ 1.04708612 1.04389834]]\n", "Error: 0.5\n", "[[ 0.19692947 0.19692947 0.19692947 0.19692947]\n", " [ 0.11467822 0.11467822 0.11467822 0.11467822]] \n", " [[ 1.04741073 1.04421735]\n", " [ 1.04741073 1.04421735]\n", " [ 1.04741073 1.04421735]\n", " [ 1.04741073 1.04421735]]\n", "Error: 0.5\n", "[[ 0.19701074 0.19701074 0.19701074 0.19701074]\n", " [ 0.11473812 0.11473812 0.11473812 0.11473812]] \n", " [[ 1.04773486 1.04453588]\n", " [ 1.04773486 1.04453588]\n", " [ 1.04773486 1.04453588]\n", " [ 1.04773486 1.04453588]]\n", "Error: 0.5\n", "[[ 0.19709188 0.19709188 0.19709188 0.19709188]\n", " [ 0.11479794 0.11479794 0.11479794 0.11479794]] \n", " [[ 1.04805851 1.04485404]\n", " [ 1.04805851 1.04485404]\n", " [ 1.04805851 1.04485404]\n", " [ 1.04805851 1.04485404]]\n", "Error: 0.5\n", "[[ 0.19717288 0.19717288 0.19717288 0.19717288]\n", " [ 0.11485768 0.11485768 0.11485768 0.11485768]] \n", " [[ 1.04838169 1.04517174]\n", " [ 1.04838169 1.04517174]\n", " [ 1.04838169 1.04517174]\n", " [ 1.04838169 1.04517174]]\n", "Error: 0.5\n", "[[ 0.19725375 0.19725375 0.19725375 0.19725375]\n", " [ 0.11491734 0.11491734 0.11491734 0.11491734]] \n", " [[ 1.04870439 1.04548895]\n", " [ 1.04870439 1.04548895]\n", " [ 1.04870439 1.04548895]\n", " [ 1.04870439 1.04548895]]\n", "Error: 0.5\n", "[[ 0.19733448 0.19733448 0.19733448 0.19733448]\n", " [ 0.11497691 0.11497691 0.11497691 0.11497691]] \n", " [[ 1.04902661 1.04580569]\n", " [ 1.04902661 1.04580569]\n", " [ 1.04902661 1.04580569]\n", " [ 1.04902661 1.04580569]]\n", "Error: 0.5\n", "[[ 0.19741508 0.19741508 0.19741508 0.19741508]\n", " [ 0.11503641 0.11503641 0.11503641 0.11503641]] \n", " [[ 1.04934835 1.04612195]\n", " [ 1.04934835 1.04612195]\n", " [ 1.04934835 1.04612195]\n", " [ 1.04934835 1.04612195]]\n", "Error: 0.5\n", "[[ 0.19749555 0.19749555 0.19749555 0.19749555]\n", " [ 0.11509582 0.11509582 0.11509582 0.11509582]] \n", " [[ 1.04966962 1.04643774]\n", " [ 1.04966962 1.04643774]\n", " [ 1.04966962 1.04643774]\n", " [ 1.04966962 1.04643774]]\n", "Error: 0.5\n", "[[ 0.19757588 0.19757588 0.19757588 0.19757588]\n", " [ 0.11515515 0.11515515 0.11515515 0.11515515]] \n", " [[ 1.04999042 1.04675305]\n", " [ 1.04999042 1.04675305]\n", " [ 1.04999042 1.04675305]\n", " [ 1.04999042 1.04675305]]\n", "Error: 0.5\n", "[[ 0.19765608 0.19765608 0.19765608 0.19765608]\n", " [ 0.11521441 0.11521441 0.11521441 0.11521441]] \n", " [[ 1.05031073 1.04706788]\n", " [ 1.05031073 1.04706788]\n", " [ 1.05031073 1.04706788]\n", " [ 1.05031073 1.04706788]]\n", "Error: 0.5\n", "[[ 0.19773614 0.19773614 0.19773614 0.19773614]\n", " [ 0.11527358 0.11527358 0.11527358 0.11527358]] \n", " [[ 1.05063057 1.04738224]\n", " [ 1.05063057 1.04738224]\n", " [ 1.05063057 1.04738224]\n", " [ 1.05063057 1.04738224]]\n", "Error: 0.5\n", "[[ 0.19781609 0.19781609 0.19781609 0.19781609]\n", " [ 0.11533267 0.11533267 0.11533267 0.11533267]] \n", " [[ 1.05094993 1.04769611]\n", " [ 1.05094993 1.04769611]\n", " [ 1.05094993 1.04769611]\n", " [ 1.05094993 1.04769611]]\n", "Error: 0.5\n", "[[ 0.1978959 0.1978959 0.1978959 0.1978959 ]\n", " [ 0.11539168 0.11539168 0.11539168 0.11539168]] \n", " [[ 1.05126894 1.04800951]\n", " [ 1.05126894 1.04800951]\n", " [ 1.05126894 1.04800951]\n", " [ 1.05126894 1.04800951]]\n", "Error: 0.5\n", "[[ 0.19797558 0.19797558 0.19797558 0.19797558]\n", " [ 0.11545061 0.11545061 0.11545061 0.11545061]] \n", " [[ 1.05158746 1.04832256]\n", " [ 1.05158746 1.04832256]\n", " [ 1.05158746 1.04832256]\n", " [ 1.05158746 1.04832256]]\n", "Error: 0.5\n", "[[ 0.19805512 0.19805512 0.19805512 0.19805512]\n", " [ 0.11550947 0.11550947 0.11550947 0.11550947]] \n", " [[ 1.05190551 1.04863513]\n", " [ 1.05190551 1.04863513]\n", " [ 1.05190551 1.04863513]\n", " [ 1.05190551 1.04863513]]\n", "Error: 0.5\n", "[[ 0.19813454 0.19813454 0.19813454 0.19813454]\n", " [ 0.11556824 0.11556824 0.11556824 0.11556824]] \n", " [[ 1.05222309 1.04894722]\n", " [ 1.05222309 1.04894722]\n", " [ 1.05222309 1.04894722]\n", " [ 1.05222309 1.04894722]]\n", "Error: 0.5\n", "[[ 0.19821383 0.19821383 0.19821383 0.19821383]\n", " [ 0.11562692 0.11562692 0.11562692 0.11562692]] \n", " [[ 1.05254018 1.04925883]\n", " [ 1.05254018 1.04925883]\n", " [ 1.05254018 1.04925883]\n", " [ 1.05254018 1.04925883]]\n", "Error: 0.5\n", "[[ 0.19829299 0.19829299 0.19829299 0.19829299]\n", " [ 0.11568554 0.11568554 0.11568554 0.11568554]] \n", " [[ 1.0528568 1.04956996]\n", " [ 1.0528568 1.04956996]\n", " [ 1.0528568 1.04956996]\n", " [ 1.0528568 1.04956996]]\n", "Error: 0.5\n", "[[ 0.19837202 0.19837202 0.19837202 0.19837202]\n", " [ 0.11574407 0.11574407 0.11574407 0.11574407]] \n", " [[ 1.05317295 1.04988062]\n", " [ 1.05317295 1.04988062]\n", " [ 1.05317295 1.04988062]\n", " [ 1.05317295 1.04988062]]\n", "Error: 0.5\n", "[[ 0.19845092 0.19845092 0.19845092 0.19845092]\n", " [ 0.11580253 0.11580253 0.11580253 0.11580253]] \n", " [[ 1.05348861 1.05019093]\n", " [ 1.05348861 1.05019093]\n", " [ 1.05348861 1.05019093]\n", " [ 1.05348861 1.05019093]]\n", "Error: 0.5\n", "[[ 0.19852969 0.19852969 0.19852969 0.19852969]\n", " [ 0.1158609 0.1158609 0.1158609 0.1158609 ]] \n", " [[ 1.05380392 1.05050075]\n", " [ 1.05380392 1.05050075]\n", " [ 1.05380392 1.05050075]\n", " [ 1.05380392 1.05050075]]\n", "Error: 0.5\n", "[[ 0.19860834 0.19860834 0.19860834 0.19860834]\n", " [ 0.1159192 0.1159192 0.1159192 0.1159192 ]] \n", " [[ 1.05411875 1.0508101 ]\n", " [ 1.05411875 1.0508101 ]\n", " [ 1.05411875 1.0508101 ]\n", " [ 1.05411875 1.0508101 ]]\n", "Error: 0.5\n", "[[ 0.19868685 0.19868685 0.19868685 0.19868685]\n", " [ 0.11597742 0.11597742 0.11597742 0.11597742]] \n", " [[ 1.05443311 1.05111897]\n", " [ 1.05443311 1.05111897]\n", " [ 1.05443311 1.05111897]\n", " [ 1.05443311 1.05111897]]\n", "Error: 0.5\n", "[[ 0.19876525 0.19876525 0.19876525 0.19876525]\n", " [ 0.11603557 0.11603557 0.11603557 0.11603557]] \n", " [[ 1.05474699 1.05142748]\n", " [ 1.05474699 1.05142748]\n", " [ 1.05474699 1.05142748]\n", " [ 1.05474699 1.05142748]]\n", "Error: 0.5\n", "[[ 0.19884351 0.19884351 0.19884351 0.19884351]\n", " [ 0.11609363 0.11609363 0.11609363 0.11609363]] \n", " [[ 1.05506039 1.05173552]\n", " [ 1.05506039 1.05173552]\n", " [ 1.05506039 1.05173552]\n", " [ 1.05506039 1.05173552]]\n", "Error: 0.5\n", "[[ 0.19892165 0.19892165 0.19892165 0.19892165]\n", " [ 0.11615162 0.11615162 0.11615162 0.11615162]] \n", " [[ 1.05537343 1.05204308]\n", " [ 1.05537343 1.05204308]\n", " [ 1.05537343 1.05204308]\n", " [ 1.05537343 1.05204308]]\n", "Error: 0.5\n", "[[ 0.19899966 0.19899966 0.19899966 0.19899966]\n", " [ 0.11620952 0.11620952 0.11620952 0.11620952]] \n", " [[ 1.055686 1.05235016]\n", " [ 1.055686 1.05235016]\n", " [ 1.055686 1.05235016]\n", " [ 1.055686 1.05235016]]\n", "Error: 0.5\n", "[[ 0.19907755 0.19907755 0.19907755 0.19907755]\n", " [ 0.11626735 0.11626735 0.11626735 0.11626735]] \n", " [[ 1.05599809 1.05265689]\n", " [ 1.05599809 1.05265689]\n", " [ 1.05599809 1.05265689]\n", " [ 1.05599809 1.05265689]]\n", "Error: 0.5\n", "[[ 0.1991553 0.1991553 0.1991553 0.1991553 ]\n", " [ 0.11632511 0.11632511 0.11632511 0.11632511]] \n", " [[ 1.0563097 1.05296314]\n", " [ 1.0563097 1.05296314]\n", " [ 1.0563097 1.05296314]\n", " [ 1.0563097 1.05296314]]\n", "Error: 0.5\n", "[[ 0.19923294 0.19923294 0.19923294 0.19923294]\n", " [ 0.11638279 0.11638279 0.11638279 0.11638279]] \n", " [[ 1.05662096 1.05326891]\n", " [ 1.05662096 1.05326891]\n", " [ 1.05662096 1.05326891]\n", " [ 1.05662096 1.05326891]]\n", "Error: 0.5\n", "[[ 0.19931045 0.19931045 0.19931045 0.19931045]\n", " [ 0.11644039 0.11644039 0.11644039 0.11644039]] \n", " [[ 1.05693173 1.05357432]\n", " [ 1.05693173 1.05357432]\n", " [ 1.05693173 1.05357432]\n", " [ 1.05693173 1.05357432]]\n", "Error: 0.5\n", "[[ 0.19938783 0.19938783 0.19938783 0.19938783]\n", " [ 0.11649791 0.11649791 0.11649791 0.11649791]] \n", " [[ 1.05724204 1.05387926]\n", " [ 1.05724204 1.05387926]\n", " [ 1.05724204 1.05387926]\n", " [ 1.05724204 1.05387926]]\n", "Error: 0.5\n", "[[ 0.1994651 0.1994651 0.1994651 0.1994651 ]\n", " [ 0.11655536 0.11655536 0.11655536 0.11655536]] \n", " [[ 1.05755198 1.05418372]\n", " [ 1.05755198 1.05418372]\n", " [ 1.05755198 1.05418372]\n", " [ 1.05755198 1.05418372]]\n", "Error: 0.5\n", "[[ 0.19954224 0.19954224 0.19954224 0.19954224]\n", " [ 0.11661273 0.11661273 0.11661273 0.11661273]] \n", " [[ 1.05786145 1.05448782]\n", " [ 1.05786145 1.05448782]\n", " [ 1.05786145 1.05448782]\n", " [ 1.05786145 1.05448782]]\n", "Error: 0.5\n", "[[ 0.19961925 0.19961925 0.19961925 0.19961925]\n", " [ 0.11667003 0.11667003 0.11667003 0.11667003]] \n", " [[ 1.05817044 1.05479145]\n", " [ 1.05817044 1.05479145]\n", " [ 1.05817044 1.05479145]\n", " [ 1.05817044 1.05479145]]\n", "Error: 0.5\n", "[[ 0.19969614 0.19969614 0.19969614 0.19969614]\n", " [ 0.11672725 0.11672725 0.11672725 0.11672725]] \n", " [[ 1.05847907 1.05509472]\n", " [ 1.05847907 1.05509472]\n", " [ 1.05847907 1.05509472]\n", " [ 1.05847907 1.05509472]]\n", "Error: 0.5\n", "[[ 0.19977291 0.19977291 0.19977291 0.19977291]\n", " [ 0.11678439 0.11678439 0.11678439 0.11678439]] \n", " [[ 1.05878723 1.05539751]\n", " [ 1.05878723 1.05539751]\n", " [ 1.05878723 1.05539751]\n", " [ 1.05878723 1.05539751]]\n", "Error: 0.5\n", "[[ 0.19984956 0.19984956 0.19984956 0.19984956]\n", " [ 0.11684147 0.11684147 0.11684147 0.11684147]] \n", " [[ 1.05909491 1.05569983]\n", " [ 1.05909491 1.05569983]\n", " [ 1.05909491 1.05569983]\n", " [ 1.05909491 1.05569983]]\n", "Error: 0.5\n", "[[ 0.19992609 0.19992609 0.19992609 0.19992609]\n", " [ 0.11689846 0.11689846 0.11689846 0.11689846]] \n", " [[ 1.05940223 1.05600178]\n", " [ 1.05940223 1.05600178]\n", " [ 1.05940223 1.05600178]\n", " [ 1.05940223 1.05600178]]\n", "Error: 0.5\n", "[[ 0.20000251 0.20000251 0.20000251 0.20000251]\n", " [ 0.11695538 0.11695538 0.11695538 0.11695538]] \n", " [[ 1.05970907 1.05630326]\n", " [ 1.05970907 1.05630326]\n", " [ 1.05970907 1.05630326]\n", " [ 1.05970907 1.05630326]]\n", "Error: 0.5\n", "[[ 0.20007879 0.20007879 0.20007879 0.20007879]\n", " [ 0.11701223 0.11701223 0.11701223 0.11701223]] \n", " [[ 1.06001544 1.05660439]\n", " [ 1.06001544 1.05660439]\n", " [ 1.06001544 1.05660439]\n", " [ 1.06001544 1.05660439]]\n", "Error: 0.5\n", "[[ 0.20015495 0.20015495 0.20015495 0.20015495]\n", " [ 0.11706901 0.11706901 0.11706901 0.11706901]] \n", " [[ 1.06032145 1.05690503]\n", " [ 1.06032145 1.05690503]\n", " [ 1.06032145 1.05690503]\n", " [ 1.06032145 1.05690503]]\n", "Error: 0.5\n", "[[ 0.20023099 0.20023099 0.20023099 0.20023099]\n", " [ 0.1171257 0.1171257 0.1171257 0.1171257 ]] \n", " [[ 1.06062698 1.0572052 ]\n", " [ 1.06062698 1.0572052 ]\n", " [ 1.06062698 1.0572052 ]\n", " [ 1.06062698 1.0572052 ]]\n", "Error: 0.5\n", "[[ 0.20030691 0.20030691 0.20030691 0.20030691]\n", " [ 0.11718233 0.11718233 0.11718233 0.11718233]] \n", " [[ 1.06093216 1.05750501]\n", " [ 1.06093216 1.05750501]\n", " [ 1.06093216 1.05750501]\n", " [ 1.06093216 1.05750501]]\n", "Error: 0.5\n", "[[ 0.20038271 0.20038271 0.20038271 0.20038271]\n", " [ 0.11723888 0.11723888 0.11723888 0.11723888]] \n", " [[ 1.06123686 1.05780435]\n", " [ 1.06123686 1.05780435]\n", " [ 1.06123686 1.05780435]\n", " [ 1.06123686 1.05780435]]\n", "Error: 0.5\n", "[[ 0.20045839 0.20045839 0.20045839 0.20045839]\n", " [ 0.11729535 0.11729535 0.11729535 0.11729535]] \n", " [[ 1.06154108 1.05810332]\n", " [ 1.06154108 1.05810332]\n", " [ 1.06154108 1.05810332]\n", " [ 1.06154108 1.05810332]]\n", "Error: 0.5\n", "[[ 0.20053396 0.20053396 0.20053396 0.20053396]\n", " [ 0.11735176 0.11735176 0.11735176 0.11735176]] \n", " [[ 1.06184494 1.05840182]\n", " [ 1.06184494 1.05840182]\n", " [ 1.06184494 1.05840182]\n", " [ 1.06184494 1.05840182]]\n", "Error: 0.5\n", "[[ 0.2006094 0.2006094 0.2006094 0.2006094 ]\n", " [ 0.11740808 0.11740808 0.11740808 0.11740808]] \n", " [[ 1.06214833 1.05869997]\n", " [ 1.06214833 1.05869997]\n", " [ 1.06214833 1.05869997]\n", " [ 1.06214833 1.05869997]]\n", "Error: 0.5\n", "[[ 0.20068473 0.20068473 0.20068473 0.20068473]\n", " [ 0.11746433 0.11746433 0.11746433 0.11746433]] \n", " [[ 1.06245136 1.05899763]\n", " [ 1.06245136 1.05899763]\n", " [ 1.06245136 1.05899763]\n", " [ 1.06245136 1.05899763]]\n", "Error: 0.5\n", "[[ 0.20075993 0.20075993 0.20075993 0.20075993]\n", " [ 0.11752051 0.11752051 0.11752051 0.11752051]] \n", " [[ 1.06275392 1.05929494]\n", " [ 1.06275392 1.05929494]\n", " [ 1.06275392 1.05929494]\n", " [ 1.06275392 1.05929494]]\n", "Error: 0.5\n", "[[ 0.20083502 0.20083502 0.20083502 0.20083502]\n", " [ 0.11757662 0.11757662 0.11757662 0.11757662]] \n", " [[ 1.06305611 1.05959177]\n", " [ 1.06305611 1.05959177]\n", " [ 1.06305611 1.05959177]\n", " [ 1.06305611 1.05959177]]\n", "Error: 0.5\n", "[[ 0.20090999 0.20090999 0.20090999 0.20090999]\n", " [ 0.11763266 0.11763266 0.11763266 0.11763266]] \n", " [[ 1.06335783 1.05988824]\n", " [ 1.06335783 1.05988824]\n", " [ 1.06335783 1.05988824]\n", " [ 1.06335783 1.05988824]]\n", "Error: 0.5\n", "[[ 0.20098485 0.20098485 0.20098485 0.20098485]\n", " [ 0.11768862 0.11768862 0.11768862 0.11768862]] \n", " [[ 1.06365919 1.06018424]\n", " [ 1.06365919 1.06018424]\n", " [ 1.06365919 1.06018424]\n", " [ 1.06365919 1.06018424]]\n", "Error: 0.5\n", "[[ 0.20105959 0.20105959 0.20105959 0.20105959]\n", " [ 0.11774451 0.11774451 0.11774451 0.11774451]] \n", " [[ 1.06396008 1.06047988]\n", " [ 1.06396008 1.06047988]\n", " [ 1.06396008 1.06047988]\n", " [ 1.06396008 1.06047988]]\n", "Error: 0.5\n", "[[ 0.20113422 0.20113422 0.20113422 0.20113422]\n", " [ 0.11780033 0.11780033 0.11780033 0.11780033]] \n", " [[ 1.0642606 1.06077516]\n", " [ 1.0642606 1.06077516]\n", " [ 1.0642606 1.06077516]\n", " [ 1.0642606 1.06077516]]\n", "Error: 0.5\n", "[[ 0.20120873 0.20120873 0.20120873 0.20120873]\n", " [ 0.11785607 0.11785607 0.11785607 0.11785607]] \n", " [[ 1.06456065 1.06106997]\n", " [ 1.06456065 1.06106997]\n", " [ 1.06456065 1.06106997]\n", " [ 1.06456065 1.06106997]]\n", "Error: 0.5\n", "[[ 0.20128311 0.20128311 0.20128311 0.20128311]\n", " [ 0.11791174 0.11791174 0.11791174 0.11791174]] \n", " [[ 1.06486034 1.06136441]\n", " [ 1.06486034 1.06136441]\n", " [ 1.06486034 1.06136441]\n", " [ 1.06486034 1.06136441]]\n", "Error: 0.5\n", "[[ 0.20135739 0.20135739 0.20135739 0.20135739]\n", " [ 0.11796734 0.11796734 0.11796734 0.11796734]] \n", " [[ 1.06515956 1.06165838]\n", " [ 1.06515956 1.06165838]\n", " [ 1.06515956 1.06165838]\n", " [ 1.06515956 1.06165838]]\n", "Error: 0.5\n", "[[ 0.20143156 0.20143156 0.20143156 0.20143156]\n", " [ 0.11802287 0.11802287 0.11802287 0.11802287]] \n", " [[ 1.06545842 1.06195199]\n", " [ 1.06545842 1.06195199]\n", " [ 1.06545842 1.06195199]\n", " [ 1.06545842 1.06195199]]\n", "Error: 0.5\n", "[[ 0.2015056 0.2015056 0.2015056 0.2015056 ]\n", " [ 0.11807834 0.11807834 0.11807834 0.11807834]] \n", " [[ 1.0657568 1.06224525]\n", " [ 1.0657568 1.06224525]\n", " [ 1.0657568 1.06224525]\n", " [ 1.0657568 1.06224525]]\n", "Error: 0.5\n", "[[ 0.20157953 0.20157953 0.20157953 0.20157953]\n", " [ 0.11813372 0.11813372 0.11813372 0.11813372]] \n", " [[ 1.06605482 1.06253803]\n", " [ 1.06605482 1.06253803]\n", " [ 1.06605482 1.06253803]\n", " [ 1.06605482 1.06253803]]\n", "Error: 0.5\n", "[[ 0.20165335 0.20165335 0.20165335 0.20165335]\n", " [ 0.11818904 0.11818904 0.11818904 0.11818904]] \n", " [[ 1.06635249 1.06283045]\n", " [ 1.06635249 1.06283045]\n", " [ 1.06635249 1.06283045]\n", " [ 1.06635249 1.06283045]]\n", "Error: 0.5\n", "[[ 0.20172705 0.20172705 0.20172705 0.20172705]\n", " [ 0.11824429 0.11824429 0.11824429 0.11824429]] \n", " [[ 1.06664968 1.06312239]\n", " [ 1.06664968 1.06312239]\n", " [ 1.06664968 1.06312239]\n", " [ 1.06664968 1.06312239]]\n", "Error: 0.5\n", "[[ 0.20180064 0.20180064 0.20180064 0.20180064]\n", " [ 0.11829947 0.11829947 0.11829947 0.11829947]] \n", " [[ 1.06694651 1.06341398]\n", " [ 1.06694651 1.06341398]\n", " [ 1.06694651 1.06341398]\n", " [ 1.06694651 1.06341398]]\n", "Error: 0.5\n", "[[ 0.20187412 0.20187412 0.20187412 0.20187412]\n", " [ 0.11835457 0.11835457 0.11835457 0.11835457]] \n", " [[ 1.06724286 1.06370521]\n", " [ 1.06724286 1.06370521]\n", " [ 1.06724286 1.06370521]\n", " [ 1.06724286 1.06370521]]\n", "Error: 0.5\n", "[[ 0.20194748 0.20194748 0.20194748 0.20194748]\n", " [ 0.11840961 0.11840961 0.11840961 0.11840961]] \n", " [[ 1.06753886 1.06399596]\n", " [ 1.06753886 1.06399596]\n", " [ 1.06753886 1.06399596]\n", " [ 1.06753886 1.06399596]]\n", "Error: 0.5\n", "[[ 0.20202073 0.20202073 0.20202073 0.20202073]\n", " [ 0.11846457 0.11846457 0.11846457 0.11846457]] \n", " [[ 1.0678345 1.06428635]\n", " [ 1.0678345 1.06428635]\n", " [ 1.0678345 1.06428635]\n", " [ 1.0678345 1.06428635]]\n", "Error: 0.5\n", "[[ 0.20209387 0.20209387 0.20209387 0.20209387]\n", " [ 0.11851947 0.11851947 0.11851947 0.11851947]] \n", " [[ 1.06812966 1.06457639]\n", " [ 1.06812966 1.06457639]\n", " [ 1.06812966 1.06457639]\n", " [ 1.06812966 1.06457639]]\n", "Error: 0.5\n", "[[ 0.2021669 0.2021669 0.2021669 0.2021669]\n", " [ 0.1185743 0.1185743 0.1185743 0.1185743]] \n", " [[ 1.06842446 1.06486595]\n", " [ 1.06842446 1.06486595]\n", " [ 1.06842446 1.06486595]\n", " [ 1.06842446 1.06486595]]\n", "Error: 0.5\n", "[[ 0.20223981 0.20223981 0.20223981 0.20223981]\n", " [ 0.11862905 0.11862905 0.11862905 0.11862905]] \n", " [[ 1.06871891 1.06515515]\n", " [ 1.06871891 1.06515515]\n", " [ 1.06871891 1.06515515]\n", " [ 1.06871891 1.06515515]]\n", "Error: 0.5\n", "[[ 0.20231262 0.20231262 0.20231262 0.20231262]\n", " [ 0.11868374 0.11868374 0.11868374 0.11868374]] \n", " [[ 1.06901288 1.06544399]\n", " [ 1.06901288 1.06544399]\n", " [ 1.06901288 1.06544399]\n", " [ 1.06901288 1.06544399]]\n", "Error: 0.5\n", "[[ 0.20238531 0.20238531 0.20238531 0.20238531]\n", " [ 0.11873836 0.11873836 0.11873836 0.11873836]] \n", " [[ 1.06930649 1.06573248]\n", " [ 1.06930649 1.06573248]\n", " [ 1.06930649 1.06573248]\n", " [ 1.06930649 1.06573248]]\n", "Error: 0.5\n", "[[ 0.20245789 0.20245789 0.20245789 0.20245789]\n", " [ 0.11879291 0.11879291 0.11879291 0.11879291]] \n", " [[ 1.06959975 1.06602049]\n", " [ 1.06959975 1.06602049]\n", " [ 1.06959975 1.06602049]\n", " [ 1.06959975 1.06602049]]\n", "Error: 0.5\n", "[[ 0.20253035 0.20253035 0.20253035 0.20253035]\n", " [ 0.11884739 0.11884739 0.11884739 0.11884739]] \n", " [[ 1.06989253 1.06630814]\n", " [ 1.06989253 1.06630814]\n", " [ 1.06989253 1.06630814]\n", " [ 1.06989253 1.06630814]]\n", "Error: 0.5\n", "[[ 0.20260271 0.20260271 0.20260271 0.20260271]\n", " [ 0.1189018 0.1189018 0.1189018 0.1189018 ]] \n", " [[ 1.07018495 1.06659544]\n", " [ 1.07018495 1.06659544]\n", " [ 1.07018495 1.06659544]\n", " [ 1.07018495 1.06659544]]\n", "Error: 0.5\n", "[[ 0.20267497 0.20267497 0.20267497 0.20267497]\n", " [ 0.11895614 0.11895614 0.11895614 0.11895614]] \n", " [[ 1.07047701 1.06688225]\n", " [ 1.07047701 1.06688225]\n", " [ 1.07047701 1.06688225]\n", " [ 1.07047701 1.06688225]]\n", "Error: 0.5\n", "[[ 0.20274711 0.20274711 0.20274711 0.20274711]\n", " [ 0.11901042 0.11901042 0.11901042 0.11901042]] \n", " [[ 1.07076859 1.06716871]\n", " [ 1.07076859 1.06716871]\n", " [ 1.07076859 1.06716871]\n", " [ 1.07076859 1.06716871]]\n", "Error: 0.5\n", "[[ 0.20281914 0.20281914 0.20281914 0.20281914]\n", " [ 0.11906462 0.11906462 0.11906462 0.11906462]] \n", " [[ 1.07105982 1.06745481]\n", " [ 1.07105982 1.06745481]\n", " [ 1.07105982 1.06745481]\n", " [ 1.07105982 1.06745481]]\n", "Error: 0.5\n", "[[ 0.20289107 0.20289107 0.20289107 0.20289107]\n", " [ 0.11911876 0.11911876 0.11911876 0.11911876]] \n", " [[ 1.07135069 1.06774056]\n", " [ 1.07135069 1.06774056]\n", " [ 1.07135069 1.06774056]\n", " [ 1.07135069 1.06774056]]\n", "Error: 0.5\n", "[[ 0.20296288 0.20296288 0.20296288 0.20296288]\n", " [ 0.11917283 0.11917283 0.11917283 0.11917283]] \n", " [[ 1.07164121 1.06802595]\n", " [ 1.07164121 1.06802595]\n", " [ 1.07164121 1.06802595]\n", " [ 1.07164121 1.06802595]]\n", "Error: 0.5\n", "[[ 0.20303458 0.20303458 0.20303458 0.20303458]\n", " [ 0.11922683 0.11922683 0.11922683 0.11922683]] \n", " [[ 1.07193124 1.06831086]\n", " [ 1.07193124 1.06831086]\n", " [ 1.07193124 1.06831086]\n", " [ 1.07193124 1.06831086]]\n", "Error: 0.5\n", "[[ 0.20310618 0.20310618 0.20310618 0.20310618]\n", " [ 0.11928076 0.11928076 0.11928076 0.11928076]] \n", " [[ 1.07222092 1.06859541]\n", " [ 1.07222092 1.06859541]\n", " [ 1.07222092 1.06859541]\n", " [ 1.07222092 1.06859541]]\n", "Error: 0.5\n", "[[ 0.20317766 0.20317766 0.20317766 0.20317766]\n", " [ 0.11933463 0.11933463 0.11933463 0.11933463]] \n", " [[ 1.07251024 1.0688796 ]\n", " [ 1.07251024 1.0688796 ]\n", " [ 1.07251024 1.0688796 ]\n", " [ 1.07251024 1.0688796 ]]\n", "Error: 0.5\n", "[[ 0.20324904 0.20324904 0.20324904 0.20324904]\n", " [ 0.11938843 0.11938843 0.11938843 0.11938843]] \n", " [[ 1.07279921 1.06916344]\n", " [ 1.07279921 1.06916344]\n", " [ 1.07279921 1.06916344]\n", " [ 1.07279921 1.06916344]]\n", "Error: 0.5\n", "[[ 0.20332031 0.20332031 0.20332031 0.20332031]\n", " [ 0.11944216 0.11944216 0.11944216 0.11944216]] \n", " [[ 1.07308769 1.06944692]\n", " [ 1.07308769 1.06944692]\n", " [ 1.07308769 1.06944692]\n", " [ 1.07308769 1.06944692]]\n", "Error: 0.5\n", "[[ 0.20339148 0.20339148 0.20339148 0.20339148]\n", " [ 0.11949583 0.11949583 0.11949583 0.11949583]] \n", " [[ 1.07337582 1.06972992]\n", " [ 1.07337582 1.06972992]\n", " [ 1.07337582 1.06972992]\n", " [ 1.07337582 1.06972992]]\n", "Error: 0.5\n", "[[ 0.20346254 0.20346254 0.20346254 0.20346254]\n", " [ 0.11954943 0.11954943 0.11954943 0.11954943]] \n", " [[ 1.07366359 1.07001257]\n", " [ 1.07366359 1.07001257]\n", " [ 1.07366359 1.07001257]\n", " [ 1.07366359 1.07001257]]\n", "Error: 0.5\n", "[[ 0.20353349 0.20353349 0.20353349 0.20353349]\n", " [ 0.11960296 0.11960296 0.11960296 0.11960296]] \n", " [[ 1.07395101 1.07029486]\n", " [ 1.07395101 1.07029486]\n", " [ 1.07395101 1.07029486]\n", " [ 1.07395101 1.07029486]]\n", "Error: 0.5\n", "[[ 0.20360433 0.20360433 0.20360433 0.20360433]\n", " [ 0.11965643 0.11965643 0.11965643 0.11965643]] \n", " [[ 1.07423806 1.07057679]\n", " [ 1.07423806 1.07057679]\n", " [ 1.07423806 1.07057679]\n", " [ 1.07423806 1.07057679]]\n", "Error: 0.5\n", "[[ 0.20367506 0.20367506 0.20367506 0.20367506]\n", " [ 0.11970983 0.11970983 0.11970983 0.11970983]] \n", " [[ 1.07452464 1.07085836]\n", " [ 1.07452464 1.07085836]\n", " [ 1.07452464 1.07085836]\n", " [ 1.07452464 1.07085836]]\n", "Error: 0.5\n", "[[ 0.20374569 0.20374569 0.20374569 0.20374569]\n", " [ 0.11976316 0.11976316 0.11976316 0.11976316]] \n", " [[ 1.07481086 1.07113957]\n", " [ 1.07481086 1.07113957]\n", " [ 1.07481086 1.07113957]\n", " [ 1.07481086 1.07113957]]\n", "Error: 0.5\n", "[[ 0.20381622 0.20381622 0.20381622 0.20381622]\n", " [ 0.11981642 0.11981642 0.11981642 0.11981642]] \n", " [[ 1.07509673 1.07142043]\n", " [ 1.07509673 1.07142043]\n", " [ 1.07509673 1.07142043]\n", " [ 1.07509673 1.07142043]]\n", "Error: 0.5\n", "[[ 0.20388664 0.20388664 0.20388664 0.20388664]\n", " [ 0.11986963 0.11986963 0.11986963 0.11986963]] \n", " [[ 1.07538223 1.07170081]\n", " [ 1.07538223 1.07170081]\n", " [ 1.07538223 1.07170081]\n", " [ 1.07538223 1.07170081]]\n", "Error: 0.5\n", "[[ 0.20395696 0.20395696 0.20395696 0.20395696]\n", " [ 0.11992276 0.11992276 0.11992276 0.11992276]] \n", " [[ 1.07566738 1.07198083]\n", " [ 1.07566738 1.07198083]\n", " [ 1.07566738 1.07198083]\n", " [ 1.07566738 1.07198083]]\n", "Error: 0.5\n", "[[ 0.20402718 0.20402718 0.20402718 0.20402718]\n", " [ 0.11997584 0.11997584 0.11997584 0.11997584]] \n", " [[ 1.07595217 1.0722605 ]\n", " [ 1.07595217 1.0722605 ]\n", " [ 1.07595217 1.0722605 ]\n", " [ 1.07595217 1.0722605 ]]\n", "Error: 0.5\n", "[[ 0.20409729 0.20409729 0.20409729 0.20409729]\n", " [ 0.12002884 0.12002884 0.12002884 0.12002884]] \n", " [[ 1.07623649 1.07253981]\n", " [ 1.07623649 1.07253981]\n", " [ 1.07623649 1.07253981]\n", " [ 1.07623649 1.07253981]]\n", "Error: 0.5\n", "[[ 0.20416729 0.20416729 0.20416729 0.20416729]\n", " [ 0.12008178 0.12008178 0.12008178 0.12008178]] \n", " [[ 1.07652044 1.07281876]\n", " [ 1.07652044 1.07281876]\n", " [ 1.07652044 1.07281876]\n", " [ 1.07652044 1.07281876]]\n", "Error: 0.5\n", "[[ 0.20423719 0.20423719 0.20423719 0.20423719]\n", " [ 0.12013466 0.12013466 0.12013466 0.12013466]] \n", " [[ 1.07680404 1.07309735]\n", " [ 1.07680404 1.07309735]\n", " [ 1.07680404 1.07309735]\n", " [ 1.07680404 1.07309735]]\n", "Error: 0.5\n", "[[ 0.20430699 0.20430699 0.20430699 0.20430699]\n", " [ 0.12018747 0.12018747 0.12018747 0.12018747]] \n", " [[ 1.07708728 1.07337558]\n", " [ 1.07708728 1.07337558]\n", " [ 1.07708728 1.07337558]\n", " [ 1.07708728 1.07337558]]\n", "Error: 0.5\n", "[[ 0.20437668 0.20437668 0.20437668 0.20437668]\n", " [ 0.12024021 0.12024021 0.12024021 0.12024021]] \n", " [[ 1.07737017 1.07365346]\n", " [ 1.07737017 1.07365346]\n", " [ 1.07737017 1.07365346]\n", " [ 1.07737017 1.07365346]]\n", "Error: 0.5\n", "[[ 0.20444627 0.20444627 0.20444627 0.20444627]\n", " [ 0.12029289 0.12029289 0.12029289 0.12029289]] \n", " [[ 1.07765269 1.07393098]\n", " [ 1.07765269 1.07393098]\n", " [ 1.07765269 1.07393098]\n", " [ 1.07765269 1.07393098]]\n", "Error: 0.5\n", "[[ 0.20451576 0.20451576 0.20451576 0.20451576]\n", " [ 0.12034551 0.12034551 0.12034551 0.12034551]] \n", " [[ 1.07793486 1.07420814]\n", " [ 1.07793486 1.07420814]\n", " [ 1.07793486 1.07420814]\n", " [ 1.07793486 1.07420814]]\n", "Error: 0.5\n", "[[ 0.20458513 0.20458513 0.20458513 0.20458513]\n", " [ 0.12039806 0.12039806 0.12039806 0.12039806]] \n", " [[ 1.07821667 1.07448494]\n", " [ 1.07821667 1.07448494]\n", " [ 1.07821667 1.07448494]\n", " [ 1.07821667 1.07448494]]\n", "Error: 0.5\n", "[[ 0.20465443 0.20465443 0.20465443 0.20465443]\n", " [ 0.12045055 0.12045055 0.12045055 0.12045055]] \n", " [[ 1.07849813 1.07476139]\n", " [ 1.07849813 1.07476139]\n", " [ 1.07849813 1.07476139]\n", " [ 1.07849813 1.07476139]]\n", "Error: 0.5\n", "[[ 0.20472361 0.20472361 0.20472361 0.20472361]\n", " [ 0.12050297 0.12050297 0.12050297 0.12050297]] \n", " [[ 1.07877922 1.07503748]\n", " [ 1.07877922 1.07503748]\n", " [ 1.07877922 1.07503748]\n", " [ 1.07877922 1.07503748]]\n", "Error: 0.5\n", "[[ 0.20479269 0.20479269 0.20479269 0.20479269]\n", " [ 0.12055533 0.12055533 0.12055533 0.12055533]] \n", " [[ 1.07905996 1.07531321]\n", " [ 1.07905996 1.07531321]\n", " [ 1.07905996 1.07531321]\n", " [ 1.07905996 1.07531321]]\n", "Error: 0.5\n", "[[ 0.20486167 0.20486167 0.20486167 0.20486167]\n", " [ 0.12060763 0.12060763 0.12060763 0.12060763]] \n", " [[ 1.07934034 1.07558858]\n", " [ 1.07934034 1.07558858]\n", " [ 1.07934034 1.07558858]\n", " [ 1.07934034 1.07558858]]\n", "Error: 0.5\n", "[[ 0.20493054 0.20493054 0.20493054 0.20493054]\n", " [ 0.12065987 0.12065987 0.12065987 0.12065987]] \n", " [[ 1.07962036 1.0758636 ]\n", " [ 1.07962036 1.0758636 ]\n", " [ 1.07962036 1.0758636 ]\n", " [ 1.07962036 1.0758636 ]]\n", "Error: 0.5\n", "[[ 0.20499931 0.20499931 0.20499931 0.20499931]\n", " [ 0.12071203 0.12071203 0.12071203 0.12071203]] \n", " [[ 1.07990003 1.07613826]\n", " [ 1.07990003 1.07613826]\n", " [ 1.07990003 1.07613826]\n", " [ 1.07990003 1.07613826]]\n", "Error: 0.5\n", "[[ 0.20506799 0.20506799 0.20506799 0.20506799]\n", " [ 0.12076414 0.12076414 0.12076414 0.12076414]] \n", " [[ 1.08017921 1.07641256]\n", " [ 1.08017921 1.07641256]\n", " [ 1.08017921 1.07641256]\n", " [ 1.08017921 1.07641256]]\n", "Error: 0.5\n", "[[ 0.20513657 0.20513657 0.20513657 0.20513657]\n", " [ 0.12081619 0.12081619 0.12081619 0.12081619]] \n", " [[ 1.08045805 1.0766865 ]\n", " [ 1.08045805 1.0766865 ]\n", " [ 1.08045805 1.0766865 ]\n", " [ 1.08045805 1.0766865 ]]\n", "Error: 0.5\n", "[[ 0.20520504 0.20520504 0.20520504 0.20520504]\n", " [ 0.12086817 0.12086817 0.12086817 0.12086817]] \n", " [[ 1.08073652 1.07696009]\n", " [ 1.08073652 1.07696009]\n", " [ 1.08073652 1.07696009]\n", " [ 1.08073652 1.07696009]]\n", "Error: 0.5\n", "[[ 0.20527342 0.20527342 0.20527342 0.20527342]\n", " [ 0.12092008 0.12092008 0.12092008 0.12092008]] \n", " [[ 1.08101463 1.07723331]\n", " [ 1.08101463 1.07723331]\n", " [ 1.08101463 1.07723331]\n", " [ 1.08101463 1.07723331]]\n", "Error: 0.5\n", "[[ 0.2053417 0.2053417 0.2053417 0.2053417 ]\n", " [ 0.12097194 0.12097194 0.12097194 0.12097194]] \n", " [[ 1.08129239 1.07750618]\n", " [ 1.08129239 1.07750618]\n", " [ 1.08129239 1.07750618]\n", " [ 1.08129239 1.07750618]]\n", "Error: 0.5\n", "[[ 0.20540987 0.20540987 0.20540987 0.20540987]\n", " [ 0.12102373 0.12102373 0.12102373 0.12102373]] \n", " [[ 1.08156979 1.0777787 ]\n", " [ 1.08156979 1.0777787 ]\n", " [ 1.08156979 1.0777787 ]\n", " [ 1.08156979 1.0777787 ]]\n", "Error: 0.5\n", "[[ 0.20547795 0.20547795 0.20547795 0.20547795]\n", " [ 0.12107546 0.12107546 0.12107546 0.12107546]] \n", " [[ 1.08184695 1.07805085]\n", " [ 1.08184695 1.07805085]\n", " [ 1.08184695 1.07805085]\n", " [ 1.08184695 1.07805085]]\n", "Error: 0.5\n", "[[ 0.20554593 0.20554593 0.20554593 0.20554593]\n", " [ 0.12112713 0.12112713 0.12112713 0.12112713]] \n", " [[ 1.08212376 1.07832265]\n", " [ 1.08212376 1.07832265]\n", " [ 1.08212376 1.07832265]\n", " [ 1.08212376 1.07832265]]\n", "Error: 0.5\n", "[[ 0.20561381 0.20561381 0.20561381 0.20561381]\n", " [ 0.12117873 0.12117873 0.12117873 0.12117873]] \n", " [[ 1.0824002 1.07859409]\n", " [ 1.0824002 1.07859409]\n", " [ 1.0824002 1.07859409]\n", " [ 1.0824002 1.07859409]]\n", "Error: 0.5\n", "[[ 0.20568159 0.20568159 0.20568159 0.20568159]\n", " [ 0.12123027 0.12123027 0.12123027 0.12123027]] \n", " [[ 1.08267629 1.07886517]\n", " [ 1.08267629 1.07886517]\n", " [ 1.08267629 1.07886517]\n", " [ 1.08267629 1.07886517]]\n", "Error: 0.5\n", "[[ 0.20574927 0.20574927 0.20574927 0.20574927]\n", " [ 0.12128176 0.12128176 0.12128176 0.12128176]] \n", " [[ 1.08295202 1.07913589]\n", " [ 1.08295202 1.07913589]\n", " [ 1.08295202 1.07913589]\n", " [ 1.08295202 1.07913589]]\n", "Error: 0.5\n", "[[ 0.20581686 0.20581686 0.20581686 0.20581686]\n", " [ 0.12133318 0.12133318 0.12133318 0.12133318]] \n", " [[ 1.0832274 1.07940626]\n", " [ 1.0832274 1.07940626]\n", " [ 1.0832274 1.07940626]\n", " [ 1.0832274 1.07940626]]\n", "Error: 0.5\n", "[[ 0.20588435 0.20588435 0.20588435 0.20588435]\n", " [ 0.12138454 0.12138454 0.12138454 0.12138454]] \n", " [[ 1.08350241 1.07967639]\n", " [ 1.08350241 1.07967639]\n", " [ 1.08350241 1.07967639]\n", " [ 1.08350241 1.07967639]]\n", "Error: 0.5\n", "[[ 0.20595174 0.20595174 0.20595174 0.20595174]\n", " [ 0.12143584 0.12143584 0.12143584 0.12143584]] \n", " [[ 1.08377707 1.07994616]\n", " [ 1.08377707 1.07994616]\n", " [ 1.08377707 1.07994616]\n", " [ 1.08377707 1.07994616]]\n", "Error: 0.5\n", "[[ 0.20601903 0.20601903 0.20601903 0.20601903]\n", " [ 0.12148707 0.12148707 0.12148707 0.12148707]] \n", " [[ 1.08405137 1.08021557]\n", " [ 1.08405137 1.08021557]\n", " [ 1.08405137 1.08021557]\n", " [ 1.08405137 1.08021557]]\n", "Error: 0.5\n", "[[ 0.20608622 0.20608622 0.20608622 0.20608622]\n", " [ 0.12153825 0.12153825 0.12153825 0.12153825]] \n", " [[ 1.08432531 1.08048463]\n", " [ 1.08432531 1.08048463]\n", " [ 1.08432531 1.08048463]\n", " [ 1.08432531 1.08048463]]\n", "Error: 0.5\n", "[[ 0.20615332 0.20615332 0.20615332 0.20615332]\n", " [ 0.12158936 0.12158936 0.12158936 0.12158936]] \n", " [[ 1.0845989 1.08075333]\n", " [ 1.0845989 1.08075333]\n", " [ 1.0845989 1.08075333]\n", " [ 1.0845989 1.08075333]]\n", "Error: 0.5\n", "[[ 0.20622031 0.20622031 0.20622031 0.20622031]\n", " [ 0.12164041 0.12164041 0.12164041 0.12164041]] \n", " [[ 1.08487213 1.08102167]\n", " [ 1.08487213 1.08102167]\n", " [ 1.08487213 1.08102167]\n", " [ 1.08487213 1.08102167]]\n", "Error: 0.5\n", "[[ 0.20628722 0.20628722 0.20628722 0.20628722]\n", " [ 0.12169141 0.12169141 0.12169141 0.12169141]] \n", " [[ 1.085145 1.08128965]\n", " [ 1.085145 1.08128965]\n", " [ 1.085145 1.08128965]\n", " [ 1.085145 1.08128965]]\n", "Error: 0.5\n", "[[ 0.20635404 0.20635404 0.20635404 0.20635404]\n", " [ 0.12174234 0.12174234 0.12174234 0.12174234]] \n", " [[ 1.08541751 1.08155739]\n", " [ 1.08541751 1.08155739]\n", " [ 1.08541751 1.08155739]\n", " [ 1.08541751 1.08155739]]\n", "Error: 0.5\n", "[[ 0.20642075 0.20642075 0.20642075 0.20642075]\n", " [ 0.12179321 0.12179321 0.12179321 0.12179321]] \n", " [[ 1.08568978 1.08182478]\n", " [ 1.08568978 1.08182478]\n", " [ 1.08568978 1.08182478]\n", " [ 1.08568978 1.08182478]]\n", "Error: 0.5\n", "[[ 0.20648737 0.20648737 0.20648737 0.20648737]\n", " [ 0.12184402 0.12184402 0.12184402 0.12184402]] \n", " [[ 1.0859617 1.08209181]\n", " [ 1.0859617 1.08209181]\n", " [ 1.0859617 1.08209181]\n", " [ 1.0859617 1.08209181]]\n", "Error: 0.5\n", "[[ 0.20655389 0.20655389 0.20655389 0.20655389]\n", " [ 0.12189478 0.12189478 0.12189478 0.12189478]] \n", " [[ 1.08623326 1.08235848]\n", " [ 1.08623326 1.08235848]\n", " [ 1.08623326 1.08235848]\n", " [ 1.08623326 1.08235848]]\n", "Error: 0.5\n", "[[ 0.20662032 0.20662032 0.20662032 0.20662032]\n", " [ 0.12194547 0.12194547 0.12194547 0.12194547]] \n", " [[ 1.08650446 1.08262479]\n", " [ 1.08650446 1.08262479]\n", " [ 1.08650446 1.08262479]\n", " [ 1.08650446 1.08262479]]\n", "Error: 0.5\n", "[[ 0.20668666 0.20668666 0.20668666 0.20668666]\n", " [ 0.1219961 0.1219961 0.1219961 0.1219961 ]] \n", " [[ 1.0867753 1.08289075]\n", " [ 1.0867753 1.08289075]\n", " [ 1.0867753 1.08289075]\n", " [ 1.0867753 1.08289075]]\n", "Error: 0.5\n", "[[ 0.2067529 0.2067529 0.2067529 0.2067529 ]\n", " [ 0.12204668 0.12204668 0.12204668 0.12204668]] \n", " [[ 1.08704579 1.08315647]\n", " [ 1.08704579 1.08315647]\n", " [ 1.08704579 1.08315647]\n", " [ 1.08704579 1.08315647]]\n", "Error: 0.5\n", "[[ 0.20681904 0.20681904 0.20681904 0.20681904]\n", " [ 0.12209719 0.12209719 0.12209719 0.12209719]] \n", " [[ 1.08731592 1.08342183]\n", " [ 1.08731592 1.08342183]\n", " [ 1.08731592 1.08342183]\n", " [ 1.08731592 1.08342183]]\n", "Error: 0.5\n", "[[ 0.2068851 0.2068851 0.2068851 0.2068851 ]\n", " [ 0.12214765 0.12214765 0.12214765 0.12214765]] \n", " [[ 1.08758581 1.08368683]\n", " [ 1.08758581 1.08368683]\n", " [ 1.08758581 1.08368683]\n", " [ 1.08758581 1.08368683]]\n", "Error: 0.5\n", "[[ 0.20695105 0.20695105 0.20695105 0.20695105]\n", " [ 0.12219805 0.12219805 0.12219805 0.12219805]] \n", " [[ 1.08785534 1.08395147]\n", " [ 1.08785534 1.08395147]\n", " [ 1.08785534 1.08395147]\n", " [ 1.08785534 1.08395147]]\n", "Error: 0.5\n", "[[ 0.20701692 0.20701692 0.20701692 0.20701692]\n", " [ 0.12224838 0.12224838 0.12224838 0.12224838]] \n", " [[ 1.08812451 1.08421576]\n", " [ 1.08812451 1.08421576]\n", " [ 1.08812451 1.08421576]\n", " [ 1.08812451 1.08421576]]\n", "Error: 0.5\n", "[[ 0.20708269 0.20708269 0.20708269 0.20708269]\n", " [ 0.12229866 0.12229866 0.12229866 0.12229866]] \n", " [[ 1.08839333 1.08447981]\n", " [ 1.08839333 1.08447981]\n", " [ 1.08839333 1.08447981]\n", " [ 1.08839333 1.08447981]]\n", "Error: 0.5\n", "[[ 0.20714837 0.20714837 0.20714837 0.20714837]\n", " [ 0.12234887 0.12234887 0.12234887 0.12234887]] \n", " [[ 1.08866179 1.0847435 ]\n", " [ 1.08866179 1.0847435 ]\n", " [ 1.08866179 1.0847435 ]\n", " [ 1.08866179 1.0847435 ]]\n", "Error: 0.5\n", "[[ 0.20721395 0.20721395 0.20721395 0.20721395]\n", " [ 0.12239903 0.12239903 0.12239903 0.12239903]] \n", " [[ 1.08893001 1.08500683]\n", " [ 1.08893001 1.08500683]\n", " [ 1.08893001 1.08500683]\n", " [ 1.08893001 1.08500683]]\n", "Error: 0.5\n", "[[ 0.20727944 0.20727944 0.20727944 0.20727944]\n", " [ 0.12244913 0.12244913 0.12244913 0.12244913]] \n", " [[ 1.08919787 1.08526981]\n", " [ 1.08919787 1.08526981]\n", " [ 1.08919787 1.08526981]\n", " [ 1.08919787 1.08526981]]\n", "Error: 0.5\n", "[[ 0.20734484 0.20734484 0.20734484 0.20734484]\n", " [ 0.12249917 0.12249917 0.12249917 0.12249917]] \n", " [[ 1.08946538 1.08553255]\n", " [ 1.08946538 1.08553255]\n", " [ 1.08946538 1.08553255]\n", " [ 1.08946538 1.08553255]]\n", "Error: 0.5\n", "[[ 0.20741016 0.20741016 0.20741016 0.20741016]\n", " [ 0.12254915 0.12254915 0.12254915 0.12254915]] \n", " [[ 1.08973253 1.08579493]\n", " [ 1.08973253 1.08579493]\n", " [ 1.08973253 1.08579493]\n", " [ 1.08973253 1.08579493]]\n", "Error: 0.5\n", "[[ 0.20747538 0.20747538 0.20747538 0.20747538]\n", " [ 0.12259907 0.12259907 0.12259907 0.12259907]] \n", " [[ 1.08999932 1.08605695]\n", " [ 1.08999932 1.08605695]\n", " [ 1.08999932 1.08605695]\n", " [ 1.08999932 1.08605695]]\n", "Error: 0.5\n", "[[ 0.2075405 0.2075405 0.2075405 0.2075405 ]\n", " [ 0.12264894 0.12264894 0.12264894 0.12264894]] \n", " [[ 1.09026587 1.08631873]\n", " [ 1.09026587 1.08631873]\n", " [ 1.09026587 1.08631873]\n", " [ 1.09026587 1.08631873]]\n", "Error: 0.5\n", "[[ 0.20760553 0.20760553 0.20760553 0.20760553]\n", " [ 0.12269875 0.12269875 0.12269875 0.12269875]] \n", " [[ 1.09053206 1.08658016]\n", " [ 1.09053206 1.08658016]\n", " [ 1.09053206 1.08658016]\n", " [ 1.09053206 1.08658016]]\n", "Error: 0.5\n", "[[ 0.20767047 0.20767047 0.20767047 0.20767047]\n", " [ 0.12274849 0.12274849 0.12274849 0.12274849]] \n", " [[ 1.0907979 1.08684123]\n", " [ 1.0907979 1.08684123]\n", " [ 1.0907979 1.08684123]\n", " [ 1.0907979 1.08684123]]\n", "Error: 0.5\n", "[[ 0.20773531 0.20773531 0.20773531 0.20773531]\n", " [ 0.12279819 0.12279819 0.12279819 0.12279819]] \n", " [[ 1.09106338 1.08710194]\n", " [ 1.09106338 1.08710194]\n", " [ 1.09106338 1.08710194]\n", " [ 1.09106338 1.08710194]]\n", "Error: 0.5\n", "[[ 0.20780008 0.20780008 0.20780008 0.20780008]\n", " [ 0.12284783 0.12284783 0.12284783 0.12284783]] \n", " [[ 1.09132862 1.08736241]\n", " [ 1.09132862 1.08736241]\n", " [ 1.09132862 1.08736241]\n", " [ 1.09132862 1.08736241]]\n", "Error: 0.5\n", "[[ 0.20786475 0.20786475 0.20786475 0.20786475]\n", " [ 0.1228974 0.1228974 0.1228974 0.1228974 ]] \n", " [[ 1.0915935 1.08762252]\n", " [ 1.0915935 1.08762252]\n", " [ 1.0915935 1.08762252]\n", " [ 1.0915935 1.08762252]]\n", "Error: 0.5\n", "[[ 0.20792933 0.20792933 0.20792933 0.20792933]\n", " [ 0.12294692 0.12294692 0.12294692 0.12294692]] \n", " [[ 1.09185803 1.08788228]\n", " [ 1.09185803 1.08788228]\n", " [ 1.09185803 1.08788228]\n", " [ 1.09185803 1.08788228]]\n", "Error: 0.5\n", "[[ 0.20799382 0.20799382 0.20799382 0.20799382]\n", " [ 0.12299638 0.12299638 0.12299638 0.12299638]] \n", " [[ 1.09212232 1.0881418 ]\n", " [ 1.09212232 1.0881418 ]\n", " [ 1.09212232 1.0881418 ]\n", " [ 1.09212232 1.0881418 ]]\n", "Error: 0.5\n", "[[ 0.20805822 0.20805822 0.20805822 0.20805822]\n", " [ 0.12304579 0.12304579 0.12304579 0.12304579]] \n", " [[ 1.09238625 1.08840096]\n", " [ 1.09238625 1.08840096]\n", " [ 1.09238625 1.08840096]\n", " [ 1.09238625 1.08840096]]\n", "Error: 0.5\n", "[[ 0.20812254 0.20812254 0.20812254 0.20812254]\n", " [ 0.12309513 0.12309513 0.12309513 0.12309513]] \n", " [[ 1.09264982 1.08865976]\n", " [ 1.09264982 1.08865976]\n", " [ 1.09264982 1.08865976]\n", " [ 1.09264982 1.08865976]]\n", "Error: 0.5\n", "[[ 0.20818676 0.20818676 0.20818676 0.20818676]\n", " [ 0.12314443 0.12314443 0.12314443 0.12314443]] \n", " [[ 1.09291303 1.08891833]\n", " [ 1.09291303 1.08891833]\n", " [ 1.09291303 1.08891833]\n", " [ 1.09291303 1.08891833]]\n", "Error: 0.5\n", "[[ 0.2082509 0.2082509 0.2082509 0.2082509 ]\n", " [ 0.12319366 0.12319366 0.12319366 0.12319366]] \n", " [[ 1.09317601 1.08917654]\n", " [ 1.09317601 1.08917654]\n", " [ 1.09317601 1.08917654]\n", " [ 1.09317601 1.08917654]]\n", "Error: 0.5\n", "[[ 0.20831494 0.20831494 0.20831494 0.20831494]\n", " [ 0.12324284 0.12324284 0.12324284 0.12324284]] \n", " [[ 1.09343863 1.08943439]\n", " [ 1.09343863 1.08943439]\n", " [ 1.09343863 1.08943439]\n", " [ 1.09343863 1.08943439]]\n", "Error: 0.5\n", "[[ 0.2083789 0.2083789 0.2083789 0.2083789 ]\n", " [ 0.12329196 0.12329196 0.12329196 0.12329196]] \n", " [[ 1.09370089 1.089692 ]\n", " [ 1.09370089 1.089692 ]\n", " [ 1.09370089 1.089692 ]\n", " [ 1.09370089 1.089692 ]]\n", "Error: 0.5\n", "[[ 0.20844276 0.20844276 0.20844276 0.20844276]\n", " [ 0.12334102 0.12334102 0.12334102 0.12334102]] \n", " [[ 1.09396291 1.08994925]\n", " [ 1.09396291 1.08994925]\n", " [ 1.09396291 1.08994925]\n", " [ 1.09396291 1.08994925]]\n", "Error: 0.5\n", "[[ 0.20850654 0.20850654 0.20850654 0.20850654]\n", " [ 0.12339003 0.12339003 0.12339003 0.12339003]] \n", " [[ 1.09422457 1.09020615]\n", " [ 1.09422457 1.09020615]\n", " [ 1.09422457 1.09020615]\n", " [ 1.09422457 1.09020615]]\n", "Error: 0.5\n", "[[ 0.20857023 0.20857023 0.20857023 0.20857023]\n", " [ 0.12343898 0.12343898 0.12343898 0.12343898]] \n", " [[ 1.09448588 1.0904628 ]\n", " [ 1.09448588 1.0904628 ]\n", " [ 1.09448588 1.0904628 ]\n", " [ 1.09448588 1.0904628 ]]\n", "Error: 0.5\n", "[[ 0.20863383 0.20863383 0.20863383 0.20863383]\n", " [ 0.12348788 0.12348788 0.12348788 0.12348788]] \n", " [[ 1.09474695 1.0907191 ]\n", " [ 1.09474695 1.0907191 ]\n", " [ 1.09474695 1.0907191 ]\n", " [ 1.09474695 1.0907191 ]]\n", "Error: 0.5\n", "[[ 0.20869733 0.20869733 0.20869733 0.20869733]\n", " [ 0.12353672 0.12353672 0.12353672 0.12353672]] \n", " [[ 1.09500766 1.09097517]\n", " [ 1.09500766 1.09097517]\n", " [ 1.09500766 1.09097517]\n", " [ 1.09500766 1.09097517]]\n", "Error: 0.5\n", "[[ 0.20876075 0.20876075 0.20876075 0.20876075]\n", " [ 0.12358551 0.12358551 0.12358551 0.12358551]] \n", " [[ 1.09526801 1.09123087]\n", " [ 1.09526801 1.09123087]\n", " [ 1.09526801 1.09123087]\n", " [ 1.09526801 1.09123087]]\n", "Error: 0.5\n", "[[ 0.20882408 0.20882408 0.20882408 0.20882408]\n", " [ 0.12363423 0.12363423 0.12363423 0.12363423]] \n", " [[ 1.09552813 1.09148622]\n", " [ 1.09552813 1.09148622]\n", " [ 1.09552813 1.09148622]\n", " [ 1.09552813 1.09148622]]\n", "Error: 0.5\n", "[[ 0.20888734 0.20888734 0.20888734 0.20888734]\n", " [ 0.12368291 0.12368291 0.12368291 0.12368291]] \n", " [[ 1.09578788 1.09174132]\n", " [ 1.09578788 1.09174132]\n", " [ 1.09578788 1.09174132]\n", " [ 1.09578788 1.09174132]]\n", "Error: 0.5\n", "[[ 0.2089505 0.2089505 0.2089505 0.2089505 ]\n", " [ 0.12373153 0.12373153 0.12373153 0.12373153]] \n", " [[ 1.0960474 1.09199607]\n", " [ 1.0960474 1.09199607]\n", " [ 1.0960474 1.09199607]\n", " [ 1.0960474 1.09199607]]\n", "Error: 0.5\n", "[[ 0.20901358 0.20901358 0.20901358 0.20901358]\n", " [ 0.12378009 0.12378009 0.12378009 0.12378009]] \n", " [[ 1.09630656 1.09225059]\n", " [ 1.09630656 1.09225059]\n", " [ 1.09630656 1.09225059]\n", " [ 1.09630656 1.09225059]]\n", "Error: 0.5\n", "[[ 0.20907657 0.20907657 0.20907657 0.20907657]\n", " [ 0.1238286 0.1238286 0.1238286 0.1238286 ]] \n", " [[ 1.09656537 1.09250474]\n", " [ 1.09656537 1.09250474]\n", " [ 1.09656537 1.09250474]\n", " [ 1.09656537 1.09250474]]\n", "Error: 0.5\n", "[[ 0.20913947 0.20913947 0.20913947 0.20913947]\n", " [ 0.12387706 0.12387706 0.12387706 0.12387706]] \n", " [[ 1.09682393 1.09275866]\n", " [ 1.09682393 1.09275866]\n", " [ 1.09682393 1.09275866]\n", " [ 1.09682393 1.09275866]]\n", "Error: 0.5\n", "[[ 0.20920229 0.20920229 0.20920229 0.20920229]\n", " [ 0.12392545 0.12392545 0.12392545 0.12392545]] \n", " [[ 1.09708214 1.09301221]\n", " [ 1.09708214 1.09301221]\n", " [ 1.09708214 1.09301221]\n", " [ 1.09708214 1.09301221]]\n", "Error: 0.5\n", "[[ 0.20926502 0.20926502 0.20926502 0.20926502]\n", " [ 0.1239738 0.1239738 0.1239738 0.1239738 ]] \n", " [[ 1.09734011 1.09326541]\n", " [ 1.09734011 1.09326541]\n", " [ 1.09734011 1.09326541]\n", " [ 1.09734011 1.09326541]]\n", "Error: 0.5\n", "[[ 0.20932767 0.20932767 0.20932767 0.20932767]\n", " [ 0.12402209 0.12402209 0.12402209 0.12402209]] \n", " [[ 1.09759772 1.09351838]\n", " [ 1.09759772 1.09351838]\n", " [ 1.09759772 1.09351838]\n", " [ 1.09759772 1.09351838]]\n", "Error: 0.5\n", "[[ 0.20939022 0.20939022 0.20939022 0.20939022]\n", " [ 0.12407032 0.12407032 0.12407032 0.12407032]] \n", " [[ 1.09785497 1.09377098]\n", " [ 1.09785497 1.09377098]\n", " [ 1.09785497 1.09377098]\n", " [ 1.09785497 1.09377098]]\n", "Error: 0.5\n", "[[ 0.2094527 0.2094527 0.2094527 0.2094527 ]\n", " [ 0.12411851 0.12411851 0.12411851 0.12411851]] \n", " [[ 1.09811199 1.09402335]\n", " [ 1.09811199 1.09402335]\n", " [ 1.09811199 1.09402335]\n", " [ 1.09811199 1.09402335]]\n", "Error: 0.5\n", "[[ 0.20951509 0.20951509 0.20951509 0.20951509]\n", " [ 0.12416663 0.12416663 0.12416663 0.12416663]] \n", " [[ 1.09836864 1.09427536]\n", " [ 1.09836864 1.09427536]\n", " [ 1.09836864 1.09427536]\n", " [ 1.09836864 1.09427536]]\n", "Error: 0.5\n", "[[ 0.2095774 0.2095774 0.2095774 0.2095774]\n", " [ 0.1242147 0.1242147 0.1242147 0.1242147]] \n", " [[ 1.09862506 1.09452713]\n", " [ 1.09862506 1.09452713]\n", " [ 1.09862506 1.09452713]\n", " [ 1.09862506 1.09452713]]\n", "Error: 0.5\n", "[[ 0.20963962 0.20963962 0.20963962 0.20963962]\n", " [ 0.12426272 0.12426272 0.12426272 0.12426272]] \n", " [[ 1.09888113 1.09477854]\n", " [ 1.09888113 1.09477854]\n", " [ 1.09888113 1.09477854]\n", " [ 1.09888113 1.09477854]]\n", "Error: 0.5\n", "[[ 0.20970176 0.20970176 0.20970176 0.20970176]\n", " [ 0.12431069 0.12431069 0.12431069 0.12431069]] \n", " [[ 1.09913695 1.09502971]\n", " [ 1.09913695 1.09502971]\n", " [ 1.09913695 1.09502971]\n", " [ 1.09913695 1.09502971]]\n", "Error: 0.5\n", "[[ 0.20976381 0.20976381 0.20976381 0.20976381]\n", " [ 0.12435859 0.12435859 0.12435859 0.12435859]] \n", " [[ 1.09939241 1.09528053]\n", " [ 1.09939241 1.09528053]\n", " [ 1.09939241 1.09528053]\n", " [ 1.09939241 1.09528053]]\n", "Error: 0.5\n", "[[ 0.20982578 0.20982578 0.20982578 0.20982578]\n", " [ 0.12440645 0.12440645 0.12440645 0.12440645]] \n", " [[ 1.09964764 1.09553111]\n", " [ 1.09964764 1.09553111]\n", " [ 1.09964764 1.09553111]\n", " [ 1.09964764 1.09553111]]\n", "Error: 0.5\n", "[[ 0.20988767 0.20988767 0.20988767 0.20988767]\n", " [ 0.12445425 0.12445425 0.12445425 0.12445425]] \n", " [[ 1.09990251 1.09578133]\n", " [ 1.09990251 1.09578133]\n", " [ 1.09990251 1.09578133]\n", " [ 1.09990251 1.09578133]]\n", "Error: 0.5\n", "[[ 0.20994946 0.20994946 0.20994946 0.20994946]\n", " [ 0.124502 0.124502 0.124502 0.124502 ]] \n", " [[ 1.10015702 1.09603131]\n", " [ 1.10015702 1.09603131]\n", " [ 1.10015702 1.09603131]\n", " [ 1.10015702 1.09603131]]\n", "Error: 0.5\n", "[[ 0.21001118 0.21001118 0.21001118 0.21001118]\n", " [ 0.1245497 0.1245497 0.1245497 0.1245497 ]] \n", " [[ 1.1004113 1.09628093]\n", " [ 1.1004113 1.09628093]\n", " [ 1.1004113 1.09628093]\n", " [ 1.1004113 1.09628093]]\n", "Error: 0.5\n", "[[ 0.21007282 0.21007282 0.21007282 0.21007282]\n", " [ 0.12459734 0.12459734 0.12459734 0.12459734]] \n", " [[ 1.10066521 1.09653032]\n", " [ 1.10066521 1.09653032]\n", " [ 1.10066521 1.09653032]\n", " [ 1.10066521 1.09653032]]\n", "Error: 0.5\n", "[[ 0.21013437 0.21013437 0.21013437 0.21013437]\n", " [ 0.12464493 0.12464493 0.12464493 0.12464493]] \n", " [[ 1.10091889 1.09677935]\n", " [ 1.10091889 1.09677935]\n", " [ 1.10091889 1.09677935]\n", " [ 1.10091889 1.09677935]]\n", "Error: 0.5\n", "[[ 0.21019584 0.21019584 0.21019584 0.21019584]\n", " [ 0.12469246 0.12469246 0.12469246 0.12469246]] \n", " [[ 1.10117221 1.09702814]\n", " [ 1.10117221 1.09702814]\n", " [ 1.10117221 1.09702814]\n", " [ 1.10117221 1.09702814]]\n", "Error: 0.5\n", "[[ 0.21025723 0.21025723 0.21025723 0.21025723]\n", " [ 0.12473994 0.12473994 0.12473994 0.12473994]] \n", " [[ 1.10142529 1.09727657]\n", " [ 1.10142529 1.09727657]\n", " [ 1.10142529 1.09727657]\n", " [ 1.10142529 1.09727657]]\n", "Error: 0.5\n", "[[ 0.21031854 0.21031854 0.21031854 0.21031854]\n", " [ 0.12478738 0.12478738 0.12478738 0.12478738]] \n", " [[ 1.10167801 1.09752476]\n", " [ 1.10167801 1.09752476]\n", " [ 1.10167801 1.09752476]\n", " [ 1.10167801 1.09752476]]\n", "Error: 0.5\n", "[[ 0.21037975 0.21037975 0.21037975 0.21037975]\n", " [ 0.12483475 0.12483475 0.12483475 0.12483475]] \n", " [[ 1.1019305 1.0977726]\n", " [ 1.1019305 1.0977726]\n", " [ 1.1019305 1.0977726]\n", " [ 1.1019305 1.0977726]]\n", "Error: 0.5\n", "[[ 0.21044089 0.21044089 0.21044089 0.21044089]\n", " [ 0.12488208 0.12488208 0.12488208 0.12488208]] \n", " [[ 1.10218263 1.0980202 ]\n", " [ 1.10218263 1.0980202 ]\n", " [ 1.10218263 1.0980202 ]\n", " [ 1.10218263 1.0980202 ]]\n", "Error: 0.5\n", "[[ 0.21050194 0.21050194 0.21050194 0.21050194]\n", " [ 0.12492935 0.12492935 0.12492935 0.12492935]] \n", " [[ 1.10243452 1.09826756]\n", " [ 1.10243452 1.09826756]\n", " [ 1.10243452 1.09826756]\n", " [ 1.10243452 1.09826756]]\n", "Error: 0.5\n", "[[ 0.21056291 0.21056291 0.21056291 0.21056291]\n", " [ 0.12497658 0.12497658 0.12497658 0.12497658]] \n", " [[ 1.10268605 1.09851456]\n", " [ 1.10268605 1.09851456]\n", " [ 1.10268605 1.09851456]\n", " [ 1.10268605 1.09851456]]\n", "Error: 0.5\n", "[[ 0.21062382 0.21062382 0.21062382 0.21062382]\n", " [ 0.12502374 0.12502374 0.12502374 0.12502374]] \n", " [[ 1.10293734 1.09876132]\n", " [ 1.10293734 1.09876132]\n", " [ 1.10293734 1.09876132]\n", " [ 1.10293734 1.09876132]]\n", "Error: 0.5\n", "[[ 0.21068463 0.21068463 0.21068463 0.21068463]\n", " [ 0.12507086 0.12507086 0.12507086 0.12507086]] \n", " [[ 1.1031884 1.09900773]\n", " [ 1.1031884 1.09900773]\n", " [ 1.1031884 1.09900773]\n", " [ 1.1031884 1.09900773]]\n", "Error: 0.5\n", "[[ 0.21074536 0.21074536 0.21074536 0.21074536]\n", " [ 0.12511791 0.12511791 0.12511791 0.12511791]] \n", " [[ 1.10343909 1.09925389]\n", " [ 1.10343909 1.09925389]\n", " [ 1.10343909 1.09925389]\n", " [ 1.10343909 1.09925389]]\n", "Error: 0.5\n", "[[ 0.21080601 0.21080601 0.21080601 0.21080601]\n", " [ 0.12516493 0.12516493 0.12516493 0.12516493]] \n", " [[ 1.10368955 1.0994997 ]\n", " [ 1.10368955 1.0994997 ]\n", " [ 1.10368955 1.0994997 ]\n", " [ 1.10368955 1.0994997 ]]\n", "Error: 0.5\n", "[[ 0.21086659 0.21086659 0.21086659 0.21086659]\n", " [ 0.12521188 0.12521188 0.12521188 0.12521188]] \n", " [[ 1.10393965 1.09974527]\n", " [ 1.10393965 1.09974527]\n", " [ 1.10393965 1.09974527]\n", " [ 1.10393965 1.09974527]]\n", "Error: 0.5\n", "[[ 0.21092707 0.21092707 0.21092707 0.21092707]\n", " [ 0.12525879 0.12525879 0.12525879 0.12525879]] \n", " [[ 1.10418952 1.09999061]\n", " [ 1.10418952 1.09999061]\n", " [ 1.10418952 1.09999061]\n", " [ 1.10418952 1.09999061]]\n", "Error: 0.5\n", "[[ 0.21098748 0.21098748 0.21098748 0.21098748]\n", " [ 0.12530565 0.12530565 0.12530565 0.12530565]] \n", " [[ 1.10443902 1.10023558]\n", " [ 1.10443902 1.10023558]\n", " [ 1.10443902 1.10023558]\n", " [ 1.10443902 1.10023558]]\n", "Error: 0.5\n", "[[ 0.21104781 0.21104781 0.21104781 0.21104781]\n", " [ 0.12535246 0.12535246 0.12535246 0.12535246]] \n", " [[ 1.10468829 1.10048032]\n", " [ 1.10468829 1.10048032]\n", " [ 1.10468829 1.10048032]\n", " [ 1.10468829 1.10048032]]\n", "Error: 0.5\n", "[[ 0.21110806 0.21110806 0.21110806 0.21110806]\n", " [ 0.12539922 0.12539922 0.12539922 0.12539922]] \n", " [[ 1.10493731 1.1007247 ]\n", " [ 1.10493731 1.1007247 ]\n", " [ 1.10493731 1.1007247 ]\n", " [ 1.10493731 1.1007247 ]]\n", "Error: 0.5\n", "[[ 0.21116823 0.21116823 0.21116823 0.21116823]\n", " [ 0.12544592 0.12544592 0.12544592 0.12544592]] \n", " [[ 1.10518599 1.10096884]\n", " [ 1.10518599 1.10096884]\n", " [ 1.10518599 1.10096884]\n", " [ 1.10518599 1.10096884]]\n", "Error: 0.5\n", "[[ 0.21122833 0.21122833 0.21122833 0.21122833]\n", " [ 0.12549257 0.12549257 0.12549257 0.12549257]] \n", " [[ 1.10543442 1.10121274]\n", " [ 1.10543442 1.10121274]\n", " [ 1.10543442 1.10121274]\n", " [ 1.10543442 1.10121274]]\n", "Error: 0.5\n", "[[ 0.21128833 0.21128833 0.21128833 0.21128833]\n", " [ 0.12553917 0.12553917 0.12553917 0.12553917]] \n", " [[ 1.10568249 1.10145628]\n", " [ 1.10568249 1.10145628]\n", " [ 1.10568249 1.10145628]\n", " [ 1.10568249 1.10145628]]\n", "Error: 0.5\n", "[[ 0.21134827 0.21134827 0.21134827 0.21134827]\n", " [ 0.12558572 0.12558572 0.12558572 0.12558572]] \n", " [[ 1.10593033 1.10169959]\n", " [ 1.10593033 1.10169959]\n", " [ 1.10593033 1.10169959]\n", " [ 1.10593033 1.10169959]]\n", "Error: 0.5\n", "[[ 0.21140812 0.21140812 0.21140812 0.21140812]\n", " [ 0.12563221 0.12563221 0.12563221 0.12563221]] \n", " [[ 1.10617781 1.10194266]\n", " [ 1.10617781 1.10194266]\n", " [ 1.10617781 1.10194266]\n", " [ 1.10617781 1.10194266]]\n", "Error: 0.5\n", "[[ 0.21146789 0.21146789 0.21146789 0.21146789]\n", " [ 0.12567866 0.12567866 0.12567866 0.12567866]] \n", " [[ 1.10642505 1.10218537]\n", " [ 1.10642505 1.10218537]\n", " [ 1.10642505 1.10218537]\n", " [ 1.10642505 1.10218537]]\n", "Error: 0.5\n", "[[ 0.21152759 0.21152759 0.21152759 0.21152759]\n", " [ 0.12572506 0.12572506 0.12572506 0.12572506]] \n", " [[ 1.10667205 1.10242784]\n", " [ 1.10667205 1.10242784]\n", " [ 1.10667205 1.10242784]\n", " [ 1.10667205 1.10242784]]\n", "Error: 0.5\n", "[[ 0.21158721 0.21158721 0.21158721 0.21158721]\n", " [ 0.1257714 0.1257714 0.1257714 0.1257714 ]] \n", " [[ 1.10691869 1.10267007]\n", " [ 1.10691869 1.10267007]\n", " [ 1.10691869 1.10267007]\n", " [ 1.10691869 1.10267007]]\n", "Error: 0.5\n", "[[ 0.21164674 0.21164674 0.21164674 0.21164674]\n", " [ 0.1258177 0.1258177 0.1258177 0.1258177 ]] \n", " [[ 1.1071651 1.10291195]\n", " [ 1.1071651 1.10291195]\n", " [ 1.1071651 1.10291195]\n", " [ 1.1071651 1.10291195]]\n", "Error: 0.5\n", "[[ 0.21170619 0.21170619 0.21170619 0.21170619]\n", " [ 0.12586395 0.12586395 0.12586395 0.12586395]] \n", " [[ 1.10741127 1.10315359]\n", " [ 1.10741127 1.10315359]\n", " [ 1.10741127 1.10315359]\n", " [ 1.10741127 1.10315359]]\n", "Error: 0.5\n", "[[ 0.21176557 0.21176557 0.21176557 0.21176557]\n", " [ 0.12591015 0.12591015 0.12591015 0.12591015]] \n", " [[ 1.10765707 1.10339499]\n", " [ 1.10765707 1.10339499]\n", " [ 1.10765707 1.10339499]\n", " [ 1.10765707 1.10339499]]\n", "Error: 0.5\n", "[[ 0.21182488 0.21182488 0.21182488 0.21182488]\n", " [ 0.1259563 0.1259563 0.1259563 0.1259563 ]] \n", " [[ 1.10790265 1.10363603]\n", " [ 1.10790265 1.10363603]\n", " [ 1.10790265 1.10363603]\n", " [ 1.10790265 1.10363603]]\n", "Error: 0.5\n", "[[ 0.21188411 0.21188411 0.21188411 0.21188411]\n", " [ 0.12600239 0.12600239 0.12600239 0.12600239]] \n", " [[ 1.10814798 1.10387683]\n", " [ 1.10814798 1.10387683]\n", " [ 1.10814798 1.10387683]\n", " [ 1.10814798 1.10387683]]\n", "Error: 0.5\n", "[[ 0.21194325 0.21194325 0.21194325 0.21194325]\n", " [ 0.12604843 0.12604843 0.12604843 0.12604843]] \n", " [[ 1.10839295 1.10411739]\n", " [ 1.10839295 1.10411739]\n", " [ 1.10839295 1.10411739]\n", " [ 1.10839295 1.10411739]]\n", "Error: 0.5\n", "[[ 0.21200232 0.21200232 0.21200232 0.21200232]\n", " [ 0.12609443 0.12609443 0.12609443 0.12609443]] \n", " [[ 1.10863769 1.1043576 ]\n", " [ 1.10863769 1.1043576 ]\n", " [ 1.10863769 1.1043576 ]\n", " [ 1.10863769 1.1043576 ]]\n", "Error: 0.5\n", "[[ 0.21206132 0.21206132 0.21206132 0.21206132]\n", " [ 0.12614037 0.12614037 0.12614037 0.12614037]] \n", " [[ 1.10888219 1.10459757]\n", " [ 1.10888219 1.10459757]\n", " [ 1.10888219 1.10459757]\n", " [ 1.10888219 1.10459757]]\n", "Error: 0.5\n", "[[ 0.21212023 0.21212023 0.21212023 0.21212023]\n", " [ 0.12618627 0.12618627 0.12618627 0.12618627]] \n", " [[ 1.10912633 1.1048373 ]\n", " [ 1.10912633 1.1048373 ]\n", " [ 1.10912633 1.1048373 ]\n", " [ 1.10912633 1.1048373 ]]\n", "Error: 0.5\n", "[[ 0.21217908 0.21217908 0.21217908 0.21217908]\n", " [ 0.12623212 0.12623212 0.12623212 0.12623212]] \n", " [[ 1.10937023 1.10507667]\n", " [ 1.10937023 1.10507667]\n", " [ 1.10937023 1.10507667]\n", " [ 1.10937023 1.10507667]]\n", "Error: 0.5\n", "[[ 0.21223785 0.21223785 0.21223785 0.21223785]\n", " [ 0.12627791 0.12627791 0.12627791 0.12627791]] \n", " [[ 1.1096139 1.1053158]\n", " [ 1.1096139 1.1053158]\n", " [ 1.1096139 1.1053158]\n", " [ 1.1096139 1.1053158]]\n", "Error: 0.5\n", "[[ 0.21229653 0.21229653 0.21229653 0.21229653]\n", " [ 0.12632366 0.12632366 0.12632366 0.12632366]] \n", " [[ 1.1098572 1.1055547]\n", " [ 1.1098572 1.1055547]\n", " [ 1.1098572 1.1055547]\n", " [ 1.1098572 1.1055547]]\n", "Error: 0.5\n", "[[ 0.21235514 0.21235514 0.21235514 0.21235514]\n", " [ 0.12636936 0.12636936 0.12636936 0.12636936]] \n", " [[ 1.11010027 1.10579336]\n", " [ 1.11010027 1.10579336]\n", " [ 1.11010027 1.10579336]\n", " [ 1.11010027 1.10579336]]\n", "Error: 0.5\n", "[[ 0.21241367 0.21241367 0.21241367 0.21241367]\n", " [ 0.12641501 0.12641501 0.12641501 0.12641501]] \n", " [[ 1.1103431 1.10603166]\n", " [ 1.1103431 1.10603166]\n", " [ 1.1103431 1.10603166]\n", " [ 1.1103431 1.10603166]]\n", "Error: 0.5\n", "[[ 0.21247213 0.21247213 0.21247213 0.21247213]\n", " [ 0.12646061 0.12646061 0.12646061 0.12646061]] \n", " [[ 1.11058557 1.10626972]\n", " [ 1.11058557 1.10626972]\n", " [ 1.11058557 1.10626972]\n", " [ 1.11058557 1.10626972]]\n", "Error: 0.5\n", "[[ 0.21253051 0.21253051 0.21253051 0.21253051]\n", " [ 0.12650616 0.12650616 0.12650616 0.12650616]] \n", " [[ 1.1108278 1.10650754]\n", " [ 1.1108278 1.10650754]\n", " [ 1.1108278 1.10650754]\n", " [ 1.1108278 1.10650754]]\n", "Error: 0.5\n", "[[ 0.21258882 0.21258882 0.21258882 0.21258882]\n", " [ 0.12655167 0.12655167 0.12655167 0.12655167]] \n", " [[ 1.1110698 1.10674512]\n", " [ 1.1110698 1.10674512]\n", " [ 1.1110698 1.10674512]\n", " [ 1.1110698 1.10674512]]\n", "Error: 0.5\n", "[[ 0.21264705 0.21264705 0.21264705 0.21264705]\n", " [ 0.12659712 0.12659712 0.12659712 0.12659712]] \n", " [[ 1.11131144 1.10698235]\n", " [ 1.11131144 1.10698235]\n", " [ 1.11131144 1.10698235]\n", " [ 1.11131144 1.10698235]]\n", "Error: 0.5\n", "[[ 0.21270521 0.21270521 0.21270521 0.21270521]\n", " [ 0.12664253 0.12664253 0.12664253 0.12664253]] \n", " [[ 1.11155283 1.10721934]\n", " [ 1.11155283 1.10721934]\n", " [ 1.11155283 1.10721934]\n", " [ 1.11155283 1.10721934]]\n", "Error: 0.5\n", "[[ 0.21276329 0.21276329 0.21276329 0.21276329]\n", " [ 0.12668788 0.12668788 0.12668788 0.12668788]] \n", " [[ 1.11179399 1.10745609]\n", " [ 1.11179399 1.10745609]\n", " [ 1.11179399 1.10745609]\n", " [ 1.11179399 1.10745609]]\n", "Error: 0.5\n", "[[ 0.2128213 0.2128213 0.2128213 0.2128213]\n", " [ 0.1267332 0.1267332 0.1267332 0.1267332]] \n", " [[ 1.11203492 1.1076926 ]\n", " [ 1.11203492 1.1076926 ]\n", " [ 1.11203492 1.1076926 ]\n", " [ 1.11203492 1.1076926 ]]\n", "Error: 0.5\n", "[[ 0.21287924 0.21287924 0.21287924 0.21287924]\n", " [ 0.12677845 0.12677845 0.12677845 0.12677845]] \n", " [[ 1.11227548 1.10792875]\n", " [ 1.11227548 1.10792875]\n", " [ 1.11227548 1.10792875]\n", " [ 1.11227548 1.10792875]]\n", "Error: 0.5\n", "[[ 0.2129371 0.2129371 0.2129371 0.2129371 ]\n", " [ 0.12682366 0.12682366 0.12682366 0.12682366]] \n", " [[ 1.11251581 1.10816467]\n", " [ 1.11251581 1.10816467]\n", " [ 1.11251581 1.10816467]\n", " [ 1.11251581 1.10816467]]\n", "Error: 0.5\n", "[[ 0.21299489 0.21299489 0.21299489 0.21299489]\n", " [ 0.12686883 0.12686883 0.12686883 0.12686883]] \n", " [[ 1.11275589 1.10840034]\n", " [ 1.11275589 1.10840034]\n", " [ 1.11275589 1.10840034]\n", " [ 1.11275589 1.10840034]]\n", "Error: 0.5\n", "[[ 0.2130526 0.2130526 0.2130526 0.2130526 ]\n", " [ 0.12691395 0.12691395 0.12691395 0.12691395]] \n", " [[ 1.11299574 1.10863578]\n", " [ 1.11299574 1.10863578]\n", " [ 1.11299574 1.10863578]\n", " [ 1.11299574 1.10863578]]\n", "Error: 0.5\n", "[[ 0.21311024 0.21311024 0.21311024 0.21311024]\n", " [ 0.12695903 0.12695903 0.12695903 0.12695903]] \n", " [[ 1.11323524 1.10887098]\n", " [ 1.11323524 1.10887098]\n", " [ 1.11323524 1.10887098]\n", " [ 1.11323524 1.10887098]]\n", "Error: 0.5\n", "[[ 0.2131678 0.2131678 0.2131678 0.2131678 ]\n", " [ 0.12700404 0.12700404 0.12700404 0.12700404]] \n", " [[ 1.11347449 1.10910583]\n", " [ 1.11347449 1.10910583]\n", " [ 1.11347449 1.10910583]\n", " [ 1.11347449 1.10910583]]\n", "Error: 0.5\n", "[[ 0.21322529 0.21322529 0.21322529 0.21322529]\n", " [ 0.12704901 0.12704901 0.12704901 0.12704901]] \n", " [[ 1.1137135 1.10934043]\n", " [ 1.1137135 1.10934043]\n", " [ 1.1137135 1.10934043]\n", " [ 1.1137135 1.10934043]]\n", "Error: 0.5\n", "[[ 0.2132827 0.2132827 0.2132827 0.2132827 ]\n", " [ 0.12709394 0.12709394 0.12709394 0.12709394]] \n", " [[ 1.11395228 1.10957479]\n", " [ 1.11395228 1.10957479]\n", " [ 1.11395228 1.10957479]\n", " [ 1.11395228 1.10957479]]\n", "Error: 0.5\n", "[[ 0.21334004 0.21334004 0.21334004 0.21334004]\n", " [ 0.12713882 0.12713882 0.12713882 0.12713882]] \n", " [[ 1.1141907 1.10980892]\n", " [ 1.1141907 1.10980892]\n", " [ 1.1141907 1.10980892]\n", " [ 1.1141907 1.10980892]]\n", "Error: 0.5\n", "[[ 0.21339731 0.21339731 0.21339731 0.21339731]\n", " [ 0.12718366 0.12718366 0.12718366 0.12718366]] \n", " [[ 1.11442888 1.11004281]\n", " [ 1.11442888 1.11004281]\n", " [ 1.11442888 1.11004281]\n", " [ 1.11442888 1.11004281]]\n", "Error: 0.5\n", "[[ 0.2134545 0.2134545 0.2134545 0.2134545 ]\n", " [ 0.12722844 0.12722844 0.12722844 0.12722844]] \n", " [[ 1.11466682 1.11027634]\n", " [ 1.11466682 1.11027634]\n", " [ 1.11466682 1.11027634]\n", " [ 1.11466682 1.11027634]]\n", "Error: 0.5\n", "[[ 0.21351162 0.21351162 0.21351162 0.21351162]\n", " [ 0.12727317 0.12727317 0.12727317 0.12727317]] \n", " [[ 1.11490452 1.11050963]\n", " [ 1.11490452 1.11050963]\n", " [ 1.11490452 1.11050963]\n", " [ 1.11490452 1.11050963]]\n", "Error: 0.5\n", "[[ 0.21356867 0.21356867 0.21356867 0.21356867]\n", " [ 0.12731786 0.12731786 0.12731786 0.12731786]] \n", " [[ 1.11514199 1.11074269]\n", " [ 1.11514199 1.11074269]\n", " [ 1.11514199 1.11074269]\n", " [ 1.11514199 1.11074269]]\n", "Error: 0.5\n", "[[ 0.21362565 0.21362565 0.21362565 0.21362565]\n", " [ 0.1273625 0.1273625 0.1273625 0.1273625 ]] \n", " [[ 1.1153791 1.1109755]\n", " [ 1.1153791 1.1109755]\n", " [ 1.1153791 1.1109755]\n", " [ 1.1153791 1.1109755]]\n", "Error: 0.5\n", "[[ 0.21368256 0.21368256 0.21368256 0.21368256]\n", " [ 0.1274071 0.1274071 0.1274071 0.1274071 ]] \n", " [[ 1.11561596 1.11120808]\n", " [ 1.11561596 1.11120808]\n", " [ 1.11561596 1.11120808]\n", " [ 1.11561596 1.11120808]]\n", "Error: 0.5\n", "[[ 0.2137394 0.2137394 0.2137394 0.2137394 ]\n", " [ 0.12745166 0.12745166 0.12745166 0.12745166]] \n", " [[ 1.11585259 1.11144042]\n", " [ 1.11585259 1.11144042]\n", " [ 1.11585259 1.11144042]\n", " [ 1.11585259 1.11144042]]\n", "Error: 0.5\n", "[[ 0.21379615 0.21379615 0.21379615 0.21379615]\n", " [ 0.12749617 0.12749617 0.12749617 0.12749617]] \n", " [[ 1.11608899 1.1116724 ]\n", " [ 1.11608899 1.1116724 ]\n", " [ 1.11608899 1.1116724 ]\n", " [ 1.11608899 1.1116724 ]]\n", "Error: 0.5\n", "[[ 0.21385284 0.21385284 0.21385284 0.21385284]\n", " [ 0.12754062 0.12754062 0.12754062 0.12754062]] \n", " [[ 1.11632514 1.11190414]\n", " [ 1.11632514 1.11190414]\n", " [ 1.11632514 1.11190414]\n", " [ 1.11632514 1.11190414]]\n", "Error: 0.5\n", "[[ 0.21390946 0.21390946 0.21390946 0.21390946]\n", " [ 0.12758502 0.12758502 0.12758502 0.12758502]] \n", " [[ 1.11656094 1.11213565]\n", " [ 1.11656094 1.11213565]\n", " [ 1.11656094 1.11213565]\n", " [ 1.11656094 1.11213565]]\n", "Error: 0.5\n", "[[ 0.21396601 0.21396601 0.21396601 0.21396601]\n", " [ 0.12762938 0.12762938 0.12762938 0.12762938]] \n", " [[ 1.11679649 1.11236691]\n", " [ 1.11679649 1.11236691]\n", " [ 1.11679649 1.11236691]\n", " [ 1.11679649 1.11236691]]\n", "Error: 0.5\n", "[[ 0.21402249 0.21402249 0.21402249 0.21402249]\n", " [ 0.1276737 0.1276737 0.1276737 0.1276737 ]] \n", " [[ 1.11703181 1.11259794]\n", " [ 1.11703181 1.11259794]\n", " [ 1.11703181 1.11259794]\n", " [ 1.11703181 1.11259794]]\n", "Error: 0.5\n", "[[ 0.21407889 0.21407889 0.21407889 0.21407889]\n", " [ 0.12771797 0.12771797 0.12771797 0.12771797]] \n", " [[ 1.11726689 1.11282873]\n", " [ 1.11726689 1.11282873]\n", " [ 1.11726689 1.11282873]\n", " [ 1.11726689 1.11282873]]\n", "Error: 0.5\n", "[[ 0.21413521 0.21413521 0.21413521 0.21413521]\n", " [ 0.1277622 0.1277622 0.1277622 0.1277622 ]] \n", " [[ 1.11750174 1.11305928]\n", " [ 1.11750174 1.11305928]\n", " [ 1.11750174 1.11305928]\n", " [ 1.11750174 1.11305928]]\n", "Error: 0.5\n", "[[ 0.21419148 0.21419148 0.21419148 0.21419148]\n", " [ 0.12780638 0.12780638 0.12780638 0.12780638]] \n", " [[ 1.11773634 1.11328948]\n", " [ 1.11773634 1.11328948]\n", " [ 1.11773634 1.11328948]\n", " [ 1.11773634 1.11328948]]\n", "Error: 0.5\n", "[[ 0.21424767 0.21424767 0.21424767 0.21424767]\n", " [ 0.12785052 0.12785052 0.12785052 0.12785052]] \n", " [[ 1.11797059 1.11351943]\n", " [ 1.11797059 1.11351943]\n", " [ 1.11797059 1.11351943]\n", " [ 1.11797059 1.11351943]]\n", "Error: 0.5\n", "[[ 0.21430379 0.21430379 0.21430379 0.21430379]\n", " [ 0.12789461 0.12789461 0.12789461 0.12789461]] \n", " [[ 1.11820459 1.11374915]\n", " [ 1.11820459 1.11374915]\n", " [ 1.11820459 1.11374915]\n", " [ 1.11820459 1.11374915]]\n", "Error: 0.5\n", "[[ 0.21435983 0.21435983 0.21435983 0.21435983]\n", " [ 0.12793866 0.12793866 0.12793866 0.12793866]] \n", " [[ 1.11843836 1.11397862]\n", " [ 1.11843836 1.11397862]\n", " [ 1.11843836 1.11397862]\n", " [ 1.11843836 1.11397862]]\n", "Error: 0.5\n", "[[ 0.21441582 0.21441582 0.21441582 0.21441582]\n", " [ 0.12798265 0.12798265 0.12798265 0.12798265]] \n", " [[ 1.11867189 1.11420786]\n", " [ 1.11867189 1.11420786]\n", " [ 1.11867189 1.11420786]\n", " [ 1.11867189 1.11420786]]\n", "Error: 0.5\n", "[[ 0.21447173 0.21447173 0.21447173 0.21447173]\n", " [ 0.12802659 0.12802659 0.12802659 0.12802659]] \n", " [[ 1.11890519 1.11443686]\n", " [ 1.11890519 1.11443686]\n", " [ 1.11890519 1.11443686]\n", " [ 1.11890519 1.11443686]]\n", "Error: 0.5\n", "[[ 0.21452756 0.21452756 0.21452756 0.21452756]\n", " [ 0.12807049 0.12807049 0.12807049 0.12807049]] \n", " [[ 1.11913824 1.11466563]\n", " [ 1.11913824 1.11466563]\n", " [ 1.11913824 1.11466563]\n", " [ 1.11913824 1.11466563]]\n", "Error: 0.5\n", "[[ 0.21458334 0.21458334 0.21458334 0.21458334]\n", " [ 0.12811434 0.12811434 0.12811434 0.12811434]] \n", " [[ 1.11937106 1.11489415]\n", " [ 1.11937106 1.11489415]\n", " [ 1.11937106 1.11489415]\n", " [ 1.11937106 1.11489415]]\n", "Error: 0.5\n", "[[ 0.21463904 0.21463904 0.21463904 0.21463904]\n", " [ 0.12815815 0.12815815 0.12815815 0.12815815]] \n", " [[ 1.11960363 1.11512244]\n", " [ 1.11960363 1.11512244]\n", " [ 1.11960363 1.11512244]\n", " [ 1.11960363 1.11512244]]\n", "Error: 0.5\n", "[[ 0.21469466 0.21469466 0.21469466 0.21469466]\n", " [ 0.12820192 0.12820192 0.12820192 0.12820192]] \n", " [[ 1.11983585 1.11535048]\n", " [ 1.11983585 1.11535048]\n", " [ 1.11983585 1.11535048]\n", " [ 1.11983585 1.11535048]]\n", "Error: 0.5\n", "[[ 0.21475023 0.21475023 0.21475023 0.21475023]\n", " [ 0.12824564 0.12824564 0.12824564 0.12824564]] \n", " [[ 1.12006783 1.11557817]\n", " [ 1.12006783 1.11557817]\n", " [ 1.12006783 1.11557817]\n", " [ 1.12006783 1.11557817]]\n", "Error: 0.5\n", "[[ 0.21480572 0.21480572 0.21480572 0.21480572]\n", " [ 0.12828931 0.12828931 0.12828931 0.12828931]] \n", " [[ 1.12029958 1.11580563]\n", " [ 1.12029958 1.11580563]\n", " [ 1.12029958 1.11580563]\n", " [ 1.12029958 1.11580563]]\n", "Error: 0.5\n", "[[ 0.21486114 0.21486114 0.21486114 0.21486114]\n", " [ 0.12833294 0.12833294 0.12833294 0.12833294]] \n", " [[ 1.12053108 1.11603284]\n", " [ 1.12053108 1.11603284]\n", " [ 1.12053108 1.11603284]\n", " [ 1.12053108 1.11603284]]\n", "Error: 0.5\n", "[[ 0.2149165 0.2149165 0.2149165 0.2149165 ]\n", " [ 0.12837653 0.12837653 0.12837653 0.12837653]] \n", " [[ 1.12076235 1.11625981]\n", " [ 1.12076235 1.11625981]\n", " [ 1.12076235 1.11625981]\n", " [ 1.12076235 1.11625981]]\n", "Error: 0.5\n", "[[ 0.21497178 0.21497178 0.21497178 0.21497178]\n", " [ 0.12842007 0.12842007 0.12842007 0.12842007]] \n", " [[ 1.12099338 1.11648655]\n", " [ 1.12099338 1.11648655]\n", " [ 1.12099338 1.11648655]\n", " [ 1.12099338 1.11648655]]\n", "Error: 0.5\n", "[[ 0.215027 0.215027 0.215027 0.215027 ]\n", " [ 0.12846357 0.12846357 0.12846357 0.12846357]] \n", " [[ 1.12122416 1.11671305]\n", " [ 1.12122416 1.11671305]\n", " [ 1.12122416 1.11671305]\n", " [ 1.12122416 1.11671305]]\n", "Error: 0.5\n", "[[ 0.21508215 0.21508215 0.21508215 0.21508215]\n", " [ 0.12850702 0.12850702 0.12850702 0.12850702]] \n", " [[ 1.12145472 1.11693931]\n", " [ 1.12145472 1.11693931]\n", " [ 1.12145472 1.11693931]\n", " [ 1.12145472 1.11693931]]\n", "Error: 0.5\n", "[[ 0.21513723 0.21513723 0.21513723 0.21513723]\n", " [ 0.12855043 0.12855043 0.12855043 0.12855043]] \n", " [[ 1.12168503 1.11716533]\n", " [ 1.12168503 1.11716533]\n", " [ 1.12168503 1.11716533]\n", " [ 1.12168503 1.11716533]]\n", "Error: 0.5\n", "[[ 0.21519224 0.21519224 0.21519224 0.21519224]\n", " [ 0.12859379 0.12859379 0.12859379 0.12859379]] \n", " [[ 1.1219151 1.11739111]\n", " [ 1.1219151 1.11739111]\n", " [ 1.1219151 1.11739111]\n", " [ 1.1219151 1.11739111]]\n", "Error: 0.5\n" ] } ], "source": [ "init = tf.initialize_all_variables()\n", "#errors=[]\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " good=tf.equal(tf.argmax(OutputTeta,1),tf.argmax(Y,1))\n", " accuracy=tf.reduce_mean(tf.cast(good,tf.float32))\n", " for i in range(1000):\n", " sess.run(optimizer,feed_dict={x1:Xd,Y:Y1})\n", " Eval=sess.run(accuracy,feed_dict={x1:Xd,Y:Y1})\n", " errors.append(1-Eval)\n", " print(sess.run(W1),\"\\n\",sess.run(W2))\n", " print(\"Error:\",errors[-1])\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

VCG/gp

fp/fp_dojo.ipynb

1

67452

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import cPickle as pickle\n", "import os; import sys; sys.path.append('../gp')\n", "# import gp\n", "# import gp.nets as nets\n", "from legacy import Legacy\n", "# import gp.Legacy\n", "# import gp.Util\n", "\n", "from sklearn.metrics import classification_report, accuracy_score, roc_curve, auc, precision_recall_fscore_support, f1_score, precision_recall_curve, average_precision_score, zero_one_loss\n", "\n", "\n", "from matplotlib.pyplot import imshow\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cnn = {}\n", "cnn['uuid'] = 'IPMLB'\n", "def run_dojo_xp_fp(cnn):\n", "\n", " # load dojo data\n", " input_image, input_prob, input_gold, input_rhoana, dojo_bbox = Legacy.read_dojo_data()\n", "\n", "\n", " original_mean_VI, original_median_VI, original_VI_s = Legacy.VI(input_gold, input_rhoana)\n", "\n", " # output folder for anything to store\n", " output_folder = '/home/d/netstatsPAPERFP/IPMLB/'\n", " if not os.path.exists(output_folder):\n", " os.makedirs(output_folder)\n", "\n", " # find merge errors, if we did not generate them before\n", " merge_error_file = output_folder+'/merges_new_cnn.p'\n", " merge_errors = []\n", " print len(merge_errors), ' merge errors found.'\n", "\n", " # we need to create a bigM for the dojo volume\n", " bigM_dojo_file = output_folder + '/bigM_fp_2D.p'\n", " if os.path.exists(bigM_dojo_file):\n", " print 'Loading dojo bigM from file..'\n", " with open(bigM_dojo_file, 'rb') as f:\n", " bigM_dojo = pickle.load(f)\n", "# else:\n", "# print 'Creating dojo bigM..'\n", "# bigM_dojo = gp.Legacy.create_bigM_without_mask(cnn, input_image, input_prob, input_rhoana, verbose=False)\n", "# with open(bigM_dojo_file, 'wb') as f:\n", "# pickle.dump(bigM_dojo, f) \n", "\n", " print bigM_dojo[0].max()\n", " \n", " dojo_vi_95_file = output_folder + '/dojo_vi_95_t6.p'\n", "\n", " dojo_merge_vis = output_folder + '/dojo_merge_auto95_vis.p'\n", " dojo_split_vis = output_folder + '/dojo_split_auto95_vis.p'\n", "\n", " dojo_merge_fixes = output_folder + '/dojo_merge_auto95_fixes.p'\n", " dojo_split_fixes = output_folder + '/dojo_split_auto95_fixes.p'\n", "\n", " dojo_output_95 = output_folder + '/dojo_auto95_output.p'\n", "\n", "\n", " if 1:\n", "\n", " bigM_dojo_05 = bigM_dojo\n", " corrected_rhoana_05 = input_rhoana.copy()\n", " print 'Correcting split errors with p > .95'\n", " bigM_dojo_after_95, out_dojo_volume_after_auto_95, dojo_auto_fixes_95, dojo_auto_vi_s_95, vi_s_per_step2 = Legacy.splits_global_from_M_automatic(cnn, bigM_dojo_05, input_image, input_prob, corrected_rhoana_05, input_gold, sureness_threshold=.5, FP=True)\n", "\n", " dojo_vi_95 = Legacy.VI(input_gold, out_dojo_volume_after_auto_95)\n", "\n", " \n", " with open(dojo_vi_95_file, 'wb') as f:\n", " pickle.dump(dojo_vi_95, f)\n", "\n", " with open(dojo_split_vis, 'wb') as f:\n", " pickle.dump(vi_s_per_step2, f)\n", "\n", " with open(dojo_split_fixes, 'wb') as f:\n", " pickle.dump(dojo_auto_fixes_95, f) \n", "\n", " with open(dojo_output_95, 'wb') as f:\n", " pickle.dump(out_dojo_volume_after_auto_95, f) \n", "\n", " print ' Mean VI improvement', original_mean_VI-dojo_vi_95[0]\n", " print ' Median VI improvement', original_median_VI-dojo_vi_95[1]\n", "\n", " \n", " dojo_vi_simuser_file = output_folder + '/dojo_vi_simuser_no_t8.p'\n", "\n", " dojo_merge_vis = output_folder + '/dojo_merge_simuser_vis.p'\n", " dojo_split_vis = output_folder + '/dojo_split_simuser_vis.p'\n", "\n", " dojo_merge_fixes = output_folder + '/dojo_merge_simuser_fixes.p'\n", " dojo_split_fixes = output_folder + '/dojo_split_simuser_fixes.p'\n", "\n", " dojo_output_simuser = output_folder + '/dojo_simuser_output.p'\n", "\n", " if 1:\n", "\n", " #\n", " # perform split correction with simulated user\n", " #\n", " print 'Correcting split errors by simulated user (er=0)'\n", " bigM_dojo_after, out_dojo_volume_after_sim_user, dojo_sim_user_fixes, dojo_sim_user_vi_s, vi_s_per_step2 = Legacy.splits_global_from_M(cnn, bigM_dojo, input_image, input_prob, input_rhoana, input_gold, hours=.5, FP=True)\n", "\n", " dojo_vi_simuser = Legacy.VI(input_gold, out_dojo_volume_after_sim_user)\n", "\n", " with open(dojo_vi_simuser_file, 'wb') as f:\n", " pickle.dump(dojo_vi_simuser, f) \n", "\n", " with open(dojo_split_vis, 'wb') as f:\n", " pickle.dump(vi_s_per_step2, f)\n", "\n", " with open(dojo_split_fixes, 'wb') as f:\n", " pickle.dump(dojo_sim_user_fixes, f)\n", "\n", " with open(dojo_output_simuser, 'wb') as f:\n", " pickle.dump(out_dojo_volume_after_sim_user, f)\n", "\n", " print ' Mean VI improvement', original_mean_VI-dojo_vi_simuser[0]\n", " print ' Median VI improvement', original_median_VI-dojo_vi_simuser[1] \n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "0 merge errors found.\n", "Loading dojo bigM from file..\n", "0.85098\n", "Correcting split errors with p > .95\n", "30 minutes done bigM_max= 0.552941\n", " Mean VI improvement -1.46607100878\n", " Median VI improvement -1.42596133803\n", "Correcting split errors by simulated user (er=0)\n", "0.419608 0.439216 0.439216\n", "0.435294 0.431373 0.435294\n", "0.184298 0.0 0.184298\n", "0.509804 0.490196 0.509804\n", "0.307451 0.4 0.4\n", "0.388235 0.0 0.388235\n", "0.643137 0.658823 0.658823\n", "0.247059 0.0 0.247059\n", "0.527489 0.686275 0.686275\n", "0.729412 0.560646 0.729412\n", "0.529412 0.0 0.529412\n", "0.486274 0.0 0.486274\n", "0.56103 0.74902 0.74902\n", "0.513726 0.0 0.513726\n", "0.0 0.0 0.0\n", "0.384314 0.287858 0.384314\n", "0.521569 0.0 0.521569\n", "0.280215 0.0 0.280215\n", "0.231373 0.0 0.231373\n", "0.247059 0.278431 0.278431\n", "0.498039 0.560784 0.560784\n", "0.505882 0.378916 0.505882\n", "0.462745 0.0 0.462745\n", "0.419608 0.0 0.419608\n", "0.494118 0.0 0.494118\n", "0.52549 0.411765 0.52549\n", "0.556863 0.0 0.556863\n", "0.517647 0.705882 0.705882\n", "0.431373 0.423529 0.431373\n", "0.276078 0.0 0.276078\n", "0.631373 0.0 0.631373\n", "0.396155 0.0 0.396155\n", "0.32549 0.0 0.32549\n", "0.482353 0.0 0.482353\n", "0.415686 0.0 0.415686\n", "0.372549 0.0 0.372549\n", "0.607843 0.701961 0.701961\n", "0.380392 0.364706 0.380392\n", "0.52549 0.0 0.52549\n", "0.376471 0.0 0.376471\n", "0.560784 0.0 0.560784\n", "0.482353 0.0 0.482353\n", "0.407843 0.427451 0.427451\n", "0.443137 0.490196 0.490196\n", "0.392157 0.0 0.392157\n", "0.172549 0.0 0.172549\n", "0.615686 0.458824 0.615686\n", "0.423529 0.427451 0.427451\n", "0.415686 0.486274 0.486274\n", "0.184314 0.173964 0.184314\n", "0.568627 0.0 0.568627\n", "0.372549 0.0 0.372549\n", "0.40526 0.564706 0.564706\n", "0.533333 0.0 0.533333\n", "0.376471 0.454902 0.454902\n", "0.435294 0.31068 0.435294\n", "0.242399 0.271511 0.271511\n", "0.627451 0.442907 0.627451\n", "0.431373 0.0 0.431373\n", "0.276078 0.533333 0.533333\n", "0.631373 0.0 0.631373\n", "0.396155 0.0 0.396155\n", "0.32549 0.0 0.32549\n", "0.482353 0.0 0.482353\n", "0.415686 0.0 0.415686\n", "0.372549 0.0 0.372549\n", "0.309219 0.25098 0.309219\n", "0.45098 0.639216 0.639216\n", "0.478431 0.517647 0.517647\n", "0.54902 0.0 0.54902\n", "0.329412 0.262745 0.329412\n", "0.458824 0.0 0.458824\n", "0.307589 0.443137 0.443137\n", "0.32549 0.329412 0.329412\n", "0.482353 0.0 0.482353\n", "0.635294 0.0 0.635294\n", "0.288997 0.0 0.288997\n", "0.368627 0.0 0.368627\n", "0.189671 0.267436 0.267436\n", "0.666667 0.65098 0.666667\n", "0.0 0.0 0.0\n", "0.494118 0.431373 0.494118\n", "0.0 0.0 0.0\n", "0.0 0.0 0.0\n", "0.0 0.0 0.0\n", "0.0 0.0 0.0\n", "0.486274 0.0 0.486274\n", "0.501961 0.0 0.501961\n", "0.427451 0.0 0.427451\n", "0.513726 0.344498 0.513726\n", "0.458824 0.447059 0.458824\n", "0.654902 0.0 0.654902\n", "0.411765 0.415686 0.415686\n", "0.462745 0.490196 0.490196\n", "0.478431 0.603922 0.603922\n", "0.576471 0.615686 0.615686\n", "0.490196 0.0 0.490196\n", "0.4 0.0 0.4\n", "0.564706 0.447059 0.564706\n", "0.505882 0.372549 0.505882\n", "0.529412 0.388235 0.529412\n", "0.356863 0.403922 0.403922\n", "0.592157 0.0 0.592157\n", "0.415686 0.4 0.415686\n", "0.54902 0.0 0.54902\n", "0.572549 0.482353 0.572549\n", "0.533333 0.509804 0.533333\n", "0.270588 0.0 0.270588\n", "0.239216 0.247059 0.247059\n", "0.231373 0.0 0.231373\n", "0.627451 0.0 0.627451\n", "0.454902 0.407843 0.454902\n", "0.466667 0.0 0.466667\n", "0.4 0.0 0.4\n", "0.498039 0.443137 0.498039\n", "0.533333 0.345098 0.533333\n", "0.631373 0.408535 0.631373\n", "0.615686 0.0 0.615686\n", "0.188235 0.177015 0.188235\n", "0.180392 0.164706 0.180392\n", "0.458824 0.482353 0.482353\n", "0.329412 0.234602 0.329412\n", "0.390927 0.607843 0.607843\n", "0.407843 0.0 0.407843\n", "0.415686 0.560784 0.560784\n", "0.403922 0.258193 0.403922\n", "0.537255 0.458824 0.537255\n", "0.541176 0.458824 0.541176\n", "0.454902 0.288997 0.454902\n", "0.521569 0.340915 0.521569\n", "0.282353 0.443137 0.443137\n", "0.466667 0.0 0.466667\n", "0.311111 0.0 0.311111\n", "0.223529 0.269373 0.269373\n", "0.572549 0.0 0.572549\n", "0.564706 0.615686 0.615686\n", "0.435294 0.419608 0.435294\n", "0.427451 0.0 0.427451\n", "0.537255 0.345098 0.537255\n", "0.6 0.537255 0.6\n", "0.552941 0.4 0.552941\n", "0.513726 0.407843 0.513726\n", "0.52549 0.0 0.52549\n", "0.435294 0.454902 0.454902\n", "0.301961 0.0 0.301961\n", "0.356863 0.498039 0.498039\n", "0.513726 0.52549 0.52549\n", "0.47451 0.0 0.47451\n", "0.396078 0.0 0.396078\n", "0.384314 0.0 0.384314\n", "0.517647 0.470588 0.517647\n", "0.47451 0.0 0.47451\n", "0.172549 0.233695 0.233695\n", "0.427451 0.0 0.427451\n", "0.447059 0.0 0.447059\n", "0.415686 0.407843 0.415686\n", "0.364706 0.337255 0.364706\n", "0.314279 0.513726 0.513726\n", "0.270588 0.0 0.270588\n", "0.494118 0.505882 0.505882\n", "0.521569 0.411765 0.521569\n", "0.0 0.0 0.0\n", "0.0 0.0 0.0\n", "0.54902 0.0 0.54902\n", "0.517647 0.454902 0.517647\n", "0.423529 0.257439 0.423529\n", "0.517647 0.0 0.517647\n", "0.407843 0.0 0.407843\n", "0.466667 0.462745 0.466667\n", "0.603922 0.0 0.603922\n", "0.490196 0.0 0.490196\n", "0.4 0.0 0.4\n", "0.423529 0.4 0.423529\n", "0.415686 0.0 0.415686\n", "0.478431 0.498039 0.498039\n", "0.560784 0.537255 0.560784\n", "0.541176 0.357416 0.541176\n", "0.592157 0.415686 0.592157\n", "0.533333 0.0 0.533333\n", "0.560784 0.447059 0.560784\n", "0.596078 0.443137 0.596078\n", "0.248858 0.0 0.248858\n", "0.266667 0.443137 0.443137\n", "0.396078 0.443137 0.443137\n", "0.462745 0.47451 0.47451\n", "0.560784 0.541176 0.560784\n", "0.462745 0.454902 0.462745\n", "0.447059 0.0 0.447059\n", "0.592157 0.0 0.592157\n", "0.392157 0.509804 0.509804\n", "0.443137 0.470588 0.470588\n", "0.552941 0.0 0.552941\n", "0.356863 0.0 0.356863\n", "0.269373 0.454902 0.454902\n", "0.419608 0.419608 0.419608\n", "0.572549 0.0 0.572549\n", "0.305882 0.0 0.305882\n", "0.311634 0.533333 0.533333\n", "0.54902 0.0 0.54902\n", "0.34842 0.435294 0.435294\n", "0.470588 0.52549 0.52549\n", "0.564706 0.0 0.564706\n", "0.470588 0.0 0.470588\n", "0.552941 0.0 0.552941\n", "0.356863 0.0 0.356863\n", "0.34902 0.486274 0.486274\n", "0.415686 0.0 0.415686\n", "0.462745 0.27451 0.462745\n", "0.568627 0.0 0.568627\n", "0.529412 0.435294 0.529412\n", "0.545098 0.0 0.545098\n", "0.388235 0.454902 0.454902\n", "0.27451 0.458824 0.458824\n", "0.54902 0.54902 0.54902\n", "0.235294 0.295086 0.295086\n", "0.458824 0.376471 0.458824\n", "0.443137 0.0 0.443137\n", "0.529412 0.34902 0.529412\n", "0.392157 0.486274 0.486274\n", "0.447059 0.0 0.447059\n", "0.360784 0.372549 0.372549\n", "0.533333 0.435294 0.533333\n", "0.454902 0.309804 0.454902\n", "0.380392 0.4 0.4\n", "0.0 0.0 0.0\n", "0.513726 0.478431 0.513726\n", "0.0 0.0 0.0\n", "0.439216 0.0 0.439216\n", "0.376471 0.533333 0.533333\n", "0.478431 0.482353 0.482353\n", "0.384314 0.458824 0.458824\n", "0.427451 0.403922 0.427451\n", "0.419608 0.321569 0.419608\n", "0.478431 0.368627 0.478431\n", "0.517647 0.0 0.517647\n", "0.486274 0.0 0.486274\n", "0.0 0.0 0.0\n", "0.0 0.0 0.0\n", "0.407843 0.0 0.407843\n", "0.376471 0.0 0.376471\n", "0.290565 0.52549 0.52549\n", "0.509804 0.0 0.509804\n", "0.427451 0.501961 0.501961\n", "0.45098 0.52549 0.52549\n", "0.447059 0.0 0.447059\n", "0.392157 0.403922 0.403922\n", "0.427451 0.0 0.427451\n", "0.360784 0.0 0.360784\n", "0.458824 0.407843 0.458824\n", "0.411765 0.47451 0.47451\n", "0.517647 0.0 0.517647\n", "0.372549 0.360784 0.372549\n", "0.478431 0.0 0.478431\n", "0.345098 0.403922 0.403922\n", "0.498039 0.0 0.498039\n", "0.419608 0.341176 0.419608\n", "0.282353 0.466667 0.466667\n", "0.415686 0.0 0.415686\n", "0.0 0.0 0.0\n", "0.121569 0.0 0.121569\n", "0.447059 0.0 0.447059\n", "0.0 0.0 0.0\n", "0.0 0.0 0.0\n", "0.513726 0.403922 0.513726\n", "0.439216 0.403922 0.439216\n", "0.329412 0.0 0.329412\n", "0.4 0.0 0.4\n", "0.415686 0.0 0.415686\n", "0.513726 0.0 0.513726\n", "0.470588 0.384314 0.470588\n", "0.364706 0.427451 0.427451\n", "0.517647 0.341176 0.517647\n", "0.47451 0.529412 0.529412\n", "0.486274 0.0 0.486274\n", "0.490196 0.486274 0.490196\n", "0.443137 0.466667 0.466667\n", "0.45098 0.447059 0.45098\n", "0.144498 0.0 0.144498\n", "0.423529 0.0 0.423529\n", "0.482353 0.0 0.482353\n", "0.32549 0.298039 0.32549\n", "0.447059 0.0 0.447059\n", "0.372549 0.0 0.372549\n", "0.27451 0.0 0.27451\n", "0.411765 0.0 0.411765\n", "0.376471 0.380392 0.380392\n", "0.521569 0.0 0.521569\n", "0.521569 0.392157 0.521569\n", "0.490196 0.0 0.490196\n", "0.411765 0.521569 0.521569\n", "0.427451 0.415686 0.427451\n", "0.4 0.282353 0.4\n", "0.462745 0.411765 0.462745\n", "0.393141 0.0 0.393141\n", "0.27451 0.415686 0.415686\n", "0.32549 0.270496 0.32549\n", "0.305882 0.0 0.305882\n", "0.517647 0.0 0.517647\n", "0.47451 0.0 0.47451\n", "0.505882 0.0 0.505882\n", "0.478431 0.494118 0.494118\n", "0.486274 0.435294 0.486274\n", "0.490196 0.439216 0.490196\n", "0.0 0.0 0.0\n", "0.498039 0.0 0.498039\n", "0.462745 0.0 0.462745\n", "0.431373 0.478431 0.478431\n", "0.509804 0.0 0.509804\n", "0.505882 0.458824 0.505882\n", "0.509804 0.364706 0.509804\n", "0.407843 0.0 0.407843\n", "0.498039 0.0 0.498039\n", "0.364706 0.329412 0.364706\n", "0.415686 0.0 0.415686\n", "0.443137 0.0 0.443137\n", "0.45098 0.235294 0.45098\n", "0.423529 0.352941 0.423529\n", "0.501961 0.490196 0.501961\n", "0.0 0.435294 0.435294\n", "0.486274 0.0 0.486274\n", "0.415686 0.0 0.415686\n", "0.376471 0.345098 0.376471\n", "0.431373 0.419608 0.431373\n", "0.466667 0.431373 0.466667\n", "0.301961 0.4 0.4\n", "0.466667 0.0 0.466667\n", "0.494118 0.0 0.494118\n", "0.4 0.0 0.4\n", "0.0 0.0 0.0\n", "0.0 0.0 0.0\n", "0.411765 0.0 0.411765\n", "0.372549 0.384314 0.384314\n", "0.45098 0.403922 0.45098\n", "0.435294 0.0 0.435294\n", "0.364706 0.458824 0.458824\n", "0.403922 0.0 0.403922\n", "0.329412 0.321569 0.329412\n", "0.305882 0.0 0.305882\n", "0.403922 0.0 0.403922\n", "0.423529 0.0 0.423529\n", "0.435294 0.0 0.435294\n", "0.423529 0.0 0.423529\n", "0.288216 0.0 0.288216\n", "0.305882 0.180392 0.305882\n", "0.415686 0.0 0.415686\n", "0.0 0.0 0.0\n", "0.360784 0.0 0.360784\n", "0.462745 0.0 0.462745\n", "0.435294 0.431373 0.435294\n", "0.227451 0.439216 0.439216\n", "0.396078 0.0 0.396078\n", "0.45098 0.0 0.45098\n", "0.144498 0.309804 0.309804\n", "0.423529 0.0 0.423529\n", "0.419608 0.0 0.419608\n", "0.321569 0.0 0.321569\n", "0.431373 0.0 0.431373\n", "0.431373 0.0 0.431373\n", "0.0 0.0 0.0\n", "0.403922 0.0 0.403922\n", "0.447059 0.0 0.447059\n", "0.447059 0.0 0.447059\n", "0.458824 0.0 0.458824\n", "0.415686 0.0 0.415686\n", "0.380392 0.0 0.380392\n", "0.184314 0.329412 0.329412\n", "0.0 0.0 0.0\n", "0.45098 0.0 0.45098\n", "0.0 0.0 0.0\n", "0.352941 0.0 0.352941\n", "0.415686 0.0 0.415686\n", "0.301961 0.0 0.301961\n", "0.0 0.0 0.0\n", "0.403922 0.0 0.403922\n", "0.419608 0.0 0.419608\n", "0.435294 0.0 0.435294\n", "0.411765 0.0 0.411765\n", "0.439216 0.411765 0.439216\n", "0.4 0.0 0.4\n", "0.333333 0.0 0.333333\n", "0.4 0.0 0.4\n", "0.0 0.0 0.0\n", "0.360784 0.0 0.360784\n", "0.298039 0.0 0.298039\n", "0.298039 0.20915 0.298039\n", "0.0 0.0 0.0\n", "0.172549 0.0 0.172549\n", "0.415686 0.0 0.415686\n", "0.415686 0.179316 0.415686\n", "0.368627 0.0 0.368627\n", "0.392157 0.0 0.392157\n", "0.423529 0.0 0.423529\n", "0.223268 0.411765 0.411765\n", "0.368627 0.233695 0.368627\n", "0.0 0.0 0.0\n", "0.372549 0.0 0.372549\n", "0.0 0.0 0.0\n", "0.360784 0.0 0.360784\n", "0.423529 0.0 0.423529\n", "0.372549 0.368627 0.372549\n", "0.388235 0.0 0.388235\n", "0.372549 0.0 0.372549\n", "0.415686 0.411765 0.415686\n", "0.396078 0.0 0.396078\n", "0.301961 0.0 0.301961\n", "0.305882 0.243137 0.305882\n", "done\n", " Mean VI improvement 0.150176602871\n", " Median VI improvement 0.123333501794\n" ] } ], "source": [ "import numpy as np\n", "run_dojo_xp_fp(cnn)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ " def run_cylinder_xp(cnn):\n", "\n", " # load cylinder data\n", " input_image = []\n", " input_prob = []\n", " input_rhoana = []\n", " input_gold = []\n", " for z in range(250, 300):\n", " image, prob, mask, gold, rhoana = gp.Util.read_section('/home/d/data/cylinderNEW/', z, verbose=False)\n", "\n", " input_image.append(image)\n", " input_prob.append(255.-prob)\n", " input_rhoana.append(rhoana)\n", " input_gold.append(gold)\n", "\n", "\n", " original_mean_VI, original_median_VI, original_VI_s = gp.Legacy.VI(input_gold, input_rhoana)\n", "\n", " print 'Original median VI', original_median_VI\n", "\n", " # output folder for anything to store\n", " output_folder = '/home/d/netstatsPAPER/'+cnn.uuid+'/'\n", " if not os.path.exists(output_folder):\n", " os.makedirs(output_folder)\n", "\n", "\n", "\n", "\n", "\n", "\n", " ### SKIPPING MERGE FOR NOW\n", " merge_errors = []\n", "\n", " print len(merge_errors), ' merge errors found.'\n", " ####\n", "\n", " # we need to create a bigM for the cylinder volume\n", " bigM_cylinder_file = output_folder + '/bigM_cylinder.p'\n", " if os.path.exists(bigM_cylinder_file):\n", " print 'Loading cylinder bigM from file..'\n", " with open(bigM_cylinder_file, 'rb') as f:\n", " bigM_cylinder = pickle.load(f)\n", " else:\n", " print 'Creating cylinder bigM..'\n", " bigM_cylinder = gp.Legacy.create_bigM_without_mask(cnn, input_image, input_prob, input_rhoana, verbose=True, max=1000000)\n", " with open(bigM_cylinder_file, 'wb') as f:\n", " pickle.dump(bigM_cylinder, f) \n", "\n", "\n", "\n", "\n", " print\n", " cylinder_vi_95_file = output_folder + '/cylinder_vi_95_w_merge.p'\n", " # cylinder_vi_auto_95_fixes_file = output_folder + '/cylinder_vi_95_fixes.p'\n", " # cylinder_auto_vis_95_file = output_folder + '/cylinder_auto_vis_95.p'\n", "\n", "\n", " dojo_merge_vis = output_folder + '/cylinder_merge_auto95_vis.p'\n", " dojo_split_vis = output_folder + '/cylinder_split_auto95_vis.p'\n", "\n", " dojo_merge_fixes = output_folder + '/cylinder_merge_auto95_fixes.p'\n", " dojo_split_fixes = output_folder + '/cylinder_split_auto95_fixes.p'\n", "\n", " dojo_output_95 = output_folder + '/cylinder_auto95_output.p'\n", "\n", " if os.path.exists(cylinder_vi_95_file):\n", " print 'Loading merge errors p < .05 and split errors p > .95 from file..'\n", " with open(cylinder_vi_95_file, 'rb') as f:\n", " cylinder_vi_95 = pickle.load(f)\n", " # with open(cylinder_auto_vis_95_file, 'rb') as f:\n", " # cylinder_auto_vi_s_95 = pickle.load(f)\n", " # with open(cylinder_vi_auto_95_fixes_file, 'rb') as f:\n", " # cylinder_auto_fixes_95 = pickle.load(f)\n", " else: \n", " #\n", " # perform merge correction with p < .05\n", " #\n", " print 'Correcting merge errors with p < .05'\n", " bigM_cylinder_05, corrected_rhoana_05, cylinder_auto_merge_fixes, vi_s_per_step = gp.Legacy.perform_auto_merge_correction(cnn, bigM_cylinder, input_image, input_prob, input_rhoana, merge_errors, .05, input_gold=input_gold)\n", "\n", " print ' Mean VI improvement', original_mean_VI-gp.Legacy.VI(input_gold, corrected_rhoana_05)[0]\n", " print ' Median VI improvement', original_median_VI-gp.Legacy.VI(input_gold, corrected_rhoana_05)[1]\n", "\n", " with open(dojo_merge_vis, 'wb') as f:\n", " pickle.dump(vi_s_per_step, f)\n", "\n", "\n", " with open(dojo_merge_fixes, 'wb') as f:\n", " pickle.dump(cylinder_auto_merge_fixes, f) \n", "\n", "\n", " #\n", " # perform split correction with p > .95\n", " #\n", " print 'Correcting split errors with p > .95'\n", " # bigM_cylinder_05 = bigM_cylinder\n", " # corrected_rhoana_05 = input_rhoana\n", " bigM_cylinder_after_95, out_cylinder_volume_after_auto_95, cylinder_auto_fixes_95, cylinder_auto_vi_s_95, vi_s_per_step2 = gp.Legacy.splits_global_from_M_automatic(cnn, bigM_cylinder_05, input_image, input_prob, corrected_rhoana_05, input_gold, sureness_threshold=.95)\n", "\n", " cylinder_vi_95 = gp.Legacy.VI(input_gold, out_cylinder_volume_after_auto_95)\n", "\n", " with open(cylinder_vi_95_file, 'wb') as f:\n", " pickle.dump(cylinder_vi_95, f)\n", "\n", " # with open(cylinder_vi_auto_95_fixes_file, 'wb') as f:\n", " # pickle.dump(cylinder_auto_fixes_95, f)\n", "\n", " # with open(cylinder_auto_vis_95_file, 'wb') as f:\n", " # pickle.dump(cylinder_auto_vi_s_95, f) \n", "\n", " with open(dojo_split_vis, 'wb') as f:\n", " pickle.dump(vi_s_per_step2, f)\n", "\n", " with open(dojo_split_fixes, 'wb') as f:\n", " pickle.dump(cylinder_auto_fixes_95, f) \n", "\n", " with open(dojo_output_95, 'wb') as f:\n", " pickle.dump(out_cylinder_volume_after_auto_95, f) \n", "\n", "\n", " print ' Mean VI improvement', original_mean_VI-cylinder_vi_95[0]\n", " print ' Median VI improvement', original_median_VI-cylinder_vi_95[1]\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " # print\n", " # cylinder_vi_0_file = output_folder + '/cylinder_vi_0.p'\n", " # cylinder_vi_auto_0_fixes_file = output_folder + '/cylinder_vi_0_fixes.p'\n", " # cylinder_auto_vis_0_file = output_folder + '/cylinder_auto_vis_0.p' \n", " # if os.path.exists(cylinder_vi_0_file):\n", " # print 'Loading split errors p >= .0 from file..'\n", " # with open(cylinder_vi_0_file, 'rb') as f:\n", " # cylinder_vi_0 = pickle.load(f)\n", " # with open(cylinder_vi_auto_0_fixes_file, 'rb') as f:\n", " # cylinder_auto_fixes_00 = pickle.load(f)\n", " # with open(cylinder_auto_vis_0_file, 'rb') as f:\n", " # cylinder_auto_vi_s_00 = pickle.load(f)\n", "\n", "\n", " # else: \n", " # # #\n", " # # # perform merge correction with p < .01\n", " # # #\n", " # # print 'Correcting merge errors with p < .01'\n", " # # bigM_dojo_05, corrected_rhoana_05 = gp.Legacy.perform_auto_merge_correction(cnn, bigM_dojo, input_image, input_prob, input_rhoana, merge_errors, .05)\n", "\n", " # # print ' Mean VI improvement', original_mean_VI-gp.Legacy.VI(input_gold, corrected_rhoana_05)[0]\n", " # # print ' Median VI improvement', original_median_VI-gp.Legacy.VI(input_gold, corrected_rhoana_05)[1]\n", "\n", " # #\n", " # # perform split correction with p > .99\n", " # #\n", " # print 'Correcting split errors with p >= .0'\n", " # bigM_cylinder_00 = bigM_cylinder\n", " # corrected_rhoana_00 = input_rhoana\n", " # bigM_cylinder_after_00, out_cylinder_volume_after_auto_00, cylinder_auto_fixes_00, cylinder_auto_vi_s_00 = gp.Legacy.splits_global_from_M_automatic(cnn, bigM_cylinder_00, input_image, input_prob, corrected_rhoana_00, input_gold, sureness_threshold=.0)\n", "\n", " # cylinder_vi_0 = gp.Legacy.VI(input_gold, out_cylinder_volume_after_auto_00)\n", "\n", " # with open(cylinder_vi_0_file, 'wb') as f:\n", " # pickle.dump(cylinder_vi_0, f)\n", "\n", " # with open(cylinder_vi_auto_0_fixes_file, 'wb') as f:\n", " # pickle.dump(cylinder_auto_fixes_00, f)\n", "\n", " # with open(cylinder_auto_vis_0_file, 'wb') as f:\n", " # pickle.dump(cylinder_auto_vi_s_00, f) \n", "\n", " # print ' Mean VI improvement', original_mean_VI-cylinder_vi_0[0]\n", " # print ' Median VI improvement', original_median_VI-cylinder_vi_0[1]\n", "\n", "\n", "\n", " print\n", " cylinder_vi_simuser_file = output_folder + '/cylinder_vi_simuser_final.p'\n", " # cylinder_fixes_simuser_file = output_folder + '/cylinder_fixes_simuser.p'\n", " # cylinder_vis_simuser_file = output_folder + '/cylinder_vi_s_simuser.p'\n", "\n", "\n", " dojo_merge_vis = output_folder + '/cylinder_merge_simuser_vis.p'\n", " dojo_split_vis = output_folder + '/cylinder_split_simuser_vis.p'\n", "\n", " dojo_merge_fixes = output_folder + '/cylinder_merge_simuser_fixes.p'\n", " dojo_split_fixes = output_folder + '/cylinder_split_simuser_fixes.p'\n", "\n", " dojo_output_simuser = output_folder + '/cylinder_simuser_output.p' \n", " if os.path.exists(cylinder_vi_simuser_file):\n", " print 'Loading merge errors and split errors (simulated user) from file..'\n", " with open(cylinder_vi_simuser_file, 'rb') as f:\n", " cylinder_vi_simuser = pickle.load(f)\n", " # with open(cylinder_fixes_simuser_file, 'rb') as f:\n", " # cylinder_sim_user_fixes = pickle.load(f)\n", " # with open(cylinder_vis_simuser_file, 'rb') as f:\n", " # cylinder_sim_user_vi_s = pickle.load(f)\n", "\n", "\n", " else:\n", " # #\n", " # # perform merge correction with simulated user\n", " # #\n", " print 'Correcting merge errors by simulated user (er=0)'\n", " bigM_cylinder_simuser, corrected_rhoana_sim_user, sim_user_fixes, vi_s_per_step = gp.Legacy.perform_sim_user_merge_correction(cnn, bigM_cylinder, input_image, input_prob, input_rhoana, input_gold, merge_errors)\n", " \n", " print ' Mean VI improvement', original_mean_VI-gp.Legacy.VI(input_gold, corrected_rhoana_sim_user)[0] \n", " print ' Median VI improvement', original_median_VI-gp.Legacy.VI(input_gold, corrected_rhoana_sim_user)[1]\n", " \n", " with open(dojo_merge_vis, 'wb') as f:\n", " pickle.dump(vi_s_per_step, f)\n", "\n", "\n", " with open(dojo_merge_fixes, 'wb') as f:\n", " pickle.dump(sim_user_fixes, f)\n", "\n", " #\n", " # perform split correction with simulated user\n", " #\n", " print 'Correcting split errors by simulated user (er=0)'\n", " # bigM_cylinder_simuser = bigM_cylinder\n", " # corrected_rhoana_sim_user = input_rhoana\n", " bigM_cylinder_after, out_cylinder_volume_after_sim_user, cylinder_sim_user_fixes, cylinder_sim_user_vi_s, vi_s_per_step2 = gp.Legacy.splits_global_from_M(cnn, bigM_cylinder_simuser, input_image, input_prob, corrected_rhoana_sim_user, input_gold, hours=-1)\n", "\n", " cylinder_vi_simuser = gp.Legacy.VI(input_gold, out_cylinder_volume_after_sim_user)\n", "\n", " with open(cylinder_vi_simuser_file, 'wb') as f:\n", " pickle.dump(cylinder_vi_simuser, f) \n", "\n", " # with open(cylinder_vis_simuser_file, 'wb') as f:\n", " # pickle.dump(cylinder_sim_user_vi_s, f)\n", "\n", " # with open(cylinder_fixes_simuser_file, 'wb') as f:\n", " # pickle.dump(cylinder_sim_user_fixes, f)\n", " with open(dojo_split_vis, 'wb') as f:\n", " pickle.dump(vi_s_per_step2, f)\n", "\n", " with open(dojo_split_fixes, 'wb') as f:\n", " pickle.dump(cylinder_sim_user_fixes, f)\n", "\n", " with open(dojo_output_simuser, 'wb') as f:\n", " pickle.dump(out_cylinder_volume_after_sim_user, f)\n", "\n", "\n", " print ' Mean VI improvement', original_mean_VI-cylinder_vi_simuser[0]\n", " print ' Median VI improvement', original_median_VI-cylinder_vi_simuser[1]\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('/home/d/netstatsPAPERFP/IPMLB/dojo_simuser_output.p', 'rb') as f:\n", " simuser_out = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(10, 474, 474)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simuser_out.shape" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fa34ff397d0>" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAFkCAYAAADi5cqQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fa350027390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imshow(simuser_out[9])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'dict' object has no attribute 'uuid'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-18-57349e36ae5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muuid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'IPMLB'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'uuid'" ] } ], "source": [ "cnn.uuid = 'IPMLB'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

BenLangmead/comp-genomics-class

notebooks/CG_LCS.ipynb

1

3062

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def traceLcs(D, x, y):\n", " ''' Backtrace for LCS; returns LCS as string. '''\n", " i, j = len(x), len(y) # start in lower-right\n", " st = []\n", " while i > 0 and j > 0:\n", " # get the three contributions\n", " diag, vert, horz = 0, 0, 0\n", " if i > 0 and j > 0:\n", " delt = -1 if x[i-1] == y[j-1] else 1\n", " diag = D[i-1, j-1] + delt\n", " if i > 0: vert = D[i-1, j]\n", " if j > 0: horz = D[i, j-1]\n", " if diag <= vert and diag <= horz:\n", " # diagonal is best, thus, this char is part of LCS\n", " st.append(x[i-1])\n", " i -= 1; j -= 1 # move up and left\n", " elif vert <= horz: i -= 1 # vertical is best; move up\n", " else: j -= 1 # horizontal is best; move left\n", " # reverse it, then return string-ized LCS\n", " return (''.join(st))[::-1]\n", "\n", "import numpy\n", "def lcsDp(x, y):\n", " ''' Return LCS of x and y. Uses bottom-up dynamic programming\n", " approach. '''\n", " D = numpy.zeros((len(x)+1, len(y)+1), dtype=int)\n", " for i in range(1, len(x)+1):\n", " for j in range(1, len(y)+1):\n", " delt = -1 if x[i-1] == y[j-1] else 1\n", " D[i, j] = min(D[i-1, j-1] + delt, D[i-1, j], D[i, j-1])\n", " return traceLcs(D, x, y), D" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'TCTA'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lcs, D = lcsDp('ATCTGAT', 'TGCATA') # example from Jones & Pevzner 6.5\n", "lcs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, -1, -1, -1],\n", " [ 0, -1, -1, -1, -1, -2, -2],\n", " [ 0, -1, -1, -2, -2, -2, -2],\n", " [ 0, -1, -1, -2, -2, -3, -3],\n", " [ 0, -1, -2, -2, -2, -3, -3],\n", " [ 0, -1, -2, -2, -3, -3, -4],\n", " [ 0, -1, -2, -2, -3, -4, -4]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "D" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-2.0

KatiRG/flyingpigeon

notebooks/WPS_ensembleRobustness_asyncall.ipynb

1

448383

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"Python WPS execute\"\"\"\n", "from owslib.wps import WebProcessingService, monitorExecution, printInputOutput\n", "from os import system" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wps_url = \"http://localhost:8093/wps\"\n", "#wps_url = \"http://birdhouse-lsce.extra.cea.fr:8093/wps\"\n", "wps = WebProcessingService(url=wps_url, verbose=False)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Flyingpigeon\n" ] } ], "source": [ "print wps.identification.title" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "subset_countries : \t Returns only the selected polygon for each input dataset\n", "subset_continents : \t Returns only the selected polygon for each input dataset\n", "subset_regionseurope : \t Returns only the selected polygon for each input dataset\n", "extractpoints : \t Extract Timeseries for specified coordinates from grid data\n", "indices_single : \t This process calculates climate indices based on one single input variable.\n", "indices_percentile : \t Calculation of climate indices based on one single input variable and based on percentils of a referece period.\n", "visualisation : \t Just testing a nice script to visualise some variables\n", "eobs_to_cordex : \t downloads EOBS data in adaped CORDEX format\n", "weatherregimes : \t Weather Regimes based on pressure patterns (kmean method)\n", "ensembleRobustness : \t Calculates the robustness as the ratio of noise to signal in an ensemle of timeseries\n", "analogs : \t Search for days with analog pressure pattern\n", "segetalflora : \t Species biodiversity of segetal flora. Imput files: variable:tas , domain: EUR-11 or EUR-44\n", "sdm : \t Species distribution model (SDM)\n", "fetch : \t This process downloads resources (limited to 50GB) to the local file system of the birdhouse compute provider\n" ] } ], "source": [ "for process in wps.processes:\n", " print '%s : \\t %s' % (process.identifier, process.abstract)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " identifier=resource, title=NetCDF Files, abstract=NetCDF Files, data type=ComplexData\n", " Supported Value: mimeType=application/x-netcdf, encoding=None, schema=None\n", " Default Value: mimeType=application/x-netcdf, encoding=None, schema=None \n", " minOccurs=1, maxOccurs=100\n", "\n", "\n", " identifier=method, title=Method of robustness calculation, abstract=Detailed information about the methods can be found in the documentation, data type=//www.w3.org/TR/xmlschema-2/#string\n", " Allowed Value: Method_A\n", " Allowed Value: Method_B\n", " Allowed Value: Method_C\n", " Default Value: None \n", " minOccurs=0, maxOccurs=1\n", "\n", "\n", " identifier=start, title=Start Year, abstract=Beginn of the analysed period (e.g 1971; if not set, the first consistend year of the ensemble will be taken), data type=//www.w3.org/TR/xmlschema-2/#integer\n", " Any value allowed\n", " Default Value: None \n", " minOccurs=0, maxOccurs=1\n", "\n", "\n", " identifier=end, title=End Year, abstract=End of the analysed period (e.g. 2050 if not set, the last consistend year of the ensemble will be taken), data type=//www.w3.org/TR/xmlschema-2/#integer\n", " Any value allowed\n", " Default Value: None \n", " minOccurs=0, maxOccurs=1\n", "\n", "\n", " identifier=timeslice, title=Time slice, abstract=Time slice (in years) for robustness reference (default=10)), data type=//www.w3.org/TR/xmlschema-2/#integer\n", " Any value allowed\n", " Default Value: None \n", " minOccurs=0, maxOccurs=1\n", "\n", "\n", " identifier=variable, title=Variable, abstract=Variable to be expected in the input files (Variable will be detected if not set, ), data type=//www.w3.org/TR/xmlschema-2/#string\n", " Any value allowed\n", " Default Value: None \n", " minOccurs=0, maxOccurs=1\n", "\n", "\n" ] } ], "source": [ "p = wps.describeprocess(identifier='ensembleRobustness')\n", "for input in p.dataInputs:\n", " printInputOutput(input)\n", " print '\\n'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Call WPS with oswlib" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "files = []\n", "\n", "for i in range(1,16): # \n", " #files.append('file:///home/estimr1/EUCLEIA/indices/RX5day/DJF/RX5day_DJF_HadGEM3-A-N216_historical_r1i1p%s_19600101-20131230.nc' % (i))\n", " files.append('file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p%s_19600101-20131230.nc' % (i))\n", " " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p1_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p2_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p3_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p4_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p5_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p6_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p7_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p8_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p9_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p10_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p11_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p12_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p13_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p14_19600101-20131230.nc',\n", " 'file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p15_19600101-20131230.nc']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "files " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " owslib.wps.WPSException : {'locator': 'None', 'code': 'NoApplicableCode', 'text': 'Failed to execute WPS process [ensembleRobustness]: failed to sort the input files'}\n", "ProcessFailed\n" ] } ], "source": [ "from os.path import join\n", "\n", "execute = wps.execute(\n", " identifier=\"ensembleRobustness\", #indices_clipping\",\n", " inputs=[\n", " (\"resource\",files[0]),\n", " (\"resource\",files[1]),\n", " # (\"resource\",files[2]),\n", " # (\"resource\",files[3]),\n", " # (\"resource\",files[4]),\n", " # (\"resource\",files[5]),\n", " # (\"resource\",files[6]),\n", " # (\"resource\",files[7]),\n", " # (\"resource\",files[8]),\n", " # (\"resource\",files[9]),\n", " # (\"resource\",files[10]),\n", " # (\"resource\",files[11]),\n", " # (\"resource\",files[12]),\n", " # (\"resource\",files[13]),\n", " # (\"resource\",files[14])\n", " # (\"timeslice\",'10')\n", " ])\n", "\n", "monitorExecution(execute, sleepSecs=5)\n", "print execute.getStatus()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://localhost:8090/wpsoutputs/flyingpigeon/output_graphic-697dee76-d722-11e5-93ae-9789bf75cf44.png\n", "http://localhost:8090/wpsoutputs/flyingpigeon/output_high-697dee76-d722-11e5-93ae-9789bf75cf44.nc\n", "http://localhost:8090/wpsoutputs/flyingpigeon/output_text-697dee76-d722-11e5-93ae-9789bf75cf44.txt\n", "http://localhost:8090/wpsoutputs/flyingpigeon/output_low-697dee76-d722-11e5-93ae-9789bf75cf44.nc\n", "http://localhost:8090/wpsoutputs/flyingpigeon/output_signal-697dee76-d722-11e5-93ae-9789bf75cf44.nc\n" ] } ], "source": [ "for o in execute.processOutputs:\n", " print o.reference " ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# call the module only " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from flyingpigeon.ensembleRobustness import worker" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/homel/nhempel/.conda/envs/birdhouse/lib/python2.7/site-packages/matplotlib/artist.py:221: MatplotlibDeprecationWarning: This has been deprecated in mpl 1.5, please use the\n", "axes property. A removal date has not been set.\n", " warnings.warn(_get_axes_msg, mplDeprecation, stacklevel=1)\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAB9AAAAPoCAYAAACGXmWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "files = []\n", "\n", "for i in range(1,16): # \n", " files.append('/home/estimr1/EUCLEIA/indices/TG/DJF/TG_DJF_HadGEM3-A-N216_historical_r1i1p%s_19600101-20131230.nc' % (i))\n", "\n", "signal, low_agreement_mask, high_agreement_mask, graphic, text = worker(resource=files, start=1960, end=2000,\n", " timeslice=20, variable='TG')\n", "\n", "from IPython.display import Image\n", "Image(filename=graphic)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# build url for asymetric WPS call:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "resource={resource1};resource={resource2};resource={resource3};resource={resource4};resource={resource5};resource={resource6};resource={resource7};resource={resource8};resource={resource9};resource={resource10};resource={resource11};resource={resource12};resource={resource13};resource={resource14};resource={resource15};\n" ] } ], "source": [ "r = ''\n", "for i in range(1,16):\n", " r= '%sresource={resource%s};' % (r,i)\n", "print r " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "async_req_url = \"{wps_url}?\" +\\\n", " \"request=Execute\" +\\\n", " \"&service=WPS\" +\\\n", " \"&version=1.0.0\" +\\\n", " \"&identifier=ensembleRobustness\" +\\\n", " \"&DataInputs=\"+r+\\\n", " \"&storeExecuteResponse=true\" +\\\n", " \"&status=true\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://localhost:8093/wps?request=Execute&service=WPS&version=1.0.0&identifier=ensembleRobustness&DataInputs=resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p1_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p2_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p3_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p4_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p5_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p6_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p7_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p8_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p9_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p10_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p11_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p12_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p13_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p14_19600101-20131230.nc;resource=file:///home/estimr1/EUCLEIA/indices/TG/yr/TG_yr_HadGEM3-A-N216_historical_r1i1p15_19600101-20131230.nc;&storeExecuteResponse=true&status=true\n" ] } ], "source": [ "url=async_req_url.format(\n", " wps_url=wps_url,\n", " resource1=files[0],\n", " resource2=files[1],\n", " resource3=files[2], \n", " resource4=files[3],\n", " resource5=files[4],\n", " resource6=files[5],\n", " resource7=files[6],\n", " resource8=files[7],\n", " resource9=files[8],\n", " resource10=files[9],\n", " resource11=files[10],\n", " resource12=files[11],\n", " resource13=files[12],\n", " resource14=files[13],\n", " resource15=files[14]\n", ")\n", "print url " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://localhost:8090/wpsoutputs/flyingpigeon/pywps-8859490c-ea10-11e5-9a62-d557c95b4ff5.xml\n" ] } ], "source": [ "r = requests.get(url)\n", "from lxml import etree\n", "from io import BytesIO\n", "tree = etree.parse(BytesIO(r.content))\n", "#print etree.tostring(tree)\n", "status_url = tree.getroot().get(\"statusLocation\")\n", "print status_url" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200\n", "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n", "<wps:ExecuteResponse xmlns:wps=\"http://www.opengis.net/wps/1.0.0\" xmlns:ows=\"http://www.opengis.net/ows/1.1\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.opengis.net/wps/1.0.0 http://schemas.opengis.net/wps/1.0.0/wpsExecute_response.xsd\" service=\"WPS\" version=\"1.0.0\" xml:lang=\"en-CA\" serviceInstance=\"http://localhost:8093/wps?service=WPS&amp;request=GetCapabilities&amp;version=1.0.0\" statusLocation=\"http://localhost:8090/wpsoutputs/flyingpigeon/pywps-8859490c-ea10-11e5-9a62-d557c95b4ff5.xml\">\n", " <wps:Process wps:processVersion=\"0.2\">\n", " <ows:Identifier>ensembleRobustness</ows:Identifier>\n", " <ows:Title>Calculation of the robustness of an ensemle</ows:Title>\n", " <ows:Abstract>Calculates the robustness as the ratio of noise to signal in an ensemle of timeseries</ows:Abstract>\n", " <ows:Metadata xlink:title=\"LSCE\" xlink:href=\"http://www.lsce.ipsl.fr/\" />\n", " </wps:Process>\n", " <wps:Status creationTime=\"2016-03-14T19:14:10Z\">\n", " <wps:ProcessFailed>\n", " <ows:ExceptionReport version=\"1.0.0\">\n", " <ows:Exception exceptionCode=\"NoApplicableCode\" locator=\"None\">\n", " <ows:ExceptionText>Failed to execute WPS process [ensembleRobustness]: (returncode:1) cdo ensmean (Abort): Input streams missing!\n", "</ows:ExceptionText>\n", " </ows:Exception>\n", " </ows:ExceptionReport>\n", " </wps:ProcessFailed>\n", " </wps:Status>\n", "</wps:ExecuteResponse>\n", "\n" ] } ], "source": [ "r = requests.get(status_url)\n", "print r.status_code\n", "print r.text" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "graphic = 'http://localhost:8090/wpsoutputs/flyingpigeon/output_graphic-5442f43a-ce61-11e5-a317-434222d428b1.png'\n", "\n", "from IPython.display import Image\n", "from IPython.core.display import HTML \n", "Image(url= graphic )" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

HackTheStacks/darwin-notes-image-processing

IPythonNotesbooks/FFT_Similarity_Clean_By_Curves.ipynb

1

72517

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Visual evaluation of top FFT matches based on similarity on clean data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import sys\n", "import csv\n", "import scipy.io as sio\n", "from scipy.fftpack import fft, ifft\n", "from sklearn.metrics import mean_squared_error\n", "from math import sqrt\n", "import os\n", "import operator\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from IPython.display import display, Image" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "#base_dir = '/home/ibanez/data/amnh/darwin_notes/'\n", "base_dir = '/data/amnh/darwin/'\n", "curves_fft_dir = base_dir + 'image_csvs_fft/'\n", "fft_similarity_dir = base_dir + 'fft_similarity_clean/'\n", "base_image_dir = base_dir + 'images/'\n", "base_fft_dir = base_dir + 'image_csvs_fft/'\n", "base_csv_dir = base_dir + 'image_csvs/'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>image1</th>\n", " <th>image2</th>\n", " <th>fft_score</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>MS-DAR-00048-000-00189_south</td>\n", " <td>MS-DAR-00048-000-00189_north_fft.mat</td>\n", " <td>0.999921</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>MS-DAR-00089-000-00017_north</td>\n", " <td>MS-DAR-00205-00002-000-00431_north_fft.mat</td>\n", " <td>0.999893</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>MS-DAR-00087-000-00010_south</td>\n", " <td>MS-DAR-00087-000-00008_north_fft.mat</td>\n", " <td>0.999853</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>MS-DAR-00085-000-00150_south</td>\n", " <td>MS-DAR-00084-00002-000-00307_north_fft.mat</td>\n", " <td>0.999843</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>MS-DAR-00209-00009-000-00088_south</td>\n", " <td>MS-DAR-00209-00009-000-00168_north_fft.mat</td>\n", " <td>0.999837</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " image1 \\\n", "0 MS-DAR-00048-000-00189_south \n", "1 MS-DAR-00089-000-00017_north \n", "2 MS-DAR-00087-000-00010_south \n", "3 MS-DAR-00085-000-00150_south \n", "4 MS-DAR-00209-00009-000-00088_south \n", "\n", " image2 fft_score \n", "0 MS-DAR-00048-000-00189_north_fft.mat 0.999921 \n", "1 MS-DAR-00205-00002-000-00431_north_fft.mat 0.999893 \n", "2 MS-DAR-00087-000-00008_north_fft.mat 0.999853 \n", "3 MS-DAR-00084-00002-000-00307_north_fft.mat 0.999843 \n", "4 MS-DAR-00209-00009-000-00168_north_fft.mat 0.999837 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_matches = pd.read_csv(base_dir + 'top_items_sorted.txt', index_col=False, header=None, sep=' ');\n", "top_matches.columns = [\"image1\",\"image2\",\"fft_score\"]\n", "top_matches.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def save_match(row_index):\n", " with open(\"/data/amnh/darwin/confirmed_matches.csv\", \"a+\") as f: \n", " image1_basename = top_matches[\"image1\"][row_index]\n", " image2_basename = top_matches[\"image2\"][row_index]\n", " fft_score = top_matches[\"fft_score\"][row_index]\n", " print(image1_basename, image2_basename, fft_score)\n", " image1_filename = image1_basename[:-6] + '.jpg'\n", " image2_filename = image2_basename[:-14] + '.jpg'\n", " print(image1_filename)\n", " print(image2_filename)\n", " \n", " if 'south' in image1_basename:\n", " f.write(\"{},{},{}\\n\".format(image2_filename, image1_filename, fft_score))\n", " else:\n", " f.write(\"{},{},{}\\n\".format(image1_filename, image2_filename, fft_score))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def check_match_curves(row_index):\n", " image1_basename = top_matches[\"image1\"][row_index]\n", " image2_basename = top_matches[\"image2\"][row_index]\n", " fft_score = top_matches[\"fft_score\"][row_index]\n", " fft1_filename = base_fft_dir + image1_basename + '_fft.mat'\n", " fft2_filename = base_fft_dir + image2_basename\n", " curve1_filename = base_csv_dir + image1_basename + '.csv'\n", " curve2_filename = base_csv_dir + image2_basename[:-8] + '.csv'\n", " if 'south' in image1_basename and 'south' in image2_basename:\n", " print('CONFLICTING BORDERS!')\n", " return\n", " if 'north' in image1_basename and 'north' in image2_basename:\n", " print('CONFLICTING BORDERS!')\n", " return\n", " fft1 = sio.loadmat(fft1_filename)['fft']\n", " fft2 = sio.loadmat(fft2_filename)['fft']\n", " curve1restored = np.real(ifft(fft1))\n", " curve2restored = np.real(ifft(fft2))\n", " curve1xy = pd.read_csv(curve1_filename)\n", " curve2xy = pd.read_csv(curve2_filename)\n", " curve1xyn = curve1xy - curve1xy.mean()\n", " curve2xyn = curve2xy - curve2xy.mean()\n", " curve1y = curve1xyn.ix[:,1] \n", " curve2y = curve2xyn.ix[:,1]\n", " commonsize = min(curve1y.size, curve2y.size)\n", " curve1yt = curve1y[:commonsize]\n", " curve2yt = curve2y[:commonsize]\n", " rms = sqrt(mean_squared_error(curve1yt,curve2yt))\n", " print(rms)\n", " print(curve1_filename)\n", " print(curve2_filename)\n", " plt.figure()\n", " plt.plot(curve1y)\n", " plt.plot(curve2y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def compute_match_curves(row_index):\n", " image1_basename = top_matches[\"image1\"][row_index]\n", " image2_basename = top_matches[\"image2\"][row_index]\n", " fft_score = top_matches[\"fft_score\"][row_index]\n", " image1_filename = image1_basename[:-6] + '.jpg'\n", " image2_filename = image2_basename[:-14] + '.jpg'\n", " fft1_filename = base_fft_dir + image1_basename + '_fft.mat'\n", " fft2_filename = base_fft_dir + image2_basename\n", " curve1_filename = base_csv_dir + image1_basename + '.csv'\n", " curve2_filename = base_csv_dir + image2_basename[:-8] + '.csv'\n", " curve1xy = pd.read_csv(curve1_filename)\n", " curve2xy = pd.read_csv(curve2_filename)\n", " curve1xyn = curve1xy - curve1xy.mean()\n", " curve2xyn = curve2xy - curve2xy.mean()\n", " curve1y = curve1xyn.ix[:,1] \n", " curve2y = curve2xyn.ix[:,1]\n", " commonsize = min(curve1y.size, curve2y.size)\n", " curve1yt = curve1y[:commonsize]\n", " curve2yt = curve2y[:commonsize]\n", " pow1 = sqrt((curve1yt**2).sum())\n", " pow2 = sqrt((curve2yt**2).sum())\n", " conflict = False\n", " verified = False\n", " rms = sqrt(mean_squared_error(curve1yt,curve2yt))\n", " if 'south' in image1_basename and 'south' in image2_basename:\n", " conflict = True\n", " if 'north' in image1_basename and 'north' in image2_basename:\n", " conflict = True\n", " return rms, fft_score, min(pow1, pow2), row_index, image1_filename, image2_filename, conflict, verified" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rmss = [compute_match_curves(x) for x in range(0,2000)]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "review = pd.DataFrame(sorted(rmss,key=operator.itemgetter(3),reverse=True),\n", " columns=[\"rms\", \"fft_score\", \"minpow1pow2\", \"row_index\", \"image1_filename\", \"image2_filename\", \"conflict\", \"verified\"])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "review = review[~review['conflict']]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>rms</th>\n", " <th>fft_score</th>\n", " <th>minpow1pow2</th>\n", " <th>row_index</th>\n", " <th>image1_filename</th>\n", " <th>image2_filename</th>\n", " <th>conflict</th>\n", " <th>verified</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4</th>\n", " <td>0.087589</td>\n", " <td>0.513633</td>\n", " <td>0.563430</td>\n", " <td>1995</td>\n", " <td>MS-DAR-00056-000-00203.jpg</td>\n", " <td>MS-DAR-00050-000-00054.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0.005059</td>\n", " <td>0.519002</td>\n", " <td>0.118997</td>\n", " <td>1994</td>\n", " <td>MS-DAR-00185-000-00418.jpg</td>\n", " <td>MS-DAR-00029-00001-000-00082.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0.019293</td>\n", " <td>0.526031</td>\n", " <td>0.301725</td>\n", " <td>1992</td>\n", " <td>MS-DAR-00059-00002-000-00031.jpg</td>\n", " <td>MS-DAR-00209-00015-000-00074.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0.061248</td>\n", " <td>0.537222</td>\n", " <td>0.508968</td>\n", " <td>1990</td>\n", " <td>MS-DAR-00209-00005-000-00121.jpg</td>\n", " <td>MS-DAR-00077-000-00254.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>0.016648</td>\n", " <td>0.560654</td>\n", " <td>0.272364</td>\n", " <td>1988</td>\n", " <td>MS-DAR-00059-00002-000-00163.jpg</td>\n", " <td>MS-DAR-00053-00001-000-00156.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rms fft_score minpow1pow2 row_index \\\n", "4 0.087589 0.513633 0.563430 1995 \n", "5 0.005059 0.519002 0.118997 1994 \n", "7 0.019293 0.526031 0.301725 1992 \n", "9 0.061248 0.537222 0.508968 1990 \n", "11 0.016648 0.560654 0.272364 1988 \n", "\n", " image1_filename image2_filename \\\n", "4 MS-DAR-00056-000-00203.jpg MS-DAR-00050-000-00054.jpg \n", "5 MS-DAR-00185-000-00418.jpg MS-DAR-00029-00001-000-00082.jpg \n", "7 MS-DAR-00059-00002-000-00031.jpg MS-DAR-00209-00015-000-00074.jpg \n", "9 MS-DAR-00209-00005-000-00121.jpg MS-DAR-00077-000-00254.jpg \n", "11 MS-DAR-00059-00002-000-00163.jpg MS-DAR-00053-00001-000-00156.jpg \n", "\n", " conflict verified \n", "4 False False \n", "5 False False \n", "7 False False \n", "9 False False \n", "11 False False " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7fb35146ed68>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAFkCAYAAACuFXjcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWBAREZGcFCyIiIhITgoWREREJKf/H9EJahBpsceCAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb3515bf780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(review['rms'], review['fft_score'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "matches = pd.read_csv(base_dir + 'confirmed_matches.csv',\n", " names = ['image1_filename', 'image2_filename', 'fft_score'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>image1_filename</th>\n", " <th>image2_filename</th>\n", " <th>fft_score</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>MS-DAR-00084-00002-000-00307.jpg</td>\n", " <td>MS-DAR-00085-000-00150.jpg</td>\n", " <td>0.999843</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>MS-DAR-00209-00009-000-00168.jpg</td>\n", " <td>MS-DAR-00209-00009-000-00088.jpg</td>\n", " <td>0.999837</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>MS-DAR-00056-000-00103.jpg</td>\n", " <td>MS-DAR-00055-000-00261.jpg</td>\n", " <td>0.999767</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>MS-DAR-00084-00002-000-00308.jpg</td>\n", " <td>MS-DAR-00085-000-00151.jpg</td>\n", " <td>0.999689</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>MS-DAR-00209-00014-000-00016.jpg</td>\n", " <td>MS-DAR-00209-00010-000-00084.jpg</td>\n", " <td>0.999662</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " image1_filename image2_filename \\\n", "0 MS-DAR-00084-00002-000-00307.jpg MS-DAR-00085-000-00150.jpg \n", "1 MS-DAR-00209-00009-000-00168.jpg MS-DAR-00209-00009-000-00088.jpg \n", "2 MS-DAR-00056-000-00103.jpg MS-DAR-00055-000-00261.jpg \n", "3 MS-DAR-00084-00002-000-00308.jpg MS-DAR-00085-000-00151.jpg \n", "4 MS-DAR-00209-00014-000-00016.jpg MS-DAR-00209-00010-000-00084.jpg \n", "\n", " fft_score \n", "0 0.999843 \n", "1 0.999837 \n", "2 0.999767 \n", "3 0.999689 \n", "4 0.999662 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matches.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "image_matches_index = pd.DataFrame(matches['image1_filename']+':'+matches['image2_filename'], columns=['image_index'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "image_review_index = pd.DataFrame(review['image2_filename']+':'+review['image1_filename'], columns=['image_index'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for index, row in review.iterrows():\n", " verified = False\n", " image_index = row['image1_filename'] + ':' + row['image2_filename']\n", " if image_index in image_matches_index.image_index.values:\n", " verified = True\n", " image_index = row['image2_filename'] + ':' + row['image1_filename']\n", " if image_index in image_matches_index.image_index.values:\n", " verified = True\n", " review.set_value(col='verified', index=index, value=verified)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "review.to_csv(base_dir + 'parametric_matches.csv', header=True, index=False,\n", " columns=[\"rms\", \"fft_score\", \"minpow1pow2\", \"row_index\", \"image1_filename\", \"image2_filename\", \"verified\"])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reviewverified = review[review['verified']]\n", "min_fft_score = min(reviewverified['fft_score'])\n", "max_rms = max(reviewverified['rms'])\n", "min_minpower = min(reviewverified['minpow1pow2'])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "candidates = review[review['verified']==False]\n", "candidates = candidates[candidates['rms'] < max_rms]\n", "candidates = candidates[candidates['fft_score'] > min_fft_score]\n", "candidates = candidates[candidates['minpow1pow2'] > min_minpower]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>rms</th>\n", " <th>fft_score</th>\n", " <th>minpow1pow2</th>\n", " <th>row_index</th>\n", " <th>image1_filename</th>\n", " <th>image2_filename</th>\n", " <th>conflict</th>\n", " <th>verified</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1646</th>\n", " <td>0.068430</td>\n", " <td>0.997431</td>\n", " <td>4.847854</td>\n", " <td>353</td>\n", " <td>MS-DAR-00010-00002-000-00283.jpg</td>\n", " <td>MS-DAR-00084-00002-000-00398.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1673</th>\n", " <td>0.056562</td>\n", " <td>0.997627</td>\n", " <td>2.662005</td>\n", " <td>326</td>\n", " <td>MS-DAR-00082-000-00314.jpg</td>\n", " <td>MS-DAR-00088-000-00088.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1706</th>\n", " <td>0.043420</td>\n", " <td>0.997828</td>\n", " <td>2.675352</td>\n", " <td>293</td>\n", " <td>MS-DAR-00083-000-00025.jpg</td>\n", " <td>MS-DAR-00018-00001-000-00077.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1750</th>\n", " <td>0.055262</td>\n", " <td>0.998073</td>\n", " <td>4.772641</td>\n", " <td>249</td>\n", " <td>MS-DAR-00209-00005-000-00116.jpg</td>\n", " <td>MS-DAR-00208-000-00214.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1868</th>\n", " <td>0.025332</td>\n", " <td>0.998839</td>\n", " <td>3.547572</td>\n", " <td>131</td>\n", " <td>MS-DAR-00018-00001-000-00190.jpg</td>\n", " <td>MS-DAR-00012-000-00165.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1909</th>\n", " <td>0.014621</td>\n", " <td>0.999101</td>\n", " <td>2.581176</td>\n", " <td>90</td>\n", " <td>MS-DAR-00081-000-00159.jpg</td>\n", " <td>MS-DAR-00209-00009-000-00158.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1940</th>\n", " <td>0.016840</td>\n", " <td>0.999316</td>\n", " <td>2.807304</td>\n", " <td>59</td>\n", " <td>MS-DAR-00205-00007-000-00281.jpg</td>\n", " <td>MS-DAR-00013-000-00030.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1997</th>\n", " <td>0.009677</td>\n", " <td>0.999853</td>\n", " <td>7.206468</td>\n", " <td>2</td>\n", " <td>MS-DAR-00087-000-00010.jpg</td>\n", " <td>MS-DAR-00087-000-00008.jpg</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rms fft_score minpow1pow2 row_index \\\n", "1646 0.068430 0.997431 4.847854 353 \n", "1673 0.056562 0.997627 2.662005 326 \n", "1706 0.043420 0.997828 2.675352 293 \n", "1750 0.055262 0.998073 4.772641 249 \n", "1868 0.025332 0.998839 3.547572 131 \n", "1909 0.014621 0.999101 2.581176 90 \n", "1940 0.016840 0.999316 2.807304 59 \n", "1997 0.009677 0.999853 7.206468 2 \n", "\n", " image1_filename image2_filename \\\n", "1646 MS-DAR-00010-00002-000-00283.jpg MS-DAR-00084-00002-000-00398.jpg \n", "1673 MS-DAR-00082-000-00314.jpg MS-DAR-00088-000-00088.jpg \n", "1706 MS-DAR-00083-000-00025.jpg MS-DAR-00018-00001-000-00077.jpg \n", "1750 MS-DAR-00209-00005-000-00116.jpg MS-DAR-00208-000-00214.jpg \n", "1868 MS-DAR-00018-00001-000-00190.jpg MS-DAR-00012-000-00165.jpg \n", "1909 MS-DAR-00081-000-00159.jpg MS-DAR-00209-00009-000-00158.jpg \n", "1940 MS-DAR-00205-00007-000-00281.jpg MS-DAR-00013-000-00030.jpg \n", "1997 MS-DAR-00087-000-00010.jpg MS-DAR-00087-000-00008.jpg \n", "\n", " conflict verified \n", "1646 False False \n", "1673 False False \n", "1706 False False \n", "1750 False False \n", "1868 False False \n", "1909 False False \n", "1940 False False \n", "1997 False False " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "candidates.sort_values('row_index', ascending=False)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.01632206704005477\n", "/data/amnh/darwin/image_csvs/MS-DAR-00088-000-00340_north.csv\n", "/data/amnh/darwin/image_csvs/MS-DAR-00088-000-00342_south.csv\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiYAAAFkCAYAAAAUtvC8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb3513ca160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "check_match_curves(368)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MS-DAR-00088-000-00340_north MS-DAR-00088-000-00342_south_fft.mat 0.997299017696\n", "MS-DAR-00088-000-00340.jpg\n", "MS-DAR-00088-000-00342.jpg\n" ] } ], "source": [ "save_match(368)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

wcmckee/niketa

markdown.ipynb

2

2623

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "gm" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a title:\n", "test\n", "Enter your paragraph. Enter a blank line to finish.\n", "this is a test\n", "\n" ] } ], "source": [ "#! /usr/bin/python\n", "\n", "import dominate # 'pip install dominate' must be run once before this\n", "from dominate.tags import *\n", "import re # Only needed because dominate doesn't let us set tab indentation\n", "import tempfile\n", "import webbrowser\n", "\n", "def generate_html(title, paragraphs):\n", " page = dominate.document(title=title)\n", " with page:\n", " for paragraph in paragraphs:\n", " p(paragraph)\n", " return str(page)\n", "\n", "def read_paragraphs():\n", " to_return = {'title': '', 'paragraphs': []}\n", " to_return['title'] = raw_input(\"Enter a title:\\n\")\n", " print \"Enter your paragraph. Enter a blank line to finish.\"\n", " for line in iter(raw_input, ''):\n", " to_return['paragraphs'].append(line)\n", " return to_return\n", "\n", "if __name__ == '__main__':\n", " user_input = read_paragraphs()\n", " html_output = generate_html(user_input['title'], user_input['paragraphs'])\n", " with tempfile.NamedTemporaryFile(mode='w+t', suffix='.html', delete=False) as out:\n", " # HACK: This is silly, but dominate doesn't provide a way to change the\n", " # number of spaces used for a tab, but since it gives us exactly half\n", " # as many as we want, we can double-up on spaces at start of line and\n", " # get what we want\n", " out.write(re.sub(r'^(\\s*)', r'\\1\\1', html_output, flags=re.MULTILINE))\n", " webbrowser.open(out.name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

regardscitoyens/consultation_an

exploitation/analyse_quanti_theme6.ipynb

1

464171

{ "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2+" }, "name": "", "signature": "sha256:afb2c5832fbbf40fff0363ee31c1af05f848b86115967cf398c6620f9b0b7dce" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import json\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Reading the data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def loadContributions(file, withsexe=False):\n", " contributions = pd.read_json(path_or_buf=file, orient=\"columns\")\n", " rows = [];\n", " rindex = [];\n", " for i in range(0, contributions.shape[0]):\n", " row = {};\n", " row['id'] = contributions['id'][i]\n", " rindex.append(contributions['id'][i])\n", " if (withsexe):\n", " if (contributions['sexe'][i] == 'Homme'):\n", " row['sexe'] = 0\n", " else:\n", " row['sexe'] = 1\n", " for question in contributions['questions'][i]:\n", " if (question.get('Reponse')) and question['titreQuestion'][-2:] != '38' and question['titreQuestion'][-2:] != '37':\n", " row[question['titreQuestion']+' : '+question['texte']] = 1\n", " for criteres in question.get('Reponse'):\n", " # print(criteres['critere'].keys())\n", " row[question['titreQuestion']+'. (R\u00e9ponse) '+question['texte']+' -> '+str(criteres['critere'].get('texte'))] = 1\n", " rows.append(row)\n", " df = pd.DataFrame(data=rows)\n", " df.fillna(0, inplace=True)\n", " return df\n", "\n", "df = loadContributions('../data/EGALITE6.brut.json', True)\n", "df.fillna(0, inplace=True)\n", "df.index = df['id']\n", "#df.to_csv('consultation_an.csv', format='%d')\n", "#df.columns = ['Q_' + str(col+1) for col in range(len(df.columns) - 2)] + ['id' , 'sexe']\n", "df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Question n\u00b0 35 : Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt;</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Autre</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L'\u00e9gal acc\u00e8s des femmes et des hommes aux mandats \u00e9lectoraux et aux fonctions \u00e9lectives, ainsi qu'aux responsabilit\u00e9s professionnelles et sociales</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L'\u00e9galit\u00e9 professionnelle et salariale et la mixit\u00e9 dans les m\u00e9tiers</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La ma\u00eetrise de la sexualit\u00e9, notamment par l'acc\u00e8s \u00e0 la contraception et \u00e0 l'interruption volontaire de grossesse</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La prostitution</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La pr\u00e9carit\u00e9 des femmes</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La recherche sur la construction sociale des r\u00f4les sexu\u00e9s</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Les pr\u00e9jug\u00e9s sur la place et le r\u00f4le des femmes et des hommes dans la soci\u00e9t\u00e9</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Les violences faites aux femmes et les atteintes \u00e0 leur dignit\u00e9</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L\u2019articulation des temps de vie et le partage des responsabilit\u00e9s parentales</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L\u2019\u00e9gal acc\u00e8s des femmes et des hommes \u00e0 la cr\u00e9ation et \u00e0 la production culturelle et artistique, ainsi qu'\u00e0 la diffusion des \u0153uvres</th>\n", " <th>Question n\u00b0 36 : Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt;</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous avez une opinion \u00e0 exprimer sur ce probl\u00e8me</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous faites des recherches approfondies sur ce probl\u00e8me</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous pouvez, par votre action, r\u00e9soudre ou am\u00e9liorer ce probl\u00e8me</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous subissez ou avez subi ce probl\u00e8me</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous \u00eates en contact avec des personnes qui subissent ou ont subi ce probl\u00e8me</th>\n", " <th>id</th>\n", " <th>sexe</th>\n", " </tr>\n", " <tr>\n", " <th>id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>lpucmWo=</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>lpucmWo=</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>lpucmmU=</th>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>lpucmmU=</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>mZmc</th>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>mZmc</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>lpucmmw=</th>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>lpucmmw=</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>lpucm2k=</th>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>lpucm2k=</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " Question n\u00b0 35 : Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 1.0 \n", "mZmc 1.0 \n", "lpucmmw= 1.0 \n", "lpucm2k= 1.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> Autre \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> L'\u00e9gal acc\u00e8s des femmes et des hommes aux mandats \u00e9lectoraux et aux fonctions \u00e9lectives, ainsi qu'aux responsabilit\u00e9s professionnelles et sociales \\\n", "id \n", "lpucmWo= 0.0 \n", "lpucmmU= 1.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> L'\u00e9galit\u00e9 professionnelle et salariale et la mixit\u00e9 dans les m\u00e9tiers \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 1.0 \n", "mZmc 1.0 \n", "lpucmmw= 1.0 \n", "lpucm2k= 1.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> La ma\u00eetrise de la sexualit\u00e9, notamment par l'acc\u00e8s \u00e0 la contraception et \u00e0 l'interruption volontaire de grossesse \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> La prostitution \\\n", "id \n", "lpucmWo= 0.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> La pr\u00e9carit\u00e9 des femmes \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 0.0 \n", "mZmc 1.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 1.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> La recherche sur la construction sociale des r\u00f4les sexu\u00e9s \\\n", "id \n", "lpucmWo= 0.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> Les pr\u00e9jug\u00e9s sur la place et le r\u00f4le des femmes et des hommes dans la soci\u00e9t\u00e9 \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 1.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> Les violences faites aux femmes et les atteintes \u00e0 leur dignit\u00e9 \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 1.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> L\u2019articulation des temps de vie et le partage des responsabilit\u00e9s parentales \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 1.0 \n", "lpucm2k= 1.0 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> L\u2019\u00e9gal acc\u00e8s des femmes et des hommes \u00e0 la cr\u00e9ation et \u00e0 la production culturelle et artistique, ainsi qu'\u00e0 la diffusion des \u0153uvres \\\n", "id \n", "lpucmWo= 0.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 36 : Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 1.0 \n", "mZmc 1.0 \n", "lpucmmw= 1.0 \n", "lpucm2k= 1.0 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous avez une opinion \u00e0 exprimer sur ce probl\u00e8me \\\n", "id \n", "lpucmWo= 0.0 \n", "lpucmmU= 1.0 \n", "mZmc 0.0 \n", "lpucmmw= 1.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous faites des recherches approfondies sur ce probl\u00e8me \\\n", "id \n", "lpucmWo= 0.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous pouvez, par votre action, r\u00e9soudre ou am\u00e9liorer ce probl\u00e8me \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 0.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous subissez ou avez subi ce probl\u00e8me \\\n", "id \n", "lpucmWo= 0.0 \n", "lpucmmU= 1.0 \n", "mZmc 0.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 1.0 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous \u00eates en contact avec des personnes qui subissent ou ont subi ce probl\u00e8me \\\n", "id \n", "lpucmWo= 1.0 \n", "lpucmmU= 0.0 \n", "mZmc 1.0 \n", "lpucmmw= 0.0 \n", "lpucm2k= 0.0 \n", "\n", " id sexe \n", "id \n", "lpucmWo= lpucmWo= 1 \n", "lpucmmU= lpucmmU= 1 \n", "mZmc mZmc 0 \n", "lpucmmw= lpucmmw= 0 \n", "lpucm2k= lpucm2k= 1 " ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build clustering model\n", "\n", "Here we build a kmeans model , and select the \"optimal\" of clusters.\n", "\n", "Here we see that the optimal number of clusters is 2." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cluster import KMeans\n", "from sklearn import metrics\n", "import numpy as np\n", "X = df.drop('id', axis=1).values\n", "\n", "def train_kmeans(nb_clusters, X):\n", " kmeans = KMeans(n_clusters=nb_clusters, random_state=0).fit(X)\n", " return kmeans\n", "#print(kmeans.predict(X))\n", "#kmeans.cluster_centers_\n", "\n", "\n", "def select_nb_clusters():\n", " perfs = {};\n", " for nbclust in range(2,10):\n", " kmeans_model = train_kmeans(nbclust, X);\n", " labels = kmeans_model.labels_\n", " # from http://scikit-learn.org/stable/modules/clustering.html#calinski-harabaz-index\n", " # we are in an unsupervised model. cannot get better!\n", " # perfs[nbclust] = metrics.calinski_harabaz_score(X, labels);\n", " perfs[nbclust] = metrics.silhouette_score(X, labels);\n", " print(perfs);\n", " return perfs;\n", "\n", "\n", "df['clusterindex'] = train_kmeans(4, X).predict(X)\n", "#df \n", "\n", "perfs = select_nb_clusters();\n", "# result :\n", "# {2: 341.07570462155348, 3: 227.39963334619881, 4: 186.90438345452918, 5: 151.03979976346525, 6: 129.11214073405731, 7: 112.37235520885432, 8: 102.35994869157568, 9: 93.848315820675438}\n", "\n", "optimal_nb_clusters = max(perfs, key=perfs.get);\n", "\n", "print(\"optimal_nb_clusters\" , optimal_nb_clusters);" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{2: 0.1022948444498088, 3: 0.089892671087531822, 4: 0.084850469187806449, 5: 0.087176553371703089, 6: 0.086745080854009643, 7: 0.088138123748739097, 8: 0.085930085539231232, 9: 0.089193013261875662}\n", "optimal_nb_clusters 2\n" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 1, "metadata": { "collapsed": false }, "source": [ "Build the optimal model and apply it" ] }, { "cell_type": "code", "collapsed": false, "input": [ "km_model = train_kmeans(optimal_nb_clusters, X);\n", "df['clusterindex'] = km_model.predict(X)\n", "lGroupBy = df.groupby(['clusterindex']).mean();" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "cluster_profile_counts = df.groupby(['clusterindex']).count();\n", "cluster_profile_means = df.groupby(['clusterindex']).mean();\n", "global_counts = df.count()\n", "global_means = df.mean()\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "cluster_profile_counts.head(10)\n" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Question n\u00b0 35 : Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt;</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Autre</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L'\u00e9gal acc\u00e8s des femmes et des hommes aux mandats \u00e9lectoraux et aux fonctions \u00e9lectives, ainsi qu'aux responsabilit\u00e9s professionnelles et sociales</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L'\u00e9galit\u00e9 professionnelle et salariale et la mixit\u00e9 dans les m\u00e9tiers</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La ma\u00eetrise de la sexualit\u00e9, notamment par l'acc\u00e8s \u00e0 la contraception et \u00e0 l'interruption volontaire de grossesse</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La prostitution</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La pr\u00e9carit\u00e9 des femmes</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La recherche sur la construction sociale des r\u00f4les sexu\u00e9s</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Les pr\u00e9jug\u00e9s sur la place et le r\u00f4le des femmes et des hommes dans la soci\u00e9t\u00e9</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Les violences faites aux femmes et les atteintes \u00e0 leur dignit\u00e9</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L\u2019articulation des temps de vie et le partage des responsabilit\u00e9s parentales</th>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L\u2019\u00e9gal acc\u00e8s des femmes et des hommes \u00e0 la cr\u00e9ation et \u00e0 la production culturelle et artistique, ainsi qu'\u00e0 la diffusion des \u0153uvres</th>\n", " <th>Question n\u00b0 36 : Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt;</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous avez une opinion \u00e0 exprimer sur ce probl\u00e8me</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous faites des recherches approfondies sur ce probl\u00e8me</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous pouvez, par votre action, r\u00e9soudre ou am\u00e9liorer ce probl\u00e8me</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous subissez ou avez subi ce probl\u00e8me</th>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous \u00eates en contact avec des personnes qui subissent ou ont subi ce probl\u00e8me</th>\n", " <th>id</th>\n", " <th>sexe</th>\n", " </tr>\n", " <tr>\n", " <th>clusterindex</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " <td>464</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " <td>371</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " Question n\u00b0 35 : Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> Autre \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> L'\u00e9gal acc\u00e8s des femmes et des hommes aux mandats \u00e9lectoraux et aux fonctions \u00e9lectives, ainsi qu'aux responsabilit\u00e9s professionnelles et sociales \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> L'\u00e9galit\u00e9 professionnelle et salariale et la mixit\u00e9 dans les m\u00e9tiers \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> La ma\u00eetrise de la sexualit\u00e9, notamment par l'acc\u00e8s \u00e0 la contraception et \u00e0 l'interruption volontaire de grossesse \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> La prostitution \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> La pr\u00e9carit\u00e9 des femmes \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> La recherche sur la construction sociale des r\u00f4les sexu\u00e9s \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> Les pr\u00e9jug\u00e9s sur la place et le r\u00f4le des femmes et des hommes dans la soci\u00e9t\u00e9 \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> Les violences faites aux femmes et les atteintes \u00e0 leur dignit\u00e9 \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> L\u2019articulation des temps de vie et le partage des responsabilit\u00e9s parentales \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : <i>(vous pouvez cocher plusieurs cases)</i> -> L\u2019\u00e9gal acc\u00e8s des femmes et des hommes \u00e0 la cr\u00e9ation et \u00e0 la production culturelle et artistique, ainsi qu'\u00e0 la diffusion des \u0153uvres \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 36 : Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous avez une opinion \u00e0 exprimer sur ce probl\u00e8me \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous faites des recherches approfondies sur ce probl\u00e8me \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous pouvez, par votre action, r\u00e9soudre ou am\u00e9liorer ce probl\u00e8me \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous subissez ou avez subi ce probl\u00e8me \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : <i>(vous pouvez cocher plusieurs cases)</i> -> Vous \u00eates en contact avec des personnes qui subissent ou ont subi ce probl\u00e8me \\\n", "clusterindex \n", "0 464 \n", "1 371 \n", "\n", " id sexe \n", "clusterindex \n", "0 464 464 \n", "1 371 371 " ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "df_profiles = pd.DataFrame();\n", "nbclusters = cluster_profile_means.shape[0]\n", "df_profiles['clusterindex'] = range(nbclusters)\n", "for col in cluster_profile_means.columns:\n", " if(col != \"clusterindex\"):\n", " df_profiles[col] = np.zeros(nbclusters)\n", " for cluster in range(nbclusters):\n", " df_profiles[col][cluster] = cluster_profile_means[col][cluster]\n", "# row.append(df[col].mean());\n", "df_profiles.head()\n", "\n", "#print(df_profiles.columns) \n", "\n", "intereseting_columns = {};\n", "for col in df_profiles.columns:\n", " if(col != \"clusterindex\"):\n", " global_mean = df[col].mean()\n", " diff_means_global = abs(df_profiles[col] - global_mean). max();\n", " # print(col , diff_means_global)\n", " if(diff_means_global > 0.05):\n", " intereseting_columns[col] = True\n", " \n", "#print(intereseting_columns)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "-c:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": true, "input": [ "%matplotlib inline\n", "\n", "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cluster Profiles\n", "\n", "Here, the optimal model ihas two clusters , cluster 0 with 399 cases, and 1 with 537 cases. \n", "\n", "As this model is based on binary inputs. Given this, the best description of the clusters is by the distribution of zeros and ones of each input (question).\n", "\n", "The figure below gives the cluster profiles of this model. Cluster 0 on the left. 1 on the right. The questions invloved as different (highest bars)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "interesting = list(intereseting_columns.keys())\n", "df_profiles_sorted = df_profiles[interesting].sort_index(axis=1)\n", "df_profiles_sorted.plot.bar(figsize =(1, 1))\n", "df_profiles_sorted.plot.bar(figsize =(16, 8), legend=False)\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9046de978>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAACx8AAAFSCAYAAAApPoN7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERERERETkkP8Hf4jvMWjaV5EAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x7fe90471be80>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA58AAAHcCAYAAABRQh6XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAgAA0J34BAAAoLv/B72HvZNNjpoJAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x7fe90471beb8>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "df_profiles_sorted.T" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L'\u00e9gal acc\u00e8s des femmes et des hommes aux mandats \u00e9lectoraux et aux fonctions \u00e9lectives, ainsi qu'aux responsabilit\u00e9s professionnelles et sociales</th>\n", " <td>0.159483</td>\n", " <td>0.417790</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L'\u00e9galit\u00e9 professionnelle et salariale et la mixit\u00e9 dans les m\u00e9tiers</th>\n", " <td>0.403017</td>\n", " <td>0.673854</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La ma\u00eetrise de la sexualit\u00e9, notamment par l'acc\u00e8s \u00e0 la contraception et \u00e0 l'interruption volontaire de grossesse</th>\n", " <td>0.140086</td>\n", " <td>0.525606</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La prostitution</th>\n", " <td>0.025862</td>\n", " <td>0.132075</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La pr\u00e9carit\u00e9 des femmes</th>\n", " <td>0.105603</td>\n", " <td>0.409704</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; La recherche sur la construction sociale des r\u00f4les sexu\u00e9s</th>\n", " <td>0.118534</td>\n", " <td>0.436658</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Les pr\u00e9jug\u00e9s sur la place et le r\u00f4le des femmes et des hommes dans la soci\u00e9t\u00e9</th>\n", " <td>0.571121</td>\n", " <td>0.886792</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Les violences faites aux femmes et les atteintes \u00e0 leur dignit\u00e9</th>\n", " <td>0.226293</td>\n", " <td>0.762803</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L\u2019articulation des temps de vie et le partage des responsabilit\u00e9s parentales</th>\n", " <td>0.265086</td>\n", " <td>0.466307</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les raisons qui vous ont incit\u00e9-e \u00e0 r\u00e9pondre \u00e0 ce questionnaire, merci d\u2019indiquer dans quel domaine vous pouvez par votre information ou votre exp\u00e9rience aider \u00e0 am\u00e9liorer l\u2019\u00e9galit\u00e9 entre les femmes et les hommes : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; L\u2019\u00e9gal acc\u00e8s des femmes et des hommes \u00e0 la cr\u00e9ation et \u00e0 la production culturelle et artistique, ainsi qu'\u00e0 la diffusion des \u0153uvres</th>\n", " <td>0.079741</td>\n", " <td>0.266846</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous avez une opinion \u00e0 exprimer sur ce probl\u00e8me</th>\n", " <td>0.497845</td>\n", " <td>0.654987</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous faites des recherches approfondies sur ce probl\u00e8me</th>\n", " <td>0.165948</td>\n", " <td>0.415094</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous pouvez, par votre action, r\u00e9soudre ou am\u00e9liorer ce probl\u00e8me</th>\n", " <td>0.299569</td>\n", " <td>0.606469</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous subissez ou avez subi ce probl\u00e8me</th>\n", " <td>0.269397</td>\n", " <td>0.541779</td>\n", " </tr>\n", " <tr>\n", " <th>Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 am\u00e9liorer le ou les probl\u00e8mes identifi\u00e9s \u00e0 la question pr\u00e9c\u00e9dente, car : &lt;i&gt;(vous pouvez cocher plusieurs cases)&lt;/i&gt; -&gt; Vous \u00eates en contact avec des personnes qui subissent ou ont subi ce probl\u00e8me</th>\n", " <td>0.301724</td>\n", " <td>0.735849</td>\n", " </tr>\n", " <tr>\n", " <th>sexe</th>\n", " <td>0.741379</td>\n", " <td>0.870620</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ " 0 1\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.159483 0.417790\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.403017 0.673854\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.140086 0.525606\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.025862 0.132075\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.105603 0.409704\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.118534 0.436658\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.571121 0.886792\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.226293 0.762803\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.265086 0.466307\n", "Question n\u00b0 35. (R\u00e9ponse) Pour pr\u00e9ciser les rai... 0.079741 0.266846\n", "Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 a... 0.497845 0.654987\n", "Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 a... 0.165948 0.415094\n", "Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 a... 0.299569 0.606469\n", "Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 a... 0.269397 0.541779\n", "Question n\u00b0 36. (R\u00e9ponse) Vous pouvez aider \u00e0 a... 0.301724 0.735849\n", "sexe 0.741379 0.870620" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "#df_profiles.sort_index(axis=1).T" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Analyse\n", "##Questions finales\n", "\n", "\n", "Deux groupes de personnes \u00e9mergent\n", " - 537 personnes n'ont pas r\u00e9pondu \u00e0 la question ouverte\n", " - 298 y ont r\u00e9pondu\n", " \n", "Ces derni\u00e8res sont plus favorable \u00e0 \u00eatre contact\u00e9e et d\u00e9clare plus largement pouvoir am\u00e9liorer ou \u00eatre en contact avec des personnes ayant subi le probl\u00e8me\n", "\n", "En ignorant les deux derni\u00e8res questions, on obtient deux autres groupes :\n", " - 464 personnes r\u00e9pondant dans la moyenne\n", " - 371 personnes d\u00e9clarant pouvoir aider plus majoritairement sur les pr\u00e9jug\u00e9s et les violences \n", " \n", "Ce dernier groupe ont d\u00e9clarer plus de motivation \u00e0 r\u00e9pondre sur d'auters th\u00e8mes et pense pouvoir plus aider" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

agpl-3.0

garth-wells/notebooks-3D7

index.ipynb

1

1074

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Finite Element Methods (3D7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These notebooks are in support of the course Finite Element Methods (3D7) at the Department of Engineering, University of Cambridge.\n", "\n", "Please report any errors to Garth N. Wells <[email protected]>.\n", "\n", "- [Basis finite element solver for an elastic bar](01-ElasticBarLinearFEM.ipynb)\n", "- [Shape functions](02-ShapeFunctions.ipynb)\n", "- [Time dependent problems](03-TimeDependentProblems.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

deepmind/deepmind-research

rl_unplugged/atari_dqn.ipynb

1

11458

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KDiJzbb8KFvP" }, "source": [ "Copyright 2020 DeepMind Technologies Limited.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n", "this file except in compliance with the License. You may obtain a copy of the\n", "License at\n", "\n", "[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n", "\n", "Unless required by applicable law or agreed to in writing, software distributed\n", "under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n", "CONDITIONS OF ANY KIND, either express or implied. See the License for the\n", "specific language governing permissions and limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ULdrhOaVbsdO" }, "source": [ "# RL Unplugged: Offline DQN - Atari\n", "## Guide to training an Acme DQN agent on Atari data.\n", "# \u003ca href=\"https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/rl_unplugged/atari_dqn.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xaJxoatMhJ71" }, "source": [ "## Installation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "colab": {}, "colab_type": "code", "id": "KH3O0zcXUeun" }, "outputs": [], "source": [ "!pip install dm-acme\n", "!pip install dm-acme[reverb]\n", "!pip install dm-acme[tf]\n", "!pip install dm-sonnet\n", "!pip install dopamine-rl==3.1.2\n", "!pip install atari-py\n", "!git clone https://github.com/deepmind/deepmind-research.git\n", "%cd deepmind-research" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "c-H2d6UZi7Sf" }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "colab": {}, "colab_type": "code", "id": "HJ74Id-8MERq" }, "outputs": [], "source": [ "import copy\n", "\n", "import acme\n", "from acme.agents.tf import actors\n", "from acme.agents.tf.dqn import learning as dqn\n", "from acme.tf import utils as acme_utils\n", "from acme.utils import loggers\n", "from rl_unplugged import atari\n", "import sonnet as snt\n", "import tensorflow as tf" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "JrOSnoWiY4Xl" }, "source": [ "## Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "Vi3_H_h1zy_0" }, "outputs": [], "source": [ "game = 'Pong' #@param\n", "run = 1 #@param\n", "\n", "tmp_path = '/tmp/atari'\n", "gs_path = 'gs://rl_unplugged/atari'\n", "\n", "!mkdir -p {tmp_path}/{game}\n", "\n", "src = f'{gs_path}/{game}/run_{run}-00000-of-00100'\n", "dest = f'{tmp_path}/{game}/run_{run}-00000-of-00001'\n", "!gsutil cp {src} {dest}" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "a9vF7LtYvLzy" }, "source": [ "## Dataset and environment" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "01AHHNd9cEX2" }, "outputs": [], "source": [ "batch_size = 10 #@param\n", "\n", "def discard_extras(sample):\n", " return sample._replace(data=sample.data[:5])\n", "\n", "dataset = atari.dataset(path=tmp_path, game='Pong', run=1, num_shards=1)\n", "# Small batch size, experiments in the paper were run with batch size 256.\n", "dataset = dataset.map(discard_extras).batch(batch_size)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KoYBhjPtI_N6" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "4b4_rHwCmQg-" }, "outputs": [], "source": [ "environment = atari.environment(game='Pong')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "BukOfOsmtSQn" }, "source": [ "## DQN learner" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 83, "status": "ok", "timestamp": 1593614657342, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "3Jcjk1w6oHVX", "outputId": "1746b0bb-5a5c-45dd-b5a1-c77852545e12" }, "outputs": [ { "data": { "text/plain": [ "TensorSpec(shape=(6,), dtype=tf.float32, name=None)" ] }, "execution_count": 20, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Get total number of actions.\n", "num_actions = environment.action_spec().num_values\n", "\n", "# Create the Q network.\n", "network = snt.Sequential([\n", " lambda x: tf.image.convert_image_dtype(x, tf.float32),\n", " snt.Conv2D(32, [8, 8], [4, 4]),\n", " tf.nn.relu,\n", " snt.Conv2D(64, [4, 4], [2, 2]),\n", " tf.nn.relu,\n", " snt.Conv2D(64, [3, 3], [1, 1]),\n", " tf.nn.relu,\n", " snt.Flatten(),\n", " snt.nets.MLP([512, num_actions])\n", "])\n", "acme_utils.create_variables(network, [environment.observation_spec()])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "9CD2sNK-oA9S" }, "outputs": [], "source": [ "# Create a logger.\n", "logger = loggers.TerminalLogger(label='learner', time_delta=1.)\n", "\n", "# Create the DQN learner.\n", "learner = dqn.DQNLearner(\n", " network=network,\n", " target_network=copy.deepcopy(network),\n", " discount=0.99,\n", " learning_rate=3e-4,\n", " importance_sampling_exponent=0.2,\n", " target_update_period=2500,\n", " dataset=dataset,\n", " logger=logger)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oKeGQxzitXYC" }, "source": [ "## Training loop" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "height": 51 }, "colab_type": "code", "executionInfo": { "elapsed": 4694, "status": "ok", "timestamp": 1593614662237, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "VWZd5N-Qoz82", "outputId": "5ee2ce7c-b3fe-483b-8893-5a6e13519f48" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Learner] Loss = 0.003 | Steps = 1 | Walltime = 0\n", "[Learner] Loss = 0.004 | Steps = 54 | Walltime = 1.126\n" ] } ], "source": [ "for _ in range(100):\n", " learner.step()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "qFQDrp0CgIzU" }, "source": [ "## Evaluation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 15099, "status": "ok", "timestamp": 1593614677360, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "DWYHBalygIDF", "outputId": "4ec412c3-810a-4208-b521-919a8ece40df" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Evaluation] Episode Length = 842 | Episode Return = -20.000 | Episodes = 1 | Steps = 842 | Steps Per Second = 265.850\n", "[Evaluation] Episode Length = 792 | Episode Return = -21.000 | Episodes = 2 | Steps = 1634 | Steps Per Second = 270.043\n", "[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 3 | Steps = 2446 | Steps Per Second = 274.792\n", "[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 4 | Steps = 3258 | Steps Per Second = 270.967\n", "[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 5 | Steps = 4070 | Steps Per Second = 274.253\n" ] } ], "source": [ "# Create a logger.\n", "logger = loggers.TerminalLogger(label='evaluation', time_delta=1.)\n", "\n", "# Create an environment loop.\n", "policy_network = snt.Sequential([\n", " network,\n", " lambda q: tf.argmax(q, axis=-1),\n", "])\n", "loop = acme.EnvironmentLoop(\n", " environment=environment,\n", " actor=actors.DeprecatedFeedForwardActor(policy_network=policy_network),\n", " logger=logger)\n", "\n", "loop.run(5)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "last_runtime": { "build_target": "//learning/deepmind/dm_python:dm_notebook3", "kind": "private" }, "name": "RL Unplugged: Offline DQN - Atari", "provenance": [ { "file_id": "1g9yTbTuk9aeERxWflOWqUGpx2M3osx0l", "timestamp": 1593685504110 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

bmeaut/python_nlp_2017_fall

course_material/13_Semantics_2/13_Semantics_2_lecture.ipynb

1

24033

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 12. Semantics 2: common tasks in computational semantics" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 12.1 [Overview](#12.1)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### 12.2 [Relation extraction](#12.2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### 12.3 [Sentiment analysis](#12.3)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### 12.4 [Question answering](#12.4)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 12.1 Overview\n", "<a id='12.1'></a>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Three common tasks in computational semantics:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - Relation extraction (RE) - obtaining structured information from language data. E.g. parsing CVs to build a database for recruitment" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - Sentiment analysis (SA) - detecting attitudes and opinions in text, e.g. user reviews of movies, products, etc." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - Question answering (QA) - detecting a particular information need in user input and fulfilling it based on some text" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 12.2 Relation extraction\n", "<a id='12.2'></a>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "obtaining structured information from language data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "RE systems typically target a particular domain, e.g.:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - professional profile information based on CVs, Linkedin pages" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - product specifications based on e-commerce sites (webshops), manufacturers' websites" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - stock price information based on news articles" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - etc." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Example" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "_\"Gryffindor values courage, bravery, nerve, and chivalry. Gryffindor's mascot is the lion, and its colours are scarlet and gold. The Head of this house is the Transfiguration teacher and Deputy Headmistress, Minerva McGonagall until she becomes headmistress, and the house ghost is Sir Nicholas de Mimsy-Porpington, more commonly known as Nearly Headless Nick. According to Rowling, Gryffindor corresponds roughly to the element of fire. The founder of the house is Godric Gryffindor.\"_" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- values(Gryffindor, courage)\n", "- mascot(Gryffindor, lion)\n", "- color(Gryffindor, scarlet)\n", "- head(Gryffindor, Minerva_McGonagall)\n", "- house_ghost(Gryffindor, Sir_Nicholas_de_Mimsy-Porpington)\n", "- founder(Gryffindor, Godric_Gryffindor)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Rule-based approaches" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Templates:\n", "- _X dropped by Y points_ -> drop_by(X, Y)\n", "- _X, CEO of Y_ -> ceo_of(X, Y)\n", "- _X was born in Y_ -> born_in(X, Y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "If parsers/NER-taggers/Chunkers are available, templates can refer to their output:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- X_NP _dropped by_ Y_NUM _points_ -> drop_by(X, Y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "or\n", "\n", "- X_PERSON, CEO _of_ Y_ORGANIZATION -> ceo_of(X, Y)\n", "- X_PERSON _was born in_ Y_LOCATION -> born_in(X, Y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Pros:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- simple and effective, yields fast results" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- high precision" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Cons:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- low recall" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- limited, no capacity for generalization" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- real-life systems may contain thousands of templates, and require continuous development by experts" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- many companies still depend on such systems" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Supervised learning" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Use parsed and annotated text to train text classifiers" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "E.g. decide for each pair of named entities (PERSON and ORGANIZATION) whether they are in the \"ceo_of\" relationship, based on context features" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Supervised learning" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Features typically include:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- headwords" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- word/POS ngrams with position information" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- NER/Chunk tags, Chunk sequences" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Paths in parse trees among candidates (e.g. N - NP - S - VP - NP - N)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- gazetteer features: whether words/phrases appear on an external list of known entities" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Pros:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Effective if large training sample is available and target texts are similar" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Cons: " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- requires a fair amount of annotated data (costly to produce)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- doesn't generalize well across genres, domains" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Semi-supervised / unsupervised approaches" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "When little or no training data is available, we must use what we have to generalize:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- a few annotated examples -> some patterns -> more examples -> more patterns" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- a few patterns -> some examples -> more patterns -> more examples" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Example" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "seed tuple: author(William_Shakespeare, Hamlet) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "found instances:\n", "- _William Shakespeare's Hamlet_\n", "- _the William Shakespeare play Hamlet_\n", "- _Hamlet by William Shakespeare_\n", "- _Hamlet is a tragedy written by William Shakespeare_" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "extracted patterns:\n", "- X's Y\n", "- the X play Y\n", "- Y by X\n", "- Y is a tragedy written by X" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Finally, use these patterns to find new seeds" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 12.3 Sentiment analysis\n", "<a id='12.3'></a>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Also called __opinion mining__ - the task of extracting opinions, emotions, attitudes from user-generated text, e.g. about __products__, __movies__, or __politics__" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Simplest version: is the attitude of a text positive or negative" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- More complex: measure attitude on a scale (e.g. from 1 to 5)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Most complex: detect __target__ of opinion (e.g. what product is it about) or __aspect__ (e.g. is it about the price, looks, or quality of a product)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Baseline approach:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Use training data, extract standard features such as:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- bag-of-words" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- ngrams" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- emoticons" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- numbers, dates" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- gazetteer features (based on _Sentiment lexicons_)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Use these to train standard classifiers, e.g. Naive Bayes, SVM, MaxEnt, etc." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Example\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Advanced techniques" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- use semi-supervised methods to learn sentiment lexicons" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- model negation explicitly" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 12.4 Question answering\n", "<a id='12.1'></a>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "One of the oldest and most popular tasks in AI" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Recent products include Apple Siri, Amazon Alexa, or IBM's Watson" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "[Watch IBM's Watson win the game show Jeopardy](https://www.youtube.com/watch?v=WFR3lOm_xhE)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Major approaches:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - IR-based: handle questions as search queries (e.g. Watson, Google)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ " - Knowledge-based: convert question into a Relation Extraction task (e.g. Watson, Siri, Wolfram Alpha)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## IR-based approaches:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- detect question type" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- generate search queries from questions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- retrieve ranked documents" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- extract relevant passages, rerank" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- extract answer candidates" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- rank answers" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Question processing:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "_They’re the two states you could be reentering if you’re crossing\n", "Florida’s northern border_" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Answer Type: US state\n", "- Query: two states, border,Florida,north\n", "- Focus: the two states\n", "- Relations: borders(Florida,?x,north)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Answer type detection" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Supervised learning can be used to train a classifier on annotated data. See also [Li & Roth 2002](https://dl.acm.org/citation.cfm?id=1072378)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Keyword extraction" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\n", "\n", "From a slide by [Mihai Surdenau](http://www.surdeanu.info/mihai/)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Answer extraction" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- matching question and answer type" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- this'll yield several answer candidates that need to be ranked" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Answer ranking" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Some features for learning to rank:\n", "\n", "- Answer type match: Candidate contains a phrase with the correct answer type.\n", "- Pattern match: Regular expression pattern matches the candidate.\n", "- Question keywords: # of question keywords in the candidate.\n", "- Keyword distance: Distance in words between the candidate and query keywords \n", "- Novelty factor: A word in the candidate is not in the query.\n", "- ..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Source: [Jurafsky-Manning slides](http://spark-public.s3.amazonaws.com/nlp/slides/qa.pdf)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Approaches to ranking can be:\n", "- pointwise\n", "- pairwise\n", "- listwise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "see [Agarwal et al. 2012](http://www.prem-melville.com/publications/ranking-Watson-QA-cikm2012.pdf) for a short survey of algorithms" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Knowledge-based approaches:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Create semantic representation of query (understand what is being asked!)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "_Whose granddaughter starred in E.T.?_" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " (acted-in ?x “E.T.”)\n", " \n", " (granddaughter-of ?x ?y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- query relevant databases and ontologies" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Hybrid systems" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- candidate answers are generated with IR-based methods, using shallow semantic represenations" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- answers are reranked using knowledge-based methods" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

CCI-Tools/sandbox

notebooks/norman/xarray-ex-2.ipynb

1

292935

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Toy weather data\n", "\n", "Here is an example of how to easily manipulate a toy weather dataset using xarray and other recommended Python libraries:\n", "\n", "* Examine a dataset with pandas and seaborn\n", "* Probability of freeze by calendar month\n", "* Monthly averaging\n", "* Calculate monthly anomalies\n", "* Fill missing values with climatology\n", "\n", "Shared setup:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns # pandas aware plotting library\n", "\n", "np.random.seed(123)\n", "\n", "times = pd.date_range('2000-01-01', '2001-12-31', name='time')\n", "annual_cycle = np.sin(2 * np.pi * (times.dayofyear / 365.25 - 0.28))\n", "\n", "base = 10 + 15 * annual_cycle.reshape(-1, 1)\n", "tmin_values = base + 3 * np.random.randn(annual_cycle.size, 3)\n", "tmax_values = base + 10 + 3 * np.random.randn(annual_cycle.size, 3)\n", "\n", "ds = xr.Dataset({'tmin': (('time', 'location'), tmin_values),\n", " 'tmax': (('time', 'location'), tmax_values)},\n", " {'time': times, 'location': ['IA', 'IN', 'IL']})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examine a dataset with pandas and seaborn" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<xarray.Dataset>\n", "Dimensions: (location: 3, time: 731)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...\n", " * location (location) <U2 'IA' 'IN' 'IL'\n", "Data variables:\n", " tmax (time, location) float64 12.98 3.31 6.779 0.4479 6.373 4.843 ...\n", " tmin (time, location) float64 -8.037 -1.788 -3.932 -9.341 -6.558 ..." ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = ds.to_dataframe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>tmax</th>\n", " <th>tmin</th>\n", " </tr>\n", " <tr>\n", " <th>location</th>\n", " <th>time</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">IA</th>\n", " <th>2000-01-01</th>\n", " <td>12.980549</td>\n", " <td>-8.037369</td>\n", " </tr>\n", " <tr>\n", " <th>2000-01-02</th>\n", " <td>0.447856</td>\n", " <td>-9.341157</td>\n", " </tr>\n", " <tr>\n", " <th>2000-01-03</th>\n", " <td>5.322699</td>\n", " <td>-12.139719</td>\n", " </tr>\n", " <tr>\n", " <th>2000-01-04</th>\n", " <td>1.889425</td>\n", " <td>-7.492914</td>\n", " </tr>\n", " <tr>\n", " <th>2000-01-05</th>\n", " <td>0.791176</td>\n", " <td>-0.447129</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " tmax tmin\n", "location time \n", "IA 2000-01-01 12.980549 -8.037369\n", " 2000-01-02 0.447856 -9.341157\n", " 2000-01-03 5.322699 -12.139719\n", " 2000-01-04 1.889425 -7.492914\n", " 2000-01-05 0.791176 -0.447129" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>tmax</th>\n", " <th>tmin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2193.000000</td>\n", " <td>2193.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>20.108232</td>\n", " <td>9.975426</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>11.010569</td>\n", " <td>10.963228</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-3.506234</td>\n", " <td>-13.395763</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>9.853905</td>\n", " <td>-0.040347</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>19.967409</td>\n", " <td>10.060403</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>30.045588</td>\n", " <td>20.083590</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>43.271148</td>\n", " <td>33.456060</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " tmax tmin\n", "count 2193.000000 2193.000000\n", "mean 20.108232 9.975426\n", "std 11.010569 10.963228\n", "min -3.506234 -13.395763\n", "25% 9.853905 -0.040347\n", "50% 19.967409 10.060403\n", "75% 30.045588 20.083590\n", "max 43.271148 33.456060" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x141373ae710>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFvCAYAAACb2bjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIIgc4P8B8U9S+BVea/wAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x14137318e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.mean(dim='location').to_dataframe().plot()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x14137318780>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV8AAAFdCAYAAACkfW6KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x14137318b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(df.reset_index(), vars=ds.data_vars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probability of freeze by calendar month" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "freeze = (ds['tmin'] <= 0).groupby('time.month').mean('time')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<xarray.DataArray 'tmin' (month: 12, location: 3)>\n", "array([[ 0.9516129 , 0.88709677, 0.93548387],\n", " [ 0.84210526, 0.71929825, 0.77192982],\n", " [ 0.24193548, 0.12903226, 0.16129032],\n", " [ 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. ],\n", " [ 0. , 0.01612903, 0. ],\n", " [ 0.33333333, 0.35 , 0.23333333],\n", " [ 0.93548387, 0.85483871, 0.82258065]])\n", "Coordinates:\n", " * location (location) <U2 'IA' 'IN' 'IL'\n", " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "freeze" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x14138f639b0>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFkCAYAAAAaKfMiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x14138ea1c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "freeze.to_pandas().plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monthly averaging" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "monthly_avg = ds.resample('1MS', dim='time', how='mean')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x14138f59860>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFvCAYAAACb2bjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4lOeV8P/vFPWGem9INAECgeggOsY2LnFsx4XYie1N\nNhv/3iTe3dgbO/F64/xcUnaT2H6TrOPEMYkLthNXXEASQgUEAnWahFDvvWva+4cQxjZVzPM8M6Pz\nuS6uOEgz5wCauedu5+hsNpsNIYQQQmhKr3UCQgghhJABWQghhHAIMiALIYQQDkAGZCGEEMIByIAs\nhBBCOAAZkIUQQggHcFUDcmdnJ+vWraOmpoa6ujruuusutm/fzhNPPGGv/IQQQogpYdIDstls5vHH\nH8fT0xOAp556ioceeogdO3ZgtVrZvXu33ZIUQgghXN2kB+RnnnmGO++8k7CwMGw2G5WVlaSnpwOQ\nkZFBQUGB3ZIUQgghXN2kBuS3336b4OBgVq1axUShL6vVevbrPj4+9Pf32ydDIYQQYgowTuZBb7/9\nNjqdjry8PI4fP87DDz9Md3f32a8PDg7i7+9/yeex2WzodLrJpCCEEEK4lEkNyDt27Dj73/fccw9P\nPPEEzz77LAcPHmTJkiXk5OSwfPnySz6PTqejvV1m0kI4qtBQP3mNCmFnoaF+5/39SQ3I5/Pwww/z\n4x//GJPJRFJSElu3brXXUwshhBAuT6d1tyf59C2E45IZshD2d6EZshQGEUIIIRyADMhCCCGEA5AB\nWQghhHAAMiALIYQQDkAGZCGEEMIByIAshBBCOAC73UMWQgghlHDf05kX/NpLj2yY1HOOjY3xyScf\nsm3bzZNNy+5khiyEEGLK6ezs4L333tE6jc+RGbIQQghNvZFZxcFjbZN67L+/kH/e318yO4zbNyRf\n8HF/+cufOH36FBkZS9m8+Rp6enrp6+vhlltuJzt7Dw0N9Tz66H+SkjKP3//+eY4fP0pvby/JyTP4\nj//4CS+88GsMBiPf+ta/8P3vf5c77ribFStWTerPMEFmyEIIIaace++9j4SE6Xzzm/+Eh4cnv/zl\nb1i7dgP79+fxzDP/zd1338uePZ8wNDSEn58/v/rVc7z44l+oqCijo6ODb33ruxw+fIgnn3yclJS5\nVz0Yg8yQhRBCaOz2DckXnc1ebA/55/+y8qrjz5w5GwBfXz8SEqYD4Ofnx+joGO7u7nR3d/HEE4/h\n6enF8PAwZrMZo9HI7bffyc9+9p+89dYHV50DyAxZCCHEFKTT6bBarWf/+0L278+nra2Fxx9/km9/\n+7uMjo4ANvr6+vjLX/7Egw/+gGee+aldcpIBWQghxJQTGBiExWJmdHT0ot83d+48mpoaefDBb/HY\nYz8kOjqWjo52nnnmp2zffi+33HIb/v4BvPXW61edk3R7EkJckHR7EsL+pNuTEEII4cBkQBZCCCEc\ngAzIQgghhAOQAVkIIYRwADIgCyGEEA5ABmQhhBDCAUilLiGEEA7tu5k/vODXnt/w7KSe83K7PXV1\ndfLnP7/IQw89PKk4V0JmyEIIIaacy+32FBQUrMpgDDJDFkIIl6dEP2F7ervqfY60lU3qsT/Of+q8\nv58WNp9bkrdd8HGX2+0pKCiYxx//Eb///Z+49947SUtbRFXVSfR6PU8//Uu8vX0mlff5yAxZCCHE\nlHO53Z7gs1rXQ0ODbN58Lc899wdCQkIpKDh/68fJkhmyEEIITd2SvO2is9mL7SH/dOV/XHX8i3V7\n+qIZM2YCEBYWztjYxetgXymZIQshhAs72dCjdQoO6XK7PZ3vcUqRGbK4ao6+PyXEVGQyW/h7Tg0f\nF9ZpnYpDutxuT5/32WCsxMAs3Z7EVZMB2XVJtyfnVNPcx4vvV9LcOURYoBdt3cMX/N4fbV9MckyA\nitkJ6fYk7GpwxERpdSdv55zSOhUhxBlmi5W3c07xs78U0dw5xMZFMTzxzaUXfczPXzvCkZPtKmUo\nLkZmyOKSbDYbrd3DnGzoobqxl6rGPpo6Bi/rsTJDdm4yQ3Ye9W0DvPh+JfVtAwT7e3DfdXOYkxB0\n0ceUVnfwwj/KMZmtfH3LLNalRauU7dR2oRmy7CFPQZdaYh41WTjd3EdVYy/VjeP/OzBsOvs9Hm4G\n5sQHkhwdQHJMAP/9RokaaQshzsNitbJrfx3v5NZgsdpYkxrJHRtn4OVx6bf31KQQfnjnIv5nZwl/\n+fg43f2j3LwmUdGDS+LCZEAWn/PTlw9R19qPxfrZwklIgCfzEoNIig4gOTqAmDAfDPrL2+0wW6wY\nDbIzIoQSmjsHefH9o9Q09xHg6843r51NalLIFT3H9Ch/Hr1nMb96vZj38k/TMzDKPVtnXfZrXNiP\nDMjic+pa+4mP8Buf/UYHkBQdQKCfx6Sf76UPj/LAthT08olbCLux2mzsPljPWzmnMJmtLJ8bzl2b\nZuLr5Tap5wsP9OZHX0/nf94oYV9pM72DY3znpnl4uBvsnLm4GNlDnoIutmT9u39di7vb1b8IR8bM\n/PL1Yqob+1i7MIp7rpkly2BOSPaQHU9b9xAvfXCUEw29+Hq5cc81s0ifHWaX5x4eNfPCP8qpqOli\nepQ//+fWVPy93e3y3OIzcspaXBZ7DMYAnu5GfnDbAuLCfdlb3MTrmVVo/NlPCKdms9nIOtLI4y8d\n5ERDL4tnhvLkA8vsNhgDeHkY+d6tqayYG8Gppj6eeqWI9p4LX5kS9iVL1kIx3p5uPPS1hTzz18N8\ncrAeT3cDN6+ZrnVaQji8i61ieXsY+acbUlieEq7IqpPRoOeBbXMI9PPgw/21/OyVIn5w2wLiI84/\nqxP2IzNkoSh/b3f+7Y40Qqd58m7eaT46IFWDhLgaP31gGSvmRii6BaTT6bh1XRJ3b55J/+AYT//t\nMBU1XYrFE+NkhjzFTNS1TYjw47F701U5bBXo58G/35HGU389zBtZVXi4G1gv9x2FmJSrOWR5pTYu\njiHAx50/vFfJ/+ws4b7r5rBiXoRq8acamSFPIVabjb/tPgnAXZtnqnryOWSaF/92x0L8vN3Y8fFx\nCspbVIsthJi89Nlh/OvXFuDuZuB/369k14FaOQ+iEJkhTyF5pc3UtvSzYm44ydHq166NDPbhX7+2\nkGf/doQ/fnAUdzcDi2eFqp6HEOLKzIoL5D+2L+K/3yhhZ1Y1O7Oqz/t9Upnv6sgMeYoYGjHz1t5q\nPNwM3LouWbM84sL9+MHXFuDmpud375RTdqpTs1yEcERmi1XrFM4rJtSXR7++WOs0XJoMyFPE+/mn\n6Rsycf2KeFX3oM4nKSqA7301Fb1ex3Nvl3G8rlvTfIRwJI7csCXI31PrFFyaLFlPAS1dQ3x6qJ6Q\nAE+uWRqrdToAzI4P5Ltfmcdv3yrj12+W8u93ppEY6a91WkJoqqSqg48O1BEe6MVPvrHksupRC9ch\nM+Qp4LU9J7FYbXxtQzJuRscphZeaFMK3b5zLqMnCr14vpqFtQOuUhNBMV98If/zgKEaDnu/cPE8G\n4ylIBmQXV1rdSWl1J3PiA1k00/EOUKXPDuO+6+YwOGLmF68X09I1pHVKQqjOYrXyh3crGBg2ccfG\nZOLCpQjHVCQDsgszW6y8tuckOh3cuXGGw9aSXjU/ku1bZtI3OMYvXjtCR6+U6hNTyzu5NZxo6CV9\nVqjc0Z/CZE3Ehe0paqCla4gNi6KJCfPVOp2L2rAohtExCzuzq/nh/y047/fIlQrhiipquvggv5aQ\nAE++ce1sh/3gPOGLr8M/vl9JXnkL375xrkYZuQ6ZIbuovsEx3s2rwcfT6DT1o69dHq91CkKoqndg\nlP99rwK9Xsd3bp6Ht+fk2idq6YZVCeh1Ot7JrcFqlYIhV0MGZBf1dk41w6MWbl4zfdI9UoUQyrFa\nbfzhvUr6hkzctj7ZaW8ZhAV6s2p+BC1dQxyobNU6HacmA7ILqm3pZ19JM9GhPqxLi9I6HSHEebxf\ncJqjtd0sTA5hc3qM1ulclRtWJmDQ63gnrwaL1TELmzgDGZBdjM1m46+7T2Bj/CCXQS//xEI4muN1\n3byTW0OQvwf3XT/H4feNLyVkmhdrUiNp6x6moFxmyZMl79YupvBoG1UNvSyaGUpKQpDW6QghvqBv\naIzfv1uBDh3/fOM8l9lS2rYyAaNBx7t5NQ5b/tPRyYDsQkbHLLyRVYXRoOf2DdrVqxZCnJ/VZuPF\n9yvpGRjjlrXTSY5Rv8mLUoL8PVm7IJqO3hHypZvbpMi1Jxey60At3f2jXL8inrBpXlqnMylfvFIx\nOmbh8ZcKae8dprqxlyQNulQJYS8fH6ij/FQX86YHsXVZnNbp2N11K+LZW9LEe3k1rJwXgdEgc74r\nMem/LavVyo9+9CPuvPNO7r77bqqqqqirq+Ouu+5i+/btPPHEE/bMU1xCR+8wuw7UMc3XnetXuM71\nIQ93A/ddPwds8McPjjJmsmidkhCTUtXQy1t7TzHN150HtqWo2o9cLYF+HqxPi6azb5R9pc1ap+N0\nJj0gZ2ZmotPpePXVV/ne977Hr371K5566ikeeughduzYgdVqZffu3fbMVVzEG1nVmMxWbluXjKe7\nay18zIydxsb0GFq6hvj7PsfthCPEhQwMm/jdu+XYsPHtG+fi7+2udUqKuW55HO5GPe/nn8Zklg/Q\nV2LSA/KmTZv46U9/CkBTUxMBAQFUVlaSnp4OQEZGBgUF56+4JOzrWG03h461kRTtz/K54Vqno4iv\nrk0iLNCLTwrrqWro1TodIS6bzWbjpQ+O0tU3yk2rEpkVF6h1SooK8PVgw6IYuvtHySmRWfKVuKoF\nfr1ezyOPPMKTTz7Jtm3bsNk+q9Li4+NDf3//VScoLs5qtfG33ScBuGvTTKe/PnEhHm4G7rtuDgB/\n/KCSUVm6Fk7i00MNFFd1MCc+kG0rE7RORxVbl8fh4Wbg/YLTss10Ba56bfPpp5+ms7OTW2+9ldHR\n0bO/Pzg4iL//pSvPhIZKV5OrsSu/hob2ATYtiWNpqmsXpQ8N9ePG+l7eyanmo4MNPHDTPK1TmhLk\nNTp5J+q6eTO7imm+HjzyjaUE+XtqnZIqQoEb1kznzcyTHKrq5KaMJK1TcgqTHpDfeecdWltb+da3\nvoWHhwd6vZ558+ZRWFjI0qVLycnJYfny5Zd8nvZ2mUVP1uCIib98eBRPdwPXL4udEn+XW5fEsL+8\nmXdzqpkTG8DM2Glap+TSQkP9psTPlRKGRsw89edCLBYb918/B8uoifZ2k9ZpqSZjfgTv5Z7ijU+P\nszgpGA93x+nFrrULfcid9JL1li1bqKysZPv27TzwwAM89thj/OQnP+G3v/0td9xxB2azma1bt046\nYXFp7+TWMDBs4oZVCQT4emidjio83Azcf2bp+qUPj8rStXBINpuNP+86SkfvCNevjGdu4tQr0uPr\n5cbm9Fj6hkxkHWnUOh2noLOdu/GrAfn0PTmNHYM8/sdCQqZ58tP7l+FmnFr3/V7PPMnHhfVsSo/h\nrk0ztU7HZckM+fLd93TmBb82VVuHDo6Y+OH/zceg1/Psd1a43A2QybL7DFlox2az8druE1htNu7Y\nMGPKDcYAX1kznYggb/YcauBEfY/W6QghzsPH040tS+IYGDaxp6hB63Qc3tR7J3cBxVUdVJzuZl5i\nEAuSg7VORxPubgbuv34O6OClD44yOiZL10I4os3psfh4GvnoQB3Do2at03FoMiA7ifuezjz767dv\nlQFQXtPF/c9kaZyZdpKiA9i6NI62nmHe3FutdTpCiPPw9jRyzdI4BkfMfHqoXut0HJoMyMKp3bwm\nkchgb/YUNXC8rlvrdIQQ57FxcQy+Xm58XFjP0MjUOWl+pWRAFk7NzWjg/utT0OnGT12PjMmSmBCO\nxsvDyLXL4hgeNfPJQZklX4gMyMLpTY/y59pl8bT3jPBmtixdC+GINiyKwc/bjU8O1jMwLLPk85Ez\n6MIl3LQ6keKqDjIPN7J4Vhhz4l27XrBwPNOj/Klp7uMX/7KKQL+pURfgSni4G7hueTyvZ1bxcWEd\nX10r1bu+SGbIwiW4GfXcf/0c9Dodf5Kla6Gypo5BTjX1MS8xWAbji1iXFk2Ajzu7DzXQPzSmdToO\nRwZk4TISI/25dnkcHb0j7MySpWuhnryy8a5Gq+ZHaJyJY/NwM3DdinhGTRY+OlCndToOR5asnUBr\n9xA6YHq0P49+PV3rdBzajavGl66zjjSSPiuUOQlTr2ShUJfFaiW/ogUfTyNpM0K0TsfhrVsYxa79\ntew53MCWpXEE+Lhub+grJTNkJ5BZ1IgN2LQ4VutUHN65S9cvfXhMChEIxVXUdNM7MMbSlHDcjNJA\n4VLcjAa2rUxgzGRl1/5ardNxKDIgO7jhUTO5ZU1M83Vn8axQrdNxCgkR/ly3Ip7OvhF2ZlVpnY5w\ncblnlqtXz4/UOBPnsSY1iiB/D7KONNIzMHrpB0wRMiA7uPzyFoZHLaxPi8ZokH+uy3XjqgRiQn3I\nLm6i4nSX1ukIFzUwbKL4ZDtRIT4kREjf6MvlZtSzbWUCJrOVDwtkljxB9pAdmNVmY09RA0aDjrUL\no7VOx6kYDXruvz6FJ/58kF++Vnze75mqHXiE/RQebcVssbF6fiQ6nU7rdJzK6vmRfFhQS3ZxE1uX\nxRHk76l1SpqTKZcDq6zpoqVriKVzwvGXgw9XLF5mLEJheWXN6HU6ls8N1zoVp2M06LlhZQJmi5UP\nZC8ZkBmyQ9t9pl3ZpvQYjTMRQnxRY/sANc39pCYFM81X7h5Pxp92HQMg63AjWYcbP/e1qbiCJTNk\nB9XaNURpdSfJ0QEkRPhrnY4Q4gvyyloAOcwl7EcGZAe157DMjoVwVOfePV6QLHePhX3IgOyAhkfN\n5JY2M83XnUUz5aqTEI6m/FQXfYNjLE+JwM0ob6PCPuQnyQHll7cwMiZXnYRwVBN3j1elSqlMYT9y\nqMvBWG02dstVJ7s592DI2znVvJ9fyzevm82a1CgNsxLObPzucQfRoT7Eh8tJfmE/Mv1yMJU1XbR2\nDbFMrjrZ3doF0eh0fOk0pxBX4kBlKxar3D0W9iczZAczcdVpoxMd5vpu5g8v+LXnNzyrYiYXFxzg\nycLkEI6c7KCmuY/ESDm9Lq5c7tm7x7JcfbW+eLXpqR1FnGzo5el/XqFRRtqSGbIDkatOytuwaPyD\nTuaZU+xCXImGtgFqW8bvHkuXIvvLWDC+lbSvpEnjTLQhA7ID2SOFQBQ3JyGQ8EAvCo+2MTBs0jod\n4WRype+xotJnh+HlYSS3rBmL1ap1OqqTAdlBjHd1cp6rTjabjeNdVTxX/KLWqVwRvU7H+rRoTGYr\nuaXNWqcjnIjZYmV/RQu+Xm5y91ghHm4GVs6NoHdgjNKqTq3TUZ0MyA7i7FWnRTEOfdXJarNyuK2U\nZw/9lt8U/4GjXSe0TumKrUqNxN2oJ/tII1abTet0hJMoP9VF35CJ5SnhDv0adXYZC8eXrfdOwWVr\n+alyAJ+76rTAMa/jjFlM7Gss4In9P+eP5Tuo729kYeh8/j39wYs+rrDlsEoZXj4fTzeWpoTT1jNM\nZY20ZhSX57PlaimVqaTYMF8SI/0pO9VJV9+I1umoSk5ZO4CKM1edVs2LcLirToOmIXIaCshuyGXA\nNIhRb2RV1DI2xWUQ5n3ppfWXK1+jfbiT6xI2OdQVkQ2LosktbSbzcCPzpgdrnY5wcH1DY5RUdRAT\n6ktcuK/W6bi8tQujqNnVx77SZm5anah1OqqRAdkB7D7keFeduka6yazfR15TIWOWMbyMnmyJX8+6\nmNUEeHy+GMKFrja1DLbyQslLfFjzKZ3DXdw1+6sY9Y7xI5cQ4U9ipD8l1R109A4TEuCldUrCgZ29\ne5zqnHePneVq4oSlc8J4dc9J9pU2ccPKBPR65/s7nwxZstZYS9cQZac6SY5xjKtOjQPNvFz5Go8X\nPENWfS7eRi9uSd7Gkyt/xE1J135pML6YCJ9w/i39QeL9YznQUsRzxS8yaBpSMPsrs2FRNDYb7C2e\nentV4srklTVj0OtYniJ9j9Xg6W5k2ZxwuvpGKZ9C20qOMV2ZwjInrjotVm92fLFPyxMifMLZHLeW\n9PCFVzWr9Xf34/tp3+blytcpbi/jl0XP853U+wj11n6ZeMnsMF7bc5J9JU3cuCpRmgSI86pr7aeu\ndYC0GSEOt6V0OSxWi9YpTMrahVHklDSRU9JEapL27xdqkHcgDU1cdQr083CYq05JAYn8c+o3eHTp\nD1gemW6XJWZ3gzv3z7ubTXFraR1q5xdFz3Gqt9YO2V5lXm4G1qRG0TdkouhEm9bpCAc10ffY2Q5z\nma1mchv385/7L74kva9xPyPmUZWyunwJEX7EhvlSUtVB74Dj5acEGZA1lFfWzMiYhXUO1NXpocXf\nYX5ICnqdffPR6/R8Jfl67ph1C0PmYX595Pccbiu1a4zJWJc2fqpd6luL8zFbrBRUtODn7eY0szST\n1cy+xgL+s+BZXj3+Nv1j/Rf9/teOv81j+T/jzZPv0jbUrlKWl6bT6chYEIXFajt7wt3VOcYoMAVZ\nbTb2FDVgNOhZu9AxrzopYU30cv459ZsYdQb+WL6DT2qzsGl4Fzgs0Jt504M42dBLfduAZnkIx1RW\n3cnAsInlKREO86H5QkwWE3sb8vnPgmd47fjfGTANsD52NU+seOSij7sucTNuejey6nN5Yv/Peb74\nj5R3HMVq075S1oq54bgb9ewraZ4SNQNkD1kj5ae6aO0eZtX8CPy9nW9f6mrMDZ7FQ4v/hRdKXuKd\n6l20D3Vyx6yvYNAbNMlnQ1oM5ae6yDrSyD3XzNIkB+GYnKFUpsliIq+pkE/rsukZ7cVN78aG2DVs\nilt3WYcwr0/czDXx6ylpLye7IZ/KruNUdh0nxDOIjJiVrIhMx9vNW4U/yZd5e7qRPjuM/PIWjtd2\nMychSJM81CIDskZ2F9UDsGlxrMaZaCPaN5J/T3+Q35X8ifzmQrpGunlg/na8jOpfP0pNCibY34OC\n8hZuW5eEl4e8LAT0DY5RWt1JXLgvcQ7Y93jMYiKv6QCf1mbRO9aPu96NTXFr2RS3Fj/3z9+VvtTV\nJqPeyOLwhSwOX0h9fyM5DfkcbD3C21Xv896pj1kakcbamFVE+6q/j56xIIr88hb2ljTJgCzsr6Vr\niPJTXSTHBBAfoe4LvaqnRtV4FzPNI4DvL/oOf6r4G+WdR/ll0Qt8J/U+gr0CVc1Dr9exLi2at/ae\nIr+8hY0qnngXjmv/mbvHjnaYa8wyxr7G/Xxal03/2ADuBnc2x61jY1zGlwbiyYj1i+buObdxc/L1\nFDQfJKchn7ymQvKaCkmelnjR9xAl7jTPiAkgMtibwyfa6R8aw8+FVxRlQNbAHg2uOsH4C3nH0TfQ\noeMHi75D0rQEVeOfj6fRg2+n3subJ99jb0MePyl46oLfq2QBgzWpUfxjXw1ZRxrZsCjaKYs/CPvS\n8u7xxa4m+rn50m8awMPgzjXxG9gQuwZfdx+75+Dj5s2muLVsiF1DRecx9jbka1K7XqcbLyn8WmYV\nBeUtbFkap3oOanHsUwouSMurTu+e+oj24U7Wx652iMF4gl6n5/aZN3HrjBs1y8Hfx50ls8No6hjk\nRH2PZnkIx1Db0k992wALkkMcbkZmsprZmrCRn678ETcmbVVkMD6XXqdnfkgKDy58gJ8s+zdFY13I\ninkRGA069pY0aXoIVGkyIKsst6yZ0TEL61W+6lTVU0N2fR5h3iHcMH2ranGvxPrY1drGXxQNQKZc\ngZry8s4c5lrtYMvVAD9d+Qg3TL8GHw0OWoX7hKkeE8DPe7wtbXPnEFWNvZrkoAYZkFV07lWnDBWv\nOk0sVQN8fc7tuBvcVIvtTJKjA4gJ9eXwiXZ6pkghAvFlZouV/ZWt+Hu7MW+64x0i0urEs9YmOuG5\ncqlbGZBVVH6qi7buYZalhKl61end6vGl6g2xa5gekKBaXGej0+nYsCgai9VGjgu/6MXFlVSduXs8\n1/HvHk8ls+IDCZvmxaFjbQyNmLRORxHy06YiLa46VfXUkN2QR7h3KNumX6NaXGe1fG44nu4Gsosb\nMVu0L4wg1OfIy9VTmV6nY82CSMbMVgoqWrVORxFyylolzZ2DlJ/qYoaKV51GLWO8cmaperssVV8W\nT3cjq+ZFsudwAyVVHSyepc2emdBG75m7x/ERfsSEadP3uL7fsc8wfPG2w+G2Uv5YvoPFYQsUj716\nfiT/2FdDTkmTS96GkAFZJZlF4y+yTenqzY7frd5Fx3AnG+MymB4Qr1rcq3Hui33YPMJjeT/Dw+DO\nf638D9VyWLcomj2HG8g83CgD8hSzv6IFq82m2ex4zGLizxWvAvDdBfeTEuz4lePSQucT7x9LUVsJ\nG/syiPdX7j0uwNeDBckhHD7RzumWfhIjtW9Za0+yZK2C4VEzueXjV53SZoSoEvNk96nPlqoTnXOp\n2svoyYqoJfSO9avaiCI6xIfZcdM4WttNc+eganGFtmy28SYGBr2OZRr1PX6n+kNahtpYG7PKKQZj\nGD97cXPStQD8o3qX4teSMlz4cJcMyAq67+lM7ns6k+/+dw6jYxa6+0f51s+zue/pTEXjjp5TAMTZ\nT1Wvi1mFDh1Z9bmq3j9cv2i8aEvWEcdePhT2U9vaT2P7IAtnhODrpf5rprLzONkNeUR4h3Fz0nWq\nx78aMwOTSQmaxYnuKo51nVQ01rzEIIL9PThwtJWRMbOisdQmA7ILeqd6Fx0jXWyMyyDRSZaqLyTE\nK5jU0LnU9Teo2kM5bUYIAT7u5JW1MDrmnA3exZXJKx3ve6zFcvWAaZAdR9/AoDNw79w7nPJD9E1J\n16JDxz+qP1S0U5Rer2NNahSjYxYKj7pWH3MZkF3Mye5q9jbkEe4dxrbELVqnYxfrY8YLhmTV71Mt\n5kRbzOFRMweOuuaJTvEZk9nK/soW/H3cVb97bLPZePXYW/SO9bMtcQtxfs5ZSz3GL4r08DQaBpoo\nai1RNNbq1Eh0OtdbtpYB2YWML1XvPLtU7eaEn7LPJ3laIrG+URS3l9M53KVa3IwFUeh1OjKLGly6\nXJ+AkqoogHFLAAAgAElEQVQOBkfMrJwbgUGv7tvi/pYiitvLSQpIZFP8WlVj29sN07dg1Bl479TH\nmK3KLScH+Xsyf3owNc19LtXHXAZkF/JO9Yd0jHSxKW4tiQGuU4Bdp9OxPnYNNmzsbchXLW6Qvydp\nM0KoaxvgVFOfanGFOibOeNz3dCYv/KMcgI8K6xQ/43GujuFOdp74B54GT+5N+Rp6nXO/JQd7BbEm\negWdI13kNh5QNNZE5S5XKuLj3P/64qwT3dXsbcgnwjuM6xM3a52O3S0KX4Cfuy/5zYWMmEdUiyv1\nrYVSrDYrL1e+zqhljNtn3kSwl+OV6ZyMaxI24GnwYNfp3Qwr+FpNTQ4mwNedgooWxkyucc5jUgOy\n2Wzmhz/8IXfffTe33347mZmZ1NXVcdddd7F9+3aeeOIJe+fpdNRc4hwxj362VJ3iOkvV53LTG1kb\nvZJh8wj7W4pUizsnPpCIIG8OHmulf2hMtbjC9X1Sm82p3tMsCktlacQirdOxGz93XzbFrWPANMie\nuhzF4hj0elbPj2Ro1Myh465xuGtShUHeffddAgMDefbZZ+nr6+Omm25i9uzZPPTQQ6Snp/P444+z\ne/duNm3aZO98nUZd6/i+xqKZoTx4y3xFY71TvYvOkS42x60jwd91lqq/aHX0cj6qzSS7PpeM6BWq\nLO/pdDrWp0Xz6p6T5JY2c+1y5z61LhxDbV89H9R8wjSPAO6YdYvLVZzaELeGvY157KnPYU30CgI8\nlKlOuGZBFB8U1JJT3MTKec5f6nRS72jXXnst3/ve9wCwWCwYDAYqKytJT08HICMjg4KCAvtl6YT2\nlY7vayh9heJEdxU5jflE+IS75FL1ufzcfVkSnkb7cCcVncdUi7tqfgTuRj1ZRxqxWuVwl7g6Y5Yx\nXq58DavNytfn3K5JG0WleRjcuT5xM2OWMT46vVuxOGHTvEhJCOREQ69LFPGZ1IDs5eWFt7c3AwMD\nfO973+MHP/jB55ZofXx86O/vt1uSzsZktnCgspUAH3fmJym3L/S5peo5t7nkUvUXTfRMzqzPVS2m\nt6cby+eG09E7QnlNp2pxhWv6e9UHtA61sz52NbODZmidjmJWRi4lzCuE3KYDtA11KBbHlSp3TbqW\ndXNzMw8++CDbt2/n+uuv5+c///nZrw0ODuLvf3k1RkND1Wm0oKZ9RxoZHDHz1fXJRIQHKBbnxUPv\n0znSzc1zrmFJ0lzF4jiS0FA/5p2eRXnbcYbceomfps6dza9smElOSTO55a1sXJ6oSkxH4Yqv0UtR\n6s98uKmcnMYCYv0juX+Zc1fRuxx3p93Mf+e/yKeNe/j+ygcUibEl0Ju/7T5JQUUr/3zrAtyMBkXi\nqGFSA3JHRwf3338/P/nJT1i+fDkAc+bM4eDBgyxZsoScnJyzv38p7e2uN5P+IO8UAIuSgxX78x3v\nquKT6hwifcJZF77WJf8eL2R1xArK247zduknbJ9zmyoxAzwMJEX5U3S0lcqTbYRO81IlrtZCQ/1c\n9mcrKdqf6sY+nnxgGVEhPp/7mhJ/5v6xAZ4vfBmjzsD2WV+jt2sEUO/GgBaSPGYQ7xdLfn0Rq6tX\nKtZ4YsXccD4urOeT/BqWztGmDvmVuNAHvkktWf/+97+nr6+PF154ga9//evcc889fP/73+c3v/kN\nd9xxB2azma1bt15Vws6qq2+EypoukqL9iQz2ufQDJmHEPMKOYzvR6/TjBUD0U6tp19zg2YR6BXOw\n9Qj9Y+oVBVi/KBobkF0sV6Cc3cmGHqob+1iYHPKlwVgJE9W4+scGuCFpKzF+UYrHdAQ6nY6bk5Vv\nPOEqy9aTeid/9NFHefTRR7/0+6+88spVJ+Ts8sqasQFrUu37gvtu5g/P+/vPHvrtl/qTujq9Ts+6\n2NXsPPEO+xoLuE6lw2wvvn8UgF3769i1v+5zX3vpkQ2q5CDsY+Lfb+sydW4lFDQfpKSjghnTprMh\ndo0qMR3FROOJyq7jHOs6yZzgmXaPERnsw8zY8Q5tbd1DhAU650E5KQxiR9Yz7dvc3fQsmS19dJW0\nPCIdL6MnOY0FmBQs0SdcT3PnIMVVHSRF+TMjRrkzHhPahzrZefJdvIye3OMC1bgmQ43GExOVu/aV\nNivy/GqYej8ZCjpZ30N7zwjps8Lw8phay8hq8zR6sDJqKf1jAxxWuJC9cC0fHZiYHccrfv/XYrXw\ncuWrjFnG+NrMrxDkGahoPEelRuOJxbNC8fYwklvajNmiXLcpJcmAbEcTn8zWpDr/BXVnsDZ6olfy\nPmn+IC5Lz8AoBRUthAd6kTYjRPF4H9dmUtNXR3r4QpZEpCkez5Ftm74Fg4KNJ9zdDKyYF0Hv4Bil\n1c55PVGmcXYyPGrm0LE2wqZ5MTN2mtbpTAnBXoEsDJ3HkfYyqnpOMSMwSeuUhIPbfagBs8XGNcvi\n0OuVnR2f7qtj1+k9TPMI4Gszb1Y0ljMI8QoiI3oFWQ255DYeYF3sKrvH2FPUAMBzb5d96WvOcM5D\nZsh2Uni0lTGzlVWpkS5XBs+RrT9zQCZLxUIhwjkNj5rJOtKIv7cbq+ZFKBpr1DLGyxWvYbPZuDfl\na3i7YDWuyVCr8YSzkhmyneSWNaMDxV/o4vOmB8QT5xdDaUclHcOdhHgFa52ScFA5JU0Mj5rZmjFd\nkeIRF7oJ8esjf5hyNyEuZKLxxPs1H7OnLodt07donZJDkQHZDpo6Bqlu7GNeYhBB/p6KxFgQOo+S\n9nJ+tPQHRPvKHvUEnU7Hhtg1/LnyVbIb8rh1xo2KxTp3yatvcIx/fT6PsEAvnnxgmWIxhX2YLVY+\nOViPh5uB9WnRWqczpanVeMIZyZK1HeSWjR/mWq3QYa5B0xAVHUeJ8omQwfg80sLmE+DuT0HTQdWW\nwfx93FkyO4zmziGO1/WoElNMXuHRVrr7R1mzIBJfL9cuV+noPAzuXJegfOMJZyQD8lUyW6zkl7fg\n42lU7NTmkbZSzDaLS/VMtSej3khGzEpGLKMUNB9ULe66MzOtrCNSucuR2Ww2PjpQh16nY8sSZUo3\niiuzKmopoV7BijeecDYyIF+l8lNd9A2OsTwlQrGi5oUtR9ChIz18oSLP7wpWRy3DTW8kuz5PscID\nXzQjJoCYUB8On2inZ2BUlZjiypXXdNHQPsjSOWGEBEyNGuSOzqA3cGPStVhtVt4/9bHW6TgMGZCv\n0tm+xwotV3cOd1HdW8OMadMJ9JTrVBfi6+7D0ojFdI50UdZRqUpMnU7H+rRoLFYb+0qcu4auK/us\nEIg6ZTLF5UkLnU+8XyxFbSXU9tXb5TlfemTD2V8vPrye+PDx/ekf35tul+dXmgzIV6HvzAX0uDBf\n4iOUOZhwsPUIgCxXX4aJXslqXoFaPjcCD3cD2cVNWKzOWR3IlZ1u6eNobTdzEwKJC1fu8NCIXOG5\nYko3ntDrdHxtQzIAr2dWOUXxIDllfRUKKlqwWG2sUmh2bLPZKGw5gpveyMKweYrEcCWRPuHMCZrJ\n0a4T1Pc3Euun/GlaLw8jK+dGkHWkkdKqTtJmhioeU1y+c8tkKmlvQz4A2xK3cG3iJkVjuZJfH/kD\nACe6q3gw6+HPfc0eV8VmxweyMDmE4qoOjpzsYJGDvz5lhjxJNpuNfaXNGA06VsxV5u5xfX8jrUNt\nzA9Jwcsoe1+XQ4tZ8sQ1mkw53OVQ2nqGOXisjbgwX1ISlKshPWIeYU9dDl5GL0WqT4mrc9v6JPQ6\nHTuzqhy+xrUMyJNU09xPU8cgC2eEKnaNorD1MCDL1VdiTtBMwr1DOdRaTO+o/ZvMn09MmC8zYwKo\nqOmitWtIlZji0j4prMNmG987VrJ63t6GfAbNQ2yMXSMfnB1QZLAP69KiaO0eJtvBPzTLgDxJZ+8e\nz1dmudpitXCotRgfN2/mBNm/f6ir0uv0rI9djcVmYV9jgWpx1y0anyVnFzv2C36q6B8aI7e0mWB/\nT9IVbIU6MTv2ltmxQ7txdSJeHgbezTvN0IhJ63QuSAbkSRg1WThQ2UKgnwfzEoMUiXGsu4r+sQEW\nhy3AqJet/iuxNGIx3kYv9jUWYLKo8+JbPDMMf283ckubGTNZVIkpLizzcCNjZitblsRiNCj3Njcx\nO94QmyGzYwfm7+3O9SsSGBg28X5BrdbpXJAMyJNw+EQ7w6MWVs6LUKxjzMEWWa6eLA+DO6uiljFg\nGuRga7EqMd2MetYsiGJwxMzBY22qxBTnN2qysKeoAR9PI2sWKFfZ7vOz45WKxRH2sTk9hmB/D3Yf\nqqejZ1jrdM5LBuRJyC1VtlTmiHmUkvZyQryCSfCXu5OTsTZmJXqdXtVeyWsXRqFjfHYmtJNX1szA\nsIn1i6LxdFdudSlbZsdOxc1o4KtrkzBbbLy5t1rrdM5L1kKvUEfPMEdru5kZE0B4oDIt1Uo7Khiz\nmlganiatHCcp0HMaaaHzKWor4UR3NbOCkhWPGRLgRWpSMCXVnZxu6SMhwl/xmOLzrFYbHxfWYTTo\n2bhYuTKZw+YRMmV2fNW+eLXpzRPvktWQyz/N+7oi8ZamhPPpoXoKj7axeUkvSVEBisSZLJkhX6HP\nGklEKRaj8Mxy9ZKINMViTAVneyU37FMv5qKY8ZgyS9ZE0Yl22ntGWDU/ggAfd8XiyN6xMlZGLQUg\nt+mAIs8/XixkBuCYxUJkQL4CVpuNvLIWPNwNpM9W5oJ572gfx7pOkuAfR5i3Y19id3SJAXEk+sdR\n3nGMtqF2VWLOmx5ESIAnBypbGXTg05yuaLyJRC064Jqlym31fH52LCer7SnKN4LpAQkc6zpJx3CX\nIjFmxk5j0cxQqhp6KTquzvvC5ZIl6ytwrLabzr4RVqdGKrY3VdRajA2bHOayk5q+8UpNT+z/+Ze+\npkTTeP2Z+tY7s6vJL2ths3QXUs3xuh5qmvtZPDOUiCBltpPgs9nxDdOvwcuoTP/zqWxV1FJO9Z6m\noKmQG5K2KhLjtnVJlFR18GZ2NQtnhCh6Ev9KOEYWTmLiMNcahQ5zARS2HkGv07MoLFWxGEJZq1Mj\nMRr0ZB1pdLglMVf2UaHyTSTOnR2vjZHZsRIWhaXiZfSkoPkgFqsyVwjDg7xZnxZNW8+wQx3ClAH5\nMg2NmCg60U54kDfJ0cocBGgebKW+v5GUoFn4ufsqEkMoz8/bnSWzQ2npGuJYbbfW6UwJDe0DlFZ3\nMjMmgCSFXp8AexvyxqtyxWXI7Fgh7gZ3lkYsonesn/LOY4rFGS8WYuS9vBqH2V6SAfkyHTjahsls\nZU1qpGInnw+2THR2ksNczm7icJfUt1bHxyo0kRg+596xzI6VtSpqGQB5Ch3uAvD1cuOGlQkMjph5\nL++0YnGuhAzIlym3tAmdDsUaSVhtVgpbDuNp8GB+SIoiMYR6kqL8iQ3z5ciJDrr7R7VOx6V19Y2w\nv7KVyGBvUpODFYuztyGPIfOwzI5VEO0bSYJ/HJWdx+kaUW6VaePiGEICPNlT1ECbAxQLkQH5MjS0\nD1DT3M/86cEE+nkoEqO65zTdoz0sDJ2Pu0G56xpCHTqdjvWLorHabOSUNGmdjkvbfagBi9XG1qVx\n6BVavZqYHfsYvWV2rJJVUcuwYSO/6aBiMdyMem5dl4TFauPNbO2LhciAfBnUOMx1UDo7uZzlKeF4\nuhvYW9zo8G3fnNXQiJns4kYCfN1ZrtDqFXw2O94gs2PVLA5fgKfBQ9HDXQBLZoeRFOXPoWNtVDX0\nKhbncsiAfAlmi5WCihZ8vdxYkByiSAyTxcThtlKmeQQwI3C6IjGmquc3PHv21/+38J8AWBy2QJEr\nT1/k6W5k1bxIegbGKKnqUDzeVLS3uJGRMQub02NxMyrzdjZsHj5ndixVudTiYXBnScQiekZ7qew6\nrlgc3eeKhZzU9GaEDMiXUFLVSf+QiRVzIxS7q1bReYxh8wjp4QvR6+SfRCmzApOJ9o3kSHuZovtS\n55poy+hIVytchcls5ZND9Xi6G1i3ULnKedn1+TI71ogah7sAkmMCSJ8VSnVTn6bNYeTd/xJyS8f3\n/5S+ewyyXK00nU7H+pjVWG1W9jbkqxIzOsSHWbHTOFrbTXPnoCoxp4r9FS30DoyxdmEU3p5uisQY\nNg+TWS+zY63E+kUR5xdDeccxukd6FI1167okDHodb2ZXYzJrs8UkA/JF9AyMUnaqi4QIP2LClLkX\nPGgaorzjKFE+EUT7Kjfoi3Hp4Qvxc/Mlr+kAI2Z1Tj+vPzNLzj4ih7uu1n1PZ5799add43dUPy6s\n576nMxWJJ7Nj7a0+c7iroFm5w10AYYHebFwcQ0fvCHuKGhSNdSEyIF9EQXkLVptNsTaLAIfbSrHY\nLDI7VombwY01MSsYNo+wv+WQKjEXzQzF38edvLJmRk3KHU4R9nXu7HidzI41szh8AR4Gd/KbDmK1\nKTtz3bYyAR9PI+/nn2ZgWP1iITIgX4DNZmNfaTNGg55lKeGKxTnYchgdOtLDFyoWQ3xeRvQKjHoj\n2fW5ir/AAYwGPRkLohgaNVNY2ap4PGEfE7PjjXEZeMrsWDOeRk/Sw9PoHu3haNcJRWNNFAsZGjXz\nbl6NorHORwbkC6hu6qOla4jFs0LxUWh/qmO4i+re08wITCLQc5oiMcSX+bn7siQ8jfbhTioULM13\nrnULo9DppHKXsxg2D7NH9o4dxuqJw12Nyh7uAtiwOIawaV5kHW6ktWtI8XjnkgH5AiYOcym5XH1o\n4jBXuJTKVNv62NUAZNap0ys5yN+Thckh1Lb0U9Pcp0pMMXnZ9XkMy+zYYcT5xxDrG0VZ51F6RpW9\nK2w0aFcsRAbkc5x7YCSnZLwYyC9fK1bkwIjNZqOw5TBueiMLw+bZ/fnFxUX7RjI7cAYneqqp71fn\nsNX6s1egtDkwIi7P+Ox4Hz5uMjt2JKuil2G1WdnfrPzZj8WzQkmOCaDoRDsn6pU93X0uGZA1Utff\nQOtQO/NDUvAyemmdzpQ0MUvOqldnlpySEETYNC8Kj7ZpcmDE2dW29KsSZ2J2vCl2rcyOHUh6eBru\nejfymwoVP/uh0+nOVu16+q+HPzdZU+pEP4BRsWcWF/VZZyc5Xa2VlOBZhHuHUtRazE1J1xHg4ado\nPL1Ox7q0aN7IqiKvrJlrlirXt9fVWG02dnwyXq3pX+9YyNyEIEXinDs7zohZoUgMMTleRk/SwxeS\n33yQ411VzAmeqXVKdiczZA1YrBYOtRbj4+ZNStAsrdOZsvQ6PetiVmO2WdjXqE6hkNWpkRgNerKP\nNGLVsESfs8krbaa6qY8ls8MUG4wBsupzZXbswFZFjx/uylW4cpdWZEDWwLHuKvpNAywOW4hBb9A6\nnSltWeRivI1e7Gvcz5hF+WVkXy83ls0Jo7V7mKOn1Snf6ewGhk3szK7Gw83A1zYkKxZnyDRMZn2u\nzI4dWLxfLNG+kZR2VNA3ps4WhppkQD5DzdlKYUsRAEsj5HS11jwM7qyOXs6AafBsxy2lrZPDXVfk\n7ZxTDAybuGl1IkH+ys1asxtkduzodDodq6LUO9ylNhmQz8hW6X7oiHmEkvYKQryCSfCXPURHsDZm\nJXqdnqz6XFU6vUyP9Cc+3I/iqg66+kYUj+fMapr72HukkagQHzalxygWR2bHzmNJeBpuejfyVDjc\npTY51AV09o6wM7saH08jT/7TcgJ83BWLVdJegclqYml4GjqFmqmLKzPNI4BFYakcai3mWPdJ5gQp\ne1hEp9OxflE0f951jL3FTXwlQ1puns/EQS4bsH3zTLt3W/tu5g/P+/v/mvMTVdpzisnxdvNicdgC\n9rcc4kR3NbODZmidkt1M+RmyzWbjLx8fZ3TMwh0bZyg6GAMcPFMMZIksVzuUDbFrAMhU6QrUsjnh\neHkYySlpwmxxrU/59pJT0kRNcz/LU8KZHR+odTrCgUwc7lKyLeNLj2z43K9vXjcbgIwFyhWLmvIz\n5P0VrZSd6mRuYhAr50UoGqt3tI9jXSdJ9I8jzDtU0VjiysT7x5IUkEBl53FaBluJ8FGufjnAd361\nF4DhUfjWz7M/97WXHtmgaGxn0D80xlvZ1Xi6G7htvXIHuYRzSvSPI9InnJL2CvrHBvBzV6Yb37lW\nzYvkk8J69pU2s2VJHFEhPnaPMaVnyH2DY/xt9wk83Azce80sxZeQi1qLsWFjidw9dkgTs+Ss+lyN\nMxFv7T3F4IiZm1cnEujnoXU6wsFMHO6y2CwcOHNIVml6vY6vrk3CZoO39ipTUnNKD8h/232CwREz\nt6ydTsg05atlFbYcRq/TsygsVfFY4sqlhs4l2DOQAy2HGTANap3OlFXd1Mu+kiaiQ33YsFi5g1zC\nuS2NWISb3khe4wFVDmMCLEgOZkZMAEdOdnCywf4lNafsgHzkZDuFR9tIivZn4yLlX/RNAy3UDzSR\nEjRLleUVceXGC4WswmQ1katCVxnxZVarjR0fn8AGfH3LLLsf5BKuw8fNm7SwVNqGOzjZc0qVmDqd\njtvWjW+h7MyutvsHgSn50z40YuaVj49jNOj4xrVz0OuVP+08cZhL7h47thVRS/E0eJDTkIfZatY6\nnSlnb3Ejta39rJgbwcxYaUkqLm5VlPKHu74oOSaAtBkhVDX0UlzVYdfnnpKHunZmV9EzMMZX1iQS\nrcDG/BdZbVYOthzB0+DB/JC5iscTk+dl9GRF1BKy6nM53FYqtcZV1Dc0xlt7T+HlYeD29UmKx3tw\nwQM8V/Iiq6OWcefsryoeT9hfUkACEd5hFLeVMTBzEF835d/PAW5dl0RxVQdv7T1FalIwBr195rZT\nboZ8tLabvcVNxIT6cO3yeFViVvecpnu0h4Vh83E3uKkSU0zeupjV6NCRWb9Ptb2pc+WVNase0xG8\nmVXN0KiZr6yZToCv8ge5DrSMV2ZbFrlY8VhCGeOHu5ZitlkobFbncBdAZLAPa1KjaOoYJL+sxW7P\ne1Uz5JKSEn7xi1/wyiuvUFdXxyOPPIJer2fGjBk8/vjj9srRbkZNFl7edQydDr553RzF96e+WHhg\nf/Ohs+XepPCA4wrxCmJB6FyK28up6qlhRqD9C3ec72pTS9cQP335EC9/dJyoEB8SI/3tHtdRVTX0\nklvWTGyY79m+0UoaMY9S0l5GiFcwif7qfDAXylgauZh3qneR21TI+tg1qhVcuml1IvsrWvhHbg1L\nU8LxcLv6vgSTHpFefPFFHnvsMUym8YL8Tz31FA899BA7duzAarWye/fuq07O3t7JraGtZ5hrlsRN\nqTc7ceXWT1yBalDvClREkDffvnEuFouV594uo3dwTLXYWrJYrWdbK27fMtNuy38XU9JezphUzHMJ\nvm4+LAybT+tQG9W9p1WLG+jnweYlsXT3j7KnyD516Sf9kx8fH8/zzz9/9v9XVFSQnp4OQEZGBgUF\nBVefnR3VNPfxcWEdYdO8uGlNotbpCAeXFJBAnF8Mpe0VdAx3qhY3NSmYW9ZOp7t/lBf+XjYlqnhl\nHW6krm2AVfMjmBGjzkGuwjPL1VITwDWsPnO4S+3bEdcui8PH08gHBbUMDF99t7hJD8ibN2/GYPhs\nin7uXpuPjw/9/Y7TGstssfKnD49is8G91862y9KCcG06nY4NsWuwYSO7Pk/V2Nctjyd9dhgnG3p5\ndc9JVWOrrXdwjL/vO4W3h/HsdRKl9Yz2cry7ikT/eMK8Q1SJKZSVPG06Yd4hHGkvZdA0pFpcb083\ntq1MYHjUzIcFtVf9fHZbG9Kfs8w0ODiIv7/jLAnv2l9LQ/sgGQuimCM1ccVlWhSWSoC7P/nNhQyb\nh1WLq9PpuP+6OcSE+pB1uJGckibVYqttZ1YVw6MWblk7HX+F68hPONhyBBs2OUHvQiYqd5mt5rOr\nH2rZsCiaYH8Pdhc10Nl7dd3b7HbtKSUlhYMHD7JkyRJycnJYvnz5ZT0uNNTPXimcV31rP+/l1xLk\n78l3bluIr5djnHJW+s8t7OO6Wet5tewdSvtK2TZrk6qxH/+nFTz0P3vZ8ckJ5iaHMjshSNX4E5T6\nWa041Ul+eQvTowO4dfNsDCrUAwA4XFSCQW9gS8pK/DykSI+ruN5vLe+e+ogDrQe5LW2rqmcD7rk+\nhf9+9QgfHarn+3dM/oOe3Qbkhx9+mB//+MeYTCaSkpLYunXrZT2uvV25pW2r1cYv/1qE2WLl7s0z\nGB4YYXhAnf6zl5pRKfnnFvaTNi2NN/Uf8v6xTNKnpWPQq7fdYQC+deNcfvV6MU/+6QA/uXeJ6nWd\nQ0P9FPlZtVitPPfGeLGcOzck09U5YPcY59PQ30RdbyMLQuYy0mdjBHkdupKFIfMoaiuhsLqc6QEJ\nqsWdGzuNmFBfMg/Ws3Z+JDFhF/+gd6EPuVc1IEdHR/Paa68BkJCQwCuvvHI1T2d3mYcbqG7sY8ns\nMNJmqNtdaf+ZO3E3Tt/KNQnSvcdZ+bh5syxyMbmN+ynpqFC9DvnchCBuX5/M65lVvPD3Mn541yLc\njM5fPmBPUeOZbaRIkqIDVIs7sZwpy9WuaWXUUoraSshtPKDqgKzX67h1XRL/s7OEN/dW8/3bFkzq\neVy2UldHzzBv7T2Fj6eRuzYr23D+i2w2G/saCzDqDKyMWqpqbGF/G2JWk9u4n6z6fZo0BtmyJJba\n1n72V7Sy45PjfOPa2U59VadnYJR/7Bt/bX51rfIVuSZYbVYOtR7B2+jF3JA5qsUV6vlt8f8CcKCl\n6EtdoJSu/TB/ehCz46ZRWt3J8bpuZsVd+Xkl5/+ofR42m42XPz7OqMnCnZtmEKDSYZEJx7uraB1q\nJy1sgTSScAHhPmHMDZ7Nqd5aTvfVqR5fp9Pxja2ziQ/3Y19pM9lHGlXPwZ7eyKxiZMzCV9cm4eet\n3mvzeFcVvWP9LApfgJveZeciQiM6nY5br7LxhEsOyPnlLVTUdDFvehAr5kaoHj+nIR+AtTErVI8t\nlDHRKzmzbp8m8d3dDDx4y3z8vN342+6TnKi3f+s3NRyr7WZ/ZSuJkX5kLIhSNfbEjGmZLFcLhUyP\n8iLPRRMAACAASURBVCd9Viinmvo4fKL9ih/vcgNy7+AYr+05iYebgXuumaX60l7XSDelHZXE+UWT\n4B+namyhnFmByUT5RHCkvYzuEW0Gw+AAT/7l5nnYbPDC38vo6lPngOLVuu/pzLO/nn11/CBXTXM/\nDzybpVoO46Uyy6VUplDcLWuT0Ot0vLn3FBbrlRX2cbkB+a+fnmBwxMyt65IICfBSPf6+xv3YsJER\nvdKp9/nE5+l0OtbHrsFqs7L3zAqIFmbFBXLnphn0DZn47dtljJksmuXiTKRUplBLRJA3GQujaO0a\nYl/JlTWKcamNlKLj7Rw61kZyTIAqBeq/yGQ1k99UiI/Rm8XhC1WPL5T112M7Afi0LptP67I/9zU1\nm4VsWBRNbUs/uWXNvPzRMR7YliKDzCVIqUyhphtXJZBf3sw7uTWsmBuBh/vlXZd0+gH5vqczv/R7\nVQ29PPBM1nk76ijpSFspA6ZBNsZlSJtFoRidTsfXr5lJY8cgBRWtxEf4s2VJrNZpOSwplSnUNs3X\ngy1L4ng//zSfHKrnhpUJl/U4px+QHcnehnx06MiIlsNcQlluxvFDXv/154O8kVlFTKgPKRpV8rqY\nkqoOrVOQUplTyLkrVVablf8seIa+sQF+tupR1XO5dlkc2Uca2bW/lnULoy7rRoHL7SFrpa6vgdN9\ndcwNnkWIV7DW6YgpINDPg+9+ZT46HfzunQrae9Srt3058sqa+e1bZVqnQWHLYQw6A4vDJ1esQTgn\nvU7P2phVmKwm8lTuAgXg5WHkhlUJjIxZeD//8hpPOPUMedSBDrTsbRw/6JMRs1LjTMRUkhwTwPYt\nM3n5o+M8/LvztzxVe+sGYNeBWnZmVePjaWRwxKx6/AkN/U00DbawIHQePm7emuUhtLEyagkf1HzC\n3sZ8NsZlqFr6FmDdwmg+PVhP5uEGNqXHEDrt4geNnXZA7u4f5TdvlWqdBgADpkGKWosJ8QpmTpC6\nVcGEWLswmpc/Oq51GgBYbTbezKrmo8I6Av08eOj2BUSHalccR0plTm1eRi9WRi4lqyGXI22lpEek\nqRrfzajnlozp/OG9Sv6+7xTfumHuRb/fKZesa1v6efIvh6htcYzC8PubD2GymsmIXoFe55R/pUJc\nNbPFyksfHOWjwjoig7350fbFmg7GFquFgxOlMoNna5aH0NbamFXo0JFZnzup6llXa2lKOHFhvuyv\naL3kmOV0o8fhE+089dcievpHuX29Og3NL8Zqs5LTUICb3o0VkelapyMU9PyGZ8/+em79M0T7RqJD\nx3+teETr1DQ3arLw3Ntl4+0Uo/x55O5FBAd4aprT8e4q+qRU5pQX6h1MakgKtf31nOq9vL1ce9Lr\ndNy6frxm+1t7qy/6vU7zU2qz2fjoQB1vZlfj5qbnwVvmkzYzlK3LtK2GVdl5nM6RLlZGLsFb9qim\nDJ1Ox8bYDP5y9HWyGnK5dcaNWqd0Qa1dQ4QHKfezOTBs4tdvllDd2Me8xCC++5X5l33vUkkTy9VS\nKlOsj11DSUcFmfX7SJqWoHr8uWduQJTXdJ29qvveL2/60vc5xQzZbLHypw+PsTO7mml+HvzH3YtJ\nm6luO8ULkcNcU9fi8AUEuPuT31TIkMmxTjif68d/PMBbe6sZHbP/IciuvhGe/uthqhv7WJ4Szv+5\nNdUhBmMplSnOlTwtkVjfKEray+kc7lI9/uUW7nH4AXlg2MQvXismt6yZhAg/HrsnnfiI8zd3Vlvb\nUAdHO08wPSCeWD/1K4MJbRn1RtbFrmLUMkZek/rXKi6Xn7c7HxTU8tiL+yk63ma3fbTmzkH+/x1F\nNHUMsik9hgduSMFocIy3FCmVKc41UfrWho3shjyt07kgh16ybu4c5Nc7S2nrGSZ9Vij3b0vBw037\nT98T9jUWnK1bLaam1VHL+ej0HrIb8lgfuxqjRnuVF7vaNDpm4b3803xcWMfzfy9nXmIQd2+eeVXL\n2NVNvfx6ZykDwya+unY61y2Pd6iBT0plii9aHL6Af1R/SH7TQa5P3IynUdszDufjGB9nz6PydBc/\n+0sRbT3DbFsZzz/fPM+hBuMxyxgFzYfwc/NlYdh8rdMRGvF2G79W0TPay+E2x7iG90Ue7gZuXZfE\nf92/lLkJgZTXdF3VMnb5qU5+/uoRBv9fe3ceFsWZrg387m72RWQHATcEISIIqMQNFZKMxt0QRSI4\nMZMTPZnJTBYnauY7TpzJF831JbnOZCRj1hN13JWjRsdEREFEBVEBQXABoUFAFhG6Wbrp7u8PldG4\ngNB0VTf376900111k8uXp+qtqudtVePX0wMwY9xgURXje60yhzqwVSb9m5nUDJFe49GqacWpyrNC\nx3kkURbk4+cr8NmOHKjaNfjNzEDMj7yznJWYnK2+gJb2FkwYMJZ3cPZxU3wmQgIJjpalCfJYRVd5\nOtvinYWj8J9zg9DPtnvT2KcLqvDfu3Oh1QJvzhtp8DWNu4KtMulxJnk9C3OpGY7L06HVPd3SiIYg\nqoKs1eqwLfkKNv1UBBsrM7wXG4rxQZ5Cx3qITqdDWnkGpBIpJno9K3QcEpiLtRNC3UaiXHEDRbeu\nCh3niSQSCUYHuOGj3zyLGeMGoUGhwoaki/h8Zw6q6puf+N0jZ+X4an8BLMxleHdhCMJEcmPlL2VW\nnYOZRIYwN7bKpAfZWdhirEcYalvrkVdbIHSch4jm1K6lrR0b9+cj91odBrjY4q2YYLh10mZMKCWN\nZZArbmCUaxAcrfoLHYdEIHpgJM7dzMVReRoCnPyEjtMpSwsZXprsi/FBHtiafAUXS+rxX9+ewa/G\nDsTMcYMfuFNap9Nhb1oxDp4qhYOtBd5ZOAo+bsI1/HgStsqkzkz1mYSTNzJxTJ6OENcgg+33/vs8\nXF0ffWOyKApy7e0W/G13LsprlBgxxAnL5wTBxkoU0R4p9e5deryZi+4Z3G8gfB0Go6CuCDcUVRhg\n5yF0pC7xdLbFOwtCcO5yDbYdvYKDp0px8NTjmyesjg/vtB+vkM5UZQNgq0x6PE9bdwQ6+eNS/WXI\nmypE9YSMoFVv1rv7HngdFeaFRc/5QSYV1Uz6AxpVTTh/Mw8eNm7wd/QVOg6JSPTASFzLu44U+Qks\nDnxZ6DhdJpFIED7cDUFDnPHjqetPLMhiLsYarQZnqy+wVSZ1aqrPJFyqv4wU+QkseSZW6DgdRFX5\nFr8wXNTFGAAybmRCo9Ngkvc4Ud1ZSsIb6fIM3KxdkFV1DrfbxNFn/Wncm8Y2VmyVSV31jJM/PGzc\nkF2dg9ttjULH6SDu6icyGq0GJypOw1JmgQiPcKHjkMhIJVJM9ZmEdp0GaXc7uJHhsFUmdZVEIsEU\nn4nQ6DRIq3j0sqVCYEF+Cnm1BWhou40Ij3BYi/ChchLes57hsDW3wYnyU1BpVELH6TPYKpOeVoRH\nGGzNbJBecRoqjVroOABYkJ9K6t0jqUle4wROQmJlIbNApNc4KNubcVqkzQdMUUerTI8wXkqiLrGQ\nWWCi17NQqJXIuju7IjQW5C6qUlbj8q2r8Os/1GjuoCVhRHqPh5nUDCnyE6JsPmCK7k1Xj3XndDV1\nXaT3nTXsU8qFWSv5l3jnQxfdu84w2XuCwElI7PpZ2GOseygyKrOQV1tg0Gcd9eGXz0vW1Ij7BrX7\nW2W62jgLHYeMSH9LB4S7hSCr+jwK668g0Nlf0DyCFuQDn84R/WAHgNb2VpypzEZ/SwcEuzwjdBwy\nAlEDI5FRmYXksjSjK8jGhq0yqSeifCYhq/o8UuQnBC/InLLugsyqc2jVtGHigAjIpOJZ4ILEy9PW\nHSOcA1B8+zpKbpcJHcdk6XQ6tsqkHhnYz/tOU5/6IlQpqwXNwoLcCZ1Oh9SKU5BJZBg/IELoOGRE\non0iAQBH5WkCJzFd5YpK3FBWYYRLIFtlUrdF+UwCAByTpwuagwW5E1cailGlrEao20g4WD66/yjR\no/g7+sLbbgAu3MxDbUu90HFMUiZbZZIeBLuOgLOVE85UnYNCrRQsBwtyJ9LK7zR4YN9qeloSiQTR\nAyOhgw7H5CeEjmNy2CqT9EUqkWKKzwSotWqcrDgjXA7B9mwEbrU2IKc2H152nhjqwGYD9PTC3ULQ\n39IBGZVZaFY/eXlDejr3WmWGu49iq0zqsXGeY2Als0RqeQbate2CZGBBfoKTN85Aq9Nistd4Nhug\nbpFJZZjiPQEqjQrpN4Q78jZFHc8ec7qa9MDazArjBozBbVUjzt3MFSQDDysfo13bjvQbZ2BtZo3R\nHqFCxyEjNmFABP51PRnH5ScR5TMJZjyb67Y3U/740HufZm8AAGyI+sTQccjETPGeiOPykzgmP4Ex\n7qEGPxHjGfJjXKi5iCaVAuM8R8NSZiF0HDJiNubWmDAgArdVjciuzhE6DhE9hou1E4JdR6CsqQLX\nbl83+P55qH6fRx19p8hPIEV+gkff1CNTvCfiePlJHJWnsd8ykYhF+UxCTs1FHJOfwLD+Qwy6b54h\nExmAs7UjQl1HokJRicJbV4SOQ0SP4eswGAPtvZBTk2/wxxVZkIkMJHrg3UYhZWwUQiRWEokEU30m\nQQcdUstPGnTfLMhEBjKonw+G9R+CS/WXcUNRJXQcInqMMLdgOFjYI+NGJlraWw22XxZkIgNiO83u\nK6znVD8ZhpnUDJHeE9CqacOpyizD7ddgexI5MayFSaYvyCUQbjYuyKo6j9lDp8HBsp/QkYyCSqPC\ntsI9kEqk+OPo38HH3kvoSGTiJnpF4PDdxxWneE+AVNL7568syHedrjwrdATqA6QSKaJ8IrG9aC9S\nyzMw23ea0JGMwqGSZNS21iN6YCSLMRnE+yc+BADUtdbjd8dWPvCz3nrqhgUZQG1LHXZd2QcrmRVW\nj30bztaOQkciExbhEY4fi3/CiYpT+NXgKD7n3gl5UwWOytPgbOWEmUNeEDoOUa/p89eQNVoNfijY\ngTaNCguHz2Uxpl5nITPHJK9xaG5vMej1KWOk1WmxtXA3tDotFgXMhwUPXsiE9fmCfKTsOIpvX0eY\nWzDGuLNFJhnGZO/xMJOa4VjZCWh1WqHjiNZxeTrKmiow1iMMgU7+Qsch6lV9esq6tFGOgyVH0N/S\nAbHD57N7EhnMyvS1AIBaA16fMjZ1LfU4UPwTbM1tMH/YTKHjEPW6PnuGrNKo8EPBdmh1WsQHLoCt\nuY3QkYjoLp1Oh+1FSVBp1Xhp2CzYW9gJHYmo1/XZgpx09RCqm2sw1XsiApz8hI5DRPfJrr6Agvoi\nBDj6cXlFEh2VRt0r2+2TU9b5dUVIq8iAh607ZvtOFzoOEd1HoVZi15X9MJeaY1EALyWRMB516WjP\nlQNIkZ/AjyU/9cpllD53hqxQKbHl0k7IJDL8+plFsJCZCx2JiO6TdOUgFGolZgx5Hi7WzkLHIeow\na+iv4GLtjJSyEyi5Xab37fepgqzT6bCtaA8aVU2YNfRX8LEfIHQkIrpPYf0VnK46Cx+7AYjymSR0\nHKIHWMgssDggBjrosKVwF9Tadr1uX68FWafTYc2aNYiNjUVCQgLkcrk+N99jpyvP4kLNRQzrP6Rj\n5R0isemrj0GpNGpsK9oLCSSIC4iBTCoTOhLRQ/wcfRHpNR5VymocLknW67b1eg05OTkZKpUK27dv\nR05ODj7++GMkJibqcxfddn83roTAWIP0JSV6nEddn/qf/O3Iqj6H4/J0RPXBA8Z/XU9GbUsdon0i\nMbCft9BxiB5rju90XKy7hJ/LjiPELQgD7fXz71WvVSk7OxuTJt2ZZgoJCcHFixf1uflu0+q0Hd24\nFvjPYTcuEqUYv1mwM7fFgeKfDL4wutDKm24guSwVzlaOmDGU7TFJ3KzMLPFKQAy0Oi22XNqFdj1N\nXeu1ICsUCtjb23e8NjMzg1Yr/PTbz6V3unGFugXzEQoSLTsLW7zkNwsqrRrbi/b2mRXI7rTH3AOt\nTovY4fPZ25uMQoCTH8Z7jkWFohI/lx7Tyzb1OmVtZ2cHpVLZ8Vqr1UIqfXLNd3W1f+LPe6q4vhSH\nSn6Go7UDfjc+AXaWtr26P6KeeNElEjn1ubhQVYDC5kuIHBwhdKReH6OHLqegtEmOiYPGYnLA6F7d\nF5E+/YdDLAoPX8bh0hRM9Y/AwP49W4lMrwU5LCwMx44dw7Rp03DhwgX4+3fee7ampkmfER6g0qjw\nedZ30Oi0eGX4y2hp1KIFvbc/In2YP2QOCmqu4vvsnfA2HyholypXV/teHaN1LbewNXcfbM1sMNNn\nWq/ui6g3LPSbhy9zv8ffMr7He+G/7dLNiI87yNXrlPXzzz8PCwsLxMbGYt26dVi1apU+N//U/vfa\nIVQ338RU74lsTE9Gw9naEbOHToOyvRm7r+wXOk6v0el02HE5CSqNCvP9ZrI9JhmlIJdARHiEo+zu\nMqE9odczZIlEgg8//FCfm+y2/LoipJazGxcZp8ne45FVfR5nqy9gjHsoglwChY6kd9k3c5BfV4gA\nRz9EeIQLHYeo217ym4VL9ZdxsOQIgl1GwMPWrVvbMclnf9iNi4ydVCLFKwExkEqk2F6UhNb2VqEj\n6ZVS3Yzdl++0x+RKa2TsbM1tEDt8Htq17dhyaVe3ewmYXEG+vxvXzKEvsBsXGS0vO0+8MGgqbrU1\n4EDxT0LH0aukqwfRpFZgxpDn4WrD9phk/EJcgxDuFoKSxlIcl6d3axsmV5BPV2XjQs1F+DoMwXMD\nJwsdh6hHpg2KgruNK1LLM1Byu1ToOHpx+dZVnKrMgpedJ9tjkkl52X8O7Mxtsb/4J9xsrn3q75tU\nQa5tqcfuy/tgJbPEkmcWshsXGT1zmTni7vbO/Wfhbr01IBCKSqPG1sI9kECCV9gek0yMvYUdFvjP\ngVqrxj8Ln37qWqITuPtATx9zeDPlj4/92aPaExIZo21Fe5FecRozh7yA6UOeM9h+9fHYE8co9SU6\nnQ5f521CTm0+FvrPRaT3+Ic+Y5DHnoiod8z1nQ4Hi344fP0oqpTVQschoseQSCRYOHwebMyskXTt\nEOqeog0uCzKREbA2s8bC4fPQrtPgn3fbTBKRODlY9kOM32yoNCpsLdzT5Ta4LMhERiLEdQRCXUei\n+PZ1pFecEToOET3BWI8wjHAOQOGtK8iozOzSd4y6IDe03RY6ApFBvew/F9Zm1th37RButTYIHadT\nGq1G6AhEgpBIJFg0fD6sZFbYe+Vgl8ar0RbklvZWJOZ8J3QMIoNysLTH/GEz0Kppw47LSaJeEUqj\n1eD7gm1CxyASjKNVf8z3m4FWTSu2dWEFN6MsyBqtBt/kbUaFolLoKEQGN85zDPz7+yKv9hLO3cwV\nOs4jabQafJe/FedFmo/IUMZ7jkWAox/y6wqRWXXuiZ81useedDodNl/aiTNV2RjpEojXgxL4LCP1\nOTeba/B/Mz+HlcwK/+fZ92BrbtMr++nOY0/t2nZ8d/GfyKnNh1//oVgespRrHFOfVtdSj/86te6B\n93Yu/PKhzxndGfLBkp9xpiobg+x98OqIV1iMqU9ys3HFi0OeR5Nagb1XfxQ6Tge1th3fXNyMnNp8\n+DsOw3+yGBPB2dqpS58zqoJ88sYZ/Ov6UbhYOWF5yKsc6NSnRftEwttuAE5XnkVh/RWh40CtUeOb\nvE3Iq72EAEc/LA/+NSw4Rom6zGgKcn5dIbYXJcHW3AZvjnqNa6dSnyeTyvBKQAwkkGBb4R6oNCrB\nsqg1anyVtwkX6woR6OSPN1iMiZ6aURTksqZyfHNxC2QSKZYFvwo3G1ehIxGJwsB+3ogaOAm1rfU4\nWHJEkAwqjRob835AQX0RRjgH4I2RS7jkKVE3mAkdoDN1LfX4Mud7qDVq/GZkPIY6DBI6EpGozBzy\nAo6WpSG5LBXJZakP/bw3+0WrNCr8I/d/UHTrKoKcA/GbkfEwl4r+zwqRKIl65CjVzdiQ8x0aVU14\n2W8ORrkGCR2JSHSEmhpu06jwj5zvcbnhGoJdRmBp0CssxkQ9INrRo9aosTH3B1Q330S0TySm+EwQ\nOhIR3dXa3oZ/5H6PKw3FCHENwtIRcTBjMSZ6rPtnqoxqtSetTotNl3bg2u0ShLkFY+6wF4WORGS0\nWtpb9bq91vZWJOZ8iysNxQh1HYnXRrzCYkykB6IsyP977RDO3cyFr8MQJAQuhFQiyphERmF1+l+w\nqWAHrjaU9LjVZkt7KzbkfItrt68j3C0Er46IYy8AIj0R3WHtcflJHC1Lg7uNG94IXgJz3q1J1CP9\nLOxxpiobZ6qy4WbjgnGeYxDhMRoOlo+eNnuclvYWbLjwLUoayzDafRQSAheyGBPpkagK8oWai9h9\nZT/6WdjjzZClvdYOkKgvWTPuj7jaUIyMG1m4UJOHfdf+hQPFP2GEcwDGeY5BkHNAp4W1Wd2Cv+d8\ng9JGOca4hyHhmQWcuSLSM9H0si6+XYq/nd8IiUSKt8OWYaC9t5CxiExSs7oFZ6vPI6MyC/KmCgB3\nzqAjPMIxbsAYuP/iGX9XV3uU3qjGFxe+QVlTOSI8wrE48GUWY6IeeNxNXaIoyDeba/D/sjegpb0V\ny4J/jRHOAUJGIuoT5E03cKoyE1lV59Hc3gIA8HUYjGu3rz/2O19MXcdiTNRDjyvIgk9ZN6kU2JDz\nHZTqZsQFvMRiTGQgPvYD4GM/F/N8ZyCn5iIyKrNQdOvqE7/DYkzUewQtyAt2LH/g9YQBEQIlIeq7\nzGXmGO0RitEeoY9cJo6IDIOHu0TUoavLxBGR/rEgExERiQALMhERkQiwIBMREYmA4HdZE5G4/LIJ\n/r1eAUTUu3iGTEREJAKiaAxCROLEM2Qi/TOq5ReJiIj6GhZkIiIiEWBBJiIiEgEWZCIiIhFgQSYi\nIhIBFmQiIiIRYEEmIiISARZkIiIiEWBBJiIiEgEWZCIiIhFgQSYiIhIBFmQiIiIRYEEmIiISARZk\nIiIiEWBBJiIiEgEWZCIiIhFgQSYiIhIBFmQiIiIRYEEmIiISgR4V5CNHjuDdd9/teJ2Tk4MFCxYg\nLi4Of//733scjoiIqK/odkH+6KOP8Pnnnz/w3po1a/DZZ59h69atyM3NRWFhYY8DEhER9QXdLshh\nYWH485//3PFaoVBArVbD29sbADBx4kRkZGT0OCAREVFfYNbZB3bv3o0ffvjhgfc+/vhjTJ8+HZmZ\nmR3vKZVK2NnZdby2tbVFeXm5HqMSERGZrk4LckxMDGJiYjrdkK2tLRQKRcdrpVKJfv36dfo9V1f7\nTj9DRMLhGCUyDL3dZW1nZwcLCwvI5XLodDqkp6cjPDxcX5snIiIyaZ2eIT+NDz/8EO+99x60Wi0m\nTJiA4OBgfW6eiIjIZEl0Op1O6BBERER9HRuDEBERiQALci/4+uuvMXHiRKhUKqGjGER8fDxKSkoe\n+bOoqCij/v9QXl6Ot956CwkJCYiLi8PatWuhVCof+dnKykocO3bMwAmpOzhG/41jVDxYkHvBgQMH\nMHPmTBw8eFDoKIKTSCRCR+i2trY2LF++HK+//jo2bdqErVu3Ijg4+IHudPc7ffo0zp07Z+CU1B0c\no//GMSoeLMh6lpmZiUGDBiE2NhZbt24FcOfodM2aNYiPj0d8fDzq6uqQmZmJBQsWYPHixdi/f7/A\nqXvuiy++wI4dOwAAxcXFiI+PBwAY8y0Kx48fR0REBEaOHNnx3ty5c9HQ0IDS0lLEx8cjNjYWr776\nKurq6vDVV1/h4MGDoj4CJ45RgGNUrGPUoAX5SdMmpmLXrl2IiYnB4MGDYW5ujtzcXABAeHg4Nm/e\njBdffBFffvklAEClUmHLli2YPXu2kJH14pdH2cZ81H2PXC6Hj4/PQ+97eXnhpZdewrJly7B9+3Yk\nJCSgqKgIb7zxBmbOnImpU6cKkLbn+sL4BDhGH/faGJnaGNXrY099XWNjI9LS0lBfX4/NmzdDoVBg\ny5YtkEgkiIiIAACEhobi6NGjAIAhQ4YIGbdHmpubYWlpCZlM9tDPjPmI+37u7u4df6zvV1paira2\nNoSEhABAx+BOSkoyaD56ehyjd3CMipPBC3J9fT3Wr18PtVqNmzdv4g9/+AOio6Mxe/ZsjB07FkVF\nRZBIJEhMTHygFacx2LdvH2JiYrBixQoAQGtrK6Kjo+Hk5IT8/Hy4u7sjOzsbfn5+AACp1HivGKxc\nuRKLFy/G6NGjUV9fj0mTJuHmzZsAgPz8fIHT6Ud0dDQ2btyIvLy8jimxXbt2wcnJCVOmTEFeXh7G\njRuHAwcOoLGxEba2ttBoNAKn7hlTHp8AxyjHqLjHqMH/tRUWFuK1117Dt99+i7Vr13Zcw1EoFJg1\naxY2b94MNzc3pKWlGTpaj+3Zswdz5szpeG1lZYUXXngB169fR1JSEuLj45GWloZly5YJmFI/li5d\nivXr12PBggWYPn06ZsyYgdTUVCQkJODSpUsdnzPmaTEbGxt8+eWXSExMRFxcHBYuXIi8vDx89tln\nWLFiBTZu3IiEhAT8+OOPmDVrFoYPH46UlBQcOnRI6OjdZsrjE+AY5RgV9xjt9cYg90+bxMfH44MP\nPsDXX38NM7M7J+eVlZXYtGkToqKicPjwYVhYWODTTz+Fr68v5s6d25vRDCY+Ph5r16416ukvMk0c\nn3dwjJIY9PoZ8sqVK5GdnQ2tVov6+nqsW7cOc+fOxfr16xEREWEy1zKexJiPQMm0cXzewTFKYtDr\n15CXLl2Kv/zlL5BIJJg2bRp8fX2xfv16fPXVV3Bzc0NDQwOABweEqQ2OTZs2CR2B6JE4Pu/gGCUx\nYC9rIiIiETDeWwiJiIhMCAsyERGRCOj9GnJ7eztWr16NiooKqNVqLFu2DMOGDcPKlSshlUrh5+eH\nNWvWAAB27tyJHTt2wNzcHMuWLcOUKVPQ1taGFStWoK6uDnZ2dli3bh0cHR31HZOoz+rpGL3nyJEj\nOHz4MD799FOBfhMi06L3grx//344Ojrik08+QWNjI+bMmYOAgAC88847GD16NNasWYPk5GSMO4pJ\ngwAAA8dJREFUGjUKmzdvRlJSElpbW7Fo0SJMmDAB27Ztg7+/P37729/i0KFDSExMxAcffKDvmER9\nVk/HqLm5OT766COcPHkSgYGBQv86RCZD7wV5+vTpmDZtGgBAo9FAJpOhoKAAo0ePBgBERkbi5MmT\nkEqlCA8Ph5mZGezs7DB48GAUFhYiOzsbr7/+esdnExMT9R2RqE/ryRgtKipCUFAQwsLC8Pzzz3cs\nVkBEPaf3a8jW1tawsbGBQqHA73//e7z99tsPPMtoa2sLhUIBpVIJe3v7jvfvfUepVHa05Lv3WSLS\nn56M0aamJgB3ijoR6Vev3NRVWVmJJUuWYN68eZgxY8YD/WCVSiX69esHOzu7B4rt/e/fW1z6l38Q\niEg/ejJGiah36L0g19bW4rXXXsOKFSswb948AEBgYCCysrIAAGlpaQgPD8fIkSORnZ0NlUqFpqYm\nFBcXw8/PD6GhoUhNTQUApKamdkyjEZF+9HSMElHv0Ps15I0bN6KxsRGJiYnYsGEDJBIJPvjgA/z1\nr3+FWq2Gr68vpk2bBolEgvj4eMTFxUGn0+Gdd96BhYUFFi1ahPfffx9xcXEdfXOJSH96OkaJqHew\nUxcREZEIsDEIERGRCLAgExERiQALMhERkQiwIBMREYkACzIREZEIsCATERGJAAsykQlRKBR48803\nUVNTgzfeeEPoOET0FFiQiUxIQ0MDCgsL4erqio0bNwodh4ieAhuDEJmQ5cuXIz09HZMnT0ZBQQFS\nUlKwatUqWFtbIzs7G01NTVi9ejX27duHoqIiREdH4/3334dWq8Unn3yCzMxMaLVazJs3D0uWLBH6\n1yHqU3iGTGRC/vSnP8HNzQ2rV6+GRCLpeL+mpgb79u3DW2+9hVWrVmHt2rVISkrCzp07oVAosHPn\nTkgkEuzduxc7d+5EcnIysrOzBfxNiPoevfeyJiLh/XLiKzIyEgAwYMAA+Pv7w9HREQDQv39/NDY2\nIiMjA0VFRTh16hQAoKWlBZcvX0Z4eLhhgxP1YSzIRCbo/rNjADA3N+/4b5lM9tDntVotVqxYgeee\new4AcOvWLdja2vZuSCJ6AKesiUyImZkZNBoNdDrdQ2fJj3LvM88++yx27NiB9vZ2KJVKxMXFIScn\np7fjEtF9eIZMZEKcnZ3h6emJVatWQSrt/Hj73pl0bGwsSktLMW/ePGg0GsTExGDMmDG9HZeI7sO7\nrImIiESAU9ZEREQiwIJMREQkAizIREREIsCCTEREJAIsyERERCLAgkxERCQCLMhEREQiwIJMREQk\nAv8f/Ri4snie/94AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x14138fab198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "monthly_avg.sel(location='IA').to_dataframe().plot(style='s-')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `MS` here refers to Month-Start; `M` labels Month-End (the last day of the month)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate monthly anomalies\n", "\n", "In climatology, “anomalies” refer to the difference between observations and typical weather for a particular season. Unlike observations, anomalies should not show any seasonal cycle." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "climatology = ds.groupby('time.month').mean('time')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "anomalies = ds.groupby('time.month') - climatology" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x14139021780>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFvCAYAAACb2bjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x14138fabda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "anomalies.mean('location').to_dataframe()[['tmin', 'tmax']].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill missing values with climatology\n", "\n", "The `fillna()` method on grouped objects lets you easily fill missing values by group:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# throw away the first half of every month\n", "some_missing = ds.tmin.sel(time=ds['time.day'] > 15).reindex_like(ds)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filled = some_missing.groupby('time.month').fillna(climatology.tmin)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "both = xr.Dataset({'some_missing': some_missing, 'filled': filled})" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<xarray.Dataset>\n", "Dimensions: (location: 3, time: 731)\n", "Coordinates:\n", " * location (location) <U2 'IA' 'IN' 'IL'\n", " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...\n", " month (time) int32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...\n", "Data variables:\n", " filled (time, location) float64 -5.163 -4.216 -4.681 -5.163 ...\n", " some_missing (time, location) float64 nan nan nan nan nan nan nan nan ..." ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "both" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = both.sel(time='2000').mean('location').reset_coords(drop=True).to_dataframe()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x14139044908>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFvCAYAAACb2bjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIAshhBAJQAJZCCGESAD/C6baUPF7/RckAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x14138ff0ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[['filled', 'some_missing']].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

EnriqueCornejo/rasdatools

work/remote_rasdaman.ipynb

1

1912

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import paramiko" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'hostname': 'WRI-Rasdaman'}\n" ] } ], "source": [ "ssh = paramiko.SSHClient()\n", "ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n", "\n", "# Get the linux config for server\n", "user_config_file = os.path.expanduser(\"~/.ssh/config\")\n", "ssh_config = paramiko.SSHConfig()\n", "if os.path.exists(user_config_file):\n", " with open(user_config_file) as f:\n", " ssh_config.parse(f)\n", " \n", "servername = 'WRI-Rasdaman'\n", "user_config = ssh_config.lookup(servername)\n", "\n", "print(user_config)\n", "\n", "# ssh.connect(user_config['hostname'], username=user_config['ecornejo'], key_filename=user_config['identityfile'])\n", "#sftp = ssh.open_sftp()\n", "#sftp.get(\"/home/aliciawyy/data/test.csv\", \"/home/alice/data/test.csv\") # filename need to be specified on server\n", "#sftp.close()\n", "#ssh.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

alantian/polyglot

notebooks/README.ipynb

2

8816

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "polyglot\n", "===============================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![Downloads](https://img.shields.io/pypi/dm/polyglot.svg \"Downloads\")](https://pypi.python.org/pypi/polyglot)\n", "[![Latest Version](https://badge.fury.io/py/polyglot.svg \"Latest Version\")](https://pypi.python.org/pypi/polyglot)\n", "[![Build Status](https://travis-ci.org/aboSamoor/polyglot.png?branch=master \"Build Status\")](https://travis-ci.org/aboSamoor/polyglot)\n", "[![Documentation Status](https://readthedocs.org/projects/polyglot/badge/?version=latest \"Documentation Status\")](https://readthedocs.org/builds/polyglot/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Polyglot is a natural language pipeline that supports massive multilingual applications.\n", "\n", "* Free software: GPLv3 license\n", "* Documentation: http://polyglot.readthedocs.org.\n", "\n", "###Features\n", "\n", "\n", "* Tokenization (165 Languages)\n", "* Language detection (196 Languages)\n", "* Named Entity Recognition (40 Languages)\n", "* Part of Speech Tagging (16 Languages)\n", "* Sentiment Analysis (136 Languages)\n", "* Word Embeddings (137 Languages)\n", "* Morphological analysis (135 Languages)\n", "* Transliteration (69 Languages)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Developer\n", "\n", "* Rami Al-Rfou @ `rmyeid gmail com`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Quick Tutorial" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import polyglot\n", "from polyglot.text import Text, Word" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Language Detection" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Language Detected: Code=fr, Name=French\n", "\n" ] } ], "source": [ "text = Text(\"Bonjour, Mesdames.\")\n", "print(\"Language Detected: Code={}, Name={}\\n\".format(text.language.code, text.language.name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tokenization" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'Beautiful', u'is', u'better', u'than', u'ugly', u'.', u'Explicit', u'is', u'better', u'than', u'implicit', u'.', u'Simple', u'is', u'better', u'than', u'complex', u'.']\n" ] } ], "source": [ "zen = Text(\"Beautiful is better than ugly. \"\n", " \"Explicit is better than implicit. \"\n", " \"Simple is better than complex.\")\n", "print(zen.words)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Sentence(\"Beautiful is better than ugly.\"), Sentence(\"Explicit is better than implicit.\"), Sentence(\"Simple is better than complex.\")]\n" ] } ], "source": [ "print(zen.sentences)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part of Speech Tagging" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Word POS Tag\n", "------------------------------\n", "O DET\n", "primeiro ADJ\n", "uso NOUN\n", "de ADP\n", "desobediência NOUN\n", "civil ADJ\n", "em ADP\n", "massa NOUN\n", "ocorreu ADJ\n", "em ADP\n", "setembro NOUN\n", "de ADP\n", "1906 NUM\n", ". PUNCT\n" ] } ], "source": [ "text = Text(u\"O primeiro uso de desobediência civil em massa ocorreu em setembro de 1906.\")\n", "\n", "print(\"{:<16}{}\".format(\"Word\", \"POS Tag\")+\"\\n\"+\"-\"*30)\n", "for word, tag in text.pos_tags:\n", " print(u\"{:<16}{:>2}\".format(word, tag))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Named Entity Recognition" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[I-LOC([u'Gro\\\\xdfbritannien']), I-PER([u'Gandhi'])]\n" ] } ], "source": [ "text = Text(u\"In Großbritannien war Gandhi mit dem westlichen Lebensstil vertraut geworden\")\n", "print(text.entities)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Polarity" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Word Polarity\n", "------------------------------\n", "Beautiful 0\n", "is 0\n", "better 1\n", "than 0\n", "ugly -1\n", ". 0\n" ] } ], "source": [ "print(\"{:<16}{}\".format(\"Word\", \"Polarity\")+\"\\n\"+\"-\"*30)\n", "for w in zen.words[:6]:\n", " print(\"{:<16}{:>2}\".format(w, w.polarity))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Embeddings" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Neighbors (Synonms) of Obama\n", "------------------------------\n", "Bush \n", "Reagan \n", "Clinton \n", "Ahmadinejad \n", "Nixon \n", "Karzai \n", "McCain \n", "Biden \n", "Huckabee \n", "Lula \n", "\n", "\n", "The first 10 dimensions out the 256 dimensions\n", "\n", "[-2.57382345 1.52175975 0.51070285 1.08678675 -0.74386948 -1.18616164\n", " 2.92784619 -0.25694436 -1.40958667 -2.39675403]\n" ] } ], "source": [ "word = Word(\"Obama\", language=\"en\")\n", "print(\"Neighbors (Synonms) of {}\".format(word)+\"\\n\"+\"-\"*30)\n", "for w in word.neighbors:\n", " print(\"{:<16}\".format(w))\n", "print(\"\\n\\nThe first 10 dimensions out the {} dimensions\\n\".format(word.vector.shape[0]))\n", "print(word.vector[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Morphology" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'Pre', u'process', u'ing']\n" ] } ], "source": [ "word = Text(\"Preprocessing is an essential step.\").words[0]\n", "print(word.morphemes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transliteration" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "препрокессинг\n" ] } ], "source": [ "from polyglot.transliteration import Transliterator\n", "transliterator = Transliterator(source_lang=\"en\", target_lang=\"ru\")\n", "print(transliterator.transliterate(u\"preprocessing\"))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

dbouquin/DATA_620

620_project2_101716.ipynb

1

69106

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 620 Project 2\n", "#### Further analysis of NASA ADS publications: two-mode network analysis\n", "Daina Bouquin\n", " \n", "Below is an analysis of affiliations between authors and journals in the 2-mode [NASA Astrophysics Data Systems](https://ui.adsabs.harvard.edu/) dataset. This project builds on work performed in Project 2. The primary objective of this project is to use clustering techniques (e.g. the island method) to try to find small sub-networks of important authors that are frequently collaborating together. In doing so we can also see which journals stand out as focal points for these types of collaborations." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import networkx as nx\n", "import os\n", "import ads as ads \n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from networkx.algorithms import bipartite as bi" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "os.environ[\"ADS_DEV_KEY\"] = \"kNUoTurJ5TXV9hsw9KQN1k8wH4U0D7Oy0CJoOvyw\"" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ads.config.token = 'ADS_DEV_KEY' " ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Search for papers (50 most cited) on stars (very general search)\n", "papers1 = list(ads.SearchQuery(q= \"stars\", sort=\"citation_count\", max_pages=1 ))" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# find author names\n", "a = []\n", "for i in papers1:\n", " authors1 = i.author\n", " a.append(authors1)\n", "author_names = a" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# find the journals\n", "j = []\n", "for i in papers1:\n", " journals1 = i.pub\n", " j.append(journals1)\n", "journals = j" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# create an initial df\n", "df = pd.DataFrame({'Author_Names' : author_names,\n", " 'Journal':journals\n", " })" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Journal</th>\n", " <th>Author_Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Physical Review B</td>\n", " <td>Monkhorst, Hendrik J.</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>Physical Review B</td>\n", " <td>Pack, James D.</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>The Astrophysical Journal</td>\n", " <td>Schlegel, David J.</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>The Astrophysical Journal</td>\n", " <td>Finkbeiner, Douglas P.</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>The Astrophysical Journal</td>\n", " <td>Davis, Marc</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Journal Author_Name\n", "0 Physical Review B Monkhorst, Hendrik J.\n", "0 Physical Review B Pack, James D.\n", "1 The Astrophysical Journal Schlegel, David J.\n", "1 The Astrophysical Journal Finkbeiner, Douglas P.\n", "1 The Astrophysical Journal Davis, Marc" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Expand the df with melt\n", "s1 = df.apply(lambda x: pd.Series(x['Author_Names']),axis=1).stack().reset_index(level=1, drop=True)\n", "s1.name = 'Author_Name'\n", "df_m = df.drop('Author_Names', axis=1).join(s1)\n", "df_m.head()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "author_nodes = pd.DataFrame(df_m.Author_Name.unique(),columns=['Author_Name'])\n", "author_nodes['node_type'] = 'Author_Name'\n", "journal_nodes = pd.DataFrame(df_m.Journal.unique(), columns=['Journal'])\n", "journal_nodes['node_type'] = 'Journal'" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Build the graph from the node sets and edges\n", "# set bipartite attribute to ensure weighted projection will work\n", "a_nodes = list(author_nodes['Author_Name'])\n", "j_nodes = list(journal_nodes['Journal'])\n", "edge_bunch = [tuple(i) for i in df_m.values]\n", "\n", "g = nx.Graph()\n", "g.add_nodes_from(a_nodes,node_type='Author_Name', bipartite=0)\n", "g.add_nodes_from(j_nodes,node_type='Jurnal', bipartite=1)\n", "g.add_edges_from(edge_bunch)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Weighted Projections/Clustering\n", "# find the largest most connected graph - 200 as cut-off \n", "big_subg = [i for i in nx.connected_component_subgraphs(g) if len(i) > 200]\n", "# Largest:\n", "sg_largest = big_subg[0] # largest connected subgraph" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# weighted_projections can be applied to this subgraph to separate the two components\n", "Journals,Author_Names = bi.sets(sg_largest) # split into bipartites" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [], "source": [ "j_proj_sg_largest = bi.weighted_projected_graph(sg_largest, Journals) " ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a_proj_sg_largest = bi.weighted_projected_graph(sg_largest, Author_Names)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Use the Island Method \n", "j = j_proj_sg_largest.edges(data=True) \n", "a = a_proj_sg_largest.edges(data=True)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "140\n" ] } ], "source": [ "# Find weights in the projections that are greater than 1\n", "print len([i for i in a if i[2]['weight'] > 1])\n", "print len([i for i in j if i[2]['weight'] > 1])" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# With a min threshold of edge weight = 1, find the nodes with strong relationships within the sub-graphs. \n", "# tidy (SNAS Ch. 4) function similar to the one presented in Social Network Analysis Chapter 4. \n", "def tidy(g, weight):\n", " g_temp = nx.Graph()\n", " edge_bunch2 = [i for i in g.edges(data=True) if i[2]['weight'] > weight] \n", " g_temp.add_edges_from(edge_bunch2)\n", " return g_temp" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a_sg_island = tidy(a_proj_sg_largest, 1)\n", "j_sg_island = tidy(j_proj_sg_largest,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have two islands of the projected authors and journals. Examining the degree centrality will help reveal which nodes are the key to the networks." ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Astronomy and Astrophysics</th>\n", " <td>0.666667</td>\n", " </tr>\n", " <tr>\n", " <th>Physics Letters B</th>\n", " <td>0.666667</td>\n", " </tr>\n", " <tr>\n", " <th>The Astrophysical Journal Supplement Series</th>\n", " <td>0.333333</td>\n", " </tr>\n", " <tr>\n", " <th>Journal of Physics G Nuclear Physics</th>\n", " <td>0.333333</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "Astronomy and Astrophysics 0.666667\n", "Physics Letters B 0.666667\n", "The Astrophysical Journal Supplement Series 0.333333\n", "Journal of Physics G Nuclear Physics 0.333333" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# degree centrality of both island clusters\n", "a_degree = nx.degree_centrality(a_sg_island)\n", "j_degree = nx.degree_centrality(j_sg_island)\n", "pd.DataFrame.from_dict(a_degree,orient='index').sort_values(0,ascending=False).head()" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Liss, T. M.</th>\n", " <td>0.761905</td>\n", " </tr>\n", " <tr>\n", " <th>Quadt, A.</th>\n", " <td>0.761905</td>\n", " </tr>\n", " <tr>\n", " <th>Cattai, A.</th>\n", " <td>0.761905</td>\n", " </tr>\n", " <tr>\n", " <th>Caso, C.</th>\n", " <td>0.761905</td>\n", " </tr>\n", " <tr>\n", " <th>Yamamoto, A.</th>\n", " <td>0.761905</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "Liss, T. M. 0.761905\n", "Quadt, A. 0.761905\n", "Cattai, A. 0.761905\n", "Caso, C. 0.761905\n", "Yamamoto, A. 0.761905" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame.from_dict(j_degree,orient='index').sort_values(0,ascending=False).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the islands are isolated, we can subset them into their largest connected subgraphs and do some basic plots. " ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# examine the connected subgraphs\n", "j_connected = [i for i in nx.connected_component_subgraphs(j_proj_sg_largest) if len(i) > 1]\n", "a_connected = [i for i in nx.connected_component_subgraphs(a_proj_sg_largest) if len(i) > 1]" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# combining the graphs \n", "def merge_graph(connected_g):\n", " g = nx.Graph()\n", " for h in connected_g:\n", " g = nx.compose(g,h)\n", " return g\n", "\n", "a_islands = merge_graph(a_connected)\n", "j_islands = merge_graph(j_connected)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAFBCAYAAACrYazjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIAYxERGRQAxiIiIigf4PqhinvruoPSEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2ab16fb50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx.draw(a_islands)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAFBCAYAAACrYazjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FFX3xz+TuukQSEJRaugIAgFfRER+SlNACCpKUbEg\noFhQ4QVepVpAQBEBsaJiQRBREaWoNH0hEV4FAU1Cr4kEDAFSd+/vj9kM2RSym2wyKefzPPNkZ+bO\nnXM3s/vde+8552pKKYUgCIIgCKbgYbYBgiAIglCVESEWBEEQBBMRIRYEQRAEExEhFgRBEAQTESEW\nBEEQBBMRIRYEQRAEExEhFgRBEAQTESEWBEEQBBMRIRYEQRAEExEhFgRBEAQTESEWBEEQBBMRIRYE\nQRAEExEhFgRBEAQTESEWBEEQBBMRIRYEQRAEExEhFgRBEAQTESEWBEEQBBMRIRYEQRAEExEhFgRB\nEAQTESEWBEEQBBMRIRYEQRAEExEhFgRBEAQTESEWBEEQBBMRIRYEQRAEExEhFgRBEAQTESEWBEEQ\nBBMRIRYEQRAEExEhFgRBsJOUlMQzTz9Nk7p1qevvTy2LhTrVq9Pz5pvZv3+/2eYJlRRNKaXMNkIQ\nBMFMYmNjefzBB/ltzx5aAMGAAjTgArAfvdfi6+/PlJdfZuzYsSZaK1Q2RIgFQajSTJowgddnz6YV\nEAdEAx2BICAViAG+BJoCfwFZQJ26dfnkyy/p2LGjSVYLlQkRYkEQqix3DhzI2tWrsQCTgRFA9QLK\nnQOWArMAG5ANZGgary5cyMjRo8vKXKGSIkIsCEKVIzY2lt433sj59HTCgc1ApBPXJQA9gbPoYuzt\n6cmsBQtEjIUSIUIsCEKVwtvLC3+rlWzAAuzAORHOIQHoDEQAiUC2tzcbfvmFqKgo9xsrVAnEa1oQ\nhCqDt6YRYLUyFbgNeA7XRBh7+YnAcSAD8MjK4vnx491qp1C1kB6xIAhVAi9NIwzYiu4V3Qw4SMFz\nwkVxFqgHNLH/3ezhQfzp04SFhbnLXKEKIT1iQRAqPb6+vgSgi3Ak8CEwkOKJMEAoMADdq7o6kG2z\nseiNN9xhqlAFESEWBKHSY8nM5HkuD0PHAZ1KWGcX4G9gJXrI0/dr1pSwRqGqIkIsCEKlplu3bmQC\nD+Q6dgE9TrgkBAEtAE/gMyAlMbGENQpVFRFiQRAqNdu2bMk3DB2IPqxcElKBtsBOoCYQf+JECWsU\nqioixIIgVGoCgRvzHGuKnjGrJMTa64kEtgD+QGBgYAlrFaoiIsSCIFRqvMg/DH0vetrKc8Ws86z9\n+nvt+5HAFMDz4sVi1ihUZUSIBUGo1GSTfxg6HD2O+INi1vkB0BfIHax0P5AJDBgwoJi1ClUViSMW\nBKFS46lp3A18nOd4LNCfyyFNzpIAdAW+AfLm0hqK7rhlla9VwQWkRywIQqXmhhtvZBX5h6E7AtOA\nXuji6gwJ9vLTyC/CADegz0kLgiuIEAuCUKnZvHkzvsD7BZwbCUxA7+G+SuFzxmeBefZyE+zXFUQQ\nejiTILiCDE0LglDp8fX1xZKZyU4KHob+FZgPrEHPuJV7PeItwHdAP+AJCu4J57AY+DeQIl+rgguI\nEAuCUOk5efIk9erWJRxdWAubE/4bPf1lHHAa+C/QDViEo2NWYQwDPkXmiAXXkKFpQRAqNefOnaNu\n3bpYgSSgAzCXgoehw4AR6AtCxAAzgRU4J8JngS+AZi1auMNsoQohPWJBECotFy9edEiyEQL4Auno\noUbR6A5WuYehvwVup+hh6LzMQ3fiSkhKklWYBJcQIRYEoVKSlpaGv7+/sd+sWTPOHTzIvKwshgLP\nAy+iZ8TyAqzoYhwA/A/XQ5o6ABc8PLBarW5qgVBVkKFpQRAqHVar1UGEGzduzPTp00nLyjKSe0xH\nT/ZxHn1YOQWwofeUb8S1kKYbgUvAjh073NMAoUohQiwIQqVCKYWXl5ex36BBA6ZPn86wYcO4CGwr\n4voMIBm9hzuPK4c0zbWX+xtYuGgRUVGuDGYLgo4MTQuCUGlQSuHhcbl/Ua9ePV544QVGjBiBp6cn\nX3zxBXf27csJHFdjKogG6CKcCQxCX384Zy55G7AK8EHvUS9ZtIiRo0e7vT1C1UCEWBCESoOmacbr\nOnXq8NJLL/Hggw/i5eXFH3/8QePGjWkUFsbYM2d4ysk6nwZeQ8+Y5Yk+l3wB0NDnllu1b8/OnTvd\n2xChSiFD04IgVApyi3CtWrWYOXMmDz74IN7e3uzfv5/GjRsDMPihh5iK83PAc9HFdyf6HHIaYLEf\nywB27dpFampJVzcWqjIixIIgVHhyi3BYWBgzZsxg5MiR+Pj4sH//fho0aADA6tWrmT17NhconkNW\n3SZNOHLyJFZfXzw9LyeznDBhgptaIlRFRIgFQajQ5Bbh0NBQZsyYwahRo/D19SUuLo769esD8M47\n7xAdHY3NZsMGJKLHCTvrkJUIhNWpQ+3atRk9erTDfRcvXozNZiuN5glVAJkjFgShwuLh4UHOV1i1\natV48cUXGTt2LH5+fsTFxVG7dm0AXn75ZSZNmmSU1TSN1NRUvvjiC16cOJGjJ08SjaND1s/oDll+\n3t6czcoy7rl161Y6depEQECALup2AV6wYAGPPfZYmbVdqDyIEAuCUCHx8vIykmcEBwczY8YMxo0b\nR0BAAPHx8YSHhwPw1FNP8dprrxnX+fj4cOHCBby9vY1j+/fvZ8Izz3Bk/34yLl7ENyCA+i1aMGvO\nHCIiIqhRo4ZRtl27duzcuZORI0fywQcfkGUXaS8vL+O1ILiCCLEgCBUOHx8fQ/QCAwOZNm0a48eP\nJzAwkISEBGrWrIlSiuHDh/Pxxx8b11WvXp3k5GSHYWVnGDZsmEM969at44YbbiA4OBibzWb0tDdu\n3MjNN9/shhYKVQkRYkEQKhT+/v6kpaUZr6dOncrEiRMJCgriwIEDhIaGYrVaufXWW1m/fr1xXYsW\nLdi3b1+x7pmZmUlAQADZ2dkAREZGEhcXx9ChQ1m5cqXxoyA8PJzExMQStlCoaoizliAIFYaQkBBD\nhC0WC8899xwTJ04kJCSEQ4cOERoaSmZmJv/6178cRHjAgAHFFmHQe+BTp0419hMSEli9ejWLFi0y\nxBkgKSmJgwcPFvs+QtVEesSCIFQIwsPD+fvvvwHw9PRk6tSpTJkyhdDQUA4cOEBwcDAXL16kffv2\nxMXFGddNnjyZmTNnlvj+SimCg4O5cOECoMcqHz9+nOjoaL777jujV9y5c2d++eWXEt9PqDqIEAuC\nUO5p0KABR44cAXSP5+eee46ZM2dSs2ZNDhw4QGBgIOfOnaN169acPHnSuO6zzz5j8ODBbrPjyy+/\nJDo62tj/8MMP6dWrFxEREQ7lLly4QEBAgNvuK1RuRIgFQSjXtGzZkv379xv7EydOZNasWYSHh3Pg\nwAH8/f1JTEykRYsWnDt3OSI4JiaGjh07ut2eq666ihMnTgAQFBREcnIyt956K5s2bTKGqR955BHe\nfPNNt99bqJyIEAuCUG657rrriImJMfafffZZ5s6dS61atUhISMDPz49Dhw7RunVrLl26ZJQ7cuQI\n9erVKxWb9uzZQ5s2bYz9RYsWceuttxrZu3KwWq0OC1AIQmHIUyIIQrmkV69eDiI8btw45s6dS506\ndThw4AB+fn7s2bOHZs2aOYjw2bNnS02EAa655ho6dOhg7D/xxBOEh4dz/fXXO6S9lB6x4CzSIxYE\nodxxzz338Nlnnxn7jz/+OAsWLKBevXrExcXh4+PDzz//TLdu3YykHgCXLl3Cz8+v1O07e/asQ5KP\nl19+mQEDBtC8eXPjmKZpkvZScAoRYqHMSUpK4sOlS4nbvZsLKSkEhoTQtE0b7hsxgrCwMLPNE0xm\n9OjRDr3JUaNGsWTJEho2bMiff/6Jt7c33377Lf369TMSaXh5eZGWloaXl1eZ2XnXXXexYsUKY//8\n+fPcdNNN/Pbbb4YA//jjj3Tv3r3MbBIqKEoQSpE//vhDRbVvr3w0TQWBqg4qCFRtULfYt+tBNQfl\nDaqar696++23zTZbMInnn39eAcb20EMPKU3TVGRkpMrKylJKKfX+++87lAkKClI2m63Mbc3IyHCw\nY9KkSWrXrl0Ox0JCQsrcLqHiIUIslApLly5VwRaLCgJlATUE1Cz7386gwu2CHA6qA6ho++YPKiTn\nr6+v6vF//6f27dtndnOEMmDBggUOInb//fcrTdNUs2bNVHZ2tlJKqVmzZjmUadKkiak2P/fccw72\nJCcnqxYtWihN04xjhw4dMtVGofwjQiy4nT49eigvUMGg5oDaCGooqEBQLUAF2MW3G6gb7X/b2YX5\nTlCPg6oB6jp72UBQV4WGqqVLl5rdNKGU+Pjjjx0EbdiwYUrTNNWqVStltVqVUko9/fTTDmV69+5t\nstVK2Ww2ZbFYDJseffRRtWXLFgc727RpY7aZQjlHhFhwK//q0EH5gKoFKh7UEvvru3KJazVQD4Ba\nDGqZ/e8I+/FO9nIzQNUDNQ/UXFA17b3kPj16mN1Ewc18++23DsI1ePBgpWmaatOmjbJarcpms6nh\nw4c7lBk/frzZZhssX77cwbaTJ0+qRo0aORy7ePGi2WYK5RgRYsFt3Dd8uPKwDy3niHAju6iGg4oA\n9Sqos6BUAdtZew+6mr0nfK399eOgtoNqaN//V4cOZjdVcBM7duxwEKxBgwYpTdNUu3btlNVqVVar\nVfXq1cuhzMcff2y22fmoVauWYd8999yjvvvuOwebR4wYYbaJQjlGvKYFtxAbG0vnTp3wA64HkoCj\nQEdgCxAG/ABEOlFXAnAz0AOIsl//HdAN2ApkAHfeey/vf/CBu5shlCH79u2jVatWxv7tt9/O119/\nTVRUFNu3b8dms9GlSxeHWOLY2FiioqLMMPeK/Pbbb7Rr187YP3ToEF26dHFIt2mz2VxeflGoGkhC\nD8EtXN+pE/6AFQgAGgHtgVSgPpAIjANinagrEl20v0UX4k+Ag+hC7AFEAF98+CG//vqrm1shlBWn\nT592EOFbb72Vr7/+mn/961/s2LGDzMxMWrVq5SDCx48fL5ciDHDttdc6ZNsaOXIkr7/+ukOZRYsW\nlbVZQgVBesRCialZrRpZKSncD5wCNgDR6L3hIHQx3gF8gS6kA4ClTtT7KrAL+CjXsQSgK3AWaNWy\nJbv27nVPI4QyIzk5mZo1axr7PXv2ZMOGDXTp0oWtW7eSmppK8+bNHXqT//zzDyEhIWaY6zRnzpxx\niIPfv38/Xbt25cyZM8Yx+boVCkJ6xEKxiY2NpWm9eninpPAs8Dn6sPRB4F1gFDDU/vd94AjwPLAa\n6OxE/fcBa4C/cx2LRB+e9gf27ttnLIsnVAwuXrzoIML/93//x/r16+nWrRtbt27lzJkz1KtXz0GE\n09PTy70IA9SsWZPbb7/d2B86dCizZ892KLNly5ayNkuoAIgQC8XircWL6dGlC0nHjvEoutBuBZ4E\nqhdyTXX04elfgcPArUXcIxQYCHyY53gk8B/AAryS54tOKL+kp6cTGBho7N944438+OOP9OjRg59+\n+omjR49Sr149/vnnH0Bfc9hqteLr62uWyS7z+eefG6937drFtddeS3BwsHGsV69eZpgllHNEiAWX\neWvxYp559FHIyuJ+YCGwDl0gk4A5wEhgiP3vHPL3alcDP6HP+14HtEGfUx6ep3xHII78jACygM8/\n+qiAs0J5w2q1OuSA7ty5M1u2bOHWW29l/fr17N+/n0aNGpGWlgZAQEAAWVlZFW71Ih8fH/79738b\n+wMHDmT69OnGfnp6OseOHTPDNKEcI3PEgkvExsbStVMngoFMoCf6cHQXYD66g1Xe+eEY4EvgNnRP\n6A32cv3Rh6hzym1FF+hg4BzQF12gP0GfJ867suwQ4FsvL1KyskqptYI7UEo5CGqnTp2IiYmhf//+\nfPXVV2zfvp3OnS9PVtSvX5/Dhw+bYKl7UErh4+NjrE28detWevXqZawQ1bBhQw4ePGimiUJ5w7TA\nKaFCEujjo0JATQB1hz2ud549aUdRMcJ32mOM5xVRbi6oUFB+9vrr2etfkqfsIlDVPTzMfkuEIiBX\nPG2HDh0UoKKjo5VS+ZN5dOvWzVxj3cRHH33kkG96+vTpDu1MS0sz20ShHFGxxn0EU+lx8814ZGYy\nBd1r2Qa0At6g6PnhFcBO9Pnhp65QLmceeQcQjt6DTkEfvp4FvJWrbFBJGiOUCbnjZtu2bcvOnTsZ\nPHgwX3zxBR988AG33Xabcf7RRx9l06ZNJljpfoYNG0b16vpTnpKSQlRUFD4+Psb5IUOGmGWaUB4x\n+5eAUDGIiYlRgej5oM+iL9DQ0Z56Mr6Q3m3OFsPllJdXKpd3i7fXvwp9tabR9npic/WIQ7y8zH5r\nhEIgVw+wVatWClBDhw5VSik1e/Zsh/Pvvfeeyda6n5iYGKN93t7e6tlnn3VosxkrRgnlE+kRlxO+\n+uorwmvWpLqHBzU0jRBNw9fDg2aNGjH1+edND9OZ9PTTZACD0Hutiegxw5MoOlvWfGCCE+XyEmm/\nbi56dq3lwN32+gD+C3hbLC7WKpQFuXvCzZs3Z+/evYwYMYJly5bx7LPPMn78eOP8jh07GDFihBlm\nliodO3akefPmAGRlZdG+fXuH9ZLnz59f2KVCVcPsXwJVnQcffFBV0zRlATUMx4UQhqIvIRgEygdU\n75tuUjExMWVuY2JiovIC1dJulwLV3j6HW9hcb86WaJ/nLapcYVuy/T7rQb0CaoD9/fjL/rdZrVpl\n/n4IV4Zcvb4mTZoo0NcVVkrlW7zh2LFjJltbupw6dcqhvY888ojDviAoJT1iU4msX58V777L80px\nEt0zOHcSjGXASWAqeszsT5s20bNLF95avLhM7fxw6VKCgWpcnpfNAm6n8Lle41r0WOCiyhVGKHAH\nMBF4AD315c3AXUAdIKxOnWLWLJQGuXvCDRs2JD4+ntGjR/PWW2/Rp08fPsoVbpaamspVV11lhpll\nRq1atejTp4+xf8011zh4kG/bts0Ms4RyhgixSUTWr8+lo0fZiXPOSzvRRSkzK4vnnniiTMU4bvdu\nPNGzWaXmOt7NmWuBTiW8f2cgHj2PdX/AEzgEHANadOhQwtoFd5FbhOvVq8ehQ4d4/PHHWbBgAddd\ndx3ff/+9cT4zM9MhuUdlZtWqVcbrxx57jLvvvtvYv+mmm0ywSChviBCbwEMPPcTfR4+yBefnTSPR\n50m9gItZWUx66qkyW/Tg999/Jw09vjcm13FnvJYvOFnuSgQBddF7113QvahbAK2Bf3KlQhTMw9PT\n03hdt25djh49yrhx43jllVdo0aIFsbH6ch+apmG1WvH29jbL1DLHYrHw1FNPGfuNGzc2frRYrVZO\nnDhhlmlCOUGE2ARWvf8+Uyme89IUwBeIzMhg/ksvudu0fLy1eDF//fEHHug/Ar5ET7aRiWPvuDAC\nnSx3JVLRQ5ni0EX5ItDcvr9u40bTHdmqOj4+PthsNkAfij1x4gQTJkxg+vTp1KtXj/j4eEAXJJvN\nVuGyZbmDuXPnGq9nzJhB3759jf22bduaYZJQjqh6nwiT+eqrr0iz2bi/mNffD1wC/gC+/vbbUhWh\n2NhYJj31FB7AevvWAz1u+Ch6rG9RNAVKOgsWi94jTs21XYM+99wsO5sPly4t4R2E4uLn50eWPbNZ\neHg4p0+fZvLkyUyYMIGIiAgSExMBiIiIMNJXVkU0TePdd9819nMvfJGcnExGRoYZZgnlBBHiMmbU\nQw8ZIUDFIRQ9hMgHaGa1lqoIzX/pJRpkZDAZPaHGbcBV6Lmg+6GnozxXRB33OlmuMM6i98LboveG\ndwAH7PV2BPytVuL27Clm7UJJCA4OJj09HdCFJSkpiSlTpjBmzBjCw8O5ePEioIfxnD592kxTywUP\nPPAA/v7+ALz//vt0797dOHfLLbeYZZZQDvAquojgTjLPneOGEtbRBX2RBf/sbF6aOpVln3xCREQE\ntWvXpkaNGlSrVo0aNWoQHh5OjRo1CA0NpUaNGtSsWdPplWySkpL4Zu1abOjeygBPoDtLtQS6A97A\nB+gZtQojHD371vvoTmeu8gF6zuk/gYbAK+g/CMLQhdkGpJ4rrswLxSUsLIzUVH3SITQ0lDNnzvDC\nCy8waNAg6tata5R74IEHHHqCVZ3169dzww36N0DuefJt27ahlHJweBOqEGbHT1U1Qu1xwsWJqc3Z\nPgIVBqorqMBcMYnu3DRQ19pjmXPfe4n93stwPmPW9+g5pouTWasWqI32WORpoKrjmFmrG6iRw4eb\n/W+tUlx11VXGcxIUFKQANXv2bLVjxw6HZ+j1118329RySb169Yz3KCoqyng9bdo0s00TTEKGpsuQ\n2NhYsnGP85JCn1e4ukEDunbtSpMmTQgLC8Pf3x8vL68S/7K2ACGQr/c+ksthTB2BaUAvIOEKdfVC\nd676vyLK5SbBft00YDdwEzAPPb46yl4mFkjVNJpec42TtQolpWXLlhw/fhzQlypMTU1l3rx5tGrV\niuuuu84ot3nzZsaOHWuWmeWaX375xXidM6oAMGXKFDPMEcoBMjRdhsx/6SU0dOelUSWo52f0mNpL\nXl48OGYMTz/7bJHXZGRkcOLECY4fP058fDxxcXEkJCRw+PBhTp48yblz5xwcRjzRxb6g0KNr0EOp\nRqELM0BXYDy6M1lB898z0ZdHvAE9bWVh5c4CS9GHoKehi/cNQDb6vPSLucp9CWR5eHDv/fcX2X6h\n5HTs2JH9+/cDupPWxYsXef311wkJCXFYvOHo0aNcffXVZplZ7qlbty5du3Zl69at/PXXX7Rs2ZJ9\n+/YB+pKJXbt2NdlCoayR9YjLiKSkJOrXqkWWUnijZ8wqjsPWWXQPYk/A09eXhGPH8PX15ejRo+zb\nt4/du3ezf/9+Dh06RFJSEikpKaSlpWG1WtE0jZx/d+7XBeGHnojjbvL/aNiL3hs+kasNv6LngF6D\n7s2cez3iWHTRbA4cQXf82nSFcn3R56OroQvxOWAsl0UY4FXgM6D+bbfx+Zo1zr59QjHp2bMnGzZs\nAMDX15eMjAwWL17MhQsXeDbXD8ELFy4QEBBglpkVhvT0dPz8/AB9jv3s2bPGOflKrnqIEJcRUW3b\nsmf3bjzQRe459IxarjIPeAE9pnivpnGxgH+fp6cnFouFwMBAatSoQUZGBgcOHHDpPh6axrVAG6V4\nv4DztdB7tnnb8Dd64o04dHENQg9huhfdweotYDLwGPrQ+vECynkC7wEz7McWc3k4GvRh6xuANA8P\nftixg6io3GcFdzN48GA+//xzQHcwysrK4p133mH//v0O8bFZWVkOixoIV2bMmDEstmfIq1+/PkeO\nHAHgxIkT1JHUrVULE+enqwxLly5VXqC8QQWD+sJJJ6eCnJeCQQWACtQ0dcstt6gpU6aolStXqj17\n9qi4uDgH5w9ntyZNmqhvvvlGWa1Ww+bExEQV7Otb6IINg0GFFrMNNUDdZnfAGmF3uvrI/neIvX11\nQT1cyPUN7ddOnDDBxP9q1WD06NH5npelS5eqe++997Jjn6bJkn7FwGazGe+hj4+P8dpisZhtmlDG\niBCXAc1q11YBds/heVz2Pm7kgpDleBD7gwr39lZjx4xR3t7eLgmup6enuuuuu9TOnTudsnvowIHq\nulw2594S0VdFauhiGxrZ265AJYGaA2okqHvsf+egr6xUzX4+59pk+7ma9vdg2N13l/J/TZgyZUq+\nZ+ijjz5SvXr1Mva9vb3NNrNCs2jRIuO9DAsLM15nZGSYbZpQhogQlzJ//PGH8raLVt5lA5fYxXUe\nhS8TmAxqrr2cD/qyiJoTohsYGKgeffRRdfTo0WLbHhMTo2r4+qqIQsR2KKg77eI4x8k2LCmkTO5t\nDqjrudxLHo7eS84ZDbi9f383/oeEgli4cGG+Z+rTTz9Vbdq0MfZDQ0PNNrNS4OHhke+9btq0qdlm\nCWWICHEp07dPH+ULyov8MbkKPSZ2GAUP046wHx9mLzekEBGuWbOmev7551VKSorb7V+yaJEK9/Yu\nsOebE0f8Pnpvv6A23JenDc70mkNAXQWqAairQUXY3z+Lvb3169d3ezuFy3z66af5nrHPP//cIX64\ndevWZptZafjpp5+M9zUnLhuQ4f4qhDhrlTJtGjYk4fBhQtAXbCgsbKkoJyfQnZYmABk+Pmzfvp12\n7dqVqu05vLV4MROeeAKvrCwmAiO47C39FjALPaQpCt0RLacNp9FjgL9F95QuigSgJ9ABPWvXd+hh\nWplA3ky88tiWDps2bXJIvQiwYsUKHnjgASPm9Y477mDFihVmmFdpCQ4OdogpBnj44Yd56623TLJI\nKFNM/iFQ6WkWHq5CQNXDPRm1qqPP9UZGRqq0tLQya0dsbKzq0727CvHwUAH2Hm5Oz3eIfdi4ZgG9\nZmeH3+dweeg6HlQd9KxhAQWMAAAqMTGxzNpeVdi1a5fDe6xpmlq+fLny8vIyjr3wwgtmm1kpOXz4\nsPEe53bcEqoGklmrlPHx98cDPSTHHRm1rFxewzT3GqelTVRUFGt//JH406d59vnnSWjfngV16/Jy\n3brsvPpqLgHp6Ik9cmfPGgl8A+wCGqHnrV4MLLP/vQdoAPxmP5aK3rO+iJ5t5lIh9owfP97dTazS\nxMfH0759e2Nf0zSWLVvG4MGDyc7OBmDdunVMmjTJLBMrNfXr16dp06YAZGZmGsfXSIx8lUCGpkuZ\nfrfeyk/ffUcjoA262BSXocCG0FDOnDuHUoqAgACWL1/ukNXILN5ctIgnHn0UT/T0mJPRRTd30pKc\n4fc9wH70Ieym6KtJ/QmsQhffnMXy0tC7BQXh5eVlLL8nlIwzZ84QFhZm7Guaxvvvv8/9uTKWSbas\n0id3ko/cyFd05UeEuJTZu3cv17RuTW/0bFK5s1G5Qk5GrRXffENycjL3338/Hh4eBAcHs2/fPmrX\nru1Gq4vHzTffzM8//oiGPsdrQ09r2ZnL2bO2oPeQG6EnJbGgL+mYjP7+WO3XObM6q81mk9VqSsjF\nixcJDAyvIBKRAAAgAElEQVQ09jVNY+HChYwZM8ahTM7yfULpMnDgQFavXu1wTBJ8VH5EiMuAejVr\nciY5mauA0RQ/o9Z0TeMfmw2AHj16sHHjRjw9PencuTObN2/Gw8PcmYbjx4879Jo80QXZB72nmwk0\nBoYAdbgszKvtZS+6eL9vvvmGvn37ltzwKkreHpimacyePdshZWV2djaenp5mmFclUUrl+xx7eHhg\ntVpNskgoC2SOuAyYMXcuHkB79IUMnF2BKIcE+3X9hw0zjn3//fcEBQVhtVrZtWsXc+bMcZu9xeWq\nq66iYcOGxr63xUI6cB49X7RCH46eif6DZBywHH1u2VURBhg3rjgrHAugC2xuEfbw8OA///mPgwjb\nbDYR4TJG0zRefvllh2M2m02mYSo50iMuI9q0asWRffvoDsSg9wQjnbguAbgRSPHx4bouXVi/fr2R\nz/evv/6iefPmaJqGn58fW7ZsoUOHDqXWBmdYtmwZw4cPL7P7yePrOnl7XR4eHjz22GO8/vrrgJ6r\nPMdBSzCHvFMuoaGhJCcnm2SNUNpIj7iM2L13L5bq1dmBvqpRFPpw87lCyp8F5qLH1CYB5y9dwsvL\ni8mTJxtlmjVrxpw5c1BKkZ6ezoABA7hw4ULpNqQIhg4dio+Pj7Ff2j0q+XJynbwifOeddxoiHBIS\nIiJcDli3bp3Dfu7VmYTKhwhxGZJ49iyBDRrwE9AbeBPdAWsYjiE9w+zHp3M5ZCkyMpJPPvmEzz77\njC+//NKo8+mnn6ZDhw7YbDYSExMZPXp02TYqD5qmMXjwYOMXfWnPbT3//POlWn9lI3dPy9PTk27d\nurF8+XJAf8b++ecfs0wTctGzZ898x3r06GGCJUKZYEbwclVn6tSpKszHR1lA9QHVBn3xhKvtSS08\nQVX38lKDBw92SLDwyCOPqJiYGBUWFqb++usvo7709HTl6+urAOXv76+WL19uYuuUOnnyZJG5sN21\nyaIDzpP7ffP09FQtWrQw9nv37m22eUIeEhIS8j3vQuVE/rMmsm3bNtWqeXNVOzBQ1fLxUbUDA1Wr\n5s3Vtm3bjDKPPPKIwwfx22+/VW+++aZq3bq1unDhglEuJibGyIYUGBioDh8+bEaTDHK+5DVNK3Ux\nlpy8RZNXhHOv9DNp0iSzzRMKwc/Pz+F/9/rrr5ttklAKiLNWBaBly5bs37/f2E9OTmbcuHFkZWWx\nbNkyY7hxwoQJzJ49Gw8PD9q2bUtsbKxpXq+fffYZ99xzD5qmlbpD1fr162XY7grkHo728vLC09OT\njAw9Unv16tXcfvvtZpkmFMGFCxcICgpyOCZf2ZUPEeIKgr+/P2lpes4pDw8PUlNTuf7663nooYd4\n7LHHjHJNmjQhISEBHx8fxo8fz4wZM0yxVymFv78/6enpeHh4YLPHP5cGLVu2ZO/evaVWf0Um75yw\nzWYzvsgTEhJo3LixWaYJThIZGcmBAweM/QMHDtCoUSMTLRLcjQhxBSFv8oWrr76an376ieuvv54v\nv/yS66+/HoDz588TGhqK1WrFYrGwceNGunTpYorNI0aM4MMPP8RisXDpUmFZo92DPMb5ySvCuR3n\nLly4QEBAgBlmCS6iCkjyIc975UK8pisIFouFmJgYY//YsWO89NJLvPvuuwwePJjExERAX05t7dq1\nAGRkZBAdHU1KSoopNs+aNQubzcalS5dKPRXluXOFBYJVTa4kwlarVUS4AqFpGnfccYfDMUnwUbkQ\nIa5AdOzYkRdffNHYf/fdd1FKcf/993P33Xcb8Z89e/bk3nvvRSnFmTNnjNdlTXh4OK1bt0bTNIdF\nBUqD3O9LVSd378nLy8tBhG02m+mpUAXXybv+s/yQqlzI0HQFpHv37mzatMnYP3XqFPfddx/XXnst\ns2bNAvShq9q1a5OYmIiPjw9vvvkmI0aMKHNbV65cyZ133lnq9/H19SU9Pb3U71PeyZkHBl2Ecyfn\nkI96xWbhwoUO/iDy/6w8iBBXUGrWrGlklfLw8CAxMZEOHTrw2muvMXDgQAASExOpXbs2Sin8/PzY\nvXs3kZHOJNZ0H0opAgMDuXTpEn5+fobDWWlQ1Vdj8vX1Nday9fb2NoYv/fz8Sn2OXigbcj/fDRs2\n5ODBgyZaI7gLGaOqoCQmJhpDjDabjTZt2rBy5UoeeeQR4uLiAIiIiODjjz8GdGevfv36OSw6XhZo\nmsaQIUPw8PAwQmZKi82bN5dq/eWZgIAA43+raZohwnXr1hURrkTs2rXLeH3o0CETLRHciQhxBcXT\n05MjR44Y+6dOneKNN95g5syZREdHc/Givp7RPffcQ+/evVFKER8fz/jx48vc1pdfftkhbKa0qKqr\nMVWvXt0Q29xx2127duX48eNmmia4mXbt2jnsjxw50iRLBHciQ9MVnE8++YShQ4ca+6tXr2b16tVk\nZGTw8ccfo2kaNpuN6tWrc/78eXx9fVmzZg233HJLmdrZpk0b/vjjj1IX46r2ONeuXZvTp0/nOz5m\nzBgWLlxogkVCaXP+/HlCQkKM/ar2zFdGpEdcwRkyZAjDcq1TPGDAAKZNm8b+/ft54403AH0O+X//\n+x+ghzTdeeednDlzpkztnDFjRpl8YZgVqmUGkZGRBYrwBx98ICJciQkODnbImPfDDz+YaI3gDqRH\nXElo3Lix4bihaRpxcXF06dLFIdnHggULePzxx9E0je7du7Nx48YydW7KmccszWX2Jk6cWCVCmdq2\nbcvu3bvzHf/9999p06aNCRYJZYnVajXWJQfpFVd0RIgrERaLxXCICgsL47333mP06NH8+uuvRERE\nANCpUydiY2Px8vJizpw5PPHEE2Vm36hRo1iyZEmp3sPf39+YH6+sdOvWjS1btuQ7fu7cOapVq2aC\nRYIZ1KhRw1in+MCBA6xauZK43bu5kJJCYEgITdu04b4RI0o9hl8oOSLElYi8CeLvueceGjduzLZt\n29iwYQNeXl5kZmYSHBxMRkYGvr6+/Prrr7Ru3bpM7Dt37hyhoaGlnnu6Mj/S0dHRDutR51DVQ7eq\nKpqm4Q/YgCEWCx3T0wlCX8c8xs+PL5Xitj59eGLiRDp27GiusUKhyBxxJSIwMNChp/Tpp5/Spk0b\nfHx8mDx5MgA+Pj5s374d0OeL+/btW6qxvbmpXr06rVq1uqIIR0VFlfg+27ZtK3Ed5ZEHH3ywQBFW\nSokIV0HeWryYah4eTAdOAu+mpzMKGAqMAt5LS+NgejodVq+m/0038dbixabaK1yBUlpeUTCRyZMn\nO6xhum/fPlW/fn31xRdf5CujaZoaMWJEmdn27bffXnFt4djY2BKvTxwVFVVm7SkrJkyYUGBbharJ\nkkWLVCN/fxUPSjmxxYNq5O+vlixaZLbpQgHIJ7mS0qlTJ+PLWtM0tX37dhUWFqb+/PNPo0zTpk0V\noLy8vNRXX31VZrblXew895aRkaHatm1bYjGuTMydOzdf+3x8fMw2SzCJmJgYVcsFEc4txrX8/VVs\nbKzZTRDyIEPTlZQdO3YYsYZKKW677TZmzpzJoEGDDGemXbt24enpSXZ2NkOGDOHUqVNlYtvw4cML\nPZecnMzKlStLfI/U1NQS11Ee+Oijj3j66acdjtWoUaPUs5QJ5Zf5L73EhLQ0XE1WGwmMT0tj/ksv\nlYZZQgkQZ61KTN4Qh+joaMNRKyfZx6ZNm+jevTugr+60ffv2Ul+dJyUlpVDv3t69e/Pdd98RERFB\nUlJSse8xZcoUpk6dWuzrywMbNmygZ8+eDsfatGnD77//bpJFgtkkJSXRrH59DqanU70Y158FGlss\nxB09Kt7U5QjpEVdiPD09iY+PN/ZXrVrF//3f/zkk+7jpppt44IEHANi5c2eZxOCGhITQtGlTADTA\nDwi0/133/ff88ccffPHFFyW6xyuvvFJSM03lt99+yyfCQ4YMERGu4ny4dCkDoVgiDBAKDNQ0Ply6\n1H1GCSVGhLiSExkZyVtvvWXs33vvvSxevJiZM2fyyy+/APq6xrVr18ZmszFt2jR27txZqjbFxsZy\ndY0aWIC7gXnAm/a/dwHXtW3Lm/PmOfTmXaUiL3Rw7NixfDmF582bZyzgIVRd4nbvplMJl/vsmJZG\n3J49brJIcAcyNF1FGDRoEKtWrTL2v/76a0aPHs3OnTuJiIggOTmZsLAwlFKEh4dz4MABAgMD3W7H\nW4sXM+WZZ5iQlsZ9ShX4y/4c8L6m8bK3N2cyMynuA/rLL7/QuXPnElhb9iQnJ1OzZk2HY9u2baNL\nly4mWSSUJenp6SQmJhrb6dOnHfZjfviBF86eZWjRVRXKMmBt37588s037jJbKCEixFWIq6++2liN\np1q1aowdO5atW7cayT5WrlzJnXfeCcAdd9zBihUr3Hr/txYvZtYzz7Du0iWnHE0SgK5AIhRLjK+7\n7jojZroicPbsWWrUqOFwLDk5mdDQUJMsEtxBWlqag5gWJLA5x9LT0wkPDyciIoKIiAhq1aplvI6I\niOCTd9/l1o0bGVUCexYDvw0fzpIPP3RXE4USIkJcxfD29jZyPfft25fMzEzatm3L7NmzAejXrx9r\n1qzBw8ODjz76iCFDhrjlvrGxsfS/6Sa2OinCOSQAHYDzxbxvRXm809PT8fPzczhmtVpL3XFOKB6X\nLl1ySlwTExNJT093ENO84pr7WLVq1a6YnGXO7NnsmzKF90owPP2Anx+tpk3j6WefLXYdgnsRIa5i\n5KSZzGHJkiW8+OKLzJs3j+joaJRSVK9enZSUFCwWC3/++Sf169cv8X2HRUcTtXo1TxbjcZsDPA8U\nJ/9XampqqQyxu5u8X77ysSx7Ll68WKCQFiSwmZmZhYpp3mMhISFuy3wmXtOVExHiKsi6devo3bu3\nsb927Vruu+8+tm7dSrNmzTh69Kghvi1btuT3338vkeOUO7486gLF6QM8//zzTJs2rRhXlh0iwqXH\nxYsXC+2p5j2enZ2dT0gLE1h3iqurDIuOpsPq1TxVjOfkVU1j18CBfFTCqATBvYgQV1GefPJJ5s+f\nb+wvXryYN954g+3btxMYGMjixYsZM2YMAM8880yJwoHcMZx2N/A5rs8VBwQEcOHChWLft7gkJSXx\n4dKlRa6Gk/vLPCe5inBlLly4cMV51tz7Vqv1ikPBubfg4OAKkbP79ddf5/knnuBXcHmap6u/P99s\n3uyWnO6C+xAhrsJce+21RlxqcHAwgwYNIi0tjU8++QRN0+jcuTPbt2/H09OTn376ia5duxbrPiOH\nDaP9xx+X2MFkHMXrFZflIx4bG8v8l17i2+++IxquuBpOp06djOvM+sFQHlBKGeLqjMAqpYqca83Z\ngoKCKoS4OsvixYuZPn06w4cM4Ys333TZ8XHaokWMHD26lK0UXEWEuIoTFBRkCMAtt9xCcnIy999/\nP48//jhWq5XAwEDS09MJCQnh8OHDxVrvdki/fty2Zk2JQy5GA8WRqu3bt3PdddeV4O7O4Wxo1lJN\nY6pSpKL38Bs0aMChQ4dK3b6yRClFamqqU57CiYmJaJrmlDNTREQEgYGBlUpcncFqtfL000+zbt06\n1qxZQ+PGjY3nbXxaGvcX8rydBd4FZoLxvMlXfvlDhLiKk5WVhY+Pj7H/yiuv8Morr7Bq1Sq6dOnC\n3r17jfWKe/bsyffff+/yl6C7esQTvLxILcbQbadOndixY0cJ7l40xQ3Nqt6sGfv+/LNUbXMXSinO\nnz/vlKfw6dOn8fT0vKK45j5eERzqzCI1NZUhQ4aQlpbGihUrqF79suT++uuvzH/pJdasXctATaNj\nWpoxArMZ+Ao9e11uR8datWqVWV55wTlEiAUHsQVYunQpkydPNpJ9TJ06lWnTpqFpGosWLWLUKNck\n1R1zxEM9PLj6mWeYZQ+zcpWZM2fSqFEjGjduTKNGjahRo4bbelUlCc0ye85OKUVKSorToTheXl5O\nh+IEBASY0qbKxLFjx+jXrx+dOnVi4cKFeHt7F1ju77//1n0S9uwh9dw5gqpXZ2tsLPsL+ZE3fPhw\nPpQ44nKDCLEAwPz583nyySeN/cmTJ/Pzzz8byT6aN2/OX3/9hZeXF/v27aNJkyZO1+0ur+nYPXvo\n1KkTaWmuBzKNGzeOY8eOceDAAQ4cOIBSykGYc7+uV69eoV94BVGS0KzS8GLNEVdnnJkSExPx8fFx\nyplJxLVs+fXXXxkwYABPPfUU48aNK9YPxytds2rVKgYOHFgSEwU3IUIsGPTp04fvv/8e0J2HunTp\nYiT7SE9PJygoiOzsbOrXr09cXJzDkHZRuCOOuFvv3kyaNIkbb7zR5TryhjGdO3eOAwcOcPDgQeNv\nzutTp05Rt27dAkW6cePGxvKSUHZxnUop/vnnH6fENSkpCV9fX6fF1d/fvxiWC6XJqlWrGDVqFG+/\n/Ta33357sespSrwTExMJDw8vdv2CexAhFhyoVasWiYmJANxwww0cO3aMuXPnMmjQIH755Rcj5/HI\nkSNZsmSJ0/WWZPj2eh8f/s7MBODkyZPUqVPHhRp0goKCOH/eufxcmZmZHDlyxEGcc//18fExRPnv\nxETq/vwzH2ZluWxTDvf7+uJ333106969UIFNSkrCYrE4HYqTN0uXUDFQSjF79mzeeOMNvvrqK9q3\nb1+i+iZPnlzkimqSwc18RIgFB2w2G97e3thsNkBf13fRokVGso9Ro0axZMkSNE1j7dq1DolBiqI4\nDk03ahpTFi5k3DPPcOnSJXr16sWwYcMYPny4y21zx6OulOLMmTOGKC+YNYv7du8usSParPBwOnXr\ndkWBtVgsJbZfKL9kZmYyevRo/ve///HNN99Qt25dt9TrzJC2yIDJKEHIw+nTpxX2SAdAvfzyy6pl\ny5YqNTVVKaVU7dq1FaD8/PxUUlKSS3UvWbRI1fL3V/M0TZ0FpQrYkkHNBhUMKjgwUK1fv159/fXX\nhj3Hjx93sM/ZLTY21u3v1T19+6plhbTD2e0jUPf07et224SKQ3Jysurevbvq37+/8TlzF5qmFfnZ\n0DTNrfcUXEPGI4R8RERE8OWXXxr7//73v4mKiuLhhx9GKcX+/fvx8PAgLS2Nfv36ufRreuTo0Xyz\neTO7Bg6kkcXC3eg9wmX2vw/4+VEXmIK+0ENGVhavvfYa/fr1IygoCICHHnqI5557zuV25WQKcyeB\nISGklrCOVCCoenGXehcqOvHx8XTu3Jn27duzatUqt4dyLVu2rMgySimuvvpqt95XcAGTfwgI5ZiH\nH37Y+MXs5+en2rVrp+bPn6+UUmr16tXGuVmzZhWr/qSkJKWBsoAKtP+dM3u2Wr58ucOv9aCgIBUX\nF6d+/PFH49jRo0eL1St2N6/MmqVGWCwl6hGP8PNTc2bPdrttQvln8+bNKiIiQi1ZsqRU7+Ps52PQ\noEGlaodQMCLEwhVp3ry58SGNiopS4eHhatu2bUoppQYMGKAA5eHhoXbv3l2s+gsTytzH/Pz81Nix\nY5VSSlWrVk0BqlevXio6OtplIb506VLJ35RcJCYmqmoWS6HD7EVtyaCqWSwuD/ELFZ8PPvhAhYeH\nqw0bNpT6vSwWi9OfkXfffbfU7REcESEWisTX19f4kD766KOqbt266tSpU8pms6mQkBAFqPDw8GKJ\nXGFCPGTIEAUoLy8vo1f8zz//qB07dhhl4+PjXRbi5557zp1vjVJKqaEDB6o5xRTieZqmhkVHu90m\nofxitVrV5MmTVaNGjdS+ffvK5J65PzfObAcOHCgTuwQd8ZoWiiQtLc0h1vTxxx/n999/Z+PGjfz9\n999GONHdd9/Np59+6lLdV1oCMOecpml4e3sza9YsnnzyScLCwjhz5gy9evXi8OHD/PXXX07fz9/f\nn4sXL7pkY1FomkYwsBNZDUe4Mmlpadx3332cPHmSL7/8skzXBHY1IUhaWpp46pcR4qwlFImfnx8x\nMTHG/uuvv46Pjw8TJ06kdu3avP322wB89tlnrFy50m33jYiIAKB+/fpkZmYyZ84crFYrmzZtAvR1\nlVesWOFSnZcuXXKbfUop48stFT13dIKT1yYAvfz9mTZnjohwFeH06dPcdNNNeHt7s3HjxjIVYcDl\nxB3+fn48PGwYQ/r1Y+SwYcyZPZu///67lKyr4pjbIRcqEi+88IIxdOXj46MaNGigVq5cqZRSqnPn\nzgpQ3t7e6sSJE07XSZ4hsaNHjxrnbDabcdzT01N5enqqr776Sil1OYSqZ8+eKjAw0KVht19//bXE\n70VmZma+ehcvXOhUaNZcTVO1/P3VkkWLSmyHUDHYvXu3ql+/vpo2bZqy2Wym2OCsg6MFlAeoIPvm\nDcoXVAioaqD87OeXLVtmSjsqIyLEgkvceOONxge2VatWKiwsTP35558qOzvbcAhp2bKlslqtTtWX\n90vgtddeczjv4eGhQHfOAlTr1q2VUkolJCQY12zatMklIe7QoUOJ3oN9+/blqzMtLU0ppVRsbKwa\nFh2tqlksajCoRehxwovQvaOrWSxqWHR0qcQ0C+WTtWvXqrCwMPXJJ5+YbcoVPxc54msB1QvUNeix\n/AGg7gW1GNQy+9/BdkH2B9W/f3+zm1XhESEWXKZ69erGh/euu+4ykn389ddfxvGJEycWWU9qamq+\nL4MePXo4lElKSnLoFQPq999/V0opVa9ePaNX7IoQl2Qg6MEHH8xXV0pKSr5yuUOz7unbV40cPlzN\nmT1bvKOrGAsWLFC1atVSP//8s9mmKKWUat26db7n1w+Ul11059m3aqBq2l8XNrpzFtQr9uvCq1c3\nu2kVGhFiwWWys7MdPsh33HGHGjx4sLLZbGrGjBnG8f/+979XrOfWW2/N96VQs2bNfOVyzt1zzz0K\nUNdcc41SSqkTJ04Y5z777DOXhDg9Pd3ldhcUAnLq1Kki3yOh6pGVlaXGjh2rWrZsqQ4ePGi2OQb/\n/POPw/MbjD7sXBtUPKgloMJBNbTvFyTAebd4ULVEjEuEfEsIxeLYsWMOH+i2bdsaw8o5scdBQUFX\nTNdX4PCYh0e+crlFNiddX1xcnFJKqcjIyGL1iv/zn/+41N6C6oiPjy+0/LZt2xSgAgICXLqPUPFJ\nSUlRffr0UT169FD//POP2ebkI2cYOgTUAPvfeFAxoGqAinBBhHOLcTCo8LAwGfUpBiLEQrH5+OOP\nDVHy8vIykn1kZGQY8b+9evUq9HpXho1zzvXs2VNpmqYaNmyolFLqzJkzxrnp06c7LcR+fn5OtTFv\n7z9n27FjxxWvy8lK1rNnT6fuI1QOjhw5oq655ho1atQolZmZabY5BRLZsKGqAep2uwjPs4vpUFDX\ngXrVRRHO2V5Bz5AXBKpacLCKiYkxu6kVBhFioUTkJN4AVMOGDY1kHzExMcbxd955p8BrXRHinCxa\nuYeHN23apJRSqlWrVgpQt9xyi0u94qJISUkp8Lp169YVeW2tWrUUoNauXVtkWaFysGPHDlWnTh31\n6quvmuYZXRQxMTEqwN7rHY8+P3wWVKK9R1uNwueEi9qS7fW1Q3fw8gKJDHASEWKhxDRo0MAQqZtv\nvll169ZNZWVlqdGjRxvDzQXNk7kqkDnnW7durTw9PVV1+5zU+fPnjXN33XWX00K8c+fOQu9VkGc0\noD788EOn3pOc8uW1VyS4lxUrVqiwsDD19ddfm23KFWlUu7a6Gn0oua+9F5zTm+0IakQxRThnux9U\nf7vQV0MfAhcxLhoRYsEt5AxFA6pr167qmWeeUUopVadOHQWoq666SmVlZTlc46oQh4WFGT3vnLni\nV155RSmlVIcOHYwfAs4Kcdu2bQu8z8KFCwssP3fuXKffD2d73ULFxmazqRdffFFdffXV6n//+5/Z\n5lyR+fPnq2AuzweHgYoG9TCoFqC6oYcmlUSIF4Eaab/H1fYesi+o4OBgs5tfrpFvCsEt5A1Fqlev\nnlq5cqVKTU01RHP06NEO17gqxBkZGUaZiIgI5eXlpby9vdWlS5dUWlqacS73QhWuDk9fe+21BZab\nNGmSS++HCHHlJyMjQ913332qQ4cOLiWxMYs6QUFqLqiJoKqjxwDnxAffaN/csrY2jg5cObHIXvJ5\nKBRJcSm4hcDAQLZu3WrsnzhxglGjRnH8+HHWrFkDwOLFi/nhhx+KfQ8fHx/jdYsWLVD6D0mGDh2K\nxWKha9eugGup/DIzMwGw2WxomsZvv/2Wr8zIkSN54YUXnK5TKeV0WaFikpycTI8ePUhJSWHz5s1G\nvvXyyt69ezmXmsp2YBHwHHAc+AAYBTQDNHDP2tr215Ho64o3B/yBQMDbxXzXVQaTfwgIlYyJEyca\nvcFatWqpFi1aqNTUVGPJRF9fX3Xu3DmH9JV5tyuxZ88eo5yfn5/y9vZWgNq7d69D2smAgACnesQT\nJ05UFy5cKPR8cdZnPXnypPSIKzF//fWXatKkiRo/frzTGeTMpm+fPqoll+OF8/ZkXwEVRcnniEeA\nw0pkyehzxdvRY5P9QdWoUcPst6PcId8Ugtvp1KmTIUTt27c3kn3kLJkYFRWlFixYUKj4XSn2WKnL\nw779+vVTPj4+qlatWqpp06ZKKWWkwszJfV3UdqV1Wrt3716s9r///vsixJWUn376SYWHhxcaCVBe\nqVejhqpWiAjHgBqEngWupF7T1UAlFSLO8ejZugLkc5EPeUeEUiF3j7Rly5bqtddeU4mJiU4J4Jo1\na65Y92uvvWaU9fDwUIGBgUrTNLV06dJC435d3dq3b1/stg8aNMjosQuVh/fee0+Fh4erH374wWxT\nXKaaphW4ZvYS9KxYr4K6E8c44kT0nvLD6PO+D9v38wptzjYP1LACjuc4cCm7IAeCuu+++8x+S8oV\nIsRCqZBXEGvWrKm2bdum3n333SJF8Iknniiy/pyyN998s/Lz81M33HCDCgwMVFlZWUbMcbt27Yol\nwpGRkSVqe04O7Hbt2pWoHqF8YLVa1b///W/VuHFj9eeff5ptjsv88ccfylJAT3cJqEZc7iXnZNYK\nRVo2DMAAACAASURBVE/2UQ3UAzgu9jDCfnyovXxOXTlpLmMLEOLcDlzJ6MPTGiI9uZF3Qyg14uPj\nHXquOck+unTpckUh7Nq1a5F154Qp5azO1LRpU+Xv768eeughZbVai90TrlOnTonbnRPKNXXq1BLX\nJZjLxYsX1aBBg1TXrl3V33//bbY5LpOdna1CgoLU3XnEMcYunDkinNP7rY2ebWtOAcKds521935r\n2cU83i7oSwopn7tHrOyibBEhdkC8poVSIzIykiVLlgC6V3JaWhqDBw8u0nP64MGDRda9fv16o97W\nrVtz9OhRxo4dy3vvvceJEye49957DRucJTQ0lBMnTjhdvjCys7MBGDBgQInrEszj1KlTdOvWDX9/\nfzZs2EDNmjXNNslpbDYb3bp1w8vLC2tqKt3ynJ8PTADOAcOAJsBCdM/pdei/SicAQ4CRwBzgb/u1\n1YGngK3ATKCjvezIQmyJBZrm2u8K+BRStqoiQiyUKiNHjjQE6ezZsxw/fpzJkydf8ZozZ84UWa+H\nhwfVqlUD9FCp9PR0/vjjDxo1akR0dDRLly5F0zQSEhKcsjMgIIDk5GSnyjrLNddc49b6hLLj999/\n51//+hcDBgzggw8+wNfX12yTnMJms9GvXz88PT3ZsmULAF5cDikCSAK+RRfbPkACYAWS0UW1N7Af\naA/cZv+7D11Mh6ELK+jhST+ii2r7Quw5C3wJ3JvrWFAhZas0ZnfJhapBTu5l7MO/Oa8L25wht/NX\nRESE8vHxUd9//73SNE19++236tFHH1WA4a19pe3ZZ591SztzJzYRKiZr1qxRYWFhavny5Wab4hK5\n877n3gJxzJj1Cqgu6MsdRtiHmRuhezS/6sKQdFFOWoWdW2S3SbiMvBtCmVGUGLoqxLnrbNmypfLw\n8FDjxo1T/fv3VzVq1LhirHLezcfHxy1t/O9//ytCXEGx2Wxq/vz5qnbt2kWupV2eGDNmTKHPdVBQ\nkAqrUUMNySWEA9BXSMpZc/gOHOeLi9ryzgkXFrZUmAPXcPQ54n379pn91pUb5NtCKDPOnj2b74vC\n09OzREK8du1a4xqLxaIsFotKTExU3t7eRTqFFfeeV+I///mPCHEFJCsrS40ZM0a1atVKHTp0yGxz\nnGLSpEmFPsuBgYGqZ8+e6q677lKapjl4TddD7/3Gg1oKRv5pZ0S4MJHNm8ijMAeuZPuPgEBQ/W69\n1ey3sNwgc8RCmVG9enW+//57h2NKqRLV2adPH+N1VFQU2dnZrFixgnr16vHzzz+7VFdB6S1dJXea\nT6FikJKSQt++fTl48CC//PILDRo0MNukKzJ79mw0TePFF1/Mdy4gIIA+ffrQrVs3NmzYwOeff45S\nCm/gffT54XPAJPQ53peA5+2vXSESGI/u9AX63HIc+pzwPHSHrIIcuD4A6qDPSR/Zv9/Fu1ZeNFXS\nb0JBcJGIiAiSkpKuWMaVx/LJJ59k/nz9K0HTtGKLe+PGjZ127iqMOnXqcOrUKXx8fMjIyChRXULp\nc/jwYfr27ctNN93Ea6+9hpeXl9kmFco777zDww8/XOA5Pz8/unfvzoULF9i6dWuBn4FgdKep94Fj\nwEl0AT2B7gntKmeBxugCvA54ETgF9AWeAKLylE9AF+jz6N7ZV4WH82diYjHuXPkQIRbKHE9PT2w2\n2xXLuPpYam5KJl/Sj4O3tzfZ2dnUr1+fw4cPu8UmoXTYvn070dHRTJw4kbFjx5ptTqGsXLmSO++8\ns8BzFouFW265hVOnTrFz584r1uMBWIBBwIdAPyAEWFYC2x4AWqEv6vAJsAoIK6BcAtAL6AD8ji7G\n4Q0b8rsToYpVARmaFsqcokQY9F//ruBKvPCVyMrKKtH1OTHEnTp1coc5QimxfPly+vfvz9tvv11u\nRXjdunVomlagCFssFvr370+DBg1Ys2ZNkSLs7e2NDT2U6Xr7sSPADSW0MWdIOgYYQH4Rzj1UPQLY\nYr9vGFC/RYsS3r3yIEIslEsefvhhjh8/7lTZt956y6khZWeGHZ9++mmn7lkQuUX8lltuKXY9Qumh\nlGLGjBmMHz+ejRs3ctttt5ltUj7++9//omkavXv3znfO19eXfv36ERERwddff82ff/55xbo8PT0B\n/QdiaGgonlyO482k5DG9Qejxx8uBncBi9B72YvTecmPgf+hLLy4BItDjjuOBWXPmlPDulQcRYqHc\n0r59+yv2nq1WK48++iiPPPJIkXVZLBYWLlxYZLlFixa5ZGNujhw5YrwWIS5/ZGRkcO+99/LNN9+w\nfft22rRpY7ZJDuzZswdN07j++uvznfPx+f/27jy6qTJ//Pj7Jt1SWtqCZSkCllVAZZdNYAQERVbF\nEb8gIMwIog4qyOKccUA9P0Yo4AJFnJFpEZVRQQREURxlG4EiIigqQlGxIEUotFC6P78/niRNaJNm\nawPl8zrnnmz35t6kzf3cZ/s8Ydx5553ExMSwfv16p/81d0pKSmjWrBmGYXDmzBkKKJ1zOIzAzD/8\nG6XVzvuAjdbbNsAuoD0wGeiFLg2bgMYJCbSSErGdBGIRVCZT+f+CZrOZU6dOueyckp2dTe/evT0K\nnIWFhURERLB582bCwtwn1ysuLq74oF3YtWuX/X5iYqLP7yMC7/fff6dfv35cvHiRzz//nPr16wf7\nkOzS09Mxm83lXhiEhYVxxx13YLFY+OCDD9x2cnT8LZlMJhITE1FKcfjwYfsFbS6wxbpOY2C7n8e+\nC9gLfAzsAdoBA6233wJd0CXi9egsXjWBC8BTc+f6uedqJiiDpsRVDUongrDdv3RJTEy03//888+d\ntj969KiqXbu2R2OD//GPfyillFq9erUyDEMtWrSowm327t3r0+caN26cjCG+DH333XeqadOmatas\nWaq4uDjYh2OXkZGhQkNDy/0fDA0NVf369VORkZEV/r8ahmG/HxkZqaKjo92ub7GOKf7G4b43Y4gd\nxwRbQP3FOjb4PvTkDrbbJJyTfNgyarVu2TLYX/1lR84Yoso5nmzsJxPrjzoWPftLBCizw8kjPT1d\nKaXUjh07PE7QERoaqmJjY1VRUZFSSqmbbrpJtWrVSoWHh7vd7tprr/Xpc914440SiC8zmzdvVnXq\n1FHLly8P9qHYnT59WlksFpf/s71791ZhYWEeB2DDMFSdOnU8/l1EUZp8oyU6DaUvgdg2W1N9XKe4\ndFxGW3/XmZmZwf4TXHbkjCGq1OnTp51OCpHWAHwfzvOe3g+qhvWkEQkq9JLA7MnSqlUrZTab1dq1\na5VSSqWnpyuTyaSefPLJCrf1RVxcnATiy8g///lPVbdu3TI1KsGSk5PjsrQaGhqqunbt6jLTnKtA\n7GnN0KWLLZtWCvrC15fMWjWtgdWTIGwrPV9br16w/wyXJTljiCo1ceJEBagw6w+5onlPF6DT8UVa\ntzGjS8/uTjIDBw603zeZTKp169b2/d9///0qOjraZYnEtthK0d6wnUTNZnMgvzLhpeLiYvXkk0+q\n5s2bqx9++CHYh6MuXryorrnmGpcBuEOHDk7Vy57U9Liq0vZ0CaE0v/RodKnWm1zT9dEXyakebjMf\nfWH98ccfB/vPcVmSQCyqlCU8XIXjfZL5RHS1dYT1JOIqGGdmZqri4mL745tuukmZzWb19ddfK6WU\nys/PV5GRkWrIkCFuT1QTJkzw6nM5TjBRq1atyvjqhAfOnz+vhg0bpnr37q1+//33oB5LYWGhuvba\na10G09atW3sVPKOiovwKvo5LJKjr0BfD80Hdgy4ZL8D1hfFp9IVzTVBtPfzt2n6/saBuatEiqH+P\ny5kEYlFlliUnqxh8TzJf13pVbSsZX3py+emnn+z7aty4sdNrQ4cOtb+2dOlSZTKZKuwI442MjAz7\ndp07dw7YdyY8l5GRoTp06KDGjRun8vPzg3YcxcXFqkWLFi4DcNOmTb0KmhXV3vi61ATVFFQLdOm2\nC6hr0VXIo9Cdq1633v4f+iI4xvob9OYiuiGoaMNQaWlpQfubXO4kEIsqsXv3blUvMlI1xXmWFm+W\nhdaThe1kEOpwUvn222+d9vfbb7/ZX2vatKkym83q1KlT9tcTExNVmzZtAhaIP/vsM/t2kydPDsh3\nJjy3d+9e1bBhQzV37lxVUlISlGMoLi5W7dq1cxmAXZWOy1vcjSgI1GKAikf3eD4Eagq685btYrcm\nqFqg4tAl2hroauxZ6BqthVRceq4LqrZhqGXJyUH5m1wpJBCLKjFq+HD1pPWq2p/hErYTgm0B1M6d\nO8vdp63nqa3tdtasWfbX9u7dqwzDcFsq9uYKfs6cOfbt3nrrLb+/L+G5devWqfj4ePXOO+8E7Rh6\n9+7tMgDHx8d7HBz9bfv1JRjXxLnndKY1iLobipRmDcrRoEbiXHoeZf2d3gwqJiREgrAHJBCLSnfy\n5EkVGx6u+lt/tK4C7Ul0e9WfrSeAP1sfO54AHgDVGX3FHgJqQP/+Lvf7wQcf2E84tWvXVhaLRRUU\nFNhfHzhwoIqNjXV5koqNjfX4Mw4YMMC+XUZGhl/fl/BMSUmJWrBggUpISFC7du0KyjEMHjzYZUB1\n97916eJNb+lALDVq1FATJkxQmZmZKiUlRcUahk/NRdHW32FNUH+wBuCW6OrtO2+9VaqjPSSBWFSq\n3bt3q5oREaoduqdl93IC7G6Hq+jxOA9jesD6/CjresmgeoNqha72igoLc7t/24knKipKGYahVq5c\naX8tOztbhYaGum2D81SjRo283kb4rqCgQE2cOFHdeOON6ueff67y/Y8ZM6bc/5ewsDBVo0aNKg2q\nni4Wi0WNHj263AvFZcnJKsGLYGzrQBkPqg6oZ0E1QLc1t23TRsYKe0nOGqLS1IiMtI81vHScsC3A\ndrH+kBfhfhjTQnS71HhQPUF1Q3cCqVlB4Js6dar9RBQeHq4SEhKcXn/mmWfctsd5OozJsYpbVK6s\nrCx12223qYEDB6rs7Owq3fdjjz1W7v9JaGhohYlibIs3Q5X8XcLDw9U999zj1JHRlWXJySrWZFLz\n3fwWbW2/16BLw7GgRlA6xHD0yJFV8FeofuSsISqFrbrK3TjhhaAa4V0PzEboqq9+6OEXcVScktJ2\nUqpfv74yDMOpGrOkpETVqVPHZdvcvffe69HndTy5ispz5MgR1apVK/WXv/xFFRYWVtl+n3nmGZcB\nOCQkJKgl3fKOaciQIerw4cNef860tDTV8frrlYWybb+2JDvR1tvG6IvsaFCNr7lGpaamVsI3f3WQ\ns4YIOMdkAa6C6m4P1nEVjGNB9bEG5Gh0+5q74Srdu3d3Ko10797d6fXNmze7PbFVJCsry+n9ReXY\nsWOHqlevnlq8eHGV7fPll192Geyqomezp4vZbFa33367OnjwYEA+d2Zmpnp48mR1TWSkiqU0i1Y4\nuv23dmioSoiLU/379g3YPq9mEohFQNWqVcujccKj0NXR3gRh2zIfPf6xNagwayeXtm3bujym3Nxc\n+wkrMTFRmUymMu1k3bt3d3lirciePXvs61osFr+/Q1HWG2+8oeLj49WHH35YZfsr738hJCSkSquW\n3S0mk0ndeuutat++fVXynYjKI4FYBFQNKh4nfBJdqvVnGFMEKJO1JGBrn126dKnL47o0J+/48eOd\nXj9+/LjLE9727dvdfuaUlBT7utddd11AvkehlZSUqNmzZ6vGjRurAwcOVPr+NmzY4LLEGezAC7rG\npUePHkHrJS4qh8xHLAJm9OjRlADjK1hvBTAciPNxP7Ws24ei5w9u2LAhAA899BAnTpwod5tPP/3U\nfr9BgwasWLGCvLw8+3P169fnkUceKXfbW265xe3xbN261X6/ZcuWnn0IUaG8vDxGjx7Nhx9+yM6d\nO7nhhhsqbV87duzAMAwGDRrk9LxhGIB/81QHQufOndmyZQslJSVs376dm2++OajHIwJLArEImDff\neIOhVBxgDwH+nkZ6AuHW+z/88AP9+vUDoHnz5iilyqzftm1b+/2MjAyKiopYtmyZ0zovvPACUVFR\nXh/Lvn377Pc7dOjg9fairFOnTtG3b1+Kior47LPPqFevXqXsZ//+/RiG4fJiq7z/parStm1bPvnk\nE5RS7N69m169egXtWETlkkAsAiYC6O3BeueBaD/3FY3+542O1u+0efNmmjZtyoULFxg+fHi52yxe\nvNh+PyYmhr///e9OJ1qz2cy//vWvcrd1VyL6+eef7fe7d+/uxacQ5Tl48CBdunShT58+vPXWW1gs\nloDvIz09HcMwnC7QLgfXX38969atQynFvn377BeYonozVDAv+US1EmsYLAFGVbDeg0AHYJIf+1oK\nTAdylLJXHwKEhIRQVFTEp59+Sp8+fcpsZ1vXtt7mzZvp27ev0zqtW7fmu+++K90GSIiLo1ePHkTF\nxNDippsY+8ADxMfHAxAaGkpRUREAZ86cIS7O10p38cknnzBq1CiSkpIYM2ZMwN8/MzOTunXrBvx9\n/dG0aVPmzp3LPffcE+xDEcESxPZpUc3URCfrqKiz1Xx0Qg9fOmrZlpHoPLlKKXXhwgV7ZxZbfmnD\nMNSFCxfKHOOIESPs64aGhqqWLVuWWefQoUMKdIKCcpORWCwqNiJCjRo+XG3fvt2pM43w3SuvvKLq\n1q2rtmzZEvD3Pnv2bNA7WjkujRo1UitWrAjaBBXi8iJnDhEwEbjPJW1bAtVr2jH4bd261f64QYMG\nCnQCj0s5zlUcHR2tDMNQR48edVpnWXKyijOb3SYjOQNqoWGouhERTnMjC+8VFRWpJ554QrVs2VL9\n+OOPAX3v3Nzcy2a4Uf369dWyZctUcXFxQD+juPLJmUMETBg6444nAdbfccRRDie46OhopZRSEydO\ntD8XFxenADV9+vQyx+k4V6xhGGrgwIH215YlJ6smkZFeZfuqB/ZgLLyTk5OjhgwZom699VZ15syZ\ngL1vQUFBlc9kVN4SHx+vXnjhBQm+wi05c4iAiUJP6uBJgPUns1bNck54M2bMUEopVbduXacgC6hD\nhw45HafjmOH4+HhlMplUTk6Ofc5kX4/JbDYH42u/Yh07dky1a9dOTZgwwW1mNG8UFRW5ncSjKpbY\n2Fg1d+7cKk3BKa5s0llLBEycYfA4uiPVNqBZBeu/CjwPbPJgXYDD6GFLZ4CCcl5PS0ujU6dOTp23\nQHfMys/Px2QqHSQQFRXFhQsX7I+nT59Oxo8/0mntWh7z4SeRBDwXGsrZgvKOTFzqyy+/ZNiwYTz6\n6KM8+eSTZf5m3iopKaFWrVqcO3cuQEfonaioKKZOncpf//pXQkNDg3IM4goW7CsBUX1Y0J2ZloFq\n4mFpdxl6OsOKZnyZZy11hlZQGsnKylKFhYVlnm/Xrp3Tsa5fv97+Wu3atVV4eLiKjYjwq93aAjL9\nmwfee+89FR8fr9asWROQ96tXr15QSr4Wi0VNnz5d5eXlBeRziKuXBGIRMCGGYe+stQxd9bywggC7\nAFRtUD0onXfYccaXB9ATOzRAtz+bPDhBFhUV2Xs+A/bZcf797387Ha/jNgao+0NDfQrCtmUkqBHD\nhwfny78ClJSUqPnz56sGDRoEZML4Jk2aVHnwDQ8PV48++qg6f/58AL4RITRJ6CECZuGLL/I+kIUe\nK7we2As0Qae9XAqstN6OB5oCXwEfAduBXcAaYCewEdgH1EUnCjkL5AElHhxHSEgIzZs3Z/78+QD2\nMb4PPPAAZ8+eta83depU+/0os5nuhYU+fW6bXsCn69axZ88ev96nOiosLOTBBx9k5cqV7Ny5k06d\nOvn8Xu3atcMwDNLT0wN4hK6FhITwpz/9iezsbPLy8njppZeoUaNGlexbXB2kjVgEVJRhMAeY6vDc\nKXR+6UNADjorVgtgDBDvsN4idOB+3fr4MNAfHdjPA0VeHotSqkxyjho1anD+/Hn7Y1vbZBTwChUn\nI3FnJbAYaH7XXby+erUf71S9ZGVlMWLECCIjI3nrrbd8SiMK0KtXL7Zt2xbgoyuf2Wzmj3/8I4sX\nL6ZWrVpVsk9x9ZISsQioDj178gw6iNrEowPzMuBN6+1UnIPwYWAeMAXdGWsB0BU4CeTjWUn4UoZh\ncPDgQcxms/25CxcucNddd9kf21JSFqMvEvyRA1wPbNi4kVOnTvn5btXDkSNH6NatG23btmXt2rU+\nBeHBgwdjGEalB2HDMBg6dCgnT56kqKiIN998U4KwqBISiEVAbd26FcNioSfOwdidw0BfoA86ADcE\nZgO56ADsayAGfXK1VU3bvPfee/zvf/8DdEpF0NXeW3zch00acCMw3DBYkZLi57td+bZt20aPHj14\n7LHHWLhwodMFkSfGjRuHYRhs2LChko5Q69+/P7/++islJSWsXbuWOnXqVOr+hLiUBGIRcGdzc7lo\nsdARPawny8V6tpJvFyAE2AC8a32twPqcp+3C7jRq1MipbRj01IaFhYVERkZSr149FLDWzbFW5Azw\nHrq6vfPFixw6cMCvY77SrVy5krvvvpsVK1YwaZJ3WcWnTZuGYRikpqZW0tFBz549SU9PRynFpk2b\naNCgQaXtS4iKSCAWleJsbi7te/fmGSABuA/nzlpjgOvQY4nPA5lAVEwMrRMT6TtwIPsOHvS7qtjm\n2LFjPPbYY7z77rv255RSNG7cGICNGzcC+sew3Md9pAKD0NXt0cDJ48f9OOIrl1KKp59+mqeffprP\nPvuM/v37e7ztvHnzMAyDBQsWVMqxdezYke+//x6lFFu3biUxMbFS9iOE14LYY1tcJZKTk1W42axq\noMcC17SOuY0LD1cPT57sduzt77//HrChJ2+++abq1auX03MzZ85USin7ZBE18S3bVz1QadbHyaBi\nwsLUmDFj1DfffFNVX3PQ5ebmqnvvvVd169ZNnTx50uPtXnvttUobbtS6dWu1f//+SvzUQvhPek2L\nK4K/mZdsDh8+TJs2bcjPz7c/99NPP7FhwwYeeeQRTEB94HM8z/Y1AJiBHrIFMDYsjGZPPYUpJISX\nXnqJbt26MXPmTLp27RqQz3A5OnnyJMOGDSMxMZHly5cTERFR4Tbr169nyJAhAT+WJk2a8J///Mev\nIVJCVCWpmhZXBKUUu3bt8vt9mjVrVqa9uGnTpkyePBnQ7dGn0Kk0F+G+fXuhdT3HIHwGeLewkNTX\nXycqKoqvvvqKfv36MXLkSG699VY2bdpEdbv2/eabb+jatSsDBgzgjTfeqDAI79ixA8MwAhqEExIS\n2LJlC0opjhw5IkFYXFEkEIsrxs033xyQIGaxWMjLy7M/Li4upn379owYMQLQncTuxbNkJOspDcIA\nqYbBXcOGkZKSwq5du2jTpg379+/nnXfeYcKECUydOpWOHTvy9ttvU1xc7PdnCbZNmzbRp08fnnvu\nOWbPnu225uLAgQMYhsEtt9wSkH3XqlWLjz76CKUUGRkZ9OrVKyDvK0SVC2K1uBA+e/HFF/1uP0xL\nS3N67NhWaWsrzgSVBOpBUPdZb5Osz5fbVhwZ6ZS+8bffflPPPfecatiwoerWrZtasWKFWr16terW\nrZtq1qyZevXVV6/YXMXJycmqXr16atu2bW7X+/nnnwPW5hsdHa1Wr15dRZ9QiKohgVhc0fw9sY8c\nOdLpsS1/sYF30zTaOmwtS04u9zgLCwvV2rVrVf/+/VWdOnXUjBkz1KpVq9Ttt9+uEhIS1Pz581V2\ndnYVf3u+KSoqUlOmTFHXX3+9OnLkiMv1Tp8+HZDgGx4erlJSUqrwEwpRtaRqWlzRlFIMHjzY5+1X\nrVpFzZo17Y+PW4cdKfSQqo7AQsNw21Y837reSWDv11+Xu15ISAhDhw5l06ZNbNu2jYKCAh5++GFC\nQkJ46qmnSEtLIzExkb/97W+XdVaunJwchg4dyjfffMMXX3xBkyZNyqyTm5uLYRjUrl3b5/2EhITw\n8ssvo5QiLy+PsWPH+nPYQlzWpNe0qDYC1bPaZDJRUlKaRqRNYiIZJ04wIC+P3uhxwjnoTFzvAwZw\n0WH7vXv30r59+wr3k5uby6pVq1iyZAnnzp1jxIgRnDhxgvXr1zN69GimTp1qH+t8OTh27BiDBg2i\na9euLF68uMy8u4WFhYSFhfn8/iaTiTlz5vDUU085zR0tRHUngVhUK3FxcWV6RXuiYcOGHDt2rMzz\nISEhmEwmfv31VxIbN6b44kVC0BNQ5KNLzuXJycnxOK+yUordu3eTnJzMunXr6N+/P2FhYWzcuJFB\ngwYxffp02rRp4/VnCqS0tDSGDx/OE088weOPP+500VNcXExISIjP7z116lTmzZsnwVdcteQ/X1Qr\nWVlZTqVZTx07dozbb7+9zPNFRUUUFBSwYcMG4uvWJQ+dCSwP10EY4LrrrvO4h7dhGHTp0oXU1FR+\n/PFHOnbsyPbt22nUqBHnz5+nT58+DBs2jJ07d3r9uQJhzZo13HnnnSxZsoQnnnjCKQgbhuFTEB4/\nfjzFxcUopUhKSpIgLK5qUiIW1ZYvVdUDBgxg06ZNTs+FhYURHR1N48aN2bt3r8fvNXPmTObOnev1\nMYAuZW7atIklS5awa9cu2rZtyw8//EDz5s2ZOXMm/fv39+nzZWZmsiIlhUP793P+3DmiYmJocdNN\njH3gAeLj453WVUrx/PPPs2TJEt5//306dOhgf82XfQ8ePJjVq1eXqdIW4mongVhUawUFBYSHh3u1\nTe3atTl9+nSZ53v06MGOHTu8eq+dO3fSpUsXr7a5VHp6OsuWLWP58uUkJCRw9uxZateuzcyZM7n7\n7rs9mtUoLS2NF+fO5YMPP+QuoHNenr2te7fFwntKcecddzBl1iw6d+5MQUEBkyZNYt++faxfv94+\nKYK3AbhHjx7897//9avtWIjqTgKxuCr425ErJCSEqKgon9qfz549S0xMjF/7B8jLy+Ptt99myZIl\nHD16FIvFgtlsZtasWYwZM8blBcerS5fy92nTmHHxImOVIq6cdbKAFMNgnsXC9DlzWPfBB8TEwDaX\nwgAADZRJREFUxLBy5UqioqK8+v5at27Nnj17sFgsvn1QIa4yEojFVSMjI4Nrr73W7/cxgAjADBRT\ncXtxrdhY7rrzTi5UUBXsjS+//JLk5GTefvttoqOjKSwsZPr06UyaNIno6Gj7eq8uXcrz06axKTfX\n49zZvQyDG/v2ZeNHH3nc/puQkMDBgwcDcsEhxNVGArG46vhaOg6zLg2AetbbdsCXlB3GZFjXNYBw\ndK/IEuttKJALREdEULdRI4aOHMnDjzziU2DOysoiJSWFRYsWkZOTQ2FhIQ8//DDTpk3jp59+Ysgf\n/sA2D4OwzWH0uOjsCtaLjo7m6NGjfo0XFkJIIBZXqTVr1nD33Xd7tG4UuuR7D9CN0nHEu4H3gDuB\nFsCz6CAbbl0/3/rYDERatwG4FudAvhdYbzLRu1cvZs+bR+fOnb3+PCUlJWzevJl58+axfft2AK6r\nU4eJv/7K4z78xJOAp3EeHw16rG96evplNb5ZiCudBGJxVXNXOjahg/DT6Ike4oBvgZnAz0ABulr6\npHX94UAj4Av0hBBDgR6UBu5t6Eki2qMDeialgfwB6zb/z2zmr3PnMvXJJ33+TL/88gvPPvssK//1\nL45bj9tbZ9AXCrapMb766ivatWvn8zEJIVyTwXviqqaUYtSoUWWeNwF10dXOU4F1wPVAZyAWmAz8\nDR2UR6DbjNOBf6MD8h6gLbq0+4H1tr31/YYBqUBX6zYdgNFATWB3cTFJ06fTtV070tLSfPpMjRo1\nomXz5oyMiPApCAPUAv4YFkbSvHkopSQIC1GJJBCLq97KlSvLJN+IArYCzYD7gSno6Q4zgNeBScAo\n6+3r6FLzCfQ0iXuALsB36CB7p/X2IHAzOhgvAWajp1lcCrQB/grMAtYCR77+mjt69uTVpUt9+kyH\n9u+ni8NUj77oWlDAoQMH/HoPIUTFfM9LJ0Q1o5TimmuuoeD0aWZTGoQ/RQdXVx2e0oB5wMPWZQbw\nMmWrhCcBC4AUh3UXoUvUN1Cav3oA0BKw5Ofz/LRpAPx50iTy8vLIzs4mOzubM2fOcOLECX755RcO\nHjzIvn37+P777zl37hygLyR6+/l9RAM5Wa6muxBCBIq0EQvh4Ntvv6XTDTdwHF0dPQX3QRh0tXKh\ndb1NFaxrcxjoD8Sg25rbooNnC3Tb8npgDroKPJmKezBfygIsRAd/Xy0F9t1/P8tWrPDjXYQQFZGq\naSEcPPzQQ9yNLs3ORVc5uwusmeihS1vxPAhjXe9j4BdgCGWrr/ei25tT0QHb29QYeejStT/SLBZa\n3Hijn+8ihKiIlIiFcFA3MpI5Fy/SE90xKwP3vY6TgNeAicBjbtbLBFYAh9CTRthKvxetz73usG4W\nuvraVt2dZF2vwMvPEgF+9ZpuGhHBoV9+8SvxiBCiYlIiFsJBSX4+0eje0HdRcRD7Gh2sXU1bn4au\num5J+Z23FqKroT922CYOeBw93OnfwG3ooOptGhITsNzLbWxSDYNBAwdKEBaiCkggFsJBMbrT1M/A\nLR6s/y16OFJ5AftVdLVzJ/Qwpddw7m293Pr836zPvXrJ9s3Q1d3b0VXN3gbiXOAZdHu0Nw4D8ywW\npsya5eWWQghfSK9pIRwUh4WxPS+PAnSv4fI4VjOfRI//nW197Ti66vkEcAA9htjd3Etx6HHKQ9G9\npUEPk7JpBkwHXkG3Jyt0xi5P5QD9LRY+vnjR405kAyIjmZOURKdOnbzYkxDCV1IiFsJB+06dWI2+\nQs255LXyqpknontMJ6EDs63q+V5gEHC7dZs0dABPQgfa/7PeJgGnKC39/h3d+9rRWHRgN/C+VFyi\nFDMXLKBnZCSLDANXg5HOAAsNg56RkcxISuLBhx7yck9CCF9JZy0hHHz77bd0u+EGGqKHFL1pff5V\ndJCcgQ6McS6eu1QWuno4FShC56vuTPn5qqege1+/DWwAHFtnx1ifL0EHfk84/rT37NnDi3PnsmHj\nRoYbBp0vXrQfQ5p1PuJBAwcyZdYsKQkLUcUkEAtxiboxMZzPzqYEXdX8DvA8zsOTXi3nufLYgvV0\nYByug3UKupf0dHSbsRkYjA7OndFjemegJ4w4TMXB2NXP+tSpU6xISeHQgQPkZGURHRdHixtvZMy4\ncdIxS4ggkUAsxCVSU1N5cNw4LOiS7tvoHsy2gJuG7oTl+Fx5w5Oy0OksP8bzJB8DgPrW2yh0cJ6D\nnr1psnU9A9cJPkJDQyko8HagkxAimKSNWIhLjB07lr633UYOuqQ6HedA+iK6dNoM18OTagH/xfMg\nDKXtxD+gZ2KyDWF6HvjQuk4+rtuJlyxZIkFYiCuQlIiFcMEwjDJJMTLRQTcdXWXtqo14NHrYkrsk\nH64sBJahAzLokvLN6PbcCOCC9XnHH25+fj5hYWE+7E0IEWwSiIVwwWQY3Au85fBcEjoRR1dctxE7\nBmtfs1o1Ao5S2mErCd3pqxXwDbrTlm1uJfkJC3Flk6ppIVyIpOwMRoeAOuiSsKuOWivQcxL7Mxfw\ncOv72IxHJxv5Geee0xKEhbjySSAWwoVQyib1OA98QWkbsSPbOOEV6Kpkf3RHB32bWugOYmfQY5NL\nkCAsRHUhgVgIF0oom9TDjO5I5Zhb+tIOW9G4zsrlKdsYX0e90FXVZryfjUkIcfmSQCyECwXoXsuO\nzqJLprZq5/LySbehbBD1Vg5lg3k0upR+DpBuWUJUHxKIhXDBANaCU1rIOEong7Al9diG7h1tC84t\n0Bmz/JFmfR9HtuB+AV1aF0JUDxKIhXDBhC6Bpjg8V4QumabhusPWGHTaSld5nStyxrr9mEue/wKd\nA/ss3s9NLIS4fEkgFsKFPHR77LOUTiUYhS6ZOib1uFQddFKPVB/3m4qeMMIx4eQZYA36IuAwuge1\nEKJ6kHHEQrhgGAYh6IBoAj5HV1V/CXyE+3HC5aXB9MRhoCewHt3ubLMQPY64yPq4EMiXn64Q1YKU\niIVwoUe3bkQAuUAM0M16/31gGO7HCXdG54geQGlpuiK2XNNzcA7Ch4G56AkfzOhq6XYyQ5IQ1YYE\nYiFcWLN2LXnoVJKxwF+AH9Htxl082P5BdPV1T2ARrtuMzwALrOvNsG5ncxjojy4B/4TONQ2QssIx\n3YcQ4komVdNCuBERGkphUREt0FMSTgJuQ09pOMrD99iDblPegM6Y5TgfcRp6dqcbgMWUloTPUDo1\noi2Tlm3Ch1wkmYcQ1YmUiIVwY9v//kcIcATYZX0uEe/GCXcCXkdnymoD7AOSgVesj59GT+bwPXre\n4fFAU3SAzgMuogNwCLp0/uCDDyKEqD6kRCxEBW7u3JmDe/ZgQud6fg098cNyP95zPDoITwVWArPQ\nAd6ETqP5HTrwFgPh6LbhPHT7cJ78ZIWoVqRELEQFdqelcQFdKk0h8OOEc9A/xGPoUnEoeg7kuegg\nXIguCRcDXXr18nGvQojLlQRiITywatUqLqB7NGcT2HHCaeiOYNOBoejEHX9HB/4S61JkXbZs2eLj\nXoUQlysJxEJ44N577yUUnV6yJ3AXOr2lp0OTbA6jO2BNsT52LB1fmtZyB7oknI9uJ35q9mwfj14I\ncTmTNmIhPJSamsrkcePIB2qghxV9CXyMZ0k7bOOEHYcoLQL2ontVN0V36IpHB+gG6OpoE1Cnfn1+\nOX48cB9GCHHZkBKxEB4aO3Ysd40cSTS609Q+4Dh67uEk3I8TXkjZccKOpeNLq6tTgEh0b+pCkCAs\nRDUmJWIhvHT/fffx7qpVhKFLsrPQVdZFwB/RyT4cxwm/hw6yUygdJ+xYOu6Dc1rLw0BHSntJy09U\niOpNArEQPpg9ezZz58yhFjqf9PvoDlYKaA/UB2qh23zHUFrStSXqmI/u+NUH5+rqw0AvdOm6ECiS\nn6cQ1Z5UTQvhg9mzZ/NycjK/o0uvClgH9Ab2Ax+gM2BFoqdKdEzU8RV67PB5SqurR6DTXHYETiNB\nWIiriZSIhfDDq0uXMnnyZCzojlVDgA7o2Zn2oQNqArqE3ABoC3yNroZuj55I4hh6isMwdO/oQqQ6\nWoiriQRiIfy0Z88e+vbsSUleHnnomZrM6DZjW9txJDpTViG6pGxBB94i6+NioHnz5hw6dCgIn0AI\nEUxSNS2Enzp16sS5ixdJz8ykR8+enAX7rE2h6IQcxdbHJnRAvgC079OHbKUoUgqllARhIa5SUiIW\nQgghgkhKxEIIIUQQSSAWQgghgkgCsRBCCBFEEoiFEEKIIJJALIQQQgSRBGIhhBAiiCQQCyGEEEEk\ngVgIIYQIIgnEQgghRBBJIBZCCCGCSAKxEEIIEUQSiIUQQoggkkAshBBCBJEEYiGEECKIJBALIYQQ\nQSSBWAghhAgiCcRCCCFEEEkgFkIIIYJIArEQQggRRBKIhRBCiCCSQCyEEEIEkQRiIYQQIogkEAsh\nhBBBJIFYCCGECCIJxEIIIUQQSSAWQgghgkgCsRBCCBFEEoiFEEKIIJJALIQQQgSRBGIhhBAiiCQQ\nCyGEEEEkgVgIIYQIIgnEQgghRBBJIBZCCCGCSAKxEEIIEUQSiIUQQoggkkAshBBCBJEEYiGEECKI\nJBALIYQQQSSBWAghhAgiCcRCCCFEEEkgFkIIIYJIArEQQggRRBKIhRBCiCCSQCyEEEIEkQRiIYQQ\nIoj+PxuENejqmQL9AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2ab1825d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx.draw(j_islands)\n", "pos=nx.circular_layout(j_islands)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

rashikaranpuria/Machine-Learning-Specialization

Classification/Week 7/module-10-online-learning-assignment-blank.ipynb

1

446437

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training Logistic Regression via Stochastic Gradient Ascent\n", "\n", "The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:\n", "\n", " * Extract features from Amazon product reviews.\n", " * Convert an SFrame into a NumPy array.\n", " * Write a function to compute the derivative of log likelihood function with respect to a single coefficient.\n", " * Implement stochastic gradient ascent.\n", " * Compare convergence of stochastic gradient ascent with that of batch gradient ascent." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fire up GraphLab Create\n", " \n", "Make sure you have the latest version of GraphLab Create. Upgrade by\n", "\n", "```\n", " pip install graphlab-create --upgrade\n", "```\n", "See [this page](https://dato.com/download/) for detailed instructions on upgrading." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "import graphlab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load and process review dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this assignment, we will use the same subset of the Amazon product review dataset that we used in Module 3 assignment. The subset was chosen to contain similar numbers of positive and negative reviews, as the original dataset consisted of mostly positive reviews." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This non-commercial license of GraphLab Create is assigned to [email protected] and will expire on October 12, 2016. For commercial licensing options, visit https://dato.com/buy/.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2016-04-21 23:19:15,324 [INFO] graphlab.cython.cy_server, 176: GraphLab Create v1.8.5 started. Logging: /tmp/graphlab_server_1461273552.log\n" ] } ], "source": [ "products = graphlab.SFrame('amazon_baby_subset.gl/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like we did previously, we will work with a hand-curated list of important words extracted from the review data. We will also perform 2 simple data transformations:\n", "\n", "1. Remove punctuation using [Python's built-in](https://docs.python.org/2/library/string.html) string manipulation functionality.\n", "2. Compute word counts (only for the important_words)\n", "\n", "Refer to Module 3 assignment for more details." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "with open('important_words.json', 'r') as f: \n", " important_words = json.load(f)\n", "important_words = [str(s) for s in important_words]\n", "\n", "# Remote punctuation\n", "def remove_punctuation(text):\n", " import string\n", " return text.translate(None, string.punctuation) \n", "\n", "products['review_clean'] = products['review'].apply(remove_punctuation)\n", "\n", "# Split out the words into individual columns\n", "for word in important_words:\n", " products[word] = products['review_clean'].apply(lambda s : s.split().count(word))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "The SFrame **products** now contains one column for each of the 193 **important_words**. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sentiment</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review_clean</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">baby</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>nonstop when I tried to ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nature's Lullabies Second<br>Year Sticker Calendar ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We wanted to get<br>something to keep track ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We wanted to get<br>something to keep track ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nature's Lullabies Second<br>Year Sticker Calendar ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My daughter had her 1st<br>baby over a year ago. ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My daughter had her 1st<br>baby over a year ago She ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lamaze Peekaboo, I Love<br>You ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">One of baby's first and<br>favorite books, and i ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">One of babys first and<br>favorite books and it is ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SoftPlay Peek-A-Boo<br>Where's Elmo A Childr ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very cute interactive<br>book! My son loves this ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very cute interactive<br>book My son loves this ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Our Baby Girl Memory Book</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Beautiful book, I love it<br>to record cherished t ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Beautiful book I love it<br>to record cherished t ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Hunnt&amp;reg; Falling<br>Flowers and Birds Kids ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Try this out for a spring<br>project !Easy ,fun and ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Try this out for a spring<br>project Easy fun and ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Blessed By Pope Benedict<br>XVI Divine Mercy Full ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">very nice Divine Mercy<br>Pendant of Jesus now on ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">very nice Divine Mercy<br>Pendant of Jesus now on ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cloth Diaper Pins<br>Stainless Steel ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We bought the pins as my<br>6 year old Autistic son ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We bought the pins as my<br>6 year old Autistic son ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cloth Diaper Pins<br>Stainless Steel ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">It has been many years<br>since we needed diaper ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">It has been many years<br>since we needed diaper ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", "</table>\n", "<table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">one</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">great</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">love</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">use</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">would</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">like</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">easy</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">little</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">seat</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">old</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">well</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">get</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">also</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">really</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">son</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">time</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bought</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", "</table>\n", "<table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">product</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">good</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">daughter</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">much</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">loves</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stroller</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">put</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">months</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">car</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">still</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">back</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">used</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">recommend</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">first</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">even</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", "</table>\n", "<table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">perfect</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">nice</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">...</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n", " </tr>\n", "</table>\n", "[53072 rows x 198 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\tname\tstr\n", "\treview\tstr\n", "\trating\tfloat\n", "\tsentiment\tint\n", "\treview_clean\tstr\n", "\tbaby\tint\n", "\tone\tint\n", "\tgreat\tint\n", "\tlove\tint\n", "\tuse\tint\n", "\twould\tint\n", "\tlike\tint\n", "\teasy\tint\n", "\tlittle\tint\n", "\tseat\tint\n", "\told\tint\n", "\twell\tint\n", "\tget\tint\n", "\talso\tint\n", "\treally\tint\n", "\tson\tint\n", "\ttime\tint\n", "\tbought\tint\n", "\tproduct\tint\n", "\tgood\tint\n", "\tdaughter\tint\n", "\tmuch\tint\n", "\tloves\tint\n", "\tstroller\tint\n", "\tput\tint\n", "\tmonths\tint\n", "\tcar\tint\n", "\tstill\tint\n", "\tback\tint\n", "\tused\tint\n", "\trecommend\tint\n", "\tfirst\tint\n", "\teven\tint\n", "\tperfect\tint\n", "\tnice\tint\n", "\tbag\tint\n", "\ttwo\tint\n", "\tusing\tint\n", "\tgot\tint\n", "\tfit\tint\n", "\taround\tint\n", "\tdiaper\tint\n", "\tenough\tint\n", "\tmonth\tint\n", "\tprice\tint\n", "\tgo\tint\n", "\tcould\tint\n", "\tsoft\tint\n", "\tsince\tint\n", "\tbuy\tint\n", "\troom\tint\n", "\tworks\tint\n", "\tmade\tint\n", "\tchild\tint\n", "\tkeep\tint\n", "\tsize\tint\n", "\tsmall\tint\n", "\tneed\tint\n", "\tyear\tint\n", "\tbig\tint\n", "\tmake\tint\n", "\ttake\tint\n", "\teasily\tint\n", "\tthink\tint\n", "\tcrib\tint\n", "\tclean\tint\n", "\tway\tint\n", "\tquality\tint\n", "\tthing\tint\n", "\tbetter\tint\n", "\twithout\tint\n", "\tset\tint\n", "\tnew\tint\n", "\tevery\tint\n", "\tcute\tint\n", "\tbest\tint\n", "\tbottles\tint\n", "\twork\tint\n", "\tpurchased\tint\n", "\tright\tint\n", "\tlot\tint\n", "\tside\tint\n", "\thappy\tint\n", "\tcomfortable\tint\n", "\ttoy\tint\n", "\table\tint\n", "\tkids\tint\n", "\tbit\tint\n", "\tnight\tint\n", "\tlong\tint\n", "\tfits\tint\n", "\tsee\tint\n", "\tus\tint\n", "\tanother\tint\n", "\tplay\tint\n", "\tday\tint\n", "\tmoney\tint\n", "\tmonitor\tint\n", "\ttried\tint\n", "\tthought\tint\n", "\tnever\tint\n", "\titem\tint\n", "\thard\tint\n", "\tplastic\tint\n", "\thowever\tint\n", "\tdisappointed\tint\n", "\treviews\tint\n", "\tsomething\tint\n", "\tgoing\tint\n", "\tpump\tint\n", "\tbottle\tint\n", "\tcup\tint\n", "\twaste\tint\n", "\treturn\tint\n", "\tamazon\tint\n", "\tdifferent\tint\n", "\ttop\tint\n", "\twant\tint\n", "\tproblem\tint\n", "\tknow\tint\n", "\twater\tint\n", "\ttry\tint\n", "\treceived\tint\n", "\tsure\tint\n", "\ttimes\tint\n", "\tchair\tint\n", "\tfind\tint\n", "\thold\tint\n", "\tgate\tint\n", "\topen\tint\n", "\tbottom\tint\n", "\taway\tint\n", "\tactually\tint\n", "\tcheap\tint\n", "\tworked\tint\n", "\tgetting\tint\n", "\tordered\tint\n", "\tcame\tint\n", "\tmilk\tint\n", "\tbad\tint\n", "\tpart\tint\n", "\tworth\tint\n", "\tfound\tint\n", "\tcover\tint\n", "\tmany\tint\n", "\tdesign\tint\n", "\tlooking\tint\n", "\tweeks\tint\n", "\tsay\tint\n", "\twanted\tint\n", "\tlook\tint\n", "\tplace\tint\n", "\tpurchase\tint\n", "\tlooks\tint\n", "\tsecond\tint\n", "\tpiece\tint\n", "\tbox\tint\n", "\tpretty\tint\n", "\ttrying\tint\n", "\tdifficult\tint\n", "\ttogether\tint\n", "\tthough\tint\n", "\tgive\tint\n", "\tstarted\tint\n", "\tanything\tint\n", "\tlast\tint\n", "\tcompany\tint\n", "\tcome\tint\n", "\treturned\tint\n", "\tmaybe\tint\n", "\ttook\tint\n", "\tbroke\tint\n", "\tmakes\tint\n", "\tstay\tint\n", "\tinstead\tint\n", "\tidea\tint\n", "\thead\tint\n", "\tsaid\tint\n", "\tless\tint\n", "\twent\tint\n", "\tworking\tint\n", "\thigh\tint\n", "\tunit\tint\n", "\tseems\tint\n", "\tpicture\tint\n", "\tcompletely\tint\n", "\twish\tint\n", "\tbuying\tint\n", "\tbabies\tint\n", "\twon\tint\n", "\ttub\tint\n", "\talmost\tint\n", "\teither\tint\n", "\n", "Rows: 53072\n", "\n", "Data:\n", "+-------------------------------+-------------------------------+--------+-----------+\n", "| name | review | rating | sentiment |\n", "+-------------------------------+-------------------------------+--------+-----------+\n", "| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 | 1 |\n", "| Nature's Lullabies Second ... | We wanted to get something... | 5.0 | 1 |\n", "| Nature's Lullabies Second ... | My daughter had her 1st ba... | 5.0 | 1 |\n", "| Lamaze Peekaboo, I Love You | One of baby's first and fa... | 4.0 | 1 |\n", "| SoftPlay Peek-A-Boo Where'... | Very cute interactive book... | 5.0 | 1 |\n", "| Our Baby Girl Memory Book | Beautiful book, I love it ... | 5.0 | 1 |\n", "| Hunnt&reg; Falling Flowers... | Try this out for a spring ... | 5.0 | 1 |\n", "| Blessed By Pope Benedict X... | very nice Divine Mercy Pen... | 5.0 | 1 |\n", "| Cloth Diaper Pins Stainles... | We bought the pins as my 6... | 4.0 | 1 |\n", "| Cloth Diaper Pins Stainles... | It has been many years sin... | 5.0 | 1 |\n", "+-------------------------------+-------------------------------+--------+-----------+\n", "+-------------------------------+------+-----+-------+------+-----+-------+------+\n", "| review_clean | baby | one | great | love | use | would | like |\n", "+-------------------------------+------+-----+-------+------+-----+-------+------+\n", "| All of my kids have cried ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 |\n", "| We wanted to get something... | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| My daughter had her 1st ba... | 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| One of babys first and fav... | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n", "| Very cute interactive book... | 0 | 0 | 1 | 0 | 0 | 0 | 0 |\n", "| Beautiful book I love it t... | 0 | 0 | 1 | 1 | 0 | 0 | 0 |\n", "| Try this out for a spring ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| very nice Divine Mercy Pen... | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| We bought the pins as my 6... | 0 | 1 | 0 | 0 | 1 | 0 | 0 |\n", "| It has been many years sin... | 0 | 1 | 0 | 0 | 0 | 0 | 1 |\n", "+-------------------------------+------+-----+-------+------+-----+-------+------+\n", "+------+--------+------+-----+------+-----+------+--------+-----+\n", "| easy | little | seat | old | well | get | also | really | ... |\n", "+------+--------+------+-----+------+-----+------+--------+-----+\n", "| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... |\n", "| 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... |\n", "| 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | ... |\n", "| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... |\n", "| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... |\n", "| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... |\n", "| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... |\n", "| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... |\n", "| 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... |\n", "| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | ... |\n", "+------+--------+------+-----+------+-----+------+--------+-----+\n", "[53072 rows x 198 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "products" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split data into training and validation sets\n", "\n", "We will now split the data into a 90-10 split where 90% is in the training set and 10% is in the validation set. We use `seed=1` so that everyone gets the same result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set : 47780 data points\n", "Validation set: 5292 data points\n" ] } ], "source": [ "train_data, validation_data = products.random_split(.9, seed=1)\n", "\n", "print 'Training set : %d data points' % len(train_data)\n", "print 'Validation set: %d data points' % len(validation_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert SFrame to NumPy array\n", "\n", "Just like in the earlier assignments, we provide you with a function that extracts columns from an SFrame and converts them into a NumPy array. Two arrays are returned: one representing features and another representing class labels. \n", "\n", "**Note:** The feature matrix includes an additional column 'intercept' filled with 1's to take account of the intercept term." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "\n", "def get_numpy_data(data_sframe, features, label):\n", " data_sframe['intercept'] = 1\n", " features = ['intercept'] + features\n", " features_sframe = data_sframe[features]\n", " feature_matrix = features_sframe.to_numpy()\n", " label_sarray = data_sframe[label]\n", " label_array = label_sarray.to_numpy()\n", " return(feature_matrix, label_array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we convert both the training and validation sets into NumPy arrays.\n", "\n", "**Warning**: This may take a few minutes." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feature_matrix_train, sentiment_train = get_numpy_data(train_data, important_words, 'sentiment')\n", "feature_matrix_valid, sentiment_valid = get_numpy_data(validation_data, important_words, 'sentiment') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Are you running this notebook on an Amazon EC2 t2.micro instance?** (If you are using your own machine, please skip this section)\n", "\n", "It has been reported that t2.micro instances do not provide sufficient power to complete the conversion in acceptable amount of time. For interest of time, please refrain from running `get_numpy_data` function. Instead, download the [binary file](https://s3.amazonaws.com/static.dato.com/files/coursera/course-3/numpy-arrays/module-10-assignment-numpy-arrays.npz) containing the four NumPy arrays you'll need for the assignment. To load the arrays, run the following commands:\n", "```\n", "arrays = np.load('module-10-assignment-numpy-arrays.npz')\n", "feature_matrix_train, sentiment_train = arrays['feature_matrix_train'], arrays['sentiment_train']\n", "feature_matrix_valid, sentiment_valid = arrays['feature_matrix_valid'], arrays['sentiment_valid']\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Quiz question**: In Module 3 assignment, there were 194 features (an intercept + one feature for each of the 193 important words). In this assignment, we will use stochastic gradient ascent to train the classifier using logistic regression. How does the changing the solver to stochastic gradient ascent affect the number of features?\n", "\n", "Stays the same" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building on logistic regression\n", "\n", "Let us now build on Module 3 assignment. Recall from lecture that the link function for logistic regression can be defined as:\n", "\n", "$$\n", "P(y_i = +1 | \\mathbf{x}_i,\\mathbf{w}) = \\frac{1}{1 + \\exp(-\\mathbf{w}^T h(\\mathbf{x}_i))},\n", "$$\n", "\n", "where the feature vector $h(\\mathbf{x}_i)$ is given by the word counts of **important_words** in the review $\\mathbf{x}_i$. \n", "\n", "\n", "We will use the **same code** as in Module 3 assignment to make probability predictions, since this part is not affected by using stochastic gradient ascent as a solver. Only the way in which the coefficients are learned is affected by using stochastic gradient ascent as a solver." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "produces probablistic estimate for P(y_i = +1 | x_i, w).\n", "estimate ranges between 0 and 1.\n", "'''\n", "def predict_probability(feature_matrix, coefficients):\n", " # Take dot product of feature_matrix and coefficients \n", " score = np.dot(feature_matrix, coefficients)\n", " \n", " # Compute P(y_i = +1 | x_i, w) using the link function\n", " predictions = 1. / (1.+np.exp(-score)) \n", " return predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Derivative of log likelihood with respect to a single coefficient\n", "\n", "Let us now work on making minor changes to how the derivative computation is performed for logistic regression.\n", "\n", "Recall from the lectures and Module 3 assignment that for logistic regression, **the derivative of log likelihood with respect to a single coefficient** is as follows:\n", "\n", "$$\n", "\\frac{\\partial\\ell}{\\partial w_j} = \\sum_{i=1}^N h_j(\\mathbf{x}_i)\\left(\\mathbf{1}[y_i = +1] - P(y_i = +1 | \\mathbf{x}_i, \\mathbf{w})\\right)\n", "$$\n", "\n", "In Module 3 assignment, we wrote a function to compute the derivative of log likelihood with respect to a single coefficient $w_j$. The function accepts the following two parameters:\n", " * `errors` vector containing $(\\mathbf{1}[y_i = +1] - P(y_i = +1 | \\mathbf{x}_i, \\mathbf{w}))$ for all $i$\n", " * `feature` vector containing $h_j(\\mathbf{x}_i)$ for all $i$\n", " \n", "Complete the following code block:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def feature_derivative(errors, feature): \n", " \n", " # Compute the dot product of errors and feature\n", " ## YOUR CODE HERE\n", " derivative = sum(feature * errors)\n", "\n", " return derivative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**. We are not using regularization in this assignment, but, as discussed in the optional video, stochastic gradient can also be used for regularized logistic regression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To verify the correctness of the gradient computation, we provide a function for computing average log likelihood (which we recall from the last assignment was a topic detailed in an advanced optional video, and used here for its numerical stability).\n", "\n", "To track the performance of stochastic gradient ascent, we provide a function for computing **average log likelihood**. \n", "\n", "$$\\ell\\ell_A(\\mathbf{w}) = \\color{red}{\\frac{1}{N}} \\sum_{i=1}^N \\Big( (\\mathbf{1}[y_i = +1] - 1)\\mathbf{w}^T h(\\mathbf{x}_i) - \\ln\\left(1 + \\exp(-\\mathbf{w}^T h(\\mathbf{x}_i))\\right) \\Big) $$\n", "\n", "**Note** that we made one tiny modification to the log likelihood function (called **compute_log_likelihood**) in our earlier assignments. We added a $\\color{red}{1/N}$ term which averages the log likelihood accross all data points. The $\\color{red}{1/N}$ term makes it easier for us to compare stochastic gradient ascent with batch gradient ascent. We will use this function to generate plots that are similar to those you saw in the lecture." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compute_avg_log_likelihood(feature_matrix, sentiment, coefficients):\n", " \n", " indicator = (sentiment==+1)\n", " scores = np.dot(feature_matrix, coefficients)\n", " logexp = np.log(1. + np.exp(-scores))\n", " \n", " # Simple check to prevent overflow\n", " mask = np.isinf(logexp)\n", " logexp[mask] = -scores[mask]\n", " \n", " lp = np.sum((indicator-1)*scores - logexp)/len(feature_matrix)\n", " \n", " return lp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Quiz Question:** Recall from the lecture and the earlier assignment, the log likelihood (without the averaging term) is given by \n", "\n", "$$\\ell\\ell(\\mathbf{w}) = \\sum_{i=1}^N \\Big( (\\mathbf{1}[y_i = +1] - 1)\\mathbf{w}^T h(\\mathbf{x}_i) - \\ln\\left(1 + \\exp(-\\mathbf{w}^T h(\\mathbf{x}_i))\\right) \\Big) $$\n", "\n", "How are the functions $\\ell\\ell(\\mathbf{w})$ and $\\ell\\ell_A(\\mathbf{w})$ related?\n", "\n", "ll_A(w) = (1/N)*ll(w)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modifying the derivative for stochastic gradient ascent\n", "\n", "Recall from the lecture that the gradient for a single data point $\\color{red}{\\mathbf{x}_i}$ can be computed using the following formula:\n", "\n", "$$\n", "\\frac{\\partial\\ell_{\\color{red}{i}}(\\mathbf{w})}{\\partial w_j} = h_j(\\color{red}{\\mathbf{x}_i})\\left(\\mathbf{1}[y_\\color{red}{i} = +1] - P(y_\\color{red}{i} = +1 | \\color{red}{\\mathbf{x}_i}, \\mathbf{w})\\right)\n", "$$\n", "\n", "\n", "** Computing the gradient for a single data point**\n", "\n", "Do we really need to re-write all our code to modify $\\partial\\ell(\\mathbf{w})/\\partial w_j$ to $\\partial\\ell_{\\color{red}{i}}(\\mathbf{w})/{\\partial w_j}$? \n", "\n", "\n", "Thankfully **No!**. Using NumPy, we access $\\mathbf{x}_i$ in the training data using `feature_matrix_train[i:i+1,:]`\n", "and $y_i$ in the training data using `sentiment_train[i:i+1]`. We can compute $\\partial\\ell_{\\color{red}{i}}(\\mathbf{w})/\\partial w_j$ by re-using **all the code** written in **feature_derivative** and **predict_probability**.\n", "\n", "\n", "We compute $\\partial\\ell_{\\color{red}{i}}(\\mathbf{w})/\\partial w_j$ using the following steps:\n", "* First, compute $P(y_i = +1 | \\mathbf{x}_i, \\mathbf{w})$ using the **predict_probability** function with `feature_matrix_train[i:i+1,:]` as the first parameter.\n", "* Next, compute $\\mathbf{1}[y_i = +1]$ using `sentiment_train[i:i+1]`.\n", "* Finally, call the **feature_derivative** function with `feature_matrix_train[i:i+1, j]` as one of the parameters. \n", "\n", "Let us follow these steps for `j = 1` and `i = 10`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gradient single data point: 0.0\n", " --> Should print 0.0\n" ] } ], "source": [ "j = 1 # Feature number\n", "i = 10 # Data point number\n", "coefficients = np.zeros(194) # A point w at which we are computing the gradient.\n", "\n", "predictions = predict_probability(feature_matrix_train[i:i+1,:], coefficients)\n", "indicator = (sentiment_train[i:i+1]==+1)\n", "\n", "errors = indicator - predictions \n", "gradient_single_data_point = feature_derivative(errors, feature_matrix_train[i:i+1,j])\n", "print \"Gradient single data point: %s\" % gradient_single_data_point\n", "print \" --> Should print 0.0\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Quiz Question:** The code block above computed $\\partial\\ell_{\\color{red}{i}}(\\mathbf{w})/{\\partial w_j}$ for `j = 1` and `i = 10`. Is $\\partial\\ell_{\\color{red}{i}}(\\mathbf{w})/{\\partial w_j}$ a scalar or a 194-dimensional vector?\n", "\n", "A scalar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modifying the derivative for using a batch of data points\n", "\n", "Stochastic gradient estimates the ascent direction using 1 data point, while gradient uses $N$ data points to decide how to update the the parameters. In an optional video, we discussed the details of a simple change that allows us to use a **mini-batch** of $B \\leq N$ data points to estimate the ascent direction. This simple approach is faster than regular gradient but less noisy than stochastic gradient that uses only 1 data point. Although we encorage you to watch the optional video on the topic to better understand why mini-batches help stochastic gradient, in this assignment, we will simply use this technique, since the approach is very simple and will improve your results.\n", "\n", "Given a mini-batch (or a set of data points) $\\mathbf{x}_{i}, \\mathbf{x}_{i+1} \\ldots \\mathbf{x}_{i+B}$, the gradient function for this mini-batch of data points is given by:\n", "$$\n", "\\color{red}{\\sum_{s = i}^{i+B}} \\frac{\\partial\\ell_{s}}{\\partial w_j} = \\color{red}{\\sum_{s = i}^{i + B}} h_j(\\mathbf{x}_s)\\left(\\mathbf{1}[y_s = +1] - P(y_s = +1 | \\mathbf{x}_s, \\mathbf{w})\\right)\n", "$$\n", "\n", "\n", "** Computing the gradient for a \"mini-batch\" of data points**\n", "\n", "Using NumPy, we access the points $\\mathbf{x}_i, \\mathbf{x}_{i+1} \\ldots \\mathbf{x}_{i+B}$ in the training data using `feature_matrix_train[i:i+B,:]`\n", "and $y_i$ in the training data using `sentiment_train[i:i+B]`. \n", "\n", "We can compute $\\color{red}{\\sum_{s = i}^{i+B}} \\partial\\ell_{s}/\\partial w_j$ easily as follows:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gradient mini-batch data points: 1.0\n", " --> Should print 1.0\n" ] } ], "source": [ "j = 1 # Feature number\n", "i = 10 # Data point start\n", "B = 10 # Mini-batch size\n", "coefficients = np.zeros(194) # A point w at which we are computing the gradient.\n", "\n", "predictions = predict_probability(feature_matrix_train[i:i+B,:], coefficients)\n", "indicator = (sentiment_train[i:i+B]==+1)\n", "\n", "errors = indicator - predictions \n", "gradient_mini_batch = feature_derivative(errors, feature_matrix_train[i:i+B,j])\n", "print \"Gradient mini-batch data points: %s\" % gradient_mini_batch\n", "print \" --> Should print 1.0\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Quiz Question:** The code block above computed \n", "$\\color{red}{\\sum_{s = i}^{i+B}}\\partial\\ell_{s}(\\mathbf{w})/{\\partial w_j}$ \n", "for `j = 10`, `i = 10`, and `B = 10`. Is this a scalar or a 194-dimensional vector?\n", "\n", "A scalar\n", "\n", "** Quiz Question:** For what value of `B` is the term\n", "$\\color{red}{\\sum_{s = 1}^{B}}\\partial\\ell_{s}(\\mathbf{w})/\\partial w_j$\n", "the same as the full gradient\n", "$\\partial\\ell(\\mathbf{w})/{\\partial w_j}$?\n", "\n", "47780" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Averaging the gradient across a batch\n", "\n", "It is a common practice to normalize the gradient update rule by the batch size B:\n", "\n", "$$\n", "\\frac{\\partial\\ell_{\\color{red}{A}}(\\mathbf{w})}{\\partial w_j} \\approx \\color{red}{\\frac{1}{B}} {\\sum_{s = i}^{i + B}} h_j(\\mathbf{x}_s)\\left(\\mathbf{1}[y_s = +1] - P(y_s = +1 | \\mathbf{x}_s, \\mathbf{w})\\right)\n", "$$\n", "In other words, we update the coefficients using the **average gradient over data points** (instead of using a summation). By using the average gradient, we ensure that the magnitude of the gradient is approximately the same for all batch sizes. This way, we can more easily compare various batch sizes of stochastic gradient ascent (including a batch size of **all the data points**), and study the effect of batch size on the algorithm as well as the choice of step size.\n", "\n", "\n", "## Implementing stochastic gradient ascent\n", "\n", "Now we are ready to implement our own logistic regression with stochastic gradient ascent. Complete the following function to fit a logistic regression model using gradient ascent:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from math import sqrt\n", "def logistic_regression_SG(feature_matrix, sentiment, initial_coefficients, step_size, batch_size, max_iter):\n", " log_likelihood_all = []\n", " \n", " # make sure it's a numpy array\n", " coefficients = np.array(initial_coefficients)\n", " # set seed=1 to produce consistent results\n", " np.random.seed(seed=1)\n", " # Shuffle the data before starting\n", " permutation = np.random.permutation(len(feature_matrix))\n", " feature_matrix = feature_matrix[permutation,:]\n", " sentiment = sentiment[permutation]\n", " \n", " i = 0 # index of current batch\n", " # Do a linear scan over data\n", " for itr in xrange(max_iter):\n", " # Predict P(y_i = +1|x_i,w) using your predict_probability() function\n", " # Make sure to slice the i-th row of feature_matrix with [i:i+batch_size,:]\n", " ### YOUR CODE HERE\n", " predictions = predict_probability(feature_matrix[i:i+batch_size,:], coefficients)\n", " \n", " # Compute indicator value for (y_i = +1)\n", " # Make sure to slice the i-th entry with [i:i+batch_size]\n", " ### YOUR CODE HERE\n", " indicator = (sentiment[i:i+batch_size]==+1)\n", " \n", " # Compute the errors as indicator - predictions\n", " errors = indicator - predictions\n", " for j in xrange(len(coefficients)): # loop over each coefficient\n", " # Recall that feature_matrix[:,j] is the feature column associated with coefficients[j]\n", " # Compute the derivative for coefficients[j] and save it to derivative.\n", " # Make sure to slice the i-th row of feature_matrix with [i:i+batch_size,j]\n", " ### YOUR CODE HERE\n", " derivative = feature_derivative(errors, feature_matrix[i:i+batch_size,j])\n", " \n", " # compute the product of the step size, the derivative, and the **normalization constant** (1./batch_size)\n", " ### YOUR CODE HERE\n", " coefficients[j] += (step_size * derivative * (1./batch_size))\n", " \n", " # Checking whether log likelihood is increasing\n", " # Print the log likelihood over the *current batch*\n", " lp = compute_avg_log_likelihood(feature_matrix[i:i+batch_size,:], sentiment[i:i+batch_size],\n", " coefficients)\n", " log_likelihood_all.append(lp)\n", " if itr <= 15 or (itr <= 1000 and itr % 100 == 0) or (itr <= 10000 and itr % 1000 == 0) \\\n", " or itr % 10000 == 0 or itr == max_iter-1:\n", " data_size = len(feature_matrix)\n", " print 'Iteration %*d: Average log likelihood (of data points in batch [%0*d:%0*d]) = %.8f' % \\\n", " (int(np.ceil(np.log10(max_iter))), itr, \\\n", " int(np.ceil(np.log10(data_size))), i, \\\n", " int(np.ceil(np.log10(data_size))), i+batch_size, lp)\n", " \n", " # if we made a complete pass over data, shuffle and restart\n", " i += batch_size\n", " if i+batch_size > len(feature_matrix):\n", " permutation = np.random.permutation(len(feature_matrix))\n", " feature_matrix = feature_matrix[permutation,:]\n", " sentiment = sentiment[permutation]\n", " i = 0\n", " \n", " # We return the list of log likelihoods for plotting purposes.\n", " return coefficients, log_likelihood_all" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**. In practice, the final set of coefficients is rarely used; it is better to use the average of the last K sets of coefficients instead, where K should be adjusted depending on how fast the log likelihood oscillates around the optimum." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checkpoint\n", "\n", "\n", "The following cell tests your stochastic gradient ascent function using a toy dataset consisting of two data points. If the test does not pass, make sure you are normalizing the gradient update rule correctly." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0: Average log likelihood (of data points in batch [0:2]) = -0.33774513\n", "Iteration 1: Average log likelihood (of data points in batch [0:2]) = -0.23455309\n", "-------------------------------------------------------------------------------------\n", "Coefficients learned : [-0.09755757 0.68242552 -0.7799831 ]\n", "Average log likelihood per-iteration : [-0.33774513108142956, -0.2345530939410341]\n", "-------------------------------------------------------------------------------------\n", "Test passed!\n" ] } ], "source": [ "sample_feature_matrix = np.array([[1.,2.,-1.], [1.,0.,1.]])\n", "sample_sentiment = np.array([+1, -1])\n", "\n", "coefficients, log_likelihood = logistic_regression_SG(sample_feature_matrix, sample_sentiment, np.zeros(3),\n", " step_size=1., batch_size=2, max_iter=2)\n", "print '-------------------------------------------------------------------------------------'\n", "print 'Coefficients learned :', coefficients\n", "print 'Average log likelihood per-iteration :', log_likelihood\n", "if np.allclose(coefficients, np.array([-0.09755757, 0.68242552, -0.7799831]), atol=1e-3)\\\n", " and np.allclose(log_likelihood, np.array([-0.33774513108142956, -0.2345530939410341])):\n", " # pass if elements match within 1e-3\n", " print '-------------------------------------------------------------------------------------'\n", " print 'Test passed!'\n", "else:\n", " print '-------------------------------------------------------------------------------------'\n", " print 'Test failed'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare convergence behavior of stochastic gradient ascent\n", "\n", "For the remainder of the assignment, we will compare stochastic gradient ascent against batch gradient ascent. For this, we need a reference implementation of batch gradient ascent. But do we need to implement this from scratch?\n", "\n", "**Quiz Question:** For what value of batch size `B` above is the stochastic gradient ascent function **logistic_regression_SG** act as a standard gradient ascent algorithm?\n", "\n", "47780" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running gradient ascent using the stochastic gradient ascent implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of implementing batch gradient ascent separately, we save time by re-using the stochastic gradient ascent function we just wrote &mdash; **to perform gradient ascent**, it suffices to set **`batch_size`** to the number of data points in the training data. Yes, we did answer above the quiz question for you, but that is an important point to remember in the future :)\n", "\n", "**Small Caveat**. The batch gradient ascent implementation here is slightly different than the one in the earlier assignments, as we now normalize the gradient update rule.\n", "\n", "We now **run stochastic gradient ascent** over the **feature_matrix_train** for 10 iterations using:\n", "* `initial_coefficients = np.zeros(194)`\n", "* `step_size = 5e-1`\n", "* `batch_size = 1`\n", "* `max_iter = 10`" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0: Average log likelihood (of data points in batch [00000:00001]) = -0.25192908\n", "Iteration 1: Average log likelihood (of data points in batch [00001:00002]) = -0.00000001\n", "Iteration 2: Average log likelihood (of data points in batch [00002:00003]) = -0.12692771\n", "Iteration 3: Average log likelihood (of data points in batch [00003:00004]) = -0.02969101\n", "Iteration 4: Average log likelihood (of data points in batch [00004:00005]) = -0.02668819\n", "Iteration 5: Average log likelihood (of data points in batch [00005:00006]) = -0.04332901\n", "Iteration 6: Average log likelihood (of data points in batch [00006:00007]) = -0.02368802\n", "Iteration 7: Average log likelihood (of data points in batch [00007:00008]) = -0.12686897\n", "Iteration 8: Average log likelihood (of data points in batch [00008:00009]) = -0.04468879\n", "Iteration 9: Average log likelihood (of data points in batch [00009:00010]) = -0.00000124\n" ] } ], "source": [ "coefficients, log_likelihood = logistic_regression_SG(feature_matrix_train, sentiment_train,\n", " initial_coefficients=np.zeros(194),\n", " step_size=5e-1, batch_size=1, max_iter=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question**. When you set `batch_size = 1`, as each iteration passes, how does the average log likelihood in the batch change?\n", "* Increases\n", "* Decreases\n", "* Fluctuates OK " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now run **batch gradient ascent** over the **feature_matrix_train** for 200 iterations using:\n", "* `initial_coefficients = np.zeros(194)`\n", "* `step_size = 5e-1`\n", "* `batch_size = len(feature_matrix_train)`\n", "* `max_iter = 200`" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0: Average log likelihood (of data points in batch [00000:47780]) = -0.68308119\n", "Iteration 1: Average log likelihood (of data points in batch [00000:47780]) = -0.67394599\n", "Iteration 2: Average log likelihood (of data points in batch [00000:47780]) = -0.66555129\n", "Iteration 3: Average log likelihood (of data points in batch [00000:47780]) = -0.65779626\n", "Iteration 4: Average log likelihood (of data points in batch [00000:47780]) = -0.65060701\n", "Iteration 5: Average log likelihood (of data points in batch [00000:47780]) = -0.64392241\n", "Iteration 6: Average log likelihood (of data points in batch [00000:47780]) = -0.63769009\n", "Iteration 7: Average log likelihood (of data points in batch [00000:47780]) = -0.63186462\n", "Iteration 8: Average log likelihood (of data points in batch [00000:47780]) = -0.62640636\n", "Iteration 9: Average log likelihood (of data points in batch [00000:47780]) = -0.62128063\n", "Iteration 10: Average log likelihood (of data points in batch [00000:47780]) = -0.61645691\n", "Iteration 11: Average log likelihood (of data points in batch [00000:47780]) = -0.61190832\n", "Iteration 12: Average log likelihood (of data points in batch [00000:47780]) = -0.60761103\n", "Iteration 13: Average log likelihood (of data points in batch [00000:47780]) = -0.60354390\n", "Iteration 14: Average log likelihood (of data points in batch [00000:47780]) = -0.59968811\n", "Iteration 15: Average log likelihood (of data points in batch [00000:47780]) = -0.59602682\n", "Iteration 100: Average log likelihood (of data points in batch [00000:47780]) = -0.49520194\n", "Iteration 199: Average log likelihood (of data points in batch [00000:47780]) = -0.47126953\n" ] } ], "source": [ "# YOUR CODE HERE\n", "coefficients_batch, log_likelihood_batch = logistic_regression_SG(feature_matrix_train, sentiment_train,\n", " initial_coefficients=np.zeros(194),\n", " step_size=5e-1, batch_size=len(feature_matrix_train), max_iter=200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question**. When you set `batch_size = len(train_data)`, as each iteration passes, how does the average log likelihood in the batch change?\n", "* Increases OK\n", "* Decreases\n", "* Fluctuates " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make \"passes\" over the dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make a fair comparison betweeen stochastic gradient ascent and batch gradient ascent, we measure the average log likelihood as a function of the number of passes (defined as follows):\n", "$$\n", "[\\text{# of passes}] = \\frac{[\\text{# of data points touched so far}]}{[\\text{size of dataset}]}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question** Suppose that we run stochastic gradient ascent with a batch size of 100. How many gradient updates are performed at the end of two passes over a dataset consisting of 50000 data points?" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1000" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 * int(50000/100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Log likelihood plots for stochastic gradient ascent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the terminology in mind, let us run stochastic gradient ascent for 10 passes. We will use\n", "* `step_size=1e-1`\n", "* `batch_size=100`\n", "* `initial_coefficients` to all zeros." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -0.68251093\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -0.67845294\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -0.68207160\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -0.67411325\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -0.67804438\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -0.67712546\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -0.66377074\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -0.67321231\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -0.66923613\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -0.67479446\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -0.66501639\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -0.65591964\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -0.66240398\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -0.66440641\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -0.65782757\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -0.64571479\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -0.60976663\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -0.54566060\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -0.48245740\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -0.46629313\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -0.47223389\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -0.52216798\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -0.52336683\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -0.46963453\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -0.47883783\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -0.46988191\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -0.46365531\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -0.36466901\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -0.51096892\n", "Iteration 4769: Average log likelihood (of data points in batch [47600:47700]) = -0.54670667\n" ] } ], "source": [ "step_size = 1e-1\n", "batch_size = 100\n", "num_passes = 10\n", "num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)\n", "\n", "coefficients_sgd, log_likelihood_sgd = logistic_regression_SG(feature_matrix_train, sentiment_train,\n", " initial_coefficients=np.zeros(194),\n", " step_size=1e-1, batch_size=100, max_iter=num_iterations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We provide you with a utility function to plot the average log likelihood as a function of the number of passes." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "def make_plot(log_likelihood_all, len_data, batch_size, smoothing_window=1, label=''):\n", " plt.rcParams.update({'figure.figsize': (9,5)})\n", " log_likelihood_all_ma = np.convolve(np.array(log_likelihood_all), \\\n", " np.ones((smoothing_window,))/smoothing_window, mode='valid')\n", " plt.plot(np.array(range(smoothing_window-1, len(log_likelihood_all)))*float(batch_size)/len_data,\n", " log_likelihood_all_ma, linewidth=4.0, label=label)\n", " plt.rcParams.update({'font.size': 16})\n", " plt.tight_layout()\n", " plt.xlabel('# of passes over data')\n", " plt.ylabel('Average log likelihood per data point')\n", " plt.legend(loc='lower right', prop={'size':14})" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAocAAAFmCAYAAAAf5DBYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxhhjjAn6fwgnfVFcidtCAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ed9f2d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_plot(log_likelihood_sgd, len_data=len(feature_matrix_train), batch_size=100,\n", " label='stochastic gradient, step_size=1e-1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Smoothing the stochastic gradient ascent curve\n", "\n", "The plotted line oscillates so much that it is hard to see whether the log likelihood is improving. In our plot, we apply a simple smoothing operation using the parameter `smoothing_window`. The smoothing is simply a [moving average](https://en.wikipedia.org/wiki/Moving_average) of log likelihood over the last `smoothing_window` \"iterations\" of stochastic gradient ascent." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAocAAAFmCAYAAAAf5DBYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nT8YAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118388790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_plot(log_likelihood_sgd, len_data=len(feature_matrix_train), batch_size=100,\n", " smoothing_window=30, label='stochastic gradient, step_size=1e-1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Checkpoint**: The above plot should look smoother than the previous plot. Play around with `smoothing_window`. As you increase it, you should see a smoother plot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stochastic gradient ascent vs batch gradient ascent\n", "\n", "To compare convergence rates for stochastic gradient ascent with batch gradient ascent, we call `make_plot()` multiple times in the same cell.\n", "\n", "We are comparing:\n", "* **stochastic gradient ascent**: `step_size = 0.1`, `batch_size=100`\n", "* **batch gradient ascent**: `step_size = 0.5`, `batch_size=len(feature_matrix_train)`\n", "\n", "Write code to run stochastic gradient ascent for 200 passes using:\n", "* `step_size=1e-1`\n", "* `batch_size=100`\n", "* `initial_coefficients` to all zeros." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -0.68251093\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -0.67845294\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -0.68207160\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -0.67411325\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -0.67804438\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -0.67712546\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -0.66377074\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -0.67321231\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -0.66923613\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -0.67479446\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -0.66501639\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -0.65591964\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -0.66240398\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -0.66440641\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -0.65782757\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -0.64571479\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -0.60976663\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -0.54566060\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -0.48245740\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -0.46629313\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -0.47223389\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -0.52216798\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -0.52336683\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -0.46963453\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -0.47883783\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -0.46988191\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -0.46365531\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -0.36466901\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -0.51096892\n", "Iteration 5000: Average log likelihood (of data points in batch [23000:23100]) = -0.43544394\n", "Iteration 6000: Average log likelihood (of data points in batch [27600:27700]) = -0.45656653\n", "Iteration 7000: Average log likelihood (of data points in batch [32200:32300]) = -0.42656766\n", "Iteration 8000: Average log likelihood (of data points in batch [36800:36900]) = -0.39989352\n", "Iteration 9000: Average log likelihood (of data points in batch [41400:41500]) = -0.45267388\n", "Iteration 10000: Average log likelihood (of data points in batch [46000:46100]) = -0.45394262\n", "Iteration 20000: Average log likelihood (of data points in batch [44300:44400]) = -0.48958438\n", "Iteration 30000: Average log likelihood (of data points in batch [42600:42700]) = -0.41913672\n", "Iteration 40000: Average log likelihood (of data points in batch [40900:41000]) = -0.45899229\n", "Iteration 50000: Average log likelihood (of data points in batch [39200:39300]) = -0.46859254\n", "Iteration 60000: Average log likelihood (of data points in batch [37500:37600]) = -0.41599369\n", "Iteration 70000: Average log likelihood (of data points in batch [35800:35900]) = -0.49905981\n", "Iteration 80000: Average log likelihood (of data points in batch [34100:34200]) = -0.45494095\n", "Iteration 90000: Average log likelihood (of data points in batch [32400:32500]) = -0.43220080\n", "Iteration 95399: Average log likelihood (of data points in batch [47600:47700]) = -0.50265709\n" ] } ], "source": [ "step_size = 1e-1\n", "batch_size = 100\n", "num_passes = 200\n", "num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)\n", "\n", "## YOUR CODE HERE\n", "coefficients_sgd, log_likelihood_sgd = logistic_regression_SG(feature_matrix_train, sentiment_train,\n", " initial_coefficients=np.zeros(194),\n", " step_size=step_size, batch_size=batch_size, max_iter=num_iterations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We compare the convergence of stochastic gradient ascent and batch gradient ascent in the following cell. Note that we apply smoothing with `smoothing_window=30`." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAFTCAYAAAC9NuGbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njDEmn7JAzhhjjDEmn7JAzhhjjDEmn/p/+fmVn5cLIlMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118394050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_plot(log_likelihood_sgd, len_data=len(feature_matrix_train), batch_size=100,\n", " smoothing_window=30, label='stochastic, step_size=1e-1')\n", "make_plot(log_likelihood_batch, len_data=len(feature_matrix_train), batch_size=len(feature_matrix_train),\n", " smoothing_window=1, label='batch, step_size=5e-1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question**: In the figure above, how many passes does batch gradient ascent need to achieve a similar log likelihood as stochastic gradient ascent? \n", "\n", "1. It's always better\n", "2. 10 passes\n", "3. 20 passes\n", "4. 150 passes or more OK" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the effects of step sizes on stochastic gradient ascent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In previous sections, we chose step sizes for you. In practice, it helps to know how to choose good step sizes yourself.\n", "\n", "To start, we explore a wide range of step sizes that are equally spaced in the log space. Run stochastic gradient ascent with `step_size` set to 1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, and 1e2. Use the following set of parameters:\n", "* `initial_coefficients=np.zeros(194)`\n", "* `batch_size=100`\n", "* `max_iter` initialized so as to run 10 passes over the data." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -0.69313622\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -0.69313170\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -0.69313585\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -0.69312487\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -0.69313157\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -0.69313113\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -0.69311121\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -0.69312692\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -0.69312115\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -0.69312811\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -0.69311286\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -0.69310301\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -0.69310725\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -0.69311567\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -0.69310836\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -0.69308342\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -0.69298918\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -0.69277472\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -0.69228764\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -0.69222554\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -0.69186710\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -0.69230650\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -0.69174220\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -0.69139955\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -0.69123818\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -0.69088883\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -0.68976850\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -0.68569701\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -0.68597545\n", "Iteration 4769: Average log likelihood (of data points in batch [47600:47700]) = -0.68736824\n", "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -0.69303759\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -0.69299241\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -0.69303389\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -0.69292442\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -0.69299113\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -0.69298668\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -0.69278828\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -0.69294460\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -0.69288708\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -0.69295651\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -0.69280480\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -0.69270635\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -0.69274924\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -0.69283249\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -0.69275924\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -0.69251197\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -0.69158805\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -0.68946852\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -0.68492418\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -0.68415366\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -0.68114554\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -0.68489867\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -0.68027821\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -0.67693088\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -0.67561867\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -0.67367588\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -0.66156206\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -0.62798175\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -0.64157978\n", "Iteration 4769: Average log likelihood (of data points in batch [47600:47700]) = -0.64571292\n", "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -0.69205420\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -0.69160695\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -0.69201686\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -0.69095428\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -0.69159348\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -0.69154386\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -0.68964000\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -0.69112685\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -0.69056997\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -0.69124730\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -0.68980179\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -0.68882576\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -0.68929536\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -0.69003572\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -0.68929307\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -0.68702353\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -0.67916061\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -0.66049079\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -0.63235099\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -0.62183600\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -0.61150928\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -0.62979300\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -0.61553432\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -0.59156014\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -0.58842264\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -0.59076267\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -0.54480104\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -0.45761063\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -0.54362587\n", "Iteration 4769: Average log likelihood (of data points in batch [47600:47700]) = -0.56306510\n", "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -0.68251093\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -0.67845294\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -0.68207160\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -0.67411325\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -0.67804438\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -0.67712546\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -0.66377074\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -0.67321231\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -0.66923613\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -0.67479446\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -0.66501639\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -0.65591964\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -0.66240398\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -0.66440641\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -0.65782757\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -0.64571479\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -0.60976663\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -0.54566060\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -0.48245740\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -0.46629313\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -0.47223389\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -0.52216798\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -0.52336683\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -0.46963453\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -0.47883783\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -0.46988191\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -0.46365531\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -0.36466901\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -0.51096892\n", "Iteration 4769: Average log likelihood (of data points in batch [47600:47700]) = -0.54670667\n", "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -0.61201447\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -0.58843678\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -0.59771677\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -0.58770466\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -0.56939710\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -0.57554451\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -0.54068090\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -0.55212916\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -0.55311029\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -0.57672007\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -0.55455807\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -0.49771894\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -0.54708765\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -0.54286814\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -0.52361054\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -0.49731367\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -0.50102061\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -0.42406927\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -0.35064478\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -0.38344116\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -0.40170047\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -0.45117863\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -0.46493371\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -0.45343350\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -0.43128394\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -0.43169967\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -0.43029376\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -0.32703099\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -0.49162447\n", "Iteration 4769: Average log likelihood (of data points in batch [47600:47700]) = -0.52452720\n", "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -0.51319004\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -2.20035379\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -3.34199720\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -3.06285156\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -2.80822162\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -2.99629286\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -2.71489944\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -3.61713200\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -1.19526584\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -0.75357081\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -0.71310829\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -0.59361318\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -1.53764659\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -2.69588686\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -1.89731473\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -0.81254441\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -1.19013437\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -0.48968363\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -0.72860037\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -0.58719556\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -0.31220572\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -1.89468446\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -0.96096585\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -0.66616640\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -0.46114004\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -0.47236476\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -0.45227508\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -0.29378688\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -2.47834692\n", "Iteration 4769: Average log likelihood (of data points in batch [47600:47700]) = -2.48776279\n", "Iteration 0: Average log likelihood (of data points in batch [00000:00100]) = -2.44471310\n", "Iteration 1: Average log likelihood (of data points in batch [00100:00200]) = -36.66862050\n", "Iteration 2: Average log likelihood (of data points in batch [00200:00300]) = -25.49870239\n", "Iteration 3: Average log likelihood (of data points in batch [00300:00400]) = -40.14565040\n", "Iteration 4: Average log likelihood (of data points in batch [00400:00500]) = -27.03748522\n", "Iteration 5: Average log likelihood (of data points in batch [00500:00600]) = -32.62294582\n", "Iteration 6: Average log likelihood (of data points in batch [00600:00700]) = -25.88017915\n", "Iteration 7: Average log likelihood (of data points in batch [00700:00800]) = -37.30720216\n", "Iteration 8: Average log likelihood (of data points in batch [00800:00900]) = -10.87360529\n", "Iteration 9: Average log likelihood (of data points in batch [00900:01000]) = -6.60878996\n", "Iteration 10: Average log likelihood (of data points in batch [01000:01100]) = -7.15375088\n", "Iteration 11: Average log likelihood (of data points in batch [01100:01200]) = -6.04741293\n", "Iteration 12: Average log likelihood (of data points in batch [01200:01300]) = -18.17389834\n", "Iteration 13: Average log likelihood (of data points in batch [01300:01400]) = -27.14619228\n", "Iteration 14: Average log likelihood (of data points in batch [01400:01500]) = -20.50685042\n", "Iteration 15: Average log likelihood (of data points in batch [01500:01600]) = -7.74332305\n", "Iteration 100: Average log likelihood (of data points in batch [10000:10100]) = -10.64501704\n", "Iteration 200: Average log likelihood (of data points in batch [20000:20100]) = -4.03699837\n", "Iteration 300: Average log likelihood (of data points in batch [30000:30100]) = -4.52977470\n", "Iteration 400: Average log likelihood (of data points in batch [40000:40100]) = -5.65641627\n", "Iteration 500: Average log likelihood (of data points in batch [02300:02400]) = -7.20280707\n", "Iteration 600: Average log likelihood (of data points in batch [12300:12400]) = -26.75060604\n", "Iteration 700: Average log likelihood (of data points in batch [22300:22400]) = -7.20637441\n", "Iteration 800: Average log likelihood (of data points in batch [32300:32400]) = -7.19668828\n", "Iteration 900: Average log likelihood (of data points in batch [42300:42400]) = -2.51593652\n", "Iteration 1000: Average log likelihood (of data points in batch [04600:04700]) = -3.55648561\n", "Iteration 2000: Average log likelihood (of data points in batch [09200:09300]) = -9.02994020\n", "Iteration 3000: Average log likelihood (of data points in batch [13800:13900]) = -1.96710287\n", "Iteration 4000: Average log likelihood (of data points in batch [18400:18500]) = -4.06013334\n", "Iteration 4769: Average log likelihood (of data points in batch [47600:47700]) = -26.54200880\n" ] } ], "source": [ "batch_size = 100\n", "num_passes = 10\n", "num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)\n", "\n", "coefficients_sgd = {}\n", "log_likelihood_sgd = {}\n", "for step_size in np.logspace(-4, 2, num=7):\n", " coefficients_sgd[step_size], log_likelihood_sgd[step_size] = logistic_regression_SG(feature_matrix_train, sentiment_train,\n", " initial_coefficients=np.zeros(194),\n", " step_size=step_size, batch_size=batch_size, max_iter=num_iterations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting the log likelihood as a function of passes for each step size\n", "\n", "Now, we will plot the change in log likelihood using the `make_plot` for each of the following values of `step_size`:\n", "\n", "* `step_size = 1e-4`\n", "* `step_size = 1e-3`\n", "* `step_size = 1e-2`\n", "* `step_size = 1e-1`\n", "* `step_size = 1e0`\n", "* `step_size = 1e1`\n", "* `step_size = 1e2`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For consistency, we again apply `smoothing_window=30`." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAFSCAYAAAB2ajI+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1188d0550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for step_size in np.logspace(-4, 2, num=7):\n", " make_plot(log_likelihood_sgd[step_size], len_data=len(train_data), batch_size=100,\n", " smoothing_window=30, label='step_size=%.1e'%step_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let us remove the step size `step_size = 1e2` and plot the rest of the curves." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAFSCAYAAAB2ajI+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw43lQEo3hbUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1184f22d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for step_size in np.logspace(-4, 2, num=7)[0:6]:\n", " make_plot(log_likelihood_sgd[step_size], len_data=len(train_data), batch_size=100,\n", " smoothing_window=30, label='step_size=%.1e'%step_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question**: Which of the following is the worst step size? Pick the step size that results in the lowest log likelihood in the end.\n", "1. 1e-2\n", "2. 1e-1\n", "3. 1e0 \n", "4. 1e1\n", "5. 1e2 OK" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question**: Which of the following is the best step size? Pick the step size that results in the highest log likelihood in the end.\n", "1. 1e-4\n", "2. 1e-2\n", "3. 1e0 OK\n", "4. 1e1\n", "5. 1e2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

janusnic/21v-python

unit_20/parallel_ml/notebooks/01 - Introduction.ipynb

1

7009

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "What is machine learning?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section we will begin to explore the basic principles of machine learning.\n", "Machine Learning is about building programs with **tunable parameters** (typically an\n", "array of floating point values) that are adjusted automatically so as to improve\n", "their behavior by **adapting to previously seen data.**\n", "\n", "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n", "algorithms can be seen as building blocks to make computers learn to behave more\n", "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n", "like a database system would do.\n", "\n", "We'll take a look at two very simple machine learning tasks here.\n", "The first is a **classification** task: the figure shows a\n", "collection of two-dimensional data, colored according to two different class\n", "labels. A classification algorithm may be used to draw a dividing boundary\n", "between the two clusters of points:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Start matplotlib inline mode, so figures will appear in the notebook\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Import the example plot from the figures directory\n", "from figures import plot_sgd_separator\n", "plot_sgd_separator()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This may seem like a trivial task, but it is a simple version of a very important concept.\n", "By drawing this separating line, we have learned a model which can **generalize** to new\n", "data: if you were to drop another point onto the plane which is unlabeled, this algorithm\n", "could now **predict** whether it's a blue or a red point.\n", "\n", "If you'd like to see the source code used to generate this, you can either open the\n", "code in the `figures` directory, or you can load the code using the `%load` magic command:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load figures/sgd_separator.py" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next simple task we'll look at is a **regression** task: a simple best-fit line\n", "to a set of data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from figures import plot_linear_regression\n", "plot_linear_regression()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, this is an example of fitting a model to data, such that the model can make\n", "generalizations about new data. The model has been **learned** from the training\n", "data, and can be used to predict the result of test data:\n", "here, we might be given an x-value, and the model would\n", "allow us to predict the y value. Again, this might seem like a trivial problem,\n", "but it is a basic example of a type of operation that is fundamental to\n", "machine learning tasks." ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "An Overview of Scikit-learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Adapted from* [*http://scikit-learn.org/stable/tutorial/basic/tutorial.html*](http://scikit-learn.org/stable/tutorial/basic/tutorial.html)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "from matplotlib import pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Loading an Example Dataset" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import datasets\n", "digits = datasets.load_digits()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "digits.data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "digits.target" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "digits.images[0]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Learning and Predicting" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import svm\n", "clf = svm.SVC(gamma=0.001, C=100.)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "clf.fit(digits.data[:-1], digits.target[:-1])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "clf.predict(digits.data[-1])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(2, 2))\n", "plt.imshow(digits.images[-1], interpolation='nearest', cmap=plt.cm.binary)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print(digits.target[-1])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

pfschus/fission_bicorrelation

methods/singles_n_sum.ipynb

1

225595

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Goal: Calculate singles sum $S_i, S_j$\n", "\n", "Patricia Schuster \n", "University of Michigan \n", "January 11, 2018 \n", "\n", "I built the singles histogram in the other methods notebook, `singles_histogram.ipynb`. Now I am going to calculate the sum on that histogram. \n", "\n", "Update: July 17, 2018. I am going to redo this method using the energy histograms.\n", "\n", "The reason we care about the singles rates:\n", "\n", "In order to correct for differences in detection efficiencies and solid angles, we will divide all of the doubles rates by the singles rates of the two detectors as follows:\n", "\n", "$ W_{i,j} = \\frac{D_{i,j}}{S_i*S_j}$\n", "\n", "I can calculate $S_i$ and $S_j$ from the singles histogram. I will write a general function to calculate the histogram, and also demonstrate how to store those sums in a pandas dataframe. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors\n", "import numpy as np\n", "import os\n", "import scipy.io as sio\n", "import sys\n", "import pandas as pd\n", "from tqdm import *\n", "\n", "# Plot entire array\n", "np.set_printoptions(threshold=np.nan)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "sns.set_style(style='white')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "sys.path.append('../scripts/')\n", "import bicorr as bicorr\n", "import bicorr_plot as bicorr_plot\n", "import bicorr_e as bicorr_e\n", "import bicorr_sums as bicorr_sums" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "det_df = bicorr.load_det_df()\n", "chList, fcList, detList, num_dets, num_det_pairs = bicorr.build_ch_lists()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (new method) ENERGY: Load `singles_histogram_e_n` data" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "bicorr_e.load_singles_hist_both?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 288x216 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 576x396 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "singles_hist_e_n, e_bin_edges, dict_det_to_index, dict_index_to_det = bicorr_e.load_singles_hist_both(filepath = r'../analysis/Cf072115_to_Cf072215b/datap', plot_flag=True, show_flag = True)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(45, 600)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "singles_hist_e_n.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate sum over a given energy range\n", "\n", "What goes in\n", "\n", "* `e_bin_edges`\n", "* `singles_hist_e_n`" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "e_min = 0.62\n", "e_max = 15" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 4\n" ] } ], "source": [ "i_min = np.digitize(e_min,e_bin_edges)-1\n", "i_max = np.digitize(e_max,e_bin_edges)-1\n", "print(i_min, i_max)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e_bin_edges[i_max]" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.1" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e_bin_edges[np.digitize(e_max,e_bin_edges)-1]" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 3693], dtype=uint64)" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "singles_hist_e_n[0,i_min:i_max]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 0, 3693, 13487, 13388, 11530, 10258, 8998, 7739,\n", " 6839], dtype=uint64)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "singles_hist_e_n[0,0:10]" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(17180, 131.07249902248756, [0.025, 0.1])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bicorr_sums.calc_n_sum_e(singles_hist_e_n, e_bin_edges, 0, e_min, e_max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize sums dataframe\n", "\n", "The method for this is described below." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ch</th>\n", " <th>Se</th>\n", " <th>Se_err</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ch Se Se_err\n", "1 2 NaN NaN\n", "2 3 NaN NaN\n", "3 4 NaN NaN\n", "4 5 NaN NaN\n", "5 6 NaN NaN" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "singles_e_df = bicorr_sums.init_singles_e_df(dict_index_to_det)\n", "singles_e_df.head()" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ch</th>\n", " <th>Se</th>\n", " <th>Se_err</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>5234051.0</td>\n", " <td>2287.804843</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>5090368.0</td>\n", " <td>2256.184390</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>5458130.0</td>\n", " <td>2336.264112</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>5773565.0</td>\n", " <td>2402.824380</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>5499777.0</td>\n", " <td>2345.160336</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ch Se Se_err\n", "1 2 5234051.0 2287.804843\n", "2 3 5090368.0 2256.184390\n", "3 4 5458130.0 2336.264112\n", "4 5 5773565.0 2402.824380\n", "5 6 5499777.0 2345.160336" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "singles_e_df = bicorr_sums.fill_singles_e_df(dict_index_to_det, singles_hist_e_n, e_bin_edges, e_min, e_max)\n", "singles_e_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate for each detector." ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 576x396 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.errorbar(singles_e_df['ch'],\n", " singles_e_df['Se'],\n", " yerr=singles_e_df['Se_err'], fmt='.')\n", "plt.xlabel('Detector channel')\n", "plt.ylabel('Singles count rate')\n", "plt.title('Singles count rate for each detector (errorbars shown)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (old method) TIME: Load `singles_histogram` data\n", "\n", "I'm going to load it from the combined data sets Cf072115 - Cf072215b. The dimensions of `singles_hist` are:\n", "\n", " Dimension 0: particle type, 0=n, 1=g\n", " Dimension 1: detector channel\n", " Dimension 2: dt bin" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0xbf82ef0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAFECAYAAADiJ9P2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTAghhCiNkgkhhBCl/X9UR1Puqm3dSAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb7d4470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "singles_hist, dt_bin_edges_sh, dict_det_to_index, dict_index_to_det = bicorr.load_singles_hist(filepath=r'../analysis/Cf072115_to_Cf072215b/datap/',\n", " plot_flag = True, show_flag =True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert energy to time" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20.9260185521 92.0622074865\n" ] } ], "source": [ "emin = 0.62\n", "emax = 12\n", "\n", "tmin = bicorr.convert_energy_to_time(emax)\n", "tmax = bicorr.convert_energy_to_time(emin)\n", "\n", "print(tmin,tmax)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 297.75, 298. , 298.25, 298.5 , 298.75, 299. , 299.25,\n", " 299.5 , 299.75, 300. ])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt_bin_edges_sh[-10:]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1284 1569\n" ] } ], "source": [ "i_min = np.min(np.argwhere(tmin<dt_bin_edges_sh))\n", "i_max = np.min(np.argwhere(tmax<dt_bin_edges_sh))\n", "\n", "print(i_min,i_max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I also need to find the time ranges for the negative sum." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "832 1117\n" ] } ], "source": [ "i_min_neg = np.min(np.argwhere(-tmax<dt_bin_edges_sh))\n", "i_max_neg = np.min(np.argwhere(-tmin<dt_bin_edges_sh))\n", "\n", "print(i_min_neg,i_max_neg)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAAH9CAYAAAD74aE/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0xd87b128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dt_bin_centers = (dt_bin_edges_sh[:-1]+dt_bin_edges_sh[1:])/2\n", "plt.plot(dt_bin_centers,np.sum(singles_hist[0,:,:],axis=(0)))\n", "plt.plot(dt_bin_centers,np.sum(singles_hist[1,:,:],axis=(0)))\n", "plt.axvline(dt_bin_edges_sh[i_min],color='r',linestyle='--')\n", "plt.axvline(dt_bin_edges_sh[i_max],color='r',linestyle='--')\n", "plt.axvline(dt_bin_edges_sh[i_min_neg],color='r',linestyle='--')\n", "plt.axvline(dt_bin_edges_sh[i_max_neg],color='r',linestyle='--')\n", "plt.xlabel('Time (ns)')\n", "plt.ylabel('Number of events')\n", "plt.title('Singles TOF distribution, all channels')\n", "plt.legend(['N','G'])\n", "plt.yscale('log')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sum over indices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this calculation, I am only working with neutron events, so selected 0 in the first dimension of `singles_hist`.\n", "\n", "For now, I will use all detector pairs. Start with the positive sum." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "223822908" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(singles_hist[[0],:,i_min:i_max])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the negative sum" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3370853" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(singles_hist[[0],:,i_min_neg:i_max_neg])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For now I am going to limit this to a single detector pair, since at the moment I only envision occasions where I have to calculate the sum for a specific pair and therefore the sums for each detector. " ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dict_det_to_index[4]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4841883\n", "5066259\n" ] } ], "source": [ "ch_1 = 10\n", "ch_2 = 11\n", "\n", "print(np.sum(singles_hist[[0],dict_det_to_index[ch_1],i_min:i_max]))\n", "print(np.sum(singles_hist[[0],dict_det_to_index[ch_2],i_min:i_max]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functionalize it\n", "\n", "I am going to perform the background subtraction and functionalize this to `bicorr.calc_n_sum_br`. " ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function calc_n_sum_br in module bicorr:\n", "\n", "calc_n_sum_br(singles_hist, dt_bin_edges_sh, det_i, emin=0.62, emax=12)\n", " Calculate background-subtracted number of neutron events within a given time range in the singles histogram. Analogous to calc_nn_sum and calc_nn_sum_br for singles events.\n", " \n", " NOTE: I AM NOT NORMALIZING THIS. PLAN ACCORDINGLY WHEN USING TOGETHER WITH CALC_NN_SUM\n", " \n", " Parameters\n", " ----------\n", " singles_hist : ndarray\n", " Histogram of singles timing information\n", " Dimension 0: particle type, 0=n, 1=g\n", " Dimension 1: detector channel\n", " Dimension 2: dt bin \n", " dt_bin_edges_sh : ndarray\n", " Singles time bin edges array\n", " det_i : int\n", " Index of detector in singles_hist. Use dict_det_to_index from `load_singles_hist`\n", " emin : float\n", " Lower energy boundary for neutron event selection in MeV\n", " Default 0.62 MeV ~ 70 keVee\n", " emax : float, optional\n", " Upper energy boundary for neutron event selection in MeV \n", " \n", " Returns\n", " -------\n", " n_sum_br : int\n", " Background-subtracted counts.\n", " n_sum_br_err : int\n", " 1-sigma error in background-subtracted counts\n", "\n" ] } ], "source": [ "help(bicorr.calc_n_sum_br)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5073249, 2286.2827909075463)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bicorr.calc_n_sum_br(singles_hist, dt_bin_edges_sh, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loop through all of the detectors and calculate it. Populate an array with these values" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "singles_rates = np.zeros((num_dets,2))\n", "# Dimension 0: Detector index\n", "# Dimension 1: 0- n_sum_br\n", "# 1- n_sum_br_err" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "for det in detList:\n", " det_i = dict_det_to_index[det]\n", " singles_rates[det_i,:] = bicorr.calc_n_sum_br(singles_hist, dt_bin_edges_sh, det_i)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAH9CAYAAAApsatnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyj+LAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xc714978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.errorbar(detList, singles_rates[:,0], yerr=singles_rates[:,1], fmt='.')\n", "plt.xlabel('Detector channel')\n", "plt.ylabel('Singles count rate')\n", "plt.title('Singles count rate for each detector (errorbars shown)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Store in pandas dataframe, `singles_df`\n", "\n", "Follow the same method as in `nn_sum_and_br_subtraction.ipynb`. I am going to store the results in a pandas dataframe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Columns:\n", "\n", "* Channel number\n", "* `Sp` - Singles counts, positive\n", "* `Sn` - Singles counts, negative\n", "* `Sd` - Singles counts, br-subtracted\n", "* `Sd_err` - Singles counts, br-subtracted, err" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "singles_df = pd.DataFrame.from_dict(dict_index_to_det,orient='index',dtype=np.int8).rename(columns={0:'ch'})\n", "chIgnore = [1,17,33]\n", "singles_df = singles_df[~singles_df['ch'].isin(chIgnore)].copy()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "singles_df['Sp']= 0.0\n", "singles_df['Sn']= 0.0\n", "singles_df['Sd']= 0.0\n", "singles_df['Sd_err'] = 0.0" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ch</th>\n", " <th>Sp</th>\n", " <th>Sn</th>\n", " <th>Sd</th>\n", " <th>Sd_err</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ch Sp Sn Sd Sd_err\n", "1 2 0.0 0.0 0.0 0.0\n", "2 3 0.0 0.0 0.0 0.0\n", "3 4 0.0 0.0 0.0 0.0\n", "4 5 0.0 0.0 0.0 0.0\n", "5 6 0.0 0.0 0.0 0.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "singles_df.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "for index in singles_df.index.values:\n", " Sp, Sn, Sd, Sd_err = bicorr.calc_n_sum_br(singles_hist, dt_bin_edges_sh, index, emin=emin, emax=emax)\n", " singles_df.loc[index,'Sp'] = Sp\n", " singles_df.loc[index,'Sn'] = Sn\n", " singles_df.loc[index,'Sd'] = Sd\n", " singles_df.loc[index,'Sd_err'] = Sd_err" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ch</th>\n", " <th>Sp</th>\n", " <th>Sn</th>\n", " <th>Sd</th>\n", " <th>Sd_err</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>5150169.0</td>\n", " <td>76920.0</td>\n", " <td>5073249.0</td>\n", " <td>2286.282791</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>5024494.0</td>\n", " <td>73426.0</td>\n", " <td>4951068.0</td>\n", " <td>2257.857391</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>5391714.0</td>\n", " <td>76594.0</td>\n", " <td>5315120.0</td>\n", " <td>2338.441361</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>5717925.0</td>\n", " <td>81136.0</td>\n", " <td>5636789.0</td>\n", " <td>2408.123959</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>5448282.0</td>\n", " <td>71482.0</td>\n", " <td>5376800.0</td>\n", " <td>2349.417800</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ch Sp Sn Sd Sd_err\n", "1 2 5150169.0 76920.0 5073249.0 2286.282791\n", "2 3 5024494.0 73426.0 4951068.0 2257.857391\n", "3 4 5391714.0 76594.0 5315120.0 2338.441361\n", "4 5 5717925.0 81136.0 5636789.0 2408.123959\n", "5 6 5448282.0 71482.0 5376800.0 2349.417800" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "singles_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use the plotting functions I developed. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0xb85cb70>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEcCAYAAAAvJLSTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnPwZY8wB/Q9+afaXZpIv7QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xc34f2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bicorr_plot.Sd_vs_angle_all(singles_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I was going to save this to disk, but actually, the singles count rate depends on the timing windows so I need to provide the timing windows. Instead, I'll call the previous loop with the energy windows I desire. " ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:Anaconda3]", "language": "python", "name": "conda-env-Anaconda3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.5" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

justttry/justttry.github.io

_tmp/2017-06-13-HIVE安装.ipynb

2

14229

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.安装JAVA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (a)安装过程省略\n", "过程参考<a href=\"https://www.tutorialspoint.com/hive/hive_installation.htm\">这里</a>, 版本略旧。\n", "\n", "## (b)查看安装版本\n", "```shell\n", "$ java –version\n", "\n", "java version \"1.8.0_131\"\n", "Java(TM) SE Runtime Environment (build 1.8.0_131-b11)\n", "Java HotSpot(TM) 64-Bit Server VM (build 25.131-b11, mixed mode\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.安装HADOOP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (a)安装过程省略\n", "\n", "## (b)查看安装版本\n", "```shell\n", "$ hadoop version\n", "\n", "Hadoop 2.8.0\n", "Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r 91f2b7a13d1e97be65db92ddabc627cc29ac0009\n", "Compiled by jdu on 2017-03-17T04:12Z\n", "Compiled with protoc 2.5.0\n", "From source with checksum 60125541c2b3e266cbf3becc5bda666\n", "This command was run using /home/ubuntu/Download/hadoop-2.8.0/share/hadoop/common/hadoop-common-2.8.0.jar\n", "```\n", "\n", "## (c) 启动YARN集群\n", "```shell\n", "$ $HADOOP_HOME/sbin/start-dfs.sh\n", "$ $HADOOP_HOME/sbin/start-yarn.sh\n", "$ $HADOOP_HOME/sbin/start-all.sh\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.安装SPARK" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (a).下载SPARK源码，省略" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (b).编译" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```shell\n", "$ cd spark-2.1.1/\n", "$ ./build/mvn -Phadoop-provided -Pyarn -Phadoop-2.7 -Phive -Phive-thriftserver -DskipTests clean package\n", "\n", "[INFO] Reactor Summary:\n", "[INFO] \n", "[INFO] Spark Project Parent POM ........................... SUCCESS [ 13.663 s]\n", "[INFO] Spark Project Tags ................................. SUCCESS [ 17.955 s]\n", "[INFO] Spark Project Sketch ............................... SUCCESS [ 6.808 s]\n", "[INFO] Spark Project Networking ........................... SUCCESS [ 11.276 s]\n", "[INFO] Spark Project Shuffle Streaming Service ............ SUCCESS [ 5.935 s]\n", "[INFO] Spark Project Unsafe ............................... SUCCESS [ 9.713 s]\n", "[INFO] Spark Project Launcher ............................. SUCCESS [ 14.214 s]\n", "[INFO] Spark Project Core ................................. SUCCESS [02:28 min]\n", "[INFO] Spark Project ML Local Library ..................... SUCCESS [ 11.149 s]\n", "[INFO] Spark Project GraphX ............................... SUCCESS [ 13.799 s]\n", "[INFO] Spark Project Streaming ............................ SUCCESS [ 32.115 s]\n", "[INFO] Spark Project Catalyst ............................. SUCCESS [01:20 min]\n", "[INFO] Spark Project SQL .................................. SUCCESS [01:45 min]\n", "[INFO] Spark Project ML Library ........................... SUCCESS [01:15 min]\n", "[INFO] Spark Project Tools ................................ SUCCESS [ 1.924 s]\n", "[INFO] Spark Project Hive ................................. SUCCESS [01:00 min]\n", "[INFO] Spark Project REPL ................................. SUCCESS [ 5.636 s]\n", "[INFO] Spark Project YARN Shuffle Service ................. SUCCESS [ 8.612 s]\n", "[INFO] Spark Project YARN ................................. SUCCESS [ 15.541 s]\n", "[INFO] Spark Project Hive Thrift Server ................... SUCCESS [ 29.356 s]\n", "[INFO] Spark Project Assembly ............................. SUCCESS [ 10.835 s]\n", "[INFO] Spark Project External Flume Sink .................. SUCCESS [ 7.775 s]\n", "[INFO] Spark Project External Flume ....................... SUCCESS [ 9.472 s]\n", "[INFO] Spark Project External Flume Assembly .............. SUCCESS [ 2.272 s]\n", "[INFO] Spark Integration for Kafka 0.8 .................... SUCCESS [ 23.306 s]\n", "[INFO] Spark Project Examples ............................. SUCCESS [ 18.441 s]\n", "[INFO] Spark Project External Kafka Assembly .............. SUCCESS [ 4.008 s]\n", "[INFO] Spark Integration for Kafka 0.10 ................... SUCCESS [ 11.553 s]\n", "[INFO] Spark Integration for Kafka 0.10 Assembly .......... SUCCESS [ 3.748 s]\n", "[INFO] Kafka 0.10 Source for Structured Streaming ......... SUCCESS [ 9.846 s]\n", "[INFO] Spark Project Java 8 Tests ......................... SUCCESS [ 5.179 s]\n", "[INFO] ------------------------------------------------------------------------\n", "[INFO] BUILD SUCCESS\n", "[INFO] ------------------------------------------------------------------------\n", "[INFO] Total time: 12:59 min\n", "[INFO] Finished at: 2017-06-13T06:11:22+00:00\n", "[INFO] Final Memory: 96M/1352M\n", "[INFO] ------------------------------------------------------------------------\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.安装HIVE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (a).安装HIVE\n", "```shell\n", "$ cd ~\n", "$ wget https://archive.apache.org/dist/hive/hive-1.2.1/apache-hive-1.2.1-bin.tar.gz\n", "$ tar zxvf apache-hive-1.2.1-bin.tar.gz\n", "$ ls\n", "apache-hive-1.2.1-bin apache-hive-1.2.1-bin.tar.gz\n", "$ sudo mv apache-hive-1.2.1-bin /usr/local/hive\n", "```\n", "<span style=\"color:red\">注意版本，根据SPARK的版本选择HIVE版本，否则会出错。</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (b).配置HIVE环境\n", "```shell\n", "$ cd ~\n", "$ vi .bashrc\n", "\n", "export HIVE_HOME=/usr/local/hive\n", "export PATH=$PATH:$HIVE_HOME/bin\n", "export CLASSPATH=$CLASSPATH:/usr/local/Hadoop/lib/*:.\n", "export CLASSPATH=$CLASSPATH:/usr/local/hive/lib/*:.\n", "\n", "$ source .bashrc\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (c).配置HIVE\n", "```shell\n", "$ cd $HIVE_HOME/conf\n", "$ cp hive-env.sh.template hive-env.sh\n", "$ vi hive-env.sh\n", "\n", "export HADOOP_HOME=/usr/local/hadoop\n", "\n", "$ cp hive-default.xml.template hive-site.xml\n", "$ vi hive-site.xml\n", "```\n", "配置服务器端口,介绍在<a href=\"https://cwiki.apache.org/confluence/display/Hive/HiveDerbyServerMode\">这里</a>\n", "```shell\n", " <property>\n", " <name>javax.jdo.option.ConnectionURL</name>\n", " <value>jdbc:derby://localhost:1527/metastore_db;create=true </value>\n", " <description>JDBC connect string for a JDBC metastore </description>\n", " </property>\n", " <property>\n", " <name>javax.jdo.option.ConnectionDriverName</name>\n", " <value>org.apache.derby.jdbc.ClientDriver</value>\n", " <description>Driver class name for a JDBC metastore</description>\n", " </property>\n", "```\n", "在hive-site.xml配置的开始行添加下列代码，原因在<a href=\"https://stackoverflow.com/questions/28536340/hive-shell-not-opening-when-i-have-hive-site-xml\">这里</a>\n", "```shell\n", " <property>\n", " <name>system:java.io.tmpdir</name>\n", " <value>/tmp/hive/java</value>\n", " </property>\n", " <property>\n", " <name>system:user.name</name>\n", " <value>${user.name}</value>\n", " </property>\n", "```\n", "\n", "```shell\n", "$ vi jpox.properties\n", "\n", "javax.jdo.PersistenceManagerFactoryClass=org.jpox.PersistenceManagerFactoryImpl\n", "org.jpox.autoCreateSchema=false\n", "org.jpox.validateTables=false\n", "org.jpox.validateColumns=false\n", "org.jpox.validateConstraints=false\n", "org.jpox.storeManagerType=rdbms\n", "org.jpox.autoCreateSchema=true\n", "org.jpox.autoStartMechanismMode=checked\n", "org.jpox.transactionIsolation=read_committed\n", "javax.jdo.option.DetachAllOnCommit=true\n", "javax.jdo.option.NontransactionalRead=true\n", "javax.jdo.option.ConnectionDriverName=org.apache.derby.jdbc.ClientDriver\n", "javax.jdo.option.ConnectionURL=jdbc:derby://localhost:1527/metastore_db;create=true\n", "javax.jdo.option.ConnectionUserName=APP\n", "javax.jdo.option.ConnectionPassword=mine\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5.安装DERBY" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (a).安装DERBY\n", "```shell\n", "$ cd ~\n", "$ wget http://archive.apache.org/dist/db/derby/db-derby-10.10.2.0/db-derby-10.10.2.0-bin.tar.gz\n", "版本和HIVE保持一致\n", "$ tar zxvf db-derby-10.10.2.0-bin.tar.gz\n", "$ ls\n", "db-derby-10.10.2.0-bin db-derby-10.10.2.0-bin.tar.gz\n", "$ sudo mv db-derby-10.10.2.0-bin /usr/local/derby\n", "```\n", "\n", "## (b).配置DERBY环境\n", "```shell\n", "$ cd ~\n", "$ vi .bashrc\n", "export DERBY_HOME=/usr/local/derby\n", "export PATH=$PATH:$DERBY_HOME/bin\n", "export CLASSPATH=$CLASSPATH:$DERBY_HOME/lib/derby.jar:$DERBY_HOME/lib/derbytools.jar\n", "$ source .bashrc\n", "$ cp $DERBY_HOME/lib/derbyclient.jar $HIVE_HOME/lib/\n", "$ cp $DERBY_HOME/lib/derbytools.jar $HIVE_HOME/lib/\n", "$ mkdir $DERBY_HOME/data\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6.验证HIVE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (a).验证DERBY\n", "```shell\n", "$ java org.apache.derby.tools.sysinfo\n", "\n", "------------------ Java Information ------------------\n", "Java Version: 1.8.0_131\n", "Java Vendor: Oracle Corporation\n", "Java home: /usr/lib/jvm/java-8-oracle/jre\n", "Java classpath: :/usr/local/Hadoop/lib/*:.:/usr/local/hive/lib/calcite-core-1.2.0-incubating.jar::/usr/local/derby/lib/derbytools.jar\n", "OS name: Linux\n", "OS architecture: amd64\n", "OS version: 4.4.0-1018-aws\n", "Java user name: ubuntu\n", "Java user home: /home/ubuntu\n", "Java user dir: /home/ubuntu\n", "java.specification.name: Java Platform API Specification\n", "java.specification.version: 1.8\n", "java.runtime.version: 1.8.0_131-b11\n", "--------- Derby Information --------\n", "[/usr/local/hive/lib/derby-10.10.2.0.jar] 10.10.2.0 - (1582446)\n", "[/usr/local/hive/lib/derbytools.jar] 10.10.2.0 - (1582446)\n", "[/usr/local/hive/lib/derbyclient.jar] 10.10.2.0 - (1582446)\n", "[/usr/local/hive/lib/hive-jdbc-1.2.1-standalone.jar] 10.10.2.0 - (1582446)\n", "[/usr/local/derby/lib/derby.jar] 10.10.2.0 - (1582446)\n", "[/usr/local/derby/lib/derbytools.jar] 10.10.2.0 - (1582446)\n", "------------------------------------------------------\n", "----------------- Locale Information -----------------\n", "Current Locale : [English/United States [en_US]]\n", "Found support for locale: [cs]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [de_DE]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [es]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [fr]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [hu]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [it]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [ja_JP]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [ko_KR]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [pl]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [pt_BR]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [ru]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [zh_CN]\n", " version: 10.10.2.0 - (1582446)\n", "Found support for locale: [zh_TW]\n", " version: 10.10.2.0 - (1582446)\n", "------------------------------------------------------\n", "------------------------------------------------------\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (b).启动YARN集群\n", "```shell\n", "$ $HADOOP_HOME/sbin/start-dfs.sh\n", "$ $HADOOP_HOME/sbin/start-yarn.sh\n", "$ $HADOOP_HOME/sbin/start-all.sh\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (c).启动DERBY服务器\n", "```shell\n", "$ startNetworkServer\n", "Tue Jun 13 06:51:35 UTC 2017 : Security manager installed using the Basic server security policy.\n", "Tue Jun 13 06:51:35 UTC 2017 : Apache Derby Network Server - 10.10.2.0 - (1582446) started and ready to accept connections on port 1527\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (b).启动HIVE\n", "```shell\n", "$ hive\n", "Logging initialized using configuration in jar:file:/usr/local/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties\n", "hive>\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

kthouz/NYC_Green_Taxi

test.ipynb

1

3627

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========Introduction=========\n", "\n", "Use this code to predict the percentage tip expected after a trip in NYC green taxi. \n", "The code is a predictive model that was built and trained on top of the Gradient Boosting Classifer and the Random Forest Gradient both provided in scikit-learn\n", "\n", "The input: \n", "pandas.dataframe with columns:This should be in the same format as downloaded from the website\n", "\n", "The data frame go through the following pipeline:\n", "\t1. Cleaning\n", "\t2. Creation of derived variables\n", "\t3. Making predictions\n", "\n", "The output:\n", "\tpandas.Series, two files are saved on disk, submission.csv and cleaned_data.csv respectively.\n", "\n", "To make predictions, run 'tip_predictor.make_predictions(data)', where data is any 2015 raw dataframe fresh from http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml\n", "Run tip_predictor.read_me() for further instructions\n", "\n" ] } ], "source": [ "import tip_predictor\n", "import pandas as pd\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Download/load the September 2015 dataset\n", "if os.path.exists('data_september_2015.csv'): # Check if the dataset is present on local disk and load it\n", " data = pd.read_csv('data_september_2015.csv')\n", "else: # Download dataset if not available on disk\n", " url = \"https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2015-09.csv\"\n", " data = pd.read_csv(url)\n", " data.to_csv(url.split('/')[-1])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cleaning ...\n", "creating features ...\n", "predicting ...\n", "submissions and cleaned data saved as submission.csv and cleaned_data.csv respectively\n", "run evaluate_predictions() to compare them\n" ] } ], "source": [ "# make predictions \n", "#tip_predictor.make_predictions(data.tail(1000))\n", "\n", "# uncomment the next line to run the entire dataset\n", "tip_predictor.make_predictions(data)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean squared error: 10.0315724521\n", "r2 score: 0.888563908067\n" ] } ], "source": [ "# compare predictions to real percentage tips\n", "tip_predictor.evaluate_predictions()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

luwei0917/awsemmd_script

notebook/GlpG_paper/May_first.ipynb

1

489303

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "import random\n", "import time\n", "from random import seed, randint\n", "import argparse\n", "import platform\n", "from datetime import datetime\n", "import imp\n", "import numpy as np\n", "import fileinput\n", "from itertools import product\n", "import pandas as pd\n", "from scipy.interpolate import griddata\n", "from scipy.interpolate import interp2d\n", "import seaborn as sns\n", "from os import listdir\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy.interpolate import griddata\n", "import matplotlib as mpl\n", "sys.path.insert(0,'..')\n", "from notebookFunctions import *\n", "# from .. import notebookFunctions\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10,6.180) #golden ratio\n", "# %matplotlib notebook\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x10fa557b8>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9AAAAI4CAYAAACCzgGzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3X101OWd///XlTszBMlwpyEBVKoG\nBRLB+N1W9GcXtok9lMKqG29q71x/tnVbUtyTFrZbvlm2Ldac72rYb631dG21tiqHIkrntHHFdX+L\ntLtEsgSwxlW8gUmCgCYlYULurt8fk8EkTGCG5DPz+cw8H+dwxrznk0+uwIB5zXVd78tYawUAAAAA\nAM4sI9kDAAAAAADACwjQAAAAAADEgAANAAAAAEAMCNAAAAAAAMSAAA0AAAAAQAwI0AAAAAAAxIAA\nDQAAAABADAjQAAAAAADEgAANAAAAAEAMspI9ADe68cYb7e9+97tkDwMAAAAA3MIkewBuwAx0FEeP\nHk32EAAAAAAALkOABgAAAAAgBgRoAAAAAABiQIAGAAAAACAGBGgAAAAAAGJAgAYAAAAAIAYEaAAA\nAAAAYkCABgAAAAAgBgRoAAAAAABiQIAGAAAAACAGBGgAAAAAAGJAgAYAAAAAIAYEaAAAAAAAYuDK\nAG2MmWKMedYY02WMedcYc8co1/3WGNM55FePMWbvkOffMcaEhjz/QuK+CwAAAABAKslK9gBG8SNJ\nPZIulHSVpIAxZo+1dv/Qi6y1nx76sTHmZUkvjbjXcmvtiw6OFQAAAACQBlw3A22MyZN0s6TvWms7\nrbU7JD0v6fNn+byLJV0v6RdOjxEAAAAAkH5cF6AlXS6p31r7xpDaHknzzvJ5X5D0H9bat0fUf2mM\nOWKMecEYUzraJxtj7jHGNBhjGo4cOXJuIwcAAAAApCw3BuiJkjpG1DoknX+Wz/uCpJ+PqH1O0sWS\nLpL0b5LqjTH+aJ9srX3UWltmrS2bPn16vGMGAAAAAKQ4NwboTkmTRtQmSTo+2icYY66TVCBp89C6\ntfYVa23IWnvCWrtBUrvCy7wBAAAAAIiLGwP0G5KyjDGXDamVSto/yvWS9EVJW6y1nWe5t5Vkxjg+\nAAAAAEAacl2AttZ2Sdoiab0xJs8Ys1jSCo3SHMwY45P0VxqxfNsYM9sYs9gYk2OMyTXGVEuaJukV\nR78BAAAAAEBKcusxVvdKekzS+5KOSfqatXa/MeZ6Sb+11k4ccu1KhfdI/9uIe5wv6ceSPiapW9J/\nS/q0tfaY04MHgKG2NgZVW9+slvaQCv0+VVcUa+XComQPCwAAAHEy1tpkj8F1ysrKbENDQ7KHASAF\nbG0Mau2WvQr19p+q+bIzteGmBYRoAADgJWyFlQuXcANAKqmtbx4WniUp1Nuv2vrmJI0IAAAA54oA\nDQAOamkPxVUHAACAexGgAcBBhX5fXHUAAAC4FwEaABxUXVEsX3bmsJovO1PVFcVJGhEAAADOlVu7\ncANASog0CqMLNwAAgPcRoAHAYSsXFhGYAQAAUgBLuAEAAAAAiAEBGgAAAACAGBCgAQAAAACIAQEa\nAAAAAIAYEKABAAAAAIgBARoAAAAAgBhwjBUkSVsbg5xTCwAAAABnQICGtjYGtXbLXoV6+yVJwfaQ\n1m7ZK0mEaAAAAAAYxBJuqLa++VR4jgj19qu2vjlJIwIAAAAA9yFAQy3tobjqcIGmTdKD86Uaf/ix\naVOyRwQAAACkPAI0VOj3xVVHkjVtkratkjoOSrLhx22rCNEAAACAwwjQUHVFsXzZmcNqvuxMVVcU\nJ2lEOKPt66XeEasDekPhOgAAAADH0EQMpxqF0YXbIzoOxVcHXIBO/wAAIBUQoCEpHKL5YdYj8mcO\nLt+OUgdciE7/AAAgVbCEG/Capeuk7BH707N94TrgQnT6BwAAqYIADXhNSaW0fKOUP0uSCT8u3xiu\nAy5Ep38AAJAqWMINeFFJJYEZnlHo9ykYJSzT6R8AAHgNM9AAAEfR6R8AAKQKZqABAI6i0z8AAEgV\nBGgAgOPo9A8AAFIBS7gBAAAAAIgBARoAAAAAgBgQoAEAAAAAiAEBGgAAAACAGBCgAQAAAACIAQEa\ngLOaNkkPzpdq/OHHpk3JHhEAAABwTjjGCoBzmjZJ21ZJvaHwxx0Hwx9LUkll8sYFAAAAnANmoAE4\nZ/v6j8JzRG8oXAcAAAA8hhloj9naGFRtfbNa2kMq9PtUXVGslQuLkj0sILqOQ/HVAQAAABcjQHvI\n1sagdjz7sJ7R0yo876haTkzTQ8/eJuleQjTcKX9meNl2tDoAAADgMSzh9pD/Djyq9eZRzcw4qgwj\nzcw4qvXmUf134NFkDw2Ibuk6Kds3vJbtC9cBAAAAjyFAe8jdPU9qgukZVptgenR3z5NJGtFZ0H0Z\nJZXS8o1S/ixJJvy4fKOrG4gFDgRUvrlcJY+XqHxzuQIHAskeEgAAAFyCJdweUphxLK56UtF9GREl\nlZ75Mw8cCKhmZ426+7slSa1drarZWSNJWjZnWRJHBgAAADdgBtpDun0FcdWTiu7L8KC63XWnwnNE\nd3+36nbXJWlEAAAAcBMCtIdM+PR69WXmDqv1ZeZqwqddGEo7DimQN0HlMwtVcvEslc8sVCBvAt2X\n4WptXW1x1QEAAJBeCNBeUlKprBX/PGw/adaKf3bl8tjA9JmqmTZFrdlZssaoNTtLNdOmKDCd7svp\nxkt7igvyoq/mGK0OAACA9MIeaK/xyH7Susl+dfd2DKt1Z2SobnK+2Ek6doEDAdXtrlNbV5sK8gpU\ntajKlXt0vbanuGpR1bDxSlJuZq6qFlUlcVQAAABwC2ag4Yi23j/FVUfsIqG0tatVVvZUKHXjzK7X\n9hQvm7NMNdfWaEbeDBkZzciboZpra1wZ9gEAAJB4zEDDEQV5BWrtao1ax9icKZS6Leg5uqe4aVO4\nKV3HISl/Zvhs6XFYnbFszjLX/T4CAADAHZiBhiOqFlUpd0TDM5bCjg8vNbpybE9x5Ji0joOS7EfH\npHHW+LjY2hjU4vtf0iVrAlp8/0va2hhM9pAAAABcgQDtMV5pyMRSWOd4qdGVY2+kcEyaY7Y2BrV2\ny14F20OykoLtIa3dspcQDQAAIJZwe4rXGjKxFNYZXmp0FfnzH/eGZ6Mdh8YxaWNWW9+sUG//sFqo\nt1+19c1aubAoSaMCAABwB1cGaGPMFEn/Iqlc0lFJa621v4pyXY2k70g6OaRcYq09MPj8VYP3uULS\nHyX9tbX2v50dvXO8tPcVznEslMqZ7t6OvJGSP3Nw+XaUOsakpT0UVx0AACCduDJAS/qRpB5JF0q6\nSlLAGLPHWrs/yrXPWGvvHFk0xuRIek7SQ5IelvQVSc8ZYy6z1vY4N3TneGnvK5zlRCj11AqHpevC\ne56HLuPO9oXrbuRQwzMnFPp9CkYJy4V+XxJGAwAA4C6u2wNtjMmTdLOk71prO621OyQ9L+nzcd7q\nkwq/QfCQtfaktXajJCNpyXiON5G8tPdVohGR13jqyKmSSmn5Ril/liQTfly+0Z2h1GMNz6oriuXL\nzhxW82VnqrqiOEkjAgAAcA/XBWhJl0vqt9a+MaS2R9K8Ua5fboz5wBiz3xjztSH1eZKarLV2SK1p\ntPsYY+4xxjQYYxqOHDkylvE7xkudrWlE5D2eW+FQUimt3ifVtIcf3RieJc81PFu5sEgbblqgIr9P\nRlKR36cNNy1g/zMAAIDcuYR7oqSOEbUOSedHuXaTpEclHZb0Z5J+bYxpt9Y+Fed9ZK19dPBeKisr\ns9GuSTYn976ONxoROWtrY1C19c1qaQ+p0O9TdUXxmH9fObvbIR5seLZyYRF/TwEAAKJwY4DulDRp\nRG2SpOMjL7TWvjbkw53GmDpJt0h6Kp77eIlXOlvTiMg5kdn9yBsUkdl9SWMKPV7q7u0pXmx45qE9\n2wAAAInkxiXcb0jKMsZcNqRWKilaA7GRrML7nDV4fYkxxgx5viTG+2CMRms45OpGRE2bpAfnSzX+\n8KNL96ieaXZ/LDi72yFL14UbnA3l9oZnHtqzDQAAkEium4G21nYZY7ZIWm+MuVvhLtwrJF078lpj\nzApJ/5+kdknXSFol6e8Gn35ZUr+kVcaYRyT9v4P1lxz9BiAp3Iho6Cyp5PJGRJHQENmrGgkNkutm\n3pyc3ffKCgdPibx+vDKje6Y9224dMwAAQIK4LkAPulfSY5Lel3RM0testfuNMddL+q21duLgdbcN\nXneepEOSfmitfVySrLU9xpiVkn4q6X6Fz4Fe6dUjrBw3zks2I0uJx3ufrmM8FBo4ZsiDSipd9zoa\nlQf3bAMAACSKKwO0tfYDSSuj1P9D4eZgkY9vP8t9GiVdPe4DTDUOzb56qhGRh0KD52b34S1e3LMN\nAACQIG7cA41E89gxO44YLRy4MDRwzBBOcWLfvtf2bAMAACSQK2egkWAemn11zNJ1w2fhJVeHBk/N\n7jskcCDgiSPdHOPUvn2v7dkGAABIIAI0WLIpERo8JnAgMOzIrdauVtXsrJGk9AnRTu7b99KebQAA\ngAQiQMNzs6+OITR4Rt3uumHnVUtSd3+36nbXpU+AZuUIAABAwrEHGuHQuHyjlD9Lkgk/Lt9ImIRr\ntXW1xVVPSR7atw8AAJAqmIFGGLOv8JCCvAK1drVGracNVo4AAAAkHDPQADynalGVcjNzh9VyM3NV\ntagqSSNKAlaOAAAAJBwz0MCgrY1B1dY3q6U9pEK/T9UVxWnf6dqtIvuc07oLt8TKEQAAgAQz1tpk\nj8F1ysrKbENDQ7KHgQTa2hjU2i17FertP1XzZWdyvjIAAAAQZpI9ADdgCTcgqba+eVh4lqRQb79q\n65uTNCIgxTRtkh6cL9X4w49Nm5I9IgAAgLgRoCEpfK5u+eZylTxeovLN5QocCCR7SAnV0h6Kqw4g\nDk2bwg3POg5KsuHHbasI0eMk3f/9BgAgkQjQUOBAQDU7a9Ta1Sorq9auVtXsrEmrH8IK/b646gDi\nsH398G7hUvjj7euTM54Uwr/fAAAkFgEaqttdp+7+7mG17v5u1e2uS9KIEq+6oli+7MxhNV92pqor\nipM0IiCFdByKr46Y8e83AACJRYCG2rra4qqnopULi7ThpgUq8vtkJBX5fTQQA8ZL/sz46vFI873V\n/PsNAEBicYwVVJBXoNau1qj1dLJyYRGBGXDC0nXhPc9Dl3Fn+8L1sYjsrY7cN7K3Wkqb47349xsA\ngMRiBhqqWlSl3MzcYbXczFxVLapK0ogApJSSSmn5Ril/liQTfly+cewhl73V/PsNAECCMQMNLZuz\nTFJ4L11bV5sK8gpUtajqVB0Axqykcvxnhdlbzb/fAAAkmLHWJnsMrlNWVmYbGhqSPQwkWOBAgB9C\nAS95cP7g0Vgj5M+SVu9L/HgAAEhtJtkDcAOWcAPiKBjAk5auU2CSX+UzC1Vy8SyVzyxUYJJ/7Hur\nAQAARkGABsRRMIAXBSbmqWbaVLVmZ8kao9bsLNVMm6rAxLxkDw0AAKQoAjQgjoIBvKhud526be+w\nWrft5Y0vAADgGAI0oNGPfOEomDSU5ucKewlvfAEAgEQjQAPiKJiIrY1BLb7/JV2yJqDF97+krY3B\nZA8psSLnCncclGQ/Olc4zUJ04EBA5ZvLVfJ4ico3l7u2FwBvfAEAgEQjQAMKHwVTc22NZuTNkJHR\njLwZqrm2Jq26cG9tDGrtlr0KtodkJQXbQ1q7ZW96hWjOFfZUQz3e+AIAAInGMVZRcIwV0tHi+19S\nsD10Wr3I79Mra5YkYURJUOOXFO3fRCPVtCd6NElRvrlcrV2tp9Vn5M3QC7e8kIQRnRnHzwEAkDAc\nYyUpK9kDAOAOLe0hfTZjh76VtUmF5qha7DQ90Fepbe3XJXtoiZM/c5RzhWcmfixJ4rV9xcvmLCMw\nAwCAhGEJNwBJ0hcn/pfuz/6pZmYcVYaRZmYc1f3ZP9UXJ/5XsoeWOEvXSdm+4bVsX1qdK8y+YgAA\ngNERoAFIkr6V/YwmmJ5htQmmR9/KfiZJI0qCkkpp+UYpf5YkE35cvjFcTxPsKwYAABgdS7gBSJIm\nhKIv0R2tnrJKKtMqMI8UWQ6d9vuKmzaFm8d1HAov4V+6Lq1fF+Nla2NQtfXNamkPqdDvU3VFsVYu\nLEr2sAAAiBkBGkAY+38xKO33FUeOM4t0ZI8cZya5M0R7JOxHOv2HevslfdTpXxIhGgDgGSzhBhzk\nlfN0JbH/14M89fryEoeOM3PknHUPnV1eW998KjxHhHr7VVvfnKQRAQAQPwI04BAvnacrif2/HuO5\n15eXdByKrx4Dx85Z99DZ5S1Rjsk7Ux0AADciQAMOqdtdp+7+7mG17v5u1e2uS9KIYlBSKa3eFz7z\nePU+wrOLefL15RWjbVsYw3YGx2ZfHQj7Tin0++KqAwDgRgRowCFeO08X3sLry0EObGdwbPbVgbDv\nlOqKYt2Ss1M7clbpwHl3aEfOKt2Ss1PVFcXJHlpKYEsHACQGARpwCOfpwkm8vhzkwHYGx2ZfPdS7\nYGXmK1HPml+Z+Uqyh+Z5bOkAgMQhQAMO4TxdOInXl8PGeTtDdUWxfNmZw2q+7Myxz76WVGrXgn9Q\nm6ZrwBq1abp2LfgHd26/2L5eWSO2HWT1d4/Pfu2mTdKD86Uaf/jRhU3UnMSWDgBIHI6xAhzCebpw\nEq8vb4kc0zTeZyBvbQxq7a6LFOr9KCj5dmVqw6zgmO8dOBAY39eXU/u1vXbsmAPY0gEAiWOstcke\ng+uUlZXZhoaGZA8DAIAzWnz/SwpG2Udd5PfplTVLzvm+kSXBQ2c1czNzVXNtzbmH6Afnj3LW/Kzw\nLP+5cuq+HlK+uVytXa2n1WfkzdALt7yQhBEBSFEm2QNwA5ZwAwDgUU41J3NkSbBT+7U91IncKWzp\nAIDEIUADAOBRTjUna4sym3mmekycOmveQ53InbJszjLVXFujGXkzZGQ0I2/G2FYLAABGxR5oAAA8\nqrqiWGu37B12xvR4NCcr6LdqzTx9pV5B/xi3fZVUjv++5KXrFHixWnWTJqgtK1MFff2q+tMJLXNh\nJ3InLZuzjMAMAAlAgIbnjHtjGwCO4++tM5xqTrbq2Af6h2mT1Z3x0UK13IEBrTr24Zju64TAxDzV\nTJuqbtsrSWrNzlLNtKnSxDyl0ytsa2Nw3F8HAIDT0UQsCpqIuZcjjW0AOIq/t97TVnOpXs3rUt1k\n/0ezuh+26+quPBXUvHnO93XijRQaaA12Y4+yEmHDTQsI0QDGE03ExB5oeAxnXQLe48W/t4EDAZVv\nLlfJ4yUq31yuwIFAsoeUUBt6/kp/3tmnFw61qOmdg3rhUIv+vLNPG3r+6pzvGXkjpbWrVVZWrV2t\nqtlZM+bfW45wCq9AGBqeJSnU26/a+uYkjQgAUhdLuOEp/KAEeI/X/t6OnDGPBD1JaTNj3jDpU1rz\nJ+lbWZtUaI6pxU7VA32VenXSp875nmd6I2Usv68FeQVRZ6AL8grO+Z5e41Q3dq9hGTucxOsLEcxA\nw1NG+4EonX5QArzGa39vvThjPt6qK4r1r5k36LqejZpz8pe6rmej/jXzhjE1J3PqjRSOcHKuG7uX\nRJaxB9tDspKC7SGt3bJXWxuDyR4aUgCvLwxFgIan8IMS4D1O/r3d2hjU4vtf0iVrAlp8/0vj8sOM\n12bMnbByYZE23LRARX6fjKQiv2/M+2mdeiOFI5zCb3j4sjOH1cajG7uXsIwdTuL1haFYwg1PifxA\nRDdfwDuc+ns7snFSZEZA0piDXrovCZbCv4fjuTyxalFV1GZy4/FGSrof4eRUN3YvYRk7nMTrC0O5\nMkAbY6ZI+hdJ5ZKOSlprrf1VlOuqJX1R0kWD1z1sra0d8vw7ki6UFHnLaKe1ttzZ0cNp6f6DEuBF\nTvy9PdOMwFiCg5NBL53xBqizxvsND68p9PsUjBJm0mkZO5zD6wtDuTJAS/qRpB6Fw+9VkgLGmD3W\n2v0jrjOSviCpSdLHJL1gjDlorX16yDXLrbUvJmLQAIDEcWpGgKDnHN4AhVOqK4qjHuWVTsvY4Rxe\nXxjKdQHaGJMn6WZJ8621nZJ2GGOel/R5SWuGXmutfWDIh83GmOckLZY0NEADAFKQkzMCBD3AW1Yu\nLFLRwd9o1u5aXWCP6H0zXQcXVeuahTcme2hIAWyTwFCuC9CSLpfUb619Y0htj6QbzvRJxhgj6XpJ\nPxnx1C+NMRmSGiVVW2v3jPL590i6R5Jmz559jkMHACQKMwIATmnapPff/IG+M3OC2rJmqaCvX1Vv\n/kBqmiyVVCZ7dEgB6b5NAh9xYxfuiZI6RtQ6JJ1/ls+rUfj7+dmQ2uckXazwHul/k1RvjPFH+2Rr\n7aPW2jJrbdn06dPPYdgAgERyolM0AG8K/Md61UyeqNbsLFlj1JqdpZrJExX4j/XJHhqAFOPGGehO\nSZNG1CZJOj7aJxhjvq7wXujrrbUnI3Vr7StDLttgjPmiwrPU28ZvuACAZGFGAIAk1Z3Xr+6M4T/W\ndmdkqO68PrEZA8B4cuMM9BuSsowxlw2plUoa2UBMkmSMuUvhvdFLrbWHznJvq3DjMQAAAKSItqzM\nuOoAcK5cNwNtre0yxmyRtN4Yc7fCXbhXSLp25LXGmM9J+oGkP7fWHhjx3GxJsyTtUviNgm9Imibp\nlZH3AQAAgHcV5PjV2jtyB2C4nk62NgZpdAU4zI0z0JJ0rySfpPclPSXpa9ba/caY640xnUOu+56k\nqZJ2GWM6B389Mvjc+ZJ+LOlDSUFJN0r6tLX2WMK+CwAAADiu6uNrlWuyh9VyTbaqPr42SSNKvK2N\nQa3dslfB9pCspGB7SGu37NXWxmCyhwakFGOtTfYYXKesrMw2NDQkexgAUkTgQIAzhQEHMesmqWmT\ntH291HFIyp8pLV2Xdt2n0/3f2sX3vxT1aL8iv0+vrFmShBEhBbEVVi5cwg0AqSRwIKCanTXq7u+W\nJLV2tapmZ40kpdUPdoBTIrNukePMIrNuktInRDdtkratknoHw1PHwfDHUlqF6HQ/v70lSng+Ux3A\nuXHrEm4ASAl1u+tOheeI7v5u1e2uS9KIgNRSW9887CxwSQr19qu2vjlJI0qC7es/Cs8RvaFwHWmj\n0O+Lqw7g3BCgAcBBbV1tcdUBxIdZN4WXbcdTR0qqriiWL3t413FfdqaqK4qTNCIgNRGgAcBBBXkF\ncdWRwpo2SQ/Ol2r84cemTckeUUpg1k3hPc/x1JGSVi4s0oabFqjI75NReO/zhpsWpM9WBiBBCNAA\n4KCqRVXKzcwdVsvNzFXVoqokjQhJEdmj2nFQkv1ojyohesyYdVO4YVj2iDcMsn3hOtLKyoVFemXN\nEr19/zK9smYJ4RlwAE3EAMBBkYY26dwZ1ovGvavzmfaoplGTJydE/lzSugt35DWU5l24ASAROMYq\nCo6xAoD0NbKrsxSe0RzTUsgav6Ro/781Uk37ud0TALyO49e8hmOsxBJuAACGcaSrM3tU4VGBAwGV\nby5XyeMlKt9crsCBQLKHhFTB1hZ4FAEaAIAhHOnqzB5VeFDkHPvWrlZZ2VPn2BOiMS44fg0eRYAG\nAGAIR7o6l1RKyzdK+bMkmfDj8o0sVYSrcY49HMXxa/AomogBADBEdUVx1D3QY+7qXFJJYIancI49\nHJU/c3D5dpQ64GLMQAMAMARnqcJJXtpTzDn2cBRbW+BRzEADADDCyoVFBGaM+3FmkT3FkWXRkT3F\nklx5tF3Voqph45U4xx7jiOPX4FEcYxUFx1gBAJDenDjOrHxzuVq7Wk+rz8iboRdueeGcx+qkwIEA\n59gDDhrvN+ocxjFWYgYaAADgNGc6zuxcf7j14p7iZXOWEZgBh4x8oy7YHtLaLXslyc0hOu2xBxoA\nAGAEJ44zY08xgKHO9EYd3IsADQAAMIITx5lVLapSbmbusBp7ioHx5aVGfU68UQfnsYQbAABghOqK\nYu149mF9U0+r0BxVi52mh3Sbrqu495zvGVkKzZ5iwBlea9RX6PcpGCUsj+WNOjiPJmJR0EQMAIA0\n17RJfc99Q1lDOlD3ZeYqa8U/0yUY7tW0Ka27WnutUZ8TzQodRhMxsYQbAADgdNvXDwvPksIfb1+f\npAEBZ9G0Sdq2Suo4KMmGH7etCtfThNca9a1cWKQNNy1Qkd8nI6nI73NzeMYglnDDe9L83VUAQAJ0\nHIqvDiTb9vVS74jlwL2hcD1Nfk4qyCuIOgPt5kZ9KxcWEZg9hhloeAvvrgIAEiF/Znx1INl404dG\nfUgIAjS85UzvrgIAMF6WrpOyRzTyyfaF64Ab8aaPls1ZppprazQjb4aMjGbkzVDNtTWubCAG72IJ\nN7yFd1cBeFTgQIDuy15SUqnAB3tVd+BZtWVIBQNS1Zy/1LI0WQoLD1q6Lrwqb+hEQxq+6bNszjL+\nbYWjmIGGt/DuKgAPihyt0trVKit76mgVN59Pmu4CBwKqOfQ7tWYaWWPUmmlUc+h3/JnBvUoqpeUb\npfxZkkz4cfnGtNn/DCQKARqesutj31DI5gyrhWyOdn3sG0ka0Vk0bZIenC/V+MOP7NUG0lLd7rpT\n55JGdPd3q253XZJGhLPhzwwRgQMBlW8uV8njJSrfXO7uN1FKKqXV+6Sa9vAj4RkYdwRoeMo3X7tM\n3+69W4cGpmnAGh0amKZv996tb752WbKHdrrBM0SHNjzre+4bhGggDXntaBXwZ4YwVo8AGIk90PCU\nlvaQgrpOz/dcN6xu2kOjfEbynPjtOk2Icoboid+u0wTeEQbSihePVkl3/JlBOvNKBPbZphf6WCCC\nGWh4SqHfF1c9mXJD0WcpRqsDSF0creI9/JlBYiUCwgIHAqrZ8d3hKxF2fJeVCGmKAA1Pqa4oli87\nc1jNl52p6oriJI1odC0DU+OqA0hdHK3iPfyZOcdLe4pHW3HASoTx4ZXXQt0fNqjb9g6rddte1f1h\nQ5JGhGRiCTc8ZeXCIklSbX2zWtpDKvT7VF1RfKruJj/NuVPf6n1YE0zPqdoJm6Of5typmuQNC0CS\ncLSK9/BnNv4ie4ojy6Ije4olufL3umpR1bDxSqxEGC9eei209bRLxkSvI+0QoOE5KxcWuTIwj3TV\nsnu07tk+fdM+rUJzTC12qh7gkbhIAAAgAElEQVTSbbpu2T3JHhoAAEnhtT3FkTGx93X8eem1UNDX\nr9bs02NTQV9/EkaDZCNAAw4Jh/x7dWv9UtfPlgMAkAhe3FPMSgRneOm1UHUyUzWZA+rO+Gj3a+7A\ngKpOZp7hs5CqCNCAg7wyWw4AQCLQ3RwRXnotLLt+nfRiteomTVBbVqYK+vpV9acTWvYXtWO/edMm\naft6qeOQlD9TWrqO87tdjiZiAAAASAi6myPCU6+Fkkot+4tavXA8U03vHNILxzPD4XmsQbdpk7Rt\nldRxUJINP25bFa7DtYy1NtljcJ2ysjLb0NCQ7GEAAACkHM7T9R6n/szS/rXw4PzB8DxC/ixp9b7E\nj+fsTu+kloYI0FEQoAEAAIDTu2VL4ZlijnUbBzV+SdGymJFqXNnhmwAtlnADAAAgkZo2hWfeavzh\nR5arutqZumVjjPJnxleHKxCgAQAAkBjs+fQcL3XL9pyl66Rs3/Bati9ch2sRoAEAABIl3Wdft6+X\nekPDa72hcB2uNFpXbDd2y/ackkpp+cbwnmeZ8OPyjXThdjmOsQIAAEiEyOxrJEBGZl+ltPmB2XYc\nirqJcrQ6kq9qUVXUPdCu7JbtRSWVafP3P1UwAw0AAJAIzL7qsKbFVUfyLZuzTDXX1mhG3gwZGc3I\nm0EDMaQ1AjQAAEAidByKr56CNvT8lU7YnGG1EzZHG3r+KkkjSi2BAwGVby5XyeMlKt9crsCBwLjc\nt7fjKnW9uUbH/7hBXW+uUW/HVeNyX8CLCNAAAACJQMddNUz6lNb03q1DA9M0YI0ODUzTmt671TDp\nU8kemudFjptq7WqVlVVrV6tqdtaMOURvbQxq7Za9CraHZCUF20Nau2WvtjYGx2fggMcQoAEAABKB\njruqrijWv2beoOt6NmrOyV/qup6N+tfMG1RdUZzsoXmeU8dN1dY3K9TbP6wW6u1XbX3zmO4LeBVN\nxAAAABIh0iho+/rwsu38meHwnEYNhFYuLJIUDmUt7SEV+n2qrig+Vce5c+q4qZb2UFx1INURoAEA\nABKFjrtaubCIwOyAgrwCtXa1Rq2PRaHfp2CUsFzo90W5Gkh9LOEGAAAAPK5qUZVyM3OH1cbjuKnq\nimL5sjOH1XzZmSy7R9pyZYA2xkwxxjxrjOkyxrxrjLljlOuMMeaHxphjg78eMMaYIc9fZYx51Rhz\nYvCRloEAAABIOU4dN7VyYZE23LRARX6fjKQiv08bblrAKgKkLWOtTfYYTmOMeUrhcP/Xkq6SFJB0\nrbV2/4jrviLpPklLJVlJ/yppo7X2EWNMjqT/kfSQpIclfUXS30q6zFrbc6avX1ZWZhsaGsb3mwIA\nAAAA7zJnvyT1uW4G2hiTJ+lmSd+11nZaa3dIel7S56Nc/kVJ/8dae8haG5T0fyR9afC5Tyq8x/sh\na+1Ja+1Ghf/Qlzj8LQAAAAAAUpDrArSkyyX1W2vfGFLbI2lelGvnDT4X7bp5kprs8Cn2plHuI2PM\nPcaYBmNMw5EjR8558AAAAACA1OTGAD1RUseIWoek82O4tkPSxMF90PHcR9baR621ZdbasunTp5/T\nwAEAAAAgVoEDAZVvLlfJ4yUq31yuwIFAsoeEs3DjMVadkiaNqE2SdDyGaydJ6rTWWmNMPPcBAAAA\ngIQJHAioZmeNuvu7JUmtXa2q2VkjSWNu/gbnuHEG+g1JWcaYy4bUSiXtj3Lt/sHnol23X1LJ0K7c\nkkpGuQ8AAAAAj9v1/E/UVnOpBv53vtpqLtWu53+S7CGNqm533anwHNHd36263XVJGhFi4boAba3t\nkrRF0npjTJ4xZrGkFZJ+EeXyJyTdZ4wpMsYUKtxl++eDz70sqV/SKmPMecaYrw/WX3Jy/AAAAMAZ\nNW2SHpwv1fjDj02bkj2ilLDr+Z9o/qt/rwIdUYaRCnRE81/9e9eG6LautrjqcAfXBehB90rySXpf\n0lOSvmat3W+MuX5waXbETyRtk7RX0j6Fj7v6iSQNHlW1UtIXJLVLukvSyrMdYQUAAAA4pmmTtG2V\n1HFQkg0/bltFiB4Hs3bXymeG/6jvMz2atbs2SSM6s4K8grjqcAdXBmhr7QfW2pXW2jxr7Wxr7a8G\n6/9hrZ045Dprrf2WtXbK4K9vDe26ba1ttNZeba31WWsXWWsbk/H9AAAAAJKk7eul3tDwWm8oXHcr\nj8yYX2Cjn6RzgT2a4JHEpmpRlXIzc4fVcjNzVbWoKkkjQizc2EQMAAAASE0dh+KrJ1tkxjwS+iMz\n5pJUUpm8cUXxvpmuAp0eot830+TGOd1Io7C63XVq62pTQV6BqhZV0UDM5QjQAAAAQKLkz1Sg75jq\nJvvVlpWpgr5+VX3YrmVZU5M9sujONGPusgB9cFG18l/9+2HLuEM2RwevrnZlgJbCIZrA7C2uXMIN\nAAAApKLAwr9UzbSpas3OkjVGrdlZqpk2VYGFf5nsoUXnoRnzaz77Fe27+ntq03QNWKM2Tde+q7+n\naz77lWQPDSmEGWgAAAAgQeqO/qe6M8ywWneGUd3R/5Qr5yHzZw42PItSd6FrPvsVaTAwFwz+AsYT\nM9AAAABAgnju6KKl66Rs3/Bati9cH6OtjUEtvv8lXbImoMX3v6StjcEx3xNwGgEaAAAASBDPHV1U\nUikt3yjlz5Jkwo/LN455//PWxqDWbtmrYHtIVlKwPaS1W/YSouF6LOEGAABAVFsbg6qtb1ZLe0iF\nfp+qK4q1cmFRsoflaVWLqlSzs0bd/d2naq4/uqikctwbhtXWNyvU2z+sFurtV219M68xuBoBGgAA\nAKeJzBBGQk5khlASAWcMOLoorKU9FFcdcAsCNAAAAE7DDKFzOLpIKvT7FIwSlgv9vihXA+7BHmgA\nAACchhlCOKm6oli+7MxhNV92pqoripM0IiA2BGgAAACcZrSZQGYIMR5WLizShpsWqMjvk5FU5Pdp\nw00LWN0A12MJNwAAAE5TXVE8bA+0xAwhxtfKhUUEZngOARoAAACniQQbunADwEcI0AAAAIiKGUIA\nGI490AAAAAAAxIAADQAAAABADAjQAAAAAADEgAANAAAAAEAMCNAAAAAAAMSAAA0AAAAAQAwI0AAA\nAAAAxIAADQAAAABADAjQAAAAAADEgAANAAAAAEAMspI9AAAAALhU0yZp+3qp45CUP1Nauk4qqUz2\nqDxva2NQtfXNamkPqdDvU3VFsVYuLEr2sADEgAANAACA0zVtkratknpD4Y87DoY/lgjRY7C1Mai1\nW/Yq1NsvSQq2h7R2y15JIkQDHsASbgAAAJxu+/qPwnNEbyhcxzmrrW8+FZ4jQr39qq1vTtKIAMSD\nAA0AAIDTdRyKr46YtLSH4qoDcBcCNAAAAE6XPzO+OmJS6PfFVQfgLgRoAAAAnG7pOil7RKjL9oXr\nOGfVFcXyZWcOq/myM1VdUZykEQGIB03EAAAAcLpIozC6cI+rSKMwunAD3mSstckeg+uUlZXZhoaG\nZA8DAAAAANzCJHsAbsASbgAAAAAAYkCABgAAAEbTtEl6cL5U4w8/Nm1K9ogAJBF7oAEAAIBomjZJ\n21Z9dB52x8HwxxJ7wYE0xQw0AAAAEM329R+F54jeULgOIC0RoAEAAIBoOg7FVweQ8gjQAAAAQDT5\nM+OrA0h5BGgAAAAgmqXrpGzf8Fq2L1wHkJYI0AAAAEA0JZXS8o1S/ixJJvy4fCMNxIA0RhduAAAA\nYDQllQRmAKcwAw0AAAAAQAwI0AAAAAAAxIAl3AAAAACAc/bqq69ekJWV9VNJ85U6k7QDkvb19fXd\nffXVV78fKRKgAQAAAADnLCsr66cFBQVXTJ8+/cOMjAyb7PGMh4GBAXPkyJEr29rafirps5E6ARoA\nAAAYxdbGoGrrm9XSHlKh36fqimKtXFiU7GEBbjM/lcKzJGVkZNjp06d3tLW1zR9aJ0ADAAAAUWxt\nDGrtlr0K9fZLkoLtIa3dsleSCNHAcBmpFJ4jBr+nYUvSU2V9OgAAADCuauubT4XniFBvv2rrm5M0\nIgDJ5roAbYyZYox51hjTZYx51xhzxxmurTbG7DPGHDfGvG2MqR7x/DvGmJAxpnPw1wvOfwcAAABI\nBS3tobjqAM5swoQJCyO/MjIyrs7NzV0U+fjHP/7xlGSPLxZnXcJtjJkt6aC1NlFT8j+S1CPpQklX\nSQoYY/ZYa/dHG56kL0hqkvQxSS8YYw5aa58ecs1ya+2LTg8aAAAAqaXQ71MwSlgu9PuSMBrA+06c\nONEY+e+ioqIFP/nJT975zGc+czyZY4pXLDPQb0uaLknGmJeMMX6nBmOMyZN0s6TvWms7rbU7JD0v\n6fPRrrfWPmCt3W2t7bPWNkt6TtJip8YHAACA9FFdUSxfduawmi87U9UVxUkaEZDaent7tXr16sKi\noqIFU6ZMKb3jjjtmnzhxwkjS5s2bJ11yySXzVq9eXZifn3/VzJkzF7z44ot5tbW106ZPn15y4YUX\nlvz617+eFLlXaWnp3Pvuu69w3rx5V5x//vlX3XjjjXOOHTuWOfpXj00sAfq4pGmD//1JSdlj/aJn\ncLmkfmvtG0NqeyTNO9snGmOMpOsljZyp/qUx5ogx5gVjTOkZPv8eY0yDMabhyJEj5zJ2AAAApJCV\nC4u04aYFKvL7ZCQV+X3acNMCGogBDvnud79b8Pvf/37if/3Xf/3xrbfe2hsMBnP+7u/+bkbk+YMH\nD+ZOmTKl7+jRo/+9YsWKDz7/+c/PefPNN3MPHjy492//9m9bv/nNb84eer+nn3566pNPPvn2oUOH\nmnp7e8299947c6xjjKUL94uSXjLG/HHw42eNMT3RLrTWLhnjeCZK6hhR65B0fgyfW6PwGwI/G1L7\nnKTdCi/1rpJUb4yZa61tH/nJ1tpHJT0qSWVlZSnXQQ4AAADxW7mwiMAMJMgvfvGL6U8++eRbRUVF\nfZK0du3atq997WsXPfTQQy2SNHHixP7vfOc772dkZOiOO+744OGHHy7YsGFDS25urv3yl7/8wbe/\n/e3ZHR0dGfn5+QOS9LnPfe7owoULuyXpBz/4QcsnP/nJuZLeHcsYYwnQn5d0l6RLJd0gqVnSiXP5\nYsaYlwfvEc0rkr4hadKI+iSFZ8HPdN+vK7wX+npr7clI3Vr7ypDLNhhjvqjwLPW2+EYOAAAAAHDK\nwMCADh8+nPPZz3728qH1zMyPVl1Pnjy5NyMjvIh6woQJNicnx06ZMmVAkvLy8gYkaWiAnj179qmJ\n30svvbSnu7s749ixY5lTp04d3l4/DrEE6OmSHrbWWmPMVZL+NtoMbiystZ880/ODe6CzjDGXWWv/\nZ7BcqtOXZQ/9nLskrZH0/1hrD51tCArPRgMAAAAAXCIjI0MXXHBBz69//es3P/GJT4xLq/v33nsv\nJ/Lfb731Vk5ubu7AWMKzFGcTMYUDqGOstV2Stkhab4zJM8YslrRC0i+iXW+M+ZykH0j6lLX2wIjn\nZhtjFhtjcowxuYNHXE1TeKYbAAAAAOAid95559E1a9YUvfPOO9mS9NZbb2U/++yzI1cox+xXv/rV\ntKampvM6OjoyvvOd7xR+5jOf+XCsY4y3idgNcraJmCTdK8kn6X1JT0n6WuQIK2PM9caYziHXfk/S\nVEm7hpz1/Mjgc+dL+rGkDyUFJd0o6dPW2mMOjx8AACCqrY1BLb7/JV2yJqDF97+krY3BZA8JAFzj\n+9//fuvHP/7xzuuvv7544sSJCz/1qU9d/sYbb5x3rve79dZbj91+++1zZs6cWWKM0cMPP3xwrGM0\nZzve2RizWdJ1kv6ocIDeqfA5zacZhyZirlBWVmYbGhqSPQwAAJBCtjYGtXbLXoV6P1o96MvOpKsz\nAK8YdSvsnj173iktLT2ayMGcTWlp6dy/+Zu/OXzPPfeMadZ5z54900pLSy+OfJzQJmIAAADpqra+\neVh4lqRQb79q65sJ0ADgEWcN0NbakKQfSdJYm4gBAACkq5b26D1xRqsDANwnlhnoU6y1f+7UQAAA\nAFJZod+nYJSwXOj3JWE0AJDa9uzZ87oT9z1rgDbGbJS01lrbNfjfo7LWrhq3kQEAAKSQ6oriqHug\nqyuKkzgqAEA8YpmBXqCPOm8vOMN1jh5xBQAA4GWRfc619c1qaQ+p0O9TdUUx+58BwENi2QP959H+\nGwAAAPFZubCIwAwAHhbLOdCSJGOMzxjzv40xTYPnLR83xuwxxvy9MYbNOwAAAACAlBZTgDbGZEl6\nSdLfSXpb0j8r3Jn7XUnrJL04eA0AAAAAAAlz4sQJs2DBgiuKi4uvvPTSS+etXr26UJJef/31nJKS\nkrkXXXTR/GXLls3p7u42khQKhcyyZcvmzJ49e35JScnc5ubmnFi/Vqwz0PcofA70ImvtCmvtWmvt\nGmvtZyUtknT54DUAAAAAACRMbm6u3bFjR3Nzc/Nr+/fvf2379u2Ttm/fnnfffffN/PrXv3743Xff\n3Zefn99XV1c3TZLq6uqm5efn97333nv7vv71rx++7777Zsb6tWIN0LdI+r61dv/IJ6y1+yRtGLwG\nAAAAAIBRPfmHd6f8r++/uOCSNYGr/9f3X1zw5B/enTKW+2VkZCg/P39Aknp6ekxfX58xxuj3v//9\n+V/+8pc/lKS77rrr2LZt2/yS9Jvf/MZ/1113HZOkL3/5yx/u3Lnz/IGBgdi+VoxjmqfwEu7RvChp\nfoz3AgAAAACkoSf/8O6Uf/zNaxe9f/xkjpX0/vGTOf/4m9cuGmuI7uvr09y5c6+88MILS2+44YY/\nXXHFFSfPP//8/uzs8IFSF198cc/hw4dzJOnw4cM5l1xySY8kZWdna+LEif2HDx+OaUtyrAF6sqQj\nZ3j+iCR/jPcCAAAAAKShjdv/p+hk38CwHHqybyBj4/b/GdMRBVlZWXr99ddfe++995p2796dt2fP\nntyR1xhjrCRZe/oJzJHnzibWAJ0pqe8Mzw8MXgMAAAAAQFRHjp+M2rBrtHq8pk2b1n/dddcdf+WV\nV/KOHz+e2dvbK0l65513ci644IJeSSooKOh5++23cySpt7dXnZ2dmRdccEF/LPePNUAbSU8aY56P\n9kvSE/F/awAAAACAdDL9/PN64qnHoqWlJevo0aOZktTZ2WlefvnlSVdeeWX3xz/+8eM/+9nPJkvS\nY489NvUzn/lMuyQtW7as/bHHHpsqST/72c8mf+ITnziekRFbNI716KnHY7iGEA0AAAAAGNWqpZcF\n//E3r100dBn3eVkZA6uWXhY813sePHgw+0tf+tIl/f39staaFStWfHD77bd3lJaWhm699daPfe97\n3yuaN2/eiaqqqqOSVFVVdfTmm2++ZPbs2fPz8/P7n3nmmbdi/Vom2vrvdFdWVmYbGhqSPQwAAAAA\ncAsz2hN79ux5p7S09GisN3ryD+9O2bj9f4qOHD+ZM/3883pWLb0seOfHL/pgfIY5vvbs2TOttLT0\n4sjHsc5AAwAAAAAwZnd+/KIP3BqYzybWPdAAAAAAAKQ1AjQAAAAAADEgQAMAAAAAEAMCNAAAAAAA\nMSBAAwAAAAAQAwI0AAAAAMCzTpw4YRYsWHBFcXHxlZdeeum81atXF0pSZWXlRcXFxVdefvnlV954\n441zOjo6MiRp48aNUydPnlw6d+7cK+fOnXvlP/3TP02L9WtxjBUAAAAAwLNyc3Ptjh07mvPz8wdO\nnjxprrnmmuLt27d3PPLIIwenTJkyIEl33333zB/+8IcX/OAHP2iTpOXLl3/4xBNPvBfv1yJAAwAA\nAAASZ9e/TNG//7BIne/naOIFPbrh20Fd89fnfC50RkaG8vPzBySpp6fH9PX1GWOMIuF5YGBAoVAo\nwxgz5qGzhBsAAAAAkBi7/mWK6tdepM7DOZKVOg/nqH7tRdr1L1PGctu+vj7NnTv3ygsvvLD0hhtu\n+NOSJUu6JOmWW265ePr06aVvvvlm7po1a96PXP/b3/7WH1na/eabb2bH+nUI0AAAAACAxPj3Hxap\n7+TwHNp3MkP//sOisdw2KytLr7/++mvvvfde0+7du/N27dqVK0mbN29+5/Dhw3suu+yy7scee2yy\nJFVWVra/9957e994443XlixZcvzOO++8JNavQ4AGAAAAACRG5/s5cdXjNG3atP7rrrvu+LZt2/Ij\ntaysLN1+++0fbN26dbIkFRQU9Pt8PitJ991335H9+/dPiPX+BGgAAAAAQGJMvKAnrnoMWlpaso4e\nPZopSZ2dnebll1+eNHfu3O59+/adJ4X3QD/33HP+yy67rFuS3n333VNLtn/1q1/558yZ0x3r16KJ\nGAAAAAAgMW74dlD1ay8atow767wB3fDt4Lne8uDBg9lf+tKXLunv75e11qxYseKDW2+9teOaa66Z\n29nZmWGtNVdcccWJn//85+9K0gMPPHBBfX29PzMz0/r9/r6f//zn78T6tQjQAAAAAIDEiHTbHscu\n3H/2Z38W+uMf//jayPru3btfj3b9j370o6CkcwrsBGgAAAAAQOJc89cfjCUwJxN7oAEAAAAAiAEB\nGgAAAACAGBCgAQAAAACIAQEaAAAAAIAYEKABAAAAAIgBARoAAAAA4FknTpwwCxYsuKK4uPjKSy+9\ndN7q1asLJenqq68unjt37pVz58698oILLij5i7/4i49J0pNPPum//PLLr5w7d+6V8+fPv6K+vn5i\nrF+LY6wAAAAAAJ6Vm5trd+zY0Zyfnz9w8uRJc8011xRv376949VXX22OXFNRUfGx5cuXt0vS8uXL\n/3THHXe0Z2Rk6D//8z99t91225y33357fyxfiwANAAAAAEiYZ5qfmfLInkeKjoWO5Uz1Te35aulX\ng7cW33rO50JnZGQoPz9/QJJ6enpMX1+fMcacev7DDz/M+P3vf3/+U0899bakU9dK0vHjxzOGXnvW\nr3WugwQAAAAAIB7PND8z5YFdD1x0NHQ0x8rqaOhozgO7HrjomeZnpozlvn19fZo7d+6VF154YekN\nN9zwpyVLlnRFnvvlL385+dprr/3TlClTTgXnJ554wn/JJZfMu/nmmy979NFH34n16xCgAQAAAAAJ\n8cieR4p6+nuG5dCe/p6MR/Y8UjSW+2ZlZen1119/7b333mvavXt33q5du3Ijz23atGnKbbfdNmyG\n+wtf+EL722+/vf/pp59+c926dTF/bQI0AAAAACAhjoWO5cRTj9e0adP6r7vuuuPbtm3Ll6S2trbM\npqamvMrKyo5o13/605/ufPfdd89rbW2NaXszARoAAAAAkBBTfVN74qnHoqWlJevo0aOZktTZ2Wle\nfvnlSVdccUW3JD3xxBNTlixZ0j5hwgQbuX7fvn3nDQyEV3Pv2LFjQm9vr7nwwgv7YvlaNBEDAAAA\nACTEV0u/Gnxg1wMXDV3GnZOZM/DV0q8Gz/WeBw8ezP7Sl750SX9/v6y1ZsWKFR/cfvvtHZK0efPm\nKd/61rdah17/1FNPTX7mmWemZmVl2dzc3IFf/OIXBzIyYptbNtbas1+VZsrKymxDQ0OyhwEAAAAA\nbjFqq+o9e/a8U1paejTWG413F24n7dmzZ1ppaenFkY+ZgQYAAAAAJMytxbd+4NbAfDbsgQYAAAAA\nIAauC9DGmCnGmGeNMV3GmHeNMXec4doaY0yvMaZzyK85Q56/yhjzqjHmxODjVYn5LgAAAAAAqcZ1\nAVrSjyT1SLpQ0uck/dgYM+8M1z9jrZ045NcBSTLG5Eh6TtKTkiZLelzSc4N1AAAAAMD4GBgYGBh1\nj7RXDX5PA0NrrgrQxpg8STdL+q61ttNau0PS85I+fw63+6TCe7wfstaetNZuVHjj+5LxGi8AAAAA\nQPuOHDmSn0ohemBgwBw5ciRf0r6hdbc1EbtcUr+19o0htT2SbjjD5yw3xnwgqVXS/7XW/niwPk9S\nkx3eZrxpsP67kTcxxtwj6R5Jmj179rl/BwAAAACQRvr6+u5ua2v7aVtb23y5bJJ2DAYk7evr67t7\naNFtAXqipI4RtQ5J549y/SZJj0o6LOnPJP3aGNNurX0q3ntZax8dvJfKyso42wsAAAAAYnD11Ve/\nL+mzyR5HIiT03QFjzMvGGDvKrx2SOiVNGvFpkyQdj3Y/a+1r1toWa22/tXanpDpJtww+Hde9AAAA\nAAA4k4QGaGvtJ621ZpRf10l6Q1KWMeayIZ9WKml/rF9CHx3wvV9SiTFm6Dr8kjjuBQAAAADAKa5a\nn26t7ZK0RdJ6Y0yeMWaxpBWSfhHtemPMCmPMZBP2vyStUrjztiS9LKlf0ipjzHnGmK8P1l9y9JsA\nAAAAAKQkVwXoQfdK8kl6X9JTkr5mrd0vScaY640xnUOuvU3Smwovy35C0g+ttY9LkrW2R9JKSV+Q\n1C7pLkkrB+sAAAAAAMTFDG9SDSncRKyhoSHZwwAAAAAAt0iZI6rGwo0z0AAAAAAAuA4BGgAAAACA\nGBCgAQAAAACIAQEaAAAAAIAYEKABAAAAAIgBARoAAAAAgBgQoAEAAAAAiAEBGgAAAACAGBCgAQAA\nAACIAQEaAAAAAIAYZCV7AADgJlsbg6qtb1ZLe0iFfp+qK4q1cmFRsocFAAAAFyBAA8CgrY1Brd2y\nV6HefklSsD2ktVv2ShIhGgAAACzh/v/bu/9guc7yPuDfJ5KCBbYjAyqJBcaFgkhgwG7U0gmQmEIq\nQ0qrxNCan4bQegpjGlJQwVMTKHYCQUwoAyHEKcYQmKRNUExJALktdls7KUTEMR4Hy+E3yHaRwTIW\nvtiu+vSP3UvWF8k6knW1u9LnM3PGu+97du+7fmaP7vee97wHYNGWbTu+H54XLdyzN1u27ZjSiAAA\nmCUCNMDYTbsXDqodAIBjiwANMHbymtUH1Q4AwLFFgAYY27xxfVavWnGvttWrVmTzxvVTGhEAALPE\nImIAY4sLhVmFGwCAfRGgASZsOn2dwAwAwD6Zwg0AAAADCNAAAAAwgAANAAAAAwjQAAAAMIBFxIBl\nddk1O61qDQDAUUGABjjAfZgAABSmSURBVJbNZdfszPlbr8vCPXuTJDt3L+T8rdcliRANAMDcMYUb\nWDZbtu34fnhetHDP3mzZtmNKIwIAgEMnQAPL5qbdCwfVDgAAs0yABpbNyWtWH1Q7AADMMgEaWDab\nN67P6lUr7tW2etWKbN64fkojAgCAQ2cRMWDZLC4UZhVuAACOBgI0sKw2nb5OYAYA4KhgCjcAAAAM\nIEADAADAAAI0AAAADCBAAwAAwAACNAAAAAwgQAMAAMAAAjQAAAAMIEADAADAAAI0AAAADCBAAwAA\nwAACNAAAAAwgQAMAAMAAAjQAAAAMIEADAADAAAI0AAAADCBAAwAAwAACNAAAAAwgQAMAAMAAMxeg\nq+rBVfVHVfXdqvpqVb3gPvb9RFXtmdjurqrrJvq/UlULE/2XH5lPAQAAwNFm5bQHsA+/meTuJA9L\nclqSP6mqa7v7+qU7dvezJp9X1ZVJPrVkt+d0939bprECAABwjJipM9BV9aAkZyV5Q3fv6e6rkvyX\nJC8e8NpTkzwtye8u5xgBAAA4Ns1UgE7y2CR7u/vGibZrkzx+wGtfkuR/dfeXl7R/uKp2VdXlVfWk\nwzVQAAAAji2zFqCPT3L7krbbk5ww4LUvSXLpkrYXJjk1ySOTXJFkW1Wt2deLq+rcqtpeVdt37dp1\nMGMGAADgGHBEA3RVXVlVvZ/tqiR7kpy45GUnJrnjAO/71CQ/muQPJ9u7++ruXujuO7v7LUl2ZzTN\n+wd098XdvaG7N6xdu/ZQPyIAAABHqSO6iFh3n3Ff/eNroFdW1WO6+6/HzU9K8gMLiC1xTpKt3b3n\nQENIUkPGCgAAAJNmagp3d383ydYkb66qB1XVU5L809zHwmBVtTrJ87Jk+nZVnVJVT6mqH66q46pq\nc5KHJrl62T4AAAAAR62ZCtBjr0yyOsk3k/xeklcs3sKqqp5WVUvPMm/K6DrpK5a0n5Dkt5LclmRn\nkjOTPKu7v7WMYwcAAOAoVd097THMnA0bNvT27dunPQwAAIBZ4VLYzOYZaAAAAJg5AjQAAAAMIEAD\nAADAAAI0AAAADCBAAwAAwAArpz0AgENx2TU7s2Xbjty0eyEnr1mdzRvXZ9Pp66Y9LAAAjmICNDB3\nLrtmZ87fel0W7tmbJNm5eyHnb70uSYRoAACWjSncwNzZsm3H98PzooV79mbLth1TGhEAAMcCARqY\nOzftXjiodgAAOBwEaGDunLxm9UG1AwDA4SBAA3Nn88b1Wb1qxb3aVq9akc0b109pRAAAHAssIgbM\nncWFwqzCDQDAkSRAA3Np0+nrBGYAAI4oU7gBAABgAAEaAAAABhCgAQAAYAABGgAAAAYQoAEAAGAA\nARoAAAAGEKABAABgAAEaAAAABhCgAQAAYAABGgAAAAYQoAEAAGAAARoAAAAGEKABAABgAAEaAAAA\nBhCgAQAAYAABGgAAAAYQoAEAAGAAARoAAAAGEKABAABgAAEaAAAABhCgAQAAYAABGgAAAAYQoAEA\nAGAAARoAAAAGEKABAABgAAEaAAAABhCgAQAAYAABGgAAAAYQoAEAAGAAARoAAAAGEKABAABgAAEa\nAAAABhCgAQAAYAABGgAAAAYQoAEAAGAAARoAAAAGmLkAXVXnVdX2qrqrqi4dsP8vV9UtVXV7VV1S\nVQ+Y6Du1qq6oqjur6oaqeuayDh4AAICj1swF6CQ3JbkoySUH2rGqNiZ5fZJnJDk1yaOS/PuJXX4v\nyTVJHpLk3yX5w6pae5jHCwAAwDFg5gJ0d2/t7suSfGvA7uckeV93X9/dtyW5MMlLk6SqHpvk7yZ5\nY3cvdPdHklyX5KzlGTkAAABHs5kL0Afp8UmunXh+bZKHVdVDxn1f6u47lvQ/fl9vVFXnjqeOb9+1\na9eyDRgAAID5NO8B+vgkt088X3x8wj76FvtP2NcbdffF3b2huzesXWuWNwAAAPd2RAN0VV1ZVb2f\n7apDeMs9SU6ceL74+I599C323xEAAAA4SEc0QHf3Gd1d+9meeghveX2SJ008f1KS/9Pd3xr3Paqq\nTljSf/2hfwIAAACOVTM3hbuqVlbVcUlWJFlRVcdV1cr97P7BJC+vqp+oqpOSXJDk0iTp7huT/GWS\nN47f4+eTPDHJR5b9QwAAAHDUmbkAnVEIXsjo9lQvGj++IEmq6pSq2lNVpyRJd38yyduSXJHkq+Pt\njRPvdXaSDUluS/LWJM/tbiuEAQAAcNCqu6c9hpmzYcOG3r59+7SHAQAAMCtq2gOYBbN4BhoAAABm\njgANAAAAAwjQAAAAMIAADQAAAAMI0AAAADCAAA0AAAADCNAAAAAwgAANAAAAAwjQAAAAMIAADQAA\nAAMI0AAAADCAAA0AAAADCNAAAAAwgAANAAAAAwjQAAAAMIAADQAAAAMI0AAAADCAAA0AAAADCNAA\nAAAwgAANAAAAAwjQAAAAMIAADQAAAAMI0AAAADCAAA0AAAADCNAAAAAwgAANAAAAAwjQAAAAMIAA\nDQAAAAMI0AAAADCAAA0AAAADCNAAAAAwgAANAAAAAwjQAAAAMIAADQAAAAMI0AAAADCAAA0AAAAD\nCNAAAAAwgAANAAAAAwjQAAAAMIAADQAAAAMI0AAAADCAAA0AAAADCNAAAAAwgAANAAAAAwjQAAAA\nMIAADQAAAAMI0AAAADCAAA0AAAADzFSArqrzqmp7Vd1VVZceYN9zquqzVfWdqvpGVb2tqlZO9F9Z\nVd+rqj3jbceyfwAAAACOWjMVoJPclOSiJJcM2PeBSV6d5KFJnpzkGUleu2Sf87r7+PG2/rCOFAAA\ngGPKygPvcuR099YkqaoNSR5+gH1/a+Lpzqr6cJKnL+PwAAAAOIbN2hno++Onk1y/pO0tVXVrVV1d\nVWfc14ur6tzx9PHtu3btWrZBAgAAMJ+OigBdVS9LsiHJ2yeaX5fkUUnWJbk4yceq6tH7e4/uvri7\nN3T3hrVr1y7reAEAAJg/RyxAjxf16v1sV92P992U5K1JntXdty62d/enu/uO7r6ruz+Q5Ookz77/\nnwQAAIBj0RG7Brq7zzjc71lVZyb5nSQ/193XHWgISepwjwFgWi67Zme2bNuRm3Yv5OQ1q7N54/ps\nOn3dtIcFAHDUmqkp3FW1sqqOS7IiyYqqOm7y1lRL9v2HST6c5Kzu/sySvjVVtXHx9VX1woyukd62\n3J8B4Ei47JqdOX/rddm5eyGdZOfuhZy/9bpcds3OaQ8NAOCoNVMBOskFSRaSvD7Ji8aPL0iSqjpl\nfD/nU8b7viHJjyT5+MS9nj8x7luV0e2wdiW5NcmrkmzqbveCBo4KW7btyMI9e+/VtnDP3mzZ5jAH\nALBcZu02Vm9K8qb99H0tyfETz/d7y6ru3pXk7x3m4QHMjJt2LxxUOwAA99+snYEGYICT16w+qHYA\nAO4/ARpgDm3euD6rV624V9vqVSuyeeP6KY0IAODoN1NTuAEYZnG1batwAwAcOQI0wJzadPo6gRkA\n4AgyhRsAAAAGEKABAABgAAEaAAAABhCgAQAAYAABGgAAAAYQoAEAAGAAARoAAAAGEKABAABgAAEa\nAAAABhCgAQAAYAABGgAAAAYQoAEAAGAAARoAAAAGEKABAABgAAEaAAAABhCgAQAAYAABGgAAAAYQ\noAEAAGAAARoAAAAGEKABAABggOruaY9h5lTVriRfnfY4DuChSW6d9iAYTL3mj5rNF/WaL+o1f9Rs\nvqjX/JmHmt3a3WdOexDTJkDPqara3t0bpj0OhlGv+aNm80W95ot6zR81my/qNX/UbH6Ywg0AAAAD\nCNAAAAAwgAA9vy6e9gA4KOo1f9RsvqjXfFGv+aNm80W95o+azQnXQAMAAMAAzkADAADAAAI0AAAA\nDCBAAwAAwAAC9Iyrqg9V1c1V9Z2qurGq/sVE3zOq6oaqurOqrqiqR05zrPyNqnpMVX2vqj400faC\nqvpqVX23qi6rqgdPc4yMVNWV41rtGW87JvrUbAZV1dlV9flxXb5YVU8btzsmzpiJ79Xitreq3jXR\nr2YzpqpOraqPV9VtVXVLVb27qlaO+06rqs+O6/XZqjpt2uM91lXVj1fVp6rq9qr6QlX9/ESf79cM\nqKrzqmp7Vd1VVZcu6dtvjarqAVV1yTgD3FJV/+aID559EqBn31uSnNrdJyb5J0kuqqqfrKqHJtma\n5A1JHpxke5L/NL1hssRvJvnzxSdV9fgkv53kxUkeluTOJO+ZztDYh/O6+/jxtj5Rs1lVVT+b5NeT\nvCzJCUl+OsmXHBNn08T36viMvkcLSf4gSdRsZr0nyTeT/FiS05L8TJJXVtUPJ/lokg8lOSnJB5J8\ndNzOFIz/sPHRJH+c0Xfo3CQfqqrH+n7NlJuSXJTkksnGATV6U5LHJHlkkqcn+bdVdeYRGC8HYBXu\nOVJV65NcmeSXkqxJ8tLu/qlx34OS3Jrk9O6+YWqDJFV1dpJfSPJXSf5Od7+oqn4toz+EvGC8z6OT\nfD7JQ7r7jumNlqq6MsmHuvs/LmlXsxlUVX+a5H3d/b4l7efGMXGmVdU5Sd6Y5NHd3Wo2m6rq80le\n090fHz/fkuTEJB9J8v4kD+/xL49V9bUk53b3J6c13mNZVT0hyf9OcsJETS5P8ukkX4/v10ypqosy\n+v68dPz8Po+BVbUzycu6+/Jx/4VJHtPdZ0/lA/B9zkDPgap6T1XdmeSGJDcn+XiSxye5dnGf7v5u\nki+O25mSqjoxyZuTvGZJ19J6fTHJ3Ukee+RGx314S1XdWlVXV9UZ4zY1mzFVtSLJhiRrx1MVvzGe\nXro6jonz4JwkH1z8RT9qNqvemeTsqnpgVa1L8qwkn8yoLp+bqF+SfC7qNU21n7YnxPdrHuy3RlV1\nUpKTJ/vHj9VvBgjQc6C7X5nRVMWnZTTV464kxye5fcmut4/3Y3ouzOjs2NeXtKvX7HpdkkclWZfk\n4iQfG59tVrPZ87Akq5I8N6Pj4WlJTk9yQdRrplXVKRlNBf7ARLOazab/kdEv6d9J8o2MppVeFvWa\nRTdkNN1+c1Wtqqp/lNH37IFRr3lwXzU6fuL50j6mTICeE929t7uvSvLwJK9IsiejKVWTTkxiaumU\njBdTeWaSd+yjW71mVHd/urvv6O67uvsDSa5O8uyo2SxaGP/3Xd19c3ffmuQ3ol7z4CVJruruL0+0\nqdmMqaofSrItoz/WPyjJQzO63vnXo14zp7vvSbIpyc8luSWj2W//OaM/fKjX7LuvGu2ZeL60jykT\noOfPyiSPTnJ9kictNo6vm1hsZzrOSHJqkq9V1S1JXpvkrKr6i/xgvR6V5AFJbjzyw+QAOqMpcGo2\nY7r7tox+MdzX4h2OibPtJbn32edEzWbRg5M8Ism7x39U/FZG1z0/O6O6PLGqJqcNPzHqNVXd/bnu\n/pnufkh3b8xoRtVn4vs1D/Zbo/G/dzdP9o8fq98MEKBnWFX9rfHtWo6vqhVVtTHJ85N8KskfJXlC\nVZ1VVccl+ZWMrk2yMMT0XJzRge+08fbeJH+SZGOSDyd5TlU9bXyAfHOSrRajmq6qWlNVG6vquKpa\nWVUvzGhV521Rs1n1/iSvGh8fT0ry6oxWoHVMnFFV9VMZXSLxB0u61GzGjGd1fDnJK8bHxDUZXbt+\nbUaLmO5N8q/Ht9c5b/yyT01lsCRJquqJ43/DHlhVr81o9fRL4/s1M8bfpeOSrEiyYvF3jhy4Rh9M\nckFVnVRVj0vyLzOqLVMmQM+2zmi69jeS3Jbk7Ule3d0f7e5dSc5K8qvjvicnsSrfFHX3nd19y+KW\n0fSb73X3ru6+Psm/yiiUfTOja1heOcXhMrIqo1tL7Mpo5ctXJdnU3TvUbGZdmNEt4m7MaFX0a5L8\nqmPiTDsn+/jjk5rNrF9IcmZGx8UvJPm/SX65u+/OaLrwS5LsTvKLGR0v757WQEkyutXizRn9O/WM\nJD87nj3g+zU7LsjoEqTXJ3nR+PEFA2r0xowWFftqRmsTbLHi/WxwGysAAAAYwBloAAAAGECABgAA\ngAEEaAAAABhAgAYAAIABBGgAAAAYQIAGAACAAQRoADgCqqqr6rnTHgcAcOgEaAC4n6rq0nFAXtxu\nrao/rqrHTez2Y0k+dhh+1kuX/Kx9bWfc358DAPyg6u5pjwEA5lpVXZpkXZIXj5tOTrIlycnd/eOH\n+WetTvIjE02/m+TbSX5pou3b3X334fy5AIAz0ABwuNzV3beMt79I8o4kjxsH3h+Ywl1Vb62qHVW1\nUFVfqaq3VdVxE/2PqKqPVtW3q+rOqrqhqs7u7oWJn3NLkruS3KttMTxX1auq6ktVdXdV3VhV5xzZ\n/yUAcHRZOe0BAMDRpqpOSPLPk1zX3Qv72e27SX4xyc4kP5HkvRmF4TeM+9+T5LgkT0/ynSTrD3IM\nz0/y9ozOTH8qyT9OcklV3dTd//WgPhAAkESABoDD5cyq2jN+/KAkX0/y7P3t3N0XTjz9SlX9WpLX\n5m8C9COTfKS7rx0///JBjue1Sd7X3e8dP/+Nqvr7SV6XRIAGgENgCjcAHB7/M8lp4+3JGZ31vbyq\nHrGvnavquVV1VVXdMg7e70hyysQu70xyQVX9WVVdVFU/eZDjeVySq5e0XZXR2W4A4BAI0ABweNzZ\n3V8Yb59J8vIkJyY5d+mOVfUPkvx+km1JnpPk9CQXJFm1uE93vy/J307y/iSPTfKnVfWmIQOpqlp8\nm310Wz0UAA6RAA0Ay6OT/L8kD9xH31OS7OzuC7v7z7v7rzOasn3vN+j+Rndf3N3/LMmvZB9hfJ8/\neHSLjRuSPHVJ11OT/NVBfAYAYIJroAHg8HhAVf3o+PFJSc5Lcnz2fe/nG5Osq6oXJvmzJBuTPH9y\nh6p6Z5JPjPc9McmZObjwuyXJB6rqL5NckdGZ7ueN3wcAOAQCNAAcHs9McvP48R0ZnQF+XndfuXTH\n7v5YVW1J8h+SrE5yeUZnmN8zsdsPJXlXkkeM3++/J3nN0MF09+9X1dokr0/y7iRfSfJyK3ADwKGr\n0SwvAAAA4L64BhoAAAAGEKABAABgAAEaAAAABhCgAQAAYAABGgAAAAYQoAEAAGAAARoAAAAGEKAB\nAABggP8PgWKrz96mA3oAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a4a0c2208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = pd.read_feather(\"/Users/weilu/Research/server/may_2018/first/rerun_1_09_May_224529.feather\")\n", "dic = {\"T0\":300, \"T1\":335, \"T2\":373, \"T3\":417, \"T4\":465, \"T5\":519, \"T6\":579, \"T7\":645, \"T8\":720, \"T9\":803, \"T10\":896, \"T11\":1000}\n", "a = data\n", "a[\"Temp\"] = a[\"Temp\"].apply(lambda x: dic[x])\n", "rerun1 = data\n", "t = a.query(\"Temp < 400\").groupby([\"BiasTo\",\"Temp\"])[[\"DisReal\",\"Run\"]].mean().reset_index()\n", "t[\"Diff\"] = t[\"DisReal\"]-t[\"BiasTo\"].apply(pd.to_numeric)\n", "t[\"BiasTo\"] = t[\"BiasTo\"].apply(pd.to_numeric)\n", "fg = sns.FacetGrid(data=t, hue='Temp', size=8, aspect=1.61)\n", "fg.map(plt.scatter, 'BiasTo', 'Diff').add_legend()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1180b2dd8>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAGJCAYAAACJojfUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrwx5eHh4eHh4bNf4/wBdC+MIJ94SAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1124e8b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rerun1.query(\"Temp == 335\").plot.hexbin(\"DisReal\", \"Qw\", cmap=\"seismic\", sharex=False)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x118110c88>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAGHCAYAAACzqFakAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMuTh4eHh4eGxQ+P/A1VPl+Cw2nfcAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x117f73b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rerun1.query(\"Temp == 300\").plot.hexbin(\"DisReal\", \"Qw\", cmap=\"seismic\", sharex=False)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a278c50>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAGHCAYAAABcaj2aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x110f31fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rerun1.query(\"Temp == 300\").plot.hexbin(\"z_h6\", \"Qw\", cmap=\"seismic\", sharex=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

autumn-lake/TalkingData-Mobile-User-Demographics

ipynb_notebooks/kaggle_kernel_4.ipynb

1

38646

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline\n", "#import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import os\n", "from sklearn.preprocessing import LabelEncoder\n", "from scipy.sparse import csr_matrix, hstack\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.cross_validation import StratifiedKFold\n", "from sklearn.metrics import log_loss" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datadir = 'input/'\n", "gatrain = pd.read_csv(os.path.join(datadir,'gender_age_train.csv'),\n", " index_col='device_id')\n", "gatest = pd.read_csv(os.path.join(datadir,'gender_age_test.csv'),\n", " index_col = 'device_id')\n", "phone = pd.read_csv(os.path.join(datadir,'phone_brand_device_model.csv'))\n", "# Get rid of duplicate device ids in phone\n", "phone = phone.drop_duplicates('device_id',keep='first').set_index('device_id')\n", "events = pd.read_csv(os.path.join(datadir,'events.csv'),\n", " parse_dates=['timestamp'], index_col='event_id')\n", "appevents = pd.read_csv(os.path.join(datadir,'app_events.csv'), \n", " usecols=['event_id','app_id','is_active'],\n", " dtype={'is_active':bool})\n", "applabels = pd.read_csv(os.path.join(datadir,'app_labels.csv'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gatrain['trainrow'] = np.arange(gatrain.shape[0])\n", "gatest['testrow'] = np.arange(gatest.shape[0])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Brand features: train shape (74645, 131), test shape (112071, 131)\n" ] } ], "source": [ "brandencoder = LabelEncoder().fit(phone.phone_brand)\n", "phone['brand'] = brandencoder.transform(phone['phone_brand'])\n", "gatrain['brand'] = phone['brand']\n", "gatest['brand'] = phone['brand']\n", "Xtr_brand = csr_matrix((np.ones(gatrain.shape[0]), \n", " (gatrain.trainrow, gatrain.brand)))\n", "Xte_brand = csr_matrix((np.ones(gatest.shape[0]), \n", " (gatest.testrow, gatest.brand)))\n", "print('Brand features: train shape {}, test shape {}'.format(Xtr_brand.shape, Xte_brand.shape))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model features: train shape (74645, 1667), test shape (112071, 1667)\n" ] } ], "source": [ "m = phone.phone_brand.str.cat(phone.device_model)\n", "modelencoder = LabelEncoder().fit(m)\n", "phone['model'] = modelencoder.transform(m)\n", "gatrain['model'] = phone['model']\n", "gatest['model'] = phone['model']\n", "Xtr_model = csr_matrix((np.ones(gatrain.shape[0]), \n", " (gatrain.trainrow, gatrain.model)))\n", "Xte_model = csr_matrix((np.ones(gatest.shape[0]), \n", " (gatest.testrow, gatest.model)))\n", "print('Model features: train shape {}, test shape {}'.format(Xtr_model.shape, Xte_model.shape))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All features: train shape (74645, 1798), test shape (112071, 1798)\n" ] } ], "source": [ "Xtrain = hstack((Xtr_brand, Xtr_model), format='csr')\n", "Xtest = hstack((Xte_brand, Xte_model), format='csr')\n", "print('All features: train shape {}, test shape {}'.format(Xtrain.shape, Xtest.shape))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "targetencoder = LabelEncoder().fit(gatrain.group)\n", "y = targetencoder.transform(gatrain.group)\n", "nclasses = len(targetencoder.classes_)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def score(clf, random_state = 667):\n", " kf = StratifiedKFold(y, n_folds=10, shuffle=True, random_state=random_state)\n", " pred = np.zeros((y.shape[0],nclasses))\n", " for itrain, itest in kf:\n", " Xtr, Xte = Xtrain[itrain, :], Xtrain[itest, :]\n", " ytr, yte = y[itrain], y[itest]\n", " clf.fit(Xtr, ytr)\n", " pred[itest,:] = clf.predict_proba(Xte)\n", " # Downsize to one fold only for kernels\n", " #return log_loss(yte, pred[itest, :])\n", " #print(\"{:.5f}\".format(log_loss(yte, pred[itest,:])), end=' ')\n", " #print('')\n", " print(\"{:.5f}\".format(log_loss(y, pred)), end=' ')\n", " return log_loss(y, pred)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.00000000e-05 3.59381366e-05 1.29154967e-04 4.64158883e-04\n", " 1.66810054e-03 5.99484250e-03 2.15443469e-02 7.74263683e-02\n", " 2.78255940e-01 1.00000000e+00]\n", "2.42752 2.42666 2.42398 2.41751 2.40801 2.40003 2.39438 2.39094 2.39218 2.40377 " ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEECAYAAADDOvgIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlBxERKQBJQcREWlAyUFERBr4/1KfR3ZmgQynAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f676eaf4b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Cs = np.logspace(-5,0,10)\n", "print(Cs)\n", "res = []\n", "for C in Cs:\n", " res.append(score(LogisticRegression(C = C, multi_class='multinomial',solver='lbfgs')))\n", "plt.semilogx(Cs, res,'-o');" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.39176 2.39117 2.39085 2.39069 2.39064 2.39066 2.39074 2.39085 2.39100 2.39116 " ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEGCAYAAACdJRn3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6771e66c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Cs = np.linspace(0.05,0.2,10)\n", "res = []\n", "for C in Cs:\n", " res.append(score(LogisticRegression(C = C, multi_class='multinomial',solver='lbfgs')))\n", "plt.plot(Cs, res,'-o');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.05 , 0.06666667, 0.08333333, 0.1 , 0.11666667,\n", " 0.13333333, 0.15 , 0.16666667, 0.18333333, 0.2 ])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linspace(0.05,0.2,10)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.44623 " ] }, { "data": { "text/plain": [ "2.4462276274420787" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "score(LogisticRegression(C=0.11666667, multi_class='multinomial',solver='lbfgs',class_weight='balanced'))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def score1(clf, random_state = 22):\n", " kf = StratifiedKFold(y, n_folds=10, shuffle=True, random_state=random_state)\n", " pred = np.zeros((y.shape[0],nclasses))\n", " for itrain, itest in kf:\n", " Xtr, Xte = Xtrain[itrain, :], Xtrain[itest, :]\n", " ytr, yte = y[itrain], y[itest]\n", " clf.fit(Xtr, ytr)\n", " pred[itest,:] = clf.predict_proba(Xte)\n", " # Downsize to one fold only for kernels\n", " #return log_loss(yte, pred[itest, :])\n", " #print(\"{:.5f}\".format(log_loss(yte, pred[itest,:])), end=' ')\n", " #print('')\n", " print(\"{:.5f}\".format(log_loss(y, pred)), end=' ')\n", " return log_loss(y, pred)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.39071 " ] }, { "data": { "text/plain": [ "2.3907051908423744" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "score1(LogisticRegression(C=0.11666667, multi_class='multinomial',solver='lbfgs'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

dragonfyre13/adventofcode2016

Dec1.ipynb

1

5539

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Taxi at (0,0) facing N, 0 steps from (0,0)\n" ] } ], "source": [ "#!/usr/bin/env python\n", "from collections import deque\n", "\n", "class Taxi(object):\n", " def __init__(self, x=0, y=0, direction='N'):\n", " # Absolute location starting from 0,0 (origination point)\n", " self.location = [0,0]\n", " self.visited_locations = []\n", " self.repeated_locations = []\n", " # North, West, South, East.\n", " # Rotate left vs right for correct cardinal direction being element 0\n", " self.cardinal_directions = deque([(0,1,'N'), (-1, 0,'E'), (0,-1,'S'), (1,0,'W')])\n", " \n", " def set_location(self, x, y):\n", " self.location = [x,y]\n", " \n", " def set_direction(self, new_direction):\n", " new_direction = new_direction.upper()\n", " if new_direction not in 'NSEW':\n", " raise ValueError('%r is not N, S, E or W (cardinal direction)' % new_direction)\n", " # Start out pointing north\n", " self.cardinal_directions = deque([(0,1,'N'), (-1, 0,'E'), (0,-1,'S'), (1,0,'W')])\n", " turns = dict(N=0, E=1, S=2, W=3)\n", " self.turn_right(turns[new_direction])\n", "\n", " def turn_left(self, number_turns=1):\n", " self.cardinal_directions.rotate(-1 * number_turns)\n", " \n", " def turn_right(self, number_turns=1):\n", " self.cardinal_directions.rotate(1 * number_turns)\n", " \n", " def step_forward(self, steps=1):\n", " for i in range(steps):\n", " self.location[0] += self.direction[0]\n", " self.location[1] += self.direction[1]\n", " if tuple(self.location) in self.visited_locations:\n", " self.repeated_locations.append(tuple(self.location + [self.direction[2], abs(self.x) + abs(self.y)]))\n", " self.visited_locations.append(tuple(self.location))\n", " \n", " def follow_directions(self, command):\n", " # Takes the form of \"L232\" for \"turn left and walk 232 steps\"\n", " if command[0] == 'L':\n", " self.turn_left()\n", " elif command[0] == 'R':\n", " self.turn_right()\n", " else:\n", " raise ValueError('Commanded to turn to the %r relative direction' % command[0])\n", " self.step_forward(int(command[1:]))\n", " \n", " @property\n", " def x(self):\n", " return self.location[0]\n", " \n", " @property\n", " def y(self):\n", " return self.location[1]\n", " \n", " @property\n", " def direction(self):\n", " return self.cardinal_directions[0]\n", " \n", " def __str__(self):\n", " return 'Taxi at (%i,%i) facing %s, %i steps from (0,0)' % (\n", " self.x, self.y, self.direction[2], abs(self.x) + abs(self.y))\n", "\n", "print Taxi()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "input_dir = \"R2, L1, R2, R1, R1, L3, R3, L5, L5, L2, L1, R4, R1, R3, L5, L5, R3, L4, L4, R5, R4, R3, L1, L2, R5, R4, L2, R1, R4, R4, L2, L1, L1, R190, R3, L4, R52, R5, R3, L5, R3, R2, R1, L5, L5, L4, R2, L3, R3, L1, L3, R5, L3, L4, R3, R77, R3, L2, R189, R4, R2, L2, R2, L1, R5, R4, R4, R2, L2, L2, L5, L1, R1, R2, L3, L4, L5, R1, L1, L2, L2, R2, L3, R3, L4, L1, L5, L4, L4, R3, R5, L2, R4, R5, R3, L2, L2, L4, L2, R2, L5, L4, R3, R1, L2, R2, R4, L1, L4, L4, L2, R2, L4, L1, L1, R4, L1, L3, L2, L2, L5, R5, R2, R5, L1, L5, R2, R4, R4, L2, R5, L5, R5, R5, L4, R2, R1, R1, R3, L3, L3, L4, L3, L2, L2, L2, R2, L1, L3, R2, R5, R5, L4, R3, L3, L4, R2, L5, R5\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "t = Taxi()\n", "# Read in the input directions\n", "for command in input_dir.split(', '):\n", " t.follow_directions(command)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Taxi at (147,-87) facing W, 234 steps from (0,0)\n" ] } ], "source": [ "# Solution for problem 1\n", "print t" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(16, -97, 'W', 113)\n" ] } ], "source": [ "# Solution for problem 2\n", "print t.repeated_locations[0]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2.7", "language": "python", "name": "python2.7" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

MiningTheDisclosures/conflict-minerals-data

notebooks/Contents parsing.ipynb

1

11316

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filing = EdgarSDFiling.objects.get(pk=711)\n", "docs = filing.edgarsdfilingdocument_set.all()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Complete submission text file'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "doc_a = docs[0]\n", "content_a = doc_a.edgardocumentcontent_set.get()\n", "doc_a.description" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'SD'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "doc_b = docs[4]\n", "content_b = doc_b.edgardocumentcontent_set.get()\n", "doc_b.description" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'<SEC-DOCUMENT>0001104659-17-036452.txt : 20170531\\n<SEC-HEADER>0001104659-17-036452.hdr.sgml : 20170531\\n<ACCEPTANCE-DATETIME>20170531113151\\nACCESSION NUMBER:\\t\\t0001104659-17-036452\\nCONFORMED SUBMISSION '" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "content_a.content[0:200]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'<DOCUMENT>\\n<TYPE>SD\\n<SEQUENCE>1\\n<FILENAME>a17-14316_1sd.htm\\n<DESCRIPTION>SD\\n<TEXT>\\n\\n\\n<html>\\n<head>\\n\\n\\n\\n\\n </head>\\n<body link=blue lang=\"EN-US\">\\n<div style=\"font-family:Times New Roman;\">\\n<div style=\"bo'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "content_b.content[0:200]" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import toolz\n", "from urlextract import URLExtract\n", "extractor = URLExtract()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1012\n", "1013\n", "1014\n", "['http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/&#160', 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/', 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/).']\n" ] } ], "source": [ "all_urls = []\n", "for doc in docs:\n", " try:\n", " # Get the doc content\n", " doc_content = doc.edgardocumentcontent_set.get()\n", " except EdgarDocumentContent.DoesNotExist:\n", " continue\n", " content = doc_content.content\n", " if content:\n", " print(doc_content.id)\n", " urls = extractor.find_urls(content)\n", " if urls:\n", " all_urls.extend(urls)\n", "unique_urls = toolz.unique(all_urls)\n", "print(list(unique_urls))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4029\n", "4028\n", "4027\n", "4026\n", "4025\n" ] } ], "source": [ "for doc in docs:\n", " print(doc.id)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "extract_urls = docs.values_list('edgardocumentcontent__urls', flat=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from toolz import filter, accumulate\n", "\n", "def compact(iter):\n", " return filter(None, iter)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/&#160',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/).'],\n", " ['http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/).'],\n", " ['http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/&#160']]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "compacted = list(compact(extract_urls))\n", "compacted" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/&#160',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/).',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/).',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/&#160']" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from itertools import chain\n", "flattened = list(chain.from_iterable(compacted))\n", "flattened" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/&#160',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/).']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(toolz.unique(flattened))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/&#160',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/',\n", " 'http://www.3m.com/3M/en_US/suppliers-direct/supplier-requirements/supplier-responsibility-expectations/).']" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filing.extracted_urls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Investigate WestRock (and others)\n", "NUCOR CORP, Owens Corning, NOVARTIS AG, James Hardie Industries plc, GENERAL DYNAMICS CORP, ESTEE LAUDER COMPANIES INC, ALBEMARLE CORP" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filing = EdgarSDFiling.objects.get(pk=775)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<QuerySet [<EdgarSDFilingDocument: WestRock Co - 2016 (SD) - 0001171843-16-010390.txt>, <EdgarSDFilingDocument: WestRock Co - 2016 (SD) - exh_101.htm>, <EdgarSDFilingDocument: WestRock Co - 2016 (SD) - fsd_053116.htm>]>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "docs = filing.edgarsdfilingdocument_set.all()\n", "docs" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n", "[]\n", "[]\n", "[]\n" ] } ], "source": [ "all_urls = []\n", "for doc in docs:\n", " try:\n", " # Get the doc content\n", " doc_content = doc.edgardocumentcontent_set.get()\n", " except EdgarDocumentContent.DoesNotExist:\n", " continue\n", " content = doc_content.content\n", " if content:\n", " extractor = URLExtract()\n", " urls = extractor.find_urls(content)\n", " print(urls)\n", " if urls:\n", " all_urls.extend(urls)\n", "unique_urls = toolz.unique(all_urls)\n", "print(list(unique_urls))" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['www.westrock.com', 'www.westrock.com', 'www.westrock.com']\n", "['www.westrock.com', 'www.westrock.com']\n", "['www.westrock.com']\n", "['www.westrock.com']\n" ] } ], "source": [ "all_urls = []\n", "for doc in docs:\n", " try:\n", " # Get the doc content\n", " doc_content = doc.edgardocumentcontent_set.get()\n", " except EdgarDocumentContent.DoesNotExist:\n", " continue\n", " content = doc_content.content\n", " if content:\n", " extractor = URLExtract()\n", " urls = extractor.find_urls(content.replace('.com.', '.com'))\n", " print(urls)\n", " if urls:\n", " all_urls.extend(urls)\n", "unique_urls = toolz.unique(all_urls)\n", "print(list(unique_urls))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Django Shell-Plus", "language": "python", "name": "django_extensions" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

rsignell-usgs/notebook

CSW/Untitled9.ipynb

2

19413

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import netCDF4\n", "nc = netCDF4.Dataset('http://geoport-dev.whoi.edu/thredds/dodsC/usgs/data2/emontgomery/stellwagen/CF-1.6/DAUPHIN/9681dw-a.nc')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'feature_type_instance',\n", " u'latitude',\n", " u'longitude',\n", " u'time',\n", " u'z',\n", " u'crs',\n", " u'platform',\n", " u'D_3',\n", " u'P_1',\n", " u'P_1ac',\n", " u'WL_NAVD88',\n", " u'dn']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nc.variables.keys()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f4065650a50>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAENCAYAAADnrmWtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfyKiHGLyJyLKISZ/IqIc+v8DRCTaahhphgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f406c683e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(nc['P_1'][:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ioos/pyoos

notebooks/NERRS.ipynb

1

102436

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "https://gist.github.com/emiliom/57e84aee123ca60c4fa3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing a NERRS station with Pyoos, via CDMO SOAP web services\n", "Illustrates querying all stations (\"features\") from a NERRS Reserve site; access to data from a NERRS station (specified by its station code); extraction of station metadata; and conversion of the returned multi-variable time series to a pandas DataFrame, followed by a time series plot from the DataFrame. Builds off the work from Dan Ramage (SECOORA), whose code is listed in the last cell, at the end. _Note that this is running from a pyoos fork with some small but key changes to the nerrs collector._ 2014 May 8-10. Emilio Mayorga." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from datetime import datetime, timedelta\n", "import pandas as pd\n", "from pyoos.collectors.nerrs.nerrs_soap import NerrsSoap" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# FROM pyoos SOS handling\n", "# Convenience function to build record style time series representation\n", "def flatten_element(p):\n", " rd = {'time':p.time}\n", " for m in p.members:\n", " rd[m['standard']] = m['value']\n", " return rd\n", "\n", "# sta.get_unique_members() serves the same function as the pyoos SOS get_unique_members method\n", "# Convenience function to extract a dict of unique members (observed properties) by standard name\n", "obsprops_bystdname = lambda sta: {m['standard']:m for m in sta.get_unique_members()}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First here's a very compact set of statements to get and plot the data for a station. No exploratory side trips." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE:** I manually removed (commented out) the NERRS/CDMO access token after running this notebook, before uploading notebook to my github gist. *Replace 'TOKEN STRING' with a [token obtained from the NERRS/CDMO office](http://nerrsdata.org/contact.cfm)*" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# NERRS/CDMO access token.\n", "accesstoken = 'TOKEN STRING'\n", "\n", "# Initialize pyoos NERRS collector object\n", "nerrsData = NerrsSoap()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Access pdbpfmet station, for the last 7 days (roughly)\n", "nerrsData.filter(features=['pdbpfmet'],\n", " start=datetime.utcnow() - timedelta(days=7),\n", " end=datetime.utcnow() - timedelta(hours=12))\n", "\n", "response = nerrsData.collect(accesstoken)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sta = response.elements[0]\n", "obsprops_bystdname_dict = obsprops_bystdname(sta)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>air_pressure</th>\n", " <th>air_temperature</th>\n", " <th>cumulative_precipitation</th>\n", " <th>relative_humidity</th>\n", " <th>total_par_LiCor</th>\n", " <th>total_precipitation</th>\n", " <th>wind_direction_from_true_north</th>\n", " <th>wind_direction_standard_deviation</th>\n", " <th>wind_sped</th>\n", " <th>wind_speed_of_gust</th>\n", " </tr>\n", " <tr>\n", " <th>time</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2014-05-10 19:30:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 12.1</td>\n", " <td> 0</td>\n", " <td> 81</td>\n", " <td> 651</td>\n", " <td> 0</td>\n", " <td> 212</td>\n", " <td> 22</td>\n", " <td> 1.8</td>\n", " <td> 3.1</td>\n", " </tr>\n", " <tr>\n", " <th>2014-05-10 19:15:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 11.6</td>\n", " <td> 0</td>\n", " <td> 83</td>\n", " <td> 462</td>\n", " <td> 0</td>\n", " <td> 192</td>\n", " <td> 17</td>\n", " <td> 2.0</td>\n", " <td> 3.7</td>\n", " </tr>\n", " <tr>\n", " <th>2014-05-10 19:00:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 11.4</td>\n", " <td> 0</td>\n", " <td> 83</td>\n", " <td> 394</td>\n", " <td> 0</td>\n", " <td> 189</td>\n", " <td> 12</td>\n", " <td> 2.2</td>\n", " <td> 4.1</td>\n", " </tr>\n", " <tr>\n", " <th>2014-05-10 18:45:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 11.4</td>\n", " <td> 0</td>\n", " <td> 84</td>\n", " <td> 451</td>\n", " <td> 0</td>\n", " <td> 193</td>\n", " <td> 14</td>\n", " <td> 2.0</td>\n", " <td> 3.8</td>\n", " </tr>\n", " <tr>\n", " <th>2014-05-10 18:30:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 11.3</td>\n", " <td> 0</td>\n", " <td> 83</td>\n", " <td> 420</td>\n", " <td> 0</td>\n", " <td> 202</td>\n", " <td> 15</td>\n", " <td> 2.0</td>\n", " <td> 4.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " air_pressure air_temperature \\\n", "time \n", "2014-05-10 19:30:00+00:00 1021 12.1 \n", "2014-05-10 19:15:00+00:00 1021 11.6 \n", "2014-05-10 19:00:00+00:00 1021 11.4 \n", "2014-05-10 18:45:00+00:00 1021 11.4 \n", "2014-05-10 18:30:00+00:00 1021 11.3 \n", "\n", " cumulative_precipitation relative_humidity \\\n", "time \n", "2014-05-10 19:30:00+00:00 0 81 \n", "2014-05-10 19:15:00+00:00 0 83 \n", "2014-05-10 19:00:00+00:00 0 83 \n", "2014-05-10 18:45:00+00:00 0 84 \n", "2014-05-10 18:30:00+00:00 0 83 \n", "\n", " total_par_LiCor total_precipitation \\\n", "time \n", "2014-05-10 19:30:00+00:00 651 0 \n", "2014-05-10 19:15:00+00:00 462 0 \n", "2014-05-10 19:00:00+00:00 394 0 \n", "2014-05-10 18:45:00+00:00 451 0 \n", "2014-05-10 18:30:00+00:00 420 0 \n", "\n", " wind_direction_from_true_north \\\n", "time \n", "2014-05-10 19:30:00+00:00 212 \n", "2014-05-10 19:15:00+00:00 192 \n", "2014-05-10 19:00:00+00:00 189 \n", "2014-05-10 18:45:00+00:00 193 \n", "2014-05-10 18:30:00+00:00 202 \n", "\n", " wind_direction_standard_deviation wind_sped \\\n", "time \n", "2014-05-10 19:30:00+00:00 22 1.8 \n", "2014-05-10 19:15:00+00:00 17 2.0 \n", "2014-05-10 19:00:00+00:00 12 2.2 \n", "2014-05-10 18:45:00+00:00 14 2.0 \n", "2014-05-10 18:30:00+00:00 15 2.0 \n", "\n", " wind_speed_of_gust \n", "time \n", "2014-05-10 19:30:00+00:00 3.1 \n", "2014-05-10 19:15:00+00:00 3.7 \n", "2014-05-10 19:00:00+00:00 4.1 \n", "2014-05-10 18:45:00+00:00 3.8 \n", "2014-05-10 18:30:00+00:00 4.0 \n", "\n", "[5 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# FROM pyoos SOS handling\n", "# For first (and only) station\n", "flattenedsta_0 = map(flatten_element, sta.elements)\n", "sta_0df = pd.DataFrame.from_records(flattenedsta_0, index=['time'])\n", "sta_0df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEYCAYAAAC0tfaFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x4bc8a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Time series plot.\n", "# \"wind_speed\" is currently mispelled; that's in pyoos, and can be fixed easily\n", "obsprop_name = 'wind_sped'\n", "obsprop = obsprops_bystdname_dict[obsprop_name]\n", "sta_0df[obsprop_name].plot()\n", "ylabel(obsprop_name + ' ('+obsprop['unit']+')');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now the same thing, but with lots of exploration in between" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# pyoos NERRS collector\n", "nerrsData = NerrsSoap()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### _May 10:_ Not sure if this will work, b/c the access token is passed via the collect method, so it hasn't been passed here yet!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['pdbbpnut',\n", " 'pdbbpwq',\n", " 'pdbbynut',\n", " 'pdbbywq',\n", " 'pdbgdnut',\n", " 'pdbgsnut',\n", " 'pdbgswq',\n", " 'pdbjenut',\n", " 'pdbjewq',\n", " 'pdbjlnut',\n", " 'pdbjlwq',\n", " 'pdbnnwq',\n", " 'pdbpfmet']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get all Padilla Bay stations (pdb)\n", "[featureid for featureid in nerrsData.list_features() if featureid.startswith('pdb')]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Access pdbpfmet station, for the last 7 days (roughly)\n", "nerrsData.filter(features=['pdbpfmet'],\n", " start=datetime.utcnow() - timedelta(days=7),\n", " end=datetime.utcnow() - timedelta(hours=12))\n", "#nerrsData.filter(variables=[\"ATemp\"])\n", "response = nerrsData.collect()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(dict, ['pdbpfmet'])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The raw response (a string) is not used outside this cell. The collect method response is what's used\n", "# I'm showing the raw response here, just for reference\n", "raw = nerrsData.raw()\n", "type(raw), raw.keys()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<paegan.cdm.dsg.features.station.Station at 0x4848a50>]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# response.elements is a one-element array with a paegan.cdm.dsg.features.station.Station element\n", "response.elements" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['_type',\n", " '_location',\n", " '_description',\n", " '_name',\n", " '_uid',\n", " '_elements',\n", " '_properties']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Looks like the station in the response doesn't include any info about the Reserve it belongs to. Too bad.\n", "# Or is one of the pieces of information below the NERRS site?\n", "sta = response.elements[0]\n", "sta.__dict__.keys()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('pdbpfmet',\n", " 'Padilla Bay Farm',\n", " None,\n", " 'timeSeries',\n", " <shapely.geometry.point.Point at 0x4848150>,\n", " <bound method Station.properties of <paegan.cdm.dsg.features.station.Station object at 0x4848a50>>)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sta.uid, sta.name, sta.description, sta.type, sta.location, sta.properties" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('pdb',\n", " {'horizontal_crs': 'EPSG:4326',\n", " 'location_description': 'Padilla Bay',\n", " 'siteid': 'pdb',\n", " 'state': 'wa',\n", " 'vertical_crs': 'EPSG:4297',\n", " 'vertical_units': 'm'})" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 'siteid' and 'location_description' seem to refer to the NERRS reserve/site\n", "sta.get_property('siteid'), sta._properties" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POINT Z (122.469303 48.463847 0) || Point || (array('d', [122.469303]), array('d', [48.463847]))\n" ] } ], "source": [ "staloc = sta.get_location()\n", "print staloc, '||', staloc.type, '||', staloc.xy" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'description': 'WSpd', 'name': 'WSpd', 'standard': 'wind_sped', 'unit': 'm/s'}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "obsprops_bystdname_dict = obsprops_bystdname(sta)\n", "obsprops_bystdname_dict['wind_sped']" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(list, 656)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The individual observations are returned in the station \"elements\"\n", "stael = sta.elements\n", "type(stael), len(stael)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "datetime.datetime(2014, 5, 10, 19, 30, tzinfo=<UTC>)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stael0 = stael[0]\n", "stael0.time\n", "# See sta.get_unique_members(), above\n", "# stael0.get_member_names() returns a list of member names for this station 'element'" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{'description': 'ATemp',\n", " 'name': 'ATemp',\n", " 'standard': 'air_temperature',\n", " 'unit': '\\xc2\\xb0C',\n", " 'value': 12.1},\n", " {'description': 'BP',\n", " 'name': 'BP',\n", " 'standard': 'air_pressure',\n", " 'unit': 'mb',\n", " 'value': 1021.0},\n", " {'description': 'CumPrcp',\n", " 'name': 'CumPrcp',\n", " 'standard': 'cumulative_precipitation',\n", " 'unit': 'mm',\n", " 'value': 0.0},\n", " {'description': 'MaxWSpd',\n", " 'name': 'MaxWSpd',\n", " 'standard': 'wind_speed_of_gust',\n", " 'unit': 'm/s',\n", " 'value': 3.1},\n", " {'description': 'RH',\n", " 'name': 'RH',\n", " 'standard': 'relative_humidity',\n", " 'unit': '%',\n", " 'value': 81.0},\n", " {'description': 'SDWDir',\n", " 'name': 'SDWDir',\n", " 'standard': 'wind_direction_standard_deviation',\n", " 'unit': 'sd',\n", " 'value': 22.0},\n", " {'description': 'TotPAR',\n", " 'name': 'TotPAR',\n", " 'standard': 'total_par_LiCor',\n", " 'unit': 'mmoles/m^2',\n", " 'value': 651.0},\n", " {'description': 'TotPrcp',\n", " 'name': 'TotPrcp',\n", " 'standard': 'total_precipitation',\n", " 'unit': 'mm',\n", " 'value': 0.0},\n", " {'description': 'Wdir',\n", " 'name': 'Wdir',\n", " 'standard': 'wind_direction_from_true_north',\n", " 'unit': '\\xc2\\xb0TN',\n", " 'value': 212.0},\n", " {'description': 'WSpd',\n", " 'name': 'WSpd',\n", " 'standard': 'wind_sped',\n", " 'unit': 'm/s',\n", " 'value': 1.8}]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stael0.members" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<bound method StationCollection.flatten of <paegan.cdm.dsg.collections.station_collection.StationCollection object at 0x4848f90>>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# From paegan: flatten Returns a Generator of Points that are part of this collection\n", "# Just exploring what this does...\n", "response.flatten" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>air_pressure</th>\n", " <th>air_temperature</th>\n", " <th>cumulative_precipitation</th>\n", " <th>relative_humidity</th>\n", " <th>total_par_LiCor</th>\n", " <th>total_precipitation</th>\n", " <th>wind_direction_from_true_north</th>\n", " <th>wind_direction_standard_deviation</th>\n", " <th>wind_sped</th>\n", " <th>wind_speed_of_gust</th>\n", " </tr>\n", " <tr>\n", " <th>time</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2014-05-10 19:30:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 12.1</td>\n", " <td> 0</td>\n", " <td> 81</td>\n", " <td> 651</td>\n", " <td> 0</td>\n", " <td> 212</td>\n", " <td> 22</td>\n", " <td> 1.8</td>\n", " <td> 3.1</td>\n", " </tr>\n", " <tr>\n", " <th>2014-05-10 19:15:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 11.6</td>\n", " <td> 0</td>\n", " <td> 83</td>\n", " <td> 462</td>\n", " <td> 0</td>\n", " <td> 192</td>\n", " <td> 17</td>\n", " <td> 2.0</td>\n", " <td> 3.7</td>\n", " </tr>\n", " <tr>\n", " <th>2014-05-10 19:00:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 11.4</td>\n", " <td> 0</td>\n", " <td> 83</td>\n", " <td> 394</td>\n", " <td> 0</td>\n", " <td> 189</td>\n", " <td> 12</td>\n", " <td> 2.2</td>\n", " <td> 4.1</td>\n", " </tr>\n", " <tr>\n", " <th>2014-05-10 18:45:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 11.4</td>\n", " <td> 0</td>\n", " <td> 84</td>\n", " <td> 451</td>\n", " <td> 0</td>\n", " <td> 193</td>\n", " <td> 14</td>\n", " <td> 2.0</td>\n", " <td> 3.8</td>\n", " </tr>\n", " <tr>\n", " <th>2014-05-10 18:30:00+00:00</th>\n", " <td> 1021</td>\n", " <td> 11.3</td>\n", " <td> 0</td>\n", " <td> 83</td>\n", " <td> 420</td>\n", " <td> 0</td>\n", " <td> 202</td>\n", " <td> 15</td>\n", " <td> 2.0</td>\n", " <td> 4.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " air_pressure air_temperature \\\n", "time \n", "2014-05-10 19:30:00+00:00 1021 12.1 \n", "2014-05-10 19:15:00+00:00 1021 11.6 \n", "2014-05-10 19:00:00+00:00 1021 11.4 \n", "2014-05-10 18:45:00+00:00 1021 11.4 \n", "2014-05-10 18:30:00+00:00 1021 11.3 \n", "\n", " cumulative_precipitation relative_humidity \\\n", "time \n", "2014-05-10 19:30:00+00:00 0 81 \n", "2014-05-10 19:15:00+00:00 0 83 \n", "2014-05-10 19:00:00+00:00 0 83 \n", "2014-05-10 18:45:00+00:00 0 84 \n", "2014-05-10 18:30:00+00:00 0 83 \n", "\n", " total_par_LiCor total_precipitation \\\n", "time \n", "2014-05-10 19:30:00+00:00 651 0 \n", "2014-05-10 19:15:00+00:00 462 0 \n", "2014-05-10 19:00:00+00:00 394 0 \n", "2014-05-10 18:45:00+00:00 451 0 \n", "2014-05-10 18:30:00+00:00 420 0 \n", "\n", " wind_direction_from_true_north \\\n", "time \n", "2014-05-10 19:30:00+00:00 212 \n", "2014-05-10 19:15:00+00:00 192 \n", "2014-05-10 19:00:00+00:00 189 \n", "2014-05-10 18:45:00+00:00 193 \n", "2014-05-10 18:30:00+00:00 202 \n", "\n", " wind_direction_standard_deviation wind_sped \\\n", "time \n", "2014-05-10 19:30:00+00:00 22 1.8 \n", "2014-05-10 19:15:00+00:00 17 2.0 \n", "2014-05-10 19:00:00+00:00 12 2.2 \n", "2014-05-10 18:45:00+00:00 14 2.0 \n", "2014-05-10 18:30:00+00:00 15 2.0 \n", "\n", " wind_speed_of_gust \n", "time \n", "2014-05-10 19:30:00+00:00 3.1 \n", "2014-05-10 19:15:00+00:00 3.7 \n", "2014-05-10 19:00:00+00:00 4.1 \n", "2014-05-10 18:45:00+00:00 3.8 \n", "2014-05-10 18:30:00+00:00 4.0 \n", "\n", "[5 rows x 10 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# FROM pyoos SOS handling\n", "# For first (and only) station\n", "flattenedsta_0 = map(flatten_element, sta.elements)\n", "sta_0df = pd.DataFrame.from_records(flattenedsta_0, index=['time'])\n", "sta_0df.head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEYCAYAAAC0tfaFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x455ef10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Time series plot.\n", "# \"wind_speed\" is currently mispelled; that's in pyoos, and can be fixed easily\n", "obsprop_name = 'wind_sped'\n", "obsprop = obsprops_bystdname_dict[obsprop_name]\n", "sta_0df[obsprop_name].plot()\n", "ylabel(obsprop_name + ' ('+obsprop['unit']+')');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The block below is Dan's code, with some tweaks I had made. I'm no longer using it directly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Change this to return arrays and/or pandas data frames that pull out individual time series per variable** \n", "**SEE THE CELL ABOVE THIS ONE!** \n", "\n", "for obsRec in response:\n", " for stationRec in response.get_elements():\n", " #stationRec = obsRec.feature\n", " print \"**** Station: %s Location: %s\" % (stationRec.name, stationRec.get_location())\n", " #The elements are a list of the observed_properties returned wrapped in a Point object.\n", " for obsProp in stationRec.get_elements():\n", " print \" -------------------\"\n", " print \" - Observation Date/Time: %s\" % (obsProp.get_time())\n", " #print \"Member names: %s\" % (obsProp.get_member_names())\n", " #I think that for a multi sensor request, there should be multiple members, each representing\n", " #a specific observed_property.\n", " for member in obsProp.get_members():\n", " #Apparently you're going to have to know how each collector parses the pieces of the data.\n", " #For an SOS query, there appear to be: name, units, value, and standard(CF MMI link).\n", " #print \" ------\\n member.keys() = %s\" % member.keys()\n", " # member.keys() = ['value', 'description', 'name', 'unit', 'standard']\n", " m = member\n", " member_values_tup = (m['name'], m['description'], m['standard'], m['value'], m['unit'])\n", " print \"name: %s (description=%s, standard=%s); value=%s, unit=%s\" % member_values_tup\n", " #for key,value in member.iteritems():\n", " # print \" %s = %s\" % (key, value)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

lgpl-3.0

bennyrowland/suspect

docs/notebooks/plot_1D_signals_tutorial.ipynb

2

26989

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting 1D MRS data using Suspect's `plot_1D_signals` module\n", "\n", "Suspect's `plot_1D_signals` module provides a set of functions for plotting MRS data. It is built on matplotlib's library of plotting functions, and automatically applies formatting and styling parameters that are useful for visualizing 1D MRS signals. This notebook provides an overview and examples of the basic functionality provided by the module, including: \n", "\n", "1. Importing data\n", "2. Plotting 1D MR spectra\n", " * Modifying default parameters\n", " * Multi-line plots\n", " * Creating subplots \n", "2. Plotting raw MR spectra\n", "3. Using `plot_1D_signals` for batch processing\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing data\n", "\n", "Start by importing the modules we will need:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import suspect\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To simplify the syntax for future calls to the funcions in the `plot_1D_signals` module, import them to the namespace:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import suspect.viz.plot_1D_signals as plot_sigs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load some data. For this example, we will use a file in the rda format that contains a single 1D complex FID signal. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: 'SVS_30.rda'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-3-9cb1dbf62859>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuspect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_rda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'SVS_30.rda'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mC:\\Users\\ljm2331\\Documents\\python\\mrs\\openmrslab\\suspect\\suspect\\io\\rda.py\u001b[0m in \u001b[0;36mload_rda\u001b[1;34m(filename)\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mload_rda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[0mheader_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfin\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 38\u001b[0m \u001b[0mheader_line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mheader_line\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34mb\">>> Begin of header <<<\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'SVS_30.rda'" ] } ], "source": [ "data = suspect.io.load_rda('SVS_30.rda')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`data` is an instance of the `MRSData` class, which is subclass of a numpy `array` that adds MRS-specific properties and methods.\n", "\n", "The axes we need for plotting the signal are attributes of the `data` object. These are:\n", "\n", "`data.time_axis` (seconds)\n", "\n", "`data.frequency_axis`: (Hz)\n", "\n", "`data.frequency_axis_ppm`: (ppm)\n", "\n", "For some datasets, it might be useful to collapse any singleton dimensions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fid = np.squeeze(data) # Time-domain signal\n", "spectrum = np.squeeze(data.spectrum()) # Frequency-domain signal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`fid` and `spectrum` are complex `numpy` arrays with 1024 data points." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting 1D MR Spectra\n", "\n", "To start, plot the magnitude of the frequency-domain signal (spectrum) using the `plot` function in the `plot_1D_signals` module, with the default plotting parameters for a spectral representation of the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# First, get the x-axis data, which in this case will be ppms\n", "ppm_axis = data.frequency_axis_ppm()\n", "\n", "fig_handle, current_axis, line_handles = plot_sigs.plot(ppm_axis,np.abs(spectrum), plot_type='spectrum')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plot` function generates a new figure, adds an axis to it, and returns the handles of the figure, current axis, and any lines that were added to the axis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modifying the figure parameters\n", "The figure we generated utilized the default styling parameters for MR spectra. These can be accessed and changed in a variety of ways.\n", "\n", "The default parameters are easy to find and change in the code. They can be accessed here:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_sigs.get_default_plot_params();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some additional default parameters have been defined for frequency and time-somain representations of the data. These can be accessed by specifying the `plot_type` when calling `get_default_plot_parameters`. The options are:\n", "\n", "`spectrum`: Frequency domain data, best for <= 5 signals plotted on the same axis\n", "\n", "`spectra`: Frequency domain data, best for > 5 signals plotted on the same axis\n", "\n", "`fid`: Time domain data, best for <= 5 signals plotted on the same axis\n", "\n", "`fids`: Time domain data, best for > 5 signals plotted on the same axis\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the default parameters for spectrum plots\n", "spectrum_defaults = plot_sigs.get_default_plot_params(plot_type='spectrum');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To change any of these parameters, pass in a dictionary of `key:value` pairs defining the parameters you want to change. The default values will be used unless otherwise specified.\n", "\n", "Let's change some things on the original plot to make it look a bit nicer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a dictionary of key:value pairs containing the changes we want to make to the figure\n", "new_params = {'xlim':[0,4],'title':'Sample MR Spectrum','xlabel':'ppm','ylabel':'abs'}\n", "\n", "# Create a new plot, with updated figure parameters\n", "fh, ax, lh = plot_sigs.plot(ppm_axis,np.abs(spectrum),plot_type='spectrum',\n", " plot_params = new_params);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Autoscaling the y-axis\n", "\n", "Note that even though we've changed the x-axis boundaries, the y-axis data has been re-scaled automatically to accommodate the data. This is not a built-in matplotlib capability. The function `autoscale_y` in the `plot_1D_signals` module does this.\n", "\n", "This function can also be applied to axes that have been modified after the fact. For example, let's change the x-limits of the plot we just generated:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fh, ax, lh = plot_sigs.plot(ppm_axis,np.abs(spectrum),plot_type='spectrum',\n", " plot_params = new_params);\n", "\n", "# Change the x-limits of the axis generated by the plot function\n", "ax.set_xlim([1.5,1.0]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks kind of terrible. Use `plot_sigs.autoscale_y` to fix it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fh, ax, lh = plot_sigs.plot(ppm_axis,np.abs(spectrum),plot_type='spectrum',\n", " plot_params = new_params);\n", "\n", "# Change the x-limits of the axis generate by the plot function\n", "ax.set_xlim([1.5,1.0]);\n", "\n", "# Autoscale y-axis to better show the range of the data\n", "plot_sigs.autoscale_y(ax);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Much better!\n", "\n", "Note that autoscaling of the y-axis is done by default, but you can always turn it off by setting `plot_params['autoscale_y'] = False`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multi-line plots\n", "\n", "There are several ways that multiple lines can be added to the same plot. If the data to be plotted on the y-axis has more than one column (or row), each column (or row) of data will be plotted as a separate line. For example, say we want to plot the real and imaginary components of the spectrum from the previous example on the same axis:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Create an array of data with size [2 x 1024] by concatenating the Re and Im components of the signal\n", "complex_signal = np.vstack((np.real(spectrum),np.imag(spectrum)))\n", "\n", "new_params = {'xlim':[0,4],'title':'Sample MR Spectrum','xlabel':'ppm','ylabel':'Re and Im'}\n", "\n", "# Add both lines to the same axis\n", "fh, ax, lh = plot_sigs.plot(ppm_axis,complex_signal,plot_type='spectrum',\n", " plot_params = new_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The color order of the lines that are sequentially added to the axis is defined in the default parameters, and can be changed.\n", "\n", "Additional lines can be added to the plot after it has been generated as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Plot the first line\n", "fh, ax, lh = plot_sigs.plot(ppm_axis,np.real(spectrum),plot_type='spectrum',\n", " plot_params = new_params);\n", "\n", "ax.hold('on') \n", "\n", "#Add the second line to the same axis, using the ax handle returned by the plot function\n", "ax.plot(ppm_axis,np.imag(spectrum),linewidth=2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Oops, we didn't rescale the y-axis to accomodate the new line!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fh, ax, lh = plot_sigs.plot(ppm_axis,np.real(spectrum),plot_type='spectrum',\n", " plot_params = new_params);\n", "\n", "ax.hold('on') \n", "ax.plot(ppm_axis,np.imag(spectrum),linewidth=2); \n", "\n", "# Apply y-axis autoscaling to accomodate the new line\n", "plot_sigs.autoscale_y(ax);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking good." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Adding a legend to the figure\n", "One of the variables returned by the plot function contains the handles of all the lines plotted in the axis. This makes it very easy to add a legend to the figure after it has been generated." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig_handle, current_axis, line_handles = plot_sigs.plot(ppm_axis,complex_signal,\n", " plot_type='spectrum',plot_params = new_params)\n", "\n", "# Add the legend to the current axis\n", "leg_handle = plt.legend(line_handles,['Real','Imaginary']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating subplots\n", "\n", "The `plot_1D_signals` module has an optional input parameter `ax`, which specifies the axis to use for plotting the data. If not specified, it will automatically create a new figure, add an axis to it, and plot the data in that axis.\n", "\n", "The ability to specify the axis for plotting makes it easy to create a grid of subplots containing different views of the data with the same styling. \n", "\n", "Say you want to create a `[2 x 1]` set of subplots, with the real component of the data plotted in the first axis, and the imaginary component plotted in the second. Here's how you might do it using the `plot_1D_signals` module:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Open up a new figure empty, with 2 rows and 1 column of subplots\n", "new_fig,(ax1,ax2) = plt.subplots(2,1) \n", "\n", "# Specify some parameters to apply to the first axis\n", "new_params = {'xlim':[0,4],'title':'Real','xlabel':'ppm','ylabel':''}\n", "\n", "# Plot the Re signal on axis 1 by passing the handle, ax1, to the plot function\n", "plot_sigs.plot(ppm_axis,np.real(spectrum),plot_type='spectrum',\n", " plot_params = new_params, ax=ax1);\n", "\n", "# Plot the Im signal on axis 2\n", "\n", "# Specify some parameters to apply to the second axis\n", "new_params = {'xlim':[0,4],'title':'Imag','xlabel':'ppm','ylabel':'','line_color':'#d55e00'}\n", "\n", "plot_sigs.plot(ppm_axis,np.imag(spectrum),plot_type='spectrum',\n", " plot_params = new_params, ax=ax2);\n", "\n", "# Make the layout look a bit nicer\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting raw MRS data\n", "\n", "A good way to get a sense of the quality of the data is to plot the raw averages collected from each coil. This is the data before averaging and coil combination turn it into the kind of 1D signal we've been working with so far. It's a very interesting view of the data as it will allow us to easily detect any signal drift that may have occurred across the averages, and identify motion-induced artifacts, which are signal quality issues that can become obscured by the averaging + coil-combination process. However, for datasets with a large number of averages (e.g. 128) it can be a lot of data to (elegantly) plot on the same axis. The `plot_1D_signals` module provides functionality for easily generating and formatting these kinds of plots.\n", "\n", "First, let's load some sample data. For this example, we will use a file in the TWIX format that contains the individual averages collected from a 32-channel head coil." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_data = suspect.io.twix.load_twix('twix_vb.dat')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`raw_data` is an instance of the `MRSData` class. The data it contains is structured as `[averages x coils x datapoints]` and its shape is `[128,32,2048]`.\n", "\n", "#### Choosing a coil\n", "\n", "For this example, we're going to plot all the averages (128) collected from a single coil. However, since the SNR of the signals detected by each coil varies greatly depending on where the coil is located relative to the signal source, some coils can have such low SNR that the informative features of the data won't even be visible.\n", "\n", "The `plot_signal` module has a function, `suggest_channel`, that can...suggest the best coil to use for creating this view of the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Find a good channel for plotting the raw data \n", "coil_idx, coil_max = plot_sigs.suggest_channel(raw_data);\n", "print('Use coil ' + str(coil_idx+1) + '!') # Inex of the coil with the highest SNR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function simply finds the coil with maximum maximum average value across all the averages. It's an easy (dumb) way of identifying the coil with the highest SNR. \n", "\n", "For this dataset, coil 9 (index 8) is probably a good one to use for plotting. Let's get the slice of the raw data from this coil:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "coil_data_spectra = raw_data.spectrum()[:,coil_idx,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the data using default parameters, like we've done before, but this time use `plot_type = spectra` instead." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# Get the ppm axis for this dataset\n", "ppm_axis = raw_data.frequency_axis_ppm()\n", "\n", "fh, ax, lh = plot_sigs.plot(ppm_axis,np.abs(coil_data_spectra),plot_type='spectra');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `plot_type = spectra` instead of `spectrum` changes a couple of the default settings, including automatically overlaying the average of the signals (plotted in gray, although kind of hard to see in this view). Like all the other parameters, these can be changed as well.\n", "\n", "Let's style this plot a bit, and zoom into the region around the residual H2O peak, which is a good place to look for evidence of signal drift and motion artifacts." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_params = {'xlim':[raw_data.ppm0-0.3, raw_data.ppm0+0.2], 'xlabel':'ppm', \n", " 'title':'128 averages, coil ' + str(coil_idx+1),'ylabel':'abs'}\n", "\n", "plot_sigs.plot(ppm_axis,np.abs(coil_data_spectra),plot_type='spectra',\n", " plot_params = new_params);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this view of the data, we can see that there is definitely some signal drift happening across the averages. It looks like the residual H2O peak starts out at 4.7 ppm and drifts about 0.11 ppm over the course of 128 averages. \n", "\n", "Let's see what this data looks like in the time-domain:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the slice of the time-domain data from the selected coil\n", "coil_data_fid = raw_data[:,coil_idx,:] \n", "\n", "# Define some new parameters that are more appropriate for time-domain data\n", "new_params = {'xlim':[-0.005,0.25], 'xlabel':'sec', 'title':'128 averages, coil ' + str(coil_idx+1),\n", " 'ylabel':'Real'}\n", "\n", "# Call the plotting function using plot_type = 'fids'\n", "plot_sigs.plot(raw_data.time_axis(),np.real(coil_data_fid),plot_type='fids',plot_params = new_params);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Using a colormap to track the averages\n", "\n", "When multiple lines are added to the same axis, they are assigned a color based on the order in which they are added. If we want to trace the drift in the averages over time, we can instead use a colormap gradient to assign colors to the lines as they are added to the axis. You can select this option by setting the parameter `use_colormap = True`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_params = {'use_colormap':True,'overlay_average':False,'xlim':[raw_data.ppm0-0.3, raw_data.ppm0+0.2], \n", " 'xlabel':'ppm', 'title':'128 averages, coil ' + str(coil_idx+1),'ylabel':'abs'}\n", "\n", "plot_sigs.plot(ppm_axis,np.abs(coil_data_spectra),plot_type='spectra',plot_params = new_params);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And in the time-domain:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_params = {'use_colormap':True,'overlay_average':False,'xlim':[-0.005,0.25], 'xlabel':'sec',\n", " 'title':'128 averages, coil ' + str(coil_idx+1),'ylabel':'Real','use_colormap':True}\n", "\n", "# Call the plotting function using plot_type = 'fids'\n", "plot_sigs.plot(raw_data.time_axis(),np.real(coil_data_fid),plot_type='fids',plot_params = new_params);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is Matplotlib's colormap `summer`, and it is the default colormap but any of the built-in colormaps can be used. For instance `viridis` looks pretty cool." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_params = {'use_colormap':True,'colormap':'viridis','overlay_average':False,\n", " 'xlim':[raw_data.ppm0-0.3, raw_data.ppm0+0.2], 'xlabel':'ppm', 'title':'128 averages, coil ' + str(coil_idx+1),'ylabel':'abs'}\n", "\n", "plot_sigs.plot(ppm_axis,np.abs(coil_data_spectra),plot_type='spectra',plot_params = new_params);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look here for all the built-in colormaps:\n", "http://matplotlib.org/examples/color/colormaps_reference.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using `plot_1D_signals` for batch processing\n", "\n", "The `plot_1D_signals` module has some additional functionality that makes it easy to do batch processing. Say you have several files you want to process, and you'd like to generate plots of the raw data and at various stages of the processing pipeline. Instead of generating a new figure window every time, there are a few options in the `plot_1D_signals` module that allow you to completely suppress rendering of the figure, automatically save it wherever you want, and automatically close it.\n", "\n", "These options work best when you use one of matplotlib's non-inline backends. For this example, we'll use a `qt` backend. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib qt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the `qt` backend ainstead of `inline` will plot new figures in a separate window, instead of inside the `ipython` console.\n", "\n", "Here is an example of how you might use the `plot_1D_signals` module to generate a new plot, suppress its rendering, save it, and automatically close it.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define some new parameters\n", "new_params = {'suppress_fig':True,'autoclose':True,'save_fig':True,'output_fig_path':'test_fig.png',\n", " 'use_colormap':True,'colormap':'viridis','xlim':[0,4], 'xlabel':'ppm', 'title':'128 averages, coil ' + str(coil_idx+1),'ylabel':'abs'}\n", "\n", "plot_sigs.plot(ppm_axis,np.abs(coil_data_spectra),plot_type='spectra',\n", " plot_params = new_params);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There should now be a `png` image file containing the plot we just created, wherever `output_fig_path` said to put it. Note that `output_fig_path` should contain the full path to the location where you'd like the file saved, as well as the file name." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

openp2pdesign/Makers-Inquiry---Analysis

Q004.ipynb

1

19480

{ "metadata": { "name": "", "signature": "sha256:9b99c278b24431396e31b3cd0f6eef4d60cb392bc7d612ed522cfa60a289e0d5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Q004 - Qual \u00e9 il tuo genere?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# -*- coding: UTF-8 -*-\n", "\n", "# Render our plots inline\n", "%matplotlib inline \n", "\n", "import pandas as pd\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn\n", "import shutil\n", "\n", "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier, overridden by seaborn\n", "pd.set_option('display.max_columns', None) # Display all the columns\n", "plt.rcParams['font.family'] = 'sans-serif' # Sans Serif fonts for all the graphs\n", "\n", "# Reference for color palettes: http://web.stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html\n", "\n", "# Change the font\n", "matplotlib.rcParams.update({'font.family': 'Source Sans Pro'})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Load csv file first\n", "data = pd.read_csv(\"data/results-makers-40.csv\", encoding=\"utf-8\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Check data\n", "#data[0:4] # Equals to data.head()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "%%capture output\n", "\n", "# Save the output as a variable that can be saved to a file\n", "#\u00a0Get the distribution of gender\n", "gender = data[\"Q004\"].value_counts(dropna=False)\n", "print \"Data:\"\n", "print gender\n", "print \"\"\n", "print \"Data %:\"\n", "print data[\"Q004\"].value_counts(normalize=True,dropna=False) * 100" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Save+show the output to a text file\n", "%save Q004-GenereMaker.py str(output)\n", "shutil.move(\"Q004-GenereMaker.py\", \"text/Q004-GenereMaker.txt\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The following commands were written to file `Q004-GenereMaker.py`:\n", "Data:\n", "Maschio 97\n", "Femmina 32\n", "NaN 5\n", "dtype: int64\n", "\n", "Data %:\n", "Maschio 72.388060\n", "Femmina 23.880597\n", "NaN 3.731343\n", "dtype: float64\n", "\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot the data\n", "plt.figure(figsize=(8,6))\n", "plt.xlabel('Genere', fontsize=16)\n", "plt.ylabel('Persone', fontsize=16)\n", "plt.title(u\"Qual \u00e9 il tuo genere?\", fontsize=18, y=1.02)\n", "my_colors = seaborn.color_palette(\"coolwarm\", len(gender)) # Set color palette\n", "gender.plot(kind=\"bar\",color=my_colors)\n", "plt.savefig(u\"svg/Q004-GenereMaker01.svg\")\n", "plt.savefig(u\"png/Q004-GenereMaker01.png\")\n", "plt.savefig(u\"pdf/Q004-GenereMaker01.pdf\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAG0CAYAAAAmUs6fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYDjKHAAAw1HmAAAYjjIHAMBw/w9xTzUq7i4s3wAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x106633510>" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 } ], "metadata": {} } ] }

gpl-3.0

ssunkara1/bqplot

examples/Marks/Object Model/Image.ipynb

1

293429

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## The `Image` Mark\n", "\n", "`Image` is a `Mark` object, used to visualize images in standard format (png, jpg etc...), in a `bqplot` `Figure`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It takes as input an [ipywidgets `Image` widget](https://github.com/jupyter-widgets/ipywidgets/blob/master/ipywidgets/widgets/widget_image.py)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The ipywidgets Image" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bfe77423b216444e9ca904519b4c3a9c", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>Image</code>.</p>\n", "<p>\n", " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another notebook frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "Image(value='\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00H\\x00H\\x00\\x00\\xff\\xe1\\x012Exif\\x00\\x00MM\\x00*\\x00\\x00\\x00\\x08\\x00\\x07\\x01\\x0f\\x00\\x02\\x00\\x00\\x00\\x12\\x00\\x00\\x00b\\x01\\x10\\x00\\x02\\x00\\x00\\x00\\x0c\\x00\\x00\\x00t\\x01\\x12\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x01\\x1a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x80\\x01\\x1b\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x88\\x82\\x98\\x00\\x02\\x00\\x00\\x00\\x07\\x00\\x00\\x00\\x90\\x87i\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x98\\x00\\x00\\x00\\x00NIKON CORPORATION\\x00NIKON D3300\\x00\\x00\\x00\\x00H\\x00\\x00\\x00\\x01\\x00\\x00\\x00H\\x00\\x00\\x00\\x01Tama66\\x00\\x00\\x00\\x08\\x82\\x9a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xfe\\x82\\x9d\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\\x06\\x88\\'\\x00\\x03\\x00\\x00\\x00\\x02\\x00d\\x00\\x00\\x90\\x03\\x00\\x02\\x00\\x00\\x00\\x14\\x00\\x00\\x01\\x0e\\x92\\n\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\"\\xa0\\x01\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xa0\\x02\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x01@\\xa0\\x03\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xd5\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x012017:06:08 17:17:46\\x00\\x00\\x00\\x00\\x18\\x00\\x00\\x00\\x01\\xff\\xe1\\n\\x1bhttp://ns.adobe.com/xap/1.0/\\x00<?xpacket begin=\"\\xef\\xbb\\xbf\" id=\"W5M0MpCehiHzreSzNTczkc9d\"?> <x:xmpmeta xmlns:x=\"adobe:ns:meta/\" x:xmptk=\"XMP Core 5.4.0\"> <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\"> <rdf:Description rdf:about=\"\" xmlns:photoshop=\"http://ns.adobe.com/photoshop/1.0/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" photoshop:DateCreated=\"2017-06-08T17:17:46\"> <dc:rights> <rdf:Alt> <rdf:li xml:lang=\"x-default\">Tama66</rdf:li> </rdf:Alt> </dc:rights> </rdf:Description> </rdf:RDF> </x:xmpmeta> <?xpacket end=\"w\"?>\\x00\\xff\\xed\\x00jPhotoshop 3.0\\x008BIM\\x04\\x04\\x00\\x00\\x00\\x00\\x002\\x1c\\x01Z\\x00\\x03\\x1b%G\\x1c\\x02\\x00\\x00\\x02\\x00\\x02\\x1c\\x027\\x00\\x0820170608\\x1c\\x02t\\x00\\x06Tama66\\x1c\\x02<\\x00\\x061717468BIM\\x04%\\x00\\x00\\x00\\x00\\x00\\x10Ab\\x95\\xfc\\xe0Y\\xc0`f\\x9a\\x1f \\xaf\\xa5!h\\xff\\xc2\\x00\\x11\\x08\\x00\\xd5\\x01@\\x03\\x01\"\\x00\\x02\\x11\\x01\\x03\\x11\\x01\\xff\\xc4\\x00\\x1f\\x00\\x00\\x01\\x05\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x03\\x02\\x04\\x01\\x05\\x00\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x10\\x00\\x01\\x03\\x03\\x02\\x04\\x03\\x04\\x06\\x04\\x07\\x06\\x04\\x08\\x06s\\x01\\x02\\x00\\x03\\x11\\x04\\x12!\\x051\\x13\"\\x10\\x06AQ2\\x14aq#\\x07\\x81 \\x91B\\x15\\xa1R3\\xb1$b0\\x16\\xc1r\\xd1C\\x924\\x82\\x08\\xe1S@%c\\x175\\xf0\\x93s\\xa2PD\\xb2\\x83\\xf1&T6d\\x94t\\xc2`\\xd2\\x84\\xa3\\x18p\\xe2\\'E7e\\xb3Uu\\xa4\\x95\\xc3\\x85\\xf2\\xd3Fv\\x80\\xe3GVf\\xb4\\t\\n\\x19\\x1a()*89:HIJWXYZghijwxyz\\x86\\x87\\x88\\x89\\x8a\\x90\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xc4\\x00\\x1f\\x01\\x00\\x03\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x01\\x02\\x00\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x11\\x00\\x02\\x02\\x01\\x03\\x03\\x03\\x02\\x03\\x05\\x02\\x05\\x02\\x04\\x04\\x87\\x01\\x00\\x02\\x11\\x03\\x10\\x12!\\x04 1A\\x13\\x050\"2Q\\x14@\\x063#aB\\x15qR4\\x81P$\\x91\\xa1C\\xb1\\x16\\x07b5S\\xf0\\xd1%`\\xc1D\\xe1r\\xf1\\x17\\x82c6p&ET\\x92\\'\\xa2\\xd2\\x08\\t\\n\\x18\\x19\\x1a()*789:FGHIJUVWXYZdefghijstuvwxyz\\x80\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x90\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa3\\xa4\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb2\\xb3\\xb4\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc2\\xc3\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd3\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe2\\xe3\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf2\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xdb\\x00C\\x00\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xdb\\x00C\\x01\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x01d)B\\x03H\\xd4\"\\xc6\\x18\\x94Q\\xb8\\x07B\\x92]\\n\\xc4U\\x85\\xc2s\\x965\\xe0P%\\xc3\\x81(\\xa9!IJ\\xe8E\\x84GBHd\\xa4\\x89*8\\x84\\xe9\\x15\\x1a(\\xd05-\\t\\x944\\xb8A\\x01JV\\x838\\x84)@\\xcd+\\xa4h\\xd5&n\\xe4\\x12\\xe5\\xa1t\\x19\\x90F\\xcf\\r\\xc0\\xf9\\xe0\\x01\\xc3=IK\\x8a\\xb0\\x923\\x1ar\\x90!\\x13(\\x86\\xc5R\\x893W-\\xd6\\x0c\\x8d{\\x81\\x9b\\x11d\\xc1\\xd2\\x85\\x9aN\\rD\\x14N)\\xbc\\xce4\\x91\\n\\x01{%c\\xb6!)&\\x11\\x83\\x1c.B\\x1de\\x02\\xd79K\\xb0dB\\xd5\\xebW+XIM\\x16\\x82h\\x10\\x95\\x8c\\x83\\xad$\\xcc\\xc3wM\\xab+)\\xc0K9\\x80\\xdc\\x8aT\\xca2\\xa0\\x10\\x10:\\x98r\\xdbTI\"\\x10IH\\x92q\\x90IT\\x14Rf\\x8eU\\x9d\\x84\\xc2\\x9d \\x9c2r\\xe6\\xad\\xc2N+\\xde\\xb6gS\\x89FS\\x04\\x9d\\xae\\xea\\xb4\\xa9\\x04\\xbf%k\\xecKy*\\x8c\\x98r\\x10\\x1a!q\\xd0\\xb2\\xb1\\x1dd\\x11\\x12$\\xa0\\xb0C\"\\x94Nds1ZW\\x80\\xd2\\x99\\x14\\xe2$D0\\xc0\\r\\xabxA\"\\x98\"\\xa0\\x96\\x92\\x1bi\\\\\\xee\\x1a\\x149\\xcal\\xe9\\xa1\\xa18\\xee\\x06\\xe4.\\x14\\xacnZ)8\\x9e\\xc9J\\x84:k\\xad\\x05\\x19\\x0c\\x89\\x93\\xac\\xdd.\\xdb\\xc2\\x00\\xa8s\\x11\\xb1%\\x1a\\xd2\\xa0\\xd2\\x89\\x16RDb\\x94K\\x13\\xd6\\xc5\\n\\x9d\\x969RA\\xae\\x0e\\x11)m \\xed\\x9d\\xe57D\\x90\\xcc\\x1f\\xb6viNB\\x97\\xf5\\xaf\\x98\\xb4\\xfd*\\x1eJ\\xcf\\x10;*q!\\xa6\\xefF\\xb51%\\x0e`K8\\x9a\\n\\xf2\\x89\\x0c\\xa0\\xad\\rB[R\\xb8R\\xd2\\xa6\\xca\\x13\\x96\\xe6\\x14\\x08\\x99\\x82\\x088X\\x88\\x95\\x84\\xbc-+2\\xcc\\xd5\\xe8\\x9a\\x17.\"J\\xc2#\\x88\\xa1c\\x93\\x9b\\x84\\xda\\x12\\xf1\\xd04\\xd1\\x8a\\xd5\\xe2\\xd8ZT\\x16\\x00Q\\rG\\t-N\\x95\\xbd\\x13\\x9b\\x89\\xd8\\x93\\x06Z\\xd0@r\\xd2\\xa0\\x9b,u\\x96\\x89\\x89\\x80\\xe7#\\x08\\xd3\\x19\\x858B\\xf5v\\xcaP\\x94$\\xca\\xca\\x10\\xd5\\xe37c6\\xb4J%:,\\xab\\xb6\\xa50W\\'N^a\\xaa\\x1cK\\x06kr\\x02Ta\\xa5Hd\\x90\\xc1z5(\\x81X\\x96\\xa1,X\\x82\\\\\\xa8K\\x9c\\xb011se\\xc7:\\xa9I)2\\x95n\\x9d\\x82\\xd5\\xf3u\\\\@\\x1f\\x1c\\xc8\\xcd\\xc0\\xc9<\\x88\\xc8C[m[\\xa0\\x13\\xe1\\xb9\\xd0\\xcbhB\\x82!\\x9c:\\xcc\\xa4\\xad\\x0c\\xe3D`\\xa2\\xc6^\\x85\\x89y8D\\x8d\"\\x87\\x08R*\\xc8\\xdb,\\'m\\xdc\\x96\\xd2#f\\x14d\\xed4\\\\\\xa7l\\xca\\x1aD$\\xc0\\xd5\\x9en\\xa5)\\xca\\x98\\x99\\x81+\\xac\\xab\\x1d\\x92\\x98V\\xc1\\xd0\\xc8\\x1c\\xc84\\xa3U\\x84JL\\xe4Y+c\\x0e\\x0cp\\x92V\\t\\xd0\\xbaY\\x10\\xa4\\x04R\\'9h\\xda\\x13\\x92q\\x01jI&^\\x8d\\xf5\\xd3\\xa5\\x8a\\x06\\xb4\\x82\\x11\\x90h\\x87#i\\xc9\\x1c(Yc\\xb1Z\\x1c\\xb7,\\xa3[ 2\\xd4eI\\x11\\x0b\\x01\\xa4dM\\x11a\"\\xc3\"R\\xd1ca\\x14\\xe3\\xd9\\x02\\xa5\\x05\\x14%yhT\\x8e\\x88a\\x91\\x9c\\x92\\x84\\xec\\xe5\\x81\\xa8\\xd92\\x80R\\x07 U\\x03\\x80\\x99s\\\\\\r*\\xa6\\x80\\xccT\\xde\\'B\\xb9\\x0c\\x92\\x96\\xe6\\x86(\\x9cX$$A\\x12\\xad\"Z2bU\\x0c\\xaa$\\x83\\x94\\x0bX\\xd4\\xb4\\xccH\\xa0\\x88\\xd1!Q\\xb5u\\xa59\\xd8\\x9a\\x13S\\x91\\x02RS\\x15\\x00(\\xc2e&\\x15cN4!HjV\\x98\\x8c\\x88\\xa32\\xf0\\xf4\"%fJ\\x93\\x84\\xb4)1!\\x06U\\x1bi@\\xa5L\\x82\\x88J\\xe9\\x04\\x1a\\xc9<(z:\\xe1PiB\\x91\\x18\\x94\\xe3e \\x80\\x05%H\\x03A\\xc5\\x05\\xae\\n&\\x89(\\x88\\x94\\xa90\\x88\\x98c\\x04\\x11@\\x1a\\xc6\\xb3\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01\\x05\\x02\\xab\\xab\\xab\\xafzkO\\xb8\\x9f\\xb8\\x03\\x0c\\xf1,h\\xfc\\xbb~o\\xcdZ0\\xce\\x8e\\xbaqz}\\xda\\xf6=\\xcb\\xa3\\x0e\\xbd\\xaa\\xea\\xc3\\xa3\\xa7a\\xa0\\xec\\x1ax!\\xf1\\xect=\\x87o\\xccx\\x84\\xb3\\xfc\\xdd{Q\\xd3G\\x8fj=X\\xfb\\x94\\x14\\xedZ\\x0f-h\\xd2h\\xce\\xa6\\x8f\\xcd\\xf9\\x97\\xe6F\\x8a\\xa7\\xdcK\\xa5Yt\\xfb\\xda\\xb0\\xc7r\\xa6\\x18\\xfb\\x81\\x8e\\x05\\xf1g\\x88e\\xf1|\\x17Z\\x83\\xdb@U\\xc1\\xd6\\xa9S\\x1a\\xb0\\x83U0*\\x03Wq\\xc7B\\xce\\x8e\\xae\\xba\\xb2\\x0b\\x0fG\\xe6\\x03\\x1d\\xd3\\xc4\\x8a\\x8c\\x19`P\\x1d\\x1au\\t\\xe1\\xe48p?\\x9b\\xf3\\x11WJ\\xa6\\xba%Z)\\xe9RY\\x1d\\xb5\\xee\\xa6\\r\\x0eA\\xf1\\xec\\x08t\\xed\\xd2\\x1e\\xbd\\xbc\\xc7b\\x18iV\\xbd\\xa9\\xd2i\\x88\\xe9H\\xe9I\\xecx\\x0e!\\xe8\\x96\\x95h\\xa4\\xd1Ht\\xd6\\x81\\xe8\\xea\\xea\\xf2g\\xb1\\xef\\xa3\\xd1\\xd1\\x9e\\xd4ut\\xec\\x8f\\xb8:X[\\x04\\x16t\\x14\\xd1CB\\x03S<Z\\x1ahR\\xb1\\xd3\\x1f\\x15\\xea\\xa8\\xd3B\\xa0Yuua5a\\x04(\\xe8h\\x1a\\x80~t\\xab\\xa7jv\\xa9z\\x97Muf\\xad,\\x8e\\xc0\\x9a\\xa9\\xa0\\xbc\\x83\\xadR\\xd6:\\x7f-4,\\xb3\\xc6=\\x14\\xb2BQG\\xfd\\xf1T\\rf\\x8e\\xacQ\\x92\\xc1\\xe9,\\xeb\\xdf\\x83\\xa9\\xee^%\\xe0\\x18\\xd1\\xa81N\\xc3W\\x93\\'\\xb6,\\x1dHh\\xabF\\xab%\\x95\\xf5\\x15t>/\\x88G\\x15pBC_\\xb6\\xd5\\x93)\\xa3\\xd0?>\\x01\\x96\\x19\\xadj\\x1dj\\xea\\x1f\\x95^ZU\\xe8\\xc2\\xbb\\x10\\xc2\\xbb\\x1e\\x01\\x96\\x01t\\x0f\\xf2\\xf9\\x1e\\x11\\x8dT(\\xd5\\xd9\\x02\\xae\\x94\\'\\x80j\\xf6\\xc3\\xe9z2u\\xea`U\\x8d\\x1e5x\\x00\\x16\\x1d\\x1e/\\x12\\xe9GP\\xc0\\xab\\xa3*\\x0cQ\\xd4\\x17\\xc5\\x90{\\x14\\xd4\\xba:\\xba\\xd0e\\xd0\\xaff\\xba$\\x10\\x07Q[\\xa3E\\x12\\xc5\\nV\\xc6\\xaf\\xf3v \\xd4:5)\\xa6\\xaf\\xca\\xa1\\x92\\x08\\xa7l^/P\\xcdK\\xc7@\\x1dT\\xf8\\xbdX\\xab\\x05\\xd6\\x8c3\\xda\\x8c\\xf0\\x91\\xa5\\xe9AP\\xd24!\\xd3\\xa9>\\xcdt\\x15c\\x89\\xa3\\xc9-K`\\xd5\\x96@z4\\x92\\xcf\\x00C4eO6K\\xab\\n`\\xbc\\x0b/\\x1a\\xbd\\x1dT\\xfc\\x8fj\\x9e\\xd5~r{C\\x89\\xf6\\xbc\\xabP\\xff\\x000\\xa69U\\xc8N)\\xe2\\xb3\\xd5\\xab\\xab\\x1a\\xbe\\rO\\x83\\x1d\\xa9\\xa8\\nd\\x16\\x03.\\x9a\\xe3\\xa9\\xa8}N\\xaf\\x8b\\x15t\\xa3$0\\x19tu\\xec\\x9fh\\xea\\xa1\\xc7\\xf3\\x16\\x84\\x82\\x92\\x9a2\\xd3\\xecbRW\\xaaS\\xc5U\\xc8\\xb2\\xea\\xea\\xc9\\xab\\xd5\\x8e\\xd5\\xa3\\xadZH},\\xbe\\xae\\xd5|~\\xe7\\x16~\\xe8\\xe2\\x9d\\x18\\x0c\\x01\\x96\\x8e\\x95|\\x1a\\xd8\\xa1\\x03B\\xafe<\\x13\\xed/\\x8d\\x1a\\x98\\xe2Od\\x9e\\xd5g^\\xdc\\x1eN\\xa1\\x87J\\xb0\\x1d\\x1dXP\\xec\\x9a\\xd4\\xeat\\xed^\\xc5\\x81\\xd3\\xdd#\\xb1\\xa3K\\x14e\\xa4Q\\xf9\\xbcR\\xd7\\x1b#\\xb7\\x9e+,\\x86\\x00\\xad\\x1e\\x0c\\xfbTK\\xe0\\xcdK\\x1d\\xb5 \\n>\\x0f\\x87b\\xc7\\x05V\\x80\\x12\\xe8\\xa0\\xc6\\xbfr\\x9fr\\xb8\\x9c\\x9dT\\xeaY\\xf6\\xabP\\xcdC \\xf6\\x08\\xd3&\\xa2\\xc2\\xc3S$\\xd4j\\xf80\\x1eE\\xd4\\xba\\x87P\\xc1z\\x93WZ0^\\xa1\\xea\\xc3:\\x04\\xbd;\\x06;\\xd1\\x94\\xbfe\\x85>\\xa7G\\xab\\xd5\\x96j\\xd2\\x03*\\xa3*k%\\xf9\\xea\\xf1i\\x18\\xb5v\\xad\\x1fQd>.\\x8f\\x83\\xab\\xd4\\xb4\\xf1t\\xd0p5$\\x07\\xa3\\xa3\\xf3\\xfb\\x99\\x06\\xa54\\xb0]^uy\\xbe!\\xd6\\x8d*,94\\x14\\x14u\\xa3\\xc9\\xf9=*\\xf8\\xb1\\xdb\"\\xd3\\xdc=^\\xaf\\x83\\x043F\\x18\\xfb\\xc7F\\x92\\x1e\\x85\\xe2\\xf1b\\xb5k\\xe2\\xc7\\x12Y\\xd4:\\x02\\xe8\\xc6\\x8e\\xbd\\xa8\\xc0\\x01\\xd1\\x97F>\\xe6\\x8e\\x94z:\\x07\\xab\\xd4\\x10\\xc0?x\\x864z:\\xbc\\xdd{,k\\xa3N-D\\xbc\\xb4K\\xa1/WC\\xdb\\x8b\\xa9\\xae\\xbd\\x8b\\xe9|\\x08\\xed\\xa8<FL\\x96;qc\\xef\\x9e\\xc2\\x8c\\x9e\\xdc\\x1eE\\xabWGD\\xb3\\xda\\xa2\\xb9\\xb2\\xa6T\\xfc\\xb1a\\xd7\\xb0$3\\xd8v\\x19=K#\\xb6\\x8c\\x11\\xdb\\x8fz\\xfd\\xdd\\x19,(>/Vx\\xb0\\xcd]k\\xd9C\\xbe/\\x80\\xfb\\x8aie\\x87\\xc1\\x8e\\xfa\\xbd_\\x1e\\xc3\\xef\\xd5\\x9e\\xd4\\xd0\\x92\\xea\\xa7^\\xd5\\xa3\\xe3\\xda\\xac\\xd5\\xf9\\x07N\\xd4z=]\\x1f\\x1f\\xb9_\\xbaHuu\\x0e\\xbf\\xcch\\xcfj\\x82\\xeaC\\xa9\\xed\\xafm\\x1e\\x9d\\xb8\\x9av\\xa7m\\x1e=\\xc0a\\xf0\\xfb\\x99:\\x87\\xa7\\xf3\\xa7\\x8d^Ut\\xed_\\xbdC\\xf7<\\xea\\xcfq\\xd8j\\xe9\\xab\\xab\\xe0Zu\\x1f\\xcdR\\x8e\\x8c\\xa4\\x06t\\xedF\\xa1\\xd8\\xff\\x001\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x01\\xed(\\xfd\\x84yJ~\\x81\\xfaQJZ\\xfa7\\xf4#\\xa8/\\x97\\xf3\\xed?\\x97e\\xb7\\xda\\x1fM}\\x11z\\x8d\\x0f\\x9e\\xd1\\xdcu\\xf4c\\xe3A\\xf5\\x8e\\xa5\\xfc\\x9f]\\x0f\\x97\\xd7\\xebG\\xcb&\\xd8\\x86A\\xbd)\\xf1\\xf4}5\\x1e4\\x1aH^\\x97\\xdb]\\xc7Rt\\x1a\\xcb\\xce\\x9c\\xf6_q\\xec\\xa4k--\\xbf\\xaa;\\n\\x7fd-}Q\\xf4Oo\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x01\\x03@\\x9bC$\\xbe-\\xf1\"\\xdd\\xf0\\xf8y%\\xfc\\xbf\\xc0\\x83\\xce\\xb5\\xd9\\xfet\\x9d\\x0b\\x16\\xe9\\xbeXRO-Q\\xfe\\xb4\\xf3\\xeb\\xa5\\xf0\\x1c`\\xff\\x00\\xb1\\xd0\\x9e\\x11/\\xba\\xb4\\'_\\xe8\\x8e\\x12\\xc5\\x97\\x94\\x9eXye\\xeb\\xfe\\x1d?4\\xb1\\xf2Aln:H\\x12\\x8e\\x08\\xe3J\\xd4\\xda|p\\x91\\xc2\\x054\\xca>C\\x01@\\xb2\\xfe\\xcf,y}\\x19D\\x89\\x8a)?y?\\xd1\\x89&@\\xfea\\xff\\x002n\\x9el[\\xcd\\xf9\\xd7\\x97\\x97\\x9a\\xa2\\x8e[:\\x14\\xbbb\\x01\\x950\\xaf\\xf5\\xd3\\xc0I\\xfb\\x83\\x11\\xf7\\x97\\x81/D\\x1b\\xd0\\x9a\\xf2\\x89\\xc4\\xbck\\xfe\\x12\\xd2S\\xa7\\x1ad\\x1fo\\xf9\\xdd\\xbfs1\\xb8S\\xb0\\x89\\x04\\x0f\\xb8\\xb2\\x07qE\\xd6\\x93\\xf1h\\xe7\\x9fWq>\\x8f\\xdcSO\\xf8\\x08\\xd3j?\\xafg\\xa3\\xfd\\xa7x\\xff\\x00cI\"\\xc6\\x9e\\xbaS\\'\\xc7\\x86\\x886\\x89ZZ\\r%\\xf4\\xd6S\\xae\\x132i\\x80\\xe3\\x96a&\\xccX\\x1eJeR\\x93\\x02$4\\xf5l~M\\x82\\x90\\x1et/\\xf9\\x92\\xee\\xe2\\x90x$\\xb7\\xcb\\x11r\\xd0\\x86\\\\04\\xcb\\x99[\\x88\\xff\\x00\\x81\\x97\\x8f\\xf3\\xa2>\\x1d\\x94\\xf0\\xf9\\xd7\\x96\\xe9&\\xf9k\\xfa\\xb3<R\\\\cKe\\xc8xh8\\xc0\\xe5\\xe1\\x03\\x90t\\xa4\\r\\x02Yi\\xcb\\xc8\\xfe\\xaf\\x96#Yxi\\xa0\\xc7\\x89r\\xdbO>\\x8f:\\x17\\x82\\xfa\\xd3\\xe7_,G=\\xa6\\x9ak\\xc0@\\xe6\\xd2\\x10\\x93\\xa8N\\x87X\\x8e\\xdak\\xfa<w\\x8f\\t}\\x13\\xfe\\xf8C\\xfe\\xf3\\xef\\x89\\xbd=;\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x06?\\x02\\xfe|\\xb2>\\xf8\\xecG\\xdc\\xe3\\xfc\\xe7\\x1e\\xdc?\\x9c\\xfbY\\xec\\x18uu\\x7f\\x17N\\xc3\\xfdE\\xaf\\xf3\\x1c~\\xe8\\xf8\\xfd\\xc0\\xe9\\xdc\\x7f\\xaa\\xce\\x9f\\xcc\\xd5\\x8e\\xe0\\xb1\\xf7x\\xf7\\x1fw\\x8f~?\\xccq\\xee;\\x1f\\xb8\\x19%\\xfc;%\\x9f\\x9b=\\xa9\\xf7\\x07\\xf3Z\\xff\\x004^\\xbd\\xcb\\x0c\\xbf\\x8f\\xdd\\xafaOS\\xd8}\\xce\\x1f\\xce\\xf1t\\xed\\xc7\\xf9\\x83\\xd8}\\xd3_\\xbb\\xab\\xd3\\xb6\\x9d\\xf5\\xfb\\xdc~\\xfd>\\xf5G\\x1a}\\xc4\\xfa\\xf6\\xa3\\'\\xb0g\\xb5\\x1d<\\xdf\\xd8\\xc7~\\x1d\\xeb_\\xe64\\xed\\xc5\\xf1\\x0f\\xcd\\xf0u\\x1fw\\xc9\\xe9\\xd8\\xf7\\xd1\\xd3\\xb1.\\xbd\\x83\\xaf\\xdc\\xe3\\xc7\\xbf\\x1f\\xe6\\xb8v\\xe2\\xe9\\xafm\\x7f\\x98\\xd3\\xee}\\xaf\\xedg\\xe4\\xcf`\\xc3/\\x87~\\x1d\\xb5t\\xfb\\xdc>\\xe7\\x17\\xc1\\xf0\\xef\\xe5\\xf3c\\xef\\x8e\\xc7\\xeeQ\\x8f\\x9b\\x0c\\xfc\\xd9c\\xbf\\x17Z\\xf6\\xf8=\\x07j}\\xfa\\xf64g\\xf9\\x90\\xc7n/\\xecc\\xb0\\xfe\\xcb\\xab\\x0c\\xfc{\\xf0\\xfb\\xba=\\x1e\\xbf\\xcc\\xea\\xf4=\\xb8j\\xf5\\x1av\\x1ft\\x0f\\xb8Xe\\xab\\xb0c\\xb1\\xd7\\xbf\\x17\\xc7\\x8fm\\x1f\\x07O\\xbd\\xc7\\xb7\\x1e\\xdeZ1\\xdbV*\\xf5\\xfe`\\x96;\\x83\\xdd?>\\xca\\xfb\\x95\\xefO\\xb8\\x07~\\x0f^\\xdeuz\\x97\\xa7\\x0e\\xe4S\\xef\\x1e\\xd5\\x1fr\\xbd\\xa8\\xcf\\xdc\\xe3\\xdb\\x83\\xd3\\xb5)O\\xbd\\xaf~,\\xea;j\\x1f\\x0e\\xf5\\xfe{\\x83\\xe1\\xf7t\\xef\\xea_\\x16\\x01|;\\x8a\\xff\\x001\\xa7\\xdd\\xd3\\xf9\\xcd\\x18\\xec~\\xf7\\x0e\\xc0\\xbe?w\\x8fz\\x7f\\x07\\xdc\\xe0\\xf5\\xd7\\xfdG\\xc3\\xee\\xe9\\xe5\\xd8W\\xf9\\xbf\\'\\xc7\\xb1z\\x8e\\xdc^\\xa1\\xe8\\xf8v\\x1f\\x7fN\\xda\\xf6/_\\xe68\\xf6\\x1d\\xf4\\xef\\xfd\\xdf\\xbd\\xeb\\xdb\\x8b\\xafn?\\xcdh\\xfe?\\xcf\\x0e\\xf5\\xfb\\xdc>\\xe1\\x7f\\x0e\\xde\\x7f\\xcfW\\xee\\xd3\\xf9\\x8e/\\x83\\xd5\\xf1\\xed\\xa7\\xfa\\x87\\xcd\\xf0\\xfb\\x95\\xfe\\x7f^\\xfc~\\xe7\\x07\\xc3\\xb5j\\xc7\\xf3:\\xf7\\xe1\\xda\\x9d\\xf5\\x1f\\xccq\\xfec\\x8fn?\\xcfk\\xd8=\\x1fP\\xfecO\\xbd_\\xb9\\xaf\\xde\\xe3\\xdb\\x87\\xf3z\\xf7\\xe3\\xfe\\xf9\\xfe\\x0fO\\xe7\\x87\\xdf=\\xeb\\xfc\\xdf\\x1e\\xc3\\xe3\\xfc\\xff\\x00\\xff\\xc4\\x003\\x10\\x01\\x00\\x03\\x00\\x02\\x02\\x02\\x02\\x02\\x03\\x01\\x01\\x00\\x00\\x02\\x0b\\x01\\x11\\x00!1AQaq\\x81\\x91\\xa1\\xb1\\xc1\\xf0\\xd1\\x10\\xe1\\xf1 0@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?!9\\xef\\xfc\\x18\\xe6\\xeb\\xb8\\xb3%A\\xcc\\xa7\\x05\\xe1\\xfe\\xac\\xc5\\xe6\\xc4\\x10Q\\xcd\\x8d\\xa3\\xee\\xe2E\\xe4{\\xae_u\\xc8\\xf3D\\x16\\x7fb\\xa8B\\xf5<oZ\\xd8\\xdd\\xfb?\\xddBO\\x9b\\xc3\\xac\\x1d\\x9d}\\xd5\\xb1\\xd7\\xf1F\\xbel\\xaba\\xfc\\xac\\x0e\\xea\\xc5\\x90\\xd2\\xc8\\xed\\x97\\xd5\\x95[\\x04\\xb1g\\xcd\\xf6\\xcb\\n\\x11\\xcf\\xd5\\x84\\xedG\\x1b\\xf9\\xae\\xa3\\xea\\x888\\x92\\xac\\xbeU\\x9f\\x15\" \\xbb\\x1f\\xe6\\xf3\\xc7\\xff\\x00o#4\\xc1F\\x8fj\\x03~[\\xe4\\x9c\\x91bJ\\xc0\\xa4\\xaf\\x86\\xd8\\xd9b\\xd7<\\x9e\\xd5^\\xb9\\xd5\\x986\\x0f\\xaa\\xe1\\x87\\xdd\\x8c\\xba\\xd6\\x1d/\\xc5 \\xf6\\xd7^?\\x16s\\x9b\\xd15\\xf0^,\\xe9N&~+\\xf2\\xaf\\x16Q\\xee\\xa1\\xf6\\xa4\\xcd)\\xd4\\x10\\xbf\\x17G\\x14\\x07\\x98\\xa1\\xee\\x85~\\xaal\\xc6YA\\xd3\\x9f6\"#\\xa2\\xc75\\xfc\\xcb,\\x99\\xe7l\\xc0\\x1fk\\x00\\x81\\xc5\\x05\\x0f\\x12\\xde\\x8d\\x96\\x1e&\\xf0\\x9b\\xf1\\xec\\xacs\\xef\\x9a\\xcc)\\x97\\xe5c\\x05\\x82\\xc7no~\\x0b!\\xc1\\x94\\x8b\\x08\\x83\\xbb\\xf8\\xa8\\x82+l\\x85:\\xa9\\x1d^z\\xbb\\xe2\\xc4&*\\x8el\\x1e\\xa2\\xc1;?U\\xf3\\x1d\\xdc\\xaf\\x9b5\\xcf\\x9f\\xf8\\xc9M\\x8a\\x0e]\\xb2\\x9c^\\xd6\\x02\\xc6\\xd9\\x06.\\x15<\\x17\\x11\\x1f-%LA?\\xcd\\x13\\xf4T\\xc4\\xea\\xee/\\xc9|GvtO5\\x07\\xb9Sc\\xdd\\xc1\\xf3\\x16\\x1f\\xbb\\x87\\xdd\\xe4\\x16, \\x8e\\x15\\xbd\\x1f\\x144\\xe7\\xca\\xb1[<O62\\xd2\\xf4\\x12\\xb2\\xee\\xe3\\xd2\\x80\\xef\\x0b\\xe5\\x16\\x04J\\xd5\\x84#\\x1eopd\\x8b\\x8c\\xfe?\\xe4\\xcd\\xbd\\xf4\\xf5q*\\xd8Qi{\\xa5\\x87\\xf8\\xa5\\xecS..\\xe8\\xf8\\xba\\xa1\\xf9\\x96\\xeb\\x99h\\x8c<\\xac\\xe2\\\\*\\xf1Y\\x10\\xf8\\xd5\\xff\\x00\\x9bh\\xa2\\xf6UDqy\\xa0\\x88O\\x8a!\\xe0\\xff\\x00W\\x82<M\\x01\\x18\\xf1\\xbf4\\xf1\\xee\\xe1\\xe6\\xb2\\xf9\\xab\\xbc\\x7f\\xc0\\x0bc\\xd8\\xae\\xbf\\xe2</\\xa3\\x94$\\xd7+&M\\x13\\xb0\\xc5\\x892l\\x7f\\x83\\xfeb\\xe9N+\\xc6\\x15\\xe2ll\\xfa\\xaf\\xc1\\x8e/a\\xc1\\x14\\xcf\\x1e\\\\\\xbd\\xfa\\x91\\x1eB/\\x0eqf\\xb5\\x814.\\xb1N\\xca\\x92\\x0f\\x8dj{T\\x91\\xf0\\x94\\xc8\\xbbj\\x97\\x1b\\x16~q\\xa7Nb\\xb0\\x81\\x17S.P1\\xb0]\\x19\\xc5~\\x7f\\xe1\\xe6\\xff\\x005\\xf3,F\\x07\\xaau\\xb0\\x086\\x9b\\x90\\xdd\\x86\\x0b\\x02y\\x94\\xc79G\\xc4\\xfdQ<\\x95\\x88\\xb8\\xba<W\\n_\\x97?U33\\x96\\xf0W\\xf15\\x04;[\\xf4\\x97\\x13\\xea\\xf33\\x89,\\x88x\\xab\\xc1\\xddD\\xe1\\x9c\\xd8\\x02\\xc6\\x9er\\x8e}O\\x14\\xc3\\xbd\\x16l\\xff\\x00\\x13L\\xbe\\x9f\\xba\\x98\\xb2\\xed\\xbd\\xd3\\x13\\'\\xdd\\xf8\\x1e\\xaa(I\\xe6o\\x91\\xd5^$v\\xf4\\xaer\\xae\\x18\\xdc\\xf4\\xbb\\xe2\\xe9\\xcf\\xfc\\xa1\\x91\\xdf\\xcd\\xd8\\x06\\x8ak\\xcdP<{\\xea\\x9d\\x8a\\x1e\\xa9\\xc19\\xccV\\xd4Q\\x8a\\x81f \\xe2\\xc3\\x1b\\xc1\\x16O-\\xea\\xac\\x8e&\\xba\\x8a\\xebsSR\\xf1X@l\\x99F\\x8a\\xc0y*r\\xbf\\x00W\\x1au\\x9a\\x90#\\x9a\\xfc\\x1e=Vd\\xbf\\xc5[\\xc7\\x99\\xbfm\\xe4{\\xcb\\xa9\\x80\\x8a\\x8eM\\xef\\x9e\\xeel\\x1bP\\xd77\\x93n/1b>\\xe8c-\\x10\\x8e)I\\xff\\x00b\\xc9\\r#\\xaa\\xfc\\xdc\\x1c>9\\xbf\\xe0S<YV\\xe8\\xe4\\xfc\\xd3\\xcb\\xda\\x0e\\xff\\x00\\xe0?\\xc0\\xebV\\x17\\x91a\\xe6\\xfc\\x03I]\\xa3\\xf7X\\x1d\\xf3H\\x85\\xd5\\xe4\\xf1i\\x88\\x877\\x95\\xb2\\xb6\\x04\\x8e\\xe9X\\xf8\\n\\xf5*\\xa9}Th\\t\\x86g\\xcd\\xc6\\x1f\\xba\\x98\\x1c*\\x17\\xa7_\\xea\\xa1\\xdb\\x91\\xb0\\xb1\\xaeVG2\\xbd%\\x98\\xe5pu\\'\\xab\\x07\\x92\\xb3\\x88>k\\x08\\xd6\\xd9\\xb9*\\x10\\xf3\\xdf\\x13r\\xd1T\\xc2o\\x88\\xee\\xb4~Otc\\xe7\\xe7\\x9a\\xbc\\x16v\\xe6\\xf1\\x95\\xd1\\xa6*0\\xads\\xaf\\xfc\\xb3~e\\xe2\\x16p\\x9e\\xear>\\xd5\\x008\\xa6\\x9f\\x92\\xc5\\xf3\\x8d\\x1c=\\xad\\x7f\\xaa\\xc4\\x0fK\\xf0jO\\x8f\\xaa\\x13\\x87=X\\xc9\\xc3G2\\x9f~\\xa8}SXc\\x0f7\\xb8\\xdf5\\x08\\x99\\xa4\\xf6v\\xa8\\xd8\\x9f\\xe6\\xb8T\\xb5\\x1c\\xd44\\x1fvw0Q\\x92\\x86/\\x01AQ\\x18O\\xcdC\\x0b\\x11\\xd4sE\\x86\\xa4y\\xa8\\x8cG\\x81\\xd5\\x82!d\\xbdT\\x96?VQ>)\\xe8kI^f\\x90\\x12j\\xb6d4K\\xff\\x008\\xa8\\xdd\\xf5g\\xaf\\x0c\\xd10tX\\x7fy\\xe1\\xa6V\\xef?T\\xf09B\\x14=\\xd7,\\xf1\\x0f\\xd5\\xc7\\xabj=\\xba\\xbb\\x127\\x97\\xe2\\x87\\xba\\x94\\xab\\xbdN\\xaf$\\xec\\xae\\xad\\xa1\\xec\\xa4\\x17\\xe6\\xfa\\xb1\\x1e\\xa8\\x1cl\\xc5t\\xc7\\xcd\\x0c\\x13\\x9e\\xaap\\x1dwG1\\xb3q4\\xd9\\xba7\\xcd1C<^\\xcfu\\x87\\x1c\\xd9\\xa4T\\xefr\\xe4\\xcf\\x11G\\xd9\\xa3\\xa3O\\xdd>?\\x166\\xf2\\xea\\x8coG\\x82\\xadg\\xc5\\xc2<&\\xc0\\xcd\\xd4\\xd8p\\xce?\\x16W8\\x88\\xfd\\xd8\\x89\\xd9D\\x97\\x81`\\x91\\xc8\\xd8&\\x1f\\xe0\\xa6\\xd9u~,\\x13\\xad\\xf3v\\x89\\xcb\\xc0\\xa2\\x7f\\xf2\\x8e\\x07c\\xf1xt+\\xe4\\x18_\\x10\\xf7A\\x19P\\xe8\\xb0p\\x14?\\x1f\\xf1\\xc1<v\\x16}\\x90\\xfa\\x9a\\x8b\\xc0\\x1e\\xcc\\xa3\\xca\\xa2\\xa4\\xce\\xdez\\xb8\\n\\x87\\xf7`\\x18\\x0f\\xc8l?\\xfc\\x15\\x82>\\\\\\xab\\xa7\\x17\\xa9\\xb2o\\xee\\xfdgW\\x13\\xeb\\xab3\\x13\\xb2\\xfe\\x92\\x9d\\xb6x\\xf8+\\x8b\\xee)\\x04r^\\xfe_\\xdd\\xea\\xf9\\xbc\\x98=\\xcd\\xe2FM\\xc8\\xb1\\xcd~\\x16H\\xd7=Y\\x1e?5\\x92\\xc9z1a\\x99\\xca\\x94\\x1a\\xfdQ\\x00\\xf2\\xaax\\xd7h\\xe5ed\\xd1\\'\\xb6\\x93\\x9f\\xdds\\xbf\\xee\\xf8\\x1f\\xba<6=\\x8f\\xab\\xe0\\xcc\\xee\\x8a\\x0e\\xfd{\\xbc\\x12%J\\x9d\\x0f\\xd4X0\\x8d\\xf3d\\xfd8\\xae4\\x1e\\x92\\xf8\\x9f\\x8a\\xb4&\\x0b\\xa9}\\xd0,\\xd7\\xab$\\x95\\x89\\xf2\\xdd\\xc6\\xf7\\xbc\\xd9A\\xfb\\xbd\\xf0\\xa7(to\\x1e\\xd9\\xff\\x00\\xca%\\x11\\xc9\\x17A\\xe3\\xaf\\xf8BQ\\xc6w\\x1fuhC\\\\\\xe7\\x8a\\x18\\xc3l1seq\\x07\\xaaN9\\x8f4G\\x0e\\xaa\\xfa\\x7f7\\x8a,\\xe5\\xf1\\xcdi\\x07\\xcdG\\xbcP\\x1b=\\xd7S\\xdb\\xd5\\xc0\\x19}X\\xf0\\x19\\xf8\\xb9\\x03\\xb3\\xea\\xc1\\x1e>\\xb9\\xb3\\xb1-sV\\x13(,\\x97\\xbc\\x9a\\xb7\\xf6.k?\\xf1N\\xe6\\x9c\\xcf\\x17\\xd1\\xd5\\xd1x*,\\xfa\\xb2\\x1f\\xb2\\xc2\\x84\\xbct\\x8e\\x05\\xcb\\x11\\xf7\\xff\\x00\\x01,\\x9c\\x9b\\x1a\\tb\\xff\\x00\\xcaC\\x82\\xcew\\xe1p\\xf2M@\\xa7\\x95\\x17\\x89G\\x90%\\xa8\\x92\\xc3k\\x1eD\\xf7R?\\xea\\xc9\\x13\\xfb\\xbb\\xc1I\\x852\\x80\\xe6\\xe0)\\x9e\\xaag\\x97\\xed\\xa0N\\xa6\\xe3A\\xad\\xf3|\\xe5}_\\x98\\xbcI\\x0cM\\x8a]U\\'\\xe9Lu\\xe0\\xb1\\xd7qY\\xc8\\xd2*\\x87<\\xdd\\x13\\xcb\\xe6\\xe6\\x18\\xd8\\'\\xd53\\xdd\\x83Z\\xb6a\\x9a\\xc79\\xabX\\xac\\x88\\x94>/B\\xe0\\xfeo:\\xf8\\xbc\\x19B%mG\\xc4\\x8d3\\xe6\\xc9\\x04Z\\x1c\\xdcGU\\xc93\\x9c\\xdc\\x03\\xe0W\\xc86\\xa3\\xaeh\\xe0<m\\x11\\xe7\\xab\\xe11\\xea\\xcc\\xe6_Tq9v>\\xf6\\xcc\\x00,\\x02,)\\xf2\\xfb\\xb2\\x98\\x12~l\\xb1\\xb7\\xff\\x00\\x9a87\\xeb9\\xb9\\x08\\xfc\\xdef^\\x11\\xbe#\\x8a\\x8f\\x9aqM>+b\\xb34\\x93\\xb3\\xb7X\\x0f\\xe2\\xe47\\xdd\\xc8\\xfd\\xdcz\\x9aE\\x86\\xeb3w >,\\xa9\\xee\\xc4\\xc5\\x80<\\xbdsf!\\x1dY\\xa4\\x1a\\x83A\\x15\\x99\\x0f\\x1bE)\\xb2\\x9e\\xcd\\x15Ph<\\x82l\\x91\\xfc\\xb7\\x95u\\xdfk!\\x9dP\\x99\\xdf\\x8aj\\x87\\xd5\\x90\\xe8}\\xd2(\\xe46\\xcck\\x9cm\\x0e\\r,C\\xb2\\xc2C\\xba\\xf0\\xf7\\xdd.\\xe4f48z\\xdb\\xeb\\xd5\\x19\\x11\\xf3u\\x95\\x19\\xb1[\\x94\\xe2\\x827Y\\xbe\\xaa\\xe1\\xf1d\\xc4\\x9b\\xe4\\xa4\\x1c\\x87\\xd7v!q\\x95I\\x07\\xb6\\x83\\xf9\\xb1\\xbf%f#\\x99\\xc6\\xc3GJ\\xb5\\n\\x1a\\xa6/c\\xcd\\x1c!L\\xd9\\xaa\\x90S\\x0e\\xdb\\x04\\xa8|X\\xe1$]\\xf2\\xc0\\xec\\xb0\\xae\\x0f\\xab\\x07\\xee\\xaa\\n\\x88U\\xe3\\xd1e\\xe4z\\xbe\\x02K!\\x1e\\xeb\\x10K\\xe6\\xb0\\xee\\x9b\\x93W\\x8b\\xc58\\xff\\x00\\x9cU\\xa8wu\\xff\\x00J\\xbf?\\x12U\\xf0\\xacL\\x8a`\\x92z\\xa3\\x18\\xe5rHv\\xb8\\xc1U<\\x9b\\x01\\x87\\x96\\x89&\\x11\\x8b)&K/\\x08nq\\xd3*h\\x93\\xf6\\xb0e(\\x07\\x16d\\x84X\\x08\\xcaA\\xd7\\x8f\\x17\\xc2?\\x85O,\\x1e\\r\\xb2@\\x87\\xab\\x89\\xf8\\xcd\\xa9\\xbd\\xd5\\xeb\\xff\\x00\\xb6S#(\\xd9\\xe7\\xee\\xe6 \\x8f\\xf3\\x8a\\xcb\\x9c\\xbf\\x8aC+\\x94\\x0e\\xd5\\xf0O\\x9a|^Q\\xff\\x00\\'\\xfe3R\\x85H\\xed\\xed\\x0c\\xbe,\\x04\\xa2|\\xfcVD\\x0cwi\\x03\\xb9\\xa0.v\\xca|\\x95k6i\\x11\\xd4\\xd1\\x8d\\xa5\\xc9<Y\\x8f\\xc5\\xd3\\xbd\\xbc\\x88\\xdf\\x9a\\xa7*y\\xd8Y\\xceSf\\xbcE\\x80\\x9b\\x0f\\xf4\\xf3T:}7\\xcd\\x0e{\\xca\\x9f1\\xf5H=^\\x16\\x0f\\x8b\\x8e\\xa3\\xe2\\x9a\\xc9\\x1f\\xd58 >\\xeaI\\xdc\\x9f\\x15|\\x16\\x0c@?\\xb5@\\x0b\\x8fq@\\x19D\\xd8\\xac\\xbd\\x7f\\xc9\\xde+R{\\xbb\\x16\\nwi.y\\xa7\\x8dQ9\\xc5$qX\\xeah/\\xc8\\xb1\\x8eo\\xb1>\\xe8b4\\xa1\\xc2<aS\\xc8\\x9b#?v\\'\\xc5l\\xcd%U W\\xdb\\xee\\x84D\\xce\\xc9$\\x11Dv\\x9a\\x10\\xc7\\xc2o)\\xbd\\xfc\\xa9\\xc0\\x04\\xaf\\x03\\xf3O{\\x80\\x9dY\\xe0\\xf0\\xd3\\x8e\\x1b&#\\xff\\x00(\\\\~)\\x00Xl\\x0f\\x7f\\xf1j\\xf3\\x14N\\xcf\\xc5\\x98\\xf36\\x00e-\\x1fO\\x92\\xe3\\xa7\\x8b\\'\\xcb\\xdd\\xd9\\xd3\\xe3\\x8a \\x1d\\xa7\\x94CP\\xef\\x8a&b\\x98\\xd0\\x1b\\x1c\\xf1f\\xc9;9v\\x8f\\xcdA\\xe6\\xf9M\\xc7t\\xc10\\xddOo\\xba\\xf1\\xb95\\x02\",\\x9ei\\xe2\\xef\\x0f\\xe2\\xf20\\ng\\xa5\\x02xl\\xb7\\xaa\\x91\\xd4~\\xec\\x1c\\xca\\xfb/\\x8a\\xec\\x9fZY\\x07\\x819\\x9aPb,\\xf9\\xbf+;__\\xf1|\\x12T\\xe5QHV\\xe1\\x81\\xe2\\xa9(\\xc7\\xf9\\xb2;\\xbawZ\\xc3\\xc5\\x97\\x93\\xcf\\x05\\xfck\\x97\\x00\\x94?\\xc8\\xbcN\\xd8vk]\\x9c\\xa2\\xc6*4\\x96\\x8f\\x9a\\x88?\\xbaH\\x9b4\\x0c\\xb3\\xdf\\x14\\x0e.\\xf5T\\xb9\\xd5\\x19\\xcf4;-\\x96\\xe9\\xb9\\xccAu\\xfe\\xd4\\x07\\xbf\\x9ayg\\xc5x0s\\xcd\\x1f\\x8d9\\xb8\\xf7\\xfa\\xba\\xfc\\xd7\\x8ay\\xff\\x00\\x9b\\x0cY\\x9ekD\\xf1\\x166~\\x95\\xe8\\x1f\\xe2\\xa3\\xa5v\\x91\\xf5D\\xbd\\x96NDk7#d\\xdc\\xd8ff\\x9d\\x1b75\\xf2\\xa3jQ\\xce\\xd5\\x92\\x82\\xc8\\xce\\xeb\\x00V\\xe8\\x93P\\xe5\\xd5R \\xa4u\\xcd\\xd8\\xf0\\xb8\\xc7K\\xc7\\x82\\xc8\\x8e\\xde\\xf9\\x7f\\x17\\x8d[(\\x0e\\x16\\x0c\\xff\\x00v@\\x11`\\xf8\\xb3g\\xcd\\xe2\\xcfW/\\xc5c\\xe6\\xcc\\xba\\xb9\\xe3\\xf7g\\xff\\x00\\x14\\xd8\\x81\\xeb\\x9b\\xa3\\xf8\\x8a\\xba\\xf4\\xf3g#\\x1fe\\x8e\\xd7\\x05b\\xca\\t?\\xe2\\x1f\\xae\\xae\\xc0\\xd8uY?\\xe7\\x9b\\x1b\\xe6\\xa9\\xee)<4\\xb2\\x12Q\\xe2\\x89\\x8f-&^\\xa9;7\\xe0\\xac\\x03>l\\x9d\\xb1w\\xdf\\xe6\\xec\\xc7\\x11g6\\xe9\\xff\\x000\\xba\\xdf\\xbb(\\xd5\\xab5\\'\\xce7\\x9f[O.\\xe8x9\\xf3u\\xae~\\x166J\\xd2/\\x19W4\\xbc\\xff\\x00\\xc8\\xdb\\x0fw|\\xdd,\\x11\\x9cVHM\\xd4?\\xf0\\xe5\\xa6_\\x0b\\xd5\\x9f6\\x88\\xe5ck\\x9cEp<\\xd7\\x87\\xb1V\\x9e\\xeae\\x1c\\xfb\\xaeX\\xdb\\xc6\\xd9\\xee\\xad\\x1b3uBW\\xd9Y\\xc2\\xa0\"\\x89g\\xaaO\\x9e\\xeb\\x1e\\xf9\\xb0O\\xaaB\\xf1\\xdf\\xfcxn\\xb7\\xdf\\xba\\xaf\\xee\\x9a6d\\xa6\\x9b\\x7f\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x10>\\xfd\\xf2\\x95\\xf9%\\xe6\\x95zI\\xff\\x00[\\x1dW\\x96\\xe5\\x9a\\xdc\\x8b\\x11\\xe2\\xfb\\xac\\xaa!\\xc9-\\xc9\\xa2\\xaaUlT\\xa5\\x17D#d\\xfc\\x9d\\xe0\\x8e\\x9eQ3\\x1e\\xac\\x99\\x1b\\xc1\\x12\\xeaU\\x8e[\\x0f\\x17\\x80:\\x82K\\xec\\x84\\xe5\\xe1\\xde\\xea\\xca\\xdc\\xb4;\\x81\\xcd/8\\xf8\\xe8\\x00\\xba\\x89\\xcc\\xaf.\\xed\\\\\\x1ec\\xb69\\xd3\\x0b\\x08\\x8e0\\xc5\\xdf\\xb5\\xban\\xb9\\xb0sw\\x08\\xe4\\xa2\\x89vvCJ&\\x86Uy8\\xd4T\\xc0\\x83\\x9f\\x83\\xa9\\xb0.&\\x04i\\xce\"\\xa0\\xba8R\\xdb>\\xfcjAXP\\x1b3\\xd5\\xe3\\xf68k\\x12\\x82p\\xb2\\xd5\\t\\x91M@\\xa7\\x9dZ\\xa2\\xbe|;\\x07\\xae\\x91BBl\\xaf1[<\\xb2\\x9ci\\xfc\\xe7\\x89\\xe6F\\xd7H7oS&\\x9c\\xc3\\'\\xbb\\xa9\\xa3\\xdf\\'\\xca\\xc3\\xe3\\x08\\xee\\xcd\\xf3)\\xdc\\x0c$\\xdd\\x18\\xef\\xff\\xc4\\x003\\x11\\x01\\x01\\x01\\x00\\x03\\x00\\x01\\x02\\x05\\x05\\x01\\x01\\x00\\x01\\x01\\t\\x01\\x00\\x11!1\\x10AQa q\\xf0\\x91\\x81\\xa1\\xb1\\xd1\\xc1\\xe1\\xf10@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x10\\xf4\\xfaOS\\xe2Y\\x1c\\xe3=Y\\xf3\\x1a\\xfeV\\xe1l\\xf9\\xcd\\xd4Y\\xe6\\xdb\\x11\\xd1t\\xbe\\x8b:nx\\xbb\\x8f\\xa7\\xc6\\xcf\\r\\x9c\\xd9\\x96\\\\c\\x889\\xb3M\\xf37\\xe7\\xf0\\x8ev\\xc4\\x8d\\xcf\\xac\\x0c\\xcb\\x00\\x0f\\xa5\\xc0\\xeb\\xe3\\x1c\\xf7\\xf5\\x90\\xc9^-9\\xb8o\\x10\\xbe\\'\\x9b\\xefyL\\x9ey\\x90{\\x87\\x1b\\x1b\\xa9-\\xf0D\\xcc\\xe6\\x0e3\\xebc\\x9f\\xa2\\xfe.\\xbe%\\x8f\\xba\\xe5\\xd4\\x17\\xd2|I\\xd5\\xa0\\xad\\xben\\xb8\\xferA\\xcf\\xcc\\xae\\xe3\\xdf\\x80]\\xdem\\xe7!\\xa9\\xfb\\xc7\\xe7w%\\x99c\\xe2\\xc8\\xb8\\xb2\\xf9\\x9f\\x01\\xe7\\xcc\\xf1:/\\x90\\xf9\\xb8si\\xa1|\\xceos\\xa7Q\\xf5\\xbb\\xd2\\xeb\\xe6\\xe9\\xee\\xfc\\xec\\xf7.\\xdf\\x97\\x87yu\\x8e\\xa6\\xff\\x00L]1\\xday[\\xe2\\x0e\\xb5\\x9c\\xf3\\xedc\\xf5\\x9e\\xfc\\xf8\\x89\\xf0o\\xe4\\xbb\\x04\\xf4\\x0b\\xe4H\\x84K\\x90}\\xa36^\\xee\\x961\\xebd\\xe2\\xd0\\x84o\\xef\\xe9g/7\\x8b\\x8a[\\xb7V=lpXxg\\x87\\x160\\xfc\\xdb\\xbf\\x0c\\x8e\\xd8\\x04\\xce\\xa1\\xea\\xd3\\xf0,\\xe3\\xc3\\xef\\xd4\\x18\\x07Q\\xdcl\\xb2\\x19\\xab\\x9f\\xac|\\xb5\\xbf\\xbd\\xb3\\xd5\\xdb\\xc3\\xaf\\xc0\\xb9\\xf1\\xcc\\x83cO\\x10\\xcb|\\x1c[\\x96\\xbe\\xb7.\\xa1\\xcf\\x9bg\\xec@\\xbfH\\x9f^\\xfc\\xc8\\x18\\xe4g\\xa3H\\x03\\xf7\\xf0\\xe1\\xe2#\\xaf\\x1e#\\xd7\\xdc\\x82??7}\\x1d\\xb7n\\xaf\\x9b\\xae\\xfc\\xcf7\\xd7\\xaf\\x0f\\xc5fbC|\\x1dx\\xf5\\xe7\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x10\\x0ed\\xfa\\x1b\\x07\\xe1\\x90w.M\\xac~\\xf7..\\x98\\xfa\\xc1\\xf6\\x00G\\xe79\\xb0\\xed\\xfag\\xe4t\\x7fFCvq\\xfe\\x91,\".\\x1f1\\xd4\\x99\\x8co\\xcd\\x8ff\\xb9\\xf8\\x97\\xa3\\x87\\xed\\x03\\xc6[\\xdf\\x898\\xbb#\\xb1\\xf1v\\x044\\xfd~b\\xe2\\x1f_\\xdet!\\xd9\\xc4\\x0b\\x01\\x9d\\xe9\\xf4e\\xfa\\x0b\\xc3\\x96\\xdf\\x83\\xb2\\x03\\xce\\xf6\\xb3\\xea\\xdd\\xb7\\x8ed\\xc6\\xb8\\x85l-\\xe3g\\x8f\\xb7\\x13\\xcf\\xcb\\xf8!\\xcf\\x9e\\'\\xa2\\xed\\xac\\xb4\\xa7x$\\xdat2V\\xee\\xeb\\xcf\\xed\\rp\\xfd\\'\\xfb\\x93s>]\\xff\\x00\\x91\\x88\\xbes\\x19bga\\x87\\xde_\\x98)\\xfcq\\x00\\x01\\x99\\xab\\xf9\\xda<\\xf3\\xb0\\x07\\xda\\x1c\\x80o\\xccw!y\\xb7\\xabu9\\xfe/\\x94\\xc0\\x96r\\x1fYz<\\xb2kK\\xe4}mp?\\x98\\x1d\\xfb\\xff\\x00\\x17@~o\\xe6f\\x03\\xa3\\x8b\\x85\\x07\\xcd\\xcb`\\xbb)\\x93\\x91\\xff\\x000\\'\\xbe\\x8f\\x88\\xd0\\x99p9\\xdf\\xday\\x08N\\x9e#\\x05V}-^\\xa3~[s\\xc4\\x88Ey\\xbb cG\\x93\\xe6\\x10\\xe5\\xdf\\xa4\\xa8\\xf1\\xf5\\xe6@\\xee\\xc3\\x87>m3\\x90:\\\\@p-\\x96\\xcf\\xc0\\x9f\\xb5\\xb2\\x9c\\xec\\xbb/8\\xfe\\xcc\\xa8\\xc7\\x87\\xc7\\xd2`\\xee\\xff\\x00kN\\xf7\\x8f\\xde$+\\x8f\\xac\\xca\\x0f\\xcc\\x1a]\\xb8\\xfa\\xc7\\\\=\\xfc\\xee\\xdcb\\xe9\\x9f=F\\x9d\\xde\\xbewXf\"\\x01\\'\\xe1\\xe6\\x1d\\x9a\\xf4~W\\x07>F\\xda\\x0f<g\\x04\\xa1\\xa7\\x1d\\xfd\\xcbH*+\\xbf\\xcc[\\xcb\\xe1\\xf9{\\xb7+\\x87\\x1dB`\\xa5\\xa7\\x00\\xe3\\xea\\xc0\\xd8\\x10}\\xe0\\xa6-\\x97!\\xe2Y\\xc5dCA\\xb7\"g\\x1fVGJ/\\xc7\\xd6(.\\xb7~y\\x8c\\x0b\\rL\\x99\\xe9\\xb0\\xa8\\xcf\\xe6\\xe7\\x0f\\x9e\\x8b,\\xbc\\xe7\\x11\\x9b\\xf7\\xc9\\x07\\xe1\\xa7pq\\xd4r\\x81\\x9c\\xf2\\xc0\\xe0\\xc76\\xe3\\xcd\\xfa\\x04+Q\\x1d\\xfaH8~>\\x90\\x9b\\x8e$ \\xe7\\x9f\\xdb,f\\x81\\xb9f\\x0e>9 \\xd3\\x99\\xef;\\x83C\\xbb\\x80.\\x9cs9\\xbe\\xdb|\\xd9\\xcc|NL\\xb5\\xfc\\xaf\\xec\\xcc@\\xf9~\\x91\\x9cg\\x82p\\x08\\xe8\\xf6\\x16\\t\\x8a\\xe8q\\xf7>ehi\\x0fD3\\xf3\\x8eN\\x9e\\xe0\\xaa\\xa7\\xc4\\x8fx[A\\x9cwl\\xc04\\x97\\x8a\\x9d\\xa7\\xbf\\xe2,>6\\x0c\\xf8\\xb6zQ\\x86\\x9e:\\xf9\\xb4*p\\x9c\\x1b5\\xb8c\\xfd$\\xc715\\x18\\xfd\\x0c\\xc7l>\\x9b\\x01n\\xf1<~6\\xe9\\xdeI\\xbc\\xe7?{P~\\xa5\\xc91\\xd3\\xe6tL\\xeb\\xaf\\x01o\\xc2@\\x03L\\xee\\xd0\\x1c\\xdc[\\xc7Y\\xe3\\x8d\\xbb\\x99rs\\xf4O\\x0f\\\\<c\\xce\\xc0H\\xbd\\xf4\\xd9\\x87\\x8f\\xd1i\\xf07\\xa8>\\xbd~\\xf01\\xfa\\xc8//,\\xeaf\\xe7\\x13\\xc7\\x9eO\\xafS\\xd3\\x11\\xe3\\xab\\x0b\\xf3\\xd5\\x8f\\xd6\\xee\\xf7*\\x07H\\xd0\\x1d?1\\x01\\x86?9\\xe3\\xd4:o\\xf1?\\x1e{\\xfc\\xa1\\xf0\\x06\\t(7\\x8e\\t/\\x1a\\xc0\\x0e{\\x9e|\\x0f\\xce7\\x9d\\\\\\x07\\xde\\x1c\\xf3\\xdd\\xf0=_\\xd0?i\\xed&}7!Ct\\xfe\\xd0\\x01\\x1f\\xefo\\xacL\\x83\\xe9g\\xd6g\\x97#\\x80\\xcf\\xf1\\x1f\\x04\\x0f\\xe6\\x1eh9\\xfc\\xf3a\\xc1\\xc5\\xc9\\xa7Rs\\x96\\xc3\\x8d\\x8e\\xb9\\x94\\xf9s\\xeb\\xb0u\\xe7\\xf2\\x90\\x1d\\xc8;\\xc7\\x84s\\xae>\\xd2\\x0e|g\\xd6\\xc7\\xe7\\xe2\\t\\x99#\\xea\\x97\\xaf\\xc21\\x9cF\\xe7<]\\xc4\\xf3\\x9c\\x9e=\\x0f\\xdb\\xc8\\xcf\\x0b\\xf3\\xb8\\xc0q\\xf9@\\'?E\\xf1\\xfc\\xc4\\xd9\\xd4\\x86\\xf5l\\x80\\xd7xo\\xf3\\x14\\xe8\\xb3\\xb9\\xe8\\x9e\\xaf\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?\\x10\\xc4f8R/\\x08M\\x98\"\\xa2<8\\x8e\\xe6\\xc5\\x02\\x91\\xc8\\x94(\\x0f\\xfa\\xa92\\x91\\xf3\\x17d\\x14\\x9a\\xf3\\x03\\x13\\x1c&>j\\x04O\\xa8\\xf5x\\xa7\\x0c<\\xbc\\xfe(\\x03\\x08\\t\\xa6\\xad&\\n\\xb00\\x04\\xd4aC3\\xc6}U\\xde8\\x9b&\\x19 9\\x9aH\\t\\x10>\\xc9\\xbd\\x83\\x00\\xe37\\xddsy\\xdd\\xb2\\x1c\\x95\\xba\\xe3\\xd5\\x17\\xda\\x00z\\xf73UR@\\xac\\xd0\\xbd\\x1e+\\x0c\\x08\\x11\\xe5c\\xca\\xee(\\x83\\x01\\t}q\\xfd\\xd1\\xe5\\t2\\x11\\xc8\\xfb\\xa6\\xaf\\xfb\\x0f\\xf4\\xa8\\xc3\\ne\\xdf\\r\"\\xa6\\x98\\x9c;+\\x01\\x9e\\xf7b\\x81\\x04I\\xbd:\\xaa\\x02\\x06\\xcb\\x15!\\x01\\xcf\\x05\\x8f\\x1e\\xfe\\xeb\\xc0\\n\\xc5\\xe8\\x88\\x07\\x8e.\\x1cP\\xc9\\xc9\\xdc\\xf3@6~\\xa2\\xac\\x07f\\xb0?\\x85\\x12\\x84\\'\\xc43\\\\\\x8eM\\x93\\x0ed\\xe1\\xdf\\xfa\\xb9$\\x18\\xd9\\xd6\\xc1\\x91\\x0e\\xa7\\xff\\x00\\x15\\xd0\\x02{\\x17\\xf0\\x81\\x13\\xfb\\xb1\\x0c\\xe4\\xdc\\xdb\\xa8A\\xf1ccN\\xef\\x1c{\\x8e\\xca\\xcc-\\xff\\x00\\x13@\"\\'&c\\x97\\xdfU:Tx\\xb0\\x07\\x1a\\xcf\\xbd\\xa01{\\x17\\xdd\\xea\\x03)\\xfe\\xe8\\xce\\'C\\xa4\\n\\x14G\\'\\x15\\xa4d3=\\x9f\\xf8\\xdd\\x18\\x01I\\x18\\xbe\\xbd\\x94pDN\\xe6\\x7f\\xed)\\x18\\xb4N\\xa7\\x9d\\xa4d\\x88\\xe1\\xeb\\xb4\\x1f\\x16Qp\\x08\\x97\\xccS\\xd2\\xa1\\xfb\\xbb\\xd4x\\xf6\\xfb\\xa5\\xbb\\xc5\\xb9\\x12\\xd2\\x1c\\x91\\xdcRb9=\\xf1\\x15\\x0b2\\x86M\\\\\\xcb\\x8e\\x14\\xd9\\t!9\\x0c\\xcf\\xee\\xe9\\x82^#\\xc7\\x96\\xb8\\x80\\x88|\\xd0d\\xa8\\x8d\\x98\\xb3\\x19\\xd5\\x91:\\x19\\xc7\\xf7dE\\x97\\xe2b\\x81\\x94\\xe4\\xf7E}\\xae\\x93\\x8b\\xc5\\x00s\\xea(\\xd0%cJR*V \\xe2\\x12\\xcc\\xbe\\xd0\\x9e\\x9f5r\\x00\\x1ec\\n\\xf0\\x08\\x86\\xae\\xef\\xa8\\xa5\\x91<\\x7f?5\\x83\\x9e\\xeaf\\x11\\x0f\\xf5Q\\x83\\x00\\x97\\xc4\\xf8\\xa8\\xfc8\\x005\\xf7d%!3\\xba\\xb0\\xcc\\'\\x1e\\xa2\\x8b\\x1a\\xba$\\xda\\xa7\\x91\\x03y\\x1d\\xff\\x00\\xed\\x00\\xa4\\x85\\x918\\xf7Wd\\x803M\\xb1\\x859\\x1dGWY\\x06\\x15\\x9e.\\x98x\\x02y\\xe6\\xf7\\x0c\\xeb{7\\xf5[\\x19JtC\\x1e\\xbd_!\\x10\\xc7\\x8a\\xeb\\x97\\xaa\\x9c\\xf3\\xfc\\xd5t\\x89@<R\\xe2\\xef\\xf1\\xee\\xa8G\\xf1E\\xfe(\\x02\\x18\\x14\\xe0\\xd7\\xdbY\\xd2c{\\xa1K\\x19\\x0cOm1@jf\\xc9\\xc2\\xe4\\xf1\\xe7\\x8ez\\xa4\\xc3:\\xf7tYI\\xe9\\xdb\\x00\\'\\x18E3\\xf7\\x7f\\xf2\\xfb]\\xe5\\xab5\\x93\\x1d\\x9c|\\xd0DL\\xccS\\xc5\\xeb\\x12v<\\xfc\\xd3\\x08JW\\x90x\\x9c\\xfcM$\\x90\\'\\x14\\x16{98\\x81TA9\\x1b\\xee\\xbe\\x80\\xf9\\xeb\\xf5s\\'\\'\\x94\\xd6\\xee\\x81\\xc7\\x9a@q?[\\xfd\\xd9\\xc1<\\xb1\\xf1V(5\\x06%\\x8f\\x13H\\x062x\\xa1\\xd9\\xd7_Ud\\xa1\\x1a\\x8f\\x191\\xf3N\\x824NBh\\r0\\x11\\xf3b\\x98\\x01p\\xce\\n\\xa50Fy\\xd4e\\x84&o\\xa1x\\x90,g\\x14\\xf1\\xf8\\xaa\\x0b\\x94\\x98\\xb1\\xe1\"\\xa4hkgz\\xe0\\xa1\\x84\\xd1\\x05\\x8c\\x16\\x95x\\x14>\\xc6)\\xca#o\\xc4\\xf7F\\xa4\\xd1%\\xe2<\\xd6\\x05M\\x93\\x99\\x8d\\x9c\\x11\\xdbMj\\xd4\\x05/:\\x97\\x180\\x80\\xf3D\\xac@\\xc3\\x0b\\x1e}W\\xcb\\xe4\\x99y\\xf8\\xda<\\\\\\xb9\\x9ex\\xbe\\xf7;\\xa7\\xbeb\\xce\\x00\\x1eLo\\x14:V\\xd9\\x8f\\xdc\\xf5@\\xe5\\xc9\\xd6 \\xfc\\xd5E\\x0f\\x8cr\\xb2\\xc8\\x9fi~\\xee*+\\xa8\\x88\\xaau\\x83b\\x7f\\x86\\xa2\\x00g\\xe6/H\\x9dS\\x9fu\\x89\\x13KH\\xce\\xec\\x00\\xf0\\xc6\\x19\\xfdP\\xc9 %\\x90\\x949\\xdf\\r\\xd4\\xeb\\xb6$h\\xd1\\xccO\\xdd\\x184\\x98\\xde\\xc0\\xf34\\xb8\\x0c\\x97?\\xab\\x1c^C\\xc2\\x99\\xb8\":\\x0c\\xb1+-\\x98~(T\\x8e\\xf5\\xdf\\xccXL\\x1d+XQ#\\xa1\\xe0\\x0cn\\x01(\\xc1\\xdb>b\\xe0\\x04\\x93\\xe8\\x9f}]\\x9aH\\x1f\\xecjI\\x8d3\\xa6\\x1d\\xb3\\xc0$\\x0f\\x0e?\\xaa4J\\x03\\x1f\\x07\\x8a\\x07\"_\\xa3\\x8b\\xdc\\x05\\x93\\xa6\\xd5\\x0e\\xf3\\x0e\"*&X\\xf4\\x7f\\xf1L\\x02=\\x9e?\\xbb\\x08\\x01\\x97\\xcdpP\\x82\\xd0\\x85\\xb2\\xc5\\xeac\\xaf\\x16j\\xeb\\xe7\\x0f\\xab0\\xc3\\xe2\\xad\\x95\\xde\\xe9\\x00y\\xe2\\xb3\\x80\\x8f\\x07\\'\\xaa\\xa6x&\\xe9\\x11\\xbfqV\\xe0\\xbc\\x87\\xf3d\\xe0\\x9c\\xa22>i@\\x19\\x8d`\\xa4r\\x83\\x9e\\x7f\\x0f\\x8aq\\xd8\\xfc}\\xd6\\x14\\xd6\\x93f\\x16\\x11:V\\x8f\\xcd\\x82\\xb0\\xe9\\x9be\\x06\\x9b\\xe6\\xc39\\xc5\\xd2\\xa3\\x90\\x86\\t%\\x0f\\\\WS{\\x8e\\xdc\\xf3a\\xa3\\x05d\\xc2\\xc4\\x95\\xe7\\xea\\x9b\\'%\\xf4*k<\\x14\\xf3\\xf7gP\\xe1w\\xe3*D\\x03(O\\x9a!\\x07\\x04\\xf8\\x9a\\xc2\\'\\xc4\\xc5\\xe3\\xb0\\xb2(T\\x06\\xcf\\x9ft\\x05\\x08$C\\xe7\\x7f\\x9b\\x83Ab\\\\\\xe7\\x9a\\x18a\\xc0\\xe31B\\x83:\\xf2\\xde5K\\x0f|\\x13\\xdf4\\xd2\\xec\\x00&\\x03\\xfb\\xb2\\xce\\x03\\xc7\\x9a\\x805<\\xf0\\xcf\\x16.)n\\xa7\\xd7\\xe2\\xaa\\x89u\\x84~\\xaa\\xf7\\xe1\\x80@\\xd8\\x00%\\xeb3\\xba\\x8c\\xce\\x98\\xe6\\xa9T\\x93\\xc64\\xca\\t\\xd8^T\\x85\\x17\\x1e\\xcaA\\xc4C\\xaf>\\xdb\\x84A!\\xc6\\xc5\\x9aaI\\x1e\\xee\\x92\\xce~\\xfe\\xca!2\\r\\xe7\\'\\xd5lCF\\xc4U\\xb9\\x84\\xf2{\\xb9\\xbc2G\\x97\\xef\\xbaI\\x00B\\x193)D.Mx\\xf2P\\x02\\xa3A\\xe49\\xe6\\xc7\\xdd\\x05\\xa9\\xd7\\x98Mq\\x9f61\\tF\\x0e_\\xe9S\\x00#\\x05\\xd8\\xfb\\xe6\\xf2\\x8c\\x8c=P(\\xd4\\xe3\\xc4M\\x0b\\r\\n\\xf7\\x13\\x97A\\xc2\\x11\\xf6X\\x90j6)\\x96\\x9c$<\\xef4\\xc6\\xb3\\xa5\\x0e=\\x1f\\x8b(\\x08rT\\x99_\\xe1\\xa8\\x8c\"+\\x8e\\xe8\\x19\\xab/\\xef\\xbaa\\x1c\\x03>2\\xb4\\x12\\x86\\'\\xed\\xad~\\x05\\x12}5\\x1c<)<E5\\xf6\\x9ev:+\\xe1\\x12\\xb1\\xa10\\x9ejS\\x90av\\xc7\\xbf\\x16I\\x80\\x81E$}\\xd7\\xc9\\xec\\xc2z\\x93*H\\x87\\n\\x11>\\xa6\\xe6\\x05\\x0c\\xa4`\\x8a\\xb2\\x04\\xb1\\x99\\x9aEC\\x9b\\xae\\x11\\xfdQ!\\x93\\x07L\\xd5P\\x0c\\r\\xe6\\x91$\\xa9\\xf5Q\\xcakE\\xf7W\\x00F&fiT\\x93Z\\x92\\x9fWd\\x00DpU\\x13b\\x04*\\xd9\\xab#\\x96&\\x1fWs)\\x80\\x0b\\x9eS\\xcd\\x1b.L\\x14}\\xd9\\x8c\\xa7AV\\t\\xceOt\\x01\\x06\\x00\\x998k$y,\\xcf\\x0f\\xab\\xce\\x00OC\\xe9\\x83.\\xc9\\xf8\\x9f\\xf7C^\\x10@\\xf5\\xe2\"\\x9e\\x1c\\x06?\\xbb\\x87R\\x04\\xe7n\\xc5\\t9\\x11\\x1f\\xc5Y\\t\\x80\\xaf\\x8f\\x8a\\x80l\\xf9\\x95\\xeb\\xbb\"\\x00_\\x8ezlb\\x13q\\x8e3\\x9aa\\x18\\x0c}\\x8dZ\\xa9f\\x12s\\xc1\\xfc\\xcd*\\x15T\\x0f\\xb1\\xca\\xb0\\xc8\\n\\xb3\\xc3\\x95\\tC)\\xe5\\xc9\\xda\\xd2&$\\xf8\\xcf5\\x94)\\\\\\x127h\\xc0\\x1eX[\\xf3\\x17H\\xd3\\xb1\\xe5|RD\\xc0\\x1b\\x91\\x95\\xc1\\xa1C\\xecG\\x96\\x86\\n\\x003\\xdco\\xe2\\x88\\x010*~\\xe8\\x8c\\t\\x98\\x9f\\x16\\x1e\\xc9:\\xf2\\x1f\\x05\\x08R\\xa2g\\xe3\\xbb\\xde9\\xc3\\xe8\\xe6\\xca\\x02CN\\xf3\\xe6\\xe8(H\\xd7\\x83+A\\xb4gG\\x9e\\xaci+\\xb8*yS<9\\xa7\\xf3f\\xc1\\x1c\\xb2(j\\xb1\\x13\\xb3\\xa31P\\x97\\x17W\"r\\xd1\\x12\\xb9\\x08\\x03\\xf9=\\x14\\x80T\\x99\\x9c\\x15y>1\\xf3\\\\\\xc1\\x02JF\\xafQR\\x01&\\x10\\xfe>(\"\\xa5\\x1a\\xe9y\\xbb\\xa4\\xaa\\xec\\xe4\\'5\\xd1;\\x89R^\\xc8\\xf7\\xf3e\\x00\\xa1\\xce\\xceW\\xbc`\\xd2\\xc4\\x02\\xf53\\xe2k\\xc8\\xf7=\\'u\\x10\\xb9\\x93\\x1c\\xca\\xc0#G\\xf9\\x08\\x8b\\xca\\x84\\x8c\\x8c\\xf9\\xfb\\xa4\\xa0\\x98\\x9c\\x7f4\\x0eC\\xa9\\xef\\xc8Y\\xc0@\\xd3\\xf0\\xf2Y\\xd8\\x0b\\xfb9Q\\x85\\x05\\x11\\xcc\\xeft\\xa9\\xc1&\\xec\\x1de\\x98\\x1e>bS\\xd6M\\xd8B\\x8c\\x8f~}VP$\\x07I\\xf3Y\\x8c\\x8df\\x14\\x85@\\xe4\\x9f\\xf3\\xba&A\\x925\\xe2\\r\\x8d\\xe5\\xaf\\xcb\\x87\\x01\\xfaR!\\xc9\\x88\\xe1\\xfa*\\x80\\xc9\\x87\\xcf\\xac\\xbbI5\\x1e\\n(I\\x1f\\xc7\\xcd\\x84\\x82&\\x8c\\xf7@\\x11\\te=\\xd6?\\xa4\\x9c>[\\x90\\x84\\xe2\\x9e\\n\"P\\x8e\\xa7\"\\xa2$\\xdc\\x113\\xbd\\xb0$\\x0fD\\x0f\\xe6\\xb8L\\x93JLNAR(\\x90\\xc8\\x99\\x7f\\x1c\\xd0$\\x04\\xe9\\x00\\xed\"2\\x15\\xfdz\\xa4\\xbc\\xc0\\x0e3\\xfd\\xd4$\\x91I\\x07\\x03\\xd8\\xff\\x00W\\x18\\xe09\\x9e}1C,$\\xa4\\x83\\x1c\\xc3\\xe7\\xcdi\\x0b\\x84\\x1b2;\\xb60H.\\xc1\\xdb\\xfcVq\\x15\\xe6)\\xbb! \\xcf.>\\xe8\\xa2\\xaa0\\x11\\xc0R\\x82y?\\x01VD\\x04\\x9eU\\xa17K\\xf4!\\t\\xf5K\\x81\\xe5>rk\\xb8$\\x10\\xd8\\xea+\\x1d#\\x86\\xf21\\x05@\\x12G\\xc0\\xfe~h1p!\\xddX\\xaanK\\xdb\\x98\\xe2\\xb3$\\x9b\\x93\\xae\\xa8\\x1a\\xad\\xf4\\r\\xfe\\x1a\\xe4\\x89b\\x07\\x8f\\x8e\\xe9,;p\\xba\\xfb9\\xa9$w\\x12s\\xb5\\x19\\x82\\x99\\x1b\\x83\\xe6\\x7f\\xaa\\x93\\x19\\t^=P\\tH09\\x0f\\x9b\\x0f\\x18\\xeau\\x9fvf\\x98\\xc1\\x8aP\\x08\\x8d\\x12\\x07\\xe2\\xc0\\x90\\xe6y%\\xf9\\xab\\x84\\xd5`\\x92E\\x110\\x9c)\\x0f\\xc5DW\\xbc<\\xeb\\xb1Kg\\x8c\\xe1\\x9a\\xa8p\\x8f\\x1c\\xfdM\\x93\\x05\\x8cQ\\xf8\\xf7Z*8\\xfe\\x04\\xde\\x90\\xb1\"H\\xf4sF\\x0c\\xcc\\xe0a\\xd3\\xe2\\x82\\xce\\x08\\xd9\\xea\\xeb\\x14Y\\x19\\x13L\\x8f\\t\\xb2\\x94{\\x9fV\\x01\\xd1\\xee\\x1b\\xf3[\\x164\\xc9\\xcf\\x98h\\x80x\\x08\\xea^<\\xfdW$\\xa8o{a\\xe8\\x15T|E\\x9aNb\\x7f\\xb5G\\xc0%\\x97\\x87\\xcd29\\t\\x9c\\xd2\\x8a8J`\\xe6{\\xaf\\x01\\xea\\\\8\\n\\xc0\\xa6\\xc7\\xb8m[\\xc1/\\xc1\\xa5\\x15\\x14\\xc9\\xe0\\xf8\\xa1!\\x04i\\xf1\\x0b\\xaaa\\xc6g\\x8a@\\x11\\xcf{\\x9bc\\x88\\xe6\\xaf\\xeb+\\xe2\\x802\\xfb\\x8f\\x14\\xe2F\\x9d\\xc7T\\x03\\x00K\\x8d~\\xec\\xd8R\\xc1\\x13\\xe2\\x97\\xf2\\r\\x97<VtD\\xc4\\xac\\xcf\\xa2i\\x057\\x1b\\x04\\xc7\\xcf\\x9a \\xc8\\x12\\x06\\x13\\xac\\xabe\\x87\\x00t\\xacr\\xd1\"q$iPhL\\xc0N\\x1b2HIS52\\x1d\\x80\\x81e\\xea\\x83\\xa8A\\xc4\\x14\\x95\\x16;\\x1e\\xe9\\xc5b\\x1d\\xe2#\\xc1\\xdd\\x94E\\xc3\\x18\\x1e\\x0ef\\x8c\\xd3\\x17S,\\x1cs\\xd3V\\na\\x01\\xf7A\\xb8\\n\\x89K\\xbb\\xaf\\x00p\\x8c\\x9fV\\x0c\\xe6x\\x96\\x19\\xf9\\xa8\\x9aI\"\\xa4={\\xb2w\\x81\\xe6a\\xd7\\xd5\\t6$w\\xbd\\xff\\x00v\\x00\\x8d$\\xa7+\\xe3oA\\x90\\x16}\\xb3\\xf9\\xa4\\xe2WPc\\x9ap`\\x8cG\\xb6\\xae\\x99\\x12\\x8f1\\x14\\xc0\\xd3c\\xee\\xe9\\xa4\\xa3~)\\x97\\xa9c\\x881\\x1f\\x9b;\"\\x85\\xd9\\xe3\\xcd\\n`e\\x85\\xf0r\\xfcV(\\x9e\\x1e\\x19`\\xaa\\xa9R2|>\\xecQ`GO\\x16\\t*\\x0c\\x13\\xd5\\x12\\x84q\\xad\\xb3 \\xd4\\xe9\\xfd\\xd5q\\x14\\xe5b#\\xc5\\xe2J\\x13<\\x07\\xe9nQ\\xc2I\\xed\\xd3\\xdd\\x14L\\x87\\xb1\\xf9hL0\\'\\x06\\xd8*B\\x9a<{k\\x10\\x10\\xf4\\xf8<\\xd8\\xc4`\\xd5$\\x82\\xaa5#\\x19\\x99Q\\x12K\\xc8\\xe5L%#H\\xf7\\xf3D\\xc2T\\xc8\\xd3\\xe2h\\x18!\\x99X3\\xf8\\xb1IE\\xc9W\\xdd\\xce)(L\\x913\\x13a`F4@\\xf2\\xc5S\\x12D>\\x9f\\x15\\x01\\x02\\x88\\xbc\\x8f1C@<\\x10aO1d\\x8e:\\xb9\\x83\\x98\\xa8\\xc1L\\xb4\"~\\xa6\\x91\\x10\"<O\\x14L\\x01%\\xc6&\\xe8\\xf9)\\xc4BD\\xc8\\xef\\xc9B\\'2\\x19\\xe5\\x95qH\\x02|\\x16\\x0fB \\xf35\\x05\\x02\\xa1\\x00\\xeewT\\xa5\\x89\\x13\\xe2&\\xb3\\xb8F|\\x114\\x04\\x12\\xcc\\xbf\\x9a2\\x82J\\r\\xdfjdE\\x98~J\\x83M\\x07\\x04\\x9b\\x9bu0h\\x00\\x07\\x1e(\\x96S\\xc3\\xcfQJ\\n$gZ\\x86\\x04\\x02:\\x01=\\xf9\\xae\\x89\\xc1\\x93\\xa9\\xf6]\\x8f$\\xe9\\xb2Q8\\x19\\xa63,P\\n\\xd8\\x17\\x83\\x8a\\x01!12\\xda\\x002\\xe1Y?\\r&\\x0b9\\xf0\\x03\\x98\\xa8\\x02\\x04\\xc8\\xf3\\xfe\\xcb\\x1d\\x07\\xa3\\x96\\xf4\\xe1\\xd9`4\\x1a\\xb61\\xe1\\x96fW\\x96Q?\\x80\\xae\\x91\\xa7\\x13?\\xea\\xea\\x9dg\\x08\\xc6\\xcc\\x12\\x03\\x82\\x13\\xf8:\\xaf3 \\x94\\xde\\x7f\\xa9\\xb3F\\xb4\\x13\\x89\\x1f\\x15i\\x1a\\x08\\x97\\xbf\\x87\\x9a\\xd1\\xb2p\\x9b=\\x9d^#9\\x89\\x0f\\xf2j\\xd0\\xa2\"|\\xad\\xd7\\x00\\xc6@\\xfc\\xf1y\\xdd\\xe5\\xf9|TN\\xe0\\xd6!\\xde\\xa9\\x101\\x85\\x83\\xb3x\\xacD\\xe3\\x08iT\\xa0\\x9d\\xce7\\x9b\\x05,L\\xef\\x8a\\xea;\\x04|g\\xf5d\\xbd\\x00\\x86(O\\x00m\\x9f\\x1f\\xfc\\xadu\\x95s\\xe5\\xea\\xe0\\x99\\x97\\xd76\\x07\\xb2Yzt\\xbd\\xa0B)\\xec\\xa0#\\xb0\\xcf\\x1f5\\xf34\\x07&\\xd9\\xac\\xe9\\x12\\x1ceQ\\xc9\\xad\\xe3<\\x91\\xa0H\\xa2&h\\xc2g\\xbfU0\\xc6F\\xf8\\xbc\\x90\\x16\\xeb\\x06.\\xcc\\xed\\tL\\x1c)\\xb3 \\t\\xf4\\xe7\\xf2Y\"3-\\x9d\\x87\\xbf\\x8a\\xe0\\xc2\\x11\\x0c\\xf2\\x9e\\xdf\\xaa\\x82\\x82\\xbc\\x07\\x1fw\\x92\\\\\\x13,\\x8b(\\x13\\x18V,\\xc3\\x1f~\\xa8F\\xa4!\\xe0\\xf8l\\xd3BA\\x90\\x9b\\x98\\x8e\\x83\\x82\\xbf\\xc5\\x19\\x9cO\\xa0G\\xbe\\xeb0\\xba\\x01\\x90i\\xe6+*\\x10\\x94\\x80E\\x88z\\xe1\\xca}Tl\\xc3R;?\\xdb`\\xc3&\\xbd\\xac\\x91y\\xb0!C\\x98h&B!\\x1d\\xef\\x96\\x9ca\\x88O\\x81\\xf1\\xb6\\x01-g\\x18<\\xeda\\x14\\xa7R\\xa3\\x1c\\x02\\x11\\xd4\\xd2I\\xe3?\\xaa\\xab0\\x15&>iq\\x081vx\\x8f6n\\x087\\xef\\x9a\\x85\\x02;\\xe3\\xaft\\x884G\\x9b\\x00iP\\xf0\\x8b\\xa6\\x19\\xdc\\xe2\\xa7\\xcd\\x8b`\\x94O\\x9b41 ?\\x8d\\xa0\\xe1\\xd2O\\x89\\xa0\\xc4\\xc6\\xc6d\\x7f\\xed!\\xb6\\xa6\\x86LP\\x9a,\\xa2\\xbb\\x9f\\x19e+\\xe2do\\xfa\\xaa2t\\x83~.\\x80\\x915\"++\\x0c\\x8c\\x04>\\xeaez\\x1c{,J\\xc9{\\x11\\xed\\xf3^)\\xc9\\x0e\\x0c\\xf7\\xf8\\xb8\\xa2\\x11\"!\\xb38\\xa1\\x14\\xf5K\\xb9\\x14\\xc6J\\xaa2C\\x08a\\xbb\\x06Dp\\x8f\\xbb1\\xca\\xce\\x7f\\x94Yq\\x0c0\\xa2?\\x15\\x12\\x87\\x9eX\\xe6\\xc4\\xd1 \\xa0\\xd6R\\xa8\\xc1\\x06)\\x92}m\\x00uI\\x86\\x9f\\x9a\\x1c\\x9a\\x08\\x02q\\xfc\\xd2\\xb40g}\\xedO?\\x1c\\xbcx\\xfe\\xea\\x050\\xe9=\\xf3?Ua\\x04\\x94\\xa5\\x96)N\\x04\\xbc\\xfb\\xf4\\xd6\\x97_\\xf7U\\xc8\\x0b\\xcb\\x8d\\x8a\\x9a\\xa7\\xe5M\\x02@\\x98\\xbd\\xcc\\xff\\x00vY\\x00t9\\xfa\\xb9I\\xd3v\\xf08z\\xee\\xc8\\xc8\\x96\\x01\\x077\\xe2\\xa8\\xe0LA\\xfd\\x940%\\x7f/\\xcdo\\x85\\xd4a\\\\\\xeaH\\xe7\\x86><\\xd1\\xf9\\xff\\x00\\x15\"\\x06\\xc3\\x12lr\\x8c\\x93\\xf1-\\x84\\x00\\x1e\\xc8\\x99\\xb2\\xc8\\xf0\\x1c\\x93T\\xb0%\\x0eu\\xf7\\xe4\\xf1`\\x83\\x9dH\\x83\\xcdXZO\\x84G\\xe6\\x9a\\xa2%\\x06X\\x1e\\x18\\x8eh\\x80e\\xd4\\xd8\\x8e\\xdb\\x1aoP\\x86\\xe7\\xdfVK\\xf5?U\\x00\\xe2\\xf0x\\xa86\\xb0\\x177\\xef\\x9b\\xb3=Q\\r\\x87\\xfa\\xa4\\x0c\\x10\\x87\\xe1D`T@DT\\x00eu\\x81\\xfc\\xc5\\xf9\\xc0\\xae\\x86\\x8cN\\x99\\xbf5$~}\\xa7\\xb8\\xf1\\x7f\\x08\\x883\\xec\\xa21\\xcaD\\xbf/\\x15\\x11\\x89\\xcf,\\x9fSO\\x07\\xc4FE\\x969\\x83\\xc3S\\x8c\\xb8\\xc9\\xb1\"Q\\x89\\x07\\xc9X\\xbb\\x04\\x8f\\xb8b?\\xd5\\x0e\\x94\\xce\\xa7\\xceV1\\x08\\x11\\xec\\x8e\\xe8\\xd2\"n\\xfb\\xf3X\\xc4D\\xc2\\xbc\\x0f\\xb3\\x1f\\xc5\\x0f\\x03\\x19Le\\x12\\x07u\\x89\\x055\\xeb\\x1f4\\xc98\\x94\\x92C\\xd3\\xf1M\\x04\\xa1Tc\\xff\\x00\\xca+D\\xbct\\xff\\x00\\xe3d\\x1e\\x05W\\xf7p,\\xa6\\x8f\\xea\\xa3\\x94\\xa6\\x1f\\xfa\\\\\\x120\\x07C\\xd8XnrW\\xc1T\\x01\\x03?\\xcf\\x9b\\xa6\\x9e#\\xacw\\xbdx\\xb3DYl\\x84\\xd8\\x00Vt\\xfcm}#\\x839#\\x9b\\x00\\xf35=6{\\xd0dV\\xd0\\xf2k\\x82\\xf5bRzx\\xfb\\xaa\\xec\\xa5SI\\x9b\\xc1Hk\\x13\\xfc\\\\\\xe8c\\xbf\\x05H\\x0c\\x00D*:\\x08a\\x92\\x7f\\x1e\\xaa\\xc2d\\xf7\\xdd\\xea\\xcc\\xb0O\\xc2}U\\x81\\x8e\\xc7}kN<R\\'=g\\xc5\\x80R\\x12\\x8d\\x0e\\xe7\\xdcP\\xd7\\x80\\xf1\\xdb\\xea\\xc9$. \\xe7\\xfc*\\x12\"<q1\\xdf\\xddp\\n\\xcfG>\\xabV\\x8d\\x0f\\x93)\\x88\\x00\\xf2yJc\\xb9\\xc0 qC\\x88\\xec\\x0e\\x86\\xbdAB$\\xf1yC\\xaa\\x0c\\x8b1\\xc5\\x9c\\rL\\x8cX\\xe8Z[\\x1fq\\xea\\xa2C\\x15\\xe18*\\x90v\\xec\\xdft2\\x8d\"\\x02\\x93\\xef\\xcd\\x8e\\x00,\\x1f\\x04\\xd8\\x83\\xc7I\\xe2\\x7f\\x9a\\x08Kra\\xeb\\xcd\\xe2\\x93\\x80\\xd8O\\x17=\\xd0$\\xc7+I6\\x83A\\xc3\\xeb\\xe6\\xa0v4\\x10\\xb3X\\xd0(O\\x9a,\\x8e\\xa0\\xf6\\xd6Q&I\\xd4\\x1c\\xd2\\xa0\\xcb!uDq\\x8b\\x1b\\xdd\\x90\\x84\\x8d\\xc7\\x19\\xef\\xd5\\xd2?9\\xa8\\xa7F\\x020\\xb3\\xc2\\xc1\\xd6~\\x8c\\xa4\\xe0\\x18$\\x84j`\\x01\\'\\\\\\x94\\x08\\xd0wA\\xd0M\\x04\\x88\\xcc\\xa1\\x86>\\xf9\\xae\\xa8\\x89J&O\\x99\\xfe\\xa8E\\x92xLC\\x9b,HU>\\x0f\\xfe\\xf3P\\x03CC\\x07\\xed\\xa4\\x05\\x82\\x86\\x98\\xd7\\xcd`\\x93J>C\\xd3Z\\x88\\x18@\\x9ei(\\x91\\xae\"\\x94\\xceS\\xddU4\\x9e|\\x94\\x88\\x8c\\xb8I\\x9f\\x9a\\xa3\\xd2`\\xb1\\x175\\xcd\\x1e\\xe0\\x8b\\x81\\xca|RD\\x19\\x1dI\\x1b\\xe6\\xa3\\x8e\\x97\\x99\\x00\\xff\\x00\\x14x\\x84o\\x18\\xcf\\x8a0\\x84\\x0cc\\xfdMB\\xc1\\x02\\x0f/\\xc5l\\x1e\\xdf|\\\\\\xec\\x81\\x19\\xef(\\x13\\xb5M\\xca@R\\x7f\\x9fV\\x00\\x93\\'\\x9f\\xa8\\xf3\\xe2\\xe7\\x80\\x89\\x9cy~}\\xd2\\\\\\xc8\\x1c\\x8ex\\xac\\x92\\xbd\\x81\\x1fQ\\xb5\\x89.\\x0e\\x98\\x87\\x9f4\\x01\\x06q\\x99\\x12k\\xca\\x16\\x19\\x13\\xe5\\xc7\\xea\\xf1(\\xbcj\\xbf\\xbb$@e\\x99e\\xf8,\\xe4\\x9d7V\\xa1\\x82\\x1eG\\x9c(\\n\\x87\\x0f\\'\\xff\\x00lU3\"4\\xbfzM\\x11\\x9e\\x18S\\x0f\\x07\\x8a\\x96\\x06\\x0c\\x91y\\xf9\\xb9Ee\\xc1\\xf0\\xba\\x10B\\x12D\\xef\\x9b\\x10.\\x08Hq\\xf7J\\x9b\\x0e$P\\xcf\\x12]\\x96$\\xf2<u\\\\\\x02\\xa2\\x8e\\xc8|,we-E\\xd1\\x8f\\xe3\\x9a\\x81\\x02C\\xdcy\\xb2W\\x0b\\x81\"\\x7f\\xba\\x81\\t\\xf2I\\x11\\x9eJ\\xa7\\xab\\x03\\x9d\\xf9\\xab\\x04\\x11\\xfd\\xb6\\x13\\xa4\\xd7Nf\\x94\\xc4\\x15\\xe4Q`\\xa5X\\x01~l\"\\x03v\\xe0\\xf3\\x13\\x974\\xb8b~W1\\xc8\\x00Fy\\'\\xbb\\n*\\x03\\xbc\\x8c\\xfab/\\x87\\x03\\x88\\x82<U\\x1c(1?\\xc6e\\x84\\x18$\\xc7\\x89\\xaaU\\x94\\xb0\\xec\\xb9\\x82\\x15\\xdf\\xfd\\xfe\\xece\\xf1\\x18\\x98\\xef\\xfd\\xd5\\x14\\xc0\\t9\\x08{\\xac\\x02\\xc8u\\xcf\\x15\\x9f\\xa2\\xe7\\x81\\xf8\\xa422\\x1f\\x8f\\xba\\xc2J\\x0f\\xc6\\xf9\\xed\\xab\\x8c\\x04\\xc8\\xa0DX\\xc1\\x00<\\xfc\\xb3\\xc3@\\xa0\\xf9C\\xdd\\x90)c2J\\xa8d\\x17@0\\xfa\\xbc\\xa8\\x92\\xb2J\\xff\\x00v\\x1b0\\xa4\\x17\\xe1\\x936#)c\\x1b\\x03\\xddm\\xe0\\xf5\\xcf\\x14\\x05\\x94\\x8c\\xa98\\xa3*\\x07\\x7f\\xda\\xf4]i!Srb\\xcc\\x1c\\xa3\\xba\\xb9%\\xa48=e\\xe0\\xc7\\x90\\xc9\\xb2\\x11\\x0e\\x8c\\xf63\\xa8\\xa5\\x05\\x83\\x10\\x17\\xf7\\xfb\\xaf\\x14\\x87\\x91\\xc8\\x7f\\xaa\\x84\\xa4.=\\x91\\x9bPz^\\xcc\\xe0n4\\xbdm\\x91>:\\x8b\\x1d\\x84v\\xf3\\xc7\\xab#\\xa0\\xfbI\\xcb%4\\xf5\\x8e\\xbf\\x15\\xe34k\\x1a~2\\xc4\\x87i\\x88c\\xf9\\xee\\xc4\\xf6\\xe1\\xe6e\\xaf \\xf2#\\x9f\\xd8\\xd4\\t\\x1eI\\xc9\\xeei\\xc1 \\x1e?\\xcf\\x15Ar;\\xe3\\xf7PBbL\\xf9z)\\xa2\\x0c\\xae\\xf3\\xe3\\xd5\\x84tx\\x0c\\x93\\xef\\xba!@=iD\\x03\\x1b\\xaf \\xe6\\xcc\\x80\\xa7\\x99\\xf1\\\\\\x87\\'\\x89\\xe0\\xf9\\xb11\\x10\\x07\\xd5\\x94\\x02}T\\x10\\x10\\xeauPb\\x06\\xcc\\x8e\\xa8H\\x81\\xe1+2\\x00U\\x19s\\xfc\\xdb\\xbb\\xb5 \\'=\\xd6BC\\xa9D\\x9e\\x0f\\x8a$\\xe5\\xf0\\xbd\\xcft\\x1e\\x898:\\x16Zs\\xc9\\xcb>\\xab\\xd0D^\\x1duQ\\x01\\xe1\\x93\\x1c\\xde\\xf0\\xf29~j\\x0c\\x1f#\\xb3?\\xc4\\\\8x\\x9e\\xcd\\x9b\\x07\\x19 \\x91\\x07\\xe9H\\x95\\x18t\\xbf\\xc3@\\\\\\xae\\xbc\\xfe\\xa6\\xcc\\xbdA@) \\xe6\\x0e\\xea\\x04O\\x9a\\x82\\xc9\\x11a<\\xc7=\\xe5\\xe65\\x06\\xb7f\\xcc0\\xb6Dp\\x1f\\x9aVD\\x14\\xd8~\\xac\\x82p\\xd8\\xc1\\x8e\\xdf\\x14\\xcc\\xa1\\x102,X\\xe0\\xaf\\xc8\\xe5#G\\x0et\\x8fuS29\\x1c\\xca\\x08\\xa0r\\xae\\xf1\\xee\\x85\\xd1\\xe0\\xa7\\x8e\\xaa\\x04Xv?\\xce\\xae\\x06f:d\\xfc\\xd1j\\x8dG\\x82\\xc5\\x92\\xa0K\\x82{\\xa6\\x11*=9\\xcb->\\xa1\\x94\\x06e\\xbc\\xbf\\x14\\x8aG<Y0/Jr\\xd8\\x05\\x00\\xf4\\xae\\x13\\x82\\xe8\\xa4\\x93.w\\x1b\\x84\\xd7\\x9cI\\xfa\\xea\\x9a\\x04C\\x89g\\xaf\\x8a\\x19$s\\x19\\x8b r\\x0f\\xdf\\xcd\\x91O;\\x8cAb\\x06v\\xcc\\x03\\x14<\\x90\\xe4\\xe6\\x1f4\\x11\\x81\\x0e)$\\xb7\\x00\\xce\\\\\\xd3\\xb5(\\x1eXp\\xbf6b,\\x83\\x86=\\xd5-H\\xb1=\\x95L\\xb3\\x93\\x9b\\xeed\\xfc~\\xa8g\\x13\\xeb\\x8b\\rF\\xf9\\xf5\\\\\\xf0c\\xf7YX\\xc7\\xd5\\x04@\\x81\\xeaO\\x86h\\x1e\\x12\\x92\\xcc\\xb1\\xfex\\xa0\\xf2\\x13\\x84\\xfe\\xeb\\x06\\xd2\\x04@\\xbfwB\\xfc =%\\x18\\x90\\xa2\\x16Ix!\\x94\\xef1\\xf3qB\\xc3\\xff\\x00\\nd{9\\x12\\x83\\xe2\\xb0O Ls\\xcd\\xc1\\x18C\\x1d{\\xf9\\xb29-$\\xcek-\\x19\\xe8\\xeb<4\\nL\\x0f>\\x9a\\x94\\xf9<\\x91Z\\xf1z\\xe2\\xe4\\r:\\xa3#2\\x19\\xf5Ya\\x9f\\x04\\xd9\\x10B\\xf67\\xfch\\x19\\x04\\x08\\xcf\\xd4X(A\\x8e\\xd5\\x82\\x10\\xe0\\xbc\\xd9\\xf91\\x0c\\x8d\\xf6Ta\\x00\\xd0\\xce\\xed\\xd4r9\\x8f>\\xee\\x8e\\x0c\\x99\\xe3\\xccW\\x10\\r\\xdcC\\xb5\\x08\\x18\\x13\\x88\\x87\\xdd\\xc0\\x19u\\x8f\\xcb\\x153\\x8b4\\x14\\xc9c\\x04\\x12\\n9\\xfc\\xd2\\xe1\\xc4\\xd0d\\xf9(\\x9a)\\x8eC\\x16Ad%\\x99\\xe2\\xa0\\x97oS\\xfa\\xadL f\\xd4\\x80\\x96*\\xcc\\x13\\x1d\\xf8\\xb2\\x0c<\\x8eudP\\xde\\x05\\x9f\\xddk\\x0c\\x86\\x91\\xcc\\xfb\\x8b\\x90\\x10O&\\x1a\\'\\x82\\x9eq\\xe2\\x81\\x10o\\x16\\x08A\\xe3S\\xff\\x00\\xb6D\\xef\\xa6A\\xe9\\xa8\\x10\\xafL\\xa5|\\x89\\xf8J\\xe1A9|\\xd4$\\x89\\x1c\\x1ce\\x18Q\\xf5\\xcf_5\\x03\\x07#l\\xe1\\x15\\xfa\\xea\\xa20\\xbf?\\xee\\xa0s\\xadFj\\x19\\xdf#\\xc7Q`/\\x8e\\xbc\\xd6\\x06\\x9f/\\x14\\x88Vr\\x99\\x01\\x04O\\x13\\x1el\\x1e\\tjwu\\x85r\\x08\\x18\\x8f~,\\t\\xc2\\x03\\xac\\x8f\\xdd\\xea\\x12\\x9e\\r\\xcf\\xae\\xe8\\x9aK\\x04\\t\\xbf\\x15A\\x88=#-\\x02\\x1b\\xcc\\xe4h\\xa8\\x94vc\\x11\\xf7P\\x8c\\x89eV \\x8b\\x02\\x0c\\x0e\\'+\\x81\\n\\x83\\x8e\\xfd\\xd0bw\\xb2}\\x94\\xe0\\x1fO5I\\x11\\x0c\\xe6\\xc7\\xf1`\\xae\\'5\\xf1c\\xa4\\xbf\\xb2\\xa7t\\xe3\\x89d\\xc9:\\x13\\xef\\xeek\\x0b) \\xf1\\xfcT\\xa7\\xb7H\\xb6n!\\x02\\xa0(d\\xe9\\xf5\\xf5\\xddN}\\x89\\xa4\\xfbc\\xab\\x10\\x83\\xa12\\xfc\\xd5<\\x89\\xe1C\\x8f\\x8a\\x08\\x82P\\xa8\\xaaWK\\xe0\\x9f\\x9a\\xf3.\\x1d\\x8d\\x92\\xca\\x0cx<\\xd4\\xa6\\x17a\\'<G\\xfe\\xd01~\\xcb T\\x0f[xH\\xeeg\\xf1@\\x86\\xec\\xe7\\x8f\\xe2\\xb9\\x99j\\xcb\\x1d}\\xc5\\x94\\xc68\\xf3t_@\\xd8\\xa5\\xa4\\x18\\xe7\\xbb\\x06TH\\xeey\\xa7!<\\xad( \\r)J\\x1d\\x1d?T!P\\x0e+[uS\\x86y\\xb0\\xc8C\\xaa\\xb6\\x10\\xac\\xc1\\xdd\\x95&N8)\\x96\\t\\xea\\x13\\x1e\\xa8r\\xb4a;\\xfc\\xdc\\xf1>2w\\xea\\xc9(\\x90\\xf1\\xd5\\x1d\\xd9\\xf8\\xea\\xcaL\\x86\\xcb\\x14\\xe4\\x87O\\xd5\\xe5\\x8eSX\\x01\\xc9\\xd96J\\x86\\x1c\\xaesR\\x1c\\xf8\\xca\\xa0}\\x81&\\xae\\xe1!\\xc3\\xc7\\xba&\\x13\\x03\\x7f\\x9b\\x02\\x00\\x94\\xc0\\xe0\\xf0{\\xaa\\x10\\x1dA\\xbb\\x05f\\x90\\x9c\\xcc\\xda\\xb3\\x81\\x0e\\xe5E\\x92\\x1eo\"\\x1f\\xbd\\x9f\\xba\\x908\\x8f\\x8d\\xb3\\x89;\\x98\\xaa\\x83\\x8f\\x1bx >\\xfa\\xa8z\\x13\\xf4V}\\xe6\\xc0C\\xe4f\\xaa\\x18k\\x93o7&U\\x04\\'\\xd5x\\xa3\\xb0i\\xa0\\xf1\\xfe\\xec\\x9c\\x0f]\\xf8\\xa2y1\\xcf\\xe3*\\xc0w\\x87\\xf5@\\xabM\\xf1I\\xd4P\\x89/=\\xbe\\xae\\x04,\\x94&>^+\\x9e\\x07\\x0cqX\\xd70\\x9e\\xf8\\xf1A\\x89\\x9c\\xfeK(\\x82\\t\\x14\\xce9\\x9b\\x02}\\xb0\\xd9D\\x1e\\xe1\\xf7T\\x9f(\\xad?uLA\\x0e2\\x92\\xf1\\x03\\xd0\\xc5\\x02\\x01#/=Y2\\x1c34\\x1c\\xa8)\\xf6FE\\x14Ks\\xe0\\xe6\\x89\\x9e\\xcf\\xcd\\x1dY\\xca\\x84\\xc1j\\x16\\x8c\\xc3\\xe9\\xf5N\\x91\\xe9\\xf3^\\x87\\xb8\\xa6\\x08\\xcc\\xcf\\xd4U\\xec9\\x8e(\\xceH\\x8e\\x15I\\xb5\\x81\\xc4d\\xd7C\\\\\\x88\\x9c\\x8f\\x05\\xff\\xd9', format=u'jpg')" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import ipywidgets as widgets\n", "import os\n", "\n", "image_path = os.path.abspath('../../data_files/trees.jpg')\n", "\n", "with open(image_path, 'rb') as f:\n", " raw_image = f.read()\n", "ipyimage = widgets.Image(value=raw_image, format='jpg')\n", "ipyimage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Displaying the image inside a bqplot Figure" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "755fcbec00654317aa22d6da462e0c38", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n", "<p>\n", " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another notebook frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "Figure(fig_margin={'top': 60, 'right': 60, 'bottom': 60, 'left': 60}, layout=Layout(min_width=u'125px'), marks=[Image(image=Image(value='\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00H\\x00H\\x00\\x00\\xff\\xe1\\x012Exif\\x00\\x00MM\\x00*\\x00\\x00\\x00\\x08\\x00\\x07\\x01\\x0f\\x00\\x02\\x00\\x00\\x00\\x12\\x00\\x00\\x00b\\x01\\x10\\x00\\x02\\x00\\x00\\x00\\x0c\\x00\\x00\\x00t\\x01\\x12\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x01\\x1a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x80\\x01\\x1b\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x88\\x82\\x98\\x00\\x02\\x00\\x00\\x00\\x07\\x00\\x00\\x00\\x90\\x87i\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x98\\x00\\x00\\x00\\x00NIKON CORPORATION\\x00NIKON D3300\\x00\\x00\\x00\\x00H\\x00\\x00\\x00\\x01\\x00\\x00\\x00H\\x00\\x00\\x00\\x01Tama66\\x00\\x00\\x00\\x08\\x82\\x9a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xfe\\x82\\x9d\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\\x06\\x88\\'\\x00\\x03\\x00\\x00\\x00\\x02\\x00d\\x00\\x00\\x90\\x03\\x00\\x02\\x00\\x00\\x00\\x14\\x00\\x00\\x01\\x0e\\x92\\n\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\"\\xa0\\x01\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xa0\\x02\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x01@\\xa0\\x03\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xd5\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x012017:06:08 17:17:46\\x00\\x00\\x00\\x00\\x18\\x00\\x00\\x00\\x01\\xff\\xe1\\n\\x1bhttp://ns.adobe.com/xap/1.0/\\x00<?xpacket begin=\"\\xef\\xbb\\xbf\" id=\"W5M0MpCehiHzreSzNTczkc9d\"?> <x:xmpmeta xmlns:x=\"adobe:ns:meta/\" x:xmptk=\"XMP Core 5.4.0\"> <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\"> <rdf:Description rdf:about=\"\" xmlns:photoshop=\"http://ns.adobe.com/photoshop/1.0/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" photoshop:DateCreated=\"2017-06-08T17:17:46\"> <dc:rights> <rdf:Alt> <rdf:li xml:lang=\"x-default\">Tama66</rdf:li> </rdf:Alt> </dc:rights> </rdf:Description> </rdf:RDF> </x:xmpmeta> <?xpacket end=\"w\"?>\\x00\\xff\\xed\\x00jPhotoshop 3.0\\x008BIM\\x04\\x04\\x00\\x00\\x00\\x00\\x002\\x1c\\x01Z\\x00\\x03\\x1b%G\\x1c\\x02\\x00\\x00\\x02\\x00\\x02\\x1c\\x027\\x00\\x0820170608\\x1c\\x02t\\x00\\x06Tama66\\x1c\\x02<\\x00\\x061717468BIM\\x04%\\x00\\x00\\x00\\x00\\x00\\x10Ab\\x95\\xfc\\xe0Y\\xc0`f\\x9a\\x1f \\xaf\\xa5!h\\xff\\xc2\\x00\\x11\\x08\\x00\\xd5\\x01@\\x03\\x01\"\\x00\\x02\\x11\\x01\\x03\\x11\\x01\\xff\\xc4\\x00\\x1f\\x00\\x00\\x01\\x05\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x03\\x02\\x04\\x01\\x05\\x00\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x10\\x00\\x01\\x03\\x03\\x02\\x04\\x03\\x04\\x06\\x04\\x07\\x06\\x04\\x08\\x06s\\x01\\x02\\x00\\x03\\x11\\x04\\x12!\\x051\\x13\"\\x10\\x06AQ2\\x14aq#\\x07\\x81 \\x91B\\x15\\xa1R3\\xb1$b0\\x16\\xc1r\\xd1C\\x924\\x82\\x08\\xe1S@%c\\x175\\xf0\\x93s\\xa2PD\\xb2\\x83\\xf1&T6d\\x94t\\xc2`\\xd2\\x84\\xa3\\x18p\\xe2\\'E7e\\xb3Uu\\xa4\\x95\\xc3\\x85\\xf2\\xd3Fv\\x80\\xe3GVf\\xb4\\t\\n\\x19\\x1a()*89:HIJWXYZghijwxyz\\x86\\x87\\x88\\x89\\x8a\\x90\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xc4\\x00\\x1f\\x01\\x00\\x03\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x01\\x02\\x00\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x11\\x00\\x02\\x02\\x01\\x03\\x03\\x03\\x02\\x03\\x05\\x02\\x05\\x02\\x04\\x04\\x87\\x01\\x00\\x02\\x11\\x03\\x10\\x12!\\x04 1A\\x13\\x050\"2Q\\x14@\\x063#aB\\x15qR4\\x81P$\\x91\\xa1C\\xb1\\x16\\x07b5S\\xf0\\xd1%`\\xc1D\\xe1r\\xf1\\x17\\x82c6p&ET\\x92\\'\\xa2\\xd2\\x08\\t\\n\\x18\\x19\\x1a()*789:FGHIJUVWXYZdefghijstuvwxyz\\x80\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x90\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa3\\xa4\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb2\\xb3\\xb4\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc2\\xc3\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd3\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe2\\xe3\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf2\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xdb\\x00C\\x00\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xdb\\x00C\\x01\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x01d)B\\x03H\\xd4\"\\xc6\\x18\\x94Q\\xb8\\x07B\\x92]\\n\\xc4U\\x85\\xc2s\\x965\\xe0P%\\xc3\\x81(\\xa9!IJ\\xe8E\\x84GBHd\\xa4\\x89*8\\x84\\xe9\\x15\\x1a(\\xd05-\\t\\x944\\xb8A\\x01JV\\x838\\x84)@\\xcd+\\xa4h\\xd5&n\\xe4\\x12\\xe5\\xa1t\\x19\\x90F\\xcf\\r\\xc0\\xf9\\xe0\\x01\\xc3=IK\\x8a\\xb0\\x923\\x1ar\\x90!\\x13(\\x86\\xc5R\\x893W-\\xd6\\x0c\\x8d{\\x81\\x9b\\x11d\\xc1\\xd2\\x85\\x9aN\\rD\\x14N)\\xbc\\xce4\\x91\\n\\x01{%c\\xb6!)&\\x11\\x83\\x1c.B\\x1de\\x02\\xd79K\\xb0dB\\xd5\\xebW+XIM\\x16\\x82h\\x10\\x95\\x8c\\x83\\xad$\\xcc\\xc3wM\\xab+)\\xc0K9\\x80\\xdc\\x8aT\\xca2\\xa0\\x10\\x10:\\x98r\\xdbTI\"\\x10IH\\x92q\\x90IT\\x14Rf\\x8eU\\x9d\\x84\\xc2\\x9d \\x9c2r\\xe6\\xad\\xc2N+\\xde\\xb6gS\\x89FS\\x04\\x9d\\xae\\xea\\xb4\\xa9\\x04\\xbf%k\\xecKy*\\x8c\\x98r\\x10\\x1a!q\\xd0\\xb2\\xb1\\x1dd\\x11\\x12$\\xa0\\xb0C\"\\x94Nds1ZW\\x80\\xd2\\x99\\x14\\xe2$D0\\xc0\\r\\xabxA\"\\x98\"\\xa0\\x96\\x92\\x1bi\\\\\\xee\\x1a\\x149\\xcal\\xe9\\xa1\\xa18\\xee\\x06\\xe4.\\x14\\xacnZ)8\\x9e\\xc9J\\x84:k\\xad\\x05\\x19\\x0c\\x89\\x93\\xac\\xdd.\\xdb\\xc2\\x00\\xa8s\\x11\\xb1%\\x1a\\xd2\\xa0\\xd2\\x89\\x16RDb\\x94K\\x13\\xd6\\xc5\\n\\x9d\\x969RA\\xae\\x0e\\x11)m \\xed\\x9d\\xe57D\\x90\\xcc\\x1f\\xb6viNB\\x97\\xf5\\xaf\\x98\\xb4\\xfd*\\x1eJ\\xcf\\x10;*q!\\xa6\\xefF\\xb51%\\x0e`K8\\x9a\\n\\xf2\\x89\\x0c\\xa0\\xad\\rB[R\\xb8R\\xd2\\xa6\\xca\\x13\\x96\\xe6\\x14\\x08\\x99\\x82\\x088X\\x88\\x95\\x84\\xbc-+2\\xcc\\xd5\\xe8\\x9a\\x17.\"J\\xc2#\\x88\\xa1c\\x93\\x9b\\x84\\xda\\x12\\xf1\\xd04\\xd1\\x8a\\xd5\\xe2\\xd8ZT\\x16\\x00Q\\rG\\t-N\\x95\\xbd\\x13\\x9b\\x89\\xd8\\x93\\x06Z\\xd0@r\\xd2\\xa0\\x9b,u\\x96\\x89\\x89\\x80\\xe7#\\x08\\xd3\\x19\\x858B\\xf5v\\xcaP\\x94$\\xca\\xca\\x10\\xd5\\xe37c6\\xb4J%:,\\xab\\xb6\\xa50W\\'N^a\\xaa\\x1cK\\x06kr\\x02Ta\\xa5Hd\\x90\\xc1z5(\\x81X\\x96\\xa1,X\\x82\\\\\\xa8K\\x9c\\xb011se\\xc7:\\xa9I)2\\x95n\\x9d\\x82\\xd5\\xf3u\\\\@\\x1f\\x1c\\xc8\\xcd\\xc0\\xc9<\\x88\\xc8C[m[\\xa0\\x13\\xe1\\xb9\\xd0\\xcbhB\\x82!\\x9c:\\xcc\\xa4\\xad\\x0c\\xe3D`\\xa2\\xc6^\\x85\\x89y8D\\x8d\"\\x87\\x08R*\\xc8\\xdb,\\'m\\xdc\\x96\\xd2#f\\x14d\\xed4\\\\\\xa7l\\xca\\x1aD$\\xc0\\xd5\\x9en\\xa5)\\xca\\x98\\x99\\x81+\\xac\\xab\\x1d\\x92\\x98V\\xc1\\xd0\\xc8\\x1c\\xc84\\xa3U\\x84JL\\xe4Y+c\\x0e\\x0cp\\x92V\\t\\xd0\\xbaY\\x10\\xa4\\x04R\\'9h\\xda\\x13\\x92q\\x01jI&^\\x8d\\xf5\\xd3\\xa5\\x8a\\x06\\xb4\\x82\\x11\\x90h\\x87#i\\xc9\\x1c(Yc\\xb1Z\\x1c\\xb7,\\xa3[ 2\\xd4eI\\x11\\x0b\\x01\\xa4dM\\x11a\"\\xc3\"R\\xd1ca\\x14\\xe3\\xd9\\x02\\xa5\\x05\\x14%yhT\\x8e\\x88a\\x91\\x9c\\x92\\x84\\xec\\xe5\\x81\\xa8\\xd92\\x80R\\x07 U\\x03\\x80\\x99s\\\\\\r*\\xa6\\x80\\xccT\\xde\\'B\\xb9\\x0c\\x92\\x96\\xe6\\x86(\\x9cX$$A\\x12\\xad\"Z2bU\\x0c\\xaa$\\x83\\x94\\x0bX\\xd4\\xb4\\xccH\\xa0\\x88\\xd1!Q\\xb5u\\xa59\\xd8\\x9a\\x13S\\x91\\x02RS\\x15\\x00(\\xc2e&\\x15cN4!HjV\\x98\\x8c\\x88\\xa32\\xf0\\xf4\"%fJ\\x93\\x84\\xb4)1!\\x06U\\x1bi@\\xa5L\\x82\\x88J\\xe9\\x04\\x1a\\xc9<(z:\\xe1PiB\\x91\\x18\\x94\\xe3e \\x80\\x05%H\\x03A\\xc5\\x05\\xae\\n&\\x89(\\x88\\x94\\xa90\\x88\\x98c\\x04\\x11@\\x1a\\xc6\\xb3\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01\\x05\\x02\\xab\\xab\\xab\\xafzkO\\xb8\\x9f\\xb8\\x03\\x0c\\xf1,h\\xfc\\xbb~o\\xcdZ0\\xce\\x8e\\xbaqz}\\xda\\xf6=\\xcb\\xa3\\x0e\\xbd\\xaa\\xea\\xc3\\xa3\\xa7a\\xa0\\xec\\x1ax!\\xf1\\xect=\\x87o\\xccx\\x84\\xb3\\xfc\\xdd{Q\\xd3G\\x8fj=X\\xfb\\x94\\x14\\xedZ\\x0f-h\\xd2h\\xce\\xa6\\x8f\\xcd\\xf9\\x97\\xe6F\\x8a\\xa7\\xdcK\\xa5Yt\\xfb\\xda\\xb0\\xc7r\\xa6\\x18\\xfb\\x81\\x8e\\x05\\xf1g\\x88e\\xf1|\\x17Z\\x83\\xdb@U\\xc1\\xd6\\xa9S\\x1a\\xb0\\x83U0*\\x03Wq\\xc7B\\xce\\x8e\\xae\\xba\\xb2\\x0b\\x0fG\\xe6\\x03\\x1d\\xd3\\xc4\\x8a\\x8c\\x19`P\\x1d\\x1au\\t\\xe1\\xe48p?\\x9b\\xf3\\x11WJ\\xa6\\xba%Z)\\xe9RY\\x1d\\xb5\\xee\\xa6\\r\\x0eA\\xf1\\xec\\x08t\\xed\\xd2\\x1e\\xbd\\xbc\\xc7b\\x18iV\\xbd\\xa9\\xd2i\\x88\\xe9H\\xe9I\\xecx\\x0e!\\xe8\\x96\\x95h\\xa4\\xd1Ht\\xd6\\x81\\xe8\\xea\\xea\\xf2g\\xb1\\xef\\xa3\\xd1\\xd1\\x9e\\xd4ut\\xec\\x8f\\xb8:X[\\x04\\x16t\\x14\\xd1CB\\x03S<Z\\x1ahR\\xb1\\xd3\\x1f\\x15\\xea\\xa8\\xd3B\\xa0Yuua5a\\x04(\\xe8h\\x1a\\x80~t\\xab\\xa7jv\\xa9z\\x97Muf\\xad,\\x8e\\xc0\\x9a\\xa9\\xa0\\xbc\\x83\\xadR\\xd6:\\x7f-4,\\xb3\\xc6=\\x14\\xb2BQG\\xfd\\xf1T\\rf\\x8e\\xacQ\\x92\\xc1\\xe9,\\xeb\\xdf\\x83\\xa9\\xee^%\\xe0\\x18\\xd1\\xa81N\\xc3W\\x93\\'\\xb6,\\x1dHh\\xabF\\xab%\\x95\\xf5\\x15t>/\\x88G\\x15pBC_\\xb6\\xd5\\x93)\\xa3\\xd0?>\\x01\\x96\\x19\\xadj\\x1dj\\xea\\x1f\\x95^ZU\\xe8\\xc2\\xbb\\x10\\xc2\\xbb\\x1e\\x01\\x96\\x01t\\x0f\\xf2\\xf9\\x1e\\x11\\x8dT(\\xd5\\xd9\\x02\\xae\\x94\\'\\x80j\\xf6\\xc3\\xe9z2u\\xea`U\\x8d\\x1e5x\\x00\\x16\\x1d\\x1e/\\x12\\xe9GP\\xc0\\xab\\xa3*\\x0cQ\\xd4\\x17\\xc5\\x90{\\x14\\xd4\\xba:\\xba\\xd0e\\xd0\\xaff\\xba$\\x10\\x07Q[\\xa3E\\x12\\xc5\\nV\\xc6\\xaf\\xf3v \\xd4:5)\\xa6\\xaf\\xca\\xa1\\x92\\x08\\xa7l^/P\\xcdK\\xc7@\\x1dT\\xf8\\xbdX\\xab\\x05\\xd6\\x8c3\\xda\\x8c\\xf0\\x91\\xa5\\xe9AP\\xd24!\\xd3\\xa9>\\xcdt\\x15c\\x89\\xa3\\xc9-K`\\xd5\\x96@z4\\x92\\xcf\\x00C4eO6K\\xab\\n`\\xbc\\x0b/\\x1a\\xbd\\x1dT\\xfc\\x8fj\\x9e\\xd5~r{C\\x89\\xf6\\xbc\\xabP\\xff\\x000\\xa69U\\xc8N)\\xe2\\xb3\\xd5\\xab\\xab\\x1a\\xbe\\rO\\x83\\x1d\\xa9\\xa8\\nd\\x16\\x03.\\x9a\\xe3\\xa9\\xa8}N\\xaf\\x8b\\x15t\\xa3$0\\x19tu\\xec\\x9fh\\xea\\xa1\\xc7\\xf3\\x16\\x84\\x82\\x92\\x9a2\\xd3\\xecbRW\\xaaS\\xc5U\\xc8\\xb2\\xea\\xea\\xc9\\xab\\xd5\\x8e\\xd5\\xa3\\xadZH},\\xbe\\xae\\xd5|~\\xe7\\x16~\\xe8\\xe2\\x9d\\x18\\x0c\\x01\\x96\\x8e\\x95|\\x1a\\xd8\\xa1\\x03B\\xafe<\\x13\\xed/\\x8d\\x1a\\x98\\xe2Od\\x9e\\xd5g^\\xdc\\x1eN\\xa1\\x87J\\xb0\\x1d\\x1dXP\\xec\\x9a\\xd4\\xeat\\xed^\\xc5\\x81\\xd3\\xdd#\\xb1\\xa3K\\x14e\\xa4Q\\xf9\\xbcR\\xd7\\x1b#\\xb7\\x9e+,\\x86\\x00\\xad\\x1e\\x0c\\xfbTK\\xe0\\xcdK\\x1d\\xb5 \\n>\\x0f\\x87b\\xc7\\x05V\\x80\\x12\\xe8\\xa0\\xc6\\xbfr\\x9fr\\xb8\\x9c\\x9dT\\xeaY\\xf6\\xabP\\xcdC \\xf6\\x08\\xd3&\\xa2\\xc2\\xc3S$\\xd4j\\xf80\\x1eE\\xd4\\xba\\x87P\\xc1z\\x93WZ0^\\xa1\\xea\\xc3:\\x04\\xbd;\\x06;\\xd1\\x94\\xbfe\\x85>\\xa7G\\xab\\xd5\\x96j\\xd2\\x03*\\xa3*k%\\xf9\\xea\\xf1i\\x18\\xb5v\\xad\\x1fQd>.\\x8f\\x83\\xab\\xd4\\xb4\\xf1t\\xd0p5$\\x07\\xa3\\xa3\\xf3\\xfb\\x99\\x06\\xa54\\xb0]^uy\\xbe!\\xd6\\x8d*,94\\x14\\x14u\\xa3\\xc9\\xf9=*\\xf8\\xb1\\xdb\"\\xd3\\xdc=^\\xaf\\x83\\x043F\\x18\\xfb\\xc7F\\x92\\x1e\\x85\\xe2\\xf1b\\xb5k\\xe2\\xc7\\x12Y\\xd4:\\x02\\xe8\\xc6\\x8e\\xbd\\xa8\\xc0\\x01\\xd1\\x97F>\\xe6\\x8e\\x94z:\\x07\\xab\\xd4\\x10\\xc0?x\\x864z:\\xbc\\xdd{,k\\xa3N-D\\xbc\\xb4K\\xa1/WC\\xdb\\x8b\\xa9\\xae\\xbd\\x8b\\xe9|\\x08\\xed\\xa8<FL\\x96;qc\\xef\\x9e\\xc2\\x8c\\x9e\\xdc\\x1eE\\xabWGD\\xb3\\xda\\xa2\\xb9\\xb2\\xa6T\\xfc\\xb1a\\xd7\\xb0$3\\xd8v\\x19=K#\\xb6\\x8c\\x11\\xdb\\x8fz\\xfd\\xdd\\x19,(>/Vx\\xb0\\xcd]k\\xd9C\\xbe/\\x80\\xfb\\x8aie\\x87\\xc1\\x8e\\xfa\\xbd_\\x1e\\xc3\\xef\\xd5\\x9e\\xd4\\xd0\\x92\\xea\\xa7^\\xd5\\xa3\\xe3\\xda\\xac\\xd5\\xf9\\x07N\\xd4z=]\\x1f\\x1f\\xb9_\\xbaHuu\\x0e\\xbf\\xcch\\xcfj\\x82\\xeaC\\xa9\\xed\\xafm\\x1e\\x9d\\xb8\\x9av\\xa7m\\x1e=\\xc0a\\xf0\\xfb\\x99:\\x87\\xa7\\xf3\\xa7\\x8d^Ut\\xed_\\xbdC\\xf7<\\xea\\xcfq\\xd8j\\xe9\\xab\\xab\\xe0Zu\\x1f\\xcdR\\x8e\\x8c\\xa4\\x06t\\xedF\\xa1\\xd8\\xff\\x001\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x01\\xed(\\xfd\\x84yJ~\\x81\\xfaQJZ\\xfa7\\xf4#\\xa8/\\x97\\xf3\\xed?\\x97e\\xb7\\xda\\x1fM}\\x11z\\x8d\\x0f\\x9e\\xd1\\xdcu\\xf4c\\xe3A\\xf5\\x8e\\xa5\\xfc\\x9f]\\x0f\\x97\\xd7\\xebG\\xcb&\\xd8\\x86A\\xbd)\\xf1\\xf4}5\\x1e4\\x1aH^\\x97\\xdb]\\xc7Rt\\x1a\\xcb\\xce\\x9c\\xf6_q\\xec\\xa4k--\\xbf\\xaa;\\n\\x7fd-}Q\\xf4Oo\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x01\\x03@\\x9bC$\\xbe-\\xf1\"\\xdd\\xf0\\xf8y%\\xfc\\xbf\\xc0\\x83\\xce\\xb5\\xd9\\xfet\\x9d\\x0b\\x16\\xe9\\xbeXRO-Q\\xfe\\xb4\\xf3\\xeb\\xa5\\xf0\\x1c`\\xff\\x00\\xb1\\xd0\\x9e\\x11/\\xba\\xb4\\'_\\xe8\\x8e\\x12\\xc5\\x97\\x94\\x9eXye\\xeb\\xfe\\x1d?4\\xb1\\xf2Aln:H\\x12\\x8e\\x08\\xe3J\\xd4\\xda|p\\x91\\xc2\\x054\\xca>C\\x01@\\xb2\\xfe\\xcf,y}\\x19D\\x89\\x8a)?y?\\xd1\\x89&@\\xfea\\xff\\x002n\\x9el[\\xcd\\xf9\\xd7\\x97\\x97\\x9a\\xa2\\x8e[:\\x14\\xbbb\\x01\\x950\\xaf\\xf5\\xd3\\xc0I\\xfb\\x83\\x11\\xf7\\x97\\x81/D\\x1b\\xd0\\x9a\\xf2\\x89\\xc4\\xbck\\xfe\\x12\\xd2S\\xa7\\x1ad\\x1fo\\xf9\\xdd\\xbfs1\\xb8S\\xb0\\x89\\x04\\x0f\\xb8\\xb2\\x07qE\\xd6\\x93\\xf1h\\xe7\\x9fWq>\\x8f\\xdcSO\\xf8\\x08\\xd3j?\\xafg\\xa3\\xfd\\xa7x\\xff\\x00cI\"\\xc6\\x9e\\xbaS\\'\\xc7\\x86\\x886\\x89ZZ\\r%\\xf4\\xd6S\\xae\\x132i\\x80\\xe3\\x96a&\\xccX\\x1eJeR\\x93\\x02$4\\xf5l~M\\x82\\x90\\x1et/\\xf9\\x92\\xee\\xe2\\x90x$\\xb7\\xcb\\x11r\\xd0\\x86\\\\04\\xcb\\x99[\\x88\\xff\\x00\\x81\\x97\\x8f\\xf3\\xa2>\\x1d\\x94\\xf0\\xf9\\xd7\\x96\\xe9&\\xf9k\\xfa\\xb3<R\\\\cKe\\xc8xh8\\xc0\\xe5\\xe1\\x03\\x90t\\xa4\\r\\x02Yi\\xcb\\xc8\\xfe\\xaf\\x96#Yxi\\xa0\\xc7\\x89r\\xdbO>\\x8f:\\x17\\x82\\xfa\\xd3\\xe7_,G=\\xa6\\x9ak\\xc0@\\xe6\\xd2\\x10\\x93\\xa8N\\x87X\\x8e\\xdak\\xfa<w\\x8f\\t}\\x13\\xfe\\xf8C\\xfe\\xf3\\xef\\x89\\xbd=;\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x06?\\x02\\xfe|\\xb2>\\xf8\\xecG\\xdc\\xe3\\xfc\\xe7\\x1e\\xdc?\\x9c\\xfbY\\xec\\x18uu\\x7f\\x17N\\xc3\\xfdE\\xaf\\xf3\\x1c~\\xe8\\xf8\\xfd\\xc0\\xe9\\xdc\\x7f\\xaa\\xce\\x9f\\xcc\\xd5\\x8e\\xe0\\xb1\\xf7x\\xf7\\x1fw\\x8f~?\\xccq\\xee;\\x1f\\xb8\\x19%\\xfc;%\\x9f\\x9b=\\xa9\\xf7\\x07\\xf3Z\\xff\\x004^\\xbd\\xcb\\x0c\\xbf\\x8f\\xdd\\xafaOS\\xd8}\\xce\\x1f\\xce\\xf1t\\xed\\xc7\\xf9\\x83\\xd8}\\xd3_\\xbb\\xab\\xd3\\xb6\\x9d\\xf5\\xfb\\xdc~\\xfd>\\xf5G\\x1a}\\xc4\\xfa\\xf6\\xa3\\'\\xb0g\\xb5\\x1d<\\xdf\\xd8\\xc7~\\x1d\\xeb_\\xe64\\xed\\xc5\\xf1\\x0f\\xcd\\xf0u\\x1fw\\xc9\\xe9\\xd8\\xf7\\xd1\\xd3\\xb1.\\xbd\\x83\\xaf\\xdc\\xe3\\xc7\\xbf\\x1f\\xe6\\xb8v\\xe2\\xe9\\xafm\\x7f\\x98\\xd3\\xee}\\xaf\\xedg\\xe4\\xcf`\\xc3/\\x87~\\x1d\\xb5t\\xfb\\xdc>\\xe7\\x17\\xc1\\xf0\\xef\\xe5\\xf3c\\xef\\x8e\\xc7\\xeeQ\\x8f\\x9b\\x0c\\xfc\\xd9c\\xbf\\x17Z\\xf6\\xf8=\\x07j}\\xfa\\xf64g\\xf9\\x90\\xc7n/\\xecc\\xb0\\xfe\\xcb\\xab\\x0c\\xfc{\\xf0\\xfb\\xba=\\x1e\\xbf\\xcc\\xea\\xf4=\\xb8j\\xf5\\x1av\\x1ft\\x0f\\xb8Xe\\xab\\xb0c\\xb1\\xd7\\xbf\\x17\\xc7\\x8fm\\x1f\\x07O\\xbd\\xc7\\xb7\\x1e\\xdeZ1\\xdbV*\\xf5\\xfe`\\x96;\\x83\\xdd?>\\xca\\xfb\\x95\\xefO\\xb8\\x07~\\x0f^\\xdeuz\\x97\\xa7\\x0e\\xe4S\\xef\\x1e\\xd5\\x1fr\\xbd\\xa8\\xcf\\xdc\\xe3\\xdb\\x83\\xd3\\xb5)O\\xbd\\xaf~,\\xea;j\\x1f\\x0e\\xf5\\xfe{\\x83\\xe1\\xf7t\\xef\\xea_\\x16\\x01|;\\x8a\\xff\\x001\\xa7\\xdd\\xd3\\xf9\\xcd\\x18\\xec~\\xf7\\x0e\\xc0\\xbe?w\\x8fz\\x7f\\x07\\xdc\\xe0\\xf5\\xd7\\xfdG\\xc3\\xee\\xe9\\xe5\\xd8W\\xf9\\xbf\\'\\xc7\\xb1z\\x8e\\xdc^\\xa1\\xe8\\xf8v\\x1f\\x7fN\\xda\\xf6/_\\xe68\\xf6\\x1d\\xf4\\xef\\xfd\\xdf\\xbd\\xeb\\xdb\\x8b\\xafn?\\xcdh\\xfe?\\xcf\\x0e\\xf5\\xfb\\xdc>\\xe1\\x7f\\x0e\\xde\\x7f\\xcfW\\xee\\xd3\\xf9\\x8e/\\x83\\xd5\\xf1\\xed\\xa7\\xfa\\x87\\xcd\\xf0\\xfb\\x95\\xfe\\x7f^\\xfc~\\xe7\\x07\\xc3\\xb5j\\xc7\\xf3:\\xf7\\xe1\\xda\\x9d\\xf5\\x1f\\xccq\\xfec\\x8fn?\\xcfk\\xd8=\\x1fP\\xfecO\\xbd_\\xb9\\xaf\\xde\\xe3\\xdb\\x87\\xf3z\\xf7\\xe3\\xfe\\xf9\\xfe\\x0fO\\xe7\\x87\\xdf=\\xeb\\xfc\\xdf\\x1e\\xc3\\xe3\\xfc\\xff\\x00\\xff\\xc4\\x003\\x10\\x01\\x00\\x03\\x00\\x02\\x02\\x02\\x02\\x02\\x03\\x01\\x01\\x00\\x00\\x02\\x0b\\x01\\x11\\x00!1AQaq\\x81\\x91\\xa1\\xb1\\xc1\\xf0\\xd1\\x10\\xe1\\xf1 0@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?!9\\xef\\xfc\\x18\\xe6\\xeb\\xb8\\xb3%A\\xcc\\xa7\\x05\\xe1\\xfe\\xac\\xc5\\xe6\\xc4\\x10Q\\xcd\\x8d\\xa3\\xee\\xe2E\\xe4{\\xae_u\\xc8\\xf3D\\x16\\x7fb\\xa8B\\xf5<oZ\\xd8\\xdd\\xfb?\\xddBO\\x9b\\xc3\\xac\\x1d\\x9d}\\xd5\\xb1\\xd7\\xf1F\\xbel\\xaba\\xfc\\xac\\x0e\\xea\\xc5\\x90\\xd2\\xc8\\xed\\x97\\xd5\\x95[\\x04\\xb1g\\xcd\\xf6\\xcb\\n\\x11\\xcf\\xd5\\x84\\xedG\\x1b\\xf9\\xae\\xa3\\xea\\x888\\x92\\xac\\xbeU\\x9f\\x15\" \\xbb\\x1f\\xe6\\xf3\\xc7\\xff\\x00o#4\\xc1F\\x8fj\\x03~[\\xe4\\x9c\\x91bJ\\xc0\\xa4\\xaf\\x86\\xd8\\xd9b\\xd7<\\x9e\\xd5^\\xb9\\xd5\\x986\\x0f\\xaa\\xe1\\x87\\xdd\\x8c\\xba\\xd6\\x1d/\\xc5 \\xf6\\xd7^?\\x16s\\x9b\\xd15\\xf0^,\\xe9N&~+\\xf2\\xaf\\x16Q\\xee\\xa1\\xf6\\xa4\\xcd)\\xd4\\x10\\xbf\\x17G\\x14\\x07\\x98\\xa1\\xee\\x85~\\xaal\\xc6YA\\xd3\\x9f6\"#\\xa2\\xc75\\xfc\\xcb,\\x99\\xe7l\\xc0\\x1fk\\x00\\x81\\xc5\\x05\\x0f\\x12\\xde\\x8d\\x96\\x1e&\\xf0\\x9b\\xf1\\xec\\xacs\\xef\\x9a\\xcc)\\x97\\xe5c\\x05\\x82\\xc7no~\\x0b!\\xc1\\x94\\x8b\\x08\\x83\\xbb\\xf8\\xa8\\x82+l\\x85:\\xa9\\x1d^z\\xbb\\xe2\\xc4&*\\x8el\\x1e\\xa2\\xc1;?U\\xf3\\x1d\\xdc\\xaf\\x9b5\\xcf\\x9f\\xf8\\xc9M\\x8a\\x0e]\\xb2\\x9c^\\xd6\\x02\\xc6\\xd9\\x06.\\x15<\\x17\\x11\\x1f-%LA?\\xcd\\x13\\xf4T\\xc4\\xea\\xee/\\xc9|GvtO5\\x07\\xb9Sc\\xdd\\xc1\\xf3\\x16\\x1f\\xbb\\x87\\xdd\\xe4\\x16, \\x8e\\x15\\xbd\\x1f\\x144\\xe7\\xca\\xb1[<O62\\xd2\\xf4\\x12\\xb2\\xee\\xe3\\xd2\\x80\\xef\\x0b\\xe5\\x16\\x04J\\xd5\\x84#\\x1eopd\\x8b\\x8c\\xfe?\\xe4\\xcd\\xbd\\xf4\\xf5q*\\xd8Qi{\\xa5\\x87\\xf8\\xa5\\xecS..\\xe8\\xf8\\xba\\xa1\\xf9\\x96\\xeb\\x99h\\x8c<\\xac\\xe2\\\\*\\xf1Y\\x10\\xf8\\xd5\\xff\\x00\\x9bh\\xa2\\xf6UDqy\\xa0\\x88O\\x8a!\\xe0\\xff\\x00W\\x82<M\\x01\\x18\\xf1\\xbf4\\xf1\\xee\\xe1\\xe6\\xb2\\xf9\\xab\\xbc\\x7f\\xc0\\x0bc\\xd8\\xae\\xbf\\xe2</\\xa3\\x94$\\xd7+&M\\x13\\xb0\\xc5\\x892l\\x7f\\x83\\xfeb\\xe9N+\\xc6\\x15\\xe2ll\\xfa\\xaf\\xc1\\x8e/a\\xc1\\x14\\xcf\\x1e\\\\\\xbd\\xfa\\x91\\x1eB/\\x0eqf\\xb5\\x814.\\xb1N\\xca\\x92\\x0f\\x8dj{T\\x91\\xf0\\x94\\xc8\\xbbj\\x97\\x1b\\x16~q\\xa7Nb\\xb0\\x81\\x17S.P1\\xb0]\\x19\\xc5~\\x7f\\xe1\\xe6\\xff\\x005\\xf3,F\\x07\\xaau\\xb0\\x086\\x9b\\x90\\xdd\\x86\\x0b\\x02y\\x94\\xc79G\\xc4\\xfdQ<\\x95\\x88\\xb8\\xba<W\\n_\\x97?U33\\x96\\xf0W\\xf15\\x04;[\\xf4\\x97\\x13\\xea\\xf33\\x89,\\x88x\\xab\\xc1\\xddD\\xe1\\x9c\\xd8\\x02\\xc6\\x9er\\x8e}O\\x14\\xc3\\xbd\\x16l\\xff\\x00\\x13L\\xbe\\x9f\\xba\\x98\\xb2\\xed\\xbd\\xd3\\x13\\'\\xdd\\xf8\\x1e\\xaa(I\\xe6o\\x91\\xd5^$v\\xf4\\xaer\\xae\\x18\\xdc\\xf4\\xbb\\xe2\\xe9\\xcf\\xfc\\xa1\\x91\\xdf\\xcd\\xd8\\x06\\x8ak\\xcdP<{\\xea\\x9d\\x8a\\x1e\\xa9\\xc19\\xccV\\xd4Q\\x8a\\x81f \\xe2\\xc3\\x1b\\xc1\\x16O-\\xea\\xac\\x8e&\\xba\\x8a\\xebsSR\\xf1X@l\\x99F\\x8a\\xc0y*r\\xbf\\x00W\\x1au\\x9a\\x90#\\x9a\\xfc\\x1e=Vd\\xbf\\xc5[\\xc7\\x99\\xbfm\\xe4{\\xcb\\xa9\\x80\\x8a\\x8eM\\xef\\x9e\\xeel\\x1bP\\xd77\\x93n/1b>\\xe8c-\\x10\\x8e)I\\xff\\x00b\\xc9\\r#\\xaa\\xfc\\xdc\\x1c>9\\xbf\\xe0S<YV\\xe8\\xe4\\xfc\\xd3\\xcb\\xda\\x0e\\xff\\x00\\xe0?\\xc0\\xebV\\x17\\x91a\\xe6\\xfc\\x03I]\\xa3\\xf7X\\x1d\\xf3H\\x85\\xd5\\xe4\\xf1i\\x88\\x877\\x95\\xb2\\xb6\\x04\\x8e\\xe9X\\xf8\\n\\xf5*\\xa9}Th\\t\\x86g\\xcd\\xc6\\x1f\\xba\\x98\\x1c*\\x17\\xa7_\\xea\\xa1\\xdb\\x91\\xb0\\xb1\\xaeVG2\\xbd%\\x98\\xe5pu\\'\\xab\\x07\\x92\\xb3\\x88>k\\x08\\xd6\\xd9\\xb9*\\x10\\xf3\\xdf\\x13r\\xd1T\\xc2o\\x88\\xee\\xb4~Otc\\xe7\\xe7\\x9a\\xbc\\x16v\\xe6\\xf1\\x95\\xd1\\xa6*0\\xads\\xaf\\xfc\\xb3~e\\xe2\\x16p\\x9e\\xear>\\xd5\\x008\\xa6\\x9f\\x92\\xc5\\xf3\\x8d\\x1c=\\xad\\x7f\\xaa\\xc4\\x0fK\\xf0jO\\x8f\\xaa\\x13\\x87=X\\xc9\\xc3G2\\x9f~\\xa8}SXc\\x0f7\\xb8\\xdf5\\x08\\x99\\xa4\\xf6v\\xa8\\xd8\\x9f\\xe6\\xb8T\\xb5\\x1c\\xd44\\x1fvw0Q\\x92\\x86/\\x01AQ\\x18O\\xcdC\\x0b\\x11\\xd4sE\\x86\\xa4y\\xa8\\x8cG\\x81\\xd5\\x82!d\\xbdT\\x96?VQ>)\\xe8kI^f\\x90\\x12j\\xb6d4K\\xff\\x008\\xa8\\xdd\\xf5g\\xaf\\x0c\\xd10tX\\x7fy\\xe1\\xa6V\\xef?T\\xf09B\\x14=\\xd7,\\xf1\\x0f\\xd5\\xc7\\xabj=\\xba\\xbb\\x127\\x97\\xe2\\x87\\xba\\x94\\xab\\xbdN\\xaf$\\xec\\xae\\xad\\xa1\\xec\\xa4\\x17\\xe6\\xfa\\xb1\\x1e\\xa8\\x1cl\\xc5t\\xc7\\xcd\\x0c\\x13\\x9e\\xaap\\x1dwG1\\xb3q4\\xd9\\xba7\\xcd1C<^\\xcfu\\x87\\x1c\\xd9\\xa4T\\xefr\\xe4\\xcf\\x11G\\xd9\\xa3\\xa3O\\xdd>?\\x166\\xf2\\xea\\x8coG\\x82\\xadg\\xc5\\xc2<&\\xc0\\xcd\\xd4\\xd8p\\xce?\\x16W8\\x88\\xfd\\xd8\\x89\\xd9D\\x97\\x81`\\x91\\xc8\\xd8&\\x1f\\xe0\\xa6\\xd9u~,\\x13\\xad\\xf3v\\x89\\xcb\\xc0\\xa2\\x7f\\xf2\\x8e\\x07c\\xf1xt+\\xe4\\x18_\\x10\\xf7A\\x19P\\xe8\\xb0p\\x14?\\x1f\\xf1\\xc1<v\\x16}\\x90\\xfa\\x9a\\x8b\\xc0\\x1e\\xcc\\xa3\\xca\\xa2\\xa4\\xce\\xdez\\xb8\\n\\x87\\xf7`\\x18\\x0f\\xc8l?\\xfc\\x15\\x82>\\\\\\xab\\xa7\\x17\\xa9\\xb2o\\xee\\xfdgW\\x13\\xeb\\xab3\\x13\\xb2\\xfe\\x92\\x9d\\xb6x\\xf8+\\x8b\\xee)\\x04r^\\xfe_\\xdd\\xea\\xf9\\xbc\\x98=\\xcd\\xe2FM\\xc8\\xb1\\xcd~\\x16H\\xd7=Y\\x1e?5\\x92\\xc9z1a\\x99\\xca\\x94\\x1a\\xfdQ\\x00\\xf2\\xaax\\xd7h\\xe5ed\\xd1\\'\\xb6\\x93\\x9f\\xdds\\xbf\\xee\\xf8\\x1f\\xba<6=\\x8f\\xab\\xe0\\xcc\\xee\\x8a\\x0e\\xfd{\\xbc\\x12%J\\x9d\\x0f\\xd4X0\\x8d\\xf3d\\xfd8\\xae4\\x1e\\x92\\xf8\\x9f\\x8a\\xb4&\\x0b\\xa9}\\xd0,\\xd7\\xab$\\x95\\x89\\xf2\\xdd\\xc6\\xf7\\xbc\\xd9A\\xfb\\xbd\\xf0\\xa7(to\\x1e\\xd9\\xff\\x00\\xca%\\x11\\xc9\\x17A\\xe3\\xaf\\xf8BQ\\xc6w\\x1fuhC\\\\\\xe7\\x8a\\x18\\xc3l1seq\\x07\\xaaN9\\x8f4G\\x0e\\xaa\\xfa\\x7f7\\x8a,\\xe5\\xf1\\xcdi\\x07\\xcdG\\xbcP\\x1b=\\xd7S\\xdb\\xd5\\xc0\\x19}X\\xf0\\x19\\xf8\\xb9\\x03\\xb3\\xea\\xc1\\x1e>\\xb9\\xb3\\xb1-sV\\x13(,\\x97\\xbc\\x9a\\xb7\\xf6.k?\\xf1N\\xe6\\x9c\\xcf\\x17\\xd1\\xd5\\xd1x*,\\xfa\\xb2\\x1f\\xb2\\xc2\\x84\\xbct\\x8e\\x05\\xcb\\x11\\xf7\\xff\\x00\\x01,\\x9c\\x9b\\x1a\\tb\\xff\\x00\\xcaC\\x82\\xcew\\xe1p\\xf2M@\\xa7\\x95\\x17\\x89G\\x90%\\xa8\\x92\\xc3k\\x1eD\\xf7R?\\xea\\xc9\\x13\\xfb\\xbb\\xc1I\\x852\\x80\\xe6\\xe0)\\x9e\\xaag\\x97\\xed\\xa0N\\xa6\\xe3A\\xad\\xf3|\\xe5}_\\x98\\xbcI\\x0cM\\x8a]U\\'\\xe9Lu\\xe0\\xb1\\xd7qY\\xc8\\xd2*\\x87<\\xdd\\x13\\xcb\\xe6\\xe6\\x18\\xd8\\'\\xd53\\xdd\\x83Z\\xb6a\\x9a\\xc79\\xabX\\xac\\x88\\x94>/B\\xe0\\xfeo:\\xf8\\xbc\\x19B%mG\\xc4\\x8d3\\xe6\\xc9\\x04Z\\x1c\\xdcGU\\xc93\\x9c\\xdc\\x03\\xe0W\\xc86\\xa3\\xaeh\\xe0<m\\x11\\xe7\\xab\\xe11\\xea\\xcc\\xe6_Tq9v>\\xf6\\xcc\\x00,\\x02,)\\xf2\\xfb\\xb2\\x98\\x12~l\\xb1\\xb7\\xff\\x00\\x9a87\\xeb9\\xb9\\x08\\xfc\\xdef^\\x11\\xbe#\\x8a\\x8f\\x9aqM>+b\\xb34\\x93\\xb3\\xb7X\\x0f\\xe2\\xe47\\xdd\\xc8\\xfd\\xdcz\\x9aE\\x86\\xeb3w >,\\xa9\\xee\\xc4\\xc5\\x80<\\xbdsf!\\x1dY\\xa4\\x1a\\x83A\\x15\\x99\\x0f\\x1bE)\\xb2\\x9e\\xcd\\x15Ph<\\x82l\\x91\\xfc\\xb7\\x95u\\xdfk!\\x9dP\\x99\\xdf\\x8aj\\x87\\xd5\\x90\\xe8}\\xd2(\\xe46\\xcck\\x9cm\\x0e\\r,C\\xb2\\xc2C\\xba\\xf0\\xf7\\xdd.\\xe4f48z\\xdb\\xeb\\xd5\\x19\\x11\\xf3u\\x95\\x19\\xb1[\\x94\\xe2\\x827Y\\xbe\\xaa\\xe1\\xf1d\\xc4\\x9b\\xe4\\xa4\\x1c\\x87\\xd7v!q\\x95I\\x07\\xb6\\x83\\xf9\\xb1\\xbf%f#\\x99\\xc6\\xc3GJ\\xb5\\n\\x1a\\xa6/c\\xcd\\x1c!L\\xd9\\xaa\\x90S\\x0e\\xdb\\x04\\xa8|X\\xe1$]\\xf2\\xc0\\xec\\xb0\\xae\\x0f\\xab\\x07\\xee\\xaa\\n\\x88U\\xe3\\xd1e\\xe4z\\xbe\\x02K!\\x1e\\xeb\\x10K\\xe6\\xb0\\xee\\x9b\\x93W\\x8b\\xc58\\xff\\x00\\x9cU\\xa8wu\\xff\\x00J\\xbf?\\x12U\\xf0\\xacL\\x8a`\\x92z\\xa3\\x18\\xe5rHv\\xb8\\xc1U<\\x9b\\x01\\x87\\x96\\x89&\\x11\\x8b)&K/\\x08nq\\xd3*h\\x93\\xf6\\xb0e(\\x07\\x16d\\x84X\\x08\\xcaA\\xd7\\x8f\\x17\\xc2?\\x85O,\\x1e\\r\\xb2@\\x87\\xab\\x89\\xf8\\xcd\\xa9\\xbd\\xd5\\xeb\\xff\\x00\\xb6S#(\\xd9\\xe7\\xee\\xe6 \\x8f\\xf3\\x8a\\xcb\\x9c\\xbf\\x8aC+\\x94\\x0e\\xd5\\xf0O\\x9a|^Q\\xff\\x00\\'\\xfe3R\\x85H\\xed\\xed\\x0c\\xbe,\\x04\\xa2|\\xfcVD\\x0cwi\\x03\\xb9\\xa0.v\\xca|\\x95k6i\\x11\\xd4\\xd1\\x8d\\xa5\\xc9<Y\\x8f\\xc5\\xd3\\xbd\\xbc\\x88\\xdf\\x9a\\xa7*y\\xd8Y\\xceSf\\xbcE\\x80\\x9b\\x0f\\xf4\\xf3T:}7\\xcd\\x0e{\\xca\\x9f1\\xf5H=^\\x16\\x0f\\x8b\\x8e\\xa3\\xe2\\x9a\\xc9\\x1f\\xd58 >\\xeaI\\xdc\\x9f\\x15|\\x16\\x0c@?\\xb5@\\x0b\\x8fq@\\x19D\\xd8\\xac\\xbd\\x7f\\xc9\\xde+R{\\xbb\\x16\\nwi.y\\xa7\\x8dQ9\\xc5$qX\\xeah/\\xc8\\xb1\\x8eo\\xb1>\\xe8b4\\xa1\\xc2<aS\\xc8\\x9b#?v\\'\\xc5l\\xcd%U W\\xdb\\xee\\x84D\\xce\\xc9$\\x11Dv\\x9a\\x10\\xc7\\xc2o)\\xbd\\xfc\\xa9\\xc0\\x04\\xaf\\x03\\xf3O{\\x80\\x9dY\\xe0\\xf0\\xd3\\x8e\\x1b&#\\xff\\x00(\\\\~)\\x00Xl\\x0f\\x7f\\xf1j\\xf3\\x14N\\xcf\\xc5\\x98\\xf36\\x00e-\\x1fO\\x92\\xe3\\xa7\\x8b\\'\\xcb\\xdd\\xd9\\xd3\\xe3\\x8a \\x1d\\xa7\\x94CP\\xef\\x8a&b\\x98\\xd0\\x1b\\x1c\\xf1f\\xc9;9v\\x8f\\xcdA\\xe6\\xf9M\\xc7t\\xc10\\xddOo\\xba\\xf1\\xb95\\x02\",\\x9ei\\xe2\\xef\\x0f\\xe2\\xf20\\ng\\xa5\\x02xl\\xb7\\xaa\\x91\\xd4~\\xec\\x1c\\xca\\xfb/\\x8a\\xec\\x9fZY\\x07\\x819\\x9aPb,\\xf9\\xbf+;__\\xf1|\\x12T\\xe5QHV\\xe1\\x81\\xe2\\xa9(\\xc7\\xf9\\xb2;\\xbawZ\\xc3\\xc5\\x97\\x93\\xcf\\x05\\xfck\\x97\\x00\\x94?\\xc8\\xbcN\\xd8vk]\\x9c\\xa2\\xc6*4\\x96\\x8f\\x9a\\x88?\\xbaH\\x9b4\\x0c\\xb3\\xdf\\x14\\x0e.\\xf5T\\xb9\\xd5\\x19\\xcf4;-\\x96\\xe9\\xb9\\xccAu\\xfe\\xd4\\x07\\xbf\\x9ayg\\xc5x0s\\xcd\\x1f\\x8d9\\xb8\\xf7\\xfa\\xba\\xfc\\xd7\\x8ay\\xff\\x00\\x9b\\x0cY\\x9ekD\\xf1\\x166~\\x95\\xe8\\x1f\\xe2\\xa3\\xa5v\\x91\\xf5D\\xbd\\x96NDk7#d\\xdc\\xd8ff\\x9d\\x1b75\\xf2\\xa3jQ\\xce\\xd5\\x92\\x82\\xc8\\xce\\xeb\\x00V\\xe8\\x93P\\xe5\\xd5R \\xa4u\\xcd\\xd8\\xf0\\xb8\\xc7K\\xc7\\x82\\xc8\\x8e\\xde\\xf9\\x7f\\x17\\x8d[(\\x0e\\x16\\x0c\\xff\\x00v@\\x11`\\xf8\\xb3g\\xcd\\xe2\\xcfW/\\xc5c\\xe6\\xcc\\xba\\xb9\\xe3\\xf7g\\xff\\x00\\x14\\xd8\\x81\\xeb\\x9b\\xa3\\xf8\\x8a\\xba\\xf4\\xf3g#\\x1fe\\x8e\\xd7\\x05b\\xca\\t?\\xe2\\x1f\\xae\\xae\\xc0\\xd8uY?\\xe7\\x9b\\x1b\\xe6\\xa9\\xee)<4\\xb2\\x12Q\\xe2\\x89\\x8f-&^\\xa9;7\\xe0\\xac\\x03>l\\x9d\\xb1w\\xdf\\xe6\\xec\\xc7\\x11g6\\xe9\\xff\\x000\\xba\\xdf\\xbb(\\xd5\\xab5\\'\\xce7\\x9f[O.\\xe8x9\\xf3u\\xae~\\x166J\\xd2/\\x19W4\\xbc\\xff\\x00\\xc8\\xdb\\x0fw|\\xdd,\\x11\\x9cVHM\\xd4?\\xf0\\xe5\\xa6_\\x0b\\xd5\\x9f6\\x88\\xe5ck\\x9cEp<\\xd7\\x87\\xb1V\\x9e\\xeae\\x1c\\xfb\\xaeX\\xdb\\xc6\\xd9\\xee\\xad\\x1b3uBW\\xd9Y\\xc2\\xa0\"\\x89g\\xaaO\\x9e\\xeb\\x1e\\xf9\\xb0O\\xaaB\\xf1\\xdf\\xfcxn\\xb7\\xdf\\xba\\xaf\\xee\\x9a6d\\xa6\\x9b\\x7f\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x10>\\xfd\\xf2\\x95\\xf9%\\xe6\\x95zI\\xff\\x00[\\x1dW\\x96\\xe5\\x9a\\xdc\\x8b\\x11\\xe2\\xfb\\xac\\xaa!\\xc9-\\xc9\\xa2\\xaaUlT\\xa5\\x17D#d\\xfc\\x9d\\xe0\\x8e\\x9eQ3\\x1e\\xac\\x99\\x1b\\xc1\\x12\\xeaU\\x8e[\\x0f\\x17\\x80:\\x82K\\xec\\x84\\xe5\\xe1\\xde\\xea\\xca\\xdc\\xb4;\\x81\\xcd/8\\xf8\\xe8\\x00\\xba\\x89\\xcc\\xaf.\\xed\\\\\\x1ec\\xb69\\xd3\\x0b\\x08\\x8e0\\xc5\\xdf\\xb5\\xban\\xb9\\xb0sw\\x08\\xe4\\xa2\\x89vvCJ&\\x86Uy8\\xd4T\\xc0\\x83\\x9f\\x83\\xa9\\xb0.&\\x04i\\xce\"\\xa0\\xba8R\\xdb>\\xfcjAXP\\x1b3\\xd5\\xe3\\xf68k\\x12\\x82p\\xb2\\xd5\\t\\x91M@\\xa7\\x9dZ\\xa2\\xbe|;\\x07\\xae\\x91BBl\\xaf1[<\\xb2\\x9ci\\xfc\\xe7\\x89\\xe6F\\xd7H7oS&\\x9c\\xc3\\'\\xbb\\xa9\\xa3\\xdf\\'\\xca\\xc3\\xe3\\x08\\xee\\xcd\\xf3)\\xdc\\x0c$\\xdd\\x18\\xef\\xff\\xc4\\x003\\x11\\x01\\x01\\x01\\x00\\x03\\x00\\x01\\x02\\x05\\x05\\x01\\x01\\x00\\x01\\x01\\t\\x01\\x00\\x11!1\\x10AQa q\\xf0\\x91\\x81\\xa1\\xb1\\xd1\\xc1\\xe1\\xf10@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x10\\xf4\\xfaOS\\xe2Y\\x1c\\xe3=Y\\xf3\\x1a\\xfeV\\xe1l\\xf9\\xcd\\xd4Y\\xe6\\xdb\\x11\\xd1t\\xbe\\x8b:nx\\xbb\\x8f\\xa7\\xc6\\xcf\\r\\x9c\\xd9\\x96\\\\c\\x889\\xb3M\\xf37\\xe7\\xf0\\x8ev\\xc4\\x8d\\xcf\\xac\\x0c\\xcb\\x00\\x0f\\xa5\\xc0\\xeb\\xe3\\x1c\\xf7\\xf5\\x90\\xc9^-9\\xb8o\\x10\\xbe\\'\\x9b\\xefyL\\x9ey\\x90{\\x87\\x1b\\x1b\\xa9-\\xf0D\\xcc\\xe6\\x0e3\\xebc\\x9f\\xa2\\xfe.\\xbe%\\x8f\\xba\\xe5\\xd4\\x17\\xd2|I\\xd5\\xa0\\xad\\xben\\xb8\\xferA\\xcf\\xcc\\xae\\xe3\\xdf\\x80]\\xdem\\xe7!\\xa9\\xfb\\xc7\\xe7w%\\x99c\\xe2\\xc8\\xb8\\xb2\\xf9\\x9f\\x01\\xe7\\xcc\\xf1:/\\x90\\xf9\\xb8si\\xa1|\\xceos\\xa7Q\\xf5\\xbb\\xd2\\xeb\\xe6\\xe9\\xee\\xfc\\xec\\xf7.\\xdf\\x97\\x87yu\\x8e\\xa6\\xff\\x00L]1\\xday[\\xe2\\x0e\\xb5\\x9c\\xf3\\xedc\\xf5\\x9e\\xfc\\xf8\\x89\\xf0o\\xe4\\xbb\\x04\\xf4\\x0b\\xe4H\\x84K\\x90}\\xa36^\\xee\\x961\\xebd\\xe2\\xd0\\x84o\\xef\\xe9g/7\\x8b\\x8a[\\xb7V=lpXxg\\x87\\x160\\xfc\\xdb\\xbf\\x0c\\x8e\\xd8\\x04\\xce\\xa1\\xea\\xd3\\xf0,\\xe3\\xc3\\xef\\xd4\\x18\\x07Q\\xdcl\\xb2\\x19\\xab\\x9f\\xac|\\xb5\\xbf\\xbd\\xb3\\xd5\\xdb\\xc3\\xaf\\xc0\\xb9\\xf1\\xcc\\x83cO\\x10\\xcb|\\x1c[\\x96\\xbe\\xb7.\\xa1\\xcf\\x9bg\\xec@\\xbfH\\x9f^\\xfc\\xc8\\x18\\xe4g\\xa3H\\x03\\xf7\\xf0\\xe1\\xe2#\\xaf\\x1e#\\xd7\\xdc\\x82??7}\\x1d\\xb7n\\xaf\\x9b\\xae\\xfc\\xcf7\\xd7\\xaf\\x0f\\xc5fbC|\\x1dx\\xf5\\xe7\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x10\\x0ed\\xfa\\x1b\\x07\\xe1\\x90w.M\\xac~\\xf7..\\x98\\xfa\\xc1\\xf6\\x00G\\xe79\\xb0\\xed\\xfag\\xe4t\\x7fFCvq\\xfe\\x91,\".\\x1f1\\xd4\\x99\\x8co\\xcd\\x8ff\\xb9\\xf8\\x97\\xa3\\x87\\xed\\x03\\xc6[\\xdf\\x898\\xbb#\\xb1\\xf1v\\x044\\xfd~b\\xe2\\x1f_\\xdet!\\xd9\\xc4\\x0b\\x01\\x9d\\xe9\\xf4e\\xfa\\x0b\\xc3\\x96\\xdf\\x83\\xb2\\x03\\xce\\xf6\\xb3\\xea\\xdd\\xb7\\x8ed\\xc6\\xb8\\x85l-\\xe3g\\x8f\\xb7\\x13\\xcf\\xcb\\xf8!\\xcf\\x9e\\'\\xa2\\xed\\xac\\xb4\\xa7x$\\xdat2V\\xee\\xeb\\xcf\\xed\\rp\\xfd\\'\\xfb\\x93s>]\\xff\\x00\\x91\\x88\\xbes\\x19bga\\x87\\xde_\\x98)\\xfcq\\x00\\x01\\x99\\xab\\xf9\\xda<\\xf3\\xb0\\x07\\xda\\x1c\\x80o\\xccw!y\\xb7\\xabu9\\xfe/\\x94\\xc0\\x96r\\x1fYz<\\xb2kK\\xe4}mp?\\x98\\x1d\\xfb\\xff\\x00\\x17@~o\\xe6f\\x03\\xa3\\x8b\\x85\\x07\\xcd\\xcb`\\xbb)\\x93\\x91\\xff\\x000\\'\\xbe\\x8f\\x88\\xd0\\x99p9\\xdf\\xday\\x08N\\x9e#\\x05V}-^\\xa3~[s\\xc4\\x88Ey\\xbb cG\\x93\\xe6\\x10\\xe5\\xdf\\xa4\\xa8\\xf1\\xf5\\xe6@\\xee\\xc3\\x87>m3\\x90:\\\\@p-\\x96\\xcf\\xc0\\x9f\\xb5\\xb2\\x9c\\xec\\xbb/8\\xfe\\xcc\\xa8\\xc7\\x87\\xc7\\xd2`\\xee\\xff\\x00kN\\xf7\\x8f\\xde$+\\x8f\\xac\\xca\\x0f\\xcc\\x1a]\\xb8\\xfa\\xc7\\\\=\\xfc\\xee\\xdcb\\xe9\\x9f=F\\x9d\\xde\\xbewXf\"\\x01\\'\\xe1\\xe6\\x1d\\x9a\\xf4~W\\x07>F\\xda\\x0f<g\\x04\\xa1\\xa7\\x1d\\xfd\\xcbH*+\\xbf\\xcc[\\xcb\\xe1\\xf9{\\xb7+\\x87\\x1dB`\\xa5\\xa7\\x00\\xe3\\xea\\xc0\\xd8\\x10}\\xe0\\xa6-\\x97!\\xe2Y\\xc5dCA\\xb7\"g\\x1fVGJ/\\xc7\\xd6(.\\xb7~y\\x8c\\x0b\\rL\\x99\\xe9\\xb0\\xa8\\xcf\\xe6\\xe7\\x0f\\x9e\\x8b,\\xbc\\xe7\\x11\\x9b\\xf7\\xc9\\x07\\xe1\\xa7pq\\xd4r\\x81\\x9c\\xf2\\xc0\\xe0\\xc76\\xe3\\xcd\\xfa\\x04+Q\\x1d\\xfaH8~>\\x90\\x9b\\x8e$ \\xe7\\x9f\\xdb,f\\x81\\xb9f\\x0e>9 \\xd3\\x99\\xef;\\x83C\\xbb\\x80.\\x9cs9\\xbe\\xdb|\\xd9\\xcc|NL\\xb5\\xfc\\xaf\\xec\\xcc@\\xf9~\\x91\\x9cg\\x82p\\x08\\xe8\\xf6\\x16\\t\\x8a\\xe8q\\xf7>ehi\\x0fD3\\xf3\\x8eN\\x9e\\xe0\\xaa\\xa7\\xc4\\x8fx[A\\x9cwl\\xc04\\x97\\x8a\\x9d\\xa7\\xbf\\xe2,>6\\x0c\\xf8\\xb6zQ\\x86\\x9e:\\xf9\\xb4*p\\x9c\\x1b5\\xb8c\\xfd$\\xc715\\x18\\xfd\\x0c\\xc7l>\\x9b\\x01n\\xf1<~6\\xe9\\xdeI\\xbc\\xe7?{P~\\xa5\\xc91\\xd3\\xe6tL\\xeb\\xaf\\x01o\\xc2@\\x03L\\xee\\xd0\\x1c\\xdc[\\xc7Y\\xe3\\x8d\\xbb\\x99rs\\xf4O\\x0f\\\\<c\\xce\\xc0H\\xbd\\xf4\\xd9\\x87\\x8f\\xd1i\\xf07\\xa8>\\xbd~\\xf01\\xfa\\xc8//,\\xeaf\\xe7\\x13\\xc7\\x9eO\\xafS\\xd3\\x11\\xe3\\xab\\x0b\\xf3\\xd5\\x8f\\xd6\\xee\\xf7*\\x07H\\xd0\\x1d?1\\x01\\x86?9\\xe3\\xd4:o\\xf1?\\x1e{\\xfc\\xa1\\xf0\\x06\\t(7\\x8e\\t/\\x1a\\xc0\\x0e{\\x9e|\\x0f\\xce7\\x9d\\\\\\x07\\xde\\x1c\\xf3\\xdd\\xf0=_\\xd0?i\\xed&}7!Ct\\xfe\\xd0\\x01\\x1f\\xefo\\xacL\\x83\\xe9g\\xd6g\\x97#\\x80\\xcf\\xf1\\x1f\\x04\\x0f\\xe6\\x1eh9\\xfc\\xf3a\\xc1\\xc5\\xc9\\xa7Rs\\x96\\xc3\\x8d\\x8e\\xb9\\x94\\xf9s\\xeb\\xb0u\\xe7\\xf2\\x90\\x1d\\xc8;\\xc7\\x84s\\xae>\\xd2\\x0e|g\\xd6\\xc7\\xe7\\xe2\\t\\x99#\\xea\\x97\\xaf\\xc21\\x9cF\\xe7<]\\xc4\\xf3\\x9c\\x9e=\\x0f\\xdb\\xc8\\xcf\\x0b\\xf3\\xb8\\xc0q\\xf9@\\'?E\\xf1\\xfc\\xc4\\xd9\\xd4\\x86\\xf5l\\x80\\xd7xo\\xf3\\x14\\xe8\\xb3\\xb9\\xe8\\x9e\\xaf\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?\\x10\\xc4f8R/\\x08M\\x98\"\\xa2<8\\x8e\\xe6\\xc5\\x02\\x91\\xc8\\x94(\\x0f\\xfa\\xa92\\x91\\xf3\\x17d\\x14\\x9a\\xf3\\x03\\x13\\x1c&>j\\x04O\\xa8\\xf5x\\xa7\\x0c<\\xbc\\xfe(\\x03\\x08\\t\\xa6\\xad&\\n\\xb00\\x04\\xd4aC3\\xc6}U\\xde8\\x9b&\\x19 9\\x9aH\\t\\x10>\\xc9\\xbd\\x83\\x00\\xe37\\xddsy\\xdd\\xb2\\x1c\\x95\\xba\\xe3\\xd5\\x17\\xda\\x00z\\xf73UR@\\xac\\xd0\\xbd\\x1e+\\x0c\\x08\\x11\\xe5c\\xca\\xee(\\x83\\x01\\t}q\\xfd\\xd1\\xe5\\t2\\x11\\xc8\\xfb\\xa6\\xaf\\xfb\\x0f\\xf4\\xa8\\xc3\\ne\\xdf\\r\"\\xa6\\x98\\x9c;+\\x01\\x9e\\xf7b\\x81\\x04I\\xbd:\\xaa\\x02\\x06\\xcb\\x15!\\x01\\xcf\\x05\\x8f\\x1e\\xfe\\xeb\\xc0\\n\\xc5\\xe8\\x88\\x07\\x8e.\\x1cP\\xc9\\xc9\\xdc\\xf3@6~\\xa2\\xac\\x07f\\xb0?\\x85\\x12\\x84\\'\\xc43\\\\\\x8eM\\x93\\x0ed\\xe1\\xdf\\xfa\\xb9$\\x18\\xd9\\xd6\\xc1\\x91\\x0e\\xa7\\xff\\x00\\x15\\xd0\\x02{\\x17\\xf0\\x81\\x13\\xfb\\xb1\\x0c\\xe4\\xdc\\xdb\\xa8A\\xf1ccN\\xef\\x1c{\\x8e\\xca\\xcc-\\xff\\x00\\x13@\"\\'&c\\x97\\xdfU:Tx\\xb0\\x07\\x1a\\xcf\\xbd\\xa01{\\x17\\xdd\\xea\\x03)\\xfe\\xe8\\xce\\'C\\xa4\\n\\x14G\\'\\x15\\xa4d3=\\x9f\\xf8\\xdd\\x18\\x01I\\x18\\xbe\\xbd\\x94pDN\\xe6\\x7f\\xed)\\x18\\xb4N\\xa7\\x9d\\xa4d\\x88\\xe1\\xeb\\xb4\\x1f\\x16Qp\\x08\\x97\\xccS\\xd2\\xa1\\xfb\\xbb\\xd4x\\xf6\\xfb\\xa5\\xbb\\xc5\\xb9\\x12\\xd2\\x1c\\x91\\xdcRb9=\\xf1\\x15\\x0b2\\x86M\\\\\\xcb\\x8e\\x14\\xd9\\t!9\\x0c\\xcf\\xee\\xe9\\x82^#\\xc7\\x96\\xb8\\x80\\x88|\\xd0d\\xa8\\x8d\\x98\\xb3\\x19\\xd5\\x91:\\x19\\xc7\\xf7dE\\x97\\xe2b\\x81\\x94\\xe4\\xf7E}\\xae\\x93\\x8b\\xc5\\x00s\\xea(\\xd0%cJR*V \\xe2\\x12\\xcc\\xbe\\xd0\\x9e\\x9f5r\\x00\\x1ec\\n\\xf0\\x08\\x86\\xae\\xef\\xa8\\xa5\\x91<\\x7f?5\\x83\\x9e\\xeaf\\x11\\x0f\\xf5Q\\x83\\x00\\x97\\xc4\\xf8\\xa8\\xfc8\\x005\\xf7d%!3\\xba\\xb0\\xcc\\'\\x1e\\xa2\\x8b\\x1a\\xba$\\xda\\xa7\\x91\\x03y\\x1d\\xff\\x00\\xed\\x00\\xa4\\x85\\x918\\xf7Wd\\x803M\\xb1\\x859\\x1dGWY\\x06\\x15\\x9e.\\x98x\\x02y\\xe6\\xf7\\x0c\\xeb{7\\xf5[\\x19JtC\\x1e\\xbd_!\\x10\\xc7\\x8a\\xeb\\x97\\xaa\\x9c\\xf3\\xfc\\xd5t\\x89@<R\\xe2\\xef\\xf1\\xee\\xa8G\\xf1E\\xfe(\\x02\\x18\\x14\\xe0\\xd7\\xdbY\\xd2c{\\xa1K\\x19\\x0cOm1@jf\\xc9\\xc2\\xe4\\xf1\\xe7\\x8ez\\xa4\\xc3:\\xf7tYI\\xe9\\xdb\\x00\\'\\x18E3\\xf7\\x7f\\xf2\\xfb]\\xe5\\xab5\\x93\\x1d\\x9c|\\xd0DL\\xccS\\xc5\\xeb\\x12v<\\xfc\\xd3\\x08JW\\x90x\\x9c\\xfcM$\\x90\\'\\x14\\x16{98\\x81TA9\\x1b\\xee\\xbe\\x80\\xf9\\xeb\\xf5s\\'\\'\\x94\\xd6\\xee\\x81\\xc7\\x9a@q?[\\xfd\\xd9\\xc1<\\xb1\\xf1V(5\\x06%\\x8f\\x13H\\x062x\\xa1\\xd9\\xd7_Ud\\xa1\\x1a\\x8f\\x191\\xf3N\\x824NBh\\r0\\x11\\xf3b\\x98\\x01p\\xce\\n\\xa50Fy\\xd4e\\x84&o\\xa1x\\x90,g\\x14\\xf1\\xf8\\xaa\\x0b\\x94\\x98\\xb1\\xe1\"\\xa4hkgz\\xe0\\xa1\\x84\\xd1\\x05\\x8c\\x16\\x95x\\x14>\\xc6)\\xca#o\\xc4\\xf7F\\xa4\\xd1%\\xe2<\\xd6\\x05M\\x93\\x99\\x8d\\x9c\\x11\\xdbMj\\xd4\\x05/:\\x97\\x180\\x80\\xf3D\\xac@\\xc3\\x0b\\x1e}W\\xcb\\xe4\\x99y\\xf8\\xda<\\\\\\xb9\\x9ex\\xbe\\xf7;\\xa7\\xbeb\\xce\\x00\\x1eLo\\x14:V\\xd9\\x8f\\xdc\\xf5@\\xe5\\xc9\\xd6 \\xfc\\xd5E\\x0f\\x8cr\\xb2\\xc8\\x9fi~\\xee*+\\xa8\\x88\\xaau\\x83b\\x7f\\x86\\xa2\\x00g\\xe6/H\\x9dS\\x9fu\\x89\\x13KH\\xce\\xec\\x00\\xf0\\xc6\\x19\\xfdP\\xc9 %\\x90\\x949\\xdf\\r\\xd4\\xeb\\xb6$h\\xd1\\xccO\\xdd\\x184\\x98\\xde\\xc0\\xf34\\xb8\\x0c\\x97?\\xab\\x1c^C\\xc2\\x99\\xb8\":\\x0c\\xb1+-\\x98~(T\\x8e\\xf5\\xdf\\xccXL\\x1d+XQ#\\xa1\\xe0\\x0cn\\x01(\\xc1\\xdb>b\\xe0\\x04\\x93\\xe8\\x9f}]\\x9aH\\x1f\\xecjI\\x8d3\\xa6\\x1d\\xb3\\xc0$\\x0f\\x0e?\\xaa4J\\x03\\x1f\\x07\\x8a\\x07\"_\\xa3\\x8b\\xdc\\x05\\x93\\xa6\\xd5\\x0e\\xf3\\x0e\"*&X\\xf4\\x7f\\xf1L\\x02=\\x9e?\\xbb\\x08\\x01\\x97\\xcdpP\\x82\\xd0\\x85\\xb2\\xc5\\xeac\\xaf\\x16j\\xeb\\xe7\\x0f\\xab0\\xc3\\xe2\\xad\\x95\\xde\\xe9\\x00y\\xe2\\xb3\\x80\\x8f\\x07\\'\\xaa\\xa6x&\\xe9\\x11\\xbfqV\\xe0\\xbc\\x87\\xf3d\\xe0\\x9c\\xa22>i@\\x19\\x8d`\\xa4r\\x83\\x9e\\x7f\\x0f\\x8aq\\xd8\\xfc}\\xd6\\x14\\xd6\\x93f\\x16\\x11:V\\x8f\\xcd\\x82\\xb0\\xe9\\x9be\\x06\\x9b\\xe6\\xc39\\xc5\\xd2\\xa3\\x90\\x86\\t%\\x0f\\\\WS{\\x8e\\xdc\\xf3a\\xa3\\x05d\\xc2\\xc4\\x95\\xe7\\xea\\x9b\\'%\\xf4*k<\\x14\\xf3\\xf7gP\\xe1w\\xe3*D\\x03(O\\x9a!\\x07\\x04\\xf8\\x9a\\xc2\\'\\xc4\\xc5\\xe3\\xb0\\xb2(T\\x06\\xcf\\x9ft\\x05\\x08$C\\xe7\\x7f\\x9b\\x83Ab\\\\\\xe7\\x9a\\x18a\\xc0\\xe31B\\x83:\\xf2\\xde5K\\x0f|\\x13\\xdf4\\xd2\\xec\\x00&\\x03\\xfb\\xb2\\xce\\x03\\xc7\\x9a\\x805<\\xf0\\xcf\\x16.)n\\xa7\\xd7\\xe2\\xaa\\x89u\\x84~\\xaa\\xf7\\xe1\\x80@\\xd8\\x00%\\xeb3\\xba\\x8c\\xce\\x98\\xe6\\xa9T\\x93\\xc64\\xca\\t\\xd8^T\\x85\\x17\\x1e\\xcaA\\xc4C\\xaf>\\xdb\\x84A!\\xc6\\xc5\\x9aaI\\x1e\\xee\\x92\\xce~\\xfe\\xca!2\\r\\xe7\\'\\xd5lCF\\xc4U\\xb9\\x84\\xf2{\\xb9\\xbc2G\\x97\\xef\\xbaI\\x00B\\x193)D.Mx\\xf2P\\x02\\xa3A\\xe49\\xe6\\xc7\\xdd\\x05\\xa9\\xd7\\x98Mq\\x9f61\\tF\\x0e_\\xe9S\\x00#\\x05\\xd8\\xfb\\xe6\\xf2\\x8c\\x8c=P(\\xd4\\xe3\\xc4M\\x0b\\r\\n\\xf7\\x13\\x97A\\xc2\\x11\\xf6X\\x90j6)\\x96\\x9c$<\\xef4\\xc6\\xb3\\xa5\\x0e=\\x1f\\x8b(\\x08rT\\x99_\\xe1\\xa8\\x8c\"+\\x8e\\xe8\\x19\\xab/\\xef\\xbaa\\x1c\\x03>2\\xb4\\x12\\x86\\'\\xed\\xad~\\x05\\x12}5\\x1c<)<E5\\xf6\\x9ev:+\\xe1\\x12\\xb1\\xa10\\x9ejS\\x90av\\xc7\\xbf\\x16I\\x80\\x81E$}\\xd7\\xc9\\xec\\xc2z\\x93*H\\x87\\n\\x11>\\xa6\\xe6\\x05\\x0c\\xa4`\\x8a\\xb2\\x04\\xb1\\x99\\x9aEC\\x9b\\xae\\x11\\xfdQ!\\x93\\x07L\\xd5P\\x0c\\r\\xe6\\x91$\\xa9\\xf5Q\\xcakE\\xf7W\\x00F&fiT\\x93Z\\x92\\x9fWd\\x00DpU\\x13b\\x04*\\xd9\\xab#\\x96&\\x1fWs)\\x80\\x0b\\x9eS\\xcd\\x1b.L\\x14}\\xd9\\x8c\\xa7AV\\t\\xceOt\\x01\\x06\\x00\\x998k$y,\\xcf\\x0f\\xab\\xce\\x00OC\\xe9\\x83.\\xc9\\xf8\\x9f\\xf7C^\\x10@\\xf5\\xe2\"\\x9e\\x1c\\x06?\\xbb\\x87R\\x04\\xe7n\\xc5\\t9\\x11\\x1f\\xc5Y\\t\\x80\\xaf\\x8f\\x8a\\x80l\\xf9\\x95\\xeb\\xbb\"\\x00_\\x8ezlb\\x13q\\x8e3\\x9aa\\x18\\x0c}\\x8dZ\\xa9f\\x12s\\xc1\\xfc\\xcd*\\x15T\\x0f\\xb1\\xca\\xb0\\xc8\\n\\xb3\\xc3\\x95\\tC)\\xe5\\xc9\\xda\\xd2&$\\xf8\\xcf5\\x94)\\\\\\x127h\\xc0\\x1eX[\\xf3\\x17H\\xd3\\xb1\\xe5|RD\\xc0\\x1b\\x91\\x95\\xc1\\xa1C\\xecG\\x96\\x86\\n\\x003\\xdco\\xe2\\x88\\x010*~\\xe8\\x8c\\t\\x98\\x9f\\x16\\x1e\\xc9:\\xf2\\x1f\\x05\\x08R\\xa2g\\xe3\\xbb\\xde9\\xc3\\xe8\\xe6\\xca\\x02CN\\xf3\\xe6\\xe8(H\\xd7\\x83+A\\xb4gG\\x9e\\xaci+\\xb8*yS<9\\xa7\\xf3f\\xc1\\x1c\\xb2(j\\xb1\\x13\\xb3\\xa31P\\x97\\x17W\"r\\xd1\\x12\\xb9\\x08\\x03\\xf9=\\x14\\x80T\\x99\\x9c\\x15y>1\\xf3\\\\\\xc1\\x02JF\\xafQR\\x01&\\x10\\xfe>(\"\\xa5\\x1a\\xe9y\\xbb\\xa4\\xaa\\xec\\xe4\\'5\\xd1;\\x89R^\\xc8\\xf7\\xf3e\\x00\\xa1\\xce\\xceW\\xbc`\\xd2\\xc4\\x02\\xf53\\xe2k\\xc8\\xf7=\\'u\\x10\\xb9\\x93\\x1c\\xca\\xc0#G\\xf9\\x08\\x8b\\xca\\x84\\x8c\\x8c\\xf9\\xfb\\xa4\\xa0\\x98\\x9c\\x7f4\\x0eC\\xa9\\xef\\xc8Y\\xc0@\\xd3\\xf0\\xf2Y\\xd8\\x0b\\xfb9Q\\x85\\x05\\x11\\xcc\\xeft\\xa9\\xc1&\\xec\\x1de\\x98\\x1e>bS\\xd6M\\xd8B\\x8c\\x8f~}VP$\\x07I\\xf3Y\\x8c\\x8df\\x14\\x85@\\xe4\\x9f\\xf3\\xba&A\\x925\\xe2\\r\\x8d\\xe5\\xaf\\xcb\\x87\\x01\\xfaR!\\xc9\\x88\\xe1\\xfa*\\x80\\xc9\\x87\\xcf\\xac\\xbbI5\\x1e\\n(I\\x1f\\xc7\\xcd\\x84\\x82&\\x8c\\xf7@\\x11\\te=\\xd6?\\xa4\\x9c>[\\x90\\x84\\xe2\\x9e\\n\"P\\x8e\\xa7\"\\xa2$\\xdc\\x113\\xbd\\xb0$\\x0fD\\x0f\\xe6\\xb8L\\x93JLNAR(\\x90\\xc8\\x99\\x7f\\x1c\\xd0$\\x04\\xe9\\x00\\xed\"2\\x15\\xfdz\\xa4\\xbc\\xc0\\x0e3\\xfd\\xd4$\\x91I\\x07\\x03\\xd8\\xff\\x00W\\x18\\xe09\\x9e}1C,$\\xa4\\x83\\x1c\\xc3\\xe7\\xcdi\\x0b\\x84\\x1b2;\\xb60H.\\xc1\\xdb\\xfcVq\\x15\\xe6)\\xbb! \\xcf.>\\xe8\\xa2\\xaa0\\x11\\xc0R\\x82y?\\x01VD\\x04\\x9eU\\xa17K\\xf4!\\t\\xf5K\\x81\\xe5>rk\\xb8$\\x10\\xd8\\xea+\\x1d#\\x86\\xf21\\x05@\\x12G\\xc0\\xfe~h1p!\\xddX\\xaanK\\xdb\\x98\\xe2\\xb3$\\x9b\\x93\\xae\\xa8\\x1a\\xad\\xf4\\r\\xfe\\x1a\\xe4\\x89b\\x07\\x8f\\x8e\\xe9,;p\\xba\\xfb9\\xa9$w\\x12s\\xb5\\x19\\x82\\x99\\x1b\\x83\\xe6\\x7f\\xaa\\x93\\x19\\t^=P\\tH09\\x0f\\x9b\\x0f\\x18\\xeau\\x9fvf\\x98\\xc1\\x8aP\\x08\\x8d\\x12\\x07\\xe2\\xc0\\x90\\xe6y%\\xf9\\xab\\x84\\xd5`\\x92E\\x110\\x9c)\\x0f\\xc5DW\\xbc<\\xeb\\xb1Kg\\x8c\\xe1\\x9a\\xa8p\\x8f\\x1c\\xfdM\\x93\\x05\\x8cQ\\xf8\\xf7Z*8\\xfe\\x04\\xde\\x90\\xb1\"H\\xf4sF\\x0c\\xcc\\xe0a\\xd3\\xe2\\x82\\xce\\x08\\xd9\\xea\\xeb\\x14Y\\x19\\x13L\\x8f\\t\\xb2\\x94{\\x9fV\\x01\\xd1\\xee\\x1b\\xf3[\\x164\\xc9\\xcf\\x98h\\x80x\\x08\\xea^<\\xfdW$\\xa8o{a\\xe8\\x15T|E\\x9aNb\\x7f\\xb5G\\xc0%\\x97\\x87\\xcd29\\t\\x9c\\xd2\\x8a8J`\\xe6{\\xaf\\x01\\xea\\\\8\\n\\xc0\\xa6\\xc7\\xb8m[\\xc1/\\xc1\\xa5\\x15\\x14\\xc9\\xe0\\xf8\\xa1!\\x04i\\xf1\\x0b\\xaaa\\xc6g\\x8a@\\x11\\xcf{\\x9bc\\x88\\xe6\\xaf\\xeb+\\xe2\\x802\\xfb\\x8f\\x14\\xe2F\\x9d\\xc7T\\x03\\x00K\\x8d~\\xec\\xd8R\\xc1\\x13\\xe2\\x97\\xf2\\r\\x97<VtD\\xc4\\xac\\xcf\\xa2i\\x057\\x1b\\x04\\xc7\\xcf\\x9a \\xc8\\x12\\x06\\x13\\xac\\xabe\\x87\\x00t\\xacr\\xd1\"q$iPhL\\xc0N\\x1b2HIS52\\x1d\\x80\\x81e\\xea\\x83\\xa8A\\xc4\\x14\\x95\\x16;\\x1e\\xe9\\xc5b\\x1d\\xe2#\\xc1\\xdd\\x94E\\xc3\\x18\\x1e\\x0ef\\x8c\\xd3\\x17S,\\x1cs\\xd3V\\na\\x01\\xf7A\\xb8\\n\\x89K\\xbb\\xaf\\x00p\\x8c\\x9fV\\x0c\\xe6x\\x96\\x19\\xf9\\xa8\\x9aI\"\\xa4={\\xb2w\\x81\\xe6a\\xd7\\xd5\\t6$w\\xbd\\xff\\x00v\\x00\\x8d$\\xa7+\\xe3oA\\x90\\x16}\\xb3\\xf9\\xa4\\xe2WPc\\x9ap`\\x8cG\\xb6\\xae\\x99\\x12\\x8f1\\x14\\xc0\\xd3c\\xee\\xe9\\xa4\\xa3~)\\x97\\xa9c\\x881\\x1f\\x9b;\"\\x85\\xd9\\xe3\\xcd\\n`e\\x85\\xf0r\\xfcV(\\x9e\\x1e\\x19`\\xaa\\xa9R2|>\\xecQ`GO\\x16\\t*\\x0c\\x13\\xd5\\x12\\x84q\\xad\\xb3 \\xd4\\xe9\\xfd\\xd5q\\x14\\xe5b#\\xc5\\xe2J\\x13<\\x07\\xe9nQ\\xc2I\\xed\\xd3\\xdd\\x14L\\x87\\xb1\\xf9hL0\\'\\x06\\xd8*B\\x9a<{k\\x10\\x10\\xf4\\xf8<\\xd8\\xc4`\\xd5$\\x82\\xaa5#\\x19\\x99Q\\x12K\\xc8\\xe5L%#H\\xf7\\xf3D\\xc2T\\xc8\\xd3\\xe2h\\x18!\\x99X3\\xf8\\xb1IE\\xc9W\\xdd\\xce)(L\\x913\\x13a`F4@\\xf2\\xc5S\\x12D>\\x9f\\x15\\x01\\x02\\x88\\xbc\\x8f1C@<\\x10aO1d\\x8e:\\xb9\\x83\\x98\\xa8\\xc1L\\xb4\"~\\xa6\\x91\\x10\"<O\\x14L\\x01%\\xc6&\\xe8\\xf9)\\xc4BD\\xc8\\xef\\xc9B\\'2\\x19\\xe5\\x95qH\\x02|\\x16\\x0fB \\xf35\\x05\\x02\\xa1\\x00\\xeewT\\xa5\\x89\\x13\\xe2&\\xb3\\xb8F|\\x114\\x04\\x12\\xcc\\xbf\\x9a2\\x82J\\r\\xdfjdE\\x98~J\\x83M\\x07\\x04\\x9b\\x9bu0h\\x00\\x07\\x1e(\\x96S\\xc3\\xcfQJ\\n$gZ\\x86\\x04\\x02:\\x01=\\xf9\\xae\\x89\\xc1\\x93\\xa9\\xf6]\\x8f$\\xe9\\xb2Q8\\x19\\xa63,P\\n\\xd8\\x17\\x83\\x8a\\x01!12\\xda\\x002\\xe1Y?\\r&\\x0b9\\xf0\\x03\\x98\\xa8\\x02\\x04\\xc8\\xf3\\xfe\\xcb\\x1d\\x07\\xa3\\x96\\xf4\\xe1\\xd9`4\\x1a\\xb61\\xe1\\x96fW\\x96Q?\\x80\\xae\\x91\\xa7\\x13?\\xea\\xea\\x9dg\\x08\\xc6\\xcc\\x12\\x03\\x82\\x13\\xf8:\\xaf3 \\x94\\xde\\x7f\\xa9\\xb3F\\xb4\\x13\\x89\\x1f\\x15i\\x1a\\x08\\x97\\xbf\\x87\\x9a\\xd1\\xb2p\\x9b=\\x9d^#9\\x89\\x0f\\xf2j\\xd0\\xa2\"|\\xad\\xd7\\x00\\xc6@\\xfc\\xf1y\\xdd\\xe5\\xf9|TN\\xe0\\xd6!\\xde\\xa9\\x101\\x85\\x83\\xb3x\\xacD\\xe3\\x08iT\\xa0\\x9d\\xce7\\x9b\\x05,L\\xef\\x8a\\xea;\\x04|g\\xf5d\\xbd\\x00\\x86(O\\x00m\\x9f\\x1f\\xfc\\xadu\\x95s\\xe5\\xea\\xe0\\x99\\x97\\xd76\\x07\\xb2Yzt\\xbd\\xa0B)\\xec\\xa0#\\xb0\\xcf\\x1f5\\xf34\\x07&\\xd9\\xac\\xe9\\x12\\x1ceQ\\xc9\\xad\\xe3<\\x91\\xa0H\\xa2&h\\xc2g\\xbfU0\\xc6F\\xf8\\xbc\\x90\\x16\\xeb\\x06.\\xcc\\xed\\tL\\x1c)\\xb3 \\t\\xf4\\xe7\\xf2Y\"3-\\x9d\\x87\\xbf\\x8a\\xe0\\xc2\\x11\\x0c\\xf2\\x9e\\xdf\\xaa\\x82\\x82\\xbc\\x07\\x1fw\\x92\\\\\\x13,\\x8b(\\x13\\x18V,\\xc3\\x1f~\\xa8F\\xa4!\\xe0\\xf8l\\xd3BA\\x90\\x9b\\x98\\x8e\\x83\\x82\\xbf\\xc5\\x19\\x9cO\\xa0G\\xbe\\xeb0\\xba\\x01\\x90i\\xe6+*\\x10\\x94\\x80E\\x88z\\xe1\\xca}Tl\\xc3R;?\\xdb`\\xc3&\\xbd\\xac\\x91y\\xb0!C\\x98h&B!\\x1d\\xef\\x96\\x9ca\\x88O\\x81\\xf1\\xb6\\x01-g\\x18<\\xeda\\x14\\xa7R\\xa3\\x1c\\x02\\x11\\xd4\\xd2I\\xe3?\\xaa\\xab0\\x15&>iq\\x081vx\\x8f6n\\x087\\xef\\x9a\\x85\\x02;\\xe3\\xaft\\x884G\\x9b\\x00iP\\xf0\\x8b\\xa6\\x19\\xdc\\xe2\\xa7\\xcd\\x8b`\\x94O\\x9b41 ?\\x8d\\xa0\\xe1\\xd2O\\x89\\xa0\\xc4\\xc6\\xc6d\\x7f\\xed!\\xb6\\xa6\\x86LP\\x9a,\\xa2\\xbb\\x9f\\x19e+\\xe2do\\xfa\\xaa2t\\x83~.\\x80\\x915\"++\\x0c\\x8c\\x04>\\xeaez\\x1c{,J\\xc9{\\x11\\xed\\xf3^)\\xc9\\x0e\\x0c\\xf7\\xf8\\xb8\\xa2\\x11\"!\\xb38\\xa1\\x14\\xf5K\\xb9\\x14\\xc6J\\xaa2C\\x08a\\xbb\\x06Dp\\x8f\\xbb1\\xca\\xce\\x7f\\x94Yq\\x0c0\\xa2?\\x15\\x12\\x87\\x9eX\\xe6\\xc4\\xd1 \\xa0\\xd6R\\xa8\\xc1\\x06)\\x92}m\\x00uI\\x86\\x9f\\x9a\\x1c\\x9a\\x08\\x02q\\xfc\\xd2\\xb40g}\\xedO?\\x1c\\xbcx\\xfe\\xea\\x050\\xe9=\\xf3?Ua\\x04\\x94\\xa5\\x96)N\\x04\\xbc\\xfb\\xf4\\xd6\\x97_\\xf7U\\xc8\\x0b\\xcb\\x8d\\x8a\\x9a\\xa7\\xe5M\\x02@\\x98\\xbd\\xcc\\xff\\x00vY\\x00t9\\xfa\\xb9I\\xd3v\\xf08z\\xee\\xc8\\xc8\\x96\\x01\\x077\\xe2\\xa8\\xe0LA\\xfd\\x940%\\x7f/\\xcdo\\x85\\xd4a\\\\\\xeaH\\xe7\\x86><\\xd1\\xf9\\xff\\x00\\x15\"\\x06\\xc3\\x12lr\\x8c\\x93\\xf1-\\x84\\x00\\x1e\\xc8\\x99\\xb2\\xc8\\xf0\\x1c\\x93T\\xb0%\\x0eu\\xf7\\xe4\\xf1`\\x83\\x9dH\\x83\\xcdXZO\\x84G\\xe6\\x9a\\xa2%\\x06X\\x1e\\x18\\x8eh\\x80e\\xd4\\xd8\\x8e\\xdb\\x1aoP\\x86\\xe7\\xdfVK\\xf5?U\\x00\\xe2\\xf0x\\xa86\\xb0\\x177\\xef\\x9b\\xb3=Q\\r\\x87\\xfa\\xa4\\x0c\\x10\\x87\\xe1D`T@DT\\x00eu\\x81\\xfc\\xc5\\xf9\\xc0\\xae\\x86\\x8cN\\x99\\xbf5$~}\\xa7\\xb8\\xf1\\x7f\\x08\\x883\\xec\\xa21\\xcaD\\xbf/\\x15\\x11\\x89\\xcf,\\x9fSO\\x07\\xc4FE\\x969\\x83\\xc3S\\x8c\\xb8\\xc9\\xb1\"Q\\x89\\x07\\xc9X\\xbb\\x04\\x8f\\xb8b?\\xd5\\x0e\\x94\\xce\\xa7\\xceV1\\x08\\x11\\xec\\x8e\\xe8\\xd2\"n\\xfb\\xf3X\\xc4D\\xc2\\xbc\\x0f\\xb3\\x1f\\xc5\\x0f\\x03\\x19Le\\x12\\x07u\\x89\\x055\\xeb\\x1f4\\xc98\\x94\\x92C\\xd3\\xf1M\\x04\\xa1Tc\\xff\\x00\\xca+D\\xbct\\xff\\x00\\xe3d\\x1e\\x05W\\xf7p,\\xa6\\x8f\\xea\\xa3\\x94\\xa6\\x1f\\xfa\\\\\\x120\\x07C\\xd8XnrW\\xc1T\\x01\\x03?\\xcf\\x9b\\xa6\\x9e#\\xacw\\xbdx\\xb3DYl\\x84\\xd8\\x00Vt\\xfcm}#\\x839#\\x9b\\x00\\xf35=6{\\xd0dV\\xd0\\xf2k\\x82\\xf5bRzx\\xfb\\xaa\\xec\\xa5SI\\x9b\\xc1Hk\\x13\\xfc\\\\\\xe8c\\xbf\\x05H\\x0c\\x00D*:\\x08a\\x92\\x7f\\x1e\\xaa\\xc2d\\xf7\\xdd\\xea\\xcc\\xb0O\\xc2}U\\x81\\x8e\\xc7}kN<R\\'=g\\xc5\\x80R\\x12\\x8d\\x0e\\xe7\\xdcP\\xd7\\x80\\xf1\\xdb\\xea\\xc9$. \\xe7\\xfc*\\x12\"<q1\\xdf\\xddp\\n\\xcfG>\\xabV\\x8d\\x0f\\x93)\\x88\\x00\\xf2yJc\\xb9\\xc0 qC\\x88\\xec\\x0e\\x86\\xbdAB$\\xf1yC\\xaa\\x0c\\x8b1\\xc5\\x9c\\rL\\x8cX\\xe8Z[\\x1fq\\xea\\xa2C\\x15\\xe18*\\x90v\\xec\\xdft2\\x8d\"\\x02\\x93\\xef\\xcd\\x8e\\x00,\\x1f\\x04\\xd8\\x83\\xc7I\\xe2\\x7f\\x9a\\x08Kra\\xeb\\xcd\\xe2\\x93\\x80\\xd8O\\x17=\\xd0$\\xc7+I6\\x83A\\xc3\\xeb\\xe6\\xa0v4\\x10\\xb3X\\xd0(O\\x9a,\\x8e\\xa0\\xf6\\xd6Q&I\\xd4\\x1c\\xd2\\xa0\\xcb!uDq\\x8b\\x1b\\xdd\\x90\\x84\\x8d\\xc7\\x19\\xef\\xd5\\xd2?9\\xa8\\xa7F\\x020\\xb3\\xc2\\xc1\\xd6~\\x8c\\xa4\\xe0\\x18$\\x84j`\\x01\\'\\\\\\x94\\x08\\xd0wA\\xd0M\\x04\\x88\\xcc\\xa1\\x86>\\xf9\\xae\\xa8\\x89J&O\\x99\\xfe\\xa8E\\x92xLC\\x9b,HU>\\x0f\\xfe\\xf3P\\x03CC\\x07\\xed\\xa4\\x05\\x82\\x86\\x98\\xd7\\xcd`\\x93J>C\\xd3Z\\x88\\x18@\\x9ei(\\x91\\xae\"\\x94\\xceS\\xddU4\\x9e|\\x94\\x88\\x8c\\xb8I\\x9f\\x9a\\xa3\\xd2`\\xb1\\x175\\xcd\\x1e\\xe0\\x8b\\x81\\xca|RD\\x19\\x1dI\\x1b\\xe6\\xa3\\x8e\\x97\\x99\\x00\\xff\\x00\\x14x\\x84o\\x18\\xcf\\x8a0\\x84\\x0cc\\xfdMB\\xc1\\x02\\x0f/\\xc5l\\x1e\\xdf|\\\\\\xec\\x81\\x19\\xef(\\x13\\xb5M\\xca@R\\x7f\\x9fV\\x00\\x93\\'\\x9f\\xa8\\xf3\\xe2\\xe7\\x80\\x89\\x9cy~}\\xd2\\\\\\xc8\\x1c\\x8ex\\xac\\x92\\xbd\\x81\\x1fQ\\xb5\\x89.\\x0e\\x98\\x87\\x9f4\\x01\\x06q\\x99\\x12k\\xca\\x16\\x19\\x13\\xe5\\xc7\\xea\\xf1(\\xbcj\\xbf\\xbb$@e\\x99e\\xf8,\\xe4\\x9d7V\\xa1\\x82\\x1eG\\x9c(\\n\\x87\\x0f\\'\\xff\\x00lU3\"4\\xbfzM\\x11\\x9e\\x18S\\x0f\\x07\\x8a\\x96\\x06\\x0c\\x91y\\xf9\\xb9Ee\\xc1\\xf0\\xba\\x10B\\x12D\\xef\\x9b\\x10.\\x08Hq\\xf7J\\x9b\\x0e$P\\xcf\\x12]\\x96$\\xf2<u\\\\\\x02\\xa2\\x8e\\xc8|,we-E\\xd1\\x8f\\xe3\\x9a\\x81\\x02C\\xdcy\\xb2W\\x0b\\x81\"\\x7f\\xba\\x81\\t\\xf2I\\x11\\x9eJ\\xa7\\xab\\x03\\x9d\\xf9\\xab\\x04\\x11\\xfd\\xb6\\x13\\xa4\\xd7Nf\\x94\\xc4\\x15\\xe4Q`\\xa5X\\x01~l\"\\x03v\\xe0\\xf3\\x13\\x974\\xb8b~W1\\xc8\\x00Fy\\'\\xbb\\n*\\x03\\xbc\\x8c\\xfab/\\x87\\x03\\x88\\x82<U\\x1c(1?\\xc6e\\x84\\x18$\\xc7\\x89\\xaaU\\x94\\xb0\\xec\\xb9\\x82\\x15\\xdf\\xfd\\xfe\\xece\\xf1\\x18\\x98\\xef\\xfd\\xd5\\x14\\xc0\\t9\\x08{\\xac\\x02\\xc8u\\xcf\\x15\\x9f\\xa2\\xe7\\x81\\xf8\\xa422\\x1f\\x8f\\xba\\xc2J\\x0f\\xc6\\xf9\\xed\\xab\\x8c\\x04\\xc8\\xa0DX\\xc1\\x00<\\xfc\\xb3\\xc3@\\xa0\\xf9C\\xdd\\x90)c2J\\xa8d\\x17@0\\xfa\\xbc\\xa8\\x92\\xb2J\\xff\\x00v\\x1b0\\xa4\\x17\\xe1\\x936#)c\\x1b\\x03\\xddm\\xe0\\xf5\\xcf\\x14\\x05\\x94\\x8c\\xa98\\xa3*\\x07\\x7f\\xda\\xf4]i!Srb\\xcc\\x1c\\xa3\\xba\\xb9%\\xa48=e\\xe0\\xc7\\x90\\xc9\\xb2\\x11\\x0e\\x8c\\xf63\\xa8\\xa5\\x05\\x83\\x10\\x17\\xf7\\xfb\\xaf\\x14\\x87\\x91\\xc8\\x7f\\xaa\\x84\\xa4.=\\x91\\x9bPz^\\xcc\\xe0n4\\xbdm\\x91>:\\x8b\\x1d\\x84v\\xf3\\xc7\\xab#\\xa0\\xfbI\\xcb%4\\xf5\\x8e\\xbf\\x15\\xe34k\\x1a~2\\xc4\\x87i\\x88c\\xf9\\xee\\xc4\\xf6\\xe1\\xe6e\\xaf \\xf2#\\x9f\\xd8\\xd4\\t\\x1eI\\xc9\\xeei\\xc1 \\x1e?\\xcf\\x15Ar;\\xe3\\xf7PBbL\\xf9z)\\xa2\\x0c\\xae\\xf3\\xe3\\xd5\\x84tx\\x0c\\x93\\xef\\xba!@=iD\\x03\\x1b\\xaf \\xe6\\xcc\\x80\\xa7\\x99\\xf1\\\\\\x87\\'\\x89\\xe0\\xf9\\xb11\\x10\\x07\\xd5\\x94\\x02}T\\x10\\x10\\xeauPb\\x06\\xcc\\x8e\\xa8H\\x81\\xe1+2\\x00U\\x19s\\xfc\\xdb\\xbb\\xb5 \\'=\\xd6BC\\xa9D\\x9e\\x0f\\x8a$\\xe5\\xf0\\xbd\\xcft\\x1e\\x898:\\x16Zs\\xc9\\xcb>\\xab\\xd0D^\\x1duQ\\x01\\xe1\\x93\\x1c\\xde\\xf0\\xf29~j\\x0c\\x1f#\\xb3?\\xc4\\\\8x\\x9e\\xcd\\x9b\\x07\\x19 \\x91\\x07\\xe9H\\x95\\x18t\\xbf\\xc3@\\\\\\xae\\xbc\\xfe\\xa6\\xcc\\xbdA@) \\xe6\\x0e\\xea\\x04O\\x9a\\x82\\xc9\\x11a<\\xc7=\\xe5\\xe65\\x06\\xb7f\\xcc0\\xb6Dp\\x1f\\x9aVD\\x14\\xd8~\\xac\\x82p\\xd8\\xc1\\x8e\\xdf\\x14\\xcc\\xa1\\x102,X\\xe0\\xaf\\xc8\\xe5#G\\x0et\\x8fuS29\\x1c\\xca\\x08\\xa0r\\xae\\xf1\\xee\\x85\\xd1\\xe0\\xa7\\x8e\\xaa\\x04Xv?\\xce\\xae\\x06f:d\\xfc\\xd1j\\x8dG\\x82\\xc5\\x92\\xa0K\\x82{\\xa6\\x11*=9\\xcb->\\xa1\\x94\\x06e\\xbc\\xbf\\x14\\x8aG<Y0/Jr\\xd8\\x05\\x00\\xf4\\xae\\x13\\x82\\xe8\\xa4\\x93.w\\x1b\\x84\\xd7\\x9cI\\xfa\\xea\\x9a\\x04C\\x89g\\xaf\\x8a\\x19$s\\x19\\x8b r\\x0f\\xdf\\xcd\\x91O;\\x8cAb\\x06v\\xcc\\x03\\x14<\\x90\\xe4\\xe6\\x1f4\\x11\\x81\\x0e)$\\xb7\\x00\\xce\\\\\\xd3\\xb5(\\x1eXp\\xbf6b,\\x83\\x86=\\xd5-H\\xb1=\\x95L\\xb3\\x93\\x9b\\xeed\\xfc~\\xa8g\\x13\\xeb\\x8b\\rF\\xf9\\xf5\\\\\\xf0c\\xf7YX\\xc7\\xd5\\x04@\\x81\\xeaO\\x86h\\x1e\\x12\\x92\\xcc\\xb1\\xfex\\xa0\\xf2\\x13\\x84\\xfe\\xeb\\x06\\xd2\\x04@\\xbfwB\\xfc =%\\x18\\x90\\xa2\\x16Ix!\\x94\\xef1\\xf3qB\\xc3\\xff\\x00\\nd{9\\x12\\x83\\xe2\\xb0O Ls\\xcd\\xc1\\x18C\\x1d{\\xf9\\xb29-$\\xcek-\\x19\\xe8\\xeb<4\\nL\\x0f>\\x9a\\x94\\xf9<\\x91Z\\xf1z\\xe2\\xe4\\r:\\xa3#2\\x19\\xf5Ya\\x9f\\x04\\xd9\\x10B\\xf67\\xfch\\x19\\x04\\x08\\xcf\\xd4X(A\\x8e\\xd5\\x82\\x10\\xe0\\xbc\\xd9\\xf91\\x0c\\x8d\\xf6Ta\\x00\\xd0\\xce\\xed\\xd4r9\\x8f>\\xee\\x8e\\x0c\\x99\\xe3\\xccW\\x10\\r\\xdcC\\xb5\\x08\\x18\\x13\\x88\\x87\\xdd\\xc0\\x19u\\x8f\\xcb\\x153\\x8b4\\x14\\xc9c\\x04\\x12\\n9\\xfc\\xd2\\xe1\\xc4\\xd0d\\xf9(\\x9a)\\x8eC\\x16Ad%\\x99\\xe2\\xa0\\x97oS\\xfa\\xadL f\\xd4\\x80\\x96*\\xcc\\x13\\x1d\\xf8\\xb2\\x0c<\\x8eudP\\xde\\x05\\x9f\\xddk\\x0c\\x86\\x91\\xcc\\xfb\\x8b\\x90\\x10O&\\x1a\\'\\x82\\x9eq\\xe2\\x81\\x10o\\x16\\x08A\\xe3S\\xff\\x00\\xb6D\\xef\\xa6A\\xe9\\xa8\\x10\\xafL\\xa5|\\x89\\xf8J\\xe1A9|\\xd4$\\x89\\x1c\\x1ce\\x18Q\\xf5\\xcf_5\\x03\\x07#l\\xe1\\x15\\xfa\\xea\\xa20\\xbf?\\xee\\xa0s\\xadFj\\x19\\xdf#\\xc7Q`/\\x8e\\xbc\\xd6\\x06\\x9f/\\x14\\x88Vr\\x99\\x01\\x04O\\x13\\x1el\\x1e\\tjwu\\x85r\\x08\\x18\\x8f~,\\t\\xc2\\x03\\xac\\x8f\\xdd\\xea\\x12\\x9e\\r\\xcf\\xae\\xe8\\x9aK\\x04\\t\\xbf\\x15A\\x88=#-\\x02\\x1b\\xcc\\xe4h\\xa8\\x94vc\\x11\\xf7P\\x8c\\x89eV \\x8b\\x02\\x0c\\x0e\\'+\\x81\\n\\x83\\x8e\\xfd\\xd0bw\\xb2}\\x94\\xe0\\x1fO5I\\x11\\x0c\\xe6\\xc7\\xf1`\\xae\\'5\\xf1c\\xa4\\xbf\\xb2\\xa7t\\xe3\\x89d\\xc9:\\x13\\xef\\xeek\\x0b) \\xf1\\xfcT\\xa7\\xb7H\\xb6n!\\x02\\xa0(d\\xe9\\xf5\\xf5\\xddN}\\x89\\xa4\\xfbc\\xab\\x10\\x83\\xa12\\xfc\\xd5<\\x89\\xe1C\\x8f\\x8a\\x08\\x82P\\xa8\\xaaWK\\xe0\\x9f\\x9a\\xf3.\\x1d\\x8d\\x92\\xca\\x0cx<\\xd4\\xa6\\x17a\\'<G\\xfe\\xd01~\\xcb T\\x0f[xH\\xeeg\\xf1@\\x86\\xec\\xe7\\x8f\\xe2\\xb9\\x99j\\xcb\\x1d}\\xc5\\x94\\xc68\\xf3t_@\\xd8\\xa5\\xa4\\x18\\xe7\\xbb\\x06TH\\xeey\\xa7!<\\xad( \\r)J\\x1d\\x1d?T!P\\x0e+[uS\\x86y\\xb0\\xc8C\\xaa\\xb6\\x10\\xac\\xc1\\xdd\\x95&N8)\\x96\\t\\xea\\x13\\x1e\\xa8r\\xb4a;\\xfc\\xdc\\xf1>2w\\xea\\xc9(\\x90\\xf1\\xd5\\x1d\\xd9\\xf8\\xea\\xcaL\\x86\\xcb\\x14\\xe4\\x87O\\xd5\\xe5\\x8eSX\\x01\\xc9\\xd96J\\x86\\x1c\\xaesR\\x1c\\xf8\\xca\\xa0}\\x81&\\xae\\xe1!\\xc3\\xc7\\xba&\\x13\\x03\\x7f\\x9b\\x02\\x00\\x94\\xc0\\xe0\\xf0{\\xaa\\x10\\x1dA\\xbb\\x05f\\x90\\x9c\\xcc\\xda\\xb3\\x81\\x0e\\xe5E\\x92\\x1eo\"\\x1f\\xbd\\x9f\\xba\\x908\\x8f\\x8d\\xb3\\x89;\\x98\\xaa\\x83\\x8f\\x1bx >\\xfa\\xa8z\\x13\\xf4V}\\xe6\\xc0C\\xe4f\\xaa\\x18k\\x93o7&U\\x04\\'\\xd5x\\xa3\\xb0i\\xa0\\xf1\\xfe\\xec\\x9c\\x0f]\\xf8\\xa2y1\\xcf\\xe3*\\xc0w\\x87\\xf5@\\xabM\\xf1I\\xd4P\\x89/=\\xbe\\xae\\x04,\\x94&>^+\\x9e\\x07\\x0cqX\\xd70\\x9e\\xf8\\xf1A\\x89\\x9c\\xfeK(\\x82\\t\\x14\\xce9\\x9b\\x02}\\xb0\\xd9D\\x1e\\xe1\\xf7T\\x9f(\\xad?uLA\\x0e2\\x92\\xf1\\x03\\xd0\\xc5\\x02\\x01#/=Y2\\x1c34\\x1c\\xa8)\\xf6FE\\x14Ks\\xe0\\xe6\\x89\\x9e\\xcf\\xcd\\x1dY\\xca\\x84\\xc1j\\x16\\x8c\\xc3\\xe9\\xf5N\\x91\\xe9\\xf3^\\x87\\xb8\\xa6\\x08\\xcc\\xcf\\xd4U\\xec9\\x8e(\\xceH\\x8e\\x15I\\xb5\\x81\\xc4d\\xd7C\\\\\\x88\\x9c\\x8f\\x05\\xff\\xd9', format=u'jpg'), interactions={'hover': 'tooltip'}, scales={'y': LinearScale(), 'x': LinearScale()}, scales_metadata={'y': {'orientation': 'vertical', 'dimension': 'y'}, 'x': {'orientation': 'horizontal', 'dimension': 'x'}}, tooltip_style={'opacity': 0.9})], padding_y=0.0, scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0), title=u'Trees')" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bqplot import *\n", "\n", "# Create the scales for the image coordinates\n", "scales={'x': LinearScale(), 'y': LinearScale()}\n", "# Define the bqplot Image mark\n", "image = Image(image=ipyimage, scales=scales)\n", "# Create the bqplot Figure to display the mark\n", "fig = Figure(title='Trees', marks=[image], padding_x=0, padding_y=0)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mixing with other marks\n", "\n", "`Image` is a mark like any other, so they can be mixed and matched together." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5bdf5d8c9b0341c381f23de4283276fe", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n", "<p>\n", " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another notebook frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "Figure(animation_duration=1000, axes=[Axis(scale=LinearScale(max=2.0, min=-1.0)), Axis(orientation='vertical', scale=LinearScale(max=2.0, min=-0.5))], fig_margin={'top': 60, 'right': 60, 'bottom': 60, 'left': 60}, layout=Layout(min_width=u'125px'), marks=[Image(image=Image(value='\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00H\\x00H\\x00\\x00\\xff\\xe1\\x012Exif\\x00\\x00MM\\x00*\\x00\\x00\\x00\\x08\\x00\\x07\\x01\\x0f\\x00\\x02\\x00\\x00\\x00\\x12\\x00\\x00\\x00b\\x01\\x10\\x00\\x02\\x00\\x00\\x00\\x0c\\x00\\x00\\x00t\\x01\\x12\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x01\\x1a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x80\\x01\\x1b\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x88\\x82\\x98\\x00\\x02\\x00\\x00\\x00\\x07\\x00\\x00\\x00\\x90\\x87i\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x98\\x00\\x00\\x00\\x00NIKON CORPORATION\\x00NIKON D3300\\x00\\x00\\x00\\x00H\\x00\\x00\\x00\\x01\\x00\\x00\\x00H\\x00\\x00\\x00\\x01Tama66\\x00\\x00\\x00\\x08\\x82\\x9a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xfe\\x82\\x9d\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\\x06\\x88\\'\\x00\\x03\\x00\\x00\\x00\\x02\\x00d\\x00\\x00\\x90\\x03\\x00\\x02\\x00\\x00\\x00\\x14\\x00\\x00\\x01\\x0e\\x92\\n\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\"\\xa0\\x01\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xa0\\x02\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x01@\\xa0\\x03\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xd5\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x012017:06:08 17:17:46\\x00\\x00\\x00\\x00\\x18\\x00\\x00\\x00\\x01\\xff\\xe1\\n\\x1bhttp://ns.adobe.com/xap/1.0/\\x00<?xpacket begin=\"\\xef\\xbb\\xbf\" id=\"W5M0MpCehiHzreSzNTczkc9d\"?> <x:xmpmeta xmlns:x=\"adobe:ns:meta/\" x:xmptk=\"XMP Core 5.4.0\"> <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\"> <rdf:Description rdf:about=\"\" xmlns:photoshop=\"http://ns.adobe.com/photoshop/1.0/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" photoshop:DateCreated=\"2017-06-08T17:17:46\"> <dc:rights> <rdf:Alt> <rdf:li xml:lang=\"x-default\">Tama66</rdf:li> </rdf:Alt> </dc:rights> </rdf:Description> </rdf:RDF> </x:xmpmeta> <?xpacket end=\"w\"?>\\x00\\xff\\xed\\x00jPhotoshop 3.0\\x008BIM\\x04\\x04\\x00\\x00\\x00\\x00\\x002\\x1c\\x01Z\\x00\\x03\\x1b%G\\x1c\\x02\\x00\\x00\\x02\\x00\\x02\\x1c\\x027\\x00\\x0820170608\\x1c\\x02t\\x00\\x06Tama66\\x1c\\x02<\\x00\\x061717468BIM\\x04%\\x00\\x00\\x00\\x00\\x00\\x10Ab\\x95\\xfc\\xe0Y\\xc0`f\\x9a\\x1f \\xaf\\xa5!h\\xff\\xc2\\x00\\x11\\x08\\x00\\xd5\\x01@\\x03\\x01\"\\x00\\x02\\x11\\x01\\x03\\x11\\x01\\xff\\xc4\\x00\\x1f\\x00\\x00\\x01\\x05\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x03\\x02\\x04\\x01\\x05\\x00\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x10\\x00\\x01\\x03\\x03\\x02\\x04\\x03\\x04\\x06\\x04\\x07\\x06\\x04\\x08\\x06s\\x01\\x02\\x00\\x03\\x11\\x04\\x12!\\x051\\x13\"\\x10\\x06AQ2\\x14aq#\\x07\\x81 \\x91B\\x15\\xa1R3\\xb1$b0\\x16\\xc1r\\xd1C\\x924\\x82\\x08\\xe1S@%c\\x175\\xf0\\x93s\\xa2PD\\xb2\\x83\\xf1&T6d\\x94t\\xc2`\\xd2\\x84\\xa3\\x18p\\xe2\\'E7e\\xb3Uu\\xa4\\x95\\xc3\\x85\\xf2\\xd3Fv\\x80\\xe3GVf\\xb4\\t\\n\\x19\\x1a()*89:HIJWXYZghijwxyz\\x86\\x87\\x88\\x89\\x8a\\x90\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xc4\\x00\\x1f\\x01\\x00\\x03\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x01\\x02\\x00\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x11\\x00\\x02\\x02\\x01\\x03\\x03\\x03\\x02\\x03\\x05\\x02\\x05\\x02\\x04\\x04\\x87\\x01\\x00\\x02\\x11\\x03\\x10\\x12!\\x04 1A\\x13\\x050\"2Q\\x14@\\x063#aB\\x15qR4\\x81P$\\x91\\xa1C\\xb1\\x16\\x07b5S\\xf0\\xd1%`\\xc1D\\xe1r\\xf1\\x17\\x82c6p&ET\\x92\\'\\xa2\\xd2\\x08\\t\\n\\x18\\x19\\x1a()*789:FGHIJUVWXYZdefghijstuvwxyz\\x80\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x90\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa3\\xa4\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb2\\xb3\\xb4\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc2\\xc3\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd3\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe2\\xe3\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf2\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xdb\\x00C\\x00\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xdb\\x00C\\x01\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x01d)B\\x03H\\xd4\"\\xc6\\x18\\x94Q\\xb8\\x07B\\x92]\\n\\xc4U\\x85\\xc2s\\x965\\xe0P%\\xc3\\x81(\\xa9!IJ\\xe8E\\x84GBHd\\xa4\\x89*8\\x84\\xe9\\x15\\x1a(\\xd05-\\t\\x944\\xb8A\\x01JV\\x838\\x84)@\\xcd+\\xa4h\\xd5&n\\xe4\\x12\\xe5\\xa1t\\x19\\x90F\\xcf\\r\\xc0\\xf9\\xe0\\x01\\xc3=IK\\x8a\\xb0\\x923\\x1ar\\x90!\\x13(\\x86\\xc5R\\x893W-\\xd6\\x0c\\x8d{\\x81\\x9b\\x11d\\xc1\\xd2\\x85\\x9aN\\rD\\x14N)\\xbc\\xce4\\x91\\n\\x01{%c\\xb6!)&\\x11\\x83\\x1c.B\\x1de\\x02\\xd79K\\xb0dB\\xd5\\xebW+XIM\\x16\\x82h\\x10\\x95\\x8c\\x83\\xad$\\xcc\\xc3wM\\xab+)\\xc0K9\\x80\\xdc\\x8aT\\xca2\\xa0\\x10\\x10:\\x98r\\xdbTI\"\\x10IH\\x92q\\x90IT\\x14Rf\\x8eU\\x9d\\x84\\xc2\\x9d \\x9c2r\\xe6\\xad\\xc2N+\\xde\\xb6gS\\x89FS\\x04\\x9d\\xae\\xea\\xb4\\xa9\\x04\\xbf%k\\xecKy*\\x8c\\x98r\\x10\\x1a!q\\xd0\\xb2\\xb1\\x1dd\\x11\\x12$\\xa0\\xb0C\"\\x94Nds1ZW\\x80\\xd2\\x99\\x14\\xe2$D0\\xc0\\r\\xabxA\"\\x98\"\\xa0\\x96\\x92\\x1bi\\\\\\xee\\x1a\\x149\\xcal\\xe9\\xa1\\xa18\\xee\\x06\\xe4.\\x14\\xacnZ)8\\x9e\\xc9J\\x84:k\\xad\\x05\\x19\\x0c\\x89\\x93\\xac\\xdd.\\xdb\\xc2\\x00\\xa8s\\x11\\xb1%\\x1a\\xd2\\xa0\\xd2\\x89\\x16RDb\\x94K\\x13\\xd6\\xc5\\n\\x9d\\x969RA\\xae\\x0e\\x11)m \\xed\\x9d\\xe57D\\x90\\xcc\\x1f\\xb6viNB\\x97\\xf5\\xaf\\x98\\xb4\\xfd*\\x1eJ\\xcf\\x10;*q!\\xa6\\xefF\\xb51%\\x0e`K8\\x9a\\n\\xf2\\x89\\x0c\\xa0\\xad\\rB[R\\xb8R\\xd2\\xa6\\xca\\x13\\x96\\xe6\\x14\\x08\\x99\\x82\\x088X\\x88\\x95\\x84\\xbc-+2\\xcc\\xd5\\xe8\\x9a\\x17.\"J\\xc2#\\x88\\xa1c\\x93\\x9b\\x84\\xda\\x12\\xf1\\xd04\\xd1\\x8a\\xd5\\xe2\\xd8ZT\\x16\\x00Q\\rG\\t-N\\x95\\xbd\\x13\\x9b\\x89\\xd8\\x93\\x06Z\\xd0@r\\xd2\\xa0\\x9b,u\\x96\\x89\\x89\\x80\\xe7#\\x08\\xd3\\x19\\x858B\\xf5v\\xcaP\\x94$\\xca\\xca\\x10\\xd5\\xe37c6\\xb4J%:,\\xab\\xb6\\xa50W\\'N^a\\xaa\\x1cK\\x06kr\\x02Ta\\xa5Hd\\x90\\xc1z5(\\x81X\\x96\\xa1,X\\x82\\\\\\xa8K\\x9c\\xb011se\\xc7:\\xa9I)2\\x95n\\x9d\\x82\\xd5\\xf3u\\\\@\\x1f\\x1c\\xc8\\xcd\\xc0\\xc9<\\x88\\xc8C[m[\\xa0\\x13\\xe1\\xb9\\xd0\\xcbhB\\x82!\\x9c:\\xcc\\xa4\\xad\\x0c\\xe3D`\\xa2\\xc6^\\x85\\x89y8D\\x8d\"\\x87\\x08R*\\xc8\\xdb,\\'m\\xdc\\x96\\xd2#f\\x14d\\xed4\\\\\\xa7l\\xca\\x1aD$\\xc0\\xd5\\x9en\\xa5)\\xca\\x98\\x99\\x81+\\xac\\xab\\x1d\\x92\\x98V\\xc1\\xd0\\xc8\\x1c\\xc84\\xa3U\\x84JL\\xe4Y+c\\x0e\\x0cp\\x92V\\t\\xd0\\xbaY\\x10\\xa4\\x04R\\'9h\\xda\\x13\\x92q\\x01jI&^\\x8d\\xf5\\xd3\\xa5\\x8a\\x06\\xb4\\x82\\x11\\x90h\\x87#i\\xc9\\x1c(Yc\\xb1Z\\x1c\\xb7,\\xa3[ 2\\xd4eI\\x11\\x0b\\x01\\xa4dM\\x11a\"\\xc3\"R\\xd1ca\\x14\\xe3\\xd9\\x02\\xa5\\x05\\x14%yhT\\x8e\\x88a\\x91\\x9c\\x92\\x84\\xec\\xe5\\x81\\xa8\\xd92\\x80R\\x07 U\\x03\\x80\\x99s\\\\\\r*\\xa6\\x80\\xccT\\xde\\'B\\xb9\\x0c\\x92\\x96\\xe6\\x86(\\x9cX$$A\\x12\\xad\"Z2bU\\x0c\\xaa$\\x83\\x94\\x0bX\\xd4\\xb4\\xccH\\xa0\\x88\\xd1!Q\\xb5u\\xa59\\xd8\\x9a\\x13S\\x91\\x02RS\\x15\\x00(\\xc2e&\\x15cN4!HjV\\x98\\x8c\\x88\\xa32\\xf0\\xf4\"%fJ\\x93\\x84\\xb4)1!\\x06U\\x1bi@\\xa5L\\x82\\x88J\\xe9\\x04\\x1a\\xc9<(z:\\xe1PiB\\x91\\x18\\x94\\xe3e \\x80\\x05%H\\x03A\\xc5\\x05\\xae\\n&\\x89(\\x88\\x94\\xa90\\x88\\x98c\\x04\\x11@\\x1a\\xc6\\xb3\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01\\x05\\x02\\xab\\xab\\xab\\xafzkO\\xb8\\x9f\\xb8\\x03\\x0c\\xf1,h\\xfc\\xbb~o\\xcdZ0\\xce\\x8e\\xbaqz}\\xda\\xf6=\\xcb\\xa3\\x0e\\xbd\\xaa\\xea\\xc3\\xa3\\xa7a\\xa0\\xec\\x1ax!\\xf1\\xect=\\x87o\\xccx\\x84\\xb3\\xfc\\xdd{Q\\xd3G\\x8fj=X\\xfb\\x94\\x14\\xedZ\\x0f-h\\xd2h\\xce\\xa6\\x8f\\xcd\\xf9\\x97\\xe6F\\x8a\\xa7\\xdcK\\xa5Yt\\xfb\\xda\\xb0\\xc7r\\xa6\\x18\\xfb\\x81\\x8e\\x05\\xf1g\\x88e\\xf1|\\x17Z\\x83\\xdb@U\\xc1\\xd6\\xa9S\\x1a\\xb0\\x83U0*\\x03Wq\\xc7B\\xce\\x8e\\xae\\xba\\xb2\\x0b\\x0fG\\xe6\\x03\\x1d\\xd3\\xc4\\x8a\\x8c\\x19`P\\x1d\\x1au\\t\\xe1\\xe48p?\\x9b\\xf3\\x11WJ\\xa6\\xba%Z)\\xe9RY\\x1d\\xb5\\xee\\xa6\\r\\x0eA\\xf1\\xec\\x08t\\xed\\xd2\\x1e\\xbd\\xbc\\xc7b\\x18iV\\xbd\\xa9\\xd2i\\x88\\xe9H\\xe9I\\xecx\\x0e!\\xe8\\x96\\x95h\\xa4\\xd1Ht\\xd6\\x81\\xe8\\xea\\xea\\xf2g\\xb1\\xef\\xa3\\xd1\\xd1\\x9e\\xd4ut\\xec\\x8f\\xb8:X[\\x04\\x16t\\x14\\xd1CB\\x03S<Z\\x1ahR\\xb1\\xd3\\x1f\\x15\\xea\\xa8\\xd3B\\xa0Yuua5a\\x04(\\xe8h\\x1a\\x80~t\\xab\\xa7jv\\xa9z\\x97Muf\\xad,\\x8e\\xc0\\x9a\\xa9\\xa0\\xbc\\x83\\xadR\\xd6:\\x7f-4,\\xb3\\xc6=\\x14\\xb2BQG\\xfd\\xf1T\\rf\\x8e\\xacQ\\x92\\xc1\\xe9,\\xeb\\xdf\\x83\\xa9\\xee^%\\xe0\\x18\\xd1\\xa81N\\xc3W\\x93\\'\\xb6,\\x1dHh\\xabF\\xab%\\x95\\xf5\\x15t>/\\x88G\\x15pBC_\\xb6\\xd5\\x93)\\xa3\\xd0?>\\x01\\x96\\x19\\xadj\\x1dj\\xea\\x1f\\x95^ZU\\xe8\\xc2\\xbb\\x10\\xc2\\xbb\\x1e\\x01\\x96\\x01t\\x0f\\xf2\\xf9\\x1e\\x11\\x8dT(\\xd5\\xd9\\x02\\xae\\x94\\'\\x80j\\xf6\\xc3\\xe9z2u\\xea`U\\x8d\\x1e5x\\x00\\x16\\x1d\\x1e/\\x12\\xe9GP\\xc0\\xab\\xa3*\\x0cQ\\xd4\\x17\\xc5\\x90{\\x14\\xd4\\xba:\\xba\\xd0e\\xd0\\xaff\\xba$\\x10\\x07Q[\\xa3E\\x12\\xc5\\nV\\xc6\\xaf\\xf3v \\xd4:5)\\xa6\\xaf\\xca\\xa1\\x92\\x08\\xa7l^/P\\xcdK\\xc7@\\x1dT\\xf8\\xbdX\\xab\\x05\\xd6\\x8c3\\xda\\x8c\\xf0\\x91\\xa5\\xe9AP\\xd24!\\xd3\\xa9>\\xcdt\\x15c\\x89\\xa3\\xc9-K`\\xd5\\x96@z4\\x92\\xcf\\x00C4eO6K\\xab\\n`\\xbc\\x0b/\\x1a\\xbd\\x1dT\\xfc\\x8fj\\x9e\\xd5~r{C\\x89\\xf6\\xbc\\xabP\\xff\\x000\\xa69U\\xc8N)\\xe2\\xb3\\xd5\\xab\\xab\\x1a\\xbe\\rO\\x83\\x1d\\xa9\\xa8\\nd\\x16\\x03.\\x9a\\xe3\\xa9\\xa8}N\\xaf\\x8b\\x15t\\xa3$0\\x19tu\\xec\\x9fh\\xea\\xa1\\xc7\\xf3\\x16\\x84\\x82\\x92\\x9a2\\xd3\\xecbRW\\xaaS\\xc5U\\xc8\\xb2\\xea\\xea\\xc9\\xab\\xd5\\x8e\\xd5\\xa3\\xadZH},\\xbe\\xae\\xd5|~\\xe7\\x16~\\xe8\\xe2\\x9d\\x18\\x0c\\x01\\x96\\x8e\\x95|\\x1a\\xd8\\xa1\\x03B\\xafe<\\x13\\xed/\\x8d\\x1a\\x98\\xe2Od\\x9e\\xd5g^\\xdc\\x1eN\\xa1\\x87J\\xb0\\x1d\\x1dXP\\xec\\x9a\\xd4\\xeat\\xed^\\xc5\\x81\\xd3\\xdd#\\xb1\\xa3K\\x14e\\xa4Q\\xf9\\xbcR\\xd7\\x1b#\\xb7\\x9e+,\\x86\\x00\\xad\\x1e\\x0c\\xfbTK\\xe0\\xcdK\\x1d\\xb5 \\n>\\x0f\\x87b\\xc7\\x05V\\x80\\x12\\xe8\\xa0\\xc6\\xbfr\\x9fr\\xb8\\x9c\\x9dT\\xeaY\\xf6\\xabP\\xcdC \\xf6\\x08\\xd3&\\xa2\\xc2\\xc3S$\\xd4j\\xf80\\x1eE\\xd4\\xba\\x87P\\xc1z\\x93WZ0^\\xa1\\xea\\xc3:\\x04\\xbd;\\x06;\\xd1\\x94\\xbfe\\x85>\\xa7G\\xab\\xd5\\x96j\\xd2\\x03*\\xa3*k%\\xf9\\xea\\xf1i\\x18\\xb5v\\xad\\x1fQd>.\\x8f\\x83\\xab\\xd4\\xb4\\xf1t\\xd0p5$\\x07\\xa3\\xa3\\xf3\\xfb\\x99\\x06\\xa54\\xb0]^uy\\xbe!\\xd6\\x8d*,94\\x14\\x14u\\xa3\\xc9\\xf9=*\\xf8\\xb1\\xdb\"\\xd3\\xdc=^\\xaf\\x83\\x043F\\x18\\xfb\\xc7F\\x92\\x1e\\x85\\xe2\\xf1b\\xb5k\\xe2\\xc7\\x12Y\\xd4:\\x02\\xe8\\xc6\\x8e\\xbd\\xa8\\xc0\\x01\\xd1\\x97F>\\xe6\\x8e\\x94z:\\x07\\xab\\xd4\\x10\\xc0?x\\x864z:\\xbc\\xdd{,k\\xa3N-D\\xbc\\xb4K\\xa1/WC\\xdb\\x8b\\xa9\\xae\\xbd\\x8b\\xe9|\\x08\\xed\\xa8<FL\\x96;qc\\xef\\x9e\\xc2\\x8c\\x9e\\xdc\\x1eE\\xabWGD\\xb3\\xda\\xa2\\xb9\\xb2\\xa6T\\xfc\\xb1a\\xd7\\xb0$3\\xd8v\\x19=K#\\xb6\\x8c\\x11\\xdb\\x8fz\\xfd\\xdd\\x19,(>/Vx\\xb0\\xcd]k\\xd9C\\xbe/\\x80\\xfb\\x8aie\\x87\\xc1\\x8e\\xfa\\xbd_\\x1e\\xc3\\xef\\xd5\\x9e\\xd4\\xd0\\x92\\xea\\xa7^\\xd5\\xa3\\xe3\\xda\\xac\\xd5\\xf9\\x07N\\xd4z=]\\x1f\\x1f\\xb9_\\xbaHuu\\x0e\\xbf\\xcch\\xcfj\\x82\\xeaC\\xa9\\xed\\xafm\\x1e\\x9d\\xb8\\x9av\\xa7m\\x1e=\\xc0a\\xf0\\xfb\\x99:\\x87\\xa7\\xf3\\xa7\\x8d^Ut\\xed_\\xbdC\\xf7<\\xea\\xcfq\\xd8j\\xe9\\xab\\xab\\xe0Zu\\x1f\\xcdR\\x8e\\x8c\\xa4\\x06t\\xedF\\xa1\\xd8\\xff\\x001\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x01\\xed(\\xfd\\x84yJ~\\x81\\xfaQJZ\\xfa7\\xf4#\\xa8/\\x97\\xf3\\xed?\\x97e\\xb7\\xda\\x1fM}\\x11z\\x8d\\x0f\\x9e\\xd1\\xdcu\\xf4c\\xe3A\\xf5\\x8e\\xa5\\xfc\\x9f]\\x0f\\x97\\xd7\\xebG\\xcb&\\xd8\\x86A\\xbd)\\xf1\\xf4}5\\x1e4\\x1aH^\\x97\\xdb]\\xc7Rt\\x1a\\xcb\\xce\\x9c\\xf6_q\\xec\\xa4k--\\xbf\\xaa;\\n\\x7fd-}Q\\xf4Oo\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x01\\x03@\\x9bC$\\xbe-\\xf1\"\\xdd\\xf0\\xf8y%\\xfc\\xbf\\xc0\\x83\\xce\\xb5\\xd9\\xfet\\x9d\\x0b\\x16\\xe9\\xbeXRO-Q\\xfe\\xb4\\xf3\\xeb\\xa5\\xf0\\x1c`\\xff\\x00\\xb1\\xd0\\x9e\\x11/\\xba\\xb4\\'_\\xe8\\x8e\\x12\\xc5\\x97\\x94\\x9eXye\\xeb\\xfe\\x1d?4\\xb1\\xf2Aln:H\\x12\\x8e\\x08\\xe3J\\xd4\\xda|p\\x91\\xc2\\x054\\xca>C\\x01@\\xb2\\xfe\\xcf,y}\\x19D\\x89\\x8a)?y?\\xd1\\x89&@\\xfea\\xff\\x002n\\x9el[\\xcd\\xf9\\xd7\\x97\\x97\\x9a\\xa2\\x8e[:\\x14\\xbbb\\x01\\x950\\xaf\\xf5\\xd3\\xc0I\\xfb\\x83\\x11\\xf7\\x97\\x81/D\\x1b\\xd0\\x9a\\xf2\\x89\\xc4\\xbck\\xfe\\x12\\xd2S\\xa7\\x1ad\\x1fo\\xf9\\xdd\\xbfs1\\xb8S\\xb0\\x89\\x04\\x0f\\xb8\\xb2\\x07qE\\xd6\\x93\\xf1h\\xe7\\x9fWq>\\x8f\\xdcSO\\xf8\\x08\\xd3j?\\xafg\\xa3\\xfd\\xa7x\\xff\\x00cI\"\\xc6\\x9e\\xbaS\\'\\xc7\\x86\\x886\\x89ZZ\\r%\\xf4\\xd6S\\xae\\x132i\\x80\\xe3\\x96a&\\xccX\\x1eJeR\\x93\\x02$4\\xf5l~M\\x82\\x90\\x1et/\\xf9\\x92\\xee\\xe2\\x90x$\\xb7\\xcb\\x11r\\xd0\\x86\\\\04\\xcb\\x99[\\x88\\xff\\x00\\x81\\x97\\x8f\\xf3\\xa2>\\x1d\\x94\\xf0\\xf9\\xd7\\x96\\xe9&\\xf9k\\xfa\\xb3<R\\\\cKe\\xc8xh8\\xc0\\xe5\\xe1\\x03\\x90t\\xa4\\r\\x02Yi\\xcb\\xc8\\xfe\\xaf\\x96#Yxi\\xa0\\xc7\\x89r\\xdbO>\\x8f:\\x17\\x82\\xfa\\xd3\\xe7_,G=\\xa6\\x9ak\\xc0@\\xe6\\xd2\\x10\\x93\\xa8N\\x87X\\x8e\\xdak\\xfa<w\\x8f\\t}\\x13\\xfe\\xf8C\\xfe\\xf3\\xef\\x89\\xbd=;\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x06?\\x02\\xfe|\\xb2>\\xf8\\xecG\\xdc\\xe3\\xfc\\xe7\\x1e\\xdc?\\x9c\\xfbY\\xec\\x18uu\\x7f\\x17N\\xc3\\xfdE\\xaf\\xf3\\x1c~\\xe8\\xf8\\xfd\\xc0\\xe9\\xdc\\x7f\\xaa\\xce\\x9f\\xcc\\xd5\\x8e\\xe0\\xb1\\xf7x\\xf7\\x1fw\\x8f~?\\xccq\\xee;\\x1f\\xb8\\x19%\\xfc;%\\x9f\\x9b=\\xa9\\xf7\\x07\\xf3Z\\xff\\x004^\\xbd\\xcb\\x0c\\xbf\\x8f\\xdd\\xafaOS\\xd8}\\xce\\x1f\\xce\\xf1t\\xed\\xc7\\xf9\\x83\\xd8}\\xd3_\\xbb\\xab\\xd3\\xb6\\x9d\\xf5\\xfb\\xdc~\\xfd>\\xf5G\\x1a}\\xc4\\xfa\\xf6\\xa3\\'\\xb0g\\xb5\\x1d<\\xdf\\xd8\\xc7~\\x1d\\xeb_\\xe64\\xed\\xc5\\xf1\\x0f\\xcd\\xf0u\\x1fw\\xc9\\xe9\\xd8\\xf7\\xd1\\xd3\\xb1.\\xbd\\x83\\xaf\\xdc\\xe3\\xc7\\xbf\\x1f\\xe6\\xb8v\\xe2\\xe9\\xafm\\x7f\\x98\\xd3\\xee}\\xaf\\xedg\\xe4\\xcf`\\xc3/\\x87~\\x1d\\xb5t\\xfb\\xdc>\\xe7\\x17\\xc1\\xf0\\xef\\xe5\\xf3c\\xef\\x8e\\xc7\\xeeQ\\x8f\\x9b\\x0c\\xfc\\xd9c\\xbf\\x17Z\\xf6\\xf8=\\x07j}\\xfa\\xf64g\\xf9\\x90\\xc7n/\\xecc\\xb0\\xfe\\xcb\\xab\\x0c\\xfc{\\xf0\\xfb\\xba=\\x1e\\xbf\\xcc\\xea\\xf4=\\xb8j\\xf5\\x1av\\x1ft\\x0f\\xb8Xe\\xab\\xb0c\\xb1\\xd7\\xbf\\x17\\xc7\\x8fm\\x1f\\x07O\\xbd\\xc7\\xb7\\x1e\\xdeZ1\\xdbV*\\xf5\\xfe`\\x96;\\x83\\xdd?>\\xca\\xfb\\x95\\xefO\\xb8\\x07~\\x0f^\\xdeuz\\x97\\xa7\\x0e\\xe4S\\xef\\x1e\\xd5\\x1fr\\xbd\\xa8\\xcf\\xdc\\xe3\\xdb\\x83\\xd3\\xb5)O\\xbd\\xaf~,\\xea;j\\x1f\\x0e\\xf5\\xfe{\\x83\\xe1\\xf7t\\xef\\xea_\\x16\\x01|;\\x8a\\xff\\x001\\xa7\\xdd\\xd3\\xf9\\xcd\\x18\\xec~\\xf7\\x0e\\xc0\\xbe?w\\x8fz\\x7f\\x07\\xdc\\xe0\\xf5\\xd7\\xfdG\\xc3\\xee\\xe9\\xe5\\xd8W\\xf9\\xbf\\'\\xc7\\xb1z\\x8e\\xdc^\\xa1\\xe8\\xf8v\\x1f\\x7fN\\xda\\xf6/_\\xe68\\xf6\\x1d\\xf4\\xef\\xfd\\xdf\\xbd\\xeb\\xdb\\x8b\\xafn?\\xcdh\\xfe?\\xcf\\x0e\\xf5\\xfb\\xdc>\\xe1\\x7f\\x0e\\xde\\x7f\\xcfW\\xee\\xd3\\xf9\\x8e/\\x83\\xd5\\xf1\\xed\\xa7\\xfa\\x87\\xcd\\xf0\\xfb\\x95\\xfe\\x7f^\\xfc~\\xe7\\x07\\xc3\\xb5j\\xc7\\xf3:\\xf7\\xe1\\xda\\x9d\\xf5\\x1f\\xccq\\xfec\\x8fn?\\xcfk\\xd8=\\x1fP\\xfecO\\xbd_\\xb9\\xaf\\xde\\xe3\\xdb\\x87\\xf3z\\xf7\\xe3\\xfe\\xf9\\xfe\\x0fO\\xe7\\x87\\xdf=\\xeb\\xfc\\xdf\\x1e\\xc3\\xe3\\xfc\\xff\\x00\\xff\\xc4\\x003\\x10\\x01\\x00\\x03\\x00\\x02\\x02\\x02\\x02\\x02\\x03\\x01\\x01\\x00\\x00\\x02\\x0b\\x01\\x11\\x00!1AQaq\\x81\\x91\\xa1\\xb1\\xc1\\xf0\\xd1\\x10\\xe1\\xf1 0@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?!9\\xef\\xfc\\x18\\xe6\\xeb\\xb8\\xb3%A\\xcc\\xa7\\x05\\xe1\\xfe\\xac\\xc5\\xe6\\xc4\\x10Q\\xcd\\x8d\\xa3\\xee\\xe2E\\xe4{\\xae_u\\xc8\\xf3D\\x16\\x7fb\\xa8B\\xf5<oZ\\xd8\\xdd\\xfb?\\xddBO\\x9b\\xc3\\xac\\x1d\\x9d}\\xd5\\xb1\\xd7\\xf1F\\xbel\\xaba\\xfc\\xac\\x0e\\xea\\xc5\\x90\\xd2\\xc8\\xed\\x97\\xd5\\x95[\\x04\\xb1g\\xcd\\xf6\\xcb\\n\\x11\\xcf\\xd5\\x84\\xedG\\x1b\\xf9\\xae\\xa3\\xea\\x888\\x92\\xac\\xbeU\\x9f\\x15\" \\xbb\\x1f\\xe6\\xf3\\xc7\\xff\\x00o#4\\xc1F\\x8fj\\x03~[\\xe4\\x9c\\x91bJ\\xc0\\xa4\\xaf\\x86\\xd8\\xd9b\\xd7<\\x9e\\xd5^\\xb9\\xd5\\x986\\x0f\\xaa\\xe1\\x87\\xdd\\x8c\\xba\\xd6\\x1d/\\xc5 \\xf6\\xd7^?\\x16s\\x9b\\xd15\\xf0^,\\xe9N&~+\\xf2\\xaf\\x16Q\\xee\\xa1\\xf6\\xa4\\xcd)\\xd4\\x10\\xbf\\x17G\\x14\\x07\\x98\\xa1\\xee\\x85~\\xaal\\xc6YA\\xd3\\x9f6\"#\\xa2\\xc75\\xfc\\xcb,\\x99\\xe7l\\xc0\\x1fk\\x00\\x81\\xc5\\x05\\x0f\\x12\\xde\\x8d\\x96\\x1e&\\xf0\\x9b\\xf1\\xec\\xacs\\xef\\x9a\\xcc)\\x97\\xe5c\\x05\\x82\\xc7no~\\x0b!\\xc1\\x94\\x8b\\x08\\x83\\xbb\\xf8\\xa8\\x82+l\\x85:\\xa9\\x1d^z\\xbb\\xe2\\xc4&*\\x8el\\x1e\\xa2\\xc1;?U\\xf3\\x1d\\xdc\\xaf\\x9b5\\xcf\\x9f\\xf8\\xc9M\\x8a\\x0e]\\xb2\\x9c^\\xd6\\x02\\xc6\\xd9\\x06.\\x15<\\x17\\x11\\x1f-%LA?\\xcd\\x13\\xf4T\\xc4\\xea\\xee/\\xc9|GvtO5\\x07\\xb9Sc\\xdd\\xc1\\xf3\\x16\\x1f\\xbb\\x87\\xdd\\xe4\\x16, \\x8e\\x15\\xbd\\x1f\\x144\\xe7\\xca\\xb1[<O62\\xd2\\xf4\\x12\\xb2\\xee\\xe3\\xd2\\x80\\xef\\x0b\\xe5\\x16\\x04J\\xd5\\x84#\\x1eopd\\x8b\\x8c\\xfe?\\xe4\\xcd\\xbd\\xf4\\xf5q*\\xd8Qi{\\xa5\\x87\\xf8\\xa5\\xecS..\\xe8\\xf8\\xba\\xa1\\xf9\\x96\\xeb\\x99h\\x8c<\\xac\\xe2\\\\*\\xf1Y\\x10\\xf8\\xd5\\xff\\x00\\x9bh\\xa2\\xf6UDqy\\xa0\\x88O\\x8a!\\xe0\\xff\\x00W\\x82<M\\x01\\x18\\xf1\\xbf4\\xf1\\xee\\xe1\\xe6\\xb2\\xf9\\xab\\xbc\\x7f\\xc0\\x0bc\\xd8\\xae\\xbf\\xe2</\\xa3\\x94$\\xd7+&M\\x13\\xb0\\xc5\\x892l\\x7f\\x83\\xfeb\\xe9N+\\xc6\\x15\\xe2ll\\xfa\\xaf\\xc1\\x8e/a\\xc1\\x14\\xcf\\x1e\\\\\\xbd\\xfa\\x91\\x1eB/\\x0eqf\\xb5\\x814.\\xb1N\\xca\\x92\\x0f\\x8dj{T\\x91\\xf0\\x94\\xc8\\xbbj\\x97\\x1b\\x16~q\\xa7Nb\\xb0\\x81\\x17S.P1\\xb0]\\x19\\xc5~\\x7f\\xe1\\xe6\\xff\\x005\\xf3,F\\x07\\xaau\\xb0\\x086\\x9b\\x90\\xdd\\x86\\x0b\\x02y\\x94\\xc79G\\xc4\\xfdQ<\\x95\\x88\\xb8\\xba<W\\n_\\x97?U33\\x96\\xf0W\\xf15\\x04;[\\xf4\\x97\\x13\\xea\\xf33\\x89,\\x88x\\xab\\xc1\\xddD\\xe1\\x9c\\xd8\\x02\\xc6\\x9er\\x8e}O\\x14\\xc3\\xbd\\x16l\\xff\\x00\\x13L\\xbe\\x9f\\xba\\x98\\xb2\\xed\\xbd\\xd3\\x13\\'\\xdd\\xf8\\x1e\\xaa(I\\xe6o\\x91\\xd5^$v\\xf4\\xaer\\xae\\x18\\xdc\\xf4\\xbb\\xe2\\xe9\\xcf\\xfc\\xa1\\x91\\xdf\\xcd\\xd8\\x06\\x8ak\\xcdP<{\\xea\\x9d\\x8a\\x1e\\xa9\\xc19\\xccV\\xd4Q\\x8a\\x81f \\xe2\\xc3\\x1b\\xc1\\x16O-\\xea\\xac\\x8e&\\xba\\x8a\\xebsSR\\xf1X@l\\x99F\\x8a\\xc0y*r\\xbf\\x00W\\x1au\\x9a\\x90#\\x9a\\xfc\\x1e=Vd\\xbf\\xc5[\\xc7\\x99\\xbfm\\xe4{\\xcb\\xa9\\x80\\x8a\\x8eM\\xef\\x9e\\xeel\\x1bP\\xd77\\x93n/1b>\\xe8c-\\x10\\x8e)I\\xff\\x00b\\xc9\\r#\\xaa\\xfc\\xdc\\x1c>9\\xbf\\xe0S<YV\\xe8\\xe4\\xfc\\xd3\\xcb\\xda\\x0e\\xff\\x00\\xe0?\\xc0\\xebV\\x17\\x91a\\xe6\\xfc\\x03I]\\xa3\\xf7X\\x1d\\xf3H\\x85\\xd5\\xe4\\xf1i\\x88\\x877\\x95\\xb2\\xb6\\x04\\x8e\\xe9X\\xf8\\n\\xf5*\\xa9}Th\\t\\x86g\\xcd\\xc6\\x1f\\xba\\x98\\x1c*\\x17\\xa7_\\xea\\xa1\\xdb\\x91\\xb0\\xb1\\xaeVG2\\xbd%\\x98\\xe5pu\\'\\xab\\x07\\x92\\xb3\\x88>k\\x08\\xd6\\xd9\\xb9*\\x10\\xf3\\xdf\\x13r\\xd1T\\xc2o\\x88\\xee\\xb4~Otc\\xe7\\xe7\\x9a\\xbc\\x16v\\xe6\\xf1\\x95\\xd1\\xa6*0\\xads\\xaf\\xfc\\xb3~e\\xe2\\x16p\\x9e\\xear>\\xd5\\x008\\xa6\\x9f\\x92\\xc5\\xf3\\x8d\\x1c=\\xad\\x7f\\xaa\\xc4\\x0fK\\xf0jO\\x8f\\xaa\\x13\\x87=X\\xc9\\xc3G2\\x9f~\\xa8}SXc\\x0f7\\xb8\\xdf5\\x08\\x99\\xa4\\xf6v\\xa8\\xd8\\x9f\\xe6\\xb8T\\xb5\\x1c\\xd44\\x1fvw0Q\\x92\\x86/\\x01AQ\\x18O\\xcdC\\x0b\\x11\\xd4sE\\x86\\xa4y\\xa8\\x8cG\\x81\\xd5\\x82!d\\xbdT\\x96?VQ>)\\xe8kI^f\\x90\\x12j\\xb6d4K\\xff\\x008\\xa8\\xdd\\xf5g\\xaf\\x0c\\xd10tX\\x7fy\\xe1\\xa6V\\xef?T\\xf09B\\x14=\\xd7,\\xf1\\x0f\\xd5\\xc7\\xabj=\\xba\\xbb\\x127\\x97\\xe2\\x87\\xba\\x94\\xab\\xbdN\\xaf$\\xec\\xae\\xad\\xa1\\xec\\xa4\\x17\\xe6\\xfa\\xb1\\x1e\\xa8\\x1cl\\xc5t\\xc7\\xcd\\x0c\\x13\\x9e\\xaap\\x1dwG1\\xb3q4\\xd9\\xba7\\xcd1C<^\\xcfu\\x87\\x1c\\xd9\\xa4T\\xefr\\xe4\\xcf\\x11G\\xd9\\xa3\\xa3O\\xdd>?\\x166\\xf2\\xea\\x8coG\\x82\\xadg\\xc5\\xc2<&\\xc0\\xcd\\xd4\\xd8p\\xce?\\x16W8\\x88\\xfd\\xd8\\x89\\xd9D\\x97\\x81`\\x91\\xc8\\xd8&\\x1f\\xe0\\xa6\\xd9u~,\\x13\\xad\\xf3v\\x89\\xcb\\xc0\\xa2\\x7f\\xf2\\x8e\\x07c\\xf1xt+\\xe4\\x18_\\x10\\xf7A\\x19P\\xe8\\xb0p\\x14?\\x1f\\xf1\\xc1<v\\x16}\\x90\\xfa\\x9a\\x8b\\xc0\\x1e\\xcc\\xa3\\xca\\xa2\\xa4\\xce\\xdez\\xb8\\n\\x87\\xf7`\\x18\\x0f\\xc8l?\\xfc\\x15\\x82>\\\\\\xab\\xa7\\x17\\xa9\\xb2o\\xee\\xfdgW\\x13\\xeb\\xab3\\x13\\xb2\\xfe\\x92\\x9d\\xb6x\\xf8+\\x8b\\xee)\\x04r^\\xfe_\\xdd\\xea\\xf9\\xbc\\x98=\\xcd\\xe2FM\\xc8\\xb1\\xcd~\\x16H\\xd7=Y\\x1e?5\\x92\\xc9z1a\\x99\\xca\\x94\\x1a\\xfdQ\\x00\\xf2\\xaax\\xd7h\\xe5ed\\xd1\\'\\xb6\\x93\\x9f\\xdds\\xbf\\xee\\xf8\\x1f\\xba<6=\\x8f\\xab\\xe0\\xcc\\xee\\x8a\\x0e\\xfd{\\xbc\\x12%J\\x9d\\x0f\\xd4X0\\x8d\\xf3d\\xfd8\\xae4\\x1e\\x92\\xf8\\x9f\\x8a\\xb4&\\x0b\\xa9}\\xd0,\\xd7\\xab$\\x95\\x89\\xf2\\xdd\\xc6\\xf7\\xbc\\xd9A\\xfb\\xbd\\xf0\\xa7(to\\x1e\\xd9\\xff\\x00\\xca%\\x11\\xc9\\x17A\\xe3\\xaf\\xf8BQ\\xc6w\\x1fuhC\\\\\\xe7\\x8a\\x18\\xc3l1seq\\x07\\xaaN9\\x8f4G\\x0e\\xaa\\xfa\\x7f7\\x8a,\\xe5\\xf1\\xcdi\\x07\\xcdG\\xbcP\\x1b=\\xd7S\\xdb\\xd5\\xc0\\x19}X\\xf0\\x19\\xf8\\xb9\\x03\\xb3\\xea\\xc1\\x1e>\\xb9\\xb3\\xb1-sV\\x13(,\\x97\\xbc\\x9a\\xb7\\xf6.k?\\xf1N\\xe6\\x9c\\xcf\\x17\\xd1\\xd5\\xd1x*,\\xfa\\xb2\\x1f\\xb2\\xc2\\x84\\xbct\\x8e\\x05\\xcb\\x11\\xf7\\xff\\x00\\x01,\\x9c\\x9b\\x1a\\tb\\xff\\x00\\xcaC\\x82\\xcew\\xe1p\\xf2M@\\xa7\\x95\\x17\\x89G\\x90%\\xa8\\x92\\xc3k\\x1eD\\xf7R?\\xea\\xc9\\x13\\xfb\\xbb\\xc1I\\x852\\x80\\xe6\\xe0)\\x9e\\xaag\\x97\\xed\\xa0N\\xa6\\xe3A\\xad\\xf3|\\xe5}_\\x98\\xbcI\\x0cM\\x8a]U\\'\\xe9Lu\\xe0\\xb1\\xd7qY\\xc8\\xd2*\\x87<\\xdd\\x13\\xcb\\xe6\\xe6\\x18\\xd8\\'\\xd53\\xdd\\x83Z\\xb6a\\x9a\\xc79\\xabX\\xac\\x88\\x94>/B\\xe0\\xfeo:\\xf8\\xbc\\x19B%mG\\xc4\\x8d3\\xe6\\xc9\\x04Z\\x1c\\xdcGU\\xc93\\x9c\\xdc\\x03\\xe0W\\xc86\\xa3\\xaeh\\xe0<m\\x11\\xe7\\xab\\xe11\\xea\\xcc\\xe6_Tq9v>\\xf6\\xcc\\x00,\\x02,)\\xf2\\xfb\\xb2\\x98\\x12~l\\xb1\\xb7\\xff\\x00\\x9a87\\xeb9\\xb9\\x08\\xfc\\xdef^\\x11\\xbe#\\x8a\\x8f\\x9aqM>+b\\xb34\\x93\\xb3\\xb7X\\x0f\\xe2\\xe47\\xdd\\xc8\\xfd\\xdcz\\x9aE\\x86\\xeb3w >,\\xa9\\xee\\xc4\\xc5\\x80<\\xbdsf!\\x1dY\\xa4\\x1a\\x83A\\x15\\x99\\x0f\\x1bE)\\xb2\\x9e\\xcd\\x15Ph<\\x82l\\x91\\xfc\\xb7\\x95u\\xdfk!\\x9dP\\x99\\xdf\\x8aj\\x87\\xd5\\x90\\xe8}\\xd2(\\xe46\\xcck\\x9cm\\x0e\\r,C\\xb2\\xc2C\\xba\\xf0\\xf7\\xdd.\\xe4f48z\\xdb\\xeb\\xd5\\x19\\x11\\xf3u\\x95\\x19\\xb1[\\x94\\xe2\\x827Y\\xbe\\xaa\\xe1\\xf1d\\xc4\\x9b\\xe4\\xa4\\x1c\\x87\\xd7v!q\\x95I\\x07\\xb6\\x83\\xf9\\xb1\\xbf%f#\\x99\\xc6\\xc3GJ\\xb5\\n\\x1a\\xa6/c\\xcd\\x1c!L\\xd9\\xaa\\x90S\\x0e\\xdb\\x04\\xa8|X\\xe1$]\\xf2\\xc0\\xec\\xb0\\xae\\x0f\\xab\\x07\\xee\\xaa\\n\\x88U\\xe3\\xd1e\\xe4z\\xbe\\x02K!\\x1e\\xeb\\x10K\\xe6\\xb0\\xee\\x9b\\x93W\\x8b\\xc58\\xff\\x00\\x9cU\\xa8wu\\xff\\x00J\\xbf?\\x12U\\xf0\\xacL\\x8a`\\x92z\\xa3\\x18\\xe5rHv\\xb8\\xc1U<\\x9b\\x01\\x87\\x96\\x89&\\x11\\x8b)&K/\\x08nq\\xd3*h\\x93\\xf6\\xb0e(\\x07\\x16d\\x84X\\x08\\xcaA\\xd7\\x8f\\x17\\xc2?\\x85O,\\x1e\\r\\xb2@\\x87\\xab\\x89\\xf8\\xcd\\xa9\\xbd\\xd5\\xeb\\xff\\x00\\xb6S#(\\xd9\\xe7\\xee\\xe6 \\x8f\\xf3\\x8a\\xcb\\x9c\\xbf\\x8aC+\\x94\\x0e\\xd5\\xf0O\\x9a|^Q\\xff\\x00\\'\\xfe3R\\x85H\\xed\\xed\\x0c\\xbe,\\x04\\xa2|\\xfcVD\\x0cwi\\x03\\xb9\\xa0.v\\xca|\\x95k6i\\x11\\xd4\\xd1\\x8d\\xa5\\xc9<Y\\x8f\\xc5\\xd3\\xbd\\xbc\\x88\\xdf\\x9a\\xa7*y\\xd8Y\\xceSf\\xbcE\\x80\\x9b\\x0f\\xf4\\xf3T:}7\\xcd\\x0e{\\xca\\x9f1\\xf5H=^\\x16\\x0f\\x8b\\x8e\\xa3\\xe2\\x9a\\xc9\\x1f\\xd58 >\\xeaI\\xdc\\x9f\\x15|\\x16\\x0c@?\\xb5@\\x0b\\x8fq@\\x19D\\xd8\\xac\\xbd\\x7f\\xc9\\xde+R{\\xbb\\x16\\nwi.y\\xa7\\x8dQ9\\xc5$qX\\xeah/\\xc8\\xb1\\x8eo\\xb1>\\xe8b4\\xa1\\xc2<aS\\xc8\\x9b#?v\\'\\xc5l\\xcd%U W\\xdb\\xee\\x84D\\xce\\xc9$\\x11Dv\\x9a\\x10\\xc7\\xc2o)\\xbd\\xfc\\xa9\\xc0\\x04\\xaf\\x03\\xf3O{\\x80\\x9dY\\xe0\\xf0\\xd3\\x8e\\x1b&#\\xff\\x00(\\\\~)\\x00Xl\\x0f\\x7f\\xf1j\\xf3\\x14N\\xcf\\xc5\\x98\\xf36\\x00e-\\x1fO\\x92\\xe3\\xa7\\x8b\\'\\xcb\\xdd\\xd9\\xd3\\xe3\\x8a \\x1d\\xa7\\x94CP\\xef\\x8a&b\\x98\\xd0\\x1b\\x1c\\xf1f\\xc9;9v\\x8f\\xcdA\\xe6\\xf9M\\xc7t\\xc10\\xddOo\\xba\\xf1\\xb95\\x02\",\\x9ei\\xe2\\xef\\x0f\\xe2\\xf20\\ng\\xa5\\x02xl\\xb7\\xaa\\x91\\xd4~\\xec\\x1c\\xca\\xfb/\\x8a\\xec\\x9fZY\\x07\\x819\\x9aPb,\\xf9\\xbf+;__\\xf1|\\x12T\\xe5QHV\\xe1\\x81\\xe2\\xa9(\\xc7\\xf9\\xb2;\\xbawZ\\xc3\\xc5\\x97\\x93\\xcf\\x05\\xfck\\x97\\x00\\x94?\\xc8\\xbcN\\xd8vk]\\x9c\\xa2\\xc6*4\\x96\\x8f\\x9a\\x88?\\xbaH\\x9b4\\x0c\\xb3\\xdf\\x14\\x0e.\\xf5T\\xb9\\xd5\\x19\\xcf4;-\\x96\\xe9\\xb9\\xccAu\\xfe\\xd4\\x07\\xbf\\x9ayg\\xc5x0s\\xcd\\x1f\\x8d9\\xb8\\xf7\\xfa\\xba\\xfc\\xd7\\x8ay\\xff\\x00\\x9b\\x0cY\\x9ekD\\xf1\\x166~\\x95\\xe8\\x1f\\xe2\\xa3\\xa5v\\x91\\xf5D\\xbd\\x96NDk7#d\\xdc\\xd8ff\\x9d\\x1b75\\xf2\\xa3jQ\\xce\\xd5\\x92\\x82\\xc8\\xce\\xeb\\x00V\\xe8\\x93P\\xe5\\xd5R \\xa4u\\xcd\\xd8\\xf0\\xb8\\xc7K\\xc7\\x82\\xc8\\x8e\\xde\\xf9\\x7f\\x17\\x8d[(\\x0e\\x16\\x0c\\xff\\x00v@\\x11`\\xf8\\xb3g\\xcd\\xe2\\xcfW/\\xc5c\\xe6\\xcc\\xba\\xb9\\xe3\\xf7g\\xff\\x00\\x14\\xd8\\x81\\xeb\\x9b\\xa3\\xf8\\x8a\\xba\\xf4\\xf3g#\\x1fe\\x8e\\xd7\\x05b\\xca\\t?\\xe2\\x1f\\xae\\xae\\xc0\\xd8uY?\\xe7\\x9b\\x1b\\xe6\\xa9\\xee)<4\\xb2\\x12Q\\xe2\\x89\\x8f-&^\\xa9;7\\xe0\\xac\\x03>l\\x9d\\xb1w\\xdf\\xe6\\xec\\xc7\\x11g6\\xe9\\xff\\x000\\xba\\xdf\\xbb(\\xd5\\xab5\\'\\xce7\\x9f[O.\\xe8x9\\xf3u\\xae~\\x166J\\xd2/\\x19W4\\xbc\\xff\\x00\\xc8\\xdb\\x0fw|\\xdd,\\x11\\x9cVHM\\xd4?\\xf0\\xe5\\xa6_\\x0b\\xd5\\x9f6\\x88\\xe5ck\\x9cEp<\\xd7\\x87\\xb1V\\x9e\\xeae\\x1c\\xfb\\xaeX\\xdb\\xc6\\xd9\\xee\\xad\\x1b3uBW\\xd9Y\\xc2\\xa0\"\\x89g\\xaaO\\x9e\\xeb\\x1e\\xf9\\xb0O\\xaaB\\xf1\\xdf\\xfcxn\\xb7\\xdf\\xba\\xaf\\xee\\x9a6d\\xa6\\x9b\\x7f\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x10>\\xfd\\xf2\\x95\\xf9%\\xe6\\x95zI\\xff\\x00[\\x1dW\\x96\\xe5\\x9a\\xdc\\x8b\\x11\\xe2\\xfb\\xac\\xaa!\\xc9-\\xc9\\xa2\\xaaUlT\\xa5\\x17D#d\\xfc\\x9d\\xe0\\x8e\\x9eQ3\\x1e\\xac\\x99\\x1b\\xc1\\x12\\xeaU\\x8e[\\x0f\\x17\\x80:\\x82K\\xec\\x84\\xe5\\xe1\\xde\\xea\\xca\\xdc\\xb4;\\x81\\xcd/8\\xf8\\xe8\\x00\\xba\\x89\\xcc\\xaf.\\xed\\\\\\x1ec\\xb69\\xd3\\x0b\\x08\\x8e0\\xc5\\xdf\\xb5\\xban\\xb9\\xb0sw\\x08\\xe4\\xa2\\x89vvCJ&\\x86Uy8\\xd4T\\xc0\\x83\\x9f\\x83\\xa9\\xb0.&\\x04i\\xce\"\\xa0\\xba8R\\xdb>\\xfcjAXP\\x1b3\\xd5\\xe3\\xf68k\\x12\\x82p\\xb2\\xd5\\t\\x91M@\\xa7\\x9dZ\\xa2\\xbe|;\\x07\\xae\\x91BBl\\xaf1[<\\xb2\\x9ci\\xfc\\xe7\\x89\\xe6F\\xd7H7oS&\\x9c\\xc3\\'\\xbb\\xa9\\xa3\\xdf\\'\\xca\\xc3\\xe3\\x08\\xee\\xcd\\xf3)\\xdc\\x0c$\\xdd\\x18\\xef\\xff\\xc4\\x003\\x11\\x01\\x01\\x01\\x00\\x03\\x00\\x01\\x02\\x05\\x05\\x01\\x01\\x00\\x01\\x01\\t\\x01\\x00\\x11!1\\x10AQa q\\xf0\\x91\\x81\\xa1\\xb1\\xd1\\xc1\\xe1\\xf10@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x10\\xf4\\xfaOS\\xe2Y\\x1c\\xe3=Y\\xf3\\x1a\\xfeV\\xe1l\\xf9\\xcd\\xd4Y\\xe6\\xdb\\x11\\xd1t\\xbe\\x8b:nx\\xbb\\x8f\\xa7\\xc6\\xcf\\r\\x9c\\xd9\\x96\\\\c\\x889\\xb3M\\xf37\\xe7\\xf0\\x8ev\\xc4\\x8d\\xcf\\xac\\x0c\\xcb\\x00\\x0f\\xa5\\xc0\\xeb\\xe3\\x1c\\xf7\\xf5\\x90\\xc9^-9\\xb8o\\x10\\xbe\\'\\x9b\\xefyL\\x9ey\\x90{\\x87\\x1b\\x1b\\xa9-\\xf0D\\xcc\\xe6\\x0e3\\xebc\\x9f\\xa2\\xfe.\\xbe%\\x8f\\xba\\xe5\\xd4\\x17\\xd2|I\\xd5\\xa0\\xad\\xben\\xb8\\xferA\\xcf\\xcc\\xae\\xe3\\xdf\\x80]\\xdem\\xe7!\\xa9\\xfb\\xc7\\xe7w%\\x99c\\xe2\\xc8\\xb8\\xb2\\xf9\\x9f\\x01\\xe7\\xcc\\xf1:/\\x90\\xf9\\xb8si\\xa1|\\xceos\\xa7Q\\xf5\\xbb\\xd2\\xeb\\xe6\\xe9\\xee\\xfc\\xec\\xf7.\\xdf\\x97\\x87yu\\x8e\\xa6\\xff\\x00L]1\\xday[\\xe2\\x0e\\xb5\\x9c\\xf3\\xedc\\xf5\\x9e\\xfc\\xf8\\x89\\xf0o\\xe4\\xbb\\x04\\xf4\\x0b\\xe4H\\x84K\\x90}\\xa36^\\xee\\x961\\xebd\\xe2\\xd0\\x84o\\xef\\xe9g/7\\x8b\\x8a[\\xb7V=lpXxg\\x87\\x160\\xfc\\xdb\\xbf\\x0c\\x8e\\xd8\\x04\\xce\\xa1\\xea\\xd3\\xf0,\\xe3\\xc3\\xef\\xd4\\x18\\x07Q\\xdcl\\xb2\\x19\\xab\\x9f\\xac|\\xb5\\xbf\\xbd\\xb3\\xd5\\xdb\\xc3\\xaf\\xc0\\xb9\\xf1\\xcc\\x83cO\\x10\\xcb|\\x1c[\\x96\\xbe\\xb7.\\xa1\\xcf\\x9bg\\xec@\\xbfH\\x9f^\\xfc\\xc8\\x18\\xe4g\\xa3H\\x03\\xf7\\xf0\\xe1\\xe2#\\xaf\\x1e#\\xd7\\xdc\\x82??7}\\x1d\\xb7n\\xaf\\x9b\\xae\\xfc\\xcf7\\xd7\\xaf\\x0f\\xc5fbC|\\x1dx\\xf5\\xe7\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x10\\x0ed\\xfa\\x1b\\x07\\xe1\\x90w.M\\xac~\\xf7..\\x98\\xfa\\xc1\\xf6\\x00G\\xe79\\xb0\\xed\\xfag\\xe4t\\x7fFCvq\\xfe\\x91,\".\\x1f1\\xd4\\x99\\x8co\\xcd\\x8ff\\xb9\\xf8\\x97\\xa3\\x87\\xed\\x03\\xc6[\\xdf\\x898\\xbb#\\xb1\\xf1v\\x044\\xfd~b\\xe2\\x1f_\\xdet!\\xd9\\xc4\\x0b\\x01\\x9d\\xe9\\xf4e\\xfa\\x0b\\xc3\\x96\\xdf\\x83\\xb2\\x03\\xce\\xf6\\xb3\\xea\\xdd\\xb7\\x8ed\\xc6\\xb8\\x85l-\\xe3g\\x8f\\xb7\\x13\\xcf\\xcb\\xf8!\\xcf\\x9e\\'\\xa2\\xed\\xac\\xb4\\xa7x$\\xdat2V\\xee\\xeb\\xcf\\xed\\rp\\xfd\\'\\xfb\\x93s>]\\xff\\x00\\x91\\x88\\xbes\\x19bga\\x87\\xde_\\x98)\\xfcq\\x00\\x01\\x99\\xab\\xf9\\xda<\\xf3\\xb0\\x07\\xda\\x1c\\x80o\\xccw!y\\xb7\\xabu9\\xfe/\\x94\\xc0\\x96r\\x1fYz<\\xb2kK\\xe4}mp?\\x98\\x1d\\xfb\\xff\\x00\\x17@~o\\xe6f\\x03\\xa3\\x8b\\x85\\x07\\xcd\\xcb`\\xbb)\\x93\\x91\\xff\\x000\\'\\xbe\\x8f\\x88\\xd0\\x99p9\\xdf\\xday\\x08N\\x9e#\\x05V}-^\\xa3~[s\\xc4\\x88Ey\\xbb cG\\x93\\xe6\\x10\\xe5\\xdf\\xa4\\xa8\\xf1\\xf5\\xe6@\\xee\\xc3\\x87>m3\\x90:\\\\@p-\\x96\\xcf\\xc0\\x9f\\xb5\\xb2\\x9c\\xec\\xbb/8\\xfe\\xcc\\xa8\\xc7\\x87\\xc7\\xd2`\\xee\\xff\\x00kN\\xf7\\x8f\\xde$+\\x8f\\xac\\xca\\x0f\\xcc\\x1a]\\xb8\\xfa\\xc7\\\\=\\xfc\\xee\\xdcb\\xe9\\x9f=F\\x9d\\xde\\xbewXf\"\\x01\\'\\xe1\\xe6\\x1d\\x9a\\xf4~W\\x07>F\\xda\\x0f<g\\x04\\xa1\\xa7\\x1d\\xfd\\xcbH*+\\xbf\\xcc[\\xcb\\xe1\\xf9{\\xb7+\\x87\\x1dB`\\xa5\\xa7\\x00\\xe3\\xea\\xc0\\xd8\\x10}\\xe0\\xa6-\\x97!\\xe2Y\\xc5dCA\\xb7\"g\\x1fVGJ/\\xc7\\xd6(.\\xb7~y\\x8c\\x0b\\rL\\x99\\xe9\\xb0\\xa8\\xcf\\xe6\\xe7\\x0f\\x9e\\x8b,\\xbc\\xe7\\x11\\x9b\\xf7\\xc9\\x07\\xe1\\xa7pq\\xd4r\\x81\\x9c\\xf2\\xc0\\xe0\\xc76\\xe3\\xcd\\xfa\\x04+Q\\x1d\\xfaH8~>\\x90\\x9b\\x8e$ \\xe7\\x9f\\xdb,f\\x81\\xb9f\\x0e>9 \\xd3\\x99\\xef;\\x83C\\xbb\\x80.\\x9cs9\\xbe\\xdb|\\xd9\\xcc|NL\\xb5\\xfc\\xaf\\xec\\xcc@\\xf9~\\x91\\x9cg\\x82p\\x08\\xe8\\xf6\\x16\\t\\x8a\\xe8q\\xf7>ehi\\x0fD3\\xf3\\x8eN\\x9e\\xe0\\xaa\\xa7\\xc4\\x8fx[A\\x9cwl\\xc04\\x97\\x8a\\x9d\\xa7\\xbf\\xe2,>6\\x0c\\xf8\\xb6zQ\\x86\\x9e:\\xf9\\xb4*p\\x9c\\x1b5\\xb8c\\xfd$\\xc715\\x18\\xfd\\x0c\\xc7l>\\x9b\\x01n\\xf1<~6\\xe9\\xdeI\\xbc\\xe7?{P~\\xa5\\xc91\\xd3\\xe6tL\\xeb\\xaf\\x01o\\xc2@\\x03L\\xee\\xd0\\x1c\\xdc[\\xc7Y\\xe3\\x8d\\xbb\\x99rs\\xf4O\\x0f\\\\<c\\xce\\xc0H\\xbd\\xf4\\xd9\\x87\\x8f\\xd1i\\xf07\\xa8>\\xbd~\\xf01\\xfa\\xc8//,\\xeaf\\xe7\\x13\\xc7\\x9eO\\xafS\\xd3\\x11\\xe3\\xab\\x0b\\xf3\\xd5\\x8f\\xd6\\xee\\xf7*\\x07H\\xd0\\x1d?1\\x01\\x86?9\\xe3\\xd4:o\\xf1?\\x1e{\\xfc\\xa1\\xf0\\x06\\t(7\\x8e\\t/\\x1a\\xc0\\x0e{\\x9e|\\x0f\\xce7\\x9d\\\\\\x07\\xde\\x1c\\xf3\\xdd\\xf0=_\\xd0?i\\xed&}7!Ct\\xfe\\xd0\\x01\\x1f\\xefo\\xacL\\x83\\xe9g\\xd6g\\x97#\\x80\\xcf\\xf1\\x1f\\x04\\x0f\\xe6\\x1eh9\\xfc\\xf3a\\xc1\\xc5\\xc9\\xa7Rs\\x96\\xc3\\x8d\\x8e\\xb9\\x94\\xf9s\\xeb\\xb0u\\xe7\\xf2\\x90\\x1d\\xc8;\\xc7\\x84s\\xae>\\xd2\\x0e|g\\xd6\\xc7\\xe7\\xe2\\t\\x99#\\xea\\x97\\xaf\\xc21\\x9cF\\xe7<]\\xc4\\xf3\\x9c\\x9e=\\x0f\\xdb\\xc8\\xcf\\x0b\\xf3\\xb8\\xc0q\\xf9@\\'?E\\xf1\\xfc\\xc4\\xd9\\xd4\\x86\\xf5l\\x80\\xd7xo\\xf3\\x14\\xe8\\xb3\\xb9\\xe8\\x9e\\xaf\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?\\x10\\xc4f8R/\\x08M\\x98\"\\xa2<8\\x8e\\xe6\\xc5\\x02\\x91\\xc8\\x94(\\x0f\\xfa\\xa92\\x91\\xf3\\x17d\\x14\\x9a\\xf3\\x03\\x13\\x1c&>j\\x04O\\xa8\\xf5x\\xa7\\x0c<\\xbc\\xfe(\\x03\\x08\\t\\xa6\\xad&\\n\\xb00\\x04\\xd4aC3\\xc6}U\\xde8\\x9b&\\x19 9\\x9aH\\t\\x10>\\xc9\\xbd\\x83\\x00\\xe37\\xddsy\\xdd\\xb2\\x1c\\x95\\xba\\xe3\\xd5\\x17\\xda\\x00z\\xf73UR@\\xac\\xd0\\xbd\\x1e+\\x0c\\x08\\x11\\xe5c\\xca\\xee(\\x83\\x01\\t}q\\xfd\\xd1\\xe5\\t2\\x11\\xc8\\xfb\\xa6\\xaf\\xfb\\x0f\\xf4\\xa8\\xc3\\ne\\xdf\\r\"\\xa6\\x98\\x9c;+\\x01\\x9e\\xf7b\\x81\\x04I\\xbd:\\xaa\\x02\\x06\\xcb\\x15!\\x01\\xcf\\x05\\x8f\\x1e\\xfe\\xeb\\xc0\\n\\xc5\\xe8\\x88\\x07\\x8e.\\x1cP\\xc9\\xc9\\xdc\\xf3@6~\\xa2\\xac\\x07f\\xb0?\\x85\\x12\\x84\\'\\xc43\\\\\\x8eM\\x93\\x0ed\\xe1\\xdf\\xfa\\xb9$\\x18\\xd9\\xd6\\xc1\\x91\\x0e\\xa7\\xff\\x00\\x15\\xd0\\x02{\\x17\\xf0\\x81\\x13\\xfb\\xb1\\x0c\\xe4\\xdc\\xdb\\xa8A\\xf1ccN\\xef\\x1c{\\x8e\\xca\\xcc-\\xff\\x00\\x13@\"\\'&c\\x97\\xdfU:Tx\\xb0\\x07\\x1a\\xcf\\xbd\\xa01{\\x17\\xdd\\xea\\x03)\\xfe\\xe8\\xce\\'C\\xa4\\n\\x14G\\'\\x15\\xa4d3=\\x9f\\xf8\\xdd\\x18\\x01I\\x18\\xbe\\xbd\\x94pDN\\xe6\\x7f\\xed)\\x18\\xb4N\\xa7\\x9d\\xa4d\\x88\\xe1\\xeb\\xb4\\x1f\\x16Qp\\x08\\x97\\xccS\\xd2\\xa1\\xfb\\xbb\\xd4x\\xf6\\xfb\\xa5\\xbb\\xc5\\xb9\\x12\\xd2\\x1c\\x91\\xdcRb9=\\xf1\\x15\\x0b2\\x86M\\\\\\xcb\\x8e\\x14\\xd9\\t!9\\x0c\\xcf\\xee\\xe9\\x82^#\\xc7\\x96\\xb8\\x80\\x88|\\xd0d\\xa8\\x8d\\x98\\xb3\\x19\\xd5\\x91:\\x19\\xc7\\xf7dE\\x97\\xe2b\\x81\\x94\\xe4\\xf7E}\\xae\\x93\\x8b\\xc5\\x00s\\xea(\\xd0%cJR*V \\xe2\\x12\\xcc\\xbe\\xd0\\x9e\\x9f5r\\x00\\x1ec\\n\\xf0\\x08\\x86\\xae\\xef\\xa8\\xa5\\x91<\\x7f?5\\x83\\x9e\\xeaf\\x11\\x0f\\xf5Q\\x83\\x00\\x97\\xc4\\xf8\\xa8\\xfc8\\x005\\xf7d%!3\\xba\\xb0\\xcc\\'\\x1e\\xa2\\x8b\\x1a\\xba$\\xda\\xa7\\x91\\x03y\\x1d\\xff\\x00\\xed\\x00\\xa4\\x85\\x918\\xf7Wd\\x803M\\xb1\\x859\\x1dGWY\\x06\\x15\\x9e.\\x98x\\x02y\\xe6\\xf7\\x0c\\xeb{7\\xf5[\\x19JtC\\x1e\\xbd_!\\x10\\xc7\\x8a\\xeb\\x97\\xaa\\x9c\\xf3\\xfc\\xd5t\\x89@<R\\xe2\\xef\\xf1\\xee\\xa8G\\xf1E\\xfe(\\x02\\x18\\x14\\xe0\\xd7\\xdbY\\xd2c{\\xa1K\\x19\\x0cOm1@jf\\xc9\\xc2\\xe4\\xf1\\xe7\\x8ez\\xa4\\xc3:\\xf7tYI\\xe9\\xdb\\x00\\'\\x18E3\\xf7\\x7f\\xf2\\xfb]\\xe5\\xab5\\x93\\x1d\\x9c|\\xd0DL\\xccS\\xc5\\xeb\\x12v<\\xfc\\xd3\\x08JW\\x90x\\x9c\\xfcM$\\x90\\'\\x14\\x16{98\\x81TA9\\x1b\\xee\\xbe\\x80\\xf9\\xeb\\xf5s\\'\\'\\x94\\xd6\\xee\\x81\\xc7\\x9a@q?[\\xfd\\xd9\\xc1<\\xb1\\xf1V(5\\x06%\\x8f\\x13H\\x062x\\xa1\\xd9\\xd7_Ud\\xa1\\x1a\\x8f\\x191\\xf3N\\x824NBh\\r0\\x11\\xf3b\\x98\\x01p\\xce\\n\\xa50Fy\\xd4e\\x84&o\\xa1x\\x90,g\\x14\\xf1\\xf8\\xaa\\x0b\\x94\\x98\\xb1\\xe1\"\\xa4hkgz\\xe0\\xa1\\x84\\xd1\\x05\\x8c\\x16\\x95x\\x14>\\xc6)\\xca#o\\xc4\\xf7F\\xa4\\xd1%\\xe2<\\xd6\\x05M\\x93\\x99\\x8d\\x9c\\x11\\xdbMj\\xd4\\x05/:\\x97\\x180\\x80\\xf3D\\xac@\\xc3\\x0b\\x1e}W\\xcb\\xe4\\x99y\\xf8\\xda<\\\\\\xb9\\x9ex\\xbe\\xf7;\\xa7\\xbeb\\xce\\x00\\x1eLo\\x14:V\\xd9\\x8f\\xdc\\xf5@\\xe5\\xc9\\xd6 \\xfc\\xd5E\\x0f\\x8cr\\xb2\\xc8\\x9fi~\\xee*+\\xa8\\x88\\xaau\\x83b\\x7f\\x86\\xa2\\x00g\\xe6/H\\x9dS\\x9fu\\x89\\x13KH\\xce\\xec\\x00\\xf0\\xc6\\x19\\xfdP\\xc9 %\\x90\\x949\\xdf\\r\\xd4\\xeb\\xb6$h\\xd1\\xccO\\xdd\\x184\\x98\\xde\\xc0\\xf34\\xb8\\x0c\\x97?\\xab\\x1c^C\\xc2\\x99\\xb8\":\\x0c\\xb1+-\\x98~(T\\x8e\\xf5\\xdf\\xccXL\\x1d+XQ#\\xa1\\xe0\\x0cn\\x01(\\xc1\\xdb>b\\xe0\\x04\\x93\\xe8\\x9f}]\\x9aH\\x1f\\xecjI\\x8d3\\xa6\\x1d\\xb3\\xc0$\\x0f\\x0e?\\xaa4J\\x03\\x1f\\x07\\x8a\\x07\"_\\xa3\\x8b\\xdc\\x05\\x93\\xa6\\xd5\\x0e\\xf3\\x0e\"*&X\\xf4\\x7f\\xf1L\\x02=\\x9e?\\xbb\\x08\\x01\\x97\\xcdpP\\x82\\xd0\\x85\\xb2\\xc5\\xeac\\xaf\\x16j\\xeb\\xe7\\x0f\\xab0\\xc3\\xe2\\xad\\x95\\xde\\xe9\\x00y\\xe2\\xb3\\x80\\x8f\\x07\\'\\xaa\\xa6x&\\xe9\\x11\\xbfqV\\xe0\\xbc\\x87\\xf3d\\xe0\\x9c\\xa22>i@\\x19\\x8d`\\xa4r\\x83\\x9e\\x7f\\x0f\\x8aq\\xd8\\xfc}\\xd6\\x14\\xd6\\x93f\\x16\\x11:V\\x8f\\xcd\\x82\\xb0\\xe9\\x9be\\x06\\x9b\\xe6\\xc39\\xc5\\xd2\\xa3\\x90\\x86\\t%\\x0f\\\\WS{\\x8e\\xdc\\xf3a\\xa3\\x05d\\xc2\\xc4\\x95\\xe7\\xea\\x9b\\'%\\xf4*k<\\x14\\xf3\\xf7gP\\xe1w\\xe3*D\\x03(O\\x9a!\\x07\\x04\\xf8\\x9a\\xc2\\'\\xc4\\xc5\\xe3\\xb0\\xb2(T\\x06\\xcf\\x9ft\\x05\\x08$C\\xe7\\x7f\\x9b\\x83Ab\\\\\\xe7\\x9a\\x18a\\xc0\\xe31B\\x83:\\xf2\\xde5K\\x0f|\\x13\\xdf4\\xd2\\xec\\x00&\\x03\\xfb\\xb2\\xce\\x03\\xc7\\x9a\\x805<\\xf0\\xcf\\x16.)n\\xa7\\xd7\\xe2\\xaa\\x89u\\x84~\\xaa\\xf7\\xe1\\x80@\\xd8\\x00%\\xeb3\\xba\\x8c\\xce\\x98\\xe6\\xa9T\\x93\\xc64\\xca\\t\\xd8^T\\x85\\x17\\x1e\\xcaA\\xc4C\\xaf>\\xdb\\x84A!\\xc6\\xc5\\x9aaI\\x1e\\xee\\x92\\xce~\\xfe\\xca!2\\r\\xe7\\'\\xd5lCF\\xc4U\\xb9\\x84\\xf2{\\xb9\\xbc2G\\x97\\xef\\xbaI\\x00B\\x193)D.Mx\\xf2P\\x02\\xa3A\\xe49\\xe6\\xc7\\xdd\\x05\\xa9\\xd7\\x98Mq\\x9f61\\tF\\x0e_\\xe9S\\x00#\\x05\\xd8\\xfb\\xe6\\xf2\\x8c\\x8c=P(\\xd4\\xe3\\xc4M\\x0b\\r\\n\\xf7\\x13\\x97A\\xc2\\x11\\xf6X\\x90j6)\\x96\\x9c$<\\xef4\\xc6\\xb3\\xa5\\x0e=\\x1f\\x8b(\\x08rT\\x99_\\xe1\\xa8\\x8c\"+\\x8e\\xe8\\x19\\xab/\\xef\\xbaa\\x1c\\x03>2\\xb4\\x12\\x86\\'\\xed\\xad~\\x05\\x12}5\\x1c<)<E5\\xf6\\x9ev:+\\xe1\\x12\\xb1\\xa10\\x9ejS\\x90av\\xc7\\xbf\\x16I\\x80\\x81E$}\\xd7\\xc9\\xec\\xc2z\\x93*H\\x87\\n\\x11>\\xa6\\xe6\\x05\\x0c\\xa4`\\x8a\\xb2\\x04\\xb1\\x99\\x9aEC\\x9b\\xae\\x11\\xfdQ!\\x93\\x07L\\xd5P\\x0c\\r\\xe6\\x91$\\xa9\\xf5Q\\xcakE\\xf7W\\x00F&fiT\\x93Z\\x92\\x9fWd\\x00DpU\\x13b\\x04*\\xd9\\xab#\\x96&\\x1fWs)\\x80\\x0b\\x9eS\\xcd\\x1b.L\\x14}\\xd9\\x8c\\xa7AV\\t\\xceOt\\x01\\x06\\x00\\x998k$y,\\xcf\\x0f\\xab\\xce\\x00OC\\xe9\\x83.\\xc9\\xf8\\x9f\\xf7C^\\x10@\\xf5\\xe2\"\\x9e\\x1c\\x06?\\xbb\\x87R\\x04\\xe7n\\xc5\\t9\\x11\\x1f\\xc5Y\\t\\x80\\xaf\\x8f\\x8a\\x80l\\xf9\\x95\\xeb\\xbb\"\\x00_\\x8ezlb\\x13q\\x8e3\\x9aa\\x18\\x0c}\\x8dZ\\xa9f\\x12s\\xc1\\xfc\\xcd*\\x15T\\x0f\\xb1\\xca\\xb0\\xc8\\n\\xb3\\xc3\\x95\\tC)\\xe5\\xc9\\xda\\xd2&$\\xf8\\xcf5\\x94)\\\\\\x127h\\xc0\\x1eX[\\xf3\\x17H\\xd3\\xb1\\xe5|RD\\xc0\\x1b\\x91\\x95\\xc1\\xa1C\\xecG\\x96\\x86\\n\\x003\\xdco\\xe2\\x88\\x010*~\\xe8\\x8c\\t\\x98\\x9f\\x16\\x1e\\xc9:\\xf2\\x1f\\x05\\x08R\\xa2g\\xe3\\xbb\\xde9\\xc3\\xe8\\xe6\\xca\\x02CN\\xf3\\xe6\\xe8(H\\xd7\\x83+A\\xb4gG\\x9e\\xaci+\\xb8*yS<9\\xa7\\xf3f\\xc1\\x1c\\xb2(j\\xb1\\x13\\xb3\\xa31P\\x97\\x17W\"r\\xd1\\x12\\xb9\\x08\\x03\\xf9=\\x14\\x80T\\x99\\x9c\\x15y>1\\xf3\\\\\\xc1\\x02JF\\xafQR\\x01&\\x10\\xfe>(\"\\xa5\\x1a\\xe9y\\xbb\\xa4\\xaa\\xec\\xe4\\'5\\xd1;\\x89R^\\xc8\\xf7\\xf3e\\x00\\xa1\\xce\\xceW\\xbc`\\xd2\\xc4\\x02\\xf53\\xe2k\\xc8\\xf7=\\'u\\x10\\xb9\\x93\\x1c\\xca\\xc0#G\\xf9\\x08\\x8b\\xca\\x84\\x8c\\x8c\\xf9\\xfb\\xa4\\xa0\\x98\\x9c\\x7f4\\x0eC\\xa9\\xef\\xc8Y\\xc0@\\xd3\\xf0\\xf2Y\\xd8\\x0b\\xfb9Q\\x85\\x05\\x11\\xcc\\xeft\\xa9\\xc1&\\xec\\x1de\\x98\\x1e>bS\\xd6M\\xd8B\\x8c\\x8f~}VP$\\x07I\\xf3Y\\x8c\\x8df\\x14\\x85@\\xe4\\x9f\\xf3\\xba&A\\x925\\xe2\\r\\x8d\\xe5\\xaf\\xcb\\x87\\x01\\xfaR!\\xc9\\x88\\xe1\\xfa*\\x80\\xc9\\x87\\xcf\\xac\\xbbI5\\x1e\\n(I\\x1f\\xc7\\xcd\\x84\\x82&\\x8c\\xf7@\\x11\\te=\\xd6?\\xa4\\x9c>[\\x90\\x84\\xe2\\x9e\\n\"P\\x8e\\xa7\"\\xa2$\\xdc\\x113\\xbd\\xb0$\\x0fD\\x0f\\xe6\\xb8L\\x93JLNAR(\\x90\\xc8\\x99\\x7f\\x1c\\xd0$\\x04\\xe9\\x00\\xed\"2\\x15\\xfdz\\xa4\\xbc\\xc0\\x0e3\\xfd\\xd4$\\x91I\\x07\\x03\\xd8\\xff\\x00W\\x18\\xe09\\x9e}1C,$\\xa4\\x83\\x1c\\xc3\\xe7\\xcdi\\x0b\\x84\\x1b2;\\xb60H.\\xc1\\xdb\\xfcVq\\x15\\xe6)\\xbb! \\xcf.>\\xe8\\xa2\\xaa0\\x11\\xc0R\\x82y?\\x01VD\\x04\\x9eU\\xa17K\\xf4!\\t\\xf5K\\x81\\xe5>rk\\xb8$\\x10\\xd8\\xea+\\x1d#\\x86\\xf21\\x05@\\x12G\\xc0\\xfe~h1p!\\xddX\\xaanK\\xdb\\x98\\xe2\\xb3$\\x9b\\x93\\xae\\xa8\\x1a\\xad\\xf4\\r\\xfe\\x1a\\xe4\\x89b\\x07\\x8f\\x8e\\xe9,;p\\xba\\xfb9\\xa9$w\\x12s\\xb5\\x19\\x82\\x99\\x1b\\x83\\xe6\\x7f\\xaa\\x93\\x19\\t^=P\\tH09\\x0f\\x9b\\x0f\\x18\\xeau\\x9fvf\\x98\\xc1\\x8aP\\x08\\x8d\\x12\\x07\\xe2\\xc0\\x90\\xe6y%\\xf9\\xab\\x84\\xd5`\\x92E\\x110\\x9c)\\x0f\\xc5DW\\xbc<\\xeb\\xb1Kg\\x8c\\xe1\\x9a\\xa8p\\x8f\\x1c\\xfdM\\x93\\x05\\x8cQ\\xf8\\xf7Z*8\\xfe\\x04\\xde\\x90\\xb1\"H\\xf4sF\\x0c\\xcc\\xe0a\\xd3\\xe2\\x82\\xce\\x08\\xd9\\xea\\xeb\\x14Y\\x19\\x13L\\x8f\\t\\xb2\\x94{\\x9fV\\x01\\xd1\\xee\\x1b\\xf3[\\x164\\xc9\\xcf\\x98h\\x80x\\x08\\xea^<\\xfdW$\\xa8o{a\\xe8\\x15T|E\\x9aNb\\x7f\\xb5G\\xc0%\\x97\\x87\\xcd29\\t\\x9c\\xd2\\x8a8J`\\xe6{\\xaf\\x01\\xea\\\\8\\n\\xc0\\xa6\\xc7\\xb8m[\\xc1/\\xc1\\xa5\\x15\\x14\\xc9\\xe0\\xf8\\xa1!\\x04i\\xf1\\x0b\\xaaa\\xc6g\\x8a@\\x11\\xcf{\\x9bc\\x88\\xe6\\xaf\\xeb+\\xe2\\x802\\xfb\\x8f\\x14\\xe2F\\x9d\\xc7T\\x03\\x00K\\x8d~\\xec\\xd8R\\xc1\\x13\\xe2\\x97\\xf2\\r\\x97<VtD\\xc4\\xac\\xcf\\xa2i\\x057\\x1b\\x04\\xc7\\xcf\\x9a \\xc8\\x12\\x06\\x13\\xac\\xabe\\x87\\x00t\\xacr\\xd1\"q$iPhL\\xc0N\\x1b2HIS52\\x1d\\x80\\x81e\\xea\\x83\\xa8A\\xc4\\x14\\x95\\x16;\\x1e\\xe9\\xc5b\\x1d\\xe2#\\xc1\\xdd\\x94E\\xc3\\x18\\x1e\\x0ef\\x8c\\xd3\\x17S,\\x1cs\\xd3V\\na\\x01\\xf7A\\xb8\\n\\x89K\\xbb\\xaf\\x00p\\x8c\\x9fV\\x0c\\xe6x\\x96\\x19\\xf9\\xa8\\x9aI\"\\xa4={\\xb2w\\x81\\xe6a\\xd7\\xd5\\t6$w\\xbd\\xff\\x00v\\x00\\x8d$\\xa7+\\xe3oA\\x90\\x16}\\xb3\\xf9\\xa4\\xe2WPc\\x9ap`\\x8cG\\xb6\\xae\\x99\\x12\\x8f1\\x14\\xc0\\xd3c\\xee\\xe9\\xa4\\xa3~)\\x97\\xa9c\\x881\\x1f\\x9b;\"\\x85\\xd9\\xe3\\xcd\\n`e\\x85\\xf0r\\xfcV(\\x9e\\x1e\\x19`\\xaa\\xa9R2|>\\xecQ`GO\\x16\\t*\\x0c\\x13\\xd5\\x12\\x84q\\xad\\xb3 \\xd4\\xe9\\xfd\\xd5q\\x14\\xe5b#\\xc5\\xe2J\\x13<\\x07\\xe9nQ\\xc2I\\xed\\xd3\\xdd\\x14L\\x87\\xb1\\xf9hL0\\'\\x06\\xd8*B\\x9a<{k\\x10\\x10\\xf4\\xf8<\\xd8\\xc4`\\xd5$\\x82\\xaa5#\\x19\\x99Q\\x12K\\xc8\\xe5L%#H\\xf7\\xf3D\\xc2T\\xc8\\xd3\\xe2h\\x18!\\x99X3\\xf8\\xb1IE\\xc9W\\xdd\\xce)(L\\x913\\x13a`F4@\\xf2\\xc5S\\x12D>\\x9f\\x15\\x01\\x02\\x88\\xbc\\x8f1C@<\\x10aO1d\\x8e:\\xb9\\x83\\x98\\xa8\\xc1L\\xb4\"~\\xa6\\x91\\x10\"<O\\x14L\\x01%\\xc6&\\xe8\\xf9)\\xc4BD\\xc8\\xef\\xc9B\\'2\\x19\\xe5\\x95qH\\x02|\\x16\\x0fB \\xf35\\x05\\x02\\xa1\\x00\\xeewT\\xa5\\x89\\x13\\xe2&\\xb3\\xb8F|\\x114\\x04\\x12\\xcc\\xbf\\x9a2\\x82J\\r\\xdfjdE\\x98~J\\x83M\\x07\\x04\\x9b\\x9bu0h\\x00\\x07\\x1e(\\x96S\\xc3\\xcfQJ\\n$gZ\\x86\\x04\\x02:\\x01=\\xf9\\xae\\x89\\xc1\\x93\\xa9\\xf6]\\x8f$\\xe9\\xb2Q8\\x19\\xa63,P\\n\\xd8\\x17\\x83\\x8a\\x01!12\\xda\\x002\\xe1Y?\\r&\\x0b9\\xf0\\x03\\x98\\xa8\\x02\\x04\\xc8\\xf3\\xfe\\xcb\\x1d\\x07\\xa3\\x96\\xf4\\xe1\\xd9`4\\x1a\\xb61\\xe1\\x96fW\\x96Q?\\x80\\xae\\x91\\xa7\\x13?\\xea\\xea\\x9dg\\x08\\xc6\\xcc\\x12\\x03\\x82\\x13\\xf8:\\xaf3 \\x94\\xde\\x7f\\xa9\\xb3F\\xb4\\x13\\x89\\x1f\\x15i\\x1a\\x08\\x97\\xbf\\x87\\x9a\\xd1\\xb2p\\x9b=\\x9d^#9\\x89\\x0f\\xf2j\\xd0\\xa2\"|\\xad\\xd7\\x00\\xc6@\\xfc\\xf1y\\xdd\\xe5\\xf9|TN\\xe0\\xd6!\\xde\\xa9\\x101\\x85\\x83\\xb3x\\xacD\\xe3\\x08iT\\xa0\\x9d\\xce7\\x9b\\x05,L\\xef\\x8a\\xea;\\x04|g\\xf5d\\xbd\\x00\\x86(O\\x00m\\x9f\\x1f\\xfc\\xadu\\x95s\\xe5\\xea\\xe0\\x99\\x97\\xd76\\x07\\xb2Yzt\\xbd\\xa0B)\\xec\\xa0#\\xb0\\xcf\\x1f5\\xf34\\x07&\\xd9\\xac\\xe9\\x12\\x1ceQ\\xc9\\xad\\xe3<\\x91\\xa0H\\xa2&h\\xc2g\\xbfU0\\xc6F\\xf8\\xbc\\x90\\x16\\xeb\\x06.\\xcc\\xed\\tL\\x1c)\\xb3 \\t\\xf4\\xe7\\xf2Y\"3-\\x9d\\x87\\xbf\\x8a\\xe0\\xc2\\x11\\x0c\\xf2\\x9e\\xdf\\xaa\\x82\\x82\\xbc\\x07\\x1fw\\x92\\\\\\x13,\\x8b(\\x13\\x18V,\\xc3\\x1f~\\xa8F\\xa4!\\xe0\\xf8l\\xd3BA\\x90\\x9b\\x98\\x8e\\x83\\x82\\xbf\\xc5\\x19\\x9cO\\xa0G\\xbe\\xeb0\\xba\\x01\\x90i\\xe6+*\\x10\\x94\\x80E\\x88z\\xe1\\xca}Tl\\xc3R;?\\xdb`\\xc3&\\xbd\\xac\\x91y\\xb0!C\\x98h&B!\\x1d\\xef\\x96\\x9ca\\x88O\\x81\\xf1\\xb6\\x01-g\\x18<\\xeda\\x14\\xa7R\\xa3\\x1c\\x02\\x11\\xd4\\xd2I\\xe3?\\xaa\\xab0\\x15&>iq\\x081vx\\x8f6n\\x087\\xef\\x9a\\x85\\x02;\\xe3\\xaft\\x884G\\x9b\\x00iP\\xf0\\x8b\\xa6\\x19\\xdc\\xe2\\xa7\\xcd\\x8b`\\x94O\\x9b41 ?\\x8d\\xa0\\xe1\\xd2O\\x89\\xa0\\xc4\\xc6\\xc6d\\x7f\\xed!\\xb6\\xa6\\x86LP\\x9a,\\xa2\\xbb\\x9f\\x19e+\\xe2do\\xfa\\xaa2t\\x83~.\\x80\\x915\"++\\x0c\\x8c\\x04>\\xeaez\\x1c{,J\\xc9{\\x11\\xed\\xf3^)\\xc9\\x0e\\x0c\\xf7\\xf8\\xb8\\xa2\\x11\"!\\xb38\\xa1\\x14\\xf5K\\xb9\\x14\\xc6J\\xaa2C\\x08a\\xbb\\x06Dp\\x8f\\xbb1\\xca\\xce\\x7f\\x94Yq\\x0c0\\xa2?\\x15\\x12\\x87\\x9eX\\xe6\\xc4\\xd1 \\xa0\\xd6R\\xa8\\xc1\\x06)\\x92}m\\x00uI\\x86\\x9f\\x9a\\x1c\\x9a\\x08\\x02q\\xfc\\xd2\\xb40g}\\xedO?\\x1c\\xbcx\\xfe\\xea\\x050\\xe9=\\xf3?Ua\\x04\\x94\\xa5\\x96)N\\x04\\xbc\\xfb\\xf4\\xd6\\x97_\\xf7U\\xc8\\x0b\\xcb\\x8d\\x8a\\x9a\\xa7\\xe5M\\x02@\\x98\\xbd\\xcc\\xff\\x00vY\\x00t9\\xfa\\xb9I\\xd3v\\xf08z\\xee\\xc8\\xc8\\x96\\x01\\x077\\xe2\\xa8\\xe0LA\\xfd\\x940%\\x7f/\\xcdo\\x85\\xd4a\\\\\\xeaH\\xe7\\x86><\\xd1\\xf9\\xff\\x00\\x15\"\\x06\\xc3\\x12lr\\x8c\\x93\\xf1-\\x84\\x00\\x1e\\xc8\\x99\\xb2\\xc8\\xf0\\x1c\\x93T\\xb0%\\x0eu\\xf7\\xe4\\xf1`\\x83\\x9dH\\x83\\xcdXZO\\x84G\\xe6\\x9a\\xa2%\\x06X\\x1e\\x18\\x8eh\\x80e\\xd4\\xd8\\x8e\\xdb\\x1aoP\\x86\\xe7\\xdfVK\\xf5?U\\x00\\xe2\\xf0x\\xa86\\xb0\\x177\\xef\\x9b\\xb3=Q\\r\\x87\\xfa\\xa4\\x0c\\x10\\x87\\xe1D`T@DT\\x00eu\\x81\\xfc\\xc5\\xf9\\xc0\\xae\\x86\\x8cN\\x99\\xbf5$~}\\xa7\\xb8\\xf1\\x7f\\x08\\x883\\xec\\xa21\\xcaD\\xbf/\\x15\\x11\\x89\\xcf,\\x9fSO\\x07\\xc4FE\\x969\\x83\\xc3S\\x8c\\xb8\\xc9\\xb1\"Q\\x89\\x07\\xc9X\\xbb\\x04\\x8f\\xb8b?\\xd5\\x0e\\x94\\xce\\xa7\\xceV1\\x08\\x11\\xec\\x8e\\xe8\\xd2\"n\\xfb\\xf3X\\xc4D\\xc2\\xbc\\x0f\\xb3\\x1f\\xc5\\x0f\\x03\\x19Le\\x12\\x07u\\x89\\x055\\xeb\\x1f4\\xc98\\x94\\x92C\\xd3\\xf1M\\x04\\xa1Tc\\xff\\x00\\xca+D\\xbct\\xff\\x00\\xe3d\\x1e\\x05W\\xf7p,\\xa6\\x8f\\xea\\xa3\\x94\\xa6\\x1f\\xfa\\\\\\x120\\x07C\\xd8XnrW\\xc1T\\x01\\x03?\\xcf\\x9b\\xa6\\x9e#\\xacw\\xbdx\\xb3DYl\\x84\\xd8\\x00Vt\\xfcm}#\\x839#\\x9b\\x00\\xf35=6{\\xd0dV\\xd0\\xf2k\\x82\\xf5bRzx\\xfb\\xaa\\xec\\xa5SI\\x9b\\xc1Hk\\x13\\xfc\\\\\\xe8c\\xbf\\x05H\\x0c\\x00D*:\\x08a\\x92\\x7f\\x1e\\xaa\\xc2d\\xf7\\xdd\\xea\\xcc\\xb0O\\xc2}U\\x81\\x8e\\xc7}kN<R\\'=g\\xc5\\x80R\\x12\\x8d\\x0e\\xe7\\xdcP\\xd7\\x80\\xf1\\xdb\\xea\\xc9$. \\xe7\\xfc*\\x12\"<q1\\xdf\\xddp\\n\\xcfG>\\xabV\\x8d\\x0f\\x93)\\x88\\x00\\xf2yJc\\xb9\\xc0 qC\\x88\\xec\\x0e\\x86\\xbdAB$\\xf1yC\\xaa\\x0c\\x8b1\\xc5\\x9c\\rL\\x8cX\\xe8Z[\\x1fq\\xea\\xa2C\\x15\\xe18*\\x90v\\xec\\xdft2\\x8d\"\\x02\\x93\\xef\\xcd\\x8e\\x00,\\x1f\\x04\\xd8\\x83\\xc7I\\xe2\\x7f\\x9a\\x08Kra\\xeb\\xcd\\xe2\\x93\\x80\\xd8O\\x17=\\xd0$\\xc7+I6\\x83A\\xc3\\xeb\\xe6\\xa0v4\\x10\\xb3X\\xd0(O\\x9a,\\x8e\\xa0\\xf6\\xd6Q&I\\xd4\\x1c\\xd2\\xa0\\xcb!uDq\\x8b\\x1b\\xdd\\x90\\x84\\x8d\\xc7\\x19\\xef\\xd5\\xd2?9\\xa8\\xa7F\\x020\\xb3\\xc2\\xc1\\xd6~\\x8c\\xa4\\xe0\\x18$\\x84j`\\x01\\'\\\\\\x94\\x08\\xd0wA\\xd0M\\x04\\x88\\xcc\\xa1\\x86>\\xf9\\xae\\xa8\\x89J&O\\x99\\xfe\\xa8E\\x92xLC\\x9b,HU>\\x0f\\xfe\\xf3P\\x03CC\\x07\\xed\\xa4\\x05\\x82\\x86\\x98\\xd7\\xcd`\\x93J>C\\xd3Z\\x88\\x18@\\x9ei(\\x91\\xae\"\\x94\\xceS\\xddU4\\x9e|\\x94\\x88\\x8c\\xb8I\\x9f\\x9a\\xa3\\xd2`\\xb1\\x175\\xcd\\x1e\\xe0\\x8b\\x81\\xca|RD\\x19\\x1dI\\x1b\\xe6\\xa3\\x8e\\x97\\x99\\x00\\xff\\x00\\x14x\\x84o\\x18\\xcf\\x8a0\\x84\\x0cc\\xfdMB\\xc1\\x02\\x0f/\\xc5l\\x1e\\xdf|\\\\\\xec\\x81\\x19\\xef(\\x13\\xb5M\\xca@R\\x7f\\x9fV\\x00\\x93\\'\\x9f\\xa8\\xf3\\xe2\\xe7\\x80\\x89\\x9cy~}\\xd2\\\\\\xc8\\x1c\\x8ex\\xac\\x92\\xbd\\x81\\x1fQ\\xb5\\x89.\\x0e\\x98\\x87\\x9f4\\x01\\x06q\\x99\\x12k\\xca\\x16\\x19\\x13\\xe5\\xc7\\xea\\xf1(\\xbcj\\xbf\\xbb$@e\\x99e\\xf8,\\xe4\\x9d7V\\xa1\\x82\\x1eG\\x9c(\\n\\x87\\x0f\\'\\xff\\x00lU3\"4\\xbfzM\\x11\\x9e\\x18S\\x0f\\x07\\x8a\\x96\\x06\\x0c\\x91y\\xf9\\xb9Ee\\xc1\\xf0\\xba\\x10B\\x12D\\xef\\x9b\\x10.\\x08Hq\\xf7J\\x9b\\x0e$P\\xcf\\x12]\\x96$\\xf2<u\\\\\\x02\\xa2\\x8e\\xc8|,we-E\\xd1\\x8f\\xe3\\x9a\\x81\\x02C\\xdcy\\xb2W\\x0b\\x81\"\\x7f\\xba\\x81\\t\\xf2I\\x11\\x9eJ\\xa7\\xab\\x03\\x9d\\xf9\\xab\\x04\\x11\\xfd\\xb6\\x13\\xa4\\xd7Nf\\x94\\xc4\\x15\\xe4Q`\\xa5X\\x01~l\"\\x03v\\xe0\\xf3\\x13\\x974\\xb8b~W1\\xc8\\x00Fy\\'\\xbb\\n*\\x03\\xbc\\x8c\\xfab/\\x87\\x03\\x88\\x82<U\\x1c(1?\\xc6e\\x84\\x18$\\xc7\\x89\\xaaU\\x94\\xb0\\xec\\xb9\\x82\\x15\\xdf\\xfd\\xfe\\xece\\xf1\\x18\\x98\\xef\\xfd\\xd5\\x14\\xc0\\t9\\x08{\\xac\\x02\\xc8u\\xcf\\x15\\x9f\\xa2\\xe7\\x81\\xf8\\xa422\\x1f\\x8f\\xba\\xc2J\\x0f\\xc6\\xf9\\xed\\xab\\x8c\\x04\\xc8\\xa0DX\\xc1\\x00<\\xfc\\xb3\\xc3@\\xa0\\xf9C\\xdd\\x90)c2J\\xa8d\\x17@0\\xfa\\xbc\\xa8\\x92\\xb2J\\xff\\x00v\\x1b0\\xa4\\x17\\xe1\\x936#)c\\x1b\\x03\\xddm\\xe0\\xf5\\xcf\\x14\\x05\\x94\\x8c\\xa98\\xa3*\\x07\\x7f\\xda\\xf4]i!Srb\\xcc\\x1c\\xa3\\xba\\xb9%\\xa48=e\\xe0\\xc7\\x90\\xc9\\xb2\\x11\\x0e\\x8c\\xf63\\xa8\\xa5\\x05\\x83\\x10\\x17\\xf7\\xfb\\xaf\\x14\\x87\\x91\\xc8\\x7f\\xaa\\x84\\xa4.=\\x91\\x9bPz^\\xcc\\xe0n4\\xbdm\\x91>:\\x8b\\x1d\\x84v\\xf3\\xc7\\xab#\\xa0\\xfbI\\xcb%4\\xf5\\x8e\\xbf\\x15\\xe34k\\x1a~2\\xc4\\x87i\\x88c\\xf9\\xee\\xc4\\xf6\\xe1\\xe6e\\xaf \\xf2#\\x9f\\xd8\\xd4\\t\\x1eI\\xc9\\xeei\\xc1 \\x1e?\\xcf\\x15Ar;\\xe3\\xf7PBbL\\xf9z)\\xa2\\x0c\\xae\\xf3\\xe3\\xd5\\x84tx\\x0c\\x93\\xef\\xba!@=iD\\x03\\x1b\\xaf \\xe6\\xcc\\x80\\xa7\\x99\\xf1\\\\\\x87\\'\\x89\\xe0\\xf9\\xb11\\x10\\x07\\xd5\\x94\\x02}T\\x10\\x10\\xeauPb\\x06\\xcc\\x8e\\xa8H\\x81\\xe1+2\\x00U\\x19s\\xfc\\xdb\\xbb\\xb5 \\'=\\xd6BC\\xa9D\\x9e\\x0f\\x8a$\\xe5\\xf0\\xbd\\xcft\\x1e\\x898:\\x16Zs\\xc9\\xcb>\\xab\\xd0D^\\x1duQ\\x01\\xe1\\x93\\x1c\\xde\\xf0\\xf29~j\\x0c\\x1f#\\xb3?\\xc4\\\\8x\\x9e\\xcd\\x9b\\x07\\x19 \\x91\\x07\\xe9H\\x95\\x18t\\xbf\\xc3@\\\\\\xae\\xbc\\xfe\\xa6\\xcc\\xbdA@) \\xe6\\x0e\\xea\\x04O\\x9a\\x82\\xc9\\x11a<\\xc7=\\xe5\\xe65\\x06\\xb7f\\xcc0\\xb6Dp\\x1f\\x9aVD\\x14\\xd8~\\xac\\x82p\\xd8\\xc1\\x8e\\xdf\\x14\\xcc\\xa1\\x102,X\\xe0\\xaf\\xc8\\xe5#G\\x0et\\x8fuS29\\x1c\\xca\\x08\\xa0r\\xae\\xf1\\xee\\x85\\xd1\\xe0\\xa7\\x8e\\xaa\\x04Xv?\\xce\\xae\\x06f:d\\xfc\\xd1j\\x8dG\\x82\\xc5\\x92\\xa0K\\x82{\\xa6\\x11*=9\\xcb->\\xa1\\x94\\x06e\\xbc\\xbf\\x14\\x8aG<Y0/Jr\\xd8\\x05\\x00\\xf4\\xae\\x13\\x82\\xe8\\xa4\\x93.w\\x1b\\x84\\xd7\\x9cI\\xfa\\xea\\x9a\\x04C\\x89g\\xaf\\x8a\\x19$s\\x19\\x8b r\\x0f\\xdf\\xcd\\x91O;\\x8cAb\\x06v\\xcc\\x03\\x14<\\x90\\xe4\\xe6\\x1f4\\x11\\x81\\x0e)$\\xb7\\x00\\xce\\\\\\xd3\\xb5(\\x1eXp\\xbf6b,\\x83\\x86=\\xd5-H\\xb1=\\x95L\\xb3\\x93\\x9b\\xeed\\xfc~\\xa8g\\x13\\xeb\\x8b\\rF\\xf9\\xf5\\\\\\xf0c\\xf7YX\\xc7\\xd5\\x04@\\x81\\xeaO\\x86h\\x1e\\x12\\x92\\xcc\\xb1\\xfex\\xa0\\xf2\\x13\\x84\\xfe\\xeb\\x06\\xd2\\x04@\\xbfwB\\xfc =%\\x18\\x90\\xa2\\x16Ix!\\x94\\xef1\\xf3qB\\xc3\\xff\\x00\\nd{9\\x12\\x83\\xe2\\xb0O Ls\\xcd\\xc1\\x18C\\x1d{\\xf9\\xb29-$\\xcek-\\x19\\xe8\\xeb<4\\nL\\x0f>\\x9a\\x94\\xf9<\\x91Z\\xf1z\\xe2\\xe4\\r:\\xa3#2\\x19\\xf5Ya\\x9f\\x04\\xd9\\x10B\\xf67\\xfch\\x19\\x04\\x08\\xcf\\xd4X(A\\x8e\\xd5\\x82\\x10\\xe0\\xbc\\xd9\\xf91\\x0c\\x8d\\xf6Ta\\x00\\xd0\\xce\\xed\\xd4r9\\x8f>\\xee\\x8e\\x0c\\x99\\xe3\\xccW\\x10\\r\\xdcC\\xb5\\x08\\x18\\x13\\x88\\x87\\xdd\\xc0\\x19u\\x8f\\xcb\\x153\\x8b4\\x14\\xc9c\\x04\\x12\\n9\\xfc\\xd2\\xe1\\xc4\\xd0d\\xf9(\\x9a)\\x8eC\\x16Ad%\\x99\\xe2\\xa0\\x97oS\\xfa\\xadL f\\xd4\\x80\\x96*\\xcc\\x13\\x1d\\xf8\\xb2\\x0c<\\x8eudP\\xde\\x05\\x9f\\xddk\\x0c\\x86\\x91\\xcc\\xfb\\x8b\\x90\\x10O&\\x1a\\'\\x82\\x9eq\\xe2\\x81\\x10o\\x16\\x08A\\xe3S\\xff\\x00\\xb6D\\xef\\xa6A\\xe9\\xa8\\x10\\xafL\\xa5|\\x89\\xf8J\\xe1A9|\\xd4$\\x89\\x1c\\x1ce\\x18Q\\xf5\\xcf_5\\x03\\x07#l\\xe1\\x15\\xfa\\xea\\xa20\\xbf?\\xee\\xa0s\\xadFj\\x19\\xdf#\\xc7Q`/\\x8e\\xbc\\xd6\\x06\\x9f/\\x14\\x88Vr\\x99\\x01\\x04O\\x13\\x1el\\x1e\\tjwu\\x85r\\x08\\x18\\x8f~,\\t\\xc2\\x03\\xac\\x8f\\xdd\\xea\\x12\\x9e\\r\\xcf\\xae\\xe8\\x9aK\\x04\\t\\xbf\\x15A\\x88=#-\\x02\\x1b\\xcc\\xe4h\\xa8\\x94vc\\x11\\xf7P\\x8c\\x89eV \\x8b\\x02\\x0c\\x0e\\'+\\x81\\n\\x83\\x8e\\xfd\\xd0bw\\xb2}\\x94\\xe0\\x1fO5I\\x11\\x0c\\xe6\\xc7\\xf1`\\xae\\'5\\xf1c\\xa4\\xbf\\xb2\\xa7t\\xe3\\x89d\\xc9:\\x13\\xef\\xeek\\x0b) \\xf1\\xfcT\\xa7\\xb7H\\xb6n!\\x02\\xa0(d\\xe9\\xf5\\xf5\\xddN}\\x89\\xa4\\xfbc\\xab\\x10\\x83\\xa12\\xfc\\xd5<\\x89\\xe1C\\x8f\\x8a\\x08\\x82P\\xa8\\xaaWK\\xe0\\x9f\\x9a\\xf3.\\x1d\\x8d\\x92\\xca\\x0cx<\\xd4\\xa6\\x17a\\'<G\\xfe\\xd01~\\xcb T\\x0f[xH\\xeeg\\xf1@\\x86\\xec\\xe7\\x8f\\xe2\\xb9\\x99j\\xcb\\x1d}\\xc5\\x94\\xc68\\xf3t_@\\xd8\\xa5\\xa4\\x18\\xe7\\xbb\\x06TH\\xeey\\xa7!<\\xad( \\r)J\\x1d\\x1d?T!P\\x0e+[uS\\x86y\\xb0\\xc8C\\xaa\\xb6\\x10\\xac\\xc1\\xdd\\x95&N8)\\x96\\t\\xea\\x13\\x1e\\xa8r\\xb4a;\\xfc\\xdc\\xf1>2w\\xea\\xc9(\\x90\\xf1\\xd5\\x1d\\xd9\\xf8\\xea\\xcaL\\x86\\xcb\\x14\\xe4\\x87O\\xd5\\xe5\\x8eSX\\x01\\xc9\\xd96J\\x86\\x1c\\xaesR\\x1c\\xf8\\xca\\xa0}\\x81&\\xae\\xe1!\\xc3\\xc7\\xba&\\x13\\x03\\x7f\\x9b\\x02\\x00\\x94\\xc0\\xe0\\xf0{\\xaa\\x10\\x1dA\\xbb\\x05f\\x90\\x9c\\xcc\\xda\\xb3\\x81\\x0e\\xe5E\\x92\\x1eo\"\\x1f\\xbd\\x9f\\xba\\x908\\x8f\\x8d\\xb3\\x89;\\x98\\xaa\\x83\\x8f\\x1bx >\\xfa\\xa8z\\x13\\xf4V}\\xe6\\xc0C\\xe4f\\xaa\\x18k\\x93o7&U\\x04\\'\\xd5x\\xa3\\xb0i\\xa0\\xf1\\xfe\\xec\\x9c\\x0f]\\xf8\\xa2y1\\xcf\\xe3*\\xc0w\\x87\\xf5@\\xabM\\xf1I\\xd4P\\x89/=\\xbe\\xae\\x04,\\x94&>^+\\x9e\\x07\\x0cqX\\xd70\\x9e\\xf8\\xf1A\\x89\\x9c\\xfeK(\\x82\\t\\x14\\xce9\\x9b\\x02}\\xb0\\xd9D\\x1e\\xe1\\xf7T\\x9f(\\xad?uLA\\x0e2\\x92\\xf1\\x03\\xd0\\xc5\\x02\\x01#/=Y2\\x1c34\\x1c\\xa8)\\xf6FE\\x14Ks\\xe0\\xe6\\x89\\x9e\\xcf\\xcd\\x1dY\\xca\\x84\\xc1j\\x16\\x8c\\xc3\\xe9\\xf5N\\x91\\xe9\\xf3^\\x87\\xb8\\xa6\\x08\\xcc\\xcf\\xd4U\\xec9\\x8e(\\xceH\\x8e\\x15I\\xb5\\x81\\xc4d\\xd7C\\\\\\x88\\x9c\\x8f\\x05\\xff\\xd9', format=u'jpg'), interactions={'hover': 'tooltip'}, scales={'y': LinearScale(max=2.0, min=-0.5), 'x': LinearScale(max=2.0, min=-1.0)}, scales_metadata={'y': {'orientation': 'vertical', 'dimension': 'y'}, 'x': {'orientation': 'horizontal', 'dimension': 'x'}}, tooltip_style={'opacity': 0.9}), Lines(colors=['red'], interactions={'hover': 'tooltip'}, scales={'y': LinearScale(max=2.0, min=-0.5), 'x': LinearScale(max=2.0, min=-1.0)}, scales_metadata={'y': {'orientation': 'vertical', 'dimension': 'y'}, 'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'color': {'dimension': 'color'}}, tooltip_style={'opacity': 0.9}, x=array([0, 1, 1, 0, 0]), y=array([0, 0, 1, 1, 0]))], padding_y=0.0, scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scales = {'x': LinearScale(min=-1, max=2), 'y': LinearScale(min=-0.5, max=2)}\n", "image = Image(image=ipyimage, scales=scales)\n", "lines = Lines(x=[0, 1, 1, 0, 0], y=[0, 0, 1, 1, 0], scales=scales, colors=['red'])\n", "fig = Figure(marks=[image, lines], padding_x=0, padding_y=0, animation_duration=1000)\n", "fig.axes = [Axis(scale=scales['x']), Axis(scale=scales['y'], orientation='vertical')]\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Its traits (attributes) will also respond dynamically to a change from the backend" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Full screen\n", "image.x = [-1, 2]\n", "image.y = [-.5, 2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pyplot\n", "\n", "It may seem verbose to first open the image file, create an `ipywidgets` `Image`, then create the scales and so forth.\n", "\n", "The `pyplot` api does all of that for you, via the `imshow` function." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f920e644b9f44e64b7c6e9f9d0509dca", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n", "<p>\n", " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another notebook frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "VBox(children=(Figure(axes=[Axis(orientation='vertical', scale=LinearScale()), Axis(scale=LinearScale())], fig_margin={'top': 60, 'right': 60, 'bottom': 60, 'left': 60}, layout=Layout(min_width=u'125px'), marks=[Image(image=Image(value='\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00H\\x00H\\x00\\x00\\xff\\xe1\\x012Exif\\x00\\x00MM\\x00*\\x00\\x00\\x00\\x08\\x00\\x07\\x01\\x0f\\x00\\x02\\x00\\x00\\x00\\x12\\x00\\x00\\x00b\\x01\\x10\\x00\\x02\\x00\\x00\\x00\\x0c\\x00\\x00\\x00t\\x01\\x12\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x01\\x1a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x80\\x01\\x1b\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x88\\x82\\x98\\x00\\x02\\x00\\x00\\x00\\x07\\x00\\x00\\x00\\x90\\x87i\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x98\\x00\\x00\\x00\\x00NIKON CORPORATION\\x00NIKON D3300\\x00\\x00\\x00\\x00H\\x00\\x00\\x00\\x01\\x00\\x00\\x00H\\x00\\x00\\x00\\x01Tama66\\x00\\x00\\x00\\x08\\x82\\x9a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xfe\\x82\\x9d\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\\x06\\x88\\'\\x00\\x03\\x00\\x00\\x00\\x02\\x00d\\x00\\x00\\x90\\x03\\x00\\x02\\x00\\x00\\x00\\x14\\x00\\x00\\x01\\x0e\\x92\\n\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\"\\xa0\\x01\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xa0\\x02\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x01@\\xa0\\x03\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xd5\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x012017:06:08 17:17:46\\x00\\x00\\x00\\x00\\x18\\x00\\x00\\x00\\x01\\xff\\xe1\\n\\x1bhttp://ns.adobe.com/xap/1.0/\\x00<?xpacket begin=\"\\xef\\xbb\\xbf\" id=\"W5M0MpCehiHzreSzNTczkc9d\"?> <x:xmpmeta xmlns:x=\"adobe:ns:meta/\" x:xmptk=\"XMP Core 5.4.0\"> <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\"> <rdf:Description rdf:about=\"\" xmlns:photoshop=\"http://ns.adobe.com/photoshop/1.0/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" photoshop:DateCreated=\"2017-06-08T17:17:46\"> <dc:rights> <rdf:Alt> <rdf:li xml:lang=\"x-default\">Tama66</rdf:li> </rdf:Alt> </dc:rights> </rdf:Description> </rdf:RDF> </x:xmpmeta> <?xpacket end=\"w\"?>\\x00\\xff\\xed\\x00jPhotoshop 3.0\\x008BIM\\x04\\x04\\x00\\x00\\x00\\x00\\x002\\x1c\\x01Z\\x00\\x03\\x1b%G\\x1c\\x02\\x00\\x00\\x02\\x00\\x02\\x1c\\x027\\x00\\x0820170608\\x1c\\x02t\\x00\\x06Tama66\\x1c\\x02<\\x00\\x061717468BIM\\x04%\\x00\\x00\\x00\\x00\\x00\\x10Ab\\x95\\xfc\\xe0Y\\xc0`f\\x9a\\x1f \\xaf\\xa5!h\\xff\\xc2\\x00\\x11\\x08\\x00\\xd5\\x01@\\x03\\x01\"\\x00\\x02\\x11\\x01\\x03\\x11\\x01\\xff\\xc4\\x00\\x1f\\x00\\x00\\x01\\x05\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x03\\x02\\x04\\x01\\x05\\x00\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x10\\x00\\x01\\x03\\x03\\x02\\x04\\x03\\x04\\x06\\x04\\x07\\x06\\x04\\x08\\x06s\\x01\\x02\\x00\\x03\\x11\\x04\\x12!\\x051\\x13\"\\x10\\x06AQ2\\x14aq#\\x07\\x81 \\x91B\\x15\\xa1R3\\xb1$b0\\x16\\xc1r\\xd1C\\x924\\x82\\x08\\xe1S@%c\\x175\\xf0\\x93s\\xa2PD\\xb2\\x83\\xf1&T6d\\x94t\\xc2`\\xd2\\x84\\xa3\\x18p\\xe2\\'E7e\\xb3Uu\\xa4\\x95\\xc3\\x85\\xf2\\xd3Fv\\x80\\xe3GVf\\xb4\\t\\n\\x19\\x1a()*89:HIJWXYZghijwxyz\\x86\\x87\\x88\\x89\\x8a\\x90\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xc4\\x00\\x1f\\x01\\x00\\x03\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x01\\x02\\x00\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x11\\x00\\x02\\x02\\x01\\x03\\x03\\x03\\x02\\x03\\x05\\x02\\x05\\x02\\x04\\x04\\x87\\x01\\x00\\x02\\x11\\x03\\x10\\x12!\\x04 1A\\x13\\x050\"2Q\\x14@\\x063#aB\\x15qR4\\x81P$\\x91\\xa1C\\xb1\\x16\\x07b5S\\xf0\\xd1%`\\xc1D\\xe1r\\xf1\\x17\\x82c6p&ET\\x92\\'\\xa2\\xd2\\x08\\t\\n\\x18\\x19\\x1a()*789:FGHIJUVWXYZdefghijstuvwxyz\\x80\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x90\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa3\\xa4\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb2\\xb3\\xb4\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc2\\xc3\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd3\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe2\\xe3\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf2\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xdb\\x00C\\x00\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xdb\\x00C\\x01\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x01d)B\\x03H\\xd4\"\\xc6\\x18\\x94Q\\xb8\\x07B\\x92]\\n\\xc4U\\x85\\xc2s\\x965\\xe0P%\\xc3\\x81(\\xa9!IJ\\xe8E\\x84GBHd\\xa4\\x89*8\\x84\\xe9\\x15\\x1a(\\xd05-\\t\\x944\\xb8A\\x01JV\\x838\\x84)@\\xcd+\\xa4h\\xd5&n\\xe4\\x12\\xe5\\xa1t\\x19\\x90F\\xcf\\r\\xc0\\xf9\\xe0\\x01\\xc3=IK\\x8a\\xb0\\x923\\x1ar\\x90!\\x13(\\x86\\xc5R\\x893W-\\xd6\\x0c\\x8d{\\x81\\x9b\\x11d\\xc1\\xd2\\x85\\x9aN\\rD\\x14N)\\xbc\\xce4\\x91\\n\\x01{%c\\xb6!)&\\x11\\x83\\x1c.B\\x1de\\x02\\xd79K\\xb0dB\\xd5\\xebW+XIM\\x16\\x82h\\x10\\x95\\x8c\\x83\\xad$\\xcc\\xc3wM\\xab+)\\xc0K9\\x80\\xdc\\x8aT\\xca2\\xa0\\x10\\x10:\\x98r\\xdbTI\"\\x10IH\\x92q\\x90IT\\x14Rf\\x8eU\\x9d\\x84\\xc2\\x9d \\x9c2r\\xe6\\xad\\xc2N+\\xde\\xb6gS\\x89FS\\x04\\x9d\\xae\\xea\\xb4\\xa9\\x04\\xbf%k\\xecKy*\\x8c\\x98r\\x10\\x1a!q\\xd0\\xb2\\xb1\\x1dd\\x11\\x12$\\xa0\\xb0C\"\\x94Nds1ZW\\x80\\xd2\\x99\\x14\\xe2$D0\\xc0\\r\\xabxA\"\\x98\"\\xa0\\x96\\x92\\x1bi\\\\\\xee\\x1a\\x149\\xcal\\xe9\\xa1\\xa18\\xee\\x06\\xe4.\\x14\\xacnZ)8\\x9e\\xc9J\\x84:k\\xad\\x05\\x19\\x0c\\x89\\x93\\xac\\xdd.\\xdb\\xc2\\x00\\xa8s\\x11\\xb1%\\x1a\\xd2\\xa0\\xd2\\x89\\x16RDb\\x94K\\x13\\xd6\\xc5\\n\\x9d\\x969RA\\xae\\x0e\\x11)m \\xed\\x9d\\xe57D\\x90\\xcc\\x1f\\xb6viNB\\x97\\xf5\\xaf\\x98\\xb4\\xfd*\\x1eJ\\xcf\\x10;*q!\\xa6\\xefF\\xb51%\\x0e`K8\\x9a\\n\\xf2\\x89\\x0c\\xa0\\xad\\rB[R\\xb8R\\xd2\\xa6\\xca\\x13\\x96\\xe6\\x14\\x08\\x99\\x82\\x088X\\x88\\x95\\x84\\xbc-+2\\xcc\\xd5\\xe8\\x9a\\x17.\"J\\xc2#\\x88\\xa1c\\x93\\x9b\\x84\\xda\\x12\\xf1\\xd04\\xd1\\x8a\\xd5\\xe2\\xd8ZT\\x16\\x00Q\\rG\\t-N\\x95\\xbd\\x13\\x9b\\x89\\xd8\\x93\\x06Z\\xd0@r\\xd2\\xa0\\x9b,u\\x96\\x89\\x89\\x80\\xe7#\\x08\\xd3\\x19\\x858B\\xf5v\\xcaP\\x94$\\xca\\xca\\x10\\xd5\\xe37c6\\xb4J%:,\\xab\\xb6\\xa50W\\'N^a\\xaa\\x1cK\\x06kr\\x02Ta\\xa5Hd\\x90\\xc1z5(\\x81X\\x96\\xa1,X\\x82\\\\\\xa8K\\x9c\\xb011se\\xc7:\\xa9I)2\\x95n\\x9d\\x82\\xd5\\xf3u\\\\@\\x1f\\x1c\\xc8\\xcd\\xc0\\xc9<\\x88\\xc8C[m[\\xa0\\x13\\xe1\\xb9\\xd0\\xcbhB\\x82!\\x9c:\\xcc\\xa4\\xad\\x0c\\xe3D`\\xa2\\xc6^\\x85\\x89y8D\\x8d\"\\x87\\x08R*\\xc8\\xdb,\\'m\\xdc\\x96\\xd2#f\\x14d\\xed4\\\\\\xa7l\\xca\\x1aD$\\xc0\\xd5\\x9en\\xa5)\\xca\\x98\\x99\\x81+\\xac\\xab\\x1d\\x92\\x98V\\xc1\\xd0\\xc8\\x1c\\xc84\\xa3U\\x84JL\\xe4Y+c\\x0e\\x0cp\\x92V\\t\\xd0\\xbaY\\x10\\xa4\\x04R\\'9h\\xda\\x13\\x92q\\x01jI&^\\x8d\\xf5\\xd3\\xa5\\x8a\\x06\\xb4\\x82\\x11\\x90h\\x87#i\\xc9\\x1c(Yc\\xb1Z\\x1c\\xb7,\\xa3[ 2\\xd4eI\\x11\\x0b\\x01\\xa4dM\\x11a\"\\xc3\"R\\xd1ca\\x14\\xe3\\xd9\\x02\\xa5\\x05\\x14%yhT\\x8e\\x88a\\x91\\x9c\\x92\\x84\\xec\\xe5\\x81\\xa8\\xd92\\x80R\\x07 U\\x03\\x80\\x99s\\\\\\r*\\xa6\\x80\\xccT\\xde\\'B\\xb9\\x0c\\x92\\x96\\xe6\\x86(\\x9cX$$A\\x12\\xad\"Z2bU\\x0c\\xaa$\\x83\\x94\\x0bX\\xd4\\xb4\\xccH\\xa0\\x88\\xd1!Q\\xb5u\\xa59\\xd8\\x9a\\x13S\\x91\\x02RS\\x15\\x00(\\xc2e&\\x15cN4!HjV\\x98\\x8c\\x88\\xa32\\xf0\\xf4\"%fJ\\x93\\x84\\xb4)1!\\x06U\\x1bi@\\xa5L\\x82\\x88J\\xe9\\x04\\x1a\\xc9<(z:\\xe1PiB\\x91\\x18\\x94\\xe3e \\x80\\x05%H\\x03A\\xc5\\x05\\xae\\n&\\x89(\\x88\\x94\\xa90\\x88\\x98c\\x04\\x11@\\x1a\\xc6\\xb3\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01\\x05\\x02\\xab\\xab\\xab\\xafzkO\\xb8\\x9f\\xb8\\x03\\x0c\\xf1,h\\xfc\\xbb~o\\xcdZ0\\xce\\x8e\\xbaqz}\\xda\\xf6=\\xcb\\xa3\\x0e\\xbd\\xaa\\xea\\xc3\\xa3\\xa7a\\xa0\\xec\\x1ax!\\xf1\\xect=\\x87o\\xccx\\x84\\xb3\\xfc\\xdd{Q\\xd3G\\x8fj=X\\xfb\\x94\\x14\\xedZ\\x0f-h\\xd2h\\xce\\xa6\\x8f\\xcd\\xf9\\x97\\xe6F\\x8a\\xa7\\xdcK\\xa5Yt\\xfb\\xda\\xb0\\xc7r\\xa6\\x18\\xfb\\x81\\x8e\\x05\\xf1g\\x88e\\xf1|\\x17Z\\x83\\xdb@U\\xc1\\xd6\\xa9S\\x1a\\xb0\\x83U0*\\x03Wq\\xc7B\\xce\\x8e\\xae\\xba\\xb2\\x0b\\x0fG\\xe6\\x03\\x1d\\xd3\\xc4\\x8a\\x8c\\x19`P\\x1d\\x1au\\t\\xe1\\xe48p?\\x9b\\xf3\\x11WJ\\xa6\\xba%Z)\\xe9RY\\x1d\\xb5\\xee\\xa6\\r\\x0eA\\xf1\\xec\\x08t\\xed\\xd2\\x1e\\xbd\\xbc\\xc7b\\x18iV\\xbd\\xa9\\xd2i\\x88\\xe9H\\xe9I\\xecx\\x0e!\\xe8\\x96\\x95h\\xa4\\xd1Ht\\xd6\\x81\\xe8\\xea\\xea\\xf2g\\xb1\\xef\\xa3\\xd1\\xd1\\x9e\\xd4ut\\xec\\x8f\\xb8:X[\\x04\\x16t\\x14\\xd1CB\\x03S<Z\\x1ahR\\xb1\\xd3\\x1f\\x15\\xea\\xa8\\xd3B\\xa0Yuua5a\\x04(\\xe8h\\x1a\\x80~t\\xab\\xa7jv\\xa9z\\x97Muf\\xad,\\x8e\\xc0\\x9a\\xa9\\xa0\\xbc\\x83\\xadR\\xd6:\\x7f-4,\\xb3\\xc6=\\x14\\xb2BQG\\xfd\\xf1T\\rf\\x8e\\xacQ\\x92\\xc1\\xe9,\\xeb\\xdf\\x83\\xa9\\xee^%\\xe0\\x18\\xd1\\xa81N\\xc3W\\x93\\'\\xb6,\\x1dHh\\xabF\\xab%\\x95\\xf5\\x15t>/\\x88G\\x15pBC_\\xb6\\xd5\\x93)\\xa3\\xd0?>\\x01\\x96\\x19\\xadj\\x1dj\\xea\\x1f\\x95^ZU\\xe8\\xc2\\xbb\\x10\\xc2\\xbb\\x1e\\x01\\x96\\x01t\\x0f\\xf2\\xf9\\x1e\\x11\\x8dT(\\xd5\\xd9\\x02\\xae\\x94\\'\\x80j\\xf6\\xc3\\xe9z2u\\xea`U\\x8d\\x1e5x\\x00\\x16\\x1d\\x1e/\\x12\\xe9GP\\xc0\\xab\\xa3*\\x0cQ\\xd4\\x17\\xc5\\x90{\\x14\\xd4\\xba:\\xba\\xd0e\\xd0\\xaff\\xba$\\x10\\x07Q[\\xa3E\\x12\\xc5\\nV\\xc6\\xaf\\xf3v \\xd4:5)\\xa6\\xaf\\xca\\xa1\\x92\\x08\\xa7l^/P\\xcdK\\xc7@\\x1dT\\xf8\\xbdX\\xab\\x05\\xd6\\x8c3\\xda\\x8c\\xf0\\x91\\xa5\\xe9AP\\xd24!\\xd3\\xa9>\\xcdt\\x15c\\x89\\xa3\\xc9-K`\\xd5\\x96@z4\\x92\\xcf\\x00C4eO6K\\xab\\n`\\xbc\\x0b/\\x1a\\xbd\\x1dT\\xfc\\x8fj\\x9e\\xd5~r{C\\x89\\xf6\\xbc\\xabP\\xff\\x000\\xa69U\\xc8N)\\xe2\\xb3\\xd5\\xab\\xab\\x1a\\xbe\\rO\\x83\\x1d\\xa9\\xa8\\nd\\x16\\x03.\\x9a\\xe3\\xa9\\xa8}N\\xaf\\x8b\\x15t\\xa3$0\\x19tu\\xec\\x9fh\\xea\\xa1\\xc7\\xf3\\x16\\x84\\x82\\x92\\x9a2\\xd3\\xecbRW\\xaaS\\xc5U\\xc8\\xb2\\xea\\xea\\xc9\\xab\\xd5\\x8e\\xd5\\xa3\\xadZH},\\xbe\\xae\\xd5|~\\xe7\\x16~\\xe8\\xe2\\x9d\\x18\\x0c\\x01\\x96\\x8e\\x95|\\x1a\\xd8\\xa1\\x03B\\xafe<\\x13\\xed/\\x8d\\x1a\\x98\\xe2Od\\x9e\\xd5g^\\xdc\\x1eN\\xa1\\x87J\\xb0\\x1d\\x1dXP\\xec\\x9a\\xd4\\xeat\\xed^\\xc5\\x81\\xd3\\xdd#\\xb1\\xa3K\\x14e\\xa4Q\\xf9\\xbcR\\xd7\\x1b#\\xb7\\x9e+,\\x86\\x00\\xad\\x1e\\x0c\\xfbTK\\xe0\\xcdK\\x1d\\xb5 \\n>\\x0f\\x87b\\xc7\\x05V\\x80\\x12\\xe8\\xa0\\xc6\\xbfr\\x9fr\\xb8\\x9c\\x9dT\\xeaY\\xf6\\xabP\\xcdC \\xf6\\x08\\xd3&\\xa2\\xc2\\xc3S$\\xd4j\\xf80\\x1eE\\xd4\\xba\\x87P\\xc1z\\x93WZ0^\\xa1\\xea\\xc3:\\x04\\xbd;\\x06;\\xd1\\x94\\xbfe\\x85>\\xa7G\\xab\\xd5\\x96j\\xd2\\x03*\\xa3*k%\\xf9\\xea\\xf1i\\x18\\xb5v\\xad\\x1fQd>.\\x8f\\x83\\xab\\xd4\\xb4\\xf1t\\xd0p5$\\x07\\xa3\\xa3\\xf3\\xfb\\x99\\x06\\xa54\\xb0]^uy\\xbe!\\xd6\\x8d*,94\\x14\\x14u\\xa3\\xc9\\xf9=*\\xf8\\xb1\\xdb\"\\xd3\\xdc=^\\xaf\\x83\\x043F\\x18\\xfb\\xc7F\\x92\\x1e\\x85\\xe2\\xf1b\\xb5k\\xe2\\xc7\\x12Y\\xd4:\\x02\\xe8\\xc6\\x8e\\xbd\\xa8\\xc0\\x01\\xd1\\x97F>\\xe6\\x8e\\x94z:\\x07\\xab\\xd4\\x10\\xc0?x\\x864z:\\xbc\\xdd{,k\\xa3N-D\\xbc\\xb4K\\xa1/WC\\xdb\\x8b\\xa9\\xae\\xbd\\x8b\\xe9|\\x08\\xed\\xa8<FL\\x96;qc\\xef\\x9e\\xc2\\x8c\\x9e\\xdc\\x1eE\\xabWGD\\xb3\\xda\\xa2\\xb9\\xb2\\xa6T\\xfc\\xb1a\\xd7\\xb0$3\\xd8v\\x19=K#\\xb6\\x8c\\x11\\xdb\\x8fz\\xfd\\xdd\\x19,(>/Vx\\xb0\\xcd]k\\xd9C\\xbe/\\x80\\xfb\\x8aie\\x87\\xc1\\x8e\\xfa\\xbd_\\x1e\\xc3\\xef\\xd5\\x9e\\xd4\\xd0\\x92\\xea\\xa7^\\xd5\\xa3\\xe3\\xda\\xac\\xd5\\xf9\\x07N\\xd4z=]\\x1f\\x1f\\xb9_\\xbaHuu\\x0e\\xbf\\xcch\\xcfj\\x82\\xeaC\\xa9\\xed\\xafm\\x1e\\x9d\\xb8\\x9av\\xa7m\\x1e=\\xc0a\\xf0\\xfb\\x99:\\x87\\xa7\\xf3\\xa7\\x8d^Ut\\xed_\\xbdC\\xf7<\\xea\\xcfq\\xd8j\\xe9\\xab\\xab\\xe0Zu\\x1f\\xcdR\\x8e\\x8c\\xa4\\x06t\\xedF\\xa1\\xd8\\xff\\x001\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x01\\xed(\\xfd\\x84yJ~\\x81\\xfaQJZ\\xfa7\\xf4#\\xa8/\\x97\\xf3\\xed?\\x97e\\xb7\\xda\\x1fM}\\x11z\\x8d\\x0f\\x9e\\xd1\\xdcu\\xf4c\\xe3A\\xf5\\x8e\\xa5\\xfc\\x9f]\\x0f\\x97\\xd7\\xebG\\xcb&\\xd8\\x86A\\xbd)\\xf1\\xf4}5\\x1e4\\x1aH^\\x97\\xdb]\\xc7Rt\\x1a\\xcb\\xce\\x9c\\xf6_q\\xec\\xa4k--\\xbf\\xaa;\\n\\x7fd-}Q\\xf4Oo\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x01\\x03@\\x9bC$\\xbe-\\xf1\"\\xdd\\xf0\\xf8y%\\xfc\\xbf\\xc0\\x83\\xce\\xb5\\xd9\\xfet\\x9d\\x0b\\x16\\xe9\\xbeXRO-Q\\xfe\\xb4\\xf3\\xeb\\xa5\\xf0\\x1c`\\xff\\x00\\xb1\\xd0\\x9e\\x11/\\xba\\xb4\\'_\\xe8\\x8e\\x12\\xc5\\x97\\x94\\x9eXye\\xeb\\xfe\\x1d?4\\xb1\\xf2Aln:H\\x12\\x8e\\x08\\xe3J\\xd4\\xda|p\\x91\\xc2\\x054\\xca>C\\x01@\\xb2\\xfe\\xcf,y}\\x19D\\x89\\x8a)?y?\\xd1\\x89&@\\xfea\\xff\\x002n\\x9el[\\xcd\\xf9\\xd7\\x97\\x97\\x9a\\xa2\\x8e[:\\x14\\xbbb\\x01\\x950\\xaf\\xf5\\xd3\\xc0I\\xfb\\x83\\x11\\xf7\\x97\\x81/D\\x1b\\xd0\\x9a\\xf2\\x89\\xc4\\xbck\\xfe\\x12\\xd2S\\xa7\\x1ad\\x1fo\\xf9\\xdd\\xbfs1\\xb8S\\xb0\\x89\\x04\\x0f\\xb8\\xb2\\x07qE\\xd6\\x93\\xf1h\\xe7\\x9fWq>\\x8f\\xdcSO\\xf8\\x08\\xd3j?\\xafg\\xa3\\xfd\\xa7x\\xff\\x00cI\"\\xc6\\x9e\\xbaS\\'\\xc7\\x86\\x886\\x89ZZ\\r%\\xf4\\xd6S\\xae\\x132i\\x80\\xe3\\x96a&\\xccX\\x1eJeR\\x93\\x02$4\\xf5l~M\\x82\\x90\\x1et/\\xf9\\x92\\xee\\xe2\\x90x$\\xb7\\xcb\\x11r\\xd0\\x86\\\\04\\xcb\\x99[\\x88\\xff\\x00\\x81\\x97\\x8f\\xf3\\xa2>\\x1d\\x94\\xf0\\xf9\\xd7\\x96\\xe9&\\xf9k\\xfa\\xb3<R\\\\cKe\\xc8xh8\\xc0\\xe5\\xe1\\x03\\x90t\\xa4\\r\\x02Yi\\xcb\\xc8\\xfe\\xaf\\x96#Yxi\\xa0\\xc7\\x89r\\xdbO>\\x8f:\\x17\\x82\\xfa\\xd3\\xe7_,G=\\xa6\\x9ak\\xc0@\\xe6\\xd2\\x10\\x93\\xa8N\\x87X\\x8e\\xdak\\xfa<w\\x8f\\t}\\x13\\xfe\\xf8C\\xfe\\xf3\\xef\\x89\\xbd=;\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x06?\\x02\\xfe|\\xb2>\\xf8\\xecG\\xdc\\xe3\\xfc\\xe7\\x1e\\xdc?\\x9c\\xfbY\\xec\\x18uu\\x7f\\x17N\\xc3\\xfdE\\xaf\\xf3\\x1c~\\xe8\\xf8\\xfd\\xc0\\xe9\\xdc\\x7f\\xaa\\xce\\x9f\\xcc\\xd5\\x8e\\xe0\\xb1\\xf7x\\xf7\\x1fw\\x8f~?\\xccq\\xee;\\x1f\\xb8\\x19%\\xfc;%\\x9f\\x9b=\\xa9\\xf7\\x07\\xf3Z\\xff\\x004^\\xbd\\xcb\\x0c\\xbf\\x8f\\xdd\\xafaOS\\xd8}\\xce\\x1f\\xce\\xf1t\\xed\\xc7\\xf9\\x83\\xd8}\\xd3_\\xbb\\xab\\xd3\\xb6\\x9d\\xf5\\xfb\\xdc~\\xfd>\\xf5G\\x1a}\\xc4\\xfa\\xf6\\xa3\\'\\xb0g\\xb5\\x1d<\\xdf\\xd8\\xc7~\\x1d\\xeb_\\xe64\\xed\\xc5\\xf1\\x0f\\xcd\\xf0u\\x1fw\\xc9\\xe9\\xd8\\xf7\\xd1\\xd3\\xb1.\\xbd\\x83\\xaf\\xdc\\xe3\\xc7\\xbf\\x1f\\xe6\\xb8v\\xe2\\xe9\\xafm\\x7f\\x98\\xd3\\xee}\\xaf\\xedg\\xe4\\xcf`\\xc3/\\x87~\\x1d\\xb5t\\xfb\\xdc>\\xe7\\x17\\xc1\\xf0\\xef\\xe5\\xf3c\\xef\\x8e\\xc7\\xeeQ\\x8f\\x9b\\x0c\\xfc\\xd9c\\xbf\\x17Z\\xf6\\xf8=\\x07j}\\xfa\\xf64g\\xf9\\x90\\xc7n/\\xecc\\xb0\\xfe\\xcb\\xab\\x0c\\xfc{\\xf0\\xfb\\xba=\\x1e\\xbf\\xcc\\xea\\xf4=\\xb8j\\xf5\\x1av\\x1ft\\x0f\\xb8Xe\\xab\\xb0c\\xb1\\xd7\\xbf\\x17\\xc7\\x8fm\\x1f\\x07O\\xbd\\xc7\\xb7\\x1e\\xdeZ1\\xdbV*\\xf5\\xfe`\\x96;\\x83\\xdd?>\\xca\\xfb\\x95\\xefO\\xb8\\x07~\\x0f^\\xdeuz\\x97\\xa7\\x0e\\xe4S\\xef\\x1e\\xd5\\x1fr\\xbd\\xa8\\xcf\\xdc\\xe3\\xdb\\x83\\xd3\\xb5)O\\xbd\\xaf~,\\xea;j\\x1f\\x0e\\xf5\\xfe{\\x83\\xe1\\xf7t\\xef\\xea_\\x16\\x01|;\\x8a\\xff\\x001\\xa7\\xdd\\xd3\\xf9\\xcd\\x18\\xec~\\xf7\\x0e\\xc0\\xbe?w\\x8fz\\x7f\\x07\\xdc\\xe0\\xf5\\xd7\\xfdG\\xc3\\xee\\xe9\\xe5\\xd8W\\xf9\\xbf\\'\\xc7\\xb1z\\x8e\\xdc^\\xa1\\xe8\\xf8v\\x1f\\x7fN\\xda\\xf6/_\\xe68\\xf6\\x1d\\xf4\\xef\\xfd\\xdf\\xbd\\xeb\\xdb\\x8b\\xafn?\\xcdh\\xfe?\\xcf\\x0e\\xf5\\xfb\\xdc>\\xe1\\x7f\\x0e\\xde\\x7f\\xcfW\\xee\\xd3\\xf9\\x8e/\\x83\\xd5\\xf1\\xed\\xa7\\xfa\\x87\\xcd\\xf0\\xfb\\x95\\xfe\\x7f^\\xfc~\\xe7\\x07\\xc3\\xb5j\\xc7\\xf3:\\xf7\\xe1\\xda\\x9d\\xf5\\x1f\\xccq\\xfec\\x8fn?\\xcfk\\xd8=\\x1fP\\xfecO\\xbd_\\xb9\\xaf\\xde\\xe3\\xdb\\x87\\xf3z\\xf7\\xe3\\xfe\\xf9\\xfe\\x0fO\\xe7\\x87\\xdf=\\xeb\\xfc\\xdf\\x1e\\xc3\\xe3\\xfc\\xff\\x00\\xff\\xc4\\x003\\x10\\x01\\x00\\x03\\x00\\x02\\x02\\x02\\x02\\x02\\x03\\x01\\x01\\x00\\x00\\x02\\x0b\\x01\\x11\\x00!1AQaq\\x81\\x91\\xa1\\xb1\\xc1\\xf0\\xd1\\x10\\xe1\\xf1 0@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?!9\\xef\\xfc\\x18\\xe6\\xeb\\xb8\\xb3%A\\xcc\\xa7\\x05\\xe1\\xfe\\xac\\xc5\\xe6\\xc4\\x10Q\\xcd\\x8d\\xa3\\xee\\xe2E\\xe4{\\xae_u\\xc8\\xf3D\\x16\\x7fb\\xa8B\\xf5<oZ\\xd8\\xdd\\xfb?\\xddBO\\x9b\\xc3\\xac\\x1d\\x9d}\\xd5\\xb1\\xd7\\xf1F\\xbel\\xaba\\xfc\\xac\\x0e\\xea\\xc5\\x90\\xd2\\xc8\\xed\\x97\\xd5\\x95[\\x04\\xb1g\\xcd\\xf6\\xcb\\n\\x11\\xcf\\xd5\\x84\\xedG\\x1b\\xf9\\xae\\xa3\\xea\\x888\\x92\\xac\\xbeU\\x9f\\x15\" \\xbb\\x1f\\xe6\\xf3\\xc7\\xff\\x00o#4\\xc1F\\x8fj\\x03~[\\xe4\\x9c\\x91bJ\\xc0\\xa4\\xaf\\x86\\xd8\\xd9b\\xd7<\\x9e\\xd5^\\xb9\\xd5\\x986\\x0f\\xaa\\xe1\\x87\\xdd\\x8c\\xba\\xd6\\x1d/\\xc5 \\xf6\\xd7^?\\x16s\\x9b\\xd15\\xf0^,\\xe9N&~+\\xf2\\xaf\\x16Q\\xee\\xa1\\xf6\\xa4\\xcd)\\xd4\\x10\\xbf\\x17G\\x14\\x07\\x98\\xa1\\xee\\x85~\\xaal\\xc6YA\\xd3\\x9f6\"#\\xa2\\xc75\\xfc\\xcb,\\x99\\xe7l\\xc0\\x1fk\\x00\\x81\\xc5\\x05\\x0f\\x12\\xde\\x8d\\x96\\x1e&\\xf0\\x9b\\xf1\\xec\\xacs\\xef\\x9a\\xcc)\\x97\\xe5c\\x05\\x82\\xc7no~\\x0b!\\xc1\\x94\\x8b\\x08\\x83\\xbb\\xf8\\xa8\\x82+l\\x85:\\xa9\\x1d^z\\xbb\\xe2\\xc4&*\\x8el\\x1e\\xa2\\xc1;?U\\xf3\\x1d\\xdc\\xaf\\x9b5\\xcf\\x9f\\xf8\\xc9M\\x8a\\x0e]\\xb2\\x9c^\\xd6\\x02\\xc6\\xd9\\x06.\\x15<\\x17\\x11\\x1f-%LA?\\xcd\\x13\\xf4T\\xc4\\xea\\xee/\\xc9|GvtO5\\x07\\xb9Sc\\xdd\\xc1\\xf3\\x16\\x1f\\xbb\\x87\\xdd\\xe4\\x16, \\x8e\\x15\\xbd\\x1f\\x144\\xe7\\xca\\xb1[<O62\\xd2\\xf4\\x12\\xb2\\xee\\xe3\\xd2\\x80\\xef\\x0b\\xe5\\x16\\x04J\\xd5\\x84#\\x1eopd\\x8b\\x8c\\xfe?\\xe4\\xcd\\xbd\\xf4\\xf5q*\\xd8Qi{\\xa5\\x87\\xf8\\xa5\\xecS..\\xe8\\xf8\\xba\\xa1\\xf9\\x96\\xeb\\x99h\\x8c<\\xac\\xe2\\\\*\\xf1Y\\x10\\xf8\\xd5\\xff\\x00\\x9bh\\xa2\\xf6UDqy\\xa0\\x88O\\x8a!\\xe0\\xff\\x00W\\x82<M\\x01\\x18\\xf1\\xbf4\\xf1\\xee\\xe1\\xe6\\xb2\\xf9\\xab\\xbc\\x7f\\xc0\\x0bc\\xd8\\xae\\xbf\\xe2</\\xa3\\x94$\\xd7+&M\\x13\\xb0\\xc5\\x892l\\x7f\\x83\\xfeb\\xe9N+\\xc6\\x15\\xe2ll\\xfa\\xaf\\xc1\\x8e/a\\xc1\\x14\\xcf\\x1e\\\\\\xbd\\xfa\\x91\\x1eB/\\x0eqf\\xb5\\x814.\\xb1N\\xca\\x92\\x0f\\x8dj{T\\x91\\xf0\\x94\\xc8\\xbbj\\x97\\x1b\\x16~q\\xa7Nb\\xb0\\x81\\x17S.P1\\xb0]\\x19\\xc5~\\x7f\\xe1\\xe6\\xff\\x005\\xf3,F\\x07\\xaau\\xb0\\x086\\x9b\\x90\\xdd\\x86\\x0b\\x02y\\x94\\xc79G\\xc4\\xfdQ<\\x95\\x88\\xb8\\xba<W\\n_\\x97?U33\\x96\\xf0W\\xf15\\x04;[\\xf4\\x97\\x13\\xea\\xf33\\x89,\\x88x\\xab\\xc1\\xddD\\xe1\\x9c\\xd8\\x02\\xc6\\x9er\\x8e}O\\x14\\xc3\\xbd\\x16l\\xff\\x00\\x13L\\xbe\\x9f\\xba\\x98\\xb2\\xed\\xbd\\xd3\\x13\\'\\xdd\\xf8\\x1e\\xaa(I\\xe6o\\x91\\xd5^$v\\xf4\\xaer\\xae\\x18\\xdc\\xf4\\xbb\\xe2\\xe9\\xcf\\xfc\\xa1\\x91\\xdf\\xcd\\xd8\\x06\\x8ak\\xcdP<{\\xea\\x9d\\x8a\\x1e\\xa9\\xc19\\xccV\\xd4Q\\x8a\\x81f \\xe2\\xc3\\x1b\\xc1\\x16O-\\xea\\xac\\x8e&\\xba\\x8a\\xebsSR\\xf1X@l\\x99F\\x8a\\xc0y*r\\xbf\\x00W\\x1au\\x9a\\x90#\\x9a\\xfc\\x1e=Vd\\xbf\\xc5[\\xc7\\x99\\xbfm\\xe4{\\xcb\\xa9\\x80\\x8a\\x8eM\\xef\\x9e\\xeel\\x1bP\\xd77\\x93n/1b>\\xe8c-\\x10\\x8e)I\\xff\\x00b\\xc9\\r#\\xaa\\xfc\\xdc\\x1c>9\\xbf\\xe0S<YV\\xe8\\xe4\\xfc\\xd3\\xcb\\xda\\x0e\\xff\\x00\\xe0?\\xc0\\xebV\\x17\\x91a\\xe6\\xfc\\x03I]\\xa3\\xf7X\\x1d\\xf3H\\x85\\xd5\\xe4\\xf1i\\x88\\x877\\x95\\xb2\\xb6\\x04\\x8e\\xe9X\\xf8\\n\\xf5*\\xa9}Th\\t\\x86g\\xcd\\xc6\\x1f\\xba\\x98\\x1c*\\x17\\xa7_\\xea\\xa1\\xdb\\x91\\xb0\\xb1\\xaeVG2\\xbd%\\x98\\xe5pu\\'\\xab\\x07\\x92\\xb3\\x88>k\\x08\\xd6\\xd9\\xb9*\\x10\\xf3\\xdf\\x13r\\xd1T\\xc2o\\x88\\xee\\xb4~Otc\\xe7\\xe7\\x9a\\xbc\\x16v\\xe6\\xf1\\x95\\xd1\\xa6*0\\xads\\xaf\\xfc\\xb3~e\\xe2\\x16p\\x9e\\xear>\\xd5\\x008\\xa6\\x9f\\x92\\xc5\\xf3\\x8d\\x1c=\\xad\\x7f\\xaa\\xc4\\x0fK\\xf0jO\\x8f\\xaa\\x13\\x87=X\\xc9\\xc3G2\\x9f~\\xa8}SXc\\x0f7\\xb8\\xdf5\\x08\\x99\\xa4\\xf6v\\xa8\\xd8\\x9f\\xe6\\xb8T\\xb5\\x1c\\xd44\\x1fvw0Q\\x92\\x86/\\x01AQ\\x18O\\xcdC\\x0b\\x11\\xd4sE\\x86\\xa4y\\xa8\\x8cG\\x81\\xd5\\x82!d\\xbdT\\x96?VQ>)\\xe8kI^f\\x90\\x12j\\xb6d4K\\xff\\x008\\xa8\\xdd\\xf5g\\xaf\\x0c\\xd10tX\\x7fy\\xe1\\xa6V\\xef?T\\xf09B\\x14=\\xd7,\\xf1\\x0f\\xd5\\xc7\\xabj=\\xba\\xbb\\x127\\x97\\xe2\\x87\\xba\\x94\\xab\\xbdN\\xaf$\\xec\\xae\\xad\\xa1\\xec\\xa4\\x17\\xe6\\xfa\\xb1\\x1e\\xa8\\x1cl\\xc5t\\xc7\\xcd\\x0c\\x13\\x9e\\xaap\\x1dwG1\\xb3q4\\xd9\\xba7\\xcd1C<^\\xcfu\\x87\\x1c\\xd9\\xa4T\\xefr\\xe4\\xcf\\x11G\\xd9\\xa3\\xa3O\\xdd>?\\x166\\xf2\\xea\\x8coG\\x82\\xadg\\xc5\\xc2<&\\xc0\\xcd\\xd4\\xd8p\\xce?\\x16W8\\x88\\xfd\\xd8\\x89\\xd9D\\x97\\x81`\\x91\\xc8\\xd8&\\x1f\\xe0\\xa6\\xd9u~,\\x13\\xad\\xf3v\\x89\\xcb\\xc0\\xa2\\x7f\\xf2\\x8e\\x07c\\xf1xt+\\xe4\\x18_\\x10\\xf7A\\x19P\\xe8\\xb0p\\x14?\\x1f\\xf1\\xc1<v\\x16}\\x90\\xfa\\x9a\\x8b\\xc0\\x1e\\xcc\\xa3\\xca\\xa2\\xa4\\xce\\xdez\\xb8\\n\\x87\\xf7`\\x18\\x0f\\xc8l?\\xfc\\x15\\x82>\\\\\\xab\\xa7\\x17\\xa9\\xb2o\\xee\\xfdgW\\x13\\xeb\\xab3\\x13\\xb2\\xfe\\x92\\x9d\\xb6x\\xf8+\\x8b\\xee)\\x04r^\\xfe_\\xdd\\xea\\xf9\\xbc\\x98=\\xcd\\xe2FM\\xc8\\xb1\\xcd~\\x16H\\xd7=Y\\x1e?5\\x92\\xc9z1a\\x99\\xca\\x94\\x1a\\xfdQ\\x00\\xf2\\xaax\\xd7h\\xe5ed\\xd1\\'\\xb6\\x93\\x9f\\xdds\\xbf\\xee\\xf8\\x1f\\xba<6=\\x8f\\xab\\xe0\\xcc\\xee\\x8a\\x0e\\xfd{\\xbc\\x12%J\\x9d\\x0f\\xd4X0\\x8d\\xf3d\\xfd8\\xae4\\x1e\\x92\\xf8\\x9f\\x8a\\xb4&\\x0b\\xa9}\\xd0,\\xd7\\xab$\\x95\\x89\\xf2\\xdd\\xc6\\xf7\\xbc\\xd9A\\xfb\\xbd\\xf0\\xa7(to\\x1e\\xd9\\xff\\x00\\xca%\\x11\\xc9\\x17A\\xe3\\xaf\\xf8BQ\\xc6w\\x1fuhC\\\\\\xe7\\x8a\\x18\\xc3l1seq\\x07\\xaaN9\\x8f4G\\x0e\\xaa\\xfa\\x7f7\\x8a,\\xe5\\xf1\\xcdi\\x07\\xcdG\\xbcP\\x1b=\\xd7S\\xdb\\xd5\\xc0\\x19}X\\xf0\\x19\\xf8\\xb9\\x03\\xb3\\xea\\xc1\\x1e>\\xb9\\xb3\\xb1-sV\\x13(,\\x97\\xbc\\x9a\\xb7\\xf6.k?\\xf1N\\xe6\\x9c\\xcf\\x17\\xd1\\xd5\\xd1x*,\\xfa\\xb2\\x1f\\xb2\\xc2\\x84\\xbct\\x8e\\x05\\xcb\\x11\\xf7\\xff\\x00\\x01,\\x9c\\x9b\\x1a\\tb\\xff\\x00\\xcaC\\x82\\xcew\\xe1p\\xf2M@\\xa7\\x95\\x17\\x89G\\x90%\\xa8\\x92\\xc3k\\x1eD\\xf7R?\\xea\\xc9\\x13\\xfb\\xbb\\xc1I\\x852\\x80\\xe6\\xe0)\\x9e\\xaag\\x97\\xed\\xa0N\\xa6\\xe3A\\xad\\xf3|\\xe5}_\\x98\\xbcI\\x0cM\\x8a]U\\'\\xe9Lu\\xe0\\xb1\\xd7qY\\xc8\\xd2*\\x87<\\xdd\\x13\\xcb\\xe6\\xe6\\x18\\xd8\\'\\xd53\\xdd\\x83Z\\xb6a\\x9a\\xc79\\xabX\\xac\\x88\\x94>/B\\xe0\\xfeo:\\xf8\\xbc\\x19B%mG\\xc4\\x8d3\\xe6\\xc9\\x04Z\\x1c\\xdcGU\\xc93\\x9c\\xdc\\x03\\xe0W\\xc86\\xa3\\xaeh\\xe0<m\\x11\\xe7\\xab\\xe11\\xea\\xcc\\xe6_Tq9v>\\xf6\\xcc\\x00,\\x02,)\\xf2\\xfb\\xb2\\x98\\x12~l\\xb1\\xb7\\xff\\x00\\x9a87\\xeb9\\xb9\\x08\\xfc\\xdef^\\x11\\xbe#\\x8a\\x8f\\x9aqM>+b\\xb34\\x93\\xb3\\xb7X\\x0f\\xe2\\xe47\\xdd\\xc8\\xfd\\xdcz\\x9aE\\x86\\xeb3w >,\\xa9\\xee\\xc4\\xc5\\x80<\\xbdsf!\\x1dY\\xa4\\x1a\\x83A\\x15\\x99\\x0f\\x1bE)\\xb2\\x9e\\xcd\\x15Ph<\\x82l\\x91\\xfc\\xb7\\x95u\\xdfk!\\x9dP\\x99\\xdf\\x8aj\\x87\\xd5\\x90\\xe8}\\xd2(\\xe46\\xcck\\x9cm\\x0e\\r,C\\xb2\\xc2C\\xba\\xf0\\xf7\\xdd.\\xe4f48z\\xdb\\xeb\\xd5\\x19\\x11\\xf3u\\x95\\x19\\xb1[\\x94\\xe2\\x827Y\\xbe\\xaa\\xe1\\xf1d\\xc4\\x9b\\xe4\\xa4\\x1c\\x87\\xd7v!q\\x95I\\x07\\xb6\\x83\\xf9\\xb1\\xbf%f#\\x99\\xc6\\xc3GJ\\xb5\\n\\x1a\\xa6/c\\xcd\\x1c!L\\xd9\\xaa\\x90S\\x0e\\xdb\\x04\\xa8|X\\xe1$]\\xf2\\xc0\\xec\\xb0\\xae\\x0f\\xab\\x07\\xee\\xaa\\n\\x88U\\xe3\\xd1e\\xe4z\\xbe\\x02K!\\x1e\\xeb\\x10K\\xe6\\xb0\\xee\\x9b\\x93W\\x8b\\xc58\\xff\\x00\\x9cU\\xa8wu\\xff\\x00J\\xbf?\\x12U\\xf0\\xacL\\x8a`\\x92z\\xa3\\x18\\xe5rHv\\xb8\\xc1U<\\x9b\\x01\\x87\\x96\\x89&\\x11\\x8b)&K/\\x08nq\\xd3*h\\x93\\xf6\\xb0e(\\x07\\x16d\\x84X\\x08\\xcaA\\xd7\\x8f\\x17\\xc2?\\x85O,\\x1e\\r\\xb2@\\x87\\xab\\x89\\xf8\\xcd\\xa9\\xbd\\xd5\\xeb\\xff\\x00\\xb6S#(\\xd9\\xe7\\xee\\xe6 \\x8f\\xf3\\x8a\\xcb\\x9c\\xbf\\x8aC+\\x94\\x0e\\xd5\\xf0O\\x9a|^Q\\xff\\x00\\'\\xfe3R\\x85H\\xed\\xed\\x0c\\xbe,\\x04\\xa2|\\xfcVD\\x0cwi\\x03\\xb9\\xa0.v\\xca|\\x95k6i\\x11\\xd4\\xd1\\x8d\\xa5\\xc9<Y\\x8f\\xc5\\xd3\\xbd\\xbc\\x88\\xdf\\x9a\\xa7*y\\xd8Y\\xceSf\\xbcE\\x80\\x9b\\x0f\\xf4\\xf3T:}7\\xcd\\x0e{\\xca\\x9f1\\xf5H=^\\x16\\x0f\\x8b\\x8e\\xa3\\xe2\\x9a\\xc9\\x1f\\xd58 >\\xeaI\\xdc\\x9f\\x15|\\x16\\x0c@?\\xb5@\\x0b\\x8fq@\\x19D\\xd8\\xac\\xbd\\x7f\\xc9\\xde+R{\\xbb\\x16\\nwi.y\\xa7\\x8dQ9\\xc5$qX\\xeah/\\xc8\\xb1\\x8eo\\xb1>\\xe8b4\\xa1\\xc2<aS\\xc8\\x9b#?v\\'\\xc5l\\xcd%U W\\xdb\\xee\\x84D\\xce\\xc9$\\x11Dv\\x9a\\x10\\xc7\\xc2o)\\xbd\\xfc\\xa9\\xc0\\x04\\xaf\\x03\\xf3O{\\x80\\x9dY\\xe0\\xf0\\xd3\\x8e\\x1b&#\\xff\\x00(\\\\~)\\x00Xl\\x0f\\x7f\\xf1j\\xf3\\x14N\\xcf\\xc5\\x98\\xf36\\x00e-\\x1fO\\x92\\xe3\\xa7\\x8b\\'\\xcb\\xdd\\xd9\\xd3\\xe3\\x8a \\x1d\\xa7\\x94CP\\xef\\x8a&b\\x98\\xd0\\x1b\\x1c\\xf1f\\xc9;9v\\x8f\\xcdA\\xe6\\xf9M\\xc7t\\xc10\\xddOo\\xba\\xf1\\xb95\\x02\",\\x9ei\\xe2\\xef\\x0f\\xe2\\xf20\\ng\\xa5\\x02xl\\xb7\\xaa\\x91\\xd4~\\xec\\x1c\\xca\\xfb/\\x8a\\xec\\x9fZY\\x07\\x819\\x9aPb,\\xf9\\xbf+;__\\xf1|\\x12T\\xe5QHV\\xe1\\x81\\xe2\\xa9(\\xc7\\xf9\\xb2;\\xbawZ\\xc3\\xc5\\x97\\x93\\xcf\\x05\\xfck\\x97\\x00\\x94?\\xc8\\xbcN\\xd8vk]\\x9c\\xa2\\xc6*4\\x96\\x8f\\x9a\\x88?\\xbaH\\x9b4\\x0c\\xb3\\xdf\\x14\\x0e.\\xf5T\\xb9\\xd5\\x19\\xcf4;-\\x96\\xe9\\xb9\\xccAu\\xfe\\xd4\\x07\\xbf\\x9ayg\\xc5x0s\\xcd\\x1f\\x8d9\\xb8\\xf7\\xfa\\xba\\xfc\\xd7\\x8ay\\xff\\x00\\x9b\\x0cY\\x9ekD\\xf1\\x166~\\x95\\xe8\\x1f\\xe2\\xa3\\xa5v\\x91\\xf5D\\xbd\\x96NDk7#d\\xdc\\xd8ff\\x9d\\x1b75\\xf2\\xa3jQ\\xce\\xd5\\x92\\x82\\xc8\\xce\\xeb\\x00V\\xe8\\x93P\\xe5\\xd5R \\xa4u\\xcd\\xd8\\xf0\\xb8\\xc7K\\xc7\\x82\\xc8\\x8e\\xde\\xf9\\x7f\\x17\\x8d[(\\x0e\\x16\\x0c\\xff\\x00v@\\x11`\\xf8\\xb3g\\xcd\\xe2\\xcfW/\\xc5c\\xe6\\xcc\\xba\\xb9\\xe3\\xf7g\\xff\\x00\\x14\\xd8\\x81\\xeb\\x9b\\xa3\\xf8\\x8a\\xba\\xf4\\xf3g#\\x1fe\\x8e\\xd7\\x05b\\xca\\t?\\xe2\\x1f\\xae\\xae\\xc0\\xd8uY?\\xe7\\x9b\\x1b\\xe6\\xa9\\xee)<4\\xb2\\x12Q\\xe2\\x89\\x8f-&^\\xa9;7\\xe0\\xac\\x03>l\\x9d\\xb1w\\xdf\\xe6\\xec\\xc7\\x11g6\\xe9\\xff\\x000\\xba\\xdf\\xbb(\\xd5\\xab5\\'\\xce7\\x9f[O.\\xe8x9\\xf3u\\xae~\\x166J\\xd2/\\x19W4\\xbc\\xff\\x00\\xc8\\xdb\\x0fw|\\xdd,\\x11\\x9cVHM\\xd4?\\xf0\\xe5\\xa6_\\x0b\\xd5\\x9f6\\x88\\xe5ck\\x9cEp<\\xd7\\x87\\xb1V\\x9e\\xeae\\x1c\\xfb\\xaeX\\xdb\\xc6\\xd9\\xee\\xad\\x1b3uBW\\xd9Y\\xc2\\xa0\"\\x89g\\xaaO\\x9e\\xeb\\x1e\\xf9\\xb0O\\xaaB\\xf1\\xdf\\xfcxn\\xb7\\xdf\\xba\\xaf\\xee\\x9a6d\\xa6\\x9b\\x7f\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x10>\\xfd\\xf2\\x95\\xf9%\\xe6\\x95zI\\xff\\x00[\\x1dW\\x96\\xe5\\x9a\\xdc\\x8b\\x11\\xe2\\xfb\\xac\\xaa!\\xc9-\\xc9\\xa2\\xaaUlT\\xa5\\x17D#d\\xfc\\x9d\\xe0\\x8e\\x9eQ3\\x1e\\xac\\x99\\x1b\\xc1\\x12\\xeaU\\x8e[\\x0f\\x17\\x80:\\x82K\\xec\\x84\\xe5\\xe1\\xde\\xea\\xca\\xdc\\xb4;\\x81\\xcd/8\\xf8\\xe8\\x00\\xba\\x89\\xcc\\xaf.\\xed\\\\\\x1ec\\xb69\\xd3\\x0b\\x08\\x8e0\\xc5\\xdf\\xb5\\xban\\xb9\\xb0sw\\x08\\xe4\\xa2\\x89vvCJ&\\x86Uy8\\xd4T\\xc0\\x83\\x9f\\x83\\xa9\\xb0.&\\x04i\\xce\"\\xa0\\xba8R\\xdb>\\xfcjAXP\\x1b3\\xd5\\xe3\\xf68k\\x12\\x82p\\xb2\\xd5\\t\\x91M@\\xa7\\x9dZ\\xa2\\xbe|;\\x07\\xae\\x91BBl\\xaf1[<\\xb2\\x9ci\\xfc\\xe7\\x89\\xe6F\\xd7H7oS&\\x9c\\xc3\\'\\xbb\\xa9\\xa3\\xdf\\'\\xca\\xc3\\xe3\\x08\\xee\\xcd\\xf3)\\xdc\\x0c$\\xdd\\x18\\xef\\xff\\xc4\\x003\\x11\\x01\\x01\\x01\\x00\\x03\\x00\\x01\\x02\\x05\\x05\\x01\\x01\\x00\\x01\\x01\\t\\x01\\x00\\x11!1\\x10AQa q\\xf0\\x91\\x81\\xa1\\xb1\\xd1\\xc1\\xe1\\xf10@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x10\\xf4\\xfaOS\\xe2Y\\x1c\\xe3=Y\\xf3\\x1a\\xfeV\\xe1l\\xf9\\xcd\\xd4Y\\xe6\\xdb\\x11\\xd1t\\xbe\\x8b:nx\\xbb\\x8f\\xa7\\xc6\\xcf\\r\\x9c\\xd9\\x96\\\\c\\x889\\xb3M\\xf37\\xe7\\xf0\\x8ev\\xc4\\x8d\\xcf\\xac\\x0c\\xcb\\x00\\x0f\\xa5\\xc0\\xeb\\xe3\\x1c\\xf7\\xf5\\x90\\xc9^-9\\xb8o\\x10\\xbe\\'\\x9b\\xefyL\\x9ey\\x90{\\x87\\x1b\\x1b\\xa9-\\xf0D\\xcc\\xe6\\x0e3\\xebc\\x9f\\xa2\\xfe.\\xbe%\\x8f\\xba\\xe5\\xd4\\x17\\xd2|I\\xd5\\xa0\\xad\\xben\\xb8\\xferA\\xcf\\xcc\\xae\\xe3\\xdf\\x80]\\xdem\\xe7!\\xa9\\xfb\\xc7\\xe7w%\\x99c\\xe2\\xc8\\xb8\\xb2\\xf9\\x9f\\x01\\xe7\\xcc\\xf1:/\\x90\\xf9\\xb8si\\xa1|\\xceos\\xa7Q\\xf5\\xbb\\xd2\\xeb\\xe6\\xe9\\xee\\xfc\\xec\\xf7.\\xdf\\x97\\x87yu\\x8e\\xa6\\xff\\x00L]1\\xday[\\xe2\\x0e\\xb5\\x9c\\xf3\\xedc\\xf5\\x9e\\xfc\\xf8\\x89\\xf0o\\xe4\\xbb\\x04\\xf4\\x0b\\xe4H\\x84K\\x90}\\xa36^\\xee\\x961\\xebd\\xe2\\xd0\\x84o\\xef\\xe9g/7\\x8b\\x8a[\\xb7V=lpXxg\\x87\\x160\\xfc\\xdb\\xbf\\x0c\\x8e\\xd8\\x04\\xce\\xa1\\xea\\xd3\\xf0,\\xe3\\xc3\\xef\\xd4\\x18\\x07Q\\xdcl\\xb2\\x19\\xab\\x9f\\xac|\\xb5\\xbf\\xbd\\xb3\\xd5\\xdb\\xc3\\xaf\\xc0\\xb9\\xf1\\xcc\\x83cO\\x10\\xcb|\\x1c[\\x96\\xbe\\xb7.\\xa1\\xcf\\x9bg\\xec@\\xbfH\\x9f^\\xfc\\xc8\\x18\\xe4g\\xa3H\\x03\\xf7\\xf0\\xe1\\xe2#\\xaf\\x1e#\\xd7\\xdc\\x82??7}\\x1d\\xb7n\\xaf\\x9b\\xae\\xfc\\xcf7\\xd7\\xaf\\x0f\\xc5fbC|\\x1dx\\xf5\\xe7\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x10\\x0ed\\xfa\\x1b\\x07\\xe1\\x90w.M\\xac~\\xf7..\\x98\\xfa\\xc1\\xf6\\x00G\\xe79\\xb0\\xed\\xfag\\xe4t\\x7fFCvq\\xfe\\x91,\".\\x1f1\\xd4\\x99\\x8co\\xcd\\x8ff\\xb9\\xf8\\x97\\xa3\\x87\\xed\\x03\\xc6[\\xdf\\x898\\xbb#\\xb1\\xf1v\\x044\\xfd~b\\xe2\\x1f_\\xdet!\\xd9\\xc4\\x0b\\x01\\x9d\\xe9\\xf4e\\xfa\\x0b\\xc3\\x96\\xdf\\x83\\xb2\\x03\\xce\\xf6\\xb3\\xea\\xdd\\xb7\\x8ed\\xc6\\xb8\\x85l-\\xe3g\\x8f\\xb7\\x13\\xcf\\xcb\\xf8!\\xcf\\x9e\\'\\xa2\\xed\\xac\\xb4\\xa7x$\\xdat2V\\xee\\xeb\\xcf\\xed\\rp\\xfd\\'\\xfb\\x93s>]\\xff\\x00\\x91\\x88\\xbes\\x19bga\\x87\\xde_\\x98)\\xfcq\\x00\\x01\\x99\\xab\\xf9\\xda<\\xf3\\xb0\\x07\\xda\\x1c\\x80o\\xccw!y\\xb7\\xabu9\\xfe/\\x94\\xc0\\x96r\\x1fYz<\\xb2kK\\xe4}mp?\\x98\\x1d\\xfb\\xff\\x00\\x17@~o\\xe6f\\x03\\xa3\\x8b\\x85\\x07\\xcd\\xcb`\\xbb)\\x93\\x91\\xff\\x000\\'\\xbe\\x8f\\x88\\xd0\\x99p9\\xdf\\xday\\x08N\\x9e#\\x05V}-^\\xa3~[s\\xc4\\x88Ey\\xbb cG\\x93\\xe6\\x10\\xe5\\xdf\\xa4\\xa8\\xf1\\xf5\\xe6@\\xee\\xc3\\x87>m3\\x90:\\\\@p-\\x96\\xcf\\xc0\\x9f\\xb5\\xb2\\x9c\\xec\\xbb/8\\xfe\\xcc\\xa8\\xc7\\x87\\xc7\\xd2`\\xee\\xff\\x00kN\\xf7\\x8f\\xde$+\\x8f\\xac\\xca\\x0f\\xcc\\x1a]\\xb8\\xfa\\xc7\\\\=\\xfc\\xee\\xdcb\\xe9\\x9f=F\\x9d\\xde\\xbewXf\"\\x01\\'\\xe1\\xe6\\x1d\\x9a\\xf4~W\\x07>F\\xda\\x0f<g\\x04\\xa1\\xa7\\x1d\\xfd\\xcbH*+\\xbf\\xcc[\\xcb\\xe1\\xf9{\\xb7+\\x87\\x1dB`\\xa5\\xa7\\x00\\xe3\\xea\\xc0\\xd8\\x10}\\xe0\\xa6-\\x97!\\xe2Y\\xc5dCA\\xb7\"g\\x1fVGJ/\\xc7\\xd6(.\\xb7~y\\x8c\\x0b\\rL\\x99\\xe9\\xb0\\xa8\\xcf\\xe6\\xe7\\x0f\\x9e\\x8b,\\xbc\\xe7\\x11\\x9b\\xf7\\xc9\\x07\\xe1\\xa7pq\\xd4r\\x81\\x9c\\xf2\\xc0\\xe0\\xc76\\xe3\\xcd\\xfa\\x04+Q\\x1d\\xfaH8~>\\x90\\x9b\\x8e$ \\xe7\\x9f\\xdb,f\\x81\\xb9f\\x0e>9 \\xd3\\x99\\xef;\\x83C\\xbb\\x80.\\x9cs9\\xbe\\xdb|\\xd9\\xcc|NL\\xb5\\xfc\\xaf\\xec\\xcc@\\xf9~\\x91\\x9cg\\x82p\\x08\\xe8\\xf6\\x16\\t\\x8a\\xe8q\\xf7>ehi\\x0fD3\\xf3\\x8eN\\x9e\\xe0\\xaa\\xa7\\xc4\\x8fx[A\\x9cwl\\xc04\\x97\\x8a\\x9d\\xa7\\xbf\\xe2,>6\\x0c\\xf8\\xb6zQ\\x86\\x9e:\\xf9\\xb4*p\\x9c\\x1b5\\xb8c\\xfd$\\xc715\\x18\\xfd\\x0c\\xc7l>\\x9b\\x01n\\xf1<~6\\xe9\\xdeI\\xbc\\xe7?{P~\\xa5\\xc91\\xd3\\xe6tL\\xeb\\xaf\\x01o\\xc2@\\x03L\\xee\\xd0\\x1c\\xdc[\\xc7Y\\xe3\\x8d\\xbb\\x99rs\\xf4O\\x0f\\\\<c\\xce\\xc0H\\xbd\\xf4\\xd9\\x87\\x8f\\xd1i\\xf07\\xa8>\\xbd~\\xf01\\xfa\\xc8//,\\xeaf\\xe7\\x13\\xc7\\x9eO\\xafS\\xd3\\x11\\xe3\\xab\\x0b\\xf3\\xd5\\x8f\\xd6\\xee\\xf7*\\x07H\\xd0\\x1d?1\\x01\\x86?9\\xe3\\xd4:o\\xf1?\\x1e{\\xfc\\xa1\\xf0\\x06\\t(7\\x8e\\t/\\x1a\\xc0\\x0e{\\x9e|\\x0f\\xce7\\x9d\\\\\\x07\\xde\\x1c\\xf3\\xdd\\xf0=_\\xd0?i\\xed&}7!Ct\\xfe\\xd0\\x01\\x1f\\xefo\\xacL\\x83\\xe9g\\xd6g\\x97#\\x80\\xcf\\xf1\\x1f\\x04\\x0f\\xe6\\x1eh9\\xfc\\xf3a\\xc1\\xc5\\xc9\\xa7Rs\\x96\\xc3\\x8d\\x8e\\xb9\\x94\\xf9s\\xeb\\xb0u\\xe7\\xf2\\x90\\x1d\\xc8;\\xc7\\x84s\\xae>\\xd2\\x0e|g\\xd6\\xc7\\xe7\\xe2\\t\\x99#\\xea\\x97\\xaf\\xc21\\x9cF\\xe7<]\\xc4\\xf3\\x9c\\x9e=\\x0f\\xdb\\xc8\\xcf\\x0b\\xf3\\xb8\\xc0q\\xf9@\\'?E\\xf1\\xfc\\xc4\\xd9\\xd4\\x86\\xf5l\\x80\\xd7xo\\xf3\\x14\\xe8\\xb3\\xb9\\xe8\\x9e\\xaf\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?\\x10\\xc4f8R/\\x08M\\x98\"\\xa2<8\\x8e\\xe6\\xc5\\x02\\x91\\xc8\\x94(\\x0f\\xfa\\xa92\\x91\\xf3\\x17d\\x14\\x9a\\xf3\\x03\\x13\\x1c&>j\\x04O\\xa8\\xf5x\\xa7\\x0c<\\xbc\\xfe(\\x03\\x08\\t\\xa6\\xad&\\n\\xb00\\x04\\xd4aC3\\xc6}U\\xde8\\x9b&\\x19 9\\x9aH\\t\\x10>\\xc9\\xbd\\x83\\x00\\xe37\\xddsy\\xdd\\xb2\\x1c\\x95\\xba\\xe3\\xd5\\x17\\xda\\x00z\\xf73UR@\\xac\\xd0\\xbd\\x1e+\\x0c\\x08\\x11\\xe5c\\xca\\xee(\\x83\\x01\\t}q\\xfd\\xd1\\xe5\\t2\\x11\\xc8\\xfb\\xa6\\xaf\\xfb\\x0f\\xf4\\xa8\\xc3\\ne\\xdf\\r\"\\xa6\\x98\\x9c;+\\x01\\x9e\\xf7b\\x81\\x04I\\xbd:\\xaa\\x02\\x06\\xcb\\x15!\\x01\\xcf\\x05\\x8f\\x1e\\xfe\\xeb\\xc0\\n\\xc5\\xe8\\x88\\x07\\x8e.\\x1cP\\xc9\\xc9\\xdc\\xf3@6~\\xa2\\xac\\x07f\\xb0?\\x85\\x12\\x84\\'\\xc43\\\\\\x8eM\\x93\\x0ed\\xe1\\xdf\\xfa\\xb9$\\x18\\xd9\\xd6\\xc1\\x91\\x0e\\xa7\\xff\\x00\\x15\\xd0\\x02{\\x17\\xf0\\x81\\x13\\xfb\\xb1\\x0c\\xe4\\xdc\\xdb\\xa8A\\xf1ccN\\xef\\x1c{\\x8e\\xca\\xcc-\\xff\\x00\\x13@\"\\'&c\\x97\\xdfU:Tx\\xb0\\x07\\x1a\\xcf\\xbd\\xa01{\\x17\\xdd\\xea\\x03)\\xfe\\xe8\\xce\\'C\\xa4\\n\\x14G\\'\\x15\\xa4d3=\\x9f\\xf8\\xdd\\x18\\x01I\\x18\\xbe\\xbd\\x94pDN\\xe6\\x7f\\xed)\\x18\\xb4N\\xa7\\x9d\\xa4d\\x88\\xe1\\xeb\\xb4\\x1f\\x16Qp\\x08\\x97\\xccS\\xd2\\xa1\\xfb\\xbb\\xd4x\\xf6\\xfb\\xa5\\xbb\\xc5\\xb9\\x12\\xd2\\x1c\\x91\\xdcRb9=\\xf1\\x15\\x0b2\\x86M\\\\\\xcb\\x8e\\x14\\xd9\\t!9\\x0c\\xcf\\xee\\xe9\\x82^#\\xc7\\x96\\xb8\\x80\\x88|\\xd0d\\xa8\\x8d\\x98\\xb3\\x19\\xd5\\x91:\\x19\\xc7\\xf7dE\\x97\\xe2b\\x81\\x94\\xe4\\xf7E}\\xae\\x93\\x8b\\xc5\\x00s\\xea(\\xd0%cJR*V \\xe2\\x12\\xcc\\xbe\\xd0\\x9e\\x9f5r\\x00\\x1ec\\n\\xf0\\x08\\x86\\xae\\xef\\xa8\\xa5\\x91<\\x7f?5\\x83\\x9e\\xeaf\\x11\\x0f\\xf5Q\\x83\\x00\\x97\\xc4\\xf8\\xa8\\xfc8\\x005\\xf7d%!3\\xba\\xb0\\xcc\\'\\x1e\\xa2\\x8b\\x1a\\xba$\\xda\\xa7\\x91\\x03y\\x1d\\xff\\x00\\xed\\x00\\xa4\\x85\\x918\\xf7Wd\\x803M\\xb1\\x859\\x1dGWY\\x06\\x15\\x9e.\\x98x\\x02y\\xe6\\xf7\\x0c\\xeb{7\\xf5[\\x19JtC\\x1e\\xbd_!\\x10\\xc7\\x8a\\xeb\\x97\\xaa\\x9c\\xf3\\xfc\\xd5t\\x89@<R\\xe2\\xef\\xf1\\xee\\xa8G\\xf1E\\xfe(\\x02\\x18\\x14\\xe0\\xd7\\xdbY\\xd2c{\\xa1K\\x19\\x0cOm1@jf\\xc9\\xc2\\xe4\\xf1\\xe7\\x8ez\\xa4\\xc3:\\xf7tYI\\xe9\\xdb\\x00\\'\\x18E3\\xf7\\x7f\\xf2\\xfb]\\xe5\\xab5\\x93\\x1d\\x9c|\\xd0DL\\xccS\\xc5\\xeb\\x12v<\\xfc\\xd3\\x08JW\\x90x\\x9c\\xfcM$\\x90\\'\\x14\\x16{98\\x81TA9\\x1b\\xee\\xbe\\x80\\xf9\\xeb\\xf5s\\'\\'\\x94\\xd6\\xee\\x81\\xc7\\x9a@q?[\\xfd\\xd9\\xc1<\\xb1\\xf1V(5\\x06%\\x8f\\x13H\\x062x\\xa1\\xd9\\xd7_Ud\\xa1\\x1a\\x8f\\x191\\xf3N\\x824NBh\\r0\\x11\\xf3b\\x98\\x01p\\xce\\n\\xa50Fy\\xd4e\\x84&o\\xa1x\\x90,g\\x14\\xf1\\xf8\\xaa\\x0b\\x94\\x98\\xb1\\xe1\"\\xa4hkgz\\xe0\\xa1\\x84\\xd1\\x05\\x8c\\x16\\x95x\\x14>\\xc6)\\xca#o\\xc4\\xf7F\\xa4\\xd1%\\xe2<\\xd6\\x05M\\x93\\x99\\x8d\\x9c\\x11\\xdbMj\\xd4\\x05/:\\x97\\x180\\x80\\xf3D\\xac@\\xc3\\x0b\\x1e}W\\xcb\\xe4\\x99y\\xf8\\xda<\\\\\\xb9\\x9ex\\xbe\\xf7;\\xa7\\xbeb\\xce\\x00\\x1eLo\\x14:V\\xd9\\x8f\\xdc\\xf5@\\xe5\\xc9\\xd6 \\xfc\\xd5E\\x0f\\x8cr\\xb2\\xc8\\x9fi~\\xee*+\\xa8\\x88\\xaau\\x83b\\x7f\\x86\\xa2\\x00g\\xe6/H\\x9dS\\x9fu\\x89\\x13KH\\xce\\xec\\x00\\xf0\\xc6\\x19\\xfdP\\xc9 %\\x90\\x949\\xdf\\r\\xd4\\xeb\\xb6$h\\xd1\\xccO\\xdd\\x184\\x98\\xde\\xc0\\xf34\\xb8\\x0c\\x97?\\xab\\x1c^C\\xc2\\x99\\xb8\":\\x0c\\xb1+-\\x98~(T\\x8e\\xf5\\xdf\\xccXL\\x1d+XQ#\\xa1\\xe0\\x0cn\\x01(\\xc1\\xdb>b\\xe0\\x04\\x93\\xe8\\x9f}]\\x9aH\\x1f\\xecjI\\x8d3\\xa6\\x1d\\xb3\\xc0$\\x0f\\x0e?\\xaa4J\\x03\\x1f\\x07\\x8a\\x07\"_\\xa3\\x8b\\xdc\\x05\\x93\\xa6\\xd5\\x0e\\xf3\\x0e\"*&X\\xf4\\x7f\\xf1L\\x02=\\x9e?\\xbb\\x08\\x01\\x97\\xcdpP\\x82\\xd0\\x85\\xb2\\xc5\\xeac\\xaf\\x16j\\xeb\\xe7\\x0f\\xab0\\xc3\\xe2\\xad\\x95\\xde\\xe9\\x00y\\xe2\\xb3\\x80\\x8f\\x07\\'\\xaa\\xa6x&\\xe9\\x11\\xbfqV\\xe0\\xbc\\x87\\xf3d\\xe0\\x9c\\xa22>i@\\x19\\x8d`\\xa4r\\x83\\x9e\\x7f\\x0f\\x8aq\\xd8\\xfc}\\xd6\\x14\\xd6\\x93f\\x16\\x11:V\\x8f\\xcd\\x82\\xb0\\xe9\\x9be\\x06\\x9b\\xe6\\xc39\\xc5\\xd2\\xa3\\x90\\x86\\t%\\x0f\\\\WS{\\x8e\\xdc\\xf3a\\xa3\\x05d\\xc2\\xc4\\x95\\xe7\\xea\\x9b\\'%\\xf4*k<\\x14\\xf3\\xf7gP\\xe1w\\xe3*D\\x03(O\\x9a!\\x07\\x04\\xf8\\x9a\\xc2\\'\\xc4\\xc5\\xe3\\xb0\\xb2(T\\x06\\xcf\\x9ft\\x05\\x08$C\\xe7\\x7f\\x9b\\x83Ab\\\\\\xe7\\x9a\\x18a\\xc0\\xe31B\\x83:\\xf2\\xde5K\\x0f|\\x13\\xdf4\\xd2\\xec\\x00&\\x03\\xfb\\xb2\\xce\\x03\\xc7\\x9a\\x805<\\xf0\\xcf\\x16.)n\\xa7\\xd7\\xe2\\xaa\\x89u\\x84~\\xaa\\xf7\\xe1\\x80@\\xd8\\x00%\\xeb3\\xba\\x8c\\xce\\x98\\xe6\\xa9T\\x93\\xc64\\xca\\t\\xd8^T\\x85\\x17\\x1e\\xcaA\\xc4C\\xaf>\\xdb\\x84A!\\xc6\\xc5\\x9aaI\\x1e\\xee\\x92\\xce~\\xfe\\xca!2\\r\\xe7\\'\\xd5lCF\\xc4U\\xb9\\x84\\xf2{\\xb9\\xbc2G\\x97\\xef\\xbaI\\x00B\\x193)D.Mx\\xf2P\\x02\\xa3A\\xe49\\xe6\\xc7\\xdd\\x05\\xa9\\xd7\\x98Mq\\x9f61\\tF\\x0e_\\xe9S\\x00#\\x05\\xd8\\xfb\\xe6\\xf2\\x8c\\x8c=P(\\xd4\\xe3\\xc4M\\x0b\\r\\n\\xf7\\x13\\x97A\\xc2\\x11\\xf6X\\x90j6)\\x96\\x9c$<\\xef4\\xc6\\xb3\\xa5\\x0e=\\x1f\\x8b(\\x08rT\\x99_\\xe1\\xa8\\x8c\"+\\x8e\\xe8\\x19\\xab/\\xef\\xbaa\\x1c\\x03>2\\xb4\\x12\\x86\\'\\xed\\xad~\\x05\\x12}5\\x1c<)<E5\\xf6\\x9ev:+\\xe1\\x12\\xb1\\xa10\\x9ejS\\x90av\\xc7\\xbf\\x16I\\x80\\x81E$}\\xd7\\xc9\\xec\\xc2z\\x93*H\\x87\\n\\x11>\\xa6\\xe6\\x05\\x0c\\xa4`\\x8a\\xb2\\x04\\xb1\\x99\\x9aEC\\x9b\\xae\\x11\\xfdQ!\\x93\\x07L\\xd5P\\x0c\\r\\xe6\\x91$\\xa9\\xf5Q\\xcakE\\xf7W\\x00F&fiT\\x93Z\\x92\\x9fWd\\x00DpU\\x13b\\x04*\\xd9\\xab#\\x96&\\x1fWs)\\x80\\x0b\\x9eS\\xcd\\x1b.L\\x14}\\xd9\\x8c\\xa7AV\\t\\xceOt\\x01\\x06\\x00\\x998k$y,\\xcf\\x0f\\xab\\xce\\x00OC\\xe9\\x83.\\xc9\\xf8\\x9f\\xf7C^\\x10@\\xf5\\xe2\"\\x9e\\x1c\\x06?\\xbb\\x87R\\x04\\xe7n\\xc5\\t9\\x11\\x1f\\xc5Y\\t\\x80\\xaf\\x8f\\x8a\\x80l\\xf9\\x95\\xeb\\xbb\"\\x00_\\x8ezlb\\x13q\\x8e3\\x9aa\\x18\\x0c}\\x8dZ\\xa9f\\x12s\\xc1\\xfc\\xcd*\\x15T\\x0f\\xb1\\xca\\xb0\\xc8\\n\\xb3\\xc3\\x95\\tC)\\xe5\\xc9\\xda\\xd2&$\\xf8\\xcf5\\x94)\\\\\\x127h\\xc0\\x1eX[\\xf3\\x17H\\xd3\\xb1\\xe5|RD\\xc0\\x1b\\x91\\x95\\xc1\\xa1C\\xecG\\x96\\x86\\n\\x003\\xdco\\xe2\\x88\\x010*~\\xe8\\x8c\\t\\x98\\x9f\\x16\\x1e\\xc9:\\xf2\\x1f\\x05\\x08R\\xa2g\\xe3\\xbb\\xde9\\xc3\\xe8\\xe6\\xca\\x02CN\\xf3\\xe6\\xe8(H\\xd7\\x83+A\\xb4gG\\x9e\\xaci+\\xb8*yS<9\\xa7\\xf3f\\xc1\\x1c\\xb2(j\\xb1\\x13\\xb3\\xa31P\\x97\\x17W\"r\\xd1\\x12\\xb9\\x08\\x03\\xf9=\\x14\\x80T\\x99\\x9c\\x15y>1\\xf3\\\\\\xc1\\x02JF\\xafQR\\x01&\\x10\\xfe>(\"\\xa5\\x1a\\xe9y\\xbb\\xa4\\xaa\\xec\\xe4\\'5\\xd1;\\x89R^\\xc8\\xf7\\xf3e\\x00\\xa1\\xce\\xceW\\xbc`\\xd2\\xc4\\x02\\xf53\\xe2k\\xc8\\xf7=\\'u\\x10\\xb9\\x93\\x1c\\xca\\xc0#G\\xf9\\x08\\x8b\\xca\\x84\\x8c\\x8c\\xf9\\xfb\\xa4\\xa0\\x98\\x9c\\x7f4\\x0eC\\xa9\\xef\\xc8Y\\xc0@\\xd3\\xf0\\xf2Y\\xd8\\x0b\\xfb9Q\\x85\\x05\\x11\\xcc\\xeft\\xa9\\xc1&\\xec\\x1de\\x98\\x1e>bS\\xd6M\\xd8B\\x8c\\x8f~}VP$\\x07I\\xf3Y\\x8c\\x8df\\x14\\x85@\\xe4\\x9f\\xf3\\xba&A\\x925\\xe2\\r\\x8d\\xe5\\xaf\\xcb\\x87\\x01\\xfaR!\\xc9\\x88\\xe1\\xfa*\\x80\\xc9\\x87\\xcf\\xac\\xbbI5\\x1e\\n(I\\x1f\\xc7\\xcd\\x84\\x82&\\x8c\\xf7@\\x11\\te=\\xd6?\\xa4\\x9c>[\\x90\\x84\\xe2\\x9e\\n\"P\\x8e\\xa7\"\\xa2$\\xdc\\x113\\xbd\\xb0$\\x0fD\\x0f\\xe6\\xb8L\\x93JLNAR(\\x90\\xc8\\x99\\x7f\\x1c\\xd0$\\x04\\xe9\\x00\\xed\"2\\x15\\xfdz\\xa4\\xbc\\xc0\\x0e3\\xfd\\xd4$\\x91I\\x07\\x03\\xd8\\xff\\x00W\\x18\\xe09\\x9e}1C,$\\xa4\\x83\\x1c\\xc3\\xe7\\xcdi\\x0b\\x84\\x1b2;\\xb60H.\\xc1\\xdb\\xfcVq\\x15\\xe6)\\xbb! \\xcf.>\\xe8\\xa2\\xaa0\\x11\\xc0R\\x82y?\\x01VD\\x04\\x9eU\\xa17K\\xf4!\\t\\xf5K\\x81\\xe5>rk\\xb8$\\x10\\xd8\\xea+\\x1d#\\x86\\xf21\\x05@\\x12G\\xc0\\xfe~h1p!\\xddX\\xaanK\\xdb\\x98\\xe2\\xb3$\\x9b\\x93\\xae\\xa8\\x1a\\xad\\xf4\\r\\xfe\\x1a\\xe4\\x89b\\x07\\x8f\\x8e\\xe9,;p\\xba\\xfb9\\xa9$w\\x12s\\xb5\\x19\\x82\\x99\\x1b\\x83\\xe6\\x7f\\xaa\\x93\\x19\\t^=P\\tH09\\x0f\\x9b\\x0f\\x18\\xeau\\x9fvf\\x98\\xc1\\x8aP\\x08\\x8d\\x12\\x07\\xe2\\xc0\\x90\\xe6y%\\xf9\\xab\\x84\\xd5`\\x92E\\x110\\x9c)\\x0f\\xc5DW\\xbc<\\xeb\\xb1Kg\\x8c\\xe1\\x9a\\xa8p\\x8f\\x1c\\xfdM\\x93\\x05\\x8cQ\\xf8\\xf7Z*8\\xfe\\x04\\xde\\x90\\xb1\"H\\xf4sF\\x0c\\xcc\\xe0a\\xd3\\xe2\\x82\\xce\\x08\\xd9\\xea\\xeb\\x14Y\\x19\\x13L\\x8f\\t\\xb2\\x94{\\x9fV\\x01\\xd1\\xee\\x1b\\xf3[\\x164\\xc9\\xcf\\x98h\\x80x\\x08\\xea^<\\xfdW$\\xa8o{a\\xe8\\x15T|E\\x9aNb\\x7f\\xb5G\\xc0%\\x97\\x87\\xcd29\\t\\x9c\\xd2\\x8a8J`\\xe6{\\xaf\\x01\\xea\\\\8\\n\\xc0\\xa6\\xc7\\xb8m[\\xc1/\\xc1\\xa5\\x15\\x14\\xc9\\xe0\\xf8\\xa1!\\x04i\\xf1\\x0b\\xaaa\\xc6g\\x8a@\\x11\\xcf{\\x9bc\\x88\\xe6\\xaf\\xeb+\\xe2\\x802\\xfb\\x8f\\x14\\xe2F\\x9d\\xc7T\\x03\\x00K\\x8d~\\xec\\xd8R\\xc1\\x13\\xe2\\x97\\xf2\\r\\x97<VtD\\xc4\\xac\\xcf\\xa2i\\x057\\x1b\\x04\\xc7\\xcf\\x9a \\xc8\\x12\\x06\\x13\\xac\\xabe\\x87\\x00t\\xacr\\xd1\"q$iPhL\\xc0N\\x1b2HIS52\\x1d\\x80\\x81e\\xea\\x83\\xa8A\\xc4\\x14\\x95\\x16;\\x1e\\xe9\\xc5b\\x1d\\xe2#\\xc1\\xdd\\x94E\\xc3\\x18\\x1e\\x0ef\\x8c\\xd3\\x17S,\\x1cs\\xd3V\\na\\x01\\xf7A\\xb8\\n\\x89K\\xbb\\xaf\\x00p\\x8c\\x9fV\\x0c\\xe6x\\x96\\x19\\xf9\\xa8\\x9aI\"\\xa4={\\xb2w\\x81\\xe6a\\xd7\\xd5\\t6$w\\xbd\\xff\\x00v\\x00\\x8d$\\xa7+\\xe3oA\\x90\\x16}\\xb3\\xf9\\xa4\\xe2WPc\\x9ap`\\x8cG\\xb6\\xae\\x99\\x12\\x8f1\\x14\\xc0\\xd3c\\xee\\xe9\\xa4\\xa3~)\\x97\\xa9c\\x881\\x1f\\x9b;\"\\x85\\xd9\\xe3\\xcd\\n`e\\x85\\xf0r\\xfcV(\\x9e\\x1e\\x19`\\xaa\\xa9R2|>\\xecQ`GO\\x16\\t*\\x0c\\x13\\xd5\\x12\\x84q\\xad\\xb3 \\xd4\\xe9\\xfd\\xd5q\\x14\\xe5b#\\xc5\\xe2J\\x13<\\x07\\xe9nQ\\xc2I\\xed\\xd3\\xdd\\x14L\\x87\\xb1\\xf9hL0\\'\\x06\\xd8*B\\x9a<{k\\x10\\x10\\xf4\\xf8<\\xd8\\xc4`\\xd5$\\x82\\xaa5#\\x19\\x99Q\\x12K\\xc8\\xe5L%#H\\xf7\\xf3D\\xc2T\\xc8\\xd3\\xe2h\\x18!\\x99X3\\xf8\\xb1IE\\xc9W\\xdd\\xce)(L\\x913\\x13a`F4@\\xf2\\xc5S\\x12D>\\x9f\\x15\\x01\\x02\\x88\\xbc\\x8f1C@<\\x10aO1d\\x8e:\\xb9\\x83\\x98\\xa8\\xc1L\\xb4\"~\\xa6\\x91\\x10\"<O\\x14L\\x01%\\xc6&\\xe8\\xf9)\\xc4BD\\xc8\\xef\\xc9B\\'2\\x19\\xe5\\x95qH\\x02|\\x16\\x0fB \\xf35\\x05\\x02\\xa1\\x00\\xeewT\\xa5\\x89\\x13\\xe2&\\xb3\\xb8F|\\x114\\x04\\x12\\xcc\\xbf\\x9a2\\x82J\\r\\xdfjdE\\x98~J\\x83M\\x07\\x04\\x9b\\x9bu0h\\x00\\x07\\x1e(\\x96S\\xc3\\xcfQJ\\n$gZ\\x86\\x04\\x02:\\x01=\\xf9\\xae\\x89\\xc1\\x93\\xa9\\xf6]\\x8f$\\xe9\\xb2Q8\\x19\\xa63,P\\n\\xd8\\x17\\x83\\x8a\\x01!12\\xda\\x002\\xe1Y?\\r&\\x0b9\\xf0\\x03\\x98\\xa8\\x02\\x04\\xc8\\xf3\\xfe\\xcb\\x1d\\x07\\xa3\\x96\\xf4\\xe1\\xd9`4\\x1a\\xb61\\xe1\\x96fW\\x96Q?\\x80\\xae\\x91\\xa7\\x13?\\xea\\xea\\x9dg\\x08\\xc6\\xcc\\x12\\x03\\x82\\x13\\xf8:\\xaf3 \\x94\\xde\\x7f\\xa9\\xb3F\\xb4\\x13\\x89\\x1f\\x15i\\x1a\\x08\\x97\\xbf\\x87\\x9a\\xd1\\xb2p\\x9b=\\x9d^#9\\x89\\x0f\\xf2j\\xd0\\xa2\"|\\xad\\xd7\\x00\\xc6@\\xfc\\xf1y\\xdd\\xe5\\xf9|TN\\xe0\\xd6!\\xde\\xa9\\x101\\x85\\x83\\xb3x\\xacD\\xe3\\x08iT\\xa0\\x9d\\xce7\\x9b\\x05,L\\xef\\x8a\\xea;\\x04|g\\xf5d\\xbd\\x00\\x86(O\\x00m\\x9f\\x1f\\xfc\\xadu\\x95s\\xe5\\xea\\xe0\\x99\\x97\\xd76\\x07\\xb2Yzt\\xbd\\xa0B)\\xec\\xa0#\\xb0\\xcf\\x1f5\\xf34\\x07&\\xd9\\xac\\xe9\\x12\\x1ceQ\\xc9\\xad\\xe3<\\x91\\xa0H\\xa2&h\\xc2g\\xbfU0\\xc6F\\xf8\\xbc\\x90\\x16\\xeb\\x06.\\xcc\\xed\\tL\\x1c)\\xb3 \\t\\xf4\\xe7\\xf2Y\"3-\\x9d\\x87\\xbf\\x8a\\xe0\\xc2\\x11\\x0c\\xf2\\x9e\\xdf\\xaa\\x82\\x82\\xbc\\x07\\x1fw\\x92\\\\\\x13,\\x8b(\\x13\\x18V,\\xc3\\x1f~\\xa8F\\xa4!\\xe0\\xf8l\\xd3BA\\x90\\x9b\\x98\\x8e\\x83\\x82\\xbf\\xc5\\x19\\x9cO\\xa0G\\xbe\\xeb0\\xba\\x01\\x90i\\xe6+*\\x10\\x94\\x80E\\x88z\\xe1\\xca}Tl\\xc3R;?\\xdb`\\xc3&\\xbd\\xac\\x91y\\xb0!C\\x98h&B!\\x1d\\xef\\x96\\x9ca\\x88O\\x81\\xf1\\xb6\\x01-g\\x18<\\xeda\\x14\\xa7R\\xa3\\x1c\\x02\\x11\\xd4\\xd2I\\xe3?\\xaa\\xab0\\x15&>iq\\x081vx\\x8f6n\\x087\\xef\\x9a\\x85\\x02;\\xe3\\xaft\\x884G\\x9b\\x00iP\\xf0\\x8b\\xa6\\x19\\xdc\\xe2\\xa7\\xcd\\x8b`\\x94O\\x9b41 ?\\x8d\\xa0\\xe1\\xd2O\\x89\\xa0\\xc4\\xc6\\xc6d\\x7f\\xed!\\xb6\\xa6\\x86LP\\x9a,\\xa2\\xbb\\x9f\\x19e+\\xe2do\\xfa\\xaa2t\\x83~.\\x80\\x915\"++\\x0c\\x8c\\x04>\\xeaez\\x1c{,J\\xc9{\\x11\\xed\\xf3^)\\xc9\\x0e\\x0c\\xf7\\xf8\\xb8\\xa2\\x11\"!\\xb38\\xa1\\x14\\xf5K\\xb9\\x14\\xc6J\\xaa2C\\x08a\\xbb\\x06Dp\\x8f\\xbb1\\xca\\xce\\x7f\\x94Yq\\x0c0\\xa2?\\x15\\x12\\x87\\x9eX\\xe6\\xc4\\xd1 \\xa0\\xd6R\\xa8\\xc1\\x06)\\x92}m\\x00uI\\x86\\x9f\\x9a\\x1c\\x9a\\x08\\x02q\\xfc\\xd2\\xb40g}\\xedO?\\x1c\\xbcx\\xfe\\xea\\x050\\xe9=\\xf3?Ua\\x04\\x94\\xa5\\x96)N\\x04\\xbc\\xfb\\xf4\\xd6\\x97_\\xf7U\\xc8\\x0b\\xcb\\x8d\\x8a\\x9a\\xa7\\xe5M\\x02@\\x98\\xbd\\xcc\\xff\\x00vY\\x00t9\\xfa\\xb9I\\xd3v\\xf08z\\xee\\xc8\\xc8\\x96\\x01\\x077\\xe2\\xa8\\xe0LA\\xfd\\x940%\\x7f/\\xcdo\\x85\\xd4a\\\\\\xeaH\\xe7\\x86><\\xd1\\xf9\\xff\\x00\\x15\"\\x06\\xc3\\x12lr\\x8c\\x93\\xf1-\\x84\\x00\\x1e\\xc8\\x99\\xb2\\xc8\\xf0\\x1c\\x93T\\xb0%\\x0eu\\xf7\\xe4\\xf1`\\x83\\x9dH\\x83\\xcdXZO\\x84G\\xe6\\x9a\\xa2%\\x06X\\x1e\\x18\\x8eh\\x80e\\xd4\\xd8\\x8e\\xdb\\x1aoP\\x86\\xe7\\xdfVK\\xf5?U\\x00\\xe2\\xf0x\\xa86\\xb0\\x177\\xef\\x9b\\xb3=Q\\r\\x87\\xfa\\xa4\\x0c\\x10\\x87\\xe1D`T@DT\\x00eu\\x81\\xfc\\xc5\\xf9\\xc0\\xae\\x86\\x8cN\\x99\\xbf5$~}\\xa7\\xb8\\xf1\\x7f\\x08\\x883\\xec\\xa21\\xcaD\\xbf/\\x15\\x11\\x89\\xcf,\\x9fSO\\x07\\xc4FE\\x969\\x83\\xc3S\\x8c\\xb8\\xc9\\xb1\"Q\\x89\\x07\\xc9X\\xbb\\x04\\x8f\\xb8b?\\xd5\\x0e\\x94\\xce\\xa7\\xceV1\\x08\\x11\\xec\\x8e\\xe8\\xd2\"n\\xfb\\xf3X\\xc4D\\xc2\\xbc\\x0f\\xb3\\x1f\\xc5\\x0f\\x03\\x19Le\\x12\\x07u\\x89\\x055\\xeb\\x1f4\\xc98\\x94\\x92C\\xd3\\xf1M\\x04\\xa1Tc\\xff\\x00\\xca+D\\xbct\\xff\\x00\\xe3d\\x1e\\x05W\\xf7p,\\xa6\\x8f\\xea\\xa3\\x94\\xa6\\x1f\\xfa\\\\\\x120\\x07C\\xd8XnrW\\xc1T\\x01\\x03?\\xcf\\x9b\\xa6\\x9e#\\xacw\\xbdx\\xb3DYl\\x84\\xd8\\x00Vt\\xfcm}#\\x839#\\x9b\\x00\\xf35=6{\\xd0dV\\xd0\\xf2k\\x82\\xf5bRzx\\xfb\\xaa\\xec\\xa5SI\\x9b\\xc1Hk\\x13\\xfc\\\\\\xe8c\\xbf\\x05H\\x0c\\x00D*:\\x08a\\x92\\x7f\\x1e\\xaa\\xc2d\\xf7\\xdd\\xea\\xcc\\xb0O\\xc2}U\\x81\\x8e\\xc7}kN<R\\'=g\\xc5\\x80R\\x12\\x8d\\x0e\\xe7\\xdcP\\xd7\\x80\\xf1\\xdb\\xea\\xc9$. \\xe7\\xfc*\\x12\"<q1\\xdf\\xddp\\n\\xcfG>\\xabV\\x8d\\x0f\\x93)\\x88\\x00\\xf2yJc\\xb9\\xc0 qC\\x88\\xec\\x0e\\x86\\xbdAB$\\xf1yC\\xaa\\x0c\\x8b1\\xc5\\x9c\\rL\\x8cX\\xe8Z[\\x1fq\\xea\\xa2C\\x15\\xe18*\\x90v\\xec\\xdft2\\x8d\"\\x02\\x93\\xef\\xcd\\x8e\\x00,\\x1f\\x04\\xd8\\x83\\xc7I\\xe2\\x7f\\x9a\\x08Kra\\xeb\\xcd\\xe2\\x93\\x80\\xd8O\\x17=\\xd0$\\xc7+I6\\x83A\\xc3\\xeb\\xe6\\xa0v4\\x10\\xb3X\\xd0(O\\x9a,\\x8e\\xa0\\xf6\\xd6Q&I\\xd4\\x1c\\xd2\\xa0\\xcb!uDq\\x8b\\x1b\\xdd\\x90\\x84\\x8d\\xc7\\x19\\xef\\xd5\\xd2?9\\xa8\\xa7F\\x020\\xb3\\xc2\\xc1\\xd6~\\x8c\\xa4\\xe0\\x18$\\x84j`\\x01\\'\\\\\\x94\\x08\\xd0wA\\xd0M\\x04\\x88\\xcc\\xa1\\x86>\\xf9\\xae\\xa8\\x89J&O\\x99\\xfe\\xa8E\\x92xLC\\x9b,HU>\\x0f\\xfe\\xf3P\\x03CC\\x07\\xed\\xa4\\x05\\x82\\x86\\x98\\xd7\\xcd`\\x93J>C\\xd3Z\\x88\\x18@\\x9ei(\\x91\\xae\"\\x94\\xceS\\xddU4\\x9e|\\x94\\x88\\x8c\\xb8I\\x9f\\x9a\\xa3\\xd2`\\xb1\\x175\\xcd\\x1e\\xe0\\x8b\\x81\\xca|RD\\x19\\x1dI\\x1b\\xe6\\xa3\\x8e\\x97\\x99\\x00\\xff\\x00\\x14x\\x84o\\x18\\xcf\\x8a0\\x84\\x0cc\\xfdMB\\xc1\\x02\\x0f/\\xc5l\\x1e\\xdf|\\\\\\xec\\x81\\x19\\xef(\\x13\\xb5M\\xca@R\\x7f\\x9fV\\x00\\x93\\'\\x9f\\xa8\\xf3\\xe2\\xe7\\x80\\x89\\x9cy~}\\xd2\\\\\\xc8\\x1c\\x8ex\\xac\\x92\\xbd\\x81\\x1fQ\\xb5\\x89.\\x0e\\x98\\x87\\x9f4\\x01\\x06q\\x99\\x12k\\xca\\x16\\x19\\x13\\xe5\\xc7\\xea\\xf1(\\xbcj\\xbf\\xbb$@e\\x99e\\xf8,\\xe4\\x9d7V\\xa1\\x82\\x1eG\\x9c(\\n\\x87\\x0f\\'\\xff\\x00lU3\"4\\xbfzM\\x11\\x9e\\x18S\\x0f\\x07\\x8a\\x96\\x06\\x0c\\x91y\\xf9\\xb9Ee\\xc1\\xf0\\xba\\x10B\\x12D\\xef\\x9b\\x10.\\x08Hq\\xf7J\\x9b\\x0e$P\\xcf\\x12]\\x96$\\xf2<u\\\\\\x02\\xa2\\x8e\\xc8|,we-E\\xd1\\x8f\\xe3\\x9a\\x81\\x02C\\xdcy\\xb2W\\x0b\\x81\"\\x7f\\xba\\x81\\t\\xf2I\\x11\\x9eJ\\xa7\\xab\\x03\\x9d\\xf9\\xab\\x04\\x11\\xfd\\xb6\\x13\\xa4\\xd7Nf\\x94\\xc4\\x15\\xe4Q`\\xa5X\\x01~l\"\\x03v\\xe0\\xf3\\x13\\x974\\xb8b~W1\\xc8\\x00Fy\\'\\xbb\\n*\\x03\\xbc\\x8c\\xfab/\\x87\\x03\\x88\\x82<U\\x1c(1?\\xc6e\\x84\\x18$\\xc7\\x89\\xaaU\\x94\\xb0\\xec\\xb9\\x82\\x15\\xdf\\xfd\\xfe\\xece\\xf1\\x18\\x98\\xef\\xfd\\xd5\\x14\\xc0\\t9\\x08{\\xac\\x02\\xc8u\\xcf\\x15\\x9f\\xa2\\xe7\\x81\\xf8\\xa422\\x1f\\x8f\\xba\\xc2J\\x0f\\xc6\\xf9\\xed\\xab\\x8c\\x04\\xc8\\xa0DX\\xc1\\x00<\\xfc\\xb3\\xc3@\\xa0\\xf9C\\xdd\\x90)c2J\\xa8d\\x17@0\\xfa\\xbc\\xa8\\x92\\xb2J\\xff\\x00v\\x1b0\\xa4\\x17\\xe1\\x936#)c\\x1b\\x03\\xddm\\xe0\\xf5\\xcf\\x14\\x05\\x94\\x8c\\xa98\\xa3*\\x07\\x7f\\xda\\xf4]i!Srb\\xcc\\x1c\\xa3\\xba\\xb9%\\xa48=e\\xe0\\xc7\\x90\\xc9\\xb2\\x11\\x0e\\x8c\\xf63\\xa8\\xa5\\x05\\x83\\x10\\x17\\xf7\\xfb\\xaf\\x14\\x87\\x91\\xc8\\x7f\\xaa\\x84\\xa4.=\\x91\\x9bPz^\\xcc\\xe0n4\\xbdm\\x91>:\\x8b\\x1d\\x84v\\xf3\\xc7\\xab#\\xa0\\xfbI\\xcb%4\\xf5\\x8e\\xbf\\x15\\xe34k\\x1a~2\\xc4\\x87i\\x88c\\xf9\\xee\\xc4\\xf6\\xe1\\xe6e\\xaf \\xf2#\\x9f\\xd8\\xd4\\t\\x1eI\\xc9\\xeei\\xc1 \\x1e?\\xcf\\x15Ar;\\xe3\\xf7PBbL\\xf9z)\\xa2\\x0c\\xae\\xf3\\xe3\\xd5\\x84tx\\x0c\\x93\\xef\\xba!@=iD\\x03\\x1b\\xaf \\xe6\\xcc\\x80\\xa7\\x99\\xf1\\\\\\x87\\'\\x89\\xe0\\xf9\\xb11\\x10\\x07\\xd5\\x94\\x02}T\\x10\\x10\\xeauPb\\x06\\xcc\\x8e\\xa8H\\x81\\xe1+2\\x00U\\x19s\\xfc\\xdb\\xbb\\xb5 \\'=\\xd6BC\\xa9D\\x9e\\x0f\\x8a$\\xe5\\xf0\\xbd\\xcft\\x1e\\x898:\\x16Zs\\xc9\\xcb>\\xab\\xd0D^\\x1duQ\\x01\\xe1\\x93\\x1c\\xde\\xf0\\xf29~j\\x0c\\x1f#\\xb3?\\xc4\\\\8x\\x9e\\xcd\\x9b\\x07\\x19 \\x91\\x07\\xe9H\\x95\\x18t\\xbf\\xc3@\\\\\\xae\\xbc\\xfe\\xa6\\xcc\\xbdA@) \\xe6\\x0e\\xea\\x04O\\x9a\\x82\\xc9\\x11a<\\xc7=\\xe5\\xe65\\x06\\xb7f\\xcc0\\xb6Dp\\x1f\\x9aVD\\x14\\xd8~\\xac\\x82p\\xd8\\xc1\\x8e\\xdf\\x14\\xcc\\xa1\\x102,X\\xe0\\xaf\\xc8\\xe5#G\\x0et\\x8fuS29\\x1c\\xca\\x08\\xa0r\\xae\\xf1\\xee\\x85\\xd1\\xe0\\xa7\\x8e\\xaa\\x04Xv?\\xce\\xae\\x06f:d\\xfc\\xd1j\\x8dG\\x82\\xc5\\x92\\xa0K\\x82{\\xa6\\x11*=9\\xcb->\\xa1\\x94\\x06e\\xbc\\xbf\\x14\\x8aG<Y0/Jr\\xd8\\x05\\x00\\xf4\\xae\\x13\\x82\\xe8\\xa4\\x93.w\\x1b\\x84\\xd7\\x9cI\\xfa\\xea\\x9a\\x04C\\x89g\\xaf\\x8a\\x19$s\\x19\\x8b r\\x0f\\xdf\\xcd\\x91O;\\x8cAb\\x06v\\xcc\\x03\\x14<\\x90\\xe4\\xe6\\x1f4\\x11\\x81\\x0e)$\\xb7\\x00\\xce\\\\\\xd3\\xb5(\\x1eXp\\xbf6b,\\x83\\x86=\\xd5-H\\xb1=\\x95L\\xb3\\x93\\x9b\\xeed\\xfc~\\xa8g\\x13\\xeb\\x8b\\rF\\xf9\\xf5\\\\\\xf0c\\xf7YX\\xc7\\xd5\\x04@\\x81\\xeaO\\x86h\\x1e\\x12\\x92\\xcc\\xb1\\xfex\\xa0\\xf2\\x13\\x84\\xfe\\xeb\\x06\\xd2\\x04@\\xbfwB\\xfc =%\\x18\\x90\\xa2\\x16Ix!\\x94\\xef1\\xf3qB\\xc3\\xff\\x00\\nd{9\\x12\\x83\\xe2\\xb0O Ls\\xcd\\xc1\\x18C\\x1d{\\xf9\\xb29-$\\xcek-\\x19\\xe8\\xeb<4\\nL\\x0f>\\x9a\\x94\\xf9<\\x91Z\\xf1z\\xe2\\xe4\\r:\\xa3#2\\x19\\xf5Ya\\x9f\\x04\\xd9\\x10B\\xf67\\xfch\\x19\\x04\\x08\\xcf\\xd4X(A\\x8e\\xd5\\x82\\x10\\xe0\\xbc\\xd9\\xf91\\x0c\\x8d\\xf6Ta\\x00\\xd0\\xce\\xed\\xd4r9\\x8f>\\xee\\x8e\\x0c\\x99\\xe3\\xccW\\x10\\r\\xdcC\\xb5\\x08\\x18\\x13\\x88\\x87\\xdd\\xc0\\x19u\\x8f\\xcb\\x153\\x8b4\\x14\\xc9c\\x04\\x12\\n9\\xfc\\xd2\\xe1\\xc4\\xd0d\\xf9(\\x9a)\\x8eC\\x16Ad%\\x99\\xe2\\xa0\\x97oS\\xfa\\xadL f\\xd4\\x80\\x96*\\xcc\\x13\\x1d\\xf8\\xb2\\x0c<\\x8eudP\\xde\\x05\\x9f\\xddk\\x0c\\x86\\x91\\xcc\\xfb\\x8b\\x90\\x10O&\\x1a\\'\\x82\\x9eq\\xe2\\x81\\x10o\\x16\\x08A\\xe3S\\xff\\x00\\xb6D\\xef\\xa6A\\xe9\\xa8\\x10\\xafL\\xa5|\\x89\\xf8J\\xe1A9|\\xd4$\\x89\\x1c\\x1ce\\x18Q\\xf5\\xcf_5\\x03\\x07#l\\xe1\\x15\\xfa\\xea\\xa20\\xbf?\\xee\\xa0s\\xadFj\\x19\\xdf#\\xc7Q`/\\x8e\\xbc\\xd6\\x06\\x9f/\\x14\\x88Vr\\x99\\x01\\x04O\\x13\\x1el\\x1e\\tjwu\\x85r\\x08\\x18\\x8f~,\\t\\xc2\\x03\\xac\\x8f\\xdd\\xea\\x12\\x9e\\r\\xcf\\xae\\xe8\\x9aK\\x04\\t\\xbf\\x15A\\x88=#-\\x02\\x1b\\xcc\\xe4h\\xa8\\x94vc\\x11\\xf7P\\x8c\\x89eV \\x8b\\x02\\x0c\\x0e\\'+\\x81\\n\\x83\\x8e\\xfd\\xd0bw\\xb2}\\x94\\xe0\\x1fO5I\\x11\\x0c\\xe6\\xc7\\xf1`\\xae\\'5\\xf1c\\xa4\\xbf\\xb2\\xa7t\\xe3\\x89d\\xc9:\\x13\\xef\\xeek\\x0b) \\xf1\\xfcT\\xa7\\xb7H\\xb6n!\\x02\\xa0(d\\xe9\\xf5\\xf5\\xddN}\\x89\\xa4\\xfbc\\xab\\x10\\x83\\xa12\\xfc\\xd5<\\x89\\xe1C\\x8f\\x8a\\x08\\x82P\\xa8\\xaaWK\\xe0\\x9f\\x9a\\xf3.\\x1d\\x8d\\x92\\xca\\x0cx<\\xd4\\xa6\\x17a\\'<G\\xfe\\xd01~\\xcb T\\x0f[xH\\xeeg\\xf1@\\x86\\xec\\xe7\\x8f\\xe2\\xb9\\x99j\\xcb\\x1d}\\xc5\\x94\\xc68\\xf3t_@\\xd8\\xa5\\xa4\\x18\\xe7\\xbb\\x06TH\\xeey\\xa7!<\\xad( \\r)J\\x1d\\x1d?T!P\\x0e+[uS\\x86y\\xb0\\xc8C\\xaa\\xb6\\x10\\xac\\xc1\\xdd\\x95&N8)\\x96\\t\\xea\\x13\\x1e\\xa8r\\xb4a;\\xfc\\xdc\\xf1>2w\\xea\\xc9(\\x90\\xf1\\xd5\\x1d\\xd9\\xf8\\xea\\xcaL\\x86\\xcb\\x14\\xe4\\x87O\\xd5\\xe5\\x8eSX\\x01\\xc9\\xd96J\\x86\\x1c\\xaesR\\x1c\\xf8\\xca\\xa0}\\x81&\\xae\\xe1!\\xc3\\xc7\\xba&\\x13\\x03\\x7f\\x9b\\x02\\x00\\x94\\xc0\\xe0\\xf0{\\xaa\\x10\\x1dA\\xbb\\x05f\\x90\\x9c\\xcc\\xda\\xb3\\x81\\x0e\\xe5E\\x92\\x1eo\"\\x1f\\xbd\\x9f\\xba\\x908\\x8f\\x8d\\xb3\\x89;\\x98\\xaa\\x83\\x8f\\x1bx >\\xfa\\xa8z\\x13\\xf4V}\\xe6\\xc0C\\xe4f\\xaa\\x18k\\x93o7&U\\x04\\'\\xd5x\\xa3\\xb0i\\xa0\\xf1\\xfe\\xec\\x9c\\x0f]\\xf8\\xa2y1\\xcf\\xe3*\\xc0w\\x87\\xf5@\\xabM\\xf1I\\xd4P\\x89/=\\xbe\\xae\\x04,\\x94&>^+\\x9e\\x07\\x0cqX\\xd70\\x9e\\xf8\\xf1A\\x89\\x9c\\xfeK(\\x82\\t\\x14\\xce9\\x9b\\x02}\\xb0\\xd9D\\x1e\\xe1\\xf7T\\x9f(\\xad?uLA\\x0e2\\x92\\xf1\\x03\\xd0\\xc5\\x02\\x01#/=Y2\\x1c34\\x1c\\xa8)\\xf6FE\\x14Ks\\xe0\\xe6\\x89\\x9e\\xcf\\xcd\\x1dY\\xca\\x84\\xc1j\\x16\\x8c\\xc3\\xe9\\xf5N\\x91\\xe9\\xf3^\\x87\\xb8\\xa6\\x08\\xcc\\xcf\\xd4U\\xec9\\x8e(\\xceH\\x8e\\x15I\\xb5\\x81\\xc4d\\xd7C\\\\\\x88\\x9c\\x8f\\x05\\xff\\xd9'), interactions={'hover': 'tooltip'}, scales={'y': LinearScale(), 'x': LinearScale()}, scales_metadata={'y': {'orientation': 'vertical', 'dimension': 'y'}, 'x': {'orientation': 'horizontal', 'dimension': 'x'}}, tooltip_style={'opacity': 0.9})], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0)), Toolbar(figure=Figure(axes=[Axis(orientation='vertical', scale=LinearScale()), Axis(scale=LinearScale())], fig_margin={'top': 60, 'right': 60, 'bottom': 60, 'left': 60}, layout=Layout(min_width=u'125px'), marks=[Image(image=Image(value='\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00H\\x00H\\x00\\x00\\xff\\xe1\\x012Exif\\x00\\x00MM\\x00*\\x00\\x00\\x00\\x08\\x00\\x07\\x01\\x0f\\x00\\x02\\x00\\x00\\x00\\x12\\x00\\x00\\x00b\\x01\\x10\\x00\\x02\\x00\\x00\\x00\\x0c\\x00\\x00\\x00t\\x01\\x12\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x01\\x1a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x80\\x01\\x1b\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x88\\x82\\x98\\x00\\x02\\x00\\x00\\x00\\x07\\x00\\x00\\x00\\x90\\x87i\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x98\\x00\\x00\\x00\\x00NIKON CORPORATION\\x00NIKON D3300\\x00\\x00\\x00\\x00H\\x00\\x00\\x00\\x01\\x00\\x00\\x00H\\x00\\x00\\x00\\x01Tama66\\x00\\x00\\x00\\x08\\x82\\x9a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xfe\\x82\\x9d\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\\x06\\x88\\'\\x00\\x03\\x00\\x00\\x00\\x02\\x00d\\x00\\x00\\x90\\x03\\x00\\x02\\x00\\x00\\x00\\x14\\x00\\x00\\x01\\x0e\\x92\\n\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x01\"\\xa0\\x01\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xa0\\x02\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x01@\\xa0\\x03\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\xd5\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x012017:06:08 17:17:46\\x00\\x00\\x00\\x00\\x18\\x00\\x00\\x00\\x01\\xff\\xe1\\n\\x1bhttp://ns.adobe.com/xap/1.0/\\x00<?xpacket begin=\"\\xef\\xbb\\xbf\" id=\"W5M0MpCehiHzreSzNTczkc9d\"?> <x:xmpmeta xmlns:x=\"adobe:ns:meta/\" x:xmptk=\"XMP Core 5.4.0\"> <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\"> <rdf:Description rdf:about=\"\" xmlns:photoshop=\"http://ns.adobe.com/photoshop/1.0/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" photoshop:DateCreated=\"2017-06-08T17:17:46\"> <dc:rights> <rdf:Alt> <rdf:li xml:lang=\"x-default\">Tama66</rdf:li> </rdf:Alt> </dc:rights> </rdf:Description> </rdf:RDF> </x:xmpmeta> <?xpacket end=\"w\"?>\\x00\\xff\\xed\\x00jPhotoshop 3.0\\x008BIM\\x04\\x04\\x00\\x00\\x00\\x00\\x002\\x1c\\x01Z\\x00\\x03\\x1b%G\\x1c\\x02\\x00\\x00\\x02\\x00\\x02\\x1c\\x027\\x00\\x0820170608\\x1c\\x02t\\x00\\x06Tama66\\x1c\\x02<\\x00\\x061717468BIM\\x04%\\x00\\x00\\x00\\x00\\x00\\x10Ab\\x95\\xfc\\xe0Y\\xc0`f\\x9a\\x1f \\xaf\\xa5!h\\xff\\xc2\\x00\\x11\\x08\\x00\\xd5\\x01@\\x03\\x01\"\\x00\\x02\\x11\\x01\\x03\\x11\\x01\\xff\\xc4\\x00\\x1f\\x00\\x00\\x01\\x05\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x03\\x02\\x04\\x01\\x05\\x00\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x10\\x00\\x01\\x03\\x03\\x02\\x04\\x03\\x04\\x06\\x04\\x07\\x06\\x04\\x08\\x06s\\x01\\x02\\x00\\x03\\x11\\x04\\x12!\\x051\\x13\"\\x10\\x06AQ2\\x14aq#\\x07\\x81 \\x91B\\x15\\xa1R3\\xb1$b0\\x16\\xc1r\\xd1C\\x924\\x82\\x08\\xe1S@%c\\x175\\xf0\\x93s\\xa2PD\\xb2\\x83\\xf1&T6d\\x94t\\xc2`\\xd2\\x84\\xa3\\x18p\\xe2\\'E7e\\xb3Uu\\xa4\\x95\\xc3\\x85\\xf2\\xd3Fv\\x80\\xe3GVf\\xb4\\t\\n\\x19\\x1a()*89:HIJWXYZghijwxyz\\x86\\x87\\x88\\x89\\x8a\\x90\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xc4\\x00\\x1f\\x01\\x00\\x03\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x01\\x02\\x00\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\xff\\xc4\\x00\\xc3\\x11\\x00\\x02\\x02\\x01\\x03\\x03\\x03\\x02\\x03\\x05\\x02\\x05\\x02\\x04\\x04\\x87\\x01\\x00\\x02\\x11\\x03\\x10\\x12!\\x04 1A\\x13\\x050\"2Q\\x14@\\x063#aB\\x15qR4\\x81P$\\x91\\xa1C\\xb1\\x16\\x07b5S\\xf0\\xd1%`\\xc1D\\xe1r\\xf1\\x17\\x82c6p&ET\\x92\\'\\xa2\\xd2\\x08\\t\\n\\x18\\x19\\x1a()*789:FGHIJUVWXYZdefghijstuvwxyz\\x80\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x90\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\xa0\\xa3\\xa4\\xa5\\xa6\\xa7\\xa8\\xa9\\xaa\\xb0\\xb2\\xb3\\xb4\\xb5\\xb6\\xb7\\xb8\\xb9\\xba\\xc0\\xc2\\xc3\\xc4\\xc5\\xc6\\xc7\\xc8\\xc9\\xca\\xd0\\xd3\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xe0\\xe2\\xe3\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xf2\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xff\\xdb\\x00C\\x00\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xdb\\x00C\\x01\\x14\\x14\\x14\\x14\\x15\\x14\\x17\\x19\\x19\\x17\\x1f\"\\x1e\"\\x1f.+\\'\\'+.F26262FjBNBBNBj^r]V]r^\\xa9\\x85vv\\x85\\xa9\\xc3\\xa4\\x9b\\xa4\\xc3\\xec\\xd3\\xd3\\xec\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x01d)B\\x03H\\xd4\"\\xc6\\x18\\x94Q\\xb8\\x07B\\x92]\\n\\xc4U\\x85\\xc2s\\x965\\xe0P%\\xc3\\x81(\\xa9!IJ\\xe8E\\x84GBHd\\xa4\\x89*8\\x84\\xe9\\x15\\x1a(\\xd05-\\t\\x944\\xb8A\\x01JV\\x838\\x84)@\\xcd+\\xa4h\\xd5&n\\xe4\\x12\\xe5\\xa1t\\x19\\x90F\\xcf\\r\\xc0\\xf9\\xe0\\x01\\xc3=IK\\x8a\\xb0\\x923\\x1ar\\x90!\\x13(\\x86\\xc5R\\x893W-\\xd6\\x0c\\x8d{\\x81\\x9b\\x11d\\xc1\\xd2\\x85\\x9aN\\rD\\x14N)\\xbc\\xce4\\x91\\n\\x01{%c\\xb6!)&\\x11\\x83\\x1c.B\\x1de\\x02\\xd79K\\xb0dB\\xd5\\xebW+XIM\\x16\\x82h\\x10\\x95\\x8c\\x83\\xad$\\xcc\\xc3wM\\xab+)\\xc0K9\\x80\\xdc\\x8aT\\xca2\\xa0\\x10\\x10:\\x98r\\xdbTI\"\\x10IH\\x92q\\x90IT\\x14Rf\\x8eU\\x9d\\x84\\xc2\\x9d \\x9c2r\\xe6\\xad\\xc2N+\\xde\\xb6gS\\x89FS\\x04\\x9d\\xae\\xea\\xb4\\xa9\\x04\\xbf%k\\xecKy*\\x8c\\x98r\\x10\\x1a!q\\xd0\\xb2\\xb1\\x1dd\\x11\\x12$\\xa0\\xb0C\"\\x94Nds1ZW\\x80\\xd2\\x99\\x14\\xe2$D0\\xc0\\r\\xabxA\"\\x98\"\\xa0\\x96\\x92\\x1bi\\\\\\xee\\x1a\\x149\\xcal\\xe9\\xa1\\xa18\\xee\\x06\\xe4.\\x14\\xacnZ)8\\x9e\\xc9J\\x84:k\\xad\\x05\\x19\\x0c\\x89\\x93\\xac\\xdd.\\xdb\\xc2\\x00\\xa8s\\x11\\xb1%\\x1a\\xd2\\xa0\\xd2\\x89\\x16RDb\\x94K\\x13\\xd6\\xc5\\n\\x9d\\x969RA\\xae\\x0e\\x11)m \\xed\\x9d\\xe57D\\x90\\xcc\\x1f\\xb6viNB\\x97\\xf5\\xaf\\x98\\xb4\\xfd*\\x1eJ\\xcf\\x10;*q!\\xa6\\xefF\\xb51%\\x0e`K8\\x9a\\n\\xf2\\x89\\x0c\\xa0\\xad\\rB[R\\xb8R\\xd2\\xa6\\xca\\x13\\x96\\xe6\\x14\\x08\\x99\\x82\\x088X\\x88\\x95\\x84\\xbc-+2\\xcc\\xd5\\xe8\\x9a\\x17.\"J\\xc2#\\x88\\xa1c\\x93\\x9b\\x84\\xda\\x12\\xf1\\xd04\\xd1\\x8a\\xd5\\xe2\\xd8ZT\\x16\\x00Q\\rG\\t-N\\x95\\xbd\\x13\\x9b\\x89\\xd8\\x93\\x06Z\\xd0@r\\xd2\\xa0\\x9b,u\\x96\\x89\\x89\\x80\\xe7#\\x08\\xd3\\x19\\x858B\\xf5v\\xcaP\\x94$\\xca\\xca\\x10\\xd5\\xe37c6\\xb4J%:,\\xab\\xb6\\xa50W\\'N^a\\xaa\\x1cK\\x06kr\\x02Ta\\xa5Hd\\x90\\xc1z5(\\x81X\\x96\\xa1,X\\x82\\\\\\xa8K\\x9c\\xb011se\\xc7:\\xa9I)2\\x95n\\x9d\\x82\\xd5\\xf3u\\\\@\\x1f\\x1c\\xc8\\xcd\\xc0\\xc9<\\x88\\xc8C[m[\\xa0\\x13\\xe1\\xb9\\xd0\\xcbhB\\x82!\\x9c:\\xcc\\xa4\\xad\\x0c\\xe3D`\\xa2\\xc6^\\x85\\x89y8D\\x8d\"\\x87\\x08R*\\xc8\\xdb,\\'m\\xdc\\x96\\xd2#f\\x14d\\xed4\\\\\\xa7l\\xca\\x1aD$\\xc0\\xd5\\x9en\\xa5)\\xca\\x98\\x99\\x81+\\xac\\xab\\x1d\\x92\\x98V\\xc1\\xd0\\xc8\\x1c\\xc84\\xa3U\\x84JL\\xe4Y+c\\x0e\\x0cp\\x92V\\t\\xd0\\xbaY\\x10\\xa4\\x04R\\'9h\\xda\\x13\\x92q\\x01jI&^\\x8d\\xf5\\xd3\\xa5\\x8a\\x06\\xb4\\x82\\x11\\x90h\\x87#i\\xc9\\x1c(Yc\\xb1Z\\x1c\\xb7,\\xa3[ 2\\xd4eI\\x11\\x0b\\x01\\xa4dM\\x11a\"\\xc3\"R\\xd1ca\\x14\\xe3\\xd9\\x02\\xa5\\x05\\x14%yhT\\x8e\\x88a\\x91\\x9c\\x92\\x84\\xec\\xe5\\x81\\xa8\\xd92\\x80R\\x07 U\\x03\\x80\\x99s\\\\\\r*\\xa6\\x80\\xccT\\xde\\'B\\xb9\\x0c\\x92\\x96\\xe6\\x86(\\x9cX$$A\\x12\\xad\"Z2bU\\x0c\\xaa$\\x83\\x94\\x0bX\\xd4\\xb4\\xccH\\xa0\\x88\\xd1!Q\\xb5u\\xa59\\xd8\\x9a\\x13S\\x91\\x02RS\\x15\\x00(\\xc2e&\\x15cN4!HjV\\x98\\x8c\\x88\\xa32\\xf0\\xf4\"%fJ\\x93\\x84\\xb4)1!\\x06U\\x1bi@\\xa5L\\x82\\x88J\\xe9\\x04\\x1a\\xc9<(z:\\xe1PiB\\x91\\x18\\x94\\xe3e \\x80\\x05%H\\x03A\\xc5\\x05\\xae\\n&\\x89(\\x88\\x94\\xa90\\x88\\x98c\\x04\\x11@\\x1a\\xc6\\xb3\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01\\x05\\x02\\xab\\xab\\xab\\xafzkO\\xb8\\x9f\\xb8\\x03\\x0c\\xf1,h\\xfc\\xbb~o\\xcdZ0\\xce\\x8e\\xbaqz}\\xda\\xf6=\\xcb\\xa3\\x0e\\xbd\\xaa\\xea\\xc3\\xa3\\xa7a\\xa0\\xec\\x1ax!\\xf1\\xect=\\x87o\\xccx\\x84\\xb3\\xfc\\xdd{Q\\xd3G\\x8fj=X\\xfb\\x94\\x14\\xedZ\\x0f-h\\xd2h\\xce\\xa6\\x8f\\xcd\\xf9\\x97\\xe6F\\x8a\\xa7\\xdcK\\xa5Yt\\xfb\\xda\\xb0\\xc7r\\xa6\\x18\\xfb\\x81\\x8e\\x05\\xf1g\\x88e\\xf1|\\x17Z\\x83\\xdb@U\\xc1\\xd6\\xa9S\\x1a\\xb0\\x83U0*\\x03Wq\\xc7B\\xce\\x8e\\xae\\xba\\xb2\\x0b\\x0fG\\xe6\\x03\\x1d\\xd3\\xc4\\x8a\\x8c\\x19`P\\x1d\\x1au\\t\\xe1\\xe48p?\\x9b\\xf3\\x11WJ\\xa6\\xba%Z)\\xe9RY\\x1d\\xb5\\xee\\xa6\\r\\x0eA\\xf1\\xec\\x08t\\xed\\xd2\\x1e\\xbd\\xbc\\xc7b\\x18iV\\xbd\\xa9\\xd2i\\x88\\xe9H\\xe9I\\xecx\\x0e!\\xe8\\x96\\x95h\\xa4\\xd1Ht\\xd6\\x81\\xe8\\xea\\xea\\xf2g\\xb1\\xef\\xa3\\xd1\\xd1\\x9e\\xd4ut\\xec\\x8f\\xb8:X[\\x04\\x16t\\x14\\xd1CB\\x03S<Z\\x1ahR\\xb1\\xd3\\x1f\\x15\\xea\\xa8\\xd3B\\xa0Yuua5a\\x04(\\xe8h\\x1a\\x80~t\\xab\\xa7jv\\xa9z\\x97Muf\\xad,\\x8e\\xc0\\x9a\\xa9\\xa0\\xbc\\x83\\xadR\\xd6:\\x7f-4,\\xb3\\xc6=\\x14\\xb2BQG\\xfd\\xf1T\\rf\\x8e\\xacQ\\x92\\xc1\\xe9,\\xeb\\xdf\\x83\\xa9\\xee^%\\xe0\\x18\\xd1\\xa81N\\xc3W\\x93\\'\\xb6,\\x1dHh\\xabF\\xab%\\x95\\xf5\\x15t>/\\x88G\\x15pBC_\\xb6\\xd5\\x93)\\xa3\\xd0?>\\x01\\x96\\x19\\xadj\\x1dj\\xea\\x1f\\x95^ZU\\xe8\\xc2\\xbb\\x10\\xc2\\xbb\\x1e\\x01\\x96\\x01t\\x0f\\xf2\\xf9\\x1e\\x11\\x8dT(\\xd5\\xd9\\x02\\xae\\x94\\'\\x80j\\xf6\\xc3\\xe9z2u\\xea`U\\x8d\\x1e5x\\x00\\x16\\x1d\\x1e/\\x12\\xe9GP\\xc0\\xab\\xa3*\\x0cQ\\xd4\\x17\\xc5\\x90{\\x14\\xd4\\xba:\\xba\\xd0e\\xd0\\xaff\\xba$\\x10\\x07Q[\\xa3E\\x12\\xc5\\nV\\xc6\\xaf\\xf3v \\xd4:5)\\xa6\\xaf\\xca\\xa1\\x92\\x08\\xa7l^/P\\xcdK\\xc7@\\x1dT\\xf8\\xbdX\\xab\\x05\\xd6\\x8c3\\xda\\x8c\\xf0\\x91\\xa5\\xe9AP\\xd24!\\xd3\\xa9>\\xcdt\\x15c\\x89\\xa3\\xc9-K`\\xd5\\x96@z4\\x92\\xcf\\x00C4eO6K\\xab\\n`\\xbc\\x0b/\\x1a\\xbd\\x1dT\\xfc\\x8fj\\x9e\\xd5~r{C\\x89\\xf6\\xbc\\xabP\\xff\\x000\\xa69U\\xc8N)\\xe2\\xb3\\xd5\\xab\\xab\\x1a\\xbe\\rO\\x83\\x1d\\xa9\\xa8\\nd\\x16\\x03.\\x9a\\xe3\\xa9\\xa8}N\\xaf\\x8b\\x15t\\xa3$0\\x19tu\\xec\\x9fh\\xea\\xa1\\xc7\\xf3\\x16\\x84\\x82\\x92\\x9a2\\xd3\\xecbRW\\xaaS\\xc5U\\xc8\\xb2\\xea\\xea\\xc9\\xab\\xd5\\x8e\\xd5\\xa3\\xadZH},\\xbe\\xae\\xd5|~\\xe7\\x16~\\xe8\\xe2\\x9d\\x18\\x0c\\x01\\x96\\x8e\\x95|\\x1a\\xd8\\xa1\\x03B\\xafe<\\x13\\xed/\\x8d\\x1a\\x98\\xe2Od\\x9e\\xd5g^\\xdc\\x1eN\\xa1\\x87J\\xb0\\x1d\\x1dXP\\xec\\x9a\\xd4\\xeat\\xed^\\xc5\\x81\\xd3\\xdd#\\xb1\\xa3K\\x14e\\xa4Q\\xf9\\xbcR\\xd7\\x1b#\\xb7\\x9e+,\\x86\\x00\\xad\\x1e\\x0c\\xfbTK\\xe0\\xcdK\\x1d\\xb5 \\n>\\x0f\\x87b\\xc7\\x05V\\x80\\x12\\xe8\\xa0\\xc6\\xbfr\\x9fr\\xb8\\x9c\\x9dT\\xeaY\\xf6\\xabP\\xcdC \\xf6\\x08\\xd3&\\xa2\\xc2\\xc3S$\\xd4j\\xf80\\x1eE\\xd4\\xba\\x87P\\xc1z\\x93WZ0^\\xa1\\xea\\xc3:\\x04\\xbd;\\x06;\\xd1\\x94\\xbfe\\x85>\\xa7G\\xab\\xd5\\x96j\\xd2\\x03*\\xa3*k%\\xf9\\xea\\xf1i\\x18\\xb5v\\xad\\x1fQd>.\\x8f\\x83\\xab\\xd4\\xb4\\xf1t\\xd0p5$\\x07\\xa3\\xa3\\xf3\\xfb\\x99\\x06\\xa54\\xb0]^uy\\xbe!\\xd6\\x8d*,94\\x14\\x14u\\xa3\\xc9\\xf9=*\\xf8\\xb1\\xdb\"\\xd3\\xdc=^\\xaf\\x83\\x043F\\x18\\xfb\\xc7F\\x92\\x1e\\x85\\xe2\\xf1b\\xb5k\\xe2\\xc7\\x12Y\\xd4:\\x02\\xe8\\xc6\\x8e\\xbd\\xa8\\xc0\\x01\\xd1\\x97F>\\xe6\\x8e\\x94z:\\x07\\xab\\xd4\\x10\\xc0?x\\x864z:\\xbc\\xdd{,k\\xa3N-D\\xbc\\xb4K\\xa1/WC\\xdb\\x8b\\xa9\\xae\\xbd\\x8b\\xe9|\\x08\\xed\\xa8<FL\\x96;qc\\xef\\x9e\\xc2\\x8c\\x9e\\xdc\\x1eE\\xabWGD\\xb3\\xda\\xa2\\xb9\\xb2\\xa6T\\xfc\\xb1a\\xd7\\xb0$3\\xd8v\\x19=K#\\xb6\\x8c\\x11\\xdb\\x8fz\\xfd\\xdd\\x19,(>/Vx\\xb0\\xcd]k\\xd9C\\xbe/\\x80\\xfb\\x8aie\\x87\\xc1\\x8e\\xfa\\xbd_\\x1e\\xc3\\xef\\xd5\\x9e\\xd4\\xd0\\x92\\xea\\xa7^\\xd5\\xa3\\xe3\\xda\\xac\\xd5\\xf9\\x07N\\xd4z=]\\x1f\\x1f\\xb9_\\xbaHuu\\x0e\\xbf\\xcch\\xcfj\\x82\\xeaC\\xa9\\xed\\xafm\\x1e\\x9d\\xb8\\x9av\\xa7m\\x1e=\\xc0a\\xf0\\xfb\\x99:\\x87\\xa7\\xf3\\xa7\\x8d^Ut\\xed_\\xbdC\\xf7<\\xea\\xcfq\\xd8j\\xe9\\xab\\xab\\xe0Zu\\x1f\\xcdR\\x8e\\x8c\\xa4\\x06t\\xedF\\xa1\\xd8\\xff\\x001\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x01\\xed(\\xfd\\x84yJ~\\x81\\xfaQJZ\\xfa7\\xf4#\\xa8/\\x97\\xf3\\xed?\\x97e\\xb7\\xda\\x1fM}\\x11z\\x8d\\x0f\\x9e\\xd1\\xdcu\\xf4c\\xe3A\\xf5\\x8e\\xa5\\xfc\\x9f]\\x0f\\x97\\xd7\\xebG\\xcb&\\xd8\\x86A\\xbd)\\xf1\\xf4}5\\x1e4\\x1aH^\\x97\\xdb]\\xc7Rt\\x1a\\xcb\\xce\\x9c\\xf6_q\\xec\\xa4k--\\xbf\\xaa;\\n\\x7fd-}Q\\xf4Oo\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x01\\x03@\\x9bC$\\xbe-\\xf1\"\\xdd\\xf0\\xf8y%\\xfc\\xbf\\xc0\\x83\\xce\\xb5\\xd9\\xfet\\x9d\\x0b\\x16\\xe9\\xbeXRO-Q\\xfe\\xb4\\xf3\\xeb\\xa5\\xf0\\x1c`\\xff\\x00\\xb1\\xd0\\x9e\\x11/\\xba\\xb4\\'_\\xe8\\x8e\\x12\\xc5\\x97\\x94\\x9eXye\\xeb\\xfe\\x1d?4\\xb1\\xf2Aln:H\\x12\\x8e\\x08\\xe3J\\xd4\\xda|p\\x91\\xc2\\x054\\xca>C\\x01@\\xb2\\xfe\\xcf,y}\\x19D\\x89\\x8a)?y?\\xd1\\x89&@\\xfea\\xff\\x002n\\x9el[\\xcd\\xf9\\xd7\\x97\\x97\\x9a\\xa2\\x8e[:\\x14\\xbbb\\x01\\x950\\xaf\\xf5\\xd3\\xc0I\\xfb\\x83\\x11\\xf7\\x97\\x81/D\\x1b\\xd0\\x9a\\xf2\\x89\\xc4\\xbck\\xfe\\x12\\xd2S\\xa7\\x1ad\\x1fo\\xf9\\xdd\\xbfs1\\xb8S\\xb0\\x89\\x04\\x0f\\xb8\\xb2\\x07qE\\xd6\\x93\\xf1h\\xe7\\x9fWq>\\x8f\\xdcSO\\xf8\\x08\\xd3j?\\xafg\\xa3\\xfd\\xa7x\\xff\\x00cI\"\\xc6\\x9e\\xbaS\\'\\xc7\\x86\\x886\\x89ZZ\\r%\\xf4\\xd6S\\xae\\x132i\\x80\\xe3\\x96a&\\xccX\\x1eJeR\\x93\\x02$4\\xf5l~M\\x82\\x90\\x1et/\\xf9\\x92\\xee\\xe2\\x90x$\\xb7\\xcb\\x11r\\xd0\\x86\\\\04\\xcb\\x99[\\x88\\xff\\x00\\x81\\x97\\x8f\\xf3\\xa2>\\x1d\\x94\\xf0\\xf9\\xd7\\x96\\xe9&\\xf9k\\xfa\\xb3<R\\\\cKe\\xc8xh8\\xc0\\xe5\\xe1\\x03\\x90t\\xa4\\r\\x02Yi\\xcb\\xc8\\xfe\\xaf\\x96#Yxi\\xa0\\xc7\\x89r\\xdbO>\\x8f:\\x17\\x82\\xfa\\xd3\\xe7_,G=\\xa6\\x9ak\\xc0@\\xe6\\xd2\\x10\\x93\\xa8N\\x87X\\x8e\\xdak\\xfa<w\\x8f\\t}\\x13\\xfe\\xf8C\\xfe\\xf3\\xef\\x89\\xbd=;\\x7f\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x06?\\x02\\xfe|\\xb2>\\xf8\\xecG\\xdc\\xe3\\xfc\\xe7\\x1e\\xdc?\\x9c\\xfbY\\xec\\x18uu\\x7f\\x17N\\xc3\\xfdE\\xaf\\xf3\\x1c~\\xe8\\xf8\\xfd\\xc0\\xe9\\xdc\\x7f\\xaa\\xce\\x9f\\xcc\\xd5\\x8e\\xe0\\xb1\\xf7x\\xf7\\x1fw\\x8f~?\\xccq\\xee;\\x1f\\xb8\\x19%\\xfc;%\\x9f\\x9b=\\xa9\\xf7\\x07\\xf3Z\\xff\\x004^\\xbd\\xcb\\x0c\\xbf\\x8f\\xdd\\xafaOS\\xd8}\\xce\\x1f\\xce\\xf1t\\xed\\xc7\\xf9\\x83\\xd8}\\xd3_\\xbb\\xab\\xd3\\xb6\\x9d\\xf5\\xfb\\xdc~\\xfd>\\xf5G\\x1a}\\xc4\\xfa\\xf6\\xa3\\'\\xb0g\\xb5\\x1d<\\xdf\\xd8\\xc7~\\x1d\\xeb_\\xe64\\xed\\xc5\\xf1\\x0f\\xcd\\xf0u\\x1fw\\xc9\\xe9\\xd8\\xf7\\xd1\\xd3\\xb1.\\xbd\\x83\\xaf\\xdc\\xe3\\xc7\\xbf\\x1f\\xe6\\xb8v\\xe2\\xe9\\xafm\\x7f\\x98\\xd3\\xee}\\xaf\\xedg\\xe4\\xcf`\\xc3/\\x87~\\x1d\\xb5t\\xfb\\xdc>\\xe7\\x17\\xc1\\xf0\\xef\\xe5\\xf3c\\xef\\x8e\\xc7\\xeeQ\\x8f\\x9b\\x0c\\xfc\\xd9c\\xbf\\x17Z\\xf6\\xf8=\\x07j}\\xfa\\xf64g\\xf9\\x90\\xc7n/\\xecc\\xb0\\xfe\\xcb\\xab\\x0c\\xfc{\\xf0\\xfb\\xba=\\x1e\\xbf\\xcc\\xea\\xf4=\\xb8j\\xf5\\x1av\\x1ft\\x0f\\xb8Xe\\xab\\xb0c\\xb1\\xd7\\xbf\\x17\\xc7\\x8fm\\x1f\\x07O\\xbd\\xc7\\xb7\\x1e\\xdeZ1\\xdbV*\\xf5\\xfe`\\x96;\\x83\\xdd?>\\xca\\xfb\\x95\\xefO\\xb8\\x07~\\x0f^\\xdeuz\\x97\\xa7\\x0e\\xe4S\\xef\\x1e\\xd5\\x1fr\\xbd\\xa8\\xcf\\xdc\\xe3\\xdb\\x83\\xd3\\xb5)O\\xbd\\xaf~,\\xea;j\\x1f\\x0e\\xf5\\xfe{\\x83\\xe1\\xf7t\\xef\\xea_\\x16\\x01|;\\x8a\\xff\\x001\\xa7\\xdd\\xd3\\xf9\\xcd\\x18\\xec~\\xf7\\x0e\\xc0\\xbe?w\\x8fz\\x7f\\x07\\xdc\\xe0\\xf5\\xd7\\xfdG\\xc3\\xee\\xe9\\xe5\\xd8W\\xf9\\xbf\\'\\xc7\\xb1z\\x8e\\xdc^\\xa1\\xe8\\xf8v\\x1f\\x7fN\\xda\\xf6/_\\xe68\\xf6\\x1d\\xf4\\xef\\xfd\\xdf\\xbd\\xeb\\xdb\\x8b\\xafn?\\xcdh\\xfe?\\xcf\\x0e\\xf5\\xfb\\xdc>\\xe1\\x7f\\x0e\\xde\\x7f\\xcfW\\xee\\xd3\\xf9\\x8e/\\x83\\xd5\\xf1\\xed\\xa7\\xfa\\x87\\xcd\\xf0\\xfb\\x95\\xfe\\x7f^\\xfc~\\xe7\\x07\\xc3\\xb5j\\xc7\\xf3:\\xf7\\xe1\\xda\\x9d\\xf5\\x1f\\xccq\\xfec\\x8fn?\\xcfk\\xd8=\\x1fP\\xfecO\\xbd_\\xb9\\xaf\\xde\\xe3\\xdb\\x87\\xf3z\\xf7\\xe3\\xfe\\xf9\\xfe\\x0fO\\xe7\\x87\\xdf=\\xeb\\xfc\\xdf\\x1e\\xc3\\xe3\\xfc\\xff\\x00\\xff\\xc4\\x003\\x10\\x01\\x00\\x03\\x00\\x02\\x02\\x02\\x02\\x02\\x03\\x01\\x01\\x00\\x00\\x02\\x0b\\x01\\x11\\x00!1AQaq\\x81\\x91\\xa1\\xb1\\xc1\\xf0\\xd1\\x10\\xe1\\xf1 0@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?!9\\xef\\xfc\\x18\\xe6\\xeb\\xb8\\xb3%A\\xcc\\xa7\\x05\\xe1\\xfe\\xac\\xc5\\xe6\\xc4\\x10Q\\xcd\\x8d\\xa3\\xee\\xe2E\\xe4{\\xae_u\\xc8\\xf3D\\x16\\x7fb\\xa8B\\xf5<oZ\\xd8\\xdd\\xfb?\\xddBO\\x9b\\xc3\\xac\\x1d\\x9d}\\xd5\\xb1\\xd7\\xf1F\\xbel\\xaba\\xfc\\xac\\x0e\\xea\\xc5\\x90\\xd2\\xc8\\xed\\x97\\xd5\\x95[\\x04\\xb1g\\xcd\\xf6\\xcb\\n\\x11\\xcf\\xd5\\x84\\xedG\\x1b\\xf9\\xae\\xa3\\xea\\x888\\x92\\xac\\xbeU\\x9f\\x15\" \\xbb\\x1f\\xe6\\xf3\\xc7\\xff\\x00o#4\\xc1F\\x8fj\\x03~[\\xe4\\x9c\\x91bJ\\xc0\\xa4\\xaf\\x86\\xd8\\xd9b\\xd7<\\x9e\\xd5^\\xb9\\xd5\\x986\\x0f\\xaa\\xe1\\x87\\xdd\\x8c\\xba\\xd6\\x1d/\\xc5 \\xf6\\xd7^?\\x16s\\x9b\\xd15\\xf0^,\\xe9N&~+\\xf2\\xaf\\x16Q\\xee\\xa1\\xf6\\xa4\\xcd)\\xd4\\x10\\xbf\\x17G\\x14\\x07\\x98\\xa1\\xee\\x85~\\xaal\\xc6YA\\xd3\\x9f6\"#\\xa2\\xc75\\xfc\\xcb,\\x99\\xe7l\\xc0\\x1fk\\x00\\x81\\xc5\\x05\\x0f\\x12\\xde\\x8d\\x96\\x1e&\\xf0\\x9b\\xf1\\xec\\xacs\\xef\\x9a\\xcc)\\x97\\xe5c\\x05\\x82\\xc7no~\\x0b!\\xc1\\x94\\x8b\\x08\\x83\\xbb\\xf8\\xa8\\x82+l\\x85:\\xa9\\x1d^z\\xbb\\xe2\\xc4&*\\x8el\\x1e\\xa2\\xc1;?U\\xf3\\x1d\\xdc\\xaf\\x9b5\\xcf\\x9f\\xf8\\xc9M\\x8a\\x0e]\\xb2\\x9c^\\xd6\\x02\\xc6\\xd9\\x06.\\x15<\\x17\\x11\\x1f-%LA?\\xcd\\x13\\xf4T\\xc4\\xea\\xee/\\xc9|GvtO5\\x07\\xb9Sc\\xdd\\xc1\\xf3\\x16\\x1f\\xbb\\x87\\xdd\\xe4\\x16, \\x8e\\x15\\xbd\\x1f\\x144\\xe7\\xca\\xb1[<O62\\xd2\\xf4\\x12\\xb2\\xee\\xe3\\xd2\\x80\\xef\\x0b\\xe5\\x16\\x04J\\xd5\\x84#\\x1eopd\\x8b\\x8c\\xfe?\\xe4\\xcd\\xbd\\xf4\\xf5q*\\xd8Qi{\\xa5\\x87\\xf8\\xa5\\xecS..\\xe8\\xf8\\xba\\xa1\\xf9\\x96\\xeb\\x99h\\x8c<\\xac\\xe2\\\\*\\xf1Y\\x10\\xf8\\xd5\\xff\\x00\\x9bh\\xa2\\xf6UDqy\\xa0\\x88O\\x8a!\\xe0\\xff\\x00W\\x82<M\\x01\\x18\\xf1\\xbf4\\xf1\\xee\\xe1\\xe6\\xb2\\xf9\\xab\\xbc\\x7f\\xc0\\x0bc\\xd8\\xae\\xbf\\xe2</\\xa3\\x94$\\xd7+&M\\x13\\xb0\\xc5\\x892l\\x7f\\x83\\xfeb\\xe9N+\\xc6\\x15\\xe2ll\\xfa\\xaf\\xc1\\x8e/a\\xc1\\x14\\xcf\\x1e\\\\\\xbd\\xfa\\x91\\x1eB/\\x0eqf\\xb5\\x814.\\xb1N\\xca\\x92\\x0f\\x8dj{T\\x91\\xf0\\x94\\xc8\\xbbj\\x97\\x1b\\x16~q\\xa7Nb\\xb0\\x81\\x17S.P1\\xb0]\\x19\\xc5~\\x7f\\xe1\\xe6\\xff\\x005\\xf3,F\\x07\\xaau\\xb0\\x086\\x9b\\x90\\xdd\\x86\\x0b\\x02y\\x94\\xc79G\\xc4\\xfdQ<\\x95\\x88\\xb8\\xba<W\\n_\\x97?U33\\x96\\xf0W\\xf15\\x04;[\\xf4\\x97\\x13\\xea\\xf33\\x89,\\x88x\\xab\\xc1\\xddD\\xe1\\x9c\\xd8\\x02\\xc6\\x9er\\x8e}O\\x14\\xc3\\xbd\\x16l\\xff\\x00\\x13L\\xbe\\x9f\\xba\\x98\\xb2\\xed\\xbd\\xd3\\x13\\'\\xdd\\xf8\\x1e\\xaa(I\\xe6o\\x91\\xd5^$v\\xf4\\xaer\\xae\\x18\\xdc\\xf4\\xbb\\xe2\\xe9\\xcf\\xfc\\xa1\\x91\\xdf\\xcd\\xd8\\x06\\x8ak\\xcdP<{\\xea\\x9d\\x8a\\x1e\\xa9\\xc19\\xccV\\xd4Q\\x8a\\x81f \\xe2\\xc3\\x1b\\xc1\\x16O-\\xea\\xac\\x8e&\\xba\\x8a\\xebsSR\\xf1X@l\\x99F\\x8a\\xc0y*r\\xbf\\x00W\\x1au\\x9a\\x90#\\x9a\\xfc\\x1e=Vd\\xbf\\xc5[\\xc7\\x99\\xbfm\\xe4{\\xcb\\xa9\\x80\\x8a\\x8eM\\xef\\x9e\\xeel\\x1bP\\xd77\\x93n/1b>\\xe8c-\\x10\\x8e)I\\xff\\x00b\\xc9\\r#\\xaa\\xfc\\xdc\\x1c>9\\xbf\\xe0S<YV\\xe8\\xe4\\xfc\\xd3\\xcb\\xda\\x0e\\xff\\x00\\xe0?\\xc0\\xebV\\x17\\x91a\\xe6\\xfc\\x03I]\\xa3\\xf7X\\x1d\\xf3H\\x85\\xd5\\xe4\\xf1i\\x88\\x877\\x95\\xb2\\xb6\\x04\\x8e\\xe9X\\xf8\\n\\xf5*\\xa9}Th\\t\\x86g\\xcd\\xc6\\x1f\\xba\\x98\\x1c*\\x17\\xa7_\\xea\\xa1\\xdb\\x91\\xb0\\xb1\\xaeVG2\\xbd%\\x98\\xe5pu\\'\\xab\\x07\\x92\\xb3\\x88>k\\x08\\xd6\\xd9\\xb9*\\x10\\xf3\\xdf\\x13r\\xd1T\\xc2o\\x88\\xee\\xb4~Otc\\xe7\\xe7\\x9a\\xbc\\x16v\\xe6\\xf1\\x95\\xd1\\xa6*0\\xads\\xaf\\xfc\\xb3~e\\xe2\\x16p\\x9e\\xear>\\xd5\\x008\\xa6\\x9f\\x92\\xc5\\xf3\\x8d\\x1c=\\xad\\x7f\\xaa\\xc4\\x0fK\\xf0jO\\x8f\\xaa\\x13\\x87=X\\xc9\\xc3G2\\x9f~\\xa8}SXc\\x0f7\\xb8\\xdf5\\x08\\x99\\xa4\\xf6v\\xa8\\xd8\\x9f\\xe6\\xb8T\\xb5\\x1c\\xd44\\x1fvw0Q\\x92\\x86/\\x01AQ\\x18O\\xcdC\\x0b\\x11\\xd4sE\\x86\\xa4y\\xa8\\x8cG\\x81\\xd5\\x82!d\\xbdT\\x96?VQ>)\\xe8kI^f\\x90\\x12j\\xb6d4K\\xff\\x008\\xa8\\xdd\\xf5g\\xaf\\x0c\\xd10tX\\x7fy\\xe1\\xa6V\\xef?T\\xf09B\\x14=\\xd7,\\xf1\\x0f\\xd5\\xc7\\xabj=\\xba\\xbb\\x127\\x97\\xe2\\x87\\xba\\x94\\xab\\xbdN\\xaf$\\xec\\xae\\xad\\xa1\\xec\\xa4\\x17\\xe6\\xfa\\xb1\\x1e\\xa8\\x1cl\\xc5t\\xc7\\xcd\\x0c\\x13\\x9e\\xaap\\x1dwG1\\xb3q4\\xd9\\xba7\\xcd1C<^\\xcfu\\x87\\x1c\\xd9\\xa4T\\xefr\\xe4\\xcf\\x11G\\xd9\\xa3\\xa3O\\xdd>?\\x166\\xf2\\xea\\x8coG\\x82\\xadg\\xc5\\xc2<&\\xc0\\xcd\\xd4\\xd8p\\xce?\\x16W8\\x88\\xfd\\xd8\\x89\\xd9D\\x97\\x81`\\x91\\xc8\\xd8&\\x1f\\xe0\\xa6\\xd9u~,\\x13\\xad\\xf3v\\x89\\xcb\\xc0\\xa2\\x7f\\xf2\\x8e\\x07c\\xf1xt+\\xe4\\x18_\\x10\\xf7A\\x19P\\xe8\\xb0p\\x14?\\x1f\\xf1\\xc1<v\\x16}\\x90\\xfa\\x9a\\x8b\\xc0\\x1e\\xcc\\xa3\\xca\\xa2\\xa4\\xce\\xdez\\xb8\\n\\x87\\xf7`\\x18\\x0f\\xc8l?\\xfc\\x15\\x82>\\\\\\xab\\xa7\\x17\\xa9\\xb2o\\xee\\xfdgW\\x13\\xeb\\xab3\\x13\\xb2\\xfe\\x92\\x9d\\xb6x\\xf8+\\x8b\\xee)\\x04r^\\xfe_\\xdd\\xea\\xf9\\xbc\\x98=\\xcd\\xe2FM\\xc8\\xb1\\xcd~\\x16H\\xd7=Y\\x1e?5\\x92\\xc9z1a\\x99\\xca\\x94\\x1a\\xfdQ\\x00\\xf2\\xaax\\xd7h\\xe5ed\\xd1\\'\\xb6\\x93\\x9f\\xdds\\xbf\\xee\\xf8\\x1f\\xba<6=\\x8f\\xab\\xe0\\xcc\\xee\\x8a\\x0e\\xfd{\\xbc\\x12%J\\x9d\\x0f\\xd4X0\\x8d\\xf3d\\xfd8\\xae4\\x1e\\x92\\xf8\\x9f\\x8a\\xb4&\\x0b\\xa9}\\xd0,\\xd7\\xab$\\x95\\x89\\xf2\\xdd\\xc6\\xf7\\xbc\\xd9A\\xfb\\xbd\\xf0\\xa7(to\\x1e\\xd9\\xff\\x00\\xca%\\x11\\xc9\\x17A\\xe3\\xaf\\xf8BQ\\xc6w\\x1fuhC\\\\\\xe7\\x8a\\x18\\xc3l1seq\\x07\\xaaN9\\x8f4G\\x0e\\xaa\\xfa\\x7f7\\x8a,\\xe5\\xf1\\xcdi\\x07\\xcdG\\xbcP\\x1b=\\xd7S\\xdb\\xd5\\xc0\\x19}X\\xf0\\x19\\xf8\\xb9\\x03\\xb3\\xea\\xc1\\x1e>\\xb9\\xb3\\xb1-sV\\x13(,\\x97\\xbc\\x9a\\xb7\\xf6.k?\\xf1N\\xe6\\x9c\\xcf\\x17\\xd1\\xd5\\xd1x*,\\xfa\\xb2\\x1f\\xb2\\xc2\\x84\\xbct\\x8e\\x05\\xcb\\x11\\xf7\\xff\\x00\\x01,\\x9c\\x9b\\x1a\\tb\\xff\\x00\\xcaC\\x82\\xcew\\xe1p\\xf2M@\\xa7\\x95\\x17\\x89G\\x90%\\xa8\\x92\\xc3k\\x1eD\\xf7R?\\xea\\xc9\\x13\\xfb\\xbb\\xc1I\\x852\\x80\\xe6\\xe0)\\x9e\\xaag\\x97\\xed\\xa0N\\xa6\\xe3A\\xad\\xf3|\\xe5}_\\x98\\xbcI\\x0cM\\x8a]U\\'\\xe9Lu\\xe0\\xb1\\xd7qY\\xc8\\xd2*\\x87<\\xdd\\x13\\xcb\\xe6\\xe6\\x18\\xd8\\'\\xd53\\xdd\\x83Z\\xb6a\\x9a\\xc79\\xabX\\xac\\x88\\x94>/B\\xe0\\xfeo:\\xf8\\xbc\\x19B%mG\\xc4\\x8d3\\xe6\\xc9\\x04Z\\x1c\\xdcGU\\xc93\\x9c\\xdc\\x03\\xe0W\\xc86\\xa3\\xaeh\\xe0<m\\x11\\xe7\\xab\\xe11\\xea\\xcc\\xe6_Tq9v>\\xf6\\xcc\\x00,\\x02,)\\xf2\\xfb\\xb2\\x98\\x12~l\\xb1\\xb7\\xff\\x00\\x9a87\\xeb9\\xb9\\x08\\xfc\\xdef^\\x11\\xbe#\\x8a\\x8f\\x9aqM>+b\\xb34\\x93\\xb3\\xb7X\\x0f\\xe2\\xe47\\xdd\\xc8\\xfd\\xdcz\\x9aE\\x86\\xeb3w >,\\xa9\\xee\\xc4\\xc5\\x80<\\xbdsf!\\x1dY\\xa4\\x1a\\x83A\\x15\\x99\\x0f\\x1bE)\\xb2\\x9e\\xcd\\x15Ph<\\x82l\\x91\\xfc\\xb7\\x95u\\xdfk!\\x9dP\\x99\\xdf\\x8aj\\x87\\xd5\\x90\\xe8}\\xd2(\\xe46\\xcck\\x9cm\\x0e\\r,C\\xb2\\xc2C\\xba\\xf0\\xf7\\xdd.\\xe4f48z\\xdb\\xeb\\xd5\\x19\\x11\\xf3u\\x95\\x19\\xb1[\\x94\\xe2\\x827Y\\xbe\\xaa\\xe1\\xf1d\\xc4\\x9b\\xe4\\xa4\\x1c\\x87\\xd7v!q\\x95I\\x07\\xb6\\x83\\xf9\\xb1\\xbf%f#\\x99\\xc6\\xc3GJ\\xb5\\n\\x1a\\xa6/c\\xcd\\x1c!L\\xd9\\xaa\\x90S\\x0e\\xdb\\x04\\xa8|X\\xe1$]\\xf2\\xc0\\xec\\xb0\\xae\\x0f\\xab\\x07\\xee\\xaa\\n\\x88U\\xe3\\xd1e\\xe4z\\xbe\\x02K!\\x1e\\xeb\\x10K\\xe6\\xb0\\xee\\x9b\\x93W\\x8b\\xc58\\xff\\x00\\x9cU\\xa8wu\\xff\\x00J\\xbf?\\x12U\\xf0\\xacL\\x8a`\\x92z\\xa3\\x18\\xe5rHv\\xb8\\xc1U<\\x9b\\x01\\x87\\x96\\x89&\\x11\\x8b)&K/\\x08nq\\xd3*h\\x93\\xf6\\xb0e(\\x07\\x16d\\x84X\\x08\\xcaA\\xd7\\x8f\\x17\\xc2?\\x85O,\\x1e\\r\\xb2@\\x87\\xab\\x89\\xf8\\xcd\\xa9\\xbd\\xd5\\xeb\\xff\\x00\\xb6S#(\\xd9\\xe7\\xee\\xe6 \\x8f\\xf3\\x8a\\xcb\\x9c\\xbf\\x8aC+\\x94\\x0e\\xd5\\xf0O\\x9a|^Q\\xff\\x00\\'\\xfe3R\\x85H\\xed\\xed\\x0c\\xbe,\\x04\\xa2|\\xfcVD\\x0cwi\\x03\\xb9\\xa0.v\\xca|\\x95k6i\\x11\\xd4\\xd1\\x8d\\xa5\\xc9<Y\\x8f\\xc5\\xd3\\xbd\\xbc\\x88\\xdf\\x9a\\xa7*y\\xd8Y\\xceSf\\xbcE\\x80\\x9b\\x0f\\xf4\\xf3T:}7\\xcd\\x0e{\\xca\\x9f1\\xf5H=^\\x16\\x0f\\x8b\\x8e\\xa3\\xe2\\x9a\\xc9\\x1f\\xd58 >\\xeaI\\xdc\\x9f\\x15|\\x16\\x0c@?\\xb5@\\x0b\\x8fq@\\x19D\\xd8\\xac\\xbd\\x7f\\xc9\\xde+R{\\xbb\\x16\\nwi.y\\xa7\\x8dQ9\\xc5$qX\\xeah/\\xc8\\xb1\\x8eo\\xb1>\\xe8b4\\xa1\\xc2<aS\\xc8\\x9b#?v\\'\\xc5l\\xcd%U W\\xdb\\xee\\x84D\\xce\\xc9$\\x11Dv\\x9a\\x10\\xc7\\xc2o)\\xbd\\xfc\\xa9\\xc0\\x04\\xaf\\x03\\xf3O{\\x80\\x9dY\\xe0\\xf0\\xd3\\x8e\\x1b&#\\xff\\x00(\\\\~)\\x00Xl\\x0f\\x7f\\xf1j\\xf3\\x14N\\xcf\\xc5\\x98\\xf36\\x00e-\\x1fO\\x92\\xe3\\xa7\\x8b\\'\\xcb\\xdd\\xd9\\xd3\\xe3\\x8a \\x1d\\xa7\\x94CP\\xef\\x8a&b\\x98\\xd0\\x1b\\x1c\\xf1f\\xc9;9v\\x8f\\xcdA\\xe6\\xf9M\\xc7t\\xc10\\xddOo\\xba\\xf1\\xb95\\x02\",\\x9ei\\xe2\\xef\\x0f\\xe2\\xf20\\ng\\xa5\\x02xl\\xb7\\xaa\\x91\\xd4~\\xec\\x1c\\xca\\xfb/\\x8a\\xec\\x9fZY\\x07\\x819\\x9aPb,\\xf9\\xbf+;__\\xf1|\\x12T\\xe5QHV\\xe1\\x81\\xe2\\xa9(\\xc7\\xf9\\xb2;\\xbawZ\\xc3\\xc5\\x97\\x93\\xcf\\x05\\xfck\\x97\\x00\\x94?\\xc8\\xbcN\\xd8vk]\\x9c\\xa2\\xc6*4\\x96\\x8f\\x9a\\x88?\\xbaH\\x9b4\\x0c\\xb3\\xdf\\x14\\x0e.\\xf5T\\xb9\\xd5\\x19\\xcf4;-\\x96\\xe9\\xb9\\xccAu\\xfe\\xd4\\x07\\xbf\\x9ayg\\xc5x0s\\xcd\\x1f\\x8d9\\xb8\\xf7\\xfa\\xba\\xfc\\xd7\\x8ay\\xff\\x00\\x9b\\x0cY\\x9ekD\\xf1\\x166~\\x95\\xe8\\x1f\\xe2\\xa3\\xa5v\\x91\\xf5D\\xbd\\x96NDk7#d\\xdc\\xd8ff\\x9d\\x1b75\\xf2\\xa3jQ\\xce\\xd5\\x92\\x82\\xc8\\xce\\xeb\\x00V\\xe8\\x93P\\xe5\\xd5R \\xa4u\\xcd\\xd8\\xf0\\xb8\\xc7K\\xc7\\x82\\xc8\\x8e\\xde\\xf9\\x7f\\x17\\x8d[(\\x0e\\x16\\x0c\\xff\\x00v@\\x11`\\xf8\\xb3g\\xcd\\xe2\\xcfW/\\xc5c\\xe6\\xcc\\xba\\xb9\\xe3\\xf7g\\xff\\x00\\x14\\xd8\\x81\\xeb\\x9b\\xa3\\xf8\\x8a\\xba\\xf4\\xf3g#\\x1fe\\x8e\\xd7\\x05b\\xca\\t?\\xe2\\x1f\\xae\\xae\\xc0\\xd8uY?\\xe7\\x9b\\x1b\\xe6\\xa9\\xee)<4\\xb2\\x12Q\\xe2\\x89\\x8f-&^\\xa9;7\\xe0\\xac\\x03>l\\x9d\\xb1w\\xdf\\xe6\\xec\\xc7\\x11g6\\xe9\\xff\\x000\\xba\\xdf\\xbb(\\xd5\\xab5\\'\\xce7\\x9f[O.\\xe8x9\\xf3u\\xae~\\x166J\\xd2/\\x19W4\\xbc\\xff\\x00\\xc8\\xdb\\x0fw|\\xdd,\\x11\\x9cVHM\\xd4?\\xf0\\xe5\\xa6_\\x0b\\xd5\\x9f6\\x88\\xe5ck\\x9cEp<\\xd7\\x87\\xb1V\\x9e\\xeae\\x1c\\xfb\\xaeX\\xdb\\xc6\\xd9\\xee\\xad\\x1b3uBW\\xd9Y\\xc2\\xa0\"\\x89g\\xaaO\\x9e\\xeb\\x1e\\xf9\\xb0O\\xaaB\\xf1\\xdf\\xfcxn\\xb7\\xdf\\xba\\xaf\\xee\\x9a6d\\xa6\\x9b\\x7f\\xff\\xda\\x00\\x0c\\x03\\x01\\x00\\x02\\x11\\x03\\x11\\x00\\x00\\x10>\\xfd\\xf2\\x95\\xf9%\\xe6\\x95zI\\xff\\x00[\\x1dW\\x96\\xe5\\x9a\\xdc\\x8b\\x11\\xe2\\xfb\\xac\\xaa!\\xc9-\\xc9\\xa2\\xaaUlT\\xa5\\x17D#d\\xfc\\x9d\\xe0\\x8e\\x9eQ3\\x1e\\xac\\x99\\x1b\\xc1\\x12\\xeaU\\x8e[\\x0f\\x17\\x80:\\x82K\\xec\\x84\\xe5\\xe1\\xde\\xea\\xca\\xdc\\xb4;\\x81\\xcd/8\\xf8\\xe8\\x00\\xba\\x89\\xcc\\xaf.\\xed\\\\\\x1ec\\xb69\\xd3\\x0b\\x08\\x8e0\\xc5\\xdf\\xb5\\xban\\xb9\\xb0sw\\x08\\xe4\\xa2\\x89vvCJ&\\x86Uy8\\xd4T\\xc0\\x83\\x9f\\x83\\xa9\\xb0.&\\x04i\\xce\"\\xa0\\xba8R\\xdb>\\xfcjAXP\\x1b3\\xd5\\xe3\\xf68k\\x12\\x82p\\xb2\\xd5\\t\\x91M@\\xa7\\x9dZ\\xa2\\xbe|;\\x07\\xae\\x91BBl\\xaf1[<\\xb2\\x9ci\\xfc\\xe7\\x89\\xe6F\\xd7H7oS&\\x9c\\xc3\\'\\xbb\\xa9\\xa3\\xdf\\'\\xca\\xc3\\xe3\\x08\\xee\\xcd\\xf3)\\xdc\\x0c$\\xdd\\x18\\xef\\xff\\xc4\\x003\\x11\\x01\\x01\\x01\\x00\\x03\\x00\\x01\\x02\\x05\\x05\\x01\\x01\\x00\\x01\\x01\\t\\x01\\x00\\x11!1\\x10AQa q\\xf0\\x91\\x81\\xa1\\xb1\\xd1\\xc1\\xe1\\xf10@P`p\\x80\\x90\\xa0\\xb0\\xc0\\xd0\\xe0\\xff\\xda\\x00\\x08\\x01\\x03\\x11\\x01?\\x10\\xf4\\xfaOS\\xe2Y\\x1c\\xe3=Y\\xf3\\x1a\\xfeV\\xe1l\\xf9\\xcd\\xd4Y\\xe6\\xdb\\x11\\xd1t\\xbe\\x8b:nx\\xbb\\x8f\\xa7\\xc6\\xcf\\r\\x9c\\xd9\\x96\\\\c\\x889\\xb3M\\xf37\\xe7\\xf0\\x8ev\\xc4\\x8d\\xcf\\xac\\x0c\\xcb\\x00\\x0f\\xa5\\xc0\\xeb\\xe3\\x1c\\xf7\\xf5\\x90\\xc9^-9\\xb8o\\x10\\xbe\\'\\x9b\\xefyL\\x9ey\\x90{\\x87\\x1b\\x1b\\xa9-\\xf0D\\xcc\\xe6\\x0e3\\xebc\\x9f\\xa2\\xfe.\\xbe%\\x8f\\xba\\xe5\\xd4\\x17\\xd2|I\\xd5\\xa0\\xad\\xben\\xb8\\xferA\\xcf\\xcc\\xae\\xe3\\xdf\\x80]\\xdem\\xe7!\\xa9\\xfb\\xc7\\xe7w%\\x99c\\xe2\\xc8\\xb8\\xb2\\xf9\\x9f\\x01\\xe7\\xcc\\xf1:/\\x90\\xf9\\xb8si\\xa1|\\xceos\\xa7Q\\xf5\\xbb\\xd2\\xeb\\xe6\\xe9\\xee\\xfc\\xec\\xf7.\\xdf\\x97\\x87yu\\x8e\\xa6\\xff\\x00L]1\\xday[\\xe2\\x0e\\xb5\\x9c\\xf3\\xedc\\xf5\\x9e\\xfc\\xf8\\x89\\xf0o\\xe4\\xbb\\x04\\xf4\\x0b\\xe4H\\x84K\\x90}\\xa36^\\xee\\x961\\xebd\\xe2\\xd0\\x84o\\xef\\xe9g/7\\x8b\\x8a[\\xb7V=lpXxg\\x87\\x160\\xfc\\xdb\\xbf\\x0c\\x8e\\xd8\\x04\\xce\\xa1\\xea\\xd3\\xf0,\\xe3\\xc3\\xef\\xd4\\x18\\x07Q\\xdcl\\xb2\\x19\\xab\\x9f\\xac|\\xb5\\xbf\\xbd\\xb3\\xd5\\xdb\\xc3\\xaf\\xc0\\xb9\\xf1\\xcc\\x83cO\\x10\\xcb|\\x1c[\\x96\\xbe\\xb7.\\xa1\\xcf\\x9bg\\xec@\\xbfH\\x9f^\\xfc\\xc8\\x18\\xe4g\\xa3H\\x03\\xf7\\xf0\\xe1\\xe2#\\xaf\\x1e#\\xd7\\xdc\\x82??7}\\x1d\\xb7n\\xaf\\x9b\\xae\\xfc\\xcf7\\xd7\\xaf\\x0f\\xc5fbC|\\x1dx\\xf5\\xe7\\xff\\xda\\x00\\x08\\x01\\x02\\x11\\x01?\\x10\\x0ed\\xfa\\x1b\\x07\\xe1\\x90w.M\\xac~\\xf7..\\x98\\xfa\\xc1\\xf6\\x00G\\xe79\\xb0\\xed\\xfag\\xe4t\\x7fFCvq\\xfe\\x91,\".\\x1f1\\xd4\\x99\\x8co\\xcd\\x8ff\\xb9\\xf8\\x97\\xa3\\x87\\xed\\x03\\xc6[\\xdf\\x898\\xbb#\\xb1\\xf1v\\x044\\xfd~b\\xe2\\x1f_\\xdet!\\xd9\\xc4\\x0b\\x01\\x9d\\xe9\\xf4e\\xfa\\x0b\\xc3\\x96\\xdf\\x83\\xb2\\x03\\xce\\xf6\\xb3\\xea\\xdd\\xb7\\x8ed\\xc6\\xb8\\x85l-\\xe3g\\x8f\\xb7\\x13\\xcf\\xcb\\xf8!\\xcf\\x9e\\'\\xa2\\xed\\xac\\xb4\\xa7x$\\xdat2V\\xee\\xeb\\xcf\\xed\\rp\\xfd\\'\\xfb\\x93s>]\\xff\\x00\\x91\\x88\\xbes\\x19bga\\x87\\xde_\\x98)\\xfcq\\x00\\x01\\x99\\xab\\xf9\\xda<\\xf3\\xb0\\x07\\xda\\x1c\\x80o\\xccw!y\\xb7\\xabu9\\xfe/\\x94\\xc0\\x96r\\x1fYz<\\xb2kK\\xe4}mp?\\x98\\x1d\\xfb\\xff\\x00\\x17@~o\\xe6f\\x03\\xa3\\x8b\\x85\\x07\\xcd\\xcb`\\xbb)\\x93\\x91\\xff\\x000\\'\\xbe\\x8f\\x88\\xd0\\x99p9\\xdf\\xday\\x08N\\x9e#\\x05V}-^\\xa3~[s\\xc4\\x88Ey\\xbb cG\\x93\\xe6\\x10\\xe5\\xdf\\xa4\\xa8\\xf1\\xf5\\xe6@\\xee\\xc3\\x87>m3\\x90:\\\\@p-\\x96\\xcf\\xc0\\x9f\\xb5\\xb2\\x9c\\xec\\xbb/8\\xfe\\xcc\\xa8\\xc7\\x87\\xc7\\xd2`\\xee\\xff\\x00kN\\xf7\\x8f\\xde$+\\x8f\\xac\\xca\\x0f\\xcc\\x1a]\\xb8\\xfa\\xc7\\\\=\\xfc\\xee\\xdcb\\xe9\\x9f=F\\x9d\\xde\\xbewXf\"\\x01\\'\\xe1\\xe6\\x1d\\x9a\\xf4~W\\x07>F\\xda\\x0f<g\\x04\\xa1\\xa7\\x1d\\xfd\\xcbH*+\\xbf\\xcc[\\xcb\\xe1\\xf9{\\xb7+\\x87\\x1dB`\\xa5\\xa7\\x00\\xe3\\xea\\xc0\\xd8\\x10}\\xe0\\xa6-\\x97!\\xe2Y\\xc5dCA\\xb7\"g\\x1fVGJ/\\xc7\\xd6(.\\xb7~y\\x8c\\x0b\\rL\\x99\\xe9\\xb0\\xa8\\xcf\\xe6\\xe7\\x0f\\x9e\\x8b,\\xbc\\xe7\\x11\\x9b\\xf7\\xc9\\x07\\xe1\\xa7pq\\xd4r\\x81\\x9c\\xf2\\xc0\\xe0\\xc76\\xe3\\xcd\\xfa\\x04+Q\\x1d\\xfaH8~>\\x90\\x9b\\x8e$ \\xe7\\x9f\\xdb,f\\x81\\xb9f\\x0e>9 \\xd3\\x99\\xef;\\x83C\\xbb\\x80.\\x9cs9\\xbe\\xdb|\\xd9\\xcc|NL\\xb5\\xfc\\xaf\\xec\\xcc@\\xf9~\\x91\\x9cg\\x82p\\x08\\xe8\\xf6\\x16\\t\\x8a\\xe8q\\xf7>ehi\\x0fD3\\xf3\\x8eN\\x9e\\xe0\\xaa\\xa7\\xc4\\x8fx[A\\x9cwl\\xc04\\x97\\x8a\\x9d\\xa7\\xbf\\xe2,>6\\x0c\\xf8\\xb6zQ\\x86\\x9e:\\xf9\\xb4*p\\x9c\\x1b5\\xb8c\\xfd$\\xc715\\x18\\xfd\\x0c\\xc7l>\\x9b\\x01n\\xf1<~6\\xe9\\xdeI\\xbc\\xe7?{P~\\xa5\\xc91\\xd3\\xe6tL\\xeb\\xaf\\x01o\\xc2@\\x03L\\xee\\xd0\\x1c\\xdc[\\xc7Y\\xe3\\x8d\\xbb\\x99rs\\xf4O\\x0f\\\\<c\\xce\\xc0H\\xbd\\xf4\\xd9\\x87\\x8f\\xd1i\\xf07\\xa8>\\xbd~\\xf01\\xfa\\xc8//,\\xeaf\\xe7\\x13\\xc7\\x9eO\\xafS\\xd3\\x11\\xe3\\xab\\x0b\\xf3\\xd5\\x8f\\xd6\\xee\\xf7*\\x07H\\xd0\\x1d?1\\x01\\x86?9\\xe3\\xd4:o\\xf1?\\x1e{\\xfc\\xa1\\xf0\\x06\\t(7\\x8e\\t/\\x1a\\xc0\\x0e{\\x9e|\\x0f\\xce7\\x9d\\\\\\x07\\xde\\x1c\\xf3\\xdd\\xf0=_\\xd0?i\\xed&}7!Ct\\xfe\\xd0\\x01\\x1f\\xefo\\xacL\\x83\\xe9g\\xd6g\\x97#\\x80\\xcf\\xf1\\x1f\\x04\\x0f\\xe6\\x1eh9\\xfc\\xf3a\\xc1\\xc5\\xc9\\xa7Rs\\x96\\xc3\\x8d\\x8e\\xb9\\x94\\xf9s\\xeb\\xb0u\\xe7\\xf2\\x90\\x1d\\xc8;\\xc7\\x84s\\xae>\\xd2\\x0e|g\\xd6\\xc7\\xe7\\xe2\\t\\x99#\\xea\\x97\\xaf\\xc21\\x9cF\\xe7<]\\xc4\\xf3\\x9c\\x9e=\\x0f\\xdb\\xc8\\xcf\\x0b\\xf3\\xb8\\xc0q\\xf9@\\'?E\\xf1\\xfc\\xc4\\xd9\\xd4\\x86\\xf5l\\x80\\xd7xo\\xf3\\x14\\xe8\\xb3\\xb9\\xe8\\x9e\\xaf\\xff\\xda\\x00\\x08\\x01\\x01\\x00\\x01?\\x10\\xc4f8R/\\x08M\\x98\"\\xa2<8\\x8e\\xe6\\xc5\\x02\\x91\\xc8\\x94(\\x0f\\xfa\\xa92\\x91\\xf3\\x17d\\x14\\x9a\\xf3\\x03\\x13\\x1c&>j\\x04O\\xa8\\xf5x\\xa7\\x0c<\\xbc\\xfe(\\x03\\x08\\t\\xa6\\xad&\\n\\xb00\\x04\\xd4aC3\\xc6}U\\xde8\\x9b&\\x19 9\\x9aH\\t\\x10>\\xc9\\xbd\\x83\\x00\\xe37\\xddsy\\xdd\\xb2\\x1c\\x95\\xba\\xe3\\xd5\\x17\\xda\\x00z\\xf73UR@\\xac\\xd0\\xbd\\x1e+\\x0c\\x08\\x11\\xe5c\\xca\\xee(\\x83\\x01\\t}q\\xfd\\xd1\\xe5\\t2\\x11\\xc8\\xfb\\xa6\\xaf\\xfb\\x0f\\xf4\\xa8\\xc3\\ne\\xdf\\r\"\\xa6\\x98\\x9c;+\\x01\\x9e\\xf7b\\x81\\x04I\\xbd:\\xaa\\x02\\x06\\xcb\\x15!\\x01\\xcf\\x05\\x8f\\x1e\\xfe\\xeb\\xc0\\n\\xc5\\xe8\\x88\\x07\\x8e.\\x1cP\\xc9\\xc9\\xdc\\xf3@6~\\xa2\\xac\\x07f\\xb0?\\x85\\x12\\x84\\'\\xc43\\\\\\x8eM\\x93\\x0ed\\xe1\\xdf\\xfa\\xb9$\\x18\\xd9\\xd6\\xc1\\x91\\x0e\\xa7\\xff\\x00\\x15\\xd0\\x02{\\x17\\xf0\\x81\\x13\\xfb\\xb1\\x0c\\xe4\\xdc\\xdb\\xa8A\\xf1ccN\\xef\\x1c{\\x8e\\xca\\xcc-\\xff\\x00\\x13@\"\\'&c\\x97\\xdfU:Tx\\xb0\\x07\\x1a\\xcf\\xbd\\xa01{\\x17\\xdd\\xea\\x03)\\xfe\\xe8\\xce\\'C\\xa4\\n\\x14G\\'\\x15\\xa4d3=\\x9f\\xf8\\xdd\\x18\\x01I\\x18\\xbe\\xbd\\x94pDN\\xe6\\x7f\\xed)\\x18\\xb4N\\xa7\\x9d\\xa4d\\x88\\xe1\\xeb\\xb4\\x1f\\x16Qp\\x08\\x97\\xccS\\xd2\\xa1\\xfb\\xbb\\xd4x\\xf6\\xfb\\xa5\\xbb\\xc5\\xb9\\x12\\xd2\\x1c\\x91\\xdcRb9=\\xf1\\x15\\x0b2\\x86M\\\\\\xcb\\x8e\\x14\\xd9\\t!9\\x0c\\xcf\\xee\\xe9\\x82^#\\xc7\\x96\\xb8\\x80\\x88|\\xd0d\\xa8\\x8d\\x98\\xb3\\x19\\xd5\\x91:\\x19\\xc7\\xf7dE\\x97\\xe2b\\x81\\x94\\xe4\\xf7E}\\xae\\x93\\x8b\\xc5\\x00s\\xea(\\xd0%cJR*V \\xe2\\x12\\xcc\\xbe\\xd0\\x9e\\x9f5r\\x00\\x1ec\\n\\xf0\\x08\\x86\\xae\\xef\\xa8\\xa5\\x91<\\x7f?5\\x83\\x9e\\xeaf\\x11\\x0f\\xf5Q\\x83\\x00\\x97\\xc4\\xf8\\xa8\\xfc8\\x005\\xf7d%!3\\xba\\xb0\\xcc\\'\\x1e\\xa2\\x8b\\x1a\\xba$\\xda\\xa7\\x91\\x03y\\x1d\\xff\\x00\\xed\\x00\\xa4\\x85\\x918\\xf7Wd\\x803M\\xb1\\x859\\x1dGWY\\x06\\x15\\x9e.\\x98x\\x02y\\xe6\\xf7\\x0c\\xeb{7\\xf5[\\x19JtC\\x1e\\xbd_!\\x10\\xc7\\x8a\\xeb\\x97\\xaa\\x9c\\xf3\\xfc\\xd5t\\x89@<R\\xe2\\xef\\xf1\\xee\\xa8G\\xf1E\\xfe(\\x02\\x18\\x14\\xe0\\xd7\\xdbY\\xd2c{\\xa1K\\x19\\x0cOm1@jf\\xc9\\xc2\\xe4\\xf1\\xe7\\x8ez\\xa4\\xc3:\\xf7tYI\\xe9\\xdb\\x00\\'\\x18E3\\xf7\\x7f\\xf2\\xfb]\\xe5\\xab5\\x93\\x1d\\x9c|\\xd0DL\\xccS\\xc5\\xeb\\x12v<\\xfc\\xd3\\x08JW\\x90x\\x9c\\xfcM$\\x90\\'\\x14\\x16{98\\x81TA9\\x1b\\xee\\xbe\\x80\\xf9\\xeb\\xf5s\\'\\'\\x94\\xd6\\xee\\x81\\xc7\\x9a@q?[\\xfd\\xd9\\xc1<\\xb1\\xf1V(5\\x06%\\x8f\\x13H\\x062x\\xa1\\xd9\\xd7_Ud\\xa1\\x1a\\x8f\\x191\\xf3N\\x824NBh\\r0\\x11\\xf3b\\x98\\x01p\\xce\\n\\xa50Fy\\xd4e\\x84&o\\xa1x\\x90,g\\x14\\xf1\\xf8\\xaa\\x0b\\x94\\x98\\xb1\\xe1\"\\xa4hkgz\\xe0\\xa1\\x84\\xd1\\x05\\x8c\\x16\\x95x\\x14>\\xc6)\\xca#o\\xc4\\xf7F\\xa4\\xd1%\\xe2<\\xd6\\x05M\\x93\\x99\\x8d\\x9c\\x11\\xdbMj\\xd4\\x05/:\\x97\\x180\\x80\\xf3D\\xac@\\xc3\\x0b\\x1e}W\\xcb\\xe4\\x99y\\xf8\\xda<\\\\\\xb9\\x9ex\\xbe\\xf7;\\xa7\\xbeb\\xce\\x00\\x1eLo\\x14:V\\xd9\\x8f\\xdc\\xf5@\\xe5\\xc9\\xd6 \\xfc\\xd5E\\x0f\\x8cr\\xb2\\xc8\\x9fi~\\xee*+\\xa8\\x88\\xaau\\x83b\\x7f\\x86\\xa2\\x00g\\xe6/H\\x9dS\\x9fu\\x89\\x13KH\\xce\\xec\\x00\\xf0\\xc6\\x19\\xfdP\\xc9 %\\x90\\x949\\xdf\\r\\xd4\\xeb\\xb6$h\\xd1\\xccO\\xdd\\x184\\x98\\xde\\xc0\\xf34\\xb8\\x0c\\x97?\\xab\\x1c^C\\xc2\\x99\\xb8\":\\x0c\\xb1+-\\x98~(T\\x8e\\xf5\\xdf\\xccXL\\x1d+XQ#\\xa1\\xe0\\x0cn\\x01(\\xc1\\xdb>b\\xe0\\x04\\x93\\xe8\\x9f}]\\x9aH\\x1f\\xecjI\\x8d3\\xa6\\x1d\\xb3\\xc0$\\x0f\\x0e?\\xaa4J\\x03\\x1f\\x07\\x8a\\x07\"_\\xa3\\x8b\\xdc\\x05\\x93\\xa6\\xd5\\x0e\\xf3\\x0e\"*&X\\xf4\\x7f\\xf1L\\x02=\\x9e?\\xbb\\x08\\x01\\x97\\xcdpP\\x82\\xd0\\x85\\xb2\\xc5\\xeac\\xaf\\x16j\\xeb\\xe7\\x0f\\xab0\\xc3\\xe2\\xad\\x95\\xde\\xe9\\x00y\\xe2\\xb3\\x80\\x8f\\x07\\'\\xaa\\xa6x&\\xe9\\x11\\xbfqV\\xe0\\xbc\\x87\\xf3d\\xe0\\x9c\\xa22>i@\\x19\\x8d`\\xa4r\\x83\\x9e\\x7f\\x0f\\x8aq\\xd8\\xfc}\\xd6\\x14\\xd6\\x93f\\x16\\x11:V\\x8f\\xcd\\x82\\xb0\\xe9\\x9be\\x06\\x9b\\xe6\\xc39\\xc5\\xd2\\xa3\\x90\\x86\\t%\\x0f\\\\WS{\\x8e\\xdc\\xf3a\\xa3\\x05d\\xc2\\xc4\\x95\\xe7\\xea\\x9b\\'%\\xf4*k<\\x14\\xf3\\xf7gP\\xe1w\\xe3*D\\x03(O\\x9a!\\x07\\x04\\xf8\\x9a\\xc2\\'\\xc4\\xc5\\xe3\\xb0\\xb2(T\\x06\\xcf\\x9ft\\x05\\x08$C\\xe7\\x7f\\x9b\\x83Ab\\\\\\xe7\\x9a\\x18a\\xc0\\xe31B\\x83:\\xf2\\xde5K\\x0f|\\x13\\xdf4\\xd2\\xec\\x00&\\x03\\xfb\\xb2\\xce\\x03\\xc7\\x9a\\x805<\\xf0\\xcf\\x16.)n\\xa7\\xd7\\xe2\\xaa\\x89u\\x84~\\xaa\\xf7\\xe1\\x80@\\xd8\\x00%\\xeb3\\xba\\x8c\\xce\\x98\\xe6\\xa9T\\x93\\xc64\\xca\\t\\xd8^T\\x85\\x17\\x1e\\xcaA\\xc4C\\xaf>\\xdb\\x84A!\\xc6\\xc5\\x9aaI\\x1e\\xee\\x92\\xce~\\xfe\\xca!2\\r\\xe7\\'\\xd5lCF\\xc4U\\xb9\\x84\\xf2{\\xb9\\xbc2G\\x97\\xef\\xbaI\\x00B\\x193)D.Mx\\xf2P\\x02\\xa3A\\xe49\\xe6\\xc7\\xdd\\x05\\xa9\\xd7\\x98Mq\\x9f61\\tF\\x0e_\\xe9S\\x00#\\x05\\xd8\\xfb\\xe6\\xf2\\x8c\\x8c=P(\\xd4\\xe3\\xc4M\\x0b\\r\\n\\xf7\\x13\\x97A\\xc2\\x11\\xf6X\\x90j6)\\x96\\x9c$<\\xef4\\xc6\\xb3\\xa5\\x0e=\\x1f\\x8b(\\x08rT\\x99_\\xe1\\xa8\\x8c\"+\\x8e\\xe8\\x19\\xab/\\xef\\xbaa\\x1c\\x03>2\\xb4\\x12\\x86\\'\\xed\\xad~\\x05\\x12}5\\x1c<)<E5\\xf6\\x9ev:+\\xe1\\x12\\xb1\\xa10\\x9ejS\\x90av\\xc7\\xbf\\x16I\\x80\\x81E$}\\xd7\\xc9\\xec\\xc2z\\x93*H\\x87\\n\\x11>\\xa6\\xe6\\x05\\x0c\\xa4`\\x8a\\xb2\\x04\\xb1\\x99\\x9aEC\\x9b\\xae\\x11\\xfdQ!\\x93\\x07L\\xd5P\\x0c\\r\\xe6\\x91$\\xa9\\xf5Q\\xcakE\\xf7W\\x00F&fiT\\x93Z\\x92\\x9fWd\\x00DpU\\x13b\\x04*\\xd9\\xab#\\x96&\\x1fWs)\\x80\\x0b\\x9eS\\xcd\\x1b.L\\x14}\\xd9\\x8c\\xa7AV\\t\\xceOt\\x01\\x06\\x00\\x998k$y,\\xcf\\x0f\\xab\\xce\\x00OC\\xe9\\x83.\\xc9\\xf8\\x9f\\xf7C^\\x10@\\xf5\\xe2\"\\x9e\\x1c\\x06?\\xbb\\x87R\\x04\\xe7n\\xc5\\t9\\x11\\x1f\\xc5Y\\t\\x80\\xaf\\x8f\\x8a\\x80l\\xf9\\x95\\xeb\\xbb\"\\x00_\\x8ezlb\\x13q\\x8e3\\x9aa\\x18\\x0c}\\x8dZ\\xa9f\\x12s\\xc1\\xfc\\xcd*\\x15T\\x0f\\xb1\\xca\\xb0\\xc8\\n\\xb3\\xc3\\x95\\tC)\\xe5\\xc9\\xda\\xd2&$\\xf8\\xcf5\\x94)\\\\\\x127h\\xc0\\x1eX[\\xf3\\x17H\\xd3\\xb1\\xe5|RD\\xc0\\x1b\\x91\\x95\\xc1\\xa1C\\xecG\\x96\\x86\\n\\x003\\xdco\\xe2\\x88\\x010*~\\xe8\\x8c\\t\\x98\\x9f\\x16\\x1e\\xc9:\\xf2\\x1f\\x05\\x08R\\xa2g\\xe3\\xbb\\xde9\\xc3\\xe8\\xe6\\xca\\x02CN\\xf3\\xe6\\xe8(H\\xd7\\x83+A\\xb4gG\\x9e\\xaci+\\xb8*yS<9\\xa7\\xf3f\\xc1\\x1c\\xb2(j\\xb1\\x13\\xb3\\xa31P\\x97\\x17W\"r\\xd1\\x12\\xb9\\x08\\x03\\xf9=\\x14\\x80T\\x99\\x9c\\x15y>1\\xf3\\\\\\xc1\\x02JF\\xafQR\\x01&\\x10\\xfe>(\"\\xa5\\x1a\\xe9y\\xbb\\xa4\\xaa\\xec\\xe4\\'5\\xd1;\\x89R^\\xc8\\xf7\\xf3e\\x00\\xa1\\xce\\xceW\\xbc`\\xd2\\xc4\\x02\\xf53\\xe2k\\xc8\\xf7=\\'u\\x10\\xb9\\x93\\x1c\\xca\\xc0#G\\xf9\\x08\\x8b\\xca\\x84\\x8c\\x8c\\xf9\\xfb\\xa4\\xa0\\x98\\x9c\\x7f4\\x0eC\\xa9\\xef\\xc8Y\\xc0@\\xd3\\xf0\\xf2Y\\xd8\\x0b\\xfb9Q\\x85\\x05\\x11\\xcc\\xeft\\xa9\\xc1&\\xec\\x1de\\x98\\x1e>bS\\xd6M\\xd8B\\x8c\\x8f~}VP$\\x07I\\xf3Y\\x8c\\x8df\\x14\\x85@\\xe4\\x9f\\xf3\\xba&A\\x925\\xe2\\r\\x8d\\xe5\\xaf\\xcb\\x87\\x01\\xfaR!\\xc9\\x88\\xe1\\xfa*\\x80\\xc9\\x87\\xcf\\xac\\xbbI5\\x1e\\n(I\\x1f\\xc7\\xcd\\x84\\x82&\\x8c\\xf7@\\x11\\te=\\xd6?\\xa4\\x9c>[\\x90\\x84\\xe2\\x9e\\n\"P\\x8e\\xa7\"\\xa2$\\xdc\\x113\\xbd\\xb0$\\x0fD\\x0f\\xe6\\xb8L\\x93JLNAR(\\x90\\xc8\\x99\\x7f\\x1c\\xd0$\\x04\\xe9\\x00\\xed\"2\\x15\\xfdz\\xa4\\xbc\\xc0\\x0e3\\xfd\\xd4$\\x91I\\x07\\x03\\xd8\\xff\\x00W\\x18\\xe09\\x9e}1C,$\\xa4\\x83\\x1c\\xc3\\xe7\\xcdi\\x0b\\x84\\x1b2;\\xb60H.\\xc1\\xdb\\xfcVq\\x15\\xe6)\\xbb! \\xcf.>\\xe8\\xa2\\xaa0\\x11\\xc0R\\x82y?\\x01VD\\x04\\x9eU\\xa17K\\xf4!\\t\\xf5K\\x81\\xe5>rk\\xb8$\\x10\\xd8\\xea+\\x1d#\\x86\\xf21\\x05@\\x12G\\xc0\\xfe~h1p!\\xddX\\xaanK\\xdb\\x98\\xe2\\xb3$\\x9b\\x93\\xae\\xa8\\x1a\\xad\\xf4\\r\\xfe\\x1a\\xe4\\x89b\\x07\\x8f\\x8e\\xe9,;p\\xba\\xfb9\\xa9$w\\x12s\\xb5\\x19\\x82\\x99\\x1b\\x83\\xe6\\x7f\\xaa\\x93\\x19\\t^=P\\tH09\\x0f\\x9b\\x0f\\x18\\xeau\\x9fvf\\x98\\xc1\\x8aP\\x08\\x8d\\x12\\x07\\xe2\\xc0\\x90\\xe6y%\\xf9\\xab\\x84\\xd5`\\x92E\\x110\\x9c)\\x0f\\xc5DW\\xbc<\\xeb\\xb1Kg\\x8c\\xe1\\x9a\\xa8p\\x8f\\x1c\\xfdM\\x93\\x05\\x8cQ\\xf8\\xf7Z*8\\xfe\\x04\\xde\\x90\\xb1\"H\\xf4sF\\x0c\\xcc\\xe0a\\xd3\\xe2\\x82\\xce\\x08\\xd9\\xea\\xeb\\x14Y\\x19\\x13L\\x8f\\t\\xb2\\x94{\\x9fV\\x01\\xd1\\xee\\x1b\\xf3[\\x164\\xc9\\xcf\\x98h\\x80x\\x08\\xea^<\\xfdW$\\xa8o{a\\xe8\\x15T|E\\x9aNb\\x7f\\xb5G\\xc0%\\x97\\x87\\xcd29\\t\\x9c\\xd2\\x8a8J`\\xe6{\\xaf\\x01\\xea\\\\8\\n\\xc0\\xa6\\xc7\\xb8m[\\xc1/\\xc1\\xa5\\x15\\x14\\xc9\\xe0\\xf8\\xa1!\\x04i\\xf1\\x0b\\xaaa\\xc6g\\x8a@\\x11\\xcf{\\x9bc\\x88\\xe6\\xaf\\xeb+\\xe2\\x802\\xfb\\x8f\\x14\\xe2F\\x9d\\xc7T\\x03\\x00K\\x8d~\\xec\\xd8R\\xc1\\x13\\xe2\\x97\\xf2\\r\\x97<VtD\\xc4\\xac\\xcf\\xa2i\\x057\\x1b\\x04\\xc7\\xcf\\x9a \\xc8\\x12\\x06\\x13\\xac\\xabe\\x87\\x00t\\xacr\\xd1\"q$iPhL\\xc0N\\x1b2HIS52\\x1d\\x80\\x81e\\xea\\x83\\xa8A\\xc4\\x14\\x95\\x16;\\x1e\\xe9\\xc5b\\x1d\\xe2#\\xc1\\xdd\\x94E\\xc3\\x18\\x1e\\x0ef\\x8c\\xd3\\x17S,\\x1cs\\xd3V\\na\\x01\\xf7A\\xb8\\n\\x89K\\xbb\\xaf\\x00p\\x8c\\x9fV\\x0c\\xe6x\\x96\\x19\\xf9\\xa8\\x9aI\"\\xa4={\\xb2w\\x81\\xe6a\\xd7\\xd5\\t6$w\\xbd\\xff\\x00v\\x00\\x8d$\\xa7+\\xe3oA\\x90\\x16}\\xb3\\xf9\\xa4\\xe2WPc\\x9ap`\\x8cG\\xb6\\xae\\x99\\x12\\x8f1\\x14\\xc0\\xd3c\\xee\\xe9\\xa4\\xa3~)\\x97\\xa9c\\x881\\x1f\\x9b;\"\\x85\\xd9\\xe3\\xcd\\n`e\\x85\\xf0r\\xfcV(\\x9e\\x1e\\x19`\\xaa\\xa9R2|>\\xecQ`GO\\x16\\t*\\x0c\\x13\\xd5\\x12\\x84q\\xad\\xb3 \\xd4\\xe9\\xfd\\xd5q\\x14\\xe5b#\\xc5\\xe2J\\x13<\\x07\\xe9nQ\\xc2I\\xed\\xd3\\xdd\\x14L\\x87\\xb1\\xf9hL0\\'\\x06\\xd8*B\\x9a<{k\\x10\\x10\\xf4\\xf8<\\xd8\\xc4`\\xd5$\\x82\\xaa5#\\x19\\x99Q\\x12K\\xc8\\xe5L%#H\\xf7\\xf3D\\xc2T\\xc8\\xd3\\xe2h\\x18!\\x99X3\\xf8\\xb1IE\\xc9W\\xdd\\xce)(L\\x913\\x13a`F4@\\xf2\\xc5S\\x12D>\\x9f\\x15\\x01\\x02\\x88\\xbc\\x8f1C@<\\x10aO1d\\x8e:\\xb9\\x83\\x98\\xa8\\xc1L\\xb4\"~\\xa6\\x91\\x10\"<O\\x14L\\x01%\\xc6&\\xe8\\xf9)\\xc4BD\\xc8\\xef\\xc9B\\'2\\x19\\xe5\\x95qH\\x02|\\x16\\x0fB \\xf35\\x05\\x02\\xa1\\x00\\xeewT\\xa5\\x89\\x13\\xe2&\\xb3\\xb8F|\\x114\\x04\\x12\\xcc\\xbf\\x9a2\\x82J\\r\\xdfjdE\\x98~J\\x83M\\x07\\x04\\x9b\\x9bu0h\\x00\\x07\\x1e(\\x96S\\xc3\\xcfQJ\\n$gZ\\x86\\x04\\x02:\\x01=\\xf9\\xae\\x89\\xc1\\x93\\xa9\\xf6]\\x8f$\\xe9\\xb2Q8\\x19\\xa63,P\\n\\xd8\\x17\\x83\\x8a\\x01!12\\xda\\x002\\xe1Y?\\r&\\x0b9\\xf0\\x03\\x98\\xa8\\x02\\x04\\xc8\\xf3\\xfe\\xcb\\x1d\\x07\\xa3\\x96\\xf4\\xe1\\xd9`4\\x1a\\xb61\\xe1\\x96fW\\x96Q?\\x80\\xae\\x91\\xa7\\x13?\\xea\\xea\\x9dg\\x08\\xc6\\xcc\\x12\\x03\\x82\\x13\\xf8:\\xaf3 \\x94\\xde\\x7f\\xa9\\xb3F\\xb4\\x13\\x89\\x1f\\x15i\\x1a\\x08\\x97\\xbf\\x87\\x9a\\xd1\\xb2p\\x9b=\\x9d^#9\\x89\\x0f\\xf2j\\xd0\\xa2\"|\\xad\\xd7\\x00\\xc6@\\xfc\\xf1y\\xdd\\xe5\\xf9|TN\\xe0\\xd6!\\xde\\xa9\\x101\\x85\\x83\\xb3x\\xacD\\xe3\\x08iT\\xa0\\x9d\\xce7\\x9b\\x05,L\\xef\\x8a\\xea;\\x04|g\\xf5d\\xbd\\x00\\x86(O\\x00m\\x9f\\x1f\\xfc\\xadu\\x95s\\xe5\\xea\\xe0\\x99\\x97\\xd76\\x07\\xb2Yzt\\xbd\\xa0B)\\xec\\xa0#\\xb0\\xcf\\x1f5\\xf34\\x07&\\xd9\\xac\\xe9\\x12\\x1ceQ\\xc9\\xad\\xe3<\\x91\\xa0H\\xa2&h\\xc2g\\xbfU0\\xc6F\\xf8\\xbc\\x90\\x16\\xeb\\x06.\\xcc\\xed\\tL\\x1c)\\xb3 \\t\\xf4\\xe7\\xf2Y\"3-\\x9d\\x87\\xbf\\x8a\\xe0\\xc2\\x11\\x0c\\xf2\\x9e\\xdf\\xaa\\x82\\x82\\xbc\\x07\\x1fw\\x92\\\\\\x13,\\x8b(\\x13\\x18V,\\xc3\\x1f~\\xa8F\\xa4!\\xe0\\xf8l\\xd3BA\\x90\\x9b\\x98\\x8e\\x83\\x82\\xbf\\xc5\\x19\\x9cO\\xa0G\\xbe\\xeb0\\xba\\x01\\x90i\\xe6+*\\x10\\x94\\x80E\\x88z\\xe1\\xca}Tl\\xc3R;?\\xdb`\\xc3&\\xbd\\xac\\x91y\\xb0!C\\x98h&B!\\x1d\\xef\\x96\\x9ca\\x88O\\x81\\xf1\\xb6\\x01-g\\x18<\\xeda\\x14\\xa7R\\xa3\\x1c\\x02\\x11\\xd4\\xd2I\\xe3?\\xaa\\xab0\\x15&>iq\\x081vx\\x8f6n\\x087\\xef\\x9a\\x85\\x02;\\xe3\\xaft\\x884G\\x9b\\x00iP\\xf0\\x8b\\xa6\\x19\\xdc\\xe2\\xa7\\xcd\\x8b`\\x94O\\x9b41 ?\\x8d\\xa0\\xe1\\xd2O\\x89\\xa0\\xc4\\xc6\\xc6d\\x7f\\xed!\\xb6\\xa6\\x86LP\\x9a,\\xa2\\xbb\\x9f\\x19e+\\xe2do\\xfa\\xaa2t\\x83~.\\x80\\x915\"++\\x0c\\x8c\\x04>\\xeaez\\x1c{,J\\xc9{\\x11\\xed\\xf3^)\\xc9\\x0e\\x0c\\xf7\\xf8\\xb8\\xa2\\x11\"!\\xb38\\xa1\\x14\\xf5K\\xb9\\x14\\xc6J\\xaa2C\\x08a\\xbb\\x06Dp\\x8f\\xbb1\\xca\\xce\\x7f\\x94Yq\\x0c0\\xa2?\\x15\\x12\\x87\\x9eX\\xe6\\xc4\\xd1 \\xa0\\xd6R\\xa8\\xc1\\x06)\\x92}m\\x00uI\\x86\\x9f\\x9a\\x1c\\x9a\\x08\\x02q\\xfc\\xd2\\xb40g}\\xedO?\\x1c\\xbcx\\xfe\\xea\\x050\\xe9=\\xf3?Ua\\x04\\x94\\xa5\\x96)N\\x04\\xbc\\xfb\\xf4\\xd6\\x97_\\xf7U\\xc8\\x0b\\xcb\\x8d\\x8a\\x9a\\xa7\\xe5M\\x02@\\x98\\xbd\\xcc\\xff\\x00vY\\x00t9\\xfa\\xb9I\\xd3v\\xf08z\\xee\\xc8\\xc8\\x96\\x01\\x077\\xe2\\xa8\\xe0LA\\xfd\\x940%\\x7f/\\xcdo\\x85\\xd4a\\\\\\xeaH\\xe7\\x86><\\xd1\\xf9\\xff\\x00\\x15\"\\x06\\xc3\\x12lr\\x8c\\x93\\xf1-\\x84\\x00\\x1e\\xc8\\x99\\xb2\\xc8\\xf0\\x1c\\x93T\\xb0%\\x0eu\\xf7\\xe4\\xf1`\\x83\\x9dH\\x83\\xcdXZO\\x84G\\xe6\\x9a\\xa2%\\x06X\\x1e\\x18\\x8eh\\x80e\\xd4\\xd8\\x8e\\xdb\\x1aoP\\x86\\xe7\\xdfVK\\xf5?U\\x00\\xe2\\xf0x\\xa86\\xb0\\x177\\xef\\x9b\\xb3=Q\\r\\x87\\xfa\\xa4\\x0c\\x10\\x87\\xe1D`T@DT\\x00eu\\x81\\xfc\\xc5\\xf9\\xc0\\xae\\x86\\x8cN\\x99\\xbf5$~}\\xa7\\xb8\\xf1\\x7f\\x08\\x883\\xec\\xa21\\xcaD\\xbf/\\x15\\x11\\x89\\xcf,\\x9fSO\\x07\\xc4FE\\x969\\x83\\xc3S\\x8c\\xb8\\xc9\\xb1\"Q\\x89\\x07\\xc9X\\xbb\\x04\\x8f\\xb8b?\\xd5\\x0e\\x94\\xce\\xa7\\xceV1\\x08\\x11\\xec\\x8e\\xe8\\xd2\"n\\xfb\\xf3X\\xc4D\\xc2\\xbc\\x0f\\xb3\\x1f\\xc5\\x0f\\x03\\x19Le\\x12\\x07u\\x89\\x055\\xeb\\x1f4\\xc98\\x94\\x92C\\xd3\\xf1M\\x04\\xa1Tc\\xff\\x00\\xca+D\\xbct\\xff\\x00\\xe3d\\x1e\\x05W\\xf7p,\\xa6\\x8f\\xea\\xa3\\x94\\xa6\\x1f\\xfa\\\\\\x120\\x07C\\xd8XnrW\\xc1T\\x01\\x03?\\xcf\\x9b\\xa6\\x9e#\\xacw\\xbdx\\xb3DYl\\x84\\xd8\\x00Vt\\xfcm}#\\x839#\\x9b\\x00\\xf35=6{\\xd0dV\\xd0\\xf2k\\x82\\xf5bRzx\\xfb\\xaa\\xec\\xa5SI\\x9b\\xc1Hk\\x13\\xfc\\\\\\xe8c\\xbf\\x05H\\x0c\\x00D*:\\x08a\\x92\\x7f\\x1e\\xaa\\xc2d\\xf7\\xdd\\xea\\xcc\\xb0O\\xc2}U\\x81\\x8e\\xc7}kN<R\\'=g\\xc5\\x80R\\x12\\x8d\\x0e\\xe7\\xdcP\\xd7\\x80\\xf1\\xdb\\xea\\xc9$. \\xe7\\xfc*\\x12\"<q1\\xdf\\xddp\\n\\xcfG>\\xabV\\x8d\\x0f\\x93)\\x88\\x00\\xf2yJc\\xb9\\xc0 qC\\x88\\xec\\x0e\\x86\\xbdAB$\\xf1yC\\xaa\\x0c\\x8b1\\xc5\\x9c\\rL\\x8cX\\xe8Z[\\x1fq\\xea\\xa2C\\x15\\xe18*\\x90v\\xec\\xdft2\\x8d\"\\x02\\x93\\xef\\xcd\\x8e\\x00,\\x1f\\x04\\xd8\\x83\\xc7I\\xe2\\x7f\\x9a\\x08Kra\\xeb\\xcd\\xe2\\x93\\x80\\xd8O\\x17=\\xd0$\\xc7+I6\\x83A\\xc3\\xeb\\xe6\\xa0v4\\x10\\xb3X\\xd0(O\\x9a,\\x8e\\xa0\\xf6\\xd6Q&I\\xd4\\x1c\\xd2\\xa0\\xcb!uDq\\x8b\\x1b\\xdd\\x90\\x84\\x8d\\xc7\\x19\\xef\\xd5\\xd2?9\\xa8\\xa7F\\x020\\xb3\\xc2\\xc1\\xd6~\\x8c\\xa4\\xe0\\x18$\\x84j`\\x01\\'\\\\\\x94\\x08\\xd0wA\\xd0M\\x04\\x88\\xcc\\xa1\\x86>\\xf9\\xae\\xa8\\x89J&O\\x99\\xfe\\xa8E\\x92xLC\\x9b,HU>\\x0f\\xfe\\xf3P\\x03CC\\x07\\xed\\xa4\\x05\\x82\\x86\\x98\\xd7\\xcd`\\x93J>C\\xd3Z\\x88\\x18@\\x9ei(\\x91\\xae\"\\x94\\xceS\\xddU4\\x9e|\\x94\\x88\\x8c\\xb8I\\x9f\\x9a\\xa3\\xd2`\\xb1\\x175\\xcd\\x1e\\xe0\\x8b\\x81\\xca|RD\\x19\\x1dI\\x1b\\xe6\\xa3\\x8e\\x97\\x99\\x00\\xff\\x00\\x14x\\x84o\\x18\\xcf\\x8a0\\x84\\x0cc\\xfdMB\\xc1\\x02\\x0f/\\xc5l\\x1e\\xdf|\\\\\\xec\\x81\\x19\\xef(\\x13\\xb5M\\xca@R\\x7f\\x9fV\\x00\\x93\\'\\x9f\\xa8\\xf3\\xe2\\xe7\\x80\\x89\\x9cy~}\\xd2\\\\\\xc8\\x1c\\x8ex\\xac\\x92\\xbd\\x81\\x1fQ\\xb5\\x89.\\x0e\\x98\\x87\\x9f4\\x01\\x06q\\x99\\x12k\\xca\\x16\\x19\\x13\\xe5\\xc7\\xea\\xf1(\\xbcj\\xbf\\xbb$@e\\x99e\\xf8,\\xe4\\x9d7V\\xa1\\x82\\x1eG\\x9c(\\n\\x87\\x0f\\'\\xff\\x00lU3\"4\\xbfzM\\x11\\x9e\\x18S\\x0f\\x07\\x8a\\x96\\x06\\x0c\\x91y\\xf9\\xb9Ee\\xc1\\xf0\\xba\\x10B\\x12D\\xef\\x9b\\x10.\\x08Hq\\xf7J\\x9b\\x0e$P\\xcf\\x12]\\x96$\\xf2<u\\\\\\x02\\xa2\\x8e\\xc8|,we-E\\xd1\\x8f\\xe3\\x9a\\x81\\x02C\\xdcy\\xb2W\\x0b\\x81\"\\x7f\\xba\\x81\\t\\xf2I\\x11\\x9eJ\\xa7\\xab\\x03\\x9d\\xf9\\xab\\x04\\x11\\xfd\\xb6\\x13\\xa4\\xd7Nf\\x94\\xc4\\x15\\xe4Q`\\xa5X\\x01~l\"\\x03v\\xe0\\xf3\\x13\\x974\\xb8b~W1\\xc8\\x00Fy\\'\\xbb\\n*\\x03\\xbc\\x8c\\xfab/\\x87\\x03\\x88\\x82<U\\x1c(1?\\xc6e\\x84\\x18$\\xc7\\x89\\xaaU\\x94\\xb0\\xec\\xb9\\x82\\x15\\xdf\\xfd\\xfe\\xece\\xf1\\x18\\x98\\xef\\xfd\\xd5\\x14\\xc0\\t9\\x08{\\xac\\x02\\xc8u\\xcf\\x15\\x9f\\xa2\\xe7\\x81\\xf8\\xa422\\x1f\\x8f\\xba\\xc2J\\x0f\\xc6\\xf9\\xed\\xab\\x8c\\x04\\xc8\\xa0DX\\xc1\\x00<\\xfc\\xb3\\xc3@\\xa0\\xf9C\\xdd\\x90)c2J\\xa8d\\x17@0\\xfa\\xbc\\xa8\\x92\\xb2J\\xff\\x00v\\x1b0\\xa4\\x17\\xe1\\x936#)c\\x1b\\x03\\xddm\\xe0\\xf5\\xcf\\x14\\x05\\x94\\x8c\\xa98\\xa3*\\x07\\x7f\\xda\\xf4]i!Srb\\xcc\\x1c\\xa3\\xba\\xb9%\\xa48=e\\xe0\\xc7\\x90\\xc9\\xb2\\x11\\x0e\\x8c\\xf63\\xa8\\xa5\\x05\\x83\\x10\\x17\\xf7\\xfb\\xaf\\x14\\x87\\x91\\xc8\\x7f\\xaa\\x84\\xa4.=\\x91\\x9bPz^\\xcc\\xe0n4\\xbdm\\x91>:\\x8b\\x1d\\x84v\\xf3\\xc7\\xab#\\xa0\\xfbI\\xcb%4\\xf5\\x8e\\xbf\\x15\\xe34k\\x1a~2\\xc4\\x87i\\x88c\\xf9\\xee\\xc4\\xf6\\xe1\\xe6e\\xaf \\xf2#\\x9f\\xd8\\xd4\\t\\x1eI\\xc9\\xeei\\xc1 \\x1e?\\xcf\\x15Ar;\\xe3\\xf7PBbL\\xf9z)\\xa2\\x0c\\xae\\xf3\\xe3\\xd5\\x84tx\\x0c\\x93\\xef\\xba!@=iD\\x03\\x1b\\xaf \\xe6\\xcc\\x80\\xa7\\x99\\xf1\\\\\\x87\\'\\x89\\xe0\\xf9\\xb11\\x10\\x07\\xd5\\x94\\x02}T\\x10\\x10\\xeauPb\\x06\\xcc\\x8e\\xa8H\\x81\\xe1+2\\x00U\\x19s\\xfc\\xdb\\xbb\\xb5 \\'=\\xd6BC\\xa9D\\x9e\\x0f\\x8a$\\xe5\\xf0\\xbd\\xcft\\x1e\\x898:\\x16Zs\\xc9\\xcb>\\xab\\xd0D^\\x1duQ\\x01\\xe1\\x93\\x1c\\xde\\xf0\\xf29~j\\x0c\\x1f#\\xb3?\\xc4\\\\8x\\x9e\\xcd\\x9b\\x07\\x19 \\x91\\x07\\xe9H\\x95\\x18t\\xbf\\xc3@\\\\\\xae\\xbc\\xfe\\xa6\\xcc\\xbdA@) \\xe6\\x0e\\xea\\x04O\\x9a\\x82\\xc9\\x11a<\\xc7=\\xe5\\xe65\\x06\\xb7f\\xcc0\\xb6Dp\\x1f\\x9aVD\\x14\\xd8~\\xac\\x82p\\xd8\\xc1\\x8e\\xdf\\x14\\xcc\\xa1\\x102,X\\xe0\\xaf\\xc8\\xe5#G\\x0et\\x8fuS29\\x1c\\xca\\x08\\xa0r\\xae\\xf1\\xee\\x85\\xd1\\xe0\\xa7\\x8e\\xaa\\x04Xv?\\xce\\xae\\x06f:d\\xfc\\xd1j\\x8dG\\x82\\xc5\\x92\\xa0K\\x82{\\xa6\\x11*=9\\xcb->\\xa1\\x94\\x06e\\xbc\\xbf\\x14\\x8aG<Y0/Jr\\xd8\\x05\\x00\\xf4\\xae\\x13\\x82\\xe8\\xa4\\x93.w\\x1b\\x84\\xd7\\x9cI\\xfa\\xea\\x9a\\x04C\\x89g\\xaf\\x8a\\x19$s\\x19\\x8b r\\x0f\\xdf\\xcd\\x91O;\\x8cAb\\x06v\\xcc\\x03\\x14<\\x90\\xe4\\xe6\\x1f4\\x11\\x81\\x0e)$\\xb7\\x00\\xce\\\\\\xd3\\xb5(\\x1eXp\\xbf6b,\\x83\\x86=\\xd5-H\\xb1=\\x95L\\xb3\\x93\\x9b\\xeed\\xfc~\\xa8g\\x13\\xeb\\x8b\\rF\\xf9\\xf5\\\\\\xf0c\\xf7YX\\xc7\\xd5\\x04@\\x81\\xeaO\\x86h\\x1e\\x12\\x92\\xcc\\xb1\\xfex\\xa0\\xf2\\x13\\x84\\xfe\\xeb\\x06\\xd2\\x04@\\xbfwB\\xfc =%\\x18\\x90\\xa2\\x16Ix!\\x94\\xef1\\xf3qB\\xc3\\xff\\x00\\nd{9\\x12\\x83\\xe2\\xb0O Ls\\xcd\\xc1\\x18C\\x1d{\\xf9\\xb29-$\\xcek-\\x19\\xe8\\xeb<4\\nL\\x0f>\\x9a\\x94\\xf9<\\x91Z\\xf1z\\xe2\\xe4\\r:\\xa3#2\\x19\\xf5Ya\\x9f\\x04\\xd9\\x10B\\xf67\\xfch\\x19\\x04\\x08\\xcf\\xd4X(A\\x8e\\xd5\\x82\\x10\\xe0\\xbc\\xd9\\xf91\\x0c\\x8d\\xf6Ta\\x00\\xd0\\xce\\xed\\xd4r9\\x8f>\\xee\\x8e\\x0c\\x99\\xe3\\xccW\\x10\\r\\xdcC\\xb5\\x08\\x18\\x13\\x88\\x87\\xdd\\xc0\\x19u\\x8f\\xcb\\x153\\x8b4\\x14\\xc9c\\x04\\x12\\n9\\xfc\\xd2\\xe1\\xc4\\xd0d\\xf9(\\x9a)\\x8eC\\x16Ad%\\x99\\xe2\\xa0\\x97oS\\xfa\\xadL f\\xd4\\x80\\x96*\\xcc\\x13\\x1d\\xf8\\xb2\\x0c<\\x8eudP\\xde\\x05\\x9f\\xddk\\x0c\\x86\\x91\\xcc\\xfb\\x8b\\x90\\x10O&\\x1a\\'\\x82\\x9eq\\xe2\\x81\\x10o\\x16\\x08A\\xe3S\\xff\\x00\\xb6D\\xef\\xa6A\\xe9\\xa8\\x10\\xafL\\xa5|\\x89\\xf8J\\xe1A9|\\xd4$\\x89\\x1c\\x1ce\\x18Q\\xf5\\xcf_5\\x03\\x07#l\\xe1\\x15\\xfa\\xea\\xa20\\xbf?\\xee\\xa0s\\xadFj\\x19\\xdf#\\xc7Q`/\\x8e\\xbc\\xd6\\x06\\x9f/\\x14\\x88Vr\\x99\\x01\\x04O\\x13\\x1el\\x1e\\tjwu\\x85r\\x08\\x18\\x8f~,\\t\\xc2\\x03\\xac\\x8f\\xdd\\xea\\x12\\x9e\\r\\xcf\\xae\\xe8\\x9aK\\x04\\t\\xbf\\x15A\\x88=#-\\x02\\x1b\\xcc\\xe4h\\xa8\\x94vc\\x11\\xf7P\\x8c\\x89eV \\x8b\\x02\\x0c\\x0e\\'+\\x81\\n\\x83\\x8e\\xfd\\xd0bw\\xb2}\\x94\\xe0\\x1fO5I\\x11\\x0c\\xe6\\xc7\\xf1`\\xae\\'5\\xf1c\\xa4\\xbf\\xb2\\xa7t\\xe3\\x89d\\xc9:\\x13\\xef\\xeek\\x0b) \\xf1\\xfcT\\xa7\\xb7H\\xb6n!\\x02\\xa0(d\\xe9\\xf5\\xf5\\xddN}\\x89\\xa4\\xfbc\\xab\\x10\\x83\\xa12\\xfc\\xd5<\\x89\\xe1C\\x8f\\x8a\\x08\\x82P\\xa8\\xaaWK\\xe0\\x9f\\x9a\\xf3.\\x1d\\x8d\\x92\\xca\\x0cx<\\xd4\\xa6\\x17a\\'<G\\xfe\\xd01~\\xcb T\\x0f[xH\\xeeg\\xf1@\\x86\\xec\\xe7\\x8f\\xe2\\xb9\\x99j\\xcb\\x1d}\\xc5\\x94\\xc68\\xf3t_@\\xd8\\xa5\\xa4\\x18\\xe7\\xbb\\x06TH\\xeey\\xa7!<\\xad( \\r)J\\x1d\\x1d?T!P\\x0e+[uS\\x86y\\xb0\\xc8C\\xaa\\xb6\\x10\\xac\\xc1\\xdd\\x95&N8)\\x96\\t\\xea\\x13\\x1e\\xa8r\\xb4a;\\xfc\\xdc\\xf1>2w\\xea\\xc9(\\x90\\xf1\\xd5\\x1d\\xd9\\xf8\\xea\\xcaL\\x86\\xcb\\x14\\xe4\\x87O\\xd5\\xe5\\x8eSX\\x01\\xc9\\xd96J\\x86\\x1c\\xaesR\\x1c\\xf8\\xca\\xa0}\\x81&\\xae\\xe1!\\xc3\\xc7\\xba&\\x13\\x03\\x7f\\x9b\\x02\\x00\\x94\\xc0\\xe0\\xf0{\\xaa\\x10\\x1dA\\xbb\\x05f\\x90\\x9c\\xcc\\xda\\xb3\\x81\\x0e\\xe5E\\x92\\x1eo\"\\x1f\\xbd\\x9f\\xba\\x908\\x8f\\x8d\\xb3\\x89;\\x98\\xaa\\x83\\x8f\\x1bx >\\xfa\\xa8z\\x13\\xf4V}\\xe6\\xc0C\\xe4f\\xaa\\x18k\\x93o7&U\\x04\\'\\xd5x\\xa3\\xb0i\\xa0\\xf1\\xfe\\xec\\x9c\\x0f]\\xf8\\xa2y1\\xcf\\xe3*\\xc0w\\x87\\xf5@\\xabM\\xf1I\\xd4P\\x89/=\\xbe\\xae\\x04,\\x94&>^+\\x9e\\x07\\x0cqX\\xd70\\x9e\\xf8\\xf1A\\x89\\x9c\\xfeK(\\x82\\t\\x14\\xce9\\x9b\\x02}\\xb0\\xd9D\\x1e\\xe1\\xf7T\\x9f(\\xad?uLA\\x0e2\\x92\\xf1\\x03\\xd0\\xc5\\x02\\x01#/=Y2\\x1c34\\x1c\\xa8)\\xf6FE\\x14Ks\\xe0\\xe6\\x89\\x9e\\xcf\\xcd\\x1dY\\xca\\x84\\xc1j\\x16\\x8c\\xc3\\xe9\\xf5N\\x91\\xe9\\xf3^\\x87\\xb8\\xa6\\x08\\xcc\\xcf\\xd4U\\xec9\\x8e(\\xceH\\x8e\\x15I\\xb5\\x81\\xc4d\\xd7C\\\\\\x88\\x9c\\x8f\\x05\\xff\\xd9'), interactions={'hover': 'tooltip'}, scales={'y': LinearScale(), 'x': LinearScale()}, scales_metadata={'y': {'orientation': 'vertical', 'dimension': 'y'}, 'x': {'orientation': 'horizontal', 'dimension': 'x'}}, tooltip_style={'opacity': 0.9})], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0)))))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import bqplot.pyplot as bqp\n", "\n", "bqp.figure()\n", "bqp.imshow(image_path, 'filename')\n", "bqp.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "The signature is \n", "\n", "`bqp.imshow(image, format)`\n", "\n", "- `image` is the `Image` data, depending on the passed `format`, can be one of:\n", " - an instance of an ipywidgets Image\n", " - a file name\n", " - a raw byte string\n", "- `format`: {'widget', 'filename', ...}\n", " Type of the input argument.\n", " If not 'widget' or 'filename', must be a format supported by the \n", " `ipywidgets` `Image`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "04d900c35c79407e8f9f57c71a3ad338": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "AxisModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "scale": "IPY_MODEL_80b4dfd4d32e46058d615a19c8ba4226", "side": "bottom", "tick_values": { "type": null, "values": null } } }, "0622ba0d24e94ed4ba3aaab8df39c611": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.0.0", "model_name": "LayoutModel", "state": {} }, "0d3e5819043a4b378d3af13648d5b3b4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.0.0", "model_name": "LayoutModel", "state": { "min_width": "125px" } }, "1ec882db9cb841fb87f907d09cad0480": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "203eb5a03efa4105adfd9b853ac49b7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.0.0", "model_name": "LayoutModel", "state": {} }, "32d48656fa234d86a504e3c01daa2a5c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.0.0", "model_name": "ImageModel", "state": { "layout": "IPY_MODEL_b72fbd0d6a36449e8691540541a6d00b", "value": {} } }, "3375f88558044daba7af8850d3f7d76e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.0.0", "model_name": "LayoutModel", "state": { "min_width": "125px" } }, "3dbaf85a158048bb8afab96efcd647dd": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "figure": "IPY_MODEL_4e6331271a734ad3831edadd5c59efad", "layout": "IPY_MODEL_203eb5a03efa4105adfd9b853ac49b7b" } }, "456831a0b1e84a8a8165dba766473edf": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "max": 2, "min": -1, "stabilized": false } }, "4db5e4b8cb6445dbb21323e987bd724d": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "stabilized": false } }, "4e6331271a734ad3831edadd5c59efad": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "FigureModel", "state": { "_dom_classes": [], "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "axes": [ "IPY_MODEL_ce3fef64c326403ba2d46dcc2ddb9c18", "IPY_MODEL_04d900c35c79407e8f9f57c71a3ad338" ], "layout": "IPY_MODEL_55514efc97d54c44974e0013a5f5cbc9", "marks": [ "IPY_MODEL_f5ab5459a15d45debea50eb61d0687b8" ], "max_aspect_ratio": 6, "scale_x": "IPY_MODEL_e0aaf68043b742aa9f59eda5706cd6f7", "scale_y": "IPY_MODEL_b97d52c4b7f6463f8b4fbea61904a477" } }, "51ee8a765fa4490693392782a05aeb28": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "max": 2, "min": -0.5, "stabilized": false } }, "55514efc97d54c44974e0013a5f5cbc9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.0.0", "model_name": "LayoutModel", "state": { "min_width": "125px" } }, "5bdf5d8c9b0341c381f23de4283276fe": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "FigureModel", "state": { "_dom_classes": [], "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "animation_duration": 1000, "axes": [ "IPY_MODEL_d39498cdd2aa4cbb92d755f4d328d391", "IPY_MODEL_ca5e304f502441c6b80a3b3d05015a34" ], "layout": "IPY_MODEL_0d3e5819043a4b378d3af13648d5b3b4", "marks": [ "IPY_MODEL_c8a4845b09b64b2482ef1f9f80ad1124", "IPY_MODEL_bb2c845b016f458ca3e6d2ce4edcc5ea" ], "max_aspect_ratio": 6, "padding_y": 0, "scale_x": "IPY_MODEL_e70a421702444d01ae2ae96b593d6872", "scale_y": "IPY_MODEL_f73f97656cff4b29a3ce2b0e1bad1dd8" } }, "755fcbec00654317aa22d6da462e0c38": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "FigureModel", "state": { "_dom_classes": [], "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "layout": "IPY_MODEL_3375f88558044daba7af8850d3f7d76e", "marks": [ "IPY_MODEL_aaf364707416448aa4d8006ec663eeea" ], "max_aspect_ratio": 6, "padding_y": 0, "scale_x": "IPY_MODEL_e15f595de79249869d36f9da368d6166", "scale_y": "IPY_MODEL_1ec882db9cb841fb87f907d09cad0480", "title": "Trees" } }, "75c841d62d024c6399b50950e30ccd55": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "stabilized": false } }, "80b4dfd4d32e46058d615a19c8ba4226": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "stabilized": false } }, "929323256cec4ef0abe15110e9cb76fa": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "stabilized": false } }, "aaf364707416448aa4d8006ec663eeea": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "ImageModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "display_legend": false, "image": "IPY_MODEL_bfe77423b216444e9ca904519b4c3a9c", "scales": { "x": "IPY_MODEL_75c841d62d024c6399b50950e30ccd55", "y": "IPY_MODEL_929323256cec4ef0abe15110e9cb76fa" }, "selected": [], "x": { "type": "float", "values": [ 0, 1 ] }, "y": { "type": "float", "values": [ 0, 1 ] } } }, "b2b338531df545928f6f8ce701fbf132": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.0.0", "model_name": "LayoutModel", "state": {} }, "b72fbd0d6a36449e8691540541a6d00b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.0.0", "model_name": "LayoutModel", "state": {} }, "b97d52c4b7f6463f8b4fbea61904a477": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "bb2c845b016f458ca3e6d2ce4edcc5ea": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinesModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "color": { "type": null, "values": null }, "colors": [ "red" ], "display_legend": false, "fill_colors": [], "labels": [ "C1" ], "scales": { "x": "IPY_MODEL_456831a0b1e84a8a8165dba766473edf", "y": "IPY_MODEL_51ee8a765fa4490693392782a05aeb28" }, "selected": [], "x": { "type": "float", "values": [ 0, 1, 1, 0, 0 ] }, "y": { "type": "float", "values": [ 0, 0, 1, 1, 0 ] } } }, "bfe77423b216444e9ca904519b4c3a9c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.0.0", "model_name": "ImageModel", "state": { "format": "jpg", "layout": "IPY_MODEL_b2b338531df545928f6f8ce701fbf132", "value": {} } }, "c8a4845b09b64b2482ef1f9f80ad1124": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "ImageModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "display_legend": false, "image": "IPY_MODEL_bfe77423b216444e9ca904519b4c3a9c", "scales": { "x": "IPY_MODEL_456831a0b1e84a8a8165dba766473edf", "y": "IPY_MODEL_51ee8a765fa4490693392782a05aeb28" }, "selected": [], "x": { "type": "float", "values": [ -1, 2 ] }, "y": { "type": "float", "values": [ -0.5, 2 ] } } }, "ca5e304f502441c6b80a3b3d05015a34": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "AxisModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "orientation": "vertical", "scale": "IPY_MODEL_51ee8a765fa4490693392782a05aeb28", "side": "left", "tick_values": { "type": null, "values": null } } }, "ce3fef64c326403ba2d46dcc2ddb9c18": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "AxisModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "orientation": "vertical", "scale": "IPY_MODEL_4db5e4b8cb6445dbb21323e987bd724d", "side": "left", "tick_values": { "type": null, "values": null } } }, "d39498cdd2aa4cbb92d755f4d328d391": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "AxisModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "scale": "IPY_MODEL_456831a0b1e84a8a8165dba766473edf", "side": "bottom", "tick_values": { "type": null, "values": null } } }, "e0aaf68043b742aa9f59eda5706cd6f7": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "e15f595de79249869d36f9da368d6166": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "e70a421702444d01ae2ae96b593d6872": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "f5ab5459a15d45debea50eb61d0687b8": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "ImageModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "display_legend": false, "image": "IPY_MODEL_32d48656fa234d86a504e3c01daa2a5c", "scales": { "x": "IPY_MODEL_80b4dfd4d32e46058d615a19c8ba4226", "y": "IPY_MODEL_4db5e4b8cb6445dbb21323e987bd724d" }, "selected": [], "x": { "type": "float", "values": [ 0, 1 ] }, "y": { "type": "float", "values": [ 0, 1 ] } } }, "f73f97656cff4b29a3ce2b0e1bad1dd8": { "model_module": "bqplot", "model_module_version": "^0.3.0", "model_name": "LinearScaleModel", "state": { "_model_module_version": "^0.3.0", "_view_module_version": "^0.3.0", "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "f920e644b9f44e64b7c6e9f9d0509dca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_4e6331271a734ad3831edadd5c59efad", "IPY_MODEL_3dbaf85a158048bb8afab96efcd647dd" ], "layout": "IPY_MODEL_0622ba0d24e94ed4ba3aaab8df39c611" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

turbomanage/training-data-analyst

courses/machine_learning/deepdive/04_advanced_preprocessing/taxicab_traffic/train.ipynb

4

11908

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Taxi Fare Prediction Using Realtime Traffic Data\n", "\n", "This will be the same as our previous taxi fare model, but with the addition of ‘trips_last_5min’ data as a feature. This is our proxy for traffic" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import shutil\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load raw data\n", "\n", "These are the same files used previously for taxi fare prediction, however an additional field `trips_last_5min` has been added" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gsutil cp gs://cloud-training-demos/taxifare/traffic/small/*.csv .\n", "!ls -l *.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train and Evaluate input functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "CSV_COLUMN_NAMES = [\"fare_amount\",\"dayofweek\",\"hourofday\",\"pickuplon\",\"pickuplat\",\\\n", " \"dropofflon\",\"dropofflat\",\"trips_last_5min\"]\n", "CSV_DEFAULTS = [[0.0],[1],[0],[-74.0],[40.0],[-74.0],[40.7],[0]]\n", "\n", "def read_dataset(csv_path):\n", " def _parse_row(row):\n", " # Decode the CSV row into list of TF tensors\n", " fields = tf.decode_csv(records = row, record_defaults = CSV_DEFAULTS)\n", "\n", " # Pack the result into a dictionary\n", " features = dict(zip(CSV_COLUMN_NAMES, fields))\n", " \n", " # NEW: Add engineered features\n", " features = add_engineered_features(features)\n", " \n", " # Separate the label from the features\n", " label = features.pop(\"fare_amount\") # remove label from features and store\n", "\n", " return features, label\n", " \n", " # Create a dataset containing the text lines.\n", " dataset = tf.data.Dataset.list_files(file_pattern = csv_path) # (i.e. data_file_*.csv)\n", " dataset = dataset.flat_map(map_func = lambda filename:tf.data.TextLineDataset(filenames = filename).skip(count = 1))\n", "\n", " # Parse each CSV row into correct (features,label) format for Estimator API\n", " dataset = dataset.map(map_func = _parse_row)\n", " \n", " return dataset\n", "\n", "def train_input_fn(csv_path, batch_size = 128):\n", " #1. Convert CSV into tf.data.Dataset with (features,label) format\n", " dataset = read_dataset(csv_path)\n", " \n", " #2. Shuffle, repeat, and batch the examples.\n", " dataset = dataset.shuffle(buffer_size = 1000).repeat(count = None).batch(batch_size = batch_size)\n", " \n", " return dataset\n", "\n", "def eval_input_fn(csv_path, batch_size = 128):\n", " #1. Convert CSV into tf.data.Dataset with (features,label) format\n", " dataset = read_dataset(csv_path)\n", "\n", " #2.Batch the examples.\n", " dataset = dataset.batch(batch_size = batch_size)\n", " \n", " return dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Engineering: feature columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 1. One hot encode dayofweek and hourofday\n", "fc_dayofweek = tf.feature_column.categorical_column_with_identity(key = \"dayofweek\", num_buckets = 7)\n", "fc_hourofday = tf.feature_column.categorical_column_with_identity(key = \"hourofday\", num_buckets = 24)\n", "\n", "# 2. Bucketize latitudes and longitudes\n", "NBUCKETS = 16\n", "latbuckets = np.linspace(start = 38.0, stop = 42.0, num = NBUCKETS).tolist()\n", "lonbuckets = np.linspace(start = -76.0, stop = -72.0, num = NBUCKETS).tolist()\n", "def bucketize_fc(key,boundaries):\n", " return tf.feature_column.bucketized_column(\n", " source_column = tf.feature_column.numeric_column(key = key), \n", " boundaries = boundaries)\n", "fc_bucketized_plat = bucketize_fc(\"pickuplon\",lonbuckets)\n", "fc_bucketized_plon = bucketize_fc(\"pickuplat\",latbuckets)\n", "fc_bucketized_dlat = bucketize_fc(\"dropofflon\",lonbuckets)\n", "fc_bucketized_dlon = bucketize_fc(\"dropofflat\",latbuckets)\n", "\n", "# 3. Cross features to get combination of day and hour\n", "fc_crossed_day_hr = tf.feature_column.crossed_column(keys = [fc_dayofweek, fc_hourofday], hash_bucket_size = 24 * 7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Engineering: input function" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_engineered_features(features):\n", " features[\"dayofweek\"] = features[\"dayofweek\"] - 1 # subtract one since our days of week are 1-7 instead of 0-6\n", " \n", " features[\"latdiff\"] = features[\"pickuplat\"] - features[\"dropofflat\"] # East/West\n", " features[\"londiff\"] = features[\"pickuplon\"] - features[\"dropofflon\"] # North/South\n", " features[\"euclidean_dist\"] = tf.sqrt(x = features[\"latdiff\"]**2 + features[\"londiff\"]**2)\n", "\n", " return features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gather list of feature columns\n", "\n", "Note we're standard normalizing our traffic proxy. The mean and standard deviation were calculated on the full dataset in BigQuery, then hardcoded here.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "feature_cols = [\n", " #1. Engineered using tf.feature_column module\n", " tf.feature_column.indicator_column(categorical_column = fc_crossed_day_hr),\n", " fc_bucketized_plat,\n", " fc_bucketized_plon,\n", " fc_bucketized_dlat,\n", " fc_bucketized_dlon,\n", " #2. Engineered in input functions\n", " tf.feature_column.numeric_column(key = \"latdiff\"),\n", " tf.feature_column.numeric_column(key = \"londiff\"),\n", " tf.feature_column.numeric_column(key = \"euclidean_dist\"),\n", " #3. Traffic proxy\n", " tf.feature_column.numeric_column(key = \"trips_last_5min\",\n", " normalizer_fn=lambda x: (x - 2070) / 616)\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Serving Input Receiver function " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def serving_input_receiver_fn():\n", " receiver_tensors = {\n", " 'dayofweek' : tf.placeholder(dtype = tf.int32, shape = [None]), # shape is vector to allow batch of requests\n", " 'hourofday' : tf.placeholder(dtype = tf.int32, shape = [None]),\n", " 'pickuplon' : tf.placeholder(dtype = tf.float32, shape = [None]), \n", " 'pickuplat' : tf.placeholder(dtype = tf.float32, shape = [None]),\n", " 'dropofflat' : tf.placeholder(dtype = tf.float32, shape = [None]),\n", " 'dropofflon' : tf.placeholder(dtype = tf.float32, shape = [None]),\n", " 'trips_last_5min' : tf.placeholder(dtype = tf.float32, shape = [None]),\n", " }\n", " \n", " features = add_engineered_features(receiver_tensors) # 'features' is what is passed on to the model\n", " \n", " return tf.estimator.export.ServingInputReceiver(features = features, receiver_tensors = receiver_tensors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train and Evaluate" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "OUTDIR = \"taxi_trained\"\n", "shutil.rmtree(path = OUTDIR, ignore_errors = True) # start fresh each time\n", "tf.summary.FileWriterCache.clear() # ensure filewriter cache is clear for TensorBoard events file\n", "tf.logging.set_verbosity(v = tf.logging.INFO) # so loss is printed during training\n", "\n", "model = tf.estimator.DNNRegressor(\n", " hidden_units = [10,10], # specify neural architecture\n", " feature_columns = feature_cols, \n", " model_dir = OUTDIR,\n", " config = tf.estimator.RunConfig(\n", " tf_random_seed = 1, # for reproducibility\n", " save_checkpoints_steps = 200 # checkpoint every N steps\n", " ) \n", ")\n", "\n", "# Add custom evaluation metric\n", "def my_rmse(labels, predictions):\n", " pred_values = tf.squeeze(input = predictions[\"predictions\"], axis = -1)\n", " return {\"rmse\": tf.metrics.root_mean_squared_error(labels = labels, predictions = pred_values)}\n", "\n", "model = tf.contrib.estimator.add_metrics(estimator = model, metric_fn = my_rmse) \n", " \n", "train_spec = tf.estimator.TrainSpec(\n", " input_fn = lambda: train_input_fn(\"./taxi-train.csv\"),\n", " max_steps = 5000)\n", "\n", "exporter = tf.estimator.FinalExporter(name = \"exporter\", serving_input_receiver_fn = serving_input_receiver_fn) # export SavedModel once at the end of training\n", "# Note: alternatively use tf.estimator.BestExporter to export at every checkpoint that has lower loss than the previous checkpoint\n", "\n", "eval_spec = tf.estimator.EvalSpec(\n", " input_fn = lambda: eval_input_fn(\"./taxi-valid.csv\"),\n", " steps = None,\n", " start_delay_secs = 1, # wait at least N seconds before first evaluation (default 120)\n", " throttle_secs = 1, # wait at least N seconds before each subsequent evaluation (default 600)\n", " exporters = exporter) # export SavedModel once at the end of training\n", "\n", "tf.estimator.train_and_evaluate(estimator = model, train_spec = train_spec, eval_spec = eval_spec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Results\n", "\n", "For me, adding the traffic information reduced the RMSE from 4.17 (without the feature) to 4.14. It looks like it helped, but the lift isn't dramatic. Perhaps this is because information about traffic is already mostly captured by the day_hr feature cross." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

MathYourLife/spouting-jibberish

Haversine/Haversine.ipynb

1

5003

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate the GPS Distance with the Haversine Formula\n", "\n", "* Dan Couture [@MathYourLife](https://twitter.com/MathYourLife), [github](https://github.com/MathYourLife)\n", "* 2015-03-05\n", "\n", "### Problem\n", "\n", "I've got the start and end gps location from an excursion across town and need to determine the travel distance\n", "\n", " start: 43.059535, -71.013171\n", " end: 43.083620, -70.892085\n", "\n", "You could always use [google maps](https://www.google.com/maps/dir/43.059535,+-71.013171/%2743.08361,-70.89202%27/), but that would just be cheating.\n", "\n", "### Haversine Formula\n", "\n", "The goal for this formula is to calculate the shortest great circle distance between two points on the globe designated by latitude and longitudes. The added benefit of the Haversine equation is that it calculates the central angle as well where $s = r\\theta$.\n", "\n", "\n", "source: https://software.intel.com/sites/default/files/great%20circle.png\n", "\n", "The Haversine formula is mainly based on calculation of the central angle, $\\theta$, between two gps coordinates. Using the formula for arc length on a sphere\n", "\n", "$$\n", "s = r \\theta\n", "$$\n", "\n", "where $r$ is the Earth's radius, and $\\theta$ is the central angle calculated as\n", "\n", "$$\n", "\\theta = 2 \\arcsin\\left( \\sqrt{\\sin^2 \\left(\\frac{\\phi_2-\\phi_1}{2}\\right) + \\cos(\\phi_1)\\cos(\\phi_2)\\sin^2 \\left( \\frac{\\lambda_2-\\lambda_1}{2} \\right) } \\right)\n", "$$\n", "\n", "with:\n", "\n", "$$\n", "\\begin{align}\n", "\\phi &= \\text{latitude}\\\\\n", "\\lambda &= \\text{longitude}\\\\\n", "\\end{align}\n", "$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import math\n", "\n", "# Mean radius of the earth\n", "EARTH_RADIUS = 6371.009\n", "\n", "def haversine(lat1, lon1, lat2, lon2):\n", " \"\"\"\n", " Calculate the great circle distance between two points\n", " on the earth (specified in decimal degrees)\n", " \n", " Return (central angle, distance between points in km)\n", " \"\"\"\n", " # convert decimal degrees to radians\n", " lat1, lon1, lat2, lon2 = [math.radians(x) for x in [lat1, lon1, lat2, lon2]]\n", "\n", " # haversine formula\n", " dlon = lon2 - lon1\n", " dlat = lat2 - lat1\n", " \n", " a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\n", " central_angle = 2 * math.asin(math.sqrt(a))\n", "\n", " # s = r * theta\n", " km = EARTH_RADIUS * central_angle\n", " return (central_angle, km)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate Excursion Distance" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Central Angle of 0.00160001 radians\n", "Arc length distance of 10.1937 km\n" ] } ], "source": [ "start = (43.059535, -71.013171)\n", "end = (43.083620, -70.892085)\n", "\n", "central_angle, km = haversine(*(start + end))\n", "\n", "print(\"Central Angle of %g radians\" % central_angle)\n", "print(\"Arc length distance of %g km\" % km)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Earth is not a sphere\n", "\n", "The Haversine is a straight forward formula that provides an approximation for the distance between gps coordinates. The Earth of course is not spherical, and elevation changes including terrain profiles will increase actual distance traveled." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References:\n", "\n", "* http://www.gcmap.com/faq/gccalc#ellipsoid\n", "* http://stackoverflow.com/questions/4913349/haversine-formula-in-python-bearing-and-distance-between-two-gps-points/4913653#4913653\n", "* http://www.gcmap.com/mapui?P=DXB-SFO%2CBINP&PM=b%3Adisc7%2B%25U%2Cp%3Adisc7%2B%25N&MS=wls&PW=2&DU=km" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

prabhamatta/Analyzing-Open-Data

notebooks/Day_23_A_Learn_Interact_Widgets.ipynb

1

8092

{ "metadata": { "name": "", "signature": "sha256:e8902703f832bd094bde1b1fb0a3ca252667a36578bc12cfed5ad2ce80e74d48" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import display, Image, HTML, clear_output" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html import widgets" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "dir(widgets)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "['AccordionWidget',\n", " 'BoundedFloatTextWidget',\n", " 'BoundedIntTextWidget',\n", " 'ButtonWidget',\n", " 'CallbackDispatcher',\n", " 'CheckboxWidget',\n", " 'ContainerWidget',\n", " 'DOMWidget',\n", " 'DropdownWidget',\n", " 'FloatProgressWidget',\n", " 'FloatSliderWidget',\n", " 'FloatTextWidget',\n", " 'HTMLWidget',\n", " 'ImageWidget',\n", " 'IntProgressWidget',\n", " 'IntSliderWidget',\n", " 'IntTextWidget',\n", " 'LatexWidget',\n", " 'PopupWidget',\n", " 'RadioButtonsWidget',\n", " 'SelectWidget',\n", " 'TabWidget',\n", " 'TextWidget',\n", " 'TextareaWidget',\n", " 'ToggleButtonWidget',\n", " 'ToggleButtonsWidget',\n", " 'Widget',\n", " '__builtins__',\n", " '__doc__',\n", " '__file__',\n", " '__name__',\n", " '__package__',\n", " '__path__',\n", " 'fixed',\n", " 'interact',\n", " 'interaction',\n", " 'interactive',\n", " 'widget',\n", " 'widget_bool',\n", " 'widget_button',\n", " 'widget_container',\n", " 'widget_float',\n", " 'widget_image',\n", " 'widget_int',\n", " 'widget_selection',\n", " 'widget_selectioncontainer',\n", " 'widget_string']" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "%%html\n", "<svg height=\"100\">\n", " <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"3\" fill=\"red\" />\n", "</svg>" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<svg height=\"100\">\n", " <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"3\" fill=\"red\" />\n", "</svg>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x10213e890>" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "def circle(r=40):\n", " \n", " cx = int(1.25*r)\n", " cy = cx\n", " height = 2*cx\n", " \n", " html = \"\"\"<svg height=\"{height}\">\n", " <circle cx=\"{cx}\" cy=\"{cy}\" r=\"{r}\" stroke=\"black\" stroke-width=\"3\" fill=\"red\" />\n", "</svg>\n", "\"\"\".format(height=height, cx=cx, cy=cy, r=r)\n", " display(HTML(html))\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "circle()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<svg height=\"100\">\n", " <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"3\" fill=\"red\" />\n", "</svg>\n" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x10213e210>" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "widgets.interact(circle, r=(0,500,5))" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<svg height=\"474\">\n", " <circle cx=\"237\" cy=\"237\" r=\"190\" stroke=\"black\" stroke-width=\"3\" fill=\"red\" />\n", "</svg>\n" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x102ec5c90>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "w = widgets.FloatSliderWidget()\n", "w.min = 0\n", "w.max = 200\n", "w.value = 30" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "w" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "w.value = 50" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "import time\n", "\n", "for m in range(0,200,2):\n", " w.value = m\n", " time.sleep(0.1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "w.keys" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "['_view_name',\n", " 'orientation',\n", " 'msg_throttle',\n", " 'min',\n", " 'max',\n", " '_css',\n", " 'value',\n", " 'readout',\n", " 'disabled',\n", " 'visible',\n", " 'step',\n", " 'description']" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "w.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "m = widgets.interact(circle, r=(0,500,5))\n", "m" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<svg height=\"100\">\n", " <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"3\" fill=\"red\" />\n", "</svg>\n" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x101dc0990>" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "<function __main__.circle>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "for r in range (0,500,5):\n", " m.widget._children[0].value = r\n", " time.sleep(0.1)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<svg height=\"1236\">\n", " <circle cx=\"618\" cy=\"618\" r=\"495\" stroke=\"black\" stroke-width=\"3\" fill=\"red\" />\n", "</svg>\n" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x102ec5fd0>" ] } ], "prompt_number": 15 } ], "metadata": {} } ] }

apache-2.0

wasit7/tutorials

django/django_generic_view/generic/formview.ipynb

1

3574

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from myapp.views import CarUpdateView" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from myapp.views import CarModelFormCreate, CarModelForm" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "global name 'FormHelper' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-11-1268bdbe4d87>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mCarModelForm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32mC:\\Users\\Wasit\\Desktop\\test_generic_view\\generic\\myapp\\views.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCarModelForm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhelper\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFormHelper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 152\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhelper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSubmit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'save'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'save'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \t\tx = Layout(\n", "\u001b[0;31mNameError\u001b[0m: global name 'FormHelper' is not defined" ] } ], "source": [ "CarModelForm()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Django Shell-Plus", "language": "python", "name": "django_extensions" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

atulsingh0/MachineLearning

scikit-learn/Matplotlib_Tutorial_02.ipynb

1

308137

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Matplotlib tutorial 02" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# generating some data points\n", "X = np.linspace(-np.pi, np.pi, 20, endpoint=True)\n", "C, S = np.cos(X), np.sin(X)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x790f278>,\n", " <matplotlib.lines.Line2D at 0x790fc88>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7764cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Simply plotting these in same plot\n", "\n", "plt.plot(X, C,\n", " X, S)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x79d2940>,\n", " <matplotlib.lines.Line2D at 0x79d2b00>,\n", " <matplotlib.lines.Line2D at 0x79d84a8>,\n", " <matplotlib.lines.Line2D at 0x79d8cc0>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhC74mqZpFvH/RxSD0PpJGysAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7939748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, C, 'oy',\n", " X, S,\n", " X, S, 'or')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Checking and Defining the Range of Axes **" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(-4.0, 4.0, -1.0, 1.0)\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhC74mqZpFvH/RxSD0PpJGysAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x79a9320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, C, 'oy',\n", " X, S,\n", " X, S, 'or')\n", "\n", "print(plt.axis()) # this will print the current plotting X and Y values" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(-4.0, 4.0, -1.0, 1.0)\n", "(-5.0, 5.0, -1.5, 1.5)\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCBv7f7czQwLGdLVtAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7c20c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# we can change it by assigning new one\n", "\n", "plt.plot(X, C,\n", " X, C, 'oy',\n", " X, S,\n", " X, S, 'or')\n", "\n", "print(plt.axis())\n", "\n", "x1, x2, y1, y2 = (-5, 5, -1.5, 1.5)\n", "plt.axis([x1, x2, y1, y2])\n", "\n", "print(plt.axis())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** \"linspace\" to Define X Values ** \n", "\n", " linspace can be used to create evenly spaced numbers over a specified interval. \n", " linspace(start, stop, num=50, endpoint=True, retstep=False) \n", " " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD7CAYAAACPDORaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wFYWCgjhRJ+bAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7c93748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = np.linspace(0, 2 * np.pi, 20, endpoint=True)\n", "F = np.sin(X)\n", "plt.plot(X,F)\n", "startx, endx = -0.1, 2*np.pi + 0.1\n", "starty, endy = -1.1, 1.1\n", "plt.axis([startx, endx, starty, endy])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAD9CAYAAABz5fboAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7cabc88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = np.linspace(-2 * np.pi, 2 * np.pi, 20, endpoint=True)\n", "F1 = 3 * np.sin(X)\n", "F2 = np.sin(2*X)\n", "F3 = 0.3 * np.sin(X)\n", "startx, endx = -2 * np.pi - 0.1, 2*np.pi + 0.1\n", "starty, endy = -3.1, 3.1\n", "plt.axis([startx, endx, starty, endy])\n", "plt.plot(X,F1)\n", "plt.plot(X,F2)\n", "plt.plot(X,F3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAD9CAYAAABz5fboAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7c24320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = np.linspace(-2 * np.pi, 2 * np.pi, 20, endpoint=True)\n", "F1 = 3 * np.sin(X)\n", "F2 = np.sin(2*X)\n", "F3 = 0.3 * np.sin(X)\n", "startx, endx = -2 * np.pi - 0.1, 2*np.pi + 0.1\n", "starty, endy = -3.1, 3.1\n", "plt.axis([startx, endx, starty, endy])\n", "plt.plot(X,F1)\n", "plt.plot(X,F2)\n", "plt.plot(X,F3)\n", "plt.plot(X, F1, 'ro')\n", "plt.plot(X, F2, 'bx')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " ** Customizing Ticks ** " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([-8., -6., -4., -2., 0., 2., 4., 6., 8.]), <a list of 9 Text xticklabel objects>)\n", "(array([-1. , -0.5, 0. , 0.5, 1. ]), <a list of 5 Text yticklabel objects>)\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Wd4VcX69/HvJHREOoKgSO8kBCmCAtKkg4BSpIUmAoL6\nt2A74vFROQpKURAEgoCAFGkKSAQiHVESeu8oHeklIZnnxQQPBxNI2XvP2ln357q4SFlZ62cMd2ZP\nVVprhBBCpH0BtgMIIYTwDSn4QgjhElLwhRDCJaTgCyGES0jBF0IIl5CCL4QQLuGRgq+UmqCUOqmU\n2nKXa0YqpfYqpaKUUsGeeK4QQoik81QLPwx4KrFPKqUaA8W01iWA54GvPPRcIYQQSeSRgq+1Xg38\ndZdLWgKT46/dAGRXSj3giWcLIYRIGl/14RcEjt72/h/xHxNCCOEjMmgrhBAukc5Hz/kDeOi29wvF\nf+wflFKyuY8QQiST1lrd6xpPFnwV/ychC4B+wHdKqerAea31ycRu5PQN3QYPHszgwYNtx7gnyZl0\nq1dD27ZQty5kzJjwNZGRg6lUafA/Pn71qvn6BQugcmXv5kyK1H4/I49H0n1Bdx7I+gBjm42lcI7C\nAETHRrPl5BbWH1v/958zV89QtWBVHiv0GNULVadaoWrkypzLJzl9xR9yKnXPWg94qOArpaYBdYDc\nSqkjwHtABkBrrcdprRcppZoopfYBV4BQTzxXCE+YMQMGDIApU+CpROeaweDB5k9C5s2DRo1g/Hho\n2dIbKb3v+s3r/PuXfzN+03g+bfApXYK6/E8hyRCYgUcffJRHH3yU/lX7A3Dqyik2HNvA+mPrGbpu\nKBv/2EiBbAWoXqg63YK68WSRJ23954gEeKTga607JuGa/p54lhCeojV8/DGMHQs//wwVK6b8Xq1a\nQcGC5u9Dh2DgQI/F9Im1R9fSY0EPyuYty5YXtpD/vvxJ+rp8WfPRvFRzmpdqDkBsXCw7Tu9g9ZHV\ntJvdjultplOvaD1vRhfJ4Ks+/DSlTp06tiMkieRMXEwM9OkDkZGwbh08+OC9v+ZeOatUgTVroGlT\n2L8fPv8cAgM9kzc5kvP9vBx9mbeXvc2sHbMY1XgUbcq2SdWzAwMCqfBABSo8UIFy+crRdmZbFnRY\nQPVC1VOV0yZ/yZkUymn95Uop7bRMIm05f97012fJAtOmwX33ef7+zzwDmTLB9Omev7+n/HzgZ3ov\n7M0ThZ/g86c+T3Lfe3Is2ruI0PmhhHcOp+IDqXgJJe5KKZWkQVuZlilc5dAhqFkTypaFuXO9U4xz\n5IBFiyBfPqhVC/780/PPSI3z18/TY34PeizowZdNvuSbVt94pdgDNCnRhJGNRtL428bsPbvXK88Q\nSScFX7jGxo2m2D//PIwc6d3ulvTpzQBu27ZQvTpsSXSXKd+av2s+5UeXJ1O6TGx7YRuNSzT2+jPb\nlW/H4NqDaTClAUcvHL33FwivkT584Qpz50Lv3jBhArRo4ZtnKgVvvQVFikD9+veeBeRN129eJ3R+\nKL//+TvT2kyjVuFaPn1+r8q9uHDjAg2mNGBV6CryZs3r0+cLQ/rwRZqmtRk8/ewzmD/f3jz5W/P8\n33/fvMLwtdeWvsbus7v5ru13ZE6f2fcB4r2z/B0W7V3Eiq4ryJ4pu7UcaU1S+/Cl4Is06+ZNMz1y\n5Ur48Ud4+GG7efbuNTN4WrWCIUMgwEcdqisPr6T97PZs7rPZestaa82Li19k88nN/NTpJ7Kkz2I1\nT1ohBV+42qVL0L69mX45axZkd0hj8uxZU/AfeMB08WT2cmP74o2LBH0VxMhGI/+eK29bnI6j67yu\nnLl6hvnt55MhMIPtSH5PZukI1zp1ysyOKVjQtOydUuwBcueG8HDIkAGefNJM4fSmV356hXpF6jmm\n2AMEqADCWoaRMTAjnb7vRGxcrO1IriEtfJHmdOhgpkQOH24GTp1Ia+jRw8zmGTvWO89YuHshA5cM\nZHOfzWTLmM07D0mF6zev03RaU4rkKMLXzb9O8n4w4p+kS0e4Ung49OoFO3aYhVVOdv68WQ/w/fdm\n6qYnnb5ymqCvgpjRdobPZ+Qkx6Ubl2gwpQE1H6rJ0IZDpeinkHTpCNe5fh369YNRo5xf7MEs0Pr0\nU7PFw82bnruv1prnf3ie5yo85+hiD5AtYzYWPbeI8APhfLjqQ9tx0jwp+CLN+OQT02Ju7pzu6nvq\n2NH063/xhefuOXXLVPae28sHdT/w3E29KFfmXCztvJRvNn/DqA2jbMdJ06RLR6QJ+/aZbpFNm+xP\nv0yuXbvg8cdh82Yz0JwaRy4cofK4yoR3Dic4f7BnAvrIofOHqBVWixGNRvB0madtx/Er0ocvXENr\naNzYHF7y+uu206TMO+/Anj0wc2bK7xGn42gwpQH1itTjrSfe8lw4H1p5eCVd5nZhV/9dZEqXyXYc\nvyF9+MI1Zs+GY8fg5ZdtJ0m5t9+G336Dn35K+T2++PULrsVc4/WafvpbD6hVuBaVClRixPoRtqOk\nSdLCF37t4kXTbz99OjzxhO00qbNokTl5a+vW5C/I2nl6J0+EPcG6HusokbuEdwL6yJ6ze6gxoQY7\n++20vjLYX0iXjnCFl1820xvDwmwn8Yw2baB8ebPnTlLFxMZQY2INugd354UqL3gvnA8NWDwArTWj\nmsggblJIwRdpXlQUNGwI27dD3jTSEDx6FCpVgrVroWTJpH3N+xHvs+7YOhY/tzjNzGM/c/UMpb8o\nzZruayiVp5TtOI4nffgiTYuLgxdegA8/TDvFHuChh+DNN816gqS0ezb+sZHRv41mQosJaabYA+TJ\nkofXa77OGz+/YTtKmiIFX/il8ePNtgk9ethO4nkDBsDJk/Ddd3e/7lrMNTrP7cyIRiMoeH8q53M6\n0IBqA4g6EcUvh36xHSXNkC4d4XdOnTL93OHhEBRkO413rFkDzz5rtohIbPO3l5a8xMkrJ5neZrpv\nw/nQ9K3TGbZuGL/2+pUAJe3TxEiXjkizXn8dOnVKu8UezFGMjRvDu+8m/PllB5Yxe8dsvmzypW+D\n+Vj78u0JDAhk+ta0+0vNl6SFL/zKypXw3HOm5ZvNeRtAetTZs1CunJmuGRLy34+fv36eimMqMq75\nOBoVb2QvoI+sOryKTnM7savfLqundTmZtPBFmhMdbQZqP/887Rd7MHvsfPyx2Vwt9rYt4wcuGUiz\nks1cUewBnij8BJULVGbEBlmMlVpS8IXf+Pxzs09Omza2k/hO166QMSOMG2fe33xiM+H7w/mkwSd2\ng/nYf+r/h6Frh3L6ymnbUfyadOkIv3D4sDmAfMMGKFbMdhrf2rbN7BO0dSsMXNWeRx98lFdrvGo7\nls8NXDyQm3E3+bJp2h63SAlZeCXSlJYt4dFHEx/ETOtefx12n9nD2jI1OTDggCNPsPK2s1fPUvrL\n0qwKXUXpPKVtx3EU6cMXacaCBWYLYX/dCdMT/vUvWHb9E5rk6efKYg+QO0tu3qj5Bq+Hu/gHIZWk\n4AtHu3LFLEQaPdr0ZbvVX7FHUWW/Z93wF4mOtp3Gnv5V+7P11FZWHFxhO4pfkoIvHO2DD6BGDahX\nz3YSu4atG8bzVXtQ6uHcDB1qO409mdJlYki9Ibwa/ipxOs52HL8jffjCsQ4cgKpVzWBlgQK209hz\n6sopSn9Rmu19t3PjbAEqVzYDuW79nmiteWzCY/Sr0o/OQZ1tx3EE6cMXfm/4cOjVy72F7ZYR60fQ\nrlw7CmQrwCOPQIcOnj0D198opRjWcBhvL3+bazHXbMfxK9LCF4507pyZfrl9Ozz4oO009ly4foGi\nI4uysddGiuYsCpjzex97DA4dgqxZ7eazqe3MtoQUCPHb4xw9SVr4wq+NHQstWri72AOM3jiaJiWa\n/F3sAYoXN6d7pZVDX1JqSP0hfLbuM05ePmk7it+QFr5wnOhoKFIEFi+GihVtp7HnasxVio4oyvKu\nyymbt+z/fG7NGujSxRx8HhhoKaADvLzkZW7E3mB009G2o1glLXzht6ZPN5uGubnYA4zfNJ4aD9X4\nR7EHM3MpXz6YP99CMAd5t/a7zNoxi52nd9qO4hek4AtH0RqGDYP/+z/bSeyKjo3m07Wf8ubjbyb4\neaXM92jYMB8Hc5hcmXPx5uNv8vrPshgrKaTgC0cJDzdFv2FD20nsmrplKmXylKFKwSqJXvP003D8\nOKxb58NgDtSvSj+2n9rOqsOrbEdxPCn4wlGGDYNXXjEtWLeKjYtlyOoh95x9EhgIL70krfyM6TLy\nao1XGb5huO0ojicFXzjG1q3mT8eOtpPYNWfnHPJmzUvtwrXveW337hARYRapuVmXoC5EHIrg8PnD\ntqM4mhR84RiffQb9+7t7zxytNR+t+oi3Hn8LlYSXOffdZxanDXd54/a+DPfRpWIXxvw2xnYUR5Np\nmcIR/vzTzMzZvx9y5bKdxp4f9/zIW8vfIur5qCQVfDDfu/LlzYIsN3/v9p3bx2MTHuPwS4fJkj6L\n7Tg+JdMyhV/54gtzVq2bC5bWmg9XfZjk1v0tDz4IzZubxWpuVjxXcaoXqs60rdNsR3EsKfjCuitX\n4Ouv4eWXbSexa+XhlZy5eoa2Zdsm+2tfeQVGjcLVWycDvFj1RUb9OgrpJUiYFHxhXVgY1KrlvqML\n7/TR6o8Y9PggAgOSv3Q2KMh0iU2f7oVgfqRB0QZEx0az8vBK21EcSQq+sCo21hxO7vaFVr/9+Rs7\nT++kU8VOKb7Hq6+aKZpubtwqpXix6ouM/HWk7SiO5JGCr5RqpJTapZTao5R6I4HP11ZKnVdKbYr/\n844nniv837x5ZouAGjVsJ7Hro1Uf8WqNV8kQmCHF92jY0BT7n3/2YDA/dGuK5pELR2xHcZxUF3yl\nVADwBfAUUA7ooJRK6IThlVrrkPg//y+1zxVpw7BhpmXqZjtO72Dt0bX0DOmZqvsoZfry3XwiFvx3\niuboje7eUC0hnmjhVwX2aq0Pa61jgBlAywSuc/HaSZGQtWvh5Elo1cp2EruGrB7CwGoDPTKVsGPH\n/y5gc7N+VfsxIXKCHJByB08U/ILA0dvePxb/sTs9ppSKUkr9qJT65/Z/wnWGDTNbA7h5e9+Dfx1k\n0d5F9K3S1yP3y5gR+vUzi9jcrHiu4lQrWE2maN4hnY+e8zvwsNb6qlKqMTAPKJnYxYMHD/777Tp1\n6lCnTh1v5xM+tn8//PILfPON7SR2fbr2U56v/DzZM2X32D379IESJczGam4+HnJAtQG8Fv4a3St1\nT9a6Bn8QERFBREREsr8u1SttlVLVgcFa60bx7w8CtNb6P3f5moNAZa31uQQ+JyttXeDFFyFbNvjo\nI9tJ7Dl+6TjlRpdjV/9d5Muaz6P37t8fsmeHDz/06G39itaasqPL8lXTr6j9yL33JfJnSV1p64mC\nHwjsBuoBx4FfgQ5a6523XfOA1vpk/NtVgZla60cSuZ8U/DTu3DlzTN+2be4+wvC1pa8RHRvNiMYj\nPH5vOffW+PLXL1lxaAWzn51tO4pX+WxrBa11LNAfWApsB2ZorXcqpZ5XSvWOv6ytUmqbUioSGA60\nS+1zhf+S82rN8YUToybyymOveOX+cu6t0SWoCysOrZApmvFk8zThUzdumPNqlyxx9xGGkzdP5rvt\n3/Fjxx+99gw599Z4ecnLZEqXiY/rf2w7itfI5mnCkaZPhwoV3F3sAcb9Po7eIb3vfWEqyLm3Rr+q\n/RgfOV6maCIFX/iQ1ma6oNu3Udh+ajsHzx+kacmmXn2OnHtryBTN/5KCL3wmPNz83aCB3Ry2fb3p\na7oHdyddgPdnRcu5t8aAagNkF02k4AsfGjpUzqu9FnONqVum0iOkh0+eJ+feGvWL1uf6zeusOuLu\ng86l4Auf2LLFTMPs0MF2Ervm7JxDlYJVeCTHIz57ppx7CwEqwOyiucHdu2hKwRc+8dlnZrGVm8+r\nBd8M1t5Jzr01ZIqmTMsUPnD6tFnqf+CAu48w3Hl6J/Um1+PwS4dJH5jep8++dWbwkSNmhbNbvbTk\nJTKny5zmpmjKtEzhGFOnmoVWbi72YAZrQ4NDfV7swSxyq10bZs70+aMdpX/V/q6eoikFX3iV1jB+\nPPRM3Vbvfu/6zetM2TIl1Xvep0bPnub/hZvdmqI5fZs7z4KUgi+8asMGiIkxy/zd7Pud3xNSIIQi\nOYtYy9CokenS2b7dWgRHuDV468auYyn4wqvGj4cePdw9FRPsDNbeKV066NYNJkywGsO6BsUauHaK\npgzaCq+5dAkefhh27oT8+W2nsWf3md3UnlSboy8ftdJ/f7v9+80umkePunvG1Je/fknE4QhmPTPL\ndhSPkEFbYd3MmWag0M3FHuwO1t6pWDEoXx4WLLCdxK4uQV1YdmCZ66ZoSsEXXjNhgunOcbMbN28w\nefNkq4O1d+rRQ7p1smXMRpegLozZOMZ2FJ+Sgi+8YscOc/hG48a2k9g1d9dcgvIHUSxXMdtR/ta6\nNWzcCIcP205iV/+q/V130LkUfOEVEyaYAcJ0vjo12aGcMFh7p8yZzRYXkybZTmJX8VzFqVSgEvN2\nzbMdxWek4AuPi46GKVPMHi5utufsHraf3k7L0i1tR/mHnj1h4kSIjbWdxK7uwd2ZGDXRdgyfkYIv\nPG7BArOMv3hx20nsGr9pPN2CupEhMIPtKP8QHAx58sCyZbaT2NWydEsij0dy6Pwh21F8Qgq+8DhZ\nWWsGa7/Z/I2jBmvvJCtvIVO6THQo34Fvor6xHcUnpOALjzpyxAwItm5tO4ld83fPp3y+8pTIXcJ2\nlER16ABLl8KZM7aT2BVaKZRJmycRp+NsR/E6KfjCo8LCoH17MzDoZk4crL1TjhzQvLkZb3GzSvkr\nkT1jdiIORdiO4nVS8IXHxMaagUC3d+fsP7efLSe30Kp0K9tR7qlnTzOjys2L25VSdK/UnYmRaX/w\nVgq+8JhlyyB3bqhUyXYSu8ZvGk/XoK5kTOf8vQtq1YIbN8wmd272XIXn+GHPD5y/ft52FK+Sgi88\nZsIEad1Hx0YTFhVGr8q9bEdJEqVk5S1A7iy5aVCsATO2zbAdxauk4AuPOHMGfvoJOna0ncSuhbsX\nUiZvGUrmLmk7SpJ17QqzZ8Ply7aT2NU9uDthUWG2Y3iVFHzhEVOnmgHAHDlsJ7Fr3CbnD9beqUAB\n07Xj9tOwGhZryB8X/2DbqW22o3iNFHyRalrLRmkAB/86yKbjm3i6zNO2oySbdOtAYEAgXYO6EhaZ\ndlv5UvBFqv36K1y/brZCdrPxm8bTuWJnMqXLZDtKsjVpAgcPmrML3Cy0UihTt04lOjbadhSvkIIv\nUm3CBLNvjptPtYqJjWFi1ER6hfjHYO2d0qUzfflub+UXz1WcUrlL8eOeH21H8Qop+CJVLl+GWbNM\nsXCzH/b8QIlcJSiTt4ztKCnWvbtZhBWdNhu3Sda9UtodvJWCL1Jl1ixzQPmDD9pOYte4TePoXdm/\nBmvvVKIElCkDCxfaTmJX27JtWXVkFccvHbcdxeOk4ItUkY3S4ND5Q2z8YyNtyrSxHSXVZEM1uC/D\nfbQp04YpW9LenhNS8EWK7dxpBvqaNLGdxK4JmybQqWInMqf3/w2E2rQxg/BHj9pOYldocCgTIyei\n09ieE1LwRYpNmABdurj7VKvYuFi+2fwNPSqljTmpmTNDu3ZmEzw3q/FQDTSadcfW2Y7iUVLwRYrc\nOtXK7XPvlx9cTr6s+ajwQAXbUTzm1mlYcWl/t+BEKaXMyts0NidfCr5IkYULoXRpM9DnZpM2T6Jb\ncDfbMTwqJARy5pTTsLoEdWH2ztlcib5iO4rHSMEXKSIbpcH56+f5cc+PdCjfwXYUj7u1bbKbFchW\ngJoP1WT2jtm2o3iMFHyRbEePwvr1ZoDPzWZun0mDYg3InSW37Sge17EjLFkCZ8/aTmJX90pp65Bz\nKfgi2SZNMqdaZcliO4ldYVFhhAaH2o7hFTlzQrNmZlM8N2tWshk7T+9k37l9tqN4hBR8kSxxcWZA\nz+2DtbvO7OLQ+UM0LNbQdhSv6dHDzMlPYzMTkyVDYAY6VezEpKhJtqN4hBR8kSzLl5stkENCbCex\na1LUJDpX7Ey6gLQ7J7V2bbh2zRxK72ahwaFMippEbFys7SipJgVfJMutbZDdvFFabFwsU7ZMSXOz\nc+4UEGD213H7ytsKD1SgQLYChB8Itx0l1aTgiyT76y9YvFhOtVq6fymF7i9E2bxlbUfxulunYV29\najuJXd2D08Yh51LwRZJNnw5PPQW5ctlOYtekzZPS7GDtnQoWhGrV4PvvbSexq0OFDizdv5SzV/17\n2pIUfJFkYWEQ6o46l6i/rv3FT/t+ol25draj+ExoqGy1kCNTDpqUaMK0rdNsR0kVKfgiSbZtg+PH\noUED20nsmr5tOo1LNCZn5py2o/hMixaweTMcOmQ7iV1pYU6+FHyRJJMmmY3SAgNtJ7FrUtQkugV1\nsx3DpzJlMusuJk+2ncSuukXqcu7aOSKPR9qOkmIeKfhKqUZKqV1KqT1KqTcSuWakUmqvUipKKRXs\niecK34iJMQtwunWzncSu7ae28+elP6lftL7tKD4XGmp+6bt5Q7UAFfD3tsn+KtUFXykVAHwBPAWU\nAzoopUrfcU1joJjWugTwPPBVap8rfGfxYihWDEqWtJ3ErklRk+gS1IXAAPe9zAkJgaxZYeVK20ns\n6hrUlenbpnP95nXbUVLEEy38qsBerfVhrXUMMANoecc1LYHJAFrrDUB2pdQDHni28AEZrDWHlE/d\nOjXNz71PjFIyeAtQJGcRgvIHsWD3AttRUsQTBb8gcPv5OMfiP3a3a/5I4BpHiYmNYen+pbZjWHf6\nNKxYAc8+azuJXT/t/4miOYtSMrd7X+Z06gTz58OlS7aT2OXPc/IduS588ODBf79dp04d6tSpYyVH\nl7ld+KXbL5TKU8rK853g22+heXO4/37bSewKiwpz3WDtnfLlgzp1zMH13bvbTmNP6zKtmbd7Hjfj\nblrbWiMiIoKIiIhkf51K7ZmNSqnqwGCtdaP49wcBWmv9n9uu+QpYobX+Lv79XUBtrfXJBO6nnXKO\n5OvhrxOgAhhSf4jtKFZoDcHB8PnnULeu7TT2nLl6huIji3P4pcNkz5Tddhyr5s2DYcNg1SrbScTt\nlFJore+54YknunQ2AsWVUoWVUhmA9sCdHVwLgC7xwaoD5xMq9k4TGhzK5M2TuRl303YUKyIj4eJF\n06pzs+lbp9OsZDPXF3uApk1hzx7Yu9d2EpESqS74WutYoD+wFNgOzNBa71RKPa+U6h1/zSLgoFJq\nHzAW6Jva5/pCmbxlKJyjMD/t+8l2FCvCwsxeKgEuX62Rlve9T6706eG558wUTeF/Ut2l42lO6tIB\n+Pr3r1myfwlznp1jO4pP3bhh9lHZuBGKFLGdxp7NJzbTYkYLDg48SIBy+W++eFu3QpMmZuWt2xfi\nOYUvu3TStHbl27HswDJOXzltO4pPLVwIFSq4u9hD/Nz7il2k2N+mQgV44AE55NwfyU/xPdyf8X5a\nlm7J1C3uOutN5t5DdGw007ZNc+3c+7vp1k3m5PsjKfhJEBocSlhUGE7qavKmP/+EtWvlkPJFexdR\nKncpiuUqZjuK43TsaFZg//WX7SQiOaTgJ0GtwrW4EnOF34//bjuKT0ydaop91qy2k9g1Kco9+94n\nV65c0LAhfPed7SQiOaTgJ0Fa2DQpqbQ2L9XdvlHaqSun+OXwL7Qt29Z2FMeSrRb8jxT8JOoa1JXv\ntn/HtZhrtqN41YYNEBsLNWvaTmLXt1u+pWWplmTLmM12FMdq2BCOHYMdO2wnEUklBT+JHsr+EI8+\n+Cjzds2zHcWrbrXu3XxIudbabKUgg7V3FRgInTtLK9+fSMFPhu7B/n/izd1cvWr2SenSxXYSuyJP\nRHI5+jK1CteyHcXxQkPNmE9MjO0kIimk4CdDy9ItiTweyaHzh2xH8Yp586BKFShUyHYSuyZFTaJr\nUFeZe58EpUqZtRo/uXMxut+Rn+hkyJQuEx3Kd+CbqG9sR/EKmXsPN27eYPq26XQJcvnLnGSQwVv/\nIQU/mUIrhTJp8yTidNo66+3IEdi0CVq1sp3Erh/2/ECFfBUoktPlS4yT4dlnzarbM2dsJxH3IgU/\nmSrlr0T2jNmJOBRhO4pHffMNtGtnDqx2MxmsTb7s2aFZM3N2gnA2KfjJpJSieyX/PfEmIVqb3Q/d\nPvf++KXjrDm6hjZlXL7EOAVuHXIunE0Kfgo8V+E5ftjzA+evn7cdxSNWrTIt+ypVbCexa8qWKbQu\n3ZqsGVy+xDgFnnzSbLMQFWU7ibgbKfgpkDtLbhoUa8B329LGuvJbg7Vun3v/9aav6VW5l+0ofikg\nwJydIIO3ziYFP4XSypz8y5dh7lxzQLWbRRyKIHO6zFQrWM12FL/VtStMmwbR0baTiMRIwU+hhsUa\n8sfFP9h2apvtKKkyaxY88QTkz287iV3jNo2jd+XeKDe/zEmlokWhXDlzloJwJin4KRQYEEjXoK6E\nRfr3a9hJk2Tu/ekrp1m8dzGdKrr8ZY4HyOCts0nBT4Vuwd2YunUqMbH+ua58/37YudNMqXOzyZsn\n06p0K3JkymE7it9r2xZWr4YTJ2wnEQmRgp8KJXKXoFTuUvy490fbUVJk0iRzkEWGDLaT2KO1/rs7\nR6Re1qzQujVMmWI7iUiIFPxU8tc5+bGxZrGV2+ferzy8kvQB6Xms0GO2o6QZt44/dMkBcX5FCn4q\ntS3bllVHVnHisn+9hl2xAnLnhuBg20nsksFaz3v8cbN75saNtpOIO0nBT6X7MtxH69KtmbLZv17D\nTpwog7Vnr57lxz0/ymCthyllWvkTJthOIu4kBd8Dulcyc/L95ZDzU6dg0SJzeIWbTd48mealmpMr\ncy7bUdKc7t1h5ky4eNF2EnE7KfgeUOOhGsTpONYfW287SpJMmGAG1nLmtJ3Enr8Ha0NksNYbChSA\n+vVl8NZppOB7gFLKrLz1g8Hb2FgYOxb69rWdxK7VR1YD8PjDj1tOkna98AKMGSODt04iBd9DOgd1\nZs7OOVzfNBdiAAAX9UlEQVSJvmI7yl0tWQJ588Kjj9pOYtet1r0M1nrPk0/CzZtmXr5wBin4HvJg\ntgep8VAN5uycYzvKXY0ZY1pebnbu2jkW7l4op1p5mVL/beULZ5CC70FOn5N/8CCsWwft29tOYtfU\nLVNpWrIpubPkth0lzevaFRYvhpMnbScRIAXfo5qVbMaO0zvYf26/7SgJGjcOunSBLFlsJ7FHa824\n32Ww1ldy5IA2bcw0YGGfFHwPyhCYgecqPMekqEm2o/zDjRvmH12fPraT2LXu2Dpi4mKoVbiW7Siu\n8cILZqJAbKztJEIKvof1COlBWFSY4zZUmzMHypeHUqVsJ7HrVuteBmt9p3JlyJfPdO0Iu6Tge1j5\nfOUplqsYc3fNtR3lf4wZI1Mx/7r2F/N2zaNrcFfbUVxHBm+dQQq+FwyoOoBRv46yHeNvW7fCgQPQ\nooXtJHZ9u/VbGpdoTJ4seWxHcZ127WDDBjNxQNgjBd8LWpZuyeHzh9l0fJPtKIBpWfXsCenT205i\njwzW2pUli5kwMHas7STuJgXfC9IFpKNvlb6OaOVfugQzZkAvl5/NveGPDVy7eY06j9SxHcW1+vQx\n2ybfuGE7iXtJwfeSniE9mbdrHqevnLaa49tvoU4dKFTIagzrZLDWvpIloWJFM4FA2CEF30vyZMlD\nmzJt+HrT19YyaC0rawEuXL/A3F1zZbDWAWTw1i4p+F70YtUXGb1xtLUpmmvXwrVrUK+elcc7xrdb\nv6VB0Qbky5rPdhTXa9HCTCDYutV2EneSgu9FQfmDKJarGPN2zbPy/DFjTL9pgIv/L2utGfv7WDmz\n1iHSpYPevaWVb4uLS4FvDKg6gJG/jvT5c0+fhh9+kDNrN/65kcvRl6lbpK7tKCJez55mIsGlS7aT\nuI8UfC+zNUVz4kR4+mnI5fLDnMb9Po5eIb0IUPKj7hQFC5qtk6dOtZ3EfeRfgZfZmKIZF2fmO7t9\nsPbijYvM2TmHbsHdbEcRd5DDUeyQgu8Dvp6i+dNP5vjCKlV88jjHmrZ1GvWK1CP/ffltRxF3qFvX\nzMdfu9Z2EneRgu8DebLkoXXp1j6bojl6tNk3x81TzmWw1tkCAsyEgtGjbSdxF6Ud9ppKKaWdlskT\nok5E0WxaMw4OPEj6QO/tcXD4MISEwJEjkDWr1x7jeL/9+RvPzHqG/QP2S/+9Q507B0WLwt695thN\nkXJKKbTW92ziyb8EHwnOH+yTKZrjxkGnTu4u9iCDtf4gVy5o3VoOR/GlVLXwlVI5ge+AwsAh4Fmt\n9YUErjsEXADigBitddW73DNNtvABZu+YzYgNI1gVusor94+OhocfhogIKF3aK4/wC5duXOLh4Q+z\no+8OCmQrYDuOuIuNG81Omnv3QmCg7TT+y1ct/EHAz1rrUsBy4M1ErosD6mitK92t2Kd1rUq34vD5\nw0Qej/TK/b//HsqWdXexBwiLCqNekXpS7P1AlSqmpf/TT7aTuENqC35L4Jv4t78BWiVynfLAs/ye\nt6doyr45EB0bzdC1Q3mj5hu2o4gkkv11fCe1RTif1vokgNb6BJDYZiUaCFdKbVRKuXqj3p4hPZm7\na67Hp2hu325eFrdK7FeuS0zdMpXSeUpTpaDL56T6kQ4dzPTMQ4dsJ0n70t3rAqVUOPDA7R/CFPB3\nErg8sc73mlrr40qpvJjCv1NrvTqxZw4ePPjvt+vUqUOdOnXuFdNv3D5F860n3vLYfeWQE4iNi2XI\n6iGMaz7OdhSRDFmyQOfOZsLBRx/ZTuMfIiIiiIiISPbXpXbQdiemb/6kUio/sEJrXeYeX/MecElr\n/Vkin0+zg7a3RJ2Iovn05hwYcMAjUzQvXzaDtZs3w0MPeSCgn5q5fSbD1w9nTfc1su+9n9m1y5zb\ncOQIZMhgO43/8dWg7QKgW/zbXYH5CQTJopS6L/7trEBDYFsqn+vXgvMHUyRHEY9N0Zw2DWrXdnex\n11rz0aqPePuJt6XY+6HSpaFcOTPxQHhPagv+f4AGSqndQD1gCIBSqoBS6of4ax4AViulIoH1wEKt\n9dJUPtfvDajmmV00tTarFd0+WLto7yI0miYlmtiOIlLohRdk5a23yUpbS27G3aToiKLMbz+fSgUq\npfg+69aZ/s89e9y7773WmpoTazKw2kDalW9nO45IoZgYKFwYli6F8uVtp/EvstLW4dIFpOOFR19I\n9RTNTz+F/v3dW+wBVh5eyemrp2lbtq3tKCIV0qc3rfyhQ20nSbukhW/RmatnKDGqBHv67yFv1uRv\nJvL77+bIuH37IHNmLwT0E09NfYpnyz5Lj5AetqOIVLpwAYoXh9WroVQp22n8h7Tw/cCtKZrjN41P\n0df/61/w5pvuLvYb/9jIztM76RzU2XYU4QHZs8PLL8NtM7OFB0kL37KUTtFct+6/e5BkzOjFgA7X\n+rvW1HmkDgOqDbAdRXjI5ctQrBgsWyZ9+UklLXw/kdIpmv/6F7zzjruL/Y7TO1hzdA09Q3rajiI8\n6L774LXX4L33bCdJe6TgO8CAagOSNXi7ciXs3w+hoV4M5QeGrB7CwGoDyZI+i+0owsP69jWvYiO9\ns8+ga0nBd4BWpVtx8PzBJO2iqTW8+65p4bt5G4UDfx1g0d5F9K3S13YU4QVZssCgQebnXHiOFHwH\nSBeQjr6P9uXz9Z/f89ply+DECXPIiZt9uuZTnq/8PDky5bAdRXhJ794QFQUbNthOknbIoK1DXLh+\ngVJflGJp56VUfKBigtdoDTVqwIsvQseOPg7oIMcvHafc6HLs6r+LfFkT26BVpAVjx5rtFmS//LuT\nQVs/kz1Tdt6t9S6vLn2VxH7hLV4MFy+a2Tlu9tm6z+hcsbMUexcIDTWryFcnureuSA4p+A7Su3Jv\nDl84zE/7/9mc0dr0Z77/vruPgjt79SwTIifwao1XbUcRPpAhg/m5f/dd20nSBin4DpI+MD2f1P+E\nV5e+ys24m//zufnz4eZNc+izm436dRRPl36ah7K7eGtQl+ncGf74A5Yvt53E/0nBd5gWpVqQJ0se\nwiLD/v5YXJxp4Xzwgbv3zLl04xJfbvySQY8Psh1F+FC6dGZO/jvvmFe6IuVcXD6cSSnF0IZDeS/i\nPS5HXwZg1iwzTa1ZM8vhLBv7+1jqFalHidwlbEcRPta+vdlnZ8kS20n8m8zScahO33eiaM6ivFfr\n35QvD8OHw1NP2U5lz/Wb1yk6oiiLn1tMUP4g23GEBbNmwSefwK+/gpxx879klo6f+6jeR3y58Uu+\nmPwHuXNDw4a2E9kVFhlGSIEQKfYu1qYNREfDggW2k/gvKfgO9XD2h+kR3Jt3l7/LBx+4u0UTExvD\nJ2s/8eih78L/BATAv/9tZu3ExdlO45+k4DvYw4ff5MbDi8hZJsp2FKtmbJvBIzkeocZDNWxHEZa1\naGGmas6ZYzuJf5I+fIeKjoaSJaHNkNFsjv6e8M7hrjycO07HUX50eUY0GkGDYg1sxxEOsGQJvPIK\nbN3q7jUpt5M+fD83YQKULg1DnunFsYvHWLxvse1IVszbNY+sGbJSv2h921GEQzz1FOTMCTNm2E7i\nf6SF70DXr5tj3r7/HqpWhYW7FzJo2SA299lMuoB0tuP5jNaaKl9X4e0n3ubpMk/bjiMcZPly6NMH\nduww8/TdTlr4fmzsWAgJMcUeoFnJZuTLmo+JkRPtBvOxKVumcDPuJi1Lt7QdRThM3bpQsCBMmWI7\niX+RFr7DXLliWveLF0Nw8H8/vun4JppOa8qe/nvIljGbvYA+cuTCESqPq0x453CC8wff+wuE66xe\nbbZd2L3bDOS6mbTw/dSXX0LNmv9b7AFCCoTQoGgDPlnziZ1gPhSn4+g2rxuvVH9Fir1I1OOPm4kN\nE931wjdVpIXvIJcumcObV6yAcuX++fmjF44SPDaYzX02U+j+Qr4P6CMj1o/gu+3fsTJ0pavGLETy\n/fqrWZC1dy9kymQ7jT3SwvdDI0ZA/foJF3uAh7I/RJ/KfXhn+Tu+DeZDO0/v5IOVHzD56clS7MU9\nVa1qXg2PG2c7iX+QFr5DnD9v+u7XrIFSpRK/7uKNi5T6ohSLOi6iUoFKvgvoAzGxMTw24TF6hvSk\nz6N9bMcRfiIyEpo2hX37zCaDbiQtfD+iNfTtC23b3r3YA9yf8X7eq/0er4YnfjKWv/pw1YfkzZqX\n5ys/bzuK8COVKsGTT5rFWOLupOA7wPDhsGsXfH7vM8wB6BnSkz8v/cmivYu8G8yHNv6xkTG/jWFC\niwmuXFEsUmfMGPjlF7NgUSROCr5lEREwZIhZZJU5c9K+Jl1AOj5t8Cmvhb/2j5Ox/NHVmKt0ntuZ\nkY1G8mC2B23HEX7o/vth7lwYNMgM5IqEScG36OhR6NABpk6FRx5J3tc2LdGUAtkKMH7TeK9k86U3\nf36TSgUq0a68y09nF6lSurQZvG3bFk6dsp3GmWTQ1pLr16FWLTOl7I03UnaPyOORNJnWhN39d3N/\nxvs9G9BHlh1YRtd5XdnywhZyZc5lO45IA95+G9auhfBw92y7kNRBWyn4lvTqBX/9ZU7xSU2Xdbd5\n3UgfkJ5xzcf5Xd/3+evnqTimIl83/5qnirv4OC/hUbGxZtZOuXIwbJjtNL4hs3QcbNw40wIJC0v9\nwSYjG48k8kSkX87NH7B4AM1KNpNiLzwqMBCmTTN9+tOn207jLC55weMc69fDO+/AqlWQzQNb4tyf\n8X6WdFpCrbBaZM+Unddrvp76m/rAnB1zWHdsHVHPu/twF+EduXKZgn9rIWPFirYTOYO08H3o5El4\n5hkYP/7e8+2TI0+WPIR3DmfMb2MY97vzlxyeuHyCfov6MbnVZLJmyGo7jkijgoLMlOfWrU33qZA+\nfJ+JiYF69aBOHXMupzfsO7eP2pNqM6zhMNqXb++dh6SS1poWM1pQMV9FPqz3oe04wgVeegn27IGF\nC9PuCVnSh+8wr71munAGD/beM4rnKs6S55YwcMlAftzzo/celAoTIydy7OIx3qvznu0owiU+/dRs\nO/7++7aT2CcF3wemToUffjB/B3j5O17hgQosaL+A0Pmh/HLoF+8+LJkO/HWAQcsGMeXpKWQIdPkG\n5sJn0qeHmTPNJIn5822nsUu6dLwsKgoaNDBHslWo4LvnLj+4nPaz2/Njxx+pUrCK7x6ciNi4WJ78\n5klalGrBqzVetR1HuNCGDdC8uZkw4ckxNCeQLh0HOHfODBiNGuXbYg9Qt0hdxrcYT/Ppzdl+artv\nH56Az9d/jlKKl6u/bDuKcKlq1eDDD+Hpp83ZE24kLXwviY2FJk2gfHm7iz++3fItg5YN4pduv1A0\nZ1GfP//6zeu8H/E+YVFhrOuxjiI5i/g8gxC389SiRyeRFr5l//qXmZnzn//YzfFcxed48/E3aTCl\nAX9e+tOnz15zZA3BXwWz99xeovpESbEXjvDFF2Yfq0/S/mmh/yALr7xg7lwzQPvbb87Yy6Nvlb5c\nuH6BhlMa8ku3X8idJbdXn3c5+jJvLXuL2TtmM6rxKNqUbePV5wmRHBkzwpw55rSskBAzxuYW0sL3\noEuXYORI6N0bZs+GvHltJ/qvQY8PommJpjT+tjGXbnivAzN8fzgVxlTg4o2LbOu7TYq9cKRChcy2\nC506wVdfmWmbbiAF3wMOHICXXzZbHK9eDYsWQRX7E2P+h1KKIfWHUCl/JVrMaMG1mGsevf9f1/6i\n+/zu9FzYkzFNxzCp1STZ/VI4Wu3a5hyKJUvMv91Bg0xXT1qWqoKvlGqrlNqmlIpVSoXc5bpGSqld\nSqk9SqkUbgbsLFqbE3aeftq8NMyQwZytOXOm84r9LUopRjcdTf778tNudjtu3LzhkfvO2zWP8mPK\nkyV9Fra9sI1GxRt55L5CeFvNmjBvntnj6sYNsx1Du3awbp3tZF6itU7xH6AUUAJYDoQkck0AsA8o\nDKQHooDSd7mndrLr17V+440VOjhY61KltB49WuvLl22nStiKFSsS/Hj0zWj9zMxndJYPs+gaE2ro\nV5a8omdum6mPXjiarPufvHxSPzvrWV1iZAm98tBKj+d0GsnpWU7MeeGC1sOHa120qNZVq2o9bZrW\n4eErbMe6p/i6ec+anaoWvtZ6t9Z6L3C36UBVgb1a68Na6xhgBtAyNc+14eRJszT7kUdg9uwIPvoI\nduyAF16ArA7d/ysiIiLBj6cPTM/MZ2Zy4v9O8MGTH5A7S26mbJlCyNgQCn1WiLYz2zJ07VBWH1md\nYNeP1ppvt3xLxTEVeST7I2zus5knCj/h8ZxOIzk9y4k5778fBg40e++89RZ8/TW0bh3Bxx/D2bO2\n06WeL+aQFARu7xk7hvkl4BeiomDECPOyr107WLbMdNs0bmw7Weply5iNukXqUrdIXcAU8oPnD7L+\n2HrWH1vPzO0z2X56O2XzlqV6wepUL1SdkrlL8v4v73P04lF+6PgDjz74qOX/CiE8LzAQWrY0f/r0\ngb17oXhxePZZ8wuhbFnbCVPmngVfKRUOPHD7hwANvK21XuitYLZdu2YWTu3dC/37w759kNu7sxmt\nU0pRNGdRiuYsSscKHQG4FnONyBORrD+2nvm75xN1IorOFTvzfbvvZT8c4Qr585tNDz/+GMaONbve\nBgWZ3TfTp7edLnk8stJWKbUC+D+t9aYEPlcdGKy1bhT//iBMf1OCS5KUUv6/zFYIIXxMJ2GlrSe7\ndBJ72EaguFKqMHAcaA90SOwmSQkthBAi+VI7LbOVUuooUB34QSm1OP7jBZRSPwBorWOB/sBSYDsw\nQ2u9M3WxhRBCJJfjNk8TQgjhHY5baauUClJKrVNKRSqlflVKOXYaiFLqRaXUTqXUVqXUENt57kYp\n9X9KqTillCOXvyqlPon/XkYppeYope63nekWf1g4qJQqpJRarpTaHv/zOMB2prtRSgUopTYppRbY\nzpIYpVR2pdSs+J/L7UqparYzJUQp9XL8AtgtSqlvlVKJzqZwXMEHPgHe01pXAt4DPrWcJ0FKqTpA\nc6CC1roCMNRuosQppQoBDYDDtrPcxVKgnNY6GNgLvGk5D2AKE/AF8BRQDuiglCptN1WCbgKvaK3L\nAY8B/Rya85aBwA7bIe5hBLBIa10GCAIc1xWtlHoQeBGz8LUiZlw20QOtnVjw44Ds8W/nAP6wmOVu\nXgCGaK1vAmitz1jOczefA6/ZDnE3WuuftdZx8e+uBwrZzHMbv1g4qLU+obWOin/7MqY4FbSbKmHx\nDZAmwHjbWRIT/wrzCa11GIDW+qbW+qLlWIkJBLIqpdIBWYBE90F3YsF/GRiqlDqCae07oqWXgJJA\nLaXUeqXUCqd2PSmlWgBHtdZbbWdJhu7AYtsh4iW0cNCRhfQWpdQjQDCwwW6SRN1qgDh5ALEIcEYp\nFRbf9TROKZXZdqg7aa3/BIYBRzCN4/Na658Tu97Kbu13W8wF1AcGaq3nKaXaAhMx3RE+d5ec72C+\ndzm11tWVUlWAmYDvj5Tinjnf4n+/f9amvSZlEZ9S6m0gRms9zUJEv6eUug+Yjfk3dNl2njsppZoC\nJ7XWUfHdok6dhp0OCAH6aa1/U0oNBwZhupkdQymVA/OKszBwAZitlOqY2L8fKwVfa51oAVdKTdFa\nD4y/brZSaoLvkv2ve+TsA3wff93G+AHR3Fprn++4kVhOpVR54BFgs1JKYbpJfldKVdVan/JhRODu\n308ApVQ3zEv9uj4JlDR/AA/f9n4hHNrNGP+SfjYwRWs933aeRNQEWiilmgCZgWxKqcla6y6Wc93p\nGOaV8W/x788GnDhgXx84oLU+B6CU+h6oASRY8J3YpfOHUqo2gFKqHrDHcp7EzCO+MCmlSgLpbRT7\nu9Fab9Na59daF9VaF8H8EFeyUezvRSnVCPMyv4XW2jP7NnvG3wsH42c/tAecOrNkIrBDaz3CdpDE\naK3f0lo/rLUuivleLndgsUdrfRI4Gv9vG6AezhxkPgJUV0plim/U1eMug8sOOIDvH3oBI5VSgcB1\noLflPIkJAyYqpbYCNwDH/dAmQOPcl9CjgAxAuPm5Zb3Wuq/dSGbhoFLq1sLBAGCCExcOKqVqAs8B\nW5VSkZj/129prZfYTebXBgDfKqXSAweAUMt5/kFr/atSajYQCcTE/z0usetl4ZUQQriEE7t0hBBC\neIEUfCGEcAkp+EII4RJS8IUQwiWk4AshhEtIwRdCCJeQgi+EEC4hBV8IIVzi/wMhkaDC0g6o2AAA\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7c98588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, S)\n", "\n", "print(plt.xticks())\n", "print(plt.yticks())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Ticks actually holding the two things, 1 - ticks value , 2 - ticks label \n", " Let's change the values first" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([<matplotlib.axis.YTick at 0x7dcd5c0>, <matplotlib.axis.YTick at 0x7dc91d0>],\n", " <a list of 2 Text yticklabel objects>)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWcAAAEACAYAAABvSbdvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2cJ5AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7db5198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, S)\n", "\n", "plt.xticks([0, 1, 2, 4, 5])\n", "plt.yticks([0, 1])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([<matplotlib.axis.YTick at 0x7e34630>,\n", " <matplotlib.axis.YTick at 0x7e25f98>,\n", " <matplotlib.axis.YTick at 0x7e2c390>,\n", " <matplotlib.axis.YTick at 0x7e1e7f0>,\n", " <matplotlib.axis.YTick at 0x7e7c2e8>,\n", " <matplotlib.axis.YTick at 0x7e84940>,\n", " <matplotlib.axis.YTick at 0x7e7c940>,\n", " <matplotlib.axis.YTick at 0x7e87518>],\n", " <a list of 8 Text yticklabel objects>)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ddc160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, S)\n", "\n", "plt.xticks(np.arange(-10, 10, 2))\n", "plt.yticks(np.arange(-2, 2, 0.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now changing the ticks lable" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([<matplotlib.axis.YTick at 0x7e2cba8>,\n", " <matplotlib.axis.YTick at 0x7dbbc50>,\n", " <matplotlib.axis.YTick at 0x8e762e8>,\n", " <matplotlib.axis.YTick at 0x8eacda0>,\n", " <matplotlib.axis.YTick at 0x8ec14a8>,\n", " <matplotlib.axis.YTick at 0x8ec1eb8>,\n", " <matplotlib.axis.YTick at 0x8ec6908>,\n", " <matplotlib.axis.YTick at 0x8ec6b70>],\n", " <a list of 8 Text yticklabel objects>)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7e84278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, S)\n", "\n", "plt.xticks(np.arange(-10, 10, 2), ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'])\n", "plt.yticks(np.arange(-2, 2, 0.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Chnaging Spine ** \n", "the gca function returns the current Axes instance on the current figure. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Axes(0.125,0.125;0.775x0.775)\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvhBCuIQUfCGEcAkp+EII4RL/Hx/wZ+hmfcFMAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x8eb4898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, S)\n", "\n", "# getting current axis and spine instance\n", "ax = plt.gca()\n", "print(ax)\n", "\n", "# making the top and right spine invisible:\n", "ax.spines['top'].set_color('none')\n", "ax.spines['right'].set_color('none')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBAu8f8BGaogIS/IBY4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x8f20278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, S)\n", "\n", "ax = plt.gca()\n", "\n", "# making the top and right spine invisible:\n", "ax.spines['top'].set_color('none')\n", "ax.spines['right'].set_color('none')\n", "\n", "# moving all top ticks tp bottom and right ticks to left\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWcAAAD3CAYAAADBqZV6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x8f42908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, S)\n", "\n", "ax = plt.gca()\n", "\n", "# making the top and right spine invisible:\n", "ax.spines['top'].set_color('none')\n", "ax.spines['right'].set_color('none')\n", "\n", "# moving all top ticks tp bottom and right ticks to left\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "\n", "# moving bottom spine up to y=0 position:\n", "ax.spines['bottom'].set_position(('data',0))\n", "\n", "# moving left spine to the right to position x == 0:\n", "ax.spines['left'].set_position(('data',0))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([<matplotlib.axis.YTick at 0x8fe55f8>,\n", " <matplotlib.axis.YTick at 0x8fe5048>,\n", " <matplotlib.axis.YTick at 0x9000710>,\n", " <matplotlib.axis.YTick at 0x9004438>,\n", " <matplotlib.axis.YTick at 0x9039470>,\n", " <matplotlib.axis.YTick at 0x9037390>,\n", " <matplotlib.axis.YTick at 0x9037fd0>,\n", " <matplotlib.axis.YTick at 0x9041f28>],\n", " <a list of 8 Text yticklabel objects>)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAD3CAYAAAAqni55AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naWAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x8fb27f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, C,\n", " X, S)\n", "\n", "ax = plt.gca()\n", "\n", "# making the top and right spine invisible:\n", "ax.spines['top'].set_color('none')\n", "ax.spines['right'].set_color('none')\n", "\n", "# moving all top ticks tp bottom and right ticks to left\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "\n", "# moving bottom spine up to y=0 position:\n", "ax.spines['bottom'].set_position(('data',0))\n", "\n", "# moving left spine to the right to position x == 0:\n", "ax.spines['left'].set_position(('data',0))\n", "\n", "# setting ticks value\n", "plt.xticks(np.arange(-8, 8, 2))\n", "plt.yticks(np.arange(-2, 2, 0.5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

tbphu/Fachkurs_Bachelor_WS1617

general/ode/Introduction_ODEs.ipynb

3

64712

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Introduction\n", "=======\n", "What is an ODE\n", "----------------\n", "Differential equations can be used to describe the time-dependent behaviour of a variable. \n", "$$\\frac{\\text{d}\\vec{x}}{\\text{d}t} = f(\\vec{x}, t)$$ \n", "The variable stands for a concentration or a number of individuals in a population. \n", "\n", "In general, a first order ODE has two parts, the increasing (birth, production,...) and the decreasing (death, degradation, consumption,...) part:\n", "\n", "$$\\frac{\\text{d}\\vec{x}}{\\text{d}t} = \\sum_{}\\text{Rates}_{\\text{production}} - \\sum_{}\\text{Rates}_{\\text{loss}}$$ \n", "\n", "\n", "You probably already know ways to solve a differential equation algebraically by 'separation of variables' (Trennung der Variablen) in the homogeneous case or 'variation of parameters' (Variation der Konstanten) in the inhomogeneous case. Here, we want to discuss the use of numerical methods to solve your ODE system." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numerical integration\n", "------------------------\n", "In principle, every numerical procedure to solve an ODE is based on the so-called \"Euler\" method. It's very easy to understand, you just have to read the $\\frac{\\text{d}\\vec{x}}{\\text{d}t}$ as a $\\frac{\\Delta \\vec{x}}{\\Delta t}$. Then you can multiply both sides of the equation with $\\Delta t$ and you have an equation describing the change of your variables during a certain time intervall $\\Delta t$:\n", "\n", "$$ \\Delta \\vec{x} = f(\\vec{x}, t)\\cdot \\Delta t$$\n", "\n", "Of course, the smaller yoy choose the time intervall $\\Delta t$, the more accurate your result will be in comparison to the analytical solution. \n", "So it's clear, we chose a tiny one, right? Well, not exactly, the smaller your time intervall the longer the simulation will take. Therefore, we need a compromise and here the provided software will help us by constantly testing and observing the numerical solution and adapt the \"step size\" $\\Delta t$ automatically." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lotka-Volterra: A prey-predator model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model equations:\n", "\n", "$$ \\frac{\\mathrm{d}\\,R}{\\mathrm{d}\\,t} = aR-bRW $$\n", "\n", "$$ \\frac{\\mathrm{d}\\,W}{\\mathrm{d}\\,t} = cWR-dW $$ \n", "\n", "### Variables:\n", "R: Rabbit population\n", "\n", "W: Wolf population\n", "\n", "### Parameters:\n", "a: rabbit's birth rate\n", "\n", "b: predation rate\n", "\n", "c: wolf's benefit\n", "\n", "d: wolf's death rate\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start\n", "------------\n", "we write a small function **f**, that receives a list of the current values of our variables **x**, the current time **t** and parameters **p**. The function has to evaluate the equations of our system or $\\frac{\\text{d}\\vec{x}}{\\text{d}t}$, respectively. Afterwards, it returns the values of the equations as another list. \n", "**Important** \n", "*Since this function **f** is used by the solver, we are not allowed to change the input (arguments) or output (return value) of this function.*" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "# define ODE (y,t,p)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we start the simulation of our model, we have to define our system. \n", "We start with our static information:\n", "1. Initial conditions for our variables\n", "2. Values of the paramters\n", "3. Simulation time and number of time points at which we want to have the values for our variables (the time grid). *Use numpy!!*" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# initial values of variables\n", "\n", "# define p = [a, b, c, d]\n", "\n", "# time grid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last but not least, we need to import and call our solver. The result will be a matrix with our time courses as columns and the values at the specified time points. Since we have a values for every time point and every species, we can directly plot the results using *matplotlib*. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from scipy.integrate import odeint\n", "\n", "# solve ODE using odeint (f, y0, t, (p,))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot results" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/matplotlib/__init__.py:901: UserWarning: could not find rc file; returning defaults\n", " warnings.warn(message)\n" ] }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f440c187588>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsvXmcXFWd//0+tfee7qSzdkIHCGsS\nAoQtIIyKCyqujDOIIyL+eFxmc9T5+cz85hl9fs78xg0dR9xGRmBUFJnxARkdRUQRg2KQEJYECFm7\nk3R671q69vP8ce6tre+9dWvrqq57P69XXre6uur24XDO/Xy/n+9yhJQSFy5cuHDhohSeZg/AhQsX\nLly0JlyCcOHChQsXhnAJwoULFy5cGMIlCBcuXLhwYQiXIFy4cOHChSFcgnDhwoULF4ZwCcKFCxcu\nXBjCJQgXLly4cGEIlyBcuHDhwoUhfM0eQC1YsWKFHB4ebvYwXLhw4WJJ4YknnpiQUg6W+9ySJojh\n4WF27drV7GG4cOHCxZKCEOKwnc+5EpMLFy5cuDCESxAuXLhw4cIQLkG4cOHChQtDLOkYhAsXLlyU\nIpVKMTIyQjweb/ZQmo5QKMTQ0BB+v7+q77sE4cKFi7bCyMgIPT09DA8PI4Ro9nCaBiklk5OTjIyM\nsHHjxqru4UpMLly4aCvE43GWL1/uaHIAEEKwfPnymjwplyBcuHDRdnA6OeiodR5cgqgWyRhks80e\nRetj6gC8+GCzR7E0EB5r9ghcuCiCSxDVIDUP/7gGfv6/mz2S1sfXroJvXwfZTLNH0tqYOgifOwN+\n/c/NHknrIzoJP/lbmB1t9khM4fV62bZtG5s3b+baa69lZmbG8vOHDh1i8+bNhr8bHh5mYmJiwftf\n/epXueuuuwC44447OHbsWO0DL4FLENXg0KPq+tTdzR3HUkBiTl1nbBVuOhdz2ub++SebO46lgHvf\nDY99CZ7/UbNHYoqOjg52797NM888w8DAALfddlvd/8b73vc+3vWudwEuQbQWXnpYXfuHmzqMJYXx\n55s9gtbG/LS6ZpIgZXPH0uqIaXMVHW/uOGzisssuY3RUeTuRSIRXvvKVXHDBBWzZsoX77rsv97l0\nOs0NN9zA2WefzXXXXUcsFsv97tOf/jRbtmzh4osvZv/+/QB8/OMf57Of/Sz33nsvu3bt4oYbbmDb\ntm3Mz8/Xbexumms1iGnuXnSh2+eiAIUxmvF9cOY1zRtLq2N+Kv86GYVgd/PG0upIaw/ASPmYzSd+\n+CzPHZur658/Z20vf3/tubY+m8lkeOihh7j55psBVZfwgx/8gN7eXiYmJrj00kt54xvfCMDzzz/P\n7bffzuWXX8573vMevvzlL/ORj3wEgL6+Pp5++mnuuusu/vIv/5IHHngg9zeuu+46vvSlL/HZz36W\n7du31/W/1fUgqkEyqq5zo661Z4XIifzrufq7v20F3YOAvAHiwhiJiLq2cFB/fn6ebdu2sXr1asbG\nxnjVq14FqNqEv/mbv2Hr1q1cffXVjI6OMjam/jvWr1/P5ZdfDsA73/lOHn300dz9rr/++tz1scce\nW7T/DteDqAapWP46Pw2dA80dT6sicjL/uvAB6GIhYgUeRHTSlS+tkNQIotAAMYFdS7/e0GMQsViM\n17zmNdx22238+Z//Od/+9rcZHx/niSeewO/3Mzw8nKtTKE1JLfzZ7HWj4XoQ1SCZ1waJTTZvHK2O\ndEGBTuED0MVCFEpMrgdhjmw2TxAt7EHo6Ozs5Itf/CKf+9znSKfTzM7OsnLlSvx+Pw8//DCHD+eT\nN44cOZLzDr7zne9wxRVX5H73ve99L3e97LLLFvydnp4ewuFw3cfvehDVIBUF4QWZgfhss0fTutAJ\nwt/lehDlEJsCX4fS193YljlSmrzr8UP0pJJ4W7wo7vzzz2fr1q3cfffd3HDDDVx77bVs2bKF7du3\nc9ZZZ+U+d+aZZ3Lbbbfxnve8h3POOYf3v//9ud9NT0+zdetWgsEgd9+9MHvy3e9+N+973/vo6Ojg\nscceo6Ojoy5jdwmiGiRj0LMG5kZcgrBCSiOI3jXFFrKLhZifgRWnw4mnXQ/CCnr8oW8dTB9SNUmB\nzqYOyQiRSKTo5x/+8Ie512YxhH379hm+f+jQIQA+9alPFb3/8Y9/PPf6bW97G29729uqGKk1XImp\nGqRi6qEHLkFYQc826VnjehDlkIpC92rwBlwPwgoJTUbpHdJ+rm+GkotiuARRDZIx6FmtXrsL1Bzp\nhLr2rlVE6lZTmyOdBF8QQstco8MKSZ0g1qpr3N1/jYRLENUgFYUed4GWRarAgwAlo7gwRiahvIdg\nT95KdrEQusSUIwiXTBuJhhKEEGKZEOJeIcQ+IcReIcRlQogBIcSDQogXtWu/9lkhhPiiEGK/EGKP\nEOKCRo6taqSTkE1D1yAIj7tArVDoQYArM1kh50H0ul6pFfQMpt516ppw918j0WgP4p+B/5ZSngWc\nB+wFPgY8JKXcBDyk/QxwDbBJ+3cL8JUGj8020pks3991lExW5rMoAl0QdDezJfQYRPdKdXU3cxGe\nPDLN/pPaA8/1IExxeDLKJx94Tu0/vUg1FwN0918j0TCCEEL0AVcCtwNIKZNSyhngTcCd2sfuBN6s\nvX4TcJdU+A2wTAixplHjqwT37Brho/fu4a7HDuVrIAKdEOpzPYgSPLDnGN/89UFSmWzeg+gaVFd3\nM+cgpeQtX97J1bf+Ur2RTigPItjrzlMJ/uK7u/nGowd5YSyselVBfk25BlpD0UgPYiMwDnxTCPGk\nEOIbQoguYJWU8rj2mRPAKu31OuBowfdHtPeajlgyDcDzJ8L5Kmp/l5ID3M1chD/9zpN84ofP8buD\nUyoG4Q2owCu4m7kAL4wVp0GSSWpz1ed6ECWYiSlSGA8n8gTRuUJdW3D/fehDH+ILX/hC7ufXvOY1\nvPe97839/OEPf5hbb73V8Lulbb+vv/56tm7dyuc///nGDdgCjSQIH3AB8BUp5flAlLycBICUUgIV\nNTMSQtwihNglhNg1Pr443Rxn51MAjM3F8y5uoBOCrgdRCFnQl2p2PqVZxR1KNoGW3MzNwuOHVF1I\nyK9twZwH0eMSaQnSWbWuRmfmIaP2Ih39KgbYgnN1+eWXs3PnTgCy2SwTExM8++yzud/v3LmTHTt2\nlL3PiRMn+N3vfseePXv40Ic+1LDxWqGRBDECjEgpf6v9fC+KMMZ06Ui76g17RoH1Bd8f0t4rgpTy\n61LK7VLK7YODgw0bfCGOz6qCr0OTsbwF4w1q1l7rLdBmIZrMFL9Oz+cDr+DOVQHGw0p+6/B7Vfqv\nzKg1FexVHoR7WmEO8ZSai9Hp+fz+08m0BY2OHTt25Irhnn32WTZv3kxPTw/T09MkEgn27t3L+eef\nz0c/+lE2b97Mli1bcq00CvHqV7+a0dFRtm3bxq9+9avF/s8AGlhJLaU8IYQ4KoQ4U0r5PPBK4Dnt\n343AP2lXvSH6/cCfCiG+C1wCzBZIUU3FCY0gYsl0AUH4NYnJ9SB0TEYSudfRRFpVUvtD6qEHLbmZ\nm4VZTTYJx9PIdBwB4AuApxOQKltHJ1YHI5XJMqGtq5HpGHQW7L+gDQPtxx9T1en1xOotcM0/mf56\n7dq1+Hw+jhw5ws6dO3PnQTz22GP09fWxZcsWHnjgAXbv3s1TTz3FxMQEF110EVdeeWXRfe6//37e\n8IY3sHv37vqOvwI0utXGnwHfFkIEgAPATSiv5R4hxM3AYeDt2md/BLwO2A/EtM+2BI7PqmyceCqb\nd3G9fi1I7T70dExGk7nX0WRa9WLyhcDjhUC3q60XYEaTLdNZSTQ2TzcoD0JvG5GYcwkCiMTTuddT\nsVR+/3n8LR0D3LFjBzt37mTnzp381V/9FaOjo+zcuZO+vj4uv/xyHn30Ua6//nq8Xi+rVq3iqquu\n4ne/+x1bt25t9tCL0FCCkFLuBoxOsHilwWcl8MFGjqda6DGIeCoDuoriDeTTXLNZ8Lg1h5ORAoJI\nFBAEaHPlels6pmOp3OvZcEQRhC+Q97ZcMgUgksgTRCKVUR68x6f2m500cwtLv5HQ4xBPP/00mzdv\nZv369Xzuc5+jt7eXm266iYcffrgp46oU7lPNBvRFmkhnyeoSk8enPAhkvvzf4SiWmDLFBNHC1l4z\noEtMAJGolvigxyDAnSsNRQSRzuazvUBbU61Znb9jxw4eeOABBgYG8Hq9DAwMMDMzw2OPPcaOHTt4\n2ctexve+9z0ymQzj4+M88sgjXHzxxc0e9gK4BFEG6UyWeCpLZ8ALQCpZEoMAdzNrmNIeess6/cUx\nCHCLCkswM59ibZ+am7BOEEUBfdfogDxBBH0ejSBSau9BS9eMbNmyJXekaOF7fX19rFixgre85S1s\n3bqV8847j1e84hV8+tOfZvXq1U0csTHcdt9lEE0oTWlFd5AjUzFSyQRByOesgxaoXm92C8cgEk/j\n8wiWdwXyMQg9xTXU6x4aVIDpaJJNq3o4NhsnmdAqzr2FEpMrx0GeIFZ0B0mkMyUeROtmEXq9Xubm\nisd2xx135F4LIfjMZz7DZz7zmaLPDA8P88wzzyx43Sy4HkQZRJL6AlWLMpXSZBSPr2Azt+YiXWxE\nE2m6gj66gz5FrNlMfjO7HkQOmaxkLp5mZU8QgKy+pvTUTWhZy3ixoQepl3cHSKSMJKY591z4BsIl\niDLQF+iKbrWZU0ltMy/wIFyEE2m6gz66gj4lMWXTKoMJWjZnvRkoXVMZ/WAlb8CVmEoQ1TyI5V0B\nzYNIF0tMMpPvbuCi7nAJogxyLq5m7aVThTEInSDcBx+ozdwd9NEZ8Kl5y6aUpwVul9IC6F5pf5ey\nhIs8iEA3INy50qDvv4GuYN6D8GgEYREDlK5XAdQ+Dy5BlEE0UWrtFWQx6XKAu5kBtZm7Qz66g15i\nyYzyIHLWXp+KSaST1jdxAPQ1NdCp5iZHEN6gOl9Zr6Z2kSOI5d2BhVlMJhJvKBRicnLS8SQhpWRy\ncpJQKFT1PdwgdRnkg2RqUWZSBRKTXy9qcjczQCSRoa/DT9DnJZnOgj9T7EGAmivf8uYNsgWgrynd\ng5B611tfibbugkg8TYffS2fASzKTRWZSCN3oMPHgh4aGGBkZYbF6tbUyQqEQQ0NDVX/fJYgyiJR4\nEEUSkzeg3F3XgwAgEk8xtKwDn1eodt+ZVEEMoiA7p8vZBJHzIHSJKV3gQYDbsK8A0aTySoM+tY6y\n6QTenAehefAldUh+v5+NGzcu5jDbFq7EVAalAcWsLpF4/Joc4B7woiOayNAV9OL3ekhmslqQurxe\n7DToBNHfqXkQqRIPws34yiEcV3GtoE89qmS6UGJyM74aDZcgyiBaoIECZNJJEN58a42QqxfriCTS\ndAf9BHwe0hmpEYTmpLopwTlEtNqa3pAfr0cgcw0gXYmpFHriQ8ivexDJgriWHgN091+j4BJEGUSS\naQI+Dz0h9aDLFlZygpu+qSGblUoOCHrx6xJTIUG4HkQOutHRpc2VzGrtJDwFD75kxOTbzkIkkaYr\n6M17EBkDD8IliIbBJYgyiGguboduwaQKFii4GScaYqkMUkJ3yIfP4yGdlerBtyAG4RJEJEcQPgJe\nDzKtE4TubblGh45IIkN30E8wd7BSgQcRcAmi0XAJogx0F9fv1aaqMLcfXL1YQ7TwoefT56qkqAnc\nBx9qrnweQdDnIeDzks3oBFFQVOg+9ACIJFJ0B725ILXMpPIGmtenjv5191/D4BJEGUQSGboKCSKT\nLvEg3IwTUMFEQCNTAUiEkcTkzlWuJYkQiiRk7owD3YPoU6fxZVLmN3EIookM3SFf7mhWkUkulHhd\nMm0YXIIoA92C8XoEQoDIugvUCLoH0RNSZOpFOzJTf+h5/ep8arctiSabqHkpjkEUSEzgriuUxNsV\nzKe5kjWIAbrz1DC4BFEG0cLN7PEgFkhM7gKFAl094MPn9eDTT1YqnCs34wvQPQj1wAv4PC5BmCCR\nzpDMZOkpSHMVhRITuPuvwXAJogwimhwAytoT2ZIFGupV5f96wzWHQieI7pCPgFcs9CDAjddoiCbz\nayrg8yjZEopjEOD4udJb7RdKvAv2n0sQDYVLEGUQSaRzKa4+r0fp6kUurtt9E/IFhXpA30eJVQxu\nfr+GiJb4ABDwelQwX3hV4SW4HoQGXbbMx7V0gnAlpsWCSxBlEE2k6QoUehClBOFae6CsYigkCM2D\nKCVTh88TlK4pT3G2F7hGh4bCxAevRxGExzCL0Nnz1Ei4BGGBTFYSS2ZycoDP49EWqNFmdvaDT9/M\nXZq1583FILz5D7keBKC3JCmQmAqzvcA9E0JDzugIFUhMMlOy/9wswkaioQQhhDgkhHhaCLFbCLFL\ne29ACPGgEOJF7dqvvS+EEF8UQuwXQuwRQlzQyLHZgb5A8xKTwCPNPAiHb+ZEGr9XpW36vR78RkFq\n14MAdIlJEWcwRxAFROp6pUBetuzSPAhBFoE0ThJxeGvvRmExPIiXSym3SSm3az9/DHhISrkJeEj7\nGeAaYJP27xbgK4swNksUFn+BkgM8RhooOJ4gIgW5/X6vB68wIQiHexBSylwdBOgeRGbhQw/cNaWn\nTgd9+DwiL1uWkqnMQGq+CSNsfzRDYnoTcKf2+k7gzQXv3yUVfgMsE0KsacL4cii0YAB8Hs2DKHRx\n3R5DQHHgtSgGUTpXqWg+a8eBSKSzpLOyyOgQskRi8neC8LhrqsBA83k9BbKlEZk6e64ahUYThAR+\nKoR4Qghxi/beKinlce31CWCV9nodcLTguyPae01DoQUDKotpocTk6sWQ71kFKpjvM4pBuPGaoswc\nUFlMotSDcNvIAwVzFVISk2Ftjbv/GopGHxh0hZRyVAixEnhQCLGv8JdSSimEqEg81IjmFoANGzbU\nb6QGiCyQmATeUmvPtWCA4tx+v1WhHKjN3DmwyCNsDRTm9gP4fR4t8Oot/mCwz/EPvVziQ8BHIp0x\nqa1x918j0VAPQko5ql1PAj8ALgbGdOlIu57UPj4KrC/4+pD2Xuk9vy6l3C6l3D44ONjI4Re1ZQZd\nYiqx9nxBdRKYwxdopCAzx18oBxh6W86dq3zFeX5NeWVJ6ia42Tmo/dcZUG1uij0Io4C+s8m0UWgY\nQQghuoQQPfpr4NXAM8D9wI3ax24E7tNe3w+8S8tmuhSYLZCimgL9YJeeoHrI+by6tWe0mZ29QKMF\nmTkBX2FA0cCDcLC2rmfGFaVOu2vKEIVdDHweG16pi7qjkRLTKuAHQlWH+oDvSCn/WwjxO+AeIcTN\nwGHg7drnfwS8DtgPxICbGjg2W4jEVTdN3YNQElOm2CoGdzNTXPylNnNJ+whwPQgWypY+r0BQktsP\nak3FJhZ7eC2FSCKdi/95PQK/YWac60E0Eg0jCCnlAeA8g/cngVcavC+BDzZqPNUgmlQLsjtUYO1h\noBe7BWDFPat8HnzCKIupT10dPFelQWolMRnFIHpg+uBiD6+lULimAIIeLVzpBqkXDW4ltQUiueKv\nvAdhLAc4u9xfz+0vzGIyTkl0PQizuJYsXVNu59uiNQUQ8BjIloFudXXwmmokXIKwgN6LXofPowVf\nXb24CPFUlqzMyyYB00pqTQ5w8JkQelyruyB12kvWXVMGCMfNPIgCb8sXAF/I8XPVKLgEYYFSC8Zn\n6kH0QMLJDz1dNtGs4qIspsICsJBq1exga6+0Ol/PzpGiVGLqhVTM0UWF0WS+kzIUeBBuDHDR4BKE\nBcIlBJFL31ygFztbDljYksQkiwkc324jmkgT0PpVgSbHiSxZoxgEOJpMlQefn5eAUQwCXIJoIFyC\nsEC0JEiW09bNFqhDG4YtKCg0S0kEx2vrkRKjw6vNlRQGawocPVfqNMe8txAUZkaHSxCNgksQFlgo\nMVnoxdm0YxuGRUoyczwegc8oJREc39G18LhRyLclMZSYwLEPPv240e7CufIYNOsDx3vwjYRLEBYo\nlZgCQhoThMOLdUolJoCAmbXn8JTgSCKTqxcBqxiEsz2I0pYkAEGjLCZwq84bCJcgLLDA2tNny8iC\nAcdu5tIgNVhtZteDKIpreZRXmlkgMTl8TcWLvVIAv5XE5GCjo5FwCcICpRpowGsmm+jWnjMzmYys\nPXMPos/Rm7mwqSHY8SCcOVelsiUoDx5wYxCLCJcgTJDNyqKTv8BqgTrb2jOSmPymKYnO9iBKg9Q+\nLfEhaxqkduZc5QjCKM3VKOPLwUkijYRLECaIpYrbbEB+gWZdvbgI+Q6lRnKAyWbOZhdreC2FUtlS\n9a3KLlxTblwLKDE6rBIfsilIxxdreI6BSxAmKD1NDvIeRMaMIBwqnRS2ZdYRMNvMoV5AQtKpD75M\ncXW+V+AVGbKUrCn9VDmHEkS45LAusKiDcHt8NQwuQZjAWANVD70Fm1lfoA7dzKW6OhR6EAYSEzhy\nM0spiSZLJCbtrOUFHoTDT5Uz8iB8VnEtcHQLl0bBJQgTlHbdBPBrFkymlCByDcOcuZkjiUzRPIFF\nxomDpZNYMoOUJQ89r2qNnhEGWzHY59iHXi6LKWQn8WGZujp0rhoJlyBMUFodDIUeRMm05RqGOXOB\nlurqUKgXm6UEO8+DMLSKdQ/CqPN+yMEEUUlcK3cQlTPnqpFwCcIERhKT7uIuiEGAo+WASMFhQTr8\nIksGj5JKCuFgvdioXsTnUVlMhmvK4QRRGtcy90r1NTWzSKNzDlyCMIGRxKQ3oFsgMYGjy/1Li78A\n/EbFX+BwD0KrFwmUSkxZlyBKUNoHDSyymNwYRMPgEoQJjCQm3YIxJgjnVnOabWbDeXKwHGDoleoe\nhNFcdSyDeWdaxYXHjepwg9SLD5cgTGC0mXULxpQgHGgVw8KjIUFt5gXZXuBwD8IoBqHmyphMnetB\nmK0pYGEMwqedM+LQuWokXIIwQTSRxusRhPz5KdJbWJvLAc576AELKs4B/GRIG82TvwOE15FzFU0a\npW6aZMaBWlPJsCMPDTKTLYGFHoQQjibTRsIlCBNE4mm6Al5EQZBVj0GkpcG0dSxzZJAsnckST2UN\nrT3Dh54Q2pkQziMIa6/UYE3p6ZsOnKvS40YB8zNGwCWIBsElCBNEEhl6QsVFXj6rGETImXpxNFl8\nxrIOv5muDo49VS4vMRVkMelGh5kHAY40PEqPGwWLGAS4BNEgNJwghBBeIcSTQogHtJ83CiF+K4TY\nL4T4nhAioL0f1H7er/1+uNFjs0IkkVqQ26+fs5w2mraOZZCeh3RiMYbXMjDS1QF8ImP80APHnioX\n0bKYOguzmKziWg4OvpYeNwoFaa5uxteiYTE8iL8A9hb8/Cng81LK04Fp4Gbt/ZuBae39z2ufaxpK\ne+ZAwWY2St/U5QCHeRFmBOHFRGICVSHsSNkkRXfQV5Tb77MyOhxMEKWt9gEt20uAx2SuHDhPjUZD\nCUIIMQS8HviG9rMAXgHcq33kTuDN2us3aT+j/f6VQpRWWS0eStsyQ2EMwiglsV9dHSYHGBV/gS4x\nmSwvh54qF4kbyCZWElOHM40Oo+NGQZFpxmjvgUsQDYItghBCXC6EeFAI8YIQ4oAQ4qAQ4oCNr34B\n+GvQ0w9YDsxIKfW0jBFgnfZ6HXAUQPv9rPb50rHcIoTYJYTYNT4+bmf4VcGYIKysPWduZqPiL1DW\nXsqofQRoRYXO28zheIVGh0M9CKPT5EDFIMxlS5cgGgG7HsTtwK3AFcBFwHbtagohxBuAk1LKJ2oa\nYQmklF+XUm6XUm4fHBys562LYFT85dU2c8ooi8mhAUWjgkLQrD3XgyhCJGEUeHUlplKENYIoTRLx\nWq6pPsgkIOWeCVFPmJh4CzArpfxxhfe+HHijEOJ1QAjoBf4ZWCaE8GlewhAwqn1+FFgPjAghfEAf\nMFnh36wbIobWnhaDMJSYnNlR0qglCajNbGgVQ/EJYM1TERcd4XiKvs5A0Xu5xAcjoyPQrdWMOMvo\nyBPEwv2Xxks2K/F4zHp8zYI/tBjDdATsehAPCyE+I4S4TAhxgf7P6gtSyv9bSjkkpRwG/hj4uZTy\nBuBh4DrtYzcC92mv79d+Rvv9z6VszhmC2awkkkzTW7JAPdpmTrkSUw5GxV+gEYTZ8gr2gsxAMtro\n4bUUwkYehFUMwqEFYOFECihu9Q06QXhIZw0eC27L74bArgdxiXbdXvCeRAWcK8X/BL4rhPgk8CRK\nvkK7/rsQYj8whSKVpiCSTCOlkYurb2aTNFdwnLVnVPwF6sGXskpzBZXJFOxu5PBaCuG4gdGhheMM\nPQhwJkFoHkRv6f6TKjMuY0gQzpR4Gw1bBCGlfHktf0RK+QvgF9rrA8DFBp+JA39Yy9+pF0xdXGkh\nB3j94O9ynAcRiS9sSQLgJW3+0Cs8Va53bYNH2DrQ01wLIbQ1ZRjXAkcTROn+82q1NelsFhac6uhM\nD77RsJvF1CeEuFXPHhJCfE4I0dfowTUL4bhycUs9iJzEZKatO7DdRlhL3SzNSPZKixiEA49oTWkt\nSUrXlN5nydTbcmBHV7P9p6+pdMbAg9DTzOenGz08R8FuDOLfgDDwdu3fHPDNRg2q2TC1YKwCiuDI\ndhtz8dQCKQDUXCWtWm2Ao1JdzVI3yWoSU9bKg3DWmjLff+oQKsMYRI4gpho9PEfBbgziNCnl2wp+\n/oQQYncjBtQKyFswpXqxIoikGUE42IMohYesBZEWSEwOgR6rWTBXGkGYkmnHgOOs4nA8Rcjvwe8t\nXj8eMiRyElMJckWFzpqrRsOuBzEvhLhC/0EIcTkw35ghNR+5IFnHwjxsMAlSgyM9iLCZByHT5rq6\nA8+EmDMxOnSCMJ2rjn710GtOQl9ToOpFjNaUagBpKDF5vMrbcgmirrDrQbwfuFOLOwhUltG7GzWo\nZmPOxMXNeRBmckDHMjjuHNkEYG4+zSnLOxe875EZUmY56w609iImxV95gjCpB+kcUJ9JzOVjN22O\nOROvVE+dNsxiAuVtxVyJqZ6wm8W0GzhPCNGr/dzWpt/cvLL2Si3jfJDawoNwnMSUsrb2spJAKUH4\nO8EbdNRmNtPVyZZLfBhQ1/lpxxBEOL7wuFFQRkfGTGKCvLflom6wJAghxDullN8SQvxVyfsASClv\nbeDYmoZwPI3fKwj6SjRQOzG1Q7TDAAAgAElEQVSIZATSSfAFjD/TZgjH0/R2GMUg0qSlSc66EMoy\ndlBA0axeJBeDMPVKteBrbAr6hxs0utaCmdHhkRaFcuASRANQLgbRpV17DP61bYWTvkCNUjezUpDJ\nWsgB4JhFmslKrTrYeDOrjBMza28AYs6YJzBP3Swbg8itKeeQqXnig0UMAhxndCwGLD0IKeXXtJc/\nk1L+uvB3WqC6LWG2QIXMkMJDykoDBYhNQs+qBo6wNaBbxaXVwZCPQZjqxQ7bzGHTLCZFHEkzoyO3\nppxFpoYEIVXxpetBLB7sZjH9i8332gJmC1Rk01qpv4lV3Kl1J3fIg88sVgNqM2fwkjKz9jr6HReD\nMJIty8YgHOaVgn5uhplXarH/OgZU1bk2py5qR7kYxGXADmCwJA7Ry4Ja9/aBCpItXKBk84FXQ3QW\neBAOQD4d2NiDSLseRA56d+AFZ2BpElPcKvEBHDNXmawkmsyYeBAZ0vitjQ5QqeZdC46ScVEFynkQ\nAVSswUdx/GGOfEfWtoOZxEQ2TQYPGVMNVFuUDrGMTXV1QMh0Qd8cA3QuV/PkkPx+s8BrLgZhJjF5\nfeqIVoesKdN0YJTEm7FMc3XbbdQb5WIQvwR+KYS4Q0p5eJHG1HSYb+YUGWHhQXQ4y4MwqxcB8GRt\n5KzLjJIE9LqINobRaXJAjiASZllMAJ3O0dZNCwrJe6VlPXiHzNViwG6hXEwI8RngXNThPwBIKatp\n993ysPYgLKxif0jl+DtkgeoexIIYRDaLIEtGWsQgCrNzHEAQc/EUfR3GsiVYpLmCFnx1hgeRb/Vt\nnCSSwUM6Y1EHAY6Zq8WA3SD1t4F9wEbgE8Ah4HcNGlNTYXZYkPplxrwfvY7O5Y7xIMyLv/IdSi09\nCHBMds5MzIwg9DoIi5P1HFQhrBsd3QYxwLxs6UpMiwW7BLFcSnk7kJJS/lJK+R6qOyyo5WF2WBCQ\n8yBMrWJwVHaOnsVklttv6W05LL9/dt6aIKwlJucE9E2NDpRsaWmgFRYVuqgL7EpMKe16XAjxeuAY\nMNCYITUXVguUbJqMcD0IHeFEmpDfQ2BB6qZaLmVjEOCYzTw7n6Kv08qDKCcxOcMqntWMjmUGcyW0\nOoiUmcQUWgbC45j9txiwSxCf1Br1fRhV/9ALfKhho2oidKvYzNrLWgWpQVl7M86I58/NmwXz9a63\nPhsBxfYniHgqQyKdNV5T2oFB8XISU3xWfdZrd8suTczoBNGxsFWNkGU8CI9HM9AmGjlER8Fus74H\ntJezQE3Hj7Y6ZmIaQRhae2UKdcBZHoTBGctAgcTkMW+LENIaAzvAgyhndEAZD6KwALN7Zb2H11KY\njSURwtyDT1utKYDOFRB1CaJeKFco9y+A6f8NKeWf131ETcbsfBIwtmB0D8I6BuEca2/OLB04o0tM\nFjEIj1c7TrP9CWK2DEGoWI3FDbpWqGt0ov0JYl6dL7KgRTx5D8LSg+9yCaKeKPcE27Uoo2gh6B6E\nkQaqE0TZGAQozbh7sAEjbB3MzqcY6DImUsC6qAnUXDlgM8/Yki0tGKJLW0fR8QaMrrUwM58y3nuo\nVjfpch581wo48XSDRuc8lCuUu7PaGwshQsAjQFD7O/dKKf9eCLER+C6wHHgC+BMpZVIIEQTuAi4E\nJoE/klIeqvbvV4sZiyAZ2TTZchZMobbe5gQxHUty2qBBU99ch1KLGARA10pHyHGzMRsEYeWV6h6E\nA7R102wv0CRej7UH70pMdYWtNFchxMNCiJ+X/ivztQTwCinlecA24LVCiEuBTwGfl1KeDkwDN2uf\nvxmY1t7/vPa5RcdMLEXA66HDb9BqKpvRNnOZGAQ4YpGa5/arILVlDALUgy9yskGjax1YS0yZ8okP\nOQ/CwWsKtBhEGQ++a4U6tCuTMv+MC9uwK5J/pOB1CHgbkLb6gpRSAhHtR7/2T6LqJ96hvX8n8HHg\nK8CbtNcA9wJfEkII7T6Lhtn5FL0dC8+CADRrr5xV7Aw5IJ3JEo6nTTytfAzCWg4YhEOPNmiErYNy\nMQhZzujo6Ffpm22+pkDN1VB/x8JfSJmrpE6VSxIBreX+6sYM0kGwm8X0RMlbvxZCPF7ue0IIL0pG\nOh24DXgJmJFS6uQyAqzTXq8Djmp/Ly2EmEXJUItqNs3OJ001UH0zW1owehCxzTdzLl/dIjPHsuoV\nFEHMT7V9QF+fq95qU6c9Xi1e095rCtRcGRsdWuq09OK19EoLvC2XIGqGrV0phCgsivOg4gRlD8iV\nUmaAbUKIZcAPgLOqGWTJWG4BbgHYsGFDrbdbgJlYyvihB9pm9peJQSwHRNtLJ3qspt8iSF1WDtBj\nNG1+wNLsvDpfxGuQmUM2pTwIq3kCR2jrUkqLinO13mxlMYEj4jWLAbtm2xMoeUigpKWD5GMHZSGl\nnBFCPAxcBiwTQvg0L2IIGNU+NgqsB0aEED4UAS2IYEopvw58HWD79u11l59mYinWLgsZ/zKbRoqQ\ntRyQs/banCCsAq+ZfBaTZUAxZ+2dbHuCMNXVM8royGQlUkpjaRMckb4ZSaTJZKVpijlQPuOrsyAl\n2EXNsBWkllJulFKeql03SSlfLaW0FI+FEIOa54AQogN4FbAXeJj8WRI3Avdpr+/Xfkb7/c8XO/4A\n+mY2WKAA2QzS47O2ikHJTG2+QGdiWr1Ip/lmTuErH4OAtpdOrDNzlGwJlJfj2nyerItU1ZqSZTO+\nnBPQXwzYlZhCwAeAK1CexK+Ar0op4xZfWwPcqcUhPMA9UsoHhBDPAd8VQnwSeBK4Xfv87cC/CyH2\nA1PAH1fzH1QrZmLlYxBl5YCuwfaXmLTN3G+xmTNW5weDSnOFtt/M1gSRIutR2zCTlRglzwGO8CDK\nZXsB5WOAekDflZjqArsS011AmPw51O8A/h34Q7MvSCn3AOcbvH8AuNjg/bjV/RYDqUyWaDJTxtrz\nWUtMoDyIkbbshp6DVc+c4iwmG3pxm1vGs/MpNq00qBeBnFcKav2FzBiiaxASs5BOgs/Ew13isJP4\nkPX4y0i8HtXNoM3X1GLBLkFsllKeU/Dzw5on0Faw6iQJKILweEmn7XgQ7b1AZyx75tisgwj1gcff\n9t6WdQwihRR5D8IUhcHX3rV1HmFrwI7EhMemB9/m3tZiwe55EL/XitwAEEJcQhu24bAMvIKy9sq5\nuKAWaCoKyWidR9g60AuajHrm5LOYfNYBRSHafjNLKZmJJY2zvUBlMeU8CGfX18xaeqUFQWqreQLl\nwUfG6j08R8KuB3EhsFMIcUT7eQPwvBDiaVRN3NaGjG6RkWvUZxR4BeVB+MoUykFxLUSgq44jbB3M\nzKfoN5unXLO+MjEIUKmubfzQCyfSpDKSAas15VEGibUHoRFEG3umM9r+s4pB4PFZF8qBqn84/Fid\nR+dM2CWI1zZ0FC2CXKM+ixgEHhsxCD34GhmH/uH6DbCFMBNLWsZqQItBlLP22jw7ZyqiHnqGTQ1B\npQR7VNzB9CAcgG4tDThyop7DaynMxFIEfR5CfgNhIycx2ckiXKXmSUrlpbqoGnbTXA8Dy4BrtX/L\npJSH9X+NHOBiYrqsxJS2qYG2f/B1JmbedTN/YJCduWpvOWAyqhFEt5XEZMOD0KuCw+1LEJORJMu7\nAqZtbgDNQLNBEJlk257Cl0xnuePXB3lxLNzwv2W3Wd9fAN8GVmr/viWE+LNGDqwZmIomAFhuupkz\nSGGzDgLaulhuZj5pLjHlctbLxCBAPfgiY1Duc0sU0zpBWM2VR6+DsJgDf4cK6rcxQUxFExZEqhGE\nsDhyVIdOpm1qeExEEnz8h8+x63DjCdCuxHQzcImUMgoghPgU8Bj5tNe2wGQ0ScDroTtoMi05icmG\nVQwQbs8FCjATtc7tB1TGVzky7Vmj5jU22Zbt0aeidiQmNY/l4zWr21pimowmWd4VNP6lVp0vvWVa\n3UCBHDcGK8+u4whbA2XXVB1hN4tJAJmCnzPae22FyUiS5d0mLi6o4KunTKk/qDz1zhUQPl7/QbYA\nEukM4USa5aaZOWozC4+vfAwiJ52051xNaRXn5l5pAUHYmas2Njp0ickQGTWPeGwQRG5NtedcTUSU\n0rHCbE3VEXY9iG8CvxVC/ED7+c3kK6DbBlPRpDkrSwkyAx4fWQnZrDRO8dTRs6Z9H3pR/aFnYu3p\nVa8eGxlfPWvUNXwC1rRFMlwRpqJJgj6T80VAeVtaJ1tbD742zs6x3H85r9ROoWp7B/QntcQHU2+r\njrDb7vtWIcQvUK02AG6SUj7ZsFE1CZORRNmHHnpbBCnxWDlRve1LEPoCNbVgtDRX4bEZg4C2nivT\nwCtoXqlGEHa09TbNzplPZphPZcxjEBmdIPzlPa1gD/g729aDyBtoTfYgtB5M70Od5/A08OWCsxza\nDpPRJKcaHaEJRVkUoOQA0745oDbzsd31HWCLQHdxzck03xbBVkoitG3wdTqWNH/ogTI8vBXEIPTs\nnM4B688uMUzqCSLlZEuvv7zRIUQ+1bUNMRFNWMdK64hyMYg7ge0ocrgG+GzDR9REWGqguQWqywHl\nrL21Ks21DY8+LOtBaHPl8dioevUFVHv0dvUgohbZXgDZFKLA6LBET/uSac4qNg1Sa/vITpAa2jpe\nUzZWWkeUI4hzpJTvlFJ+DdWC+8qGj6hJiCXTzKcy5laxHiTz2shZB006kW2Zapez9sp4EHhtpASD\nikO04TyBSnM1NToAMilEzoMoZ3To8Zr2I1Pd6LCqFwElMVm2JNHRxh6EksIXp2FjOYLImb/tLC1B\nYeCnTB62lnFSdpHqDdXm2nMzB30eugJmgdc0IPB6bQSpQbP22m+eQA+8WgQTs5m8V2qnAAzakkwn\no2X2n5bm6vGWOWNER88atfcW/0iZhmPKKh24zihHEOcJIea0f2Fgq/5aCDG3GANcLJQN/OgurtZq\n2Z4HQVs++CYiSVZ0B81d3GwavH68HlHeKgaNINrP2kukM0QSaQa6TOpFQElMdmMQbexB6EWq5bKY\n8AbKEylA3zrVMDM+U6cRtg4mrKTwOsMyyiGltArDthUmyy3QgjxssBmDgLbczBPlXFytoNDnEfY2\nsy4xZTO5quJ2wHRUPdSsPYi0fYkp0AmhZTB3rF5DbBmULVLVDDSP11++WR9A7zp1nR1Vhwi1ESaj\nrSMxOQb5wKu1rm5bDuhcrsikDQliMpooo6trBOEVNiWmNSCzbedF5LO9ysUgbJwHoaNvPcyO1GN4\nLYWpiKqBMPdKNQ/CTicDgL4hdZ0btf7cEkMsmSaeyprH/+oMlyA0TJZtiaAHqdXvyz74PB4lnbRp\nDMJygeY8CBvtvkE99KDtNvPJsDqRd2WPVW2NRGhrylbwtW+oPQnCqkgOcjEI4QvYXFMaQbTZXJWN\nldYZLkFomIomCfk9dJoFXnUX124MApSb22YLVEqZS7MzhUYQfq8glbYhB+ibeeaI9eeWGE7OKQ9i\nZW/I+AN6OnAuM87mXM0ercv4WgnjkQSDZkQKOQ/C4ytz5KiO7lWqZqnNjA5bXmkd4RKEhvFwonzg\nFezrxQDL1sNsez30wok0yUyWQUsPIqURhI3Om9C21t7JsNrMpnOV09VtnCino28I4rOQaHyr58XE\n2FycVb0Wa6qwDsLOPHm8SrqcbS+CyKUDt0gWk2OgFqiJpQc5iSlHELY283oVUMxmyn92iWCy3AE4\noAWbNYKw42mFelUr6zazjE+G4/R3+gn4TLaZZhULn83aGigg0/Z58GWykvFwwnr/6d6Wz2ahHCgP\nvs08CN3osCTTOsIlCA12LRhdYrK1SJetVwu7jQLVY3O6rl6GTH0B+xITQN+G9vMg5hLW86QZDrrE\n5FRvazKaICstYjWQ239eO/29dPS1n8Q7NhdHCItkmjqjYQQhhFgvhHhYCPGcEOJZ7dAhhBADQogH\nhRAvatd+7X0hhPiiEGK/EGKPEOKCRo3NCOU3cxUxiL4N6tpG2rpOEKv7rDZzErwB+xITqAffTLt5\nEAlW2jA6vFV5EO0zV2VjNaDJln58Pi+pjETaKYDrXad58O1zGNXYXJwV3UH83sWx7Rv5V9LAh6WU\n5wCXAh8UQpwDfAx4SEq5CXhI+xlUr6dN2r9bgK80cGxFiCbShBPpMhKTJgfkJCY7MQidINpnM+sE\nUXauvH78XptZTKDFa9rL2hsPJ8rEavTMHJuFcqAa9glvW0knZbO9ILemfFqLfVvLqm8IMgmITdRh\nlK2BskpHndEwgpBSHpdS/l57HQb2AuuAN6GaAKJd36y9fhNwl1T4DbBMCLGmUeMrhC1dL5PPogCb\nmzln7bWPB3FiNkFnwGvdSVLzIHxeQdK2xDQEiVkVgG0DSKl09UGrNaV5pd5KJCavTwu+tg+Zjs3p\n+69MDMLjx+dVBGFrrpadoq5t5cEnWGWldNQZi+KnCCGGgfOB3wKrpJS6KH8C0BrMsA4oNLVHtPdK\n73WLEGKXEGLX+Ph4XcZnyyrWJSa/2vC2dNBApzpZrs08iNW9IetOkpkUeAMEKpKYtFqINnnwzcRS\nJDPZMrEaPfAaQAibEhMob2v6cB1G2RrQ95+lrp5RByv5PeqRZctA6x9W1+lDtQ2whTA2F2dVXxsR\nhBCiG/gP4C+llEX9m6QSEivqpiWl/LqUcruUcvvgYH3OMM4TRHkPwueroKgJNOmkfQjiRLlsL9A8\niAolJp0g2sTa071SS9kk1wDSi9/jsb+m+je21UPvZFhV5ptme4FaUwUeREUS7/TBOoyy+Uims0xG\nk+3jQQgh/Chy+LaU8j+1t8d06Ui7ntTeHwXWF3x9SHuv4bAVJNMJwq8Iwr50sr7tPIiyGmiBxGQ7\ni2lgo7q2yYPPlq6eax+hGhvaKpQDNVfhY5Car3GUrYGTc3HrIjnINYDUYxC2DI9Ap4rZtMmaGo8s\nboorNDaLSaDOrd4rpby14Ff3Azdqr28E7it4/11aNtOlwGyBFNVQjM3F6fB76SmnqwM+TWKyLZ30\nD8PM4baohZBScnIuUd7FLZSY7D70OpdDsBemDtQ+0BaAPaMjf0qhzysq8CCG1bVNvK2xuTI1EJAP\nUmvZO7bqkEDNVZvIcSdmNaWjTSSmy4E/AV4hhNit/Xsd8E/Aq4QQLwJXaz8D/Ag4AOwH/hX4QAPH\nVoSxcIJVvRZV1JCTA/wBXWKy+eBbfpoilzbIOpmKJklmsqyuQGKy/dATQm3mqfaQA47NKOt+jdVm\n1vt7+QL4PMJ+DKJf87baZK5Ohm14pXqaq6eCIDVoBHGopvG1Ck7qUvgiSkwNO9RUSvkoYPbEfaXB\n5yXwwUaNxwpjs3FrSw8KJCa1kG1LTAOnquvkS3lNdInihJ1gPhRJTJmsJJuVeDw2jkccOBVOPF2H\nkTYfx2bnWdEdIGR1cHlGeRl4g/i8WfsFYDk5bukTRCqTZTycsGF0pHNGB9iUmEARxJ7vQTqZO8tl\nqcJWrLTOcCupUZvZ0tKDnLXn1wnCrmU8cJq6toF0ctJOOiIU1UEA9mWmgY1KjtOllyWMkel51i3r\nsP5Qrr9QwP7ZGaDkuEBPW3gQJ2bjZCWs6y8zVwX9vaBCDwLZFokiJ+YSBLwe6zY3dYbjCSKdyXJ8\nNs5Q2QWqHlqBYIUxiJ414Au1BUEcn9WrqO15EIHcZrZLpqeqeZ5b+qmux2bmWVuOINKaB+EL2D87\nA5QcNzDcFh7EyLSS4tYt67T+oGZ06JlOtj14PV7TBmQ6OjPP2mVlUszrDMcTxFg4QSYrGeq3sUAR\n+H1KlbO9QD0epRm3AUGMTMfweYTNGEQgX9RUqRy3xOdKSsmxmXh5giiUmOyenaGjTVJdR7VYTXkP\nQhXK6QSRcNiaArX/yj6n6gzHE8TIVAzAhhygAq9erwchKvAgQAWqJ1+qYZStgaPTyir2losnVCsx\n5YKvS3szT8dSzKcyNjyI4iC1rdx+HctPU1bxEpfjRqdtBPMhVyine6W2DbTulRDsg4kXahlmS2Bk\ner680lFnuAShLVBbEpPHjxCCgNdDspLNPLBRyQFLPNVVWTA2FmiuWZ+ecWLTMu5ZA76OJU+megaT\nLaMDwBvA66lAYgJYcYbS5Ze4FzEyHWNlT9A6mA+5LKacxGR3/wkBKzYteYKIpzKMhxMuQSw2dBe3\nvBygrGJAEYRdCwZUoDqTXPKBsqNT86wv5+Jms1pRUyDvQVQix63YBOPP1zjS5mK0YoJQ3Tkr8iBW\nnKmuE0t/rsrKS5Dz4Cv2IECR6cSLVY6wNaCvKVdiWmSMTMcYtGPBaAsUwO+roMcQwOBZ6jq+dK2Y\neCrDRMSGBaNXBxelJFY4V0ucII7ljI4ysklBkLpyD+J0dV3iczU6YyPbC7Q01WDlQWpQRkf42JI+\nhc+20lFnuARhV9fTXFxQHkQqXcFmHtSsvfG9VYywNTAyrWI16wfKBfPzsokuMSUrnau5kSW9mUen\n5wn5baQjFgSpA74KvdJQn5LklrBlnM1Kjs/E7XkQ6Tj4QjmCqMhAW7FJXZewzKTvP9eDWGTYtmAK\nJCa/T1QWg+gcUIeon9xX5Sibj6N2LZiC3P6Kc9YhT6ZLeDMfnoqxvr+zfDpiwVwFfRXGtUCTTpau\nBzEeSZDMZBmy5UEkavAgzlDXJUymI9Pz+L3CurdXA+BogshkJcdsa6AFBFFpkBo06WTpEoSe7WXf\ng6hBYoIlLZ0cmogyvKKr/AfTCfD4wOMh6POSSFW6ps5UsqWd09VaEEe0NTVUbk2B8rZ8oVwMIlHJ\n/uvfqA5ZWsJrSi+8tNWRoI5wNEEcm5knlZEML7ezmeMqwwZdYqpwM688Wy3QJXr84cj0PAGfx/qE\nNCiSmHzVSEz9G5WUt0Q3czYrOTwVY6MdgsgkwavmM+jzkEhXmOW24gxIhtWxmksQB8ejAJxql0wL\nii8r8iB8ASUznVy6Eu+RqcWvgQCHE8SBiUoWaBx8ajMHqpEDBs+EVHTJZjIdnIiyvt+GBVMgmwSq\nkZi8Pi2TaWl6W8dm50mmszaNjkSuP5AiiArX1Kpz1fXkcxWOsjVwYCKK3ytsBqmLYxAVEQTAqs0w\n9kwVo2w+pJQcGI/YMzrqDEcTxMHxCAAbB21MfCoOfrWQ/ZWclKZj8Gx1XaIPvgMTUU4b7C7/wVol\nJoCV58CJpbmZD08q2WR4hU3ZxKsRhL/CIDXkCeLEnsq+1yI4OBFhw0BnroW3KTJpkNnaCGL1ZmWc\nzU9XOdrmYSKSJBxPc6qd51Sd4WiCODARpSfoKy+bQLEHUWkWEyzpzZzOZDk8GeXUiggiH6SueDOv\n2aoymWJTFY60+TioeaW2PIhMqkBi8lbuQYT6VK+hJdoB99BEjI0rbKyptOoBhi+I1yPwekTlBtqq\nLeo69mxl32sBHNAMWVv7r85wNEEcnIiycbDLXvOrtAqSgaqDqChIBhDqVQVzx5+qYqTNxdFpFas5\nzY4FU5iZ46+wb46O1VvVdQnO1aGJKEGfp3y/KiiSmALVxCBASSdLkCCyWcnByag9qzhXL5I30CqW\neFdvVtelSBCVSOF1hqMJ4sB41P6kp+dzBBGo5CjNQqw5b0k+9HQL5rSVdqw9bTN7fLniw3iqwgff\nmvPUdQl6W4cmowwv77KXbbIgSJ1FVpqRtHqrak2SjFYx2uahslhN3oMA8HtF5V5p9yrVJn0JkumB\n8QhBn8derKbOcCxBxFMZRmfm7bm4UORBBCqtpNax5jx1TOQSk05e0gnClhygnZPs7ySk6cXxStM3\nOwegdwiOLz2CODAetR9MLAlSS1nBQTg6Vm8BJIwtrUC1LsXZy/bSPQh9/1Uhxwmh5moJGh0HJ9Sa\nWuwUV3AwQegL1HbgJx0HvyYxVePiwpK1jA+MR1nRHaCv01/+wymdIDqq9yBAxSGW2DzFUxkOTUY5\nY3WPvS9oTQ1BxSCgCjlujSbHHXuysu81GS+d1HX1CiQmb55MK/YgANZeoCQmfY0uERwYtynFNQCO\nJYgXxlQrh9PtyCagsph8eYKoWmICOLa78u82ES+NRzjVrqeV0uQAfwfBaj0IgDXbVOVrfLby7zYJ\nL45FyEo4qwqCyJ1zUCmZ9q6D7tUwuquy7zUZ+06E6e/026sMzklMeQ++KgNtaLtqJLmEPNNEOsPh\nqZi9DMIGwLEEsfd4GL9X2J/4giymqnLWQUkn/Rth5HeVf7dJkFKy70SYTavsEoRK88Tfgc/rwecR\nxKsJvq6/CJAw+kTl320S9p2YA+BMuwShtY8AcmRalXQytH1JrSmAvSfCnL2m136CCBQHqatZU+u2\nq+sSItMXxyJkspKzVvc25e87mCDmOH1lT85ys0QmBTKTq6Tu8Hurk00A1l8CRx9fMu0RRqbnCcfT\nnLu2z94XUvkYBECo2rlatx0QcHTpPPiePxEm6PPYC7xCscRUbcYXwPqL1SFL0cnKv9sEZLKSF06E\n7T/00sUxCL9P2D9jpBA9q6BvPYwsHYJ47rgyOs5eY9PoqDMaRhBCiH8TQpwUQjxT8N6AEOJBIcSL\n2rVfe18IIb4ohNgvhNgjhLigUePSsff4nP1JL8mi6Ah4iaUylWecAGy4BKInl8x5wvoCPWet3c2s\nEYS2mUN+T3USU6hXFcwd/W3l320Snh9TnlbZE/d0pGK54ks9BlGVtj50kbouES/i8GSU+VSGs2zv\nPyMPosqWNesuXFIexN7jc3QGvJxi1+ioMxrpQdwBvLbkvY8BD0kpNwEPaT8DXANs0v7dAnylgeNi\nMpLgZDjBOWuqs2BCfi9SVmvtXaKuR5bGg++5Y3N4BJy5yuZmThUThGpCV623dZGy9pZI/6p9J8Kc\nuaoCKSAZy3lauSZ01Ugna7apZnQjj1f+3SZg3wkV/zvbtgdRbKCposJq19TFKpNwdrS67y8ynjs2\nx5mre+wbHXVGwwhCSvkIUJrP+SbgTu31ncCbC96/Syr8BlgmhFjTqLHtPa4tULsEkZNN1EOvM1BD\nds7g2RDshaO/qfy7TS9XhYkAACAASURBVMBzx+c4dbCbjkCZA5V0pOaVFOdRSyvk91QXgwBFponZ\nJXGOxng4wXg4UZkUkIpBQMV2apKYAp2wdhsc+nXl320C9h1XRoftuJZene/L779Ysso1NXyFuh5u\n/bmSUmpKR3PiD7D4MYhVUsrj2usTwCrt9TqgsIvdiPZeQ7A3p+tV50F0aOmbVS1Sj0c9+JbIZn7u\n2Jx9TwsUQfjzVcQhfxVtrHXom/ngI9V9fxGx++gMANvWL7P3BSlVcVtAeRC5NNdq52rjlUo6SUSq\n+/4i4unRWU5f2V3+FEcdugehxWs6Al7mqyWIVZtVi5IlsKaOzcaZi6cr2391RtOC1FIJ+BWL+EKI\nW4QQu4QQu8bHx6v6239w5iD/8JbN5U/80lGSZqdb0/PVSienXgWTL7a8mzsVTTI6M28//gAaQeQb\n1YX83uo9iGUbYOBUOPDL6r6/iHjq6Axej7AfzM8kVeKDXycIrW9Vpsq52nilSuE80tqeqZSSJ4/O\ncP76fvtfKpEtOwNeosl0dQPweOGUy+HQo9V9fxGx+4gyOrass7mmGoDFJogxXTrSrie190eB9QWf\nG9LeWwAp5dellNullNsHBwerGsSmVT3ccMkp9r9QShCa5VO1FXPqy9X1wMPVfX+R8MRh1fnygg0V\nbOb0fC7wCjUEqXVsvEpt5kyVD4RFwu6jM5y1use+FKe3xgio4GOglpoRgPWXqnM0DrY2mR6ciDIT\nS3H+BpueFuTnKqgkqc6Ar3qJCWD4ZSpJZHak+nssAp44PE3I76nMQKszFpsg7gdu1F7fCNxX8P67\ntGymS4HZAimq+TDIYoIaPIhV50LXSnip9QnC7xVsHarAgtFjEBpCvhpSgkF5W8kwHPt99fdoMLJZ\nyVMjM5xnV16CBQRRs9ER6FQB2AO/qO77i4QnNav4/EqMjmQEhCfnbXXWIjGBWlMA+39W/T0WAb8/\nMs3WoWW5rsjNQCPTXO8GHgPOFEKMCCFuBv4JeJUQ4kXgau1ngB8BB4D9wL8CH2jUuKpCbjMrC6bm\nzSwEnPZytZlbOEPnicNTbF7XZ18rhqLUTaihDkLHxqvUw+HFB6u/R4NxYCJKOJ62H3+AgoJC9dDr\nDvkAqpdOAE6/WrUnaeET5p48Ok1P0Mcmux0MQMVVAt1q36AIIp2V1ae6rjxH1UO88NPqvr8IiKcy\nPHtstjLvvQFoZBbT9VLKNVJKv5RySEp5u5RyUkr5SinlJinl1VLKKe2zUkr5QSnlaVLKLVLK1kpU\nTqisJ4IqQ0V/YFbtQQBsejXEJlo2dz2RzvDUyCwXVrpAU/GiIHWwVompc0DJJ8//qPp7NBi/PagK\n1C48pRKruNiD6A4qgogkaiCIM69R1xf+u/p7NBhPHJ5h6/q+yhrPJcM54wygI6DmqiYD7YzXKIlX\nbw3TYnh6dJZURla2phoAx1ZSVwS9H5BGEHqaa01u7qZXKc143w9rHV1D8MzoHMl0lu3DlRJErChI\nXVPOuo6zXqeOi5w+VNt9GoSd+ydZ3RuqrF9/iQcR9HnwegTRWghi8Cx1gNDzrUkQU9Eke4/Pcdmp\nyyv7YiKciz8AdGn7ryZv64zXqv8HLRqsfvygqhC4oJJYTQPgEoQd6B5ESAWLao5BgEq1O/Uq2PtA\nS7bd+NWL4wgBl2yscDOnioPU3UEv0USNBHHm69T1+R/Xdp8GIJuVPHZgkh2nL7fXV0hHiWwphKAr\nUONcCQFnXKMC1fqabSH8ev8EAJefvqKyL+oSkwZ9/9UWqL5C3XPvfeU/2wT88oVxzlnTy3I7p102\nEC5B2EEiXBQkqzkGoeOsN6hsihY8xOSRF8bZOrSMfrupwDris6oQUENPyM98KlPd+Rk6lp8GK8+F\nZ/6z+ns0CPtOhJmKJtlxWoUPvRxB5L2t7qCPcLzGbK1z36ySKvb9V233aQAefXGCnpCPrUMVWsXJ\nSJEH0VmrxATKiDnzdfDc/ZBOVn+fBiCSSPP7w9NceUZ1WZr1hEsQdpAIK3lJsxDr4kEAnP1GJTPt\n+V6tI6wrZmJJdh+d4apKF6iUEJ+BjvwDoFcLvtb84Nv6h6qVxNSB2u5TZzy6X9Xi7DitUk+rWGIC\n6Ar6apOYQBVhLtsAe+6p7T51hpSSR/dPsOO05ZW3jUhEIJCvUO/MeRA1ztWW69R6fenntd2nznjs\npUnSWcmVmyo0OhoAlyDsIDEHwXyqZ8BbB70YoGu5Cpbtuael8vwf3T9BVsJVZ1S4QNNxVQAWys9V\nT0gdMhSOp2ob1Ja3A6LlHnw/fXaMs9f0srbS4yCTGkEE8nGLrqCvNl0dlBGz5Q9VADZysvznFwkv\njEUYnZnnZZuqsIqT4RIPog4SE6h6pI7+ljPQHnlhnA6/lwsrjf81AC5B2IHuQWgQQrCsw8/sfI0P\nPYDzrlfdXV96qPZ71Qk/eXaM/k4/51UqBcyrHHdCBR5EhyKIufkaH3x962Djy2D3dyBb44OhThgP\nJ3jiyDSvOXdV+Q+XYl4VIRaSaXfQV1sWk46tfwQyC09+q/Z71Qk/fuY4QsCrq5mrkhiELjHVTBC+\ngJqrvT+ESHVdGeqNTFbyk2dPcOUZK3LtV5oJlyDsIDFXRBAAfZ1+ZupBEJterYrmHv/X2u9VB8SS\naX723Biv3bwGX6UFOnGNIAokpp6cxFSHubrwJpg53DI1EQ8+N4aU8NrNqyv/8vyU8kq9+WNcu+sh\nMQEMnqmqhXd9s2XI9MdPn+CiUwZY2RMq/+FSlMQguuu9prIp2P3t2u9VB+w6NMXJcII3bF3b7KEA\nLkHYQ3wul8GkY1mHn9lYHRaoLwAXvRf2P6iO2Gwyfr7vJPOpDNeeV0UzXT0dOLSQIOZqjUEAnH0t\n9KyFx79W+73qgP96+hjDyzvtt0IvRGwKOoslBBWDqNMD/aL3wuwReOEn9blfDXhpPMLzY2Gu2VIF\nkaYTSrYsMNAGOlXixHQ99t/Ks+CUK2DX7S0h8/7X08cJ+jy84qyVzR4K4BKEPSSKC3UAlnUGmJmv\nU/bD9ptUp8rHbqvP/WrAfbuPsaI7WHl6KxhLTFoMYq4e1p7XDxe9RwUVm5z5dXQqxq/3T/LWC4Yq\nS2/VEZuEjoGit7qD3vpYxQBnvV5VC//6C01Po/7+rhG8HsHrtlRhdEQ16acrH7voCHgJ+T1Mx+q0\n/y77gDoj4tkf1Od+VSKRzvBfe47zirNW0qUVTjYbLkHYQWwCuooDtss6/MzUw4IB6F4J225QmvHM\nkfrcswocm5nnob1jXHfhUHUHlBhITL25IHWdrLOL3qvSaH/5qfrcr0rcs+soQsB1Fw5Vd4P5Kegs\nJuFlnQHCiXRtKcE6vH64/C/UiXxNbG2dTGe594mjvOKslazqrUJe0gPtXcUW9UBngKlonQjijGvU\nOS2/+lxTW9/85NkxJqNJ/vjiDU0bQylcgiiH1LySTrqLg2t9nXWSmHRc+RGVgfLIZ+p3zwpx9+NH\nkMANl1S5QA08iLrqxaCyTi59vwosHn+qPvesEMl0lnt2HeXKTYOVZy/piE2qNiIFGOwJIiX1e/Cd\n/yfQvRoe/oemeREPPjfGRCTJO6p96EVVcV2hBwGKTKfrNU8ej9p/43vh6e/X555V4Fu/Ocwpyzt5\nWaWFhA2ESxDlEBlT1xKCWNZRR2sPoG8Itr9HeRHH99TnnhVgPpnh7seP8IozV7J+oLP8F4wQPamO\nvizwILweQV+Hn8lIHYuRLv2Asr5//LGmPPj+vydHGZtLcNPlw9XfJDa9QGJaoVXNjocTNYyuAP4Q\nvPxvlBfx7OIXGUop+dojL7FhoLP6oq+o5kF0F39/oCtQP4kJ4Ny3qqNbH/pEPgV5EbFnZIbHD05x\nwyUbKutT1WC4BFEOYY0geooDbMs6lXRSl1RXHX/wMfXQ+NFHFt3V/dZvDjMRSfK+Pzit+pvMjkLv\nWnUoSwFW94Y4MVfHpmgdy+CV/w8c2bnoFl8mK/nqL1/i3LW9lRcS6kgnVW7/Ag9CBV/HI3UiCIDz\n3wmrt8BP/04lWywiHnlxgj0js3zgD06r/kxlE4mpvytQnyC1Do8HXvOPMDcKj3y6fve1iX/+2Yss\n6/RzfQvJS+ASRHlETqhriQcx2KOsvZNzddzMHf3wqv9XWXy//Ur97lsGkUSar/7yJV62aQUXDQ+U\n/4IZ5kahd+FJsav7QozVkyBAySfrtsOPPgpzi3d0yPd3HeXARJQPvvz06oLTALPa6bolczXYrTT6\niXp5EKDI+g1fgPBx+Onf1u++ZZDNSj730+dZ0xfirRdUGacBFaQOdBe1JAEY6PQzWU8iBRi+XBHq\nr78Io4t3/siTR6Z5aN9J/sfLTs0VlrYKXIIoBxMPYqhfac8j03V2R7e9Q/WI+dnH4diT9b23CW79\n6QtMxZJ8+NVn1naj2RFV0FaC1b0hjs/WmSA8XnjLV1Ua5H/+D8jU0Zo0wVw8xWd+8jzbT+nnmmpq\nH3ToXWn7h4veXtEIDwJgaDvs+HP4/V3w9L31vbcJ7tl1lD0js/zP156VOy2vKswdU0kcJVjZG2Iu\nnq5P3UghXv0Paq9//0aVitxgZLKSv7vvGVb2BLlxx3DD/16lcAmiHGYOq6NGO4sDR0P9yqI5Oj1f\n378nBLzxX5THcvf1DT+3es/IDHfsPMg7Lt5Q2YE3pZBSbWYDD2JVX4iJSKJ+8RodKzbB6z8Hh34F\nP/7rhscjPvnAc0zHkvz9tedW7z2AKUF0Bnx0B32M1ZtMAV7+t7BhB9z3wYZbxydm4/zTf+/jouF+\n3rStxoKvyf2wfNOCt09Zrvbfkak6G2gdy+Dtd0H4BNx7U8NrI+7YeYhnRuf4uzeckzsTpJXgEkQ5\nnNwLK85QGmUB+jv9dAW89fcgQKXUvuN7qsXAd/6oYZbM7HyKP/3Ok6zqDfHXrz2rtpvNHYNMQjWK\nK8GavhBSUn+ZCeD8G5R1vOvf4NHP1//+Gn709HHu2TXC+//gNLZUcgSrEaYPqbqXnoV1ARtXdHFg\nIlrb/Y3gC6gHX9egMjzGX6j/30BZxH91z24SqSz/561bayPSbFYRxIqFBDG8XPWwOjzZgLka2g6v\nv1Wd+HjfBxtWjf70yCyf+vE+XnnWSt6wtYoakUWASxDlcHIvrDx7wdtCCIb6OzlabwtGx6pz4e13\nwMTz8M3X1f0YyWQ6y5/d/STHZub50jvOp6+jRu1TTzldc96CX502qIoMXxhr0BkFV38cNr9NZaA8\n9L/r7knsGZnhw/c8xbb1y/jLq8+o/YYTL0D/xgVGB8Cmld3sPxmp/W8YoXsQbvg+yAx88xo48Uxd\nby+l5BM/fJadL03yiTeey+mVHCtqhNmjqgGkAUFs0DyIw5MN2n8X/Am8/H/Bnu/Cf95S95bgx2fn\n+b/+fRfLuwN89g/Pq41IGwiXIKwQm4LwMXVSlwHOWN3Ds8camBly+tXwzv9Q2v43robDO+ty20Q6\nw19890keeWGcf3jLZi48pYbAtI7ju9WZGavOXfCrs9eoNgnPjjZorjxeeOu/wgXvgl99Fu59T77t\nR414ZnSWG//tcQa6Anz9XRfWfoC8lHD0cRi6yPDXp6/q5vhsvH51I6VYeTbc9GPlwfzba+oWk5BS\n8umfPM9djx3mlitP5e0Xra/9psd3q+vgQgOtN+RnRXeAF8YaRKYAV31UGR/P3At3vL5ucu/x2Xn+\n5PbHCcfTfOPG7ZWfubKIcAnCCgd+oa6nXG746ws2LOP4bJzjs3WOQxRi45Vw04/Uhr7j9fDg3yvp\nqUqcDMd51+2P8+NnTvC/Xn82f3RRndLqjvwGVpxZ1L5aR0/Iz/DyTp45Vp+HtiE8Xrj2iyr99bn7\n4GtXwos/q+mWP3zqGH/89d/QGfDx7fdeUl2juVJMvKiqqDdcYvjrs1ernl97Rho4Vys2wXt/Bqs2\nw3/crCxkPRmjCkQSaT70vd185Rcvcf3FG/hYrXKljgO/VBlM6y4w/PUFG/rZdbjBgeQrPgTXfRNO\nPgdfvQJ+/+81paDvPjrD2768kxOzcb5x43bOXVujXNlguARhhRf+W1UFD203/LV+oPhjL002dhxr\ntsL7fgXnvUP11vmXC1X316R9/TWbldz7xAjXfOFXPDUywxf+aBvvfdmp9RlfdFKd7XvmNaYf2T48\nwM6XJms/n9oKQsDLPqwIVXjh22+Db12nyKsCnAzH+ej3n+LP7n6SM1Z1c+/7L2O4kvOmraAfcbnx\nSsNfX7xxgIDXwy9faHD76b518O4H4MqPqpP6/uVCePj/qP+XFeDRFyd4/Rd/xf1PHePDrzqDf3zL\n5voUemUz8OJPlXHmNZY/L944wOHJGMdmGmigAWx+K9zyC9Ul9/4/hW+8Avb9qCKiiCbSfOYn+7ju\nKzsRQvDdWy79/9u79+C4qvuA49/f3l2tXpZsSX7Kb2NMjIvtxDG2cRoDgTgEglsyHSgwbksKSXCc\nTsNkQqczTdPJhEwZaCZlypCEhMkQQhIK4dVg4phXGBvbCOLEyK9g2ZIfkm1J1sPa1e799Y9zpV2b\nlS2LlVbe/X1mNPehleec43P3d+6555zL5ef7bu4cGFUBQkRWi8guEdkrIt/IaWLam9ziXQtu+sDE\nrz4LplQydVwJv9reOPzpiY6BNQ/BHb+FcTPcZLoHL4UX7oH9vx/wQVoskeSZuiZu+O83uOeX7zK1\nqpTn1q1kzeIPjjYasq0/cP3aC24a8CPX/cUkOnoSbKofgXX3py+DL292c0qatrmulEdWucUQz/Is\n5+CJbu77v3pW/ecrPF3XxJdWzeHJu5YzuXKIy2mcqfcUbH/MrR56xgimPmXRMJfPruKFPxzO/qiv\nM3kRuOpf4e4t7v3or97n6tRTX3CrwA7Q7570lU27mrnth1u47UdbSPrKk3ct5ytXz81eX/p7z7pn\nEItvHfAjV3/EzU0akeuvZq7rmlvzsFsm5ee3wPc/6oLqsb0D/llrV5yHX93Hlfe/wkOb9nHDwim8\nuP4TLKgd3XcOfURzvNJjHxHxgN3ANUAjsBW4RVV3DvQ3S5Ys0W3btmU/Mafa4Imb4dA7cPfmAS9m\ngP95ZR/f/U09j9z+Ma699EOMjT8fqsFkuodh128gccrNwJ6xAp2xgpayeWzrmcxv3+9lY30z7ad6\nmV1Txvqr5/K5hVOyO5V/3yY30mreajdKZgC9SZ9PPfAq4ZDwzN1XjNyEoHgX1D0OdT+FI8ESJhPm\nw8yVxCcv4f3QDH53rJKNu1vZfqAVAT6zYDL3fHoes7J11wDuy/a59fDuE7D2uQHvIAA27Wrm73+8\nla+vnseXV12UvTScS3O9q1M7n3EvNIqUuWA78wrax86nLjaFDQeETbtaONzeQ015EV/85BxuWzaD\n4kgWX27Tsgt+cr0bzXfX6+ANPPxz7aNvUXeglRfWf2LoS8Scr2TCNR7rfhoshKhu9N6sT+LXfpz9\n4Vls7hzPhj0dvLn3OPGkz4o51Xzt2nn9vQ65JiLbVTVz10j650ZRgFgOfFNVPx0c3wugqt8Z6G+G\nHCCSCfel2tuT2nYfdw+DG99yryCMd8FfP3LWVjG4FvpfPfQme5o7+Nul0/nsZVOYPb6McaVFQ19e\n4Ay+r8QSPrFEkp5en654gtauOCe64nScbKP8wEaqD7/G9I46JiRTfcnHqKSrpJbSmmnUTJ6JjJnk\n3mBWXOlWRC2ucH28XpFrTYajqX3x3F2BnwQ/EWx73RdHZzMc3wd7XnJLb4+/xLWuSs/+sPv1PS38\n3Y+3MrO6lH9YOYslM6qYUV2a1S8XVaWn16cj1ktXLElnT4LOWILW7jjdh+qpPvgSk05sZVb3Dopx\nE9J61aPZm4g/ppbqKTMprZ7m1nrqK6viSldW4eJU+XhFqX1VV0bqu3LSpLtb6Gpx4+kPv+MeBrcf\nhFX3uiVVzpGHdT+r44Udh7lx0RTWLK7lkkljmDimOKvBPZH06Yon6Yq5CWedsQQdPQma2zoobniF\nmqOvM7VtO1MTDf1/065ltEcnE62aRk3tbLyKyW7uQHpZRUqDuhQBL61OhbygfPxUWfkJV6e6j0Pr\n++65Q/3z7t9Z+7x7X8NZ7D/WxQ3ff4NoJMQdK2fzlxfXMHVcKRXF4azdzSSSPj0Jn1hvkp6ET2dP\nghPB9XfqxEGqGl6iumUzc7rqKFfX7ZtUoSVUQ7xsCmMnzaJi4gzXiOsvq7GuTnnR1HWXfv2punqk\nmqpT8S7XeO086kY3zrnadT8PwYUYID4PrFbVLwTHtwOXq+q6gf5myAHijQfdTOVMwiUw9xq3umOG\nIZuZtHXH+c6L9Tz1diMJ35WniBtpEfFChENC2BMiXoiQBP/3gK+Kr+qOlf59d94Fn1ivT3wQXQ0T\nxkSZWV3GZZVdLCtvZkGkiQk9+wmdbHLdKh2H3Zvxsqlymhs5tOxLH3jj3kBe293Cfzy/kz1pQzmL\nvBClUY/SiIeIEAqBIIiA4IYUS1BuCd8nmVQSvpL03db3U8e9vn/WUa7RcIhJlcXMqylmxdhWFkab\nuJgDlHUddOXU3uTKSrP4rEQ897rU5etc3RqEWCLJAy/v5vHNB057DWlJxKMsGiYaDrnyEQiJILgt\nwXdiepn0b5P+6cf+2a/9caURaseVsLgGlpUf4VKvkWmJBryOQ25ZlfbG1BLv2VJa7RplK/8ZKgY3\nN6D+yEm+9dxO3kx7FhjxhPJomLAXIhISPE+IhEL99ajvGlMU33dBuf9YXfn19CaJJfxzllNIoHZc\nCbOrS/loRTvLSo9wiTRQ0dOInDzkGgYnD7kXH2XTdffD0n8c0p/mbYAQkTuBOwGmT5/+sYaGhg/8\nW+fUuB0afg+REtcqjJS4yF4xFapmu0lFQ3CiK87bDa00tZ3ieGeMtlO99CaVpO+TSCq9wZdZ30Ud\nktSXX/pF7obHC9FwiOKI178tjoSIhj1KizyqyopO+xlUKzze7YJET7tbuC3W7lolyV5XeZPx1L6f\ngFA4+PHcl1wo7NaLKp/gVp+tGNosWVVl91H3lrGDJ7rp6EnQHU/QHU/iB9HTV0U5PZh6IoRDghcE\n3FD/cYiwF5wPSTAj2aO8OEx5NEJZ1GNsSRGTK4sZWxo5d8vS991iej3taWV1MvV2s/SySsTc8N7+\nMgq5baTEdZGUjXczgYuG1v3RFUuwo6mdPUc7ONYZd639eIJ4QlH6Ghd9X26unIBUOfWVT3CcOidE\nwx5lUY/yaJjy4jBl0TBjomEmjClmQkV0cHUqEXdlc6otKKtWd/eUjLvfpZeXn0yVk4RcWYXCrjVd\nVuPqVOX0jPNDBuPA8W7ebWzj6MkejnfF6ejpDQKjC4Z9jYlQKLj2CK67/mvx9OP0ay/9GiwtClNd\nVkRVubv2xpUWnXv4syr0dgfl1JaqV8lYUE6x08tMQmn1KtiPlLi7kLIaqL7otFWTz9eFGCBGrovJ\nGGMK2GADxGgaxbQVmCsis0SkCLgZeDbHaTLGmII1alaHUtWEiKwDXgI84FFV/VOOk2WMMQVr1AQI\nAFV9EXgx1+kwxhgzurqYjDHGjCIWIIwxxmRkAcIYY0xGFiCMMcZkZAHCGGNMRqNmotxQiEgLMISp\n1ADUAMeymJwLgeW5MFieC8OHyfMMVR1/rg9d0AHiwxCRbYOZSZhPLM+FwfJcGEYiz9bFZIwxJiML\nEMYYYzIq5ADxSK4TkAOW58JgeS4Mw57ngn0GYYwx5uwK+Q7CGGPMWRRkgBCR1SKyS0T2isjZ3/94\ngRKRR0WkWUT+mHauSkReFpE9wXZ0vCA3C0RkmohsEpGdIvInEflqcD6f81wsIm+JyLtBnv89OD9L\nRLYE9fvJYPn8vCIinojUicjzwXFe51lE9ovIDhF5R0S2BeeGvW4XXIAQEQ94CPgMMB+4RUTm5zZV\nw+InwOozzn0D2Kiqc4GNwXG+SABfU9X5wDLg7uD/NZ/zHAOuUtWFwCJgtYgsA74LPKiqFwGtwB05\nTONw+SrwXtpxIeT5SlVdlDa0ddjrdsEFCGApsFdV/6yqceDnwI05TlPWqeprwIkzTt8IPBbsPwas\nGdFEDSNVPayqbwf7Hbgvj1ryO8+qqn0v944EPwpcBfwqOJ9XeQYQkanAZ4EfBsdCnud5AMNetwsx\nQNQCB9OOG4NzhWCiqh4O9o8AE3OZmOEiIjOBxcAW8jzPQVfLO0Az8DKwD2hT1UTwkXys3/8FfB3w\ng+Nq8j/PCmwQke0icmdwbtjr9qh6YZAZOaqqIpJ3Q9hEpBx4CvgnVT3pGpdOPuZZVZPAIhEZCzwN\nXJLjJA0rEbkeaFbV7SKyKtfpGUErVbVJRCYAL4tIffovh6tuF+IdRBMwLe14anCuEBwVkckAwbY5\nx+nJKhGJ4ILD46r6v8HpvM5zH1VtAzYBy4GxItLX+Mu3+n0F8DkR2Y/rHr4K+B75nWdUtSnYNuMa\nAksZgbpdiAFiKzA3GPVQBNwMPJvjNI2UZ4G1wf5a4Nc5TEtWBf3QPwLeU9UH0n6Vz3keH9w5ICIl\nwDW4Zy+bgM8HH8urPKvqvao6VVVn4q7d36nqreRxnkWkTETG9O0D1wJ/ZATqdkFOlBOR63D9mB7w\nqKp+O8dJyjoReQJYhVvx8Sjwb8AzwC+A6bhVcP9GVc98kH1BEpGVwOvADlJ90/+Cew6Rr3m+DPdw\n0sM19n6hqt8Skdm41nUVUAfcpqqx3KV0eARdTPeo6vX5nOcgb08Hh2HgZ6r6bRGpZpjrdkEGCGOM\nMedWiF1MxhhjBsEChDHGmIwsQBhjjMnIAoQxxpiMLEAYY4zJyGZSGzMIwZDCjcHhJCAJtATH3aq6\nIicJM2YY2TBXY86TiHwT6FTV+3OdFmOGk3UxGfMhiUhnsF0lIq+KyK9F5M8icp+I3Bq8s2GHiMwJ\nPjdeRJ4Ska3BzxW5zYExmVmAMCa7FgJfBD4C3A5crKpLcUtTfyX4zPdw7y74OHBT8DtjRh17BmFM\ndm3tW4JZRPYB8wbNGQAAAJhJREFUG4LzO4Arg/1PAfPTVpqtEJHytHc7GDMqWIAwJrvS1//x0459\nUtdbCFimqj0jmTBjzpd1MRkz8jaQ6m5CRBblMC3GDMgChDEjbz2wRET+ICI7cc8sjBl1bJirMcaY\njOwOwhhjTEYWIIwxxmRkAcIYY0xGFiCMMcZkZAHCGGNMRhYgjDHGZGQBwhhjTEYWIIwxxmT0/7Di\nmtgQoLVHAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f44091c8cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Phase space diagram" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f69438cecc0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAF5CAYAAABEPIrHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6947e066d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercises\n", "======" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Implement the Goldbeter model:\n", "---------------\n", "\n", "\n", "$\\frac{\\mathrm{d}C}{\\mathrm{d}t}=v_i-v_dX\\frac{C}{K_d+C}-k_dC$\n", "\n", "$\\frac{\\mathrm{d}M}{\\mathrm{d}t}=V_1\\frac{1-M}{K_1+(1-M)}-V_2\\frac{M}{K_2+M}$\n", "\n", "$\\frac{\\mathrm{d}X}{\\mathrm{d}t}=V_3\\frac{1-X}{K_3+(1-X)}-V_4\\frac{X}{K_4+X}$\n", "\n", "\n", "with $V_1=\\frac{C}{K_c+C}V_{M1}$ and $V_3=MV_{M3}$ \n", "\n", "and $M+M^*=1$ and $X+X^*=1$\n", "\n", "### Tasks:\n", "\n", "* Plot the trajectories and the phase space diagrams\n", "* Perform parameter sampling (20 random parameter sets for the interval [0, 1] ) and plot trajectories and phase space diagrams for each parameter set\n", "* Scan the parameter 'Kd' in a range where the solution oscillates (~20 values) and plot trajectories and phase space diagrams for each parameter set\n", "* Determine frequency and amplitude of those solutions and plot them in dependency of the 'Kd'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Solution" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# solve ODE using odeint\n", "y = odeint(f, y0, t, (p,))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

turbomanage/training-data-analyst

courses/ai-for-finance/practice/aapl_regression_scikit_learn.ipynb

1

9379

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building a Regression Model for a Financial Dataset\n", "\n", "In this notebook, you will build a simple linear regression model to predict the closing AAPL stock price. The lab objectives are:\n", "* Pull data from BigQuery into a Pandas dataframe\n", "* Use Matplotlib to visualize data\n", "* Use Scikit-Learn to build a regression model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "\n", "bq mk -d ai4f\n", "bq load --autodetect --source_format=CSV ai4f.AAPL10Y gs://cloud-training/ai4f/AAPL10Y.csv" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn import linear_model\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.metrics import r2_score\n", "\n", "plt.rc('figure', figsize=(12, 8.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pull Data from BigQuery\n", "\n", "In this section we'll use a magic function to query a BigQuery table and then store the output in a Pandas dataframe. A magic function is just an alias to perform a system command. To see documentation on the \"bigquery\" magic function execute the following cell:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The query below selects everything you'll need to build a regression model to predict the closing price of AAPL stock. The model will be very simple for the purposes of demonstrating BQML functionality. The only features you'll use as input into the model are the previous day's closing price and a three day trend value. The trend value can only take on two values, either -1 or +1. If the AAPL stock price has increased over any two of the previous three days then the trend will be +1. Otherwise, the trend value will be -1.\n", "\n", "Note, the features you'll need can be generated from the raw table `ai4f.AAPL10Y` using Pandas functions. However, it's better to take advantage of the serverless-ness of BigQuery to do the data pre-processing rather than applying the necessary transformations locally. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bigquery df\n", "WITH\n", " raw AS (\n", " SELECT\n", " date,\n", " close,\n", " LAG(close, 1) OVER(ORDER BY date) AS min_1_close,\n", " LAG(close, 2) OVER(ORDER BY date) AS min_2_close,\n", " LAG(close, 3) OVER(ORDER BY date) AS min_3_close,\n", " LAG(close, 4) OVER(ORDER BY date) AS min_4_close\n", " FROM\n", " `ai4f.AAPL10Y`\n", " ORDER BY\n", " date DESC ),\n", " raw_plus_trend AS (\n", " SELECT\n", " date,\n", " close,\n", " min_1_close,\n", " IF (min_1_close - min_2_close > 0, 1, -1) AS min_1_trend,\n", " IF (min_2_close - min_3_close > 0, 1, -1) AS min_2_trend,\n", " IF (min_3_close - min_4_close > 0, 1, -1) AS min_3_trend\n", " FROM\n", " raw ),\n", " train_data AS (\n", " SELECT\n", " date,\n", " close,\n", " min_1_close AS day_prev_close,\n", " IF (min_1_trend + min_2_trend + min_3_trend > 0, 1, -1) AS trend_3_day\n", " FROM\n", " raw_plus_trend\n", " ORDER BY\n", " date ASC )\n", "SELECT\n", " *\n", "FROM\n", " train_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "View the first five rows of the query's output. Note that the object `df` containing the query output is a Pandas Dataframe." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(type(df))\n", "df.dropna(inplace=True)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simplest plot you can make is to show the closing stock price as a time series. Pandas DataFrames have built in plotting funtionality based on Matplotlib. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.plot(x='date', y='close');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also embed the `trend_3_day` variable into the time series above. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "start_date = '2018-06-01'\n", "end_date = '2018-07-31'\n", "\n", "plt.plot(\n", " 'date', 'close', 'k--',\n", " data = (\n", " df.loc[pd.to_datetime(df.date).between(start_date, end_date)]\n", " )\n", ")\n", "\n", "plt.scatter(\n", " 'date', 'close', color='b', label='pos trend', \n", " data = (\n", " df.loc[df.trend_3_day == 1 & pd.to_datetime(df.date).between(start_date, end_date)]\n", " )\n", ")\n", "\n", "plt.scatter(\n", " 'date', 'close', color='r', label='neg trend',\n", " data = (\n", " df.loc[(df.trend_3_day == -1) & pd.to_datetime(df.date).between(start_date, end_date)]\n", " )\n", ")\n", "\n", "plt.legend()\n", "plt.xticks(rotation = 90);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build a Regression Model in Scikit-Learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section you'll train a linear regression model to predict AAPL closing prices when given the previous day's closing price `day_prev_close` and the three day trend `trend_3_day`. A training set and test set are created by sequentially splitting the data after 2000 rows. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "features = ['day_prev_close', 'trend_3_day']\n", "target = 'close'\n", "\n", "X_train, X_test = df.loc[:2000, features], df.loc[2000:, features]\n", "y_train, y_test = df.loc[:2000, target], df.loc[2000:, target]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create linear regression object. Don't include an intercept,\n", "# TODO" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Train the model using the training set\n", "# TODO" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Make predictions using the testing set\n", "# TODO" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Print the root mean squared error of your predictions\n", "# TODO" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Print the variance score (1 is perfect prediction)\n", "# TODO" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot the predicted values against their corresponding true values\n", "# TODO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model's predictions are more or less in line with the truth. However, the utility of the model depends on the business context (i.e. you won't be making any money with this model). It's fair to question whether the variable `trend_3_day` even adds to the performance of the model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('Root Mean Squared Error: {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, X_test.day_prev_close))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, the RMSE is actually lower if we simply use the previous day's closing value as a prediction! Does increasing the number of days included in the trend improve the model? Feel free to create new features and attempt to improve model performance!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 4 }

apache-2.0

KMFleischer/PyEarthScience

Visualization/PyNGL/PyEarthScience_contour_unstructured_PyNGL.ipynb

1

118666

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PyEarthScience: Python examples for Earth Scientists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## contour plots\n", "\n", "### Using PyNGL\n", "\n", "#### Contour plot with\n", " - unstructured data (ICON)\n", " - CellFill\n", " - filled contour areas\n", " - without contour line labels\n", " - labelbar\n", " - title\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import math, time\n", "import Ngl,Nio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Retrieve time for wallclock time computation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "t1 = time.time() #-- retrieve start time\n", "print \"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open and read variable and grid." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------------------\n", "Variable: ta\n", "Type: float\n", "Total Size: 983040 bytes\n", " 245760 values\n", "Number of Dimensions: 3\n", "Dimensions and sizes:\t[time | 12] x [lev | 1] x [ncells | 20480]\n", "Coordinates: \n", " time: [19790131..19791231]\n", " lev: [85000..85000]\n", " ncells: not a coordinate variable\n", "Number of Attributes: 7\n", " standard_name :\ttemperature\n", " long_name :\tabsolute temperature\n", " units :\tK\n", " grid_type :\tunstructured\n", " number_of_grid_in_reference :\t1\n", " _FillValue :\t-9e+33\n", " missing_value :\t-9e+33\n", "\n", "-----------------------\n" ] } ], "source": [ "#-- define variables\n", "diri = \"/Users/k204045/NCL/PyNGL/User_Guide_examples/\" #-- data path\n", "fname = \"ta_ps_850.nc\" #-- data file\n", "gname = \"r2b4_amip.nc\" #-- grid info file\n", "\n", "#-- open file and read variables\n", "f = Nio.open_file(diri + fname,\"r\") #-- add data file\n", "g = Nio.open_file(diri + gname,\"r\") #-- add grid file (not contained in data file!!!)\n", "\n", "#-- read a timestep of \"ta\" \n", "var = f.variables[\"ta\"][0,0,:] #-- first time step, lev, ncells\n", "\n", "print \"-----------------------\"\n", "print f.variables[\"ta\"] #-- like printVarSummary\n", "print \"-----------------------\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define title string, minimum and maximum contour values, interval and levels." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "title = \"ICON: Surface temperature\" #-- title string\n", "varMin = 230 #-- data minimum\n", "varMax = 310 #-- data maximum\n", "varInt = 5 #-- data increment\n", "levels = range(varMin,varMax,varInt) #-- set levels array\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the x-, y-values and the polygon points." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Cell points: 3\n", "Cells: 20480\n", "Variable ta min/max: 238.28 / 294.22\n", "\n" ] } ], "source": [ "rad2deg = 45./np.arctan(1.) #-- radians to degrees\n", "\n", "x = g.variables[\"clon\"][:] #-- read clon\n", "y = g.variables[\"clat\"][:] #-- read clat\n", "vlon = g.variables[\"clon_vertices\"][:] #-- read clon_vertices\n", "vlat = g.variables[\"clat_vertices\"][:] #-- read clat_vertices\n", "\n", "ncells = vlon.shape[0] #-- number of cells\n", "nv = vlon.shape[1] #-- number of edges\n", "\n", "x = x * rad2deg #-- cell center, lon\n", "y = y * rad2deg #-- cell center, lat\n", "vlat = vlat * rad2deg #-- cell lattitude vertices\n", "vlon = vlon * rad2deg #-- cell longitude vertices\n", "\n", "#-- longitude values -180. - 180.\n", "for j in range(1,ncells):\n", " for i in range(1,nv):\n", " if vlon[j,i] < -180. :\n", " vlon[j,i] = vlon[j,i] + 360.\n", " if vlon[j,i] > 180. :\n", " vlon[j,i] = vlon[j,i] - 360.\n", "\n", "#-- print some information\n", "print \"\"\n", "print \"Cell points: \", nv\n", "print \"Cells: \", str(ncells)\n", "print \"Variable ta min/max: %.2f \" % np.min(var) + \"/\" + \" %.2f\" % np.max(var)\n", "print \"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open a workstation, here x11 window." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#-- open a workstation\n", "wks_type = \"png\" #-- graphics output type\n", "wks = Ngl.open_wks(wks_type,\"plot_contour_unstructured_PyNGL\") #-- open a workstation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set resources." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res = Ngl.Resources() #-- plot mods desired.\n", "res.cnFillOn = True #-- color plot desired\n", "res.cnFillMode = \"CellFill\" #-- set fill mode\n", "res.cnFillPalette = \"BlueWhiteOrangeRed\" #-- choose colormap\n", "res.cnLinesOn = False #-- turn off contour lines\n", "res.cnLineLabelsOn = False #-- turn off contour labels\n", "res.cnLevelSelectionMode = \"ExplicitLevels\" #-- use explicit levels\n", "res.cnLevels = levels #-- set levels\n", "\n", "res.lbOrientation = \"Horizontal\" #-- vertical by default\n", "res.lbBoxLinesOn = False #-- turn off labelbar boxes\n", "res.lbLabelFontHeightF = 0.01 #-- labelbar label font size\n", "\n", "res.mpFillOn = False #-- don't use filled map\n", "res.mpGridAndLimbOn = False #-- don't draw grid lines\n", "\n", "res.sfXArray = x #-- transform x to mesh scalar field\n", "res.sfYArray = y #-- transform y to mesh scalar field\n", "res.sfXCellBounds = vlon #-- needed if set cnFillMode = \"CellFill\"\n", "res.sfYCellBounds = vlat #-- needed if set cnFillMode = \"CellFill\"\n", "\n", "res.tiMainString = \"Unstructured grid: ICON\" #-- title string\n", "res.tiMainOffsetYF = 0.03 #-- move main title towards plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw the plot." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#-- create the plot\n", "plot = Ngl.contour_map(wks,var,res) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the wallclock time" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wallclock time: 0.593 seconds\n", "\n" ] } ], "source": [ "t2 = time.time()\n", "print \"Wallclock time: %0.3f seconds\" % (t2-t1)\n", "print \"\"\n", "\n", "Ngl.delete_wks(wks) #-- this need to be done to close the graphics output file\n", "Ngl.end()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show the plot in this notebook." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAJYCAIAAAAxBA+LAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nAAAAKO3/AApftBd0FImUAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='plot_contour_unstructured_PyNGL.png') " ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

siddhartha-gadgil/ProvingGround

notes/2019-09-17-simple-optimization-constant-probs.ipynb

1

13583

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple learning after _sums of term probabilites_\n", "\n", "This is to check behaviour after making the sums of term probabilities constant by taking gradient perpendicular to these." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36m$cp.$ \u001b[39m" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import $cp.bin.`provingground-core-jvm-dcc4d5d.fat.jar`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36mprovingground._ , interface._, HoTT._, learning._ \n", "\u001b[39m" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import provingground._ , interface._, HoTT._, learning._ \n", "repl.pprinter() = {\n", " val p = repl.pprinter()\n", " p.copy(\n", " additionalHandlers = p.additionalHandlers.orElse {\n", " translation.FansiShow.fansiHandler\n", " }\n", " )\n", "}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[36mA\u001b[39m: \u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m] = \u001b[32mA\u001b[39m\n", "\u001b[36mB\u001b[39m: \u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m] = \u001b[32mB\u001b[39m\n", "\u001b[36ma\u001b[39m: \u001b[32mTerm\u001b[39m = \u001b[32ma\u001b[39m\n", "\u001b[36mf\u001b[39m: \u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m] = \u001b[32mf\u001b[39m\n", "\u001b[36mlp0\u001b[39m: \u001b[32mLocalProver\u001b[39m = \u001b[33mLocalProver\u001b[39m(\n", " \u001b[33mTermState\u001b[39m(\n", " \u001b[33mFiniteDistribution\u001b[39m(\n", " \u001b[33mVector\u001b[39m(\n", " \u001b[33mWeighted\u001b[39m(\u001b[32ma\u001b[39m, \u001b[32m0.3333333333333333\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf(a)\u001b[39m, \u001b[32m0.3333333333333333\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf\u001b[39m, \u001b[32m0.3333333333333333\u001b[39m)\n", " )\n", " ),\n", " \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32mA\u001b[39m, \u001b[32m0.01\u001b[39m), \u001b[33mWeighted\u001b[39m(\u001b[32mB\u001b[39m, \u001b[32m0.99\u001b[39m))),\n", " \u001b[33mVector\u001b[39m(),\n", " \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n", " \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n", " Empty\n", " ),\n", " \u001b[33mTermGenParams\u001b[39m(\n", " \u001b[32m0.1\u001b[39m,\n", " \u001b[32m0.1\u001b[39m,\n", " \u001b[32m0.1\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.05\u001b[39m,\n", " \u001b[32m0.05\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.3\u001b[39m,\n", " \u001b[32m0.7\u001b[39m,\n", " \u001b[32m0.5\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m\n", " ),\n", " \u001b[32m1.0E-4\u001b[39m,\n", " 12 minutes,\n", " \u001b[32m1.01\u001b[39m,\n", " \u001b[32m1.0\u001b[39m,\n", "..." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val A = \"A\" :: Type\n", "val B = \"B\" :: Type\n", "val a = \"a\" :: A\n", "val f = \"f\" :: (A ->: B)\n", "val lp0 = LocalProver(TermState(FiniteDistribution.unif(a, f(a), f), FiniteDistribution(A -> 0.01, B -> 0.99)), hW = 0, klW = 10).noIsles" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36mmonix.execution.Scheduler.Implicits.global\u001b[39m" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import monix.execution.Scheduler.Implicits.global" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[36mgT0\u001b[39m: \u001b[32mmonix\u001b[39m.\u001b[32meval\u001b[39m.\u001b[32mTask\u001b[39m[\u001b[32mList\u001b[39m[(\u001b[32mFiniteDistribution\u001b[39m[\u001b[32mTerm\u001b[39m], \u001b[32mInt\u001b[39m)]] = \u001b[33mFlatMap\u001b[39m(\n", " \u001b[33mAsync\u001b[39m(<function2>, false, true, true),\n", " ammonite.$sess.cmd4$Helper$$Lambda$2723/1016219062@2ca9ec62\n", ")" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val gT0 = lp0.expressionEval.flatMap(e => e.generatorIterant(0, 1, 0.000001, e.finalDist).take(100).toListL.map(_.zipWithIndex.filter(_._2 % 15 == 0)) )" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[36mgF0\u001b[39m: \u001b[32mmonix\u001b[39m.\u001b[32mexecution\u001b[39m.\u001b[32mCancelableFuture\u001b[39m[\u001b[32mList\u001b[39m[(\u001b[32mFiniteDistribution\u001b[39m[\u001b[32mTerm\u001b[39m], \u001b[32mInt\u001b[39m)]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n", " \u001b[33mList\u001b[39m(\n", " (\n", " \u001b[33mFiniteDistribution\u001b[39m(\n", " \u001b[33mVector\u001b[39m(\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf(a)\u001b[39m, \u001b[32m0.34762395733133417\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf\u001b[39m, \u001b[32m0.3194693144987902\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32ma\u001b[39m, \u001b[32m0.3329067281698756\u001b[39m)\n", " )\n", " ),\n", " \u001b[32m0\u001b[39m\n", " ),\n", " (\n", " \u001b[33mFiniteDistribution\u001b[39m(\n", " \u001b[33mVector\u001b[39m(\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf(a)\u001b[39m, \u001b[32m0.3476239573313341\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf\u001b[39m, \u001b[32m0.3194692909272\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32ma\u001b[39m, \u001b[32m0.3329067517414659\u001b[39m)\n", " )\n", " ),\n", " \u001b[32m15\u001b[39m\n", " ),\n", " (\n", " \u001b[33mFiniteDistribution\u001b[39m(\n", " \u001b[33mVector\u001b[39m(\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf(a)\u001b[39m, \u001b[32m0.3476239573313341\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf\u001b[39m, \u001b[32m0.3194692909272\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32ma\u001b[39m, \u001b[32m0.3329067517414659\u001b[39m)\n", " )\n", " ),\n", " \u001b[32m30\u001b[39m\n", " ),\n", " (\n", " \u001b[33mFiniteDistribution\u001b[39m(\n", " \u001b[33mVector\u001b[39m(\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf(a)\u001b[39m, \u001b[32m0.3476239573313341\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf\u001b[39m, \u001b[32m0.3194692909272\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32ma\u001b[39m, \u001b[32m0.3329067517414659\u001b[39m)\n", " )\n", "...\u001b[39m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val gF0 = gT0.runToFuture" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[36mlp1\u001b[39m: \u001b[32mLocalProver\u001b[39m = \u001b[33mLocalProver\u001b[39m(\n", " \u001b[33mTermState\u001b[39m(\n", " \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32ma\u001b[39m, \u001b[32m0.5\u001b[39m), \u001b[33mWeighted\u001b[39m(\u001b[32mf(a)\u001b[39m, \u001b[32m0.5\u001b[39m))),\n", " \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32mA\u001b[39m, \u001b[32m0.8\u001b[39m), \u001b[33mWeighted\u001b[39m(\u001b[32mB\u001b[39m, \u001b[32m0.2\u001b[39m))),\n", " \u001b[33mVector\u001b[39m(),\n", " \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n", " \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n", " Empty\n", " ),\n", " \u001b[33mTermGenParams\u001b[39m(\n", " \u001b[32m0.1\u001b[39m,\n", " \u001b[32m0.1\u001b[39m,\n", " \u001b[32m0.1\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.05\u001b[39m,\n", " \u001b[32m0.05\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.3\u001b[39m,\n", " \u001b[32m0.7\u001b[39m,\n", " \u001b[32m0.5\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m,\n", " \u001b[32m0.0\u001b[39m\n", " ),\n", " \u001b[32m1.0E-4\u001b[39m,\n", " 12 minutes,\n", " \u001b[32m1.01\u001b[39m,\n", " \u001b[32m1.0\u001b[39m,\n", " \u001b[32m10000\u001b[39m,\n", " \u001b[32m10\u001b[39m,\n", " \u001b[32m1.0\u001b[39m,\n", " \u001b[32m1.0\u001b[39m\n", ")" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val lp1 = LocalProver(TermState(FiniteDistribution.unif(a, f(a)), FiniteDistribution(A -> 0.8, B -> 0.2))).noIsles" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[36mtF\u001b[39m: \u001b[32mmonix\u001b[39m.\u001b[32mexecution\u001b[39m.\u001b[32mCancelableFuture\u001b[39m[\u001b[32mFiniteDistribution\u001b[39m[\u001b[32mTerm\u001b[39m]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n", " \u001b[33mFiniteDistribution\u001b[39m(\n", " \u001b[33mVector\u001b[39m(\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf(a)\u001b[39m, \u001b[32m0.018886148869072937\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32ma\u001b[39m, \u001b[32m0.9811138511309271\u001b[39m)\n", " )\n", " )\n", ")\u001b[39m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val tF = lp1.tunedGenerators.runToFuture" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[36mtF0\u001b[39m: \u001b[32mmonix\u001b[39m.\u001b[32mexecution\u001b[39m.\u001b[32mCancelableFuture\u001b[39m[\u001b[32mFiniteDistribution\u001b[39m[\u001b[32mTerm\u001b[39m]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n", " \u001b[33mFiniteDistribution\u001b[39m(\n", " \u001b[33mVector\u001b[39m(\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf(a)\u001b[39m, \u001b[32m0.4737319922622689\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32mf\u001b[39m, \u001b[32m0.2051787292005911\u001b[39m),\n", " \u001b[33mWeighted\u001b[39m(\u001b[32ma\u001b[39m, \u001b[32m0.32108927853714\u001b[39m)\n", " )\n", " )\n", ")\u001b[39m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val tF0 = lp0.tunedGenerators.runToFuture" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "* The behaviour is now as expected, though the tuning is slow.\n", "* While initial values are fixed for a while in the earlier experiments, other values could be changing with the change propagating." ] } ], "metadata": { "kernelspec": { "display_name": "Scala", "language": "scala", "name": "scala" }, "language_info": { "codemirror_mode": "text/x-scala", "file_extension": ".scala", "mimetype": "text/x-scala", "name": "scala", "nbconvert_exporter": "script", "version": "2.12.9" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

amozie/amozie

quantzie/research/match_trading.ipynb

1

26431

{ "cells": [ { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import operator\n", "import numpy as np\n", "import statsmodels.tsa.stattools as sts\n", "import matplotlib.pyplot as plt\n", "import tushare as ts\n", "import pandas as pd\n", "from datetime import datetime\n", "from scipy.stats.stats import pearsonr\n", "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Getting data:]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] }, { "name": "stdout", "output_type": "stream", "text": [ "#" ] } ], "source": [ "concept = ts.get_concept_classified()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "select_concept = concept[concept.c_name=='国企改革']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "select_code = select_concept.code" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "select_data = []\n", "for i in select_code:\n", " select_data.append(ts.get_k_data(i, start='2016-09-01', end='2017-09-01'))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:1110: RuntimeWarning: Mean of empty slice.\n avg = a.mean(axis)\nD:\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:73: RuntimeWarning: invalid value encountered in true_divide\n ret, rcount, out=ret, casting='unsafe', subok=False)\nD:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3154: RuntimeWarning: Degrees of freedom <= 0 for slice\n c = cov(x, y, rowvar)\nD:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3088: RuntimeWarning: divide by zero encountered in double_scalars\n c *= 1. / np.float64(fact)\nD:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3088: RuntimeWarning: invalid value encountered in multiply\n c *= 1. / np.float64(fact)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "7\n8\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "9\n10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "11\n12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "13\n14\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "15\n16\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "17\n18\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "19\n20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "21\n22\n23\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "24\n25\n26\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "27\n28\n29" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n30\n31\n32\n33" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n34\n35\n36\n37\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "38\n39\n40\n41\n42\n43\n44\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "45\n46\n47\n48\n49\n" ] } ], "source": [ "rank = {}\n", "for i in range(50):\n", " print(i)\n", " for j in range(i+1,50):\n", " if i != j:\n", " # get the price of stock from TuShare\n", " price_of_i = select_data[i]\n", " price_of_j = select_data[j]\n", " # combine the close price of the two stocks and drop the NaN\n", " closePrice_of_ij = pd.concat([price_of_i['close'], price_of_j['close']], axis = 1)\n", " closePrice_of_ij = closePrice_of_ij.dropna()\n", " # change the column name in the dataFrame\n", " closePrice_of_ij.columns = ['close_i', 'close_j']\n", " # calculate the daily return and drop the return of first day cause it is NaN.\n", " ret_of_i = ((closePrice_of_ij['close_i'] - closePrice_of_ij['close_i'].shift())/closePrice_of_ij['close_i'].shift()).dropna()\n", " ret_of_j = ((closePrice_of_ij['close_j'] - closePrice_of_ij['close_j'].shift())/closePrice_of_ij['close_j'].shift()).dropna()\n", " # calculate the correlation and store them in rank1\n", " if len(ret_of_i) == len(ret_of_j):\n", " correlation = np.corrcoef(ret_of_i.tolist(), ret_of_j.tolist())\n", " m = '{0}|{1}+{2}|{3}'.format(i, select_code.iloc[i], j, select_code.iloc[j])\n", " #m = select_code.iloc[i] + '+' + select_code.iloc[j]\n", " rank[m] = correlation[0,1]\n", " rank1 = sorted(rank.items(), key=operator.itemgetter(1))\n", " potentialPair = [list(item[0].split('+')) for item in rank1]\n", " potentialPair = potentialPair[-5:]" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('29|600708+48|000705', 0.46697200954440821), ('31|600748+33|600776', 0.47688134428945161), ('31|600748+37|600826', 0.47733774727612649), ('31|600748+48|000705', 0.4778279438380168), ('33|600776+37|600826', 0.47826849402882404), ('36|600825+49|000969', 0.48988498716333195), ('24|600662+33|600776', 0.49145599351497693), ('33|600776+49|000969', 0.49446497212287593), ('27|600689+36|600825', 0.51303918354026201), ('29|600708+31|600748', 0.51437394982953766), ('24|600662+48|000705', 0.52751952426256044), ('31|600748+36|600825', 0.53276417494675998), ('27|600689+37|600826', 0.53396198645395043), ('33|600776+48|000705', 0.54361963744863118), ('36|600825+48|000705', 0.56894157806571533), ('24|600662+37|600826', 0.57166966506245509), ('24|600662+36|600825', 0.5908464178767493), ('33|600776+36|600825', 0.59290300338353685), ('24|600662+31|600748', 0.60194926560589934), ('24|600662+27|600689', 0.67590967349480546)]\n" ] } ], "source": [ "print(rank1[-20:])" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['24|600662', '37|600826'], ['24|600662', '36|600825'], ['33|600776', '36|600825'], ['24|600662', '31|600748'], ['24|600662', '27|600689']]\n" ] } ], "source": [ "print(potentialPair)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n2.73739928501\n1\n2\n3\n0.341047783921\n4\n1.38024977087\n" ] } ], "source": [ "Rank = {}\n", "for i in range(len(potentialPair)):\n", " print(i)\n", " m = int(potentialPair[i][0].split('|')[0])\n", " n = int(potentialPair[i][1].split('|')[0])\n", " price_of_1 = select_data[m]\n", " price_of_2 = select_data[n]\n", "\n", " closeprice_of_1 = price_of_1['close']\n", " closeprice_of_2 = price_of_2['close']\n", "\n", " if len(closeprice_of_1) != 0 and len(closeprice_of_2) != 0 and len(closeprice_of_1) == len(closeprice_of_2):\n", " y = closeprice_of_2\n", " x = closeprice_of_1\n", " x = sm.add_constant(x)\n", " res = sm.OLS(y, x).fit()\n", " print(res.params.close)\n", " # model = pd.ols(y=closeprice_of_2, x=closeprice_of_1, intercept=True) # perform ols on these two stocks\n", " spread = closeprice_of_2 - closeprice_of_1*res.params.close\n", " spread = spread.dropna()\n", " sta = sts.adfuller(spread, 1)\n", " pair = str(select_code.iloc[m]) + '+' + str(select_code.iloc[n])\n", " Rank[pair] = sta[0]\n", " rank2 = sorted(Rank.items(), key=operator.itemgetter(1))" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'600662+600826': -3.0217050661884217, '600662+600748': -3.6736896104202232, '600662+600689': -3.0571929684838479}\n(-3.0571929684838479, 0.029887605550865008, 1, 242, {'1%': -3.4576641321552009, '5%': -2.8735585105960224, '10%': -2.5731749894132916}, 143.98447973809539)\n" ] } ], "source": [ "print(Rank)\n", "print(sta)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

xiongzhenggang/xiongzhenggang.github.io

AI/ML/week2逻辑回归doc.ipynb

1

38443

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 逻辑回归\n", "引入一个新的模型，逻辑回归，该模型的输出变量范围始终在0和1之间。 逻辑回归模型的假设是： $h_\\theta \\left( x \\right)=g\\left(\\theta^{T}X \\right)$ 其中： $X$ 代表特征向量 $g$ 代表逻辑函数（logistic function)是一个常用的逻辑函数为S形函数（Sigmoid function），公式为： $g\\left( z \\right)=\\frac{1}{1+{{e}^{-z}}}$。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "def sigmoid(z):\n", " return 1 / (1 + np.exp(-z))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对模型的理解： $g\\left( z \\right)=\\frac{1}{1+{{e}^{-z}}}$。\n", "\n", "$h_\\theta \\left( x \\right)$的作用是，对于给定的输入变量，根据选择的参数计算输出变量=1的可能性（estimated probablity）即$h_\\theta \\left( x \\right)=P\\left( y=1|x;\\theta \\right)$ 例如，如果对于给定的$x$，通过已经确定的参数计算得出$h_\\theta \\left( x \\right)=0.7$，则表示有70%的几率$y$为正向类，相应地$y$为负向类的几率为1-0.7=0.3。\n", "\n", "### 判定边界\n", "在逻辑回归中，我们预测：\n", "\n", "当${h_\\theta}\\left( x \\right)>=0.5$时，预测 $y=1$。\n", "\n", "当${h_\\theta}\\left( x \\right)<0.5$时，预测 $y=0$ 。\n", "\n", "根据上面绘制出的 S 形函数图像，我们知道当\n", "\n", "$z=0$ 时 $g(z)=0.5$\n", "\n", "$z>0$ 时 $g(z)>0.5$\n", "\n", "$z<0$ 时 $g(z)<0.5$\n", "\n", "又 $z={\\theta^{T}}x$ ，即： ${\\theta^{T}}x>=0$ 时，预测 $y=1$ ${\\theta^{T}}x<0$ 时，预测 $y=0$\n", "\n", "### 代价函数\n", "对于线性回归模型，我们定义的代价函数是所有模型误差的平方和。理论上来说，我们也可以对逻辑回归模型沿用这个定义，但是问题在于，当我们将${h_\\theta}\\left( x \\right)=\\frac{1}{1+{e^{-\\theta^{T}x}}}$带入到这样定义了的代价函数中时，我们得到的代价函数将是一个非凸函数（non-convexfunction）。\n", "\n", "这意味着我们的代价函数有许多局部最小值，这将影响梯度下降算法寻找全局最小值。\n", "\n", "线性回归的代价函数为：$J\\left( \\theta \\right)=\\frac{1}{m}\\sum\\limits_{i=1}^{m}{\\frac{1}{2}{{\\left( {h_\\theta}\\left({x}^{\\left( i \\right)} \\right)-{y}^{\\left( i \\right)} \\right)}^{2}}}$ 。 我们重新定义逻辑回归的代价函数为：$J\\left( \\theta \\right)=\\frac{1}{m}\\sum\\limits_{i=1}^{m}{{Cost}\\left( {h_\\theta}\\left( {x}^{\\left( i \\right)} \\right),{y}^{\\left( i \\right)} \\right)}$，其中\n", "\n", "${h_\\theta}\\left( x \\right)$与 $Cost\\left( {h_\\theta}\\left( x \\right),y \\right)$\n", "\n", "这样构建的$Cost\\left( {h_\\theta}\\left( x \\right),y \\right)$函数的特点是：当实际的 $y=1$ 且${h_\\theta}\\left( x \\right)$也为 1 时误差为 0，当 $y=1$ 但${h_\\theta}\\left( x \\right)$不为1时误差随着${h_\\theta}\\left( x \\right)$变小而变大；当实际的 $y=0$ 且${h_\\theta}\\left( x \\right)$也为 0 时代价为 0，当$y=0$ 但${h_\\theta}\\left( x \\right)$不为 0时误差随着 ${h_\\theta}\\left( x \\right)$的变大而变大。 将构建的 $Cost\\left( {h_\\theta}\\left( x \\right),y \\right)$简化如下： $Cost\\left( {h_\\theta}\\left( x \\right),y \\right)=-y\\times log\\left( {h_\\theta}\\left( x \\right) \\right)-(1-y)\\times log\\left( 1-{h_\\theta}\\left( x \\right) \\right)$ 带入代价函数得到： $J\\left( \\theta \\right)=\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[-{{y}^{(i)}}\\log \\left( {h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)-\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1-{h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)]}$ 即：$J\\left( \\theta \\right)=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[{{y}^{(i)}}\\log \\left( {h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)+\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1-{h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)]}$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "def cost(theta, X, y): \n", " theta = np.matrix(theta)\n", " X = np.matrix(X)\n", " y = np.matrix(y)\n", " first = np.multiply(-y, np.log(sigmoid(X* theta.T)))\n", " second = np.multiply((1 - y), np.log(1 - sigmoid(X* theta.T)))\n", " return np.sum(first - second) / (len(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在得到这样一个代价函数以后，我们便可以用梯度下降算法来求得能使代价函数最小的参数了。算法为：\n", "\n", "Repeat { $\\theta_j := \\theta_j - \\alpha \\frac{\\partial}{\\partial\\theta_j} J(\\theta)$ (simultaneously update all ) }\n", "\n", "求导后得到：\n", "\n", "Repeat { $\\theta_j := \\theta_j - \\alpha \\frac{1}{m}\\sum\\limits_{i=1}^{m}{{\\left( {h_\\theta}\\left( \\mathop{x}^{\\left( i \\right)} \\right)-\\mathop{y}^{\\left( i \\right)} \\right)}}\\mathop{x}_{j}^{(i)}$ (simultaneously update all ) }\n", "\n", "在这个视频中，我们定义了单训练样本的代价函数，凸性分析的内容是超出这门课的范围的，但是可以证明我们所选的代价值函数会给我们一个凸优化问题。代价函数$J(\\theta)$会是一个凸函数，并且没有局部最优值。\n", "\n", "推导过程：\n", "\n", "$J\\left( \\theta \\right)=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[{{y}^{(i)}}\\log \\left( {h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)+\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1-{h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)]}$ 考虑： ${h_\\theta}\\left( {{x}^{(i)}} \\right)=\\frac{1}{1+{{e}^{-{\\theta^T}{{x}^{(i)}}}}}$ 则： ${{y}^{(i)}}\\log \\left( {h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)+\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1-{h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)$ $={{y}^{(i)}}\\log \\left( \\frac{1}{1+{{e}^{-{\\theta^T}{{x}^{(i)}}}}} \\right)+\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1-\\frac{1}{1+{{e}^{-{\\theta^T}{{x}^{(i)}}}}} \\right)$ $=-{{y}^{(i)}}\\log \\left( 1+{{e}^{-{\\theta^T}{{x}^{(i)}}}} \\right)-\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1+{{e}^{{\\theta^T}{{x}^{(i)}}}} \\right)$\n", "\n", "所以： $\\frac{\\partial }{\\partial {\\theta_{j}}}J\\left( \\theta \\right)=\\frac{\\partial }{\\partial {\\theta_{j}}}[-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[-{{y}^{(i)}}\\log \\left( 1+{{e}^{-{\\theta^{T}}{{x}^{(i)}}}} \\right)-\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1+{{e}^{{\\theta^{T}}{{x}^{(i)}}}} \\right)]}]$ $=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[-{{y}^{(i)}}\\frac{-x_{j}^{(i)}{{e}^{-{\\theta^{T}}{{x}^{(i)}}}}}{1+{{e}^{-{\\theta^{T}}{{x}^{(i)}}}}}-\\left( 1-{{y}^{(i)}} \\right)\\frac{x_j^{(i)}{{e}^{{\\theta^T}{{x}^{(i)}}}}}{1+{{e}^{{\\theta^T}{{x}^{(i)}}}}}}]$ $=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{{y}^{(i)}}\\frac{x_j^{(i)}}{1+{{e}^{{\\theta^T}{{x}^{(i)}}}}}-\\left( 1-{{y}^{(i)}} \\right)\\frac{x_j^{(i)}{{e}^{{\\theta^T}{{x}^{(i)}}}}}{1+{{e}^{{\\theta^T}{{x}^{(i)}}}}}]$ $=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{\\frac{{{y}^{(i)}}x_j^{(i)}-x_j^{(i)}{{e}^{{\\theta^T}{{x}^{(i)}}}}+{{y}^{(i)}}x_j^{(i)}{{e}^{{\\theta^T}{{x}^{(i)}}}}}{1+{{e}^{{\\theta^T}{{x}^{(i)}}}}}}$ $=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{\\frac{{{y}^{(i)}}\\left( 1\\text{+}{{e}^{{\\theta^T}{{x}^{(i)}}}} \\right)-{{e}^{{\\theta^T}{{x}^{(i)}}}}}{1+{{e}^{{\\theta^T}{{x}^{(i)}}}}}x_j^{(i)}}$ $=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{({{y}^{(i)}}-\\frac{{{e}^{{\\theta^T}{{x}^{(i)}}}}}{1+{{e}^{{\\theta^T}{{x}^{(i)}}}}})x_j^{(i)}}$ $=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{({{y}^{(i)}}-\\frac{1}{1+{{e}^{-{\\theta^T}{{x}^{(i)}}}}})x_j^{(i)}}$ $=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[{{y}^{(i)}}-{h_\\theta}\\left( {{x}^{(i)}} \\right)]x_j^{(i)}}$ $=\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[{h_\\theta}\\left( {{x}^{(i)}} \\right)-{{y}^{(i)}}]x_j^{(i)}}$\n", "\n", "注：虽然得到的梯度下降算法表面上看上去与线性回归的梯度下降算法一样，但是这里的${h_\\theta}\\left( x \\right)=g\\left( {\\theta^T}X \\right)$与线性回归中不同，所以实际上是不一样的。另外，在运行梯度下降算法之前，进行特征缩放依旧是非常必要的。\n", "\n", "一些梯度下降算法之外的选择： 除了梯度下降算法以外，还有一些常被用来令代价函数最小的算法，这些算法更加复杂和优越，而且通常不需要人工选择学习率，通常比梯度下降算法要更加快速。这些算法有：共轭梯度（Conjugate Gradient），局部优化法(Broyden fletcher goldfarb shann,BFGS)和有限内存局部优化法(LBFGS) ，fminunc是 matlab和octave 中都带的一个最小值优化函数，使用时我们需要提供代价函数和每个参数的求导" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 简化的成本函数和梯度下降\n", "\n", "$Cost\\left( {h_\\theta}\\left( x \\right),y \\right)=-y\\times log\\left( {h_\\theta}\\left( x \\right) \\right)-(1-y)\\times log\\left( 1-{h_\\theta}\\left( x \\right) \\right)$ 即，逻辑回归的代价函数： $Cost\\left( {h_\\theta}\\left( x \\right),y \\right)=-y\\times log\\left( {h_\\theta}\\left( x \\right) \\right)-(1-y)\\times log\\left( 1-{h_\\theta}\\left( x \\right) \\right)$ $=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[{{y}^{(i)}}\\log \\left( {h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)+\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1-{h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)]}$ 根据这个代价函数，为了拟合出参数，该怎么做呢？我们要试图找尽量让$J\\left( \\theta \\right)$ 取得最小值的参数$\\theta $。 $\\underset{\\theta}{\\min }J\\left( \\theta \\right)$ 所以我们想要尽量减小这一项，这将我们将得到某个参数$\\theta $。 如果我们给出一个新的样本，假如某个特征 $x$，我们可以用拟合训练样本的参数$\\theta $，来输出对假设的预测。 另外，我们假设的输出，实际上就是这个概率值：$p(y=1|x;\\theta)$，就是关于 $x$以$\\theta $为参数，$y=1$ 的概率，你可以认为我们的假设就是估计 $y=1$ 的概率，所以，接下来就是弄清楚如何最大限度地最小化代价函数$J\\left( \\theta \\right)$，作为一个关于$\\theta $的函数，这样我们才能为训练集拟合出参数$\\theta $。\n", "\n", "最小化代价函数的方法，是使用梯度下降法(gradient descent)。这是我们的代价函数： $J\\left( \\theta \\right)=-\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[{{y}^{(i)}}\\log \\left( {h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)+\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1-{h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)]}$\n", "\n", "如果我们要最小化这个关于$\\theta$的函数值，这就是我们通常用的梯度下降法的模板。\n", "\n", "Want ${{\\min }_\\theta}J(\\theta )$：\n", "\n", "我们要反复更新每个参数，用这个式子来更新，就是用它自己减去学习率 $\\alpha$ 乘以后面的微分项。求导后得到：\n", "\n", "Want ：\n", "\n", "如果你计算一下的话，你会得到这个等式： ${\\theta_j}:={\\theta_j}-\\alpha \\frac{1}{m}\\sum\\limits_{i=1}^{m}{({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}}){x_{j}}^{(i)}}$ 我把它写在这里，将后面这个式子，在 $i=1$ 到 $m$ 上求和，其实就是预测误差乘以$x_j^{(i)}$ ，所以你把这个偏导数项$\\frac{\\partial }{\\partial {\\theta_j}}J\\left( \\theta \\right)$放回到原来式子这里，我们就可以将梯度下降算法写作如下形式： ${\\theta_j}:={\\theta_j}-\\alpha \\frac{1}{m}\\sum\\limits_{i=1}^{m}{({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}}){x_{j}}^{(i)}}$\n", "\n", "所以，如果你有 $n$ 个特征，也就是说：，参数向量$\\theta $包括${\\theta_{0}}$ ${\\theta_{1}}$ ${\\theta_{2}}$ 一直到${\\theta_{n}}$，那么你就需要用这个式子：\n", "\n", "${\\theta_j}:={\\theta_j}-\\alpha \\frac{1}{m}\\sum\\limits_{i=1}^{m}{({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}}){{x}_{j}}^{(i)}}$来同时更新所有$\\theta $的值。\n", "\n", "现在，如果你把这个更新规则和我们之前用在线性回归上的进行比较的话，你会惊讶地发现，这个式子正是我们用来做线性回归梯度下降的。\n", "\n", "那么，线性回归和逻辑回归是同一个算法吗？要回答这个问题，我们要观察逻辑回归看看发生了哪些变化。实际上，假设的定义发生了变化。\n", "\n", "对于线性回归假设函数：\n", "\n", "${h_\\theta}\\left( x \\right)={\\theta^T}X={\\theta_{0}}{x_{0}}+{\\theta_{1}}{x_{1}}+{\\theta_{2}}{x_{2}}+...+{\\theta_{n}}{x_{n}}$\n", "\n", "而现在逻辑函数假设函数：\n", "\n", "${h_\\theta}\\left( x \\right)=\\frac{1}{1+{{e}^{-{\\theta^T}X}}}$\n", "\n", "因此，即使更新参数的规则看起来基本相同，但由于假设的定义发生了变化，所以逻辑函数的梯度下降，跟线性回归的梯度下降实际上是两个完全不同的东西。\n", "\n", "在先前的视频中，当我们在谈论线性回归的梯度下降法时，我们谈到了如何监控梯度下降法以确保其收敛，我通常也把同样的方法用在逻辑回归中，来监测梯度下降，以确保它正常收敛。\n", "\n", "当使用梯度下降法来实现逻辑回归时，我们有这些不同的参数$\\theta $，就是${\\theta_{0}}$ ${\\theta_{1}}$ ${\\theta_{2}}$ 一直到${\\theta_{n}}$，我们需要用这个表达式来更新这些参数。我们还可以使用 for循环来更新这些参数值，用 for i=1 to n，或者 for i=1 to n+1。当然，不用 for循环也是可以的，理想情况下，我们更提倡使用向量化的实现，可以把所有这些 $n$个参数同时更新。\n", "\n", "最后还有一点，我们之前在谈线性回归时讲到的特征缩放，我们看到了特征缩放是如何提高梯度下降的收敛速度的，这个特征缩放的方法，也适用于逻辑回归。如果你的特征范围差距很大的话，那么应用特征缩放的方法，同样也可以让逻辑回归中，梯度下降收敛更快。\n", "\n", "就是这样，现在你知道如何实现逻辑回归，这是一种非常强大，甚至可能世界上使用最广泛的一种分类算法。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 高级优化" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "假设我们已经完成了可以实现这两件事的代码，那么梯度下降所做的就是反复执行这些更新。 另一种考虑梯度下降的思路是：我们需要写出代码来计算$J\\left( \\theta \\right)$ 和这些偏导数，然后把这些插入到梯度下降中，然后它就可以为我们最小化这个函数。 对于梯度下降来说，我认为从技术上讲，你实际并不需要编写代码来计算代价函数$J\\left( \\theta \\right)$。你只需要编写代码来计算导数项，但是，如果你希望代码还要能够监控这些$J\\left( \\theta \\right)$ 的收敛性，那么我们就需要自己编写代码来计算代价函数$J(\\theta)$和偏导数项$\\frac{\\partial }{\\partial {\\theta_j}}J\\left( \\theta \\right)$。所以，在写完能够计算这两者的代码之后，我们就可以使用梯度下降。 然而梯度下降并不是我们可以使用的唯一算法，还有其他一些算法，更高级、更复杂。如果我们能用这些方法来计算代价函数$J\\left( \\theta \\right)$和偏导数项$\\frac{\\partial }{\\partial {\\theta_j}}J\\left( \\theta \\right)$两个项的话，那么这些算法就是为我们优化代价函数的不同方法，共轭梯度法 BFGS (变尺度法) 和L-BFGS (限制变尺度法) 就是其中一些更高级的优化算法，它们需要有一种方法来计算 $J\\left( \\theta \\right)$，以及需要一种方法计算导数项，然后使用比梯度下降更复杂的算法来最小化代价函数。这三种算法的具体细节超出了本门课程的范畴。实际上你最后通常会花费很多天，或几周时间研究这些算法，你可以专门学一门课来提高数值计算能力，不过让我来告诉你他们的一些特性：\n", "\n", "这三种算法有许多优点：\n", "\n", "一个是使用这其中任何一个算法，你通常不需要手动选择学习率 $\\alpha$，所以对于这些算法的一种思路是，给出计算导数项和代价函数的方法，你可以认为算法有一个智能的内部循环，而且，事实上，他们确实有一个智能的内部循环，称为线性搜索(line search)算法，它可以自动尝试不同的学习速率 $\\alpha$，并自动选择一个好的学习速率 $a$，因此它甚至可以为每次迭代选择不同的学习速率，那么你就不需要自己选择。这些算法实际上在做更复杂的事情，不仅仅是选择一个好的学习速率，所以它们往往最终比梯度下降收敛得快多了，不过关于它们到底做什么的详细讨论，已经超过了本门课程的范围。\n", "\n", "实际上，我过去使用这些算法已经很长一段时间了，也许超过十年了，使用得相当频繁，而直到几年前我才真正搞清楚共轭梯度法 BFGS 和 L-BFGS的细节。\n", "\n", "我们实际上完全有可能成功使用这些算法，并应用于许多不同的学习问题，而不需要真正理解这些算法的内环间在做什么，如果说这些算法有缺点的话，那么我想说主要缺点是它们比梯度下降法复杂多了，特别是你最好不要使用 L-BGFS、BFGS这些算法，除非你是数值计算方面的专家。实际上，我不会建议你们编写自己的代码来计算数据的平方根，或者计算逆矩阵，因为对于这些算法，我还是会建议你直接使用一个软件库，比如说，要求一个平方根，我们所能做的就是调用一些别人已经写好用来计算数字平方根的函数。幸运的是现在我们有Octave 和与它密切相关的 MATLAB 语言可以使用。\n", "\n", "Octave 有一个非常理想的库用于实现这些先进的优化算法，所以，如果你直接调用它自带的库，你就能得到不错的结果。我必须指出这些算法实现得好或不好是有区别的，因此，如果你正在你的机器学习程序中使用一种不同的语言，比如如果你正在使用C、C++、Java等等，你可能会想尝试一些不同的库，以确保你找到一个能很好实现这些算法的库。因为在L-BFGS或者等高线梯度的实现上，表现得好与不太好是有差别的，因此现在让我们来说明：如何使用这些算法：\n", "\n", "比方说，你有一个含两个参数的问题，这两个参数是${\\theta_{0}}$和${\\theta_{1}}$，因此，通过这个代价函数，你可以得到${\\theta_{1}}$和 ${\\theta_{2}}$的值，如果你将$J\\left( \\theta \\right)$ 最小化的话，那么它的最小值将是${\\theta_{1}}=5$ ，${\\theta_{2}}=5$。代价函数$J\\left( \\theta \\right)$的导数推出来就是这两个表达式：\n", "\n", "$\\frac{\\partial }{\\partial {{\\theta }{1}}}J(\\theta)=2({{\\theta }{1}}-5)$\n", "\n", "$\\frac{\\partial }{\\partial {{\\theta }{2}}}J(\\theta)=2({{\\theta }{2}}-5)$\n", "\n", "如果我们不知道最小值，但你想要代价函数找到这个最小值，是用比如梯度下降这些算法，但最好是用比它更高级的算法，你要做的就是运行一个像这样的Octave 函数：\n", "```Octave\n", "function [jVal, gradient]=costFunction(theta)\n", " \n", "　　jVal=(theta(1)-5)^2+(theta(2)-5)^2;\n", " \n", "　　gradient=zeros(2,1);\n", " \n", "　　gradient(1)=2*(theta(1)-5);\n", " \n", "　　gradient(2)=2*(theta(2)-5);\n", " \n", "end\n", "```\n", "这样就计算出这个代价函数，函数返回的第二个值是梯度值，梯度值应该是一个2×1的向量，梯度向量的两个元素对应这里的两个偏导数项，运行这个costFunction 函数后，你就可以调用高级的优化函数，这个函数叫 fminunc，它表示Octave 里无约束最小化函数。调用它的方式如下：\n", "```\n", "options=optimset('GradObj','on','MaxIter',100);\n", "\n", "initialTheta=zeros(2,1);\n", " \n", "[optTheta, functionVal, exitFlag]=fminunc(@costFunction, initialTheta, options);\n", "```\n", "你要设置几个options，这个 options 变量作为一个数据结构可以存储你想要的options，所以 GradObj 和On，这里设置梯度目标参数为打开(on)，这意味着你现在确实要给这个算法提供一个梯度，然后设置最大迭代次数，比方说100，我们给出一个$\\theta$ 的猜测初始值，它是一个2×1的向量，那么这个命令就调用fminunc，这个@符号表示指向我们刚刚定义的costFunction 函数的指针。如果你调用它，它就会使用众多高级优化算法中的一个，当然你也可以把它当成梯度下降，只不过它能自动选择学习速率$\\alpha$，你不需要自己来做。然后它会尝试使用这些高级的优化算法，就像加强版的梯度下降法，为你找到最佳的${\\theta}$值。\n", "\n", "让我告诉你它在 Octave 里什么样：\n", "\n", "所以我写了这个关于theta的 costFunction 函数，它计算出代价函数 jval以及梯度gradient，gradient 有两个元素，是代价函数对于theta(1) 和 **theta(2)**这两个参数的偏导数。\n", "\n", "我希望你们从这个幻灯片中学到的主要内容是：写一个函数，它能返回代价函数值、梯度值，因此要把这个应用到逻辑回归，或者甚至线性回归中，你也可以把这些优化算法用于线性回归，你需要做的就是输入合适的代码来计算这里的这些东西。\n", "\n", "现在你已经知道如何使用这些高级的优化算法，有了这些算法，你就可以使用一个复杂的优化库，它让算法使用起来更模糊一点。因此也许稍微有点难调试，不过由于这些算法的运行速度通常远远超过梯度下降。\n", "\n", "所以当我有一个很大的机器学习问题时，我会选择这些高级算法，而不是梯度下降。有了这些概念，你就应该能将逻辑回归和线性回归应用于更大的问题中，这就是高级优化的概念。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 多类别分类：一对多\n", "第一个例子：假如说你现在需要一个学习算法能自动地将邮件归类到不同的文件夹里，或者说可以自动地加上标签，那么，你也许需要一些不同的文件夹，或者不同的标签来完成这件事，来区分开来自工作的邮件、来自朋友的邮件、来自家人的邮件或者是有关兴趣爱好的邮件，那么，我们就有了这样一个分类问题：其类别有四个，分别用$y=1$、$y=2$、$y=3$、$y=4$ 来代表。\n", "\n", "第二个例子是有关药物诊断的，如果一个病人因为鼻塞来到你的诊所，他可能并没有生病，用 $y=1$ 这个类别来代表；或者患了感冒，用 $y=2$ 来代表；或者得了流感用$y=3$来代表。\n", "\n", "第三个例子：如果你正在做有关天气的机器学习分类问题，那么你可能想要区分哪些天是晴天、多云、雨天、或者下雪天，对上述所有的例子，$y$ 可以取一个很小的数值，一个相对\"谨慎\"的数值，比如1 到3、1到4或者其它数值，以上说的都是多类分类问题，顺便一提的是，对于下标是0 1 2 3，还是 1 2 3 4 都不重要，我更喜欢将分类从 1 开始标而不是0，其实怎样标注都不会影响最后的结果。\n", "\n", "\n", "我用3种不同的符号来代表3个类别，问题就是给出3个类型的数据集，我们如何得到一个学习算法来进行分类呢？\n", "\n", "我们现在已经知道如何进行二元分类，可以使用逻辑回归，对于直线或许你也知道，可以将数据集一分为二为正类和负类。用一对多的分类思想，我们可以将其用在多类分类问题上。\n", "\n", "下面将介绍如何进行一对多的分类工作，有时这个方法也被称为\"一对余\"方法。\n", "\n", "现在我们有一个训练集，好比上图表示的有3个类别，我们用三角形表示 $y=1$，方框表示$y=2$，叉叉表示 $y=3$。我们下面要做的就是使用一个训练集，将其分成3个二元分类问题。\n", "\n", "我们先从用三角形代表的类别1开始，实际上我们可以创建一个，新的\"伪\"训练集，类型2和类型3定为负类，类型1设定为正类，我们创建一个新的训练集，如下图所示的那样，我们要拟合出一个合适的分类器。\n", "\n", "这里的三角形是正样本，而圆形代表负样本。可以这样想，设置三角形的值为1，圆形的值为0，下面我们来训练一个标准的逻辑回归分类器，这样我们就得到一个正边界。\n", "\n", "为了能实现这样的转变，我们将多个类中的一个类标记为正向类（$y=1$），然后将其他所有类都标记为负向类，这个模型记作$h_\\theta^{\\left( 1 \\right)}\\left( x \\right)$。接着，类似地第我们选择另一个类标记为正向类（$y=2$），再将其它类都标记为负向类，将这个模型记作 $h_\\theta^{\\left( 2 \\right)}\\left( x \\right)$,依此类推。 最后我们得到一系列的模型简记为： $h_\\theta^{\\left( i \\right)}\\left( x \\right)=p\\left( y=i|x;\\theta \\right)$其中：$i=\\left( 1,2,3....k \\right)$\n", "\n", "最后，在我们需要做预测时，我们将所有的分类机都运行一遍，然后对每一个输入变量，都选择最高可能性的输出变量。\n", "\n", "总之，我们已经把要做的做完了，现在要做的就是训练这个逻辑回归分类器：$h_\\theta^{\\left( i \\right)}\\left( x \\right)$， 其中 $i$ 对应每一个可能的 $y=i$，最后，为了做出预测，我们给出输入一个新的 $x$ 值，用这个做预测。我们要做的就是在我们三个分类器里面输入 $x$，然后我们选择一个让 $h_\\theta^{\\left( i \\right)}\\left( x \\right)$ 最大的$ i$，即$\\mathop{\\max}\\limits_i,h_\\theta^{\\left( i \\right)}\\left( x \\right)$。\n", "\n", "你现在知道了基本的挑选分类器的方法，选择出哪一个分类器是可信度最高效果最好的，那么就可认为得到一个正确的分类，无论$i$值是多少，我们都有最高的概率值，我们预测$y$就是那个值。这就是多类别分类问题，以及一对多的方法，通过这个小方法，你现在也可以将逻辑回归分类器用在多类分类的问题上。\n", "\n", "### 过拟合的问题 正则化(Regularization)\n", "\n", "\n", "到现在为止，我们已经学习了几种不同的学习算法，包括线性回归和逻辑回归，它们能够有效地解决许多问题，但是当将它们应用到某些特定的机器学习应用时，会遇到过拟合(over-fitting)的问题，可能会导致它们效果很差。\n", "\n", "在这段视频中，我将为你解释什么是过度拟合问题，并且在此之后接下来的几个视频中，我们将谈论一种称为正则化(regularization)的技术，它可以改善或者减少过度拟合问题。\n", "\n", "如果我们有非常多的特征，我们通过学习得到的假设可能能够非常好地适应训练集（代价函数可能几乎为0），但是可能会不能推广到新的数据。\n", "\n", "下图是一个回归问题的例子：\n", "\n", "第一个模型是一个线性模型，欠拟合，不能很好地适应我们的训练集；第三个模型是一个四次方的模型，过于强调拟合原始数据，而丢失了算法的本质：预测新数据。我们可以看出，若给出一个新的值使之预测，它将表现的很差，是过拟合，虽然能非常好地适应我们的训练集但在新输入变量进行预测时可能会效果不好；而中间的模型似乎最合适。\n", "\n", "分类问题中也存在这样的问题：\n", "\n", "就以多项式理解，$x$ 的次数越高，拟合的越好，但相应的预测的能力就可能变差。\n", "\n", "问题是，如果我们发现了过拟合问题，应该如何处理？\n", "\n", " 丢弃一些不能帮助我们正确预测的特征。可以是手工选择保留哪些特征，或者使用一些模型选择的算法来帮忙（例如PCA）\n", "\n", " 正则化。 保留所有的特征，但是减少参数的大小（magnitude）。\n", "\n", "### 代价函数\n", "\n", "参考视频: 7 - 2 - Cost Function (10 min).mkv\n", "\n", "上面的回归问题中如果我们的模型是： ${h_\\theta}\\left( x \\right)={\\theta_{0}}+{\\theta_{1}}{x_{1}}+{\\theta_{2}}{x_{2}^2}+{\\theta_{3}}{x_{3}^3}+{\\theta_{4}}{x_{4}^4}$ 我们可以从之前的事例中看出，正是那些高次项导致了过拟合的产生，所以如果我们能让这些高次项的系数接近于0的话，我们就能很好的拟合了。 所以我们要做的就是在一定程度上减小这些参数$\\theta $ 的值，这就是正则化的基本方法。我们决定要减少${\\theta_{3}}$和${\\theta_{4}}$的大小，我们要做的便是修改代价函数，在其中${\\theta_{3}}$和${\\theta_{4}}$ 设置一点惩罚。这样做的话，我们在尝试最小化代价时也需要将这个惩罚纳入考虑中，并最终导致选择较小一些的${\\theta_{3}}$和${\\theta_{4}}$。 修改后的代价函数如下：$\\underset{\\theta }{\\mathop{\\min }},\\frac{1}{2m}[\\sum\\limits_{i=1}^{m}{{{\\left( {{h}_{\\theta }}\\left( {{x}^{(i)}} \\right)-{{y}^{(i)}} \\right)}^{2}}+1000\\theta _{3}^{2}+10000\\theta _{4}^{2}]}$\n", "\n", "通过这样的代价函数选择出的${\\theta_{3}}$和${\\theta_{4}}$ 对预测结果的影响就比之前要小许多。假如我们有非常多的特征，我们并不知道其中哪些特征我们要惩罚，我们将对所有的特征进行惩罚，并且让代价函数最优化的软件来选择这些惩罚的程度。这样的结果是得到了一个较为简单的能防止过拟合问题的假设：$J\\left( \\theta \\right)=\\frac{1}{2m}[\\sum\\limits_{i=1}^{m}{{{({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}})}^{2}}+\\lambda \\sum\\limits_{j=1}^{n}{\\theta_{j}^{2}}]}$\n", "\n", "其中$\\lambda $又称为正则化参数（Regularization Parameter）。 注：根据惯例，我们不对${\\theta_{0}}$ 进行惩罚。经过正则化处理的模型与原模型的可能对比如下图所示：\n", "\n", "如果选择的正则化参数$\\lambda$ 过大，则会把所有的参数都最小化了，导致模型变成 ${h_\\theta}\\left( x \\right)={\\theta_{0}}$，也就是上图中红色直线所示的情况，造成欠拟合。 那为什么增加的一项$\\lambda =\\sum\\limits_{j=1}^{n}{\\theta_j^{2}}$ 可以使$\\theta $的值减小呢？ 因为如果我们令 $\\lambda$ 的值很大的话，为了使Cost Function 尽可能的小，所有的 $\\theta $ 的值（不包括${\\theta_{0}}$）都会在一定程度上减小。 但若$\\lambda$ 的值太大了，那么$\\theta $（不包括${\\theta_{0}}$）都会趋近于0，这样我们所得到的只能是一条平行于$x$轴的直线。 所以对于正则化，我们要取一个合理的 $\\lambda$ 的值，这样才能更好的应用正则化。 回顾一下代价函数，为了使用正则化，让我们把这些概念应用到到线性回归和逻辑回归中去，那么我们就可以让他们避免过度拟合了。\n", "### 正则化线性回归\n", "\n", "参考视频: 7 - 3 - Regularized Linear Regression (11 min).mkv\n", "\n", "对于线性回归的求解，我们之前推导了两种学习算法：一种基于梯度下降，一种基于正规方程。\n", "\n", "正则化线性回归的代价函数为：\n", "\n", "$J\\left( \\theta \\right)=\\frac{1}{2m}\\sum\\limits_{i=1}^{m}{[({{({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}})}^{2}}+\\lambda \\sum\\limits_{j=1}^{n}{\\theta _{j}^{2}})]}$\n", "\n", "如果我们要使用梯度下降法令这个代价函数最小化，因为我们未对$\\theta_0​$进行正则化，所以梯度下降算法将分两种情形：\n", "\n", "$Repeat$ $until$ $convergence${\n", "\n", "​ ${\\theta_0}:={\\theta_0}-a\\frac{1}{m}\\sum\\limits_{i=1}^{m}{(({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}})x_{0}^{(i)}})$\n", "\n", "​ ${\\theta_j}:={\\theta_j}-a[\\frac{1}{m}\\sum\\limits_{i=1}^{m}{(({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}})x_{j}^{\\left( i \\right)}}+\\frac{\\lambda }{m}{\\theta_j}]$\n", "\n", "​ $for$ $j=1,2,...n$\n", "\n", "​ }\n", "\n", "对上面的算法中$ j=1,2,...,n$ 时的更新式子进行调整可得：\n", "\n", "${\\theta_j}:={\\theta_j}(1-a\\frac{\\lambda }{m})-a\\frac{1}{m}\\sum\\limits_{i=1}^{m}{({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}})x_{j}^{\\left( i \\right)}}​$ 可以看出，正则化线性回归的梯度下降算法的变化在于，每次都在原有算法更新规则的基础上令$\\theta $值减少了一个额外的值。\n", "\n", "我们同样也可以利用正规方程来求解正则化线性回归模型，方法如下所示：\n", "\n", "图中的矩阵尺寸为 $(n+1)*(n+1)$。\n", "### 正则化的逻辑回归模型\n", "\n", "参考视频: 7 - 4 - Regularized Logistic Regression (9 min).mkv\n", "\n", "针对逻辑回归问题，我们在之前的课程已经学习过两种优化算法：我们首先学习了使用梯度下降法来优化代价函数$J\\left( \\theta \\right)$，接下来学习了更高级的优化算法，这些高级优化算法需要你自己设计代价函数$J\\left( \\theta \\right)$。\n", "\n", "自己计算导数同样对于逻辑回归，我们也给代价函数增加一个正则化的表达式，得到代价函数：\n", "\n", "$J\\left( \\theta \\right)=\\frac{1}{m}\\sum\\limits_{i=1}^{m}{[-{{y}^{(i)}}\\log \\left( {h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)-\\left( 1-{{y}^{(i)}} \\right)\\log \\left( 1-{h_\\theta}\\left( {{x}^{(i)}} \\right) \\right)]}+\\frac{\\lambda }{2m}\\sum\\limits_{j=1}^{n}{\\theta _{j}^{2}}$\n", "\n", "Python代码：\n", "\n", "```py\n", "import numpy as np\n", "\n", "def costReg(theta, X, y, learningRate):\n", " theta = np.matrix(theta)\n", " X = np.matrix(X)\n", " y = np.matrix(y)\n", " first = np.multiply(-y, np.log(sigmoid(X*theta.T)))\n", " second = np.multiply((1 - y), np.log(1 - sigmoid(X*theta.T)))\n", " reg = (learningRate / (2 * len(X))* np.sum(np.power(theta[:,1:theta.shape[1]],2))\n", " return np.sum(first - second) / (len(X)) + reg\n", "```\n", "\n", "要最小化该代价函数，通过求导，得出梯度下降算法为：\n", "\n", "$Repeat$ $until$ $convergence${\n", "\n", "​ ${\\theta_0}:={\\theta_0}-a\\frac{1}{m}\\sum\\limits_{i=1}^{m}{(({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}})x_{0}^{(i)}})$\n", "\n", "​ ${\\theta_j}:={\\theta_j}-a[\\frac{1}{m}\\sum\\limits_{i=1}^{m}{({h_\\theta}({{x}^{(i)}})-{{y}^{(i)}})x_{j}^{\\left( i \\right)}}+\\frac{\\lambda }{m}{\\theta_j}]$\n", "\n", "​ $for$ $j=1,2,...n$\n", "\n", "​ }\n", "\n", "注：看上去同线性回归一样，但是知道 ${h_\\theta}\\left( x \\right)=g\\left( {\\theta^T}X \\right)​$，所以与线性回归不同。 Octave 中，我们依旧可以用 fminuc 函数来求解代价函数最小化的参数，值得注意的是参数${\\theta_{0}}​$的更新规则与其他情况不同。 注意：\n", "\n", " 虽然正则化的逻辑回归中的梯度下降和正则化的线性回归中的表达式看起来一样，但由于两者的${h_\\theta}\\left( x \\right)​$不同所以还是有很大差别。\n", "\n", " ${\\theta_{0}}$不参与其中的任何一个正则化。\n", "\n", "目前大家对机器学习算法可能还只是略懂，但是一旦你精通了线性回归、高级优化算法和正则化技术，坦率地说，你对机器学习的理解可能已经比许多工程师深入了。现在，你已经有了丰富的机器学习知识，目测比那些硅谷工程师还厉害，或者用机器学习算法来做产品。\n", "\n", "接下来的课程中，我们将学习一个非常强大的非线性分类器，无论是线性回归问题，还是逻辑回归问题，都可以构造多项式来解决。你将逐渐发现还有更强大的非线性分类器，可以用来解决多项式回归问题。我们接下来将将学会，比现在解决问题的方法强大N倍的学习算法。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[文档来自](https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes)" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": 3 }, "orig_nbformat": 2 }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

rfinn/LCS

notebooks/MSpaper_final.ipynb

1

1368072

gpl-3.0

gsd-ufal/Juliabox

container/interactive/IJulia/tutorial/Plotting in Julia.ipynb

1

288806

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting in Julia\n", "\n", "We will use the PyPlot package to plot with Julia. This notebook has a few examples to get you started. The [PyPlot.jl](https://github.com/stevengj/PyPlot.jl) site has excellent documentation for plotting.\n", "\n", "Loading the PyPlot module may take a few seconds.\n", "\n", "In general, all of the arguments, including keyword arguments, are exactly the same as in Python. (With minor translations, of course, e.g. Julia uses `true` and `nothing` instead of Python's `True` and `None`.)\n", "\n", "The full matplotlib.pyplot API is far too extensive to describe here; see the [matplotlib.pyplot documentation](http://matplotlib.org/api/pyplot_api.html) for more information. The Matplotlib version number is returned by PyPlot.version." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Loading help data...\n" ] } ], "source": [ "using PyPlot" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7f3751cb5d90>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.text.Text object at 0x7f3790bdc310>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = linspace(0,2*pi,1000); y = sin(3*x + 4*cos(2*x));\n", "plot(x, y, color=\"red\", linewidth=2.0, linestyle=\"--\")\n", "title(\"A sinusoidally modulated sinusoid\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7f3790baed90>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "1-element Array{Any,1}:\n", " PyObject <matplotlib.lines.Line2D object at 0x7f37515b47d0>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Draw (x, y) points\n", "figure(figsize=(5, 5))\n", "θ = [0:0.1:2π]\n", "plot(0,0,\"b.\")\n", "plot(cos(θ), sin(θ), \"r.\")\n", "plot(0.5cos(θ), 0.5sin(θ), \"g.\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7f3790bc5290>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "([2.0,2.0,2.0,7.0,17.0,46.0,84.0,183.0,346.0,592.0 … 1333.0,776.0,416.0,229.0,114.0,50.0,20.0,14.0,5.0,1.0],[-4.91619,-4.72466,-4.53313,-4.3416,-4.15007,-3.95855,-3.76702,-3.57549,-3.38396,-3.19243 … 2.93652,3.12805,3.31958,3.51111,3.70264,3.89417,4.0857,4.27723,4.46876,4.66029],{PyObject <matplotlib.patches.Rectangle object at 0x7f3750602f50>,PyObject <matplotlib.patches.Rectangle object at 0x7f3750612590>,PyObject <matplotlib.patches.Rectangle object at 0x7f3750612c10>,PyObject <matplotlib.patches.Rectangle object at 0x7f375059f2d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f375059f950>,PyObject <matplotlib.patches.Rectangle object at 0x7f375059ffd0>,PyObject <matplotlib.patches.Rectangle object at 0x7f37505ab690>,PyObject <matplotlib.patches.Rectangle object at 0x7f37505abd10>,PyObject <matplotlib.patches.Rectangle object at 0x7f37505b83d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f37505b8a50> … PyObject <matplotlib.patches.Rectangle object at 0x7f37505534d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f3750553b50>,PyObject <matplotlib.patches.Rectangle object at 0x7f37504df210>,PyObject <matplotlib.patches.Rectangle object at 0x7f37504df890>,PyObject <matplotlib.patches.Rectangle object at 0x7f37504dff10>,PyObject <matplotlib.patches.Rectangle object at 0x7f37504eb5d0>,PyObject <matplotlib.patches.Rectangle object at 0x7f37504ebc50>,PyObject <matplotlib.patches.Rectangle object at 0x7f37504f7310>,PyObject <matplotlib.patches.Rectangle object at 0x7f37504f7990>,PyObject <matplotlib.patches.Rectangle object at 0x7f37504f7e90>})" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Draw a histogram\n", "\n", "y = randn(10^6)\n", "plt[:hist](y, 50) # We use plt.hist, because it conflicts with the built-in hist" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7f37515ca350>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.legend.Legend object at 0x7f37503fc9d0>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Draw a stacked bar chart\n", "\n", "N = 5\n", "menMeans = (20, 35, 30, 35, 27)\n", "womenMeans = (25, 32, 34, 20, 25)\n", "menStd = (2, 3, 4, 1, 2)\n", "womenStd = (3, 5, 2, 3, 3)\n", "ind = [1:N] # the x locations for the groups\n", "width = 0.35 # the width of the bars: can also be len(x) sequence\n", "\n", "p1 = bar(ind, menMeans, width, color=\"r\", yerr=womenStd)\n", "p2 = bar(ind, womenMeans, width, color=\"y\", bottom=menMeans, yerr=menStd)\n", "\n", "ylabel(\"Scores\")\n", "title(\"Scores by group and gender\")\n", "xticks(ind+width/2., (\"G1\", \"G2\", \"G3\", \"G4\", \"G5\") )\n", "yticks([0:10:81])\n", "legend( (p1[1], p2[1]), (\"Men\", \"Women\") )\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7f37515d0c50>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x7f3751546110>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Plot a random surface\n", "\n", "surf(rand(30,40))" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.11", "language": "julia", "name": "julia-0.3" }, "language": "Julia", "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.3.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

jviada/QuantEcon.py

solutions/jv_solutions.ipynb

7

108644

{ "metadata": { "name": "", "signature": "sha256:508943d1ce2372490699818cedf35a0997172a9adcaf9c6de4b3eeb42efd275d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "quant-econ Solutions: On-the-Job Search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solutions for http://quant-econ.net/py/jv.html" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import random\n", "from quantecon import compute_fixed_point\n", "from quantecon.models import JvWorker" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here\u2019s code to produce the 45 degree diagram" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "wp = JvWorker(grid_size=25)\n", "G, pi, F = wp.G, wp.pi, wp.F # Simplify names\n", "\n", "v_init = wp.x_grid * 0.5\n", "print(\"Computing value function\")\n", "V = compute_fixed_point(wp.bellman_operator, v_init, max_iter=40, verbose=False)\n", "print(\"Computing policy functions\")\n", "s_policy, phi_policy = wp.bellman_operator(V, return_policies=True)\n", "\n", "# Turn the policy function arrays into actual functions\n", "s = lambda y: np.interp(y, wp.x_grid, s_policy)\n", "phi = lambda y: np.interp(y, wp.x_grid, phi_policy)\n", "\n", "def h(x, b, U):\n", " return (1 - b) * G(x, phi(x)) + b * max(G(x, phi(x)), U)\n", "\n", "plot_grid_max, plot_grid_size = 1.2, 100\n", "plot_grid = np.linspace(0, plot_grid_max, plot_grid_size)\n", "fig, ax = plt.subplots(figsize=(8,8))\n", "ax.set_xlim(0, plot_grid_max)\n", "ax.set_ylim(0, plot_grid_max)\n", "ticks = (0.25, 0.5, 0.75, 1.0)\n", "ax.set_xticks(ticks)\n", "ax.set_yticks(ticks)\n", "ax.set_xlabel(r'$x_t$', fontsize=16)\n", "ax.set_ylabel(r'$x_{t+1}$', fontsize=16, rotation='horizontal')\n", "\n", "ax.plot(plot_grid, plot_grid, 'k--') # 45 degree line\n", "for x in plot_grid:\n", " for i in range(50):\n", " b = 1 if random.uniform(0, 1) < pi(s(x)) else 0\n", " U = wp.F.rvs(1)\n", " y = h(x, b, U)\n", " ax.plot(x, y, 'go', alpha=0.25)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Computing value function\n", "Computing policy functions" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAHvCAYAAABe0gYYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x10b1f57d0>" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the dynamics, we can see that \n", "\n", "* If $x_t$ is below about 0.2 the dynamics are random, but $x_{t+1} > x_t$ is very likely\n", "* As $x_t$ increases the dynamics become deterministic, and $x_t$ converges to a steady state value close to 1\n", "\n", "Referring back to the figure here\n", "\n", "http://quant-econ.net/py/jv.html#solving-for-policies\n", "\n", "we see that $x_t \\approx 1$ means that $s_t = s(x_t) \\approx 0$ and $\\phi_t = \\phi(x_t) \\approx 0.6$\n", "\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure can be produced as follows" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "wp = JvWorker(grid_size=25)\n", "\n", "def xbar(phi):\n", " return (wp.A * phi**wp.alpha)**(1 / (1 - wp.alpha))\n", "\n", "phi_grid = np.linspace(0, 1, 100)\n", "fig, ax = plt.subplots(figsize=(9, 7))\n", "ax.set_xlabel(r'$\\phi$', fontsize=16)\n", "ax.plot(phi_grid, [xbar(phi) * (1 - phi) for phi in phi_grid], 'b-', label=r'$w^*(\\phi)$')\n", "ax.legend(loc='upper left')\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAG/CAYAAAByhizjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x115e1ce10>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observe that the maximizer is around 0.6\n", "\n", "This this is similar to the long run value for $\\phi$ obtained\n", "in exercise 1\n", "\n", "Hence the behaviour of the infinitely patent worker is similar to that of the\n", "worker with $\\beta = 0.96$\n", "\n", "This seems reasonable, and helps us confirm that our dynamic programming\n", "solutions are probably correct\n" ] } ], "metadata": {} } ] }

bsd-3-clause

AISpace2/AISpace2

notebooks/bayesian_network/bayes_builder.ipynb

1

3044

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building a Belief Network\n", "\n", "## About\n", "This notebook helps to construct a belief network visually. You can export the belief network and run query algorithms on it afterwards. You can create new variables and edges, remove existing ones, and change the names and factors of variables.\n", "\n", "You can run each cell by selecting it and pressing *Ctrl+Enter* in Windows or *Shift+Return* in MacOS. Alternatively, you can click the *Play* button in the toolbar, to the left of the stop button. For more information, check out our AISpace2 [Tutorial](https://aispace2.github.io/AISpace2/tutorial.html).\n", "\n", "Feel free to modify our codes either in this notebook or somewhere outside (e.g. python files in `/aipython/`). If you want to modify our codes outside, you might find [this](https://aispace2.github.io/AISpace2/tutorial.html#tutorial-faq-why-update-aipython-not-reflect) helpful for how your changes can take effect.\n", "\n", "You need to run the following command to import our pre-defined problems." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Run this to import pre-defined problems\n", "from aipython.probGraphicalModels import bn_empty, bn_simple1, bn_simple2, bn_simple3, bn_grass_watering, bn_fire_alarm, bn_diagnosis, bn_diagnosis_extended, bn_conditional_independence, bn_car_starting, bn_electrical_diagnosis, bn_hailfinder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from aispace2.jupyter.bayes import BayesBuilder\n", "\n", "builder = BayesBuilder(bn_simple3)\n", "\n", "# Visualization options\n", "# For more explanation please visit: https://aispace2.github.io/AISpace2/tutorial.html#tutorial-common-visualization-options\n", "builder.text_size = 13 # The fontsize of the text\n", "builder.line_width = 2.0 # The thickness of edges\n", "builder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtaining the Belief Network\n", "\n", "The following method enerates the Python code that, once run, constructs a `Belief_Network`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "builder.py_code(need_positions=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

ireapps/cfj-2017

exercises/08. Working with APIs (Part 1)-working.ipynb

1

8242

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Let's post a message to Slack\n", "\n", "In this session, we're going to use Python to post a message to Slack. I set up [a team for us](https://ire-cfj-2017.slack.com/) so we can mess around with the [Slack API](https://api.slack.com/).\n", "\n", "We're going to use a simple [_incoming webhook_](https://api.slack.com/incoming-webhooks) to accomplish this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hello API\n", "\n", "API stands for \"Application Programming Interface.\" An API is a way to interact programmatically with a software application.\n", "\n", "If you want to post a message to Slack, you could open a browser and navigate to your URL and sign in with your username and password (or open the app), click on the channel you want, and start typing.\n", "\n", "OR ... you could post your Slack message with a Python script." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hello environmental variables\n", "\n", "The code for this boot camp [is on the public internet](https://github.com/ireapps/cfj-2017). We don't want anyone on the internet to be able to post messages to our Slack channels, so we're going to use an [environmental variable](https://en.wikipedia.org/wiki/Environment_variable) to store our webhook.\n", "\n", "The environmental variable we're going to use -- `IRE_CFJ_2017_SLACK_HOOK` -- should already be stored on your computer.\n", "\n", "Python has a standard library module for working with the operating system called [`os`](https://docs.python.org/3/library/os.html). The `os` module has a data attribute called `environ`, a dictionary of environmental variables stored on your computer.\n", "\n", "(Here is a new thing: Instead of using brackets to access items in a dictionary, you can use the `get()` method. The advantage to doing it this way: If the item you're trying to get doesn't exist in your dictionary, it'll return `None` instead of throwing an exception, which is sometimes a desired behavior.)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hello JSON\n", "\n", "So far we've been working with tabular data -- CSVs with columns and rows. Most modern web APIs prefer to shake hands with a data structure called [JSON](http://www.json.org/) (**J**ava**S**cript **O**bject **N**otation), which is more like a matryoshka doll.\n", "\n", "![](https://media.giphy.com/media/Ud5r7tzmG4De0/giphy.gif \"russian nesting dolls\")\n", "\n", "Python has a standard library module for working with JSON data called [`json`](https://docs.python.org/3/library/json.html). Let's import it." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using `requests` to post data\n", "\n", "We're also going to use the `requests` library again, except this time, instead of using the `get()` method to get something off the web, we're going to use the `post()` method to send data _to_ the web." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Formatting the data correctly\n", "\n", "The JSON data we're going to send to the Slack webhook will start its life as a Python dictionary. Then we'll use the `json` module's `dumps()` method to turn it into a string of JSON." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# build a dictionary of payload data\n", "\n", "\n", "# turn it into a string of JSON\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Send it off to Slack" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# check to see if you have the webhook URL\n", "\n", "\n", " # send it to slack!\n", "\n", "\n", "\n", "\n", " # if you don't have the webhook env var, print a message to the terminal\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### _Exercise_\n", "\n", "Read through the [Slack documentation](https://api.slack.com/incoming-webhooks) and post a message to a Slack channel ...\n", "\n", "- with a different emoji\n", "- with an image URL instead of an emoji\n", "- with a link in it\n", "- with an attachment\n", "- with other kinds of fancy formatting" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### _Extra credit: Slack alert_\n", "\n", "Scenario: You cover the Fort Calhoun Nuclear Power Station outside of Omaha, Nebraska. Every day, you'd like to check [an NRC website](https://www.nrc.gov/reading-rm/doc-collections/event-status/event/) to see if your plant had any \"Event Notifications\" in the agency's most recent report. You decide to write a Slack script to do this for you. (Ignore, for now, the problem of setting up the script to run daily.)\n", "\n", "Breaking down your problem, you need to:\n", "\n", "- Fetch [the page with the latest reports](https://www.nrc.gov/reading-rm/doc-collections/event-status/event/en.html) using `requests`\n", "- Look through the text and see if your reactor's name appears in the page text (you could just use an `if` statement with `in`)\n", "- If it's there, use `requests` to send a message to Slack\n", "\n", "Notice that we don't need to parse the page with BeautifulSoup -- we're basically just checking for the presence of a string inside a bigger string.\n", "\n", "### _Extra, extra credit_\n", "\n", "Let's extend the script you just wrote with a function that would allow you to check for the presence of _any string_ on an NRc page for _any date_ of reports -- most days have their own page, though I think weekends are grouped together.\n", "\n", "Let's break it down. Inside our function, we need to:\n", "\n", "- Figure out the URL pattern for each day's report page. [Here's the page for Sept. 29, 2017](https://www.nrc.gov/reading-rm/doc-collections/event-status/event/2017/20170929en.html)\n", "- Decide how you want to accept the two arguments in your function -- one for the date and one for the string to search for (me, I'd use a date object for the default date argument to keep things explicit, but you could also pass a string)\n", "- Fill in the URL using `format()` and the date being passed to the function\n", "- Fetch the page using `requests`\n", "- Not every day has a page, so you'll need to check to see if the request was successful (hint: use the requests [`status_code` attribute](http://docs.python-requests.org/en/master/user/quickstart/#response-status-codes) -- 200 means success)\n", "- If the request was successful, check for the presence of the string in the page text\n", "- If the text we're looking for is there, send a message to Slack" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

JasonTam/ndsb2015

theano/sandbox.ipynb

2

27539

{ "metadata": { "name": "", "signature": "sha256:db2ec0b213949698a257740a9f84e0211fbf808ac21a145b9e9c7343f7f5897d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# Code via\n", "# https://github.com/benanne/kaggle-galaxies\n", "\n", "import numpy as np\n", "# import pandas as pd\n", "import theano\n", "import theano.tensor as T\n", "import layers\n", "import cc_layers\n", "import custom\n", "import load_data\n", "import realtime_augmentation as ra\n", "import time\n", "import csv\n", "import os\n", "import cPickle as pickle\n", "from datetime import datetime, timedelta" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "Using gpu device 0: GeForce GTX 580\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%run copy_data_to_shm.py" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Copying /media/raid_arr/data/ndsb/train to /dev/shm...\n", " took 9.38 seconds." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Copying /media/raid_arr/data/ndsb/test to /dev/shm...\n", " took 45.02 seconds." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "BATCH_SIZE = 16\n", "NUM_INPUT_FEATURES = 1 # Number of channels\n", "\n", "LEARNING_RATE_SCHEDULE = { # keyed by chunk num\n", " 0: 0.04,\n", " 1800: 0.004,\n", " 2300: 0.0004,\n", "}\n", "MOMENTUM = 0.9\n", "WEIGHT_DECAY = 0.0\n", "CHUNK_SIZE = 10000 # 30000 # this should be a multiple of the batch size, ideally.\n", "NUM_CHUNKS = 2500 # 3000 # 1500 # 600 # 600 # 600 # 500 \n", "VALIDATE_EVERY = 20 # 12 # 6 # 6 # 6 # 5 # validate only every 5 chunks. MUST BE A DIVISOR OF NUM_CHUNKS!!!\n", "# else computing the analysis data does not work correctly, since it assumes that the validation set is still loaded.\n", "\n", "NUM_CHUNKS_NONORM = 1 # train without normalisation for this many chunks, to get the weights in the right 'zone'.\n", "# this should be only a few, just 1 hopefully suffices.\n", "\n", "GEN_BUFFER_SIZE = 1\n", "\n", "\n", "TARGET_PATH = \"predictions/preds.csv\"\n", "ANALYSIS_PATH = \"analysis/analysis.pkl\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Set up data loading\"\n", "\n", "input_sizes = [(64, 64), (64, 64)]\n", "\n", "ds_transforms = [\n", " ra.build_ds_transform(3.0, target_size=input_sizes[0]),\n", " ra.build_ds_transform(3.0, target_size=input_sizes[1]) + ra.build_augmentation_transform(rotation=45)\n", " ]\n", "\n", "num_input_representations = len(ds_transforms)\n", "\n", "augmentation_params = {\n", " 'zoom_range': (1.0 / 1.3, 1.3),\n", " 'rotation_range': (0, 360),\n", " 'shear_range': (0, 0),\n", " 'translation_range': (-4, 4),\n", " 'do_flip': True,\n", "}\n", "\n", "augmented_data_gen = ra.realtime_augmented_data_gen(\n", " num_chunks=NUM_CHUNKS, chunk_size=CHUNK_SIZE,\n", " augmentation_params=augmentation_params, ds_transforms=ds_transforms,\n", " target_sizes=input_sizes)\n", "\n", "post_augmented_data_gen = ra.post_augment_brightness_gen(augmented_data_gen, std=0.5)\n", "\n", "train_gen = load_data.buffered_gen_mp(post_augmented_data_gen, buffer_size=GEN_BUFFER_SIZE)\n", "\n", "\n", "y_train = np.load(\"data/train_lbls.npy\")\n", "train_ids = load_data.train_ids\n", "test_ids = load_data.test_ids\n", "\n", "\n", "# todo: should probably use stratified K-fold (k=5 or 10)\n", "# split training data into training + a small validation set\n", "num_train = len(train_ids)\n", "num_test = len(test_ids)\n", "\n", "num_valid = num_train // 10 # integer division\n", "num_train -= num_valid\n", "\n", "y_valid = y_train[num_train:]\n", "y_train = y_train[:num_train]\n", "\n", "valid_ids = train_ids[num_train:]\n", "train_ids = train_ids[:num_train]\n", "\n", "train_indices = np.arange(num_train)\n", "valid_indices = np.arange(num_train, num_train + num_valid)\n", "test_indices = np.arange(num_test)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Set up data loading\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "def create_train_gen():\n", " \"\"\"\n", " this generates the training data in order, for postprocessing. Do not use this for actual training.\n", " \"\"\"\n", " data_gen_train = ra.realtime_fixed_augmented_data_gen(train_indices, 'train',\n", " ds_transforms=ds_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes)\n", " return load_data.buffered_gen_mp(data_gen_train, buffer_size=GEN_BUFFER_SIZE)\n", "\n", "\n", "def create_valid_gen():\n", " data_gen_valid = ra.realtime_fixed_augmented_data_gen(valid_indices, 'train',\n", " ds_transforms=ds_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes)\n", " return load_data.buffered_gen_mp(data_gen_valid, buffer_size=GEN_BUFFER_SIZE)\n", "\n", "\n", "def create_test_gen():\n", " data_gen_test = ra.realtime_fixed_augmented_data_gen(test_indices, 'test',\n", " ds_transforms=ds_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes)\n", " return load_data.buffered_gen_mp(data_gen_test, buffer_size=GEN_BUFFER_SIZE)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Preprocess validation data upfront\"\n", "start_time = time.time()\n", "xs_valid = [[] for _ in xrange(num_input_representations)]\n", "\n", "for data, length in create_valid_gen():\n", " for x_valid_list, x_chunk in zip(xs_valid, data):\n", " x_valid_list.append(x_chunk[:length])\n", "\n", "xs_valid = [np.vstack(x_valid) for x_valid in xs_valid]\n", "xs_valid = [x_valid.transpose(0, 3, 1, 2) for x_valid in xs_valid] # move the colour dimension up\n", "\n", "\n", "print \" took %.2f seconds\" % (time.time() - start_time)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Preprocess validation data upfront\n" ] } ] }, { "cell_type": "code", "collapsed": false, "input": [ "for data, length in create_valid_gen():\n", " for x_valid_list, x_chunk in zip(xs_valid, data):\n", " x_valid_list.append(x_chunk[:length])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "Exception in thread Thread-4:\n", "Traceback (most recent call last):\n", " File \"/usr/lib/python2.7/threading.py\", line 810, in __bootstrap_inner\n", " self.run()\n", " File \"/usr/lib/python2.7/site-packages/zmq/utils/garbage.py\", line 37, in run\n", " s.bind(self.gc.url)\n", " File \"zmq/backend/cython/socket.pyx\", line 444, in zmq.backend.cython.socket.Socket.bind (zmq/backend/cython/socket.c:4092)\n", " File \"zmq/backend/cython/checkrc.pxd\", line 21, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/socket.c:6251)\n", " raise ZMQError(errno)\n", "ZMQError: Address already in use\n", "\n", "Exception in thread Thread-4:\n", "Traceback (most recent call last):\n", " File \"/usr/lib/python2.7/threading.py\", line 810, in __bootstrap_inner\n", " self.run()\n", " File \"/usr/lib/python2.7/site-packages/zmq/utils/garbage.py\", line 37, in run\n", " s.bind(self.gc.url)\n", " File \"zmq/backend/cython/socket.pyx\", line 444, in zmq.backend.cython.socket.Socket.bind (zmq/backend/cython/socket.c:4092)\n", " File \"zmq/backend/cython/checkrc.pxd\", line 21, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/socket.c:6251)\n", " raise ZMQError(errno)\n", "ZMQError: Address already in use\n", "\n", "Exception in thread Thread-4:\n", "Traceback (most recent call last):\n", " File \"/usr/lib/python2.7/threading.py\", line 810, in __bootstrap_inner\n", " self.run()\n", " File \"/usr/lib/python2.7/site-packages/zmq/utils/garbage.py\", line 37, in run\n", " s.bind(self.gc.url)\n", " File \"zmq/backend/cython/socket.pyx\", line 444, in zmq.backend.cython.socket.Socket.bind (zmq/backend/cython/socket.c:4092)\n", " File \"zmq/backend/cython/checkrc.pxd\", line 21, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/socket.c:6251)\n", " raise ZMQError(errno)\n", "ZMQError: Address already in use\n", "\n", "Exception in thread Thread-4:\n", "Traceback (most recent call last):\n", " File \"/usr/lib/python2.7/threading.py\", line 810, in __bootstrap_inner\n", " self.run()\n", " File \"/usr/lib/python2.7/site-packages/zmq/utils/garbage.py\", line 37, in run\n", " s.bind(self.gc.url)\n", " File \"zmq/backend/cython/socket.pyx\", line 444, in zmq.backend.cython.socket.Socket.bind (zmq/backend/cython/socket.c:4092)\n", " File \"zmq/backend/cython/checkrc.pxd\", line 21, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/socket.c:6251)\n", " raise ZMQError(errno)\n", "ZMQError: Address already in use\n", "\n", "Exception in thread Thread-4:\n", "Traceback (most recent call last):\n", " File \"/usr/lib/python2.7/threading.py\", line 810, in __bootstrap_inner\n", " self.run()\n", " File \"/usr/lib/python2.7/site-packages/zmq/utils/garbage.py\", line 37, in run\n", " s.bind(self.gc.url)\n", " File \"zmq/backend/cython/socket.pyx\", line 444, in zmq.backend.cython.socket.Socket.bind (zmq/backend/cython/socket.c:4092)\n", " File \"zmq/backend/cython/checkrc.pxd\", line 21, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/socket.c:6251)\n", " raise ZMQError(errno)\n", "ZMQError: Address already in use\n", "\n", "Process Process-5:\n", "Traceback (most recent call last):\n", " File \"/usr/lib/python2.7/multiprocessing/process.py\", line 258, in _bootstrap\n", " self.run()\n", " File \"/usr/lib/python2.7/multiprocessing/process.py\", line 114, in run\n", " self._target(*self._args, **self._kwargs)\n", " File \"load_data.py\", line 564, in _buffered_generation_process\n", " time.sleep(sleep_time)\n", " File \"realtime_augmentation.py\", line 336, in realtime_fixed_augmented_data_gen\n", " for k, imgs_aug in enumerate(gen):\n", " File \"/usr/lib/python2.7/multiprocessing/pool.py\", line 269, in <genexpr>\n", " return (item for chunk in result for item in chunk)\n", " File \"/usr/lib/python2.7/multiprocessing/pool.py\", line 659, in next\n", " raise value\n", "IOError: [Errno 2] No such file or directory: '/dev/shm/images_train_rev1/909833.jpg'\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Build model\"\n", "# INPUT LAYERS\n", "l0 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, \n", " input_sizes[0][0], input_sizes[0][1])\n", "l0_45 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, \n", " input_sizes[1][0], input_sizes[1][1])\n", "\n", "l0r = layers.MultiRotSliceLayer([l0, l0_45], \n", " part_size=45, include_flip=True)\n", "\n", "l0s = cc_layers.ShuffleBC01ToC01BLayer(l0r) \n", "# CONVOLUTION & POOLING\n", "l1a = cc_layers.CudaConvnetConv2DLayer(l0s, \n", " n_filters=32, filter_size=6, \n", " weights_std=0.01, init_bias_value=0.1, \n", " dropout=0.0, partial_sum=1, untie_biases=True)\n", "l1 = cc_layers.CudaConvnetPooling2DLayer(l1a, pool_size=2)\n", "\n", "l2a = cc_layers.CudaConvnetConv2DLayer(l1, \n", " n_filters=64, filter_size=5, \n", " weights_std=0.01, init_bias_value=0.1, \n", " dropout=0.0, partial_sum=1, untie_biases=True)\n", "l2 = cc_layers.CudaConvnetPooling2DLayer(l2a, pool_size=2)\n", "\n", "l3a = cc_layers.CudaConvnetConv2DLayer(l2, \n", " n_filters=128, filter_size=3, \n", " weights_std=0.01, init_bias_value=0.1, \n", " dropout=0.0, partial_sum=1, untie_biases=True)\n", "l3b = cc_layers.CudaConvnetConv2DLayer(l3a, \n", " n_filters=128, filter_size=3, \n", " pad=0, weights_std=0.1, init_bias_value=0.1, \n", " dropout=0.0, partial_sum=1, untie_biases=True)\n", "l3 = cc_layers.CudaConvnetPooling2DLayer(l3b, pool_size=2)\n", "\n", "l3s = cc_layers.ShuffleC01BToBC01Layer(l3)\n", "\n", "j3 = layers.MultiRotMergeLayer(l3s, num_views=4) # 2) # merge convolutional parts\n", "# FULLY CONNECTED\n", "l4a = layers.DenseLayer(j3, \n", " n_outputs=1024, weights_std=0.001, \n", " init_bias_value=0.01, dropout=0.5, \n", " nonlinearity=layers.identity)\n", "l4b = layers.FeatureMaxPoolingLayer(l4a, \n", " pool_size=2, feature_dim=1, \n", " implementation='reshape')\n", "l4c = layers.DenseLayer(l4b, \n", " n_outputs=1024, weights_std=0.001, \n", " init_bias_value=0.01, dropout=0.5, \n", " nonlinearity=layers.identity)\n", "l4 = layers.FeatureMaxPoolingLayer(l4c, \n", " pool_size=2, feature_dim=1, \n", " implementation='reshape')\n", "\n", "l5 = layers.DenseLayer(l4, \n", " n_outputs=121, weights_std=0.01, \n", " init_bias_value=0.1, dropout=0.5, \n", " nonlinearity=layers.identity)\n", "\n", "# OUTPUT LAYER\n", "l6 = custom.OptimisedDivGalaxyOutputLayer(l5) # this incorporates the constraints on the output (probabilities sum to one, weighting, etc.)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Build model\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "train_loss_nonorm = l6.error(normalisation=False)\n", "train_loss = l6.error() # but compute and print this!\n", "valid_loss = l6.error(dropout_active=False)\n", "all_parameters = layers.all_parameters(l6)\n", "all_bias_parameters = layers.all_bias_parameters(l6)\n", "\n", "xs_shared = [theano.shared(np.zeros((1,1,1,1), dtype=theano.config.floatX)) for _ in xrange(num_input_representations)]\n", "y_shared = theano.shared(np.zeros((1,1), dtype=theano.config.floatX))\n", "\n", "learning_rate = theano.shared(np.array(LEARNING_RATE_SCHEDULE[0], dtype=theano.config.floatX))\n", "\n", "idx = T.lscalar('idx')\n", "\n", "givens = {\n", " l0.input_var: xs_shared[0][idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],\n", " l0_45.input_var: xs_shared[1][idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],\n", " l6.target_var: y_shared[idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],\n", "}\n", "\n", "# updates = layers.gen_updates(train_loss, all_parameters, learning_rate=LEARNING_RATE, momentum=MOMENTUM, weight_decay=WEIGHT_DECAY)\n", "updates_nonorm = layers.gen_updates_nesterov_momentum_no_bias_decay(\n", " train_loss_nonorm, all_parameters, all_bias_parameters, \n", " learning_rate=learning_rate, momentum=MOMENTUM, weight_decay=WEIGHT_DECAY)\n", "\n", "updates = layers.gen_updates_nesterov_momentum_no_bias_decay(\n", " train_loss, all_parameters, all_bias_parameters, \n", " learning_rate=learning_rate, momentum=MOMENTUM, weight_decay=WEIGHT_DECAY)\n", "\n", "train_nonorm = theano.function([idx], train_loss_nonorm, givens=givens, updates=updates_nonorm)\n", "train_norm = theano.function([idx], train_loss, givens=givens, updates=updates)\n", "compute_loss = theano.function([idx], valid_loss, givens=givens) # dropout_active=False\n", "compute_output = theano.function([idx], l6.predictions(dropout_active=False), givens=givens, on_unused_input='ignore') # not using the labels, so theano complains\n", "compute_features = theano.function([idx], l4.output(dropout_active=False), givens=givens, on_unused_input='ignore')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Train model\"\n", "start_time = time.time()\n", "prev_time = start_time\n", "\n", "num_batches_valid = x_valid.shape[0] // BATCH_SIZE\n", "losses_train = []\n", "losses_valid = []\n", "\n", "param_stds = []\n", "\n", "for e in xrange(NUM_CHUNKS):\n", " print \"Chunk %d/%d\" % (e + 1, NUM_CHUNKS)\n", " chunk_data, chunk_length = train_gen.next()\n", " y_chunk = chunk_data.pop() # last element is labels.\n", " xs_chunk = chunk_data\n", "\n", " # need to transpose the chunks to move the 'channels' dimension up\n", " xs_chunk = [x_chunk.transpose(0, 3, 1, 2) for x_chunk in xs_chunk]\n", "\n", " if e in LEARNING_RATE_SCHEDULE:\n", " current_lr = LEARNING_RATE_SCHEDULE[e]\n", " learning_rate.set_value(LEARNING_RATE_SCHEDULE[e])\n", " print \" setting learning rate to %.6f\" % current_lr\n", "\n", " # train without normalisation for the first # chunks.\n", " if e >= NUM_CHUNKS_NONORM:\n", " train = train_norm\n", " else:\n", " train = train_nonorm\n", "\n", " print \" load training data onto GPU\"\n", " for x_shared, x_chunk in zip(xs_shared, xs_chunk):\n", " x_shared.set_value(x_chunk)\n", " y_shared.set_value(y_chunk)\n", " num_batches_chunk = x_chunk.shape[0] // BATCH_SIZE\n", "\n", " print \" batch SGD\"\n", " losses = []\n", " for b in xrange(num_batches_chunk):\n", " # if b % 1000 == 0:\n", " # print \" batch %d/%d\" % (b + 1, num_batches_chunk)\n", "\n", " loss = train(b)\n", " losses.append(loss)\n", " # print \" loss: %.6f\" % loss\n", "\n", " mean_train_loss = np.sqrt(np.mean(losses))\n", " print \" mean training loss (RMSE):\\t\\t%.6f\" % mean_train_loss\n", " losses_train.append(mean_train_loss)\n", "\n", " # store param stds during training\n", " param_stds.append([p.std() for p in layers.get_param_values(l6)])\n", "\n", " if ((e + 1) % VALIDATE_EVERY) == 0:\n", " print\n", " print \"VALIDATING\"\n", " print \" load validation data onto GPU\"\n", " for x_shared, x_valid in zip(xs_shared, xs_valid):\n", " x_shared.set_value(x_valid)\n", " y_shared.set_value(y_valid)\n", "\n", " print \" compute losses\"\n", " losses = []\n", " for b in xrange(num_batches_valid):\n", " # if b % 1000 == 0:\n", " # print \" batch %d/%d\" % (b + 1, num_batches_valid)\n", " loss = compute_loss(b)\n", " losses.append(loss)\n", "\n", " mean_valid_loss = np.sqrt(np.mean(losses))\n", " print \" mean validation loss (RMSE):\\t\\t%.6f\" % mean_valid_loss\n", " losses_valid.append(mean_valid_loss)\n", "\n", " now = time.time()\n", " time_since_start = now - start_time\n", " time_since_prev = now - prev_time\n", " prev_time = now\n", " est_time_left = time_since_start * (float(NUM_CHUNKS - (e + 1)) / float(e + 1))\n", " eta = datetime.now() + timedelta(seconds=est_time_left)\n", " eta_str = eta.strftime(\"%c\")\n", " print \" %s since start (%.2f s)\" % (load_data.hms(time_since_start), time_since_prev)\n", " print \" estimated %s to go (ETA: %s)\" % (load_data.hms(est_time_left), eta_str)\n", " print\n", "\n", "\n", "del chunk_data, xs_chunk, x_chunk, y_chunk, xs_valid, x_valid # memory cleanup" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Compute predictions on validation set for analysis in batches\"\n", "predictions_list = []\n", "for b in xrange(num_batches_valid):\n", " # if b % 1000 == 0:\n", " # print \" batch %d/%d\" % (b + 1, num_batches_valid)\n", "\n", " predictions = compute_output(b)\n", " predictions_list.append(predictions)\n", "\n", "all_predictions = np.vstack(predictions_list)\n", "\n", "# postprocessing: clip all predictions to 0-1\n", "all_predictions[all_predictions > 1] = 1.0\n", "all_predictions[all_predictions < 0] = 0.0\n", "\n", "print \"Write validation set predictions to %s\" % ANALYSIS_PATH\n", "with open(ANALYSIS_PATH, 'w') as f:\n", " pickle.dump({\n", " 'ids': valid_ids[:num_batches_valid * BATCH_SIZE], # note that we need to truncate the ids to a multiple of the batch size.\n", " 'predictions': all_predictions,\n", " 'targets': y_valid,\n", " 'mean_train_loss': mean_train_loss,\n", " 'mean_valid_loss': mean_valid_loss,\n", " 'time_since_start': time_since_start,\n", " 'losses_train': losses_train,\n", " 'losses_valid': losses_valid,\n", " 'param_values': layers.get_param_values(l6),\n", " 'param_stds': param_stds,\n", " }, f, pickle.HIGHEST_PROTOCOL)\n", "\n", "\n", "del predictions_list, all_predictions # memory cleanup\n", "\n", "\n", "print \"Computing predictions on test data\"\n", "predictions_list = []\n", "for e, (xs_chunk, chunk_length) in enumerate(create_test_gen()):\n", " print \"Chunk %d\" % (e + 1)\n", " xs_chunk = [x_chunk.transpose(0, 3, 1, 2) for x_chunk in xs_chunk] # move the colour dimension up.\n", "\n", " for x_shared, x_chunk in zip(xs_shared, xs_chunk):\n", " x_shared.set_value(x_chunk)\n", "\n", " num_batches_chunk = int(np.ceil(chunk_length / float(BATCH_SIZE))) # need to round UP this time to account for all data\n", "\n", " # make predictions for testset, don't forget to cute off the zeros at the end\n", " for b in xrange(num_batches_chunk):\n", " # if b % 1000 == 0:\n", " # print \" batch %d/%d\" % (b + 1, num_batches_chunk)\n", "\n", " predictions = compute_output(b)\n", " predictions_list.append(predictions)\n", "\n", "\n", "all_predictions = np.vstack(predictions_list)\n", "all_predictions = all_predictions[:num_test] # truncate back to the correct length\n", "\n", "# postprocessing: clip all predictions to 0-1\n", "all_predictions[all_predictions > 1] = 1.0\n", "all_predictions[all_predictions < 0] = 0.0\n", "\n", "\n", "print \"Write predictions to %s\" % TARGET_PATH\n", "# test_ids = np.load(\"data/test_ids.npy\")\n", "\n", "with open(TARGET_PATH, 'wb') as csvfile:\n", " writer = csv.writer(csvfile) # , delimiter=',', quoting=csv.QUOTE_MINIMAL)\n", "\n", " # write header\n", " writer.writerow(['GalaxyID', 'Class1.1', 'Class1.2', 'Class1.3', 'Class2.1', 'Class2.2', 'Class3.1', 'Class3.2', 'Class4.1', 'Class4.2', 'Class5.1', 'Class5.2', 'Class5.3', 'Class5.4', 'Class6.1', 'Class6.2', 'Class7.1', 'Class7.2', 'Class7.3', 'Class8.1', 'Class8.2', 'Class8.3', 'Class8.4', 'Class8.5', 'Class8.6', 'Class8.7', 'Class9.1', 'Class9.2', 'Class9.3', 'Class10.1', 'Class10.2', 'Class10.3', 'Class11.1', 'Class11.2', 'Class11.3', 'Class11.4', 'Class11.5', 'Class11.6'])\n", "\n", " # write data\n", " for k in xrange(test_ids.shape[0]):\n", " row = [test_ids[k]] + all_predictions[k].tolist()\n", " writer.writerow(row)\n", "\n", "print \"Gzipping...\"\n", "os.system(\"gzip -c %s > %s.gz\" % (TARGET_PATH, TARGET_PATH))\n", "\n", "\n", "del all_predictions, predictions_list, xs_chunk, x_chunk # memory cleanup\n", "\n", "\n", "print \"Done!\"" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

LSSTC-DSFP/LSSTC-DSFP-Sessions

Sessions/Session13/Day4/NonparametricMeasuresOfPeriodicity.ipynb

1

63233

{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "b6cddf8f", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import emcee\n", "import george\n", "from george import kernels\n", "import corner\n", "\n", "%matplotlib notebook" ] }, { "cell_type": "markdown", "id": "34db36ae", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Non-parametric Measures of Periodicity – Return of the Gaps\n", "\n", "**Version 0.1**\n", "\n", "* * *\n", "\n", "By AA Miller (Northwester/CIERA) \n", "20 Sep 2021" ] }, { "cell_type": "markdown", "id": "aca999b6", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "In this lecture we will examine non-parametric methods to search for periodic signals in astronomical time series. Lecture III focused extensively on the Lomb-Scargle periodogram. LS is the \"standard\" in astronomy, in part because it was the first (good) method developed for noisy and sparse data." ] }, { "cell_type": "markdown", "id": "07c36a54", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "LS is not without warts, however, (i) LS does not handle outliers well, and (ii) LS works best on purely sinusoidal signals.\n", "\n", "Given non-Gaussian noise and that some signals (e.g., transiting planets) are not sinusoidal, we will now explore alternative methods to search for periodicity." ] }, { "cell_type": "markdown", "id": "89b12a7c", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We begin today with a simulated signal (we will use multiple harmonics to make things a bit more challenging than a pure sinusoid). The cell below creates a periodic signal with $P = 0.7\\,\\mathrm{d}$, sampled over two months ($60\\,\\mathrm{d}$), with an average of two observations per night." ] }, { "cell_type": "markdown", "id": "334ca732", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "(If the slide below does not execute properly, jump to the end of this notebook and execute the cells with the various helper functions)" ] }, { "cell_type": "code", "execution_count": null, "id": "7f59d7da", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "np.random.seed(185)\n", "t_obs = np.random.uniform(60, size=120)\n", "phi = np.pi\n", "var_y = 0.81\n", "\n", "y = gen_periodic_data(t_obs, period=0.7, \n", " amplitude=3, phase=phi, \n", " noise=var_y) \n", "y += gen_periodic_data(t_obs, period=0.7/2, \n", " amplitude=3, phase=phi+np.pi/2)\n", "y += gen_periodic_data(t_obs, period=0.7/3, \n", " amplitude=2, phase=phi)\n", "\n", "y_unc = np.ones_like(y)*np.sqrt(var_y)\n", "\n", "phase_plot(t_obs, y, 0.7, y_unc = y_unc)" ] }, { "cell_type": "markdown", "id": "db84827c", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "For this simulated data, lets run LS and see what we find." ] }, { "cell_type": "code", "execution_count": null, "id": "9138cdf5", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "from astropy.timeseries import LombScargle\n", "\n", "freq, psd = LombScargle(t_obs, y, y_unc).autopower(maximum_frequency=5)\n", "\n", "best_period = 1/freq[np.argmax(psd)]\n", "print('Top LS period is {}'.format(best_period))\n", "\n", "phase_plot(t_obs, y, best_period, y_unc = 0.1*np.ones_like(y))" ] }, { "cell_type": "markdown", "id": "9d37ffee", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "LS only recovers the \"half-period\" as the correct answer! \n", "\n", "This is a common problem for eclipsing binaries, which are not purely sinusoidal signals." ] }, { "cell_type": "markdown", "id": "f11dde3a", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "(As you might expect, the input period in the data that was simulated with 3 harmonic terms can be recovered with [`LombScargle`](https://docs.astropy.org/en/stable/api/astropy.timeseries.LombScargle.html) if more than one harmonic is searched." ] }, { "cell_type": "markdown", "id": "b18b5914", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Now we will explore several alternatives to LS, and demonstrate that they can be implemented in `python` without too much overhead." ] }, { "cell_type": "markdown", "id": "1b2bec8f", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Method 1) String Length\n", "\n", "The string length method ([Dworetsky](http://adsabs.harvard.edu/abs/1983MNRAS.203..917D)), phase folds the data at trial periods and then minimizes the distance to connect the phase-ordered observations." ] }, { "cell_type": "markdown", "id": "2a47c2c0", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<img style=\"display: block; margin-left: auto; margin-right: auto\" src=\"./images/StringLength.png\" align=\"middle\">\n", "\n", "<div align=\"right\"> <font size=\"-3\">(credit: Gaveen Freer - http://slideplayer.com/slide/4212629/#) </font></div>" ] }, { "cell_type": "markdown", "id": "0382f913", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Probelm 1a**\n", "\n", "Write a function, `calc_string_length`, that calculates the string length for a phase-folded light curve with observations `x`, `y`, and frequency `f`." ] }, { "cell_type": "code", "execution_count": null, "id": "1d8cc481", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def calc_string_length(x, y, f=1):\n", " '''Calculate string length for observations at frequency f\n", " \n", " Parameters\n", " ----------\n", " x : array-like\n", " input time of observations\n", " \n", " y : array-like\n", " measured signal at input x\n", " \n", " f : float (default=1)\n", " frequency of the test period\n", " \n", " Returns\n", " -------\n", " sl : float\n", " String length for the phase-ordered observations\n", " \n", " '''\n", " \n", " phases = x*f % 1\n", " sl = # complete\n", " return sl" ] }, { "cell_type": "markdown", "id": "5a9a224c", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 1b** \n", "\n", "Write a function `sl_periodogram` to measure the string length for input data `x`, `y`, over a frequency grid `f_grid`." ] }, { "cell_type": "code", "execution_count": null, "id": "8e7e024e", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def sl_periodogram(x, y, f_grid = np.linspace(0.1,10,10)):\n", " '''Calculate the string length \"periodogram\"\n", " \n", " Parameters\n", " ----------\n", " x : array-like\n", " input time of observations\n", " \n", " y : array-like\n", " measured signal at input x\n", " \n", " f_grid : array_like (default=np.linspace(0.1,10,10))\n", " frequency grid for the period\n", " \n", " Returns\n", " -------\n", " sl_psd : array_like\n", " String length at every test frequency f\n", " \n", " '''\n", " \n", " sl_psd = np.zeros_like(f_grid)\n", " for f_num, f in enumerate(f_grid):\n", " sl_psd[f_num] = # complete\n", " \n", " return sl_psd" ] }, { "cell_type": "markdown", "id": "9d5e53ab", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 1c**\n", "\n", "Plot the string length periodogram for the simulated data. Does it make sense?\n", "\n", "*Hint - think about the optimal grid from Notebook III*" ] }, { "cell_type": "code", "execution_count": null, "id": "30600939", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "f_grid = np.arange(1/np.ptp(t_obs), 10, 1/5/np.ptp(t_obs))\n", "sl_psd = sl_periodogram( # complete\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(1/f_grid, sl_psd, '0.2', lw=2)\n", "ax.set_xlabel('Period (d)')\n", "ax.set_ylabel('String Length')\n", "\n", "ax.axvline(0.7, color='DarkOrange', \n", " lw=1, ls='--')\n", "\n", "axins = plt.axes([.29, .22, .65, .27])\n", "axins.plot(1/f_grid, sl_psd, '0.2', lw=2)\n", "axins.axvline(0.7, color='DarkOrange', lw=1, ls='--')\n", "axins.set_xlim(0,3)\n", "\n", "fig.subplots_adjust(left=0.1, right=0.99, top=0.98, bottom=0.1)" ] }, { "cell_type": "markdown", "id": "ad52e709", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The string length method was able to recover the correct period for the simulated data. \n", "\n", "The main downside to this method is that it does not account for the observational uncertainties at all." ] }, { "cell_type": "markdown", "id": "6910c0ec", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Method 2) Phase Dispersion Minimization\n", "\n", "Phase Dispersion Minimization (PDM; [Jurkevich 1971](http://adsabs.harvard.edu/abs/1971Ap%26SS..13..154J), [Stellingwerth 1978](http://adsabs.harvard.edu/abs/1978ApJ...224..953S)), like LS, folds the data at a large number of trial frequencies $f$." ] }, { "cell_type": "markdown", "id": "a3d27225", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The phased data are then binned, and the variance is calculated in each bin, combined, and compared to the overall variance of the signal. No functional form of the signal is assumed, and thus, non-sinusoidal signals can be found.\n", "\n", "<img style=\"display: block; margin-left: auto; margin-right: auto\" src=\"./images/PDM.jpg\" align=\"middle\">\n", "\n", "<div align=\"right\"> <font size=\"-3\">(credit: Gaveen Freer - http://slideplayer.com/slide/4212629/#) </font></div>" ] }, { "cell_type": "markdown", "id": "368a46b3", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 2a** \n", "\n", "Write a function called `calc_pdm`, that calculates the average dispersion/scatter in $N$ equally spaced bins for a phase-folded light curve with observations `x`, `y`, and frequency `f`. Specify the number of bins $N$ via keyword argument `bins`." ] }, { "cell_type": "code", "execution_count": null, "id": "cb3b8fb6", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def calc_pdm(x, y, f=1, bins=10):\n", " '''Calculate the phase dispersion minimization for observations at frequency f\n", " \n", " Parameters\n", " ----------\n", " x : array-like\n", " input time of observations\n", " \n", " y : array-like\n", " measured signal at input x\n", " \n", " f : float (default=1)\n", " frequency of the test period\n", " \n", " bins : int (default=10)\n", " \n", " Returns\n", " -------\n", " pdm : float\n", " the sum of the scatter in each bin\n", " '''\n", " phases = x*f % 1\n", " # complete\n", " # complete \n", " # complete\n", " # complete\n", "\n", " return pdm\n" ] }, { "cell_type": "markdown", "id": "1f52349e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 2b** \n", "\n", "Write a function `pdm_periodogram` to measure the relative reduction in the scatter for a phase-folded light curve with input data `x`, `y`, over a frequency grid `f_grid`.\n", "\n", "Plot the periodogram." ] }, { "cell_type": "code", "execution_count": null, "id": "2632b6cd", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def pdm_periodogram(x, y, f_grid = np.linspace(0.1,10,10), **kwargs):\n", " '''Calculate the phase dispersion minimization \"periodogram\"\n", " \n", " Parameters\n", " ----------\n", " x : array-like\n", " input time of observations\n", " \n", " y : array-like\n", " measured signal at input x\n", " \n", " f_grid : array_like (default=np.linspace(0.1,10,10))\n", " frequency grid for the period\n", " \n", " Returns\n", " -------\n", " pdm_psd : array_like\n", " PDM at every test frequency f\n", " \n", " '''\n", " \n", " pdm_psd = np.zeros_like(f_grid)\n", " total_rms = np.std(y, ddof=1)\n", " for f_num, f in enumerate(f_grid):\n", " pdm_psd[f_num] = calc_pdm( # complete\n", " \n", " return pdm_psd" ] }, { "cell_type": "code", "execution_count": null, "id": "e77a5eea", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "f_grid = np.arange(1/np.ptp(t_obs), 10, 1/5/np.ptp(t_obs))\n", "pdm_psd = pdm_periodogram( # complete\n", "\n", "print(f'The best-fit period is {1/f_grid[np.argmin(pdm_psd)]:.4f} d')\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(1/f_grid, pdm_psd, '0.2', lw=2)\n", "ax.set_xlabel('Period (d)')\n", "ax.set_ylabel('PDM statistic')\n", "ax.axline((0.7,np.mean(pdm_psd)), (0.7, np.mean(pdm_psd)+1e-3), \n", " color='DarkOrange', lw=1, ls='--')\n", "\n", "\n", "axins = plt.axes([.29, .22, .65, .27])\n", "axins.plot(1/f_grid, pdm_psd, '0.2', lw=2)\n", "axins.axline((0.7,np.mean(pdm_psd)), (0.7, np.mean(pdm_psd)+1e-3), \n", " color='DarkOrange', lw=1, ls='--')\n", "axins.set_xlim(0,3)\n", "fig.subplots_adjust(left=0.1, right=0.99, top=0.98, bottom=0.1)\n" ] }, { "cell_type": "markdown", "id": "a58b5b4e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "PDM finds the correct period! \n", "\n", "Like string length, PDM does not incorporate observational uncertainties, though a slight modification to measure the $\\chi^2$ rather than the scatter can correct that. \n", "\n", "The main challenge for PDM is deciding the number of bins to adopt. For some light curves the choice of 10 or 100 bins can result in different measurements of the best period." ] }, { "cell_type": "markdown", "id": "242b48e3", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Method 3) Analysis of Variance\n", "\n", "Analysis of Variance (AOV; [Schwarzenberg-Czerny 1989](http://adsabs.harvard.edu/abs/1989MNRAS.241..153S)) is similar to PDM. Optimal periods are defined via hypothesis testing, and these methods are found to perform best for certain types of astronomical signals." ] }, { "cell_type": "markdown", "id": "280e9852", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Method 4) Supersmoother\n", "\n", "Supersmoother ([Reimann](http://adsabs.harvard.edu/abs/1994PhDT........20R)) is a least-squares approach wherein a flexible, non-parametric model is fit to the folded observations at many trial frequncies. The use of this flexible model reduces aliasing issues relative to models that assume a sinusoidal shape, however, this comes at the cost of requiring considerable computational time. " ] }, { "cell_type": "markdown", "id": "d9ddd4c8", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Briefly, supersmoother provides a smooth estimate of the data via localized linear regression. Observations are then compared to the smooth model value to identify the model that optimally reduces the sum of the square of the residuals (normalized by the uncertainties when available). " ] }, { "cell_type": "markdown", "id": "7942fbd2", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Supersmoother requires a user-selected smoothing window (called the \"span\"), a sliding region over which the linear fit is performed. \n", "\n", "The \"magic\" in supersmoother is that it uses cross-validation to identify the optimal span at every location within the data set." ] }, { "cell_type": "markdown", "id": "d8fa9a37", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The supersmoother psuedo-code is: \n" ] }, { "cell_type": "markdown", "id": "9170abbc", "metadata": {}, "source": [ " 1. create 3 smooth local linear estimations of `y` at every input `x` with `span` = 0.05, 0.2, and 0.5" ] }, { "cell_type": "markdown", "id": "70c73f8d", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " 2. identify optimal `span` at every `x` based on residuals\n" ] }, { "cell_type": "markdown", "id": "af2dbbf9", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " 3. smooth the above \"optimal\" span curve with `span` = 0.2\n" ] }, { "cell_type": "markdown", "id": "8fdfea06", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " 4. create a \"final\" smooth estimate by interpolating bewtween two smooth curves closest in value to (3)" ] }, { "cell_type": "markdown", "id": "c2958758", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We will now illustrate how this works via several examples (before putting everything together into a single function). " ] }, { "cell_type": "markdown", "id": "28b22812", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 4a**\n", "\n", "Write a function `smooth` that estimates the value of `y` at every phase `phase` via a linear least squares fit to all the observations within $\\pm$`span`/2 of `phase`. The observed value of `y` at phase `phase` should be excluded from the fit. \n", "\n", "*Hint* - it may be helpful to input `x` and `f` so the phase can be calculated within the function. Note - you can \"supersmooth\" any series of data, a frequency is not strictly required." ] }, { "cell_type": "code", "execution_count": null, "id": "b368d51f", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def smooth(y, x, f=None, span=0.05, y_unc=None):\n", " '''Calculate the smooth\n", " \n", " Parameters\n", " ----------\n", " x : array-like\n", " input time of observations\n", " \n", " y : array-like\n", " measured signal at input x\n", " \n", " f : float (optional; default=None)\n", " frequency for which to calculate the smooth.\n", " if None, then the x values are normalized \n", " between 0 and 1.\n", "\n", " y_unc : array-like (optional; default=None)\n", " uncertainties on the input signal\n", " \n", " Returns\n", " -------\n", " smooth : array_like\n", " smooth estimate of the phase folded frequency\n", " '''\n", " \n", " if type(y_unc) == int:\n", " y_unc = np.ones_like(y)*y_unc\n", " \n", " if f is None:\n", " phases = (x - np.min(x))/(np.ptp(x))\n", " else:\n", " phases = (x*f) % 1\n", " \n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " # complete\n", " \n", " return smooth\n", "\n" ] }, { "cell_type": "markdown", "id": "3ed79f46", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 4b**\n", "\n", "Plot the smooth representation of the data with spans of 0.05, 0.2, and 0.5 folded at a period of 0.7 d. \n", "\n", "*pseudocode 1*" ] }, { "cell_type": "code", "execution_count": null, "id": "f21e13ea", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "phases = (t_obs/0.7) % 1\n", "\n", "smooth_tweeter = smooth(y, t_obs, 1/0.7, span=0.05, y_unc=y_unc)\n", "smooth_midrange = smooth(y, t_obs, 1/0.7, span=0.2, y_unc=y_unc)\n", "smooth_woofer = smooth(y, t_obs, 1/0.7, span=0.5, y_unc=y_unc)\n", "\n", "phase_plot(t_obs, y, 0.7, y_unc = 0.1*np.ones_like(y))\n", "plt.plot(np.sort(phases), smooth_tweeter[np.argsort(phases)], \n", " label=\"span = 0.05\")\n", "plt.plot(np.sort(phases), smooth_midrange[np.argsort(phases)], \n", " label=\"span = 0.2\")\n", "plt.plot(np.sort(phases), smooth_woofer[np.argsort(phases)], \n", " label=\"span = 0.5\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "id": "d3abcb5e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We now have 3 different \"smooth\" representations of the data. But even these \"smooth\" representations are a bit jagged, and in places we can see the influence of noise.\n", "\n", "We will now identify the \"optimal\" span at every phase." ] }, { "cell_type": "markdown", "id": "a032f22b", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Probem 4c**\n", "\n", "Identify the best span at every phase via the residuals. \n", "\n", "*pseudocode 2*" ] }, { "cell_type": "code", "execution_count": null, "id": "46c1db97", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "smooth_list = np.vstack([[smooth_tweeter], \n", " [smooth_midrange],\n", " [smooth_woofer]])\n", "span_list = np.vstack([[np.ones_like(smooth_tweeter)*0.05], \n", " [np.ones_like(smooth_midrange)*0.2],\n", " [np.ones_like(smooth_woofer)*0.5]])\n", "resid = np.abs(y - smooth_list)\n", "\n", "best_span = span_list[np.argmin(resid, axis=0), 0]" ] }, { "cell_type": "markdown", "id": "04e4facd", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 4d**\n", "\n", "Smooth the `best_span` array using a `span = 0.2`, to create a smooth representation of the best smooth.\n", "\n", "*pseudocode 3*" ] }, { "cell_type": "code", "execution_count": null, "id": "07ae7b1c", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "span_midrange = smooth(best_span, t_obs, 1/0.7, span=0.2)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(np.sort(phases), span_midrange[np.argsort(phases)])\n", "ax.set_xlabel('phase')\n", "ax.set_ylabel('optimal smooth')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "a8ed79fc", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 4e**\n", "\n", "Calculate the \"super\" smooth representation of the data by interpolating from the 3 initial smooth estimates to the `span_midrange` estimate.\n", "\n", "*pseudocode 4*" ] }, { "cell_type": "code", "execution_count": null, "id": "a847b29b", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "supersmooth = np.empty_like(smooth_midrange)\n", "for sm_num, sm in enumerate(span_midrange):\n", " supersmooth[sm_num] = np.interp(sm, \n", " span_list.T[sm_num], \n", " smooth_list.T[sm_num])" ] }, { "cell_type": "markdown", "id": "737d9d8d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 4f**\n", "\n", "Plot the supersmooth over the phase-folded data." ] }, { "cell_type": "code", "execution_count": null, "id": "503bb10b", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "phases = (t_obs/0.7) % 1\n", "\n", "phase_plot(t_obs, y, 0.7, y_unc = 0.1*np.ones_like(y))\n", "plt.plot(np.sort(phases), smooth_tweeter[np.argsort(phases)], \n", " label=\"span = 0.05\")\n", "plt.plot(np.sort(phases), smooth_midrange[np.argsort(phases)], \n", " label=\"span = 0.2\")\n", "plt.plot(np.sort(phases), smooth_woofer[np.argsort(phases)], \n", " label=\"span = 0.5\")\n", "plt.plot(np.sort(phases), supersmooth[np.argsort(phases)], \n", " lw=4, color='0.3', zorder=10, \n", " label='supersmooth')\n", "plt.legend()" ] }, { "cell_type": "markdown", "id": "43f9bf82", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 4g**\n", "\n", "Wrap everything from above into a single function `calc_supersmooth`." ] }, { "cell_type": "code", "execution_count": null, "id": "f07bb7ca", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def calc_supersmooth(y, x, f=None, spans=[0.05, 0.2, 0.5], y_unc=None):\n", " '''Calculate the smooth\n", " \n", " Parameters\n", " ----------\n", " x : array-like\n", " input time of observations\n", " \n", " y : array-like\n", " measured signal at input x\n", " \n", " f : float (default=None)\n", " frequency for which to calculate the smooth.\n", " if None, then the x values are normalized \n", " between 0 and 1.\n", "\n", " spans : list (default=[0.05, 0.2, 0.5])\n", " list of the individual spans to use for the \n", " initial smooth representations of the data\n", "\n", " y_unc : array-like (default=None)\n", " uncertainties on the input signal\n", " \n", " Returns\n", " -------\n", " supersmooth : array_like\n", " smooth estimate of the phase folded frequency\n", " '''\n", " if type(y_unc) == int:\n", " y_unc = np.ones_like(y)*y_unc\n", " \n", " if f is None:\n", " phases = (x - np.min(x))/(np.ptp(x))\n", " else:\n", " phases = (x*f) % 1\n", "\n", " smooth_list = np.vstack([[smooth(y, x, f, span=s, y_unc=y_unc) for s in spans]])\n", " span_list = np.ones_like(smooth_list)*np.array(spans)[:,None]\n", " \n", " resid = np.abs(y - smooth_list)\n", " \n", " span_midrange = smooth(span_list[np.argmin(resid, axis=0), 0], \n", " x, f, span=np.median(spans))\n", " \n", " supersmooth = np.empty_like(smooth_midrange)\n", " for sm_num, sm in enumerate(span_midrange):\n", " supersmooth[sm_num] = np.interp(sm, \n", " span_list.T[sm_num], \n", " smooth_list.T[sm_num])\n", " \n", " return supersmooth\n", "\n" ] }, { "cell_type": "markdown", "id": "770c9a38", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 4h**\n", "\n", "Write a function `supersmooth_periodogram` to calculate the Supersmoother periodogram. \n", "\n", "Unlike the other methods above, we can in this case calculate $\\chi^2$ at each frequency. Use $\\chi^2_0$ as a relative baseline as we did in calculating the LS periodogram." ] }, { "cell_type": "code", "execution_count": null, "id": "bb02e0d4", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def supersmooth_periodogram(y, y_unc, x, f_grid):\n", " psd = np.empty_like(f_grid)\n", " chi2_0 = np.sum(((y - np.mean(y))/y_unc)**2)\n", " \n", " for f_num, f in enumerate(f_grid):\n", " supersmooth = calc_supersmooth(y, x, f, y_unc=y_unc)\n", " chi2 = np.sum((y - supersmooth)**2/y_unc**2)\n", " psd[f_num] = 0.5*(chi2_0 - chi2)\n", " \n", " return psd" ] }, { "cell_type": "markdown", "id": "e6f1fa43", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 4i**\n", "\n", "Calculate and plot the supersmoother periodogram for the simulated data. \n", "\n", "*Hint* - this is much slower than above, use a frequency grid that goes from 0.6 to 3 and has 1000 points." ] }, { "cell_type": "code", "execution_count": null, "id": "162dcd8b", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "f_grid = np.linspace(0.6, 3, 1000)\n", "\n", "ss_psd = supersmooth_periodogram(y, y_unc, t_obs, f_grid)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(1/f_grid, ss_psd)\n", "ax.axvline(0.7, color='DarkOrange', \n", " lw=1, ls='--')\n", "ax.set_xlabel('Period (d)')\n", "ax.set_ylabel('Power')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "7567c4a7", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The grand downside of supersmoother should now be obvious - this thing is S - L - O - W, slow. \n", "\n", "We only ran a frequency grid with only 1000 points and it still took significantly longer than the other methods." ] }, { "cell_type": "markdown", "id": "1fc7de4d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Fortunately, Jake VanderPlas has created a faster implementation of [SuperSmoother](https://www.astroml.org/gatspy/periodic/supersmoother.html), if you are interested in implementing this method. " ] }, { "cell_type": "markdown", "id": "cbceb20d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We have just now covered 4 (4!) alternatives to LS for measuring periodicity (and Notebook IV involves yet another). " ] }, { "cell_type": "markdown", "id": "8ad1c026", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "There is something that should really be bothering you at this point though..." ] }, { "cell_type": "markdown", "id": "774135fc", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\n", "Absolutely none of the methods we have covered provide any sort of measurement of uncertainty on the best-fit period estimates. And uncertainty is at the heart of all meaningful analysis." ] }, { "cell_type": "markdown", "id": "087d1cf8", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Bayesian Methods\n", "\n", "There have been some efforts to frame the period-finding problem in a Bayesian framework. [Bretthorst 1988](https://www.springer.com/us/book/9780387968711) developed Bayesian generalized LS models, while [Gregory & Loredo 1992](http://adsabs.harvard.edu/abs/1992ApJ...398..146G) applied Bayesian techniques to phase-binned models. " ] }, { "cell_type": "markdown", "id": "d743f3c8", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "More recently, efforts to use Gaussian processes (GPs) to model and extract a period from the light curve have been developed ([Wang et al. 2012](http://adsabs.harvard.edu/abs/2012ApJ...756...67W)). These methods have proved to be especially useful for detecting stellar rotation in Kepler light curves ([Angus et al. 2018](http://adsabs.harvard.edu/abs/2018MNRAS.474.2094A)). \n" ] }, { "cell_type": "markdown", "id": "2c38e321", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Method 5) GPs + the Quasi-Periodic Kernel\n", "\n", "Our desired goal is to get some form of probabilistic estimate on the period. This can be done using a GP plus some Bayesian inference techniques, as the references above demonstrate." ] }, { "cell_type": "markdown", "id": "6d41159f", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "(I realize that for half of you we have not yet had a chance to cover Bayesian statistics. I'm going to provide a woefully short intro/review here, but I'm otherwise writing things in a way that I hope is general enough to see the utility of the following method)." ] }, { "cell_type": "markdown", "id": "31b6826d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Here is my ridiculously short review/intro to Bayesian analysis for this problem: the posterior $P(\\theta|x)$ (i.e., the probability distribution for the model parameters) is proportional to the likelihood multiplied by the prior:\n", "\n", "$$P(\\theta|x) \\propto P(x|\\theta)P(\\theta)$$\n", "\n", "where $P(x|\\theta)$ is the likelihood, $P(\\theta)$ is the prior, $\\theta$ is a vector of all the model parameters, and $x$ is the data." ] }, { "cell_type": "markdown", "id": "e4df8074", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In many applications, including this one, the likelihood $\\mathcal L$, specifically the $\\ln \\mathcal{L}$, is essentially the familiar form of what we often call $\\chi^2$. There can be important implications for the prior but for this example we will find that the prior does not significantly the final probability densities for the model parameters (in part because we have really good data for constraining the period of this particulat eclipsing binary). Thus, we will adopt \"wide and flat\" priors, which are adopted by many, but I'll warn not always the absolute best idea for analysis like this." ] }, { "cell_type": "markdown", "id": "1bc04ff6", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We will adopt a \"standard\" gaussian likelihood (which reduces to the $\\chi^2$ when we calculate the log of the likelihood). \n", "\n", "All we need now is a model for the signal at every time, $t$, or position, $x$." ] }, { "cell_type": "markdown", "id": "4b299d24", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We will model the data as a Gaussian process (GP). In this case we know the signal is periodic (we have an EB). \n", "\n", "For periodic signals it is common to adopt a cosine kernel for the GP covariance function. As we saw in Notebook III, this particular EB does not have a purely sinusoidal signal (due to the difference in the depths of the two eclipses). " ] }, { "cell_type": "markdown", "id": "d3887293", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Instead, we will adopt the quasi-periodic kernel: \n", "\n", "$$K_{ij} = k(x_i - x_j) = \\exp \\left(-\\Gamma \\sin^2\\left[\\frac{\\pi}{P} \\left|x_i - x_j\\right|\\right]\\right)$$\n", "\n", "which is extremely useful for periodic (or quasi-periodic) data with non-sinusoidal signals." ] }, { "cell_type": "markdown", "id": "c340a73e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5a**\n", "\n", "Load the light curve from the example EB from Notebook III, and plot the phase folded light curve at the previously identified \"optimal\" period $0.735085\\,\\mathrm{d}$." ] }, { "cell_type": "code", "execution_count": null, "id": "b6d9656e", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "lc = pd.read_csv(\"example_asas_lc.dat\")\n", "\n", "phase_plot(lc['hjd'], lc['mag'], 0.735085, lc['mag_unc'], \n", " mag_plot=True)" ] }, { "cell_type": "markdown", "id": "0a36a82e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Evaluating the quasi-periodic (really any) GP kernel when the number of observations is large is computationally expensive. For this purpose we will use [`george`](https://github.com/dfm/george), which performs the necessary matrix algebra quickly. \n", "\n", "We do not have time for a true introduction to `george`, but I encourage you to [read the docs](https://george.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "id": "2fb8cb91", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "For the MCMC sampling we will use [`emcee`](https://emcee.readthedocs.io/en/stable/index.html), again there is not time for a full introduction. This software will be covered in greater detail elsewhere in the DSFP. \n", "\n", "It's advantage for our present purposes is that it is written in pure python, and can accept any user defined function for the posterior. It also integrates very nicely with `george`." ] }, { "cell_type": "markdown", "id": "c706774d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "In brief, `emcee` uses multiple MCMC chains that simultaneously explore the posterior probability. During each step within the chain, individual chains (called \"walkers\") will effectively query the other walkers in order to identify the optimal next step in the chain. \n", "\n", "Multiple chains enables \"easy\" parallelization, though we will not focus on that today as `george` is already highly optimized to use mutliple CPU cores." ] }, { "cell_type": "markdown", "id": "682a9ecb", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5b**\n", "\n", "Write a function `model` that returns the \"mean model\" for the GP. The two arguments for this function should be a tuple `theta` of length 4, where the 3rd element is the \"mean\" `b`, and the time of observation `t`." ] }, { "cell_type": "code", "execution_count": null, "id": "ab3f1653", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def model(theta, t):\n", " _, _, b, _ = theta\n", " return b" ] }, { "cell_type": "markdown", "id": "414256a7", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5c**\n", "\n", "Write a function `lnlike` to calculate the log likelihood for the data given the model parameters `theta`. `theta` should include the log of the period, the log of the amplitude of the signal, the GP mean value `b`, and the log of $\\Gamma$.\n", "\n", "*Hint* - execute cell below." ] }, { "cell_type": "markdown", "id": "884f6545", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "$\\log$ in all instances here and below refers to the natural logarithm, or $\\log$ base $\\mathcal{e}$." ] }, { "cell_type": "code", "execution_count": null, "id": "fb6416c2", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def lnlike(theta, t, y, yerr):\n", " lnper, lna, b, lngamma = theta\n", " gp = george.GP(np.exp(lna) * \n", " kernels.ExpSine2Kernel(np.exp(lngamma), lnper))\n", " gp.compute(t, yerr)\n", " return gp.lnlikelihood(y - model(theta, t), quiet=True)\n" ] }, { "cell_type": "markdown", "id": "736ba33b", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5d**\n", "\n", "Write a function `lnprior` to calculate the log of the prior on `theta`. Use a wide and flat prior on every parameter. For numerical reasons, the function should return `-np.inf` if the prior probability is equal to zero.\n", "\n", "*Hint* - execute the cell below" ] }, { "cell_type": "code", "execution_count": null, "id": "559021db", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def lnprior(theta):\n", " lnper, lna, b, lngamma = theta\n", " if (-20 < lna < 20 and \n", " -20 < b < 20 and \n", " -20 < lngamma < 20 and\n", " -10 < lnper < np.log(10)):\n", " return 0.0\n", " return -np.inf" ] }, { "cell_type": "markdown", "id": "a5b53b67", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5e**\n", "\n", "Write a function `lnprob` to calculate the log of the product of the likelihood with the prior. " ] }, { "cell_type": "code", "execution_count": null, "id": "c6552b1a", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def lnprob(p, x, y, yerr):\n", " lp = lnprior(p)\n", " return lp + lnlike(p, x, y, yerr) if np.isfinite(lp) else -np.inf" ] }, { "cell_type": "markdown", "id": "b18d23eb", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We need a starting position for our MCMC chains/walkers. The use of a quasi-periodic kernel means the posterior is highly non-linear, so we cannot expect to start the walkers anywhere and still achieve reasonable results. \n", "\n", "In this case we will use a little common sense, plus a little computational \"brute force\". " ] }, { "cell_type": "markdown", "id": "737c8727", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5f**\n", "\n", "Run `LombScarge` on the data and determine the top three peaks in the periodogram. Set `nterms` = 2, and the maximum frequency to 5 (this is arbitrary but sufficient in this case, since the period is > 0.2 d).\n", "\n", "*Hint* - you may need to search more than the top 3 periodogram values to find the 3 peaks." ] }, { "cell_type": "markdown", "id": "bdfb0c93", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In [Angus et al. 2018](http://adsabs.harvard.edu/abs/2018MNRAS.474.2094A) a far more sophisticated approach is used for initializing the walkers using the autocorrelation function and adjusting the prior on $P$ - if you want to use GPs to infer periods for real observations I cannot recommend that source enough." ] }, { "cell_type": "code", "execution_count": null, "id": "1df7f953", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "frequency, power = LombScargle(lc['hjd'], lc['mag'], lc['mag_unc'], nterms=2).autopower(maximum_frequency=5)\n", "\n", "print('Top LS period is {}'.format(1/frequency[np.argmax(power)]))\n", "print(1/frequency[np.argsort(power)[::-1][0:5]])" ] }, { "cell_type": "markdown", "id": "9d831cca", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5g**\n", "\n", "Initialize one third of your 150 walkers around each of the periods identified in the previous problem. \n", "\n", "Run the MCMC for 500 steps following this initialization." ] }, { "cell_type": "code", "execution_count": null, "id": "71aec449", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "initial1 = np.array([np.log(0.735), 1, 10, 1])\n", "ndim = len(initial1)\n", "nwalkers = 50\n", "p1 = [np.array(initial1) + 1e-4 * np.random.randn(ndim)\n", " for i in range(nwalkers)]\n", "\n", "initial2 = np.array([np.log(0.367), 1, 10, 1])\n", "ndim = len(initial2)\n", "nwalkers = 50\n", "p2 = [np.array(initial2) + 1e-4 * np.random.randn(ndim)\n", " for i in range(nwalkers)]\n", "\n", "initial3 = np.array([np.log(0.211), 1, 10, 1])\n", "ndim = len(initial3)\n", "nwalkers = 50\n", "p3 = [np.array(initial3) + 1e-4 * np.random.randn(ndim)\n", " for i in range(nwalkers)]\n", "p0 = p1+p2+p3\n", "\n", "nwalkers = len(p0)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "30c18b9e", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, \n", " args=(lc['hjd'],lc['mag'],lc['mag_unc']))\n", "for sample in sampler.sample(p0, iterations=500, progress=True):\n", " continue" ] }, { "cell_type": "markdown", "id": "008f25b2", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5h**\n", "\n", "Plot the chains using the `plotChains()` helper function from the end of this notebook." ] }, { "cell_type": "code", "execution_count": null, "id": "a2151d8d", "metadata": { "scrolled": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "paramsNames = ['ln(P)', 'ln(a)', 'b', '$ln(\\gamma)$']\n", "nburn = 350\n", "plotChains(sampler, nburn, paramsNames)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "d0cfb84a", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5i** \n", "\n", "Plot $\\ln P$ vs. log posterior. " ] }, { "cell_type": "code", "execution_count": null, "id": "356935fb", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "chain_lnp_end = sampler.chain[:,-1,0]\n", "chain_lnprob_end = sampler.lnprobability[:,-1]\n", "fig, ax = plt.subplots()\n", "ax.scatter(chain_lnp_end, chain_lnprob_end, alpha=0.1)\n", "ax.set_xlabel('ln(P)')\n", "ax.set_ylabel('ln(Probability)')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "f8cbdd9a", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5j**\n", "\n", "Now we will start a new set of MCMC chains all initialized around a \"random ball\" centered on the maximum a posteriori value from the previous set of simulations.$^\\dagger$\n", "\n", "Run a new MCMC with 150 walkers for 500 steps. \n", "\n", "Plot the chains. Have they converged?" ] }, { "cell_type": "markdown", "id": "8de2554b", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "$^\\dagger$It is important to remember there are no rules about where an MCMC chain is initialized. We can do whatever we want. I also caution to be mindful of this in future research endeavors, because there is also no guarantee that a finite MCMC chain has identified a global maximum for the posterior. " ] }, { "cell_type": "code", "execution_count": null, "id": "98ad11bb", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "p = p0[np.argmax(chain_lnprob_end)]\n", "sampler.reset()" ] }, { "cell_type": "code", "execution_count": null, "id": "998204a8", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "p0 = [p + 1e-8 * np.random.randn(ndim) for i in range(nwalkers)]\n", "sampler.reset() # do not continue the existing chains\n", "for sample in sampler.sample(p0, iterations=500, progress=True):\n", " continue" ] }, { "cell_type": "code", "execution_count": null, "id": "cc6725fe", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "paramsNames = ['ln(P)', 'ln(a)', 'b', '$ln(\\gamma)$']\n", "nburn = 250\n", "plotChains(sampler, nburn, paramsNames)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "9231cf3a", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Problem 5k**\n", "\n", "Make a corner plot of the samples. What is the marginalized estimate$^\\dagger$ for the period of this source? \n", "\n", "How does this estimate compare to LS?" ] }, { "cell_type": "markdown", "id": "fc44f816", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Given that we ultimately used wide and flat priors, which effectively played no role in the analysis of this problem, the discerning viewer might ask \"Why did we bother to use Bayesian analysis at all?\"\n", "\n", "This problem answers that question – the data cannot be \"marginalized\" (i.e., we ignore everything but the final distribution on the period $P$) in any context except a Bayesian one. Ultimately, marginalization requires an integral where we \"integrate out\" our ignorance of the model parameters that we do not care about. These sorts of integrals require a Bayesian view of probability." ] }, { "cell_type": "code", "execution_count": null, "id": "fbda15e3", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "samples = sampler.chain[:, nburn:, :].reshape((-1, ndim))\n", "plot_samples = samples.copy()\n", "plot_samples[:,0] = np.exp(samples[:,0])\n", "fig = corner.corner(plot_samples, \n", " labels=paramsNames, \n", " quantiles=[0.16,0.50,0.84])" ] }, { "cell_type": "code", "execution_count": null, "id": "bc3fa730", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "p16, p50, p84 = np.percentile(samples[:,0], [16,50,84])\n", "\n", "print('ln(P) = {:.6f} +{:.6f} -{:.6f}'.format(p50, p84-p50, p50-p16))\n", "\n", "print('GP Period = {:.6f}'.format(np.exp(p50)))" ] }, { "cell_type": "markdown", "id": "8f253c0c", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The cell below shows marginalized samples overplotted on the actual data. How well does the model perform?" ] }, { "cell_type": "code", "execution_count": null, "id": "f0b7d2f7", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(12,6))\n", "ax.errorbar(lc['hjd'], lc['mag'], lc['mag_unc'], fmt='o')\n", "ax.set_xlabel('HJD (d)')\n", "ax.set_ylabel('mag')\n", "\n", "hjd_grid = np.linspace(4790, 4850,5000)\n", "\n", "for s in samples[np.random.randint(len(samples), size=5)]:\n", " # Set up the GP for this sample.\n", " lnper, lna, b, lngamma = s\n", " gp = george.GP(np.exp(lna) * \n", " kernels.ExpSine2Kernel(np.exp(lngamma), lnper))\n", " gp.compute(lc['hjd'], lc['mag_unc'])\n", " # Compute the prediction conditioned on the observations and plot it.\n", " m = gp.sample_conditional(lc['mag'] - model(s, lc['hjd']), hjd_grid) + model(s, hjd_grid)\n", " \n", " ax.plot(hjd_grid, m, color=\"0.2\", alpha=0.3)\n", "ax.set_xlim(4803, 4832)\n", "ax.set_ylim(11.35, 10.8)\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "af9a9d67", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "Now you have the tools to fit a GP to a light curve and get an estimate of the best fit period (and to get an estimate of the uncertainty on that period to boot!). \n", "\n", "As previously noted, you should be a bit worried about \"burn in\" and how the walkers were initialized throughout. If you plan to use GPs to search for periods in your own work, I highly recommend you read [Angus et al. 2018](http://adsabs.harvard.edu/abs/2018MNRAS.474.2094A) on the GP periodogram. Angus et al. provide far more intelligent methods for initializing the MCMC than what is presented here." ] }, { "cell_type": "markdown", "id": "57ae873f", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Helper Functions\n", "\n", "We developed useful helper functions as part of [Lecture III](https://github.com/LSSTC-DSFP/LSSTC-DSFP-Sessions/tree/main/Session13/Day3) from this session. \n", "\n", "These functions generate periodic data, and phase fold light curves on a specified period. These functions will once again prove useful, so we include them here in order to simulate data above. \n", "\n", "We also add a helper function that can visualize the invidual MCMC chains that are produced by the [`emcee`](https://emcee.readthedocs.io/en/stable/) MCMC sampler." ] }, { "cell_type": "markdown", "id": "252dc9a0", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "**Helper 1**\n", "\n", "Create a function, `gen_periodic_data`, that creates simulated data (including noise) over a grid of user supplied positions:\n", "\n", "$$ y = A\\,cos\\left(\\frac{x}{P} - \\phi\\right) + \\sigma_y$$\n", "\n", "where $A, P, \\phi$ are inputs to the function. `gen_periodic_data` should include Gaussian noise, $\\sigma_y$, for each output $y_i$." ] }, { "cell_type": "code", "execution_count": null, "id": "3f689592", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def gen_periodic_data(x, period=1, amplitude=1, phase=0, noise=0):\n", " '''Generate periodic data given the function inputs\n", " \n", " y = A*cos(x/p - phase) + noise\n", " \n", " Parameters\n", " ----------\n", " x : array-like\n", " input values to evaluate the array\n", " \n", " period : float (default=1)\n", " period of the periodic signal\n", " \n", " amplitude : float (default=1)\n", " amplitude of the periodic signal\n", " \n", " phase : float (default=0)\n", " phase offset of the periodic signal\n", " \n", " noise : float (default=0)\n", " variance of the noise term added to the periodic signal\n", " \n", " Returns\n", " -------\n", " y : array-like\n", " Periodic signal evaluated at all points x\n", " '''\n", " \n", " y = amplitude*np.sin(2*np.pi*x/(period) - phase) + np.random.normal(0, np.sqrt(noise), size=len(x))\n", " return y" ] }, { "cell_type": "markdown", "id": "18086ce3", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "**Helper 2**\n", "\n", "Create a function, `phase_plot`, that takes x, y, and $P$ as inputs to create a phase-folded light curve (i.e., plot the data at their respective phase values given the period $P$).\n", "\n", "Include an optional argument, `y_unc`, to include uncertainties on the `y` values, when available." ] }, { "cell_type": "code", "execution_count": null, "id": "378dbe84", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def phase_plot(x, y, period, y_unc = 0.0, mag_plot=False):\n", " '''Create phase-folded plot of input data x, y\n", " \n", " Parameters\n", " ----------\n", " x : array-like\n", " data values along abscissa\n", "\n", " y : array-like\n", " data values along ordinate\n", "\n", " period : float\n", " period to fold the data\n", " \n", " y_unc : array-like\n", " uncertainty of the \n", " ''' \n", " phases = (x/period) % 1\n", " if type(y_unc) == float:\n", " y_unc = np.zeros_like(x)\n", " \n", " plot_order = np.argsort(phases)\n", " fig, ax = plt.subplots()\n", " ax.errorbar(phases[plot_order], y[plot_order], y_unc[plot_order],\n", " fmt='o', mec=\"0.2\", mew=0.1)\n", " ax.set_xlabel(\"phase\")\n", " ax.set_ylabel(\"signal\")\n", " if mag_plot:\n", " ax.set_ylim(ax.get_ylim()[::-1])\n", " fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "2f0f7519", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "**Helper 3**\n", "\n", "Write a function `plot_chains` to show the individual chains from the multi-chain MCMC sampler `emcee`." ] }, { "cell_type": "code", "execution_count": null, "id": "4e30f140", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "#define function to plot walker chains \n", "def plotChains(sampler, nburn, paramsNames, nplot=None):\n", " '''Plot individual chains from the emcee MCMC sampler\n", " \n", " Parameters\n", " ----------\n", " sampler : emcee EnsembleSampler object\n", " emcee affine-invariant multi-chain MCMC sampler\n", " \n", " nburn : int\n", " number of \"burn-in\" steps for the MCMC chains\n", " \n", " paramsNames : list\n", " names of the parameters to be shown\n", " \n", " nplot : int (default=None)\n", " number of chains to show in the visualization.\n", " In instances where the number of chains is \n", " very large (>> 100), then it can be helpful to \n", " downsample to provide more clarity.\n", " \n", " Returns\n", " -------\n", " ax : maptlotlib axes object\n", " multi panel plot showing the evoltion of \n", " each chain for the parameters in the model\n", " \n", " '''\n", " \n", " Nparams = len(paramsNames)\n", " nwalkers = sampler.get_chain().shape[1]\n", " \n", " fig, ax = plt.subplots(Nparams+1,1, figsize = (8,2*(Nparams+1)), sharex = True)\n", " fig.subplots_adjust(hspace = 0)\n", " ax[0].set_title('Chains')\n", " xplot = np.arange(sampler.get_chain().shape[0])\n", "\n", " if nplot is None:\n", " nplot=nwalkers\n", " selected_walkers = np.random.choice(range(nwalkers), nplot, replace=False)\n", " for i,p in enumerate(paramsNames):\n", " for w in selected_walkers:\n", " burn = ax[i].plot(xplot[:nburn], sampler.get_chain()[:nburn,w,i], \n", " alpha = 0.4, lw = 0.7, zorder = 1)\n", " ax[i].plot(xplot[nburn:], sampler.get_chain(discard=nburn)[:,w,i], \n", " color=burn[0].get_color(), alpha = 0.8, lw = 0.7, zorder = 1)\n", " \n", " ax[i].set_ylabel(p)\n", " if i==Nparams-1:\n", " ax[i+1].plot(xplot[:nburn], sampler.get_log_prob()[:nburn,w], \n", " color=burn[0].get_color(), alpha = 0.4, lw = 0.7, zorder = 1)\n", " ax[i+1].plot(xplot[nburn:], sampler.get_log_prob(discard=nburn)[:,w], \n", " color=burn[0].get_color(), alpha = 0.8, lw = 0.7, zorder = 1)\n", " ax[i+1].set_ylabel('ln P')\n", " \n", " return ax" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "livereveal": { "height": 768, "scroll": true, "start_slideshow_at": "selected", "theme": "solarized", "width": 1024 } }, "nbformat": 4, "nbformat_minor": 5 }

mit

K3D-tools/K3D-jupyter

examples/snapshots_in_js.ipynb

1

4432

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import k3d\n", "import base64" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Produce a plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "iteration = 4\n", "size = 3**iteration\n", "\n", "voxels = np.ones((size, size, size));\n", "\n", "def iterate(length, x, y, z):\n", "\n", " nl = length // 3 \n", "\n", " if nl < 1:\n", " return\n", "\n", " margin = (nl-1) // 2\n", "\n", " voxels[z-margin:z+margin+1, y-margin:y+margin+1, :] = 0\n", " voxels[z-margin:z+margin+1, :, x-margin:x+margin+1] = 0\n", " voxels[:, y-margin:y+margin+1, x-margin:x+margin+1] = 0 \n", "\n", " for ix,iy,iz in np.ndindex((3,3,3)):\n", " if (1 if ix !=1 else 0) + (1 if iy != 1 else 0) + (1 if iz != 1 else 0) !=2:\n", " iterate(nl, x + (ix-1) * nl, y + (iy-1) * nl , z + (iz-1) * nl)\n", "\n", "iterate(size, size//2, size//2, size//2)\n", "\n", "plot = k3d.plot()\n", "plot += k3d.voxels(voxels.astype(np.uint8), compression_level=9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create and save to *.k3d file" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "template = \"\"\"\n", "<!doctype html>\n", "<html>\n", "<head>\n", " <meta charset=\"utf-8\">\n", " <title>K3D in js</title>\n", " <style>\n", " #canvasTarget {\n", " width: 100%;\n", " height: 100%;\n", " position: absolute;\n", " }\n", " </style>\n", " <script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"></script>\n", " <script src=\"https://unpkg.com/k3d/dist/standalone.js\"></script>\n", "</head>\n", "<body>\n", "<div id=\"canvasTarget\"></div>\n", "\n", "<script>\n", " var K3DInstance;\n", " var data = '[DATA]';\n", "\n", " // base64 is only for embeding data in html normally recommeded way is to load data via ajax\n", " function _base64ToArrayBuffer(base64) {\n", " var binary_string = window.atob(base64);\n", " var len = binary_string.length;\n", " var bytes = new Uint8Array(len);\n", " for (var i = 0; i < len; i++) {\n", " bytes[i] = binary_string.charCodeAt(i);\n", " }\n", " return bytes;\n", " }\n", "\n", " require(['k3d'], function (lib) {\n", " try {\n", " K3DInstance = new lib.CreateK3DAndLoadBinarySnapshot(\n", " _base64ToArrayBuffer(data),\n", " document.getElementById('canvasTarget'),\n", " );\n", "\n", " K3DInstance.then(function(K3DInstance) {\n", " console.log(K3DInstance);\n", " });\n", " } catch (e) {\n", " console.log(e);\n", " return;\n", " }\n", " });\n", "</script>\n", "</body>\n", "</html>\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = plot.get_binary_snapshot()\n", "\n", "with open(\"k3d_in_javascript.html\", \"w\") as f: \n", " # base64 is only for embeding data in html normally recommeded way is to load data via ajax\n", " f.write(template.replace(\"[DATA]\", base64.b64encode(data).decode(\"utf-8\")))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

probml/pyprobml

deprecated/vae_compare_results.ipynb

1

2487626

mit

JuliaX/IJuliaNotebooks

julia-0.2/Julia tutorial.ipynb

1

34334

{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A Julia tutorial\n", "\n", "If you can read this and see the IJulia logo in the top left corner of the browser window, congratulations! You are ready to start using Julia.\n", "\n", "This IJulia notebook is fully executable. The Julia code is grouped into units called cells. To run all the commands in a cell, click inside and press Shift+Enter." ] }, { "cell_type": "code", "collapsed": false, "input": [ "1+1" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try changing the code to see what it will do!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Printing things\n", "\n", "The basic way to print things is to use the `println()` (print line) function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"Hello world\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Hello world\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use the `print()` function, which does the same thing, but leaves out the newline at the end." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"Hello\")\n", "print(\"world\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Helloworld" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Don't forget the enclosing parentheses, or you will get an error:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 1+2" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "syntax: extra token \"1\" after end of expression\nat In[16]:1", "output_type": "pyerr", "traceback": [ "syntax: extra token \"1\" after end of expression\nat In[16]:1" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "print(1+2)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can `print` multiple things together. Separate each piece with commas:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"1 + 1 = \", 1+1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 + 1 = 2" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** Julia does not automatically insert spaces when printing multiple items together:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"For\",\"sooth!\",\"There is no \",1,\" I trust more\", \".\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Forsooth!" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "There is no 1 I trust more.\n" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Printing in interactive mode\n", "\n", "In a [REPL](http://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop) such as this IJulia notebook, Julia will automatically print the result of the last statement. This will **not** happen if you run julia [in a non-interactive mode](http://docs.julialang.org/en/latest/manual/getting-started/)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "1+1\n", "2+2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "4" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, a `print` or `println` function will always produce printable output." ] }, { "cell_type": "code", "collapsed": false, "input": [ "1\n", "2\n", "print(\"3\")\n", "4\n", "5" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "5" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Escaping characters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some special characters are produced by **escape sequences**. An escape sequence starts with a backslash `\\` and ends with a single character that follows it.\n", "\n", "Here are two very common examples:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"1\\n2\") #Newline\n", "println(\"1\\t2\") #Tab" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "2\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1\u00072\n", "1\t2\n" ] } ], "prompt_number": 53 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To print the baskslash character itself, it has to be escaped with another backslash." ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"1\\\\2\") #backslash" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\\2\n" ] } ], "prompt_number": 54 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dollar sign has special meaning in Julia and must be escaped to print correctly." ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"\\$\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "$\n" ] } ], "prompt_number": 74 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (Optional) Unicode characters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Julia supports [Unicode characters](http://www.unicode.org/standard/standard.html), which are used to encode non-English texts and special symbols." ] }, { "cell_type": "code", "collapsed": false, "input": [ "println('\u2211')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\u2211\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Julia, a single character is quoted with single quotes ('), whereas strings are enclosed by double quotes (\").\n", "\n", "The following will produce an error in Julia:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "println('Hello')" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "syntax: invalid character literal\nat In[8]:1", "output_type": "pyerr", "traceback": [ "syntax: invalid character literal\nat In[8]:1" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the first article of the [UN Declaration of Human Rights](http://www.un.org/en/documents/udhr/index.shtml#a1) [in Chinese](http://www.un.org/zh/documents/udhr/index.shtml#a1):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"\u4eba\u4eba\u751f\u800c\u81ea\u7531\uff0c\u5728\u5c0a\u4e25\u548c\u6743\u5229\u4e0a\u4e00\u5f8b\u5e73\u7b49\u3002\u4ed6\u4eec\u8d4b\u6709\u7406\u6027\u548c\u826f\u5fc3\uff0c\u5e76\u5e94\u4ee5\u5144\u5f1f\u5173\u7cfb\u7684\u7cbe\u795e\u76f8\u5bf9\u5f85\u3002\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\u4eba\u4eba\u751f\u800c\u81ea\u7531\uff0c\u5728\u5c0a\u4e25\u548c\u6743\u5229\u4e0a\u4e00\u5f8b\u5e73\u7b49\u3002\u4ed6\u4eec\u8d4b\u6709\u7406\u6027\u548c\u826f\u5fc3\uff0c\u5e76\u5e94\u4ee5\u5144\u5f1f\u5173\u7cfb\u7684\u7cbe\u795e\u76f8\u5bf9\u5f85\u3002" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To specify a Unicode character by its [code point](http://www.unicode.org/charts/charindex.html):\n", "\n", "a) use `\\U` followed by its hexadecimal code point." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print('\\U263A')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\u263a" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "or, b) use the `char()` function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "char(0xa22d)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "'\ua22d'" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "Julia provides the `is_valid_char()` function to check if a valid character was specified." ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "println(is_valid_char(0x110000))\n", "println(is_valid_char(0x1100))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Starting kernel event loops.\n", "false" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "true\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes the character won't display correctly. This is usually a problem with the font you are using.\n", "\n", "You can try your luck with the Unicode character for a slice of pizza:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "println('\\U1f355')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\ud83c\udf55\n" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#2. Comments\n", "\n", "Julia ignores the entire part of a line after a `#` symbol. <small><small><small>(What is this symbol [really called](http://en.wiktionary.org/wiki/octothorpe) anyway?)</small></small></small>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"The jail still held the heart I lost and the finger I stole\") #from you" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The jail still held the heart I lost and the finger I stole" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<small><small><small>(This example sentence was taken from [here](http://www.essayscam.org/Forum/17/ellipsis-4109/).)</small></small></small>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comments are useful for leaving notes about code that is tricky or potentially confusing, and for temporarily disabling part of Julia programs." ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"Roses are red\")\n", "println(\"Violets are blue\")\n", "#println(\"I don't like bread\")\n", "println(\"One plus one is \", 1+1) #Compute the answer" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Roses are red\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Violets are blue\n", "One plus one is 2\n" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Basic math\n", "\n", "Here's the very basics." ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"+1 = \", +1)\n", "println(\"-1 = \", -1)\n", "println(\"1 + 1 = \", 1+1)\n", "println(\"1 - 1 = \", 1-1)\n", "println(\"2 * 3 = \", 2*3)\n", "println(\"2 / 3 = \", 2/3) #The answer is not a whole number!\n", "println(\"3 \\\\ 2 = \", 3\\2) #Same as above\n", "println(\"2 ^ 3 = \", 2^3) #This is exponentiation" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "+1 = 1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "-1 = -1\n", "1 + 1 = 2\n", "1 - 1 = 0\n", "2 * 3 = 6\n", "2 / 3 = 0.6666666666666666\n", "3 \\ 2 = 0.6666666666666666\n", "2 ^ 3 = 8\n" ] } ], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remainders are slightly trickier, particularly when it comes to negative numbers." ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"5 % 2 = \", 5%2)\n", "println(\"rem(5,2) = \", rem(5,2)) #Same as above\n", "println(\"mod(5,2) = \", mod(5,2))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5 % 2 = 1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "rem(5,2) = 1\n", "mod(5,2) = 1\n" ] } ], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"5 % 2.5 = \", 5%2.5)\n", "println(\"rem(5,2.5) = \", rem(5,2.5)) #Same as above\n", "println(\"mod(5,2.5) = \", mod(5,2.5))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5 % 2.5 = 0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ".0\n", "rem(5,2.5) = 0.0\n", "mod(5,2.5) = 0.0\n" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"-5 % 2 = \", -5%2)\n", "println(\"rem(-5,2) = \", rem(-5,2)) #Same as above\n", "println(\"mod(-5,2) = \", mod(-5,2))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "-5 % 2 = -1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "rem(-5,2) = -1\n", "mod(-5,2) = 1\n" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For convenience, you can use underscores (`_`) to separate groups of digits." ] }, { "cell_type": "code", "collapsed": false, "input": [ "525_600 #Five hundred twenty five thousand six hundred " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "525600" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "1_00_00_000 #One crore = 10^7" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "10000000" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TODO Floating-point" ] }, { "cell_type": "code", "collapsed": false, "input": [ "println(\"1 / 0 = \", 1/0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 / 0 = Inf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 65 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Complex numbers\n", "\n", "The imaginary unit is called `im` in Julia." ] }, { "cell_type": "code", "collapsed": false, "input": [ "z=3+4im\n", "z' #conjugate" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "3 - 4im" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "z*z'" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "25 + 0im" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Variables\n", "\n", "Variables allow you to store and reuse the results from earlier calculations.\n", "\n", "There are very few limits to what names you can use for your variables. There is no implicit type of any variable.\n", "\n", "## Naming variables and Assigning them values\n", "\n", "Use a single equal `=` sign to assign variables." ] }, { "cell_type": "code", "collapsed": false, "input": [ "i=2.0" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "2.0" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "supercalifragilisticexpialidocious = pi\n", "supercalifragilisticexpialidocious/2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "1.5707963267948966" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "e = 1+1im" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "1 + 1im" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "z=3+4im\n", "\u03be=1/z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "0.09090909090909091" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "\u30a2\u30eb\u30b3\u30fc\u30eb = 0.1337\n", "\u30a2\u30eb\u30b3\u30fc\u30eb^2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "0.017875690000000003" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can even use names of built-in variables and functions, if you so choose. (This is usually not a good idea since it is very easy to write confusing code.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "im=2\n", "3+4im" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "11" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You *cannot*, however, use the names of Julia keywords for your variable names." ] }, { "cell_type": "code", "collapsed": false, "input": [ "end=0.5im" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "syntax: unexpected end\nat In[23]:1", "output_type": "pyerr", "traceback": [ "syntax: unexpected end\nat In[23]:1" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing variables\n", "\n", "Use the double equals `==` operator to compare variables or values." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x=1\n", "x==2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "false" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To test inequality, use `!=`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x!=x+1" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "true" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "Printing variables" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "There are a few ways to print variables in Julia:" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "g=9.81" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Multiple dispatch" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "f(x::Number, y::Number) = (x,y)\n", "f(1,2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "(1,2)" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "f(x::String, y::String) = string(x,y)\n", "f(\"foo\",\"bar\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "\"foobar\"" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "f(x,y) = f(string(x),string(y))\n", "f(\"I like \",1\u03c0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "\"I like 3.141592653589793\"" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Introspection" ] }, { "cell_type": "code", "collapsed": false, "input": [ "methods(f)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "# 3 methods for generic function \"f\":\n", "f(x::Number,y::Number) at In[1]:1\n", "f(x::String,y::String) at In[2]:1\n", "f(x,y) at In[4]:1" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Declaring types" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type LP\n", " c # Types are optional\n", " A::Matrix{Float64}\n", " b::Vector{Float64}\n", "end\n", "randlp(n,m)=LP(rand(n),rand(n,m),rand(m))\n", "mylp = randlp(10,5)\n", "println(mylp.c)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ ".23562710945428877\n", ".23951372465002896\n", ".7525737066697606\n", ".010800546955753054\n", ".5230908626626509\n", ".2716179537331287\n", ".7217869017869374\n", ".8982447615369507\n", ".5718451267150129\n", ".5881888772951562\n", "\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parametric types" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type LP2{T}\n", " c::Vector{T}\n", " A::Matrix{T}\n", " b::Vector{T}\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "invalid redefinition of constant LP2\nat In[9]:5", "output_type": "pyerr", "traceback": [ "invalid redefinition of constant LP2\nat In[9]:5" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "lp = LP2{Float64}(mylp.c,mylp.A,mylp.b) # dbl precision\n", "lp = LP2{Rational}(mylp.c,mylp.A,mylp.b) # exact" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "no method LP2{Rational{T<:Integer}}(Array{Float64,1},Array{Float64,2},Array{Float64,1})\nat In[10]:2", "output_type": "pyerr", "traceback": [ "no method LP2{Rational{T<:Integer}}(Array{Float64,1},Array{Float64,2},Array{Float64,1})\nat In[10]:2" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lists" ] }, { "cell_type": "code", "collapsed": false, "input": [ "L = {} # empty Vector{Any}\n", "push!(L,3.0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "1-element Array{Any,1}:\n", " 3.0" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "L2 = Float64[] # Vector{Float64}\n", "push!(L2,:Hello) # Error" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "no method convert(Type{Float64},Symbol)\nat In[12]:2", "output_type": "pyerr", "traceback": [ "no method convert(Type{Float64},Symbol)\nat In[12]:2", " in push! at array.jl:659" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "push!(L2,10.7) # Okay\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "1-element Array{Float64,1}:\n", " 10.7" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "L3 = Float64[2,3,4]# conversion" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "3-element Array{Float64,1}:\n", " 2.0\n", " 3.0\n", " 4.0" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dictionaries" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d1 = Dict() # Empty untyped\n", "d1[\u201ckey\u201d] = 10 # Okay" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "\u201ckey\u201d not defined\nat In[15]:2", "output_type": "pyerr", "traceback": [ "\u201ckey\u201d not defined\nat In[15]:2" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "d2 = (Symbol=>Int)[] # Empty typed\n", "d2[:x] = 8 # Okay\n", "d2[10] = \"value\" # Error" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "LoadError", "evalue": "no method convert(Type{Symbol},Int64)\nat In[17]:3", "output_type": "pyerr", "traceback": [ "no method convert(Type{Symbol},Int64)\nat In[17]:3", " in setindex! at dict.jl:412" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "d3 = [:Cat=>13, :Dog=>14]\n", "d3[:Dog]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "14" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise!\n", "\n", "Write a function that takes a vector and normalizes it in place by its L2 norm. Use for loops." ] }, { "cell_type": "code", "collapsed": false, "input": [ "f(v) = v/norm(v)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "f (generic function with 4 methods)" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

saifrahmed/bokeh

examples/plotting/notebook/animated.ipynb

42

2950

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import time\n", "from numpy import pi, cos, sin, linspace, roll, zeros_like\n", "from bokeh.plotting import cursession, figure, show, output_notebook" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = 50 + 1\n", "r_base = 8\n", "theta = linspace(0, 2*pi, N)\n", "r_x = linspace(0, 6*pi, N-1)\n", "rmin = r_base - cos(r_x) - 1\n", "rmax = r_base + sin(r_x) + 1\n", "colors = [\n", " \"FFFFCC\", \"#C7E9B4\", \"#7FCDBB\", \"#41B6C4\", \"#2C7FB8\", \n", " \"#253494\", \"#2C7FB8\", \"#41B6C4\", \"#7FCDBB\", \"#C7E9B4\"\n", "] * 5\n", "cx = cy = zeros_like(rmin)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*To run these examples you must execute the command `python bokeh-server` in the top-level Bokeh source directory first.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output_notebook(url=\"default\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = figure(x_range=[-11, 11], y_range=[-11, 11])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p.annular_wedge(cx, cy, rmin, rmax, theta[:-1], theta[1:],\n", " fill_color = colors, line_color=\"black\", name=\"aw\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "renderer = p.select(dict(name=\"aw\"))[0]\n", "ds = renderer.data_source\n", "show(p)\n", "while True:\n", " rmin = ds.data[\"inner_radius\"]\n", " rmin = roll(rmin, 1)\n", " ds.data[\"inner_radius\"] = rmin\n", " \n", " rmax = ds.data[\"outer_radius\"]\n", " rmax = roll(rmax, -1)\n", " ds.data[\"outer_radius\"] = rmax\n", " \n", " cursession().store_objects(ds)\n", " time.sleep(.10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

zoltanctoth/bigdata-training

python/Python basics.ipynb

1

31241

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python tutorial\n", "\n", "Launch this notebook with:\n", "1. Open terminal\n", "2. type ```cd training/python```\n", "3. type ```ipython notebook```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## basics" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 + 1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "528" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "12 * 44" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-3-ffc4546b2fc9>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-3-ffc4546b2fc9>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m Hello Data Skills!\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "Hello Data Skills!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Hello Data Skills!'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'Hello Data Skills!'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello Data Skills!\n" ] } ], "source": [ "print 'Hello Data Skills!'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello Data Skills!\n", "Hello Data Skills!\n", "Hello Data Skills!\n" ] } ], "source": [ "print \"Hello Data Skills!\"\n", "print 'Hello Data Skills!'\n", "print \"\"\"Hello Data Skills!\"\"\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## types" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(1 + 1)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(\"hello\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 == 2" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 == 1" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bool" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(1 == 1)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "True" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "False" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bool" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-20-756092b166ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m12\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "12 + 12 + \"hello\"" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str(1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(str(1))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "float" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(3.14)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3 / 2" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.5" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3 / 2.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## variables" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = 1" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print x" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y = x + 2" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print y" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = \"hello\"" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "w = \"python\"" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hellopython\n" ] } ], "source": [ "print z + w" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello python\n" ] } ], "source": [ "print z + \" \" + w" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = x + 1" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print x" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## control statements" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "i = 10\n", "print i % 2" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "i = 9\n", "print i % 2" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "even\n" ] } ], "source": [ "if i % 2 == 0:\n", " print \"even\"" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "odd\n" ] } ], "source": [ "if i % 2 == 0:\n", " print \"even\"\n", "else:\n", " print \"odd\"" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "i = 10" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# execute \"if\" the code above with the new value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Excercise: Write a statement which say \"it's small\" for v < 10 and it says \"it's big\" for v >= 10" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v = 1" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "it's small\n" ] } ], "source": [ "if v < 10:\n", " print \"it's small\"\n", "else:\n", " print \"it's big\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def printeven(num):\n", " if num % 2 == 0:\n", " print \"even\"\n", " else:\n", " print \"false\"\n" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "even\n" ] } ], "source": [ "printeven(10)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "false\n" ] } ], "source": [ "printeven(9)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "even\n" ] } ], "source": [ "printeven(i)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def printhuf(usd):\n", " print \"$\" + str(usd) + \" is \" + str(272.9 * usd) + \" Ft\"" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "$10 is 2729.0 Ft\n" ] } ], "source": [ "printhuf(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: Write a function that converts hufs to dollars and prints them" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def printusd(huf):\n", " print str(huf) + \" Ft\" + \" is $\" + str(huf / 272.9)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 Ft is $3.66434591425\n" ] } ], "source": [ "printusd(1000)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def printconvert(amount, currency):\n", " if currency == \"usd\":\n", " printhuf(amount)\n", " else:\n", " printusd(amount)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "$10 is 2729.0 Ft\n" ] } ], "source": [ "printconvert(10, \"usd\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### return" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def tohuf(usd):\n", " return 272.9 * usd" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2729.0" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tohuf(10)" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "$10 is 2729.0 Ft\n" ] } ], "source": [ "x = printhuf(10)" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print x" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x is None" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": true }, "outputs": [], "source": [ "isnone = x is None" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print isnone" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y = tohuf(10)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2729.0\n" ] } ], "source": [ "print y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### exercise\n", "1. create the tousd() function\n", "2. Create a function that takes an amount and \"what\" and returns the converted value." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2729.0\n" ] } ], "source": [ "# solution\n", "def tousd(huf):\n", " return huf / 272.9\n", "\n", "def convert(amount, currency):\n", " if currency == \"usd\":\n", " return tohuf(amount)\n", " else:\n", " return tousd(amount)\n", "\n", "print convert(10,\"usd\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### lists" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l = [2,4,6]" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(l)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "6\n" ] } ], "source": [ "print l[0]\n", "print l[2]" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-102-e30cce092d00>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print l[3]" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l.append(8)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 4, 6, 8]\n" ] } ], "source": [ "print l" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l[3]" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "4\n", "6\n", "8\n" ] } ], "source": [ "for elem in l:\n", " print elem" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7, 8, 9]" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(1,10)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(1,11)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tenelements = range(1,11)" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n" ] } ], "source": [ "for e in tenelements:\n", " print e" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "272.9\n", "545.8\n", "818.7\n", "1091.6\n", "1364.5\n", "1637.4\n", "1910.3\n", "2183.2\n", "2456.1\n", "2729.0\n" ] } ], "source": [ "for e in tenelements:\n", " print tohuf(e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### exercise:\n", "1. create a loop which goes from 1 to 10 but only print the even numbers\n", "2. transform this loop so it uses an \"even\" function which returns if a number is even or not (bool)" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "4\n", "6\n", "8\n", "10\n" ] } ], "source": [ "# solution\n", "for e in range(1,11):\n", " if e % 2 == 0:\n", " print e" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def even(num):\n", " return num % 2 == 0" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "4\n", "6\n", "8\n", "10\n" ] } ], "source": [ "for e in range(1,11):\n", " if even(e):\n", " print e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### dictionaries" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [], "source": [ "u = {\n", " \"username\": \"lili\",\n", " \"password\": \"1982\"\n", "}" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'lili'" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u[\"username\"]" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1982'" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u[\"password\"]" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'username': 'lili', 'password': '1982'}\n" ] } ], "source": [ "print u" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(\"passwords.json\") as f:\n", " d = json.load(f)\n" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{u'password': u'1982', u'username': u'lili'},\n", " {u'password': u'skateordie', u'username': u'cucu'},\n", " {u'password': u'imserious', u'username': u'maxwell'}]" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(d)" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{u'password': u'imserious', u'username': u'maxwell'}" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[2]" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{u'username': u'lili', u'password': u'1982'}\n", "{u'username': u'cucu', u'password': u'skateordie'}\n", "{u'username': u'maxwell', u'password': u'imserious'}\n" ] } ], "source": [ "for u in d:\n", " print u" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lili\n", "cucu\n", "maxwell\n" ] } ], "source": [ "for u in d:\n", " print u[\"username\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise:\n", "create a function that gets a username as an argument and prints \"user found!!\" that user is in the json file or not" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Solution\n", "def printifinfile(username):\n", " with open(\"passwords.json\") as f:\n", " j = json.load(f)\n", " for record in j:\n", " if record[\"username\"] == username:\n", " print \"user found!!\"\n", " " ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "user found!!\n" ] } ], "source": [ "printifinfile(\"lili\")" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": true }, "outputs": [], "source": [ "printifinfile(\"newuser\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-2.0

JAmarel/Phys202

ODEs/ODEsEx03.ipynb

2

148385

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Ordinary Differential Equations Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "from scipy.integrate import odeint\n", "from IPython.html.widgets import interact, fixed" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Damped, driven nonlinear pendulum" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The equations of motion for a simple [pendulum](http://en.wikipedia.org/wiki/Pendulum) of mass $m$, length $l$ are:\n", "\n", "$$\n", "\\frac{d^2\\theta}{dt^2} = \\frac{-g}{\\ell}\\sin\\theta\n", "$$\n", "\n", "When a damping and periodic driving force are added the resulting system has much richer and interesting dynamics:\n", "\n", "$$\n", "\\frac{d^2\\theta}{dt^2} = \\frac{-g}{\\ell}\\sin\\theta - a \\omega - b \\sin(\\omega_0 t)\n", "$$\n", "\n", "In this equation:\n", "\n", "* $a$ governs the strength of the damping.\n", "* $b$ governs the strength of the driving force.\n", "* $\\omega_0$ is the angular frequency of the driving force.\n", "\n", "When $a=0$ and $b=0$, the energy/mass is conserved:\n", "\n", "$$E/m =g\\ell(1-\\cos(\\theta)) + \\frac{1}{2}\\ell^2\\omega^2$$" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "### Basic setup" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Here are the basic parameters we are going to use for this exercise:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [], "source": [ "g = 9.81 # m/s^2\n", "l = 0.5 # length of pendulum, in meters\n", "tmax = 50. # seconds\n", "t = np.linspace(0, tmax, int(100*tmax))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function `derivs` for usage with `scipy.integrate.odeint` that computes the derivatives for the damped, driven harmonic oscillator. The solution vector at each time will be $\\vec{y}(t) = (\\theta(t),\\omega(t))$." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "nbgrader": { "checksum": "c7256bdd25791dfa8322d3b828cec74d", "solution": true } }, "outputs": [], "source": [ "def derivs(y, t, a, b, omega0):\n", " \"\"\"Compute the derivatives of the damped, driven pendulum.\n", " \n", " Parameters\n", " ----------\n", " y : ndarray\n", " The solution vector at the current time t[i]: [theta[i],omega[i]].\n", " t : float\n", " The current time t[i].\n", " a, b, omega0: float\n", " The parameters in the differential equation.\n", " \n", " Returns\n", " -------\n", " dy : ndarray\n", " The vector of derviatives at t[i]: [dtheta[i],domega[i]].\n", " \"\"\"\n", " theta = y[0]\n", " dtheta = y[1]\n", " dw = -(g/l)*np.sin(theta) - a*dtheta - b*np.sin(omega0*t)\n", " return [dtheta,dw]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "3509b75989fc0ec30fa07c7a9331e14e", "grade": true, "grade_id": "odesex03a", "points": 2 } }, "outputs": [], "source": [ "assert np.allclose(derivs(np.array([np.pi,1.0]), 0, 1.0, 1.0, 1.0), [1.,-1.])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true, "nbgrader": { "checksum": "eb552816913899d79298c64989e872d4", "solution": true } }, "outputs": [], "source": [ "def energy(y):\n", " \"\"\"Compute the energy for the state array y.\n", " \n", " The state array y can have two forms:\n", " \n", " 1. It could be an ndim=1 array of np.array([theta,omega]) at a single time.\n", " 2. It could be an ndim=2 array where each row is the [theta,omega] at single\n", " time.\n", " \n", " Parameters\n", " ----------\n", " y : ndarray, list, tuple\n", " A solution vector\n", " \n", " Returns\n", " -------\n", " E/m : float (ndim=1) or ndarray (ndim=2)\n", " The energy per mass.\n", " \"\"\"\n", " theta = y[0]\n", " omega = y[1]\n", " if y.ndim == 1:\n", " theta = y[0]\n", " omega = y[1]\n", " EperM = g*l*(1-np.cos(theta))+.5*(l**2)*omega**2\n", " return EperM\n", " if y.ndim == 2:\n", " theta = y[:,0]\n", " omega = y[:,1]\n", " EperM = g*l*(1-np.cos(theta))+.5*(l**2)*omega**2\n", " return EperM" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "3eda6ae22611b37df76850d7cdc960d0", "grade": true, "grade_id": "odesex03b", "points": 2 } }, "outputs": [], "source": [ "assert np.allclose(energy(np.array([np.pi,0])),g)\n", "assert np.allclose(energy(np.ones((10,2))), np.ones(10)*energy(np.array([1,1])))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "### Simple pendulum" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use the above functions to integrate the simple pendulum for the case where it starts at rest pointing vertically upwards. In this case, it should remain at rest with constant energy.\n", "\n", "* Integrate the equations of motion.\n", "* Plot $E/m$ versus time.\n", "* Plot $\\theta(t)$ and $\\omega(t)$ versus time.\n", "* Tune the `atol` and `rtol` arguments of `odeint` until $E/m$, $\\theta(t)$ and $\\omega(t)$ are constant.\n", "\n", "Anytime you have a differential equation with a a conserved quantity, it is critical to make sure the numerical solutions conserve that quantity as well. This also gives you an opportunity to find other bugs in your code. The default error tolerances (`atol` and `rtol`) used by `odeint` are not sufficiently small for this problem. Start by trying `atol=1e-3`, `rtol=1e-2` and then decrease each by an order of magnitude until your solutions are stable." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "y0 = [np.pi,0]\n", "a = 0\n", "b = 0\n", "omega0 = 0\n", "soln = odeint(derivs,y0,t,args=(a,b,omega0),atol=1e-5, rtol=1e-4)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theta = soln[:,0]\n", "omega = soln[:,1]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEMCAYAAAAlGRZyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4e8e1bbfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(t,energy(soln));\n", "plt.title('Energy per Mass vs time');" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEbCAYAAADXk4MCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIJCkmjMIJKnmDAJJqrn/D5taFWgV1FqrAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4e8e30eda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(t,theta,label='$\\Theta(t)$');\n", "plt.title('Theta and Omega vs Time');\n", "plt.ylim((-np.pi,2*np.pi));\n", "plt.plot(t,omega,label='$\\omega(t)$');\n", "plt.legend();\n", "plt.xlabel('Time');\n", "plt.ylabel('Omega,Theta');" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "afb5bca3311c3e9c7ac5070b15f2435c", "grade": true, "grade_id": "odesex03c", "points": 3 } }, "outputs": [], "source": [ "assert True # leave this to grade the two plots and their tuning of atol, rtol." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Damped pendulum" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a `plot_pendulum` function that integrates the damped, driven pendulum differential equation for a particular set of parameters $[a,b,\\omega_0]$.\n", "\n", "* Use the initial conditions $\\theta(0)=-\\pi + 0.1$ and $\\omega=0$.\n", "* Decrease your `atol` and `rtol` even futher and make sure your solutions have converged.\n", "* Make a parametric plot of $[\\theta(t),\\omega(t)]$ versus time.\n", "* Use the plot limits $\\theta \\in [-2 \\pi,2 \\pi]$ and $\\theta \\in [-10,10]$\n", "* Label your axes and customize your plot to make it beautiful and effective." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true, "nbgrader": { "checksum": "82dc6206b4de351b8afc48dba9d0b915", "solution": true } }, "outputs": [], "source": [ "def plot_pendulum(a=0.0, b=0.0, omega0=0.0):\n", " \"\"\"Integrate the damped, driven pendulum and make a phase plot of the solution.\"\"\"\n", " y0 = [-np.pi + .1,0]\n", " soln = odeint(derivs,y0,t,args=(a,b,omega0),atol=1e-5, rtol=1e-4)\n", " theta = soln[:,0]\n", " omega = soln[:,1]\n", " plt.figure(figsize=(10,6))\n", " plt.plot(theta,omega)\n", " \n", " plt.title('Pendlum Motion')\n", " plt.xlabel('Theta')\n", " plt.ylabel('Omega')" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Here is an example of the output of your `plot_pendulum` function that should show a decaying spiral." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAGLCAYAAAD5+Pe5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4e8c779c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_pendulum(0.5, 0.0, 0.0)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use `interact` to explore the `plot_pendulum` function with:\n", "\n", "* `a`: a float slider over the interval $[0.0,1.0]$ with steps of $0.1$.\n", "* `b`: a float slider over the interval $[0.0,10.0]$ with steps of $0.1$.\n", "* `omega0`: a float slider over the interval $[0.0,10.0]$ with steps of $0.1$." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAGLCAYAAAD5+Pe5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4e8e117cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interact(plot_pendulum,a=(0.0,1.0,.1),b=(0.0,10.0,.1),omega0=(0.0,10.0,.1));" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use your interactive plot to explore the behavior of the damped, driven pendulum by varying the values of $a$, $b$ and $\\omega_0$.\n", "\n", "* First start by increasing $a$ with $b=0$ and $\\omega_0=0$.\n", "* Then fix $a$ at a non-zero value and start to increase $b$ and $\\omega_0$.\n", "\n", "Describe the different *classes* of behaviors you observe below." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "40364759d02737525e2503b814608893", "grade": true, "grade_id": "odesex03d", "points": 3, "solution": true } }, "source": [ "'a' is the damping coefficient, so as we increase 'a' with the other two parameters 0, the pendulum will spiral into the center sooner. Corresponding to the pendulum stopping sooner." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a fixed at 0.5, if we increase b and $\\omega$ together, we are increasing the amplitude of our driving force and changing the intial radial velocity. At some values b and $\\omega$ we notice resonance where the pendulum motion and the driving force are in phase." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I worked with Hunter, Jessica, and Brett." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

camm/SHUG2015

sassena/examples/me8t8/me8t8.ipynb

1

19478

{ "metadata": { "name": "", "signature": "sha256:61c0fbfd656da0101a745e69b0bd7a5f1e2631a7f05f20bfd277773d1996e736" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook contains a tutorial to carry out calculation of the intermediate structure factor $I(Q,t)$ with the Sassena program. It is recommended that you do the molecular dynamics tutorial for this same molecule before trying this tutorial.\n", "\n", "<h1>Sassena calculations on a mPOSS simulation</h1>\n", "<a id='Table of Contents'></a><h3>Table of Contents</h3>\n", " \n", "<a href='#me8t8'>Octa-methyl Silsesqioxane</a> \n", "<a href='#calc_fqt'>Calculation of $I(Q,t)$ and quick view with hdfview</a> \n", "<a href='#load'>Load Sassena output into Mantid</a> \n", "<a href='#view'>View/Edit the contents on an ASCII file</a>\n", "\n", "<a href='#Section'><h4>Section</h4></a>\n", "\n", "* <a href='#Section.subsection'>subsection</a> \n", "\n", "<a href='#Syntax'>Examples of HTML and Markdown syntax</a></br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='me8t8'></a><h3>Octa-methyl Silsesqioxane (mPOSS)</h3>\n", "The mPOSS molecule is composed of a cubic cage where silicon atoms occupy the cube vertices, and oxygen atoms are located in the cube edges (see below figure). Thus, each Si atom has a tetrahedral coordination to three O atoms and one methyl group. Methyl substitution by other chemical species makes POSS molecules highly versatile, with applications as organic solvents, polymer dispersants, catalysts, nanocomposites, diodes, and many other uses. In particular, mPOSS has found application as a coating for carbon fibers and low-dielectric films. \n", "\n", "<center><a href=\"files/supporting/me8t8_molecule.png\"><img src=\"files/supporting/me8t8_molecule.png\" width=\"300\" height=\"300\" alt=\"me8t8_molecule.png\"></a></center> \n", "\n", "In the figure above, mPOSS molecule composed of Si (yellow), O (red), C (cyan), and H (grey) atoms. Nine different chains of consecutive O-Si-C-H covalent bonds can be constructed for each methyl group, due to the three different oxygen and hydrogen atoms that can be selected at the extremes of the chain. \n", "\n", "<center><a href=\"files/supporting/me8t8_crystal.png\"><img src=\"files/supporting/me8t8_crystal.png\" width=\"300\" height=\"300\" alt=\"me8t8_crystal.png\"></a></center> \n", "\n", "mPOSS molecule non-vibrational degrees of freedom are restricted to discrete rotational diffusion of the methyl groups (-CH3). In the AMBER force field the barrier to rotation is described by a dihedral 4-body term. \n", "<center>$V(\\phi)=K[1+cos(3\\phi)]$</center> \n", "Where $\\phi$ is the dihedral angle defined by one of the nine combinations that can be formed with the four linked atoms O, Si, C, and H. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='calc_fqt'></a><h3>Calculation of $I(Q,t)$ and quick view with hdfview</h3>\n", "\n", "The necessary files to carry out the simulation are: \n", "\n", "* me8t8.pdb (list of atoms and molecule name)\n", "* hydrogens.pdb (selection of atoms over which to calculate $I(Q,t)$)\n", "* run1_rms2first.dcd (molecular dynamics trajectory with removed global rotations and translations. 4000 frames, with frames recorded every 1ps, for a total of 4ns).\n", "* [sassena.xml](files/sassena.xml) (input options for the Sassena program)\n", "\n", "If you completed the tutorial on molecular dynamics for this molecule, you should have produced file <i>run1_rms2first.dcd</i> yourself.\n", "\n", "Inspect file <i>sassena.xml</i> (can be viewed in the web browser): \n", "\n", "* How many Q-values are we sampling?\n", "* For a given Q-value, how many vectors are being sampled to perform the orientational average?\n", "* Are we calculating the coherent of the incoherent cross section? How can you tell?\n", "* If we inspect file <i>hydrogens.pdb</i> (if necessary, refer to <a href='#view'>View/Edit the contents on an ASCII file</a>), how can we tell which atoms are selected for the $I(q,t)$ calculation?\n", "\n", "Inspect file [hydrogens.pdb](files/hydrogens.pdb). Find out what atoms are being selected for the calculation of $I(Q,t)$.\n", "\n", "All necessary files are contained in directory <i>&#36;HOME/SHUG2015/sassena/examples/me8t8/</i>. Let's copy them to the scratch area: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "mkdir -p /SNSlocal/scratch/$USER/me8t8/\n", "cd $HOME/SHUG2015/sassena/examples/me8t8/\n", "/bin/cp hydrogens.pdb me8t8.pdb run1_rms2first.dcd sassena.xml /SNSlocal/scratch/$USER/me8t8/\n", "cd /SNSlocal/scratch/$USER/me8t8/" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the sassena module. This will make available the sassena command to actually run the calculation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "module load sassena\n", "which sassena #will print the path to the namd executable" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now run the calculation. We ask that you limit to four cores. It should take about 1 minute" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "time mpirun -np 4 sassena --config sassena.xml &> sassena.log" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the calculation is done, new files are generated: \n", "\n", "* sassena.log #several info messages regarding details of the computaion\n", "* fqt_inc.h5 #structure factor $I(Q,t)$, in binary format.\n", "\n", "File <i>fqt_inc.h5</i> can be inspected with command <code>hdfview</code>. In the terminal, type: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "hdfview &" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load file <i>fqt_inc.h5</i> thought the $File \\rightarrow Open$ browser. \n", "\n", "<center><a href=\"files/supporting/hdfview.1.png\"><img src=\"files/supporting/hdfview.1.png\" width=\"600\" height=\"600\" alt=\"hdfview.1.png\"></a></center> \n", "\n", "Now double-click in the <i>fqt</i> block data to display the nine structure factors (one per row). Highlight the first row (row in index=0, corresponding to $I(Q=0.3A^{-1},t)$) and click in the \"Line Plot\" icon: \n", "\n", "<center><a href=\"files/supporting/hdfview.2.png\"><img src=\"files/supporting/hdfview.2.png\" width=\"600\" height=\"600\" alt=\"hdfview.2.png\"></a></center> \n", "\n", "In the <i>Line Plot Options</i> be sure to select \"Row\" and then click \"OK\": \n", "\n", "<center><a href=\"files/supporting/hdfview.3.png\"><img src=\"files/supporting/hdfview.3.png\" width=\"200\" height=\"200\" alt=\"hdfview.3.png\"></a></center> \n", "\n", "A simple plot of $I(Q=0.3A^{-1},t)$ will be displayed. It shows a fast decay (in less than 200ps) follow by a fluctuating plateau. \n", "\n", "<center><a href=\"files/supporting/hdfview.4.png\"><img src=\"files/supporting/hdfview.4.png\" width=\"300\" height=\"300\" alt=\"hdfview.4.png\"></a></center> \n", "\n", "The spike at $t$~4000ps is an artifact due to poor statistics. In order to calculate $I(Q,t)$ we have to compare many pairs of frames separated in time by $t$, and there are very few pairs when $t$ becomes comparable to the simulation span. These scarcity produces an artificially high correlation, hence the spike. In general, the statistical significance degrades with increasing $t$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='load'></a><h3>Load Sassena output into Mantid</h3>\n", "Now we are ready to load file <i>fqt_inc.h5</i> in MantidPlot. \n", "\n", "A tutorial for Basic introduction and usage of Mantid is available [here](http://www.mantidproject.org/Mantid_Basic_Course). Of special relevance is section \"Loading and Displaying Data\". \n", "\n", "We start by opening MantidPlot: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "module load mantid\n", "MantidPlot &" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Mantid session will start. Select <i>SNS</i> as \"Default Facility\" and <i>BASIS</i> as \"Default Instrument\": \n", "\n", "<center><a href=\"files/supporting/starting.2.png\"><img src=\"files/supporting/starting.2.png\" width=\"500\" height=\"500\" alt=\"supporting/starting.2.png\"></a></center>\n", "\n", "* Click on the \"Manage User Directories\" button. \n", "* Click \"Browse To Directory\" and navigate to the location of the data files (/SNSlocal/scratch/&#36;USER/me8t8/). \n", "* Do the same for the default save directory. \n", "\n", "<center><a href=\"files/supporting/starting.3.png\"><img src=\"files/supporting/starting.3.png\" width=\"400\" height=\"400\" alt=\"supporting/starting.3.png\"></a></center>\n", "\n", "* Click \"OK\". \n", "* Click \"Set\". \n", "\n", "The initial Mantid session should look like the picture below, but don't worry if some of the panels are not showing up. Panels can be brought to view from the <b>View</b> menu. \n", "\n", "<center><a href=\"files/supporting/starting.4.png\"><img src=\"files/supporting/starting.4.png\" width=\"800\" height=\"800\" alt=\"supporting/starting.4.png\"></a></center> \n", "\n", "Mantid can plot data with the intensity normalized by the bin width, and this is the default behavior for all histogram and event data. As we are not going to use this normalization in this course we need to change the default settings. To do that, go to \"View\"->\"Preferences...\" and in the new window select \"2D Plots\" and untick \"Normalize histograms to bin width\". \n", "\n", "<center><a href=\"files/supporting/starting.5.png\"><img src=\"files/supporting/starting.5.png\" width=\"600\" height=\"600\" alt=\"supporting/starting.5.png\"></a></center>\n", "\n", "As a first impression, you can think of Mantid as a collection of <i>Algorithms</i> and <i>Workspaces</i>. An algorithm is a piece of code that performs a specific task. A workspace is a (multidimensional) matrix where data are stored.\n", "\n", "We will use algorithm \"LoadSassena\" :\n", "\n", "* Type LoadSassena in the Algorithm panel and click in \"Execute\", the Algorithm dialog window will popup (see picture below).\n", "* Browse to the location of the file <i>fqt_inc.h5</i>\n", "* We are storing the contents of file <i>fqt_inc.h5</i> into an output workspace which we name as \"fqt\"\n", "* Recall that our trajectory has consecutive frames separated by 1ps. In the algorithm popup, \"TimeUnit\" is the separation between consecutive frames and the units are precisely picoseconds. Thus, we enter 1.0.\n", "* Click in \"Run\" to do the actual loading.\n", "\n", "<center><a href=\"files/supporting/load_sassena.1.png\"><img src=\"files/supporting/load_sassena.1.png\" width=\"600\" height=\"600\" alt=\"supporting/load_sassena.1.png\"></a></center> \n", "\n", "Workspace \"fqt\" (in the figure, within the orange dashed circle) is instantiated in the workspace panel. \n", "\n", "Let's expand the contents of fqt. If you click in the \"+\" sign, we see that \"fqt\" is actually made up of six workspaces. \n", "\n", "<center><a href=\"files/supporting/load_sassena.2.png\"><img src=\"files/supporting/load_sassena.2.png\" width=\"200\" height=\"200\" alt=\"supporting/load_sassena.2.png\"></a></center> \n", "\n", "* fqt_qvectors - List of generating Q-vectors, each one was orientationally averaged, each one has a different modulus (from 0.3$A^{-1}$ to 1.9$A^{-1}$). \n", "* fqt_fq0 - This is $I(Q,t=0)$. We calculated the incoherent signal, thus $I(Q,t=0)=\\sum_i |b_i^{incoh}|^2$, independent of $Q$.\n", "* fqt_fqt.Re - Real part of $I(Q,t)$.\n", "* fqt_fqt.Im - Imaginary part of $I(Q,t)$. For a perfect orientational average, this should be exactly zero. \n", "* fqt_fq - $\\int dt I(Q,t) = S(Q,E=0)$\n", "* fqt_fq2 - $\\int dt |I(Q,t)|^2$\n", "\n", "Clicking in the \"+\" of each workspace will show a brief report of its contents. For instance, <i>fqt_fqt.Re</i> will inform this workspace contains 9 histograms (one per Q-value, values are 0.3$A^{-1}$, 0.5$A^{-1}$, 0.7$A^{-1}$,...,1.9$A^{-1}$) and each histogram contains 7999 bins corresponding to 7999 time points. \n", "\n", "<center><a href=\"files/supporting/load_sassena.3.png\"><img src=\"files/supporting/load_sassena.3.png\" width=\"300\" height=\"300\" alt=\"supporting/load_sassena.3.png\"></a></center> \n", "\n", "Let's plot the histogram corresponding to Q=0.5 and Q=0.9. First left-click in workspace <i>fqt_fqt.Re</i> and select \"Plot Spectrum\" \n", "\n", "<center><a href=\"files/supporting/load_sassena.4.png\"><img src=\"files/supporting/load_sassena.4.png\" width=\"300\" height=\"300\" alt=\"supporting/load_sassena.4.png\"></a></center> \n", "\n", "In the popup enter the appropriate indexes for these two Q-values,separated by a comma,then press OK.\n", "\n", "<center><a href=\"files/supporting/load_sassena.5.png\"><img src=\"files/supporting/load_sassena.5.png\" width=\"200\" height=\"200\" alt=\"supporting/load_sassena.5.png\"></a></center> \n", "\n", "The two histograms are depicted on the same plot.\n", "\n", "<center><a href=\"files/supporting/load_sassena.6.png\"><img src=\"files/supporting/load_sassena.6.png\" width=\"300\" height=\"300\" alt=\"supporting/load_sassena.6.png\"></a></center> \n", "\n", "Why does the plateau diminishes with increasing Q-value? (Hint: $I(Q,t)$ is the Fourier transform of the self-correlation function $G(r,t)$. What is the physical meaning of this correlation function?) \n", "\n", "Notice that $I(Q,t)$ extends to negative times, but we do not have negative times in the simulation, nor in the sassena output file <i>fqt_inc.h5</i>. The LoadSassena algorithm assumes that $I(Q,t)$ is an even function of time, and will symmetrize the data loaded from file <i>fqt_inc.h5</i>. This step is justified because Newton's equations are invariant under time reversal (at least for potentials that are time-independent, as was the case of our simulation).\n", "\n", "<b>Exercise</b> Plot the imaginary part of $I(Q,t)$ (workspace <i>fqt_fqt.Im</i>). \n", "\n", "* How does the maximum of the imaginary part of $I(Q,t)$ compare with the maximum of the real part of $I(Q,t)$ ?\n", "* Is the imaginary part an even function of time? Why not? (Hint: $S(Q,E)$, the Fourier transform of $I(q,t)$, is a real function) \n", "\n", "We can inspect the actual data of workspace <i>fqt_fqt.Re</i> by double-clicking on the workspace. It also shows the Q-values of each histogram on the left column. \n", "\n", "<center><a href=\"files/supporting/load_sassena.7.png\"><img src=\"files/supporting/load_sassena.7.png\" width=\"500\" height=\"500\" alt=\"supporting/load_sassena.7.png\"></a></center> \n", "\n", "At this point, it is useful to check out the [Mantid help on displaying data](http://www.mantidproject.org/MBC_Displaying_data) to be aware of other ways of plotting data, or adding a curve to and existing plot.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='view'></a><h3>View/Edit the contents on an ASCII file</h3>\n", "For those not familiar with the Linux operating system, we enumerate here a few ways to view and/or edit an ASCII file: \n", "\n", "1 File is shown in the terminal: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "cat file #will dump all contents of the file to the terminal\n", "less file #will fill the terminal with beginning of the file, and wait for user input\n", "vi file #the best Linux text editor according to vi fans\n", "emacs -nw file #the best Linux text editor according to emacs fans" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2 File is shown in a separate window" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "emacs file &\n", "gedit file & #recommended for those familiar with windows, similar to notepad\n", "openoffice.org file & #like WORD, maybe to much for a simple ASCII file" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='Section'></a><h2>Section</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='Section.subsection'></a><h3>Subsection</h3>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='Syntax'></a><h3>Markdown Syntax Examples</h3>\n", "local link: [link](files/link)</br>\n", "remote link: <a href=\"http://ambermd.org/\">http://ambermd.org</a>\n", "<font face=\"courier new\"> font face=\"courier new\" </font><br/>\n", "$$S_{model}(Q,E)=A(Q)\\cdot S_{elastic}(E) + B(Q)\\cdot S_{simulation}(Q,E)\\otimes S_{elastic}(E) + C(Q)+D(Q)\\cdot E$$\n", "<pre> Quoted text </pre>\n", "<center><table><tr>\n", "<td><a href=\"files/image.png\"><img src=\"files/image.png\" width=\"300\" height=\"250\" alt=\"image here\"></a> <br/>\n", " <i>image caption</i></td>\n", "<td>some text</td>\n", "</tr></table></center>" ] } ], "metadata": {} } ] }

mit

pwer21c/pwer21c.github.io

python/pythoncodes/.ipynb_checkpoints/2_turtle-checkpoint.ipynb

1

81098

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 거북이 turtle 게임" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 오늘 할 과제입니다. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/jpeg": "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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 5, "metadata": { "image/jpeg": { "height": 300, "width": 600 } }, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "# Load image from local storage\n", "Image(filename = \"2_img01.jpg\", width = 600, height = 300)\n", "\n", "### 위의 코드는 run 하지 않아도 됩니다.\n", "### 첫번째 그림과 세번째 그림을 공부해 보고 두번째와 네번째는 숙제입니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 거북이 만들기입니다. 몰라도 일단 그대로 써봅시다. 다음에 이것이 무엇인지를 설명할것입니다." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import turtle\n", "t=turtle.Turtle()\n", "t.shape('turtle')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 거북이 앞으로 100걸음 가기" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "t.forward(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 거북이가 왼쪽으로 90도 몸을 틀어서 앞으로 100걸음 갑니다." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "t.left(90)\n", "t.forward(100)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 거북이를 다시 오른쪽으로 몸을 틀어서 앞으로 100걸음 갑니다." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "t.right(90)\n", "t.forward(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 거북이를 다시 오른쪽으로 몸을 틀어서 앞으로 100걸음 갑니다." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "t.right(90)\n", "t.forward(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 거북이가 왼쪽으로 90도 몸을 틀어서 앞으로 100걸음 갑니다." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "t.left(90)\n", "t.forward(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 그림을 지우고 다시 시작하는 명령어입니다." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "t.reset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 원을 그려요" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "t.circle(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 세번째 그림" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "t.reset() ## 그림을 지웁니다.\n", "t.forward(100) ## 앞으로 100걸음 간다.\n", "t.circle(100) ## 한바퀴 돌아요\n", "t.forward(100) ## 앞으로 100걸음 갑니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "거북이를 움직여 봅시다\n", "forward : 앞으로 \n", "backward: 뒤로\n", "left: 왼쪽으로\n", "right: 오른쪽으로\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 재미있는 거북이" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "## 빙글빙글 세번 도는 거북이.\n", "t.reset()\n", "t.circle(10)\n", "t.circle(20)\n", "t.circle(30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 숙제는 두번째와 세번째 그림입니다. 두번째 그림은 조금 어렵고 네번째 계단은 쉬워요." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## 참고 피똥 거북이가 문제 있으면 \n", "exit() \n", "## 실행시키세요. 강제 종료입니다." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

google/or-tools

examples/notebook/examples/tasks_and_workers_assignment_sat.ipynb

1

7521

{ "cells": [ { "cell_type": "markdown", "id": "google", "metadata": {}, "source": [ "##### Copyright 2021 Google LLC." ] }, { "cell_type": "markdown", "id": "apache", "metadata": {}, "source": [ "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " http://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License.\n" ] }, { "cell_type": "markdown", "id": "basename", "metadata": {}, "source": [ "# tasks_and_workers_assignment_sat" ] }, { "cell_type": "markdown", "id": "link", "metadata": {}, "source": [ "<table align=\"left\">\n", "<td>\n", "<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/examples/tasks_and_workers_assignment_sat.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n", "</td>\n", "<td>\n", "<a href=\"https://github.com/google/or-tools/blob/master/examples/python/tasks_and_workers_assignment_sat.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n", "</td>\n", "</table>" ] }, { "cell_type": "markdown", "id": "doc", "metadata": {}, "source": [ "First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab." ] }, { "cell_type": "code", "execution_count": null, "id": "install", "metadata": {}, "outputs": [], "source": [ "!pip install ortools" ] }, { "cell_type": "code", "execution_count": null, "id": "code", "metadata": {}, "outputs": [], "source": [ "# Copyright 2010-2021 Google LLC\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# http://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "\"\"\"Tasks and workers to group assignment to average sum(cost) / #workers\"\"\"\n", "\n", "\n", "from ortools.sat.python import cp_model\n", "\n", "\n", "class ObjectivePrinter(cp_model.CpSolverSolutionCallback):\n", " \"\"\"Print intermediate solutions.\"\"\"\n", "\n", " def __init__(self):\n", " cp_model.CpSolverSolutionCallback.__init__(self)\n", " self.__solution_count = 0\n", "\n", " def on_solution_callback(self):\n", " print('Solution %i, time = %f s, objective = %i' %\n", " (self.__solution_count, self.WallTime(), self.ObjectiveValue()))\n", " self.__solution_count += 1\n", "\n", "\n", "def tasks_and_workers_assignment_sat():\n", " \"\"\"Solve the assignment problem.\"\"\"\n", " model = cp_model.CpModel()\n", "\n", " # CP-SAT solver is integer only.\n", " task_cost = [24, 10, 7, 2, 11, 16, 1, 13, 9, 27]\n", " num_tasks = len(task_cost)\n", " num_workers = 3\n", " num_groups = 2\n", " all_workers = range(num_workers)\n", " all_groups = range(num_groups)\n", " all_tasks = range(num_tasks)\n", "\n", " # Variables\n", "\n", " ## x_ij = 1 if worker i is assigned to group j\n", " x = {}\n", " for i in all_workers:\n", " for j in all_groups:\n", " x[i, j] = model.NewBoolVar('x[%i,%i]' % (i, j))\n", "\n", " ## y_kj is 1 if task k is assigned to group j\n", " y = {}\n", " for k in all_tasks:\n", " for j in all_groups:\n", " y[k, j] = model.NewBoolVar('x[%i,%i]' % (k, j))\n", "\n", " # Constraints\n", "\n", " # Each task k is assigned to a group and only one.\n", " for k in all_tasks:\n", " model.Add(sum(y[k, j] for j in all_groups) == 1)\n", "\n", " # Each worker i is assigned to a group and only one.\n", " for i in all_workers:\n", " model.Add(sum(x[i, j] for j in all_groups) == 1)\n", "\n", " # cost per group\n", " sum_of_costs = sum(task_cost)\n", " averages = []\n", " num_workers_in_group = []\n", " scaled_sum_of_costs_in_group = []\n", " scaling = 1000 # We introduce scaling to deal with floating point average.\n", " for j in all_groups:\n", " n = model.NewIntVar(1, num_workers, 'num_workers_in_group_%i' % j)\n", " model.Add(n == sum(x[i, j] for i in all_workers))\n", " c = model.NewIntVar(0, sum_of_costs * scaling,\n", " 'sum_of_costs_of_group_%i' % j)\n", " model.Add(c == sum(y[k, j] * task_cost[k] * scaling for k in all_tasks))\n", " a = model.NewIntVar(0, sum_of_costs * scaling,\n", " 'average_cost_of_group_%i' % j)\n", " model.AddDivisionEquality(a, c, n)\n", "\n", " averages.append(a)\n", " num_workers_in_group.append(n)\n", " scaled_sum_of_costs_in_group.append(c)\n", "\n", " # All workers are assigned.\n", " model.Add(sum(num_workers_in_group) == num_workers)\n", "\n", " # Objective.\n", " obj = model.NewIntVar(0, sum_of_costs * scaling, 'obj')\n", " model.AddMaxEquality(obj, averages)\n", " model.Minimize(obj)\n", "\n", " # Solve and print out the solution.\n", " solver = cp_model.CpSolver()\n", " solver.parameters.max_time_in_seconds = 60 * 60 * 2\n", " objective_printer = ObjectivePrinter()\n", " status = solver.Solve(model, objective_printer)\n", " print(solver.ResponseStats())\n", "\n", " if status == cp_model.OPTIMAL:\n", " for j in all_groups:\n", " print('Group %i' % j)\n", " for i in all_workers:\n", " if solver.BooleanValue(x[i, j]):\n", " print(' - worker %i' % i)\n", " for k in all_tasks:\n", " if solver.BooleanValue(y[k, j]):\n", " print(' - task %i with cost %i' % (k, task_cost[k]))\n", " print(' - sum_of_costs = %i' %\n", " (solver.Value(scaled_sum_of_costs_in_group[j]) // scaling))\n", " print(' - average cost = %f' %\n", " (solver.Value(averages[j]) * 1.0 / scaling))\n", "\n", "\n", "tasks_and_workers_assignment_sat()\n", "\n" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }

apache-2.0

marwin-ko/projects

aerialintel-data_science_challenge/3_feature_selection.ipynb

1

265791

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# FEATURE SELECTION" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor\n", "from sklearn.cross_validation import train_test_split, cross_val_score\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.preprocessing import StandardScaler\n", "from sequential_backward_selection import SBS\n", "from pprint import pprint\n", "from time import time\n", "import pandas as pd\n", "import numpy as np\n", "import pickle\n", "\n", "from warnings import filterwarnings\n", "filterwarnings('ignore')\n", "import matplotlib.pyplot as plt\n", "% matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LOAD DATA" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>apparentTemperatureMax</th>\n", " <th>apparentTemperatureMin</th>\n", " <th>cloudCover</th>\n", " <th>dewPoint</th>\n", " <th>humidity</th>\n", " <th>precipIntensity</th>\n", " <th>precipIntensityMax</th>\n", " <th>precipProbability</th>\n", " <th>...</th>\n", " <th>precipTypeIsSnow</th>\n", " <th>pressure</th>\n", " <th>temperatureMax</th>\n", " <th>temperatureMin</th>\n", " <th>visibility</th>\n", " <th>windBearing</th>\n", " <th>windSpeed</th>\n", " <th>NDVI</th>\n", " <th>DayInSeason</th>\n", " <th>Yield</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>46.811686</td>\n", " <td>-118.695237</td>\n", " <td>35.70</td>\n", " <td>20.85</td>\n", " <td>0.00</td>\n", " <td>29.53</td>\n", " <td>0.91</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.00</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1027.13</td>\n", " <td>35.70</td>\n", " <td>27.48</td>\n", " <td>2.46</td>\n", " <td>214</td>\n", " <td>1.18</td>\n", " <td>134.110657</td>\n", " <td>0</td>\n", " <td>35.7</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>46.929839</td>\n", " <td>-118.352109</td>\n", " <td>35.10</td>\n", " <td>26.92</td>\n", " <td>0.00</td>\n", " <td>29.77</td>\n", " <td>0.93</td>\n", " <td>0.0001</td>\n", " <td>0.0019</td>\n", " <td>0.05</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1026.87</td>\n", " <td>35.10</td>\n", " <td>26.92</td>\n", " <td>2.83</td>\n", " <td>166</td>\n", " <td>1.01</td>\n", " <td>131.506592</td>\n", " <td>0</td>\n", " <td>35.7</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>47.006888</td>\n", " <td>-118.510160</td>\n", " <td>33.38</td>\n", " <td>26.95</td>\n", " <td>0.00</td>\n", " <td>29.36</td>\n", " <td>0.94</td>\n", " <td>0.0001</td>\n", " <td>0.0022</td>\n", " <td>0.06</td>\n", " <td>...</td>\n", " <td>1</td>\n", " <td>1026.88</td>\n", " <td>33.38</td>\n", " <td>26.95</td>\n", " <td>2.95</td>\n", " <td>158</td>\n", " <td>1.03</td>\n", " <td>131.472946</td>\n", " <td>0</td>\n", " <td>35.7</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>47.162342</td>\n", " <td>-118.699677</td>\n", " <td>28.05</td>\n", " <td>25.93</td>\n", " <td>0.91</td>\n", " <td>29.47</td>\n", " <td>0.94</td>\n", " <td>0.0002</td>\n", " <td>0.0039</td>\n", " <td>0.15</td>\n", " <td>...</td>\n", " <td>1</td>\n", " <td>1026.37</td>\n", " <td>33.19</td>\n", " <td>27.17</td>\n", " <td>2.89</td>\n", " <td>153</td>\n", " <td>1.84</td>\n", " <td>131.288300</td>\n", " <td>0</td>\n", " <td>35.7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>47.157512</td>\n", " <td>-118.434056</td>\n", " <td>28.83</td>\n", " <td>25.98</td>\n", " <td>0.91</td>\n", " <td>29.86</td>\n", " <td>0.94</td>\n", " <td>0.0003</td>\n", " <td>0.0055</td>\n", " <td>0.24</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1026.19</td>\n", " <td>33.85</td>\n", " <td>27.07</td>\n", " <td>2.97</td>\n", " <td>156</td>\n", " <td>1.85</td>\n", " <td>131.288300</td>\n", " <td>0</td>\n", " <td>35.7</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 22 columns</p>\n", "</div>" ], "text/plain": [ " Latitude Longitude apparentTemperatureMax apparentTemperatureMin \\\n", "0 46.811686 -118.695237 35.70 20.85 \n", "1 46.929839 -118.352109 35.10 26.92 \n", "2 47.006888 -118.510160 33.38 26.95 \n", "3 47.162342 -118.699677 28.05 25.93 \n", "4 47.157512 -118.434056 28.83 25.98 \n", "\n", " cloudCover dewPoint humidity precipIntensity precipIntensityMax \\\n", "0 0.00 29.53 0.91 0.0000 0.0000 \n", "1 0.00 29.77 0.93 0.0001 0.0019 \n", "2 0.00 29.36 0.94 0.0001 0.0022 \n", "3 0.91 29.47 0.94 0.0002 0.0039 \n", "4 0.91 29.86 0.94 0.0003 0.0055 \n", "\n", " precipProbability ... precipTypeIsSnow pressure temperatureMax \\\n", "0 0.00 ... 0 1027.13 35.70 \n", "1 0.05 ... 0 1026.87 35.10 \n", "2 0.06 ... 1 1026.88 33.38 \n", "3 0.15 ... 1 1026.37 33.19 \n", "4 0.24 ... 0 1026.19 33.85 \n", "\n", " temperatureMin visibility windBearing windSpeed NDVI \\\n", "0 27.48 2.46 214 1.18 134.110657 \n", "1 26.92 2.83 166 1.01 131.506592 \n", "2 26.95 2.95 158 1.03 131.472946 \n", "3 27.17 2.89 153 1.84 131.288300 \n", "4 27.07 2.97 156 1.85 131.288300 \n", "\n", " DayInSeason Yield \n", "0 0 35.7 \n", "1 0 35.7 \n", "2 0 35.7 \n", "3 0 35.7 \n", "4 0 35.7 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/wheat-2013-supervised-edited.csv')\n", "df.drop(df.columns[0],axis=1,inplace=True)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ALGORITHMS" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "models = {}\n", "models['Linear Regression'] = LinearRegression()\n", "models['Gradient Boost'] = GradientBoostingRegressor(random_state=42)\n", "models['Random Forest'] = RandomForestRegressor(random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FEATURE EVALUATION" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def show_feat_importances(name, trained_model,feat_labels):\n", " plt.figure(figsize=(10,5))\n", " if name == 'Linear Regression':\n", " importances = trained_model.coef_\n", " plt.ylabel('Coefficients')\n", " else:\n", " importances = trained_model.feature_importances_\n", " plt.ylabel('Importances')\n", " indices = np.argsort(importances)[::-1]\n", " plt.bar(range(len(feat_labels)),importances[indices],color='lightblue',align='center')\n", " plt.xticks(range(len(feat_labels)),feat_labels[indices],rotation=90)\n", " plt.xlim([-1,len(feat_labels)])\n", " plt.tight_layout()\n", " plt.title('FEATURE IMPORTANCE | MODEL:{}'.format(name))\n", " plt.grid()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FEATURE EVALUATION: Round#1\n", "\n", "The model scores alone tipped me off to further investigate the longitude and latitude features. In my review, I concluded that longitude and latitude was a source of leakage (where features are directly related to the target). As a result, I removed longitude and latitude as features in Round#2 of model/feature evaluation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "################################### Gradient Boost ##########################################\n", "RUN_TIME:437.020871878sec \t TEST_SCORE:0.93 \t TRAIN_SCORE:0.93\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuis7wE0mAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116ebacd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "################################### Random Forest ##########################################\n", "RUN_TIME:586.046509981sec \t TEST_SCORE:1.0 \t TRAIN_SCORE:1.0\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103dee210>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "################################### Linear Regression ##########################################\n", "RUN_TIME:587.957453966sec \t TEST_SCORE:0.22 \t TRAIN_SCORE:0.23\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1174dc310>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time = time()\n", "X = np.matrix(df.ix[:,:-1])\n", "y = np.array(df.ix[:,-1])\n", "X_std = StandardScaler().fit_transform(X)\n", "y_std = StandardScaler().fit_transform(y)\n", "X_train,X_test,y_train,y_test = train_test_split(X_std,y_std,test_size=0.25,random_state=42)\n", "for name,model in models.items():\n", " results= model.fit(X_train,y_train)\n", " test_score = model.score(X_test,y_test)\n", " train_score = np.mean(cross_val_score(model,X_train,y_train,cv=8))\n", " print '################################### {} ##########################################'.format(name)\n", " print 'RUN_TIME:{}sec \\t TEST_SCORE:{} \\t TRAIN_SCORE:{}'.format(time()-start_time,test_score.round(2),train_score.round(2))\n", " show_feat_importances(name,results,df.columns[:-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FEATURE EVALUATION: Round#2\n", "\n", "In this round, it is apparent that the \"precip...\" features are on the lower end of importance. I will use this information to help me remove features after I run the sequential backward selection (SBS), a feature selection algorithm." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "################################### Gradient Boost ##########################################\n", "RUN_TIME:415.282940865sec \t TEST_SCORE:0.27 \t TRAIN_SCORE:0.27\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxCY5hBBCCCGEUWKTHEIIIYQQwij/HxSm/oZrlOPnAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116ec5b50>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "################################### Random Forest ##########################################\n", "RUN_TIME:640.647248983sec \t TEST_SCORE:0.84 \t TRAIN_SCORE:0.83\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x117906d10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "################################### Linear Regression ##########################################\n", "RUN_TIME:642.419631004sec \t TEST_SCORE:0.13 \t TRAIN_SCORE:0.14\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAR5biVs/IarOAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116dfcf90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time = time()\n", "X = np.matrix(df.ix[:,2:-1])\n", "y = np.array(df.ix[:,-1])\n", "X_std = StandardScaler().fit_transform(X)\n", "y_std = StandardScaler().fit_transform(y)\n", "X_train,X_test,y_train,y_test = train_test_split(X_std,y_std,test_size=0.25,random_state=42)\n", "for name,model in models.items():\n", " results= model.fit(X_train,y_train)\n", " test_score = model.score(X_test,y_test)\n", " train_score = np.mean(cross_val_score(model,X_train,y_train,cv=8))\n", " print '################################### {} ##########################################'.format(name)\n", " print 'RUN_TIME:{}sec \\t TEST_SCORE:{} \\t TRAIN_SCORE:{}'.format(time()-start_time,test_score.round(2),train_score.round(2))\n", " show_feat_importances(name,results,df.columns[2:-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sequential Backward Selection (SBS)\n", "\n", "The sequential backward selection (SBS) is a feature selection technique that utilizes the greedy algorithm approach to optimize the score for a model given k features.\n", "\n", "In the graph shown below, the (test) scores plateau at the...\n", "* MODEL: Random Forest ==> plateau: 5th iteration (4 features removed)\n", "* MODEL: Linear Regression ==> plateau: 9th iteration (8 features removed)\n", "* MODEL: Gradient Boost ==> plateau: 9th iteration (8 features removed)\n", "\n", "_See below the graph for a print out of the specific feature removed for each model._" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAFRCAYAAAA1jNoBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103dff510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sbs_dict = {}\n", "plt.figure(figsize=(10,5))\n", "names = []\n", "for name,model in models.items():\n", " names.append(name)\n", " sbs = SBS(model, k_features=1)\n", " sbs.fit(X,y)\n", " k_feat = [len(k)-1 for k in sbs.subsets_]\n", " sbs_dict[name] = sbs.subsets_\n", " sbs_feat_subset = sbs.subsets_\n", " plt.plot(k_feat,sbs.scores_,marker='o')\n", " plt.xlabel('# of Features Removed')\n", " plt.ylabel('Test Score')\n", " plt.grid(True)\n", "plt.title('Sequential Backward Selection') \n", "plt.legend(names,loc=0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "################################### Gradient Boost ##########################################\n", "['precipIntensity',\n", " 'precipAccumulation',\n", " 'apparentTemperatureMax',\n", " 'visibility',\n", " 'precipTypeIsSnow',\n", " 'temperatureMin',\n", " 'precipTypeIsRain',\n", " 'precipProbability']\n", "\n", "################################### Random Forest ##########################################\n", "['apparentTemperatureMax', 'apparentTemperatureMin', 'temperatureMin', 'NDVI']\n", "\n", "################################### Linear Regression ##########################################\n", "['precipIntensity',\n", " 'precipAccumulation',\n", " 'visibility',\n", " 'pressure',\n", " 'cloudCover',\n", " 'precipTypeIsRain',\n", " 'precipIntensityMax',\n", " 'precipProbability']\n", "\n" ] } ], "source": [ "for name,model in models.items():\n", " print '################################### {} ##########################################'.format(name)\n", " if name == 'Random Forest':\n", " k = 4\n", " else:\n", " k = 8\n", " remove_feats = list(set(list(df.columns[2:-1])) - set(list(df.columns[2:-1][list(sbs_dict[name][k])])))\n", " pprint(remove_feats)\n", " print ''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SAVE SBS FEATURE GROUPINGS" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('data/SBS_feat_set.plk','wb') as f:\n", " pickle.dump(sbs_dict,f)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

wootencl/D3_Jupyter_Data_Visualization

PokitDok_Data_Visualization.ipynb

2

830621

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### PokitDok Interview Homework - Part 1 - Data Visualization\n", "#### Data Set: Geographic Variation Public Use File - [URL](https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/GV_PUF.html)\n", "\n", "#### Sources: \n", "[[1]](http://stackoverflow.com/questions/26207668/how-to-use-python-defined-variables-in-javascript-code-within-ipython-notebook)\n", "\n", "[[2]](https://github.com/cmoscardi/embedded_d3_example/blob/master/Embedded_D3.ipynb) (Used throughout)\n", "\n", "[[3]](http://bl.ocks.org/dbuezas/9306799) (The visualization I used for D3.js)\n", "\n", "##### Setup: \n", "Following commands import a function from Github to parse the excel file into a readable dictionary. Then move that file and rename it for proper use in the Jupyter notebook.\n", "```\n", "git clone https://gist.github.com/639082.git\n", "mv 639082/* .\n", "rm -rf 639082/\n", "mv xls-dict-reader.py xls_dict_reader.py\n", "```\n", "I ended up having to do some manual editing of the data set before parsing into JSON. I originally was going to display the statistics for US > State > County, but the resulting json file was too large for simple analysis. \n", "\n", "The following code opens the respective excel file. Then uses the recently retrieved github gist to convert that excel file to a dictionary which is then converted to JSON via python. Lastly that data is then associated with the front-end 'window' for access (typically not a good idea but figured it would be safe as everything is constricted to within the notebook) . [1]" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "window.data=[{\"PQI12 UTI Admission Rate (age < 65)\": 355.0, \"% of Beneficiaries Using PAC: HH\": 0.0938, \"PAC: LTCH Standardized Costs\": 5179396794.6, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.92, \"E&M Per Capita Standardized Costs\": 945.89, \"E&M Per User Standardized Costs\": 1077.95, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1376.0, \"IP Covered Days Per 1000 Beneficiaries\": 1530.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 44.0, \"Count of Medicare beneficiaries with lung cancer\": 350953.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3375, \"Percent Eligible for Medicaid\": 21.3, \"Imaging Per Capita Standardized Costs\": 221.01, \"% of Beneficiaries Using Tests\": 0.7703, \"Imaging Per Capita Actual Costs\": 217.97, \"% of Beneficiaries Using PAC: SNF\": 0.0505, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0336, \"Count of Medicare beneficiaries with colorectal cancer\": 426175.0, \"Hospice Actual Costs\": 10473734910.65, \"# PAC: HH Users\": 3218301.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23625.26, \"Total Actual Costs\": 324414816394.78, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 3528548.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0095, \"ASC Standardized Costs\": 2918933336.46, \"DME Events Per 1000 Beneficiaries\": 1723.0, \"PQI08 CHF Admission Rate (age < 65)\": 867.0, \"ASC Events Per 1000 Beneficiaries\": 158.0, \"PAC: LTCH Actual Costs\": 4953698329.44, \"Count of Medicare beneficiaries with depression\": 5426189.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1525.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.29, \"Outpatient Dialysis Facility Per User Actual Costs\": 23716.13, \"Beneficiaries with Part A and Part B\": 50180674.0, \"% of Beneficiaries Using Part B Drugs\": 0.5166, \"Percent of Medicare beneficiaries with diabetes\": 26.9, \"% of Beneficiaries Using PAC: IRF\": 0.0096, \"E&M Per Capita Actual Costs\": 904.22, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0246, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0355, \"PAC: SNF Actual Costs\": 26566473947.58, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 535.0, \"Percent Female\": 54.87, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 296.0, \"Percent of Medicare beneficiaries with osteoporosis\": 6.07, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 246.95, \"# Outpatient Dialysis Facility Users\": 358577.0, \"FQHC/RHC Per User Actual Costs\": 404.17, \"Count of Medicare beneficiaries with ischemic heart disease\": 9508827.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 174.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.83, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 404.0, \"Percent of Medicare beneficiaries with depression\": 15.82, \"Emergency Department Visits per 1000 Beneficiaries\": 646.0, \"IP Actual Costs\": 109476039782.31, \"% of Beneficiaries Using OP\": 0.637, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0154, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1053, \"Count of Medicare beneficiaries with stroke\": 1283045.0, \"PQI12 UTI Admission Rate (age 75+)\": 1122.0, \"# OP Users\": 21850415.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 28.0, \"# Procedure Users\": 20943047.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 27.72, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0687, \"Count of Medicare beneficiaries with diabetes\": 9228867.0, \"ASC Per User Actual Costs\": 850.2, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1026.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0237, \"Percent of Medicare beneficiaries with high cholesterol\": 44.91, \"Standardized Per Capita Costs\": 8984.97, \"Ambulance Actual Costs\": 4688922050.48, \"FQHC/RHC Per Capita Actual Costs\": 35.93, \"Part B Drugs Per User Standardized Costs\": 617.8, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0207, \"# PAC: SNF Users (with a covered stay)\": 1731949.0, \"Ambulance Per Capita Actual Costs\": 136.69, \"PQI12 UTI Admission Rate (age 65-74)\": 268.0, \"Hospice Standardized Costs\": 10610637565.12, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 247.9, \"% of Beneficiaries Using E&M\": 0.8775, \"PQI10 Dehydration Admission Rate (age 65-74)\": 199.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 200.0, \"Tests Per Capita Actual Costs\": 254.47, \"# DME Users\": 9476736.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.089, \"PAC: SNF Per User Actual Costs\": 15339.06, \"State\": \"National\", \"OP Per User Actual Costs\": 1893.97, \"PAC: HH Episodes Per 1000 Beneficiaries\": 182.0, \"Part B Drugs Actual Costs\": 10892803079.38, \"FQHC/RHC Actual Costs\": 1232700321.39, \"OP Standardized Costs\": 40462164622.91, \"DME Per User Standardized Costs\": 769.94, \"OP Actual Costs as % of Total Actual Costs\": 0.1276, \"PAC: SNF Per Capita Standardized Costs\": 799.93, \"% of Beneficiaries Using Ambulance\": 0.1138, \"Hospice Per User Standardized Costs\": 11484.26, \"# Imaging Users\": 23245271.0, \"Part B Drugs Per User Actual Costs\": 614.65, \"Total Standardized Costs\": 308220444037.58, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.24, \"Count of Medicare beneficiaries with chronic kidney disease\": 5499471.0, \"E&M Standardized Costs\": 32447696868.14, \"Percent Hispanic\": 5.95, \"ASC Per Capita Actual Costs\": 82.57, \"Count of Medicare beneficiaries who have had a heart attack\": 284037.0, \"Tests Actual Costs\": 8729216498.73, \"# PAC: LTCH Users (with a covered stay)\": 117713.0, \"% of Beneficiaries Using Procedures\": 0.6105, \"PAC: HH Actual Costs\": 16782933418.62, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 44.0, \"PAC: LTCH Per Capita Standardized Costs\": 150.99, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0289, \"Emergency Department Visits\": 22148055.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0889, \"Procedures Actual Costs\": 20754567087.77, \"# FQHC/RHC Users\": 3049925.0, \"Number of Acute Hospital Readmissions\": 1677359.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 135.0, \"Outpatient Dialysis Facility Standardized Costs\": 8471473651.96, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0201, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1313, \"Ambulance Per User Standardized Costs\": 1216.62, \"Imaging Per User Standardized Costs\": 326.16, \"Percent of Medicare beneficiaries with asthma\": 4.99, \"Part B Drugs Standardized Costs\": 10948496301.83, \"FFS Beneficiaries\": 34303998.0, \"# Hospice Users (with a covered stay)\": 923929.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0105, \"Count of Medicare beneficiaries with osteoporosis\": 2083819.0, \"PQI08 CHF Admission Rate (age 75+)\": 1964.0, \"PAC: IRF Per Capita Standardized Costs\": 186.33, \"Procedures Standardized Costs\": 21163543936.54, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2871, \"IP Per Capita Actual Costs\": 3191.35, \"DME Actual Costs\": 6837179856.83, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0517, \"Count of Medicare beneficiaries with prostate cancer\": 1037536.0, \"PAC: HH Per Capita Standardized Costs\": 509.19, \"Count of Medicare beneficiaries with heart failure\": 4828573.0, \"Tests Per User Standardized Costs\": 337.47, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0153, \"Percent of Medicare beneficiaries with prostate cancer\": 3.02, \"PAC: IRF Per Capita Actual Costs\": 189.8, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 321.67, \"Percent of Medicare beneficiaries with breast cancer\": 2.9, \"Procedures Per User Standardized Costs\": 1010.53, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.03, \"PAC: HH Per User Actual Costs\": 5214.84, \"Count of Medicare beneficiaries with high cholesterol\": 15404240.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0819, \"Hospice Per Capita Standardized Costs\": 309.31, \"# Part B Drugs Users\": 17721835.0, \"Average HCC Score\": 1.0, \"Standardized Risk-Adjusted Per Capita Costs\": 9419.37, \"# PAC: IRF Users (with a covered stay)\": 328533.0, \"Ambulance Standardized Costs\": 4749470532.23, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0323, \"Percent of Medicare beneficiaries with heart failure\": 14.08, \"Tests Actual Costs as % of Total Actual Costs\": 0.0269, \"FQHC/RHC Per Capita Standardized Costs\": 38.8, \"PQI07 Hypertension Admission Rate (age 65-74)\": 80.0, \"Test Events Per 1000 Beneficiaries\": 9477.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 103.0, \"ASC Actual Costs\": 2832606189.04, \"Part B Drugs Per Capita Standardized Costs\": 319.16, \"Imaging Actual Costs\": 7477285040.22, \"Tests Per User Actual Costs\": 330.33, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0145, \"Hospice Per User Actual Costs\": 11336.08, \"Tests Standardized Costs\": 8917861227.02, \"IP Standardized Costs\": 88480217378.73, \"IP Per Capita Standardized Costs\": 2579.3, \"Outpatient Dialysis Facility Actual Costs\": 8504059462.32, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 70.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1887.0, \"Percent of Medicare beneficiaries with hypertension\": 55.43, \"IP Covered Stays Per 1000 Beneficiaries\": 282.0, \"# Ambulance Users\": 3903823.0, \"# ASC Users\": 3331702.0, \"ASC Per Capita Standardized Costs\": 85.09, \"Procedures Per Capita Actual Costs\": 605.02, \"Procedures Per Capita Standardized Costs\": 616.94, \"IP Users (with a covered stay)\": 6011520.0, \"Total Standardized Risk-Adjusted Costs\": 323122154381.62, \"Actual Per Capita Costs\": 9457.06, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0168, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 11.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 4.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.02, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.19, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 235.0, \"OP Per User Standardized Costs\": 1851.78, \"Count of Medicare beneficiaries with atrial fibrillation\": 2716806.0, \"Procedure Events Per 1000 Beneficiaries\": 4612.0, \"Percent of Medicare beneficiaries with stroke\": 3.74, \"PAC: IRF Per User Actual Costs\": 19818.38, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 3838929.0, \"% of Beneficiaries Using ASC\": 0.0971, \"PAC: IRF Standardized Costs\": 6391856941.95, \"Hospice Covered Days Per 1000 Beneficiaries\": 1889.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 295.0, \"PAC: SNF Per User Standardized Costs\": 15843.79, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0038, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 123.0, \"County\": \"NATIONAL TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0344, \"MA Participation Rate\": 31.64, \"OP Per Capita Standardized Costs\": 1179.52, \"Percent of Medicare beneficiaries with arthritis\": 29.19, \"Ambulance Per Capita Standardized Costs\": 138.45, \"PAC: HH Visits Per 1000 Beneficiaries\": 3062.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.064, \"Imaging Actual Costs as % of Total Actual Costs\": 0.023, \"PAC: HH Per Capita Actual Costs\": 489.24, \"E&M Events Per 1000 Beneficiaries\": 13316.0, \"Count of Medicare beneficiaries with asthma\": 1713093.0, \"# Test Users\": 26425521.0, \"E&M Actual Costs\": 31018395972.28, \"% of Beneficiaries Using Imaging\": 0.6776, \"PAC: SNF Standardized Costs\": 27440629779.73, \"DME Per Capita Actual Costs\": 199.31, \"E&M Per User Actual Costs\": 1030.47, \"Percent Male\": 45.13, \"OP Visits Per 1000 Beneficiaries\": 4221.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0211, \"OP Per Capita Actual Costs\": 1206.39, \"Hospital Readmission Rate\": 0.1807, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0043, \"Tests Per Capita Standardized Costs\": 259.97, \"Hospice Per Capita Actual Costs\": 305.32, \"E&M Actual Costs as % of Total Actual Costs\": 0.0956, \"PQI07 Hypertension Admission Rate (age < 65)\": 133.0, \"Percent African American\": 9.81, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0567, \"PAC: LTCH Per Capita Actual Costs\": 144.41, \"MA Beneficiaries\": 15876676.0, \"FQHC/RHC Per User Standardized Costs\": 436.37, \"PAC: HH Per User Standardized Costs\": 5427.47, \"Procedures Per User Actual Costs\": 991.0, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 704.0, \"Ambulance Per User Actual Costs\": 1201.11, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1331.0, \"PAC: HH Standardized Costs\": 17467234237.25, \"ASC Actual Costs as % of Total Actual Costs\": 0.0087, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 726.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0034, \"Percent Other/Unknown\": 4.32, \"% of Beneficiaries Using Hospice\": 0.0269, \"PAC: LTCH Per User Actual Costs\": 42082.85, \"Percent Non-Hispanic White\": 79.92, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 695.0, \"Count of Medicare beneficiaries with breast cancer\": 994977.0, \"DME Per Capita Standardized Costs\": 212.7, \"PQI08 CHF Admission Rate (age 65-74)\": 637.0, \"% of Beneficiaries Using DME\": 0.2763, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0262, \"% of Beneficiaries Using IP\": 0.1752, \"DME Standardized Costs\": 7296503030.89, \"FQHC/RHC Standardized Costs\": 1330908803.62, \"PAC: SNF Per Capita Actual Costs\": 774.44, \"Imaging Standardized Costs\": 7581575584.62, \"# E&M Users\": 30101306.0, \"Count of Medicare beneficiaries with arthritis\": 10012901.0, \"IP Per User Standardized Costs\": 14718.44, \"IP Per User Actual Costs\": 18211.04, \"PQI10 Dehydration Admission Rate (age 75+)\": 511.0, \"PAC: IRF Per User Standardized Costs\": 19455.75, \"DME Per User Actual Costs\": 721.47, \"PAC: IRF Actual Costs\": 6510992257.54, \"Imaging Events Per 1000 Beneficiaries\": 4034.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0275, \"PAC: LTCH Per User Standardized Costs\": 44000.21, \"OP Actual Costs\": 41384079332.28, \"Count of Medicare beneficiaries with hypertension\": 19015500.0, \"ASC Per User Standardized Costs\": 876.11, \"Part B Drugs Per Capita Actual Costs\": 317.54, \"Ambulance Events Per 1000 Beneficiaries\": 408.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 209.0, \"% of Beneficiaries Using PAC: HH\": 0.0338, \"PAC: LTCH Standardized Costs\": 12031078.74, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.01, \"E&M Per Capita Standardized Costs\": 538.76, \"E&M Per User Standardized Costs\": 667.65, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1008.0, \"IP Covered Days Per 1000 Beneficiaries\": 1095.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 51.0, \"Count of Medicare beneficiaries with lung cancer\": 596.0, \"IP Actual Costs as % of Total Actual Costs\": 0.419, \"Percent Eligible for Medicaid\": 23.73, \"Imaging Per Capita Standardized Costs\": 163.99, \"% of Beneficiaries Using Tests\": 0.627, \"Imaging Per Capita Actual Costs\": 183.86, \"% of Beneficiaries Using PAC: SNF\": 0.0134, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0295, \"Count of Medicare beneficiaries with colorectal cancer\": 765.0, \"Hospice Actual Costs\": 7214181.29, \"# PAC: HH Users\": 2369.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23611.99, \"Total Actual Costs\": 578269123.97, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 4840.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0142, \"ASC Standardized Costs\": 5898136.24, \"DME Events Per 1000 Beneficiaries\": 1027.0, \"PQI08 CHF Admission Rate (age < 65)\": 511.0, \"ASC Events Per 1000 Beneficiaries\": 136.0, \"PAC: LTCH Actual Costs\": 14729260.41, \"Count of Medicare beneficiaries with depression\": 7851.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1356.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 6.9, \"Outpatient Dialysis Facility Per User Actual Costs\": 25841.67, \"Beneficiaries with Part A and Part B\": 70905.0, \"% of Beneficiaries Using Part B Drugs\": 0.3671, \"Percent of Medicare beneficiaries with diabetes\": 20.34, \"% of Beneficiaries Using PAC: IRF\": 0.0028, \"E&M Per Capita Actual Costs\": 635.86, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0276, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0411, \"PAC: SNF Actual Costs\": 20718772.01, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 498.0, \"Percent Female\": 50.17, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 3.62, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 162.99, \"# Outpatient Dialysis Facility Users\": 484.0, \"FQHC/RHC Per User Actual Costs\": 347.4, \"Count of Medicare beneficiaries with ischemic heart disease\": 12526.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 116.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.6, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 322.0, \"Percent of Medicare beneficiaries with depression\": 11.2, \"Emergency Department Visits per 1000 Beneficiaries\": 551.0, \"IP Actual Costs\": 242322200.63, \"% of Beneficiaries Using OP\": 0.5927, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0143, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0906, \"Count of Medicare beneficiaries with stroke\": 1451.0, \"PQI12 UTI Admission Rate (age 75+)\": 805.0, \"# OP Users\": 41562.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 12.0, \"# Procedure Users\": 32588.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 17.86, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0768, \"Count of Medicare beneficiaries with diabetes\": 14259.0, \"ASC Per User Actual Costs\": 1074.44, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 884.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0217, \"Percent of Medicare beneficiaries with high cholesterol\": 26.71, \"Standardized Per Capita Costs\": 5944.48, \"Ambulance Actual Costs\": 14185889.68, \"FQHC/RHC Per Capita Actual Costs\": 29.79, \"Part B Drugs Per User Standardized Costs\": 665.05, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0085, \"# PAC: SNF Users (with a covered stay)\": 939.0, \"Ambulance Per Capita Actual Costs\": 202.31, \"PQI12 UTI Admission Rate (age 65-74)\": 145.0, \"Hospice Standardized Costs\": 6444705.51, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 178.38, \"% of Beneficiaries Using E&M\": 0.8069, \"PQI10 Dehydration Admission Rate (age 65-74)\": 123.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 142.0, \"Tests Per Capita Actual Costs\": 153.37, \"# DME Users\": 13380.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0428, \"PAC: SNF Per User Actual Costs\": 22064.72, \"State\": \"AK\", \"OP Per User Actual Costs\": 2528.08, \"PAC: HH Episodes Per 1000 Beneficiaries\": 51.0, \"Part B Drugs Actual Costs\": 17034241.94, \"FQHC/RHC Actual Costs\": 2088575.37, \"OP Standardized Costs\": 72093089.53, \"DME Per User Standardized Costs\": 674.64, \"OP Actual Costs as % of Total Actual Costs\": 0.1817, \"PAC: SNF Per Capita Standardized Costs\": 254.17, \"% of Beneficiaries Using Ambulance\": 0.1016, \"Hospice Per User Standardized Costs\": 8168.19, \"# Imaging Users\": 39896.0, \"Part B Drugs Per User Actual Costs\": 661.78, \"Total Standardized Costs\": 416815041.47, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.09, \"Count of Medicare beneficiaries with chronic kidney disease\": 8204.0, \"E&M Standardized Costs\": 37776553.09, \"Percent Hispanic\": 2.39, \"ASC Per Capita Actual Costs\": 94.71, \"Count of Medicare beneficiaries who have had a heart attack\": 424.0, \"Tests Actual Costs\": 10753837.06, \"# PAC: LTCH Users (with a covered stay)\": 194.0, \"% of Beneficiaries Using Procedures\": 0.4648, \"PAC: HH Actual Costs\": 10617909.36, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 44.0, \"PAC: LTCH Per Capita Standardized Costs\": 171.58, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0246, \"Emergency Department Visits\": 38642.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0857, \"Procedures Actual Costs\": 37450765.53, \"# FQHC/RHC Users\": 6012.0, \"Number of Acute Hospital Readmissions\": 2028.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 35.0, \"Outpatient Dialysis Facility Standardized Costs\": 11428201.25, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0071, \"OP Standardized Costs as % of Total Standardized Costs\": 0.173, \"Ambulance Per User Standardized Costs\": 837.07, \"Imaging Per User Standardized Costs\": 288.22, \"Percent of Medicare beneficiaries with asthma\": 3.7, \"Part B Drugs Standardized Costs\": 17118418.09, \"FFS Beneficiaries\": 70118.0, \"# Hospice Users (with a covered stay)\": 789.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0069, \"Count of Medicare beneficiaries with osteoporosis\": 2541.0, \"PQI08 CHF Admission Rate (age 75+)\": 1240.0, \"PAC: IRF Per Capita Standardized Costs\": 50.59, \"Procedures Standardized Costs\": 32006413.63, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3495, \"IP Per Capita Actual Costs\": 3455.92, \"DME Actual Costs\": 8739631.35, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0184, \"Count of Medicare beneficiaries with prostate cancer\": 1691.0, \"PAC: HH Per Capita Standardized Costs\": 133.98, \"Count of Medicare beneficiaries with heart failure\": 6690.0, \"Tests Per User Standardized Costs\": 233.25, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0255, \"Percent of Medicare beneficiaries with prostate cancer\": 2.41, \"PAC: IRF Per Capita Actual Costs\": 58.23, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 323.14, \"Percent of Medicare beneficiaries with breast cancer\": 2.44, \"Procedures Per User Standardized Costs\": 982.15, \"Percent of Medicare beneficiaries with chronic kidney disease\": 11.7, \"PAC: HH Per User Actual Costs\": 4482.02, \"Count of Medicare beneficiaries with high cholesterol\": 18730.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0358, \"Hospice Per Capita Standardized Costs\": 91.91, \"# Part B Drugs Users\": 25740.0, \"Average HCC Score\": 0.8208, \"Standardized Risk-Adjusted Per Capita Costs\": 7598.23, \"# PAC: IRF Users (with a covered stay)\": 199.0, \"Ambulance Standardized Costs\": 5964670.3, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0125, \"Percent of Medicare beneficiaries with heart failure\": 9.54, \"Tests Actual Costs as % of Total Actual Costs\": 0.0186, \"FQHC/RHC Per Capita Standardized Costs\": 29.56, \"PQI07 Hypertension Admission Rate (age 65-74)\": \"*\", \"Test Events Per 1000 Beneficiaries\": 5377.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 100.0, \"ASC Actual Costs\": 6641127.88, \"Part B Drugs Per Capita Standardized Costs\": 244.14, \"Imaging Actual Costs\": 12892000.19, \"Tests Per User Actual Costs\": 244.59, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0245, \"Hospice Per User Actual Costs\": 9143.45, \"Tests Standardized Costs\": 10255156.96, \"IP Standardized Costs\": 145688926.2, \"IP Per Capita Standardized Costs\": 2077.77, \"Outpatient Dialysis Facility Actual Costs\": 12507369.16, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 16.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 423.0, \"Percent of Medicare beneficiaries with hypertension\": 39.35, \"IP Covered Stays Per 1000 Beneficiaries\": 201.0, \"# Ambulance Users\": 7126.0, \"# ASC Users\": 6181.0, \"ASC Per Capita Standardized Costs\": 84.12, \"Procedures Per Capita Actual Costs\": 534.11, \"Procedures Per Capita Standardized Costs\": 456.47, \"IP Users (with a covered stay)\": 9649.0, \"Total Standardized Risk-Adjusted Costs\": 532772674.84, \"Actual Per Capita Costs\": 8247.09, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0289, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 3.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.85, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 8.04, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 157.0, \"OP Per User Standardized Costs\": 1734.59, \"Count of Medicare beneficiaries with atrial fibrillation\": 4217.0, \"Procedure Events Per 1000 Beneficiaries\": 2975.0, \"Percent of Medicare beneficiaries with stroke\": 2.07, \"PAC: IRF Per User Actual Costs\": 20516.09, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 5637.0, \"% of Beneficiaries Using ASC\": 0.0882, \"PAC: IRF Standardized Costs\": 3547184.11, \"Hospice Covered Days Per 1000 Beneficiaries\": 590.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 234.0, \"PAC: SNF Per User Standardized Costs\": 18979.71, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0036, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": \"*\", \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0155, \"MA Participation Rate\": 1.11, \"OP Per Capita Standardized Costs\": 1028.17, \"Percent of Medicare beneficiaries with arthritis\": 22.53, \"Ambulance Per Capita Standardized Costs\": 85.07, \"PAC: HH Visits Per 1000 Beneficiaries\": 699.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0648, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0223, \"PAC: HH Per Capita Actual Costs\": 151.43, \"E&M Events Per 1000 Beneficiaries\": 8137.0, \"Count of Medicare beneficiaries with asthma\": 2596.0, \"# Test Users\": 43967.0, \"E&M Actual Costs\": 44585491.8, \"% of Beneficiaries Using Imaging\": 0.569, \"PAC: SNF Standardized Costs\": 17821945.68, \"DME Per Capita Actual Costs\": 124.64, \"E&M Per User Actual Costs\": 787.99, \"Percent Male\": 49.83, \"OP Visits Per 1000 Beneficiaries\": 4079.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0151, \"OP Per Capita Actual Costs\": 1498.51, \"Hospital Readmission Rate\": 0.1471, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.005, \"Tests Per Capita Standardized Costs\": 146.26, \"Hospice Per Capita Actual Costs\": 102.89, \"E&M Actual Costs as % of Total Actual Costs\": 0.0771, \"PQI07 Hypertension Admission Rate (age < 65)\": \"*\", \"Percent African American\": 2.64, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0225, \"PAC: LTCH Per Capita Actual Costs\": 210.06, \"MA Beneficiaries\": 787.0, \"FQHC/RHC Per User Standardized Costs\": 344.81, \"PAC: HH Per User Standardized Costs\": 3965.57, \"Procedures Per User Actual Costs\": 1149.22, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 444.0, \"Ambulance Per User Actual Costs\": 1990.82, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 779.0, \"PAC: HH Standardized Costs\": 9394436.49, \"ASC Actual Costs as % of Total Actual Costs\": 0.0115, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 593.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0028, \"Percent Other/Unknown\": 20.15, \"% of Beneficiaries Using Hospice\": 0.0113, \"PAC: LTCH Per User Actual Costs\": 75924.02, \"Percent Non-Hispanic White\": 74.82, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 427.0, \"Count of Medicare beneficiaries with breast cancer\": 1713.0, \"DME Per Capita Standardized Costs\": 128.74, \"PQI08 CHF Admission Rate (age 65-74)\": 323.0, \"% of Beneficiaries Using DME\": 0.1908, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0216, \"% of Beneficiaries Using IP\": 0.1376, \"DME Standardized Costs\": 9026691.08, \"FQHC/RHC Standardized Costs\": 2073019.89, \"PAC: SNF Per Capita Actual Costs\": 295.48, \"Imaging Standardized Costs\": 11498903.33, \"# E&M Users\": 56581.0, \"Count of Medicare beneficiaries with arthritis\": 15797.0, \"IP Per User Standardized Costs\": 15098.86, \"IP Per User Actual Costs\": 25113.71, \"PQI10 Dehydration Admission Rate (age 75+)\": 264.0, \"PAC: IRF Per User Standardized Costs\": 17825.05, \"DME Per User Actual Costs\": 653.19, \"PAC: IRF Actual Costs\": 4082701.82, \"Imaging Events Per 1000 Beneficiaries\": 2983.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0274, \"PAC: LTCH Per User Standardized Costs\": 62015.87, \"OP Actual Costs\": 105072211.64, \"Count of Medicare beneficiaries with hypertension\": 27590.0, \"ASC Per User Standardized Costs\": 954.24, \"Part B Drugs Per Capita Actual Costs\": 242.94, \"Ambulance Events Per 1000 Beneficiaries\": 240.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 359.0, \"% of Beneficiaries Using PAC: HH\": 0.0994, \"PAC: LTCH Standardized Costs\": 90961509.85, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.38, \"E&M Per Capita Standardized Costs\": 893.61, \"E&M Per User Standardized Costs\": 995.83, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1484.0, \"IP Covered Days Per 1000 Beneficiaries\": 1664.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 66.0, \"Count of Medicare beneficiaries with lung cancer\": 6693.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3088, \"Percent Eligible for Medicaid\": 20.88, \"Imaging Per Capita Standardized Costs\": 250.71, \"% of Beneficiaries Using Tests\": 0.821, \"Imaging Per Capita Actual Costs\": 225.26, \"% of Beneficiaries Using PAC: SNF\": 0.0457, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0417, \"Count of Medicare beneficiaries with colorectal cancer\": 8151.0, \"Hospice Actual Costs\": 274929722.17, \"# PAC: HH Users\": 67632.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23987.18, \"Total Actual Costs\": 5710976499.68, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 71250.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0092, \"ASC Standardized Costs\": 57340856.82, \"DME Events Per 1000 Beneficiaries\": 1943.0, \"PQI08 CHF Admission Rate (age < 65)\": 892.0, \"ASC Events Per 1000 Beneficiaries\": 155.0, \"PAC: LTCH Actual Costs\": 77536531.92, \"Count of Medicare beneficiaries with depression\": 94713.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1766.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.47, \"Outpatient Dialysis Facility Per User Actual Costs\": 22350.93, \"Beneficiaries with Part A and Part B\": 907413.0, \"% of Beneficiaries Using Part B Drugs\": 0.5882, \"Percent of Medicare beneficiaries with diabetes\": 29.06, \"% of Beneficiaries Using PAC: IRF\": 0.0112, \"E&M Per Capita Actual Costs\": 786.55, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0273, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0384, \"PAC: SNF Actual Costs\": 346698304.38, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 727.0, \"Percent Female\": 55.63, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 284.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.2, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 264.79, \"# Outpatient Dialysis Facility Users\": 7513.0, \"FQHC/RHC Per User Actual Costs\": 307.11, \"Count of Medicare beneficiaries with ischemic heart disease\": 197123.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 178.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.83, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 380.0, \"Percent of Medicare beneficiaries with depression\": 13.92, \"Emergency Department Visits per 1000 Beneficiaries\": 666.0, \"IP Actual Costs\": 1763640725.7, \"% of Beneficiaries Using OP\": 0.6444, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0154, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0975, \"Count of Medicare beneficiaries with stroke\": 27636.0, \"PQI12 UTI Admission Rate (age 75+)\": 1245.0, \"# OP Users\": 438540.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 35.0, \"# Procedure Users\": 447718.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 28.96, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.069, \"Count of Medicare beneficiaries with diabetes\": 197801.0, \"ASC Per User Actual Costs\": 758.49, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1326.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0266, \"Percent of Medicare beneficiaries with high cholesterol\": 46.23, \"Standardized Per Capita Costs\": 9169.6, \"Ambulance Actual Costs\": 94245146.62, \"FQHC/RHC Per Capita Actual Costs\": 28.32, \"Part B Drugs Per User Standardized Costs\": 598.58, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0258, \"# PAC: SNF Users (with a covered stay)\": 31101.0, \"Ambulance Per Capita Actual Costs\": 138.48, \"PQI12 UTI Admission Rate (age 65-74)\": 353.0, \"Hospice Standardized Costs\": 311724001.96, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 246.73, \"% of Beneficiaries Using E&M\": 0.8974, \"PQI10 Dehydration Admission Rate (age 65-74)\": 276.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 219.0, \"Tests Per Capita Actual Costs\": 279.17, \"# DME Users\": 215129.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0682, \"PAC: SNF Per User Actual Costs\": 11147.5, \"State\": \"AL\", \"OP Per User Actual Costs\": 1634.07, \"PAC: HH Episodes Per 1000 Beneficiaries\": 207.0, \"Part B Drugs Actual Costs\": 238246768.68, \"FQHC/RHC Actual Costs\": 19271866.89, \"OP Standardized Costs\": 788929974.33, \"DME Per User Standardized Costs\": 770.55, \"OP Actual Costs as % of Total Actual Costs\": 0.1255, \"PAC: SNF Per Capita Standardized Costs\": 625.73, \"% of Beneficiaries Using Ambulance\": 0.1185, \"Hospice Per User Standardized Costs\": 14378.41, \"# Imaging Users\": 490125.0, \"Part B Drugs Per User Actual Costs\": 595.09, \"Total Standardized Costs\": 6240725823.47, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.2, \"Count of Medicare beneficiaries with chronic kidney disease\": 104842.0, \"E&M Standardized Costs\": 608183245.33, \"Percent Hispanic\": 0.53, \"ASC Per Capita Actual Costs\": 73.19, \"Count of Medicare beneficiaries who have had a heart attack\": 5638.0, \"Tests Actual Costs\": 189999528.55, \"# PAC: LTCH Users (with a covered stay)\": 2093.0, \"% of Beneficiaries Using Procedures\": 0.6578, \"PAC: HH Actual Costs\": 319731284.36, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 55.0, \"PAC: LTCH Per Capita Standardized Costs\": 133.65, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0323, \"Emergency Department Visits\": 452966.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0922, \"Procedures Actual Costs\": 382038321.49, \"# FQHC/RHC Users\": 62752.0, \"Number of Acute Hospital Readmissions\": 32711.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 167.0, \"Outpatient Dialysis Facility Standardized Costs\": 180215708.1, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0249, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1264, \"Ambulance Per User Standardized Costs\": 1193.42, \"Imaging Per User Standardized Costs\": 348.14, \"Percent of Medicare beneficiaries with asthma\": 4.81, \"Part B Drugs Standardized Costs\": 239640698.88, \"FFS Beneficiaries\": 680589.0, \"# Hospice Users (with a covered stay)\": 21680.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.011, \"Count of Medicare beneficiaries with osteoporosis\": 35358.0, \"PQI08 CHF Admission Rate (age 75+)\": 2034.0, \"PAC: IRF Per Capita Standardized Costs\": 236.65, \"Procedures Standardized Costs\": 430478384.2, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2894, \"IP Per Capita Actual Costs\": 2591.34, \"DME Actual Costs\": 158509956.37, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.056, \"Count of Medicare beneficiaries with prostate cancer\": 18466.0, \"PAC: HH Per Capita Standardized Costs\": 575.02, \"Count of Medicare beneficiaries with heart failure\": 103568.0, \"Tests Per User Standardized Costs\": 360.3, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0136, \"Percent of Medicare beneficiaries with prostate cancer\": 2.71, \"PAC: IRF Per Capita Actual Costs\": 208.64, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 312.8, \"Percent of Medicare beneficiaries with breast cancer\": 2.64, \"Procedures Per User Standardized Costs\": 961.49, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.4, \"PAC: HH Per User Actual Costs\": 4727.51, \"Count of Medicare beneficiaries with high cholesterol\": 314659.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0607, \"Hospice Per Capita Standardized Costs\": 458.02, \"# Part B Drugs Users\": 400352.0, \"Average HCC Score\": 0.9753, \"Standardized Risk-Adjusted Per Capita Costs\": 10093.05, \"# PAC: IRF Users (with a covered stay)\": 7613.0, \"Ambulance Standardized Costs\": 96268883.2, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0481, \"Percent of Medicare beneficiaries with heart failure\": 15.22, \"Tests Actual Costs as % of Total Actual Costs\": 0.0333, \"FQHC/RHC Per Capita Standardized Costs\": 33.78, \"PQI07 Hypertension Admission Rate (age 65-74)\": 90.0, \"Test Events Per 1000 Beneficiaries\": 10270.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 89.0, \"ASC Actual Costs\": 49811507.07, \"Part B Drugs Per Capita Standardized Costs\": 352.11, \"Imaging Actual Costs\": 153311393.38, \"Tests Per User Actual Costs\": 340.05, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0165, \"Hospice Per User Actual Costs\": 12681.26, \"Tests Standardized Costs\": 201314590.58, \"IP Standardized Costs\": 1806235379.33, \"IP Per Capita Standardized Costs\": 2653.93, \"Outpatient Dialysis Facility Actual Costs\": 167922532.93, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 61.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1515.0, \"Percent of Medicare beneficiaries with hypertension\": 61.52, \"IP Covered Stays Per 1000 Beneficiaries\": 293.0, \"# Ambulance Users\": 80666.0, \"# ASC Users\": 65672.0, \"ASC Per Capita Standardized Costs\": 84.25, \"Procedures Per Capita Actual Costs\": 561.33, \"Procedures Per Capita Standardized Costs\": 632.51, \"IP Users (with a covered stay)\": 125760.0, \"Total Standardized Risk-Adjusted Costs\": 6869218122.46, \"Actual Per Capita Costs\": 8391.23, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0146, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 12.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.98, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 12.56, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 268.0, \"OP Per User Standardized Costs\": 1798.99, \"Count of Medicare beneficiaries with atrial fibrillation\": 50245.0, \"Procedure Events Per 1000 Beneficiaries\": 4907.0, \"Percent of Medicare beneficiaries with stroke\": 4.06, \"PAC: IRF Per User Actual Costs\": 18651.93, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 85513.0, \"% of Beneficiaries Using ASC\": 0.0965, \"PAC: IRF Standardized Costs\": 161063755.34, \"Hospice Covered Days Per 1000 Beneficiaries\": 2933.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 351.0, \"PAC: SNF Per User Standardized Costs\": 13693.05, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0034, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 126.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0499, \"MA Participation Rate\": 25.0, \"OP Per Capita Standardized Costs\": 1159.19, \"Percent of Medicare beneficiaries with arthritis\": 32.35, \"Ambulance Per Capita Standardized Costs\": 141.45, \"PAC: HH Visits Per 1000 Beneficiaries\": 3247.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0669, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0268, \"PAC: HH Per Capita Actual Costs\": 469.79, \"E&M Events Per 1000 Beneficiaries\": 12432.0, \"Count of Medicare beneficiaries with asthma\": 32765.0, \"# Test Users\": 558742.0, \"E&M Actual Costs\": 535320120.42, \"% of Beneficiaries Using Imaging\": 0.7201, \"PAC: SNF Standardized Costs\": 425867470.01, \"DME Per Capita Actual Costs\": 232.9, \"E&M Per User Actual Costs\": 876.53, \"Percent Male\": 44.37, \"OP Visits Per 1000 Beneficiaries\": 3172.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0278, \"OP Per Capita Actual Costs\": 1052.92, \"Hospital Readmission Rate\": 0.1703, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0037, \"Tests Per Capita Standardized Costs\": 295.79, \"Hospice Per Capita Actual Costs\": 403.96, \"E&M Actual Costs as % of Total Actual Costs\": 0.0937, \"PQI07 Hypertension Admission Rate (age < 65)\": 169.0, \"Percent African American\": 20.13, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0627, \"PAC: LTCH Per Capita Actual Costs\": 113.93, \"MA Beneficiaries\": 226824.0, \"FQHC/RHC Per User Standardized Costs\": 366.33, \"PAC: HH Per User Standardized Costs\": 5786.45, \"Procedures Per User Actual Costs\": 853.3, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 678.0, \"Ambulance Per User Actual Costs\": 1168.34, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1429.0, \"PAC: HH Standardized Costs\": 391349481.9, \"ASC Actual Costs as % of Total Actual Costs\": 0.0087, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 975.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0031, \"Percent Other/Unknown\": 1.07, \"% of Beneficiaries Using Hospice\": 0.0319, \"PAC: LTCH Per User Actual Costs\": 37045.64, \"Percent Non-Hispanic White\": 78.27, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 779.0, \"Count of Medicare beneficiaries with breast cancer\": 17941.0, \"DME Per Capita Standardized Costs\": 243.57, \"PQI08 CHF Admission Rate (age 65-74)\": 731.0, \"% of Beneficiaries Using DME\": 0.3161, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0294, \"% of Beneficiaries Using IP\": 0.1848, \"DME Standardized Costs\": 165767968.36, \"FQHC/RHC Standardized Costs\": 22987942.63, \"PAC: SNF Per Capita Actual Costs\": 509.41, \"Imaging Standardized Costs\": 170633250.97, \"# E&M Users\": 610728.0, \"Count of Medicare beneficiaries with arthritis\": 220194.0, \"IP Per User Standardized Costs\": 14362.56, \"IP Per User Actual Costs\": 14023.86, \"PQI10 Dehydration Admission Rate (age 75+)\": 617.0, \"PAC: IRF Per User Standardized Costs\": 21156.41, \"DME Per User Actual Costs\": 736.81, \"PAC: IRF Actual Costs\": 141997128.41, \"Imaging Events Per 1000 Beneficiaries\": 4394.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0289, \"PAC: LTCH Per User Standardized Costs\": 43459.87, \"OP Actual Costs\": 716604545.46, \"Count of Medicare beneficiaries with hypertension\": 418667.0, \"ASC Per User Standardized Costs\": 873.14, \"Part B Drugs Per Capita Actual Costs\": 350.06, \"Ambulance Events Per 1000 Beneficiaries\": 430.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 373.0, \"% of Beneficiaries Using PAC: HH\": 0.0754, \"PAC: LTCH Standardized Costs\": 74577202.64, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.51, \"E&M Per Capita Standardized Costs\": 759.81, \"E&M Per User Standardized Costs\": 865.62, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1136.0, \"IP Covered Days Per 1000 Beneficiaries\": 1496.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 39.0, \"Count of Medicare beneficiaries with lung cancer\": 4787.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3239, \"Percent Eligible for Medicaid\": 22.37, \"Imaging Per Capita Standardized Costs\": 182.88, \"% of Beneficiaries Using Tests\": 0.7906, \"Imaging Per Capita Actual Costs\": 163.44, \"% of Beneficiaries Using PAC: SNF\": 0.0412, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0399, \"Count of Medicare beneficiaries with colorectal cancer\": 5208.0, \"Hospice Actual Costs\": 110623104.54, \"# PAC: HH Users\": 33785.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 22864.92, \"Total Actual Costs\": 3543650897.17, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 48200.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0098, \"ASC Standardized Costs\": 37104961.06, \"DME Events Per 1000 Beneficiaries\": 2036.0, \"PQI08 CHF Admission Rate (age < 65)\": 748.0, \"ASC Events Per 1000 Beneficiaries\": 167.0, \"PAC: LTCH Actual Costs\": 65106557.74, \"Count of Medicare beneficiaries with depression\": 69946.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2108.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.76, \"Outpatient Dialysis Facility Per User Actual Costs\": 21326.68, \"Beneficiaries with Part A and Part B\": 564704.0, \"% of Beneficiaries Using Part B Drugs\": 0.5069, \"Percent of Medicare beneficiaries with diabetes\": 24.15, \"% of Beneficiaries Using PAC: IRF\": 0.0192, \"E&M Per Capita Actual Costs\": 664.57, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0217, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0377, \"PAC: SNF Actual Costs\": 246380523.15, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 748.0, \"Percent Female\": 54.75, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 148.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.55, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 185.87, \"# Outpatient Dialysis Facility Users\": 3642.0, \"FQHC/RHC Per User Actual Costs\": 315.09, \"Count of Medicare beneficiaries with ischemic heart disease\": 134487.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 191.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.85, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 495.0, \"Percent of Medicare beneficiaries with depression\": 15.61, \"Emergency Department Visits per 1000 Beneficiaries\": 637.0, \"IP Actual Costs\": 1147796453.93, \"% of Beneficiaries Using OP\": 0.6381, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0121, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0901, \"Count of Medicare beneficiaries with stroke\": 18267.0, \"PQI12 UTI Admission Rate (age 75+)\": 1396.0, \"# OP Users\": 285892.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 27.0, \"# Procedure Users\": 261351.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 30.02, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0651, \"Count of Medicare beneficiaries with diabetes\": 108187.0, \"ASC Per User Actual Costs\": 706.44, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1166.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0309, \"Percent of Medicare beneficiaries with high cholesterol\": 37.92, \"Standardized Per Capita Costs\": 8431.55, \"Ambulance Actual Costs\": 52806613.93, \"FQHC/RHC Per Capita Actual Costs\": 36.37, \"Part B Drugs Per User Standardized Costs\": 626.5, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0437, \"# PAC: SNF Users (with a covered stay)\": 18468.0, \"Ambulance Per Capita Actual Costs\": 117.87, \"PQI12 UTI Admission Rate (age 65-74)\": 320.0, \"Hospice Standardized Costs\": 123438112.8, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 173.37, \"% of Beneficiaries Using E&M\": 0.8778, \"PQI10 Dehydration Admission Rate (age 65-74)\": 249.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 142.0, \"Tests Per Capita Actual Costs\": 229.61, \"# DME Users\": 134571.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0783, \"PAC: SNF Per User Actual Costs\": 13340.94, \"State\": \"AR\", \"OP Per User Actual Costs\": 1730.81, \"PAC: HH Episodes Per 1000 Beneficiaries\": 147.0, \"Part B Drugs Actual Costs\": 141383542.02, \"FQHC/RHC Actual Costs\": 16292514.33, \"OP Standardized Costs\": 520432404.4, \"DME Per User Standardized Costs\": 866.15, \"OP Actual Costs as % of Total Actual Costs\": 0.1396, \"PAC: SNF Per Capita Standardized Costs\": 660.57, \"% of Beneficiaries Using Ambulance\": 0.1062, \"Hospice Per User Standardized Costs\": 10475.95, \"# Imaging Users\": 304023.0, \"Part B Drugs Per User Actual Costs\": 622.6, \"Total Standardized Costs\": 3777446150.59, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.16, \"Count of Medicare beneficiaries with chronic kidney disease\": 59113.0, \"E&M Standardized Costs\": 340406021.09, \"Percent Hispanic\": 1.03, \"ASC Per Capita Actual Costs\": 73.21, \"Count of Medicare beneficiaries who have had a heart attack\": 3824.0, \"Tests Actual Costs\": 102869316.18, \"# PAC: LTCH Users (with a covered stay)\": 1731.0, \"% of Beneficiaries Using Procedures\": 0.5834, \"PAC: HH Actual Costs\": 148674318.65, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 49.0, \"PAC: LTCH Per Capita Standardized Costs\": 166.46, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0288, \"Emergency Department Visits\": 285599.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1154, \"Procedures Actual Costs\": 215895751.02, \"# FQHC/RHC Users\": 51707.0, \"Number of Acute Hospital Readmissions\": 20980.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 263.0, \"Outpatient Dialysis Facility Standardized Costs\": 83274027.92, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0426, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1378, \"Ambulance Per User Standardized Costs\": 959.39, \"Imaging Per User Standardized Costs\": 269.5, \"Percent of Medicare beneficiaries with asthma\": 3.84, \"Part B Drugs Standardized Costs\": 142268902.3, \"FFS Beneficiaries\": 448013.0, \"# Hospice Users (with a covered stay)\": 11783.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0081, \"Count of Medicare beneficiaries with osteoporosis\": 24854.0, \"PQI08 CHF Admission Rate (age 75+)\": 2088.0, \"PAC: IRF Per Capita Standardized Costs\": 368.5, \"Procedures Standardized Costs\": 245820772.93, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3015, \"IP Per Capita Actual Costs\": 2561.97, \"DME Actual Costs\": 110972789.2, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.042, \"Count of Medicare beneficiaries with prostate cancer\": 11086.0, \"PAC: HH Per Capita Standardized Costs\": 393.32, \"Count of Medicare beneficiaries with heart failure\": 64885.0, \"Tests Per User Standardized Costs\": 306.98, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0184, \"Percent of Medicare beneficiaries with prostate cancer\": 2.47, \"PAC: IRF Per Capita Actual Costs\": 336.88, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 240.84, \"Percent of Medicare beneficiaries with breast cancer\": 2.55, \"Procedures Per User Standardized Costs\": 940.58, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.19, \"PAC: HH Per User Actual Costs\": 4400.6, \"Count of Medicare beneficiaries with high cholesterol\": 169906.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0695, \"Hospice Per Capita Standardized Costs\": 275.52, \"# Part B Drugs Users\": 227086.0, \"Average HCC Score\": 0.9345, \"Standardized Risk-Adjusted Per Capita Costs\": 9402.4, \"# PAC: IRF Users (with a covered stay)\": 8589.0, \"Ambulance Standardized Costs\": 45655192.64, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0312, \"Percent of Medicare beneficiaries with heart failure\": 14.48, \"Tests Actual Costs as % of Total Actual Costs\": 0.029, \"FQHC/RHC Per Capita Standardized Costs\": 43.63, \"PQI07 Hypertension Admission Rate (age 65-74)\": 71.0, \"Test Events Per 1000 Beneficiaries\": 9884.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 112.0, \"ASC Actual Costs\": 32799105.64, \"Part B Drugs Per Capita Standardized Costs\": 317.56, \"Imaging Actual Costs\": 73221949.65, \"Tests Per User Actual Costs\": 290.44, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0149, \"Hospice Per User Actual Costs\": 9388.37, \"Tests Standardized Costs\": 108726692.03, \"IP Standardized Costs\": 1138815909.21, \"IP Per Capita Standardized Costs\": 2541.93, \"Outpatient Dialysis Facility Actual Costs\": 77671756.9, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 58.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1559.0, \"Percent of Medicare beneficiaries with hypertension\": 54.58, \"IP Covered Stays Per 1000 Beneficiaries\": 287.0, \"# Ambulance Users\": 47588.0, \"# ASC Users\": 46429.0, \"ASC Per Capita Standardized Costs\": 82.82, \"Procedures Per Capita Actual Costs\": 481.9, \"Procedures Per Capita Standardized Costs\": 548.69, \"IP Users (with a covered stay)\": 80804.0, \"Total Standardized Risk-Adjusted Costs\": 4212398787.17, \"Actual Per Capita Costs\": 7909.71, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0197, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 21.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 4.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.07, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.61, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 184.0, \"OP Per User Standardized Costs\": 1820.38, \"Count of Medicare beneficiaries with atrial fibrillation\": 33657.0, \"Procedure Events Per 1000 Beneficiaries\": 3710.0, \"Percent of Medicare beneficiaries with stroke\": 4.08, \"PAC: IRF Per User Actual Costs\": 17571.94, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 52012.0, \"% of Beneficiaries Using ASC\": 0.1036, \"PAC: IRF Standardized Costs\": 165094900.63, \"Hospice Covered Days Per 1000 Beneficiaries\": 1628.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 274.0, \"PAC: SNF Per User Standardized Costs\": 16024.69, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0046, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 135.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0327, \"MA Participation Rate\": 20.66, \"OP Per Capita Standardized Costs\": 1161.65, \"Percent of Medicare beneficiaries with arthritis\": 27.28, \"Ambulance Per Capita Standardized Costs\": 101.91, \"PAC: HH Visits Per 1000 Beneficiaries\": 2515.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0609, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0207, \"PAC: HH Per Capita Actual Costs\": 331.85, \"E&M Events Per 1000 Beneficiaries\": 11236.0, \"Count of Medicare beneficiaries with asthma\": 17191.0, \"# Test Users\": 354187.0, \"E&M Actual Costs\": 297736579.03, \"% of Beneficiaries Using Imaging\": 0.6786, \"PAC: SNF Standardized Costs\": 295943939.52, \"DME Per Capita Actual Costs\": 247.7, \"E&M Per User Actual Costs\": 757.11, \"Percent Male\": 45.25, \"OP Visits Per 1000 Beneficiaries\": 3654.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0313, \"OP Per Capita Actual Costs\": 1104.49, \"Hospital Readmission Rate\": 0.172, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0052, \"Tests Per Capita Standardized Costs\": 242.69, \"Hospice Per Capita Actual Costs\": 246.92, \"E&M Actual Costs as % of Total Actual Costs\": 0.084, \"PQI07 Hypertension Admission Rate (age < 65)\": 134.0, \"Percent African American\": 10.61, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0466, \"PAC: LTCH Per Capita Actual Costs\": 145.32, \"MA Beneficiaries\": 116691.0, \"FQHC/RHC Per User Standardized Costs\": 377.99, \"PAC: HH Per User Standardized Costs\": 5215.68, \"Procedures Per User Actual Costs\": 826.08, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 499.0, \"Ambulance Per User Actual Costs\": 1109.67, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1270.0, \"PAC: HH Standardized Costs\": 176211677.02, \"ASC Actual Costs as % of Total Actual Costs\": 0.0093, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 893.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0039, \"Percent Other/Unknown\": 1.43, \"% of Beneficiaries Using Hospice\": 0.0263, \"PAC: LTCH Per User Actual Costs\": 37612.11, \"Percent Non-Hispanic White\": 86.93, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 781.0, \"Count of Medicare beneficiaries with breast cancer\": 11412.0, \"DME Per Capita Standardized Costs\": 260.17, \"PQI08 CHF Admission Rate (age 65-74)\": 663.0, \"% of Beneficiaries Using DME\": 0.3004, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0219, \"% of Beneficiaries Using IP\": 0.1804, \"DME Standardized Costs\": 116558090.52, \"FQHC/RHC Standardized Costs\": 19544732.25, \"PAC: SNF Per Capita Actual Costs\": 549.94, \"Imaging Standardized Costs\": 81934428.9, \"# E&M Users\": 393252.0, \"Count of Medicare beneficiaries with arthritis\": 122231.0, \"IP Per User Standardized Costs\": 14093.56, \"IP Per User Actual Costs\": 14204.7, \"PQI10 Dehydration Admission Rate (age 75+)\": 596.0, \"PAC: IRF Per User Standardized Costs\": 19221.67, \"DME Per User Actual Costs\": 824.64, \"PAC: IRF Actual Costs\": 150925404.47, \"Imaging Events Per 1000 Beneficiaries\": 3974.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.022, \"PAC: LTCH Per User Standardized Costs\": 43083.31, \"OP Actual Costs\": 494823810.98, \"Count of Medicare beneficiaries with hypertension\": 244535.0, \"ASC Per User Standardized Costs\": 799.18, \"Part B Drugs Per Capita Actual Costs\": 315.58, \"Ambulance Events Per 1000 Beneficiaries\": 282.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 287.0, \"% of Beneficiaries Using PAC: HH\": 0.0611, \"PAC: LTCH Standardized Costs\": 65458088.71, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.56, \"E&M Per Capita Standardized Costs\": 991.06, \"E&M Per User Standardized Costs\": 1143.79, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1234.0, \"IP Covered Days Per 1000 Beneficiaries\": 1123.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 27.0, \"Count of Medicare beneficiaries with lung cancer\": 5417.0, \"IP Actual Costs as % of Total Actual Costs\": 0.325, \"Percent Eligible for Medicaid\": 10.32, \"Imaging Per Capita Standardized Costs\": 302.64, \"% of Beneficiaries Using Tests\": 0.7809, \"Imaging Per Capita Actual Costs\": 291.66, \"% of Beneficiaries Using PAC: SNF\": 0.0334, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0451, \"Count of Medicare beneficiaries with colorectal cancer\": 6593.0, \"Hospice Actual Costs\": 235578999.64, \"# PAC: HH Users\": 36189.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23217.56, \"Total Actual Costs\": 5142920635.12, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 41693.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0154, \"ASC Standardized Costs\": 74446151.84, \"DME Events Per 1000 Beneficiaries\": 1358.0, \"PQI08 CHF Admission Rate (age < 65)\": 606.0, \"ASC Events Per 1000 Beneficiaries\": 230.0, \"PAC: LTCH Actual Costs\": 65355247.53, \"Count of Medicare beneficiaries with depression\": 70076.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1306.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 7.03, \"Outpatient Dialysis Facility Per User Actual Costs\": 23489.72, \"Beneficiaries with Part A and Part B\": 1008588.0, \"% of Beneficiaries Using Part B Drugs\": 0.5436, \"Percent of Medicare beneficiaries with diabetes\": 22.11, \"% of Beneficiaries Using PAC: IRF\": 0.012, \"E&M Per Capita Actual Costs\": 938.59, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0372, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0483, \"PAC: SNF Actual Costs\": 266379440.64, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 385.0, \"Percent Female\": 52.15, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 243.0, \"Percent of Medicare beneficiaries with osteoporosis\": 6.24, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 218.05, \"# Outpatient Dialysis Facility Users\": 5566.0, \"FQHC/RHC Per User Actual Costs\": 360.23, \"Count of Medicare beneficiaries with ischemic heart disease\": 143903.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 133.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.69, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 150.0, \"Percent of Medicare beneficiaries with depression\": 11.82, \"Emergency Department Visits per 1000 Beneficiaries\": 527.0, \"IP Actual Costs\": 1671409048.45, \"% of Beneficiaries Using OP\": 0.5003, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0093, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1218, \"Count of Medicare beneficiaries with stroke\": 20065.0, \"PQI12 UTI Admission Rate (age 75+)\": 760.0, \"# OP Users\": 296513.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 33.0, \"# Procedure Users\": 374530.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 24.28, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0956, \"Count of Medicare beneficiaries with diabetes\": 131033.0, \"ASC Per User Actual Costs\": 898.23, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 778.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0229, \"Percent of Medicare beneficiaries with high cholesterol\": 43.45, \"Standardized Per Capita Costs\": 8134.62, \"Ambulance Actual Costs\": 63131416.92, \"FQHC/RHC Per Capita Actual Costs\": 15.81, \"Part B Drugs Per User Standardized Costs\": 723.38, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0279, \"# PAC: SNF Users (with a covered stay)\": 19824.0, \"Ambulance Per Capita Actual Costs\": 106.52, \"PQI12 UTI Admission Rate (age 65-74)\": 182.0, \"Hospice Standardized Costs\": 226351570.4, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 220.6, \"% of Beneficiaries Using E&M\": 0.8665, \"PQI10 Dehydration Admission Rate (age 65-74)\": 151.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 165.0, \"Tests Per Capita Actual Costs\": 313.03, \"# DME Users\": 140488.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0554, \"PAC: SNF Per User Actual Costs\": 13437.22, \"State\": \"AZ\", \"OP Per User Actual Costs\": 1983.59, \"PAC: HH Episodes Per 1000 Beneficiaries\": 92.0, \"Part B Drugs Actual Costs\": 231967636.74, \"FQHC/RHC Actual Costs\": 9368228.43, \"OP Standardized Costs\": 548892501.93, \"DME Per User Standardized Costs\": 784.74, \"OP Actual Costs as % of Total Actual Costs\": 0.1144, \"PAC: SNF Per Capita Standardized Costs\": 450.92, \"% of Beneficiaries Using Ambulance\": 0.1005, \"Hospice Per User Standardized Costs\": 12486.3, \"# Imaging Users\": 399703.0, \"Part B Drugs Per User Actual Costs\": 719.95, \"Total Standardized Costs\": 4821144476.85, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.11, \"Count of Medicare beneficiaries with chronic kidney disease\": 96379.0, \"E&M Standardized Costs\": 587370398.5, \"Percent Hispanic\": 7.97, \"ASC Per Capita Actual Costs\": 124.97, \"Count of Medicare beneficiaries who have had a heart attack\": 4075.0, \"Tests Actual Costs\": 185523029.07, \"# PAC: LTCH Users (with a covered stay)\": 1440.0, \"% of Beneficiaries Using Procedures\": 0.6319, \"PAC: HH Actual Costs\": 152112926.72, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 26.0, \"PAC: LTCH Per Capita Standardized Costs\": 110.45, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0399, \"Emergency Department Visits\": 312110.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0439, \"Procedures Actual Costs\": 444743392.12, \"# FQHC/RHC Users\": 26006.0, \"Number of Acute Hospital Readmissions\": 22703.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 168.0, \"Outpatient Dialysis Facility Standardized Costs\": 129228962.71, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0266, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1139, \"Ambulance Per User Standardized Costs\": 749.48, \"Imaging Per User Standardized Costs\": 448.75, \"Percent of Medicare beneficiaries with asthma\": 5.11, \"Part B Drugs Standardized Costs\": 233072043.32, \"FFS Beneficiaries\": 592670.0, \"# Hospice Users (with a covered stay)\": 18128.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0094, \"Count of Medicare beneficiaries with osteoporosis\": 36969.0, \"PQI08 CHF Admission Rate (age 75+)\": 1247.0, \"PAC: IRF Per Capita Standardized Costs\": 226.85, \"Procedures Standardized Costs\": 460836606.5, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2798, \"IP Per Capita Actual Costs\": 2820.13, \"DME Actual Costs\": 104483909.0, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0296, \"Count of Medicare beneficiaries with prostate cancer\": 20714.0, \"PAC: HH Per Capita Standardized Costs\": 255.31, \"Count of Medicare beneficiaries with heart failure\": 59573.0, \"Tests Per User Standardized Costs\": 416.09, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0127, \"Percent of Medicare beneficiaries with prostate cancer\": 3.5, \"PAC: IRF Per Capita Actual Costs\": 230.44, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 432.47, \"Percent of Medicare beneficiaries with breast cancer\": 2.99, \"Procedures Per User Standardized Costs\": 1230.44, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.26, \"PAC: HH Per User Actual Costs\": 4203.29, \"Count of Medicare beneficiaries with high cholesterol\": 257527.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0518, \"Hospice Per Capita Standardized Costs\": 381.92, \"# Part B Drugs Users\": 322200.0, \"Average HCC Score\": 0.9126, \"Standardized Risk-Adjusted Per Capita Costs\": 9693.99, \"# PAC: IRF Users (with a covered stay)\": 7091.0, \"Ambulance Standardized Costs\": 44631824.45, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0458, \"Percent of Medicare beneficiaries with heart failure\": 10.05, \"Tests Actual Costs as % of Total Actual Costs\": 0.0361, \"FQHC/RHC Per Capita Standardized Costs\": 15.34, \"PQI07 Hypertension Admission Rate (age 65-74)\": 60.0, \"Test Events Per 1000 Beneficiaries\": 10855.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 69.0, \"ASC Actual Costs\": 74067948.52, \"Part B Drugs Per Capita Standardized Costs\": 393.26, \"Imaging Actual Costs\": 172859329.52, \"Tests Per User Actual Costs\": 400.83, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0123, \"Hospice Per User Actual Costs\": 12995.31, \"Tests Standardized Costs\": 192586845.96, \"IP Standardized Costs\": 1349083767.11, \"IP Per Capita Standardized Costs\": 2276.28, \"Outpatient Dialysis Facility Actual Costs\": 130743759.67, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 43.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1023.0, \"Percent of Medicare beneficiaries with hypertension\": 50.06, \"IP Covered Stays Per 1000 Beneficiaries\": 235.0, \"# Ambulance Users\": 59550.0, \"# ASC Users\": 82460.0, \"ASC Per Capita Standardized Costs\": 125.61, \"Procedures Per Capita Actual Costs\": 750.41, \"Procedures Per Capita Standardized Costs\": 777.56, \"IP Users (with a covered stay)\": 91505.0, \"Total Standardized Risk-Adjusted Costs\": 5745339088.31, \"Actual Per Capita Costs\": 8677.55, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0136, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 13.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.91, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.37, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 185.0, \"OP Per User Standardized Costs\": 1851.16, \"Count of Medicare beneficiaries with atrial fibrillation\": 44828.0, \"Procedure Events Per 1000 Beneficiaries\": 5468.0, \"Percent of Medicare beneficiaries with stroke\": 3.39, \"PAC: IRF Per User Actual Costs\": 19259.93, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 55537.0, \"% of Beneficiaries Using ASC\": 0.1391, \"PAC: IRF Standardized Costs\": 134448538.89, \"Hospice Covered Days Per 1000 Beneficiaries\": 2311.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 262.0, \"PAC: SNF Per User Standardized Costs\": 13481.09, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0018, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 141.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0469, \"MA Participation Rate\": 41.24, \"OP Per Capita Standardized Costs\": 926.14, \"Percent of Medicare beneficiaries with arthritis\": 27.99, \"Ambulance Per Capita Standardized Costs\": 75.31, \"PAC: HH Visits Per 1000 Beneficiaries\": 1399.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0865, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0336, \"PAC: HH Per Capita Actual Costs\": 256.66, \"E&M Events Per 1000 Beneficiaries\": 13176.0, \"Count of Medicare beneficiaries with asthma\": 30287.0, \"# Test Users\": 462844.0, \"E&M Actual Costs\": 556273348.66, \"% of Beneficiaries Using Imaging\": 0.6744, \"PAC: SNF Standardized Costs\": 267249188.93, \"DME Per Capita Actual Costs\": 176.29, \"E&M Per User Actual Costs\": 1083.23, \"Percent Male\": 47.85, \"OP Visits Per 1000 Beneficiaries\": 2604.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0203, \"OP Per Capita Actual Costs\": 992.39, \"Hospital Readmission Rate\": 0.1643, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0019, \"Tests Per Capita Standardized Costs\": 324.95, \"Hospice Per Capita Actual Costs\": 397.49, \"E&M Actual Costs as % of Total Actual Costs\": 0.1082, \"PQI07 Hypertension Admission Rate (age < 65)\": 121.0, \"Percent African American\": 2.4, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0314, \"PAC: LTCH Per Capita Actual Costs\": 110.27, \"MA Beneficiaries\": 415918.0, \"FQHC/RHC Per User Standardized Costs\": 349.6, \"PAC: HH Per User Standardized Costs\": 4181.15, \"Procedures Per User Actual Costs\": 1187.47, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 822.0, \"Ambulance Per User Actual Costs\": 1060.14, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 815.0, \"PAC: HH Standardized Costs\": 151311748.53, \"ASC Actual Costs as % of Total Actual Costs\": 0.0144, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 440.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0024, \"Percent Other/Unknown\": 5.98, \"% of Beneficiaries Using Hospice\": 0.0306, \"PAC: LTCH Per User Actual Costs\": 45385.59, \"Percent Non-Hispanic White\": 83.66, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 553.0, \"Count of Medicare beneficiaries with breast cancer\": 17740.0, \"DME Per Capita Standardized Costs\": 186.02, \"PQI08 CHF Admission Rate (age 65-74)\": 356.0, \"% of Beneficiaries Using DME\": 0.237, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0254, \"% of Beneficiaries Using IP\": 0.1544, \"DME Standardized Costs\": 110247202.76, \"FQHC/RHC Standardized Costs\": 9091674.46, \"PAC: SNF Per Capita Actual Costs\": 449.46, \"Imaging Standardized Costs\": 179366702.71, \"# E&M Users\": 513532.0, \"Count of Medicare beneficiaries with arthritis\": 165912.0, \"IP Per User Standardized Costs\": 14743.28, \"IP Per User Actual Costs\": 18265.77, \"PQI10 Dehydration Admission Rate (age 75+)\": 388.0, \"PAC: IRF Per User Standardized Costs\": 18960.45, \"DME Per User Actual Costs\": 743.72, \"PAC: IRF Actual Costs\": 136572179.08, \"Imaging Events Per 1000 Beneficiaries\": 4198.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0268, \"PAC: LTCH Per User Standardized Costs\": 45457.01, \"OP Actual Costs\": 588161166.79, \"Count of Medicare beneficiaries with hypertension\": 296672.0, \"ASC Per User Standardized Costs\": 902.82, \"Part B Drugs Per Capita Actual Costs\": 391.39, \"Ambulance Events Per 1000 Beneficiaries\": 201.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 329.0, \"% of Beneficiaries Using PAC: HH\": 0.0914, \"PAC: LTCH Standardized Costs\": 479427743.65, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.19, \"E&M Per Capita Standardized Costs\": 984.96, \"E&M Per User Standardized Costs\": 1179.41, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1746.0, \"IP Covered Days Per 1000 Beneficiaries\": 1362.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 35.0, \"Count of Medicare beneficiaries with lung cancer\": 22918.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3621, \"Percent Eligible for Medicaid\": 29.74, \"Imaging Per Capita Standardized Costs\": 254.09, \"% of Beneficiaries Using Tests\": 0.7424, \"Imaging Per Capita Actual Costs\": 271.36, \"% of Beneficiaries Using PAC: SNF\": 0.0457, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0329, \"Count of Medicare beneficiaries with colorectal cancer\": 32178.0, \"Hospice Actual Costs\": 801169946.71, \"# PAC: HH Users\": 259045.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24251.62, \"Total Actual Costs\": 29742884785.75, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 281211.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0119, \"ASC Standardized Costs\": 288350495.66, \"DME Events Per 1000 Beneficiaries\": 1317.0, \"PQI08 CHF Admission Rate (age < 65)\": 808.0, \"ASC Events Per 1000 Beneficiaries\": 185.0, \"PAC: LTCH Actual Costs\": 549497771.46, \"Count of Medicare beneficiaries with depression\": 388133.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1204.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.92, \"Outpatient Dialysis Facility Per User Actual Costs\": 27143.03, \"Beneficiaries with Part A and Part B\": 4942019.0, \"% of Beneficiaries Using Part B Drugs\": 0.4931, \"Percent of Medicare beneficiaries with diabetes\": 26.41, \"% of Beneficiaries Using PAC: IRF\": 0.006, \"E&M Per Capita Actual Costs\": 1010.11, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0298, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0406, \"PAC: SNF Actual Costs\": 2710772439.99, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 365.0, \"Percent Female\": 53.67, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 202.0, \"Percent of Medicare beneficiaries with osteoporosis\": 7.06, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 310.65, \"# Outpatient Dialysis Facility Users\": 36314.0, \"FQHC/RHC Per User Actual Costs\": 479.03, \"Count of Medicare beneficiaries with ischemic heart disease\": 713900.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 122.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.68, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 508.0, \"Percent of Medicare beneficiaries with depression\": 13.69, \"Emergency Department Visits per 1000 Beneficiaries\": 566.0, \"IP Actual Costs\": 10770275070.99, \"% of Beneficiaries Using OP\": 0.5414, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0182, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1155, \"Count of Medicare beneficiaries with stroke\": 99327.0, \"PQI12 UTI Admission Rate (age 75+)\": 926.0, \"# OP Users\": 1534884.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 23.0, \"# Procedure Users\": 1671588.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 25.18, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0787, \"Count of Medicare beneficiaries with diabetes\": 748771.0, \"ASC Per User Actual Costs\": 1042.77, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 802.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0205, \"Percent of Medicare beneficiaries with high cholesterol\": 42.03, \"Standardized Per Capita Costs\": 8524.31, \"Ambulance Actual Costs\": 433801608.99, \"FQHC/RHC Per Capita Actual Costs\": 45.22, \"Part B Drugs Per User Standardized Costs\": 702.05, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0141, \"# PAC: SNF Users (with a covered stay)\": 129494.0, \"Ambulance Per Capita Actual Costs\": 153.02, \"PQI12 UTI Admission Rate (age 65-74)\": 212.0, \"Hospice Standardized Costs\": 657433976.39, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 347.69, \"% of Beneficiaries Using E&M\": 0.8351, \"PQI10 Dehydration Admission Rate (age 65-74)\": 149.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 188.0, \"Tests Per Capita Actual Costs\": 274.01, \"# DME Users\": 667828.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0915, \"PAC: SNF Per User Actual Costs\": 20933.58, \"State\": \"CA\", \"OP Per User Actual Costs\": 1931.97, \"PAC: HH Episodes Per 1000 Beneficiaries\": 159.0, \"Part B Drugs Actual Costs\": 979441601.77, \"FQHC/RHC Actual Costs\": 128196282.04, \"OP Standardized Costs\": 2580946092.78, \"DME Per User Standardized Costs\": 742.42, \"OP Actual Costs as % of Total Actual Costs\": 0.0997, \"PAC: SNF Per Capita Standardized Costs\": 780.19, \"% of Beneficiaries Using Ambulance\": 0.1142, \"Hospice Per User Standardized Costs\": 10603.94, \"# Imaging Users\": 1816666.0, \"Part B Drugs Per User Actual Costs\": 700.7, \"Total Standardized Costs\": 24165766597.12, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.14, \"Count of Medicare beneficiaries with chronic kidney disease\": 462959.0, \"E&M Standardized Costs\": 2792298613.21, \"Percent Hispanic\": 17.23, \"ASC Per Capita Actual Costs\": 115.75, \"Count of Medicare beneficiaries who have had a heart attack\": 19204.0, \"Tests Actual Costs\": 776784249.82, \"# PAC: LTCH Users (with a covered stay)\": 10171.0, \"% of Beneficiaries Using Procedures\": 0.5896, \"PAC: HH Actual Costs\": 1441197311.04, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 36.0, \"PAC: LTCH Per Capita Standardized Costs\": 169.11, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0318, \"Emergency Department Visits\": 1605699.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0944, \"Procedures Actual Costs\": 1991387014.82, \"# FQHC/RHC Users\": 267618.0, \"Number of Acute Hospital Readmissions\": 125785.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 82.0, \"Outpatient Dialysis Facility Standardized Costs\": 880673369.46, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0146, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1068, \"Ambulance Per User Standardized Costs\": 1358.18, \"Imaging Per User Standardized Costs\": 396.5, \"Percent of Medicare beneficiaries with asthma\": 5.27, \"Part B Drugs Standardized Costs\": 981326441.31, \"FFS Beneficiaries\": 2834924.0, \"# Hospice Users (with a covered stay)\": 61999.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0128, \"Count of Medicare beneficiaries with osteoporosis\": 200210.0, \"PQI08 CHF Admission Rate (age 75+)\": 1548.0, \"PAC: IRF Per Capita Standardized Costs\": 120.39, \"Procedures Standardized Costs\": 1902532803.61, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2884, \"IP Per Capita Actual Costs\": 3799.14, \"DME Actual Costs\": 468763436.94, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0485, \"Count of Medicare beneficiaries with prostate cancer\": 82168.0, \"PAC: HH Per Capita Standardized Costs\": 420.52, \"Count of Medicare beneficiaries with heart failure\": 388529.0, \"Tests Per User Standardized Costs\": 364.72, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0185, \"Percent of Medicare beneficiaries with prostate cancer\": 2.9, \"PAC: IRF Per Capita Actual Costs\": 152.77, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 423.46, \"Percent of Medicare beneficiaries with breast cancer\": 2.84, \"Procedures Per User Standardized Costs\": 1138.16, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.33, \"PAC: HH Per User Actual Costs\": 5563.5, \"Count of Medicare beneficiaries with high cholesterol\": 1191635.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0911, \"Hospice Per Capita Standardized Costs\": 231.91, \"# Part B Drugs Users\": 1397807.0, \"Average HCC Score\": 1.0241, \"Standardized Risk-Adjusted Per Capita Costs\": 8385.16, \"# PAC: IRF Users (with a covered stay)\": 17082.0, \"Ambulance Standardized Costs\": 439761044.47, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0269, \"Percent of Medicare beneficiaries with heart failure\": 13.71, \"Tests Actual Costs as % of Total Actual Costs\": 0.0261, \"FQHC/RHC Per Capita Standardized Costs\": 40.47, \"PQI07 Hypertension Admission Rate (age 65-74)\": 55.0, \"Test Events Per 1000 Beneficiaries\": 9687.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 113.0, \"ASC Actual Costs\": 328149732.99, \"Part B Drugs Per Capita Standardized Costs\": 346.16, \"Imaging Actual Costs\": 769286804.8, \"Tests Per User Actual Costs\": 369.09, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0146, \"Hospice Per User Actual Costs\": 12922.3, \"Tests Standardized Costs\": 767576942.68, \"IP Standardized Costs\": 6969838988.44, \"IP Per Capita Standardized Costs\": 2458.56, \"Outpatient Dialysis Facility Actual Costs\": 985671821.66, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 64.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1819.0, \"Percent of Medicare beneficiaries with hypertension\": 51.21, \"IP Covered Stays Per 1000 Beneficiaries\": 250.0, \"# Ambulance Users\": 323787.0, \"# ASC Users\": 314690.0, \"ASC Per Capita Standardized Costs\": 101.71, \"Procedures Per Capita Actual Costs\": 702.45, \"Procedures Per Capita Standardized Costs\": 671.11, \"IP Users (with a covered stay)\": 441383.0, \"Total Standardized Risk-Adjusted Costs\": 23771283478.74, \"Actual Per Capita Costs\": 10491.6, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0198, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 7.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 4.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.81, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.31, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 232.0, \"OP Per User Standardized Costs\": 1681.53, \"Count of Medicare beneficiaries with atrial fibrillation\": 203971.0, \"Procedure Events Per 1000 Beneficiaries\": 5124.0, \"Percent of Medicare beneficiaries with stroke\": 3.5, \"PAC: IRF Per User Actual Costs\": 25353.58, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 263853.0, \"% of Beneficiaries Using ASC\": 0.111, \"PAC: IRF Standardized Costs\": 341289492.46, \"Hospice Covered Days Per 1000 Beneficiaries\": 1457.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 223.0, \"PAC: SNF Per User Standardized Costs\": 17080.18, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0043, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 105.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0272, \"MA Participation Rate\": 42.64, \"OP Per Capita Standardized Costs\": 910.41, \"Percent of Medicare beneficiaries with arthritis\": 27.53, \"Ambulance Per Capita Standardized Costs\": 155.12, \"PAC: HH Visits Per 1000 Beneficiaries\": 2427.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.067, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0259, \"PAC: HH Per Capita Actual Costs\": 508.37, \"E&M Events Per 1000 Beneficiaries\": 13593.0, \"Count of Medicare beneficiaries with asthma\": 149514.0, \"# Test Users\": 2104581.0, \"E&M Actual Costs\": 2863590358.45, \"% of Beneficiaries Using Imaging\": 0.6408, \"PAC: SNF Standardized Costs\": 2211780545.17, \"DME Per Capita Actual Costs\": 165.35, \"E&M Per User Actual Costs\": 1209.52, \"Percent Male\": 46.33, \"OP Visits Per 1000 Beneficiaries\": 3143.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0158, \"OP Per Capita Actual Costs\": 1046.01, \"Hospital Readmission Rate\": 0.1844, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0047, \"Tests Per Capita Standardized Costs\": 270.76, \"Hospice Per Capita Actual Costs\": 282.61, \"E&M Actual Costs as % of Total Actual Costs\": 0.0963, \"PQI07 Hypertension Admission Rate (age < 65)\": 90.0, \"Percent African American\": 5.72, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0493, \"PAC: LTCH Per Capita Actual Costs\": 193.83, \"MA Beneficiaries\": 2107095.0, \"FQHC/RHC Per User Standardized Costs\": 428.68, \"PAC: HH Per User Standardized Costs\": 4602.07, \"Procedures Per User Actual Costs\": 1191.31, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 720.0, \"Ambulance Per User Actual Costs\": 1339.77, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 833.0, \"PAC: HH Standardized Costs\": 1192143019.65, \"ASC Actual Costs as % of Total Actual Costs\": 0.011, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 468.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0036, \"Percent Other/Unknown\": 12.84, \"% of Beneficiaries Using Hospice\": 0.0219, \"PAC: LTCH Per User Actual Costs\": 54025.93, \"Percent Non-Hispanic White\": 64.21, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 484.0, \"Count of Medicare beneficiaries with breast cancer\": 80579.0, \"DME Per Capita Standardized Costs\": 174.89, \"PQI08 CHF Admission Rate (age 65-74)\": 484.0, \"% of Beneficiaries Using DME\": 0.2356, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0331, \"% of Beneficiaries Using IP\": 0.1557, \"DME Standardized Costs\": 495811916.74, \"FQHC/RHC Standardized Costs\": 114723016.15, \"PAC: SNF Per Capita Actual Costs\": 956.21, \"Imaging Standardized Costs\": 720315642.42, \"# E&M Users\": 2367534.0, \"Count of Medicare beneficiaries with arthritis\": 780522.0, \"IP Per User Standardized Costs\": 15790.91, \"IP Per User Actual Costs\": 24401.2, \"PQI10 Dehydration Admission Rate (age 75+)\": 399.0, \"PAC: IRF Per User Standardized Costs\": 19979.48, \"DME Per User Actual Costs\": 701.92, \"PAC: IRF Actual Costs\": 433089775.73, \"Imaging Events Per 1000 Beneficiaries\": 3753.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0364, \"PAC: LTCH Per User Standardized Costs\": 47136.74, \"OP Actual Costs\": 2965347402.24, \"Count of Medicare beneficiaries with hypertension\": 1451736.0, \"ASC Per User Standardized Costs\": 916.3, \"Part B Drugs Per Capita Actual Costs\": 345.49, \"Ambulance Events Per 1000 Beneficiaries\": 466.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 233.0, \"% of Beneficiaries Using PAC: HH\": 0.0704, \"PAC: LTCH Standardized Costs\": 44417425.7, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.42, \"E&M Per Capita Standardized Costs\": 755.61, \"E&M Per User Standardized Costs\": 893.21, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 941.0, \"IP Covered Days Per 1000 Beneficiaries\": 1062.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 25.0, \"Count of Medicare beneficiaries with lung cancer\": 3042.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3141, \"Percent Eligible for Medicaid\": 14.61, \"Imaging Per Capita Standardized Costs\": 159.99, \"% of Beneficiaries Using Tests\": 0.7131, \"Imaging Per Capita Actual Costs\": 156.69, \"% of Beneficiaries Using PAC: SNF\": 0.0436, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0412, \"Count of Medicare beneficiaries with colorectal cancer\": 3949.0, \"Hospice Actual Costs\": 128158159.81, \"# PAC: HH Users\": 29572.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24186.66, \"Total Actual Costs\": 3316429403.76, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 35588.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0156, \"ASC Standardized Costs\": 49506649.15, \"DME Events Per 1000 Beneficiaries\": 2038.0, \"PQI08 CHF Admission Rate (age < 65)\": 489.0, \"ASC Events Per 1000 Beneficiaries\": 216.0, \"PAC: LTCH Actual Costs\": 44319472.19, \"Count of Medicare beneficiaries with depression\": 61470.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1252.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.47, \"Outpatient Dialysis Facility Per User Actual Costs\": 24569.53, \"Beneficiaries with Part A and Part B\": 686059.0, \"% of Beneficiaries Using Part B Drugs\": 0.4999, \"Percent of Medicare beneficiaries with diabetes\": 18.18, \"% of Beneficiaries Using PAC: IRF\": 0.0069, \"E&M Per Capita Actual Costs\": 712.7, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0212, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0434, \"PAC: SNF Actual Costs\": 268010753.55, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 375.0, \"Percent Female\": 52.89, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 201.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.48, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 164.33, \"# Outpatient Dialysis Facility Users\": 2854.0, \"FQHC/RHC Per User Actual Costs\": 449.23, \"Count of Medicare beneficiaries with ischemic heart disease\": 81305.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 110.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.54, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 487.0, \"Percent of Medicare beneficiaries with depression\": 14.63, \"Emergency Department Visits per 1000 Beneficiaries\": 583.0, \"IP Actual Costs\": 1041594711.9, \"% of Beneficiaries Using OP\": 0.6337, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.01, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1002, \"Count of Medicare beneficiaries with stroke\": 10789.0, \"PQI12 UTI Admission Rate (age 75+)\": 763.0, \"# OP Users\": 266219.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 27.0, \"# Procedure Users\": 241289.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 19.36, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0767, \"Count of Medicare beneficiaries with diabetes\": 76388.0, \"ASC Per User Actual Costs\": 850.32, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 660.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0341, \"Percent of Medicare beneficiaries with high cholesterol\": 32.27, \"Standardized Per Capita Costs\": 7544.01, \"Ambulance Actual Costs\": 33408693.28, \"FQHC/RHC Per Capita Actual Costs\": 49.95, \"Part B Drugs Per User Standardized Costs\": 654.45, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0175, \"# PAC: SNF Users (with a covered stay)\": 18295.0, \"Ambulance Per Capita Actual Costs\": 79.53, \"PQI12 UTI Admission Rate (age 65-74)\": 137.0, \"Hospice Standardized Costs\": 125695171.1, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 166.93, \"% of Beneficiaries Using E&M\": 0.846, \"PQI10 Dehydration Admission Rate (age 65-74)\": 134.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 94.0, \"Tests Per Capita Actual Costs\": 178.72, \"# DME Users\": 112822.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.086, \"PAC: SNF Per User Actual Costs\": 14649.4, \"State\": \"CO\", \"OP Per User Actual Costs\": 1820.76, \"PAC: HH Episodes Per 1000 Beneficiaries\": 109.0, \"Part B Drugs Actual Costs\": 136632179.89, \"FQHC/RHC Actual Costs\": 20983877.53, \"OP Standardized Costs\": 460886413.54, \"DME Per User Standardized Costs\": 958.06, \"OP Actual Costs as % of Total Actual Costs\": 0.1462, \"PAC: SNF Per Capita Standardized Costs\": 649.0, \"% of Beneficiaries Using Ambulance\": 0.087, \"Hospice Per User Standardized Costs\": 11597.64, \"# Imaging Users\": 264019.0, \"Part B Drugs Per User Actual Costs\": 650.68, \"Total Standardized Costs\": 3169033470.24, \"Percent of Medicare beneficiaries with colorectal cancer\": 0.94, \"Count of Medicare beneficiaries with chronic kidney disease\": 55868.0, \"E&M Standardized Costs\": 317413063.49, \"Percent Hispanic\": 9.57, \"ASC Per Capita Actual Costs\": 114.59, \"Count of Medicare beneficiaries who have had a heart attack\": 2254.0, \"Tests Actual Costs\": 75073622.49, \"# PAC: LTCH Users (with a covered stay)\": 1000.0, \"% of Beneficiaries Using Procedures\": 0.5744, \"PAC: HH Actual Costs\": 135738473.03, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 27.0, \"PAC: LTCH Per Capita Standardized Costs\": 105.74, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0243, \"Emergency Department Visits\": 244784.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1112, \"Procedures Actual Costs\": 237666150.22, \"# FQHC/RHC Users\": 46711.0, \"Number of Acute Hospital Readmissions\": 12843.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 97.0, \"Outpatient Dialysis Facility Standardized Costs\": 69028726.08, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.017, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1454, \"Ambulance Per User Standardized Costs\": 865.52, \"Imaging Per User Standardized Costs\": 254.56, \"Percent of Medicare beneficiaries with asthma\": 4.26, \"Part B Drugs Standardized Costs\": 137425258.86, \"FFS Beneficiaries\": 420073.0, \"# Hospice Users (with a covered stay)\": 10838.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0068, \"Count of Medicare beneficiaries with osteoporosis\": 23000.0, \"PQI08 CHF Admission Rate (age 75+)\": 1207.0, \"PAC: IRF Per Capita Standardized Costs\": 131.74, \"Procedures Standardized Costs\": 243112156.04, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2786, \"IP Per Capita Actual Costs\": 2479.56, \"DME Actual Costs\": 102495713.37, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0409, \"Count of Medicare beneficiaries with prostate cancer\": 12461.0, \"PAC: HH Per Capita Standardized Costs\": 325.65, \"Count of Medicare beneficiaries with heart failure\": 41331.0, \"Tests Per User Standardized Costs\": 257.5, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0134, \"Percent of Medicare beneficiaries with prostate cancer\": 2.97, \"PAC: IRF Per Capita Actual Costs\": 134.34, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 249.31, \"Percent of Medicare beneficiaries with breast cancer\": 2.7, \"Procedures Per User Standardized Costs\": 1007.56, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.3, \"PAC: HH Per User Actual Costs\": 4590.1, \"Count of Medicare beneficiaries with high cholesterol\": 135541.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0808, \"Hospice Per Capita Standardized Costs\": 299.22, \"# Part B Drugs Users\": 209985.0, \"Average HCC Score\": 0.8851, \"Standardized Risk-Adjusted Per Capita Costs\": 9132.54, \"# PAC: IRF Users (with a covered stay)\": 2915.0, \"Ambulance Standardized Costs\": 31635092.91, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0386, \"Percent of Medicare beneficiaries with heart failure\": 9.84, \"Tests Actual Costs as % of Total Actual Costs\": 0.0226, \"FQHC/RHC Per Capita Standardized Costs\": 50.36, \"PQI07 Hypertension Admission Rate (age 65-74)\": 44.0, \"Test Events Per 1000 Beneficiaries\": 6762.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 70.0, \"ASC Actual Costs\": 48134680.04, \"Part B Drugs Per Capita Standardized Costs\": 327.15, \"Imaging Actual Costs\": 65821423.26, \"Tests Per User Actual Costs\": 250.61, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0101, \"Hospice Per User Actual Costs\": 11824.89, \"Tests Standardized Costs\": 77139271.86, \"IP Standardized Costs\": 882756434.35, \"IP Per Capita Standardized Costs\": 2101.44, \"Outpatient Dialysis Facility Actual Costs\": 70121436.06, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 57.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1430.0, \"Percent of Medicare beneficiaries with hypertension\": 41.3, \"IP Covered Stays Per 1000 Beneficiaries\": 217.0, \"# Ambulance Users\": 36551.0, \"# ASC Users\": 56608.0, \"ASC Per Capita Standardized Costs\": 117.85, \"Procedures Per Capita Actual Costs\": 565.77, \"Procedures Per Capita Standardized Costs\": 578.74, \"IP Users (with a covered stay)\": 61956.0, \"Total Standardized Risk-Adjusted Costs\": 3836334228.98, \"Actual Per Capita Costs\": 7894.89, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.014, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 8.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.72, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.18, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 120.0, \"OP Per User Standardized Costs\": 1731.23, \"Count of Medicare beneficiaries with atrial fibrillation\": 26983.0, \"Procedure Events Per 1000 Beneficiaries\": 4139.0, \"Percent of Medicare beneficiaries with stroke\": 2.57, \"PAC: IRF Per User Actual Costs\": 19358.96, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 38572.0, \"% of Beneficiaries Using ASC\": 0.1348, \"PAC: IRF Standardized Costs\": 55341065.4, \"Hospice Covered Days Per 1000 Beneficiaries\": 1846.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 213.0, \"PAC: SNF Per User Standardized Costs\": 14901.67, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0063, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 102.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0397, \"MA Participation Rate\": 38.77, \"OP Per Capita Standardized Costs\": 1097.16, \"Percent of Medicare beneficiaries with arthritis\": 26.45, \"Ambulance Per Capita Standardized Costs\": 75.31, \"PAC: HH Visits Per 1000 Beneficiaries\": 1958.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0717, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0198, \"PAC: HH Per Capita Actual Costs\": 323.13, \"E&M Events Per 1000 Beneficiaries\": 10503.0, \"Count of Medicare beneficiaries with asthma\": 17890.0, \"# Test Users\": 299567.0, \"E&M Actual Costs\": 299385827.72, \"% of Beneficiaries Using Imaging\": 0.6285, \"PAC: SNF Standardized Costs\": 272626135.74, \"DME Per Capita Actual Costs\": 244.0, \"E&M Per User Actual Costs\": 842.48, \"Percent Male\": 47.11, \"OP Visits Per 1000 Beneficiaries\": 3948.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0309, \"OP Per Capita Actual Costs\": 1153.9, \"Hospital Readmission Rate\": 0.1446, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0067, \"Tests Per Capita Standardized Costs\": 183.63, \"Hospice Per Capita Actual Costs\": 305.09, \"E&M Actual Costs as % of Total Actual Costs\": 0.0903, \"PQI07 Hypertension Admission Rate (age < 65)\": 70.0, \"Percent African American\": 3.19, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0432, \"PAC: LTCH Per Capita Actual Costs\": 105.5, \"MA Beneficiaries\": 265986.0, \"FQHC/RHC Per User Standardized Costs\": 452.84, \"PAC: HH Per User Standardized Costs\": 4625.92, \"Procedures Per User Actual Costs\": 984.99, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 585.0, \"Ambulance Per User Actual Costs\": 914.04, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 772.0, \"PAC: HH Standardized Costs\": 136797776.77, \"ASC Actual Costs as % of Total Actual Costs\": 0.0145, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 372.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0024, \"Percent Other/Unknown\": 3.27, \"% of Beneficiaries Using Hospice\": 0.0258, \"PAC: LTCH Per User Actual Costs\": 44319.47, \"Percent Non-Hispanic White\": 83.98, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 565.0, \"Count of Medicare beneficiaries with breast cancer\": 11354.0, \"DME Per Capita Standardized Costs\": 257.31, \"PQI08 CHF Admission Rate (age 65-74)\": 276.0, \"% of Beneficiaries Using DME\": 0.2686, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0211, \"% of Beneficiaries Using IP\": 0.1475, \"DME Standardized Costs\": 108090094.94, \"FQHC/RHC Standardized Costs\": 21152782.28, \"PAC: SNF Per Capita Actual Costs\": 638.01, \"Imaging Standardized Costs\": 67207863.19, \"# E&M Users\": 355362.0, \"Count of Medicare beneficiaries with arthritis\": 111089.0, \"IP Per User Standardized Costs\": 14248.12, \"IP Per User Actual Costs\": 16811.85, \"PQI10 Dehydration Admission Rate (age 75+)\": 396.0, \"PAC: IRF Per User Standardized Costs\": 18984.93, \"DME Per User Actual Costs\": 908.47, \"PAC: IRF Actual Costs\": 56431375.65, \"Imaging Events Per 1000 Beneficiaries\": 3304.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0218, \"PAC: LTCH Per User Standardized Costs\": 44417.43, \"OP Actual Costs\": 484720790.61, \"Count of Medicare beneficiaries with hypertension\": 173492.0, \"ASC Per User Standardized Costs\": 874.55, \"Part B Drugs Per Capita Actual Costs\": 325.26, \"Ambulance Events Per 1000 Beneficiaries\": 208.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 385.0, \"% of Beneficiaries Using PAC: HH\": 0.1129, \"PAC: LTCH Standardized Costs\": 31983132.88, \"Percent of Medicare beneficiaries with atrial fibrillation\": 10.52, \"E&M Per Capita Standardized Costs\": 1093.68, \"E&M Per User Standardized Costs\": 1202.15, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1191.0, \"IP Covered Days Per 1000 Beneficiaries\": 1787.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 34.0, \"Count of Medicare beneficiaries with lung cancer\": 4996.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3706, \"Percent Eligible for Medicaid\": 26.7, \"Imaging Per Capita Standardized Costs\": 209.83, \"% of Beneficiaries Using Tests\": 0.8208, \"Imaging Per Capita Actual Costs\": 221.09, \"% of Beneficiaries Using PAC: SNF\": 0.075, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0322, \"Count of Medicare beneficiaries with colorectal cancer\": 5922.0, \"Hospice Actual Costs\": 108378795.43, \"# PAC: HH Users\": 47649.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24729.53, \"Total Actual Costs\": 4590153041.68, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 52108.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.006, \"ASC Standardized Costs\": 23240037.27, \"DME Events Per 1000 Beneficiaries\": 1541.0, \"PQI08 CHF Admission Rate (age < 65)\": 696.0, \"ASC Events Per 1000 Beneficiaries\": 106.0, \"PAC: LTCH Actual Costs\": 34770822.49, \"Count of Medicare beneficiaries with depression\": 72582.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1462.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 12.34, \"Outpatient Dialysis Facility Per User Actual Costs\": 26607.74, \"Beneficiaries with Part A and Part B\": 572359.0, \"% of Beneficiaries Using Part B Drugs\": 0.5582, \"Percent of Medicare beneficiaries with diabetes\": 25.24, \"% of Beneficiaries Using PAC: IRF\": 0.0038, \"E&M Per Capita Actual Costs\": 1100.89, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0228, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0382, \"PAC: SNF Actual Costs\": 494726675.93, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 454.0, \"Percent Female\": 56.84, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 336.0, \"Percent of Medicare beneficiaries with osteoporosis\": 6.98, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 205.94, \"# Outpatient Dialysis Facility Users\": 3516.0, \"FQHC/RHC Per User Actual Costs\": 502.81, \"Count of Medicare beneficiaries with ischemic heart disease\": 112442.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 135.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.89, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 250.0, \"Percent of Medicare beneficiaries with depression\": 17.19, \"Emergency Department Visits per 1000 Beneficiaries\": 712.0, \"IP Actual Costs\": 1701273320.03, \"% of Beneficiaries Using OP\": 0.6814, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0208, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1191, \"Count of Medicare beneficiaries with stroke\": 15586.0, \"PQI12 UTI Admission Rate (age 75+)\": 1319.0, \"# OP Users\": 287716.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 27.0, \"# Procedure Users\": 271026.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 26.63, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0637, \"Count of Medicare beneficiaries with diabetes\": 106563.0, \"ASC Per User Actual Costs\": 801.43, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 980.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.02, \"Percent of Medicare beneficiaries with high cholesterol\": 48.68, \"Standardized Per Capita Costs\": 9184.3, \"Ambulance Actual Costs\": 75106935.2, \"FQHC/RHC Per Capita Actual Costs\": 22.66, \"Part B Drugs Per User Standardized Costs\": 629.33, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.008, \"# PAC: SNF Users (with a covered stay)\": 31667.0, \"Ambulance Per Capita Actual Costs\": 177.89, \"PQI12 UTI Admission Rate (age 65-74)\": 286.0, \"Hospice Standardized Costs\": 96976554.78, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 221.58, \"% of Beneficiaries Using E&M\": 0.9098, \"PQI10 Dehydration Admission Rate (age 65-74)\": 202.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 204.0, \"Tests Per Capita Actual Costs\": 258.12, \"# DME Users\": 108545.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1138, \"PAC: SNF Per User Actual Costs\": 15622.78, \"State\": \"CT\", \"OP Per User Actual Costs\": 1932.58, \"PAC: HH Episodes Per 1000 Beneficiaries\": 188.0, \"Part B Drugs Actual Costs\": 147999693.08, \"FQHC/RHC Actual Costs\": 9565439.43, \"OP Standardized Costs\": 509453416.15, \"DME Per User Standardized Costs\": 715.24, \"OP Actual Costs as % of Total Actual Costs\": 0.1211, \"PAC: SNF Per Capita Standardized Costs\": 1045.18, \"% of Beneficiaries Using Ambulance\": 0.1486, \"Hospice Per User Standardized Costs\": 8978.48, \"# Imaging Users\": 295191.0, \"Part B Drugs Per User Actual Costs\": 627.96, \"Total Standardized Costs\": 3877748016.0, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.4, \"Count of Medicare beneficiaries with chronic kidney disease\": 66116.0, \"E&M Standardized Costs\": 461769333.81, \"Percent Hispanic\": 5.2, \"ASC Per Capita Actual Costs\": 59.09, \"Count of Medicare beneficiaries who have had a heart attack\": 3739.0, \"Tests Actual Costs\": 108982592.25, \"# PAC: LTCH Users (with a covered stay)\": 857.0, \"% of Beneficiaries Using Procedures\": 0.6419, \"PAC: HH Actual Costs\": 241138443.83, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 36.0, \"PAC: LTCH Per Capita Standardized Costs\": 75.75, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0281, \"Emergency Department Visits\": 300624.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0451, \"Procedures Actual Costs\": 260223728.08, \"# FQHC/RHC Users\": 19024.0, \"Number of Acute Hospital Readmissions\": 21843.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 54.0, \"Outpatient Dialysis Facility Standardized Costs\": 86949017.36, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0078, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1314, \"Ambulance Per User Standardized Costs\": 1287.51, \"Imaging Per User Standardized Costs\": 300.13, \"Percent of Medicare beneficiaries with asthma\": 5.86, \"Part B Drugs Standardized Costs\": 148321490.31, \"FFS Beneficiaries\": 422215.0, \"# Hospice Users (with a covered stay)\": 10801.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0083, \"Count of Medicare beneficiaries with osteoporosis\": 29480.0, \"PQI08 CHF Admission Rate (age 75+)\": 2222.0, \"PAC: IRF Per Capita Standardized Costs\": 73.86, \"Procedures Standardized Costs\": 247132158.67, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2958, \"IP Per Capita Actual Costs\": 4029.4, \"DME Actual Costs\": 72515288.28, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0525, \"Count of Medicare beneficiaries with prostate cancer\": 13913.0, \"PAC: HH Per Capita Standardized Costs\": 509.35, \"Count of Medicare beneficiaries with heart failure\": 63224.0, \"Tests Per User Standardized Costs\": 313.98, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0076, \"Percent of Medicare beneficiaries with prostate cancer\": 3.3, \"PAC: IRF Per Capita Actual Costs\": 85.17, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 316.23, \"Percent of Medicare beneficiaries with breast cancer\": 3.58, \"Procedures Per User Standardized Costs\": 911.84, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.66, \"PAC: HH Per User Actual Costs\": 5060.72, \"Count of Medicare beneficiaries with high cholesterol\": 205545.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.1078, \"Hospice Per Capita Standardized Costs\": 229.69, \"# Part B Drugs Users\": 235682.0, \"Average HCC Score\": 1.0426, \"Standardized Risk-Adjusted Per Capita Costs\": 9225.34, \"# PAC: IRF Users (with a covered stay)\": 1598.0, \"Ambulance Standardized Costs\": 80777757.99, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0236, \"Percent of Medicare beneficiaries with heart failure\": 14.97, \"Tests Actual Costs as % of Total Actual Costs\": 0.0237, \"FQHC/RHC Per Capita Standardized Costs\": 22.23, \"PQI07 Hypertension Admission Rate (age 65-74)\": 50.0, \"Test Events Per 1000 Beneficiaries\": 10398.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 61.0, \"ASC Actual Costs\": 24948581.18, \"Part B Drugs Per Capita Standardized Costs\": 351.29, \"Imaging Actual Costs\": 93347769.29, \"Tests Per User Actual Costs\": 314.49, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0164, \"Hospice Per User Actual Costs\": 10034.14, \"Tests Standardized Costs\": 108805769.46, \"IP Standardized Costs\": 1147181455.39, \"IP Per Capita Standardized Costs\": 2717.06, \"Outpatient Dialysis Facility Actual Costs\": 93552800.68, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 105.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2618.0, \"Percent of Medicare beneficiaries with hypertension\": 58.2, \"IP Covered Stays Per 1000 Beneficiaries\": 299.0, \"# Ambulance Users\": 62739.0, \"# ASC Users\": 31130.0, \"ASC Per Capita Standardized Costs\": 55.04, \"Procedures Per Capita Actual Costs\": 616.33, \"Procedures Per Capita Standardized Costs\": 585.32, \"IP Users (with a covered stay)\": 77842.0, \"Total Standardized Risk-Adjusted Costs\": 3895077528.83, \"Actual Per Capita Costs\": 10871.6, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0082, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 4.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.18, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.61, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 223.0, \"OP Per User Standardized Costs\": 1770.68, \"Count of Medicare beneficiaries with atrial fibrillation\": 44427.0, \"Procedure Events Per 1000 Beneficiaries\": 4856.0, \"Percent of Medicare beneficiaries with stroke\": 3.69, \"PAC: IRF Per User Actual Costs\": 22502.11, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 44794.0, \"% of Beneficiaries Using ASC\": 0.0737, \"PAC: IRF Standardized Costs\": 31184758.55, \"Hospice Covered Days Per 1000 Beneficiaries\": 1283.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 352.0, \"PAC: SNF Per User Standardized Costs\": 13935.4, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0021, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 111.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.025, \"MA Participation Rate\": 26.23, \"OP Per Capita Standardized Costs\": 1206.62, \"Percent of Medicare beneficiaries with arthritis\": 27.57, \"Ambulance Per Capita Standardized Costs\": 191.32, \"PAC: HH Visits Per 1000 Beneficiaries\": 3580.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0567, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0203, \"PAC: HH Per Capita Actual Costs\": 571.13, \"E&M Events Per 1000 Beneficiaries\": 15408.0, \"Count of Medicare beneficiaries with asthma\": 24761.0, \"# Test Users\": 346534.0, \"E&M Actual Costs\": 464812717.52, \"% of Beneficiaries Using Imaging\": 0.6991, \"PAC: SNF Standardized Costs\": 441292329.13, \"DME Per Capita Actual Costs\": 171.75, \"E&M Per User Actual Costs\": 1210.08, \"Percent Male\": 43.16, \"OP Visits Per 1000 Beneficiaries\": 4642.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0158, \"OP Per Capita Actual Costs\": 1316.95, \"Hospital Readmission Rate\": 0.184, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0024, \"Tests Per Capita Standardized Costs\": 257.7, \"Hospice Per Capita Actual Costs\": 256.69, \"E&M Actual Costs as % of Total Actual Costs\": 0.1013, \"PQI07 Hypertension Admission Rate (age < 65)\": 105.0, \"Percent African American\": 6.51, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0555, \"PAC: LTCH Per Capita Actual Costs\": 82.35, \"MA Beneficiaries\": 150144.0, \"FQHC/RHC Per User Standardized Costs\": 493.35, \"PAC: HH Per User Standardized Costs\": 4513.3, \"Procedures Per User Actual Costs\": 960.14, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 793.0, \"Ambulance Per User Actual Costs\": 1197.12, \"Average Age\": 73.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1430.0, \"PAC: HH Standardized Costs\": 215054423.92, \"ASC Actual Costs as % of Total Actual Costs\": 0.0054, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 628.0, \"% of Beneficiaries Using PAC: LTCH\": 0.002, \"Percent Other/Unknown\": 3.29, \"% of Beneficiaries Using Hospice\": 0.0256, \"PAC: LTCH Per User Actual Costs\": 40572.72, \"Percent Non-Hispanic White\": 85.01, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 631.0, \"Count of Medicare beneficiaries with breast cancer\": 15116.0, \"DME Per Capita Standardized Costs\": 183.88, \"PQI08 CHF Admission Rate (age 65-74)\": 558.0, \"% of Beneficiaries Using DME\": 0.2571, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0204, \"% of Beneficiaries Using IP\": 0.1844, \"DME Standardized Costs\": 77635842.59, \"FQHC/RHC Standardized Costs\": 9385416.11, \"PAC: SNF Per Capita Actual Costs\": 1171.74, \"Imaging Standardized Costs\": 88594311.11, \"# E&M Users\": 384118.0, \"Count of Medicare beneficiaries with arthritis\": 116403.0, \"IP Per User Standardized Costs\": 14737.31, \"IP Per User Actual Costs\": 21855.47, \"PQI10 Dehydration Admission Rate (age 75+)\": 589.0, \"PAC: IRF Per User Standardized Costs\": 19514.87, \"DME Per User Actual Costs\": 668.07, \"PAC: IRF Actual Costs\": 35958374.22, \"Imaging Events Per 1000 Beneficiaries\": 4028.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0224, \"PAC: LTCH Per User Standardized Costs\": 37319.88, \"OP Actual Costs\": 556034643.57, \"Count of Medicare beneficiaries with hypertension\": 245736.0, \"ASC Per User Standardized Costs\": 746.55, \"Part B Drugs Per Capita Actual Costs\": 350.53, \"Ambulance Events Per 1000 Beneficiaries\": 570.0}, {\"PQI12 UTI Admission Rate (age < 65)\": \"*\", \"% of Beneficiaries Using PAC: HH\": 0.079, \"PAC: LTCH Standardized Costs\": 13569024.77, \"Percent of Medicare beneficiaries with atrial fibrillation\": 5.0, \"E&M Per Capita Standardized Costs\": 1013.15, \"E&M Per User Standardized Costs\": 1184.13, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 3178.0, \"IP Covered Days Per 1000 Beneficiaries\": 2176.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 79.0, \"Count of Medicare beneficiaries with lung cancer\": 554.0, \"IP Actual Costs as % of Total Actual Costs\": 0.4181, \"Percent Eligible for Medicaid\": 40.61, \"Imaging Per Capita Standardized Costs\": 213.43, \"% of Beneficiaries Using Tests\": 0.7736, \"Imaging Per Capita Actual Costs\": 235.52, \"% of Beneficiaries Using PAC: SNF\": 0.0553, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0201, \"Count of Medicare beneficiaries with colorectal cancer\": 738.0, \"Hospice Actual Costs\": 16716608.89, \"# PAC: HH Users\": 4826.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24384.31, \"Total Actual Costs\": 702368420.63, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 7746.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0053, \"ASC Standardized Costs\": 3060119.96, \"DME Events Per 1000 Beneficiaries\": 1317.0, \"PQI08 CHF Admission Rate (age < 65)\": 2590.0, \"ASC Events Per 1000 Beneficiaries\": 108.0, \"PAC: LTCH Actual Costs\": 13999962.82, \"Count of Medicare beneficiaries with depression\": 7392.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1114.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 12.69, \"Outpatient Dialysis Facility Per User Actual Costs\": 25102.75, \"Beneficiaries with Part A and Part B\": 71253.0, \"% of Beneficiaries Using Part B Drugs\": 0.395, \"Percent of Medicare beneficiaries with diabetes\": 29.16, \"% of Beneficiaries Using PAC: IRF\": 0.0075, \"E&M Per Capita Actual Costs\": 1071.95, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0224, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0243, \"PAC: SNF Actual Costs\": 56030765.52, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 398.0, \"Percent Female\": 57.54, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 511.0, \"Percent of Medicare beneficiaries with osteoporosis\": 4.62, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 609.84, \"# Outpatient Dialysis Facility Users\": 1527.0, \"FQHC/RHC Per User Actual Costs\": 454.4, \"Count of Medicare beneficiaries with ischemic heart disease\": 13796.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 340.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.78, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 460.0, \"Percent of Medicare beneficiaries with depression\": 12.11, \"Emergency Department Visits per 1000 Beneficiaries\": 882.0, \"IP Actual Costs\": 293659598.2, \"% of Beneficiaries Using OP\": 0.6143, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0141, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1066, \"Count of Medicare beneficiaries with stroke\": 2936.0, \"PQI12 UTI Admission Rate (age 75+)\": 1143.0, \"# OP Users\": 37510.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 24.0, \"# Procedure Users\": 33264.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 22.6, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0639, \"Count of Medicare beneficiaries with diabetes\": 17804.0, \"ASC Per User Actual Costs\": 717.14, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": \"*\", \"DME Standardized Costs as % of Total Standardized Costs\": 0.0182, \"Percent of Medicare beneficiaries with high cholesterol\": 37.46, \"Standardized Per Capita Costs\": 9508.67, \"Ambulance Actual Costs\": 7719045.66, \"FQHC/RHC Per Capita Actual Costs\": 43.05, \"Part B Drugs Per User Standardized Costs\": 585.99, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0135, \"# PAC: SNF Users (with a covered stay)\": 3375.0, \"Ambulance Per Capita Actual Costs\": 126.42, \"PQI12 UTI Admission Rate (age 65-74)\": \"*\", \"Hospice Standardized Costs\": 15685496.52, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 627.81, \"% of Beneficiaries Using E&M\": 0.8556, \"PQI10 Dehydration Admission Rate (age 65-74)\": 216.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 313.0, \"Tests Per Capita Actual Costs\": 275.75, \"# DME Users\": 13943.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0942, \"PAC: SNF Per User Actual Costs\": 16601.71, \"State\": \"DC\", \"OP Per User Actual Costs\": 1851.76, \"PAC: HH Episodes Per 1000 Beneficiaries\": 118.0, \"Part B Drugs Actual Costs\": 14116190.56, \"FQHC/RHC Actual Costs\": 2628225.8, \"OP Standardized Costs\": 66532631.64, \"DME Per User Standardized Costs\": 757.97, \"OP Actual Costs as % of Total Actual Costs\": 0.0989, \"PAC: SNF Per Capita Standardized Costs\": 896.1, \"% of Beneficiaries Using Ambulance\": 0.1498, \"Hospice Per User Standardized Costs\": 11627.5, \"# Imaging Users\": 40229.0, \"Part B Drugs Per User Actual Costs\": 585.3, \"Total Standardized Costs\": 580570701.7, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.21, \"Count of Medicare beneficiaries with chronic kidney disease\": 10844.0, \"E&M Standardized Costs\": 61859909.05, \"Percent Hispanic\": 3.54, \"ASC Per Capita Actual Costs\": 50.41, \"Count of Medicare beneficiaries who have had a heart attack\": 474.0, \"Tests Actual Costs\": 16836451.89, \"# PAC: LTCH Users (with a covered stay)\": 317.0, \"% of Beneficiaries Using Procedures\": 0.5448, \"PAC: HH Actual Costs\": 21347682.77, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": \"*\", \"PAC: LTCH Per Capita Standardized Costs\": 222.24, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0289, \"Emergency Department Visits\": 53832.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0947, \"Procedures Actual Costs\": 40604713.19, \"# FQHC/RHC Users\": 5784.0, \"Number of Acute Hospital Readmissions\": 4462.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 100.0, \"Outpatient Dialysis Facility Standardized Costs\": 37234846.26, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0133, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1146, \"Ambulance Per User Standardized Costs\": 896.49, \"Imaging Per User Standardized Costs\": 323.93, \"Percent of Medicare beneficiaries with asthma\": 6.31, \"Part B Drugs Standardized Costs\": 14132801.03, \"FFS Beneficiaries\": 61057.0, \"# Hospice Users (with a covered stay)\": 1349.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.025, \"Count of Medicare beneficiaries with osteoporosis\": 2821.0, \"PQI08 CHF Admission Rate (age 75+)\": 1983.0, \"PAC: IRF Per Capita Standardized Costs\": 128.53, \"Procedures Standardized Costs\": 37084638.75, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3238, \"IP Per Capita Actual Costs\": 4809.6, \"DME Actual Costs\": 9631843.91, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0304, \"Count of Medicare beneficiaries with prostate cancer\": 2023.0, \"PAC: HH Per Capita Standardized Costs\": 338.95, \"Count of Medicare beneficiaries with heart failure\": 9174.0, \"Tests Per User Standardized Costs\": 354.74, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0199, \"Percent of Medicare beneficiaries with prostate cancer\": 3.31, \"PAC: IRF Per Capita Actual Costs\": 153.27, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 357.46, \"Percent of Medicare beneficiaries with breast cancer\": 3.48, \"Procedures Per User Standardized Costs\": 1114.86, \"Percent of Medicare beneficiaries with chronic kidney disease\": 17.76, \"PAC: HH Per User Actual Costs\": 4423.47, \"Count of Medicare beneficiaries with high cholesterol\": 22873.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0798, \"Hospice Per Capita Standardized Costs\": 256.9, \"# Part B Drugs Users\": 24118.0, \"Average HCC Score\": 1.1507, \"Standardized Risk-Adjusted Per Capita Costs\": 8561.2, \"# PAC: IRF Users (with a covered stay)\": 459.0, \"Ambulance Standardized Costs\": 8201495.39, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0238, \"Percent of Medicare beneficiaries with heart failure\": 15.03, \"Tests Actual Costs as % of Total Actual Costs\": 0.024, \"FQHC/RHC Per Capita Standardized Costs\": 43.34, \"PQI07 Hypertension Admission Rate (age 65-74)\": 178.0, \"Test Events Per 1000 Beneficiaries\": 9675.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 153.0, \"ASC Actual Costs\": 3077967.74, \"Part B Drugs Per Capita Standardized Costs\": 231.47, \"Imaging Actual Costs\": 14380240.81, \"Tests Per User Actual Costs\": 356.45, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.011, \"Hospice Per User Actual Costs\": 12391.85, \"Tests Standardized Costs\": 16755613.97, \"IP Standardized Costs\": 187966788.28, \"IP Per Capita Standardized Costs\": 3078.55, \"Outpatient Dialysis Facility Actual Costs\": 38331906.3, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 75.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2159.0, \"Percent of Medicare beneficiaries with hypertension\": 56.59, \"IP Covered Stays Per 1000 Beneficiaries\": 343.0, \"# Ambulance Users\": 9149.0, \"# ASC Users\": 4292.0, \"ASC Per Capita Standardized Costs\": 50.12, \"Procedures Per Capita Actual Costs\": 665.03, \"Procedures Per Capita Standardized Costs\": 607.38, \"IP Users (with a covered stay)\": 11740.0, \"Total Standardized Risk-Adjusted Costs\": 522721251.79, \"Actual Per Capita Costs\": 11503.49, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0234, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 8.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 6.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.91, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 7.41, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 352.0, \"OP Per User Standardized Costs\": 1773.73, \"Count of Medicare beneficiaries with atrial fibrillation\": 3051.0, \"Procedure Events Per 1000 Beneficiaries\": 4034.0, \"Percent of Medicare beneficiaries with stroke\": 4.81, \"PAC: IRF Per User Actual Costs\": 20388.13, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 4524.0, \"% of Beneficiaries Using ASC\": 0.0703, \"PAC: IRF Standardized Costs\": 7847684.49, \"Hospice Covered Days Per 1000 Beneficiaries\": 1598.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 316.0, \"PAC: SNF Per User Standardized Costs\": 16211.26, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0037, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 158.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.027, \"MA Participation Rate\": 14.31, \"OP Per Capita Standardized Costs\": 1089.68, \"Percent of Medicare beneficiaries with arthritis\": 26.23, \"Ambulance Per Capita Standardized Costs\": 134.33, \"PAC: HH Visits Per 1000 Beneficiaries\": 2002.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0578, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0205, \"PAC: HH Per Capita Actual Costs\": 349.64, \"E&M Events Per 1000 Beneficiaries\": 14182.0, \"Count of Medicare beneficiaries with asthma\": 3850.0, \"# Test Users\": 47234.0, \"E&M Actual Costs\": 65450244.86, \"% of Beneficiaries Using Imaging\": 0.6589, \"PAC: SNF Standardized Costs\": 54712999.39, \"DME Per Capita Actual Costs\": 157.75, \"E&M Per User Actual Costs\": 1252.85, \"Percent Male\": 42.46, \"OP Visits Per 1000 Beneficiaries\": 3966.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0137, \"OP Per Capita Actual Costs\": 1137.62, \"Hospital Readmission Rate\": 0.2262, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0046, \"Tests Per Capita Standardized Costs\": 274.43, \"Hospice Per Capita Actual Costs\": 273.79, \"E&M Actual Costs as % of Total Actual Costs\": 0.0932, \"PQI07 Hypertension Admission Rate (age < 65)\": 294.0, \"Percent African American\": 65.31, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0356, \"PAC: LTCH Per Capita Actual Costs\": 229.29, \"MA Beneficiaries\": 10196.0, \"FQHC/RHC Per User Standardized Costs\": 457.52, \"PAC: HH Per User Standardized Costs\": 4288.3, \"Procedures Per User Actual Costs\": 1220.68, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 1001.0, \"Ambulance Per User Actual Costs\": 843.75, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": \"*\", \"PAC: HH Standardized Costs\": 20695314.78, \"ASC Actual Costs as % of Total Actual Costs\": 0.0044, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 914.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0052, \"Percent Other/Unknown\": 3.14, \"% of Beneficiaries Using Hospice\": 0.0221, \"PAC: LTCH Per User Actual Costs\": 44163.92, \"Percent Non-Hispanic White\": 28.01, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": \"*\", \"Count of Medicare beneficiaries with breast cancer\": 2125.0, \"DME Per Capita Standardized Costs\": 173.09, \"PQI08 CHF Admission Rate (age 65-74)\": 1172.0, \"% of Beneficiaries Using DME\": 0.2284, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0546, \"% of Beneficiaries Using IP\": 0.1923, \"DME Standardized Costs\": 10568344.37, \"FQHC/RHC Standardized Costs\": 2646277.93, \"PAC: SNF Per Capita Actual Costs\": 917.68, \"Imaging Standardized Costs\": 13031382.63, \"# E&M Users\": 52241.0, \"Count of Medicare beneficiaries with arthritis\": 16016.0, \"IP Per User Standardized Costs\": 16010.8, \"IP Per User Actual Costs\": 25013.59, \"PQI10 Dehydration Admission Rate (age 75+)\": 534.0, \"PAC: IRF Per User Standardized Costs\": 17097.35, \"DME Per User Actual Costs\": 690.8, \"PAC: IRF Actual Costs\": 9358150.21, \"Imaging Events Per 1000 Beneficiaries\": 3795.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0641, \"PAC: LTCH Per User Standardized Costs\": 42804.49, \"OP Actual Costs\": 69459357.4, \"Count of Medicare beneficiaries with hypertension\": 34553.0, \"ASC Per User Standardized Costs\": 712.98, \"Part B Drugs Per Capita Actual Costs\": 231.2, \"Ambulance Events Per 1000 Beneficiaries\": 398.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 407.0, \"% of Beneficiaries Using PAC: HH\": 0.083, \"PAC: LTCH Standardized Costs\": 9413243.81, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.97, \"E&M Per Capita Standardized Costs\": 1034.38, \"E&M Per User Standardized Costs\": 1121.86, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1391.0, \"IP Covered Days Per 1000 Beneficiaries\": 1541.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 66.0, \"Count of Medicare beneficiaries with lung cancer\": 1670.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3461, \"Percent Eligible for Medicaid\": 16.2, \"Imaging Per Capita Standardized Costs\": 232.6, \"% of Beneficiaries Using Tests\": 0.8133, \"Imaging Per Capita Actual Costs\": 233.53, \"% of Beneficiaries Using PAC: SNF\": 0.0469, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0352, \"Count of Medicare beneficiaries with colorectal cancer\": 1776.0, \"Hospice Actual Costs\": 51071221.58, \"# PAC: HH Users\": 12369.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23891.03, \"Total Actual Costs\": 1443112120.34, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 13266.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.015, \"ASC Standardized Costs\": 19811088.24, \"DME Events Per 1000 Beneficiaries\": 1683.0, \"PQI08 CHF Admission Rate (age < 65)\": 1080.0, \"ASC Events Per 1000 Beneficiaries\": 264.0, \"PAC: LTCH Actual Costs\": 9574713.64, \"Count of Medicare beneficiaries with depression\": 21828.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1566.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.9, \"Outpatient Dialysis Facility Per User Actual Costs\": 24504.15, \"Beneficiaries with Part A and Part B\": 162857.0, \"% of Beneficiaries Using Part B Drugs\": 0.5736, \"Percent of Medicare beneficiaries with diabetes\": 29.81, \"% of Beneficiaries Using PAC: IRF\": 0.0087, \"E&M Per Capita Actual Costs\": 1003.88, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0262, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0385, \"PAC: SNF Actual Costs\": 114317996.05, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 604.0, \"Percent Female\": 55.27, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 515.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.44, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 247.78, \"# Outpatient Dialysis Facility Users\": 1546.0, \"FQHC/RHC Per User Actual Costs\": 314.44, \"Count of Medicare beneficiaries with ischemic heart disease\": 44261.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 131.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.76, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 61.0, \"Percent of Medicare beneficiaries with depression\": 14.64, \"Emergency Department Visits per 1000 Beneficiaries\": 598.0, \"IP Actual Costs\": 499391509.18, \"% of Beneficiaries Using OP\": 0.696, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0166, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1166, \"Count of Medicare beneficiaries with stroke\": 6955.0, \"PQI12 UTI Admission Rate (age 75+)\": 1210.0, \"# OP Users\": 103748.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 30.0, \"# Procedure Users\": 96536.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 29.69, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0738, \"Count of Medicare beneficiaries with diabetes\": 44433.0, \"ASC Per User Actual Costs\": 831.29, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 828.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0241, \"Percent of Medicare beneficiaries with high cholesterol\": 58.77, \"Standardized Per Capita Costs\": 8871.48, \"Ambulance Actual Costs\": 20947674.66, \"FQHC/RHC Per Capita Actual Costs\": 5.74, \"Part B Drugs Per User Standardized Costs\": 595.95, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0183, \"# PAC: SNF Users (with a covered stay)\": 6998.0, \"Ambulance Per Capita Actual Costs\": 140.53, \"PQI12 UTI Admission Rate (age 65-74)\": 262.0, \"Hospice Standardized Costs\": 49111229.45, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 254.14, \"% of Beneficiaries Using E&M\": 0.922, \"PQI10 Dehydration Admission Rate (age 65-74)\": 182.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 199.0, \"Tests Per Capita Actual Costs\": 254.8, \"# DME Users\": 41180.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0849, \"PAC: SNF Per User Actual Costs\": 16335.81, \"State\": \"DE\", \"OP Per User Actual Costs\": 1811.98, \"PAC: HH Episodes Per 1000 Beneficiaries\": 125.0, \"Part B Drugs Actual Costs\": 50757944.69, \"FQHC/RHC Actual Costs\": 855898.59, \"OP Standardized Costs\": 181782458.53, \"DME Per User Standardized Costs\": 773.8, \"OP Actual Costs as % of Total Actual Costs\": 0.1303, \"PAC: SNF Per Capita Standardized Costs\": 753.43, \"% of Beneficiaries Using Ambulance\": 0.1252, \"Hospice Per User Standardized Costs\": 11701.51, \"# Imaging Users\": 107191.0, \"Part B Drugs Per User Actual Costs\": 593.6, \"Total Standardized Costs\": 1322418897.31, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.19, \"Count of Medicare beneficiaries with chronic kidney disease\": 24811.0, \"E&M Standardized Costs\": 154188998.36, \"Percent Hispanic\": 2.06, \"ASC Per Capita Actual Costs\": 132.15, \"Count of Medicare beneficiaries who have had a heart attack\": 1130.0, \"Tests Actual Costs\": 37980892.52, \"# PAC: LTCH Users (with a covered stay)\": 212.0, \"% of Beneficiaries Using Procedures\": 0.6476, \"PAC: HH Actual Costs\": 51592656.78, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 32.0, \"PAC: LTCH Per Capita Standardized Costs\": 63.15, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0289, \"Emergency Department Visits\": 89157.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0183, \"Procedures Actual Costs\": 96898818.3, \"# FQHC/RHC Users\": 2722.0, \"Number of Acute Hospital Readmissions\": 6827.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 119.0, \"Outpatient Dialysis Facility Standardized Costs\": 36935524.68, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0183, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1375, \"Ambulance Per User Standardized Costs\": 1175.12, \"Imaging Per User Standardized Costs\": 323.46, \"Percent of Medicare beneficiaries with asthma\": 5.36, \"Part B Drugs Standardized Costs\": 50958786.48, \"FFS Beneficiaries\": 149064.0, \"# Hospice Users (with a covered stay)\": 4197.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0104, \"Count of Medicare beneficiaries with osteoporosis\": 8108.0, \"PQI08 CHF Admission Rate (age 75+)\": 2005.0, \"PAC: IRF Per Capita Standardized Costs\": 162.73, \"Procedures Standardized Costs\": 97601853.67, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2906, \"IP Per Capita Actual Costs\": 3350.18, \"DME Actual Costs\": 29489485.67, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0358, \"Count of Medicare beneficiaries with prostate cancer\": 5534.0, \"PAC: HH Per Capita Standardized Costs\": 337.15, \"Count of Medicare beneficiaries with heart failure\": 18921.0, \"Tests Per User Standardized Costs\": 315.21, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0066, \"Percent of Medicare beneficiaries with prostate cancer\": 3.71, \"PAC: IRF Per Capita Actual Costs\": 176.71, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 324.76, \"Percent of Medicare beneficiaries with breast cancer\": 3.12, \"Procedures Per User Standardized Costs\": 1011.04, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.64, \"PAC: HH Per User Actual Costs\": 4171.13, \"Count of Medicare beneficiaries with high cholesterol\": 87603.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0792, \"Hospice Per Capita Standardized Costs\": 329.46, \"# Part B Drugs Users\": 85509.0, \"Average HCC Score\": 0.9875, \"Standardized Risk-Adjusted Per Capita Costs\": 9575.02, \"# PAC: IRF Users (with a covered stay)\": 1292.0, \"Ambulance Standardized Costs\": 21927337.15, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0354, \"Percent of Medicare beneficiaries with heart failure\": 12.69, \"Tests Actual Costs as % of Total Actual Costs\": 0.0263, \"FQHC/RHC Per Capita Standardized Costs\": 5.85, \"PQI07 Hypertension Admission Rate (age 65-74)\": 59.0, \"Test Events Per 1000 Beneficiaries\": 8775.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 42.0, \"ASC Actual Costs\": 19698247.0, \"Part B Drugs Per Capita Standardized Costs\": 341.86, \"Imaging Actual Costs\": 34810895.72, \"Tests Per User Actual Costs\": 313.29, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0145, \"Hospice Per User Actual Costs\": 12168.51, \"Tests Standardized Costs\": 38212836.19, \"IP Standardized Costs\": 384235133.6, \"IP Per Capita Standardized Costs\": 2577.65, \"Outpatient Dialysis Facility Actual Costs\": 37883421.53, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 65.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1802.0, \"Percent of Medicare beneficiaries with hypertension\": 62.11, \"IP Covered Stays Per 1000 Beneficiaries\": 272.0, \"# Ambulance Users\": 18660.0, \"# ASC Users\": 23696.0, \"ASC Per Capita Standardized Costs\": 132.9, \"Procedures Per Capita Actual Costs\": 650.05, \"Procedures Per Capita Standardized Costs\": 654.76, \"IP Users (with a covered stay)\": 25452.0, \"Total Standardized Risk-Adjusted Costs\": 1427291308.74, \"Actual Per Capita Costs\": 9681.16, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0071, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 10.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.12, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.96, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 245.0, \"OP Per User Standardized Costs\": 1752.15, \"Count of Medicare beneficiaries with atrial fibrillation\": 13373.0, \"Procedure Events Per 1000 Beneficiaries\": 5595.0, \"Percent of Medicare beneficiaries with stroke\": 4.67, \"PAC: IRF Per User Actual Costs\": 20387.71, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 16333.0, \"% of Beneficiaries Using ASC\": 0.159, \"PAC: IRF Standardized Costs\": 24256893.45, \"Hospice Covered Days Per 1000 Beneficiaries\": 1934.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 267.0, \"PAC: SNF Per User Standardized Costs\": 16048.78, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0006, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 127.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0371, \"MA Participation Rate\": 8.47, \"OP Per Capita Standardized Costs\": 1219.49, \"Percent of Medicare beneficiaries with arthritis\": 30.84, \"Ambulance Per Capita Standardized Costs\": 147.1, \"PAC: HH Visits Per 1000 Beneficiaries\": 1942.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0671, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0241, \"PAC: HH Per Capita Actual Costs\": 346.11, \"E&M Events Per 1000 Beneficiaries\": 14732.0, \"Count of Medicare beneficiaries with asthma\": 7996.0, \"# Test Users\": 121231.0, \"E&M Actual Costs\": 149642661.31, \"% of Beneficiaries Using Imaging\": 0.7191, \"PAC: SNF Standardized Costs\": 112309377.84, \"DME Per Capita Actual Costs\": 197.83, \"E&M Per User Actual Costs\": 1088.78, \"Percent Male\": 44.73, \"OP Visits Per 1000 Beneficiaries\": 4442.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0204, \"OP Per Capita Actual Costs\": 1261.13, \"Hospital Readmission Rate\": 0.1746, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0007, \"Tests Per Capita Standardized Costs\": 256.35, \"Hospice Per Capita Actual Costs\": 342.61, \"E&M Actual Costs as % of Total Actual Costs\": 0.1037, \"PQI07 Hypertension Admission Rate (age < 65)\": 131.0, \"Percent African American\": 14.91, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.038, \"PAC: LTCH Per Capita Actual Costs\": 64.23, \"MA Beneficiaries\": 13793.0, \"FQHC/RHC Per User Standardized Costs\": 320.26, \"PAC: HH Per User Standardized Costs\": 4063.14, \"Procedures Per User Actual Costs\": 1003.76, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 831.0, \"Ambulance Per User Actual Costs\": 1122.62, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1361.0, \"PAC: HH Standardized Costs\": 50257029.14, \"ASC Actual Costs as % of Total Actual Costs\": 0.0136, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 614.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0014, \"Percent Other/Unknown\": 2.81, \"% of Beneficiaries Using Hospice\": 0.0282, \"PAC: LTCH Per User Actual Costs\": 45163.74, \"Percent Non-Hispanic White\": 80.22, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 827.0, \"Count of Medicare beneficiaries with breast cancer\": 4645.0, \"DME Per Capita Standardized Costs\": 213.77, \"PQI08 CHF Admission Rate (age 65-74)\": 726.0, \"% of Beneficiaries Using DME\": 0.2763, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0263, \"% of Beneficiaries Using IP\": 0.1707, \"DME Standardized Costs\": 31865086.92, \"FQHC/RHC Standardized Costs\": 871752.51, \"PAC: SNF Per Capita Actual Costs\": 766.91, \"Imaging Standardized Costs\": 34672373.36, \"# E&M Users\": 137441.0, \"Count of Medicare beneficiaries with arthritis\": 45964.0, \"IP Per User Standardized Costs\": 15096.46, \"IP Per User Actual Costs\": 19620.91, \"PQI10 Dehydration Admission Rate (age 75+)\": 527.0, \"PAC: IRF Per User Standardized Costs\": 18774.69, \"DME Per User Actual Costs\": 716.11, \"PAC: IRF Actual Costs\": 26340923.64, \"Imaging Events Per 1000 Beneficiaries\": 4071.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0279, \"PAC: LTCH Per User Standardized Costs\": 44402.09, \"OP Actual Costs\": 187988890.46, \"Count of Medicare beneficiaries with hypertension\": 92589.0, \"ASC Per User Standardized Costs\": 836.05, \"Part B Drugs Per Capita Actual Costs\": 340.51, \"Ambulance Events Per 1000 Beneficiaries\": 446.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 471.0, \"% of Beneficiaries Using PAC: HH\": 0.1427, \"PAC: LTCH Standardized Costs\": 320439798.48, \"Percent of Medicare beneficiaries with atrial fibrillation\": 9.5, \"E&M Per Capita Standardized Costs\": 1277.35, \"E&M Per User Standardized Costs\": 1411.31, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1262.0, \"IP Covered Days Per 1000 Beneficiaries\": 1685.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 39.0, \"Count of Medicare beneficiaries with lung cancer\": 28267.0, \"IP Actual Costs as % of Total Actual Costs\": 0.2821, \"Percent Eligible for Medicaid\": 18.89, \"Imaging Per Capita Standardized Costs\": 352.86, \"% of Beneficiaries Using Tests\": 0.8366, \"Imaging Per Capita Actual Costs\": 350.17, \"% of Beneficiaries Using PAC: SNF\": 0.0553, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.045, \"Count of Medicare beneficiaries with colorectal cancer\": 30900.0, \"Hospice Actual Costs\": 961182274.53, \"# PAC: HH Users\": 320364.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23023.38, \"Total Actual Costs\": 24093389522.59, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 266848.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0135, \"ASC Standardized Costs\": 324932261.31, \"DME Events Per 1000 Beneficiaries\": 1708.0, \"PQI08 CHF Admission Rate (age < 65)\": 1052.0, \"ASC Events Per 1000 Beneficiaries\": 270.0, \"PAC: LTCH Actual Costs\": 297466030.87, \"Count of Medicare beneficiaries with depression\": 376050.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1292.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 11.89, \"Outpatient Dialysis Facility Per User Actual Costs\": 22534.68, \"Beneficiaries with Part A and Part B\": 3689240.0, \"% of Beneficiaries Using Part B Drugs\": 0.5633, \"Percent of Medicare beneficiaries with diabetes\": 28.29, \"% of Beneficiaries Using PAC: IRF\": 0.01, \"E&M Per Capita Actual Costs\": 1258.72, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0329, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0452, \"PAC: SNF Actual Costs\": 1894071836.23, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 442.0, \"Percent Female\": 54.53, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 328.0, \"Percent of Medicare beneficiaries with osteoporosis\": 8.03, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 224.75, \"# Outpatient Dialysis Facility Users\": 21915.0, \"FQHC/RHC Per User Actual Costs\": 364.53, \"Count of Medicare beneficiaries with ischemic heart disease\": 807570.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 228.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.84, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 214.0, \"Percent of Medicare beneficiaries with depression\": 16.75, \"Emergency Department Visits per 1000 Beneficiaries\": 612.0, \"IP Actual Costs\": 6797397513.69, \"% of Beneficiaries Using OP\": 0.5616, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0115, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.119, \"Count of Medicare beneficiaries with stroke\": 100600.0, \"PQI12 UTI Admission Rate (age 75+)\": 1271.0, \"# OP Users\": 1260742.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 36.0, \"# Procedure Users\": 1563475.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 35.97, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0819, \"Count of Medicare beneficiaries with diabetes\": 635108.0, \"ASC Per User Actual Costs\": 868.26, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1177.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.021, \"Percent of Medicare beneficiaries with high cholesterol\": 55.54, \"Standardized Per Capita Costs\": 10737.58, \"Ambulance Actual Costs\": 253029482.02, \"FQHC/RHC Per Capita Actual Costs\": 16.84, \"Part B Drugs Per User Standardized Costs\": 861.21, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0188, \"# PAC: SNF Users (with a covered stay)\": 124260.0, \"Ambulance Per Capita Actual Costs\": 112.71, \"PQI12 UTI Admission Rate (age 65-74)\": 308.0, \"Hospice Standardized Costs\": 997570648.41, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 219.98, \"% of Beneficiaries Using E&M\": 0.9051, \"PQI10 Dehydration Admission Rate (age 65-74)\": 181.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 199.0, \"Tests Per Capita Actual Costs\": 416.07, \"# DME Users\": 638145.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0865, \"PAC: SNF Per User Actual Costs\": 15242.81, \"State\": \"FL\", \"OP Per User Actual Costs\": 1674.83, \"PAC: HH Episodes Per 1000 Beneficiaries\": 280.0, \"Part B Drugs Actual Costs\": 1084927693.68, \"FQHC/RHC Actual Costs\": 37795883.86, \"OP Standardized Costs\": 2180693155.34, \"DME Per User Standardized Costs\": 793.94, \"OP Actual Costs as % of Total Actual Costs\": 0.0876, \"PAC: SNF Per Capita Standardized Costs\": 928.62, \"% of Beneficiaries Using Ambulance\": 0.1143, \"Hospice Per User Standardized Costs\": 13102.66, \"# Imaging Users\": 1647545.0, \"Part B Drugs Per User Actual Costs\": 857.92, \"Total Standardized Costs\": 24105793859.21, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.38, \"Count of Medicare beneficiaries with chronic kidney disease\": 417208.0, \"E&M Standardized Costs\": 2867636722.97, \"Percent Hispanic\": 8.77, \"ASC Per Capita Actual Costs\": 135.7, \"Count of Medicare beneficiaries who have had a heart attack\": 18814.0, \"Tests Actual Costs\": 934070409.19, \"# PAC: LTCH Users (with a covered stay)\": 6984.0, \"% of Beneficiaries Using Procedures\": 0.6964, \"PAC: HH Actual Costs\": 1996983391.69, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 37.0, \"PAC: LTCH Per Capita Standardized Costs\": 142.74, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0393, \"Emergency Department Visits\": 1374214.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0462, \"Procedures Actual Costs\": 2004805693.09, \"# FQHC/RHC Users\": 103684.0, \"Number of Acute Hospital Readmissions\": 128011.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 149.0, \"Outpatient Dialysis Facility Standardized Costs\": 504557387.45, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0186, \"OP Standardized Costs as % of Total Standardized Costs\": 0.0905, \"Ambulance Per User Standardized Costs\": 1076.71, \"Imaging Per User Standardized Costs\": 480.81, \"Percent of Medicare beneficiaries with asthma\": 5.24, \"Part B Drugs Standardized Costs\": 1089086205.62, \"FFS Beneficiaries\": 2244994.0, \"# Hospice Users (with a covered stay)\": 76135.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0098, \"Count of Medicare beneficiaries with osteoporosis\": 180345.0, \"PQI08 CHF Admission Rate (age 75+)\": 1925.0, \"PAC: IRF Per Capita Standardized Costs\": 202.05, \"Procedures Standardized Costs\": 1973868630.46, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2429, \"IP Per Capita Actual Costs\": 3027.8, \"DME Actual Costs\": 468670064.75, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0829, \"Count of Medicare beneficiaries with prostate cancer\": 91411.0, \"PAC: HH Per Capita Standardized Costs\": 945.12, \"Count of Medicare beneficiaries with heart failure\": 326626.0, \"Tests Per User Standardized Costs\": 504.96, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0123, \"Percent of Medicare beneficiaries with prostate cancer\": 4.07, \"PAC: IRF Per Capita Actual Costs\": 199.1, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 477.15, \"Percent of Medicare beneficiaries with breast cancer\": 3.37, \"Procedures Per User Standardized Costs\": 1262.49, \"Percent of Medicare beneficiaries with chronic kidney disease\": 18.58, \"PAC: HH Per User Actual Costs\": 6233.48, \"Count of Medicare beneficiaries with high cholesterol\": 1246948.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0786, \"Hospice Per Capita Standardized Costs\": 444.35, \"# Part B Drugs Users\": 1264604.0, \"Average HCC Score\": 1.0702, \"Standardized Risk-Adjusted Per Capita Costs\": 10618.98, \"# PAC: IRF Users (with a covered stay)\": 22461.0, \"Ambulance Standardized Costs\": 276285612.15, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0399, \"Percent of Medicare beneficiaries with heart failure\": 14.55, \"Tests Actual Costs as % of Total Actual Costs\": 0.0388, \"FQHC/RHC Per Capita Standardized Costs\": 18.59, \"PQI07 Hypertension Admission Rate (age 65-74)\": 102.0, \"Test Events Per 1000 Beneficiaries\": 13336.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 89.0, \"ASC Actual Costs\": 304646714.68, \"Part B Drugs Per Capita Standardized Costs\": 485.12, \"Imaging Actual Costs\": 786132132.75, \"Tests Per User Actual Costs\": 497.33, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0105, \"Hospice Per User Actual Costs\": 12624.71, \"Tests Standardized Costs\": 948411044.53, \"IP Standardized Costs\": 5855191213.38, \"IP Per Capita Standardized Costs\": 2608.11, \"Outpatient Dialysis Facility Actual Costs\": 493847520.39, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 78.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2146.0, \"Percent of Medicare beneficiaries with hypertension\": 60.83, \"IP Covered Stays Per 1000 Beneficiaries\": 310.0, \"# Ambulance Users\": 256601.0, \"# ASC Users\": 350869.0, \"ASC Per Capita Standardized Costs\": 144.74, \"Procedures Per Capita Actual Costs\": 893.01, \"Procedures Per Capita Standardized Costs\": 879.23, \"IP Users (with a covered stay)\": 421194.0, \"Total Standardized Risk-Adjusted Costs\": 23839555663.35, \"Actual Per Capita Costs\": 10732.05, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0133, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 11.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.26, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 13.4, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 241.0, \"OP Per User Standardized Costs\": 1729.69, \"Count of Medicare beneficiaries with atrial fibrillation\": 213204.0, \"Procedure Events Per 1000 Beneficiaries\": 5992.0, \"Percent of Medicare beneficiaries with stroke\": 4.48, \"PAC: IRF Per User Actual Costs\": 19900.12, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 300744.0, \"% of Beneficiaries Using ASC\": 0.1563, \"PAC: IRF Standardized Costs\": 453603484.81, \"Hospice Covered Days Per 1000 Beneficiaries\": 2393.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 316.0, \"PAC: SNF Per User Standardized Costs\": 16777.23, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0016, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 134.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0414, \"MA Participation Rate\": 39.15, \"OP Per Capita Standardized Costs\": 971.36, \"Percent of Medicare beneficiaries with arthritis\": 33.9, \"Ambulance Per Capita Standardized Costs\": 123.07, \"PAC: HH Visits Per 1000 Beneficiaries\": 5891.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0832, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0326, \"PAC: HH Per Capita Actual Costs\": 889.53, \"E&M Events Per 1000 Beneficiaries\": 17340.0, \"Count of Medicare beneficiaries with asthma\": 117571.0, \"# Test Users\": 1878176.0, \"E&M Actual Costs\": 2825817903.58, \"% of Beneficiaries Using Imaging\": 0.7339, \"PAC: SNF Standardized Costs\": 2084738650.7, \"DME Per Capita Actual Costs\": 208.76, \"E&M Per User Actual Costs\": 1390.73, \"Percent Male\": 45.47, \"OP Visits Per 1000 Beneficiaries\": 3073.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0195, \"OP Per Capita Actual Costs\": 940.55, \"Hospital Readmission Rate\": 0.1908, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0017, \"Tests Per Capita Standardized Costs\": 422.46, \"Hospice Per Capita Actual Costs\": 428.14, \"E&M Actual Costs as % of Total Actual Costs\": 0.1173, \"PQI07 Hypertension Admission Rate (age < 65)\": 186.0, \"Percent African American\": 7.86, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.088, \"PAC: LTCH Per Capita Actual Costs\": 132.5, \"MA Beneficiaries\": 1444246.0, \"FQHC/RHC Per User Standardized Costs\": 402.58, \"PAC: HH Per User Standardized Costs\": 6623.02, \"Procedures Per User Actual Costs\": 1282.28, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 916.0, \"Ambulance Per User Actual Costs\": 986.08, \"Average Age\": 73.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1648.0, \"PAC: HH Standardized Costs\": 2121777947.55, \"ASC Actual Costs as % of Total Actual Costs\": 0.0126, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 788.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0031, \"Percent Other/Unknown\": 2.46, \"% of Beneficiaries Using Hospice\": 0.0339, \"PAC: LTCH Per User Actual Costs\": 42592.5, \"Percent Non-Hispanic White\": 80.91, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 703.0, \"Count of Medicare beneficiaries with breast cancer\": 75715.0, \"DME Per Capita Standardized Costs\": 225.68, \"PQI08 CHF Admission Rate (age 65-74)\": 618.0, \"% of Beneficiaries Using DME\": 0.2843, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0205, \"% of Beneficiaries Using IP\": 0.1876, \"DME Standardized Costs\": 506647081.2, \"FQHC/RHC Standardized Costs\": 41741011.3, \"PAC: SNF Per Capita Actual Costs\": 843.69, \"Imaging Standardized Costs\": 792158914.77, \"# E&M Users\": 2031895.0, \"Count of Medicare beneficiaries with arthritis\": 761149.0, \"IP Per User Standardized Costs\": 13901.41, \"IP Per User Actual Costs\": 16138.4, \"PQI10 Dehydration Admission Rate (age 75+)\": 446.0, \"PAC: IRF Per User Standardized Costs\": 20195.16, \"DME Per User Actual Costs\": 734.43, \"PAC: IRF Actual Costs\": 446976592.19, \"Imaging Events Per 1000 Beneficiaries\": 4843.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0209, \"PAC: LTCH Per User Standardized Costs\": 45881.99, \"OP Actual Costs\": 2111533160.1, \"Count of Medicare beneficiaries with hypertension\": 1365530.0, \"ASC Per User Standardized Costs\": 926.08, \"Part B Drugs Per Capita Actual Costs\": 483.27, \"Ambulance Events Per 1000 Beneficiaries\": 340.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 360.0, \"% of Beneficiaries Using PAC: HH\": 0.083, \"PAC: LTCH Standardized Costs\": 144822670.39, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.09, \"E&M Per Capita Standardized Costs\": 938.06, \"E&M Per User Standardized Costs\": 1054.18, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 2203.0, \"IP Covered Days Per 1000 Beneficiaries\": 1541.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 56.0, \"Count of Medicare beneficiaries with lung cancer\": 9624.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3147, \"Percent Eligible for Medicaid\": 20.59, \"Imaging Per Capita Standardized Costs\": 222.38, \"% of Beneficiaries Using Tests\": 0.8074, \"Imaging Per Capita Actual Costs\": 209.33, \"% of Beneficiaries Using PAC: SNF\": 0.0397, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0324, \"Count of Medicare beneficiaries with colorectal cancer\": 11043.0, \"Hospice Actual Costs\": 418933515.21, \"# PAC: HH Users\": 79448.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24190.86, \"Total Actual Costs\": 8515019063.75, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 97725.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0119, \"ASC Standardized Costs\": 103888432.5, \"DME Events Per 1000 Beneficiaries\": 1819.0, \"PQI08 CHF Admission Rate (age < 65)\": 1201.0, \"ASC Events Per 1000 Beneficiaries\": 217.0, \"PAC: LTCH Actual Costs\": 131031418.87, \"Count of Medicare beneficiaries with depression\": 142328.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1580.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.21, \"Outpatient Dialysis Facility Per User Actual Costs\": 23396.39, \"Beneficiaries with Part A and Part B\": 1370012.0, \"% of Beneficiaries Using Part B Drugs\": 0.5502, \"Percent of Medicare beneficiaries with diabetes\": 27.61, \"% of Beneficiaries Using PAC: IRF\": 0.0068, \"E&M Per Capita Actual Costs\": 867.23, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0244, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0318, \"PAC: SNF Actual Costs\": 517429009.13, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 578.0, \"Percent Female\": 54.96, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 283.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.04, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 389.73, \"# Outpatient Dialysis Facility Users\": 15427.0, \"FQHC/RHC Per User Actual Costs\": 339.48, \"Count of Medicare beneficiaries with ischemic heart disease\": 248737.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 164.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.79, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 241.0, \"Percent of Medicare beneficiaries with depression\": 14.86, \"Emergency Department Visits per 1000 Beneficiaries\": 688.0, \"IP Actual Costs\": 2679715083.14, \"% of Beneficiaries Using OP\": 0.6254, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0212, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1029, \"Count of Medicare beneficiaries with stroke\": 37650.0, \"PQI12 UTI Admission Rate (age 75+)\": 1238.0, \"# OP Users\": 598903.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 35.0, \"# Procedure Users\": 599010.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 25.98, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0723, \"Count of Medicare beneficiaries with diabetes\": 264423.0, \"ASC Per User Actual Costs\": 806.56, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1009.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0263, \"Percent of Medicare beneficiaries with high cholesterol\": 46.28, \"Standardized Per Capita Costs\": 9114.9, \"Ambulance Actual Costs\": 174068177.63, \"FQHC/RHC Per Capita Actual Costs\": 19.99, \"Part B Drugs Per User Standardized Costs\": 527.42, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0149, \"# PAC: SNF Users (with a covered stay)\": 37970.0, \"Ambulance Per Capita Actual Costs\": 181.78, \"PQI12 UTI Admission Rate (age 65-74)\": 280.0, \"Hospice Standardized Costs\": 444009919.02, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 376.93, \"% of Beneficiaries Using E&M\": 0.8898, \"PQI10 Dehydration Admission Rate (age 65-74)\": 205.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 201.0, \"Tests Per Capita Actual Costs\": 318.62, \"# DME Users\": 280972.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0672, \"PAC: SNF Per User Actual Costs\": 13627.31, \"State\": \"GA\", \"OP Per User Actual Costs\": 1787.78, \"PAC: HH Episodes Per 1000 Beneficiaries\": 147.0, \"Part B Drugs Actual Costs\": 276137839.6, \"FQHC/RHC Actual Costs\": 19142992.88, \"OP Standardized Costs\": 1119411759.28, \"DME Per User Standardized Costs\": 816.24, \"OP Actual Costs as % of Total Actual Costs\": 0.1257, \"PAC: SNF Per Capita Standardized Costs\": 612.17, \"% of Beneficiaries Using Ambulance\": 0.1215, \"Hospice Per User Standardized Costs\": 14199.68, \"# Imaging Users\": 663758.0, \"Part B Drugs Per User Actual Costs\": 524.1, \"Total Standardized Costs\": 8728188002.73, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.15, \"Count of Medicare beneficiaries with chronic kidney disease\": 170075.0, \"E&M Standardized Costs\": 898257341.98, \"Percent Hispanic\": 1.58, \"ASC Per Capita Actual Costs\": 100.56, \"Count of Medicare beneficiaries who have had a heart attack\": 7564.0, \"Tests Actual Costs\": 305103971.71, \"# PAC: LTCH Users (with a covered stay)\": 2932.0, \"% of Beneficiaries Using Procedures\": 0.6255, \"PAC: HH Actual Costs\": 370011354.81, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 60.0, \"PAC: LTCH Per Capita Standardized Costs\": 151.24, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0366, \"Emergency Department Visits\": 658475.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0589, \"Procedures Actual Costs\": 595517129.55, \"# FQHC/RHC Users\": 56389.0, \"Number of Acute Hospital Readmissions\": 45142.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 96.0, \"Outpatient Dialysis Facility Standardized Costs\": 373192362.2, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0146, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1283, \"Ambulance Per User Standardized Costs\": 1589.95, \"Imaging Per User Standardized Costs\": 320.82, \"Percent of Medicare beneficiaries with asthma\": 4.47, \"Part B Drugs Standardized Costs\": 277887207.11, \"FFS Beneficiaries\": 957574.0, \"# Hospice Users (with a covered stay)\": 31269.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0161, \"Count of Medicare beneficiaries with osteoporosis\": 48297.0, \"PQI08 CHF Admission Rate (age 75+)\": 2047.0, \"PAC: IRF Per Capita Standardized Costs\": 135.87, \"Procedures Standardized Costs\": 630934533.88, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2835, \"IP Per Capita Actual Costs\": 2798.44, \"DME Actual Costs\": 215904102.94, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0435, \"Count of Medicare beneficiaries with prostate cancer\": 27522.0, \"PAC: HH Per Capita Standardized Costs\": 429.99, \"Count of Medicare beneficiaries with heart failure\": 128625.0, \"Tests Per User Standardized Costs\": 413.49, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0154, \"Percent of Medicare beneficiaries with prostate cancer\": 2.87, \"PAC: IRF Per Capita Actual Costs\": 129.48, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 301.99, \"Percent of Medicare beneficiaries with breast cancer\": 2.77, \"Procedures Per User Standardized Costs\": 1053.3, \"Percent of Medicare beneficiaries with chronic kidney disease\": 17.76, \"PAC: HH Per User Actual Costs\": 4657.28, \"Count of Medicare beneficiaries with high cholesterol\": 443156.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0608, \"Hospice Per Capita Standardized Costs\": 463.68, \"# Part B Drugs Users\": 526884.0, \"Average HCC Score\": 1.0156, \"Standardized Risk-Adjusted Per Capita Costs\": 9572.18, \"# PAC: IRF Users (with a covered stay)\": 6484.0, \"Ambulance Standardized Costs\": 184925032.46, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0492, \"Percent of Medicare beneficiaries with heart failure\": 13.43, \"Tests Actual Costs as % of Total Actual Costs\": 0.0358, \"FQHC/RHC Per Capita Standardized Costs\": 23.62, \"PQI07 Hypertension Admission Rate (age 65-74)\": 80.0, \"Test Events Per 1000 Beneficiaries\": 10574.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 96.0, \"ASC Actual Costs\": 96289302.01, \"Part B Drugs Per Capita Standardized Costs\": 290.2, \"Imaging Actual Costs\": 200451244.99, \"Tests Per User Actual Costs\": 394.62, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0204, \"Hospice Per User Actual Costs\": 13397.73, \"Tests Standardized Costs\": 319697091.9, \"IP Standardized Costs\": 2474284487.46, \"IP Per Capita Standardized Costs\": 2583.91, \"Outpatient Dialysis Facility Actual Costs\": 360936144.97, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 54.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1473.0, \"Percent of Medicare beneficiaries with hypertension\": 59.8, \"IP Covered Stays Per 1000 Beneficiaries\": 275.0, \"# Ambulance Users\": 116309.0, \"# ASC Users\": 119382.0, \"ASC Per Capita Standardized Costs\": 108.49, \"Procedures Per Capita Actual Costs\": 621.9, \"Procedures Per Capita Standardized Costs\": 658.89, \"IP Users (with a covered stay)\": 164988.0, \"Total Standardized Risk-Adjusted Costs\": 9166068957.59, \"Actual Per Capita Costs\": 8892.28, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0166, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 7.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.01, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.65, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 220.0, \"OP Per User Standardized Costs\": 1869.1, \"Count of Medicare beneficiaries with atrial fibrillation\": 67859.0, \"Procedure Events Per 1000 Beneficiaries\": 4539.0, \"Percent of Medicare beneficiaries with stroke\": 3.93, \"PAC: IRF Per User Actual Costs\": 19121.85, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 111556.0, \"% of Beneficiaries Using ASC\": 0.1247, \"PAC: IRF Standardized Costs\": 130105281.91, \"Hospice Covered Days Per 1000 Beneficiaries\": 2812.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 305.0, \"PAC: SNF Per User Standardized Costs\": 15438.34, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0022, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 156.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0509, \"MA Participation Rate\": 30.1, \"OP Per Capita Standardized Costs\": 1169.01, \"Percent of Medicare beneficiaries with arthritis\": 28.87, \"Ambulance Per Capita Standardized Costs\": 193.12, \"PAC: HH Visits Per 1000 Beneficiaries\": 2473.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0699, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0235, \"PAC: HH Per Capita Actual Costs\": 386.41, \"E&M Events Per 1000 Beneficiaries\": 13002.0, \"Count of Medicare beneficiaries with asthma\": 42845.0, \"# Test Users\": 773166.0, \"E&M Actual Costs\": 830441478.08, \"% of Beneficiaries Using Imaging\": 0.6932, \"PAC: SNF Standardized Costs\": 586193842.93, \"DME Per Capita Actual Costs\": 225.47, \"E&M Per User Actual Costs\": 974.59, \"Percent Male\": 45.04, \"OP Visits Per 1000 Beneficiaries\": 3281.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0254, \"OP Per Capita Actual Costs\": 1118.15, \"Hospital Readmission Rate\": 0.1777, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0026, \"Tests Per Capita Standardized Costs\": 333.86, \"Hospice Per Capita Actual Costs\": 437.49, \"E&M Actual Costs as % of Total Actual Costs\": 0.0975, \"PQI07 Hypertension Admission Rate (age < 65)\": 172.0, \"Percent African American\": 21.42, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0472, \"PAC: LTCH Per Capita Actual Costs\": 136.84, \"MA Beneficiaries\": 412438.0, \"FQHC/RHC Per User Standardized Costs\": 401.14, \"PAC: HH Per User Standardized Costs\": 5182.64, \"Procedures Per User Actual Costs\": 994.17, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 790.0, \"Ambulance Per User Actual Costs\": 1496.6, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1329.0, \"PAC: HH Standardized Costs\": 411750734.79, \"ASC Actual Costs as % of Total Actual Costs\": 0.0113, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 733.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0031, \"Percent Other/Unknown\": 2.18, \"% of Beneficiaries Using Hospice\": 0.0327, \"PAC: LTCH Per User Actual Costs\": 44690.12, \"Percent Non-Hispanic White\": 74.81, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 754.0, \"Count of Medicare beneficiaries with breast cancer\": 26562.0, \"DME Per Capita Standardized Costs\": 239.5, \"PQI08 CHF Admission Rate (age 65-74)\": 748.0, \"% of Beneficiaries Using DME\": 0.2934, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0424, \"% of Beneficiaries Using IP\": 0.1723, \"DME Standardized Costs\": 229339678.4, \"FQHC/RHC Standardized Costs\": 22620151.0, \"PAC: SNF Per Capita Actual Costs\": 540.35, \"Imaging Standardized Costs\": 212946633.04, \"# E&M Users\": 852094.0, \"Count of Medicare beneficiaries with arthritis\": 276453.0, \"IP Per User Standardized Costs\": 14996.75, \"IP Per User Actual Costs\": 16241.88, \"PQI10 Dehydration Admission Rate (age 75+)\": 522.0, \"PAC: IRF Per User Standardized Costs\": 20065.59, \"DME Per User Actual Costs\": 768.42, \"PAC: IRF Actual Costs\": 123986085.34, \"Imaging Events Per 1000 Beneficiaries\": 4157.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0428, \"PAC: LTCH Per User Standardized Costs\": 49393.82, \"OP Actual Costs\": 1070708294.04, \"Count of Medicare beneficiaries with hypertension\": 572644.0, \"ASC Per User Standardized Costs\": 870.22, \"Part B Drugs Per Capita Actual Costs\": 288.37, \"Ambulance Events Per 1000 Beneficiaries\": 621.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 137.0, \"% of Beneficiaries Using PAC: HH\": 0.0248, \"PAC: LTCH Standardized Costs\": 491059.88, \"Percent of Medicare beneficiaries with atrial fibrillation\": 5.81, \"E&M Per Capita Standardized Costs\": 738.91, \"E&M Per User Standardized Costs\": 890.46, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 2444.0, \"IP Covered Days Per 1000 Beneficiaries\": 1070.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 30.0, \"Count of Medicare beneficiaries with lung cancer\": 732.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3795, \"Percent Eligible for Medicaid\": 12.04, \"Imaging Per Capita Standardized Costs\": 136.75, \"% of Beneficiaries Using Tests\": 0.7438, \"Imaging Per Capita Actual Costs\": 141.02, \"% of Beneficiaries Using PAC: SNF\": 0.0261, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0253, \"Count of Medicare beneficiaries with colorectal cancer\": 1229.0, \"Hospice Actual Costs\": 23256167.23, \"# PAC: HH Users\": 2526.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 25600.36, \"Total Actual Costs\": 696475253.19, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 9806.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0105, \"ASC Standardized Costs\": 6239885.45, \"DME Events Per 1000 Beneficiaries\": 628.0, \"PQI08 CHF Admission Rate (age < 65)\": 914.0, \"ASC Events Per 1000 Beneficiaries\": 110.0, \"PAC: LTCH Actual Costs\": 511939.73, \"Count of Medicare beneficiaries with depression\": 7350.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 886.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.62, \"Outpatient Dialysis Facility Per User Actual Costs\": 27650.49, \"Beneficiaries with Part A and Part B\": 212999.0, \"% of Beneficiaries Using Part B Drugs\": 0.4377, \"Percent of Medicare beneficiaries with diabetes\": 26.96, \"% of Beneficiaries Using PAC: IRF\": 0.0049, \"E&M Per Capita Actual Costs\": 729.22, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0236, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0299, \"PAC: SNF Actual Costs\": 44817895.12, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 209.0, \"Percent Female\": 52.28, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 8.46, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 445.21, \"# Outpatient Dialysis Facility Users\": 1772.0, \"FQHC/RHC Per User Actual Costs\": 418.25, \"Count of Medicare beneficiaries with ischemic heart disease\": 20309.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 96.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.84, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 250.0, \"Percent of Medicare beneficiaries with depression\": 7.21, \"Emergency Department Visits per 1000 Beneficiaries\": 451.0, \"IP Actual Costs\": 264335809.82, \"% of Beneficiaries Using OP\": 0.5216, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0098, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1273, \"Count of Medicare beneficiaries with stroke\": 3665.0, \"PQI12 UTI Admission Rate (age 75+)\": 478.0, \"# OP Users\": 53143.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 21.0, \"# Procedure Users\": 49855.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 19.93, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0782, \"Count of Medicare beneficiaries with diabetes\": 27471.0, \"ASC Per User Actual Costs\": 838.9, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 506.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.012, \"Percent of Medicare beneficiaries with high cholesterol\": 54.16, \"Standardized Per Capita Costs\": 5805.33, \"Ambulance Actual Costs\": 7670519.03, \"FQHC/RHC Per Capita Actual Costs\": 22.33, \"Part B Drugs Per User Standardized Costs\": 396.04, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0163, \"# PAC: SNF Users (with a covered stay)\": 2662.0, \"Ambulance Per Capita Actual Costs\": 75.28, \"PQI12 UTI Admission Rate (age 65-74)\": 92.0, \"Hospice Standardized Costs\": 20595447.43, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 480.86, \"% of Beneficiaries Using E&M\": 0.8298, \"PQI10 Dehydration Admission Rate (age 65-74)\": 73.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 130.0, \"Tests Per Capita Actual Costs\": 217.57, \"# DME Users\": 13395.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0675, \"PAC: SNF Per User Actual Costs\": 16836.17, \"State\": \"HI\", \"OP Per User Actual Costs\": 1674.29, \"PAC: HH Episodes Per 1000 Beneficiaries\": 33.0, \"Part B Drugs Actual Costs\": 17634986.01, \"FQHC/RHC Actual Costs\": 2274887.51, \"OP Standardized Costs\": 82978218.41, \"DME Per User Standardized Costs\": 530.19, \"OP Actual Costs as % of Total Actual Costs\": 0.1278, \"PAC: SNF Per Capita Standardized Costs\": 391.7, \"% of Beneficiaries Using Ambulance\": 0.0845, \"Hospice Per User Standardized Costs\": 10056.37, \"# Imaging Users\": 60260.0, \"Part B Drugs Per User Actual Costs\": 395.38, \"Total Standardized Costs\": 591527910.39, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.21, \"Count of Medicare beneficiaries with chronic kidney disease\": 17858.0, \"E&M Standardized Costs\": 75290506.66, \"Percent Hispanic\": 5.75, \"ASC Per Capita Actual Costs\": 64.56, \"Count of Medicare beneficiaries who have had a heart attack\": 853.0, \"Tests Actual Costs\": 22168685.99, \"# PAC: LTCH Users (with a covered stay)\": 12.0, \"% of Beneficiaries Using Procedures\": 0.4893, \"PAC: HH Actual Costs\": 9901662.62, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 38.0, \"PAC: LTCH Per Capita Standardized Costs\": 4.82, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0376, \"Emergency Department Visits\": 45971.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0534, \"Procedures Actual Costs\": 46310946.02, \"# FQHC/RHC Users\": 5439.0, \"Number of Acute Hospital Readmissions\": 2673.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 62.0, \"Outpatient Dialysis Facility Standardized Costs\": 45363841.52, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0157, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1403, \"Ambulance Per User Standardized Costs\": 674.84, \"Imaging Per User Standardized Costs\": 231.23, \"Percent of Medicare beneficiaries with asthma\": 5.17, \"Part B Drugs Standardized Costs\": 17664638.46, \"FFS Beneficiaries\": 101894.0, \"# Hospice Users (with a covered stay)\": 2048.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0174, \"Count of Medicare beneficiaries with osteoporosis\": 8617.0, \"PQI08 CHF Admission Rate (age 75+)\": 1118.0, \"PAC: IRF Per Capita Standardized Costs\": 94.52, \"Procedures Standardized Costs\": 46262338.55, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3049, \"IP Per Capita Actual Costs\": 2594.22, \"DME Actual Costs\": 6975091.97, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0142, \"Count of Medicare beneficiaries with prostate cancer\": 2824.0, \"PAC: HH Per Capita Standardized Costs\": 86.5, \"Count of Medicare beneficiaries with heart failure\": 9603.0, \"Tests Per User Standardized Costs\": 293.21, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0007, \"Percent of Medicare beneficiaries with prostate cancer\": 2.77, \"PAC: IRF Per Capita Actual Costs\": 107.54, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 238.46, \"Percent of Medicare beneficiaries with breast cancer\": 3.13, \"Procedures Per User Standardized Costs\": 927.94, \"Percent of Medicare beneficiaries with chronic kidney disease\": 17.53, \"PAC: HH Per User Actual Costs\": 3919.9, \"Count of Medicare beneficiaries with high cholesterol\": 55187.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0643, \"Hospice Per Capita Standardized Costs\": 202.13, \"# Part B Drugs Users\": 44603.0, \"Average HCC Score\": 0.9065, \"Standardized Risk-Adjusted Per Capita Costs\": 6595.1, \"# PAC: IRF Users (with a covered stay)\": 497.0, \"Ambulance Standardized Costs\": 5812533.17, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0334, \"Percent of Medicare beneficiaries with heart failure\": 9.42, \"Tests Actual Costs as % of Total Actual Costs\": 0.0318, \"FQHC/RHC Per Capita Standardized Costs\": 22.27, \"PQI07 Hypertension Admission Rate (age 65-74)\": 41.0, \"Test Events Per 1000 Beneficiaries\": 10366.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 5.0, \"ASC Actual Costs\": 6577820.11, \"Part B Drugs Per Capita Standardized Costs\": 173.36, \"Imaging Actual Costs\": 14369596.06, \"Tests Per User Actual Costs\": 292.49, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.011, \"Hospice Per User Actual Costs\": 11355.55, \"Tests Standardized Costs\": 22223444.2, \"IP Standardized Costs\": 180333044.34, \"IP Per Capita Standardized Costs\": 1769.81, \"Outpatient Dialysis Facility Actual Costs\": 48996675.28, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 31.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 897.0, \"Percent of Medicare beneficiaries with hypertension\": 55.09, \"IP Covered Stays Per 1000 Beneficiaries\": 168.0, \"# Ambulance Users\": 8613.0, \"# ASC Users\": 7841.0, \"ASC Per Capita Standardized Costs\": 61.24, \"Procedures Per Capita Actual Costs\": 454.5, \"Procedures Per Capita Standardized Costs\": 454.02, \"IP Users (with a covered stay)\": 11683.0, \"Total Standardized Risk-Adjusted Costs\": 672000682.66, \"Actual Per Capita Costs\": 6835.29, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0008, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 5.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 0.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.72, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 5.84, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 145.0, \"OP Per User Standardized Costs\": 1561.41, \"Count of Medicare beneficiaries with atrial fibrillation\": 5923.0, \"Procedure Events Per 1000 Beneficiaries\": 3229.0, \"Percent of Medicare beneficiaries with stroke\": 3.6, \"PAC: IRF Per User Actual Costs\": 22047.22, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 5947.0, \"% of Beneficiaries Using ASC\": 0.077, \"PAC: IRF Standardized Costs\": 9631008.71, \"Hospice Covered Days Per 1000 Beneficiaries\": 1309.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 146.0, \"PAC: SNF Per User Standardized Costs\": 14993.27, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0033, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 113.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0348, \"MA Participation Rate\": 52.16, \"OP Per Capita Standardized Costs\": 814.36, \"Percent of Medicare beneficiaries with arthritis\": 17.58, \"Ambulance Per Capita Standardized Costs\": 57.04, \"PAC: HH Visits Per 1000 Beneficiaries\": 412.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0665, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0206, \"PAC: HH Per Capita Actual Costs\": 97.18, \"E&M Events Per 1000 Beneficiaries\": 10791.0, \"Count of Medicare beneficiaries with asthma\": 5270.0, \"# Test Users\": 75793.0, \"E&M Actual Costs\": 74303084.48, \"% of Beneficiaries Using Imaging\": 0.5914, \"PAC: SNF Standardized Costs\": 39912072.27, \"DME Per Capita Actual Costs\": 68.45, \"E&M Per User Actual Costs\": 878.79, \"Percent Male\": 47.72, \"OP Visits Per 1000 Beneficiaries\": 2828.0, \"DME Actual Costs as % of Total Actual Costs\": 0.01, \"OP Per Capita Actual Costs\": 873.23, \"Hospital Readmission Rate\": 0.1572, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0038, \"Tests Per Capita Standardized Costs\": 218.1, \"Hospice Per Capita Actual Costs\": 228.24, \"E&M Actual Costs as % of Total Actual Costs\": 0.1067, \"PQI07 Hypertension Admission Rate (age < 65)\": \"*\", \"Percent African American\": 1.01, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0149, \"PAC: LTCH Per Capita Actual Costs\": 5.02, \"MA Beneficiaries\": 111105.0, \"FQHC/RHC Per User Standardized Costs\": 417.14, \"PAC: HH Per User Standardized Costs\": 3489.25, \"Procedures Per User Actual Costs\": 928.91, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 712.0, \"Ambulance Per User Actual Costs\": 890.56, \"Average Age\": 73.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 720.0, \"PAC: HH Standardized Costs\": 8813833.69, \"ASC Actual Costs as % of Total Actual Costs\": 0.0094, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 343.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0001, \"Percent Other/Unknown\": 63.57, \"% of Beneficiaries Using Hospice\": 0.0201, \"PAC: LTCH Per User Actual Costs\": 42661.64, \"Percent Non-Hispanic White\": 29.67, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 380.0, \"Count of Medicare beneficiaries with breast cancer\": 3189.0, \"DME Per Capita Standardized Costs\": 69.7, \"PQI08 CHF Admission Rate (age 65-74)\": 331.0, \"% of Beneficiaries Using DME\": 0.1315, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0703, \"% of Beneficiaries Using IP\": 0.1147, \"DME Standardized Costs\": 7101906.01, \"FQHC/RHC Standardized Costs\": 2268842.06, \"PAC: SNF Per Capita Actual Costs\": 439.85, \"Imaging Standardized Costs\": 13933966.16, \"# E&M Users\": 84552.0, \"Count of Medicare beneficiaries with arthritis\": 17908.0, \"IP Per User Standardized Costs\": 15435.51, \"IP Per User Actual Costs\": 22625.68, \"PQI10 Dehydration Admission Rate (age 75+)\": 293.0, \"PAC: IRF Per User Standardized Costs\": 19378.29, \"DME Per User Actual Costs\": 520.72, \"PAC: IRF Actual Costs\": 10957470.0, \"Imaging Events Per 1000 Beneficiaries\": 2753.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0767, \"PAC: LTCH Per User Standardized Costs\": 40921.66, \"OP Actual Costs\": 88976888.7, \"Count of Medicare beneficiaries with hypertension\": 56136.0, \"ASC Per User Standardized Costs\": 795.8, \"Part B Drugs Per Capita Actual Costs\": 173.07, \"Ambulance Events Per 1000 Beneficiaries\": 149.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 314.0, \"% of Beneficiaries Using PAC: HH\": 0.0554, \"PAC: LTCH Standardized Costs\": 22931303.76, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.72, \"E&M Per Capita Standardized Costs\": 670.26, \"E&M Per User Standardized Costs\": 750.38, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 761.0, \"IP Covered Days Per 1000 Beneficiaries\": 1254.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 32.0, \"Count of Medicare beneficiaries with lung cancer\": 4108.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3319, \"Percent Eligible for Medicaid\": 17.19, \"Imaging Per Capita Standardized Costs\": 155.12, \"% of Beneficiaries Using Tests\": 0.744, \"Imaging Per Capita Actual Costs\": 141.47, \"% of Beneficiaries Using PAC: SNF\": 0.0554, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0414, \"Count of Medicare beneficiaries with colorectal cancer\": 5979.0, \"Hospice Actual Costs\": 130418181.8, \"# PAC: HH Users\": 25423.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 22837.0, \"Total Actual Costs\": 3566630296.58, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 42603.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0069, \"ASC Standardized Costs\": 24169034.25, \"DME Events Per 1000 Beneficiaries\": 1856.0, \"PQI08 CHF Admission Rate (age < 65)\": 610.0, \"ASC Events Per 1000 Beneficiaries\": 84.0, \"PAC: LTCH Actual Costs\": 21093767.89, \"Count of Medicare beneficiaries with depression\": 69636.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1966.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.29, \"Outpatient Dialysis Facility Per User Actual Costs\": 22527.34, \"Beneficiaries with Part A and Part B\": 542103.0, \"% of Beneficiaries Using Part B Drugs\": 0.4816, \"Percent of Medicare beneficiaries with diabetes\": 23.69, \"% of Beneficiaries Using PAC: IRF\": 0.0045, \"E&M Per Capita Actual Costs\": 588.04, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0203, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0423, \"PAC: SNF Actual Costs\": 332821443.25, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 617.0, \"Percent Female\": 55.86, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 219.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.15, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 133.2, \"# Outpatient Dialysis Facility Users\": 2675.0, \"FQHC/RHC Per User Actual Costs\": 436.74, \"Count of Medicare beneficiaries with ischemic heart disease\": 113236.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 112.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.82, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 756.0, \"Percent of Medicare beneficiaries with depression\": 15.18, \"Emergency Department Visits per 1000 Beneficiaries\": 612.0, \"IP Actual Costs\": 1183589221.87, \"% of Beneficiaries Using OP\": 0.7172, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0082, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0876, \"Count of Medicare beneficiaries with stroke\": 12062.0, \"PQI12 UTI Admission Rate (age 75+)\": 848.0, \"# OP Users\": 328915.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 34.0, \"# Procedure Users\": 276862.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 24.69, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0645, \"Count of Medicare beneficiaries with diabetes\": 108670.0, \"ASC Per User Actual Costs\": 891.31, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 875.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0267, \"Percent of Medicare beneficiaries with high cholesterol\": 40.0, \"Standardized Per Capita Costs\": 7655.37, \"Ambulance Actual Costs\": 31549058.95, \"FQHC/RHC Per Capita Actual Costs\": 72.97, \"Part B Drugs Per User Standardized Costs\": 672.9, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0108, \"# PAC: SNF Users (with a covered stay)\": 25422.0, \"Ambulance Per Capita Actual Costs\": 68.79, \"PQI12 UTI Admission Rate (age 65-74)\": 224.0, \"Hospice Standardized Costs\": 138285287.06, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 131.39, \"% of Beneficiaries Using E&M\": 0.8932, \"PQI10 Dehydration Admission Rate (age 65-74)\": 152.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 124.0, \"Tests Per Capita Actual Costs\": 138.93, \"# DME Users\": 136148.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1057, \"PAC: SNF Per User Actual Costs\": 13091.87, \"State\": \"IA\", \"OP Per User Actual Costs\": 2172.77, \"PAC: HH Episodes Per 1000 Beneficiaries\": 83.0, \"Part B Drugs Actual Costs\": 147731473.27, \"FQHC/RHC Actual Costs\": 33465487.91, \"OP Standardized Costs\": 659313525.07, \"DME Per User Standardized Costs\": 688.66, \"OP Actual Costs as % of Total Actual Costs\": 0.2004, \"PAC: SNF Per Capita Standardized Costs\": 808.95, \"% of Beneficiaries Using Ambulance\": 0.0833, \"Hospice Per User Standardized Costs\": 9272.8, \"# Imaging Users\": 304089.0, \"Part B Drugs Per User Actual Costs\": 668.82, \"Total Standardized Costs\": 3510999769.65, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.3, \"Count of Medicare beneficiaries with chronic kidney disease\": 62794.0, \"E&M Standardized Costs\": 307402439.73, \"Percent Hispanic\": 0.98, \"ASC Per Capita Actual Costs\": 49.79, \"Count of Medicare beneficiaries who have had a heart attack\": 3748.0, \"Tests Actual Costs\": 63717487.12, \"# PAC: LTCH Users (with a covered stay)\": 514.0, \"% of Beneficiaries Using Procedures\": 0.6037, \"PAC: HH Actual Costs\": 91765492.37, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 34.0, \"PAC: LTCH Per Capita Standardized Costs\": 50.0, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0193, \"Emergency Department Visits\": 280576.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1671, \"Procedures Actual Costs\": 202072050.02, \"# FQHC/RHC Users\": 76625.0, \"Number of Acute Hospital Readmissions\": 17927.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 61.0, \"Outpatient Dialysis Facility Standardized Costs\": 61088966.45, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0105, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1878, \"Ambulance Per User Standardized Costs\": 753.33, \"Imaging Per User Standardized Costs\": 233.95, \"Percent of Medicare beneficiaries with asthma\": 3.51, \"Part B Drugs Standardized Costs\": 148633550.65, \"FFS Beneficiaries\": 458632.0, \"# Hospice Users (with a covered stay)\": 14913.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0058, \"Count of Medicare beneficiaries with osteoporosis\": 23624.0, \"PQI08 CHF Admission Rate (age 75+)\": 1693.0, \"PAC: IRF Per Capita Standardized Costs\": 82.35, \"Procedures Standardized Costs\": 226418182.03, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2958, \"IP Per Capita Actual Costs\": 2580.69, \"DME Actual Costs\": 89147363.7, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0257, \"Count of Medicare beneficiaries with prostate cancer\": 11687.0, \"PAC: HH Per Capita Standardized Costs\": 218.63, \"Count of Medicare beneficiaries with heart failure\": 56876.0, \"Tests Per User Standardized Costs\": 198.61, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0059, \"Percent of Medicare beneficiaries with prostate cancer\": 2.55, \"PAC: IRF Per Capita Actual Costs\": 81.35, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 213.36, \"Percent of Medicare beneficiaries with breast cancer\": 2.65, \"Procedures Per User Standardized Costs\": 817.8, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.69, \"PAC: HH Per User Actual Costs\": 3609.55, \"Count of Medicare beneficiaries with high cholesterol\": 183453.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0933, \"Hospice Per Capita Standardized Costs\": 301.52, \"# Part B Drugs Users\": 220885.0, \"Average HCC Score\": 0.9025, \"Standardized Risk-Adjusted Per Capita Costs\": 9115.14, \"# PAC: IRF Users (with a covered stay)\": 2056.0, \"Ambulance Standardized Costs\": 28770113.38, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0366, \"Percent of Medicare beneficiaries with heart failure\": 12.4, \"Tests Actual Costs as % of Total Actual Costs\": 0.0179, \"FQHC/RHC Per Capita Standardized Costs\": 83.23, \"PQI07 Hypertension Admission Rate (age 65-74)\": 40.0, \"Test Events Per 1000 Beneficiaries\": 7198.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 33.0, \"ASC Actual Costs\": 22835360.27, \"Part B Drugs Per Capita Standardized Costs\": 324.08, \"Imaging Actual Costs\": 64880375.01, \"Tests Per User Actual Costs\": 186.74, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0088, \"Hospice Per User Actual Costs\": 8745.27, \"Tests Standardized Costs\": 67767331.17, \"IP Standardized Costs\": 1038435891.0, \"IP Per Capita Standardized Costs\": 2264.2, \"Outpatient Dialysis Facility Actual Costs\": 60260628.16, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 74.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1603.0, \"Percent of Medicare beneficiaries with hypertension\": 51.15, \"IP Covered Stays Per 1000 Beneficiaries\": 255.0, \"# Ambulance Users\": 38191.0, \"# ASC Users\": 25620.0, \"ASC Per Capita Standardized Costs\": 52.7, \"Procedures Per Capita Actual Costs\": 440.6, \"Procedures Per Capita Standardized Costs\": 493.68, \"IP Users (with a covered stay)\": 78158.0, \"Total Standardized Risk-Adjusted Costs\": 4180493628.77, \"Actual Per Capita Costs\": 7776.67, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0065, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 5.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.9, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.4, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 131.0, \"OP Per User Standardized Costs\": 2004.51, \"Count of Medicare beneficiaries with atrial fibrillation\": 39995.0, \"Procedure Events Per 1000 Beneficiaries\": 3724.0, \"Percent of Medicare beneficiaries with stroke\": 2.63, \"PAC: IRF Per User Actual Costs\": 18146.72, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 47709.0, \"% of Beneficiaries Using ASC\": 0.0559, \"PAC: IRF Standardized Costs\": 37767426.52, \"Hospice Covered Days Per 1000 Beneficiaries\": 1877.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 255.0, \"PAC: SNF Per User Standardized Costs\": 14593.99, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0094, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 118.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0394, \"MA Participation Rate\": 15.4, \"OP Per Capita Standardized Costs\": 1437.57, \"Percent of Medicare beneficiaries with arthritis\": 25.65, \"Ambulance Per Capita Standardized Costs\": 62.73, \"PAC: HH Visits Per 1000 Beneficiaries\": 1407.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0567, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0182, \"PAC: HH Per Capita Actual Costs\": 200.09, \"E&M Events Per 1000 Beneficiaries\": 10190.0, \"Count of Medicare beneficiaries with asthma\": 16105.0, \"# Test Users\": 341209.0, \"E&M Actual Costs\": 269695977.63, \"% of Beneficiaries Using Imaging\": 0.663, \"PAC: SNF Standardized Costs\": 371008370.42, \"DME Per Capita Actual Costs\": 194.38, \"E&M Per User Actual Costs\": 658.34, \"Percent Male\": 44.14, \"OP Visits Per 1000 Beneficiaries\": 5766.0, \"DME Actual Costs as % of Total Actual Costs\": 0.025, \"OP Per Capita Actual Costs\": 1558.24, \"Hospital Readmission Rate\": 0.1563, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0109, \"Tests Per Capita Standardized Costs\": 147.76, \"Hospice Per Capita Actual Costs\": 284.36, \"E&M Actual Costs as % of Total Actual Costs\": 0.0756, \"PQI07 Hypertension Admission Rate (age < 65)\": 89.0, \"Percent African American\": 1.58, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0286, \"PAC: LTCH Per Capita Actual Costs\": 45.99, \"MA Beneficiaries\": 83471.0, \"FQHC/RHC Per User Standardized Costs\": 498.19, \"PAC: HH Per User Standardized Costs\": 3944.02, \"Procedures Per User Actual Costs\": 729.87, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 465.0, \"Ambulance Per User Actual Costs\": 826.09, \"Average Age\": 73.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1224.0, \"PAC: HH Standardized Costs\": 100268827.82, \"ASC Actual Costs as % of Total Actual Costs\": 0.0064, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 579.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0011, \"Percent Other/Unknown\": 1.71, \"% of Beneficiaries Using Hospice\": 0.0325, \"PAC: LTCH Per User Actual Costs\": 41038.46, \"Percent Non-Hispanic White\": 95.74, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 906.0, \"Count of Medicare beneficiaries with breast cancer\": 12139.0, \"DME Per Capita Standardized Costs\": 204.43, \"PQI08 CHF Admission Rate (age 65-74)\": 479.0, \"% of Beneficiaries Using DME\": 0.2969, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0169, \"% of Beneficiaries Using IP\": 0.1704, \"DME Standardized Costs\": 93760323.72, \"FQHC/RHC Standardized Costs\": 38174106.61, \"PAC: SNF Per Capita Actual Costs\": 725.68, \"Imaging Standardized Costs\": 71142923.64, \"# E&M Users\": 409660.0, \"Count of Medicare beneficiaries with arthritis\": 117645.0, \"IP Per User Standardized Costs\": 13286.37, \"IP Per User Actual Costs\": 15143.55, \"PQI10 Dehydration Admission Rate (age 75+)\": 390.0, \"PAC: IRF Per User Standardized Costs\": 18369.37, \"DME Per User Actual Costs\": 654.78, \"PAC: IRF Actual Costs\": 37309649.37, \"Imaging Events Per 1000 Beneficiaries\": 3420.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0174, \"PAC: LTCH Per User Standardized Costs\": 44613.43, \"OP Actual Costs\": 714657548.03, \"Count of Medicare beneficiaries with hypertension\": 234596.0, \"ASC Per User Standardized Costs\": 943.37, \"Part B Drugs Per Capita Actual Costs\": 322.11, \"Ambulance Events Per 1000 Beneficiaries\": 168.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 186.0, \"% of Beneficiaries Using PAC: HH\": 0.0722, \"PAC: LTCH Standardized Costs\": 23325107.12, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.8, \"E&M Per Capita Standardized Costs\": 561.56, \"E&M Per User Standardized Costs\": 676.51, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 937.0, \"IP Covered Days Per 1000 Beneficiaries\": 988.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 31.0, \"Count of Medicare beneficiaries with lung cancer\": 1228.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3232, \"Percent Eligible for Medicaid\": 18.8, \"Imaging Per Capita Standardized Costs\": 116.6, \"% of Beneficiaries Using Tests\": 0.6661, \"Imaging Per Capita Actual Costs\": 107.38, \"% of Beneficiaries Using PAC: SNF\": 0.0403, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0194, \"Count of Medicare beneficiaries with colorectal cancer\": 1567.0, \"Hospice Actual Costs\": 64748292.9, \"# PAC: HH Users\": 12419.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23113.42, \"Total Actual Costs\": 1285244964.74, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 13298.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0134, \"ASC Standardized Costs\": 16969361.38, \"DME Events Per 1000 Beneficiaries\": 1911.0, \"PQI08 CHF Admission Rate (age < 65)\": 292.0, \"ASC Events Per 1000 Beneficiaries\": 184.0, \"PAC: LTCH Actual Costs\": 21648196.14, \"Count of Medicare beneficiaries with depression\": 28090.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1271.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 7.73, \"Outpatient Dialysis Facility Per User Actual Costs\": 22500.76, \"Beneficiaries with Part A and Part B\": 255776.0, \"% of Beneficiaries Using Part B Drugs\": 0.3993, \"Percent of Medicare beneficiaries with diabetes\": 21.93, \"% of Beneficiaries Using PAC: IRF\": 0.0048, \"E&M Per Capita Actual Costs\": 494.86, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0158, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.02, \"PAC: SNF Actual Costs\": 92285230.25, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 429.0, \"Percent Female\": 52.06, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 249.0, \"Percent of Medicare beneficiaries with osteoporosis\": 4.58, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 149.73, \"# Outpatient Dialysis Facility Users\": 1114.0, \"FQHC/RHC Per User Actual Costs\": 437.32, \"Count of Medicare beneficiaries with ischemic heart disease\": 34089.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 83.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.63, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 836.0, \"Percent of Medicare beneficiaries with depression\": 16.33, \"Emergency Department Visits per 1000 Beneficiaries\": 565.0, \"IP Actual Costs\": 415450991.65, \"% of Beneficiaries Using OP\": 0.7123, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0086, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0761, \"Count of Medicare beneficiaries with stroke\": 3902.0, \"PQI12 UTI Admission Rate (age 75+)\": 653.0, \"# OP Users\": 122498.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 31.0, \"# Procedure Users\": 94690.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 19.82, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0695, \"Count of Medicare beneficiaries with diabetes\": 37717.0, \"ASC Per User Actual Costs\": 812.78, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 579.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0328, \"Percent of Medicare beneficiaries with high cholesterol\": 31.08, \"Standardized Per Capita Costs\": 7381.03, \"Ambulance Actual Costs\": 13662908.75, \"FQHC/RHC Per Capita Actual Costs\": 83.45, \"Part B Drugs Per User Standardized Costs\": 369.48, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0128, \"# PAC: SNF Users (with a covered stay)\": 6930.0, \"Ambulance Per Capita Actual Costs\": 79.45, \"PQI12 UTI Admission Rate (age 65-74)\": 132.0, \"Hospice Standardized Costs\": 68788661.32, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 145.76, \"% of Beneficiaries Using E&M\": 0.8301, \"PQI10 Dehydration Admission Rate (age 65-74)\": 140.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 103.0, \"Tests Per Capita Actual Costs\": 133.87, \"# DME Users\": 46252.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0823, \"PAC: SNF Per User Actual Costs\": 13316.77, \"State\": \"ID\", \"OP Per User Actual Costs\": 2141.76, \"PAC: HH Episodes Per 1000 Beneficiaries\": 119.0, \"Part B Drugs Actual Costs\": 24993079.52, \"FQHC/RHC Actual Costs\": 14350158.9, \"OP Standardized Costs\": 251373123.24, \"DME Per User Standardized Costs\": 899.86, \"OP Actual Costs as % of Total Actual Costs\": 0.2041, \"PAC: SNF Per Capita Standardized Costs\": 607.19, \"% of Beneficiaries Using Ambulance\": 0.0855, \"Hospice Per User Standardized Costs\": 13812.98, \"# Imaging Users\": 103748.0, \"Part B Drugs Per User Actual Costs\": 364.0, \"Total Standardized Costs\": 1269271448.0, \"Percent of Medicare beneficiaries with colorectal cancer\": 0.91, \"Count of Medicare beneficiaries with chronic kidney disease\": 21611.0, \"E&M Standardized Costs\": 96568385.98, \"Percent Hispanic\": 3.51, \"ASC Per Capita Actual Costs\": 91.22, \"Count of Medicare beneficiaries who have had a heart attack\": 1079.0, \"Tests Actual Costs\": 23020985.29, \"# PAC: LTCH Users (with a covered stay)\": 509.0, \"% of Beneficiaries Using Procedures\": 0.5506, \"PAC: HH Actual Costs\": 56571630.36, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 22.0, \"PAC: LTCH Per Capita Standardized Costs\": 135.64, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0191, \"Emergency Department Visits\": 97107.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1908, \"Procedures Actual Costs\": 80621134.17, \"# FQHC/RHC Users\": 32814.0, \"Number of Acute Hospital Readmissions\": 4659.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 63.0, \"Outpatient Dialysis Facility Standardized Costs\": 25748354.73, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0125, \"OP Standardized Costs as % of Total Standardized Costs\": 0.198, \"Ambulance Per User Standardized Costs\": 740.03, \"Imaging Per User Standardized Costs\": 193.27, \"Percent of Medicare beneficiaries with asthma\": 4.27, \"Part B Drugs Standardized Costs\": 25368889.11, \"FFS Beneficiaries\": 171964.0, \"# Hospice Users (with a covered stay)\": 4980.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0065, \"Count of Medicare beneficiaries with osteoporosis\": 7882.0, \"PQI08 CHF Admission Rate (age 75+)\": 1136.0, \"PAC: IRF Per Capita Standardized Costs\": 94.24, \"Procedures Standardized Costs\": 88276155.24, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2825, \"IP Per Capita Actual Costs\": 2415.92, \"DME Actual Costs\": 39535242.01, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.044, \"Count of Medicare beneficiaries with prostate cancer\": 4631.0, \"PAC: HH Per Capita Standardized Costs\": 368.65, \"Count of Medicare beneficiaries with heart failure\": 18915.0, \"Tests Per User Standardized Costs\": 211.24, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0168, \"Percent of Medicare beneficiaries with prostate cancer\": 2.69, \"PAC: IRF Per Capita Actual Costs\": 93.33, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 177.99, \"Percent of Medicare beneficiaries with breast cancer\": 2.31, \"Procedures Per User Standardized Costs\": 932.26, \"Percent of Medicare beneficiaries with chronic kidney disease\": 12.57, \"PAC: HH Per User Actual Costs\": 4555.25, \"Count of Medicare beneficiaries with high cholesterol\": 53443.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0718, \"Hospice Per Capita Standardized Costs\": 400.02, \"# Part B Drugs Users\": 68662.0, \"Average HCC Score\": 0.8677, \"Standardized Risk-Adjusted Per Capita Costs\": 9130.92, \"# PAC: IRF Users (with a covered stay)\": 818.0, \"Ambulance Standardized Costs\": 10879624.38, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0504, \"Percent of Medicare beneficiaries with heart failure\": 11.0, \"Tests Actual Costs as % of Total Actual Costs\": 0.0179, \"FQHC/RHC Per Capita Standardized Costs\": 96.41, \"PQI07 Hypertension Admission Rate (age 65-74)\": 40.0, \"Test Events Per 1000 Beneficiaries\": 5572.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 86.0, \"ASC Actual Costs\": 15686645.47, \"Part B Drugs Per Capita Standardized Costs\": 147.52, \"Imaging Actual Costs\": 18465939.33, \"Tests Per User Actual Costs\": 200.99, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0106, \"Hospice Per User Actual Costs\": 13001.67, \"Tests Standardized Costs\": 24195556.2, \"IP Standardized Costs\": 358596835.63, \"IP Per Capita Standardized Costs\": 2085.3, \"Outpatient Dialysis Facility Actual Costs\": 25065851.09, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 51.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1314.0, \"Percent of Medicare beneficiaries with hypertension\": 42.36, \"IP Covered Stays Per 1000 Beneficiaries\": 211.0, \"# Ambulance Users\": 14702.0, \"# ASC Users\": 19300.0, \"ASC Per Capita Standardized Costs\": 98.68, \"Procedures Per Capita Actual Costs\": 468.83, \"Procedures Per Capita Standardized Costs\": 513.34, \"IP Users (with a covered stay)\": 25345.0, \"Total Standardized Risk-Adjusted Costs\": 1570190375.8, \"Actual Per Capita Costs\": 7473.92, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0184, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 5.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.71, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 8.77, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 109.0, \"OP Per User Standardized Costs\": 2052.06, \"Count of Medicare beneficiaries with atrial fibrillation\": 11690.0, \"Procedure Events Per 1000 Beneficiaries\": 3725.0, \"Percent of Medicare beneficiaries with stroke\": 2.27, \"PAC: IRF Per User Actual Costs\": 19619.91, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 15077.0, \"% of Beneficiaries Using ASC\": 0.1122, \"PAC: IRF Standardized Costs\": 16205608.69, \"Hospice Covered Days Per 1000 Beneficiaries\": 2599.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 199.0, \"PAC: SNF Per User Standardized Costs\": 15067.01, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0112, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 63.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0542, \"MA Participation Rate\": 32.77, \"OP Per Capita Standardized Costs\": 1461.78, \"Percent of Medicare beneficiaries with arthritis\": 25.88, \"Ambulance Per Capita Standardized Costs\": 63.27, \"PAC: HH Visits Per 1000 Beneficiaries\": 2257.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0627, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0144, \"PAC: HH Per Capita Actual Costs\": 328.97, \"E&M Events Per 1000 Beneficiaries\": 8474.0, \"Count of Medicare beneficiaries with asthma\": 7348.0, \"# Test Users\": 114540.0, \"E&M Actual Costs\": 85098240.57, \"% of Beneficiaries Using Imaging\": 0.6033, \"PAC: SNF Standardized Costs\": 104414351.61, \"DME Per Capita Actual Costs\": 229.9, \"E&M Per User Actual Costs\": 596.16, \"Percent Male\": 47.94, \"OP Visits Per 1000 Beneficiaries\": 5825.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0308, \"OP Per Capita Actual Costs\": 1525.67, \"Hospital Readmission Rate\": 0.1339, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0131, \"Tests Per Capita Standardized Costs\": 140.7, \"Hospice Per Capita Actual Costs\": 376.52, \"E&M Actual Costs as % of Total Actual Costs\": 0.0662, \"PQI07 Hypertension Admission Rate (age < 65)\": 50.0, \"Percent African American\": 0.28, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0499, \"PAC: LTCH Per Capita Actual Costs\": 125.89, \"MA Beneficiaries\": 83812.0, \"FQHC/RHC Per User Standardized Costs\": 505.25, \"PAC: HH Per User Standardized Costs\": 5104.62, \"Procedures Per User Actual Costs\": 851.42, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 315.0, \"Ambulance Per User Actual Costs\": 929.34, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 586.0, \"PAC: HH Standardized Costs\": 63394295.05, \"ASC Actual Costs as % of Total Actual Costs\": 0.0122, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 410.0, \"% of Beneficiaries Using PAC: LTCH\": 0.003, \"Percent Other/Unknown\": 2.88, \"% of Beneficiaries Using Hospice\": 0.029, \"PAC: LTCH Per User Actual Costs\": 42530.84, \"Percent Non-Hispanic White\": 93.33, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 548.0, \"Count of Medicare beneficiaries with breast cancer\": 3972.0, \"DME Per Capita Standardized Costs\": 242.03, \"PQI08 CHF Admission Rate (age 65-74)\": 342.0, \"% of Beneficiaries Using DME\": 0.269, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0195, \"% of Beneficiaries Using IP\": 0.1474, \"DME Standardized Costs\": 41620092.85, \"FQHC/RHC Standardized Costs\": 16579203.64, \"PAC: SNF Per Capita Actual Costs\": 536.65, \"Imaging Standardized Costs\": 20051845.2, \"# E&M Users\": 142744.0, \"Count of Medicare beneficiaries with arthritis\": 44506.0, \"IP Per User Standardized Costs\": 14148.62, \"IP Per User Actual Costs\": 16391.83, \"PQI10 Dehydration Admission Rate (age 75+)\": 316.0, \"PAC: IRF Per User Standardized Costs\": 19811.26, \"DME Per User Actual Costs\": 854.78, \"PAC: IRF Actual Costs\": 16049088.15, \"Imaging Events Per 1000 Beneficiaries\": 2929.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0203, \"PAC: LTCH Per User Standardized Costs\": 45825.36, \"OP Actual Costs\": 262360911.56, \"Count of Medicare beneficiaries with hypertension\": 72837.0, \"ASC Per User Standardized Costs\": 879.24, \"Part B Drugs Per Capita Actual Costs\": 145.34, \"Ambulance Events Per 1000 Beneficiaries\": 171.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 417.0, \"% of Beneficiaries Using PAC: HH\": 0.1091, \"PAC: LTCH Standardized Costs\": 176564154.8, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.37, \"E&M Per Capita Standardized Costs\": 988.69, \"E&M Per User Standardized Costs\": 1112.3, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1303.0, \"IP Covered Days Per 1000 Beneficiaries\": 1580.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 38.0, \"Count of Medicare beneficiaries with lung cancer\": 18357.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3335, \"Percent Eligible for Medicaid\": 18.79, \"Imaging Per Capita Standardized Costs\": 191.22, \"% of Beneficiaries Using Tests\": 0.7696, \"Imaging Per Capita Actual Costs\": 191.94, \"% of Beneficiaries Using PAC: SNF\": 0.0592, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0332, \"Count of Medicare beneficiaries with colorectal cancer\": 23674.0, \"Hospice Actual Costs\": 433874670.6, \"# PAC: HH Users\": 179425.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23242.29, \"Total Actual Costs\": 16279367597.36, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 169377.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0063, \"ASC Standardized Costs\": 98091207.13, \"DME Events Per 1000 Beneficiaries\": 1756.0, \"PQI08 CHF Admission Rate (age < 65)\": 1046.0, \"ASC Events Per 1000 Beneficiaries\": 104.0, \"PAC: LTCH Actual Costs\": 170769965.96, \"Count of Medicare beneficiaries with depression\": 247429.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1532.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.3, \"Outpatient Dialysis Facility Per User Actual Costs\": 23574.79, \"Beneficiaries with Part A and Part B\": 1886993.0, \"% of Beneficiaries Using Part B Drugs\": 0.5098, \"Percent of Medicare beneficiaries with diabetes\": 27.09, \"% of Beneficiaries Using PAC: IRF\": 0.0104, \"E&M Per Capita Actual Costs\": 981.86, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0203, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0351, \"PAC: SNF Actual Costs\": 1613318143.44, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 516.0, \"Percent Female\": 56.21, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 503.0, \"Percent of Medicare beneficiaries with osteoporosis\": 6.1, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 235.74, \"# Outpatient Dialysis Facility Users\": 16681.0, \"FQHC/RHC Per User Actual Costs\": 344.43, \"Count of Medicare beneficiaries with ischemic heart disease\": 463264.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 183.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.85, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 499.0, \"Percent of Medicare beneficiaries with depression\": 15.04, \"Emergency Department Visits per 1000 Beneficiaries\": 648.0, \"IP Actual Costs\": 5428415315.47, \"% of Beneficiaries Using OP\": 0.7038, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0143, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1051, \"Count of Medicare beneficiaries with stroke\": 64540.0, \"PQI12 UTI Admission Rate (age 75+)\": 1143.0, \"# OP Users\": 1157522.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 26.0, \"# Procedure Users\": 997225.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 28.17, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0621, \"Count of Medicare beneficiaries with diabetes\": 445566.0, \"ASC Per User Actual Costs\": 895.23, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1157.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0216, \"Percent of Medicare beneficiaries with high cholesterol\": 46.77, \"Standardized Per Capita Costs\": 9403.97, \"Ambulance Actual Costs\": 207525434.9, \"FQHC/RHC Per Capita Actual Costs\": 40.06, \"Part B Drugs Per User Standardized Costs\": 648.28, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0208, \"# PAC: SNF Users (with a covered stay)\": 97340.0, \"Ambulance Per Capita Actual Costs\": 126.18, \"PQI12 UTI Admission Rate (age 65-74)\": 275.0, \"Hospice Standardized Costs\": 427669499.29, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 239.11, \"% of Beneficiaries Using E&M\": 0.8889, \"PQI10 Dehydration Admission Rate (age 65-74)\": 229.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 220.0, \"Tests Per Capita Actual Costs\": 193.96, \"# DME Users\": 466759.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1069, \"PAC: SNF Per User Actual Costs\": 16574.05, \"State\": \"IL\", \"OP Per User Actual Costs\": 1834.1, \"PAC: HH Episodes Per 1000 Beneficiaries\": 235.0, \"Part B Drugs Actual Costs\": 540202614.88, \"FQHC/RHC Actual Costs\": 65885046.54, \"OP Standardized Costs\": 2098742632.73, \"DME Per User Standardized Costs\": 716.78, \"OP Actual Costs as % of Total Actual Costs\": 0.1304, \"PAC: SNF Per Capita Standardized Costs\": 1005.13, \"% of Beneficiaries Using Ambulance\": 0.1126, \"Hospice Per User Standardized Costs\": 10221.3, \"# Imaging Users\": 1120679.0, \"Part B Drugs Per User Actual Costs\": 644.28, \"Total Standardized Costs\": 15466123450.71, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.44, \"Count of Medicare beneficiaries with chronic kidney disease\": 268520.0, \"E&M Standardized Costs\": 1626030500.48, \"Percent Hispanic\": 5.43, \"ASC Per Capita Actual Costs\": 57.88, \"Count of Medicare beneficiaries who have had a heart attack\": 13934.0, \"Tests Actual Costs\": 318995045.77, \"# PAC: LTCH Users (with a covered stay)\": 3485.0, \"% of Beneficiaries Using Procedures\": 0.6063, \"PAC: HH Actual Costs\": 1032244491.39, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 46.0, \"PAC: LTCH Per Capita Standardized Costs\": 107.36, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0209, \"Emergency Department Visits\": 1066009.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1163, \"Procedures Actual Costs\": 982667219.56, \"# FQHC/RHC Users\": 191285.0, \"Number of Acute Hospital Readmissions\": 96419.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 149.0, \"Outpatient Dialysis Facility Standardized Costs\": 387704620.47, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0212, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1357, \"Ambulance Per User Standardized Costs\": 1197.98, \"Imaging Per User Standardized Costs\": 280.62, \"Percent of Medicare beneficiaries with asthma\": 5.19, \"Part B Drugs Standardized Costs\": 543556411.1, \"FFS Beneficiaries\": 1644637.0, \"# Hospice Users (with a covered stay)\": 41841.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0101, \"Count of Medicare beneficiaries with osteoporosis\": 100323.0, \"PQI08 CHF Admission Rate (age 75+)\": 2100.0, \"PAC: IRF Per Capita Standardized Costs\": 195.8, \"Procedures Standardized Costs\": 960304221.74, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2936, \"IP Per Capita Actual Costs\": 3300.68, \"DME Actual Costs\": 311813133.4, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0634, \"Count of Medicare beneficiaries with prostate cancer\": 52833.0, \"PAC: HH Per Capita Standardized Costs\": 624.26, \"Count of Medicare beneficiaries with heart failure\": 244772.0, \"Tests Per User Standardized Costs\": 254.94, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0105, \"Percent of Medicare beneficiaries with prostate cancer\": 3.21, \"PAC: IRF Per Capita Actual Costs\": 209.61, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 281.69, \"Percent of Medicare beneficiaries with breast cancer\": 3.16, \"Procedures Per User Standardized Costs\": 962.98, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.33, \"PAC: HH Per User Actual Costs\": 5753.07, \"Count of Medicare beneficiaries with high cholesterol\": 769217.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0991, \"Hospice Per Capita Standardized Costs\": 260.04, \"# Part B Drugs Users\": 838459.0, \"Average HCC Score\": 1.0023, \"Standardized Risk-Adjusted Per Capita Costs\": 9759.57, \"# PAC: IRF Users (with a covered stay)\": 17159.0, \"Ambulance Standardized Costs\": 221935128.1, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0267, \"Percent of Medicare beneficiaries with heart failure\": 14.88, \"Tests Actual Costs as % of Total Actual Costs\": 0.0196, \"FQHC/RHC Per Capita Standardized Costs\": 44.93, \"PQI07 Hypertension Admission Rate (age 65-74)\": 95.0, \"Test Events Per 1000 Beneficiaries\": 8263.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 67.0, \"ASC Actual Costs\": 95197358.52, \"Part B Drugs Per Capita Standardized Costs\": 330.5, \"Imaging Actual Costs\": 315678472.34, \"Tests Per User Actual Costs\": 252.03, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0127, \"Hospice Per User Actual Costs\": 10369.61, \"Tests Standardized Costs\": 322675852.79, \"IP Standardized Costs\": 4541589002.19, \"IP Per Capita Standardized Costs\": 2761.45, \"Outpatient Dialysis Facility Actual Costs\": 393251096.49, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 84.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2412.0, \"Percent of Medicare beneficiaries with hypertension\": 57.5, \"IP Covered Stays Per 1000 Beneficiaries\": 312.0, \"# Ambulance Users\": 185258.0, \"# ASC Users\": 106339.0, \"ASC Per Capita Standardized Costs\": 59.64, \"Procedures Per Capita Actual Costs\": 597.5, \"Procedures Per Capita Standardized Costs\": 583.9, \"IP Users (with a covered stay)\": 309090.0, \"Total Standardized Risk-Adjusted Costs\": 16050956320.08, \"Actual Per Capita Costs\": 9898.46, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0114, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 12.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.12, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.24, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 254.0, \"OP Per User Standardized Costs\": 1813.13, \"Count of Medicare beneficiaries with atrial fibrillation\": 137689.0, \"Procedure Events Per 1000 Beneficiaries\": 4542.0, \"Percent of Medicare beneficiaries with stroke\": 3.92, \"PAC: IRF Per User Actual Costs\": 20090.34, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 184870.0, \"% of Beneficiaries Using ASC\": 0.0647, \"PAC: IRF Standardized Costs\": 322022907.89, \"Hospice Covered Days Per 1000 Beneficiaries\": 1575.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 318.0, \"PAC: SNF Per User Standardized Costs\": 16982.47, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.004, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 118.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0277, \"MA Participation Rate\": 12.84, \"OP Per Capita Standardized Costs\": 1276.11, \"Percent of Medicare beneficiaries with arthritis\": 31.65, \"Ambulance Per Capita Standardized Costs\": 134.94, \"PAC: HH Visits Per 1000 Beneficiaries\": 3428.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0604, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0194, \"PAC: HH Per Capita Actual Costs\": 627.64, \"E&M Events Per 1000 Beneficiaries\": 13791.0, \"Count of Medicare beneficiaries with asthma\": 85301.0, \"# Test Users\": 1265687.0, \"E&M Actual Costs\": 1614799118.0, \"% of Beneficiaries Using Imaging\": 0.6814, \"PAC: SNF Standardized Costs\": 1653073933.78, \"DME Per Capita Actual Costs\": 189.59, \"E&M Per User Actual Costs\": 1104.62, \"Percent Male\": 43.79, \"OP Visits Per 1000 Beneficiaries\": 4943.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0192, \"OP Per Capita Actual Costs\": 1290.87, \"Hospital Readmission Rate\": 0.1937, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0048, \"Tests Per Capita Standardized Costs\": 196.2, \"Hospice Per Capita Actual Costs\": 263.81, \"E&M Actual Costs as % of Total Actual Costs\": 0.0992, \"PQI07 Hypertension Admission Rate (age < 65)\": 136.0, \"Percent African American\": 11.54, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0664, \"PAC: LTCH Per Capita Actual Costs\": 103.83, \"MA Beneficiaries\": 242356.0, \"FQHC/RHC Per User Standardized Costs\": 386.34, \"PAC: HH Per User Standardized Costs\": 5722.03, \"Procedures Per User Actual Costs\": 985.4, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 739.0, \"Ambulance Per User Actual Costs\": 1120.2, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1726.0, \"PAC: HH Standardized Costs\": 1026674971.48, \"ASC Actual Costs as % of Total Actual Costs\": 0.0058, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 791.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0021, \"Percent Other/Unknown\": 3.33, \"% of Beneficiaries Using Hospice\": 0.0254, \"PAC: LTCH Per User Actual Costs\": 49001.43, \"Percent Non-Hispanic White\": 79.7, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 767.0, \"Count of Medicare beneficiaries with breast cancer\": 51937.0, \"DME Per Capita Standardized Costs\": 203.43, \"PQI08 CHF Admission Rate (age 65-74)\": 674.0, \"% of Beneficiaries Using DME\": 0.2838, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0242, \"% of Beneficiaries Using IP\": 0.1879, \"DME Standardized Costs\": 334561234.13, \"FQHC/RHC Standardized Costs\": 73900602.7, \"PAC: SNF Per Capita Actual Costs\": 980.96, \"Imaging Standardized Costs\": 314482993.37, \"# E&M Users\": 1461864.0, \"Count of Medicare beneficiaries with arthritis\": 520554.0, \"IP Per User Standardized Costs\": 14693.42, \"IP Per User Actual Costs\": 17562.57, \"PQI10 Dehydration Admission Rate (age 75+)\": 584.0, \"PAC: IRF Per User Standardized Costs\": 18767.0, \"DME Per User Actual Costs\": 668.04, \"PAC: IRF Actual Costs\": 344730113.6, \"Imaging Events Per 1000 Beneficiaries\": 4084.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0251, \"PAC: LTCH Per User Standardized Costs\": 50664.03, \"OP Actual Costs\": 2123008805.86, \"Count of Medicare beneficiaries with hypertension\": 945612.0, \"ASC Per User Standardized Costs\": 922.44, \"Part B Drugs Per Capita Actual Costs\": 328.46, \"Ambulance Events Per 1000 Beneficiaries\": 384.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 389.0, \"% of Beneficiaries Using PAC: HH\": 0.0719, \"PAC: LTCH Standardized Costs\": 146531341.94, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.78, \"E&M Per Capita Standardized Costs\": 867.11, \"E&M Per User Standardized Costs\": 966.6, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1234.0, \"IP Covered Days Per 1000 Beneficiaries\": 1521.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 41.0, \"Count of Medicare beneficiaries with lung cancer\": 9133.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3289, \"Percent Eligible for Medicaid\": 19.08, \"Imaging Per Capita Standardized Costs\": 171.96, \"% of Beneficiaries Using Tests\": 0.7567, \"Imaging Per Capita Actual Costs\": 160.57, \"% of Beneficiaries Using PAC: SNF\": 0.0592, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0361, \"Count of Medicare beneficiaries with colorectal cancer\": 10967.0, \"Hospice Actual Costs\": 222689588.13, \"# PAC: HH Users\": 59653.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23213.68, \"Total Actual Costs\": 7550169000.51, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 84629.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0101, \"ASC Standardized Costs\": 76937604.25, \"DME Events Per 1000 Beneficiaries\": 2030.0, \"PQI08 CHF Admission Rate (age < 65)\": 862.0, \"ASC Events Per 1000 Beneficiaries\": 152.0, \"PAC: LTCH Actual Costs\": 136748778.32, \"Count of Medicare beneficiaries with depression\": 144257.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1743.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.2, \"Outpatient Dialysis Facility Per User Actual Costs\": 22966.51, \"Beneficiaries with Part A and Part B\": 1080040.0, \"% of Beneficiaries Using Part B Drugs\": 0.5448, \"Percent of Medicare beneficiaries with diabetes\": 27.47, \"% of Beneficiaries Using PAC: IRF\": 0.0116, \"E&M Per Capita Actual Costs\": 781.03, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0188, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0361, \"PAC: SNF Actual Costs\": 795043462.07, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 643.0, \"Percent Female\": 55.78, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 364.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.89, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 218.11, \"# Outpatient Dialysis Facility Users\": 7795.0, \"FQHC/RHC Per User Actual Costs\": 295.71, \"Count of Medicare beneficiaries with ischemic heart disease\": 238951.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 158.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.9, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 249.0, \"Percent of Medicare beneficiaries with depression\": 17.39, \"Emergency Department Visits per 1000 Beneficiaries\": 689.0, \"IP Actual Costs\": 2483484506.6, \"% of Beneficiaries Using OP\": 0.719, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0159, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0946, \"Count of Medicare beneficiaries with stroke\": 31363.0, \"PQI12 UTI Admission Rate (age 75+)\": 1235.0, \"# OP Users\": 596532.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 28.0, \"# Procedure Users\": 499114.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 28.8, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0585, \"Count of Medicare beneficiaries with diabetes\": 227861.0, \"ASC Per User Actual Costs\": 940.87, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1226.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0258, \"Percent of Medicare beneficiaries with high cholesterol\": 43.96, \"Standardized Per Capita Costs\": 9163.74, \"Ambulance Actual Costs\": 108332514.48, \"FQHC/RHC Per Capita Actual Costs\": 19.02, \"Part B Drugs Per User Standardized Costs\": 607.47, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0238, \"# PAC: SNF Users (with a covered stay)\": 49087.0, \"Ambulance Per Capita Actual Costs\": 130.58, \"PQI12 UTI Admission Rate (age 65-74)\": 302.0, \"Hospice Standardized Costs\": 233178995.05, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 215.79, \"% of Beneficiaries Using E&M\": 0.8971, \"PQI10 Dehydration Admission Rate (age 65-74)\": 231.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 203.0, \"Tests Per Capita Actual Costs\": 184.99, \"# DME Users\": 252785.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1139, \"PAC: SNF Per User Actual Costs\": 16196.62, \"State\": \"IN\", \"OP Per User Actual Costs\": 1856.74, \"PAC: HH Episodes Per 1000 Beneficiaries\": 127.0, \"Part B Drugs Actual Costs\": 272679662.75, \"FQHC/RHC Actual Costs\": 15776187.71, \"OP Standardized Costs\": 1112642001.42, \"DME Per User Standardized Costs\": 775.87, \"OP Actual Costs as % of Total Actual Costs\": 0.1467, \"PAC: SNF Per Capita Standardized Costs\": 1043.86, \"% of Beneficiaries Using Ambulance\": 0.1099, \"Hospice Per User Standardized Costs\": 10398.17, \"# Imaging Users\": 563880.0, \"Part B Drugs Per User Actual Costs\": 603.24, \"Total Standardized Costs\": 7602544527.67, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.32, \"Count of Medicare beneficiaries with chronic kidney disease\": 136322.0, \"E&M Standardized Costs\": 719384786.76, \"Percent Hispanic\": 1.67, \"ASC Per Capita Actual Costs\": 87.87, \"Count of Medicare beneficiaries who have had a heart attack\": 7457.0, \"Tests Actual Costs\": 153470578.17, \"# PAC: LTCH Users (with a covered stay)\": 3196.0, \"% of Beneficiaries Using Procedures\": 0.6016, \"PAC: HH Actual Costs\": 284901034.92, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 47.0, \"PAC: LTCH Per Capita Standardized Costs\": 176.62, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0211, \"Emergency Department Visits\": 571952.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0643, \"Procedures Actual Costs\": 409029618.31, \"# FQHC/RHC Users\": 53351.0, \"Number of Acute Hospital Readmissions\": 41131.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 159.0, \"Outpatient Dialysis Facility Standardized Costs\": 180950660.45, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0234, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1464, \"Ambulance Per User Standardized Costs\": 1324.76, \"Imaging Per User Standardized Costs\": 253.0, \"Percent of Medicare beneficiaries with asthma\": 4.76, \"Part B Drugs Standardized Costs\": 274592241.86, \"FFS Beneficiaries\": 829633.0, \"# Hospice Users (with a covered stay)\": 22425.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0094, \"Count of Medicare beneficiaries with osteoporosis\": 48899.0, \"PQI08 CHF Admission Rate (age 75+)\": 2082.0, \"PAC: IRF Per Capita Standardized Costs\": 218.45, \"Procedures Standardized Costs\": 445097664.39, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2944, \"IP Per Capita Actual Costs\": 2993.47, \"DME Actual Costs\": 183343001.0, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0377, \"Count of Medicare beneficiaries with prostate cancer\": 21993.0, \"PAC: HH Per Capita Standardized Costs\": 368.87, \"Count of Medicare beneficiaries with heart failure\": 123668.0, \"Tests Per User Standardized Costs\": 255.09, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0181, \"Percent of Medicare beneficiaries with prostate cancer\": 2.65, \"PAC: IRF Per Capita Actual Costs\": 212.8, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 236.25, \"Percent of Medicare beneficiaries with breast cancer\": 2.83, \"Procedures Per User Standardized Costs\": 891.78, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.43, \"PAC: HH Per User Actual Costs\": 4775.97, \"Count of Medicare beneficiaries with high cholesterol\": 364725.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.1053, \"Hospice Per Capita Standardized Costs\": 281.06, \"# Part B Drugs Users\": 452024.0, \"Average HCC Score\": 1.0004, \"Standardized Risk-Adjusted Per Capita Costs\": 9554.65, \"# PAC: IRF Users (with a covered stay)\": 9642.0, \"Ambulance Standardized Costs\": 120765742.39, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0295, \"Percent of Medicare beneficiaries with heart failure\": 14.91, \"Tests Actual Costs as % of Total Actual Costs\": 0.0203, \"FQHC/RHC Per Capita Standardized Costs\": 21.64, \"PQI07 Hypertension Admission Rate (age 65-74)\": 78.0, \"Test Events Per 1000 Beneficiaries\": 7515.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 116.0, \"ASC Actual Costs\": 72897326.89, \"Part B Drugs Per Capita Standardized Costs\": 330.98, \"Imaging Actual Costs\": 133216511.06, \"Tests Per User Actual Costs\": 244.48, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0143, \"Hospice Per User Actual Costs\": 9930.42, \"Tests Standardized Costs\": 160131881.32, \"IP Standardized Costs\": 2238475584.95, \"IP Per Capita Standardized Costs\": 2698.15, \"Outpatient Dialysis Facility Actual Costs\": 179023917.58, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 82.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2578.0, \"Percent of Medicare beneficiaries with hypertension\": 56.8, \"IP Covered Stays Per 1000 Beneficiaries\": 297.0, \"# Ambulance Users\": 91160.0, \"# ASC Users\": 77479.0, \"ASC Per Capita Standardized Costs\": 92.74, \"Procedures Per Capita Actual Costs\": 493.02, \"Procedures Per Capita Standardized Costs\": 536.5, \"IP Users (with a covered stay)\": 153770.0, \"Total Standardized Risk-Adjusted Costs\": 7926855877.06, \"Actual Per Capita Costs\": 9100.61, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0193, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 13.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 4.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.1, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 13.46, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 237.0, \"OP Per User Standardized Costs\": 1865.18, \"Count of Medicare beneficiaries with atrial fibrillation\": 64551.0, \"Procedure Events Per 1000 Beneficiaries\": 3685.0, \"Percent of Medicare beneficiaries with stroke\": 3.78, \"PAC: IRF Per User Actual Costs\": 18310.46, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 111635.0, \"% of Beneficiaries Using ASC\": 0.0934, \"PAC: IRF Standardized Costs\": 181234124.59, \"Hospice Covered Days Per 1000 Beneficiaries\": 1755.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 345.0, \"PAC: SNF Per User Standardized Costs\": 17642.65, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0021, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 132.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0307, \"MA Participation Rate\": 23.18, \"OP Per Capita Standardized Costs\": 1341.13, \"Percent of Medicare beneficiaries with arthritis\": 29.5, \"Ambulance Per Capita Standardized Costs\": 145.57, \"PAC: HH Visits Per 1000 Beneficiaries\": 2285.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0542, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0176, \"PAC: HH Per Capita Actual Costs\": 343.41, \"E&M Events Per 1000 Beneficiaries\": 12470.0, \"Count of Medicare beneficiaries with asthma\": 39491.0, \"# Test Users\": 627747.0, \"E&M Actual Costs\": 647967471.51, \"% of Beneficiaries Using Imaging\": 0.6797, \"PAC: SNF Standardized Costs\": 866024525.39, \"DME Per Capita Actual Costs\": 220.99, \"E&M Per User Actual Costs\": 870.64, \"Percent Male\": 44.22, \"OP Visits Per 1000 Beneficiaries\": 5075.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0243, \"OP Per Capita Actual Costs\": 1335.05, \"Hospital Readmission Rate\": 0.1737, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0024, \"Tests Per Capita Standardized Costs\": 193.02, \"Hospice Per Capita Actual Costs\": 268.42, \"E&M Actual Costs as % of Total Actual Costs\": 0.0858, \"PQI07 Hypertension Admission Rate (age < 65)\": 135.0, \"Percent African American\": 6.97, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0403, \"PAC: LTCH Per Capita Actual Costs\": 164.83, \"MA Beneficiaries\": 250407.0, \"FQHC/RHC Per User Standardized Costs\": 336.44, \"PAC: HH Per User Standardized Costs\": 5130.07, \"Procedures Per User Actual Costs\": 819.51, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 672.0, \"Ambulance Per User Actual Costs\": 1188.37, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1888.0, \"PAC: HH Standardized Costs\": 306023969.71, \"ASC Actual Costs as % of Total Actual Costs\": 0.0097, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 983.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0039, \"Percent Other/Unknown\": 1.66, \"% of Beneficiaries Using Hospice\": 0.027, \"PAC: LTCH Per User Actual Costs\": 42787.48, \"Percent Non-Hispanic White\": 89.7, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 908.0, \"Count of Medicare beneficiaries with breast cancer\": 23449.0, \"DME Per Capita Standardized Costs\": 236.4, \"PQI08 CHF Admission Rate (age 65-74)\": 689.0, \"% of Beneficiaries Using DME\": 0.3047, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0237, \"% of Beneficiaries Using IP\": 0.1853, \"DME Standardized Costs\": 196128986.96, \"FQHC/RHC Standardized Costs\": 17949367.13, \"PAC: SNF Per Capita Actual Costs\": 958.31, \"Imaging Standardized Costs\": 142662500.81, \"# E&M Users\": 744245.0, \"Count of Medicare beneficiaries with arthritis\": 244762.0, \"IP Per User Standardized Costs\": 14557.3, \"IP Per User Actual Costs\": 16150.64, \"PQI10 Dehydration Admission Rate (age 75+)\": 547.0, \"PAC: IRF Per User Standardized Costs\": 18796.32, \"DME Per User Actual Costs\": 725.29, \"PAC: IRF Actual Costs\": 176549457.29, \"Imaging Events Per 1000 Beneficiaries\": 3922.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0238, \"PAC: LTCH Per User Standardized Costs\": 45848.35, \"OP Actual Costs\": 1107604388.73, \"Count of Medicare beneficiaries with hypertension\": 471215.0, \"ASC Per User Standardized Costs\": 993.01, \"Part B Drugs Per Capita Actual Costs\": 328.68, \"Ambulance Events Per 1000 Beneficiaries\": 444.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 368.0, \"% of Beneficiaries Using PAC: HH\": 0.0621, \"PAC: LTCH Standardized Costs\": 49933137.45, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.17, \"E&M Per Capita Standardized Costs\": 758.67, \"E&M Per User Standardized Costs\": 858.43, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 937.0, \"IP Covered Days Per 1000 Beneficiaries\": 1399.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 28.0, \"Count of Medicare beneficiaries with lung cancer\": 3720.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3166, \"Percent Eligible for Medicaid\": 15.95, \"Imaging Per Capita Standardized Costs\": 185.76, \"% of Beneficiaries Using Tests\": 0.7637, \"Imaging Per Capita Actual Costs\": 170.97, \"% of Beneficiaries Using PAC: SNF\": 0.0583, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0439, \"Count of Medicare beneficiaries with colorectal cancer\": 4912.0, \"Hospice Actual Costs\": 110291648.51, \"# PAC: HH Users\": 24171.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23880.89, \"Total Actual Costs\": 3311027522.96, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 40928.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0103, \"ASC Standardized Costs\": 34992428.63, \"DME Events Per 1000 Beneficiaries\": 1874.0, \"PQI08 CHF Admission Rate (age < 65)\": 789.0, \"ASC Events Per 1000 Beneficiaries\": 162.0, \"PAC: LTCH Actual Costs\": 45446576.69, \"Count of Medicare beneficiaries with depression\": 64626.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2088.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.52, \"Outpatient Dialysis Facility Per User Actual Costs\": 22969.7, \"Beneficiaries with Part A and Part B\": 453999.0, \"% of Beneficiaries Using Part B Drugs\": 0.5077, \"Percent of Medicare beneficiaries with diabetes\": 24.62, \"% of Beneficiaries Using PAC: IRF\": 0.0118, \"E&M Per Capita Actual Costs\": 681.43, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0214, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0432, \"PAC: SNF Actual Costs\": 330684297.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 664.0, \"Percent Female\": 55.64, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 218.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.78, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 168.29, \"# Outpatient Dialysis Facility Users\": 2742.0, \"FQHC/RHC Per User Actual Costs\": 417.99, \"Count of Medicare beneficiaries with ischemic heart disease\": 101771.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 182.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.87, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 1231.0, \"Percent of Medicare beneficiaries with depression\": 16.61, \"Emergency Department Visits per 1000 Beneficiaries\": 613.0, \"IP Actual Costs\": 1048422906.98, \"% of Beneficiaries Using OP\": 0.6378, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0092, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0872, \"Count of Medicare beneficiaries with stroke\": 12325.0, \"PQI12 UTI Admission Rate (age 75+)\": 952.0, \"# OP Users\": 248148.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 31.0, \"# Procedure Users\": 232945.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 26.16, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0631, \"Count of Medicare beneficiaries with diabetes\": 95789.0, \"ASC Per User Actual Costs\": 816.2, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 850.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0269, \"Percent of Medicare beneficiaries with high cholesterol\": 39.59, \"Standardized Per Capita Costs\": 8697.06, \"Ambulance Actual Costs\": 38022177.48, \"FQHC/RHC Per Capita Actual Costs\": 104.95, \"Part B Drugs Per User Standardized Costs\": 740.33, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0248, \"# PAC: SNF Users (with a covered stay)\": 22681.0, \"Ambulance Per Capita Actual Costs\": 97.72, \"PQI12 UTI Admission Rate (age 65-74)\": 208.0, \"Hospice Standardized Costs\": 117913177.6, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 161.87, \"% of Beneficiaries Using E&M\": 0.8838, \"PQI10 Dehydration Admission Rate (age 65-74)\": 212.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 147.0, \"Tests Per Capita Actual Costs\": 183.37, \"# DME Users\": 109832.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1117, \"PAC: SNF Per User Actual Costs\": 14579.79, \"State\": \"KS\", \"OP Per User Actual Costs\": 2107.17, \"PAC: HH Episodes Per 1000 Beneficiaries\": 102.0, \"Part B Drugs Actual Costs\": 145288000.77, \"FQHC/RHC Actual Costs\": 40835806.81, \"OP Standardized Costs\": 511675407.12, \"DME Per User Standardized Costs\": 827.95, \"OP Actual Costs as % of Total Actual Costs\": 0.1579, \"PAC: SNF Per Capita Standardized Costs\": 971.41, \"% of Beneficiaries Using Ambulance\": 0.1, \"Hospice Per User Standardized Costs\": 10332.39, \"# Imaging Users\": 266608.0, \"Part B Drugs Per User Actual Costs\": 735.42, \"Total Standardized Costs\": 3383946955.25, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.26, \"Count of Medicare beneficiaries with chronic kidney disease\": 55673.0, \"E&M Standardized Costs\": 295193215.46, \"Percent Hispanic\": 2.72, \"ASC Per Capita Actual Costs\": 82.68, \"Count of Medicare beneficiaries who have had a heart attack\": 3384.0, \"Tests Actual Costs\": 71346164.63, \"# PAC: LTCH Users (with a covered stay)\": 1145.0, \"% of Beneficiaries Using Procedures\": 0.5987, \"PAC: HH Actual Costs\": 102990729.89, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 27.0, \"PAC: LTCH Per Capita Standardized Costs\": 128.33, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0223, \"Emergency Department Visits\": 238439.0, \"% of Beneficiaries Using FQHC/RHC\": 0.2511, \"Procedures Actual Costs\": 198124894.71, \"# FQHC/RHC Users\": 97696.0, \"Number of Acute Hospital Readmissions\": 16684.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 158.0, \"Outpatient Dialysis Facility Standardized Costs\": 65481402.29, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0242, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1512, \"Ambulance Per User Standardized Costs\": 797.22, \"Imaging Per User Standardized Costs\": 271.1, \"Percent of Medicare beneficiaries with asthma\": 3.91, \"Part B Drugs Standardized Costs\": 146258155.7, \"FFS Beneficiaries\": 389091.0, \"# Hospice Users (with a covered stay)\": 11412.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.007, \"Count of Medicare beneficiaries with osteoporosis\": 22473.0, \"PQI08 CHF Admission Rate (age 75+)\": 1697.0, \"PAC: IRF Per Capita Standardized Costs\": 215.26, \"Procedures Standardized Costs\": 213637406.62, \"IP Standardized Costs as % of Total Standardized Costs\": 0.291, \"IP Per Capita Actual Costs\": 2694.54, \"DME Actual Costs\": 85568854.72, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0311, \"Count of Medicare beneficiaries with prostate cancer\": 12085.0, \"PAC: HH Per Capita Standardized Costs\": 293.51, \"Count of Medicare beneficiaries with heart failure\": 52686.0, \"Tests Per User Standardized Costs\": 254.44, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0137, \"Percent of Medicare beneficiaries with prostate cancer\": 3.11, \"PAC: IRF Per Capita Actual Costs\": 205.67, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 249.52, \"Percent of Medicare beneficiaries with breast cancer\": 2.8, \"Procedures Per User Standardized Costs\": 917.12, \"Percent of Medicare beneficiaries with chronic kidney disease\": 14.31, \"PAC: HH Per User Actual Costs\": 4260.92, \"Count of Medicare beneficiaries with high cholesterol\": 154045.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0999, \"Hospice Per Capita Standardized Costs\": 303.05, \"# Part B Drugs Users\": 197557.0, \"Average HCC Score\": 0.9304, \"Standardized Risk-Adjusted Per Capita Costs\": 9929.16, \"# PAC: IRF Users (with a covered stay)\": 4589.0, \"Ambulance Standardized Costs\": 31031336.79, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0333, \"Percent of Medicare beneficiaries with heart failure\": 13.54, \"Tests Actual Costs as % of Total Actual Costs\": 0.0215, \"FQHC/RHC Per Capita Standardized Costs\": 117.55, \"PQI07 Hypertension Admission Rate (age 65-74)\": 79.0, \"Test Events Per 1000 Beneficiaries\": 8866.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 87.0, \"ASC Actual Costs\": 32169576.53, \"Part B Drugs Per Capita Standardized Costs\": 375.9, \"Imaging Actual Costs\": 66523419.88, \"Tests Per User Actual Costs\": 240.11, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0115, \"Hospice Per User Actual Costs\": 9664.53, \"Tests Standardized Costs\": 75605046.5, \"IP Standardized Costs\": 984756678.49, \"IP Per Capita Standardized Costs\": 2530.92, \"Outpatient Dialysis Facility Actual Costs\": 62982928.67, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 80.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1902.0, \"Percent of Medicare beneficiaries with hypertension\": 52.82, \"IP Covered Stays Per 1000 Beneficiaries\": 281.0, \"# Ambulance Users\": 38924.0, \"# ASC Users\": 39414.0, \"ASC Per Capita Standardized Costs\": 89.93, \"Procedures Per Capita Actual Costs\": 509.2, \"Procedures Per Capita Standardized Costs\": 549.07, \"IP Users (with a covered stay)\": 70937.0, \"Total Standardized Risk-Adjusted Costs\": 3863345962.24, \"Actual Per Capita Costs\": 8509.65, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0148, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 13.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.96, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.08, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 171.0, \"OP Per User Standardized Costs\": 2061.98, \"Count of Medicare beneficiaries with atrial fibrillation\": 31797.0, \"Procedure Events Per 1000 Beneficiaries\": 3932.0, \"Percent of Medicare beneficiaries with stroke\": 3.17, \"PAC: IRF Per User Actual Costs\": 17438.54, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 43099.0, \"% of Beneficiaries Using ASC\": 0.1013, \"PAC: IRF Standardized Costs\": 83754481.36, \"Hospice Covered Days Per 1000 Beneficiaries\": 1900.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 395.0, \"PAC: SNF Per User Standardized Costs\": 16664.55, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0123, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 112.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0348, \"MA Participation Rate\": 14.3, \"OP Per Capita Standardized Costs\": 1315.05, \"Percent of Medicare beneficiaries with arthritis\": 28.04, \"Ambulance Per Capita Standardized Costs\": 79.75, \"PAC: HH Visits Per 1000 Beneficiaries\": 1824.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0598, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0201, \"PAC: HH Per Capita Actual Costs\": 264.7, \"E&M Events Per 1000 Beneficiaries\": 10919.0, \"Count of Medicare beneficiaries with asthma\": 15231.0, \"# Test Users\": 297145.0, \"E&M Actual Costs\": 265139489.36, \"% of Beneficiaries Using Imaging\": 0.6852, \"PAC: SNF Standardized Costs\": 377968550.26, \"DME Per Capita Actual Costs\": 219.92, \"E&M Per User Actual Costs\": 771.03, \"Percent Male\": 44.36, \"OP Visits Per 1000 Beneficiaries\": 4626.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0258, \"OP Per Capita Actual Costs\": 1343.87, \"Hospital Readmission Rate\": 0.1596, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0135, \"Tests Per Capita Standardized Costs\": 194.31, \"Hospice Per Capita Actual Costs\": 283.46, \"E&M Actual Costs as % of Total Actual Costs\": 0.0801, \"PQI07 Hypertension Admission Rate (age < 65)\": 137.0, \"Percent African American\": 4.26, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0337, \"PAC: LTCH Per Capita Actual Costs\": 116.8, \"MA Beneficiaries\": 64908.0, \"FQHC/RHC Per User Standardized Costs\": 468.18, \"PAC: HH Per User Standardized Costs\": 4724.74, \"Procedures Per User Actual Costs\": 850.52, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 543.0, \"Ambulance Per User Actual Costs\": 976.82, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1234.0, \"PAC: HH Standardized Costs\": 114201792.38, \"ASC Actual Costs as % of Total Actual Costs\": 0.0097, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 625.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0029, \"Percent Other/Unknown\": 2.35, \"% of Beneficiaries Using Hospice\": 0.0293, \"PAC: LTCH Per User Actual Costs\": 39691.33, \"Percent Non-Hispanic White\": 90.67, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 951.0, \"Count of Medicare beneficiaries with breast cancer\": 10904.0, \"DME Per Capita Standardized Costs\": 233.71, \"PQI08 CHF Admission Rate (age 65-74)\": 536.0, \"% of Beneficiaries Using DME\": 0.2823, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.019, \"% of Beneficiaries Using IP\": 0.1823, \"DME Standardized Costs\": 90935206.83, \"FQHC/RHC Standardized Costs\": 45738907.11, \"PAC: SNF Per Capita Actual Costs\": 849.89, \"Imaging Standardized Costs\": 72276415.63, \"# E&M Users\": 343877.0, \"Count of Medicare beneficiaries with arthritis\": 109083.0, \"IP Per User Standardized Costs\": 13882.13, \"IP Per User Actual Costs\": 14779.63, \"PQI10 Dehydration Admission Rate (age 75+)\": 597.0, \"PAC: IRF Per User Standardized Costs\": 18251.14, \"DME Per User Actual Costs\": 779.09, \"PAC: IRF Actual Costs\": 80025464.06, \"Imaging Events Per 1000 Beneficiaries\": 3831.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0194, \"PAC: LTCH Per User Standardized Costs\": 43609.73, \"OP Actual Costs\": 522889207.64, \"Count of Medicare beneficiaries with hypertension\": 205506.0, \"ASC Per User Standardized Costs\": 887.82, \"Part B Drugs Per Capita Actual Costs\": 373.4, \"Ambulance Events Per 1000 Beneficiaries\": 212.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 389.0, \"% of Beneficiaries Using PAC: HH\": 0.0898, \"PAC: LTCH Standardized Costs\": 75630037.65, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.48, \"E&M Per Capita Standardized Costs\": 887.14, \"E&M Per User Standardized Costs\": 1008.46, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 994.0, \"IP Covered Days Per 1000 Beneficiaries\": 1683.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 47.0, \"Count of Medicare beneficiaries with lung cancer\": 7703.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3555, \"Percent Eligible for Medicaid\": 26.88, \"Imaging Per Capita Standardized Costs\": 173.78, \"% of Beneficiaries Using Tests\": 0.7897, \"Imaging Per Capita Actual Costs\": 157.91, \"% of Beneficiaries Using PAC: SNF\": 0.0523, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.025, \"Count of Medicare beneficiaries with colorectal cancer\": 7920.0, \"Hospice Actual Costs\": 112977838.95, \"# PAC: HH Users\": 54443.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23020.24, \"Total Actual Costs\": 5295803232.53, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 56571.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0063, \"ASC Standardized Costs\": 34673283.57, \"DME Events Per 1000 Beneficiaries\": 2369.0, \"PQI08 CHF Admission Rate (age < 65)\": 801.0, \"ASC Events Per 1000 Beneficiaries\": 107.0, \"PAC: LTCH Actual Costs\": 67191804.34, \"Count of Medicare beneficiaries with depression\": 115420.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2594.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.33, \"Outpatient Dialysis Facility Per User Actual Costs\": 22062.84, \"Beneficiaries with Part A and Part B\": 810400.0, \"% of Beneficiaries Using Part B Drugs\": 0.5292, \"Percent of Medicare beneficiaries with diabetes\": 28.47, \"% of Beneficiaries Using PAC: IRF\": 0.0108, \"E&M Per Capita Actual Costs\": 783.04, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0191, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0243, \"PAC: SNF Actual Costs\": 418920527.25, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 1122.0, \"Percent Female\": 53.47, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 294.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.3, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 175.95, \"# Outpatient Dialysis Facility Users\": 4633.0, \"FQHC/RHC Per User Actual Costs\": 371.59, \"Count of Medicare beneficiaries with ischemic heart disease\": 183438.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 207.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 1.05, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 755.0, \"Percent of Medicare beneficiaries with depression\": 19.04, \"Emergency Department Visits per 1000 Beneficiaries\": 760.0, \"IP Actual Costs\": 1882476631.13, \"% of Beneficiaries Using OP\": 0.6768, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0178, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0976, \"Count of Medicare beneficiaries with stroke\": 21873.0, \"PQI12 UTI Admission Rate (age 75+)\": 1544.0, \"# OP Users\": 410228.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 22.0, \"# Procedure Users\": 357925.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 30.26, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0572, \"Count of Medicare beneficiaries with diabetes\": 172588.0, \"ASC Per User Actual Costs\": 808.86, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1544.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0295, \"Percent of Medicare beneficiaries with high cholesterol\": 46.56, \"Standardized Per Capita Costs\": 9085.27, \"Ambulance Actual Costs\": 105748270.83, \"FQHC/RHC Per Capita Actual Costs\": 56.77, \"Part B Drugs Per User Standardized Costs\": 417.35, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0224, \"# PAC: SNF Users (with a covered stay)\": 31687.0, \"Ambulance Per Capita Actual Costs\": 174.46, \"PQI12 UTI Admission Rate (age 65-74)\": 411.0, \"Hospice Standardized Costs\": 124668100.44, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 168.63, \"% of Beneficiaries Using E&M\": 0.8797, \"PQI10 Dehydration Admission Rate (age 65-74)\": 291.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 232.0, \"Tests Per Capita Actual Costs\": 301.61, \"# DME Users\": 199441.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0884, \"PAC: SNF Per User Actual Costs\": 13220.58, \"State\": \"KY\", \"OP Per User Actual Costs\": 1808.07, \"PAC: HH Episodes Per 1000 Beneficiaries\": 175.0, \"Part B Drugs Actual Costs\": 132496215.88, \"FQHC/RHC Actual Costs\": 34411372.35, \"OP Standardized Costs\": 782243459.8, \"DME Per User Standardized Costs\": 814.9, \"OP Actual Costs as % of Total Actual Costs\": 0.1401, \"PAC: SNF Per Capita Standardized Costs\": 803.25, \"% of Beneficiaries Using Ambulance\": 0.1327, \"Hospice Per User Standardized Costs\": 9784.8, \"# Imaging Users\": 413082.0, \"Part B Drugs Per User Actual Costs\": 413.03, \"Total Standardized Costs\": 5507011001.36, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.31, \"Count of Medicare beneficiaries with chronic kidney disease\": 97555.0, \"E&M Standardized Costs\": 537739148.26, \"Percent Hispanic\": 0.51, \"ASC Per Capita Actual Costs\": 52.26, \"Count of Medicare beneficiaries who have had a heart attack\": 6364.0, \"Tests Actual Costs\": 182822364.97, \"# PAC: LTCH Users (with a covered stay)\": 1671.0, \"% of Beneficiaries Using Procedures\": 0.5905, \"PAC: HH Actual Costs\": 258075402.67, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 52.0, \"PAC: LTCH Per Capita Standardized Costs\": 124.77, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0348, \"Emergency Department Visits\": 460684.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1528, \"Procedures Actual Costs\": 283965057.77, \"# FQHC/RHC Users\": 92607.0, \"Number of Acute Hospital Readmissions\": 36242.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 145.0, \"Outpatient Dialysis Facility Standardized Costs\": 106652753.81, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0228, \"OP Standardized Costs as % of Total Standardized Costs\": 0.142, \"Ambulance Per User Standardized Costs\": 1215.49, \"Imaging Per User Standardized Costs\": 255.0, \"Percent of Medicare beneficiaries with asthma\": 4.99, \"Part B Drugs Standardized Costs\": 133881052.18, \"FFS Beneficiaries\": 606147.0, \"# Hospice Users (with a covered stay)\": 12741.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0076, \"Count of Medicare beneficiaries with osteoporosis\": 32114.0, \"PQI08 CHF Admission Rate (age 75+)\": 2623.0, \"PAC: IRF Per Capita Standardized Costs\": 203.29, \"Procedures Standardized Costs\": 315265840.26, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3105, \"IP Per Capita Actual Costs\": 3105.64, \"DME Actual Costs\": 153888890.44, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0487, \"Count of Medicare beneficiaries with prostate cancer\": 13805.0, \"PAC: HH Per Capita Standardized Costs\": 486.86, \"Count of Medicare beneficiaries with heart failure\": 92032.0, \"Tests Per User Standardized Costs\": 400.43, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0127, \"Percent of Medicare beneficiaries with prostate cancer\": 2.28, \"PAC: IRF Per Capita Actual Costs\": 198.86, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 231.71, \"Percent of Medicare beneficiaries with breast cancer\": 2.42, \"Procedures Per User Standardized Costs\": 880.82, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.09, \"PAC: HH Per User Actual Costs\": 4740.29, \"Count of Medicare beneficiaries with high cholesterol\": 282237.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0791, \"Hospice Per Capita Standardized Costs\": 205.67, \"# Part B Drugs Users\": 320790.0, \"Average HCC Score\": 0.9998, \"Standardized Risk-Adjusted Per Capita Costs\": 9690.37, \"# PAC: IRF Users (with a covered stay)\": 6561.0, \"Ambulance Standardized Costs\": 97759815.31, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0213, \"Percent of Medicare beneficiaries with heart failure\": 15.18, \"Tests Actual Costs as % of Total Actual Costs\": 0.0345, \"FQHC/RHC Per Capita Standardized Costs\": 66.59, \"PQI07 Hypertension Admission Rate (age 65-74)\": 118.0, \"Test Events Per 1000 Beneficiaries\": 9992.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 85.0, \"ASC Actual Costs\": 31679056.4, \"Part B Drugs Per Capita Standardized Costs\": 220.87, \"Imaging Actual Costs\": 95714664.85, \"Tests Per User Actual Costs\": 381.95, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.02, \"Hospice Per User Actual Costs\": 8867.27, \"Tests Standardized Costs\": 191667276.52, \"IP Standardized Costs\": 1709896039.75, \"IP Per Capita Standardized Costs\": 2820.93, \"Outpatient Dialysis Facility Actual Costs\": 102217124.93, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 74.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1931.0, \"Percent of Medicare beneficiaries with hypertension\": 59.56, \"IP Covered Stays Per 1000 Beneficiaries\": 318.0, \"# Ambulance Users\": 80428.0, \"# ASC Users\": 39165.0, \"ASC Per Capita Standardized Costs\": 57.2, \"Procedures Per Capita Actual Costs\": 468.48, \"Procedures Per Capita Standardized Costs\": 520.11, \"IP Users (with a covered stay)\": 116057.0, \"Total Standardized Risk-Adjusted Costs\": 5873787158.84, \"Actual Per Capita Costs\": 8736.83, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0137, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 12.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.27, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 16.62, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 231.0, \"OP Per User Standardized Costs\": 1906.85, \"Count of Medicare beneficiaries with atrial fibrillation\": 45325.0, \"Procedure Events Per 1000 Beneficiaries\": 3730.0, \"Percent of Medicare beneficiaries with stroke\": 3.61, \"PAC: IRF Per User Actual Costs\": 18372.09, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 100755.0, \"% of Beneficiaries Using ASC\": 0.0646, \"PAC: IRF Standardized Costs\": 123221563.97, \"Hospice Covered Days Per 1000 Beneficiaries\": 1185.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 343.0, \"PAC: SNF Per User Standardized Costs\": 15365.44, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0065, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 114.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0226, \"MA Participation Rate\": 25.2, \"OP Per Capita Standardized Costs\": 1290.52, \"Percent of Medicare beneficiaries with arthritis\": 32.27, \"Ambulance Per Capita Standardized Costs\": 161.28, \"PAC: HH Visits Per 1000 Beneficiaries\": 2819.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0536, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0181, \"PAC: HH Per Capita Actual Costs\": 425.76, \"E&M Events Per 1000 Beneficiaries\": 12962.0, \"Count of Medicare beneficiaries with asthma\": 30221.0, \"# Test Users\": 478656.0, \"E&M Actual Costs\": 474636690.41, \"% of Beneficiaries Using Imaging\": 0.6815, \"PAC: SNF Standardized Costs\": 486884569.0, \"DME Per Capita Actual Costs\": 253.88, \"E&M Per User Actual Costs\": 890.12, \"Percent Male\": 46.53, \"OP Visits Per 1000 Beneficiaries\": 3995.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0291, \"OP Per Capita Actual Costs\": 1223.67, \"Hospital Readmission Rate\": 0.1937, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0073, \"Tests Per Capita Standardized Costs\": 316.21, \"Hospice Per Capita Actual Costs\": 186.39, \"E&M Actual Costs as % of Total Actual Costs\": 0.0896, \"PQI07 Hypertension Admission Rate (age < 65)\": 158.0, \"Percent African American\": 5.63, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0536, \"PAC: LTCH Per Capita Actual Costs\": 110.85, \"MA Beneficiaries\": 204253.0, \"FQHC/RHC Per User Standardized Costs\": 435.86, \"PAC: HH Per User Standardized Costs\": 5420.49, \"Procedures Per User Actual Costs\": 793.36, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 560.0, \"Ambulance Per User Actual Costs\": 1314.81, \"Average Age\": 69.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1816.0, \"PAC: HH Standardized Costs\": 295107477.66, \"ASC Actual Costs as % of Total Actual Costs\": 0.006, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 1433.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0028, \"Percent Other/Unknown\": 1.15, \"% of Beneficiaries Using Hospice\": 0.021, \"PAC: LTCH Per User Actual Costs\": 40210.54, \"Percent Non-Hispanic White\": 92.71, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 1079.0, \"Count of Medicare beneficiaries with breast cancer\": 14675.0, \"DME Per Capita Standardized Costs\": 268.13, \"PQI08 CHF Admission Rate (age 65-74)\": 983.0, \"% of Beneficiaries Using DME\": 0.329, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0193, \"% of Beneficiaries Using IP\": 0.1915, \"DME Standardized Costs\": 162523779.18, \"FQHC/RHC Standardized Costs\": 40363979.57, \"PAC: SNF Per Capita Actual Costs\": 691.12, \"Imaging Standardized Costs\": 105337562.05, \"# E&M Users\": 533230.0, \"Count of Medicare beneficiaries with arthritis\": 195599.0, \"IP Per User Standardized Costs\": 14733.24, \"IP Per User Actual Costs\": 16220.28, \"PQI10 Dehydration Admission Rate (age 75+)\": 696.0, \"PAC: IRF Per User Standardized Costs\": 18780.91, \"DME Per User Actual Costs\": 771.6, \"PAC: IRF Actual Costs\": 120539251.44, \"Imaging Events Per 1000 Beneficiaries\": 4191.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0194, \"PAC: LTCH Per User Standardized Costs\": 45260.35, \"OP Actual Costs\": 741721730.69, \"Count of Medicare beneficiaries with hypertension\": 360997.0, \"ASC Per User Standardized Costs\": 885.31, \"Part B Drugs Per Capita Actual Costs\": 218.59, \"Ambulance Events Per 1000 Beneficiaries\": 480.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 460.0, \"% of Beneficiaries Using PAC: HH\": 0.1343, \"PAC: LTCH Standardized Costs\": 356707170.32, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.5, \"E&M Per Capita Standardized Costs\": 921.6, \"E&M Per User Standardized Costs\": 1041.06, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1921.0, \"IP Covered Days Per 1000 Beneficiaries\": 1847.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 71.0, \"Count of Medicare beneficiaries with lung cancer\": 4965.0, \"IP Actual Costs as % of Total Actual Costs\": 0.2959, \"Percent Eligible for Medicaid\": 30.91, \"Imaging Per Capita Standardized Costs\": 226.03, \"% of Beneficiaries Using Tests\": 0.7573, \"Imaging Per Capita Actual Costs\": 205.69, \"% of Beneficiaries Using PAC: SNF\": 0.042, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0225, \"Count of Medicare beneficiaries with colorectal cancer\": 6761.0, \"Hospice Actual Costs\": 186440240.12, \"# PAC: HH Users\": 69718.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23300.91, \"Total Actual Costs\": 5319408061.35, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 55621.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0082, \"ASC Standardized Costs\": 46113729.76, \"DME Events Per 1000 Beneficiaries\": 1834.0, \"PQI08 CHF Admission Rate (age < 65)\": 1145.0, \"ASC Events Per 1000 Beneficiaries\": 181.0, \"PAC: LTCH Actual Costs\": 309547168.13, \"Count of Medicare beneficiaries with depression\": 84178.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2037.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.71, \"Outpatient Dialysis Facility Per User Actual Costs\": 22111.35, \"Beneficiaries with Part A and Part B\": 733648.0, \"% of Beneficiaries Using Part B Drugs\": 0.4987, \"Percent of Medicare beneficiaries with diabetes\": 29.17, \"% of Beneficiaries Using PAC: IRF\": 0.015, \"E&M Per Capita Actual Costs\": 828.29, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0209, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0215, \"PAC: SNF Actual Costs\": 341041488.6, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 818.0, \"Percent Female\": 54.64, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 203.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.36, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 352.39, \"# Outpatient Dialysis Facility Users\": 7853.0, \"FQHC/RHC Per User Actual Costs\": 412.28, \"Count of Medicare beneficiaries with ischemic heart disease\": 165184.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 196.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.76, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 459.0, \"Percent of Medicare beneficiaries with depression\": 16.21, \"Emergency Department Visits per 1000 Beneficiaries\": 786.0, \"IP Actual Costs\": 1573896789.38, \"% of Beneficiaries Using OP\": 0.6942, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0132, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0852, \"Count of Medicare beneficiaries with stroke\": 23845.0, \"PQI12 UTI Admission Rate (age 75+)\": 1688.0, \"# OP Users\": 360478.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 32.0, \"# Procedure Users\": 309081.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 31.81, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0545, \"Count of Medicare beneficiaries with diabetes\": 151486.0, \"ASC Per User Actual Costs\": 765.62, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1205.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0221, \"Percent of Medicare beneficiaries with high cholesterol\": 44.56, \"Standardized Per Capita Costs\": 10820.69, \"Ambulance Actual Costs\": 71541331.77, \"FQHC/RHC Per Capita Actual Costs\": 42.18, \"Part B Drugs Per User Standardized Costs\": 465.95, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0279, \"# PAC: SNF Users (with a covered stay)\": 21816.0, \"Ambulance Per Capita Actual Costs\": 137.78, \"PQI12 UTI Admission Rate (age 65-74)\": 463.0, \"Hospice Standardized Costs\": 208648332.92, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 334.4, \"% of Beneficiaries Using E&M\": 0.8852, \"PQI10 Dehydration Admission Rate (age 65-74)\": 303.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 244.0, \"Tests Per Capita Actual Costs\": 227.58, \"# DME Users\": 151871.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0731, \"PAC: SNF Per User Actual Costs\": 15632.63, \"State\": \"LA\", \"OP Per User Actual Costs\": 1993.95, \"PAC: HH Episodes Per 1000 Beneficiaries\": 395.0, \"Part B Drugs Actual Costs\": 119764485.31, \"FQHC/RHC Actual Costs\": 21903668.76, \"OP Standardized Costs\": 765279306.21, \"DME Per User Standardized Costs\": 818.36, \"OP Actual Costs as % of Total Actual Costs\": 0.1351, \"PAC: SNF Per Capita Standardized Costs\": 791.04, \"% of Beneficiaries Using Ambulance\": 0.1182, \"Hospice Per User Standardized Costs\": 13172.24, \"# Imaging Users\": 362688.0, \"Part B Drugs Per User Actual Costs\": 462.48, \"Total Standardized Costs\": 5618675718.0, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.3, \"Count of Medicare beneficiaries with chronic kidney disease\": 90110.0, \"E&M Standardized Costs\": 478542362.83, \"Percent Hispanic\": 1.84, \"ASC Per Capita Actual Costs\": 78.47, \"Count of Medicare beneficiaries who have had a heart attack\": 3961.0, \"Tests Actual Costs\": 118173497.31, \"# PAC: LTCH Users (with a covered stay)\": 8709.0, \"% of Beneficiaries Using Procedures\": 0.5952, \"PAC: HH Actual Costs\": 458712119.6, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 63.0, \"PAC: LTCH Per Capita Standardized Costs\": 686.96, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0221, \"Emergency Department Visits\": 408127.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1023, \"Procedures Actual Costs\": 279538242.51, \"# FQHC/RHC Users\": 53128.0, \"Number of Acute Hospital Readmissions\": 27836.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 223.0, \"Outpatient Dialysis Facility Standardized Costs\": 182982017.52, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0284, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1362, \"Ambulance Per User Standardized Costs\": 1210.15, \"Imaging Per User Standardized Costs\": 323.61, \"Percent of Medicare beneficiaries with asthma\": 4.49, \"Part B Drugs Standardized Costs\": 120663088.62, \"FFS Beneficiaries\": 519253.0, \"# Hospice Users (with a covered stay)\": 15840.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0151, \"Count of Medicare beneficiaries with osteoporosis\": 27853.0, \"PQI08 CHF Admission Rate (age 75+)\": 2561.0, \"PAC: IRF Per Capita Standardized Costs\": 301.81, \"Procedures Standardized Costs\": 306431594.86, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2625, \"IP Per Capita Actual Costs\": 3031.08, \"DME Actual Costs\": 117799381.7, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0862, \"Count of Medicare beneficiaries with prostate cancer\": 14466.0, \"PAC: HH Per Capita Standardized Costs\": 1039.43, \"Count of Medicare beneficiaries with heart failure\": 85316.0, \"Tests Per User Standardized Costs\": 316.23, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0582, \"Percent of Medicare beneficiaries with prostate cancer\": 2.79, \"PAC: IRF Per Capita Actual Costs\": 290.9, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 294.48, \"Percent of Medicare beneficiaries with breast cancer\": 2.61, \"Procedures Per User Standardized Costs\": 991.43, \"Percent of Medicare beneficiaries with chronic kidney disease\": 17.35, \"PAC: HH Per User Actual Costs\": 6579.54, \"Count of Medicare beneficiaries with high cholesterol\": 231383.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0641, \"Hospice Per Capita Standardized Costs\": 401.82, \"# Part B Drugs Users\": 258962.0, \"Average HCC Score\": 1.0621, \"Standardized Risk-Adjusted Per Capita Costs\": 10440.83, \"# PAC: IRF Users (with a covered stay)\": 7801.0, \"Ambulance Standardized Costs\": 74269010.01, \"Hospice Actual Costs as % of Total Actual Costs\": 0.035, \"Percent of Medicare beneficiaries with heart failure\": 16.43, \"Tests Actual Costs as % of Total Actual Costs\": 0.0222, \"FQHC/RHC Per Capita Standardized Costs\": 48.32, \"PQI07 Hypertension Admission Rate (age 65-74)\": 133.0, \"Test Events Per 1000 Beneficiaries\": 8199.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 512.0, \"ASC Actual Costs\": 40744017.11, \"Part B Drugs Per Capita Standardized Costs\": 232.38, \"Imaging Actual Costs\": 106803892.9, \"Tests Per User Actual Costs\": 300.53, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0134, \"Hospice Per User Actual Costs\": 11770.22, \"Tests Standardized Costs\": 124347260.87, \"IP Standardized Costs\": 1475175208.53, \"IP Per Capita Standardized Costs\": 2840.96, \"Outpatient Dialysis Facility Actual Costs\": 173640430.21, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 62.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1942.0, \"Percent of Medicare beneficiaries with hypertension\": 62.19, \"IP Covered Stays Per 1000 Beneficiaries\": 317.0, \"# Ambulance Users\": 61372.0, \"# ASC Users\": 53217.0, \"ASC Per Capita Standardized Costs\": 88.81, \"Procedures Per Capita Actual Costs\": 538.35, \"Procedures Per Capita Standardized Costs\": 590.14, \"IP Users (with a covered stay)\": 98196.0, \"Total Standardized Risk-Adjusted Costs\": 5421433637.23, \"Actual Per Capita Costs\": 10244.35, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0635, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 17.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 20.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.96, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 12.27, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 268.0, \"OP Per User Standardized Costs\": 2122.96, \"Count of Medicare beneficiaries with atrial fibrillation\": 38954.0, \"Procedure Events Per 1000 Beneficiaries\": 4189.0, \"Percent of Medicare beneficiaries with stroke\": 4.59, \"PAC: IRF Per User Actual Costs\": 19363.12, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 63722.0, \"% of Beneficiaries Using ASC\": 0.1025, \"PAC: IRF Standardized Costs\": 156715863.06, \"Hospice Covered Days Per 1000 Beneficiaries\": 2508.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 373.0, \"PAC: SNF Per User Standardized Costs\": 18828.02, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0041, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 138.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0371, \"MA Participation Rate\": 29.22, \"OP Per Capita Standardized Costs\": 1473.81, \"Percent of Medicare beneficiaries with arthritis\": 31.85, \"Ambulance Per Capita Standardized Costs\": 143.03, \"PAC: HH Visits Per 1000 Beneficiaries\": 6284.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0526, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0201, \"PAC: HH Per Capita Actual Costs\": 883.41, \"E&M Events Per 1000 Beneficiaries\": 13336.0, \"Count of Medicare beneficiaries with asthma\": 23316.0, \"# Test Users\": 393213.0, \"E&M Actual Costs\": 430090297.29, \"% of Beneficiaries Using Imaging\": 0.6985, \"PAC: SNF Standardized Costs\": 410752147.52, \"DME Per Capita Actual Costs\": 226.86, \"E&M Per User Actual Costs\": 935.65, \"Percent Male\": 45.36, \"OP Visits Per 1000 Beneficiaries\": 5199.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0221, \"OP Per Capita Actual Costs\": 1384.25, \"Hospital Readmission Rate\": 0.1857, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0045, \"Tests Per Capita Standardized Costs\": 239.47, \"Hospice Per Capita Actual Costs\": 359.05, \"E&M Actual Costs as % of Total Actual Costs\": 0.0809, \"PQI07 Hypertension Admission Rate (age < 65)\": 181.0, \"Percent African American\": 27.12, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0961, \"PAC: LTCH Per Capita Actual Costs\": 596.14, \"MA Beneficiaries\": 214395.0, \"FQHC/RHC Per User Standardized Costs\": 472.26, \"PAC: HH Per User Standardized Costs\": 7741.56, \"Procedures Per User Actual Costs\": 904.42, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 710.0, \"Ambulance Per User Actual Costs\": 1165.7, \"Average Age\": 69.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1351.0, \"PAC: HH Standardized Costs\": 539725822.05, \"ASC Actual Costs as % of Total Actual Costs\": 0.0077, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 987.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0168, \"Percent Other/Unknown\": 1.75, \"% of Beneficiaries Using Hospice\": 0.0305, \"PAC: LTCH Per User Actual Costs\": 35543.37, \"Percent Non-Hispanic White\": 69.29, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 753.0, \"Count of Medicare beneficiaries with breast cancer\": 13530.0, \"DME Per Capita Standardized Costs\": 239.36, \"PQI08 CHF Admission Rate (age 65-74)\": 1046.0, \"% of Beneficiaries Using DME\": 0.2925, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0326, \"% of Beneficiaries Using IP\": 0.1891, \"DME Standardized Costs\": 124285794.05, \"FQHC/RHC Standardized Costs\": 25090247.8, \"PAC: SNF Per Capita Actual Costs\": 656.79, \"Imaging Standardized Costs\": 117368393.2, \"# E&M Users\": 459668.0, \"Count of Medicare beneficiaries with arthritis\": 165399.0, \"IP Per User Standardized Costs\": 15022.76, \"IP Per User Actual Costs\": 16028.12, \"PQI10 Dehydration Admission Rate (age 75+)\": 805.0, \"PAC: IRF Per User Standardized Costs\": 20089.2, \"DME Per User Actual Costs\": 775.65, \"PAC: IRF Actual Costs\": 151051672.27, \"Imaging Events Per 1000 Beneficiaries\": 4376.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0326, \"PAC: LTCH Per User Standardized Costs\": 40958.45, \"OP Actual Costs\": 718776451.18, \"Count of Medicare beneficiaries with hypertension\": 322909.0, \"ASC Per User Standardized Costs\": 866.52, \"Part B Drugs Per Capita Actual Costs\": 230.65, \"Ambulance Events Per 1000 Beneficiaries\": 407.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 357.0, \"% of Beneficiaries Using PAC: HH\": 0.121, \"PAC: LTCH Standardized Costs\": 162750075.11, \"Percent of Medicare beneficiaries with atrial fibrillation\": 9.22, \"E&M Per Capita Standardized Costs\": 1033.65, \"E&M Per User Standardized Costs\": 1139.42, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 762.0, \"IP Covered Days Per 1000 Beneficiaries\": 1679.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 38.0, \"Count of Medicare beneficiaries with lung cancer\": 10813.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3646, \"Percent Eligible for Medicaid\": 27.67, \"Imaging Per Capita Standardized Costs\": 175.9, \"% of Beneficiaries Using Tests\": 0.7749, \"Imaging Per Capita Actual Costs\": 181.2, \"% of Beneficiaries Using PAC: SNF\": 0.0644, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0186, \"Count of Medicare beneficiaries with colorectal cancer\": 11451.0, \"Hospice Actual Costs\": 242895727.62, \"# PAC: HH Users\": 103730.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23007.95, \"Total Actual Costs\": 9071020295.76, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 88136.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0055, \"ASC Standardized Costs\": 42316708.56, \"DME Events Per 1000 Beneficiaries\": 1427.0, \"PQI08 CHF Admission Rate (age < 65)\": 635.0, \"ASC Events Per 1000 Beneficiaries\": 94.0, \"PAC: LTCH Actual Costs\": 176031712.29, \"Count of Medicare beneficiaries with depression\": 176139.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1603.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.28, \"Outpatient Dialysis Facility Per User Actual Costs\": 24416.54, \"Beneficiaries with Part A and Part B\": 1090290.0, \"% of Beneficiaries Using Part B Drugs\": 0.509, \"Percent of Medicare beneficiaries with diabetes\": 23.8, \"% of Beneficiaries Using PAC: IRF\": 0.01, \"E&M Per Capita Actual Costs\": 1009.36, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0197, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0221, \"PAC: SNF Actual Costs\": 832335871.97, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 512.0, \"Percent Female\": 56.04, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 378.0, \"Percent of Medicare beneficiaries with osteoporosis\": 6.69, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 138.96, \"# Outpatient Dialysis Facility Users\": 5179.0, \"FQHC/RHC Per User Actual Costs\": 558.7, \"Count of Medicare beneficiaries with ischemic heart disease\": 207667.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 166.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.79, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 289.0, \"Percent of Medicare beneficiaries with depression\": 20.54, \"Emergency Department Visits per 1000 Beneficiaries\": 735.0, \"IP Actual Costs\": 3307508361.06, \"% of Beneficiaries Using OP\": 0.7795, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0238, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1159, \"Count of Medicare beneficiaries with stroke\": 29912.0, \"PQI12 UTI Admission Rate (age 75+)\": 1303.0, \"# OP Users\": 668435.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 25.0, \"# Procedure Users\": 535135.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 24.22, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0563, \"Count of Medicare beneficiaries with diabetes\": 204085.0, \"ASC Per User Actual Costs\": 819.63, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1133.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.018, \"Percent of Medicare beneficiaries with high cholesterol\": 44.68, \"Standardized Per Capita Costs\": 8919.31, \"Ambulance Actual Costs\": 169832689.15, \"FQHC/RHC Per Capita Actual Costs\": 27.3, \"Part B Drugs Per User Standardized Costs\": 387.92, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.022, \"# PAC: SNF Users (with a covered stay)\": 55219.0, \"Ambulance Per Capita Actual Costs\": 198.06, \"PQI12 UTI Admission Rate (age 65-74)\": 287.0, \"Hospice Standardized Costs\": 217884303.86, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 147.47, \"% of Beneficiaries Using E&M\": 0.9072, \"PQI10 Dehydration Admission Rate (age 65-74)\": 212.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 209.0, \"Tests Per Capita Actual Costs\": 193.94, \"# DME Users\": 217435.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0998, \"PAC: SNF Per User Actual Costs\": 15073.36, \"State\": \"MA\", \"OP Per User Actual Costs\": 2071.82, \"PAC: HH Episodes Per 1000 Beneficiaries\": 206.0, \"Part B Drugs Actual Costs\": 168411796.62, \"FQHC/RHC Actual Costs\": 23405569.27, \"OP Standardized Costs\": 1253457170.2, \"DME Per User Standardized Costs\": 632.86, \"OP Actual Costs as % of Total Actual Costs\": 0.1527, \"PAC: SNF Per Capita Standardized Costs\": 889.91, \"% of Beneficiaries Using Ambulance\": 0.1541, \"Hospice Per User Standardized Costs\": 10556.92, \"# Imaging Users\": 601454.0, \"Part B Drugs Per User Actual Costs\": 385.88, \"Total Standardized Costs\": 7648121525.01, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.34, \"Count of Medicare beneficiaries with chronic kidney disease\": 141369.0, \"E&M Standardized Costs\": 886336870.57, \"Percent Hispanic\": 5.41, \"ASC Per Capita Actual Costs\": 51.9, \"Count of Medicare beneficiaries who have had a heart attack\": 6771.0, \"Tests Actual Costs\": 166298150.59, \"# PAC: LTCH Users (with a covered stay)\": 4717.0, \"% of Beneficiaries Using Procedures\": 0.6241, \"PAC: HH Actual Costs\": 519608105.19, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 27.0, \"PAC: LTCH Per Capita Standardized Costs\": 189.8, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0218, \"Emergency Department Visits\": 630183.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0489, \"Procedures Actual Costs\": 439979521.69, \"# FQHC/RHC Users\": 41893.0, \"Number of Acute Hospital Readmissions\": 43695.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 154.0, \"Outpatient Dialysis Facility Standardized Costs\": 119158181.75, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0212, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1639, \"Ambulance Per User Standardized Costs\": 1376.62, \"Imaging Per User Standardized Costs\": 250.78, \"Percent of Medicare beneficiaries with asthma\": 6.15, \"Part B Drugs Standardized Costs\": 169301944.59, \"FFS Beneficiaries\": 857479.0, \"# Hospice Users (with a covered stay)\": 20639.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.006, \"Count of Medicare beneficiaries with osteoporosis\": 57401.0, \"PQI08 CHF Admission Rate (age 75+)\": 2511.0, \"PAC: IRF Per Capita Standardized Costs\": 196.6, \"Procedures Standardized Costs\": 430541085.38, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2832, \"IP Per Capita Actual Costs\": 3857.25, \"DME Actual Costs\": 126668459.27, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0573, \"Count of Medicare beneficiaries with prostate cancer\": 27757.0, \"PAC: HH Per Capita Standardized Costs\": 553.23, \"Count of Medicare beneficiaries with heart failure\": 110496.0, \"Tests Per User Standardized Costs\": 250.73, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0194, \"Percent of Medicare beneficiaries with prostate cancer\": 3.24, \"PAC: IRF Per Capita Actual Costs\": 223.9, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 258.34, \"Percent of Medicare beneficiaries with breast cancer\": 3.36, \"Procedures Per User Standardized Costs\": 804.55, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.49, \"PAC: HH Per User Actual Costs\": 5009.24, \"Count of Medicare beneficiaries with high cholesterol\": 383143.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0918, \"Hospice Per Capita Standardized Costs\": 254.1, \"# Part B Drugs Users\": 436438.0, \"Average HCC Score\": 1.0105, \"Standardized Risk-Adjusted Per Capita Costs\": 9148.82, \"# PAC: IRF Users (with a covered stay)\": 8537.0, \"Ambulance Standardized Costs\": 181901446.15, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0268, \"Percent of Medicare beneficiaries with heart failure\": 12.89, \"Tests Actual Costs as % of Total Actual Costs\": 0.0183, \"FQHC/RHC Per Capita Standardized Costs\": 25.92, \"PQI07 Hypertension Admission Rate (age 65-74)\": 60.0, \"Test Events Per 1000 Beneficiaries\": 8244.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 163.0, \"ASC Actual Costs\": 44507359.86, \"Part B Drugs Per Capita Standardized Costs\": 197.44, \"Imaging Actual Costs\": 155379261.49, \"Tests Per User Actual Costs\": 250.27, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0187, \"Hospice Per User Actual Costs\": 11768.77, \"Tests Standardized Costs\": 166602507.62, \"IP Standardized Costs\": 2166151719.06, \"IP Per Capita Standardized Costs\": 2526.19, \"Outpatient Dialysis Facility Actual Costs\": 126453241.97, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 90.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2141.0, \"Percent of Medicare beneficiaries with hypertension\": 54.74, \"IP Covered Stays Per 1000 Beneficiaries\": 301.0, \"# Ambulance Users\": 132136.0, \"# ASC Users\": 54302.0, \"ASC Per Capita Standardized Costs\": 49.35, \"Procedures Per Capita Actual Costs\": 513.11, \"Procedures Per Capita Standardized Costs\": 502.1, \"IP Users (with a covered stay)\": 155277.0, \"Total Standardized Risk-Adjusted Costs\": 7844921287.29, \"Actual Per Capita Costs\": 10578.71, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0213, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 11.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 6.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.26, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.04, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 269.0, \"OP Per User Standardized Costs\": 1875.21, \"Count of Medicare beneficiaries with atrial fibrillation\": 79093.0, \"Procedure Events Per 1000 Beneficiaries\": 4163.0, \"Percent of Medicare beneficiaries with stroke\": 3.49, \"PAC: IRF Per User Actual Costs\": 22489.34, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 86075.0, \"% of Beneficiaries Using ASC\": 0.0633, \"PAC: IRF Standardized Costs\": 168583229.69, \"Hospice Covered Days Per 1000 Beneficiaries\": 1599.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 328.0, \"PAC: SNF Per User Standardized Costs\": 13819.14, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0026, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 65.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0285, \"MA Participation Rate\": 21.35, \"OP Per Capita Standardized Costs\": 1461.79, \"Percent of Medicare beneficiaries with arthritis\": 26.87, \"Ambulance Per Capita Standardized Costs\": 212.14, \"PAC: HH Visits Per 1000 Beneficiaries\": 3531.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0485, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0171, \"PAC: HH Per Capita Actual Costs\": 605.97, \"E&M Events Per 1000 Beneficiaries\": 14891.0, \"Count of Medicare beneficiaries with asthma\": 52761.0, \"# Test Users\": 664469.0, \"E&M Actual Costs\": 865507025.91, \"% of Beneficiaries Using Imaging\": 0.7014, \"PAC: SNF Standardized Costs\": 763079242.72, \"DME Per Capita Actual Costs\": 147.72, \"E&M Per User Actual Costs\": 1112.65, \"Percent Male\": 43.96, \"OP Visits Per 1000 Beneficiaries\": 6989.0, \"DME Actual Costs as % of Total Actual Costs\": 0.014, \"OP Per Capita Actual Costs\": 1615.05, \"Hospital Readmission Rate\": 0.1838, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0029, \"Tests Per Capita Standardized Costs\": 194.29, \"Hospice Per Capita Actual Costs\": 283.27, \"E&M Actual Costs as % of Total Actual Costs\": 0.0954, \"PQI07 Hypertension Admission Rate (age < 65)\": 69.0, \"Percent African American\": 4.51, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.062, \"PAC: LTCH Per Capita Actual Costs\": 205.29, \"MA Beneficiaries\": 232811.0, \"FQHC/RHC Per User Standardized Costs\": 530.56, \"PAC: HH Per User Standardized Costs\": 4573.23, \"Procedures Per User Actual Costs\": 822.18, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 551.0, \"Ambulance Per User Actual Costs\": 1285.28, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1474.0, \"PAC: HH Standardized Costs\": 474381416.21, \"ASC Actual Costs as % of Total Actual Costs\": 0.0049, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 819.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0055, \"Percent Other/Unknown\": 4.1, \"% of Beneficiaries Using Hospice\": 0.0241, \"PAC: LTCH Per User Actual Costs\": 37318.57, \"Percent Non-Hispanic White\": 85.97, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 666.0, \"Count of Medicare beneficiaries with breast cancer\": 28853.0, \"DME Per Capita Standardized Costs\": 160.48, \"PQI08 CHF Admission Rate (age 65-74)\": 653.0, \"% of Beneficiaries Using DME\": 0.2536, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0139, \"% of Beneficiaries Using IP\": 0.1811, \"DME Standardized Costs\": 137604888.42, \"FQHC/RHC Standardized Costs\": 22226839.47, \"PAC: SNF Per Capita Actual Costs\": 970.68, \"Imaging Standardized Costs\": 150830376.62, \"# E&M Users\": 777881.0, \"Count of Medicare beneficiaries with arthritis\": 230444.0, \"IP Per User Standardized Costs\": 13950.24, \"IP Per User Actual Costs\": 21300.7, \"PQI10 Dehydration Admission Rate (age 75+)\": 568.0, \"PAC: IRF Per User Standardized Costs\": 19747.36, \"DME Per User Actual Costs\": 582.56, \"PAC: IRF Actual Costs\": 191991495.69, \"Imaging Events Per 1000 Beneficiaries\": 4040.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0156, \"PAC: LTCH Per User Standardized Costs\": 34502.88, \"OP Actual Costs\": 1384874495.55, \"Count of Medicare beneficiaries with hypertension\": 469410.0, \"ASC Per User Standardized Costs\": 779.28, \"Part B Drugs Per Capita Actual Costs\": 196.4, \"Ambulance Events Per 1000 Beneficiaries\": 638.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 435.0, \"% of Beneficiaries Using PAC: HH\": 0.0793, \"PAC: LTCH Standardized Costs\": 27262680.69, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.9, \"E&M Per Capita Standardized Costs\": 1036.42, \"E&M Per User Standardized Costs\": 1160.82, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1464.0, \"IP Covered Days Per 1000 Beneficiaries\": 1673.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 47.0, \"Count of Medicare beneficiaries with lung cancer\": 7955.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3934, \"Percent Eligible for Medicaid\": 16.4, \"Imaging Per Capita Standardized Costs\": 293.22, \"% of Beneficiaries Using Tests\": 0.8086, \"Imaging Per Capita Actual Costs\": 308.87, \"% of Beneficiaries Using PAC: SNF\": 0.0547, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0291, \"Count of Medicare beneficiaries with colorectal cancer\": 9400.0, \"Hospice Actual Costs\": 157988920.79, \"# PAC: HH Users\": 57446.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23188.3, \"Total Actual Costs\": 8026725125.67, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 74896.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0137, \"ASC Standardized Costs\": 88980999.07, \"DME Events Per 1000 Beneficiaries\": 1462.0, \"PQI08 CHF Admission Rate (age < 65)\": 1376.0, \"ASC Events Per 1000 Beneficiaries\": 265.0, \"PAC: LTCH Actual Costs\": 32048706.95, \"Count of Medicare beneficiaries with depression\": 102916.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1361.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.34, \"Outpatient Dialysis Facility Per User Actual Costs\": 23528.4, \"Beneficiaries with Part A and Part B\": 809029.0, \"% of Beneficiaries Using Part B Drugs\": 0.5484, \"Percent of Medicare beneficiaries with diabetes\": 28.76, \"% of Beneficiaries Using PAC: IRF\": 0.003, \"E&M Per Capita Actual Costs\": 1045.69, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0326, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0359, \"PAC: SNF Actual Costs\": 591046027.85, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 480.0, \"Percent Female\": 56.92, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 337.0, \"Percent of Medicare beneficiaries with osteoporosis\": 6.01, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 268.87, \"# Outpatient Dialysis Facility Users\": 8395.0, \"FQHC/RHC Per User Actual Costs\": 367.91, \"Count of Medicare beneficiaries with ischemic heart disease\": 197924.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 205.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.82, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 128.0, \"Percent of Medicare beneficiaries with depression\": 14.21, \"Emergency Department Visits per 1000 Beneficiaries\": 662.0, \"IP Actual Costs\": 3157390869.87, \"% of Beneficiaries Using OP\": 0.4874, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0112, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1151, \"Count of Medicare beneficiaries with stroke\": 30912.0, \"PQI12 UTI Admission Rate (age 75+)\": 1203.0, \"# OP Users\": 352902.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 24.0, \"# Procedure Users\": 453671.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 27.34, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0736, \"Count of Medicare beneficiaries with diabetes\": 208244.0, \"ASC Per User Actual Costs\": 786.07, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 993.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0207, \"Percent of Medicare beneficiaries with high cholesterol\": 49.24, \"Standardized Per Capita Costs\": 9001.84, \"Ambulance Actual Costs\": 72981273.86, \"FQHC/RHC Per Capita Actual Costs\": 11.67, \"Part B Drugs Per User Standardized Costs\": 588.65, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0064, \"# PAC: SNF Users (with a covered stay)\": 39606.0, \"Ambulance Per Capita Actual Costs\": 100.8, \"PQI12 UTI Admission Rate (age 65-74)\": 277.0, \"Hospice Standardized Costs\": 154295880.35, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 272.82, \"% of Beneficiaries Using E&M\": 0.8928, \"PQI10 Dehydration Admission Rate (age 65-74)\": 226.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 227.0, \"Tests Per Capita Actual Costs\": 292.88, \"# DME Users\": 189107.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0924, \"PAC: SNF Per User Actual Costs\": 14923.14, \"State\": \"MD\", \"OP Per User Actual Costs\": 3563.56, \"PAC: HH Episodes Per 1000 Beneficiaries\": 117.0, \"Part B Drugs Actual Costs\": 233258307.55, \"FQHC/RHC Actual Costs\": 8446752.28, \"OP Standardized Costs\": 951972305.84, \"DME Per User Standardized Costs\": 714.8, \"OP Actual Costs as % of Total Actual Costs\": 0.1567, \"PAC: SNF Per Capita Standardized Costs\": 831.74, \"% of Beneficiaries Using Ambulance\": 0.1105, \"Hospice Per User Standardized Costs\": 9258.12, \"# Imaging Users\": 501149.0, \"Part B Drugs Per User Actual Costs\": 587.52, \"Total Standardized Costs\": 6517368973.58, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.3, \"Count of Medicare beneficiaries with chronic kidney disease\": 119260.0, \"E&M Standardized Costs\": 750371451.21, \"Percent Hispanic\": 2.05, \"ASC Per Capita Actual Costs\": 120.3, \"Count of Medicare beneficiaries who have had a heart attack\": 5941.0, \"Tests Actual Costs\": 212048633.96, \"# PAC: LTCH Users (with a covered stay)\": 912.0, \"% of Beneficiaries Using Procedures\": 0.6266, \"PAC: HH Actual Costs\": 242460103.4, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 39.0, \"PAC: LTCH Per Capita Standardized Costs\": 37.66, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0324, \"Emergency Department Visits\": 479292.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0317, \"Procedures Actual Costs\": 502157719.81, \"# FQHC/RHC Users\": 22959.0, \"Number of Acute Hospital Readmissions\": 41467.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 43.0, \"Outpatient Dialysis Facility Standardized Costs\": 194665758.33, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0054, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1461, \"Ambulance Per User Standardized Costs\": 911.53, \"Imaging Per User Standardized Costs\": 423.61, \"Percent of Medicare beneficiaries with asthma\": 5.08, \"Part B Drugs Standardized Costs\": 233705664.9, \"FFS Beneficiaries\": 724004.0, \"# Hospice Users (with a covered stay)\": 16666.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0116, \"Count of Medicare beneficiaries with osteoporosis\": 43524.0, \"PQI08 CHF Admission Rate (age 75+)\": 2038.0, \"PAC: IRF Per Capita Standardized Costs\": 57.73, \"Procedures Standardized Costs\": 479664995.24, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3032, \"IP Per Capita Actual Costs\": 4361.01, \"DME Actual Costs\": 124090171.15, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0302, \"Count of Medicare beneficiaries with prostate cancer\": 24219.0, \"PAC: HH Per Capita Standardized Costs\": 337.52, \"Count of Medicare beneficiaries with heart failure\": 94754.0, \"Tests Per User Standardized Costs\": 360.2, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.004, \"Percent of Medicare beneficiaries with prostate cancer\": 3.35, \"PAC: IRF Per Capita Actual Costs\": 59.76, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 446.21, \"Percent of Medicare beneficiaries with breast cancer\": 3.28, \"Procedures Per User Standardized Costs\": 1057.3, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.47, \"PAC: HH Per User Actual Costs\": 4220.66, \"Count of Medicare beneficiaries with high cholesterol\": 356526.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0736, \"Hospice Per Capita Standardized Costs\": 213.11, \"# Part B Drugs Users\": 397021.0, \"Average HCC Score\": 1.0082, \"Standardized Risk-Adjusted Per Capita Costs\": 9567.47, \"# PAC: IRF Users (with a covered stay)\": 2139.0, \"Ambulance Standardized Costs\": 72906655.71, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0197, \"Percent of Medicare beneficiaries with heart failure\": 13.09, \"Tests Actual Costs as % of Total Actual Costs\": 0.0264, \"FQHC/RHC Per Capita Standardized Costs\": 12.42, \"PQI07 Hypertension Admission Rate (age 65-74)\": 93.0, \"Test Events Per 1000 Beneficiaries\": 10569.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 34.0, \"ASC Actual Costs\": 87098320.64, \"Part B Drugs Per Capita Standardized Costs\": 322.8, \"Imaging Actual Costs\": 223619734.99, \"Tests Per User Actual Costs\": 362.21, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0091, \"Hospice Per User Actual Costs\": 9479.71, \"Tests Standardized Costs\": 210874164.93, \"IP Standardized Costs\": 1975760049.76, \"IP Per Capita Standardized Costs\": 2728.94, \"Outpatient Dialysis Facility Actual Costs\": 197520922.42, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 76.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2004.0, \"Percent of Medicare beneficiaries with hypertension\": 59.5, \"IP Covered Stays Per 1000 Beneficiaries\": 295.0, \"# Ambulance Users\": 79983.0, \"# ASC Users\": 110802.0, \"ASC Per Capita Standardized Costs\": 122.9, \"Procedures Per Capita Actual Costs\": 693.58, \"Procedures Per Capita Standardized Costs\": 662.52, \"IP Users (with a covered stay)\": 130062.0, \"Total Standardized Risk-Adjusted Costs\": 6926885386.33, \"Actual Per Capita Costs\": 11086.58, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0042, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 3.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.1, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.84, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 282.0, \"OP Per User Standardized Costs\": 2697.55, \"Count of Medicare beneficiaries with atrial fibrillation\": 57216.0, \"Procedure Events Per 1000 Beneficiaries\": 5151.0, \"Percent of Medicare beneficiaries with stroke\": 4.27, \"PAC: IRF Per User Actual Costs\": 20229.09, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 71229.0, \"% of Beneficiaries Using ASC\": 0.153, \"PAC: IRF Standardized Costs\": 41799896.76, \"Hospice Covered Days Per 1000 Beneficiaries\": 1283.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 397.0, \"PAC: SNF Per User Standardized Costs\": 15204.37, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0011, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 124.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0237, \"MA Participation Rate\": 10.51, \"OP Per Capita Standardized Costs\": 1314.87, \"Percent of Medicare beneficiaries with arthritis\": 29.23, \"Ambulance Per Capita Standardized Costs\": 100.7, \"PAC: HH Visits Per 1000 Beneficiaries\": 1793.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0626, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0279, \"PAC: HH Per Capita Actual Costs\": 334.89, \"E&M Events Per 1000 Beneficiaries\": 14179.0, \"Count of Medicare beneficiaries with asthma\": 36757.0, \"# Test Users\": 585435.0, \"E&M Actual Costs\": 757084596.77, \"% of Beneficiaries Using Imaging\": 0.6922, \"PAC: SNF Standardized Costs\": 602184422.76, \"DME Per Capita Actual Costs\": 171.39, \"E&M Per User Actual Costs\": 1171.2, \"Percent Male\": 43.08, \"OP Visits Per 1000 Beneficiaries\": 2841.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0155, \"OP Per Capita Actual Costs\": 1736.99, \"Hospital Readmission Rate\": 0.1968, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0014, \"Tests Per Capita Standardized Costs\": 291.26, \"Hospice Per Capita Actual Costs\": 218.22, \"E&M Actual Costs as % of Total Actual Costs\": 0.0943, \"PQI07 Hypertension Admission Rate (age < 65)\": 190.0, \"Percent African American\": 22.92, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0375, \"PAC: LTCH Per Capita Actual Costs\": 44.27, \"MA Beneficiaries\": 85025.0, \"FQHC/RHC Per User Standardized Costs\": 391.51, \"PAC: HH Per User Standardized Costs\": 4253.78, \"Procedures Per User Actual Costs\": 1106.88, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 944.0, \"Ambulance Per User Actual Costs\": 912.46, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1642.0, \"PAC: HH Standardized Costs\": 244362715.37, \"ASC Actual Costs as % of Total Actual Costs\": 0.0109, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 732.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0013, \"Percent Other/Unknown\": 4.74, \"% of Beneficiaries Using Hospice\": 0.023, \"PAC: LTCH Per User Actual Costs\": 35141.13, \"Percent Non-Hispanic White\": 70.29, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 736.0, \"Count of Medicare beneficiaries with breast cancer\": 23757.0, \"DME Per Capita Standardized Costs\": 186.7, \"PQI08 CHF Admission Rate (age 65-74)\": 725.0, \"% of Beneficiaries Using DME\": 0.2612, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0246, \"% of Beneficiaries Using IP\": 0.1796, \"DME Standardized Costs\": 135173153.73, \"FQHC/RHC Standardized Costs\": 8988614.21, \"PAC: SNF Per Capita Actual Costs\": 816.36, \"Imaging Standardized Costs\": 212291933.9, \"# E&M Users\": 646416.0, \"Count of Medicare beneficiaries with arthritis\": 211612.0, \"IP Per User Standardized Costs\": 15190.91, \"IP Per User Actual Costs\": 24276.04, \"PQI10 Dehydration Admission Rate (age 75+)\": 506.0, \"PAC: IRF Per User Standardized Costs\": 19541.79, \"DME Per User Actual Costs\": 656.19, \"PAC: IRF Actual Costs\": 43270030.3, \"Imaging Events Per 1000 Beneficiaries\": 4178.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0299, \"PAC: LTCH Per User Standardized Costs\": 29893.29, \"OP Actual Costs\": 1257587974.17, \"Count of Medicare beneficiaries with hypertension\": 430774.0, \"ASC Per User Standardized Costs\": 803.06, \"Part B Drugs Per Capita Actual Costs\": 322.18, \"Ambulance Events Per 1000 Beneficiaries\": 304.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 170.0, \"% of Beneficiaries Using PAC: HH\": 0.0841, \"PAC: LTCH Standardized Costs\": 1882277.66, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.33, \"E&M Per Capita Standardized Costs\": 678.5, \"E&M Per User Standardized Costs\": 806.81, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 556.0, \"IP Covered Days Per 1000 Beneficiaries\": 1242.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 31.0, \"Count of Medicare beneficiaries with lung cancer\": 2352.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3258, \"Percent Eligible for Medicaid\": 38.75, \"Imaging Per Capita Standardized Costs\": 113.11, \"% of Beneficiaries Using Tests\": 0.6525, \"Imaging Per Capita Actual Costs\": 108.1, \"% of Beneficiaries Using PAC: SNF\": 0.0502, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0279, \"Count of Medicare beneficiaries with colorectal cancer\": 2436.0, \"Hospice Actual Costs\": 51348541.29, \"# PAC: HH Users\": 19092.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23202.97, \"Total Actual Costs\": 1798598449.21, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 19551.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0053, \"ASC Standardized Costs\": 9109091.06, \"DME Events Per 1000 Beneficiaries\": 1761.0, \"PQI08 CHF Admission Rate (age < 65)\": 398.0, \"ASC Events Per 1000 Beneficiaries\": 76.0, \"PAC: LTCH Actual Costs\": 2007902.32, \"Count of Medicare beneficiaries with depression\": 47947.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1764.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.62, \"Outpatient Dialysis Facility Per User Actual Costs\": 22985.71, \"Beneficiaries with Part A and Part B\": 282966.0, \"% of Beneficiaries Using Part B Drugs\": 0.3287, \"Percent of Medicare beneficiaries with diabetes\": 23.19, \"% of Beneficiaries Using PAC: IRF\": 0.0092, \"E&M Per Capita Actual Costs\": 612.93, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0149, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0293, \"PAC: SNF Actual Costs\": 143054120.26, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 619.0, \"Percent Female\": 54.28, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 162.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.3, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 103.39, \"# Outpatient Dialysis Facility Users\": 1011.0, \"FQHC/RHC Per User Actual Costs\": 629.65, \"Count of Medicare beneficiaries with ischemic heart disease\": 52274.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 102.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 1.07, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 1384.0, \"Percent of Medicare beneficiaries with depression\": 21.13, \"Emergency Department Visits per 1000 Beneficiaries\": 781.0, \"IP Actual Costs\": 585936000.41, \"% of Beneficiaries Using OP\": 0.8075, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.017, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0896, \"Count of Medicare beneficiaries with stroke\": 6531.0, \"PQI12 UTI Admission Rate (age 75+)\": 816.0, \"# OP Users\": 183212.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 26.0, \"# Procedure Users\": 120242.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 23.04, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0565, \"Count of Medicare beneficiaries with diabetes\": 52608.0, \"ASC Per User Actual Costs\": 847.56, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1028.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0263, \"Percent of Medicare beneficiaries with high cholesterol\": 42.16, \"Standardized Per Capita Costs\": 7574.34, \"Ambulance Actual Costs\": 31160390.17, \"FQHC/RHC Per Capita Actual Costs\": 142.87, \"Part B Drugs Per User Standardized Costs\": 675.74, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0231, \"# PAC: SNF Users (with a covered stay)\": 11389.0, \"Ambulance Per Capita Actual Costs\": 137.34, \"PQI12 UTI Admission Rate (age 65-74)\": 200.0, \"Hospice Standardized Costs\": 53211252.29, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 102.42, \"% of Beneficiaries Using E&M\": 0.841, \"PQI10 Dehydration Admission Rate (age 65-74)\": 138.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 139.0, \"Tests Per Capita Actual Costs\": 127.16, \"# DME Users\": 60273.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0898, \"PAC: SNF Per User Actual Costs\": 12560.73, \"State\": \"ME\", \"OP Per User Actual Costs\": 2172.47, \"PAC: HH Episodes Per 1000 Beneficiaries\": 127.0, \"Part B Drugs Actual Costs\": 50134514.85, \"FQHC/RHC Actual Costs\": 32414419.28, \"OP Standardized Costs\": 381078709.84, \"DME Per User Standardized Costs\": 749.8, \"OP Actual Costs as % of Total Actual Costs\": 0.2213, \"PAC: SNF Per Capita Standardized Costs\": 680.07, \"% of Beneficiaries Using Ambulance\": 0.1051, \"Hospice Per User Standardized Costs\": 9471.57, \"# Imaging Users\": 146319.0, \"Part B Drugs Per User Actual Costs\": 672.22, \"Total Standardized Costs\": 1718527247.85, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.07, \"Count of Medicare beneficiaries with chronic kidney disease\": 31406.0, \"E&M Standardized Costs\": 153944238.17, \"Percent Hispanic\": 0.47, \"ASC Per Capita Actual Costs\": 38.56, \"Count of Medicare beneficiaries who have had a heart attack\": 2431.0, \"Tests Actual Costs\": 28850450.31, \"# PAC: LTCH Users (with a covered stay)\": 47.0, \"% of Beneficiaries Using Procedures\": 0.53, \"PAC: HH Actual Costs\": 72794374.17, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 32.0, \"PAC: LTCH Per Capita Standardized Costs\": 8.3, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0173, \"Emergency Department Visits\": 177242.0, \"% of Beneficiaries Using FQHC/RHC\": 0.2269, \"Procedures Actual Costs\": 91946709.15, \"# FQHC/RHC Users\": 51480.0, \"Number of Acute Hospital Readmissions\": 8736.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 133.0, \"Outpatient Dialysis Facility Standardized Costs\": 23458198.06, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0228, \"OP Standardized Costs as % of Total Standardized Costs\": 0.2217, \"Ambulance Per User Standardized Costs\": 1227.88, \"Imaging Per User Standardized Costs\": 175.39, \"Percent of Medicare beneficiaries with asthma\": 4.71, \"Part B Drugs Standardized Costs\": 50397083.55, \"FFS Beneficiaries\": 226888.0, \"# Hospice Users (with a covered stay)\": 5618.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0045, \"Count of Medicare beneficiaries with osteoporosis\": 12033.0, \"PQI08 CHF Admission Rate (age 75+)\": 1931.0, \"PAC: IRF Per Capita Standardized Costs\": 175.11, \"Procedures Standardized Costs\": 97134691.69, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2763, \"IP Per Capita Actual Costs\": 2582.49, \"DME Actual Costs\": 43256353.06, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0405, \"Count of Medicare beneficiaries with prostate cancer\": 5604.0, \"PAC: HH Per Capita Standardized Costs\": 339.8, \"Count of Medicare beneficiaries with heart failure\": 26170.0, \"Tests Per User Standardized Costs\": 201.35, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0011, \"Percent of Medicare beneficiaries with prostate cancer\": 2.47, \"PAC: IRF Per Capita Actual Costs\": 180.93, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 167.62, \"Percent of Medicare beneficiaries with breast cancer\": 2.5, \"Procedures Per User Standardized Costs\": 807.83, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.84, \"PAC: HH Per User Actual Costs\": 3812.82, \"Count of Medicare beneficiaries with high cholesterol\": 95647.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0795, \"Hospice Per Capita Standardized Costs\": 234.53, \"# Part B Drugs Users\": 74581.0, \"Average HCC Score\": 0.9346, \"Standardized Risk-Adjusted Per Capita Costs\": 8382.97, \"# PAC: IRF Users (with a covered stay)\": 2087.0, \"Ambulance Standardized Costs\": 29272112.05, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0285, \"Percent of Medicare beneficiaries with heart failure\": 11.53, \"Tests Actual Costs as % of Total Actual Costs\": 0.016, \"FQHC/RHC Per Capita Standardized Costs\": 156.69, \"PQI07 Hypertension Admission Rate (age 65-74)\": 37.0, \"Test Events Per 1000 Beneficiaries\": 5249.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 6.0, \"ASC Actual Costs\": 8747701.38, \"Part B Drugs Per Capita Standardized Costs\": 222.12, \"Imaging Actual Costs\": 24526395.7, \"Tests Per User Actual Costs\": 194.87, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0173, \"Hospice Per User Actual Costs\": 9140.0, \"Tests Standardized Costs\": 29810040.08, \"IP Standardized Costs\": 474898284.5, \"IP Per Capita Standardized Costs\": 2093.1, \"Outpatient Dialysis Facility Actual Costs\": 23238557.55, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 67.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1476.0, \"Percent of Medicare beneficiaries with hypertension\": 48.93, \"IP Covered Stays Per 1000 Beneficiaries\": 244.0, \"# Ambulance Users\": 23840.0, \"# ASC Users\": 10321.0, \"ASC Per Capita Standardized Costs\": 40.15, \"Procedures Per Capita Actual Costs\": 405.25, \"Procedures Per Capita Standardized Costs\": 428.12, \"IP Users (with a covered stay)\": 36069.0, \"Total Standardized Risk-Adjusted Costs\": 1901995083.12, \"Actual Per Capita Costs\": 7927.25, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0011, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 10.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 0.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.04, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 12.11, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 148.0, \"OP Per User Standardized Costs\": 2079.99, \"Count of Medicare beneficiaries with atrial fibrillation\": 18893.0, \"Procedure Events Per 1000 Beneficiaries\": 3354.0, \"Percent of Medicare beneficiaries with stroke\": 2.88, \"PAC: IRF Per User Actual Costs\": 19669.78, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 27472.0, \"% of Beneficiaries Using ASC\": 0.0455, \"PAC: IRF Standardized Costs\": 39731378.0, \"Hospice Covered Days Per 1000 Beneficiaries\": 1425.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 184.0, \"PAC: SNF Per User Standardized Costs\": 13548.14, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.018, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 80.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.031, \"MA Participation Rate\": 19.82, \"OP Per Capita Standardized Costs\": 1679.59, \"Percent of Medicare beneficiaries with arthritis\": 23.81, \"Ambulance Per Capita Standardized Costs\": 129.02, \"PAC: HH Visits Per 1000 Beneficiaries\": 1910.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0511, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0136, \"PAC: HH Per Capita Actual Costs\": 320.84, \"E&M Events Per 1000 Beneficiaries\": 10256.0, \"Count of Medicare beneficiaries with asthma\": 10677.0, \"# Test Users\": 148050.0, \"E&M Actual Costs\": 139065351.11, \"% of Beneficiaries Using Imaging\": 0.6449, \"PAC: SNF Standardized Costs\": 154299773.31, \"DME Per Capita Actual Costs\": 190.65, \"E&M Per User Actual Costs\": 728.83, \"Percent Male\": 45.72, \"OP Visits Per 1000 Beneficiaries\": 8900.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0241, \"OP Per Capita Actual Costs\": 1754.27, \"Hospital Readmission Rate\": 0.165, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0207, \"Tests Per Capita Standardized Costs\": 131.39, \"Hospice Per Capita Actual Costs\": 226.32, \"E&M Actual Costs as % of Total Actual Costs\": 0.0773, \"PQI07 Hypertension Admission Rate (age < 65)\": 46.0, \"Percent African American\": 0.4, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0449, \"PAC: LTCH Per Capita Actual Costs\": 8.85, \"MA Beneficiaries\": 56078.0, \"FQHC/RHC Per User Standardized Costs\": 690.56, \"PAC: HH Per User Standardized Costs\": 4038.13, \"Procedures Per User Actual Costs\": 764.68, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 444.0, \"Ambulance Per User Actual Costs\": 1307.08, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1010.0, \"PAC: HH Standardized Costs\": 77096068.69, \"ASC Actual Costs as % of Total Actual Costs\": 0.0049, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 808.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0002, \"Percent Other/Unknown\": 2.03, \"% of Beneficiaries Using Hospice\": 0.0248, \"PAC: LTCH Per User Actual Costs\": 42721.33, \"Percent Non-Hispanic White\": 97.09, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 569.0, \"Count of Medicare beneficiaries with breast cancer\": 5670.0, \"DME Per Capita Standardized Costs\": 199.19, \"PQI08 CHF Admission Rate (age 65-74)\": 592.0, \"% of Beneficiaries Using DME\": 0.2657, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0129, \"% of Beneficiaries Using IP\": 0.159, \"DME Standardized Costs\": 45192817.17, \"FQHC/RHC Standardized Costs\": 35550231.02, \"PAC: SNF Per Capita Actual Costs\": 630.51, \"Imaging Standardized Costs\": 25663164.99, \"# E&M Users\": 190807.0, \"Count of Medicare beneficiaries with arthritis\": 54014.0, \"IP Per User Standardized Costs\": 13166.38, \"IP Per User Actual Costs\": 16244.86, \"PQI10 Dehydration Admission Rate (age 75+)\": 314.0, \"PAC: IRF Per User Standardized Costs\": 19037.56, \"DME Per User Actual Costs\": 717.67, \"PAC: IRF Actual Costs\": 41050840.91, \"Imaging Events Per 1000 Beneficiaries\": 3093.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0137, \"PAC: LTCH Per User Standardized Costs\": 40048.46, \"OP Actual Costs\": 398022479.04, \"Count of Medicare beneficiaries with hypertension\": 111011.0, \"ASC Per User Standardized Costs\": 882.58, \"Part B Drugs Per Capita Actual Costs\": 220.97, \"Ambulance Events Per 1000 Beneficiaries\": 365.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 327.0, \"% of Beneficiaries Using PAC: HH\": 0.1146, \"PAC: LTCH Standardized Costs\": 191502596.88, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.96, \"E&M Per Capita Standardized Costs\": 1027.98, \"E&M Per User Standardized Costs\": 1155.85, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1295.0, \"IP Covered Days Per 1000 Beneficiaries\": 1704.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 41.0, \"Count of Medicare beneficiaries with lung cancer\": 14198.0, \"IP Actual Costs as % of Total Actual Costs\": 0.361, \"Percent Eligible for Medicaid\": 21.11, \"Imaging Per Capita Standardized Costs\": 206.37, \"% of Beneficiaries Using Tests\": 0.7382, \"Imaging Per Capita Actual Costs\": 202.57, \"% of Beneficiaries Using PAC: SNF\": 0.0511, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0287, \"Count of Medicare beneficiaries with colorectal cancer\": 15812.0, \"Hospice Actual Costs\": 380792531.68, \"# PAC: HH Users\": 145081.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23568.84, \"Total Actual Costs\": 12679195275.02, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 138240.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0069, \"ASC Standardized Costs\": 83159681.5, \"DME Events Per 1000 Beneficiaries\": 1953.0, \"PQI08 CHF Admission Rate (age < 65)\": 854.0, \"ASC Events Per 1000 Beneficiaries\": 117.0, \"PAC: LTCH Actual Costs\": 179820667.73, \"Count of Medicare beneficiaries with depression\": 223912.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1394.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.92, \"Outpatient Dialysis Facility Per User Actual Costs\": 23275.6, \"Beneficiaries with Part A and Part B\": 1799696.0, \"% of Beneficiaries Using Part B Drugs\": 0.5055, \"Percent of Medicare beneficiaries with diabetes\": 29.8, \"% of Beneficiaries Using PAC: IRF\": 0.0077, \"E&M Per Capita Actual Costs\": 991.72, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0216, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0303, \"PAC: SNF Actual Costs\": 931793267.69, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 527.0, \"Percent Female\": 54.93, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 488.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.32, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 237.11, \"# Outpatient Dialysis Facility Users\": 12735.0, \"FQHC/RHC Per User Actual Costs\": 396.61, \"Count of Medicare beneficiaries with ischemic heart disease\": 418266.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 217.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 1.01, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 436.0, \"Percent of Medicare beneficiaries with depression\": 17.69, \"Emergency Department Visits per 1000 Beneficiaries\": 750.0, \"IP Actual Costs\": 4576932704.98, \"% of Beneficiaries Using OP\": 0.7422, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0146, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1077, \"Count of Medicare beneficiaries with stroke\": 51453.0, \"PQI12 UTI Admission Rate (age 75+)\": 1163.0, \"# OP Users\": 939504.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 31.0, \"# Procedure Users\": 790267.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 33.04, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0641, \"Count of Medicare beneficiaries with diabetes\": 377211.0, \"ASC Per User Actual Costs\": 843.42, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1192.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0239, \"Percent of Medicare beneficiaries with high cholesterol\": 45.26, \"Standardized Per Capita Costs\": 9544.79, \"Ambulance Actual Costs\": 165511290.99, \"FQHC/RHC Per Capita Actual Costs\": 43.12, \"Part B Drugs Per User Standardized Costs\": 572.03, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0154, \"# PAC: SNF Users (with a covered stay)\": 64642.0, \"Ambulance Per Capita Actual Costs\": 130.75, \"PQI12 UTI Admission Rate (age 65-74)\": 300.0, \"Hospice Standardized Costs\": 392299455.13, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 234.16, \"% of Beneficiaries Using E&M\": 0.8894, \"PQI10 Dehydration Admission Rate (age 65-74)\": 213.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 232.0, \"Tests Per Capita Actual Costs\": 194.16, \"# DME Users\": 393329.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0829, \"PAC: SNF Per User Actual Costs\": 14414.67, \"State\": \"MI\", \"OP Per User Actual Costs\": 1846.77, \"PAC: HH Episodes Per 1000 Beneficiaries\": 202.0, \"Part B Drugs Actual Costs\": 364040419.1, \"FQHC/RHC Actual Costs\": 54586241.04, \"OP Standardized Costs\": 1742007622.8, \"DME Per User Standardized Costs\": 734.88, \"OP Actual Costs as % of Total Actual Costs\": 0.1368, \"PAC: SNF Per Capita Standardized Costs\": 791.68, \"% of Beneficiaries Using Ambulance\": 0.1078, \"Hospice Per User Standardized Costs\": 10314.98, \"# Imaging Users\": 891727.0, \"Part B Drugs Per User Actual Costs\": 568.86, \"Total Standardized Costs\": 12082545912.4, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.25, \"Count of Medicare beneficiaries with chronic kidney disease\": 221613.0, \"E&M Standardized Costs\": 1301293434.33, \"Percent Hispanic\": 1.78, \"ASC Per Capita Actual Costs\": 62.63, \"Count of Medicare beneficiaries who have had a heart attack\": 12773.0, \"Tests Actual Costs\": 245785140.45, \"# PAC: LTCH Users (with a covered stay)\": 4416.0, \"% of Beneficiaries Using Procedures\": 0.6243, \"PAC: HH Actual Costs\": 680741661.43, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 48.0, \"PAC: LTCH Per Capita Standardized Costs\": 151.28, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0208, \"Emergency Department Visits\": 948911.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1087, \"Procedures Actual Costs\": 775052948.96, \"# FQHC/RHC Users\": 137632.0, \"Number of Acute Hospital Readmissions\": 76179.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 104.0, \"Outpatient Dialysis Facility Standardized Costs\": 300149163.81, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0149, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1442, \"Ambulance Per User Standardized Costs\": 1293.1, \"Imaging Per User Standardized Costs\": 292.96, \"Percent of Medicare beneficiaries with asthma\": 6.02, \"Part B Drugs Standardized Costs\": 366065355.04, \"FFS Beneficiaries\": 1265878.0, \"# Hospice Users (with a covered stay)\": 38032.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0101, \"Count of Medicare beneficiaries with osteoporosis\": 67287.0, \"PQI08 CHF Admission Rate (age 75+)\": 2176.0, \"PAC: IRF Per Capita Standardized Costs\": 147.17, \"Procedures Standardized Costs\": 774837553.28, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3086, \"IP Per Capita Actual Costs\": 3615.62, \"DME Actual Costs\": 269915301.12, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0537, \"Count of Medicare beneficiaries with prostate cancer\": 40366.0, \"PAC: HH Per Capita Standardized Costs\": 572.18, \"Count of Medicare beneficiaries with heart failure\": 214364.0, \"Tests Per User Standardized Costs\": 268.97, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0142, \"Percent of Medicare beneficiaries with prostate cancer\": 3.19, \"PAC: IRF Per Capita Actual Costs\": 149.5, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 287.57, \"Percent of Medicare beneficiaries with breast cancer\": 2.74, \"Procedures Per User Standardized Costs\": 980.48, \"Percent of Medicare beneficiaries with chronic kidney disease\": 17.51, \"PAC: HH Per User Actual Costs\": 4692.15, \"Count of Medicare beneficiaries with high cholesterol\": 572920.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0735, \"Hospice Per Capita Standardized Costs\": 309.9, \"# Part B Drugs Users\": 639942.0, \"Average HCC Score\": 1.0574, \"Standardized Risk-Adjusted Per Capita Costs\": 9615.93, \"# PAC: IRF Users (with a covered stay)\": 9774.0, \"Ambulance Standardized Costs\": 176435506.11, \"Hospice Actual Costs as % of Total Actual Costs\": 0.03, \"Percent of Medicare beneficiaries with heart failure\": 16.93, \"Tests Actual Costs as % of Total Actual Costs\": 0.0194, \"FQHC/RHC Per Capita Standardized Costs\": 46.42, \"PQI07 Hypertension Admission Rate (age 65-74)\": 103.0, \"Test Events Per 1000 Beneficiaries\": 7089.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 103.0, \"ASC Actual Costs\": 79287817.9, \"Part B Drugs Per Capita Standardized Costs\": 289.18, \"Imaging Actual Costs\": 256433292.94, \"Tests Per User Actual Costs\": 263.03, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0131, \"Hospice Per User Actual Costs\": 10012.42, \"Tests Standardized Costs\": 251340020.79, \"IP Standardized Costs\": 3728268885.67, \"IP Per Capita Standardized Costs\": 2945.2, \"Outpatient Dialysis Facility Actual Costs\": 296414794.61, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 71.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1914.0, \"Percent of Medicare beneficiaries with hypertension\": 57.32, \"IP Covered Stays Per 1000 Beneficiaries\": 326.0, \"# Ambulance Users\": 136443.0, \"# ASC Users\": 94007.0, \"ASC Per Capita Standardized Costs\": 65.69, \"Procedures Per Capita Actual Costs\": 612.27, \"Procedures Per Capita Standardized Costs\": 612.1, \"IP Users (with a covered stay)\": 248454.0, \"Total Standardized Risk-Adjusted Costs\": 12172599366.67, \"Actual Per Capita Costs\": 10016.13, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0158, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 8.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 4.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.12, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 14.06, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 276.0, \"OP Per User Standardized Costs\": 1854.18, \"Count of Medicare beneficiaries with atrial fibrillation\": 100759.0, \"Procedure Events Per 1000 Beneficiaries\": 4664.0, \"Percent of Medicare beneficiaries with stroke\": 4.06, \"PAC: IRF Per User Actual Costs\": 19363.01, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 177945.0, \"% of Beneficiaries Using ASC\": 0.0743, \"PAC: IRF Standardized Costs\": 186305457.25, \"Hospice Covered Days Per 1000 Beneficiaries\": 1950.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 282.0, \"PAC: SNF Per User Standardized Costs\": 15503.34, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0043, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 115.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0325, \"MA Participation Rate\": 29.66, \"OP Per Capita Standardized Costs\": 1376.13, \"Percent of Medicare beneficiaries with arthritis\": 31.92, \"Ambulance Per Capita Standardized Costs\": 139.38, \"PAC: HH Visits Per 1000 Beneficiaries\": 3009.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0611, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0202, \"PAC: HH Per Capita Actual Costs\": 537.76, \"E&M Events Per 1000 Beneficiaries\": 14098.0, \"Count of Medicare beneficiaries with asthma\": 76168.0, \"# Test Users\": 934442.0, \"E&M Actual Costs\": 1255394719.04, \"% of Beneficiaries Using Imaging\": 0.7044, \"PAC: SNF Standardized Costs\": 1002167071.57, \"DME Per Capita Actual Costs\": 213.22, \"E&M Per User Actual Costs\": 1115.09, \"Percent Male\": 45.07, \"OP Visits Per 1000 Beneficiaries\": 5505.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0213, \"OP Per Capita Actual Costs\": 1370.63, \"Hospital Readmission Rate\": 0.1922, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0049, \"Tests Per Capita Standardized Costs\": 198.55, \"Hospice Per Capita Actual Costs\": 300.81, \"E&M Actual Costs as % of Total Actual Costs\": 0.099, \"PQI07 Hypertension Admission Rate (age < 65)\": 171.0, \"Percent African American\": 12.74, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0599, \"PAC: LTCH Per Capita Actual Costs\": 142.05, \"MA Beneficiaries\": 533818.0, \"FQHC/RHC Per User Standardized Costs\": 426.96, \"PAC: HH Per User Standardized Costs\": 4992.49, \"Procedures Per User Actual Costs\": 980.75, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 705.0, \"Ambulance Per User Actual Costs\": 1213.04, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1492.0, \"PAC: HH Standardized Costs\": 724315944.71, \"ASC Actual Costs as % of Total Actual Costs\": 0.0063, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 923.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0035, \"Percent Other/Unknown\": 2.83, \"% of Beneficiaries Using Hospice\": 0.03, \"PAC: LTCH Per User Actual Costs\": 40720.26, \"Percent Non-Hispanic White\": 82.65, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 577.0, \"Count of Medicare beneficiaries with breast cancer\": 34669.0, \"DME Per Capita Standardized Costs\": 228.34, \"PQI08 CHF Admission Rate (age 65-74)\": 797.0, \"% of Beneficiaries Using DME\": 0.3107, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0234, \"% of Beneficiaries Using IP\": 0.1963, \"DME Standardized Costs\": 289049097.86, \"FQHC/RHC Standardized Costs\": 58762896.94, \"PAC: SNF Per Capita Actual Costs\": 736.08, \"Imaging Standardized Costs\": 261243827.41, \"# E&M Users\": 1125828.0, \"Count of Medicare beneficiaries with arthritis\": 404094.0, \"IP Per User Standardized Costs\": 15005.87, \"IP Per User Actual Costs\": 18421.65, \"PQI10 Dehydration Admission Rate (age 75+)\": 521.0, \"PAC: IRF Per User Standardized Costs\": 19061.33, \"DME Per User Actual Costs\": 686.23, \"PAC: IRF Actual Costs\": 189254088.55, \"Imaging Events Per 1000 Beneficiaries\": 4331.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0248, \"PAC: LTCH Per User Standardized Costs\": 43365.62, \"OP Actual Costs\": 1735047139.92, \"Count of Medicare beneficiaries with hypertension\": 725644.0, \"ASC Per User Standardized Costs\": 884.61, \"Part B Drugs Per Capita Actual Costs\": 287.58, \"Ambulance Events Per 1000 Beneficiaries\": 400.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 279.0, \"% of Beneficiaries Using PAC: HH\": 0.0526, \"PAC: LTCH Standardized Costs\": 23397843.92, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.42, \"E&M Per Capita Standardized Costs\": 698.26, \"E&M Per User Standardized Costs\": 826.21, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1068.0, \"IP Covered Days Per 1000 Beneficiaries\": 1327.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 30.0, \"Count of Medicare beneficiaries with lung cancer\": 3524.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3807, \"Percent Eligible for Medicaid\": 22.5, \"Imaging Per Capita Standardized Costs\": 153.15, \"% of Beneficiaries Using Tests\": 0.6915, \"Imaging Per Capita Actual Costs\": 146.62, \"% of Beneficiaries Using PAC: SNF\": 0.0536, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0349, \"Count of Medicare beneficiaries with colorectal cancer\": 4368.0, \"Hospice Actual Costs\": 94267823.42, \"# PAC: HH Users\": 20558.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23588.34, \"Total Actual Costs\": 3215135645.9, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 32027.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0073, \"ASC Standardized Costs\": 21484024.57, \"DME Events Per 1000 Beneficiaries\": 1731.0, \"PQI08 CHF Admission Rate (age < 65)\": 549.0, \"ASC Events Per 1000 Beneficiaries\": 91.0, \"PAC: LTCH Actual Costs\": 23945592.1, \"Count of Medicare beneficiaries with depression\": 72894.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1373.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.2, \"Outpatient Dialysis Facility Per User Actual Costs\": 24585.6, \"Beneficiaries with Part A and Part B\": 841129.0, \"% of Beneficiaries Using Part B Drugs\": 0.4574, \"Percent of Medicare beneficiaries with diabetes\": 20.39, \"% of Beneficiaries Using PAC: IRF\": 0.0039, \"E&M Per Capita Actual Costs\": 633.45, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0203, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0383, \"PAC: SNF Actual Costs\": 270812354.99, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 417.0, \"Percent Female\": 53.01, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 214.0, \"Percent of Medicare beneficiaries with osteoporosis\": 4.58, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 192.59, \"# Outpatient Dialysis Facility Users\": 3189.0, \"FQHC/RHC Per User Actual Costs\": 519.3, \"Count of Medicare beneficiaries with ischemic heart disease\": 76406.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 145.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.79, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 344.0, \"Percent of Medicare beneficiaries with depression\": 18.66, \"Emergency Department Visits per 1000 Beneficiaries\": 652.0, \"IP Actual Costs\": 1223982458.18, \"% of Beneficiaries Using OP\": 0.6536, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0082, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0925, \"Count of Medicare beneficiaries with stroke\": 10100.0, \"PQI12 UTI Admission Rate (age 75+)\": 730.0, \"# OP Users\": 255289.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 26.0, \"# Procedure Users\": 206672.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 19.56, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0604, \"Count of Medicare beneficiaries with diabetes\": 79649.0, \"ASC Per User Actual Costs\": 969.86, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 764.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0267, \"Percent of Medicare beneficiaries with high cholesterol\": 30.64, \"Standardized Per Capita Costs\": 7548.12, \"Ambulance Actual Costs\": 27828901.7, \"FQHC/RHC Per Capita Actual Costs\": 40.46, \"Part B Drugs Per User Standardized Costs\": 631.35, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0095, \"# PAC: SNF Users (with a covered stay)\": 20927.0, \"Ambulance Per Capita Actual Costs\": 71.25, \"PQI12 UTI Admission Rate (age 65-74)\": 154.0, \"Hospice Standardized Costs\": 90335813.74, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 200.73, \"% of Beneficiaries Using E&M\": 0.8451, \"PQI10 Dehydration Admission Rate (age 65-74)\": 148.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 157.0, \"Tests Per Capita Actual Costs\": 128.46, \"# DME Users\": 104973.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0917, \"PAC: SNF Per User Actual Costs\": 12940.81, \"State\": \"MN\", \"OP Per User Actual Costs\": 2269.33, \"PAC: HH Episodes Per 1000 Beneficiaries\": 78.0, \"Part B Drugs Actual Costs\": 112086323.65, \"FQHC/RHC Actual Costs\": 15804250.95, \"OP Standardized Costs\": 531610784.56, \"DME Per User Standardized Costs\": 749.94, \"OP Actual Costs as % of Total Actual Costs\": 0.1802, \"PAC: SNF Per Capita Standardized Costs\": 692.09, \"% of Beneficiaries Using Ambulance\": 0.0804, \"Hospice Per User Standardized Costs\": 9338.97, \"# Imaging Users\": 244428.0, \"Part B Drugs Per User Actual Costs\": 627.33, \"Total Standardized Costs\": 2948251994.95, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.12, \"Count of Medicare beneficiaries with chronic kidney disease\": 57606.0, \"E&M Standardized Costs\": 272736929.7, \"Percent Hispanic\": 1.21, \"ASC Per Capita Actual Costs\": 55.69, \"Count of Medicare beneficiaries who have had a heart attack\": 3099.0, \"Tests Actual Costs\": 50175247.09, \"# PAC: LTCH Users (with a covered stay)\": 528.0, \"% of Beneficiaries Using Procedures\": 0.5291, \"PAC: HH Actual Costs\": 82874154.44, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 32.0, \"PAC: LTCH Per Capita Standardized Costs\": 59.9, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0176, \"Emergency Department Visits\": 254635.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0779, \"Procedures Actual Costs\": 165758968.03, \"# FQHC/RHC Users\": 30434.0, \"Number of Acute Hospital Readmissions\": 17275.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 50.0, \"Outpatient Dialysis Facility Standardized Costs\": 75223219.08, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0094, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1803, \"Ambulance Per User Standardized Costs\": 771.66, \"Imaging Per User Standardized Costs\": 244.73, \"Percent of Medicare beneficiaries with asthma\": 4.17, \"Part B Drugs Standardized Costs\": 112804865.82, \"FFS Beneficiaries\": 390594.0, \"# Hospice Users (with a covered stay)\": 9673.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0082, \"Count of Medicare beneficiaries with osteoporosis\": 17894.0, \"PQI08 CHF Admission Rate (age 75+)\": 1858.0, \"PAC: IRF Per Capita Standardized Costs\": 71.52, \"Procedures Standardized Costs\": 178011829.43, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3235, \"IP Per Capita Actual Costs\": 3133.64, \"DME Actual Costs\": 73121745.14, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0258, \"Count of Medicare beneficiaries with prostate cancer\": 10800.0, \"PAC: HH Per Capita Standardized Costs\": 203.78, \"Count of Medicare beneficiaries with heart failure\": 41180.0, \"Tests Per User Standardized Costs\": 192.33, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0074, \"Percent of Medicare beneficiaries with prostate cancer\": 2.77, \"PAC: IRF Per Capita Actual Costs\": 77.43, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 234.3, \"Percent of Medicare beneficiaries with breast cancer\": 2.44, \"Procedures Per User Standardized Costs\": 861.33, \"Percent of Medicare beneficiaries with chronic kidney disease\": 14.75, \"PAC: HH Per User Actual Costs\": 4031.24, \"Count of Medicare beneficiaries with high cholesterol\": 119686.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0842, \"Hospice Per Capita Standardized Costs\": 231.28, \"# Part B Drugs Users\": 178672.0, \"Average HCC Score\": 0.9274, \"Standardized Risk-Adjusted Per Capita Costs\": 8568.72, \"# PAC: IRF Users (with a covered stay)\": 1510.0, \"Ambulance Standardized Costs\": 24242656.61, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0293, \"Percent of Medicare beneficiaries with heart failure\": 10.54, \"Tests Actual Costs as % of Total Actual Costs\": 0.0156, \"FQHC/RHC Per Capita Standardized Costs\": 42.4, \"PQI07 Hypertension Admission Rate (age 65-74)\": 56.0, \"Test Events Per 1000 Beneficiaries\": 6744.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 40.0, \"ASC Actual Costs\": 21752893.88, \"Part B Drugs Per Capita Standardized Costs\": 288.8, \"Imaging Actual Costs\": 57269137.04, \"Tests Per User Actual Costs\": 185.77, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0087, \"Hospice Per User Actual Costs\": 9745.46, \"Tests Standardized Costs\": 51946770.85, \"IP Standardized Costs\": 953629367.12, \"IP Per Capita Standardized Costs\": 2441.48, \"Outpatient Dialysis Facility Actual Costs\": 78403492.41, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 71.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1533.0, \"Percent of Medicare beneficiaries with hypertension\": 41.51, \"IP Covered Stays Per 1000 Beneficiaries\": 266.0, \"# Ambulance Users\": 31416.0, \"# ASC Users\": 22429.0, \"ASC Per Capita Standardized Costs\": 55.0, \"Procedures Per Capita Actual Costs\": 424.38, \"Procedures Per Capita Standardized Costs\": 455.75, \"IP Users (with a covered stay)\": 66633.0, \"Total Standardized Risk-Adjusted Costs\": 3346891614.74, \"Actual Per Capita Costs\": 8231.4, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0079, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 4.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.9, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 7.27, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 143.0, \"OP Per User Standardized Costs\": 2082.39, \"Count of Medicare beneficiaries with atrial fibrillation\": 28984.0, \"Procedure Events Per 1000 Beneficiaries\": 3231.0, \"Percent of Medicare beneficiaries with stroke\": 2.59, \"PAC: IRF Per User Actual Costs\": 20028.62, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 28405.0, \"% of Beneficiaries Using ASC\": 0.0574, \"PAC: IRF Standardized Costs\": 27935818.33, \"Hospice Covered Days Per 1000 Beneficiaries\": 1502.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 276.0, \"PAC: SNF Per User Standardized Costs\": 12917.55, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0049, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 76.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0306, \"MA Participation Rate\": 53.56, \"OP Per Capita Standardized Costs\": 1361.03, \"Percent of Medicare beneficiaries with arthritis\": 22.1, \"Ambulance Per Capita Standardized Costs\": 62.07, \"PAC: HH Visits Per 1000 Beneficiaries\": 1188.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0516, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0178, \"PAC: HH Per Capita Actual Costs\": 212.17, \"E&M Events Per 1000 Beneficiaries\": 10098.0, \"Count of Medicare beneficiaries with asthma\": 16307.0, \"# Test Users\": 270098.0, \"E&M Actual Costs\": 247422448.54, \"% of Beneficiaries Using Imaging\": 0.6258, \"PAC: SNF Standardized Costs\": 270325599.04, \"DME Per Capita Actual Costs\": 187.21, \"E&M Per User Actual Costs\": 749.53, \"Percent Male\": 46.99, \"OP Visits Per 1000 Beneficiaries\": 5310.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0227, \"OP Per Capita Actual Costs\": 1483.21, \"Hospital Readmission Rate\": 0.1712, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0056, \"Tests Per Capita Standardized Costs\": 132.99, \"Hospice Per Capita Actual Costs\": 241.34, \"E&M Actual Costs as % of Total Actual Costs\": 0.077, \"PQI07 Hypertension Admission Rate (age < 65)\": 82.0, \"Percent African American\": 3.72, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.027, \"PAC: LTCH Per Capita Actual Costs\": 61.31, \"MA Beneficiaries\": 450535.0, \"FQHC/RHC Per User Standardized Costs\": 544.17, \"PAC: HH Per User Standardized Costs\": 3871.81, \"Procedures Per User Actual Costs\": 802.04, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 475.0, \"Ambulance Per User Actual Costs\": 885.81, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 964.0, \"PAC: HH Standardized Costs\": 79596698.62, \"ASC Actual Costs as % of Total Actual Costs\": 0.0068, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 477.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0014, \"Percent Other/Unknown\": 4.11, \"% of Beneficiaries Using Hospice\": 0.0248, \"PAC: LTCH Per User Actual Costs\": 45351.5, \"Percent Non-Hispanic White\": 90.95, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 558.0, \"Count of Medicare beneficiaries with breast cancer\": 9516.0, \"DME Per Capita Standardized Costs\": 201.55, \"PQI08 CHF Admission Rate (age 65-74)\": 486.0, \"% of Beneficiaries Using DME\": 0.2688, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0244, \"% of Beneficiaries Using IP\": 0.1706, \"DME Standardized Costs\": 78723832.62, \"FQHC/RHC Standardized Costs\": 16561329.61, \"PAC: SNF Per Capita Actual Costs\": 693.33, \"Imaging Standardized Costs\": 59818618.21, \"# E&M Users\": 330105.0, \"Count of Medicare beneficiaries with arthritis\": 86315.0, \"IP Per User Standardized Costs\": 14311.67, \"IP Per User Actual Costs\": 18369.01, \"PQI10 Dehydration Admission Rate (age 75+)\": 386.0, \"PAC: IRF Per User Standardized Costs\": 18500.54, \"DME Per User Actual Costs\": 696.58, \"PAC: IRF Actual Costs\": 30243210.66, \"Imaging Events Per 1000 Beneficiaries\": 3318.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0255, \"PAC: LTCH Per User Standardized Costs\": 44314.1, \"OP Actual Costs\": 579334240.6, \"Count of Medicare beneficiaries with hypertension\": 162138.0, \"ASC Per User Standardized Costs\": 957.87, \"Part B Drugs Per Capita Actual Costs\": 286.96, \"Ambulance Events Per 1000 Beneficiaries\": 164.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 333.0, \"% of Beneficiaries Using PAC: HH\": 0.0806, \"PAC: LTCH Standardized Costs\": 87339651.9, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.96, \"E&M Per Capita Standardized Costs\": 788.65, \"E&M Per User Standardized Costs\": 912.23, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1219.0, \"IP Covered Days Per 1000 Beneficiaries\": 1530.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 41.0, \"Count of Medicare beneficiaries with lung cancer\": 8862.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3421, \"Percent Eligible for Medicaid\": 19.37, \"Imaging Per Capita Standardized Costs\": 170.86, \"% of Beneficiaries Using Tests\": 0.7716, \"Imaging Per Capita Actual Costs\": 159.99, \"% of Beneficiaries Using PAC: SNF\": 0.0516, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0302, \"Count of Medicare beneficiaries with colorectal cancer\": 10336.0, \"Hospice Actual Costs\": 247111401.26, \"# PAC: HH Users\": 62358.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23442.62, \"Total Actual Costs\": 6726852034.66, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 79669.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0091, \"ASC Standardized Costs\": 62006933.6, \"DME Events Per 1000 Beneficiaries\": 1918.0, \"PQI08 CHF Admission Rate (age < 65)\": 923.0, \"ASC Events Per 1000 Beneficiaries\": 137.0, \"PAC: LTCH Actual Costs\": 78335699.61, \"Count of Medicare beneficiaries with depression\": 147542.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1849.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.29, \"Outpatient Dialysis Facility Per User Actual Costs\": 22871.6, \"Beneficiaries with Part A and Part B\": 1063352.0, \"% of Beneficiaries Using Part B Drugs\": 0.4723, \"Percent of Medicare beneficiaries with diabetes\": 26.01, \"% of Beneficiaries Using PAC: IRF\": 0.0094, \"E&M Per Capita Actual Costs\": 722.04, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0194, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.03, \"PAC: SNF Actual Costs\": 517510111.75, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 681.0, \"Percent Female\": 54.82, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 246.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.97, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 211.95, \"# Outpatient Dialysis Facility Users\": 6997.0, \"FQHC/RHC Per User Actual Costs\": 403.71, \"Count of Medicare beneficiaries with ischemic heart disease\": 213929.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 183.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.85, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 1209.0, \"Percent of Medicare beneficiaries with depression\": 19.07, \"Emergency Department Visits per 1000 Beneficiaries\": 685.0, \"IP Actual Costs\": 2301059737.45, \"% of Beneficiaries Using OP\": 0.6927, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0112, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0895, \"Count of Medicare beneficiaries with stroke\": 28285.0, \"PQI12 UTI Admission Rate (age 75+)\": 1012.0, \"# OP Users\": 536092.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 31.0, \"# Procedure Users\": 460280.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 27.64, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0621, \"Count of Medicare beneficiaries with diabetes\": 201312.0, \"ASC Per User Actual Costs\": 850.25, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 999.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0272, \"Percent of Medicare beneficiaries with high cholesterol\": 42.57, \"Standardized Per Capita Costs\": 8816.7, \"Ambulance Actual Costs\": 91311659.66, \"FQHC/RHC Per Capita Actual Costs\": 101.2, \"Part B Drugs Per User Standardized Costs\": 559.98, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0212, \"# PAC: SNF Users (with a covered stay)\": 39929.0, \"Ambulance Per Capita Actual Costs\": 117.99, \"PQI12 UTI Admission Rate (age 65-74)\": 260.0, \"Hospice Standardized Costs\": 263380974.33, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 206.79, \"% of Beneficiaries Using E&M\": 0.8645, \"PQI10 Dehydration Admission Rate (age 65-74)\": 211.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 194.0, \"Tests Per Capita Actual Costs\": 199.75, \"# DME Users\": 222556.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0861, \"PAC: SNF Per User Actual Costs\": 12960.76, \"State\": \"MO\", \"OP Per User Actual Costs\": 1973.3, \"PAC: HH Episodes Per 1000 Beneficiaries\": 128.0, \"Part B Drugs Actual Costs\": 203290681.3, \"FQHC/RHC Actual Costs\": 78314745.59, \"OP Standardized Costs\": 1083207078.67, \"DME Per User Standardized Costs\": 835.27, \"OP Actual Costs as % of Total Actual Costs\": 0.1573, \"PAC: SNF Per Capita Standardized Costs\": 758.87, \"% of Beneficiaries Using Ambulance\": 0.1068, \"Hospice Per User Standardized Costs\": 11499.85, \"# Imaging Users\": 526358.0, \"Part B Drugs Per User Actual Costs\": 556.14, \"Total Standardized Costs\": 6823136685.72, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.34, \"Count of Medicare beneficiaries with chronic kidney disease\": 123216.0, \"E&M Standardized Costs\": 610329676.91, \"Percent Hispanic\": 0.94, \"ASC Per Capita Actual Costs\": 74.2, \"Count of Medicare beneficiaries who have had a heart attack\": 6591.0, \"Tests Actual Costs\": 154583993.44, \"# PAC: LTCH Users (with a covered stay)\": 1893.0, \"% of Beneficiaries Using Procedures\": 0.5948, \"PAC: HH Actual Costs\": 247256732.56, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 42.0, \"PAC: LTCH Per Capita Standardized Costs\": 112.86, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0236, \"Emergency Department Visits\": 530305.0, \"% of Beneficiaries Using FQHC/RHC\": 0.2507, \"Procedures Actual Costs\": 399111440.45, \"# FQHC/RHC Users\": 193990.0, \"Number of Acute Hospital Readmissions\": 40138.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 135.0, \"Outpatient Dialysis Facility Standardized Costs\": 164028004.82, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0212, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1588, \"Ambulance Per User Standardized Costs\": 926.97, \"Imaging Per User Standardized Costs\": 251.2, \"Percent of Medicare beneficiaries with asthma\": 4.66, \"Part B Drugs Standardized Costs\": 204696208.44, \"FFS Beneficiaries\": 773888.0, \"# Hospice Users (with a covered stay)\": 22903.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.009, \"Count of Medicare beneficiaries with osteoporosis\": 46204.0, \"PQI08 CHF Admission Rate (age 75+)\": 2073.0, \"PAC: IRF Per Capita Standardized Costs\": 187.03, \"Procedures Standardized Costs\": 424014359.54, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3118, \"IP Per Capita Actual Costs\": 2973.38, \"DME Actual Costs\": 173927069.26, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0368, \"Count of Medicare beneficiaries with prostate cancer\": 22037.0, \"PAC: HH Per Capita Standardized Costs\": 353.58, \"Count of Medicare beneficiaries with heart failure\": 107511.0, \"Tests Per User Standardized Costs\": 269.63, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0116, \"Percent of Medicare beneficiaries with prostate cancer\": 2.85, \"PAC: IRF Per Capita Actual Costs\": 184.43, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 235.22, \"Percent of Medicare beneficiaries with breast cancer\": 2.93, \"Procedures Per User Standardized Costs\": 921.21, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.92, \"PAC: HH Per User Actual Costs\": 3965.12, \"Count of Medicare beneficiaries with high cholesterol\": 329441.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0769, \"Hospice Per Capita Standardized Costs\": 340.33, \"# Part B Drugs Users\": 365539.0, \"Average HCC Score\": 0.9954, \"Standardized Risk-Adjusted Per Capita Costs\": 9455.82, \"# PAC: IRF Users (with a covered stay)\": 7305.0, \"Ambulance Standardized Costs\": 76628782.09, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0367, \"Percent of Medicare beneficiaries with heart failure\": 13.89, \"Tests Actual Costs as % of Total Actual Costs\": 0.023, \"FQHC/RHC Per Capita Standardized Costs\": 116.85, \"PQI07 Hypertension Admission Rate (age 65-74)\": 79.0, \"Test Events Per 1000 Beneficiaries\": 8572.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 73.0, \"ASC Actual Costs\": 57419300.74, \"Part B Drugs Per Capita Standardized Costs\": 264.5, \"Imaging Actual Costs\": 123811593.09, \"Tests Per User Actual Costs\": 258.88, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0136, \"Hospice Per User Actual Costs\": 10789.48, \"Tests Standardized Costs\": 161002650.01, \"IP Standardized Costs\": 2127335477.22, \"IP Per Capita Standardized Costs\": 2748.89, \"Outpatient Dialysis Facility Actual Costs\": 160032596.55, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 72.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1856.0, \"Percent of Medicare beneficiaries with hypertension\": 54.98, \"IP Covered Stays Per 1000 Beneficiaries\": 301.0, \"# Ambulance Users\": 82666.0, \"# ASC Users\": 67532.0, \"ASC Per Capita Standardized Costs\": 80.12, \"Procedures Per Capita Actual Costs\": 515.72, \"Procedures Per Capita Standardized Costs\": 547.9, \"IP Users (with a covered stay)\": 144526.0, \"Total Standardized Risk-Adjusted Costs\": 7317748869.42, \"Actual Per Capita Costs\": 8692.28, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0128, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 10.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.15, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 13.1, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 213.0, \"OP Per User Standardized Costs\": 2020.56, \"Count of Medicare beneficiaries with atrial fibrillation\": 61629.0, \"Procedure Events Per 1000 Beneficiaries\": 3818.0, \"Percent of Medicare beneficiaries with stroke\": 3.65, \"PAC: IRF Per User Actual Costs\": 19538.48, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 101361.0, \"% of Beneficiaries Using ASC\": 0.0873, \"PAC: IRF Standardized Costs\": 144741738.13, \"Hospice Covered Days Per 1000 Beneficiaries\": 2194.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 343.0, \"PAC: SNF Per User Standardized Costs\": 14708.13, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0116, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 106.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0386, \"MA Participation Rate\": 27.22, \"OP Per Capita Standardized Costs\": 1399.69, \"Percent of Medicare beneficiaries with arthritis\": 31.26, \"Ambulance Per Capita Standardized Costs\": 99.02, \"PAC: HH Visits Per 1000 Beneficiaries\": 1901.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0593, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0184, \"PAC: HH Per Capita Actual Costs\": 319.5, \"E&M Events Per 1000 Beneficiaries\": 11305.0, \"Count of Medicare beneficiaries with asthma\": 36047.0, \"# Test Users\": 597133.0, \"E&M Actual Costs\": 558777735.53, \"% of Beneficiaries Using Imaging\": 0.6801, \"PAC: SNF Standardized Costs\": 587281035.93, \"DME Per Capita Actual Costs\": 224.74, \"E&M Per User Actual Costs\": 835.18, \"Percent Male\": 45.18, \"OP Visits Per 1000 Beneficiaries\": 4923.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0259, \"OP Per Capita Actual Costs\": 1366.96, \"Hospital Readmission Rate\": 0.1811, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0133, \"Tests Per Capita Standardized Costs\": 208.04, \"Hospice Per Capita Actual Costs\": 319.31, \"E&M Actual Costs as % of Total Actual Costs\": 0.0831, \"PQI07 Hypertension Admission Rate (age < 65)\": 142.0, \"Percent African American\": 7.83, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0401, \"PAC: LTCH Per Capita Actual Costs\": 101.22, \"MA Beneficiaries\": 289464.0, \"FQHC/RHC Per User Standardized Costs\": 466.15, \"PAC: HH Per User Standardized Costs\": 4388.06, \"Procedures Per User Actual Costs\": 867.11, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 623.0, \"Ambulance Per User Actual Costs\": 1104.59, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1314.0, \"PAC: HH Standardized Costs\": 273630446.08, \"ASC Actual Costs as % of Total Actual Costs\": 0.0085, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 769.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0024, \"Percent Other/Unknown\": 1.64, \"% of Beneficiaries Using Hospice\": 0.0296, \"PAC: LTCH Per User Actual Costs\": 41381.77, \"Percent Non-Hispanic White\": 89.58, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 891.0, \"Count of Medicare beneficiaries with breast cancer\": 22676.0, \"DME Per Capita Standardized Costs\": 240.21, \"PQI08 CHF Admission Rate (age 65-74)\": 728.0, \"% of Beneficiaries Using DME\": 0.2876, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0238, \"% of Beneficiaries Using IP\": 0.1868, \"DME Standardized Costs\": 185894985.5, \"FQHC/RHC Standardized Costs\": 90428716.99, \"PAC: SNF Per Capita Actual Costs\": 668.71, \"Imaging Standardized Costs\": 132223024.88, \"# E&M Users\": 669051.0, \"Count of Medicare beneficiaries with arthritis\": 241915.0, \"IP Per User Standardized Costs\": 14719.4, \"IP Per User Actual Costs\": 15921.42, \"PQI10 Dehydration Admission Rate (age 75+)\": 535.0, \"PAC: IRF Per User Standardized Costs\": 19814.06, \"DME Per User Actual Costs\": 781.5, \"PAC: IRF Actual Costs\": 142728623.87, \"Imaging Events Per 1000 Beneficiaries\": 4069.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.024, \"PAC: LTCH Per User Standardized Costs\": 46138.22, \"OP Actual Costs\": 1057870994.93, \"Count of Medicare beneficiaries with hypertension\": 425466.0, \"ASC Per User Standardized Costs\": 918.19, \"Part B Drugs Per Capita Actual Costs\": 262.69, \"Ambulance Events Per 1000 Beneficiaries\": 270.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 414.0, \"% of Beneficiaries Using PAC: HH\": 0.1184, \"PAC: LTCH Standardized Costs\": 158786910.7, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.63, \"E&M Per Capita Standardized Costs\": 807.76, \"E&M Per User Standardized Costs\": 914.2, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1967.0, \"IP Covered Days Per 1000 Beneficiaries\": 1785.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 93.0, \"Count of Medicare beneficiaries with lung cancer\": 4533.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3087, \"Percent Eligible for Medicaid\": 30.77, \"Imaging Per Capita Standardized Costs\": 210.3, \"% of Beneficiaries Using Tests\": 0.799, \"Imaging Per Capita Actual Costs\": 189.25, \"% of Beneficiaries Using PAC: SNF\": 0.0476, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0295, \"Count of Medicare beneficiaries with colorectal cancer\": 5370.0, \"Hospice Actual Costs\": 167032259.14, \"# PAC: HH Users\": 53632.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24415.03, \"Total Actual Costs\": 4240353882.3, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 46718.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0128, \"ASC Standardized Costs\": 57777166.31, \"DME Events Per 1000 Beneficiaries\": 2227.0, \"PQI08 CHF Admission Rate (age < 65)\": 1217.0, \"ASC Events Per 1000 Beneficiaries\": 269.0, \"PAC: LTCH Actual Costs\": 134148939.43, \"Count of Medicare beneficiaries with depression\": 67103.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2217.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.31, \"Outpatient Dialysis Facility Per User Actual Costs\": 22726.33, \"Beneficiaries with Part A and Part B\": 532635.0, \"% of Beneficiaries Using Part B Drugs\": 0.5306, \"Percent of Medicare beneficiaries with diabetes\": 29.13, \"% of Beneficiaries Using PAC: IRF\": 0.0067, \"E&M Per Capita Actual Costs\": 712.37, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0211, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.028, \"PAC: SNF Actual Costs\": 336456082.25, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 847.0, \"Percent Female\": 55.61, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 287.0, \"Percent of Medicare beneficiaries with osteoporosis\": 4.53, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 349.03, \"# Outpatient Dialysis Facility Users\": 6475.0, \"FQHC/RHC Per User Actual Costs\": 331.83, \"Count of Medicare beneficiaries with ischemic heart disease\": 125276.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 176.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.86, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 961.0, \"Percent of Medicare beneficiaries with depression\": 14.82, \"Emergency Department Visits per 1000 Beneficiaries\": 792.0, \"IP Actual Costs\": 1308945554.78, \"% of Beneficiaries Using OP\": 0.654, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0105, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.081, \"Count of Medicare beneficiaries with stroke\": 17388.0, \"PQI12 UTI Admission Rate (age 75+)\": 1660.0, \"# OP Users\": 296230.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 33.0, \"# Procedure Users\": 272533.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 27.66, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0538, \"Count of Medicare beneficiaries with diabetes\": 131936.0, \"ASC Per User Actual Costs\": 749.11, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1263.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0291, \"Percent of Medicare beneficiaries with high cholesterol\": 39.75, \"Standardized Per Capita Costs\": 9972.13, \"Ambulance Actual Costs\": 52053698.46, \"FQHC/RHC Per Capita Actual Costs\": 72.15, \"Part B Drugs Per User Standardized Costs\": 525.51, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0137, \"# PAC: SNF Users (with a covered stay)\": 21574.0, \"Ambulance Per Capita Actual Costs\": 114.93, \"PQI12 UTI Admission Rate (age 65-74)\": 397.0, \"Hospice Standardized Costs\": 190405972.99, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 324.89, \"% of Beneficiaries Using E&M\": 0.8836, \"PQI10 Dehydration Admission Rate (age 65-74)\": 304.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 237.0, \"Tests Per Capita Actual Costs\": 247.14, \"# DME Users\": 152315.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0906, \"PAC: SNF Per User Actual Costs\": 15595.44, \"State\": \"MS\", \"OP Per User Actual Costs\": 1912.05, \"PAC: HH Episodes Per 1000 Beneficiaries\": 307.0, \"Part B Drugs Actual Costs\": 125237523.4, \"FQHC/RHC Actual Costs\": 32678327.26, \"OP Standardized Costs\": 591942567.14, \"DME Per User Standardized Costs\": 861.92, \"OP Actual Costs as % of Total Actual Costs\": 0.1336, \"PAC: SNF Per Capita Standardized Costs\": 903.26, \"% of Beneficiaries Using Ambulance\": 0.0957, \"Hospice Per User Standardized Costs\": 14005.59, \"# Imaging Users\": 312792.0, \"Part B Drugs Per User Actual Costs\": 521.15, \"Total Standardized Costs\": 4516724604.93, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.19, \"Count of Medicare beneficiaries with chronic kidney disease\": 66508.0, \"E&M Standardized Costs\": 365862334.79, \"Percent Hispanic\": 0.54, \"ASC Per Capita Actual Costs\": 111.22, \"Count of Medicare beneficiaries who have had a heart attack\": 3878.0, \"Tests Actual Costs\": 111939638.45, \"# PAC: LTCH Users (with a covered stay)\": 3314.0, \"% of Beneficiaries Using Procedures\": 0.6017, \"PAC: HH Actual Costs\": 310899059.35, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 75.0, \"PAC: LTCH Per Capita Standardized Costs\": 350.57, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0263, \"Emergency Department Visits\": 358729.0, \"% of Beneficiaries Using FQHC/RHC\": 0.2174, \"Procedures Actual Costs\": 218580314.51, \"# FQHC/RHC Users\": 98479.0, \"Number of Acute Hospital Readmissions\": 24215.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 96.0, \"Outpatient Dialysis Facility Standardized Costs\": 158087301.56, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0136, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1311, \"Ambulance Per User Standardized Costs\": 1090.89, \"Imaging Per User Standardized Costs\": 304.52, \"Percent of Medicare beneficiaries with asthma\": 4.14, \"Part B Drugs Standardized Costs\": 126286564.39, \"FFS Beneficiaries\": 452935.0, \"# Hospice Users (with a covered stay)\": 13595.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0143, \"Count of Medicare beneficiaries with osteoporosis\": 20523.0, \"PQI08 CHF Admission Rate (age 75+)\": 2256.0, \"PAC: IRF Per Capita Standardized Costs\": 136.13, \"Procedures Standardized Costs\": 242878481.92, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2788, \"IP Per Capita Actual Costs\": 2889.92, \"DME Actual Costs\": 125441454.36, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0733, \"Count of Medicare beneficiaries with prostate cancer\": 11606.0, \"PAC: HH Per Capita Standardized Costs\": 822.34, \"Count of Medicare beneficiaries with heart failure\": 70922.0, \"Tests Per User Standardized Costs\": 327.86, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0316, \"Percent of Medicare beneficiaries with prostate cancer\": 2.56, \"PAC: IRF Per Capita Actual Costs\": 127.07, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 274.04, \"Percent of Medicare beneficiaries with breast cancer\": 2.38, \"Procedures Per User Standardized Costs\": 891.19, \"Percent of Medicare beneficiaries with chronic kidney disease\": 14.68, \"PAC: HH Per User Actual Costs\": 5796.89, \"Count of Medicare beneficiaries with high cholesterol\": 180062.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0793, \"Hospice Per Capita Standardized Costs\": 420.38, \"# Part B Drugs Users\": 240312.0, \"Average HCC Score\": 0.9963, \"Standardized Risk-Adjusted Per Capita Costs\": 10455.15, \"# PAC: IRF Users (with a covered stay)\": 3036.0, \"Ambulance Standardized Costs\": 47306840.13, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0394, \"Percent of Medicare beneficiaries with heart failure\": 15.66, \"Tests Actual Costs as % of Total Actual Costs\": 0.0264, \"FQHC/RHC Per Capita Standardized Costs\": 85.27, \"PQI07 Hypertension Admission Rate (age 65-74)\": 110.0, \"Test Events Per 1000 Beneficiaries\": 9584.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 219.0, \"ASC Actual Costs\": 50374950.37, \"Part B Drugs Per Capita Standardized Costs\": 278.82, \"Imaging Actual Costs\": 85715956.69, \"Tests Per User Actual Costs\": 309.3, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0123, \"Hospice Per User Actual Costs\": 12286.3, \"Tests Standardized Costs\": 118657924.09, \"IP Standardized Costs\": 1259363856.35, \"IP Per Capita Standardized Costs\": 2780.45, \"Outpatient Dialysis Facility Actual Costs\": 147152997.26, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 67.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1959.0, \"Percent of Medicare beneficiaries with hypertension\": 61.26, \"IP Covered Stays Per 1000 Beneficiaries\": 312.0, \"# Ambulance Users\": 43365.0, \"# ASC Users\": 67246.0, \"ASC Per Capita Standardized Costs\": 127.56, \"Procedures Per Capita Actual Costs\": 482.59, \"Procedures Per Capita Standardized Costs\": 536.23, \"IP Users (with a covered stay)\": 85687.0, \"Total Standardized Risk-Adjusted Costs\": 4735503638.45, \"Actual Per Capita Costs\": 9361.95, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0352, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 7.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 8.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.0, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 12.06, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 305.0, \"OP Per User Standardized Costs\": 1998.25, \"Count of Medicare beneficiaries with atrial fibrillation\": 30030.0, \"Procedure Events Per 1000 Beneficiaries\": 3886.0, \"Percent of Medicare beneficiaries with stroke\": 3.84, \"PAC: IRF Per User Actual Costs\": 18957.49, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 54633.0, \"% of Beneficiaries Using ASC\": 0.1485, \"PAC: IRF Standardized Costs\": 61656911.12, \"Hospice Covered Days Per 1000 Beneficiaries\": 2632.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 355.0, \"PAC: SNF Per User Standardized Costs\": 18963.53, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0077, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 174.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0422, \"MA Participation Rate\": 14.96, \"OP Per Capita Standardized Costs\": 1306.9, \"Percent of Medicare beneficiaries with arthritis\": 31.47, \"Ambulance Per Capita Standardized Costs\": 104.45, \"PAC: HH Visits Per 1000 Beneficiaries\": 4603.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0515, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0202, \"PAC: HH Per Capita Actual Costs\": 686.41, \"E&M Events Per 1000 Beneficiaries\": 11718.0, \"Count of Medicare beneficiaries with asthma\": 18773.0, \"# Test Users\": 361915.0, \"E&M Actual Costs\": 322658536.91, \"% of Beneficiaries Using Imaging\": 0.6906, \"PAC: SNF Standardized Costs\": 409119219.69, \"DME Per Capita Actual Costs\": 276.95, \"E&M Per User Actual Costs\": 806.24, \"Percent Male\": 44.39, \"OP Visits Per 1000 Beneficiaries\": 4308.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0296, \"OP Per Capita Actual Costs\": 1250.52, \"Hospital Readmission Rate\": 0.1829, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0086, \"Tests Per Capita Standardized Costs\": 261.98, \"Hospice Per Capita Actual Costs\": 368.78, \"E&M Actual Costs as % of Total Actual Costs\": 0.0761, \"PQI07 Hypertension Admission Rate (age < 65)\": 163.0, \"Percent African American\": 27.65, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0825, \"PAC: LTCH Per Capita Actual Costs\": 296.18, \"MA Beneficiaries\": 79700.0, \"FQHC/RHC Per User Standardized Costs\": 392.17, \"PAC: HH Per User Standardized Costs\": 6944.85, \"Procedures Per User Actual Costs\": 802.03, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 798.0, \"Ambulance Per User Actual Costs\": 1200.36, \"Average Age\": 69.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1484.0, \"PAC: HH Standardized Costs\": 372466454.75, \"ASC Actual Costs as % of Total Actual Costs\": 0.0119, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 974.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0073, \"Percent Other/Unknown\": 1.07, \"% of Beneficiaries Using Hospice\": 0.03, \"PAC: LTCH Per User Actual Costs\": 40479.46, \"Percent Non-Hispanic White\": 70.73, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 909.0, \"Count of Medicare beneficiaries with breast cancer\": 10791.0, \"DME Per Capita Standardized Costs\": 289.85, \"PQI08 CHF Admission Rate (age 65-74)\": 849.0, \"% of Beneficiaries Using DME\": 0.3363, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0347, \"% of Beneficiaries Using IP\": 0.1892, \"DME Standardized Costs\": 131283305.21, \"FQHC/RHC Standardized Costs\": 38620565.83, \"PAC: SNF Per Capita Actual Costs\": 742.84, \"Imaging Standardized Costs\": 95251629.97, \"# E&M Users\": 400201.0, \"Count of Medicare beneficiaries with arthritis\": 142557.0, \"IP Per User Standardized Costs\": 14697.26, \"IP Per User Actual Costs\": 15275.89, \"PQI10 Dehydration Admission Rate (age 75+)\": 813.0, \"PAC: IRF Per User Standardized Costs\": 20308.6, \"DME Per User Actual Costs\": 823.57, \"PAC: IRF Actual Costs\": 57554943.19, \"Imaging Events Per 1000 Beneficiaries\": 4240.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.035, \"PAC: LTCH Per User Standardized Costs\": 47913.97, \"OP Actual Costs\": 566405388.05, \"Count of Medicare beneficiaries with hypertension\": 277486.0, \"ASC Per User Standardized Costs\": 859.19, \"Part B Drugs Per Capita Actual Costs\": 276.5, \"Ambulance Events Per 1000 Beneficiaries\": 297.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 281.0, \"% of Beneficiaries Using PAC: HH\": 0.0413, \"PAC: LTCH Standardized Costs\": 7810185.16, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.8, \"E&M Per Capita Standardized Costs\": 529.56, \"E&M Per User Standardized Costs\": 639.22, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 685.0, \"IP Covered Days Per 1000 Beneficiaries\": 1060.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 22.0, \"Count of Medicare beneficiaries with lung cancer\": 1336.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3348, \"Percent Eligible for Medicaid\": 15.47, \"Imaging Per Capita Standardized Costs\": 108.13, \"% of Beneficiaries Using Tests\": 0.5922, \"Imaging Per Capita Actual Costs\": 105.96, \"% of Beneficiaries Using PAC: SNF\": 0.0429, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0243, \"Count of Medicare beneficiaries with colorectal cancer\": 1641.0, \"Hospice Actual Costs\": 30683132.13, \"# PAC: HH Users\": 6321.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23916.57, \"Total Actual Costs\": 1074956491.18, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 11964.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0109, \"ASC Standardized Costs\": 11199082.67, \"DME Events Per 1000 Beneficiaries\": 1738.0, \"PQI08 CHF Admission Rate (age < 65)\": 320.0, \"ASC Events Per 1000 Beneficiaries\": 116.0, \"PAC: LTCH Actual Costs\": 7141332.6, \"Count of Medicare beneficiaries with depression\": 23124.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1822.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 7.81, \"Outpatient Dialysis Facility Per User Actual Costs\": 22924.28, \"Beneficiaries with Part A and Part B\": 184669.0, \"% of Beneficiaries Using Part B Drugs\": 0.3756, \"Percent of Medicare beneficiaries with diabetes\": 19.06, \"% of Beneficiaries Using PAC: IRF\": 0.0042, \"E&M Per Capita Actual Costs\": 495.56, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0161, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0256, \"PAC: SNF Actual Costs\": 90606254.06, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 516.0, \"Percent Female\": 51.94, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 5.48, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 119.62, \"# Outpatient Dialysis Facility Users\": 766.0, \"FQHC/RHC Per User Actual Costs\": 471.83, \"Count of Medicare beneficiaries with ischemic heart disease\": 29587.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 87.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.61, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 921.0, \"Percent of Medicare beneficiaries with depression\": 15.1, \"Emergency Department Visits per 1000 Beneficiaries\": 527.0, \"IP Actual Costs\": 359861170.08, \"% of Beneficiaries Using OP\": 0.758, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0081, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0787, \"Count of Medicare beneficiaries with stroke\": 3747.0, \"PQI12 UTI Admission Rate (age 75+)\": 772.0, \"# OP Users\": 116089.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 23.0, \"# Procedure Users\": 80979.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 19.32, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0675, \"Count of Medicare beneficiaries with diabetes\": 29190.0, \"ASC Per User Actual Costs\": 911.72, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 777.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0303, \"Percent of Medicare beneficiaries with high cholesterol\": 27.68, \"Standardized Per Capita Costs\": 6726.22, \"Ambulance Actual Costs\": 11329837.75, \"FQHC/RHC Per Capita Actual Costs\": 103.32, \"Part B Drugs Per User Standardized Costs\": 459.26, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0114, \"# PAC: SNF Users (with a covered stay)\": 6563.0, \"Ambulance Per Capita Actual Costs\": 73.98, \"PQI12 UTI Admission Rate (age 65-74)\": 173.0, \"Hospice Standardized Costs\": 33069095.04, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 114.66, \"% of Beneficiaries Using E&M\": 0.8284, \"PQI10 Dehydration Admission Rate (age 65-74)\": 154.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 90.0, \"Tests Per Capita Actual Costs\": 97.1, \"# DME Users\": 39403.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0975, \"PAC: SNF Per User Actual Costs\": 13805.62, \"State\": \"MT\", \"OP Per User Actual Costs\": 2198.06, \"PAC: HH Episodes Per 1000 Beneficiaries\": 58.0, \"Part B Drugs Actual Costs\": 26127474.85, \"FQHC/RHC Actual Costs\": 15824155.27, \"OP Standardized Costs\": 229517345.66, \"DME Per User Standardized Costs\": 791.14, \"OP Actual Costs as % of Total Actual Costs\": 0.2374, \"PAC: SNF Per Capita Standardized Costs\": 656.11, \"% of Beneficiaries Using Ambulance\": 0.0802, \"Hospice Per User Standardized Costs\": 9697.68, \"# Imaging Users\": 94080.0, \"Part B Drugs Per User Actual Costs\": 454.23, \"Total Standardized Costs\": 1030120679.4, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.07, \"Count of Medicare beneficiaries with chronic kidney disease\": 17470.0, \"E&M Standardized Costs\": 81102210.6, \"Percent Hispanic\": 1.07, \"ASC Per Capita Actual Costs\": 66.88, \"Count of Medicare beneficiaries who have had a heart attack\": 934.0, \"Tests Actual Costs\": 14870282.5, \"# PAC: LTCH Users (with a covered stay)\": 173.0, \"% of Beneficiaries Using Procedures\": 0.5288, \"PAC: HH Actual Costs\": 22004677.5, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 28.0, \"PAC: LTCH Per Capita Standardized Costs\": 51.0, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0148, \"Emergency Department Visits\": 80692.0, \"% of Beneficiaries Using FQHC/RHC\": 0.219, \"Procedures Actual Costs\": 68606139.68, \"# FQHC/RHC Users\": 33538.0, \"Number of Acute Hospital Readmissions\": 4769.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 53.0, \"Outpatient Dialysis Facility Standardized Costs\": 18320088.77, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0106, \"OP Standardized Costs as % of Total Standardized Costs\": 0.2228, \"Ambulance Per User Standardized Costs\": 680.63, \"Imaging Per User Standardized Costs\": 176.03, \"Percent of Medicare beneficiaries with asthma\": 3.57, \"Part B Drugs Standardized Costs\": 26416989.25, \"FFS Beneficiaries\": 153150.0, \"# Hospice Users (with a covered stay)\": 3410.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.005, \"Count of Medicare beneficiaries with osteoporosis\": 8385.0, \"PQI08 CHF Admission Rate (age 75+)\": 1469.0, \"PAC: IRF Per Capita Standardized Costs\": 76.5, \"Procedures Standardized Costs\": 69535663.34, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3079, \"IP Per Capita Actual Costs\": 2349.73, \"DME Actual Costs\": 29562408.08, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0205, \"Count of Medicare beneficiaries with prostate cancer\": 4538.0, \"PAC: HH Per Capita Standardized Costs\": 159.71, \"Count of Medicare beneficiaries with heart failure\": 16281.0, \"Tests Per User Standardized Costs\": 167.56, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0066, \"Percent of Medicare beneficiaries with prostate cancer\": 2.96, \"PAC: IRF Per Capita Actual Costs\": 74.22, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 172.49, \"Percent of Medicare beneficiaries with breast cancer\": 2.5, \"Procedures Per User Standardized Costs\": 858.69, \"Percent of Medicare beneficiaries with chronic kidney disease\": 11.41, \"PAC: HH Per User Actual Costs\": 3481.2, \"Count of Medicare beneficiaries with high cholesterol\": 42389.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0843, \"Hospice Per Capita Standardized Costs\": 215.93, \"# Part B Drugs Users\": 57521.0, \"Average HCC Score\": 0.8361, \"Standardized Risk-Adjusted Per Capita Costs\": 8701.97, \"# PAC: IRF Users (with a covered stay)\": 638.0, \"Ambulance Standardized Costs\": 8361070.64, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0285, \"Percent of Medicare beneficiaries with heart failure\": 10.63, \"Tests Actual Costs as % of Total Actual Costs\": 0.0138, \"FQHC/RHC Per Capita Standardized Costs\": 101.63, \"PQI07 Hypertension Admission Rate (age 65-74)\": 33.0, \"Test Events Per 1000 Beneficiaries\": 3706.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 32.0, \"ASC Actual Costs\": 10243210.16, \"Part B Drugs Per Capita Standardized Costs\": 172.49, \"Imaging Actual Costs\": 16228161.71, \"Tests Per User Actual Costs\": 163.95, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0105, \"Hospice Per User Actual Costs\": 8997.99, \"Tests Standardized Costs\": 15197702.65, \"IP Standardized Costs\": 317145863.12, \"IP Per Capita Standardized Costs\": 2070.82, \"Outpatient Dialysis Facility Actual Costs\": 17559997.27, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 54.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1269.0, \"Percent of Medicare beneficiaries with hypertension\": 40.37, \"IP Covered Stays Per 1000 Beneficiaries\": 222.0, \"# Ambulance Users\": 12284.0, \"# ASC Users\": 11235.0, \"ASC Per Capita Standardized Costs\": 73.12, \"Procedures Per Capita Actual Costs\": 447.97, \"Procedures Per Capita Standardized Costs\": 454.04, \"IP Users (with a covered stay)\": 23553.0, \"Total Standardized Risk-Adjusted Costs\": 1332706824.02, \"Actual Per Capita Costs\": 7018.98, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0076, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 4.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.87, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.86, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 112.0, \"OP Per User Standardized Costs\": 1977.08, \"Count of Medicare beneficiaries with atrial fibrillation\": 10416.0, \"Procedure Events Per 1000 Beneficiaries\": 3229.0, \"Percent of Medicare beneficiaries with stroke\": 2.45, \"PAC: IRF Per User Actual Costs\": 17816.51, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 15104.0, \"% of Beneficiaries Using ASC\": 0.0734, \"PAC: IRF Standardized Costs\": 11716325.81, \"Hospice Covered Days Per 1000 Beneficiaries\": 1371.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 281.0, \"PAC: SNF Per User Standardized Costs\": 15310.51, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0147, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 83.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0321, \"MA Participation Rate\": 17.07, \"OP Per Capita Standardized Costs\": 1498.64, \"Percent of Medicare beneficiaries with arthritis\": 24.32, \"Ambulance Per Capita Standardized Costs\": 54.59, \"PAC: HH Visits Per 1000 Beneficiaries\": 875.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0638, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0151, \"PAC: HH Per Capita Actual Costs\": 143.68, \"E&M Events Per 1000 Beneficiaries\": 8201.0, \"Count of Medicare beneficiaries with asthma\": 5465.0, \"# Test Users\": 90699.0, \"E&M Actual Costs\": 75895008.75, \"% of Beneficiaries Using Imaging\": 0.6143, \"PAC: SNF Standardized Costs\": 100482845.07, \"DME Per Capita Actual Costs\": 193.03, \"E&M Per User Actual Costs\": 598.18, \"Percent Male\": 48.06, \"OP Visits Per 1000 Beneficiaries\": 6615.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0275, \"OP Per Capita Actual Costs\": 1666.14, \"Hospital Readmission Rate\": 0.1432, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0151, \"Tests Per Capita Standardized Costs\": 99.23, \"Hospice Per Capita Actual Costs\": 200.35, \"E&M Actual Costs as % of Total Actual Costs\": 0.0706, \"PQI07 Hypertension Admission Rate (age < 65)\": \"*\", \"Percent African American\": 0.22, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0237, \"PAC: LTCH Per Capita Actual Costs\": 46.63, \"MA Beneficiaries\": 31519.0, \"FQHC/RHC Per User Standardized Costs\": 464.1, \"PAC: HH Per User Standardized Costs\": 3869.54, \"Procedures Per User Actual Costs\": 847.21, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 381.0, \"Ambulance Per User Actual Costs\": 922.31, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 728.0, \"PAC: HH Standardized Costs\": 24459350.27, \"ASC Actual Costs as % of Total Actual Costs\": 0.0095, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 506.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0011, \"Percent Other/Unknown\": 5.59, \"% of Beneficiaries Using Hospice\": 0.0223, \"PAC: LTCH Per User Actual Costs\": 41279.38, \"Percent Non-Hispanic White\": 93.13, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 671.0, \"Count of Medicare beneficiaries with breast cancer\": 3835.0, \"DME Per Capita Standardized Costs\": 203.55, \"PQI08 CHF Admission Rate (age 65-74)\": 383.0, \"% of Beneficiaries Using DME\": 0.2573, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0163, \"% of Beneficiaries Using IP\": 0.1538, \"DME Standardized Costs\": 31173245.95, \"FQHC/RHC Standardized Costs\": 15564884.75, \"PAC: SNF Per Capita Actual Costs\": 591.62, \"Imaging Standardized Costs\": 16560850.65, \"# E&M Users\": 126877.0, \"Count of Medicare beneficiaries with arthritis\": 37239.0, \"IP Per User Standardized Costs\": 13465.2, \"IP Per User Actual Costs\": 15278.78, \"PQI10 Dehydration Admission Rate (age 75+)\": 471.0, \"PAC: IRF Per User Standardized Costs\": 18364.15, \"DME Per User Actual Costs\": 750.26, \"PAC: IRF Actual Costs\": 11366935.83, \"Imaging Events Per 1000 Beneficiaries\": 2767.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0178, \"PAC: LTCH Per User Standardized Costs\": 45145.58, \"OP Actual Costs\": 255170022.36, \"Count of Medicare beneficiaries with hypertension\": 61832.0, \"ASC Per User Standardized Costs\": 996.8, \"Part B Drugs Per Capita Actual Costs\": 170.6, \"Ambulance Events Per 1000 Beneficiaries\": 146.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 328.0, \"% of Beneficiaries Using PAC: HH\": 0.0826, \"PAC: LTCH Standardized Costs\": 108170689.58, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.4, \"E&M Per Capita Standardized Costs\": 898.27, \"E&M Per User Standardized Costs\": 987.13, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1498.0, \"IP Covered Days Per 1000 Beneficiaries\": 1452.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 57.0, \"Count of Medicare beneficiaries with lung cancer\": 13427.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3456, \"Percent Eligible for Medicaid\": 21.59, \"Imaging Per Capita Standardized Costs\": 198.65, \"% of Beneficiaries Using Tests\": 0.8267, \"Imaging Per Capita Actual Costs\": 184.29, \"% of Beneficiaries Using PAC: SNF\": 0.0455, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0371, \"Count of Medicare beneficiaries with colorectal cancer\": 14363.0, \"Hospice Actual Costs\": 371465919.31, \"# PAC: HH Users\": 105722.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24533.61, \"Total Actual Costs\": 10875144817.39, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 122933.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0073, \"ASC Standardized Costs\": 79789200.61, \"DME Events Per 1000 Beneficiaries\": 2006.0, \"PQI08 CHF Admission Rate (age < 65)\": 1075.0, \"ASC Events Per 1000 Beneficiaries\": 123.0, \"PAC: LTCH Actual Costs\": 98230576.07, \"Count of Medicare beneficiaries with depression\": 203057.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1439.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.61, \"Outpatient Dialysis Facility Per User Actual Costs\": 23644.91, \"Beneficiaries with Part A and Part B\": 1651000.0, \"% of Beneficiaries Using Part B Drugs\": 0.5764, \"Percent of Medicare beneficiaries with diabetes\": 28.48, \"% of Beneficiaries Using PAC: IRF\": 0.0057, \"E&M Per Capita Actual Costs\": 810.67, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0234, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0373, \"PAC: SNF Actual Costs\": 764661202.61, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 527.0, \"Percent Female\": 56.07, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 314.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.43, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 267.35, \"# Outpatient Dialysis Facility Users\": 13947.0, \"FQHC/RHC Per User Actual Costs\": 344.34, \"Count of Medicare beneficiaries with ischemic heart disease\": 310663.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 153.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.86, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 331.0, \"Percent of Medicare beneficiaries with depression\": 15.87, \"Emergency Department Visits per 1000 Beneficiaries\": 694.0, \"IP Actual Costs\": 3758269525.75, \"% of Beneficiaries Using OP\": 0.6381, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.017, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1056, \"Count of Medicare beneficiaries with stroke\": 45044.0, \"PQI12 UTI Admission Rate (age 75+)\": 1074.0, \"# OP Users\": 816637.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 27.0, \"# Procedure Users\": 803736.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 24.27, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0701, \"Count of Medicare beneficiaries with diabetes\": 364560.0, \"ASC Per User Actual Costs\": 664.3, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 899.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.029, \"Percent of Medicare beneficiaries with high cholesterol\": 46.73, \"Standardized Per Capita Costs\": 8504.54, \"Ambulance Actual Costs\": 176760694.72, \"FQHC/RHC Per Capita Actual Costs\": 26.68, \"Part B Drugs Per User Standardized Costs\": 550.01, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0133, \"# PAC: SNF Users (with a covered stay)\": 58298.0, \"Ambulance Per Capita Actual Costs\": 138.11, \"PQI12 UTI Admission Rate (age 65-74)\": 252.0, \"Hospice Standardized Costs\": 397566904.58, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 257.66, \"% of Beneficiaries Using E&M\": 0.91, \"PQI10 Dehydration Admission Rate (age 65-74)\": 214.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 192.0, \"Tests Per Capita Actual Costs\": 258.9, \"# DME Users\": 401845.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0791, \"PAC: SNF Per User Actual Costs\": 13116.42, \"State\": \"NC\", \"OP Per User Actual Costs\": 1850.58, \"PAC: HH Episodes Per 1000 Beneficiaries\": 132.0, \"Part B Drugs Actual Costs\": 403138980.01, \"FQHC/RHC Actual Costs\": 34142901.7, \"OP Standardized Costs\": 1556321532.52, \"DME Per User Standardized Costs\": 785.12, \"OP Actual Costs as % of Total Actual Costs\": 0.139, \"PAC: SNF Per Capita Standardized Costs\": 672.45, \"% of Beneficiaries Using Ambulance\": 0.1282, \"Hospice Per User Standardized Costs\": 11970.94, \"# Imaging Users\": 902013.0, \"Part B Drugs Per User Actual Costs\": 546.47, \"Total Standardized Costs\": 10884795417.24, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.12, \"Count of Medicare beneficiaries with chronic kidney disease\": 210288.0, \"E&M Standardized Costs\": 1149681502.69, \"Percent Hispanic\": 1.21, \"ASC Per Capita Actual Costs\": 57.46, \"Count of Medicare beneficiaries who have had a heart attack\": 10949.0, \"Tests Actual Costs\": 331357726.85, \"# PAC: LTCH Users (with a covered stay)\": 2344.0, \"% of Beneficiaries Using Procedures\": 0.628, \"PAC: HH Actual Costs\": 420122110.27, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 49.0, \"PAC: LTCH Per Capita Standardized Costs\": 84.52, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0317, \"Emergency Department Visits\": 888306.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0775, \"Procedures Actual Costs\": 704609367.75, \"# FQHC/RHC Users\": 99154.0, \"Number of Acute Hospital Readmissions\": 58254.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 83.0, \"Outpatient Dialysis Facility Standardized Costs\": 342170287.27, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0136, \"OP Standardized Costs as % of Total Standardized Costs\": 0.143, \"Ambulance Per User Standardized Costs\": 1130.06, \"Imaging Per User Standardized Costs\": 281.87, \"Percent of Medicare beneficiaries with asthma\": 4.76, \"Part B Drugs Standardized Costs\": 405748165.35, \"FFS Beneficiaries\": 1279881.0, \"# Hospice Users (with a covered stay)\": 33211.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0109, \"Count of Medicare beneficiaries with osteoporosis\": 69510.0, \"PQI08 CHF Admission Rate (age 75+)\": 2001.0, \"PAC: IRF Per Capita Standardized Costs\": 113.51, \"Procedures Standardized Costs\": 762852994.52, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2926, \"IP Per Capita Actual Costs\": 2936.42, \"DME Actual Costs\": 294164634.9, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0386, \"Count of Medicare beneficiaries with prostate cancer\": 37750.0, \"PAC: HH Per Capita Standardized Costs\": 363.85, \"Count of Medicare beneficiaries with heart failure\": 161381.0, \"Tests Per User Standardized Costs\": 326.0, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.009, \"Percent of Medicare beneficiaries with prostate cancer\": 2.95, \"PAC: IRF Per Capita Actual Costs\": 115.83, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 261.49, \"Percent of Medicare beneficiaries with breast cancer\": 2.92, \"Procedures Per User Standardized Costs\": 949.13, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.43, \"PAC: HH Per User Actual Costs\": 3973.84, \"Count of Medicare beneficiaries with high cholesterol\": 598090.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0703, \"Hospice Per Capita Standardized Costs\": 310.63, \"# Part B Drugs Users\": 737711.0, \"Average HCC Score\": 0.9745, \"Standardized Risk-Adjusted Per Capita Costs\": 9393.2, \"# PAC: IRF Users (with a covered stay)\": 7357.0, \"Ambulance Standardized Costs\": 185404372.82, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0342, \"Percent of Medicare beneficiaries with heart failure\": 12.61, \"Tests Actual Costs as % of Total Actual Costs\": 0.0305, \"FQHC/RHC Per Capita Standardized Costs\": 30.21, \"PQI07 Hypertension Admission Rate (age 65-74)\": 81.0, \"Test Events Per 1000 Beneficiaries\": 10436.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 56.0, \"ASC Actual Costs\": 73547446.03, \"Part B Drugs Per Capita Standardized Costs\": 317.02, \"Imaging Actual Costs\": 235864547.66, \"Tests Per User Actual Costs\": 313.15, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0163, \"Hospice Per User Actual Costs\": 11185.03, \"Tests Standardized Costs\": 344955810.16, \"IP Standardized Costs\": 3184546795.54, \"IP Per Capita Standardized Costs\": 2488.16, \"Outpatient Dialysis Facility Actual Costs\": 329775569.56, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 61.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1685.0, \"Percent of Medicare beneficiaries with hypertension\": 57.9, \"IP Covered Stays Per 1000 Beneficiaries\": 269.0, \"# Ambulance Users\": 164066.0, \"# ASC Users\": 110715.0, \"ASC Per Capita Standardized Costs\": 62.34, \"Procedures Per Capita Actual Costs\": 550.53, \"Procedures Per Capita Standardized Costs\": 596.03, \"IP Users (with a covered stay)\": 219739.0, \"Total Standardized Risk-Adjusted Costs\": 12022183473.84, \"Actual Per Capita Costs\": 8497.0, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0099, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 6.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.05, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.19, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 219.0, \"OP Per User Standardized Costs\": 1905.77, \"Count of Medicare beneficiaries with atrial fibrillation\": 94768.0, \"Procedure Events Per 1000 Beneficiaries\": 4142.0, \"Percent of Medicare beneficiaries with stroke\": 3.52, \"PAC: IRF Per User Actual Costs\": 20150.78, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 143167.0, \"% of Beneficiaries Using ASC\": 0.0865, \"PAC: IRF Standardized Costs\": 145276377.5, \"Hospice Covered Days Per 1000 Beneficiaries\": 1820.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 336.0, \"PAC: SNF Per User Standardized Costs\": 14763.12, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0031, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 150.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0365, \"MA Participation Rate\": 22.48, \"OP Per Capita Standardized Costs\": 1215.99, \"Percent of Medicare beneficiaries with arthritis\": 27.63, \"Ambulance Per Capita Standardized Costs\": 144.86, \"PAC: HH Visits Per 1000 Beneficiaries\": 1953.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0648, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0217, \"PAC: HH Per Capita Actual Costs\": 328.25, \"E&M Events Per 1000 Beneficiaries\": 12641.0, \"Count of Medicare beneficiaries with asthma\": 60924.0, \"# Test Users\": 1058136.0, \"E&M Actual Costs\": 1037563134.55, \"% of Beneficiaries Using Imaging\": 0.7048, \"PAC: SNF Standardized Costs\": 860660235.35, \"DME Per Capita Actual Costs\": 229.84, \"E&M Per User Actual Costs\": 890.86, \"Percent Male\": 43.93, \"OP Visits Per 1000 Beneficiaries\": 3927.0, \"DME Actual Costs as % of Total Actual Costs\": 0.027, \"OP Per Capita Actual Costs\": 1180.78, \"Hospital Readmission Rate\": 0.174, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0036, \"Tests Per Capita Standardized Costs\": 269.52, \"Hospice Per Capita Actual Costs\": 290.23, \"E&M Actual Costs as % of Total Actual Costs\": 0.0954, \"PQI07 Hypertension Admission Rate (age < 65)\": 175.0, \"Percent African American\": 19.01, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0428, \"PAC: LTCH Per Capita Actual Costs\": 76.75, \"MA Beneficiaries\": 371119.0, \"FQHC/RHC Per User Standardized Costs\": 389.99, \"PAC: HH Per User Standardized Costs\": 4404.85, \"Procedures Per User Actual Costs\": 876.67, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 793.0, \"Ambulance Per User Actual Costs\": 1077.38, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1315.0, \"PAC: HH Standardized Costs\": 465689365.69, \"ASC Actual Costs as % of Total Actual Costs\": 0.0068, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 657.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0018, \"Percent Other/Unknown\": 2.55, \"% of Beneficiaries Using Hospice\": 0.0259, \"PAC: LTCH Per User Actual Costs\": 41907.24, \"Percent Non-Hispanic White\": 77.24, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 743.0, \"Count of Medicare beneficiaries with breast cancer\": 37426.0, \"DME Per Capita Standardized Costs\": 246.51, \"PQI08 CHF Admission Rate (age 65-74)\": 706.0, \"% of Beneficiaries Using DME\": 0.314, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0303, \"% of Beneficiaries Using IP\": 0.1717, \"DME Standardized Costs\": 315497734.86, \"FQHC/RHC Standardized Costs\": 38668700.57, \"PAC: SNF Per Capita Actual Costs\": 597.45, \"Imaging Standardized Costs\": 254248466.44, \"# E&M Users\": 1164671.0, \"Count of Medicare beneficiaries with arthritis\": 353596.0, \"IP Per User Standardized Costs\": 14492.41, \"IP Per User Actual Costs\": 17103.33, \"PQI10 Dehydration Admission Rate (age 75+)\": 510.0, \"PAC: IRF Per User Standardized Costs\": 19746.69, \"DME Per User Actual Costs\": 732.04, \"PAC: IRF Actual Costs\": 148249315.29, \"Imaging Events Per 1000 Beneficiaries\": 4110.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0314, \"PAC: LTCH Per User Standardized Costs\": 46147.91, \"OP Actual Costs\": 1511255988.93, \"Count of Medicare beneficiaries with hypertension\": 741050.0, \"ASC Per User Standardized Costs\": 720.67, \"Part B Drugs Per Capita Actual Costs\": 314.98, \"Ambulance Events Per 1000 Beneficiaries\": 426.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 265.0, \"% of Beneficiaries Using PAC: HH\": 0.036, \"PAC: LTCH Standardized Costs\": 7381423.42, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.16, \"E&M Per Capita Standardized Costs\": 599.93, \"E&M Per User Standardized Costs\": 683.92, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 822.0, \"IP Covered Days Per 1000 Beneficiaries\": 1318.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": \"*\", \"Count of Medicare beneficiaries with lung cancer\": 892.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3443, \"Percent Eligible for Medicaid\": 15.88, \"Imaging Per Capita Standardized Costs\": 123.39, \"% of Beneficiaries Using Tests\": 0.6758, \"Imaging Per Capita Actual Costs\": 118.24, \"% of Beneficiaries Using PAC: SNF\": 0.0543, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0287, \"Count of Medicare beneficiaries with colorectal cancer\": 1348.0, \"Hospice Actual Costs\": 11863891.48, \"# PAC: HH Users\": 3386.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 20374.07, \"Total Actual Costs\": 732497030.77, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 9109.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0068, \"ASC Standardized Costs\": 4938562.43, \"DME Events Per 1000 Beneficiaries\": 1660.0, \"PQI08 CHF Admission Rate (age < 65)\": 428.0, \"ASC Events Per 1000 Beneficiaries\": 87.0, \"PAC: LTCH Actual Costs\": 6162151.36, \"Count of Medicare beneficiaries with depression\": 15651.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2179.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.68, \"Outpatient Dialysis Facility Per User Actual Costs\": 18238.75, \"Beneficiaries with Part A and Part B\": 111427.0, \"% of Beneficiaries Using Part B Drugs\": 0.3745, \"Percent of Medicare beneficiaries with diabetes\": 23.14, \"% of Beneficiaries Using PAC: IRF\": 0.0081, \"E&M Per Capita Actual Costs\": 549.08, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.016, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0292, \"PAC: SNF Actual Costs\": 60823825.85, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 637.0, \"Percent Female\": 55.1, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 5.68, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 141.76, \"# Outpatient Dialysis Facility Users\": 655.0, \"FQHC/RHC Per User Actual Costs\": 463.37, \"Count of Medicare beneficiaries with ischemic heart disease\": 23455.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 101.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.88, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 931.0, \"Percent of Medicare beneficiaries with depression\": 16.63, \"Emergency Department Visits per 1000 Beneficiaries\": 568.0, \"IP Actual Costs\": 252173491.25, \"% of Beneficiaries Using OP\": 0.8026, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0071, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.078, \"Count of Medicare beneficiaries with stroke\": 2250.0, \"PQI12 UTI Admission Rate (age 75+)\": 722.0, \"# OP Users\": 75553.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 18.0, \"# Procedure Users\": 49866.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 24.92, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0571, \"Count of Medicare beneficiaries with diabetes\": 21784.0, \"ASC Per User Actual Costs\": 826.42, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 743.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0239, \"Percent of Medicare beneficiaries with high cholesterol\": 36.69, \"Standardized Per Capita Costs\": 7692.95, \"Ambulance Actual Costs\": 7784998.92, \"FQHC/RHC Per Capita Actual Costs\": 93.19, \"Part B Drugs Per User Standardized Costs\": 600.11, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0183, \"# PAC: SNF Users (with a covered stay)\": 5108.0, \"Ambulance Per Capita Actual Costs\": 82.7, \"PQI12 UTI Admission Rate (age 65-74)\": 140.0, \"Hospice Standardized Costs\": 13478809.18, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 126.9, \"% of Beneficiaries Using E&M\": 0.8772, \"PQI10 Dehydration Admission Rate (age 65-74)\": 179.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 117.0, \"Tests Per Capita Actual Costs\": 118.98, \"# DME Users\": 25736.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1073, \"PAC: SNF Per User Actual Costs\": 11907.56, \"State\": \"ND\", \"OP Per User Actual Costs\": 2485.29, \"PAC: HH Episodes Per 1000 Beneficiaries\": 47.0, \"Part B Drugs Actual Costs\": 21053491.6, \"FQHC/RHC Actual Costs\": 8772992.5, \"OP Standardized Costs\": 175246804.69, \"DME Per User Standardized Costs\": 673.44, \"OP Actual Costs as % of Total Actual Costs\": 0.2563, \"PAC: SNF Per Capita Standardized Costs\": 825.28, \"% of Beneficiaries Using Ambulance\": 0.0779, \"Hospice Per User Standardized Costs\": 8340.85, \"# Imaging Users\": 61803.0, \"Part B Drugs Per User Actual Costs\": 597.18, \"Total Standardized Costs\": 724191640.32, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.43, \"Count of Medicare beneficiaries with chronic kidney disease\": 14373.0, \"E&M Standardized Costs\": 56475617.78, \"Percent Hispanic\": 0.55, \"ASC Per Capita Actual Costs\": 45.26, \"Count of Medicare beneficiaries who have had a heart attack\": 831.0, \"Tests Actual Costs\": 11200733.02, \"# PAC: LTCH Users (with a covered stay)\": 168.0, \"% of Beneficiaries Using Procedures\": 0.5297, \"PAC: HH Actual Costs\": 9041745.11, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 47.0, \"PAC: LTCH Per Capita Standardized Costs\": 78.41, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.016, \"Emergency Department Visits\": 53438.0, \"% of Beneficiaries Using FQHC/RHC\": 0.2011, \"Procedures Actual Costs\": 38800164.57, \"# FQHC/RHC Users\": 18933.0, \"Number of Acute Hospital Readmissions\": 3648.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 98.0, \"Outpatient Dialysis Facility Standardized Costs\": 13345015.88, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0162, \"OP Standardized Costs as % of Total Standardized Costs\": 0.242, \"Ambulance Per User Standardized Costs\": 706.02, \"Imaging Per User Standardized Costs\": 187.95, \"Percent of Medicare beneficiaries with asthma\": 3.74, \"Part B Drugs Standardized Costs\": 21156844.55, \"FFS Beneficiaries\": 94137.0, \"# Hospice Users (with a covered stay)\": 1616.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.007, \"Count of Medicare beneficiaries with osteoporosis\": 5350.0, \"PQI08 CHF Admission Rate (age 75+)\": 1807.0, \"PAC: IRF Per Capita Standardized Costs\": 141.02, \"Procedures Standardized Costs\": 41332630.48, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3086, \"IP Per Capita Actual Costs\": 2678.79, \"DME Actual Costs\": 16356148.7, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0123, \"Count of Medicare beneficiaries with prostate cancer\": 2904.0, \"PAC: HH Per Capita Standardized Costs\": 119.04, \"Count of Medicare beneficiaries with heart failure\": 12871.0, \"Tests Per User Standardized Costs\": 182.38, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0084, \"Percent of Medicare beneficiaries with prostate cancer\": 3.08, \"PAC: IRF Per Capita Actual Costs\": 126.24, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 180.11, \"Percent of Medicare beneficiaries with breast cancer\": 2.58, \"Procedures Per User Standardized Costs\": 828.87, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.27, \"PAC: HH Per User Actual Costs\": 2670.33, \"Count of Medicare beneficiaries with high cholesterol\": 34537.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.083, \"Hospice Per Capita Standardized Costs\": 143.18, \"# Part B Drugs Users\": 35255.0, \"Average HCC Score\": 0.8952, \"Standardized Risk-Adjusted Per Capita Costs\": 9434.92, \"# PAC: IRF Users (with a covered stay)\": 762.0, \"Ambulance Standardized Costs\": 5176568.12, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0162, \"Percent of Medicare beneficiaries with heart failure\": 13.67, \"Tests Actual Costs as % of Total Actual Costs\": 0.0153, \"FQHC/RHC Per Capita Standardized Costs\": 96.47, \"PQI07 Hypertension Admission Rate (age 65-74)\": 41.0, \"Test Events Per 1000 Beneficiaries\": 6319.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 52.0, \"ASC Actual Costs\": 4260209.98, \"Part B Drugs Per Capita Standardized Costs\": 224.75, \"Imaging Actual Costs\": 11131027.78, \"Tests Per User Actual Costs\": 176.06, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0106, \"Hospice Per User Actual Costs\": 7341.52, \"Tests Standardized Costs\": 11602687.35, \"IP Standardized Costs\": 223487074.43, \"IP Per Capita Standardized Costs\": 2374.06, \"Outpatient Dialysis Facility Actual Costs\": 11946383.62, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 71.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1581.0, \"Percent of Medicare beneficiaries with hypertension\": 48.48, \"IP Covered Stays Per 1000 Beneficiaries\": 252.0, \"# Ambulance Users\": 7332.0, \"# ASC Users\": 5155.0, \"ASC Per Capita Standardized Costs\": 52.46, \"Procedures Per Capita Actual Costs\": 412.17, \"Procedures Per Capita Standardized Costs\": 439.07, \"IP Users (with a covered stay)\": 15917.0, \"Total Standardized Risk-Adjusted Costs\": 888175088.31, \"Actual Per Capita Costs\": 7781.18, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0102, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 9.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.95, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.69, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 120.0, \"OP Per User Standardized Costs\": 2319.52, \"Count of Medicare beneficiaries with atrial fibrillation\": 6743.0, \"Procedure Events Per 1000 Beneficiaries\": 2977.0, \"Percent of Medicare beneficiaries with stroke\": 2.39, \"PAC: IRF Per User Actual Costs\": 15596.01, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9124.0, \"% of Beneficiaries Using ASC\": 0.0548, \"PAC: IRF Standardized Costs\": 13275356.31, \"Hospice Covered Days Per 1000 Beneficiaries\": 926.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 381.0, \"PAC: SNF Per User Standardized Costs\": 15209.38, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.012, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": \"*\", \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0186, \"MA Participation Rate\": 15.52, \"OP Per Capita Standardized Costs\": 1861.61, \"Percent of Medicare beneficiaries with arthritis\": 25.09, \"Ambulance Per Capita Standardized Costs\": 54.99, \"PAC: HH Visits Per 1000 Beneficiaries\": 663.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.053, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0152, \"PAC: HH Per Capita Actual Costs\": 96.05, \"E&M Events Per 1000 Beneficiaries\": 9498.0, \"Count of Medicare beneficiaries with asthma\": 3521.0, \"# Test Users\": 63619.0, \"E&M Actual Costs\": 51688726.05, \"% of Beneficiaries Using Imaging\": 0.6565, \"PAC: SNF Standardized Costs\": 77689516.67, \"DME Per Capita Actual Costs\": 173.75, \"E&M Per User Actual Costs\": 625.95, \"Percent Male\": 44.9, \"OP Visits Per 1000 Beneficiaries\": 7917.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0223, \"OP Per Capita Actual Costs\": 1994.66, \"Hospital Readmission Rate\": 0.1597, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0125, \"Tests Per Capita Standardized Costs\": 123.25, \"Hospice Per Capita Actual Costs\": 126.03, \"E&M Actual Costs as % of Total Actual Costs\": 0.0706, \"PQI07 Hypertension Admission Rate (age < 65)\": \"*\", \"Percent African American\": 0.32, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0155, \"PAC: LTCH Per Capita Actual Costs\": 65.46, \"MA Beneficiaries\": 17290.0, \"FQHC/RHC Per User Standardized Costs\": 479.67, \"PAC: HH Per User Standardized Costs\": 3309.49, \"Procedures Per User Actual Costs\": 778.09, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 420.0, \"Ambulance Per User Actual Costs\": 1061.77, \"Average Age\": 73.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 927.0, \"PAC: HH Standardized Costs\": 11205928.85, \"ASC Actual Costs as % of Total Actual Costs\": 0.0058, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 559.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0018, \"Percent Other/Unknown\": 4.42, \"% of Beneficiaries Using Hospice\": 0.0172, \"PAC: LTCH Per User Actual Costs\": 36679.47, \"Percent Non-Hispanic White\": 94.72, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 809.0, \"Count of Medicare beneficiaries with breast cancer\": 2426.0, \"DME Per Capita Standardized Costs\": 184.11, \"PQI08 CHF Admission Rate (age 65-74)\": 464.0, \"% of Beneficiaries Using DME\": 0.2734, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0163, \"% of Beneficiaries Using IP\": 0.1691, \"DME Standardized Costs\": 17331645.26, \"FQHC/RHC Standardized Costs\": 9081673.09, \"PAC: SNF Per Capita Actual Costs\": 646.12, \"Imaging Standardized Costs\": 11615969.13, \"# E&M Users\": 82576.0, \"Count of Medicare beneficiaries with arthritis\": 23621.0, \"IP Per User Standardized Costs\": 14040.78, \"IP Per User Actual Costs\": 15843.03, \"PQI10 Dehydration Admission Rate (age 75+)\": 537.0, \"PAC: IRF Per User Standardized Costs\": 17421.73, \"DME Per User Actual Costs\": 635.54, \"PAC: IRF Actual Costs\": 11884162.62, \"Imaging Events Per 1000 Beneficiaries\": 3466.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0184, \"PAC: LTCH Per User Standardized Costs\": 43937.04, \"OP Actual Costs\": 187771136.78, \"Count of Medicare beneficiaries with hypertension\": 45638.0, \"ASC Per User Standardized Costs\": 958.01, \"Part B Drugs Per Capita Actual Costs\": 223.65, \"Ambulance Events Per 1000 Beneficiaries\": 146.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 387.0, \"% of Beneficiaries Using PAC: HH\": 0.0585, \"PAC: LTCH Standardized Costs\": 26445989.32, \"Percent of Medicare beneficiaries with atrial fibrillation\": 9.09, \"E&M Per Capita Standardized Costs\": 694.94, \"E&M Per User Standardized Costs\": 790.87, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 814.0, \"IP Covered Days Per 1000 Beneficiaries\": 1225.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 28.0, \"Count of Medicare beneficiaries with lung cancer\": 2288.0, \"IP Actual Costs as % of Total Actual Costs\": 0.321, \"Percent Eligible for Medicaid\": 15.53, \"Imaging Per Capita Standardized Costs\": 150.99, \"% of Beneficiaries Using Tests\": 0.7626, \"Imaging Per Capita Actual Costs\": 138.32, \"% of Beneficiaries Using PAC: SNF\": 0.0643, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.048, \"Count of Medicare beneficiaries with colorectal cancer\": 3484.0, \"Hospice Actual Costs\": 64445835.46, \"# PAC: HH Users\": 14635.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23665.64, \"Total Actual Costs\": 2107938776.03, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 23309.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0107, \"ASC Standardized Costs\": 21884391.15, \"DME Events Per 1000 Beneficiaries\": 1844.0, \"PQI08 CHF Admission Rate (age < 65)\": 545.0, \"ASC Events Per 1000 Beneficiaries\": 162.0, \"PAC: LTCH Actual Costs\": 24514726.91, \"Count of Medicare beneficiaries with depression\": 35982.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1861.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.32, \"Outpatient Dialysis Facility Per User Actual Costs\": 23497.1, \"Beneficiaries with Part A and Part B\": 289780.0, \"% of Beneficiaries Using Part B Drugs\": 0.5257, \"Percent of Medicare beneficiaries with diabetes\": 22.44, \"% of Beneficiaries Using PAC: IRF\": 0.0056, \"E&M Per Capita Actual Costs\": 608.87, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0185, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0498, \"PAC: SNF Actual Costs\": 232622981.18, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 581.0, \"Percent Female\": 55.86, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 249.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.62, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 144.45, \"# Outpatient Dialysis Facility Users\": 1527.0, \"FQHC/RHC Per User Actual Costs\": 460.03, \"Count of Medicare beneficiaries with ischemic heart disease\": 61622.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 136.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.67, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 1012.0, \"Percent of Medicare beneficiaries with depression\": 14.38, \"Emergency Department Visits per 1000 Beneficiaries\": 511.0, \"IP Actual Costs\": 676743681.36, \"% of Beneficiaries Using OP\": 0.6637, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0082, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.085, \"Count of Medicare beneficiaries with stroke\": 6500.0, \"PQI12 UTI Admission Rate (age 75+)\": 867.0, \"# OP Users\": 166031.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 29.0, \"# Procedure Users\": 149744.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 24.63, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0668, \"Count of Medicare beneficiaries with diabetes\": 56130.0, \"ASC Per User Actual Costs\": 784.26, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 985.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0265, \"Percent of Medicare beneficiaries with high cholesterol\": 35.25, \"Standardized Per Capita Costs\": 8177.63, \"Ambulance Actual Costs\": 19998727.43, \"FQHC/RHC Per Capita Actual Costs\": 95.83, \"Part B Drugs Per User Standardized Costs\": 773.97, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0129, \"# PAC: SNF Users (with a covered stay)\": 16092.0, \"Ambulance Per Capita Actual Costs\": 79.94, \"PQI12 UTI Admission Rate (age 65-74)\": 217.0, \"Hospice Standardized Costs\": 66620605.11, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 143.43, \"% of Beneficiaries Using E&M\": 0.8787, \"PQI10 Dehydration Admission Rate (age 65-74)\": 161.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 135.0, \"Tests Per Capita Actual Costs\": 160.98, \"# DME Users\": 70389.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.122, \"PAC: SNF Per User Actual Costs\": 14455.82, \"State\": \"NE\", \"OP Per User Actual Costs\": 2282.31, \"PAC: HH Episodes Per 1000 Beneficiaries\": 90.0, \"Part B Drugs Actual Costs\": 101223088.0, \"FQHC/RHC Actual Costs\": 23972390.55, \"OP Standardized Costs\": 335986292.85, \"DME Per User Standardized Costs\": 771.22, \"OP Actual Costs as % of Total Actual Costs\": 0.1798, \"PAC: SNF Per Capita Standardized Costs\": 997.78, \"% of Beneficiaries Using Ambulance\": 0.0952, \"Hospice Per User Standardized Costs\": 9567.8, \"# Imaging Users\": 166784.0, \"Part B Drugs Per User Actual Costs\": 769.73, \"Total Standardized Costs\": 2045757739.85, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.39, \"Count of Medicare beneficiaries with chronic kidney disease\": 33376.0, \"E&M Standardized Costs\": 173850073.7, \"Percent Hispanic\": 2.06, \"ASC Per Capita Actual Costs\": 82.88, \"Count of Medicare beneficiaries who have had a heart attack\": 1680.0, \"Tests Actual Costs\": 40271575.01, \"# PAC: LTCH Users (with a covered stay)\": 664.0, \"% of Beneficiaries Using Procedures\": 0.5986, \"PAC: HH Actual Costs\": 60241290.63, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 31.0, \"PAC: LTCH Per Capita Standardized Costs\": 105.71, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.021, \"Emergency Department Visits\": 127877.0, \"% of Beneficiaries Using FQHC/RHC\": 0.2083, \"Procedures Actual Costs\": 122302883.36, \"# FQHC/RHC Users\": 52111.0, \"Number of Acute Hospital Readmissions\": 9789.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 82.0, \"Outpatient Dialysis Facility Standardized Costs\": 36137429.09, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0129, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1642, \"Ambulance Per User Standardized Costs\": 706.76, \"Imaging Per User Standardized Costs\": 226.47, \"Percent of Medicare beneficiaries with asthma\": 3.41, \"Part B Drugs Standardized Costs\": 101779893.03, \"FFS Beneficiaries\": 250165.0, \"# Hospice Users (with a covered stay)\": 6963.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0061, \"Count of Medicare beneficiaries with osteoporosis\": 14047.0, \"PQI08 CHF Admission Rate (age 75+)\": 1493.0, \"PAC: IRF Per Capita Standardized Costs\": 105.55, \"Procedures Standardized Costs\": 136736838.69, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2861, \"IP Per Capita Actual Costs\": 2705.19, \"DME Actual Costs\": 51266008.3, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0286, \"Count of Medicare beneficiaries with prostate cancer\": 7222.0, \"PAC: HH Per Capita Standardized Costs\": 250.71, \"Count of Medicare beneficiaries with heart failure\": 30993.0, \"Tests Per User Standardized Costs\": 225.51, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0116, \"Percent of Medicare beneficiaries with prostate cancer\": 2.89, \"PAC: IRF Per Capita Actual Costs\": 109.04, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 207.47, \"Percent of Medicare beneficiaries with breast cancer\": 2.82, \"Procedures Per User Standardized Costs\": 913.14, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.34, \"PAC: HH Per User Actual Costs\": 4116.25, \"Count of Medicare beneficiaries with high cholesterol\": 88194.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.1104, \"Hospice Per Capita Standardized Costs\": 266.31, \"# Part B Drugs Users\": 131504.0, \"Average HCC Score\": 0.9006, \"Standardized Risk-Adjusted Per Capita Costs\": 9802.35, \"# PAC: IRF Users (with a covered stay)\": 1394.0, \"Ambulance Standardized Costs\": 16824535.07, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0306, \"Percent of Medicare beneficiaries with heart failure\": 12.39, \"Tests Actual Costs as % of Total Actual Costs\": 0.0191, \"FQHC/RHC Per Capita Standardized Costs\": 104.55, \"PQI07 Hypertension Admission Rate (age 65-74)\": 60.0, \"Test Events Per 1000 Beneficiaries\": 8273.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 74.0, \"ASC Actual Costs\": 20732610.02, \"Part B Drugs Per Capita Standardized Costs\": 406.85, \"Imaging Actual Costs\": 34602566.22, \"Tests Per User Actual Costs\": 211.1, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0095, \"Hospice Per User Actual Costs\": 9255.47, \"Tests Standardized Costs\": 43019126.26, \"IP Standardized Costs\": 585309018.16, \"IP Per Capita Standardized Costs\": 2339.69, \"Outpatient Dialysis Facility Actual Costs\": 35880076.52, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 84.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2061.0, \"Percent of Medicare beneficiaries with hypertension\": 48.41, \"IP Covered Stays Per 1000 Beneficiaries\": 260.0, \"# Ambulance Users\": 23805.0, \"# ASC Users\": 26436.0, \"ASC Per Capita Standardized Costs\": 87.48, \"Procedures Per Capita Actual Costs\": 488.89, \"Procedures Per Capita Standardized Costs\": 546.59, \"IP Users (with a covered stay)\": 43397.0, \"Total Standardized Risk-Adjusted Costs\": 2452204967.3, \"Actual Per Capita Costs\": 8426.19, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0129, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 6.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.91, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.2, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 145.0, \"OP Per User Standardized Costs\": 2023.64, \"Count of Medicare beneficiaries with atrial fibrillation\": 22744.0, \"Procedure Events Per 1000 Beneficiaries\": 4416.0, \"Percent of Medicare beneficiaries with stroke\": 2.6, \"PAC: IRF Per User Actual Costs\": 19568.89, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 25523.0, \"% of Beneficiaries Using ASC\": 0.1057, \"PAC: IRF Standardized Costs\": 26405475.52, \"Hospice Covered Days Per 1000 Beneficiaries\": 1717.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 342.0, \"PAC: SNF Per User Standardized Costs\": 15511.48, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0114, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 89.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0326, \"MA Participation Rate\": 13.67, \"OP Per Capita Standardized Costs\": 1343.06, \"Percent of Medicare beneficiaries with arthritis\": 27.72, \"Ambulance Per Capita Standardized Costs\": 67.25, \"PAC: HH Visits Per 1000 Beneficiaries\": 1451.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.058, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0164, \"PAC: HH Per Capita Actual Costs\": 240.81, \"E&M Events Per 1000 Beneficiaries\": 10452.0, \"Count of Medicare beneficiaries with asthma\": 8532.0, \"# Test Users\": 190766.0, \"E&M Actual Costs\": 152317497.24, \"% of Beneficiaries Using Imaging\": 0.6667, \"PAC: SNF Standardized Costs\": 249610788.6, \"DME Per Capita Actual Costs\": 204.93, \"E&M Per User Actual Costs\": 692.92, \"Percent Male\": 44.14, \"OP Visits Per 1000 Beneficiaries\": 4758.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0243, \"OP Per Capita Actual Costs\": 1514.74, \"Hospital Readmission Rate\": 0.1539, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0128, \"Tests Per Capita Standardized Costs\": 171.96, \"Hospice Per Capita Actual Costs\": 257.61, \"E&M Actual Costs as % of Total Actual Costs\": 0.0723, \"PQI07 Hypertension Admission Rate (age < 65)\": 104.0, \"Percent African American\": 2.62, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0307, \"PAC: LTCH Per Capita Actual Costs\": 97.99, \"MA Beneficiaries\": 39615.0, \"FQHC/RHC Per User Standardized Costs\": 501.9, \"PAC: HH Per User Standardized Costs\": 4285.46, \"Procedures Per User Actual Costs\": 816.75, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 491.0, \"Ambulance Per User Actual Costs\": 840.1, \"Average Age\": 73.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1349.0, \"PAC: HH Standardized Costs\": 62717640.79, \"ASC Actual Costs as % of Total Actual Costs\": 0.0098, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 746.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0027, \"Percent Other/Unknown\": 2.19, \"% of Beneficiaries Using Hospice\": 0.0278, \"PAC: LTCH Per User Actual Costs\": 36919.77, \"Percent Non-Hispanic White\": 93.13, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 780.0, \"Count of Medicare beneficiaries with breast cancer\": 7052.0, \"DME Per Capita Standardized Costs\": 217.0, \"PQI08 CHF Admission Rate (age 65-74)\": 410.0, \"% of Beneficiaries Using DME\": 0.2814, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.017, \"% of Beneficiaries Using IP\": 0.1735, \"DME Standardized Costs\": 54285481.59, \"FQHC/RHC Standardized Costs\": 26154607.74, \"PAC: SNF Per Capita Actual Costs\": 929.88, \"Imaging Standardized Costs\": 37772083.96, \"# E&M Users\": 219820.0, \"Count of Medicare beneficiaries with arthritis\": 69351.0, \"IP Per User Standardized Costs\": 13487.32, \"IP Per User Actual Costs\": 15594.25, \"PQI10 Dehydration Admission Rate (age 75+)\": 419.0, \"PAC: IRF Per User Standardized Costs\": 18942.23, \"DME Per User Actual Costs\": 728.32, \"PAC: IRF Actual Costs\": 27279038.21, \"Imaging Events Per 1000 Beneficiaries\": 3616.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0177, \"PAC: LTCH Per User Standardized Costs\": 39828.3, \"OP Actual Costs\": 378933888.71, \"Count of Medicare beneficiaries with hypertension\": 121110.0, \"ASC Per User Standardized Costs\": 827.83, \"Part B Drugs Per Capita Actual Costs\": 404.63, \"Ambulance Events Per 1000 Beneficiaries\": 182.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 198.0, \"% of Beneficiaries Using PAC: HH\": 0.0949, \"PAC: LTCH Standardized Costs\": 8556124.26, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.45, \"E&M Per Capita Standardized Costs\": 752.29, \"E&M Per User Standardized Costs\": 874.58, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 487.0, \"IP Covered Days Per 1000 Beneficiaries\": 1242.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 36.0, \"Count of Medicare beneficiaries with lung cancer\": 2160.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3285, \"Percent Eligible for Medicaid\": 15.44, \"Imaging Per Capita Standardized Costs\": 131.8, \"% of Beneficiaries Using Tests\": 0.6393, \"Imaging Per Capita Actual Costs\": 131.99, \"% of Beneficiaries Using PAC: SNF\": 0.0495, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0192, \"Count of Medicare beneficiaries with colorectal cancer\": 2274.0, \"Hospice Actual Costs\": 48436711.33, \"# PAC: HH Users\": 20702.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 22789.0, \"Total Actual Costs\": 1822616845.2, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 20193.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0078, \"ASC Standardized Costs\": 12894654.22, \"DME Events Per 1000 Beneficiaries\": 1521.0, \"PQI08 CHF Admission Rate (age < 65)\": 539.0, \"ASC Events Per 1000 Beneficiaries\": 112.0, \"PAC: LTCH Actual Costs\": 8827534.03, \"Count of Medicare beneficiaries with depression\": 39726.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1574.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.25, \"Outpatient Dialysis Facility Per User Actual Costs\": 23302.96, \"Beneficiaries with Part A and Part B\": 234290.0, \"% of Beneficiaries Using Part B Drugs\": 0.3999, \"Percent of Medicare beneficiaries with diabetes\": 21.83, \"% of Beneficiaries Using PAC: IRF\": 0.0116, \"E&M Per Capita Actual Costs\": 711.58, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0174, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0212, \"PAC: SNF Actual Costs\": 176057546.68, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 530.0, \"Percent Female\": 54.94, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 142.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.53, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 87.21, \"# Outpatient Dialysis Facility Users\": 835.0, \"FQHC/RHC Per User Actual Costs\": 498.63, \"Count of Medicare beneficiaries with ischemic heart disease\": 45998.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 122.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.77, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 478.0, \"Percent of Medicare beneficiaries with depression\": 18.21, \"Emergency Department Visits per 1000 Beneficiaries\": 637.0, \"IP Actual Costs\": 598675337.69, \"% of Beneficiaries Using OP\": 0.7879, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0154, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0991, \"Count of Medicare beneficiaries with stroke\": 6513.0, \"PQI12 UTI Admission Rate (age 75+)\": 918.0, \"# OP Users\": 171909.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 23.0, \"# Procedure Users\": 121497.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 21.08, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0584, \"Count of Medicare beneficiaries with diabetes\": 47634.0, \"ASC Per User Actual Costs\": 834.72, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 963.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0233, \"Percent of Medicare beneficiaries with high cholesterol\": 43.28, \"Standardized Per Capita Costs\": 7593.62, \"Ambulance Actual Costs\": 25342514.91, \"FQHC/RHC Per Capita Actual Costs\": 50.04, \"Part B Drugs Per User Standardized Costs\": 403.49, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0292, \"# PAC: SNF Users (with a covered stay)\": 10807.0, \"Ambulance Per Capita Actual Costs\": 116.15, \"PQI12 UTI Admission Rate (age 65-74)\": 177.0, \"Hospice Standardized Costs\": 46783665.25, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 89.18, \"% of Beneficiaries Using E&M\": 0.8602, \"PQI10 Dehydration Admission Rate (age 65-74)\": 154.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 130.0, \"Tests Per Capita Actual Costs\": 114.1, \"# DME Users\": 53622.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1049, \"PAC: SNF Per User Actual Costs\": 16291.07, \"State\": \"NH\", \"OP Per User Actual Costs\": 2164.87, \"PAC: HH Episodes Per 1000 Beneficiaries\": 149.0, \"Part B Drugs Actual Costs\": 34951645.62, \"FQHC/RHC Actual Costs\": 10918066.02, \"OP Standardized Costs\": 337326538.23, \"DME Per User Standardized Costs\": 720.11, \"OP Actual Costs as % of Total Actual Costs\": 0.2042, \"PAC: SNF Per Capita Standardized Costs\": 796.24, \"% of Beneficiaries Using Ambulance\": 0.1194, \"Hospice Per User Standardized Costs\": 9575.04, \"# Imaging Users\": 141001.0, \"Part B Drugs Per User Actual Costs\": 400.61, \"Total Standardized Costs\": 1656881975.15, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.04, \"Count of Medicare beneficiaries with chronic kidney disease\": 29446.0, \"E&M Standardized Costs\": 164145185.24, \"Percent Hispanic\": 1.0, \"ASC Per Capita Actual Costs\": 58.57, \"Count of Medicare beneficiaries who have had a heart attack\": 1671.0, \"Tests Actual Costs\": 24895769.17, \"# PAC: LTCH Users (with a covered stay)\": 234.0, \"% of Beneficiaries Using Procedures\": 0.5568, \"PAC: HH Actual Costs\": 86965690.62, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 29.0, \"PAC: LTCH Per Capita Standardized Costs\": 39.21, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0152, \"Emergency Department Visits\": 139011.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1004, \"Procedures Actual Costs\": 96157961.68, \"# FQHC/RHC Users\": 21896.0, \"Number of Acute Hospital Readmissions\": 7962.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 162.0, \"Outpatient Dialysis Facility Standardized Costs\": 19028812.04, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0288, \"OP Standardized Costs as % of Total Standardized Costs\": 0.2036, \"Ambulance Per User Standardized Costs\": 976.88, \"Imaging Per User Standardized Costs\": 203.96, \"Percent of Medicare beneficiaries with asthma\": 4.61, \"Part B Drugs Standardized Costs\": 35202902.04, \"FFS Beneficiaries\": 218194.0, \"# Hospice Users (with a covered stay)\": 4886.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0038, \"Count of Medicare beneficiaries with osteoporosis\": 12059.0, \"PQI08 CHF Admission Rate (age 75+)\": 2095.0, \"PAC: IRF Per Capita Standardized Costs\": 221.83, \"Procedures Standardized Costs\": 96735643.25, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2803, \"IP Per Capita Actual Costs\": 2743.78, \"DME Actual Costs\": 36380072.01, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0477, \"Count of Medicare beneficiaries with prostate cancer\": 6533.0, \"PAC: HH Per Capita Standardized Costs\": 392.3, \"Count of Medicare beneficiaries with heart failure\": 22181.0, \"Tests Per User Standardized Costs\": 179.98, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0048, \"Percent of Medicare beneficiaries with prostate cancer\": 2.99, \"PAC: IRF Per Capita Actual Costs\": 240.82, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 204.25, \"Percent of Medicare beneficiaries with breast cancer\": 2.76, \"Procedures Per User Standardized Costs\": 796.2, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.5, \"PAC: HH Per User Actual Costs\": 4200.84, \"Count of Medicare beneficiaries with high cholesterol\": 94429.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0966, \"Hospice Per Capita Standardized Costs\": 214.41, \"# Part B Drugs Users\": 87246.0, \"Average HCC Score\": 0.8726, \"Standardized Risk-Adjusted Per Capita Costs\": 9045.62, \"# PAC: IRF Users (with a covered stay)\": 2537.0, \"Ambulance Standardized Costs\": 25447470.77, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0266, \"Percent of Medicare beneficiaries with heart failure\": 10.17, \"Tests Actual Costs as % of Total Actual Costs\": 0.0137, \"FQHC/RHC Per Capita Standardized Costs\": 48.58, \"PQI07 Hypertension Admission Rate (age 65-74)\": 42.0, \"Test Events Per 1000 Beneficiaries\": 4283.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 31.0, \"ASC Actual Costs\": 12778693.01, \"Part B Drugs Per Capita Standardized Costs\": 161.34, \"Imaging Actual Costs\": 28799400.48, \"Tests Per User Actual Costs\": 178.47, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0139, \"Hospice Per User Actual Costs\": 9913.37, \"Tests Standardized Costs\": 25106345.02, \"IP Standardized Costs\": 464349044.12, \"IP Per Capita Standardized Costs\": 2128.15, \"Outpatient Dialysis Facility Actual Costs\": 19457972.24, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 67.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1743.0, \"Percent of Medicare beneficiaries with hypertension\": 49.23, \"IP Covered Stays Per 1000 Beneficiaries\": 235.0, \"# Ambulance Users\": 26050.0, \"# ASC Users\": 15309.0, \"ASC Per Capita Standardized Costs\": 59.1, \"Procedures Per Capita Actual Costs\": 440.7, \"Procedures Per Capita Standardized Costs\": 443.35, \"IP Users (with a covered stay)\": 33440.0, \"Total Standardized Risk-Adjusted Costs\": 1973700915.91, \"Actual Per Capita Costs\": 8353.19, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0052, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 13.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.99, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.35, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 165.0, \"OP Per User Standardized Costs\": 1962.24, \"Count of Medicare beneficiaries with atrial fibrillation\": 18444.0, \"Procedure Events Per 1000 Beneficiaries\": 3431.0, \"Percent of Medicare beneficiaries with stroke\": 2.99, \"PAC: IRF Per User Actual Costs\": 20711.54, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 22592.0, \"% of Beneficiaries Using ASC\": 0.0702, \"PAC: IRF Standardized Costs\": 48401497.23, \"Hospice Covered Days Per 1000 Beneficiaries\": 1297.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 211.0, \"PAC: SNF Per User Standardized Costs\": 16076.1, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.006, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 53.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0282, \"MA Participation Rate\": 6.87, \"OP Per Capita Standardized Costs\": 1545.99, \"Percent of Medicare beneficiaries with arthritis\": 24.96, \"Ambulance Per Capita Standardized Costs\": 116.63, \"PAC: HH Visits Per 1000 Beneficiaries\": 2380.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0528, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0158, \"PAC: HH Per Capita Actual Costs\": 398.57, \"E&M Events Per 1000 Beneficiaries\": 11078.0, \"Count of Medicare beneficiaries with asthma\": 10068.0, \"# Test Users\": 139493.0, \"E&M Actual Costs\": 155262005.4, \"% of Beneficiaries Using Imaging\": 0.6462, \"PAC: SNF Standardized Costs\": 173734435.45, \"DME Per Capita Actual Costs\": 166.73, \"E&M Per User Actual Costs\": 827.25, \"Percent Male\": 45.06, \"OP Visits Per 1000 Beneficiaries\": 7745.0, \"DME Actual Costs as % of Total Actual Costs\": 0.02, \"OP Per Capita Actual Costs\": 1705.64, \"Hospital Readmission Rate\": 0.164, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0064, \"Tests Per Capita Standardized Costs\": 115.06, \"Hospice Per Capita Actual Costs\": 221.99, \"E&M Actual Costs as % of Total Actual Costs\": 0.0852, \"PQI07 Hypertension Admission Rate (age < 65)\": 66.0, \"Percent African American\": 0.54, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0517, \"PAC: LTCH Per Capita Actual Costs\": 40.46, \"MA Beneficiaries\": 16096.0, \"FQHC/RHC Per User Standardized Costs\": 484.11, \"PAC: HH Per User Standardized Costs\": 4134.7, \"Procedures Per User Actual Costs\": 791.44, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 401.0, \"Ambulance Per User Actual Costs\": 972.85, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1276.0, \"PAC: HH Standardized Costs\": 85596615.51, \"ASC Actual Costs as % of Total Actual Costs\": 0.007, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 639.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0011, \"Percent Other/Unknown\": 2.24, \"% of Beneficiaries Using Hospice\": 0.0224, \"PAC: LTCH Per User Actual Costs\": 37724.5, \"Percent Non-Hispanic White\": 96.23, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 658.0, \"Count of Medicare beneficiaries with breast cancer\": 6014.0, \"DME Per Capita Standardized Costs\": 176.97, \"PQI08 CHF Admission Rate (age 65-74)\": 558.0, \"% of Beneficiaries Using DME\": 0.2458, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0107, \"% of Beneficiaries Using IP\": 0.1533, \"DME Standardized Costs\": 38613768.73, \"FQHC/RHC Standardized Costs\": 10600179.73, \"PAC: SNF Per Capita Actual Costs\": 806.89, \"Imaging Standardized Costs\": 28757992.81, \"# E&M Users\": 187684.0, \"Count of Medicare beneficiaries with arthritis\": 54471.0, \"IP Per User Standardized Costs\": 13886.04, \"IP Per User Actual Costs\": 17902.97, \"PQI10 Dehydration Admission Rate (age 75+)\": 376.0, \"PAC: IRF Per User Standardized Costs\": 19078.24, \"DME Per User Actual Costs\": 678.45, \"PAC: IRF Actual Costs\": 52545186.77, \"Imaging Events Per 1000 Beneficiaries\": 3311.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0115, \"PAC: LTCH Per User Standardized Costs\": 36564.63, \"OP Actual Costs\": 372160784.82, \"Count of Medicare beneficiaries with hypertension\": 107417.0, \"ASC Per User Standardized Costs\": 842.29, \"Part B Drugs Per Capita Actual Costs\": 160.19, \"Ambulance Events Per 1000 Beneficiaries\": 329.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 374.0, \"% of Beneficiaries Using PAC: HH\": 0.0847, \"PAC: LTCH Standardized Costs\": 108291517.97, \"Percent of Medicare beneficiaries with atrial fibrillation\": 9.75, \"E&M Per Capita Standardized Costs\": 1275.33, \"E&M Per User Standardized Costs\": 1409.32, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1308.0, \"IP Covered Days Per 1000 Beneficiaries\": 1797.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 50.0, \"Count of Medicare beneficiaries with lung cancer\": 12852.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3314, \"Percent Eligible for Medicaid\": 14.02, \"Imaging Per Capita Standardized Costs\": 302.5, \"% of Beneficiaries Using Tests\": 0.8333, \"Imaging Per Capita Actual Costs\": 328.43, \"% of Beneficiaries Using PAC: SNF\": 0.0667, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.037, \"Count of Medicare beneficiaries with colorectal cancer\": 17058.0, \"Hospice Actual Costs\": 313041075.49, \"# PAC: HH Users\": 92418.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23252.88, \"Total Actual Costs\": 12008300313.22, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 129460.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0112, \"ASC Standardized Costs\": 118991741.55, \"DME Events Per 1000 Beneficiaries\": 1455.0, \"PQI08 CHF Admission Rate (age < 65)\": 893.0, \"ASC Events Per 1000 Beneficiaries\": 217.0, \"PAC: LTCH Actual Costs\": 115585552.17, \"Count of Medicare beneficiaries with depression\": 142732.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1277.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 11.86, \"Outpatient Dialysis Facility Per User Actual Costs\": 24524.33, \"Beneficiaries with Part A and Part B\": 1344772.0, \"% of Beneficiaries Using Part B Drugs\": 0.5716, \"Percent of Medicare beneficiaries with diabetes\": 31.79, \"% of Beneficiaries Using PAC: IRF\": 0.0117, \"E&M Per Capita Actual Costs\": 1324.94, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0311, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0418, \"PAC: SNF Actual Costs\": 1231121596.49, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 435.0, \"Percent Female\": 57.21, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 465.0, \"Percent of Medicare beneficiaries with osteoporosis\": 6.94, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 244.49, \"# Outpatient Dialysis Facility Users\": 11479.0, \"FQHC/RHC Per User Actual Costs\": 358.85, \"Count of Medicare beneficiaries with ischemic heart disease\": 378975.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 189.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.92, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 49.0, \"Percent of Medicare beneficiaries with depression\": 13.07, \"Emergency Department Visits per 1000 Beneficiaries\": 591.0, \"IP Actual Costs\": 3979381748.39, \"% of Beneficiaries Using OP\": 0.5528, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.022, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1311, \"Count of Medicare beneficiaries with stroke\": 51198.0, \"PQI12 UTI Admission Rate (age 75+)\": 1151.0, \"# OP Users\": 603499.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 27.0, \"# Procedure Users\": 741150.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 34.71, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0769, \"Count of Medicare beneficiaries with diabetes\": 347030.0, \"ASC Per User Actual Costs\": 832.84, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1162.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0186, \"Percent of Medicare beneficiaries with high cholesterol\": 53.94, \"Standardized Per Capita Costs\": 9727.7, \"Ambulance Actual Costs\": 210399602.46, \"FQHC/RHC Per Capita Actual Costs\": 4.73, \"Part B Drugs Per User Standardized Costs\": 712.1, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0232, \"# PAC: SNF Users (with a covered stay)\": 72837.0, \"Ambulance Per Capita Actual Costs\": 192.72, \"PQI12 UTI Admission Rate (age 65-74)\": 266.0, \"Hospice Standardized Costs\": 286918773.8, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 257.86, \"% of Beneficiaries Using E&M\": 0.9049, \"PQI10 Dehydration Admission Rate (age 65-74)\": 183.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 274.0, \"Tests Per Capita Actual Costs\": 382.53, \"# DME Users\": 280826.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.107, \"PAC: SNF Per User Actual Costs\": 16902.42, \"State\": \"NJ\", \"OP Per User Actual Costs\": 1996.78, \"PAC: HH Episodes Per 1000 Beneficiaries\": 127.0, \"Part B Drugs Actual Costs\": 443949515.05, \"FQHC/RHC Actual Costs\": 5165241.66, \"OP Standardized Costs\": 1136467950.35, \"DME Per User Standardized Costs\": 702.01, \"OP Actual Costs as % of Total Actual Costs\": 0.1004, \"PAC: SNF Per Capita Standardized Costs\": 1040.62, \"% of Beneficiaries Using Ambulance\": 0.0988, \"Hospice Per User Standardized Costs\": 10284.93, \"# Imaging Users\": 770593.0, \"Part B Drugs Per User Actual Costs\": 711.36, \"Total Standardized Costs\": 10620194352.6, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.56, \"Count of Medicare beneficiaries with chronic kidney disease\": 172130.0, \"E&M Standardized Costs\": 1392334632.63, \"Percent Hispanic\": 6.97, \"ASC Per Capita Actual Costs\": 113.06, \"Count of Medicare beneficiaries who have had a heart attack\": 10063.0, \"Tests Actual Costs\": 417621177.43, \"# PAC: LTCH Users (with a covered stay)\": 2234.0, \"% of Beneficiaries Using Procedures\": 0.6789, \"PAC: HH Actual Costs\": 385494875.15, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 41.0, \"PAC: LTCH Per Capita Standardized Costs\": 99.19, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0386, \"Emergency Department Visits\": 645384.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0132, \"Procedures Actual Costs\": 873597220.45, \"# FQHC/RHC Users\": 14394.0, \"Number of Acute Hospital Readmissions\": 60078.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 170.0, \"Outpatient Dialysis Facility Standardized Costs\": 266919809.68, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0225, \"OP Standardized Costs as % of Total Standardized Costs\": 0.107, \"Ambulance Per User Standardized Costs\": 2165.16, \"Imaging Per User Standardized Costs\": 428.57, \"Percent of Medicare beneficiaries with asthma\": 5.57, \"Part B Drugs Standardized Costs\": 444406459.88, \"FFS Beneficiaries\": 1091748.0, \"# Hospice Users (with a covered stay)\": 27897.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0105, \"Count of Medicare beneficiaries with osteoporosis\": 75766.0, \"PQI08 CHF Admission Rate (age 75+)\": 2198.0, \"PAC: IRF Per Capita Standardized Costs\": 225.69, \"Procedures Standardized Costs\": 817138873.6, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2793, \"IP Per Capita Actual Costs\": 3644.96, \"DME Actual Costs\": 181202780.12, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0321, \"Count of Medicare beneficiaries with prostate cancer\": 41286.0, \"PAC: HH Per Capita Standardized Costs\": 326.94, \"Count of Medicare beneficiaries with heart failure\": 187523.0, \"Tests Per User Standardized Costs\": 450.2, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0096, \"Percent of Medicare beneficiaries with prostate cancer\": 3.78, \"PAC: IRF Per Capita Actual Costs\": 246.94, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 465.31, \"Percent of Medicare beneficiaries with breast cancer\": 3.44, \"Procedures Per User Standardized Costs\": 1102.53, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.77, \"PAC: HH Per User Actual Costs\": 4171.21, \"Count of Medicare beneficiaries with high cholesterol\": 588896.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.1025, \"Hospice Per Capita Standardized Costs\": 262.81, \"# Part B Drugs Users\": 624083.0, \"Average HCC Score\": 1.0556, \"Standardized Risk-Adjusted Per Capita Costs\": 9700.09, \"# PAC: IRF Users (with a covered stay)\": 12769.0, \"Ambulance Standardized Costs\": 233465434.01, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0261, \"Percent of Medicare beneficiaries with heart failure\": 17.18, \"Tests Actual Costs as % of Total Actual Costs\": 0.0348, \"FQHC/RHC Per Capita Standardized Costs\": 4.54, \"PQI07 Hypertension Admission Rate (age 65-74)\": 91.0, \"Test Events Per 1000 Beneficiaries\": 12944.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 62.0, \"ASC Actual Costs\": 123436423.36, \"Part B Drugs Per Capita Standardized Costs\": 407.06, \"Imaging Actual Costs\": 358566425.1, \"Tests Per User Actual Costs\": 459.06, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0175, \"Hospice Per User Actual Costs\": 11221.32, \"Tests Standardized Costs\": 409560546.05, \"IP Standardized Costs\": 2965820636.27, \"IP Per Capita Standardized Costs\": 2716.58, \"Outpatient Dialysis Facility Actual Costs\": 281514752.26, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 96.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2431.0, \"Percent of Medicare beneficiaries with hypertension\": 61.33, \"IP Covered Stays Per 1000 Beneficiaries\": 298.0, \"# Ambulance Users\": 107828.0, \"# ASC Users\": 148211.0, \"ASC Per Capita Standardized Costs\": 108.99, \"Procedures Per Capita Actual Costs\": 800.18, \"Procedures Per Capita Standardized Costs\": 748.47, \"IP Users (with a covered stay)\": 197424.0, \"Total Standardized Risk-Adjusted Costs\": 10590057951.02, \"Actual Per Capita Costs\": 10999.15, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0102, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 13.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.18, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.86, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 324.0, \"OP Per User Standardized Costs\": 1883.13, \"Count of Medicare beneficiaries with atrial fibrillation\": 106441.0, \"Procedure Events Per 1000 Beneficiaries\": 6815.0, \"Percent of Medicare beneficiaries with stroke\": 4.69, \"PAC: IRF Per User Actual Costs\": 21113.11, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 118527.0, \"% of Beneficiaries Using ASC\": 0.1358, \"PAC: IRF Standardized Costs\": 246398139.62, \"Hospice Covered Days Per 1000 Beneficiaries\": 1641.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 261.0, \"PAC: SNF Per User Standardized Costs\": 15597.81, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0004, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 122.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.027, \"MA Participation Rate\": 18.82, \"OP Per Capita Standardized Costs\": 1040.96, \"Percent of Medicare beneficiaries with arthritis\": 30.93, \"Ambulance Per Capita Standardized Costs\": 213.85, \"PAC: HH Visits Per 1000 Beneficiaries\": 1880.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0727, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0299, \"PAC: HH Per Capita Actual Costs\": 353.1, \"E&M Events Per 1000 Beneficiaries\": 17320.0, \"Count of Medicare beneficiaries with asthma\": 60802.0, \"# Test Users\": 909735.0, \"E&M Actual Costs\": 1446504227.23, \"% of Beneficiaries Using Imaging\": 0.7058, \"PAC: SNF Standardized Costs\": 1136097876.67, \"DME Per Capita Actual Costs\": 165.97, \"E&M Per User Actual Costs\": 1464.15, \"Percent Male\": 42.79, \"OP Visits Per 1000 Beneficiaries\": 3248.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0151, \"OP Per Capita Actual Costs\": 1103.78, \"Hospital Readmission Rate\": 0.1926, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0005, \"Tests Per Capita Standardized Costs\": 375.14, \"Hospice Per Capita Actual Costs\": 286.73, \"E&M Actual Costs as % of Total Actual Costs\": 0.1205, \"PQI07 Hypertension Admission Rate (age < 65)\": 151.0, \"Percent African American\": 9.9, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0336, \"PAC: LTCH Per Capita Actual Costs\": 105.87, \"MA Beneficiaries\": 253024.0, \"FQHC/RHC Per User Standardized Costs\": 344.0, \"PAC: HH Per User Standardized Costs\": 3862.14, \"Procedures Per User Actual Costs\": 1178.71, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 900.0, \"Ambulance Per User Actual Costs\": 1951.25, \"Average Age\": 73.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1398.0, \"PAC: HH Standardized Costs\": 356931100.34, \"ASC Actual Costs as % of Total Actual Costs\": 0.0103, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 691.0, \"% of Beneficiaries Using PAC: LTCH\": 0.002, \"Percent Other/Unknown\": 4.81, \"% of Beneficiaries Using Hospice\": 0.0256, \"PAC: LTCH Per User Actual Costs\": 51739.28, \"Percent Non-Hispanic White\": 78.32, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 590.0, \"Count of Medicare beneficiaries with breast cancer\": 37515.0, \"DME Per Capita Standardized Costs\": 180.58, \"PQI08 CHF Admission Rate (age 65-74)\": 655.0, \"% of Beneficiaries Using DME\": 0.2572, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0234, \"% of Beneficiaries Using IP\": 0.1808, \"DME Standardized Costs\": 197143230.95, \"FQHC/RHC Standardized Costs\": 4951546.11, \"PAC: SNF Per Capita Actual Costs\": 1127.66, \"Imaging Standardized Costs\": 330249580.25, \"# E&M Users\": 987948.0, \"Count of Medicare beneficiaries with arthritis\": 337656.0, \"IP Per User Standardized Costs\": 15022.59, \"IP Per User Actual Costs\": 20156.52, \"PQI10 Dehydration Admission Rate (age 75+)\": 553.0, \"PAC: IRF Per User Standardized Costs\": 19296.59, \"DME Per User Actual Costs\": 645.25, \"PAC: IRF Actual Costs\": 269593311.05, \"Imaging Events Per 1000 Beneficiaries\": 4504.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0251, \"PAC: LTCH Per User Standardized Costs\": 48474.27, \"OP Actual Costs\": 1205051927.51, \"Count of Medicare beneficiaries with hypertension\": 669560.0, \"ASC Per User Standardized Costs\": 802.85, \"Part B Drugs Per Capita Actual Costs\": 406.64, \"Ambulance Events Per 1000 Beneficiaries\": 654.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 265.0, \"% of Beneficiaries Using PAC: HH\": 0.0694, \"PAC: LTCH Standardized Costs\": 26593556.23, \"Percent of Medicare beneficiaries with atrial fibrillation\": 4.9, \"E&M Per Capita Standardized Costs\": 687.42, \"E&M Per User Standardized Costs\": 828.9, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1599.0, \"IP Covered Days Per 1000 Beneficiaries\": 1058.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 28.0, \"Count of Medicare beneficiaries with lung cancer\": 1298.0, \"IP Actual Costs as % of Total Actual Costs\": 0.328, \"Percent Eligible for Medicaid\": 22.45, \"Imaging Per Capita Standardized Costs\": 167.81, \"% of Beneficiaries Using Tests\": 0.6612, \"Imaging Per Capita Actual Costs\": 156.02, \"% of Beneficiaries Using PAC: SNF\": 0.0324, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0312, \"Count of Medicare beneficiaries with colorectal cancer\": 2095.0, \"Hospice Actual Costs\": 69488808.79, \"# PAC: HH Users\": 15455.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24175.27, \"Total Actual Costs\": 1646799835.32, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 18543.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0095, \"ASC Standardized Costs\": 14793597.27, \"DME Events Per 1000 Beneficiaries\": 1786.0, \"PQI08 CHF Admission Rate (age < 65)\": 394.0, \"ASC Events Per 1000 Beneficiaries\": 119.0, \"PAC: LTCH Actual Costs\": 24883116.24, \"Count of Medicare beneficiaries with depression\": 33327.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1369.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.33, \"Outpatient Dialysis Facility Per User Actual Costs\": 23729.56, \"Beneficiaries with Part A and Part B\": 331614.0, \"% of Beneficiaries Using Part B Drugs\": 0.4024, \"Percent of Medicare beneficiaries with diabetes\": 24.57, \"% of Beneficiaries Using PAC: IRF\": 0.0104, \"E&M Per Capita Actual Costs\": 623.16, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0241, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0333, \"PAC: SNF Actual Costs\": 98201005.29, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 451.0, \"Percent Female\": 52.03, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 259.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.71, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 277.74, \"# Outpatient Dialysis Facility Users\": 2559.0, \"FQHC/RHC Per User Actual Costs\": 413.58, \"Count of Medicare beneficiaries with ischemic heart disease\": 47045.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 132.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.72, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 577.0, \"Percent of Medicare beneficiaries with depression\": 14.96, \"Emergency Department Visits per 1000 Beneficiaries\": 554.0, \"IP Actual Costs\": 540201997.99, \"% of Beneficiaries Using OP\": 0.6349, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0109, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0988, \"Count of Medicare beneficiaries with stroke\": 5845.0, \"PQI12 UTI Admission Rate (age 75+)\": 863.0, \"# OP Users\": 141415.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 27.0, \"# Procedure Users\": 114916.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 21.12, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0667, \"Count of Medicare beneficiaries with diabetes\": 54737.0, \"ASC Per User Actual Costs\": 784.36, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 792.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0313, \"Percent of Medicare beneficiaries with high cholesterol\": 35.5, \"Standardized Per Capita Costs\": 6958.94, \"Ambulance Actual Costs\": 32618775.46, \"FQHC/RHC Per Capita Actual Costs\": 54.46, \"Part B Drugs Per User Standardized Costs\": 575.73, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0291, \"# PAC: SNF Users (with a covered stay)\": 7217.0, \"Ambulance Per Capita Actual Costs\": 146.44, \"PQI12 UTI Admission Rate (age 65-74)\": 243.0, \"Hospice Standardized Costs\": 71280631.18, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 272.62, \"% of Beneficiaries Using E&M\": 0.8293, \"PQI10 Dehydration Admission Rate (age 65-74)\": 181.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 193.0, \"Tests Per Capita Actual Costs\": 158.16, \"# DME Users\": 58566.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0679, \"PAC: SNF Per User Actual Costs\": 13606.9, \"State\": \"NM\", \"OP Per User Actual Costs\": 1639.2, \"PAC: HH Episodes Per 1000 Beneficiaries\": 136.0, \"Part B Drugs Actual Costs\": 51298263.51, \"FQHC/RHC Actual Costs\": 12130982.87, \"OP Standardized Costs\": 219615544.29, \"DME Per User Standardized Costs\": 827.39, \"OP Actual Costs as % of Total Actual Costs\": 0.1408, \"PAC: SNF Per Capita Standardized Costs\": 472.6, \"% of Beneficiaries Using Ambulance\": 0.0977, \"Hospice Per User Standardized Costs\": 12663.11, \"# Imaging Users\": 134406.0, \"Part B Drugs Per User Actual Costs\": 572.29, \"Total Standardized Costs\": 1550027114.23, \"Percent of Medicare beneficiaries with colorectal cancer\": 0.94, \"Count of Medicare beneficiaries with chronic kidney disease\": 27985.0, \"E&M Standardized Costs\": 153115682.57, \"Percent Hispanic\": 29.55, \"ASC Per Capita Actual Costs\": 62.93, \"Count of Medicare beneficiaries who have had a heart attack\": 1608.0, \"Tests Actual Costs\": 35228880.1, \"# PAC: LTCH Users (with a covered stay)\": 610.0, \"% of Beneficiaries Using Procedures\": 0.5159, \"PAC: HH Actual Costs\": 78622548.38, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 41.0, \"PAC: LTCH Per Capita Standardized Costs\": 119.39, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0237, \"Emergency Department Visits\": 123437.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1317, \"Procedures Actual Costs\": 97048733.74, \"# FQHC/RHC Users\": 29332.0, \"Number of Acute Hospital Readmissions\": 7277.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 148.0, \"Outpatient Dialysis Facility Standardized Costs\": 61864526.51, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0275, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1417, \"Ambulance Per User Standardized Costs\": 779.36, \"Imaging Per User Standardized Costs\": 278.09, \"Percent of Medicare beneficiaries with asthma\": 4.7, \"Part B Drugs Standardized Costs\": 51606499.5, \"FFS Beneficiaries\": 222739.0, \"# Hospice Users (with a covered stay)\": 5629.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0115, \"Count of Medicare beneficiaries with osteoporosis\": 12721.0, \"PQI08 CHF Admission Rate (age 75+)\": 1383.0, \"PAC: IRF Per Capita Standardized Costs\": 202.65, \"Procedures Standardized Costs\": 103436750.98, \"IP Standardized Costs as % of Total Standardized Costs\": 0.276, \"IP Per Capita Actual Costs\": 2425.27, \"DME Actual Costs\": 46037848.61, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0477, \"Count of Medicare beneficiaries with prostate cancer\": 5214.0, \"PAC: HH Per Capita Standardized Costs\": 376.66, \"Count of Medicare beneficiaries with heart failure\": 25630.0, \"Tests Per User Standardized Costs\": 249.89, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0151, \"Percent of Medicare beneficiaries with prostate cancer\": 2.34, \"PAC: IRF Per Capita Actual Costs\": 203.08, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 258.57, \"Percent of Medicare beneficiaries with breast cancer\": 2.26, \"Procedures Per User Standardized Costs\": 900.11, \"Percent of Medicare beneficiaries with chronic kidney disease\": 12.56, \"PAC: HH Per User Actual Costs\": 5087.19, \"Count of Medicare beneficiaries with high cholesterol\": 79075.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0596, \"Hospice Per Capita Standardized Costs\": 320.02, \"# Part B Drugs Users\": 89637.0, \"Average HCC Score\": 0.9176, \"Standardized Risk-Adjusted Per Capita Costs\": 7899.62, \"# PAC: IRF Users (with a covered stay)\": 2311.0, \"Ambulance Standardized Costs\": 16955691.74, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0422, \"Percent of Medicare beneficiaries with heart failure\": 11.51, \"Tests Actual Costs as % of Total Actual Costs\": 0.0214, \"FQHC/RHC Per Capita Standardized Costs\": 57.63, \"PQI07 Hypertension Admission Rate (age 65-74)\": 46.0, \"Test Events Per 1000 Beneficiaries\": 5784.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 78.0, \"ASC Actual Costs\": 14016440.41, \"Part B Drugs Per Capita Standardized Costs\": 231.69, \"Imaging Actual Costs\": 34752807.01, \"Tests Per User Actual Costs\": 239.19, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0198, \"Hospice Per User Actual Costs\": 12344.79, \"Tests Standardized Costs\": 36804780.13, \"IP Standardized Costs\": 427786166.48, \"IP Per Capita Standardized Costs\": 1920.57, \"Outpatient Dialysis Facility Actual Costs\": 60723932.29, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 42.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1128.0, \"Percent of Medicare beneficiaries with hypertension\": 46.2, \"IP Covered Stays Per 1000 Beneficiaries\": 210.0, \"# Ambulance Users\": 21756.0, \"# ASC Users\": 17870.0, \"ASC Per Capita Standardized Costs\": 66.42, \"Procedures Per Capita Actual Costs\": 435.71, \"Procedures Per Capita Standardized Costs\": 464.39, \"IP Users (with a covered stay)\": 31158.0, \"Total Standardized Risk-Adjusted Costs\": 1759553459.72, \"Actual Per Capita Costs\": 7393.41, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0172, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 11.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.58, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 8.97, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 193.0, \"OP Per User Standardized Costs\": 1552.99, \"Count of Medicare beneficiaries with atrial fibrillation\": 10911.0, \"Procedure Events Per 1000 Beneficiaries\": 3133.0, \"Percent of Medicare beneficiaries with stroke\": 2.62, \"PAC: IRF Per User Actual Costs\": 19572.84, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 19990.0, \"% of Beneficiaries Using ASC\": 0.0802, \"PAC: IRF Standardized Costs\": 45137501.69, \"Hospice Covered Days Per 1000 Beneficiaries\": 2014.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 174.0, \"PAC: SNF Per User Standardized Costs\": 14585.89, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0074, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 118.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.046, \"MA Participation Rate\": 32.83, \"OP Per Capita Standardized Costs\": 985.98, \"Percent of Medicare beneficiaries with arthritis\": 26.85, \"Ambulance Per Capita Standardized Costs\": 76.12, \"PAC: HH Visits Per 1000 Beneficiaries\": 2334.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0589, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0211, \"PAC: HH Per Capita Actual Costs\": 352.98, \"E&M Events Per 1000 Beneficiaries\": 9901.0, \"Count of Medicare beneficiaries with asthma\": 10464.0, \"# Test Users\": 147286.0, \"E&M Actual Costs\": 138802462.28, \"% of Beneficiaries Using Imaging\": 0.6034, \"PAC: SNF Standardized Costs\": 105266395.29, \"DME Per Capita Actual Costs\": 206.69, \"E&M Per User Actual Costs\": 751.41, \"Percent Male\": 47.97, \"OP Visits Per 1000 Beneficiaries\": 4309.0, \"DME Actual Costs as % of Total Actual Costs\": 0.028, \"OP Per Capita Actual Costs\": 1040.71, \"Hospital Readmission Rate\": 0.16, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0083, \"Tests Per Capita Standardized Costs\": 165.24, \"Hospice Per Capita Actual Costs\": 311.97, \"E&M Actual Costs as % of Total Actual Costs\": 0.0843, \"PQI07 Hypertension Admission Rate (age < 65)\": 53.0, \"Percent African American\": 1.64, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0541, \"PAC: LTCH Per Capita Actual Costs\": 111.71, \"MA Beneficiaries\": 108875.0, \"FQHC/RHC Per User Standardized Costs\": 437.62, \"PAC: HH Per User Standardized Costs\": 5428.39, \"Procedures Per User Actual Costs\": 844.52, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 661.0, \"Ambulance Per User Actual Costs\": 1499.3, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 653.0, \"PAC: HH Standardized Costs\": 83895795.38, \"ASC Actual Costs as % of Total Actual Costs\": 0.0085, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 509.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0027, \"Percent Other/Unknown\": 8.68, \"% of Beneficiaries Using Hospice\": 0.0253, \"PAC: LTCH Per User Actual Costs\": 40791.99, \"Percent Non-Hispanic White\": 60.12, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 421.0, \"Count of Medicare beneficiaries with breast cancer\": 5028.0, \"DME Per Capita Standardized Costs\": 217.55, \"PQI08 CHF Admission Rate (age 65-74)\": 426.0, \"% of Beneficiaries Using DME\": 0.2629, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0369, \"% of Beneficiaries Using IP\": 0.1399, \"DME Standardized Costs\": 48457127.56, \"FQHC/RHC Standardized Costs\": 12836381.35, \"PAC: SNF Per Capita Actual Costs\": 440.88, \"Imaging Standardized Costs\": 37377220.88, \"# E&M Users\": 184722.0, \"Count of Medicare beneficiaries with arthritis\": 59797.0, \"IP Per User Standardized Costs\": 13729.58, \"IP Per User Actual Costs\": 17337.51, \"PQI10 Dehydration Admission Rate (age 75+)\": 394.0, \"PAC: IRF Per User Standardized Costs\": 19531.59, \"DME Per User Actual Costs\": 786.08, \"PAC: IRF Actual Costs\": 45232823.63, \"Imaging Events Per 1000 Beneficiaries\": 3129.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0399, \"PAC: LTCH Per User Standardized Costs\": 43595.99, \"OP Actual Costs\": 231807518.83, \"Count of Medicare beneficiaries with hypertension\": 102908.0, \"ASC Per User Standardized Costs\": 827.85, \"Part B Drugs Per Capita Actual Costs\": 230.31, \"Ambulance Events Per 1000 Beneficiaries\": 214.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 432.0, \"% of Beneficiaries Using PAC: HH\": 0.0939, \"PAC: LTCH Standardized Costs\": 61454847.0, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.22, \"E&M Per Capita Standardized Costs\": 977.4, \"E&M Per User Standardized Costs\": 1175.73, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1340.0, \"IP Covered Days Per 1000 Beneficiaries\": 1373.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 30.0, \"Count of Medicare beneficiaries with lung cancer\": 2431.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3152, \"Percent Eligible for Medicaid\": 14.58, \"Imaging Per Capita Standardized Costs\": 295.84, \"% of Beneficiaries Using Tests\": 0.7169, \"Imaging Per Capita Actual Costs\": 296.97, \"% of Beneficiaries Using PAC: SNF\": 0.0327, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.035, \"Count of Medicare beneficiaries with colorectal cancer\": 2655.0, \"Hospice Actual Costs\": 82778773.56, \"# PAC: HH Users\": 23708.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24532.06, \"Total Actual Costs\": 2396372342.75, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 19237.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0139, \"ASC Standardized Costs\": 30396176.45, \"DME Events Per 1000 Beneficiaries\": 1563.0, \"PQI08 CHF Admission Rate (age < 65)\": 1035.0, \"ASC Events Per 1000 Beneficiaries\": 207.0, \"PAC: LTCH Actual Costs\": 66302152.53, \"Count of Medicare beneficiaries with depression\": 30927.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1351.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 7.62, \"Outpatient Dialysis Facility Per User Actual Costs\": 26064.19, \"Beneficiaries with Part A and Part B\": 391476.0, \"% of Beneficiaries Using Part B Drugs\": 0.4696, \"Percent of Medicare beneficiaries with diabetes\": 23.86, \"% of Beneficiaries Using PAC: IRF\": 0.0229, \"E&M Per Capita Actual Costs\": 960.25, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0342, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0386, \"PAC: SNF Actual Costs\": 137995380.89, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 475.0, \"Percent Female\": 51.01, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 209.0, \"Percent of Medicare beneficiaries with osteoporosis\": 4.91, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 250.03, \"# Outpatient Dialysis Facility Users\": 2572.0, \"FQHC/RHC Per User Actual Costs\": 472.54, \"Count of Medicare beneficiaries with ischemic heart disease\": 58928.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 153.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.66, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 183.0, \"Percent of Medicare beneficiaries with depression\": 12.26, \"Emergency Department Visits per 1000 Beneficiaries\": 557.0, \"IP Actual Costs\": 755438929.42, \"% of Beneficiaries Using OP\": 0.4728, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.011, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.113, \"Count of Medicare beneficiaries with stroke\": 9109.0, \"PQI12 UTI Admission Rate (age 75+)\": 956.0, \"# OP Users\": 119303.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 25.0, \"# Procedure Users\": 141498.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 23.35, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0806, \"Count of Medicare beneficiaries with diabetes\": 60204.0, \"ASC Per User Actual Costs\": 1039.49, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 933.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0249, \"Percent of Medicare beneficiaries with high cholesterol\": 42.05, \"Standardized Per Capita Costs\": 8649.77, \"Ambulance Actual Costs\": 27914811.33, \"FQHC/RHC Per Capita Actual Costs\": 22.12, \"Part B Drugs Per User Standardized Costs\": 711.62, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0571, \"# PAC: SNF Users (with a covered stay)\": 8240.0, \"Ambulance Per Capita Actual Costs\": 110.62, \"PQI12 UTI Admission Rate (age 65-74)\": 230.0, \"Hospice Standardized Costs\": 74379929.15, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 265.65, \"% of Beneficiaries Using E&M\": 0.8313, \"PQI10 Dehydration Admission Rate (age 65-74)\": 163.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 169.0, \"Tests Per Capita Actual Costs\": 288.74, \"# DME Users\": 61788.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0584, \"PAC: SNF Per User Actual Costs\": 16747.01, \"State\": \"NV\", \"OP Per User Actual Costs\": 1771.73, \"PAC: HH Episodes Per 1000 Beneficiaries\": 179.0, \"Part B Drugs Actual Costs\": 83885550.5, \"FQHC/RHC Actual Costs\": 5581611.7, \"OP Standardized Costs\": 194012614.8, \"DME Per User Standardized Costs\": 878.77, \"OP Actual Costs as % of Total Actual Costs\": 0.0882, \"PAC: SNF Per Capita Standardized Costs\": 504.94, \"% of Beneficiaries Using Ambulance\": 0.1055, \"Hospice Per User Standardized Costs\": 12433.96, \"# Imaging Users\": 159396.0, \"Part B Drugs Per User Actual Costs\": 707.92, \"Total Standardized Costs\": 2182803267.84, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.05, \"Count of Medicare beneficiaries with chronic kidney disease\": 40671.0, \"E&M Standardized Costs\": 246651081.62, \"Percent Hispanic\": 8.76, \"ASC Per Capita Actual Costs\": 123.6, \"Count of Medicare beneficiaries who have had a heart attack\": 1666.0, \"Tests Actual Costs\": 72865826.15, \"# PAC: LTCH Users (with a covered stay)\": 1406.0, \"% of Beneficiaries Using Procedures\": 0.5607, \"PAC: HH Actual Costs\": 138609945.58, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 38.0, \"PAC: LTCH Per Capita Standardized Costs\": 243.53, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0339, \"Emergency Department Visits\": 140684.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0468, \"Procedures Actual Costs\": 176906737.48, \"# FQHC/RHC Users\": 11812.0, \"Number of Acute Hospital Readmissions\": 10716.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 349.0, \"Outpatient Dialysis Facility Standardized Costs\": 63096452.03, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0597, \"OP Standardized Costs as % of Total Standardized Costs\": 0.0889, \"Ambulance Per User Standardized Costs\": 898.52, \"Imaging Per User Standardized Costs\": 468.38, \"Percent of Medicare beneficiaries with asthma\": 4.52, \"Part B Drugs Standardized Costs\": 84323950.1, \"FFS Beneficiaries\": 252354.0, \"# Hospice Users (with a covered stay)\": 5982.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0102, \"Count of Medicare beneficiaries with osteoporosis\": 12399.0, \"PQI08 CHF Admission Rate (age 75+)\": 1436.0, \"PAC: IRF Per Capita Standardized Costs\": 493.66, \"Procedures Standardized Costs\": 176026501.37, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2817, \"IP Per Capita Actual Costs\": 2993.57, \"DME Actual Costs\": 51910531.34, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0578, \"Count of Medicare beneficiaries with prostate cancer\": 7639.0, \"PAC: HH Per Capita Standardized Costs\": 491.94, \"Count of Medicare beneficiaries with heart failure\": 27295.0, \"Tests Per User Standardized Costs\": 408.88, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0277, \"Percent of Medicare beneficiaries with prostate cancer\": 3.03, \"PAC: IRF Per Capita Actual Costs\": 567.05, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 470.17, \"Percent of Medicare beneficiaries with breast cancer\": 2.51, \"Procedures Per User Standardized Costs\": 1244.02, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.12, \"PAC: HH Per User Actual Costs\": 5846.55, \"Count of Medicare beneficiaries with high cholesterol\": 106106.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0576, \"Hospice Per Capita Standardized Costs\": 294.74, \"# Part B Drugs Users\": 118496.0, \"Average HCC Score\": 0.9392, \"Standardized Risk-Adjusted Per Capita Costs\": 9596.24, \"# PAC: IRF Users (with a covered stay)\": 5778.0, \"Ambulance Standardized Costs\": 23924731.24, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0345, \"Percent of Medicare beneficiaries with heart failure\": 10.82, \"Tests Actual Costs as % of Total Actual Costs\": 0.0304, \"FQHC/RHC Per Capita Standardized Costs\": 19.92, \"PQI07 Hypertension Admission Rate (age 65-74)\": 82.0, \"Test Events Per 1000 Beneficiaries\": 9097.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 178.0, \"ASC Actual Costs\": 31191824.96, \"Part B Drugs Per Capita Standardized Costs\": 334.15, \"Imaging Actual Costs\": 74942522.44, \"Tests Per User Actual Costs\": 402.77, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0116, \"Hospice Per User Actual Costs\": 13837.98, \"Tests Standardized Costs\": 73970381.79, \"IP Standardized Costs\": 614921460.48, \"IP Per Capita Standardized Costs\": 2436.74, \"Outpatient Dialysis Facility Actual Costs\": 67037087.84, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 44.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1180.0, \"Percent of Medicare beneficiaries with hypertension\": 49.64, \"IP Covered Stays Per 1000 Beneficiaries\": 248.0, \"# Ambulance Users\": 26627.0, \"# ASC Users\": 30007.0, \"ASC Per Capita Standardized Costs\": 120.45, \"Procedures Per Capita Actual Costs\": 701.03, \"Procedures Per Capita Standardized Costs\": 697.54, \"IP Users (with a covered stay)\": 38883.0, \"Total Standardized Risk-Adjusted Costs\": 2421650001.86, \"Actual Per Capita Costs\": 9496.07, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0282, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 27.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 6.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.96, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.13, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 200.0, \"OP Per User Standardized Costs\": 1626.22, \"Count of Medicare beneficiaries with atrial fibrillation\": 15702.0, \"Procedure Events Per 1000 Beneficiaries\": 4688.0, \"Percent of Medicare beneficiaries with stroke\": 3.61, \"PAC: IRF Per User Actual Costs\": 24766.02, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 28091.0, \"% of Beneficiaries Using ASC\": 0.1189, \"PAC: IRF Standardized Costs\": 124578276.77, \"Hospice Covered Days Per 1000 Beneficiaries\": 1798.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 255.0, \"PAC: SNF Per User Standardized Costs\": 15463.99, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0023, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 88.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0341, \"MA Participation Rate\": 35.54, \"OP Per Capita Standardized Costs\": 768.81, \"Percent of Medicare beneficiaries with arthritis\": 25.21, \"Ambulance Per Capita Standardized Costs\": 94.81, \"PAC: HH Visits Per 1000 Beneficiaries\": 3064.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0738, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0313, \"PAC: HH Per Capita Actual Costs\": 549.27, \"E&M Events Per 1000 Beneficiaries\": 13285.0, \"Count of Medicare beneficiaries with asthma\": 11405.0, \"# Test Users\": 180911.0, \"E&M Actual Costs\": 242322271.48, \"% of Beneficiaries Using Imaging\": 0.6316, \"PAC: SNF Standardized Costs\": 127423300.99, \"DME Per Capita Actual Costs\": 205.71, \"E&M Per User Actual Costs\": 1155.1, \"Percent Male\": 48.99, \"OP Visits Per 1000 Beneficiaries\": 2393.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0217, \"OP Per Capita Actual Costs\": 837.6, \"Hospital Readmission Rate\": 0.1805, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0023, \"Tests Per Capita Standardized Costs\": 293.12, \"Hospice Per Capita Actual Costs\": 328.03, \"E&M Actual Costs as % of Total Actual Costs\": 0.1011, \"PQI07 Hypertension Admission Rate (age < 65)\": 164.0, \"Percent African American\": 6.91, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0569, \"PAC: LTCH Per Capita Actual Costs\": 262.73, \"MA Beneficiaries\": 139122.0, \"FQHC/RHC Per User Standardized Costs\": 425.57, \"PAC: HH Per User Standardized Costs\": 5236.38, \"Procedures Per User Actual Costs\": 1250.24, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 648.0, \"Ambulance Per User Actual Costs\": 1048.37, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1210.0, \"PAC: HH Standardized Costs\": 124144190.52, \"ASC Actual Costs as % of Total Actual Costs\": 0.013, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 634.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0056, \"Percent Other/Unknown\": 7.46, \"% of Beneficiaries Using Hospice\": 0.0237, \"PAC: LTCH Per User Actual Costs\": 47156.58, \"Percent Non-Hispanic White\": 76.87, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 680.0, \"Count of Medicare beneficiaries with breast cancer\": 6340.0, \"DME Per Capita Standardized Costs\": 215.16, \"PQI08 CHF Admission Rate (age 65-74)\": 486.0, \"% of Beneficiaries Using DME\": 0.2448, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.028, \"% of Beneficiaries Using IP\": 0.1541, \"DME Standardized Costs\": 54297312.29, \"FQHC/RHC Standardized Costs\": 5026867.1, \"PAC: SNF Per Capita Actual Costs\": 546.83, \"Imaging Standardized Costs\": 74657638.86, \"# E&M Users\": 209785.0, \"Count of Medicare beneficiaries with arthritis\": 63620.0, \"IP Per User Standardized Costs\": 15814.66, \"IP Per User Actual Costs\": 19428.51, \"PQI10 Dehydration Admission Rate (age 75+)\": 405.0, \"PAC: IRF Per User Standardized Costs\": 21560.8, \"DME Per User Actual Costs\": 840.14, \"PAC: IRF Actual Costs\": 143098039.38, \"Imaging Events Per 1000 Beneficiaries\": 3960.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0289, \"PAC: LTCH Per User Standardized Costs\": 43709.0, \"OP Actual Costs\": 211372808.15, \"Count of Medicare beneficiaries with hypertension\": 125267.0, \"ASC Per User Standardized Costs\": 1012.97, \"Part B Drugs Per Capita Actual Costs\": 332.41, \"Ambulance Events Per 1000 Beneficiaries\": 243.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 283.0, \"% of Beneficiaries Using PAC: HH\": 0.0862, \"PAC: LTCH Standardized Costs\": 55925140.43, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.51, \"E&M Per Capita Standardized Costs\": 1163.72, \"E&M Per User Standardized Costs\": 1320.92, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1375.0, \"IP Covered Days Per 1000 Beneficiaries\": 2002.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 46.0, \"Count of Medicare beneficiaries with lung cancer\": 21593.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3985, \"Percent Eligible for Medicaid\": 27.19, \"Imaging Per Capita Standardized Costs\": 314.73, \"% of Beneficiaries Using Tests\": 0.7863, \"Imaging Per Capita Actual Costs\": 338.93, \"% of Beneficiaries Using PAC: SNF\": 0.0526, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0296, \"Count of Medicare beneficiaries with colorectal cancer\": 26488.0, \"Hospice Actual Costs\": 306078946.36, \"# PAC: HH Users\": 161181.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23445.04, \"Total Actual Costs\": 20170705077.51, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 217297.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0049, \"ASC Standardized Costs\": 81753206.06, \"DME Events Per 1000 Beneficiaries\": 1550.0, \"PQI08 CHF Admission Rate (age < 65)\": 687.0, \"ASC Events Per 1000 Beneficiaries\": 80.0, \"PAC: LTCH Actual Costs\": 64272592.38, \"Count of Medicare beneficiaries with depression\": 271484.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1384.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 11.62, \"Outpatient Dialysis Facility Per User Actual Costs\": 24955.52, \"Beneficiaries with Part A and Part B\": 3032122.0, \"% of Beneficiaries Using Part B Drugs\": 0.5106, \"Percent of Medicare beneficiaries with diabetes\": 31.4, \"% of Beneficiaries Using PAC: IRF\": 0.0071, \"E&M Per Capita Actual Costs\": 1192.89, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0352, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0357, \"PAC: SNF Actual Costs\": 1758560495.6, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 434.0, \"Percent Female\": 56.08, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 316.0, \"Percent of Medicare beneficiaries with osteoporosis\": 7.15, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 254.56, \"# Outpatient Dialysis Facility Users\": 20299.0, \"FQHC/RHC Per User Actual Costs\": 453.12, \"Count of Medicare beneficiaries with ischemic heart disease\": 609851.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 220.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.85, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 159.0, \"Percent of Medicare beneficiaries with depression\": 14.52, \"Emergency Department Visits per 1000 Beneficiaries\": 588.0, \"IP Actual Costs\": 8038442399.98, \"% of Beneficiaries Using OP\": 0.5747, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.015, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.13, \"Count of Medicare beneficiaries with stroke\": 73556.0, \"PQI12 UTI Admission Rate (age 75+)\": 1099.0, \"# OP Users\": 1074458.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 17.0, \"# Procedure Users\": 1191124.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 32.62, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0791, \"Count of Medicare beneficiaries with diabetes\": 587089.0, \"ASC Per User Actual Costs\": 857.3, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1119.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0203, \"Percent of Medicare beneficiaries with high cholesterol\": 48.89, \"Standardized Per Capita Costs\": 8951.74, \"Ambulance Actual Costs\": 235965473.97, \"FQHC/RHC Per Capita Actual Costs\": 14.54, \"Part B Drugs Per User Standardized Costs\": 626.76, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0148, \"# PAC: SNF Users (with a covered stay)\": 98300.0, \"Ambulance Per Capita Actual Costs\": 126.21, \"PQI12 UTI Admission Rate (age 65-74)\": 236.0, \"Hospice Standardized Costs\": 278556317.98, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 270.96, \"% of Beneficiaries Using E&M\": 0.881, \"PQI10 Dehydration Admission Rate (age 65-74)\": 189.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 271.0, \"Tests Per Capita Actual Costs\": 371.33, \"# DME Users\": 479688.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.094, \"PAC: SNF Per User Actual Costs\": 17889.73, \"State\": \"NY\", \"OP Per User Actual Costs\": 1846.62, \"PAC: HH Episodes Per 1000 Beneficiaries\": 135.0, \"Part B Drugs Actual Costs\": 597227392.65, \"FQHC/RHC Actual Costs\": 27175116.06, \"OP Standardized Costs\": 1878380135.04, \"DME Per User Standardized Costs\": 707.55, \"OP Actual Costs as % of Total Actual Costs\": 0.0984, \"PAC: SNF Per Capita Standardized Costs\": 841.83, \"% of Beneficiaries Using Ambulance\": 0.1166, \"Hospice Per User Standardized Costs\": 9086.22, \"# Imaging Users\": 1299044.0, \"Part B Drugs Per User Actual Costs\": 625.66, \"Total Standardized Costs\": 16735748284.15, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.42, \"Count of Medicare beneficiaries with chronic kidney disease\": 281928.0, \"E&M Standardized Costs\": 2175635367.13, \"Percent Hispanic\": 7.27, \"ASC Per Capita Actual Costs\": 45.39, \"Count of Medicare beneficiaries who have had a heart attack\": 15815.0, \"Tests Actual Costs\": 694224022.9, \"# PAC: LTCH Users (with a covered stay)\": 2241.0, \"% of Beneficiaries Using Procedures\": 0.6371, \"PAC: HH Actual Costs\": 747411823.14, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 43.0, \"PAC: LTCH Per Capita Standardized Costs\": 29.91, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0405, \"Emergency Department Visits\": 1099261.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0321, \"Procedures Actual Costs\": 1413907591.42, \"# FQHC/RHC Users\": 59973.0, \"Number of Acute Hospital Readmissions\": 103402.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 103.0, \"Outpatient Dialysis Facility Standardized Costs\": 475910782.4, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0146, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1122, \"Ambulance Per User Standardized Costs\": 1153.69, \"Imaging Per User Standardized Costs\": 452.95, \"Percent of Medicare beneficiaries with asthma\": 5.9, \"Part B Drugs Standardized Costs\": 598282981.8, \"FFS Beneficiaries\": 1869553.0, \"# Hospice Users (with a covered stay)\": 30657.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0109, \"Count of Medicare beneficiaries with osteoporosis\": 133707.0, \"PQI08 CHF Admission Rate (age 75+)\": 2185.0, \"PAC: IRF Per Capita Standardized Costs\": 132.31, \"Procedures Standardized Costs\": 1323865341.72, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3118, \"IP Per Capita Actual Costs\": 4299.66, \"DME Actual Costs\": 314777921.24, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0371, \"Count of Medicare beneficiaries with prostate cancer\": 60608.0, \"PAC: HH Per Capita Standardized Costs\": 360.75, \"Count of Medicare beneficiaries with heart failure\": 310399.0, \"Tests Per User Standardized Costs\": 460.93, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0032, \"Percent of Medicare beneficiaries with prostate cancer\": 3.24, \"PAC: IRF Per Capita Actual Costs\": 157.5, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 487.78, \"Percent of Medicare beneficiaries with breast cancer\": 3.38, \"Procedures Per User Standardized Costs\": 1111.44, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.08, \"PAC: HH Per User Actual Costs\": 4637.1, \"Count of Medicare beneficiaries with high cholesterol\": 914071.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0872, \"Hospice Per Capita Standardized Costs\": 149.0, \"# Part B Drugs Users\": 954560.0, \"Average HCC Score\": 1.0777, \"Standardized Risk-Adjusted Per Capita Costs\": 8750.88, \"# PAC: IRF Users (with a covered stay)\": 13325.0, \"Ambulance Standardized Costs\": 251466747.03, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0152, \"Percent of Medicare beneficiaries with heart failure\": 16.6, \"Tests Actual Costs as % of Total Actual Costs\": 0.0344, \"FQHC/RHC Per Capita Standardized Costs\": 15.36, \"PQI07 Hypertension Admission Rate (age 65-74)\": 95.0, \"Test Events Per 1000 Beneficiaries\": 12471.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 33.0, \"ASC Actual Costs\": 84857767.36, \"Part B Drugs Per Capita Standardized Costs\": 320.01, \"Imaging Actual Costs\": 633644247.38, \"Tests Per User Actual Costs\": 472.27, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0117, \"Hospice Per User Actual Costs\": 9983.98, \"Tests Standardized Costs\": 677557762.68, \"IP Standardized Costs\": 5218144368.59, \"IP Per Capita Standardized Costs\": 2791.12, \"Outpatient Dialysis Facility Actual Costs\": 506572115.12, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 73.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2030.0, \"Percent of Medicare beneficiaries with hypertension\": 56.48, \"IP Covered Stays Per 1000 Beneficiaries\": 299.0, \"# Ambulance Users\": 217967.0, \"# ASC Users\": 98983.0, \"ASC Per Capita Standardized Costs\": 43.73, \"Procedures Per Capita Actual Costs\": 756.28, \"Procedures Per Capita Standardized Costs\": 708.12, \"IP Users (with a covered stay)\": 337635.0, \"Total Standardized Risk-Adjusted Costs\": 16360228853.13, \"Actual Per Capita Costs\": 10789.05, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0033, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 8.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.16, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.53, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 342.0, \"OP Per User Standardized Costs\": 1748.21, \"Count of Medicare beneficiaries with atrial fibrillation\": 159158.0, \"Procedure Events Per 1000 Beneficiaries\": 6382.0, \"Percent of Medicare beneficiaries with stroke\": 3.93, \"PAC: IRF Per User Actual Costs\": 22098.17, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 196884.0, \"% of Beneficiaries Using ASC\": 0.0529, \"PAC: IRF Standardized Costs\": 247367358.21, \"Hospice Covered Days Per 1000 Beneficiaries\": 870.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 216.0, \"PAC: SNF Per User Standardized Costs\": 16010.69, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0013, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 96.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0166, \"MA Participation Rate\": 38.34, \"OP Per Capita Standardized Costs\": 1004.72, \"Percent of Medicare beneficiaries with arthritis\": 28.47, \"Ambulance Per Capita Standardized Costs\": 134.51, \"PAC: HH Visits Per 1000 Beneficiaries\": 2739.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0701, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0314, \"PAC: HH Per Capita Actual Costs\": 399.78, \"E&M Events Per 1000 Beneficiaries\": 16635.0, \"Count of Medicare beneficiaries with asthma\": 110220.0, \"# Test Users\": 1469976.0, \"E&M Actual Costs\": 2230167294.64, \"% of Beneficiaries Using Imaging\": 0.6948, \"PAC: SNF Standardized Costs\": 1573850822.12, \"DME Per Capita Actual Costs\": 168.37, \"E&M Per User Actual Costs\": 1354.03, \"Percent Male\": 43.92, \"OP Visits Per 1000 Beneficiaries\": 3998.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0156, \"OP Per Capita Actual Costs\": 1061.28, \"Hospital Readmission Rate\": 0.1974, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0017, \"Tests Per Capita Standardized Costs\": 362.42, \"Hospice Per Capita Actual Costs\": 163.72, \"E&M Actual Costs as % of Total Actual Costs\": 0.1106, \"PQI07 Hypertension Admission Rate (age < 65)\": 115.0, \"Percent African American\": 10.25, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0403, \"PAC: LTCH Per Capita Actual Costs\": 34.38, \"MA Beneficiaries\": 1162569.0, \"FQHC/RHC Per User Standardized Costs\": 478.91, \"PAC: HH Per User Standardized Costs\": 4184.4, \"Procedures Per User Actual Costs\": 1187.04, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 688.0, \"Ambulance Per User Actual Costs\": 1082.57, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1136.0, \"PAC: HH Standardized Costs\": 674445914.44, \"ASC Actual Costs as % of Total Actual Costs\": 0.0042, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 729.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0012, \"Percent Other/Unknown\": 5.98, \"% of Beneficiaries Using Hospice\": 0.0164, \"PAC: LTCH Per User Actual Costs\": 28680.32, \"Percent Non-Hispanic White\": 76.49, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 509.0, \"Count of Medicare beneficiaries with breast cancer\": 63222.0, \"DME Per Capita Standardized Costs\": 181.54, \"PQI08 CHF Admission Rate (age 65-74)\": 633.0, \"% of Beneficiaries Using DME\": 0.2566, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0251, \"% of Beneficiaries Using IP\": 0.1806, \"DME Standardized Costs\": 339405516.04, \"FQHC/RHC Standardized Costs\": 28721690.01, \"PAC: SNF Per Capita Actual Costs\": 940.63, \"Imaging Standardized Costs\": 588399527.5, \"# E&M Users\": 1647058.0, \"Count of Medicare beneficiaries with arthritis\": 532347.0, \"IP Per User Standardized Costs\": 15454.99, \"IP Per User Actual Costs\": 23808.08, \"PQI10 Dehydration Admission Rate (age 75+)\": 554.0, \"PAC: IRF Per User Standardized Costs\": 18564.15, \"DME Per User Actual Costs\": 656.21, \"PAC: IRF Actual Costs\": 294458104.03, \"Imaging Events Per 1000 Beneficiaries\": 4395.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0284, \"PAC: LTCH Per User Standardized Costs\": 24955.44, \"OP Actual Costs\": 1984115983.67, \"Count of Medicare beneficiaries with hypertension\": 1055875.0, \"ASC Per User Standardized Costs\": 825.93, \"Part B Drugs Per Capita Actual Costs\": 319.45, \"Ambulance Events Per 1000 Beneficiaries\": 403.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 401.0, \"% of Beneficiaries Using PAC: HH\": 0.0906, \"PAC: LTCH Standardized Costs\": 225913435.78, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.37, \"E&M Per Capita Standardized Costs\": 959.25, \"E&M Per User Standardized Costs\": 1081.77, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1374.0, \"IP Covered Days Per 1000 Beneficiaries\": 1645.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 43.0, \"Count of Medicare beneficiaries with lung cancer\": 13194.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3481, \"Percent Eligible for Medicaid\": 22.68, \"Imaging Per Capita Standardized Costs\": 160.85, \"% of Beneficiaries Using Tests\": 0.7367, \"Imaging Per Capita Actual Costs\": 152.78, \"% of Beneficiaries Using PAC: SNF\": 0.0647, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.029, \"Count of Medicare beneficiaries with colorectal cancer\": 15818.0, \"Hospice Actual Costs\": 463405245.57, \"# PAC: HH Users\": 109371.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23168.14, \"Total Actual Costs\": 11604591444.32, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 126571.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0072, \"ASC Standardized Costs\": 82975018.44, \"DME Events Per 1000 Beneficiaries\": 1935.0, \"PQI08 CHF Admission Rate (age < 65)\": 1024.0, \"ASC Events Per 1000 Beneficiaries\": 134.0, \"PAC: LTCH Actual Costs\": 204689552.83, \"Count of Medicare beneficiaries with depression\": 223482.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1698.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.49, \"Outpatient Dialysis Facility Per User Actual Costs\": 22552.12, \"Beneficiaries with Part A and Part B\": 2001823.0, \"% of Beneficiaries Using Part B Drugs\": 0.5069, \"Percent of Medicare beneficiaries with diabetes\": 27.77, \"% of Beneficiaries Using PAC: IRF\": 0.0086, \"E&M Per Capita Actual Costs\": 891.1, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0167, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0292, \"PAC: SNF Actual Costs\": 1122953377.11, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 668.0, \"Percent Female\": 54.34, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 355.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.9, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 249.32, \"# Outpatient Dialysis Facility Users\": 12985.0, \"FQHC/RHC Per User Actual Costs\": 371.52, \"Count of Medicare beneficiaries with ischemic heart disease\": 345910.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 198.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.98, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 170.0, \"Percent of Medicare beneficiaries with depression\": 18.52, \"Emergency Department Visits per 1000 Beneficiaries\": 775.0, \"IP Actual Costs\": 4039977679.88, \"% of Beneficiaries Using OP\": 0.7253, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.019, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0998, \"Count of Medicare beneficiaries with stroke\": 49032.0, \"PQI12 UTI Admission Rate (age 75+)\": 1268.0, \"# OP Users\": 875226.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 35.0, \"# Procedure Users\": 729204.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 28.67, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0561, \"Count of Medicare beneficiaries with diabetes\": 335124.0, \"ASC Per User Actual Costs\": 759.29, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1224.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0227, \"Percent of Medicare beneficiaries with high cholesterol\": 46.32, \"Standardized Per Capita Costs\": 9613.24, \"Ambulance Actual Costs\": 203831681.97, \"FQHC/RHC Per Capita Actual Costs\": 15.41, \"Part B Drugs Per User Standardized Costs\": 553.55, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0164, \"# PAC: SNF Users (with a covered stay)\": 78064.0, \"Ambulance Per Capita Actual Costs\": 168.93, \"PQI12 UTI Admission Rate (age 65-74)\": 336.0, \"Hospice Standardized Costs\": 488263939.83, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 242.69, \"% of Beneficiaries Using E&M\": 0.8867, \"PQI10 Dehydration Admission Rate (age 65-74)\": 240.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 233.0, \"Tests Per Capita Actual Costs\": 184.99, \"# DME Users\": 346492.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1075, \"PAC: SNF Per User Actual Costs\": 14385.04, \"State\": \"OH\", \"OP Per User Actual Costs\": 1807.56, \"PAC: HH Episodes Per 1000 Beneficiaries\": 164.0, \"Part B Drugs Actual Costs\": 336212711.38, \"FQHC/RHC Actual Costs\": 18594405.96, \"OP Standardized Costs\": 1643253260.49, \"DME Per User Standardized Costs\": 759.83, \"OP Actual Costs as % of Total Actual Costs\": 0.1363, \"PAC: SNF Per Capita Standardized Costs\": 1033.68, \"% of Beneficiaries Using Ambulance\": 0.1417, \"Hospice Per User Standardized Costs\": 12179.2, \"# Imaging Users\": 810715.0, \"Part B Drugs Per User Actual Costs\": 549.63, \"Total Standardized Costs\": 11599716528.27, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.31, \"Count of Medicare beneficiaries with chronic kidney disease\": 206183.0, \"E&M Standardized Costs\": 1157469700.17, \"Percent Hispanic\": 1.27, \"ASC Per Capita Actual Costs\": 63.95, \"Count of Medicare beneficiaries who have had a heart attack\": 11807.0, \"Tests Actual Costs\": 223214417.99, \"# PAC: LTCH Users (with a covered stay)\": 4978.0, \"% of Beneficiaries Using Procedures\": 0.6043, \"PAC: HH Actual Costs\": 500727625.59, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 46.0, \"PAC: LTCH Per Capita Standardized Costs\": 187.23, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.02, \"Emergency Department Visits\": 935642.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0415, \"Procedures Actual Costs\": 623388323.77, \"# FQHC/RHC Users\": 50049.0, \"Number of Acute Hospital Readmissions\": 70810.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 114.0, \"Outpatient Dialysis Facility Standardized Costs\": 300838238.14, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0156, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1417, \"Ambulance Per User Standardized Costs\": 1288.17, \"Imaging Per User Standardized Costs\": 239.4, \"Percent of Medicare beneficiaries with asthma\": 5.57, \"Part B Drugs Standardized Costs\": 338607870.09, \"FFS Beneficiaries\": 1206640.0, \"# Hospice Users (with a covered stay)\": 40090.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0108, \"Count of Medicare beneficiaries with osteoporosis\": 71248.0, \"PQI08 CHF Admission Rate (age 75+)\": 2327.0, \"PAC: IRF Per Capita Standardized Costs\": 157.76, \"Procedures Standardized Costs\": 650846591.52, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3008, \"IP Per Capita Actual Costs\": 3348.12, \"DME Actual Costs\": 243213160.36, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0431, \"Count of Medicare beneficiaries with prostate cancer\": 34180.0, \"PAC: HH Per Capita Standardized Costs\": 452.55, \"Count of Medicare beneficiaries with heart failure\": 183379.0, \"Tests Per User Standardized Costs\": 261.33, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0176, \"Percent of Medicare beneficiaries with prostate cancer\": 2.83, \"PAC: IRF Per Capita Actual Costs\": 150.31, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 227.39, \"Percent of Medicare beneficiaries with breast cancer\": 2.74, \"Procedures Per User Standardized Costs\": 892.54, \"Percent of Medicare beneficiaries with chronic kidney disease\": 17.09, \"PAC: HH Per User Actual Costs\": 4578.25, \"Count of Medicare beneficiaries with high cholesterol\": 558948.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0968, \"Hospice Per Capita Standardized Costs\": 404.65, \"# Part B Drugs Users\": 611706.0, \"Average HCC Score\": 1.0452, \"Standardized Risk-Adjusted Per Capita Costs\": 9658.27, \"# PAC: IRF Users (with a covered stay)\": 10413.0, \"Ambulance Standardized Costs\": 220185807.27, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0399, \"Percent of Medicare beneficiaries with heart failure\": 15.2, \"Tests Actual Costs as % of Total Actual Costs\": 0.0192, \"FQHC/RHC Per Capita Standardized Costs\": 17.0, \"PQI07 Hypertension Admission Rate (age 65-74)\": 92.0, \"Test Events Per 1000 Beneficiaries\": 7459.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 125.0, \"ASC Actual Costs\": 77170296.06, \"Part B Drugs Per Capita Standardized Costs\": 280.62, \"Imaging Actual Costs\": 184349244.75, \"Tests Per User Actual Costs\": 251.11, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0176, \"Hospice Per User Actual Costs\": 11559.12, \"Tests Standardized Costs\": 232299842.96, \"IP Standardized Costs\": 3488806345.81, \"IP Per Capita Standardized Costs\": 2891.34, \"Outpatient Dialysis Facility Actual Costs\": 292839279.69, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 92.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2443.0, \"Percent of Medicare beneficiaries with hypertension\": 57.59, \"IP Covered Stays Per 1000 Beneficiaries\": 323.0, \"# Ambulance Users\": 170930.0, \"# ASC Users\": 101635.0, \"ASC Per Capita Standardized Costs\": 68.77, \"Procedures Per Capita Actual Costs\": 516.63, \"Procedures Per Capita Standardized Costs\": 539.39, \"IP Users (with a covered stay)\": 234149.0, \"Total Standardized Risk-Adjusted Costs\": 11654054049.32, \"Actual Per Capita Costs\": 9617.28, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0195, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 9.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 5.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.09, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 13.54, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 246.0, \"OP Per User Standardized Costs\": 1877.52, \"Count of Medicare beneficiaries with atrial fibrillation\": 100948.0, \"Procedure Events Per 1000 Beneficiaries\": 3976.0, \"Percent of Medicare beneficiaries with stroke\": 4.06, \"PAC: IRF Per User Actual Costs\": 17417.58, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 163413.0, \"% of Beneficiaries Using ASC\": 0.0842, \"PAC: IRF Standardized Costs\": 190359420.82, \"Hospice Covered Days Per 1000 Beneficiaries\": 2406.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 322.0, \"PAC: SNF Per User Standardized Costs\": 15977.6, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0016, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 123.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0421, \"MA Participation Rate\": 39.72, \"OP Per Capita Standardized Costs\": 1361.84, \"Percent of Medicare beneficiaries with arthritis\": 31.27, \"Ambulance Per Capita Standardized Costs\": 182.48, \"PAC: HH Visits Per 1000 Beneficiaries\": 2925.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0537, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0159, \"PAC: HH Per Capita Actual Costs\": 414.98, \"E&M Events Per 1000 Beneficiaries\": 13542.0, \"Count of Medicare beneficiaries with asthma\": 67151.0, \"# Test Users\": 888903.0, \"E&M Actual Costs\": 1075238735.74, \"% of Beneficiaries Using Imaging\": 0.6719, \"PAC: SNF Standardized Costs\": 1247275389.93, \"DME Per Capita Actual Costs\": 201.56, \"E&M Per User Actual Costs\": 1004.91, \"Percent Male\": 45.66, \"OP Visits Per 1000 Beneficiaries\": 5519.0, \"DME Actual Costs as % of Total Actual Costs\": 0.021, \"OP Per Capita Actual Costs\": 1311.1, \"Hospital Readmission Rate\": 0.1892, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0018, \"Tests Per Capita Standardized Costs\": 192.52, \"Hospice Per Capita Actual Costs\": 384.05, \"E&M Actual Costs as % of Total Actual Costs\": 0.0927, \"PQI07 Hypertension Admission Rate (age < 65)\": 162.0, \"Percent African American\": 9.73, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0471, \"PAC: LTCH Per Capita Actual Costs\": 169.64, \"MA Beneficiaries\": 795183.0, \"FQHC/RHC Per User Standardized Costs\": 409.92, \"PAC: HH Per User Standardized Costs\": 4992.74, \"Procedures Per User Actual Costs\": 854.89, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 717.0, \"Ambulance Per User Actual Costs\": 1192.49, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1930.0, \"PAC: HH Standardized Costs\": 546060478.72, \"ASC Actual Costs as % of Total Actual Costs\": 0.0066, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 1050.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0041, \"Percent Other/Unknown\": 2.19, \"% of Beneficiaries Using Hospice\": 0.0332, \"PAC: LTCH Per User Actual Costs\": 41118.83, \"Percent Non-Hispanic White\": 86.81, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 889.0, \"Count of Medicare beneficiaries with breast cancer\": 33117.0, \"DME Per Capita Standardized Costs\": 218.19, \"PQI08 CHF Admission Rate (age 65-74)\": 852.0, \"% of Beneficiaries Using DME\": 0.2872, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0252, \"% of Beneficiaries Using IP\": 0.1941, \"DME Standardized Costs\": 263275530.01, \"FQHC/RHC Standardized Costs\": 20516190.46, \"PAC: SNF Per Capita Actual Costs\": 930.64, \"Imaging Standardized Costs\": 194086658.84, \"# E&M Users\": 1069981.0, \"Count of Medicare beneficiaries with arthritis\": 377265.0, \"IP Per User Standardized Costs\": 14899.94, \"IP Per User Actual Costs\": 17253.88, \"PQI10 Dehydration Admission Rate (age 75+)\": 593.0, \"PAC: IRF Per User Standardized Costs\": 18280.94, \"DME Per User Actual Costs\": 701.93, \"PAC: IRF Actual Costs\": 181369231.12, \"Imaging Events Per 1000 Beneficiaries\": 4088.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0259, \"PAC: LTCH Per User Standardized Costs\": 45382.37, \"OP Actual Costs\": 1582026149.63, \"Count of Medicare beneficiaries with hypertension\": 694954.0, \"ASC Per User Standardized Costs\": 816.4, \"Part B Drugs Per Capita Actual Costs\": 278.64, \"Ambulance Events Per 1000 Beneficiaries\": 542.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 406.0, \"% of Beneficiaries Using PAC: HH\": 0.1218, \"PAC: LTCH Standardized Costs\": 160284262.42, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.89, \"E&M Per Capita Standardized Costs\": 779.82, \"E&M Per User Standardized Costs\": 880.03, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1106.0, \"IP Covered Days Per 1000 Beneficiaries\": 1507.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 43.0, \"Count of Medicare beneficiaries with lung cancer\": 5158.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3163, \"Percent Eligible for Medicaid\": 20.38, \"Imaging Per Capita Standardized Costs\": 168.28, \"% of Beneficiaries Using Tests\": 0.7626, \"Imaging Per Capita Actual Costs\": 151.61, \"% of Beneficiaries Using PAC: SNF\": 0.0426, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0293, \"Count of Medicare beneficiaries with colorectal cancer\": 6058.0, \"Hospice Actual Costs\": 198791516.87, \"# PAC: HH Users\": 63468.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 22290.84, \"Total Actual Costs\": 4625677971.43, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 51679.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0076, \"ASC Standardized Costs\": 36762426.31, \"DME Events Per 1000 Beneficiaries\": 1906.0, \"PQI08 CHF Admission Rate (age < 65)\": 768.0, \"ASC Events Per 1000 Beneficiaries\": 125.0, \"PAC: LTCH Actual Costs\": 141563836.49, \"Count of Medicare beneficiaries with depression\": 95787.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1871.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.91, \"Outpatient Dialysis Facility Per User Actual Costs\": 20971.65, \"Beneficiaries with Part A and Part B\": 635511.0, \"% of Beneficiaries Using Part B Drugs\": 0.5292, \"Percent of Medicare beneficiaries with diabetes\": 26.36, \"% of Beneficiaries Using PAC: IRF\": 0.0123, \"E&M Per Capita Actual Costs\": 682.99, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0181, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0283, \"PAC: SNF Actual Costs\": 278510988.39, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 750.0, \"Percent Female\": 55.05, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 354.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.24, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 192.05, \"# Outpatient Dialysis Facility Users\": 4491.0, \"FQHC/RHC Per User Actual Costs\": 354.66, \"Count of Medicare beneficiaries with ischemic heart disease\": 161646.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 147.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.9, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 270.0, \"Percent of Medicare beneficiaries with depression\": 18.38, \"Emergency Department Visits per 1000 Beneficiaries\": 710.0, \"IP Actual Costs\": 1463101181.82, \"% of Beneficiaries Using OP\": 0.682, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0106, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0841, \"Count of Medicare beneficiaries with stroke\": 18994.0, \"PQI12 UTI Admission Rate (age 75+)\": 1185.0, \"# OP Users\": 355475.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 34.0, \"# Procedure Users\": 306198.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 31.01, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0591, \"Count of Medicare beneficiaries with diabetes\": 137423.0, \"ASC Per User Actual Costs\": 805.32, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1163.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0273, \"Percent of Medicare beneficiaries with high cholesterol\": 39.7, \"Standardized Per Capita Costs\": 9276.58, \"Ambulance Actual Costs\": 62702474.81, \"FQHC/RHC Per Capita Actual Costs\": 22.3, \"Part B Drugs Per User Standardized Costs\": 495.71, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0243, \"# PAC: SNF Users (with a covered stay)\": 22182.0, \"Ambulance Per Capita Actual Costs\": 120.29, \"PQI12 UTI Admission Rate (age 65-74)\": 321.0, \"Hospice Standardized Costs\": 220769081.5, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 180.69, \"% of Beneficiaries Using E&M\": 0.8861, \"PQI10 Dehydration Admission Rate (age 65-74)\": 258.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 176.0, \"Tests Per Capita Actual Costs\": 222.72, \"# DME Users\": 152679.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.068, \"PAC: SNF Per User Actual Costs\": 12555.72, \"State\": \"OK\", \"OP Per User Actual Costs\": 1873.27, \"PAC: HH Episodes Per 1000 Beneficiaries\": 362.0, \"Part B Drugs Actual Costs\": 135440014.93, \"FQHC/RHC Actual Costs\": 11621901.36, \"OP Standardized Costs\": 676219118.63, \"DME Per User Standardized Costs\": 864.35, \"OP Actual Costs as % of Total Actual Costs\": 0.144, \"PAC: SNF Per Capita Standardized Costs\": 631.24, \"% of Beneficiaries Using Ambulance\": 0.1038, \"Hospice Per User Standardized Costs\": 13312.98, \"# Imaging Users\": 349603.0, \"Part B Drugs Per User Actual Costs\": 491.01, \"Total Standardized Costs\": 4835492974.82, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.16, \"Count of Medicare beneficiaries with chronic kidney disease\": 78516.0, \"E&M Standardized Costs\": 406489347.43, \"Percent Hispanic\": 1.87, \"ASC Per Capita Actual Costs\": 62.99, \"Count of Medicare beneficiaries who have had a heart attack\": 4692.0, \"Tests Actual Costs\": 116093459.36, \"# PAC: LTCH Users (with a covered stay)\": 3793.0, \"% of Beneficiaries Using Procedures\": 0.5874, \"PAC: HH Actual Costs\": 429505729.28, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 45.0, \"PAC: LTCH Per Capita Standardized Costs\": 307.5, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0252, \"Emergency Department Visits\": 370228.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0629, \"Procedures Actual Costs\": 255494224.84, \"# FQHC/RHC Users\": 32769.0, \"Number of Acute Hospital Readmissions\": 24093.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 159.0, \"Outpatient Dialysis Facility Standardized Costs\": 100108177.34, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0238, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1398, \"Ambulance Per User Standardized Costs\": 952.01, \"Imaging Per User Standardized Costs\": 250.91, \"Percent of Medicare beneficiaries with asthma\": 5.03, \"Part B Drugs Standardized Costs\": 136736054.01, \"FFS Beneficiaries\": 521258.0, \"# Hospice Users (with a covered stay)\": 16583.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0086, \"Count of Medicare beneficiaries with osteoporosis\": 27338.0, \"PQI08 CHF Admission Rate (age 75+)\": 1868.0, \"PAC: IRF Per Capita Standardized Costs\": 225.47, \"Procedures Standardized Costs\": 285877200.87, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2858, \"IP Per Capita Actual Costs\": 2806.87, \"DME Actual Costs\": 124519487.34, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0929, \"Count of Medicare beneficiaries with prostate cancer\": 13135.0, \"PAC: HH Per Capita Standardized Costs\": 962.52, \"Count of Medicare beneficiaries with heart failure\": 83601.0, \"Tests Per User Standardized Costs\": 307.0, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0306, \"Percent of Medicare beneficiaries with prostate cancer\": 2.52, \"PAC: IRF Per Capita Actual Costs\": 211.62, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 226.05, \"Percent of Medicare beneficiaries with breast cancer\": 2.6, \"Procedures Per User Standardized Costs\": 933.64, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.06, \"PAC: HH Per User Actual Costs\": 6767.28, \"Count of Medicare beneficiaries with high cholesterol\": 206917.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0602, \"Hospice Per Capita Standardized Costs\": 423.53, \"# Part B Drugs Users\": 275841.0, \"Average HCC Score\": 0.9714, \"Standardized Risk-Adjusted Per Capita Costs\": 10008.26, \"# PAC: IRF Users (with a covered stay)\": 6389.0, \"Ambulance Standardized Costs\": 51486269.38, \"Hospice Actual Costs as % of Total Actual Costs\": 0.043, \"Percent of Medicare beneficiaries with heart failure\": 16.04, \"Tests Actual Costs as % of Total Actual Costs\": 0.0251, \"FQHC/RHC Per Capita Standardized Costs\": 25.4, \"PQI07 Hypertension Admission Rate (age 65-74)\": 84.0, \"Test Events Per 1000 Beneficiaries\": 8366.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 214.0, \"ASC Actual Costs\": 32833007.29, \"Part B Drugs Per Capita Standardized Costs\": 262.32, \"Imaging Actual Costs\": 79028198.77, \"Tests Per User Actual Costs\": 292.06, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0136, \"Hospice Per User Actual Costs\": 11987.67, \"Tests Standardized Costs\": 122031397.87, \"IP Standardized Costs\": 1382016608.67, \"IP Per Capita Standardized Costs\": 2651.31, \"Outpatient Dialysis Facility Actual Costs\": 94183662.69, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 59.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1520.0, \"Percent of Medicare beneficiaries with hypertension\": 56.84, \"IP Covered Stays Per 1000 Beneficiaries\": 287.0, \"# Ambulance Users\": 54082.0, \"# ASC Users\": 40770.0, \"ASC Per Capita Standardized Costs\": 70.53, \"Procedures Per Capita Actual Costs\": 490.15, \"Procedures Per Capita Standardized Costs\": 548.44, \"IP Users (with a covered stay)\": 94396.0, \"Total Standardized Risk-Adjusted Costs\": 5216886064.67, \"Actual Per Capita Costs\": 8874.07, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0331, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 13.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 8.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.99, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 13.36, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 212.0, \"OP Per User Standardized Costs\": 1902.3, \"Count of Medicare beneficiaries with atrial fibrillation\": 35892.0, \"Procedure Events Per 1000 Beneficiaries\": 3673.0, \"Percent of Medicare beneficiaries with stroke\": 3.64, \"PAC: IRF Per User Actual Costs\": 17265.05, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 69665.0, \"% of Beneficiaries Using ASC\": 0.0782, \"PAC: IRF Standardized Costs\": 117526169.66, \"Hospice Covered Days Per 1000 Beneficiaries\": 2731.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 384.0, \"PAC: SNF Per User Standardized Costs\": 14833.69, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0025, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 146.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0457, \"MA Participation Rate\": 17.98, \"OP Per Capita Standardized Costs\": 1297.28, \"Percent of Medicare beneficiaries with arthritis\": 31.95, \"Ambulance Per Capita Standardized Costs\": 98.77, \"PAC: HH Visits Per 1000 Beneficiaries\": 6417.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0552, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0171, \"PAC: HH Per Capita Actual Costs\": 823.98, \"E&M Events Per 1000 Beneficiaries\": 11434.0, \"Count of Medicare beneficiaries with asthma\": 26203.0, \"# Test Users\": 397496.0, \"E&M Actual Costs\": 356016219.32, \"% of Beneficiaries Using Imaging\": 0.6707, \"PAC: SNF Standardized Costs\": 329040860.64, \"DME Per Capita Actual Costs\": 238.88, \"E&M Per User Actual Costs\": 770.75, \"Percent Male\": 44.95, \"OP Visits Per 1000 Beneficiaries\": 4135.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0269, \"OP Per Capita Actual Costs\": 1277.48, \"Hospital Readmission Rate\": 0.1693, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0027, \"Tests Per Capita Standardized Costs\": 234.11, \"Hospice Per Capita Actual Costs\": 381.37, \"E&M Actual Costs as % of Total Actual Costs\": 0.077, \"PQI07 Hypertension Admission Rate (age < 65)\": 153.0, \"Percent African American\": 5.52, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.1038, \"PAC: LTCH Per Capita Actual Costs\": 271.58, \"MA Beneficiaries\": 114253.0, \"FQHC/RHC Per User Standardized Costs\": 404.11, \"PAC: HH Per User Standardized Costs\": 7905.12, \"Procedures Per User Actual Costs\": 834.41, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 625.0, \"Ambulance Per User Actual Costs\": 1159.41, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1544.0, \"PAC: HH Standardized Costs\": 501722012.77, \"ASC Actual Costs as % of Total Actual Costs\": 0.0071, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 962.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0073, \"Percent Other/Unknown\": 9.68, \"% of Beneficiaries Using Hospice\": 0.0318, \"PAC: LTCH Per User Actual Costs\": 37322.39, \"Percent Non-Hispanic White\": 82.93, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 912.0, \"Count of Medicare beneficiaries with breast cancer\": 13560.0, \"DME Per Capita Standardized Costs\": 253.17, \"PQI08 CHF Admission Rate (age 65-74)\": 623.0, \"% of Beneficiaries Using DME\": 0.2929, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0204, \"% of Beneficiaries Using IP\": 0.1811, \"DME Standardized Costs\": 131967881.25, \"FQHC/RHC Standardized Costs\": 13242300.84, \"PAC: SNF Per Capita Actual Costs\": 534.31, \"Imaging Standardized Costs\": 87717503.35, \"# E&M Users\": 461906.0, \"Count of Medicare beneficiaries with arthritis\": 166534.0, \"IP Per User Standardized Costs\": 14640.63, \"IP Per User Actual Costs\": 15499.61, \"PQI10 Dehydration Admission Rate (age 75+)\": 619.0, \"PAC: IRF Per User Standardized Costs\": 18395.08, \"DME Per User Actual Costs\": 815.56, \"PAC: IRF Actual Costs\": 110306412.14, \"Imaging Events Per 1000 Beneficiaries\": 3804.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0207, \"PAC: LTCH Per User Standardized Costs\": 42257.91, \"OP Actual Costs\": 665899057.31, \"Count of Medicare beneficiaries with hypertension\": 296292.0, \"ASC Per User Standardized Costs\": 901.7, \"Part B Drugs Per Capita Actual Costs\": 259.83, \"Ambulance Events Per 1000 Beneficiaries\": 265.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 176.0, \"% of Beneficiaries Using PAC: HH\": 0.0562, \"PAC: LTCH Standardized Costs\": 11054930.17, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.33, \"E&M Per Capita Standardized Costs\": 596.86, \"E&M Per User Standardized Costs\": 733.6, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 989.0, \"IP Covered Days Per 1000 Beneficiaries\": 970.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 25.0, \"Count of Medicare beneficiaries with lung cancer\": 3120.0, \"IP Actual Costs as % of Total Actual Costs\": 0.363, \"Percent Eligible for Medicaid\": 17.67, \"Imaging Per Capita Standardized Costs\": 137.06, \"% of Beneficiaries Using Tests\": 0.6869, \"Imaging Per Capita Actual Costs\": 131.6, \"% of Beneficiaries Using PAC: SNF\": 0.0331, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0383, \"Count of Medicare beneficiaries with colorectal cancer\": 3542.0, \"Hospice Actual Costs\": 98311036.85, \"# PAC: HH Users\": 20796.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24472.42, \"Total Actual Costs\": 2671439640.69, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 28581.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0139, \"ASC Standardized Costs\": 33430400.7, \"DME Events Per 1000 Beneficiaries\": 1553.0, \"PQI08 CHF Admission Rate (age < 65)\": 551.0, \"ASC Events Per 1000 Beneficiaries\": 158.0, \"PAC: LTCH Actual Costs\": 11655262.72, \"Count of Medicare beneficiaries with depression\": 53872.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1178.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 7.73, \"Outpatient Dialysis Facility Per User Actual Costs\": 25617.02, \"Beneficiaries with Part A and Part B\": 679358.0, \"% of Beneficiaries Using Part B Drugs\": 0.4549, \"Percent of Medicare beneficiaries with diabetes\": 20.94, \"% of Beneficiaries Using PAC: IRF\": 0.0025, \"E&M Per Capita Actual Costs\": 553.62, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.021, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0427, \"PAC: SNF Actual Costs\": 181327515.45, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 355.0, \"Percent Female\": 51.5, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 168.0, \"Percent of Medicare beneficiaries with osteoporosis\": 4.51, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 171.56, \"# Outpatient Dialysis Facility Users\": 2593.0, \"FQHC/RHC Per User Actual Costs\": 410.03, \"Count of Medicare beneficiaries with ischemic heart disease\": 70192.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 97.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.73, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 612.0, \"Percent of Medicare beneficiaries with depression\": 14.56, \"Emergency Department Visits per 1000 Beneficiaries\": 551.0, \"IP Actual Costs\": 969769231.46, \"% of Beneficiaries Using OP\": 0.6516, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0131, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0915, \"Count of Medicare beneficiaries with stroke\": 10226.0, \"PQI12 UTI Admission Rate (age 75+)\": 693.0, \"# OP Users\": 241012.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 28.0, \"# Procedure Users\": 194488.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 18.98, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0756, \"Count of Medicare beneficiaries with diabetes\": 77468.0, \"ASC Per User Actual Costs\": 897.03, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 636.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0303, \"Percent of Medicare beneficiaries with high cholesterol\": 33.74, \"Standardized Per Capita Costs\": 6521.63, \"Ambulance Actual Costs\": 38154026.74, \"FQHC/RHC Per Capita Actual Costs\": 59.98, \"Part B Drugs Per User Standardized Costs\": 611.49, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0075, \"# PAC: SNF Users (with a covered stay)\": 12256.0, \"Ambulance Per Capita Actual Costs\": 103.15, \"PQI12 UTI Admission Rate (age 65-74)\": 143.0, \"Hospice Standardized Costs\": 90980030.21, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 179.58, \"% of Beneficiaries Using E&M\": 0.8136, \"PQI10 Dehydration Admission Rate (age 65-74)\": 134.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 118.0, \"Tests Per Capita Actual Costs\": 158.28, \"# DME Users\": 92213.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0719, \"PAC: SNF Per User Actual Costs\": 14795.0, \"State\": \"OR\", \"OP Per User Actual Costs\": 1880.24, \"PAC: HH Episodes Per 1000 Beneficiaries\": 89.0, \"Part B Drugs Actual Costs\": 102193690.35, \"FQHC/RHC Actual Costs\": 22186266.08, \"OP Standardized Costs\": 410680803.4, \"DME Per User Standardized Costs\": 791.53, \"OP Actual Costs as % of Total Actual Costs\": 0.1696, \"PAC: SNF Per Capita Standardized Costs\": 469.22, \"% of Beneficiaries Using Ambulance\": 0.1056, \"Hospice Per User Standardized Costs\": 9215.03, \"# Imaging Users\": 221429.0, \"Part B Drugs Per User Actual Costs\": 607.38, \"Total Standardized Costs\": 2412253560.43, \"Percent of Medicare beneficiaries with colorectal cancer\": 0.96, \"Count of Medicare beneficiaries with chronic kidney disease\": 48951.0, \"E&M Standardized Costs\": 220769377.32, \"Percent Hispanic\": 2.99, \"ASC Per Capita Actual Costs\": 92.43, \"Count of Medicare beneficiaries who have had a heart attack\": 2683.0, \"Tests Actual Costs\": 58543719.33, \"# PAC: LTCH Users (with a covered stay)\": 211.0, \"% of Beneficiaries Using Procedures\": 0.5258, \"PAC: HH Actual Costs\": 90057527.24, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 34.0, \"PAC: LTCH Per Capita Standardized Costs\": 29.89, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0251, \"Emergency Department Visits\": 203728.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1463, \"Procedures Actual Costs\": 173164571.06, \"# FQHC/RHC Users\": 54109.0, \"Number of Acute Hospital Readmissions\": 10879.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 33.0, \"Outpatient Dialysis Facility Standardized Costs\": 63456981.12, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0075, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1702, \"Ambulance Per User Standardized Costs\": 807.71, \"Imaging Per User Standardized Costs\": 228.95, \"Percent of Medicare beneficiaries with asthma\": 4.33, \"Part B Drugs Standardized Costs\": 102884375.02, \"FFS Beneficiaries\": 369885.0, \"# Hospice Users (with a covered stay)\": 9873.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.007, \"Count of Medicare beneficiaries with osteoporosis\": 16684.0, \"PQI08 CHF Admission Rate (age 75+)\": 1547.0, \"PAC: IRF Per Capita Standardized Costs\": 49.07, \"Procedures Standardized Costs\": 182316590.19, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3104, \"IP Per Capita Actual Costs\": 2621.81, \"DME Actual Costs\": 69870110.82, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0337, \"Count of Medicare beneficiaries with prostate cancer\": 10386.0, \"PAC: HH Per Capita Standardized Costs\": 231.66, \"Count of Medicare beneficiaries with heart failure\": 41558.0, \"Tests Per User Standardized Costs\": 238.11, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0044, \"Percent of Medicare beneficiaries with prostate cancer\": 2.81, \"PAC: IRF Per Capita Actual Costs\": 53.92, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 219.84, \"Percent of Medicare beneficiaries with breast cancer\": 2.45, \"Procedures Per User Standardized Costs\": 937.42, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.23, \"PAC: HH Per User Actual Costs\": 4330.52, \"Count of Medicare beneficiaries with high cholesterol\": 124806.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0679, \"Hospice Per Capita Standardized Costs\": 245.97, \"# Part B Drugs Users\": 168253.0, \"Average HCC Score\": 0.8708, \"Standardized Risk-Adjusted Per Capita Costs\": 7960.72, \"# PAC: IRF Users (with a covered stay)\": 938.0, \"Ambulance Standardized Costs\": 31548514.12, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0368, \"Percent of Medicare beneficiaries with heart failure\": 11.24, \"Tests Actual Costs as % of Total Actual Costs\": 0.0219, \"FQHC/RHC Per Capita Standardized Costs\": 65.33, \"PQI07 Hypertension Admission Rate (age 65-74)\": 29.0, \"Test Events Per 1000 Beneficiaries\": 6535.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 18.0, \"ASC Actual Costs\": 34186868.82, \"Part B Drugs Per Capita Standardized Costs\": 278.15, \"Imaging Actual Costs\": 48678491.12, \"Tests Per User Actual Costs\": 230.41, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0143, \"Hospice Per User Actual Costs\": 9957.56, \"Tests Standardized Costs\": 60499283.29, \"IP Standardized Costs\": 748797233.03, \"IP Per Capita Standardized Costs\": 2024.41, \"Outpatient Dialysis Facility Actual Costs\": 66424926.86, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 42.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1016.0, \"Percent of Medicare beneficiaries with hypertension\": 42.83, \"IP Covered Stays Per 1000 Beneficiaries\": 205.0, \"# Ambulance Users\": 39059.0, \"# ASC Users\": 38111.0, \"ASC Per Capita Standardized Costs\": 90.38, \"Procedures Per Capita Actual Costs\": 468.16, \"Procedures Per Capita Standardized Costs\": 492.9, \"IP Users (with a covered stay)\": 51957.0, \"Total Standardized Risk-Adjusted Costs\": 2944552627.09, \"Actual Per Capita Costs\": 7222.35, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0046, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 3.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.84, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 8.45, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 115.0, \"OP Per User Standardized Costs\": 1703.98, \"Count of Medicare beneficiaries with atrial fibrillation\": 27095.0, \"Procedure Events Per 1000 Beneficiaries\": 3544.0, \"Percent of Medicare beneficiaries with stroke\": 2.76, \"PAC: IRF Per User Actual Costs\": 21262.59, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 31249.0, \"% of Beneficiaries Using ASC\": 0.103, \"PAC: IRF Standardized Costs\": 18149187.72, \"Hospice Covered Days Per 1000 Beneficiaries\": 1583.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 181.0, \"PAC: SNF Per User Standardized Costs\": 14161.15, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0083, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 84.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0377, \"MA Participation Rate\": 45.55, \"OP Per Capita Standardized Costs\": 1110.29, \"Percent of Medicare beneficiaries with arthritis\": 23.2, \"Ambulance Per Capita Standardized Costs\": 85.29, \"PAC: HH Visits Per 1000 Beneficiaries\": 1197.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0648, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0182, \"PAC: HH Per Capita Actual Costs\": 243.47, \"E&M Events Per 1000 Beneficiaries\": 8563.0, \"Count of Medicare beneficiaries with asthma\": 16019.0, \"# Test Users\": 254083.0, \"E&M Actual Costs\": 204774030.37, \"% of Beneficiaries Using Imaging\": 0.5986, \"PAC: SNF Standardized Costs\": 173559053.84, \"DME Per Capita Actual Costs\": 188.9, \"E&M Per User Actual Costs\": 680.45, \"Percent Male\": 48.5, \"OP Visits Per 1000 Beneficiaries\": 4585.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0262, \"OP Per Capita Actual Costs\": 1225.14, \"Hospital Readmission Rate\": 0.1474, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.01, \"Tests Per Capita Standardized Costs\": 163.56, \"Hospice Per Capita Actual Costs\": 265.79, \"E&M Actual Costs as % of Total Actual Costs\": 0.0767, \"PQI07 Hypertension Admission Rate (age < 65)\": 58.0, \"Percent African American\": 1.14, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0355, \"PAC: LTCH Per Capita Actual Costs\": 31.51, \"MA Beneficiaries\": 309473.0, \"FQHC/RHC Per User Standardized Costs\": 446.59, \"PAC: HH Per User Standardized Costs\": 4120.33, \"Procedures Per User Actual Costs\": 890.36, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 510.0, \"Ambulance Per User Actual Costs\": 976.82, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 651.0, \"PAC: HH Standardized Costs\": 85686471.73, \"ASC Actual Costs as % of Total Actual Costs\": 0.0128, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 440.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0006, \"Percent Other/Unknown\": 4.16, \"% of Beneficiaries Using Hospice\": 0.0267, \"PAC: LTCH Per User Actual Costs\": 55238.21, \"Percent Non-Hispanic White\": 91.71, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 414.0, \"Count of Medicare beneficiaries with breast cancer\": 9080.0, \"DME Per Capita Standardized Costs\": 197.33, \"PQI08 CHF Admission Rate (age 65-74)\": 413.0, \"% of Beneficiaries Using DME\": 0.2493, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0249, \"% of Beneficiaries Using IP\": 0.1405, \"DME Standardized Costs\": 72989599.7, \"FQHC/RHC Standardized Costs\": 24164586.51, \"PAC: SNF Per Capita Actual Costs\": 490.23, \"Imaging Standardized Costs\": 50695451.01, \"# E&M Users\": 300938.0, \"Count of Medicare beneficiaries with arthritis\": 85799.0, \"IP Per User Standardized Costs\": 14411.86, \"IP Per User Actual Costs\": 18664.84, \"PQI10 Dehydration Admission Rate (age 75+)\": 312.0, \"PAC: IRF Per User Standardized Costs\": 19348.81, \"DME Per User Actual Costs\": 757.7, \"PAC: IRF Actual Costs\": 19944309.93, \"Imaging Events Per 1000 Beneficiaries\": 2960.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0263, \"PAC: LTCH Per User Standardized Costs\": 52393.03, \"OP Actual Costs\": 453159611.17, \"Count of Medicare beneficiaries with hypertension\": 158412.0, \"ASC Per User Standardized Costs\": 877.19, \"Part B Drugs Per Capita Actual Costs\": 276.29, \"Ambulance Events Per 1000 Beneficiaries\": 221.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 322.0, \"% of Beneficiaries Using PAC: HH\": 0.1005, \"PAC: LTCH Standardized Costs\": 193320007.11, \"Percent of Medicare beneficiaries with atrial fibrillation\": 9.43, \"E&M Per Capita Standardized Costs\": 989.8, \"E&M Per User Standardized Costs\": 1118.66, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1159.0, \"IP Covered Days Per 1000 Beneficiaries\": 1647.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 45.0, \"Count of Medicare beneficiaries with lung cancer\": 13898.0, \"IP Actual Costs as % of Total Actual Costs\": 0.342, \"Percent Eligible for Medicaid\": 19.47, \"Imaging Per Capita Standardized Costs\": 177.51, \"% of Beneficiaries Using Tests\": 0.7413, \"Imaging Per Capita Actual Costs\": 172.96, \"% of Beneficiaries Using PAC: SNF\": 0.0571, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0351, \"Count of Medicare beneficiaries with colorectal cancer\": 19586.0, \"Hospice Actual Costs\": 396697048.7, \"# PAC: HH Users\": 135867.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23308.6, \"Total Actual Costs\": 13025964753.81, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 145059.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0087, \"ASC Standardized Costs\": 109128680.25, \"DME Events Per 1000 Beneficiaries\": 1648.0, \"PQI08 CHF Admission Rate (age < 65)\": 808.0, \"ASC Events Per 1000 Beneficiaries\": 167.0, \"PAC: LTCH Actual Costs\": 180094066.66, \"Count of Medicare beneficiaries with depression\": 224266.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1476.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.73, \"Outpatient Dialysis Facility Per User Actual Costs\": 23090.74, \"Beneficiaries with Part A and Part B\": 2361022.0, \"% of Beneficiaries Using Part B Drugs\": 0.5295, \"Percent of Medicare beneficiaries with diabetes\": 26.3, \"% of Beneficiaries Using PAC: IRF\": 0.0176, \"E&M Per Capita Actual Costs\": 943.09, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0191, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0365, \"PAC: SNF Actual Costs\": 1109702671.12, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 514.0, \"Percent Female\": 55.77, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 292.0, \"Percent of Medicare beneficiaries with osteoporosis\": 7.04, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 210.55, \"# Outpatient Dialysis Facility Users\": 12216.0, \"FQHC/RHC Per User Actual Costs\": 367.31, \"Count of Medicare beneficiaries with ischemic heart disease\": 390512.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 179.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.94, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 186.0, \"Percent of Medicare beneficiaries with depression\": 16.58, \"Emergency Department Visits per 1000 Beneficiaries\": 646.0, \"IP Actual Costs\": 4455308992.79, \"% of Beneficiaries Using OP\": 0.7262, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.019, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1065, \"Count of Medicare beneficiaries with stroke\": 59527.0, \"PQI12 UTI Admission Rate (age 75+)\": 1089.0, \"# OP Users\": 982133.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 30.0, \"# Procedure Users\": 860387.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 28.88, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.066, \"Count of Medicare beneficiaries with diabetes\": 355709.0, \"ASC Per User Actual Costs\": 748.23, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1066.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0211, \"Percent of Medicare beneficiaries with high cholesterol\": 48.88, \"Standardized Per Capita Costs\": 9297.0, \"Ambulance Actual Costs\": 216204945.13, \"FQHC/RHC Per Capita Actual Costs\": 15.78, \"Part B Drugs Per User Standardized Costs\": 640.69, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0376, \"# PAC: SNF Users (with a covered stay)\": 77265.0, \"Ambulance Per Capita Actual Costs\": 159.87, \"PQI12 UTI Admission Rate (age 65-74)\": 256.0, \"Hospice Standardized Costs\": 407840350.57, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 208.58, \"% of Beneficiaries Using E&M\": 0.8848, \"PQI10 Dehydration Admission Rate (age 65-74)\": 191.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 223.0, \"Tests Per Capita Actual Costs\": 190.9, \"# DME Users\": 365045.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0946, \"PAC: SNF Per User Actual Costs\": 14362.29, \"State\": \"PA\", \"OP Per User Actual Costs\": 1682.59, \"PAC: HH Episodes Per 1000 Beneficiaries\": 162.0, \"Part B Drugs Actual Costs\": 456788472.75, \"FQHC/RHC Actual Costs\": 21346850.65, \"OP Standardized Costs\": 1694057842.75, \"DME Per User Standardized Costs\": 728.04, \"OP Actual Costs as % of Total Actual Costs\": 0.1269, \"PAC: SNF Per Capita Standardized Costs\": 879.69, \"% of Beneficiaries Using Ambulance\": 0.128, \"Hospice Per User Standardized Costs\": 10413.39, \"# Imaging Users\": 912024.0, \"Part B Drugs Per User Actual Costs\": 637.87, \"Total Standardized Costs\": 12573086223.33, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.45, \"Count of Medicare beneficiaries with chronic kidney disease\": 221405.0, \"E&M Standardized Costs\": 1338580991.79, \"Percent Hispanic\": 2.13, \"ASC Per Capita Actual Costs\": 76.85, \"Count of Medicare beneficiaries who have had a heart attack\": 12716.0, \"Tests Actual Costs\": 258167150.19, \"# PAC: LTCH Users (with a covered stay)\": 4561.0, \"% of Beneficiaries Using Procedures\": 0.6362, \"PAC: HH Actual Costs\": 554556226.21, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 41.0, \"PAC: LTCH Per Capita Standardized Costs\": 142.95, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0209, \"Emergency Department Visits\": 873959.0, \"% of Beneficiaries Using FQHC/RHC\": 0.043, \"Procedures Actual Costs\": 818817279.26, \"# FQHC/RHC Users\": 58117.0, \"Number of Acute Hospital Readmissions\": 70391.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 253.0, \"Outpatient Dialysis Facility Standardized Costs\": 284737909.83, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0354, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1347, \"Ambulance Per User Standardized Costs\": 1380.0, \"Imaging Per User Standardized Costs\": 263.22, \"Percent of Medicare beneficiaries with asthma\": 4.99, \"Part B Drugs Standardized Costs\": 458805664.08, \"FFS Beneficiaries\": 1352381.0, \"# Hospice Users (with a covered stay)\": 39165.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.009, \"Count of Medicare beneficiaries with osteoporosis\": 95155.0, \"PQI08 CHF Admission Rate (age 75+)\": 2374.0, \"PAC: IRF Per Capita Standardized Costs\": 349.2, \"Procedures Standardized Costs\": 830227007.91, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2909, \"IP Per Capita Actual Costs\": 3294.42, \"DME Actual Costs\": 245719806.13, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0426, \"Count of Medicare beneficiaries with prostate cancer\": 45261.0, \"PAC: HH Per Capita Standardized Costs\": 434.75, \"Count of Medicare beneficiaries with heart failure\": 192855.0, \"Tests Per User Standardized Costs\": 262.02, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0138, \"Percent of Medicare beneficiaries with prostate cancer\": 3.35, \"PAC: IRF Per Capita Actual Costs\": 340.74, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 256.48, \"Percent of Medicare beneficiaries with breast cancer\": 3.19, \"Procedures Per User Standardized Costs\": 964.95, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.37, \"PAC: HH Per User Actual Costs\": 4081.61, \"Count of Medicare beneficiaries with high cholesterol\": 661096.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0852, \"Hospice Per Capita Standardized Costs\": 301.57, \"# Part B Drugs Users\": 716114.0, \"Average HCC Score\": 1.0253, \"Standardized Risk-Adjusted Per Capita Costs\": 9463.37, \"# PAC: IRF Users (with a covered stay)\": 23797.0, \"Ambulance Standardized Costs\": 238933864.4, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0305, \"Percent of Medicare beneficiaries with heart failure\": 14.26, \"Tests Actual Costs as % of Total Actual Costs\": 0.0198, \"FQHC/RHC Per Capita Standardized Costs\": 17.44, \"PQI07 Hypertension Admission Rate (age 65-74)\": 76.0, \"Test Events Per 1000 Beneficiaries\": 7576.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 101.0, \"ASC Actual Costs\": 103926698.93, \"Part B Drugs Per Capita Standardized Costs\": 339.26, \"Imaging Actual Costs\": 233911940.67, \"Tests Per User Actual Costs\": 257.52, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0166, \"Hospice Per User Actual Costs\": 10128.87, \"Tests Standardized Costs\": 262679747.58, \"IP Standardized Costs\": 3656975194.62, \"IP Per Capita Standardized Costs\": 2704.1, \"Outpatient Dialysis Facility Actual Costs\": 282076497.44, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 80.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2139.0, \"Percent of Medicare beneficiaries with hypertension\": 57.18, \"IP Covered Stays Per 1000 Beneficiaries\": 303.0, \"# Ambulance Users\": 173140.0, \"# ASC Users\": 138896.0, \"ASC Per Capita Standardized Costs\": 80.69, \"Procedures Per Capita Actual Costs\": 605.46, \"Procedures Per Capita Standardized Costs\": 613.9, \"IP Users (with a covered stay)\": 253631.0, \"Total Standardized Risk-Adjusted Costs\": 12798080142.06, \"Actual Per Capita Costs\": 9631.88, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0154, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 20.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 4.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.03, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.02, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 251.0, \"OP Per User Standardized Costs\": 1724.88, \"Count of Medicare beneficiaries with atrial fibrillation\": 127529.0, \"Procedure Events Per 1000 Beneficiaries\": 5308.0, \"Percent of Medicare beneficiaries with stroke\": 4.4, \"PAC: IRF Per User Actual Costs\": 19363.98, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 149048.0, \"% of Beneficiaries Using ASC\": 0.1027, \"PAC: IRF Standardized Costs\": 472244891.81, \"Hospice Covered Days Per 1000 Beneficiaries\": 1870.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 281.0, \"PAC: SNF Per User Standardized Costs\": 15397.42, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0016, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 114.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0324, \"MA Participation Rate\": 42.72, \"OP Per Capita Standardized Costs\": 1252.65, \"Percent of Medicare beneficiaries with arthritis\": 31.01, \"Ambulance Per Capita Standardized Costs\": 176.68, \"PAC: HH Visits Per 1000 Beneficiaries\": 2547.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0629, \"Imaging Actual Costs as % of Total Actual Costs\": 0.018, \"PAC: HH Per Capita Actual Costs\": 410.06, \"E&M Events Per 1000 Beneficiaries\": 13970.0, \"Count of Medicare beneficiaries with asthma\": 67430.0, \"# Test Users\": 1002532.0, \"E&M Actual Costs\": 1275412669.82, \"% of Beneficiaries Using Imaging\": 0.6744, \"PAC: SNF Standardized Costs\": 1189681848.25, \"DME Per Capita Actual Costs\": 181.69, \"E&M Per User Actual Costs\": 1065.87, \"Percent Male\": 44.23, \"OP Visits Per 1000 Beneficiaries\": 5244.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0189, \"OP Per Capita Actual Costs\": 1221.94, \"Hospital Readmission Rate\": 0.1791, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0019, \"Tests Per Capita Standardized Costs\": 194.24, \"Hospice Per Capita Actual Costs\": 293.33, \"E&M Actual Costs as % of Total Actual Costs\": 0.0979, \"PQI07 Hypertension Admission Rate (age < 65)\": 105.0, \"Percent African American\": 7.18, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0468, \"PAC: LTCH Per Capita Actual Costs\": 133.17, \"MA Beneficiaries\": 1008641.0, \"FQHC/RHC Per User Standardized Costs\": 405.91, \"PAC: HH Per User Standardized Costs\": 4327.4, \"Procedures Per User Actual Costs\": 951.68, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 695.0, \"Ambulance Per User Actual Costs\": 1248.73, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1372.0, \"PAC: HH Standardized Costs\": 587950206.87, \"ASC Actual Costs as % of Total Actual Costs\": 0.008, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 780.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0034, \"Percent Other/Unknown\": 2.49, \"% of Beneficiaries Using Hospice\": 0.029, \"PAC: LTCH Per User Actual Costs\": 39485.65, \"Percent Non-Hispanic White\": 88.21, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 606.0, \"Count of Medicare beneficiaries with breast cancer\": 43118.0, \"DME Per Capita Standardized Costs\": 196.52, \"PQI08 CHF Admission Rate (age 65-74)\": 723.0, \"% of Beneficiaries Using DME\": 0.2699, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0217, \"% of Beneficiaries Using IP\": 0.1875, \"DME Standardized Costs\": 265768895.99, \"FQHC/RHC Standardized Costs\": 23590429.98, \"PAC: SNF Per Capita Actual Costs\": 820.55, \"Imaging Standardized Costs\": 240059510.47, \"# E&M Users\": 1196592.0, \"Count of Medicare beneficiaries with arthritis\": 419435.0, \"IP Per User Standardized Costs\": 14418.49, \"IP Per User Actual Costs\": 17566.11, \"PQI10 Dehydration Admission Rate (age 75+)\": 522.0, \"PAC: IRF Per User Standardized Costs\": 19844.72, \"DME Per User Actual Costs\": 673.12, \"PAC: IRF Actual Costs\": 460804614.54, \"Imaging Events Per 1000 Beneficiaries\": 3983.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0226, \"PAC: LTCH Per User Standardized Costs\": 42385.44, \"OP Actual Costs\": 1652526330.93, \"Count of Medicare beneficiaries with hypertension\": 773245.0, \"ASC Per User Standardized Costs\": 785.69, \"Part B Drugs Per Capita Actual Costs\": 337.77, \"Ambulance Events Per 1000 Beneficiaries\": 511.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 382.0, \"% of Beneficiaries Using PAC: HH\": 0.0501, \"PAC: LTCH Standardized Costs\": 878377.4, \"Percent of Medicare beneficiaries with atrial fibrillation\": 2.81, \"E&M Per Capita Standardized Costs\": 873.16, \"E&M Per User Standardized Costs\": 1081.64, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 4525.0, \"IP Covered Days Per 1000 Beneficiaries\": 1668.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 69.0, \"Count of Medicare beneficiaries with lung cancer\": 206.0, \"IP Actual Costs as % of Total Actual Costs\": 0.2959, \"Percent Eligible for Medicaid\": 1.27, \"Imaging Per Capita Standardized Costs\": 235.95, \"% of Beneficiaries Using Tests\": 0.7483, \"Imaging Per Capita Actual Costs\": 177.14, \"% of Beneficiaries Using PAC: SNF\": 0.0063, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0366, \"Count of Medicare beneficiaries with colorectal cancer\": 1153.0, \"Hospice Actual Costs\": 8698138.92, \"# PAC: HH Users\": 3710.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23223.21, \"Total Actual Costs\": 365609911.4, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 10006.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0071, \"ASC Standardized Costs\": 3357228.07, \"DME Events Per 1000 Beneficiaries\": 1314.0, \"PQI08 CHF Admission Rate (age < 65)\": 678.0, \"ASC Events Per 1000 Beneficiaries\": 67.0, \"PAC: LTCH Actual Costs\": 843987.0, \"Count of Medicare beneficiaries with depression\": 8560.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1299.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 13.52, \"Outpatient Dialysis Facility Per User Actual Costs\": 18990.18, \"Beneficiaries with Part A and Part B\": 612951.0, \"% of Beneficiaries Using Part B Drugs\": 0.2756, \"Percent of Medicare beneficiaries with diabetes\": 45.96, \"% of Beneficiaries Using PAC: IRF\": 0.0054, \"E&M Per Capita Actual Costs\": 692.41, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0371, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0287, \"PAC: SNF Actual Costs\": 3063613.31, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 407.0, \"Percent Female\": 54.31, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 10.54, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 763.79, \"# Outpatient Dialysis Facility Users\": 2434.0, \"FQHC/RHC Per User Actual Costs\": 314.83, \"Count of Medicare beneficiaries with ischemic heart disease\": 23051.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 199.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.77, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 14.0, \"Percent of Medicare beneficiaries with depression\": 11.57, \"Emergency Department Visits per 1000 Beneficiaries\": 486.0, \"IP Actual Costs\": 108187503.24, \"% of Beneficiaries Using OP\": 0.4217, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.02, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1371, \"Count of Medicare beneficiaries with stroke\": 3647.0, \"PQI12 UTI Admission Rate (age 75+)\": 1109.0, \"# OP Users\": 31207.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 12.0, \"# Procedure Users\": 37614.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 31.15, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0866, \"Count of Medicare beneficiaries with diabetes\": 34015.0, \"ASC Per User Actual Costs\": 759.62, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 997.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0229, \"Percent of Medicare beneficiaries with high cholesterol\": 47.37, \"Standardized Per Capita Costs\": 6367.96, \"Ambulance Actual Costs\": 6909819.8, \"FQHC/RHC Per Capita Actual Costs\": 1.26, \"Part B Drugs Per User Standardized Costs\": 662.67, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0174, \"# PAC: SNF Users (with a covered stay)\": 467.0, \"Ambulance Per Capita Actual Costs\": 93.37, \"PQI12 UTI Admission Rate (age 65-74)\": 382.0, \"Hospice Standardized Costs\": 12878493.93, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 624.57, \"% of Beneficiaries Using E&M\": 0.8073, \"PQI10 Dehydration Admission Rate (age 65-74)\": 235.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 157.0, \"Tests Per Capita Actual Costs\": 273.09, \"# DME Users\": 16938.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0077, \"PAC: SNF Per User Actual Costs\": 6560.2, \"State\": \"PR\", \"OP Per User Actual Costs\": 891.34, \"PAC: HH Episodes Per 1000 Beneficiaries\": 85.0, \"Part B Drugs Actual Costs\": 13390848.34, \"FQHC/RHC Actual Costs\": 93505.63, \"OP Standardized Costs\": 36587635.84, \"DME Per User Standardized Costs\": 638.18, \"OP Actual Costs as % of Total Actual Costs\": 0.0761, \"PAC: SNF Per Capita Standardized Costs\": 49.29, \"% of Beneficiaries Using Ambulance\": 0.0632, \"Hospice Per User Standardized Costs\": 14871.24, \"# Imaging Users\": 45948.0, \"Part B Drugs Per User Actual Costs\": 656.58, \"Total Standardized Costs\": 471267540.67, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.56, \"Count of Medicare beneficiaries with chronic kidney disease\": 13290.0, \"E&M Standardized Costs\": 64619365.9, \"Percent Hispanic\": 99.07, \"ASC Per Capita Actual Costs\": 32.75, \"Count of Medicare beneficiaries who have had a heart attack\": 569.0, \"Tests Actual Costs\": 20210433.73, \"# PAC: LTCH Users (with a covered stay)\": 20.0, \"% of Beneficiaries Using Procedures\": 0.5083, \"PAC: HH Actual Costs\": 10590516.39, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 64.0, \"PAC: LTCH Per Capita Standardized Costs\": 11.87, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0475, \"Emergency Department Visits\": 35998.0, \"% of Beneficiaries Using FQHC/RHC\": 0.004, \"Procedures Actual Costs\": 32066392.1, \"# FQHC/RHC Users\": 297.0, \"Number of Acute Hospital Readmissions\": 2787.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 74.0, \"Outpatient Dialysis Facility Standardized Costs\": 56525298.46, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0141, \"OP Standardized Costs as % of Total Standardized Costs\": 0.0776, \"Ambulance Per User Standardized Costs\": 2020.26, \"Imaging Per User Standardized Costs\": 380.03, \"Percent of Medicare beneficiaries with asthma\": 8.37, \"Part B Drugs Standardized Costs\": 13515166.6, \"FFS Beneficiaries\": 74006.0, \"# Hospice Users (with a covered stay)\": 866.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0329, \"Count of Medicare beneficiaries with osteoporosis\": 7800.0, \"PQI08 CHF Admission Rate (age 75+)\": 1299.0, \"PAC: IRF Per Capita Standardized Costs\": 110.85, \"Procedures Standardized Costs\": 40808611.14, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3001, \"IP Per Capita Actual Costs\": 1461.87, \"DME Actual Costs\": 11684609.29, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.029, \"Count of Medicare beneficiaries with prostate cancer\": 2883.0, \"PAC: HH Per Capita Standardized Costs\": 239.25, \"Count of Medicare beneficiaries with heart failure\": 11443.0, \"Tests Per User Standardized Costs\": 404.15, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0023, \"Percent of Medicare beneficiaries with prostate cancer\": 3.9, \"PAC: IRF Per Capita Actual Costs\": 69.89, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 285.32, \"Percent of Medicare beneficiaries with breast cancer\": 2.48, \"Procedures Per User Standardized Costs\": 1084.93, \"Percent of Medicare beneficiaries with chronic kidney disease\": 17.96, \"PAC: HH Per User Actual Costs\": 2854.59, \"Count of Medicare beneficiaries with high cholesterol\": 35058.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0084, \"Hospice Per Capita Standardized Costs\": 174.02, \"# Part B Drugs Users\": 20395.0, \"Average HCC Score\": 1.1271, \"Standardized Risk-Adjusted Per Capita Costs\": 5591.37, \"# PAC: IRF Users (with a covered stay)\": 398.0, \"Ambulance Standardized Costs\": 9447401.91, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0238, \"Percent of Medicare beneficiaries with heart failure\": 15.46, \"Tests Actual Costs as % of Total Actual Costs\": 0.0553, \"FQHC/RHC Per Capita Standardized Costs\": 1.57, \"PQI07 Hypertension Admission Rate (age 65-74)\": 89.0, \"Test Events Per 1000 Beneficiaries\": 12013.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 7.0, \"ASC Actual Costs\": 2423962.37, \"Part B Drugs Per Capita Standardized Costs\": 182.62, \"Imaging Actual Costs\": 13109772.12, \"Tests Per User Actual Costs\": 364.97, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0189, \"Hospice Per User Actual Costs\": 10044.04, \"Tests Standardized Costs\": 22380130.38, \"IP Standardized Costs\": 141438303.89, \"IP Per Capita Standardized Costs\": 1911.17, \"Outpatient Dialysis Facility Actual Costs\": 46222105.91, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 7.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 130.0, \"Percent of Medicare beneficiaries with hypertension\": 61.68, \"IP Covered Stays Per 1000 Beneficiaries\": 221.0, \"# Ambulance Users\": 4676.0, \"# ASC Users\": 3191.0, \"ASC Per Capita Standardized Costs\": 45.36, \"Procedures Per Capita Actual Costs\": 433.29, \"Procedures Per Capita Standardized Costs\": 551.42, \"IP Users (with a covered stay)\": 10615.0, \"Total Standardized Risk-Adjusted Costs\": 413795246.86, \"Actual Per Capita Costs\": 4940.27, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0019, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 6.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 0.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.28, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 6.73, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 162.0, \"OP Per User Standardized Costs\": 1172.42, \"Count of Medicare beneficiaries with atrial fibrillation\": 2080.0, \"Procedure Events Per 1000 Beneficiaries\": 3389.0, \"Percent of Medicare beneficiaries with stroke\": 4.93, \"PAC: IRF Per User Actual Costs\": 12996.23, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 4982.0, \"% of Beneficiaries Using ASC\": 0.0431, \"PAC: IRF Standardized Costs\": 8203196.99, \"Hospice Covered Days Per 1000 Beneficiaries\": 1088.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 382.0, \"PAC: SNF Per User Standardized Costs\": 7810.49, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0003, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 281.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0273, \"MA Participation Rate\": 87.93, \"OP Per Capita Standardized Costs\": 494.39, \"Percent of Medicare beneficiaries with arthritis\": 30.74, \"Ambulance Per Capita Standardized Costs\": 127.66, \"PAC: HH Visits Per 1000 Beneficiaries\": 1275.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0877, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0359, \"PAC: HH Per Capita Actual Costs\": 143.1, \"E&M Events Per 1000 Beneficiaries\": 12118.0, \"Count of Medicare beneficiaries with asthma\": 6191.0, \"# Test Users\": 55376.0, \"E&M Actual Costs\": 51242612.02, \"% of Beneficiaries Using Imaging\": 0.6209, \"PAC: SNF Standardized Costs\": 3647498.7, \"DME Per Capita Actual Costs\": 157.89, \"E&M Per User Actual Costs\": 857.73, \"Percent Male\": 45.69, \"OP Visits Per 1000 Beneficiaries\": 1673.0, \"DME Actual Costs as % of Total Actual Costs\": 0.032, \"OP Per Capita Actual Costs\": 375.86, \"Hospital Readmission Rate\": 0.1812, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0002, \"Tests Per Capita Standardized Costs\": 302.41, \"Hospice Per Capita Actual Costs\": 117.53, \"E&M Actual Costs as % of Total Actual Costs\": 0.1402, \"PQI07 Hypertension Admission Rate (age < 65)\": 101.0, \"Percent African American\": 0.05, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0376, \"PAC: LTCH Per Capita Actual Costs\": 11.4, \"MA Beneficiaries\": 538945.0, \"FQHC/RHC Per User Standardized Costs\": 390.78, \"PAC: HH Per User Standardized Costs\": 4772.45, \"Procedures Per User Actual Costs\": 852.51, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 685.0, \"Ambulance Per User Actual Costs\": 1477.61, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 671.0, \"PAC: HH Standardized Costs\": 17705796.86, \"ASC Actual Costs as % of Total Actual Costs\": 0.0066, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 585.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0003, \"Percent Other/Unknown\": 0.05, \"% of Beneficiaries Using Hospice\": 0.0117, \"PAC: LTCH Per User Actual Costs\": 42199.35, \"Percent Non-Hispanic White\": 0.83, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 361.0, \"Count of Medicare beneficiaries with breast cancer\": 1836.0, \"DME Per Capita Standardized Costs\": 146.06, \"PQI08 CHF Admission Rate (age 65-74)\": 446.0, \"% of Beneficiaries Using DME\": 0.2289, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.1264, \"% of Beneficiaries Using IP\": 0.1434, \"DME Standardized Costs\": 10809546.14, \"FQHC/RHC Standardized Costs\": 116060.84, \"PAC: SNF Per Capita Actual Costs\": 41.4, \"Imaging Standardized Costs\": 17461588.86, \"# E&M Users\": 59742.0, \"Count of Medicare beneficiaries with arthritis\": 22746.0, \"IP Per User Standardized Costs\": 13324.38, \"IP Per User Actual Costs\": 10191.95, \"PQI10 Dehydration Admission Rate (age 75+)\": 392.0, \"PAC: IRF Per User Standardized Costs\": 20611.05, \"DME Per User Actual Costs\": 689.85, \"PAC: IRF Actual Costs\": 5172499.64, \"Imaging Events Per 1000 Beneficiaries\": 2925.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.1199, \"PAC: LTCH Per User Standardized Costs\": 43918.87, \"OP Actual Costs\": 27816033.35, \"Count of Medicare beneficiaries with hypertension\": 45646.0, \"ASC Per User Standardized Costs\": 1052.09, \"Part B Drugs Per Capita Actual Costs\": 180.94, \"Ambulance Events Per 1000 Beneficiaries\": 376.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 223.0, \"% of Beneficiaries Using PAC: HH\": 0.1129, \"PAC: LTCH Standardized Costs\": 3738745.14, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.4, \"E&M Per Capita Standardized Costs\": 950.14, \"E&M Per User Standardized Costs\": 1079.48, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 849.0, \"IP Covered Days Per 1000 Beneficiaries\": 1646.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 34.0, \"Count of Medicare beneficiaries with lung cancer\": 1389.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3608, \"Percent Eligible for Medicaid\": 26.61, \"Imaging Per Capita Standardized Costs\": 209.5, \"% of Beneficiaries Using Tests\": 0.773, \"Imaging Per Capita Actual Costs\": 213.75, \"% of Beneficiaries Using PAC: SNF\": 0.0576, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0214, \"Count of Medicare beneficiaries with colorectal cancer\": 1491.0, \"Hospice Actual Costs\": 35032425.99, \"# PAC: HH Users\": 12468.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23241.48, \"Total Actual Costs\": 1046681228.99, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 11062.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0067, \"ASC Standardized Costs\": 6204295.56, \"DME Events Per 1000 Beneficiaries\": 1441.0, \"PQI08 CHF Admission Rate (age < 65)\": 554.0, \"ASC Events Per 1000 Beneficiaries\": 118.0, \"PAC: LTCH Actual Costs\": 4117256.87, \"Count of Medicare beneficiaries with depression\": 22498.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1277.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.02, \"Outpatient Dialysis Facility Per User Actual Costs\": 24051.33, \"Beneficiaries with Part A and Part B\": 182185.0, \"% of Beneficiaries Using Part B Drugs\": 0.466, \"Percent of Medicare beneficiaries with diabetes\": 25.32, \"% of Beneficiaries Using PAC: IRF\": 0.0063, \"E&M Per Capita Actual Costs\": 931.76, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0248, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0241, \"PAC: SNF Actual Costs\": 93123914.81, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 396.0, \"Percent Female\": 55.76, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 5.97, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 162.3, \"# Outpatient Dialysis Facility Users\": 771.0, \"FQHC/RHC Per User Actual Costs\": 461.19, \"Count of Medicare beneficiaries with ischemic heart disease\": 28281.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 130.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 1.02, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 367.0, \"Percent of Medicare beneficiaries with depression\": 20.38, \"Emergency Department Visits per 1000 Beneficiaries\": 744.0, \"IP Actual Costs\": 377675384.32, \"% of Beneficiaries Using OP\": 0.7039, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0294, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1126, \"Count of Medicare beneficiaries with stroke\": 3877.0, \"PQI12 UTI Admission Rate (age 75+)\": 949.0, \"# OP Users\": 77723.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 31.0, \"# Procedure Users\": 68484.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 25.61, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0661, \"Count of Medicare beneficiaries with diabetes\": 27960.0, \"ASC Per User Actual Costs\": 712.76, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 954.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.019, \"Percent of Medicare beneficiaries with high cholesterol\": 49.4, \"Standardized Per Capita Costs\": 8439.13, \"Ambulance Actual Costs\": 24238822.49, \"FQHC/RHC Per Capita Actual Costs\": 35.69, \"Part B Drugs Per User Standardized Costs\": 437.26, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0147, \"# PAC: SNF Users (with a covered stay)\": 6358.0, \"Ambulance Per Capita Actual Costs\": 219.53, \"PQI12 UTI Admission Rate (age 65-74)\": 252.0, \"Hospice Standardized Costs\": 33011955.38, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 167.95, \"% of Beneficiaries Using E&M\": 0.8802, \"PQI10 Dehydration Admission Rate (age 65-74)\": 149.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 212.0, \"Tests Per Capita Actual Costs\": 223.34, \"# DME Users\": 27223.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0971, \"PAC: SNF Per User Actual Costs\": 14646.73, \"State\": \"RI\", \"OP Per User Actual Costs\": 1813.83, \"PAC: HH Episodes Per 1000 Beneficiaries\": 189.0, \"Part B Drugs Actual Costs\": 22375772.9, \"FQHC/RHC Actual Costs\": 3940367.12, \"OP Standardized Costs\": 133488162.21, \"DME Per User Standardized Costs\": 649.19, \"OP Actual Costs as % of Total Actual Costs\": 0.1347, \"PAC: SNF Per Capita Standardized Costs\": 819.35, \"% of Beneficiaries Using Ambulance\": 0.1526, \"Hospice Per User Standardized Costs\": 10145.04, \"# Imaging Users\": 75745.0, \"Part B Drugs Per User Actual Costs\": 434.87, \"Total Standardized Costs\": 931764181.15, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.35, \"Count of Medicare beneficiaries with chronic kidney disease\": 15834.0, \"E&M Standardized Costs\": 104905272.55, \"Percent Hispanic\": 6.72, \"ASC Per Capita Actual Costs\": 57.08, \"Count of Medicare beneficiaries who have had a heart attack\": 1130.0, \"Tests Actual Costs\": 24659280.35, \"# PAC: LTCH Users (with a covered stay)\": 117.0, \"% of Beneficiaries Using Procedures\": 0.6203, \"PAC: HH Actual Costs\": 58632259.71, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 28.0, \"PAC: LTCH Per Capita Standardized Costs\": 33.86, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0267, \"Emergency Department Visits\": 82126.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0774, \"Procedures Actual Costs\": 62562216.71, \"# FQHC/RHC Users\": 8544.0, \"Number of Acute Hospital Readmissions\": 5381.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 93.0, \"Outpatient Dialysis Facility Standardized Costs\": 17919181.32, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0139, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1433, \"Ambulance Per User Standardized Costs\": 1627.06, \"Imaging Per User Standardized Costs\": 305.38, \"Percent of Medicare beneficiaries with asthma\": 6.89, \"Part B Drugs Standardized Costs\": 22498734.33, \"FFS Beneficiaries\": 110410.0, \"# Hospice Users (with a covered stay)\": 3254.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.007, \"Count of Medicare beneficiaries with osteoporosis\": 6592.0, \"PQI08 CHF Admission Rate (age 75+)\": 2179.0, \"PAC: IRF Per Capita Standardized Costs\": 124.14, \"Procedures Standardized Costs\": 61557403.31, \"IP Standardized Costs as % of Total Standardized Costs\": 0.293, \"IP Per Capita Actual Costs\": 3420.66, \"DME Actual Costs\": 16229427.31, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.056, \"Count of Medicare beneficiaries with prostate cancer\": 3484.0, \"PAC: HH Per Capita Standardized Costs\": 511.65, \"Count of Medicare beneficiaries with heart failure\": 14452.0, \"Tests Per User Standardized Costs\": 291.74, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0039, \"Percent of Medicare beneficiaries with prostate cancer\": 3.16, \"PAC: IRF Per Capita Actual Costs\": 132.03, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 311.57, \"Percent of Medicare beneficiaries with breast cancer\": 3.37, \"Procedures Per User Standardized Costs\": 898.86, \"Percent of Medicare beneficiaries with chronic kidney disease\": 14.34, \"PAC: HH Per User Actual Costs\": 4702.62, \"Count of Medicare beneficiaries with high cholesterol\": 54537.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.089, \"Hospice Per Capita Standardized Costs\": 298.99, \"# Part B Drugs Users\": 51454.0, \"Average HCC Score\": 1.0002, \"Standardized Risk-Adjusted Per Capita Costs\": 8644.5, \"# PAC: IRF Users (with a covered stay)\": 694.0, \"Ambulance Standardized Costs\": 27420597.06, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0335, \"Percent of Medicare beneficiaries with heart failure\": 13.09, \"Tests Actual Costs as % of Total Actual Costs\": 0.0236, \"FQHC/RHC Per Capita Standardized Costs\": 33.74, \"PQI07 Hypertension Admission Rate (age 65-74)\": 56.0, \"Test Events Per 1000 Beneficiaries\": 9630.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 28.0, \"ASC Actual Costs\": 6302267.45, \"Part B Drugs Per Capita Standardized Costs\": 203.77, \"Imaging Actual Costs\": 23599961.82, \"Tests Per User Actual Costs\": 288.92, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0232, \"Hospice Per User Actual Costs\": 10765.96, \"Tests Standardized Costs\": 24900646.35, \"IP Standardized Costs\": 272960431.64, \"IP Per Capita Standardized Costs\": 2472.24, \"Outpatient Dialysis Facility Actual Costs\": 18543572.25, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 81.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1994.0, \"Percent of Medicare beneficiaries with hypertension\": 56.67, \"IP Covered Stays Per 1000 Beneficiaries\": 287.0, \"# Ambulance Users\": 16853.0, \"# ASC Users\": 8842.0, \"ASC Per Capita Standardized Costs\": 56.19, \"Procedures Per Capita Actual Costs\": 566.64, \"Procedures Per Capita Standardized Costs\": 557.53, \"IP Users (with a covered stay)\": 19041.0, \"Total Standardized Risk-Adjusted Costs\": 954439644.42, \"Actual Per Capita Costs\": 9479.95, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.004, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 7.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.26, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.18, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 246.0, \"OP Per User Standardized Costs\": 1717.49, \"Count of Medicare beneficiaries with atrial fibrillation\": 9278.0, \"Procedure Events Per 1000 Beneficiaries\": 4946.0, \"Percent of Medicare beneficiaries with stroke\": 3.51, \"PAC: IRF Per User Actual Costs\": 21005.65, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 12343.0, \"% of Beneficiaries Using ASC\": 0.0801, \"PAC: IRF Standardized Costs\": 13706327.61, \"Hospice Covered Days Per 1000 Beneficiaries\": 1774.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 193.0, \"PAC: SNF Per User Standardized Costs\": 14228.5, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0038, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 74.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0354, \"MA Participation Rate\": 39.4, \"OP Per Capita Standardized Costs\": 1209.02, \"Percent of Medicare beneficiaries with arthritis\": 26.68, \"Ambulance Per Capita Standardized Costs\": 248.35, \"PAC: HH Visits Per 1000 Beneficiaries\": 3226.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0598, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0225, \"PAC: HH Per Capita Actual Costs\": 531.04, \"E&M Events Per 1000 Beneficiaries\": 13420.0, \"Count of Medicare beneficiaries with asthma\": 7605.0, \"# Test Users\": 85351.0, \"E&M Actual Costs\": 102876151.19, \"% of Beneficiaries Using Imaging\": 0.686, \"PAC: SNF Standardized Costs\": 90464813.36, \"DME Per Capita Actual Costs\": 146.99, \"E&M Per User Actual Costs\": 1058.6, \"Percent Male\": 44.24, \"OP Visits Per 1000 Beneficiaries\": 4967.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0155, \"OP Per Capita Actual Costs\": 1276.85, \"Hospital Readmission Rate\": 0.184, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.004, \"Tests Per Capita Standardized Costs\": 225.53, \"Hospice Per Capita Actual Costs\": 317.29, \"E&M Actual Costs as % of Total Actual Costs\": 0.0983, \"PQI07 Hypertension Admission Rate (age < 65)\": 71.0, \"Percent African American\": 3.75, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0606, \"PAC: LTCH Per Capita Actual Costs\": 37.29, \"MA Beneficiaries\": 71775.0, \"FQHC/RHC Per User Standardized Costs\": 435.99, \"PAC: HH Per User Standardized Costs\": 4530.9, \"Procedures Per User Actual Costs\": 913.53, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 532.0, \"Ambulance Per User Actual Costs\": 1438.26, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1572.0, \"PAC: HH Standardized Costs\": 56491223.65, \"ASC Actual Costs as % of Total Actual Costs\": 0.006, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 812.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0011, \"Percent Other/Unknown\": 3.47, \"% of Beneficiaries Using Hospice\": 0.0295, \"PAC: LTCH Per User Actual Costs\": 35190.23, \"Percent Non-Hispanic White\": 86.06, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 432.0, \"Count of Medicare beneficiaries with breast cancer\": 3721.0, \"DME Per Capita Standardized Costs\": 160.07, \"PQI08 CHF Admission Rate (age 65-74)\": 646.0, \"% of Beneficiaries Using DME\": 0.2466, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0177, \"% of Beneficiaries Using IP\": 0.1725, \"DME Standardized Costs\": 17672870.97, \"FQHC/RHC Standardized Costs\": 3725108.74, \"PAC: SNF Per Capita Actual Costs\": 843.44, \"Imaging Standardized Costs\": 23130888.9, \"# E&M Users\": 97181.0, \"Count of Medicare beneficiaries with arthritis\": 29462.0, \"IP Per User Standardized Costs\": 14335.4, \"IP Per User Actual Costs\": 19834.85, \"PQI10 Dehydration Admission Rate (age 75+)\": 384.0, \"PAC: IRF Per User Standardized Costs\": 19749.75, \"DME Per User Actual Costs\": 596.17, \"PAC: IRF Actual Costs\": 14577919.55, \"Imaging Events Per 1000 Beneficiaries\": 3954.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0192, \"PAC: LTCH Per User Standardized Costs\": 31955.09, \"OP Actual Costs\": 140976623.2, \"Count of Medicare beneficiaries with hypertension\": 62572.0, \"ASC Per User Standardized Costs\": 701.68, \"Part B Drugs Per Capita Actual Costs\": 202.66, \"Ambulance Events Per 1000 Beneficiaries\": 732.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 313.0, \"% of Beneficiaries Using PAC: HH\": 0.0752, \"PAC: LTCH Standardized Costs\": 65821220.94, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.36, \"E&M Per Capita Standardized Costs\": 857.57, \"E&M Per User Standardized Costs\": 954.56, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1630.0, \"IP Covered Days Per 1000 Beneficiaries\": 1461.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 65.0, \"Count of Medicare beneficiaries with lung cancer\": 6692.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3204, \"Percent Eligible for Medicaid\": 15.85, \"Imaging Per Capita Standardized Costs\": 222.13, \"% of Beneficiaries Using Tests\": 0.8315, \"Imaging Per Capita Actual Costs\": 202.53, \"% of Beneficiaries Using PAC: SNF\": 0.0395, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0367, \"Count of Medicare beneficiaries with colorectal cancer\": 7508.0, \"Hospice Actual Costs\": 289580557.83, \"# PAC: HH Users\": 50144.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 24302.43, \"Total Actual Costs\": 5691145480.39, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 63166.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0121, \"ASC Standardized Costs\": 70544384.95, \"DME Events Per 1000 Beneficiaries\": 1752.0, \"PQI08 CHF Admission Rate (age < 65)\": 1026.0, \"ASC Events Per 1000 Beneficiaries\": 202.0, \"PAC: LTCH Actual Costs\": 59570876.82, \"Count of Medicare beneficiaries with depression\": 95553.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1377.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.47, \"Outpatient Dialysis Facility Per User Actual Costs\": 23396.56, \"Beneficiaries with Part A and Part B\": 864349.0, \"% of Beneficiaries Using Part B Drugs\": 0.5659, \"Percent of Medicare beneficiaries with diabetes\": 27.18, \"% of Beneficiaries Using PAC: IRF\": 0.0118, \"E&M Per Capita Actual Costs\": 767.4, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0254, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0361, \"PAC: SNF Actual Costs\": 359681519.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 522.0, \"Percent Female\": 54.87, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 309.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.09, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 292.11, \"# Outpatient Dialysis Facility Users\": 8020.0, \"FQHC/RHC Per User Actual Costs\": 319.45, \"Count of Medicare beneficiaries with ischemic heart disease\": 171468.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 165.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.74, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 525.0, \"Percent of Medicare beneficiaries with depression\": 14.32, \"Emergency Department Visits per 1000 Beneficiaries\": 622.0, \"IP Actual Costs\": 1823468627.16, \"% of Beneficiaries Using OP\": 0.6663, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0259, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.098, \"Count of Medicare beneficiaries with stroke\": 24825.0, \"PQI12 UTI Admission Rate (age 75+)\": 1121.0, \"# OP Users\": 444569.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 35.0, \"# Procedure Users\": 426244.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 25.7, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0734, \"Count of Medicare beneficiaries with diabetes\": 181376.0, \"ASC Per User Actual Costs\": 786.87, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 910.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0274, \"Percent of Medicare beneficiaries with high cholesterol\": 49.24, \"Standardized Per Capita Costs\": 8751.57, \"Ambulance Actual Costs\": 138082177.83, \"FQHC/RHC Per Capita Actual Costs\": 39.07, \"Part B Drugs Per User Standardized Costs\": 557.71, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0262, \"# PAC: SNF Users (with a covered stay)\": 26377.0, \"Ambulance Per Capita Actual Costs\": 206.95, \"PQI12 UTI Admission Rate (age 65-74)\": 244.0, \"Hospice Standardized Costs\": 310705179.81, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 281.23, \"% of Beneficiaries Using E&M\": 0.8984, \"PQI10 Dehydration Admission Rate (age 65-74)\": 207.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 173.0, \"Tests Per Capita Actual Costs\": 281.42, \"# DME Users\": 195245.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0692, \"PAC: SNF Per User Actual Costs\": 13636.18, \"State\": \"SC\", \"OP Per User Actual Costs\": 1625.51, \"PAC: HH Episodes Per 1000 Beneficiaries\": 119.0, \"Part B Drugs Actual Costs\": 209145763.01, \"FQHC/RHC Actual Costs\": 26070875.04, \"OP Standardized Costs\": 763334055.72, \"DME Per User Standardized Costs\": 819.49, \"OP Actual Costs as % of Total Actual Costs\": 0.127, \"PAC: SNF Per Capita Standardized Costs\": 605.46, \"% of Beneficiaries Using Ambulance\": 0.1254, \"Hospice Per User Standardized Costs\": 14444.01, \"# Imaging Users\": 470748.0, \"Part B Drugs Per User Actual Costs\": 553.9, \"Total Standardized Costs\": 5839259263.85, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.13, \"Count of Medicare beneficiaries with chronic kidney disease\": 102317.0, \"E&M Standardized Costs\": 572189316.55, \"Percent Hispanic\": 0.87, \"ASC Per Capita Actual Costs\": 96.76, \"Count of Medicare beneficiaries who have had a heart attack\": 4932.0, \"Tests Actual Costs\": 187768714.1, \"# PAC: LTCH Users (with a covered stay)\": 1437.0, \"% of Beneficiaries Using Procedures\": 0.6388, \"PAC: HH Actual Costs\": 205050851.85, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 57.0, \"PAC: LTCH Per Capita Standardized Costs\": 98.65, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0338, \"Emergency Department Visits\": 415339.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1223, \"Procedures Actual Costs\": 389042020.92, \"# FQHC/RHC Users\": 81612.0, \"Number of Acute Hospital Readmissions\": 28229.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 170.0, \"Outpatient Dialysis Facility Standardized Costs\": 194905527.99, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0256, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1307, \"Ambulance Per User Standardized Costs\": 1811.26, \"Imaging Per User Standardized Costs\": 314.84, \"Percent of Medicare beneficiaries with asthma\": 4.43, \"Part B Drugs Standardized Costs\": 210585311.36, \"FFS Beneficiaries\": 667224.0, \"# Hospice Users (with a covered stay)\": 21511.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.012, \"Count of Medicare beneficiaries with osteoporosis\": 33979.0, \"PQI08 CHF Admission Rate (age 75+)\": 1761.0, \"PAC: IRF Per Capita Standardized Costs\": 229.34, \"Procedures Standardized Costs\": 428842203.63, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2818, \"IP Per Capita Actual Costs\": 2732.92, \"DME Actual Costs\": 151302112.45, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.036, \"Count of Medicare beneficiaries with prostate cancer\": 21254.0, \"PAC: HH Per Capita Standardized Costs\": 342.06, \"Count of Medicare beneficiaries with heart failure\": 81961.0, \"Tests Per User Standardized Costs\": 355.75, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0105, \"Percent of Medicare beneficiaries with prostate cancer\": 3.19, \"PAC: IRF Per Capita Actual Costs\": 218.61, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 287.06, \"Percent of Medicare beneficiaries with breast cancer\": 2.91, \"Procedures Per User Standardized Costs\": 1006.1, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.33, \"PAC: HH Per User Actual Costs\": 4089.24, \"Count of Medicare beneficiaries with high cholesterol\": 328558.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0632, \"Hospice Per Capita Standardized Costs\": 465.67, \"# Part B Drugs Users\": 377588.0, \"Average HCC Score\": 0.9473, \"Standardized Risk-Adjusted Per Capita Costs\": 9885.1, \"# PAC: IRF Users (with a covered stay)\": 7865.0, \"Ambulance Standardized Costs\": 151500928.06, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0509, \"Percent of Medicare beneficiaries with heart failure\": 12.28, \"Tests Actual Costs as % of Total Actual Costs\": 0.033, \"FQHC/RHC Per Capita Standardized Costs\": 45.16, \"PQI07 Hypertension Admission Rate (age 65-74)\": 79.0, \"Test Events Per 1000 Beneficiaries\": 10369.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 66.0, \"ASC Actual Costs\": 64561781.61, \"Part B Drugs Per Capita Standardized Costs\": 315.61, \"Imaging Actual Costs\": 135131081.8, \"Tests Per User Actual Costs\": 338.43, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0243, \"Hospice Per User Actual Costs\": 13461.98, \"Tests Standardized Costs\": 197378051.96, \"IP Standardized Costs\": 1645382189.96, \"IP Per Capita Standardized Costs\": 2466.01, \"Outpatient Dialysis Facility Actual Costs\": 187640408.47, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 52.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1514.0, \"Percent of Medicare beneficiaries with hypertension\": 59.95, \"IP Covered Stays Per 1000 Beneficiaries\": 256.0, \"# Ambulance Users\": 83644.0, \"# ASC Users\": 82049.0, \"ASC Per Capita Standardized Costs\": 105.73, \"Procedures Per Capita Actual Costs\": 583.08, \"Procedures Per Capita Standardized Costs\": 642.73, \"IP Users (with a covered stay)\": 110308.0, \"Total Standardized Risk-Adjusted Costs\": 6595578654.84, \"Actual Per Capita Costs\": 8529.59, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0113, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 13.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.0, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.58, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 217.0, \"OP Per User Standardized Costs\": 1717.02, \"Count of Medicare beneficiaries with atrial fibrillation\": 49121.0, \"Procedure Events Per 1000 Beneficiaries\": 4659.0, \"Percent of Medicare beneficiaries with stroke\": 3.72, \"PAC: IRF Per User Actual Costs\": 18545.65, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 70609.0, \"% of Beneficiaries Using ASC\": 0.123, \"PAC: IRF Standardized Costs\": 153022252.01, \"Hospice Covered Days Per 1000 Beneficiaries\": 2890.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 307.0, \"PAC: SNF Per User Standardized Costs\": 15315.44, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0046, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 159.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0532, \"MA Participation Rate\": 22.81, \"OP Per Capita Standardized Costs\": 1144.04, \"Percent of Medicare beneficiaries with arthritis\": 28.66, \"Ambulance Per Capita Standardized Costs\": 227.06, \"PAC: HH Visits Per 1000 Beneficiaries\": 1826.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0684, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0237, \"PAC: HH Per Capita Actual Costs\": 307.32, \"E&M Events Per 1000 Beneficiaries\": 12328.0, \"Count of Medicare beneficiaries with asthma\": 29547.0, \"# Test Users\": 554830.0, \"E&M Actual Costs\": 512030546.27, \"% of Beneficiaries Using Imaging\": 0.7055, \"PAC: SNF Standardized Costs\": 403975474.41, \"DME Per Capita Actual Costs\": 226.76, \"E&M Per User Actual Costs\": 854.2, \"Percent Male\": 45.13, \"OP Visits Per 1000 Beneficiaries\": 3631.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0266, \"OP Per Capita Actual Costs\": 1083.07, \"Hospital Readmission Rate\": 0.17, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0052, \"Tests Per Capita Standardized Costs\": 295.82, \"Hospice Per Capita Actual Costs\": 434.01, \"E&M Actual Costs as % of Total Actual Costs\": 0.09, \"PQI07 Hypertension Admission Rate (age < 65)\": 138.0, \"Percent African American\": 20.1, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0391, \"PAC: LTCH Per Capita Actual Costs\": 89.28, \"MA Beneficiaries\": 197125.0, \"FQHC/RHC Per User Standardized Costs\": 369.21, \"PAC: HH Per User Standardized Costs\": 4551.49, \"Procedures Per User Actual Costs\": 912.72, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 705.0, \"Ambulance Per User Actual Costs\": 1650.83, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1262.0, \"PAC: HH Standardized Costs\": 228229839.61, \"ASC Actual Costs as % of Total Actual Costs\": 0.0113, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 626.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0022, \"Percent Other/Unknown\": 1.52, \"% of Beneficiaries Using Hospice\": 0.0322, \"PAC: LTCH Per User Actual Costs\": 41455.03, \"Percent Non-Hispanic White\": 77.51, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 692.0, \"Count of Medicare beneficiaries with breast cancer\": 19403.0, \"DME Per Capita Standardized Costs\": 239.8, \"PQI08 CHF Admission Rate (age 65-74)\": 617.0, \"% of Beneficiaries Using DME\": 0.2926, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.033, \"% of Beneficiaries Using IP\": 0.1653, \"DME Standardized Costs\": 160001351.14, \"FQHC/RHC Standardized Costs\": 30132166.0, \"PAC: SNF Per Capita Actual Costs\": 539.07, \"Imaging Standardized Costs\": 148212545.91, \"# E&M Users\": 599427.0, \"Count of Medicare beneficiaries with arthritis\": 191199.0, \"IP Per User Standardized Costs\": 14916.25, \"IP Per User Actual Costs\": 16530.7, \"PQI10 Dehydration Admission Rate (age 75+)\": 549.0, \"PAC: IRF Per User Standardized Costs\": 19456.1, \"DME Per User Actual Costs\": 774.93, \"PAC: IRF Actual Costs\": 145861542.88, \"Imaging Events Per 1000 Beneficiaries\": 4105.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0334, \"PAC: LTCH Per User Standardized Costs\": 45804.61, \"OP Actual Costs\": 722651431.35, \"Count of Medicare beneficiaries with hypertension\": 400005.0, \"ASC Per User Standardized Costs\": 859.78, \"Part B Drugs Per Capita Actual Costs\": 313.46, \"Ambulance Events Per 1000 Beneficiaries\": 750.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 382.0, \"% of Beneficiaries Using PAC: HH\": 0.0324, \"PAC: LTCH Standardized Costs\": 5586669.48, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.69, \"E&M Per Capita Standardized Costs\": 583.35, \"E&M Per User Standardized Costs\": 678.37, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 836.0, \"IP Covered Days Per 1000 Beneficiaries\": 1267.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": \"*\", \"Count of Medicare beneficiaries with lung cancer\": 1015.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3484, \"Percent Eligible for Medicaid\": 16.84, \"Imaging Per Capita Standardized Costs\": 135.58, \"% of Beneficiaries Using Tests\": 0.682, \"Imaging Per Capita Actual Costs\": 130.05, \"% of Beneficiaries Using PAC: SNF\": 0.0532, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0251, \"Count of Medicare beneficiaries with colorectal cancer\": 1585.0, \"Hospice Actual Costs\": 16810749.24, \"# PAC: HH Users\": 3894.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23030.15, \"Total Actual Costs\": 921672664.89, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 10285.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0077, \"ASC Standardized Costs\": 6840746.78, \"DME Events Per 1000 Beneficiaries\": 1729.0, \"PQI08 CHF Admission Rate (age < 65)\": 511.0, \"ASC Events Per 1000 Beneficiaries\": 104.0, \"PAC: LTCH Actual Costs\": 4930101.86, \"Count of Medicare beneficiaries with depression\": 17622.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2142.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.57, \"Outpatient Dialysis Facility Per User Actual Costs\": 22436.14, \"Beneficiaries with Part A and Part B\": 143813.0, \"% of Beneficiaries Using Part B Drugs\": 0.4224, \"Percent of Medicare beneficiaries with diabetes\": 22.12, \"% of Beneficiaries Using PAC: IRF\": 0.0055, \"E&M Per Capita Actual Costs\": 534.72, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0184, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0264, \"PAC: SNF Actual Costs\": 86251192.04, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 642.0, \"Percent Female\": 54.31, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 5.83, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 151.51, \"# Outpatient Dialysis Facility Users\": 790.0, \"FQHC/RHC Per User Actual Costs\": 526.85, \"Count of Medicare beneficiaries with ischemic heart disease\": 27548.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 139.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.73, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 863.0, \"Percent of Medicare beneficiaries with depression\": 14.68, \"Emergency Department Visits per 1000 Beneficiaries\": 483.0, \"IP Actual Costs\": 321099182.01, \"% of Beneficiaries Using OP\": 0.7274, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.008, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0793, \"Count of Medicare beneficiaries with stroke\": 2824.0, \"PQI12 UTI Admission Rate (age 75+)\": 810.0, \"# OP Users\": 87344.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 19.0, \"# Procedure Users\": 66789.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 22.94, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0651, \"Count of Medicare beneficiaries with diabetes\": 26563.0, \"ASC Per User Actual Costs\": 771.55, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 878.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0268, \"Percent of Medicare beneficiaries with high cholesterol\": 35.39, \"Standardized Per Capita Costs\": 7358.12, \"Ambulance Actual Costs\": 10154456.18, \"FQHC/RHC Per Capita Actual Costs\": 95.88, \"Part B Drugs Per User Standardized Costs\": 460.61, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0122, \"# PAC: SNF Users (with a covered stay)\": 6386.0, \"Ambulance Per Capita Actual Costs\": 84.56, \"PQI12 UTI Admission Rate (age 65-74)\": 205.0, \"Hospice Standardized Costs\": 17976425.2, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 147.61, \"% of Beneficiaries Using E&M\": 0.8599, \"PQI10 Dehydration Admission Rate (age 65-74)\": 188.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 116.0, \"Tests Per Capita Actual Costs\": 131.25, \"# DME Users\": 32327.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1119, \"PAC: SNF Per User Actual Costs\": 13506.29, \"State\": \"SD\", \"OP Per User Actual Costs\": 2392.32, \"PAC: HH Episodes Per 1000 Beneficiaries\": 47.0, \"Part B Drugs Actual Costs\": 23154636.07, \"FQHC/RHC Actual Costs\": 11513729.07, \"OP Standardized Costs\": 192379097.71, \"DME Per User Standardized Costs\": 732.21, \"OP Actual Costs as % of Total Actual Costs\": 0.2267, \"PAC: SNF Per Capita Standardized Costs\": 823.55, \"% of Beneficiaries Using Ambulance\": 0.0829, \"Hospice Per User Standardized Costs\": 8104.79, \"# Imaging Users\": 78295.0, \"Part B Drugs Per User Actual Costs\": 456.48, \"Total Standardized Costs\": 883570385.83, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.32, \"Count of Medicare beneficiaries with chronic kidney disease\": 16129.0, \"E&M Standardized Costs\": 70048875.5, \"Percent Hispanic\": 0.58, \"ASC Per Capita Actual Costs\": 53.12, \"Count of Medicare beneficiaries who have had a heart attack\": 876.0, \"Tests Actual Costs\": 15760672.99, \"# PAC: LTCH Users (with a covered stay)\": 146.0, \"% of Beneficiaries Using Procedures\": 0.5562, \"PAC: HH Actual Costs\": 14387917.06, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 30.0, \"PAC: LTCH Per Capita Standardized Costs\": 46.52, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0184, \"Emergency Department Visits\": 58033.0, \"% of Beneficiaries Using FQHC/RHC\": 0.182, \"Procedures Actual Costs\": 53928092.96, \"# FQHC/RHC Users\": 21854.0, \"Number of Acute Hospital Readmissions\": 4286.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 68.0, \"Outpatient Dialysis Facility Standardized Costs\": 18193822.17, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0118, \"OP Standardized Costs as % of Total Standardized Costs\": 0.2177, \"Ambulance Per User Standardized Costs\": 706.62, \"Imaging Per User Standardized Costs\": 207.94, \"Percent of Medicare beneficiaries with asthma\": 3.73, \"Part B Drugs Standardized Costs\": 23364003.76, \"FFS Beneficiaries\": 120081.0, \"# Hospice Users (with a covered stay)\": 2218.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0066, \"Count of Medicare beneficiaries with osteoporosis\": 7005.0, \"PQI08 CHF Admission Rate (age 75+)\": 1614.0, \"PAC: IRF Per Capita Standardized Costs\": 89.66, \"Procedures Standardized Costs\": 57493589.43, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3085, \"IP Per Capita Actual Costs\": 2674.02, \"DME Actual Costs\": 22619428.83, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0156, \"Count of Medicare beneficiaries with prostate cancer\": 3401.0, \"PAC: HH Per Capita Standardized Costs\": 134.48, \"Count of Medicare beneficiaries with heart failure\": 14691.0, \"Tests Per User Standardized Costs\": 198.1, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0053, \"Percent of Medicare beneficiaries with prostate cancer\": 2.83, \"PAC: IRF Per Capita Actual Costs\": 90.84, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 199.46, \"Percent of Medicare beneficiaries with breast cancer\": 2.68, \"Procedures Per User Standardized Costs\": 860.82, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.43, \"PAC: HH Per User Actual Costs\": 3694.89, \"Count of Medicare beneficiaries with high cholesterol\": 42494.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0936, \"Hospice Per Capita Standardized Costs\": 149.7, \"# Part B Drugs Users\": 50724.0, \"Average HCC Score\": 0.8709, \"Standardized Risk-Adjusted Per Capita Costs\": 9208.64, \"# PAC: IRF Users (with a covered stay)\": 656.0, \"Ambulance Standardized Costs\": 7032344.72, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0182, \"Percent of Medicare beneficiaries with heart failure\": 12.23, \"Tests Actual Costs as % of Total Actual Costs\": 0.0171, \"FQHC/RHC Per Capita Standardized Costs\": 94.77, \"PQI07 Hypertension Admission Rate (age 65-74)\": 49.0, \"Test Events Per 1000 Beneficiaries\": 6235.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 32.0, \"ASC Actual Costs\": 6378436.29, \"Part B Drugs Per Capita Standardized Costs\": 194.57, \"Imaging Actual Costs\": 15616697.63, \"Tests Per User Actual Costs\": 192.45, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.011, \"Hospice Per User Actual Costs\": 7579.24, \"Tests Standardized Costs\": 16224146.02, \"IP Standardized Costs\": 272575139.21, \"IP Per Capita Standardized Costs\": 2269.93, \"Outpatient Dialysis Facility Actual Costs\": 17724552.43, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 70.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1684.0, \"Percent of Medicare beneficiaries with hypertension\": 47.38, \"IP Covered Stays Per 1000 Beneficiaries\": 253.0, \"# Ambulance Users\": 9952.0, \"# ASC Users\": 8267.0, \"ASC Per Capita Standardized Costs\": 56.97, \"Procedures Per Capita Actual Costs\": 449.1, \"Procedures Per Capita Standardized Costs\": 478.79, \"IP Users (with a covered stay)\": 20437.0, \"Total Standardized Risk-Adjusted Costs\": 1105782256.54, \"Actual Per Capita Costs\": 7675.42, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0063, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 6.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.85, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.85, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 143.0, \"OP Per User Standardized Costs\": 2202.55, \"Count of Medicare beneficiaries with atrial fibrillation\": 9232.0, \"Procedure Events Per 1000 Beneficiaries\": 3518.0, \"Percent of Medicare beneficiaries with stroke\": 2.35, \"PAC: IRF Per User Actual Costs\": 16628.02, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11828.0, \"% of Beneficiaries Using ASC\": 0.0688, \"PAC: IRF Standardized Costs\": 10766601.62, \"Hospice Covered Days Per 1000 Beneficiaries\": 950.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 314.0, \"PAC: SNF Per User Standardized Costs\": 15485.93, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0125, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 92.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0203, \"MA Participation Rate\": 16.5, \"OP Per Capita Standardized Costs\": 1602.08, \"Percent of Medicare beneficiaries with arthritis\": 24.4, \"Ambulance Per Capita Standardized Costs\": 58.56, \"PAC: HH Visits Per 1000 Beneficiaries\": 797.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0585, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0169, \"PAC: HH Per Capita Actual Costs\": 119.82, \"E&M Events Per 1000 Beneficiaries\": 9238.0, \"Count of Medicare beneficiaries with asthma\": 4481.0, \"# Test Users\": 81897.0, \"E&M Actual Costs\": 64209984.79, \"% of Beneficiaries Using Imaging\": 0.652, \"PAC: SNF Standardized Costs\": 98893144.39, \"DME Per Capita Actual Costs\": 188.37, \"E&M Per User Actual Costs\": 621.83, \"Percent Male\": 45.69, \"OP Visits Per 1000 Beneficiaries\": 6440.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0245, \"OP Per Capita Actual Costs\": 1740.12, \"Hospital Readmission Rate\": 0.1446, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0129, \"Tests Per Capita Standardized Costs\": 135.11, \"Hospice Per Capita Actual Costs\": 140.0, \"E&M Actual Costs as % of Total Actual Costs\": 0.0697, \"PQI07 Hypertension Admission Rate (age < 65)\": \"*\", \"Percent African American\": 0.41, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0183, \"PAC: LTCH Per Capita Actual Costs\": 41.06, \"MA Beneficiaries\": 23732.0, \"FQHC/RHC Per User Standardized Costs\": 520.71, \"PAC: HH Per User Standardized Costs\": 4147.03, \"Procedures Per User Actual Costs\": 807.44, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 511.0, \"Ambulance Per User Actual Costs\": 1020.33, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 882.0, \"PAC: HH Standardized Costs\": 16148550.01, \"ASC Actual Costs as % of Total Actual Costs\": 0.0069, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 549.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0012, \"Percent Other/Unknown\": 6.18, \"% of Beneficiaries Using Hospice\": 0.0185, \"PAC: LTCH Per User Actual Costs\": 33767.82, \"Percent Non-Hispanic White\": 92.82, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 776.0, \"Count of Medicare beneficiaries with breast cancer\": 3221.0, \"DME Per Capita Standardized Costs\": 197.12, \"PQI08 CHF Admission Rate (age 65-74)\": 410.0, \"% of Beneficiaries Using DME\": 0.2692, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0192, \"% of Beneficiaries Using IP\": 0.1702, \"DME Standardized Costs\": 23670094.12, \"FQHC/RHC Standardized Costs\": 11379599.98, \"PAC: SNF Per Capita Actual Costs\": 718.28, \"Imaging Standardized Costs\": 16280625.47, \"# E&M Users\": 103260.0, \"Count of Medicare beneficiaries with arthritis\": 29300.0, \"IP Per User Standardized Costs\": 13337.34, \"IP Per User Actual Costs\": 15711.66, \"PQI10 Dehydration Admission Rate (age 75+)\": 462.0, \"PAC: IRF Per User Standardized Costs\": 16412.5, \"DME Per User Actual Costs\": 699.71, \"PAC: IRF Actual Costs\": 10907979.84, \"Imaging Events Per 1000 Beneficiaries\": 3398.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0206, \"PAC: LTCH Per User Standardized Costs\": 38264.86, \"OP Actual Costs\": 208955059.25, \"Count of Medicare beneficiaries with hypertension\": 56891.0, \"ASC Per User Standardized Costs\": 827.48, \"Part B Drugs Per Capita Actual Costs\": 192.83, \"Ambulance Events Per 1000 Beneficiaries\": 157.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 407.0, \"% of Beneficiaries Using PAC: HH\": 0.0955, \"PAC: LTCH Standardized Costs\": 82941115.27, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.54, \"E&M Per Capita Standardized Costs\": 939.12, \"E&M Per User Standardized Costs\": 1045.56, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1368.0, \"IP Covered Days Per 1000 Beneficiaries\": 1598.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 54.0, \"Count of Medicare beneficiaries with lung cancer\": 8365.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3162, \"Percent Eligible for Medicaid\": 22.13, \"Imaging Per Capita Standardized Costs\": 202.28, \"% of Beneficiaries Using Tests\": 0.8189, \"Imaging Per Capita Actual Costs\": 182.85, \"% of Beneficiaries Using PAC: SNF\": 0.0521, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0354, \"Count of Medicare beneficiaries with colorectal cancer\": 8770.0, \"Hospice Actual Costs\": 193846665.8, \"# PAC: HH Users\": 73819.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23378.33, \"Total Actual Costs\": 6647621908.61, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 82591.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.011, \"ASC Standardized Costs\": 78790188.53, \"DME Events Per 1000 Beneficiaries\": 2179.0, \"PQI08 CHF Admission Rate (age < 65)\": 995.0, \"ASC Events Per 1000 Beneficiaries\": 187.0, \"PAC: LTCH Actual Costs\": 72894773.18, \"Count of Medicare beneficiaries with depression\": 131581.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1985.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 10.69, \"Outpatient Dialysis Facility Per User Actual Costs\": 22225.99, \"Beneficiaries with Part A and Part B\": 1154303.0, \"% of Beneficiaries Using Part B Drugs\": 0.6014, \"Percent of Medicare beneficiaries with diabetes\": 27.43, \"% of Beneficiaries Using PAC: IRF\": 0.0097, \"E&M Per Capita Actual Costs\": 824.73, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0219, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0332, \"PAC: SNF Actual Costs\": 557806540.44, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 783.0, \"Percent Female\": 54.97, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 188.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.44, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 246.02, \"# Outpatient Dialysis Facility Users\": 8132.0, \"FQHC/RHC Per User Actual Costs\": 298.3, \"Count of Medicare beneficiaries with ischemic heart disease\": 218992.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 216.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.99, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 202.0, \"Percent of Medicare beneficiaries with depression\": 17.03, \"Emergency Department Visits per 1000 Beneficiaries\": 684.0, \"IP Actual Costs\": 2101666689.53, \"% of Beneficiaries Using OP\": 0.6296, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0182, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1017, \"Count of Medicare beneficiaries with stroke\": 27576.0, \"PQI12 UTI Admission Rate (age 75+)\": 1438.0, \"# OP Users\": 486500.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 26.0, \"# Procedure Users\": 490329.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 28.34, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0624, \"Count of Medicare beneficiaries with diabetes\": 211950.0, \"ASC Per User Actual Costs\": 801.84, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1095.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0291, \"Percent of Medicare beneficiaries with high cholesterol\": 43.82, \"Standardized Per Capita Costs\": 9235.05, \"Ambulance Actual Costs\": 131492859.7, \"FQHC/RHC Per Capita Actual Costs\": 15.42, \"Part B Drugs Per User Standardized Costs\": 510.46, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0206, \"# PAC: SNF Users (with a covered stay)\": 40261.0, \"Ambulance Per Capita Actual Costs\": 170.16, \"PQI12 UTI Admission Rate (age 65-74)\": 339.0, \"Hospice Standardized Costs\": 214137891.44, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 233.89, \"% of Beneficiaries Using E&M\": 0.8982, \"PQI10 Dehydration Admission Rate (age 65-74)\": 244.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 187.0, \"Tests Per Capita Actual Costs\": 340.25, \"# DME Users\": 246147.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0939, \"PAC: SNF Per User Actual Costs\": 13854.76, \"State\": \"TN\", \"OP Per User Actual Costs\": 1657.11, \"PAC: HH Episodes Per 1000 Beneficiaries\": 211.0, \"Part B Drugs Actual Costs\": 235291788.31, \"FQHC/RHC Actual Costs\": 11914878.88, \"OP Standardized Costs\": 869662534.39, \"DME Per User Standardized Costs\": 843.19, \"OP Actual Costs as % of Total Actual Costs\": 0.1213, \"PAC: SNF Per Capita Standardized Costs\": 866.76, \"% of Beneficiaries Using Ambulance\": 0.1225, \"Hospice Per User Standardized Costs\": 11147.21, \"# Imaging Users\": 542305.0, \"Part B Drugs Per User Actual Costs\": 506.31, \"Total Standardized Costs\": 7136533855.65, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.13, \"Count of Medicare beneficiaries with chronic kidney disease\": 129032.0, \"E&M Standardized Costs\": 725720906.68, \"Percent Hispanic\": 0.71, \"ASC Per Capita Actual Costs\": 90.21, \"Count of Medicare beneficiaries who have had a heart attack\": 7682.0, \"Tests Actual Costs\": 262936530.97, \"# PAC: LTCH Users (with a covered stay)\": 1623.0, \"% of Beneficiaries Using Procedures\": 0.6345, \"PAC: HH Actual Costs\": 410079674.99, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 46.0, \"PAC: LTCH Per Capita Standardized Costs\": 107.33, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0388, \"Emergency Department Visits\": 528866.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0517, \"Procedures Actual Costs\": 398722465.24, \"# FQHC/RHC Users\": 39942.0, \"Number of Acute Hospital Readmissions\": 39365.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 139.0, \"Outpatient Dialysis Facility Standardized Costs\": 190112581.16, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0204, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1219, \"Ambulance Per User Standardized Costs\": 1375.74, \"Imaging Per User Standardized Costs\": 288.25, \"Percent of Medicare beneficiaries with asthma\": 4.36, \"Part B Drugs Standardized Costs\": 237220482.42, \"FFS Beneficiaries\": 772766.0, \"# Hospice Users (with a covered stay)\": 19210.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0105, \"Count of Medicare beneficiaries with osteoporosis\": 42013.0, \"PQI08 CHF Admission Rate (age 75+)\": 2080.0, \"PAC: IRF Per Capita Standardized Costs\": 190.24, \"Procedures Standardized Costs\": 445069811.42, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2918, \"IP Per Capita Actual Costs\": 2719.67, \"DME Actual Costs\": 196527232.77, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0617, \"Count of Medicare beneficiaries with prostate cancer\": 19707.0, \"PAC: HH Per Capita Standardized Costs\": 626.61, \"Count of Medicare beneficiaries with heart failure\": 114692.0, \"Tests Per User Standardized Costs\": 437.95, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.011, \"Percent of Medicare beneficiaries with prostate cancer\": 2.55, \"PAC: IRF Per Capita Actual Costs\": 175.83, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 260.56, \"Percent of Medicare beneficiaries with breast cancer\": 2.7, \"Procedures Per User Standardized Costs\": 907.7, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.7, \"PAC: HH Per User Actual Costs\": 5555.21, \"Count of Medicare beneficiaries with high cholesterol\": 338641.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0839, \"Hospice Per Capita Standardized Costs\": 277.11, \"# Part B Drugs Users\": 464716.0, \"Average HCC Score\": 1.0048, \"Standardized Risk-Adjusted Per Capita Costs\": 9703.77, \"# PAC: IRF Users (with a covered stay)\": 7500.0, \"Ambulance Standardized Costs\": 130235662.43, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0292, \"Percent of Medicare beneficiaries with heart failure\": 14.84, \"Tests Actual Costs as % of Total Actual Costs\": 0.0396, \"FQHC/RHC Per Capita Standardized Costs\": 18.53, \"PQI07 Hypertension Admission Rate (age 65-74)\": 102.0, \"Test Events Per 1000 Beneficiaries\": 11678.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 65.0, \"ASC Actual Costs\": 69709027.25, \"Part B Drugs Per Capita Standardized Costs\": 306.98, \"Imaging Actual Costs\": 141300547.23, \"Tests Per User Actual Costs\": 415.51, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0198, \"Hospice Per User Actual Costs\": 10090.92, \"Tests Standardized Costs\": 277134209.37, \"IP Standardized Costs\": 2082478723.59, \"IP Per Capita Standardized Costs\": 2694.84, \"Outpatient Dialysis Facility Actual Costs\": 180741711.86, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 72.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 2140.0, \"Percent of Medicare beneficiaries with hypertension\": 58.34, \"IP Covered Stays Per 1000 Beneficiaries\": 292.0, \"# Ambulance Users\": 94666.0, \"# ASC Users\": 86936.0, \"ASC Per Capita Standardized Costs\": 101.96, \"Procedures Per Capita Actual Costs\": 515.97, \"Procedures Per Capita Standardized Costs\": 575.94, \"IP Users (with a covered stay)\": 138735.0, \"Total Standardized Risk-Adjusted Costs\": 7498744408.08, \"Actual Per Capita Costs\": 8602.37, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0116, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 11.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.08, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 13.28, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 216.0, \"OP Per User Standardized Costs\": 1787.59, \"Count of Medicare beneficiaries with atrial fibrillation\": 58252.0, \"Procedure Events Per 1000 Beneficiaries\": 4299.0, \"Percent of Medicare beneficiaries with stroke\": 3.57, \"PAC: IRF Per User Actual Costs\": 18116.75, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 102600.0, \"% of Beneficiaries Using ASC\": 0.1125, \"PAC: IRF Standardized Costs\": 147009427.66, \"Hospice Covered Days Per 1000 Beneficiaries\": 1737.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 365.0, \"PAC: SNF Per User Standardized Costs\": 16636.57, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0018, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 128.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.03, \"MA Participation Rate\": 33.05, \"OP Per Capita Standardized Costs\": 1125.39, \"Percent of Medicare beneficiaries with arthritis\": 30.67, \"Ambulance Per Capita Standardized Costs\": 168.53, \"PAC: HH Visits Per 1000 Beneficiaries\": 3511.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.06, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0213, \"PAC: HH Per Capita Actual Costs\": 530.66, \"E&M Events Per 1000 Beneficiaries\": 13509.0, \"Count of Medicare beneficiaries with asthma\": 33667.0, \"# Test Users\": 632805.0, \"E&M Actual Costs\": 637321951.12, \"% of Beneficiaries Using Imaging\": 0.7018, \"PAC: SNF Standardized Costs\": 669804868.58, \"DME Per Capita Actual Costs\": 254.32, \"E&M Per User Actual Costs\": 918.2, \"Percent Male\": 45.03, \"OP Visits Per 1000 Beneficiaries\": 3260.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0296, \"OP Per Capita Actual Costs\": 1043.25, \"Hospital Readmission Rate\": 0.1823, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.002, \"Tests Per Capita Standardized Costs\": 358.63, \"Hospice Per Capita Actual Costs\": 250.85, \"E&M Actual Costs as % of Total Actual Costs\": 0.0959, \"PQI07 Hypertension Admission Rate (age < 65)\": 170.0, \"Percent African American\": 10.78, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0679, \"PAC: LTCH Per Capita Actual Costs\": 94.33, \"MA Beneficiaries\": 381537.0, \"FQHC/RHC Per User Standardized Costs\": 358.45, \"PAC: HH Per User Standardized Costs\": 6559.62, \"Procedures Per User Actual Costs\": 813.17, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 642.0, \"Ambulance Per User Actual Costs\": 1389.02, \"Average Age\": 70.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1597.0, \"PAC: HH Standardized Costs\": 484224679.81, \"ASC Actual Costs as % of Total Actual Costs\": 0.0105, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 919.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0021, \"Percent Other/Unknown\": 1.41, \"% of Beneficiaries Using Hospice\": 0.0249, \"PAC: LTCH Per User Actual Costs\": 44913.6, \"Percent Non-Hispanic White\": 87.1, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 995.0, \"Count of Medicare beneficiaries with breast cancer\": 20884.0, \"DME Per Capita Standardized Costs\": 268.58, \"PQI08 CHF Admission Rate (age 65-74)\": 765.0, \"% of Beneficiaries Using DME\": 0.3185, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0272, \"% of Beneficiaries Using IP\": 0.1795, \"DME Standardized Costs\": 207548026.83, \"FQHC/RHC Standardized Costs\": 14317180.34, \"PAC: SNF Per Capita Actual Costs\": 721.83, \"Imaging Standardized Costs\": 156317074.66, \"# E&M Users\": 694100.0, \"Count of Medicare beneficiaries with arthritis\": 237036.0, \"IP Per User Standardized Costs\": 15010.48, \"IP Per User Actual Costs\": 15148.79, \"PQI10 Dehydration Admission Rate (age 75+)\": 584.0, \"PAC: IRF Per User Standardized Costs\": 19601.26, \"DME Per User Actual Costs\": 798.41, \"PAC: IRF Actual Costs\": 135875611.25, \"Imaging Events Per 1000 Beneficiaries\": 4258.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0266, \"PAC: LTCH Per User Standardized Costs\": 51103.58, \"OP Actual Costs\": 806186145.02, \"Count of Medicare beneficiaries with hypertension\": 450805.0, \"ASC Per User Standardized Costs\": 906.3, \"Part B Drugs Per Capita Actual Costs\": 304.48, \"Ambulance Events Per 1000 Beneficiaries\": 518.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 456.0, \"% of Beneficiaries Using PAC: HH\": 0.1334, \"PAC: LTCH Standardized Costs\": 1026440544.3, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.0, \"E&M Per Capita Standardized Costs\": 994.16, \"E&M Per User Standardized Costs\": 1145.17, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 2109.0, \"IP Covered Days Per 1000 Beneficiaries\": 1527.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 60.0, \"Count of Medicare beneficiaries with lung cancer\": 20358.0, \"IP Actual Costs as % of Total Actual Costs\": 0.2887, \"Percent Eligible for Medicaid\": 21.51, \"Imaging Per Capita Standardized Costs\": 262.53, \"% of Beneficiaries Using Tests\": 0.7829, \"Imaging Per Capita Actual Costs\": 248.66, \"% of Beneficiaries Using PAC: SNF\": 0.0458, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0333, \"Count of Medicare beneficiaries with colorectal cancer\": 25781.0, \"Hospice Actual Costs\": 923550715.1, \"# PAC: HH Users\": 301710.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23753.34, \"Total Actual Costs\": 23610543575.21, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 275304.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0088, \"ASC Standardized Costs\": 209922526.94, \"DME Events Per 1000 Beneficiaries\": 1701.0, \"PQI08 CHF Admission Rate (age < 65)\": 1038.0, \"ASC Events Per 1000 Beneficiaries\": 168.0, \"PAC: LTCH Actual Costs\": 964191566.77, \"Count of Medicare beneficiaries with depression\": 374063.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1641.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 12.17, \"Outpatient Dialysis Facility Per User Actual Costs\": 23202.19, \"Beneficiaries with Part A and Part B\": 3274680.0, \"% of Beneficiaries Using Part B Drugs\": 0.5223, \"Percent of Medicare beneficiaries with diabetes\": 28.67, \"% of Beneficiaries Using PAC: IRF\": 0.0185, \"E&M Per Capita Actual Costs\": 918.58, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.025, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0333, \"PAC: SNF Actual Costs\": 1665014275.69, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 559.0, \"Percent Female\": 54.59, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 290.0, \"Percent of Medicare beneficiaries with osteoporosis\": 6.69, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 378.69, \"# Outpatient Dialysis Facility Users\": 36051.0, \"FQHC/RHC Per User Actual Costs\": 380.07, \"Count of Medicare beneficiaries with ischemic heart disease\": 681837.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 225.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.81, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 396.0, \"Percent of Medicare beneficiaries with depression\": 16.54, \"Emergency Department Visits per 1000 Beneficiaries\": 643.0, \"IP Actual Costs\": 6815766159.92, \"% of Beneficiaries Using OP\": 0.5754, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0128, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0946, \"Count of Medicare beneficiaries with stroke\": 94075.0, \"PQI12 UTI Admission Rate (age 75+)\": 1339.0, \"# OP Users\": 1301149.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 34.0, \"# Procedure Users\": 1357212.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 30.15, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0633, \"Count of Medicare beneficiaries with diabetes\": 648429.0, \"ASC Per User Actual Costs\": 848.85, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1057.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0217, \"Percent of Medicare beneficiaries with high cholesterol\": 45.59, \"Standardized Per Capita Costs\": 10505.27, \"Ambulance Actual Costs\": 308689058.12, \"FQHC/RHC Per Capita Actual Costs\": 34.84, \"Part B Drugs Per User Standardized Costs\": 668.8, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0344, \"# PAC: SNF Users (with a covered stay)\": 103681.0, \"Ambulance Per Capita Actual Costs\": 136.51, \"PQI12 UTI Admission Rate (age 65-74)\": 331.0, \"Hospice Standardized Costs\": 971129029.55, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 369.9, \"% of Beneficiaries Using E&M\": 0.8681, \"PQI10 Dehydration Admission Rate (age 65-74)\": 212.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 244.0, \"Tests Per Capita Actual Costs\": 301.36, \"# DME Users\": 620884.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0773, \"PAC: SNF Per User Actual Costs\": 16059.01, \"State\": \"TX\", \"OP Per User Actual Costs\": 1785.46, \"PAC: HH Episodes Per 1000 Beneficiaries\": 404.0, \"Part B Drugs Actual Costs\": 785059511.07, \"FQHC/RHC Actual Costs\": 78791861.9, \"OP Standardized Costs\": 2398328090.49, \"DME Per User Standardized Costs\": 830.57, \"OP Actual Costs as % of Total Actual Costs\": 0.0984, \"PAC: SNF Per Capita Standardized Costs\": 812.37, \"% of Beneficiaries Using Ambulance\": 0.1031, \"Hospice Per User Standardized Costs\": 13405.24, \"# Imaging Users\": 1528643.0, \"Part B Drugs Per User Actual Costs\": 664.66, \"Total Standardized Costs\": 23755714662.72, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.14, \"Count of Medicare beneficiaries with chronic kidney disease\": 392915.0, \"E&M Standardized Costs\": 2248098317.96, \"Percent Hispanic\": 18.11, \"ASC Per Capita Actual Costs\": 86.96, \"Count of Medicare beneficiaries who have had a heart attack\": 18343.0, \"Tests Actual Costs\": 681478689.64, \"# PAC: LTCH Users (with a covered stay)\": 23476.0, \"% of Beneficiaries Using Procedures\": 0.6002, \"PAC: HH Actual Costs\": 2264622354.54, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 62.0, \"PAC: LTCH Per Capita Standardized Costs\": 453.91, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0298, \"Emergency Department Visits\": 1453382.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0917, \"Procedures Actual Costs\": 1417175626.17, \"# FQHC/RHC Users\": 207309.0, \"Number of Acute Hospital Readmissions\": 107137.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 258.0, \"Outpatient Dialysis Facility Standardized Costs\": 856331708.41, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0336, \"OP Standardized Costs as % of Total Standardized Costs\": 0.101, \"Ambulance Per User Standardized Costs\": 1306.33, \"Imaging Per User Standardized Costs\": 388.36, \"Percent of Medicare beneficiaries with asthma\": 5.07, \"Part B Drugs Standardized Costs\": 789948130.99, \"FFS Beneficiaries\": 2261314.0, \"# Hospice Users (with a covered stay)\": 72444.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0159, \"Count of Medicare beneficiaries with osteoporosis\": 151286.0, \"PQI08 CHF Admission Rate (age 75+)\": 1877.0, \"PAC: IRF Per Capita Standardized Costs\": 361.22, \"Procedures Standardized Costs\": 1503867339.49, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2511, \"IP Per Capita Actual Costs\": 3014.07, \"DME Actual Costs\": 482705884.93, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0959, \"Count of Medicare beneficiaries with prostate cancer\": 60602.0, \"PAC: HH Per Capita Standardized Costs\": 1091.95, \"Count of Medicare beneficiaries with heart failure\": 367638.0, \"Tests Per User Standardized Costs\": 399.58, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0408, \"Percent of Medicare beneficiaries with prostate cancer\": 2.68, \"PAC: IRF Per Capita Actual Costs\": 350.47, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 367.84, \"Percent of Medicare beneficiaries with breast cancer\": 2.62, \"Procedures Per User Standardized Costs\": 1108.06, \"Percent of Medicare beneficiaries with chronic kidney disease\": 17.38, \"PAC: HH Per User Actual Costs\": 7505.96, \"Count of Medicare beneficiaries with high cholesterol\": 1030901.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0705, \"Hospice Per Capita Standardized Costs\": 429.45, \"# Part B Drugs Users\": 1181137.0, \"Average HCC Score\": 1.0582, \"Standardized Risk-Adjusted Per Capita Costs\": 10255.56, \"# PAC: IRF Users (with a covered stay)\": 41785.0, \"Ambulance Standardized Costs\": 304493822.3, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0391, \"Percent of Medicare beneficiaries with heart failure\": 16.26, \"Tests Actual Costs as % of Total Actual Costs\": 0.0289, \"FQHC/RHC Per Capita Standardized Costs\": 39.09, \"PQI07 Hypertension Admission Rate (age 65-74)\": 104.0, \"Test Events Per 1000 Beneficiaries\": 11053.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 314.0, \"ASC Actual Costs\": 196651585.29, \"Part B Drugs Per Capita Standardized Costs\": 349.33, \"Imaging Actual Costs\": 562290948.05, \"Tests Per User Actual Costs\": 384.93, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0131, \"Hospice Per User Actual Costs\": 12748.48, \"Tests Standardized Costs\": 707411928.33, \"IP Standardized Costs\": 5964505145.73, \"IP Per Capita Standardized Costs\": 2637.63, \"Outpatient Dialysis Facility Actual Costs\": 836462161.32, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 64.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1928.0, \"Percent of Medicare beneficiaries with hypertension\": 57.67, \"IP Covered Stays Per 1000 Beneficiaries\": 281.0, \"# Ambulance Users\": 233092.0, \"# ASC Users\": 231667.0, \"ASC Per Capita Standardized Costs\": 92.83, \"Procedures Per Capita Actual Costs\": 626.7, \"Procedures Per Capita Standardized Costs\": 665.04, \"IP Users (with a covered stay)\": 394837.0, \"Total Standardized Risk-Adjusted Costs\": 23191034522.85, \"Actual Per Capita Costs\": 10441.07, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0432, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 21.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 12.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.9, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 11.21, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 273.0, \"OP Per User Standardized Costs\": 1843.24, \"Count of Medicare beneficiaries with atrial fibrillation\": 158340.0, \"Procedure Events Per 1000 Beneficiaries\": 4168.0, \"Percent of Medicare beneficiaries with stroke\": 4.16, \"PAC: IRF Per User Actual Costs\": 18966.5, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 253450.0, \"% of Beneficiaries Using ASC\": 0.1024, \"PAC: IRF Standardized Costs\": 816830320.43, \"Hospice Covered Days Per 1000 Beneficiaries\": 2661.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 312.0, \"PAC: SNF Per User Standardized Costs\": 17718.01, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0033, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 202.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0409, \"MA Participation Rate\": 30.95, \"OP Per Capita Standardized Costs\": 1060.59, \"Percent of Medicare beneficiaries with arthritis\": 30.99, \"Ambulance Per Capita Standardized Costs\": 134.65, \"PAC: HH Visits Per 1000 Beneficiaries\": 6869.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.06, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0238, \"PAC: HH Per Capita Actual Costs\": 1001.46, \"E&M Events Per 1000 Beneficiaries\": 13933.0, \"Count of Medicare beneficiaries with asthma\": 114719.0, \"# Test Users\": 1770377.0, \"E&M Actual Costs\": 2077195598.07, \"% of Beneficiaries Using Imaging\": 0.676, \"PAC: SNF Standardized Costs\": 1837021208.82, \"DME Per Capita Actual Costs\": 213.46, \"E&M Per User Actual Costs\": 1058.12, \"Percent Male\": 45.41, \"OP Visits Per 1000 Beneficiaries\": 3287.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0204, \"OP Per Capita Actual Costs\": 1027.34, \"Hospital Readmission Rate\": 0.1755, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0037, \"Tests Per Capita Standardized Costs\": 312.83, \"Hospice Per Capita Actual Costs\": 408.41, \"E&M Actual Costs as % of Total Actual Costs\": 0.088, \"PQI07 Hypertension Admission Rate (age < 65)\": 181.0, \"Percent African American\": 9.93, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.1039, \"PAC: LTCH Per Capita Actual Costs\": 426.39, \"MA Beneficiaries\": 1013366.0, \"FQHC/RHC Per User Standardized Costs\": 426.34, \"PAC: HH Per User Standardized Costs\": 8184.16, \"Procedures Per User Actual Costs\": 1044.18, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 1011.0, \"Ambulance Per User Actual Costs\": 1324.33, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1173.0, \"PAC: HH Standardized Costs\": 2469244203.81, \"ASC Actual Costs as % of Total Actual Costs\": 0.0083, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 703.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0104, \"Percent Other/Unknown\": 3.05, \"% of Beneficiaries Using Hospice\": 0.032, \"PAC: LTCH Per User Actual Costs\": 41071.37, \"Percent Non-Hispanic White\": 68.91, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 687.0, \"Count of Medicare beneficiaries with breast cancer\": 59139.0, \"DME Per Capita Standardized Costs\": 228.05, \"PQI08 CHF Admission Rate (age 65-74)\": 664.0, \"% of Beneficiaries Using DME\": 0.2746, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0354, \"% of Beneficiaries Using IP\": 0.1746, \"DME Standardized Costs\": 515685637.87, \"FQHC/RHC Standardized Costs\": 88384359.07, \"PAC: SNF Per Capita Actual Costs\": 736.3, \"Imaging Standardized Costs\": 593665107.98, \"# E&M Users\": 1963105.0, \"Count of Medicare beneficiaries with arthritis\": 700716.0, \"IP Per User Standardized Costs\": 15106.25, \"IP Per User Actual Costs\": 17262.23, \"PQI10 Dehydration Admission Rate (age 75+)\": 543.0, \"PAC: IRF Per User Standardized Costs\": 19548.41, \"DME Per User Actual Costs\": 777.45, \"PAC: IRF Actual Costs\": 792515324.48, \"Imaging Events Per 1000 Beneficiaries\": 4424.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.036, \"PAC: LTCH Per User Standardized Costs\": 43722.97, \"OP Actual Costs\": 2323144653.9, \"Count of Medicare beneficiaries with hypertension\": 1304110.0, \"ASC Per User Standardized Costs\": 906.14, \"Part B Drugs Per Capita Actual Costs\": 347.17, \"Ambulance Events Per 1000 Beneficiaries\": 392.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 251.0, \"% of Beneficiaries Using PAC: HH\": 0.0955, \"PAC: LTCH Standardized Costs\": 16538852.62, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.91, \"E&M Per Capita Standardized Costs\": 672.92, \"E&M Per User Standardized Costs\": 773.38, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 973.0, \"IP Covered Days Per 1000 Beneficiaries\": 932.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 22.0, \"Count of Medicare beneficiaries with lung cancer\": 793.0, \"IP Actual Costs as % of Total Actual Costs\": 0.2974, \"Percent Eligible for Medicaid\": 11.04, \"Imaging Per Capita Standardized Costs\": 137.51, \"% of Beneficiaries Using Tests\": 0.7314, \"Imaging Per Capita Actual Costs\": 127.99, \"% of Beneficiaries Using PAC: SNF\": 0.049, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0467, \"Count of Medicare beneficiaries with colorectal cancer\": 1527.0, \"Hospice Actual Costs\": 90369379.71, \"# PAC: HH Users\": 18239.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23656.67, \"Total Actual Costs\": 1497880121.0, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 16056.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0117, \"ASC Standardized Costs\": 17798176.66, \"DME Events Per 1000 Beneficiaries\": 1859.0, \"PQI08 CHF Admission Rate (age < 65)\": 491.0, \"ASC Events Per 1000 Beneficiaries\": 147.0, \"PAC: LTCH Actual Costs\": 15413853.68, \"Count of Medicare beneficiaries with depression\": 30074.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1182.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.41, \"Outpatient Dialysis Facility Per User Actual Costs\": 22793.62, \"Beneficiaries with Part A and Part B\": 303158.0, \"% of Beneficiaries Using Part B Drugs\": 0.5083, \"Percent of Medicare beneficiaries with diabetes\": 22.94, \"% of Beneficiaries Using PAC: IRF\": 0.0064, \"E&M Per Capita Actual Costs\": 611.66, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0173, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0463, \"PAC: SNF Actual Costs\": 125284899.09, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 480.0, \"Percent Female\": 53.69, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 349.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.21, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 173.09, \"# Outpatient Dialysis Facility Users\": 1397.0, \"FQHC/RHC Per User Actual Costs\": 389.94, \"Count of Medicare beneficiaries with ischemic heart disease\": 38747.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 61.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.54, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 195.0, \"Percent of Medicare beneficiaries with depression\": 15.75, \"Emergency Department Visits per 1000 Beneficiaries\": 517.0, \"IP Actual Costs\": 445490437.89, \"% of Beneficiaries Using OP\": 0.663, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0071, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0844, \"Count of Medicare beneficiaries with stroke\": 4885.0, \"PQI12 UTI Admission Rate (age 75+)\": 700.0, \"# OP Users\": 126586.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 39.0, \"# Procedure Users\": 119700.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 20.29, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0785, \"Count of Medicare beneficiaries with diabetes\": 43792.0, \"ASC Per User Actual Costs\": 891.38, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 418.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0334, \"Percent of Medicare beneficiaries with high cholesterol\": 29.39, \"Standardized Per Capita Costs\": 7968.75, \"Ambulance Actual Costs\": 11061734.33, \"FQHC/RHC Per Capita Actual Costs\": 17.52, \"Part B Drugs Per User Standardized Costs\": 725.45, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0161, \"# PAC: SNF Users (with a covered stay)\": 9357.0, \"Ambulance Per Capita Actual Costs\": 57.94, \"PQI12 UTI Admission Rate (age 65-74)\": 190.0, \"Hospice Standardized Costs\": 94240466.83, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 166.78, \"% of Beneficiaries Using E&M\": 0.8701, \"PQI10 Dehydration Admission Rate (age 65-74)\": 157.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 105.0, \"Tests Per Capita Actual Costs\": 160.91, \"# DME Users\": 54085.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0907, \"PAC: SNF Per User Actual Costs\": 13389.43, \"State\": \"UT\", \"OP Per User Actual Costs\": 1695.27, \"PAC: HH Episodes Per 1000 Beneficiaries\": 171.0, \"Part B Drugs Actual Costs\": 70014096.74, \"FQHC/RHC Actual Costs\": 3344140.84, \"OP Standardized Costs\": 223234390.6, \"DME Per User Standardized Costs\": 940.02, \"OP Actual Costs as % of Total Actual Costs\": 0.1433, \"PAC: SNF Per Capita Standardized Costs\": 722.99, \"% of Beneficiaries Using Ambulance\": 0.0766, \"Hospice Per User Standardized Costs\": 13378.83, \"# Imaging Users\": 118949.0, \"Part B Drugs Per User Actual Costs\": 721.45, \"Total Standardized Costs\": 1521473696.65, \"Percent of Medicare beneficiaries with colorectal cancer\": 0.8, \"Count of Medicare beneficiaries with chronic kidney disease\": 24986.0, \"E&M Standardized Costs\": 128479858.72, \"Percent Hispanic\": 4.29, \"ASC Per Capita Actual Costs\": 86.15, \"Count of Medicare beneficiaries who have had a heart attack\": 1034.0, \"Tests Actual Costs\": 30721811.57, \"# PAC: LTCH Users (with a covered stay)\": 364.0, \"% of Beneficiaries Using Procedures\": 0.6269, \"PAC: HH Actual Costs\": 99074072.55, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 30.0, \"PAC: LTCH Per Capita Standardized Costs\": 86.62, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0213, \"Emergency Department Visits\": 98617.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0449, \"Procedures Actual Costs\": 112052972.73, \"# FQHC/RHC Users\": 8576.0, \"Number of Acute Hospital Readmissions\": 5304.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 93.0, \"Outpatient Dialysis Facility Standardized Costs\": 33048366.68, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0153, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1467, \"Ambulance Per User Standardized Costs\": 733.52, \"Imaging Per User Standardized Costs\": 220.73, \"Percent of Medicare beneficiaries with asthma\": 4.27, \"Part B Drugs Standardized Costs\": 70402909.44, \"FFS Beneficiaries\": 190930.0, \"# Hospice Users (with a covered stay)\": 7044.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0073, \"Count of Medicare beneficiaries with osteoporosis\": 9945.0, \"PQI08 CHF Admission Rate (age 75+)\": 1053.0, \"PAC: IRF Per Capita Standardized Costs\": 128.32, \"Procedures Standardized Costs\": 119361475.55, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2641, \"IP Per Capita Actual Costs\": 2333.27, \"DME Actual Costs\": 48394700.29, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0661, \"Count of Medicare beneficiaries with prostate cancer\": 5314.0, \"PAC: HH Per Capita Standardized Costs\": 568.82, \"Count of Medicare beneficiaries with heart failure\": 22172.0, \"Tests Per User Standardized Costs\": 231.75, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0103, \"Percent of Medicare beneficiaries with prostate cancer\": 2.78, \"PAC: IRF Per Capita Actual Costs\": 119.91, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 205.44, \"Percent of Medicare beneficiaries with breast cancer\": 2.27, \"Procedures Per User Standardized Costs\": 997.17, \"Percent of Medicare beneficiaries with chronic kidney disease\": 13.09, \"PAC: HH Per User Actual Costs\": 5431.99, \"Count of Medicare beneficiaries with high cholesterol\": 56117.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0836, \"Hospice Per Capita Standardized Costs\": 493.59, \"# Part B Drugs Users\": 97047.0, \"Average HCC Score\": 0.8924, \"Standardized Risk-Adjusted Per Capita Costs\": 9689.09, \"# PAC: IRF Users (with a covered stay)\": 1214.0, \"Ambulance Standardized Costs\": 10727501.03, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0603, \"Percent of Medicare beneficiaries with heart failure\": 11.61, \"Tests Actual Costs as % of Total Actual Costs\": 0.0205, \"FQHC/RHC Per Capita Standardized Costs\": 18.94, \"PQI07 Hypertension Admission Rate (age 65-74)\": 15.0, \"Test Events Per 1000 Beneficiaries\": 6590.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 55.0, \"ASC Actual Costs\": 16448682.05, \"Part B Drugs Per Capita Standardized Costs\": 368.74, \"Imaging Actual Costs\": 24437160.83, \"Tests Per User Actual Costs\": 220.0, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0074, \"Hospice Per User Actual Costs\": 12829.27, \"Tests Standardized Costs\": 32361995.02, \"IP Standardized Costs\": 401795704.92, \"IP Per Capita Standardized Costs\": 2104.41, \"Outpatient Dialysis Facility Actual Costs\": 31842685.38, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 61.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1554.0, \"Percent of Medicare beneficiaries with hypertension\": 40.66, \"IP Covered Stays Per 1000 Beneficiaries\": 211.0, \"# Ambulance Users\": 14625.0, \"# ASC Users\": 18453.0, \"ASC Per Capita Standardized Costs\": 93.22, \"Procedures Per Capita Actual Costs\": 586.88, \"Procedures Per Capita Standardized Costs\": 625.16, \"IP Users (with a covered stay)\": 28011.0, \"Total Standardized Risk-Adjusted Costs\": 1849937765.69, \"Actual Per Capita Costs\": 7845.18, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0109, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 7.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.42, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 5.63, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 109.0, \"OP Per User Standardized Costs\": 1763.5, \"Count of Medicare beneficiaries with atrial fibrillation\": 13199.0, \"Procedure Events Per 1000 Beneficiaries\": 4497.0, \"Percent of Medicare beneficiaries with stroke\": 2.56, \"PAC: IRF Per User Actual Costs\": 18858.1, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10748.0, \"% of Beneficiaries Using ASC\": 0.0966, \"PAC: IRF Standardized Costs\": 24499339.93, \"Hospice Covered Days Per 1000 Beneficiaries\": 3186.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 261.0, \"PAC: SNF Per User Standardized Costs\": 14752.73, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0022, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 66.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0619, \"MA Participation Rate\": 37.02, \"OP Per Capita Standardized Costs\": 1169.19, \"Percent of Medicare beneficiaries with arthritis\": 26.28, \"Ambulance Per Capita Standardized Costs\": 56.19, \"PAC: HH Visits Per 1000 Beneficiaries\": 4448.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0748, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0163, \"PAC: HH Per Capita Actual Costs\": 518.9, \"E&M Events Per 1000 Beneficiaries\": 9451.0, \"Count of Medicare beneficiaries with asthma\": 8147.0, \"# Test Users\": 139643.0, \"E&M Actual Costs\": 116784986.28, \"% of Beneficiaries Using Imaging\": 0.623, \"PAC: SNF Standardized Costs\": 138041277.72, \"DME Per Capita Actual Costs\": 253.47, \"E&M Per User Actual Costs\": 702.99, \"Percent Male\": 46.31, \"OP Visits Per 1000 Beneficiaries\": 3896.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0323, \"OP Per Capita Actual Costs\": 1123.96, \"Hospital Readmission Rate\": 0.1349, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0024, \"Tests Per Capita Standardized Costs\": 169.5, \"Hospice Per Capita Actual Costs\": 473.31, \"E&M Actual Costs as % of Total Actual Costs\": 0.078, \"PQI07 Hypertension Admission Rate (age < 65)\": 52.0, \"Percent African American\": 0.58, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0714, \"PAC: LTCH Per Capita Actual Costs\": 80.73, \"MA Beneficiaries\": 112228.0, \"FQHC/RHC Per User Standardized Costs\": 421.56, \"PAC: HH Per User Standardized Costs\": 5954.53, \"Procedures Per User Actual Costs\": 936.12, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 386.0, \"Ambulance Per User Actual Costs\": 756.37, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 602.0, \"PAC: HH Standardized Costs\": 108604582.12, \"ASC Actual Costs as % of Total Actual Costs\": 0.011, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 298.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0019, \"Percent Other/Unknown\": 3.48, \"% of Beneficiaries Using Hospice\": 0.0369, \"PAC: LTCH Per User Actual Costs\": 42345.75, \"Percent Non-Hispanic White\": 91.65, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 647.0, \"Count of Medicare beneficiaries with breast cancer\": 4335.0, \"DME Per Capita Standardized Costs\": 266.28, \"PQI08 CHF Admission Rate (age 65-74)\": 332.0, \"% of Beneficiaries Using DME\": 0.2833, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0213, \"% of Beneficiaries Using IP\": 0.1467, \"DME Standardized Costs\": 50840773.24, \"FQHC/RHC Standardized Costs\": 3615300.52, \"PAC: SNF Per Capita Actual Costs\": 656.18, \"Imaging Standardized Costs\": 26255434.19, \"# E&M Users\": 166127.0, \"Count of Medicare beneficiaries with arthritis\": 50180.0, \"IP Per User Standardized Costs\": 14344.21, \"IP Per User Actual Costs\": 15904.12, \"PQI10 Dehydration Admission Rate (age 75+)\": 377.0, \"PAC: IRF Per User Standardized Costs\": 20180.68, \"DME Per User Actual Costs\": 894.79, \"PAC: IRF Actual Costs\": 22893738.6, \"Imaging Events Per 1000 Beneficiaries\": 3126.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0217, \"PAC: LTCH Per User Standardized Costs\": 45436.41, \"OP Actual Costs\": 214597144.03, \"Count of Medicare beneficiaries with hypertension\": 77636.0, \"ASC Per User Standardized Costs\": 964.51, \"Part B Drugs Per Capita Actual Costs\": 366.7, \"Ambulance Events Per 1000 Beneficiaries\": 148.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 352.0, \"% of Beneficiaries Using PAC: HH\": 0.0879, \"PAC: LTCH Standardized Costs\": 61349294.04, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.74, \"E&M Per Capita Standardized Costs\": 886.86, \"E&M Per User Standardized Costs\": 986.07, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1317.0, \"IP Covered Days Per 1000 Beneficiaries\": 1431.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 55.0, \"Count of Medicare beneficiaries with lung cancer\": 10085.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3424, \"Percent Eligible for Medicaid\": 16.32, \"Imaging Per Capita Standardized Costs\": 185.21, \"% of Beneficiaries Using Tests\": 0.81, \"Imaging Per Capita Actual Costs\": 182.23, \"% of Beneficiaries Using PAC: SNF\": 0.048, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0392, \"Count of Medicare beneficiaries with colorectal cancer\": 12103.0, \"Hospice Actual Costs\": 249084196.55, \"# PAC: HH Users\": 87887.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23874.38, \"Total Actual Costs\": 8346291207.88, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 98608.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0076, \"ASC Standardized Costs\": 62683693.14, \"DME Events Per 1000 Beneficiaries\": 1733.0, \"PQI08 CHF Admission Rate (age < 65)\": 949.0, \"ASC Events Per 1000 Beneficiaries\": 95.0, \"PAC: LTCH Actual Costs\": 56780860.03, \"Count of Medicare beneficiaries with depression\": 147677.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1297.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.86, \"Outpatient Dialysis Facility Per User Actual Costs\": 23476.37, \"Beneficiaries with Part A and Part B\": 1209021.0, \"% of Beneficiaries Using Part B Drugs\": 0.5389, \"Percent of Medicare beneficiaries with diabetes\": 26.89, \"% of Beneficiaries Using PAC: IRF\": 0.0084, \"E&M Per Capita Actual Costs\": 840.82, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0224, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0398, \"PAC: SNF Actual Costs\": 636635956.66, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 453.0, \"Percent Female\": 55.8, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 327.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.3, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 237.92, \"# Outpatient Dialysis Facility Users\": 9967.0, \"FQHC/RHC Per User Actual Costs\": 344.52, \"Count of Medicare beneficiaries with ischemic heart disease\": 239082.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 172.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.78, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 228.0, \"Percent of Medicare beneficiaries with depression\": 14.77, \"Emergency Department Visits per 1000 Beneficiaries\": 663.0, \"IP Actual Costs\": 2857961350.69, \"% of Beneficiaries Using OP\": 0.6494, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0181, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1074, \"Count of Medicare beneficiaries with stroke\": 36509.0, \"PQI12 UTI Admission Rate (age 75+)\": 1122.0, \"# OP Users\": 649492.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 25.0, \"# Procedure Users\": 605200.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 23.9, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0695, \"Count of Medicare beneficiaries with diabetes\": 268953.0, \"ASC Per User Actual Costs\": 961.4, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 890.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0248, \"Percent of Medicare beneficiaries with high cholesterol\": 47.34, \"Standardized Per Capita Costs\": 8260.62, \"Ambulance Actual Costs\": 147644340.91, \"FQHC/RHC Per Capita Actual Costs\": 18.55, \"Part B Drugs Per User Standardized Costs\": 609.64, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0198, \"# PAC: SNF Users (with a covered stay)\": 48019.0, \"Ambulance Per Capita Actual Costs\": 147.62, \"PQI12 UTI Admission Rate (age 65-74)\": 247.0, \"Hospice Standardized Costs\": 256597114.91, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 233.96, \"% of Beneficiaries Using E&M\": 0.8994, \"PQI10 Dehydration Admission Rate (age 65-74)\": 209.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 198.0, \"Tests Per Capita Actual Costs\": 227.65, \"# DME Users\": 277906.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.084, \"PAC: SNF Per User Actual Costs\": 13258.0, \"State\": \"VA\", \"OP Per User Actual Costs\": 1641.06, \"PAC: HH Episodes Per 1000 Beneficiaries\": 150.0, \"Part B Drugs Actual Costs\": 326969051.81, \"FQHC/RHC Actual Costs\": 18550195.1, \"OP Standardized Costs\": 1095245574.72, \"DME Per User Standardized Costs\": 738.3, \"OP Actual Costs as % of Total Actual Costs\": 0.1277, \"PAC: SNF Per Capita Standardized Costs\": 694.01, \"% of Beneficiaries Using Ambulance\": 0.1241, \"Hospice Per User Standardized Costs\": 10764.2, \"# Imaging Users\": 681405.0, \"Part B Drugs Per User Actual Costs\": 606.65, \"Total Standardized Costs\": 8261773934.97, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.21, \"Count of Medicare beneficiaries with chronic kidney disease\": 154289.0, \"E&M Standardized Costs\": 886983568.19, \"Percent Hispanic\": 1.68, \"ASC Per Capita Actual Costs\": 59.42, \"Count of Medicare beneficiaries who have had a heart attack\": 7793.0, \"Tests Actual Costs\": 227682106.41, \"# PAC: LTCH Users (with a covered stay)\": 1398.0, \"% of Beneficiaries Using Procedures\": 0.6051, \"PAC: HH Actual Costs\": 389557363.5, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 48.0, \"PAC: LTCH Per Capita Standardized Costs\": 61.34, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0282, \"Emergency Department Visits\": 663099.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0538, \"Procedures Actual Costs\": 555849698.43, \"# FQHC/RHC Users\": 53843.0, \"Number of Acute Hospital Readmissions\": 48504.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 117.0, \"Outpatient Dialysis Facility Standardized Costs\": 237955970.91, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0198, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1326, \"Ambulance Per User Standardized Costs\": 1205.53, \"Imaging Per User Standardized Costs\": 271.85, \"Percent of Medicare beneficiaries with asthma\": 4.97, \"Part B Drugs Standardized Costs\": 328582005.86, \"FFS Beneficiaries\": 1000140.0, \"# Hospice Users (with a covered stay)\": 23838.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.01, \"Count of Medicare beneficiaries with osteoporosis\": 52980.0, \"PQI08 CHF Admission Rate (age 75+)\": 2013.0, \"PAC: IRF Per Capita Standardized Costs\": 163.32, \"Procedures Standardized Costs\": 573902757.2, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3027, \"IP Per Capita Actual Costs\": 2857.56, \"DME Actual Costs\": 189434064.52, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0467, \"Count of Medicare beneficiaries with prostate cancer\": 30088.0, \"PAC: HH Per Capita Standardized Costs\": 422.21, \"Count of Medicare beneficiaries with heart failure\": 120186.0, \"Tests Per User Standardized Costs\": 287.67, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0068, \"Percent of Medicare beneficiaries with prostate cancer\": 3.01, \"PAC: IRF Per Capita Actual Costs\": 164.99, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 267.46, \"Percent of Medicare beneficiaries with breast cancer\": 3.08, \"Procedures Per User Standardized Costs\": 948.29, \"Percent of Medicare beneficiaries with chronic kidney disease\": 15.43, \"PAC: HH Per User Actual Costs\": 4432.48, \"Count of Medicare beneficiaries with high cholesterol\": 473431.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0763, \"Hospice Per Capita Standardized Costs\": 256.56, \"# Part B Drugs Users\": 538975.0, \"Average HCC Score\": 0.9446, \"Standardized Risk-Adjusted Per Capita Costs\": 9241.22, \"# PAC: IRF Users (with a covered stay)\": 8370.0, \"Ambulance Standardized Costs\": 149627758.29, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0298, \"Percent of Medicare beneficiaries with heart failure\": 12.02, \"Tests Actual Costs as % of Total Actual Costs\": 0.0273, \"FQHC/RHC Per Capita Standardized Costs\": 21.26, \"PQI07 Hypertension Admission Rate (age 65-74)\": 75.0, \"Test Events Per 1000 Beneficiaries\": 9632.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 43.0, \"ASC Actual Costs\": 59430571.13, \"Part B Drugs Per Capita Standardized Costs\": 328.54, \"Imaging Actual Costs\": 182251096.26, \"Tests Per User Actual Costs\": 281.06, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0177, \"Hospice Per User Actual Costs\": 10449.04, \"Tests Standardized Costs\": 233042288.81, \"IP Standardized Costs\": 2501116372.27, \"IP Per Capita Standardized Costs\": 2500.77, \"Outpatient Dialysis Facility Actual Costs\": 233989018.53, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 65.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1745.0, \"Percent of Medicare beneficiaries with hypertension\": 57.21, \"IP Covered Stays Per 1000 Beneficiaries\": 271.0, \"# Ambulance Users\": 124118.0, \"# ASC Users\": 61817.0, \"ASC Per Capita Standardized Costs\": 62.67, \"Procedures Per Capita Actual Costs\": 555.77, \"Procedures Per Capita Standardized Costs\": 573.82, \"IP Users (with a covered stay)\": 169977.0, \"Total Standardized Risk-Adjusted Costs\": 9242514029.34, \"Actual Per Capita Costs\": 8345.12, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0074, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 9.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.01, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.93, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 239.0, \"OP Per User Standardized Costs\": 1686.31, \"Count of Medicare beneficiaries with atrial fibrillation\": 77362.0, \"Procedure Events Per 1000 Beneficiaries\": 4135.0, \"Percent of Medicare beneficiaries with stroke\": 3.65, \"PAC: IRF Per User Actual Costs\": 19714.73, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 99317.0, \"% of Beneficiaries Using ASC\": 0.0618, \"PAC: IRF Standardized Costs\": 163340667.69, \"Hospice Covered Days Per 1000 Beneficiaries\": 1631.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 326.0, \"PAC: SNF Per User Standardized Costs\": 14454.92, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0022, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 116.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0311, \"MA Participation Rate\": 17.28, \"OP Per Capita Standardized Costs\": 1095.09, \"Percent of Medicare beneficiaries with arthritis\": 27.71, \"Ambulance Per Capita Standardized Costs\": 149.61, \"PAC: HH Visits Per 1000 Beneficiaries\": 2436.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0666, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0218, \"PAC: HH Per Capita Actual Costs\": 389.5, \"E&M Events Per 1000 Beneficiaries\": 12730.0, \"Count of Medicare beneficiaries with asthma\": 49695.0, \"# Test Users\": 810096.0, \"E&M Actual Costs\": 840941759.35, \"% of Beneficiaries Using Imaging\": 0.6813, \"PAC: SNF Standardized Costs\": 694111026.74, \"DME Per Capita Actual Costs\": 189.41, \"E&M Per User Actual Costs\": 934.88, \"Percent Male\": 44.2, \"OP Visits Per 1000 Beneficiaries\": 3928.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0227, \"OP Per Capita Actual Costs\": 1065.7, \"Hospital Readmission Rate\": 0.1832, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0026, \"Tests Per Capita Standardized Costs\": 233.01, \"Hospice Per Capita Actual Costs\": 249.05, \"E&M Actual Costs as % of Total Actual Costs\": 0.1008, \"PQI07 Hypertension Admission Rate (age < 65)\": 150.0, \"Percent African American\": 16.3, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0511, \"PAC: LTCH Per Capita Actual Costs\": 56.77, \"MA Beneficiaries\": 208881.0, \"FQHC/RHC Per User Standardized Costs\": 394.86, \"PAC: HH Per User Standardized Costs\": 4804.7, \"Procedures Per User Actual Costs\": 918.46, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 760.0, \"Ambulance Per User Actual Costs\": 1189.55, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1420.0, \"PAC: HH Standardized Costs\": 422270294.51, \"ASC Actual Costs as % of Total Actual Costs\": 0.0071, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 653.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0014, \"Percent Other/Unknown\": 3.93, \"% of Beneficiaries Using Hospice\": 0.0238, \"PAC: LTCH Per User Actual Costs\": 40615.78, \"Percent Non-Hispanic White\": 78.09, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 622.0, \"Count of Medicare beneficiaries with breast cancer\": 30823.0, \"DME Per Capita Standardized Costs\": 205.15, \"PQI08 CHF Admission Rate (age 65-74)\": 659.0, \"% of Beneficiaries Using DME\": 0.2779, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.028, \"% of Beneficiaries Using IP\": 0.17, \"DME Standardized Costs\": 205178435.54, \"FQHC/RHC Standardized Costs\": 21260543.15, \"PAC: SNF Per Capita Actual Costs\": 636.55, \"Imaging Standardized Costs\": 185237626.06, \"# E&M Users\": 899517.0, \"Count of Medicare beneficiaries with arthritis\": 277165.0, \"IP Per User Standardized Costs\": 14714.44, \"IP Per User Actual Costs\": 16813.81, \"PQI10 Dehydration Admission Rate (age 75+)\": 560.0, \"PAC: IRF Per User Standardized Costs\": 19515.01, \"DME Per User Actual Costs\": 681.65, \"PAC: IRF Actual Costs\": 165012303.83, \"Imaging Events Per 1000 Beneficiaries\": 3865.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0288, \"PAC: LTCH Per User Standardized Costs\": 43883.62, \"OP Actual Costs\": 1065853478.22, \"Count of Medicare beneficiaries with hypertension\": 572162.0, \"ASC Per User Standardized Costs\": 1014.02, \"Part B Drugs Per Capita Actual Costs\": 326.92, \"Ambulance Events Per 1000 Beneficiaries\": 458.0}, {\"PQI12 UTI Admission Rate (age < 65)\": \"*\", \"% of Beneficiaries Using PAC: HH\": 0.0336, \"PAC: LTCH Standardized Costs\": 844280.51, \"Percent of Medicare beneficiaries with atrial fibrillation\": 2.71, \"E&M Per Capita Standardized Costs\": 521.58, \"E&M Per User Standardized Costs\": 683.71, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1476.0, \"IP Covered Days Per 1000 Beneficiaries\": 1205.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": \"*\", \"Count of Medicare beneficiaries with lung cancer\": 46.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3902, \"Percent Eligible for Medicaid\": 1.22, \"Imaging Per Capita Standardized Costs\": 145.3, \"% of Beneficiaries Using Tests\": 0.6586, \"Imaging Per Capita Actual Costs\": 144.29, \"% of Beneficiaries Using PAC: SNF\": 0.0103, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0171, \"Count of Medicare beneficiaries with colorectal cancer\": 157.0, \"Hospice Actual Costs\": 4592572.42, \"# PAC: HH Users\": 559.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 21684.3, \"Total Actual Costs\": 81813387.71, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 934.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0045, \"ASC Standardized Costs\": 340787.95, \"DME Events Per 1000 Beneficiaries\": 548.0, \"PQI08 CHF Admission Rate (age < 65)\": 692.0, \"ASC Events Per 1000 Beneficiaries\": 35.0, \"PAC: LTCH Actual Costs\": 842715.9, \"Count of Medicare beneficiaries with depression\": 369.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1122.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 5.62, \"Outpatient Dialysis Facility Per User Actual Costs\": 19007.03, \"Beneficiaries with Part A and Part B\": 16835.0, \"% of Beneficiaries Using Part B Drugs\": 0.1832, \"Percent of Medicare beneficiaries with diabetes\": 31.96, \"% of Beneficiaries Using PAC: IRF\": 0.0031, \"E&M Per Capita Actual Costs\": 500.38, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0317, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0185, \"PAC: SNF Actual Costs\": 2153589.27, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 211.0, \"Percent Female\": 52.93, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 0.0, \"Percent of Medicare beneficiaries with osteoporosis\": 1.73, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 275.31, \"# Outpatient Dialysis Facility Users\": 211.0, \"FQHC/RHC Per User Actual Costs\": 269.83, \"Count of Medicare beneficiaries with ischemic heart disease\": 2278.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 326.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.29, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 163.0, \"Percent of Medicare beneficiaries with depression\": 2.22, \"Emergency Department Visits per 1000 Beneficiaries\": 449.0, \"IP Actual Costs\": 31927423.49, \"% of Beneficiaries Using OP\": 0.3944, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0072, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.1136, \"Count of Medicare beneficiaries with stroke\": 491.0, \"PQI12 UTI Admission Rate (age 75+)\": 507.0, \"# OP Users\": 6555.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 20.0, \"# Procedure Users\": 6848.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 13.71, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0824, \"Count of Medicare beneficiaries with diabetes\": 5311.0, \"ASC Per User Actual Costs\": 847.27, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": \"*\", \"DME Standardized Costs as % of Total Standardized Costs\": 0.0175, \"Percent of Medicare beneficiaries with high cholesterol\": 28.15, \"Standardized Per Capita Costs\": 4590.54, \"Ambulance Actual Costs\": 458968.55, \"FQHC/RHC Per Capita Actual Costs\": 13.56, \"Part B Drugs Per User Standardized Costs\": 462.85, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0131, \"# PAC: SNF Users (with a covered stay)\": 172.0, \"Ambulance Per Capita Actual Costs\": 27.62, \"PQI12 UTI Admission Rate (age 65-74)\": \"*\", \"Hospice Standardized Costs\": 5243771.66, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 241.32, \"% of Beneficiaries Using E&M\": 0.7629, \"PQI10 Dehydration Admission Rate (age 65-74)\": 168.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 284.0, \"Tests Per Capita Actual Costs\": 153.33, \"# DME Users\": 2372.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0294, \"PAC: SNF Per User Actual Costs\": 12520.87, \"State\": \"VI\", \"OP Per User Actual Costs\": 1711.97, \"PAC: HH Episodes Per 1000 Beneficiaries\": 48.0, \"Part B Drugs Actual Costs\": 1401302.86, \"FQHC/RHC Actual Costs\": 225309.32, \"OP Standardized Costs\": 8136950.04, \"DME Per User Standardized Costs\": 562.41, \"OP Actual Costs as % of Total Actual Costs\": 0.1372, \"PAC: SNF Per Capita Standardized Costs\": 134.84, \"% of Beneficiaries Using Ambulance\": 0.0349, \"Hospice Per User Standardized Costs\": 17421.17, \"# Imaging Users\": 8475.0, \"Part B Drugs Per User Actual Costs\": 460.2, \"Total Standardized Costs\": 76290255.56, \"Percent of Medicare beneficiaries with colorectal cancer\": 0.94, \"Count of Medicare beneficiaries with chronic kidney disease\": 1395.0, \"E&M Standardized Costs\": 8668093.7, \"Percent Hispanic\": 9.92, \"ASC Per Capita Actual Costs\": 19.63, \"Count of Medicare beneficiaries who have had a heart attack\": 49.0, \"Tests Actual Costs\": 2548255.39, \"# PAC: LTCH Users (with a covered stay)\": 16.0, \"% of Beneficiaries Using Procedures\": 0.4121, \"PAC: HH Actual Costs\": 2057417.33, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": \"*\", \"PAC: LTCH Per Capita Standardized Costs\": 50.8, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0344, \"Emergency Department Visits\": 7470.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0502, \"Procedures Actual Costs\": 6173657.98, \"# FQHC/RHC Users\": 835.0, \"Number of Acute Hospital Readmissions\": 395.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 48.0, \"Outpatient Dialysis Facility Standardized Costs\": 4575386.79, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0126, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1067, \"Ambulance Per User Standardized Costs\": 942.18, \"Imaging Per User Standardized Costs\": 284.92, \"Percent of Medicare beneficiaries with asthma\": 1.41, \"Part B Drugs Standardized Costs\": 1409365.59, \"FFS Beneficiaries\": 16619.0, \"# Hospice Users (with a covered stay)\": 301.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0127, \"Count of Medicare beneficiaries with osteoporosis\": 287.0, \"PQI08 CHF Admission Rate (age 75+)\": 1302.0, \"PAC: IRF Per Capita Standardized Costs\": 59.94, \"Procedures Standardized Costs\": 6289429.2, \"IP Standardized Costs as % of Total Standardized Costs\": 0.351, \"IP Per Capita Actual Costs\": 1921.14, \"DME Actual Costs\": 1226781.2, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0251, \"Count of Medicare beneficiaries with prostate cancer\": 763.0, \"PAC: HH Per Capita Standardized Costs\": 139.92, \"Count of Medicare beneficiaries with heart failure\": 1199.0, \"Tests Per User Standardized Costs\": 240.01, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0103, \"Percent of Medicare beneficiaries with prostate cancer\": 4.59, \"PAC: IRF Per Capita Actual Costs\": 62.07, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 282.95, \"Percent of Medicare beneficiaries with breast cancer\": 1.73, \"Procedures Per User Standardized Costs\": 918.43, \"Percent of Medicare beneficiaries with chronic kidney disease\": 8.39, \"PAC: HH Per User Actual Costs\": 3680.53, \"Count of Medicare beneficiaries with high cholesterol\": 4678.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0263, \"Hospice Per Capita Standardized Costs\": 315.53, \"# Part B Drugs Users\": 3045.0, \"Average HCC Score\": 0.6925, \"Standardized Risk-Adjusted Per Capita Costs\": 6504.2, \"# PAC: IRF Users (with a covered stay)\": 51.0, \"Ambulance Standardized Costs\": 546463.69, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0561, \"Percent of Medicare beneficiaries with heart failure\": 7.21, \"Tests Actual Costs as % of Total Actual Costs\": 0.0311, \"FQHC/RHC Per Capita Standardized Costs\": 14.22, \"PQI07 Hypertension Admission Rate (age 65-74)\": 147.0, \"Test Events Per 1000 Beneficiaries\": 6774.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 31.0, \"ASC Actual Costs\": 326200.16, \"Part B Drugs Per Capita Standardized Costs\": 84.8, \"Imaging Actual Costs\": 2398013.2, \"Tests Per User Actual Costs\": 232.8, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0056, \"Hospice Per User Actual Costs\": 15257.72, \"Tests Standardized Costs\": 2627169.58, \"IP Standardized Costs\": 26774609.84, \"IP Per Capita Standardized Costs\": 1611.08, \"Outpatient Dialysis Facility Actual Costs\": 4010484.15, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 13.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 322.0, \"Percent of Medicare beneficiaries with hypertension\": 47.9, \"IP Covered Stays Per 1000 Beneficiaries\": 149.0, \"# Ambulance Users\": 580.0, \"# ASC Users\": 385.0, \"ASC Per Capita Standardized Costs\": 20.51, \"Procedures Per Capita Actual Costs\": 371.48, \"Procedures Per Capita Standardized Costs\": 378.45, \"IP Users (with a covered stay)\": 1716.0, \"Total Standardized Risk-Adjusted Costs\": 108093294.04, \"Actual Per Capita Costs\": 4922.88, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0111, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 3.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.28, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 1.47, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 434.0, \"OP Per User Standardized Costs\": 1241.33, \"Count of Medicare beneficiaries with atrial fibrillation\": 450.0, \"Procedure Events Per 1000 Beneficiaries\": 2550.0, \"Percent of Medicare beneficiaries with stroke\": 2.95, \"PAC: IRF Per User Actual Costs\": 20226.85, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 244.0, \"% of Beneficiaries Using ASC\": 0.0232, \"PAC: IRF Standardized Costs\": 996195.95, \"Hospice Covered Days Per 1000 Beneficiaries\": 2026.0, \"PQI10 Dehydration Admission Rate (age < 65)\": \"*\", \"PAC: SNF Per User Standardized Costs\": 13028.5, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0028, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": \"*\", \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0687, \"MA Participation Rate\": 1.28, \"OP Per Capita Standardized Costs\": 489.62, \"Percent of Medicare beneficiaries with arthritis\": 12.99, \"Ambulance Per Capita Standardized Costs\": 32.88, \"PAC: HH Visits Per 1000 Beneficiaries\": 790.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0755, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0293, \"PAC: HH Per Capita Actual Costs\": 123.8, \"E&M Events Per 1000 Beneficiaries\": 6928.0, \"Count of Medicare beneficiaries with asthma\": 234.0, \"# Test Users\": 10946.0, \"E&M Actual Costs\": 8315853.22, \"% of Beneficiaries Using Imaging\": 0.51, \"PAC: SNF Standardized Costs\": 2240901.6, \"DME Per Capita Actual Costs\": 73.82, \"E&M Per User Actual Costs\": 655.93, \"Percent Male\": 47.07, \"OP Visits Per 1000 Beneficiaries\": 1529.0, \"DME Actual Costs as % of Total Actual Costs\": 0.015, \"OP Per Capita Actual Costs\": 675.25, \"Hospital Readmission Rate\": 0.1622, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0031, \"Tests Per Capita Standardized Costs\": 158.08, \"Hospice Per Capita Actual Costs\": 276.34, \"E&M Actual Costs as % of Total Actual Costs\": 0.1016, \"PQI07 Hypertension Admission Rate (age < 65)\": \"*\", \"Percent African American\": 73.02, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0305, \"PAC: LTCH Per Capita Actual Costs\": 50.71, \"MA Beneficiaries\": 216.0, \"FQHC/RHC Per User Standardized Costs\": 282.97, \"PAC: HH Per User Standardized Costs\": 4159.88, \"Procedures Per User Actual Costs\": 901.53, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 755.0, \"Ambulance Per User Actual Costs\": 791.33, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": \"*\", \"PAC: HH Standardized Costs\": 2325371.27, \"ASC Actual Costs as % of Total Actual Costs\": 0.004, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 189.0, \"% of Beneficiaries Using PAC: LTCH\": 0.001, \"Percent Other/Unknown\": 3.04, \"% of Beneficiaries Using Hospice\": 0.0181, \"PAC: LTCH Per User Actual Costs\": 52669.74, \"Percent Non-Hispanic White\": 14.01, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": \"*\", \"Count of Medicare beneficiaries with breast cancer\": 287.0, \"DME Per Capita Standardized Costs\": 80.27, \"PQI08 CHF Admission Rate (age 65-74)\": 526.0, \"% of Beneficiaries Using DME\": 0.1427, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.049, \"% of Beneficiaries Using IP\": 0.1033, \"DME Standardized Costs\": 1334028.48, \"FQHC/RHC Standardized Costs\": 236279.36, \"PAC: SNF Per Capita Actual Costs\": 129.59, \"Imaging Standardized Costs\": 2414707.75, \"# E&M Users\": 12678.0, \"Count of Medicare beneficiaries with arthritis\": 2158.0, \"IP Per User Standardized Costs\": 15602.92, \"IP Per User Actual Costs\": 18605.72, \"PQI10 Dehydration Admission Rate (age 75+)\": 326.0, \"PAC: IRF Per User Standardized Costs\": 19533.25, \"DME Per User Actual Costs\": 517.19, \"PAC: IRF Actual Costs\": 1031569.26, \"Imaging Events Per 1000 Beneficiaries\": 2125.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.06, \"PAC: LTCH Per User Standardized Costs\": 52767.53, \"OP Actual Costs\": 11221993.15, \"Count of Medicare beneficiaries with hypertension\": 7960.0, \"ASC Per User Standardized Costs\": 885.16, \"Part B Drugs Per Capita Actual Costs\": 84.32, \"Ambulance Events Per 1000 Beneficiaries\": 90.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 207.0, \"% of Beneficiaries Using PAC: HH\": 0.0859, \"PAC: LTCH Standardized Costs\": 924467.09, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.01, \"E&M Per Capita Standardized Costs\": 599.54, \"E&M Per User Standardized Costs\": 711.38, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 470.0, \"IP Covered Days Per 1000 Beneficiaries\": 1153.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 29.0, \"Count of Medicare beneficiaries with lung cancer\": 1081.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3644, \"Percent Eligible for Medicaid\": 26.37, \"Imaging Per Capita Standardized Costs\": 83.93, \"% of Beneficiaries Using Tests\": 0.6167, \"Imaging Per Capita Actual Costs\": 81.83, \"% of Beneficiaries Using PAC: SNF\": 0.0466, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0137, \"Count of Medicare beneficiaries with colorectal cancer\": 1063.0, \"Hospice Actual Costs\": 18058310.06, \"# PAC: HH Users\": 9452.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23423.31, \"Total Actual Costs\": 870133872.03, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 8558.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0027, \"ASC Standardized Costs\": 2026434.66, \"DME Events Per 1000 Beneficiaries\": 1460.0, \"PQI08 CHF Admission Rate (age < 65)\": 276.0, \"ASC Events Per 1000 Beneficiaries\": 26.0, \"PAC: LTCH Actual Costs\": 950331.18, \"Count of Medicare beneficiaries with depression\": 20032.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2000.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 7.78, \"Outpatient Dialysis Facility Per User Actual Costs\": 23120.99, \"Beneficiaries with Part A and Part B\": 120048.0, \"% of Beneficiaries Using Part B Drugs\": 0.3274, \"Percent of Medicare beneficiaries with diabetes\": 20.64, \"% of Beneficiaries Using PAC: IRF\": 0.0035, \"E&M Per Capita Actual Costs\": 552.48, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0122, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0158, \"PAC: SNF Actual Costs\": 78046485.21, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 600.0, \"Percent Female\": 54.3, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 3.99, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 88.81, \"# Outpatient Dialysis Facility Users\": 417.0, \"FQHC/RHC Per User Actual Costs\": 496.7, \"Count of Medicare beneficiaries with ischemic heart disease\": 22673.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 100.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.92, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 1318.0, \"Percent of Medicare beneficiaries with depression\": 18.21, \"Emergency Department Visits per 1000 Beneficiaries\": 646.0, \"IP Actual Costs\": 317051260.96, \"% of Beneficiaries Using OP\": 0.8225, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.018, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0869, \"Count of Medicare beneficiaries with stroke\": 2830.0, \"PQI12 UTI Admission Rate (age 75+)\": 780.0, \"# OP Users\": 90461.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 18.0, \"# Procedure Users\": 55642.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 20.62, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0557, \"Count of Medicare beneficiaries with diabetes\": 22698.0, \"ASC Per User Actual Costs\": 1037.54, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 963.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0234, \"Percent of Medicare beneficiaries with high cholesterol\": 34.3, \"Standardized Per Capita Costs\": 6901.47, \"Ambulance Actual Costs\": 14153754.89, \"FQHC/RHC Per Capita Actual Costs\": 133.35, \"Part B Drugs Per User Standardized Costs\": 334.02, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0104, \"# PAC: SNF Users (with a covered stay)\": 5128.0, \"Ambulance Per Capita Actual Costs\": 128.69, \"PQI12 UTI Admission Rate (age 65-74)\": 132.0, \"Hospice Standardized Costs\": 17986037.72, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 87.66, \"% of Beneficiaries Using E&M\": 0.8428, \"PQI10 Dehydration Admission Rate (age 65-74)\": 136.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 105.0, \"Tests Per Capita Actual Costs\": 87.65, \"# DME Users\": 25353.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.1036, \"PAC: SNF Per User Actual Costs\": 15219.67, \"State\": \"VT\", \"OP Per User Actual Costs\": 2201.85, \"PAC: HH Episodes Per 1000 Beneficiaries\": 145.0, \"Part B Drugs Actual Costs\": 11928275.98, \"FQHC/RHC Actual Costs\": 14666108.68, \"OP Standardized Costs\": 184327776.51, \"DME Per User Standardized Costs\": 699.15, \"OP Actual Costs as % of Total Actual Costs\": 0.2289, \"PAC: SNF Per Capita Standardized Costs\": 715.11, \"% of Beneficiaries Using Ambulance\": 0.0723, \"Hospice Per User Standardized Costs\": 9701.21, \"# Imaging Users\": 68035.0, \"Part B Drugs Per User Actual Costs\": 331.23, \"Total Standardized Costs\": 759044333.41, \"Percent of Medicare beneficiaries with colorectal cancer\": 0.97, \"Count of Medicare beneficiaries with chronic kidney disease\": 12328.0, \"E&M Standardized Costs\": 65938859.41, \"Percent Hispanic\": 0.63, \"ASC Per Capita Actual Costs\": 18.06, \"Count of Medicare beneficiaries who have had a heart attack\": 1014.0, \"Tests Actual Costs\": 9640182.66, \"# PAC: LTCH Users (with a covered stay)\": 20.0, \"% of Beneficiaries Using Procedures\": 0.5059, \"PAC: HH Actual Costs\": 40107936.03, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 25.0, \"PAC: LTCH Per Capita Standardized Costs\": 8.41, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0131, \"Emergency Department Visits\": 71020.0, \"% of Beneficiaries Using FQHC/RHC\": 0.2685, \"Procedures Actual Costs\": 40915116.15, \"# FQHC/RHC Users\": 29527.0, \"Number of Acute Hospital Readmissions\": 3476.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 48.0, \"Outpatient Dialysis Facility Standardized Costs\": 9767521.0, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0098, \"OP Standardized Costs as % of Total Standardized Costs\": 0.2428, \"Ambulance Per User Standardized Costs\": 1718.46, \"Imaging Per User Standardized Costs\": 135.68, \"Percent of Medicare beneficiaries with asthma\": 4.5, \"Part B Drugs Standardized Costs\": 12028841.78, \"FFS Beneficiaries\": 109983.0, \"# Hospice Users (with a covered stay)\": 1854.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0038, \"Count of Medicare beneficiaries with osteoporosis\": 4389.0, \"PQI08 CHF Admission Rate (age 75+)\": 1729.0, \"PAC: IRF Per Capita Standardized Costs\": 71.53, \"Procedures Standardized Costs\": 42292225.18, \"IP Standardized Costs as % of Total Standardized Costs\": 0.2788, \"IP Per Capita Actual Costs\": 2882.73, \"DME Actual Costs\": 16748590.42, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0461, \"Count of Medicare beneficiaries with prostate cancer\": 2956.0, \"PAC: HH Per Capita Standardized Costs\": 367.59, \"Count of Medicare beneficiaries with heart failure\": 10217.0, \"Tests Per User Standardized Costs\": 146.34, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0011, \"Percent of Medicare beneficiaries with prostate cancer\": 2.69, \"PAC: IRF Per Capita Actual Costs\": 77.29, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 132.29, \"Percent of Medicare beneficiaries with breast cancer\": 2.61, \"Procedures Per User Standardized Costs\": 760.08, \"Percent of Medicare beneficiaries with chronic kidney disease\": 11.21, \"PAC: HH Per User Actual Costs\": 4243.33, \"Count of Medicare beneficiaries with high cholesterol\": 37719.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0897, \"Hospice Per Capita Standardized Costs\": 163.53, \"# Part B Drugs Users\": 36012.0, \"Average HCC Score\": 0.8613, \"Standardized Risk-Adjusted Per Capita Costs\": 8355.48, \"# PAC: IRF Users (with a covered stay)\": 388.0, \"Ambulance Standardized Costs\": 13666947.44, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0208, \"Percent of Medicare beneficiaries with heart failure\": 9.29, \"Tests Actual Costs as % of Total Actual Costs\": 0.0111, \"FQHC/RHC Per Capita Standardized Costs\": 137.92, \"PQI07 Hypertension Admission Rate (age 65-74)\": 29.0, \"Test Events Per 1000 Beneficiaries\": 3335.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 6.0, \"ASC Actual Costs\": 1985845.92, \"Part B Drugs Per Capita Standardized Costs\": 109.37, \"Imaging Actual Costs\": 9000117.96, \"Tests Per User Actual Costs\": 142.13, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0163, \"Hospice Per User Actual Costs\": 9740.19, \"Tests Standardized Costs\": 9926065.43, \"IP Standardized Costs\": 211659148.87, \"IP Per Capita Standardized Costs\": 1924.47, \"Outpatient Dialysis Facility Actual Costs\": 9641453.38, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 61.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1549.0, \"Percent of Medicare beneficiaries with hypertension\": 44.46, \"IP Covered Stays Per 1000 Beneficiaries\": 215.0, \"# Ambulance Users\": 7953.0, \"# ASC Users\": 1914.0, \"ASC Per Capita Standardized Costs\": 18.43, \"Procedures Per Capita Actual Costs\": 372.01, \"Procedures Per Capita Standardized Costs\": 384.53, \"IP Users (with a covered stay)\": 15925.0, \"Total Standardized Risk-Adjusted Costs\": 918960339.32, \"Actual Per Capita Costs\": 7911.53, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0012, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 4.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 0.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.98, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 9.35, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 114.0, \"OP Per User Standardized Costs\": 2037.65, \"Count of Medicare beneficiaries with atrial fibrillation\": 8808.0, \"Procedure Events Per 1000 Beneficiaries\": 2941.0, \"Percent of Medicare beneficiaries with stroke\": 2.57, \"PAC: IRF Per User Actual Costs\": 21908.61, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10284.0, \"% of Beneficiaries Using ASC\": 0.0174, \"PAC: IRF Standardized Costs\": 7867046.19, \"Hospice Covered Days Per 1000 Beneficiaries\": 1017.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 178.0, \"PAC: SNF Per User Standardized Costs\": 15337.32, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0169, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 59.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0237, \"MA Participation Rate\": 8.38, \"OP Per Capita Standardized Costs\": 1675.97, \"Percent of Medicare beneficiaries with arthritis\": 24.09, \"Ambulance Per Capita Standardized Costs\": 124.26, \"PAC: HH Visits Per 1000 Beneficiaries\": 2298.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.047, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0103, \"PAC: HH Per Capita Actual Costs\": 364.67, \"E&M Events Per 1000 Beneficiaries\": 9143.0, \"Count of Medicare beneficiaries with asthma\": 4953.0, \"# Test Users\": 67828.0, \"E&M Actual Costs\": 60763078.71, \"% of Beneficiaries Using Imaging\": 0.6186, \"PAC: SNF Standardized Costs\": 78649778.86, \"DME Per Capita Actual Costs\": 152.28, \"E&M Per User Actual Costs\": 655.54, \"Percent Male\": 45.7, \"OP Visits Per 1000 Beneficiaries\": 7701.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0192, \"OP Per Capita Actual Costs\": 1811.02, \"Hospital Readmission Rate\": 0.155, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.02, \"Tests Per Capita Standardized Costs\": 90.25, \"Hospice Per Capita Actual Costs\": 164.19, \"E&M Actual Costs as % of Total Actual Costs\": 0.0698, \"PQI07 Hypertension Admission Rate (age < 65)\": \"*\", \"Percent African American\": 0.4, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0533, \"PAC: LTCH Per Capita Actual Costs\": 8.64, \"MA Beneficiaries\": 10065.0, \"FQHC/RHC Per User Standardized Costs\": 513.73, \"PAC: HH Per User Standardized Costs\": 4277.25, \"Procedures Per User Actual Costs\": 735.33, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 335.0, \"Ambulance Per User Actual Costs\": 1779.67, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1378.0, \"PAC: HH Standardized Costs\": 40428604.0, \"ASC Actual Costs as % of Total Actual Costs\": 0.0023, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 606.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0002, \"Percent Other/Unknown\": 2.6, \"% of Beneficiaries Using Hospice\": 0.0169, \"PAC: LTCH Per User Actual Costs\": 47516.56, \"Percent Non-Hispanic White\": 96.37, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 710.0, \"Count of Medicare beneficiaries with breast cancer\": 2874.0, \"DME Per Capita Standardized Costs\": 161.17, \"PQI08 CHF Admission Rate (age 65-74)\": 409.0, \"% of Beneficiaries Using DME\": 0.2305, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0111, \"% of Beneficiaries Using IP\": 0.1448, \"DME Standardized Costs\": 17725481.74, \"FQHC/RHC Standardized Costs\": 15168798.45, \"PAC: SNF Per Capita Actual Costs\": 709.62, \"Imaging Standardized Costs\": 9231245.21, \"# E&M Users\": 92691.0, \"Count of Medicare beneficiaries with arthritis\": 26499.0, \"IP Per User Standardized Costs\": 13291.0, \"IP Per User Actual Costs\": 19909.03, \"PQI10 Dehydration Admission Rate (age 75+)\": 359.0, \"PAC: IRF Per User Standardized Costs\": 20275.89, \"DME Per User Actual Costs\": 660.62, \"PAC: IRF Actual Costs\": 8500539.95, \"Imaging Events Per 1000 Beneficiaries\": 2723.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0129, \"PAC: LTCH Per User Standardized Costs\": 46223.35, \"OP Actual Costs\": 199181922.89, \"Count of Medicare beneficiaries with hypertension\": 48903.0, \"ASC Per User Standardized Costs\": 1058.74, \"Part B Drugs Per Capita Actual Costs\": 108.46, \"Ambulance Events Per 1000 Beneficiaries\": 343.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 193.0, \"% of Beneficiaries Using PAC: HH\": 0.0561, \"PAC: LTCH Standardized Costs\": 29169083.44, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.88, \"E&M Per Capita Standardized Costs\": 694.59, \"E&M Per User Standardized Costs\": 834.8, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1041.0, \"IP Covered Days Per 1000 Beneficiaries\": 1088.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 45.0, \"Count of Medicare beneficiaries with lung cancer\": 6490.0, \"IP Actual Costs as % of Total Actual Costs\": 0.353, \"Percent Eligible for Medicaid\": 19.14, \"Imaging Per Capita Standardized Costs\": 171.47, \"% of Beneficiaries Using Tests\": 0.7191, \"Imaging Per Capita Actual Costs\": 171.58, \"% of Beneficiaries Using PAC: SNF\": 0.0423, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0333, \"Count of Medicare beneficiaries with colorectal cancer\": 7205.0, \"Hospice Actual Costs\": 161198638.65, \"# PAC: HH Users\": 40479.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23661.33, \"Total Actual Costs\": 5763453847.06, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 62108.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0142, \"ASC Standardized Costs\": 73456379.82, \"DME Events Per 1000 Beneficiaries\": 1471.0, \"PQI08 CHF Admission Rate (age < 65)\": 553.0, \"ASC Events Per 1000 Beneficiaries\": 190.0, \"PAC: LTCH Actual Costs\": 30585908.5, \"Count of Medicare beneficiaries with depression\": 103217.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1123.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.6, \"Outpatient Dialysis Facility Per User Actual Costs\": 24923.58, \"Beneficiaries with Part A and Part B\": 1057854.0, \"% of Beneficiaries Using Part B Drugs\": 0.4685, \"Percent of Medicare beneficiaries with diabetes\": 21.83, \"% of Beneficiaries Using PAC: IRF\": 0.0051, \"E&M Per Capita Actual Costs\": 668.35, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0239, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0373, \"PAC: SNF Actual Costs\": 510832182.28, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 370.0, \"Percent Female\": 53.27, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 168.0, \"Percent of Medicare beneficiaries with osteoporosis\": 4.49, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 186.0, \"# Outpatient Dialysis Facility Users\": 5675.0, \"FQHC/RHC Per User Actual Costs\": 489.81, \"Count of Medicare beneficiaries with ischemic heart disease\": 140182.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 121.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.69, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 798.0, \"Percent of Medicare beneficiaries with depression\": 14.3, \"Emergency Department Visits per 1000 Beneficiaries\": 574.0, \"IP Actual Costs\": 2034643008.39, \"% of Beneficiaries Using OP\": 0.6299, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0131, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0967, \"Count of Medicare beneficiaries with stroke\": 20739.0, \"PQI12 UTI Admission Rate (age 75+)\": 773.0, \"# OP Users\": 454760.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 24.0, \"# Procedure Users\": 397311.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 19.42, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0727, \"Count of Medicare beneficiaries with diabetes\": 157590.0, \"ASC Per User Actual Costs\": 868.42, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 578.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0253, \"Percent of Medicare beneficiaries with high cholesterol\": 34.67, \"Standardized Per Capita Costs\": 7182.46, \"Ambulance Actual Costs\": 72208816.06, \"FQHC/RHC Per Capita Actual Costs\": 84.14, \"Part B Drugs Per User Standardized Costs\": 571.12, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0134, \"# PAC: SNF Users (with a covered stay)\": 30558.0, \"Ambulance Per Capita Actual Costs\": 100.02, \"PQI12 UTI Admission Rate (age 65-74)\": 154.0, \"Hospice Standardized Costs\": 148418359.26, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 195.93, \"% of Beneficiaries Using E&M\": 0.832, \"PQI10 Dehydration Admission Rate (age 65-74)\": 141.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 113.0, \"Tests Per Capita Actual Costs\": 177.58, \"# DME Users\": 180394.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0925, \"PAC: SNF Per User Actual Costs\": 16716.81, \"State\": \"WA\", \"OP Per User Actual Costs\": 2025.86, \"PAC: HH Episodes Per 1000 Beneficiaries\": 86.0, \"Part B Drugs Actual Costs\": 192155833.93, \"FQHC/RHC Actual Costs\": 60744625.51, \"OP Standardized Costs\": 840832510.96, \"DME Per User Standardized Costs\": 727.17, \"OP Actual Costs as % of Total Actual Costs\": 0.1598, \"PAC: SNF Per Capita Standardized Costs\": 664.38, \"% of Beneficiaries Using Ambulance\": 0.111, \"Hospice Per User Standardized Costs\": 9143.57, \"# Imaging Users\": 448633.0, \"Part B Drugs Per User Actual Costs\": 568.09, \"Total Standardized Costs\": 5185098462.78, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.0, \"Count of Medicare beneficiaries with chronic kidney disease\": 102028.0, \"E&M Standardized Costs\": 501431353.99, \"Percent Hispanic\": 3.31, \"ASC Per Capita Actual Costs\": 103.98, \"Count of Medicare beneficiaries who have had a heart attack\": 5005.0, \"Tests Actual Costs\": 128196986.56, \"# PAC: LTCH Users (with a covered stay)\": 536.0, \"% of Beneficiaries Using Procedures\": 0.5504, \"PAC: HH Actual Costs\": 181778004.05, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 39.0, \"PAC: LTCH Per Capita Standardized Costs\": 40.41, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0251, \"Emergency Department Visits\": 414434.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1718, \"Procedures Actual Costs\": 373149174.17, \"# FQHC/RHC Users\": 124016.0, \"Number of Acute Hospital Readmissions\": 25073.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 67.0, \"Outpatient Dialysis Facility Standardized Costs\": 134278066.58, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0135, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1622, \"Ambulance Per User Standardized Costs\": 844.58, \"Imaging Per User Standardized Costs\": 275.92, \"Percent of Medicare beneficiaries with asthma\": 4.1, \"Part B Drugs Standardized Costs\": 193181078.9, \"FFS Beneficiaries\": 721911.0, \"# Hospice Users (with a covered stay)\": 16232.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0079, \"Count of Medicare beneficiaries with osteoporosis\": 32415.0, \"PQI08 CHF Admission Rate (age 75+)\": 1607.0, \"PAC: IRF Per Capita Standardized Costs\": 96.53, \"Procedures Standardized Costs\": 377200001.15, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3046, \"IP Per Capita Actual Costs\": 2818.41, \"DME Actual Costs\": 123642833.81, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0315, \"Count of Medicare beneficiaries with prostate cancer\": 20142.0, \"PAC: HH Per Capita Standardized Costs\": 235.27, \"Count of Medicare beneficiaries with heart failure\": 83242.0, \"Tests Per User Standardized Costs\": 250.61, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0053, \"Percent of Medicare beneficiaries with prostate cancer\": 2.79, \"PAC: IRF Per Capita Actual Costs\": 107.88, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 276.1, \"Percent of Medicare beneficiaries with breast cancer\": 2.69, \"Procedures Per User Standardized Costs\": 949.38, \"Percent of Medicare beneficiaries with chronic kidney disease\": 14.13, \"PAC: HH Per User Actual Costs\": 4490.67, \"Count of Medicare beneficiaries with high cholesterol\": 250265.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0886, \"Hospice Per Capita Standardized Costs\": 205.59, \"# Part B Drugs Users\": 338250.0, \"Average HCC Score\": 0.899, \"Standardized Risk-Adjusted Per Capita Costs\": 8455.06, \"# PAC: IRF Users (with a covered stay)\": 3710.0, \"Ambulance Standardized Costs\": 67681239.06, \"Hospice Actual Costs as % of Total Actual Costs\": 0.028, \"Percent of Medicare beneficiaries with heart failure\": 11.53, \"Tests Actual Costs as % of Total Actual Costs\": 0.0222, \"FQHC/RHC Per Capita Standardized Costs\": 85.81, \"PQI07 Hypertension Admission Rate (age 65-74)\": 49.0, \"Test Events Per 1000 Beneficiaries\": 7263.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 25.0, \"ASC Actual Costs\": 75067280.3, \"Part B Drugs Per Capita Standardized Costs\": 267.6, \"Imaging Actual Costs\": 123867480.99, \"Tests Per User Actual Costs\": 246.95, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0125, \"Hospice Per User Actual Costs\": 9930.92, \"Tests Standardized Costs\": 130096937.62, \"IP Standardized Costs\": 1579268810.45, \"IP Per Capita Standardized Costs\": 2187.62, \"Outpatient Dialysis Facility Actual Costs\": 141441309.19, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 55.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1476.0, \"Percent of Medicare beneficiaries with hypertension\": 43.64, \"IP Covered Stays Per 1000 Beneficiaries\": 223.0, \"# Ambulance Users\": 80136.0, \"# ASC Users\": 86441.0, \"ASC Per Capita Standardized Costs\": 101.75, \"Procedures Per Capita Actual Costs\": 516.89, \"Procedures Per Capita Standardized Costs\": 522.5, \"IP Users (with a covered stay)\": 107528.0, \"Total Standardized Risk-Adjusted Costs\": 6103802796.88, \"Actual Per Capita Costs\": 7983.61, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0056, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 6.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.9, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 8.19, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 136.0, \"OP Per User Standardized Costs\": 1848.96, \"Count of Medicare beneficiaries with atrial fibrillation\": 56906.0, \"Procedure Events Per 1000 Beneficiaries\": 4026.0, \"Percent of Medicare beneficiaries with stroke\": 2.87, \"PAC: IRF Per User Actual Costs\": 20992.72, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 59125.0, \"% of Beneficiaries Using ASC\": 0.1197, \"PAC: IRF Standardized Costs\": 69686281.92, \"Hospice Covered Days Per 1000 Beneficiaries\": 1295.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 225.0, \"PAC: SNF Per User Standardized Costs\": 15695.42, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0105, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 126.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0286, \"MA Participation Rate\": 31.76, \"OP Per Capita Standardized Costs\": 1164.73, \"Percent of Medicare beneficiaries with arthritis\": 23.31, \"Ambulance Per Capita Standardized Costs\": 93.75, \"PAC: HH Visits Per 1000 Beneficiaries\": 1202.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0647, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0215, \"PAC: HH Per Capita Actual Costs\": 251.8, \"E&M Events Per 1000 Beneficiaries\": 9720.0, \"Count of Medicare beneficiaries with asthma\": 29630.0, \"# Test Users\": 519113.0, \"E&M Actual Costs\": 482492609.77, \"% of Beneficiaries Using Imaging\": 0.6215, \"PAC: SNF Standardized Costs\": 479620675.79, \"DME Per Capita Actual Costs\": 171.27, \"E&M Per User Actual Costs\": 803.27, \"Percent Male\": 46.74, \"OP Visits Per 1000 Beneficiaries\": 4347.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0215, \"OP Per Capita Actual Costs\": 1276.17, \"Hospital Readmission Rate\": 0.1592, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0119, \"Tests Per Capita Standardized Costs\": 180.21, \"Hospice Per Capita Actual Costs\": 223.29, \"E&M Actual Costs as % of Total Actual Costs\": 0.0837, \"PQI07 Hypertension Admission Rate (age < 65)\": 64.0, \"Percent African American\": 2.63, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0328, \"PAC: LTCH Per Capita Actual Costs\": 42.37, \"MA Beneficiaries\": 335943.0, \"FQHC/RHC Per User Standardized Costs\": 499.49, \"PAC: HH Per User Standardized Costs\": 4195.77, \"Procedures Per User Actual Costs\": 939.19, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 552.0, \"Ambulance Per User Actual Costs\": 901.08, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 689.0, \"PAC: HH Standardized Costs\": 169840745.83, \"ASC Actual Costs as % of Total Actual Costs\": 0.013, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 368.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0007, \"Percent Other/Unknown\": 7.38, \"% of Beneficiaries Using Hospice\": 0.0225, \"PAC: LTCH Per User Actual Costs\": 57063.26, \"Percent Non-Hispanic White\": 86.69, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 443.0, \"Count of Medicare beneficiaries with breast cancer\": 19413.0, \"DME Per Capita Standardized Costs\": 181.71, \"PQI08 CHF Admission Rate (age 65-74)\": 482.0, \"% of Beneficiaries Using DME\": 0.2499, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0245, \"% of Beneficiaries Using IP\": 0.1489, \"DME Standardized Costs\": 131176904.33, \"FQHC/RHC Standardized Costs\": 61944133.6, \"PAC: SNF Per Capita Actual Costs\": 707.61, \"Imaging Standardized Costs\": 123787451.33, \"# E&M Users\": 600658.0, \"Count of Medicare beneficiaries with arthritis\": 168276.0, \"IP Per User Standardized Costs\": 14687.05, \"IP Per User Actual Costs\": 18921.98, \"PQI10 Dehydration Admission Rate (age 75+)\": 370.0, \"PAC: IRF Per User Standardized Costs\": 18783.36, \"DME Per User Actual Costs\": 685.4, \"PAC: IRF Actual Costs\": 77882986.34, \"Imaging Events Per 1000 Beneficiaries\": 3238.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0259, \"PAC: LTCH Per User Standardized Costs\": 54419.93, \"OP Actual Costs\": 921279296.34, \"Count of Medicare beneficiaries with hypertension\": 315010.0, \"ASC Per User Standardized Costs\": 849.79, \"Part B Drugs Per Capita Actual Costs\": 266.18, \"Ambulance Events Per 1000 Beneficiaries\": 263.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 267.0, \"% of Beneficiaries Using PAC: HH\": 0.0529, \"PAC: LTCH Standardized Costs\": 51741505.85, \"Percent of Medicare beneficiaries with atrial fibrillation\": 8.04, \"E&M Per Capita Standardized Costs\": 704.81, \"E&M Per User Standardized Costs\": 805.52, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 1042.0, \"IP Covered Days Per 1000 Beneficiaries\": 1245.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 34.0, \"Count of Medicare beneficiaries with lung cancer\": 5914.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3508, \"Percent Eligible for Medicaid\": 21.09, \"Imaging Per Capita Standardized Costs\": 144.43, \"% of Beneficiaries Using Tests\": 0.7396, \"Imaging Per Capita Actual Costs\": 136.68, \"% of Beneficiaries Using PAC: SNF\": 0.0517, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.031, \"Count of Medicare beneficiaries with colorectal cancer\": 7148.0, \"Hospice Actual Costs\": 207353938.62, \"# PAC: HH Users\": 33393.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 23340.14, \"Total Actual Costs\": 5101567724.41, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 58629.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0074, \"ASC Standardized Costs\": 36042527.39, \"DME Events Per 1000 Beneficiaries\": 1660.0, \"PQI08 CHF Admission Rate (age < 65)\": 575.0, \"ASC Events Per 1000 Beneficiaries\": 105.0, \"PAC: LTCH Actual Costs\": 51157431.55, \"Count of Medicare beneficiaries with depression\": 100269.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1454.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 9.29, \"Outpatient Dialysis Facility Per User Actual Costs\": 23422.59, \"Beneficiaries with Part A and Part B\": 984106.0, \"% of Beneficiaries Using Part B Drugs\": 0.4972, \"Percent of Medicare beneficiaries with diabetes\": 23.29, \"% of Beneficiaries Using PAC: IRF\": 0.0057, \"E&M Per Capita Actual Costs\": 641.55, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0186, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0325, \"PAC: SNF Actual Costs\": 465294791.53, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 458.0, \"Percent Female\": 54.91, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 198.0, \"Percent of Medicare beneficiaries with osteoporosis\": 5.27, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 189.74, \"# Outpatient Dialysis Facility Users\": 5130.0, \"FQHC/RHC Per User Actual Costs\": 505.78, \"Count of Medicare beneficiaries with ischemic heart disease\": 146964.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 133.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.79, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 227.0, \"Percent of Medicare beneficiaries with depression\": 15.89, \"Emergency Department Visits per 1000 Beneficiaries\": 615.0, \"IP Actual Costs\": 1789734746.19, \"% of Beneficiaries Using OP\": 0.6792, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0127, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0909, \"Count of Medicare beneficiaries with stroke\": 16987.0, \"PQI12 UTI Admission Rate (age 75+)\": 887.0, \"# OP Users\": 428602.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 31.0, \"# Procedure Users\": 355825.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 23.29, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0608, \"Count of Medicare beneficiaries with diabetes\": 146967.0, \"ASC Per User Actual Costs\": 945.68, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 821.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0232, \"Percent of Medicare beneficiaries with high cholesterol\": 40.82, \"Standardized Per Capita Costs\": 7757.47, \"Ambulance Actual Costs\": 59855025.6, \"FQHC/RHC Per Capita Actual Costs\": 28.2, \"Part B Drugs Per User Standardized Costs\": 507.43, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0136, \"# PAC: SNF Users (with a covered stay)\": 32611.0, \"Ambulance Per Capita Actual Costs\": 94.85, \"PQI12 UTI Admission Rate (age 65-74)\": 191.0, \"Hospice Standardized Costs\": 207812319.28, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 190.41, \"% of Beneficiaries Using E&M\": 0.875, \"PQI10 Dehydration Admission Rate (age 65-74)\": 169.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 163.0, \"Tests Per Capita Actual Costs\": 154.4, \"# DME Users\": 168441.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0987, \"PAC: SNF Per User Actual Costs\": 14268.03, \"State\": \"WI\", \"OP Per User Actual Costs\": 2182.97, \"PAC: HH Episodes Per 1000 Beneficiaries\": 79.0, \"Part B Drugs Actual Costs\": 157923177.1, \"FQHC/RHC Actual Costs\": 17793480.55, \"OP Standardized Costs\": 891031837.69, \"DME Per User Standardized Costs\": 674.22, \"OP Actual Costs as % of Total Actual Costs\": 0.1834, \"PAC: SNF Per Capita Standardized Costs\": 765.34, \"% of Beneficiaries Using Ambulance\": 0.1104, \"Hospice Per User Standardized Costs\": 11169.7, \"# Imaging Users\": 407713.0, \"Part B Drugs Per User Actual Costs\": 503.35, \"Total Standardized Costs\": 4895256728.37, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.13, \"Count of Medicare beneficiaries with chronic kidney disease\": 103097.0, \"E&M Standardized Costs\": 444761573.58, \"Percent Hispanic\": 1.65, \"ASC Per Capita Actual Costs\": 55.52, \"Count of Medicare beneficiaries who have had a heart attack\": 4996.0, \"Tests Actual Costs\": 97430072.87, \"# PAC: LTCH Users (with a covered stay)\": 1093.0, \"% of Beneficiaries Using Procedures\": 0.5639, \"PAC: HH Actual Costs\": 126803272.44, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 43.0, \"PAC: LTCH Per Capita Standardized Costs\": 81.99, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0206, \"Emergency Department Visits\": 388218.0, \"% of Beneficiaries Using FQHC/RHC\": 0.0557, \"Procedures Actual Costs\": 277525134.04, \"# FQHC/RHC Users\": 35180.0, \"Number of Acute Hospital Readmissions\": 26080.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 76.0, \"Outpatient Dialysis Facility Standardized Costs\": 119734932.72, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0135, \"OP Standardized Costs as % of Total Standardized Costs\": 0.182, \"Ambulance Per User Standardized Costs\": 889.25, \"Imaging Per User Standardized Costs\": 223.54, \"Percent of Medicare beneficiaries with asthma\": 4.87, \"Part B Drugs Standardized Costs\": 159203238.2, \"FFS Beneficiaries\": 631038.0, \"# Hospice Users (with a covered stay)\": 18605.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0081, \"Count of Medicare beneficiaries with osteoporosis\": 33249.0, \"PQI08 CHF Admission Rate (age 75+)\": 1824.0, \"PAC: IRF Per Capita Standardized Costs\": 105.62, \"Procedures Standardized Costs\": 297521766.29, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3069, \"IP Per Capita Actual Costs\": 2836.18, \"DME Actual Costs\": 105703975.79, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0249, \"Count of Medicare beneficiaries with prostate cancer\": 17465.0, \"PAC: HH Per Capita Standardized Costs\": 205.45, \"Count of Medicare beneficiaries with heart failure\": 78637.0, \"Tests Per User Standardized Costs\": 216.05, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.01, \"Percent of Medicare beneficiaries with prostate cancer\": 2.77, \"PAC: IRF Per Capita Actual Costs\": 109.19, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 211.54, \"Percent of Medicare beneficiaries with breast cancer\": 2.8, \"Procedures Per User Standardized Costs\": 836.15, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.34, \"PAC: HH Per User Actual Costs\": 3797.3, \"Count of Medicare beneficiaries with high cholesterol\": 257571.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0912, \"Hospice Per Capita Standardized Costs\": 329.32, \"# Part B Drugs Users\": 313747.0, \"Average HCC Score\": 0.9499, \"Standardized Risk-Adjusted Per Capita Costs\": 8700.61, \"# PAC: IRF Users (with a covered stay)\": 3580.0, \"Ambulance Standardized Costs\": 61928207.77, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0406, \"Percent of Medicare beneficiaries with heart failure\": 12.46, \"Tests Actual Costs as % of Total Actual Costs\": 0.0191, \"FQHC/RHC Per Capita Standardized Costs\": 29.63, \"PQI07 Hypertension Admission Rate (age 65-74)\": 53.0, \"Test Events Per 1000 Beneficiaries\": 8144.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 55.0, \"ASC Actual Costs\": 35036486.87, \"Part B Drugs Per Capita Standardized Costs\": 252.29, \"Imaging Actual Costs\": 86248040.4, \"Tests Per User Actual Costs\": 208.77, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0117, \"Hospice Per User Actual Costs\": 11145.07, \"Tests Standardized Costs\": 100832186.43, \"IP Standardized Costs\": 1502377923.28, \"IP Per Capita Standardized Costs\": 2380.8, \"Outpatient Dialysis Facility Actual Costs\": 120157901.5, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 68.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1769.0, \"Percent of Medicare beneficiaries with hypertension\": 48.48, \"IP Covered Stays Per 1000 Beneficiaries\": 259.0, \"# Ambulance Users\": 69641.0, \"# ASC Users\": 37049.0, \"ASC Per Capita Standardized Costs\": 57.12, \"Procedures Per Capita Actual Costs\": 439.79, \"Procedures Per Capita Standardized Costs\": 471.48, \"IP Users (with a covered stay)\": 106428.0, \"Total Standardized Risk-Adjusted Costs\": 5490417382.17, \"Actual Per Capita Costs\": 8084.41, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0106, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 6.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 2.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.94, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 8.78, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 166.0, \"OP Per User Standardized Costs\": 2078.93, \"Count of Medicare beneficiaries with atrial fibrillation\": 50704.0, \"Procedure Events Per 1000 Beneficiaries\": 3495.0, \"Percent of Medicare beneficiaries with stroke\": 2.69, \"PAC: IRF Per User Actual Costs\": 19246.9, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 55380.0, \"% of Beneficiaries Using ASC\": 0.0587, \"PAC: IRF Standardized Costs\": 66650281.91, \"Hospice Covered Days Per 1000 Beneficiaries\": 2069.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 267.0, \"PAC: SNF Per User Standardized Costs\": 14809.71, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0035, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 100.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0425, \"MA Participation Rate\": 35.88, \"OP Per Capita Standardized Costs\": 1412.01, \"Percent of Medicare beneficiaries with arthritis\": 26.1, \"Ambulance Per Capita Standardized Costs\": 98.14, \"PAC: HH Visits Per 1000 Beneficiaries\": 1190.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0544, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0169, \"PAC: HH Per Capita Actual Costs\": 200.94, \"E&M Events Per 1000 Beneficiaries\": 10258.0, \"Count of Medicare beneficiaries with asthma\": 30750.0, \"# Test Users\": 466697.0, \"E&M Actual Costs\": 404839510.98, \"% of Beneficiaries Using Imaging\": 0.6461, \"PAC: SNF Standardized Costs\": 482959313.19, \"DME Per Capita Actual Costs\": 167.51, \"E&M Per User Actual Costs\": 733.21, \"Percent Male\": 45.09, \"OP Visits Per 1000 Beneficiaries\": 5628.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0207, \"OP Per Capita Actual Costs\": 1482.67, \"Hospital Readmission Rate\": 0.1638, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0038, \"Tests Per Capita Standardized Costs\": 159.79, \"Hospice Per Capita Actual Costs\": 328.59, \"E&M Actual Costs as % of Total Actual Costs\": 0.0794, \"PQI07 Hypertension Admission Rate (age < 65)\": 59.0, \"Percent African American\": 4.08, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0265, \"PAC: LTCH Per Capita Actual Costs\": 81.07, \"MA Beneficiaries\": 353068.0, \"FQHC/RHC Per User Standardized Costs\": 531.51, \"PAC: HH Per User Standardized Costs\": 3882.48, \"Procedures Per User Actual Costs\": 779.95, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 477.0, \"Ambulance Per User Actual Costs\": 859.48, \"Average Age\": 71.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 914.0, \"PAC: HH Standardized Costs\": 129647611.2, \"ASC Actual Costs as % of Total Actual Costs\": 0.0069, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 509.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0017, \"Percent Other/Unknown\": 3.15, \"% of Beneficiaries Using Hospice\": 0.0295, \"PAC: LTCH Per User Actual Costs\": 46804.6, \"Percent Non-Hispanic White\": 91.12, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 623.0, \"Count of Medicare beneficiaries with breast cancer\": 17666.0, \"DME Per Capita Standardized Costs\": 179.97, \"PQI08 CHF Admission Rate (age 65-74)\": 517.0, \"% of Beneficiaries Using DME\": 0.2669, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0236, \"% of Beneficiaries Using IP\": 0.1687, \"DME Standardized Costs\": 113567056.52, \"FQHC/RHC Standardized Costs\": 18698463.75, \"PAC: SNF Per Capita Actual Costs\": 737.35, \"Imaging Standardized Costs\": 91139225.41, \"# E&M Users\": 552144.0, \"Count of Medicare beneficiaries with arthritis\": 164700.0, \"IP Per User Standardized Costs\": 14116.38, \"IP Per User Actual Costs\": 16816.39, \"PQI10 Dehydration Admission Rate (age 75+)\": 406.0, \"PAC: IRF Per User Standardized Costs\": 18617.4, \"DME Per User Actual Costs\": 627.54, \"PAC: IRF Actual Costs\": 68903916.83, \"Imaging Events Per 1000 Beneficiaries\": 3500.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0245, \"PAC: LTCH Per User Standardized Costs\": 47338.98, \"OP Actual Costs\": 935623712.7, \"Count of Medicare beneficiaries with hypertension\": 305929.0, \"ASC Per User Standardized Costs\": 972.83, \"Part B Drugs Per Capita Actual Costs\": 250.26, \"Ambulance Events Per 1000 Beneficiaries\": 270.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 369.0, \"% of Beneficiaries Using PAC: HH\": 0.0715, \"PAC: LTCH Standardized Costs\": 33694509.74, \"Percent of Medicare beneficiaries with atrial fibrillation\": 7.16, \"E&M Per Capita Standardized Costs\": 803.68, \"E&M Per User Standardized Costs\": 941.36, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 849.0, \"IP Covered Days Per 1000 Beneficiaries\": 1675.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": 49.0, \"Count of Medicare beneficiaries with lung cancer\": 3291.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3746, \"Percent Eligible for Medicaid\": 25.98, \"Imaging Per Capita Standardized Costs\": 175.4, \"% of Beneficiaries Using Tests\": 0.7395, \"Imaging Per Capita Actual Costs\": 159.97, \"% of Beneficiaries Using PAC: SNF\": 0.0409, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0207, \"Count of Medicare beneficiaries with colorectal cancer\": 3811.0, \"Hospice Actual Costs\": 64143394.3, \"# PAC: HH Users\": 21081.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 21749.13, \"Total Actual Costs\": 2435369866.78, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 26120.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0042, \"ASC Standardized Costs\": 10428256.8, \"DME Events Per 1000 Beneficiaries\": 2237.0, \"PQI08 CHF Admission Rate (age < 65)\": 685.0, \"ASC Events Per 1000 Beneficiaries\": 68.0, \"PAC: LTCH Actual Costs\": 28628846.76, \"Count of Medicare beneficiaries with depression\": 56354.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 2238.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 8.85, \"Outpatient Dialysis Facility Per User Actual Costs\": 20322.84, \"Beneficiaries with Part A and Part B\": 400302.0, \"% of Beneficiaries Using Part B Drugs\": 0.4641, \"Percent of Medicare beneficiaries with diabetes\": 29.58, \"% of Beneficiaries Using PAC: IRF\": 0.0121, \"E&M Per Capita Actual Costs\": 717.61, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0208, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0205, \"PAC: SNF Actual Costs\": 153235201.42, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 977.0, \"Percent Female\": 51.96, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": 260.0, \"Percent of Medicare beneficiaries with osteoporosis\": 4.83, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 149.72, \"# Outpatient Dialysis Facility Users\": 2031.0, \"FQHC/RHC Per User Actual Costs\": 360.95, \"Count of Medicare beneficiaries with ischemic heart disease\": 88476.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 227.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 1.12, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 935.0, \"Percent of Medicare beneficiaries with depression\": 19.1, \"Emergency Department Visits per 1000 Beneficiaries\": 769.0, \"IP Actual Costs\": 912192383.36, \"% of Beneficiaries Using OP\": 0.6839, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0233, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0953, \"Count of Medicare beneficiaries with stroke\": 9990.0, \"PQI12 UTI Admission Rate (age 75+)\": 1337.0, \"# OP Users\": 201791.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 23.0, \"# Procedure Users\": 162671.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 29.99, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0573, \"Count of Medicare beneficiaries with diabetes\": 87276.0, \"ASC Per User Actual Costs\": 755.01, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 1695.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0286, \"Percent of Medicare beneficiaries with high cholesterol\": 47.9, \"Standardized Per Capita Costs\": 8434.42, \"Ambulance Actual Costs\": 62589604.34, \"FQHC/RHC Per Capita Actual Costs\": 78.22, \"Part B Drugs Per User Standardized Costs\": 372.15, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0292, \"# PAC: SNF Users (with a covered stay)\": 12074.0, \"Ambulance Per Capita Actual Costs\": 212.14, \"PQI12 UTI Admission Rate (age 65-74)\": 415.0, \"Hospice Standardized Costs\": 72031772.16, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 139.9, \"% of Beneficiaries Using E&M\": 0.8537, \"PQI10 Dehydration Admission Rate (age 65-74)\": 218.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 219.0, \"Tests Per Capita Actual Costs\": 197.32, \"# DME Users\": 90373.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0747, \"PAC: SNF Per User Actual Costs\": 12691.34, \"State\": \"WV\", \"OP Per User Actual Costs\": 1869.15, \"PAC: HH Episodes Per 1000 Beneficiaries\": 124.0, \"Part B Drugs Actual Costs\": 50341463.69, \"FQHC/RHC Actual Costs\": 23079286.48, \"OP Standardized Costs\": 394435066.05, \"DME Per User Standardized Costs\": 788.2, \"OP Actual Costs as % of Total Actual Costs\": 0.1549, \"PAC: SNF Per Capita Standardized Costs\": 629.94, \"% of Beneficiaries Using Ambulance\": 0.1477, \"Hospice Per User Standardized Costs\": 11002.26, \"# Imaging Users\": 195173.0, \"Part B Drugs Per User Actual Costs\": 367.61, \"Total Standardized Costs\": 2488509596.09, \"Percent of Medicare beneficiaries with colorectal cancer\": 1.29, \"Count of Medicare beneficiaries with chronic kidney disease\": 48211.0, \"E&M Standardized Costs\": 237118987.69, \"Percent Hispanic\": 0.41, \"ASC Per Capita Actual Costs\": 31.45, \"Count of Medicare beneficiaries who have had a heart attack\": 3307.0, \"Tests Actual Costs\": 58216905.85, \"# PAC: LTCH Users (with a covered stay)\": 715.0, \"% of Beneficiaries Using Procedures\": 0.5513, \"PAC: HH Actual Costs\": 89425254.64, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 57.0, \"PAC: LTCH Per Capita Standardized Costs\": 114.2, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0255, \"Emergency Department Visits\": 226936.0, \"% of Beneficiaries Using FQHC/RHC\": 0.2167, \"Procedures Actual Costs\": 131489996.2, \"# FQHC/RHC Users\": 63940.0, \"Number of Acute Hospital Readmissions\": 17086.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 184.0, \"Outpatient Dialysis Facility Standardized Costs\": 44172481.26, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0268, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1585, \"Ambulance Per User Standardized Costs\": 1328.86, \"Imaging Per User Standardized Costs\": 265.15, \"Percent of Medicare beneficiaries with asthma\": 4.95, \"Part B Drugs Standardized Costs\": 50962897.34, \"FFS Beneficiaries\": 295042.0, \"# Hospice Users (with a covered stay)\": 6547.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0069, \"Count of Medicare beneficiaries with osteoporosis\": 14241.0, \"PQI08 CHF Admission Rate (age 75+)\": 2386.0, \"PAC: IRF Per Capita Standardized Costs\": 246.21, \"Procedures Standardized Costs\": 142711820.15, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3272, \"IP Per Capita Actual Costs\": 3091.74, \"DME Actual Costs\": 67638219.01, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.0367, \"Count of Medicare beneficiaries with prostate cancer\": 6098.0, \"PAC: HH Per Capita Standardized Costs\": 364.54, \"Count of Medicare beneficiaries with heart failure\": 39484.0, \"Tests Per User Standardized Costs\": 291.1, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0118, \"Percent of Medicare beneficiaries with prostate cancer\": 2.07, \"PAC: IRF Per Capita Actual Costs\": 221.46, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 241.83, \"Percent of Medicare beneficiaries with breast cancer\": 2.26, \"Procedures Per User Standardized Costs\": 877.3, \"Percent of Medicare beneficiaries with chronic kidney disease\": 16.34, \"PAC: HH Per User Actual Costs\": 4241.98, \"Count of Medicare beneficiaries with high cholesterol\": 141325.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.0629, \"Hospice Per Capita Standardized Costs\": 244.14, \"# Part B Drugs Users\": 136942.0, \"Average HCC Score\": 0.9733, \"Standardized Risk-Adjusted Per Capita Costs\": 9004.33, \"# PAC: IRF Users (with a covered stay)\": 3573.0, \"Ambulance Standardized Costs\": 57909325.75, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0263, \"Percent of Medicare beneficiaries with heart failure\": 13.38, \"Tests Actual Costs as % of Total Actual Costs\": 0.0239, \"FQHC/RHC Per Capita Standardized Costs\": 91.46, \"PQI07 Hypertension Admission Rate (age 65-74)\": 119.0, \"Test Events Per 1000 Beneficiaries\": 7547.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 70.0, \"ASC Actual Costs\": 9279830.15, \"Part B Drugs Per Capita Standardized Costs\": 172.73, \"Imaging Actual Costs\": 47199290.67, \"Tests Per User Actual Costs\": 266.83, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0257, \"Hospice Per User Actual Costs\": 9797.37, \"Tests Standardized Costs\": 63512433.63, \"IP Standardized Costs\": 814156599.4, \"IP Per Capita Standardized Costs\": 2759.46, \"Outpatient Dialysis Facility Actual Costs\": 41275695.67, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 56.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1475.0, \"Percent of Medicare beneficiaries with hypertension\": 58.81, \"IP Covered Stays Per 1000 Beneficiaries\": 304.0, \"# Ambulance Users\": 43578.0, \"# ASC Users\": 12291.0, \"ASC Per Capita Standardized Costs\": 35.35, \"Procedures Per Capita Actual Costs\": 445.67, \"Procedures Per Capita Standardized Costs\": 483.7, \"IP Users (with a covered stay)\": 53837.0, \"Total Standardized Risk-Adjusted Costs\": 2656655339.88, \"Actual Per Capita Costs\": 8254.32, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0135, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 14.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 3.0, \"Percent of Medicare beneficiaries with lung cancer\": 1.12, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 15.72, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 256.0, \"OP Per User Standardized Costs\": 1954.67, \"Count of Medicare beneficiaries with atrial fibrillation\": 21111.0, \"Procedure Events Per 1000 Beneficiaries\": 3735.0, \"Percent of Medicare beneficiaries with stroke\": 3.39, \"PAC: IRF Per User Actual Costs\": 18287.44, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 46367.0, \"% of Beneficiaries Using ASC\": 0.0417, \"PAC: IRF Standardized Costs\": 72642597.69, \"Hospice Covered Days Per 1000 Beneficiaries\": 1494.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 282.0, \"PAC: SNF Per User Standardized Costs\": 15393.23, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0095, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 92.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0289, \"MA Participation Rate\": 26.3, \"OP Per Capita Standardized Costs\": 1336.88, \"Percent of Medicare beneficiaries with arthritis\": 30.9, \"Ambulance Per Capita Standardized Costs\": 196.27, \"PAC: HH Visits Per 1000 Beneficiaries\": 1934.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.054, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0194, \"PAC: HH Per Capita Actual Costs\": 303.09, \"E&M Events Per 1000 Beneficiaries\": 11655.0, \"Count of Medicare beneficiaries with asthma\": 14612.0, \"# Test Users\": 218179.0, \"E&M Actual Costs\": 211724985.12, \"% of Beneficiaries Using Imaging\": 0.6615, \"PAC: SNF Standardized Costs\": 185857851.54, \"DME Per Capita Actual Costs\": 229.25, \"E&M Per User Actual Costs\": 840.55, \"Percent Male\": 48.04, \"OP Visits Per 1000 Beneficiaries\": 4496.0, \"DME Actual Costs as % of Total Actual Costs\": 0.0278, \"OP Per Capita Actual Costs\": 1278.39, \"Hospital Readmission Rate\": 0.1974, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.0108, \"Tests Per Capita Standardized Costs\": 215.27, \"Hospice Per Capita Actual Costs\": 217.4, \"E&M Actual Costs as % of Total Actual Costs\": 0.0869, \"PQI07 Hypertension Admission Rate (age < 65)\": 127.0, \"Percent African American\": 2.64, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.0432, \"PAC: LTCH Per Capita Actual Costs\": 97.03, \"MA Beneficiaries\": 105260.0, \"FQHC/RHC Per User Standardized Costs\": 422.01, \"PAC: HH Per User Standardized Costs\": 5102.0, \"Procedures Per User Actual Costs\": 808.32, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 526.0, \"Ambulance Per User Actual Costs\": 1436.26, \"Average Age\": 69.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1994.0, \"PAC: HH Standardized Costs\": 107555200.89, \"ASC Actual Costs as % of Total Actual Costs\": 0.0038, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 1439.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0024, \"Percent Other/Unknown\": 1.03, \"% of Beneficiaries Using Hospice\": 0.0222, \"PAC: LTCH Per User Actual Costs\": 40040.35, \"Percent Non-Hispanic White\": 95.93, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 899.0, \"Count of Medicare beneficiaries with breast cancer\": 6679.0, \"DME Per Capita Standardized Costs\": 241.43, \"PQI08 CHF Admission Rate (age 65-74)\": 895.0, \"% of Beneficiaries Using DME\": 0.3063, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0169, \"% of Beneficiaries Using IP\": 0.1825, \"DME Standardized Costs\": 71231862.14, \"FQHC/RHC Standardized Costs\": 26983063.29, \"PAC: SNF Per Capita Actual Costs\": 519.37, \"Imaging Standardized Costs\": 51749448.62, \"# E&M Users\": 251890.0, \"Count of Medicare beneficiaries with arthritis\": 91165.0, \"IP Per User Standardized Costs\": 15122.62, \"IP Per User Actual Costs\": 16943.6, \"PQI10 Dehydration Admission Rate (age 75+)\": 476.0, \"PAC: IRF Per User Standardized Costs\": 20330.98, \"DME Per User Actual Costs\": 748.43, \"PAC: IRF Actual Costs\": 65341023.19, \"Imaging Events Per 1000 Beneficiaries\": 4096.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0178, \"PAC: LTCH Per User Standardized Costs\": 47125.19, \"OP Actual Costs\": 377177855.25, \"Count of Medicare beneficiaries with hypertension\": 173523.0, \"ASC Per User Standardized Costs\": 848.45, \"Part B Drugs Per Capita Actual Costs\": 170.62, \"Ambulance Events Per 1000 Beneficiaries\": 602.0}, {\"PQI12 UTI Admission Rate (age < 65)\": 380.0, \"% of Beneficiaries Using PAC: HH\": 0.0428, \"PAC: LTCH Standardized Costs\": 4369945.22, \"Percent of Medicare beneficiaries with atrial fibrillation\": 6.26, \"E&M Per Capita Standardized Costs\": 613.76, \"E&M Per User Standardized Costs\": 722.75, \"Outpatient Dialysis Facility Events Per 1000 Beneficiaries\": 501.0, \"IP Covered Days Per 1000 Beneficiaries\": 1066.0, \"PQI16 Lower Extremity Amputation Admission Rate (age 75+)\": \"*\", \"Count of Medicare beneficiaries with lung cancer\": 596.0, \"IP Actual Costs as % of Total Actual Costs\": 0.3758, \"Percent Eligible for Medicaid\": 13.83, \"Imaging Per Capita Standardized Costs\": 153.48, \"% of Beneficiaries Using Tests\": 0.6511, \"Imaging Per Capita Actual Costs\": 150.01, \"% of Beneficiaries Using PAC: SNF\": 0.0382, \"Part B Drugs Actual Costs as % of Total Actual Costs\": 0.0302, \"Count of Medicare beneficiaries with colorectal cancer\": 764.0, \"Hospice Actual Costs\": 9044770.82, \"# PAC: HH Users\": 3552.0, \"Outpatient Dialysis Facility Per User Standardized Costs\": 22904.52, \"Total Actual Costs\": 635935025.25, \"Count of Medicare beneficiaries with Alzheimer's and related disorders\": 6167.0, \"ASC Standardized Costs as % of Total Standardized Costs\": 0.0158, \"ASC Standardized Costs\": 8961035.22, \"DME Events Per 1000 Beneficiaries\": 2194.0, \"PQI08 CHF Admission Rate (age < 65)\": 344.0, \"ASC Events Per 1000 Beneficiaries\": 203.0, \"PAC: LTCH Actual Costs\": 4157309.21, \"Count of Medicare beneficiaries with depression\": 11202.0, \"PQI11 Bacterial Pneumonia Admission Rate (age 75+)\": 1873.0, \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\": 7.44, \"Outpatient Dialysis Facility Per User Actual Costs\": 22401.83, \"Beneficiaries with Part A and Part B\": 86920.0, \"% of Beneficiaries Using Part B Drugs\": 0.4782, \"Percent of Medicare beneficiaries with diabetes\": 18.99, \"% of Beneficiaries Using PAC: IRF\": 0.0108, \"E&M Per Capita Actual Costs\": 577.11, \"Imaging Standardized Costs as % of Total Standardized Costs\": 0.0224, \"Part B Drugs Standardized Costs as % of Total Standardized Costs\": 0.0342, \"PAC: SNF Actual Costs\": 47682207.84, \"PQI11 Bacterial Pneumonia Admission Rate (age 65-74)\": 676.0, \"Percent Female\": 52.53, \"PQI15 Asthma in Younger Adults Admission Rate (age < 40)\": \"*\", \"Percent of Medicare beneficiaries with osteoporosis\": 4.31, \"Outpatient Dialysis Facility Per Capita Standardized Costs\": 87.02, \"# Outpatient Dialysis Facility Users\": 315.0, \"FQHC/RHC Per User Actual Costs\": 381.81, \"Count of Medicare beneficiaries with ischemic heart disease\": 15948.0, \"PQI07 Hypertension Admission Rate (age 75+)\": 145.0, \"Percent of Medicare beneficiaries who have had a heart attack\": 0.67, \"FQHC/RHC Visits Per 1000 Beneficiaries\": 428.0, \"Percent of Medicare beneficiaries with depression\": 13.51, \"Emergency Department Visits per 1000 Beneficiaries\": 556.0, \"IP Actual Costs\": 238970556.57, \"% of Beneficiaries Using OP\": 0.616, \"Ambulance Standardized Costs as % of Total Standardized Costs\": 0.0068, \"E&M Standardized Costs as % of Total Standardized Costs\": 0.0895, \"Count of Medicare beneficiaries with stroke\": 1957.0, \"PQI12 UTI Admission Rate (age 75+)\": 916.0, \"# OP Users\": 51072.0, \"Hospice Covered Stays Per 1000 Beneficiaries\": 15.0, \"# Procedure Users\": 46874.0, \"Percent Medicare beneficiaries with ischemic heart disease\": 19.24, \"Procedures Standardized Costs as % of Total Standardized Costs\": 0.0815, \"Count of Medicare beneficiaries with diabetes\": 15748.0, \"ASC Per User Actual Costs\": 817.5, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 75+)\": 919.0, \"DME Standardized Costs as % of Total Standardized Costs\": 0.0381, \"Percent of Medicare beneficiaries with high cholesterol\": 24.24, \"Standardized Per Capita Costs\": 6857.05, \"Ambulance Actual Costs\": 7365202.52, \"FQHC/RHC Per Capita Actual Costs\": 41.68, \"Part B Drugs Per User Standardized Costs\": 490.48, \"PAC: IRF Standardized Costs as % of Total Standardized Costs\": 0.0311, \"# PAC: SNF Users (with a covered stay)\": 3167.0, \"Ambulance Per Capita Actual Costs\": 88.83, \"PQI12 UTI Admission Rate (age 65-74)\": 218.0, \"Hospice Standardized Costs\": 9157094.1, \"Outpatient Dialysis Facility Per Capita Actual Costs\": 85.11, \"% of Beneficiaries Using E&M\": 0.8492, \"PQI10 Dehydration Admission Rate (age 65-74)\": 145.0, \"PQI03 Diabetes LT Complication Admission Rate (age 65-74)\": 93.0, \"Tests Per Capita Actual Costs\": 130.43, \"# DME Users\": 22631.0, \"PAC: SNF Standardized Costs as % of Total Standardized Costs\": 0.0882, \"PAC: SNF Per User Actual Costs\": 15055.95, \"State\": \"WY\", \"OP Per User Actual Costs\": 2139.45, \"PAC: HH Episodes Per 1000 Beneficiaries\": 70.0, \"Part B Drugs Actual Costs\": 19208586.52, \"FQHC/RHC Actual Costs\": 3456140.01, \"OP Standardized Costs\": 96452464.59, \"DME Per User Standardized Costs\": 956.36, \"OP Actual Costs as % of Total Actual Costs\": 0.1718, \"PAC: SNF Per Capita Standardized Costs\": 604.57, \"% of Beneficiaries Using Ambulance\": 0.0668, \"Hospice Per User Standardized Costs\": 7450.85, \"# Imaging Users\": 49053.0, \"Part B Drugs Per User Actual Costs\": 484.44, \"Total Standardized Costs\": 568524518.62, \"Percent of Medicare beneficiaries with colorectal cancer\": 0.92, \"Count of Medicare beneficiaries with chronic kidney disease\": 8578.0, \"E&M Standardized Costs\": 50887643.96, \"Percent Hispanic\": 3.98, \"ASC Per Capita Actual Costs\": 103.31, \"Count of Medicare beneficiaries who have had a heart attack\": 558.0, \"Tests Actual Costs\": 10814365.21, \"# PAC: LTCH Users (with a covered stay)\": 93.0, \"% of Beneficiaries Using Procedures\": 0.5654, \"PAC: HH Actual Costs\": 15919059.59, \"PQI16 Lower Extremity Amputation Admission Rate (age 65-74)\": 43.0, \"PAC: LTCH Per Capita Standardized Costs\": 52.71, \"Tests Standardized Costs as % of Total Standardized Costs\": 0.0196, \"Emergency Department Visits\": 46134.0, \"% of Beneficiaries Using FQHC/RHC\": 0.1092, \"Procedures Actual Costs\": 45504364.89, \"# FQHC/RHC Users\": 9052.0, \"Number of Acute Hospital Readmissions\": 2600.0, \"PAC: IRF Covered Days Per 1000 Beneficiaries\": 155.0, \"Outpatient Dialysis Facility Standardized Costs\": 7214924.44, \"PAC: IRF Actual Costs as % of Total Actual Costs\": 0.0286, \"OP Standardized Costs as % of Total Standardized Costs\": 0.1697, \"Ambulance Per User Standardized Costs\": 698.61, \"Imaging Per User Standardized Costs\": 259.42, \"Percent of Medicare beneficiaries with asthma\": 3.29, \"Part B Drugs Standardized Costs\": 19447869.27, \"FFS Beneficiaries\": 82911.0, \"# Hospice Users (with a covered stay)\": 1229.0, \"% of Beneficiaries Using Outpatient Dialysis Facility\": 0.0038, \"Count of Medicare beneficiaries with osteoporosis\": 3574.0, \"PQI08 CHF Admission Rate (age 75+)\": 1387.0, \"PAC: IRF Per Capita Standardized Costs\": 213.59, \"Procedures Standardized Costs\": 46311982.34, \"IP Standardized Costs as % of Total Standardized Costs\": 0.3148, \"IP Per Capita Actual Costs\": 2882.25, \"DME Actual Costs\": 21008260.75, \"PAC: HH Actual Costs as % of Total Actual Costs\": 0.025, \"Count of Medicare beneficiaries with prostate cancer\": 2270.0, \"PAC: HH Per Capita Standardized Costs\": 198.97, \"Count of Medicare beneficiaries with heart failure\": 8641.0, \"Tests Per User Standardized Costs\": 206.47, \"PAC: LTCH Actual Costs as % of Total Actual Costs\": 0.0065, \"Percent of Medicare beneficiaries with prostate cancer\": 2.74, \"PAC: IRF Per Capita Actual Costs\": 219.46, \"State and County FIPS Code\": \".\", \"Imaging Per User Actual Costs\": 253.55, \"Percent of Medicare beneficiaries with breast cancer\": 2.15, \"Procedures Per User Standardized Costs\": 988.01, \"Percent of Medicare beneficiaries with chronic kidney disease\": 10.35, \"PAC: HH Per User Actual Costs\": 4481.72, \"Count of Medicare beneficiaries with high cholesterol\": 20097.0, \"PAC: SNF Actual Costs as % of Total Actual Costs\": 0.075, \"Hospice Per Capita Standardized Costs\": 110.44, \"# Part B Drugs Users\": 39651.0, \"Average HCC Score\": 0.8149, \"Standardized Risk-Adjusted Per Capita Costs\": 9147.14, \"# PAC: IRF Users (with a covered stay)\": 893.0, \"Ambulance Standardized Costs\": 3869072.5, \"Hospice Actual Costs as % of Total Actual Costs\": 0.0142, \"Percent of Medicare beneficiaries with heart failure\": 10.42, \"Tests Actual Costs as % of Total Actual Costs\": 0.017, \"FQHC/RHC Per Capita Standardized Costs\": 41.41, \"PQI07 Hypertension Admission Rate (age 65-74)\": 48.0, \"Test Events Per 1000 Beneficiaries\": 5002.0, \"PAC: LTCH Covered Days Per 1000 Beneficiaries\": 32.0, \"ASC Actual Costs\": 8565786.33, \"Part B Drugs Per Capita Standardized Costs\": 234.56, \"Imaging Actual Costs\": 12437341.87, \"Tests Per User Actual Costs\": 200.31, \"Ambulance Actual Costs as % of Total Actual Costs\": 0.0116, \"Hospice Per User Actual Costs\": 7359.46, \"Tests Standardized Costs\": 11146613.29, \"IP Standardized Costs\": 178980694.39, \"IP Per Capita Standardized Costs\": 2158.71, \"Outpatient Dialysis Facility Actual Costs\": 7056575.73, \"PAC: SNF Covered Stays Per 1000 Beneficiaries\": 49.0, \"PAC: SNF Covered Days Per 1000 Beneficiaries\": 1264.0, \"Percent of Medicare beneficiaries with hypertension\": 38.2, \"IP Covered Stays Per 1000 Beneficiaries\": 225.0, \"# Ambulance Users\": 5538.0, \"# ASC Users\": 10478.0, \"ASC Per Capita Standardized Costs\": 108.08, \"Procedures Per Capita Actual Costs\": 548.83, \"Procedures Per Capita Standardized Costs\": 558.57, \"IP Users (with a covered stay)\": 12899.0, \"Total Standardized Risk-Adjusted Costs\": 758398248.63, \"Actual Per Capita Costs\": 7670.09, \"PAC: LTCH Standardized Costs as % of Total Standardized Costs\": 0.0077, \"PAC: IRF Covered Stays Per 1000 Beneficiaries\": 12.0, \"PAC: LTCH Covered Stays Per 1000 Beneficiaries\": 1.0, \"Percent of Medicare beneficiaries with lung cancer\": 0.72, \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\": 10.22, \"PQI03 Diabetes LT Complication Admission Rate (age 75+)\": 117.0, \"OP Per User Standardized Costs\": 1888.56, \"Count of Medicare beneficiaries with atrial fibrillation\": 5191.0, \"Procedure Events Per 1000 Beneficiaries\": 4142.0, \"Percent of Medicare beneficiaries with stroke\": 2.36, \"PAC: IRF Per User Actual Costs\": 20375.91, \"Count of Medicare beneficiaries with chronic obstructive pulmonary disease\": 8470.0, \"% of Beneficiaries Using ASC\": 0.1264, \"PAC: IRF Standardized Costs\": 17709016.26, \"Hospice Covered Days Per 1000 Beneficiaries\": 695.0, \"PQI10 Dehydration Admission Rate (age < 65)\": 265.0, \"PAC: SNF Per User Standardized Costs\": 15827.5, \"FQHC/RHC Actual Costs as % of Total Actual Costs\": 0.0054, \"PQI16 Lower Extremity Amputation Admission Rate (age < 65)\": 132.0, \"County\": \"STATE TOTAL\", \"Hospice Standardized Costs as % of Total Standardized Costs\": 0.0161, \"MA Participation Rate\": 4.61, \"OP Per Capita Standardized Costs\": 1163.33, \"Percent of Medicare beneficiaries with arthritis\": 22.03, \"Ambulance Per Capita Standardized Costs\": 46.67, \"PAC: HH Visits Per 1000 Beneficiaries\": 1405.0, \"Procedures Actual Costs as % of Total Actual Costs\": 0.0716, \"Imaging Actual Costs as % of Total Actual Costs\": 0.0196, \"PAC: HH Per Capita Actual Costs\": 192.0, \"E&M Events Per 1000 Beneficiaries\": 9195.0, \"Count of Medicare beneficiaries with asthma\": 2724.0, \"# Test Users\": 53987.0, \"E&M Actual Costs\": 47848642.3, \"% of Beneficiaries Using Imaging\": 0.5916, \"PAC: SNF Standardized Costs\": 50125704.02, \"DME Per Capita Actual Costs\": 253.38, \"E&M Per User Actual Costs\": 679.59, \"Percent Male\": 47.47, \"OP Visits Per 1000 Beneficiaries\": 3943.0, \"DME Actual Costs as % of Total Actual Costs\": 0.033, \"OP Per Capita Actual Costs\": 1317.87, \"Hospital Readmission Rate\": 0.1426, \"FQHC/RHC Standardized Costs as % of Total Standardized Costs\": 0.006, \"Tests Per Capita Standardized Costs\": 134.44, \"Hospice Per Capita Actual Costs\": 109.09, \"E&M Actual Costs as % of Total Actual Costs\": 0.0752, \"PQI07 Hypertension Admission Rate (age < 65)\": \"*\", \"Percent African American\": 0.58, \"PAC: HH Standardized Costs as % of Total Standardized Costs\": 0.029, \"PAC: LTCH Per Capita Actual Costs\": 50.14, \"MA Beneficiaries\": 4009.0, \"FQHC/RHC Per User Standardized Costs\": 379.26, \"PAC: HH Per User Standardized Costs\": 4644.25, \"Procedures Per User Actual Costs\": 970.78, \"PQI03 Diabetes LT Complication Admission Rate (age < 65)\": 397.0, \"Ambulance Per User Actual Costs\": 1329.88, \"Average Age\": 72.0, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 40-64)\": 1353.0, \"PAC: HH Standardized Costs\": 16496390.27, \"ASC Actual Costs as % of Total Actual Costs\": 0.0135, \"PQI05 COPD or Asthma in Older Adults Admission Rate (age 65-74)\": 716.0, \"% of Beneficiaries Using PAC: LTCH\": 0.0011, \"Percent Other/Unknown\": 2.82, \"% of Beneficiaries Using Hospice\": 0.0148, \"PAC: LTCH Per User Actual Costs\": 44702.25, \"Percent Non-Hispanic White\": 92.61, \"PQI11 Bacterial Pneumonia Admission Rate (age < 65)\": 883.0, \"Count of Medicare beneficiaries with breast cancer\": 1782.0, \"DME Per Capita Standardized Costs\": 261.04, \"PQI08 CHF Admission Rate (age 65-74)\": 398.0, \"% of Beneficiaries Using DME\": 0.273, \"Outpatient Dialysis Facility Actual Costs as % of Total Actual Costs\": 0.0111, \"% of Beneficiaries Using IP\": 0.1556, \"DME Standardized Costs\": 21643429.69, \"FQHC/RHC Standardized Costs\": 3433069.81, \"PAC: SNF Per Capita Actual Costs\": 575.1, \"Imaging Standardized Costs\": 12725156.36, \"# E&M Users\": 70408.0, \"Count of Medicare beneficiaries with arthritis\": 18264.0, \"IP Per User Standardized Costs\": 13875.55, \"IP Per User Actual Costs\": 18526.29, \"PQI10 Dehydration Admission Rate (age 75+)\": 436.0, \"PAC: IRF Per User Standardized Costs\": 19830.93, \"DME Per User Actual Costs\": 928.3, \"PAC: IRF Actual Costs\": 18195689.58, \"Imaging Events Per 1000 Beneficiaries\": 3013.0, \"Outpatient Dialysis Facility Standardized Costs as % of Total Standardized Costs\": 0.0127, \"PAC: LTCH Per User Standardized Costs\": 46988.66, \"OP Actual Costs\": 109265995.73, \"Count of Medicare beneficiaries with hypertension\": 31674.0, \"ASC Per User Standardized Costs\": 855.22, \"Part B Drugs Per Capita Actual Costs\": 231.68, \"Ambulance Events Per 1000 Beneficiaries\": 129.0}];" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import csv, json, xlrd, mmap\n", "from xls_dict_reader import XLSDictReader\n", "from IPython.display import Javascript\n", "from IPython.display import HTML\n", "\n", "xlsFile = open('Geographic_Healthcare_Data_States.xlsx','r')\n", "\n", "reader = XLSDictReader(xlsFile)\n", "out = json.dumps( [row for row in reader])\n", "\n", "javascript = 'window.data={};'.format(out);\n", "Javascript(javascript)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importing D3 for use in the javascript." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "require.config({\n", " paths: {\n", " d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min'\n", " }\n", "});" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript \n", "require.config({\n", " paths: {\n", " d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min'\n", " }\n", "});" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualization creation." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "application/javascript": [ "require(['d3'], function(d3) {\n", " //Control the size of the pie charts\n", " var wrapperWidth = 900;\n", " var wrapperHeight = 1200;\n", " \n", " //Clean-up in case you run this command multiple times\n", " while (element.firstChild) {\n", " element.removeChild(element.firstChild);\n", " }\n", " //Creation of HTML elements\n", " var dropDownDiv = document.createElement(\"div\");\n", " var wrapper = document.createElement(\"div\");\n", " wrapper.id = \"wrapper\";\n", " var wrapperCss = 'font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif; width:'+wrapperWidth.toString()+'px; height: '+wrapperHeight.toString()+'px; position: relative;';\n", " wrapper.style.cssText = wrapperCss;\n", " \n", " //sexPieChartDiv\n", " var sexVisualizationDiv = document.createElement(\"div\");\n", " sexVisualizationDiv.id = \"sexVisualizationDiv\";\n", " sexVisualizationDiv.style.height = (wrapperHeight/3).toString()+\"px\";\n", " sexVisualizationDiv.style.width = (wrapperWidth/2).toString()+\"px\";\n", " sexVisualizationDiv.style.display = \"inline-block\";\n", " \n", " //ethnicityPieChartDiv\n", " var ethnicityVisualizationDiv = document.createElement(\"div\");\n", " ethnicityVisualizationDiv.id = \"ethnicityVisualizationDiv\";\n", " ethnicityVisualizationDiv.style.height = (wrapperHeight/3).toString()+\"px\";\n", " ethnicityVisualizationDiv.style.width = (wrapperWidth/2).toString()+\"px\";\n", " ethnicityVisualizationDiv.style.display = \"inline-block\";\n", " \n", " //diseasePieChartDiv\n", " var diseaseVisualizationDiv = document.createElement(\"div\");\n", " diseaseVisualizationDiv.id = \"diseaseVisualizationDiv\";\n", " diseaseVisualizationDiv.style.height = (wrapperHeight*(2/3)).toString()+\"px\";\n", " diseaseVisualizationDiv.style.width = (wrapperWidth).toString()+\"px\";\n", " \n", " var dropDown = document.createElement('Select'); \n", " dropDown.id = 'dropDown';\n", " var option;\n", " for (var index in data) {\n", " if(!data.hasOwnProperty(index)) continue;\n", " option = new Option(data[index][\"State\"],data[index][\"State\"]);\n", " dropDown.options.add(option);\n", " }\n", " dropDownDiv.appendChild(dropDown);\n", " element.append(dropDownDiv);\n", " element.append(wrapper);\n", " wrapper.appendChild(sexVisualizationDiv);\n", " wrapper.appendChild(ethnicityVisualizationDiv);\n", " wrapper.appendChild(diseaseVisualizationDiv);\n", " \n", " //sexPieChart Creation\n", " var sexPieChartLabelsColors = {\n", " labels: [\"Male\", \"Female\"],\n", " colors: [\"#98abc5\", \"#8a89a6\"]};\n", " var sexPieChart = createPie(\"#sexVisualizationDiv\",wrapperWidth/2,wrapperHeight/3,sexPieChartLabelsColors);\n", " var sexPieChartSearch = ['Percent Male', 'Percent Female'];\n", " \n", " //ethnicityPieChart Creation\n", " var ethnicityPieChartLabelsColors = {\n", " labels: [\"White\", \"African American\",\"Hispanic\",\"Other\"],\n", " colors: [\"#7b6888\", \"#6b486b\", \"#a05d56\", \"#d0743c\"]};\n", " var ethnicityPieChart = createPie(\"#ethnicityVisualizationDiv\",wrapperWidth/2,wrapperHeight/3,ethnicityPieChartLabelsColors);\n", " var ethnicityPieChartSearch = ['Percent Non-Hispanic White', 'Percent African American', 'Percent Hispanic','Percent Other/Unknown'];\n", " \n", " //diseasePieChart Creation\n", " var diseasePieChartLabelsColors = {\n", " labels: [\"HA\", \"AF\",\"CKD\",\"OPD\",\"Depression\",\"Diabetes\",\"HF\",\"IHD\",\"BC\",\"CC\",\"LC\",\"PC\",\"Asthma\",\"HyperTension\",\"HC\",\"Arthritis\",\"Osteoporosis\",\"ARD\",\"Stroke\"],\n", " colors: ['#3182bd', '#6baed6', '#9ecae1', '#c6dbef',\n", " '#e6550d', '#fd8d3c', '#fdae6b', '#fdd0a2',\n", " '#31a354', '#74c476', '#a1d99b', '#c7e9c0',\n", " '#756bb1', '#9e9ac8', '#bcbddc', '#dadaeb',\n", " '#636363', '#969696', '#bdbdbd']};\n", " var diseasePieChart = createPie(\"#diseaseVisualizationDiv\",wrapperWidth,wrapperHeight*(2/3)+100,diseasePieChartLabelsColors);\n", " var diseasePieChartSearch = ['Percent of Medicare beneficiaries who have had a heart attack',\n", " \"Percent of Medicare beneficiaries with atrial fibrillation\",\n", " \"Percent of Medicare beneficiaries with chronic kidney disease\",\n", " \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\",\n", " \"Percent of Medicare beneficiaries with depression\",\n", " \"Percent of Medicare beneficiaries with diabetes\",\n", " \"Percent of Medicare beneficiaries with heart failure\",\n", " \"Percent Medicare beneficiaries with ischemic heart disease\",\n", " \"Percent of Medicare beneficiaries with breast cancer\",\n", " \"Percent of Medicare beneficiaries with colorectal cancer\",\n", " \"Percent of Medicare beneficiaries with lung cancer\",\n", " \"Percent of Medicare beneficiaries with prostate cancer\",\n", " \"Percent of Medicare beneficiaries with asthma\",\n", " \"Percent of Medicare beneficiaries with hypertension\",\n", " \"Percent of Medicare beneficiaries with high cholesterol\",\n", " \"Percent of Medicare beneficiaries with arthritis\",\n", " \"Percent of Medicare beneficiaries with osteoporosis\",\n", " \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\",\n", " \"Percent of Medicare beneficiaries with stroke\"]\n", " \n", " function generateData (pieChart, state, searchData){\n", " var labels = pieChart.color.domain();\n", " var stateData = data.filter( function(obj) {\n", " return obj[\"State\"] === state;\n", " })[0];\n", " var i = 0;\n", " return labels.map(function(label){\n", " var retVal = { label: label, value: stateData[searchData[i]]}\n", " i++;\n", " return retVal;\n", " });\n", " }\n", "\n", " //Initialization for National\n", " change(generateData(sexPieChart, \"National\", sexPieChartSearch), sexPieChart);\n", " change(generateData(ethnicityPieChart, \"National\", ethnicityPieChartSearch), ethnicityPieChart);\n", " change(generateData(diseasePieChart, \"National\", diseasePieChartSearch), diseasePieChart);\n", " \n", " d3.select(\"#dropDown\")\n", " .on(\"change\",function(){\n", " var dropDown = document.getElementById('dropDown');\n", " var state = dropDown[dropDown.selectedIndex].value;\n", " change(generateData(sexPieChart, state , sexPieChartSearch), sexPieChart);\n", " change(generateData(ethnicityPieChart, state, ethnicityPieChartSearch), ethnicityPieChart);\n", " change(generateData(diseasePieChart, state, diseasePieChartSearch), diseasePieChart);\n", " });\n", "\n", " var key = function(d){ return d.data.label; };\n", " \n", " function createPie(appendElement, pieWidth, pieHeight, labelsColor) {\n", " var svg = d3.select(appendElement)\n", " .append(\"svg\")\n", " .style(\"width\",\"100%\")\n", " .style(\"height\",\"100%\")\n", " .append(\"g\");\n", "\n", " svg.append(\"g\")\n", " .attr(\"class\", \"slices\");\n", " svg.append(\"g\")\n", " .attr(\"class\", \"labels\");\n", " svg.append(\"g\")\n", " .attr(\"class\", \"lines\");\n", "\n", " var width = pieWidth,\n", " height = pieHeight-150,\n", " radius = Math.min(width,height)/2;\n", "\n", " var pie = d3.layout.pie()\n", " .sort(null)\n", " .value(function(d) {\n", " return d.value;\n", " });\n", "\n", " var arc = d3.svg.arc()\n", " .outerRadius(radius * 0.8)\n", " .innerRadius(radius * 0.4);\n", "\n", " var outerArc = d3.svg.arc()\n", " .innerRadius(radius * 0.9)\n", " .outerRadius(radius * 0.9);\n", "\n", " svg.attr(\"transform\", \"translate(\" + width / 2 + \",\" + height / 2 + \")\");\n", "\n", " var color = d3.scale.ordinal()\n", " .domain(labelsColor.labels)\n", " .range(labelsColor.colors);\n", "\n", " return {\n", " svg: svg,\n", " radius: radius,\n", " pie: pie,\n", " arc: arc,\n", " outerArc: outerArc,\n", " color:color\n", " };\n", " }\n", "\n", " \n", " function change(data, pieChart) {\n", "\n", " /* ------- PIE SLICES -------*/\n", " var slice = pieChart.svg.select(\".slices\").selectAll(\"path.slice\")\n", " .data(pieChart.pie(data), key)\n", " .style(\"stroke-width\",\"2px\");\n", "\n", " slice.enter()\n", " .insert(\"path\")\n", " .style(\"fill\", function(d) { return pieChart.color(d.data.label); })\n", " .attr(\"class\", \"slice\");\n", "\n", " slice\n", " .transition().duration(1000)\n", " .attrTween(\"d\", function(d) {\n", " this._current = this._current || d;\n", " var interpolate = d3.interpolate(this._current, d);\n", " this._current = interpolate(0);\n", " return function(t) {\n", " return pieChart.arc(interpolate(t));\n", " };\n", " })\n", "\n", " slice.exit()\n", " .remove();\n", " \n", " /* ------- TEXT LABELS -------*/\n", " var i = 0;\n", " var text = pieChart.svg.select(\".labels\").selectAll(\"text\")\n", " .data(pieChart.pie(data), key);\n", " \n", " text.enter()\n", " .append(\"text\")\n", " .attr(\"y\", \".35em\")\n", " .text(function(d) {\n", " return d.data.label;\n", " });\n", "\n", " function midAngle(d){\n", " return d.startAngle + (d.endAngle - d.startAngle)/2;\n", " }\n", " text.transition().duration(1000)\n", " .attrTween(\"transform\", function(d) {\n", " var i = 0;\n", " this._current = this._current || d;\n", " var interpolate = d3.interpolate(this._current, d);\n", " this._current = interpolate(0);\n", " return function(t) {\n", " var d2 = interpolate(t);\n", " var pos = pieChart.outerArc.centroid(d2);\n", " //labelArray.push(pos);\n", " //Changing things slightly to fix overlapping. Not ideal but it works\n", " //pos[0] = pieChart.radius * (midAngle(d2) < Math.PI ? 1 : -1);\n", " return \"translate(\"+ pos +\")\";\n", " };\n", " })\n", " .styleTween(\"text-anchor\", function(d){\n", " this._current = this._current || d;\n", " var interpolate = d3.interpolate(this._current, d);\n", " this._current = interpolate(0);\n", " return function(t) {\n", " var d2 = interpolate(t);\n", " return midAngle(d2) < Math.PI ? \"start\":\"end\";\n", " };\n", " });\n", " d3.selectAll(\"#diseaseVisualizationDiv text\").each( function() {\n", " console.log(d3.select(this).attr(\"transhform\"));\n", " });\n", " text.exit()\n", " .remove();\n", " \n", " /* ------- SLICE TO TEXT POLYLINES -------*/\n", "\n", " var polyline = pieChart.svg.select(\".lines\").selectAll(\"polyline\")\n", " .data(pieChart.pie(data), key);\n", "\n", " polyline.enter()\n", " .append(\"polyline\")\n", " .style(\"opacity\",\".3\")\n", " .style(\"stroke\",\"black\")\n", " .style(\"stroke-width\",\"2px\")\n", " .style(\"fill\",\"none\");\n", "\n", " polyline.transition().duration(1000)\n", " .attrTween(\"points\", function(d){\n", " this._current = this._current || d;\n", " var interpolate = d3.interpolate(this._current, d);\n", " this._current = interpolate(0);\n", " return function(t) {\n", " var d2 = interpolate(t);\n", " var pos = pieChart.outerArc.centroid(d2);\n", " // Changing things slightly to fix overlapping. Not ideal but it works\n", " //pos[0] = pieChart.radius * 0.95 * (midAngle(d2) < Math.PI ? 1 : -1);\n", " return [pieChart.arc.centroid(d2), pieChart.outerArc.centroid(d2), pos];\n", " };\n", " });\n", " polyline.exit()\n", " .remove();\n", " }; \n", "});" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "require(['d3'], function(d3) {\n", " //Control the size of the pie charts\n", " var wrapperWidth = 900;\n", " var wrapperHeight = 1200;\n", " \n", " //Clean-up in case you run this command multiple times\n", " while (element.firstChild) {\n", " element.removeChild(element.firstChild);\n", " }\n", " //Creation of HTML elements\n", " var dropDownDiv = document.createElement(\"div\");\n", " var wrapper = document.createElement(\"div\");\n", " wrapper.id = \"wrapper\";\n", " var wrapperCss = 'font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif; width:'+wrapperWidth.toString()+'px; height: '+wrapperHeight.toString()+'px; position: relative;';\n", " wrapper.style.cssText = wrapperCss;\n", " \n", " //sexPieChartDiv\n", " var sexVisualizationDiv = document.createElement(\"div\");\n", " sexVisualizationDiv.id = \"sexVisualizationDiv\";\n", " sexVisualizationDiv.style.height = (wrapperHeight/3).toString()+\"px\";\n", " sexVisualizationDiv.style.width = (wrapperWidth/2).toString()+\"px\";\n", " sexVisualizationDiv.style.display = \"inline-block\";\n", " \n", " //ethnicityPieChartDiv\n", " var ethnicityVisualizationDiv = document.createElement(\"div\");\n", " ethnicityVisualizationDiv.id = \"ethnicityVisualizationDiv\";\n", " ethnicityVisualizationDiv.style.height = (wrapperHeight/3).toString()+\"px\";\n", " ethnicityVisualizationDiv.style.width = (wrapperWidth/2).toString()+\"px\";\n", " ethnicityVisualizationDiv.style.display = \"inline-block\";\n", " \n", " //diseasePieChartDiv\n", " var diseaseVisualizationDiv = document.createElement(\"div\");\n", " diseaseVisualizationDiv.id = \"diseaseVisualizationDiv\";\n", " diseaseVisualizationDiv.style.height = (wrapperHeight*(2/3)).toString()+\"px\";\n", " diseaseVisualizationDiv.style.width = (wrapperWidth).toString()+\"px\";\n", " \n", " var dropDown = document.createElement('Select'); \n", " dropDown.id = 'dropDown';\n", " var option;\n", " for (var index in data) {\n", " if(!data.hasOwnProperty(index)) continue;\n", " option = new Option(data[index][\"State\"],data[index][\"State\"]);\n", " dropDown.options.add(option);\n", " }\n", " dropDownDiv.appendChild(dropDown);\n", " element.append(dropDownDiv);\n", " element.append(wrapper);\n", " wrapper.appendChild(sexVisualizationDiv);\n", " wrapper.appendChild(ethnicityVisualizationDiv);\n", " wrapper.appendChild(diseaseVisualizationDiv);\n", " \n", " //sexPieChart Creation\n", " var sexPieChartLabelsColors = {\n", " labels: [\"Male\", \"Female\"],\n", " colors: [\"#98abc5\", \"#8a89a6\"]};\n", " var sexPieChart = createPie(\"#sexVisualizationDiv\",wrapperWidth/2,wrapperHeight/3,sexPieChartLabelsColors);\n", " var sexPieChartSearch = ['Percent Male', 'Percent Female'];\n", " \n", " //ethnicityPieChart Creation\n", " var ethnicityPieChartLabelsColors = {\n", " labels: [\"White\", \"African American\",\"Hispanic\",\"Other\"],\n", " colors: [\"#7b6888\", \"#6b486b\", \"#a05d56\", \"#d0743c\"]};\n", " var ethnicityPieChart = createPie(\"#ethnicityVisualizationDiv\",wrapperWidth/2,wrapperHeight/3,ethnicityPieChartLabelsColors);\n", " var ethnicityPieChartSearch = ['Percent Non-Hispanic White', 'Percent African American', 'Percent Hispanic','Percent Other/Unknown'];\n", " \n", " //diseasePieChart Creation\n", " var diseasePieChartLabelsColors = {\n", " labels: [\"HA\", \"AF\",\"CKD\",\"OPD\",\"Depression\",\"Diabetes\",\"HF\",\"IHD\",\"BC\",\"CC\",\"LC\",\"PC\",\"Asthma\",\"HyperTension\",\"HC\",\"Arthritis\",\"Osteoporosis\",\"ARD\",\"Stroke\"],\n", " colors: ['#3182bd', '#6baed6', '#9ecae1', '#c6dbef',\n", " '#e6550d', '#fd8d3c', '#fdae6b', '#fdd0a2',\n", " '#31a354', '#74c476', '#a1d99b', '#c7e9c0',\n", " '#756bb1', '#9e9ac8', '#bcbddc', '#dadaeb',\n", " '#636363', '#969696', '#bdbdbd']};\n", " var diseasePieChart = createPie(\"#diseaseVisualizationDiv\",wrapperWidth,wrapperHeight*(2/3)+100,diseasePieChartLabelsColors);\n", " var diseasePieChartSearch = ['Percent of Medicare beneficiaries who have had a heart attack',\n", " \"Percent of Medicare beneficiaries with atrial fibrillation\",\n", " \"Percent of Medicare beneficiaries with chronic kidney disease\",\n", " \"Percent of Medicare beneficiaries with chronic obstructive pulmonary disease\",\n", " \"Percent of Medicare beneficiaries with depression\",\n", " \"Percent of Medicare beneficiaries with diabetes\",\n", " \"Percent of Medicare beneficiaries with heart failure\",\n", " \"Percent Medicare beneficiaries with ischemic heart disease\",\n", " \"Percent of Medicare beneficiaries with breast cancer\",\n", " \"Percent of Medicare beneficiaries with colorectal cancer\",\n", " \"Percent of Medicare beneficiaries with lung cancer\",\n", " \"Percent of Medicare beneficiaries with prostate cancer\",\n", " \"Percent of Medicare beneficiaries with asthma\",\n", " \"Percent of Medicare beneficiaries with hypertension\",\n", " \"Percent of Medicare beneficiaries with high cholesterol\",\n", " \"Percent of Medicare beneficiaries with arthritis\",\n", " \"Percent of Medicare beneficiaries with osteoporosis\",\n", " \"Percent of Medicare beneficiaries with Alzheimer's and related disorders\",\n", " \"Percent of Medicare beneficiaries with stroke\"]\n", " \n", " function generateData (pieChart, state, searchData){\n", " var labels = pieChart.color.domain();\n", " var stateData = data.filter( function(obj) {\n", " return obj[\"State\"] === state;\n", " })[0];\n", " var i = 0;\n", " return labels.map(function(label){\n", " var retVal = { label: label, value: stateData[searchData[i]]}\n", " i++;\n", " return retVal;\n", " });\n", " }\n", "\n", " //Initialization for National\n", " change(generateData(sexPieChart, \"National\", sexPieChartSearch), sexPieChart);\n", " change(generateData(ethnicityPieChart, \"National\", ethnicityPieChartSearch), ethnicityPieChart);\n", " change(generateData(diseasePieChart, \"National\", diseasePieChartSearch), diseasePieChart);\n", " \n", " d3.select(\"#dropDown\")\n", " .on(\"change\",function(){\n", " var dropDown = document.getElementById('dropDown');\n", " var state = dropDown[dropDown.selectedIndex].value;\n", " change(generateData(sexPieChart, state , sexPieChartSearch), sexPieChart);\n", " change(generateData(ethnicityPieChart, state, ethnicityPieChartSearch), ethnicityPieChart);\n", " change(generateData(diseasePieChart, state, diseasePieChartSearch), diseasePieChart);\n", " });\n", "\n", " var key = function(d){ return d.data.label; };\n", " \n", " function createPie(appendElement, pieWidth, pieHeight, labelsColor) {\n", " var svg = d3.select(appendElement)\n", " .append(\"svg\")\n", " .style(\"width\",\"100%\")\n", " .style(\"height\",\"100%\")\n", " .append(\"g\");\n", "\n", " svg.append(\"g\")\n", " .attr(\"class\", \"slices\");\n", " svg.append(\"g\")\n", " .attr(\"class\", \"labels\");\n", " svg.append(\"g\")\n", " .attr(\"class\", \"lines\");\n", "\n", " var width = pieWidth,\n", " height = pieHeight-150,\n", " radius = Math.min(width,height)/2;\n", "\n", " var pie = d3.layout.pie()\n", " .sort(null)\n", " .value(function(d) {\n", " return d.value;\n", " });\n", "\n", " var arc = d3.svg.arc()\n", " .outerRadius(radius * 0.8)\n", " .innerRadius(radius * 0.4);\n", "\n", " var outerArc = d3.svg.arc()\n", " .innerRadius(radius * 0.9)\n", " .outerRadius(radius * 0.9);\n", "\n", " svg.attr(\"transform\", \"translate(\" + width / 2 + \",\" + height / 2 + \")\");\n", "\n", " var color = d3.scale.ordinal()\n", " .domain(labelsColor.labels)\n", " .range(labelsColor.colors);\n", "\n", " return {\n", " svg: svg,\n", " radius: radius,\n", " pie: pie,\n", " arc: arc,\n", " outerArc: outerArc,\n", " color:color\n", " };\n", " }\n", "\n", " \n", " function change(data, pieChart) {\n", "\n", " /* ------- PIE SLICES -------*/\n", " var slice = pieChart.svg.select(\".slices\").selectAll(\"path.slice\")\n", " .data(pieChart.pie(data), key)\n", " .style(\"stroke-width\",\"2px\");\n", "\n", " slice.enter()\n", " .insert(\"path\")\n", " .style(\"fill\", function(d) { return pieChart.color(d.data.label); })\n", " .attr(\"class\", \"slice\");\n", "\n", " slice\n", " .transition().duration(1000)\n", " .attrTween(\"d\", function(d) {\n", " this._current = this._current || d;\n", " var interpolate = d3.interpolate(this._current, d);\n", " this._current = interpolate(0);\n", " return function(t) {\n", " return pieChart.arc(interpolate(t));\n", " };\n", " })\n", "\n", " slice.exit()\n", " .remove();\n", " \n", " /* ------- TEXT LABELS -------*/\n", " var i = 0;\n", " var text = pieChart.svg.select(\".labels\").selectAll(\"text\")\n", " .data(pieChart.pie(data), key);\n", " \n", " text.enter()\n", " .append(\"text\")\n", " .attr(\"y\", \".35em\")\n", " .text(function(d) {\n", " return d.data.label;\n", " });\n", "\n", " function midAngle(d){\n", " return d.startAngle + (d.endAngle - d.startAngle)/2;\n", " }\n", " text.transition().duration(1000)\n", " .attrTween(\"transform\", function(d) {\n", " var i = 0;\n", " this._current = this._current || d;\n", " var interpolate = d3.interpolate(this._current, d);\n", " this._current = interpolate(0);\n", " return function(t) {\n", " var d2 = interpolate(t);\n", " var pos = pieChart.outerArc.centroid(d2);\n", " //labelArray.push(pos);\n", " //Changing things slightly to fix overlapping. Not ideal but it works\n", " //pos[0] = pieChart.radius * (midAngle(d2) < Math.PI ? 1 : -1);\n", " return \"translate(\"+ pos +\")\";\n", " };\n", " })\n", " .styleTween(\"text-anchor\", function(d){\n", " this._current = this._current || d;\n", " var interpolate = d3.interpolate(this._current, d);\n", " this._current = interpolate(0);\n", " return function(t) {\n", " var d2 = interpolate(t);\n", " return midAngle(d2) < Math.PI ? \"start\":\"end\";\n", " };\n", " });\n", " text.exit()\n", " .remove();\n", " \n", " /* ------- SLICE TO TEXT POLYLINES -------*/\n", "\n", " var polyline = pieChart.svg.select(\".lines\").selectAll(\"polyline\")\n", " .data(pieChart.pie(data), key);\n", "\n", " polyline.enter()\n", " .append(\"polyline\")\n", " .style(\"opacity\",\".3\")\n", " .style(\"stroke\",\"black\")\n", " .style(\"stroke-width\",\"2px\")\n", " .style(\"fill\",\"none\");\n", "\n", " polyline.transition().duration(1000)\n", " .attrTween(\"points\", function(d){\n", " this._current = this._current || d;\n", " var interpolate = d3.interpolate(this._current, d);\n", " this._current = interpolate(0);\n", " return function(t) {\n", " var d2 = interpolate(t);\n", " var pos = pieChart.outerArc.centroid(d2);\n", " // Changing things slightly to fix overlapping. Not ideal but it works\n", " //pos[0] = pieChart.radius * 0.95 * (midAngle(d2) < Math.PI ? 1 : -1);\n", " return [pieChart.arc.centroid(d2), pieChart.outerArc.centroid(d2), pos];\n", " };\n", " });\n", " polyline.exit()\n", " .remove();\n", " }; \n", "});" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Quick Explanation of the Data:\n", "##### Chart 1 (Top Left):\n", "This pie chart represents the percentages of the seperate genders of the medicare beneficaries in either the nation or the selected state.\n", "\n", "##### Chart 2 (Top Right):\n", "This pie chart shows the percentages of the seperate ethnicities of the medicare beneficaries in either the nation or the selected state. \n", "\n", "##### Chart 3 (Bottom):\n", "This pie chart represents the percentage of each seperate disease that affected the medicare beneficaries in either the nation or the selected state. \n", "\n", "Acronyms used in Chart:\n", "* __HA__ (Heart Attack), __AF__ (Atrial Fibrillation), __CKD__ (Chronic Kidney Disease), __OPD__ (Obstructive Pulmonary Disease), __HF__ (Hear Failure), __IHD__ (Ischemic Heart Disease), __BC__ (Breast Cancer), __CC__ (Colorectal Cancer), __LC__ (Lung Cancer), __PC__ (Prostate Cancer), __HC__ (High Cholesteral), __ARD__ (Alzheimer's and Related Diseases)\n", "\n", "#### Additional Notes:\n", "I was planning on creating a pie chart for costs as well (represented in the excel file sheet), but after completing the first three I felt they did a well enough job at getting the point across I was trying to convey. On another note I probably spent the longest amount of time trying to fix overlapping labels with frustratingly no success. Ended up having to comment out some lines to get minimum overlapping. Sadly this took away from the pie chart design I originally was shooting for." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ES-DOC/esdoc-jupyterhub

notebooks/niwa/cmip6/models/sandbox-1/land.ipynb

1

173498

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "**MIP Era**: CMIP6 \n", "**Institute**: NIWA \n", "**Source ID**: SANDBOX-1 \n", "**Topic**: Land \n", "**Sub-Topics**: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n", "**Properties**: 154 (96 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/land?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:30" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'niwa', 'sandbox-1', 'land')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Conservation Properties](#2.-Key-Properties---&gt;-Conservation-Properties) \n", "[3. Key Properties --&gt; Timestepping Framework](#3.-Key-Properties---&gt;-Timestepping-Framework) \n", "[4. Key Properties --&gt; Software Properties](#4.-Key-Properties---&gt;-Software-Properties) \n", "[5. Grid](#5.-Grid) \n", "[6. Grid --&gt; Horizontal](#6.-Grid---&gt;-Horizontal) \n", "[7. Grid --&gt; Vertical](#7.-Grid---&gt;-Vertical) \n", "[8. Soil](#8.-Soil) \n", "[9. Soil --&gt; Soil Map](#9.-Soil---&gt;-Soil-Map) \n", "[10. Soil --&gt; Snow Free Albedo](#10.-Soil---&gt;-Snow-Free-Albedo) \n", "[11. Soil --&gt; Hydrology](#11.-Soil---&gt;-Hydrology) \n", "[12. Soil --&gt; Hydrology --&gt; Freezing](#12.-Soil---&gt;-Hydrology---&gt;-Freezing) \n", "[13. Soil --&gt; Hydrology --&gt; Drainage](#13.-Soil---&gt;-Hydrology---&gt;-Drainage) \n", "[14. Soil --&gt; Heat Treatment](#14.-Soil---&gt;-Heat-Treatment) \n", "[15. Snow](#15.-Snow) \n", "[16. Snow --&gt; Snow Albedo](#16.-Snow---&gt;-Snow-Albedo) \n", "[17. Vegetation](#17.-Vegetation) \n", "[18. Energy Balance](#18.-Energy-Balance) \n", "[19. Carbon Cycle](#19.-Carbon-Cycle) \n", "[20. Carbon Cycle --&gt; Vegetation](#20.-Carbon-Cycle---&gt;-Vegetation) \n", "[21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis](#21.-Carbon-Cycle---&gt;-Vegetation---&gt;-Photosynthesis) \n", "[22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration](#22.-Carbon-Cycle---&gt;-Vegetation---&gt;-Autotrophic-Respiration) \n", "[23. Carbon Cycle --&gt; Vegetation --&gt; Allocation](#23.-Carbon-Cycle---&gt;-Vegetation---&gt;-Allocation) \n", "[24. Carbon Cycle --&gt; Vegetation --&gt; Phenology](#24.-Carbon-Cycle---&gt;-Vegetation---&gt;-Phenology) \n", "[25. Carbon Cycle --&gt; Vegetation --&gt; Mortality](#25.-Carbon-Cycle---&gt;-Vegetation---&gt;-Mortality) \n", "[26. Carbon Cycle --&gt; Litter](#26.-Carbon-Cycle---&gt;-Litter) \n", "[27. Carbon Cycle --&gt; Soil](#27.-Carbon-Cycle---&gt;-Soil) \n", "[28. Carbon Cycle --&gt; Permafrost Carbon](#28.-Carbon-Cycle---&gt;-Permafrost-Carbon) \n", "[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n", "[30. River Routing](#30.-River-Routing) \n", "[31. River Routing --&gt; Oceanic Discharge](#31.-River-Routing---&gt;-Oceanic-Discharge) \n", "[32. Lakes](#32.-Lakes) \n", "[33. Lakes --&gt; Method](#33.-Lakes---&gt;-Method) \n", "[34. Lakes --&gt; Wetlands](#34.-Lakes---&gt;-Wetlands) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Land surface key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of land surface model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of land surface model code (e.g. MOSES2.2)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Land Atmosphere Flux Exchanges\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Fluxes exchanged with the atmopshere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"water\" \n", "# \"energy\" \n", "# \"carbon\" \n", "# \"nitrogen\" \n", "# \"phospherous\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Atmospheric Coupling Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Land Cover\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Types of land cover defined in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bare soil\" \n", "# \"urban\" \n", "# \"lake\" \n", "# \"land ice\" \n", "# \"lake ice\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Land Cover Change\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how land cover change is managed (e.g. the use of net or gross transitions)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover_change') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Tiling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Conservation Properties \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Energy\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Water\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestepping Framework \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dependent On Atmosphere\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a time step dependent on the frequency of atmosphere coupling?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overall timestep of land surface model (i.e. time between calls)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Timestepping Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of time stepping method and associated time step(s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Software Properties \n", "*Software properties of land surface code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Grid \n", "*Land surface grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --&gt; Horizontal \n", "*The horizontal grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the horizontal grid (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Matches Atmosphere Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the horizontal grid match the atmosphere?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --&gt; Vertical \n", "*The vertical grid in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the vertical grid in the soil (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Total Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The total depth of the soil (in metres)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.total_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Soil \n", "*Land surface soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of soil in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Heat Water Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the coupling between heat and water in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_water_coupling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Number Of Soil layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.number_of_soil layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the soil scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Soil --&gt; Soil Map \n", "*Key properties of the land surface soil map*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of soil map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Structure\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil structure map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.structure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Texture\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil texture map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.texture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Organic Matter\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil organic matter map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Albedo\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil albedo map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Water Table\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil water table map, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.water_table') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.7. Continuously Varying Soil Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the soil properties vary continuously with depth?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.8. Soil Depth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil depth map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Soil --&gt; Snow Free Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow free albedo prognostic?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, describe the dependancies on snow free albedo calculations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"soil humidity\" \n", "# \"vegetation state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Direct Diffuse\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, describe the distinction between direct and diffuse albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"distinction between direct and diffuse albedo\" \n", "# \"no distinction between direct and diffuse albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Number Of Wavelength Bands\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, enter the number of wavelength bands used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Soil --&gt; Hydrology \n", "*Key properties of the land surface soil hydrology*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the soil hydrological model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river soil hydrology in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil hydrology tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Number Of Ground Water Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers that may contain water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Lateral Connectivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe the lateral connectivity between tiles*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"perfect connectivity\" \n", "# \"Darcian flow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.7. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The hydrological dynamics scheme in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bucket\" \n", "# \"Force-restore\" \n", "# \"Choisnel\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Soil --&gt; Hydrology --&gt; Freezing \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Number Of Ground Ice Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How many soil layers may contain ground ice*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Ice Storage Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of ice storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Permafrost\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of permafrost, if any, within the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Soil --&gt; Hydrology --&gt; Drainage \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General describe how drainage is included in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Different types of runoff represented by the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gravity drainage\" \n", "# \"Horton mechanism\" \n", "# \"topmodel-based\" \n", "# \"Dunne mechanism\" \n", "# \"Lateral subsurface flow\" \n", "# \"Baseflow from groundwater\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Soil --&gt; Heat Treatment \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of how heat treatment properties are defined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of soil heat scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil heat treatment tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Heat Storage\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the method of heat storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Force-restore\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe processes included in the treatment of soil heat*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"soil moisture freeze-thaw\" \n", "# \"coupling with snow temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Snow \n", "*Land surface snow*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of snow in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Number Of Snow Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of snow levels used in the land surface scheme/model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.number_of_snow_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Density\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow density*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Water Equivalent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the snow water equivalent*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.water_equivalent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Heat Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the heat content of snow*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.heat_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.7. Temperature\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow temperature*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.temperature') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.8. Liquid Water Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow liquid water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.liquid_water_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.9. Snow Cover Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify cover fractions used in the surface snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ground snow fraction\" \n", "# \"vegetation snow fraction\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.10. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Snow related processes in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"snow interception\" \n", "# \"snow melting\" \n", "# \"snow freezing\" \n", "# \"blowing snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.11. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Snow --&gt; Snow Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of snow-covered land albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"prescribed\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"snow age\" \n", "# \"snow density\" \n", "# \"snow grain type\" \n", "# \"aerosol deposition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Vegetation \n", "*Land surface vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of vegetation in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of vegetation scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Dynamic Vegetation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there dynamic evolution of vegetation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.4. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vegetation tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.5. Vegetation Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Vegetation classification used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation types\" \n", "# \"biome types\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.6. Vegetation Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of vegetation types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"broadleaf tree\" \n", "# \"needleleaf tree\" \n", "# \"C3 grass\" \n", "# \"C4 grass\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.7. Biome Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of biome types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biome_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"evergreen needleleaf forest\" \n", "# \"evergreen broadleaf forest\" \n", "# \"deciduous needleleaf forest\" \n", "# \"deciduous broadleaf forest\" \n", "# \"mixed forest\" \n", "# \"woodland\" \n", "# \"wooded grassland\" \n", "# \"closed shrubland\" \n", "# \"opne shrubland\" \n", "# \"grassland\" \n", "# \"cropland\" \n", "# \"wetlands\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.8. Vegetation Time Variation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How the vegetation fractions in each tile are varying with time*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed (not varying)\" \n", "# \"prescribed (varying from files)\" \n", "# \"dynamical (varying from simulation)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.9. Vegetation Map\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.10. Interception\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is vegetation interception of rainwater represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.interception') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.11. Phenology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic (vegetation map)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.12. Phenology Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.13. Leaf Area Index\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.14. Leaf Area Index Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.15. Biomass\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biomass *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.16. Biomass Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biomass*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.17. Biogeography\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.18. Biogeography Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.19. Stomatal Resistance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify what the vegetation stomatal resistance depends on*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"light\" \n", "# \"temperature\" \n", "# \"water availability\" \n", "# \"CO2\" \n", "# \"O3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.20. Stomatal Resistance Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation stomatal resistance*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.21. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the vegetation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Energy Balance \n", "*Land surface energy balance*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of energy balance in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the energy balance tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Number Of Surface Temperatures\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.4. Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify the formulation method for land surface evaporation, from soil and vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"alpha\" \n", "# \"beta\" \n", "# \"combined\" \n", "# \"Monteith potential evaporation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.5. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe which processes are included in the energy balance scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"transpiration\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Carbon Cycle \n", "*Land surface carbon cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of carbon cycle in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the carbon cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of carbon cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Anthropogenic Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Describe the treament of the anthropogenic carbon pool*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grand slam protocol\" \n", "# \"residence time\" \n", "# \"decay time\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the carbon scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Carbon Cycle --&gt; Vegetation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Forest Stand Dynamics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of forest stand dyanmics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Maintainance Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for maintainence respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Growth Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for growth respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Carbon Cycle --&gt; Vegetation --&gt; Allocation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the allocation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Allocation Bins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify distinct carbon bins used in allocation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"leaves + stems + roots\" \n", "# \"leaves + stems + roots (leafy + woody)\" \n", "# \"leaves + fine roots + coarse roots + stems\" \n", "# \"whole plant (no distinction)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Allocation Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the fractions of allocation are calculated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"function of vegetation type\" \n", "# \"function of plant allometry\" \n", "# \"explicitly calculated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Carbon Cycle --&gt; Vegetation --&gt; Phenology \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the phenology scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Carbon Cycle --&gt; Vegetation --&gt; Mortality \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the mortality scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Carbon Cycle --&gt; Litter \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Carbon Cycle --&gt; Soil \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Carbon Cycle --&gt; Permafrost Carbon \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Is Permafrost Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is permafrost included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.2. Emitted Greenhouse Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the GHGs emitted*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.4. Impact On Soil Properties\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the impact of permafrost on soil properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Nitrogen Cycle \n", "*Land surface nitrogen cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the nitrogen cycle in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the notrogen cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of nitrogen cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the nitrogen scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. River Routing \n", "*Land surface river routing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of river routing in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the river routing, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river routing scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Grid Inherited From Land Surface\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the grid inherited from land surface?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.5. Grid Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of grid, if not inherited from land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.6. Number Of Reservoirs\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of reservoirs*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.7. Water Re Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *TODO*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"flood plains\" \n", "# \"irrigation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.8. Coupled To Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Is river routing coupled to the atmosphere model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.9. Coupled To Land\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the coupling between land and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.10. Quantities Exchanged With Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.11. Basin Flow Direction Map\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of basin flow direction map is being used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"adapted for other periods\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.12. Flooding\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the representation of flooding, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.flooding') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.13. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the river routing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. River Routing --&gt; Oceanic Discharge \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Discharge Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify how rivers are discharged to the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"direct (large rivers)\" \n", "# \"diffuse\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Quantities Transported\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Quantities that are exchanged from river-routing to the ocean model component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Lakes \n", "*Land surface lakes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of lakes in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Coupling With Rivers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are lakes coupled to the river routing model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of lake scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Quantities Exchanged With Rivers\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If coupling with rivers, which quantities are exchanged between the lakes and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Vertical Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vertical grid of lakes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.vertical_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the lake scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Lakes --&gt; Method \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Ice Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is lake ice included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.ice_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of lake albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Dynamics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which dynamics of lakes are treated? horizontal, vertical, etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No lake dynamics\" \n", "# \"vertical\" \n", "# \"horizontal\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Dynamic Lake Extent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a dynamic lake extent scheme included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.5. Endorheic Basins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basins not flowing to ocean included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Lakes --&gt; Wetlands \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of wetlands, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.wetlands.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

gpl-3.0

DRVV/jupyter-notebooks

.ipynb_checkpoints/PoissonProcesses-checkpoint.ipynb

1

66956

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import random" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16.666666666666668" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2000/120" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "14.234746363126334" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "random.expovariate(1/40)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "epochs = []\n", "for i in range(2000):\n", " epochs.append(random.expovariate(2000/120))\n", " \n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[0.038188958937845636,\n", " 0.04567362240087953,\n", " 0.05444850366597455,\n", " 0.12245184891851517,\n", " 0.021401618833711233,\n", " 0.04579854492860237,\n", " 0.007110259002255721,\n", " 0.09667307525675924,\n", " 0.26409947205295187,\n", " 0.005945439891086437,\n", " 0.0009619366583299904,\n", " 0.035586872999171036,\n", " 0.07822278682639569,\n", " 0.049851819160784676,\n", " 0.02746496258797135,\n", " 0.09500878253483946,\n", " 0.036933884048088894,\n", " 0.06675183568157819,\n", " 0.11330647300814511,\n", " 0.04092748687582202,\n", " 0.023600054527223004,\n", " 0.015652970934719772,\n", " 0.03825444769790678,\n", " 0.13844929405473877,\n", " 0.012197723011375877,\n", " 0.07752151394217702,\n", " 0.005915189050950566,\n", " 0.010027365692421731,\n", " 0.03438234914761147,\n", " 0.19755828570598824,\n", " 0.028497102111501835,\n", " 0.06923819617151812,\n", " 0.04332967694683593,\n", " 0.0188858468385757,\n", " 0.018931863645574473,\n", " 0.07615518075291364,\n", " 0.06401821864732672,\n", " 0.004552828306070285,\n", " 0.07522022451356482,\n", " 0.03586838673166861,\n", " 0.007568866548640607,\n", " 0.08342788047493226,\n", " 0.11177490379599342,\n", " 0.11115586956465129,\n", " 0.010204821964346597,\n", " 0.11783086498310517,\n", " 0.026134297878853522,\n", " 0.08168254674813108,\n", " 0.023931131929794593,\n", " 0.0799133603658837,\n", " 0.3096472984904035,\n", " 0.03767123554027636,\n", " 0.03576110446858449,\n", " 0.05185472198845367,\n", " 0.0022718051768604417,\n", " 0.011141543285863132,\n", " 0.011842848485165806,\n", " 0.05430697975425911,\n", " 0.01949856808745779,\n", " 0.01147459037496924,\n", " 0.05054151448417052,\n", " 0.021311270100826007,\n", " 0.034573751781648177,\n", " 0.04628338516217519,\n", " 0.01497735181230834,\n", " 0.018656016804417575,\n", " 0.06242320846585585,\n", " 0.1468806715896249,\n", " 0.005733595035705134,\n", " 0.07721537446148609,\n", " 0.176275074084628,\n", " 0.27559094880045404,\n", " 0.09065508223415361,\n", " 0.20562504308627977,\n", " 0.12545456615981415,\n", " 0.005199778006593633,\n", " 0.07022974816975892,\n", " 0.0006751305525807848,\n", " 0.0011533710845561807,\n", " 0.07054068621235064,\n", " 0.04184031236326555,\n", " 0.05080661987033239,\n", " 0.016705510648276984,\n", " 0.03948989303960003,\n", " 0.10606491103248257,\n", " 0.03754184199646285,\n", " 0.012621094089322498,\n", " 0.08790225704764129,\n", " 0.01675820004985765,\n", " 0.15219842226604172,\n", " 0.029805734083123186,\n", " 0.0325265527671848,\n", " 0.09368390621710242,\n", " 0.008066043186955175,\n", " 0.005512774035941801,\n", " 0.0495245685856797,\n", " 0.005656245268188658,\n", " 0.23881225627822103,\n", " 0.008146012765641066,\n", " 0.006980811268653613,\n", " 0.014370975829561546,\n", " 0.042041264520958826,\n", " 0.0011549420692363783,\n", " 0.004864022989016169,\n", " 0.05415089500252503,\n", " 0.012783561082890658,\n", " 0.07736614862072028,\n", " 0.02164219523827658,\n", " 0.061172427002665876,\n", " 0.006934701226923573,\n", " 0.018548176420831862,\n", " 0.09185692559157506,\n", " 0.03111145440197742,\n", " 0.06161215648199556,\n", " 0.0037180310730833793,\n", " 0.022525498632344213,\n", " 0.03833925704142594,\n", " 0.007634278569911308,\n", " 0.10231164988640078,\n", " 0.005629456091240166,\n", " 0.011023385341009108,\n", " 0.006575964873562368,\n", " 0.06820778806481807,\n", " 0.03146982446847058,\n", " 0.01866217522552457,\n", " 0.11220591307897279,\n", " 0.07635954051579552,\n", " 0.02407698717176014,\n", " 0.06415468534590964,\n", " 0.010936649098921815,\n", " 0.10967161131819815,\n", " 0.18393804499697122,\n", " 0.000692792127611996,\n", " 0.0020014457388260417,\n", " 0.0026104475147190093,\n", " 0.10284761836202931,\n", " 0.1494010670304919,\n", " 0.005193324167342477,\n", " 0.01343155894399322,\n", " 0.02801869793329833,\n", " 0.003338714637993629,\n", " 0.024365927024013925,\n", " 0.012377729519785023,\n", " 0.044148630463068515,\n", " 0.03825193861492199,\n", " 0.037180177194533294,\n", " 0.07377881700933839,\n", " 0.21553131601930126,\n", " 0.2512575488872711,\n", " 0.025204070218241587,\n", " 0.20422772990920302,\n", " 0.03499976346970192,\n", " 0.015998073516035424,\n", " 0.2679265625685119,\n", " 0.15955630215764638,\n", " 0.10828712339913474,\n", " 0.05697122230625198,\n", " 0.05656919603477219,\n", " 0.04682116555786874,\n", " 0.07277593969681652,\n", " 0.009027620344443352,\n", " 0.04395157475085556,\n", " 0.04771138162844278,\n", " 0.0001815376728134051,\n", " 0.1423773851546034,\n", " 0.0769762444188212,\n", " 0.037813540966962336,\n", " 0.005174712088568227,\n", " 0.07733441469000046,\n", " 0.09826365208943051,\n", " 0.014632972779955813,\n", " 0.15219887963374681,\n", " 0.0743750959592783,\n", " 0.05310993676327006,\n", " 0.051486632172834515,\n", " 0.029690256092089713,\n", " 0.019962073198138024,\n", " 0.03691286502117878,\n", " 0.11223949003332206,\n", " 0.107717052031977,\n", " 0.020649384024416857,\n", " 0.07538591075864388,\n", " 0.05005111438751669,\n", " 0.0047372196143366735,\n", " 0.039829833227374094,\n", " 0.16049399852363141,\n", " 0.04626907264792853,\n", " 0.12533832262553316,\n", " 0.1656743283432876,\n", " 0.1353336826004452,\n", " 0.110834466610978,\n", " 0.1995684188462435,\n", " 0.03739638582978376,\n", " 0.002739439650320624,\n", " 0.04278988967171726,\n", " 0.06478787312012493,\n", " 0.05576991415760287,\n", " 0.2346460159166563,\n", " 0.006210744577743716,\n", " 0.08690730400471107,\n", " 0.025069099449118726,\n", " 0.0336545581214146,\n", " 0.05091470638401613,\n", " 0.03721175006975256,\n", " 0.06113281223862663,\n", " 0.04698048349819118,\n", " 0.0817602067928795,\n", " 0.017098148396682677,\n", " 0.016007262241550916,\n", " 0.00955086105083056,\n", " 0.16427653528085673,\n", " 0.04892284522107832,\n", " 0.00866511163322911,\n", " 0.09434074322329061,\n", " 0.00258292163718381,\n", " 0.04438101874397026,\n", " 0.02848360371452731,\n", " 0.029799346141369133,\n", " 0.2477439802721951,\n", " 0.005088855503978442,\n", " 0.08775622325342987,\n", " 0.0001336517479799692,\n", " 0.010701292766487286,\n", " 0.047893985619831436,\n", " 0.10626038885747109,\n", " 0.09564884530225613,\n", " 0.09991899554732546,\n", " 0.10605290796155657,\n", " 0.09012669332019767,\n", " 0.010221768324162519,\n", " 0.02761626762808391,\n", " 0.02169154868266972,\n", " 0.075250015229927,\n", " 0.1767178137511029,\n", " 0.04343624769276975,\n", " 0.019580787018325385,\n", " 0.01275646981808337,\n", " 0.03195033348681586,\n", " 0.2096531159868549,\n", " 0.03597412428319374,\n", " 0.0698791107680573,\n", " 0.05456333676123745,\n", " 0.03347370863971854,\n", " 0.02186325066075193,\n", " 0.08656306167687737,\n", " 0.007301093984500821,\n", " 0.06279661811981546,\n", " 0.023472799762869194,\n", " 0.07489530924755783,\n", " 0.0008914402462160901,\n", " 0.01733561973602962,\n", " 0.2105291652951954,\n", " 0.005026439633783395,\n", " 0.043918686997461036,\n", " 0.10547904478105166,\n", " 0.028006348596203223,\n", " 0.005936935013168338,\n", " 0.05585966424398998,\n", " 0.003057392081090225,\n", " 0.017315678629453678,\n", " 0.03389628526222826,\n", " 0.01084413613228243,\n", " 0.07573916517321506,\n", " 2.6588362965357223e-05,\n", " 0.17166473513154135,\n", " 0.018127102597425993,\n", " 0.11235888185091324,\n", " 0.10270255006756761,\n", " 0.038732059455499616,\n", " 0.04308993019920674,\n", " 0.05518758018441053,\n", " 0.08324036907023177,\n", " 0.06292455767764446,\n", " 0.06143924449620759,\n", " 0.009112311131908005,\n", " 0.03313837334403605,\n", " 0.051271465048606024,\n", " 0.4775815012947525,\n", " 0.05856998639921078,\n", " 0.06279066331032927,\n", " 0.05459638933900067,\n", " 5.098662359162552e-05,\n", " 0.2372404489765313,\n", " 0.06615291844490878,\n", " 0.2533933383539444,\n", " 0.12683700719791435,\n", " 0.018760153294028463,\n", " 0.08824404044432943,\n", " 0.021401192364407243,\n", " 0.1812902648193767,\n", " 0.5477679013847037,\n", " 0.003139262363153855,\n", " 0.03977392139985515,\n", " 0.15873206826110792,\n", " 0.054185912785326036,\n", " 0.0446406414582013,\n", " 0.11176184820961438,\n", " 0.09990071532883756,\n", " 0.07677693767177889,\n", " 0.007211307232568504,\n", " 0.03001077969236259,\n", " 0.018382257558803024,\n", " 0.040071667147314936,\n", " 0.07039698043909308,\n", " 0.03241836332104694,\n", " 0.0216698734205629,\n", " 0.04292239534476124,\n", " 0.010175920092400405,\n", " 0.016334247745736483,\n", " 0.2298804431215361,\n", " 0.059820058700494796,\n", " 0.09254531065213238,\n", " 0.0032710539449418673,\n", " 0.0940527769487869,\n", " 0.02157611823314414,\n", " 0.09963688453619167,\n", " 0.0008355169554250321,\n", " 0.02649597263749392,\n", " 0.014329185631902216,\n", " 0.008229363461702341,\n", " 0.042491223951702614,\n", " 0.0002431405925546625,\n", " 0.013286513905873442,\n", " 0.05758535561484568,\n", " 0.018501715057361413,\n", " 0.28225895278385893,\n", " 0.057612590307036915,\n", " 0.0032618186609024453,\n", " 0.026181049189100584,\n", " 0.018130746103609395,\n", " 0.04814120017936884,\n", " 0.028033837453545683,\n", " 0.016018952159159962,\n", " 0.10786244489622294,\n", " 0.0834781768620232,\n", " 0.022854535251391913,\n", " 0.015962650158433622,\n", " 0.07385429028281207,\n", " 0.01585260381694802,\n", " 0.10713143810303755,\n", " 0.16434986905748017,\n", " 0.053196749127254785,\n", " 0.07198281118803972,\n", " 0.06529979031720769,\n", " 0.07449011311316245,\n", " 0.08752410119085728,\n", " 0.2816055797102072,\n", " 0.04347393604002157,\n", " 0.17907922424929817,\n", " 0.01490412243409682,\n", " 0.0477068969571282,\n", " 0.003125176634761368,\n", " 0.147365277243595,\n", " 0.02378176231653818,\n", " 0.0317203316735914,\n", " 0.00888263266513068,\n", " 0.01289556785901546,\n", " 0.010265212589804711,\n", " 0.018080221088379866,\n", " 0.21576138392391997,\n", " 0.007517776565924957,\n", " 0.006047411778817634,\n", " 0.17776085998593621,\n", " 0.09529986401732875,\n", " 0.11049881072358364,\n", " 0.06462928735323874,\n", " 0.010355927619680247,\n", " 0.0386496416895755,\n", " 0.012622701026531015,\n", " 0.027391605825429165,\n", " 0.05388070169154412,\n", " 0.12461257745386922,\n", " 0.01810511082312453,\n", " 0.0175526978804259,\n", " 0.08783847075900593,\n", " 0.01361508438154978,\n", " 0.04676134976871421,\n", " 0.007397573866253584,\n", " 0.13202577433888127,\n", " 0.01132020669468271,\n", " 0.12832589197935043,\n", " 0.04437461317876895,\n", " 0.12845654882591606,\n", " 0.11582753760823448,\n", " 0.07309772947137406,\n", " 0.018830527715500214,\n", " 0.035333067409162984,\n", " 0.056864919285666206,\n", " 0.005368697339630413,\n", " 0.040291747838744604,\n", " 0.033316035411091426,\n", " 0.007716146638381505,\n", " 0.0003930941633237081,\n", " 0.07515774355126258,\n", " 0.04276175202646662,\n", " 0.03474282700200875,\n", " 0.15126973663125468,\n", " 0.06635488407941134,\n", " 0.01730345749962059,\n", " 0.02394255375850988,\n", " 0.1357381368298741,\n", " 0.15850501669586578,\n", " 0.07892485435015333,\n", " 0.01582154287590615,\n", " 0.16707019161931755,\n", " 0.02236961693240029,\n", " 0.09351194279337109,\n", " 0.18505588682301166,\n", " 0.13773883921289104,\n", " 0.03572282743377015,\n", " 0.03338028264882304,\n", " 0.028207860178104557,\n", " 0.024713646956453086,\n", " 0.21901040117238055,\n", " 0.0016245678366825934,\n", " 0.02812674224463469,\n", " 0.06207950446720738,\n", " 0.08253596683108745,\n", " 0.09621822312055597,\n", " 0.007918107934213959,\n", " 0.05904738936652541,\n", " 0.1698262620349022,\n", " 0.028915195613451707,\n", " 0.09318403892358103,\n", " 0.07128782673038109,\n", " 0.007925790632116193,\n", " 0.12967837198611154,\n", " 0.17276076784387986,\n", " 0.012324509677551037,\n", " 0.033539591181539874,\n", " 0.07250071559325715,\n", " 0.019068320785266085,\n", " 0.17483601443601635,\n", " 0.11664804362632596,\n", " 0.017089974116468846,\n", " 0.010144395346020975,\n", " 0.00901659027989611,\n", " 0.015619667245122974,\n", " 0.05290896204142636,\n", " 0.13147162144385238,\n", " 0.005454481067866377,\n", " 0.004253329132621117,\n", " 0.02361155664566811,\n", " 0.0017153572388056643,\n", " 0.04081351884994781,\n", " 0.09371147687792068,\n", " 0.03343233456697503,\n", " 0.01584420672613682,\n", " 0.058695380337141514,\n", " 0.06256187444580125,\n", " 0.0075520786938369925,\n", " 0.07741929505791237,\n", " 0.03536004275721634,\n", " 0.17489206122116432,\n", " 0.02535509332169783,\n", " 0.18830063072671957,\n", " 0.03077388440297053,\n", " 0.024176616769182785,\n", " 0.03835924387355441,\n", " 0.0005918134909739369,\n", " 0.04315171164588778,\n", " 0.34342769004820817,\n", " 0.00949977722015926,\n", " 0.09486534713329366,\n", " 0.02196164345130267,\n", " 0.00163446151705672,\n", " 0.1176258605337431,\n", " 0.06836918411909254,\n", " 0.02771730124493453,\n", " 0.0032764357113829125,\n", " 0.007841192406600951,\n", " 0.01747496502915831,\n", " 0.15277635299349684,\n", " 0.037339128406504135,\n", " 0.13764668607833072,\n", " 0.13842428932933026,\n", " 0.34177562289120056,\n", " 0.018004738994762806,\n", " 0.01726598108214283,\n", " 0.05389490031558953,\n", " 0.030941888020514823,\n", " 0.02363873956297599,\n", " 0.06811704326354026,\n", " 0.017213405253107456,\n", " 0.07772609088729454,\n", " 0.03474441989865715,\n", " 0.025142012768472548,\n", " 0.09826756618547912,\n", " 0.019604491723430064,\n", " 0.0044533666895359226,\n", " 0.01259608547765949,\n", " 0.13759383500404226,\n", " 0.008982600076736352,\n", " 0.05761436601789566,\n", " 0.09571774671968611,\n", " 0.06830193848360605,\n", " 0.06519434784743955,\n", " 0.07873514834137374,\n", " 0.015789259454323468,\n", " 0.1390188548925317,\n", " 0.0160748571333401,\n", " 0.061542942958879125,\n", " 0.005195696136548641,\n", " 0.04699300509700979,\n", " 0.0036719711838100284,\n", " 0.0011296542650983054,\n", " 0.010373284138620428,\n", " 0.010716638600308352,\n", " 0.008180501662157546,\n", " 0.003037446375496399,\n", " 0.030376262716253894,\n", " 0.11715884559504283,\n", " 0.054339570099615736,\n", " 0.04930502923607483,\n", " 0.09825146668526819,\n", " 0.0744942461500982,\n", " 0.08415685248558985,\n", " 0.23999275109280155,\n", " 0.030873354078486837,\n", " 0.09874832842706652,\n", " 0.007914688323334093,\n", " 0.05695863780662854,\n", " 0.028582080929921774,\n", " 0.1163962521049647,\n", " 0.09040402021080894,\n", " 0.07837235301674397,\n", " 0.018991758250118108,\n", " 0.06965642228563412,\n", " 0.11617311786273812,\n", " 0.15936569653879093,\n", " 0.005328751639630378,\n", " 0.13199999920911512,\n", " 0.06374533104207339,\n", " 0.019102183277462794,\n", " 0.007309244020837152,\n", " 0.06597835108160796,\n", " 0.044886371931244735,\n", " 0.10867306128077907,\n", " 0.1997077206102412,\n", " 0.14682743590035588,\n", " 0.005525185424535208,\n", " 0.024364859407311156,\n", " 0.0935051751215348,\n", " 0.002581097434324894,\n", " 0.03822130550884379,\n", " 0.030848456466034693,\n", " 0.07999072625643787,\n", " 0.05724656344251598,\n", " 0.023625242067680755,\n", " 0.03344401255905948,\n", " 0.08830193300861543,\n", " 0.0056261269450389225,\n", " 0.026763193801551947,\n", " 0.006364792202095674,\n", " 0.12307235011996998,\n", " 0.07251240513118784,\n", " 0.039583615179130005,\n", " 0.03902661640273232,\n", " 0.04064147427386682,\n", " 0.05596833903877591,\n", " 0.007312318365597766,\n", " 0.07933716096876867,\n", " 0.17815577221671558,\n", " 0.009366916980114093,\n", " 0.018306267980130124,\n", " 0.02552681802766124,\n", " 0.041834421035625145,\n", " 0.06867037174513749,\n", " 0.0034039558349684062,\n", " 0.1385327880823339,\n", " 0.0027592612048691176,\n", " 0.016980845245221633,\n", " 0.08066629254734528,\n", " 0.09408624425198371,\n", " 0.040181841960918216,\n", " 0.031893791140374314,\n", " 0.05442341212283659,\n", " 0.27951659792515327,\n", " 0.0465456113991994,\n", " 0.003624920990410939,\n", " 0.07437201263748489,\n", " 0.045379564076541146,\n", " 0.09491871695750517,\n", " 0.017779172769020364,\n", " 0.09786641581409125,\n", " 0.046987372428895696,\n", " 0.11786188996072133,\n", " 0.0006843102798217846,\n", " 0.10769141970030222,\n", " 0.051551370247694725,\n", " 0.10343568725939226,\n", " 0.09099030909302533,\n", " 0.0006364344569372858,\n", " 0.11603816228956948,\n", " 0.07810846746587588,\n", " 0.06747891321444946,\n", " 0.03124973017791591,\n", " 0.09975561658418966,\n", " 0.10613327145092943,\n", " 0.030868266149580378,\n", " 0.07130464598489332,\n", " 0.010831751476718488,\n", " 0.06795155014194397,\n", " 0.0849228072453086,\n", " 0.23244074034329923,\n", " 0.011360233702307241,\n", " 0.19078991494111097,\n", " 0.004982882609227137,\n", " 0.058457085711226896,\n", " 0.03652346412868897,\n", " 0.019326710732070002,\n", " 0.06213896267526133,\n", " 0.14216099996150267,\n", " 0.044048586033315934,\n", " 0.029193797949268966,\n", " 0.10688919715953672,\n", " 0.05752385187702821,\n", " 0.15324535596601713,\n", " 0.02712645296952388,\n", " 0.005838831315330486,\n", " 0.02748156569087206,\n", " 0.08261866111524592,\n", " 0.0696626956511742,\n", " 0.06640547461989672,\n", " 0.009915047399410204,\n", " 0.001255707466267965,\n", " 0.03376561432315163,\n", " 0.048738942251450465,\n", " 0.00861313132927425,\n", " 0.005743554134014069,\n", " 0.08401738447914843,\n", " 0.07564292973167815,\n", " 0.05046612226637836,\n", " 0.04317723772484261,\n", " 0.08540260249672019,\n", " 0.023915633761266833,\n", " 0.01135443395579627,\n", " 0.053148476755855045,\n", " 0.05542534321351843,\n", " 0.0009075983436091371,\n", " 0.005095876020771002,\n", " 0.04971524662205007,\n", " 0.017980411759507407,\n", " 0.20010576582218,\n", " 0.2513852460775641,\n", " 0.03225115610454214,\n", " 0.005305087127849043,\n", " 0.07288747060336302,\n", " 0.02272380097211539,\n", " 0.023907495486764282,\n", " 0.0013254124999266426,\n", " 0.023575962950710995,\n", " 0.03204810307766892,\n", " 0.03811803572260532,\n", " 0.10042711082541808,\n", " 0.11526819003280259,\n", " 0.022303439318800702,\n", " 0.060127999203787405,\n", " 0.028268260382587087,\n", " 0.037590642672625525,\n", " 0.0029652601131710623,\n", " 0.15014145383584435,\n", " 0.04208226874718443,\n", " 0.017158743785458497,\n", " 0.00547294612315318,\n", " 0.03480621114401159,\n", " 0.022295622692906917,\n", " 0.06999159502907827,\n", " 0.04178017784707431,\n", " 0.005692446036791035,\n", " 0.041707198062546864,\n", " 0.007145854595781261,\n", " 0.044554217493107826,\n", " 0.12787244524552746,\n", " 0.014408737686137131,\n", " 0.10302036134792078,\n", " 0.049758742580125165,\n", " 0.027987475719212517,\n", " 0.028085113945399146,\n", " 0.033119151398710196,\n", " 0.02155839639109083,\n", " 0.13637758062859412,\n", " 0.059948265491590186,\n", " 0.06645425003855722,\n", " 0.04019461281119669,\n", " 0.0021953293771129444,\n", " 0.0463159222067459,\n", " 0.000932252183692524,\n", " 0.005061131582356685,\n", " 0.05963643917810013,\n", " 0.011600319916847561,\n", " 0.10568876186790245,\n", " 0.030102336900264193,\n", " 0.01843879873277151,\n", " 0.005313421612462337,\n", " 0.007900822145144225,\n", " 0.06934951198394482,\n", " 0.040890947097128205,\n", " 0.057269647763408406,\n", " 0.11209590502338855,\n", " 0.07782804442442894,\n", " 0.001044890390902744,\n", " 0.022732143571587662,\n", " 0.1859862485612928,\n", " 0.010182198898881816,\n", " 0.09244665848966689,\n", " 0.022910513669710968,\n", " 0.10190693177953153,\n", " 0.132351873135519,\n", " 0.025335119453477956,\n", " 0.022211186200827036,\n", " 0.00996651581319825,\n", " 0.014936406750349754,\n", " 0.026176503907688935,\n", " 0.012520107697075948,\n", " 0.122100256506301,\n", " 0.029119256322961654,\n", " 0.007546093007245901,\n", " 0.028869098776218988,\n", " 0.037564321724282944,\n", " 0.0036696759931069195,\n", " 0.049722280578567195,\n", " 0.01566923887590691,\n", " 0.012055027733427962,\n", " 0.010257721702723932,\n", " 0.016855216621574395,\n", " 0.056220525417235614,\n", " 0.051626015452095095,\n", " 0.04646819368460349,\n", " 0.030225399859196634,\n", " 0.0380829944021235,\n", " 0.11277223529173716,\n", " 0.012374894894527411,\n", " 0.11669065827908633,\n", " 0.0513235633124395,\n", " 0.05532090179634949,\n", " 0.02325623916704761,\n", " 0.05450730457624424,\n", " 0.019826324104614736,\n", " 0.07237817445347063,\n", " 0.04369808451167148,\n", " 0.07105449856684025,\n", " 0.005989164375105483,\n", " 0.08398005662195569,\n", " 0.051764559461228106,\n", " 0.004187136241250456,\n", " 0.012504898564251358,\n", " 0.04717399738499172,\n", " 0.2016781471549755,\n", " 0.09464675597832291,\n", " 0.0298386423982813,\n", " 0.24084453474677703,\n", " 0.013378538813525022,\n", " 0.08842289121003263,\n", " 0.0354598846298383,\n", " 0.06240997964901875,\n", " 0.007029409476979748,\n", " 0.10955453295060376,\n", " 0.14675753365987426,\n", " 0.0812610961859522,\n", " 0.0020720124907177184,\n", " 0.012365753100144415,\n", " 0.16098761051172714,\n", " 0.00016654633950932506,\n", " 0.07347599069833181,\n", " 0.36952741285942603,\n", " 0.09172540042210525,\n", " 0.0230170030322268,\n", " 0.240731015736934,\n", " 0.07109973614072948,\n", " 0.03917519182260257,\n", " 0.018666154010077908,\n", " 0.04851988423822812,\n", " 0.008488602179320837,\n", " 0.10453475058439121,\n", " 0.025743840613652303,\n", " 0.1652761554390935,\n", " 0.008757747345074758,\n", " 0.12333879673059428,\n", " 0.10973651999064132,\n", " 0.04519238973922455,\n", " 0.08488022528010326,\n", " 0.09727832624791945,\n", " 0.003239753774409666,\n", " 0.04560410703231033,\n", " 0.04053193469317116,\n", " 0.12136419270073905,\n", " 0.07434219064683764,\n", " 0.028833416243939577,\n", " 0.000946557339209708,\n", " 0.010749460632669934,\n", " 0.12500957751963837,\n", " 0.007005119880928155,\n", " 0.09627049249088689,\n", " 0.01656662065912043,\n", " 0.030527450515431726,\n", " 0.11959749367614357,\n", " 0.006333486086411952,\n", " 0.030883202066574446,\n", " 0.03207565261334512,\n", " 0.1844746128409591,\n", " 0.1204648022644105,\n", " 0.04956549158517971,\n", " 0.027189527839651815,\n", " 0.011788578144008199,\n", " 0.04037926716620046,\n", " 0.05191314431892716,\n", " 0.00039834422618859056,\n", " 0.044845722514293834,\n", " 0.0013406998001181485,\n", " 0.13915755708034125,\n", " 0.025804221628810565,\n", " 0.016625006337755377,\n", " 0.1004207707992065,\n", " 0.0110461991576931,\n", " 0.0005624162029610176,\n", " 0.02218827893890363,\n", " 0.04513924234830089,\n", " 0.0524335258014399,\n", " 0.04771079535303877,\n", " 0.016968502391290323,\n", " 0.26161696975192894,\n", " 0.007363237533763179,\n", " 0.02308518394451976,\n", " 0.04513202681555214,\n", " 0.07006194463329805,\n", " 0.14029359717926201,\n", " 0.014381378840256576,\n", " 0.03555597016265209,\n", " 0.018657535757983852,\n", " 0.031766676814429386,\n", " 0.06075944769045604,\n", " 0.08331255978606869,\n", " 0.045645172937360264,\n", " 0.02461081914653503,\n", " 0.07381426102037852,\n", " 0.22397542932751477,\n", " 0.004992507677112961,\n", " 0.08864470762569429,\n", " 0.027779586273450306,\n", " 0.2848107279231186,\n", " 0.04630695191862104,\n", " 0.017011369308842075,\n", " 0.15952499671284268,\n", " 0.03463221552269786,\n", " 0.08578767264164745,\n", " 0.12910567697821315,\n", " 0.05781212267037459,\n", " 0.10891720614251975,\n", " 0.146423553132656,\n", " 0.09763325395557741,\n", " 0.16859086429179537,\n", " 0.024729283374874494,\n", " 0.04470282305977166,\n", " 0.18883202093348495,\n", " 0.0028667597248430154,\n", " 0.11482687167531963,\n", " 0.060860702504767675,\n", " 0.14081893198888162,\n", " 0.024484030989604954,\n", " 0.03453447472018027,\n", " 0.03671514483192041,\n", " 0.0652186533009921,\n", " 0.1009556388723195,\n", " 0.010646572952264713,\n", " 0.19199343032052324,\n", " 0.029608180505835188,\n", " 0.10784646766356636,\n", " 0.022439340202445698,\n", " 0.23246130825560182,\n", " 0.008394868156295407,\n", " 0.027827311099374884,\n", " 0.04145422453178862,\n", " 0.10415595818939992,\n", " 0.007659782526669153,\n", " 0.004264704675250825,\n", " 0.11170964926336935,\n", " 0.024633190329006165,\n", " 0.0033323555404036454,\n", " 0.08722590956293633,\n", " 0.09880772435662095,\n", " 0.051736398477979305,\n", " 0.05456284348095279,\n", " 0.05922779643849195,\n", " 0.08630258786795603,\n", " 0.01211540096346939,\n", " 0.038769566686892465,\n", " 0.03577626472037746,\n", " 0.04822176559153868,\n", " 0.11865063287090627,\n", " 0.1316371549918439,\n", " 0.0788642466819838,\n", " 0.0868610393088276,\n", " 0.005311068379085502,\n", " 0.004769257830017382,\n", " 0.09285618244565486,\n", " 0.03315419894507164,\n", " 0.03534193184656062,\n", " 0.03993504664191436,\n", " 0.09841114508154215,\n", " 0.019053097895672007,\n", " 0.016968814880337385,\n", " 0.032235189969098584,\n", " 0.044244080285606335,\n", " 0.001418913692462551,\n", " 0.036187567349041695,\n", " 0.057724347556453705,\n", " 0.01898263941506022,\n", " 0.028899731320974856,\n", " 0.050876953074515334,\n", " 0.06713943059838287,\n", " 0.022879690303597974,\n", " 0.00966646173365474,\n", " 0.05337592664195556,\n", " 0.05073488249320381,\n", " 0.04105499095268792,\n", " 0.10120178364136453,\n", " 0.10715531858641573,\n", " 0.0035603074254617613,\n", " 0.017803197434089483,\n", " 0.15189181294157955,\n", " 0.006647091062676414,\n", " 0.06342040542484459,\n", " 0.022392015714865775,\n", " 0.29988905816191935,\n", " 0.006983456553617498,\n", " 0.003408924136986515,\n", " 0.14747751776604665,\n", " 0.06375127664908338,\n", " 0.005900808147907294,\n", " 0.010431010086377025,\n", " 0.01620017997035337,\n", " 0.017828484934035393,\n", " 0.019313306738542927,\n", " 0.06673699689980025,\n", " 0.08499887921193867,\n", " 0.0038478454118828203,\n", " 0.04701815205947114,\n", " 0.07437440340918938,\n", " 0.061624971545332786,\n", " 0.10492065109178188,\n", " 0.06709385802487085,\n", " 0.046127962858330714,\n", " 0.05711762075269766,\n", " 0.09525506157722224,\n", " 0.054370950654008016,\n", " 0.13718366018692565,\n", " 0.019671618384428345,\n", " 0.15516309586517213,\n", " 0.021408083368284078,\n", " 0.018214064649179944,\n", " 0.0828144291810782,\n", " 0.010673318935660323,\n", " 0.1544692666212407,\n", " 0.12863786406466254,\n", " 0.002769975742531964,\n", " 0.0026862432959808284,\n", " 0.030629766254641026,\n", " 0.13826715604036388,\n", " 0.09342747468240838,\n", " 0.055270588502692314,\n", " 0.05060823752734416,\n", " 0.019867506977313248,\n", " 0.06453963836809115,\n", " 0.03465454303595934,\n", " 0.15216603998278494,\n", " 0.03674293110810991,\n", " 0.08626985220162152,\n", " 0.04657748940042151,\n", " 0.019357083446233592,\n", " 0.006605222729382264,\n", " 0.013158027329384826,\n", " 0.03153894153522901,\n", " 0.01988072082352704,\n", " 0.00525449520758363,\n", " 0.03922772764730287,\n", " 0.042706166987718244,\n", " 0.03323988743509692,\n", " 0.05917085648982662,\n", " 0.00844354044362862,\n", " 0.043642886062953294,\n", " 0.061282464916661984,\n", " 0.011217650339457624,\n", " 0.000996266382697946,\n", " 0.03001334033835828,\n", " 0.15623005315979493,\n", " 0.11448010225720932,\n", " 0.02919964631278445,\n", " 0.006218378151982562,\n", " 0.003862858719273705,\n", " 0.056665638825001564,\n", " 0.10581214475313877,\n", " 0.02957985688302192,\n", " 0.033620473036023404,\n", " 0.007118170987232178,\n", " 0.07457001207681127,\n", " 0.003787888894919096,\n", " 0.028689365961345487,\n", " 0.10569168819287358,\n", " 0.13000348719072144,\n", " ...]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "epochs" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.05986339357761045" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(epochs)/2000" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXd0JOd5p/t8XZ0j4uScA8OQHAZZ2ZJpSVbc1fVSztde\ny/KVrmTLSV4fn/XZXVuOq/Wu00orXUvrVRYlKlqZoiSSQw45w+HkiJkBBsAAg9CxqrurvvtHVWEQ\nGuhqoNHdaHzPOTgDVOj+Jv36rfd7398rpJQoFAqFon3xNXsBCoVCoVhelNArFApFm6OEXqFQKNoc\nJfQKhULR5iihVygUijZHCb1CoVC0OUroFQqFos1RQq9QKBRtjhJ6hUKhaHP8zV4AQE9Pj9y2bVuz\nl6FQKBQriueee25UStlb7bqWEPpt27Zx9OjRZi9DoVAoVhRCiKterlOpG4VCoWhzlNArFApFm6OE\nXqFQKNocJfQKhULR5iihVygUijZHCb1CoVC0OUroFQqFos1RQt+mHP/Opxi6dqHZy1AoFC2AEvo2\nRFoWB374Hq4+9l+avRSFQtECKKFvQ0qlIkFRJpk+3+ylKBSKFkAJfRtSyGcB2FS8grSsJq9GoVA0\nm6pCL4T4mBDiphDi5LRjnxFCHHe++oQQx53j24QQhWnn/mk5F6+oTLFgC31CFBi6rvL0CsVqx4up\n2T8Dfwd8wj0gpfx37vdCiL8BJqddf0lKeaheC1TUjp7PTH0/fOF51m/d28TVKBSKZlM1opdSPgGM\nVTonhBDAzwKfqvO6FEugWMhNfa/3v9jElSgUilZgqTn6lwPDUsrp+YHtQohjQogfCCFevsTXVyyC\nkp6d+j5w63QTV6JQKFqBpfrRv4OZ0fwgsEVKeUsIcR/wJSHEQSllevaNQoh3Au8E2LJlyxKXoZhO\nyYnoJ4jTk7vY5NUoFIpms+iIXgjhB/4N8Bn3mJTSkFLecr5/DrgE7Kl0v5Tyw1LKw1LKw729VQek\nKGrANGyhvxrez0ZzAH1aKkehUKw+lpK6eS1wVkrZ7x4QQvQKITTn+x3AbuDy0paoqJVy0Rb2/Jp7\n8QuL/vPHm7wihULRTLyUV34KeArYK4ToF0L8mnPqEeZuwr4COOGUW34eeJeUsuJGrmL5kEYegPjO\nhwAYv6KEXqFYzVTN0Usp3zHP8V+pcOwLwBeWvizFUnBTN+v3Hkb/XgBzUFXeKBSrGdUZ24bIUgGA\nWKKTfv8WYhPnmrwihULRTJTQtyMlO3UTjsQYT+xhvaG2SRSK1YwS+jZEFHPkZQjh82H2HqCHCW4N\n91e/UaFQtCVK6NsQUS5giBAAsc13AzB4/rlmLkmhUDQRJfRtiK+sY2AL/Ya99wGQvX6imUtSKBRN\nRAl9G6KZeQxfGIDutZu4RQrfTWWFoFCsVpTQtyGaqVN0UjcAN0I76Mwqu2KFYrWihL4N8Zs6JSei\nB8h17GNzqQ+zXG7iqhQKRbNQQt+GBCydshaZ+tm37iBhUWLg8skF7lIoFO2KEvo2JGjplLXbEX3X\njnsAGL10rFlLUigUTUQJfRsSlAbWNKHftPsQphQUB5QVgkKxGlFC34YEpYHpv526CUfj9GsbCY2d\nbeKqFApFs1BC34ZEpI4MRGccG43uYk1BDSFRKFYjSujbDGlZhDGQ0yJ6gGL3PjbKYbLp8SatTKFQ\nNAsl9G1GsaijCYmYFdFHNtlWCP3nn2/GshQKRRNRQt9m6HlnbGBwptCv2XUvAJNqCIlCsepQQt9m\n6Hl7DrtvltCv27KbrIzA8KlmLEuhUDQRJfRtRrGQBeYKvU/T6A9uJ5E+34xlKRSKJqKEvs0oFuzU\njRaOzzk3mdzNpuJlpGU1elkKhaKJeBkO/jEhxE0hxMlpx/5ECDEghDjufL1h2rk/FEJcFEKcE0L8\n9HItXFGZkhPR+0PRuSfXHCRJjps3rjR4VQqFopl4iej/GXhdheMfklIecr6+DiCEOAA8Ahx07vkH\nIYRWr8UqqlNyBoP7w7E55xJb7cqbITWERKFYVVQVeinlE8CYx9d7C/BpKaUhpbwCXAQeWML6FDVS\n1m2hD1QQ+o17DwOQv/5CQ9ekUCiay1Jy9O8RQpxwUjudzrGNwPVp1/Q7x+YghHinEOKoEOLoyMjI\nEpahmI5ZtAeDByNzc/Spzh6G6CVwS1khKBSricUK/T8CO4FDwCDwN7W+gJTyw1LKw1LKw729vYtc\nhmI2lpO6qST0AMORHXSrISQKxapiUUIvpRyWUppSSgv4CLfTMwPA5mmXbnKOKRqE5UT04XmEPt+5\nj01mP0VDb+SyFApFE1mU0Ash1k/78W2AW5HzZeARIURICLEd2A08s7QlKmpBukIfS1Q8H9hwBwFh\n0n9B5ekVitWCv9oFQohPAa8CeoQQ/cB/BF4lhDgESKAP+A0AKeUpIcRngdNAGXi3lNJcnqUrKlLK\nY0lBKBSpeLpn571wFMYuP8+OOx5s8OIUCkUzqCr0Usp3VDj80QWu/1PgT5eyKMXiEaUCOkGivsoP\naxt33klR+ikNqrGCCsVqQXXGthmiXEAXoXnPB4Ihrvs3Exs/18BVKRSKZqKEvs3wlfPoIrzgNWPx\n3azTLzVoRQqFotkooW8ztLJOcYGIHsDs2c8axpgYHWrQqhQKRTNRQt9maGaBkm/hiD662bZCGDin\nrBAUitWAEvo2w2/pFKsIffeWfQDkR5S5mUKxGlBC32YELZ1yFaGPd64FwMzdasSSFApFk1FC32YE\nLB1Tq1xD75JMdVGWPmTeq1edQqFYySihbzOC0sD0LxzRC5+PSZHAp483aFUKhaKZKKFvM8JSx/JX\nGDoyi6wvSUAJvUKxKlBC32aEpYH0L5y6AchrSUKliQasSKFQNBsl9G2EtCzCFJGB6kKvB1JEy+kG\nrEqhUDQbJfRthGEU8AkJgeqpm1Kwg5ilhF6hWA0ooW8j9FwGABGsLvRmpIuUzCAta7mXpVAomowS\n+jZCL2QB8HkQehHpIiRK5HMqqlco2h0l9C3K05/6M0792ctquqfoCn1o7mDw2fhi3QCkx4ZrX5xC\noVhRKKFvUbShF9hp1DbE2xV6zUNEH0j0AJCbUIPZFYp2Rwl9i+IvZwmLUk2zXUu6PRjcH64e0YdT\n9kD2wqQSeoWi3VFC36IEynZ0nkt7b2oqO0If8JC6iTpCb6SV0CsU7U5VoRdCfEwIcVMIcXLasb8S\nQpwVQpwQQnxRCNHhHN8mhCgIIY47X/+0nItfKreG++k7c7TZy6hI0LSHfOcz3puayoZ9TyASr3pt\nvHMNAGZWGZspFO2Ol4j+n4HXzTr2beAOKeVdwHngD6eduySlPOR8vas+y1weLn7ujwl95pFmL6Mi\nYcuOzgsZ78Zjpm4/BQQj1SP6VJct9JYyNlMo2p6qQi+lfAIYm3XsW1LKsvPj08CmZVjbshMs3KRT\ntqYNQMSyo3MjN+n5Hqto3xP0ENH7A0HSRPEVlNArFO1OPXL0vwp8Y9rP24UQx4QQPxBCvLwOr79s\nBEuTNW94NoqYtEW7lK9d6MPRpKfr0yKJZrTmB51CoagfSxJ6IcQfAWXg/ziHBoEtUsp7gPcDnxRC\nVFQdIcQ7hRBHhRBHR0aasyEYLdudpLVseDYCs1wmKgwAyvkahHhK6KunbsA2NgsWW+v3rlAo6s+i\nhV4I8SvAG4Gfl1JKACmlIaW85Xz/HHAJ2FPpfinlh6WUh6WUh3t7exe7jCXher3ka8iDN4LstA1Y\ns+C9c1WW8phSEAwu7EfvUvCniJS8PzEoFIqVyaKEXgjxOuD3gTdL6eQY7OO9QgjN+X4HsBu4XI+F\nLgdx6W54tlb6opC5HWVbunehF+UCOiGEz9tfqzI2UyhWB17KKz8FPAXsFUL0CyF+Dfg7IAF8e1YZ\n5SuAE0KI48DngXdJKVsrXHbQC7mp9IiRbS2h16dvwBoZz/eJcoGC8BbNA5TDnSQt76+vUChWJv5q\nF0gp31Hh8EfnufYLwBeWuqhGkB0fxZXEYq618tT6tA8eX9G7EGulPEUR8ny9jHQRFwWKhk4w5P0D\nQqFQrCxWbWdsdlrrf7mGPHgjKE6L6LUahN5n6jUJ/ZSx2S1lbKZQtDOrVugL6dsdoVahtTYkS3n7\ng6coNbRyzvN9flOn6PMemfvjttBnJm7WtkCFQrGiWLVCb0wXer21hN50PnhGfd0Ey94jer9ZoFSD\n0IccB8u8crBUKNqaVSv0xekeLzVseDYCt9Jm0t9LyMxXufo2AUunrHkX+kjHWgCKGSX0CkU7s2qF\n3srbG7CGDNS04dkIpPPBkw+vnfK88UJQGpha9cHgLvFOu3+hmB6tbYEKhWJFsWqFXhbGMaVgxNeN\nv8WEXhgZ8jJEOZicskLwQsjSaxL6VJcd0Vt55WCpULQzq1boffoEaRGn4Ivjd7zfWwVfMUNORLFq\nFXoMLL/31E04GqcggwjlYKlQtDWrVuj9xgRZkcDwxwm2mNBrpSwFXxTCCYKijKF7E/uQNJB+7xE9\nQFoklLGZQtHmrFqhD5TSFLQ4JS1GuIYNz0YQKOfQfTF8YdsPLjtZPeKWlkVUGMigN0Mzl6yWImC0\nVsOYQqGoL6tW6MPlNLo/RTmQIFLDhmcjCJo5ilp0Sui9ePHoBef3EKg+GHw6BX+SsDI2UyjamlUr\n9FEzTTGYwgrGidJaQh8yc5S0GIFoBwB6zoPQ5+30kwjUlroxAh3ETCX0CkU7s2qFPiEzmMEUVihJ\nXBaQltXsJU0RsfKUA3EC0RQAerZ6asUo2EKveRgMPp1yqIO4bK2qI4VCUV9WpdBbpklC5pHhDkQ4\niU9IctnWiWqj5LECcUJxW+hLHsYJGnlbrEWottSNjHSTlFnMcrn6xQqFYkWyKoU+MzmGT0iIdiJC\ndh48l26NEkNpWcRkHiuUIByzUzdeTNeKup1+8te4GUu0C01IMhOqaUqhaFdWpdBnHRMvLdqF302P\ntMjwkUI+gyYkhJJEk52At+EjJUfoa03dTBmbjSsHS4WiXanqR9+O5JzoNZDoxuez/wgKHvLgjSCf\nniAK+MIJYo7QSw8RfdkR+kCkNqEPOMZmOWVsplC0LatS6I2M3fIfjneBEAAUW2TKVN75wNHCSULh\nKEXpn/K+WQjTsDdjA+F4Te8XSdl+N7ryu1Eo2pbVKfSOc2Uk1YOUdrVNqUU86XVnU9hNKWVFFF+x\nekRvGnbTV7DGiD7mOlimVUSvULQrq1Lozay98Rrr6MUsF+1j+dYQ+qKzDre0Mi+iaKXqFg2u0Iej\niZreL9G1BgArp4zNFIp2xdNmrBDiY0KIm0KIk9OOdQkhvi2EuOD82ukcF0KI/y6EuCiEOCGEuHe5\nFr9YLMfEK9nZSzThbni2htC706VCMWeT2BfD70HoZdHO0QcjtaVuEslOSlJDKmMzhaJt8Vp188/A\n62Yd+wDwXSnlbuC7zs8Arwd2O1/vBP5x6cusL0KfJC9DBENhYvEUlhSgt0bTUNmJ6CMJu7TS0GIE\nzeqdu7JkR/SRaG1CL3w+0iKOT2+NzWiFQlF/PAm9lPIJYHbI9xbg4873HwfeOu34J6TN00CHEGJ9\nPRZbLzRjgrSwUxw+TSMrImC0xoBwt5QyEreFvuiPEzI9uGuWCpSkRjDk3abYJetLElAOlgpF27KU\nOvq1UspB5/shYK3z/Ubg+rTr+p1jMxBCvFMIcVQIcXRkpLEbgf7iJDntdi47j7c8eCOQzpOFW1pp\n+mNErOrumqJUQCe4qPfMaSlCJRXRK5qHVytuxeKoS8OUlFICssZ7PiylPCylPNzb21uPZXgmXJpE\n15JTPxd8Mfyl1kjdSCNDUfoJhW0rAzOYIOJh+IivnMcQoUW9pxFIES23xhONYvXx4hNfRHxwE9cv\nvtjspbQtSxH6YTcl4/x60zk+AGyedt0m51jLEDEzGIHbQm9oMQItMnzEV0yTE7f9aqxggrjMVzVd\n85ULixb6UqiDuKWEXtF4pGUReOIvCAqTsWtnmr2ctmUpQv9l4Jed738ZeGza8V9yqm8eAianpXha\ngpiVoRxMTf1saDFCHjY8G4FWypIX06yGQwkCwqz6aKuZOkVRm0WxixnuJCUzLeXgqVgdnH7qG+wr\n2wJfzKimveXCa3nlp4CngL1CiH4hxK8Bfw78lBDiAvBa52eArwOXgYvAR4D/p+6rXgLSsmy3xnDH\n1LFyIEG4RYTe70yXcpmaMpVeOIeumQWKvsVF9CLaTVCUW8rBU7E6sJ74a9I4acpc+5X4SstqiQDK\nU8OUlPId85x6TYVrJfDupSxqOdELOSKiBJGuqWPlQJyobA2hD5SzGNptodci9pNHITvBzIzYrPtM\nnbKv9oobAC1mG5ulx24SdzaBFYrl5vzzP+BO43me2vFeHrz0P6ANezmO/NNvEJu8yJ1/+P2mrmPV\nuVe6dry+yO2IXgYTxDxseDaCkJmnOE3ob7trLhzRByydkra41I3rYJmbuFnlSoWifuS+8xdMEuPO\nt76fjIgi2qyXo5DLcOfwl9lkXGj2Ulaf0LtiFojfjuhlOElYlCgaerOWNUXYylEO3G56cq0QjCrj\nBINSx9QWF9GHXWOzSeV3o2gMfWeOck/+x5ze/HPEk51kRBJ/m/VynHr808SETqoFBvusOqHPT9qe\nLsFE99Qxd/hIdrL5fi9RmcecJvRhp3HKtUaYj6BlYPkXF9FHHaE31GaYokGMfuOD5GWI/W/5XQBy\nWpJgsb2EPnDycwD4hGRyrLnzHlad0BcdQ7NIsmfq2Mw8eHOJyQJWcLrQ2znzcn7htYVYvNAnuuxe\nt7ISekUDGLh8hnsmv8uJ9W+no2cdALo/SbiNejnGbg5wMP8sN4RtGphRQt9Yyo5FcTR1O6J38+CF\nKnnw5cbQ84RECaYJfdTxvDGrDB8JSwO5SKFPdtoRvTI2UzSC/q99EBONXW/+g6ljpWAHsTbq5bjw\nvU/gFxbXdv0i0Pz9r1Un9KYjZonONVPHAo5TpNHk8sJ8xn5/Eb7dzDU1ZWoBLx7LNImIIjJQ22Bw\nF38gSJoYvoISesXycnPgCveMfo1jPW+kZ8PWqePlUAdJqzW60+tBx8UvcUnbTu+drwXAmFQRfUOR\n+XFKUiMWv90w5aZHirnmRvR5Z26tb5rQB0NhdBlALDA3Vi/YXb0isLiIHiAtEmhtthmmaD0uf/kv\n8GGx+Y1/OOO4jHQRFwVKRaNJK6sf/RdPsrd8lpHtbyHe5Q72aW5adNUJvc+YJC3iCN/t33ok4ebB\nmxvR684YwUA0OeN4TkQRC5iu6XlH6GscDD6dvJYkWFJCr1g+xkcGuWvoUY51vJYN2/fNOOeL2VVw\nk2Mrv8T3+hMfx5KCHa/+FVLd9h6ElWtuRduqE3q/MUHON9Oz3bUEthaImhuBkXPGCE6r8QcoiCha\n0YPQBxeXugEoBDqIKqFXLCPnHvsrosJgzes+MOec5gh9bnxlC720LDZf/wqnw3ezZuN2wpEYORlG\nNHn/a9UJfbA0Sd43M2J28+DNnjLljhEMTUsrge2uGSjPn78sOakbbQlCXwx2EDPbZzNM0VpkJsc4\n0P9pno+9nK3775tzPpSwq+DyK3xI/fnnH2eTHCS/799OHZv0pfDrzS3dXnVCHymnZzhXAoTCUYwq\nefBGUC64YwRnRvRFLUawPL9FQ1G3z2nh2qZLTccMdZKU7bMZpmgtTj72IZLkSLz29yueb5emvYmn\n/wVdBtj36p+fOpbVUk3vEVh1Qh+1shSDqTnHs1Xy4I3ALaF0Sypdiv6F3TVLjtD7Q4uP6GWkk5jQ\n1QAIRd0pGjp7Ln+cE+H72H3PKypeE3OE3i1/XomUiga7R7/NqcRLSXbcLt+206LNLfRYdUIflxnM\nCkKfFzH8xeZGtFKvLPTlQILwAl48Jd3+gPKHF78Z64vZj87pNtgMU7QWQ1fP0s0kxrR0xmwSXXa5\ns7mCezlO/+gxukijHXpkxvFisJN4k9Oiq0roy6UiSfLIyFyHRt0XbfqUKWlksKQgGpuZWrICcWIL\nuGuahn0uuITUjT9uC312vLn1vor2Y3LwMgCxdTvnvSae6KAktRXdtFc69mnGSXDg5W+bcdwMd5GS\nzd3/W1VCn5mwHwtFBaE3/HGCTfakF0aGrIjMKP0EsEIJYrIwr6+1adjRfii6eKEPJe1HzfzEyt4M\nU7QehZE+ALo2zC/0wucjLeL49JVZ+ZVNj3Mw/UPO9/wUwdBMc0EZ7SYiihRyzQskV5XQZydtEfPH\n5gp9yR9v+vARrZQlz9w8uwgl8QsLvVB5fZYT0YeWENFHUvajs5FZ2ZthitbDHL9KWfroWb91weuy\nviSBFdq0d+Z7nyQiiqQe+Lk557S4vf8wMXqj0cuaYlUJfX7CFrFAvHvOubI/TsRqvtDrvgpC73TK\n5iYrP9bKkh3RB5cQ0ccdv5tSZuVuhilak0CmnxHRjT8QXPC6vJYktEJ7OcJnPs8NsZa9h+fMYiKQ\ncNOizdv/WlVCrzsi5qYppmMGE0RpbsVJoJydMUbQRYvYQp/PVt65l0V73ZElCH3KadWWOSX0ivoS\nKwwyFlxX9To9kCKyAh0sR29c5YB+jGsb3zgn7QoQ6bD/bxWauP+1aKEXQuwVQhyf9pUWQvyWEOJP\nhBAD046/oZ4LXgputBpL9cw9GUoSlwUs02zwqqYtwcxR9M8V+qkpU/OZrpUKFKVGILi4mbEA4Wic\nvAyBMjZT1JnO0jD5yIaq15WCHcRXoIPlxe9/HE1INrzilyqejzkGisUmpkUXLfRSynNSykNSykPA\nfUAe+KJz+kPuOSnl1+ux0Hpg5u2IuKLQhxP4hCSfa94/tJCVp1xB6INOA5Uxj1++KOXRxeJF3iUt\nEmhtNs5N0VxKRYNeeQszsanqtVYotSKb9nouf4nz/j1s2XOo4vlkt/0hV16JQj+L1wCXpJRX6/R6\ny4LlCL3rvz4dX9iOmnPp5kW0ESs/Y4ygi9spWy5UFnpfuYDB0oU+pyUJtNmUH0VzGbnRhyYkvs4t\nVa+V0S7CojTl3bQSuHrmOXaZlxjb+dZ5r0mkuihLHzLfvLRovYT+EeBT035+jxDihBDiY0KIuSUu\nTULo42RkpOKmkNYCw0diMo8VTMw5HqkyTtBn6hhicfNip5P3p4is0M0wRWsyfuMSAJHe7VWv9UXt\nvbNmj92rhcGjjwGw69W/OO81Pk1jUiSaOu9hyUIvhAgCbwY+5xz6R2AncAgYBP5mnvveKYQ4KoQ4\nOjLSmEcazZgg45srpACBiJsHb47QmeUyMaEjKwj9lOlaoXKOXivnKdYhdVMMdhBVxmaKOpK/eQWA\njg07ql4biNsOltnxlVPiKyauMkGcnnULP7FkfCmCxgoWeuD1wPNSymEAKeWwlNKUUlrAR4AHKt0k\npfywlPKwlPJwb+/cVMpyECimyc8j9EEnai42SehzWXe61Nz1uZYI0qicv/SbOiXf0iP6cqiThFRC\nr6gf5TE7m9u7sbrQB505zoX0yhH6cH6QUW1t1ety/hShJqZF6yH072Ba2kYIsX7aubcBJ+vwHnUh\nXE6j+ysLfXgqPdKcv4y8kzKaPl3KJRAMUZBBxHxCb+mUtKULvQx3kpQ5zHJ5ya+lUABo6euM0kE4\nUt2HKeo07TV7GlMtJI1hMqHqQm8EO4mZK1TohRAx4KeAR6cd/kshxItCiBPAq4HfXsp71JOImaYY\nmGtoBrenTFUbwr1cuCkjt2Z+NjkRxVesvLaAZVDWFj9GcIpoFz4hySgbBEWdiBZucMtfvYYeINZh\nR/Sl7Mop8e0xhynGPJSOhrpINrF01L+Um6WUOaB71rH5dyWaTNzKMBTqqHgulrTzg80aPuIKvbtX\nMJuCiKLNY6McsgpM1iGid43N0mNDdPR4+8+pUCxER3GY4fi+6hdyu2nPamJ1Si2kJ26RFAVkanPV\na2Wkm5TMYJbLaP4lye6iWDWdsdKySMgsVqiykEZjSSwppqyCG03RGSMYjFaO6HUthn8eoQ9KA7MO\nEX3QnfIzsXJypIrWxTJN1lgjFOMbPV0fjsbtFOUKcbAc7b8IQKBrYQ8fAGLd+IQk3aSN5lUj9Plc\nmqAwKzpXgl0ClRWRefPgy41bOjl7jKCLocXmddcMYSD9Sxf6qSk/KyhHqmhdxob7CYoyvo7qNfQu\nGRHHZzTX0tcr6WG7oiixrnrpqD9h/99K3xpc1jXNx6oR+ozzSeqLds17TZ4YWpOGj5hO6aS7KTyb\nhdw1I9LACixd6GMdbqu2EnrF0hm9YUe84V4PEa9D1rdymvaM0T4AujyUjoaSttDnJppjbLZqhD43\naef9/BWcK10KTRw+Yk1Nl6r8QWS7a841XTPLZUKiBIHFjxF0caf8WDkl9Iqlk3Mi3uQCA0dmU/Cn\nCK+Qpj1r4jqGDNDVWz01FXWMzYzJ5jSDrRqhd2tzQ/H5I3pdixFokie9NOz8eyxROXVjBeMV3TX1\ngn2fCNZB6JOdK37Kj6J1KN1yaug37fJ8jxFIETVXht9NMHuDEV8PPk2rem3c2WhuVunoqhH6ouNc\nGalkaOZe448TKjfHZ0MYafIyNK9ntxVKEpP5OVOm3Kk1og4RvfD5mFTGZoo6ISavMUmMeNK7C0op\ntHIcLGOFQSaC1WvoAVLddhWblVObsctK2anNdWt1K17jjxOukB5pBL5SlryYP88uQgm0Cu6aRWfq\nVD0ieoCsL4F/hU75UbQW4fwNT12j07HCHSRldt6xma1EV9mb/TJAOBIjJ8NNqyhaNUJ/27lyzbzX\nlIMJogsM4V5OtFKWgphfrN0pU/nMTBEu6vYTiBZa/NCR6eS11Iqd8qNoLVLGEOnQ+uoXTkNEuwgI\nk2ymtf8NFg2dHjmOmfBWOgow6Uvh15vTI7BqhF4WJihK/4Kt2FYwQVw2J6L3l7Lo2vxrc90187Ns\nlN2I3h+uQ2csYAQ7iJVXRnmbonWRlkWveRPDYw29iy9mF0tkxpo3ds8Lozf68AmJ1uW9dDSrpQg2\nqaJo1Qi9ZoyTFvGKo76mCCUIiRKG3nixD5o5jAWEfj53zVLBjeire4l4YaVO+VG0FunxEWJCBw9d\no9MJxOxLUPp5AAAgAElEQVRiidxkazftufbL0R7vpaOFQAfRUnP2v1aN0PuNSbLzOFe63B4+0vi/\njLCZp7SA0Adj9tqK+ZnRdtmwI/pAnYTeDHeRlJkVkSNdDGePfpfTT/9rs5fR9ow4XaOhGoQQpjXt\ntbjQ50f6AOhY7710tBjsJN4kG/BVI/TBcpqCVtlewMV1jsw3Q+itXMXpUi7ulKlSbmZEbzpCH4wu\n/CHmFRHtJCjMKdvkdsP3rx8g9O0PNHsZbU/GqaGPr63eTDSdqCP0Ros37ZXHrwHQs7F6V6yLGe4i\nJZvz/2rVCH2knMYILCz0U0O4c43Po0XJYy0g9BHHk362u+aU0IfrsxmrOTnS9K2VM+XHK2a5zOZS\nH+vLN9r2iaVVcLtGezZ6r6EHiHXYQm/mWruXQ0v3e7ZfdpHRbiKiOFUS3UhWjdDHzAyleSyKXYIx\nu95XzzY2opeWRUwWsELzR+Vux6w5y3TNKtr7CaFofVI3gURzW7WXkxt9Z4iIIlFhMDp0rdnLaW8m\nrpGXITq6ayuvTE11Z7e20IcLg4z556/gq4TmuMNO3hpajiUtyKoR+oTMYIYr+8i4uIZipVxjH6/0\nQg6/sCA0/xOH2zEr9ZnRgHSEPlyn1E3YnfLT4jnSxTB66fmp72/2nW74+5dLRSbHWzslUS9CuQFG\ntDULFz9UIBAMkZERRBPnq3qhozhENlyblbcbRGXGlNAvC6WiYc9jrSL07vCRcr6xQp+bmi41v1j7\nA0HyMoQwZkb0slQAqOkRciEibWxspg/cHnaWGzzX8Pc/+qn/jPW391A09Ia/d6NJ6INMBhc30yDT\n4k17dunoCMVYbaWjEWeCVqEJT8urQujTU86VC7diT6VH5hnCvVwUHKHXKowRnI49ZWpWfq+Ypyj9\n81on1Eqi0446ytmVMfyhFkK3zjIg1lKUGubo5Ya/f2TwaTpJc/nFHzf8vRtNt3mTQo1C6JJrcQfL\n8dFBIqIIHbWVjsam/G6U0C8LWWc0nhab39AMIOZ4csgGT5nSnQoXdzN4Pgq+uVOmfOU8ugjVbS1J\nR+jlAlN+nvrEH3PsL19ft/dsFL35iwxH9zCkrSOUvtLw999YOA/AxNkfNvy9G0k+O0knGazkpkXd\nXwikiLRw096tG3aQEOqurXQ02WU/4ZSzjX9aXrLQCyH6nBmxx4UQR51jXUKIbwshLji/enc1Wgbc\nfHNwAYtigGAojC4DDR8+YjhVPoEqQq/7YgRmma6JcgGd+gm9PxBkkhi+eXKkT3/ij3nJ5f/OPfkn\nV9SGpp7PssEaxOjax3hoE52F6w19/9EbV+nB/nsODT7b0PduNCPXnclLNQqhS6nFHSyzw7bQJ9Z6\nL60ESHT0UJY+ZBNswOsV0b9aSnlISnnY+fkDwHellLuB7zo/Nw3DSUOEkwsLPUBWxBANHj4yNV0q\ntrDQG1qMYHmmF49WLmCIpc+LnU5GJCvmSI98+oM8dPm/c1Gzm0QGTj9Z1/ddTq6fP4YmJKGNd1BI\nbGOd2dgSy4GzR+xfxVq25F5s6/LOiSFbCONrahNCl3Kog4Rs3e5s45Yd4NRivwz2FLtJkZg3iFpO\nlit18xbg4873Hwfeukzv44lixv6DjSbnd650KYjovLNZlwt389etlZ+Pkj9O2Jop9D5Tp+irX0QP\nkNOSczw5nn30b3nw7J9zLPoT9L77m1hSkO97rq7vu5yMXzkOQO/OexDdO4mIIiODVxv2/vlrdsXP\n9R2P0M0kA5cbX/XTKHSna7Srxhp6FxnuJCHzmOVyHVdVRyb7ycvQVJqzFjK+FEFjZQq9BL4lhHhO\nCPFO59haKaU7HHEIqK2Yts64zRcJD38x9hDuxkb07nSpyDxjBF3MQJzILKH3mwVKvvoYmrnos3Kk\nR7/6Ye574T9yInyYA+/9AqmuXq5pm4iMnKjr+y4n1tApCjLIhu0Hia7bDcBIA0ssQyMn6RfrWXvf\nmwAYPPl4w9670VgT1yhKjZ513g2/ZhDtwickmYnWrPwKZhdXOgqQ86cINWGjuR5C/zIp5b3A64F3\nCyFeMf2klFJifxjMQAjxTiHEUSHE0ZGR5a3ZloVxLCmIp6qnbgwtRqjBU6bc2vholYjeDCaIznLX\nDFgGpTpH9NM9OY5961849OwfcDZ0B7v/3y8RCttWyiOJA2wqnF0xKYjYxDn6/VvQ/H66txwAIDd0\nvmHvvy5/npuxPWzdey9pYshrTzfsvRtNMNPvefJSJdxxn5nx1uzOThhDiy4dNYKdxMwVKPRSygHn\n15vAF4EHgGEhxHoA59c59URSyg9LKQ9LKQ/39tb+CFQLPn2CrIii+f1Vry35440XeiNDUWqEQlUi\n82CCGDqWaU4dClgFylp9I3oz3ElSpjnx+Bc4+OP3cSmwmy3v+QqR2O06f3PdIXqYaGj6YymsNy4z\nkbAj+bWbd1GUfszRiw1578mxETbIYYzeO/FpGlfCB1gzcbwh790MYoVBJgKLE0KAgCP0ucnWjOi7\ny8MUorX57LuUQl0km+AOuyShF0LEhBAJ93vgYeAk8GXgl53Lfhl4bCnvs1Q0Y4KM8OYFUw4k5qRH\nlhtfKUtORKs/CoYT+IScYTgWtAzMOgu9jHQSFQZ7vv8bXPdvYd27vz5nHFzHrgcAGDjV+jXh4yOD\n9DCB2bsfAM3vZ1BbRzjd15D3v37Gjt5jW+8FIL/uMNus60zW6CeUmRyjXCrWfX31pqs8TD7qbfJS\nJdzubCPdet3Zej5LF2lksrYaehcZ6SYlMw3ff1hqRL8W+JEQ4gXgGeBrUsp/Bf4c+CkhxAXgtc7P\nTSNQnCRfxbnSxQzEiTV4ypRWzCw4XcrFtVHOZ2578YSkjuWvb9WNL2pHVEPaOjp/46ukOuduYm89\n8CBl6UO/1vobsgPnjwIQ23zX1LHx0CY6Cv0Nef9sn70Ru3H/gwAk99jZzb4Xvu/5NcqlItkPPcDV\nP3+AkRt9dV9jvXAnL5UXKYQAMbc7O916TXs3Hftlf9cif3+xbnxCTjVxNoolCb2U8rKU8m7n66CU\n8k+d47eklK+RUu6WUr5WStlU44pwOU3B703oCSXnpEeWG62co+CrLvRaxP496NPGrIUxkP76RvQb\n7n0dRxOvIfbvv0rXmsrdjZFYgmvaFmKjL9b1vZeD7DV703j9nvumjulJu8SyEX/P/uET3KSL7rV2\nA9GOu19OSWrkL3ovTz37zDdZzwjby32YH34NfWeOLtdyl8TIwCV78lLnIjdiuV00YTZpvupCTA7Z\njXbRRZaO+h2/m/StwSpX1pdV0RkbNTOUqlgUTxFOzkmPLDfBchbDV92rxu2cLUyzUQ5LA8tfn8Hg\nLpt33cnh33mU3g3bFrxuNHWQzfq5lt+Q9d08zThJutfc7tS8XWLZt+zv35M9x43InqmfI7EEVwI7\nSY0+v8BdM8kde5SCDHL+DZ/FT5muz7yJkz/+ynIsd0lMTV5as23Rr5Ho6MGUAtmCQl8YsfekOtcv\nTuhDyea4w64KoY/LLOXgws1ILu7wkVy6cf/IQmaOkr+60LvDR4rOOMFyqUhQlCFYX6H3ilx/D52k\nGbp+oSnv75VU5gI3gttn7IG4JZajV88u63sXchk2m/0Ueu6YcXys+152GGc9GZxZpsnO0e9xJv4Q\n+x58mNL//S3GfN3s+dYvc/TL/7RcS18U7uSlzvWLq6EHu7EoLeJNaSyqhjV+DVMKetZvW9T90Q67\n0tyYbGxFUdsLvbQsEjKLFfbmwuAO4Z49m3U5CVt5Sv7qm8Uhp87e7aQt5O3GLhGob+rGK1277Zzz\n4JmnmvL+XrBMk82lPjKpPTOOT5VY1uhief75x2vaEL165hk0IQlvvmfG8eD2lxAWJU8GZ+eOfoce\nJrAOvBmA9Vv30vme73MhdJDDz/8BT338P7TMU5U5dg1LCnprmLxUiaxI4C+2nt+NlhlgRHQTCC6u\npDk+ZWzW2Iqithf6bGYCv7AQVZwrXQJRW0z1TOOGj0RkHjNYXejdhirXXdNwBoOLJkX0W/Yfpig1\njKutuyE7ePU8UWHgW3dwxvG1m3baJZa3Lnl+rWvnj7Pny2/h6Of/2vM9k5ftP5v1+x6ccXzLoZ8E\nvBmcTT73eQwZYN/L3z51LNXVy673f5Ojydfykit/zzN//ystUZHjz/QzKjoJhpZWIJCv0J3dCkQL\nNxgP1DZwZDqpbrvsVOYau9Hc9kKf8WhR7OIO4TYaOE4wJvPIBcYIukSTtvum5bhrGnm7OsgXrI8X\nfa2EwlGu+reTGGvdDtmbF22hTW29e8bxxZRYDp/8AQDxvm96vkcMvsAEcdZumjlEumfdFgbE2qoG\nZ5Zpsv3mdzkdu39OiWsoHOXe932Wpzb+Cg/eeoxT//VnmjKmbjrR/ABjS6ihd7G7s1vP76ajNEwu\nsoTS0UiMnAxDXkX0dSXvOFcGqlgUu0TijR0+UjR0wqKEXGCMoEssPnPKVFG3I3ot1JyIHmCs4yBb\njPMtkzqYjT5gVwVt2nvvnHPj4c101OBiKa8/A8A+/QSTY97K4zrTZ+kP7arYI3EjeXdVg7Pzxx5n\nLbcw97254nmfpvGSX/9bjhz8Y+4uPMOJr/6Dp3UtF52lYbLhxTUTTacYTBEzW0voLdOk1xqlFF+8\n0ANM+pL49cbuP7S90Bcm7UekkAdDM4CIEzU3SujzTqmkqDJ0BOwoNCfDU+6axbz9qxZqTkQPIDbc\nQ5J8y5p0BZ1hI7EK9hJ6YhvrzEHPJZZrJk8wQid+YXHhyS9Wvb5UNNha7iPbebDieWvTg1UNziae\n/RxFqbHnFT+74Hs98G/fzy1S+AaPVV3XcmGWy7YQJhbnQz/jtUKdJGVrWRXfGr5OUJj4OhZfOgqQ\n1ToIFhs7l7rthb6YdZwrU96E3vWbkUZjoglX6H0ehB6cKVPO2kqGnbrxN1Hou50N2aFzrbkh25O/\nxM3IzornRPcOzyWW6YlbbDGvc3Hz27lFCnHuG1XvuXbuGEFRxr/pUMXzaw6+Epjf4ExaFluHv8OZ\n6H0kOxb2aRI+HwPh3XRllreKaCFGh64SECa+JdTQu8hIJzGht9TYxVsDdrNUuGdxPvsuBX+KSKmx\n+w9tL/RlZ9Mj5lHoY/GUXcOrN0boC1l36Ig3oS/4Yvid4SOmI/SBiDd7h+Vgy777MGSAcgt2yBp6\nno3mAHrXvorno+v2AjBytfrTyNUTT+ATkvjul3Kp82XszjxdVYRuXbTz7727H6h4vprB2cUTP2Y9\nIxh73lR1fQC5roNsKV/D0PPVL14Gxgfsje1Iz7Ylv5Yvaj9Zp8cbP3ZvPnI3+wBIrltaRVEx1EXC\nbGxFUdsLveU0XSQ6vAm98PlmRM3Ljbvp648s7Fzpovui+J3hI2Xd/jVYp8HgiyEQDNEX2EFi/GT1\nixtM//nj+IVFcMMdFc/3bLU/APKD1fsAspeexpKCrXe9guCBN5Ikz/lnFt6UlTeOk5chNu2s/P7V\nDM5Gn/ksJamx9xX/rur6AIKb7iEgTK6f896IVU+yN+2u0dT6HUt+LdfBMttCQl8asweO9Gys/ITo\nFTPcRbLBg1XaXuhFYYKCDBKOeo9680TxFRszfKSUd8bLxb01dBW1GKGpiN6O3ILh5kX0ABMdB9lm\nXGiobYQXxpxhIz077ql4fs1Gu8TS8uBiGR1+jmvaZpId3ez9iTehywDZEwt3piYnznAtuHNB19T5\nDM6kZbF58NuciRwi1e1tnMPaPfcDMHaxOfYIpTG7a7R309KEECCYsIU+30IOlmLyOmliJFLeCjvm\nQ0a7iQqjoRVSbS/0vhqcK10KvsYNH7k9RtBbRF8KxAlbtsDLov1rqIYPseVAbLyXmNC5frG6742h\n57n2nw7w9L/8ybKvyxw8iSEDbJwnorZLLNcTqlJiaZkm2/TT3EzZpmiRWIKzscNsGf3BvBUzlmmy\npXiJydT+BV97PoOzy6eeYZMcRN/1xgXvn86G7fvJyghy8AXP99QTX7qfcZJEPQYtCxFJ2VYBhofG\nossnj/DC9z675PesRig/yIi2+Bp6Fy1uZxcmbw0t+bW80vZCHyhOkvNVL12cjq5FCZYbE9GbBVvo\nqw0dcSn7b0+ZkiVb6Gt5WlkOevc+BMCIhw3ZE//6MbZYA3RfWX6flujEefr9m/EHgvNeMxbeTIe+\nsItl/6UXSZGDTfdPHSvu/Gk2yJv0nalcBz9w+SQxoePbcHfF8y7zGZzdPPIZTCnY5TFtA3Yq6Fpw\nJ6nJ5mzIRnIDjNZBCAFiHbbQlzLVhb7w5d/l7id+nSOf/mBd3ns+kvogmdDSewQCjrFZZkwJfd0I\nlia9O1c6FP1xgg0aPuJu+noVeiuYICZnRvThJm7GAmzefTd5GaLcv3BuWFoWPSc+AsDO8iXGbg4s\n67rW6ZcYiy/suWIktrK+iovl0Cm7e3XtgZdPHdvx0n9rn3u2cpnlzfP2B0DnzsMLvn8lgzNpWWy8\n8U3Ohu6a1z10PtId+9lSvNSUeaup4hCZOtTQAyQ67Q8Mq4qxmVkus904R16GePDsn3PkM8vniN5j\njWAswWffJZKyf2+FBhqbtb3QR8sZjBqFvuRv3PARaWSwpCAa87ZGGUoSE7r9H7mUR5cBT5OzlhN/\nIMjV4C5SVTZkT/7oK2y3+jjS/VZ8QnL5ma8t25omx0ZYwxhmd+WKGxfRvZOwKHHzxpV5r5H9z5Im\nyuY9t8ske9Zt4Zx/L9393614T7H/GEWpsWXffRXPT2e2wdnVc8+zxRogu/Nnqt47G9+Gu4kKg/5L\njd0cl5bFGvMmxXhtH0zzEYunKEoNmV+43vzaueeICoNTd/8Rx6I/wYNnPsiRz/xFXdYwnczkGEly\nyNTSewRiU343SujrRtTKUPLoXOliBuNEGzR8RBgZsiLiedCwCNtpqFx2El+pgC7qOy92sUx23sHW\n4qUF/VasJ/8Ho3Rw16/+HRPEkRcqi2Q9GDhnb0hGpg0bqURsvW12NnrtzLzX9I6/QF94/5wZqOOb\nXsue8nlGb8wdpxgfO801/zZPni+zDc4Gn/oMlhTsfPkjVe+dTfcuO700cuGZmu9dCuOjg0REEZbY\nTOQifD7SIoFWpYN05KydLlx356s4+L4vOmL/Zxz57F/WZR0uo07paKBr6b+/ZJed/ilnG7fR3PZC\nn5BZrFBtQm+nRwrLtKKZaKUsebxbGLiNVfnMOKJcwKA1hN6/6V6iwuD6+cqdmVfPPMfd+rNc2PoI\nkViCS4n72TZ5ZNmsEzLX7A3JdbvnWh9Mp3uLHfHnblQeFJ5Nj7PVvEqud+7rrH3gbQBcfvILM45L\ny2KTcYGx5MJPEy6zDc7W9X+Ls8GD9GyovTFny957KEo/5f7GzqS95QhhqHtpzUTTyfo8OFgOHCVN\njI077iAYCnPwfV/kePQlPHj6Tzny2b+q21rSzsCR2Nql1dCDXepdlj5kTgl9XdALOaLCgEht5VAi\nlCIkSktqPJm8Ncwz/+0dDPcv7I6olXLoPu82w+6UqUJmHM0sUGyRiH7NPmdD9vyRiueHv/0hdBlg\n78+8FwBzx0/SyzhXTi9s6rVobp4mTYw1Gxb+j7l20y4MGUDO42LZd+KHaEIS2/HQnHPb9t3HDbGW\n4KWZ9fTDA5fpJINct/DThMt0g7Nr54+z3eojveMNnu6dTSAY4qp/G7HxxlpSZIYvA5Bct/Qaepe8\nliJUpYO0e/IkfeF9U09bwVCY/e99lOORh3jw9H/hyOe8O40uhD7aZ7/fhqWXjvo0jUmRaKjfflsL\nfXbc/sT0alHs4qZHspOL/4s4+8nf44GJr3Pl+x9f8LqAx+lSU9c7NspGdgLN1DFq+JBYTjbtvNMu\n7RuYG9GP3Rzg7lv/ygs9b5jaXNz2gN3tefP415dlPan0BfoD26umxHyaxqC2bt4Sy4xTDbP17lfO\nOSd8Pq71vpJ9+efIT5tINnTWTpuktlfPz7u4BmcDT34agO2LSNu4jCf3scm42FCjueItO33Vs2l3\n3V7TCKSIlucvc85nJ9lW7iPXM7OyKRSOsv99X+SFyIM8eOo/c+Rzf7PktVgT1ylKje61i5+FO52M\nL0XQWAFCL4TYLIT4vhDitBDilBDifc7xPxFCDAghjjtfiwtN6kDOabbwx2uL6H0RZ2RfZnF+FBeO\n/5D7R78MQGRg4bmgQTOH4WG61NT1jo1yMTeJ3yxQ8rVGRO/TNK6GdtM5MXcT8NxX/5aQKLHu4fdP\nHVuzcTt9vi3Erv+g7muRlsXG4pU5w0bmYzy8mU69sotlZPgYV32bSHX1Vjwfv+tNhEWJc0/eLhct\nXHseSwq2HKhsfVAJ1+Bsd9+nOOffN8fWuBbkurvoJMPwwOVFv0bNTFwnKyNVPXlqoRRMEbPm7yDt\nO/kUmpBEtj8451woHGXf+77kiP1/4uhXP7yktQQyA4z4eubs0yyWnD9FqIF++0uJ6MvA70gpDwAP\nAe8WQhxwzn1ISnnI+VqekM0DeafZIlij0LtRcyFbu8OcZZpYX/1dxkWSo4nXsKvwIqWiMe/1YStP\nuQahD09NmZogYOqUfUsb8FBPMl13srV0ZYYHjF7Isefap3kh8iBb98409xrqfSl79JN17xAcun6B\nhCjAmgPVL8YusazkYikti62F0wwn50/B7H3gp0kTpXT6dgVR+NYprmsba2occg3OephgfNvrPd9X\nidQO+0li8ExlD53lIJQbYERb47mowAtWuJOUzMz7ZJK+aG/Ebr7jZZXX5Ij9JW07qWP/c0lriemD\nTNTBZ9/FCHYSM1eA0EspB6WUzzvfZ4AzQH1qq+qEkbYNzcIeLYpdAtHFDx85+uV/YG/5LJcP/T5i\n/88QEzqXT8w/Li5i5Sh7GCM4dX3CTkOZhTQBS6estUbqBiCw+V5CosS1s7cNzk58/SN0M4n20vfM\nuT524GFCosT5Z/61rusYvminj2YPG5kP0bOrYonlwOXTdJKe0Sg1m0AwxPnES9g18aOp2vUN+XOM\nxPbWtGbX4Axgy8veUdO9s9my/34sKdCvN25DNmkMkq5DM9F0ZLSbkCihFypXwAWHnueGWEP32vlL\nHkPhKCOb38Bu82LF6iivdJWGyUfq0yMAUAp1kVzgaaXe1OXjVwixDbgHcHfi3iOEOCGE+JgQomKC\nXAjxTiHEUSHE0ZERb0McaqXolC/FUpUfu+djajZrrjaHucnxUXa+8Fec9e/nvjf9Jtvu+2kAxk7N\nX0YYlQWsoPfO3agj9JaeJigNTH/rCP26/S8BYOyC/c9AWhZrTv0vLmnbOfgTc1v5d9//MLoMUDjz\n7bquo9BvT7zasNdbjnyqxHKWi+XgqScA6N3/0oVfYN/r6SLNhWOPM3ZzgLXcorz2zprW7NM0LiQe\n4HTwTjZsq+1DYjbReIrr2kbCt04t6XW8cv75x9le7iPf423z2Suas7c2OVZ5kPaG7BkG45W9/qfT\ne589tOXyU9VnCFSiVDTokWOUk0uvoXexIl0kZaZhjW1LFnohRBz4AvBbUso08I/ATuAQMAhU3AmR\nUn5YSnlYSnm4t7c2Ifa8tqtPkpERejZsq+m+SMIdPlJbRH/mU39Ip0zjf9Nf49M0utduos+3mdhg\nZWsAyzSJi4Kn6VIusXgKSwrQ04SkjqW1Tupmw7b9tu3uDTuifvGJL7LNus7Ynb9e8ZE+HI1zPnIX\n60cX3seolcDoWYbo9Zwv7tli+9HkBmeWWFrXjpCVEbZU+cDY/RNvoyQ1xo89xsAZ+0MuvnXhss5K\n3PmeT7Pjt72PKVyIkdhe1ucrl4zWE8s04eu/x5hIccfb/0NdXzvgGJvlKjhYjg5dYx0jlNZVNqyb\nzo6DDzBMN4HLiwsoRgf70IRE66jPRiyAiPWgCUl6fHmC3NksSeiFEAFskf8/UspHAaSUw1JKU0pp\nAR8BvO9I1ZGiobN34gec7XhFzYOKXTsC14fGC1dOHeHw8Od5tuct7Lr7ds5wuOt+dhVOVszT55xK\nDVGD0Ps0jRxhKGYIYWC1UEQvfD6uhvbQPelEkk//PSN0cvfrf23ee/KbX8lW6zpD16s7SHqlO3eR\n4Yj3Mr81G3egywDy1szNy+6JF+kL763aeZzq7OFc+E7WD32f7FX7Q27zgbnlmNUIhsKE62Q5XV5z\nB+sYZWLUm5/K6NB1nv/G/1dzpc7RL/0P9pTP03fvB5bs6jiboGP+VahgbNb/4o8A6Nj1kqqvI3w+\n+rpfxp7s0UWVTI/fsP9dRHrr1yPgd35v6XmeVurNUqpuBPBR4IyU8r9OOz49kfU2oClG5ad/9CWS\n5Ane/faa7405Q5il7i11Iy2L/GO/Q1ZE2fuOme3XgV2vJCoMLh1/Ys59+Yy92et1utTUfcK2UQ5L\nAxlo3rzYSmS772RLuY8Lx3/IXfpzXNz+cwt+0K67127zv/ZMfUzOiobOJrOffKf39IdP0xjS1hNK\n387R26V7V8hUaJSqRHbbw2yzrtNz7RsM0uvZWni5iG21n0Kun67c1zCbvk/+Fvce+S2effS/eX6P\nybERdr/4N5wJHOS+N/7Gota5EFFnhoS71zadQt8RytLH1juqCz1AaP/riQmd88/UHtXnR/oA6KiD\nz/7Uehy/m9x4Y4zNlhLRvxT4ReAnZ5VS/qUQ4kUhxAng1cBv12OhtVJ+4fNMEmP/SysPVV6IYCiM\nLgMIw1s1yHPf+CgHiy9y7uBv09Ezc0Nqu5Onnzj9/Tn36U75ptsE5ZWCL0qgOEFQmBBonYgeILTl\nPnuu5lfeS16GOPDG9y14/da993KTLvxX5v75LIaBiy8QECaB9ZWtiedjPLxpRonllRM/xi8soju8\nCcmWl9gBxe7yBYai3so6l5PNB+ySw9zV6pO/MpNjHJx8gqL0c+jFP+XCsblBSSXOfvIPSMoMwTf9\ndV2rbVzinfaHZSWrgPjoC/T5txOJeXsa3vPQGzBkgNzJ2v2Vpnz2lzhwZDqusZk+2eKpGynlj6SU\nQqyfmtEAABTKSURBVEp51/RSSinlL0op73SOv1lKOVjPBXtBL+TYN/kjznW8sua0jUtWxKaGcC9E\nLjPBlmf/jIvaTg6/7bfmnO/sXc8V3zZig3Pz0AV3ulS0NosGwxcjVnSawYKtFdGvdzZkd5qXebH3\nZ6pGtsLno6/jIXZmj9ZlY+rWZbvSpHtH5Tmt82Ekt7PeHJoqsUxfsCultt71Ck/3b9i2l8u+bQDo\nvbVtxC4HHT3rGKKXwM3qMwLOfOcTRESRM6/8e8ZEJ/HHfnXOIJTZXD55hMMjj3K0923svOsn6rXs\nGSQ67b272cZm9nyAs9zq8P7nHI2nOBc5xMaRH9a8Dl96gFuk6moHnui2A8JSg4zN2rIz9swPv0hc\nFIjc838t+jUKIupp+MiJT/4xaxij/Lq/nDeXO9x9P7v0U3Pyg25VT6hWoffHSZbtrjoRbN4YwUqs\n27ybMZJYUrDhp+d+8FVC2/0aUuS4cHzxzVOjQ9d46p8/wObn/oKCDLJxl7fSShfRvZOQKHHTaTIK\nDz/PdbFhzhPaQgxvsD1rIluqbxA2gsHobnpz56pelzj7Wa76NnHXq36W9Js/Srccp+8jPz+vdbO0\nLPTH3k9axNn3juWzBQ5HYuRlCGZZBVy/+CIJUUBs8t55DFDY9ho2yxtcv1DbYJZI/gZj/vr47Luk\nHKGXublpqeWgLYXefPFRxkmwv0JJn1d0LUagtPDwkWvnj3Pfjf/Dsx2vZ9/9r533uuCuVxIRRS6/\nMDOaKOUdoa9xIk/ZH6NLOvn9Fovohc/Hhd6HOdr9Rjbv9ia2Ox98I5YUjJ+orZ7eMk1O/vAxnv/r\nN5H6x0O8pO8fuRnawqWf+mjNT3Kx9Xbr/ujV00jLYkv+FEPJ2iLzra99F8/FX8Wu+3+6pvuWC73n\nDjaZN8gt0OHdf/Ek+0unuLHt3yB8Pvbc+0qOHfwAd+vPcuQTlatonvvaRzhQOsmFO96/7HsRaZFA\nM2au/+YZeyN27b4qZa+z2PygbUI38MxjNd2XKg6TCdWvhh7sD7GcDEO+McZmbSf0hVyGA+kfcb7r\n1QtOFqqGocWqDh+ZePR3MAiy/ZGFXfJ2Hn4YSwomTn9vxnG3qsftdvVKORC38/OAFmotoQd48N0f\n5YH3/ovn6zt61nExsJvOwR95un58ZJCn/+VPGPgvd3DHd3+J7dljPLf+Ea7/wo+48w8f546X1b4v\n07PV7qLNDV3gRt85upnE2jh/o1QlNmzby32/+xjxZG3eSstFZPM9+ITk+jxTsACuP/5RTCnY+Zpf\nnTr2wNt/h2dTD/Ng3//kxR88OuP6bHqcrc99kAv+3dz31vcu29pdclqCwCyrAKv/ObIywiaPgYTL\nhu376PNtJn7Nuz22tCx6zZsU40sfODKbSV8SfxUb5nrRdkJ/5onPExUGsXsXn7YBKPoThM35I/pT\nT36du/SjnNrzm/SsW7i+NtW9liv+7SRn1dObU9OlaitLs4K3N2+1UGulbhbLrXUvY1fxLJPjC0c4\nR7/6YaJ/dycPXfwQWX8nR+/9CyJ/cI6H3vUPbN61+Nz4mg3b7RLL0YtTjVI9+19e5a7WZt0+u7J5\n8krlDVnLNNne/2VORQ6zZuNtl0/h83HHOz/GVW0rm77/XoauXZg6d/KTf0Qv41gLpCrrScGfIlya\nWf3WNX7CU9lrJYbWvII9+otk097sTSbHbtoOuHUYODKbrNZBsFi7zcpiaDuh59QXuUWK/Q8tzUvN\nnDaEuyKP/zmjdHDobb/j6fVGuu9np3F6Rju31O09gFiittTN9AarQLi5YwTrRcedr8MvLC4d+eq8\n1zz/zf/NoWf/gEuhfVz52e+w/4+e5PCb31WX2vOpEsvMVcyrR8jLEFv31d701Eqs3biDcRKIoRMV\nz59+8qusY5TSnXOdMiOxBP6f+xf8skz6Ez+Hoee5eu449w1+mmc63sDewz+53MsHoBhIETFv97Po\n+axd9tq9uC7cxF1vJChMLjz1ZU/Xj/TbH3LB7m2Ler+FKPhTRKrYMNeLthL6XGaC/ZmnuNjzmiVH\nG2YwQWyeKVOnn/oGB4svcHHPv/e8Ex/e/SrCosSlY7c3HIWRJi9DNaeYxAyhb4+Iftc9ryIjI5TP\nf6fi+Re+/znuePJ9XAzsZct7vsL2A7WlVbzgulh2jb/AldC+JaX+WgHh89Ef2kVXuvKwcP3Z/02a\nKAd/srK3zuZdd3LxpX/NnvJ5jn/kN0k/+tsURIgdj9R3etNClEIdJKZ5wvSdepqAMAlvm+tY6YU9\nh19jm9Cd8bYfNP74P1CUGhv2194AV41iqIuEWZvNymJpK6E/88TniIgiift+dsmvJUNJYugVKw8s\nN5p/q/cWge1Onj595nae3lfKkhe118FrkdtPAME2EfpAMMTF+H1sGXt6TnfmyR9/hb2P/ybX/NtY\n/+6vLlsO3EhuY4M5yLbyFdI9tZVntiq5zgNsKffNcBQFt3b+B5zpfnjBJ6J7Hv4Fnlr/Czx460vc\naTzP6b3vqZqqrCdWuJOkzE79m5i4YKc/N83jWFmNQDDEhcSD7Jh4csGB8GDbjR8e/wbPr3+EdVvq\n57PvYoa7SMrGGJu1ldBrp7/ECJ3se+DhJb+WCCXwCTllU+By5sg3ucM4zsXdv1ZTXW2qs4fL/h0k\nh29bx2qlLAVR+2bq9AarYB1re5tNceurWMcI1y/eTjWcfebb7PjWrzGkraf7XV8j1VmbE2ktiO6d\nBEWZgDCJeGyUanX8mw4RFCbXzz0/47hbO596yS9VfY37f/VDnAgf5rx/D4ff/nvLtdSKiGgXfmGR\ncXLq/sFjDNNNb43+VdOxdj1MDxNcWsBVVloW5a/9PuMiyYFH/vOi32shZLSbqDDqbtNdibYR+szk\nGAeyR7jU+9q6DAdwh4/k0jN3xcvf+yCjdHB3DdG8y2jPA+w2zqDn7U1efymLrtUekQem1d2HIu0j\n9Jvvt6dO3XjO7l68+P+3d+axUdxXHP+8Xe8ae21DDAZsgzE22EAwdzgaVJEDQtMoJApVUdPmUCOU\nqqmapm1CVKlNKzVpKvVSlbZKkzRRjyQkTQjN0UJCSJSKcpvbAXMEzGWMMfgC28uvf8wsLMQY73p2\nZ3b1PtJqZ3+2Z7487Tx+837v996WTyh+5xs0+vLJeeBtrilwNsXtckJFF3e0lvRyo5TXGVxhLcie\n3LvhkvGcmtc46CumcsoNVz1HRiBI1aMrKXvsv0kPZ/myrUSF5kZrY1Fhy3YOh65esbInymct4LwR\nGjZfuezGxveeZ2znTvZWfc/RZirR+O16N6dPJr4MQto4+k8/WkqmdDLguq86cj5/lt18pPniqnjN\n2hVUndtM7aj7e731OpqsyjkEpYvaTVb4Jhhu41w8jj500dE7uVvPbYpGjuGQFJF1cDX7d65n4JuL\naJUcMu7/V1yNsmOlwE6xrJOhF1oepjrDyqtoM5mYIxc3CdXVbmdc53YOj7iz16ULxOdzZc0imGs5\nw9amek6dOEqxOU7HkL6F1fIHF7MnUMnAI6u7/Xl7azPD1v+CWn85Uxd8p0/X6olArrXzt7lRHX2v\nyahZxjEGUTHVmWyAQLYVHjnXcnFVvHPVU5ykPxPimM0DjJwyl7ARWmpWA5AZbqUzDkeflXMxRt0v\njWb0AEcGzqKirZrcpQvpIoPwPW8lJD7aHQWFpbSZTI720FEq1fD5/RwMlpPXtOvCWN3qF6zc+Zsf\ncFFZ7+hnlyo+e6aBg9usDYe5vahYeTUai+dQ0bWbhmOfbyFZ/erPGEoDHXOfTGgKaaTeTXtT4ssg\npIWjP32qgXGt6zgwZK5jPR2D9iamSJepmnUrqTq3iT2j7o+pRVw0eQMGsjcwmv52nL6faaMrELuj\njnSZajdBx/69XiFzzFyypAMf52lb9AbFZX17TI8Fn9/PvrnPMfyuJ5N2zWRwuv8YSjr2Wm0uw2FK\n65azI2vqJbnzXiVrgOUMO5obaNu/jrARSqv6Xltn8NQFAOxbs+yS8WOHapn02YtszJnDuJnz+3yd\nngjlW7uKO84kvrBZWjj63R+9QlDC5M/4fD5wvGSFLvZmBehY9RSN5DHhjkd6+rOrcrJgOuUdNbS3\nNpNt2jgfiH1GH6mXf1a80RjcScbOXsD/ht5N08LXXcljHz/79qQ9QSQLKZxISM5yeP9Odq55h6Gc\noKOb3HkvEilsFm5tJHSimoP+EYRyY9tJ3h1l42daVVNrV1wyXrf0UQRD0VcSn0Kal2/Vu+lqUUff\nK4I1yzgigxk9ybkFtKw8axEo3HaGmg0fMOHsRnaX3Rf3bD5CqOIGghKmduP7hGJsI3jhHHaXqXOk\nn6PP7JfNzAf/QNn4+PKklc+TP2oaAPW719Fu586Pv6FvfWmTRZ7t6M+3nqTk7C5O9HfmCU98Pg7k\nX09Fy/oLqac1a1cwrfkDqoffQ+GIvrVz7A25AwbRZXxJKWyW8o6+qeEY49o38dnQeY7WxI5uPtLx\n/lOcIo+qXu6C7YmyqTfRZXy07vg3AQlDDN2lIojPR4tkcc7nnTaCincZXjmFTuOna98njG9aza6B\nc1NmET8jEOQM2WQ3bGEALVA8zbFzB8bMJ0fa2b1+BefDYfwrHqeefCYs+olj1+gJn9/PacnF166O\n/qrs/ugVAhKmYKazM5TsUB5hI4TqPmbC2fXUlN3nyCNjTt417A1UMLzearQRa3epCG1k05mGoRvF\neTL7ZXMwo4TJJ5bbufP3ui0pJpoll8o2q0XjoBgrVvZExazb6DAZtGx7lw3Ln2F0uJaDUx7r81N7\nLDT78j5XtC0RJL4qUYLJ2v0WdVJIuQMLNNGIz0erZFN1bjOnyGWCA7P5CI2DZ1B55CUg9u5SEdp9\nITp1Rq/0ksbcMZQ37e917ryXaPPnUWyO02YyKal0rtZ/KHcAW7MmMrx+FZnH3+PTjDFMvW2xY+fv\nDUO+v4YRDvUJ7omUntE31h9m7NlqDhXdkpBWZq1Yu1ZrRt7jyGw+Qk7lnAvHGVnxzR5OZZfSHEp8\nbrmSHoSHWCmjseTOe4X2DOseOZBZ4Xguf9uImyk2xxlEE3Lr00m3TVYoNynXTNgVRGS+iHwqIrUi\nsiQR12g8up9D/uEMmfW1RJyedl+IJnKoutPZbd/lU2+iw1hpkYEYu0tFmPzIMqY//LKTspQ0pmTW\nXWztN43RtzzotpSY6Qhak6wz+c7vb4g0I1nf/xYqpsxx/PxeISGhGxHxA88Ac4E6YL2ILDfG7HTy\nOqMmzoaJ3ZdgdYLTM39IS6AfExwuopWd059dwUrGdu6MubtUhFSblSnuUlRaSdGS3jfc8BLhoHWP\nBEc4X7G0uGwsO+a9zDiHQ79eI1Ex+ulArTFmH4CIvAIsABx19Ilm8ryvJ+zcTYNnwOGdZIaSt/Cj\nKKnI+Wxrd2zRtfFVrLwa136hb70rUoFEOfpiIHpvcR2gidFRjJz/EGveCzNj5Di3pSiKpxlx4wOs\nXTeY6cPK3ZaSsriWdSMii4HFACUlJW7JcI2hw0cxdPHv3ZahKJ6nqLSSotLklkdONxIV6D0MRHcn\nGGaPXcAY86wxZpoxZlpBQUGCZCiKoiiJcvTrgdEiMlJEgsAioHdNGhVFURRHSUjoxhjTJSIPAf8B\n/MALxpgdibiWoiiK0jMJi9EbY94F3k3U+RVFUZTeocnYiqIoaY46ekVRlDRHHb2iKEqao45eURQl\nzRFjjNsaEJETwGd9OMUgoMEhOU6j2uJDtcWHaouPVNU2whhz1Y1InnD0fUVENhhjnGs94yCqLT5U\nW3yotvhId20aulEURUlz1NEriqKkOeni6J91W0APqLb4UG3xodriI621pUWMXlEURbky6TKjVxRF\nUa5ASjv6ZPSljRcROSAi20SkWkQ2uKzlBRGpF5HtUWP5IrJSRPbY7872S+ybtidE5LBtu2oRcaUF\nkIgMF5EPRWSniOwQke/a467brgdtrttORPqJyDoR2WJr+6k9PlJE1tr366t2ZVuvaHtRRPZH2W1S\nsrVFafSLyGYRedv+3He7GWNS8oVVFXMvUAYEgS3AOLd1Rek7AAxyW4et5YvAFGB71NgvgSX28RLg\naQ9pewL4gQfsVghMsY9zgd3AOC/YrgdtrtsOECDHPg4Aa4GZwFJgkT3+J+BbHtL2IrDQ7e+cresR\n4B/A2/bnPtstlWf0F/rSGmM6gEhfWuUyjDEfA42XDS8AXrKPXwLuSKoomyto8wTGmKPGmE32cTOw\nC6tNpuu260Gb6xiLFvtjwH4Z4EbgdXvcLbtdSZsnEJFhwJeB5+zPggN2S2VH311fWk980W0MsEJE\nNtptE73GEGPMUfv4GDDETTHd8JCIbLVDO66ElaIRkVJgMtYM0FO2u0wbeMB2dvihGqgHVmI9fTcZ\nY7rsX3Htfr1cmzEmYref23b7jYhkuqEN+C3wKHDe/jwQB+yWyo7e68w2xkwBvgR8W0S+6LagK2Gs\nZ0LPzGqAPwLlwCTgKPArN8WISA7wT+BhY8yZ6J+5bbtutHnCdsaYsDFmElYb0enAGDd0dMfl2kRk\nPPA4lsbrgHzgsWTrEpHbgHpjzEanz53Kjv6qfWndxBhz2H6vB97E+rJ7ieMiUghgv9e7rOcCxpjj\n9s14HvgzLtpORAJYjvTvxpg37GFP2K47bV6yna2nCfgQmAUMEJFIsyPX79cobfPtUJgxxpwD/oI7\ndrseuF1EDmCFom8EfocDdktlR+/ZvrQiEhKR3MgxMA/Y3vNfJZ3lwL328b3AWy5quYSIE7W5E5ds\nZ8dHnwd2GWN+HfUj1213JW1esJ2IFIjIAPs4C5iLtYbwIbDQ/jW37Nadtpqo/7gFKwaedLsZYx43\nxgwzxpRi+bNVxpi7ccJubq8w93F1+lasbIO9wI/c1hOlqwwrC2gLsMNtbcDLWI/xnVgxvm9ixf4+\nAPYA7wP5HtL2V2AbsBXLqRa6pG02VlhmK1Btv271gu160Oa67YAJwGZbw3bgx/Z4GbAOqAVeAzI9\npG2VbbftwN+wM3PcegFzuJh102e76c5YRVGUNCeVQzeKoihKL1BHryiKkuaoo1cURUlz1NEriqKk\nOeroFUVR0hx19IqiKGmOOnpFUZQ0Rx29oihKmvN/dcxmlJz+5I8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x8067828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(epochs)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def next_time(rate):\n", " return " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

NicovincX2/Battleship

battleship.ipynb

1

25546

{ "cells": [ { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# 1.1 Import tensorflow and other libraries.\n", "# http://efavdb.com/battleship/#mjx-eqn-rewards\n", "import tensorflow as tf\n", "import numpy as np\n", "%matplotlib inline\n", "import pylab" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"Placeholder_63:0\", shape=(1, 10), dtype=float32)\n", "Tensor(\"Placeholder_64:0\", dtype=int64)\n", "Tensor(\"Placeholder_65:0\", shape=(), dtype=float32)\n", "Tensor(\"Variable_84/read:0\", shape=(10, 10), dtype=float32)\n", "Tensor(\"Variable_85/read:0\", shape=(1, 10), dtype=float32)\n", "Tensor(\"Tanh_21:0\", shape=(1, 10), dtype=float32)\n", "Tensor(\"add_44:0\", shape=(1, 10), dtype=float32)\n", "Tensor(\"Softmax_21:0\", shape=(1, 10), dtype=float32)\n" ] } ], "source": [ "# 1.2 Define the nn variable network.\n", "# Input is array of BOARD_SIZE values.\n", "# ---------------------------------------\n", "# -1 value -> Not yet checked\n", "# 0 value -> Checked, no ship\n", "# 1 value -> Checked, is ship location.\n", "# ---------------------------------------\n", "BOARD_SIZE = 10\n", "SHIP_SIZE = 3\n", "\n", "hidden_units = BOARD_SIZE\n", "output_units = BOARD_SIZE\n", "\n", "input_positions = tf.placeholder(tf.float32, shape=(1, BOARD_SIZE))\n", "print(input_positions)\n", "labels = tf.placeholder(tf.int64)\n", "print(labels)\n", "learning_rate = tf.placeholder(tf.float32, shape=[])\n", "print(learning_rate)\n", "# Generate hidden layer\n", "W1 = tf.Variable(tf.truncated_normal([BOARD_SIZE, hidden_units], stddev=0.1 / np.sqrt(float(BOARD_SIZE))))\n", "print(W1)\n", "b1 = tf.Variable(tf.zeros([1, hidden_units]))\n", "print(b1)\n", "h1 = tf.tanh(tf.matmul(input_positions, W1) + b1)\n", "print(h1)\n", "# Second layer -- linear classifier for action logits\n", "W2 = tf.Variable(tf.truncated_normal([hidden_units, output_units], stddev=0.1 / np.sqrt(float(hidden_units))))\n", "b2 = tf.Variable(tf.zeros([1, output_units]))\n", "logits = tf.matmul(h1, W2) + b2 \n", "print(logits)\n", "probabilities = tf.nn.softmax(logits)\n", "print(probabilities)\n" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"xentropy_9/xentropy:0\", shape=(1,), dtype=float32)\n", "name: \"GradientDescent_9\"\n", "op: \"NoOp\"\n", "input: \"^GradientDescent_9/update_Variable_60/ApplyGradientDescent\"\n", "input: \"^GradientDescent_9/update_Variable_61/ApplyGradientDescent\"\n", "input: \"^GradientDescent_9/update_Variable_62/ApplyGradientDescent\"\n", "input: \"^GradientDescent_9/update_Variable_63/ApplyGradientDescent\"\n", "\n" ] } ], "source": [ "# 1.3 Define the operations we will use\n", "init = tf.global_variables_initializer()\n", "cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='xentropy')\n", "print(cross_entropy)\n", "train_step = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy)\n", "print(train_step)\n", "# Start TF session\n", "sess = tf.Session()\n", "sess.run(init)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "([[[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1]],\n", " [[-1, -1, -1, -1, -1, -1, -1, -1, 0, -1]],\n", " [[-1, -1, -1, -1, -1, -1, 1, -1, 0, -1]],\n", " [[-1, -1, 0, -1, -1, -1, 1, -1, 0, -1]],\n", " [[0, -1, 0, -1, -1, -1, 1, -1, 0, -1]],\n", " [[0, 0, 0, -1, -1, -1, 1, -1, 0, -1]],\n", " [[0, 0, 0, -1, 1, -1, 1, -1, 0, -1]],\n", " [[0, 0, 0, -1, 1, -1, 1, -1, 0, 0]],\n", " [[0, 0, 0, -1, 1, -1, 1, 0, 0, 0]],\n", " [[0, 0, 0, 0, 1, -1, 1, 0, 0, 0]]],\n", " [8, 6, 2, 0, 1, 4, 9, 7, 3, 5],\n", " [0, 1, 0, 0, 0, 1, 0, 0, 0, 1])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1.4 Game play definition.\n", "TRAINING = True\n", "def play_game(training=TRAINING):\n", " \"\"\" Play game of battleship using network.\"\"\"\n", " # Select random location for ship\n", " ship_left = np.random.randint(BOARD_SIZE - SHIP_SIZE + 1)\n", " ship_positions = set(range(ship_left, ship_left + SHIP_SIZE))\n", " # Initialize logs for game\n", " board_position_log = []\n", " action_log = []\n", " hit_log = []\n", " # Play through game\n", " current_board = [[-1 for i in range(BOARD_SIZE)]]\n", " while (sum(hit_log) < SHIP_SIZE) and (len(action_log) < BOARD_SIZE):\n", " board_position_log.append([[i for i in current_board[0]]])\n", " probs = sess.run([probabilities], feed_dict={input_positions:current_board})[0][0]\n", " probs = [p * (index not in action_log) for index, p in enumerate(probs)]\n", " probs = [p / sum(probs) for p in probs]\n", " if training == True:\n", " bomb_index = np.random.choice(BOARD_SIZE, p=probs) \n", " else:\n", " bomb_index = np.argmax(probs)\n", " # update board, logs\n", " hit_log.append(1 * (bomb_index in ship_positions))\n", " current_board[0][bomb_index] = 1 * (bomb_index in ship_positions)\n", " action_log.append(bomb_index)\n", " return board_position_log, action_log, hit_log\n", "# Example:\n", "play_game()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[-0.16904761904761903,\n", " 0.2619047619047619,\n", " 1.1904761904761905,\n", " 1.130952380952381,\n", " 0.8333333333333334]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1.5 Reward function definition\n", "def rewards_calculator(hit_log, gamma=0.5):\n", " \"\"\" Discounted sum of future hits over trajectory\"\"\" \n", " hit_log_weighted = [(item - float(SHIP_SIZE - sum(hit_log[:index])) / float(BOARD_SIZE - index)) * (gamma ** index) for index, item in enumerate(hit_log)]\n", " return [((gamma) ** (-i)) * sum(hit_log_weighted[i:]) for i in range(len(hit_log))]\n", "\n", "# Example\n", "rewards_calculator([0,0,1,1,1])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 1.6 Training loop: Play and learn\n", "game_lengths = []\n", "TRAINING = True # Boolean specifies training mode\n", "ALPHA = 0.06 # step size\n", "\n", "for game in range(10000):\n", " board_position_log, action_log, hit_log = play_game(training=TRAINING)\n", " game_lengths.append(len(action_log))\n", " rewards_log = rewards_calculator(hit_log)\n", " for reward, current_board, action in zip(rewards_log, board_position_log, action_log):\n", " # Take step along gradient\n", " if TRAINING:\n", " sess.run([train_step], \n", " feed_dict={input_positions:current_board, labels:[action], learning_rate:ALPHA * reward})" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1ef59252860>]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8VOXd9/HPLxsJkIUl7GBAUcAqAhFBUREUAbm1T7Vu\n1VatD7VqXap3i3VrsVZb7/tu3Qp1uVtr3bUuj7iLO4gGQVRk35ElrGFLyHI9f8zJMAkJmYRJzsyZ\n7/v1mhdzlsz8zkz45sw117kuc84hIiLBkuJ3ASIiEnsKdxGRAFK4i4gEkMJdRCSAFO4iIgGkcBcR\nCSCFu4hIACncRUQCSOEuIhJAaX49cceOHV1BQYFfTy8ikpBmz569yTmX39B+voV7QUEBRUVFfj29\niEhCMrOV0eynZhkRkQBSuIuIBJDCXUQkgBTuIiIBpHAXEQmgBsPdzI4ws7kRtxIzu67WPmZm95nZ\nEjObZ2aDm69kERFpSINdIZ1zC4FjAMwsFVgLvFhrt3FAX+92HDDF+1dERHzQ2GaZ0cBS51ztfpZn\nAf90IZ8CeWbWNSYV1lJaXskzn6+iqkrTA4qI1Kex4X4+8FQd67sDqyOW13jrYu6eNxfy6xe+4oNF\nxc3x8CIigRB1uJtZBnAm8FxTn8zMJppZkZkVFRc3LZzHH9UFgAqduYuI1KsxZ+7jgC+ccxvq2LYW\n6Bmx3MNbV4Nz7iHnXKFzrjA/v8GhEerUsW0rALbvKW/Sz4uIJIPGhPsF1N0kA/AK8GOv18wwYLtz\nbt1BV1eHvNYZAGzbvbc5Hl5EJBCiGjjMzNoApwE/i1h3BYBzbirwGjAeWALsBi6NeaWenMw0UlOM\nrQp3EZF6RRXuzrldQIda66ZG3HfAVbEtrW5mRl5WOlt3q1lGRKQ+CXmFal7rdDXLiIgcQEKGe7vW\nGWzdpTN3EZH6+DZZx8EoWrnV7xJEROJaQp65V6uorPK7BBGRuJSQ4X7RsF4AzF9X4nMlIiLxKSHD\nvW+nbAC+Wrvd50pEROJTQob7cX3aA3Dzi1/7XImISHxKyHA/NL+t3yWIiMS1hAz39NQUzji6K707\ntvG7FBGRuJSQ4Q7QrnU6yzft8rsMEZG4lLDhXq20vNLvEkRE4k7ChvuArrkAbNMYMyIi+0nYcG/f\nJh2AzbvKfK5ERCT+JGy4t/PGddcYMyIi+0vYcO/QNhTuOnMXEdlfwob7vjN3Df0rIlJbwoZ79XR7\nny7b4nMlIiLxJ2HDPTXFAHjjm/U+VyIiEn8SNtxFRKR+CR3uV59yGCkGVVXO71JEROJKQod7fnYr\nqhxs1peqIiI1JHS457UOXci0fY/6uouIRErocM/JCoV7SanCXUQkUkKHe1Z6KgBfrt7mcyUiIvEl\nocO92ktz1vpdgohIXEnocB/UKw+A0wZ09rkSEZH4ktDh3iotlaz0VH2hKiJSS0KHO4R6zGhMdxGR\nmhI+3NdtL+W52Wv8LkNEJK4kfLiLiMj+ogp3M8szs+fNbIGZfWtmw2ttH2lm281srne7rXnK3d95\nhT3plN2qpZ5ORCQhpEW5373AG865c8wsA2hdxz4fOecmxK606GRnprGzrKKln1ZEJK41GO5mlguc\nBFwC4JzbC8TNYC6pKcbuvZVUVFaRlqpWJhERiK5ZpjdQDPzdzOaY2SNm1qaO/Yab2Zdm9rqZHVnX\nA5nZRDMrMrOi4uLig6k77INFocdZtGFnTB5PRCQIogn3NGAwMMU5NwjYBUyqtc8XwCHOuYHA/cBL\ndT2Qc+4h51yhc64wPz//IMre5+pRhwFQqWF/RUTCogn3NcAa59wsb/l5QmEf5pwrcc7t9O6/BqSb\nWceYVlqP/LahL1M1eJiIyD4Nhrtzbj2w2syO8FaNBuZH7mNmXczMvPtDvcfdHONa61Q9MqTGdBcR\n2Sfa3jK/AJ7wesosAy41sysAnHNTgXOAn5tZBbAHON851yLtJG1bhQ7hmc9XcebAbi3xlCIicS+q\ncHfOzQUKa62eGrH9AeCBGNYVte55WQD075Ljx9OLiMSlhO87mJJidMnJ1OBhIiIREj7cITSXavHO\nMr/LEBGJG4EI945tM9hYonAXEakWiHDvkpvFd9v3+F2GiEjcCES4bygpZdvucv70xgK/SxERiQuB\nCPcF60oA+Ov7S32uREQkPgQi3B+//Ljw/YrKKh8rERGJD4EI90Pz23LlyEMB1CVSRISAhDvAEV2y\nAdimcBcRCU64V48xozN3EZEghXtmaCSFHaWalUlEJDDhnp0ZOnPfoaF/RUSCE+7Vo0PqzF1EJEDh\nnh1ultGZu4hIYMK9TUYo3J8tWuNzJSIi/gtMuKekGADpqYE5JBGRJot2JqaEcFzv9miabBGRAJ25\ngzeu+w4N/SsiErhwX75pF5VVOn8XkeQWqHDv0CYDgM2alUlEklygwr13x7YAbN611+dKRET8Fahw\n79i2+sxd4S4iyS1Q4Z6ZngrA/36y3OdKRET8FahwL+jQBoBtu3XmLiLJLVDhnts6nezMNI7uked3\nKSIivgpUuAPktU7XmO4ikvSCF+5ZGWqWEZGkF7hwz81KZ96a7X6XISLiq8CF+8dLNrF5116Wb9rl\ndykiIr4JXLgPLWgPwAPTl/hciYiIf6IKdzPLM7PnzWyBmX1rZsNrbTczu8/MlpjZPDMb3DzlNuze\nC44B9k3eISKSjKJNwHuBN5xz55hZBtC61vZxQF/vdhwwxfu3xXXJySQ91cjKSPXj6UVE4kKDZ+5m\nlgucBDwK4Jzb65zbVmu3s4B/upBPgTwz6xrzaqNgZpRXOqZ+sNSPpxcRiQvRNMv0BoqBv5vZHDN7\nxMza1NqnO7A6YnmNt64GM5toZkVmVlRcXNzkoqPhNOqviCSxaMI9DRgMTHHODQJ2AZOa8mTOuYec\nc4XOucL8/PymPESjlJZXNvtziIjEo2jCfQ2wxjk3y1t+nlDYR1oL9IxY7uGt89UmjesuIkmqwXB3\nzq0HVpvZEd6q0cD8Wru9AvzY6zUzDNjunFsX21Kjd+/5oR4zJXsq/CpBRMRX0faW+QXwhNdTZhlw\nqZldAeCcmwq8BowHlgC7gUubodao5bdtBaAxZkQkaUUV7s65uUBhrdVTI7Y74KoY1nVQcrLSAYW7\niCSvwF2hCqHxZQBKFO4ikqQCGe7VZ+4lpQp3EUlOgQz37FZpmKlZRkSSVyDDPSXFyG6VpmYZEUla\ngQx3CE25pzN3EUlWgQ33nMx0SkrVz11EklNgwz03S2fuIpK8Ah3uanMXkWQV2HDPydSZu4gkr8CG\ne27rdDbuKMNp7F8RSUKBDffUFANgQ4lGhhSR5BPYcB/UMw+AjTtKfa5ERKTlBTbcO+VkAvDR4k0+\nVyIi0vICG+752aFhf+95c6HPlYiItLzAhnv3vCwAxn2vi8+ViIi0vMCGO8AhHVqTnhroQxQRqVOg\nk885WLl5l99liIi0uGin2UtIq7bsZtUWv6sQEWl5gT5zH92vk98liIj4ItDhPqBbDoCuUhWRpBPo\ncF+5eTcAL81d63MlIiItK9DhvmjDDgDe/Xajz5WIiLSsQIf7PecMBOCo7rk+VyIi0rICHe5Hdssh\nxWBnmWZkEpHkEuhwT0kxqhw89OEyv0sREWlRgQ73amUVVX6XICLSogIf7kd1z6Vd63S/yxARaVGB\nD/chh7SjvFL93EUkuQQ+3Nu1zmBnWQUlpZpPVUSSR+DDvW1maPicxV6fdxGRZBBVuJvZCjP7yszm\nmllRHdtHmtl2b/tcM7st9qU2TdtWqQCcPWWmz5WIiLScxpy5n+KcO8Y5V1jP9o+87cc45ybHorhY\nOLewp98liIi0uMA3y5hZ+H5ZRaWPlYiItJxow90Bb5nZbDObWM8+w83sSzN73cyOrGsHM5toZkVm\nVlRcXNykgpviz+eFhiGoHkhMRCToog33Ec65wcA44CozO6nW9i+AQ5xzA4H7gZfqehDn3EPOuULn\nXGF+fn6Ti26sKu8apjF//rDFnlNExE9Rhbtzbq3370bgRWBore0lzrmd3v3XgHQz6xjjWpvs+4O6\nAzDyiJb7gyIi4qcGw93M2phZdvV9YAzwda19upjXuG1mQ73H3Rz7cpsmNcU4vHNbTZYtIkkjmjlU\nOwMvetmdBjzpnHvDzK4AcM5NBc4Bfm5mFcAe4HwXZ9Mf5Wal8/b8DX6XISLSIhoMd+fcMmBgHeun\nRtx/AHggtqXF1ucrtgJQUlpOTqbGmhGRYEuadorbJgwAYPGGnT5XIiLS/JIm3Pt3DU2WffaUGT5X\nIiLS/JIm3POzM/wuQUSkxSRNuB/WKTt8P86+6xURibmkCXeAVmmhw91QUuZzJSIizSupwn3C0d0A\nuPfdxT5XIiLSvJIq3M8eHLpS9anPVvlciYhI80qqcD+2d3sAjuic3cCeIiKJLanCvXr4gYWalUlE\nAi6pwj1SeWWV3yWIiDSbpA33J2ep3V1Egivpwv0v5x0DwIrNu3yuRESk+SRduI/q3wmA77bt8bkS\nEZHmk3Thnt0qNBDmm99o+F8RCa6kC/fICbNFRIIq6cIdoHVGKqAxZkQkuJIy3K87tS8AW3eX+1yJ\niEjzSMpwr56JaaV6zIhIQCVluHds2wqA377yjc+ViIg0j6QM96F9QmPMDDmkvc+ViIg0j6QM9+ru\nkKlJefQikgySMt6qu0M+/NFy9ZgRkUBKynCPNGv5Fr9LEBGJuaQP90c+Wu53CSIiMZe04b7i7jMA\nyMlK87kSEZHYS9pwB+jXJZutu/b6XcZ+vvluOwWTplEwaRpVVfpOQEQaL6nDvV3rDN5bWBx3AXr7\ny/v630/5YKmPlYhIokrqcJ+5bDMA/5ixwt9CIlRWOYpWbg0v3/PmQvZWaNYoEWmcpA73q045FIDJ\nr86Piy6RVVWOQ3/z2n7r73r9W3bvrfChIhFJVFGFu5mtMLOvzGyumRXVsd3M7D4zW2Jm88xscOxL\njb1rRvcN35+/rsTHSqCsopI+EcH++c2nMu+3YwD4+ycrGHDbm/zvx+rZIyLRacyZ+ynOuWOcc4V1\nbBsH9PVuE4EpsSiuubVKS6VbbiYAZ9z3sS9frn6+YgsFk6ZxxC1vhNedf2xP8rNbkZOZTqfsVuH1\nk1+dT2WcfT8gIvEpVs0yZwH/dCGfAnlm1jVGj92s3r1hZPj+oDvebvHnv/KJL2osT71oMHeffXR4\nefqNI+nQJiO8/OKctS1Wm4gkrmjD3QFvmdlsM5tYx/buwOqI5TXeuriXlbHv7B1o0e6HzjmKd5SF\nl/t3zWHs92r+TWzbKo3Zt57Gl7eHmmhufO5Ldpap/V1EDizacB/hnBtMqPnlKjM7qSlPZmYTzazI\nzIqKi4ub8hDNYsZNo2ss9/nNay0S8Nu8yUIG9cpjxd1n8Pq1J9a7b25Wevj+zS9+1ey1iUhiiyrc\nnXNrvX83Ai8CQ2vtshboGbHcw1tX+3Eecs4VOucK8/Pzm1ZxC7niX7Ob/TlWbtkNwHmFPRvYM+S9\nG0cC8PLc7yiYNI0/v72ouUoTkQTXYLibWRszy66+D4wBvq612yvAj71eM8OA7c65dTGvthn97KQ+\nNZbfmr+BH/z1k2Z7Pucc338w9PjtI9rUD6R3xzY1lu99dzGrNu+OeW0ikviiOXPvDHxsZl8CnwHT\nnHNvmNkVZnaFt89rwDJgCfAwcGWzVNuMbhrfnxV3n8HC348Nr/ti1Ta27Y5tDxrnHAWTptH7pn3d\nHk/p1ynqn3/juppNNyfd817MahOR4Ghw1Czn3DJgYB3rp0bcd8BVsS3NH63SUhncK48vVm0D4JjJ\nb/P1706nbavYDDD2Wa0hhjPTU0hvxKwh/brksOwP43EQvuBpzqqtDOrVLib1iUgwJPUVqvV54MLB\nTDh6X6+V793+5n77PPP5Kh5twkVF1e3s1b6dPLaePeuXkmKkphgXDzsEgKtqdac8kCdmraTw92+z\nZOOORj+viCQO8+uy+8LCQldUtN/FrnFlxB+ns2brHgC+vG0Mua1DPVZKyyvpd2vooqNXfzGC73XP\njfoxCyZNA+CCob34weDuHFvQ9HlcnXM1mnc+u3k0nbIz69zPzMLPXdu8344hJzO9zm0iEl/MbHY9\nF5PWoDP3A/j416PC9wdOfouzp8xgZ1kFc7wmG4AJ938c9eNNm7fvO+a7fnDUQQU77JsusNrQO9/l\n+mfm8s78DeF1M5dupvdNr9Ub7ABH//atg6pDROKPztwb8OY36/nZ4wfuFvnpTaP5bvseBjfQ7l0d\nsNeO7sv1px0ek/q27y5n0cYd/HDqzKj2v2lcP0b378Sp//NhjfVHdsth2jX197MXkfigM/cYOf3I\nLtx3waA6t/3HwG4ADLvrXX7w1xlc9/Sceh9n8859V6Jed2rfevdrrNzW6Rxb0J4HL4xurLbLRvTm\nsE7ZLLhjLEW3nMpDFw8B4JvvStiztzJmdYmIvxTuUThzYLfwtHzVstJTuWlcvxrrvtteWu9jDPn9\nO0Cor3rt5pRYGH9UF844qivTbziZn50c6rN/2oDOfHn7GFbcfUb4Vt0zJzM9lY5tW3HagM7065IN\nwKn/80FcDH0sIgdPzTKN8NpX61i/vZT0VOO0AV3okpvJqP96n2WbdoX3ifzitdr1z8wND/j17eSx\nZGWkNnut5ZVVUXexrKpy4eGGjz+0AwvX72DKRUMY2vvgvhMQkdhTs0wzGH9UVy4b0ZuLhxfQxRts\nbPqNI2uc1V/9VM1uiU/OWlVjJMeWCHagUX3nU1KMYX1CQT5j6WY279rLuX+bycrNuxr4SRGJVwr3\nGPnXT48D4KPFm3h/4cbw+t9EDPLVlD7tLeXpicM5/cjONdY99dnqevYWkXincI+REX07hu9f8vfP\na2zLSEthxd1ntNhZe1P97eJCbp0wgI9+dQoAUz9YqvlbRRKUwj2GPvjPkTWWd5SGhvTtnNOqjr3j\n009H9KZn+9bhCUIOv+V1nysSkaZQuMfQIR3a0D0vC4Btu/fyXNEaACaN7e9nWU0y3RteWEQSk8I9\nxtJSQ90cj5n8NpNfnQ/AyCPie+z6uuRmpfOb8aGuntMXbGhgbxGJNwr3GHvl6hH7rWsToxElW1r1\nFbeX/SOxuqyKiMI95nKz0pkUcXHTtGv2D/tEURgx9o0ubmp+c1dv48lZq/wuQwJC4d4MLjm+AAiN\n1X5kt+hHjIxnVz0Z/bDCUlNFZRXzvyvh3KkzKZg0jWc+3z/A567exvcf/ITfvPiVJkCXmEjM9oI4\nl5meut9wBYlqxqRRHH/3dCoqdebeGM45pnywlP5dcrj0HzW7xv76ha84/tCO9GzfmmufnsPLc78j\nPXXfkBTj7v2QS4/vTeecTM6ImFdApDE0/IA06OR73mPl5t288PPhDDlEQxI0ZPWW3Zz4p4anP3zh\n58M5e0rDo3ku/cN4UlNiPx6RJCYNPyAxc8HQXgCcPWUmxTvKGtg7uS3ZuKPeYH9m4rAan+jqCvY+\n+W32W3fWg9HPGdCcKqscf3lnEc8WrWbxhh2UlifWKKLFO8p485v13PvOYn44dYbf5TQ7NctIgy4f\n0Zu7X18AwLF3vsOcW0+jnXeRk9Q0fcG+oScuGNqTbbvLef3r9Xzzu9PDvabOLezBs941EEB4Rq7x\nR3Vl9ZbdTLj/Y3q1b81ffzSYCfd/zNdrS3hvwUaO7J7DyX96nxvGHM7wQzsc9Pc5peWV3PDcl5zU\ntyNpKSmcPaRHeNvc1dsoWrGF30/7lg//8xRat0ql0BvZNNIrV59AilmjZiNraeWVVTw+cyV/fX8J\nm3bum/C+en6FHw7pwT0/3G+a6JhavWU3N/37Kx7/6dBmGRW2LmqWkahEjhx5zajD+OWYI3yuKD79\n6JFP+WTJ5nBTinMO50KDs1Urq6jkiFtC0zS+d+NIenfc/2y92oFm0Hry8uM4/rCO9W4/kJLS8v1m\n4LrvgkGcObAbX6/d3qgZxgD+39UjOKpH/AV87ako63P/BYPC8zMcjNLySv7yzmKuGX0YrTNCf8y3\n7y5n4OR9r/WQQ9rx1P8dRkZa0xpOom2WUbhL1DaWlDL0D+8C8PwVw2t0lZSagRmrL9S37yln4O8O\nPA3iPy8bSk5Weuj96d2epcW7GHJI3bOCOed4tmg1v37hqzq3/+3iIUx6YR5bd5fXub3ollPZWFLG\nB4uK+eMbC+rc59GfFHLS4fmUVVTRtpHXeOwsq2DTjjJ27a3gyG65/Or5L3m2aA3D+3Rg0YYdfDJp\nFJnpdY/R5Jxj8669dGwbGu5jb0XVfsNnXDnyUM4t7EluVjoPfbSMKe8vrbH9l6cdzjWjmz6ZzoH+\nGEcyg+V3Ne13ROEuzSLyl/eWM/pz+Yl9fKwmfkz9YGm46ap7XhafTBrVwE9Er7LKsXtvBdt2l/Pt\nuhImNjDtI8DTE4cxrE+H8PKtL33N45+uZNK4fuE6IdRd97ITejPlg6VUR0F2Zho7Siu45Yz+nHBY\nR8bd+xGnDejMbRMG0LN96xrPM2vZZs576NN66/jZSX24aXz0w29EE45vXX8Sh3cOTTBzy0tf8c78\njcyYNCr8ybIu/bpks2D9DmZMGkU3b4gQCI3/dPaUGSzasDO8rrGT3ldVOSa/Op9/zFhxwP0O69SW\nJRtDz/PQxUMYc2SXqJ8jksJdmsXWXXsZdMfb4eWgdPlsrF1lFXy+Yst+I4BC3RO2xNrGHaUMvfPd\nA+4T+d7UFZpFt5waPsu9/eWveWzmyvC2gT3zePmqE6Ku58KHP2XG0s0H3GfxneNqzDOws6yC7XvK\n6Z6XxSMfLaNkTzn3TV8S1fMt/cN4xv7lQxZv3Nngvm9ffxJ9O2fjnKu3vfvZotX86vl5+63//OZT\nyc8+8MB/97+7mP9+e1F4+a4fHMWHi4p5/ev14XV3nHUkFw8vaLDWaCjcpdlUVFZx2M2hj7vJGO7f\nbdvD8XdPr3d7S70m5ZVV9PXehzOO7sq0eetqbH/ssqEc17s9Fz86i89XbK2x7Y3rTqRfl5zwsnOO\nBet3MO7ej4DGn71W21Fazv3Tl/DQh8vq3P7qL0Zw7dNz6JSdSUlpOd98V7LfPumpRrl3XcXt/zGA\n/zOoOxc/+hl98tvw8tzvDvj83fOyWLttT3j5bxcP4fQoz5B3lVVw5O1v7re+rvdzb0UVe8oryc1K\nr/GH8+zBPfjvcweG9znrwU8Y1qc9t00YELMvUhXu0qyqz1Zm33IqHdomzpDGsXDX69/ytw9qhtcN\npx3OLw6irTYWFqwvIS0lhXP/NpMtu/but/3Evh3ZvbeSKRcNplN2Zp2P8d6CjWRlpNZo0mmsHaXl\n/PGNBXTNzeKeNxc2+uffveFkAD5cVMxPhhfU+DL6qie+YNpXNf+IfTt5LDc+9yWnf68LZw7sRkVl\nFRt2lIVHaG2MsopKbnnxa56bvabG+s9uHh1+zSI7F3Rok8Fm77VuqT/qCndpVh8v3sRFj87iH5ce\ny8gjOvldTrOZsWQTFz4yi8cuG8rJh4dG9zz/oZl8umwLxx/agR8PP4SjeuQ1KUiaS2WV49Ba7c8X\nDevF779/lE8VwZ3T5vPwR8vr3X5sQTsmjevPoJ55NcK8tuIdZRx7Z6hL5le/HUN2ZvM1fz0+cwW3\nvvxNeHnSuH6U7Cnnr7W+hAV47ZoTGdAtZ7/1zUHhLs1q086ycL/nBXeMrbcHQ6KL/Mj9/o0jKSkt\n58wHPgHiu0mqqsox8fHZLNxQwotXnhBuW/fTnr2VXPGv2fx6bL8WC8KDVVpeSb9b32hwv9rfJzSn\naMNdFzFJk0SGxbw12xnaO/bdIiP7Bz922VCOLWhHZlrqAc/sDkZVlWN3eWW4+9767aU1to/8r/e5\nZtRhANw2YUCz1BArKSnGIz9p8P9/i8rKSOWxy4b6XUajZKan8szEYfv1CFp+13jMjOIdZWSkpbRY\nsDeGztylySIvdqndxSwW6usWd0zPPOavK2He7WNi9olh884yhnifRCafdSSn9u8c/tL0omG9+Nen\nNUdyXPaH8c32R0biz96KKj5cVEz7thnheQ78EvOxZcws1czmmNmrdWy7xMyKzWyud7u8sQVL4qnu\nawxw/N3TKZg0jTF//oBNOxs3/syC9SUUTJrGL56aE153oGFv567ext6KKk7/y4eNL7qWisoqCiZN\nCwc7wG0vf1OjN8x1px7OlB8NrvFzCvbkkpGWwqkDOvse7I0R9Zm7mf0SKARynHMTam27BCh0zl0d\n7RPrzD0YInsOVBvepwNPTRzW4M/+91sLub+Bfs1dczP530uODXfRq61vp7YMP7QD//T6aF9yfAHj\nvteFbnlZNS64qa8fc0Nm3jSKrrmhTyQzl27mgoc/5ecjD+XXY/s18JMizSOmX6iaWQ/gMeBO4JcK\nd4lUV/PJ4z8dytE98sjNCvVmqKisIi2iXTJyfJUDWfT7cTXG4HDO8fb8DVFdpQnw8lUncHSP3AbH\nFzEL9Xjo3zX0Rd/Xa7cDxPWAWJKcYh3uzwN3AdnAjfWE+11AMbAIuN45t/pAj6lwD47S8kqen72G\nC4f24rCbX6Mq4ldq6R/Gc+HDnzJr+ZYa42m8NGct1z0zN7zfgjvGMn3BRj5bviV8GfeBeuG8v3Bj\nnVeHNsW7N5zMofltY/JYIs0tZuFuZhOA8c65K81sJHWHewdgp3OuzMx+BpznnNtvcA0zmwhMBOjV\nq9eQlStX1t5FEtyarbsZ8ceGJ6qo9smkUU3uIz5z6WYGdM2hTavU8KeCB99bwqzlW/hwUfF++8/7\n7Ri27SqnZ/sslhbv4rBOCnRJPLEM97uAi4EKIBPIAf7tnLuonv1TgS3OuQN+ntWZe3A9PnMFX6za\nxotz1h5wv/5dc3j92hObrY7S8ko+WryJf8xYzsM/LgwPwSqSyGLWW8Y5d5NzrodzrgA4H5heO9jN\nLHKixzOBbxtZrwTIxcML+PN5x9RY9/q1J+43H+iKTbuatY7M9FROG9CZJy4fpmCXpNPk33gzmwwU\nOedeAa4xszMJnd1vAS6JTXmSyBbcMZa5q7dxZLccsjPTefDCwTx4IVzz1Bxe+fI7XmrEqIMi0ji6\niElEJIESqrxgAAAEmUlEQVRogmwRkSSmcBcRCSCFu4hIACncRUQCSOEuIhJACncRkQBSuIuIBJDC\nXUQkgHy7iMnMioGmjhzWEdgUw3ISUbK/Bjp+HX+yHv8hzrn8hnbyLdwPhpkVRXOFVpAl+2ug49fx\nJ/PxR0PNMiIiAaRwFxEJoEQN94f8LiAOJPtroONPbsl+/A1KyDZ3ERE5sEQ9cxcRkQNIuHA3s7Fm\nttDMlpjZJL/riRUz62lm75nZfDP7xsyu9da3N7O3zWyx9287b72Z2X3e6zDPzAZHPNZPvP0Xm9lP\n/DqmpjCzVDObY2avesu9zWyWd5zPmFmGt76Vt7zE214Q8Rg3eesXmtnp/hxJ45lZnpk9b2YLzOxb\nMxueTO+/mV3v/e5/bWZPmVlmMr3/MeecS5gbkAosBfoAGcCXwAC/64rRsXUFBnv3s4FFwADgT8Ak\nb/0k4I/e/fHA64ABw4BZ3vr2wDLv33be/XZ+H18jXodfAk8Cr3rLzwLne/enAj/37l8JTPXunw88\n490f4P1etAJ6e78vqX4fV5TH/hhwuXc/A8hLlvcf6A4sB7Ii3vdLkun9j/Ut0c7chwJLnHPLnHN7\ngaeBs3yuKSacc+ucc19493cQmoe2O6Hje8zb7THg+979s4B/upBPgTxvLtvTgbedc1ucc1uBt4Gx\nLXgoTWZmPYAzgEe8ZQNGAc97u9Q+/urX5XlgtLf/WcDTzrky59xyYAmh35u4Zma5wEnAowDOub3O\nuW0k0ftPaNrPLDNLA1oD60iS9785JFq4dwdWRyyv8dYFivcRcxAwC+jsnFvnbVoPdPbu1/daJPJr\n9BfgV0CVt9wB2Oacq/CWI48lfJze9u3e/ol6/L2BYuDvXrPUI2bWhiR5/51za4H/AlYRCvXtwGyS\n5/2PuUQL98Azs7bAC8B1zrmSyG0u9LkzkN2bzGwCsNE5N9vvWnySBgwGpjjnBgG7CDXDhAX8/W9H\n6Ky7N9ANaEPifOKIS4kW7muBnhHLPbx1gWBm6YSC/Qnn3L+91Ru8j9t4/2701tf3WiTqa3QCcKaZ\nrSDU3DYKuJdQc0Oat0/ksYSP09ueC2wmcY9/DbDGOTfLW36eUNgny/t/KrDcOVfsnCsH/k3odyJZ\n3v+YS7Rw/xzo632DnkHoi5RXfK4pJrz2wkeBb51z/xOx6RWgusfDT4CXI9b/2Os1MQzY7n18fxMY\nY2btvLOhMd66uOacu8k518M5V0DofZ3unPsR8B5wjrdb7eOvfl3O8fZ33vrzvd4UvYG+wGctdBhN\n5pxbD6w2syO8VaOB+STJ+0+oOWaYmbX2/i9UH39SvP/Nwu9vdBt7I9RLYBGhb8Fv9rueGB7XCEIf\nuecBc73beELtiO8Ci4F3gPbe/gY86L0OXwGFEY91GaEvkpYAl/p9bE14LUayr7dMH0L/OZcAzwGt\nvPWZ3vISb3ufiJ+/2XtdFgLj/D6eRhz3MUCR9zvwEqHeLknz/gO/AxYAXwOPE+rxkjTvf6xvukJV\nRCSAEq1ZRkREoqBwFxEJIIW7iEgAKdxFRAJI4S4iEkAKdxGRAFK4i4gEkMJdRCSA/j8+Gh0KXtJX\nYwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1ef591324a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1.7 Plot running average game lengths\n", "window_size = 500\n", "running_average_length = [np.mean(game_lengths[i:i+window_size]) for i in range(len(game_lengths)- window_size)]\n", "pylab.plot(running_average_length)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([[ 7.25901186e-01, 1.04886639e+00, 6.20839112e-02,\n", " -3.41061354e-02, 4.55370486e-01, 2.02341691e-01,\n", " -2.12955847e-01, 3.58229399e-01, 7.24420965e-01,\n", " 3.38534296e-01],\n", " [ -1.57648444e+00, -1.72019684e+00, -1.80868149e+00,\n", " -7.12780356e-02, 1.11409567e-01, 1.63963601e-01,\n", " 1.47185652e-02, 1.30413938e+00, -2.37215042e+00,\n", " 1.21751346e-01],\n", " [ 1.37824535e+00, -1.32748854e+00, -1.55953240e+00,\n", " 1.73858631e+00, -3.07726669e+00, -3.41630965e-01,\n", " -1.37973142e+00, 2.52039552e+00, 2.36320090e+00,\n", " -2.60692453e+00],\n", " [ -2.57445884e+00, 2.02244043e+00, 2.29665589e+00,\n", " 1.36782777e+00, -2.47151569e-01, -2.57568288e+00,\n", " -1.09048522e+00, -1.92701316e+00, -2.55728292e+00,\n", " -4.62185621e-01],\n", " [ 2.34399986e+00, -9.83024359e-01, 2.24991977e-01,\n", " -1.41239774e+00, 2.62054294e-01, 4.87807579e-02,\n", " 4.96334672e-01, 2.22372866e+00, 2.49350381e+00,\n", " 1.24225557e+00],\n", " [ -6.45392179e-01, 3.22920609e+00, -2.60062885e+00,\n", " 1.91084027e+00, -1.12026894e+00, -1.70617259e+00,\n", " -2.64522958e+00, -8.12052429e-01, -2.31542993e+00,\n", " 3.47127318e-01],\n", " [ -2.34244061e+00, -1.58395278e+00, 9.90208447e-01,\n", " 1.73158303e-01, -3.54572773e-01, 2.88874793e+00,\n", " -1.95340112e-01, -7.65791774e-01, 5.15411556e-01,\n", " -2.77374339e+00],\n", " [ -6.06560588e-01, 1.22526658e+00, -8.06299269e-01,\n", " -4.10335332e-01, -2.49871969e+00, -1.05831993e+00,\n", " 1.84081817e+00, -6.14631951e-01, -4.36943889e-01,\n", " 1.05194032e+00],\n", " [ 1.39860177e+00, -7.86226571e-01, -1.10314333e+00,\n", " -4.63514149e-01, 2.99973726e-01, -2.54053384e-01,\n", " 2.37958416e-01, 1.81838661e-01, -1.48128510e+00,\n", " 3.10174823e-01],\n", " [ -8.47620487e-01, 7.17755735e-01, 2.06155315e-01,\n", " 2.71693528e-01, 4.05549586e-01, 1.87696156e-03,\n", " 2.58052349e-01, 4.01220560e-01, 1.02901244e+00,\n", " 1.32583559e-01]], dtype=float32)]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1.8 Example showing how to print current coefficient values\n", "sess.run([W1], feed_dict={input_positions:board_position_log[0]})" ] } ], "metadata": { "kernelspec": { "display_name": "An0n1mX2", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

h-mayorquin/time_series_basic

presentations/2016-04-13(Letters columns diagrams).ipynb

1

34579

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Letter columns diagrams\n", "Here we plot the columns of the diagrams in order to show how the task works." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import h5py\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "signal_location = '../data/wall_street_data_30.hdf5'\n", "\n", "# Access the data and load it into signal\n", "with h5py.File(signal_location, 'r') as f:\n", " dset = f['signal']\n", " signals = np.empty(dset.shape, np.float)\n", " dset.read_direct(signals)\n", "\n", "letter = signals[0, ...]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5sAAAKCCAYAAABfxc33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe3880dbef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Nletters = 8\n", "length = 10\n", "cmap = 'seismic'\n", "cmap = 'gray'\n", "interpolation = 'nearest'\n", "\n", "\n", "gs = gridspec.GridSpec(2, Nletters)\n", "middle = (Nletters // 2 )\n", "fig = plt.figure(figsize=(16, 12))\n", "ax = fig.add_subplot(gs[0, (middle - 1):(middle + 1)])\n", "ax.imshow(letter, cmap=cmap)\n", "ax.set_axis_off()\n", "\n", "for i in range(Nletters):\n", " ax = fig.add_subplot(gs[1, i])\n", " ax.imshow(letter[:, i:(i+length)], cmap=cmap)\n", " ax.set_axis_off()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

gregnordin/ECEn360_W15

transmission_lines/01a_simple_forward_reverse_pulses.ipynb

1

22560

{ "metadata": { "name": "", "signature": "sha256:fd273becc3054dac3f43549a1f885b224acd2470dd231b043ba5b9930c6d2c52" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Forward and Backward Propagating Pulses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The wave equation for the voltage on a lossless transmission line that we discussed in class is\n", "\n", "$$\\frac{\\partial^2 v}{\\partial z^2} = \\frac{1}{u^2}\\frac{\\partial^2 v}{\\partial t^2}.$$\n", "\n", "It has a general solution of the form\n", "\n", "$$v(z,t) = v^+(z - ut) + v^-(z + ut),$$\n", "\n", "where $z$ is position along the transmission line, $t$ is time, and $u$ is the phase velocity. $v^+$ and $v^-$ represent forward and reverse propagating waves, respectively (i.e., in the $+z$ and $-z$ directions). We'll use the following code to illustrate this." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Import needed modules\n", "\n", "from IPython.html.widgets import interact, fixed\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Define a simple arbitrary pulse function. Let's use 1/2 cycle of a cosine centered at 0,\n", "# and zero everywhere else.\n", "\n", "def pulse(a):\n", " if a < np.pi/2.0 and a > -np.pi/2.0:\n", " return np.cos(a)\n", " else:\n", " return 0.0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a function to plot a forward propagating pulse. Use interact() to make it interactive.\n", "\n", "zmin = -30\n", "zmax = 30\n", "numpnts = 500\n", "def plotpulse(u,t):\n", " x = np.linspace(zmin,zmax,numpnts)\n", " y = np.zeros(numpnts)\n", " for i in range(0,numpnts): \n", " y[i] = pulse(x[i] - u*t)\n", " plt.plot(x,y)\n", " plt.ylim(0,1.2)\n", " plt.xlabel('z')\n", " plt.figtext(0.15,0.82,'t = ' + str(t))\n", "interact(plotpulse, u=fixed(1.0), t=(-30,30,0.25));" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEPCAYAAAC9RFRvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x10881efd0>" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a function to plot both forward and backward propagating pulses, \n", "# arbitrarily starting them at z=0.\n", "\n", "zmin = -30\n", "zmax = 30\n", "numpnts = 500\n", "def plotpulses(u,t):\n", " x = np.linspace(zmin,zmax,numpnts)\n", " yforward = np.zeros(numpnts)\n", " ybackward = np.zeros(numpnts)\n", " for i in range(0,numpnts): \n", " yforward[i] = pulse(x[i] - u*t)\n", " ybackward[i] = pulse(x[i] + u*t)\n", " plt.plot(x,yforward, 'b')\n", " plt.plot(x,ybackward, 'r')\n", " plt.ylim(0,1.2)\n", " plt.xlabel('z')\n", " plt.figtext(0.15,0.82,'t = ' + str(t))\n", "interact(plotpulses, u=fixed(1.0), t=(-30,30,0.25));" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEPCAYAAAC9RFRvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x109113090>" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.get_backend()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "'module://IPython.kernel.zmq.pylab.backend_inline'" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

yoon-gu/tensorflow-tutorial

Tutorial02.ipynb

1

15038

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "%matplotlib inline\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "sess = tf.InteractiveSession()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def weight_variable(shape):\n", " initial = tf.truncated_normal(shape, stddev=0.1)\n", " return tf.Variable(initial)\n", "\n", "def bias_variable(shape):\n", " initial = tf.constant(0.1, shape=shape)\n", " return tf.Variable(initial)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def conv2d(x, W):\n", " return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n", "\n", "def max_pool_2x2(x):\n", " return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n", " strides=[1, 2, 2, 1], padding='SAME')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, 784])\n", "y_ = tf.placeholder(tf.float32, shape=[None, 10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First Convolutional Layer" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "W_conv1 = weight_variable([5, 5, 1, 32])\n", "b_conv1 = bias_variable([32])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_image = tf.reshape(x, [-1,28,28,1])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n", "h_pool1 = max_pool_2x2(h_conv1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Second Convolutional Layer" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "W_conv2 = weight_variable([5, 5, 32, 64])\n", "b_conv2 = bias_variable([64])\n", "\n", "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n", "h_pool2 = max_pool_2x2(h_conv2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Densely Connected Layer" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "W_fc1 = weight_variable([7 * 7 * 64, 1024])\n", "b_fc1 = bias_variable([1024])\n", "\n", "h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])\n", "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "keep_prob = tf.placeholder(tf.float32)\n", "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Readout Layer" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "W_fc2 = weight_variable([1024, 10])\n", "b_fc2 = bias_variable([10])\n", "\n", "y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "step 0, training accuracy 0.12\n", "step 100, training accuracy 0.82\n", "step 200, training accuracy 0.96\n", "step 300, training accuracy 0.88\n", "step 400, training accuracy 1\n", "step 500, training accuracy 0.94\n", "step 600, training accuracy 1\n", "step 700, training accuracy 0.94\n", "step 800, training accuracy 0.88\n", "step 900, training accuracy 1\n", "step 1000, training accuracy 0.94\n", "step 1100, training accuracy 0.94\n", "step 1200, training accuracy 0.98\n", "step 1300, training accuracy 1\n", "step 1400, training accuracy 0.98\n", "step 1500, training accuracy 0.96\n", "step 1600, training accuracy 0.96\n", "step 1700, training accuracy 0.94\n", "step 1800, training accuracy 0.96\n", "step 1900, training accuracy 0.96\n", "step 2000, training accuracy 1\n", "step 2100, training accuracy 1\n", "step 2200, training accuracy 0.98\n", "step 2300, training accuracy 0.96\n", "step 2400, training accuracy 1\n", "step 2500, training accuracy 1\n", "step 2600, training accuracy 0.98\n", "step 2700, training accuracy 0.98\n", "step 2800, training accuracy 0.96\n", "step 2900, training accuracy 0.98\n", "step 3000, training accuracy 1\n", "step 3100, training accuracy 0.98\n", "step 3200, training accuracy 0.94\n", "step 3300, training accuracy 0.98\n", "step 3400, training accuracy 1\n", "step 3500, training accuracy 1\n", "step 3600, training accuracy 1\n", "step 3700, training accuracy 1\n", "step 3800, training accuracy 1\n", "step 3900, training accuracy 0.98\n", "step 4000, training accuracy 1\n", "step 4100, training accuracy 0.98\n", "step 4200, training accuracy 0.98\n", "step 4300, training accuracy 0.98\n", "step 4400, training accuracy 0.98\n", "step 4500, training accuracy 1\n", "step 4600, training accuracy 1\n", "step 4700, training accuracy 0.98\n", "step 4800, training accuracy 0.98\n", "step 4900, training accuracy 1\n", "step 5000, training accuracy 0.98\n", "step 5100, training accuracy 1\n", "step 5200, training accuracy 0.98\n", "step 5300, training accuracy 0.98\n", "step 5400, training accuracy 1\n", "step 5500, training accuracy 1\n", "step 5600, training accuracy 1\n", "step 5700, training accuracy 0.98\n", "step 5800, training accuracy 1\n", "step 5900, training accuracy 1\n", "step 6000, training accuracy 1\n", "step 6100, training accuracy 0.98\n", "step 6200, training accuracy 0.98\n", "step 6300, training accuracy 1\n", "step 6400, training accuracy 1\n", "step 6500, training accuracy 0.98\n", "step 6600, training accuracy 1\n", "step 6700, training accuracy 1\n", "step 6800, training accuracy 1\n", "step 6900, training accuracy 1\n", "step 7000, training accuracy 1\n", "step 7100, training accuracy 1\n", "step 7200, training accuracy 0.98\n", "step 7300, training accuracy 1\n", "step 7400, training accuracy 1\n", "step 7500, training accuracy 1\n", "step 7600, training accuracy 1\n", "step 7700, training accuracy 1\n", "step 7800, training accuracy 1\n", "step 7900, training accuracy 0.98\n", "step 8000, training accuracy 1\n", "step 8100, training accuracy 1\n", "step 8200, training accuracy 1\n", "step 8300, training accuracy 1\n", "step 8400, training accuracy 1\n", "step 8500, training accuracy 1\n", "step 8600, training accuracy 1\n", "step 8700, training accuracy 1\n", "step 8800, training accuracy 1\n", "step 8900, training accuracy 1\n", "step 9000, training accuracy 0.98\n", "step 9100, training accuracy 1\n", "step 9200, training accuracy 0.98\n", "step 9300, training accuracy 1\n", "step 9400, training accuracy 1\n", "step 9500, training accuracy 1\n", "step 9600, training accuracy 1\n", "step 9700, training accuracy 1\n", "step 9800, training accuracy 1\n", "step 9900, training accuracy 1\n", "step 10000, training accuracy 0.98\n", "step 10100, training accuracy 1\n", "step 10200, training accuracy 1\n", "step 10300, training accuracy 1\n", "step 10400, training accuracy 1\n", "step 10500, training accuracy 1\n", "step 10600, training accuracy 0.98\n", "step 10700, training accuracy 1\n", "step 10800, training accuracy 1\n", "step 10900, training accuracy 1\n", "step 11000, training accuracy 1\n", "step 11100, training accuracy 1\n", "step 11200, training accuracy 1\n", "step 11300, training accuracy 1\n", "step 11400, training accuracy 1\n", "step 11500, training accuracy 1\n", "step 11600, training accuracy 1\n", "step 11700, training accuracy 1\n", "step 11800, training accuracy 0.98\n", "step 11900, training accuracy 0.98\n", "step 12000, training accuracy 0.98\n", "step 12100, training accuracy 1\n", "step 12200, training accuracy 1\n", "step 12300, training accuracy 1\n", "step 12400, training accuracy 1\n", "step 12500, training accuracy 0.98\n", "step 12600, training accuracy 1\n", "step 12700, training accuracy 1\n", "step 12800, training accuracy 1\n", "step 12900, training accuracy 1\n", "step 13000, training accuracy 1\n", "step 13100, training accuracy 1\n", "step 13200, training accuracy 1\n", "step 13300, training accuracy 1\n", "step 13400, training accuracy 1\n", "step 13500, training accuracy 1\n", "step 13600, training accuracy 1\n", "step 13700, training accuracy 1\n", "step 13800, training accuracy 1\n", "step 13900, training accuracy 1\n", "step 14000, training accuracy 1\n", "step 14100, training accuracy 1\n", "step 14200, training accuracy 1\n", "step 14300, training accuracy 1\n", "step 14400, training accuracy 1\n", "step 14500, training accuracy 0.98\n", "step 14600, training accuracy 1\n", "step 14700, training accuracy 0.98\n", "step 14800, training accuracy 1\n", "step 14900, training accuracy 1\n", "step 15000, training accuracy 1\n", "step 15100, training accuracy 1\n", "step 15200, training accuracy 1\n", "step 15300, training accuracy 0.98\n", "step 15400, training accuracy 1\n", "step 15500, training accuracy 0.98\n", "step 15600, training accuracy 1\n", "step 15700, training accuracy 1\n", "step 15800, training accuracy 1\n", "step 15900, training accuracy 1\n", "step 16000, training accuracy 1\n", "step 16100, training accuracy 1\n", "step 16200, training accuracy 1\n", "step 16300, training accuracy 1\n", "step 16400, training accuracy 1\n", "step 16500, training accuracy 1\n", "step 16600, training accuracy 1\n", "step 16700, training accuracy 1\n", "step 16800, training accuracy 1\n", "step 16900, training accuracy 1\n", "step 17000, training accuracy 1\n", "step 17100, training accuracy 1\n", "step 17200, training accuracy 1\n", "step 17300, training accuracy 1\n", "step 17400, training accuracy 1\n", "step 17500, training accuracy 1\n", "step 17600, training accuracy 1\n", "step 17700, training accuracy 1\n", "step 17800, training accuracy 1\n", "step 17900, training accuracy 1\n", "step 18000, training accuracy 1\n", "step 18100, training accuracy 1\n", "step 18200, training accuracy 1\n", "step 18300, training accuracy 1\n", "step 18400, training accuracy 1\n", "step 18500, training accuracy 1\n", "step 18600, training accuracy 1\n", "step 18700, training accuracy 1\n", "step 18800, training accuracy 1\n", "step 18900, training accuracy 1\n", "step 19000, training accuracy 1\n", "step 19100, training accuracy 1\n", "step 19200, training accuracy 1\n", "step 19300, training accuracy 1\n", "step 19400, training accuracy 1\n", "step 19500, training accuracy 1\n", "step 19600, training accuracy 1\n", "step 19700, training accuracy 1\n", "step 19800, training accuracy 0.98\n", "step 19900, training accuracy 1\n", "test accuracy 0.9921\n" ] } ], "source": [ "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))\n", "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n", "correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "sess.run(tf.global_variables_initializer())\n", "for i in range(20000):\n", " batch = mnist.train.next_batch(50)\n", " if i%100 == 0:\n", " train_accuracy = accuracy.eval(feed_dict={\n", " x:batch[0], y_: batch[1], keep_prob: 1.0})\n", " print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n", " train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n", "\n", "print(\"test accuracy %g\"%accuracy.eval(feed_dict={\n", " x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

tabakg/potapov_interpolation

consolidated_taylor_perturbation.ipynb

1

45107

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.sparse.linalg import eigs\n", "import scipy.sparse as sparse\n", "import numpy as np\n", "import numpy.linalg as la\n", "import sympy as sp\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import Potapov_Code.Time_Delay_Network as networks\n", "from Potapov_Code.functions import spatial_modes\n", "from decimal import Decimal\n", "\n", "from sympy import init_printing\n", "init_printing() \n", "\n", "from sympy.utilities.autowrap import ufuncify\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import Potapov_Code.Time_Delay_Network" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from Potapov_Code.functions import gcd_lst" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X1 = Potapov_Code.Time_Delay_Network.Example3()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def _find_commensurate(delays):\n", " '''\n", " Find the 'gcd' but for Decimal numbers.\n", "\n", " Args:\n", " delays(list of Demicals): numbers whose gcd will be found.\n", "\n", " Returns:\n", " Decimal gcd.\n", " '''\n", " mult = min([d.as_tuple().exponent for d in delays])\n", " power = 10**-mult\n", " delays = map(lambda x: x*power,delays)\n", " int_gcd = gcd_lst(delays)\n", " return int_gcd/power\n", "\n", "Decimal_delays = map(lambda x: Decimal(str(x)),X1.delays)\n", "Decimal_gcd = _find_commensurate(Decimal_delays)\n", "\n", "xs = [sp.symbols('x_'+str(i)) for i in range(4) ]\n", "E_sym = sp.Matrix(np.zeros_like(X1.M1))\n", "for i,delay in enumerate(Decimal_delays):\n", " E_sym[i,i] = xs[i]\n", "M1_sym = sp.Matrix(X1.M1)\n", "num, den = (E_sym - M1_sym).det().as_numer_denom()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Alternatively, we can Taylor expand about the $z$ variable only. This gives just Newton's method. To first order we get\n", "$$\n", "\\Delta z = - \\frac{f(z,T+\\Delta T)}{\\frac{\\partial f}{\\partial z (z,T+\\Delta T}}.\n", "$$\n", "This can be used as an iterative procedure with the substitution $z \\mapsto z + \\Delta_z$. In addition, dependence on $\\Delta T $ can be incorporated by evaluation $\\Delta T \\equiv \\Delta T(z + \\Delta z)$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2YAAAAcBAMAAAD4qrH3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left ( 1.0 x_{0}^{2} x_{1}^{2} x_{2} x_{3} + 0.32 x_{0}^{2} x_{1}^{2} - 0.72 x_{0} x_{1} x_{2} x_{3} - 0.8352 x_{0} x_{1} + 0.1296 x_{2} x_{3} + 0.2592, \\quad 1.0 x_{0} x_{1} - 0.36\\right )$$" ], "text/plain": [ "⎛ 2 2 2 2 \n", "⎝1.0⋅x₀ ⋅x₁ ⋅x₂⋅x₃ + 0.32⋅x₀ ⋅x₁ - - -0.72⋅x₀⋅x₁⋅x₂⋅x₃ - - -0.8352⋅x₀⋅x₁ + 0.\n", "\n", " ⎞\n", "1296⋅x₂⋅x₃ + 0.2592, 1.0⋅x₀⋅x₁ - 0.36⎠" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num, den" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z, z_Delta = sp.symbols('z dz',complex=True)\n", "Ts = [sp.symbols('T_'+str(i),real=True) for i in range(4)]\n", "x,y = sp.symbols('x y', real = True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "num2 = num.subs({x: sp.exp(-z*T) for x,T in zip(xs,Ts)})" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABD8AAAAVBAMAAACzn1g+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", "text/latex": [ "$$0.2592 + 0.1296 e^{- T_{2} z} e^{- T_{3} z} - 0.8352 e^{- T_{0} z} e^{- T_{1} z} - 0.72 e^{- T_{0} z} e^{- T_{1} z} e^{- T_{2} z} e^{- T_{3} z} + 0.32 e^{- 2 T_{0} z} e^{- 2 T_{1} z} + 1.0 e^{- 2 T_{0} z} e^{- 2 T_{1} z} e^{- T_{2} z} e^{- T_{3} z}$$" ], "text/plain": [ " -T₂⋅z -T₃⋅z -T₀⋅z -T₁⋅z -T₀⋅z -T₁⋅\n", "0.2592 + 0.1296⋅ℯ ⋅ℯ - - -0.8352⋅ℯ ⋅ℯ - - -0.72⋅ℯ ⋅ℯ \n", "\n", "z -T₂⋅z -T₃⋅z -2⋅T₀⋅z -2⋅T₁⋅z -2⋅T₀⋅z -2⋅T₁⋅z -T₂⋅z -T₃⋅z\n", " ⋅ℯ ⋅ℯ + 0.32⋅ℯ ⋅ℯ + 1.0⋅ℯ ⋅ℯ ⋅ℯ ⋅ℯ " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num2" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "num3 = num2.subs({sp.exp(-z*T): sp.cos(-x*T)*(1j*sp.sin(y*T)+sp.cos(y*T)) for T in Ts})" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAE1oAAAAaBAMAAADyyvSXAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nP3uyocAAAAAASUVORK5CYII=\n", "text/latex": [ "$$1.0 \\left(1.0 i \\sin{\\left (T_{0} y \\right )} + \\cos{\\left (T_{0} y \\right )}\\right)^{2} \\left(1.0 i \\sin{\\left (T_{1} y \\right )} + \\cos{\\left (T_{1} y \\right )}\\right)^{2} \\left(1.0 i \\sin{\\left (T_{2} y \\right )} + \\cos{\\left (T_{2} y \\right )}\\right) \\left(1.0 i \\sin{\\left (T_{3} y \\right )} + \\cos{\\left (T_{3} y \\right )}\\right) \\cos^{2}{\\left (T_{0} x \\right )} \\cos^{2}{\\left (T_{1} x \\right )} \\cos{\\left (T_{2} x \\right )} \\cos{\\left (T_{3} x \\right )} + 0.32 \\left(1.0 i \\sin{\\left (T_{0} y \\right )} + \\cos{\\left (T_{0} y \\right )}\\right)^{2} \\left(1.0 i \\sin{\\left (T_{1} y \\right )} + \\cos{\\left (T_{1} y \\right )}\\right)^{2} \\cos^{2}{\\left (T_{0} x \\right )} \\cos^{2}{\\left (T_{1} x \\right )} - 0.72 \\left(1.0 i \\sin{\\left (T_{0} y \\right )} + \\cos{\\left (T_{0} y \\right )}\\right) \\left(1.0 i \\sin{\\left (T_{1} y \\right )} + \\cos{\\left (T_{1} y \\right )}\\right) \\left(1.0 i \\sin{\\left (T_{2} y \\right )} + \\cos{\\left (T_{2} y \\right )}\\right) \\left(1.0 i \\sin{\\left (T_{3} y \\right )} + \\cos{\\left (T_{3} y \\right )}\\right) \\cos{\\left (T_{0} x \\right )} \\cos{\\left (T_{1} x \\right )} \\cos{\\left (T_{2} x \\right )} \\cos{\\left (T_{3} x \\right )} - 0.8352 \\left(1.0 i \\sin{\\left (T_{0} y \\right )} + \\cos{\\left (T_{0} y \\right )}\\right) \\left(1.0 i \\sin{\\left (T_{1} y \\right )} + \\cos{\\left (T_{1} y \\right )}\\right) \\cos{\\left (T_{0} x \\right )} \\cos{\\left (T_{1} x \\right )} + 0.1296 \\left(1.0 i \\sin{\\left (T_{2} y \\right )} + \\cos{\\left (T_{2} y \\right )}\\right) \\left(1.0 i \\sin{\\left (T_{3} y \\right )} + \\cos{\\left (T_{3} y \\right )}\\right) \\cos{\\left (T_{2} x \\right )} \\cos{\\left (T_{3} x \\right )} + 0.2592$$" ], "text/plain": [ " 2 2 \n", "1.0⋅(1.0⋅ⅈ⋅sin(T₀⋅y) + cos(T₀⋅y)) ⋅(1.0⋅ⅈ⋅sin(T₁⋅y) + cos(T₁⋅y)) ⋅(1.0⋅ⅈ⋅sin(T\n", "\n", " 2 2 \n", "₂⋅y) + cos(T₂⋅y))⋅(1.0⋅ⅈ⋅sin(T₃⋅y) + cos(T₃⋅y))⋅cos (T₀⋅x)⋅cos (T₁⋅x)⋅cos(T₂⋅x\n", "\n", " 2 \n", ")⋅cos(T₃⋅x) + 0.32⋅(1.0⋅ⅈ⋅sin(T₀⋅y) + cos(T₀⋅y)) ⋅(1.0⋅ⅈ⋅sin(T₁⋅y) + cos(T₁⋅y)\n", "\n", " 2 2 2 \n", ") ⋅cos (T₀⋅x)⋅cos (T₁⋅x) - - -0.72⋅(1.0⋅ⅈ⋅sin(T₀⋅y) + cos(T₀⋅y))⋅(1.0⋅ⅈ⋅sin(T₁\n", "\n", " \n", "⋅y) + cos(T₁⋅y))⋅(1.0⋅ⅈ⋅sin(T₂⋅y) + cos(T₂⋅y))⋅(1.0⋅ⅈ⋅sin(T₃⋅y) + cos(T₃⋅y))⋅c\n", "\n", " \n", "os(T₀⋅x)⋅cos(T₁⋅x)⋅cos(T₂⋅x)⋅cos(T₃⋅x) - - -0.8352⋅(1.0⋅ⅈ⋅sin(T₀⋅y) + cos(T₀⋅y\n", "\n", " \n", "))⋅(1.0⋅ⅈ⋅sin(T₁⋅y) + cos(T₁⋅y))⋅cos(T₀⋅x)⋅cos(T₁⋅x) + 0.1296⋅(1.0⋅ⅈ⋅sin(T₂⋅y)\n", "\n", " \n", " + cos(T₂⋅y))⋅(1.0⋅ⅈ⋅sin(T₃⋅y) + cos(T₃⋅y))⋅cos(T₂⋅x)⋅cos(T₃⋅x) + 0.2592" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num3" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_real,num_imag = num3.expand().as_real_imag()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAARYAAAAUBAMAAAC63A/DAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n1PdZDlLbp00MVKqgVPkPmZXItVvYhvIAAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left [ x, \\quad y, \\quad \\left [ T_{0}, \\quad T_{1}, \\quad T_{2}, \\quad T_{3}\\right ]\\right ]$$" ], "text/plain": [ "[x, y, [T₀, T₁, T₂, T₃]]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[x,y]+[Ts]" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f_r = ufuncify( [x,y]+Ts, num_real)\n", "f_i = ufuncify( [x,y]+Ts, num_imag)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.14730830564958425-0.40346430265728966j)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f_r(1,2,3,4,5,6)+f_i(1,2,3,4,5,6)*1j" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAAPBAMAAAAypujKAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nNsn/j8C/8Yop/XPH4poAAAAASUVORK5CYII=\n", "text/latex": [ "$$0.147308305649584 - 0.40346430265729 i$$" ], "text/plain": [ "0.147308305649584 - - -0.40346430265729⋅ⅈ" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## testing\n", "num3.subs({x:1,y:2,Ts[0]:3,Ts[1]:4,Ts[2]:5,Ts[3]:6}).evalf()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Consolidating\n", "def get_real_imag_func(expression):\n", " '''\n", " Takes an expression in terms of Ts and sp.exp(-z*T) for T in Ts. \n", " Here :math:`z = x + i y` is a complex number.\n", " '''\n", " D = {sp.exp(-z*T): sp.cos(-x*T)*(1j*sp.sin(y*T)+sp.cos(y*T)) for T in Ts}\n", " expression2 = expression.subs(D)\n", " num_real,num_imag = expression2.expand().as_real_imag()\n", " f_r = ufuncify( [x,y]+Ts, num_real)\n", " f_i = ufuncify( [x,y]+Ts, num_imag)\n", " return lambda x,y,Ts: f_r(x,y,*Ts)+f_i(x,y,*Ts)*1j" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": true }, "outputs": [], "source": [ "F = get_real_imag_func(num2)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.14730830564958425-0.40346430265728966j)" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## testing\n", "F(1,2,(3,4,5,6))" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.33226636779973417+1.7256217430980794j)" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_real_imag_func(sp.diff(num2,z))(1,2,(3,4,5,6))" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def blah(args):\n", " for i in range(500):\n", " F(*args)" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls = [[0.]*5]*5" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls[1][2] = 4" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABWUAAAAUBAMAAAAXe0LaAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", "text/latex": [ "$$\\left [ \\left [ 0.0, \\quad 0.0, \\quad 4, \\quad 0.0, \\quad 0.0\\right ], \\quad \\left [ 0.0, \\quad 0.0, \\quad 4, \\quad 0.0, \\quad 0.0\\right ], \\quad \\left [ 0.0, \\quad 0.0, \\quad 4, \\quad 0.0, \\quad 0.0\\right ], \\quad \\left [ 0.0, \\quad 0.0, \\quad 4, \\quad 0.0, \\quad 0.0\\right ], \\quad \\left [ 0.0, \\quad 0.0, \\quad 4, \\quad 0.0, \\quad 0.0\\right ]\\right ]$$" ], "text/plain": [ "[[0.0, 0.0, 4, 0.0, 0.0], [0.0, 0.0, 4, 0.0, 0.0], [0.0, 0.0, 4, 0.0, 0.0], [0\n", ".0, 0.0, 4, 0.0, 0.0], [0.0, 0.0, 4, 0.0, 0.0]]" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls2 = np.zeros((5,5))" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0.]])" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ls2" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls2[2,3] = 4" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 4., 0.],\n", " [ 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0.]])" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ls2" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 4., 0.])" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ls2[2]" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [], "source": [ "delays = [1,4,5,6,7]" ] }, { "cell_type": "code", "execution_count": 179, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAAUBAMAAABhZ6XhAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAAAElFTkSuQmCC\n", "text/latex": [ "$$\\left [ 1.0, \\quad 4.0, \\quad 5.0, \\quad 10.0, \\quad 7.0\\right ]$$" ], "text/plain": [ "[1.0, 4.0, 5.0, 10.0, 7.0]" ] }, "execution_count": 179, "metadata": {}, "output_type": "execute_result" } ], "source": [ "map(sum,zip(ls2[2],delays))" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAACIAAAAPBAMAAABzWwAZAAAAIVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAADdcGRXAAAACnRSTlMAVO8Qq5l2zWbd0hufRgAAAAlwSFlzAAAOxAAA\nDsQBlSsOGwAAADpJREFUGBljYMAEjMoQMTANIkxCFoNFwDSEwwYRYQDTCAKoDMEZeDVWBUjuYXVa\n7sHA6cAApsEEhtcBrYgcUf0WzaQAAAAASUVORK5CYII=\n", "text/latex": [ "$$1.11$$" ], "text/plain": [ "1.11" ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "round(1.111111,2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

rucka/NeuralNetworkPlayground

notebook/mnist-tutorial.ipynb

1

13254

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A simple MNIST classifier which displays summaries in TensorBoard.\n", "\n", "This is an unimpressive MNIST model, but it is a good example of using\n", "tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of\n", "naming summary tags so that they are grouped meaningfully in TensorBoard.\n", "\n", "It demonstrates the functionality of every TensorBoard dashboard." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import argparse\n", "import sys\n", "\n", "import tensorflow as tf\n", "from tensorflow.python.platform import flags\n", "\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "FLAGS = flags.FLAGS\n", "#FLAGS = None" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "FLAGS.log_dir = '/logs/mnist_with_summaries'\n", "FLAGS.data_dir = '/book/mnist-data'\n", "FLAGS.fake_data = False\n", "FLAGS.max_steps = 1000\n", "FLAGS.learning_rate = 0.001\n", "FLAGS.dropout = 0.9\n", "\n", "if tf.gfile.Exists(FLAGS.log_dir):\n", " tf.gfile.DeleteRecursively(FLAGS.log_dir)\n", "tf.gfile.MakeDirs(FLAGS.log_dir)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train():\n", " # Import data\n", " mnist = input_data.read_data_sets(FLAGS.data_dir,\n", " one_hot=True,\n", " fake_data=FLAGS.fake_data)\n", "\n", " sess = tf.InteractiveSession()\n", " # Create a multilayer model.\n", "\n", " # Input placeholders\n", " with tf.name_scope('input'):\n", " x = tf.placeholder(tf.float32, [None, 784], name='x-input')\n", " y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')\n", "\n", " with tf.name_scope('input_reshape'):\n", " image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])\n", " tf.summary.image('input', image_shaped_input, 10)\n", "\n", " # We can't initialize these variables to 0 - the network will get stuck.\n", " def weight_variable(shape):\n", " \"\"\"Create a weight variable with appropriate initialization.\"\"\"\n", " initial = tf.truncated_normal(shape, stddev=0.1)\n", " return tf.Variable(initial)\n", "\n", " def bias_variable(shape):\n", " \"\"\"Create a bias variable with appropriate initialization.\"\"\"\n", " initial = tf.constant(0.1, shape=shape)\n", " return tf.Variable(initial)\n", "\n", " def variable_summaries(var):\n", " \"\"\"Attach a lot of summaries to a Tensor (for TensorBoard visualization).\"\"\"\n", " with tf.name_scope('summaries'):\n", " mean = tf.reduce_mean(var)\n", " tf.summary.scalar('mean', mean)\n", " with tf.name_scope('stddev'):\n", " stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))\n", " tf.summary.scalar('stddev', stddev)\n", " tf.summary.scalar('max', tf.reduce_max(var))\n", " tf.summary.scalar('min', tf.reduce_min(var))\n", " tf.summary.histogram('histogram', var)\n", "\n", " def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):\n", " \"\"\"Reusable code for making a simple neural net layer.\n", "\n", " It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.\n", " It also sets up name scoping so that the resultant graph is easy to read,\n", " and adds a number of summary ops.\n", " \"\"\"\n", " # Adding a name scope ensures logical grouping of the layers in the graph.\n", " with tf.name_scope(layer_name):\n", " # This Variable will hold the state of the weights for the layer\n", " with tf.name_scope('weights'):\n", " weights = weight_variable([input_dim, output_dim])\n", " variable_summaries(weights)\n", " with tf.name_scope('biases'):\n", " biases = bias_variable([output_dim])\n", " variable_summaries(biases)\n", " with tf.name_scope('Wx_plus_b'):\n", " preactivate = tf.matmul(input_tensor, weights) + biases\n", " tf.summary.histogram('pre_activations', preactivate)\n", " activations = act(preactivate, name='activation')\n", " tf.summary.histogram('activations', activations)\n", " return activations\n", "\n", " hidden1 = nn_layer(x, 784, 500, 'layer1')\n", "\n", " with tf.name_scope('dropout'):\n", " keep_prob = tf.placeholder(tf.float32)\n", " tf.summary.scalar('dropout_keep_probability', keep_prob)\n", " dropped = tf.nn.dropout(hidden1, keep_prob)\n", "\n", " # Do not apply softmax activation yet, see below.\n", " y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)\n", "\n", " with tf.name_scope('cross_entropy'):\n", " # The raw formulation of cross-entropy,\n", " #\n", " # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),\n", " # reduction_indices=[1]))\n", " #\n", " # can be numerically unstable.\n", " #\n", " # So here we use tf.nn.softmax_cross_entropy_with_logits on the\n", " # raw outputs of the nn_layer above, and then average across\n", " # the batch.\n", " diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)\n", " with tf.name_scope('total'):\n", " cross_entropy = tf.reduce_mean(diff)\n", " tf.summary.scalar('cross_entropy', cross_entropy)\n", "\n", " with tf.name_scope('train'):\n", " train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(\n", " cross_entropy)\n", "\n", " with tf.name_scope('accuracy'):\n", " with tf.name_scope('correct_prediction'):\n", " correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n", " with tf.name_scope('accuracy'):\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " tf.summary.scalar('accuracy', accuracy)\n", "\n", " # Merge all the summaries and write them out to\n", " # /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)\n", " merged = tf.summary.merge_all()\n", " train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)\n", " test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')\n", " tf.global_variables_initializer().run()\n", "\n", " # Train the model, and also write summaries.\n", " # Every 10th step, measure test-set accuracy, and write test summaries\n", " # All other steps, run train_step on training data, & add training summaries\n", "\n", " def feed_dict(train):\n", " \"\"\"Make a TensorFlow feed_dict: maps data onto Tensor placeholders.\"\"\"\n", " if train or FLAGS.fake_data:\n", " xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)\n", " k = FLAGS.dropout\n", " else:\n", " xs, ys = mnist.test.images, mnist.test.labels\n", " k = 1.0\n", " return {x: xs, y_: ys, keep_prob: k}\n", "\n", " for i in range(FLAGS.max_steps):\n", " if i % 10 == 0: # Record summaries and test-set accuracy\n", " summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))\n", " test_writer.add_summary(summary, i)\n", " print('Accuracy at step %s: %s' % (i, acc))\n", " else: # Record train set summaries, and train\n", " if i % 100 == 99: # Record execution stats\n", " run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n", " run_metadata = tf.RunMetadata()\n", " summary, _ = sess.run([merged, train_step],\n", " feed_dict=feed_dict(True),\n", " options=run_options,\n", " run_metadata=run_metadata)\n", " train_writer.add_run_metadata(run_metadata, 'step%03d' % i)\n", " train_writer.add_summary(summary, i)\n", " print('Adding run metadata for', i)\n", " else: # Record a summary\n", " summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))\n", " train_writer.add_summary(summary, i)\n", " train_writer.close()\n", " test_writer.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting /book/mnist-data/train-images-idx3-ubyte.gz\n", "Extracting /book/mnist-data/train-labels-idx1-ubyte.gz\n", "Extracting /book/mnist-data/t10k-images-idx3-ubyte.gz\n", "Extracting /book/mnist-data/t10k-labels-idx1-ubyte.gz\n", "Accuracy at step 0: 0.1194\n", "Accuracy at step 10: 0.748\n", "Accuracy at step 20: 0.8399\n", "Accuracy at step 30: 0.8697\n", "Accuracy at step 40: 0.8816\n", "Accuracy at step 50: 0.8914\n", "Accuracy at step 60: 0.8974\n", "Accuracy at step 70: 0.9069\n", "Accuracy at step 80: 0.9086\n", "Accuracy at step 90: 0.9107\n", "Adding run metadata for 99\n", "Accuracy at step 100: 0.9133\n", "Accuracy at step 110: 0.917\n", "Accuracy at step 120: 0.9255\n", "Accuracy at step 130: 0.9284\n", "Accuracy at step 140: 0.9263\n", "Accuracy at step 150: 0.929\n", "Accuracy at step 160: 0.9306\n", "Accuracy at step 170: 0.9317\n", "Accuracy at step 180: 0.9301\n", "Accuracy at step 190: 0.9327\n", "Adding run metadata for 199\n", "Accuracy at step 200: 0.9357\n", "Accuracy at step 210: 0.9377\n", "Accuracy at step 220: 0.9392\n", "Accuracy at step 230: 0.933\n", "Accuracy at step 240: 0.9387\n", "Accuracy at step 250: 0.9377\n", "Accuracy at step 260: 0.9432\n", "Accuracy at step 270: 0.9404\n", "Accuracy at step 280: 0.941\n", "Accuracy at step 290: 0.9462\n", "Adding run metadata for 299\n", "Accuracy at step 300: 0.9453\n", "Accuracy at step 310: 0.9446\n", "Accuracy at step 320: 0.949\n", "Accuracy at step 330: 0.9473\n", "Accuracy at step 340: 0.9472\n", "Accuracy at step 350: 0.9437\n", "Accuracy at step 360: 0.9475\n", "Accuracy at step 370: 0.9494\n", "Accuracy at step 380: 0.9502\n", "Accuracy at step 390: 0.9506\n", "Adding run metadata for 399\n", "Accuracy at step 400: 0.9518\n", "Accuracy at step 410: 0.9511\n", "Accuracy at step 420: 0.9501\n", "Accuracy at step 430: 0.9509\n", "Accuracy at step 440: 0.9468\n", "Accuracy at step 450: 0.9547\n", "Accuracy at step 460: 0.9503\n", "Accuracy at step 470: 0.9527\n", "Accuracy at step 480: 0.9553\n", "Accuracy at step 490: 0.9571\n", "Adding run metadata for 499\n", "Accuracy at step 500: 0.9566\n", "Accuracy at step 510: 0.9564\n", "Accuracy at step 520: 0.9554\n", "Accuracy at step 530: 0.9533\n", "Accuracy at step 540: 0.9572\n", "Accuracy at step 550: 0.9602\n", "Accuracy at step 560: 0.9586\n", "Accuracy at step 570: 0.9567\n", "Accuracy at step 580: 0.96\n", "Accuracy at step 590: 0.9593\n", "Adding run metadata for 599\n", "Accuracy at step 600: 0.9576\n", "Accuracy at step 610: 0.9599\n", "Accuracy at step 620: 0.9591\n", "Accuracy at step 630: 0.959\n", "Accuracy at step 640: 0.9606\n", "Accuracy at step 650: 0.9594\n", "Accuracy at step 660: 0.9612\n", "Accuracy at step 670: 0.9627\n", "Accuracy at step 680: 0.9612\n", "Accuracy at step 690: 0.9635\n", "Adding run metadata for 699\n", "Accuracy at step 700: 0.9642\n", "Accuracy at step 710: 0.9645\n", "Accuracy at step 720: 0.9627\n", "Accuracy at step 730: 0.9614\n", "Accuracy at step 740: 0.9654\n", "Accuracy at step 750: 0.9653\n", "Accuracy at step 760: 0.9657\n", "Accuracy at step 770: 0.9632\n" ] } ], "source": [ "train()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

GiladAmar/Crash_courses

Python/drafts/numpy learning.ipynb

1

30318

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "start_time": "2019-07-02T14:25:18.374Z" } }, "outputs": [], "source": [ "import numpy as np\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "start_time": "2019-07-02T14:24:02.775Z" } }, "outputs": [], "source": [ "np." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "np.array2string?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "np.atleast_3d?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "np.convolve?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "np.compress?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 6], dtype=int32)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.cumproduct((1,2,3))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "np.digitize?" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "np.ediff1d?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "np.einsum?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "np.flatiter?" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "np.flatnonzero?" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "np.fmax?" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "np.format_float_scientific?" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "np.format_float_positional?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "np.fromfunction?" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "np.full?" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "np.fv?\n", "np.pv?\n", "np.ipmt?\n", "np.irr?\n", "np.mirr?\n", "np.nper?\n", "np.npv?\n", "np.pmt?\n", "np.ppmt?" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "np.gcd?\n", "np.lcm?" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "np.geomspace?\n", "np.logspace\n", "np.linspace" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "np.gradient?\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "np.histogram?\n", "np.histogram2\n", "np.histogram_bin_edges\n", "np.histogramdd\n" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "np.i0?" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(iinfo(min=-2147483648, max=2147483647, dtype=int32),\n", " iinfo(min=-9223372036854775808, max=9223372036854775807, dtype=int64))" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.iinfo(int),np.iinfo(np.int64)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "np.isin" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "np.info?" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "np.inner?" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "np.interp?" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "np.intersect1d?" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "np.isneginf\n", "np.isposinf\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.ix_?" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "np.lexsort?" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "np.linalg?" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "np.ma?" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "np.meshgrid?" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "np.mgrid?" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [], "source": [ "np.moveaxis?" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "np.nanargmax\n", "np.nanargmin\n", "np.nancumprod\n", "np.nancumsum\n", "...\n" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<function numpy.core.fromnumeric.ndim(a)>" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.nanpercentile\n", "np.nanquantile\n" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "np.ndim?" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "np.nested_iters?" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "np.newaxis\n" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [], "source": [ "np.ogrid?" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "np.outer?" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "np.pad\n", "np.partition\n", "np.piecewise?\n", "np.place?\n", "np.poly?" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-111-39779e405f90>, line 1)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-111-39779e405f90>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m np.poly...\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "np.poly...\n", "\n" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "np.ptp?" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<function numpy.core.multiarray.where>" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "np.vander?" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "np.unique\n", "np.union1d\n", "np.unwrap?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.r...\n" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [], "source": [ "np.searchsorted\n", "np.select\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "np.save?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "np.savez?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "np.searchsorted?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "np.swapaxes?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "np.take?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "np.tensordot?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "np.tile?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "np.trace?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "np.trapz?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "np.tri?" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "np.trim_zeros?" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "np.linalg?" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import scipy as sci\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "import numexpr" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "import mkl\n", "import mkl_random\n", "import mkl_fft\n", "import glob2\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "import glob\n", "variable = ('')\n", "glob.glob" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "pd.wide_to_long\n", "pd.concat\n", "pd.crosstab\n", "pd.cut\n", "pd.date_range?\n", "pd.datetime\n", "pd.factorize?\n", "pd.get_dummies\n", "pd.get_option?\n", "pd.groupby?\n", "pd.infer_freq\n", "pd.interval_range\n", "pd.lreshape?\n", "pd.match?\n", "pd.melt?\n", "pd.merge\n", "pd.merge_asof\n", "pd.merge_ordered?\n", "pd.notna\n", "pd.notnull?\n", "pd.period_range\n", "pd.pivot\n", "pd.pivot_table\n", "pd.qcut?\n", "pd.set_option?\n", "pd.timedelta_range\n", "pd.to_datetime\n", "pd.unique\n", "pd.value_counts\n", "pd.wide_to_long" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "from PIL import Image\n", "import PIL" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "PIL.Image.composite\n", "PIL.Image.effect_mandelbrot?" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [], "source": [ "img = PIL.Image.effect_mandelbrot((1000,1000),(-0.5,0.5,0,1),250)" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [], "source": [ "img = np.array(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " PIL" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [], "source": [ "import skimage" ] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [], "source": [ "viewer = skimage.viewer.ImageViewer(img)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [], "source": [ "import numexpr" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [], "source": [ "numexpr.evaluate?" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "122 ms ± 2.11 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "%timeit numexpr.evaluate('a+b')" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 109 ms\n" ] } ], "source": [ "%time _ = numexpr.evaluate('a+b')" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [], "source": [ "a = np.random.randint(0,255,(80000,6000,3))\n", "\n", "b = np.random.randint(0,255,(80000,6000,3))" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 1min 51s\n" ] } ], "source": [ "%time _ = a**2 + 3*b" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 22.8 s\n" ] } ], "source": [ "%time _ = numexpr.evaluate('a**2 + 3*b')" ] }, { "cell_type": "code", "execution_count": 238, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.868421052631579" ] }, "execution_count": 238, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(60 + 51) / 22.8" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%time _ = numexpr.evaluate" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(8000, 6000, 3)" ] }, "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_.shape" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "248 ms ± 4.09 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit a+b" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 261 ms\n" ] } ], "source": [ "%time _ = a+b" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numexpr.detect_number_of_cores()" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numexpr.detect_number_of_threads()" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "64" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numexpr.MAX_THREADS" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numexpr.nthreads" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mkl_info:\n", " libraries = ['mkl_rt']\n", " library_dirs = ['C:/Users/TAG_server/Anaconda3\\\\Library\\\\lib']\n", " define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\n", " include_dirs = ['C:/Users/TAG_server/Anaconda3\\\\Library\\\\include']\n" ] } ], "source": [ "numexpr.show_config()" ] }, { "cell_type": "code", "execution_count": 229, "metadata": {}, "outputs": [], "source": [ "from numba import jit\n", "jit?\n" ] }, { "cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [], "source": [ "\n", "def add(a,b):\n", " return a +b" ] }, { "cell_type": "code", "execution_count": 222, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 38 s\n" ] }, { "data": { "text/plain": [ "array([[[420, 108, 207],\n", " [264, 242, 466],\n", " [266, 320, 367],\n", " ...,\n", " [209, 404, 332],\n", " [ 73, 409, 184],\n", " [175, 169, 70]],\n", "\n", " [[218, 446, 379],\n", " [152, 276, 118],\n", " [ 95, 202, 175],\n", " ...,\n", " [190, 305, 288],\n", " [156, 285, 417],\n", " [419, 182, 364]],\n", "\n", " [[396, 264, 145],\n", " [125, 11, 295],\n", " [102, 363, 251],\n", " ...,\n", " [281, 259, 258],\n", " [276, 366, 445],\n", " [270, 117, 226]],\n", "\n", " ...,\n", "\n", " [[219, 215, 207],\n", " [152, 244, 10],\n", " [206, 330, 265],\n", " ...,\n", " [458, 136, 343],\n", " [103, 371, 165],\n", " [365, 85, 414]],\n", "\n", " [[383, 340, 348],\n", " [456, 409, 325],\n", " [167, 135, 291],\n", " ...,\n", " [231, 269, 252],\n", " [431, 245, 364],\n", " [390, 178, 282]],\n", "\n", " [[306, 235, 218],\n", " [253, 289, 274],\n", " [270, 272, 130],\n", " ...,\n", " [237, 36, 342],\n", " [311, 478, 253],\n", " [191, 226, 347]]])" ] }, "execution_count": 222, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time add(a,b)" ] }, { "cell_type": "code", "execution_count": 225, "metadata": {}, "outputs": [], "source": [ "@jit\n", "def add(a,b):\n", " return a +b" ] }, { "cell_type": "code", "execution_count": 226, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 55.1 s\n" ] }, { "data": { "text/plain": [ "array([[[420, 108, 207],\n", " [264, 242, 466],\n", " [266, 320, 367],\n", " ...,\n", " [209, 404, 332],\n", " [ 73, 409, 184],\n", " [175, 169, 70]],\n", "\n", " [[218, 446, 379],\n", " [152, 276, 118],\n", " [ 95, 202, 175],\n", " ...,\n", " [190, 305, 288],\n", " [156, 285, 417],\n", " [419, 182, 364]],\n", "\n", " [[396, 264, 145],\n", " [125, 11, 295],\n", " [102, 363, 251],\n", " ...,\n", " [281, 259, 258],\n", " [276, 366, 445],\n", " [270, 117, 226]],\n", "\n", " ...,\n", "\n", " [[219, 215, 207],\n", " [152, 244, 10],\n", " [206, 330, 265],\n", " ...,\n", " [458, 136, 343],\n", " [103, 371, 165],\n", " [365, 85, 414]],\n", "\n", " [[383, 340, 348],\n", " [456, 409, 325],\n", " [167, 135, 291],\n", " ...,\n", " [231, 269, 252],\n", " [431, 245, 364],\n", " [390, 178, 282]],\n", "\n", " [[306, 235, 218],\n", " [253, 289, 274],\n", " [270, 272, 130],\n", " ...,\n", " [237, 36, 342],\n", " [311, 478, 253],\n", " [191, 226, 347]]])" ] }, "execution_count": 226, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time _ = add(a, b)" ] }, { "cell_type": "code", "execution_count": 227, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 49.1 s\n" ] } ], "source": [ "%time _ = add(a ,b)" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [], "source": [ "@jit(target='cpu')\n", "def add(a, b):\n", " return a + b" ] }, { "cell_type": "code", "execution_count": 232, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 49 s\n" ] } ], "source": [ "%time _ = add(a, b)" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 1min 11s\n" ] } ], "source": [ "%time _ = add(a, b)" ] }, { "cell_type": "code", "execution_count": 239, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Zen of Python, by Tim Peters\n", "\n", "Beautiful is better than ugly.\n", "Explicit is better than implicit.\n", "Simple is better than complex.\n", "Complex is better than complicated.\n", "Flat is better than nested.\n", "Sparse is better than dense.\n", "Readability counts.\n", "Special cases aren't special enough to break the rules.\n", "Although practicality beats purity.\n", "Errors should never pass silently.\n", "Unless explicitly silenced.\n", "In the face of ambiguity, refuse the temptation to guess.\n", "There should be one-- and preferably only one --obvious way to do it.\n", "Although that way may not be obvious at first unless you're Dutch.\n", "Now is better than never.\n", "Although never is often better than *right* now.\n", "If the implementation is hard to explain, it's a bad idea.\n", "If the implementation is easy to explain, it may be a good idea.\n", "Namespaces are one honking great idea -- let's do more of those!\n" ] } ], "source": [ "import this" ] }, { "cell_type": "code", "execution_count": 257, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'The Zen of Python by Tim Peters Beautiful is better than ugly Explicit is better than implicit Simple is better than complex Complex is better than complicated Flat is better than nested Sparse is better than dense Readability counts Special cases aren t special enough to break the rules Although practicality beats purity Errors should never pass silently Unless explicitly silenced In the face of ambiguity refuse the temptation to guess There should be one and preferably only one obvious way to do it Although that way may not be obvious at first unless you re Dutch Now is better than never Although never is often better than right now If the implementation is hard to explain it s a bad idea If the implementation is easy to explain it may be a good idea Namespaces are one honking great idea let s do more of those '" ] }, "execution_count": 257, "metadata": {}, "output_type": "execute_result" } ], "source": [ "''.join([this.d.get(i,' ') for i in this.s])\n" ] }, { "cell_type": "code", "execution_count": 255, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Gur Mra bs Clguba, ol Gvz Crgref\\n\\nOrnhgvshy vf orggre guna htyl.\\nRkcyvpvg vf orggre guna vzcyvpvg.\\nFvzcyr vf orggre guna pbzcyrk.\\nPbzcyrk vf orggre guna pbzcyvpngrq.\\nSyng vf orggre guna arfgrq.\\nFcnefr vf orggre guna qrafr.\\nErnqnovyvgl pbhagf.\\nFcrpvny pnfrf nera'g fcrpvny rabhtu gb oernx gur ehyrf.\\nNygubhtu cenpgvpnyvgl orngf chevgl.\\nReebef fubhyq arire cnff fvyragyl.\\nHayrff rkcyvpvgyl fvyraprq.\\nVa gur snpr bs nzovthvgl, ershfr gur grzcgngvba gb thrff.\\nGurer fubhyq or bar-- naq cersrenoyl bayl bar --boivbhf jnl gb qb vg.\\nNygubhtu gung jnl znl abg or boivbhf ng svefg hayrff lbh'er Qhgpu.\\nAbj vf orggre guna arire.\\nNygubhtu arire vf bsgra orggre guna *evtug* abj.\\nVs gur vzcyrzragngvba vf uneq gb rkcynva, vg'f n onq vqrn.\\nVs gur vzcyrzragngvba vf rnfl gb rkcynva, vg znl or n tbbq vqrn.\\nAnzrfcnprf ner bar ubaxvat terng vqrn -- yrg'f qb zber bs gubfr!\"" ] }, "execution_count": 255, "metadata": {}, "output_type": "execute_result" } ], "source": [ "this.s" ] }, { "cell_type": "code", "execution_count": 263, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13" ] }, "execution_count": 263, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mkl_random, mk\n", "mkl_random.randint(0,100)" ] }, { "cell_type": "code", "execution_count": 274, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7 ms ± 10.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%timeit mkl_random.randint(0,100,(1000,1000))" ] }, { "cell_type": "code", "execution_count": 275, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.62 ms ± 49.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit np.random.randint(0,100,(1000,1000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%mkdir\n", "\n", "\n", "\n", "\n", "np.testing.assert_allclose(model.predict(x), predict(x), atol=1e-5)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false }, "pycharm": { "stem_cell": { "cell_type": "raw", "source": [], "metadata": { "collapsed": false } } } }, "nbformat": 4, "nbformat_minor": 2 }

mit

jna29/SymGP

symgp/notebooks/KalmanFilter.ipynb

1

107280

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Kalman Filter" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "# Add the symgp folder path to the sys.path list\n", "module_path = r'/Users/jaduol/Documents/Uni (original)/Part II/IIB/MEng Project/'\n", "if module_path not in sys.path:\n", " sys.path.append(module_path)\n", "\n", "from symgp import SuperMatSymbol, utils, MVG, Variable, SuperDiagMat, Kernel, Covariance, Constant, Mean\n", "from sympy import symbols, ZeroMatrix, Identity\n", "from IPython.display import display, Math, Latex, clear_output" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Shapes\n", "m, n, t = symbols('m n t')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Variables" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Variables\n", "x_prev, x_t, y_prev, y_t, b_t, d_t, w_t, e_t = utils.variables('x_{t-1} x_t y_{1:t-1} y_t b_t d_t w_t e_t', \n", " [m, m, (n,t-1), n, m, n, m, n])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Constant Parameters" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Constant parameters\n", "A, C = utils.constants('A C',[(m,m), (n,m)]) \n", "Q = Covariance(w_t, name='Q')\n", "R = Covariance(e_t, name='R')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. p(x_(t-1)|y_(1:t-1))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_xprev:\n" ] }, { "data": { "text/latex": [ "\\begin{align*}\n", "p\\left(\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}\\right)&= \\mathcal{N}\\left(\\mathbf{x_{t-1}};\\mathbf{m}_{\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}},\\mathbf{\\Sigma}_{\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}}\\right)\\\\\n", "\\mathbf{m}_{\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}} &= \\mathbf{m_{t-1|t-1}}\\\\\n", "\\mathbf{\\Sigma}_{\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}} &= \\mathbf{P_{t-1|t-1}}\\\\\n", "\\end{align*}" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# p(x_(t-1)|y_(1:t-1))\n", "f_prev = Mean(x_prev, name='m_{t-1|t-1}') \n", "F_prev = Covariance(x_prev, name='P_{t-1|t-1}') \n", "p_xprev = MVG([x_prev], mean=f_prev, cov=F_prev, cond_vars=[y_prev])\n", "\n", "print(\"p_xprev:\")\n", "display(Latex(utils.matLatex(p_xprev)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P_{t-1|t-1}\n" ] } ], "source": [ "print(p_xprev.covar.expanded)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. p(x_t|x_(t-1))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_xt:\n" ] }, { "data": { "text/latex": [ "\\begin{align*}\n", "p\\left(\\mathbf{x_t}|\\mathbf{x_{t-1}}\\right)&= \\mathcal{N}\\left(\\mathbf{x_t};\\mathbf{m}_{\\mathbf{x_t}|\\mathbf{x_{t-1}}},\\mathbf{\\Sigma}_{\\mathbf{x_t}|\\mathbf{x_{t-1}}}\\right)\\\\\n", "\\mathbf{m}_{\\mathbf{x_t}|\\mathbf{x_{t-1}}} &= \\mathbf{A} \\mathbf{x_{t-1}}\\\\\n", "\\mathbf{\\Sigma}_{\\mathbf{x_t}|\\mathbf{x_{t-1}}} &= \\mathbf{Q}\\\\\n", "\\end{align*}" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p_xt = MVG([x_t], mean=A*x_prev, cov=Q, cond_vars=[x_prev])\n", "\n", "print(\"p_xt:\")\n", "display(Latex(utils.matLatex(p_xt)))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['x_{t-1}', 'x_t', 'y_{1:t-1}', 'y_t', 'b_t', 'd_t', 'w_t', 'e_t', 'A', 'C', 'Q', 'R', 'm_{t-1|t-1}', 'P_{t-1|t-1}', 'm_{x_{t-1}|y_{1:t-1}}', 'S_{x_{t-1},x_{t-1}|y_{1:t-1}}', 'm_{x_t|x_{t-1}}', 'S_{x_t,x_t|x_{t-1}}']\n" ] } ], "source": [ "print(Covariance._used_names)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "S_{x_{t-1},x_{t-1}|y_{1:t-1}}\n" ] } ], "source": [ "print(p_xprev.covar)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. p(x_t,x_(t-1)|y_(1:t-1))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_xt_xprev:\n" ] }, { "data": { "text/latex": [ "\\begin{align*}\n", "p\\left(\\mathbf{x_t},\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}\\right)&= \\mathcal{N}\\left(\\left[\\begin{smallmatrix}\\mathbf{x_t}\\\\\\mathbf{x_{t-1}}\\end{smallmatrix}\\right];\\mathbf{m}_{\\mathbf{x_t},\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}},\\mathbf{\\Sigma}_{\\mathbf{x_t},\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}}\\right)\\\\\n", "\\mathbf{m}_{\\mathbf{x_t},\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}} &= \\left[\\begin{smallmatrix}\\mathbf{A} \\mathbf{m_{t-1|t-1}}\\\\\\mathbf{m_{t-1|t-1}}\\end{smallmatrix}\\right]\\\\\n", "\\mathbf{\\Sigma}_{\\mathbf{x_t},\\mathbf{x_{t-1}}|\\mathbf{y_{1:t-1}}} &= \\left[\\begin{smallmatrix}\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T&\\mathbf{A} \\mathbf{P_{t-1|t-1}}\\\\\\mathbf{P_{t-1|t-1}} \\mathbf{A}^T&\\mathbf{P_{t-1|t-1}}\\end{smallmatrix}\\right]\\\\\n", "\\end{align*}" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p_xt_xprev = p_xt*p_xprev\n", "\n", "print(\"p_xt_xprev:\")\n", "display(Latex(utils.matLatex(p_xt_xprev)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. p(x_t|y_(1:t-1))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_xt_predict:\n" ] }, { "data": { "text/latex": [ "\\begin{align*}\n", "p\\left(\\mathbf{x_t}|\\mathbf{y_{1:t-1}}\\right)&= \\mathcal{N}\\left(\\mathbf{x_t};\\mathbf{m}_{\\mathbf{x_t}|\\mathbf{y_{1:t-1}}},\\mathbf{\\Sigma}_{\\mathbf{x_t}|\\mathbf{y_{1:t-1}}}\\right)\\\\\n", "\\mathbf{m}_{\\mathbf{x_t}|\\mathbf{y_{1:t-1}}} &= \\mathbf{A} \\mathbf{m_{t-1|t-1}}\\\\\n", "\\mathbf{\\Sigma}_{\\mathbf{x_t}|\\mathbf{y_{1:t-1}}} &= \\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\\\\n", "\\end{align*}" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p_xt_predict = p_xt_xprev.marginalise([x_prev])\n", "\n", "print(\"p_xt_predict:\")\n", "display(Latex(utils.matLatex(p_xt_predict)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. p(y_t|x_t)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_yt:\n" ] }, { "data": { "text/latex": [ "\\begin{align*}\n", "p\\left(\\mathbf{y_t}|\\mathbf{x_t}\\right)&= \\mathcal{N}\\left(\\mathbf{y_t};\\mathbf{m}_{\\mathbf{y_t}|\\mathbf{x_t}},\\mathbf{\\Sigma}_{\\mathbf{y_t}|\\mathbf{x_t}}\\right)\\\\\n", "\\mathbf{m}_{\\mathbf{y_t}|\\mathbf{x_t}} &= \\mathbf{C} \\mathbf{x_t}\\\\\n", "\\mathbf{\\Sigma}_{\\mathbf{y_t}|\\mathbf{x_t}} &= \\mathbf{R}\\\\\n", "\\end{align*}" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p_yt = MVG([y_t], mean=C*x_t, cov=R, cond_vars=[x_t])\n", "\n", "print(\"p_yt:\")\n", "display(Latex(utils.matLatex(p_yt)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. p(y_t ,x_t|y_(1:t-1))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_yt_xt:\n" ] }, { "data": { "text/latex": [ "\\begin{align*}\n", "p\\left(\\mathbf{y_t},\\mathbf{x_t}|\\mathbf{y_{1:t-1}}\\right)&= \\mathcal{N}\\left(\\left[\\begin{smallmatrix}\\mathbf{y_t}\\\\\\mathbf{x_t}\\end{smallmatrix}\\right];\\mathbf{m}_{\\mathbf{y_t},\\mathbf{x_t}|\\mathbf{y_{1:t-1}}},\\mathbf{\\Sigma}_{\\mathbf{y_t},\\mathbf{x_t}|\\mathbf{y_{1:t-1}}}\\right)\\\\\n", "\\mathbf{m}_{\\mathbf{y_t},\\mathbf{x_t}|\\mathbf{y_{1:t-1}}} &= \\left[\\begin{smallmatrix}\\mathbf{C} \\mathbf{A} \\mathbf{m_{t-1|t-1}}\\\\\\mathbf{A} \\mathbf{m_{t-1|t-1}}\\end{smallmatrix}\\right]\\\\\n", "\\mathbf{\\Sigma}_{\\mathbf{y_t},\\mathbf{x_t}|\\mathbf{y_{1:t-1}}} &= \\left[\\begin{smallmatrix}\\mathbf{R} + \\mathbf{C} \\left(\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\right) \\mathbf{C}^T&\\mathbf{C} \\left(\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\right)\\\\\\left(\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\right) \\mathbf{C}^T&\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\end{smallmatrix}\\right]\\\\\n", "\\end{align*}" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p_yt_xt = p_yt*p_xt_predict\n", "\n", "print(\"p_yt_xt:\")\n", "display(Latex(utils.matLatex(p_yt_xt)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. p(x_t|y_(1:t))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_xt_update:\n" ] }, { "data": { "text/latex": [ "\\begin{align*}\n", "p\\left(\\mathbf{x_t}|\\mathbf{y_{1:t-1}},\\mathbf{y_t}\\right)&= \\mathcal{N}\\left(\\mathbf{x_t};\\mathbf{m}_{\\mathbf{x_t}|\\mathbf{y_{1:t-1}},\\mathbf{y_t}},\\mathbf{\\Sigma}_{\\mathbf{x_t}|\\mathbf{y_{1:t-1}},\\mathbf{y_t}}\\right)\\\\\n", "\\mathbf{m}_{\\mathbf{x_t}|\\mathbf{y_{1:t-1}},\\mathbf{y_t}} &= \\mathbf{A} \\mathbf{m_{t-1|t-1}} + \\left(\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\right) \\mathbf{C}^T \\left(\\mathbf{R} + \\mathbf{C} \\left(\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\right) \\mathbf{C}^T\\right)^{-1} \\left(\\mathbf{y_t} - \\mathbf{C} \\mathbf{A} \\mathbf{m_{t-1|t-1}}\\right)\\\\\n", "\\mathbf{\\Sigma}_{\\mathbf{x_t}|\\mathbf{y_{1:t-1}},\\mathbf{y_t}} &= \\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T - \\left(\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\right) \\mathbf{C}^T \\left(\\mathbf{R} + \\mathbf{C} \\left(\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\right) \\mathbf{C}^T\\right)^{-1} \\mathbf{C} \\left(\\mathbf{Q} + \\mathbf{A} \\mathbf{P_{t-1|t-1}} \\mathbf{A}^T\\right)\\\\\n", "\\end{align*}" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p_xt_update = p_yt_xt.condition([y_t])\n", "\n", "print(\"p_xt_update:\")\n", "display(Latex(utils.matLatex(p_xt_update)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numerical Evaluation" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import Matrix, Identity\n", "import numpy as np\n", "\n", "d = {'A': np.array([[1, 0, 1, 0],[0, 1, 0, 1],[0, 0, 1, 0],[0, 0, 0, 1]],dtype=np.float32),\n", " 'C': np.array([[1, 0, 0, 0],[0, 1, 0, 0]], dtype=np.float32),\n", " 'Q': 0.001*np.eye(4),#Matrix(Identity(4)),\n", " 'R': np.eye(2),#Matrix(Identity(2)),\n", " 'm_{t-1|t-1}': np.array([[8],[10],[1],[0]],dtype=np.float32),\n", " 'P_{t-1|t-1}': np.eye(4),#Matrix(Identity(4)),\n", " 'b_t': np.array([[0],[0],[0],[0]],dtype=np.float32),\n", " 'd_t': np.array([[0],[0]],dtype=np.float32)}\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def ldsSample(A, C, Q, R, initmu, T):\n", " \"\"\"\n", " Simulates a run of a linear dynamical system\n", " \"\"\"\n", " \n", " A, C = np.array(A.tolist(), dtype=np.float32), np.array(C.tolist(), dtype=np.float32)\n", " Q, R = np.array(Q.tolist(), dtype=np.float32), np.array(R.tolist(), dtype=np.float32)\n", " initmu = np.array(initmu.tolist(), dtype=np.float32).reshape((-1,))\n", " \n", " ss = A.shape[0]\n", " os = C.shape[0]\n", " \n", " x = np.zeros((ss,T))\n", " y = np.zeros((os,T))\n", " \n", " x[:,0] = np.dot(A,initmu) + np.random.multivariate_normal(np.zeros(ss),Q)\n", " y[:,0] = np.dot(C,x[:,0]) + np.random.multivariate_normal(np.zeros(os),R)\n", " \n", " for t in range(1,T):\n", " x[:,t] = np.dot(A,x[:,t-1]) + np.random.multivariate_normal(np.zeros(ss),Q)\n", " y[:,t] = np.dot(C,x[:,t-1]) + np.random.multivariate_normal(np.zeros(os),R)\n", " \n", " return x,y\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "T = 15\n", "x, y = ldsSample(d['A'], d['C'], d['Q'], d['R'], d['m_{t-1|t-1}'], T)\n", "\n", "d['y_t'] = Matrix(y[:,0].reshape((-1,1)))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def kalmanFilter(y, A, C, Q, R, init_m, init_V, debug=False):\n", " \n", " A, C = np.array(A.tolist(), dtype=np.float32), np.array(C.tolist(), dtype=np.float32)\n", " Q, R = np.array(Q.tolist(), dtype=np.float32), np.array(R.tolist(), dtype=np.float32)\n", " init_m = np.array(init_m.tolist(), dtype=np.float32).reshape((-1,))\n", " init_V = np.array(init_V.tolist(), dtype=np.float32)\n", " \n", " os, T = y.shape #Observations shape \n", " ss = A.shape[0] #State space size\n", " \n", " m = np.zeros((ss, T))\n", " mpred = np.zeros((ss, T))\n", " V = np.zeros((ss, ss, T))\n", " Vpred = np.zeros((ss, ss, T))\n", " \n", " for t in range(T):\n", " if t==0:\n", " d['m_{t-1|t-1}'] = init_m\n", " d['P_{t-1|t-1}'] = init_V\n", " else:\n", " d['m_{t-1|t-1}'] = m[:,t-1]\n", " d['P_{t-1|t-1}'] = V[:,:,t-1]\n", " \n", " d['y_t'] = Matrix(y[:,t].reshape((-1,1)))\n", " \n", " if debug:\n", " print(\"t: \", t)\n", " print(\"d['m_{t-1|t-1}']: \",d['m_{t-1|t-1}'])\n", " print(\"d['P_{t-1|t-1}']: \",d['P_{t-1|t-1}'])\n", " print(\"d['y_t']: \", d['y_t'])\n", " \n", " \n", " m[:,t] = utils.replace_with_num(p_xt_update.mean.to_full_expr(), d, debug)\n", " V[:,:,t] = utils.replace_with_num(p_xt_update.covar.to_full_expr(), d, debug)\n", " mpred[:,t] = utils.replace_with_num(p_xt_predict.mean.to_full_expr(), d, debug)\n", " Vpred[:,:,t] = utils.replace_with_num(p_xt_predict.covar.to_full_expr(), d, debug)\n", " \n", " \n", " return m, V, mpred, Vpred" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mfilt, Vfilt, mpred, Vpred = kalmanFilter(y, d['A'], d['C'], d['Q'], d['R'], d['m_{t-1|t-1}'], d['P_{t-1|t-1}'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Configure the matplotlib backend as plotting inline\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "\n", "#Enables latex\n", "from matplotlib import rc\n", "rc('font',**{'family':'sans-serif','sans-serif':['Helvetica'],'size':'20'})\n", "## for Palatino and other serif fonts use:\n", "#rc('font',**{'family':'serif','serif':['Palatino']})\n", "rc('text', usetex=True)\n", "\n", "def plotGaussianEllipse(ax, m, V, ellipsecolor, markercolor,label=None,t=None):\n", " \"\"\"\n", " Plots an ellipse representing the covariance matrix of a Gaussian.\n", " \n", " Args:\n", " ax - The Axes object on which to plot the ellipses\n", " m - The centre of the ellipse\n", " V - Covariance matrix\n", " ellipsecolor - Ellipse color border colour\n", " markercolor - Centre marker colour\n", " (Optional)\n", " label - label for the plot containing this point\n", " t - point at which to add new data\n", " \"\"\"\n", " \n", " s = 4.605 #90% confidence interval\n", " eigVals, eigVecs = np.linalg.eig(V)\n", " \n", " #Sort eigvalues to make sure that large eigval is first\n", " idx = eigVals.argsort()[::-1]\n", " eigVals = eigVals[idx]\n", " eigVecs = eigVecs[:,idx]\n", " \n", " #Add ellipse to axes\n", " border = patches.Arc(xy=m,width = 2*(s*eigVals[0])**0.5,height = 2*(s*eigVals[1])**0.5,\n", " angle=np.degrees(np.arctan2(eigVecs[1,0],eigVecs[0,0])), \\\n", " linewidth=0.5, linestyle='--',color=ellipsecolor,fill=False, zorder=2)\n", " ax.add_patch(border)\n", " \n", " #Either plot points that are joined by a line or plot them individually\n", " if t != None:\n", " data = ax.lines[-1].get_data()\n", " if t >= len(data[0]):\n", " new_xdata = np.append(data[0],m[0])\n", " new_ydata = np.append(data[1],m[1])\n", " ax.lines[-1].set_data((new_xdata, new_ydata))\n", " else:\n", " data[0][t] = m[0]\n", " data[1][t] = m[1]\n", " ax.lines[-1].set_data((data[0],data[1])) \n", " else: \n", " ax.plot(m[0],m[1],'x-',ms=10,mew=2,c=markercolor,label=label)\n", " \n", "def plotLine(ax, a, b, color):\n", " \"\"\"\n", " Plots a line between two points\n", " \"\"\"\n", " ax.plot([a[0], b[0]],[a[1], b[1]], color=color,linestyle='solid')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABXoAAAHbCAYAAAB1OSJZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAADlhF4AAAAAgHIvb+dsOJGKaB4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10e039a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Set up figure\n", "fig = plt.figure()\n", "fig.set_size_inches(20,7)\n", "ax = fig.add_subplot(111)\n", "plt.axis('equal')\n", "\n", "#Plot observations and true data\n", "ax.set_xlim(left=5,right=25)\n", "ax.set_ylim(bottom=5,top=15)\n", "ax.plot(y[0,:],y[1,:],'ko',label=r'Obs') \n", "ax.plot(x[0,:],x[1,:],'g-s',label=r'True')\n", "ax.set_xlabel('$x_{1}$')\n", "ax.set_ylabel('$x_{2}$')\n", "fig.tight_layout()\n", "\n", "runTillEnd = False\n", "\n", "#Plot Kalman filter estimate\n", "for t in range(mfilt.shape[1]):\n", " if not runTillEnd:\n", " if input(\"Press enter to continue (Predict). Type 'rte' to run simulation to end.\") == \"rte\":\n", " runTillEnd = True\n", " \n", " clear_output(wait=True)\n", " \n", " #Plot prediction\n", " plotGaussianEllipse(ax, mpred[:,t], Vpred[:,:,t], \\\n", " ellipsecolor='green', markercolor='green')\n", " display(plt.gcf())\n", " \n", " if not runTillEnd:\n", " if input(\"Press enter to continue (Measurement). Type 'rte' to run simulation to end.\") == \"rte\":\n", " runTillEnd = True\n", " \n", " clear_output(wait=True)\n", " \n", " #Remove last prediction ellipse\n", " del ax.patches[-1]\n", " del ax.lines[-1]\n", " \n", " #Plot update\n", " if t == 0:\n", " plotGaussianEllipse(ax, mfilt[:2,t],Vfilt[:2,:2,t],\\\n", " ellipsecolor='blue', markercolor='red',label=r'Filter')\n", " ax.legend()\n", " else:\n", " plotGaussianEllipse(ax, mfilt[:2,t],Vfilt[:2,:2,t],\\\n", " ellipsecolor='blue', markercolor='red',t=t)\n", " display(plt.gcf())\n", "\n", "clear_output(wait=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simplification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Covariance of p(x_t|y_{1:t})" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "simp_exprs, subs = utils.simplify(p_xt_update.covar.to_full_expr())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q + A*P_{t-1|t-1}*A' + (-1)*(Q + A*P_{t-1|t-1}*A')*C'*(R + C*(Q + A*P_{t-1|t-1}*A')*C')^-1*C*(Q + A*P_{t-1|t-1}*A')\n" ] } ], "source": [ "print(p_xt_update.covar.to_full_expr())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "s: (I + (-1)*A_{2}*C)*A_{1}\n", "s: (-1)*A_{2}*C*A_{1} + A_{1}\n", "s: A_{1}*(I + (-1)*C'*(R + C*A_{1}*C')^-1*C*A_{1})\n", "s: (I + (-1)*A_{1}*C'*(R + C*A_{1}*C')^-1*C)*A_{1}\n", "s: (-1)*A_{1}*C'*(R + C*A_{1}*C')^-1*C*A_{1} + A_{1}\n", "s: (Q + A_{0})*(I + (-1)*C'*(R + C*(Q + A_{0})*C')^-1*C*(Q + A_{0}))\n", "s: (I + (-1)*(Q + A_{0})*C'*(R + C*(Q + A_{0})*C')^-1*C)*(Q + A_{0})\n", "s: Q + (-1)*(Q + A_{0})*C'*(R + C*(Q + A_{0})*C')^-1*C*(Q + A_{0}) + A_{0}\n", "Q + A_{0}: A_{1}\n", "A*P_{t-1|t-1}*A': A_{0}\n", "A_{1}*C'*(R + C*A_{1}*C')^-1: A_{2}\n" ] } ], "source": [ "for s in simp_exprs:\n", " print(\"s: \", s)\n", "\n", "for s in subs.keys():\n", " print(\"%s: %s\"%(s,subs[s]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1.0, "eqLabelWithNumbers": true, "eqNumInitial": 0.0 } }, "nbformat": 4, "nbformat_minor": 0 }

mit

QuantConnect/Tutorials

05 Introduction to Financial Python[]/13 Market Risk/13 Market Risk.ipynb

1

512400

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import quandl\n", "import pandas as pd\n", "import numpy as np\n", "from googlefinance import getQuotes\n", "import json\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from scipy.stats.mstats import normaltest\n", "import time\n", "from cvxopt import matrix\n", "import seaborn as sns\n", "import statsmodels.tsa.stattools as ts" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class stock(object):\n", " def __init__(self,ticker):\n", " self.ticker = ticker\n", "tickers = [\"MMM\", \"AXP\", \"AAPL\", \"BA\", \"CAT\", \"CVX\", \"CSCO\",\"KO\",\n", " \"DIS\",\"DD\",\"XOM\",\"GE\",\"GS\",\"HD\",\"IBM\",\"INTC\",\"JPM\",\"MCD\",\n", " \"MRK\",\"MSFT\",\"NKE\",\"PFE\",\"PG\",\"TRV\",\"UTX\",\"UNH\",\"VZ\",\"WMT\"] \n", "stocks = []\n", "for i in tickers:\n", " vars()[i] = stock(i)\n", " stocks.append(vars()[i])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in stocks:\n", " table = quandl.get('WIKI/%s'%i.ticker,start_date = '2012-03-21',end_date = '2015-01-01')\n", " i.rate = np.log(table['Adj. Close']).diff().dropna()\n", " i.mean = np.mean(i.rate)*252\n", " i.std = np.std(i.rate)*np.sqrt(252)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "spy = quandl.get('LSE/SPY5')\n", "spy = np.log(spy['Last Close']).diff().dropna()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class rolling(object):\n", " def __init__(self,ticker,series,spy_series):\n", " self.ticker = ticker\n", " self.prices = series\n", " self.spy = spy_series\n", " self.df = pd.concat([self.prices,self.spy],axis = 1).dropna()\n", " self.df.columns = ['SPY','%s'%self.ticker]\n", " self.prices = self.df['%s'%self.ticker]\n", " self.spy = self.df['SPY']\n", " \n", " def roll(self, length):\n", " df_leng = self.df.shape[0]\n", " beta, beta_p, inter, inter_p,resid = [],[],[],[],[]\n", " loop = df_leng - length\n", " for i in range(loop):\n", " x = sm.add_constant(self.spy[i:i+length])\n", " model = sm.OLS(self.prices[i:i+length],x).fit()\n", " beta.append(model.params[1])\n", " beta_p.append(model.pvalues[1])\n", " inter.append(model.params[0])\n", " inter_p.append(model.pvalues[0])\n", " beta_df = pd.DataFrame({'beta':beta,'beta_p':beta_p,'inter':inter,'inter_p':inter_p},index = self.df.index[length:])\n", " self.beta_df = beta_df\n", " self.mean_beta = np.mean(beta)\n", " self.std_beta = np.std(beta)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "for i in stocks:\n", " i.r = rolling(i.ticker, i.rate,spy)\n", " i.r.roll(21*6)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mean_beta sd_beta sd_beta_p\n", "MMM 0.391424 0.106368 0.000174\n", "AXP 0.260624 0.055665 0.000784\n", "AAPL 0.095790 0.041038 0.095875\n", "BA 0.190906 0.070859 0.023826\n", "CAT 0.191012 0.063119 0.019710\n", "CVX 0.280610 0.089215 0.011553\n", "CSCO 0.132116 0.067679 0.115558\n", "KO 0.181293 0.166132 0.309038\n", "DIS 0.254268 0.063585 0.000340\n", "DD 0.247352 0.057332 0.002610\n", "XOM 0.292914 0.150067 0.026876\n", "GE 0.256557 0.075137 0.010253\n", "GS 0.237748 0.029856 0.000318\n", "HD 0.219472 0.061830 0.012097\n", "IBM 0.212634 0.112679 0.055888\n", "INTC 0.152051 0.073508 0.057509\n", "JPM 0.222741 0.057927 0.008795\n", "MCD 0.243713 0.113179 0.189099\n", "MRK 0.170320 0.062014 0.049013\n", "MSFT 0.211149 0.104175 0.008382\n", "NKE 0.115547 0.066427 0.252076\n", "PFE 0.247528 0.092036 0.012432\n", "PG 0.197096 0.159905 0.280305\n", "UTX 0.318099 0.081374 0.000436\n", "UNH 0.135254 0.032307 0.036826\n", "VZ 0.170965 0.087304 0.082649\n", "WMT 0.193440 0.062965 0.062210\n" ] } ], "source": [ "tickers = [x.ticker for x in stocks]\n", "mean_betas = [x.r.mean_beta for x in stocks]\n", "sd_betas = [x.r.std_beta for x in stocks]\n", "beta_list = [x.r.beta_df['beta'] for x in stocks if len(x.r.beta_df['beta']) != 0]\n", "sd_beta_p = [np.std(x.r.beta_df['beta_p']) for x in stocks]\n", "df = pd.DataFrame({'mean_beta':mean_betas,'sd_beta':sd_betas,'sd_beta_p':sd_beta_p},index = tickers).dropna()\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABCAAAAJCCAYAAAARALYOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwWAwGAwGt/N/S3w2JE4ATzkAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11663fbd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cov_matrix = np.corrcoef(beta_list)\n", "cov_df = pd.DataFrame(cov_matrix)\n", "plt.figure(figsize = (20,10))\n", "sns.heatmap(cov_df, xticklabels = df.index, yticklabels = df.index,annot=True,cmap=\"YlGnBu\")" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAJCCAYAAAB0wYY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7f8DDi7hGT7PuDIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1164b3110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (20,10))\n", "p_df = pd.DataFrame({'XOM':PG.r.beta_df['beta'],'PG':XOM.r.beta_df['beta']})\n", "plt.plot(KO.r.beta_df['beta'])\n", "plt.plot(PG.r.beta_df['beta'])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: beta R-squared: 0.870\n", "Model: OLS Adj. R-squared: 0.870\n", "Method: Least Squares F-statistic: 3363.\n", "Date: Tue, 01 Aug 2017 Prob (F-statistic): 6.92e-225\n", "Time: 12:43:08 Log-Likelihood: 704.82\n", "No. Observations: 505 AIC: -1406.\n", "Df Residuals: 503 BIC: -1397.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0097 0.004 -2.285 0.023 -0.018 -0.001\n", "beta 0.9690 0.017 57.988 0.000 0.936 1.002\n", "==============================================================================\n", "Omnibus: 11.655 Durbin-Watson: 0.068\n", "Prob(Omnibus): 0.003 Jarque-Bera (JB): 11.842\n", "Skew: 0.369 Prob(JB): 0.00268\n", "Kurtosis: 3.133 Cond. No. 6.50\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "-3.19434626171\n" ] } ], "source": [ "x = sm.add_constant(PG.r.beta_df['beta'])\n", "coint = sm.OLS(KO.r.beta_df['beta'],x).fit()\n", "print(coint.summary())\n", "adf = ts.adfuller(coint.resid,autolag = 'BIC')[0]\n", "print(adf)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABIsAAAJCCAYAAABAuEcoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3W+Mped93+fvL7MreRojHtkigu5QEumanpoODW09otuq\nlls51tAoIG5YuaaTIHIhQElbvXIzBRd+IYB+QdmTNkFRFRULu7BdBLKjbgcbOM5EsJQGMCKHQ4+s\nBaVMvWYciWeNmhY1DlIdWMvV3Rdz5ubsasmd/+ecmesCFpzznOcsfws8Mzvz2ee+T7XWAgAAAABJ\n8ufGPQAAAAAAk0MsAgAAAKATiwAAAADoxCIAAAAAOrEIAAAAgE4sAgAAAKATiwAAAADoxCIAAAAA\nOrEIAAAAgO7cuAe401vf+tb2wAMPjHsMAAAAgFPj+eef/5PW2n17OXfiYtEDDzyQ9fX1cY8BAAAA\ncGpU1b/e67mWoQEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1Y\nBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAA\nAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAn\nFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdOfGPQAAAADAYa1uDLKytpkb\nW8NcmJvN8tJCLl2cH/dYU0ksAgAAAKba6sYgl69cy/DmrSTJYGuYy1euJYlgdACWoQEAAABTbWVt\ns4eiHcObt7KytjmmiaabWAQAAABMtRtbw30d542JRQAAAMBUuzA3u6/jvDGxCAAAAJhqy0sLmT0/\nc9ux2fMzWV5aGNNE080G1wAAAMBU29nE2ruhHQ2xCAAAAJh6ly7Oi0NHxDI0AAAAADqxCAAAAIBO\nLAIAAACgE4sAAAAA6MQiAAAAADqxCAAAAIBuT7Goqh6rqs2qul5VT93l+Z+pqi9W1Req6req6h27\nnrtVVZ8f/bp6lMMDAAAAcLTO3euEqppJ8vEkP5bkpSTPVdXV1toXd522kWSxtfb1qvqvk/xCkp8c\nPTdsrb3ziOcGAAAA4Bjs5c6iR5Ncb6292Fr7RpJPJnl89wmttc+21r4+evi5JPcf7ZgAAAAAnIS9\nxKL5JF/Z9fil0bHX86Ekv7nr8bdV1XpVfa6qLt3tBVX14dE56y+//PIeRgIAAADgONxzGdp+VNVf\nT7KY5Ed2HX5Ha21QVd+d5DNVda219ge7X9daezbJs0myuLjYjnImAAAAAPZuL3cWDZK8bdfj+0fH\nblNVfznJzyZ5f2vtz3aOt9YGo/++mOSfJrl4iHkBAAAAOEZ7iUXPJXmoqh6sqjcleTLJbe9qVlUX\nk3wi26Hoj3cdf0tVvXn08VuTvDvJ7o2xAQAAAJgg91yG1lp7tao+kmQtyUySX2qtvVBVTydZb61d\nTbKS5NuT/IOqSpIvt9ben+T7knyiqr6Z7TD1sTveRQ0AAACACVKtTdYWQYuLi219fX3cYwAAAACc\nGlX1fGttcS/n7mUZGgAAAABnhFgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAA\nANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJ\nRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAA\nAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0\nYhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEA\nAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAA\nnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCdG/cAAADAeK1uDLKytpkb\nW8NcmJvN8tJCLl2cH/dYAIyJWAQAAGfY6sYgl69cy/DmrSTJYGuYy1euJYlgBHBGWYYGAABn2Mra\nZg9FO4Y3b2VlbXNMEwEwbmIRAACcYTe2hvs6DsDpJxYBAMAZdmFudl/HATj9xCIAADjDlpcWMnt+\n5rZjs+dnsry0MKaJABg3G1wDAMAZtrOJtXdDA2CHWAQAAGfcpYvz4hAAnWVoAAAAAHRiEQAAAACd\nWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQA\nAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABA\nJxYBAAAA0O0pFlXVY1W1WVXXq+qpuzz/M1X1xar6QlX9VlW9Y9dzH6yq3x/9+uBRDg8AAADA0bpn\nLKqqmSQfT/LjSR5O8lNV9fAdp20kWWyt/UCSTyX5hdFrvzPJR5P8UJJHk3y0qt5ydOMDAAAAcJT2\ncmfRo0mut9ZebK19I8knkzy++4TW2mdba18fPfxckvtHHy8l+XRr7ZXW2teSfDrJY0czOgAAAABH\nbS+xaD7JV3Y9fml07PV8KMlv7ue1VfXhqlqvqvWXX355DyMBAAAAcByOdIPrqvrrSRaTrOznda21\nZ1tri621xfvuu+8oRwIAAABgH/YSiwZJ3rbr8f2jY7epqr+c5GeTvL+19mf7eS0AAAAAk2Evsei5\nJA9V1YNV9aYkTya5uvuEqrqY5BPZDkV/vOuptSTvq6q3jDa2ft/oGAAAAAAT6Ny9TmitvVpVH8l2\n5JlJ8kuttReq6ukk6621q9ledvbtSf5BVSXJl1tr72+tvVJVP5ft4JQkT7fWXjmWPwkAAAAAh1at\ntXHPcJvFxcW2vr4+7jEAAAAATo2qer61triXc490g2sAAAAApptYBAAAAEAnFgEAAADQ3XODawB4\nPasbg6ysbebG1jAX5mazvLSQSxfnxz0WAABwCGIRAAeyujHI5SvXMrx5K0ky2Brm8pVrSSIYAQDA\nFLMMDYADWVnb7KFox/DmraysbY5pIgAA4CiIRQAcyI2t4b6OAwAA00EsAuBALszN7us4AAAwHcQi\nAA5keWkhs+dnbjs2e34my0sLY5oIAAA4Cja4BuBAdjax9m5oAABwuohFABzYpYvz4hAAAJwylqEB\nAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAd27cAwAA\ncHasbgyysraZG1vDXJibzfLSQi5dnB/3WADALmIRAAAnYnVjkMtXrmV481aSZLA1zOUr15JEMAKA\nCWIZGgAAJ2JlbbOHoh3Dm7eysrY5pokAgLsRiwAAOBE3tob7Og4AjIdlaAD7ZL8NgIO5MDebwV3C\n0IW52TFMAwC8HncWAezDzn4bg61hWl7bb2N1YzDu0QAm3vLSQmbPz9x2bPb8TJaXFsY0EQBwN2IR\nwD7YbwPg4C5dnM8zTzyS+bnZVJL5udk888Qj7s4EgAljGRrAPthvA+BwLl2cF4cAYMK5swhgH15v\nXw37bQAAAKeFWASwD/bbAAAATjvL0AD2YWfphHdDAwAATiuxCGCf7LcBAACcZpahAQAAANCJRQAA\nAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0\nYhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEA\nAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAA\nnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgE\nAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAA\nQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQLen\nWFRVj1XVZlVdr6qn7vL8e6rqd6vq1ar6wB3P3aqqz49+XT2qwQEAAAA4eufudUJVzST5eJIfS/JS\nkueq6mpr7Yu7Tvtykp9O8rfv8lsMW2vvPIJZAQAAADhm94xFSR5Ncr219mKSVNUnkzyepMei1tof\njp775jHMCAAAAMAJ2csytPkkX9n1+KXRsb36tqpar6rPVdWlu51QVR8enbP+8ssv7+O3BgAAAOAo\nncQG1+9orS0m+atJ/l5V/Xt3ntBae7a1tthaW7zvvvtOYCQAAAAA7mYvsWiQ5G27Ht8/OrYnrbXB\n6L8vJvmnSS7uYz4AAAAATtBeYtFzSR6qqger6k1Jnkyyp3c1q6q3VNWbRx+/Ncm7s2uvIwAAAAAm\nyz1jUWvt1SQfSbKW5EtJfr219kJVPV1V70+SqnpXVb2U5CeSfKKqXhi9/PuSrFfV7yX5bJKP3fEu\nagAAAABMkGqtjXuG2ywuLrb19fVxjwEAAABwalTV86M9pe/pJDa4BgAAAGBKiEUAAAAAdGIRAAAA\nAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1Y\nBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAA\nAEAnFgEAAADQnRv3AADA4a1uDLKytpkbW8NcmJvN8tJCLl2cH/dYAABMIbEIAKbc6sYgl69cy/Dm\nrSTJYGuYy1euJYlgBADAvlmGBgBTbmVts4eiHcObt7KytjmmiQAAmGZiEQBMuRtbw30dBwCANyIW\nAcCUuzA3u6/jAADwRsQiAJhyy0sLmT0/c9ux2fMzWV5aGNNEAABMMxtcA8CU29nE2ruhAQBwFMQi\nADgFLl2cF4cAADgSlqEBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAA\nAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACd\nWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQA\nAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABA\nJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYB\nAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA\n0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHR7ikVV9VhVbVbV9ap66i7Pv6eqfreq\nXq2qD9zx3Aer6vdHvz54VIMDAAAAcPTuGYuqaibJx5P8eJKHk/xUVT18x2lfTvLTSf7+Ha/9ziQf\nTfJDSR5N8tGqesvhxwYAAADgOOzlzqJHk1xvrb3YWvtGkk8meXz3Ca21P2ytfSHJN+947VKST7fW\nXmmtfS3Jp5M8dgRzAwAAAHAM9hKL5pN8Zdfjl0bH9mJPr62qD1fVelWtv/zyy3v8rQEAAAA4ahOx\nwXVr7dnW2mJrbfG+++4b9zgAAAAAZ9ZeYtEgydt2Pb5/dGwvDvNaAAAAAE7YXmLRc0keqqoHq+pN\nSZ5McnWPv/9akvdV1VtGG1u/b3QMAAAAgAl0z1jUWns1yUeyHXm+lOTXW2svVNXTVfX+JKmqd1XV\nS0l+IsknquqF0WtfSfJz2Q5OzyV5enQMAAAAgAlUrbVxz3CbxcXFtr6+Pu4xAAAAAE6Nqnq+tba4\nl3MnYoNrAAAAACaDWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAA\nAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRiEQAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHRi\nEQAAAACdWAQAAABAd27cAwCHt7oxyMraZm5sDXNhbjbLSwu5dHF+3GMBAAAwhcQimHKrG4NcvnIt\nw5u3kiSDrWEuX7mWJIIRAAAA+2YZGky5lbXNHop2DG/eysra5pgmAgAAYJqJRTDlbmwN93UcAAAA\n3ohlaDDlLszNZnCXMHRhbnYM0wCJfcQAAJhu7iyCKbe8tJDZ8zO3HZs9P5PlpYUxTQRn284+YoOt\nYVpe20dsdWMw7tEAAGBPxCKYcpcuzueZJx7J/NxsKsn83GyeeeIRdzHAmNhHDACAaWcZGpwCly7O\ni0MwIewjBgDAtHNnEQAcodfbL8w+YgAATAuxCACOkH3EAACYdpahAcAR2lkS6t3QAACYVmIRABwx\n+4gBADDNLEMDAAAAoBOLAAAAAOjEIgAAAAA6sQgAAACATiwCAAAAoBOLAAAAAOjEIgAAAAA6sQgA\nAACATiwCAAAAoBOLAAAAAOjEIgAAAAA6sQgAAACATiwCAAAAoBOLAAAAAOjEIgAAAAC6c+MegHtb\n3RhkZW0zN7aGuTA3m+WlhVy6OD/usQAAAIBTSCyacKsbg1y+ci3Dm7eSJIOtYS5fuZYkghEAAABw\n5CxDm3Ara5s9FO0Y3ryVlbXNMU0EAAAAnGZi0YS7sTXc13EAAACAwxCLJtyFudl9HQcAAAA4DLFo\nwi0vLWT2/Mxtx2bPz2R5aWFMEwEAAACnmQ2uJ9zOJtbeDQ0AAAA4CWLRFLh0cV4cAgAAAE6EZWgA\nAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAA\ndGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIR\nAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAA\nAJ1YBAAAAEB3btwDAIe3ujHIytpmbmwNc2FuNstLC7l0cX7cYwEAADCFxCKYcqsbg1y+ci3Dm7eS\nJIOtYS5fuZYkghEAAAD7ZhkaTLmVtc0einYMb97KytrmmCYCAABgmu0pFlXVY1W1WVXXq+qpuzz/\n5qr6tdHzv1NVD4yOP1BVw6r6/OjX/3q04wM3tob7Og4AAABv5J7L0KpqJsnHk/xYkpeSPFdVV1tr\nX9x12oeSfK219j1V9WSSn0/yk6Pn/qC19s4jnhsYuTA3m8FdwtCFudkxTAMAAMC028udRY8mud5a\ne7G19o0kn0zy+B3nPJ7kl0cffyrJj1ZVHd2YwOtZXlrI7PmZ247Nnp/J8tLCmCYCAABgmu0lFs0n\n+cquxy+Njt31nNbaq0n+NMl3jZ57sKo2qur/rqofvtv/oKo+XFXrVbX+8ssv7+sPAGfdpYvzeeaJ\nRzI/N5tKMj83m2eeeMTm1gAAABzIcb8b2h8leXtr7atV9YNJVqvq+1tr/2b3Sa21Z5M8mySLi4vt\nmGeCU+fSxXlxCAAAgCOxlzuLBknetuvx/aNjdz2nqs4l+Y4kX22t/Vlr7atJ0lp7PskfJPneww4N\nAAAAwPHYSyx6LslDVfVgVb0pyZNJrt5xztUkHxx9/IEkn2mttaq6b7RBdqrqu5M8lOTFoxkdAAAA\ngKN2z2VorbVXq+ojSdaSzCT5pdbaC1X1dJL11trVJL+Y5Fer6nqSV7IdlJLkPUmerqqbSb6Z5G+1\n1l45jj8IAAAAAIdXrU3WFkGLi4ttfX193GMAAAAAnBpV9XxrbXEv5+5lGRoAAAAAZ4RYBAAAAEAn\nFgEAAADQiUUAAAAAdGIRAAAAAN25cQ8AwPFa3RhkZW0zN7aGuTA3m+WlhVy6OD/usQAAgAklFgGc\nYqsbg1y+ci3Dm7eSJIOtYS5fuZYkghEAAHBXlqEBnGIra5s9FO0Y3ryVlbXNMU0EAABMOrEI4BS7\nsTXc13EAAACxCOAUuzA3u6/jAAAAYhHAKba8tJDZ8zO3HZs9P5PlpYUxTQQAAEw6G1wDnGI7m1h7\nNzQAAGCvxCKAU+7SxfkzGYdWNwYiGQAAHIBYBMCps7oxyOUr1/o7wQ22hrl85VqSCEYAAHAP9iwC\n4NRZWdvsoWjH8OatrKxtjmkiAACYHmIRAKfOja3hvo4DAACvEYsAOHUuzM3u6zgAAPAasQiAU2d5\naSGz52duOzZ7fibLSwtjmggAAKaHDa4BOHV2NrH2bmgAALB/YhEAp9Kli/PiEAAAHIBlaAAAAAB0\nYhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEAAAAAnVgEAAAAQCcWAQAAANCJRQAAAAB0YhEA\nAAAAnVgEAAAAQHdu3APASVndGGRlbTM3toa5MDeb5aWFXLo4P+6xAAAAYKKIRZwJqxuDXL5yLcOb\nt5Ikg61hLl+5liSCEQAAAOxiGRpnwsraZg9FO4Y3b2VlbXNMEwEAAMBkEos4E25sDfd1HAAAAM4q\nsYgz4cLc7L6OAwAAwFklFnEmLC8tZPb8zG3HZs/PZHlpYUwTAQAAwGSywTVnws4m1t4NDQAAAN6Y\nWMSZcenivDgEAAAA92AZGgAAAACdWAQAAABAJxYBAAAA0IlFAAAAAHQ2uAYAzpTVjYF3xwQAeANi\nEQBwZqxuDHL5yrUMb95Kkgy2hrl85VqSCEYAACOWoQEAZ8bK2mYPRTuGN29lZW1zTBMBAEwedxYB\nMBaWAjEON7aG+zoOAHAWubMIgBO3sxRosDVMy2tLgVY3BuMejVPuwtzsvo4DAJxFYhEAJ85SIMZl\neWkhs+dnbjs2e34my0sLY5oIAGDyWIYGwImzFIhx2VnqaAkkAMDrE4sAOHEX5mYzuEsYshSIk3Dp\n4rw4BADwBixDA+DEWQoEAACTy51FAJw4S4EAAGByiUUAjIWlQAAAMJksQwMAAACgE4sAAAAA6MQi\nAAAAADp7FpHVjYFNZgEAAIAkYtGZt7oxyOUr1zK8eStJMtga5vKVa0kiGAEAAMAZZBnaGbeyttlD\n0Y7hzVtZWdsc00QAAADAOIlFZ9yNreG+jgMAAACnm2VoZ9yFudkM7hKGLszNjmEaOBz7bwEAABye\nO4vOuOWlhcyen7nt2Oz5mSwvLYxpIjiYnf23BlvDtLy2/9bqxmDcowEAAEwVseiMu3RxPs888Ujm\n52ZTSebnZvPME4+4G4OpY/8tAACAo2EZGrl0cV4cYurZfwsAAOBouLMIOBVeb58t+28BAADsj1gE\nnAr/2b9/X+qOY/bfAgAA2D+xCJh6qxuD/J/PD9J2Hask/8UPWmIJAACwX/YsAqbe3Ta3bkk++y9f\nHs9A2Q5YK2ububE1zIW52SwvLQhX3JVrBQCASSMWAVNv0ja3Xt0Y5PKVaz1gDbaGuXzlWpKIANzG\ntQIAwCSyDA2YepO2ufXd7nQa3ryVlbXNsczD5HKtAAAwicQiYOotLy1k9vzMbcfGubn1pN3pxORy\nrQAAMInEImDqrG4M8u6PfSYPPvUbeffHPpMkeeaJRzI/N5tKMj83m2eeeGRsy3gm7U4nJpdrBQCA\nSWTPImAsDrqp7+vt8fLME4/kt59673GPvSfLSwu3zZiM904nJpdrBQCASeTOIuDE7QSfwdYwLa8F\nn9WNwT1fOw17vFy6OD9RdzoxuVwrAABMIncWASfujYLPvX5InpY9Xi5dnPcDP3tyWq6Vg94tCADA\n5HFnEXDiDhN87PECk+cwdwsCADB5xCLgxB0m+EzaO58B07E8FACAvROLgBN3mOBznHu83Pkua+6K\ngL2ZluWhAADsjT2LgBO3E3YOur/Jcezx8nrvsrZ7XuDuLszNZnCXMGR5KADAdBKLThkbjDItJm1T\n34Nuuu1zDrbvFtwdWxPLQwEApplYdIq4MwIO7iDLaHzOwbbD3i0IAMBkEYtOkcO8HTmcdQdZRuNz\nDl4zaXcLAgBwcHva4LqqHquqzaq6XlVP3eX5N1fVr42e/52qemDXc5dHxzeraunoRudONhhlr2zk\n/K0Osum2zzkAAOA0umcsqqqZJB9P8uNJHk7yU1X18B2nfSjJ11pr35Pk7yb5+dFrH07yZJLvT/JY\nkv9l9PtxDA7zduScHTtLpwZbw7S8tnTqrAejg7zLms85AADgNNrLnUWPJrneWnuxtfaNJJ9M8vgd\n5zye5JdHH38qyY9WVY2Of7K19mettX+V5Pro9+MYHObtyDk73mjp1Fl36eJ8fvup9+Zffew/z28/\n9d57LqnxOQcAAJxGe9mzaD7JV3Y9finJD73eOa21V6vqT5N81+j45+547bf89FVVH07y4SR5+9vf\nvtfZuYMNRtkLS6eOjs85AADgNJqIDa5ba88meTZJFhcX25jHmWo2GOVeDrKRM6/P5xwAAHDa7GUZ\n2iDJ23Y9vn907K7nVNW5JN+R5Kt7fC1wgiydAgAA4I3sJRY9l+Shqnqwqt6U7Q2rr95xztUkHxx9\n/IEkn2mttdHxJ0fvlvZgkoeS/IujGR04iINs5AwAAMDZcc9laKM9iD6SZC3JTJJfaq29UFVPJ1lv\nrV1N8otJfrWqrid5JdtBKaPzfj3JF5O8muS/ba3duuv/CDgxlk4BAADwemr7BqDJsbi42NbX18c9\nBgAAAMCpUVXPt9YW93LuXpahAQAAAHBGiEUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAA\ndGIRAAD/qIMQAAAGkUlEQVQAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEA\nAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQ\niUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQVWtt\n3DPcpqpeTvKvxz0HB/LWJH8y7iE4VVxTHDXXFEfNNcVRc01x1FxTHDXX1PR6R2vtvr2cOHGxiOlV\nVeuttcVxz8Hp4ZriqLmmOGquKY6aa4qj5priqLmmzgbL0AAAAADoxCIAAAAAOrGIo/TsuAfg1HFN\ncdRcUxw11xRHzTXFUXNNcdRcU2eAPYsAAAAA6NxZBAAAAEAnFnFPVfVYVW1W1fWqeuouz7+5qn5t\n9PzvVNUDu577gar651X1QlVdq6pvO8nZmVwHva6q6nxV/fLoevpSVV0+6dmZTHu4pt5TVb9bVa9W\n1QfueO6DVfX7o18fPLmpmWQHvaaq6p27/u77QlX95MlOzqQ6zNep0fN/oapeqqr/+WQmZtId8u++\nt1fVPxl9P/XF3d/Dc3Yd8pr6hdHffV+qqv+pqurkJueoiUW8oaqaSfLxJD+e5OEkP1VVD99x2oeS\nfK219j1J/m6Snx+99lyS/yPJ32qtfX+S/zTJzRManQl2mOsqyU8keXNr7ZEkP5jkb/rmhj1eU19O\n8tNJ/v4dr/3OJB9N8kNJHk3y0ap6y3HPzGQ7zDWV5OtJ/sbo777Hkvy9qpo73omZdIe8pnb8XJJ/\ndlwzMl2O4Jr6lSQrrbXvy/bff398fNMyDQ75/dR/nOTdSX4gyV9K8q4kP3LMI3OMxCLu5dEk11tr\nL7bWvpHkk0kev+Ocx5P88ujjTyX50VFFfl+SL7TWfi9JWmtfba3dOqG5mWyHua5akj8/ipGzSb6R\n5N+czNhMsHteU621P2ytfSHJN+947VKST7fWXmmtfS3Jp7P9Az5n24Gvqdba/9Na+/3Rxzey/QPY\nfSczNhPsMF+nUlU/mOQvJvknJzEsU+HA19QoAJxrrX16dN6/ba19/YTmZnId5utUS/JtSd6U5M1J\nzif5f49/ZI6LWMS9zCf5yq7HL42O3fWc1tqrSf40yXcl+d4krarWRrcq/vcnMC/T4TDX1aeS/H9J\n/ijb/7Lxd1prrxz3wEy8vVxTx/FaTq8juS6q6tFsf+P8B0c0F9PrwNdUVf25JP9Dkr99DHMxvQ7z\ndep7k2xV1ZWq2qiqldFdJZxtB76mWmv/PMlns/09+h8lWWutfenIJ+TEiEUcp3NJ/pMkf230379S\nVT863pE4BR5NcivJhSQPJvnvquq7xzsSwLeqqn83ya8m+a9aa99ypwjsw3+T5B+11l4a9yCcGueS\n/HC2A+S7knx3tpcWwYFU1fck+b4k92c7ML23qn54vFNxGGIR9zJI8rZdj+8fHbvrOaOlQd+R5KvZ\nLtH/rLX2J6PbWv9Rkv/g2CdmGhzmuvqrSf5xa+1ma+2Pk/x2ksVjn5hJt5dr6jhey+l1qOuiqv5C\nkt9I8rOttc8d8WxMp8NcU/9Rko9U1R8m+TtJ/kZVfexox2MKHeaaeinJ50fLjV5Nshrfp3O4a+qv\nJPncaEnjv03ym9n+2sWUEou4l+eSPFRVD1bVm5I8meTqHedcTbLz7kEfSPKZ1lpLspbkkar6d0Y/\n7P9Iki+e0NxMtsNcV19O8t4kqao/n+Q/TPIvT2RqJtlerqnXs5bkfVX1ltHG1u8bHeNsO/A1NTr/\n/0ryK621Tx3jjEyXA19TrbW/1lp7e2vtgWzfCfIrrbVveZcizpzD/N33XJK5qtrZT+298X06h7um\nvpzkR6rqXFWdz/bPfpahTTGxiDc0+peGj2T7B6cvJfn11toLVfV0Vb1/dNovJvmuqrqe5GeSPDV6\n7deS/I/Z/qLz+SS/21r7jZP+MzB5DnNdZfsdGr69ql7I9rX1v4822eMM28s1VVXvqqqXsv2Oep8Y\nXUMZ7Xn1c9m+np5L8rR9sDjMNZXkv0zyniQ/XVWfH/165xj+GEyQQ15T8C0O+XffrWyHx9+qqmtJ\nKsn/No4/B5PjkF+nPpXt/fmuJfm9JL/XWvuHJ/6H4MjU9j/UAwAAAIA7iwAAAADYRSwCAAAAoBOL\nAAAAAOjEIgAAAAA6sQgAAACATiwCAAAAoBOLAAAAAOjEIgAAAAC6/x+Qt4oOxfzD7QAAAABJRU5E\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116b40890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (20,10))\n", "plt.scatter(df['sd_beta'],df['sd_beta_p'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABIsAAAIuCAYAAAA/jogJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERCRPxSIREREREREREcn7/0SUm9TtupAjAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10e3db810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (20,10))\n", "GE.r.beta_df['beta_p'].plot()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

wenduowang/git_home

python/MSBA/textanalytics/HW3/Page-Rank-and-Weighted-Page-Rank.ipynb

1

12243

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import networkx as nx\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import pylab\n", "\n", "G = nx.DiGraph()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ES</th>\n", " <th>LS</th>\n", " <th>RX</th>\n", " <th>A8</th>\n", " <th>A6</th>\n", " <th>3series</th>\n", " <th>5series</th>\n", " <th>7series</th>\n", " <th>XJ</th>\n", " <th>Sclass</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>4.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>-1.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>4.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>-2.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ES LS RX A8 A6 3series 5series 7series XJ Sclass\n", "0 NaN NaN 4.0 3.0 NaN NaN NaN 5.0 2.0 1.0\n", "1 NaN NaN 3.0 2.0 2.0 2.0 2.0 2.0 NaN NaN\n", "2 NaN 4.0 NaN NaN NaN 3.0 NaN 3.0 NaN 3.0\n", "3 NaN 2.0 NaN 2.0 NaN NaN NaN 2.0 4.0 2.0\n", "4 NaN 3.0 3.0 NaN NaN NaN 2.0 NaN NaN 3.0\n", "5 NaN NaN NaN 1.0 NaN NaN NaN 3.0 4.0 3.0\n", "6 NaN 2.0 NaN 3.0 NaN NaN NaN 4.0 -1.0 3.0\n", "7 NaN NaN NaN 3.0 NaN NaN NaN 1.0 4.0 2.0\n", "8 NaN 2.0 NaN 4.0 3.0 -2.0 NaN NaN NaN 3.0\n", "9 NaN 4.0 NaN 2.0 NaN NaN NaN 2.0 NaN 2.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Scores = pd.read_csv(\"scores.csv\")\n", "Scores[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Page Rank" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G = nx.DiGraph()\n", "for i in range(len(Scores)):\n", " for x in Scores.columns.values:\n", " if Scores.ix[i][x] > 0:\n", " for y in Scores.columns.values:\n", " if Scores.ix[i][y] > 0:\n", " if Scores.ix[i][x] > Scores.ix[i][y]:\n", " if (x,y) not in G.edges():\n", " G.add_edges_from([(x,y)])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pos=nx.spring_layout(G)\n", "nx.draw(G,pos,node_color = 'blue', node_size=2000)\n", "node_labels = {node:node for node in G.nodes()}\n", "nx.draw_networkx_labels(G, pos, labels=node_labels)\n", "pylab.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'3series': 0.07910322957982689,\n", " '5series': 0.09573750117209036,\n", " '7series': 0.1216537730762872,\n", " 'A6': 0.05892813273464103,\n", " 'A8': 0.09662922372038388,\n", " 'ES': 0.09559802761764902,\n", " 'LS': 0.1645243436346072,\n", " 'RX': 0.076621128657151,\n", " 'Sclass': 0.12734701517178246,\n", " 'XJ': 0.08385762463558098}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nx.pagerank(G)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['A6', 'A8', 'XJ', 'ES', 'LS', 'RX', 'Sclass', '7series', '5series', '3series']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(list(nx.pagerank(G)), key=lambda tup: tup[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weighted Page Rank" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "W = nx.DiGraph()\n", "for i in range(len(Scores)):\n", " for x in Scores.columns.values:\n", " if Scores.ix[i][x] > 0:\n", " for y in Scores.columns.values:\n", " if Scores.ix[i][y] > 0:\n", " if Scores.ix[i][x] > Scores.ix[i][y]:\n", " W.add_edges_from([(x,y)])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'3series': 0.07910322957982689,\n", " '5series': 0.09573750117209036,\n", " '7series': 0.1216537730762872,\n", " 'A6': 0.05892813273464103,\n", " 'A8': 0.09662922372038388,\n", " 'ES': 0.09559802761764902,\n", " 'LS': 0.1645243436346072,\n", " 'RX': 0.076621128657151,\n", " 'Sclass': 0.12734701517178246,\n", " 'XJ': 0.08385762463558098}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nx.pagerank(W)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sales = {\n", " \"A6\": 20,\n", " \"A8\": 12,\n", " \"3series\": 220,\n", " \"5series\": 60,\n", " \"7series\": 14,\n", " \"XJ\": 6.6,\n", " \"ES\": 135,\n", " \"LS\": 30,\n", " \"RX\": 120,\n", " \"Sclass\": 25\n", "}" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test = pd.DataFrame({\"Sales\": sales, \"Weighted\": nx.pagerank(W), \"Unweighted\": nx.pagerank(G)})" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-753.4467295]\n", "0.103980708337\n" ] } ], "source": [ "model_uw = LinearRegression()\n", "model_uw.fit(test[[\"Unweighted\"]], test.Sales)\n", "print model_uw.coef_\n", "print model_uw.score(test[[\"Unweighted\"]], test.Sales)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_w = LinearRegression()\n", "model_w.fit(test[[\"Weighted\"]], test.Sales)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.10398070833650408" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_w.score(test[[\"Weighted\"]], test.Sales)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

jdeldre/whirl2d.jl

examples/3.-Applying-pulse-forcing-to-a-flow.ipynb

1

1114337

gpl-3.0

GoogleCloudPlatform/vertex-ai-samples

notebooks/community/migration/UJ15 AutoML for vision with Vertex AI Video Object Tracking.ipynb

1

77111

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "title:migration,new" }, "source": [ "# Vertex SDK: AutoML video object tracking model\n" ] }, { "cell_type": "markdown", "metadata": { "id": "install_aip" }, "source": [ "## Installation\n", "\n", "Install the latest (preview) version of Vertex SDK.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KDLhKMzGn5Hx" }, "outputs": [], "source": [ "! pip3 install -U google-cloud-aiplatform --user" ] }, { "cell_type": "markdown", "metadata": { "id": "install_storage" }, "source": [ "Install the Google *cloud-storage* library as well.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XKUq0zsKn5H3" }, "outputs": [], "source": [ "! pip3 install google-cloud-storage" ] }, { "cell_type": "markdown", "metadata": { "id": "restart" }, "source": [ "### Restart the Kernel\n", "\n", "Once you've installed the Vertex SDK and Google *cloud-storage*, you need to restart the notebook kernel so it can find the packages.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kvM_QWtcn5H5" }, "outputs": [], "source": [ "import os\n", "\n", "if not os.getenv(\"AUTORUN\"):\n", " # Automatically restart kernel after installs\n", " import IPython\n", "\n", " app = IPython.Application.instance()\n", " app.kernel.do_shutdown(True)" ] }, { "cell_type": "markdown", "metadata": { "id": "before_you_begin" }, "source": [ "## Before you begin\n", "\n", "### GPU run-time\n", "\n", "*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\n", "\n", "### Set up your GCP project\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "1. [Select or create a GCP project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", "\n", "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n", "\n", "3. [Enable the Vertex APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n", "\n", "4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebooks.\n", "\n", "5. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_project_id" }, "outputs": [], "source": [ "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_project_id" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n", " # Get your GCP project id from gcloud\n", " shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n", " PROJECT_ID = shell_output[0]\n", " print(\"Project ID:\", PROJECT_ID)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_gcloud_project_id" }, "outputs": [], "source": [ "! gcloud config set project $PROJECT_ID" ] }, { "cell_type": "markdown", "metadata": { "id": "region" }, "source": [ "#### Region\n", "\n", "You can also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend when possible, to choose the region closest to you.\n", "\n", "- Americas: `us-central1`\n", "- Europe: `europe-west4`\n", "- Asia Pacific: `asia-east1`\n", "\n", "You cannot use a Multi-Regional Storage bucket for training with Vertex. Not all regions provide support for all Vertex services. For the latest support per region, see [Region support for Vertex AI services](https://cloud.google.com/vertex-ai/docs/general/locations)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pTJUl6sSn5H7" }, "outputs": [], "source": [ "REGION = \"us-central1\" # @param {type: \"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "timestamp" }, "source": [ "#### Timestamp\n", "\n", "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Gn2q7I4yn5H8" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "markdown", "metadata": { "id": "gcp_authenticate" }, "source": [ "### Authenticate your GCP account\n", "\n", "**If you are using Google Cloud Notebooks**, your environment is already\n", "authenticated. Skip this step.\n", "\n", "*Note: If you are on an Vertex notebook and run the cell, the cell knows to skip executing the authentication steps.*\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TFasBw7Cn5H9" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "# If you are running this notebook in Colab, run this cell and follow the\n", "# instructions to authenticate your Google Cloud account. This provides access\n", "# to your Cloud Storage bucket and lets you submit training jobs and prediction\n", "# requests.\n", "\n", "# If on Vertex, then don't execute this code\n", "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n", " if \"google.colab\" in sys.modules:\n", " from google.colab import auth as google_auth\n", "\n", " google_auth.authenticate_user()\n", "\n", " # If you are running this tutorial in a notebook locally, replace the string\n", " # below with the path to your service account key and run this cell to\n", " # authenticate your Google Cloud account.\n", " else:\n", " %env GOOGLE_APPLICATION_CREDENTIALS your_path_to_credentials.json\n", "\n", " # Log in to your account on Google Cloud\n", " ! gcloud auth login" ] }, { "cell_type": "markdown", "metadata": { "id": "bucket:batch_prediction" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "This tutorial is designed to use training data that is in a public Cloud Storage bucket and a local Cloud Storage bucket for your batch predictions. You may alternatively use your own training data that you have stored in a local Cloud Storage bucket.\n", "\n", "Set the name of your Cloud Storage bucket below. It must be unique across all Cloud Storage buckets.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bucket" }, "outputs": [], "source": [ "BUCKET_NAME = \"[your-bucket-name]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_bucket" }, "outputs": [], "source": [ "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n", " BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP" ] }, { "cell_type": "markdown", "metadata": { "id": "create_bucket" }, "source": [ "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KTRHxxatn5H-" }, "outputs": [], "source": [ "! gsutil mb -l $REGION gs://$BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "validate_bucket" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OBbXRqZPn5H_" }, "outputs": [], "source": [ "! gsutil ls -al gs://$BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "setup_vars" }, "source": [ "### Set up variables\n", "\n", "Next, set up some variables used throughout the tutorial.\n", "### Import libraries and define constants\n" ] }, { "cell_type": "markdown", "metadata": { "id": "import_aip" }, "source": [ "#### Import Vertex SDK\n", "\n", "Import the Vertex SDK into our Python environment.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z7wda4l9n5IA" }, "outputs": [], "source": [ "import os\n", "import sys\n", "import time\n", "\n", "from google.cloud.aiplatform import gapic as aip\n", "from google.protobuf import json_format\n", "from google.protobuf.json_format import MessageToJson, ParseDict\n", "from google.protobuf.struct_pb2 import Struct, Value" ] }, { "cell_type": "markdown", "metadata": { "id": "aip_constants" }, "source": [ "#### Vertex AI constants\n", "\n", "Setup up the following constants for Vertex AI:\n", "\n", "- `API_ENDPOINT`: The Vertex AI API service endpoint for dataset, model, job, pipeline and endpoint services.\n", "- `PARENT`: The Vertex AI location root path for dataset, model and endpoint resources.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T6CQX7uwn5IA" }, "outputs": [], "source": [ "# API Endpoint\n", "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)\n", "\n", "# Vertex AI location root path for your dataset, model and endpoint resources\n", "PARENT = \"projects/\" + PROJECT_ID + \"/locations/\" + REGION" ] }, { "cell_type": "markdown", "metadata": { "id": "automl_constants:automl" }, "source": [ "#### AutoML constants\n", "\n", "Next, setup constants unique to AutoML video object tracking datasets and training:\n", "\n", "- Dataset Schemas: Tells the managed dataset service which type of dataset it is.\n", "- Data Labeling (Annotations) Schemas: Tells the managed dataset service how the data is labeled (annotated).\n", "- Dataset Training Schemas: Tells the Vertex AI Pipelines service the task (e.g., classification) to train the model for.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "automl_constants:automl,icn" }, "outputs": [], "source": [ "# Video Dataset type\n", "VIDEO_SCHEMA = \"google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml\"\n", "# Video Labeling type\n", "IMPORT_SCHEMA_VIDEO_OBJECT_TRACKING = \"gs://google-cloud-aiplatform/schema/dataset/ioformat/video_object_tracking_io_format_1.0.0.yaml\"\n", "# Video Training task\n", "TRAINING_VIDEO_OBJECT_TRACKING_SCHEMA = \"gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_object_tracking_1.0.0.yaml\"" ] }, { "cell_type": "markdown", "metadata": { "id": "clients" }, "source": [ "## Clients\n", "\n", "The Vertex SDK works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the server (Vertex).\n", "\n", "You will use several clients in this tutorial, so set them all up upfront.\n", "\n", "- Dataset Service for managed datasets.\n", "- Model Service for managed models.\n", "- Pipeline Service for training.\n", "- Endpoint Service for deployment.\n", "- Job Service for batch jobs and custom training.\n", "- Prediction Service for serving. *Note*: Prediction has a different service endpoint.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "H8mUvtiTn5IB" }, "outputs": [], "source": [ "# client options same for all services\n", "client_options = {\"api_endpoint\": API_ENDPOINT}\n", "\n", "\n", "def create_dataset_client():\n", " client = aip.DatasetServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_model_client():\n", " client = aip.ModelServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_pipeline_client():\n", " client = aip.PipelineServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_endpoint_client():\n", " client = aip.EndpointServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_prediction_client():\n", " client = aip.PredictionServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "def create_job_client():\n", " client = aip.JobServiceClient(client_options=client_options)\n", " return client\n", "\n", "\n", "clients = {}\n", "clients[\"dataset\"] = create_dataset_client()\n", "clients[\"model\"] = create_model_client()\n", "clients[\"pipeline\"] = create_pipeline_client()\n", "clients[\"endpoint\"] = create_endpoint_client()\n", "clients[\"prediction\"] = create_prediction_client()\n", "clients[\"job\"] = create_job_client()\n", "\n", "for client in clients.items():\n", " print(client)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_file:flowers,csv,icn" }, "outputs": [], "source": [ "IMPORT_FILE = \"gs://automl-video-demo-data/traffic_videos/traffic_videos_labels.csv\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ptL5nC02n5IC" }, "outputs": [], "source": [ "! gsutil cat $IMPORT_FILE | head -n 10" ] }, { "cell_type": "markdown", "metadata": { "id": "trainingpipelines_create:migration,new,response,icn" }, "source": [ "*Example output*:\n", "```\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,sedan,1565750291672021,11.933333,0.509205,0.594283,,,0.728737,0.760959,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,pickup_suv_van,1565750291672171,17.566666,0.761241,0.498466,,,0.948839,0.668524,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,pickup_suv_van,1565750291672223,20.433333,0.000000,0.465235,,,0.142638,0.665644,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,pickup_suv_van,1565750291672347,25.766666,0.486523,0.592331,,,0.720611,0.776687,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,pickup_suv_van,1565750291672575,28.966666,0.578534,0.652778,,,0.828647,0.862967,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,pickup_suv_van,1565750291672549,28.966666,0.000000,0.518571,,,0.148841,0.737677,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,pickup_suv_van,1565750291672599,28.966666,0.106979,0.458078,,,0.377877,0.678937,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,pickup_suv_van,1565715798494273,32.466666,0.333083,0.485473,,,0.542722,0.647774,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,sedan,1565715798494439,36.433333,0.935638,0.564839,,,1.000000,0.672182,,\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4,pickup_suv_van,1565715798494381,36.433333,0.000000,0.455703,,,0.164878,0.660083,,\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "create_a_dataset:migration" }, "source": [ "## Create a dataset\n" ] }, { "cell_type": "markdown", "metadata": { "id": "datasets_create:migration,new" }, "source": [ "### [projects.locations.datasets.create](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.datasets/create)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "request:migration" }, "source": [ "#### Request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "datasets_create:migration,new,request" }, "outputs": [], "source": [ "DATA_SCHEMA = VIDEO_SCHEMA\n", "\n", "dataset = {\n", " \"display_name\": \"traffic_\" + TIMESTAMP,\n", " \"metadata_schema_uri\": \"gs://\" + DATA_SCHEMA,\n", "}\n", "\n", "print(\n", " MessageToJson(\n", " aip.CreateDatasetRequest(parent=PARENT, dataset=dataset).__dict__[\"_pb\"]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "sPupiwqN_jAB" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"dataset\": {\n", " \"displayName\": \"traffic_20210310013516\",\n", " \"metadataSchemaUri\": \"gs://google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml\"\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "call:migration" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "datasets_create:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"dataset\"].create_dataset(parent=PARENT, dataset=dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "response:migration" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "print:migration,new,response" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "OC-Yc89In5IH" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/datasets/7534187925055995904\",\n", " \"displayName\": \"traffic_20210310013516\",\n", " \"metadataSchemaUri\": \"gs://google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml\",\n", " \"labels\": {\n", " \"aiplatform.googleapis.com/dataset_metadata_schema\": \"VIDEO\"\n", " },\n", " \"metadata\": {\n", " \"dataItemSchemaUri\": \"gs://google-cloud-aiplatform/schema/dataset/dataitem/video_1.0.0.yaml\"\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dataset_id:migration,new,response" }, "outputs": [], "source": [ "# The full unique ID for the dataset\n", "dataset_id = result.name\n", "# The short numeric ID for the dataset\n", "dataset_short_id = dataset_id.split(\"/\")[-1]\n", "\n", "print(dataset_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "datasets_import:migration,new" }, "source": [ "### [projects.locations.datasets.import](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.datasets/import)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "qYXlUMfWn5II" }, "source": [ "#### Request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "datasets_import:migration,new,request" }, "outputs": [], "source": [ "LABEL_SCHEMA = IMPORT_SCHEMA_VIDEO_OBJECT_TRACKING\n", "\n", "import_config = {\n", " \"gcs_source\": {\"uris\": [IMPORT_FILE]},\n", " \"import_schema_uri\": LABEL_SCHEMA,\n", "}\n", "\n", "print(\n", " MessageToJson(\n", " aip.ImportDataRequest(\n", " name=dataset_short_id, import_configs=[import_config]\n", " ).__dict__[\"_pb\"]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "AVJY95yXn5II" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"7534187925055995904\",\n", " \"importConfigs\": [\n", " {\n", " \"gcsSource\": {\n", " \"uris\": [\n", " \"gs://automl-video-demo-data/traffic_videos/traffic_videos_labels.csv\"\n", " ]\n", " },\n", " \"importSchemaUri\": \"gs://google-cloud-aiplatform/schema/dataset/ioformat/video_object_tracking_io_format_1.0.0.yaml\"\n", " }\n", " ]\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "2qSg5OOWn5II" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "datasets_import:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"dataset\"].import_data(\n", " name=dataset_id, import_configs=[import_config]\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "y6bxHeSWn5IJ" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ojm6e7dTn5IJ" }, "outputs": [], "source": [ "result = request.result()\n", "\n", "print(MessageToJson(result.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "sMdZTOKsn5IK" }, "source": [ "*Example output*:\n", "```\n", "{}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "train_a_model:migration" }, "source": [ "## Train a model\n" ] }, { "cell_type": "markdown", "metadata": { "id": "trainingpipelines_create:migration,new" }, "source": [ "### [projects.locations.trainingPipelines.create](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.trainingPipelines/create)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ZJvJeQ5mn5IK" }, "source": [ "#### Request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "trainingpipelines_create:migration,new,request,icn" }, "outputs": [], "source": [ "TRAINING_SCHEMA = TRAINING_VIDEO_OBJECT_TRACKING_SCHEMA\n", "\n", "task = Value(struct_value=Struct(fields={\"model_type\": Value(string_value=\"CLOUD\")}))\n", "\n", "training_pipeline = {\n", " \"display_name\": \"traffic_\" + TIMESTAMP,\n", " \"training_task_definition\": TRAINING_SCHEMA,\n", " \"training_task_inputs\": task,\n", " \"input_data_config\": {\n", " \"dataset_id\": dataset_short_id,\n", " \"fraction_split\": {\"training_fraction\": 0.8, \"test_fraction\": 0.2},\n", " },\n", " \"model_to_upload\": {\"display_name\": \"traffic_\" + TIMESTAMP},\n", "}\n", "\n", "print(\n", " MessageToJson(\n", " aip.CreateTrainingPipelineRequest(\n", " parent=PARENT, training_pipeline=training_pipeline\n", " ).__dict__[\"_pb\"]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "SMWsytANn5IK" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"trainingPipeline\": {\n", " \"displayName\": \"traffic_20210310013516\",\n", " \"inputDataConfig\": {\n", " \"datasetId\": \"7534187925055995904\",\n", " \"fractionSplit\": {\n", " \"trainingFraction\": 0.8,\n", " \"testFraction\": 0.2\n", " }\n", " },\n", " \"trainingTaskDefinition\": \"gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_object_tracking_1.0.0.yaml\",\n", " \"trainingTaskInputs\": {\n", " \"model_type\": \"CLOUD\"\n", " },\n", " \"modelToUpload\": {\n", " \"displayName\": \"traffic_20210310013516\"\n", " }\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "SdESf_Xjn5IL" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "trainingpipelines_create:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"pipeline\"].create_training_pipeline(\n", " parent=PARENT, training_pipeline=training_pipeline\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "jk2KsljSn5IL" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "print:migration,new,request" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "gUIK_1mcn5IM" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/trainingPipelines/4612961451915608064\",\n", " \"displayName\": \"traffic_20210310013516\",\n", " \"inputDataConfig\": {\n", " \"datasetId\": \"7534187925055995904\",\n", " \"fractionSplit\": {\n", " \"trainingFraction\": 0.8,\n", " \"testFraction\": 0.2\n", " }\n", " },\n", " \"trainingTaskDefinition\": \"gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_object_tracking_1.0.0.yaml\",\n", " \"trainingTaskInputs\": {\n", " \"modelType\": \"CLOUD\"\n", " },\n", " \"modelToUpload\": {\n", " \"displayName\": \"traffic_20210310013516\"\n", " },\n", " \"state\": \"PIPELINE_STATE_PENDING\",\n", " \"createTime\": \"2021-03-10T13:09:36.473816Z\",\n", " \"updateTime\": \"2021-03-10T13:09:36.473816Z\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "training_pipeline_id:migration,new,response" }, "outputs": [], "source": [ "# The full unique ID for the training pipeline\n", "training_pipeline_id = request.name\n", "# The short numeric ID for the training pipeline\n", "training_pipeline_short_id = training_pipeline_id.split(\"/\")[-1]\n", "\n", "print(training_pipeline_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "trainingpipelines_get:migration,new" }, "source": [ "### [projects.locations.trainingPipelines.get](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.trainingPipelines/get)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "egJoW5sUn5IM" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "trainingpipelines_get:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"pipeline\"].get_training_pipeline(name=training_pipeline_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "INONfT8Ln5IN" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xYKvFpAVn5IN" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "eeT7dtSvn5IN" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/trainingPipelines/4612961451915608064\",\n", " \"displayName\": \"traffic_20210310013516\",\n", " \"inputDataConfig\": {\n", " \"datasetId\": \"7534187925055995904\",\n", " \"fractionSplit\": {\n", " \"trainingFraction\": 0.8,\n", " \"testFraction\": 0.2\n", " }\n", " },\n", " \"trainingTaskDefinition\": \"gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_object_tracking_1.0.0.yaml\",\n", " \"trainingTaskInputs\": {\n", " \"modelType\": \"CLOUD\"\n", " },\n", " \"modelToUpload\": {\n", " \"displayName\": \"traffic_20210310013516\"\n", " },\n", " \"state\": \"PIPELINE_STATE_PENDING\",\n", " \"createTime\": \"2021-03-10T13:09:36.473816Z\",\n", " \"updateTime\": \"2021-03-10T13:09:36.473816Z\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "trainingpipelines_get:migration,new,wait" }, "outputs": [], "source": [ "while True:\n", " response = clients[\"pipeline\"].get_training_pipeline(name=training_pipeline_id)\n", " if response.state != aip.PipelineState.PIPELINE_STATE_SUCCEEDED:\n", " print(\"Training job has not completed:\", response.state)\n", " model_to_deploy_name = None\n", " if response.state == aip.PipelineState.PIPELINE_STATE_FAILED:\n", " break\n", " else:\n", " model_id = response.model_to_upload.name\n", " print(\"Training Time:\", response.end_time - response.start_time)\n", " break\n", " time.sleep(60)\n", "\n", "print(model_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "evaluate_the_model:migration" }, "source": [ "## Evaluate the model\n" ] }, { "cell_type": "markdown", "metadata": { "id": "models_evaluations_list:migration,new" }, "source": [ "### [projects.locations.models.evaluations.list](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.models.evaluations/list)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "xTM1xhTzn5IO" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "models_evaluations_list:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"model\"].list_model_evaluations(parent=model_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "qRTiZUwYn5IO" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AxEY7WFHj2YC" }, "outputs": [], "source": [ "import json\n", "\n", "model_evaluations = [json.loads(MessageToJson(me.__dict__[\"_pb\"])) for me in request]\n", "# The evaluation slice\n", "evaluation_slice = request.model_evaluations[0].name\n", "\n", "print(json.dumps(model_evaluations, indent=2))" ] }, { "cell_type": "markdown", "metadata": { "id": "rBPb8NvWn5IP" }, "source": [ "*Example output*:\n", "```\n", "[\n", " {\n", " \"name\": \"projects/116273516712/locations/us-central1/models/6125898247828406272/evaluations/305090287452028928\",\n", " \"metricsSchemaUri\": \"gs://google-cloud-aiplatform/schema/modelevaluation/video_object_tracking_metrics_1.0.0.yaml\",\n", " \"metrics\": {\n", " \"boundingBoxMetrics\": [\n", " {\n", " \"meanAveragePrecision\": 0.34263912,\n", " \"iouThreshold\": 0.5,\n", " \"confidenceMetrics\": [\n", " {\n", " \"precision\": 0.36842105,\n", " \"recall\": 1.0,\n", " \"f1Score\": 0.53846157\n", " },\n", " {\n", " \"precision\": 0.088,\n", " \"confidenceThreshold\": 0.032954127,\n", " \"recall\": 0.16541353,\n", " \"f1Score\": 0.11488251\n", " },\n", " {\n", " \"precision\": 0.08835341,\n", " \"confidenceThreshold\": 0.035069585,\n", " \"recall\": 0.16541353,\n", " \"f1Score\": 0.11518325\n", " },\n", " {\n", " \"precision\": 0.088709675,\n", " \"recall\": 0.16541353,\n", " \"confidenceThreshold\": 0.036181003,\n", " \"f1Score\": 0.115485564\n", " },\n", " {\n", " \"recall\": 0.16541353,\n", " \"f1Score\": 0.11578947,\n", " \"confidenceThreshold\": 0.037186295,\n", " \"precision\": 0.08906882\n", " },\n", " {\n", " \"recall\": 0.16541353,\n", " \"precision\": 0.08943089,\n", " \"confidenceThreshold\": 0.038205147,\n", " \"f1Score\": 0.116094984\n", " },\n", " \n", " # REMOVED FOR BREVITY\n", " {\n", " \"recall\": 0.007518797,\n", " \"precision\": 1.0,\n", " \"confidenceThreshold\": 0.66486305,\n", " \"f1Score\": 0.014925373\n", " },\n", " {\n", " \"precision\": 1.0,\n", " \"confidenceThreshold\": 1.0\n", " }\n", " ]\n", " }\n", " ],\n", " \"boundingBoxMeanAveragePrecision\": 0.34263912\n", " },\n", " \"createTime\": \"2021-03-10T14:18:31.880535Z\",\n", " \"sliceDimensions\": [\n", " \"annotationSpec\"\n", " ]\n", " }\n", "]\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "models_evaluations_get:migration,new" }, "source": [ "### [projects.locations.models.evaluations.get](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.models.evaluations/get)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Othev0Hnn5IP" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "models_evaluations_get:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"model\"].get_model_evaluation(name=evaluation_slice)" ] }, { "cell_type": "markdown", "metadata": { "id": "LUV4WStsn5IQ" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pktv6Vcxn5IQ" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "v9HtXNL4n5IQ" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/models/6125898247828406272/evaluations/305090287452028928\",\n", " \"metricsSchemaUri\": \"gs://google-cloud-aiplatform/schema/modelevaluation/video_object_tracking_metrics_1.0.0.yaml\",\n", " \"metrics\": {\n", " \"boundingBoxMetrics\": [\n", " {\n", " \"confidenceMetrics\": [\n", " {\n", " \"recall\": 1.0,\n", " \"precision\": 0.36842105,\n", " \"f1Score\": 0.53846157\n", " },\n", " {\n", " \"recall\": 0.16541353,\n", " \"precision\": 0.088,\n", " \"f1Score\": 0.11488251,\n", " \"confidenceThreshold\": 0.032954127\n", " },\n", " \n", " # REMOVED FOR BREVITY\n", " \n", " {\n", " \"confidenceThreshold\": 1.0,\n", " \"precision\": 1.0\n", " }\n", " ],\n", " \"meanAveragePrecision\": 0.34263912,\n", " \"iouThreshold\": 0.5\n", " }\n", " ],\n", " \"boundingBoxMeanAveragePrecision\": 0.34263912\n", " },\n", " \"createTime\": \"2021-03-10T14:18:31.880535Z\",\n", " \"sliceDimensions\": [\n", " \"annotationSpec\"\n", " ]\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_predictions:migration" }, "source": [ "## Make batch predictions\n" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_prediction_file:migration,new" }, "source": [ "### Prepare batch prediction data\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "get_test_items:automl,icn,csv" }, "outputs": [], "source": [ "test_items = ! gsutil cat $IMPORT_FILE | head -n25\n", "\n", "cols_1 = test_items[0].split(\",\")\n", "cols_2 = test_items[-1].split(\",\")\n", "\n", "if len(cols_1) > 12:\n", " test_item_1 = str(cols_1[1])\n", " test_item_2 = str(cols_2[1])\n", " test_label_1 = str(cols_1[5:])\n", " test_label_2 = str(cols_2[5:])\n", "else:\n", " test_item_1 = str(cols_1[0])\n", " test_item_2 = str(cols_2[0])\n", " test_label_1 = str(cols_1[4:])\n", " test_label_2 = str(cols_2[4:])\n", "\n", "\n", "print(test_item_1, test_label_1)\n", "print(test_item_2, test_label_2)" ] }, { "cell_type": "markdown", "metadata": { "id": "6fRc0HuWn5IS" }, "source": [ "*Example output*:\n", "```\n", "gs://automl-video-demo-data/traffic_videos/highway_005.mp4 ['0.509205', '0.594283', '', '', '0.728737', '0.760959', '', '']\n", "gs://automl-video-demo-data/traffic_videos/highway_006.mp4 ['0.621857', '0.561570', '', '', '0.825726', '0.699151', '', '']\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "make_batch_file:automl,image" }, "source": [ "### Make the batch input file\n", "\n", "Let's now make a batch input file, which you store in your local Cloud Storage bucket. The batch input file can be either CSV or JSONL. You will use JSONL in this tutorial. For JSONL file, you make one dictionary entry per line for each video. The dictionary contains the key/value pairs:\n", "\n", "- `content`: The Cloud Storage path to the video.\n", "- `mimeType`: The content type. In our example, it is an `avi` file.\n", "- `timeSegmentStart`: The start timestamp in the video to do prediction on. *Note*, the timestamp must be specified as a string and followed by s (second), m (minute) or h (hour).\n", "- `timeSegmentEnd`: The end timestamp in the video to do prediction on.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0rWoXOq9n5IS" }, "outputs": [], "source": [ "import json\n", "\n", "import tensorflow as tf\n", "\n", "gcs_input_uri = \"gs://\" + BUCKET_NAME + \"/test.jsonl\"\n", "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n", " data = {\n", " \"content\": test_item_1,\n", " \"mimeType\": \"video/avi\",\n", " \"timeSegmentStart\": \"0.0s\",\n", " \"timeSegmentEnd\": \"inf\",\n", " }\n", " f.write(json.dumps(data) + \"\\n\")\n", " data = {\n", " \"content\": test_item_2,\n", " \"mimeType\": \"video/avi\",\n", " \"timeSegmentStart\": \"0.0s\",\n", " \"timeSegmentEnd\": \"inf\",\n", " }\n", " f.write(json.dumps(data) + \"\\n\")\n", "\n", "print(gcs_input_uri)\n", "!gsutil cat $gcs_input_uri" ] }, { "cell_type": "markdown", "metadata": { "id": "trainingpipelines_get:migration,new,response,icn" }, "source": [ "*Example output*:\n", "```\n", "gs://migration-ucaip-trainingaip-20210310013516/test.jsonl\n", "{\"content\": \"gs://automl-video-demo-data/traffic_videos/highway_005.mp4\", \"mimeType\": \"video/avi\", \"timeSegmentStart\": \"0.0s\", \"timeSegmentEnd\": \"inf\"}\n", "{\"content\": \"gs://automl-video-demo-data/traffic_videos/highway_006.mp4\", \"mimeType\": \"video/avi\", \"timeSegmentStart\": \"0.0s\", \"timeSegmentEnd\": \"inf\"}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "batchpredictionjobs_create:migration,new" }, "source": [ "### [projects.locations.batchPredictionJobs.create](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.batchPredictionJobs/create)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "S5Odu9ogn5IS" }, "source": [ "#### Request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_create:migration,new,request,icn" }, "outputs": [], "source": [ "batch_prediction_job = {\n", " \"display_name\": \"traffic_\" + TIMESTAMP,\n", " # Format: 'projects/{project}/locations/{location}/models/{model_id}'\n", " \"model\": model_id,\n", " \"model_parameters\": json_format.ParseDict(\n", " {\"confidenceThreshold\": 0.5, \"maxPredictions\": 2}, Value()\n", " ),\n", " \"input_config\": {\n", " \"instances_format\": \"jsonl\",\n", " \"gcs_source\": {\"uris\": [gcs_input_uri]},\n", " },\n", " \"output_config\": {\n", " \"predictions_format\": \"jsonl\",\n", " \"gcs_destination\": {\n", " \"output_uri_prefix\": \"gs://\" + f\"{BUCKET_NAME}/batch_output/\"\n", " },\n", " },\n", " \"dedicated_resources\": {\n", " \"machine_spec\": {\"machine_type\": \"n1-standard-4\", \"accelerator_count\": 0},\n", " \"starting_replica_count\": 1,\n", " \"max_replica_count\": 1,\n", " },\n", "}\n", "\n", "print(\n", " MessageToJson(\n", " aip.CreateBatchPredictionJobRequest(\n", " parent=PARENT, batch_prediction_job=batch_prediction_job\n", " ).__dict__[\"_pb\"]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "nj-dxOHen5IT" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n", " \"batchPredictionJob\": {\n", " \"displayName\": \"traffic_20210310013516\",\n", " \"model\": \"projects/116273516712/locations/us-central1/models/6125898247828406272\",\n", " \"inputConfig\": {\n", " \"instancesFormat\": \"jsonl\",\n", " \"gcsSource\": {\n", " \"uris\": [\n", " \"gs://migration-ucaip-trainingaip-20210310013516/test.jsonl\"\n", " ]\n", " }\n", " },\n", " \"modelParameters\": {\n", " \"maxPredictions\": 2.0,\n", " \"confidenceThreshold\": 0.5\n", " },\n", " \"outputConfig\": {\n", " \"predictionsFormat\": \"jsonl\",\n", " \"gcsDestination\": {\n", " \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210310013516/batch_output/\"\n", " }\n", " },\n", " \"dedicatedResources\": {\n", " \"machineSpec\": {\n", " \"machineType\": \"n1-standard-4\"\n", " },\n", " \"startingReplicaCount\": 1,\n", " \"maxReplicaCount\": 1\n", " }\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "nLnR65Omn5IT" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_create:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"job\"].create_batch_prediction_job(\n", " parent=PARENT, batch_prediction_job=batch_prediction_job\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "86xo791tn5IU" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eHpRHUQNn5IU" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "UJnCkCcfn5IU" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/batchPredictionJobs/6806214470445039616\",\n", " \"displayName\": \"traffic_20210310013516\",\n", " \"model\": \"projects/116273516712/locations/us-central1/models/6125898247828406272\",\n", " \"inputConfig\": {\n", " \"instancesFormat\": \"jsonl\",\n", " \"gcsSource\": {\n", " \"uris\": [\n", " \"gs://migration-ucaip-trainingaip-20210310013516/test.jsonl\"\n", " ]\n", " }\n", " },\n", " \"modelParameters\": {\n", " \"maxPredictions\": 2.0,\n", " \"confidenceThreshold\": 0.5\n", " },\n", " \"outputConfig\": {\n", " \"predictionsFormat\": \"jsonl\",\n", " \"gcsDestination\": {\n", " \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210310013516/batch_output/\"\n", " }\n", " },\n", " \"state\": \"JOB_STATE_PENDING\",\n", " \"completionStats\": {\n", " \"incompleteCount\": \"-1\"\n", " },\n", " \"createTime\": \"2021-03-10T14:23:59.862541Z\",\n", " \"updateTime\": \"2021-03-10T14:23:59.862541Z\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batch_job_id:migration,new,response" }, "outputs": [], "source": [ "# The fully qualified ID for the batch job\n", "batch_job_id = request.name\n", "# The short numeric ID for the batch job\n", "batch_job_short_id = batch_job_id.split(\"/\")[-1]\n", "\n", "print(batch_job_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "batchpredictionjobs_get:migration,new" }, "source": [ "### [projects.locations.batchPredictionJobs.get](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.batchPredictionJobs/get)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "OJpZjkX1n5IZ" }, "source": [ "#### Call\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_get:migration,new,call" }, "outputs": [], "source": [ "request = clients[\"job\"].get_batch_prediction_job(name=batch_job_id)" ] }, { "cell_type": "markdown", "metadata": { "id": "h3la42lGn5Ia" }, "source": [ "#### Response\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9oovDLACn5Ia" }, "outputs": [], "source": [ "print(MessageToJson(request.__dict__[\"_pb\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "PgOg5Qqln5Ia" }, "source": [ "*Example output*:\n", "```\n", "{\n", " \"name\": \"projects/116273516712/locations/us-central1/batchPredictionJobs/6806214470445039616\",\n", " \"displayName\": \"traffic_20210310013516\",\n", " \"model\": \"projects/116273516712/locations/us-central1/models/6125898247828406272\",\n", " \"inputConfig\": {\n", " \"instancesFormat\": \"jsonl\",\n", " \"gcsSource\": {\n", " \"uris\": [\n", " \"gs://migration-ucaip-trainingaip-20210310013516/test.jsonl\"\n", " ]\n", " }\n", " },\n", " \"modelParameters\": {\n", " \"maxPredictions\": 2.0,\n", " \"confidenceThreshold\": 0.5\n", " },\n", " \"outputConfig\": {\n", " \"predictionsFormat\": \"jsonl\",\n", " \"gcsDestination\": {\n", " \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210310013516/batch_output/\"\n", " }\n", " },\n", " \"state\": \"JOB_STATE_RUNNING\",\n", " \"completionStats\": {\n", " \"incompleteCount\": \"2\"\n", " },\n", " \"createTime\": \"2021-03-10T14:23:59.862541Z\",\n", " \"startTime\": \"2021-03-10T14:24:00.012555Z\",\n", " \"updateTime\": \"2021-03-10T14:24:00.520535Z\"\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "batchpredictionjobs_get:migration,new,wait" }, "outputs": [], "source": [ "def get_latest_predictions(gcs_out_dir):\n", " \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n", " folders = !gsutil ls $gcs_out_dir\n", " latest = \"\"\n", " for folder in folders:\n", " subfolder = folder.split(\"/\")[-2]\n", " if subfolder.startswith(\"prediction-\"):\n", " if subfolder > latest:\n", " latest = folder[:-1]\n", " return latest\n", "\n", "\n", "while True:\n", " response = clients[\"job\"].get_batch_prediction_job(name=batch_job_id)\n", " if response.state != aip.JobState.JOB_STATE_SUCCEEDED:\n", " print(\"The job has not completed:\", response.state)\n", " if response.state == aip.JobState.JOB_STATE_FAILED:\n", " break\n", " else:\n", " folder = get_latest_predictions(\n", " response.output_config.gcs_destination.output_uri_prefix\n", " )\n", " ! gsutil ls $folder/prediction**\n", "\n", " ! gsutil cat $folder/prediction**\n", " break\n", " time.sleep(60)" ] }, { "cell_type": "markdown", "metadata": { "id": "Xgx0imoHn5Ib" }, "source": [ "*Example output*:\n", "```\n", "gs://migration-ucaip-trainingaip-20210310013516/batch_output/prediction-traffic_20210310013516-2021-03-10T14:23:59.650333Z/predictions_00001.jsonl\n", "gs://migration-ucaip-trainingaip-20210310013516/batch_output/prediction-traffic_20210310013516-2021-03-10T14:23:59.650333Z/predictions_00002.jsonl\n", "{\"instance\":{\"content\":\"gs://automl-video-demo-data/traffic_videos/highway_005.mp4\",\"mimeType\":\"video/avi\",\"timeSegmentStart\":\"0.0s\",\"timeSegmentEnd\":\"inf\"},\"prediction\":[{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"35.300s\",\"timeSegmentEnd\":\"35.900s\",\"confidence\":0.5733388,\"frames\":[{\"timeOffset\":\"35.300s\",\"xMin\":0.92079705,\"xMax\":1.0,\"yMin\":0.52717,\"yMax\":0.6845663},{\"timeOffset\":\"35.400s\",\"xMin\":0.86325824,\"xMax\":0.999999,\"yMin\":0.5247129,\"yMax\":0.68186224},{\"timeOffset\":\"35.500s\",\"xMin\":0.79357177,\"xMax\":0.99050075,\"yMin\":0.5186033,\"yMax\":0.68388295},{\"timeOffset\":\"35.600s\",\"xMin\":0.7312134,\"xMax\":0.935794,\"yMin\":0.5121129,\"yMax\":0.68021643},{\"timeOffset\":\"35.700s\",\"xMin\":0.6609115,\"xMax\":0.8773811,\"yMin\":0.50215065,\"yMax\":0.6793843},{\"timeOffset\":\"35.800s\",\"xMin\":0.593415,\"xMax\":0.816827,\"yMin\":0.4967009,\"yMax\":0.677144},{\"timeOffset\":\"35.900s\",\"xMin\":0.51087815,\"xMax\":0.75138736,\"yMin\":0.4922755,\"yMax\":0.6732076}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"46.900s\",\"timeSegmentEnd\":\"47.900s\",\"confidence\":0.5721089,\"frames\":[{\"timeOffset\":\"46.900s\",\"xMin\":0.8938238,\"xMax\":1.0,\"yMin\":0.53446406,\"yMax\":0.6901284},{\"timeOffset\":\"47s\",\"xMin\":0.81357133,\"xMax\":0.99999857,\"yMin\":0.5293013,\"yMax\":0.6901114},{\"timeOffset\":\"47.100s\",\"xMin\":0.733154,\"xMax\":0.94289106,\"yMin\":0.52340066,\"yMax\":0.69049513},{\"timeOffset\":\"47.200s\",\"xMin\":0.6544491,\"xMax\":0.87583166,\"yMin\":0.51654726,\"yMax\":0.68932515},{\"timeOffset\":\"47.300s\",\"xMin\":0.56814355,\"xMax\":0.7984497,\"yMin\":0.50629544,\"yMax\":0.6880638},{\"timeOffset\":\"47.400s\",\"xMin\":0.47772846,\"xMax\":0.7148553,\"yMin\":0.49765483,\"yMax\":0.68850183},{\"timeOffset\":\"47.500s\",\"xMin\":0.3756373,\"xMax\":0.6187881,\"yMin\":0.49258503,\"yMax\":0.68797386},{\"timeOffset\":\"47.600s\",\"xMin\":0.28856453,\"xMax\":0.5317154,\"yMin\":0.48884195,\"yMax\":0.6842308},{\"timeOffset\":\"47.700s\",\"xMin\":0.2014918,\"xMax\":0.44464266,\"yMin\":0.48509887,\"yMax\":0.6804877},{\"timeOffset\":\"47.800s\",\"xMin\":0.11441906,\"xMax\":0.35756993,\"yMin\":0.48135576,\"yMax\":0.6767446},{\"timeOffset\":\"47.900s\",\"xMin\":0.0075410376,\"xMax\":0.16627955,\"yMin\":0.4600201,\"yMax\":0.67653906}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"30.700s\",\"timeSegmentEnd\":\"31.800s\",\"confidence\":0.5617838,\"frames\":[{\"timeOffset\":\"30.700s\",\"xMin\":0.9150882,\"xMax\":1.0,\"yMin\":0.5151331,\"yMax\":0.6645079},{\"timeOffset\":\"30.800s\",\"xMin\":0.86258525,\"xMax\":0.99999905,\"yMin\":0.5172026,\"yMax\":0.6603533},{\"timeOffset\":\"30.900s\",\"xMin\":0.801704,\"xMax\":0.9690958,\"yMin\":0.5114948,\"yMax\":0.65620536},{\"timeOffset\":\"31s\",\"xMin\":0.7319551,\"xMax\":0.90261525,\"yMin\":0.5059969,\"yMax\":0.65359175},{\"timeOffset\":\"31.100s\",\"xMin\":0.66124815,\"xMax\":0.83811176,\"yMin\":0.49920097,\"yMax\":0.65197486},{\"timeOffset\":\"31.200s\",\"xMin\":0.5840557,\"xMax\":0.77017546,\"yMin\":0.49158275,\"yMax\":0.6492404},{\"timeOffset\":\"31.300s\",\"xMin\":0.49507296,\"xMax\":0.7009768,\"yMin\":0.48103508,\"yMax\":0.6466039},{\"timeOffset\":\"31.400s\",\"xMin\":0.405905,\"xMax\":0.61765563,\"yMin\":0.4749309,\"yMax\":0.6409255},{\"timeOffset\":\"31.500s\",\"xMin\":0.3119387,\"xMax\":0.5226898,\"yMin\":0.46739954,\"yMax\":0.639586},{\"timeOffset\":\"31.600s\",\"xMin\":0.21487714,\"xMax\":0.4266367,\"yMin\":0.46247935,\"yMax\":0.6337805},{\"timeOffset\":\"31.700s\",\"xMin\":0.104759425,\"xMax\":0.32538062,\"yMin\":0.4524644,\"yMax\":0.63389504},{\"timeOffset\":\"31.800s\",\"xMin\":0.018637476,\"xMax\":0.18262094,\"yMin\":0.438457,\"yMax\":0.6318268}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"19.200s\",\"timeSegmentEnd\":\"20.400s\",\"confidence\":0.5343286,\"frames\":[{\"timeOffset\":\"19.200s\",\"xMin\":0.53855574,\"xMax\":0.7483454,\"yMin\":0.7698453,\"yMax\":0.88802785},{\"timeOffset\":\"19.300s\",\"xMin\":0.7066706,\"xMax\":0.9046804,\"yMin\":0.5262296,\"yMax\":0.68281764},{\"timeOffset\":\"19.400s\",\"xMin\":0.6643509,\"xMax\":0.8667256,\"yMin\":0.52155364,\"yMax\":0.683776},{\"timeOffset\":\"19.500s\",\"xMin\":0.60929006,\"xMax\":0.81281793,\"yMin\":0.51661617,\"yMax\":0.6828631},{\"timeOffset\":\"19.600s\",\"xMin\":0.5457967,\"xMax\":0.75832534,\"yMin\":0.51252514,\"yMax\":0.6830068},{\"timeOffset\":\"19.700s\",\"xMin\":0.48583922,\"xMax\":0.7025621,\"yMin\":0.5050785,\"yMax\":0.6833887},{\"timeOffset\":\"19.800s\",\"xMin\":0.42353436,\"xMax\":0.65267944,\"yMin\":0.499186,\"yMax\":0.68389857},{\"timeOffset\":\"19.900s\",\"xMin\":0.36298347,\"xMax\":0.5871576,\"yMin\":0.49524042,\"yMax\":0.68120617},{\"timeOffset\":\"20s\",\"xMin\":0.28549758,\"xMax\":0.51240987,\"yMin\":0.48895967,\"yMax\":0.6785187},{\"timeOffset\":\"20.100s\",\"xMin\":0.20944653,\"xMax\":0.439814,\"yMin\":0.48409462,\"yMax\":0.6736581},{\"timeOffset\":\"20.200s\",\"xMin\":0.12498511,\"xMax\":0.3544973,\"yMin\":0.47413808,\"yMax\":0.6721386},{\"timeOffset\":\"20.300s\",\"xMin\":0.047281284,\"xMax\":0.26844072,\"yMin\":0.46798372,\"yMax\":0.66819036},{\"timeOffset\":\"20.400s\",\"xMin\":-9.64848E-4,\"xMax\":0.1562836,\"yMin\":0.45929697,\"yMax\":0.66720545}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"21.100s\",\"timeSegmentEnd\":\"22.500s\",\"confidence\":0.5130946,\"frames\":[{\"timeOffset\":\"21.100s\",\"xMin\":0.96134996,\"xMax\":0.9994743,\"yMin\":0.5534551,\"yMax\":0.69061935},{\"timeOffset\":\"21.200s\",\"xMin\":0.70565313,\"xMax\":0.9051543,\"yMin\":0.52271163,\"yMax\":0.68131983},{\"timeOffset\":\"21.300s\",\"xMin\":0.66035485,\"xMax\":0.8695388,\"yMin\":0.5142326,\"yMax\":0.68292105},{\"timeOffset\":\"21.400s\",\"xMin\":0.60553956,\"xMax\":0.81872016,\"yMin\":0.5056492,\"yMax\":0.6834419},{\"timeOffset\":\"21.500s\",\"xMin\":0.54476696,\"xMax\":0.7676598,\"yMin\":0.5011589,\"yMax\":0.6836144},{\"timeOffset\":\"21.600s\",\"xMin\":0.48482427,\"xMax\":0.7100822,\"yMin\":0.49725318,\"yMax\":0.68293834},{\"timeOffset\":\"21.700s\",\"xMin\":0.42285097,\"xMax\":0.65375054,\"yMin\":0.49218974,\"yMax\":0.6809865},{\"timeOffset\":\"21.800s\",\"xMin\":0.35869005,\"xMax\":0.5910624,\"yMin\":0.48708224,\"yMax\":0.68020374},{\"timeOffset\":\"21.900s\",\"xMin\":0.29066974,\"xMax\":0.52481556,\"yMin\":0.4808314,\"yMax\":0.68033195},{\"timeOffset\":\"22s\",\"xMin\":0.22343048,\"xMax\":0.460608,\"yMin\":0.4752792,\"yMax\":0.68010074},{\"timeOffset\":\"22.100s\",\"xMin\":0.15115453,\"xMax\":0.39462602,\"yMin\":0.46882644,\"yMax\":0.67740893},{\"timeOffset\":\"22.200s\",\"xMin\":0.07956007,\"xMax\":0.32599604,\"yMin\":0.46620452,\"yMax\":0.6739611},{\"timeOffset\":\"22.300s\",\"xMin\":0.019409377,\"xMax\":0.20294939,\"yMin\":0.4610349,\"yMax\":0.67052174},{\"timeOffset\":\"22.400s\",\"xMin\":-0.017409453,\"xMax\":0.13916901,\"yMin\":0.45393682,\"yMax\":0.66754013},{\"timeOffset\":\"22.500s\",\"xMin\":-0.02455799,\"xMax\":0.0710371,\"yMin\":0.45522687,\"yMax\":0.66398114}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"7.200s\",\"timeSegmentEnd\":\"7.800s\",\"confidence\":0.50423145,\"frames\":[{\"timeOffset\":\"7.200s\",\"xMin\":0.77211607,\"xMax\":0.9202541,\"yMin\":0.55106527,\"yMax\":0.67469126},{\"timeOffset\":\"7.300s\",\"xMin\":0.9063811,\"xMax\":0.9998785,\"yMin\":0.52542484,\"yMax\":0.68584275},{\"timeOffset\":\"7.400s\",\"xMin\":0.8464132,\"xMax\":0.9988885,\"yMin\":0.5182979,\"yMax\":0.6811758},{\"timeOffset\":\"7.500s\",\"xMin\":0.7691617,\"xMax\":0.9857696,\"yMin\":0.5142189,\"yMax\":0.6805917},{\"timeOffset\":\"7.600s\",\"xMin\":0.7101507,\"xMax\":0.91866463,\"yMin\":0.50963223,\"yMax\":0.6773922},{\"timeOffset\":\"7.700s\",\"xMin\":0.6297891,\"xMax\":0.85534894,\"yMin\":0.505215,\"yMax\":0.6743725},{\"timeOffset\":\"7.800s\",\"xMin\":0.5598867,\"xMax\":0.7586217,\"yMin\":0.5045158,\"yMax\":0.67020833}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"60.800s\",\"timeSegmentEnd\":\"60.900s\",\"confidence\":0.5034977,\"frames\":[{\"timeOffset\":\"60.800s\",\"xMin\":0.6538362,\"xMax\":0.88029855,\"yMin\":0.59181464,\"yMax\":0.769181},{\"timeOffset\":\"60.900s\",\"xMin\":0.53221023,\"xMax\":0.7752164,\"yMin\":0.58824813,\"yMax\":0.77228796}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"49.800s\",\"timeSegmentEnd\":\"50s\",\"confidence\":0.5016138,\"frames\":[{\"timeOffset\":\"49.800s\",\"xMin\":0.5714027,\"xMax\":0.8481284,\"yMin\":0.63446057,\"yMax\":0.88131857},{\"timeOffset\":\"49.900s\",\"xMin\":0.3961952,\"xMax\":0.6958802,\"yMin\":0.6273483,\"yMax\":0.8836541},{\"timeOffset\":\"50s\",\"xMin\":0.20160168,\"xMax\":0.50914085,\"yMin\":0.6205848,\"yMax\":0.89831626}]}]}\n", "{\"instance\":{\"content\":\"gs://automl-video-demo-data/traffic_videos/highway_006.mp4\",\"mimeType\":\"video/avi\",\"timeSegmentStart\":\"0.0s\",\"timeSegmentEnd\":\"inf\"},\"prediction\":[{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"33.600s\",\"timeSegmentEnd\":\"34.300s\",\"confidence\":0.57430255,\"frames\":[{\"timeOffset\":\"33.600s\",\"xMin\":0.7709672,\"xMax\":0.95710844,\"yMin\":0.50214607,\"yMax\":0.663173},{\"timeOffset\":\"33.700s\",\"xMin\":0.6879625,\"xMax\":0.8816852,\"yMin\":0.5102545,\"yMax\":0.67594415},{\"timeOffset\":\"33.800s\",\"xMin\":0.60038805,\"xMax\":0.7979881,\"yMin\":0.5179747,\"yMax\":0.6842574},{\"timeOffset\":\"33.900s\",\"xMin\":0.49664575,\"xMax\":0.7060626,\"yMin\":0.5262368,\"yMax\":0.69601244},{\"timeOffset\":\"34s\",\"xMin\":0.40653434,\"xMax\":0.62025917,\"yMin\":0.5326814,\"yMax\":0.7117975},{\"timeOffset\":\"34.100s\",\"xMin\":0.29995015,\"xMax\":0.5285418,\"yMin\":0.53684795,\"yMax\":0.7238653},{\"timeOffset\":\"34.200s\",\"xMin\":0.18591899,\"xMax\":0.42276734,\"yMin\":0.5479096,\"yMax\":0.7368871},{\"timeOffset\":\"34.300s\",\"xMin\":0.06478273,\"xMax\":0.3098502,\"yMin\":0.5567089,\"yMax\":0.7530978}]},{\"id\":\"8899756077986349056\",\"displayName\":\"large_veh_bus\",\"timeSegmentStart\":\"42.800s\",\"timeSegmentEnd\":\"43.600s\",\"confidence\":0.5635853,\"frames\":[{\"timeOffset\":\"42.800s\",\"xMin\":0.896148,\"xMax\":0.99750274,\"yMin\":0.08421734,\"yMax\":0.27437803},{\"timeOffset\":\"42.900s\",\"xMin\":0.61661655,\"xMax\":0.9991679,\"yMin\":0.34556246,\"yMax\":0.7192955},{\"timeOffset\":\"43s\",\"xMin\":0.5206372,\"xMax\":0.9830786,\"yMin\":0.35251692,\"yMax\":0.72906935},{\"timeOffset\":\"43.100s\",\"xMin\":0.415886,\"xMax\":0.95419466,\"yMin\":0.3468655,\"yMax\":0.74923456},{\"timeOffset\":\"43.200s\",\"xMin\":0.33259684,\"xMax\":0.89674544,\"yMin\":0.3457283,\"yMax\":0.7659639},{\"timeOffset\":\"43.300s\",\"xMin\":0.24448554,\"xMax\":0.813682,\"yMin\":0.34896624,\"yMax\":0.77039707},{\"timeOffset\":\"43.400s\",\"xMin\":0.14297022,\"xMax\":0.7258636,\"yMin\":0.34973124,\"yMax\":0.78382397},{\"timeOffset\":\"43.500s\",\"xMin\":0.035293583,\"xMax\":0.6205815,\"yMin\":0.3503142,\"yMax\":0.797604},{\"timeOffset\":\"43.600s\",\"xMin\":-0.012641478,\"xMax\":0.5095917,\"yMin\":0.34936112,\"yMax\":0.7985512}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"14.300s\",\"timeSegmentEnd\":\"14.800s\",\"confidence\":0.5230016,\"frames\":[{\"timeOffset\":\"14.300s\",\"xMin\":0.6586052,\"xMax\":0.92509496,\"yMin\":0.56249046,\"yMax\":0.7750921},{\"timeOffset\":\"14.400s\",\"xMin\":0.54897916,\"xMax\":0.83694077,\"yMin\":0.57280374,\"yMax\":0.7998729},{\"timeOffset\":\"14.500s\",\"xMin\":0.42660663,\"xMax\":0.7059393,\"yMin\":0.5838014,\"yMax\":0.81943566},{\"timeOffset\":\"14.600s\",\"xMin\":0.28328148,\"xMax\":0.5759178,\"yMin\":0.5971082,\"yMax\":0.8422871},{\"timeOffset\":\"14.700s\",\"xMin\":0.14642228,\"xMax\":0.47787058,\"yMin\":0.60969377,\"yMax\":0.86163664},{\"timeOffset\":\"14.800s\",\"xMin\":0.13050511,\"xMax\":0.393794,\"yMin\":0.6079176,\"yMax\":0.8719975}]},{\"id\":\"8899756077986349056\",\"displayName\":\"large_veh_bus\",\"timeSegmentStart\":\"36.400s\",\"timeSegmentEnd\":\"37.200s\",\"confidence\":0.5050303,\"frames\":[{\"timeOffset\":\"36.400s\",\"xMin\":0.45120007,\"xMax\":0.6234123,\"yMin\":0.4991348,\"yMax\":0.68943614},{\"timeOffset\":\"36.500s\",\"xMin\":0.52608997,\"xMax\":0.97820985,\"yMin\":0.37224957,\"yMax\":0.70904154},{\"timeOffset\":\"36.600s\",\"xMin\":0.43248644,\"xMax\":0.9457052,\"yMin\":0.3685041,\"yMax\":0.7249851},{\"timeOffset\":\"36.700s\",\"xMin\":0.32448632,\"xMax\":0.8723778,\"yMin\":0.3692387,\"yMax\":0.736981},{\"timeOffset\":\"36.800s\",\"xMin\":0.24751942,\"xMax\":0.7811472,\"yMin\":0.36809424,\"yMax\":0.76570237},{\"timeOffset\":\"36.900s\",\"xMin\":0.11482327,\"xMax\":0.6963299,\"yMin\":0.36990193,\"yMax\":0.7694417},{\"timeOffset\":\"37s\",\"xMin\":0.014343479,\"xMax\":0.5851304,\"yMin\":0.37472367,\"yMax\":0.7721312},{\"timeOffset\":\"37.100s\",\"xMin\":-0.008719128,\"xMax\":0.47485036,\"yMin\":0.37370107,\"yMax\":0.77210236},{\"timeOffset\":\"37.200s\",\"xMin\":-0.0062835654,\"xMax\":0.35681728,\"yMin\":0.36186668,\"yMax\":0.811519}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"30.700s\",\"timeSegmentEnd\":\"31.300s\",\"confidence\":0.50222003,\"frames\":[{\"timeOffset\":\"30.700s\",\"xMin\":0.5281936,\"xMax\":0.68637884,\"yMin\":0.52747375,\"yMax\":0.68398994},{\"timeOffset\":\"30.800s\",\"xMin\":0.57842255,\"xMax\":0.78849596,\"yMin\":0.5235871,\"yMax\":0.6842843},{\"timeOffset\":\"30.900s\",\"xMin\":0.4813683,\"xMax\":0.69776064,\"yMin\":0.5304373,\"yMax\":0.69869477},{\"timeOffset\":\"31s\",\"xMin\":0.36337966,\"xMax\":0.5917747,\"yMin\":0.53825593,\"yMax\":0.7169143},{\"timeOffset\":\"31.100s\",\"xMin\":0.23924252,\"xMax\":0.4805882,\"yMin\":0.53838265,\"yMax\":0.7325624},{\"timeOffset\":\"31.200s\",\"xMin\":0.112733364,\"xMax\":0.37042505,\"yMin\":0.5456531,\"yMax\":0.7470921},{\"timeOffset\":\"31.300s\",\"xMin\":0.021284297,\"xMax\":0.18838634,\"yMin\":0.5539143,\"yMax\":0.7607572}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"22.400s\",\"timeSegmentEnd\":\"23s\",\"confidence\":0.5017651,\"frames\":[{\"timeOffset\":\"22.400s\",\"xMin\":0.8683299,\"xMax\":1.0,\"yMin\":0.56906176,\"yMax\":0.7567184},{\"timeOffset\":\"22.500s\",\"xMin\":0.75663006,\"xMax\":0.9971202,\"yMin\":0.5708618,\"yMax\":0.77356946},{\"timeOffset\":\"22.600s\",\"xMin\":0.65595376,\"xMax\":0.9007106,\"yMin\":0.582666,\"yMax\":0.7840566},{\"timeOffset\":\"22.700s\",\"xMin\":0.54099375,\"xMax\":0.7999944,\"yMin\":0.5906615,\"yMax\":0.80249023},{\"timeOffset\":\"22.800s\",\"xMin\":0.41166627,\"xMax\":0.68448514,\"yMin\":0.59567124,\"yMax\":0.8259323},{\"timeOffset\":\"22.900s\",\"xMin\":0.2598535,\"xMax\":0.54294544,\"yMin\":0.6135365,\"yMax\":0.84419787},{\"timeOffset\":\"23s\",\"xMin\":0.081104435,\"xMax\":0.38947436,\"yMin\":0.6304356,\"yMax\":0.8707452}]},{\"id\":\"6593913068772655104\",\"displayName\":\"pickup_suv_van\",\"timeSegmentStart\":\"45.200s\",\"timeSegmentEnd\":\"45.800s\",\"confidence\":0.5006547,\"frames\":[{\"timeOffset\":\"45.200s\",\"xMin\":0.93331534,\"xMax\":0.99947244,\"yMin\":0.5425535,\"yMax\":0.7291488},{\"timeOffset\":\"45.300s\",\"xMin\":0.85503596,\"xMax\":0.9993583,\"yMin\":0.5599785,\"yMax\":0.75284433},{\"timeOffset\":\"45.400s\",\"xMin\":0.7337758,\"xMax\":0.9910556,\"yMin\":0.5646295,\"yMax\":0.7706001},{\"timeOffset\":\"45.500s\",\"xMin\":0.6204056,\"xMax\":0.89361084,\"yMin\":0.5727049,\"yMax\":0.79307556},{\"timeOffset\":\"45.600s\",\"xMin\":0.49371505,\"xMax\":0.795267,\"yMin\":0.58835214,\"yMax\":0.81640065},{\"timeOffset\":\"45.700s\",\"xMin\":0.35610467,\"xMax\":0.6617675,\"yMin\":0.6014027,\"yMax\":0.8416757},{\"timeOffset\":\"45.800s\",\"xMin\":0.1996267,\"xMax\":0.5096757,\"yMin\":0.6206687,\"yMax\":0.8625379}]}]}\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "id": "cleanup:migration,new" }, "source": [ "# Cleaning up\n", "\n", "To clean up all GCP resources used in this project, you can [delete the GCP\n", "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", "\n", "Otherwise, you can delete the individual resources you created in this tutorial.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bILJnBmvn5Ib" }, "outputs": [], "source": [ "delete_dataset = True\n", "delete_model = True\n", "delete_pipeline = True\n", "delete_batchjob = True\n", "delete_bucket = True\n", "\n", "# Delete the dataset using the Vertex AI fully qualified identifier for the dataset\n", "try:\n", " if delete_dataset:\n", " clients[\"dataset\"].delete_dataset(name=dataset_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the model using the Vertex AI fully qualified identifier for the model\n", "try:\n", " if delete_model:\n", " clients[\"model\"].delete_model(name=model_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the training pipeline using the Vertex AI fully qualified identifier for the training pipeline\n", "try:\n", " if delete_pipeline:\n", " clients[\"pipeline\"].delete_training_pipeline(name=training_pipeline_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "# Delete the batch job using the Vertex AI fully qualified identifier for the batch job\n", "try:\n", " if delete_batchjob:\n", " clients[\"job\"].delete_batch_prediction_job(name=batch_job_id)\n", "except Exception as e:\n", " print(e)\n", "\n", "if delete_bucket and \"BUCKET_NAME\" in globals():\n", " ! gsutil rm -r gs://$BUCKET_NAME" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "UJ15 unified AutoML for vision with Vertex AI Video Object Tracking.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

nljubesi/python-for-linguists

3-Regularni_izrazi.ipynb

1

13437

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Regularni izrazi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Regularni izrazi ([Regular expression](https://en.wikipedia.org/wiki/Regular_expression)) su sastavni dio svakog naprednog alata za obradu teksta te svakog visokorazinskog programskog jezika. Oni su zapravo niz znakova koji predstavljaju obrazac koji se najčešće koristi za pronalaženje svih nizova znakova koji odgovaraju tom obrascu ili pak zamjenu tih nizova znakova. Primjer regularnog izraza koji opisuje većinu elektroničkih adresa je ovaj: `r'[\\w.-]+@(?:\\w+[.])+\\w+'`. Taj obrazac opisuje niz znakova koji se sastoji od niza alfanumeričkih znakova koji mogu sadržavati i crticu, znaka @ te niza alfanumeričkih znakova i crtice koji završavaju točkom, ta se sekvenca može pojavljivati više puta, te konačno alfanumerički niz znakova. Iz ovog je primjera vidljivo da smo kroz nekoliko znakova definirali obrazac koji se riječima ne može tako kratko opisati.\n", "\n", "Regularni izrazi imaju izražajnost regularnih jezika koji se pak mogu definirati regularnim gramatikama, najjednostavnijim gramatikama u Chomskyjevoj hijerarhiji formalnih jezika i gramatika ([Chomsky hierarchy](http://en.wikipedia.org/wiki/Chomsky_hierarchy)).\n", "\n", "U *Pythonu* se podrška za regularne izraze nalazi u modulu `re` te ćemo ovdje koristiti redovito funkciju `findall(pattern, string[, flags])` koja za neki uzorak, niz znakova te moguće zastavice vraća listu nizova znakova koji odgovaraju našem uzorku, odnosno regularnom izrazu.\n", "\n", "Krenimo od početka s upoznavanjem znakova koji nose posebno značenje.\n", "\n", "To su za početak znak točke `.` koja predstavlja bilo koji znak osim znaka prelaska u novi red, uglate zagrade `[]` koje omogućuju definiranje skupa znakova te znakova `^` i `$` koji predstavljaju oznake početka i kraja niza znakova.\n", "\n", "U sljedećem primjeru tražimo svako pojavljivanje dva znaka, od kojeg je prvi znak `a`, a drugi bilo koji znak osim prelaska u novi red u zadanom nizu znakova `anafora`:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['an', 'af']\n" ] } ], "source": [ "import re\n", "print re.findall(r'a.','anafora')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Module je potrebno jednom učitati naredbom `import`, nakon kojeg slijedi naziv modula (`re`). Nakon toga moguće je u programu pozivati funkcije i metode iz učitanog modula.\n", "\n", "Na sljedeći način možemo u nizu `anafora` pronaći svako pojavljivanje dva znaka, od kojeg prvi znak mora biti jedan od samoglasnika (`a` ili `e` ili `i` ili `o` ili `u`), a drugi znak bilo koji znak osim prelaska u novi red:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['an', 'af', 'or']\n" ] } ], "source": [ "print re.findall(r'[aeiou].','anafora')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U sljedećem primjeru tražimo bilo koja dva znaka na početku niza znakova:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['an']" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r'^..','anafora')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Na sljedeći način tražimo bilo koja tri znaka koji se nalaze na kraju niza znakova:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['ora']" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r'...$','anafora')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Druga važna skupina posebnih znakova je ona koja predstavlja kvantifikatore. Kvantifikatori su simboli koji označavaju koliko puta se neki znak, skup ili grupa znakova može, odnosno mora pojaviti. Razlikujemo četiri kvantifikatora:\n", "1. `+` kvantifikator odgovara jednoj ili više pojava\n", "2. `*` kvantifikator odgovara nula, jednoj ili više pojava\n", "3. `?` kvantifikator odgovara nula ili jednoj pojavi\n", "4. `{m,n}` kvantifikator odgovara od m do n pojava" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U sljedećem primjeru tražimo jedan ili više (`+`) samoglasnika (`[aeiou]`), odnosno najdulju sekvencu samoglasnika u nizu znakova (`neaktivan`):" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ea', 'i', 'a']\n" ] } ], "source": [ "print re.findall(r'[aeiou]+','neaktivan')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Na sljedeći način tražimo bilo koji znak (`.`) iza kojeg slijedi nula, jedan ili više (`*`) samoglasnika (`[aeiou]`):" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['nea', 'k', 'ti', 'va', 'n']\n" ] } ], "source": [ "print re.findall(r'.[aeiou]*','neaktivan')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U sljedećem primjeru tražimo pojavu znaka `e` kojemu može, a ne mora slijediti znak `a`." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ea']\n" ] } ], "source": [ "print re.findall(r'ea?','neaktivan')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Na sljedeći način tražimo sekvencu samoglasnika duljine `2` do `5`." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['ea', 'aaaa']" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r'[aeiou]{2,5}','neaktivaaaan')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U *Pythonovoj* implementaciji regularnih izraza postoji i niz predefiniranih skupova znakova:\n", "* `\\d` predstavlja znamenku, odnosno `[0-9]` dok `\\D` predstavlja komplement prethodnog skupa, odnosno `[^0-9]` (znak `^` na početku popisa znakova predstavlja negaciju, odnosno komplement)\n", "* `\\s` predstavlja skup svih praznina dok je `\\S` komplement tog skupa\n", "* `\\w` predstavlja skup alfanumeričkih znakova dok je `\\W` komplement tog skupa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Kao treći argument funkciji `findall()` moguće je proslijediti i jednu ili više zastavica. Razlikujemo tri osnovne:\n", "* `re.DOTALL` čini da točka predstavlja svaki znak, pa i prelazak u novi red\n", "* `re.MULTILINE` čini da znakovi `^` i `$` predstavljaju početak, tj. kraj svakog retka\n", "* `re.UNICODE` čini da skup alfanumeričkih znakova sadrži sve Unicode alfanumeričke znakove." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U sljedećem primjeru tražimo dva znaka, od kojih je prvi slovo `s`, a drugi bilo koji znak (`.`). Dodavanjem trećeg argumenta `re.DOTALL`, točka `.` može predstavljati bilo koji znak, pa i prelazak u novi red:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['s\\n', 'su']" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r's.','danas\\nsutra',re.DOTALL)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ako ne dodamo `re.DOTALL`, rezultat će biti sljedeći:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['su']" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r's.','danas\\nsutra')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U sljedećem primjeru tražimo prvi znak na početku reda. Dodavanje trećeg argumenta `re.MULTILINE` omogućuje pronalaženje prvog znaka na početku svakog reda, a ne samo prvog:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['d', 's']" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r'^.','danas\\nsutra',re.MULTILINE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ako izostavimo treći argument, rezultat će biti sljedeći:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['d']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r'^.','danas\\nsutra')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U sljedećem primjeru tražimo sve Unicode alfanumeričke znakove: " ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'Kuhamo', u'\\u010daj']" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r'\\w+',u'Kuhamo čaj.',re.UNICODE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ako izostavimo treći argument, slova s dijakritičkim znakovima biti će izostavljeni, a rezultat će biti sljedeći:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'Kuhamo', u'aj']" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(r'\\w+',u'Kuhamo čaj.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nama je posebno važna posljednja zastavica `re.UNICODE` jer nam omogućava da regularnim izrazom `+\\w+` identificiramo najdulji niz alfanumeričkih znakova što će nam ugrubo predstavljati postupak rastavljanja niza znakova na riječi koje često nazivamo i pojavnicama. Iz tog razloga taj postupak nazivamo **opojavničenje** (*tokenization*) ([Tokenization (lexical analysis)](https://en.wikipedia.org/wiki/Tokenization_(lexical_analysis)))." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U sljedećem primjeru možemo opojavničiti sadržaj datoteke `tekst.txt`. Prvo je potrebno datoteku pročitati i ispravno dekodirati te taj sadržaj pohraniti u varijablu `tekst` nad kojom ćemo pokrenuti upit. Zatim je potrebno pomoću regularnih izraza pronaći sve alfanumeričke znakove (`\\w+`) te omogućiti da budu pronađeni svi Unicode alfanumerički znakovi (`re.UNICODE`)." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'Prvi', u'red', u'Drugi', u'red', u'Tre\\u0107i', u'red']\n" ] } ], "source": [ "tekst=open('tekst.txt').read().decode('utf8')\n", "print re.findall(r'\\w+',tekst,re.UNICODE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zadatke možete naći ovdje: [Zadaci](6-Zadaci.ipynb)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

manumartin/examples

ml/mouse_recognition/model/data_analysis.ipynb

1

914677

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle\n", "\n", "import numpy as np\n", "import numpy.polynomial.polynomial as poly\n", "import pandas as pd\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "45\n" ] } ], "source": [ "hists = []\n", "for i in range(50):\n", " try:\n", " with open('results/model_{}.hist'.format(i),\"rb\") as f:\n", " hists.append(pickle.load(f))\n", " except:\n", " pass\n", " \n", "\n", "print(len(hists)) " ] }, { "cell_type": "code", "execution_count": 251, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model trained the following hyperparameters:\n", "learning_rate: 0.0001\n", "seq_size: 300\n", "batch_size: 30\n", "epochs: 200\n", "\n", "Training stats:\n", "biggest training accuracy: 0.937\n", "biggest validation accuracy: 0.901\n", "least training loss: 0.361\n", "least validation loss: 0.476\n", "mean training / validation accuracy difference: 0.047\n", "validation accuracy variance: 0.001\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/data/installs/miniconda3/envs/dl-python35/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABIcAAAMZCAYAAACaulQmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3fcf7e0ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "def fit(y, grade=4):\n", " x = range(len(y))\n", " x_new = np.linspace(x[0], x[-1], num=len(x)*10)\n", " coefs = poly.polyfit(x, y, grade)\n", " ffit = poly.polyval(x_new, coefs)\n", " return x_new, ffit\n", "\n", "def print_history(history):\n", " hyper = history['hyperparameters']\n", " hist = history['history']\n", " print('Model trained the following hyperparameters:')\n", " print('learning_rate: {}'.format(hyper[0]))\n", " print('seq_size: {}'.format(int(hyper[1])))\n", " print('batch_size: {}'.format(int(hyper[2])))\n", " print('epochs: {}'.format(int(hyper[3])))\n", " print('\\nTraining stats:')\n", " print('biggest training accuracy: {:.3f}'.format(np.max(hist['acc'])))\n", " print('biggest validation accuracy: {:.3f}'.format(np.max(hist['val_acc'])))\n", " print('least training loss: {:.3f}'.format(np.min(hist['loss'])))\n", " print('least validation loss: {:.3f}'.format(np.min(hist['val_loss'])))\n", " print('mean training / validation accuracy difference: {:.3f}'.format(np.mean(hist['acc']) - np.mean(hist['val_acc'])))\n", " print('validation accuracy variance: {:.3f}'.format(np.var(hist['val_acc'])))\n", "\n", " plt.figure(1, figsize=(20, 14))\n", " plt.subplot(211)\n", " plt.plot(hist['acc'])\n", " plt.plot(hist['val_acc'])\n", "# plt.plot(*fit(hist['val_acc']))\n", "\n", " plt.subplot(212)\n", " plt.plot(hist['loss'])\n", " plt.plot(hist['val_loss'])\n", "# plt.plot(*fit(hist['val_loss']))\n", " plt.show()\n", "\n", "print_history(hists[18])\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 249, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/data/installs/miniconda3/envs/dl-python35/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABJUAAAM2CAYAAABPG7IQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XecXGW9+PHP9NmZnZ3tJW3Tn5AQEnoXpKggICACF0FE\nQfGKDbyo4O+CFfSCXiyoVwQUBESkRiAJJQGSEEJICCRw0sv2NnWnz5zfH+fMZrbvhvR8368Xr9fu\nnPY8ZybMs9/zfb6PRdd1hBBCCCGEEEIIIYQYDeu+boAQQgghhBBCCCGEOPBIUEkIIYQQQgghhBBC\njJoElYQQQgghhBBCCCHEqElQSQghhBBCCCGEEEKMmgSVhBBCCCGEEEIIIcSoSVBJCCGEEEIIIYQQ\nQoyaBJXEIUEptVYpdfoI992qlDprDzdJjIJS6nal1MP7uh2iN6XU55VSC/Z1O4QQQghxYFNKnayU\n2qCUiiqlLlRKvaCUunqU5xjxeF8IsfvY93UDhPiolFJbgWs1TXup4LUvmq+dAqBp2qx90rid7bkd\nmKpp2pX7sh37C6XUlcBPgXLgA+CzmqY17NtWidHSNO3vwN/3dTuEEEKI/dlAY9V9re9YeT/wY+B3\nmqbdY/7+dH7DQG1VSj0INGia9sP8a/t6vC/EoUoylYQQKKUsSqm98v8DpVQx8ADwFaAUuAFI7I1r\n76+UUgdcgP9AbLMQQggh9i8F44l6YO2+bIsQYtfIHwXikFD4hEgpVQT8EbgAaMEIcHxT07RxBYfM\nVUr9CuML7kXgak3TEua5zsPIspkIrAOu1zRtjbnte8A3gRKgCfhPwAHcAliUUhcCmzRNmzNAG78P\nXAdUAzuAWzVNe6pg+3XAjcA4c/uVmqa9o5QaD9wDnIoRKH5U07Qb+mZHKaUmAlsAh6ZpGaXUImAJ\ncDpwFDBbKXUqcLN5jXbgF5qm/amgDZ8BfgRMNrd/HfAB39c07eiC/W4ETtM07TMDvB06kAG2aJqW\nA1YMsM+QlFIXAHcAY4HVwNc0TftAKXUNcLGmaeeb+20AVmua9jnz9x3A+ZqmrR7gnP/EuIdFwLvm\nOdea24ow3vNLMAJh7wFna5oWV0qdAvwSmAlEgP+nadqD5v19WNO0+8xzfJGCp2xKKR0joPZtjP8X\nT1JK3QNcDPiBDcC3NU173dzfBnwP+DLGZ2Q9cCHwfSChadpNBX15FnhV07Rf9+njH4BuTdO+W/Da\nM8BiTdN+NdRn0Gz/dcBbwBeAPyilNvbp01Dtv928RwngImA7xr+rt83tA36OzW1fAv4LqDWv/xVN\n07b1fQ+FEEKIA405vvseRvb2Gxjjyial1I+Ack3TvqGUcgBB4F5N0/7LHJcEgDGapnUppU4AfoXx\nPbsN+JamaYvM838R+G+gCugAfgi8gzEWdiilokBG07TSAdo2xtzvFKALY1z4Z/P1TcBYTdO6zH2P\nBBYCdZqmpYf67u47BlJK5YBJwHNKqSxQAcwHHsYYq/ZqK8ZY9fOArpT6NsaY5/w+4/3bGXrccRTw\nF2Aqxlg/B2wozHwSQoyMZCqJQ9FtGAGhycDZwEBT0i4FPoXxBXcE8EXo+cK8H/gqxhfen4BnlVIu\npZTC+II8VtM0H/BJYKumaS8CPwf+oWla8UABJdMmjD+o/RiBm4eVUnXmdT8H3I7xx3wJRkCs0ww0\nzMMYQEzECLI8Nop7cRVGxpDPPEcbcJ55jWuAX5tfuiiljgP+hjFAKAU+BmwFnsUIiBzW57x/G+Sa\nKYxA0ONKqfJRtBWzHdOBRzEGIlXA8xiDECewGDhVKWU1BzxO4ETzuMlAMbBmkFO/AEzDCKi8Q+9p\nXXcBRwMnYQz6bgZySql687jfmm2Za/ZtpC4EjscY9IARYJtrXuMR4J9KKbe57UbgP4BzMd6fLwEx\n4K/Af+QzzZRSlcBZ5vF9PQpcppSymPuWAZ9g52dm0M+g6XhgM1AD/GyA8w/VfjA+t49hfH6eBX5n\ntmPQz7EZyLwFI1hVBbxu9kMIIYQ4oCmlzsB4SHYpUIfxPZj/Tl6M8eAP4FiMB6EfM38/EdDMgNJY\n4N/sLCvwXeBfSqkqpZQX+A1wjjk2PQnjYdsHwPXAMnNs2i+gZHoMaADGYDxY+7lS6gxN05qAZcBn\nC/a9AnjCDCiN5Lu7ZwykadoUjKDP+WZ7kvmdBmqrpmn/hzFO+6X52vmDtH+wcYcTeAp40Lxnj2IE\nnoQQu0AylcTB4mmlVKbgdydGYGAgl2JkoQSAgFLqNxgBm0K/Mb8wUUo9h/GHMhgBmD9pmrbc/P2v\nSqlbgBOARsAFzFRKtWuatnU0HdA07Z8Fv/5DKfUD4DjgGeBajC/OfFbPRrNtJ2J80f+Xpmn5/r8x\niss+mM/GMf274OfFZhHmUzHu5ZeB+zVNW2hub8zvqJT6B0Zw7lal1CyMwMC8Qa75W4xMoM3AQqXU\n2eag6KdAUWHGzSAuA/6db4dS6i7gW8BJmqYtUkpFMN6v6RhPueYqpWZgDMBeN7Oj+tE07f6C/tyO\n8dnwY2QffQk4QdO0fJ+XmvtdAbykaVp+oNRp/jdSd+Sf8JltKCxGfrdS6oeAwrhf1wI3a5qmmdvf\nzV9TKRUCzsR4Qng5sEjTtNYBrvc6RqbYqcBrGAPEZfnP+jCfQYAmTdN+a/6cMeKoOw3TfoA3NE17\nHkAp9RBGYBDzGoN9jq8379MH5nE/B25RStVLtpIQQogD3OcxxlbvAJjfuwEzu3wZME0pVYERTPoL\n8J9mGYHTMIJOYIy/ns9/v2KMrd7GeAj1BEYGzuFKqe2apjUDzSNpmJlBfDLwaTNbf7VS6j6MB5yv\nYDw8ugL4s/mw6nKzPzCy7+5eY6A9ZLBxxwkYfwf/RtM0HXhSKfXWHm6LEActCSqJg8WFAxXqHmTf\nMRhTe/J2DLBPS8HPMfMYMKbDXa2U+kbBdidG+vFiMwX3dmCWUmo+cGP+D/bhKKW+gJGNMtF8qRio\nNH8ej5FF0td4YFvBH+Kj1avvSqlzMDK5pmNkMnowpnrlr/U8A/sr8KgZRLgKeLzwKVPB+b0YwakJ\nmqY1m5lKLyljtb2TMTKChjMG40keAJqm5cxpbWPNl/JP9qaaPwcxBl8nsnMA1rddNozMm89hPFHL\nB54qMQKFbga//wO9PlJ97/93Me7PGIzgTwnDfwbAuP9XYgSVrsSYRtaPpmm6UuoxjIyn1zAGgz2B\noGE+g/3a29cw7Yf+/67cyqilMNTnuB64Ryl1d8FrFoz3W4JKQgghDmRjKHgIqmlaVCnViTGtbKsZ\nHDoNI6j0M4yHZiebr+Uf8tQDn1NKFWbrODCmhHUrpS7DyF76i1JqCXCTpmkfjrBtXZqmRQpe2wYc\nY/78L+C3ZkbzdIyx0+sFbRruu3vIMcVuMti4YwzQaAaU8vZGe4Q4KMn0N3EoasaoGZQ3fhTH7gB+\nZqbe5v/z5DNVNE17xKwvU4/xR/UvzOP0Qc4HgDmN6s8Y0+cqzDTk9zG+gPPXnTJIeyaogYsmd2ME\nhfJqB9inp11KKRfGAOEuoMZsw/MjaAOapr2JMa3tVIxAxUMD7Yfx/xwbxmAHTdO+jzFl6k2M9OMX\nBjmuUBPG/c2324LxHuaziPJBpVPNnxdjDL4Kn+r1dQXwGYxpY352BlUsGPUHEgx+/we8J4z+/ufr\nWV0KlJn3P8QI7j9GYOgzSqk5wGEUrJgygEeBS8zP3PEY7/lIPoO92tvXCNo/lKE+xzuAr/b5N1ek\nadrSEZxXCCGE2J/1HdN4McorFI5pzgCOxBgvLcYor3AcxsMhML4nH+rzPenVNO1OAE3T5muadjbG\n9LoPMb7rYZixqdm2cqWUr+C1Cfm2mRn/CzAyyK8AHisI0ozku3u46xcaaN/RHN9XMzA2Xw7ANJq/\nB4QQBSRTSRyKHgd+oJRagfFH/w2jOPbPwFNKqZcwig56MAIYr2E89RiLUVAwAcQxAigArcDZSinr\nINOvvBhfju0Ayig4fXjB9vuAXyml3sB4ojUFSJttaAbuVErdBmSBozVNW4JR2+d7SqkJGH/c/2CY\nvjkxsnLaMaY2nYNRb+d9c/tfgAVKqXnAqxiDE1/B066/YcxVT2uaNuAUPE3TIkqpF4F7lVJfxigy\n+QpGdssyjP8npYZp5+PA95VSZ2Lc928BScwpaRgDrl8BrZqmNSilwhhBLjuwapBz+sxzdGK8pz8v\naHNOKXU/xv2/CuO9PI6ddZduUUpdCjyJEZAarxmFwFcDF5up4mPMPg40Ja2wDRmM+29XRtHskoLt\n9wE/UUqtw5j+OBvjKVun2c8VZj//pWlafLCLaJq2SinVYZ5vvqZpQXPTcJ/B4QzX/qEM9Tn+o9nv\n1ZqmrTWnJH6iz1Q9IYQQYn/n6FNnMIPxoOdRpdQjwAcY44/lBSUUFmNMYVuhaVpKGYuA3IGx2Em7\nuc/DwAql1CeBlzAe3J2AMVZImz+/hDEujbIzG7sVGKeUcmqa1m/spWnaDqXUUuAOMxN5OsZY5vMF\nuz2CUWS8HiP4lbe7v7sHamsrRn3UXbEMY6xxgzIWMfk0xthu0S6eT4hDmmQqiUPRjzGKDm7B+JJ9\nAiOgMCzNWDHiOozgSQDjC/uL5mYXcCdGZksLRsHnfCAn/yXaqZTqV+tJ07R1wN0YX3KtGAGDJQXb\n/4mR9vwIRo2fpzFWBMkC52NM9dpu9usy85iFwD8wClOvZPAaR/lrRDBWrnvc7NsVGEUN89vfwize\njRGkWkzB0zWMgMbhFEynGsSVZh/fxbhX12CkclsxiqAPyawpdCVG2ncHRv/Pzw8yNE1bjzFoet38\nPYxRv2mJeb8G8jeMdOxGjBX93uyz/bsY0wBXYK5+Alg1TduOUbPgJvP11UC+EPuvMQJkrRjT0/7O\n0OZjrD6y3mxLgt6p2L/CeG8WAGGMIF9Rwfa/YnxuBssSK/QIfYp5D/cZHIHh2j+oYT7HT2Hc78fM\nAOH7wDmjaJcQQgixP3geI7CT/+92s3TD/8PIGm7GeGh4ecExSzG+6/NZSeswvl/zv6Np2g6MbOtb\nMB7s7MBYVMVq/ncjRtZRF0bW9tfMQ18B1gIt5sOmgfwHRvZ2E0Zh69sKy01gjBOnAS2apuXrJ+6J\n7+6B2voXjDqmQaXUUBna/ZhjxosxgmRBjHHlPEb494AQojeLrn+UzEEhDnxKqa8Bl2uadtq+bsuB\nTBnL27YBR2matmFft+dQo5T6GEZAr75PjQAhhBBCCDEEpdRy4I+apj2wr9sixIFGpr+JQ45ZUHAy\n5qoaGFkmv9unjTo4fA0jPVsCSnuZUsqBMQ3wPgkoCSGEEEIMTSl1GqBhZL1/HjgCI+NaCDFKElQS\nhyIn8CdgEkbK62PAvfu0RQc4pdRWjILMF+7blhx6lFKHAW9jTCe8Zh83RwghhBDiQKAwygp4Mcok\nXKJpWvO+bZIQByaZ/iaEEEIIIYQQQgghRk0KdQshhBBCCCGEEEKIUZOgkhBCCCGEEEIIIYQYtYOq\nplJ7e2SPzeUrK/MQCMT21On3G9LPg8eh0EeQfh5spJ8Hlz3Rz6oqn2W3nlB8ZDL++uiknwcX6efB\nRfp5cJF+7rrBxmCSqTRCdrttXzdhr5B+HjwOhT6C9PNgI/08uBwq/RR7zqHyGZJ+HlyknwcX6efB\nRfq5+0lQSQghhBBCCCGEEEKMmgSVhBBCCCGEEEIIIcSoSVBJCCGEEEIIIYQQQoyaBJWEEEIIIYQQ\nQgghxKhJUEkIIYQQQgghhBBCjJoElYQQQgghhBBCCCHEqElQSQghhBBCCCGEEEKMmgSVDiCXXHI+\nwWBwxPv8/Oc/4rzzzuaqqy7dG80TQgghhBBCCCHEIUSCSgexc889n7vv/u2+boYQQgghhBBCCCEO\nQvZ93YCDXXNzEzfd9A1mzZrNe++t4bDDZnLuuedz//1/IhAI8N///RPGjRvPHXf8mKamRlwuNzff\nfCtTp04jFApy++230t7ezuGHz0bX9Z7zzp//PE888RjpdIaZM2dx003fx2az9br23LlH0dzctLe7\nLIQQQgghhBBCiEPAIRVUevyVjaz4sG2XjrXZLGSzer/Xj51RzaVnTB3y2MbGBn7yk1/wgx9M5tpr\nv8DChS9y771/4Y03FvPQQw9QXV3DtGmKO+64m5UrV/DTn97Ggw8+wgMP/JkjjpjLNddcx9KlbzBv\n3jMAbN26hZdfXsgf/nA/drudu+66kwULXuCcc87bpb4JIYQQQgghhBBCjNYeDSoppT4F3APYgPs0\nTbuzz/Yy4H5gCpAAvqRp2vsjOfZAUlc3hilTjMDTpEmTOeaY47BYLEyePJXm5mZaWpr56U9/CcDR\nRx9LOByiuzvK6tWr+NnPjNdPOukUfL4SAFaufAtN+4Brr/0CAMlkgrKysn3QMyGEEEIIIYQQQhyq\n9lhQSSllA34PnA00ACuUUs9qmrauYLdbgNWapl2klJph7n/mCI8dtUvPmDpsVtFgqqp8tLdHdulY\nh8PR87PVau353Wq1ks1msNtH9zbous4555zH9dffsEvtEUIIsfvk9BzrOjWmlU3BZXPu6+YIIYQQ\nQoj9mK7rJMIbcXnHY7W793VzPrI9Waj7OGCjpmmbNU1LAY8Bn+mzz0zgFQBN0z4EJiqlakZ47EFj\nzpwjWbjwRQDeeedt/H4/Xm8xc+fufH3ZsiVEImEAjj76OBYteplAoAuAcDhES0vzvmm8EEIc4jYG\nt/CHNQ+wrGnFvm6KEEIIIYTYS3K5NF07nice3jSq40LNr9C++VGCza/uoZbtXXsyqDQW2FHwe4P5\nWqF3gYsBlFLHAfXAuBEee9D40pe+gqZ9wNVXX84f//g7br31RwBcc811vPvuKq688lJee+1Vampq\nAWMK3XXXfY3vfOcGrr76cr797a/T0dHR77y33XYL119/Ddu3b+Oii85l3ryn92q/hBDiUBDLxAGI\npHYtm1UIIYQQQux9up4jHlqPnsvuwrE6XdvnEe14m2DTKyM+LtL+FuHWJQDEQx/2Woyrr1wuvUtt\n29v2daHuO4F7lFKrgfeAVcAu37WyMg92u234HXdRVZVvF45RvPjiCz2//+//3j3gtvvu+78Br/fw\nw38b8LyXX/5ZLr/8s/1eX7x4Uc/P997721G3N3/dQ8Gh0M9DoY8g/TzYHGj99MbNKc7O3KjafqD1\nc1cdKv0UQgghxJ6h57JgsWKxWHbreUPNiwi3vkHpmDMpqTl5VMdGO1YQC7wHQDreTDrRicNdMeQx\n3YG1BBpexGr34iyqIRHZTCrWhMvbP38mEdlCx9Z/YbHYKB1zJp6y2b36r+s66DmwWAGdbDqKnktj\nd5Xv9vs0nD0ZVGoExhf8Ps58rYemaWHgGgCllAXYAmwGioY7diCBQOyjtXgIH6Wm0oFE+nnwOBT6\nCNLPg82B2M+uYBSAQCQy4rYfiP3cFXuinxKkEkIIIQ4dkbblBBrnA2Cx2CnyT6ek5iScnjGjPlcu\nm0TPeQBIxZp6MoainavwVZ804mBMsnsHgYYFWO1efFXHEWp+lVhwLf7ajw16THfXGjq3PYPF6qJ6\nyhVkUmESkc3EQx/i8o5F13XS8RZy2STJ7h2EmheBxYIFK53bnibasZLKyZdjsxeRTUdp2/gQ6UQ7\nkG+zkfFUNeXzFJVMGfW9+Sj2ZFBpBTBNKTUJIyB0OXBF4Q5KqVIgZtZNuhZ4TdO0sFJq2GOFEEKI\n/UFGzwCQyCb3cUuEEEIIIfatdLILu6MEi3XgUEMumyLY/ApFJdOGDX5kUkGCza9gsblxFtWQTUeJ\nBdcRC67DVTwBb/kcPKWHYbUNXuw6HW8n2PwqqVgT2XSYVpcfX81pRNqWAzoOdzXpRBup7h24iicM\n2N62jX/DVTyRsrFnoetZurbPA3JUTvwsTk8toZbXiAXWDRhU0nWdaOdKAjuex2JzUz3l8zg9ddjd\nlVgsdmJBDX/dGXRue4pY4P2e46z2YqomfQ6bw0egcT7xkEbH5seomvIftG95nHSiHadnLBaLUdHI\n5ijB7irD5dn7VYP2WFBJ07SMUuoGYD5gA+7XNG2tUup6c/sfgcOAvyqldGAt8OWhjt1TbRVCCCF2\nVdac657IJPZxS/a+ZDZFIpPE75LsISGEEGJPigXWYXdX4Cyq2aXjc9kkYME6gpVqdT1HLLCOIv80\nrDYXANl0N6l4M27flEEzesKtSwk2vQQWK86iWnzVJ+Itm7WzDbk07ZsfIxndSjK6Y9igUqDhRfRc\nmor6T+MtPwJd10lGthBqXUIyuoVkdDvBxpeonXEddmep2XYddGNsFu1aTbBhAbqewebw4SqeSCrW\nQNf2ZwHwVhyFt3QmbZseJtr17oBBpWjnKlKxJlKxJhzuSnKZbtKJdoorjsbtmwhAUclU4iGNVLwN\nZ1F1z7GZVIiuHf8mEd6I1VZE9dQrcXrqALBaHbhLphIPfUiw6SVigfdxFNVS5J+O1ebCW3Y4Nocx\nvqqcdCmdW/9FLLiOpnW/J5fpxlM6i4qJF+/1qW4D2aM1lTRNex54vs9rfyz4eRkwfaTHCiGEEPub\nTM7MVMoceplK/9CeYk3HOn584vfxOIr2dXOEEEKIg1Iq1kTH1iewWJ1UT7miV/BD13XiofVG9ouz\nZMDjdT1L6/r7yaTDlI87B0/ZbGKB94h0vI2/5lSK/NN67R9pf4tg4wK85XOpqL8AXddp3/wYqVgj\nZePPxVd5TL9rJCJbCTa9jNXuxe70k4q30Ln1X6DreMsPJ5dJ0L7lnySjWwEr6XgzmWQAu6us5xyZ\nVJjObU9itbqxu8qIh9bjKq7HUzYbAIvFgrtkMu6SyWSSQcLtbxJtf4tI23LKxn3S7OcDpGJNPee0\n2oqomHAxntIZAPh9WTa//28yqRBlY8/CYnVhc/iJBdbirz2NYNPLZFIBqiZfjtXqItK2DIvVARYb\nXTv+jQULVruH0jFn9FzDUzaLeEgjFlyL3VlKOt5KtHMVscD76HoGt28y5ePPw+4q7XXPivyKeOhD\nIm3LsNrcVE2+DLvT3+/eWiwWKuovJJvpJhndhqOojvL6C/aLgBLs+0LdQgghxAEtYz4NS2QPrkyl\nHZFGPHYPFUVlg+7TlQgQz8R5t2MtJ9b1H2AKIYQQYic9lyXU+hoOVyXe8tn9t5srgfUNFkS71pjH\np2jb9AjVU6/E5R0HQHfXu3Rtfxa3bxLVU6/qOSabiWOzGw98op2rzfo70LntaYKNL5HNGDUhu7Y/\nR93MrwNGVkwuEyfc8pp57jX4az9GOtlJKmaUOA40vGhmS1kJtbwG6Lh9k836RBaqJl2Kq3g8qVgz\nrRv/Rue2p0hENhMLfoCeS1LkV7h9kwk0vEAs+CElNSca180mad/0KOlE686OW6yUjz93wOCJ3VVK\n2ZiziQfWEe1chb/udLoD75sZRVXYHD5sjmL8dWf0CrY53aVU1F/Q61zeiiMIt7xO8we/R8+ljfu0\n9Uk8ZbPIpsP4qo7H7ZtM++ZH0YGyMZ/Gat/5MK2oZDoWi51wy+uEW17ved3mLMVf+zG85XMG7EOR\nfzpGTSSd8gkXDBhQ2nkr7FRNvoxo52q8ZYdjtToG3Xdvk6CSEEII8RHsrelvbzS+SSAZ4vzJn+y3\n7bWGZaRyKc6acNounfuDzvWsbHuXy6ZfiMPmIJKKctfK3zO9bApfn/PlQY9LmQOvla2rJagkhBDi\nkGIEgPSemjaFr0faloLFhq/quJ7tuUyc9i2Pk4xuA8Bic+Lxq57jMqkwrRseJJdN4HCV4/ZNwV93\nOug5YoH3sdo9lI39FJ3bnqJt48NUTroEu6ucQMOLgLFaWH76Vah5MaGWxZSNOxdvxRzCLa9hsdip\nnnYVwaaXSUa34ymbjdXmJtqxglDL69TUXgRAqPV1ctkELu8Ekt3bCbW+QTrRBkDpmLMJNr1E28a/\no+dSPW1PhDca28d+Alexsd6W01NH9ZQraNv4MN1dq7HavfhrT8FXdQK5bIJAw4vEgusoqTkRXc/S\nseWfpBOtFFceQ0nNySSj27A5inG4qwZ9DyxWG8VVxxJqfpVI+1tE2ldgsTqonnoVNkfxiN/L4vK5\nhFteR89l8NeeRjLWRCK8gUR0G2DFV30CdqefivoLSSc78ZYf0et4q82Jv+50YsEPsNqLsDl8eEtn\n4fJNGjKbyGYvonTsWQA9mVRDsdrclFSfMOJ+7S0SVDqAXHLJ+dx330OUlpaOaJ+f//xHLF36BmVl\nZTz00OM9+2zYoPE//3MHqVQKm83GTTd9j5kzD98bXRBCiINOfvpbfA8W6tZ1nXlbFhBJRTl93Mn4\nnL0HSgu3LyKa7ubM8R/bpVToN1ve5u3W1UwrnczxdUezonUVmVyG7vTQq6rm+64FNhJNdVPs9I76\n2kIIIcRgsukoVrt3n03zScVbiQY7gd5LxWeSAdo2PYLN4aN66lW92hfteJtg08sAxAJrKa37OKlE\nO9GOt8kkO3H7JpOMbqdz61PYp1+Ds6iGXDZF++bHyKaC2J1lpOKtpGJN2BxebE4/uUwMX9XxeMsP\nx2K10bH1Sdo3PYrdWYqeS+EpO4JYYA2R9uX4a04h1PoGAIGG54mHNLLpCCU1J+Pyjqd66tXksnFs\ndg+5XJp4eD2R9jeJR04k2R0g0r4Cm9NP1ZQraNH+THfnO4CRjWNkFekEm17CUVRD2dhPGtPUwhtB\nz1HcZ1qcyzuemmlfJJ1ox1M6s6d4t83qxVVcTzK6lUwqRKh5EYnIZtwl0ygb9yksFiv2PoGbwRRX\nHk245XVCza8CUFL7sVEFlADsrjKqpnwem92D01NHLpOgRfszmVQAb/mcngyivsGkQiU1J1FSc9Ko\nrgtQUn3iqI/Z31iH30UcqM4993zuvvu3/V6/997fcM011/Hgg49w7bVf5d57f7MPWieE2N/l9Bzb\nww37uhn7vfz0t1Q2RU7P7ZFrBJMhIikjTX1DcHO/7bF0nFQ2tctT8JJZ42nj4oalALzZ/DYAOTML\nazDprJGplNNzrGp/b5euLYQQ4uCSCG8mEdky5D7pRAehljfQB/ieMWoEbaB1/YM0vv8rIu1v7qmm\nAtC14wXW59icAAAgAElEQVRa1t9PLPhhz/QzMFYxa13/ANpbvyfQ+BJ6/vs+3kbr+gfIJDvNgtPb\neo5JdjcQaJyP1e7BUzqTVKyRtk0PE2ycTybZia/6BKqmfJ6K+guNqWwbH6Jrx/N0bH2CdLwFb8WR\n1M28gbGzvoXVVkSw6eWeZe/zAQ1P6WHUTP0CVnsRmVQAT9lsKuovwO4so7trDV075oGepaTmVKw2\nN4nIJiw2F75qI+BhsViw2T2AUSy6bOwnQM+xbtndtK6/H/QspXVnYLU5Kak5padvJXXGymYlNSdR\nd9jXqVXX4fZNxO7046s8Gl/VsQMG/5yeOrzlR/RbDc5TehgA7Zsfo7vrXZyeMVRO/Gy/zK/h2Owe\nPOa9sdq9uxykKSqZsrOItt1N1ZTL8ZTOMrLFxJAkU2kPa25u4qabvsGsWbN57701HHbYTM4993zu\nv/9PBAIB/vu/f8K4ceO5444f09TUiMvl5uabb2Xq1GmEQkFuv/1W2tvbOfzw2b3+Jzd//vM88cRj\npNMZZs6cxU03fR+bzdbr2nPnHkVzc1PfJmGxWIjFugGIRqNUVg6eUiiEOHS92bySv3/4T2486j+Z\nUjpxt5xzsFoBA+032D7ZXBab1Tbgtn0hWzAgTmSSe6Rg9bbwjp6fNwQ2cVT1zidlOT3XE0wKJEIU\nFY/++vmg0rbIDpY0Lqcx2gzsDJgNJp3L4La5SGSTrGxdzalj97+UbCGEEHtHfkpTd9casNgYO+vb\n2BwDZ7AGGheQCG9Ez6V6FTwGo2ZPtGOF8YvFRqjlNbzlc3vqA/WVijVjc5T0XCse3kTntqfw+GdQ\nUnvqkHVqYiGt51odWx7HUVRHxYRP4yiqpXPb0+i5FHZnMZG2pSTCG7BY7KSTHei5NN6KI+nuXEWk\n/S3cvolk0910bPkn6DqVEy/G7ZtMLPghicgWnJ4xPQEYAE/ZTMoynyLYvIhoh/Egx1VcT/k4o36Q\nzVFM2fhz6Nz6JKnuBhzuahxFtT3tdhWPp3b6l4kF11FceQwWixVf1XEEGueTiGzB5R2Pv+50ivzT\n6dz6JCU1Jw16/4r8M4wMo0wnutWPq3gCnjJjFou3fDbdne9gd5Xj8ozpOcbhrhjwXKNRVDqDQMML\npOOt2JylRmHsEaxMN5CS6hNJhDdQOubMntXqPiqHu4rKSZ/dLec62B1SQaUnN85jVduuPUm1WS1k\nc3q/14+sns3FU88b8tjGxgZ+8pNf8IMfTObaa7/AwoUvcu+9f+GNNxbz0EMPUF1dw7RpijvuuJuV\nK1fw05/exoMPPsIDD/yZI46YyzXXXMfSpW8wb94zAGzduoWXX17IH/5wP3a7nbvuupMFC17gnHOG\nbkfeN795EzfeeAO///095HI5/vjH+0d/Q4QQB73WmDF/PpAM7rZzvrrjdV7c+gq3nXgzXodnwH2a\noi3cueIevnrE1cyq6D2/fHnzSh7R/sXX53yZ6WVDL0O7t6T1TM/PiWxijwSVthYEldb3yVQqrOUU\nSoYZU1zLaKWyO+siPL7+6Z6fs8NlKuXSlLr8eB0eNga3EEqG8bsGXnlGCCHEwSuXidOs/ZlsKojV\n7iGXidHdtZqSmpP77ZtJhXpq8IRbl1DkV7i8YwGIh9YT7ViBw11FRf1FJCJbCDYtJNy6hDKz9kxe\nNh0h0LDAWHHLVU6t+goWi5VAwwvkMjGine8Q7VqNyzsBh6scq92LrqexYMVbeRQ2u5fAjhfAYqVy\n4iXEAmuJBdfSot2Pq3gCqe4GPGWHM/3Iy1j/zmPEgmuxWOzYnH78tafiKZtNOtZCPKSRTnbRtX0e\n2XQEf90ZuH2TAaNOzmC1cnxVx1FceQzJ7h2kYs0Ul8/FUvDQzFM6i1jpB8SDHxiZPn0ettldZb3u\nr7diLsHmRei5JGXjPonFYsHlHcuYWd8Y8r2zWCyUjz+Xqiof7e2RPtus1Ey/Zsjjd5Xd4cPtm0Iq\n3kz1lCtGPWWtkMNdwdjDv7MbWydG45AKKu0rdXVjmDJlKgCTJk3mmGOOw2KxMHnyVJqbm2lpaean\nP/0lAEcffSzhcIju7iirV6/iZz8zXj/ppFPw+YyB+sqVb6FpH3DttV8AIJlMUFY2+Oo8fT399BN8\n85s3cvrpZ/Lyywu5446fcM899+7OLgshDgLhlDGwSJlTnHaHVe3v0Z2J0RHvHDSo1BhtJqtnebf9\n/V5BpaZoC49qT5LJZdgW3rHfBJX6ZirtCflMpUklE9gS3k44FaHEaazSEsvEe/YLJEO7dP5kNkmR\nvQifw0tbvAOfoxirxUJ22EylNA6bg6Oq57AptJU1HWs5deyBXxtACCEOZrlcmmjHShyuip6l5HOZ\nOLHQh3hKZ/VkiwQaF5KIbKai/iKcRdVDnjPauYpsKkhx5TH4a0+jae09RDpW4qs+qf9KZp2rAPCW\nz6G76106tz9D7fRr0fUMndufA4uNiokX4yyqweGuJNK+nGj7W/iqjsPuLCGXTRJpX064dRl6LonV\n7iGT7CLY9BJ2ZymZZBfFlcfi8o4l3LrEnKK2tVcbwu3LcRbVkE2HKak5tSf4kwgfSef2Z0lGt2Jz\n+Ckfdy42u5vKSZ8ll7sAi8Xeqz++6uPp3PY0bRv+RjYdpsivBgykDcZiseIursddXD/ANgsVE86n\nu3gixRVHDnsuq81ltDMTx1mQVbQ/q5p8Gbqe3W3ZRWLfOKSCShdPPW/YrKLBDBS5HSmHY+dyf1ar\nted3q9VKNpvBbh/d26DrOueccx7XX3/DLrXnhRfm8a1vfReAM844i1/84qe7dB4hxIEvlo7z8vbF\nfGLiGbj6pByHk2ZQqWB1j48ik8uwPWIsRxsfYqW0pFnweltBPadEJsl97z9M2lxtLB/w2h9kCoNK\nu1jTaCg5Pcf2SAM1nmqOqJzFlvB2NgQ2c3TNHKB3UCm0i0GlVDaF2+bi1HEn8q8Nz3Fs7ZG817Fu\n2IBiOpfBYbUzs0LBBviwa4MElYQQYi9IxVtxuCp7ZbYMR9d1YsF1xnLyaeP7wl93OkUl0+nY8k8y\nqQCpWAvl488hnegg0rYMgNYND1A16VLcvkmDnDdHpONtLBY7/rqPY7MX4SmfTXfnKhLhjT2Bq/y+\n3Z2rsVidlI07B4vNRbT9LRrW/AKrvYhcJkbpmLPMJeuNZdT9dafRtf05Wjc8gN1RQjrRTi6bwGor\nonT8p/GWH0Gr9heiZhustiJK607Hai/CW34EuVyaTDJALhvHYrGbAaiXScWasLvK8dee2tM+d8lk\n6mZcT6RjBUV+hdXu7tk20BLuntKZBBoXkk2HsbvKqaj/zG4tKm61ufFVHTvi/YtKpu62a+8NFqsd\ny0EWktBzOZINO3CNn7DPCszvbVKoez8wZ86RLFxoLAX5zjtv4/f78XqLmTt35+vLli0hEgkDcPTR\nx7Fo0csEAl0AhMMhWlqaR3y9ysoqVq1aCcDKlSsYN2787uyOEOIAsqptDS9ue4WVre/225YP3KR3\nU6bSjkhTz2phhYGQvvJTsZq6W3p+nrd5Pq2xNo40awnt7aBSKBnhh0t+zpr2tf22ZQumv8X3QKZS\nW6ydRDbJxJLxTCsz0ukLi3XH0zsDWbueqZTCZXPysbEncrm6iHMnnYXNah8yUymby5LTczisDqqK\nKqhwl6EFNu2xYuVCCCEM3YG1tHz4J9o3P4Zu/j832LyIprW/JZsZeNXOTCpE++ZH6dz6L7KZKL6q\n47E5/ISaF/WscmUsL/826Xg7oZbXAPBWHIWey9C28WFa1z9AsPEl4qH15Aq+x+PhDWRTQTzlR/TU\n7fGZq4BFzHpBeYnwRrLpMN6y2VhtTkrHnElJ9Um4vOPQc1ncvin4+iyZ7i2fg6t4ErlMjGR3A2DB\nX3cGY2Z9E1/l0VitDirqLwSLFV3P4K/7ONaC+kFWqwNnUTXu4npc3rF4y2dTN/PrlI//NFWT/6Nf\nAWmr3Y2/9tRhs7PADHrVnILNXkzlpM9htbmHPUYc3Nr+/je2//g2Gn91F+n29hEfp+dyZMLhXq8l\ntm0l1drSb99cIkH4rTcJLJhPYMF8Iivf7lV/eW87uMKCB6gvfekr3HHHj7n66stxudzceuuPALjm\nmuu4/fZbufLKS5k9+whqaow6GZMmTea6677Gd75zA7qew2azc+ON36O2tq7XeW+77RZWr15JMBjk\noovO5ctf/grnnXchN9/8Q+655y6y2SxOp5Obb751r/dZCLF/SJhZQQPVTdo5/W33ZCptCe9cHSUx\nZKbSzhXFGqJNTCyZwMq2d/E5irnqsEtZ1baGUDI86PF7QlO0mUAyyAddGziialavbb0ylYbo167K\n11OqLxnPBN84nDYnGwKberYXBuiCHyGoVO4uw26192Qa2SzWXn3rK20GCB1WYyrAjPJpLGl6i23h\nBib5J+xSO4QQQgwtl00QbJgPQCKyiVDzYmx2D2EzCBQPfkBx5dG9jokFP6Bz2zPouRRu32TKxp+L\nw1VOSc0pdGx5nFS8jYr6z4DFQsfmf9C57WlS8WYcRbWUj/803vLZBBsXkuxuINm9A9qMlUKDOyZR\nXHMm0Xaj0HVhRo3TU4fTM5ZEeAPRztV4y+eQTUd6VjIrrjSmc1mtDkr71Erqy2KxUjPtKmDwBT+c\nnloq6i8i2b2D4sqjhr2PVquj333aVb7q4/FVH79bziX2L8mmRsLLlhJ9522KjzyaqksuHXL/7rXv\nE1q8CIvTSeyDtWy97VZqrryakpOGnxLZ9ujfCS1+lTFfu4HiI48ivnkTO37xcxzlFUz82Z1YrFZy\niThtj/ydyNtvoad6j8+9c4+k9otfxla867WpdpUElfawuroxPPTQ4z2/33rr7QNuu+OOu/sd6/eX\n8utf/37A85555ic488xP9Hv9iSee6/n5Rz/6+YDHzpkzl/vvf3hE7RfiUJXOpnm96U1OqD1mjxRe\n3l/kpzcFE72DEdlclmjaWCUylds9mUpbQ9t7fh4yU6lgut3W8A7sVjvhVITja4/GZXPidXj2eqZS\n0mxTKNU/mJXpU6h7pOKZBEualnPymOMpsg/+ZDM/DbC+ZBw2q40p/ol80LWeUDKC3+Uj/hGDSjk9\nRzqX7jf90W4ZOlMpPxXRYU4HmFE+nSVNb/Fh1wYJKgkhDkqJyGasdm/P1KzdKZuJgZ4btlhxsHmR\nkWlUfQLxoEa49XWAnuLYsZDWK1iSirfRufUpsFgpn3AB3vI5PQEZm8NL9bQvgp7FYrWj6zqu4ok9\n9Yf8dadhsVhwF9dTq64ll032BJaS0a1Eg1uIBv8C6LiK6/vdF3/d6XRs/gdd258l3LaUTDIAeha3\nb9Iu1/wZajqRt2wW3rJZg24X+69MMEh46RsUH30MzprRLzgCkAmHsRUXY7H2noyVjUbpnPcsnpmz\n8M42Mt5TzU2ku7rwqBlY7HZyySSRt9/CNXYc7onGNM/ASwtof+yRnvMEXnweR0UlpR/vvVphz3Vi\nMVofvB9sNsZ//1ZSjY20PfowLff/mVR7GxUXXDjo5zfV1kZo8auQy9F83/8x9lvfoeW+P0E2S7q9\njfiG9XjUDAILFxBe+gaOqip8J5yEe4Ix3gq8/BLdq1ex7ce3Mf77t+IoL9+le7irJKgkhBADeK1x\nGU9unIfD6hjVMumbQ9t4+IPH+eoRX6TGU7UHW7h75AM4fTOVIunozn120/S3zaGdmUpD11TaGVTa\nFt5B0pxSli/a7XeW7NYV6UYin601UIbUrhbqfqftXZ7a+G864wEuUxcC8OSGebTG2rj+iGt6Bh7b\nwjuwWWyMLTYG4JP99XzQtZ6GaCN+14yPnKmU71vfoJLNahsyqJSfymg3g0rTy6ZgwcKHgfWcM+nM\nUbdD7B1KqU8B9wA24D5N0+7ss70MuB+YAiSAL2ma9v5eb6gQ+5lsOkLbxr9jc/gYM/OGflOmdumc\nmTiJ8Ea6A++RCG/CYnVQMfEiPH414P6J8Gai7SuwuyoorTsDb/kcWrW/gMVK9ZTP07n9ORKRLeSy\nSaw2F7lsio4tT6DrGSonfg5P6WH9zmmxWMBi7/m5bOwnaNH+jLOolqKS6b32tdpcFJVMoajEWCjD\nZW1hy/v/IpPs7DdlDaCoZAp1M79OsOllYoH3sTlL8deeirf8iI9668R+IpeIg9WG1ekcfudB6Nks\nTX/4HYlNG+l46l8Uzz2Kigs+g2v8wA+o9FyOyFtvgg6+408Ai4Wuec/S+cxT2Px+fEcfg/+0M3CN\nNVYUbPvHI0SWLSX40gKKpk2n1eUg/L5RzsBWUoJ39hyi764iF41isdupvfarYLXS/o9HsflLqb78\nClzjxrHjl3fQ9ujDOKqr8c463GiLrpNqaiS2bi2RFcvJBLqouOBC3BPqjf8mTaLxf39F13PPkNiy\nBf+pH8M763ByiTjZ7hjO2losNhtd856FXA7fCScSeXMZDb+8AwDvnLl0v7ua8JLXKZoyleCiV7AW\nFVF/24+xunc+9PbOOZKuec8SWDifTKBLgkpCCLE/yNcYGu3Ur82hrbTG2tkRbtjtQaV0Ns2TG+fx\n8fGnUL2bzp3PNukbjMgX6YbdM/0tmAwRSAYpc5USSAZHVKgbjIBKZ7wLq8XKYeVGoc8Sp8+st5TG\naetfNHNPSA4RVMoHV2B009/CSSNw90bTm3x8/Cl0Jrp4eYcxfSGjZ3FY7KRzGRqiTYwrHoPD/AOm\n1OUHIJIyjo+njaCSw2qnOx0jnTVWZBt534zPgLNvUMliJafnyOk5rJb+JRjzGWxOm9GuYoeX8b4x\nbAltJ5FJ4rbLSi77G6WUDfg9cDbQAKxQSj2radq6gt1uAVZrmnaRUmqGub9ECcUhr7trDaCTTYfp\n7lrTM8VK1/Uhs2d0PUsivBk9lzYKUWeTpLobSHRvJ9XdCBjTuRxFdWQS7XRs/ge+6hPM/RqxOUtw\nF08k2d1APPQhAOXjz8ViteMsqqF2xlewWGzYXWV4/IpQvIV4eCOe0pl07ZhHJtmBr+r4AQNKA3F6\naqmZ/iXszpJhiwyXVEyjbsZXSSe7Bq0/ZHf6qZx4Mdlxn8Jqc2GxjLywuBi56Jp3cU+ox15aOuy+\n2XicVHMT7kmT+73Hei6HnsmMKEiUCYfZdtsPySUTeGYdjuewmdhLSrCXleOePGXERaq7np9HYtNG\nimYcRi6RILpqJdHV7+A/9TR8J5xIur2dbDSCs6YWm9dLx5NPEN+w3jj2xedxVFbS/e5qbP5S9GyG\n4CsvE1qyhHE3fhc9lSKybCmu8eOxV1TSvXoVccBz2EwctXVElr9JeMnrWL1eSs84i9CSN2j+071Y\nbDYsTidjv/lt3PUTARjzn99gx12/oPHXd+GoqcVZV0di8yayBXWQPLMOp/zcnQuDOWvrGH/L/6P5\nD78j9v4aYu+v6dV314R6ys+7gPCyJTjHjKX2S9fhKK+g6/l5eA4/gjFf/yZbb/kekbdX4J4ylWwo\nROnZn+wVUAKwWK1UXHAh5efv3kLxIyVBJSGE6KM91sm2iFHHJjtETZmB5ItaJ3fTimmFNga38Frj\nMmwWG5dMv2C3nLNn+lvfoFLB9LLdsfrbFnPq26wKxRtNy3tN2erfJuN6Y7y1NHW30BHvYrJ/Ih6H\nBwC/q8RsY5jKooqP3LaR6MlUSoX7BVkyeuHqbyPPVOo2pxfm9BxPb/w3jd07CzEms0kcVjuhZIis\nnqXWu3Ow7nMaUyPyQaWYGciq9dawI9JIMBmmyjPy+5IP4rn6LOdrN4NY2UGCSn0zlcCYArc90sjG\n4GYOrxzZHzBirzoO2Khp2mYApdRjwGeAwqDSTOBOAE3TPlRKTVRK1Wia1rrXWyvEbqTrOeKh9biK\nJ2CzewpeHzoolN8n2vkumAGRUOsbeCvm0N31HoGGF/CWzcY/5kzQs4RblxDY2kHOWoLF6iAWWEsu\n0z3AWS24vONwl0zFU3oYDnclqVgT7Zv/QaTtTWMPi510oo1EeCMATu84Ssechbt4ZwaHw13Z83NR\n6QxCLYuJBz9EzyaIBd7H6RlL6Zihaxb15fKOHfG+RnBr+ILWhfdc7F7da9+n6Te/xj11GuO/dwsW\ni4Xga4voeOJxxv/XD3CNNxZl0nWdyJvLaP/nY2TDYUrP/iRVn7sMi9WKnsvRvvh1tv79MdJtrZSc\ndAoVF1yIo2Lw8UT7Px8jGwlj85fSveodule907Ot7FPn9tQfCi15ncSmjRQfcxyeGYeRSyZJNTeh\np1JkQkE6n3sGe1k5Y752A1aPh9ja92l//FFCry0i9NqiAa9dfNTRWD0e1jR1sHrasVyczlB/3Vew\nFXkIL19G618foPF/78bq8YDFQs0Xv4y7fiLJpkYqqkuJ2r0AVH3uMhJbt+Cun4jV5aLklFNpvOdX\nZMNhxnzthp6AEkDRtOmMveFbBF5eSGLjBrpbW7CVlOA7/kQ8M2cagary/vfLXlLCuJt/QHL7NiIr\n3iKxdQt2n49cOk336lU03/tbAGN6nNVKxYUX45k5ywjMWa2UnHwKnc88ZUzFs1goPWPw5zz7arU5\nCSoJIUQf77TtXAmtsF7OSOSzN3bXlLFC+WlOrbGRryQxnHw745kEiUwCt1nbp1dQaRf7EkyGeHDt\no0wsmUB32liNZmZPUGnwjJ58AGd62RSaulvQ0Tm8ckbP9hKnDzBWZNtbQaV84CWn5+hOx3oCO9B3\n+tvIM5XyNavK3WW822GkYVvN7KBkJkWxw9sznc5dUHOp2Ky3kZ+imA/QjfHWmkGlEJVF5bzyTiNH\nTqukvGTolWjy97t/ppLN7F+mJ0uq93FmplJhUKlsGgu2vYoW2ChBpf3TWGBHwe8NQN/qsu8CFwOv\nK6WOA+qBccCgQaWyMg92+57LPqiq8u2xc+9PpJ8jo+eyNG1agK98KiUV04Y/wNS0aQEdWxbi9dej\njvtPLBYrTZsW0tHwJlPmfhGvf/DVkKPBbWSSHZTVzsHu8NC+YxmR5ucItr4H6EQ73yEe/pBcNo3e\npw6hzeGhesKpuDzlZFIxLFYbXv8EvCXjsPWr2aiorv0OoY4PKSquxVMylnQyQjSwGZvDQ0nF9GGy\noorp2lZOIryeeFjD5vCgjr4aZ9Hw2Su7Sj63/em6TuOTT6Nns4y54Dxs7l1fES60di1tryxm/Ocu\nxl3bv9aQrus0zXsagMTGDdi2aPimT2XTE4+Ti8WIzJ/HuB/cjK7raL/4HzqXLcfqdOKqria4cD72\nZDeuyko6liwl2dqGxWbDVV1FeMnrRJYvo2jcWFxVlVjtDrKJBPZiL+Mvu5R0KERk2VK8kycx565f\nEG9uoXvTZjLRKE3PzSPw4vNUz55BOhSi9YG/GH15bTFWt5tcos9YyWJB3fhNSiea/as+iQmnHkfb\ny68Qb2qmqK4Oe4mPRFMziZZWyo87hvLjjKLwK5auJRhM4vvWt6irNaZ8VV94Lv4yH+t/fQ+5eJwx\nnzmf8cfMNs5dZYwne/3LG1swlqw6nNrf/S+pQBBvff/pd1VnnMzEM05Gz2ZJBYI4K8pHHsipng35\ndpi63l7Jpt//EVdNNZM+efrOelA1x/Xs4/v0J+h85in0dJry449l7MwpI7see+/fpwSVhBCij5UF\nQaXCgMGW0DYaok09q2MNJP8HenI3rZhWKL4HgkrpgiykYDJE7UBBpV0s1P3vzQvYENzMhuBmACxY\nmF42FQuWITOV8vduWtkUFjUYq8Tk6ykBlLh8/dq4pxUG1oLJcK+gUuH0t/goMpXyQaXLpl/IH9Y8\nQLm7jGmlk1nesrIniJW/F+6CLKL8taMp4/h8sLHOW2O2L8SW5gh/X7ieznCCSz8+dch2JIeoqQS9\nM7EKZczPhb0g4DSldCKHlU9nTHHdgMeIA8KdwD1KqdXAe8AqYMiUzUBg4CXMd4eqKh/t7Xu3MP++\nIP0cuUj7CgINr9C67XVq1XW9MnUAcrk0FosNS0GGZTy8kfZNCwHoDm1j89qF2F3ldGxZAMCGlfdT\no67FYrESbH4Vq62I0rrTe+omdW03Vjuze2fhcFcBywm2rsFqc1M15fMko1sJtbyG1VZEydhPUD/t\nBFqbmshmunF5x/WcJx9DSuYgGcwAg9wL5wxiKYh1dANWsE8lq0NHR3Tg/Qu4iqeTihuZTuUTP0co\naofonvlsyed2YOE3l9LyN2NRpKbn51N58SX4jjsei2344Hu2u5tsJIyeyRBc/CqhV18BIPDuGsZ/\n79Z+09uiq94humEj7ilTSWzexOYH/4ZrQj3ZWAyr203Xm8tpeGcdiW1b6Fy2HPfUadRd+xWs7iIa\nf/NrOl57AwCLy03N2WfhOeOT2CsqiCxfRmDhAhItrcS2but1zY6lb2LzeMFiofzyq+joioGrBGbO\nxQ7UjJvE9p/9hPW//g16JoOtpITqK64itm4tsfUf4q6oxFk3BpvHyF5zT5lKum5iv3tsO+pEis2F\n/HTANe1wXBhfSPl9oxYHkGRbZ4yawqn/M+dS95Wv0f3eGjxnf7rXuYd/Py3gKSM27HvuhBH8mxxS\n/XTq77wLcjk6OgfKaASsRRTNOIz4hx/gOfWMnrY3dSfwOmz4nQOXPNgT/z4HC1JJUEkIIYDfrb6P\n7nQ3x9cdQ2O0Gb+zhFAq3OsP6vnbXuW9jnXMrZrdK6hQKB98SI4iuDBS+eyerkRg1HVzBlMYMAok\nQtSagYlQQU2l9C4EyJq7W1nW/Da13hpOrDuGBVtfpb5kPEV2Ny6ba9hC3Q6rg0klxhOiUpefMd6d\nT+f8+UylAVZi21MKg4ShZIjxvp2r1vx/9t4zQI6zzvb+VegcpiePZhRHsmTZlmRZzjkQjTE2wRjj\nHAgL7Lssu+x62cuysKy53OUuvrDEBRscYMEkYywHnJNkSZZkK6cJmjzTOVZ+P1RXTfdMz2hkS7KB\nOV+mp7uq+qmqp6ue59Q5569bBkE5QEEvHpZSKa/l8YoeTmpazs0nXsOcUBvrhzZVfV/JtaaNEz4T\n7XiGAXgAACAASURBVG9FvYgoiG6GV0pJ49Hs9bN5lR++9lN8ko/rT/gwAMlSiv/Y9F98ZNn7Oalp\n+ZRB3bKrVDJrtl8tk2mVKiZZlPn0ybfO+BjM4pijH6iUY8wtv+di9+7dGeAmgGXLlglAF3DgWDVw\nFrOohGVZpPofQy0M0rToQwiiTHroGRAkLFNjrOsBWpfdgih6MLQ86eHnyI1tBAQ8/hY8/kYkT4R8\nfAsIEs2dHybe8ztSg08hCDKCIBNqXE1ubAMj++7F1POYhk3UK7luGua/F8vUyCe3I3mi+COdCIJI\ntPUs8olXae68Gm9wDr5QB+GmUxFEGUGQkGQ/nkAzHo59wY5Qw0lkR9cTbTuXQN3MlVx/iVAOHsTT\n0oLosx/cqMNDJP/4ON6WFrztHahDgxR27SQ3v4PQZe9HEATU4WH6v/kNGt5zGXXnng+AnkqS376d\n8ClrMIsFRu67B8Hno+78C0k/9QRDP/oBY795gNhFl1B3wYU2IVMBU1NJP/M0uU0bKe7bC5blfuZt\nbydw3FLSzzxN33/+B/M+fztSyF7fMk3GfvtrEATabryZxKOPkHn+WdSBAXzz5tN4xfsZ+NY3Gfmf\n+1F6uhH9fuZ87JNuiPPcz32e1BOP42mbQ+ikFbR2NLokRPSsc4iedQ6WZWEWClimgejzU9i+jZGf\n34cej1N3wYUEOjsnHVdfewdtN9/K4He/jRSJMvfv/gFfeweRU0878uewPEZJKpMfgEZOO53IaadP\nev/1YKigEPXKBGuocoeLCnVeGf8MSMNaSGoGIhArD6cUw+Sl4RSrmyIuYdR6w02UDhwgsMx+yGpa\nFj/c1Y9XEvjUCfOJet9cWmeWVPoTwgc/+F7++7/vITZNAJuzTCAQ4NOfvg1V1TAMg4suuoRbbvk4\nAJlMmi9+8XaGhgZpa5vDl7/8NaLR6LHajVnM4i0H0zLZmbAD/3qz9vzqtLbV/LH3mSqlkpOXlCgl\npyaVysqfIxFuPREOEWNhMVIco+MIqEG0CgVOsiJXyVEBCQivS6n04P5HsLB4X+e7WNl8IhfPO8/9\nLCD7D2l/80le6nxR3rPo7bQGW6qkxVFvOVNJOZZKpQpSaQKZZZgGAdlPyVAOq/pbTisQ8tgDwzWt\nJwPjiqRxpVKZVKoIvfZJXryix7W/FfQSQTlAzG8HeKeUNGHVJnwyxSL7R7dX9dfeskVuX6qLk5qW\nT5mp5CiVjCksoI5S6UiQm7M4ZtgAHLds2bJF2GTS1cA1lQssW7YsBhR2796tArcCz5aJplnM4g3B\nsiysisnyTJBPbCE7uh6AkX334gvPw9QL1M25CEPLkBvbxMjenyAIEmpxGMtUkbwxJCmAVhpFKw66\n26qfdymB6BIa5l3GWNf/YFkGDfPfR6hhJZalk49vRhA9xDreiVYcJp/YwtCu77vrh5pPc9VPsfZL\nqJtzcdW9SZTeGsUJvMF25q78/FumPW8EpqqSfvopwqecgqdp5gSdqankXtmEFI7gaWpGOdhLbvMr\nWKpK5Iwz8c2dx+ivfkF+8ysEli5j7t/9AwBDP/oBpQOTOfT85ldoa+kgcvoZjNz7U7TREUb/52eE\nVp6MFAzS/607UXq6Gf2f+5FjMcxikZbrbyR2/oXUX/w2Eo+tJfPiC4z96pckHn6I2EWXEHv7O5Aj\nUUxVZeDbd1LYsR0EAf/iJfjaO1A8XqzmFuZdcAGCLIMokX7qCXr+9X/RdOUH8M2dT2LtQ6j9fUTP\nPgfvnHYa33cl2fUvYWkaLddch3/JEnwLF1HctROAlutuqKoKJvp8VaHStSAIgktiAYRXn0LwhBMp\n7NxBsFwBrRYia05F/ocvIDc2HtVKZCXdJpUS6pGPnXAwUlT59vZeOkJ+Pr58LmLF7z6j6nx7ey8n\nN0b5wKLW17X9u3b3k9MNbl7aQXvQx/37BtmbKWBicXG7bc3zNrfgbR7PLisZJoppophw375Bbju+\nA1mcnH95rDBLKv2Zwuv1cued3yMYDKLrOp/85C2cccbZnHTSCu69927WrDmd6667kXvuuZt7772b\nv/qrv36zmzyLWbxpcNRF88LtNPjrSakZVres4I+9z1QplZx8paSSZgG1sxcckubokErjlrHhwugR\nIZWqbV0p93VGzSIKIiFP8LCtfAfS3bw6tp3OuoWsaDoBoCroOSD7qwisiVAM1c33uXTR2yd97tjf\njq1SaZwsmlgBTrd0glKAgOSnZBxeptLECoGOWkiZYKOcSPhEvOGK6m8FArLfrQqXUtLUl5/YpY0k\nFpZrkYNxu5zTnxQ3U6maHHKUSvoUYfVOX6+VtzSLtyZ2796tL1u27NPAo4AE/Hj37t3bly1b9ony\n598DlgM/WbZsmQVsB2550xo8iz95WJbJ6P77UPJ9WKbGoCdEpPV8wk1rsCwDNd+H7GtE9o4/3NSV\nJKIcQleTJA+uRZD8BKLHUUi+hlYaRvJEiLSciYCAkh9ALQwAArI3RqTlYsKNaxBECcsyMbQshpbB\nsiw33DoYW0as4+2ASLhxFQANcy/FF5qPP7wA2Wc/vPVHFlJI7UL21iH7GwnVr6zatzcrEHcm+HMg\nlMCuCpZ46EESj65l3t99Hu+c9kOuY1kWwz+9m+xLL9b8PLd5k/taDAQo7tlN6sknELweSgcOEFp9\nCpFTT0ft78PT3IyntY2Bb36D0Z/dh5HNUti5HSkSxchmGPv1A3iamlB6uvF3dqKOjKAODBBauYq6\n8y4AwNPcTOtHr6fpyg+QfuZpko89SuLhh0j+8THqzr8Qtb+Pws4dhFauovWGm5Hr7Hv5/fsG2Z8p\ncLskIQsCLR/5KFIwSPLRtQz96IfuPnjbO2i84v32d9XX03bbJzBzOQLH2Sq1xve+j4FvfZPA0mW8\ntnQV4lCSc9rqpzx+XdkiW+IZ3jOvGa80PnbTTJOXhtOkVZ33zG8ifPLqQ54Lpw1HEyVjaqXSkcLT\ngwlM4GC+xOaxDGua69zPhosKhgV70vkZhf5PhGFZJBQNC7hrTz8LwwH2ZmxLeVGvrRQHyGv22EwU\n7HY92DPK+18nqXUkMDsSPMoYHBzgc5/7DCeeuILXXnuV5ctP4NJL38uPf/x9kskkX/ziV5g7dx53\n3PFlBgb68fn8fP7zX2DJkuNIp1N86UtfYHR0lJNOWlH1dOfRRx/mgQd+jqbpnHDCiXzuc/+IVCG5\nEwSBYNmnqus6hqG7nfy5557hW9/6AQDvfvdlfOYzH5sllWbxpsK0THoyB1kYnf+mDNKcSXVzsIlb\nTroWgJHCGGCHFDtwVEvJUoqp4Kh6jk6m0jhhMXKEcpUqK7slS9VKpYgnhFfyHjZBtmnYzqR6z6K3\n1zyfATnAYH54mlL1KhHv1MGCjv3tWGYqVVbzm0QqmQaSIOGXfTNWKqm6imrYYdyV8E1UKumT7W8A\nYW+Y/uwAlmVR0EvU+eoIe0JIgkRKyVAo2f02ZyXKbdRRDQ2v5KGoOYOVUvm7ahNXoqtUmoJUcu1v\ns0qlPyXs3r37YeDhCe99r+L1S8DSY92uWfx5Ih/fQinbheStQ/ZE0ZRRkn1ryYy8iKnlsSwdyRtj\nzvEfR5R8ZEZeItVvZx8hSGAZNM3/AIG6pQiCSD6xlVj72xDL1522pTdh6AUkT7gqQwlAEESbEPLW\nTWwW0ZbqbERBlFyCyUGoYSWhhmoiaRYzw/BP70JPJmn/zN+MBw8fJvR0muTjjyL4fBjpFAe/fgeN\nl1+BWVKwdM22rEkS2vAw6vAQ/gULabj0PWTWr7NLyC9YSGjlKrSRETzNTYRXr0GQPWSef5ZSTzd1\n511A8IQT6P6Xf2bs179EkD2Ifj+tH70OOVZNusy/9hq6f3w3oz+/D8HrZd4//hMD3/k2mReeA1FE\nisXo+JvPIUgy+W2vEjrxpEnjHykYouHd77HL1j//LMlH1rJz+y6GOhZyzsmraf/Ep2xFEra1aV+m\nQMkwyWkGMZ+IIIo0XfkB6s6/gPjvf4eRyVB3/oWEVq6qOsaRU9ZUfW941cm0//VnSbfPZ23XGF5R\n4OzW2JTj7Q2jabbEszT7vZxbJp92pnL8vmeUVFkFfXJjhHnh1x8+frgo6gbd2SLHx0KT2j1OKh1e\nYZ2ZYqyksjWepcnvIa3qPNIX58T6MP6yDW6sZI/7s5rBWEmjOeCdbnOTkNN0LKDeK5NSdXal89R7\nZZKqjmJMTSoVdHtsdlZLjK5skY1jGY6PhTihvraT4mjjL4pUGv3lz8lu3PC61u2RRIwaJzZy6mk0\nf+jqadft7+/jK1/539x+eye33no9jz/+CN/5zo94/vlnuOeeu2hpaeW445Zxxx3fYNOmDfzbv/0L\nd999P3fd9UNWrjyZm266jRdffJ6HHvodAN3dXTzxxON897s/RpZl/uM/vsZjj63l3e+uli8ahsEt\nt1xHf/9BrrzyQ5xYligmkwmamuxQw8bGRpLJxOs6JrP4y8BTB5/HK3k4p31igaAjh5eHXuGenb/g\n06tuZXnjsZ/L1LL/yDVCip1A5qQyDankKJXMo6tUGsofGVJJMzQCcoCiXiQ1wf7WGmjCxHIDpWcK\nh3SbG679RDEg+7GwUAyFgDyx8s24/W0q+GU/Xsn7FrK/6ciihCD4SZSSM9pethyyHfJUl1ierFQq\nV3+bqFTyhNEtg5yWRzM1gnIAURCJ+aKklDSF8uCqJI63p6AX8Ep15Mv9yFEsOX116kylQ5FKf1FD\niVnM4i8euppCyfdjmSpYJv7oEpe40UpxtNIogbplWJZOeugZBEGmdenNyJ4IsajA/m2/Jx/fjMff\nhCiHUXJdJPseIdy0hlT/E4hyCG+gFV1JEmxYQTC2DICG+ZdTN+fCKpJIEOUqldMsji7U0RGGf/zf\n+DsX03TlBwAo7t1L9pWNNF52OVIohDo0RPrZZwBIPfUE9ZdMVhzXgp7JkNu0ASkURjj5FHY9+QxR\nRaHlo9eDACP3/pSR++6Zcv3C9m2kXnweI19ACodp/6vP4GmcXCG2+cMfqfq/9ZrrGPzBd7FUlear\nr5lEKAG0X3YpQ089S6nrAI2XXY63tY2Wj3yUvv/432AYtF5/k5uTFFkzfW6Q6PNRf8nbiV1wEU9t\n3Ml+yc+5y9pdQglsosIhS7KaQcw3/vDG09hE242HJyANr1zFb/YOAKCaFjndIOKpfe/OlImj54aS\nnNFSx3BB5b69gwgCdEYCHMgW6ckVjymp9OJwiicGEty8rIMl0epxU8mwxyh53UAxTHzSzEjMjKoz\nXFRYEg26RJVqmMiiUGVve3owgQW8vaOReEnjsf44TwwkeM98W2k+WhofHx7IFg6bVMqodvtPrA8z\nL+xnWzLHhXMa+Nb2XjcvqhYcUqnOK3NVZxvf2t7Dgz2jdEYDrzvb6Y1gdiR4DDBnTjuLF9vVdxYt\n6uTUU09HEAQ6O5cwODjI0NAg//ZvXwdgzZrTyGTS5PM5tmzZzFe/ar9/9tnnEonYN81Nm15m9+6d\n3Hrr9QAoSon6+skXQEmSuPvu+8lms/zTP/0dBw7so7OzugqQ/SN668p3Z/Hm45HuJwjI/qNKKnVl\negFmPCE/0qhV/UoS7Mtj5YTaIZimUyo51dQU/egolQQEJEE8gkoljYjXHgg5pFJJV1ANlYgvQlEr\nVlnkZoKEksIjypMIEwcOkVTUS5NIJdM00Uwdnzj9TTnqjRxT+5tatuRZllXD/mYgizIeBEq6MiP5\nc1axrWuTlEryxEylqe1vMF4JMFAuKVTnq6M700uuZK+vezI4Q4uCViTmq6OgOfa3slJpCjWUdEil\nkmN/m1UqzWIWf+4wjRK5+BYKyW1lu1kFBJFw42o70DrxGmDhjy7B42/B0LJEW89F9tgKU48vTOP8\ny2iYdymCIGKZBsN77yKf2EohvRuwaFr4fvyRRZPaIAhCTdXRLF4/LMsi9cTjFPfuIXj8CYRWrXKr\nejnh1Q6Ug730ffMbGOk0xb17KHUdoHDi8fT/9kE7WNowaLnmWlJPP2GvIAjEf/trImtOm1SxDOyQ\n6VLXAUr791PYs4v8a6+CYaDJHh6+6mMk5y7nI/MXUXfe+QiyjG/efNShIaRwGNHrxVQULE3D09SE\np6mZ5JN/5JFEke6Fy/hMk7cmoVQL4dNOp767Cy0RJ3bRJTWXESQJ/8c+xY59PSxaY5eEDx6/nMYr\n3o8gSYRXrqq53nQQZJlSpA4KCgfyKh3R8fFAb278IWJOe+MKnN5ckZ2p8QeE8ZI2NalU/r6sZvDS\ncIpNYxlM4OalHTT6PPyfV7vpyZU49w23auZIlK1tBzKFKlLJtCxUc9zJk1Q02oIzs37+oXeU15I5\nTmmMcPmCFjbHM6w9OMaKhoibjZRQNLaMZWnxezmxPoxpWWwYS7NuJMU75jbiEUXipfEx8oFskTNa\nps4+roV0mcSLemVWNERY0RBxFUrTKZXyZVIpKEu0BLxcMKeBJwcSPN4X570LWqZc72jhL4pUav7Q\n1YdUFU257hsoyefxjA+4RVF0/xdFEcPQkeXDOw2WZfHud1/GJz7x6RktH4lEOOWUU1m37iU6O5dQ\nX9/A2NgYTU1NjI2N1SSkZvHmIKNm8YgeAvKxY/8PBcVQqkqGHw0M5oYAKB4ijyZRShKUg/jlI5sV\nMFOl0rj9beo8ILf621FRKpUIyH6ivijDhZEZe7cTpSQ/2fFzrjn+g5MyfFRDI+INU++rcxVYjq0s\n6o1gmAaGZWCYhksyHArJUop639TS6qDH7+7PRDjVzrzTKJXAtsAdSCcwTINn+1+iN9vH9cs/fFj2\nyYcOPEZBL3LV0vcdclnF0PCJXnyyr4pUMi0T0zKRBMkmnbBQDPWQfTQzFak0QalUq/pb5XqOTTNY\nvmbU++o4YJnu9oXA+H2r4CqUHPtbOVPJdDKVJpBKEzKVRgqj/HTHL7jm+A/QHm6btb/NYhZ/AbAs\nk9TAE+TGNmKZGiDgj3Tijy5GlAJYpkp2ZD25MTunxuNvQZSDlDL7KGX2IUoBoq1nT9quY1UTRInG\nBVcytPsHWEaJurYLahJKszjyMEtFhu76EblNGwHsv/eNfx45/Qzabv04gihS3L+P/m9+A7NYpOlD\nH6Z0YD+5TRvp37MbT3MzlmmSeuYpoueeR+aF55HqYjS85zJG77+X0V/8jNabbkH0jN9j9GyGwe9/\n1w2QtgDf3HlEzzmX3xAmGbEn5t53XeoqeJS5C/DMX0TIU3ss0vS+K8m+eoCCYmAct3DGx0EQBJqv\nOvT8cH3B5CU5QmumyOom+77XeNnlM/6eWsiVc3H2ZwucN2d8PtabGx8fZbXaD3YOhVfGMmway9Dk\n9zCQt8cSqxsjbI5niZdUFkYmK8UBsqpBzCtT0A0e6YsDtsVqSTSIZVlEPBK9ueLryg96vXCIru5c\n9bhxIumSVGdOKvUV7G29Es+yM5WnWN5Wf378O3an8pjAOW0xRMFWMB1fF+alkRSDBYX54QCjJZVI\nuU92ZezjkkkVeXVDH2detBjPFP114r5VVm/zigJCjf2rRKGCVAK4cE49ryayrBtJc1pz3YyPw5HC\nXxSp9FbFqlWrefzxR7jxxlt55ZWN1NXVEQqFOfnk8fdfeukFsll7IrNmzencfvvn+PCHr6G+voFM\nJk2hUKCtbTy0N5lMIssykUgERSmxYcN6PvrRGwA499wLWLv2Ia677kbWrn2I88pBcrN4c2GYBne8\n/E066xZw24rr3+zmAHabNFN3bV9HA5ZlMZAfBpg2jyalpPnXdf8Hv+TjXQsv4dyOM4+Y7caZwFda\njCTX+jO+79pM7G/m0QzqtkmltmAzQ/lhMmqWOt+hZf874rvZl+piZ3zPZFLJVPGKXgKyn4H8ECW9\nVEUq5cvWN9XUCMyAVNIMjZyWnzZEPCBNTSo5Cq/p7G8AUV8UC4uUkuEPXY9TLJNDtex0taAYKo/3\nPo1hGlze+U78hyByFUPBK3mp80Y5kO52STaHaJRF2e0/JaN0SFLJCdkOHSpTybG/ybWVSo5iLSjb\nT+6cCnBZIwmihOgbP8aFcpZSYYL9bSo1lGt/KxOr+9M9dGV62JPaXyaVZqu/zWIWf+4oJLeTHXkJ\nyRMl3Hoe4aZTkORqFWq46VSKqV0gSgSitoU9M/w86aFniXW8DVGa/vrq8TfStOgq1EI/0dZjqX/4\n80Bu6xb0dIrIKacihQ+dp6L095Fdv47MupfQE3ECS5fRfPU1FPfuobhrF5aho42Okn15PVIkSvSs\ns21CSVFou+3jRM84y1Y4PflHvKUcgUveRX7bawx+7zv0/6dNPDW87R3ELryY7Esvkn15vb2taBT/\ngoX4OxeTfu5Z9ESc4Ekr4axz+LHcQECWqfN56M4WEbCJJmnpcrfd3915kEa/l48dP3fKfcsjAgYZ\nTafBf2TvTXHFvlduiWdZ3fTGLZeWZdvQwA7H1k0LWbRJmt58Jal0eGNw3bT4Q+8o60fT7rYBjosG\nWdMUtUmlKUKtS7qBYposCPhpDfh4bihJo8/DO+faqi9BEJgfDrA9mSOl6tT7js3937GI9eVK6Kbp\nVjlzLIIeUUAzrRnnKhU1g6SisyDsp9nvZeNYhqV1QUaKqqscAtwMqRb/+Ji0I2SPlfrzCnOCPtKq\nzsJIgKhHZmsiy0hJZf/mAba9MkC0I8parciF7fWcWVYwbSkroj51wnyiXtm1G9ZVKMcEQcAriYdQ\nKtmfhcqkkiyKfHBRK7/uHuHwamweGcySSm8B3Hzzx7jjji9zww1X4/P5+cIX/hWAm266jS996Qtc\ne+1VrFixktbWNsC20N122yf57Gc/jWWZSJLM3/7tP1SRSvH4GF/96r9gmiamaXLxxW/nnHPskt7X\nXnsDX/zi7fzhD7+jtXUOX/nKHcd+p2cxCYNlkiBefOtkXDlZK9rrKCk/U6TVjKuYmK5yVl92AN3U\nyZk6D+x9kM0jr/G3az55RNrgTNy98vhNw1UqVdjfnMl1WslMqdxxyKSjVf2tKdBIS5kYGi6MzohU\ncjKRKiuYgU0ampaJV/JQ75ajz1SRSk5/VA1tRgo6p6pbvW9q+a9j1arMiHIwlRVrIpyw7peHNrnb\nyar5GZNKe5L7XLK0LzfIktj0T8ZVQyXmqyNWJrOyWo6Yr85Vssmi5BJTJV2BQzwgypTKSiXv9Eql\n8eMxBalUtJVKzrk5vv44nuh9lox8ECFgZ+f5RB+KqYyTSY79TbOfqDl91StOtL9VW0Cd46Xq1deF\n2UylWczirQ/L1EkO/BF/eAHB2PJDr1BGdnQ9AK3H3YDsq61sFwSRYP0JVe/VtZ1HtPWcSeHZUyEQ\nXUwgunjG7fpTgmVZZJ5/FjEQILRqNaJn5hPxnKYTkCQksbYiRBsbZeA73wLDYOS+ewivPoWWa65D\njtql6sd+8yu8ra3ELrzY3t6rWxn41jfBshC8Xurf+S7ES99Hj2ay7JIFbv6RUShw8GtfJfXE46Sf\newZL02xC6fQzAXvSW3/J210nR3jNafgXdVLqOgCSROyCCxFEkTkf/ySJhx9CGx1FHR0h/9qrts1N\nEGi84v00XHoZ21MFSvsHUTWdpKoT88qsaojwzFAStTw71k2TrGaQ04oUdMNVZ0yEQ8Bk1CP/MNSx\nOe3LFMioepWy5PWgZJgY5SJMmmnRly+xMBKgpBuMFlVCskReN1w100zxQNcQryZytAW8fGTxHFTT\nZLCgsLQu5JINY6Xa4/pU+f2oR+aCOfUUdIOzW2NVVeAWhP1sT+boyRWPGanknFfdsujLK67KyiGV\nWgNe+vLKjCvA9ZWJtnkhP5fOb+aSjkaiHom79gywL1NANUy8kkhatbdXV3GuXVKpUGJRKYAFNPk9\nzA352ZrIciBTJBW3t78lkyMjW+xJFVxSaVcqT1Yz6MkVWdEQqalUAvCL4owylYLy+LmZHw7wNyct\nmNExONKYHQkeZcyZ08499/zC/f8LX/hSzc/uuOMbk9atq4vxn//5XzW3e8kl7+CSS94x6f0HHvg9\nALFYjLvuur/munV1Me6887sz3odZHBv0ZA4C40oXsNUMP9v9az6w5DIaAw3HvE3OxFY7ikqlwdyw\n+7qWcsXBUGEEgKuXvZ/1g5vYn+4iXkzSzNRVwmaKWkoNSZicJ+NMri0s0mqGBv/kAbZ2lKq/maZJ\nyVAIyH5XbTRcGGFp/aEH4Y4qpjSBVHL6mlfyuOXok0rKDcCOeiOuCmWmJJmTN+WQVLUwnVKppNe2\nYk1EtEwqPdM/XjLYVlU1zaid2+K73NcHs/0zIpV8ktcl8dJKxiaVyr8NSZBdNdF05KgD55yEJwV1\nl5VKuhPUXVu5FfFUK5UcMm1Z/RJCniD50EHEoL1Om28uPcX9FUol+69u2UpE59z65In2N7G8XHkw\nV95Xpx9pxqz9bRaz+FNBauAJcqMvkxvdQPPia/BHOskMP092dD3BuuVEmk/HE6hWsir5PtTCAIG6\npVMSStNhpoTSWwUl3cAniTOy9JilIlo8jq/DVs3o6RT937qT4LJlk6I2Mi8+z/BP7gJADIeJXXQJ\nje99X82qaKaiUNy7ByObxVx5Mv935wCXdDRwhpLG0jQCx1UXM4k/9CAYBtGzzqHU20Nu4wZK3V20\n3XgLY79+gNKB/SCKBI9fjqellbFfPwBA260fI7x6DaLPx3d29NKfV7j95EWEy2oJKRik4//7W3r/\n/csY6TQtN90KJ5+KaVlVIcYOBEGg6UMfpu/rd5QzlOz+4mlqpvX6m9zl9FSK4v69eBqb8C+077vO\nxP2qzjbagz6CssSOpDNuMav+WsD+TIEVDZPHfophopXzdTJHIIeoErppuYSFBWxNZDmvbWa/iZym\nM1xUWTwhYNqxtTnk0f5MgYWRAAfzJSxgeSzExrEMOX3m+6IaJtuTOZr9Xj6xfJ5LBnWE7HGXaVl4\nRGFKpVKq/H7EKxOUJTdbqBLzywHdvbkSJzdGOZApUOeVafQfXkD1nnSe1oCXOu/0YwjVMCkZj/QE\nRQAAIABJREFUJqIApgXd2eIkUmlO0Hd4pFLGJn0ci5hDGjl/M5pOk+QlreoI2MfDQbPfi1cU6M8r\njJVDupv8XjrLberKFiFRwBKgCxMQGKkI8x4tE3cOSekQoBMzrryS6OYmgU2sjRZVOsv9yPksNAXB\neqwxSyrNYhZvEXQ7pFJFKPKO+G62jm5jYXQe71hw0TFvk6OSMC3zsDJ1DgcD+SH39XT2t+FytbPF\ndQvRDJWuTA/7Ugc4fv78N9wGpUZujSAISIJUpVTSrPEbe7KUnkQq2aqPo0MqOSqTgBygNWgH8A3P\nMKx7KqWS01aP6CFWVhalSukqpZKjXplpNTvHGjgTpVKhhlLJ6QOHJJXK5I5DzgAzrlJnWRbbx3Yh\nCiKmZdKb7Zt2ecM00C3Dtr+VvzelZFjAOOkoi5JLlk3Xjx04mUchTwhDN7n/B+s57oRWVp5jK1LV\n8rkqaiWw4JF1/Vx+zjjxFS4rlUaLdt5BsHxMJVFidfMKnh9Yj9xiX1MapDn0sH+SUglstZhLXE1Q\nKslTKJUcUkkvk5JHO3NtFrOYxcxgWRbJvkfQSiNYpobkCRNtOx9Ty5EdXY/kjWFoWca6fok/sohi\nejcgkItvIhffhCiHkb1RfKF51LVdQHbEVilFmo9eoY63CnqyRf57dx/vW9DCqc32QxHLsjAsXEuS\nA0vXOfgfX0fp7qLxyg9Q/7Z30P//vonS043S3UXwhJNovvAsALR4nNGf34/o9xM99zyy69eR+P3v\n0FNJWq+70SWWTE1l5L57ya57EatMIoyddQH6ynPp6+ljzl13uuRR84c/ghQOow4PkXnxBbzt7bTe\ndAsIAonf/474g7+1q5IB/sVLKO3fx+gDvyB61tmofQeJnHkW0TPtnKv+fIm+ct5OQtFcUgnA09jI\n/H/+Enoyye66Jn6xtQufKDIv7OeS9gYWTMjkCS5dxvwv/iue5qlDguVYbFJ1NIcAinllmsrEhFPB\nS5lAKoGtFKpFKlUGWh+uZexQSBQVTGBZXZC9mQJb4jMnldYeHGNLPMvfr1xYVcXNIQVOagjz8kia\n/ZkCl3Q0unlKx8dCvDKWOaxMpe5cEcOyCSlvjSpooiDQ6PMQL6k1M5FSZfIjOkWIN0B70I8sCPTm\nSuxM5rhn3yALI4FpbYkTsS2R5f79QywI+/n48nnTLuv0j+OiQXanC3RXhJgr5cpvjT4PXlGYMal0\nMGs/XJszIXfIIZXSqk6T3yaVoh4ZqeI4iYLAnKCP3lyJwYJ9vJr9Hhp8Huq8MnvTBRqLKkqjH1W2\n10sqGpppIgmCS0SNle2UGU0nJEuTrjM+SSChjPf7J/rjbBjNuP2ooBmIAjOudne0MTsSnMUs3iLo\nyTpKpfHJu/M6/iZXRQNb1SBxtEmlqRUew4URBASag00Yln2R3ZPcD1z4htug6rUzZSRRwqggkior\nwdXKVdItAwtHyqxhWibiEXpSW1AdUqlSqTQzUslVKk0gO5xKdV7R6yqLkkrKJZXqfBG8rlJpZjdq\nJ8S83j8NqeSSLzUylaYIpp4Ix/4GEPPVkVLS5NSZkUqD+WGSSopTWlayPb6L3mz/pGXGFVcx93fg\nZCoBbli3QzpKQqX9bQZKJcVua9gTIpctkcsoDPalOVWySVKHuMkoRSxDpmuguuKcU7HPIXoqbX+r\nm1fy/MB6xJB9HiOm/aSxoNt2t0oyzyGVREGcRA5NVOs5++qco0ql2yxmMYs3H/nEFnJjGwAQBBm1\nMEAxvQdBkEGQaF50FZoSJ979K4rp3XhDc2la9CHUfD+5+CvopThqcRi1MEAhtQNDy+Pxt+ALL3xz\nd+wY4PH+OIYFA7ki2e7dyPX1rA828txQkr9ZsaBKRRB/8Lco3V0gisR/8yvSTz+FnkwQWrGS/PZt\njNzzE+aduRrLNBm++8eYxSKtN95C3bnn0fjeK+j7xtfJPPcsAI3vfR+ix0v/f/0/Svv24mlrI7xq\nNerQIHsT9n0o1d+P6PPjaWoi89IL5F97lei556EODYJp0nj5FS451Xj5FXja2hj9+f3UXXARje99\nH33/52vkt2y2VUuCQONl48UpXh4dLzySVHTmT4hk8tTX46mvZ/3Og2XFhsS+TIHhosJnV0y22fjn\nH771xsmwqbQYTUcq7c9MfiAFVNnE0kfY/jZcJt7mhQOIgsDOVJ6hgjKjMGRHeRRXtCpSySHBmv1e\n2oM+DuZLqIbJwXKe0vywn7BHOiz72760TZYsidauvgvQ6PcwVFTJ6cYkdYxrf/NOPd6XRYGOkE2q\n/OKA7TYYyJemVLFNRE7T+W2PPX7tyZXoyRYnEZSVcJQ87UE/YyWNntz4dzn9widJ1Ps8JFR9RgHi\nBzNFJME+9pWoJJVMyyKt6oRLBplUkWhsvI0dIT89uRKvJe1xVpPfiyAIXDCnngd7Rhld0YBYzjya\nF/JxMK8QL2n4JNFV042VNNsaq+o1VV5+ScSwLDdDKq3qWNjkb8znIa8bhGTpmIWlHwqzpNIsZvEW\ngGqoDJbDqisn787rRPHNIpXGSQjd1A850X89GMwNIwsSFpPtWZUYLozSFGjAI8p0hNsIygH2pvYf\nkTZMRWTIFUol+4llBalUmkwqaRPUSaqhHbFKdY66JCD7CXoCRLxhV711KIwrlSa3D2xSoL5sf3tx\nYINLVES9EVcxpB2uUmk6Uqlc/a2WUkmZof2tMkvqvI4z+f2BRw+pVHJIvu1l69tJjctJK1kOpLtR\nyvY2B9979W4Abj/9b1xyt8r+ptokjxPkXhnUXZymHzvIukqlIKN5u92FnF1pURYk91wVtRKYEplC\nNak3sWpcsCLvqj0wH0v1IXgVTMWP5SjDtCKKoWBa44Pzgl6yQ8hF76SBycRcMUeZ5CgY9Yp9n8Us\nZnF0YRoqWnGIUq4HJX8Qf6STaMuZFZ8rpAaeRBA9zFn+KWRvlFK2i9TAE6iFAernvhtvsA1vsA3L\n1NGVOHVt5yOIMnLseIKx4wGwLIPM8Aukh54DTCLNp79lJi1HC93ZIgfKGStDr7zC4NpfgChy4ObP\nUpT87E7lXfVSYc9uEmv/gNXWTt0nPo3y4++j9PYQXH4i7Z/6a8Z++2uSjzzMzn+7g8LIKNrwMKGV\nq4ieYweQS6EQc//2711iKfPcsyBJYBhETjud1ptvRfR4sQyDjY88BYAaijDvH7+At7WV5GOPklj7\nEMlHHgbAO3ce4VNOrdqf6OlnutlHAE0fupqD//5ljEyG6Nnn4G2zFbGKYbI1Pl4hNDGFyiNeUunJ\nlVgcDXDLsrk80R/niYEETw0kuL7t8Mqn14JjMapUSfml6iDmSlIpoWgkStqkIO5clVXo9VVMmwoj\nBfu+1+Tz0Oz3sDOVZ0s8y7sOQSqVDMPNL5pIdDlkUViWWBwN0l9Q+PaOXpKKToPPQ9gjE/bIjE6h\nKqqFfZkCsiCwIDJ1BmajzwvkiZe0yaSSMp6pNB0WhAP05Eoopkm9Tyap6CQUzVWaTURvrkhXtkhH\n0M9LIykKusHKhjCvJnI8M5Tk+gpSqagbfHNbD+e31XNOW72rVIp4JRZFAmwcyzBUUGgP+d1+4ZdE\n6n0ehosqRcOcMnMLbAvgQLZIi987SR3k7Hda1clqBhagpxX27RzhlLPGCdOO8nkfK2mIAtSXLXxn\nNNexayDNnvIQ1ZtS6IxFOIjCaEmtUhXFSxqKYaKaFtEaFeK8okOsWsgibnU6Jzy8oBtvONfrSOKt\n05JZzOIvGAezA+5Er1Lh4mSd1FIqaYbGxpGtnN66+qjY0qCahDgaYd2mZTKYH6I11EJayUyp8Mip\neXJankV1topDFESWxDp5dWw7I/k4Am+M7Jqq+pWtVLJv+s7fsCdETsvXVCqpE47RTErLzxR5bdz+\nBtAabGZ/qhvN0A5ZfStXVipNtL9VVu9q9DfQ6K93+9q8SAc+yYdXPFylkmN/m0GmklYrU2lmSiUn\nU6k12MKy+iX8nulJpc0jr/GjbfeypnUVw4VRBAROaFzGwWw/+9Nd9OcG6Kxb6C4fLyUo52eOK5VE\nLzHfBKVSzaDumSiVcvglP7IoU8zb2y/k7IGjT/KNq4EMFcuQyRaqST1ZlAnKgSpbpIOiYmAk2pDb\nerCKEVRZAskm8SYSeUW9WM6LmtyHJiqVHPtnaYJSaTZTaRazOHrIxbeQGX4eXaku4lHK7McXmocv\n1AFAeug5TD1P3ZwLkcuKSn9kEa1Lb8HQMsje8WtyuHHVlN8nCBJ1becTjC2nlOsh1HjyUdir1wfT\nskip9oT79UBLxBn83n/h71xM43uvQAwEKOzcwdqBDMRsBXDRFyB28SVk1q8jGU9ASztbd+yhY99m\n1IEB1H7bLr3lQzexfajAZz/7eaQdrxFedTKCLNN4+RXkNm0k/do2BK+XyOln0vyRa6oIASkcZu7f\n/QPpZ56i1NONOjRE+JQ1VTlLgiRhnLACxjIYbe342tsBaHj3pcTe9jZyr7xCfutmYm97Z81spkoE\nOjsJnnMeozt2sPA9l7vvb4lnUU2LVQ0RtiayU1qHNpeJp9WNdr86f049r4xleGE4xdtzpTc8maxl\nMZqkVCqrPpr8HsZKGnszBc6YkN1YaX873KDuTWMZ0qrOxe2180tHykqlRr+HloAXnySyNZ7lHXMb\nEQWB/ZkCr4xleN+Clirb2WDFvTulVh9fl1TySKxsjLA5blvdPKLAmnJ1uYhHYqBgoZoWPml6Uimr\n6QwVVZZEg3im6RONZTIuXlKZH/azOZ5laV2QiEeuUCpNf1aPqwvy7FCS89vqCXskHj44xmBBocnv\nRTNNXhnLcHJjFJ8kopsWP9s3RLri/CyKBLiqs42U0seuVJ7hokJrwFc+ZgpZzWB3umCTSuXKb1GP\njDcisnEsQ1e2SHvI7/YPvyRSX25zUtGmJZXGShqqadVUmVUqlfrH7LGzXDIYHcpVLedkVAE0+Dxu\nkL4gCJxUgu6cilrnJdxfwNNq7/dIcZxUEgXb/jhcVKc83g6xqpgmISSK5d9AWtUxLIuiYdJoWpNU\nVG8WZkmlWcziLYCeTC8AAgIWFrqp45W87qQtUUpMekqxcXgL9+76JSICZ8xZc1TaVWV/Owph3YlS\nEtXUmBNqRdGVKYO6nZBuJ0sIYGn9Yl4d286OkT2cGD7pDbWjNKVSSa5QaZRlyoFGm1QqpZmIiWHW\nh1MBzjANkkqapikC2fOqLWl2qny1BpvZl+pipDhGR9iu/DhcGKU50FhlubMsi2yZbJlofxuv+uXB\nI3n40ln/4Fr8JFEqlzR1MpVmSCopKQKy3yVYasGt/lYj0NollcTpSaWIN8wHj7ucueE5hMuh1dPZ\n314b24GFxcbhLUiah/kNc4l4w8yL2BOy3ky/SypZlkVJV7CwMEyjKsg6Osn+5gR1S+NB3TPJVFJz\nbuW3QplU0nUTVbF/+1bcx0//60XE+fZxyBQm96WIN+ySRE6mEkChpKPH25FbezGzMYoeCzEsUtCK\nruJNFiR0y6Co2fY3vzR5cOWQ1VPZ33Q3qHt2KDGLWbwemOVroCjVvl7m4ltI9D6IIHrwhRfgDbTh\nC80DQWSs6xckeh+i7fhbUfL9dmaSp45Iy1lV2xAEoYpQmik8/mY8/uZDL3iEYWoauY0bCJ962qQq\nadsSOX5+YIhrFs9hqV5Aro8heibfKyxdp9TdRXHvXuSGeqJn2Mdk9Bc/p3TgAKUDB8isewkpFKLf\nkjh45U10xIdINTSjz1tIy6qLiF10CaU9toK8xxMktX49sijgnTOH2MVvo0/woFsaOwsaZ58xrgoS\nvV7mfu7v8WfjaO2LEH21HyxJoRANl1427bFwFBpFs7pAuOjxEj3jTKIV33sobL7oMtaddD6iHGQV\nNlmzbiSFCLyto4GtiWxNpZJpWWyOZ/CKAifW2/dajyjynvnN3LtvkP/Z0cdHa4Q5zxSmZZHRdDqC\n1b8B/wRSyamCdVJ9mKcHk+zLFDijpbpfV6qTMpptgwJ4tC/Ogoif5TG7/btTeX7XM8KNSztoCdj9\n58n+OClV55zWWM2MGkep1Ojz4BFFVtSHXXJjUSTA73pGGCtpzAv73SpfYNvCHExSKpWzs8IemZaA\nl9tP7pz0vY56K6vpeEUPj/XH6YwEOK4uNGnZ/RnH+jY9wdBYJmXjisbmsQy/6h7hnNYY50XDxPMK\nIocOf14cDfL5lQup88quym+woLCiIcLLI2n+UCaZrljYytZElrSmc2J9iEafl6Si8e55TYiCwPlz\n6rl33yBP9ydo3Zuhe98Yiy47DrBJGGffwSZeGsrzICf42iEbHaUS2Eq2StJnIobK53JinhJUBHWr\nOq8dSEJERFIMRjPZquWa/HaGk2pak9RZ2XiRpp1xFly0gINDBUgqELT7kNO3OiMB9mXGFZK1lGHe\nSRZQu3+nVI1iWZWX6Mvw66f7+cANa4jUHbpC89HE7EhwFrN4C6CnHBTcHm6jPzeIamh4JW9V9bWs\nlnPVGWAHBcM44XI0UK1UOvKkkmP5mxNqYyg/QrZcHn0ihmuQSsfF7Jvv9iNAKo3b3yYrlRzSwFGk\nRH1RPKJcW6k0Qc0z03BrgGf7X+JXe3/P/zrjc7SGJodcFiYplcbDujvCc9ib3M83N3+fjx7/Ic5u\nHw/BLOpFVwU3KajbycQpEziiICJOGEw5SqWZBo/bAebTy+HHlUr2Pq0b3EjEG+HExmUztr8BXDTP\nthMUy8SKo1SyLIs/9j7DcfWdLIza6raD2X68kperFr6fzT9P0TDPh3WqxfyoHSxZGdatGIqbjVWY\nEGTtl334Jd+4/c1VKsn4nayoQ1R/syyLrJJnbth+8uyQSgD5nIpP9iEnIuSzKpFsA8VgBlUzUVQD\nX0XOQdgTZpjRqmMKNqlk5es4U/wwTw0lKMgKUX89Bb3gklAN/npGimMU9BKqoVZdWxzIwkT7m14+\nPs51SUMSJPpHC8xrCU9afxazmMXUsEydoV0/xNCyhBpXEw2/DbCvt5ZlUUhuI9H7e0TJT8txN+AN\nVE/cQ42rycc3M7znbtSCnQtXP/ediH/iysHko2uJ//bXNCUTk0iX0YJ9bX38lW14fv49QstPpOOz\nn6tS6hR27mDg+9/BzI0rCyxFxdPcTG7jBvydiwmfsobEQw+ix+McvOJaAN559hoe7h0lqZRtvW1z\nKPbZ9xTd48X65y+zpKMNQZJQDJPEK7b9flsiy9mt1fc8T1MzDcs7GR2tnogeLhy1TckwZ5xXMxUG\niioW8IsDQ+Q0nY1jGYaLKqsbIzT6vUQ8Uk2lUk+uRFLRWd0YqSJblsdCzA/72T6WoTCvaVplyHTI\naQamVZ2nBONKpZLpKJXs+1B70EfMK3MgU5h0TBzlT8wrk1J1FMMkrxs8O5TENyrytyv8BCSRB3tH\nSKk6e9N5WgJeVMMkVc6rGS4qzA9PJmVG8gohWcJf3s+TGyNsHMuwJZ5FMUzX4rZuOM0ZzXXuQ+D+\nwvi4K6VMYX+rYX1yECl/ltUMDMvimcEkO5N5/mbFZFJpJnlKgJvfM1rS2F6usncwV+J3D+1j4OQG\nIkHvjPqakw/lkDMD5X3dU27HhtEMpzXX8exgElGA98xrrsqUAjuMPCZJvBrP0vHqIIIF+/vT4LOJ\nwZJhuL+DqEd27WrOsXPsb8VUiYawve2kMv18ZXAaUskviXbgd0lFHc5CpI6WaIDswTFKRQ1/wP4O\nURBoD/rozpVomrBPqUQB2TC5eGkbP328i+JoAc+iAPsGM4imhRCSWVoXYl+mSFc5MHxi/4fJar1K\npVKh/FpQTIoFjbW/eo0rr12N5020w7014sJnMSN88IPvJZWaPJGttYyiKNx22/XccMNHuPbaq/jR\nj77vLvPkk3/k2muv4rzzTmPXrh1Hu9l/VijpJf7lxa/xXP+6I7rd7sxBgnKA9pCtOHHIiMqMnnix\nWvqe0+wbwVi5+tPRQCUJ8Ubtb73ZPj7/3JfYk9znvjeQs0O620OtBGQ/iqFW5b04cLKD2kLjT03b\nw22E5CA7Rva8oXbB1GXb7Uwl++bkKHhkQaLeF6udqVTD/jYV9iT3VSlrhgujWFiMTEGsVWYqAW5Y\nt1NSfk/qAAD7011V61VWR5tkfzPG7W9TwQlhnpgXVQtFvUjJKE2bp+R8nyzKFI0SiqFy785f8tt9\nf6hq40xIJQd+yY8kSC6pFC8l+O3+h/ntPjtzwsksmxdup9OzBMESSfZqbHium9ZgM17Jy8FsP4Zp\n8sSmPgYS4yq0SlLJaZNf9k/OFRIkAq5SaXpSSTEUdFMn7LEHfsUKUqmQU/BJXoSSfdx9xQgY9qBy\nolop4nWeGstV5zBfHty2R1uQRRnPQI62HasoqEUKmj2AaSwr4pz9q2U3nKxUKk9wyvuumTqWKfKV\nn2wgnTu0OmsWs5jFOHLxV9BV226cG9vAtue+xsj+n5GLb2Zk30+J9/wGQfTQvPijkwglgPr2tyPK\nYdRCP7KvkZbjbnRzkd6qMAoFcvumzkK0TJP0s08DkH72GSyzIv9t5w6GX3gBgNG6RuLHnUBh53aS\njz3iLpN+4Xn6vvkNrFKJugsvJn3TJ+k68RSG77mboR//EASBlmuuo+Fdl9L5jTvp/M9vUVxkqyLm\nhfxEPDKKaaIaJgXdwGRcsbHf8iBI9uuhgoKjHerJlWZktTItq+b7umly955+tsQzkz5zlC125uTk\nsdHhIKFoBCQRryjyh4NjDBdVzmyp48qFdt9q8HlcW00lnHad0hStel8QBJe86M3VDs6eCTI1Qrqh\nMk+mOlPJL9n5Q0XDdMkBB479rb1MFqQ13bWfKYbJY31x1o2kXdJhxK3Cpbnnc6iGKtgwLcaKimsb\nA1gYCRDzymxL5HhqwB6fzw/5GSmprvoEYCCv4BUFApLoZuGMt9dAEiAwTfUuR6mU03R3f0dKqqu2\ncWBZFvsyBUKydMjw8IhHwiMK7Erl3fL2AwWFbFZBlQQi04R010JQlqjzygwWFDTTpCtbJCCJWMBP\n9gwwWlJZ1RCZRCgBCIA1nMcSBZaeNQ+PV2KkYjwxWtTIaE7mlkRAEpEFwVUvOYqdxx/Yhpy392W0\nVDF3Gs2x9eWDrmoNYLA4NakkCAJ1XplkSUMrH4e2srVsdKiaJHbUUBOVSqlEgWgsQCjiIxD0kBzN\nExVESl6Bokcg5pFpK1v9esqV/hz7m2GY5DL2e5VqPc000cv7kFJ0t3KgpBlEY37iI3meeGhX1X4e\na8ySSn+m8Hq93Hnn9/jJT37G3Xffz7p1L7Jt22sAdHYu5t///eusWrX6TW7lnx7ipSRjpQSbR149\nYtvMaXnGinEWROe5uSa1ytJPzFVyJtCjR5VUOnL2t57MQfJagUe6n3Tf688NAjZB5FiHJhIfMK5U\nagmOk0qiILKkvpPRQoKxCYTb4WKcyKi+4VVmKlWGEtf7Y+S0/GRlUvl/J4vGsU0937+O3+1f6y7X\nnxvkzs0/4NGe8WPhTPZz5b8TMZFUco7FUJlwO1hW2gyUj6mDbEXO0MQg9JlU75pof3uu/yX+cOCx\nmsu6ld+myVNyEJD9FPUi/bkBLKyKCnW1Cb7pIAgCYU+QnJpndCjrErBdmV40Q6MvN4iFxbxIB6Xi\n+Dnb9GIP3XvizA23M1QYYe36Lu57fA9/3DJOzBW0wrhNsNwmr+hxj4db/U2UxzOVavRhy7LY8Hw3\n3XvH3HMc8lTb3wDyWduKJpfsbfkLYfd7pyKVgnL1U9VCye6rQb9M2C8hGRZy0Y+WHw9Hd0iljJrB\nwqpJ4rmZSpOUSgobnu9GGAlhmSK6YfHyzqOnmJzFLP7cYJk6meEXEEQP7Sd+hsYFVxCMzqWU2Uui\n9/couR780SW0Lr3JzUyaCFH207L4I9TPfRdzjv84/vD8Y7wXh4+hH/+QrZ/7PIM/+B5GrjqjpKgb\nHNz6KnoiAZKENjZKYaf94HP0l/9D3ze+TqGCZDpw+dVIdXWM/eZXpJ99hv5v38nwXf+N6PPT8dm/\nI3L1R3nI38gL574by+tDTyaJnn8BrwRiJBUN0edDCgRIKBohWcInia5iJKcbrhJieSyETxLZlc67\nEzZHkdER9GEBO1KT98XJ4HH+/99bu3j04OSHRkNFlT3pAk8PVo/xVMN0g3mdbbxeqIZJVjPoCPm5\naVk788N+PrSolcsXtLjKj3qfB5PJFq3ubAmfJLKoRnWuBWH7PuVMjCuxK5Xj1fihlVpOzs5EUkkU\nBHyiOG5/q8jO6Sy3pStbTWbldAMRXFIlqxoMlwkEryiwaSzDH/vj+CURkXF71Whx/N7qEA6VSKoa\npjVuG3Pat6oxgmKa9BcUlsdCXDq/CYB1I/ZDR9UwGS2ptrrK5yGtalWT/pyuE5LlaQO4K5VKlflM\nryaqj+1AQSGjGSyOBg6pMhIFgUafxyUQF4T96JaFEvOBKBB6HTmt7UEfWc1gWyKHblmsaYpyYn3Y\nDU8/f059zfX6upOQKv+eVrSyYHEjJXm8/SNFhYymE/FIiIJQHu9JZFUDy7LoG7RJT1E3SfekkATc\nc25ZFk/+fhcvPrmfsWH7N5rTdPrzCjG/Z0p1XZ1XRhVAD9p9cl6zPdYaGaw+5qc0RVkY9rMsNq4a\nKxU1SkWdWINNuDY0h8imS5iJEpYkYnolQoZtnwPcSnCO/W3TCz3c97315LJKFbHqqJTAtr/ly9cn\nUTN52+Un0D4/RteeMXZurZ4DHEvMkkpHGYODA1xzzQf46le/xNVXv59//dd/ZsOG9Xzykzdz9dVX\nsmPHNjKZNLff/jluuOFqPvaxG9m3by8A6XSKz372U1x77VV87WtfqboQPfrow9x22/XceOM1fP3r\nX8Uwqm82giAQDNodWtd1DEN3L1oLFy5i/vyFx+YA/JnBmVT15QaOGBvclx0AYH5kbsUEXi3/HZ8A\nT6wA56hcRgvxo8ZMVymVjDdGKjl5SbuT+xjKD5Mspdg6tp16X4wGf/24dahGHs1QfoRTs96YAAAg\nAElEQVSwJzSp4tX8yNzy58NvqG2KoeKVvFVZRGBnKrkT6opA5nqfrcRJTbDAOefNUaA4x+/pvhd4\nrOcp0op9Q9qTtJ/UVqqdnMl+foqw6bxaTSo1+uuRBclVKvVmbPvDYH7YbTOMh3Q7+1nV3vL/0wUt\ne0UvginS+5jBnm1DPNn7HGu7n6hJMs6k8psDm1Qque3OaXle3DbAc6/2AJOtiIdC2BuGwTAP3L2J\nni6baNVNna5MLwez9nfMj8ylVK6itmJNB7JH5NlH9zA33IFpmTy02SbeK73z+QpSySG6vJLXfc+o\nDOqWps5USiUKbHy+m60b+txz7PTnYn78d57PKXZAumoPmn3FCA1Be7lsvprEjJTXD0wklcpPYYM+\nuWqg7s/Usa3Pvt40+W1SKVUmAmsdb7f62wRiVVN0Nj7fTXD/XCzD/s2s2/HGfoOzmMWfKyzLpJDe\nzfDee+h77f+SGX6B7OgGDC1LuOk0JE+EUMNKlp/517Qt+xh1cy6mdenNtCy+pqZCqRLe4By7Otuf\nQK6ZNjpKfusWEEWyL6+j+0v/TL78sBPgN90j/EjxocsyLR+9DoD0s0/z/7P33tF2nfWd92e309vt\nTbpX1ZJsWZK7cY1tYowpgZAQUqgDgdRhMpO8ybCSIQMvDJNCEiCwwtBCgDcEHIqpxsZdlm1ZvV/d\n3k8v+5Rd3z92OefcIskYAxnuby0v656yy7OfffbzfJ9vKT93kPz3voPS3494xR7AiQE/VWlQefM7\nObXzKp59+DHUw4cIbd7C8J+9h8iOnTy9VES3bAxA+d3/TOKW2yj+4qu4fyrNowvOeMq0bQqa7ht/\nt7JCPCZEKiizPREh3zB8VofHGHnpUBfgeD15NV9t8HfHJ/nLx0762zhZUCnrZhuDxStPcrZU08i0\nMCxKevsztmpcGlNptfFg1t1HZ1BhOBbmXbs2ctUy5pHnR9MqgTMsi2xdoy+8uhxqYyyEAEwtA5Wm\nK3X+ZXSeL48tkK2vZP4szBZZmHWePR6ItZpRcVASV6S/heQmwNUKKuWzVcqaQVSRmr44rnE14DOy\nNMvmjoFOOkNOUpht223MluUMIHBSuoAVse/7upqy8dsHOtgYDTEYCXIyr1Jo6My7jLahaIhUQEaz\n7DagsKKbF5S+wXKmktPOiihwLFdpu9YHMw64srdzpZR9tfJYV7tSUd+AvdbjLmb9CFMKj/XzmHtv\nbU9GuHugE8my2Yjkm3Avr1NH5pHVJnNsy45ujLCERx1brGmUNbMtpS6uyJQNg/0Pn6dS18G2ES2b\n6bE8PaEAizUNy7aZHs+RWXLuzfRCmelKnY+emEY1TLYFmtfSNC1OHZlHd4GapJvkpiUDSAIMDyT8\nbSw/59/etbFtnFXIOouGqS4XVOp2xmlmttlXZVUnEZCRW+4pr/9Pj+ewLJtirtpm1F1rmedrlk2u\n4fRZybDp7otx1yt3EghKPPnQecrFi4fFvBj1s/8U+jHWkw+dZ+z0j7aaKkoi1irU0y07e7npzq0X\n/O7s7Azve9+H+LM/28Lb3/4mHnjgu/zjP36Kxx9/hM9//jP09vaxffsOPvjBv+HgwWd4//v/B5/9\n7Bf5zGc+yZ49+3jrW9/Bk08+zv33fx2AiYlxHnzwAT7+8U8jyzJ//df/i+9//zu8/OXt2nPTNPlP\n/+mNzM5O89rX/ipXXPHCfGfWq+krpOpVCo3iJU2eL1Ye4ygVTFJsOA9Zj/GitTGV2tk43sS0btap\n6KrPWvhxVhtTyf7xgErg+Ac1TA3DMnjFlrsRBdFnedSMOq3rGbqpk63n2ZratGKbXrx7SXthvgUN\ns7Gm/MebUPsG1oJMIuQMaPL1Yht7yrtusUCMolb228+7xqOF81zTt4/zxQkA1JYkLo+JpF6UqRT2\nj6070s1iNU2xUfY9fnTLIF3L0u/6MrUylQzLwLRMX9bUZCqtzQoKSAqBRphGRmRqLEclpWJjk6nl\n/H141Ux+W3lfnDm2gKyIbN3Z659Hrl7wAR8bmwNnZ0jrZeSe5yd/A4f143qZk8+p4I7TzuXPk3PB\nro3xIdLTzjkPjaSoqhrnT6fp0Z1+ZIh1BBLkqhVw/dLbPJU8UMkIYzRsbNv2fxNkQSYgBRAQVjWc\nnzrv3L+mYfn9IbYKU6la0QjEApiacz9IlkynnGCKtZlKy0ElT/4WDSnEWrxGouVODk5MIHc3mUqe\nN9uq/V9w6dhWe/qbhy8rtTBy3Rk4jc+XWMxV6eu8sJfDeq3Xz1PZtsnS6BdoVCYAEESFwtyD/r8T\nfTe1fT4Q6ScQ6f9JH+aLXo/O5xAPHaLXttn2+79LfmaB7De+xuzf/Q3J2++g45dew7lCBV2SsXbu\nJnnr7RQeepDK4UNUT59CUBQGf/cPaBQsJL3OHYMdfHlskX9tKHDLy5Atkz/tkohsc6RshmWzf6m5\naJPuGeSGt7yNY7POgoPHUClqRhsDxWOFVHTTBzHiikwqpXA8X+FkvkJvuJP5agPZlX8NR0OMl2uM\nlqrUDJP7JpZ8Vs2hTInbBjp9xk62sRJgaTXHPpmvcNtAp39sALIgYNg21UtgKj0yn+PZdIm3XDbY\nBoB4+2iVby2vzhaTY29Wk67rWEBfePXncUiSGIqHmVHrmJaNJArUDJP/7/w8nrf4w/N5Xtdi5G3b\nNt+97ziCIPDm37/JP8/UGqCSJ/PxrkdQEokrMh0BmYlyDcu2sUyLr37uIMWb++iLhXwAoqQZLFQb\nhCWRPZ0xpiop5qp1buxLMqXWyNRVvvQvz1Ha5Yw6w5LIggs0tbKHPFBuuXdOXzjIlZ0xRATfh+kl\nvUm+OrHEN6fSPqNqMBL0WUEFzSAiS66kyb4oqNTKVJqranQGFYaiQY7lKsxVGwxFQ+iWxZFsmbgi\ncVlqpdfSarUhGuJUXuX2gQ4f3Kh1O+MO5RIBzNbyQKWFmoYiCmyKh9n/g1H6j84jSyKNK4cJhtqv\nca2qMX42Q/dgjCwOY+zqkS6scpGwalCLykxW6hi23QY6xhUJy4bnnptDvK6HoCQyMJRkfqZI9w19\nLNQ08g2d556c8r9zqFDh9GnHhys5WkRTC7DD8bU8d2KRh79zBtO02H31kL8vSxHpDCjEEiHCEWUF\nqLRaFXIeqORc+84e53ooanMOpaeriIJAd0hhoaYhC4480jBMn1FVq+oEE56nqeUbkns16zIhU+EA\nkiQSS4S46c5tPPydMzzy3TO84vV7LsiAezFqnan0E6iBgUG2bt2GKIps3ryFa6+9HkEQ2LJlG/Pz\n8xw9epiXvexeAK655jpKpSKqWuHw4UPcfffLAbjppluIx52Jz8GDT3PmzCne/naHqXTw4NPMzc2u\n2K8kSXz2s1/kvvu+zalTJxgbG13xmfV6ftXqmTNTmfuxbNObgIblEIrHVHInsZqpI+D8KCyXv7WC\nBS+Wr1K7p9ILA5U8nxlJkNg/9wwH5g8yEO3jhv6rAZosj2Umx0u1DDZ2m0m3V565sMcA+lGrYWir\nMzUECcu2sGyrRf7WZCrlVjCVnP4Rd9PINNMZnHhA0dnCGLZtM1Zw5FXVFgCparjytzUSzLxthFtS\n1foiPdTNOieyp4EmSDHbIoHztucZbtfb2Gd623urVUAKIJrOA1bTDJ9RlV7F+ynvgqKrga1PPjTK\nEw82f4PCUgjDMhgrTfivlRplBMkZQD4f+Rs4rB1Zd76jtqStnC2cd0y6RYX+aK8vfwuGFXoGnP6T\nmXIe1hv6g2zdkKRcb4J9ql5d4fMUfnYzG0b3YdimD7jMLFVpaBYbx/chnOzCXLYIMT3ugEqGYfrX\nJBqIYNs2NVUjGnePvaKhaCH/vgcIaw5QU14GKsU8+Zuytvwt1DKoiJY7EYJOP/KZSm4fXh1UdYYI\nHqDsSf28/gAQK3b5UboH1tlK6/VzXrXiWeZP/xP18gQAxYXHaFQmCMW30L/znQxe8W5iXU5aa6L3\nJiT5pwPCGpbtJwm90NLz+QuypQ3L4nszWfbLcaRYnJ7bbsG+62V8451/Sm73VRQf+SHPfuCDNNxN\nBG68CUEQSN12O5gmlqrS8/pfJzg4RNUwicgSV3bEua4nwXU9CfrDAQxRwh5pJmcdy5Up66afgjXj\nPhMmXWaLByp5YEtHyGMqeRP4JlMprkjsSkWRBYFD2TKmZbNYc9g7kiiwuzOGDXz6zCxfOr+AYVm8\neqQHRRR4NlNC1U0/latqWCtkbK2g0ol88/nvgS0eoHMxUOlcUeV7M1myDZ2vjC+2eTj5TJtVPG28\nao1j98qTEfWvwTIB2NoRRbdsh5Vj2/z7xBJ5zSAxWUZWnXSx1nOslBrUVJ1qRaOqahQ1N8J+lfSr\nkLS6/A2cSPqaabFY0yhkazQMC0sUCAuCDwpkGzq5hk5/JIggCLxqpId37tqIIor0uue0WNVI13UC\nosDWRISGa9rdWh7bajVQ7te3DvBrW5tA8L6uBFviYU4VVH4w6zz3B6NBUi77pei2hef/5PW5Qq7K\nlz/1DNmldiml9/5ctUHVMBmIBNjjspGOuQy5k3mVmmmxXQnQqF6a/+kt/R388d7NDMfC9IYCCJaN\nGXbaTW786KASONdmbjzPiefmkG0wGiYnD6+cM509vohl2ezd0YsoOCBm2VvAzdWJSqJ/7yaWMZUA\nzKCIEg0QkiWGtzpjmkDV+f7p6TzzM0UGN3WQ35XiRFQgIArcE42RmKyQz6g03LHS4pyzuOYxfFoX\n45IBR57YMxB3+u4qnlutlfeYSp3tTKWeFmBWX6pSVTUf+E24+8guqVguGluram3pbx5TyfPfmnF/\ny7paEt927uln45ZOpsfzjqzwJ1w/V0ylm+7celFW0VrV0xP/kVMclJZIVFEU/b9FUcQ0DWT5+V0G\n27Z5+ctfybve9fuX9Pl4PM7VV1/LU0/tZ8uWbc9rX+vVXq2Sn5nyHFd2X/6Ct1lvAZU8bxsPnNAs\nB+yQRYlcC6hk27bPdgDHV2lzcuRHPobJ0jSmbbFl2TbamErmpT2o1ioPPLtx4BqemHsagF/a+nJf\nchb2mUrt1ONFV97VH1kZbZx0QaUXzlTS/Hj31moaFVst8jfZZ0h5sfJeeWbW3rY0U6NuNnzz8XP5\n82RqOYru8aptoNKF5W/VZfI3aJp1P7N4CIDr+q7ihzOPM1eZ55q+vQCUXUP37nAXc+oCdaNB1JXn\nXQpTSREVH0SotwwMl6qrgEprMJVs26ZRN7Bth6kjySJhFwhp3U5Zr0DQvOgxrVaxQBTJBeZqVQ0S\nTkLeRHEKC5tNiY2IguiDSuGwQm+/C0ouWLAJrtweJz8eZrzlPFuBv6AUoFE3EKoBAoEImqn5/eKR\nQ/N01KZJpB2z/W//2zFe9torCARldN1kbsppG8OwqLiss5gSRddMDMNioCdGtZKjWmn4fko1uUHY\nCBJw/y4vGyw25W8hfvDNk2h1k3t/9co2UEmxbUxAiFgEqlECgoUJJIJxFFGm5MojV/dUclfrXB8T\n7/evFVSKlzsY3N7N0fNZ9p9c5FU3b/qJr46t13r9LJRlauSmv4Wpl0mf/yKJ/lsoLTyGFEjSvelX\nEN3f7s7hV5AavAvheUp8f5z15bEFJis13r17hPAaviJGIU/17Bni117flq7mlVWvs/SvX6T02KMk\nb7udvje9FYDs/d+gtP8JBt7xLkKbNvupWrVgiMQttyIGAkyUa+RMGH/Fr3DZZVs517KKb293zMbj\nN76EM4/vp3Ogj+Qv3AE4wEo8ICOJgi9n+vrkEgs1jZLuSJ9s2+aJxQIC8EsjvXzkxJTPpJl2J6iq\nYVI1TB9AWSl/M32ZUlyRCcsOsHQsX+FwtoRh2/4k+rqeJJploZk2ArCrI8pwLMyCbvD0XJ7vTKex\nwI8fzzZ0NrS0uXcMG6JBplXH9DsRkH0D6/5IkNlqo002tbzKusGXxxaRBMdwfKJS54mFAre6Pjae\nVKbzAqBSK1PJK8+0ei2mEjig0iNTGSYrNQqawfF8hV5BJHC+hFzWye3u5JH5nH+9WtkeubRKUXNM\nmONrMJVM28awLOqmhSQIKGITVHouW2a8XKMn28AKOK9bqk7CBWLOl6rYaxx/rzuh12MydUVgMBRg\nMBLkeL7CQrXhywGhyVS6ECjnlSQK/Na2Af7p9IzP2ukJBVgIOG3pAVZ+8ps7Bxw/myGbVpmZyNPV\nkqYaFEUUUfAll8aCSlQKEhRFDmVL7O2KczDjLOjNPTjBgW0V7rjXuYcMw2RpvszgxpULfZJrSA1Q\nVzWUso6WdNvpEoGp1uoIyA6AJED1TI6HxkuIosCrfm0v3/7KMY49O8Oe6zYguaCIbducPDKPJAns\n3N1P1+gcS3XNb2u5ZqDoNqr709PKVNIKzn3cs6WDUQGSksjI1i4OPDKOsaRCh8zR887YsrGni4pa\nI1DW+J0bt3HuQDPpN7NYZmikw++TatlNgjabgKzXRj19cabO50gvlBne0rVmO/hMJRdU6htKcM3N\nIwxv7+bM9AI2jvxtfrpIt5tW552bB26BY4vQsYqn0kAkyFi5Rt4Fmfu6mgsTgiBw5707OPz0dFsf\n+knVOlPpZ6D27r2KBx5w0iuee+5Zkskk0WiMffuar+/f/wTlstPZrrnmeh5++EHyeQcBL5WKLCy0\nG3Pl83nKZecmaTTqPPPMAUZGNv2Ezuj/3mpl61wKU+l45hQfOfTJC6aAeaBSSA75jJEmU0kjKCl0\nhjrI1ZurgZ50zPPCSa8ywX8+9c+nvswnj/3zitd/nEwlD1S6a/h2AqLC9tQWdnft8t/3TY6XSYcy\nVYeF1RPpXrHNpvxtZWrKpZZt2678bW1PGdMyMN3zlwSJlGtE7ckVvfJAGs+AuWFqbSDRYjXdZvKu\nuiCIZVu+vO1CRt0B0UlN88oDlc65Hk03DlwLwKzaylRqgkrOMTWvadNTaW1gOyA1QaWG1gIqrcZU\nckGlVKjdqFvXTLxF07KbahGWmuCYB2jUTBVE86LHtFpFlSiy4TL9GiYxJcoVXTswbBPLttgYd8xu\nW5lK3X0OqGSVnEdhIGTRmwojSM2+rho1GlbTqLtUcK6TaMlopub3C2yRxUXnN9cSDWYm8nz9i4dJ\n56p84ouHMN1BSqv8LapEfelbNBogEgugVjQ/+a0UdfqXUHWp/MtWyHoi3ciCRF+kh8nRLFNjWUzT\nourL32RE3RmI2D0u4Fn3zL0jhOUwtmtcsBpTaS1PJclqTohiaoruRIh927pZzFWZuAR6+Hqt13/k\nsi2TWmkU225njpSX9mPqZUIJR4JVnH8YgK6R1/iAkleiHPqpgq+LtQZl3eTh+dVDLmpjY0y+770s\n/NMnKD726Ir3q6dOMvmXf0HpsUdBFCk++gil/U9QOrCf7NfuQ19cZOZv/je10XMsTDg+eY1QhMRt\nvwA4Uh6AM6Ua8XvuJbfven/bdZelaQTDfOveX+fgHa9CEAQs26ZuWkSWJWV5rAWPWZSp68y5xsld\noQCD0RBLNY2JSs03xQWHreQxeDqXyd/KuulvzwOavPSzB1wJnQcqBSWROwe7uGdjNy/b2O3LoG7Z\n4Dxzn3Olbzf0Os9Fb59e5RqOCfE+19fGM/32mErefi7EVLpvfBHVMLlnQze/uW2QmCzx/dmszzTK\nLgPPVqtEQEYS2uPYve+v5YcDsDXlPFPOl2p8ezqNJMDgpIpgQ2SpRhyB5zIlHzzz5D3gJHOVNIO4\nIiOtcj94kep106Jumv7fAJvjrvS6XCOfqWK6SV31XI2ILCEJTQBnNaaVBzTVukPYokBXQKY/4rw2\n32LcrVsWM2qDZFAmtAYAu7xCssSbLxuiM6iwLRFBFARf3jefrzoJX+719JhIubRrabGsf3jG1H6b\nncpy4tlZbhvooKyb/OPJKc6XasRrJkrVILPQbN8jT8/w9S8c9v2r1qr0QoVAqXnOVvniab/LSxAE\nAhXn2M3JEvWqzg23b2ZwOMXlewdQKxrnTjTZzKVCnUK2yvDWLkJhhZ6QQsO0fH8uuW4iFJvjVQ8o\nNHST+bPO+HP4ij4apkVIEunsiRKNByiNOQvwGcNgaFMHY5qGYtn0PJtBqOjMzzTbYmmhjGlYZJec\ntvdAJVlrArg+qOQy29PzFx7jZJdUgiGZcETx2+X6WzfT3x+nLxwgLolIhs38dMFPjfPObakFVKqq\nDbK5GrZlu55KTVAJwFB1Grk6+bpBpthk14eiATbvHfD3/5OsdVDpZ6De9rbf5syZU7z5zW/gE5/4\nKO95z18C8Na3voMjRw7xW7/1eh599If09Tn0ys2bt/COd/wO/+W//D5vfvMbePe7f49Mpn2Cl81m\n+MM/fCdvfvMbePvb38R1193AzTffCsAjj/yQ1772Xk6cOMYf//G7+aM/ujTG03qtZCpdrJ6cf4bT\n+XMX/GzNbGUquSsnLZ5KihSgK9SBbhk+I8eblHpG1elLSD87njnFgfmDq75X1sqUtDI1o91EsmG0\nPlhfOKgkINAT7uLPb/xvvGvPW9oG1WvJ35r+MytR96gSQRTEFyR/0y0DG9vff2t5TA3DNn3pjyw2\nQaXCCqaSJ39rBZUckMgDSR6cftQ9nyiaqaFbBg2z4U/u1zTq1mttLCVogko2NolAnA3xQRKBOLOV\nBf8znkyyK9zhHlMLqHQpnkpiAMlyjl1voYSvBmTm6nniSmwFIORRjKFJLw4rzXPZ0eEwKDXqCJKJ\nLCgrTNMvVq3yN6PuSPAu62gyUze694pn1B0KywRDMsmOMHJdBBtqeg3yNQJ2s1866W+eTLAFVDIl\nNFMjrzp/25ZIOe/8e2HjabZd0UNmscL37j9FtmUQYhhWm1F3zQWVwrEA0VgQtdKAmrtaHi1giSZm\n2WmLsto+0EsFk7z3Jf8Pt/XditZwgLtysU61YaDIIoosYWsmGjYF0QWvyp0ItkRAUtq8mLw+sFSo\n8acfe4Kz03kk9xp4wNlyppIlmEi2hKKbXL/LkaceGX1hAPd6rdfPeuXnHiB9/ovkZ77nv2ZoJUpL\nTyLKUbo3/TI9234LKZAkNXgnodiPziJ+scozfX5ysUiuoWNWKky+/y+Z/J//g/l/+jgzf/VBzFIJ\nJIncd+7HNpx7X1uYZ/YfPsyZT3ycYz0bSN1zLyPvfT9iOMzi5z/H4mc/jRgO0/2612M1Gkx/6AOM\nf+vbANiiiNbhSFQ8wEazbM4U1DbDZQ88KesGhm37gEjdtLBhRWKTNyEru8+nvLv4MejGfW+MOglt\nTy46ix5bXUlcuq75rByfqSS3GnWbfow5wLZkhKgsUXIBsdXiyFtrR1fcl5QNR0NscUGQbAsTyLRt\nCg2djqDC5a4XzklXAuelovVfRP5WaOicKVYZiYW4qS9FVJF49UgPpm3z1JIzgc7VHfZOQFr7ueoA\nH0obU2mxphFXJKIX8P3piQSIyRKniyoFzeCGzgS50RzRWADBho1FA9OGH84549RWplI2rVLSjRXJ\nb161RqrXXfDAq46gTFJxfJVyWRXTZSrVsjVqFa3N2NkDi1orKUlguYlnQNRogk+tZt0HMyWqhslN\nG9Zmp6xWyYDMu3eP8JvbHPZyKugcz8lzac4eX1whf8tlXFBpFZZQvEXRolQ01HKDOwY7efP2QSKy\nhA0oY861zmdVn128OOuMUVuBvNUqvVBuA5V0d5xWKtT4wieeYuISnutqpUHsdJ4tOZ13/fYNvOEd\n17P3+o0A7LluA6IocPjpaX9x3DO07nEZ471uPz9TdNqhNxrEWGousnpMtsxiBcMFm6qun3dIEhEE\ngeEtXRgFDcGwsJNBdt+9lZJuskGSES2bhdkS6fkykaizr8xCmWy64kvO1Iprfl1v3mte3+ztj2Nj\nMzWWW1Puq5YblIt1+oeSqy4avHH7IG/bMYQkCczPFP172wOXFudKSJJABZtvn0nz9188RObAAnNz\nZV822xsKUDqbJ/PUAvlDab7y+AR//qmnOXwug1rX+ehXj/GeTx7g2NiLlwy+Vv1cyd9+GjUwMMjn\nP/9l/+/3vOe9q773wQ/+zYrvJpMpPvzhj6263bvuupu77rp7xetf+co3AUilUnzmM19c9bu3334H\nt99+xyWfw3o1q9VTKVPPUTNqK0xyW8sDk8ot8qyZ8hyKpPiAgJcUFZKCK+LbNUsnFojR6QIC2Xqe\nZDDhT0qH40OMlybJrMIaWV5fOfcNio0SNwxc0/a6bdv+MWRqOZ/RAaC1ABCG9fzpsK1VN+uE5CCi\nINIZWhkt2mQqtcvfVD9+faX3hCiIJEPxVZlKVb3GeGmSK7p2XvC4PJDlQkwN0zJ9XxlZlIkqEWRR\nprAGU8nzutEszWceXd65gyOZE1R0lZAUZFtqM4fTx6nqtba2rawlf9NrRJf5b/S1SAKH3es2FBvg\nVO6s3zcrWoWwHPK/+3w9lYItnkq63ly9WS5/0y5gqK61rHyWCh5TqXnfXNG1g2OZkwiKBqKJxPNf\nYYkqUSTDi5aVSQZTbE1uRkDAxvbbp143CARlRJdWnOyOUMzXCDQilKdssieW6I0n8dbTqvU6lssc\nCkpBFvLuPmyJmt5gdM711bJF6hWNAFCPlNn7C/3k01Wyc2W6AUEU6OiKUC7WybmMrpgSZVF1+kck\n4jCVrHkbPS8CFnqkTCNUQSolCSkipVUGmx2hVNsgvZivodYNIkEZ07Qw6gYNYLpUIy6GiZY7EW3n\nfCItIKXH1Dv47AzDZY3DB+e462WOiaVpL5O/uUylSipNIt+PWWxw+aZOJFHg2FiW19za9DZZr/X6\nv6kMrUgl4yzOVDLPEogMEkpsJTf1DWxLp2PoZYhSkFBsmKEr/vNP+WhXlp7NoGWyVA3BN4D+/kyG\nuw4/TmNiHESRxtQkYijE4O/+AZWjRyj+8EFKB54iNDLC9P/+IFa1yulXvJ5jG7aze8cQvYkIfW95\nG/Mf/xgIAgO/83vE9uwj0N/Pwuc+TWPbDn//nuTH+z/AQ3M56qZFMiBT1AwfPCe7IkwAACAASURB\nVPEisz1Wk/f6clCpyVRy3l9u/LzBBZdOF5xn67XdSc6Xaiy5Zr6yIPgMpbAsIgrO8VVN02W8OJND\nSRDY1xXnCRecuhioJAoC1/Qk+MFsjj1dcd+PJ+f68xiGyYnRDBYOqJUKKgxFgoyVqlR0g5JmoIiC\n/73aGubJJ/IOYLCvK0GjbpBdqrBrY4qgJHK2qGJYNgXNYCQWWvX7rdURVBgtVdFMCwvne9sSF/b9\nEgSB4ViIkwWVhCIxkNWYtmHfDcM8/dg40kSJnhv6eC5T4tZ+53kVjQeoVXWWclXMnviqyW+wElRq\nBYoEQWBzPMzhXJmFSh0h6rwnaSZjZ9LEI7LPVFqNaZVbKCPXDIyo076BukkyIBOSRBZchpZp2zy+\nUEAWBF66qZdG6fmlasliE1iIKzKCbWOEJEqFGpVuZ8wZk2Usy/a9eDw2dWt5wJOomUgNy2fU7EhF\neffuEf79gTMUFmokO8IU8zWK+RodXVE/+cwDcNaq9GKZQNnZr2DZ1ArO9qfGcpQKdZ55dIKRrV0X\nZFcuzBQJlnSuTkRRAjIdXS3ei4kQG7d0MjmapapqRGPBFpmYMxbscYGVjMvU2tSfIH26yWzyPJWq\nqobkMom8NEaPwTaytYtTR+YJ1U0aMYWzrtx1VyrKMeDk4Tksy2brrh7OnVhiab7MUsuiX6Xs+IIZ\nqgNMWZJArVDnyydGOT9bZEywmZwtcN1kgQ2bVs5jPEZY/4bEivcqNZ3jZ9PEwgq9gwnmp4t0iRJ3\nRGMkVZNj5zKMFmpUghJZ04a6zqaBOBPzZQ4+OsVUX5Z6QuHB8xWq0xWkiEykO8xdw918+6lJPvLV\noySiAYqqxq6RDi5bRfL4Ytc6qLRe6+VWuprlvtH7+c2dv7Kqvw402TpdoQ6y9Twz5Xm2d6w+garq\nNT+xrdQS6/6xI5+iM9TBH1/rMMRajbpXyt90AqJCl2uqm6vl2JIcoexuLxGI0x3qJL3MqHumPMen\nTv2AX936WhKBOKZlkq3nfcPpVgmVYRl+LPpyUKlVtvfjYCqFpLUHNWE56H+utVTDY3WsPrDpCCWZ\nLs6tSOv42vlv88TcAf7k2j9gJLFxzf02k71WYyq5dFvLbEl/kxAEgWQgscJTybtu8VXkbzs7t3Mm\nP0rdbLA5OeIDT1Wj2saAqxo1LNtqY+rYtk1Vq9Id72zbX0SJEFdilPWKz8QZjPVzKneW2coC21Kb\nqegqMSVK0G3f1mt6KUwlSZSQTKdfmi2gUqFRRDd1FNcHbKmaxrahb5UI7HamkusN5TKVFFcKCSAo\nDRdUen5+SuD0D8mVv8lGgI5QiogSZnNyhEV1iX7X6L1e1dtowZI7oAypSbSc00aS1Wx7fTSKMN5N\ncHecoBSgmG9ZUa/WmVgoQC90xsLYRaePNEIqdWq89FWX86VPPYOEgBALEAhIGLrJ6dw5Nqc2ElUi\n1FRnghJxmUoA1YyNjY0eLlGPlAlXk3RGlBXyt2abNu+ZYr5GtW4Qjyj+6w1A0ySq8TzxYi9ydWVq\nXFAKYNs2s6eWEBBQM2pT/ub2T30ZU6mcXCJW6KWarRIOymwbSnJ2ukCpqpFYZWV4vdbrP2KZehlB\nDCJKAUoLj4Ntkui7lXLmGXLT30IQRGxLJxgbIdq170U7jqWaxmfPzvIrm/vYsmyiXx8fI/utb9L1\nilcR2rz6mEQ9dpTZj/49DVnBfst/ZWByFGPrdo7mKmx+7hDdqRSb3/+/MIoFpFgcKRolMDRE8dGH\nyd3/dSxdx6pW6XvTWzEHtkNBZUatsyURIX7NddhvewdCKERsj9MGsauuZttVV/Ps+XlwDYU9dkZF\nd3x0kgHZj3y/PBVj/1LBZ1F58qCKYWC1pJ8t94DyAImSu+2Cu4iRXAYq2TgT0x1Jp+3Srn9LR1BB\ndMcOoiAQk5248qpu0RFsnyZd3Z3gicUCXUGlTYq1Vt3a30FHQGFPZ5ylhRICTabSkw+d5+B4Fq7u\n9v179nbFma02OJ6vUNQcBo8Hoi1nKjXqOt/96nEmL08hAJd3RNn//VFOH1vg9W+7lm2JCCfyFc6V\nVGyg8wLJb151uueba+hoLtvlQn5KXm1PRjhZULl3Yw+j3zqHIMC2y3sZO5tmfrrInX0p/nVyie9N\nZqhVdTZf1k0pX2NJbQDxVZPfoAkWVN2kNA9kmpsqMD2eY2RnJ4dzZdIyhF3DYkmzOH86TeLGflAd\nRtNq12puuoiiNkElinUEQaA/EmSyXEMzLU4VVHINnet7EiSCCml+9Kh2wT02MyRRV/U2+VupUMN0\n+3291hwvHTs4w5GnZ5gbjkBvmKhms2EkxexkAUM3kRWJerFB8cgSvf0xtu3qZf8Px8ilVcKRgA8+\nFfK1FcfjlW3bpBfKpCSJgiQiaAbVcgPLsvwFq8xShYWZIgMXACoWZpzxcP+G5Krvd/fGmBzNkkur\ny0Al535sNbKOyRKbNqU4fLAZQuXd57WqhtRw2i7tArRev9i0vYs77t3BsbDNoYLK0+miAwZv6OS4\n0GRs9Q8lqBTrjJ/L+obWiVSIUqFOo25QU3X0QpXCTIXPVxwPJkGAgCyR1k2++cBZ3vX26/05R6ZQ\n40sPnmNuvkQdi1C6Qmy6wBPH5omEZM7NFBmfL/k2EDdu6sDC5qP/doSTc8sWxRsGUWBIkrjz+mH+\nbS5D5Uye9KIKi1AGlGSAjr09SHWDnSMd7N3WzT989Sj5coNX3jTCa27Zgij+5OXV6/K39Vovtw4u\nHeFo5gSn8+fW/Iw3udqUGAYu7KvUmsDlMZUapkZJK7cxXDzJWahF/qZZOqZlYtomAVf+Bs0EOJ+9\nE4jQHemioqtt0rX7Ru/nufnjHMucBBxWlWcWvRy0qbXIzZanyLUZdf8YQKXl8q3W8plKy+Rvql5F\nQPDfX16pcBLdMtrOy7ItjqSPAzBWnLzgcflMJfkCTCXbaEt/A8fPqaSVfbAJ8GVSPlOpRf4WD8TZ\nmtoMwNbkZp85pOpV30/JO/blvlK6pWPa1qqsuF6XreQzlaIO1XquMo9lO/498UDMl/c1jFam0sU9\nlQAU22kbU3eeiF2hTmzsNjBzQV1k05nr0J5cSRFvrMpUcq7nhtiA740lyBqCZCL+COsdQTuMaDuP\nNMlQ/AS6awL3cL3yWiTRMXCt13SC4eb2Gy5jqSMzBPmwe77udgQJs+r8O1JJOZ5KLYOz83N5arrT\nht2JCAHbRlBsTFmjqtcIxYNM4SbXhCQkWcS2wbbgns33IAiC76kUjihEY247N2wMpQHBOvWw89uR\nUCQqVb0t0cer1UClaEjxAbA6NrahUOx0fpO65zY5+2xjKgU4fzqNrjp9uFGsI7pDBA909th6Hqhk\nKBpqUKVaatCo6+ze0okNnBi/uBx3vdbrP0IZepm5kx9l7uRHKC0+QSV7CDnYSXLgdrpHXgu2iSAq\ndG58Jb3b3ojwPGW7z6eO5MoUNMNPfPKqdn6Umb/9K9TDh5j5+79FW+axCVCfmGDuEx9DEEWCbqpw\nSK1w2ZMPAbDYNUDXva9EDIUI9PUjRd0Y7M4ukjffgp5OYxYK9Lz+DSRvu90HcGbUFs+Tm24mfvU1\nLK9Wj55KC/soIkvs7mjK2q/ocPZZW8ZUsmwHUPGYOhF5uadSM7EN8NPEPKAiFZCJusDMSCxESJaI\nKxLTlTp10/KBFK9iikRRM2hYDjPGbGEIDUSC3D7QwR2D7Qs8a5UiilzVncDQDL7xL4dRGibZuk4+\nq3Ly0BxG2DkuT363pzOOABxMl1ANk4Qio4gisiD46U9ezU4WmFoqs2AYbIqHickSE6POM3l+puiD\nZ08tOuPNrmBzjNPKHm6tjhaz7llXkngpoNJ1PUn+aPcIwniRxbkSQyMdRKIBPwyjp24xGAlyslxF\ni8n09Mfp7I3SkJ2J71pMJQ8M8kzLPfDg0IEpnts/RXfdQgLKAxFEFxzq64gwP1Mk6Eqa1kqum58u\noLjPOywbLV1zPx/ABj57Zob7Ts4i4ICDL7RyGRWpamAGJdSq3jTqViTfTwnamUqjJ5coF+so7oLe\nZQMJ4gnnua1WnHtv7IwTZrPnuo109jj3Uy6ttkneLsRUUisaNVWnty/OG7cPsq/mjFOqFa3Nn+nY\nwVls2+bR753lC594qs0OARyWjigK/jVfXl29Uf/YWo8p2dHOVAIHAE10R7FNCyoakt1MPaupOqJm\nOQCty1QKSc59JAgCO/cMMJTwmPkWW2Ih0vka9ViAOWzOYvHh75zmq6MZDmPxjbNLHMPiQKVOERu1\n3GAuUyF7No+h6mwajPML+wb529+/hT9/87WIAhzKqvzp3z3KB/7lIH/1pef47598ikPnMixWNIrA\nYycX+dAXnuOxo/N87+lpxuaagBLAUxN5jmNzcq7EQFeEgCISA4YR2DuQ4DJFJmZafOLrJ5AiMqmd\nHfTu6iCxq4OYCANbU4iKiKVbfPpbpwgGJO68aoi9W7uZWVL56H3HyBTWBhJfrFoHldZrvdzyTJcv\nBJ7opvOel7R2OneWz5/8Mh94+sMrZEutgFPJTeDymC2tqV91s+HEtgtiG1NJ882BFbrCzgDGA5W8\nRK+YEqPHNWD2JviTpWnO5J3o9gV1yXmvRaq03DepFcC4EKj0QphKjsTuIqCStLb8zfNOWq06XFPo\nVgncWHHSvx4TpakV3zl4Js3f/uthdMO8CFPJnTxbZjP9zX0tFUxgY/vXYi6jkil712UlUymqRNjX\nsxsBgSu6d/hyPlWv+obdXi3vS14y3Grtt71jCyEp6PfJoZgDKs1W5qkZdSzbIqbEfHlfvc1Tybmm\nygXkbwCK5T7sLQFswU8JbDXrnissES11UUvb6Hr74FdbxVMp4qa/bYxvICSFEJFc+ZuBaF8cVLJt\nm+9/7YQfUysbzesnmQopJYVl2fz7D2f59iNZqnUDXTOxLJtwuHm+OU3HxiZe7PVfk21ngNIRSmFr\nzqA3XE2iiDLFQvN+OTedB8EZKSTCQUKAHAMExxtrcrFMGjiCRVEUqNvOd+PGIB//3Byabvqg0tf2\nTzDbMgjQglUE0abhgkpRG0zL9pPdWqsVVMpnq1i2TSQk+/5PDcA2FArds9QiJToKvczPFv0EPgBZ\nUHjmsXEAKtjYukWt4uzLB5VcANWTv1miSV1xjr9UqHOlm4hy/Keg5V+v9XoxSs0ewrZ0LEOlMPcg\nYJHsvx1BEAkntzOw6/cYvPwPiHVf/aICSgCjRec54UVsgwMozX74r7EaDRK33IpVqTDz4b+mNnYe\nPZelMTtD4YcPMfsPf4utaQz89rsIudYJqQ2DJCadkIfiwAYSt96+6n47730lSk8vna98NR133wNA\nWXN+C2bVizM3WiPqm+wjk7gisbvTmQQnAzJDLqOoapptnwVXjraG/C0iS4hC85g8yZMHVAiCwEZ3\n2yPx5gTWM77tDLaDJnFFwvPzbuRrfPYjT7SxbV+2ods37b7UqpQaWJaNWHEYKo8/PIZtg+UCISn3\nnMxSg6GAwqzr6eOxrSKy6DO4vMpnVGo9zvnsjEdYmi/7gMTibInLks445Fyp6p6ns6/5mSKf+vDj\nHHpq5djIY3U9MJXhkWengSYoU1U1Djw61tYWXtmWzYlHxnn8B6OEIwo3/oLDlut2AYb0YoWXDjnj\nWHUgSk9/jM7uKGbQXaRTLix/866pBzJ5CyZLY3lGZAUjqpANO+/tucJhS5fnym3H31qmabE4WyLl\nLhIqDZNixmmnfV1xuoIKE2oDPSQRWaoRtV8462NqLOd49QgCZd3w2XoRWSKfWR1UqqoakViAO291\n/CF39iWJxp3zqZScPuItdPX0x+jscYGbjEp2qQkIlYt1jDU8ueanHbZ072CczfEww3GnDxRyNXIZ\nld7BOF29UcbOpHnw/lOcODRHqVBnYbY55tZ1k8xihe7+GPIa/lud3ctApVyNeDLkfz4oiSQVGdu2\nKU2U+G8ff5JDlsXCgUXKB5doeDLYqua0myRiGCb1dI2nnpziTz7+JAs5Z9seEGo2TI49PMX7Pvcs\nR8t1ZrEpAvFIgMGuCCJguP9VDItz2Bw4ucgjk3lsCwLxABNzZR4+PMe3908y2B3l1sv7MIBCw2R0\npsipyQKGm/x4DXBDLEh3MoSHISUiAa7d0csb776M//7Ga+hKugu8QAqoZqsYhs01XTH6EPjtX93D\npqEkEgKvuGGYsCyhyCLhsEJkIMpOS0Rx5X+peIilQo2//PijVL7yBa545AuMnp7i1GSe4hrM9hez\n1uVv6/VzUbZtO2lp0tqTZw/wuRB44nnfDMUGUESF49nT/nuz5Xl2dG7z/2415/bkap7Rtm7pvnSo\nZtR91kbAPT7N0n2wIyAGfA+irGvIXdGaRr89YScVLV3NMhzfwPcmf+jvd1519Mitk/8VTKU2UKnJ\nMPBS0QJSwIlOvwRQqaKp3Dd6Py/f9FJ6Ik3GimdEfSFQKbxG+psHKq1VqZCXAFemP+oMJjyWEsBU\naWbFd545vcjx8RxzmSqNwCV4Ktkt8jf3taZZd5FUMMmXfnCWUSWL2NEElVqZSlElwvbUFvZ0X0Es\nEPXNtE9OL6C7g+hkIE5RK7eBjtAukVxe9256KS8dvs1nMfVFe5EEibHipN/vYkrUB83aEv1MDVmQ\n/HNaq2Szed+IpsTm5AjPLBxmbinDPtfWaX6uhIDzRyFb9c0XYXWm0rbUFvb1XMktQzcgCAJBIYwZ\nqCOINlgXfzTVqjrnT6dRyw0u3zeIqC1bbSbO+HyJsutDNL1UZsBd4Qu1gEpz+RphIAyYikZUjqJZ\nErYlEpGioLlMpWoS02h6GQBk8ipe10xKIQpUEdy/Vb3K0oLzm6IBpZrGgpVGIYWytINa3aSoar5R\n9+GJPGZ/wneT0gNOO9WijnEj+TrbEJifK7F9azsbzAOVZFn0B9uRkEwp35S/YSogwMLwKTafvoEn\nfjBK+OYmqJQ7r1PI1dBjAXKVOjEEMrPO74wHJnm/f6LpgkqSQUNyXisVamzZ0UMyFuD4eA7Ltn1J\nyXqt13/Esm2LSuYQghigf8fbKS0+iW0bRDqu8D+jhJ6fee+PWjXD9MGk+VoD3bIQVZW5f/wolgsW\nxa+9HqW7h+zX7mP6A+9bsY3e3/gtYlddw7SbLta1cxeDp55DsCzKW7YjKquPj5TuHjZ94EO+1MOy\nbZ8VlNecyXFsDVBAt5yUq6Ao0rAsKrqJZlo0TIuYIrMhGmJfV5yN0RABUUAShBWeSuCwkHz5m9T+\nvBIFgbgs++ypomYQlSU/eh5gV0eU0VLVZ+/0hgOMuUyclUyl5t/1bA27YVIp1QmGfvSIbm/xQHal\nTWPzJUY2JjH6YxQBPVtDj4b4+pcOoyYUuNwZ8zVBJckHVrzKZavUesNg2yTyDabyTQb84lyJRECm\nL6Sw6CfcudH1LrPlqYfH6O6LsXFzk3W1NRFhbyrKkYIKfe623et+8IkJjj83hySKXHvLprZjOXVk\nnpOH5+nujXHP63YTd6VovW5iVmahws1XOR59RkSmuy+ObYHpJumtZdQddK+hxz4LSSKWZVF2xxET\no1k2XNHNmAI1wZGY7dzRy9HOSdJncnB1N9uSkRX2COmFMoZhMZyKMAXETBd40U2GY2H+655NfO2+\nY4xnKgSKGoej02wcvjR22lo1PZZDkl1pp3svRGUJURB8k+5INEC9qvvHW1U1Uh0RruyM0RHcwMZo\niFMJd3HaHYsU3cWjRDKMKAkEQzLZtIrsApWDG5PMTRcp5es+6NRaU+edcf/wFuf8kqmw+3oWy7Lp\n6Y/T0xfn4e+c4dyJJQJBGa1hsDBT9PtOer6MZdkMDK0ufQOIp0JYopP4pzUMqqrGxs1OP1/KVzk5\nkUefr5CfKbOYqxNUJDYmgmTyVUrFBl97bJw33LXdHzMFDZuJA4uYNQPX2ZL3ffZZ/vr3bqIvHEAr\nNigcy2K5UrmEINBrQ39vDHkwTkc0yEzGUTIsYFMRoWjZfOUp57UeIBhUuOH2DTx2ZJ4HD85w694B\ncjWdCFAFIkGJnlSEV9+yCaPU4NkHRpmtaGSw2butm5dc0cdV27tRWoDwP/71q3n/555BqxlcZpkM\nlU6S2LuXk5NVOrojhCMBwi7YfPfVG5gYnyevGSgRBQWBa28aYXEowv5cicv64wxHslx++mHC7rzw\n3dXH2PB7f0qgY+1r8WLVOlNpvX4u6quj3+TP93/wgsCIl+R1QaaS5a2WBLi6dw99kV4u73JMKD1m\nkVczlTkUUUYURF/+1hpB77FP6kbdl3YpYjP9zU+ckgIEpQCpYJLFqjMY8NOjAtE2ptKCusiR9HFG\n4hvpCCfXYCq1gzatzKBWppKXiuazbmoGU2MXlrU8Ofc0BxYO8sOZx9pe9/a5loQNWtPfmsdj2zZV\no3ZBUKkj7IBKXgKcbdscSZ9AQsEqdbBUy6wAaTygoVLXL9lTyWcquVIxT7LlgZEFVcMWTHdbARRR\noWFqLel1USca1vXrirog0IGzMzx91gG+eiIOQLicqdQElVbK3yRRantdEWV2d+9iTl3gVO4sgCN/\nc9u+Vf6mWTrKBfyU/H1YLaCSJbE5OUxHeiOT37T9eNbKYnN1rXXVDZpMJUkWqdd0dM0gLId4x5Vv\n9JlVkh1CcAE+rAuDXNBczSu7xpmNevsqXNiKceR8s99PLjRXcT1QybZt5jJVLDeKON8zQziqIJky\nGDKSFfB9moJqvM1PCaDe0EglFHd/Tr+w3QQaVa8y6foRREMylcA0FdOZzNXzTh8oVTVn1U0UMIF6\nCz9aDzp91lQ0fvFXd6LEA3Qg8Ni3Tq9IHikV6ygBia6+GGqp4aziBWV/sFkHsEQkQUJNZCmGnQQU\nfdLpE8FajFOPZ5EVkawi4NlWLs06/zIt0wXmTQQERPdcLclEl512LxUcP4orN3dRrur+ua/Xev1H\nrXrpPKZeJNqxGyXUTdfIq+ne9MsvOiNptRov17BxzKItG+bUBguf+zRmsUD3a19H/NrrAeh8xavo\nf9s7SN35UuI33Ejippvpe9Nb2fT/fojUnS8FoOqCNVFFYvidv0OnLJJRwmsmGgFtE3LVMGnlzMxV\nGyu/4JYnfRuKuuwK3aDU8FJSnQn167f085K+FIIgtDFyVKOF4aqbVM3V5W8A8YBEWTexbJuiZqzw\n6Lm2O8FfXL2VrlCAek1vk9p0LfMaao1v11xgfi252KWWByoNdzsgixGRuOnOrZgRJ4EsPZ7n1OF5\naqpONFPDo0p55sRhWaJuWpjuNco3dE5j0EgFCBQ1ls7nmRrLIYoCfYMJivkatapGj968bqJrxDwz\nkUeUBERJ4IGvn/QZreAweORnFpFVHQQBuWYydSaDrpmcdePgTx2d99OyvPLMjl/66l0+oASOtEkJ\nSCwtlAnJEpJuYUVlItEAnT1RDFeKvpbfk89M8uVvks/6AmcBq3Iu7xwvEJUlZEnk6huHCeQbvLwI\n5kyZz33kSe7/1yNUKw2qqsbjDzg2FzuGktyzoZud7kKJ5/Oj6yZLY3n6EImHFY4dnGlbULpY1apa\n2/2kNQzmp4t+v5ztC1HQ9JbktypKQKK7P4Zl2eiaia4ZGLpFOBZAFASGY2EEQWgyldzjKRXqxBJB\nJNlJQOvsjlLK11icKyErIsPuIpR3bq1lWU6aWSQWoKvXAU0TqTAGNs+dWsTApqcvztadPcghGSWi\n8PLXXwk4wKVX3jhwLT8lw7T4+68c5aBl8Z2FEu/5PwcoYJPqijCxUOK9n3mGf/7eGcaOptFydaLx\nAA3dpFhpcBkCiaDMA89O88lvnqBUbKBjM3ZgHrNmEB6MEugMEgpI1DSTv/jUM/yfr50g9+wSVsMk\nIIv8yu1bkIMSUeDoksM8+vcnxtFdPlFHT5TfeOllbEdAAKJAHwJ/+Lo9vOIlm/iNX9yOZdt87L5j\nHBvLMdIX4xoE7upJ8BdvuZartveAO6/oHUywa6SDd736Cq7f1Ye1MMf4n/0J+QectNDeVJg/+Y2r\nuevGYa6sHGJH5hB9D36O7eVj3HmvM58Mu56UtaqGZNvYkogdEImFZK6/bTM9ssXGiTNs++xHuebo\nt4hYDbpe+zo67rkXY2mR2b/5EGblwol/L0atM5XW6+ei5ioLlLUKNaNO3PW7ydcLhOWQP9n2fI70\nC6Sc6b6vjsybLv81AB6d2c/J7BnfpBkcYGpeXWRDfJBCveAbdRdbUuCqRo1kMEHdqNPtAkM+U8nU\n/O15Pkv9kV5O589RNxp+THxMiWKHnR/F708+xA+mHgHg7k13cGDpGY4unqJm1NuSulaASi0eRrlG\nAdMykUTJZ7TElAi5ep7qmSDfGjvKa994Ff1rrEYczjgMoROZ09jbmytDFwJFvJJFGUmQ2o7Pk29d\nmKnkHEvRlb/NqQtk6znijRFyFQkxkWeqNMOursv875RdWqha07GDl8ZU8sBGD2hqMpVK/rboagJP\nQZfhtVZ6XcT9W9MbYDvU2d5wN6OFcT8xrtkOa8vfVqsb+6/hSPo4jx89QmdpmOi2yOryN1Mj0OKn\nVFQ1oiEZeZmppdjCHAoTYSDSR7AeBQRGTy7SMxjFyjfbL5dpP36PqdTVE2VpvkypUPcHMP4+zCB4\nY/lLAJUaLkCkljVM0/JjeLVAjYAWRjJkDp9rgqSTi2V2dDlgTsg16i5UNGoNg/CGJHY5TaZ3nCvS\n+xANGduQsU3FNykXbNH3q1AiInrV8XJIxGQKBsi602a6a46o6lUm5stEQzIj/VHOJ07DgkNhr3qA\nWFWnVtFQghLUTOqWjcfv0oLNgf7G4S6Gr2/w7IPn6ajqVEoNf+Bu2zblYp14MkSyI8zibIkAEAkp\nlCaLBIIysmViGxYROUxZrzCTyNBppMg8Y9E7uJ1kdhBTt7n7Nbv4+++doQrYAizMlhC2CBi2iWVb\n2NhE5YgDugGWaGAHnImeNzHZvaWTx4/Nc3wsy+aB5ycRWa/1+lmqSvY5UEeN5gAAIABJREFUAGLd\nV/9U9m81GpiqipxI+BKmqxNBninWOfvMQUYOHyK8cxcdL3u5/x1BEEjcdDOJm25ec7seaBNRJARZ\npj8Z40S+4ka7X9zM2fO36QjK5BsGM2rdl1otL0/6NhwLMVauoRomxUZ7lHprhWWJsrt91VidqbRc\n/lZVNcIImLZNpq5j2PYK5osgCMiC8zv1pX96mv7rBsF9BHk+Ql61JoxJrqRO01aXDl1qeaDShq4I\nJ1WVLddtoHcgQWU+jVI3mTxbwLRsZEXkNb95FZ86NkUpFUAv1KGvCaTVDYtThQr3TSzBgMMk6ks3\nmJhXMXSLweEUgxuTLM6VWJwtIc2r0Ksg6hZLkwU6EiGyaZUNmzrYurOHR757licfOs89v7wbcPx5\nlqaK7I33cDQmEChqnJ1cRJJEtIaJEnBAndnJfBvDKZ9REUWBREf7GE8QBHr648xNFXjku2eQZAM9\noWDZNrFEECMqI5o2s6fSPP3YODfcvoUdu/v974dWgEpNNm48EaRcapCeL5MIQm6r4vep7Vf08fRj\nExx/dpbjz84iCDA9nuffPnMQWREpFers3NPPlh29bBMFjs6qzODIx7v74sxO5DENiy2X9RBLBHns\n++d4/KFRrr5p+KLXemG2yNf+5RA33bWNPdc6ASqzUwUsy2ZTV5RpLOpxBcF20ttM06KQrdLdH/MX\nvOo13ffgiUTa+6cX6KGWGxiGiVpuMDjcNNDu7IkyP1OkmK/RP5Qg1eWMNVcDldLuYtvOPf3+eL2g\nGZzARnM9mxYPTKI9OU62rmEDh758hL0hmYXZkiPpFAUWvdSzIeeZb5gWZ6cL1BoGhmlx6FyGExN5\nUkEZvWGQKzfIAvlj82RcmaUAjGxKUUnIZE/nGOyOMpdR2YTIoGVz2ob9JxYdyRigqTrR/5+99wyT\n9CzvfH9vrlzd1Tn3JE2WRmmUkECATDIgBIZdYxwwGIPBXq93fXzsa/dc6/Wu7bWPsbENNsFaJ6QF\nk0wGSQSjPBrNaDS5Z6ZzqOrqyuHN58Mbqqq7RzNgkq/T96fu6uo3h/v5P/8wmSK5I00mZ/DLL9nF\nf/7QY+TLTfL+ZGMipvB//fQNjPTGuevQCI89v8zhmMJIbxwTgYf+6TmcisG/e81eNEUkhcAYLn0I\nJDIxen3W1rU7erluRw+1546Rcl3ecO/LOPd8kZmpPAszBUYnM6Ec8B0/dW14Hq1SkYUP/CnWWp7c\nP32C2P4DaMMjjPYleEU0x3L2FFW1CwWL8ZVnqPy/UzT7+unWBQ6v5Cj+wae4qWeQ85O7EF2XbStz\nXPz7RTKFNV4GuIJA8ubD9Lz+DaiDQx6Q6boUvvplmjPTxPcfuOL1+v2sLVBpq/5/UQEbpz1V7X88\n9X6u69vP2/a+Gdux26RpV5a/tRsbB0CQbrfAqOVaFtu1GU0MYzs2OV9+VtZboFLNrGPaJpZrh2BB\nu1F3mMzl+90MxPs4UzhPtp6jZtYQBdEDxSSNfT27Wal5LKZrundwbe8+FppzPLdympV6Nlw/XF7+\nJiDguA4FvUhvtCdk8MR9ppLT8F44l86tbgoqFfUSM2Xv5bDaXCNbzzEQ7+9YxwuBIoIgEJUjHfK3\nEJCRN29YAbqjgaeSd2wD6ZtVGMCpesdwpjK3DlTymUoNEzkZMJU2gkotTyUrlL+FTCU1Fe43QK1p\nIUo2EgqCIKBKqu+pVEeT1I7EPWiBTNtWh5EciQujF0KmUu2yTKWrA5X29+xhaHUXmYs7ERCQr42h\n9W5kgpmOFTKVmobFb/3V49x+cJC3/cTujuWJVquJjwkJFEkh6nrbf+n8KttvSxGtdiFoDq4ubmAq\nBR4MvQMJD1QqbQSVMFugkmNfBVOpzdehVtFDFpIeqaIaUfL5BvO5Kvu3ZZhaKDGzUkXf1slUWvS3\nc3i8m0LmDFZWR9a8e0E0NWxdRrJaDd3FM949luxRWKvrSAIk4xKUwG14HWDN8u6Tkl4lW0ywf7Ib\nPX0JUavTF+vFwmueAMpVnXrdRI0r0ICmZSOKgjdTqXqNsyRIyKJMOq5Rw6UbjyofgEq67xUVgEoA\nGh4te7XYINMXZ0IRUSSBuhKjYlbRgZEbR1g+MU//4i4A9h8eZGJXL7XPevePo8kUVuuoExFs1w6f\ni3E1FsrfbMlCi3R6XBzYliEekTEuE3+9VVv1b6Eso0SjdA41OoQaG/6hr79+9gyLH/xznJr3jDr9\n796NEo0z/JG/hje/i5nlLNticQbf/k4E8btjTgVgTWBePRhVOVmAlYZBWlVYquvotsNkcvNJoED6\ntq8rwaMrxQ6zboAns0Uez5b4pT2jFHzZUn9URRUFqqYdMpU2k8zFZIlcw8Bx3XXyNzs08F4PKn39\nc6dYTggwFAslgl3a5uBY1pfpzD27BHd6LNnMuu8m2pYfRJf/a5lKjarXZwzEI1Cr4SYUmpbHvsqI\nIhXfH+f6W8foG0zyilIvXzyzhNSUYHdL8tewbU4VvWui+0yBg91JEv0pTsx6/c/4jgw9vlnz4lyR\n8rk8clcfUsVkdq1BIuX1AaOT3ey9boinvzPdYehc8CeEbjk4zCtGkjxy8SQLi2XqNQNBgLtfvZuv\nffYUp48vhaCS67oU8nXSmSjSJilrA8MeqHTq2BLKgQxGWg2T7ayojFw1+cY3zwLw2MNTTO7sQfOZ\nSxuYSrJIqeBt74EbR3n8G54n2IgBNUmk3/fSkSSRG24b51++dp7B0RR3v3oPM1N5nvjmRZyayw23\nj3P4zm0hkNLtAy/B/gcTSJM7e+gbTHLsiVme+s4l+oY65YKblWdoDaeeXeTgjSMIgsD0ea8HPzTR\nQ+OZeRYuFXjHL99CNKqylqvhOC6Z3jiaL1FsNkxs2+spYonO3jQ4h9WKHsoAU12t3rBd4tbTnwjT\n1YprG42bZy94+zmxo4eGbvHf/vfT5AoeK7IHT7q/2AZGiaKAadlM2RZDCPzlA8+S1y2Ws1UE4C/+\n+STJmMqJi3ma64DYwUyMl4x3c+HYEnkc5oBV/74a709guy7T056Qbftwit94yyEePbHE0YemSJgu\nUZ+JFEjd1G6NxHavD3/14XESMZXfedtNfOBTzzGYifHiQ8Psnegm5l9LUdfk5sIpkrsOI3cl6etL\n0iV4z4QeuUn2k/9EqtmLGe9HtF1GMgrGygrqgGercV8qS2PJsxdx/vibXNPbD+4oz35NoH7jOPmZ\nZbozXWF/6TSbLP7lB7DW8sQPXU/t2LNk//5vGf3Pv4U+O8vK3/0tghah9xffQ+9EP+XP/RP1M6dp\nXphCdl2igoyrJeifPU//bCtAyk2nie0/AIND9N55F5HRVrq1IAj0/dRb6L7nFchdl0/q+0HVFqi0\nVT+SalhNLpam2ZfZ3UGr/kFVwMYJgJq6UadhNUK/nYpZxfVpkFcjf2s3Ng6BoDam0pxv0j2aGGat\nWWC+uohhGyGrBaBu1sMBfiD9CsCqdqZSAHYM+JHoy/UsVaNGXPbNqwX4let+ccO2jqa9hmmussha\nsxh+3txg1O1tw2C8n6XaCquNNR9UCphKPqhktEClW1+yfcN5ey7nJc2NJ0eZrcxzMn+mDVTymTbS\nC4MiEUnrAD1qVsvk+nIVeir5gN1zq6eQBInSYgpH8M7XtA92ge8H0QYqRYP0t03kbx1MJT/5Sl7H\nVCrpZQzTxrQcNNFG8E2eVUmlolc8doeyERSL+elvqqUi+cycwB9ro6dSwFRqNfmWZSMIwqZN3NFH\nZ+m5uAsXBxBorAhEdvmgkm7iOA6iKGLYBjF/maWqgW7arJWaG5YntoE8cdHb7ii+0XjF4MTxOSRH\nRk80UK1Y6A8QlNEGKgFhI9RetqGAf3m41pUHSXqbmWWl1KThs8/0aJVkuY8LvvnkoZ29mKbN+YUS\nVb+xD176C/52jvTF0f39ElTvOSAZUYyajICAqTRRzAirvvFlujfC2pyOIghEND/yuGTiAH6/z2q1\nDAwyOhjlcfs4ri0xGBtmnkKoOy9VdC9ZyD+HuuUQS6hUy3rIVAqeDamYQnDnrq3WmPAp7YGfUqoN\nVIoAqgu27ZLqivKbr9uH47r82bHHvAVY3rJueH0fD3/+NKba5KYX3UqlbobmkoYqEm1CotqDYMY5\nd9LzAIvLcdxA/ibaaJpCLK6GXlmxiMIf/8odKFcRt71VW/XjWHptntXpTwEuib6bfmjrdW0bq1ig\nevwY2Y//AwCJG26kaLmU0xkm80uM7dqBZlusbbuGsbtuRslcfoA7Vaqz0tC5Y11y1XrGT2Bqu1w3\n2JmK8XfnFqlYFu/aM8ZYYuM7u+QPFodjGmlFZqHWDD1gLMfl4YU1qpbNc2uVUP7WrSokFNmTvxkt\n+dv6ikkiLtCwHGqWjSoKGI5LxbBCY+1om/zNdV2yS2WEEe8dO1f1nkOX8+gp+QNk0XCQLId4VOnw\nXoJOBpXo+7GY3yem0mAqipCFvG6y5gMl/XGNGiArItfe7A0Q945neOIzp1n1MjHCc1W3bOarTeKC\nQGKhTv/OAQaGU5w44kWvT2zvIZ70zufp40tYus1LdIlstsbycjV8941Odnustq4oKwslbNtBksSQ\ncZrqipBQZPbsH2ThYoFKqcnEjh627+4j0xfn0rlVGnWDaMx79puGHRoxr69Dt4zTlYnR3RvnuG3w\nzeUCed1j4riigFK36B1IMDiS5vmjCxx9fJbb7vZYvQGo1PTPvSaJoTH14GiK/uEk2cUKfd1RXnNg\nIvw+wP7rhxkaS9PdE0cUBboOxxiZ6KZW1cP3Z1AhqJSv47ouM1N5IlGF/uEUoijw8tft458fOM7X\nPnuKN/7cDSFQs74adSNMYyvk66yuVEl1RZg6lSWZ0hgYSdN9YpkV3cZoWh6o5Pchmb54mDTYbJhY\nfuJbIIUKaq2mI0kitYre8lPqausN2+6Ps9kKtyZVRFHYlKkUSCaHJ7r4k08cJ+sf2x1ABpEqLqdx\nuXlPP31dUZ49n2MpX2cVWMUFv88SARc4M1vcsI6gsoU6j5R1JoAeRBK4sK2bQ9f0cVuvjV2vc1HZ\nydm5Iq84PE5Uk3n5TWOsnMpSXqzwX996AyXd4ksfP0aqK4p04yBnXT/p0T9Go/0J/te7b9+wbqtY\nYP5P/wRjfo7St7/J2G/9DvQl6elP0NOfYOHP/oTaiee4XlQ4PvxyXBdGvvUppr9apfuVrya2dx+N\nTz+IEIuTvvtlmHMz1E+fYqeZhfxReApuB+xokuyDFwCX8mOP4tTrpG67g4G3v4OlD/0F1aPPMPcH\nv0fz4kUABt/5LlI3eBPesbe/AwDXspg5s8zDnz/PrXfv4OncItHcRRxRILFvP/fdfPCK4+YfBaAE\nW6DSVv2I6jsLT/DZC1/ifYfeyZ7Mrh/4+gL2i+mziQIWTq6Rx3Xd0BcHrl7+FpS2Cai04Jt0jyWH\nw/SxslHtSCirW40NsiZREFFE2WMqBXHvPhNqwI+OX6nnqJq10NPncjWa8kCl51dP4+LSG8mw2lyj\nfhmm0mhihKXaCrlGnj3sCo9RACq5poiAx0hYW62Fs2FBBQyht+y+lz868hc8nz/DS8fvAlrH/4U8\nlYK/t/s6XU461l4BqFQyKlTNGnOVBSYTk8xaMn0oFJ0kM+W5lvFh0wpj2asNE9G+vPwtMLC2HDs0\nKw4+a/dUqgWsGdHGdYIkC5VVx8BwTIbi/ayvuJ+8JbkikiuBC/2Bp5JxZabSp//2KMl0hFe96WDH\nd+curXHk0RliaYUTI99i+6nbKM1bRCQN0ZIxHxng20vnecmrdmM4ZgiKVn2QxrQ3YZjYrSZFsaNk\nC3VUN0IAv84+UwUEluomvY4LRc/wMkj1COVvPjupXNo4Y2Y2FfAvadu6CqZSo81vo6zT8IFCPeod\nu4vzXnNz3Y4eVgp1zs2XyOW9v21gKvXEWa5458P2jadlS8OoeNtRS66RLgwh+AkwkZR3zGKqjO0a\n4EKl0MSWBPIlC1WQKOseACWms5iFBtbKdoS0L6Xzt7vsU7RtXzLXNGwSvVFqFT006g7OTzKutkCl\ntvjhAFTqZCoJ6D7Ik+nzGmoRIQQQXVuhWNXpSie4tPdJb5+Uf09urS2BRhKJAgNT+xBtmUefu4R0\nvUpCjVGzJRzBBtElpqqkuiOsLJTDgYl2mfSXrdqqH+dyXZdK9nGKi48ADqnBu4hnDn1f1+GY5qaG\n2Gtf/iLnP/tpXD+0QYzHGX73e4nt2ctirgTTWQ5cfy3Dr7yLsbMLTJXr2IMvzKD60lyO5YbBtT3J\nDknXelBpMOYB1ysNnZlqk5LPRHrwwhLv3e+lD7VXwFRKqTIjcY1TxRpl0yatypwsVMPUtmP5SugJ\n1K0pxGWJhbpJ0TeOTsibM5XAY1PVLJvhmMZCTadi2Zi2gyIKHSBQtaxjmU4oU5v2E/IuByoFbI3x\nbd00T6yx9+Dghu8kOuRv3vtQ/z55KqWSGmlVZqmu8+iyl+Y71pdgIR1h73VDxOLe8z4SVejuibGy\nWMZxnFD+tljXqVo2I47PsOmNMzSWJhpTUFSJ7t4YgiDQ3RsLWTe7d/SSkGWeWaxw8WwOLSKHEzzp\n7ijL8yUqpSZdmRilYgNRFEj4oRbbrulFUSVMw2bf9UMIgsDe64Z49KEpzp5Y4dAtY+RWvAm9AJhZ\nX5Gowp5rvX60b9XrgfNNM+zDdg6nue9V47iuy/TUKieOzHPghhGS6UgofwuXJYlhSmq6O8rkzl6y\nixW6e+Jh2l9QgiBs6FN7BxLhvrdXPKmhqBIri2WOfGeaes1g94EBRP/dPDia5ifffC2fe+AYX/rk\nCX7q7TehbPKeO3tiGcd2GZnoYmGmyPmTK8QSKqZlc91144iiEPYf52eLDFpO+D7P9MbD93mzboaS\ny4CpVKrqfOIbUzx+coVrERALzXAyJ3j3m5bDZ56cYRBwcDmyUOKpP/0XBMeFhSLn//EoGcvBqltk\nRZd6ocFoIsI/PnSeKV/C9kv3HuTpz58C2yXZHeV37zvAqH8c3/ji7Szkahw5ucyjT87iRmQyokiy\nbnL3vft58sIqlbrJPTePsXMkzXK+znzOYzF9/KHzvvTVO6cJVebtbzpI4UtfYOZDnwPXZfwX3sGB\nF3vSXdeyQJLYNZnhmcUKVtNiuDuKhsD4cIqhsSQTH/wLZMtA7nkH7rZJKk88xtqXvkj3K15J+kXe\n+MNYXmb+/X+Elc+jjY2hz82x8IH30/f7vwtA9blj1E4858nHVlY4tPB1BNebtJW7uih8+YsUvvxF\nkCRG3uM9kwHseo3cN/+FlcefQdFkIhEZcfYCxYe+BoCUTpN57evJvPonPQbRv/8Z6qdO0rx4EW1y\nG733voH4gWs3XEOCLBPrSYEg0KgZVJUE0wdvBuDmvtQPhYjxvdYWqLRVP5IKjIjPFS78UEClhg8c\nBEwl3fJe8KZjUjLKoYTJ+6zVPJwrTFHUyxwe9DwVrE1AJdU319bbjLrnq4sICAwnhkIPp4pR6fRU\nMuubGliroropU2nQByYWq8vUrUZocHy5Gkl5zdLZgkebnEiNsdpc25CuFrC4RpNDPL3SSpgLmEpB\n9Dttho/T51Y7XtZ1s8G54gXGkyNMpsYZS44wVbwUmpA37KuTb0Vkj6nkuA6iIF4VqKRICnE5Rlkv\nM1X0ItF7pVFcBJIICGvjLPeepKiX6I50hX5K4PkgaS9g1B2wktqZSgGbTPXXW9RL1PwmWRAdHMv7\nuyaq4fWyGVNJFmVkVwnj2VUnGgJVNety6W/euTB0i3yuFho1BmWaNt/+6jkEAV79hmtZnjtCY6ZE\nYVnANQW68yMIhkw+V8VxHS8R0d+fYB/MzWRLbcyhfM7mjx44xkE1gimYCALYDe/vFUsm4rrEEXxv\nggSPnlimUTdRVClsfDZjKjXrUsjgsc0rvzT1ZltMdakZyt+MiAfmVCoGI31xeruiTAx4TkV5fxYu\nBJXyNURBYCATI9b0mzLJZw8SQa9658ZUmwhxE6oqkagSRldHZAnbtZBNDdNwkOIKek0nLceoNr1z\nKEUbUACn0o2Z8JroYD+D8xfsiWHa3Hr3dqplnRPzXwdsNDlgKqnoeF5HVwKVIsDKVB5REth7bes5\nEfXZcVgKhYpOTPZATFVUEAWRctu9URdc+mQRy5JxZBPRUpAthZgco+HIOJLf8CoaqXSU5fky1bIe\nbsNWbdW/pbLNGvnZz9EsTyHJCXom30Akue37tnzXdck9+HGKjzyElEyijYzSdc9PkLj2ENVjz7L6\nqU+idHdRO3A939l2gJdPDhEb90CjC2WfPZDy7q3RuMZUuc5Crcku38uooJt8djrLayf66I2oNCyb\nlYZ3P6/UDZLpVs9Ss2wEWn41GU1BFgRWGgYn1irhui6UG3x6OstP7xjsGMgEnkpJP73tVLHGQq1J\nWk3weNYD8/siKrPVJilFQhYEEopEQpFwXFj2JWqbeSoFoNKabuK43jpiskXF8CaD1kvfCv5EQU9C\nowCs6AYIAs9/a5qu60bCRKugimt1RMljnuQ+/CTZI0twW+d5DhhUguUw0Jcgt1zB1P/1TCUtIiPJ\nImOJCCfWqjyb9471cDLK3e++dcP/DI6mKRyvk8/Wwv0+54Nm0ZqFDmR6Y4iiyOvfej2SJITnaWA4\n5cmXNYnB0TSSJPLMY16qVcBSAkj7sqlysUFXJka50CDZFQnBFEWRuPGOCZbnS4xv99g91+wf4LGH\np7h0LsehW8ZYDUClyzCV2iuQGq7pZmg6vneyB8kHzW65axsPf+EMT337Ei977V7UTUClUqGBqklE\nogr7Dg1RLjbYfWDgiut+oWr3fjryqHecJnf1dnznupvGOHtymTPPLbOyUGJ0svPacl2XU8eWkGSR\nl79uHw9+5CnOn8oiSgKXBDj22CXMlEYsppDF5YNf9tKj+6IKaVy6e2IhI67ZsGj6PU40pnLy0hof\n/OwJGrqNIMBp12VAN8k/MYOOy5HZNey4wtFzOebydRKSxKzteL2F4xJMmZ71mUUChKzkXLUJz3tM\n5NfcNsFr79xO7tgi89MF7rp1PASUguM02p9gpG8H+WNLOLaL1bTYtquPA3v6ObCncwJ1YjDJWLeK\noCjsncxQquo89MBx1Moq49Yqc7//CPr0JeTuDI6us3z/R7FrVfT5ecpPPIaUSJIa2UlXvYdKaTeK\nH6oSjcpoH//fDC94PX/xD3+P5sREyABa+dv7EaMx5O4Mix94P3a1Qs+995F59U+y/DcfofLE45z4\n7f9C+jWvI/fgAyCKDL37vTz2qScYeu4LmJKG9oafZfIl15N98AHKjz/KwFt/NgSUAKRYnMFXv5LB\nV7+ydQ1YFvXTp3Btm/iBgwhtwLnS3c3ob/7f2NUqsb37XhAcCthp9bqBZZrQ4/WB65Mvf9xqC1Ta\nqh9JBcDNhdKlH/i6HNdp81JqeSoFlavnO2Rplt0ClT534SssVpc2gErKFZhK2foqmUgXmqSGoFLZ\nqHQwoupWI5SetYNKiqRg2O2eSt7y02qKiKRxsTQNtBhEl6ukliCpJsJY+fHUKM9kj2+S/ub9PpYY\n8Y6HzxQKmEoROeKZU5sSiZRGvWpw8dwqN94xGS7jZP4MjutwXZ9nCre/Zw9zlQXOFqa4ru/AVXsC\nRXx5nG7rROVoCCrFXgBUAkhpSUp6mXMFT1+vlHpJ4m9/sQd6YaYyT3eki3KtdZ6qDYv4VTGVWp5K\ngVE3eGylcrFJOQB3RBvbEDEtO2SYwOVBMcWJ+VkToDkxYnIUAWEDUykAPYPkuMC/Rm9aNBtmCJI8\n8+gM5WKT6w6P0jeY5G2Jt/Dk6kWWjxsszBTJZD0ufbNuhvegakU8IKrfNw5fByq5rgtW6+Wn1yWK\nVR2lKwayTjmeJ1UcwJZMGqZGExcQKKzWKDsOf/Ol09yiKcQiMpGoN5taXiex000bs6kQwHqWb3qt\nNy2eeWyageE0O/b0dfxPs13+Vm56MbyCG8rG+pMah+/wBgsTgx6oVPHPU8RPnClWdNIJFUUWQzli\nU6gDUSJulGbT2w5bNpC6LOyq6s3srnnLUQQRy7HRmt49Hk1qUNORXBXTLbFjJEXd9Z5xrh7F8Flg\nQZvcqJlogO54n9uOS+9giqFREXdWQsAM5W+JqIIAOLJIIV8PDTKDmcpkOuL5UEgCadulUTXYd2go\nTIoBDzg+nlMQSFGs6kR9wDgAVEvV1r1h2C6vf+shPnTifqK5HpJzo0iWiiLKSLaMI/qAqaaRinjL\nKRcbW6DSVv2bK8cxWT73MWyjSCS5g56Je5Gu8H69XDVnZyg+8jCuZSIIItE9e0kePkz+s5+h+PDX\nkTMZBFGifvoU9dOnSL3oTqrPHEFQFHp+67f5+GyVmmVz3JbY6S9zptogJov0+4llY3HvPTlX00NQ\n6VShyvlynSezJV4z3sdstRkOGpcbOjvTrXdQ3bKJ+VHmAKIg0B9VyTYMyoZFTJb42V3D3H92gZOF\nKvM1vUMGV/aZSmlVDj//2kIey3WZrTa5Jh3jQHeCT09nKZs2vZqC6ANLAPM+SHbkoSnuumt7h3Qn\nYOTkmr6noyyRUiTyuomAQEbrHLas5bweYf/ufqbqVfD3KT9d4KwkdYBKrutSXKuT7o4SiSoMjqaZ\nPp+nXtWJJVrPyYgkIrqeRG5iR4bccuVfn/5WNUIW0pu3D/KSIYOSYWI5LrvSm/cHgyMpTh9fYnmh\nRHS7J2e56Bu2C6sNJEkgmfaO3XqW0OBImjPPLTO+vQdJEukfTqJFZPSmxehkSw4ZGGuXCg2/n7Do\nXxewcP0t43BL6/dAFrayWEZvWuR8T6bLyd/aK0jayzcNDP+919eW/LZr/wBHH59l6kyWF92zE1mR\nEGwH1weXBNOhXGzS3eMxsqIxlbtfveeK672aeuV9+8kuecbVgiCw7ZreDd8Jjmu1vDEJbn66QKnQ\n4JoDA8TiKkPbupk+nWMRlzwu2PCxL55me1+cGTyPoInBJGdmi+RCR3MDAAAgAElEQVSA3/qbp+jR\nFDRc/unxafriChIu+ZrOB798Gp/EiCyJOLbDvOuCb6Y9/ewiX3t2MdyW0/6Xbz8wyB0HBzn55BzZ\ni2uUBIEl10GLq/Qi4DZMVqIypZrBzpEUb7hzOwDpTJT56QIDlwnbEAQvZXDukse2O3j9QKgGaK/6\nmdMs/uUH0EbHGPm1/0jXYJI9lWP0zz0DgA4kbryJgbf9PGYuy/yf/BG5//MAAEpfH06jiXjqCDcC\nzYdK1F/zBmRbp+vZr+CcO8HC6DbO7L+Rn3jiIZoXLxLbt5/0S17K8sc+zPJH/gpEEdc0GfjZXyB9\n14u9c/jznl1I5YnHqX3gTwHoevk9aCMjuDv283hJxRJVXrVnL2IkyuDPv53+t/4MonLlpGRBlokf\n3Mg+CioyPnHFZQBEfXP2wmodt23YFN0k+fLHqbZApa36kVRgeD1TnvPMgsUf3KWot6ddOZ3yN/BA\nlE6mUmuw2rR1DMcMmTOm4zU27cDCZp5Kuq2TVL1ZnZTqDWjXmkUaViMEempmY1MGjyopNKxmuI3B\n8gVBYCDWz0zF8weKq1d+gQ/FBlqgUtJLoWjYm8vfBuJ9KKJCPgCVrBbYoggygiWRSkfo7okxd6kQ\nJk4BYXT9wd59ABzo2cNXph/m+dUzHaDSleRvwXFoWgGoFKTcXQFUUpMs1VY4nT+LIio0Lsngg0pq\n1WuapkuzHOo7EPopgSf56gqYSvJmTCXvurQdu42l1gYqSWnSx6/luewlwA3lb7liswOkCphKhm4x\ncyHPzr39HrXWbDvvdhRREIkp0Q1G3bOVBSKyRp+fEtgeAVwqNIhEFYprdY4/NUcypXHzizwwZTgx\nyG3XRvnM8Wd55tEZtIYHfjQbLXmltJzm5KlF+vZ6oI21DlSyTJuWYAskW8F2XAzdJhGLsZw5Tao4\ngJ6oQSnVkmjl6yE93LEcBLPI8kc/TDJ9A5VSs6MBqdQNXLN1vExTYHGuyCOfP02lrNM/VNoAKult\nRt2ep5KJKwkYFa/5nuxPcLM/azbUE0ORRZp1ExXQfBCu3rTo9k0vA0ZeTagCUaJEqPjJb7ZsoiRc\n7HlIdUc4trzKECC5ArpjIRveMpKpCCyXqVYEhKjFXdcNcbThNU+uHg0Bu6AtaDY9UKnedsx100YS\nBVxbQqAFdoqiQCKm0LRcJNMJZ5ZDTyV/xtmRRUTbRRA8L4v2eunYnbxo+Fb+54Vj5Mt66HEWPGPa\nmUqGadM/lMKdbmKv+deKpSCLMqIjYyreeuOaRirWmu3eqq36t1b1wvPYRpF4z/Vkxn7ye5YXVI8f\nY+mvP4hrtO6j8uOPknvwH3EaDZTBQcZ+87eRUyn0uTmWPvIhyt/5FwC0X/xlPrRQo2bZSAJMV7x7\nqWRYFA2LPV3xcLtGfFApMKUGz6MH4FypxmvoY7rSYrsu1TsHwDXLIb5ucDIQVUNp1eG+FIoocm1P\nkulqk7xudIBKFcNCFQU0SaTXhvRinewwPHjBYzvc1t/FRCLC56az2IDss3wCWdlKTUdwYfZUjrPd\nMW6+s8UUChg5uUYQFCKRVGWWGgbgbpDiBUylkd44zHq9jgDIhkNt3cC/UTcxdJuRCa+f6O1PMH0+\nT26lykQbqCQIApOrBqXlKuOvHOHIozPfNaj07JOzrGVrvPQn9+DYLnrTCuXfkiAwFNMYim3sOdor\niGdfni+zY5cHjhmOlxRrLFbp7omFjKL1te2aXi6dX+XQLZ5HkyiKTOzoYep0tgNUCiYByoVmm5/S\nlScGxia7WVkoszhbILdSQRA8IOJKFZclVFFgTTdDUKkn0nr3C4LANQcGePJbl7h4dpWuTBTBcvGt\nKlm+tIZtOT+QyQstolzRhNtVRNZwKa2bGDs/U+BvP/kcJRyOPr/EJ86sgOXQj8ACLpIocN+Lt/Pw\nkXku5moowM6ExvnlCruBZaDUtKgHfU3dYL5uEAWOf/E0puNBxN1JjV9947VMP7/MN47Mo+MSQWAG\nF00RAYGedITdY11ct7OXa33vKLfQJHexQJcLL7llgtvu3oHrujiO5yb7/KU1do91hdfTTbdPML49\nszFQpa0G+zT05y4xYcxQ+8O/Z6Z/gKF3vRvNN46unXyexb/4M1zTpHH+HAt//qdEd+ykf+4Z6koK\n4daXsf/1dyF3edejlEgw8uv/ieJDXyd5883Er7segPyzzzP30Y8SP/UE+swp7qpVEHBRR8f45j1v\nhEiEydfcjT4/T3TXNQiCgKi9j4UPvB/BdRl+z/tIXN9K8BRkmaF3vIvtb3kjU3/3caxCgZ7X3QtA\nIqnRVLwxWwACA1cFKH0/S1YkFFUin62GfnEAkS2m0lZt1cYyfDaQ6VjMVebZnp78ga0rYANBm6eS\n1Q4qrXYwiNqNuoPvG7ZBRI5gOhayKHc0nevT31zXRbeNcPY/YCrN+z5LQ/FBKsYUdave8hqSOuVv\nZbsSrjtYPnjATwAqJa9iJnUw3s+5osfeGU96TKSGuc6oOzQLj9AbzZBrrIX7AB6LQXW87dMiCmPb\nu5m7VGDmQp4DN3jLzNZziILIoG8mPpEaQxEV5iqeEXqwnzH5hRuBAHRqWE26gZq/rZvJx9or5Sex\nZRur7O7aibHawMXFEAQihoRkKaG31Xr5m341TCXXxnI7098AknqGuiNTXKkB3kAeRyRbaKwDlbwm\n9sQzCzz17UvE4iojE90IZquplCxv3xNKvMOoW7cNVmpZ9vTt8IzZaTGVgp8HhlPMTxdwHJcbbp8I\nKcIA/UMptIgcmkzbooWhQ9Mf+EimgkMrnWa9/M3wBwSWZCDbKoKfhqbrFplkjJuv3c3p1RyVVL0D\nVCrkakTT3v65toukV6jOPEvq7ttZy9VoNsyQ4lupm7hW63hpTY1//vgxb/skMQRO2itgKqmaTLXs\npb85iFjT1yNKAs028FASRcb6E9iLFVRNRpJEHNeloVuMat61FVybFbdMgj40QUO2AqaSSbQfmkDf\nQJLCuRWGEHAsTxop2z6b0N9fvSEhRWH/jgRfP75GUknScCWafpqRCCRjanhsa20DFt3wQCXswJur\ndY2kYiqVQpM4XoRzVyZGpdxE1SRUfwbfEDz52449fRsab1EQicgaXQmN+VwNxxZRRYWID6gGLD5B\naDHWJFHGlNeBSraEo/kSmGiUdFfAVNp4nrZqq36cy/NRegoQSA/e9T0BSq7rUnzoa+Q+8SCCojD0\nrvcQ2bEDp9mk/NijlL79LZS+Pkb/428ip7x3lTY2xvjv/D+sffHzSIkEjwxuo7Ra5jVjvUyV65wt\n1SkbFrNV74k60QbqpFSZuCyRbbTeZWs+qJRrmqw1DY7PFkABXFiqtu5Lx3VpWHYHOwRgMKoBnozp\nYMYbVAV+SGt1gy8+/BwHbxphfHsPJdMO/WtmptZInS4wloxwJiXRrSnsSscQBYFU2aSQUrALvtzN\nB4RcQLU9cCSf65xACUGlNqZSu0xug/xttY4oCgz0xBFnwcFjUMXj6gZ5eDHvvVe7fPCjN5BFZ6sd\nxs2O48KZNYYTagjWG9+FUXe9qvP0ty9h2y433zkZDtTXp3hdqQJG1fJCiYNt+92jyjhNm+7tl++L\nIlGFV6/zW3zRPbs4dMtYB2iUbmMqhaBS95VTZke3ZTjy6AxzlwqsrlRJZ2KbhoasL0EQ6NEU8rpJ\nw3JIKXKHuTbAzr39PPmtS0ydzjIwkkK0HRzN2/9Lz2c7tvv7WaWagW5Y9HdvnMR0XZeHnprhw185\nQwOX8rPz7Dg4iKpIfPQLpzh7aQ2z7fuB4fYC3nVuOy6f9JPqVGAPAmtrDRJJhZThsn04xd4XTfDM\nmSwXnltmqD/OxXydvO2A46IpErfuH+DeF20jndCoLJQZQAAE+gYT/MZbriMekS/7/AqMxeNJjZvu\n8NgyXtCLgF2vM/zUV7BKe3BvuBGAWEJjcufmoGft1EnWvvQFUufOctAHBpWBQYylRWb/x++SvvPF\nGNksjTOnQBAYfu+vUX7sO1SPPkPj7BncVIajmZdx9+23hIBSUNHtO4j+0o6Oz7oPHeAz46/lutpR\n0rnzlCJ99By+kbH7XoN8bgVZFJBicWLXtFKL4/sPMP7b/wVRVVGHNveeS2zfxsiv/GrHZ4HJvXcM\nfrhA0vqKxhTKRRvRclufbTGVtmqrNpbVxga6UJz+nkCllXqOvmhPONC+XLXLvQynUwYHsNrIU28D\nWto9lcyQ2WQSkSMdHjRBhUylYNmOiYuLJnufB0yl+WoAKg1wrjBF3WxsKgtTJQXDMUOPpkD+Bq0E\nOLgy0AIwGPd05t1aFxE5gioqmzKVBAQ0SaU3mmGptkLNqneALarjy2Sicqibr1VbDVuukac3kglB\nGFEQyUS6w9S5q5e/dcbeB4ydF/JUAkhryfDnUWMHK7ZLSRGRVRmhZjJqbmOmfAnbsSmvYyoFAGP7\ncQ4q9FRy7E3lbxE9QR1wLZeo6IMxtuQZWXdtBJUKvjF0YNpJsw1U8oGJuBIj18iH7Lj58gKZpUkS\nyzsw99goitQxeA8ApsBnp2+wdSzAY7iMbetm6nQOO9qkEinQVRiiVvf+TzBaEbYAhtXZPAfNtKXq\nyA0V0Za9JslyUDWZV17zUlAe5vNf9PbFAmRVYm21Rno4iYQ/c2zpuHqTrpS3vvnpArv2eddnpW5A\nG1MpZkZwXbjj5Tv5+iMXiNbN0AQ6KL1phT5N+WwVx3ExJIGupErU7pTHAUwMJFlbrCD5gFtTt3Ah\njJsNQKWiUyABRFwFKTilskG8V+YV7zxMqitC85EpABxTwHJsVMu7JzJ+o+v6wFvTbbDWLDKRHCcL\nNEwbBZAFkZG+OJXZIiBQbfPraJo2siyGCWvtoNJgT4yp1RqDiKzlakzu6qVSapJKR8Jmsux6TesN\nt12eZt3lz8oXqzo/dc3rQyltyb8uM0mNQsX7WRYkGlIAKqnIrozgiqGnUjKihQOvdrBzq7bq30Lp\n1RnM5gqxrn3Iavq7/n+rUmbl/o+xfGkGZ2SSgz/3NiLbtod/73vTm+m99z5c191g0C1qGr33vQmA\n2RMzRGSR2wa6sFyXs6U609VGmGY2nugcRPdHVaYrDUzHQRFF8m0ec195ZpaSBErNAsclJwo4roso\nCDRtBxcPrGmvAR/gTyoS25LeugLgaHa5TOPiGrIiMjzZTd2yGfQT4xZ9j5bUqs5/vnMfIgKiILA0\nX0K6WIZDPZjZOrWq3umh5DMy1taBStEQVPLNvBWJpNXquWLrkt8K+RrpTBRFlkgoMmXTokuViSc1\nVrPVDkZssRCASj5TyTdsXvXlW0EV8jVMw2ZwJN0C678LptJzR+bDOPi11VrIeGhnPlxNCYLA4EiK\n6ak8dlvfkjJcTCBzGWPsy5UWkdEiiXWfKWgRmXKxET6/01fBVOofSqKoEhfOZGk2LIbGrv7eyURU\nlhoGhmOxPblxXamuKAMjKRZmClTKTcQd/ja7LquLFQRabCrTcnji5DI37eknuk4aadkOz19aY+9E\n9xXDIxzH5X99/CjLa3Vec9sEr7tjG7Lfb+SKdf78UyeYz9UQBC9PJFc3+e2PPIFltwb8o5rMf3rn\nLaQSGqblMJutcGmxzFh/AlEUePb8KqbpYDZMGqdz3HJNL7e8aJJP/s0R+vvjHNjew96Jbj783Aoj\nUZVkwqGiW1xz5yQvPjQcbg90gh+priiJ6Ebzf6fZoPC1r1I7+Tzpl93DvusGuebAIPI6YCL3wD9S\nfvxRio88RGz/AZLvfRcoyQ3LA495uXz/x8Bx0Ca3ET94LcmbD6MNj1B99ijL93+U4iMPAaAMDDDw\nMz9HbO8+4gcOsvTRD2MszDP0q/+BeE1mZKJ703WsL0kSiaQTnE3fxfYX38dzzyzwpp+4ESmR4CXD\nJpebBohMTF7V8tsr7vdGkiSgRX60EEmQqiu0hedEf8xTdbdApa36kVS7xOxC6RL38JLv6v/nKgv8\nwdN/xlv3vInbhw+/4Hc75G+bMpXymLZJVI7SsBod2xZ833Raxt7rQSVN7JS/hT5J65hKS7UVAPqi\nPciC5HsqbQIqiSqO64TJcO1MpcG2FLGrYSoFqWN9fqqYt48bPZUisjco7Y14s3W5er7NKFxDtb19\n0SJKKGmy/FSUhtWgatYYT412LDcT6WKlnqVp6Vctf4uE8jfv+6FRt3wFUEltewHOJYE6SiaGLApQ\nM8nUh5mJnmOhthQyleIRmVrTomnrKKISAmLt1WIqWZuatIu1COANrlNAGXAdiZVCg1RP66UfDtr9\npi1gqLh669yKRgAqxXFch6bVxGmIfOczswxl99IAFmeLTOzo6Ri8B/G6eb/h2SyBZXJXL1Onc+jD\nBWwfLKjV/evA8PfRb/LXM5VM35TVUnRoJBGdlqG2qskoosz+6G18pn6EqCbR0G3UhEp5rYFQNQiO\nquzfh5M9DseAU8eWQlCpXDMBEVWIYLhNVNs7xtGURt1xiCFQLTdJt80gNhsmkYhMMq2RW/Zm2Ju2\nQzqhEjFcKn6y2re/eo58tkr/7l7KgOg3VEFiX8xvHAL5W13wBhiqqyD77DRLNtEkle6eGJbtYOPi\n4oLlMRtl//7o9wHXAFSary7i4tIXy3BOFKgbNmkgpkp0JTWqfj9q02pMdcNGkcUWU0luXUfbh1Kc\n8OOK11Y9tpdp2KEM1XFd5gwLeTjxgrT1rmQLVLp9vPX8DJhKPakI+bKOZTtIooQpedeYZCuIPthl\nS34CVCxKNK4iK+KmBuxbtVU/zlXJPQVAsu+F+4iO/zEtns6VOByVyP7P36VZKfOVn/lV3GiMmyY3\nmnsLsgfEXyjX+cbiGi8ZzrAz1fYss2xWmwbX9CQQBYFJH0CarjSYrzURBc+cu736IgqXKg1Wmyb9\nEZWCYZJWZUqGxWnHxFVlJhNRFhZL1FMq+aZJX1TdkPwW1GjcS9q6uS8dei0FTKVsqUkSD3yp+GzL\nlCLjui5LPqhULTc7EuaOPTlLNN/k+mWD3EKNmak8iR2tQaTY9JZTKjQwTTvsKwLQKNjOuCx1LDfS\nlvxWqxoYus3opPfcTSoSZdMirSrEkxrZpQqNuhmCOcW89xwLQKVESvNYvOtApZVFj7k+MJJCkkQk\nWQwNlNeXbTs89/Q8p48vcejWMXbs7uP5oy1vm0K+Hjoif7egEngSuOmpPF/6h2Nwh/e+LJxZJcHV\nGWNfTQUTM0FfkboKFpAkiYxMdDF93rNLuBo/paB6tFbf0xvZ/Jjs2tfPykKZ0lqDyDUpDEByWkL8\ngKn01adm+fS3L/Ls+VXe98bOqPXPfPsiX35yltG+OO95w0EGMxt7o7Vyk7/655OosshSvo4gwBce\nm+Hx51fo7YpQb1rMZVvXhyKJbLdccrgs+IDSkCgwIEm8/RcPk/BBCUUW2TGcZsdwC2zbNepJ85sN\nk/tP5xBdl5rPpgvS9iRJRNUkmnWTes2grzfGy27s7K2BDq/E9XJF13Eofeub5D/3Geyq3xtdmGLb\nzl3Yp1WmLkwhd3cz9I53YeZXKT/+KNr4BFIiQf3k8zz7vl+n+5WvInXHnZQfe5Tq008hJZNI6TTV\nI08jxmKMvO8/EN11Tcd6E9ffwOT230efn0MbH0dOtvyYBFlm+JffE4K8o51uBlesZCrC8kKJqs+o\nDzyH7hy8OmDqais4rrG4+iNPWQuY/B1MpS3521Zt1cYK2EBdWpoLxemQlXG1FSSUBUDNC9Wm8rd2\nT6X6KiCQiXRh2Ean/G2dB5Mnf+ucEZBFGQEh/E6L4eM9nOKyZ8Zs+wPUtJYipsS89Dc7AFtaLwjF\nB5ECs+Z2GdVgrPUkTqiXHzQGNZYcIakk2NPtWX5G5QgVs7OBatp6CIAFkfa5xmqb/E1FCUElOYyJ\nN/3mMjD27ot2mhpmIt7Dfq1Z6GBDvVAFxyEAoWpWHVVUwmNyuQrYYKqoUFw0MHDpG0piGDb6UgVx\nLQ49cKk0S7nuveiGeuJMLZRomvplt6vDUymUv7Ue6m5VJgCVkgiUAWyJ1cUSvdd0MpUCk1DwZj0t\n28HR2x7BprePAQBVMWo88n8uUc+7NKMVIo0kueUKEzt6KBcbxBIqzbpJsVDHdV3WclXS3dHw/LTX\nzr39JFIR7r90BrvhRzYHbCkfVLL9ptm0LyN/U3xzalcJXxzBLO70steE753IcPRcDimiAA2qxXoL\nVPKB2Vh9NYzbLeTrdPfEQqAvJscxzCaKD1y4kkhwpy5nax2gkt60SHdHPR+j4BACfXENTbLJ52qY\nps3Z55exTIfJwQQiAvgzPfX1oJIPXFq+1Et2JWR8A23ZaDESTe8zR7JxbRHbtdF86V53OkJ/dxQt\n3cUqMOvLP3ujPSRjCjXdIg1EVYl0QmPJ3+724Ypu2qimiOsE8rc2UGnYa64RBfK5Gk9+yzMB7/Fn\n3Ct1L6a5K/nC4G23T+surJOHlGsG8YgcsrcM00ESJAzR+55ktdIKA6PurlgUQRBIdUUpFRubmnVu\n1Vb9OFVt7QSV7BMIkoZenUGNDqHGxy77fddxaJw7S/mJx0GA8y9/PQ8trjFbzHJ7YY2Lb/kFaloU\nHJeqZXeAIO312EqRi5UGF88ucFN3gldP9hORJRbqOi6wzTfdHolHkAWBqXKdNd1kOKahiJ39UZ8/\nGM81DFRRwHFhezLKdKlOwb/9d2biZP1B/3JDf0FQKSZL/M7122lfS0LxfN3qrkMST96a9xnKSVX2\ngW3vOVBre5YU8jWmz+fpH05yz53b+ceTOS6dX+WWPa0eQTJsYnGVes2gsFoLzaHXb1dckWm0TXRY\nbUEbAfO3u9d7dqdUmYW6TlqVSfiDw1pFb4FK/vs38P4RBIHegQQLM0UM3QrfZysLPqg07G2Tqkro\nmzCVVleqfP2fT4Wyum99+RwnjixgGjZ7rh3kzHPLFFfraP5yvxdQacj3VbJ1C8kFW/AAQKfLCT2X\n/rWV6oqSXaqwNOd5i6bSV5a/AYxNZkJQKTgHV1M9kXZQaWNv57ou23f38ehDU7guxCMKZVzUtvdK\nujuKbtp8/YhnB3FsapVPfGOKe24aI5OKsLxW52tPz6EpEvO5Gv/t/qe45+YxbNsLuPiJm8cQRYH3\nf+I4C6stttzkYJLp5Qr5cpN8uTVJMtoX57d+7jARCT51/zNo+RrX3jTC3tE0//Kpk0xs6w6BoSuV\nFpERBA9cak9vDSoSVSiXmtiWQ/Qy10yiHVRKSjRnphG1CK5tk33gH2icOY0YjdJz730krrue1c99\nmtqxZwGPQWSurDD7+7+HqHrJbEPvfBfK4BDVo0fIf+JB1r7weda+8HkABFXFWFkG10VKdzH6678R\n+iatLzmdRk5f/rr8XnuDZDrC0nwpnEAMAJfvd4WgUmJz6d8Ps6Jx794QnTam0pb8bau2amOZjoWA\nwDXdO3hq+SjLtSzDicGr/v8A8AhMqF+o2uVegVF3aFIsSKHUKq2lWGsWQsDLcZ0QCAoYS5ZjEVE6\nHzaC4IElxjpQKQBqJFEirsSo+lKutJoiJkepmNUWU6nDU8kHlfzvK22yrF5f7ue4zlXJ36JylD+4\n87+2/R4h21j1Er2Abx1fpG42yES8GZR+H7Raqec6wLHAMyYSlVEU76FmBaBSPQCVerBsh28cXeCO\ng0MdoFLTbhKRtSsCh8FxaNotptLV7GdK85q/7alJ9IYXtTvYE6dY1ZkGxKKLaEtcKs1SrXuzK4OZ\nmAcq2XqHxKi9JFECFxa/CXYyCd0gCa3Hpl52sCUTRIGE6X13tDRMpFjHXGk1S3El5plj+wCN3rQo\n1wxEq62hMuXwuwDLSwWK+TpGX4GF8RPseOYucksVbMuhWtYZHu+ipuqUCw2qZR1DtxnbtjnQKAgC\nQ6NpnCkpBE0aPpDj6P5L3m/cTdPpAAaCGdrAmLkrGmHJZzupvpRs2n/R35o/TjyfxZl4KeAl3gRH\nSwnkoSsr7Du0l4WZIqePLXL7y3aG5ukJJUHRzKP6wIUlgOGnyc0tlNi927s+bdvBNGy0iNzRyJm4\npBMqUR/wmLu4huWDQPlpb0bdlbz9qvvyirjf1GqS6t1bkoWLd73IvkuCLZstRqI/KHMkG8Hy0t8k\n/zxGYir//Rdv4cllkQfOPctM2QOVeqIZUjGHtWyVYUQiikg6oYWAmwOosohhOeiGjaqI4LO1Im3X\n5sRgEkEASxYorNYprNbpHUhw/a2eIXfJH/B1xV+4IQrkb4VqJ6hUqhmk4l4aHoBp2chC65qRLAXB\nkgGrQ/4GnlH4eq+srdqqH8cqr3wHs5kLf08Nvuiygx27XmPuD38fY2E+/GylbwL6xjnX1U/fi17O\nkcwI+Ea6a01zU1DJcV0uVRokFQlVEDhSqFItNPnZm7eFhtvburxnvywKjCUiXKoEfkobmSN9vvws\n1zSI+PdrRlMwkCj4MPXu/hRHqt4zbLlhcBCotTGA1pe07hiIgoDmgKlJpLoilItNFte8viSlSCzN\ntgJODN1Gb1poEZlTxzy4/NDhcVJdUTJ9cRamC2hu27INh33XD3PkO9Os5Vqg0vqZ+LgsUTJbg6pa\nrgF+qndh1QNzApZM0p9Q6dJkND+AoVrRQ0l4aa2OFpE7nk+9/R6olM9WGRrz+qCVxTKyIoZMIFWT\nMfWNTKVHH56imK9z4IZh9lw7xMNfOM1aroaqydx29w7OnVxhLV8j6cuDvxePloGRFC977V4yvXE+\ntpyjZtq8+d6Dnu/e96nafZXiSXXTianNanRbiyXS3XP1TKXMJkyl5bU6f/eVM7zsxlG+9MQsMU1i\n17g3+SQ7UFuoIMYUHFwUWSKWUHn4mXkqdZOdI2kuLJb46lNzfOPoAm+95xqOnM1iOy6/9No9nJkt\n8o1nF/jCYzPher9xdIFMSmNhtcaNu/t45mwOSRS4tFRhrD/BWH+CuWyVxdUat+wb4OdftYehwRS5\nXIVEypNWvvbWiZZP13chRRQEAS2qoFcbVHxJZgeoFJGxs/ejstQAACAASURBVMt0GUX65y9RfrxK\n8tbbOr1cBYuRynl6KzPIH1lh1uqU+8cPXc/A234OOe1d0yPv/TWMlWWkWBwpmaR28nmWP/rX2JUK\nfT/9M6HvUPLGm5l48W2c+7sHaVy8QPKmm0nddgeIAmY2i9LTixi5OvDs+1nB8amUPA9J6QcErkRj\nCnsODjIwsnnq3Q+zgudUQlMI6BPrgwp+3OoHCirt3r37lcCfARLw0bNnz/7Bur+ngX8Axv1t+eOz\nZ8/e7/9tGs810Aass2fP3vSD3Naturp6avkog7H+DVKn77ZMx0SRFHamt/HU8lEulKZ/YKBSO1Mp\n8D0K5G+D8X4Wql4D1KWlkUU5BJUCIMn7+fLyN/B8lVqgUovhE1RKTbZAJS1JTImxUs9R30QWFjAi\ngu+3L0cWZXqjGbL1VZJXkf62vqJyFMd1MB2T6UXvJR69WQ/XP+CDStl6LvQO0iQ1HDSrmhyaQJuG\nTa7YYL7UkvU9e36VBx4+j+u6ZMa9l5mXetfsMCO/XEXa0t/A81Raz4DarMaSI0wkxzicOcxRCpjA\nYCaK47hUgKT7/7H33vGSnPWZ77dy5+4T+uRzJk/PjCZoGEWEhCSCMRkbAwZ8PxcnjLHBa3v37l3v\n7vU13jW+a3987YW99uIArLHBQYCEEEImKKcR0sxoQk88OXWfzqny/aOqq/uECUpYxvP7Z+Z0V1W/\nVfXWW7/3eZ/n+UGqMcSFyhR2YwvRkEzCXwHSbYOYunFCIIsSkqXSWhShEYWeDlPJcRwaZQs9XMNW\nIV7sIVrpo7fsVQ9x6mLA1Y4pUcr5jmRN1y2KNX01qOQzhtpMpZlzHgiylJxkuLefeCJEbrFKpdyu\n0BJCVkSmC00WZr3kvjd96T5hmSK24ss0fZNXp+WXlfYnRS6eqaQsCVi2w7lZr2SspXr3JKooSO3K\nem2m0kKVkAyJHzzIzabFY+5tAJgNs0v+5j9Dy0tseU8/4YjCqWOL3PD6LUHVsR4tyWwDVEdECcnU\ndCtgKi3nOs+67vslhcIK8WQHQLGAZFRFc71zOnfKmzgKAlT95M/xb0ogf/PPQRAEInKYmlnHlkwc\nU0MRBBxsXNHpMJV88M0VHURLwXY9UEmSxUDCEdfa5vxzAPSHeohHKrSnsZokkYypASvABgZTYebz\ndVqmhWaIEDCVukpdqzKj/V5f6vPP/y0/sTf43ZJPDU9eZvIy4MsAFvIdQ3jLdqg1TcbSUQ/U8s9V\nEiVs2bvesqUg2t53ji9/a1+XRJdZ91VQ6Wq8WsPSS5itHKHEDtJb3ofrWogXWVQAyP3D3/HE8Da2\nbN/N3kPXsvLVf6RcKEF6AsFxePSaG8FxGY1ozDV0VnSTTRv4xCw0dFq2wzU9CXbXHL5kVjgveszC\ntm/S5mQU2wd6N8fDAag0EVv/7hzwJ+PLLSNg9/SFFFpNGxSIiiID8RAJRyAHLPkV4Br+YlB5ocpj\nJ1a4+c5tFwXUHMeFhokdkTl40wQPfus0y20zZ1UO/JTGNvcwO1mkVm2hhWKs+HKhia0e6LBlRz/P\nPDbF0mQJSQDbhZgsMr65JwCV2iHivYscHzSJyiJ6d2EKfwEDOpXf2pLvlOq9T3s1BaGLqQTeQkSl\n1CI9vNorpttXaXg8hd6yKOYbjEx0KmGpmrTKQxI8tvHibJmB4Ti3vtlbqPqJn3kNTz88ydBYklBY\noS8do7TSID24vprUlYYgCOy8xpO9vVFyMB3nZQWUoFM5FK7MTynYtidMPBmiVtUD8/MriW75Wzqk\n0DIsPnPXMebzdc7NVwIJvj2coBBXWH7IL7KCVyUtbFn84Vee46yf95ydKyMKXu5iWA5/dd8pAFIx\nlUeOLXDsfAFVFpFlkUbLIhqSaRo2s7m69z717+2/ed8BRtMxkl33yXYcpDUswfZCVrXc8uSNdPqg\n6zg0s6eonzhOeNt2ovsPIIjrAZCU0GDHc19FfF5iLLabUGszxW8/Qf3YUfacOYPUtuhYhMXjD2Ku\n5Ol7+zuxq1WWv/I31A4/zS7Lt2QYGiG6exeuZeE0m8SuPUj8xpvXPdfqYGeOFb1mL5t++1O0pqaI\n7tu/ajspHA683rpDG31p876XEt2g28XYWy9HCILAHW/b9Yod/4VEm6mU6GJNhf61eiplMhkJ+Czw\nJmAWeDqTydydzWZPdG32ceBENpt9R8Zbgs5mMpkvZbPZ9jzijmw2m3+l2ng1Xlg0zAZfOPFldvfu\n5Feu/fmXdCzTN7yeSHgUyjawc6XRBofWSrk2im5PpbXyt9HYcBeolEARlcBEfJW3UuCptF7+Bh67\nqM2CarOPukvUx9UY+HlTwmcqubiUWt5Lca1RN3Tkb+oa6dfO1DZ0ywjAhysN13UD1kPTanFmtgSi\nDYIb/H5SS6CKCkuNHH0hDxzRJDUwkVZC4ir52+//zQ8QJ85BxAOVDvurLoWqztY18rce7fJU7bB/\nzVpWC8ux0G3jsibd3n4h/t31v0puscoPeAYTGOyNUG2YVH2my7AxzvHmE4itKoloLDA2NB3j4kwl\nQULV/WRJlxAQEAWRhZU6f3HX8/Q5LlakRT1UJV7sYez8AUTXG/RbVdczWsLzSVouFoLjGrpFqWog\n2p0h2DWlYFuA3GQLURKoJpe5Nn4TfeNJTh9fCijqyZ5wAPBdOO3BFX2XAZUMXcDyAQK9aSFIIm21\np4D3QrDwfJVkSeSx5xd54KkZNiMG8jfBJQCKVE3GMG3m83UOxVtgmoiAWlpEJ4aj2+vkb8bSEpIk\nsmv/MM8+Mc35bD4Ald629Y3Ez45RwkUNyVQbZgAqlbv8elo+IKSFlVVMJQtIxjRC/qLy5Fnv9bH/\n+nGOPOXR5E3f4KKhr5a/geerVDPr2LKJpTuogoAtegfTAvmb74clOQi6hOVYiJZCKLyamQYdZmRf\nuJd4pEV7vV2RBFIxLQCVHCCdDDGfr2OYDrrl4G7AVAJPAnc0V2ckrPJj77lmVbJVajOVLkPdHuoN\no8gi08udCVqbLZaIqqj+JNUwbSRBwhUdBNkz6hbstvzNuw5tkL292l3M1wPZyNW4Gq+2aFbOABBO\nbEcQJQQuvvLbyJ5i5sgxjn7gYzTjYW7cNYY2Oob+0GEAblqe5PGhraRDCm8a6+Pzp+eDKmxrow0Q\nbY2HqUzniZSa1EeiTNdazNZ14opET0hheqnKvX93lPHXdeQlGzGVEqqMIgrkm0bgfdSnqeQKOkrU\nZd+Y9/7tjWlcMGwWmz6o1K5KdWaFmQtldu4dCoAV8BisS3MVysUmpWITwbIgphAf9rZZqesQFYnL\nEs9Ml4jGVEYmUh6oVNHpS8cCxoviG31v3tHHM49NceK5ecI74tRwSCfDwSJIdwW4Z5+YRrBsCMtI\ntossitRzdfAvQWPFq1KWSIUp5D0PnLZH0o0DSWKKxPZEhKWKdx/aFeCq5RaO45Ja4xfUlg63q6Pm\nFldL3wAUVcYynVXFItrVVse3dkrQq5rMLW/cHvzdPxAjt1glt+RLdl7iZPhQ/yszrnZ7KF2Jn1I7\nBEHg9h/PIPu+U3P5Os+cWuaho/NsH0nyvju307uBJCwiieCz5TUX/vKbp5jP11EVEcN0GO33FjZO\nLXj3QorIRDfFkWsWzcU6ddPhxKS32BXRZN58/Ti3HhghO13kW09OM+3fy1LNoFQrkBlP8ZG37SYe\nVvibB05z+HTOA0whkL5tH02yZ3Pv2qauA5TA8+ICqC7kKOW8vCQZgZVv3E35we9j+bleEVDSaaL7\nr0WKxZB7+4gdfA2uabAj+w1Uq4HjSGTyT5H79FPB8Z1YL8tCD1W1l82HthN55tusfO0uXMuk8thj\nWIUV1KFhFuLbuCCN8oFff0sAgL6QkJMpYvtTL3i/f47oznMi/0oWrdqLcz3+M6RJYuB392qNV5Kp\ndANwNpvNngfIZDJfBt4FdINKLhDPZDICEAMKePOCq/EqjHYlr7JeecnHsmwTRVQCtk27yteVxgtj\nKnXJ3+x2hStv/5FoB7lPagkUUQ7kcKsNuw0c18FxnVWeOu1QJZW6Xl7Vtm6gom3WHZbDqJISTDoL\nrQKiIKJ0AVXtKmT1QP62GlR6385385M73rHKMPpK4nP3nCDrViHumWufnimDzzZoT1xFQWQgkma5\nkQvMsVVJRfKrr0iqgCSJiKKAadgUKjphp4yAQG+4l5Wy9zKt1A36Qh7raaVVoGW1CPuV6C4VoUD+\npndMuq8AVGpHw2dqWAL0J0MsF5tBifuo7pskynlGwz1EwzIIDg7OxT2VRBnF8NokGFLgsXRissjK\nSp0+REJJkcXwAsMzO1HMEK7kItgCes0DlWRRRhUVysUOK8RoWZRqemB6DOCaYnC+aitCq+gQGfZk\nVhPxUZLjKU4fX+LsyU5J3TaoNH3eu+696Uv7bLWaAkLIZ9Q1bWRt9XmrdEClsObJqdq93ZYsXNHG\nddwOqBSSWCw0cFyXbU4xOE6iME1N2YNoOcFLpg0qmbllXMdhYlsvzz4xTSFXp9owUWWRscQgO8Mi\nhzmBpMlUG0YAKjX9e+u6LjN+sukZda/2VErFVNx2xTrToX8wxv7rxzj69AyuC4afTLY9laJdng49\nhsK77srx2GYdw7KRXJfWGlCpvYrqSi6iK6KbBqIpE0p07mWsq89KgkRKSxKP5AM7bkUUAvmb41l+\nk/aT+ZZhE1JtZBO2T7fQdq1+/rcMJ3joyAJbbtvMyMTqhDCQv12GqSSJImPpKNNLNSzbAxDbJt2J\niIrkSwQNqzPeiaqLZCueqQcdplJ7fGpX/5mZLLJr//Alf/9qXI1/rmhWvMqN4cSOS27nmAZLX/wr\nCv1ejlDzvebkZBJ7YjOSbvGW219Lf8NmSzyM6k8+LwYqna/4oFIizOP5BuEVD1R6dKlIxbTYnYoi\nCAILMyXySzViJ/OIIyoJRQ6qsHWHKAikQyrLTYOo5D33fSGFWrHJyIkqb3+9t9qe6g2j1CwKqoRu\nO4H8TS+3UIDTx5foH4yht0y++Q/HWJxdnd9JO73nWogqyLJIybAgqkLdpNkw2b5nYJV/kWnagTy7\nHemhOAMjceamSugDCiRURtIxVE0mltACptLyQoXDj0wi35DGBgTdxrYciot1hHENVxYRTYfp8wWu\nOThCMV8nkQoHUpiwLHFd2mtv2xulXvGuzcVkSj19ESRZDMy6O35KHUZT2xPJNGyksPdbMxe8d+7E\n1vVARHDeg3FOskBuoYogsGrh4dUUyW5Q6QUwlVzXZbLS4rnzK2T/sRCwfwGeqixzOLvMxGCchm7h\nui4/+fpt3LB7kK9896yXZzsu//FzT1JpmMTCCjWfgdzQLRLRzn3acU2aUkLmQG+c928b4txcmc99\n4wTlmsFvf+R6+v0233TNEDddM8RSsUG5ZhANKximzaaheDAZ/7m37+Hn8LwLSzWdpUKTfLnJvq19\nlzxXM5ej0SxBOEU0rrK5cATrs5+nVxC4Xu2l/sdfodZqIobDJG97PdF9+6kdPUL1iccpfeeB4DjL\nX1KRYjHUVpnzPQdY6LuGLfVT7BwRiVyzl9iBa3ny6TwnDnty21379jD6un3MfPq/eB5HgkDfu95D\n79vewZBus9ewXxSg9C8tutl0bQbPj3r0DUQRBBgdSYBee9VXfoNXFlQaBWa6/p4FblyzzWeAu4F5\nIA68P5vNthdzXeCfMpmMDfxZNpv9n5f7wZ6eCPIrqDdMpzcusfijFhc7z2nTm3TUrNpLvhY2NiFF\nZfOwBzSYgv6Cjin7xTVqZp2+/uglvXqEuY4eX1Rc0uk4+qT38tozuo2vnfO+m0gPEVrUqDfqpNNx\nzEoH6NIiUkDvjYZC69oaDYVZauZIp+OoVW+AT6eSwXaDyT5Ygt6I91lfPAGLUDaqxNQoAwOdFaie\nZQ8YsFwbTVJXffdCo7udcyt1qopXKVSJipyfLyP4E8NUNBZsO94zzGxtnpyeRxZlhgd7kB0VC+gf\njHjnqMlByVxXrdMX7mFksIeKn1Q0DJvto6NIgkhOz+PikozELnuPLb/6nCvbtH3I++Opy+7X/v7o\ns17H6OkJMzSYpGZ6pXcFWURseC8iIVaivzfC6GACxHYFq+iGv2FodRTDN/V0RTTXu/eiLLUXThkY\nSnBar2NJFrItk9gtUH0eDB/NSmgxBgYStOqdhMuxXQzHRbK7xitLJNUTZUIYIF70novzomfEfGBi\nJ7q/Gj0/7YG7m7b0Ua8ZwFks00FRJbZtTyNcJMFwXZdm00WN+7I3CyTRS7wFQcB1Xdqv6ngiTLo3\ngqzKSHSBCLKLSIeplE7HsX1vj/5axzS/tzBLLXMIvdgI9m+DSq5hkJQspHHfj8HxpGipuOb19WPe\ncUIxFdPxpGEO4Jo2yVSEh56d4wv3nmQHIn39McbGe1BUCdOwsYDNYz3Uu0p3b981wJat/WzZkeb8\n6Rym4LXb9a/TyFCi0/dLNpGWS8jQ0UUXAbAEbxI2nO4j3RNn0Z+ktK21XENAsCXvmvnHUeId85B0\ntJfBgSRD6VjAVAqpcsBUan+2ZTQFh2eRFQktpLBnpsqbZyr0X9cifaDTNw9dM8wXvpVlsdha12db\n/jO5ZaL3ss/Mzk29XFio0nJgy1CcKV8KNzIYR/dBuUhUI1L3K8tJNpKuoPjGvI7o+eINDXqTx/7+\nGPFkiLnJIn19sVWJ7r+Wd+fVeHWH45jo1QsooTSydukV+vzf/x3m0hKN178VgJrZGb+biMQ1FSUe\n52a/a9uuiyh4nkprw3ZdLtSa9GkKSVWhsFInVNQRDZvjRS/PGI96z1nd96tbnCzxU7fsIyp30nTH\nccgeW2LbrjSqJpMOqcw3dM6V6oQ0mYgsUS42iSdDiD7IleqNoMyvoPdqLDeNwKgb/zk+e2KJm27f\nypGnZlmcrTA4mmBiSy+96SihsMLztsH382Wqlk1vOsqCD41XFjz2zch4KmBu1Cp6UI20G7wRBIF3\nf/AgJ47Mc/dKCRPYPOpd/750lKlzBRo1ne99M4vjuKRTEWZNE9GwWZqvkFuoogyrGDIeqHSugKrJ\n6C2LkfGN72MbVGozlUqF1ZXf2iGKIn3pKPnlGrbtsDTvnddAN1NJ85mbukUorOC6LtPnC2ghOfCC\n2ijaDDDX9fyUXq2T/4hfvdMynVUA09ooVnUefG6O1/mLBl+47xTHfcZQOhUiHlFZLDRIp8LkSk1E\nUWB6yeOKuy786deP8xf3nsS0HOIRBdNyaPnvmlrT80Ya7o/w8JEFilWdneMp3nhoFKtH4+7pXCD9\n2Taa5L/+wk3opk1YWz+NHeyJMNhz6cVIxTZJ5OcY2L49kIm5to25vIRr2SBJqMPDCIJA9fDTLP7l\n53ANg+i+/cgobCs8ix1J0BAjxOrLiLEYPW97B6k77kAMedcwdvAQ6Z96P2Y+j1Ov0zx/jsojD2Pm\nlqlvOcgFcT8gUNlzGyM/fW3QtlCk41UWiapoIwOMfuLXWLnn6/S8+S1Er9nrbRcWX7VA5csd0biG\nIHj96JWUv72aoqcvykc+eQuqJpM4coGUKmPaJjWzTk/o1ckw++c26v4x4DngTmAb8EAmk3k4m81W\ngNdls9m5TCYz4H9+KpvNPnSpgxW7mAAvd6TTcXK56uU3/BcelzrPySVvwl7V6ywulTYswX6loVsG\nYSlMqdBClVSK9eoLur7Fqreq5LgOU/PLxC7hL1Sodkk8mg1yuSq679kTMqNIgoTt2gi6guCIGLZJ\nLldlsdoZ2PPlCgvL3svTtYR1bRUdCduxWVwqkS95K11Gwwm2axtdx6QYuVzVN7v1QhO1VcczW50J\nqSIqZM/l6IlrL7hqwtp72WiaIMooeojvfP2sx9SIeollq06wbUryBqtis0xUjpDLVQMz53Ldu36S\nLNBsmiDaCKpOTBwhl6uy4Pve5EtNVlbqpLQUc5VF/xrJwW9U6gaaKqGtMYQ0/Hx8trjIuTkPXBBt\n5ZJ9o/s8H33GW905uHuQXK6K7a8au4pIvWQhOAJirIQmidimheCbDWOLG/5GpdkKmEoAihkil6uS\nL9QJ+WBJTyoOy1AcWEBtKezYPUHpeR277h07LIXJ5aosL1aQZY8mXq/p1JeqQSUtAMmVmJotkoz0\nMVrfho1LceAcri3SKimM+pVe3HYZetcFsdNXevoj5Fcuztwr1w0sU0JqG3XXTOSE3y+TGtVSi/ar\nenG5gmDbFMvNjpm0ZCHIYBkdSVtLN8n7JrPa4jRiLEbO1uitLrEckVGLAqo/CVFsA7m3D6uwwuLJ\nc0ibPZlAoVCnVG0xPuA9G46fZBquS8E3hRVUEcWwOXJqka9+/0zw4rJsm3y+RiyhUcw3sADXtDCt\nzgSwdyBKLlflwA1jPH82R8m0vXvovy+MlhHc++iK73Fm6ej+xTBNjdf23k7UTHr3se3t5F/7Nugo\nyZ1xwe6q1pFSUuRyVUTXCQAkx3ICplLb/rVd1aNQauLaDtGa/7zPlFb1zYgkoCkSJy6srOuzi37b\nHMO87Hg64E8Ej5xaIqaIzMx7453kulj+BDqXr2EZXqsrRou4HcO++2FI34gj2ajS6mdzbHMPJ48s\ncOLYfCAfeSXenVdBqqvxYkKvTuK6FqHE9nXfGUuLCIqK0ttL9fDTlL77T6gjI5QntkK1Rd20cVwP\naK5bduBp1A5JEEipCisbMJUWGjq67bCvJ4ZtOVSKTQQXwsst6mNe7jLWBpV8tmGrYTJmi/R1gTNT\nZwt8/74sywsVXv+WDMn2ZFgWiSNg6BbNhkn/UOf5SPaEUU95Y/6pcj1gKkm++XW9ZnA+m+Po4VnC\nEYV3vP9AwIAFWMpXIF+mYlj0D8awVJuQKHDimXkEAca39ASFP2qVVlBhba3MTJJF9h0aQ1+KcbLa\nZMQ3Je9Nx5g6V+Ch+89QyNXZtX+IxbjGbMFEMh2yxxZpNU2iCLiiQF8ixMyFAlPnVlA1iYM3T2x4\nryVJJBxVAk+ltgRtI9/B9HCc5YUq3/7acZbmy8QTGtEuCbHqM8XahTaKKw1qFZ3tu9OXBIraXkrw\n4vyUfhixXGoyl6uhhhUsU1/FCGmHaTmcmi7y+ftOUazq3PPYZJCH7N6U4taD4zxxbJ6j51YY7ovw\nWz9ziLseOs93f+B5CgrA/q19nJgqBEzfasNEAMYHYiwXG7zntm288dAYtabJUyeXkUWBj77zGnri\nGs+t+MzkLqaGKAobAkobhd1ssvCnnwXXJbwzg1OvU37kIZxmk963vp3+n3gvdqPB7H/7NPrMdLCf\n3NdHaPMWas8cRtA04pkM1WNHAahofRRvfh/nppsMD0d554cPbeidJEWiSBNen4vs3kPvW9+OubzE\nD07W4QmPdxFfU22vGyhqs3LC23cw9m9+84rO90cxJEkkGteoVfR/NfI3AM1n0v9sZgxFFPj6+ft4\nZO5JfveW//CCLVB+GPFKgkpzQHfNwTH/s+74CPDpbDbrAmczmcwFYBfwVDabnQPIZrPLmUzmq3hy\nukuCSlfjlY2iL+9ycamZDZLai0/sPcNr72GJypFA6nSl0TbFBqj4bJ+LxUbyt7ZELSSH6Av3sNzI\nrzLqdl13lfzNtM3g741kZ22zWt02ApPp1Z5K3rVKqO2yuZ1EMSSv9j9Ru+Rugivzm//jMT753v0c\n2H55w+pLhW56Pi29y5tYWdBJAXZcwAQsq5NEtivAdZ+XYEq4uAiKLwFQJJoNE0Hz7lvITeC6Lit+\nedS2DKc3lGKl5dHEw7KXZBqmzW997gn2bO7lY+/eu6qNESXM5sQE58qTFKaOQBIi8pXRsWeWayzn\n6wwgsH+nd63i/svHlAQUxyVppimoVeIRxfNUEmzixQHUoYt7Kil65/dV0/t/Q7cI4dEph3r7YRly\no1kc0eBNsV3YgoFgeFlXVI7gui6lQoNETxjLtD1PpZqO5HYlSY5Io2WiCuAUVBJpFaPag9OMMbVQ\nZ+fmAWIJ76UaCitoIRlF9aSIjuPSdxnpW6HSAtvzx7EFB6sFss8yiqTCVEstFATApVxsIpuOV1Le\n339XehvqvIZRcwL2karJNOs6UauJUisR3n+AfB765ldQXa8PtHu67BhoEzuwCisYS4skM7sQRYFa\nVcey3cB7wfa9jlxZpFo3EIBoTKNRaPLQs/NML9VoCyk1P/kamUiRLzaxbe+e6/7ngtApyTw0liSf\nCtH0mQQbyd/iJa/NYaNF2c9bLEdmq7yvUw3Pn5S1mUqKHlnVFvCqBoakEC27RV/Yk0ckImoAKglA\nPOoZdVt4UpZeH+TRDRvDtAm15YIrq20FRVFg01CcMzMlmrq1KrEu1QwkUQj8wi4V4/6EZ3qpxi37\noFz3q2DGVJo+sKebDrJv2G/LFujQkr2xVnTNddLc8S29nDyywPS5lau+SlfjVRcXk76ZxSJTv/2f\ncG2b+HXXUz92FEFVGf6lj7O47DM7gYZlo4gipuMS3aBCVp+mcKbSQLcdtK4JcLf0rVRs4Lre2BlZ\nbgSg0qhfsbHbEHp2skjfQGdcbxdpOHV0kUOv3URjrgr+fFTTHco+S6jbbDnZEya83ELe7fL0cpmU\nJiMAguWyffcAZ08u871vnsIyHa6/dfMqQAkIpHcVwyY9EMOy6oSqJsWVBnsOjpBIhbH8MbFW1S/K\nCGrHdYMpfnzveAA0t0GeC2fyaCGZm27fyrdzHhtXNBxOn/YWl25WQkxkBpgtw5GnZonGVN72vv2r\nrs/aiMU1CvkGruuyOFsmFFE2ZOJc99pNFPMNJs94lWzHNves+l4N+Uwlo1NVFLzx7lLRl44G7IpX\nG6i0sFJnZrnG5+45ju3AFgR6gIWqTrTHJKLJCIJArWnyB19+lumlzoKV21nL4uRUiZNT3v3aMpzg\no++6hkhI4f137mA+X6dQ0fnIW3eRmeihXNP5xuNT7N3aCy70JkKMD8RwXDeQpyWiKv/hw4eQJYEe\nn20W81nasUtUpXNdF2wbq1LGWikghkNoY+O4rsvSzLK0bQAAIABJREFU5/+CxvHnAWicOA6AlEwi\nh8IUvvkN1JERKo8+ij4zTeSavaiDQ9i1GvWjz1F75jBKf5qRX/kEYwf3MPXoYZpnz/L9Iyrykonr\nQjKd2BBQ2igEQUAdHCI02RHyxBNrQaUun8dXWb/554x4MkStov/Iyd8c17lsZewBP1c/UzyP6ZgU\nmsV/daDS08COTCazBQ9M+gDwwTXbTANvAB7OZDKDQAY4n8lkooCYzWar/v/fDPzOK9jWq3EFUfQ9\nlQBqZu1Fg0oeYGMF4EzMr4T2QkLvApVqlzHr7jbqDjyV/P1VUWFXz05ERGJKNDCdtV17FXCl2waW\ns9pDpDsCE1/HCH6v21Mp4YNKKc0HlZROYtNt0g2gdPn7uI430ORKTV5qGJYNtoJm+KXrERjcFOWE\nC6ZfAWy51OTsOQvZ0Nh68iZa2z2WEabomRe7vjmvIlG1dISYByqJZoRq0wwqY9VbFqbl0BvqJGea\nL7VaqbSotyx+cDpHo2USCa2+ngfS1zBZmWZZOOMBGpZ3PZq6xX/9X8/whkNj3H5wdN35fe3h84F8\nq019V2SRsCbTdEEBQq0EQrRIPKISDSvEBdh05jpM2YA9669Zt6cSeEyldltCgAEkZE+y54h+n5JU\nZE2GlgkuRNUojbqBZTqkesNBxZBizSAliuCAI9hIrki9aeH43kGp/iTGyYMAnPdZJP3pCLWKTjzi\n9QtRFIknQ5SLzcuadK+UW7i+h5Ml2Ni6hOxfW8334Gn32GcfvECt1MTZ3huYSX9433u57/TzLBer\nXUbdEk3dZtQvzx3auo2qYMD8EeRqHkgQ6ZK/hTZtpv7cs5hLSwiCQCii0PBNuvv91TrT91WwBIFq\n0yQaVkj1hGkUmjxxxGNLyv4xQ77J9q1v2sG953IkbAlRFIKVvoHhRFChDiAWlsmXmriuS71lcqB8\nGvPBOrz9HQBEfPZSyOgA0TZQqHYB0/4Kvyj7wJoPOnYnguB5Y7XsFv0+qNQGOB1c8Cv4SAjouMSj\nCiF/MtcyLXRTJtT2dsuvr1WxdSTB6ZkSU4tVdm3qPGPluk4qpl4Rq3EsHUUAZpbb7MGOUXfBX903\nLRvJl984vsF7G1TaPlvn7OhqMHZscw+CANMXClx/65bLtuFqXI1XOsxWjuWzf43rWDiOjiBpaLHx\nVduUvvMArmkiJRJUn3oSgKGf+wWs9BCVufPBdlXTRpPaiwXrJ7jtUukF3WQ4ouG4LtO1FkcK3jO2\nNR4h5wMSm7f3kT2+RNiG3rgWlIpuy98A5qaKHLih09Y268ZxXJ5+ZJKlmSK8xltAcSt6B1TqWQ0q\niY5Lf9lisUegZtlorgds79w7yNJcmWpFJxpTuebakXXnlGyDSqZFT48G+QZCvomqydxw62YAZFki\nFFGoVfQOU6nvyhaDut9bN75+K+GIGlSziylSYKi8eSjOWCxMz6ExbMvl2hvH1zE81kY0rpFbrLGy\nXKdW0dm8o2/DsTES03jnTx/g2DNzPPfENDv2rPZ/bDOVTJ+p1PYwHL+EnxJ4i2/xZIhKqfVDBwcc\n1yU7XWLrcALNf7fM5+ucmS2RL7e49/GpYNvhvgj7dvbznSdnOH7PcXTTYbgvwnWZNI8+v0jBl3z3\nJ0N88r37kWWRf3zwPI2W54W0ZSzFvk09jPR37qUii/zmTx/0AEz/midjGh96085V7aw8+TjF+7/F\nyC//Ckq/t6A5vgYo3BYP876tg+xOrQcQa0eeY+nzf4Fdq61Gu4D4za9F6U9Te+Yw4Z0Zhj/6MZrn\nzoHrEjtwLcbyEtP/5VMs/rnnrhI9cC0jH/9EABA5pkHr7Fm0iU1IUZ9ttDNDZGeG8OTj1Pzr0tN3\n5b6f7QhFuhazLsJUEiVhVf7yoxau6/LdmYfZkdp6RRXF48kQCzPll7W6bM2sU2yVGI+vn1P8MKKs\nV/idJ/6Ad217C7eNvfaS21qOxULdA9k3KlL1nemHeC73PLeM3MB1g9e+YN/dlyNesV/MZrNWJpP5\nFeB+PAuOv8xms8czmcwv+d//KfAp4POZTOYY3jvu/8hms/lMJrMV+Gomk2m38W+y2ey3Xqm2Xo0r\ni6LeAZUqRpVRXpwZawBM+B0+qkQxnHlM20SRrgyB7gZ8LmfW3bR0BARUSQnYRrplICAgizLvz7w7\n2FaR/OTBsdZVfzN9UEmWNmAq+UCTYXdApe6qTTt7tnH72C28duR6YDX7Zi2otIqp1AYB7NUvyxca\nrutimA6iLaO2vJdjQhQY6lM4kYdWy3uJ3vfEFA8eKzIynkbVowgrXtLkmgK2ZHaANUXCsRxEn6lk\nNSMBS6kdlbqxClSy/epmxXaJX8fl2TN5btm3uh8d6L+Gr5+7DynhyQ1bDa9tFxYqXnWR7PI6UClX\navLsmTwHFRnRdlfRhxMRhWrDJAGojRgkLOIRhWhIDkAUY2HjficJUiBvApBNb49GwyCMQBGXakXA\nNVQE1euTiqgQiaq0WhYHw7dw5/gByoV2sh9Bb1lYpkO50iItCjhYOJKFZKrUWyaCD8wZXbf8/LxH\n/+6RW0wCYaNjpproCVMuNjek9XdHvtwCv6KYLdk4uozkn4/oAyJhWUSwbMr5Oq4LdsPsAEiqjKJI\nq7yXVE2mqVuM+KBSeNt29GYdjoCanwZtrwf/uK7HVBr3pArGsvdiDIcViv5EqM9frdN9w2hT8PpQ\nMqYxkI4yf66AAsSSIeSy/4x13edy3WS4z7sG8WSI/dePrVtJjoYUbMelqds0WxZvKj5P6etP0v/G\nNyFoGppPs1e6xhcLNwBZAPS2fMSXq7X7R2gNOBpVIqy0CkEVxXiQRApYlldJSMQDrRIRFc2fuOg+\nQyzhM5WslQ1AJd/H4/xCJQCVHNelXDPYNHRlYH9IlRnsjTC9VMNxXU5OFREE7z7M+xVxDMsJqj3a\nig8q+cD4rgtVlg6sBpW0kMzQaJKF2TLNhvGyJn9X42q8mKguP4ltVpG1XkQ3RLTvAILQAYScVpPy\ng99DiifY8uk/oHn6FE6jSfyGGzlb8d5vsiBguS4108LyJcsbgkr+GHDX147xrjt28A/5Esst7zne\nkYiQUGWyvnfZ5h19nD6+xDUzDX7s3VuDY9SrOuGIghqSmZ8pr6o41gaVQmGZU0cXcT1iKQigLzco\nRb1jJ7vKu8uKRCyhoU9Xoccbi2Q/n0ikQuzcO8Qzj03xmps3Bc96d7Qry1UMi0rU+79aNrjudZtW\nPd+xuEZxpYFWaCCKwmUBn3ak+iKEwjLJ3gi7D3i5QMQ/375EmLZwNu2Pa/FkiFvffGmT9U6bvDac\nPeUVtxgavXgFWkEQ2H/dGPuvWz+5VbUOU8lxXBZmSvSlo6skcheLnv4olVKL8GWKJ7yQmFmu8fDR\ned5+82YSPlilmzYC3qLdsfMFTkwWOHpuhbF0lE+8dz+5YpP/ftexwMNIEgVsxyURVVlYadCyXF53\n3Rj3PzWDIMBSocE9j3WAp0RU5d/+9EHSPgvul7tY5mslzo5pICrqZStVOYZB7u++gl0usfC5P2P8\n3/2fIAgeyOs4JG+9DSniGdhf27ee+do4eYKF/+8zIIqEt+9AkGWkeBy5t4/GyRNUH3/MO9dEguFf\n/BhyMkX8NYeC/bWRUYZ/4aPMf/ZP0CY2MfyLH1vFOBIVlcjuDVYbIWCNw3rz9yuJcPjyoFIkemUL\nRP9SY76+yF1nv8G+/t380v6PXHb7geE4Z44v0dP/8jF0/vHMPTy9+Cy/ceiX2ZLc5BWCqc3Ro6WC\nAkuvZCw1crTsFvdeeICbhq9fV+177ba2P39eO+89VTjDV8/ei4vL+fIk95y/n08c/EUGu5QnP4x4\nRWGsbDb7TeCbaz77067/z+OxkNbudx448Eq27Wq88OhmKl1J1bWLhemzhdoAUru6V91qkJIuX3Ye\nVrOPKpdpS8tuoUkqqqRi+BM13TZQJWXdgC37gI7lWAGI1G5zG2RSLil/M4Pqcd0VxRRR5qd2viv4\nu7uiWbviWedYnUHFsb0XXLdHy4uJto59OJFALXu/F0FA1rzPGw0vybwwXwFbIVrxBiKpEcZ1XRxD\nwA6bHWBNbVeE8gCBZkULQCVFFrEthwfueh51MBrQ8y0fVGqvegE8fWp5Hag0EEkjGnEc1UtSar63\nzJxfIWZ+Zb1UslDxfX1EgVBo9X2NR1Vmiy1GEVAaEQTBJRzyKmCF/SpXZkmgVmmtKk8PILgCsql5\n5eNtEdn05XR1kzDQAqYWqzjNGJLqrV6qkkoqFWJxpUGquoutyc2cnFwAvFXj9kqubtjIsuwZZIoi\nkgn1lomk+/ekLbMSPFDJdlz6I95nCakD4G3N9NOsGwwMXxpMWKm0cH1jcEsywQwFLBtLErFx0QSR\nKJ0FP6vpgUqiJCDJYiCNWAsqjbZyIAiEtmxBnZ0hp6ZI5C7A2F7/OnoJr5JOI0YimEu+GXdEwcnV\nEYD+pC8trBm4uDRth3rLYiwdC1beVeD1145w/JEpsN1ActbUbQzLIekn7oIgcMsb1vumtGVh9ZZJ\nvWkSs5vgurQmL6AODSH6fiiK0+mjFlCs6LiOQ/Fb38QJbwa8yZpNN1PJO3bliceQk6ngGV/LVEL0\nQCXTT+4dPOAz5E/odMNGN+0OU2klj+u6q/r0Vl9a1gYbwTM6tR2X1BVMdNoxMRjjqZPLfO8Hc8zm\naty0Z5B4REX1J8yGaSNGvTFI8Jl4up9ohSyLvccKHte4+5jbelmYLTM7WVy34n81rsYPMxxbp158\nnmVxE9dmPowsrQdNyo88jNNs0veutyCqKtG9+4PvFhveM7g5HuJspUnVtD0vOzaW4vT5TKUacM/h\nKZaHwmSSEW4eTLEt7o0HpRXvPTYwnCAaU2nkGoGUznVd6jWdVG+EgZEEJ56dJ7dYDcCQWlVHEOCG\n27by0P2nEYFeVaZgWog1k/NZD9xfK/FK9oSZmyqxIx7mTLWJ6LMt48kQr7l5gvRwnM3bN65+pUoi\nIUmkbFrMNb3rMawp7H3N6oWdWEIjv1Qjv1QjkeoYhV8uJEnk/T9/A4oiBf5E47EQogA7B+I8gwd+\naaErW3Bc2yaAc37F1KGxK8sv10abLWLoFo26gW279PRfGZDQ0xdh6uzKS2IqFas6h08t88zpHI7j\nkCu3KNcMnjuT52Pv3ssz2RzfeWYWx3VxHBfb6axIzebq/M7nD9MyLJwuJo/tuOzb2scvvHMPv/e/\nnuHbT8+gqZ6w3XUJKpXumkjxntu2Mj4QI7RBFcK10ZqaZPYPfp/4DTcy+DP/+6rvHNOg8ugjRPbs\nRR0YoPzQg9jlEmIkSuvcWfJfuwtzeYnaM4cBWLn766TuuJP+d/8Egs+YNZYWaV04j1UosHLvPQCM\nfPwTgXl1O1zbpvS971B59BEGPvhh5NTGxsaxaw+y+Xd/D7mnF1G98nvk5Yr+Yt/LzVSKdEClH+WY\nqngSwI2UKq7r8p2Zh9jTm2Ek5lXgvObgKFt29K/L0zfatztfupS59VRlFheXvz9zN2+euINvT3+P\nqcoMW5Ob2Nu3m1PFs/zKgZ8LPITPli7QF+q5pFH292YeYb62yFu3vDHYbm2b2tGey9bMOn91/Evs\n7NnOHeOv2/C4s9X54P/dc/CKUeXzJ/4WURD5ub0f4mzpAkdzx4O59g8zfnR5dVfjZQ3HdSjpnQlM\nxXjxxqtmICNrM5V8UMlskNKu7KXfzVSqXQ5UsnRCcghJkDryN8tEFdcP2O02mY656oG8nPytDSp5\nTCWvbZp88cldpAtUWs9U6rTLsbzE7KUyldqytJQcRfQTC8FxcXxfmVrVW+ma9YGbcNVjP4gNFct0\nwBGwZaMDrCleuyTVGxDLBcljwgCbhuJYsxWKSzWipgz+3N7wJXZtKZEkChy/UFgngZteqqHn0ygj\nXh8rl732zuW9+1ys6uv2qfmVbGzDJrrGyyERUWnhIskScsO71krIO6balfhOnVvhmjUMqFbNRkDA\nTTShGEHyQSXLl2i1cJlaquLqcUj6oJKokO6PsniuwPyidw5LvklosjeMNuf1MQkQgXgkgum6VJtN\n6i0L2T+Xqu8ttGuih5NTRWaXq/SHDA7O3c/YYGeldkukTn/kDLL8Gi4VK+UWtJlvftU/reUBBLrj\nYAKa49K9BuS2LCQ69H/Fn/yoeEmnLIu0mjpD+gri4AhiKEwsrDATGuBA9VzwIhVc7/fEUAhlYBBj\ndgbXcYIqHjLQl2xXP2phAkW/P8WjarDiHJVEXrdvmDOPT3ugki9/a/sBjbeWOfebv8bYJ389YEV1\nRxtUqjVNrEYd2V/1aZ0/FyBppZiI0gVaI4kUqi2ap7Pk7/oHIgduBbagqB6opHSBSq5lsfiXf446\nMMj4R25jpjoXeJSFNYk3Xz9O/fllbMvB8O+v459j+5nSDcsDlbqq5dnVKnKiq0JkXCMZU7mwUKFx\nOos2MkrZx1qTL2BFfHwghvX4Qxy5exrCE/z4TZu8++u3xbAcZN88KmT5YKjP75Mck1Rx/bg0vqWX\nJx+8wMJs+Sqo9CqNTCbzFuCP8YahP89ms59e830S+GtgAu/x/INsNvtXP/SGvsRoFJ9nzk5yt/1a\nrJUqNw2sngg4uk7xgfsRVJXU7Xeu23/BB5V2JKOcrTSpmRaue3GmUo8/TlphiaWQ9wy9Z/Ng4EsE\nUMg3UFSPPZTsjTA/XcIybWRFClis0ZjG2KYUJ56dZ26yGIBK9YpOJKaxa/8Qp48vMTAcZyoWolis\nITcsVsoGgrB+kprsjTA3VWJvOMSZahNaFpGYGlRL3rLj0n6NCUWmYlg0LZuEIvGBDx5cBxq1x2jH\ndklexE/pYrF28jwRC/O71+3ANCxORBUmLlPu/WLRlsFXSi1ESSA99OKYB91G3TV/AetyE9t2jG3u\n4bknZxi4QgZpd5ybL3P/UzM8k13G9SWL3SNuvtziU1/wABhZElblifGwgmV71dVqTRNFFsEFRRb4\n+Hv2UWsaHNyRJqzJfOK9+/ndLxym3rL4wBu205cI05vQGOmPriumAt4EufrEY2ibtqCNdCSTjq6z\n8D//FKfZpPzg94ns3kP8uhsAMAsrzP+Pz6BPXkCKJxj51V+jcN+9CJrGxG/9Z2b/8Pcp3ncvAOGd\nGaL79lP8zgMUv/VNjMUFhj/6y9QOP83i5/8CbL+8hSgy8rGPrwOUAARJoueNb6bnjet4C+tCHRy6\n7DZrI+b3LUkSrrgvdEeoy/cxGlfXfRdLaJddKPyXHm1QKd8sYDv2quJPk5Vpvnr2Xk72nOZXD/4C\njuvw1XP38tTiD/j3139yHaizWF/iW5PfY642T765wod3v49Dgx435Z4L9/Pg7GP8pxt/M1jkA88O\nJdfMB2353PNfREAgpkQ5X55ivrZIy9ZZaRUYiKSpGjX++Nk/42B6Hz+790MbntN8bZF/PHMPLi6H\nl57lLZvfgOu6/NPMQ2xLbuI929/GULSTF3VbuRzNn+B08RyvH3vthh5Ls7UuUKlL/vbFE1+hatT4\nie1v50B6Lw2zyXdnHn5J8/QXG1dBpatxRVE1atiuTY+WoqiXXhJTyQoYP2uYSi/ArFt3uo26L++p\nFFM8Cm1Fb/mfGRvSDDugkoWxSv5mYto+S2cDppLWDSpZ6z2V1ka3/C20FlTqapdlekCMZb80plK7\nRLhmK4CBJZnItkJjxfu8VHWYXqriuC7Xbu5FmfRZaZZI0V9ZtSUrANbaNHlZbWGaGislK/B9Ggsp\n1H3PG6PRSXJaTR9U8plK1+8e4InjS+skcE+cWMQuDqCMeF4Web+NbaYSeGyl7V1U9nrLl2ltYIiZ\n8Fd9YskQVsECF2TVBwjprBxMnV0PKjV8fws70UQuRhAN/97417MFTC5WcUKdZFWVFHp91V9+pYFu\n2Bw+uuCBNYqEpnWBSq6LqkkIjouEQK1hoDX9CjNNE1kSeM3ONCenipyeKjLRaNDbXEDUOxT90oPf\npfrE46Ruv+OSyVGh0kIRvPbbWgPqPWitKLIs0jBsDCBku8S6rolreKbcbYaS3MVUckUBQRAQl+dR\nXBt1yzbvOocVluUokmvjuDaSICP5z5KohVAHB9EnL2AVVgIKuIInu3Icl1pFxwByviltIqIEK863\n7BokGdNQELBxMX1pSMn3oRpZOoNdKlF57FHS718PKkXbTKWmiVjvvHCb584ihr1ncmpYY9NMZ3yJ\nRFSKVR27Pcw0vH6oahItQDXaRt0yZqEAjoOxtMjbRm/nxze/oWN2Lwh84A07+NvzRVpNMwCVEkaF\nRGgEUfCqunnyN5twF7Bl5vOrQCVBENg6nKB45Ciz/8/nSN5xJ6Wb3wZAn2xRfepJYtffcFnq/ERK\nYVv+aWpyGPENBwMvi26mkuS7akWN+qp9XUxkc33i0z8Y4+Y7tjE0dtWo+9UYmUxGAj4LvAmYBZ7O\nZDJ3Z7PZE12bfRw4kc1m35HJZNJANpPJfCmbzRobHPJVG9X8Dyi7Xj9ss44Ayo8+zMrX7sIqehLr\n5O13IsXXT+AWGjqKKLA55o0NVdMOJvVRZX0eILe8sdtORzA0kXBJR7Ec2mU1HcehXGjQm44hCAKp\n3jDz0yXPE28gRtUH0qNxlVFf1jo7VeLQLW0Wk0F6KI4kibznw57f3j7D4tpomAdNrw5OLBEK5HLt\naFdiS9Ys3jDSy/OHsySuUJ4Gnln3csugZcM1PdENWUixZCffuZhJ9wsNRZX50C/dhCS9OAlQe+IP\nnnxO3gAIvKJ2dMnf2pKn9jvpcjG+pZef/41bgwWZK4l8qcnffucMz57xJrwTgzFuOzDCgW19fPpL\nP6BY1TmwvZ/lYpM5X6ps2S59iRA37hmkoVu89/VbWSw0+W9ffhbLcrBsB02R+OR795OZWG1EPtgT\n4d9/6DXM5GrcuHvwku8N13XJ/d2XKT1wP2I0ysS//y1IZwDIfeVvMZcWiV9/g+d19MUvoA4O0Txz\nmpV7vo5drRLO7KKZPcXM730KXJeeH38b6uAgQz//Ueb/5I+IXX8Dgx/63xBkmdSdb2T+M39C/bln\nmf7U/4UxP48YDtP3znej9Pejjo2jpgeu+Lq+nNE21072Ri5ZAfBi0QaVYnFt3fMkSSIf/OiNP9LS\nN4Cpqlet2XGdALhpx6QPOGWLZ5mpznPP+W9xfOUUAOfLUxxaAyrdc/5+nss9jyoqGI7J0fzxAFQ6\nUzyP5VgczT3PnRO3BfvMV5ZwXIfdvTs4WTgDwK8f+mV0S+czR/48UJ1MV+e4UJ4mrsZwXId8s3DR\nc/rauW/i4nL72C08tfgsd5/3nHtkUeb5lVOcKJzm3dveyhv8dnTbpbRsnZat893phwnJGq8duWEV\nuNTNVKr5+dhcbYGThdPsTG3jzvFbMR2Ley88gCLKjMZenEXNS4mroNLVuKJo+ylNJMYo5l4aqLRW\nRhb1HexfCKhk2CYROUzDam5oWNYdLatFf7jP8xVqeyrZBlF5vS53tfytC1SyjSuTv/lG3ZIgbbgd\neD4xrtD5LrxO/tYBRUzDe6nYL5mp5CW7qiVjYyCPNmFaobXiQh/Uai6nZ7x7vKM/wuRkCVu0kByZ\nxTmPoeYxlXxGkOBdC0myUc1hVhyXM7NlZECfLuPgomgyZsvGdQQE0aXuz0nbTKU3Xz/OE8eXVkng\nHNflyRNLhKw+kmqCsl5lKWeQOzsbJE/gGU6uBZXaUFxkDVOjLTsKxVSEFRFFDyOIPjjm+obPcYnZ\nqRKmaa9K/toeFlaoBZKBqHu/Ilve/WjiSRJEtwMqeZ5KPhhoWHzh/lOIloMN3Hd4hp1+e9IxDadm\noqhywB5r1E0iPnssV9PpS4SC88xOFxlres+I0+pMkJyWdz1dozPfK9V0VFkiEur0s5VKi95YjDLg\nRGpQAMH1yi5Xmibt3p4EJFXCtR0k0/aZSj6Y5F8bAQF/rZDE0iQA0d27vH9DMk2/D7uOAWIXqBQK\nBWac5soK4YjPQFK8ttarukffFwWavilqPKIGkwPdZ4iJrosOlGsGoV45qDYYL3oyw/qxo6Tf/9Os\njTZTqVDVCRkd8/vWuXMofd5qeGlLP9snO6zMWExleqGCUfGur6A3QQVNUwAb0fdYCYUVrCW/wKnr\nYs7OEt6+3vtD9uWhhg9MbqvPEM/JQAZNlWiZNrrRYSoBWPkcbN266jhbh+NEvvtc0P7SPl+acuIx\nFp57lCFcEjfctO73u2OwmaeES8Jq8Nb9HTaA0sVU0vwbHW/W6R5pHdlBsdaPS4IgcO2N4+s+vxqv\nmrgBOOvbDJDJZL4MvAvoBpVcIJ7JZAQgBhTwlKD/YkJvzGM2FzC110MTCnqn+YX77sWqVIjs3oM6\nOkbfO961bn/Lccm1DIYjGnF/3Kv5vjWwMVNpZa6CqNu0fBAiNN/g2OE5brjNM62vlFqrpFNt8KVU\naNA3EKPSBpViGqGwV6ms4L/3mnUDx3ED9k07EqrM7qEkT0UUmg2TVO96g+y27Gt+usz1oxOcLhvE\nxy4u31gbia6KcOPRjcGobgBnoza82HghYMza6L5Wl/JTulwETKWWTfUyTKVnz+TQDZstIwn6+728\n4HLncHq6yH+/6xiW4xLVZEo1Hcf12Nyv3TvEh960E1kWuf/JaVYqOm88NMYHfcPrWtOk4fft3riG\n3AUobh1R+ORP7ueP/v4IIUnkkwMLqH/zCPYnfg0psjr/HU3HGL1IBVmrVMJYXkJOJKg8+QSlB+5H\nSqWwSyVm/+gPiX7y4yx95yHKD30fbXycwZ/9BcKPPszyX3+Rqf/7P3sHkSQGPvhhkne8gcpjj7D0\n+b9E0DR63/wWwDPA3vbHn0XokqiKmsbIr36S+c/+CY3jzyP39TH6yV9HG/nnMVXujjao+GKkbwDI\nDrZq0IzqG369Fhi+krAci/snv0s60s+hgQOrmD8bxbenvs/9k9/lYwc+wvbU5QtrPDr/JFElyrXp\n9cywS0XDbKJIyqo5kWmbzNUWgr+XGrlVoFK0GfObAAAgAElEQVS26FXqdHH59NP/LwB9oV5WWgVy\nzZVVx9dtg+MrWQYjaf7jjb/Bv33ot5nxARjbsZmvewWHjuVPBqDSVGWGab3tGdYB706snOKtW94U\nVO8F+Pq5+yi0iuzp83LccpenaXecKpzh+Mopdqa28d4d72SlWeTYygm2JTfzS/s/wtnSeb548it8\ne+p7XaCSl+eNxoY5V54E4KvnPMbefH2Jn9rxTgRBwHVd5moL3rzIqARz8MNLXg5469jNCILA43NP\nU9RL3Dl+K0nth7+wdxVUuhpXFAXfT2kiPsaR3PMvk/xtLVOpftF91oZuG/RoSXTbuCTAZToWlmsT\nkjRs18Z0TBzXwbAMelQvqTry9Awry3XueGvmovI343LV3/5/9t48zK6rPPP97fmMNc9zaSrNkmVb\n8gie7YBJIBAIhO50k046ySVwkyd90/Ptm3ToXNIh3BDSZKIJQyCMjgFjbPAsW5I1WLNKpVINqrnq\n1JmHPd8/1j77VJVKtsEkkG59z6OnVHX23mfttYf1rXe97/vJNaZSxTVX+Smtjf/2heMkoiqRPoFM\nr2UqrTy+Y1flb2+MqVStWKVYwhjYbFvGmKzDzsjQDL6jcvSC0DUrwWprtnmGpsU+5meqoJIw6s6a\neY6lXqGBPvylLm7ovYPvMcPUYoE2VcaxXOaAvqgGpotaTuDG8+SDy5TOmUQNlYGOOnpa45wdW8Zx\nPVRFJpM3yRQsbtraxj3b38PfPX+GxuVZvvpVnzZzmVJbB0vZSmgkXI1i2akuBhNb4ylTNUhWAkAh\nUk5iBp45iivjyg7N/XVMn8nx6Hcv8s6Ht4X75oNVSVMvgW5iWDFcz0P3fTxZonpZvFJtlVtXdGKJ\nwMQZicNn57kRGVuVefHMHIXmODqwd6CJmTPz6LpCUOSPYskiXu3/ikNPZx09bXF0VWZ4Is1dqgBC\nfLPmqVQFlbwAVHI9j//yP19mQ2cdH3qX8Acpmw7FisNgVxPd6puZytWemWhMZ2YFqKQgEak30H1w\nlkrCCylgV60sN+0G43DD0iQAdTt2AAK4KQcsPcU1QY2huhauJCOpKmqTAC+c5RSR2AAAjVVZWrW6\nkSJD4CNWF9PQDRXdUELpgeT6OEC2aNHeFCNTsJB9D21JJCnW3CzW4sJVq5jxAGRbSJeJuwGoJEm4\nhTzF06cA+KWf/vece+53wn0a6iMwm6OwLCrwyRUBKkUMHQEriohENcylmjdAZXLimqCSY7shU0nx\nHZJe4AmmyZiWg18W/Y6qguNgr2fWXZ5BMcXfzekpcoH+LTovVvjST3yX5M2vvtqpzNSMWLudDCAY\ncFWmkm17aJ7YvyFfpBDkz6omo0diqN4PP+G7Hj+26AaurPh9CjiwZps/BR4FZoAk8J7h4eE3Ngj9\nI0dh8WUAbKMrAJUC6fvcLPbcHPEb9tH9f3zomvsvVixcHzpjRuiflLcdqqSE+DpAwexUFlVzsAwF\nRYLYQplMfW2xLB34AVYnojVQSbxHakwlI/x8YjRFpWyH78ZE8mqGjCRJdHTXMzayRF3j1YBOa0cS\nI6JyZWyZTdvEO7Gu/vUDP3UrWFm9ifX3Wwmy/KiYSm80VsqKOt8Ac3KlUXdBDAMk12EqXbyS4RNf\nOx3+3tue5LfevYf6uI7tuHz9ucts7Kqnrz3BQqbMlp4GDp6e5fNPXAwZcFVWuapI6JrC86dmOT+R\nxnE9MgULQ1N4+LaB8DsSUS1cLFkvtvY38nv/6gD2049T/PbjVIDM00/R/Na3veo5+55H6fw5ss8+\nTeGVE+F4DKC1tNLzO/+e3IsvkPrG1zj7f4vi3GpzMx2//GvImkb9m++mMj6GOTVF3f4DJPcfQG0Q\nDKn62+9E7+hEkuVVDEFpHc8zWdfp+uCHKLz8MrGdu1Yxdn+c0dKRRNVkegYaX3vjdeLQ3FEu7niW\nqB7B9x94w6wkz/f43PkvhyDDNy9/l4cHH+BA543X3OfZqYNU3AqfOPEX/McDv01ci/Kd8e+zIdvD\nDfU3XHX8vxt+BIB/e/OHQ5+j14qKY/L/HPoou1t28Avb3hX+faowg+d7IbNoobQ6xxlJj4b/j6pR\n7u29k71tu/ivh/8olKxV43xqGNuz2du6C1mS6U12cSkzhulapMrL4dztUnaMkl3CdC0+duzPwqJR\nF9Oj1GlJfHy+f+V57ui+Bc93w+MvV9Kr2pSz8ni+hyzJnFw8yyuLp3nHpof5xiUBBr1j81sFEBQ8\n1a2xFmJalN2tO2ibaF0FppmOyNtX2stsbxoiY2Z5duogjUY99/ffRcbMUnRK3NC2m7NL58nbBXzf\n59j8KxiKzs7mbdiuzXcnnkKTNe7vv+t1XZ8fdVwHla7H64pMACp1xFrRFf1Hw1QKZF5VKdgPJH9z\nTQzFIKknXrUtFUckaRHVCP2UbM8J5W+lgsnhZy7juj53PrC5Biq5a5lKK0yq16D/nudjBzI1M/BU\nupb0zfN9FtIl5pehczAagEqrt10pf/ODSZvjvTGmkmmLF6QfJCylWJaO5hjLGU+sSbsqE/N54hGV\n7GIRWZFYNko0AfPTNVDJ9mzGsuNYiARXWthEzw11iLkH1EU1yNtY+FgIzyClWIcbz5PNiqRkOV+h\nKUhA2xpjTC0WKZsOyZgerrglYxpbmzYzlIAyx6kAza7J9q1tPH54chVrCVYzleJrmErV6iiHLi3R\nBxiVOOXgvpAdGVezWLDFtR4+v4D/1q3hAF9N5MtaAUUziZST5PKWcJXRVeoUwTzDU6nX6snaWXRZ\nw49XQSWIItZB+gcaOXlpkalUkQ3IdCQNZhAmoFJA7y+XHcplBz2iQsWitT6CIssMdCS5NJ3FbhD3\nehVIAvDKAdAUgEoL6TK5osXoTDbcJhWAMS11EdqMXVzOXQo/i8Z0ivkyca8MspgM6EmDRk0hHVQq\n0nSZxa99BaIbw/1c38ezLZozMywZjWypFyvB8ahGRRb3tOFXsADNt7ADoFVrEpp2e3kZ+gT7JhGs\nBFdBIzSZKspVZZol6iIUchVcx8P3BKiUCRhK2aJJq5lGch3kaBSvXKZ4+hT6PfetuheqyfdCpkzC\nEf0W3byF8sVh7MVFYdYZiRJrrCW6dcEkzsoJIF0xRZ/EoitBJeHvVFiqJTvmZA2wWRmKKuP7UAlY\nV4pn01Mvzt/QVArlCn5wfY3uHsyJceyl1UmU7/tEX/weFrBU30VLdgZregrFd5EXxLNojo9RuXSJ\n6ObNVCYnkBQVo3uNZ9jlWuJmTk6E3hRVTyXTcYkE6GFLrsRUMFfUdIVYvB4nm+F6/C8ZDwKvAPcA\nG4Enh4aGnh8eHl5/iRZobIz90PKi1xOtra/fX8Qqp5lMn8aIteJG6oEsGcumqTnB7POCkNV5562v\nesyRqaBqW2s9vuUTUxXKvk8sQJUGOuox1pzv4mwevcPAAva01bPoTWNbbvg9w6fEivnAhhZaW5Mo\ngbShUrRpbU1y4aSYbHT1NNDamqSrt4GJ0RS4hNu2d9at2+6NQ22MjSzRHey73ufnTs6wNFdY9R2v\nJ7pLFZhNI0uwp6/5qvOGGhANsHFz62v6zPwg1/ONRCyuUypabN/dvS4g93oiERf7SUjhYsDAhpZV\n1e9cz+fvPiv8jd593xZGpzIcu7DAX337PL/7K7fysS8e5+DJGeAKsiTh+X5YgQ3gwI4O/tXP7GQ5\nV8HQFQY66ymbDn/73Qt8+4XLxKMa997cy1tuG2Rj32sDGb7rsvDMs7ilErHFJWa+/U2M1hacUpnc\nU0+y+b3vRDGu7g/Ptpl97HHmHv8ulRlxP8YHB2i4YS92Lg++T+973kWkvZ3Oze/lSkSlePkybffe\nQ9NNN64Chtr+zf957Qa23nDtz9aJ9p956Afa/h8qqvdta2uSf/eRt4DEDwwIOa7D9w89i6vZFHwb\nP2bSlnhjVbo+f/IbHJ1/hS3NG9jQ1MdTlw/y2fN/R0tjHbf07mMqO8ufv/x53rb1fvb37MX3/ZAc\n4Pguf3D04xiKTtbMc3BG5+533LqqGnfOLISVx7448lV+/77/6zWZUADnFmYp2EWGMyOrnvlvjJ8E\nCNUjOT8bfj6ZmabimuiKxtaWjZyav8AD2++gJdqIdEQiY6dXHev8JSGL0yMyLS0JNrcNMJK5TFHN\nkJWEVK0l1sRSaZkr9gQXU2M4vosmq9ieg+u7vP+Gd2A6Fn99/Ev81dnPYnk2iqTg+i5RNcLdg7fy\n2MjTgADYLKPE1849xouT4pnvbmhlqjDDgZ4buHGDWJiWVPFsm1TC9kY0FcdzaGyOocoKyozYJlVZ\npq++m8nsNLGIwQdv/w3+4/f+kEdGH2OwvSskGwy1DzBVnKZcMTlyfJhMvkBTfZI/OvGnqLJCxszy\n01vvZ2N3zesM/vHet9dBpevxuiJtiglqY6SBOi1B/o0wldZ4E/2g8jfHc/B8D0PRSWpx5teg1p7v\ncXLxLDtbtlFxqnrVWoJTCr5Hl3VOHZ0KpWVWxVklf1vtqVRjKqlrmEpPn5jm745eRtsMduCplDTW\nf4ArphtW1pI8cZxXM+qmCio5r2+R+PzJWRbn89x5/2qGRFX+5lUcXN2kTInWjiTpVIlIJUnZFwnr\nQHuS1GSW9u56jk0JenV19dRVBLCWswq4gdFzTJfDErMAyYgAlZAkKp5HDFDyzUgNabIZAf6UTZem\nwNQzGqwAVkGlcpCwxQJmTGdzjMuKOH7U99jYVU9j0riaqVRxavK3NZ5K1WpYWccFZIxykopTwfd9\nZEfBjBV46WKWvWhEXJ+55VJYmr4KcpTVPBFN3EtTk2kkJOSoSmtUFaASsL1pKxOFCVRZRY6J8zIk\niT3d9ThTOTZvbGaP74lJAqLKGQgySlVuVKk4VEoWiqZApWZevbm3gYtTWXLLOVTWgEpVppItjlf1\nnsqXbIoVm3hEC6vjNdVF0FR5lY4lGtdIXB7mxsVhhttvF22K6zSsSMBVxyL9xLcxt96D8O4F2xey\nK9V3ma2vgRWJqEYlAJDifhELMNwKdvC8r2Qqmd3iYYipctDfoo9lXYHgdVBlmiXrDJYXi8xNi3eR\ni5C/EfzsDFg7jfc/SOrRRyieOkXjNUClxXSZwYCplNh3E+WLwwDoHUKGabQ0oxRtZMMIfZ/cQlGA\npFbAJojV+kfWRWK5EvwxJ9YHlaoT72LQdsVzILiGEV3BtFyk4OQj/f0CVFrDVCqdPYM1McZE0wYu\nGp3cn51BmpuizdTAdTD6+jEnJ0g/+Tj2coq5T/8lels7A7/3kfAYvu9TvjyKHIngVSpUVrQ3ZCo5\nHp4roVseDcXaPadpCnIkgr+wPnX/evxExzSwUp/YE/xtZfxL4A+Gh4d94NLQ0NAYsBU4cq2DptOv\nf1HoB421JctfK5anvge+R7zlVlLBPer5cGkmTeHgIZAk/MGtr3rMM7NiddooWnz6M4fhjg4yvo/s\n+WiyRC5dwvd9Dj1zmZ6BRprbEqQWizR3xygAO5IxDsc0suly+D1zVaBfhsXFPJ7nIcsSczNZFhfz\n4VjruC6Li3mMqHhnjo0u4QRjuC/567a7d2Mju27spmugYd3P27qSnDsJJw6L51xSeN19KgfjcntE\nJ3eN6+x5HpIk/BZLFYuyee3KQz/o9Xwj0buhiXLJolwR/36YcANKciZTplK2UVSZdLbEZ759jhfP\nzLJvSyuaKjM2k+OWHW1EVInJuRyqInP2copf+4Pvs5ApB555Lp7vo6tyWEDlbbcN8I43bQDPozVY\nFFtOCfDvHbcP8NBNPeiajBJ477yevss8/X0WvvC58HclmaTzw79N7qWDLH/7m4w+8hgNb76b7AvP\nozU3E9+5C99xmPnUJym+cgJJVam79Xbq77qbyIaNq4CTPJAP2hC996fo+3lxPZeW/+HeAT8J8aO6\nb1+cOUKqlCahxSnYRY6PX+Cm9vVB2DNL55kuzPLgQK2QgOVaIs8MgOZTi2d59MITtMVa+KXt/4yE\nFufGxn3892N/yp8e+gwL6SzfuPQtinYJ7cJzDBobmS3M4fkeUSWCoRpkzCwVx0RCwnQtDo2cZmtz\nbR4xUxCAuCzJXE5P8rfHvsVDQZtcz+UPj36CDQ0DvHvL21e1/+SViwCkymkuTc2gSDKPXv4OB2fE\nUFL16R1ZGAv79nOnvgHAloaN7G7azan5Czxx7iA/NXgvzUYjM7mFcFvbc3h56hSKpPDN4e+xMbaR\nFkUAdH9z9GuhvOy+3rv40vDXefzCc4zlJmiONCLJEkWrxL29b2JbfDue79ESeYKxjCDy3tVzOy/M\nHAIkbm25JQSVAP7L9z9G3i7QnehkujDLKzPnAeiOdIdtSxfFGsxcbpFzp2doaIoynhGLfn928HO8\nc/PbyBRqpIi9zbvIlHOMpibxixof2PTP+dpXD/PXpUcYGhR5d6PUTEyKI5/u5Hhujq7GXVzZfFwU\nFMInocW5reXWVffpP8T79log1Q8u2rwe/1tGOmAqNRgNJPUkebuI5/9wbPgqA0i/hlG367lkzWs/\nANXKb7qik9STqyquAbyyeIa/OvM5Xpw5EhqtRVQDLQBrCsH3aJ7OmeM14zPTdF5V/ra2al01ZlJF\nPDdY2XdtTNckcg2mUmlFomWbQVWlFYDXt79yipMHV+T3IVPp9fX16WNTnD0+w+Tl1UZypuUhAa7p\n4sUsyk6Z1qAaSaLcRFVT3BXX8X3o7K0nHtdDiRPU5G95K48viwQ3qim0rjD8TAQV2SKGSjFgR2np\nHnaa78Lz5LAEelNAHY8G4FHVP6cKKlX/3haVqGiBL4EEPa1xulviQQW4GjRSLNuh6fZaT6XNPfX8\n4kND/PY/vxFJAqMcp+xWsEwHyZdxNAvfV3AUmQgwPFljXxRyJp7iUJZKOAGoNDspJgZ6TKclANQk\n4L1b38F/OPBbSJKEosoYEZUuw2dLwO5obovzL96yjTfvExKjqjeDeeoE5UPPA2CZNpWygx8wl6qA\n3c1bhWQhnxb9txpUWs1UWsnimgvkFrPBz7bGKLoqszLd16wib730HaJO8Mz5PlJUWyVhkO3At2mp\nRtu1fZ/iebHqv9RYM8WORzTKAVOpwU3T2pGgqTyDKVVBpYCplFoO7xF9DTNMNWrPWJWptHW3AHye\nfkwAQI7vkw0AveVcha6KAF4S+25E7+6hPHwez1wNetSYSiUSAagU37UrLFesdwpKt9rSQtTOk4ir\nRALJn1sUg78a9EUyWgNTg9MV4I8sC1BnZhrPvnpipQQAWimoWKd4dngNDU3G8338wDtLbWpGTiRw\n1jCVypdEopbbeiNTqpDxaosz9KwA1oz+AQonjjP3V38Orou1MI+/4j1iz8/jFQrEd+9FjsUxr6wA\nlaqeSraL40Bz1kHxXQictHRdRTYi+I6D7/yTstq5HvAysHloaGhwaGhIB34eIXVbGZPAvQBDQ0Pt\nwBBw+R+1lT9kuHaR4tJxFK2eeNMuCnZNxrC4nKEyeonops3rGnOvjIl8BV2WMKfzeJ6PX3Qoux5Z\nywn9lFILBV45fIXHvnKaoy+MA7C3Ls4vDXWztSEesmSqUQwKP1RlWbIs09QaJ7VQwLHdq+VvzTXP\npeKryN9AyG/vuH/zKvbMyugdFOyWSlBddG2FuFeLpmBc709eWzInyzJdfQ30bWj6iTIYvuetW3nr\nz+1+Q8dQFBlJkbg8lSGVKlJwXP7zXx/hsUMTZAoWTx2f5rtHxET00NkFPvv4sCju4HnIkmDGKrKE\nabvs3thMXVzDdj3ecks/n/jwnQJQepWIGmoIKL2e8EyT1LceRTIMOv/1r9P5679B/3/5PfSODhru\nvR9J00h/5zEmf/93WfjcZ5j++B8x9+m/ZPYvP0XxlRPEtu1gw3//OB2/9MtEN276ibqe/xTC8Rye\nnHiG3DrzGNdzeWLiaVRJ4ec2/zRQq4K2Nkp2ic+c+xKPXn48nIMV7RL/6cX/FkqtAC6khcn0+7e+\nm0SwSN+V6OAXt78Xy7P5/PkvU7LL6IrOeG4S3/c5uXgWgPZ4G/9h/2+xs3kbQ42bwsrUj1x+bFVb\nqqymN3ffRr2e5DtjT5INJFuXMmNcKcxwcOYIJVuA7dVYeW7PTr3A7x76Qw7OHEGRFHRZ520bHgRg\nriSsAwpWkdMpAdDc1nWAva07UGWVI/PH8HyP1lgLOSsfEgaGl0cwPTNkUY1lJ+lNikXOS9kxrhTE\nfOrm9r00Rxq5mBnF9hzu6rmDpdIy/YleHui9B1mSUWWVh4P21OtJ3r7pLQw1bqbslLH91XlO3i7w\n8OCD/NubP0ydngx9mzriNcuFgl1EkRTySxZf/cwxXnh6JDTmfmHmMP/t5Y+TLtcUBVsaN9GT6CJt\nZijaJdwlnXi2mfhCOy/PHxfXNd5B/Hwf8VwzvuRRn+4gnmvmzT238fG7PsJ/vf0/kNR/uCqXP4q4\nDipdj9cVy2YGRVJI6nHqAgf8kl1+7R3XiZqMTEzkElVQyRGT4aeuPM9/evEjITK+NswQVNLCh2el\nBG4yJyoKzBRmV8jfIqGsrOrdJE02YFtuWJbcqjjrVn9TJAXLtVdUrVsNKhXLdgj+lJwSju9e01Np\nFRASzP2rTCXbcpkcXWZyNB2uQITyt9dp1F1NPE+8NLnq75bjUk0hpbhDxTFp6RCDT6JUK7EZD+ac\nnT311Cd0KiuO4aqiAl7OLuAp4gUeUWUa6wyUQBJQZRhFIyq5ACiqN9SQcTMyJQbGpiAxrm5flb2V\n1oBKytw8BH0hSwqtDVG6WkS7Z1I18KRYsYlU27DGU8nN5eh/5FO0LY4RrVPRTSF/K5fE9XQUh/qE\nwcbBJiQkRkZrJoCFXAXPsPB8LwSVFmfFQBpJ6rQE5xWLqFcZK8aiCqWixexpMeA3t8apj+vcvkfQ\nUqsAilzMQl6sirtBn7lBEtcS+F70tiXobU/iFAKj7nU8lXxLnM/0Yu1ZmAtWDq8sFMLjrGUq2WeO\nofouI5uErYruFHCBuobapEOuiL6Wy7U+d4HiuXN4SORaa8SHqKFgaWLfpFvinb94I+2FSUxUXM9D\niUaRYzGc5RS50FdI7JsLKgjqKzwiqvLFwS0tDGxqDideO3MjKCNn8DyPK4sFep1lJMNA7+omvms3\nvm1TGj6/6ppUq7+VTZd4IH9Tm5ow+gfE93aKa6O1tLJ79inu2q0RDaR5VaBHc4R/U2KFYW1Vweqk\nllCbmogMbhBAzsxaAojwVIIVTDXfwSsHcrfqd5XEdynxOFpzC3ZqaVWi5pVE29t621nUG3AkmWRm\njk2I5yuyYQON9z8Avo+STIbtcTLp8BjlUSGBjGzciNHXhz0/jxtIKXW1ZtTtONCcqZZwDtqsK0gR\ncdJrgbvr8ZMdw8PDDvBB4LvAeeDLw8PDZ4eGhn51aGjoV4PNfg+4bWho6DTwfeB3hoeHrzb2+gmM\n/OJhfN+hrv1WJEkhb9fednNj4+D7xPe+uvSm5LgsVCx6ExGuBAs0UjB2Fxw3BJWWA3mw5/mcPSEW\nqHp7GthYF0OSJGIJA9tysQPZebFgCj+yFaB5d18DruszN50jn6ugKFKYk1S9l9KpUjherDXqfr2R\nqIuEBuGyLL3u6mUA7VGDf7Gli/u7m191u59+714eePuOH6p9/9jh+z6Fss3YbI7D5+Y5eHqWsulg\n2i6j01l+/3NH+cMvnmB4Ms3zJ2cwXQ8DCRUJxVCZT4t35W/87C5agsp3va0JtvU38tD+Pj76a7fx\noXffEDLTXc/n7XcM8uF37eb3fukAH/nlW3jXXRvDMemHCXs5xdzffJq5v/k0qW89SvmSyDUyT30P\nN5ul8f4HSN68n+S+G1HrxeKDWldH/Z1vwkkvY05OkLz1Noz+AXIvHqRw7CjRLUN0ffBDKIkf38T0\nJyVM1+Jz57/MxfSl1944CM/3+MSJv+SR0cf4m/N/d9XnF9IjLJZTHOi8iZ0t25GQmAjmLGvjycln\nKQd5ymRQKe1s6gIFu8iFoFoZCBaRhERvcrXkaU/rDt4ycB+arHFL5020x1op2EVSlWUuZoT0fWP9\nADEtyq/t+Zd86IZf4d/f/JsAXMlP8/3J58JjVUGl9ngrDw7ci+O7HAv8m04unQEEmPY/Tn2G//zS\nH4TztJWg0pOTz2L7Dm/b8CCu7zJY38fu1h0BO8qk4pg8P30oJCxsbBggqka5sW0PC6UlTiycpjUq\n3kEXlkdIlZc5NCvkZ1XLkcu5CdpjraFcuBpThVl2tWwHoF6voycAnurG+vnsJ18K5043tu/hTd23\n8fZNb0WWZNpjrahWhKefPoNi1+Z1d3Tdwk8N3ossyWxsGAzPtyPWDoj7IGfliSgG8SUBNI2PLFE1\nT9vfvo+F0hJnls+H7e9LdtMTXMPpwkyYD7fZoq1xNcbfPv495Nk6Sok0Y1sP4+PTObGNZqMJp+Jh\nFn68i3zX5W/X43VFppKhwahHluQQyMlZeRL61RXUquF6LpezE2xqGFy10rG2ilpEjSAhhUylsdwk\nru9yfOHkumZw1YfXUIzQjyhv5WmJCnBkuijYFAulpdC9P6IYmLJ4aRTsIvgS7lgc3VDYcUMXx1+a\nxDQd1OiK6m8BUymhxSi7ZgiGKaj4vh+eU6ni4LtK0CeF8PvWiyqoJAFWNkGiwaApKI1ZXdE0yza6\nrAmWVXBc93UYddu2G65Czk5lmRxbJpoQ52PZNVBJTXr4+CRbDTzJJZprQFNlbMfDylaQpABUihvk\n/SLxgAEkmEo2eauAFzCVDFVBkWWa6gwWM5VQzhaLaJQzZUAmrim0BP4KI1cEKl/1VKoxlZzgZ8CA\nCtgh2aklCIRtvqwhy1INVFpRAa5YdqiTJMAnFl+dpJUunMeaukL6iceJd99DKetQLGcpa4GxteLw\nC/dtQc2ZTF9KMRNIFCzTwTJdvKbAiysAlfIB6yfZEKUuYBKtrLJWjYgukVYM8jQScwp4SwvQ1RVO\nGIpVqZdVRg4kVVguIGEGjJKWANiRJH8ngyEAACAASURBVIm79vWgHxZt9i0rZJ34waTes8TPVUyl\nAFSaWiigqzLtjTFmlkrU1u5BWZrjdHIDxf5N1L0yjFFZxHSGiCRXgEolAaQpKySh+C7WxBizRjNa\nvMZqkiQJPRrBQSbiVvAdG9n3sGSNUkXIHNXGJpzlFOmShYdP1fF8ebFILK5TCWQfsiSFfStJEnc+\nsJmp0UUcX6beztJ3+CUm3Vnc8iAN5TSRLUNIskx89x7Sjz9G6cxpErv3hm3TVRlVkXFcj4Rbxjei\nyJpOdMsQldFLGD0CHNNaWog6BYzCMtHmfrHzCkAt5tskIhF8yUPyZRRDwrNtnEyG6JYhjH6xjzk5\nQSQArKqhBiygYmEFUykArIzA/NcIVrPkeBytpQVzYhw3lw0nBm6wfX9/K96xHIt6I+12GtX2IR5H\na2tHC0zKo5u3kH32GSpjl7GXltAC+WHlskiUoxs34aRSlC+cx7wySWzLEKoii3fUCqYSgKs6KJbw\nVJKNKqhUQYlfexy4Hj95MTw8/Bjw2Jq/fWrF/2eAB/6x2/VGw7Gy5BePIKtx4s03YLoeludj4GMi\nsTAzRyeQeA1QabIgkvm+WITxcQHEylZtDK6adKeDd+2e/T2cPjqNJEFbZ40BVZVil4oW9XqUQt4k\nnjBW5UNd/Q2cfHmK6ck0uWyFeLL2eSSqEYlqZFKl0CvwhwWVAPoGm0gvlUjUXV3G/LViS/3/Gs/4\nUqbMpx87z8R8Icw7qvH5J4axbI+VS3jnJ8T134WEjqhG5dYbsCDGwv/5nQsUyjb37uvhffdvXnVt\nNw+2kMuX+cZzl3n7nRt4U7CglIzpIQP3WmHNzpA7cpimBx9CjlzNECtdOM/sn/8Zbr7GhkkByf23\nUDxzGjkWp/GB9X2Imh7+GTzbJnnjzaHsLf3E41hzs7S97/3hu/1/93h++iUOzR6laBfZ0rjpNbf3\nfI8vXPgql7JjgFjYXhvVRe/dLduJqAad8Xau5KdwPXeVR9GlzBhPX3keWZLxfI/J/DR7Wndyakmw\nw+dKC9iujSqrzBTnaIk2raocDaLc/OG5Y9iezUuzL4d/H8tOhmbRO4KKZtVoiNSxrXUz5xdH+Mal\nb3FD6y7ydiFkJdXpSTbWD/LVkUc5Mnecu3vv5OTi2dBw+3JQwezM0nm2NG4kVUnTm+zmSn4az/f4\nwI73hf5A/XW9RNUITZEGUpU051PDvBTI4pojTSHr6qGBe3l5/gSPjT3JbZ03A/CXZz67qt3v2/pO\nHrn0GGPZCWRJJq7FVxWU+tSpzzBYJxj1D/TfxWJpEXywpw3sss3xlya584HNyJLMe4ZqEr46q5mN\nZ28lbUsMRg/AgVkulS5Rt8LiZGP9ACcWhASvMSLmJCW7jOd7xNUYdctiHlspuOiVOFa0yJ09t7Kz\nZSv/8+wXAdjcMIgiK/QkBCN/Kj+DlBY5nJ9X6U/0slBZwpxWieIzsfkY2zo2EC27SON1TD4Gw9kX\nAdi2p5MDbx68Jmv1HzKuM5Wux2uGE/joVB+WpC4eptcy635x9mU+fuJTXMqsZs2vraImXgCxEFRa\nCGiQVXrm2rBCUEmnbp22VBlO86XFmqeSaoSmcwW7iG5G8U2ZgU0toaGkWVkjfwvaGdfi2K4wqVYc\njRc/N88rh2voe7FSYyqlioE06hqgUrHisB2JXZqKM7uBB2IfCOmmpWCSWS7btQpwPwBTqYq0V1c3\nD36/tpJh2V4IKul1IumxfJNSMo1SiDLYmqC3JUZqvkBrZxLdUAOmUu17XUVIAPNWIZS/6UFiWpVp\nRYNkOx5TQ4mVIUkhU2ksYPlUmUpXg0qrmUqpgGGD7+MoETzbonsFqFTrV2HUHY1pVyXL1rQYxMsX\nh0kGXkflnBMylRL1KjdtbaO5VRzXLjmk82bo7+NHAkZTACr5Pnj4JOsjIVMpHrl6tTGiikmIo+jE\nK0vMfPJPRJ8F51b1yVKsEmpQPr46BBRtD0NTSK5YxXzTDd0YK8rMe6a5SgbnWza24zG/XKY1AKPm\nUiUc12MmVaS7NY4sS2iqjA/IgcROk12ead5HIqZz8/wTtOVHsF0XXwIzuP5SQTBgFK+WhCftPHge\nE7HOkHFWjXhMp6IY6LaJXxH9ZslayETTmpvxymVyqRwO4Foutu2Sy1Ro7UgSCY6XiGnIK5L0RF2E\nIV8kbEcat5KKt2IdPcSDC4eQgMiAKItbBXKshYVV7ZIkiUQAWMWdMiRFJZnmtz5M1wc/TCSo1qY1\nC02+vbRIJGAPyZUaMzMp2xiqEYKrqiHjLKfA99GaW4j0CVCpso5Zd1X+VsiWwz6tXkcjAFMjAaik\nxBNoLS1BW2pEES9gMnX3tPA777uBgX3bkT0PL5MmMij8LyRZpu6W29CaW1CbxTFWyugql0eRNA2j\np3cVCFbtJ02TQ6ZSe8rGk8AxxD2r6wqyIe4xr3KdqXQ9fvzh+z7LVx7D9ywauu5BljVyeTFGNF8R\nOciy46N3daG3v3rlovF8UDa+7ODYHu1ddShmDYpPqFVQSTyHe/f38raf38ODP7sTdUVVuKoUu1Qw\ncR2PSsm+ChTq6m1AkmBqLE0xAJ1WRkNzjFymTC5bIRrXfqhS49Xo3SAW3n4Q6ds/5Tg7tsxvfuIF\nfuPjz3F5JstyrsJHv3iCC5MZ6uMaeze1cP9NvfzC/Vt4+52DuJ6o16QqEl0tMQyt1tejCszicx6f\nMwsF6uM6dTGNQtnmvhuvBpSqcefuLj72wTtCQOn1hJPNMPXH/53lb/49i19ZzXaxlxZZ+OIXmPrY\nH+KWSrS+7/30/+5H6PrghzEGBskfOYRXKtL0U29Fia0PBKp1dXT84geI79wFgKSqNL3lYTo+8Mvr\nAlj/EPHJk3/Nn77yV69r26yZ5+PHP8V3xr4X5v8/6vB8j69c/Hs+c/ZL+L6P5Vp8b+JZQIAwK5nC\na+Pw7DFemD7El4a/HjJnQMw11u43VRCMxu4APOir68HybOZKIlcpO2X+9sJX+ePj/wPbc0J52GRu\nCsdzOJ8aDtv7yOh3ODJ3nKJdois4Hoh34fGFU/zRsU+SqqS5v+8uPrDjF2iLibzm5NJZsbgO9NWt\nLt4BcHP3HnEc4Nvj3+OjRz/B2ZQww07qSRJ6nB3NQ1wpzHB47hgZM8u2pi1I1O7/EwunQnZVlXmk\nSio7m7dxZkmwc6ogz8Z6kbc9MfkMKVOAuIP1NUuFtlgLt3TcyFxpIQTsVElle9OQ+Dzayo1texis\n7wuZWCv7fU/LTiTg3LLou1cWzzCeu4JuxrBLYrtzJ2dCFnw15qazjD5uotlRisllIuUkjae3Irsq\nuRXV2jbUi/wpohqhyqQKaNXbrRhmnKrAJZkV18B0TG5s30tXXIxF9/S+CYCehHhPTBVmGZsT94rn\nwOziMhXLJFZqINIo4WoWt3XtR9uaw1EsyjmXjp56GltinD85yxf/4gjp1OuvqP6jiutMpevxmpE1\nc/j4NBpCk18XSs5e3firCg4tVdKstI221lR/A0JQyfM9lspCfjRTnGOxlKI1VqNcj11c4uUjU0jt\nsvBUCvx28rYAHwp2kUxgKp61cmStQKqkREID7KJdQjPFwFnXEKnJ38zV8rcqoyquxfDxKTkV9Eoc\n1/IZv7TEDbeIl16x7IQytVSgj11b0a0axYpFDJBsjxgSFyZyPLRffFZlKnmuj0GEPIUV8rfXZipV\nQaUNQ61MT2YYOb/Avtv7aWqJB/I38cKP1qmQQ5SoTKZI5Fr4mRt7UFWZ7z1yju6gskhDQmelwNFV\nHRxfeCppmjg/LZCc7d7QzFK2EoIL8ZiOg1jVU32f5gC4qxpTVplKofytsj6olC6BJHsoThFLjWMt\npegMJvxVUMlxPSqWiyLJV/kpAZhVCZLvEy+mARVmiiycOwQkaSxV8D0vBOOiwKXpLG3B5MAzVoNK\nACZCShWCSutQ2CNSDYBpatSxz8/h5HPoidVeHoprhQygaqqfM21amqKrktT2xiiXVjCFvEoFVkqi\nLJO55RKe77Otv4lCeZ7Z5VIALPn0tolnpSptUj0TCx1tz16KszESEQ1JVVFcD8sR/yqAAUjZZfTu\nHtTl2qDbFngYjUc72bgGVEpENcqKTtSqhICJJauUKg7LuQopokSAyuISSFHMsk2m6vvUkSQfeBHV\nxVb3q++6dE4eprVtls923su3jXt439ij7CiIRCMyKLwpZMNAMgzc3NXFqhJRjVy+TMwzkQJQSY5E\nV7EXakDOIhFDAd9HNmsmpEkcdEXHVRwUV0OLyCHoo7W0oHd3g6Ksa9YdGnXnAlDJt0PZWSS456IB\neKjEYmgBIGSnlohuFKul1Yp/ciTKUF+czOaNLLwkfLmiG6725wjPJzD89iplzKkpops2I6kqRu9q\nUAmEWbdlu7imQ1vaYbFRxdcDnzRNQQ7kb765OhG7HtfjxxGlzFkquRGMxCDxpr1YCwuMfv4LcOfD\ntPkOC75HpX8DnXfuW3f/QwsZWiI6m+piTBTKgql3ReQ4+27rY+xgbXGsylRaXipiRFSicf0q2TWs\nZipVmYkry9yDWGRo7UiyMJsPPl99nMbmGHNTWQo5k9aONyZJ6uptoHewkY1b215745/QuDyT48tP\nX+LhW/vZuUHkhp7n47geuqbg+z7nJ9I8dmiCc+M1ue/vf/YYdXE99OFbzFSIRzT2bGrmzXu7ubJQ\n4JHnx+huifPv3r+PWETD9TzGZvO8cGqW50/OUBUpbelI8otv244kSUzM5dm/re1H5jvk2RYzn/wE\nzvIycjRK9tlnSN58gMjAIItf/hLZF54Dz0Ntaqbzl/810c2ioIrR1UV89x6yLzyHOTlJw733vcY3\n/fjC931G0qO4vofjOaEVxrXibOoCI5nLjGQu88LMYf7ZtneztWnzVdu9OHOEtlgrmxoGf+A2PTr6\nOM9MHQTg7t7bGc2Ok7cLKJJCwS7yx8f/B3WxOB/Y+s9C4KDimEiSxDcufTucg8TUGCWnhISE53ss\nVzI0R2vV+i5nxBg7lp2gMdLAQF0vh2aPMpGbQkLikyf/OpzDSEjc3XMHL0wfYjI/xcX0aOgTC/DM\n1AthW7riQna1WErxlZG/52zqApqs8Us738++tt3B8eCvz36B04uC7RRRIkTVq0HEvZ3b+axQtnEo\nYDgtlETuUF3Iv7l9H6eXzvPVEWHHN1Ocxw8WIRNanDOpC7QEUrXpwiwRxaDimkwXZnlp9ij1WpL4\ncgvfeuok7lIX20rNlONZ2Cqesv5kLycOTdK3sYnm1gQPDdzH4bnjnJ0boSnVT0dhkO4dMc4xzEMD\n9yBJEgN1fZy+cpHjF4YpmqWQNnNr+37e1fWzWNEij17+LicXzzCSuUxjTjDTO3rqmZvKcvylCd78\nkACqXNfj+988j2t7TG14hUzzDN1ju2Cpl05pG9l2kVvatsvipYpQv3i1hYcqqJRItWIDie02uVMa\niWwLqY7x8DpKkkREMRhqErlda6wFTdY4vXSOnswBdMS85ED8Vvra2jl5NE1du8iL81aBZT/FyO7j\n/O5t/5aGZALX9ThzbJrhM3N4b7Bq+A8T10Gl6/GasRwYxK1lKuXsV2cqVemSVQ+jajjrGF7HtRiL\n5RTpShbbc8JSjyeXznBf35vD7cZGlkhNVYglGjBk/SpPpbU+TFWqaUQ1Vnkq6QGolKyPhMwRs+Jg\nBG1yPAfLtZGQakwiu4QaaGqX5gtB5RaZYsVGl8SxS04RJK7pqVQsWCGa329oDE9mcFwPVZFDjxUA\nw6syf3RKXFv+tlKGV2XWJOoMNm9vY24qy9J8gaaWOKbtCnBAgni9Djn41uXvUqhL0T4NizM5tCBR\n7u4XEpv6uBF6KimqhC972K5gKtWrot/VAFR6YH8fD+zv48WnhE47EdAubSDieCGIVI3GKlMpspqp\nVFpR/c0plchLCaJ+ibLv4EsyhZlFWjo7aUwaocyrULKRAcm/uvIbCKaSpOv4loU+MwZspv1EioyZ\nguYb2HbkEtneZ6h/890omkzUdrl4JYNviGtqR0W/rgSVKgjgq6U+yi072tk52HTV9xq+RfUV2xwA\nVtbsLLEtdeiGghWsfKueHYJK1elE2fXor1892LvlMivTVt+srDJe9m079FPqaY1zZSHGlYUCE/Ni\ncOttE8+tFoBKulXE0nTsnTfC7CXiUQ1JU1EcV0ghbZcKUA/IjonR20tUr5kK9pRmseuamIy2s3Mt\nUymiUZENZDMXmlBX5W9PHr0C0yZ3AW4mhRbvw7JcFudEO1s7kkzNie9ZKxGwZmfwLYvGgS7qNYOR\ntMHx7Q+y/8QjAEQGa8mkWlePk82yNhJRjXjgtaY2NFz1OQjZmRyNYi8tEdVVNN9BXtHXcSxUScEL\nqiDqETkEbLSWVmRNx+jqwrwySepbj6Ikk1RGRylfGsHadA9gUAqYcorn1ORva5hKcjyB2nI1y8gt\nlZCjUaSAlbdSYhdZD1RqXs12qoyPg+8T2bBRtL+jA0nXV1eA04QkNjafQvFgpkUDLQCVdAXZDZhK\n1z2VrsePOTynQnrqcSRJpanvrUiSxOKXvkA+8Crruu02ZpbyZG0Vo7vnqv0LtsOjE4vossSvb+9j\nqmjSGTOYOTGLqsn0DjTR9MoUVbe9uKriOC65TJn27vprAgohqFSwwmqPa5lIAN39jStApTXVS1cU\nTHgj0jcQLMmH37PnDR3jxxm+7/P5J4YZn8vzx1MZ3nXXRlRF5vHDk2TyJq2NURRZCgtTNCR0MgUL\nVZFwXFHYQZJElc2Opjhjs3lGZ3IUynboO/hzd28iFjCPFVlmU3c9m7rrqcuZnB5bJgn8/D2bwwqx\nHSuuz3rh2RalM2dEQQNZMEirPpF+UAlZb+tAa2ujdO4s6Se/S+XyKMkDt9J43/1MfuT3mP/Mp5FU\nFWtuFr2ri6afepjkzfvDAhPVkGSZhjfd9aPq7n+wKNqlUAmwWE7RGQAi64Xv+8wGJsj72nbzyuIZ\nvnzxEf7zLf9m1XYVx+QLF75KX7Kb37n5w9c83oXlEdJmlls7bwr/9szUQZ6cfIaoGqXslHlh5jBn\nls5jKDq3dR7g6annGc2OQxbOtJxnd+sOZgpz/L9H/yScz9TaUQk8fRRminNcWB7h9m6xclx2KuRs\n8Zxfzk2wr30P/UkBbDwx8RSu55Ixs9zQuosTi6fx8VkoL9Gb7OGVxdMcDTyMqlFv1IXzrK5EJyW7\nzEdf/hNKbpktjZt4z5a3rzKO3tu2C+28Gvb9ys9WRneyI6xMJ0syMTUagmZVUsGulu1EFIOyU0FC\nYrG8xN7WXZxcPIMqKxRsm+emXwyP2RRpZKY4x1dG/h41lWRw5iaeKAjGUiSugg+JXAuKrZNMROh2\nB3jqmVFmr2R5y8/tojnayH7lDrJHIsi+yJNmDnoY2xJsadzIsRcnGH3RZ8i5h9GTFu1tW5kdEODZ\n+HMVXhw7xi/86gE+sON9/Pnpv+FcapjmomAF3fXQFr7z9TNcODXH3gN91DdGGT4zRy5TYee+LsZj\nz4EFhaFxes1NuOkOsmVx7FcOTXL04DRN/X0st0+Qtwok9URNejcfx5NcSh3zmCMNxPPNSJ4cgkqm\na2LIOlPjaUbOzdPanqQ70clE5gqaFUVWJDzXZ4BNRAsakKalMw5FyFtFlirLxGI6DUlxXRRFZs/+\nXvbsX1ng9R8vroNK1+OqWAlUACFq3mhUQaWrzbHXiypLqCprq0bVqyiUeCFAJc/3uBLQJQ903MjB\nmSOcWjy7ClSqGl4a5WRQ/W11W6o64cG6fsZyE0wEx4soRvh9BauIZolEIFkfCenqlumQCLYR8jeh\nV17JcFIc8X/H9kinSjQ2x5ErDtuNCCOOiqMEJejPRzk6P85Ntw+sOvdCvgYcJWwP23NZSJfpaolT\nXFElRnciIEFdJIqpyDjrIM7lkYtM/+n/R89v/jaRgcFVRp5OUPHGLIu+tmwPDWGAvKO1n8PzLwuj\nvriEpPhMT2TQNBlZlujoEde5IaFjAUigB+BPySlTcU2aA9BMYXV4AfhVF9c5kD6DVTeEbcpEdIV4\nRKVYcUhENfSgz9cada9kKi2eH8WVNVoSNnK0gfFFyM0v0wK0NUS5eEUAcpWSRdI1QYmiFTOr22Oa\nAhgY2opvmigTw9CzGdeJ4He1gQmaW8Gen0eSJJpbEzgzOUaupDFkBVmWsJJF8IWnFJIPvkQF4aMk\nyxK/8rb1jUl1twwIIKelt5kiYM1ME9syhGGoIaikrACV5AA2shGV2laGW1r9HHmV1aCSZ1kh0Nbd\nmqCjKcbYbJ5XRgSQ0BPI+6qgUkN5Hl/RsQIdfzyiIqkail8OQCWPLD7tQNJcRu+4gbiRDIuPRzyT\nuU03QEEKvbSqIZhKBpLvY6eF0a0lqRQrNufH03Sroi1GOY/cpIDtMR1U3WvrSBJZFueRXMNUqoyP\ni+8eGKAhq+MDL1QaMLtv5eGNRijzAlDq6rDHLuN7Xgi+gGCVxYPKb0bj+qCSJEmoTc04yynqdIWo\nuxo4ifu2eEcqov+1iBKCPlUQKLZ9B+aVK6Qe+fqqfZ3IBOhbKFdq198ri+e76qkUqTKV4vGaFC+1\nQv5WLiFHa5MZo7sHSVXxHYfIwNWgktosVgztJcEeNSeFkX8VjJJkGaO3j8rYZTzLQtZ1dFWhVLFJ\nLoh9Zls10IPz1RUkL/BUqlxnKl2PH29UCuN4Tom69jvQjCacbJbimdPYtwu2RlLXaDI0FioWJccl\npq5+X82WAomu5/OZi9O4vk+nprGwXGZgUzOKKtPeGKcqKI9rCplUGd+HppZrgwqh/K1ohUzi9YCh\n7v4GThwSz+Ra0KlxxfGvVfntf5c4MbLE+FyeLT31zGfKfOVpsYilazKbe+qZXipSMh1u3trGLTs6\n+LNvnKazOcZ7793Mx758EhBA0YfeuZuhvkYWM2U++rfH+dqzgoXW355k14arF4gAGhIGbcH4fC2T\nc9/3Wf7m3yOpKnW330GxkGLyox9bt2DDq0Vsx07a/8W/RNZ0Gh94kPR3HxdtuO8BWt/17qvApH9q\nkTZredpscf6aoNIT40/z/SvP0R4T4Md7h95JyS5zIT1CwSqS0OMsV9J84fxXuaf3TkAsLK/1J6rG\n6aVz/MXpz+L5HrtbthPXYuSsPF8b+SZJPcFv7fs1Pn78U7w8dwLbs7mr53ZOLIr7piXazFI5xePj\nT7GrZTtPTj6D4zlsrB9gNDuOoeh4noftO9zdewejmXFminNczIyGoNK5QLoGhIqMrkRHAMqI339q\n4D6yZm0x7HJ2gv4AVDqbuoDqqmw4fQeZlmmsjTV5f1e8g8sLU/QcO0C8S+IDdz14lRWELMlsrB8M\nq8VtrB9Yt98lSWJb0xAvzx9nY/0gTZEGDs8dQ5f10LdJVzR2tmzj6Pwr+Pjs79jH+7f+HH/yyl9w\nKTOGXo7TfXk32S1jpLQ5EnoCOadSPp5gYGkbniyxdXc7e/b30tAc5fc/9xlaZjZwV/3d/Owt93Lu\npJB+zVzJ4LoeiiLTMNdD3k+TG5zAkiu0jA4xOLqfV+Q5LpyaIxbXmaufIJKvp2mhn3TbFer8BqZG\nxf02P5Nj49Y2fnnnP+dvL3wN+1QzRtKgoTnG/jsHefLvz/H418/wtp/fw7GDEyiKxL5b+zk20kbe\nKtCRaKdvUzPnTpiUg3RsdFjkR83z/Sy3TTCaHWdv607yVgG9HMfOSpQaU0wULpGs76NlbgOxfGNY\nRMqpeHSevpFvHhT32ai+yMBb+5hZWEJCom+wifFLKZYWCujBomNHdwNchJyVY7mSpj959ULJjyv+\nab+ZrsePPGzX5iNH/phNDRtCs7IqOFRvCKlI3Qqj7rX7Hei8kYcG7hWfB+U01zKV1lZ/A4gHk8yx\nnEisNtQPMFtc4HJ2PER+gRAsiZSSGOuASlVjvBvadjGWmwhf3Kurv5XQzRqoVGUBWaaDFnghVeVv\nuqxhVPdzakwlgMW5AkbCoB0JzXRJZlvJNs+CL5E+J3FEnrgKVCqVgupJmoxjezQD+ZIFxCmvYCop\njgEa1EWjpBVpXflbZXICr1ikMjGxClRKJI3QL6gcgkouKmBEVDY1DPL7t/9HZovzTOSuML+kMDsu\nrnFnb33IWKpPGPhApC3Ohv4kLwPLlXRw7YKVvDULtNW+TEYV7k4d56jRSlbpxDJdmusiFCsFmlYk\nZNFQ/mZTKphhe6OGytSlGUCjpSMJRpTxxQLFpZrRtw9k8iaoKl1WFqJRlKXVyZs1OwO+j9HVjdrU\nROHr3wCgEKkj0tkD42V0t4KTC47bEmNhJkdmocgiokTyNEHfS6J0vFeBMn7Y9muFbheBJLom0zjQ\nJUClWXF/6hEVAmbZSqZSNR64pZ87blo9UDjF1c+RV6nguzW6rW9ZTC9WQaV4uIJ66rJ4BqryN02V\nMVyLoYVDxNtLnLGFr0LMUJE1DdUvYjsepuOSBXY2zBC/lEXv7ERqtGA6aKvncKltKxTyV/VFe1OU\ncgDGOinx/ZasMTqdI1u02NLeBvNQ5xRxohp2wWZmQgz8rR1JIqNioK5bw1SqjAcyt4FB6i7VANP8\nzlto/7nVK/BqfT14Hm6xgBrI3ECwqBJVUKmpkWuF2tiENT2F4dk1kKehATeTIeYHhumBb5YRUWtM\npQDYannXe6i/883Yi4s42QxGXz8Ln/sb/EwGmqGKB6qejVex8X0/ZCpFXeFmJUejqIHE0Vkh5fNK\nJdSmmixYUlUS+27Cq5TXrdojaxpKQ0PYxsoVwUgyAu8nAL2jk8roJZzlZfSODnRVJuN4NCzVQKWu\nimiLpivIVJlK10Gl6/HjDbsiJldGXLwz80cOiWd/o5DIJDWFpogGWVg27XVAJSvcLmMFCxt58X6p\n+hB1tyegLPKMuKqQDpgtjS3XNrBeyVSKBIzX9ZhKHd31yLKE5/nryt+q8UaZSj9J4f3/7L13uBzn\nfd/7mT7b92w5vQEHHQRJkBQ7pitwFgAAIABJREFUKVGiTFEWKcsWJUtusn0td8eWr+PcG98ndmzn\nOo7tOImS6+tYsS3HRVazZIoSVdh7BdFxgAMcnF6219nZKfljZmd3gQNQlGKVBL/nwQPgnN3ZnfbO\n7/2+3+K6nF2pMDUURZEvBgC2ev1nnzyLIMCP3bsHXZX5+MMnScc17rt1G4oscvRsgcXNGrsnkhw6\nk8N2XO69yZPJfe8tUzz8wiIfvm8fu32JfzYZ4tc+eB3/9q9foVht8b23TF2SddaZ0MGlz0PlmafI\nf95jzeY+91kEQcC1LOJ33Ik+MYnruOA4uL7HjCDLYDuYa6uY62toY+Mk7rgTbaLrJ5O+/z3gQmj3\nHqLXXLvl5363VdHogkrr9Y0tX+O4Do8vP0OtXadZWSChxgkrIbYnpjhZPM25ynkOZPbx3OpLnCye\nDhQAlmuzWl8PUrQ6dapwhj87+t8Df5+FyhJ707sCA+k7xm5hMJzlxuHr+crCY4AH6JRaFUQ8idKN\n49fywtIhXlx7lZfWDzEcGeLDB36MX3/qt9g1MMPbJu7kSP4ENw/fgIDAi+uvsux7KAE8vfJ88O9c\n01tsk0UZURCxXZuh9jib/xhmaXqO0IBO0zI4Vz7PjcOeZLfWrhMvDaOaYdL5SU6Pe/2QKIhkQ2me\n/tppVDNMex5efGqem+7sLjAVNus8++gcu7Yd4KQPj89cRib43p3v4sX1V5AEkZnktAcqSf2LfB1/\nq33p3fzI3vchCiJR35JkuDZNuD5AKC+SH14jpkQYn7uGeGkIId7mve+9hfRgt1fRkwKswKjjMWzy\n614v2zZtNlerpIeirCyUSWUjWLtMFopzuHWZ7NoMJw+vkRmM8r3vO8B/OfkqKwtLTM++ias27gBT\nxPB7+M21GjN7BlElhXdl3sUnjZeY2ZFEEARm9mRZXRzj6CvL/P1/e5Fmvc3VN4wTiWkMhbOcLp1l\nODLI9Eya46+uIOVi5HO1wFdPM6JEyxnmSufYrsyw/JLBtnNeorI7VKduNRDiOTJr24mWs7T8hUpl\nM4ncCDM1k6bdtllZKHF78namJnby8uF1siMxchs18us1RElADysMZTxQqXPtpkNbA+HfjroCKl2p\nvnp+6VVW6mv05l90wKG46k3MtjLqLpsVNpo5ThRmA1CpdCmmkj+BVi9gKoFniAeervSa7H7myuc4\nkjvOraMe0t9lKkX7PJU6wNdybQ1ZkDiQ2cdnzjwYbF+X9ABhr7frKK00CN6Kk+EDMC3DQha9BrGT\n/qZISs/7GshWNtjm5mqVxEiUznQ13ExSZhWtGUF0BVzbvYj1Zfigycz+IU69tkrWFaj4DKVeppJo\nKgGoJEsm9hZG3UHqly+dqffI3zrVYSoZLRsZAd33/hEEgdHoMKPRYQ5tWwhApbGp7iQ76TfEVibM\nm+6c5hNPdEElQfB+J1yAddm+Z1JccbCBSLtKmREadZN0Qmdho0aqJ1WsA0YYhSZ/+dFnEVWJGKBr\nEptrFSDN0MwojZYD1AJzY71uMgrkKwZaSCVut3AAOb/cx0xp+Sbd6tg44X37kD/198h2i2I0QdJn\nCim2EcikUv4EobMiGT//KnG5Sb5DgFFdMIRA/na5UlpVYJhUJow+5jU45qrXYPRGSktOG9ntB5Xu\nnNaJhfq3b9W989wWJBTX3hJUWqrViIcV4mGVYZ+e37Yc0nE9oPSrskTSp2Ar2cGAHaZrMoKiILld\n+RtAqOo1P+rwKKokwRMnQBA4FxokZ/g+XRcci7ffMMHS7DTWk3O0e0ClV2a9BvLAwRk4DAmrgTsQ\nZn2zQaNuEomphMJq4C10MVPpHEgS6tg4ybVuszYxeDGQIsU9xp1dLveBSp78zbuOlMTWTCUAxQec\npHo5YCop2UHsUinwPBIkn2Gky560TJICSZ0gCKjDI6jDXQNNdWQUMb8e/F9wHeRYFKdawTXNYL91\nx8RWdQRRRAyHQRBwat546zoOjmEghfsZEiMf/hkuV0o6g3HuLK5t01pYQNA0lMEu/V30t9eRK6qK\nRLttky5uUgmL1MISgtMDKgkdptIV+duV+vZW2/d3U3Tv+Vx59hmQJMzBESg3iSoyKV/OXGi1GY/0\ny7HXfKbSB2dG+KszqzQsGynv3QdDo97YMTWWgNkqiAJRWWLFn0ykLgMqRXqYSlonICB2sURbUSUG\nR+OsLZWD93QqGteRJAHbdr9rmErVhsnCRo3901tPdmzb4WMPnuDZY2tkkzrvu2sH1+3KBr1Sudby\n0l3HEwHg9NyxNZY369x21TCZRIjPPnmWw2fyuMATr63iOB7wBPCFZz3QfCCmcfN+jwXzA2+e4V23\nTgds0E4NJkP8xo/ewPxqhWt3ZrhUdZ7ZkaiKW6/hhHREpXuu7GqVzU9+AkHTSN//fVSfewbBtkg/\n8AEiB67+Bo6iV6KmkX3fD37D7/+nLtd1+7xkeuto7gQNqxmAIp0q9DCVOgbVF9Zc6RylVhlJkLBd\nO7CU2O6za47nZ9EklRMFDyA5XZoL3rtYW+kDlZqWwZ8e+Ti4Lm8Zv43Hlp5mvrLI3vSuwDJjzDdM\nvmXkhgBUWqgusT+9h0a7yfnqIh++8QO8sHSIz519CMd1uHvizmA+o0kaOwdm2DngScq3+ebNeR88\natttT0IHZPQUuaZnJt122tiu34uuJTGaFsnZGUbekuRk8wTnyuf5gZ33BfsSL3nXs9TSEA0VdIuo\nEqHdcticNWkrLeKhCK88s4AkiuzcP0gx3+Crnz9B27QJbyoIuwVc0Q1MobeqqBolqkQoNMo0j4VQ\nrFAniBmA85VFjuROMBIZ4mcOfCjwdpL8vwfdYQxA2ojBEEimSqyUoRku8+4fvJZ0qr9nu/uqW3nu\n+CrNojeHCEJ6gKX5Iq2WhW05TM2kcMJpKJ5mfWKWfZF9xOQ4d71zN6omsy0xybnkk5ipMmwkAIeZ\nPVnmTm6SW+8SIZb9VMfOnEcQBG67ewf1WotzszlkReTgLR64O+wz6YbDQ4wNJUF0iJaynDzuLWKb\nEznUxQzp9WmWU3k+9YWXaRkqotRm18EMs0PzUIB6vIAgeWbdhtXCdV30gjdG3v72HZw/k2dloURl\nwyTcjgPrxJMhMoNR5s94ffT0jnRAplisLgfX03dKXQGVrlRfPXzaSzso9zjbd5lKHpjkScnkPqPu\npk/l6/gvGZYRoNi1r4ep5INKnbSAwVAm8I/pfei0O0ylZgxVVPwIxlHmSvMs11ZZqa8xHBkiE0oh\ni3Kgd9ZlLQCxau0Gg60QogbLuQYjKU9m5Bl1d+RvFqbTRpe0AFRqtBsM9DGVqoQWw0gdA+xmPPhu\n4JniWW0bRe3up2lYyHgSn9XVKqzXKPr6/16mktD2vkcqHEWWilsylZwAVPKa31rVQFZEVE1GC3mv\nN5re/rdNCxHQL5ikQz+Q1PFTAkj4zW2pZgb+V51zKTj+cbgg2aIDfsUkhxKg2t6+NWqtwKy7l6kU\n9mVTVs1EAWTTZg8i//UPnsCxUiDA0K5x/2GwTL1u4rouzeUKIwjkywYxVyDs2tQAtVnCXF0JfDPM\nZW/Q1cbGULODJN78FsQVA9eO0mpYWLIJIRXbZyp1Vp0H/HOaWHyNCUnnXKfX1B3cqojhdr97a2UZ\nOZ64iCGi1/MkmnF2HdiBqIc85ovPVNJ6QBjZMZHcblMmCLD6O/+S6oGrGf3FXw4Aso78rSaHGWhX\ncVotXKsLRlmtFrmKwZ7JJObqCoP1AoLr4ApiH+iiyCJJqwMqZTF8oFZXJQRZRnadQP4GoJU3QRRR\nBgc9I2/3CLagcCy6DdM3n70QVFIViWR2gBxg+eyYtiiT94HPnfumKAsC1w3J1LalWJ/1XtOZoO0Y\nSzCSDrN/W5eN41oW5tIi2vgEoqKQ6PHPmhzqNz8Hn6mEx/DpnYpFQ12m0qU8lcBjKgHYpRIx0fc/\nymTh9Cy645ssKj6oFPZAJWUghSBdetVdHRlFOtwFwySnjTY8TLNawWk2+zyV7LA3LgmiiBiJYNe9\nJqtj0C6G3lhKj5LJYMydob25gbm2ij69rU8WKPnb64wniiwSb1XQ2y3Oj3pHUEybJNNhRieSiMve\nuHzFqPtKfbur3dxEEBUkNUlraZHWwnki1x6k7k9wYopEyh+jCkabQqtNvW0zEfWeSavNFqooMBHV\n+fFdo+SMNmcenEUUBVK+bDiRDCFZDrYqEZZFCr7UeOAy8jdFlZEVzy+xA0pcChjaf3AUXLdv9R5A\nFAUSqTCFzfp3DVPp///8MY7PF/n1Dx4MWEGdsmyHf/fXL/PssTUGkyHyFYP//NmjxMJe8EXbcljy\nGbfJqMq9N00xv1bluWNrSKLAHdeM8jsff4nFjRqDyRDbx+KsF5pIosBV21NMDsaYXSxxarHEPTdO\nIPek5fUCSq5lsfzR/4ggS4z+7C9wcJcHSJobG8jJJKKq4hhNCg99gfrxY8QsiatKFkmryNmP/Cna\n9DYmfv3/CoClzU//PU6tRuaB95O6515S99xLNhtjc/PygTbfTXWmdI4/P/Y3/MzVH2Ii5qWG/eXx\nv2Olsco/v/6XLjLc/vvZz1EwiuxMbmdA7z5rS0ZX3rV2CabSi75/0L3Tb+PBc18mZxTYaOSYTkwg\nIPDqxmGeXH42eL3V00MtVZehxzPpVPEMhm3wjqm3cuf4rTy29DTnq94C9orv1zQS9UClocggcTVG\nxawSU2L86L738/D8I5yrnMd2HXYP7OBU8QxRJcINwwe7aghJo1RocH4uz9U3jDMY8ppGuwX5aokz\ntTksx0JAYCw6Si53lLJZ6Vt018reMZJtFe34GNuubjC7Pk+xVENAwHUgVuouBEUqaUy9gSZpHHlp\nCdcSyE2e5Xvf8m6e+NQ8Lz7l/QHPT21kIsHqYpnh4gyVoRVS+qX7H4ABPUnrjM7p8wUmIgdZuspL\ntnNch7+f/RwuLu/dcT9L50rMndhgciYdyA4jVhKDJo4hEqoncIwQAgITexLMpKYu+qxrpnfygrRG\nfrOG67rkN2tEYiqNmsnSfJGmzyad3J6mhtcXiqLAu95zEF3uLhJsS0zB4pOkrnNoPCIgKyJ3fM9O\nNteqbK7VgoX+pQ6oNN0dn0RR4O779vL0184wMp4ImKY3j1xPvd3gxuHrkGUJddCGtRizr20iigIL\nw0eZqtxArDyI86JFy7Zw9mxyMvoyH77rtymdPQcFcEWH9LhO7jzU8xWMiRaRSho3YhJPhsiOeH3s\nxmoVSfYXtpM66aEuqDQ0FkcWZcJyiIa/OJoOdfvkb3ddAZWuVFCL1WVO5T1tedNqYtomqqQGTKUO\nQ0kQBGJqjEoPU6npX9ylVhnbsSn3AE6XYir1eip1zLDbTpuQHPK1zl4DZdrdiXOHqSTZCq7hDV7v\n2v49/Mnhv+Avj/8dbafNWHQEURAZDGWCB4Yu68Hn1Vt15LZO2TH57BNn+aX3Xo0kCbSM3vQ3z1Mp\npkYDMKppGWR9T6XEQIjcRg35XHdiqzW8ZlBvdFkRzWa7D1RqtzxQKRbTGBiMUFmvUSr4TKN6d8U/\n40xw5GyD7HUJIm4Rw7ocqOS/v9oiGtO8NAF/VbTDjGq3PKPucFj1ojYdJ5j8pgejaLqMbTkMjXS/\nuyJ7PkjluokkSN5DzWewOZaKg+vRuXuqI39TfSaH5l8XjboZmHX3mnYrsoQsCdimjQJshiRCbYeZ\nwRjG+XMk3QqhyFuIxH3PJcOhWjaw2w4iApu5OoIsofogkG7Vac6dCUClgKk06jVAQz/yIVp/8SDi\nmoRRcbC0Fm4silXqyt+C82nViZglBkoJOhbazpRJyYkhFJsosoTdaLDwO79F9PobGPnJD/efoHqV\nG8tPsOO693vfYWSExvFjOEYz8KgCkFwLAZBFF8sR0FSPyF0/cpjNT36Cwfd/AOjK36qSDyoZRh+o\n1Kx5x3osE2Hh934Xp17nn4kKp6JTSLd8oOeYiyR900UlO4ix2gWVLEVBdm3MthUwlZRSDiWbRVS8\n+0DRZOw2rGtJBB8IDW/B2hIj3v3QYSq1/PsoHdcZTEWoJ5PYpQKhHqCzM4HLJEP87k/d3Le91tKS\n7xnkUbYT0V5QaSumknct2xeYdUdCcsBU6rCZtqoOqGQV8sTwPa/SXkOnWb6UJbSGTJF4W8Mul1D3\n7L3k9sC7BkS3a+wpuRbK4BDN07M4RhPNT1XUHRO7Z/VJikSwfaZS534Xw5c3iL2wOrK8+uHXwLb7\nJBZAAFJ1wEtVFhn3ZUUrGe8cyTGHD/yUxxpt5K8YdV+pb3+5rkO7lUPVvQSuyrOeOWz8lluptS1k\nQUCXRFKaN168sFnmayt5HBc+cmCKuCqzaZiMh3VEQWAsojMSUnl+s85AJozkgxKCIBASRGrA2lyB\nop/8tlU4RG9FohqNuonSkbZe4vW79g9x21t2bAlCpDIRCpt1Ygl9i3d+Z9Wxc4Ugde2h5xYCUOnY\nfIFnjqxxfL7gSaAnkvyz915NuW7yD0+eZX6t6ptlC+yfHiCd0Hnu+Dp/+zWPiTIxGOX9d+3g04/P\nsbhR485rRvjBt+1EVy9+9lyOcdSp/IOfp3H0MACFL36B9Lvup/To19j4679C0DTC+/ZjnJ3znh+i\niOQ4DAGOrKEOj9CaP8fmJ/6OoR/+UcpPP0nlqSfRJiYYuPt7/uccyO/AenXjMKVWmUcXn+JH970f\n13U5mj9B0zJ4bfMo1w915Xltu03BKOLi8tTK89y3/Z7gdx1PpagSYb2xgeM6AdMFPKXAoY0jJNQY\nMV8h4bgOL68f4t5tdzMSGWK1J23sTUMHeXH91eD9HQZHp04UZgHYn9lDQoszoCWZryziui4rtTUU\nUSHbMzEfjQ5TKVR5YNf9RJVIAFQcWT8ZzHuuzuxDEWUMvxfQZI3HvniK1cUy6WyU8ekBQlKIyUM3\n8w+nX2NunzcuDUcGGQx712euWQhsRK6JX4PdiFOL5XBEB1YHkYvb2WPM8I9HjqLsD6G0Qki2wvj0\nAEvzRSKVNMXBRURL4sjLy7iKRTG7yOTwEA98KM3Z2Rzry2WMpsVNb95GJKrx13/yHOMbe7nprrte\nN7FwQE3SXPeOS7ieJL48QalV5mjuBKvrBa6u38Hzf7NJo+4d79x6DelWb5xrVrp9Trw4jNXSEQR4\n5623bPlZoiiSSkco5BpUywZmy2ZiW4pq2WB9pUKl3ETVJIbG4uQK3vGbjk/2AUrgGYi/ffIt3DZ6\nE62MiKyIhMIqmaEYZ09tUq+20MOKf54iF4H8siIFCXCdCskh3rW9e18nxhU218CoWwxPxTgsNNBn\nWtivRBFsiVveMc1D5qtoTRVV6l5bMSXK7gND5M6fpzEvsZgqIDky4qB3DWSGooiiwMZaJZBJd5hK\nnRoe63obd0ClTOjSNg7f6hJf/yVX6n+XemLJG/QGNA+9LvtgUtmsEpHDfWltMTVKzfRQX+gylRzX\noWxW+phOVfMS6W/SxUwlgMFwBkEQ0KQOqNRl8HQ8lQBMf6743HMuA8JwYNI96q84ZHS/sXBFFFHu\n6oENBQGBluAE8bKqLtNqdaNNA/mb2JW/ubhIlhog/rblsHa2gINLaED36KiWjN7osiZ6jbkBbB8U\nC0dUUr48qV5p4TgOzXob1We/OE0FOzcONZPRukWkdTG9uONlYjebWG0bo2kR9QGbxsIComvTKHrn\nsN1JVYuoVF94jtM/92HMdU+GI4oCb7tvL3ffvxdJ7h8SElGNcs2LTe1dhbJNFQdwL2BQBSl1/ndT\nfUZIvWayf1uKZFRl33T/ABjSZFwfNMs5DpUBnfd88GpuWHiQA7oHCnYkAYYtsbHcBQkKhSbVmgE+\nYKFZDYy5Lg3aXFlGTqX6pEJyxAfCXLAVEyEexWnUcdptIjENWfGOwUBzDSkaJVXpgqJNx6HquAFL\nqb2xjmuaAQOp71jUqkjR7rWgjnQkcKsBU0mW3ICRJ4ve99I7GIsgUPrKw5Sf8NiDti9/qynedeMY\nzT6T5HrV+/22rI5TryPF4iCIXFM5w1QP5qLIIgM98jfD7JqjdwxA7baFaTmEbAPRaPRJuLRoGARw\nANsHFXX1YnaOFPHldx35m5+QuHdqoGuEXSwGAChcXkpinO/4KU0DXjoheIlp2eTFrB053mEq9YNK\nfUylxGVApZQPKhWLRH1QiZQ3pmi+HG7X2bPcduYphD/+M8BjA12u1JFRxJ4VVRkbKerts93wmEqy\nY6G4Nq7W3ScpEsWu13FdF6fhA2JvFFTKeCvxtVdf8fZh8kJQyZe/NbvytzEfVFrN+qCS0D1XguaD\nSleMuq/Ut7GsVhFcG1nP4rou1ReeQwyHiVx9LdW2TVSREASBAU1GAEqmhSQIuMDxYp2Nponjwki4\nO7koFZrYlkPmAgbkzaNJIqsNXnrsHOVik4FM5HUnZqGISrNhUqsYhCNqAFK9kbrxzm3cff9e4luM\nc99J5bgun3rMe/4ODYQ4cjbP4kaNE+eL/NEnDvHssTVc4N5bpvmVB64hpMkMp8Lcc+Mk/8f37uO/\nfOTN/N5P30w6ofPU4TVkUSQRUblx7yC/8WPXs7BR4/RSmRt2Z/mxd+zZElDqLddxsMolL3mtp5pn\n5yg89CByKo08MED+Hz9H7nOfZeNv/jtSNIacTFJ/9RWcZpP0u9/Djv/0/8Ev/GuemnovxQf+Tyb/\nn99EHRun/NgjLP3xH7L+5x9DDIUY+rGfuCxT9X9WPb/6Mv/3U78dhOh8q6rjefrq5hEMq8VmMx/0\n/o8sPsWX5r8W9Pc5oxCAPk+vPI/lWCzXVvmNp/8N85VFBAR2D+yg7ViBrUKnThRmqVsNrhu6ho0e\npUJHCbE9MRVsG+D2sZuDqHtJkFiqrQTeSa7rciI/S0jWmYpNcPzQCuMLB6i2auSaBdYaGyTUGMUe\n9lTCB7Km45PMndxgWPJ6t88c/2KQRDeT9PyKOt44YklnddHbxuI5T/I23J5AMUOYNZfUiV0Ijhd7\nn/E9cHLNPBsNj6W90/EWpOqJPPZVa0RiGqIrUo8VwBYZnb+KeNGTYR28eZJQRCFaSYML6nKGlmFR\nG10lFoqgiDLRuM7VN4zz9nfv574fvIbBkTiRmMbea0ZpVm2cxfAlU6U7Faul0Ywo4VGXtmIwuLyD\nT3/pSV74zBo7j9yJczaGZTnsv26UZCpEMd9AdEUER6RRbZMdjiHJAsncOFZBZnQyuaWnXKdS2Qi2\n5XC2Y3496IFzjuNSr5pMbEshSSJT8UkicvgiWSV4qeLft+OdZMNpxqcHAhAmO+w1wZtrNRbmCrRN\nm91XDV92/y9Vw9Pdhjo+6Y3no9tjxPZYnN/1EtqkSaVVJa55r8v4oNJYdITpHRksuYW1pDPvh+io\nw756R5ZIZSPk12uU8g0fEFPI+IumguB5jgKBdxVAWr/CVLpS32HVaDd4cf0QQ5EM12QO8OXzj1I2\nK2TDaSpmlaQW73t9TIlguTaGbfgxnN2JRcEoUekBlRrtRp+30Fbpb9FeUMmnjXY01K2e5KV2D6jU\nLNq0LZtnj64zNL4HRjvaaG8CrHfcjmzvMu+AQ4pv0t0SXCyfyaNpsu+pJPufaWK5dj8YBShtjVBY\nYXAkxsnDa9iWQxUYSYdpFg30RryPqVSr9a/iOz54oocUBge9yWSrbgbG2qlMhLXlSsAwcn0wKNq+\nePB3Wx5g5TSbfclvAEe/9ASqlaaea1J69BFs05sAhsIK9UOHwbZpLZxHHfIeUpnWGtg2kO37jERE\nZSVXp23ZyKIcsMxaTZkwXQ+lTnX+L7R88zrHuy5qa3muvXGCP/qF2y/aj5AmIzZbqJpEw7QY1mTP\n48h1kQc8AErVZCQcWlKI9fnN4L3VchNz3cCUw0jYqKqIcdZrau16HatYJHxVv5+BEhXoXEWWYiLF\n47iAXamgpNPEaFJEoxiLowxMET95DNV0MFWRpuHQbFmB3Ku96X0Xu9yfOue6LnathpLtUpXVEe+6\nNFdXUTWvSVHEbmOkiC4GoPk+Pen73k3xy18i/4XPk7jzzVg+g6SpRaDqeWo5Zhe0bFQbSDGBA+NR\nVoHQrl2cW20wuXKckXj3GpZEoeuplMlgmN6DTVcl6j6o5FhtzLZN2kdue0GlaFyjbdlQ7d7zWzGV\nOqBS59i0/Xtrry+3VFIpjLkzKHZ3Ox2m0lbVa9INnjQCvBVscYuJneQDRvYFoFJE9zyVHMGTlV2q\nFP/aaxcLRHxjbjuWwEFAafv+ZZUWlgiypkKjGQA3l9xmNkvvnFIWQdR92ZnRRI9KgSm40JPuJkWj\nYHs+WnaHqRR6Y6BSJ5WuecZb+dcvYip1QCIfVJJFxpubmIJELumDoD1jtqj77L0LmEquZbH8n/6Y\n2I03kbjtjjf0Ha/UlXqj1Ta8MVgNZTFXVrCKRWI33Ywgy9TaNiO+2b8iitw/lcVyXK5KRfn91+Y5\nVqqBD6oP94BKuXWPjZC5gAH51ulBwudrvHrcmwRfLvmtU5GoiutCtdIKJjZvtBIDIRID39mAEsBL\nJzc4v17lxr1e+tp//NRhPv34HOfXqoiCwD974Gr2b0sxOBhnc7NKq23zd187zeOHPElwRJdxXJdm\nyyab1FFkiVypyQsnNlgrNFjJNYiHFX74nt2XBfPMjQ3W/uxPaC0s4FoWUiLJ+K/8Ktr4BFa5xNrH\n/hRcl+Gf/ClvvPr3f+Cltmk6Y7/yq2iTU7Q3NhDDocCPLzuRpq3FGJ1KIWoaoz/785z/7d+icfQI\n6ugYoz//i6hD39gk9Y3Ws6svUjarHM/Pcuvom77p7Tmuwysbh2laBneM3bzla9qOxXLVO0+mbXJo\n80iw0CwIAvOVBeYrC2RDaa4fupaNhndfhmSdqlnj2dUX+erCExRbJSRBIqHFGYkMA6/xhXNf4d7p\nu/nTI3/J/dvfwcsbXhLWm4YO8uDZLwffocNwmo5P8pRvei0LMtvikyS0OBWzyoCeJNfMk2vmGQxn\n2WzmyBsFrs1ehSRKHHqIcZ7xAAAgAElEQVRhEbcQQ0qpHMkdx3IsckaBL81/lR/a+4C3f36fW143\n+PI/nGLbrgzp4RR5o8De1C5OFGaxfcZxJ8WreqrbAy2eLXDLXTOkKqO0gJZWJ1JNMzp/FWM7RwKQ\nIdfMB2BWa8MDI2vxPLcOX817br8ZwzL4tSd+k8nZ64mVB4nW0qiaxMhEgonpFLPH2kQrGWILY+gh\nmZOZU0yGLu2TBHDw5gmOv7bCEw+f5omHTyPLIgheFP0td82w95pur2cveONVa2qDlcgiU6dvwDwR\nRcclPaFz/fXbmdqRRpYlHn3oJKVCExoKSisELqSzEcSww/pZr6fduW/rlL9OpQcjcAxmj60H/1cU\niVee9cDMST80IaHF+P07f/Oy27qwOgsEm+tVyr46ZP+1lz9Wl6psKkkzvEyoGac6sA5rMBofZuQW\nl2ePbbJcW6XWrjMY9vrBidgYIVlnf2YPYTVEKbNMZm07Z48WsEWL8GB3LBsciZHzbVFSWW/RIpbQ\nicQ04gk9YLx2fJVEQWRAv/Ti6Le6roBKVwqA2eKcF6G57RZc07toy60ypt2maTUviizsyNUa7eYW\noFKR9YrXdLmugCM4GHaLkE9T7HgqKX1G3d3JXTZ8IajkJy25rid/Ux0wRWqFNkVffuPUkhzMHuBo\n/mSg9XaaPqPDkrBsJ/g8teV7KAENH7xRdZlK2QhW4jtyPkVSUMUuXV2yVPSQEqDFAGVckpkI62cK\nRCtplHaXjlmr9zOVBN9zKBRWGPFXVqxmm4a/H6msByp1mEVuh9nkQrNhEupJw+owlZxGI4gr7lA5\nG5t5FDdKXU2w8dcfZ2jbvRjSEHpIobXkScKsHiBk4+N/gdM2mfnD/9D3fTsT97Lvq9T0f95qSLiC\n0AfyQddTqcN2iE+OgA3V9TyXqpAmI7ktwlENJ98mrMlYJT9lLtk10dMVl5YVZnO1C1gaNRPXLWDI\nEUIqhLZtp3HiOHa9HsT4amP9D45QXKJztVpyCyWSxASschklnWawvUazneKVUIY3ZwzgGJmSxcqg\nSqPpgUop/zi3Nzf8Y1nuMwh3mg1wnD6fpQ5TqbWyHLBE5B6ncxkbkNAE79xrk1Nok1M0T8/iWha2\nL39r6d42HcPAaXevL9v02GCaz4QRNZ2pMR13BVKh7kNLEAQGrBoNJYyoaTRbnvxOUyQE2btH3LZF\nq22T8v3UOoAYwD3vuYq1fJ3H/+rl7jHVtwCVLvCYsmXvmO3xQaVOeplidOUeA+lLgzxWzjfjHfYa\n91hY5YfevmtLk24A2Ze/9aamgWfeWrabtEPRy05MuvK3AiE/qs1UQjQlDaXdxHVdQuUmpbjE5Ec+\nQvjQKeK3XQya9pYgiqjJbgMgy0IXzGk2UVUJvQOih7vHQvKlhE6thtP45uRvuC4IAupY/5h+EVNJ\nEki3yyyFMriid5yUnphmUds6/a155jSNY0cDyemVulL/lNUBlWQ9Q+PF4wCE9+7HsB1s1yWmdMem\nmwa7HiJTsRDz1SabcwUYCpPpSSALQKUtxpbrbp7k1JE1GjXzsslvneqVx11ulf47uRzH5eEXFrh2\nZ4aRC8bolmnzycfOcGqxxFq+gSQKvOfO7WSTIcYyEQ7Pec/+9921g6u2d1fUV/N1PvqZI6zmG4xn\no8yMxTk8l8e2Hb7/7TO85eAokihSOHqMV7/wOO7L52mJCiM//XMXpYJWnn0ac32NxJ134bZNlv7g\n97GKBbSpaaRYnMbRwyz+/u+Rvu9+Cg89iF2tMvCOdxLevQeAgXe8k9KjjzD687+IPjUNECy4dWog\nHeHDv3Zn8MxQh0cY+4VfonHiOKl3vgtR/9ZIEw3L4GzZMyI/V57/ukClL577KjE1yu1bAEZHcyf4\n3NwXA6uIfaldW6ZJLVWXsVybq9J7OJo/yfOrLzMV95K67t15Fw/NPuK9rrbqg0re8/qeqbfyD3MP\n8YlT/4CLiyIqtJ02cTXKcMRbdHth7RVqZp3V+jpfWXiMleIGWW2Qydg4q/V1Emoc02lT9JlZnTkC\neL4/kigF/X1nQXuxusxgOMtxX/pWNMo8u/gy5YIf9tKIMVuaQ3BEpmZvoGYI4KvXLR9UWjvvjQPn\n5/L8xJ0/QiKrsrCxwYnCbCB7M+wWqhGmtuixYTRdYWm+SL3WQsxFcASbwrXHGD15kIHcBGuPSmy7\nx2fNNPMUjCKiIJJfaqKoIjOTI9w+drMnt1VCjMVGWJk+xt5jQzgWTM2kkSSRsakks8fWmZg7iOjI\n7LxxgJeaJunXMW2OxnXuunc35+fyNBttTH+uUS42eeLhWZLpMNlsjGbDpL4Ihl4lJ52hOlBmdfoY\nkqPwnjvezNUT/RKxzoKgU5WDdO34QAhl0GL9bB1El+27L8/kTmW945Lf8HrdzGAUPawgyyKW5QSg\n0jdSnQWC1cUyG6sVEgMhhscS5HK113nnxZXQYizOHOKq2H6ezh0hooTZl9odgJ6nimdwcYn7PsQx\nNcq/u+O3AK//rmRXyaxtx3WgnswxoXZ7wsGROMcPecqHhM9MFQSBH/ix6/pYrh1QKaUP9ElHv911\nBVS6UgCBB9JwLEuj5g8yrUqg9+3cHJ0Ky97F3rCapOmCMAD5ZpFTq94DyjXCCKE69XajB1TyBmy5\nZ4IS2YKpJIuyJ1OzTf7wE4eIarLnCx0zcQoKlULLi5QHDNPmQ/s/QK1dJ6r6srKSCipgyxQqBqrv\n3aL4oFLLdWm0LGzHQdNkHNtFsL2GodH2J1Y98jfBFhEdiVBYIZ2NBhHAZSA7FOMUkMx7EylBFXFN\nh0a9x0jZdhBdF1cQkBUJWZEwAcl0aPjgUyyhIysiLdNGAEzfaFsAzp/Js+fq7uS+16i7cUHym1Mp\no0RaOKKMLcnEGyWM2BC6JmL4CWRWyRsAXcehXSyA6/YBI+DJ3wBK9a5Zt4BAoy4hiC7WBQwqx3aQ\nZBHXj15OHzwAL0Gt2O+r1VthRUIGND+ZLqTJWEUfVBroSuXCIYlNUydXbAcPGcuwEJobWNIOojEJ\nPTtD48RxjHNzXhoXoI72T54jcZUO2dpSTNRkFpMuo2Vq81VSpsuTY+9mU0+RggBUqtYt2pZD2AdR\nTB9UwnGwK5XA+Nmuevu/pfxtbRV1p8/8oBdUsgAVtePfEw6jpDM0Z0/RLhQCppIZ9j0GWkYfS0Rx\nLW7YPRgYJ4u6TjIme/va7l6HrmURa9fZjHlNs2Ha6JonERH8CZjTbmNaDmGfaSb3pKRpukw83t9A\nh7aQIVzIAooPRFEiUQZ8QK4jLxOrRURRIBLTglWYrcqqVBBDob7EnbddP37J1/emv/VWJqFTcA2U\nzMQl3wve8RPDYU+iZ3v7W0HBEFXi7RZ2tYps2pQGNfakMqTeufOy2+uUnk2Dj6Mpshh4GTlGE12R\ngmQ5qQdUEn2Azq7Xup5Kb9CoW06lPP6063reTmr/xCww6vavM901EYCGpONB8P3hCqLmnUf3gvS3\nxkl/Yu9P2K7UlfqnrA6opOhZiie+yMm911EZn2HSX/CIKhIto42qyX0g8r5EhPlqk+ZgCFyX5z97\nnMz3H/D8Ete7HhcXlqrJ3PH2nTz+8Czj06/vZRHu8X77bjHavrBeO5Pjk4/N8eyxdf7Vj9+A1NMj\nfOmFBR55ZRlNkZgejnHXdWNkkyFaps07bprkY184wbU7MtxzY3e8PTqX49/81cvUDYu7rx/ngbtm\nUGQpsFPonKfKC8+R+9M/oXekTrz8MOz/ccBbaMx/7rMUHvw8AIUvPoSo6TiNemCYDVB++inW/+Jj\nbH7ibxFkmewP/hDJt90dbDP73veR+b7vD+Tfl6oLFyHCe/cR3rvvDR7Nb65Ol84GaWFnfTlabzUt\ng43GZgD4rNc3ePDclxEFkYnYOIIAk7Fx6u0Gn5z9HC+uv4qAwFB4kPXGBi+tv8YTy8/wk1f9UJC0\nBl3p2/VD12LYLWZLc1g+W+e+3W8PQKXzlUWAAFTan97DS+uHWKqtMJOYZk9qJ1849xVwIax0n2En\nix6Ddj6/zM4jd6Ci86o2T9Eosye1g4pZDWRyktDtEzpzkXq7gSqqmP7zc7G6wvVD13Ii74FK56uL\nqLXDaHhMZ70ZZ7Y4R6ieIFrJYJ/tUUTY3n6tnvcAKsd2aSwJvGnvdjYL5eA4gwcqpde8bV570yS1\nSoul+SKzR9dpFlwa8QKWYnJ8+2NML14Ha2m+8jenie5Ik4sXPEYVw1RKBlM70rzz2h/vO5/3z9zL\n4uASo5ntPP21uYDt0wnYkSyFZriCMSLA2a/PX2fXVcPsukD6tbJQ4vN/e4gv/8MxNEXm2SfmcB0o\nDi5S8hnrb7vtINsSk0zGL+670h1QqSyjGt58LjEQIjRgYkkm8TEJTb84KGirbYDXZ0Z8j9jrbp2i\n1WwT/iZA+XBEJRJTWVnw5j079g6+rnT5UpXQ4pihOofsl3Bch+/d9nZ0WWNQzCALEmeKnjdxR5IJ\n/WOHEG/TjtVRqhGqyU00qctwHxzpviee7PbZFy5IxHwixndS8htcAZWulF+dJLeEFkfx6eAlswdU\nUi8BKvngSz9TqcBiMQ9xcBoxxFCderse6IjbjoUsSH3oakTu91QCAl+lltXizLkCqYjCDODKFq1Q\ni3JeoehLcFqmJ89Kal3Ed3NDhnFwbZnNksE2P+lFNTugkvf5dcNC80ECq+0iCmJggOZ5Kvl+Ir5J\ndyjs+SpNbBtgYaWC0TQZGoyA6AYIvZJQMTeNILEAoGFYXiJnD9psywKq5VIuep8XjnhMKKPWIhpW\nqFdauHig0rnZXB+o5PaASr3yt1KthWrUkHRf6z22DaXhgRdSvezL3LqTbbtWC37mNJuBbAkgGekw\nlVqB9CWihCk0bQRZom3afdJG23aQJCFgO4QnJ5BfXKBp2OQ+91ki+69Cn9nRN8CG/eMhdoxMVeki\nplJn3zYrLdo2bNuV4txsDslxoVKGOMTTMfTtHuBSevSRQAbXkUt1KprQcGkjIGApJnoyQw2PbeS0\nWtjlEs6w955TrRC34IFKAI2mdxwvlL+BB9IFoFLNu2962TpSLIYYiXjpdHoHVOoxbfYZfKrrnTcp\nEgkkS1Y+F3gqtSM+qGR0QaWWrCG7Ngd2ZXAWPZmYqOvg+zv0yuTahQIiLmXFu6cN0wq8KYRA/uYx\nlWQ/KlhQ+puBXg8lTZUQxS3kZ5H+CdmHf+BgIEkDUHymklUscNvdV/et6Lu2TeXpp4jdeFOwAmxX\nK4H59tdToqoihkKelLKnnEYDwbbRUq/ffMkDKaxCHj2cxgUqlogtqqRaBdobHkV7ctsBUvrXb5QY\nGsx2QSVN7srfmk3CPUwlMdIjf+tICWs17OY35qkkKgpyMolVLKJNXJy+0mEqdbbfAbcMUWVLUEnf\nmqnUOHECRJHQFVDpSn0Lqm3kEAQZSYxSPXOG5z/4i4i5Ovd3nmMNiz//D0/ztvv29skvokV/EikI\nRGwobzb40meO8sCP30B+o0Y8qQeJbRfW9t1Ztu++vNS1U31Mpe9SUOmFk97iydJmjcdeXeFt149T\nrrV47vg6X3z+PLGwwu/99C3Bc/HTj8/x5RcX+aUfOMAvP3A1O8aTfO6pcyysez6cx+aLuK7LT7xz\nL7f39DS9fUHz9GnW/9ufIeo6wz/1M+jbt7P8R39A+YnHCe3cjTY5SfHhL1F55imUbJbk276H8mOP\nYK6tkn3/Bxh4e9cYOnHb7UiRCJWnnyJ9/7svCikAXhdQ+kZrobLE35z6ND+y932MRb19XauvUzVr\nQfz8VrXR2OTRhaf4/p3vQumxYDhR8MCXsBxirb5Oo90grIT5m5OfQpVUHNfh8aVn+JXrfpa4GuX5\nNY9R7LgOf3L4v1Exa7x35/185fyjlM0qU7EJfnjvA5RaZf7zax/jmZXnAzPuT85+nr2pXdw/8w7m\nyx6otC0+he3YnCmdY7G6TDaUZrPeZaIvVDyG+GYzh4BANpQmrsaBFfZn9jAU9thJNavB2dL54H2O\n6zAcHsQ9mUJph0CA5x85z0z4VobvFpBEiZX6Gk2rSbnHS6ozj6iZNVJ6ElVSWagucbxwinc538Ns\naY6klqDUKtPI20ES7EA7S94+R7runROhplEtG8QSOm2njWQpbK5WSaRClAtNZo+u8+a37w6MoTuy\nN6PdIpkfRY2IbN+doZhv8Oyj8Mqz3r5VE5uUzQqDiQwfuvOt5M6afO0fTzC2eBXn4s9gum2mmx5F\nanzq4j5if3o3+9O7YRpm9gwGY0gsoZMYCFEuNlmdPEa44u1H6hsEGUYnk9xy1wzPPDLHJ/78RQCS\n2RDHM0vBa67K7CV9CdCq44dpVyQ020/MHAiRV8ucvuZx3rH9rtf9DuGoih6SMZoW6WzXr+76Wy/u\nV76RygzFqFe9a3Vm79c3fm9VuqSjigqm00aTVN48fhsAkigxFBkM/H3j6tYsel3SKE+eZ1vuaioD\na2jSweB3A5lwsGh+OQ+9DlNpK1bht7O+czhTV+rbWp1Eg6QeD/yTyq1KQCVNXAgq+cyiuuVNdI0e\nUGmpnKOF93PX8AaXWk8CXNtp9z0koSMz836WDXUpkpqkUPf9S5oNb9JtixatUA3bctj0aZKm5WA7\nXdaHZTusrpuY5/dgrW5ns9RECTyVQrg4mI4PKjXbQfNo+glwvaBSR4Yntf1kJp/xdO97D9Aa8W7s\naERDinWlYJEBb7873kgAdaONDIhKz23nT85XFz30PBzV0HUFwYF4RKVWNXAVkQYui/PFIP0Oukwl\n+wL52/xalajVBH81Sxjfju1L+MTievcY+fK3DoADBAlTnQqYSrUuUymqRLEdF9EHg6weXyXb9n7e\nSZCSwmFCuoQp6Ww++CCn/+CPqb78Yt9naP6Dw5G8vy/FVIomu2DXyHgCQRZRAck3Co5nE4S2e01a\n/bVD2I0G2Q/+MNpYvwwnrIRoq975dVUL3Qc37HKJdt5bXZN8MOeVvIAtCGSKPvjjA5FdUKlrIrnV\ncexlKgmCgDoySntjg47SQna610fn36oP1IqhcCBZauc2g/Q3J+ozcAwjMEmuCRq64BDRleBnoq4H\niW1uL6jkf+eSf083W3YAEnXAI9m1aRoWsrs1qNQbzRy6BLtIUNW+Rj2RTvTJFjoAnFUqcdV1Y32T\ntNorL7P+8T+n/PST3vd3HI8ZdJm0tq1KiiewL5C/dc5xL2B5qZIHUjjNJnqzgiGqlBsWTUlDcB1a\ni16TPT69/w19J32467OlaEqXqdRsosoiIce7l+VeQLKXqdT4xjyVoGvWfaFJt7e9fk8lvUPvF7sT\n4V5QSZBlkKQ+tpxjNDHOnUWfng6YT1fqSv1Tles6WEYOWc/QOn+eXDSBK4rYLnxp0bvPKytVXBc2\n16o973M5/dwSih+ksS0TZfeBYQqbdV5+eh6jaW3JUvpG6rudqWS2bQ6dzjEQ0whpMp994izleov/\n+uBxPvHIGcy2gyqL5Ct+cIjj8OThVdqWw0c/e5SQJvNfP3+Mzz89z6EzOV6byxPSZH7lfdf0AUoA\nzbNn2fzk37H25x9j+aN/jOu6jPzsLxC95lrkWJyRn/l5RF1n7WN/yvl/9RtUnnkKbWKCiX/xLxm4\n++1M/evfZfsf/oc+QKlT0WsPMvrzv7gloPQ/qxzX4fde+GM+Ofu54GcPzX+FxeoyXzz3VcALgfno\noY/xn1/7WGBqvVX92dG/5omVZ/s8hQBOFmZRJZXbRm8CPAbRSm2Np1de4NHFpzjuM3M+ffof+a3n\n/h2PLz2DLmmMRIaCPv9Tpz9Pxaxx3/Z7+NXrf47R6HBgG5EzPJPpw7njLFSX+NriE9TMOvOVBaJK\nhEwoxcHBAyiCTNuxmIiNcTJ3Jvh+TbtJzayz0cgxoCdRJIWSLwtaqCxh+wtVRaPEs6v9veCB0NWk\n16cxtSY//LM3k5iUCDUSyEspBvwF46JRDmRwAA2rge3Y1K0GMTXKh/b9IADLtVU+eujPMC0zmHg7\nlZ7EacPbXqjeZWEvnPX2ve1YxKuDuC7s3j/E6GSS1aUypUID1dVQWl3Lj3rVRLIVBoZ1L8EsEyES\nUzH9cJ3YmMBkbJyPXPdzpEID7No/xI69gyi1CFohBa6Asup9v7Gp7nfZqi4cP95y72623R6mES9y\npuQtJn4zSWBXv2mcg7dMcvCmSd79Q9fyvh+/AUHp+n5eCiQBL5BAD8lYFTFgKsWTIWzXwZbbyPLr\nG9gLghBI4NKXsDX4Zirrj+kDmTDp7De+fUEQSPjz5NvHbu5T2oxGugywC8kYndIkjXJijZ3vCGEr\n7WCOCV4KXsb33osPXFpS2yFQDIZfP+nyW1lXQKX/RapgFPny/KNB4sEbrar/sEnoseBGKLcqgSwu\nfoFRd4ep1PQnwA1/gBUFkY1aHkFpoQkhXNMbBOvtbgJc22l3U9Ysm0e+cJK1pTJxLU5cjfVRYjtM\nJSCgRltiGyvifW4h191uy+zu+0qujmW7DNv7ccpZNstNZEFCQEBthWmrBri+MV6zHTBHWi0LRVSC\nxDlFUlB8QKbLVPIeTIIgUDdtREEgpEkoCd9PSLSJpr1Bxmh2m4ZKrYWIgNwjFVJ8ydeKnxoRjqio\nIRkJSOiK935ZpIRngn3o+QUe/9IpnvzyaeyOp5LRpOY3dNG4xvxyiajdxPalVe7QGG0/SY+NbtRq\nh8HRB4bU+0GlwFOp3qJQ9k26G95xk3xwzGrb2PU6xsJ5bMtBksQ+35fYUIq2pPPYzA/z9PQDzB/v\nT0rrPOY7sEdIkwNpXgd4AIhmuoBCdjiGElZQXVD9YSwa15GiUbTpbUjxOOO/+s8ZeGuX4t6psBLC\n1LzvJ2tdmZRVLgfMo7Dvp1A1XQqhEOmy7XnRuB7wFdZkXMvCKhR6jmfXoyoAlWL9Dy51ZARcF6nh\nHfteUEnymSFyuwvIddLE2vmcB9RJEkLY26bV8NLfHEmmJSooPgDUYY4Iuo6g+nLILUClouRtxzDt\nHqaSdzYk16HWbAeg0oVSKVEUUOV+gO3CEgQBscNWEoSLgCnJl9RdKE8DaC37vl9+cpxTr3vHLbb1\nQ/pSJcfj2LUqrt0FYztgkDZ+aelcpxSfzaQ1KjQljUrdxPDvJeOcR3FWBy9vPnlh6SPdpkMNqV3Z\nmdFEEAQivqeWEtsCVKr1yt/eOKgkpz0AdauJ1YWeSprdYSptDSqB56vUm/7WmD0FjkN4z7dWEnKl\n/vcsyyzhuhaKnqVx4ji5bCcAQaDpJxsVlzxQuVbpgp+L5wrkNmqM+4sEI2GNG+/chiyLvPS0xzC4\nMPntG61eplI0pl7mld+earYsDs/lcRx3y98fnsvTatvcsn+Y77tjG42WxR/+7SGOz3t9gyIJ5Cst\n/varHoPmxPkilbrJ1FAM07T5f//7K7w2l2f/thT//hdv56O/fCcf/8172Dfdv8JefvpJFv/t73rs\no6efxDEMhn7kQ0T2XxW8Rh0aYvgnP4w8kCJ6w5sY/qmfZuJf/EYgzxZE8bKJnhfWXx3/e/721Ge+\nrtcWjCK/+/wfMVeav+RrKmaVxdoKjy89w2p9nfXaJkdzJwE4tHmUfLPAC2uvUmyV/NSz0pbbKRol\nlmueTcGjS0+x6BtkF4wi641NdiVnApbT2fJ5nlt9KXjvZtMDUxeq3jPUsFvsGpghqvTL0cejI9wz\n9VYksWv821nYnYiOBoCX5Vg8svgEeaPIdHzC87eUdaYSnrwuooQ5lfNY4aLfi712+gzVRoPBUIa2\nY7HmJ7idLp4NJGwuLnmj0KcuOP1cGdEVWZs4Tk0qU959Dltqkz/ukJC9c1xslYLUOwGBcqtCte31\nW3E1xlBkkMFQBtGWqB0Kse/le4hWvGtNb8ZA8GVFVQVciDVTuL635YLv/2U5FvGyt/gzsT3Frv3e\nM/6hzxzh4Y/Psuu1t9Asej1FI+/9nRr0np+CIDDhX9uxhM5H7vwpfu2GXwiYJQA33D4FuAwt7WR4\nYQ/tnMzkTOqyQSVb1ehkkt0HvH6iY0Hyep5KlytBELj5zdu5733XMDrheVV1zk9I1i8iA1z43lQm\nglXz/KoUXUTTZZxODyl+fXBDRwL3TwEqjUx4+9I5n99MDYWzKKLCWyf6w0g66eNwaVBJlzUMqxWE\nUPWCSuD5ZsmKeNljsD+9hw/u/gFuH93aWP/bVVfkb/+L1BfPfZVnVl9kx8B2tie2pgq6rsup4hlm\nEtMXDQ5Vs4ooiETUMIZYJ6ZE+5hKF8nflK6nEnSZSsPhQVaqGwiqSCqUoeIDMfVeppJtBQ+vtaUK\np46sIYoCP3LL+3AvAMVUScV0vAdIB+e2hTZuxLsZ6+XuhKbVtgOvm/P+quT1u7Ms5+pslgyPKSJo\nKG2dWiwPfiJarYep1JsAB56nknaB/E0PdY9dvdkmrHteDfqAgLEA7XCDiN9IdkzwAKpl7zsrWo90\nKKpi55sYfvqbmF9F9IGGmCR4sJAiUWyajCIEDS9A0lI8Kq/jsL5eRZJFVE1mdXGDfbiBsMpKDmJK\nGwiug73i6d2leDyYzFvFXjCku5oLXabS00fWqI/ZSHHYzHvNpyJLtIG2aVN96LOUHn8U+6qfQFak\nYOIrhcNM70hTzNfRVYli0SCfa/Z9huQ3sw27Ky0L5G89Xj6Rjo+P65IejBCOqpiVFjXNmyh3/KQm\nfu1feMdS23pVWJd0WuEakWoaJSIETahV6YJKyYlROO9dW5uRCNlGg0TNZt0WUJz/wd57x0tyFXa+\n38pVnW/Ok7MmaBRGEhIKICGDBRgjsDEGA15nw+767T68u9731v6s972367Ss/YzjejEYG5CxQSaL\nYEmMwijNaHION4fOqeL+caqqu28YzSgZyff3z4Tu27e6urrOOb/zCyJM3JmfEw113T24C/MxEdZ+\nHtuVSgB6vxhwEnaJvsE0PZPHQZaRDQPDqYIGll0EWUYyjBapNDeHW62hWAkMU8eRFJRGA9lp4io6\nrqQiuY6onI+USkncRA4AACAASURBVIYZ2xqXUyrNqylcz8f1fKzwmoyURUrgCWVdmJmwmBACYXuz\nXX/Z5rcISiqFVywgm+YS/7qayYAkdZBxEewpkckWq+lCtZGSvnL7G4QNcEGAVynH11LzQkgqXcGO\ndRTWDVCXDYpVGykkmaM2Oq2/f9mfXQnW0CBwDgA9aXUolQBSYdOc3k4qJVukUrsK8GqRu+MuJFnB\n2rxlyWOSpgnlUfjd1cN7el0xkJBEyKq0iFQyjdiGC6H1DV71nJFV/PNEe55S6ejDzI0Ky+U71vbz\nwNlQlRtu7ERqXoDjh8T95d5twzzjNLm+N0NKV9mzb4ynvh+RSi+XUqk1Dv2gBXXPFOp84gsHmZir\n8tab1/CeOzfhej6f/PvDlGs2v/SuXbH1bd/2fkb6kjxxZJrTEy3150/eu5XHj0xz5Fye81NlHg+b\nm953x1pmj5zguUeeY2O3yrVkCJ6YR7/uBlSlpaYIXJe5Lz5A/utfRU4kGfipD2OMjqFmM7E1uB2p\nvdeR2ru0SvxqMVGZ4rGpA0hI/MjGt2Kpl1dWHl84xUR1iscmn2Rjbt2yz4nIjoCAr579FkOFPgIC\nrunZxuH5Y3z74sMcnj8WP3++vrCs0uDh8cfiv3uBxx8d/Av+7Q0f5VhofdvevYX1GTF+ncyfYaY2\nS1JN4AYeTa/JYKI/JnJAWNIKdpGklqDuNhhI9HOxMsH3J5+IFU9luxKX6Lxr03184tk/BoRaP2pa\nW5dprS2yuhiL5+oLXKqM0212ocs68/kSB7+8wGDfNvrXppmqzsSb3VW3xpFT50kVe3FVh2aizHX9\ne3hq+hlS5V5S+T4q6TlKXdP8twO/T8NrsnFMQzk3hHsuARKcPTrPQuiEGEoOMFGdYrYmyKBUSNzs\ns27hxGPlOI7COZeAMUF2WBmF/uEMpSMNzHoapWZSz+bRHJNL5/N4ro/jOWQK3ZiWSu9AmmyXxcPf\nOMGpo+KcSkjY0+GaYUHMX/va7hdjG7o5dmiKtRu7Y9KuHV09SdLrJDiXwaxnSORU7n77jheV8dNt\ntubIsiR3kHQvB7rNHPONhQ5SbMXn9iWZuFhEcyzMLvG+I2Vaew7W5bB11yALc1XWbup54SdfJUbX\ndfOuD+ztyC16sfjJ7e+l5taXnO92pdJK58xQDAICyqHYwlA6x4W9N69h1/Wjl80YVWSFW0duerGH\n/4phlVR6DcLzPf7mxBfZN3g9m3Ii++V08RwANWflQOSzpfP8j2f/hBsGruXD1/xEx2Mlu0JaS8X+\n5KyRYaY+FyuVltjfwgykiFSquw00WaU/0ctEdQoJny4zw3lvGVLJd+K2t3yoNGrUHTblOtsEQDC4\norozQEbccF3ZIfoONhot0qZhuxA6ps+HQZu7N/bylccuMFsIc0Ic8SW39RYZVak7dEfBy01hf7Ma\nPk1NEra82P7WqVQCkceUjAKmuxUKgJ+qY1kaAUHc4gZQCie1RhsplcqaFAl3XSSY/+TvEgzcBsow\npgc1RNZQDdi8awBFkiCAY4emKJAi4tsrZZtAkfH8gIVxMfB54c5AQ7GwFRPNb9C8dBG1qxutv5/6\n8WNCbdOmVPIrLeUXQDYkx/LlJmZI6yXkBEVA0wWp5Do+zsI8eB6e42FYGl6hJtQphsmefWPs2TfG\n/EyFz/35Aapt4eUAckgmlcJzFbW/KelMh30qGaqmEk4RpVEhk7MoTJQpWOIsRKTSSmRSBEs1mR45\nQaFngsFURrRoKQpesYgzJ85demSQpHmJasOlkM3A7CyjMw73nH6ErkaVuTv/bUzOWJu3UH58/wr2\nt85BJcoEkutl7v/QHZz7v7+Ia5rIhsma6il2/NL9NP/4G3gJ4SdXu7pBlnHm5vCqVWTLwjQUbFlD\nbzTAbuIoGk44YAeO02Z/MwjCgO5OpZJYiM1JSerhOY+VSmFQtxp4VOsuajgZlLSlO+yGplDGWVGp\nBK0soOWacSRVRUmlViCVhJotIuq8cqnj/F0p4ga4YrFFKl28AJKEMXr5oG7oJJUaoVLJDEkle3IS\nSVU7nnMl0KzW9amnrI5MJYBEID4zo02VFV1Hfof97ertZdbmLcsSSiB2GhUrgV8Lrb9RZoSsI6Pg\n4XaUK4AgLtuJ6PqxI0iqirlx01Uf2ypWcbVw6mHzm5qjfvoUc/vuxVRkrutJc6JQ5fmFMmrdRVXl\nOHcQRNORosqMDWdY07aQu/YmUbddrzovG6lkWlqUj/8DZX87NV7kE184SKUu7uFffewCm0dzPHl0\nhqdPiPP6m395gPlig76cxVi/aMu8ddcgpydKbBnLcvM1g9y6c4hcyuDIuTxf/v45Jo6f5T0Lz8L/\n+2n6fJ+7AeagKFxZzPzVp5nfeQ3GnuvQh0eY/exnaF68gDYwwMhH/zX64OCKx/xy4tGQKAkIOJE/\nw56+y9uY50OFTWQzWg6FRkt1+9TMc5gLBmktxUeueT+/vv//47uXHgUgraUoO5XYatYOz/d4ZHw/\nIEiClJYk3yzw0IXvxe1S27s3k9AshpIDnC6K47lj9FYulC5xtnSeYrNF+umyHgctv3/b/WzMrcfx\nHP7z47/NF099hV29O8joab598WECQjeA78YbCTcO7uX7E08AMJQa4EzxHGOpkXhOf2zhpHjewBZM\n1aAycRqQSFS66EvkYsXV2swYU9ML6IfWsg5BTjnJKu+4817evfk+/tenv0MNULaWUGSFhtfk5qEb\neOdN9/G5P3qKmcMOG6VbuVhzCNRutJ0mawZHKU64fPdTF0iu7YntWQtPK2hNi+RWB3kqTXHWQ+9L\nongaWi6I839yc8Ly56VruEENddxi4mKBoKKh2gaj27uRZQnD1Ljtns3YDY/htVke+F9P4y+IeYBb\nEGNi/1CLXNiwtZdb37yJTTtW3nDafGMXT52fx5Nd7vqR7bFb4mqR1lOokoIbeHQZ2WVJrJeCrpC0\nWkl1047uNkuZmRbrjyhUXrnChrK+wTTveN+1V3uYV4zBkZeHdEvrqWVJoytSKoUL2Ei0oS9SKkmS\ndFlC6QcZq6TSaxBTtRkenXiC+Xqej+79Gcp2hemamAS0B2YvxnTYxnBg+lluGryeHT0tEqdslxlI\ntDJNckaGS5UJZsLXXWJ/i5RK4cBS9+qYqtkRWJsxMpiyhU+n/c313TifJz8fZjLVO4mGCLqigxSA\n5KOEdjVHstF1cYNqVwI12vKGzk+XUWSJsf4kfTmTuYhUssVNwNGapBMa5ZpDte4yGE72mg2XhCNx\n/5fmObbORN+iocs6wzM2W054zFlghrkwQRBQrTv0ZcWCOTus8czo8yRHfUxDRDC7bcdUDRvezDZS\nKpuzyIeEmZXUCWo1tFoe0sNIdmQ9Eu/7ujeupztjMnGhwLFDU5TUHAOAJymokkTR9/nK/vNQCRsr\nQiKgVnOwFZOkU8QrFEju2h1bXdxS8bKZSpahYmgKTcdjtDfDuD3Lm3ZvwO1bR6bqcGKqguN4MZEh\ngrpl/Hod2Up07LykQqVRzZEIPA9JiYiQ0KYQXgOWruDm8+iDnXkL6eg8N2axJyfp6UlwAaiFdZyp\n9JVV+lqqia+61FMFLHVASOYz2Q6lktbXz2D3AqcnSlTDXKc3PVFGDh0CmenzhFFfWFsEqeRdgVIp\nIjm8snjcb9RF9pFhEpTLDI3lOF2rxnXxkqKgdnXhzs/h12pog0OYmoItqQTNJn6zga2m8UIFSWDb\nbaSShR8O5IHTqVRyFY2aYlIJz3mcqbTI/hZZ6iR9qVIp+pnLkUpRA1xUP78YSiaLuzDf8X+B78ch\n2BHhFOUiqVdpf4sb4MKfD4KA5sUL6AODL0g+QmemV13WKVVt0hGjHQSovb0dbYlXAlmOpusSRiYZ\nk0NeXXxuiTBTyci23qscK5WqMfkkJ17+zCLZsvDCTCXVDjOVFANFEjk1i+1vkmHghxlVbrlE8+JF\nrG3bl9glV7GKVwJ2TSxY7ROTNFWNUjLN5qRQRd432M3cV04x1JNEkiVmJ8v4foAsSyKQN2MsUQbo\nhsq979pJfq76sqmKZFmM7Y7trRj8/WrDcX0++ffPU2u4fPDerWwYzvCfP/UUf/C3h/D8gPVDGTaN\nZPnmgYvh8z1sx+f8dJnPf/c0uibzc+/YGTd57lzfzUhfktT+r/PB/GFkAvSRURI7rsFctx41m0XS\ndRpnzlB+8nGKh56HQ8/Hx5N54+30vfd9r1oOm+M5PDH1NLIk4wc+x/MnX5BUimxbM/U5is1SnKnS\njkK4SLxp8Hoen3qKhtvkrnW3YaoGO3q28lgYmh2R8/P1paTSwbkjVCMrk9HV8XvPFs+TM7L0h3P1\n9Zm1TFbFWHnL0A1cDC1vda9Bv9XLTH2OO0bfwP7JJ+k2u9jde4245jV4+4Yf4vMn/54HTn6ZzbkN\nfOP8d0ioFjW3zhPTT8cE07nixfjY/uz5T+MHPgnVwvEdLNWKbVcbsuvotbo53BAbQkY9RY/Ww6my\nsMbdNXobf3v2uwCUu2bYnNrI1EU4cWiaNRt6qI9LuKkad++5CUm6mSAI2B1+JrtuGOHp71/AIovc\nZePnddacv5bMcIqxU9fietA9vVaQdcUGpRmbamaOYEOJa7O3c/CJBgNTYqPDT9Vjm1k2bGv2Mw2q\nUpn0+AiHDoyTmxHWwrH1rfF/x7XD9PWlmZ0t4xoNlEJSKMOLGq5eJ9e2gSjLMrtvvLy9fnSgn89t\nfxBZ9xkdWBrTcKWQJZmcmWOuPv+SrG8rocsQpFL6ikilln1Pz0SkUlgSdIVKpdc6uowcpmLS8Bor\nK5VUcd+MRBuL7W+vZfxgjHCruCpEeT8nC2douM1YpQSXJ5XaGxP++vgX+bWbfgVd0Wm4TWzf6bhp\nRAPmxcoEpm9x4DsXufG2dbH1K67xjJRKTgNLM+MbEAh5rKXoVFmsVBK5RfDCpJIRqgJQPGRX3JRs\nqUkyzIpx2irtmyEJ4/sBF6crDPcm0VSFvpzF5HyNYqlO5ryQDNeNGmP9KY6cy4tMpfBmaDddugsu\nuhuw9XyDeV9GVzRuOFKj7upgtZRKTcfD84OWUqkZcO8zT3Le2IC5XsEDPKdFKtVCUinRRkqlExpN\nwAISSfE6eqMCafDC5jgltCa5oaIn2kEthbavotkXnheJLz16jl1heHpgGhBAtWITyCp66N/VR8fA\nDUPPi8VlyZB2vPn6UXw/oJybYHwGepM5brl9A08+ck4cVxup5PvEmUqL7Tm6oaBKPk0lgTM3hx7m\nFnm2i0/AbNjkZ+EQ2HZHnhKI0L837VZw/u4A9tRGBsZ2x49JypUz+5ZqLvm7ks1ij1/C0Q1RW59M\nMtCd4PRECbtXnGc5gDPJATZUp0lcPIFji+MzxtYgGeYi+9sKSqXQvhXZufxGAzWbQzaMWPnk12od\n6hetp5f6yRMQBEKppCs4skbQqIHdpKl3QWjR9NtJJcMQ2+O07G9BEODMzlJPZEGSKIXXpLWo/U0N\nPGpNF52Q2FzB/gbE1rnlENm2pGWUSiAys+zxS/jNZkzyNOfm4uONrk03Vipd3S6T2paXBSLw3K/X\nMXbtvtyPxdC6O5VKxapNT5tU+WrzlOLX1VVs20O3DCRdB1mObWdmmK3VTiq1B3V7tRqSpiEvox57\nqZAtC7cU5n3ZobVZ1lFkFTx7aaaSaRLYNoHvUz9+HIDEtu0v+3GtYhWLEQQBzcp5FC1D/m++zvyg\nUB6OJsW95uyxWdSax+Y3DDB1qcj0eIl6zUbTFBp1l76h5VWPQ6NZhkZfXgvJtfvGOgot/qnx6KFJ\nFkpN3nLjGHfuFQvrn7h7M5/6+nH6cib/8v7dTM5XY1KpULH5rb95hovTFTw/4GfeviMmlEDsrL+9\nu0wu/zx5NcXAe3+MtXfdtoS0szZspOvue8jQ5Pw3vkPj7Bkyb7iN5BXcjy+UL5FvFNjTt3PF5zi+\ny7niBTZ3bbjsaz0ze4iaW+fNY7fz8MRjHFs4ddnnA8y3qYpOFc5y/cCeJc+J7G+3Dt/ETG2OS5Vx\nbhsRmSdRxmWXkY2DpmfrnRsqNafO1849BAiL1ZrMKLONeTRZY6Y2R8WpcsPAtRx5doL93znDpreN\nAU8wkhpiJDXEeGUSXdaxfZt3bnwrPVYPw8kB7l5zB4qsdHwet4/ewhNTT3Ng+lkOTD9LSkvywR0/\nzv//3J/x9MxBAKxKlhl7AdMyaHhNRlPDjKWHOTR3lJpbZ0f3Vp6bOwzAxtw6esyu2HImIaFXk1wK\nm7B29W7nO64gEt2RBe6960f4zCcf48Aj51mYrREEcM/te9nW17mZCLD3pjV4ns8XS59ndLQb5/Ee\nkvleZr4NqgeB4pEu9GEFCc6EKju7r0ihMs1d6zR4ArJzIm+tZpXi3B4tLN+RsjYVfx5NVzh/eh6T\nLE5vkU3bl1caObkK1nQv4+cLSLZKIze/ZGx8IfRaPTRSRYaTgx1N2C8G3UZIKr0CTWBXpVTqbc35\njXDqG5FK8susoPpBhSRJ7Ozdxlx9YcVrIlYqxaTSD46C9aVilVR6DcIJM3e8wON4/iSn2+S4DW9l\nUikayCJ/9+ePfo3373xHHNLdzqpGfmnbsxkrbeX5E+N09STYeZ2YgCwO6q57DbqtLjJaazKWNTIk\nNLmDVPIDHy/wWkqlNvvbcgj8cOFqghKKaDzZRdfFzUsOWuGSkVJpcqGG7fqsDYM2e7MmEvD1Lx5B\nK6fI91yilMyzN9kileL2t6ZLthTaT5yA5LkppL4GayZtnh8Uk9WIVKqGWQ3JULZqTudJFD3qk1UM\nXcEFfNcnCAIkSYrfYzoM61z4yoP0f/Ob2APvxJIUEiE5pYXkT6MiFpd7//FTzHftwvWEf1Y3VHI5\ng7Inas4XLDFYrt/Uw7mTs6Q88ZlIqRSUobgQ2lnCa8MYHYvVSW6xeNmgboD77xS7Np86InbZoutE\nC4O6HccjqNcJAD8ARZHwarWYNIogSRIJPaDmJrCnJuPHnYaLAzRCgtBoVLDpVIlEWLNthIt+E3t6\nktzulp9YvQrpsNlGKkUthmo2S/PcWezpKYzhESRJYqA7fKwrw7NbLEophf21N/KxE1/CPHccWxbW\nU62vT1S1d9gIKyBJseIogpIR16RXLsX5R/KAiWwYBK6L36gTOA5KWwiz1ttH/YRYsCtWAkNXacoq\nUiNsRUSFtpa3qI1LNk2CsBHRD1UnQbOJ32jQzIiJWznM8jKNpUolAI3L29/g8kqly9nfgI48K71P\nTODql1ph8n7YcPdi7W9KNlIqiXtfK0/pyipqOzKVFIN606Uut87F1eYpxcelymB76LqY6MumFZOB\nfdTxjARam5JK1nUkTRNB3Y3Gi7K+XQlkyyJoNgk8D9kO7W+KgRbuMi4N6g6D4BsN7GmRU2OseXnq\nf1exisvBbczhe3W0egp3bo7ynW8DYDRUGM2EuT/rNvVQLkZNTU2UsLU0Ur6+Gtiz74Wttq8kTl0q\n8sD3TvOOW9exeSzHP+w/h6bKvPWmVq7cHdcOk03qrB9Kk0nq/MmXBVnw8Z/Yyz/sP8/zZxfQVJlf\nfucOdm/szDvxqlV6Hv4ytiSzf887+Fdv6gyvBfjOxUfIGhmu69+N0ddL11t+6Krew+eO/x3ny5f4\n7dt/Y4ldJMJ3Lz7C353+Cr+456dFDfsKiOxcbxy5hanaDIfnj5FvFOIFNECxWUaVlbjZab7eGt9P\nF1cilcQ1lzMy/MKeD6OnArRmEs/3ODJ/nLSW4tdv+VU+fezzPDH1NFPVVhtv2a7w+8/+KZcqE4JQ\nSo8ylh7hqZnnSGoJ8mGo98bsOg59dVw0AV/MMJju54fWvZnJ6jRNz2Zv32529Gxhd981MVmR0pcG\nQMuSzPu2vZv/euATWIrJx/b+LEPJgVhloTcSbDx6K4mcyo9/ZB8ubnwuPN/jXOkiQ8l+nMMus41Z\nhpIDyJJMwm6N0faCxHhjgl6zG1M16fL6cIFkTieR1Nlzo8gwe/7pcUxLW9Euphsqb7hrE1962OFi\ndQJ3/ThbD9+J25BZ6LuIa9Tpv7SF6rjE3EmhnM2tUZmoFikYM9h6Dd0Wx17QZ0hmdHzZRfZVdENF\nSQbYJZvrblnD1HiJ7+r/wMBIBlVbngiRuhowDYcOhIUiqdpV5yEZis57Nr+T3peBCIocIq+EUmkw\nIT6TPuuFM44MU0NLSDi1AC0dtjr7V2d/ez3gQzved9nroWV/e/0plf75fMqvI9hei4A5PH/sqpVK\nP7Ht3eBqPD7xLABlJ2x4W0apBGD5gkRotuUXiVY0jZpbw/EcXN/FUkwScttr6GmShkHgKVRC+1sU\nBKgqKo26Qz1c1Dbrbtzu1g7HFl/MtcNWfLH6iosWKitkQJHFcxqOeO3JkKgaDXcj+nIWY0jMT5bx\n+kuMbzhEECiMhcn61bqD0RbUnS61rELmkbNUnn4KJQBbMQE/JqCqDXHsCTMkg2phELcnrEEuQCCU\nPAB2SEKlMwZ+s0n+a19BrpTwQ5LQSogBTAvtL6H6mIRd5O7ZJ3BqrYDrnh4DT9GpaxkWEsPIErzt\nTZswNIVUqB5Tc+KzKBciUim01YyOtRbzxQJuoRCTH4vtb+2IJnIZQ7xuNOA6toffqOOHC09ZkQia\njSWECkAyqeEqJrUJsXvl+z52w8Vue47RqITHv5RUivIW7MlJzPbcAGvlVorFkCU5volHSqW4Mcbz\n0PpCaflQREqm+N4NaZ7ZlkDXDM4kRpBKBWpHjyKbJkoqjZrL4ZXLBJECrFxGSaWWWKOi9jKvVCZw\nHfA8EWIdki5O2CYnJ9tJpVaQp5xIxEqlCPVAFWoXhM0tiO1vZmxDCuwwWylshgu0cFAL1XCx/U1r\nBXUDwv6mKLFVsR0vB6kUN8AVWirK+vhEx8+4xcKLtr+1SCvx83Hz25orq5WWTTO+jhtRQHdbG5r2\nIpVKaticF6nrZMvEr9cFmVPKkxxZulurpFL41Sp+rbbsd+vlQHtouNwMNwxkPbZrLNf+BuA3m7j5\nsA2q++Wf2K5iFYvRqIpA7foz55B0nfk1YvMjUipFyuBkyiAVqmoqpWZMMGVeRVLp1cbZyRLFSlhm\n0nD4w79/nuMXC/zO557jTx88wnypye4NPTx3eh7PFxtfTx2f5Y++fJjf+dxBvvLYec6emuCtykW6\nHnmQn7t7DT98y1r+zX2byH76v3P6Vz7GzF9/hsa5s9hTk8z+9V/hlYp03fdOfv6n37TkeDzf44GT\nX+aBk19edp53OZwqnGW+nme8OoUf+B118otxpiiuicPzR5c8diJ/mt947Lf4P773HzlZOMOWrk30\nJXrY1iVsUVEINggV3G8/9Qf8yaFPxcdfaBZZmxlDk7UVc5UKYeZR1siQ1BIMZwap12y++vVn8PMa\ne/t3o8gKazOCZIysbXWnzq8/9l+5VJlge/cWAgK2dG1kKCnGF01WsUMFa587HCv8zx5d4N/f+Ctc\n17+bM+E6YHvPZt4wvO+K1C9j6WH+7fW/zK/u+5eMpIaQJZmRlBh7hme3QgC1vMvJQ7MdtemKrLAx\nt46EluBnd32Q3/6h/xj/PtNubU5PThWoOjVG0mLj02gkCSSfXE7MC/bsG8O0xJiy49qhF6yd7zKz\n1N06jtEgva/Out05JtceJt8tNqIuPV9h6lKRobEswz1iHvfc3GGK3eF8U3WZDaaZrc/TsMQ8s38o\nja6IDNTdN41w74/uoJpaiDe+l4PaI+Z5506F7bSZldddl8OdY7eys/elK3sjMrTHWjpnfqnY3LWB\nf7n3Z7lt+MpCoRM9CoHko6bF9/xqM5VeD3ghgjGyv60qlVbxA4GImIHQf+3UYm9z4zKkUqFRRJc1\nkkqK3gvb8JMlPN9bXqnURiqZvshFas8vOnF4muHz11DddIF6qICxVBOTTmIqYZQIXI2yHZFKYnGr\ny1qsUgJhWbOb3pKwumZTfDlHB0yKJ8TzfdnD0HQasoTiB/R3CXtbpFSqhGRPOgyZ7smY9CBsZN61\nM1AIwFcYDUPlKnUHvS2oO1EQ76epSehHz1CqCrWGI5uAQwBIiJBuaCmVtIr4Oc0TC+7obDXqggRz\nbBcJyGZMSo/vj7NRPN8FBSwzJJXa1Ga65KIEHmmvjv3db8AHRcB6T1blNDCZXEPZ6KEvBT1dFh9+\n2zbUzz8KJVGH7l+cwwmPU1ZFrbs+MBArN9z5ebxyGWvzFuonT1yWVLpt+CaSWoLRVFjbHJIKruOj\nNhoE4aAhh2yYskzleaorCQtlihNz9AO1qgMBHaSSUgsJhEX2NxCNV0o2iz09hXPhLIZbpakmsVJX\nx/RbqkXTs2PFXbutKiKVdq7v4T99+EbmpXM8Es5jcwmT08kRrqmcJWg20MdExW50rG6xiNbTg1ep\nxARSO2RNFxajcqllU7OsmBRyQ1Kp3Tqo9rSRSqH9rSC1SKWmrGKEP99pfzPjgO74z1DdFIT20cj+\nZi5jfwNBKkXqpcW4okyl0La1UqZSO7kZoT4uJojWlq1UDz4n1HQvtv0tyrAqRkolsei4kua3+Bi7\nurFrNerhwN9o21XSX6xSKfzuRN8h2Urgzs/hzM2B56EPLA2rlZMp3Pk5AsfpIBpfTkTfWb9RR27U\n8IBmG6m0eKIdEX9Bs4GbF9fu1QaXr2IVLwbNiiCIvVMLZN90D+NNj5yukg6J8XpNKJAVVY4Dsqvl\nZrRX86oqlV4tnJ8q84Xvnebw2QWSpspP37eDxw5PkS83uXFbP4fOzPPE0RlkSeKZk7M8dWKWSw9+\nhT2Tz/LF3tsJUv2Mz1WY/8J3+eXCUWQCCseheugQa++/C+VvHsWenEA2TQrf+iaFb30z/t3G2BgD\nP/zDHeUaEQrNEgEBhWaR+cYC/VzZffxU4Sy/+/QfsiGzLo59yDcKHfmf7bhYFmPH0YUTSx57ZuYg\n07UZBhP9dFtd/PD6ewDY2r0ZgM8ef4BdvTtI6UmqTo35xgLFZhHP98g3iwQE9Ft9GLLOycIZqk6t\ng2jxA59iQLsunwAAIABJREFUs0TO7yZwgXCYeOrR81x8rsJGbkWpWZQG6vEcyvYdak6d7088GW8I\nR86Drd2b6LfEfT66Zk3FIH9WzOe6ehPk52qcOznHxm39MaG2Ibvuis5thDWZzuyfsfQwZ+cvkpoZ\nIJnSsW2PJx8+y6bt/XH8hef6PPi5g2zc1sfO60YwNZMyjsgsq+vUE0WMRoqZyRJsIiaq3AoElsPe\nAWFhNEyVW960iWcfvxC7IC6HLiPHhfAzHlib5OahTTz4iI9j1qkl8zArSJUNW/popMRZO7ZwArU7\nRd/URuSMQ91rcCx/kqZVJlHN0TeYYlwR16zjO0hhIdDlSCUjK9NUm6huWA6TXd5p8WrhxsG9TNdm\n2Nmz7RV5/S1dV168sfZmi2eef5hd+p3AP79MpStBpFSKCLdVpdIq/kkRETOKpFC2K/iBz45u0ehz\nOaVSvlkkZ2apNT3650fomVrHXGOB0guQSporJl/tSqUjz06QnhymWWn9Tku18B2VwBM3j4yewTJV\ncHVqYc6PE6qsVFllekr8XlkJlUbLWOBC3oXBPhMlvNn7ioehaMiKhAIMhjalKFOp3mi1iOW/9U04\ndRoVCTltoEcLZF+mO2OQMFSRqdSmVDLzdWxV4uh6E6nRpH7iOJ4UKpUCm3K4EK+GxxtlKqnVFqlk\n6gpRmlL0vrzw+HJZk8K3H4rfo+01CHSFseHQ5+21WmpMv04gyZTUBMGj38YOc3d6QmnpeG47SBID\nSXFM+7YP0CPZSLpOMpeh9YlBc9MOhn/pY6J1K1zMN86fE8fe3SNCei9DKo2mh3n7hnvjHamWUskV\ntqpIqRRappZTU6T7xO+tzIpFflTz3P7Jy6HVaTn7G4A+OIQ7P0/t+DFMRxzv1bbqRBa4iFSKlUqA\n1tsiCtYMpDsmF7mkyZnEsKjqQwR6Q4sAcwt5At/Hq1aW5ClFUNIZvHIJv94if+RYqSR2vmRrBaVS\nSCrZbcdky1psQxL2N/G6kmnGuTtBZH+L/gz/v7w4qFvrtL+pgYe8TEg3gBESUVEe07LvNVYqLf/5\nRI1snaSSUCpZm4V9wS3kRbC5oly1QqeVYRWSShcvouRycWD6lSC6DiOFUr1tVyn6/K8Wi5VKimV1\nWMi0gaUKKCWVEmom130FlUqh8qheh0adpqyLJpJw0q1KK9nfhFJJ0vVX7NhWsYoIUZ4StkRQcJDe\neBdV14tVSgD1qh3nFEbNoJVyS6n0WieV6k03zlkEeOipS/zGXzzJ4bMLbBrJ0nR8PvGFgzxxdIZc\nSufAsZl4zuMHAYaucl9mgRvOPoLWqHB34Rn+wweu59fvHeSmwhGcZIa+976Prrf+MM7sDJk//Bvc\ni5fI3n4HG3/v9xn6uV8ke8ddZG+/k9yb72H4Fz+2LKEExK1lcPn2tHY4vstnjz0AiObiCAuNfGwF\na0fZrsS/Z6Y2tyQEO3rsV67/RX5pz0+zLiM2FoaTg6iyihf4zIU/MxO2CrqBx3RtloUwT6nH6mJT\nbj0BQawMAjg4e5iPP/zrFIt1Rp65ka984Xm+dOpr/NrXf4sjh8bxNJtmusTsuTpf+uxz9CktUmy+\nsRArq3qtHmzfQZVVNmTWYgVJdFmjHCoaBqx+Th+dRTcU3nyfULgcfU6ocM4VL2CpVky4+X7A80+P\nrxgtsRI2ZNfRPbsGPJldN45y/a1radRdDjzSer/zsxUmLhR49rELHcqzarkJvoRt1mgkSlTyDpIv\nM5oaolF3aDY81g8NxwHcANt2DfLj/2LfFc3hcm32xJyRJaUlUcN5Z6VvpvUetvbG1e5u4NFMlth3\nxzqyO8Uc/NmZQ9RS4noYGs3FGa+258ab9pq8svrd0kxq6TBCQrXRE/+0S+mBRB8/vfMn4ziHf0pY\naY1aOo8XRi/ESqV/JplKV4L2CA5VVl9X52aVVHoN4G9PPhjLcKFFzGztbrHH14QM9UqkkuO7VJwq\nOT1LpWojBzKabTJVnokHrA77m95aZMtueMNtUypFBJNStKiFuUqWatKwPYKmWKhnjDQJQyVwNRzf\nwfac+Ia9UHD5u4eE3Lg/tBktN/hVQ5VQV1Yh+tr5sivkgnJIKvV0kkqRgighecz+9WeYOSQmMRVN\nQgsDjaVAJWVppCyNSt1BUWVkRaLZcEmWGhTSCifXtAa5M4NpPEVHDmzmonyGUBGVCu1vZk383h4l\nja4puOH+UnSuAtcnAJTJ89iXWo0aiuSR77Xo7xUL/XZSyWgUsTPdfKfnevBcFr78JQByVgCBj6OK\nRXu/1gpCdwsF1GyOVFLvIJWU/n6SO3cBrcV887yYrKldXSip9LKZSitBixr4amFId6RUioL5lsl9\nSefEZ9WecQFgh+fK0BW8kGBYzv4GoQUuCKgceBLTE+977VUGq1pKmI+lraxUitB+w3/Ljet4821b\nMTds6HiumhXH6hYKovI9CGKVzmIo6TReuYwftmxF7W/QUiq1L8zbSSUlkcDUVey2CY8ta6ih8ihW\nKikKktqyxfmR/S1UKkX/HxGkkdqolakkvktq4C6bpwRgXon9LWy/k83lM4DizKNiu/1tHLW7J84r\n8gpFvFIJJZ2++qY1TUNOJPFKRbxyGTe/gHkVKiUArVtkCcRKpShTSZbRel6cYmiJ/c00IWymg+UD\nwCOCDjpJx5cT0et69TrUa9RlHdNQ411GddHkJ7Jt+qFSSe3qvupsiVWs4mrh2QU8p4w/1UTJZsmb\n4rodDEswfD+gUXewwn/H9rc2UimTe+2SSpW6w7//48f4+Cf38+SxGb514CKf+eYJ0kmdX/mxPfz7\nD1zPr33wevpzJoosUajYpBIaU/N1ak2XlKXxb25Ksuvg1/B1nelUhpHKJL0LF+GhBwHY+As/T9db\n7qXv3e9Bvv/t+BJMb+qj/yd/CklVSd+4j4EP/BQDH/wQ/e97/5Jxsx2FxtWTSt86/z2majOktGTc\nRAbw7Ozz/Nr3/8uS14lUStE8tt3OBrDQKKAreryRFEGSJDKNbvovbWG6IjKOoqZkgBNnL3Lo0WkI\noMfsYlNOjP0nC2cAKDZLfPrY56m5dfovbEbyFCYuFHj04CFmTjbwbJjvu8Dgmz323rKGcrHBI189\nQ1JNYNSTHH52gvGy2Ez4hd0f5qPX/gy/sPvDnD48z6f+x36GgzU0Q5VWrt5PpdRk/ZY++gbTDI5k\nuHg2z0K+wmx9viP0+cLpeR7+xkmefbw117wS7OnZydqFa1A1mR17hth9/SiZnMnhZyZw7HD+Pitc\nA+VSk9mpVrlLKYxauGnjHtx0DSmQMGtpRlLDFBbEXC3b/eLzALuM1jwtZ2SRJCneAA8GK8iyxMBI\nhlTGZCDZH5+LLivH9besY2hMzHtPFc5S6BvnbT9+DWs2dscEkuOLKA8A9XKkkmpSTQlSqZEoxXam\nVbQUSX4QFSeF64FVpVKMdrvb60mlBKuk0msCh+aO8PxcyyNuh0qlPb3XxFLNHT1bkZBWJJWKUYCg\nmaVUDYN8A4WLC7NL7G++H5BUE/ENWbLFzaBdqdQMCRWrko13gCzVpNZ0cSc2sMO4GU1WSZgqgSu+\nNFWnGqusGk2faEo3NCpu9MuRSqVKxHY7mOFCzFc8dEXHlyRkWkqlyP5WC8kvs1khAKbpxgOmGy56\nOFAYqtiBT4akEoBhqDRqTTTPp5BWmOzVIJ0CSeLYcNQAZjNfikil0P4WesLj3BcvQFXkmGSpzYcL\nZi/Ak6D4HaFSssKWpJQuUa45BGEmlBI4EBIzRrOEk+vlaGodgaxgT4ldKdlpkrTDliavSTYQA3vg\n+3ilImouRyrRSSpF3nUQpIWkqnHbm5rLCSVEpXLFmQexUqkRWqsipVK0M7GMYiHaMa67Ml61GpNK\nXniuLF2JA69XJpWElNqv18mG85PevuUJnJUQZSnFmUptVrvFk+NoJ0yRFPZu7uPdd2wkuWtP+NxQ\nqRSqWdxiIT6nKyqVMhnwfdywil22rFZ+UGx/axEIaq4LlJZNytA6lUpNWUMNlUCBI0gl2RC12rLR\nqVSKVExE9rfaIqWSGmUqhZJl34vVS4uRDlUA2ctYD61Nm+n6obeRfeMdyz7eUiqJa9lv1LHnF9CH\nhtoshXm8cgn1Kq1vrd+RxZmdZeZznwWuzvoGkHvzPXS99YeZDkMwA0lGthJoPb0r7sy/EBS1U+0X\nkTnNc+eAlZVK8d8Tr1xQN4gGwqBWo6EYJAwlJlYXT7QjpZJXKeOVyyuqC1exipcTjYrYDPHOlTDG\n1lINcwsj61uj7hAEYIUWeCupI0lQLTUpFxqomhxbeV6LePD75yhWbfLlJn/4d8/zV986STap8/Gf\n2MvO9WKukksbpBM6nh+wZTTLb/7Mzfzex27jZ+7bwf+5O8D91B8SBAHj776Vb98qvt9Tf/6n1I4e\nIXHNzo4Wx+CmvfzRu3t5+K6hqyb2gY4cpFMhGdOOufo8j44/zl8f/yKfPPgXfPLgX/C18w+R1dP8\ni50f6HhuZA+LSKQI0b+3hlado/lOUmm+vgABfO3cQ1wsT3TMc3IX1tE/sYnzF8SYPNtGKh17bpqZ\n5x30RpJus4v12TWoksKh2SN4vsdfHv0cVafGHvl6sgvDNM0KAQHD4zvY07wZCNDXNrh99Bb2vXE9\nY+u7uHBmgbUHb2bzoTs4+2gVbaoLRVIYTPazrXsz27o3M36uQBBAd7NlhZYnBWG2OQy03r5HzIW+\n8GdPsfboPrryLQvZfEj8TE+0sievBKeOztKsemzfPYRhaiiqzNpNPfh+wPyMeM2FudYm5pnjrXNV\nDEmlwd4ubtu+F4Bso48esysujMm9FFKpTanUZQqCKRsSTamUwbs+sJe3vHMHIOxrkWorshH2hIHW\nAQEDyV7WrusLlbgtUilaX13O/mYpJpXsLEhQzcxjrpJKMaLspMj29s8xU+mFYLY3CMuvL1JpNVPp\nNYCKU8UNPDzfQ5GVWKmUM7LsG7wO27NJ6ykMxVix/S2qOs0ZWcrVFnkzPb+AnxUDRUZPEwQBv/an\nj7NpNEsmm6bQLOKHuUbLKZUSla5YZmypFrWmi7cwzHU5cWNPGCqESqeqU4tvMJ4rkwQkTSbTJRbU\njVrruIIgYGKuSrMhoQNN30ZXZHA9kamk6HgEHfa3RjixrIWEl14rM2300JBMHEtlvtRADu0bVhhU\nnLI0PD+gYXvopkqtLAa+QlohkCWkH7ufxlSTytwhEoDitymVIvtbqFSK6s+j/BpZlcAJGP/rv2H9\nmo8g+wG+IlF57lm0gQGSO66hfuwoSU3ibN0h8MQ5lQDda2KrFqZbxe3qh4pEYFmxkqhSqpJuzlM1\nuuiqTxKEChSvVIIgQMnmSCe0DlLJslo3L0mSULJZ3Hlht1JzXcjJFIErrGwrVcC3I8qDccLzEJNK\n4ftYNqg73DFuqknsqUkqZXFtKboMTR/LUHGn8kiqipxc2lgCLVIJYPtajZvuu4VU7uoGdWux/S1S\nKknSEvWJEk4u2hVLuTfdDb5P5uZbxM/nosDpAl5ZfEaRSmcxInLEnhFybdk0W5lK+aVKJUlR0Lq6\nceZmhf3NULClTqWSFpJKflPY3yKSKlIZxZlKkTUuJAPKtU6lkhwSSFGmkuK7K9rf7rx2hJHeJBuH\nVyZ7JEWh7/73rvj44kwle0rsFOsDg/E5dWZn8RuNq25+i5Daex0LX/0Hyvu/D4Cx9urayYyREfre\n/R7M3/3HmLAe+NBHXlIDWzpjYphqnCEX2c4a58WCaXFzIoCSbJFKr7hSqVwicGwalo5lqDGxulJQ\ntzMtPjdtNU9pFa8Cojwlf6KBefNaqq64XyXDoN96qMCM7G+KIpNI6lTKTVHGkTVfs4q6mXyNh566\nRE/G4NZdQ3zrkZOYTo0P3P9GhnrEmDm1UOP3PvccM4U6N+8Y4MNv246mygSuy+ZT32fhwS8hmybD\nP/+LPKmcYEbSmNvQR+8ZYfvqffd7On6nF3jYunzZkOzLIdp47DJyzNbnWagXgEjR4PP/PPHfl8xd\nJSR+bOu72JRbjyIp8dyxESq5o5DrCCdDsunJ6WfQZZ3zp+c4a87S3ZNESbZ+7sGz3+DBs9/gPVve\nyZ2jt+J5PkZRjDUL82IuPFObjV+3UC+RJYXq6vSY3eiKzr7B6/j+5JN85om/Z/447Oi+AXNymCou\nlzY8R//sBtKzQ8xX66zZ0MMv3PHL8evd/Y4dfOF/HqBcglqyQKKaI7MwiL628/3Pz4bWfjcb5zO5\n0zqGqTKyVhzv5msGKCzUOH58gmS+G+cQBG8WbcMLYWbp7FRZZB3JL3y9u47IT1IUqaOxsC9sUp6b\nrjA4mo1fW1FlzhyfjQm6Ul68h2yXRb+Z5iAz7DVuQJIkivl6+NiLH7u6jBapFDVUR0qllJ6if6hz\njjCcHGSyOk1fQszpetsazMbSLQIu2mx2PCee50WW7+VgqSbNRIX190kcnjzDBuW6F/2eXm+IlErx\nWm+VVFqCdhLy9aZyWyWVfsDh+R61sM3L8cUNL2LSdUXjgzt+LH6upZorBnW3k0rVuZa9qlCqIFkV\nJCSSWgLX85laqBEAXX0ZCs0ibjO0cYULKs/1ccP6d7OeZq6aj3//QvicaJEqlEotUim6YQe2jIGE\np8qYISnTqDsUK02+9dUnsQ88xkO5a6E7VEl5NqoUZSq56IqO47sYSGTCn2+G0txo4afWiswmhSqh\neziNe3qeI2dKYEJCj0ilsMktzFUqLYj3WkiHk9ORDRQtFX9G7Hqpns38IvtblKkU5bYEEamkKeC4\nOKhUTp9GASTJJ7BtzLXrY5VDSpeo11xcu0WqaV4jJpW87m1QgcBMxplHlWKFbGOWqcwmemrj+HUx\nmXQLkXUsR9rqVCpFu7YR1GyujVTKoaTFotWrVlZs62pHZN1xmhGpJAaNoCEmHMstfFPpcPGsJrCn\nJqlWxCCvGCo0XRKGilvIo3Z1rTjpbyeVUhs3sH5zL7Oz5WWfuxKGkgPoshZXsUaEhdrVtUR9Ei2o\ntbY8GSWRoOcdP9L6d1um0gsrlcQEzYlJJQspJG6iTKXFKi+1txdnbnZZ+1tT1tASYWByqFSKyBpJ\nUUBR4msysr9FZEArqHt5pZLsrWx/swyV3RtfWmC0bJpIhhlbHu0pkaekDw3FlsTIErZc8PmVoPdH\n76frh95K/fhxnPl5Ute+uAmgaSjUmi66JpO+/oYX9RoRbr93C297927qocovsge6Cwso2eyydsH2\n6+mlEFqXQ0RuOeF9wVaF2iEiVrVF9rcoK8ueFArKVaXSKl4NNKsXwFcI5m2MNWuoOItIpYgsT7Tu\nXcm0wexUmSCAodEXR1D/IOAL3zuD5wfomsKXHjnL+ye+zUh9hke+k2T3xnuYLTb4L3/5FJW6w31v\nWMe73rgePI/ygaeYe+DzOLMzqL29jHz0X2OMjFA++DQA+3cnePt5hfQN+zDXdBLvkSWo5tZpuM2r\nVmbkG2JudMPAtXzzwnc5NnuKzZbIzGt6Ng2vwZr0KO/b+qP0WsJCq0iKUKQHPn44HklIsRVuoc1S\nd6k8wbEwnDurZ2jmYfjotXzt6GEAjJ4ANkKf1cs9a+7gr44/wLniRRiF8UsLKF4Y7l4Q19FMfU60\n3QYB+OHGl6vF6ph71t7F/skDTD/r0V/YBJNQxSXfe4mbtu5k743X8dBfnsH3A3ZcO9xxLkxL40d/\n6nqevvg8n534CpsO3Uaq1MNwWy6P5/qxskdrmpAAyZfx6wo9Y0nkUC2mKDI337mRyoZLHPjqNNn8\nINWKTSptkA+VSo7tUVio0d27/Cbd+VPzqJrMyNouDh64RKXUZO/NYx2ZY70DYuyZnRZzm4XZKsm0\nwcBwhjPHZ5mZKiOrUmx/y3RZJJJC5VSbF+c0sr+9NKVSqErSkrG6KBeSShl96XxrJDXEUzPP0R+S\nSj1Wa9OjnVSKNkts30ENM0Evl6lkhpuRVb1EIAerSqU2KOG16fkRqRSq3l9HuUEvFav2t1X8k6Hq\ntmSmzVCh5MTyzM6L0VLNFe1v7aRSpU0RVK82KdsVUnoSWZJjC1m+3KDH6kIOFFxb3BTsUJ0UWd8A\npEBmYVoco6ma1NpCssWfWsv+5tZilZVUC//PDzAT4uZ94twCH/+j/Tj7/5Hr55/nnt4Gb9gwIoL/\nPBtVErLVQPLRFZ2mF7aMhdxDbH9ruKiKhF8sMpNai+y7vPtt2xntSzEdEmrJcFEdEUKVhmiKCZDw\nJIVCOiSban5Yux7apLxGrFSSpicYasyRMFWRZVMT58EPrUaxPUw2KF4Qiy49jKTWh4biBXxCE2+g\nWml9dpovXsN0q/g9QursG5awpfg+tXKV4dJJ1gy6DJVOxU1yLetYjuQipVIy2TlIRtYjCDOVQiXE\n5cK6O34+fH92SOYFkWe6Fu5iLaNU0g0FVZVCpdIU+bkasiyhW9G5kPFKpRWtbwBqT0987swNG6/o\nWBfjnrV38htv+Hex5VM2DMxNm0nu3L3kudFgeLlBMbZx5Qs4oa1tJaVSFB7tzLaUSovtb4tVXlGu\nkpxIYGoKTntQt6RhWK2g7qDZ7CAFZV0ncOzwcXFdKSEZEFk4l2t/kwIfOfBXtL+9XFBzWdyCuD/Z\nUyJbQh8cEnlIqRTOrNg1frH2NxB2wtTe6+i6+x5BtL0IRIHk+gvUHl/R8ahynPMCnSTRcnlKINrf\n4p9/hcKwo/a36BrevHmIn3zLlniXcbFSSQrvo5Etd7X5bRWvNHzfwW0uIFXEuGmuWRcrlVLhmFQL\n1djtGynJtEHkeHothnQHQcB3nxnnwLEZBrtF4+1dyQJj9WlkAjY9+3UeOzTJJ75wkErd4f33bOEt\nmSLjv/PfOPWxX2Tyk3+AszBP7k13s/bX/hPGiFhUV8L4gzOJOn2//n8x8KGPLPndbuDFfy80l4Zk\nvxDyzQKarMUBzUdmW9a0qNUtCAL2X3iKbx3dj6mYgtQBZuvzMZEUECAhCKdIqeR4Dp88+D+FpSnR\nx7/b96/oK4nNRHeoQKbLpDkvodomu3q3c/PQDUhI5Jvi50+fmo6PxSsr+IHPTG2OnJ7G9h2kQFxn\numfF97/+RC87erai11L4msudb91KbpfH5Joj7OzdzqahMW66Yz1brxlg7aal98REUmfXetE6V+qa\nRg4U1jY3t87XfA3fDy/WukaP2U3KySEhkelaSsrM1uZoJMQYOjdVxvd98guttcPMCha4Rt3hqw8c\n4kuffY5vP3iUp/dfwLRU9t7cSSrmehIoisTcdIVmw6VabtLdm2DDVjEvOXpQ3P+L+bpoW0zpyLLM\nwHCG+Zkq+blq67GrLFVpR1bPoEgK3YsCuwHS2lJS6YaBvezq3cHePpElaih6/Lw1yymVfAfHi4K6\nV9ZcRCRStK56PVXCv1TIsVJpsf1tlVSK0KFUep1dO6uk0g84KnY1/rvj2+GfLaVSO0zVpOE1l83E\naZFKGept2UVuQ9S8RuGG9VDlYzs+947ey89u/VD83GbTFa0rUUV9OF+rzYmbR0I145+PSCXLUMDR\n4vcSHbtUE/+Xt91Y8XLiXB5FltkR8gn7+nWaT/v0zKyl6dnIgC8FIIEmaTTD5hMpfLvtpFJCV3jy\nrERNz9FXvUDSkPnou3fF5ywTLuLSEalUd+K2NlfWY6VSuepRrjngiS++4bcylbY/+zXeM/EQCUPB\nK7Vk4UGoOFKNkFRSDEqTYpFmBmJRrw8NQUQqhWNXLQy8rskGhivCnk2nAt0hqWQmIAjwazXqlRoy\nPmvGUqimgV8XE4gom0bN5Ugn9Dgs3CPAWpQhoeRaoYdqNhcrIa6UVNI0cftwI1tkMiRRamI3azn7\nmyRJpFI6DTVBZWI2llNbodps/exxCAK0FRbWAJIsY6xb3xHmfLVQZbWj7RBgza/+BwY++KGlz23L\nVFoJsq4jJ5I0zp9l7gufA0XBXL9++d8dkUoz7aSSuB4DJ2x2XHTuuu65l9H33o8xtgZdk5cEdRth\nxo4XtYMZrYmnpOst+9sipVKEllKpFdQdWeBWUiq9XFCzObxKmcDzaF4QWSn60FD8WIT2MPV/Cpjh\n91nXXv5hs51UWi5PCRYplV6x9rdQMTUn7le9gz0M9STjgO6l9rdQqTS1qlRaxasDtyFUdN5sHTmR\nQO3tjTOVVrK/AR0kbjr7yij9Xin843MTfPyP9vOprx8naYqSESnwuXnyCZAklA2bGW7O89Rf/R0T\nc1XuvmGUfd44E3/wCWpHj6D19pG9602s+43fpP8nfrLjXhJlagJcUquxBRrgQukSf/78Z6i2zUMj\n1VG+UeAfL+2/ogzGfKNAl5llTXoEXdY4Onsqfiyam16sjHP20RoXvhZwIT8RPz5ZERsN0TgcEJAz\nMnED3HcvPRrb8vb07SStpxipbMSXPI4PP86pzEEAkqVuhpKDKLJC1sjESqeJc0UCycdVbbR6gkvl\nCRzfoRIW0EhBqApyOudPPWoPum3hJKts2z1IsLGAr7qxHevam9bwYx/ZF6uKFiNrpFEkmVKXeH+N\n8da9dWG29ZnUSja/8YZfpS8Q2UpRXEQ7Zurz1JOCOJqdrlDM1/G9gK5eMU7MTC6v5J68VCQIRHHE\n8eencWyPG25bF9uyIyiKTE9/ioXZKnOhWqm7L8najT0oisSR50RGValQJ5NrWUt3XieIm4MHLlHM\n18l2WS/JdqrICh/Z+X7es+Wd8f9FlrZIdd6OHquLn9/9oY4spr5ELxISo+mWgizOVPKceI3yQkHd\n0Lp2zdcZMfBSsNj+thrUvRSvZ6XSqv3tBxxVpzWY25FSyVs+SM5UDfzAp+nZS+SYhSio28hRr7d2\nmjTbxPadmL2PGtQAAttiWB8BxILB9wI8149JpeywSv6ci7egQY+QhNbC3Z+W/a1NqeTUSIaVl1pI\nKlWDgJob1uL6Addt6SXzZB0bmBwvQdCLUU/T9GykAHxJPNexpZgE8l0fVZHbSCWHIV/ifDNNujHH\ntpkIIxEkAAAgAElEQVT9+I330pdLcfuuMb43d5S1A2IASrWRSuXQxtU0EtQNMfCVKx6lmoPsi/Np\nBQ3minWCIEBr1kj4TSiXYzIHWvk1WngOHNmgtJAHC5KOmCzog0M0Lwhbj6WK39WoNVGAeT3Dxvmn\nGSyfIZkxqYaEgWtYGAh7WrMSyYzTeFZCtDXRplRalKnkArrWeVOPFuxKOo2kqldNKsVKrHBCL3eJ\nwT0oi2ttJTVFKpegUGhycSEAA9Zs6ObSRJG+Zp6tZx5CTiToefs7Lvu7h3/po+B5r0ouRsv6c/nb\npZrLYU+MI+k6I7/0MYzhkWWfF1ntIqubbFlL1ECLSQNjZJS+a7e3bH566/ttyxpm0qJJKyxeMttU\nMLresr+FmUqKaQKtnUwjIpW0yP7WIpVWylR6uaBmsxAEuPkFasePYY0Mx0o1NZfDHr8kjinz4uxv\nLxfMl1GptBjtdreVlEqd9rdXNlPJCUml6DpMaylUWcVUOhc0kSIuUkqq3atKpVW8snAa4tr0JkoY\na9YiSRIV10WRJAxFLOCXs79FJRHwg69UKlSanB4vcmaixDMnZ5laqAESG6QC7zn2Vc5rPVzb1U8w\nO0329jvpeee7OPGrH+f2hWcY6TZ5o11n8k8fQDZNRv71v8G6jKK35LTG+3Oli+zq3RH/+7Gpp3hq\n5rkO4ijKR/rauYd4ZOJxRtNDnC1e4PqBPbFqpB2O97/Ze88oSc77vPdXuarz5LRhdrEZi8UiEwQD\nmElBzCIlSlTkUbIvLVk60jm2dWzL15Ytyb4+sqWja5tXVqKyRCqTlhgAEgQIEnmB3VluDpND93Ss\nfD+8b1V3z8zuDsCdRerny+50qK6uqq73/z7v8zx/n5pfZzw7ShRH7Chs51T5DM2giaM7HFs6AcCo\nPUKpOkocKTxz/iQ7+8X4OV0XpMstQzdzunyWVa9KzshyvnWJlVaFz5//IoYqOgzvyG+jstKktuLT\n6lvBMg1q+UWG2Ue2OkCfXSSKYoYXdnHBPE297rK64NHIrxArMbnVQR6feRaAVtii3y6lpJISaFS9\nWroYdWFmjhxFqtYy56sXqXS4ATaLrJFlNVMlsFpcOLNMGERoupqGbOu6Sm3VJQwjcn6RCDBz60mq\nhcYiZp8YnxZnq6nVbe+hEb758LkrhnXPXBTn8l0fOsz8zCrVcmudXS/B4EiO+Zkqp08I5XD/YBbT\n0tl9YIhvPTfPYw+dxXNDxra3x7Jd+wbJF21OPDNLFMUUN1BZvVAcHTrc9feRwUN84vDHu67bq+Ej\n+97HSquC09EFsLP7W5w+dvVMJWjPq15tuTjfDlL7Wy9T6YroJJJebaRS7yy/zFHz2xM/TyqV2t0J\nuid7SZv0jcK6y60KqqKSN7O0Ouxrhifek5dKpZbXNkytVFs0O6xyAKculllKAveGbHzDJVMXg6ij\n2zQT+5ud2N86MpUCqVSKwWnYhMTUgeWa+F46cNN4MbUALVdkNpJn4YYuShwTSlLJbSnS+Sw847ap\n4UpyQ2uFFNyQXFTn6PQ/oMd+2r59clhMerYNiElrYn+rNwNW5OqmW+oDSVaUawHVuoculUrZsIHn\nRzxxchFNyra92ZkuUin2PeI4FsqGOMbXLOZCcYyGmxdFGPTICMjJqaOJYawplUrLRhEnqDHYuIQ5\nOoYuC+VQqk/CWg1fTuL6BvKomQxRQ/ydZNPopRKOpRPJ7oABYOndP/cknDoJRE5JpfrmSCVNU1FV\nhUCSgmpRrhR54ntcKfclWTG+gJg879jdT04N+cDsg2hRwOiP/CjG4JXbE4NQ+1zNInc9oW/C/gZg\n75xEzWbZ9rM/T/bmw1d8XWJ/I1nB6bC/JbimEsXqJpWcnLw2JKHXuT3FtDqCuqX9zWk/b5saqrze\n2/a3CD1KlEpbSyppMv+p+vg3iV2X0u23pc8l2VDQcdxeIjiSeDP1l0ipdAPsb0mmUkJOazIs/0N7\n38u/uOun1y1WqFb33z2lUg9bDb8lJrXxsostOznWg5CsrqWLDFeyvyUolF5aUimMQv7xwoNpk5NO\nPH9umZ//za/xG585xt9//QKzy01QYhSrwb654yiBz2Rzlj3Tz6CYJgPv+wB6scjo93wMO/K5+eRD\nLP/Fn6KaJhM//bNXJZTc0MMLPXbmRSjzucqFrucXm2Lh41Sl3bEtUfhclgqipxae4y9O/Q2fOfW3\nG35GoiKars/yzx/8hbT722xdnMfjS1MA7GQPcSDO3+mZS+n7Z+rCnvahPQ/wth1vAsCSk/q/PvM5\nmkEr7fK1PT/BuW8J0tEcD2iFLX76TT8MekR2tZ9+q8Sp4/OYJ8aZfO4evvHIaQCqxUVcW9Q9x84L\nFVUUR2SNbGp/0wKD86sX0+NSXhI1l+vUeGz2CcruKqZmpoTDZjBg94EC+piH74VcOi/uuwmpNDEp\n7qe1VRfLlZlIWa9rG17os+KWGSgWyORMFuZqrMgg7cGRHIMjQmEU+CFrMXOxgqoqjG0vcud9k7zl\ngQNo2sbjW5KrdOq4UFj3D4n9ue9te8jmLZ54RFw7xVJ7LFNVhVvunEitfN9OntKVoCoqtw8fueai\nX4Id+W3cKm2YCZK5lBcFBFeYX3UimWslSiVHe3mT1DcSiVIp6nV/uyJURU3tvT37Ww83FLWNlEoy\nNNFYY39LBrONcpXKboWiWRC5SW57cNElqZSE3HU+t7zq0pJWuUQQ8pt//ixf+IYYPDIZi2a2jOE5\n6J6Fo9s03ACFtvIhY+vEvvjRrLTK+FGA2cpiBjoVIAbmyk3QFHRg16BFJDN5Kr740Rm+hRd6RGFM\npIr9Wy77hHJNwfMCLEPD9QI8P8SWk/U9q09jymyiqCWOyZHBQ3xk3/u5a0K0hE+USuWaS7UsVCCB\nJCviSKVS9ag2fMxQhDZmQkHy/dE/nsSU58GbmSHsIJWIIghDLEtHizxcPcNSZgIvDulfOY8xOIRq\nmKnVyJahUDWpPlo225NnY3QMTT4fmOJchfU6QVN8HyeXRXUcomaDOI7ToG6tJIKudXkeNlIqJfa3\nhJxJM5Wqmw+9NkyNIBDnQSkIckpNFC5XmPhm5Ypx1Rokk9HpH8oyMXOcAX+V5ZtfR+7obRu+76VC\nMkjq15DvjvzwJ9j9q/8V56Y9V9/eGsXNWlJJ0XXUa1jOOu1rrmqQySakUnXd84phpEqlWCqVjDWk\nUudr4cbb3wBWH/4KAH23HW0/10Ec6i+y+9v1gi2Vh2t/R9cDWlem0ujGr+nohrh1Qd3yNyuVCWpG\nfKaj24xm11tNu64zXb9ijlgPPVwv+K4gDaJlP+3kWPfDNE8JNmN/e2kngU/MP8NnTv0tD08/1vV4\n0w347b8/QRTBW2+foCDzJkv7TmPvfpL9tfPUNIc/3vdBCm99O6M/9Il0Uaj/zW9m57/7JcZ+8v9i\n6GPfx/Z/+a+vORYleUqj2WGGnAHOVy+lk0GAhaY41qsdFrkVt0wcx8xIBdG5irAsP73wHMcWj3Nu\ntZuYSjKY6n4DR3dSVfxXLz9Kw29yvioIJHOlfe9YWamnte90fQ5bs+izSu3GGnIs/sbsEww6A7iB\nS0Z3GLD7OCtJpeGd4l7mxT5RXwPLzWJ4Ds8/Jax1Wmhw/JuCIKkVF3Ad8R2TsG6ArJ5p298Ck3OS\nVHpq4RhWU+yvlg/55txTrLTKlKzCC1JPv33Hm8noDvfeJlQ2CWGzvFAjmzcZkkROtdJMc0hdq9G1\njYT4G84MMjSSo151mZYKpP7BLCNjBaIoZnG+e7HQcwMWZqsMj+XTTr5Xw6DsAJc4Ffpkp0EnY/Le\nj96avm6tPe/gkTFMaR3/djq/bSVMSUgFkd+eX101U0mMv8lvpadUaiO1v/WCuq+KxDLZUyr1cEPR\nbX+TmUqSXDLXKpXkjW5tB7gojqh4q6ks1+tQI7WVSpJU6nhuuerSkkqlXEHaHMKIBblCk884NHNi\n8HLqJWzNoukG2JaeKh8sU0MJLJTIZLo2ix/55MtictKUK/6zy3UCwEBhWJUkEAo1XZIevoUbeERB\nRKSKG9TF2WZqf/O9ENvSaHkhDTfAluocpzLTPgaSVLJ1m/u33YdjiO+TkErHTi3gyFWHhiPCB5VI\nY6XqstrwsHwLM2riyHqrUqmjJqTW7EzaEj21g3gutqGhRx4tI0+oGiheBb1VT/NiFKlUGszqmIbK\nsVNi5a6h2ShycmeOtZVKvtFWKsVuko1jiQlpHBO7LbyZadRsNt0PQwZg+6zPgknafyeWlWRCGG1S\nqQSCVPJlYLqSF+dLjUNQlA07WEF3cT/er6IoCoWyzBW4+a5Nf/aNwpXyZNZCUVVU89oDhJbNtVla\nhPWpc3KuZjfu0tL1WR0kVKibmFmpMJHZXuuDun3iKEqVSnoHKZFYVaEzUym6gfY3MSnypqdRdJ3C\n4fYqovYyUiol5NuWZypdISdMzWbT62bLMpUsq+va1K5xLXbaLK/WsbGHHq4X/NYChAo0QqwdO/HC\nCC+K0zwlgEbdQ9fV1KINbaWSaWnrMmNuNL4++zjQzscECMKI3/izp1mstLhj/xCPPDfHasPng2+a\nxC2eZmd1ESfyOJHbyS333sLo936c/N33dG3XGh8nf8ed9L3tHVjjG9uYOpGQRTkzy87CdppBkwVJ\nUkRxxFJzZd3kutyqsNwq0wrFWDLbmE+/y/937NN86tnf77bLyQymmJjx7Cj3jN0BwJPTx/ibz30d\n1RXbdxfa91WzleH5pSlqfp35xgJj2REUReloKS+2HxHz+rE7WWgtsT0/QavpM3upwshEgW2DQvE5\nXZ+hURAKoG89s8DMxQrZUYVLu58GBRQrpJVZxXVErW0124pQSzMxFXHd6FKpFMcxT84/iy1fd3jy\nJup+g3rQoGS+sNy/o8O38Ktv+kVu3XcTpX6HU8/Ps7RQo171GBjKkZeqn9Vyi7Cm4hstVsPuoPR5\nSfwNOYMp8XP5fBnD1MgVLIbHxGPz092LhXPTq6IT4vYSm8HAUDYdGvJFO81CBdh3aISbbxPX2+Bw\nd1alaenpc0OjG3fEfamxUabS1e1v3SRSL1OpDW1tULdc5O8FdXejRyr18JKgS6kUdXd/WzvJta+g\nVKp6NaI4omQnpFKHUik0USI1DeruVCqtrLbSbILEC60DcSgnm5qBa4v9M90smqrRlG3hE6iKQsYy\n0NwCC80l6n6DQnlYhC2O5lAUODdTpRVG6EC4IuTgzcwAUTKZ9008GX4daeLfsxc9rCSzyAuxDUkq\ntQIcAAWcjqyAxP4WRxHNM2fSoifnGLxh6Sk++sj/4J0zX0CJIxY8QbIoaCxXW1TrPnqk4sReagky\n4jb55s3OpEHdxpCQYceej2Vq6LLwAti2KmTV5mhCKiVKpZgPvnE3gcx0ijUdc0gQW9bYeJtUMsU5\naJZX0ZIuepbVzkFZWMBfWMCe3JVO7uysyTkiZojXKSzMbdsZ/t6P0/eu7xDnKs1UqrNZGKZGIFvu\nKjkx6VfjSLSKv0JAZacNYVgXhY5TnsNXtCtaf15KaIrMGbpOKy2Kqnbn46xRKmmbyMvR5WQ+UDQc\nx0SVGUtJptJa+xuIEPCEVDIybRKjS6mkd2YqiWv8RtnfAJx9+9E6LFXJKjy89JlK17P721okBKze\n17/OUpZAUdX0t75VmUqKqnZfi9cglTpf2+v81sNWI45DgtYycSVEMU3M0bG081u2U6nU8HGyZhfJ\nmYw7+YL9kpKfZbfCiWXR/ezMdJsg+M3PHuPBJ4Rq5xsn5vH8kB/9zkMcvUXs9/7z4t49+o6jPHDv\n5Iv6bC/0+OOpz3J8+SQANVkj5Y0ckwVhJUwscCutCmEccsvgoS57z7K7wnS9vWBX9xuMZsW4rdYs\n6mU/7cwWRRHnnqmkIdeaopKRi5/F6e0sPQejFw6hRCrNhbYt0XQzHFs8zu89/ydEccTRYdG9K+n6\nlahJAAZscd/Znp/gzNQCcQy79g4ynhV11nRtlqXMZQCefFR8t+2H8lQGpxl/a4x/9DIopPY3q5VL\n99HQDCzZkcYMHc6vXuRLl77KudUL5Nw+MlmT1+24I92X4gvIU+qEoijcevd2oijmoc+Jc9M/lKUg\nFXXl5SZuPcKzGiyv6b630JDKrMxgF2nTN5BBURSGx0VdNj/TnauUqJnGtm9un3VDozSQSfdtLd74\nzr189Efu3JCkuvtNu/noJ+5MSa+XGzrtb8m1pWtXrnsM1UDtsHO92ixM3w6S45IGdSeLkz37WxcS\nddur7drpneWXKeI45tEvn6Yy017JSpRKXuijq/q6H+mV7G+dnd8AAkkqJZF0hmdfWakk7W8JqaQB\nmlQC1V0IDDlJdcXzDTfoUj6AUELEzTwxMReWp8lU+6irAbm8xWDR5sJ8jQBQgNaiIJXcHe3QPQUV\nvy5XprQAYpXVasSYHNg8V2QqhVHMat3FBgwjRiFOW14nSqX6009x8Zf+HUsPfw0QmUo3184SKSon\nsxOM5EPqTQOrmUVTdJpuiBLHKICteKiBOB47+9rssjczndrOjEHZqc3zsAwttd85/ir7VkV2QJtU\nEscpDgPefuc2BnPib8s2McfGQVUxx8bRpf3Nk+qq+nIZIw6IFFVYpaTKof78cwDYu3an+5a1DRaA\nUFdT9Vh6XBWF0lvfjimVEVpOHM+wViVyXeZ+73doyW5cnYh8j7lP/x7e/DyG0SaVkPY8NQ6vqqRI\nAlOVOKKvOUMcBFjlBeqFIQ7veXHd3LYSuqqRM7IvKIDzWkhVN4qCYlkoup5eD5tRoWiShPBUg4xt\npAqpJDC5k5hIlEax56W/g277W4dSSRJIeodSacvtbx3EUfbwLd3PSRWT6jjXtARuNZzU/rZ1SqVr\nkapaTqjcrkQ8XZ99aV9/if3tSlB0AyR53COVethqBO4yEBHNNzDHJ1BUNe38lpNkbxzHNBseTrZ7\nUqhpKm96117uuX/32s3eMMRxzCOXniSWtdep6Qqf/coZ/s83LvDkyQX6vFXuG/B520jAz330Fu49\nPMpcfR4tjNl72Wc1o3J6fI5mWON/PvM7/PI3/hufPv5nPDH/zDU/O4xCfuu5P+Chy19LbXdVrwYx\nZGmTSs8vizoltVU5g0zkx9LtrLQqTNdm0XyT0sI2lFDj7pHb2OUdYPdz97H99FFOlc8CcOHMMstP\nqgzMiU6oqqJi6RZKpDIwL6yLpeVx+ud3EIew86YBMjkT28vxxPwzHFs6zv6+Pbx1+xtZmK2ycLaF\npmhUZECyozusemJhalQd49Evn0U3VPYcHGZMEl2ny2ep2sugh8QxOBmDPfuFxbhZKFPJCIV4YLhE\nWoDVzImsI4QbIK8LIsQILepBgz//1l9T0AooLYO+wQzbcmOMZ8X2+uwXXyPsOzyCkzWYvSy+28Bw\nLrVpTl9YgRg8u8FKq8zZynm+evlRwihkvtFWKg2NtkmbJKy72OdgmNo6+9vMBTEvGJ3Y/D4PSVJo\nI1JJURQGhjdWIqmqwsDQy1OlBN1B3UEYdD22ERRF6crOWps1+FpGGtQdrQ3q7imVOvFqVSr1ur+9\nTFGttHjy0YvE2TwcApTOTCV/wxtecpGutb+VO7pSxHEsg5UVXMBA5Cq1g7o7lEpVl6YjLvhOUim5\naJarAb5UDumeTRTHtNwwDelOkLF1qrUcaj8sXmgyjEpFazFk6oz2Z1kot9IuZfUlaUEb2AFzUNRd\nKoFFWBfbjHSfyDMBhe2jeS5eqopMJTkxnpuro6HgyP0yx8Zwz51NJ9P+gpBqrx4/QX7/EYzQp8+v\nct4Z5dlb38P33r2D2b86TnFpnOq4KDaSn7yj+iiBzwfesIsjeRfvCfF4sLyMaloohpHmFMXS/hbJ\nczFUu4Apb67mmJACp6SSH6CpKnfvHyQ6C9mszdBHHqD45regl0roTVFkeAlpWF7FjAJiQ6zEJhPS\nxvHnxXXQSSo5ibri2hNh1TBRLIuwVmP14a9QefBLBCvLTPyzf971usbx41S+9AU0x8Ew9xChEqGk\nyjI1jq4aJJwv2KiqQqE+jzI/jTt9GcKQnbceoJh9+d1gVUXlF+752esqcdYKBZi+LBRdiaXJdghr\n1U2RSqZjEaHgqgZZW0dZY7vrVipJwsnziF0XxTAwrfb9o0uppKqgqkKpFCX2ty0mlQrtojZz8xpS\nKQmRf4mtb9Bhf9sCpZJWKND/ne/F2bv/qq/LHLoZf37wiirA64FOK9417W+S4IqazV5Idw9biiiO\n+dL0EqNxjuzyEs74PoB1SiXPDYjCuKvzW4Kbb9u4I+eNwucfu8CffjnAOpRHzVaxLZW/evgcVujx\n3bMPsqs5AzKSyLrwFdwf+VFmvXn2XHDRvZDTe3I8tfgcF2vTLLVWUFC4UL3E12Ye48Abf5GMsbHl\nPI5j/uRbf8mzi6JGSCZ8Va9GYXmUp/+oxs6P3cR4dpRvzj3FGyfuTUmlQWeAut/kVPksqqLiRz6n\nymcZubSP/oUdDE3fhJUZIvuMArGC2cpyqnyOe8buoFqW2Y+ymYumaliaRWlxAi0wqeWXyFUHGL14\nAICJnSUW5qo0LrvEEeStHD946GN4rZC/+eNnaDV9hm4fYzYWii5Hs7hQvQwxXH4kxHMD3vSufSkZ\nM2D3c7E2DQoYgyH+rMaBI2MMZsW9arm1wqpbxdJM3NAjzrpY1Qy2UeIi0xiaQdKcGE+DGHRN57vH\nP8ojTNM/mEVRFO4evZ3Pnv67VEn1YqDrGrfcsY3HHhKE3MBQlmzeQlUVFmYFIRQ4TZ5ZPM1TC8cA\n0chnobmIgsKQMyA6dDoGraZPnySVFEWhULKprIiuxYqiEAQh8zOrDI7kXpAVdGS8wMnn5tKsp1cL\nzA77myoXza8V/O1oNnXZSKlnf2tjnf0tIZW2sGZ5JcLSekqlHm4gAl/8IJW6SaYmBsDO7m/mBje8\nVKkUriWVBCnRZxVpeWF60gNpq+pjgCFHtIPvViq1UqVSYQNSaabcwJPd2DTPotEKiKHL/ob826uK\nAc5cEsVFWQ1wLI3RfjGBTj61sSxWnVYVQXIN+IIEiutyFVL3QAZ/75qQUmgvxJIF5cK8eH9OFcfA\nkgROouAIZQh4/YwYuF3Zrnze7OPgzj4m9wyi6QqlpXGMRPacHF8tJA4C3nvvDkay3RNLb3YGrVhs\nK0Y8H0uHrF+BOCbrLaSvbWcqtZVKAH2O2OZb7t6JXuojs09MMDVVDHKuXA3xq1WMyE9tTQmB0zwp\nVhjtyV3pZ2UlebDZcGEtmyOs1Sg/+GVxnI49S1Dtlk0nmUtBuZwGPIaaQSSvLCUOr2rPMS2dBz56\nhCP+cbzZGdyLooq2ZCeflyPyZm5dMP63Az0vru+u7CP5/8109rItnVU9y6qeIesYKKqaXk/QHaCs\npvY3j8h1USyri2TsVCqBuC71OMJIlEr61trf1FwOxTDQ+/ox1+SAaMUiqGpXF7iXCrYMGzW2QKmk\nKAqDH/jwVbsGAox8/AfY9jM/d90/vxMpqaRpKJtQRCXXbZLN1kMPW4FL9RZfXtJ4KLqbeNlL7xW1\nhFSSZG/S+S2zhQsUDb/BQ5ceWbeAdyX4YcDlyiJffOocTuDhXxSE2JE9/ey2XL7/0t+zqzmDfWAv\nfe94F/m7X4d78SLn//0vsuPXP8O7HxFj8LcmxW9zqbXC60bv5L/e/x+4dVBk0LkdVvu1eGbxOb56\n+VFGM0IJHEhrc9WrYTfzEMOxx6fZ378XgD+c+gvmGqJmGXQGMOUYkAQTnyyfJlsbIFYiTDfD8a8I\nAsozG2iRztlFMabXqlKpXS9CLLseqQYDs7tAhTc/sAdrOErDsMd3lCiUHIgVxrRxfvjm76Vo5fna\nF0+ntWjf5cn0e4VxxMXqZUYW9rBwsc7OmwY4dLStqhrPtZseDOw1GBjKcvj2cRzdwdFtFhqL1IMG\ng7L+VfIBSqwyjNiGqZpEoWSVYhi1RviBg9+N3RKLHAlxc//2N/A9+z/EXSO3X/EcbAaHbx9HN0RX\n3dJABlVVUmU3QLZoEsURhwcOUDTz/N3Zf+BidZqSVcTQDBRFSS1wnWqifNEm8KP0GM7PVAnDeNPW\ntwQHj47x7g8dZvf+q3fnfaWhU6m0maBuoEup9GojBr4dtEmlNUHdPaVSF2y9p1Tq4QYiCNqKof75\nHTTyK2lAtx9urFTayP4Wx3HajaNoFak3/ZRUik0NmgHvGXtXeoEnSqWsrVNvBTTqHqal4cguJDoK\nGiIm8fx8DSY0Qs1H903mlgVr71jdNw9hf8uhRhr58jCe2aSJyCgpyMLPsDRwIxqVOjlgpeyTiRrY\nlRno244qO19EekDsm+QzBuNy0EyCugEqKzJEPBLER0LgJEqlsC5JpbPniKMI95IglRasEm/b2Ydh\naozsyjL9rZhcs8gcbVIpo4tjk0zOAbRSiVBa3/RiKbUKxZ6Lg8Vo+TlKJZVlqUbRcvk0T6dTqQQQ\nB+Lfof7uVaAkU6klz29Yq2PEAaqV5Ks48jM99P6Brgl4qlTaLKmUy+FKy5tiWcSuS/Ubj9H31ren\nrwnr4jwHlTLGkCT77Bwyj++a9jeAbZN9KKMl6s+cp3HiOADWjpcvqXS9kShvurpnycn5tSxHAJah\n8elt78JXNI7KlUbFMNJrSOlSKskJgecRuS1Uy8LoIJXW/l4V3UAPQ7SEVNrioG5FURj90Z9Ay+XW\nZZ2ohsnYj/0kev/Alu7DZpBkKllboFR6OSHpRKdlspvKnkmIJ6OnVOphC1GWC17T8Qjz6iCj40J1\nVJfjZ9L9Len8ttb+dj3x4KVH+Juzn+crlx/hJ2/94bQr2UZ4/PRF/vfU76L7Tb7jWMCO5hx/2Xwj\n53Y5ZJst3n/qK1h+nVM37+KhXSo/8853MJYZpPD6+5j7/d9Bra5weodD7u57mDGF0mjIGeT7Dn4X\nqqKSNWSXs47Q77U4XT4HwHftex+//tSnWGmJmqXq19BCcZzOfmuB5zNfpz/fx2x9juSXP5QZIF9O\n3LAAACAASURBVFjobkcfuypWM0u1sMDCxCnua7yL6ug0588uMTC/k+VKjZpXT0klPTAxPBtNUWnM\ngN3KkZuMuXvyVs4d/hzuF20KQyaWbaQt6X9w8vvZ3t/PpXMrTD07y+CIGB8WZmOsoRxBtsmqV8Ut\nx9x04XbsjMH937G/6541kR1N1Vnbbipx133tzrJ9Volp2cFu0O7ncm0GNSeupeFIEJaGqhNF7dDx\nnzr8TyiUHB55/rTYxmAmfd0bJ153xeO/WVi2wdvfewjXDdBk3Zcv2qxKxdeHb303ucH3Muj08+zi\n8/y/z/w2YRiys7At3caeg8PUax4j420rXKEojmm10sLJmCwviDq40y63GWiayq59g9/Wd3w5op2p\n5KexIvpV7G/QzrCFVx8x8O0gyR5NM5V6Qd0boqdU6uGGIgjabV0Ly6NovtEV1L2RaiJpc1lp1Yni\nGDf0+K3nPs1js0/QZ5XYlhun3grSk67JyWh9tb3C1XLFoDoqA/kadR8nY6ah2BqiS1tIzMxyE0u1\n8Y0WRmAysyTIhkyHtabV9IUdLtIZre9CCw0qA9MQa9iWniqV+mWr0Vbdxc8N0moFFE0foynscFpT\nZiOpIbFvsW97CTMN6g6wZUHZqspgcU8ERSZWs4RUiiSpFDab+EuLuJdEi9hydpC92wQZs+OAUEBl\nl8VE1ky6yclxI3Lb2TSdqiC90FYqxb6PpYiJua6GzGp5uT/tVbREAZIolQgTZUg315tkKvmoYgLX\nrGNGAbrMxem0q9i7dnW9N2uLz7A2qa7oDJAe+fgPgKpSffRrXa8JE6VSpZIqlSI7SyhX9NQ42lTY\ntDkiVhHrTz4BioK1bfum9vHVAK0gSaWOc/eClEqmRlXP0tLsVI2WKNc6twUdSiVXkKGqZWN0ECPr\nlEpGolRKgrq3vmDK335Hqsxb99ydd+HsfulyUBL0yaDfwsvQonk9kYaBZzcXBp4Qo71MpR62Eqsd\nKupn9t6NNSFJpVSpJO5jSXORjexv1wunK0LpPF2f5Ve++d+5XJtZ95o4jvmDB5/iU1OfouAv8bF/\nXGayOYtKzHfOPczIVInJP3kSy63z+K7beTp3EztP3MvT54XiOHv4Fnb+0i/zqe8a5ql37+MvM2fT\n5iyThe3p5Lezc9WVMFOfA9odglNSyauhBnJxK4LS4gR1v05Gd5hrLGCoBkWzQCjVG1ld3BMyNVEj\nNfIrNPIr3P7AMLX+eXxTZvZ5Nmcq56hWmuk+2PUiqqIyd0Ja4vaI8xQXm5zf+01ue5uojRJVfGWl\nSRRFPPT5kygK3P+e/dx5305AYWh6D312CSKFidO3okQq979n/zp12liHUqlvjTWt06o24Ih7l58R\n+1ZbETWxqZmEYZtUSpQ+K4uijkxyi64ndu0b5MAt7f0ulNo1wtjQAINyX28ZPMTdo0IZNZRpEz0H\njozx3Z+4C8tu1+GJHTAhpyrL4ryU+rem4cMrDYYm5xJhW6m0kRukEwmpZKjGdWvi8mqAJu9LCZkU\n9oK6N0TSDKCTnHw1oHeWX6YIJankGy3UWKO0uC0N6vYjH1NdXzAlSqWvHrvI5x+7wP969nd5Yv4Z\nbipO8nN3fhJbt6i32kolU6qPqqttZVOiVErIHq8VYDsGpt0mlSxNSe1qWcMhMF30SIe//3P21C/i\n2OIG+7UvnOK3/9vD6A3x6tKiKALLA9PEkYpjauzZVuQtt09wVMppW02fep9YdenvMzFDMfgZLfED\njLSA2LfYv72UtjT1vDDNOwmaPjEx2cYCaFranjvp/pYolQDcC+fxLl0EReEnPvG2dHI9tqNApIQY\nq9Kyl3xXS5A7kee2SaWdk+n2tGIxndhHnouJOJb1UGVJtprtJpWkyidRKvmyo9s6UkkG34URWjaL\n7jYw4k5SqV0YdOYpAWm+1QtRKgHog4Pk77mXzKGbaZ05gzc3m74mJeYq5fQcdJNK11YqARgdKjJj\nZGRLw4dfbkiVShvY3zbT2auTCErOsdqhKNowU8n3iFstVNtC15R0JbozUwkS+1tHptIWd397pWD7\ncI5/9f138NbbX9pclq2G6iTk5uYmTMnvtpep1MNWoiJJJSt2uTC5nxVHjFU1vztTqSGVSltlf4vi\niLOV8wxnBvngngeoejW+fPGr6173yPFLfKXxFxhGg+95sEVfs8GjpZvJ/tBPoMchHzv2NP2VCsf7\n91F+ex+FZTEenp27nG5jxa3gxyFu6NEMmrxjx/0ANIM2WdNp3UlQcassNVfSLrcz9TmKZiGtId3Q\nI4ojql4NM5JjhRLTt7ANN/DIGTmiOGLQ6RcZPHIsuHlAZB9lquK33sivpPu52FohtsT2Dc/hVOVs\nV23pNIqoocbSpRYtu0pcaKX7Uu2bZ3hEjIlJB7jVcpNL51aorDQ5cGSModE8O/cMkBvQKS6PMXHu\nMBNnjuA0CwzuM9i1d72CJgnQBqFM6kSnuqzPEoRXqMmICa8d1pxMjgFaTfH48mIDJ2tgO1s/NiaE\nkJ0x1uUffWTv+3j92N28YfyeTW2jWhHHvLwiFoBL/RtncL3WYKa/oYAg6f52DaVSMt/q5Sl1Y233\ntzAOURX1Je24+XLEGyfu5YN7HuhSGb4a0COVXqZIMpVWhi4RqxH9C9vxQo84jvGjIGXWO5F6fFWf\nx6dmmVo5xURujH92249RtIRSplOpZOdMImKqnUolOZiODWSFzS2OsR0dpSw6TOiAErUzkApWLu0A\nlz97gtsqJ8lYBieeneXpb1wijsG9tIoOqIsZWk4V16lCpOFYOrqm8v3v3M+k7EDhRRo12bVjaKKE\nJYsnw5OkkhqCb3Jwsj8lNHw3TIO6NT+iBWjVMnqxlCpB1trfANwLF3AvXcQYGWF0tF1w5OwsrUwV\ntSY0SsnQ4jjiyMWeR+yK7ZmjY+1MkWIxtQrFnoclB6daqHDeGaNZHCJ3+53p56xVKiX/riWVEhl0\nEMZo2Ry210ChrRDoUipNrlEqyaLH2kRQN4CaFYV68Y1vRlFVCq+7F4DVRx9JX5Mcw7BaTVVUkeUQ\nBqKA3SyplHTBA7B37NzU/r1aoBfE71HZiFTapFIpQTaxv3UqlTptdZIUippN4iBAtUQ4eGKBW9ut\nUdENtM7ub1sc1P1KgaIo3DRR3DRB+0pFQmpeK6Q7Qe7obWRvOfKyCFPv4dWLSktMhG9ffhwUha/M\nCRVzfU2mUlNmKiWW/euN6dosrdDlpuIu7hw5CghipBNhFPGZJx9D0UIeuDyEvdrg8dJBzt58PxNv\neB0Ld72Vlp7jgjPM9p/8CNNnK6ix2P/plfk0u2iuITIlF5pLjGdHeefOt6AqKo2OiIO1pFIQBfz7\nr/9n/vUj/5Gf+8q/5beO/QErbpmx7EjatCUmZrm1QtWrYURyIWykht3K49RKtGQuZ5I1lGQw3TN2\nBwDZWj8xMa8/eCsgmsEsN5fJyvwf07U5vniSRs3DtUS94NQLxEsOURBT7ZtPj1lCdCU2kESZs1pu\n8a3nxfdPlDuKovDWd96MaeqE57OUlsdxrTr3vuWmDc/VSGYITdFQUNLuxwk6lUtFq4Ch6gTIYyhV\n46ZmdNnfWk0f3w+pVlr0DVx/ldJGSAihpFlOJzJGhu87+F1sz199oSMl6iptpZIgqXoLRtAmkPzI\nTx0hG82xOpHMt6xe57cuqIqKgtIO6o6iVL3UQxsDTh9v3/HmV52C69X1bV5FSDKVAqOF2u9itXK4\nbtARIrdx9zclUphY3Mb80iJRHLE9P5FKpoGuTKWMY+AD9Vqn/U187lh/Jg3cUpfnufhv/xUKsSBY\n4phYss4D2Ty+IQYqT3fIBw1o+Tz0uSlMS+emA0OEbsA+RGeQ8oDoxEGspjlIAJbM/mnpOc5Fw5iW\nxvYjuzHDFsQxia4iUkM++PqDTMiuG4ap4XkBtqmhI5RUrTgmXq2g9/Wh2t2kUlSvpRkgtaefImo2\n19muckaWA7t2QKxQ0lRMxCqRbknFh9vOVFJtG0OSI2uDuhP70KoHdd1h5qOf7GqZniqVgiRT6er2\ntyCM0HI5VHmzVuX+pFYzRcGenOz+LvYLC+rO334HmYOHKL7pzeL9t92BYprUnng8fU0nMafJgjAy\nnS77m1649gSzk1R6OYd0bwWSCbhmb2B/24TtqJNUyshzrBgbK5US+1tYEyH2aQaOJJXWKZUMQ9g2\nE1Kpp1R6TSEhqTeT7QXQ9853M/FTP7OlHel66GHVC1CIODD9LAWvybHlGmEcU/dDDFXBlA0tUvvb\nFimVzlTOATBZ2NHV6ehz577AH019BoCvPTtL2SsTf/P1jD9ymtC0+UrfEY5KNc32B97Nw5Mf5sQt\nb+aJ2tcpLbRJgcCNma4JZfA3Zp8EIKNn+Ce3/gimZuDoNo0OpVKisqj7DY4vnaQRNGkETfJGDhWF\nx+efAmAsN0LFbTfduFC9TM2vo4cGpqWzMCi+V2l5nJovxvgBqeZJlEpj2VGUSMWuFxkcyXJ4RFiW\nL1Wn8SKfUlHcMwzPYb68ArGCXgqxczpOvUg0K8ae1b65NFjclQ1oTJlL42QMDFNjeaHO2ZOL5AsW\nIxPtemJiZx8//FP3cev7+7g8eYzpg08xXhre8Fxpqsbe0m6258fXWZQ6lUoFs4ChGvixIBSSWsZQ\nDaIO+5vb9KnI7NC+gRtjHUsIoWLpxauKOpVKYRixWm72VEodSEK5vdAnSEilTXR/g55SaSNoqtbO\nVIrDXp7Sawi9oO4bgNn6HDkjR87c/MpGYn+L1BC7pNFcBK/SXo0yNyCVNFUjWxtksDxKaNVZAEac\n7i4N9ZaPBqiaQtY2mAPchk8UxaiqkiqVRgfapFJ4dkooY5QIS3bpcBwD3fUYLhQ4bwq/vqs55Lw5\nzp5cIgxj3v3hg4yMFzh3epmslKhXBqZBEkxOh4XHkYqaufwu4kjlrru2kxsfwRofw4ha+Frb/rZ3\ndCR9n2lqIqjb1EiGyDAOIIokqSSzmJpt+5sxOASthrC+wYZZPnsmx7lwbJUBS8ds+OQLdqoEiTvs\nb4plYY6N4Z47i14oEkupdOy5GFaAC1R9UeyWct1FbqpUSkklaX9bs0Kiy8laGMWpkgg6lEoZ8c3N\nsfGUREvwQu1vmYOHyBw81PEZFsbgEMHKSvpY1Ki1n28IoiIy7LQQG/7IRym8/q5rfpaWz6NmMkSN\nxmuOVDJHx1CzWawO++QLsb9ZGyiV1A5F0Ub2t7AqzpW6jlRa3/1N7ZFKr1mkQd2bVCr10MONwGoQ\n4dBC9QMmI49nIoeZhkstCMnqWmqv2Gr722lJKv3uPz7D3SMmmEId9MULX6ERNPnArgf47FfPELmj\n3Lv8PFbo8sXS7bQ0i6N7BKlUXfJQUNCWsyw9XqZQ78M0NTwvRA9MTq6cImdmeXz+aQC+78CHU2VN\nRndoylbm0A7G/fSJP6cZNHnr9jcCcKB/Hzkjw5cufRVihWJziJVoPn3f1MopYmLUQMewVMraIiNA\nQS0yIxeuks8M5QKZqRmMettRY5WJ7f2UrKI8JiJjqr8vzyIxhmeTCwURdNuOgywsrdI6E+BfjnGy\nBs1sGTcU6qNEqZRM4hVFoVCyWZoXxNbh2yfWWWc0TWXHtiFWZi6wuzh51dX+Hz/yQxs+3t+lVMpj\nqEZbqSQX+NYGdbeaPuUkj+gGkUrDYwVue9129hzcmDjbDExLx7J1qpUWq+UWcQylvl6eUgJFUTBU\nXXR/C8V1uNHCfSeSLJxXW9Dy9YCmqERRu/tbj1R67aC3tLjFCKOQX/nmf+cPp/7iBb0vsb9FakRx\nQNy8glW1PQBfob256YmBQlfED3o4O0QQhGlXtMT+pukaGVvHB+K4vbrX8gJ0TWWwaGPKwsKUq2Iq\nEZpUDO2b7OPnv/d2hrJ9+NL+5uoZnMilWWnRN5hhx+4BLNtg+yFBbFlFCyUTSpm3gt1hu7EkqRQr\nKqYOR+4SPtPsLUewgnYBFakhBbNNrBiyELMMjWQarcey40hfv2i1bppErRZxFBE1GmjZLNmOQOuN\nSKXhMWFPmnBMVERb17YKSXTRAjF5zx29Db2/H3tyV5prE/s+uizEfFksFXPdg0/a/W2tUslYa3/r\nUCp1TPRUW2aZFEtY27eTv2d995HhPofb9g5y5/4XX5BohQJRo57uZ6dSiZqQ04e6nbbeLd1916by\nkRRFSdVKrzVSScvl2PNrv0Hf29+RPubsO4A+MLCpwPKNMpVSm5qidFnWkus2XBWr1AkZmZJKa7q/\nqYaBFkVMDtpd7+/htYFUqdQjlXp4mSCOY6pBTI4GBDGTOXFvOlttUvfD1PoG0Gz4qKqyLn/maoji\niF978n/yuXNfTB87v3qRf/O1/8RzS1Ndrz1dPocSaRjbv8XXLz0LwHK9Rj1oEBPz9597jCOzZ/j+\nM1/i7srzRPkSJ0YOMzGUZceIqF3Ki6KmitWIwqxQKR26TTQV0XyTk+XT/PWZz5NbHCFbGUizjECE\nbXcqlY4vnwREyLCt2Tw8/RggCKCEOCksj3Lq71zKp9td3I4vifcRaChGTKwI8qRotFVBCWmUKJV0\nVedeR5BWo9uKFKWlbK6xAMBgph/dUTE8m3v7hHU+V7ApDst6IFSY3DOAoiipUskLPUzV6CKGOsOp\n9x7auHYZyQ5zoG8vrx+/e8PnE5iagblBvdyZsVQwCxiajk+iVBLHQlfE+xIrZavps7KU5BHdGFJG\nVRVed/9NDI68sE5ta5Ev2lQrrVRpVewplbpgqIYglaJAEL7XIEISUsnu2d/WQVO0tv1NZir18NpA\n70xvMbzIww291Mu+WSQrJbEapt7tqKqnSqUrseiazB7SVTEojmSGeOrRi/zR/3qM2mortb8ZhkrG\n1tNsJLcl/tfyQixDxdA1DrVERxNbDqgK7cDCUtFmz0SRO0eO8q79wirlaQ4tPUsYRAwMtSckkweH\nuUxMdkeJo0OHyYSCZHI61BaapqKrYvu37Mumnd2yt9ya5iqBUCrlzfbgaph6h1JJkC9ZxOv1kiga\nVMchcltEDTGYqtks2d2dpNL6oLRSfwbT0mhJMi6Xt9KJeux5RC1pf7Ns8nfcxe5f+X/QS6WuoG5d\ndmPxFPFd1imVVBVUNQ3oTjOV1iiVNCnrD4Koa6KXfJai6+z8N/83Aw+8d9330DWVT374CHfsH1r3\n3Gah58XxTuxTXaTSqlAwhbrVzlTSNn9bGfyujzL88R9ALxZf9P69WpC//Q52//J/2dSx6M5UEr/P\nhPxRLatrZbetVFpNnwfSDnAbBXUrxBwcE0Xzjej+1sPLB8aIIHo7mwr00MNLiZoXEMYKWaUJQcye\ncUE0nCjXCeKYXIcSt1n3sDPGCwqGvVi9zMmVUzw+91T62PNLUyy2lvmtY59mVnZPW2mVWXHLxIG4\n5960V9RpM2XRTS272s/csRaN7A4io4Q30seOH/9xfuWTb+ZffvwOHp19nJ958BdYmBf3Yu/Wy8RK\nBEbIgSNCuZMlx4nlb/H16cfZduYI2y4e7lpEzOgOfhSk3d6WZSe39+5+F+/Z9Tbc0GX0/EHiWSe1\neBm+uOf7pzMQg4LCUmsZYgUChUgPxH4AOb29aFc0BWkUhAH55RGe/fplFk6LBbWxbSKHKG+0Xz9g\n95HPW+iezezisthe3iI/2N7/XfuGsHQTN5CLkaGXWt8SJKRS32CGgeEcG8FQdT55249y79idGz5/\nLRStAqqiYmsWtm4JUgFp55cLZHosarFE9dZqBpSXbqz97XohX7QJg4iZS2IuUuwplbpgqIbs/uaj\nq/o17x+9oO4rQ5BKUqkUhb3ueK8h9EilLUYSRtjsCFbcDIKgrVQaGpYkSs24aqYSgObJCaOiEMfg\nUKC83CCKYlYrLRpSqWSYGhlLJ1m38tw2qZRMMkuRGPT777wNACVur3Ilq4C6qnPbdrGK5uoZ6qYo\nYjpbrWYdg2liAlPjBw99D+OVtwLrA4JzqocV1Lm5o7uSs2cvRtzOfFJ1pesmbpgaYRBh6m2lUkZ2\n8EisYKptEzWbKRkilEqT6XP6wPquIYqiMDSaT6XP2Q6lkiCVEqVS94CSdMqKXQ9FkkWpUim7fvBR\ndJ1YhkIm5BJrMpUURUHXFIIoJu6wRnVanLYSWkGuVq6uCrVXvQ7JgFteAiBSjdT+pr0AUimzbz+l\n+996fXf4NYDuTCWpVJLkj7Lmukiu26C6caaSs4H9DSCUJGzP/vbagrN7N7v+46+Sv/PqCoAeerhR\nWGmJsVEsGGkMDA3Sbxmcq4pFn0SpVF5uUK20upQuV4MfBTx46WupGmmhudgRki3UN62wxf945ndo\n+A0efvgEE6ePEIVyoccWxElIQH5lmJ1TdxFLhcMj+/fg/Ow/JXPgIK26h9fwOFU+gxt6rCzW8Y0m\nXv8K5/Y/hn1PhaxUMucp4kcBaqijxhq63103ZAzx3RK1UqL40VSNN03cS5Ycg3O7aJ4x26SSbDei\n1m1ytcFUOaBJq4+ntABR62T0DKqcGuSMDGEYoR4bZeepO/j6g2dZWWwwMJwlI/e3ZLcXQQacfkql\nHGqssboo9itXsMkPybFJi5nYWcLWrbQ2dkNvnYUoyfvZe2iErYKqqEwWtrMjLxYVDdXASzKVZIyB\nLhcEM7mEVPIpLzfQdZVc4ZVFJiTZTBfPiGu2l6nUDUMz0u5v18pTgo6g7h6ptA4iUylRKvWCul9L\n6GUqbTE8uZrUepGkUqyGlHJ5AtNFrVvp9q7UmUDzpFohUohdh9OXqqkKyW0G1Fs+GUQWUdY2CIkB\nBd8TxEbLDdrdpGRxZGTFzVOJQ5I+5J1dIzKSLHG1DHVTqIP6OkilnLS2VaXFrumFKHTnwgDcxXGa\nF0/iDLctQYquoxUMEpFU1na6VhBMuQ1NARvwiClocfpeEGqiYGVlDakklErmxLYrrkgMjxW4fF6s\nAubyFkq1rUJKur91dtmCtnoo8n1iTxRVgaJh6iqOtZ6tV3S9Q6m0cVA3CMVREEaEVgepZN6YwUxL\nlEqrq0StJsQxxvAI/vwc8co8FA8SdJJKeq916Faj0/6WKpVkcPtasnF9ptIa+9s6pZLsFpco+8we\nqfRagzH04pWNPfRwvZGSSkoDPV9CUVV25R2WXfm4tIw/8cgF4hiO3Hn1blgJvnr5Uf7sW3/FaFYo\nn/woYKVVYcDpY76xiK5ovHn7fXzhwkP8wsO/xO6n3kSfv43Z0TOEwEx9DgUFJTCYOHsEJY45Ov0P\nPD3xFnLNApOFHcRxzF/9wVNEUUzl9RXUQMetR7jFGkEcUC8skx/WMUwNVVOwIjHZv8nZA4Dia0RR\nhCqzFTO6eL4ZNClahbS29CMfUzM5OniECtDyXBz5Wj1uK4EGZndyuViGGNRAHLemUk+3TwTb8xOc\nr17E80M+93fHMGZKNHMVPvTu+7Ado2vRsGQVuVi9DIjw61xBZC5qFVGr5PIWtVBheegCO0fG0HUN\nW7doerLBS+ilNrsEe28eIQwjDhzZWrXkJ4/+WFLSikydVKkkakhNTpEMU0M31JRUKvY7r7gW6UlY\n99KCqIM36ib3WoapGjT8Bn7kXzNPCdq/w7Trdg8pNEUljNpB3YbaI95eK+jRh1uMxK7WDF8YqRT6\n7aDunJElzLTQXIumtF1tFNQdhFGbVAp14laW4+dXaMmCzG351Bo+GgqGITKVNlIqWVJKHklSKU6L\njSD9rM68AsPUUOIQT3eoSVKpv8P+VsyZqIrC8qrY95YbYFsaasegHIch6txFMo6+LsNFH2nLn7Nr\nJsyJTY4gwkKhBTjS+pdkE6mOQ+x5hDUhOdeyOezREQbe9wEG3veBdccxQZKrBKIwSmxDsSe7v63J\nrgE61EwusSdb5qo6xZy5YREilEpJplJif1tPPumaShjGBGa7EFDsG0QqyU5uYXWVqC6IBnNc5D9Q\nliu1ik4YRigK7eK0hy1D8hvVVAXTEMc7USqtJTrVtfY3ed2YVwrqNtYqlXr2tx566OGlQ9kVY2mW\nJlqhn7/702dpnmo3j8jqGqvlJiePzdI3mGH3Ju3ezyw8B8BCYzF9bL65QBzHzDcXGMwM8oGbvoP3\nTL6d/nAYzRf3wozXYR1ycwxUB9ADkwH3OAPNaer5JUw3Q7MaMHOpQnXVpV7zqC55WE1Rz3iZOr6c\neDmGICkyWRPNN3j/7vdwX//r5QcoNBt++nEZI4MSqXzpT8/w0P85SUsqlRI73K7cpNgt3+fZxefF\nFiJRe4RqQG5lGKMlO42Goo6sxVWGc0KxHcUxe0pi0e3ssWUunFnG668wd/PTTO4ZZHSi2K67gD5J\nCGX1DI5uk8uLbZtuFkUVXfiiOGR61zFKh0Rd6+h2qlTyNrC/GYbGLXdsw9hkg5EXC1MzUmuhoRpp\nrlRCKumJwktVsR2DlcU6gR+94qxvAIViu3bMFSz0LT62rzR0ZiptRqm0I7+N90y+jfuuken1WoSq\nqERxO6hb7dnfXjPYUqXS/v373w38GqLT+6empqb+05rni8DvAzvkvvznqamp/72Z975S4HUMnC/E\nW9rOVIrIGhnirAtlWF0R5FTCpB974jKBH3L0nh2s1j106fXXQh3Fy3HyYoUDcoB0WwFNubKnm2tJ\npZAoivH8tv2tqeUgjtpXSbwxqQRgKAGu5qCYfahK3CU/11SVvrzJ0qrY96YXrJvIrn7tqwQry2kr\n+06YO4dhRnx2fk1nLEPua1V25GgBdqpUkgWBJKKCJUGAqNksiqJclVCCblIpm7dQqjKo2xXd3xTT\nWtdGW5GqjsjzBfEE+IqxLqQ7fb2udwR1B6BpG7bm1jSFIIwIOjoIbiYM+3pAz8tchdXVVO1lDA2j\n6Dq6lIsHikYYxC/I+tbDi0fyG83abe9/QnBeUalUEyvICel0/9EJtg3lUvtc+nqplIuaPftbDz28\nGrGJ2uzngO+Tf+rAQWBoampq+YbuqETb/tbgvD/E+fNLBLYG94kcopyhpSqlO16/c1Mq2t9+vQAA\nIABJREFUkrrf4JTsWhbGETkjS82vM9dYYCI3RjNosa90E6qi8p2738muymEeQoRbF8s57jt/mZOD\nEWeyfQzO7yRWIm6efo7FsRzlviUy1VGmL5RZmK2mnxkuWNiqqCv8TD3tkJTVRV2TyZiUV5q8c/It\nPPr4ifR9zbqX2uMyusPgzG7Ksy7l2WkyB/to5FfSaIREIaDEKp85/bcAyIdYGbrI4NwuinPbaG4/\nkZJKnuayMztCC4ijOJ1UJ2RWfedlVGPjY1qUpNKAI6x2nbYwO6ujqkpqhUlsd7Yh7G9hFBLE4TpS\n6aWAoepprlQSeyCayohuybZtUJMLo8UbFNJ9PZEoleDGhYy/kmBoeppVZlvXVh9pqsZ37n7XDdiz\nVx7WBnX37G+vHWzZmd6/f78G/AbwHuAQ8LH9+/cfWvOyfwo8PzU1dStwP/Bf9u/fb27yva8IJHY1\naHvfN4PE/mboOrqqQ15sZ3VJkkpydeXxr53nG189RxzHlGseepiQSgZ2VKDe8tsh3C2fZlP8X9fX\nZyq5vvjLNMSqX83sJ9taJEzyucNOUql7omni4esOdbNEXnVR1e4CZKBgU665BGFE0w278pQi12Xp\nrz6LYpr0v3c90ZMZktLoOCJvd4c2JqRSeVGQHS1iLGWN/U1Osv0lsRq52VbZ2byVeumz+fXd39bm\nKUHb/iaUSpJUUnVKV2hvrOhGF6m0kUoJQFdVgjDGM9qfuVaRslVoK5WqhHUpbc9m0UoltKSQjVWR\nvdAjlW4IVFXB1FUyHb/DzqDuTqRKI5kTkWQq3XlgmO952951215rf+uRSj308OrBZuqrqampX52a\nmjo6NTV1FPgXwIMvFaEEUO6wv12sOCgK6K2QrCSPNC9k6tlZiv0ONx3YXKfT55ZOpPlJAPeO3QXA\nfGORealcGs60FU/TF8vp/7dfzrD/+RXe+1CFDz3iYroZRuNpzLDF1O4M9YI4VH/7ja9y4vlpTGl9\nt8pF7GZCKjXTMNskJ8nJmnhuQBhG1GptdXuj3q4j1abJ0PRNJDzM2PlDEIMnVfGeJ8ZkGyv9fkkc\nZnlgWuxHI4eu6GjS/hZpARN5QdBFUYwuFy0TS3sQh6IO3QCJUqnf7gfWkEo5qXpNSSXZHEK3iImp\n+qKesK7QzfhGwtAMkLVjcrx0ub+qqmA5HeqsV6BSKV9sn5ee9W09koX6ZtjalFKphytDZCp1BHX3\nSKXXDLbyTN8NnJqamjozNTXlAX8EvH/Na2Igv3//fgXIActAsMn3viLgRV76/xcS1h1KUikjJ4Bq\nThQKtRVROJiqQRhENGoegR+JVqfVFnrYvhk6QRHPC3Cb0oLX8Ntklez+lpJKXognSSXb1Dj13KzY\nRmuepnyPErULm7VKJTuWXnRVIx9VWYv+ok0cQ7nq0vKCrs5v5S/8A8HKCn1vfydGX9+69+bkCp0W\nBxSt7raqSabS4pwoTlqAKe1vSWh2W6kkCkU1szlSSVEU7nrDJLe/fgeapq7r/rZRUHbymYJ4EsfE\nV/WrKJW0blJJ37i4EkHdEc0O//aNUiqlpNJqRYR0IyyEerFNKgWSVOrlKd04PHDvTt559/b077ZS\nqbtgXGsnvdZ1k5BIUbOJYrywLko99NDDyx4vtL76GPCHN2TProCVVtv+pqDyjvcLDizfEDXLwtky\nURRz2z071i1oXQmJ9S1B0pp+vrHAXGMeaJNKcRwzc6GMqqsQe/hKH4vjE3zzphKXC4chjtl9/usE\nusKJbQauU0W1YpzFAYIW7Dk4TGHAIlvrx67L8TTbJJASoiSfJeky5rYC6rX2ImSz7qX7Mf/NGDXW\nGL1LY2SPg9MoUlrcltrfvED8ayk2/XafyHyKRakfGh6RFqL7NruLO8kgFulCzWckJ75rFMZpZmei\negoJUoJlLZIw8CFnABBRAQmsrIxSkBPMZHJpyTbsq56oFV8OYce6qoMiCKREqaSlSiVhf0vwSlT6\nGKaednIu9kK61yGJFIniaFOZSj1cGZ2ZSiKou2d/e63gmqTS/v371/db3xwmgIsdf1+Sj3Xi1xGy\n6mngWeCnpqamok2+9xWBTqXSCyGVAknwZOQE0SiKQS4hlQzV6Co6qpUWKytNVNoFlRMV8P2YWLyV\net0j+WnrpoamqmgyV8V3A1odSqXTJxZQ4gjdW6Hlx8lOpdteTyq1v1u2ubTu+wwUBBkyt9IkCGNs\n6/9n782DJLnu+87Pe3nU1VXV51yYATDggAWAJA4dPADqWOqgZUsULUuyLVk+JJmSw7texzpsOWR5\nHV7bIUfYYUfY6/VqN2JX1tqra7XhQ5Ytk6JFESJFUVqCBwgWAeIczNXT3dNHXXm8t3+8l1lZ1dXd\n1T3TmO7p942YmK6qrKyXWZWZv/y+7/f7G+a2rP6n/4is1Zj7I981cV/MzJj3+iqmziiRE1gb3c3r\npjjpA6EYt7+ZfRivmHFNq1QCeOzJc7znmx8y6wmHQd2q35+oFBK+D56Hjov2N4/ZmZ2VSoyQSjso\nlWymUl8EtkfLUHFy2Bja3zZHws792VkkCqFTEiVQiXL2t7cQ3/PMRb71yeFpcZxEzSBK46TS7gq3\nTOGnk8SplBwcjigOuTbLPqMK/BHg1w74WXcEa/2YUMcEIuXBJZ+HWktUayGVL6/y4QeWuPqF6/iB\n5NKj02UpxWnM86ttFisLCEAgOFVdpBHWudFdLiiVFtFK8eK/+iU6WxFRf4PZ/k36QZ1frj/JFx54\nik64SKd5jQf/4o/w6Q+/gy0vBgHeQpSTOZcePUXjrI9UHrWteXQlRvhDsqUaWPtbbdhlrFdQJ3Vt\nk5Nrb25w63LMVuMm3n19zjzlo2TC6cutnEyKs3oiFfzVp36Sp8+9G6HMOLRIoZQQRCX+zKM/yLed\n/VYAUj+m5IWGUNE6VyUNlUrJjkqlS7MX+VOt7+MD93+T2YaZUq74GZJKtolHZn+zpNJmZCYDQ3n3\n7W8ZqSA8gVbWqqetEk6KY08qATSsBe64jv8wERTUck6pdHso2t+UVrlC0eHexzRHzu+3Wq1PA/+i\n3W5//A5//geB54APAG8DPtpqtT550JXNzVXxd7gpvxNYWqrvvdAYSpvDm+xyXU69DnstY64+w9JS\nnXqzzC1/QLRRgvtgca6BFxVu4JWg109H1lH3Z0Ctk3GHSaxyFrHRqLC0VDcXys0YKSQ1S97UQ4+b\ny13me1eJhURn3cisUklKwbn7ZkcUDLUCqVTdus7i4szI6w+cMxLpW9Z+N1svs7RU59YXXkX1etz3\nfR/mzANnJu6LuLyI0G08lXC+MjOyD+cXDEEUDUwfuwiYCc3nzi01qC/V6S80WQPSNUMqLV0wbWr3\n+30ORMSrQEiKjgaUZqoT1/G1UglPJQRWBxZLn/NnmhOXvVIOiZKEpaU6r2mFCMOJy5VKPunmgKBc\noi9DKipi4cw8tT224SC/2XFoPcPLYYjodShjfgNz5xbhzSW2MGSfRqC1Jiz5d+Qz94u78Zl3A7tt\np1pocgOozdVHltNa85IQZOzywtndfzfdZo0sAtcrldz3eYhw2+lwGzjM2izD9wC/O4317TDrr1uf\ni6lqM0lz5lSdU6caPPzYaT7/2TeYuzFga73Pk994gXP3bVc6T8L/d+VLRGlEa+kiv/v6CqCpz4Wc\nb57hheWXWI6WAfi5X3mV2eu/zzOrL8Ppszy29iX6fo1blXPM1s7wRCPgOn1Wz1zh4Q9+hOqnX4PX\njcopWdiEKyWSYMA7n7qPr22+wuUvmc9XM32b4WLIovOnFllq1KlYJUk5DEgGQ2seyhxDr7YN2bW2\neBnCGYKaYrO5THPtLEILlpbqZA56reCR++9nWT3GG+rzZjVSIcsK0S1z39I818o94Cqpl7A4X0d6\nwmRgNoyCKZskSkVKKSzveBx/X6FjL4BfFSQdmF0014+ZviGRGnVTM5XfsBNiJVsPztTu+jmicdkQ\nLZ4vQAlKXkijYZ6rzZTyqIXGbJlz981Ovd67vV1FnD7b4MbVTd728Kk7buE7Stt5ENSrw/1RK+/8\nWz/u2zktbmc7y6WQdCNlYaGGRlMuBUd2vx3Vcd1pvFXbOQ2p9CDwJ4F/YIO1/wXwC+12e7vHaRRv\nAhcKj8/b54r4C8A/bLfbGnip1Wq9Ajwy5Xu3YW2tu9ciB8bSUp3l5b02eTtW1ofvuXpzlUWmW0en\n00ejCSixvLyJTgSDyhb+ZohQkt5WwuVrw+4nb76xxvUbZt1aKISWiFRTLPE2N/o5qRTHCcvLm9Y+\nFrOx0ePqddMdqnfDqFFOb77K617I2saAU4BnSaWw7HPz5tbIeMNoC6yitrp1g+uv38ArnKRD+8Ht\nVw2xI9AsL29y68VXAUiaCzvu384gpir/kLetdNGrSyPLDaLhbF7qCXQK2j63vhXRX96klxqSKV4z\nmQi3BnAG9v19plumAOyu3AKtSWQweR2+T9Tto0rm9xgLH0+ricumSHSScOPGBkkUIcPS5HVqTZwo\nbq52aHolKipivZvQ3WUbDvqbnQRZrzNYW2Pzhrm32EokUWiLMJXQHyjiRBGW/Tv2mdPiTm7nUcZe\n27k1MCRmX8lty4kgyLsR3uqmu/5uelHhZsZ33+dhwW3n7a3T4VBrswx/iimtb4dVfw1SRS9JadoO\numEpZHl5k1PnzG/gv/4nE2j9wNt3riHG8cmvfI5yp4FIhiXw1968wlwwh0bz/I2v4umQlVXFN/df\nZ61iJqKulev0ZzuQwh974gIvPPcmqRfTm11jeXmTCkMV9I3KZRbEHGuLb/DylStsldbQwljR+uUN\n0JDYnLv+pmJ5sJkrla5dXae/lZBNCK7e7LC8vMmVN9cBiEpdVjc2SAemQzBApztgeXmTza0+UCZN\nTM3hx2WEzjr5KigpBILXXlthbdXUesqP2VyPEEIQRQn9jqmZ+tZ2mKgYUjH1/vUqmqQjSKSpM9du\nmXqx141ZXt7MlUpvrhjyLo2mX/dhIRlYDbiw/Wmkz+qKGfdgkOSq/sZsZeqxHrVz/BPvucB9D86S\nqPSOjuuobedBkA6TSlDJ5N/jvbCd0+B2tzOL371yw9yjqmT/91tvBdz3eXvrnIQ9vSrtdjtqt9v/\nV7vdfh/w48BPAW+2Wq1/3mq1dktE/CzwcKvVuthqtUJMcfLvx5Z5Hfg2gFardRpoAS9P+d5jgSg9\nWKZSFCcomTJbbvClV1ZYW0/oV7YAQalXI5DDThRg7G+ZHS6yZEYgxAhrGA2S/Av37axLJukdFIK6\no9UeQsBS53X6MqQb25BFm58zbn0DKFtvvC9SSkmH5NYtomtXufq//xxpt8O8tb+9uWwu0hVrW4tv\nmFm98NTpHfdFyQtRlTdY6rxOdSzrPCh0kdO2tXqWFJXb3yoF/7iUE7OQpkFmI0o2stbsk9cjwxI6\nitCDAcrzENLj9A5yY2GzC0jT3e1vUpCmin6U0pMlO563LofAbzRJNzbyoG5Zq+E3zWydp2LiRJlM\nJc/l79wtZDZFv9Hc9lpm3YQpMpX84THl7G8ODkcTh1ybZd15vwX4d4cw/KmxYYOnK9p0eK3PGRXN\n+QfnEALSVNOcq3D2/Pbz3iRorVl9NuBtX36a9Y1O/vz6YCPPUEpUgupVaVR8Wt03WKueJdGaT863\n+NJjZhxf+/INupsx8eJ6ruiZLQ3HcIOrtJ/8r1w//yLLvRXW1TrdmpnY2irfsm23bXbmWKZSvxeT\n9CAKTS3XtZlKm+umhoxLPbpJj81oyxBFQBKb/RTbOzqtzLYulOeQ1v4W+j5e2UYpbA0YDGyjDS/G\nl36eJ5RZgVJLeiViZ/vbJARVUwcElmMb7/5WsdmQWabSUej+loWTCwkoY4fLu8BJQdkGdR9n61i9\nWebi26eziJ40ZDli4Oxvt4vM5prlvLmg7pODqb7pVqv1QKvV+lnMjNXHMB7768Bv7vSedrudAP+t\nXeYF4Ffa7fbzrVbrJ1ut1k/axf4e8HSr1foi8FvAT7Xb7Zs7vfdAW3iXERXCrfvJ9N3f4jhBy5RG\nWOcXP/YiL7yywaBiO2X06gRewNbGkKTaXO/nHvyoYgoRX4xK0ZJBmn/hgSUvqiUfhWbQTxhECgHE\nWxGLDUGgBgy8Iank2fbxk0iloGdm0JpljQDS9Vus/uffYPMzn6b7/PN5ptKbtktbxXZDiZcNqRSc\n2rkGDmVIv2QDHgejFr+wEPgtbE6Tjw0Wn5Ax49VqBw4eFn4AQpBuZqTSDuHbYWhylwYD/HKZv/fj\n72ZpdnIwogiy/JoYkmRIMo3B8yQa6PRj1sIGlCvbApkPE169jk4S4pvDDnr+rCmgPZ0Qxympy1S6\nq6i0HuG+v/o/0Hj6mW2vibDQJW5PUmm4bN45zsHB4cjhEGszgD8O/Jd2u92ZtJ63ChtxRiqZesev\nmBnSciXg1DlDpLfedWaq67rSiq8+fw1vvYbQkutXNnKCYz3a4HR1iWBQYfbmOcLVJd5THvCZ2W9l\n4NcQ9ZD/7ocvEZU74GlurZg6Kz2zjrI1x1x5SCppNEFZgNAs925yq7/O+sIVtFSsV28gkMYy7oX4\n0qefDNjShmTpdiJ0JIlKPfxQ5kHdm+s9PE+QBAO6SZfNeAstLPFj84/ieFgjpYmiWWoglIcSKbPl\nJtKSSp2tAVE/I5USAukhpEArnd9UZ6QK6H2F7c69XbBy6jXKczLf7zDa/Q2GmUqlI0AqZaSCIZUE\ngRfmmVKeJ1g8U0cIOH9xOoulw/FCMZzbBXXfHjISKWtU5YK6Tw72pGNbrdavA+8Afg74una7naUw\nf6rVav2p3d7bbrd/A/iNsef+18LfV4DvnPa9xxFxWiSV9hHUnSiUVDTDOivra6iyZFA3ZEapP2OV\nSobI0cKQSrGddXr/w1/H53//Mh6M2N/SRA2Duq2qp1YJ6AD9fsIgTvLXy56VZcsQae0wXhqBFDSa\no2SGVorS1k0WWOfimSZ80YRid557znzu5gb1kk+15NO1YyznSqXriFIJb4K6IoMnPSJLZInO6D4M\nCqSSV/ZhHTxtSrxMcVEkleQ+QrrHIYRAhCXSTVP47RR4LMLQBHVHA2RY4uzCzp+ZkUg6sUqlYPIh\n6VuyptOL+eTiu3n3DzyaBzO/Fcg6wEXXroIQyHIlVyr5QqFSO6PnSKW7BiEltXc+Pvm1Qgc4Ee5e\nwBd/g+Od4xwcHI4GDrM2s49/Hvj5OzXeg2LdKpWquodKwasMlSKPPXmOQS/mkccn5zGO42c//c+Y\n/8w7yUpfsV7mzPlTvLLxOuuDDd65+CgXXnqKasdc224BlOaJdI8/+2few6vRSyCgPCvor0C54rO5\n0CXtGCKnqFQCeOfio/zB9edY7q6wPlhn6+xNvEfrRKs9pKij0blK6WOvf4JPPP+HPMT7WLtpCKsk\nGFDxgjyoe3O9T71ZphKU6cY902FJmvoiSdKR/8EEbftBQECAlopmqYlfgRjodjKlkkZ5CZ7w8axS\nKVMlqVQBEi30vpRKM2d8rm48jxZPmfXsENS9Mci6v93960yQK5U0QgtC6Q+VSp5kfrHGR/76t0zd\nXdDheGGEVNphctdhOmQkUtaoSkpHKp0UTHMH+PPApXa7/Q8LRQsA7Xb7nYcyqnsIGVML0EunJ5VU\notEyxddVokSB8hiU7QW4N0MofbY2B6RAV2vWb/UgMRfu+UVTZHh6yBpmtqTs0p2RMbP1EinGGjeI\nVb58iJ3BCkt0M1IJxeITZ3j/d1waHWu/j0TzTO0VHn3UFGObf/BZ0i0z3sSSMJkFDoxSSWtNdOMG\n4alTe84yqpp5b7bODEVS6cnHTvMd33CBMLO/BRmpNCTBvNrMrp+zF2QYgpWE72x/C439rT/Y1nlr\nHEWlkt5FqeTb72+zFzPwQqpndrYLHgY8a61KNzaQtRpCSrycVNLD5XxHKh1FZPY3USoh5O7f0Yj9\nzXczdg4ORxQ/zwmozTatUqkmuuh09Lr7yLvO8Kc/8h5qM3tbwZVWJC/VYOCzcvpVQFPZmuWBhomX\nWh9sIDZCqp1ZurVbLNdvcnrrFR678lG+7rvexdxshWXbFW52yYzhodYSvjfsdDRbGg1wfmLJfA3L\nvZusDdaZKzeZKRlSTMMIqbQ+2CDxjJp91Sq6k2BArVai340Z9BP6vYR6s0zVr+T2t8Cer1Nb/2Xq\nGjCTkwCe9u0kZQP7cfS2YqNUCjQI8K1SSSmd32BnpIoWCn8fN4bZTeWwrXhqn7ekUmBJpdh2fztC\npBISUJJAhsPJMkskOULp3kVYIE33Q6A6bEdGIsXK2d9OGqb5pm8B+Z14q9WabbVaHzi8Id1bGBQy\nlfajVFKpRsmUpG8utlpJkiAi9WLKvZnc/hYBA7t8Rp1knm+hND7mIli3rURD+zjr0jJfL6GAOEoZ\nRCnZLWSgzbhFpUrHWs6EVszMlqlURwsA1TcZA7JcwZ810uDu81/MX8+IoIXGsPCrlHzSjXX0YECw\ntFv8g8F3PPpddl1jAeGl4cn/8UdP8ae//WG0bambZyqN2d9uB0WSaCcbUWa7S7udkSybicvaglAN\nBiOPx+HlSqVRpddbhSyvB4b70JuZAc/D9wqkkstUOpLIraBT5HCN2N9CRyo5OBxRnIja7P6ZCmek\nx2luomN94EzEfhSzeO0icdDn+vk2uhZT6TS51LwIGPvbV75wHYC1Uy9yqfs7vPPaJ9hqzvPME/cB\nhhwCuPjwEp4vefSJs3jCQ6NRWlEPawiG18BH5h4m9EKudq6zFXdolppUA0sq2W6cNftYaUXi22YK\n1lpHKaVSC9Ealq+ZOqo+WxmSSvEWgZ2YyuxvI0qlAqmkRcpsqYFfMePrdWKjVPLNMsVMpYxAGpJK\n+1Mq5aSSzkilnexvR0mpZCf4hDJKJS9AZROIrq6555HliIGzv90ucvtbnqnklEonBdOQSv8I2Cg8\n3gD+8eEM595D0f62n6BunQq0VAx69kKuPBDQr2wS9qukfYgGKQM0GW1VBZCCWt2qEtSw+5v/5kvA\nUKmU2d/m6mUSQCWKfpQMlUqpITlktUrHKpUkmnKw/eSgema7ZGVIKqE1WW/bzC423xwWg+XQJ75h\nOn8Eu4R0Z2idf8Ksawelkh/IfLuHpNIE+1v19kIWi3YgsUtQNwBKTR2KrPr9kcfjyJRKnZ75PZXD\nt/Yk7TW3k0pCSk7/8J9l5sEHhq85+9uRRPabnIpUClymkoPDMcCJqM0u1iu8ex2q3gCRKoR3sGvf\n1ctrSOWzPn8V5aUM6ht4yud0eh6A9e4mX33+Glr2+ROfeIFvaG/Q8wMe+4HvoRt3+X9f/HV+//rn\n8KXPOx65n7/4176JU2cbeHKoypFC5uTLXKlJNaiwVFngenc5f25IIhnCJVMqpTpFeQmIIZkjyzoP\n775+xXzVjWaZSlAlSiMGaUQpm8TaRakktERLxWypSVgx1+heJyYaJOCbcfgjmUrjSqX9ZSplN5UZ\nqaTGgrozUqkTG/IslHf/OpORCloopPYIRECaK5VcXXOvw2Uq3Tnk9rc8U8kdPycF03zTot1u51KE\ndrutGI3qcdgFRftbf0r7m1JmpgQPbm3Y3oyp2eWDyhYCyfJlczGO7D8AgcAPvWGQttI5SVQZmK4j\nuRLJkhJzVqkEpgNctryfGPWRX6nm9jepNeEkUilTKlUquXoFYOZJ46dPbbe0hTH7W3TDzAruFtKd\nISMyMoIqf96TlMo+84vDAG6dJOB5uc3nTtrfxEgXrZ0zlfK/pyWVrFKJHUklsy1bvZgwkG+5DNsr\nKJVkdaj2an7zt1A9NyQFHal0NJEp7MQOv9mRZQu/wbcyt8vBwWFfODG12fL1TTwvRaR674V3wJuv\nmfbWW02jNlotXwNgfXlAPZihf8UnHijOrb9ImMDvPV7n//mB+/gN/Un+1qf+Ab/1xu9QD2b4C+/4\nIQIvyOuNcQIlU/mcq5mcp1OVxXwMswVSKVu+EgxJJQTIcLiNQUVQsaTSDUsqZfa3DCVL/Kt0O6mU\nEU1CSZRUXGzej1+SKJHS6yREgxSdKZVEUak0FtS9b6WS3SdW6TNuf8vC0fNt8I8AqZRts0jt49Hu\nbw73NkZJJWd/ux24TKWTi2nuADdbrdZ7sgf277vaDeQ4ITqAUimx3dZ8X7BiW8hqNSSVAF79hLGX\nRWjuL7TSLVUCgtBDCNDpMJi7EtvWrfZxZn+bs5lKAP1egVSKzFcczMyQ2uJJajVRIaN6huDyKhWE\nlHmAc/0b3o2s1Qr2twKpFPrEy4ZUCqdQKgnfR1ar2+xvAH/sBx/nA9/9aP5Yx/GIhedO2t+KSqWd\nu78VPnuvUGQvUyr1Rh6PIyOVuoNkolrssDHJ/pYhKIzHZSodTch92N+KRJKzvzk4HFmciNpMa83N\nG1t4UiHUwW/ur76+iRaKbn0VgG7NTLRdv7JBs9QgeNOorB9c/SqvXniYue/+blZFly+vtDldXeJP\nPPw9/J33/nWeXBqNqxpavdTI43MzZwFYqhZJpQY135BKsbIB5LlSybxfh0P7WliVVGtBPk7ISKVh\nTZORSmlq9lVaIN4yUokUHmjexwONC/ieRxJEdG+Z2lT5Zhye9JAiy1SyVrADZiplN5HjSiVvzP6W\nb+dRUCpZUiG1pFJIkBN1ztZ/7yP0HKl0p+Bty1RypNJJwTRHzt8A/m2r1XrePn4M+L7DG9K9hSiN\n8YRHIP2pSaV+ZLRHvu/lpBJjpNLVNQW+USktLla5edkUHLWZECEEpbKPSkzwttQpYaY8yjKVrP1t\nphqgBKBNWHf+en8LEQRUamWU5R4lmtJEUsna3yx5E54+Q9rZovaud+HV67m6qEgqlUse8Y0bwHRK\nJQBvpr7N/gZw+lxj5LFO4pEOVsL3Eb6PTpLb6v4GoyqknZRKxRylPZVK9gY+t7/t1P2tMFP2Vucp\nAXiN+vDvcVKp2IHPFV9HEpnCbpo8kpGgbmd/c3A4qjgRtVm3E9HvRUhPo/XBri/Xzye3AAAgAElE\nQVTRIGHtepdu7RbKM6RBv7qJ9IwCqKHPITfmqcTXqcabvP17P8xDD76dC/X7uDBzHwuVndvIjxMo\n2Q3U+YxUqizky86WmrlKJ7GkUsWSSpkdTvkxwpbm5WqQZ1j2uuYGrTFbptIdKpUqYYktQChBotM8\nXBqM/S0jmnw74eMJjyTokHbMOrSfIIU0/zyBUionWHQmehJGyTQtsn2QkUnpDva3DEcpUyklRQC+\nCEe6vznc2ygqlXxnf7stZMd5nLqg7pOGPa8S7Xb7061W6zHgffapT7fb7bXDHda9g0hFhF5AySvR\nTwZTvedW1xBEQeBzbaNP4EtSSyr1LakU2dmuCGg0K9y0711cMM+HJZ9e3yiPfB0TFGx4MCQCpBBG\naRIpSyoZ+P0NZLVKtRygikqlCaSH6g3tbwCn/8KPofo9E9xdb9C7fh2tFPNjQd2dGzcQvj/MYdoD\n3swM8cpNtNa7dovTcbItm0hWKqSbm3dWqTSF/W2voO7MKpiTSnsoleCtz1MCQ+gZ+ZtGjlkIR0kl\nd/E4ish+k9MFdRdJJVdcOTgcRZyU2mztZhfPy7KBDnbtu/L6LbSGTsM0yVuqLHCzt8r86RorVzuI\nlUWUjHnyyu/yxqkK3/a4UT6Pq5ImYWj1snY2v8xmtMk7Fh/JPyvDbLmZkzPZLH7ZK9n3m22M/QEh\nFTSKajXMM5XATAaWKwFVf5gNWQ3LwAChJXEaDy1rGCtcRjJlKmJPeiTBcIIz9ZPc2pZlKmWPtdZk\n94Pevrq/TZeplOEodX9LiAmAgGBb9zeHexdFUil0SqXbQh7U7ZRKJw5THTm2UPmNQx7LPYk4jQll\nQNkvsznYrrKZhFs9a1ULfFZu9Dk9V2U5MkRTEvRBxKDNCXAA1Osh5YpPv5fk/vtS2Wdrc4AH+Coi\nSEcJrcz+BlAu+xBFo5lK3XW8apVa2d9bqVTo/gYQzM/nr3kzddCatLPF7EzdyKu1phwapVKwdGrP\nFufDdc1AmqJ6PbxdArd1kmzLgpHlsiWVbjNTqXBTvpPqY5R42v0mPhun3iOou6gAuhukkvA8vNoM\n6dZ2Yq5IKklnfzuSkNbGtpdyDka7v+1l33RwcLh7OAm12dpKB09mlrCDXV8u2zyljFS6FD6Jfh3e\nWI+oaBNk3eB3qcYbtJ/Ym0gqYpv9TXqU/HKuQBq1vzXzrm/Z8plyKSNgBrJHyCxJEFELKnlNB8b6\nJoQYyVSqliyppCSxikeUSmmi8rDubMLHFz5xOKwHlZfg223IMpWG9rchobKvTKU8vNwqlSzhlj0f\njmUoHSWlUkJCAPj4rvvbCUJQmND1PTeZdjsYZiq5oO6Thj2vEq1W63Hg54AngPyOpN1uO+pxCgzS\niMALqXhlbqTLe6psADZ6Ro3k+wH9KGWxWaa/UTZhCQKk10Els6A1sYBaOaDeLNPvbeWzWqWymWXx\nEfgqwldjpFIwPMgr1RC9EbGxFVHDFhGdDeTiPNWyD0KgMUqlSaRSOqZUKsKrG9tUurmJX28wVy+x\n2Yug20V1OwQPPzzlnrQEFZBube1BKsXbCJ/s8e3a30a7v+2UqTR9UPc2pdIeQd0A5dLdmUXxGvXJ\npFLglEpHHbn9bZqg7sAplRwcjjpOSm1WqYZUKqZmEtPNg27Dm6/dQnqC7ozJUXrpRY83XpVsyYhL\nCFZKK3xT+2U2K5KF93z9vtY9JJUMcZKodOQmqhk2TOizVswEtZxMypCpdrL392WXOpAEAyp+Jc9U\nAtP5DaAaFEmlCrCO1JIojdFj9rcsuHuoVJIkwbAeTLwoJ4ykEGgN0pJ3WhmBMpATTwfZJ8NMJTtB\nKSShFxKlEQJxJLptZd3fEoy6IiB03d9OEEIX1H3HkJHHmVLJBXWfHExzpvyXwM8ALwLngZ8Ffvow\nB3UvIVJGqVTxyyitcsnzbtjoG1JJ2gvzQqNMvdDBLBRWyaT6aKBWCag3zesZqRQWiIcg7Y8olfxA\njhBbtao5mfa6EYEQlMs+Ik2R1Rq1ss38QSLQeUh0dP0a3a+8YF7bjVRqDEklgO955kE+9MxFojxP\nae+Q7nxdGUE1IVepCJ1MsL/Z/Xe79rdpur8VVVJ7BnWPZyrtSCoNv6/SXQjqhmEHuHFizmUqHX1I\nZ39zcLjXcCJqs0uPnuLP/hkz+ST2keuTYWV9g9XlDnquh5aG3Lj2puD8Uo2//Zfexws+PHH9DwkT\nzR+8o8qFxoV9rX+oyjEESqrTEVWPEIJH5h/mbbMXkUISSH/E7lUas78lvpndT4IBlaBCEPr5JGA9\nI5UKSqVaaP4WStJNugg9LOvTROVh3TmpJPwxUikesb9lCKSP1uT2t/11f5tMKskC2Zapk8JCJ727\niYzYii2p5OG77m8nCK77252D5zKVTiym+abL7Xb7twDZbrevttvtnwG+/5DHdc8gTiNKXpjPRu0W\n1p1deLcsSYM2J7aFZplmtRDMqAypUo63QGtqZZ85m6WUFR2lcoFUSvp4OkHorKvcKCkxM2NVDICv\noVQ2r3vVqlEqAUoIpNY5obH8q7/M5X/6j0m73W32tyK8GUNEZKTSNz9xjj/63gcKnd+mC+k265qS\nVIrjbYHXGRFyu/a30e5vO2Uq7SOoO+/+lpFKkwkjrzBTdjfsbzAklcb3octUOvrIArenC+oukKIu\nqNvB4ajixNRmad80tRMHuNn7wksvAXCt9BoAFa9CFAsePNvgt5+7wtL6G7RuXuHqgs8XL1U4Xz+3\nr/WP5welKt2WIfKTj/95/vunPpI/rhUykTJSKQ+1LpJKljzKwrqzycNKgVSaqZjaRmhJN+4h1Cip\nlNnfhkHdE5RKBfsbgFLahBVrsL1b9pWpJMf3yVj3NxhmSR2FPCUYEgnKdn8r2t/cZNm9j2Ck+5ub\nTLsd5PY3l6l04jDNFTozs6+2Wq0ngMvA4i7LO1ikKiXRqbG/Wd98P+nTLDW2LfuLX/k1vnrra/zM\nu/8anX4XCPNw7sVmmRvrhZmpdIM1oJx08MMmtUrAk++5wNkLTZbOGOKlqFQKk77pZpEOiP0KQTB6\n49+sl7iG6QznAaXABnNXqwWlkkCi8veqXg/SlOjqld2VSvVRpVKGweXLwH6VSjN2XVs7LqO1tkql\n0YvCwoc+TP3rv9HkMt0GcmubEDuqOOQ+grozVciQVJq8Tt+7u93fAMr330/nC88RLC6NPD9CKrlM\npSOJfSmVCr9rEbriysHhiOLE1GZJRiodgIDodgyB0pytsQwseRdYBR44U+fjz7b58M3PgJR8/N0N\n5qrz1IKdrfWT4I11f0t1OpLPMgm1oMrawFjxynabcvtcMCSVqrZurM6EbK73h0ola38LvZCyJf6F\nknTizohSKSkqlbJMJekRF0klGVHK7G8ZqZTaXCVVUCrtx/5mJ8GG3d/Mto0qlcy1qCSPCqlk8y2F\nVSdpbxjU7SbL7nm47m93Dtk50SmVTh6muTv9pVartYCRVj8LeMD/eKijukeQWd1CGVD2TDHQSycr\nlV7ZeJ0b3Zu8vP4anaiHxyxRbA7EhWaZ2WoFPTD+9uZghWXZYa57hUppgVrZx5OSCxeHAdkjSiWb\npxSoiJgK/pjSZW7WjC27tJd82y2koFTSQuKhkVamrFNTJERXr+aEiKxsV0EMSaWN/DmtNVuf/X1E\nqUTl0kEylXZWKunEtOkdJ3zK9z9A+f4Hpv6snZDfnJfLO0q2xT6CujNFlRoc7e5vAHMf/C6a3/yt\n24g5l6l09JEr9ewxtBuc/c3B4VjgxNRmab8LgDxAgO6ga2qC+UYDUvC7p/FVQv0Pfps/+aVPUFYx\ns3/kuyhduEJr7tK+159bvQqh1P4eNr0icRWMkUqdxgpb566ytng5VyRVrVKpMTtqf6sHM/k1V2qP\nrXH7WzopU8knCYd1aOT1qUqzviw7SGtt7W8iVyodyP5m98nu9rejQSp50kMKiRZ2f+G77m8nCEXL\nm7O/3R6GSiVDkLtMpZODXY+cVqslgY+12+0V4D+3Wq15jOR6ujZmJxyZ9C/0glyptJP9rROboun5\nla/QH5jA7F7fXNAWmmVm62XoeeCl1Po93r/8qwDMLj06Yo3KUCSVfJvAn+UqjdvfFmZNQZHRH6HN\nHZC2+xsYpZLHMAASlZFKRqkkfH+iVca3pFJSUCr1v/YS8c1l6u97eirlRIaMzBhXPRWRk0o7ZBPd\nLvLA411sRKNB3XtkKk1rfysqlUp35wQtPG+i0stlKh19zDzxJGf/0l+m9q4n9lx2RKnk7G8ODkcO\nJ602SwZGDS286euFDIOeVcmUFHTh1nKPH7ryCWZfvklPhsgPfi9Lf/xD/LR3sOvqeH5QqtM9Z+ZH\n1VBZNzjzfi0Vr57/HDAkjx5qLRJFCXOL1fx5gaAR1vF8G2KuJJ24ixyzv23PVJK5GgoglgN8Yeo0\nMcH+diCl0h5B3VBQKh3gOz0s+NLPc7c87Q27vzlS6Z6HJzwEAo12pNJtQm7LVHKk0knBrkdOu91W\nrVbrXwOP28cxsHfStAMwbKcYypBybn8bbFtOa81WZCxdz698BT2YoQZs9WPCQFKvBDRqIShDKgX9\nJH/vrJduWx+MBXVbpVLWAS6zsCW31lj5j/+BeHULeGxIKgmzflmtUs3sb0LiiSGppJNRUmlSnhIM\nc3iKRNDG730agMZ7n574np1Q7P62E3Rifp6HRSplSqXdspKK5Nqe9rdgWvtbUal0tC54zv529CF8\nn/rXf+N0y0oJUoJSewbNOzg4vPU4abVZGvVBgvT3T0BEvRTwkCWNt6n5lj/4Pc71V/ly/SJfevQD\n/K3vf/9tBUVv6/6mU7w9bkqrBVJJaVNXZQRGEZlS6e3vPMPb33lm+JnS40ce/UHmy7M5USO0IZXG\n7W/jmUq+8EFoZEmjBoJIDvDlpEwlD5QAW/fdTlB39n8xlylTKpWOiFIJjKugaH+Lnf3txEAIQeAF\nRGk0kq/ksH9k56RhppI7fk4KpvmmX2q1Wg8e9kDuRUTpUKlU3kWpNEgHJPaie6VzjciqbTZ6MQsN\nY7Nq1kK0nYEKesPasSkn15GjSqUBolQeKpVCj7WPfZRXfvqnWP+vHyf6wh+acVqdc6DtiaBaJfAl\noS/zoO4Mo/a33kTrGxTURdayppOEzc9+Bq/ZpPrIoxPfsxPG1zUJOs5IpcO5KIg8m2YXpVJBnST2\nIpWmVCqNkEp3qfvbTpBS5mSSK77uDWSkrLO/OTgcWZyY2iyNbW5jsHejgXEkVvEtQsUHP7XB/Zur\nvDZ3kf9w6hne9/UXb7vzWKYUz7u/qb2VSjN+kVTKcofUtjDs6i7b+56zX8/Dc2/Lr72TMpXSCZlK\n2Xi9iiWzvCQnjDJSSStNIANEwf62n6Du4T4ZbhtMzlQ6KvY3sEql3P7m5d3fnAL7ZCC0WUpOqXR7\nyO1vTql04jDNkVMHvtBqtZ4FcolIu93+wUMb1T2CzE8ajgR197Ytt2Wtb57wSHWKtAHd3TjlAdvt\no1kLIfXxEo2XDNVJ9R0mJ0czlSLk4hLBwIzHl7D8K7+IrFYpnT9P7+WXKbb5CDMfbMVKrcs+Ckkw\nYn8zF9745jLC9wnPnJ04DuH7yGqVdMNkKnW+9EVUp8Psd3wQsU+5uaxWQcrdlUpxlql0SEql0t72\nN7mvTCWrBMtJpZ0ylYpB3UfvBB0EHmmiXKbSPQLhB+gocqSSg8PRxYmpzVQ8gBLIcHpSKVYJ64N1\nkj6kXkx5Y4uH3xjwZmmRX5l/mka9zHvfMX2jkJ0wVOUolFZo9J5KpaL9LS2EWc+Xmyx3VwEQiKms\nYdk113R/6yBUYd2TMpVs3tPMYzGXSm/nSx1ypVLR/hZI35aF+1cqZeSRGrO/jZBKfkYqHZ1rTCgD\nEksqSS1Ryqr2nf3tRCAL63ZB3beHPKjb3ks6pdLJwTRXiX9t/znsE7lSSQZ5+9Reut3+1olNZ5N3\nLjzC528+n7eE1cBc3baSrYagJJXIXPBEpYrudZnZiVQq2t/SAXr+fvzL5rNF1AelaLz3abyZGfov\nv4wQ2oQyAoElvryqKU5q5QAlBB5DeXamVEJrdBxP7PyWwavXc/vbxu99CoDGe9+34/I7QUiJV5s5\nUFD3nYKYootWMYdG7pmpZE6+Oal0hIO6d0MQevR7sZvRu0eQkbLO/ubgcGRxYmqz1MYGyGDnOmMc\nv/7yb/LxNz7JY4PvIPEH1F64DsDnm5f4wLsf5EPPPJh3t70dFK1eiVUr7Z2pVMv/VgWLWMmfoeZX\n6SRdyn55hITZ8fOzoG6bqeTrYXfWdJL9zd7whediWvcvwbPkweKj9jcfoYfB1QfLVMqCurfvl6No\nf/OlT5xlimoPZWt4p8A+GQg8H4HY12/dYTty+1t2/Lig7hODPUmldrv9r96KgdyLyDOVRpRK2+1v\nW5ZUeqBxgWvdZYRVKiksmQQEvkTiUx5YUmlhCX35NSpqGLio05SNT3+Kjd/9JJUPfih/3lcRanaR\n4PU3zHs7hpQpX3woJ2g8AYkVIvlWOSWrpvDJlEpCDxVSOh3mOgG7k0ozdeLlZVQU0fniFwhOn6Z0\nwE5sXn2GZGNjx9eHmUqHQyrJKYK65UhQ93RKJZ11f9tBYeXLYlD30ZPmZrlKLlPp3oCzvzk4HG2c\npNpM2eu6LO1NKv37X3yO6kzI2sVbKKXQkSSpDWi0r6EQvDJ3mr/ygUt5J9vbRWb1UirNs4P8PW6i\nqgVyLLe/KYUnJPVSnU7SpepPp8rKg7q1ZCvuMKuG25Uk6bagbpl3ZktJrBLHm5CpFMiAFIES2Tbd\nRqaSUiPPw9EM6g68gK4l0YSWrvvbCUNJhgTSv21L7EnHsPubs7+dNOx5lWi1Wr8KRd+Twb0osb7T\nyA6ooND9bSKpFBlSaSao8Y6FFi8oI39WQL0yvKnzZUBlYH3w80tw+TUqqVlfdP0aV/7nf0Z09YpZ\n9r7ngPOAxlcRSWMOL/2a/cBbAJQfvEj/ay/ZdSuSLLMp6qAZVSppIZB6glLJYqegbgCv0QCl6Dz3\nOfRgQO3xJw980vZm6kRXr9J/9RU2P/v7zH77dxLMzQ3HFR9yULfdJ1mL9kkodn/bM6h7XJm0g1LJ\nOwZKJcDZ3+4RZKSs6/7m4HA0cZJqM5XZKEo7X3czXHtzgyD0GNwf4SU2F0gOmF9Z5Y3yaebPzd8x\nQgmGN0yJTvNcpcxithMWyvP530U1jyc9GmGFa53reUj3XpBSgrCZSkmXeS3xAkjjyZlKGeGValUg\nwbZnKvnCR2mJsmr4/WUqTe7+JicolY5SplIgfbSwin4tXfe3E4bvfPADrA92nrR2mA65/S11Qd0n\nDdPcef964e8y8P3Alw9nOPcW4tz+Fu4a1J3Z32phjW9f/BY2qp+hz6hSyawnyJVKUWOeEAitLPzW\nb32U6OoVak8+Ree5z0FvC88TqDRFAH0RoDL72q0VZLVGcOoU0ZU37boV2ci83iYJQ/VRreyjbLPN\nHKlClMq5wmY3pZJfN13b1j/1rFnfux7fc9/tBG9mBrTm9b//d83nlkosfOjD+eu5/e2QSKXw1CnO\n/PhHqDzc2nGZ0Zbsuys9xse5c6ZSoRg7YkHdYDKVAKSzv90TyH63MnRKJQeHI4oTU5vl2Tal6h7L\nqZxIGQwi/MTUT7VBDwG8OHuaxx9avKNjK6py0gk2r0k4N3OG9575Bn7v2h9sC+pu+KYhSWVKpRKY\nIGmhJb2kj9ASP5SksbG+bc9UykK0k1yplNl9iplKvvCJAJXb3/ajVMrUW8O8KJgc1F2SR4lUCtDS\nxD8IJQpB3e6m+CTg604d/N7EYYjsOI+dUunEYd/2t1ar9X8C/+XQRnQPYZDb3wIq3s6kUhbUPRPU\naJYanK/cx0vcQAEz1eFNXeiFVCyp1KvNEgBBbJVKN24AcPpH/hwvP/c50k6H6kyJ3rp5vaM8/GiD\nB9a+yOn1r1Buma4nGRlUDWAjMm01ZW8TUSrlBEe1HKCERKhRpVKwsEC8soIe9He1g3kzhlTqPv8l\nRKlE5eG3729HFhCcOp3/H9+4TrJ+a+T1zP4mD9G203jv07u+LqQ0N+VCmPbsuy07NalUDOo+wvY3\nV3zdE8jtb4dkI3VwcLg9nKTaTGtLKpVHlUqfX/4SH33tt/nLT/44Fb9MHA1rlKij8WJDXMxtmRrr\na+dDvv3iEncSuSpHqTxTaRqr2GJlATAqHq01qU7xpUcjNPVSNdidQCtCegKphl3gvEAihCGUxjOV\nMhVVUsiAGlcqZaQSgGI6S9/IeDLiKs+LMtOSRVKpHprvshZOv52HjUAGeYaUVoI0s7+5yTIHh6mx\nzf62x32Qw72Dg3zTGrjvTg/kXkTW/a3khfjSxxMe/QlB3Vmm0owtIhLb3c0olYY3dWU/pGztb12v\nQl+G+AMzqxIvLyNnZvCbs4ggIN3a4js//BhnMXa4Tiro+mUurfwh5aRD+eJFYGjnCmzRVq0F6G4X\nrzos3nKl0pj9Tfg+4VnT9S2zyk2CZ5VKaE31sXfcFuGz8N3fw/0/83e4/6f/NgDJ+vrI63n3t7t8\nMyyCcM/ObzA9qXTU7W8z9RJCQLniSIh7Af7sLF69fmiKPwcHhzuOe7Y2y0mlMavU5258kVc2Xufy\npqlz4miY9bi5qnKl0sL6FtfDObbObHJhoXFHxzaqVLIZRdMEbOfEi8rVSp4Ykkr7UyrJPItTaInn\nSTxfjtrfxoK6U5WS2PFmpJIo2N8CSyql7D9TSQqJQIzY38b3yWPzLX78nT/CN55+aur1HjaM/c3a\nEZVCKYUQuIwdB4d9IFcq5rbXo3fP4nA42G+mkgQeBz56mIO6V5DZ3wIZIISg4pfpJX0ub17h11/5\nTX7g4e9loTI/tL/ZjiBZEWAylYZFVC0s5/a3LRFQ8kpUBj20UsQ3lynb8GtZq6E6HU6dbTCjTafh\njUTieUOLWvnBh8yyVqnk6wioUq2FpN0ufiGnqFr2UUIgVMH+plKQktLZcwxefWV3pVJGKnF71jcw\n2U3lBy+itQbPI90YI5Xy7m9392bYq1Vhipm98XHupVQKA3kk/f3v+ZaLPPbUOUcq3SM482MfyTsS\nOjg4HD2cpNpMkwI+YqzV90rf5E9uxqbOieNC1mPPw7cWq3LS56v188j6DYI73MLeK6hysnwkbwoC\nRmYB3zodkkpSUg+N/a06ZaYSGMJIJAWlkifwfc/Y38YylSZ1q8vsb1nWlFIaH7OfMlJpr5yobWOS\n3khe1HgnO096PHXqXfta52Ej8AK0NIdUmmpUql3nNweHfWI8f83Z304O9puplAD/qN1uf+aQxnNP\nIZP+ZUGEZb/MVrTF//bFX2Clv8ojc2/nWy88w1bcQSCoZUqlWKEx1WLR/rbUqOX2t01KVGUJemsk\na6uQpgRLRtbt1WZI1tbMZ9sxbMSCwBsSP5lSyauYz/SsVa9SDVG9Lt59wwnPpdkKy0iElWkLIYxS\nyfNypdKu3d/qw5nB2jvvjGdZCIHfaE5QKh1uUPe0OPOjfxGmmN0aD+rekVSyBehRtL4BBKHP/OLR\nHJvD/uFVKni7HNMODg53HSemNtNoBCDHSaWeqXM2ItPFNo6GpFIQh2hrCQvTPi8tnUeKG/sKnJ4G\n2fpUwf42jVJJFmbzh1lMHgtlM6GXKZamGoMnEVqCtkol38PzzQRlko7Z32SRVBpVKmU2L6U0nr09\nyNRXwT73myckSg3tb8dBrVC0v6lUGVLpCE7iOTgcZYyTSC6o++Rg35lKDtMjyjKVbCFU8Urc7K3Q\nSYy//2ZvBTCZStWgkhcZmQc+DORIKPPFxTN0IzOLckv5NL0SDFIGly8DECydAsCr1YiuvIlWCt+S\nSuuxyLuJ+PPz+M1ZgFxh5NnA73JJgtYj3c0ef9sCL55twivXQSm0lKAUwvNoPPNNxCs3qT3x5I77\nIVMqlS5cIJif33G5/cJrNonevJwTXTBsPXy37W/T5kaNB3lv6wZnkSmVykcwpNvBwcHB4a3FSarN\ntFCgNRRuVuI0Zj0ynZo2I6tUKpJKURklTD3QD2JWz3WpyjtfFxSVPyojh6YgYGTB/jZUOHlcmn2I\nH33HD/PYws7NQMbh+9JkKmmBQOB7Et/XJEm6zf6Wd6tTE0ilCZlKKdbStw/7W/Y56UgI+dG/sSza\n39JUkyrlSCUHh31inEC+00S+w9HFnmf5Vqv1bKvVmis8nm+1Wr9zuMO6NxBl3d+s3DrrAPdA/QIA\nN/uGVOpEHWaCIYmTJClKjFrfAJ45925mO01i4XF5LaJnpd39V18BGFEqoTWq28W3JMvaQNOx9rfy\ngxfzdQrfR4QhXmSymcq25srCtcGogqq2C51WKaSmcBKeh99scvpH/vxIBtM4wjNnKV96mNlv/869\ndtm+4Dca6DhG9Xr5c0P72/GwYQkpRxRNe2UqHcU8JQcHBweHtxYnpTbTSoFnunEVs21WB8MmHZsT\nlEphVGFmtQnAGwsh1cVNgh0mbW4HRfvb0E629+d4k5RK0kMIwdeffmJ/mUq+h9BWrQT4vofnS5J4\nr0ylURKsmKnkY57TQttt2q9SyStkKm23vx1FbFMqKe2ajzg47BPjyiSnVDo5mOabnmm322vZg3a7\nvQpMr8s9wciCujP726XZiyxVFviJx/8cFb/Mcm8VpRWdpJvnKYGRLKd6NKQbDLlTUzE9r8SVmx3i\nwJJKr2SkklEqZSqjtLOFn8ZoYK2vuF6aBympPf7EyHplpZqTSqG0RcbMzOhne7agSBXakkp40xUZ\nMgy5/2/+LZrPfNNUy08Lr2kKxmKu0lGxv+0HRQJsr0wlRyo5ODg4OHBCajPV74MvQI0qRlZ6q/nf\nG9H2TKWgN0M1BrTi5aUqYcmQBncaQztZ0ca2T/vbPmxzE8fgSYSSyCKp5EnSdEgqZfa3Yoh2mimV\nLAlWzFTK7G9a6G2d26Yak/Ty7VJKHYtclUAGaGmVSirLVHJKJQeH/WD8PPpivfoAACAASURBVHYc\nrK8OdwbTXCVkq9XKW3u1Wq0Z4HjIQO4ycqWSNKTSdz/0Qf7Oe/8GzVKDxcoCK70VekkfpRW1y2f4\n2L//Mlpr4lih0CN5ShmCqEdPGjIpLZmvZbBNqZSRSh1kEhEJn16kWA2bnP7Zf0JjjNzxKhXCvpGR\n17zJpBJWuqxVmpNKYkpS6bDgW1KpmKt03JRKMLofdyKVwsDjvsUabzvffKuG5eDg4OBwdHEiarMd\nSaV+zqdNzlRSPlL4BGrAxaffjufpfXUwmxa5/U2lQ3JomqBuMSSjVMH+dqAxeMb2JlPzft/z8u5v\nWaZSMXDakx5JUVllP7dof8uCubVQuYJqP5BCjtrfjoFaIfD8XJlllErO/ubgsF9sD+o++se+w53B\nNFfYXwQ+2mq1/qV9/JeAf314Q7p3kGcqFbqNZBfmxfI8b2y+yZWtqwDIa3VeXLvB0992iSROUUBj\nzP6m4hiiAYOqzSUqm3oy3dpE+D7+rFHCZ6SS6nTwkpheYXau2qxvKw5ktUJz+VU++GM/yfnOK7zK\nqP0NrE0LQGUR4keAVGpYpdL6MVcq+QHQs39PHrcUgv/px97tWts6ODg4OMAJqc1Uv4/wRW7tyrBa\nIJWyTKX1DZMNmQqNpwWRX8PXtyiHZRKV5M1Q7iQycijRKYkNtZ7mJmpof0tzhdN+LWb5ujIVUmrq\nB8+T+L5E6yHRVrRx+cJDqeF4M7JN5KTSUFmkhZ7KzrdtTELman2lU3wZ7vGOu4+i/S21Qd1B4Gou\nB4f9YFtQt8tUOjGYJqj7Z1ut1hXgQ/apn2u3279wuMO6NxCpGCnkRNnvYmUBgM+82gZAKLPM2s0O\naaJQbLe/qY4pnLLgawo5Rv7iYk78SKsySjtbiHhAlBUMAvwJ/nBZqSLSlAcvNok/Y2b8tiuVbIGR\npuRdjO/yicKzpFKyMUGpdKxIJX/i39uWc4SSg4ODgwMnpzYzSiUJarTeyOxv9XCGzWgTrTVffd0Q\nTaLiQdeQA/1yRMlrEKv4kJRKw0ylTJkzzeeMdn+7XaWSrf2UJZV8mT8XDRKkFCOKG0+MK5VGu79p\npfGySUOhcyXTvsY0FtR9HCwwgfRRMstU0iilRxReDg4Oe2NbUPcxOPYd7gymusLaLiMnptPInUKc\nRoQymEgGLFaM2ujZr72ANw8iNReutZUuSumJpFK6ZUil6lwTEkY6tIU2TwkK9retDiKOiKR5XA4n\nS5ilbR2uel2STUsq1ceUSl5mf1NgC4W7rlSaaH+zSqXjZH/LiCQhcpuhg4ODg4PDbjgJtZnq98AX\niHiMVOqv4QmPCzP38eXVNhv9Lq9f3WAR8OIOGlPXdKoxoQyIVUJwCKRSRrgoNcwo2k+mUlrMVDoo\nqZR1dkuG5FD23KCf5H/ny9u8oyQfr7W/FTKVpJ8pldSByDhPFoO6j4n9bUyplKbO/ubgsF+Md3o8\nDse+w53BNN3ffq3Vas0XHi+0Wq1fOdxh3RuIVEzgTSY3MqWSqBlCJImM+ufmdUMcGVJpVC6ckUpz\np83XUWoM1URZnhLY7m9YW1wUEVvpcrhDO/ohqdQj3tgcWUcGkRU7KoX0aJBKmVIp3djIn9NxplQ6\nfqSS8PafW+Dg4ODgcPJwUmozNegipECMWbBWeqvMl2dplhoA/M7zr6BSU0fdf/X5fLk4jAi9AI0+\nnKDuQjbSfhRHxe5veabSQe1v40ola38Do1TaRirZzmzpuFIps79pjaeH9reDjMsTMl9/qtNjcWMZ\nyGKmUqZUcjWZg8N+MH6+OA4qRYc7g2nO8g/ZriIAtNvtFeDS4Q3p3kGUxnlI9zgyUkmW+gDYTO+c\nVNJAvTJZqbR0doEf/aOP8vS735a/FkxQKiW31gBNZAup8g6kklex2UzdHsmmIWi22d8ypdJI97e7\nWyTsHtR9/Oxvx0ld5eDg4OBwV3EiajPV7wIgCmqZKI3YjLdYKM9TD02t8uwLr5JVOIv96/myaRDl\nwdmHYn/Lu78V7GRTZBCN2t9uU6lkiY9iplJGJMVRmhNMxTEnKiW2mUqB/dwsU0krjbR7U3OwgPOi\n/U0dF/ubtz1TaVx14eDgsDu2ZSodA0LZ4c5gmm/ab7Va+S+k1WoFQOnwhnS88dW1l/i3L/0GSiui\nNBoJ6S5irtQEbWdANNjJEVaXh0ql8e5v6dbQmvb+x8+yeGYhf61IKmW2uGTF1JtZplJpT6VSl2Rz\nC4RAVkcDLYtKpWH3t7tL3MhyGVEqkW5MCuo+PgRNRoDd7f3p4ODg4HBscCJqs3SQkUrDCbqs89tC\nZY5GaKz6K90NFmfMMgOtMSECkPhRHoB9GPa3ke5vGTk0lf0ts82pQhbTwW6+MqWSlw7tb0UiyRub\nAPStUmlofxtTKimNl90eHDBTSQqZK7BMptLRv7GcCWp5MZ4kWdc8p1RycNgPxs9/Lqj75GCas/x/\nBn651Wq9v9VqvR/4JeA/He6wji8+/sazfPT13+by5hUiFRF6Ic+9dJPLy1t89tlX+bf/5nPGpy0k\nRIbMEdq0gwVIrXx7kv0tunYNAK9u5N6yUs0zeEaVSmbmLl5dMf/bgqEU7kQqGQLJ2N828Gozw25v\n+UqHSiVURird/ROF32hOzFSSxymoOyOT/Lu/Px0cHBwcjgVORG2mItsZtTBBl4V0zxeUSsEDLxDa\nLqpXy/NEVgWeBIOc0NgpjuB24E3MRtq7/pj4vjvY/a1oeZuYqTRif7OZSgVSSeT2N3XA7m8eGp3b\n+44DqXR//Tx/5amPID1BYrvmuUwlB4f9oXisC8SxOPYd7gymuVL8tP33TzCurF8HfvsQx3SssREZ\nNdFLt14mVgk+Pv/8177Au+6bpfzmBlrDay+tMHu2Ttqv4JW6eee3IsaDulW/z8annsWrN6g8/DBg\nuoF51Rrp1ibB4mK+rAhDhO+TWFIps79No1SKNza3W9/YQal0BGTBXqNB/MrLaKUQUhbsb8dIqWRV\nVfIYqascHBwcHO4qTkRtpq1FS/rl/LlMqbRYnqNhSSUZDkj7EUILBqdniUtdSoMqiR8h5eEplaQo\n2N/0/oO6lU7vgP1tVKnkeXJEnbTN/iaM/S0b7zBTyY5JabzM/ib0gcaVvSfO1FDHQK0ghKA1f4lP\neleJY0sque5vDg77ghAiVyo669vJwp7fdrvdjtvt9t8FPgz8O+BHgP/jsAd2XLEZGfvaC6svAqBS\nD61BLHfQ1uL2lS9e49Vrm+iBUQj5ejuZoIFqaVgArX/qWVS3y+x/8wFkMFQwBadPE549hywNVe9C\nCGRtJreC5fa3HZRKXtWQSmm3S7K1ta3zGwwJpNFMpbtfJPjNJiiV500Ng7qPkVIpUyg5pZKDg4OD\nwxQ4KbVZ7aknAQhmh3b/lb5VKlXmc/sbgI7AVzGVRxv0K5tooYhLvfzGxj+MoG45tL8plQV17y9T\n6baDurcplcSoUmnc/ia9sQyo7ZlKmf1Ni4NmKtmg8DQaeXwc4HmCJLb2N6dUcnDYN/KOkseATHa4\nc9j1StFqtXzge4EfBd5rl/9gu93+vbdgbMcOWms2rVLpxVsvA5AmgiYQDlLOPzhHvxfz+tdW6MyW\n0H1DKs1IUxQN0JSsDc4PZN4JTCvFrY99FOH7NL/1AyOfee4v/xWwhUwRXq1Gun4LgFjspVQy40hW\nVkAp5ASlUmaz00Wl0hEgQTwb1p2ur+M3Gsb+JsSRILymRaZUcplKDg4ODg574STVZrJiJtGK9rfV\nns1UKs/n1i2d+Agl8HQCFyvcKD/PxumrJOEAbWf0DidTKbOx7U+pNGJ/u9NB3b7EV3r4+gSlktIq\nz1Ta1v1NaUQxU+lA3d8ypZKNJDhGpJL0JLG1v3kuU8nBYd/whEdMfKzIZIfbx47fdqvV+qfAZeAn\ngH8DnAdW78Wi5U6hnw5yqW92IY0GgvMINPD0B97GI+86g9Zw9eVV1MAohGrCBGv3CzMiYUFV1Pn8\nc8Q3rlN/39P4jcbIZ/qNBv7s7LaxFC1syiqbds5UMuOIl2+Y99Ym2N8ykkYpyO1vd5+48Ru2A5wN\n61ZxjPD9nJA7DsjIueOkrnJwcHBweOtx0mozbWspIQqZSv1VAunTCGdIIqMG13EAQuIJRc9LUV6K\nrplcpaweOxz7m0QgTEZRHri93+5v2fvujP1N7pWpZAmfQTowj3fNVDqYUilTKMS2tfFBVVh3A54U\nOakkj0DMg4PDcUPWNfE4HfcOt4/dzpY/ATwP/Gy73f6/2+12D+PKctgBmUqpeBBFHUkVQUfCwqkZ\nHn7HaaQUpGt9mr4hgyrCKIVqjTKp3cVFUunWb38cgLlv/+DUY8k6wEGBVNpDqRQvL5vx76pUOlr2\nt1ypZEklnSTHjpzJlUrHbNwODg4ODm85TlRtlmcqFaxrq/1bzJVmEUJw9WYXtKDeT0ilT1jyGVjL\n1TixcRj2N7DB16oYuL2P7m+HEtQ92v1tW6aSzEgls58y0qhof5O2O7EW6mCZSpn9zU60Hjel0vDv\n4zNB6eBwVJCdy5xS6WRht7vYc8APAf+o1WrNA7+wx/InHhs2T6k1d4kvr7YBSDrmwOrZkq9cCTj7\n4CxvvrzGxdop3ghqLIlF1oGF2TJXbvWoAaWSKX601vRf/hrhmbOU7rtv6rF4BVKJ0OQtlXcglbxM\nqXTTkkqTMpUyAikt2N+OAKmUK5XWM1IpPlYh3UCepeRIJQcHBweHPXCiarNcqWQJoVSlbMUdztZO\nA/Da8i1IfU5tdNHCI6yEeY6PtHECA2UeH4ZSCUwmUbrPwO2i/S3PVDqgKmZSUPek14vjhaFSKevu\nVlQq6ZxU0gfu/gZDldhxurksEkkuU8nBYf/IM5WcUulEYcezfLvdvtVut/+Xdrv9DZggyFmg3Gq1\nfqfVav3EWzbCY4QspLs1f4myZzqVxFt2tkYrktQUDuGsIXFOhWX+7vt+ivcsfYN5vFC1DXGhXDIH\nYrK6gur1KF24sK+xFEklbZVK4R7d37LOaZPsb0WlEuoI2d8KmUqQKZWOF6nklEoODg4ODtPgpNVm\n46RS1mE3C+h+ZeUaOi6xuG5V3rUKg3SAQGA5JbqxqayCw1IqifHg6/3Y3wpk1B1TKo12f9tmf7O1\nWz+xpNIE+5vOMpmEOpAtLyORMpXYcVIqjew71/3NwWHfyI7340QmO9w+pvq22+32F9rt9l8F7gP+\nOSYg0mEMmf2tGTZ4qPkAAF5sipgYiGyL0r4llyqhpOyXSQ2Xw+n5GpHNAqpWzPsGb7wBQHh+v6TS\nkBgSmVJph0wl4fuIcNhRbpL9LSOQdEGpdBS6lXljmUo6ThDB8SJnMjLJkUoODg4ODtPiJNRmytqn\nhFUZZaRSs2TyJa9s3UANKsyv2yYnMxUGaUToBSgb0N2Ju8DhKZWklKNKpansb8VMpTsb1C3Hur9t\ns7+JUfvbsFPT0P6mLKmkhT7QuHLroTp+mUpOqeTgcHvIM5WOgPjA4a3Dvq6w7XY7Bn7V/nMYw0a0\nRdiv0r/i8bb5B/nyahs/HZJK/SilWg5IbaEjU8XLf/P/Z+/e4x2r63v/v9Ylyd7Zt9nD3BmEuRmZ\n4XoG0FI9WlGOQ6EeOJRDLYKDYPEUrQyVymP6oxTQUStaThXwqMDj0J/19PBrteWBwIgKB6EPERCo\nl9VjFYG5yAyzZ9+TrNvvj7VWdrJnDzvZ2clOMu/nP5OsrKx8V2Ym+eazPp/P9085+KbfAXrIdNn0\nrOzlld0jrD86CpYUXomCSpkag0pmeVApk4HJw/dUgihbyS/GE4wZy9/iSUlZT6VWKH+z4sblpfI3\n18WYqSdUC1NQSURE5qqT52bTM5WGCyNAlKkUhiGvTQwRmr0MjMCupWBmbQp+gbSVLq1uNu41Nqhk\nGdN6KlXxOhWrvwX1NepOegBZwVSmkmm+zupv5lT5m21YpYVNjLJMpSAeU2iEpOoofyu24epv5WWI\n6qkkUrupnkoL/ztRmqd9PuXbwGhxlKW71vOvDx9gU3YTR1lHY09GAQ4XKMSZSsUkrXhiAm//fvKv\nRcvj2rbJhmMH2QMM9EaZQ4VXXgFqDyqVl78ZXVF52+FWf4OpErjouTOVv01lKhFnWrVCUMlMpTCz\nPWWNutuvp1IpqGQpqCQiIlISJuX20ZxouCxTaWTCpei7pCYssvl4jmIHFH2XjJUpBXkm4kwl22pw\n+VvcVLzZmUpJJpIRxsEle9rqb9N7LJX1VCpf2c2sCCrVl6l0SPlbG62iVpmp1D7jFmkVatR9ZNKv\n2Hk0WhzD8qNeSla+iw359/BrfzcwlakEU0GlcDJa7tb3o/uptMW7TjuGIAg5ef2SaN9XXsbs7sZe\nvLimsZQHlaxMBph43UwlqzuLm9yeqfytIlMpSUdf+KASRH2VvOF2Xv1NmUoiIiLT9Sw+iWw2jd0T\nLVQyUpaptHvfGAQ2y4bH8ONMpsD0KfgFelLZUrCm0eVvlmlS9Ir4YZJxVE1PpanV30qNuuf4A2x6\n0CgKDk0FRqZnKtml0jSPTCoz7XmV5W8QVnU+h4zpkEbdrTFfrIal1d9E6jJVUts+/++lfgohzqNR\ndwwziP4DjY7k2XtgguS6mAsU4qBSwYv+9PNRuZkbzSewbZOBnjS//zvryaQsgmKR4m/2kll9TCk9\nuVrlgSGzOwp0VZWpZJqY2ewMOySNust6KrVAphKANTBAMD5O4BYhCNouOKOgkoiIyKHszCCr1p+N\nEf9IKc9U2rV/nLDYxarhMfw48OGbbtRTyUyXgjzj7jjQ2PK3oLz8rYrgUEX5W709laaXt9lmRR+l\nw/VUmn47mWeWN+oOjaC0WlxNY0oylYLqs7daRUVQST2VRGpW6qnURv/vpX76FTuPRopjLGI1AKPD\nUVDpDaaBaZmEbkA+Ln8r+CE24BWjKzi+D1hgT8skKu7eDWFYc5NuALMsU6krGwWMspnD/3UnQaVU\nXy/GDOm+pW1BEK0AR2uUvwHYSbPu1w4AtOHqb0lQqTXeTxER6Vy5XO49wG2ABXzVcZxPz7DPO4C/\nBlLAfsdx3t7UQR7GSDHKVBpI9/Hy/l8STvZy9PgQnhFdSCsaBYIwqAggJZlKdoNXf5sKDtWy+lt5\nMGquPZUqAx+WZVJ+HfKQoFPZ61RT/jaXTKUkQ6EYNwNvp55KKn8TqY96Kh2Z9Gk5j0aLo9hhNGk5\nODTJ0GiBFJCKgzlJptJk0SMkxI3v+2G8asm0oNJcm3TDVF8kI5Ph7De/gQ9seRMrj5ohAylmdkeP\n2TM06Y52SHoqBeAljbpb459P0qzbfW0/QBuu/paq+FNERKQRcrmcBXwJ2AJsBP4gl8ttnLbPIuB2\n4Pccx9kE/H7TB3oYw4VRbNOm2+7m3/e9SpjPsnpiiLHuqOfSJEkAaWoe4MXBnlQDg0pe6JeCQ9Vk\n9kwFlfyysrm5rv42LWhkGRXZSdMfL3+d8ttJMCUIQgK/3p5KleVv7RRUKn+/LJW/idRMQaUjU/t8\nyre4ol+k4Bexwug/0IHXJjAAI4BUVxxUijOVJt2AAAPPi760vSD60kqlKv86poJKq2sej5FOY9g2\nZibDkoFu/uPJq163hM5KMpXiAM0hx7Nat/zNHogylZKgktl2jbqt+M/2CoaJiEjbOQP4heM4v3Qc\npwh8A3jvtH3eB/yD4zgvATiO82qTx3hYI8VRBtJ9hCG8OnKQJcVhujyXob6oN9BYMAbM3NeokT2V\n/GAqU6maAIpZVv5W6qlUZ6Pu0rGtaY26a8xUCoOQIF6lGCPEntPqb9FrFtuwp1JFppKCSiI1Sz7f\nlOl3ZNGv2HkyUowmMmZgEQJjI4VSP6VMd3QradSdL3gEBrhxMCnJVEqlD5OpdPQcgkqGgb34KMxM\nuqr9kz5Kh8tUKjXl9gPCIMlUao1/Plap/O01oP2CM6UMpRYJ0omISMc6Gni57P4rwJun7fNGIJXL\n5b4P9AG3OY7zP1/voIODWewGlnAvXdpHEAaMFkdZt/g4XMPAD0JW5/cBMJrNQB4mieZi2a5D5z7L\nly5iac9hsrHr0JVOExKCGWCZFsuWzXxxrpyXZDWlDNJdUyslLV1a+/imn+vy5f0VgabFi7MVx+3f\nP5W13pVOlx4r5L14TBZdmWheEhoBgwM9NY9r4EA8p0xH89u+3u6KY8zlPJulp2eqefnAQLausbby\nec4nnWdnqfc8u+PPpGwm09LvWSuPbT416zzb69d3CxuNm0cagUkIFCddkq/57p7oVqEYfWFPFn0M\nI8SLr/548TKw5ROyMAwpvPIyqaXLMLu65jSmVX/8UYwqmwwmPZUOW/5WlqmEnyzx2xoR6OmZSu1W\nRpZkKrVbhpWIiHQkG9gMnAV0A0/mcrl/cRzn3w73hKGhiYYNZunSPvbtG2W0OIYfBmTNLM/9/DcQ\nmqzOR0lUY90ZevNwoDgEXeAWg0OOMzJUwJgYnffxxb2oGS/ksTDZt2/210iyk/IFl7GJSSDKVKrm\nudMlwaDE0NB4RXbS+Hix4riFian9Q98oPebF2fT5vMv4WCF63AiZHPdqHtfERJShNDIe/bvIT7il\nYyR/n62qWJx6f8YnCnMea6uf53zReXaW+ThP340rcdygZd8z/X3Wd8yZKKg0T5JMJfwk+BKSXAvK\nxkGlpFF3vuhhEeCbaULAJ2qqWJ5m648ME4yN0b3hjXMeU+boo6vet9Sou7+KTKUWLX9r10wle2AR\nMJVxJSIi0iC7gPJGjavjbeVeAV5zHGccGM/lco8BJwOHDSo1w3AhatLdn+7n5b1jhF6a1fnfYPb0\nUIwvJo2G0eR5phK0xpW/TTWlrqZJN0TjMzCi1d+Celd/m9ao2zYxDAPLMvD98JDyN9OcufzNKC9/\nK2vUPZfStek9leZ6bgvB1OpvInVRT6UjU3v9+m5hSaZSXFIPQA/Rl1FvXOtfKPr4QUDRDUgZHp6R\nITAsfMPCTlkVPY/c/VHWTXrZsqaMP1lBLX3UUTPvMENPpVZZ/S0JxrhJUKnNGnV3rVvPcTd/itTy\nFQs9FBER6WxPARtyudwaomDSxUQ9lMp9C/hiLpezgTRRedwXmjrKGQzH86yBTB/P/2Y/vRMhi9xx\nuo8/ubRISmDF85MZekg2LKgU/3AqBm5NS2hbhhmt/pY06p7jDzBrWhAkOXfLNvF9/5Cgkl3RU8mq\neC5A4AdlPZWCOa3+Vgoq+e3YqFurv4nUw4z//5sKKh1R9Gk5T0aLYxBCWJZx3Zv8WRZUSvoqpcLo\ni9a30viGfUiT7qSUyz5qSWMHHstuOoEVH7yS5e9654yPlzKVgmCq/K1Vgkp9fWAYeAeHADDarIzM\nMAzSK1e1TDmhiIh0JsdxPOBq4CHgZ8DfO47zk1wud1Uul7sq3udnwIPA88APga86jvOvCzXmxEhZ\nptJLB/dydFz61r3hjdhBlBEemN5hnz+X4Eg1Sk2p/WJNr2EaJkHo192o2zCMUqZ7ecZ7Ekya3si7\nolF3WRNuwzAwDAjCkMCPxhQa4ZxWpZveqLudgkqmVn8TqYsV/56x9LvmiNJeKR0tbKQ4hhH3RjIM\nCEPoijOVBhZFPZHyrs9kIZrwZII8k2YvHLWCwLRJT1vyNSnlSjUpqGSYJv2/9dtYXV0w6h66Q/zB\nEPqtl6lkmCZWXx/+SDThbLeeSiIiIs3iOM4DwAPTtt057f5fAX/VzHHNJslUSofd5IMJjs5HF9+6\n1q3HevHnwFSmUsLEJCAgZdqvuwJuPZJgUMEv0pPKzrJ32dgMiyAMS6vGWYaFP8tzDjsGyyTw/Yqs\nJTu+bU2bX1rmzJlKEGUrBUFIWFb+NqfV30olgcnqb+3z49LS6m8idVH525GpfT7lW9xocRQziP7z\n9C/qrnhs0UAUVCoUffKFaMqQdqPGjMbS5VGm0rSa+KT8LXW4crQmS7JowiAgDOJ0rBYJKsFUXyVo\nv55KIiIi8vpGitGFo/ExCwgZdKP7mZWrsAKbwPDBiMu24j+SzCHbbNzFpuSHU0hYU7ZRqfyt1FNp\n7lNya4YAkpWKf9hNL38ry6aypgWMDNOIeirF5W/LsktY2bu89vHE74kXdzFvpx+XleWE+pkkUqsk\niNxOwWSpX0N/fedyufcAtwEWUfr0p6c9/nHgD8vGcjyw1HGcA7lc7kVgFPABz3Gc0xo51nqNFMdK\nQaVFi7sZHoqCRqYRks2mMIwoUykpf8u4E5CBINOLP2ljTw8qxZlKzSp/m00pKykICH2vclsLiPoq\nRaskK1NJRESkswwXokylAwcCQi/DgDuG0dWF2duL6VsVWUoB0cWvtJWiGBQb1k8JKn841ZLVYxpm\n1Ki7ovzt0FXrqhqDfWj5WylT6ZDyt6n7qcNkKgV+FFT62OYPkU1nah5PUu7mtmX5mzKVROqhTKUj\nU8O+ZXO5nAV8CXg30UoiT+VyuX9yHOenyT7l6dW5XO484BrHcQ6UHeZ3HMfZ36gxzqdRd5QesweA\nrmwaE58Aiy7TxzRNutIWhaLPZNHDCAMy+VHoBddKExoW07KT8Q7sx8xmsbq7Z3i1BVBW/lbqqdRC\nq3lUZCq1WaNuEREReX0jxRFMw+RXr71KONHDIm+M1KqVUS+gwCKwpkr3kz5FKSsFbuOadEPlamq1\nXJmf6qkUzamiBtpzDCrNlKlUTU+lae9LKagUl78Zc1z9rLzPFLRXUGl643MRqU2S4adMvyNLI/+2\nzwB+4TjOLx3HKQLfAN77Ovv/AfB3DRxPQ40Wx+iz+oDoC9wimtxkiL5QMykLN18kP3SQfm8CO4i2\nF4iaS9plV0PCMMR97bWWKX2D6ZlKrVf+lqwAB8pUEhER6TTDhVH6Ur28MrKXbjcgHXiklkTZ3IZn\nVjTpTrJ/MmY8x2pC+RuAVXOj7qlMJbOOC3VJAKk8K6m07XV6Kk0v1zOm9VSa64/C5LhJplI7lcGU\nB5Kmv3ciMjtlKh2ZGpnScTRJPVLkFaJlaQ+Ry+WywHuIViRJhMB3RrFUVQAAIABJREFUcrmcD3zZ\ncZz/MdsLDg5mse3G/QNeurRvxu1hGDLp5UsNGvv6u0iFRVyji24KLF3aR093itP+7XsMPPNzLkwP\n4KcXAxDYUb+l7oxVOr47MkpYKNCzasVhX7ORZnrNsdE+XgK6MhbFOM16yfIB0ouaP76ZuEcvZyi+\nPbC4r6r3bSHe22Y7Es4RdJ6dRufZWY6U85TGCcOQkeIIK3uW85o3zhI3muulli4jDEPwTYKuqfK3\npJdPxopKt5pV/lZL8MQyTLzQL/VUsuv4AZYEP8oDIstX9TE5UcSetrqwfZjV36Lnm1FPpVJQaa6Z\nSnGj7jbsqWQqU0mkLlNBJQVljyStUid0HvCDaaVvb3UcZ1cul1sG7Mzlcj93HOex1zvI0NBEwwa4\ndGkf+/aNzviYG69uYXjR21ksetj+JNj92IUx9u0bxTZNFo/vi45VHGa/3QvAWDH6wgo8t3T8/K9f\njLb1LjrsazbK4c6zMJIHYGIsjz9RAODA0CSW2xoThUlrquZ/dNKDWd631/v77BRHwjmCzrPT6Dw7\nSyPOU0GqI8+kl8cNPLJ2D6uHljIQ/1ZJLVmC7wcQGvhlmUpJ2VWXnQSVGpepVNH4upZMJdMk8Fz8\n0MfAqKtUxJqhf9Jb3rGON7997SGr3pWPcbbyt7n2FEp+VLptWf6mnkoi9VCm0pGpkZ/yu4Bjyu6v\njrfN5GKmlb45jrMr/vNV4B+JyulaUjFO77XDaNJi2yYpL2rUnc7Hq5OkLbLuBMW+Qb78hv+Meebb\nAcgHyX+8sHS8Vlv5DaZWfyPwS426W6n8ze7X6m8iIiKdKFn5reB6DOb7SNlRZnhq6VLceAGU8kbd\nRd/FMqyWzlQyDatU/lbvFf0kEDK9XGt6QGn6GO0ZGnXPS6ZSPGf04n5R7RRUMrX6m0hdkv//Ciod\nWRr5afkUsCGXy63J5XJposDRP03fKZfLDQBvB75Vtq0nl8v1JbeBs4F/beBY61KqGQ+jSYtlm2QL\nr2EGPv3DLxMGAd22QY+fJ5/pYSjdT/eJJwGQ9+JGimVBJa+08lvrBJWIJx6hHxAGUf1/663+FjFT\n6qkkIiLSKSa8KFt6aHwYKzQxiIIdqSVlQaU4U8nAoOAX6La7SkEl22pkUKm88XX18yIrXv0tCP26\n+ilBef+k2YNA9utkKhnTG3XPEJSqajzTfkxO793UyioylVT+JlKzJIisoOyRpWHfso7jeLlc7mrg\nIcAC7nIc5ye5XO6q+PE7413PBx52HGe87OnLgX/M5XLJGL/uOM6DjRprvYpx+ZuFjQfYtkU2f4Df\n2XcvAP74GH1BHpOQcTtaIa63J5ro5L346pIxdYXNPZBkKi1p1inMyrCmMpWmVn9rnQ+LitXf1Khb\nRESkYyRtBsYnoyzw0LAIieZJY8PRY0mmUsbKMOnl6bK7yFjRfKCR5W8VjbqNWht1+/OUqRT/iKui\nsXRlZtWhmUpJo+56Sr+mZya1U2+V8h/C1QTpRKSSyt+OTA2tE3Ic5wHggWnb7px2/x7gnmnbfgmc\n3MixzackU8kObApEV4xsf2ppW39khF4/6vc0bEaNuft6ognOpBsHlcKpZWTdlsxUir5kQz8gjINK\nrVT+ZmazGLZN6HkYKZW/iYiIdIpivGKuVYj7JxoGxsBizEwG142ymAIrac6dIu/n6U/3krai1d8a\nWf5WEYSoqfwtXv0t8Ov+8XW4ld5m3Nd4nZ5KxlSmUj1ZOocEq9rox2VlT6X2CYaJtAo16j4y6W97\nHiRBJTOM/xPZJqm4OSHEQSU3Ciq9RhRU6u2LJkZhXPVmUbZqyWuvYaTTWL2t04zUSFKXAz8KKlnW\nnNOiG8EwjFIJnHoqiYiIdA43XkXMLmRL24wly6PHysrfDAzSZpqCX6TL7ioFlaYHT+ZTZflb9a+T\nlL/5oT9vmUrVlb8dfrymlfRUCuY5qNQ+PzfKm52r/E2kduqpdGRqn0/5FpakZSc9lWzbJFWWqeSN\nDNPtRtV9rwVpTMOgO2NXfnEFU6uWuK/tJ3XUkpYK2hBPWMIgylRqpdK3RFICp/I3ERGRznFgcgiA\nTH5RaZsxGLUIKBamGnWv6FnG2kXHAkTlb2aSqdSs8rc5ZCqFQd2ZPOZhGnXP/LplQaVpr1veU6mu\noJLZGeVvWv1NpHaLuwYBGOxaNMue0kna51O+hSWrvxlB9HYalkE6qCx/6y5EQaVRK0tXOsrySaXL\nvtjDKKgU5CcJxsdbq/SNqUylMAjA91uqSXfC6u8HUPmbiIhIBxmOV3+z8j1TGxdF8yS3GM2ffMvj\nLStP43fX/CcAussylRq6+lt5+VsNr5MEd7zAq7uRtR1fpDTt2af1r5upZEz1VDJU/qZGwyJzcPzi\nN/Lpt97AxqNyCz0UaSL9+p4HSaaSGVhASBhCOnAJDAMzDPFGRkjnxwAYtbN0Z6Iv13TaIj+RlM5F\nk6Kkn1Kq1YJKydUv348CSy0YVBp463/E7O7GHly80EMRERGReTLhRg26Tbcs46g/uhpeTMrfLI+C\nXyTvRz2WKoNKrZeplOzrBi7ddld9Y0jK36oIBJWPcfpqdUkgyffnt6dSW2UqWSp/E6lXX7p3oYcg\nTaagUh3GRgs89I//yuAp8WpogQn4BL6HSchk9wDdE8P4IyOkJkaj51jdLM9Eb3s6M/X2W3Fgyn2t\n9VZ+A6AsUyn0vakeSy2k99T/QO+p/2GhhyEiIiLzaDIOFNllQaWwJyp5L++pVPSLTHrRvl1W+epv\nzempVEvGUdJnyPXdunuPJBlKVhWZSuXZVPa01eqSIIrvBVUd67DjaePV36xSKaECSiIi1WqfT/kW\n9OruEV7dPcrB3VFT7qT8zS8WAMj3RlfR/NER7IlRJs00nmnTnY6DSmXlb2ZcLue9dgBosZXfYKqH\nkh+Xv9mtF1QSERGRzpOPA0W2PxUECXuixUzKy9+KfrG0b7fdRW8qulqeTWVplPJAUi3BoSTw4s1r\no+5qVn87fKZSElTyPL/OnkrtW/6WZCpp5TcRkeopU6kOrhtdHXMLAaSBOKgUFApYgJvtx7BtvJER\nzPERxuxoUtMVl7+lyjKVzDhTyZ+Iei+10spvAJhJo26f0G/N8jcRERHpPAU/uliXCqbmHkG6Gyhv\n1B2Vv5UylewMGwbX8uGTtrJhcF3Dxma9TuPr139eeZlVfXOqJKummsbSpmGWmoRP7wE1FVQK6uyp\nVBmQaavV35L3UqVvIiJVa59P+RaUpFx7cXAJP/oCCorRhMbIdGH1D+Dt34+Rn2TUiiZAXTNkKlle\nlO0UTMZ9A7rqq6+fb4ZpgmFAsvqbgkoiIiLSBEXfhRBS4dS01XNDoKz8LclUSnoqWV2YhskJS44n\nE/dWaoTyAEptjbrLnldvppJdfaZS9HrRHO5wPZWC+e6p1EYNr5Pm3Fr5TUSkeu3zKd+CkmCSVwii\nDUlQqRBdUSOdwervxx+L+ynFmUrd6RkylbxC/NxoMmR2dTd28HNhmoS+H5W/tWBPJREREek8Rb+I\n6duYGBhhnCUez8GKcflbYPkUgvJMpeZcnKssf6t+Wl0ZVKo3U2mOQaXD9FSafrtWhmHM6/k1k1nK\nVNJPJBGRaukTsw7J1bG4co0wji2FhSjbyOjKYMfL3ENZUCkzU6ZSHFRKMpW6WytTCcCwrKhRd+Cr\n/E1ERESaohi42G4GgG43Wk03CSaV5mLTG3U3K6hUXv5WQ6ZS5apx8xNUqja7JslQOlxPpem35zSm\n8vK+Nip/S85b5W8iItVrn0/5FpRcJfPjFOz44hlBPi5/6+rGKgsqjdpJ+Vv0JV5a/S0MwS1WPLfV\nyt8gLoFT+ZuIiIg0kRd42G5UwtbtjgBTwaRiwceyTdK2HTfqji7SdS9AUGmumUr1ZsUMLslip0wW\nL+mpav+p8rfKIJgxj0Els02DSoZhYFmGyt9ERGqgRt11KNXxJ5lKfvQlnASGrK4u7NRAaf8xK27U\nPa2nkhV64EUHmQoqtWL5mzVV/qZVMURERKQJvNCnq5SpFLUUSOZgbtEjlbZIW2kKvjuVqWQ1q/yt\nvKdS7au/Qf2ZSiuOHuCD17yt6kCQZc4cVCp/fj2NumHuwbZWYFqmMpVERGqgoFIdPDeqdwvjoFLg\nR80Sg/xUCZuVTZX2Hy2Vv8U9lUpBJZ8wCSpNTmKk01FWUIsxrPJMJf3TERERkcbzA79U/paNM5WS\nvpZu0SedtshY6cpG3QuSqTS31d/mI+hSSxDELvVUamT52/yV9zXbplNXke1pXHN3EZFOo8hAHZKr\nZKEXN+j2wjioFE1o7K4sVv/Ul3IxE6Ull3oqxX+a+IRukqk0idndgllKEGUqeV5UrqfyNxEREWmC\nIAywi1GQaHqmUrHoM5BNk7bSjBRGyZd6KmWaMraKnko1BE8WspG1WUWmUt1BpbKsLcNor6yf3/qd\ndQs9BBGRttJ66TBtJOmpFHomhNESrLZtEsYruNnZLuy+uKeSYeBle4Gy8rc4Y8kOfYKynkotWfpG\nlKmUjLMVM6lERESks/iBT0iI7UZBpWxZUCkMQ9yiP1X+Fq/+lrHSTevjU5FxVEOj7sqeQ80NKiXB\nr+nBrPnsqZS8LwZGW/VUEhGR2ulTvg7JVTIjNDBCE98L4qBS1CQylc1i9Uc9laz+AdLpqBRuqvwt\nmnxYRjCVqTQ52ZJNugEM0yIsRuNUo24RERFptAk3aimQlL+lvQls26BY9EvzsFTGImOm8QKPCW+S\nbrt5F+fKM3JqKWOrKA9r8oU6y7QwMA7pAVWZqVTfmJLzq6XPlIiItCeVv9UhyVQCSAcZPC8ga5sY\nxShTKdWTxY5Xf7MXLSqt+jY9U8kiJHRdwiAgLBZbt/zNMku9n1T+JiIiIo02ngSVihmMwMcKPVJp\nG9edCiql0zYZO+qBM1IYYUn3UU0bX2VwaG6ZSs0uf3vzis0c23fMIdvns1F3cn7KUhIR6XwKKtUh\nmcwAZMLuOFPJgmKUqZTu6cbs6SF7wklk3/hGMq9Fk4bu9LRMJTPKVCo1+G7VTCXDLGVUKVNJRERE\nGm2iOAFAykuTCvKYmQyptIVX9CkWveixtEXajIJKXug3rUk3zD1TyZznRt21ePvqM2fcPq/lb2ZS\nYqegkohIp9MnfR3KM5UyQYYgiBp1m0lQKduNYZqs/tg2Fp9zLl2pOFMpbtDdnU2xZFkvS8yxKKg0\n2dpBJSwratKNgkoiIiLSeCOFMQjB9tKkvUns/n5SaWtaplK0+luiq5lBpfJG3TVkKlkLmKl0OKYx\n/z2VlKkkItL59ElfB68sUyntRSVrtm1iukUKhk0mXTm5+O0TV/K2k1bS0xVnKFkmv3/5aaxP7wPA\nHx0DaNnyt4rm3AoqiYiISIONFsawvBQGBl3eOFb/QBRUKvoUC0lPJZv0ggWV5pZxVNGou0UWPzGt\n8jHVG1SauRm4iIh0HpW/zVEQhHheULqf8qIJjGWbmG6Bopkinar8In3LphW8ZdOKQ45lpKIG3v7o\nCABmpoUzlWKGGi+KiIhIgw3nR0tNujP+JHb/AKm0TRjC5ES0Im06Xv0t0W0tTKZSLU2pF7Kn0uHM\nb08lK/6zNQJmIiLSOPqknyOvrPQNwPaiCY9tm1hekaKZImVX9/aacVDJG4mDSm2QqaTyNxEREWm0\n0eI4lh/Nk1J+Aau/n1R80W58NAoqpQ4pf8s0bXyVPZXaO6hklJe/WeqpJCIi1dEn/Rwl/ZSSqzrJ\nVTTLNrF9l6KZwraqe3uNVDQRKmUqtWpQqTyQpKCSiIiINNhoYQzTjwMUoRcFleIFTybGox6WqemZ\nSk0sfzMNE4N4LjjnoFJrTMfLM5XK+yvNRXJOrRIwExGRxmmNb7E24Qc+XhCtNJI0h+zuia6emcXo\nT9sysAMPz05Vfdy2KX9TppKIiIg00Zg7gRnEvSgDLyp/izOVJsbi8reMTcYsDyo19+JcKYDS7o26\n53P1NzXqFhE5YuiTvga3Pn07X3nhXqAsqNQbB5Xc+E8jWh3NK7tiNptSUGlkNDpGq2YqlaV4G1Vm\nYYmIiIjM1URxYipTKXCxBgZKmUrjY1PlbxWNuq3mlb8BmHMo9WrFRt3lfZTqLn9TTyURkSOGGnXX\nYPf4HsKxEC/wSuVvmWz0FhoFixCwwqh5t2/XHlTykkylrhbNVLLKM5X0T0dEREQaa9ydxAymyt/s\n/n5S+SRTKSp/S6ct0ubClL/BVADFriFTyTTm1oupkea1Ubd6KomIHDH0SV+lIAhwAw8v9Nkz/ptS\no+5MNn4Li/EVmTDaXktQyUzHPZWSRt1drZqpVPbPReVvIiIi0mCTbr6s/M3F6h8o66lUVv5W0ai7\nuUGlpJdSLQEU6wjpqWRqtWARkY7XGt9ibSDvF0q3Xxp9pVT+lu6JvizDYvzlGUY9l4J09anXRtx/\nyR+Lyt+s7hbNVCrvqdQiqdoiIiLSuSa9fFn5W5ypFPdUKhaiudhCNuqGspXOaspUar2eSpXlb/XN\n86w5BNpERKQ96ZO+SnmvPKi0qxRUmj5vMeNG3mGqhqBSqadSlKlktGqmkmXNeFtERESkEfJuAdOP\ngjWmGbUISDKVEqm0taCZSlMrnc21p1JrzKnUqFtEROZCn/RVKg8qvTy6q9RTCTvAN93SY6EX3Q7T\nNfRUSqfi50YBqVbNVFL5m4iIiDRTwS+WeiqZmWhuVR5UMk0DyzIreip1WQvTU6mWjKNWL38z6ix/\nm+qppPmiiEina41vsTZQ8Iql27vG9lAsxhlJVkBgeVM7uvF+6eonNGacqZQwaiidaypTmUoiIiLS\nHK7vEoTBVPlbNppbJeVvEAWYDMNY0PI307SwDKumQEzrl79p9TcREamOPumrlPfypdte4DE0HpWq\nhVaAXxFUijOaMtVPaIyyoJLZ1dWy/YqMitXfWmMCJCIiIp1pIp572V4SVMoClZlK6fh2xormUqZh\nkqqht9F8sA2r5mwjs8Uzlear/K1Vzk1ERBpH68JXKclUWtw1yMC/72V893PAMYSWX5GpFBSjoJJR\nU1Bp6uqa2d2a/ZSAikwllb+JiIhII016kwCk3GjOke7vie6XBZVSmWgqm2QqddtddZdu1ertq3+b\n4cJwTc8pz06yOrKnUpKp1BrnJiIijaOgUpWSnkrrF61h0a9/DeOTMACB6VUElYzhAwCYXbU36gYw\nawhGNVtFplKLTIBERESkMyVBJTOwMQOfdO+hQaUkUynJUOpucj8lgDNXnV7zcyoadbdI4GVeg0ql\nnkrKVBIR6XQKKlUpCSqt6T+WcS9kMk6t9kyvsvzNeQEAs4YV3Mp7KrVLppLK30RERKSRkvI3M7Cw\nQhcrXgRlek+lxMqe5Qxk+ps7yDlqxfK38p5KRp1BJVOrv4mIHDEUVKpSElTqSWUxwxRjcVDJNzww\nplZ/M8NoVbhaVnCr7KnUukEl9VQSERGRZpl0JwAwAgsr8LDi1d/siqDS1FR22+Y/xqS5pW9zZZmt\n16hb5W8iIjIXunxQpSSolLHSZH0L34gCQZ7pglHWU+n4k9ifGsDoX1T1sSt6KnW1cPlb+dUmBZVE\nRESkgZJMJSO0sQIXOw4qmaaBnYrmJOlMWYDJtFumP9FsKjKVWmSBFjXqFhGRudAnfZWSRt0ZK03K\nA9+0Mc0Qj8qgUv63z+arx76XdFf6cIc6REWmUg0ZTk1nqfxNREREmiPpqURgY4UeVtl8KSmBKy9/\nayeV5W+tcQ4N6anUIgEzERFpHJW/VWkqUymD5/p42RSmEeAGLgZT5W9uEAKQtqufIFSWv7VuUMkw\nVf4mIiLSznK53HuA2wAL+KrjOJ+e9vg7gG8Bv4o3/YPjODc1dZCxo7oGMUMTA6siUwmiYNLkhEs6\n3Z5T2fJAUquUiBkVQaX6gkEqfxMROXK05zfxAigvf5ssuASGjWEEFP3KoJIfxkGlVPVfxmab9FSq\nKHlTUElERKSt5HI5C/gS8G7gFeCpXC73T47j/HTarv/HcZxzmz7AaU5bcSqPvfQvAFiBh5muDCqV\n/9luWrFRtzmPjbotNeoWETli6JO+SoU4qJQyUxhFF8+0MQzv0Ewlv85MpRZe/a0iU0npzCIiIu3m\nDOAXjuP80nGcIvAN4L0LPKbX5RbjBVBCr2K+lASTynsqtZNWDyrVW/6WZCi1yrmJiEjjKFOpSqVM\npcCEIMA3U1jhOK7vYoVRUMk0QlwvAGrLVGqb8reKnkr6pyMiItJmjgZeLrv/CvDmGfY7M5fLPQ/s\nAv7UcZyfvN5BBwez2DVcTKuF68ZBpcBlcEk/g0v7AOjpyQBw1FG9LI23tZNgLF+6vXRJf/TnAp9H\nqmyet3hxT13jGRzvAaC3p/uQ4yz0eTaLzrOz6Dw7i85zfikyUKUkqJTyQgIMQsOC0KUYuJhxUMky\noVgKKtWQqWSaGLZN6HmtXf5mavU3ERGRDvcM8AbHccZyudw5wDeBDa/3hKGhiYYNxi1E8yor9BiZ\n8PD2jQIQEmWGTxZc9sXb2snB/GTp9vBQnuW9LPh5jI1MBbpGRibrGs/4WLTATWHSqzjO0qV9C36e\nzaDz7Cw6z86i86zvmDNRTmqVCl4B27CgWMQ348yisIgbuNhB9MVpmQbF+Ipa2q7trU2ylVo6U8ks\nz1TSPx0REZE2sws4puz+6nhbieM4I47jjMW3HwBSuVxuSfOGWMl3o+CRFbiV5W/xxbt0B/RUapVm\n1qZV3uag3p5KSaNuzRdFRDqdMpWqlPcKZKwMYT6Pb0RvmxEUKfhFesIAH7Atg6IXB5VqyFQCMOwU\nMNnSPZWwtPqbiIhIG3sK2JDL5dYQBZMuBt5XvkMul1sB/MZxnDCXy51BdAHytaaPNOZ7UVDJDip7\nKqUz0Vws09WeU9ny1d+sFulTOZ89lZJeSlaLBMxERKRx2vObeAHk/SJpK02Qz5cylczA5UB+iJWB\nxwRgWQaTblz+NudMpdYNKlU06lZQSUREpK04juPlcrmrgYcAC7jLcZyf5HK5q+LH7wQuBD6cy+U8\nYBK42HGccCHG6wc+eFFwwwy9+AJc5ITNR9PTl+GoZb0LMbS6tWKjbsOYv6BSlx1l3mfs9Cx7iohI\nNS688Dy++tV7WbRo0az7FAp5tm37b7z66j7A4Pd+73wuuugPALjhhut56aVfAzA2Nkpvbx/33PP1\nusamoFKV8l6BHisbB5Wit80KPMbdCTJBEAWVbJPfDE1gmQZ92dq+RI1065e/UVb+VnFbRERE2kJc\n0vbAtG13lt3+IvDFZo9rJm7gYgbRnMsOXMyyTKVFi7Oc+pY3LNTQ6lYZVGqNOdV8ZiqtGziOrZve\nx6ajcvUOS0REamRZNp/4xCdYtuwNTEyMc/nl7+f009/MmjVruemmHaX9/uZvvkBvb/0XZxRUqlLe\nKzCYXkRQmMpUsoOoQXcmhN7CAQYXreall8d4w/JeUjVmKiUTJbO7dYNK5X2UjAat8iIiIiICRIuh\n+PHS9IGHkeqcaavV6kElq87yN9PitOWn1DskEZG2tmfPbq699iNs2nQiL7zwPMcfv5FzzjmPu+76\nMkNDQ9xww82sXn0MO3bcxO7du8hkurjuuu2sX7+B4eGD3Hjjdvbt28cJJ5xIGE4lDT/00APcd983\ncF2PjRs3ce21n8AqqyRasmRJqVF3NtvDcccdx/79r7JmzdrSPmEY8r3vfYfbbruj7vPsnG/nBgrC\nANd3yVhpgompnkpW4AGQ9uGMl/8Z+7yP4/96jDUr+2t+jfYofytv1N0aEyARERHpTEXfxYozlazQ\nrSh/a3eVjbpbpPytLKhUXgonItLu/uEX9/Psqy8A0eJaflB/Vfepy07kgvXnzrrfrl2vcPPNn+H6\n69dyxRWXsnPng9x++9d4/PFHuffeu1m2bDkbNuTYseNWnn76KW655S+4556vc/fdX+Gkk05h69Yr\neeKJx7n//m8B8OKLv+KRR3Zyxx13Yds2n/vcp3n44W+zZcvMY9mzZzf/9m8OGzeeULH9ueeeZXBw\nMcccU3/Wr4JKVSj40epuGStTUf6W8qNMpbQPBiGvHIzuzyWoZC9egrV/P0a6hWvPy1d8U/mbiIiI\nNJAbuBgVmUqdF1QyDbNlAjjlw6i3/E1ERCIrV65i3br1AKxZs5bTTjsDwzBYu3Y9e/bsYe/ePdxy\ny2cB2Lz5dEZGhhkfH+PHP36WT34y2n7mmW+lry+KMTz99A9xnJ9xxRWXAlAo5BkcHJzxtScmJti+\n/Tr+5E+upaenssztO995iHe96z/NyzkqqFSFYimoFDfqNqJJTRJUSkUJS/z6tTwwt6DSig9eSVgo\ntMzEYibKVBIREZFmKfpFzKBzg0oGRss06YYoO8k0DYIgxLRaZ1wiIvW6YP25payipCysWVJl312m\naZbum6aJ73vYdm0hmTAM2bLlXK666urX3c91Xf78z6/j7LPfw9vf/s6KxzzP49FHv8fXvnZvTa99\nOPrGqELBLwBTQSVvWqaS7Ufpc7/cn6crbbHiqGzNr2GmUljz0CSrobT6m4iIiDSJG3hYfjTnMkK/\nYhXaTmAaZsv0U0okGUrKVBIRaY6TTz6VnTsfBOCZZ37EwMAAPT29nHLK1PYnn/wBo6MjAGzefAbf\n//4jDA0dAGBkZJi9e/dUHDMMQ7Zv386xx67h4osvOeQ1f/SjH3LsscexbNnyeTkHZSpVISl/S1tp\ngsIwQRxUyrhxUMmLgkq7DrpsOHYxZgtnG9WjIpCkoJKIiIg0UHmmkmEECzya+deKQaWkr1IrZ86L\niHSSyy//EDt23MRll11MJtPF9u1/CcDWrVdy443bueSSizjxxJNYvnwFEJXQXXnlh7nmmqsJwwDL\nstm27c9YsWJl6ZjPP/8c3/rWt1i3bj0f+MD7APijP/pv/NZvvRWARx55mHe96+x5OwcFlapQ3lNp\ncmQcb1r5m+0FhKaJb5isWVV76VvbUKaSiIiINEnUUynOVOoCMDYOAAAgAElEQVTAGIdlmJgtln1V\nylSqc/U3ERGJ+inde+/fl+5v337jjI/t2HHrIc8dGFjEF77wpRmPe9ZZZ3PWWYcGhe67758BWLTo\nFBzHOWyZX/k45kNrfZO1qEJZT6Wh/SOlRt1p14UwxPZCAjtqsL1mRecGlUpp54bRcSnoIiIi0lqi\n1d/ii1gdGONo5Uwllb+JiEi1FBmoQtJTKW2lGR0exTOjAFIqcLECMD0fL54UrO3gTKUkO0lZSiIi\nItJoxaCI6duYgQdW5yXXR0Gl1pqKq6eSiIjUqrW+yVpUsvqbWzDwJ/MU7KgRd9qfJOWFWG5AHouB\nnjSDfZmFHGpjJdlJylISERGRBnN9DzOwsEKXsMbVcdpBNtVNNlX74i6NZJoGhqGeSiIiUr3O+4Zu\ngKT87ZXf5MmFLoVUD0bgYoU+thdiuh6FMMMblvd19JewYSpTSURERJojylSysDo0U+mPTvwAZgtm\nKhnKUhIRkRo09Bs6l8u9B7gNsICvOo7z6WmPfxz4w7KxHA8sdRznwGzPbaYkU+mXu8Y5KfAoWt1Y\nYdyk2w8xPB/Xtlm1pLWuNs07K5r4GB04sRMREZHW4vouZmBjBZPQgZlKK3qWLfQQDmGYhkrfRESk\nJg27PJLL5SzgS8AWYCPwB7lcbmP5Po7j/JXjOKc4jnMKcD3waBxQmvW5zZT0VNo/5JImwDPT2IYP\nwIVHb8H0A1zTZuVRPQs1xKZIMpWS4JKIiIhIo6xftBbTt7ADD8NOLfRwjgimgkoiIlKjRkYHzgB+\n4TjOLx3HKQLfAN77Ovv/AfB3c3xuQxXKeyoZ8cpvVgDAkuIAAJ5hs6rTg0qlTCWVv4mIiEhjre9f\ni4GJFboYKQWVmuG49Udx3IYlCz0MERGZ5sILz+PgwYNV73P99ddz7rnv5v3vv6jhY2tkLvHRwMtl\n918B3jzTjrlcLgu8B7i61ueWGxzMYtvzH/AwXwwBsDwD1+oGINsVBVi88XEAXNPixNwyerPpeX/9\nZlu6tG/G7ROTffwasNOpw+7TTjrhHGZzJJwj6Dw7jc6zsxwp5ynzz3WjrHAr8DAVVGqKt7xj3UIP\nQURE5sEFF1zA7/7uBdxyyw0Nf61WKVA/D/iB4zgH6jnI0NDEPA2n0sHxMQBSbkghDipl4rnN/l2v\nMgCQyjA5XmByvNCQMTTL0qV97Ns3OuNjxeE8AH5oHHafdvF659kpjoRzBJ1np9F5dpZGnKeCVEcO\nt5gElVzMdPtftBMRkSPLnj27ufbaj7Bp04m88MLzHH/8Rs455zzuuuvLDA0NccMNN7N69THs2HET\nu3fvIpPp4rrrtrN+/QaGhw9y443b2bdvHyeccCJhGJaO+9BDD3Dffd/AdT02btzEtdd+AmtaNdHp\np5/O8887TTnPRgaVdgHHlN1fHW+bycVMlb7V+tyGK/pRU+60F1Kwo2bcfb1RVGniwDADQCbbtVDD\nax6t/iYiIiJNUspUCj2stDKVRERkbp747r/zy5+/CoBpmQR+UPcx175pGWe+c/bszl27XuHmmz/D\n9dev5YorLmXnzge5/fav8fjjj3LvvXezbNlyNmzIsWPHrTz99FPccstfcM89X+fuu7/CSSedwtat\nV/LEE49z//3fAuDFF3/FI4/s5I477sK2bT73uU/z8MPfZsuWc+s+p7lqZFDpKWBDLpdbQxQQuhh4\n3/SdcrncAPB24JJan9ssSaPujB9QjINKi/qjINLYa0MAdPd2L8zgmihZYlZBJREREWm0qUwlDyvd\n+fMsERHpPCtXrmLduvUArFmzltNOOwPDMFi7dj179uxh79493HLLZwHYvPl0RkaGGR8f48c/fpZP\nfjLafuaZb6Wvrx+Ap5/+IY7zM6644lIACoU8g4ODC3BmUxoWVHIcx8vlclcDDwEWcJfjOD/J5XJX\nxY/fGe96PvCw4zjjsz23UWOdTcEvYmKS9j0KVhRUWry4m3EgjEvjsn3ZhRpe85RWf1NQSURERBqr\nvPzNygws8GhERKRdnfnOdaWsoma3IEiV9QQ0TbN03zRNfN/DtmsLyYRhyJYt53LVVVfPvnOTNLSn\nkuM4DwAPTNt257T79wD3VPPchVL0i9hGmlTgUbCjK2V9A12MA91xFlPfQGev/AZa/U1ERESap7L8\nTT2VRESk85x88qns3PkgH/jAFTzzzI8YGBigp6eXU06Z2v7kkz9gdHQEgM2bz+D666/lv/7X9zE4\nuJiRkWEmJiZYsWLlgp2DuWCv3EYKfgELm3ToUrCzpO0Quzsqf8v6UfPqgUW9CznEpjDUU0lERESa\npLL8TT2VRESk81x++YdwnJ9x2WUXc+edX2T79r8EYOvWK3nuuWe55JKLeOyx77F8+QogKqG78soP\nc801V3PZZRfzsY/9Mfv37z/kuNu2beOqq7by0ku/5vzzz+H++7/ZsHNoldXfWlrBL2KEKTKBS9HK\n0ps2SquQJEGl7iOh/E2ZSiIiItIknltW/qZMJRERaTMrV67i3nv/vnR/+/YbZ3xsx45bD3nuwMAi\nvvCFL8143LPOOpuzzjr7kO333ffPpduf//znm1bmp0ylWRQLHoPOBtJj/aQDH89K091tYaQzAHQF\nRQCs+H4nSzKVMPXPRkRERBoryVSyQw87o6CSiIhIK1Km0izGxvIM7F+F6WdI8Rs8IJu1MePJjRHv\nZ2Q6P6hkdnWx6J1n0f3G3EIPRURERDqcW5appKCSiIhIa1JQaRbd/RZuKk92ZADb3I8H9PSmMaal\nYZtHSFr2sve9f6GHICIiIkeA/kXdhGFA1h1R+ZuIiEiLUh3TLIqBy9jAPizfxkz3A5Dty2BOK3eb\nHmQSERERkbnbsHEZwfDP6fLGMVJq1C0iItKKFFSaRcEvMLpoHwD59BIAevuzh0xupgeZRERERGTu\nPD/EDqMSOAWVREREWpOCSrMo+C5j/fsJCQnMaELTuyiLYZoVE5wjoaeSiIiISLO4XqCgkoiISItT\nUGkWXuAS2B7j6cnStt7FfUBlIOlI6akkIiIi0gyuH2DFQSVTQSURETmCXXjheRw8eLDqfa6//nrO\nPffdvP/9F1Xs83//r8OHPvQBPvCB9/HBD76fn/70X+sem4JKs1jdu4ozl53JcGCVtmUHuoHKQJJ6\nKomIiIjMH9fzlakkIiIyBxdccAG33vo3h2y//fb/ztatV3LPPV/niiv+iNtv/+91v5ZWf5tFykrx\n20veyZPejzgaSPt5LCuKxZUHkpSpJCIiIjJ/KsrfbE1ZRUSkvezZs5trr/0ImzadyAsvPM/xx2/k\nnHPO4667vszQ0BA33HAzq1cfw44dN7F79y4ymS6uu24769dvYHj4IDfeuJ19+/ZxwgknEoZh6bgP\nPfQA9933DVzXY+PGTVx77SewLKvitU8//XSef945ZEyGYTAxMQ7A2NgYS5Ysrfs89Q1dhfFJjwmg\n2xslG06VwZWac1uWJjsiIiIi80g9lUREZD58++V9vHBgDADLMvH9oO5jnri4ly3HzB6Q2bXrFW6+\n+TNcf/1arrjiUnbufJDbb/8ajz/+KPfeezfLli1nw4YcO3bcytNPP8Utt/wF99zzde6++yucdNIp\nbN16JU888Tj33/8tAF588Vc88shO7rjjLmzb5nOf+zQPP/xttmw5t6pxf/Sj17Jt29V86Uu3EQQB\nd955V13vAyioVJXxvAvAaXsepmvp4tL2JFNJWUoiIiIi86tQ9LECBZVERKR9rVy5inXr1gOwZs1a\nTjvtDAzDYO3a9ezZs4e9e/dwyy2fBWDz5tMZGRlmfHyMH//4WT75yWj7mWe+lb6+fgCefvqHOM7P\nuOKKSwEoFPIMDg5WPZ5vfvM+PvrRbbzjHWfxyCM72bHjZm677fa6zlFBpSqM5z3M0CddGCXTu7q0\nPQkmGWmt/CYiIiIynyaLnjKVRESkbluOWVrKKlq6tI99+0ab9tqpsu8v0zRL903TxPc97BornsIw\nZMuWc7nqqqvnNJ5vf/t+/uRP/hSAd77zXXzmM7fM6Tjl1Ki7CuOTLlm/AIDV21fanqz+ZmYUVBIR\nERGZT/mijx1GJQpa/U1ERDrRySefys6dDwLwzDM/YmBggJ6eXk45ZWr7k0/+gNHREQA2bz6D73//\nEYaGDgAwMjLM3r17qn69JUuW8uyzTwPw9NNPsXr1MXWfgzKVqjCR9+hOgkp9U0GlqUwllb+JiIiI\nzKd80ccqNepWUElERDrP5Zd/iB07buKyyy4mk+li+/a/BGDr1iu58cbtXHLJRZx44kksX74CiEro\nrrzyw1xzzdWEYYBl2Wzb9mesWLGy4rjbtm3jX/7lXzh48CDnn38OH/zghzj33P/Mddf9Obfd9jl8\n3yedTnPdddvrPgcFlaowlnfJ+nkA7LKgknoqiYiIiDRGlKnkE2LAtFVtREREWt3Klau4996/L93f\nvv3GGR/bsePWQ547MLCIL3zhSzMe96yzzuass84+ZPt99/1z6fbnP//5Gcv8Tj75FO6662+rPodq\nqPytClH5WxRUqsxUisrelKkkIiIiMr8KcVDJNy0Mw1jo4YiIiMgMFFSqwkTeIxscGlQqZSqpp5KI\niIjIvCq4UflbYCmxXkREpFUpqFSF8bzLgOEBlY26TZW/iYiIiDREwY0ylQJTQSUREZFWpaBSFcbz\nHv3M0Kg7k5S/KVNJREREZD4V46BSqEwlERGRlqWgUhXG8y69YRGozFQytPqbiIiISEMUXR878AnV\npFtERKRl6dLPLFwvoOgGU426e3tLjyWNutVTSURERGR+LRvMYoU+YTq10EMRERGRw1Cm0iwm8i4A\nXV4eM9uDUXa1zFBPJREREZGGWHFUFjv0MVOaZ4mIyJHtwgvP4+DBg1Xvc/3113Puue/m/e+/qGKf\n7373O1xyyUW87W2n8/Of/3Rexqag0izG8lGD7lRxsqKfEoA9OBj9uWiw6eMSERER6WRu0cMiBFuJ\n9SIiIrW44IILuPXWvzlk+9q16/jUpz7LySefOm+vpW/pWRRdH8IQuzCJ1beq4rGutes47uZPkVq+\nYoFGJyIiItKZvELUz9JIqfxNRETaz549u7n22o+wadOJvPDC8xx//EbOOec87rrrywwNDXHDDTez\nevUx7NhxE7t37yKT6eK667azfv0GhocPcuON29m3bx8nnHAiYRiWjvvQQw9w333fwHU9Nm7cxLXX\nfgJrWv/B008/neefdw4Z03HHrZn381RQaRbHLOvlD9+2GuPfg0MylQzDIL1y1WGeKSIiIiJzpaCS\niIjMh3/4xf08++oLAFimgR+EszxjdqcuO5EL1p876367dr3CzTd/huuvX8sVV1zKzp0PcvvtX+Px\nxx/l3nvvZtmy5WzYkGPHjlt5+umnuOWWv+Cee77O3Xd/hZNOOoWtW6/kiSce5/77vwXAiy/+ikce\n2ckdd9yFbdt87nOf5uGHv82WLbOPpVEUVJqFbZm8bV0fLwL2tKCSiIiISDvJ5XLvAW4DLOCrjuN8\n+jD7nQ48CVzsOM59TRxiie8qqCQiIu1t5cpVrFu3HoA1a9Zy2mlnYBgGa9euZ8+ePezdu4dbbvks\nAJs3n87IyDDj42P8+MfP8slPRtvPPPOt9PX1A/D00z/EcX7GFVdcCkChkGdwcGHb8SioVAV/bAwA\nq1dBJREREWlPuVzOAr4EvBt4BXgql8v9k+M4P51hv88ADzd/lFOCYrRYiqWgkoiI1OGC9eeWsoqW\nLu1j377Rpr12quw7zDTN0n3TNPF9D7vGvoFhGLJly7lcddXV8zrOeqhRdxW80egfnYJKIiIi0sbO\nAH7hOM4vHccpAt8A3jvDfh8B/j/g1WYObrqTjx0AoK+/ZyGHISIi0jAnn3wqO3c+CMAzz/yIgYEB\nenp6OeWUqe1PPvkDRkdHANi8+Qy+//1HGBo6AMDIyDB79+5ZmMHHFFSqgp8ElVT+JiIiIu3raODl\nsvuvxNtKcrnc0cD5wB1NHNeMlvRGV2+tTHqBRyIiItIYl1/+IRznZ1x22cXceecX2b79LwHYuvVK\nnnvuWS655CIee+x7LI8XB1uzZi1XXvlhrrnmai677GI+9rE/Zv/+/Yccd9u2bVx11VZeeunXnH/+\nOdx//zcBePTR73H++efwk5+8wMc//jG2bas/40nlb1Xw46iggkoiIiLS4f4a+DPHcYJcLlfVEwYH\ns9i2NfuONRrZH5UI9PRnWbq08+dgR8I5gs6z0+g8O4vOc75fJ8eDD367dP+v//rWGR/76lf/xwzP\n7eNv//Z/znjciy/+L1x88X85ZPujj36/dPvzn//8jM+98MLf48ILf6+q8VdLQaUq+GPKVBIREZG2\ntws4puz+6nhbudOAb8QBpSXAOblcznMc55uHO+jQ0MR8jxOAiX3DAOTdsKn9LxZCs3t8LBSdZ2fR\neXYWnWdnacR5Hi4Yp6BSFfxRNeoWERGRtvcUsCGXy60hCiZdDLyvfAfHcdYkt3O53D3A/a8XUGqk\nwI0adWv1NxERkdalnkpVUKaSiIiItDvHcTzgauAh4GfA3zuO85NcLndVLpe7amFHd6hQQSUREZGW\np0ylKvijo5hdXZhpNYoUERGR9uU4zgPAA9O23XmYfT/QjDEdTugpqCQiItLqlKlUBX90lFR//0IP\nQ0REROSIEboeAIatoJKIiEirUlBpFmEY4o+NkhpQUElERESkWZLyN1OZSiIiIi1LQaVZhIUCoeuS\n6lc/JREREZFmUU8lERGRyIUXnsfBgwdr2sf3fbZufR/XXfex0rbvfvc7XHLJRbztbafz85//dF7G\npqDSLPzRqEm33T+wwCMREREROXKop5KIiMjc/e///Xcce+yaim1r167jU5/6LCeffOq8vY4adc8i\nWflN5W8iIiIizRMkmUq2pqsiItJ+9uzZzbXXfoRNm07khRee5/jjN3LOOedx111fZmhoiBtuuJnV\nq49hx46b2L17F5lMF9ddt5316zcwPHyQG2/czr59+zjhhBMJw7B03IceeoD77vsGruuxceMmrr32\nE1iWVfHae/fu5cknf8Cll17O//pf/29p+3HHVQaZ5oO+pWeRWrac7MZNHPXmMygs9GBEREREjhA9\nGzcRvPwimTe8YaGHIiIibeyJ7/47v/z5qwCYlkngB3Ufc+2blnHmO9fNut+uXa9w882f4frr13LF\nFZeyc+eD3H7713j88Ue59967WbZsORs25Nix41aefvopbrnlL7jnnq9z991f4aSTTmHr1it54onH\nuf/+bwHw4ou/4pFHdnLHHXdh2zaf+9ynefjhb7Nly7kVr/upT32KD3/4o0xMjNd9rrNRUGkWVk8P\nq7d9nP6lfezbN7rQwxERERE5InRveCNvOHOz5l8iItK2Vq5cxbp16wFYs2Ytp512BoZhsHbtevbs\n2cPevXu45ZbPArB58+mMjAwzPj7Gj3/8LJ/8ZLT9zDPfSl9fVDn19NM/xHF+xhVXXApAoZBncHCw\n4jV/8IP/w+LFi3nTm47nmWd+1PBzVFBJRERERERERDrSme9cV8oqWtrkZJFUWV9A0zRL903TxPc9\n7BpLvMMwZMuWc7nqqqsPu88LLzzHd7/7Xb73ve9TLBYZHx/jppv+H2644ea5ncQs1KhbRERERERE\nRKTJTj75VHbufBCAZ575EQMDA/T09HLKKVPbn3zyB4yOjgCwefMZfP/7jzA0dACAkZFh9u7dU3HM\nq666mscee4z77vtnbrzxk2zefHrDAkqgoJKIiIiIiIiISNNdfvmHcJyfcdllF3PnnV9k+/a/BGDr\n1it57rlnueSSi3jsse+xfPkKICqhu/LKD3PNNVdz2WUX87GP/TH79++v+vUeffR7nH/+OfzkJy/w\n8Y9/jG3bDp/xVC2jvIt4u9u3b7RhJ9PsNLmFovPsHEfCOYLOs9PoPDtLI85z6dI+Y14PKHXT/Kt+\nOs/OovPsLDrPzqLzrOuYM87BlKkkIiIiIiIiIiI1U1BJRERERERERERqpqCSiIiIiIiIiIjUTEEl\nERERERERERGpmYJKIiIiIiIiIiJSMwWVRERERERERESkZgoqiYiIiIiIiIhIzRRUEhERERERERGR\nmimoJCIiIiIiIiIiNVNQSUREREREREREamaEYbjQYxARERERERGR/5+9+46PqkofP/6ZlkknHQIE\nCO3QBVEQu+iq2LG7urq61l11i9t0f2tZC7r7davrWtEV+2JHEMGCIkUgdOFISK+kTzKZlpn7++Pe\nxAAJECkh4Xm/XrzI3HrO3JncJ889RYgeRloqCSGEEEIIIYQQQoguk6SSEEIIIYQQQgghhOgySSoJ\nIYQQQgghhBBCiC6TpJIQQgghhBBCCCGE6DJJKgkhhBBCCCGEEEKILpOkkhBCCCGEEEIIIYToMmd3\nF+Bwp5Q6G/gH4ACe01o/2s1FOiCUUlnAS0BfwACe0Vr/Qyl1P3ATUGVteo/Wen73lPLAUEoVAI1A\nGGjRWh+jlEoB3gCGAAXA5Vrrum4q4n5TSinM+rQaCtwLJNHDr6dSajZwHrBDaz3OWtbp9VNK3Q38\nBPN636m1XtgNxe6yTur5F+B8IAhsB67XWtcrpYYAWwBt7b5Ca33roS9113VSz/vp5HPay67nG4Cy\nNkkC6rXWE3vq9dzDfaTXfT9F95AYrOfds3clMVjPvp4Sg0kMRg+7nkdC/AWHXwwmLZX2QCnlAP4N\nzADGAFcppcZ0b6kOmBbgLq31GOA44Gft6vY3rfVE61+PuvntwWlWfY6xXv8e+ERrPQL4xHrdY2nT\nRK31RGAy0Ay8Y63u6dfzReDsXZZ1eP2sz/CVwFhrnyet73FP8CK713MRME5rPQH4Fri73brt7a5r\nj7gBWl5k93pCB5/T3nY9tdZXtPuevgW83W51T7yend1HeuP3UxxiEoP12Ht2RyQG67nX80UkBpMY\nrGddzxfp/fEXHGYxmCSV9mwKkKu1ztNaB4HXgQu7uUwHhNa6XGudY/3ciJmlHdC9pTqkLgT+a/38\nX+CibizLgXY65i/Iwu4uyIGgtf4CqN1lcWfX70Lgda11QGudD+Rifo8Pex3VU2v9sda6xXq5Ahh4\nyAt2gHVyPTvTq65nK6WUDbgceO2QFuoA28N9pNd9P0W3kBis95IYrIeQGExisJ52PY+E+AsOvxhM\nkkp7NgAobve6hF5407ea/k0CVlqL7lBKbVBKzVZKJXdfyQ4YA1islFqjlLrZWtZXa11u/VyB2XSw\nt7iSnX9Z9rbrCZ1fv978nb0BWNDudbZSap1SaolS6qTuKtQB1NHntLdez5OASq31tnbLevT13OU+\nciR+P8WBd0R8XiQGkxisBzoSf8dLDNY7rmevi7/g8IjBJKl0hFNKxWM2A/yF1toD/AezL/hEoBx4\nvBuLd6CcaDV3nIHZNPDk9iu11gZm0NPjKaWigAuA/1mLeuP13Elvun6dUUr9AbOZ6yvWonJgkPW5\n/hXwqlIqsbvKdwD0+s/pLq5i5z86evT17OA+0uZI+H4K8X1JDNa7fkdIDNY7SQzWq/Sq+AsOnxhM\nkkp7VgpktXs90FrWKyilXJgfwle01m8DaK0rtdZhrXUEeJYe0Mxxb7TWpdb/OzD7uE8BKpVSmQDW\n/zu6r4QH1AwgR2tdCb3zelo6u3697jurlPox5oCDV1s3B6ymqzXWz2swB5Ac2W2F3E97+Jz2xuvp\nBC6m3aCuPfl6dnQf4Qj6foqDqld/XiQGkxisBztifsdLDNZ7rmdvi7/g8IrBJKm0Z6uAEUqpbOvp\nw5XA+91cpgPC6lP6PLBFa/3Xdssz2202E9h0qMt2ICml4pRSCa0/A2di1ul94Dprs+uA97qnhAfc\nThn43nY92+ns+r0PXKmUciulsoERwNfdUL4Dwpr56LfABVrr5nbL01sH11NKDcWsZ173lHL/7eFz\n2quup+UMYKvWuqR1QU+9np3dRzhCvp/ioJMYrIffsyUG613Xs50j4ne8xGC963rSi+IvOPxiMJth\n9OoWi/tNKXUO8HfM6Wxna60f7uYiHRBKqROBL4GNQMRafA/mDXEiZlO5AuCWdv0yexzrF0TrDBxO\n4FWt9cNKqVTgTWAQUIg53eK+Dlx3WLICtiJgqNa6wVo2hx5+PZVSrwGnAmlAJXAf8C6dXD+rmfIN\nmE2Vf6G1XtDBYQ87ndTzbsAN1FibrdBa36qUugT4ExDC/P7ep7X+4JAX+nvopJ6n0snntDddT631\n80qpFzGv41Pttu2R13MP95GV9LLvp+geEoP1vHt2exKDSQzWU37HSwzWe2KwIyH+gsMvBpOkkhBC\nCCGEEEIIIYToMun+JoQQQgghhBBCCCG6TJJKQgghhBBCCCGEEKLLJKkkhBBCCCGEEEIIIbpMkkpC\nCCGEEEIIIYQQosskqSSEEEIIIYQQQgghuszZ3QUQQhy5lFIFgN/61+oirXXBATzHEGC11jrtQB1T\nCCGEEKInkxhMCHGgSFJJCNHdLtVab+ruQgghhBBCHGEkBhNC7DdJKgkhDjtKKQP4E3AhEAPco7V+\ny1p3NjALcABVwC1a61xr3Q3Az63DBIHz2h3zYeAcIBb4idZ66aGpjRBCCCFEzyAxmBCiq2RMJSFE\nd5urlFpn/VvdbnlYaz0RuAB4RimVoZTKAOYAV2utJwCvAq8AKKVOBe4BztJaHwWcBjRYx0oFlmut\nJ2EGSo8diooJIYQQQhzGJAYTQuw3aakkhOhunTW9fh5Aa62VUjnAcYABrNdaf2Nt8wLwpFIqATgX\neElrXWHt1wSglAJo0lrPs/ZZATx+sCojhBBCCNFDSAwmhNhv0lJJCHEkCLT7OYwk1IUQQgghDgWJ\nwYTo5SSpJIQ4XF0PoJQaAUzCfLq1AjhKKTXK2uY6YK3WuhH4ELhWKdXX2i9eKRV96IsthBBCCNGj\nSQwmhNhnkikWQnS3uUqp9tPZ3mj971RKrcUc1PEWrULLGcsAACAASURBVPUOAKXUj4BXlVJOzEEi\nrwHQWn+ulJoFLFZKRTCfjJ1/qCohhBBCCNHDSAwmhNhvNsMwursMQgixE2vmkYTWPvlCCCGEEOLg\nkxhMCNFV0v1NCCGEEEIIIYQQQnSZtFQSQgghhBBCCCGEEF0mLZWEEEIIIYQQQgghRJdJUkkIIYQQ\nQgghhBBCdJkklYQQQgghhBBCCCFEl0lSSQghhBBCCCGEEEJ0mSSVhBBCCCGEEEIIIUSXSVJJCCGE\nEEIIIYQQQnSZJJWEEEIIIYQQQgghRJdJUkkIIYQQQgghhBBCdJkklYQQQgghhBBCCCFEl0lSSQgh\nhBBCCCGEEEJ0mSSVhBBCCCGEEEIIIUSXSVJJCCGEEEIIIYQQQnSZJJWEEEIIIYQQQgghRJdJUkkI\nIYQQQgghhBBCdJkklYQQQgghhBBCCCFEl0lSSQghhBBCCCGEEEJ0mSSVhBBCCCGEEEIIIUSXSVJJ\nCCGEEEIIIYQQQnSZJJWEEEIIIYQQQgghRJdJUkkIIYQQQgghhBBCdJkklYQQQgghhBBCCCFEl0lS\nSQixG6XUZqXUqfu4bYFS6oyDXCQhhBBCCCH2m1KqSSk1tLvLIURvIUklIY4wHSWBlFI/VkotbX2t\ntR6rtf78kBfuu/Lcr5R6ubvOL4QQQogjx+H4gGzX2EwcOFrreK11XneXQ4jeQpJKQgjRRUopm1JK\nfn8KIYQQ4oillHJ0dxm6Qinl7O4yCNEbyRdLCLEbpVQBcKPWerFSKgZ4CrgAqABeAO7UWg9st8tE\npdRfgcHAR8B1Wmu/dazzgIeAIcA3wK1a6w3Wut8BdwKJQBnwU8AF3APYlFIXAdu11kd1UMbfAzcB\nGUAx8Aet9Tvt1t8E/AoYaK2/Rmudo5TKAv4BnISZWH9Na327Uup+YLjW+hpr/yFAPuDSWrcopT4H\nvgJOBY4GxiulTgJ+a52jCnhMa/10uzJcCDwADLXW/wxIAH6vtZ7cbrtfAadorS/s9KIIIYQQ4pCz\n4onfASnAUsw4pkwp9QCQorW+QynlAuqBJ7XWv7Fipzqgv9a6Vil1HPBXYAxQCPy8tUW4UurHwL1A\nOlAN/D8gBzP2cimlmoAWrXVSB2W7ni7GIVrrj5RSKcDjwFlADLBEa32RVZYbtdYntjuGAYzQWucq\npV4EfJjx3inAhUopN2acNwxoAJ7XWt/fbv8TgT9bdW8E/ghsBuZZ70/Y2u5i4L5dYz6l1FTgPWBA\nu21nAg9orScopaZgxnWjrbK9BfxKax1sV/7bgV9g/u2bvUudzu2s/O1iwR8DDwKxwN+01g9b6x2Y\nn42fYMaj3wIXaa2LlVKjgH8Bk633/o9a6zd3vYZC9AbypF0IsTf3YSaEhgI/AK7pYJvLgbOBbGAC\n5s0XpdQkYDZwC5AKPA28r5RyK6UU5k3+WK11AmZgU6C1/gh4BHjDap68W0LJsh0zMdQHM2B6WSmV\naZ33MuB+4FrMhNUFQI1185+HGdANAQYAr3fhvfgRcDNmYqgQ2AGcZ53jeuBvSqmjrTJMAV4CfgMk\nAScDBcD7mAHN6F2O+1IXyiGEEEKIg0wpNR2YhRnnZGLe+1vjhiWYD5oAjsV88Hay9XoaoK2E0gDg\nQ8zERQrwa+AtpVS6UioO+Ccww4qFjgfWaa23ALcCy61YaLeEkuX7xCEAczATJGMxkyF/68Lb8kPg\nYcxYaCngxYy3koBzgdush4IopQYDCzCTK+nARKt+q4Aa4Mx2x+0wFtJar7TOMX2XMrxq/RwGfgmk\nYb7vp2M+pGzvImAqZmJrV52Wv50TAWUd+952MdyvgKuAczCvwQ1As3VdF1llzACuBJ5USnV0fiF6\nPGmpJMSR6V2lVEu711GYT8U6cjlwm9a6DqhTSv0TM2HT3j+11mUASqkPMIMGMBMwT1sBAcB/lVL3\nAMcBpYAbGKOUqtJaF3SlAlrr/7V7+YZS6m5gCubTrBuBP1tBC0CuVbZpQH/gN1rr1vp3ZbyCF7XW\nm9u9/rDdz0uUUh9jJrpyMJ9azdZaL7LWl7ZuqJR6AzM59wel1FjMBNe8LpRDCCGEEAff1Zj38hwA\nK9aos1qwLAdGKKVSMRM2zwM/VUrFY7biWWId4xpgvtZ6vvV6kVJqNWYiYi4QAcYppYq01uVA+b4W\nTmvd5TjEegA3A0i1YjvalXVfvKe1/sr62Q983m7dBqXUa5j1fxcz+bNYa/2atb7G+gfwX8z3ZoHV\ncuosdk8GtXoNM3mzSCmVgPne/RpAa72m3XYFSqmnrfP/vd3yWVrr2o4OvMsYoruWv9UDWmsfsF4p\ntR44CtiCGW/+Vmutre3WAyilrsB8UPqCtXytUuot4DLMB6FC9CqSVBLiyHSR1npx64vW5s6dbNsf\ns/tYq+IOtqlo93OztQ+YzaOvU0rd0W59FGZz5yVKqV9gJqjGKqUWYjZXLtuXCiilrsV8QjTEWhSP\n+ZQKIAuzJdOusoDCdgmlrtqp7kqpGZgtuUZitvyMBTa2O9d8OvZf4DWl1P/DfDL3ptY68D3LJIQQ\nQoiDoz/tHrpprZuUUjWYXbEKrOTQKZhJpYcxH6qdYC37l7XbYOAypdT57Y7rAj7TWnutBMSvgeeV\nUl8Bd2mtt+5L4b5nHJIF1LZLKHXVrrHQVOBRYBxmjOcGWh/8dRaPAbwMbLFa9VwOfGkl1TryKrBM\nKXUbcDGQo7UutM4/ErNr4TGY9XcCa3bZv6PYdV/K32rXODd+L/UbDExVStW3W+bEbCEmRK8j3d+E\nEHtTjtlXv1VWF/YtBh7WWie1+xfb+sRKa/2q1W9/MGAAj1n7GXs6qNWc+lnM7nOpVrPwTYCt3XmH\ndVKeQZ0M1OjFDEZa9etgm7ZyWWMIvAX8H9DXKsP8fSgDWusVQBDzaeIPkSBDCCGEOByVYcYoAFgJ\nkFS+a328BLNb1iRglfX6LMyW019Y2xQDc3aJheK01o8CaK0Xaq1/gNm9bitmfAN7j4W+bxxSDKQo\npTrqUrdTLKSU2mMsZHkVs2t/lta6D+ZYUPsSC5Vitva6GPMBW6exkNb6G8yuhzPYuesbwH8w37cR\nWutErHE591LmfS3/3uzpPV6yyzWP11rfto/HFaJHkZZKQoi9eRO4Wym1CjPQuL0L+z4LvKOUWgx8\nbe1/Kmag1R9zTKOvMJtP+4DWWUQqgR8opexa60gHx43DDBCqoG2gynHt1j8H/NWaijcH84YfsspQ\nDjyqlLoPsx/+ZKsZ9zrgd0qpQZgDNd69l7q1Ps2qAlqsp4VnYia3wGwG/7FSah7wGWawmNDu6eNL\nwBNASGstUwYLIYQQ3cullIpu97oFs9vVa0qpVzG7Oz0CrGzXZX8JZhe2VVrroDWpxywgX2tdZW3z\nMrBKKXUWsBizldJxmF3zQ9bPizHjoCbM7nBgxkIDlVJRrYNO7+J7xyFKqQWYY/z8zDrnNK31F5jd\nt8YqpSZiJmru34f3LQGz5ZPfGsfph8DH1rpXgHuUUpcDb2OOg5mltV5nrX8J+D1m4u7tvZznVeDn\nmO/X1buc3wM0WYNj32a9J/tqT+Xfm+eAB5VS32Bez/GYCcd5mLHmj/huDK6JQJM1XpYQvYq0VBJC\n7M2fgBLM2S8WYwZP+9RVS2u9GnOGticwZ0HJxRrEGzMQehRzppMKzIEMWxM5rc2Oa5RSu431ZD2x\nehzzCVcl5k38q3br/4fZDP1VzJlG3sWcoSUMnA8MB4qsel1h7bMIeAPYgNlseo9jHGmtGzFnrnvT\nqtsPMZ90ta7/GmvQTMwk1RLaPe3EfCI3DjPYFEIIIUT3mo+Z2Gn9d781VMAfMVsElWM+pLqy3T7L\nMGdPa22V9A3mg7LW12iti4ELMVvQVGG2YvkN5t9hdsyu/GVALWa3udbWLJ9izpJWoZSq3rWw+xmH\n/AgzobUVc7DvX1j7fIsZ9y0GtrFv407+FPiTUqoRcxa7thnOtNZFmOMf3WXVbx3meESt3rHK9I7W\nunkv52kd6+hTrXX79+PXVt0bMR9mvrEPZd6n8u+Dv1rbf4yZ2HoeiLGuzZmYn5UyzDj3MczYV4he\nx2YYe2xZKYQQO7H6s1+ptT6lu8vSkylzuuEdwNFa623dXR4hhBBCiENNKbUduKX9WJ9CiJ5Fur8J\nIfbImiVkKNYsJ5hPm57o1kL1DrdhNpeXhJIQQgghjjhKqUswhzP4tLvLIoT4/iSpJITYmyjgaSAb\nqMfsG/5kt5aoh1NKFWAOAnlR95ZECCGEEOLQs8afGgP8qJPxM4UQPYR0fxNCCCGEEEIIIYQQXSYD\ndQshhBBCCCGEEEKILpPub0IIIYQQvYxS6mzgH4ADeE5r/egu65OB2ZizWfmBG7TWm6x1BZgzKYWB\nFq31MYeu5EIIIYToSXpVUqmqqvGg9eVLTo6lrm5vM132fFLP3uNIqCNIPXsbqWfvcjDqmZ6eYDug\nB+yFlFIO4N/AD4ASYJVS6n2t9TftNrsHWKe1nqmUGmVtf3q79aftMm13pyT+2n9Sz95F6tm7SD17\nF6nn99dZDCbd3/aR0+no7iIcElLP3uNIqCNIPXsbqWfvcqTU8zA0BcjVWudprYOYEyxcuMs2Y7Bm\nXNJabwWGKKX6Htpi7t2R8hmSevYuUs/eRerZu0g9DzxJKgkhhBBC9C4DgOJ2r0usZe2tBy4GUEpN\nAQYDA611BrBYKbVGKXXzQS6rEEIIIXqwXtX9TQghhBBC7JNHgX8opdYBG4G1mGMoAZyotS5VSmUA\ni5RSW7XWX3R2oOTk2IP6RDQ9PeGgHftwIvXsXaSevYvUs3eReh5YklQSQgghhOhdSoGsdq8HWsva\naK09wPUASikbkA/kWetKrf93KKXewexO12lS6WCOTZGenkBVVeNBO/7hQurZu0g9exepZ+8i9dy/\nY3ZEur8JIYQQQvQuq4ARSqlspVQUcCXwfvsNlFJJ1jqAG4EvtNYepVScUirB2iYOOBPYdAjLLoQQ\nQoge5KC2VJLpbIUQQgghDi2tdYtS6nZgIWYMNltrvVkpdau1/ilgNPBfpZQBbAZ+Yu3eF3hHKQVm\nnPiq1vqjQ10HIYQQQvQMBy2pdKinsxVCCCGEECat9Xxg/i7Lnmr383JgZAf75QFHHfQCCiGEEKJX\nOJjd33rNdLZCCCGEEEIIIYQQYmcHs/tbR9PZTt1lm9bpbL/cZTrbSr6bzjYMPK21fmZvJ5TZRw4M\nqWfvcSTUEaSevY3Us3c5UuoperdLLz2f556bQ1JS0j5t88gjD7Bs2VKSk5OZM+fNQ1hSIYQQ4tDq\n7tnfDth0tiCzjxwIUs/e40ioI0g9exupZ+9yKGceEeJwcs4553PJJVfw0EP3dndRhBBCiIPqYCaV\nDul0tkIIIYQQQrQqLy/jrrvuYOzY8WzcuIHRo8dwzjnnM3v209TV1XHvvQ8ycGAWs2b9ibKyUtzu\naH772z8wfPgIGhrquf/+P1BVVcW4ceMxDKPtuAsXzmfu3NcJhVoYM2Ysd931exyOnVvKT5x4NOXl\nZYe6ykIIIcQhdzCTSm3T2WImk64Efth+A6VUEtBsjbm003S2gF1r3dhuOts/HcSyCiGEEEKIg+Dt\n3Hms3bHxe+3rsNsIR4zdlk/KGM/Fw8/b6/6lpSU8+OBj3H33UG688VoWLfqIJ598nqVLlzBnzgtk\nZPRlxAjFrFmPs2bNKh566D5efPFVXnjhWSZMmMj119/EsmVLmTfvPQAKCvL55JNF/Oc/s3E6nfzf\n/z3Kxx8vYMaMvZdFCCGE6I0OWlJJprMVQgghhBDdKTOzP8OGDQcgO3soxxwzBZvNxtChwykvL6ei\nopyHHvozAJMnH4vH04DX28S6dWt5+GFz+fHHn0hCQiIAa9Z8jdZbuPHGawEIBPwkJyd3Q82EEEKI\nw8NBHVNJprMVQgghjiyhQC0OZxx2h7u7iyIOExcPP2+fWhV1ZH/H5XK5XG0/2+32ttd2u51wuAWn\ns2uhsGEYzJhxHrfeevv3LpMQQggRKCvDlZ6Ovd19qqeyd3cBhBBCCNG9ymu8fLG+bKdxY76PkL+G\n8i1P0lD++YEpmBAH2VFHTWLRIrMxfE7Oavr06UNcXDwTJ363fPnyr2hs9AAwefIUPv/8E+rqagHw\neBqoqCjvnsILIYTodhG/HyMS6dI+3s2bKLz3HuoWLjhIpTq0JKkkhBBC9EKGEd77RpbXFm/jxQVb\nKazcv5naPDuWgxHBHZe1942FOAzccMPNaL2F6667kqeeeoI//OEBAK6//ibWr1/LNddczhdffEbf\nvv0AswvdTTfdxi9/eTvXXXclv/jFz6iurt7tuPfddw+33no9RUWFzJx5DvPmvXtI6yWEEOLgC9VU\nk/ebX1Lz7tv7vI8RiVD91v8A8OVuO1hFO6QOavc3IYQQQhx6/sYCqvJeJyFjKkmZp3W4jWFEqC2a\nR8TmZmuR2fT6m4I6hvRL3OfzfFtcT16Zh9MnD8BuNOOtXY/TnUJM0qgDUg8h9kdmZn/mzHmz7fUf\n/nB/h+tmzXp8t3379Enib3/7d4fHPf30Mzn99DN3Wz537gdtPz/wwCPft9hCCCF6iLqPFxLx+fBu\n2kjaxZfu0z5NOasJFBUCECguOpjFO2SkpZIQQghxCAVDYZr9oX3evqtd0lqCDVQXzMWIBPFUfImv\noeOnYJ7Kr/DWrsNXs5Lpw/MAg8aajVTo5/B5tu+0bbjFR9BXic+zHZ8nF59nO8FQgKfe28Sbn+Xy\n8EtrKC9aCkaYhIxp2GwSXgghhBDi8BasrKDo0YcJFBd3ed9wYyMNXy4BIFBaQiS099jOCIepfvdt\nsNtxZ2URbmigpaGh0+19udsIlHS9bIeatFQSQggh9kNLoI660kXEp06ioD6N5AQ3/VJid9vum4Ja\nPsspZWN+DdFRTmbdfBwxbvM23OQL4XY5cDl3TsZ4/SHuff5rjh2VwcXTEohEgrjjsoi0eHG44nc7\nRyQSojrvTSItzTicCYRbvNQUvUe/UbfgcMRgYNDkN3j3k+WcMvBLbAEbjWE3xw8pIzulkczERoLN\nUFPwtrmPM46awndprt+827lCthSamhXpSXHUVlcRqMnBGRVDfIrMsyGEEEKIw1/dwo/w527Ds+Ir\n0rOu7Nq+ny7GCAaxx8UR8XoJlpYQPSR7j/t4li0lVFFBn5NPxZGYSKC4mEBJMc4+fXbb1rdtG8V/\nmYXN4aD/z+4kbtz4LpXvUJKkkhBCiCNKfrmHvsmxxEbv/RYYbC6nuUHTp99J2GyO3dZHIiGq8v9H\nyFeBt17zyeZhFDYOZtYt03A6vksQebxB/vbmesIRgxi3E483yLrcaqaN7YfHG+TuZ5YzZnAKP7t4\n54Bhja6irjHAx6uK6RPSjO1bjTs2m0BzHvEpx5A86CzATul//0YkPYB9kItISzNRsQNZletneL9o\n4lqqKNv0t7Zj+iN9mJzmAyOC96NK5iT8gB+doMlMbCRQEcJR3ITzWNAbXsXpjCXGKMDeEktM2kic\n0X0wIuAtXg/uWs4alc8JJ1yBd/W/cLkMmtZ6iIwM4IiV8EIIIYQQ+y+4Ywf1ny7G5nLhiI8n4dip\nuFJS9vu4kUCAxq9XAODbvn0vW+++b/2ni7HHxZF64UyqXn0Zf0H+HpNKYa+X6rfnYnO7STn/Qvx5\nuYDZBS5u7Lidt21qovzZ/4DVWr3siX+Q+dPbiZ8wsW2b+iWfESwvxxEbi9HSQqC4iJb6evrdeAvu\nAQO6VJ/9JVGfEEKIw0o41ERtyQKS+p2KKyb9ex/HF2hh4ddFRAyYeVI2NpuNbSX1zHo5h4nD07jz\n0gnW+bz4PNuISzkKm83Wtn/IV8WO3DlEwn5c0enEJY81l7eEsdlsOB126koWEvJVsL0mlcyEBi4Y\nl8vCrWFWbB7KiRMy2461fns14YjBzJOHMKmvZuO3eazZksy0sf1YtqkCXyDMmm+r0BveICnaR8qg\n83FFp7Jq6w4AXPYwH2weyojUGmjOwwgbNNWuJtBUQktdLcZRQQCMsJ2EjGm8/7WPJToeNhpcMimR\nMZlNxMXGEomECXpKSYgOszR/APmRIewIJVK6LoPsutUYeV78rhhiUrwkDNsBBoTLAvg/yCeYWEbS\n9DOo+3Qh4QYPrssGcGxWBS0Fz+HKiNBS5MO5ooLCwkcZdNdvcCbs+9hMQgghhBAdqXn3LRq/Xtn2\num7BfDJvuY3Y0WO6fCzDMNqGFWhc/TURvx+AQEE+RksLNue+pUc8y74i4vWScv6FxI5QAPgLC/a4\nT/U7bxFubCTtkstxJSdjZA02z211vTMiEYKlpYSbvdQtXEBLbS2pF84kZvgISv/1d8qffILBDzxE\nVN9++AsK2DHnv7udw5WWDrbdFh90klQSQghxWPHWrsdXv4VIOERUdCqumAziUyd16RgrNlfwxqe5\nNHjNZMuQfgkcPTKdd7/MB2BdbjXbyxoYmpnAju0vE/JVYrO7vkscBeqp2PYyRtgMNrw1aylsyOSL\n9aX4G7YydoCXowa78DdupzGYwGs5inMmhZncdxPTRxTy2ppvOX5cP+x2886+bls18VFBJiZ9RsRT\nyth+ULFtI83+8SzJycNui6DSa4kJawJeqNj6NFHRk+hfrDkmUkPiSBe+5HiinBEiZT6aP2skZmos\nDK/AiDUIF/qJbGrA8MZRcNlItq1bwY/rluCNj+eL5eN4P17x+0uTKSr6hpeWHceU+Go2NcThjY3F\nGWlh0IaVYLfxWv+zSEuN4wefvUs4yU1LYwT7wjJsWXGECuuonvsmEZuN7cmDGLqwgtjLMom4vITz\nvGzwDyAQF8+kkm3Uzv+QjCuuOlAfCSGEEEL0IoHSEpzJyThi4/a4Xdjno2ltDq6+fel3/Y34tudS\n/fZcSv76F9JmXkLymWfvNRFkGAbedTk0rl6Nd+MGdgzLJu2mn+JZ+iXYbMSOHU/zpg34i4qIGTp0\n9/0jEarefI1YNYr4SZMB8CxfCjYbSaechiMhAZvLRaCgoNMy+AvyaVjyGVGZ/Un+gTnRgystDZs7\num2w7uq5b1L38Udt+8SMGk3Kuedjs9vp++MbqHjmKWree5fMm2+ldr45MUTfH9+AKzUNbDbcAwbi\nSEjY43txsEhSSQghxAHhC7QAtI0T9L2P48kDINCYS6AxF2wO3PGDcbm/a+ocCQepzn8Td/wg+vQ7\neaf988o8PPPBN0Q57Zx5bBafrCnhjU+34Y5yoItquGT0tzS0uPk6x0fciEpCvkoAGso+Iy55LNuL\nK6nJf5l+CV7riDZ2rNd4v/yaUacMYuj4ZgD8jWB3JvDysuHEuEJMzswjqe+p1JV8yMjkbaz5djRj\nMnZQVfwpuUUjuGXaOoxAiJik0TTV53NidjHvLFpKdV2IYweUcProElpabPhzfcRlRxEwVnHC6QAu\nqxxNhAp8NC+q5vnM8xmSU8bZWzdS6uvDO7EnclpNDuMbtxN44Umu8e3AjgHNNQyjkNzYAbzy0TE4\nXS4MbIzOXUX/acN4J18xsWkb9qCf5HPOw9k0hDVlHtLdQ5n4ei52YE0fxcbEMQQHRhjvyWVzwlCq\n3ckcZ8tj+oLV2NLc2JLdjB1j588lU2hI6sc1U6bu12dACCGEED1PJBSkceUKoocOx92/f4fb+PK2\nUzzrIRKPP5F+1/9kj8drylmNEQqReNzxxAwfYf4bNpyy/zxB9dtz8Xy9kr4/uo6YYcM7PUbN++9S\n+8F7ANhjYmjYuInmR/5EqKKC2NFjSZw61Uwq5eV2mFQKFBZQv3gRjStXEjt2PC21tfjz8ogdOw5n\nUhIA7qxB+AsLiISC2F1RO78nfh8VL84GwyDj6h+1JcFsdjvugQPx5+fR4vFQv+RzHImJbeMtJR53\nPDa7OZRCwjFTqPtoAY1fryDuqIk05azBPSSbxBNO2qmVfXeRpJIQQoj9FjEMHnl5Df5AmPuuP5b4\nGDMRYhhGl252kXCQgLcIm92FEbFm0TDC1JcuIn3oFW3beSq/wt+Yh78xj6iYfsT0GUm4pRlPxZdU\nFRRy41QvGf0U2SMGYQu3sDCnnCfe2sgJqfmMH1RtHaWUYDN4gy5CYTtJ1LLg0w+JZxuDk72UNsST\n7LATY68jcaiLhMGp2BzNBKtaeDF3MomxSYzqW4en3sHJ2WVEgtU4o1PAkcgxA8t5fdlq0ieuZntN\nOqcOLyIhOgQ2B6mDZuKMX09jyYcc1zSfqS2NODNTsLvchD6twrmlEX8fF83js6iOimXIwGai8iME\n1hRDc5jo6WO5PDMXlyNCgIGkN4f59YAENuam4FlUwSBfJeEYF2nXXUpj2XJCq3cwvKwUvoG3M09l\nkm07/dObyXIXUja4P9OWbQSHjcTTTmb0xka2l3lYlTmRiUYBkQCsSBpHozeOvtQwNqWRGVdOoZJY\n+qccjWdxPkaTn3BeE7bcLYwbksqKsmzGfZ7D8dm7B2ZC9FSXXno+zz03hyTrD4i9bfPIIw+wbNlS\nkpOTmTPnzbZttm3T/OUvswgGgzgcDu6663eMGTOu02MKIUSrQHERztTUvbbuOVBaPB6zFY4Vxxkt\nLUT8fhzxu08U0qp2/odtCRz3kGzSL72c2FGj29YbkQhVr70ChoFve8ez07bnWb4MgITjprUtixk+\ngiEPPEzV3DfxLP2C4j/PYsiDs4jKyNht/8Y1q6j94D2caWn0v+123FmDaHz7dSo++hiAxJNOInqI\nGa/4t+fCGWfudgzv5k0AhBs9eJYtpaW+3tz3uOPbtnEPHoI/bzuB4pKdElNGSwtl/36CYEkxfU45\nbaf3AsA9aBD+7blUz30DI+An6ZxzST33/N3KYLPbSbv4Ukr//jgVzz0NQOq55x0WCSWQpJIQQnQL\nw4gAtsPiZhCJhAAnC1cVMaRfIqMHJ3e6bUs4gmEYuJw7D1q9Prea0iqzZc9LCzW3XTgW7/p1VM55\nkdQLLiLplNP2qSyNdXlghIlLPZqm6lUARMX2hqgpIgAAIABJREFUx9egyc3bwPChE2gJ1OHZsQy7\nM45I2E9N0ftkjrqV2pIF+Oq3kBkHEQPs/tWUrPiaYzc0s851OpXBCCcNKcaIOAh9vIP6tBRWuhVr\ny/uREuPjpmkbGJu8BgCPP4r5a0ZyuX6PSJSNimnDGTrSR3izB9YGiI+uYbBvM6M83zIKIB8Cy+1U\nZj9HzLGjIN7DxMRcti1ykhxTw4iTIoSMGFz4qPjgOVw7WohkB3CNiodRZnAW3tLINv9Ilg1Ioy4h\nCV+lm8zERo4+0U7ytLOoDL5AxNdMxiU3kFG3nkBBGZ6FS0gcexIZ40/GVvEZkUuSCefG4Z4QR9Kk\n0zAygjT1z8H3YRXDi0u5teAdEsPNRIBIfjPnjs+l2efHMS6RpsYcJqaP5mOjhVNGF+Ea15fQW6Xc\nNDmeiozhTKhopv7dIgJzX2bcb36Pb9u31Cwuw9k3lei4LIxQiGOTq1lf1hfdJ5PjO77EQhwRzjnn\nfC655AoeeujenZY/+eQ/uf76m5g27QSWL1/Kk0/+kyeeeKabSimE6ClaGhoofOgBorOHkvW7e75X\n/Fi7cAGR5mZSL7q4bf9wUxP2uLjdjufbnkvxow+Tcc21bTFc1Zuv0/DVlwy5/yFc6buPeRkJBmn4\n7FPssXHEDBuGd9NGyp/+D9mzHsMeHQOYSSJ/vtkiPVRZScTvxx4dvXNdPR7sMTGEGxvx6a1EDx9B\nVPrOCSNHfDz9fnwD7oFZVL3+Ct5NG4iafgYAzVu+wZ+fhxEOU/vRfGxuNwNu/znugVkADL31Zlri\n+uAvLCB+0tHYnC4cCQltg3W3eDyEG+pxZw0yj7d5E9hs2BwO6hYuAANsbjfxR09uK0/0kCE0AIHC\n/LakUiQUonL2szRv2UzcxElk/PCa3d4z90DzHJ5lX2FzOulz8imdXr/YseOIGanwfauJ6j+AuKO6\nNjTEwSRJJSGEOMTCoSbKtvybPn1PJLHvCd/rGOtyq3HabYwbmrpfZWlu0FTnvUF9MIM16zN46/Mk\nrj17FCcftXuT5Yhh8JfX1tLgDXLfpaMIbFxL4gkn0LDjCxauMPtw90+LY/XWHSyrXUr6iq8hEqHq\nzdeJHTeOSJQXX4OmJdhAXPI4YpIUYCccaiTkqyDQVEzIk0eFJ5aC4gaOy4JwBLaXhxiYCMGKeawq\nW07/5BAYYZIHnkUw0MQXy3Pos/4Zho5tIlAV5vMvUxk12c+A5iocKh7HqXH86Msl5MQPJCrVCZUQ\nHTecxJXrGRMfYNKPT2bCUCfFWzbiBiIRSE7sw1W+DTgjIeYnTKOkoj+3DcuBnACGz89FcQXYPUXU\nuhIIRsUwuG8Cwcoigt+UEtxaStTVWYwdWocRE8AW78AwnESWlsNJSfjKt9C0pgGKovGeMQRfZYQx\no09gx6cvMH5yNrajRjN3aTURA8b2qyYhbQY2pwtf7re01NVh1DWT2Pckip65n0hpgOQ7zgIgKpBJ\naEAFjilRuOOzsTuiiEkcgbdmLTHnpOGZ6ySxph4SEog5YxzBz7fQvHEjAI4R8TTVrCZ607fcZf8W\n94AMjK0u0q/8IclnnIbZjiKLUMF2vOvW4lm2lPpPP4EWg8zrbiNm6DAA7KW5zLQvwhWdtl+fSyEO\nhPLyMu666w7Gjh3Pxo0bGD16DOeccz6zZz9NXV0d9977IAMHZjFr1p8oKyvF7Y7mt7/9A8OHj6Ch\noZ777/8DVVVVjBs3vm1gV4CFC+czd+7rhEItjBkzlrvu+j0Ox86J9okTj6a8vGy3MtlsNpqbzQR8\nU1MTaWnffzICIcSRw5+XC+Ew/txtNK5cvlMrGcMwaMpZQ/SQbFypHceFYZ+P6rfnQjiMzeEg9YKL\nqF24gOr/vUHaZVeQctaMnbb3LF8GhkHj1ytJOuU0jEiExlVfYwQC1H78EX2v/tFu5/As/4pwUyMp\n555P2sxLqPngPWree4fa+R+SdvGlRPw+qt/+H7aoKOLGT6BpzWoCxcXEjBjRdoxAaQlFD96PPSaW\nqAEDwDBInNb5Y6r4iROpev0Vmrd8Q/L0M4gEApT+6+8YwWDbNpm33d6WUALz93DKjHN3Ok70sOF4\n160lUFpC2RP/JFRbw5CHZuGIT8C3PZfo7GzcWYNoWPI5YLacsrvd3+0/eIh5nazBuv1FhVQ8/yzB\n0hKih48g8+bbsDl2n0W4NXEFkDB12h4nObHZbKRffiWl//oHaZde1tY17nAgSSUhhDjEfJ7tGOEA\n3rpN+5RUagnUUVmQQ8Q1FGdUErqojn/N3YABnDQ+iYumxJCUNmqfnlq1/mHUum1TtdkyJylqB9ce\nu4Ovi7N4cQFU1fu44IRsXE7zhhVoKiKnwMa2kgYAtj77NAkUE/CWkx9fQV75BK6eUs6ogbEs/KCK\n9GUrCUc5iZ+s8K3cQsXmZyAh/N170LAVuzMWI9KCEQm2LyJxLhcjU4sIR2w8s3wSdR4nt/UrJnlS\nNPFUEg5CY72bwuIUvlhaxambNQMuiscwouCzck6pKoR50AIYNSGcxycTN8XJifXFQAzJoy/A59lK\n84b1DGjagfvDF4n+xV3ENzsIbqyCYASnKxZnwbfU9h3KpvhhXOH5ApfbCcf3w//RNuxlRVQnJPNS\n+llcVbaYQN52Em88HX+zxqgKYpS0YMuKw5Zpw2aDlvUNGBtqaYl3EF7nMa/BhCS2rXMz1R1Nn/En\nU5v8Hn69hR/cfAtpPMemUjfTRpottfz5ebTU1ABQ/dZcks88m0BRIfGTj8GVYgaQcZkTqWuchy3B\nSUyiObZAdMJQsDmwOcDt8+BI7IPt1l/zwDu5HDVhAjN963Fg4M3ZQFRmNKEh1UT1M//I7X/Rz3ab\nfS/jqqsp+GYzlS+9COEwCVOPa0soBUNhnvmoFiPYn5mnTUSIVss+3U6eNZNhV9kddiLhyG7Lh47K\n4Pjpw/a6f2lpCQ8++Bh33z2UG2+8lkWLPuLJJ59n6dIlzJnzAhkZfRkxQjFr1uOsWbOKhx66jxdf\nfJUXXniWCRMmcv31N7Fs2VLmzTO7cxQU5PPJJ4v4z39m43Q6+b//e5SPP17AjBnn7VN97rzzLn71\nq9v597//QSQS4amnZnftDRFC9HjhpiZsLtdOSYm9aT/lfdXcN4mfeHRbCx+f3kr5f54gfvIx9L/t\n9g73b968EcJmHFbz/rv4C/LxblgPQMPnn5F85tlt2xqRCE05q81j524j3NxMqLKCcKMZv3i++pLU\nCy7EmZBI45rV2GNiiB01mrqPP8LmdJI0/XQAks88m4YvPqfu44+IG38UO954lXBDA6kXXIQrPZ2m\nNavxFxfulFSqfvdts5tdMIBv6xZsTicJk4/t9H1xpaXjSkvHp7diRCJ4N23ACAZJPOEkEo6dgis1\nlajMjsd2ai9m6DC869ZS8te/EG4wY926jz8ibsxYiESIHTOOxGkn0PDFEivRtXP8HpXZH1tUFE05\naygsLCBQVgbhMH1OPpX0y6/AHhXV0WlxDxgANhsYBkmnn7HXckYPyWbY43/f63aHmiSVhBBHtJCv\nCmd02iHthhZoKrDOXUk45MXh2nPf+NqSj/B7tgE2jJhRPPtJJjabjaz0WEbEfEFjiYdg4wQyss/H\nZvvuKYiZQDKw2czEUChQS+W3s3HHDSJ96OWEW3z4GrZT7onj83zF1cfkMmVQORsqs/lweSGrtuzg\n8unDGZm+g+r8t8j5ZgR2I53BgXKSp3qw9+uL36Mpqx3AT6ZuIDPRS9gLZ0yH8NC+vLLlaFxhBxce\nU0d0Qpjyymg+Lc7G3mJw1fEGUIojqg8t/jpoNgh+UoK9XzTxxyRjs4Wo9g+g2uPisoolxOSVEa4Y\nTLXXS9qJUST0g28/f43TthTRdxzY09wUV/ThjfjJXGtfRmJlGa6MDFq2NdHiqMU1LRVbrBNbswtn\nYjz1ixZ+dz3y88j/7V0YAX/bsmYasEW5mXTHTdzx938R66kmJuEHNGd/Q9SoLMLhRlJPSudX/ccz\n2DGGwgfuJbSqDPuJbmypcSTaT6Lp89Xk25LIccZzyroviAFallVBtB33OQOxDXIxrTpE35lXYrPZ\niB09Gs+yrwiVleK0p3Ha8M3UBU5gMNC4xgzu7LFxNK1dQ7DK/AM96bTT28ocO1xRNXsOrhPTiU0a\nZX4G/CEchYn4tm7FFZ/OwLt+w9y1dUQMg7WlPspTJnLHGVlEHllJaEUtrmkpEA9OdyrODlobuVLT\nSL3gIqrnvonN5SLt4sva1r3xaS4lVU2cOmkUR48avLevgRCHRGZmf4ZZA7hmZw/lmGOmYLPZGDp0\nOOXl5VRUlPPQQ38GYPLkY/F4GvB6m1i3bi0PP2wuP/74E0mwnh6vWfM1Wm/hxhuvBSAQ8JOc3HmX\n4V29++5c7rzzV5x66ul88skiZs16kH/848kDWWUhxGEsVFtD4f334h44kIG/+f1u8acRMZPou7ZC\n8efnmbONnX4G9YsXUTt/HmkXXwpA/WefANC8dQtGJILNbicSDOIvyCdmxEhsNhtN69YC0O/Gm9nx\nyhy8G9bj6tsPV0oKzVu+IVBYABkTACuR5PFgc7kwQiGav9lMsKwUMGck823dQv0ni7E5HNS89w4A\nrr59CVVWknjiSTj7mGPP2d1u0mZeSsXsZyl+7GEAEo8/gZRzziNYWQFAoKjwuzoW5ONdm0P0sOEM\n/NVvaFy1Ekd8wh7HcGotk2fpFwSKimjKsR6WTj+9rfXQvoi27hPhhgbiJhxFoLQEz9IvCXvMRFrc\n2PFE9e1L0vQzCBQVEjt6zE772xwOYkePwbt+HcFwGPeAgaRdfClx48bv8bx2t5uEY44Fm53oQZ3H\nTp7mIOXVXtSgfb/fHEqSVBJCHLH8TUXs2PYiif1OJinz1L1ubxgGZTXN1DX6CQTDTByRhsO66S9Y\nWUh+eSM3nz8Gp2PPzVH9VlKp9ee45LEEm8tpqs4hqf907M6YtvUtQQ9+Ty7u2DTCERevr4hQ2xjk\nohOzmT4uSG2+h3DERqBhA1XbvaRlX4bdYT4NqSuej7duEylZM4hOHMGO3JeJtDTja9hKc/1WNuUW\n0s9hoHekcs5ESOp3PHUlH3HH2QaLdRaf5pQw+4McfjJ1A6lxcP7YbQyLriQ7XIm9XwyhugjOPg6O\nSay0CuuAoI1IQxOOIXFcmFHJK6uHEXN8PIY/RPIHW5kZ2MIOdzKvVk3kth9eiHfrQhjQQvDTCiJF\nPnzFLUSVBehz1TSyGhL5Rf6buIww0SNGknXXb7H97XFKltaQNdPg6KMbiPRz4xgRj80RzXFn/IRx\nmblUPlFG3FETGXDHLwhWVlL4wB8JZ/hwDIshIeNEKp5/FoC0iy+l+u25RGVlESwrI3bCOEIDa4hJ\nVXheW4oRDFD0p/uICQZJPOU0UoecR3BrCeHTG7GTQnL60aQMHACYg1H6Nn3LgCt+iSM2iYrHniRY\nXkYGcLbVzNy7fi22eDeRyV5ssU6iE0eQ8dOr2q517OgxeJZ9RcOyr2j5cjnB1CBb/GsYNOIEmnLW\nYHO76X/bzyh5/M8ES4qJGjCQGGUmj5p8ISKuGOzVcQRfKif4swoai1ZSM38ekaYmogZmMfB3d+FI\n7MPabd8SHeXg5KP68/GqYh55fzu3JyQSzqnHOSgZ2wAbsUmjO020Jp9xJoHSEmKGj2hrZv/5ulI+\nW1vKwPR4rpze+Qws4sh0/PRh+9SqqCPp6QlUVTV+73O7XK62n+12e9tru91OONyCcy9TUe/KMAxm\nzDiPW2/tuDXA3ixYMI+f//zXAEyffgaPPfbQ9zqOEOLw1qy3YndHEz1kSNsywzCofGE2kWYvvm81\nzd9sJm7sdwP1h30+Cu/7f0SCAWJGjCRx2gkkHD0ZIxLBX5BPVGZ/0mZeStOaNdQuXEDClKnY4+Jp\nWpsDQMTrJVBSTPSgwdTOe5/a+fPo95ObSDh2Kt4NG3CmpJAwdRqu9Aw8K5aTesGF+Ldvp3nLNzSu\nXAHHmkmlJutBVsq551Pz7tt4N6wnUFYKDgeZN95Cwf3/j9oFH0I4jCstHXfWIJrWrgGbjeQffNfi\nCcxuYvWffYK/qJCMK66iz2mnY7PZiOqXic3lIlBU1LZt9TtvAZA28xLsbjd9Ttx5dt/OxI4eg2fp\nF3g3bcC7YT3O1FTce0jQdCR6SDb2mBicScn0u+lWPMu/ourVl2myWmJFW+MkZVx1dafH6P+zO4k0\nN3c4RtWeZN7y0z2ujxgGT7y1kdzSBn73w0mHZWLp8OmIJ4QQh1hri6HGHSsIt/gAc/axkL+qw+1n\nz9/CH59byV/fWM+/39nEvGXm05XqBh9vL8lj9dYdzF9e2OG+rVoC9YSDDbTYUzAM8DeagxXWlX5M\nU80aaos/3GnsDm/tesAgFH88r6w7hk0V6WQlNTJj6gAayz8D4L+rj6KwPg1/43Zr/wj+xgKaatZg\nRALUFL5LyeZ/Ew7WU1hnjn301crFeOs0AOMyq0m2bSA6cSQ2h5tAfQ5XTs/mj1Na+OXRy0iN87Gp\nPI3GgIsxwzzEjIzBvyPMEysm0/xWBYF8P9VN6eAME6lvJPh2GfaGeNJi67nzlA1EOSPERU8kdvhY\n7A7oF6jlnJIvefupdwi4SgmX+YgUNlMUk8Hy9PHYSppxbo3H894nuAyzqXbsmLGEvU0Ev91K35pa\nWr6uwxbnxDEiHjx2gu9UUPfhQurefQtsNtJmXgJAVN++pF10CaGF5RhLDar+/jKhHZWkzDiXpDP+\nP3vnGSfHWeXrp6pz7p6cgyZrgjQa5WDZipZzwhmDDTYm3yUYfnh3MbB3YQkLi8GEBYNxxElylmQF\nK2s0mqTJOeeezjnV/dDy2GPJAQyGa8/zqUPVW3Wqq6tOnfec/9kGMhmiXEHRA78l60tfI+fybxFp\ndUIkgr5mOTKdDkGhwLx5K6JcQ0JOvCOHIMgxval0Ub9kKUSjPP94G08/1U5oYhx9zXIKf/ErCn/x\nKxIvvZycb/07WV/8OjJjPONBnzhfYFFTGp/1cry6B0PARXA8TNVsJ3sefoXw9BS6yiq0ZYvRV8eF\nIc2btiAIAv5ghPv+cIp7f3sSISefmN/P6I//i5knn4BIhKRrryfn3n9HbjIzOuPF6gxQVZDIjZuL\n+NSlZQTDUbqkuIOiTNqOJfsyjKlv6BeMzXg41jKBzRUgJkk4/FH6VlzOQEYFwXCUV+tG+NPuLvQa\nBZ+9qhyl4lzNgAUW+GdlyZJqXn11NwANDacxmUzodHqWLn3j8xMnjuE+W/ZRU7OS117bj91uA8Dl\ncjI5OfGet5eUlExjY3wmvb6+jqw36XwssMAC/3/h6+zA33NuB7Owzcbof/+IkR/8B/7eN753vnYQ\nX0fbXMBj9oXn5vl8jgP7iNhmIRrF29jAxAP3E5qcJDQ2hhQMol5UgKhSkXLrbRCNMvng73Ae3A+x\n2Nwkk6+jHUmScNWeAMD67NN421uJ+bzollQjCAKagkJSb/k4coMRbXkFolaL+/QppFjsbOlbPaJW\ni2X7DmRGI56mRoKDA2gKi5CbzVg2bYkHlJJTyLrnm2R8/ovkfuf/kn3Pt+LlXG9CEEWyvvp18r//\nozm/BeKZPcrMLIJjo0iRCL7uLnxtrWhKy87pkPZuaEvjttv37iHm96NftvwvrkAQVSpyv/1dcu79\nN2QaDaZ1G5Dp4z6ztmzxefWQ3oogisj0+r9J9cO41Us4Es9aO9E6Se9YvCTvyYN9886ZfxYWMpUW\nWGCBDz3RiB9Rpj7nIh/yxUVUpVgI98xJjClrmer5I+HADJnlX0amMMwtOzjp4ljLJOmJWlaVpXKg\ncYxXaodYktrL/jYd0ZiEXCbwwvFBlhUnk5XyRqpu7OzFXxQEAp5BWiaS2NVayqrcSXaoBgj5pxgY\nszLtSaYs0kks9mcsmZuRq5LwzDYybDcicx5gxlpMWaaGHYUduKdUhAPT6BKWkJVZyEOntNy7Q4bP\n3oLP0QFngzF6/TIcvlHksWlaJ5J4pSOP21e2UZQcfyCKhGRkepIIqgbxO9rRJy7DPX2CoRe+i5Ai\noFDLCDc7mXGUYBT96Cq8IApYT4joVEFmdRZSXh4noVCA7SqiI/HgnMl0AV5VC6HgCGJER+LyK5np\neAxfWwvKLWVE93ex2tGG9UU9eimIDDiSUM0tt1Qi/qIL20svQiwaT70+61zJdDqQJBKvugbnoYNE\n9C7EmJrQsVEQZdhGXgDAsGYtyswsmnqt7D45hEqRwrWLCgg09yFqNKR+6i4Mq9cgCALq3FwCQ0NI\nZ4Ur/R19+Bob0RQVk37350GSiAWDyDTx7DFPLJMmazVqlY4s+Ru/sX5pNbPP7UQ90ElQjGdCyGrW\nzHU7eR1BEDGmbiAWGKJrykxLbRd5aQaWFCZhslhQpqUTmpyg1ryYmo1LkT/3GBWNL8W3cbbLSMpt\nn0BbXo5p/QYAnj7Uh80VjL+W53HLRg0Kgx65JQH90mrkJtPc9ht74gHT6qK4VtK6ynRSLVr+9LiP\nLkcOHQe9mE9HWFM+ykXVmdR2TLHryADR2OtaXPBmX0YuE4lEY5h0Sr52UzXpiR9Mm+MFFvhbcccd\nd/H973+XT3ziRlQqNffe+x0Abr/9Tu67715uvfV6KiurSE1NA+IldHfe+Vn+5V++gCTFkMnkfOUr\n3yAtLX3euN/+9rdoaqrH4XBw9dWX8KlP3cVll13FPff8K//zPz8mGo2iVCq55557P3CbF1hggfdO\naGoKn98BGvP8zyfGGfvZTxA1Whb99//M8zEd+/dCNIoEjP/i52R97R78vT3MPP1nRK2OzC/9H6Ye\nfghvcxP+zg60ZYuJBfzY9+5G1OrI/68f42moZ+oPv8N55DUUKfHrz+sahvolSzGu34Dr6BGCw0Nx\n3+a2TzJ47zfxd3agKSwiMjuLoFIRsduZ+sPv4+stPbdbmKhQoF9Wg+voEVwdHQQ8YSJ2G8a16xAV\nCnQVVbiOHwVAVxnPZLLsuASZwYi+ehlyc/y4vDWYNG8bas05/hCAOieX4OAAwfExZnc9CzA3KfiX\nIDeZUWZkEBqP+/WGN3Vl+0tQvKlxgqhSYd6yldldz87Z/UHRNmDjJ39uIjfVwO2XlPLUwV6UCpGC\nDBMdQ3ZOd82wojTl3Qf6AFkIKi2wwAIfarz2NmYHn8WcsXle9gVAyDuOKNcBEu7pU4S8Y4T98VIu\nj3OQjulkKvIT0WsU7Dw8AMCtW4spy0vAbFDxx1c6eeyQk/5ZgWSjjBu3VnD/My38/qUO7r2tBrlM\nJBSO8tMnmxmzetlSk0XMN87zZ0oAaBxNZF3eILHuh8gwBcgwxTsCBVzdTLr7MaauZ9bpI8cSF7L+\n3IYuUvIvx9p/LC6wLcjQqqtYrZ3muCSypzOdHYVTIEUQBIh5IthP7OM5cTteMYXViWN8ceRZxJAK\n8eJUAKK1U7jO9CDm6XBdWYdRvx4pJiHmyJEiMaKjPqInHayJvBI/ZmdAtsJMyfWbSas7hKxAS3BY\nJNQ7hiIvk2i3BwBtyWJ8L3YRnpklNjhGKG0sXhtvMJB5xR30qO7Hu9+OMhiEWIRWwyJKPYPUFF7J\nwKZN2F6MB4hSbr4Vd309vtYzOF71gyBgXL0GeUICk7/9NQCWrdtJvPIq3KdO4e/pxrf+Yr77x9MM\nTb1RMnP11Z8goase04aNKBISAAiGo0TSc6G/n/GWTjIqS5l57FEQRVJuvQ1BEJiy+znWOoFWpSAU\njvJy7RChcDxwojVPsr4q/iCpzMrGpzJQ4BtDJgq4ZRr+t87Hv5aHMOmU+IMRfvxEIy5vmGSzGoc3\nh8nZ9rn9E4AVZSlcedWNvLLzOAOZVdx8yWoGTh9BOTZCRBAZMeawWJJoGPVzypbMml4bBq2Cgw1j\nZCTpKMw0crh5gudzarjr8sXnnSlr7LYiEwUq39Q1sDDLxL/cvYWTbZNox110jTh4pXaYV2rjKekm\nvZI15Wm8WjdCNCYhEwWuWJdPKBKlscdKLCbx5euqSE3Qvsd/5QILfDCkp2fw8MNPzr2/9977zvvd\n97//k3PWNZnM/PSnvzzvuJs3b2Pz5m3nfP700y/Mvf7Od/7zvOsuWbKUBx985D3t/wILfNRx159m\n9oXnSLn5VrTFJX+37QSGBok4Heir5jeZkCIRRn/8A4Z9PrK/9W+oMrPin8diTD38EFIkQtTtIjQ+\nNvdd1OfDeeg1ZCYzCTsuZeaJRxm679/iA8pkpN1xJ3KzhcTLr8Tb3IR117Nk5efjOHiAmNdL4pVX\nI9NoMKxcxcxTT+A6dgzt2RK510uwAJJvuBlfezsR2yzGdRtQpqahSEvD192N/GQ8Syn1ttuZeeIx\nom5XXEz7bDbTWzGsXI3r6BF673+AsDvui+rPimPrqt4UVKpaAoCoUGK+aNP7Pu6qnHjnM/vul/F3\nd6GrWoKm4K8rodeWlhEaH0dmMM7pI/21RGMxIlGJhB2XosrKnrP7g+JQczw4NjTl5r4/1AFwzQWL\nWFGWwr/+by3PHOqjuijpXeU2PkgWgkoLLLDAB0L/uIsHdrXwyR2lVOSfv93p35qgZ4TZoV2AhHum\nFkPKagRBxO0L0dg1xuSQEVGZwrryRMK2/QTc/chVSUSCVp47PsFr7TMkGtVcvDKLlv5ZynItlOXF\nAxJrSg28ctRHrzX+fn1uJwUmDWsr0jjeOsn9z7RwxxYjTx210jXiRhQEdh0dANTkWpxcudSGUTaO\nXCYhxSIM2oy0TWVg9chZnj1BedoszsnXMGvA4Vehk2WhUPadtQekWBRT0gVM/vzXKKamyKy5kbp+\nHUJ0ETtK+4lGIPT0GII3SpX6Nc7kLievoxnJEyHmiRCbDSFYFET7vMhMJqKDToJ7e5kOdiMmKxGz\ntKh0OXgbm5FbEpBCYaJOB5qKMlgt4Y92oSxPJSp54ayopDZchNM1Fk9nQcJ97DhSOIwUCjH63z8k\n6naj2XARpwei5FdVo89rYdy3iGPTS0gbUaSOAAAgAElEQVQK2KjZ/wem9u7DtPFC7Ht2IyhV8e4a\nogxf6xnCM9NoikuQ9EYMy1cSnpxEkZqGcdVqAHTrNrA3msnuXT1IEqwsSyEzWc/Ow/00zUS46sqr\ngbj+0L7TI+yvHyXLGuVq4KUnX6OmrhGjdQbLtotRZWYhSRK/e7GdvnHX3Dml1yi49oICdh3t59FX\nuynKNpFq0TI646VDnUFNsAti4KxYy6w7zB9e7uDL11XxyN4uBibc6NRyOocdKOUiFyzJYE15KkNT\nHk60TnKqY5qGbpGIoYRrlmYik8nIuPEmRn/yQwZ0mbzwYjfFzdO0DsSzzBq6ZxAFAQH45MWl5KUb\nGJvxUts+RWmOmY1L588a2lwBhqbclOcnoFXPv/1bDCp2rI6n44cjUQ43TfDY/m5EQWDHqlxa+meJ\nxiSKs810jziYsvv49GWLuXbjX6eTs8ACCyywwIcTSZKYfW4nUjRK8rUfe9vl3KdPYXv5JTK//JV5\nGbUQD9rMPrcT20vxQK3j1b1/t6CSFIsxdv/PiDocmDdvJfmGm+ZEsj1NDUTsdgDGH7ifnHu/jUyr\nxXX8KP7uLkS9npjHg6+zYy6o5Dz0GrFAgKRLL8eyZStRrwf3ieMY167DtPHCORFrdV4+uqXVeJsa\n6f/G1yAWQ9Ro5jqAiQoFprXrse/djfvUSQSVGmXGG/d1mUZD+l13M/v8LizbtgPxMi3nwQM4D7+G\nqNViqFlOLBhg+k9/RFdRifA2GnLa0jLkiYkEJiaRWyyYLtg4p/WkXVwBMhlyi2VeFzV/MIJG9f5C\nCaqcXPyiEld9C0Yg8apr/uqxtGXlOA7sR1+97ByR8/fK2IyHw80T1LZPEo7GuOOSxdScJ7vrfEiS\nxAM7W+kctlOUZaaqIJELlmYgvk0pnCRJODwhzHrlvElAbyBMU88M6YlaLl6Zw8N7u0kyqdm+MgeF\nXGTj0gwONIyxt26ES97kt7l9YRKM6r/K7r8Fsvvuu+8ftvG/NT5f6L6/19g6nQqfL/TuC/5/zoKd\nHx7+2Wz884EeesdcdAzZ2VCVjkL+Rm1y17Cd//uneoqyjOB6DaQYbSMSHUM28tON7zju29kZ9E0y\n0/cYUiyMUpdFJGBFqUlDoU7ilztb2Ht6gl6rhZ4pJZI8mYKEcRRKCylFt9HX38Izjelo1Arc3hBn\n+uMP8XddXj53wbaNvIRBHOTMeCqpFiWXlQ8TcHawbMkqxmwgC3SQrzpIumYQlSaJ/3PzRSSo7VQl\nNrCpeBitzI09oKNuOJX9PbnUDuVwnTRNfTSHuuEEZgNG8gxWgkGBvjozibuPoMrNI6b1QlBG6Pkx\n/Mfaic46UKy0oMiSMz6mxjDtJingQVk/TnA2hifZQqprhqLZHgjFEBKVmDdsRZexBHnATLh/mqjT\nicxiJDriQrKHwSuQfeu3MZWsI9g9SqC7C115OaGJCSxbLkaVl0PA1YskRhC9KiLNVgBCY6NzAaaI\nzU5woB/L1u0oEhLjXUuAPUkreaHFTsOAQGGKn/Sc9WxeVU5ZSQaO1w7gam3DvncPRKOYN21CV1mF\nIikp3q0tFsNXs5Fv755EEEWWblmNKitr7jff3zDKziMDJJvVfP7qSnasyiUnRc/eumE8/ggXVWdi\ncwX4t9+foqXfhkoho7wkg4zeOnSEMI50gt5I9ue/gCBXUN81w966EaoKErn+osI5HaLFeQkkmtSc\n6pimZ9RBYaaJfadHGJj2UnFWI6v0C3cz6JJo7bcxPOXhdNcM+elG/uPOVVy2NpdbLy1ncbaZJJOG\ngkwTFyzJQCEX6RyyIwCXrs1lcNJNekE2htJSIpUrqO11MGX3U5Jt5pM7ShEEGJ3xsnVFdtxxEQWK\nskwcPTNBa7+NFaXJiILIw3u62HV0gKNnJvD4w2xfmf2O/yuZKNI5bKd90A4StA7YmHH4qVyUyFdu\nWMKZvlla+20UZppIsZybzv5e/p/vB51O9Z2/6YALvG8W/K/3z4KdHy4+ynbaX3mJ2ed2Eujrxbxl\nK6Li/K3UZ558gkBPN6JWe07AaHbnM9hefhFFcjKiWkNwbATLlm1vGxR5K4GhQUZ/+mNEtRpV9jvr\nlvnaWnG+dhBEkUB/H8HhoXhgQiZj+pE/EZmdJXHdWjwdnQQG+vH3dMd9EpmczC9+GdexowgKBcaV\nq4iFw0yczaJOv/MziAol2tIyLFu2oS0pRVTPf+jXVy1FUCoJ9PUR8/uw7LgU/ZtKrRQJCXOd3TRF\nRZjWbZi3viIhEeOadcg08UxhKRzCc7oOJAnDytUYapajys5BbrZgvvAiZNrzZxQLgoBh1RoWXXMZ\n2m2XoV9aPReYERUKVLl5GNesm8v03l07zI+faCTRqCYn1XDeMd8LokbL/c0RjiVWUZNnJGPr5ndf\n6W1QpKYiN5owb9qMqFK947LnO2+PtUzws6fO0DvmRCGXEYnEONk+RTAUxWJQoVHJEcW310qq75rh\n+WODCAKMWb00982SaFSTmxY/Pi8cG+CF44N0Djs40zfLY/t62HVkgBmHn+ri5LnA0rGWSZp6rFy8\nKocty7O5YEk6F1ZnolbGz/38dCMnWic50zdLVUEioiDwn4808PyxQZaXJGPQvvF/+yB9sIWg0nvk\no3xz+DDyUbDzn8lGjz/MH1/pBMAfiuLyhec0XQCeOzJAz5gTm9NNkfYQTsckv9wD9d1WVi1ORa9R\nzBsvGI4SjsRQyEV0OhUT027u/d9ahkb6MEcO4J18Fc/MKSQpgsG4HlPWejzW08Sifkbc2ew8MsCi\nxCjbSjtJVnsYnvKzdfONmFJWgKjgd3sdOHxyblB2s1J9hhU1dsrTrGSoB4lFvcwOv0TQPUCiAsry\nati0tpikxHR89lZCHV0UtR6mbG2AYESGQhYj3zJOyNmCRWwlURdAJteRlP8xhgM1PFsbwRVQcwHD\n5LYeJss1ynBiAcX9bWSc7EI8YyN5Ot7GNdw+SWw4RPjYJHgi8XbxlSYUaxNI7B6npq2RIvcQqhEb\n0z4df86+mPbkxcgDPtKCNmSVRrI+8y1MK9eizshDm1OKMi0d96laJJ8flALIROpX3cJzbW42VGWg\nSk3FeeggoYm4EG3ydTegTSnGZT0NUhRD1jo0lgLkRiPBkZG53yg0NgpA2u2fxrCsBtfJ44QNFp6m\nmPwME2q1lr1tJup6g1QuSsRk1CBJEBweRJ2/COPqNSRediWCXI6oUBAaHydstfKceTkzvhidQ3aG\nJl0kGjVzgb4nD/Ridwf5j0+vmtO0UshFesdc9Iw6WVeRxksnh+kecbBjVQ5fuKaS6spsek800KTI\nIjNgZU/iCnR5+SSZ1fxiZwuBUJQvXVdFUZaZ7BQDKmU8GJqVrGfWFaCl38bBxjFGpj2oExOp8fai\nzs0j8eJLWJxn4VjLJMPTHlRKGV+7cSkGrRKZKGIyaub9PwUhHhCKRGNMOwLsrx/jdOc0dR3TLCrP\np7wkg5JsM+X5CVy/qZBUi5ZlxclsX5XDkoJEBEFAkiQefKmD8Vkf0ZjEwcZxjrVM0D3qJBCM4PSG\n0KhkbKnJQiYT6R5xcKxlAn8wSkbSG1pIkWiM377QjiTBt29fgd0dRAK+cE0lWpWCRenxMrvWARsr\nSlPQquUMT7l5/tggOamGeTOXC0GljwYL/tf7Z8HODxcfVTvdp08x/fBDc+81RcUoz+qRvRkpFmPm\nsUeQIhHCM9OYN2+de6iOhUJM/PZXiBoNud/+HrFAAH9XJ+pFBSjfol0GEAsEmHr0T0S9XtRny6ms\nzzyFr70NT2M9kiShKSl9W/Hk2V07CY2NkvnlrxB1ufC1thCanECZno71mafQli2m4l+/ibWpBX9X\nJ8HheEOW1I9/Av3SapzHjxKenMSyfQfukydw157AsnkL+qXL3vX4CXI52uISzBdtQl1YhGn9BfOy\nbGQGA77ODiKzsxhXrj6njf1bkRtN2He/DEDStR9DmZIa14/My3vbgNLriCoVptTE8563ytQ0FJZ4\nQOm1pjEe2xcXHx+Z9rCpJvNts3HeSveIg5/8uYmCTCNmvYrOURd7O11EBRnTpkzWL816R5FrSZKo\n7ZjCoFHMBVleRxAE1Pn5CEolD+xspWvEwZKCpHPGGJ32IFPIEM5qRUqSxAvHB3l8Xw8apZxPXVbG\nJ3eUsqw4mfZBG819sxxoGOPlk0PIRIHibPM5YwZDUX7+zBnCkRjf/dQqLlyawaGmcaYdfi6qzmRs\nxsuvnmtjxhFgeMrD0KQbCTDrlXSPOAmEopTnJyAIAo/t68bhDvKpSxejUclRK+Uo3zQRr1LIyErR\ncbx1ks4hO7XtU4xZvcRiEk5PiJVlqXPLLgSV/koWnJr3z4KdHx7+mWw83DRMc5+di4oG8YcVtI8E\nybYESE9JJiZJPLy3m2A4yowzRFmqlYZhI73WeCaFUi5QkORCpjASCMXYXTvEL3e2cKpjmk3LMtHp\nVBxuGMYSO8YFue3IJDehiIRMPCuOPa3AWLSGgLufgHuIwf4RqppOsWb8NGlr5OSneFiSOYVtph+F\nLEJ7by8OxxRbSwZYlDuNaZEcgyqMRRskGnHid/QjxXzxm54simakDpmtE1+4FYghEUAs1CDIwOFW\nIFeAQpSQYnEhZUGmIb30TlTaDDKTdbx6ahAZUa7seQm5QoE27KNmupl0/wzKtHRkegNRjwdRrUaK\nRpE8IZBArDQieSNIo34kd5RoswO5wUDC5VcQGh2lTZ1Fmz4ff1hAW6iiYmMQVeUiLHnzZ4GUKamY\nNmxE1GiIWG3IVmzkD8MaHJ4QZbkWUnNSCQz0E56eQmYwknTd9Uw7wxxonCTbZKfHU03ZhRegys3H\ntn8fEUEkKChQEEUsKCb54h2IKhXK5av5eb+WsCDjGzdXs2N1LhaDilMd09R1TmNzBXmiN8pRw2I2\n3HYlaTVL581I6ioqmS1YyvPNNooyTbh8IcZnfZzunGZzTRbeQITH9/VQlGViy/L5s5LBUJTmvlkE\nQeBgwyg6jYLrLyog0aRBkiR+0+inS5XBhDmbfmUqJzumqOucZsoedwbWVsx3YoPhKIIQF7vOTTPg\n9oWZcQS4blMRlVdfjHHNOkZmA7QP2lhXmU7nkJ1bt5XMtYCVJAm1WoHfH54b0+MP85vn2jh8ZgJJ\nguriZEqyzbQP2TjaMklTr5UZh5/UBC05KW/MCspl4pwDdqhpnL11I+SlGea6wgVCUbatyObrN1cz\nPuNleNrDsdZJ9taNUNs+Rc+ok7rOaeSyNxyl013THD0zwcalGayvymDV4lS21GTNOXAmvQqtWs7p\nrhk6huwYtAp+/my81e34rJfVi1Pn9mkhqPTRYMH/ev8s2Pnh4qNoZ8TpZPRHP0BUKkm65jp8ba3I\nLQnoFpefs15odBTHvr0AxPx+NAWFKFPiD8Oukyfw1NVi2bwV/ZKliGo1riOHEeQK9NXzAzWxUIix\n+3+Gt6Eef1cH5s1bkaQYUw/9AVGnR24w4G1qwFN/mtDEBOGZaXwd7fj7+1BmZCKFw0w99CCK5GSS\nb7wZw4qVcW3G1hY8TY1IgQBJ199IQtEihOIK1Hl5JF52BcnX34Q6Nw+A4MgIgf5e9EursT71Z6J+\nH+l33T2XPfReEOQKlKlp5y3bkukNuOvrSLr2Y3OZQm+HqFTi62gHJFJuuPkvLgN7t/O2vmua37/Y\ngUGrYHFuAv0TLjKTdGQm6992nTfz+5faGZx0Mzjh5oIlGTy2r4cpu58ck4xBWwi5TDxv0OZ1Grqt\n/Pq5NjqHHayvTD9v5lBz7yy7jg4wOOFmZVnKvMydwUkX33voNK/WDlGUZcKkU/Lwni72nBoh0ajm\nnpurKc21IIoCRp2StRXpGHVKzAYVDneQph4rFYsSSDDMzzh77ugAZ/pmuWRNLitKUzHqlEzYfHQM\nxUvhdp8aZmLWx+evruTqCxaxrjKNGzYVsq4ynaZeK829s7h8YXzBCPtOj1KeZ2FTTdZbTZsjxaIl\nEIrQ3Bdfb9OyTERRoG3QTnl+wtxk60JQ6a9kwal5/yzY+eHhb23j8JSbV2qH6Z9wYXUGSDZrUMjf\n/WYV8o3z0CsteEJybljhoCDFz+khE0PjM2yqyWdk2sfeuhGSTGp8wQjeoILWyRSUCgGlQsHIlJ0K\n/UtMTHTyw10+mvtspOpdhEMBFi/KJC3ZwPFTR1iW1kFI0vJKew5PNpVRZbWiyYoQcTkxFV5AJOgh\n5B0k0eDBnBhBTJAjy9QQnAgzGEwkRWsl4O5DyxDFyXYsmiCIAqJcBRJEez2ICUoEUSBmCyE0yBAS\n1AipIpIujOQJEZsKIBgVCGoZkcOzKBunET0hxHQ1giig8KViUq8jMu0gMDhIQKnj5fopto8fIyNs\nh1h0XmutQFI6jA6hLCxm1f8+gKxiGeFZK6qMTHSZFRhXrcfb3IRkjQesEi6+FFGpwtNYT/GGGhoi\nFgKhKFetFDBop9ElVKIxniteKKrVaEtKsWzeylMD8ZIqAKVCxpKCJOQJCbiOHUVfXY0zp4wfPt5I\n16SGUyNZ9ExG2bwsk8ZhL6/2B7CmLEKpUmDyWOkuXk9xTbwt7HOnxmgedHHZmlyWlcQ7VuSmGTDr\nldR1TjMw4UJ2NhDSNeJgXUX6PAFCQS7nscPDTNp8aFRynN6zTmxUQhQEXL4QTb1WtizPpjBzvjaD\n2aBiz6kR+sZdSEAoEqOl38b6ynRaB2zsb7chl6LYBQ0rylIw6ZUMTLhRKWV84erKuewkgFA4yn1/\nqONQ0zirFqeSm2pgXWU621bEtyuqVEQFkR882sDJ9ilWlaXyiYtL5jKnorEYv9zZyoMvtpOVrCPF\noqV3zMlPnmhkYMJNWa6Fb9yyjA1V6SwpTKJiUQLjM15Gpr2MTHto6J4hK1k3L7MIYNLm4xc7W1Ap\nZHzjlmVsWZ5Nc98sbl+YVYtTCYSiPHu4n6xkPctKkkk0qakpSWFLTRZ9404auq14/WGMOiXPHR3A\n5g5y5+Xl52QJvk5+uhG3P8yZvlnqOqcRBYGMRC29Yy7SErRz9i4ElT4aLPhf758FOz9cfFjsDAwO\n4uvunNMLeitvttPT1IDndB2JV16N+aLN2Pa8AtEIpg0bz1nP03Aab8sZTBsvJDg0iBQKYVixCoDp\nR/5IxOEg7dN3IdNqkZvMOA8fIjQ5jmXr9rlASSwcZuJXv8DX3oZMbyDm86JITibqcOI6fgzzxgtJ\nvf3ThKYmCQ70E+jrxdtyBl9HO772Nnwd7fFW9i1nsGzdjrakFEEmQ7+kGk9jPZHZWeQJCaTechs6\nvRp/WEKVkYncaJoXrIkF/HgaGwjPTBMY6Me4ei2mdev/Zr+BMi2NhEsvR5H43vRI9TXLMV9w4Tml\ndm9meMrNM4f76R+P+/TpiVpkMvEdz1tvIMxP/tyMIAh8/aZqlhYlcaBhlBl7gI1LM94xw+j1bT59\nqB8BcHhD+INRTrRNxpuF3LycE23xkq6a4mSMuvOXTD60uxObK4jTEyISjVGePz/I9roWpsMTt0GC\nuWylQCjCT55owu0PE41J1LZN0TZoo7HHSm6qga/fXE2yeX5Jv0IuUpBporoomfx0A0dbJukfd3HB\nkrjkQCgc5bljA+yuHSbBoOLuKyvmfNdEo5rDzeMMTbnpHnFQkGHk+k2FGLRKzHoVoiigVMioLkqi\nvmuajiE7Dd3xDr1XbVhEdso7B+pKsi1M230sLUri+k2FpCfoONoywaTNx/rKdARB+EB9sAWh7gUW\nWOBdGZ328MPHGvEFI3OfPXd0gDu2JVKYlYpcFZ9VcPtCvHRiiK3Ls0k0qQkHZmlueJZx52LKs6Fo\nye0Igkhl/37ODGto6ahn2BVPi752YwHPHmymYzp+8d+8aIyYpoKDzXa6p5I5OZLC4uQRLigYQ6sI\nEQjL6BwqY+nidHRSHwAqWYwCs4360XRa8y9jg/NJSIzy0KP7qJEdxlgiJ2oLI8vRQo6WmCOM8oyM\nA8E09ibksTg1zPLRoygjYSRbCFmhAZYY0KdU4+1rITw5g5ikRCtfQsrdNyKJApOv/i+++jPEBnzx\nu5cAyhsyiQ77wRXGUFpBYO8gQeskAU8/bk7MHUNJJqc8cRWV3nhnueSbbsG0/gLC1hlGfvNrFIPx\n9OLHQvmcerqZogwDJXd+YZ7IcszrxXH4EOHJCXzdnQRHhuOtZS+9jDtnw/SMOlhaZcQxYkef9M6p\n2FN2H7UdU2Ql63B4QtR3zXDLlmK0xSVkffUe3PpE/uuJJlzeELdsLcbmDvDKyWH21Y9yoH4Mp3kR\n/3HnakxRL4/8Yif1TjObzpZd7T01QqLxDTHoQCjCriMDiKLAhdUZ5KcZWVORxgsnh3nhSD+P7uvm\njkvK5vZtYtZLU68VnVrOmNXLmvJUTDolu0+N8ErtEMVZ8XOwujj5HLvMehWLMoz0j7uQiQLRmMSs\nK8Cjr3YxPOVBEOBbn1zFgy91cKpjmk9dWsala/JQK2XnODYHGsaYsvkAeGBnC1+5YSlymTiv5GvP\nqRGszgAAu470U1WYyOuu1hP7e2nsiWtQ/fSpZlaWpXK6c5qYJHHVhnwuW5M3b+atIMPEvbctJyZJ\nDE64+eHjDTz4cifZqQZSzBpikkRdxzTPHOojFI5xx5VlczNU99xczTd+fYIXTwyhUckRBPj0ZWXn\n6B/kpxv58RON7KsfZV99vGyxqiCRtHfo5iYIAjdvKcLuCjIw4eLz11Ri1Cn599/V8vj+HioWJb5t\nQGqBBRZYYIF/LLGAn6lH/oQ6J29O5Dlsm8X20oskXn4FcnM8s3byj78nNDqCqFCekyX0VnxdcZkD\nbdliRJUKdW4ugaEhYoHAOQEOf2/cv7Fs2Uagvx9PcxMRp4OI00mgvx9d1RIUiXF/UBDFeJnZoYP4\ne7rRlpYR9XgYf+B+/N1daCsqSbnpVgb/9Zs4Dr2GMjWe8WRYsRK50UjmF75MLBwmMNBPxGGPC23X\nnsR94jjBwYF4V9k16+b2TabXk/mlrzD+q19g2bIVQSbjndCUxH2VeIYQWLZf/O4/wF/IX5JxJNO8\ns9YhwK4jAzT1Wufetw7Y+NxVFe+4zs7D/Xj8YT52YcGcTlBNSQqnO+MBkcVnG9k4vSH2nBqmPC+B\nxXmWuWDTvtNx/+ITO0p5Yn8Pr56OSyZsX5GNXqPg49tLuP+ZFl44PsjdV567L/3jcSmD0hwzNneQ\nV2qHWZyfQHneG4Gl1gEbAxNuqouSGJ5yc6xlgmsuWIROreDxs1lR21dms7Iig+8/VEfPqJOKRQl8\n7qqKc8rp3kpJjoULl2bwWtM4D77cgU6toLnXitUZIMGo4q4rylEp3jhX8tONFGeZ6B51AvFA0fkC\nbwlGNd/79Co6hx10DzsIhCIsLznXl30rCrk47zgVZ5upLkqiscdK94hjLjv+g2IhU+k98mGZcXg3\nFuz8xxINewEJQXz/8d63s9EbCNPYbSXJpD6nFaUUixD0DCNTmuYufKMjHfzkmV48/ii3bitm87J0\nNMI0HSNhTnQ4ibqbKSuqQAIe2NXKibYp7J4gywr1TPc+zIFOC+MuAzduqSQ9KR51N2jVnGi34nOO\nM9Axi0tQcdvFpUQctXRNGVHLY1xb1Y5RPkPdcDK9kwaSjQGuquxBLkaJSQJKeYzhKS+JFg0y9wm8\nYTUqWQBj5yTN4QLkQz2UCrOIaSIZfWfQlmuIBKB1JoW0BD9SMEr4uQmU5nTkScmcdhrJ72kjZ2QE\nyRmDcJTYkI9omwt52Ii/tYPYhB+1roCMT91NLBhk+o8P4nn1JLiZE6gGGKAE08goglJJcHCQqMON\nKNOgSE4GUUAKBkEUEWJRSrwjCIDPkEjeZz6DqFAgNxrZ600kODRIMDmD5pQqWvpmOdUxzSu1Qxw9\nM0FTzww2V4DitdUkb9uG69RJgoODSOEwiVdcha68gmSzhpIcC3K5Fn3iEmTy+dktb+Xp1/oYmnRz\n89ZiFPK45k5ZroUkk4ag3syPn2ln1hXglq3FbK7JIjtFz8GGMc70zeIPRti6IptVZanItVrGlYm0\nDNixGFQcaBhl0ubj9h1vBDT214/y/LFBesecDE666R93smFJBhuXZ3OyZYKW/lnCkRjF2WY8/jAP\nvtSB1RkgHIlRmGXiS9dWkZdu5NXTI0SiElZXgKxkPZetzSMYjvLI3i56xpyYdUqMOiWBUJTWARuS\nBOur0pGJAi39Nty+MOsr07loWRYVixI50jxB17Cd6y4sOGfGyh+M8KtdrchkAovzEmgbtGN3B6lY\nlIDsrMNndwf59XNtaNVyFucm0DXiICtZT0aSjn2nR3j+2CCZSTru+fhyGrtm6B1zYtQq+eK1VXMz\nS+dDEAQsBtVcyWDbgI3uEQc7D/dzqHmcYDjKpWvy5pX+qRQywuEYLf2zeP1hNi/LYsOSjHPG1qrl\nrK1II8WixaxXolbKuO7CQsz6dxa6FAWBlWUpbFuRQ6JJjU6tQC4TaeyxIgjxY7SQqfTR4KPgf113\n3eVs23YJ6neY/X99GUEQ+Oxn7+CZZ57kmWeexG63sWzZcgBcLiff+MZX+OMff8fRo4dZu3YDKpXq\nn8bOvzcLdv7jifp8jP30J/hazuBra0VdUIgiIYGxn/033qZGEGXoFpcTnp1l9tmnAPB1dmBat/4c\nAeQ32znz5BMQi5F8480IgkBoaopATzea0jKUySnz1pv58+Mgk5F03fUgSXibm3DX1eJtbSHqdpF8\n/U0o097QYhJkMtwnTxC22Yi63Uw/+nBcULtmOemf+Rxyk4nA8BD+zg5CkxMokpJIuvb6uXuqIJOh\nSExClZmFMiUV/dJqInY7weEhNKVlWLZsnbd/Mr0e84WbUOfknmPnW5FpNLhOniDm9aKtqCJh298+\nqPR2dI84+M0LbZTlWNCq39tEjjcQ5k+7u8hI0vG5qyoYt3ppH7RTkm0mN8N0XjuHp9z8cXcnaQla\nPn3Z4rnJryRTPBtn3OplbUUaggr07iAAACAASURBVCDwy2dbONE2xYm2ybOZzPHOuX/a00WyWc3t\nl5ShlIu0DthIMqn5+LYSBEEgLUFLU4+VjmE7q8/qqUaiMaIxCZko8OcDPYxZvXxyRymry9M41jJB\nbfsU3kCEzGQ9/mCER1/txu4OcveV5ei1Clr6bMhEkZNtkxw5M0FuqoHPXFlO6aIk8lJ0ZCXpuXFz\nEUrFOwcOX6coy8zx1gl6x1wMTLgIhqNsX5HNx9cFSE3QI1PMzy7SqhWc6pimOMvE1RecP6gEcSmD\ntAQt5fkJLClMmvMp/1IKMow4PCGWl6SgVSsWyt/+Wj4KTs3fmwU7/3FIUoyJ9l8S9AyhS6h61+Uj\n0RgN3fGW928NDsH5bfQHI/zoiSb2149ypHkcmSiSk2pAdvbmYB95GfvYbqIhJxpTMX29J7n/+XEc\nPhk3rNewtlhAGThFlqqB3EQ/vVYzbRNGwiEvIzMBXmscB2DC6mWR7hQut53n24pINGm4eWvx3E3I\nIkY5eaaPYbcRp6QiS/CyeU0OKs9upq0qVvoHyCkOoFP46R4z44hquK6yA70qQlLu1cQiToJBD2qZ\nn5HhLizaIJNuLSZZkMjL41T5him3dhCb8SBbYkKWqUaQiyTkbqKopAKfI15vHjlhI+JyU/bx62ms\n7+OS6eMgCAixGLprlhBTB5BmQoT6RxBkMgS5nIjNjq/1DLaXXyTQ24M8N5/UO+/GW1cL0SgApulx\nJEEg99vfQ1taiqhQEvV6CU9PIQWDGNeuI+sr93CyYZBEbzzV9aWyq1i/uggAlzfEb3f3MJaxmE9+\n7Ua2rshmzZJMtGdvep5AmNEZL53DDg43jxONSSTjJzY8gMxsJv3Tn0GQy+kcsvP88UFerRvhQMMo\nKWbNvECJJEn4ghFeOj7E4/t6aOmfJcWi4ePbSlAqZJxonUSlkJGXZuTnz5xhZNrDxatyuHxtHhAP\nWkzZfYxMe1DIRb54bRVKuYxAKILHH6axZ4aeUSejM17Kci1cd2HBG4LSL3cSCEX56g1LMGgVtA/a\nsTr8XLQ8h7wUXbzGvG+W5l4rr9QOMWb1IQrxVOGv3bgUvUaBUiHDH4zSOxafBSrONrG8NIXfvdjO\n8dYpekedHGwco6nHSnayjtEZL5FojC9cU8mSwiSOnomLj3/+6or4zVetQJKks/pLsDgvAZsrQHOv\nFbVKxqHGcc70z3L52jxu3FRES/8sLf02DjeN4w9GGZ328PLJISZtPm7cXMTGpZkcaBhldMZDS/8s\n++pHMWoV3HNTNZXFKVTmmjHrVdy6vYSs96hHkJNqYNYVoH3QzrjVSygSY/XiVD53dSXLS1LOWT43\n1cDh5nFUShmfv6Zyntjjm1HIZeSmGVhSmMS6yvR3DSi9jiAI85yk/AwD0ahEeZ6FZLNmIaj0EeGj\n4H89+eTjXHHFNe8YVHp9GZ1Ox5YtF3P99TdxxRVX85vf/JJFiwpJSUnl97//DXl5BXzvez9gZmaG\n06drWbFi1T+NnX9vFuz82xALBpl58nHkZgtyU7zkW5IkpEj4HTNroj4fo//9I4ID/WgrqgjPWvG1\ntRGemcLb3BRfxuHAvHkr7tqTeFuaUeXkEp6ZJmydQb98xbxr/ut2hm02Znc9i3ZxOcbVa+P7Ewnj\nrj2JIjEJbekbmcdh2yy253fFl121GmVmFlGPm+DwCBHbLPKkJFJuvnXedhQJiTgPvUZofAxfexsx\nrwfLjktJvfUTiGf1F2UaLe7aEyBJmDZedF4tp9cRBAFd1RJU6RmYN2991+yed/s9w1OTBAYGSL3t\nkyiS3j3L5GDjGG5fiNQ3ZQRP231o1PJ3LSN7nUg0xs+eamZ4yoMEVC6Kl8f99vk2dh4ZwKRTkZ6o\nPWe82rYpGnqsbFuRzZqKdLJTDGfLtDzsWJuP3z/fTkmSeGBXKzZXkM9cUT4vi9liUDHj8NPSb8Pu\nCmJzBTnYOEZZroWiLBPdI06aemfZXz9KLCZx1YZFFGSYyE0zEAxH2bYiZ+4YCIKATqOgrnOaYDje\nQOQHjzSw83A/47Ne6jpmyEzW87GLCrAY1GQn6+kdc9LSb2Nv3Qh760awu4MsLUxi28ocMpJ0HKgf\no2PIzvCUh+wUPZ+7phKDVolOp0IlEyjINL1jR7e3opCLVBUkUpBh4tI1uVy/qZCiJCvOsRcIeofR\nJy1HEASmbB4O1TVRU5ZHWoKW7Stz0L3P7G239TT2kVfQWsrfNvlAq1acbaAS39ZC+dsCC3wECfun\niUY8RN1eohEfMrkWlzfEkwd7uWpDPkmm+Te8548N8OLxIUpzzNy0tBG5EEFlyEVrLkOtz51bzn+2\nZE0uE/nlzhaGJt0sShUYt0V4fH8P7YM2vnhtFcSCeO0tAHhtzXQNO3ikNo1ARMOFhSOU6UaYHYxn\n46j0OawvvwFT5094WKzmlboZYAaTTsEWg5NnJrXsb5UTppKYJHDDpiLkMhEpFiE8Y2X0pz9mqZjM\nPuMKAPKsPQz/7CiKbXo+ZmwgcsYKLAIkdujPcFxfSprJT7THg/PEQVJv/zSnG/9Mum4EdShenqRC\nJPTkKERBHfTSbCykwVjCFusERSkOEGToE5chU+hIyLkS6+EnEFUaYn4/Mz/4HncolICEqNUhyOWY\nlm4mmrKT5KtugqEIyoxMvK0t2F95CX9PN8hkiBu28MOpdBaddvPZT92Jc3CE8b37MUe9DBmyKUxL\nQ5WRgaEmbmcsGCTm9yM3m3H5Quw0Lmd13lKEWJQuhwKPP4xeo2Bv3QjhSIwdq3PmAoaVBUmkGVXz\nftd99aPsrh3m2cP9HA1q+LgoZ6j8Ioz+GEdq+3jp+BCvKzQJwP3PtvDNW5aRk2rg0b3dNPbMIBMF\nZpwBZKJAkknNqrIUBAFKc8zoNQpq26c43TmN66w2z3UXFsztQyQaY3DCPfdeOFvk9YeXO6nrnEap\nEPEFIwjAzVuK5hybrmEHkzYfa8pTKctLoCTHQv+4i9NdMxxqHKM828R37ljJY692c6x1EpkokGLW\nMO3wc/PWonmBsYtX57CvPp6t1NBt5TfPt3GqY5rCLBNbarI40TpJS7+Noan4fm6pyZorD/vKDUsJ\nRaIkvWm8bStzeK1pnD2nRjDrVTx7uH/uPyQKAnqNgi3Ls892c6tmz6lhDjaM8cLxwbkx8tIMXFAV\nr7dfW5HGsZZJJmZ9lOaYuWVbydz2THoV21fmnOdq8M584uISLlyaiVkfF498p64rWrWce2+rQRQE\ndO9xFvP9IBPFeefIAgv8I5mYGOerX/0i5eWVtLScoaxsMZdccjkPPvgb7HY7//7v3yMrK5vvf/+7\njI+PoVKpueeeeyksLMLpdHDfffcyMzNDRUUl0pv07vbseZmnn36CcDjC4sXlfPWr30T2pgd5QRDQ\nnu20FIlEiEYjc9e/I0cOcf/9vwVgx47L+OIX7+Jzn/vSB3hUFvgw4K49iePAfgLDw+R8814AZnc+\ng33/q+T/538hN50rdhwLhxj/xf8QHBzAuHYdqZ/8FPbdL2N99mmchw+hSE1DmZ6Ot6mR4NAQ3pZm\nANI/+3mmHvwdnvrTjP7oB5jPdjZ7c/DK3x0vfdOUlM59piksAkHA3901bz9eL33TFMYn0kSlktSP\nf5KUm24lMNCP3GI5p9xLkMvJve97hCYniPl8yAyGufVfR1tegTwpiYjVimHFync9hoIoYli56l2X\ney8kXn0dhtVr0Sx69/vftMPPw3u6UCtlfP+u1Zj0KvbXj/Loq90sK07mzssXzyujejteaxxjYjZe\njn+8ZZLrNhYwZvVysn0KgF/ubKEs18LdV5bPE6s+1RH//vUuYYsyjKyrjPsqe08Osrxofse0010z\n9I46WVacfI6GEcBt20uYmPVyrHWS422TGLQK7rqiHJNOyfUXFfJa0zgHG8eQiQLrKuPZZ3KZyI2b\ni84Zq6Y4mbQELSdaJ2nqseLxhzHplJxsi+/z1hVvdIerLk6mYlEih5rGaOm3oVXLMemUbDkrcK1T\nK9iyPIs9p4a5fG0eO1bnnncS/nxEw16iYSdK7bnZ3emJOtIT45n/kiQxOXEIgHBgBr+zE625jN62\nZ6g0DPH0nlluvnTbe86EOh+RaAxBiOGcOEws4sFnb5uTs3B4gtjdcV3VRKMalTSJY3w/luxLUWrO\nnWj8e7IQVFpggX8Sgr7Rs68kAq5edAlVHGud4HjrJDq1gpu2vHHxDYQiHGyIt5nvHHbwiM/EDdUd\nhANTeKz1ZFV+HYhr5Nz3YB3BcBS5TCASlVicGebaxbUEInKeOVNKcx88faiPHRU2pFgYY+p6ajvt\nPF1nQkLgtq3ZLMvW4RiLt1AVRBVJ+Tfga2pB29LH7beIPNK0hBmvhuuUwyTV7ye15GpaJ+IzNeXZ\nBspMdqb7XiXg6iU2FSam9bKhch2H+yRCEagyzRIZtSKMiJiXbsQ+swfOhkOyqiVujPYhAZHTdv4f\ne+cZGFd15+1n7vSqkUbSaNSrR7aqC7bcu7ExxhRTAoQUaghLkn2TTdvsZjckJFsgbEJCCIHQi0PA\nBmPHxsa9W5Zs9V5GvU7vc98PI48RtsEQyGYTPV9szZxz59wzd+49519+/+DoIK4z/4Bv7jyYBVpF\ngPBQgPg3axADAaSGODL/+V94a1c3A+2jBIbtkDyOSpmNVB59CMicBgLbetHPX4DMEIenvg6/rRtN\nUTGpX/vHWPSFqLag1SUgpEYfQqqsbEzrNyBGIvh8Af7t+dMEIl4ausZpmFVMT7KJdtMwVw0e5ojO\nSkr7GKV5JkbsPs60DjM0obNzVYWWpq5xACzF0wiHIzCRCpafFseeShsGrYJFJReWzj2HWiljw4Js\nVsxKo7IpWoXrN3HJuAZCvPxEVLfJZFByw9I8ZluTqGoZ4ddv1vDo5mqunp/FntO2mCb4uopMAsEI\nu0/ZeOtwNCroXDnV/dW9yKQSbl6Rz+orMiYZMN4+3EHPsJt4vZIxp5/3TtsozIznRMMg8Xolzgnv\nSGKcapI3bm9V9NpdWp4GgCBIuHP9dP716RM88cczfPPmcrJS9Nx59QwWlVqiaV4H2inPT7xgTgwa\nBbevsVLdOkxt2yjH6wdJMCj56nUlxGkVzJ1uxuEOcKx+gO4BFxsWZsf6XqzCiFIuZdPSPH77dh0v\n7mpCIRdYPz+Lzv6o0OKmZXkx/SSdWs4NS/NYPz+LM60jE4Yb2STP1w1Lo9FZs6YlUZZnumwP5Ich\nFQRyUw2X3d4cf/kVaKaY4rNge/cQZ0ddn6ivVCpE75EfoCRBx7qMj44I6Omx8aMf/YzvfjeXu+66\ng127dvCrX/2Ogwf38fzzz5CcbKagwMrDD/83p06d4KGH/pXf//4lnnnmt5SWlvOlL93N4cMHefvt\nLQB0dLSze/cufv3rp5HJZPzXf/2UnTu3s27d1ZM+NxwOc+edn6enp5vrrruRoqKo9sXY2CiJidFN\nm8lkYmxs9BPNyxR/3ziOHgbA19KMt7kJWUICYzt3IIZCeFtb0c+aPam9GInQ/9vf4G1qRDd7DuYv\n3olEEIhfexWe+nq8bS2k3nc/gaEh3FWncRw+iKe+DkVaOoqkZFLuvpeBZ5/BU1uDt6kRqcGAYd58\ntNdeBco4PA0TekrW8xFJUo0WZUYmvrZWIsEAgjxq2PA2TxiVCqZNGqNEJrvgtfcji4uLRWVdDIkg\nkPKlu/B3dqLM+PgOmz8HqVp9gUGpqmUYCVCWP9lIc2zC6OMLhHnjQBvXLMzhD/ui2qCVTUP8x0uV\n/MMNpR8aMezyBtlysB21UsoVhcnsr+7jZOMgp5uiOkl3rLVS1TzMmdbo2u+c/qPDHaCuc4zcVMMk\nB92mpXmcahzid2/VknDLzNgaIxiK8Ie9LUgFCTcuv7jBTCGX8sD1pfz7709gdwf44tpC4ib0KON0\nSjYuyuHqBVlEIiLyS0RLn0MQJFxVkcXT79Tj8YX4wlori8tSae4ep2/Uw8IPVOOVywRWzcm4oOrv\nOa5fkss1C3Muq7DQOURRZKjtFQKeHuIsyzGYo6Lrbb0OkuPVkwx03vF6gr5BlLos/K4u7H378QYi\nZOiie6YMTQtPbEnjq9cXX1ZK29nmDlrbzjK/IETEP4g+ZTkP/2GUmenjzE2JPkNdo1Uo4sp4+0gH\n2492EY5EF/MWg497FpxFIgYB8dIf8hkxZVSaYoq/EgLuntj/vfYmtAmlMcPD6eYhblmZH9uQHjzT\nh9sXYv38LFq7OmnoSeD56mWsK3GRIjuB390NmHh1dwv+YJhp6XF4/GHSEpWsSn8HpSqe+Phibpaf\n4clDCnYc6yJoHyI33sBpZzpbjoNKDl/ZWEBJfgZjPTtjYxMjIbp/8iPCdjcERbSDDu6uOI0griT4\nm13Ir0xmU0o9GkUIrSKEGBIZ7ouOW3SEEMxyFBssiJzl21kyxEgYmVSFGMkidHIc1eLpyGafQgyL\nhPaOIVsWjygPEun0YZy3hrHt2wBIPHKMSKoZIUWFuyOMSiZDDARIuu125Akmblmh5Fj9IHMkUhzv\nVSMzJSFOC4MgYD90AABNcSmNhhxKN96AQhJBIpXFvGM17SM8+uoZslL0rJ2XyWxrUuyBIBEEXn6v\nncExLxVFZk7UD/LanhZC4QiBpEIiX7iazjcaOHi2D4tJw0PPncTpOV8+vq3HTsqEl6Mw00ggFN0w\nNdvGOV43gC8QZuOiHN4+0kHXgIu0JC3zS9NIT7gwPFurkrO4NJXFpakEQ2GO1Q2yt6oHc7wahyfI\nk2/VUdFm5sZl+dyyIp9X9rTw4q7mScfwB8LsqYz2idcrqWwaorXHzopZaSwptbBqTkasmtc5ugac\nbDvSSYJByfdun80PfnecXSe6qZoQob73miLM8Wqe2FpLY9c4v9lay30bi3B7Q5xqHCItUUtB+vnF\nYXK8hjvWWnnq7Tr+4+VKvrapjGkZRpKMav7n9TOolVI+f6X1okaZJWWpLClLpa5jlO1HO7lxeX5s\nQQNg0CpYfYkFx8WYV2TmWP0Abm+QL6+fHvNIXQqVQhbz+H0Qo045SXB8iimm+MtisaSSlxetepmT\nk8ucOXORSCTk5ubT19dHf38fDz30HwDMnn0FDocdt9tFVdVpfvzj6OsLFixCr49usk6dOk5jYz13\n3XUHAH6/j/j4CwVRpVIpv//9SzidTr73vW/S1tZCbu7k6pvR+9mfb2ie4u+L4PAQ3qZGZPEJhMZG\nGd2+DalOjxiKRtX6u7suMCqNbH0TV+Up1NZCUu66J7bWkQgCad/4f9HoH50OuTkFiVLF+N49EImg\nLS0Douln6d/4JoG+Xsbf243j2FHGdv0J+773SH3wG3gbGxDUapSZk4056mlW/F2djL61FdN1NxC2\n23GfqUIik6HMzOLTRmMtRPO+aKlPypjTz89erEQqlZCdYmDDkjxS4i4vLRxg1OHjl6+fJSKKzJth\n5vY102Ip9sfqBpBJBRLjVByo7qN70I0/EOaOK6209to5dLafHzx1jBuX57Oo1HLRaOSth9px+0Lc\ntDyf2dYk9lf38dahDgbHvORY9CydWBc9/seznG4e5tU9Ldy2ehonGwcRRZj3gTVLnE7J3VfP4PE3\nzvLzzdV89/ZZWEzaaHW3cR+r5qR/qIMqXq/k+5+fzcCY96LRTFJB4DKDhJhfbGbc5acgPS4mNm3N\njP9EwtMSiQS57OPdY/3ubgKe6J7M3vceLtcom09ncaZ1lIL0OL5z26yYlIO9fz8gISHjauz9+/CM\n1eC3vUlElBBGT0HSGO/Ud7P5PfVFI7PO4RqpwjV0gjhvH7OSwR9VdWCwYyuj46UkZEfX7TKliYDb\nxiMvvkdTv0C8XskVhUnIsVOgPoFE9KMxr0ehvvia9LNkyqg0xRQTRCIiLT12CtLjPnE0gS8QQimX\nxvr73TbcI9UY01YiSC+tw3CurURQIsjUeB2thMOhWMWAYbuP7kEXmWY94UiEnSe6kcsEVl+Rwdzk\ng7wWVlHbn8QTu5XkJ87gNn0nw6FUqlqGWDHdz42rZ6DUpGDvP4i9L4g+aS765Hmo9Dnc6nmNp46V\ns7shid0kATZMBhVfv7GUtCQdkXAA10gVgkyLzjQTx8BBwhoH4S5XVH+oW4k8J0Lo5OvIliQizdeR\nhI9AWE5oMIwgCRHxRxDr/YSaRkn80o2I6SFCgTEiYT+BwR7CI36EFBWyOUbsNTuRGCSEG5yEG8YR\nw0EU880kzrqN3V0aZizyIR7cjRxwnPBgr0ii1aZnEXYkMhlP1oSRtVXz9RvLuD5JR8Rnwf3uLlx1\nx+gZcSGRyXCfqUYaZ+RU2MQLW2pZOSud29ZM9o5tP9qFCHT2O3liSy2JcSqunJtJqknD7soeKpuG\nyErR8+WrpmPUKtlxvAuA1XMyKJxmwWLqoqp5iJ4hF05PkA0LsinJNbHzZDcnGwZpstlRyARyLAaC\noQiCRML+ql7cvhA5FgMpJg2PbT4DwJnWEbYf7eLrN5ZRmnfpkrJymZRFpRYWlVpo6bHzk+dPIRUk\nHK0d4HTzMF/ZWMRXri3muR0N+INhvnpdCb96s4Y9lT3IpBK+cm0x6Uk6th3tZNuRDt440E6SUcXS\nmWmTPicSEXl2RyPhiMgX1xaSYFCxYlYa24504vAEmVmQGIsC+vqNZfz8tWpONQ7xnSeOEopERRcv\nVn52flEKcQY1j75cySOvVpGbasA+UXb2i+sKidd/+IJuRnZCrPrIn4MgkfD1G8v+7ONMMcUUUdZl\nJF1WVNHFSErSMzTk/OiGl0AuP5/2KQhC7G9BEAiHQ8hkH28pLIoi69ZdzX33PXBZ7fV6PbNmzeHo\n0SPk5uYTH5/A8PAwiYmJDA8PX9QgNcUUH4bj6EQ08sZrsR/Yj/tMNUgkyEwmQiMj+G3dk9qHvV7G\nd+9CGhdH6lcfjEUMnUMiCEh1UceRoFCgKyvDefwYALrSyc9ChSWV5Fs/T9JNn8Nx7CiDz/+env95\nFDEQQFtWfkHaWvyV63BXVzH6ztuEXS7cNWcIjY4Sv2Ytgvyvt0LotiMdDI57kUkF+kY8nGoc5D/v\nXzApSuXDeO90DxFRxKhTcKxugGbbON+7fTZuX4jeYTezpiWxbGYqj7xaTXufA2uGkaXlqSwtTyU7\nxcDr+1r5/fYGDtf08+ANJZNEuIftXt6r7CExTsXK2enIZQJFOQnUtkejHq+qyI5G3AN3XT2Dnzx/\nit2nbFQ1D+H0BJEAcwovTI2aOS2J+zeV88vNVTz8QiXpSVo6B5xolDKuWZjzkeecaFRPkhP4pEgF\ngasntDs/Ln63DYU65aKaQx5fCLVSyphtB94hGfK4ecgUF0Z9Owcnfl/ZNzDUvQ+c1QheDypFKs02\nO83NJzAKTYT8Y4QCY2gTSpGrTMSlLMYzVoMgCfNeSzbXLCnB3fcWSwuGefOEmtmZDkzKfkKBaMBA\nfNoa5Ook3KNnGe3aCgi0jRhpHExgwG3i/nVKPEMHuaa4mTzTGKLcQpxlPiMdfyRT30WaeTZrpjUS\ncB1EjERT4PY0ZzJYL+H/3RL5xGLfn5S/7KdNMcVfMVsPtfPTFys51Tj0sfoNj3v5zdZa/unXh7n/\nkf38068P89K7TRyrG+BY5RHaOusZbn8dUbwwfB+gZ8jF09tqGBxzo9Smoo6bhhjx097VgtcfipU0\nr2yKjmvvyzsYtvtYWGRGp5Ig+m3cWuHgB1+Yw/SsOFqGE/ivrWEe/0M1eYnjLMk8yUDjkwx3/BHn\n0AkkghytKbpIUOmzycmdzT3zT7N+eguLs2G+0c/3bikhbUI42Gk7hhj2oTPNQimJepWkRVGDgaBS\nEaizERkPIp2pR1ZiABGCx0aRve0itLkTVX8BQrWaUNMoCksq8QuuwpS1kaSMm9G5Z+F/rovQ0bGo\ns1aEoC56nqHqqEHNNHMDGYt/QLM/mS0H2/nlaDrSzGxkJhPtM2/jmcoy0gNuIh4PzuQs6vo8nGkd\nieUYj/jgycRVjFvy8dTV4j5TjWZ6EZbv/oC3KqMhyAfP9uHxRSOJQuEITd3j1HeOUZhp5Cf3VLCs\nPJVxV4AXdzXxn69UUdk0RHaKnq9sLEImjT789JroA3/5rDQkEgmLSi2EwiJ9Ix5Wz8nguiW55KfH\ncdf66eRYog+x/PS4WDn6DLMOty+ETCrwhbVWXnm3GYkEvn3rTL56XTRt4o0DbZN0Pd7PmNPPk2/V\n0tg1BsCWA20AfPOWcr6w1ooYEXn8jRq6B124fSEWFKdQlp8YFc8GbllZQKZZjyBI2LAgm5/dt4CV\ns9MZtvv42UuVnG4+/7vYX91Le5+DeTPMFE8IQ66ak4FcJiBIJJM0dZRyKQ9uKqU0z4Q/GEaQSMhP\nj2NB8cVT+5bOSuer15egUclo6Bqnb8RDcW4Ci0svnQo4xRRTTPFJKSubya5dOwCorDxJXFwcWq2O\n8vLzrx85cgin0wHA7Nlz2bt3dyxtzeGw09/fN+mYY2NjOJ1RQ5jf7+PEiWNkZWUDsGjRUrZvfxuA\n7dvfZvHipZ/5OU7x6dH/7NOM7dzxmRzb19GOt7XlQ9uIoojj6GEkcjm6WXNIWLf+3Bsk33IbUr0B\nf3fXpD6OA/uJeL0Yl69EqvnodGjdhA6koNWiuoRGkEQmI27hIqzf/MdYhNTFIoTk8fGk/9N3kZvN\n2PfvJTQ6SuL1m0i88eaPHMf/FiN2H/ure0kyqnj8G0u4YWkugVCEfVW9l+zTN+Jm25EOAsEwgWCY\nfVW9aFUyfnx3BevnZzHq8POrN2s4dDZ6r6iYYaY4x8TMgkQUcoE71lpj0gsrZ6fz47srmFmQSFP3\nOI+8Vh3TdgTYeqiDcETk2sXn07qWlUe1f1ISNMycdj7dTq2U8Q+bSsmxRCvvmuJUXDkv85JOuisr\nsrhlRT6iKNLQNY7XH+baxTno/kyR6XOEAuOMdr9DOHiho6C6ZZhv/epwbI4+Lh57MwNNTzPQ+toF\na+Wm7nEefOwAr/7pIK7hcc8JswAAIABJREFUEwzZjmCr/QWHDr3GmCO63xgY8/DE6wfxjDei0KSi\njpvOG7XR9ffaUjff+txMBEkE7HvwOduIRPwoddnEWZYDIFclEdJcwZneJPzKmSQklyDINJSlDrCu\nsBWlaxuukVP4nK34nK30Nz+NY/AoI11bkQhKBuU38NzJYprGcugYUfLKsXhG3CqKUqLFY/p9eWji\nCgmE5ZSnDbAs4wB+RwNSmQaNsYj4jKtxyWbR0DVOTdtfPq16KlJpiimI5ibvPBH17FS3Dl/Ugn8p\nnt3RQG3HGFqVQFa8nQGXgXdP2ngXG2ACTMzPtnGtYidJmefLjHodLRypbua142oCwQie9HRuz7Gg\n1GXiGjpOXWs3IGX9/Cw2v9fC6eZhVs1JZ1tHGASRRUYvAXc3iGFU+iwsFgPfvGUWb25/jXfOmnCP\nurlt+XlvlWesBgC5KhkxEoaJtGaDeSlJg43E6wbwHzqBdJoOz+stGO/8KmG/i/GOd0EnIHPG4zh+\nkLDRizRdjTwrmYQVGxh45ndETnmRrYwaSmTeZHwn2wgzjmHRYt5NnkfmyVpSgLDHzeALz+JpaCA4\n0B8dgESCfFkiEqUUn02CKl0kbPOi0KYiyzRhXH0lEomEA9XRh4wzIPJC+lq+c+tMUgUppRV+0iim\n6+XX2DJ23hNe2z7KolILR2r76XdH+I2mgrU5ySycmYll7Rp2nrRhdwUwGVSMOHzsq+5l3bwsnnmn\ngWMTIoYrZ2dgTtBwx9pCrl2cy55KG2NOP4tKLeSnnY9o06hkfG1TGaMOX6wqxoKiFN461MH0rHhu\nXnE+5UEhl/LgDSU8s72BJWUW7C4/IjAtPY7OfifXL8nlbNsIA2NeVs5Oj4X7LipL5WB1L6ebh5k1\n7UKP/ws7GzndPMzJhkGunJtJbccY07PiYyHD8Xolv3j9LG9PCEovnxkVMlw9J4MFxSkXiDjHaRXc\ntnoaRdkJPLG1hl/+8SwrZ6czd7qZ1/e1olJIJ51XnFbBfdcUEYqIF6SLqZWyjxX5U56fSPkDiwhH\nInj9YTTKy6+GMsUUU0zxcfjyl+/h4Yf/nS984RaUShXf/360sM2XvnQ3P/zh97n99psoKSnFbI4K\nzObk5HL33V/hG994AFGMIJXK+Md//DYpKecN3yMjw/z4x/9KJBIhEomwYsVqFi5cDMDtt3+Bf/mX\n77Jt2xbMZgs/+tHDf/mTnuITERwdwXFgP4JGi3Hl6g+tsvZxCDkdDG9+DcfhgyCVkvndH6DKzr5o\nW39HO8H+fnRz5iLVaNCWlqEumIZEoUBbPhPle7vx1NUS9riRarSI4TBju3ciUSgwLltxWePRlpSi\nSLFEI48+4hxN8+dhueteRne8g272nIu2kcfHk/Gt7zK0+VV0s2ajv0S7c1XG9BoFd1xpvayxfha8\nfaSDUFiMafGsmJXOO0e72FNpY+28zAvEniOiyG+21tI14KLFZqesIBGXN8hVFVmolTKuX5LLqMPP\nkdp+2nodqBTSWNT5fRuLJzmQzxGvV/LV60t4els9h2v6eXRzNV/ZWIwvEOLw2X5SE7VUzEiJtS8v\nSGT9/CxK80wXpMslG9X84AtXXPb5r5mbyZq5mYTCEfzB8Mcu8iGKIn53F0pt5gVrt/HePXjGagh6\nB0kuuAOJ5PxcbjnYzojDx++21dPaY+dzq6Z9qBbSuMuPhGjqHkBn60G0Egi4WjhTvY2iolU4B/YR\n8PZzsLGUiCgi99cD0OHMI17WTYamgaGGFhrFYg7URyhM7EMiAaekhIG2Uc52hlienUCCqpe0RAmr\nioOoZAFCqnJyp18zaTyjDh+vV6ZQ36nkW7ekIRFkExkeh5iX1ceQS02zay4bls4BXysjXVsZ79kJ\nSEjMvYF39kad4XdfPYPH/nCGyuYxxkfyueOKGnxBKdX9CcyZJVDTn8SstF7E4Cj65AqMqati83j3\n1WGONwxQ+AlSBf9cPlOjktVqXQs8RnT7+lRjY+NPP/B+PPA0kAf4gC83NjbWXE7fKab4NNl5ogtf\nIFoOvrZ9FFEUL2sT297noLZjjMIsI7eXnSDkHyCMEpf+C9j6OhgdrOPsQDZHOtLptdu5dflRCqZV\nMDY+xotvn6LSloRSFkElh6aheOSaVFS6LCSCnOYeBxCPhZ3kp+TQ0OPisZdPMS7VsmD0DJp6Lb6s\n6KZepcsGwDV8ioqcUdJ13bhlJcQpop5VmSqZkG8QgKBvkPbqx9Dqy1H2SHHs3UegtxeUAqrbc0Al\nEhwdYGjHqwRMPUgMUkKnx+k78ThiJIJQoIkaldYloihIQ25JIRxyIYoGZPI4gnuinhy52Yz+ptvp\n/M/XmesZIhKXAPZR7Pv2IqjVqAuno0xPR1NSRn9gNwrsnAxYkL9rxxZIw5dTzq2rpiGRSBh3+TnT\nOkJWij5WIv2Fd1u58+oZzJ0RT2+fnee0V2DzuLllZQGv7G7mZOMALm+QUw2DSAUJs6alsL1BYG+j\nlKviu9h5ohuVQsq3PlfOvzx9nN2nbJTkJnC0th8RUMoFygvOp5oZtAquXZx7wTVw7lrJTTVMEk/W\nT1S9cHoDvHuym0SjmuKcBGQygfrOMYbtPn79Zm1MYE+QgFop5U8nunB5ghg0cq5bfD7c+NYrCzl0\nppc3D7RRXpBIIBhGIZMiCBKqmoc53TxMepKWIbuPbUeiAoHXvq9/aV4id66fzpNv1ZGXZiArRR97\n78MWDOUFiXz71ln88o9no8bSk1FB+c+tKrhARHLmRYxdfw5SQUCnngqonWKKT8LUGiyqp/T886/F\n/v7+93940fcefvi/L+gbF2fk0Ucfv+hxV65cw8qVay54/Q9/eAsAo9HIM8+8dNG+cXFGHnvs15d9\nDlP89eBrjYopRzxufO1tF1Qf+7iE3W7Gd+9i7N2dRDweFCkWAv199D31BFk/+DcEpZKwx42gUiMR\nBMJOJwMvPg+AYf4CIJq6lvHt78WOqczIwFNXi99mQzPNiqvyFKGREeKWrYiluH0UgkJB9kOXb+zU\nz533kVXUZEYjlrvv/dA2LT32WLbA2rkZJH9GRSZc3iBHavsZGvMy6vSjUkgxGVToNHLCYZGDZ/ow\nJ2ioKIrq0qiVMlbPzWTrgTZONgxSUZQy6XiVjUN0DbhQyASqW0c42zaKIJGwfEI6QCKRcMdaK7Yh\nF92DLmYWJMWqgcllAnLZxVPqBImEL181nXAkqsP07SeOYIpTERFFrlucEysIAtH10g1LP53Kqz5n\nO+7RsyRkXBVbH/rdNoK+ISQSGXJVIgrNeSP63qoepBIJ84tTkEkFxnvfxTl4BGPqagzm+bF2If84\nnrHaieN1Ye/fh3Eiyqe9z0FHv5NpGUa8/hB7q3rpH/XwtU1lKBVSfK4u3COVGFNXIpXr6Rpw8ovN\nxxCBezbOQSPzoKGbAZcWpTREvLqStqo6VLJogRx9OESqyUp52jAOn4JnD6dQlG1lYe4ARqpJVVRx\n84Tv0+5V8tLxIEp5GxLAlFKGOP4e7rE6KrKHIQDbz+q40eIl2ajGNuhid6WNg2f6CEdE8tIMWLOi\nRh1d4hxcI1UotJnsOJNOa5+XYy2nWTUnHWlwBenK46ji5yDX5nK29SAmgxJrppHlM9PYcbwLhS4b\nY3oGz/6pmy6Hm85+F4faLBQke8nMXYA+cbJ2mlIhZXHphRXr/hJ8ZkYlq9UqBR4HVgM24ITVat3a\n2NhY975m3wOqGhsbr7NarYUT7VdeZt8ppvhUcHmD7DoZrbaVl2rgdPMwvcPuWPrXh3Fu876qKELI\nP4BEqkIa9pFttGGWtOPT2diwegPP7uzidAs8/EcP6YkHGBr34w8lkaxzc9PMVg535lDZpafPGU++\nUYZKP42OES16pR+9tJ+CuBANPQW0DnrJNoyyfHo/rsNOWCgFJAQ7HIimesaGdgARzHoQpFVEwiBX\np2BUr2a49mVCTgeeoAxNcYSg5yQBZYhwohuGpChWp4BKRK5MJpgwiI+oKFyo3kno8PkwykiTC/mC\nEoLqfobaXkB6nRapJPrgd73RgqTLGdVakis43jDEwsFTALTO28CiVBkKiwVVds4k71fbngYK4+1Y\n86zorihm4KSNs01D/Pj5U3zrc+U0dY8TEUWWlFpYVJpK96CLQzX9zMhO4Jrleja/14JtyM2y8lRW\nz0ln54kuatvHONMaHXdxbgL3bSwiK0XHO0e7eH1fNDXsmoXZJBrVLCqxsKeyhye21MbqJQRDIk5P\nEKNOSUQUqesY5eCZPnyBMHddPQOdWs7guJfHNlfjcAdIjteQl2bgmoU5aFQynt3ewIEzk0N4VQop\nCQYVvcNuZFIJ2RY98TolTBjOnJ4giCKaeBnXL8mblEOfYdZTMSOFI7X93P/IPgLBCEadgrXzsnj3\nZDdSQcK91xQRCov8z+tnyEuLoyB9cnWziqIU0pJ0GHWXpwlwjhyLgZ/eO59TjYPsrepFo5SxYlba\nR3f8O0EURfq67ZjTDEgvV4lyiik+Q6bWYFNM8enz/tQ0d82Zj2VU8tu6ifj9yE0mgiMjOI4cxnns\nCBGvF0GnI+mWWzEuX8nwH15jbNef6HvqN4iBAJ7aGmQmE3FLluE8cphAfx+GBYvQlpRe9HPOVT3z\nd3ehLpgWTdWTSIhfdaER9LPkV2/WMO70893bZ112pPG5qsYA+6p6uXH5ZGF7hyeAUiZFqYiuH0Ph\nCFXNwxi0ClITtZNStE43D/HCziZC4QhKuZT8tDiWzUzD7g7w4s5GHO8rnnIxNi7KnqRJc/WiXN46\n0BbVz5SAwx2kJDcBc7yGNw60IUgkfO/zs3lhVxMtNjtzrEmY4s5rqSrlUv7h+hI2723lqorLr0wn\nCBLuvnoGhZlGth3pZGDUQ1aK/qIR6822cSwm7YemqgV9w4iREDJVMi5fCMMHNKJEUWTMtoOgbwh1\n3DQ0xkIi4QCDLc8jRs7PWWLOTWiMhfSNuHluRyMA7xzt5Nq5AhaimkSOwUPoEmcjSKOf4Rg6CojE\np6/FMXgUR/8BlJp01HEF7Km0kWca48ayASy56/jd9i5ONw/z6OZq7lqbhqvrZQT8uJyDyM038eK2\nQ9w1t4qIKOGZrUGuyBpmThpoE+aSbM7C0fkccsGPRz4LebCZOem9ZFlMyKUhRiPFfGVTObPyEhAk\nEmwD82hoPM60VDl6NdS2ahkY8wEB5k5PJjU9m57xvbiGTyIERhnzGTnVBpVPHMGcoKF/1ANAcrya\nq+dnU1FkjkWLyRRxpBVHq0p/Mz3MjuNdbD/aGduHQClqZZjrl/Ti8YeYV2RGIpGwriKTUaePdfOy\nMCTpEVVKRmzDHK3tZ8SjxqW7CX3iX16M+8P4LCOV5gItjY2NbQBWq/UVYCPw/kXJDOCnAI2NjQ1W\nqzXbarWagdzL6DvFFJ8K7xzpxB8Ic92iHDQqOaebh6ltHyUtSUdVyzB2lz9W+vz9tHe1RbV1zEqS\nJQeJSKQk593GQNMzOAePEQqMIVebMRiSeOCGRI5W17PvVB3Nw/GoZGE2lo+xdGYmjt7T5CfYqOya\nztl2F/kZyQT1K3AHTnNFoYmM0pXIEk6xrd6DVhFg04wG5HEaRKOcgKcHqWig/xe/RLExFSFdhUqX\nh8/VSiTsRxRFgieH6d4bDedHKkUSFvGfBv/cNOJLFcgXm5DNNiLRSJGRQMr0exhr2YnTeYxIixuh\nQY7lvvsZ2/UnAv0DWO67H+30GfjdPYz37sLtceOtsqFoHsU7CPZ568gebsbf1kr37r1U+Edo0GVR\n7dWzftGFIc+iKPKnumRaTRLuvXkBgiDFmhnP6aYhHn+jhv/5wxmUCikKmcC8GWbkMoF7Nxbxw6eP\n8+yOBt6r6qHFZsdi0nDzigIkEgmFmUYO1wzEPmPWRCjyjmPdhMIRinMTiERE/MEw9z+yj7K8aP55\n73D0waBRyfD4Quw83s0saxK/21bPwMRDA+DR16q495oiHnmtmsExL8nxaroHnbT3OThaO0C2RU9N\n2yhZZj3XLcklGIrQ1mfneN0gvcNu5k5P5oaleZPKuV4O1y7OoWvQiSCRYNAqaLHZeWV31Pi3bl5m\nzBD6n/cvuOQxMpIvz1P5QeQygYqilAs8dFNAf4+DLS9VsXhNAcWfsrHNFwrz85pO5puNLLV8MhFy\nfziCBFBMGbz+nphag00xxaeMr7UFJhxi7rNnSbz2BoKjI4xs3UL8ylUxg07Y7cY52kdEZ0IMhxl6\n9WUcB/dfcDxpnJHE9RswLluBoIoaIEzXb8JdX4f7dCUAyswsAv19jLzxOgDxV64lcdPNlzTUvN+o\n5GttwdfehrZ8JoqUv9yz2+UNcmqiylizzc60DCORiMjBs30UpMfFUuSDoQgjE7IBdneAEw2DWEwa\nnJ4gB870ce3iXOQygY5+B3863s2J+uj73/v8bNRKGa/uaWH3KVvsc4tyErhz/XRGHD6e2BKNiEmM\nU+H2hThaN8DRuui6UC4TuH5JLsW5CSToVfgCIUbsPty+qG6RWiVjRtbk9CFLopbygkRONw/z5Nbo\nrXDze9H1Zt+IhyVlFjLNer6+qYw9lTYWllyoA5loVPOVa4s/9nwKgoSl5WksLLFQ0zZKpll3wfff\nM+Ti4RcqmTfDzL3XFAFgG3Kx60Q3t6wsQCaOY+/bi2c8OvZxfxyHWpNYtmA103POG6gco60EfdFo\nseGBGjKNhXgdzYiRIBpjEUpdBuO9uxnp3IJclcTR2qgeUWGmkd7BIfT+SsJyCY2DJmakDOMaPoHB\nvJCA341r+DRSuQFd4mwUmjQGmp9hqO0V1ImLkLh7uG12G0IQ3P1SvnLtJp58q46qpn5azu4lNc5P\nn0OLxdBPY9Vz3FDsRCkPRzVBy8+ilIYJRuQUTq9AEOT4g3fw8801SJXxFJlFlmadJkWIFr8pL1tG\nanpWrABEujmRdPNVsTlYZQ5zoOEEA2MerlmYg1SuRaXPxudsByArdz536zN4r6qHVpud4twElpen\nUZpvuqg49rnvSqmQsnFRDotLLTR0jZEYp2ZgzMMz7zTw4q6m6Njyo/sRvUbBfRvPXyt5aQaqWobZ\nX90bm++/Nj5Lo1Ia8P7yAzbgg7GR1cD1wAGr1ToXyALSL7PvBcTHa5DJPp385ouRlKT/6EZ/A/wt\nnafPH0Ihl04KET1HUpKeA6d72HG8i6R4NZvWFOLyBHj6nXqaeh2sFASe2FJDIBjBmpNISf554TtR\njPDz504BJipSTxMJOTBnLyc9uxC/vZjxgeiNKymtPDaf16yexxzzOO3d7yFEIpTMux9tYgY1Q8fI\nM40hFURqOka554YyKicibOYUpWFOMWFOWcNDhgFsP/oh2mEw3bWMEU4CIsG6PqS5eoR0FRGbF+/R\nasLWMBK1lFCrG7HNjVSjIezxQDiMKJFyRDcDY7WL4ppepKVaZCVxiN4QssYIyWviUGquof+JfmQH\n9lLwjQdJXrYU1q38wAQWkppp5WuP7MXdmcI9s+S80R9H90iYezR2EsQWZjbtQ0RCT8lSOgecaPWq\nSdE3ALZBJ2OuEOqCYszm8zfJNUl6BLmUx16twu0LsXx2OlkZCbHv7t7rS3nsldO0TFTIE0XwhEXS\nk/QkGCeHTKelxLHjhA2XN4ggSKhpG0WvUVDXMYZUkHCiYRBBAhERPrd6GhuW5PLAf+5ld6WNnSei\nVeBWXZHJlRVZ/OloJ++e6OKfnzpGKCxy48oC7rhqBqFwhK3723hpZwM1baNYM+P54T3zJ3mM7ouI\n+AKhC+bgcplRkMwT31kV+9vu8rP1QBv9I26+vLEElfJvQybv/9o9aKg3ujAJ+sMfa+yX07Z+2Ikj\nGKbHH/zE8/LvB+vRyKV8c960j278GfB/7fv8G+EvvgabYoq/diI+H4Mvv0jckqWo8/I/usP7+wYD\n+Lo6UWZkIqhUeBvqCdnHGXzxedzVVbirTpP+T99BDATo+cXPCdujFWklSiURtxtlRgaaGUUER0YQ\n5Ar08+ahmV50gWaRIJeT9tUHsR/cj272HFSZWYQ9bhyHDyOoVcRNaHNdCoU5BYlMhr+7OyYoHr/6\nyo83UZfJ1oPtdA27WTM7PVbxFaCmbYRzOskHz/YxLcPIkdp+fr+9AbVSyv3XlmDUKXhiSy09w27W\nzctEqZASjoismJXOiN3HjuNdnGwYpHfEHcsKMGjk9Ay7+e1bdcydkczuUzYsJg3lBYk02+zUto/y\nr08fRxSjUUxf21RKaV4ioijS1D3OvupeQmGR65fkxvQvISpvcDmpdresLCAtSUecVoFcJvDOkU5q\nO8aQSSVsWBCVG9CoZJ+4ctlHIZMKlBckXvS9c8V8qpqHCYbCyGVSthxsp6+vjY66k2iJGkTkagtd\nw5Cs7mf9DDtDPf24TDehM6QTCIY5cfIdzCoIhgWC9iYaOkZIFBsAMJgXotCkIEjVjHS+wXD7a5xq\nKEIpl/LAxlxGOk4gBoJ0eGaytU5JXuI4joEjqI0zOHxkC7lxQbo9ZZgjEgKSJEblGzAE3sU7fIBV\nBRAU1SjVcXjtDehcrdxz9TROnziJWeViOJhHwLiEMe9W8hJHEEUJidk3EPQOwsABACSqcgQhur5O\ns2RSPt3H7lM2Bsc0zEhKIEkzilKXhVz54Q46uUzKtz43k3GXn9TEqAFUE1+Mz9mORCLDYCphvlnF\n/OIUIhHxonvMDyPBoIoVq5mWYcTpCfKHva0o5MIljUV5qXEABEIRUhO1MR2pvyb+t3cgPwUes1qt\nVcBZ4DQQ/qQHGxvzfHSjT8ifW9L2/wp/S+fp8YX456eOolbKePCGUszve4AkJek5VNnNI6+cRqWQ\n8sB1JTjGo9dPWqKWmpZhHnv5FIFgtGLbL187xQ+/XBET52tpa+BMbwLJKiczc9MRFJnIDXMZGnKi\n0JfChFFJlOdNms/BzccQXL3gD2MbPkTC+g0EjjpRlIfJZpjWXgknz/aydV80xNpiVMX6G8f7cTgG\nUVyxgLAmnkhNAIlZhqgHxZWpiIQIH3cRHnAicSsQXQEAvFIlao8HVU4uvlmLCL3xCgvHzgIQ0BrI\nnH4j0kIrtkf+C19nFY/+Zg+HWp3c3XIUmaDgp8cDLBObmVOYhPwDRtsjNf109DlYOGs6eVfP4EG7\nj/9+tYp9XTKuA5RikNbpS3BpjESGxjl82kZZfiJ9I260ajkGjYIj1dFw56xkHYODDo7XD5KWpCU9\nSUdZTgLXLcll68F2FhalTJ7LIVfs/3lpBtp6HfzsuRP86M55dPTYJ43zxe31dA+6sJg0PHB9CU+9\nXU9Hv4Mr52awfn422492suNYFxaThpUz0/C5A6y5IoNXdjdjMqi4e8OM2ILpluV5OFw+jtcPsrAk\nhbVz0mPjWlxsZnq6gdMtwywqseB1+fC6fBdcm27nha99FJf6ba6dExXcdjq8/C38cv8v3oNGRtzR\nf4ddlz32yz3Phv5oNb8Rl+8Tzcu4P0i3w4tBLvtfmdfP4vucMlJ9anxqa7App96nw9R5Xj6jJ06i\nzc5CmXT5Wn49b+zBcegAjI+Q+ZMfAdD71jZsr/+R0p89jMp86SItjvoGCIdJKJ6OMjGRjoZ6HFte\nx11dhTI5Cf/gEL3//R+EvV4iwSBJS5fgsfXg6+8n4+YbSb/xBgT5ZTqUkvSkzni/hqOelKzrL/s8\ne7My8XR24e/qRJuXS+bCOZ96sYveIRdbD7UTEaGyYZA5081887bZaNVyGm3RqAu1UsapxkEeuGkm\n7xztQiaVEAqLPLq5GplUIBAME6dTsP1YFxJJVCLgmmX5jLv87DjexTPbGwiFI1hMWu67oZSy/ER+\n+NRRqpqGqG4dRq2U8oM7K8gw6xFFkbcOtvHMW3WEwhHu31TGyors2HiTkw0smn35aWcXY0ZBMjMK\nzl8j1yzL593jXRj1SgrzL7wOm7rGUMqlZFkuLFv/cfD6Q5xuHGRuUVSzSBQj9LfvweceJCV7OWq9\nhZqO6FrBHwzRY6smTmhjWUoL+uzoXkCjTyMldyXP7w2y63g3c61lXJHWTJK2lZGWZ3CaFvDKESXX\nFtgY9xtQ6tLRBOt4cfd+bi5vRBQMnLFJsXt6idOlMiN9PsO2I3y+/CBDwXzsnb9HDDgwpV7BrKJN\ntDorOdzRy/L8LvrqfkFuHDh8Cn5/UMarxw/jcAcQRVDLi1lX2IZeFWTllfegkoWoO/pz7L07kCm0\nmFU2NIYMVl9xJ4JUTjCQia1xK/HmMozJRYiiyIG3e1HJ2jEYZ026t3x5YwnH6qL6qqqUNSgCO8go\nWEncRJsPuw998L144xwcfXuIN5ditny6+qF3XF2EXhfV9Eq1XNyopDeoEV6tIhIRmWlN/tQdmJ8G\nn6VRqQfIeN/f6ROvxWhsbHQAXwKwWq0SoB1oA9Qf1XeKKT6KPZU2xl0Bxl0BfvTsSe7dWETJRPnz\nniEXv3j9DKIIX72uhIxkHaIoEg6MU5STwM4T3dS0j5GdME6C2kdlTwp7TtlYMzf6QPrD7jZAxXJX\nFeFD6SR98c7Y5yp1OchVKUhECXLVeY+Ct7kJ95lqVHn5BMZ6Gd+9C2VaGr6jzQhn1eRJ5bQmJ/GT\n508SDIuUuNqIH06BpBkAeOqjIavhPAfjvbsQ0qM5ytJMDSIh4izL0Hx9Bp66WgaefRpFUQot40Yy\nexqwKw3k/MPX2NNg552MDdyeG2FLewgxOZUH8op55PfVZLlSWSPa8B49xLWiHV3YR3vWTJoHPDS/\nXcfLu+VUFJnJtRiI1ys53TzMgTN9yKQSNk4IQpviVHzv87P53Ys+6N9PozaDreEcwp3jADR2jRMO\nizz+xllkMoHrFufSMRAVE5+WYaTZZuc3W2tRK6X80+dmkZWiZ8OCbNZ9oNqGwx3gzYPtaJQy/u2e\nCkxaBS+928S7J228sb+N+s4xFHKBQDBCgkFJ12DUAHXrqmlYTFq+//nZuH1B9BO55Dcuz2dRqQWN\nUhbzOKyak445Xs20wVdVAAAgAElEQVS0DCPq90UACYKEuzfMYPUVGeSkGC5YqCUa1ayek8EUfz+E\ngtF9uNf94RoNn4R+b7QaiCN48b1+KBJBOlGG+GJ0uLwABCKRT31sAAO9DkaH3EwvuzDUf4r/Vf6i\na7App96fz9R5Xj5+WzedDz2MumDaJJHqD0MMhbBtiQqpO2rr6DnTiMyUSNcrmwm7nLS88ArmO750\nyf6jpyachamZiGnRn8fw/gMgkZDylQfxNDYw9MqLSBQKUr/6IDmrl0w6z5FxH1E9/M8eqSUNsTWq\n2TI+ZwV1zYOTInEuVYwmIoq09TrItRg+Mvri2bfriIhw6xorJ+sHOFk/wHNv13L9klxO1vcTr1ey\nqMTCW4c7eOh3R+kbcbN8VhoVM8z84vWzRCIid19XwvQsI09sraWmbZSKohTcTh9yoCg7ntqOMYqy\n47l3YzE6tZzRUTdfXlfIQ4MuBse93HFlISqB2DzPL0wm06Rh2O6jLN/0ia4zURQRw34EmWrS65e6\nbudMRA598D2XN8h3Hj+EKMID1xdTmnfxCKNz+Bxt2AcOEp9+JQr1ea2cYCjMo69V09A1ztULsrlu\nURYjXVvxTDiHR/tOI9NNR/BBeoKRivRmhLHhCSejgtp+E9X9afzj7RsYdAV59/hh0pO0fGn9bJDM\n5snN21maXYtx9BDrcmRIBZGs3EUolHEMtdVxpbUNmRDiYLued5vOxsa1uCSVHH0xKcoGMjUNBP1g\nTF2FJnk+w8NuVs5M5afPp3JF5hCBkMDJbjMrF69mYek4R+v6KUg3UpKbQIJBBZSRlqglFFLhCoE+\naR7OwSMEfGNoE8qIz7iKkdHzvx9dygaC75vznsFy2hpTWLruQufZ7WumcbR2gPzMPJTyBwlM9Psk\n9yHLjK+BRPhM7tOLi6Pf+YcdOz1JS9eAi6wk7afuwPw4XMpI9VkalU4ABVarNYfoYuQW4Nb3N7Ba\nrUbA09jYGADuAvY3NjY6rFbrR/ad4u+LcMiDBOGCmzyA0xNAKZfGKikA+INhdp7oQiULsaZolHdq\nUvj55mo2LctjYbGFn75UidsX4kvrCinKiYZBukerGO16i0z9fECKVIiwoagTvc5A/WCQP+5pICni\nQmE20zSiIid+jPy2cRyHO0lYuz6Wr+4+ewb376qIeDx4k1vRWAtRpKbhPBYVrku66RbcZ6oZ3fYW\nfU8+AUDag/8P52+fYScQDItsUFVSZumj91cn0M2cQ/zylXjq65GkqojIPYQ7PYSrnQhKLdq8GSgt\nuWi1Zcj0Bnwd0RDXQet6XqoOkJOWwYDcSJJHQnufA6dcS9ZVFVjea+VU0xAPPXcSrz9MaUUF4vZK\nlo5WAaC2FjLnS19ky3NVhCMiEVGMVf46h14j5+YVVnRqOZ39TrJS9OjUcr7yhUW8OdrBzkgGMqkE\ng0bBmMvPkbr+WDpZMBThtfdakAoStCoZqSYNv307ajjz+sM8+loVX7xqOm29dnqG3MyxJnPF9GSG\nxr28vLsZrz/ErasKKMyOLhyuXZTL8frBqIgiMLMgkY5+J2vnZfLCziZmFiTGvmtBkMQMSuc4l+N/\nDkEioSz/4osAqSDEQlE/DhFRpMPppcfjZ9gXYF5SHKnaC6/pKf5vcd6oFPjUj93viR7THQpfYEBy\nBkP895kO5ibFcVXmxT1nHc6oUSl4GUalD4Zx93aNo9YqiDddOi3gxIF2utvH0BmUZOR8Ms2nKT4T\nptZgU/zN4jx5Aog663wd7aiycz6iBzhPHCc0NobCkkqgrxf7/n3IU1IIu6IbLvuhgySs34BUp6f/\nmaeQGQwkfe722P3WNyHSrc4rQJaQgCzBRGh0hLily1FmZKDMyEBhsSA3mVCk/O8a2ZXpUQdoV3IB\nr1QG0NSd4qG75mHQKjjbNsKTW2uZX5TCpmV5k9bOh8728cw7DayYlcbta6yXPP7AqIcjtf2kJWm5\nebWVJSUpfOc3R3j3VDe5qQbcvhCzrcksLEnhrcMd1LSPIpNKWF+RRYJBxU/vrQCISQF8fVMZtR2j\nWN+XQvfl9TNo6Bpj7vTkSTo1OrWc731+Nv2jnkkpd+dIS9JdVqGdS+EeqWS0exsKtQWtqQydaRYS\n4eNvlw+e6UOvcOMPSfnlH89y/7Ull0xdc4+eYaRzKxBhvHc3yXnR220kIvLkW3W02kYotYwQGO2k\npyFIxN+PQpuOIakC+8BBgq56Pjfr/PFs9jjOjMzkeHOQsrxEmgZGaO6x09brQARWzk6PiZ2vWriQ\nx19XctPMNrKNAwgyFXpTKRKJgESQk6iNRmLPKJxLrjUJnVrOGwfaOHB2gAMYSTQs4J83aVCok1Dp\nz/8OM8168jOT+a/3otXJ1s3LJCfNTE6amc9feelrCyAuZSmRsA+lNhOdqewj5zoUEgmHpfi8Fzr2\n5k43M3f6pyNo/Umug0+TudPNOD1Bpmef1/sK+EMIUslnGil8uXxms9PY2BiyWq0PAH8iWpL26cbG\nxlqr1XrfxPtPANOBZ61WqwjUAnd+WN/PaqxT/HUjiiIDjb8DQYql8F4kkvM/HI8vyA+eOoZCLuX7\nd8whThs1FOyv7sXlDbEkt5dZKV1Y80v47Z/G2PxeK+8c6cTtC3H1gmwWl50vu3iuzKVZOEZOYjmF\niX38f/bOOzCu8sz6v3vv9NFoZtR7t2zZcjcuGBcwGEyH0ENCEkLKhuTbbLZls1+S3bRN+dI2S5JN\nIQmBEEIvpthgMO7dsmRbzep9iqa3W74/7mhk4YJNMCS7On+N5vare+d93vOc5zzVNQuw2utYX/88\nz7TM5D9fH8Zk0I3+rsjrpmDJXQz99L/wPP0E7iuuJHzoIP6XNiJIErbZjcQ6OghsfT1zDPuChVhr\n6zDmF+B/5SU0JYXt2rn4Qi9SvKKW2za/QlI0MvtWMwa7EwQIbd1OaMd2AIwb0u1NtTriajfJjn4C\nHaPA6ww6XEgf/jTC7t3INgcPtqRw2E1cctVaHnq5lQNtHrqHQtgtBvJdVi5qKGB/2xjxpMJHNsxi\n9fwShkYXE9qzG1vjXIo/fT//9Vwr8aQ+YZ5Z7uKqZRUMeCKMjceYWe5ifl0ekijw7UcO0tY3zqdu\nmMPShkIGvVFeFaswiHqwYLMY+Lff7CWQLsmzmCTiSYUsq4FwTMaVZSYUS7EvbdK4bnEZv3+ljR8/\n3pS5dwfbPfzh1XbC6UGjtjSbS08yRbZZDNy6tpZfvXAMgFsuraPAZUXTNJx2Ew2V7/+Ed+9YgGd6\nxqZ8d9M0qfRXDzldJhuNvrukkqJpjMYm97nxhWPER6Pc8tHFiKJIbzhOUtXYNjJOuHkMazDFdXfo\nAZiiarzc76EjEEvvS/9OOkP2+eCuXg7v6ePGuxfiyrExNhzi2T8corTSndnn6ZBIG5vueK2TWz/q\nPm9vgWlcGEzHYO8+brnlOn75y4dwuc5skDqxjtVq5f777yOZTKEoCpdeuo5779Xbqb/22mZ+/ev/\npqeni1/84rfMmjX7vbqEv1okBgaQ/V7sjXrHs/CBfZll/k2vTGlVLwcCjD36MJLTRdaChVhn1IMo\n4n/lRRAESj7zOfq+/Q0CO7YhZWUhGAzk3ngznscfw/vcsyjBAJGmwwAYcvPIuXIDmqYR6+xAcrkw\n5OQgCALONXo3trwbbsoc2z7n/E2Yz4ZgJMlPnjzCpQtLWdF47kbbtoYGPCYnT7qXoyq6auZ3L7dy\nx7o6/vvZFiJxmc37+2np9vHpGxopSzfw2Nk8DMBrBwaoKcnm4sZiwrEUsqLiOsnD5bkd3Wga3LBS\nb2tvMkpcs6KKhze18eBGPQabX5tLgdtGfbmLtr5xVs0vSatSOMVXUhSFTCXBBNwOMyvO0Bgk224i\n235+XWzPFSHPfkAgGRsm2T9EKu7FmLsOy0nju6ppbGsaYl5t7pT7MoF4ZBBL5AU+t2oMRXDwg9fn\n85Mnj3DT6mo2LK/MdAYDCI3tw9+/EUGyYDA6iAc7+O1zb9IxaiIal8mSxvjs6k6yzTq5oybA6mog\nt/JGRNGI1dXAQ89vQUj0cOkcgQ6Pi9/vdqBqMtXFTi5fUs7hTi+HOzwc7vBgMohTSJaGSjff+Zu1\nmE3rSARbycnNI67o99aSXUds/BiSMZv5DY0ZgrW6JJvvPnKQ3tEwi2aWkl1w+i6I1yyvpPmED6fd\ndF4+U6JkIrfiunNeX5H1GOx0pNL/JFy9vJKrl1dm/tY0jUd/uRd3ru2scdp7hQtKubW2tm4ENr7l\nu5+d9HkncFrn0NNtO43/nUjFhpGTeq1w2HsIR97izLJX9val24Km+PHjh/nHuxahqhov7e7FKKks\nq9QHyBwO8KVbruaBF9roGouxdnEZN62aZNRVJU483I3BkocRkXsWH0CUrGQXrCD45g7mBjopWhHl\n1UOldMQKmF8yQm1xOVkLF2OuqCS8by/hdObMkJtL4Sc/g6WqClFVSQ4NkhgaRPb6yF6hd+UyZGfj\nuHolCdcJ1KwIaBFS0iA1sWGSxQ5Mdl11YJjrRB2Ko3ZEEbIkxGobEk4Kb7wX4SaB0NAITz2zF6Hj\nKMvGj5J84LuYNJn9rtmkNIH7rprF7Co3f9jczs7mYbzBOHOq3AiCwMIZ+ay/qJzZVe6MLDf/zg9i\na5iNY/nF7G33cajDw6wKF5Io0NzlY2F9/imlXXuPj9LWp5e3PbjxOE67iV8+fxRZVvnsLfOYle6g\nsWhGPvvbxrCYJP793qX89OkWuob00rcRf5QXdvQgKxprF5Ry2aIyBKBrOMT82jyKcm28cXCAnS3D\n1JZkc+XSChbV558ygb24sYhDHR6MkkhBurOaIAgsnnlmn4T3EmNxfcC7tDiHLUM+EsqFKUl6t5CI\nyxw/MkTjolKk6c5hZ4Qs68RrPJo6J9PG4cgIPckuKk1nz6574knkCbdT4ERfAHMwSU+nj+oZeQxF\nE5llLVkCBc3jpJIyRpOBE6Eo20bGp+wvqapYRQlV09g1GmB+jgN7Oks9MhgkFk2x5YXjXH/XAra+\n3IamvX2QlkzopJJvLELrkeHpMri/IEzHYO8fTCYTP/rRz7DZbMiyzKc/fS/Lll1MY+Ncampq+eY3\nv8N3vvPN9/s0/yqgqSqDP/kRqbFRKv71qwgmI8nBQewLFpIaHSG0bw95t9yG0e0mOTbKwPe/R2ps\nFIDxza8gGI0YCwpJDvTjuGgppqIisi++BP/LL6JGImRfshr3FVcS2PpGpkObdVYDyaEhPE/8CXNZ\nOZosowQCZC2e9CbKveY6cq8594nvO8HT27roGAgw4Akzuzonkzh9K57d3oVREtmQnnCmcgp5Zu5t\nxAMJ7r2mgW1NQxxoG+PEYIBIXObOy2cw5o+xeX8/P3nqCN+4bxnBSIrW3nFK8uz4Qwl++1Ir+46P\nceSEF1XVuLixiEvmFfPqgQH2Hde9LxfNnFTIrp5fwou7e/AFExgkIaOmuH5lFc9t7+baFVUX9F6d\nDpqmIghnj106BwOU5Nqxmg0kY6OkYsOc8OXRm1jOquLX8I0e5ruPmshx2vn6x5ciiSIH2zz85sXj\nrJhTyN2rTWiajDdRxq5jw6yt7SfmeZMaN8QVCxYpxOevCvOfm3N44o1O+gf7uOPKi3BmWUhE+vH3\nv4RosJNdcSev7GxhaeEYuWIz+0KzWV3Tw9KybgQB7HlL+cM2lfZhiX/+8CUZQ+pkSmXbcZGinNl8\nqGEZgS4fqqZXHCybXUh9uQuzUWLr4UGSKZUVc4qmWDrAJMlnczXgyHEQT5dL2ZwziY0fw+qaNaVc\n0m4x8vd3LuS1A/2sPU137AnUl7v48JUzKS/IOuWY7ybkDKkkX7BjgK7eznZZyMo+NRk82DeOK8eG\n7R2SnZ6RMKIokJOvV02EAnG2bDzO0tXVFJWevjJCkVUioQSRUILBvnFKTqPcey/xfht1T2MaBKNJ\nHFZj5gdL0zRCsRTZ6fKkWLA9s+7mXS0cHpX5+HVzcDvMbNrXh8NmZE51DrtaRvjKr/bgCyWQFZXl\nlUPkF85ESYVIhLsI/uqL3JKCwbpK6o1mRlpd2PMWYLPPIepvBk3F7ppDVt4S/AMvY3PNBkXE+/yz\nqKkYFQtmcPfqNrzhHpyWBK6GzyKIIoUfugffSxsx5uTiNWfxhlzEnqf6qC0Z4VPrbSTUHlIuD4ot\niIUafI9uxOB0IszUECIGEt1GjMYgYqmV4Rl1lJXqE8HeQ2bKZ8cwXl7M0OpGlOFWyoUEjvI1CIKA\nP5Tg+y90MzBuo/Gi9UTCJdh3bgZg1nWXc/e6ixDSk93G6hwOdXgAqEobBhoNInes07MLPcMhFFWj\npiQb56o1BCNJHt7Uhskg8pENszAaJL78q9388dV23FnmjIQ3mVJ47DW9hO2m1TU8/non337kIAA3\nrarOtMYEuGVtLZF4ig+sqSXPaeWDV9Tz9d/p2UZZ0di0rw+TQWTlXD0zdemiMi496Tm564p67rri\n7N2rBEHgMzfNPZ/H7z1FOKUPePNzHWwZ8pFUtLfZ4v1Fy8EBdr/RhdNlpeoMsu3zRV+Xj+NHhrns\nmllnJKpi0SSvv9jK8jU1uPOmliUO9Pg5tKePlevqcOW8fbeW9wKppB7QaBok4imstrMHFU91vECL\nr5VvX/IV7MYzX8NE6ZvbbMCfkFEsIgTh6MHBKaTSfHcWhwkTrHHg80QpLMmeonDKnKeqYQVOhGI8\n3ztGMClzVbn+f50gj4YHgjz50AE8w7oP2UQGEPQyvyP7B2hcVILRZEhfu4LFakBOqex5s4u6hvzM\nsmlM4/3G0NAgX/jCZ5kzZy5HjjTR0DCbq6++jl//+uf4/X6+/OWvUVZWzre+9e8MDg5gNlv4x3/8\nEnV1MwgExvnqV7/E2NgYjY1z0U4ieF9+eSOPP/4oqZTM7Nlz+MIX/hnppC5egiBgs+nvtizLKIqc\niXGqzqFUaxqTiBw+lCGJxh77A7YGXdnlWHIRajLJ6O9+g+eJxzAVlzD+2qsogXFyrrkOa/1MwocO\nEO/sJDk4AJKE+yq9bbhz1Rr8L78IgHv9lQiSRM411zHy4C8xV1VTev/nSPT10ffd/2DgB9/LnIu1\n/uxlO+8mBjwRth4axGQQiSUUHn+9g3uvOVXV1jMc4uk3dduD8sIsGqtzeXhTG2OBBNesqGTl3GLq\ny118+Vd7GA8nWTGniMsXlyEIArKq8frBAfYcHSUYTaIBly0qJSfbwo8fb+JQh4eyfDsasL15mO1p\nJVN1cTYf2TBriuLGaBC59uIqfvdSKzPLXVjS48DsqhxmV/15SvFoXMZmOb9xJRbsYKzzEUzWYmzu\n2RgsBZwYihCM21k+rw5RFNi4q4fHX+9kQV0en7tlXsanaF9vHkdHxrEknSyvHKImN0DrqN45eH5d\nHjuah/TzGj/K2AldmeWNOXHGJWIeH+GklScP13Ln1WswBB+F2GG+dNvNtLdupyRrmOMHDlBWew2M\nvwCo5FXdzCNvBtjWJDJjTRZzSzwsndlPPNSNZHKRW3kjlqwKLl48xoEnj/Dfzx7l726fj8NmYnvz\nELKiZuLymRUubGYDsYTM0oYCjAaR2VVuDrbrc4BV88498WNzN6IqCWzuOacsy7IauX7l2X/LBEFg\n7cIzk07vFiYSe4kLqFQKBeI8+4dDlFS4uP7OBVOWRcIJnn3kEDPnFnHp1bPe0f5ffqqZeCzFXZ9c\nhtVmYs/WLgZ6xuk94TsjqZQ6yWvz4M7eaVJpGv+70Tca5mu/3cfsKjf33zwXQYD//NNeWnrC/Mvd\ni6gucRMLdgACAXExL7SYULUI33nkII01OcQSCrddWs3lS8oIRpIc7fZTlm+nodDH0qIesnLvRolH\nSIS7MW0oxpwlMUNU0IiSjEVJ9gwx/OivMCx3I9VYsLpmQUIj/Ot9hLV9SA4Hyvg47quuxlixAe3E\nQ+RmeRBCZoxZehbGUl1Dyafv50RvF8bRh7lMPMiyMiM2Ywpf78SVCoDGePdmwpv3gcOE5cNlmGzl\nhF/aQaQym5xSKw23ziTqOYoWkinY3Utw1EHW5TmUWJqgCkIJI0c7srncrfLAU0cY8ES4fHEZt6+r\nQxIXMF5XhhIMUL9mEfluW8acbeGMvAypVP2WLhQ9wyG++fv9qKrGP9+9iJribH7z4nHCsRR3XFaX\nMXe877rZPPBUMz9+ookbLqmmsTqH3cdG8AbjXLW0gquXVxKMJHllbx8LZ+RxzVukroU5Nv7xrsmi\n75qSbK5ZUUnnQIBwLEX/WISlswtPkUX/T0IoPQC40xmbxAUyTz4fqKpK93CImtN4RPk9uvluLHpu\nA/Xjbc+SVFPcNesDZ1xn9xsnGBsOM/+iMgrO0BGlr8tPd7sXs8XIZdfoA7SmabQcHGTbpnY0DYrK\nR3lZfIp5ebO5tubCtEs+V0wENADRSPK0pJKmaRze00d+kQNP3I+maXjjvrchlXTSqN5pZ/doAMUk\n4c6z0XvCR3A8xlA0gcMocXlONkeGA0SKbfjGIjqpFJ8klXLMRnyJVMasO5TUyc3+yKRpbCIuYzCK\niJKAZziMZBCQJCnjFwXQcWyUXa+fQBQF5i/VFYvJpILTbaWyNpf9O3poPjCIqmqUVbkpLPnzOt5M\n438Onux4noOjR95+xdNAEgUU9VQCfmHBXG6uu/Zttx8Y6OdrX/s2X/xiDR//+IfZtOklHnjgV2zb\n9gYPPfQgBQWFzJgxk2996/+xf/9evv71r/Cb3zzCgw/+gnnzFvDRj97Hjh3beP75ZwDo7u7i1Vc3\n8dOf/hqDwcD3vvcfvPLKi2zYMPVcFEXh3ns/xMBAHzfddCtz3uXSqL8maIqCEgqiRGMkjYXoVZXn\nBv+mlwEwV1QSa2sl0dsDkoR93gIEgwHvk08Q2qV7ViII5N9xF+7L1wOT5WiaLKMmE0g2PUlhKirC\nvf4qEEXMJfqkN/vilRhcLizVNYgWK9YZ9RR+6B4Cb27FUlODbeYs7PMW8F7h8S0dqJrGfdc18tz2\nLrYfGWbNglLq3jLBfG5Hd+bzb188zvUrq9l9dITakmxuTCvy811WPnXDHA53eLj9shkZgvPq5RW8\neXiQ53d2YzJIiILAklkFZNtM/MvdizEZRSoKHaiqxu6jIzSd8LJkZgGL6vNOa/R9ydxifME4C07T\nCe2donMwwDcf2s9tl9Zx5dJz79421q8/E/HoMMmYTgJlAzZF4JHn5mNxzmHjrh4ADnd68AZiRL1N\nxFMSiqmKf/3wLLp67MAQt1+s8u9P6z5JtaVOmjq9VOeMc/3s46gYkaxV5NJOrhV6/Nn88WADuW43\nNaU5JJzXMNrxOyIDT1CSBQnVTrEjgDL6iH5ORasZCOWwrWk/ZfkOaurX4e99hnioE8FYxnhyLaVZ\n+nUvnJHHqnnFvNk0xLcfOUhDpZtX9/djMoqZUkGDpCeDw/FUpjRvXm0uB9s95Lss1J+hZf3pIAgi\njvyLznn99wsTya9zjVXfCXo6vWgaDPSMEw4lyHJMlj2Ggwk0DbyjkXe8/0g4iSKr7Hmzm7mLS2lr\n0a1W1LNUNKSSk/FZ7wkfY8Mh8ovevw6i06TSNN5XvLq/D1lRaer06nXYWpymrggg8PKOw9x341KS\nkQEkSxmPbc9B1aIsKR9jX18+u1pGyLYZuXRhKQZJ5G9vnU8omsJp1Rho/j6SyYU5q5LA4a2o8SRi\nrgmDyU12/iXEXtpJcPQoxnUFGJa7EEtNCCkjRksB3qeeIDk4CJIEQ4OINhtHSxbwuwf28k933Upu\nbAf26lMDC9/AGxRYVJJCPhZznBG/ESy1LFiwApMln9HOR0iEuxCyDYgVunRSDdgxKCk6UhUUmA3E\nAscQjAJyUwRkmXD+Cn7+upNVs2RWzlTZ1KzQMtTD64dHGPXHWD6nkDsvnwwQXGvWnvY+CyeV4+Sc\n9EMYiib5yZNNGenoA081s25xWabs7fKLJkvd5tXm8cW7F/OTJ4/wzLYuntmmZ8YcNmOmVvq2S+uY\nV5vLjDLnlAzWmfCBNbWAHjQ8urmdq5ZW8FT3CLOcdhrc79xo8S8V4ZSCzSBiFEWMokDyL6D87Y2j\nw2yKRbguEGNFw1T/gnGfTipN+OacCQlFxSQK7B7eT0JJcnv9jUjiqZMGvyfCWFoBEwmf2X9oItvU\n1TaGcmU9kkHkwM5e9mztwmiSSCUVRnw+Bix6oPhekEq+UJyfHeji8pIcls6Yavo44akE6Q5wb4mn\nNU1j+6sdHNk3QFFpNoHKgL7PmJ8KR9kZjznR+a3CaGQ3YM23sbA0l9eeP86hQ4OM21XqnTaSkRTG\nqEzCbWZsLEwDMHaSUqkyy5ImlfSJeSRNgg1GE5kuQLFYCrPDTGWlm6MHBymtcBMcj0353wfGdX8m\nz4j+P1RVjVRSwWSSmHdRGYd297J3WzeKrJJKytOk0jT+IlBcXEJtbR0A1dU1LFmyFEEQqKmpY2ho\niOHhIb7+9e8AsHjxRQSDASKRMIcOHeQb39C/v/jiS3A49Od5//49tLYe4+Mf/zAAiUQct9t9ynEl\nSeI3v3mEUCjEv/zL33PiRAc1NXXvxSX/RUFTVXr+7cu6WgjoNRgouu+TOBZPTlbP1I0s3ttDrK0V\n25xGCu78IN1f+VfUeBz7vPlIaSVY8af+hlhHO+bSMsxV1RhP878QDAYkw9QpT/5td0xdRxBO8URy\nrlqDc9Wad3bh54G+0TAFLmvGPPlYt4/DnV5mlrtYVJ+H027im7/fz4Mbj/GPdy3KlMH1jYY50DZG\ndXE2c6pzeH5HNw++eByTQeTea2dPMbieX5d3SvORPKeVFY1FbGvSx9K5NbmZKoG6sknyShQFVjQW\nva2vk0ESuXl17Z99PzRNIxHpQ0kF2dViRNPgqa0nWFyfT17a2gBAlWNEA63YnLOmNPJRUhHUeDdD\nwSx+v38O9fk+sswpKgvM1GR3srriEK+2+8lxVLOisZQXdvZwqOUwM6whjo0UsGx2KTUl2VQXL2Ww\nZQdC8gQ1JVdOc64AACAASURBVBUc6vBQvb+d1TXdrKweRAN2Dy9DNFfQdNzG7ascWIobsLV2ccMl\n1QiCgMVRhSN/GWHvQVwll5GVdxGHjuzAHN3KcMhOPFHJ3uNtgN6pLCvHQTzYhsHk4o0tOQz191I/\npwKjyYAgCNyzYRYWk4FN+/oY9EQozLFx/02NU5rNLJk11fJh4Yx8Xtrdy4bllbQcGAAN5i45c+wx\n1B+grWWES9bVIRmmKsrbWkYY7B1n9ZX1fzEeihNzmAupVOrt9GU+dx4bzSTWQE8mAgT80TP+lp0N\nsqxkiLFjhwbxDIdOWnYWUimd9HPmWAn4Yhzc1cv6G09Vlb1XmCaVpvG+IRpPsatlhDynhWy7iZ1p\nVrYkO0RcNnLghImx4RZAY0tnNQOeKBfPMrC+spXKAjPPHMxmQ5lGbOdWqKnFXF6B0wZjHY+iaTK2\n7LkIgkBo+3ZSYyMUfu5jOMqWIogSVZ+/hI5nXyTCQajRz0du9pMqH8K/+RUkp4vqb36blNeDaLbw\nxxe60IA3m8a499obAEjFPWhqEpOthHh0lHxzLyPhLBrrb2bwu/+Be9wPNNPv2ELeLbchmWxgAMva\nGSiS7iU09mY3VsC1dDmOfAF//0sAOOvWEgu3U3f3jVSHZQpcVkRRwHjkCIo6xqg/hskgcsdldQiC\nwLAvSvdQkP6xCLGkbnxdUuBgPBBjyBvhtQOT3aBf2tPLp25oJCWr/OyZFrzBBDeu0ge/p7ae4PHX\nO7GaDXz82tmnEEOVRQ6+/JElvLK3D1XVsFuNLK7Pz8iSRVF4RzLn2hInX/rwEnyJFHu7ggSS8vtO\nKmmaRl+Xn5IK51m7KrQHIuSaTeScg8IqnJJxGPV7ZRbFvwil0lAkDiL0esOsOOl7TdMypNKEb85p\nt48m+NmxPlYXOYnKOungjfspsJ1aLtd2dCTzOXoWUimeJjKSCYW+Lh9FZU4O7OzBnmXihg8u4NFf\n7MUbCIAFRqJjqJqK+Da+CeeLwbAu8y/J0oPo5gE/YZuB/UOB05BKk9mi/lAMeyILt3nyeTiws5cj\n+/R3cNwfI1aqK4Q8ER89nV7KqtxTSgEngpLhaJJso4FYfxAEMOdaqZ2Vz/bNHRzp9sIcN8VWM5Fw\nEjFNbI369EzZyUqlCY1HKk1iRtLnG1dU/AkZt9nAcLGVcJmd/Lh+HharkWg4SUSe3E9oPH3eozqp\nNJElM5oMSJKIOb1NXmEWi1dWnfvNnsb/eNxcd+05qYpOhz+3JbLROPkuiqKY+VsURRRFxmA4v1BY\n0zQ2bLiWT33q/nNa3+FwsGjREnbt2vm/klSKn9DLz0ylZVhrawnt2c3Qz3+K+qEost+P/9VNZC1c\nRNFH7gUgOTLC6B8exlRUSHJIJzvcl6/HVFSMa82ljL+2GceSpZn922Y1YJvV8L5c2/ngTJPNpk4P\nP/xTE3WlTv75g4tQNY1HNrcjALev02O8ujInVy4t5+U9ffzH7/fz93csJNdp4fm0Sun6lVXMqc7h\nYPsYA2MRPrCmlqJzKA+XUyE2LC1i+5EhNA2WzX73/CdHfFFSspoxAT9XhD0HCI5sz3ipStFaBKGY\npKzyyOY27r9ZLwGMjR/DP7AJVY4Qsu7GVXEnJksWkigy1H8AUdAYiFTwL/dcwomBAGUFWVQXZ5OM\njTDQ+jDrZvSwrn4Is6MWqTGAS9Xj8ubhQv7P5fp9EAQBm2s2obHdXLNIpfl4NzMt2zHWqgiilZfa\n6tnXI2I2DgE5zGpYidEgclHD1BIzV+l6XKVXZPydFs5bSUffbJ5/ppnxsJ6gXTGnMNPNLr/6VgCC\n4zsAPRaaKCsXBYE71tWR67Qw6o/ygTW1b+tXlG038a1PriCZkHnwx9sxGqWzkkrHDw9x/MgwlTU5\nU6wPmvb2s/1VvQvivCVlGf+f9xsZo+74hSGVZFlhoEfvchsJJeh4C6kUS1sVJBMKsUgS22nM28+G\nZEKPpewOff+jQyFsWSaiafXSmTARg1XPyKO/20/n8TFCgTgO5/vTAGiaVJrGewpV1dDQiI838+K2\nJpJyid7VYnY23/n9DlRV5TPXlbCnLcozexVe39tMji2H15pV8l0W7rx8AeNHDzI3r4k6yYuwMcAo\ngCiSc/N1JEsGkBUfSmeE4Ks7sN5ZR6y9DVvDbLIrVjDgifD01hN8+tYFuFauxhKuZLT9twAoHUH6\nDn0bLZkk99bbEc1mzCWl+EMJhnpHaIwO0tUUJL6+HrNBoP/YbxCJklN2FSMjXZgE8MVnMvyj76OM\n+xkpqCUUjlOX8DDy4C/BbMB8TxnxIg2zwYw6FMd67ATjBjuu+losrizE4TcxWQvIXXgdrNXvWVGO\niWRK4dmt3exvHcNoEFFUjaSs8qPHj5CSFfrHzi65zHNauP/mufz2pVb2HBtl2ewxNu/r51iPn4Uz\n8jJKoxMDAQ53evnQ+vpMl463wmEzZRRG7zai6cnuePLCmu2dCw7u6mX3G11ces0sZs09fXYurij8\ntm2QWS47d8/QOwkO9wd45ZmjXH/n/CmeP7KqElNUim06QWWSxHekVAr4Y2S7LKcEp0lFZcugj+WF\nTpymtye4Ng14aQ9EMCoKiBAMJ6Ysj0aSmYEucdJA3RGMEpcVGnMcaJrGxr4xUqpG6/jkpG8kOnoK\nqaRpGu0to5m/I2853sk4OdvUcXyUseEQckpl6epynG4bDqcZT2gUCkFWZbwxP/m23DPu753gV82/\nR0Pjy8v/AYBAmugKKsop605kkjQBNsYjHOlSuW+WHrAN9QfYs7ULR7YZs9WomzEqEqqkMNQRoX3/\nEeoa8rn8+tkossrmZ48RCSeYeU02ff6HaSy4hYGmBDRkg82IwSBRPreQHSmd4Cm2mYkMjSOmgwtP\nIE44JRM9KRCZeM6ichJfPJ5RKgG0jQSYn59NJN+CKgrsNankGAQi4QSSUUSRVVRNRVZlQgH9mOPe\nKLKskEq/pyazRFvLSIYoNBhFjMb3v73tNKZxLpg/fyGbNr3ERz7ycQ4c2IfT6cRuz2LBgsnvd+7c\nTiikTzoXL17KF7/4BW6//S7c7hyCwQDRaJSik9rI+/1+DAYDDoeDRCLO3r27+eAH73m/LvF9RfjA\nfgDyPnArWfPmU3HNlbR89WuM/PbBzDrB7dvIve5GjLm5eJ97mmhzE9FmfZmpqBhbWkGUd+vt2Bpm\nY5//3pWhvRX9Y2HsFiNux7lPHHtHQjzwdDNLGwq5eXVN5vtwLMWDLx4HoGMgwOb9/aBpDHgirJ5f\nQlXRpNrztkvrkESRjbt6+L+/2o0kCkTiMpWFDubV5iIIAn932wJa+/zn1EJdlWMMHfspJmsxly5c\nysF2DwtnvHtla0+8soVYIsn9d12P2SghJwOMnfgjWbkLcOTrpKAix0hGB7A4ahEEgUSkH1/f8wii\nEZt7LpFgNxdXdGK15xOPx5mds4f+w89kjqFoEmGlGGdsiGMHfs7W/hV88qblBMYOYZMEqmsuojTP\nTulJ3owmayEVcz5BcGQ70UArieAxJhoJj4WtZOfUkGWdjKFsbp1Uyk1tZk2tSiBmojM4k+vXX0th\nqJ9UZxcpWWX9ReUYDadPbunx2tSYra7czVc+uozfPHwAJZCY8lyATmRMKLqTSRk7k8+bIAisv2hq\n05xzQV+XD1XRSCgyqqoiiqc/30Q6kdjT6c2QSod297Jzy4nMOsHx2HtKKkUjSXo6vMyaV3RK/Duh\nFo9HU+9IKfR2GOwNIMsqtbMK8I2F6evyE/BHcabtQWKRyZh13Bc7b1JpQhFeUZNDLJKku8PL0lXV\nvP5i61lJpYnrNhol5iwq4Y0X22hrGWHxxZVn3OZCYppUmsa7BlWOI0hGBOH0k4lBT4QHnm4mHE2y\nvr6V3d1FGESVxVURYgMv8YnlIzgLV+MuvYhL3RFe2L+TXT1FJGSDLuVdYGf0W/9GKuLBfHsZ1rV5\nWFYsQ1KyCbZvI+I6jKCIyC1BhOMG4sOt9H33W4BeK59IKjzw1BGGvFE27enhysVlmO3lGC2FyMlx\nhKARJRTAkJs7Re7cvOMwHx3diDNHQT0Rp+uLW7Fc1ohUoqs4/P0vYQJC4yLVW7aRGh0h5+pr8dZf\nwuPPH+WWhW7mHNlE4vhRRvsMFNbog4TcEUYDNpevpeNPTVzcWMRHr/o0gjD5WqqaxuZ9/by4q4dA\nJEme08I/3LEQi1niwY3HOdThQRIFFtTlMbvKTVl+FllWI+PhBBgkopEEJqPEzHIXVrOBu9fX8/Xf\n7uM/n9C9LRbU5fHJ6+dkFEn3f2Auo/7YFBnthUCLt5U8aw6FtqkBTDQ9YQ+8R6RSXFbY2OdhdbGb\nPMukD04smuTAzt7M5zNvr6ICnsTkgDLYN04klGBkIDiFVAqnCbOs9GTbLApE5PMz6vaOhnns1/u4\neF0t898SUBzweHhjOEBvOMh9DTVn2MMkjvhCeOIpcjUNEAi9pRZ93BvNfE6cpFR6smuEcEphtjuL\n9kCUzqCuThqOJREECyAwEh1jwi49mZAxmiSG+gOEAnEKih2MDoXOqlSaGGBNZonudi+iKGC2GJg9\nv5jB3nEC/jgp+yQxMhwdeddJpagcIybHMwFKKG2yHhU0dm7pxGozsmCZ7nMwIUFOZhmRYYpJ9lha\nxrxsbQ2DfQE8I2GMCRsJW4iwPwnY6Dg2hs3eiXcszECPbtQfGulH06Jo8hCeQSOGegfh9PvR6pJI\nqDrpW2wz0x5OZJRKMVWlzz/5vwNdkQSwZ2gb+0e20ZDz8cyyLXt66dZElGo7kqoRNwj4Zrtx9sVw\nZptRVY2n2jeyd+QADYF1gJ4c8HuiGVm80Wxg1BMhXGKjWpAY7g++77X905jGueJjH/sE3/rWv3PP\nPXdgNlv40pf+DYCPfvQ+vvrVL3H33bcxd+48Cgv15EJ1dQ333fdpPv/5+9E0FUky8Hd/909TSCWv\n18M3vvEVVFVFVVUuu+wKVq5cBcAbb2zhhz/8LuPjfv7hH/6WGTPq+f73f/LeX/h7AE3TCB/Yj2ix\nZAy2s2fNpPTz/4Dnicewz5uPYDAw9oeHCbyxBddl6wjt3YOppISCu+8h3tmJbc4chPTkVzQayVq4\n6GyHvKBo7vLyw8eayLIZ+b8fXkLuWxQBqqYhwJRJbXv/OD/8UxOxhMyLu3pYNa+Y/HQJ18Ob2giE\nk1y5tJztR4Z58o1ORFHAbjHwgTVTx3FBELhlbS0Om5HN+/owmwyUF2Rx8+razPHcDjPLZ5+9RG0C\nYd9hNCVOItzFbauv5oNX1J/TZFzVNLYcGKCu1Ell+jc+ER3BM7iTvJKlmG0ljPTt5Jr6/WgaHGlt\nYEnjLILD20jFhvH3v4TBnIPJWsRI+2+RE16yi1bjLFqNr09vOJlfcxcWRyV7t+1nhvlFFubuAiCl\niHR4XGiaQChhYmtnOeNxM1fUi6ysHuCq6q1s2tLHgkI/Xf48Vi08vQeTZMzCXXYlrtL1yAkvfaNB\nfvxkK+GEkU/dMFVlZLKVYTC5kZPjnAjW8OieAu7ZMA9RNHJRQwFPp60gVs8vOaf7fjKcdhMz3DZ6\nfHG0uAIn2WVNJHFgUsny56K73Zv5HI/JZ+xUNqFO7z3hQ9M0IuEku14/gd1hYs7CUvZs7SI4Hj/t\nthcKzQcG2L+9h2yXhdLKqSWuSjrGURQNOaViNL27Sa3eTv2+VdbmkJNvp6/LT8fR0YwiOxaZjPnG\n/VFKzsO3Cibvt9liYOW6WnyeKFnZOjGlnIOnktEkUTuzgO2bOmg9MsyiFRXvOrF2LpgmlabxriCZ\nCPGbJ5+jpMDFNZdtOOVh3nd8lF9tPEYiqSCJGo8f0gfL+SWjxId0eWd2/kW4SnQyJzvLzuJaI7vb\n9UDijoJx+OVvSAkCrssux1oxE//oi8StugxTWmiDlICyO0T+sjux3z6XgR9+n1jrcVSjCXH2fH6/\nqZWh9ES5qd3DFQsL8XY/RSqul+Q4rruY8UdeJvf6mxDScvjQwT0UdT2K6a48BLNE/x4V54FBUNsB\nKyMvhclfZkJ0mzDvHkIdjeBccym5N30AW0LBIAlsbo/wkmE5tvIayhNmrkPvjtY7aKfLXUWHpE+G\ndzQP01DpZuXcyQFt875+Hn21HbNJYsPyCq5aWoEjXe/+Nzc1crzHT3VJNva3lF6VFWSdtmSgujib\nNQtLef3gAKvmFfPhq2ZOqbmXRPGCE0pxOc7Pmh5kpruO+xd8fMqy6EQHB0UlrihYpAurdugKx9jn\nCZJjNrK2ZLJsb9+27syP9RS/nGiSaCRJbr4u506lPWrGE5PZkYnB4a0y3HD62ibK3yaUSueTVfF5\ndEVaT4f3FFKpPTAMWOkKKwxE4pTazyx/TSoq3vT5RUWdVEooKvFYCks6Qzfui2XWnyB5gkk5oyIL\nJGVe7PMgALLiASkPu/U6BMHEYFjPZnW3e3j5qRZsWSZMaXn2gmUVvPJ0y9uUv+nnVj+niOZ06eaS\nS6owmgwMD+p+RKIyOXwNR0aZm3dqV5w/B7Iqk1JTJJQkFoOZsKKCBLJF4tAbfTgc5gyplEyfb9Kp\nv5sRWSGpqJgkkXBQD7yyXVYiIV2dZYrrpFIsbCE6x40jkOTwvn4E9BJSVdUYCOlBjCqHEMnBikAo\nqdAdijGo6s+SoEGuxcihUJJYgT5JUYwSvd7wlGuZeK/GYqMomoIvHAejEQQBxWVmwKufY0NcoDOZ\nJJZvZcSbIDdd9tk3Pkg4ESUannymPSNhsnOtjNdmc8QhEE/I+BvczLdaGRsI0dY8Mk0qTeN9R3Fx\nCQ899Fjm7y996aunXfatb/2/U7Z1Ol384Af/ddr9rlu3nnXr1p/y/eOPPweAy+XiwQcfOe22a9Zc\nypo1l5522V8DdKJoH+F9e1EiEbRUCte6K3As0T2SAtu2EmlupvDuD5PyeUl5xnAsXY54UhmitaaG\n8n/4ZwDURALvM08TePMNNEUGRcF1+Xps9TOxvYfd1t4OvSMhHniqGQ2NYCTJjx5v4ot3L2LYF2VX\nywhdQ0F6R0IIgkCu04LLYSYSSzHoiaAoGivmFLGzZZjndnTzsasb2NY0lDHUvmVtLVVF2fz82RYA\nPrS+PhPrvRVXLq04L8Pq00HTNMKefZm/o/4juIrXntO2bxwa5OFNbdgtBv7tY0txOjR6jj6ERYoy\n3NqE1VFNItRFShExGVRk/1bkZBFh30EkowNFjuLpfhLJkIWc8CKIJoLDW0lGB0nFhmn1FPNIi4db\nl9po2RulxdXAbQtbsTpnkJSW48gyk+e0YDMbmLsoRSwpU1m4CjV8CLlvEwsK9Y7RFufct/X8EQQB\noyWP6vJcct0+CMaZX5d7yjoFMz6Ey2nCNiRwnWmEixr08rjiXDtLGwqQRJGSvHcWO08QRuO+KHmF\nk6WCJ5M2qXch0aqqKj2dk6RSLJI8I6k0EfOFgwl8nghdrR40DZasrCK/yJEmlWKn3fZCIZ5OfI77\noqeQSidbEMRjqXMmlXo6vXS1ebjk8joM6YSv3xvFajNmYmFN0+jp9GI0SRSVOcmXVba+JNB+7CRS\n6aTkc8B3/vdlYt5gMhswmgwUlmRnKgTOxVPJYJQwWwxU1+fRfnSUkYEgRWWn7xh3ITFNKk3jHeHE\nYJARXzRj2new+SC7eoqgB/qDh/noNXMxGAS6hkI8tfUEx3r8mI0iH7uykKzoC2w83kCP38FVF8+G\ncAc2ZwPusiunTK43rJzPvs4DXNbopuqphzDm5VP8qc9gqaoCwJxbRSLcTSLch8HsJrtgOcJF5sw+\nzHd/guZvfo8+ayH7f7GPlKzqWRUN2vt8jLT/nlS0j5Q3hTHXSMTipfIrX4OiEh54uplG0yAV1jcx\nL3ORTEqYJSOFi1P8iXXcVdaKPJggu8vLibE8AsXZ2Eo2cOm9SzHlF+ANxGnt85PjsDA6HsNkFNlw\n5UVcvqSUA7u9HB/Nob0yh8GArj6677rZ/PBPh/n9K23UlGRTnGsnmVJ4cVcPZpPEtz6xPNPFYQIG\nSaSx5vzVGR+8Ygar5xdTWeh4X5jscCqKqqkMhodOWXZyyU4gKWOxXlhSKZ4+XkrT2PinI4SCccqr\nc2g5OIhk0Et/lJNKhXa82kln6xj33L8Cs8WY6aaVVDWisordKGUG40RsahAQTitdMkolSURDJ6ZM\n0rn9HyaImOGBIIqsTjFQ9MVTgE4sbB7wck/9mdu4DscSGZ+dZDro0iQB72g4M1hP+CkBJNPX1BeJ\nIyYUBE1j31iA0XiSOodGs+cEkpSHJOnZma5oHqNDQTY9exRB1Im2cDCB3WGmuj4Pg0FkPJHke03d\nVNgtrC3JocA6GdzEYylEUaBhvk4qGYwicxfr19PmHSNQ48A8PBlw9YUGGAwPZ/yP3gkSioqqaYiC\ngFkSkdV0UJWKYDGYiao6qaRJIopBJBpJTjG5BkhmT17DeFKmwGoiHNSJpCyHmex0dtqUsAEC41Vl\nyA4z0SIbpkoH88MaeZKBpkODjCV0PwnkMJCDTRAIqSqv9Hsyx5BkFVEQCIUTyEV6QBvMHecNvxeM\nMwCwSCK+cAIkgXAqlL5WAREVu8VIzGFETb8HJaKEt9lHbGUh4SIrcrqqNpyIYUzo556Tb8c3FmFg\nKMgrapxQlYMQwARZmmNl+dqa9yWgmcY0pnFhEe86weijjxDv7Jjyfay9jeSNN6NGo/hf0b0htWQC\nc7lOfmQtWnzGfYpmM85LVuF/5SX8r7yMlOUge/nFF+4i3gJfME5r7zjLZheekYToGAjw06ebiScV\nPnXDHFp7x9lycIB/+tlOwunff1EQKMmzIwp6GfKQN4LJIOG0m7jrinrm1eTSMxJix5FhSvPs/GlL\nJzazIWOovbShgK6hIOPhBGsWXNg27PFQJ3LCh9XVQDzYgX/kIM8dKWL9kgoK3+LFpCpJVCWGweTE\nF4zzpy0dmA0qqVScXzx3hJvmHMAiRdnXV8SsohhCqIu4YuWXuxq4obGTctcwI52Pg6biKlmHpqn4\nep9FVuI48peTlbeIodZfEw92EE9JPHuknEgywMbBo9hVjZS5hvL5NyEI4lt7YDClyM+2FLOjjiM7\n/4hJjDNn8ZmfubdCEAS+cPt8UrKK8TQemgaTC2uWA4fFz9zCbAwneSB+6oY/r6vjRALz5JgL3n2l\n0nB/kERcRhBA087eKe1kH82eDi/HmoYwmiTqGgrQND16fK+VShOK+YB/KmmjKCraSaL/eCx1zp5C\nzQcG6O30IYgCa66sp6/LxwuPNVFdn8eVNzVmjhccj1Ndn4ckiUiSSHG5i/5uP8mEjMlsIHpy+Zs3\neqbDnfna4pNKpQlM+GyevfxtUqkEMHNuEe1HR2ltHp4mlabx14FQNMkP/3SYcCxFWUEWZfl2dh0b\nA9zk2qLsaYV9bVtRT3rL6/L8rJ95ggJiYIO/u3UOkq0Ko0FEVWYiiKZTCI6KIhc/+ds1JPbtYljT\ncK5ZmyGUhrwRvvnQUW5cVcO6xUtOOUc5GaCl109+chx7to02m5FEUuHTN8zhzaYhsoQTpKJ9KGEH\nymOHkW4pRcz1kor52HkIggPNVCw8jmg10tLhhLINrCqN4et9htsW610azI03UbV+Hi8+fYymTi8r\n3IX4pSy2vdHJy3t6kZXJ6189r4QNyyvpHwvzy+2V6fbISYpzbfyfW+ZR4LZxz1Wz+NkzLfzkySN8\n8e7F7GwZJhBJcvXyylMIpT8HkihOqdN/rxFN6T+4gWSImBzDapjs5BE9icAJJGUKred/3aqm/wCf\ni3HzhFF2Sp7M4PjS/lSLllewd1v3FKVSJJxAkVU8Izr5kjypzbU/mcJulM6oVAql3qJUSivEkqqu\naDkXTPgQKbLKyFCQkvJJia0/HZhIRGkNQG84RkWW9bT7GYpO+hkp6ddOEwW8Y5FTSCVJEjKDeV84\nTm6zD0NcYaBQf4ZKbXEOK5PZL02TSVDEY5tbMcgqV97cSEVNDoO942RlWxBFAVuWCY8EvkQKXyLF\nYV+IJfnZXFuhh4yJmIzFaiS3IIt5F5WRk2fHaDGwdcjHkbxstAIBZ3IyM3TU18qB0SY+1HAby4rP\nPZCcwCFvkD+dGMkQbdeU5yFr+v0Mp8LkmN3E0BVdAIpFQgmlSCZknVxME4bJ7MlMvD+RosBqIhSM\nZ675ZFLJbV+BLJqxjsQQZZVIqZ1AoY0Kj0yk1Ias6gRQMBXABWRJIiOaQnc4jiuhEjAIkP6N8adk\nSE+Gkk4jimBi4kxcBonh9HsVTYUwxewobgkpoZCFSsgkIOeYMYRT2A0GDAkFsz9BIsfCgKw/J7F4\nDFOaVKqsy8XviXCsx8dIbh7JeBtGczUCBhAEgimFtcv/vCz6NKYxjb8saKqKb+PzeJ99GlSVrIWL\nyb3xJowFBaRGRxn40Q/wPv0koHsgSU4nkabDRFqaEQwG7I1zz7p/59rL8G96GTQN59q1iKbTKyje\nbUTjKb7zh4OM+mNEEzLrFk81Lw5Gkvzh1XZ2p5tM3H5ZHUsbCllUn89YIEZLl4+FM/JYs6CUWRUu\nTCd5yeXlZeHxTFWN3nBJNT99upk/vtaB2Sjx+dvmZwy1BUHgjnUzLvAV6wiP6Sql7IKLiSUFpOhR\nTnQd45Ghw6xvGGFUXUaMIubV5mDw/ZFkdJjcqpv4/asKlc5Rbl/UjohMUhYxaSqtYwW80T2Lrd0a\n3/hQAd9/eoRwSiBlvwR4ASU+iMGci83diCCIaGoSTVPxyg385I/tyLF6bmxso2lsBn97xwq2bOkk\n1ad7mM0qd2YMrt8OFmsOA0PLGeoLULv4/JKStnNottK0r59927q58e6FFL9Lk/ZkWoX0VlLpZCXQ\n2ZqlvB0mEpDd7XpCqrI2l+4O71mtHZIJGavdSCyS4uCuXpIJhdkLijOKc7PF8J4rlSaSm6eQSm8h\nXeLn0QEukk76HT04iM1u4vCePjSNTDIQYKhPV8eXV0+qo7JdOmkVDibIyTcQiyQzhNC4/89TKk1g\nIml89gpd/AAAIABJREFULkbdEx6WpZVu7A4THcdGWbluUn31XmGaVJrGeeOPr3VkMjOb9vXxgWUG\njg05cNsVPnPJcV5rL2EgOgOzSSLbbmJZdZhcZRvmrEoSJ/pQxxKY5pQhpl8YUTozcWA2SXibDgFM\nMWY80DZGJC7zyOY2Ct3WKYqdaKAVz4k/InQXkK1EyR7p4Guf/ghSYTEmo0RDhYsy+tE0iD/fQQoT\nR/uLWJTvZ/TNh3GELdx5sQSiQNMeO0/65/KNy0oxZZvZM9jJjg4LSyrGuHP+QhKyRteQPvDtbBnJ\ndLBzO8xcubSC8nw7v954jC0HB1g1v4QHNx5DUTU+smEWi+rzp5gBLm0o5MRgkFf29vGDxw7jD8Ux\nGyWuXHr+ZnzvJsLBOAgCWedhSnk2ROTJgXM4Mkq1s5JwKMH+7d2EZkwO0uPvcBB94PCv0TSNzy68\n723XTaRrleNpUqCyNpf6xkJSSYXicid7t3Vn5KUwWQrnHdXJl9RJ3dv8iRRldstZlEpv8VRKE0lJ\nRYO3j2VQNXVKydhg7/gUUimpGtBIEE91YzTNZuuQn7tnnIlU0veTbzEylia/JpRKExhPS4CNpkn1\nVW8kjjGcQpI1QmkfKVWNoaiTpJJDbCGszGW42sFNs4qoTps8lldPlhfaskz0izohsqrIRXsgyt6x\nIMPRJJ9z2ojHk8g5YTQ0Vq7TuyU90TXCfk9w8iIMRkRFAoNGTNYzZr879keSapJVpStQFBVBIGNE\n6fdFOdE/zqK5xacQ2MPRJBpQ5bAy1jPODlnvKCcoIvteGeDVvh7iywuYIJVkq4QplCIaSWK2GFEV\nDVUSke1GPQUoCPiT+v2ZUGgJgpAJREQxF1WsR4rL5LX6MYki3flWxmJJjBYTwXIbWvo9CaaCuEiT\nkekAwtUfIVJgJmU3oqgqQXGS3BQs+UiCjIiMigFrSj8fTdOIyWHc45XIBSJSOIlFEcBkAFHA4kug\n5uvnZxuJkcixMGrVM8PxZApL0kUsz8Ixh4g534bijVEFtIkigmAkuzNIsDab4F+Ayf40pjGNdw+a\nLDPwnz8k2tKMwe2m6N5PTOm2Zi4to+JL/5ehX/wc0WKh6KMfB1Wl52tfRfZ5sS1YiGg5u3LAVFCA\nfcFCokeP4lq77kJfEqB7A/3iuaOM+mMIwBNvdLKoPn+KAfdvXzrOwXYPVUUO7rx8BjPK9DHXIIn8\n7S3ziSXlU+wHJnA6JfjimflUFjoY8IT57AfmUlv63qsJ5ISfWLANk60EjEU8d9DGdTPhziVdWEQ9\nBpCjr/PAjkV4h8ZZUzUIgKf7SSrN+cxfOIokGjH8f/beM06O8zrz/Vfq6jg9OScAg5wJEswJjGIU\nFShZsmRZwZYtp5V37ZW1vr/rvfc6rO+117JsWaIk0soSJVHMYhZJEARA5IzBAANMnunpnCrXfqjq\nnhkAQwIUFa53ni8D9HT3VL31Vr3nfc5znhNaRGp6kqlUmKVr388mJ8HP9o1xaLyewclx1i5uYOOq\nFWx9ZQdr26aJt15bJYdiTZvJFQ2+8OBOcgWD1Yt70euv4MNXNiOJArvKNpUG7ktnjZGhWwii8KaN\nIExf1WNoFrxDcWsFFfXQxEj2HSSVfKVS8mxSSTvnPReL3dvOsPOVQboW1ZGaLnreOyubOT2QrJaT\nnQ3XddE1i+b2GmLxIFNjXoJr1YYZz6ia2iCp6dIvxBR7PhjzKJXOLg+7KFKpoBMMK1imza6tpwHP\ngqAS887+vsisuRT1Gxnlcxr1TRHKJa+UUFElpicKXqlhfoRHTz7FJ9d8hGjgzUsjq0qlWaSSIAiI\nkoB1gZ5KlWNftrqVvduHGB3K0LPknfUafSsskEoLOC8SmTI/2ztKrmTwoZuXVdtVHhpMsu3QBD0t\nMcqGxfbDkzTIOQw7ypaV9dQ2b+BmtlHfvYpog0cCTZ38LloOQtpysg+/BEAq9iSN996HPjzM6D//\nT0J9S2n6wG8gx+c+pF3LonToIKVQDf++K83v3OOXwPjMsSQKfOnRw/zlb11azfbkJz2Ppo6miary\nILX3RygrG6nvvouueIp0TZE3jjTRVMhwqmU1HWtuwzS/g7wmTHtAxNUdlJE2Sku3cLPjUtQs/t/v\n7SOd9wKKF/u7uCZR4uCpJPmSyZ1X9tBUG2L74Qn6OuPceUUvqn+Tf/jW5Xzhhwf422/voaxbXLm6\ndV5Dv/u39FEsm7x2yGtnfvvl3fPW1f+y8MT3DxBQZd7z0XfGHLNonksqHdw1wpF940hNM+f6ds26\nRwpjVXnuW6FiYKxXCJ8alT6/Vr7g+9/MrtWuEEyVlurGLDVahQSrqHr0c5RK3utPDCX4wGKJgK8s\n0Z35F4wKXh7ZxuOv/pSr8vdWX9tWKpEfS3Fjez2u6+ISxHFyWE6GsCjMaSkPXs13MKQgCALjJR1R\ngA0NMZ4b9UI3VxZJTXkqLdtyyGc1WjviWJZNabqE7bqM5Uq0+ObiOc0ABMpmDtct4zhlRFR6nRAD\nk9NoHc1YXedvJRyJqliCNz6XNsa5uaOBR05PsS+Z53Mv7UGtTzPWsZWto7Vc13klk2WdPdM5WkMB\n7J0jJNY2YysSajlKXUuIseIEbYE2Cm6e7x1/hBa1lde+PYbjOHT01GGZNsODXjmZZttcvdEjak3D\nwnVnvLGuUYO8vHcaV4DW5pVE8vUkSl6poDmrRNHyM1KjmRJHLRPXAaPOm7tqxkCvU8noFrbtEYHt\nXd5z7UDmELZiovX0AgL1RzOoqsC6jV0Ml4tkFJFhVcZUy+BP/4LrkWtxVQZDJyJL2GMFlIiEGQtw\nZjyHFpoViEhBn/oqIgkyYlqDuIyLiePaRAvtZAAsA0VzIe59NpjUsOs89ZlQmEawazFUCa1ORZBb\noOMypoNRpm2T5vYw6lQJKT+KFG7AdUxqTucpLK4hZy6QSgtYwH8kFI8cpnT4EKEVK2n/9GeQouc+\n1+V4LV3/+c/nvNb++3/AxFe/Qu2NF0YStX3yd3HKJeTaizO5nQ/ZosHp8Rx1MZXulhl/t0LZ5NRY\nljeOTbH/ZJLVvXVsWt7MN545znee6+cz7/FUVdOZMvtOTLOoLcbnP3pptaFJBZ6h9gVkhGZ/RhD4\nsw9tRDPsi+oe907BdV1Sw08CEKrfzINPH2XPaZVb+sIEpQJSoA6dZuo4zkevGKUhMIaDQkPPuxkf\n+AkbO6dADNHc9yHUSAfxbouNsSCuaVHSBX62b4wf/swrjVzeXUs8qnKysJmDewYRj5osahvgkmVN\nLGqv4atPHCFbMHj/jUu45ZJOXv5pP/vSGuFIgNR0sVqmNVvJ/cOHdlPfFOH298xfclZR/vw86p75\noPkJucmx3Fu888Jh6hWlUnkOSZOf7an0Ns5l5HSana8MIstiNf5ZsqKpmiCer/zNNGxc1yM4Wtpr\nmBrzmm7M9kisqQ2RmChQKhhzyJZfJCqxdS6jzRmnipJHkgRs271gUsk0bbSyRWdvHSvWtfLSU8fZ\nfO0ijh0Yn6PiqsTy6qx7PeabaBdyOrbtoJUt6hsjRGs8Ei6f1dmfPMSJzCn6kyfJ71dZvqa1WpI2\nPZnn4O5Rrrt1GZIsVs8tEJxLy8i+DcebnQN4HXcrWL+5E8u0aWo5f/z9i8QCqbSAObBshwceP8z2\nwzOlIBOpEp+9fwPDUwUefOoYoiDwsXetoH8kw3efP8ET+zwy56p1fURrF5Gbep3c6KsIUwEECfTS\nIHKwicyPnwVAjEZJP/0kkTVrGf/yl7BSSfI7kxQPHaD2pltQO7sI9vSgNDZRPtGPo2kcifey48gU\nH7zJIBpSODGSoaU+zF1X9vC1J4/y19/czftuWMLmPgG9OAyAWieSXdFFVDOxWjPY+SyT/V9HlCNM\nF0M8ObwcoXMpS9preP/aLh57uoNF9Wkilkl73wdpuKKX+/Hq7f/7Q29QKFu86/JuultifPmxw3z1\niSOk8zqRoMy7Lu8mHFTOSxZt6Gtk07ImdvcniEcDfOiW88ubXddl2/MDXN4Rx7QdBkaz3P5zmjH+\nvHBdl2y6TDB8cYHTm6E0i1QaL3nKruFTHrFRsu1q19WsMXdhsCybF584hhKQWLy8ic7eumrN8Wzo\ntlEtgXsr6FVSyfsZnKUcSyU84mj2wl4hmCqKnjlKJf94KxLdsxe2ioJjWjP59sA4q+q8zIXxJlmI\nCobzo5RNjVyuRDAkE46q7I/LTI+luKqllrSmIQgKjpNHFGxs1yVnWNWFd2wow6Pf2ccd719L1+J6\nJss6zcHAnPJCKSiRShRxHO+auy7UNoTJZzUsy2HnRAZ7lvpKMy1cWWbcv99sJ4kod5LdGaPGMdA6\n4FC2yOWtdeecTzgSwPKJnFpVRhFF7uqKs3viZVx5A9ayZbSdDnMsOMh1nVfywmgKF9jSVsdLOa8r\nn6OIqOUY9WqMseIEwaOd3HzHIr55/AecnhhFK1uIosBgvyf5luMBrKzBiRNJrt7Yheu6/OTb+zAN\nm9Btvd41mvLmpitA4+QiAILNLsWMBIKAotmYQQk76BHGr2ULnElZ1LVFcALeXAxNldHrVNK6WTXm\njtYE0SyNhw5/l9aeO7GiKi2mQSClI7XAuks7eea5IxhxOGobOPZMwOpgY8sGkYC3XCsAhoNc9K7F\nifEsZvTc+9OxS9QEayiNpiFeB66FZAYImPX+95YRkhZ0hRBsFzWjV81AtWCC4HQH5ZYw6RW1KKEb\ncPGIJ60hSFbRaAbS6VHE6CUIehEBCIviglJpAQv4D4by8aMANNx593kJpfkQ7F1E7//9Nxf8flFV\nEdWL36DuH5hmbLrIDRs7CKkyh0+n+Naz/Uz65URqQOLvPn0lNeEAQ5N5/ubbe9D9DH9TbZDfvXcN\n4aDM9sMT7O5PsOvYFJeuaOalvaO4wE2bOs8hlH4ehFS5mqh9p/DQ08fIFT2CprU+NG+5WCG5Gy1/\nCk3s5gsP50jlDBa11dC+5C6MwinibTcgiAoTx75MF95a+9qZZURKQbbtXcN7NuXZeMntKKq3joSD\nMo21IRKJPMu7awmrMkm/dGi5r6S+7YolfOMZi/GhLMeGsjy9Y4iGGpVkTmft4gZu29zN6Ok0/b7S\nv4Klq1roPzxZVaJU4tGzE3Zno6Lq0X8BpFKFiJkaf2dIJdt2sP3kpGnYc0iaXHZW+dtFKpVKRYMX\nHj+KKArc86ENXifdgSRLljdVSYr5yt8q1g0BVWbp6maO7B87p0V9RXWdy5R/aaRShSS0LYdiXq+q\nhSy/vD8SU8llNLTyhV33iuosElNZuqqFxcuakGSRU/0JMqkZFVZFRRScRfjMVipVFF+hSIDaeq86\nIJMqkTM8hdfYUJbhfTa27VZJpWMHJjh2YIJlq1s8K43zKJXA81Wy7fmT5OZZnkoAoXCAa+bZZ/6i\nsUAqLWAOHnz8MK8fnqSrOcqtl3VxeDDF9iOT/OXXdpDK6QjAe29YTE9rjOa6EI+83I9minTUi3Q2\n+8HGpIDVkmb8sS+C6xK4qw2pGKEwuIPiolVMdyynZ+sjDP/dX4Pj0HDvfRAKM/XDh0k9/qj3HYJA\n84c/gjHhmTkPRDpx8YKHrpYommGzuSvO1Wvb0E2bh186yUNPH8PafIa+OkgPR6nrKhC5ooWgomDK\nk4ilKHY4j23mOXCiUjcvMDBe4PNf3Umu2Av00loX4K8u95UMls2/PHKIXMnkQzcv5eZLvdePnknx\nyn7v2N5z3eK3rMX+8K3LEEWBmzZ1zpvZSk4VObh7lMaWKJ/+7UtxHBdB8LosNTRHfumm2olSkogQ\nwXHcqsTynUDRnFkoJ4tTFPM6Sd/HSHNcJFnAdt1ql7EKxoYynDyWALwHcnNb7Bz1lOu6mLaJi4vl\nWMiijO24vDSe4pLGGurVuWNfIZUqxM5sUmlixAsaZmdzKsFNarqIbTtzPZX8crBKIHP2wjbm+xg1\nqApJ3aQ/6wW8F6JUKuua1zGsZFNbG6GhpxYEsFyXY5kiY0W/U5hboF6VKLpguC667RCUJSZGPWVf\ncqpApCOG4bi0hVUCs6aUGJCxLIdcplyt7a+tD6FrFmZI5vGR6Tkmpg4iLjpp31DaMZMgd6IFFIJp\nh2BZ5xQemVYTmLvUhKMBLNNCwiRvZKkP1vHSyFZKxhmCZgxF7aW0uIeRrMqpXIlD6QKdEZVOQUI0\nvPFyFJFgMoY1EaAxuRi5ECZoekRdNl8CAlxyZTfL1rTiui4PDp9Aec6kMJb3pMkDKaYnPXLQ9ssK\nc9PeeZcuqyM7+SquaHPJuiUUXvSIMcl1MQErJOMCk5YFAmSX1KD4Ro3BhAZLXYYLGb419BIC7UTj\nKkktDUKIUmczounQfLpEBnBiOgFVpkFVKAKZoolUzoAMETlM0SzhiDapvHcNi0WDGKD483IkU8KM\nKgimhiMLgIAgBJBTBpFcBjIVbwCXWKYZ1y8bsMQiejbEuroop/dPIDozEuxyNE3DeJlySxg7LIOt\nEz86SjQRYPTGTsoBb7yKuosgyEh+EFoTkBjXTWzXRfoVNAFYwAIWMD9c1yX32laCvb2onRdeVl86\nehRBlgku6fsFHt0FHotm0j+SZd3iBkRRQDMsvvL4Ecq6xbNvDLOyt47thyeRRIH1SxpQZJFdxxM8\nse00v3HTUr77/Al0w+b2zd0s765lWVdtleD56O0r+O8PvcGDTx+lpT7MK/vHiIUVLlvR/I4ce75k\nEAzIKPKF+QNdKFI5jVf2jwEuXep2NnQkaVp0D9G61QDsOrCPWLBMZ2OUzOhzOKj8y4stlC2LO6/s\n4Y4reggGJCK1y6txZn333Uyd+HeKVg0vHG/EOTZINFTLmo23o6jnj2FlSWTD0ka2HZpAVSSvMQ6w\nvLuO/+dTV1DSLAZGs7x6YIy9/dPUxVQ+cddKxFkb996lDRi6TVNrlGgsSP/hySoJUvmplS3KJYPQ\nPCr+Srz6i1AqVeLBYt6gkNd/bluIs2PrTKpEJKaiayaGblNTGySX0S76XLY+d4JS0eCqLUtoafcU\nyHV+Z+cKmTSfUqli3RAIysTrwnz0M+ea5lf8IbMZjbZfkkPH7JK0bLpcJXYq8yJaIZXexIB8Niqe\nUJVrWPEwUoOyp2A3bAKqfF4T7WhVqaRVxzMcCRD3K2YyqRJ5xYsv0+M64PkuVVAqzr0G8ymVpLdS\nKp3lqfSrxjv7ZFvA/6/x2sFRHnv1FO2NEf7rhy9hVd0Bbut+jE2LIZXTaauDT1xxhEsbX8VxTER7\nig3tHrFy1VqPxdbHxtBfPIPrugRv6SWw3itXyz3qlaT9iD6+Ox7F7e0DxyG2+Qoit93JN9PNfLHr\n3TzctoXEZbcgRaNMfesbZF95GUtSGAp5PR4OHhnhzA7PY2l5l7fRa4wH+fS9q9m8vIaemlGyWojy\ns0PYaRM5ksEMTOFkLMrf7UdNdiOUFY6M1SK6DpFwgDWL6rFthxXdtdREAkykDf7+u/t4/LVB/vbb\nexkcz3HVmtY5Bo4f2LKUhhqVmkhgzusTJX2OeqWC2qjK7717Dcu65pd1DxybAmYWQlEUGDmd5uEH\nd52TwflFY6qU4K+2/w9eHtwOeA+u0/kSPzk9hX2BpWXzoXSWp9LwYKr6f911iSkSEVk6p/xt9EwG\ngCtvXEx7dy1T43lGTqfnvMd0PEIJQLO9zfRArsSLYyl2J87NLGlnk0qzFFmpaY/oMuZ4Knn/dmyX\nTLJ0llLJl1xXPJVmZdPyplU9n99f1UVfTYhp//f6BSiVjBMRlh64DseESDRArH0mW3woXeBU1hsb\nxynQoM4sLjn/eCu1+VrJrJp0t4ZV8rMWaUfxgsnkVIFUqogjCtQ2hFGDMqUGv0OcOyuzJQi4rk5O\n97IxUsm7rkZMwZJMLGMvjutyMJU/53yCkQB2UEK3k7w2tpOCUeTFoVeIBjchqg0op/ei5A2seCtf\nPT4KwM0dDRTzBoLjguNiB0SCpRjm8ShNo32oegQ75y3IxYJ3vuFogHhdiNr6MEUb9PogkmZzcizL\nnu1ncAUHR7TI5rz3Z6eLOIrAsq4o6eYhMo2jHEofQavxzrtEGsF0sHy1kiaA69o4qoReH0SwDKyo\njKzZ5AyH0Wnvno7VBElpaYLqFSBJxAeyFMa9gEaLZXFch0TwFQyzH6VkIpS967mkdhEBLULACHO8\n3yuJFUreNVN8pdKEbWMHJVwjhyRYKGmPfKo/E0A+nUMqe936QKJxfBGOL5O2JK+D4H2djTQNeXO9\nEuiUohmCKY2A5s99LUEsLWFLGo5TgIAXOCl2o38sNi4uZTuJy4x/2AIWsIBfH5RP9DP50NcY/7d/\nxfXXLtdx0MfmLxm3i0X04SGCi5f80syzzwfXdXn98AR/8cAOvvDDAzy94wwArx2coKxbLO2MU9Y9\nS4a2hjCf/+gm/vj96/mde1bTGA/ys72jPPfGMMeHM2zoa+T+LX2s72ucoxhqb4zwW7evoKzb/PW3\ndlPULK5b337ebmDzIZXTKJ+HAMgUdP78317n8w9s58TRp0kMPox7AYpqx3HZ/vKpOX6HrutgWzPJ\nuf0Dnhr3w1ek2NQ5gSSYpE7/iMzYi5w8+BDN9mOEis+RPPMIrmNyOL2BvK7ypx9Yz3uvX0JIldn+\ns1N860vbqxvYYLSHlqUfo3nJhxFFb4zuu3bRWyZQNy33mm30dcbndEgDT9W0bkkDn7lvLf/wh1fz\nVx/fTI1PDFU27kuWN3HvhzZw1Za+6ia/ksyb7Z2TnqfDlm071XOYTUK8U5it7pl6B0rgKvG+5JfX\nV86rErM1+Mn6i+3+NnomTSweZN1lnef8zrNBeBOlkq+yV9X5532FVPplmXU7jjOnac5sX6XKvIj4\nRM/ZTXLmQ26WUmk2KuRR1R+1SirNzH3PJxMKWb3a+S0UVqpKpWyqXFUqlaa84yvNJpX8RGaFaHpT\npdKFkEqBXw9SaUGptAAA9h85yL8/PUFIlfnD96xFlSymp3cjuBZ39m1lY1OUtpoikiig5VNMHX4I\nx9G4YUmO9o41bLnUK9PKvvoybspEddsxAuPQLYIl4k5q2Cs3MGZ6pMoznTfw0auvQrn0Cv7hB/sZ\nGMmyekkb46k4X0vr/NH9n6bmx1/DSqc4Ge2hszWObbt0vPFT2nKn6G6/lWVdV7H36AD//OgQquzw\nJzedQsFh144Yr7ffwVXHRthy5QgA0cgmMsJPyX7vZyQCcaa7L6c2onD3NYu5ceNM61bTcvj6U0fZ\ncWSSAV/dsWZRPR+9bfkcpVBIlfnLj12G47jVoCSpGfzz4SFu6mhgS/uMKfGFwHVdTh71NqBns/Hg\nqXSWr3n77dIvFolyEheXRCYNeOTdG1NZ9qYKXNpUQ2fkwtp1ng8VT6U6tZakluZ0IlH9nSlCgywh\nAJNlY07d9OiZDKIosPqSDtq6avnxN/ZweM8Yl2yekeXq9qw6aEsnqkSYLHuvnY/sqxA6pu0gAcFZ\n3jSVIG72A332opacKmDUz2ohr5uYplWVqhq6jeM4iKLIcyPeZrtGkQjJEvcvbuV/HjxDyXYYyJVY\nWz9Tq34+ODkJ2c8BhCMB1IYQFL3j688WcZwSEMFx80hCFBFw8FRCzaFAdR6VyyYjGY9AaAurjKSK\nM+fmq5D6D09yuFYie0UzsboQiiqS7iwg0QyCvxHBM/Z2HZOSH9wG8hZOA5gxBSNYICkeJF7sYv/3\np2i5dVnVqwrADcugCzhOgaxe4Nmhl9BsnUa1jenSUYJ5iJ9JMLXSwWrrpCcWZmlNmONDOQRAcFzs\ngIBajiFZCqI/NskMhIO3kEtqKDAnk2k4EkK9SniqzK79Y2TG8gyu3o4lGTQWmyAe4lB3CHNVDXoJ\nRLEOx0kzVZ6mKewFH4F0EiEWxwrJ6LV+MKzvIyxuwAlIoOcw4gGksoUVDqL4yqmE4JDLpwkoS0DP\nEBkrVSyTyIQmSZSmmTBPItl5RLMTLC9YXVLby+l+T4VmOUF/nL35rJQtcFyK/nE4dpqwoRI/pFNq\nGifRMkiT04eaN4nYUBADqFoMcbVKEjDkNC6NFPNGVYGWmMhjyw6GWgbXpe9whv4VNdhqK4IxiRkp\n4DhZZLmDYuw0Ml65r5oHK2AwUTqDGlhLzrCIBxbCiwUs4NcJmReeA8CYGCe/Yzs1V15F4vvfJfPC\nc0Qv2UTLb30cKTLXULbcfxxcl9DyFe/oseRLBj96+SRbLumc43c0Hx586hhbD46jyCIhVeLJ189w\n9do2nt81jCwJfOa+tVi2w9EzaS5d0YzqZ+5lSeS+axfzwBNH+N6LA0iiwP1b5ldcXbmmlcHxHM/v\nHkEQmBMfng3HdTEtB1WRcF2XdF7n8w/sYFlXLZ/aYjM9sBfTshEllQPTa9EMm6ZwGlU7QFmDwvQe\nYk2X4rouxdQ+AuEOAiFvnUzlNM5M5umIqex9fQhDs1h7VSdG9hB2bieWkaF5yW8SjPWybyDJxo4J\nlsYHEJQ4D+/p5dalR2FyKwowmIxzeKKR2zZ30NrcxnOvTlITEVk6K8k5cHSKQk6nWNCrhIEa7UYF\n7r9xCafGc1y34fxeoLOxdnEDWy7peEt1V81ZKqMKuTJ7435296vZpFImWZrTqKSC2cqfd1qpZOgW\nlukgKyKW6TA1nmexT6JdyGeLBb2qFjr7eBuao0yN56sq8QpZ09gSZbB/uuoTdSEolwy0skVrR/y8\nFQ6CIBAMK/MqlWaXv82HSvlbxR4imy4RUOV51WPzIZUoEompc1RA50OFVAuGFbSSOZdUMmeUSgD6\nBXoqna1UqkD1lXjGLH9UWRar8xE8siccVcnntCoxFIp4SUzwlUqRPIIjYqcVBM4ilSoJPJ/Y03XP\nrmG2NxJ498Ds62ToFrmMRqPvl1RJdP+6KJUWor4FsP3QKF97chIXgfs3nqG57noK03twXYualmsQ\nBAk1eATF7YFBl6J4EKPNUyjVxZdz12bPkNsxDXLbtkIkyo7MBjbUTSDgoNatpPX/+iTf2zUNB6dY\nfk1EAAAgAElEQVRoqAmyb0Ljphs28NQTxxkYybJ5ZTOfvGsVo4kif/Ot3Xz59Wk+96k/ofzwd9hp\n9LBxSSOCXmbp0VHkq+p5X/QYmaEf8vXnGoEAuiXyxP44t3Ym2JntwRJltuZ7WZYrsbgtTMPSe6hf\ndyvZba+xfc8UWHD/TUu5YtVcokaRRT519youXd4ECCztip+z+FVw9utZw8LFI5cuFomJfDUzYejW\nObW8ifFz1R5a2Wv1ecmVPW/5QD4fypbNPx48xE0dXVzePJcEqxA/xfKsh6C/+BV/ThVC5buX1Pay\na3IfJyeGUeuCjNQOYAtNhOUwAVFktKRTtGyiiic/nZ7M09IRR1EkmttiNLZEOT0wPSdTMptUqiiV\npjTv5/kUVmXLRNPfQBNXEcTL4Bi2wXQuU70eVem17eA4bjWgmJ4qoNfEyBd/TCSwBENZT7Y499rr\nmsUJTWeX37WsKejNmagic0N7PU8NT7MrkWNdfYwlNZ76w3FdXhhNsqEhRlAyEQQBV5u5vqFIgMoZ\nyyULMyxjWTay7CmVTCeI7ZoIglI1Ta6MUVY32Zf05lJbSOWonq1+rw0IApw+kSR7dQt2UCYrw0F5\nFwQ8fyFEb7F1JQEEAVkQcJEACzUvoVkORkyhxvACjvpECaEc5ujxiTmkkun7DzlOnrSWZdfkPurU\nWmxXxbSGUfRuBBdCYwma+9J8eOl9CIJAOjNDgjmKiGKpuMwEmqcsGyXUy3S8SKuQIRwJVMfUchTE\nkDcHpkZyBIByMAeCSyFQRBDCmCGZmOEwDUTD96Hp23Dtk1h+E71osgZdsTFjAcqNPrFaniI+kSO9\nsg7XymBEO5A1Gx2QXS/ofSGVI2J718Iqn0TAe+64uEw7CabKXqbZdjK4AjiSDi4ERIVA2ZsXdmjG\nCBwB5BobuWx5HecAy0kSHapHNgXygX3k63Vi+mqCBRM3rUFTCCNs0dIRgSLYYol8sxfo4qv7LNPB\nitgguFiKjqHL1JwukOvxAxhFw3ayyHSQa8kT8kMINS+iBws+ucmCWfcCFvArhKOVMZNJnFIZua4W\npbEJM5WksHcPSnMLZnKa5OOPIoZCHtEkSRT27EY7c5q6W25H7eoi2LsIUVUp+X5K4ZWr3tFj3Hpw\nnFf2j7Pz6BR/8J61rOqtJ5XTMCyn2nSlgp1HJ9l6cJyelhi/f98aDp1K8s1n+/mH7+9nMl3mmrVt\n1PjP+qvXtp3zty5f3cLTO4YYSRS4aVPnOd9fQalo8PxjR7ju6l7yZZPGeJD6mvkTaN9+tp8dRyb5\ni/fHsVM/ZX/ycnTT4ejpBKnRPeDoCKKC6xh0iBM0xzbw21eNgg2mLZIceZFI3Rpyk1vJTW1DEAOI\njffy5G6HN45NYTsu92zoQJJsgvIRxg4/QkzVcRERcEkNPU7dkk9SzA7ym5tOIkohWvo+zI2qywNP\nBLh28QgnEnUEYks4PJKmtrWdy6Mt5EqjXLOureoTlc9q1RbqWtmskkoVVGwfklMFfvb0cdq7a1mx\nrvUcggQ8Eu83b10+75jNhxkz5JlYR66QSvbc8jeYX6k0m0h6p0mlChHQ2VvH6RPJizLr3vnqIIf3\njvGR37+yGpfAjFdSU1vMJ5W8WK2qVGryvTcvQqlUGZvahvPPc/ASbpVrfjaq5W9vQipFazylTi5T\nppjX+cHXdtHZW8e73rf2oo7zB19/g6WrWrjp7pVv+t7KtWxqjTF8KjWHVKrMCzWoIJ9FwrwZZjyV\n5u7lzlYqaWXrvHusWI3K5FjOj6G82FwJyERiAdLJEvmWIqFiHMH17rNy0cBxXERRmCl/K854sQZU\n+RwSUJbF6vwH2PnKIIf2jPLRP7iKcCSAadoIAnMIr18lFkil/83x2sFxvvbkcVTZ4Tc2DdIbn6SU\nOUoxuRcQiDZdhnl6gsSXf4BT9DZ0giqj3tONo5o4J3Tw14/Cnt04xSKH2jfyxBt5WNPExo4pjk3W\nce3iFvYODhANKXzq7lX87bf38E8P78eyXTYubeRTd69CEkV6WmN84q5VfOknh3jsR69x6+AxVsZh\nfd8duHu2Id/RhFgfIObCd3YGKRoBtlj7Oe50cniiifFhFV1RuWZ1M68dmeJHB9fzZ2vWkc7rBBSZ\n2htv4vjJHQSyGhv6Gs87JqIgsGn5/FkW23IQJeGcm9/wlTBvp/Rj4Kin1qkQFmfX8qami5imPYeN\n7j80yb4dw4QjAdZvvvii5pPZKUbT3+EV9woub75vzu9Kvu+RVjKphFRF3wyv8vPtwDRsSlYJURDp\nrelm1+Q+ckIWFg2RkAYJWV2E5I1Efbl51rCIKjLjwxlcFzq6vU26IAis3tjOyz/tZ++OIVZd4mXQ\njNmkkuWTSj4xdj5SKV0eRDf2kZMVamkiGFJ4ZOAp9h4/Tg+XA5783HXdajakuTXG2HCW5FSRQpeF\n4yTB9TbdU8W5i/SZdI4fT2RQRAHTced4CzWHZhayR05P8cGaOLu3naHzxhZeGi9wPDNBvvQ8AUlB\n1Wcyq5ZlUzIsLGuU0ISOtXgxCN54uW6eQimCK9oIgkJK17Eth0JOxwpKDEa9hUcAkuVxTuWOA57K\n0HJtSjcco0fsY9hfGnYmxjhoHaIm6HlXubKCC9hBb+7HAlGyRd+guhTDzRhojUEQI4iWjDTujdnQ\n5EyJI4Ame5933DyTpQSBdJz1nRvZY+nY9gQB3dvAKLbKmezxaveXTHamBABJwhVBcGYW06RgAhJW\nKEK+26oaOBYtGwQRyXQxgjDavJXG5hYWH7kKBJfJy0Qk16V96zhXX7OIXaETnCw1E1QvxywPY6ue\nek4tSlhl7zu1xhA4DlK5RGSshO1kSLdMYEZ7Uce8Z6UoePPCDkrkrQguLrozAD6pJMgOmqMxkh/z\nz8DACBkohRByWOH7/T+hQfKUeFZYBtclmDGQoy5JYQql2FQllRwrRWDYwAiUyTSOIrgxZLxygunS\nNI5jMdU7ToOzBLBw3TKFhiLFgoFtzdwbpuoFOWZAwygHCU+UKDV4Y2yGHRzHC6SvveQath/zCEq5\nbFGoK+C6fkC8YNa9gAX8SuBoZQb/659hF/xElCTR+vFPYoyOguNQf8ddaIOnyL78EmNf+iKCLNP1\nuf9GYd9eUk88RuJ73wZArq+n68//wvNTUhSCixa/o8d58KSvwLQd/vEH+2mtDzM6XUQUBH7v3Wuq\nZVTZgs63nu0nIIt8+t7VNNWGuG5DOy/uGWXEb6Zx86XnlvjMhigI/PYdK3hxzwj3XN077/sGjk4x\neiZDc3uK371ndfX187VNz5cMXj0wRkOogDb5KrJo0yjsQRI3sKplGhyNlt4bUeuu5fjR54myjU9d\nsQfRNhEia3llX56blp5mcuCbmOVxJKUG2yqiT/wQsdTD1X01WKZOzD3O2uvTKIqFYYm8frqdg5M9\n/N4tJlZuF8P9j/LetYMANC56P0qwkY1LYf+KXp7ar7KorYY/fc86PvvF19h9PFFVcK1fMhP/jo/M\nJJfezOT4+KFJpsbzTI3n2bdjmCtvXMyGy9+ZZjKVWHe2p0y1/M2sKJVmYs/MPKTSbKWSfpElY2+F\nol+yFK8NUdcYJjGRr5IEb4VUoohjuxTz+lxSySdLojGVcCRQPa9KaVZNbYiAKl1U97fKd9S9Kamk\nkEp43qBnN7zRzkPwnQ1RFInFg2QzZfa/MYxlOfMSffPh6L4xXBfOnEy+5ThW5kdtfYjxYZHcecrf\nJFkkGFYuXKn0luVvZvVvn008AUTjQSZGc9WqhpBvn9HWVcvAkSnCmXqCJc+YW5ZFLMtBK5soilSd\np+VZSqXzjXel/K3yDCoVDVwXSgVvHpmGjRKQfumeu/Ph14PaWsCvBJbt8OOXT6BINp+6+gxXXHEf\nIJAZfRajPE4ovhTRCTDx9a/glErEr7+Btt/7DIv//gt03fQXOE8WyL28Ddv3Usm+/DMAtso99HXG\nWbfxvTx7YgXf3y5zYCBJtmCwvq+BZV21rFlUj2W7LO+q5dP3rkYSZ6biZSuauWdzB5ef2QrAxuxx\nOsQSAX03Yn2AU6fCHHrY5kSint7SGFdpk3zy4zchCpBSali/pIHfvms17752MemCyece2M1//tdt\n/NE/vcp/+dI2xpMlLl3VQvBtlGdoZZMHv/Aae7adOed3ul/2dLGki+u6nDw2RUCV6FnS4H3XWb48\nrsucmnqYUZ9MjL69uu7h/AjgktMT5/yu4ntkzCrFK/ts+dtVKo0NZfjaP75KupAnLIdojXjEXbZ+\nnBHJC4pct0xYEqkNeA/nig9RxU+po2dG7rx0VTNKQGLPjiEcn9DT7RlSR7N1XNedRSqde0wlv8zI\ncn2T5ZBCzsijFOdm4AaSBSaK3gI0bo1TUxtkerJAzvA8nWTB7wo3S9llKjYPjZzCcFwui3uLe7Ri\nkmzaHHnDK82sCcikdZN9u0YYG8qwd8gjYKY0gbHiBKlyBtEI4PilZ4lEjqxpUdJeZLp2K2HHQUDB\ndU1cVyefL+P6xVVjxSy5rIZWF2D86laS9d7i6QJf3P91+vPPYFme6tB2SpwsnmKAkZnzzpWRpMaZ\nTjKiiB200EO+VF1SvW9zIViOEfXHSG8MUjvdQbDskSpOETRrpote3r9ejpMnZ+Tp6b+U3LE4lj2O\n4IBiegt40AmTKCfJ+r5Ned/7yBH9zKUvFZZadFygFJQR0RAMk+ziGl583btPk77HkuuUKLRMU4wn\nOBM/xGTXcSQrjCjVIWo6kuVS06hyYmIblnkGQQhgumFcyTdyNBwk/55wJYFA3kA2AwhAUC9hK57X\nkqR7xydIURzZwZVFbGowrQEsOYct+j4KvnJqpFAhlcAI5AkW6nD97JahlrEFBzMsI5dNRNNBitlY\nio7s+yvhOMQnaxAcmG47iSu6uK7Ozbcs46Z7VjIVf4FC6XFKcZtKvOW6GqXgMMl0rtpBBGZIJUvR\ncR2PgFQS3vFpERPH8TYgO5J70UMWguUgmg5GsEhQ9M5ngVRawAJ+NSjs34ddyBNavoK6296FGAgw\n8cCXST//LFI0Ruzyy6m/824EWQbbpvE97yPY00vjvffR+9d/R+unfpeaa67DSqUY+f/+HmN0hFDf\nUkRlfi+dbEHHuQivxZJmcmIky6K2GP/p/g0EFJGpTJk1i+pRFJF/e/QQe/oTDIxm+crjRyiUTd5/\nYx8tvsJIEkU+cJOXaFnRXXtB5XOL2mr4xJ2r3tQTaMgnumZv4PftGOLf/3nbOR3Hth4YIxYo86FN\nR5FFm1QpTHu8wCduiXFZt+d/19ixGYBnjzTxxlArimgiymHaF99GuGEz6ZKKWR5HlEI09X2E7eNX\n4DgCty4/zZZFB7h12XGWtiawLImDJ7v4Sf8WGrtvZywj8a8vxECqJWj3E1FN3JobCMZ6q8f3Gzct\n44Nb+viD96wloEhsXNpIOq/z0p5RZElg9aKZrqxzSaX5N+RTYzkEAbbcuQI1KLNv53A19vp5cT4z\n5J9bqfQOeyrNVqQ0t9VgGva85NbZqKiCzvZ5qpALgYBMbX3I67Zr2uT9+D4WDxJQ5Yvq/nZhSiXv\nPjifqXVlDryZUgk8wqtcNDm814sPCnl9Xm+2s2FZNscPefeJrlkkJs6txpiNanmkKhOvC5HNlKt/\ny/b3XLIsEgwqaBd43fPZMpIk+BUKJj89/QJlqzxHqeS6LoZuUaJYTVRXEPM9nKb8SpIKWbjBT/I3\njS8hkvfus87F3s9y0ZjjZVVRVRm6ReA8HlYVYtWxK2pyP6npk7+W6fzalL7BAqn0vzV2H58gXbDY\n0D7JiiVXEwg1U9e6Dtv0bpBIw0amH/kRZiJB3a230/KRjxHbdBlSOMyRkRyJZZfi6hrZV18m+eTj\nlPuPY/csJROoYXVvPYs6mljSdxWa4fDAE0cA2NDnZZ9+6/YV3HfdYv7wvevOMUEcHM8xdPQUr9et\n5cmmK9EklakfPYjbY2OXXH54ZgP7atYBsCW1l/ZP/i49nfXcf2MfSzpq+NgdKxEEgTuv7OHWy1tZ\n11fHFataWLekAcPPeNx6+dz2mBeKfFbDNGyOHpg45+F5sUqlQ6k8Dx4fZXw8RyGn09vXWH0onW0Q\nBzMPrgoqLPvkaPaCH+QAbySy/M2+U4wUPEJEs859mFeUSpY+870VU+u3q1RKTOZxXchrBYJiiJjt\nEUT5+hkTctfVCbhU/VgqHeBGh9JIkkBLR031vUpAZunqFvJZrdqpbXb5W0pLkzWsaoc2yzl3jEy7\n6P9dT32mBCRs1yJY8v6OokhodQEeGpzgy6fGmV5bT0IqEW0IoJVN8ppHAIk+qVTpABcIyaTWNCCK\ndejGIbae+i6uaxLEIwpOHJli+MAkcsFEEQRcYML3N8oU/MXCFXFcB1cTERAwVC9QmB4vkikbyFIn\nBFuIpIYRxRCuXaJ5ZClG0QWfJJsul8hlyuhxtTLA1XOvdOHT9B2IuOAfW0KfIX9KdhBF7sF1HUrl\nlzCtIbSwgSB489QwBcBEMYJItkJM972bGoI0j3ktTV0BZAN2juyvfm/lujpOHtu1AZeC6WJZoyj6\njPReNL15cCp72juevIkjgeuPt6OIOILDikuasYMSJlOk898mdHoARIEzMZHpyTwDPilpymmysWEA\nJDNAoTbB6ZU7AQEl490HJ+yjmLqN5XjzUpQaEaQIOB55gj0TRAbS3rmDJyd3FcWrIawkjeQQRqBC\nDBcpay+DAKZ/LYO13vmNFiaq32kLWaLFFlyfRCxHsmjRMq4iIhe98xZjFlZAr3acw8pTm2pHCAik\nmzxS0EUnqoik9DS2pOG6RQRBqJLCohBDU4Y5MX2K2TD9zm5mYGYeBEreZ2wpj+PkcV2L8fxOcsKL\nSCUTAdCDReIVf7lZc2gBC1jALw/5N3YC0Pzhj9D0/g/Q9WefQ6qpwTUM4tddj6gEUOrrafqND1N7\n863U3nxr9bOBpmZqLr+S1o99nLrbbsec8p6Bb+andPh0is/+y2s8+upg9bWjZ9J845njc5pczMa+\n/gS247J2cQMre+r4+9+7mn/+42v57Ac28CfvW4ckCXzxxwf562/u5uiZNKt767jxkrneRmsWNfCf\n7l/PJ+96+2V55dwA5dwA4G3uR4cyRCNFEuk8/8fXdvKvjxxkajxPuWRW4y/bzDN54pusUL7JH1+3\ni3hQ4/Uzvfxwv7fedatv0FWbY2C6loEJSGY19g+kOJRaR7ztRhoX3Y8kh7n76iU8f3IFE/kow/ZN\nvHakzE/3Czx16jrquu6mrusO6jrv5PmDV/PiK5s5MrCID922nps2dfLuaxYxnjL59+3dmLbI3rFO\nepZcM+fc1IDErZu7qfNVGBX1fUm3WNFdNyepOj6cqf57vs5Ztu2QmMhT3xhh+dpWlqxoolw0GRvK\nnPf9F4vzkUrneCrN8rTMZ7U5yZAKjF+gp1LJJ5WCIYWWdo/InBp/66Su67oU/ITY2eRkxStJUaUq\nCZRNl8llNIIhhYAqowSkizqXC1MqeTHc+UrF9HlMo89GxVfJMh1EUcC2nAs2Rz91fBqtbFWPcWQw\ndc57UtNFTGPuXqjSkc4ynWoJ2WylkhqSMQ37Tc2tK8hlNN9wW2Dv1AEeP/UM28beqJJpumZVxz1h\nTbFnav+cz1e6z1VKFStj2tQao6ErSCTfQCTXiK4WiTZ431kqGnO8lcpFA9v2TMjPR+KdbVZvmnNN\n6E3DRl4glRbwi0JZt/jO8/28sn8M/S3IjWe2eUTPpVo/43/3FQoH9tPaeyMAkhyFSZfMC8+htLTS\ncO9MeVRJM/nyo4d5aKoelADJR35E8pEfIdc30L/hFsDr/ABw7fp2mmtDlHULWRKr2ZGaiNd1LaRW\nynYcMqPPkxx5ga/+5AD78ir74ss4GF/KI0tuw15WQpAEIqzhsx+8jFNCHR1aglV33Eiw1/N7uXVz\nN5//yKXEq9JSl73yw0SWH+J37lnNn7x/Pf/0R9fwr5+9jk0rWt7W+FayCvmsVu0OVoFeJV2sCyJ5\njmaKnMiVOD3tKZBaO+PVh8qMQdzMA/psX6WKUqlYMOatjT4bQ4Uyj52ZIm/ajGtesGHY55JKRV+p\n5Bozjwjd+flIpVLBwMXFkkzMnMurjw4i2t751qrefHHcMpI9QyplDQutbJKcKtLSEUc+i4Bs9Umm\nyrUoz1LDDOdGmJrlb3V2+ZvtuNiON4aO4PgdMQQsx0YtxxBEEGsCJNf4flNWhnJziPLSNQhx77rk\nNY+Yc13v/4OajlavkllZh1EfwbSGMIw3yMuTlMovkJ/wrvW+HUPIhkPtyRyyL/ktVNRWTqXTmn9v\naN5POxTySs9Ml/xYhlDwesKhLUxrgwiCily2aR5bSm2iHcH2SvZypk0uU8aK+IuVINBUbQfsZ3mc\nBJadRBQjSEIA2/UW+Y6wCgioykocJ4NpDWCaJzBCNmbQC0QKvjJJLXsBliRqyEUTvV5FdAI4go3Q\n7s2hR089y1cOfgPwyTfXxXW88bAUHUMRsZ0UgVmkkmsI4MLJjLdh0Ys2VlCsjrcZtMk2jNHX2YFR\nE8CyRwEH25xG1G2MuMrhvWOcnvAysWZdE6WQ9+8may24YKhZHCdJeNpEDgj8LPEKqhPCtr0NlSS1\ngBwE20AAbHJo+hvoxiGCWaNKQMYiYZBUTHOQQngY13WxgwqWauG6DrpxqDrmWiyLJet0tjcQEBUS\n5SQVJspx0tiBAI7sk4sBnXyjF7gqvjLJjRqYio7iE5CYWQJGCLVewRVngilZMBlIz2z2bGfau39t\nF1X1Shr7pUPee33Vl6nq1WtSQcD0romj5BF1Ddcpe+PsTOHqntLNCBYJ+bdnUlsglRawgF8EXMsi\nt3M7I//w96SeeXrO7+xSidKhgwQ6OlHbPRJG7eqm63P/jYZ776Pu9juq7629/kaaP/ghBPH824DG\n995PzZVXgyAQXb/hvO8xLZtvPnMc14Vndg6RzuuUdYsHHj/Mz/aO8uhrg+f93G6/0+1aX5kdDsoE\n/I3R8u46/vh961nUFuP6De38wXvW8kfvW1/1/5mNtYsb3tTz6M3g2AbTgw+TOPU9jPIko2fStLeM\nc/01u1nb+wwrag9xbHCUCT+2SCWKGOVJJo5/Db0wSKIQZrLcSW3HraxY/S6my3FMqQNL9/zxdg+3\n8tXHDvF/PrgTx3W5YWMX8dZrCUa7/XNWuPrSy/nytg088EyGbz7bT0AR+cCtlxJr3Eis8VJiTZtY\n3NoFCDTWqLQ3egrqe65ZxGfvX0/WauJ/vHQ5afHK847PbKxeVEfQ7xC1fpb1g1Y2SU+Xqs//+ZRK\nqUQRy3Kqib2lq7wY+sSRqbcz/OdA1ywkSZgT48nVDbWf1DirU242da5KaG752zutVKoYMis0t3nj\ncCG+SuWSWW3gco5SSa8olSRqfSXe49/fTzZdrpI2AdUjSi40eZxOlryGLm+iyqt0Oj5fB7iLUSqB\nRwQuXe3Nhzfbi+QyZQb7pz2l/j5P3bTlrhUIAgwNzu3kXMzrPPz1Xex6zVObn61UgplGRhUCSZbF\nGQXWW5TA2bZDoaBXTbq9GAymytNzlEqV62XLJklt7jHGZj17ZEWc04GtaY33b9EVKcXSuAHvOpcK\nRrXzG3hzY8ak/tzxPlutV7kXKuSkadq/Np3fYMFT6T8UHNflgcePsM9vL/qDFwfobIqQKRgoishv\nv2slPU0itlXi5NAwp6dF+mqmaTiRxyrkGfvCP2JcczXxG2/EGk0y+qV/AKD1Y5+Y00r2qe1DFMom\nSCoT3WtoPbkHpaWVzj/9L3z/qdMIAiz2H7iyJHLfdYv58mOHWdU7kx15evsZfrJ1kHdd0c17r+0h\neeYRytljvDHUyni2j7W5Ae68bSWPD5U4PBpnT7mbSw6doOG9t/Parklc4Na7r6B+0/yKo5JVpmAW\nGc6PVl8TBOFtlb1VMLsDw+kTSRqaZlq7V0gl2/VUPSFZwjRtUokiLe0153xXheRI+yVT4UigKm08\nkTnFbiuBrtUjKyKCIMyRh7quWzWZA5gYzRKLv3lwVbJsvntyAscFVRTR7AZAxHJKHN4/gmsLrPGz\ngSXfTFu2fGNmkWq3qsLbNOEtFQ0c0QLRxSmLZKbL1C2qJ8kU9/XdyYOHv4PrakimM4tUmsmEzS59\nq6CS5ajWoRszYzRWmqyWvsG5pJLuOLiuT57hEgz5JXdmN8XLetGLJo4k4gQk4plREuVtRAO3YzTE\nmBbTgErZyvp/N0NEgUnHgY1ewCYVNbLOC6jKchxtApNhxk5Okl7cVDVjVFMzZQOWKqEClh/kC4KA\nIIQQdL8TRWM9ilEkULQwcgZCbQgQMZq7UQCl6H1PJN9AwRRxFNAdiWyujBlRPJWSINAoJJhyw4CD\nKNTiuDksO0FAamRTy/UcSHtB9Lp6ldGSDoLskwjguBpm0MVVvHEVDRNEqsRKWSyiJksUu+PotSqm\nM0RjbSP6qIhGgQOJIxiWQVq3EGyD5ftuINF+CivgeT45Th7VmO1nJiA7Kqdzw5iGhWOAHZXBNYAI\no30nkThDa92HMGpGcBw/m6xoqDmDclOII/smybWGoSGAKMcRJBXXKWE0SODHpKZ5inCyEydikjcK\nrAwu4aizH8fVkKUWEBRc2wsoyvIEunECQYgRyDaA4p17NKqCoFDSXqBU7yKWTiAENyGGHVzXwDA9\nEl9EQFlS4nj3S2TlFt63+B6+c/xHSFIrtj2B7WQo1Xhkm2Qq2IpJMZYmANVyNztSxsrrBAom0thh\nsKcY6ssj1syd4wI6A9lT/r8D2M4kJbeA4ESR5U5kmsjGpmiMZKgXGj0/t4CvUpylVJL1IB09tRwP\nlJAMGUGbIdQ16Qh2cANGoIwkgONo5MyF0GIBC3in4DoOpaNHKOzbS2HPbuystyaWTw5Qe/2NiEFv\n7S/u24trWcQu2zzn84GmZhruvvei/qYgirR8/JM0vu9+5Hj8vO95evsQU+ky7Y0RxqaLPNLIDVgA\nACAASURBVP7aIEFVJlMwEAWBZ3YMs3lFCz2tM+Vpruuy6+gk0ZDCotZz4yKAlT11/OVvXXZRx3sx\nGJrMUyOcxHW8jVnyzGP0n1jF6pUDmJYECFyzeIRVrUlefG0TIUT+F3vvHSDZdV53/u5Llas693SY\nnIEBMINEgAgESVAkKFGMlihxFWwF27KCRcv22lrZWgd6V7ISJJlrS5RESQwSwQiKJEiQyHGAGcxg\ncuqens5VXbnq1Yt3/7ivQnfPIJCidinN908DU1Uv3Hffvd8995zz1UtzLJ15FBm6HF7azRdfGuLH\nb93MN75a4d0fGuCjH34Trdok+fOfRDPSFJxx8ssVEjGDH37LDu66YX3ltNuu2cCuyT5ePJ3n5Qsr\n3Hn9GKP9q9klbcgvvsaId9+2Qf7zT72B504sceNrqEBmGjo37x7h2ROLq/xEFyPp26ZtAxF75PKL\n8TZ40s5lxzbmSGUsLpzOr/I6+nbDdfx1IEjb62ctUymTi1OrtCitNBlaI33szdG/a/K3pMXAcArd\n0FYpCKSUSMk6b6A2SwnWgx1upyS8wabtA5w8uoDnBmSyMXbtU0CNZemEoSTww1dlpXheQK3SYnzT\n+ny5N14LU8l6lQJAgyMK5Lzhlkm06FnVa93KZGvj4QdPsjRXxYrpOI7P5OZ+RsayDI9lWJqrRBIw\ndc5apUUYyo6Urw0QWr2gUtFmfGNfh8VjGDqxRBdUWuuV1BvNugsSUpGErRgBRoXmCrE+dYw5Z5aF\n2XOASaB7lFqrWXnpbPf4vT5ZAHKgRSNdJFUfoJEpdjbpmg0X0+s+Q88NaEQVfS/LVLrCO9CW5nlu\n8P8r+dvVzO/vUXzhiQu8dK7A3s39bJ/I8djhGc7OVsimLPIVm9/45It84PoTDKUafO3UNmCAm+aO\nMfDOd5PYup3Fj/8JhSefgqefgTBEJJIU3/oBHj7usPj489x1wxgHdg7xjRcudSi1n2/u4d+9dzsD\nd92FTKaZWniZjSNpEj0vxy17R3C8gF095T8PnVU+Pl99dga7dJw3bT6FPRfwyNlNWLrPu74/STz7\nPO+IuZxfupmHT23jpuu3QTzBE0fnScR0br3ulc0Z28BIySkThAG69p2/eL27INNnC9z0xi6o5fZo\nyxt+QMLQefaRCxw7NMcHf+aWdZUy2mqsthQombY6E87B6vNMFS5wk/8O4okE2Vyc+UvdQdduevhe\nSCpt0ai7LM5WOztHV4q/PjFHxfW5d2IQ2/d5ainEMDbj+1M8/expZNVg7/Vj6IbWkUbpflQ9qyeh\n+U6YSoEReRfpCXbtG+We/e9mrjHPjSPX86fHP4WULaTtkTKUMXLF9Snk1YScHtX4b8//LtcM7mb3\nwF34UrIr2tkp5ht888GTlEa6i918s0C+h6nkhxLbD9CEIKZr2L5PGIFKREwlACcYAF3QiIz5Eks2\n7vI04xPj2A1wB6Hk2CSJ4YQVktV+fMuBFCAhO1UlsDTSM2Wa25LIfhMRHwNvhXJ9ia99Vp1SAloo\nCSouxARBxNoLekwTNZHBcGI0RhM0xpII18FquYQ9u8umpQxUrcgw2/TixMsCOyWQMsZXgi9ARi0o\nrLLDKf8ZwoHbAdD1EeLuOL6MJK+xcTRNMXSyRgV8F4wYMpJ8SemAYeJFnkq640KCjndSXVSIVftp\nkKM1FKfuL9AXN/DiGRR0J3lueZqqJzAbIaaXYOziNfiDNZpxgZR1ErbyynDjPlbLYEgbJG8XOjtg\nQdxQ1wFgJBlLjFL3Q7yc1QGVvJhNqqZApWbKwLd6EnLZBn8FKmXX8N0LiGAc26hF/VMt0kJ/BcNU\nQKsnakzveolGZiVqCwWAxn117+lMHBnYgEQPMwQUqWjfwNm0H+mdQtXXA4TggPdGiqUmC4ML/NXp\nz6u21AYQsk4QlGilVALfl59kZXwKN1bGAoRrI5F4iVYnSYmtVPATAaXhZaS2GlTyQ5upyoxqKnMn\nnnecVniGpHYAITTi+o3Ug4fIj50nU0vgeoJWUgFarrmabXTTHZv5+lmPeJBAazUhyqGa5hyNu98A\nZYkX+krm579yQns1rsbVeO2x9Bd/RvWJxwHQkkn67n0b0vOoPPYotRcOkrvzLgBqB58DWAcqfbsh\nhLgioLRUavLlZy6SS1v87x+6kY/8xYs8fmQBIWAoF+dH793F/Z89yp9+9ST/9AevxfNDcukYlbpD\nsdritmtHX5PB8dqQUmI3XJLpKy8YXynOzVb4yF++yE/fforJLMTSW3HqU2zfsIxhhBw+uhdPbuLN\nb1kGDrNz0xyz05Ok488gQ5fPHd3F0YVh9mzqo7JYZ3mhRmGpzoaJHPHMdrIb7sJKjPET940wu2Jz\n297hK1YQBhjIxnnbLRt52y2XL7bSkbl46+U8iZjBPQcm1v37leJH3rKDew+MM9izAdn2U9q6a/g1\ngUojEagkhGDH3hGOPD/L+VN5BkbXV4J7PeG0vHXl6NvsqTZo0AavhkbTHVBpbXjOty9/++aDJymt\nNPnAT9502c/bsqVE0kTXNYZH0yzNVztFdI4enOWFp6b54E/fugrQ6GXvrPdUaoMlOv2DKT740+vf\n3Y6C4TVIndrsrVeSvrXvARRTKQhCXnhqmt37NtA3kOz0gdhlPH56Y+PWAd77YwcYHc92GGuNWreK\n4KFnLnLgtk0kkmptszRXJZWJUUkucWbyecaG3tw5zvJ8jbmLZbbuUoCnHV1Dm9XTBgitmNHJ1dtq\njVVG3fHXxlSqR9fZZiqtRFYWeXulwxg6zPOsLC+xy7qHwPA6wFM70j1MpbV9t+bWWdh8gmvKt1Lt\nX+RieAHfyK4ClVIZi0bNpVJSz+yyRt1rmUpel6nUvm9XOCw1lhlNXbnA1N9VXJW//T2J508u8eWn\nLzLcF+efv2cf771rCx9+0zP8H297io/8xAZ+7t17CMOATx7ay/1P3MyZ/ACjWp0thTkyN95MbONG\nNv37X2PnL/0CRq6PxM5dfOOmD/K/zhq8cGqZ+UKDTz18lv/4J8/j+SHvvWsbd10/RiUwOL3xAEY2\ny8XFGn4QsnNy9YJCE4K7bxjvlHEt1RxmlupsHcswlPJ59HSGP3x0P3/+0vU0fYu7ti9gJS8QBi02\nbr2bH3rrXpxQ46PTSe5/4GXKdZfbrt1A7FUof02/za4IKTmvrvsO/JBzJ5cJe7x3FpsOU7VulYFe\nUGl5odYZQKFr1A3KV0lKyYUzCjxbWe6CHc8+doEjz1/qMGfq0cCQTFloVtvsWw0ytucQixsMR8yv\nwpJa8LUH0627htF0weJc12gR1GR69MXZVSaK0+UGmhfypg19bM+o81jmLgBqfpUgkBQiM/C2Ubce\nMZVC87WBSlXX509OzzHfaHGu0mTJ7rZPo+HiTKQAwb69m3jrD+xl7+BO7t30JjShYYo4wvO48Mg0\nBx+7QNY0qLh+x5z52cYzzNbneXjmRf7i3ByfnVrCtHQyuTgr+Tpnji+xclBrK4xwaiEnZoodexs3\nCPndYxf5+Nl5PC9gvljsMpVEiBVpdwKRxGj63Nass6fgMnCihOUmGAxH0KMqWa2WQDMEOB5bT93G\njmN3QijR3IDcVI2xiw0sW7D9xBtJ55PoupLQeXq1w1Jql4qVS+oagri+rq01LYPhxmiOJpCmhj2S\nwMs5+CkLKUNM2ZUXWLYkNNVEmiiqZySERj1ugiaIlRxSC038VgsRgS+aliLj3MDmlPKDQGTQtCwa\nLnPL8yQK6nh6Q/ULKVuEhkFgqVbVI7PAWDNDqPnUwiqDSQ8ZujgbYrjDJVbEInaum0g9MncGCRiN\nkGrfElKEmJUMgWgCklhLjRNeVrXDoD5M3WtQKKmkNojpHVBpMD7Ou7Z9H5+bWsRJm53KZG7MxowY\nddUNDuX0yc75JT6CGH4YousbMI2NBKKGk6ix1K/ac0EqdqP0Svj+Ik37EYKwRr0vj9Tb75Sn5H1S\nI9RCMqlkx8Q6pb2Rfu+dCJGiFbyE475AzwWQn7bZeP4AP7HrRwiITL1FhtHUGBKHZlJJynLFMeKN\nLIGoIKWHtanA/JbjNMN6B1RKhElCyRpASbVdxamy1FRjkCZSgInrnyGUqn/o1gS6Z+Ek6swkzlG4\nvYyPGiudZI8s1goYnkghRYjum4g2k1FXwH7dOQVY1L0GoWwQotMK/nYr71yNq/EPMVrTU1SfeBxr\nYpLJX/m3bP/t+xn54IcYuO/7Aag+9QQAQb1O48RxYps2Y41u+K5f11efvYgfhHzwLTtJJ0zed/c2\nQikJQsmPvHUn+3cOccd1G5hZqvOrf/Qcv/6nB/nl33+S3/zUYQCu3zb4bZ136kyBj//BM8xOl179\nyygQqtdE/OljC6Qtl/FMgeVGji8cv4Zay8I0AvLljZRrE7gO9I/fi6YnuGb7DLt2XKQ/W+PU8gjx\nvn381Pfv5Rc/cH0n/2vnZUII+sbeTLJvD9dsGeBD79jzioDSa4n2Ytr/Nguk9MbhZ2b42iePsJLv\nFn1ZuFRBCNi8XeUorwQqKeCjC1a0NzKPHZ677G9ea0gpcVrrq191WBprjLrbTJjLmWT3Akm98rdS\nobHKz6hcbPKNLx5fdb8LsxXyi7UrtnUvUwkUwCZl9/mfObaE6wRcWuMP9Eqgkut0mUpXira8qX1v\npZXGOm+mzn2+BpNudQ9do+6pMwUOPT3D8UNRQY6WhxC8KoAlhGDDRA4hRAecad/rmeNLHHl+liPP\nK4/H9vt67YFxtr4pSaj7PFz6Bl86/zUmtyhblEvTRY4VTvLbL/4PilWVyzWjisrtdorFjXUSt1VG\n3a8if1tq5gnCoPPupiJwesVW11dsldAttQVaEerfGpkige5RXMNUisWNjrl2IrWaZVd1a7RSVa55\naz+h4fNC/SALm0/QbLjYETg5OBL147Zy4TJMJWOtr1j0t2X7zFYWkEimG9P8wZGPEYT/3+dcV0Gl\nvwcxs1TjT75ykpil84vvv550wsSzFxHSQ9ckK9OfZ6PxOD9xy8tsNIrsSzl88NYRPnjub0jvuw49\nrTq20DRG3nIPW3/jt3A+9C94YdFn12SOj/zsbfzWz9/BzXtGsJ2AyeEUb9y3gTuvH0MAjx9RA9ET\nR9XfMJTMLF3Zyf/lC2qn/7qxAj9+8yG2pQrUvCSLZj9DuTjvuucOkn3XsmH3z5AdvYO7909wx3Ub\nKNVcXr6wghBwz/5X351pm00DFOz1JnBr4/hL83zjiye4dKH73S9eXObPz853tMxtUKlNLb14foVm\nwyW/WMPt0XvXvYDlhVoHZW9rf30/4KVnZ3j5xblOolOPmBNWQuevpxRrwQ4jymfgEoubjIy1TQGj\nKliR9K1/MMnIhgwry3U81+eFp6b55oMn+foXjvOFTx7m3Ml85/u+JtC8EN8N8IIV/GAZQ59EiCRO\nxEpo70bVXDVJ1rMr1MeTNEe6PjevVP3tVLnBuWqTh2ZX+POz8/z5mfnOfS7FBeVt41jmNaTM9ROe\nrsVJVOMEXsiZY0tkTZ2q61OrOrQSNV4oHGJT6Vr64m9FouGEklYQMjSS7lRCCOs62eIYAEmxn0Zc\np72XkG+51LyAi9UmH/u9J3nqE6cxWhHkJAMwoeUHSM3EaPr09wkmQw0tlJhuHLOZVEbNgHCSpId1\n0tUcAoHAAE1g1j0kcM99u6j1VUAK+uYTaKj+EouSpNxAgsnNfYS6QCyotg5iOlKsB5VMN4GXUROW\nn4pRyzZxcha4Va7v7/o56XZAc9MioRYQq3STGENX70p8xSFWdtE9i5iu+pcQSXQXNmbULmnd09FE\nBhnWuPS0S2bWxqx7mNEEL6WDNCxk5Htg2B6EgngrQytRp9aXZ2rkJfxgFs802ZDdzoKcw0l3E6mi\nrcYJzWtxafth5rceA1+Qu6jeFdOOIQUEGfXkskLtlC+uqHEjiOtoqPszdYtNmUnmmg6hHiBR/diz\nbJyogqDXb+GZbUN2iZQtNC2NH7oY+gYMQ3myLW48RTUyjW+/f5pdw/VP4/nnCIJ5huNd2QBAYERG\nkYZPKhYnCAuAjq6Pki5Y9IXvxJCKpaVCEBJSLDS46fbN3Dp5gMn0eHRtDfosNRa7+hKab5Bo5khX\nhgBJGMxz674tlEZmKLUqhFZk8OnFkazexdaEOs7F2hwyOreUNSxzF1I2aPpPqjHN0NEDg0D38Q2H\nwXgCiMYq0+n81sx0QXo9MCD6b8vcTSrMUrJPIUSaYquEJSIw73VUq7kaV+MfarQuTtOavrz3kJSS\n/F99CoCRH/kQyT17VdU2wBwaJrFnL/bZM7hLixQ+9xkIgr8VltLZ2TJfefYijx+Z5/yaDStQlYNf\nPJ2nL21xy161O37T7mFu3j3MXdePsX+nGid/5K07efONE9x9wxhvvWmSfdsG8ENJOmGy7xVApYWV\nBv/1L15gYaWx7rN2LnXhTJ6njy2QL9vrvgNQrLb43OMX+PyXP85zj/8PqssHcZwaB08tc/PmEpqA\nF2YGee5UhYNn93N+apL00L3KFNn10YwEubF7MI2Andtn8DyDW256P//s3fu447oxYqbeYTu0QYXv\nRrSBkcuZUr/eKBUaBH7IUw+fU5VxF6osL1QZHssQi5tYMf2K1cAqRZuRseyq0uVDo2ly/QnOnFh6\nXcVi1obyC1ovt+qYFHurF9S5/gSGqV2eqdSuphbT8b2QIAhxHZ8H/uxFvvnlU53vvfTcJc6dzHPx\nfJt5LDuL/TZ4tDaadRfD6HrndPLy+SrNhtvZlF1rXt4rf1sLBnWrv10ZwLFiBr7h8uDMV6nUa3zm\nT1/kyYfPXfa7pddg0g0Q75G/tUGwWqXFcwsvUqrWiMWN11Wmvi0FO2wf4ncOfZSVglqrnDu5jJSy\nc46NW/upOCqfTJspHrr4LV7yXsCK6UydW+aPj/0l5yvTzBRUnmg3PAU69sjfut6zqu1WMZU68rf1\nLLVjhZP8p2d/k+eXDndBpUwMP/QpO2qck0iqYZXAcvA11R+amRJ96TRlp0IoV+dabbbSWvlbew01\nmemuVat9yxRrFYoV1TZt+5RyxC67nNywLSvssPWicWDem+e/H72fldFpfM2n2Crx3OKhdb//u46r\n8rfv8ag2XX7/sy/jeiE//77rmIg6qdNQ6HA8s41W7QKt2hkmPIcPnTkCIRgz/fihQ+bW29YdUwjB\ng09PA/C+N23vMIx+7j37OD9XYSgX5+sHLzG9WOWarf0cnyrx9YMzPHFU7a4/cniOxw7P8uO3b+CO\nW7ZSefRblB97hOwbbmfwve/n6Dm1cNuUfJl0rcaHVg6x+Vf/M6VmQCKmq5KvAzs616MJwU99/zX8\n5H17WC7ZBKFkcvjymt3eaC+CAPKNAtvTW19xN6CdGPQ689e8ACcI8UKJpYuO/nnntSPMz5R55pHz\nPPa1MwBYP7Ct87uG79M41wWn2pTUStFGSkURNaNJuCWgL6az7CxTDkoMAS1pg0ABBHGD4ciTIL+o\nBuNquYUE/IzJyESWxbkqxw7Nc/CJ6VX3dGmqyK5rR1mYrSANgXACJZdrLON5FzDiI5jmTrx4F1SS\nUnYMr6uDBVojfWg9lRTcUOKFIWaPBMsLPNzQYynaVThbjaSHrs/JcoM92SS2qSYow9hI0lzv/2Ro\nCRK1NKEhaNoe6YZPKKDccMhvOcPQwjYMdzfBpm4yWvV8BodTTJ0toBsavh8wMr+D6sACMjmMEAJb\nSoQQNCPQTwqBMZDAW27SVxgnP3EeCAljPoWoepth+1hZAWaUwLtxqBto0TMzgjSi3ya1rK5ldsdp\nYKLTTkOjGdxEi5YriNXTDJ2RNDZBzFfg3C13bsVuurT6YyQKLQgkflwnsHQQAilDhNDQRAY9DAja\nk42uUx9OITSBWWuQ3Wgx5K9QlP2YDY/W3hr1Yp5saQOaExDGdExDTWpmw0N3AgzfIqmr+9S0FJob\nMpCwoAQLtoMQGtQcwhULd+ASm5bjTMn2u+SDriN1EylDEl6dZKMPIQW1XB7fcliReQx/GtPcRjq2\nlVA/hpPu8bYKledbI3sJqYeUh+bYVN1DvBDDbCXQPJMwpuObCcDFDKKdpKg6mx/XiestXOlzqfxl\nPnHqAg3/lo70DSAwPXwzQACh3ochFaNGpwkECC2FxMPUJ9H1IWw06n2Fzu8dGUkuWy52IgL9wiIb\nM9vIt7rf800X00sQWB6BIQnDEro+jtAMrJU8E5dyNN50Cxedo4Rhgaw7SNUqMLY9zTveu49iscFQ\nYoDZ+jyud47z5W4Sl2gqwDJZHYTxC9itp1ls3owhdGpuHUs3SKRMQtdaZaqtnmuGMKgyX1/oafcy\nqcTbCYI8Xnge4SSIx25DhAah6ZLIGpwvPa3aSegEBFT7FknVB0hYsQ5Ir/smUqj/1sIYk2zhFEfR\nhIUXeiQNQUPCkl1nJDHA1bgaV+PyIX2f2d/6TUK7yeC738vQT/7oqs/rh17EPnuG1P4DJPfsXff7\n3B13YZ86ydz9v4O3tERs02b63vyW7+iabMfn/geO0uhhVPzce/Zx856utOLEdJFGy+femyc7BtFC\nCH7uvdetOlYybvJj37d71b8FYcjAQJpK+cql2J8+tsj5uSrPHF/kfXdvX/VZexF+5OgChw/Nsnsy\nzT954xR29RyaHsOw+nBTb+Ujn77Ipr4C/9tNSv5bnvsqcu4h7ts1wLZhFxBs334Lm7fHiRWanHo5\nzoF7BrGsZRoR2yI1eCMzpx4jk25y8sxWru2D/mhfwWn5HfZA4RU2Ub/TaDNbwkAShiHaFYzVX0u0\n/XPmLpY5fWyJF56cRkq49S61sRJPmLQuw4BpVzhb6w0qhGBoNM35U3kadbfDVnm9cbnKb7DepDjo\n8c7pH0xSzDcIQ7lKRtnO0dPZuDJXd3yaDRffD5k5v4LddInFDabOqHm8vfHruUFn4V6vOuT614My\njbrTYcn0tsfyQpVED6iwFlSq9TCV1oIdbo/87UphWTqVgXlOFU8wNruBwA9ZuLQe7IUrV37zQ59P\nnHqAO8bfwI6+rSQjZo3dcMlHa59atcXXz3yBbY276U9c3u/sStFm/FxiinI5z2hpnzpmpcXSfJXZ\n6RKxuMHQaIbysrr2X7np5/lvB3+Hg8uHuHb4zeRnG/i+DxqUanUgThhKWvZqM2tNE1QG5qm5Kp/u\n9AtTI55QAFyzuR4YfHzuGQCKdpGwpjZ609kYZafS2UADKNgrBLnu+NTMFNkcHyOQAVW31iksBJDJ\nxijmG5eRv9UwNYPRZHcjUuoBZ73TZMqDxPUsfQNdbyh4jUylCGCtRB6fxdGLDCwqG5aHLn6LN2y4\n8W/F6uXbjatMpe/hmFmq8duffomVaov33LmVG3d1zfqcuiqb3Td5H1wE6QTEalvY8l9+g/i27fil\nEsKySO8/sO64UwtVXr6wwu6Nfat8kAC2T+Qo110+8+g5nj+5zBuvHUPXBJ/+pkLNJ9wV3pZ/DjNw\n+bNnlvjr/3A/hc89gL+yQvErX2b+W3/O8QsF+hM2g76H++ACQz/4j9BMk8FcXAFKVwhd0xgbTL0m\nQAlWM5UuPFfnE//zuVcsM1mKqnz0StzaEg47+tvWPw8OpxmdyOI6QYf22OrZSap7AdNnV9B1gaYJ\nytHuWnsXIQwlXnQtjiFIpizOV6YJDCWp8YSa2ENdgUrZvjiJpMnCpYqq6lW2aQ3F+etKBblBTR4H\nn5zutlUcfMPl4lQBKSXzsxWkrqH5kmbdZbG5jOerqgqGNoIbVXxamqvSCroMBS+bBk0Qmhq+v4Bt\nP4aUXoet9KenXuTfPvVH/Ktv/Rd+/Wu/z5ml9Yywp5bK2A0XP6EGTEMfI64l1n1PaHESzjDzd2yg\nvCOHWFZttWgs4uEyNL+b8s4+RChJCFWCfaXV6kzmA8MpvA1l4naGbGkUYpGBo1Q8EQGkZtXkefMP\n7EZqktzKePR5gGt4FCIPJqPpI42wo+nXfQu3DiKi2UrdpGaUSVUH8XWXer9KuuyhBFJTuxahHtBM\nl9CkRmYxoN/qQ6vEicUNduwdJp2J4eZMBGA4AUFco9WvnoOI+q6mZRBWNLm3ZZkZNYEkCiEx3eJf\nvuF2dhwqk06YaOmAar8Cba2Ki5QuQqi2Nhs+WiCJuWni0Q6MJlJovkcimrhq0XNNlmIEmoe4tkh9\nNIlvdEGhUJcIzcJ1T3Bmy+Oky2rcKQ/Ndr4TBBFrUSjQzYu3f68RhmWkdGnG5hFRpTt7VPWb/vxG\nNKmDpRNG1Ot2BcJqJZIJxnQsrRWVtnc5W7oQNU+t87wBPKtJKCVCGJjGVqT0GInbnfuWMkDXRxiI\nxdA0dZ3xRpaRxDCupp5DTk91JG1SNogZq5PmNlPJszxmXAXgGPo4UkAzW+L73nMNfbk2sKIjGwpM\nvf37tnSo/WWniuqdLVpBd8yK2SpxibmDmOZuAtnkGzOPqgqF0kfXdJIpC9EycRINtFCnPxaN10KN\nCXm7C4CFYRkhTJLJt6OJPlzvGHbrm+qZ6D55Yz7ahTNIm+rdubTjJYpDs+hS74ynWmAiI9DNcAxG\nDSW1aaf1qQg8vlR7dXbo1bga/5DDPnuGsNkAKVn5wuc49X/9JkE98jRbXCD/6U+CrjP8gR++7O/T\nN96ElkjgLS1hDo8w8UsfRouvn1svFw8+NcX9DxzFWcMofPSlORotn3v2j/Pj79iNrgkeeOw8fg8T\n+/mTyj/l1r2vv4qurmmdSm9XitPRovzspQpea4XA6zKW5iI2iOFLBhJw6+iz2JXTGFYOTU/gNuep\nzj5AwmjyQzfOAIJPHd7HY1M7qLlp9o0VSBpV4tntvO0Nu7nvDZtxooV+IqnYOr4fEoYhnis59NI1\nHD+1k0uzG1jJd6+jV9K0km+sq0z2eqPtbbM2es2mPfc7O0fL9jAtHU0TPPI3p6hVWtx4+yY2blVz\nVDxh0mp661hHS3OXB5WgWwGsegXG2GuJbgn7NUbdV5D+GKZG32CSIJCrWEDQzdHbo637NgAAIABJ\nREFUAJfrBB3gSEo4fyrP/EylI49qf9a7mdzoqc7VDilV7hzvAQ8yuTjxhMnSfK2jcMj1J6hVnVXt\nUa+10DShWHBXqP72SlIzK2bgR8VRyo2uWuFyEq/Siqrkt9ak+lJtjucXD/HY7FOdY2qaYHGu2mHt\nVCs2TuAiPP1VK7+tDT2qvGYL9Y4U6iXaRKeDT0xTrzpMbulXgJBTJaZbDCcH2dW/g+VmgUuRlcO7\nNn4/mtCoN7rt16y7XfPwmM4le5ZLO17inHUc6AItf3LiExRY5tT+b/Kl1meYrc1326VV5sTKaUAp\nQHqZSm0ly2hS5bIFu4iXit51KXASDeLROm+tBC4d+ZPFkwaPXnqKuWgjr+rWyVgZLN1C64Faapk8\nODqJpNlZu7wSU6nXUykIwo49ix3lim68iZOskzKSFOwVXlw+cqVH9HcSV0Gl78EIQ8kDj57nP/3Z\nC8ws17n7hnF+4I4tAHgrBS786w9jr5xFM5K45xdoffkC+qEBxt7/M1gjI0z+q39D9i33Ur3jPv7T\nJ4/wy7//JOUeuueXI5bSu6Jjrj33x792ivacYxga//Vnb2PTsFrE3F54idszTX7xpgRp4fONoVv5\n7P4P0fq5f4d17wRnZRkn0NmTbOL85Vni49tIH7jxu9JOvUwleyXEbnid8plBEPJXHzvI84+rgUxK\n2SlL356UpJS0okmsGf3tpaq+64ev5yd/8Y284W7FUHJ6EoJSw6GYbzC5pZ9MLt5BotvAFdABlXxL\nI5GymKpchNgwdq57HN+EI8tVLizXGN/UR6PuUinZ1CotvKhMvIhop72AWW1ylnq2gNMIePSrpzl/\nXi0uNT+kWVembkgHKSWa3o8XVXqqVVqcOdEdiE1Tlb9FCFzvFK5/Bsc9Tt0P8MKQF+e/QL11ls2n\nDrD1+C0UAx/NDdBbUaIqJdM1m+lSAz/yDBLCoBZchpor4wR940hDwxmI4S2otmomKmw8vx97Q5Ig\nrpOeqZPx1HPM282uqWDCZGm8iEQyuLQLoatzZKZrICVJx1esIGDF82kNNIi3MsTtDMgQR3dZiXbp\nzKZPoPmdiV4gsGt+5PujjMtLxQaWm6SZLSPaIJkuELpANzRC3cNOl9vNx7gxgeEkGJ5II4Qgk4vj\nZCzmtrxM3fsWoanTTEcMopZLGDYRWoYwpkDU9rULoZ57eklH9y2qpRathsf4pj4MzaDat0Sg+Xj2\nEWr1T1Nr/DWhV8ZHJSN9ziBhxDwSIgmBixeuTsxiNUGtL8/YwBD5pIZvdZMsqYcIEUf6aqekOrBA\n34SJF7M7gI6UNgkdym4MTej4ujp+3FUARNhawgsKaLogZSYp5uYJRcjgsgLMzJjekQGu1NT9NquR\nfj6uo+MShqpta16VMGwSBur+YrYCRHzjfEcaKISmmDoRCCREEk3EEcJgPGlgmWo3PFsaIWdlCTXV\nf3OxbMd3C6R6R6ED1HlRoudbHhftCKQ1xkFAjDjb94wwnBxChjU0LYNlRu+q3h1jlpp5LD2HoW/g\nLZN3ddrZ0NQOl9ufJBm/m8ncXdw4cj1eGO1cy5BkOoYUEidRIxGkGYwrbwIRTe2lVoWkkeg8Eykl\nmoiTStyHrg3j+VM4CdWOcT3G3qF3A5KMlUITGmgSJ1lFhBoNV7VDQo8Tiighy8O4Fcn3iExMo9x4\n2f7uSUKuxtX4+xD1o2oBsOGnfpbE7j0Un32O6f/4qxQf+iozH/kv+KUiQ+95P9aGy3skabEYfW+9\nVwFKv/wrVzTVXhuhlHz94CVeOlfgj798oiNRd72Ah56/RNzS+cA927ln/wT37J9guWTzWFQC3PMD\nDp/NM5iNsf0yIMOrhe34zOXXjw1SStzmArXKRaYiZkyjNs/CyY8yf+J+yvPf5OVTR9Dki9x84Bg3\nHzjGz77xGNuHyiy3xhnb888Zv+ZfMNe6jmzM5hfuOoIl6jhyH2PxrTxyZgO//ch1fOHETaSHbqZv\nrMvosm3lIWPFDKyIze65AZ7rU28kccI9gKDYCyrV2vOxYhGVCldmXr1atGyPv/zos518tDd65VLf\nqa+S3XTJ9SW47ibFXh6bzHHLXVs6n8cTJkEg151nOao8PDK+utIa0Cl7Xy19J6BSlMOtWVRrmoam\niQ6Y1M5xdV3rMInafjTtaEui2nIs1/FXeZ+ePbHc8TqFrmdPb5n33u+3o81k6vXOEUIwOp6hVmkx\nfa5AImVy7Y1qPpzvYRLVqw6pTIx43FjHBHNdHyumv6LUzIzpnSI3tZ7Kq70VoUGtzyrFJv2DyXXH\nq0ZyrPn6Yufa40mzkz8LAW4rQPMNtFB/RebUlSKZMXEM9Twass7GrQPEE0bHT6kNXpadSoftc82A\nYjLmE2pT8kB2PxuSIx2gFxTg5zo+hqmhaRozNfVdO5Lqu5F/5sXmDC/XXwZNUhQF/u8X7udr09/k\nXGmKjx37y85muRM41GsOmiZIJK2OAffufuUtmrcLnYIliYYa44Io1yutMevO5lR+1YrX+MzZL/Lg\nha8hpaTu1slYaUIZIgkRCBJumkZuBREYGAnRkcw5PSbkoMbCtj+S3iN/613jtXpy9ma6xDu3vQ1N\naHxt+pvrJHp/l3EVVPoejAefnuYrz15kIBvjwz90Az95354OBbny2KP4bgWpuxgMUPrylwAYeme3\nnOxS3ed3q9v5w0s5ZpbqVBouX3tOUYRPXyxy+GyB7RNZ9m7uX3fubx2aZXqxxlhErZxZqjHSl2DA\nUC/F5u3jbPr3v8bed9zDr/7snVy7pZ+zdZ3f/foc/yN/Ow+fVIvGjS8fJtBN0u/6wOvS7b6eaJtN\nA0Rqro4ut1l3KeYbnD2hWB21SquDdrccl9859FGenD/YcSqxI4ZKm4I515rHtAwSSaszEbpSdnbr\n85Hn0ZadQ+QGErRsD6flrdKAt0ElNIGRtThXvkQifS+l7d2dEMtLE1tscvjFWSai5zF3sUy13EKL\nJjevZxK95sA4oQZlr0wjq4CkU0cXaUZgmPBDnnt8Cl7YQMaeiE6fxbc8hiLTuKe/MkOsqZIHQ+9W\nJAmlmsxc9yjFVpOC7SJxmcjfQLLRh9R0pGmR8iXJCBBKR0DIwVK9w1QCyDvrGWl6PU1zTF2DnzJo\nFW00JyBXGMR0E/i7sgggPdsgFgENK61WZ/fwVL9OM+5RGVjA0AYJwwaOe5rkYh3h+WiVJmZdPb8l\n26U8qLT0uZVxIMTWWh1QSbQcAhl0mErtcE2VKISmILmkdjXsjN0FlQBhSmpeHV/3aKbVBKTpgr6G\nWhgkIhVBKhNDaILqQB5HzEZyRpWg6U2HUNbQRBov2nFKLHeTJ+E4lIbOcXp+iplIqz6xqQ9TMwgN\nn7M3PElx4GUkHhDg2S+ztPEEEsly/zSl1jIgECKhTLX91QmxYfvU+pfIxkYIhSCIdRMhaQQIEetU\nzmulasxNnACBMlzX1LOdSBrU/ZBrhu7pfLdvQTFpQn9Zyc80i5yVpRqWqfeX0AP121TcJIjavmEn\nEGj4DYFv+UhdAxyCsLtjFIQrGBFolqkoMKYp5noAIfCDRUzRXgikEZrq45OpOJoWjXVSIym7VWwS\ncZOoTh8Ai83lzu8BvJg6vmc5XGpcBAx0TfWLuKuOozkGEgdNpBmfjPpMNCA1vCa2b6NrfUz0vZf3\n73oXfbHoWqKO4g2pvmWwws2jXWapE7hqZzBZAQEZPUM2phIg9dzBlz5Zq70Q6I6zmp4mlXw3ifhb\nEFK9S/9s3z/GMieBgLgR6wBU9VyBI2OP8JlzXwQgHUt2GFp9F1364jmSRqYjP8xFQGzRubwvxdW4\nGldDRePoEUQsRvrmW5j88L9m8499iLDRoPCZvyJ0Woz+459m4L53vuIxht7zfrb+t9/AGnntlX/m\n8w0aLR8BvHgmz2cfO08oJU8cXaDacHnLjZMd1vi77thCzNL50lNT2I7PsQtFbCfglj2j31bu9qmH\nz/BL//0hDvUs7Gv5F1g4+Ycsnv4jShc+zq7hPJqQvGP3GSBEaBbVpafI2V/kwI4ZRkeKjI4UiRtl\nzheH+djT22g4IUfPF/jjx7OcXRnB0Dx0M8sTj+dozdVIxXRAMLlxLwMb34mV7AJ1raZHPGkihMCM\ntU2Rg06+2D+Uworpq0ClNvAwOqEWx68mgQuCkIc+f4zTxxbXfdbOP+curmZB+H5A0FMAxv82K++C\nAqR8LySeNLnl7q3c/fadvP19166S013Jj6ZedbBi+jqJD/QylVrrPnut0SttWhu6ofUwldqsHq0r\nHSqtzl1c10cIOtUBnVYXVDJMjcXZCmePLxOP8tF2OfdVTKXLgEpt8CWRWJ27jkRFdFwnYOOWASY2\nqXmzLYELgpBm3SWTjRFLmJep/hZ0PJquFJZlEESWBXWne79rQaVKySYI5GVNuusRqLRsF/CC6F56\npHybIo+zeDv3t14/PGDmJAjVX32rxeBIim09stmNW/vxAo+G1yTXBpUGVbEgO6GA5LPFKRpeE9lT\nMKlRV6BSu3+0GUhutLm1XFdrHamFzNoKcNpTupmcleXBCw/x8ROfYqo6g4hyoFbg0Kg5ZHJxNE2w\n0gaVItuVvL2CHasiQkGmrK6/iWq/tUyla/aPcefbdsCg6v+XavPYvo0vA7JWmuVmAYnyakrbA0gt\npNGXR8TCVe0PXfnbgxce4sOP/Ro1t75K/tYL9jpRsRoRaDiJBhOpMW4dvZGlZp7Tpcv7bf1dxFVQ\n6Xssjk8V+dKTUwxm4/yHn7xlldmhDAIqTz2JvlEteJrPnsQ+e4bEvutZig9SqNicnC7ykb94keWy\nzZtvnOAjP3sb/ZkYj740R7Xp8qdfPgHAB960fV3C0Gz5fO7xC6TiBj//PqWdn16o0Cge5VK+QTxw\n2PKOt6JZauIZ7U/yi+/dzD+9a5q9owUqrQTzjSyWLsje/U6e3PQBzhW/PQ32awk7kmsYmoFwo0Qh\nGtDb5Sqr5RYrlSYXl6qd31W9GufKUxzNn+geK2IhVW2VVLy4crjzWXug86QkG3nwlG01QW3eMUiu\nX01+5aK9GlTqqczmxaHmaQihdxBxzTdI1FT71BbrTGxWC/JLU0Xq1RZaNCBVImryDbdu5OJ4nMXb\nRjHcJPXcSuf4YTQwaYGkXnNIlYfY+PI+rKqHEIIwZrFlZ7cvWU4STRtC05Id2ZUMogpgOBxcfJq8\n4xCzk/RdVMbYflL5Ae2czNFX9yGUuHGd4ZjBOc8ljOlYYRUpfebXbOqFUmL4Q8o7SEqkEHgpk8Fj\nRZL1FOUNDWq6zo50HMMJ8CNVT9lxmZ0uITVBqc9ChDb58XM4aYOm/TAt53Gs8RKaL9AaAboTYCFY\naLao5FRFh9zKOFIGNLEpOC6EIcJz8KWPoa8eIh1LTT6hqWF6CgByMxJNdEEl3/CYqy8Q6j5urEmg\nq+ozRlm9l0Gf6kOJpIkIQwLDAREAHkEymtCbDmFYQwiN1mAMwpB4oZu0hX6RpY2nOVI/wpPfPIsU\nIQOTiU5VL99s0r+8iUzqRxEiTYuz1PqWyY+dpzB0kYpTQhPRblZgU2ytTnT1lkctl8fU2lXruuf2\nNRdBDCkbRLMlU5xV10XYYcYMRYCL0HcRyiYi1JUsEZBmEQiJGTFysSxe6FAemOmcYzhl8aG2tELW\n0MihtSzceMQYDJ1OpTeAICigR5N+qhpJ7rQGKd3uVIoL/XmCUH1HE2k0TSWDm9JpBJHUTgsxnB6/\nr1j7HQ07Uj0AgXr33Ijh58TqVL0VDH0DQmhIAbqj3t2FFQVEaVqGWCRNaUWg0rKtFlZSZMlEY8fN\no9dj6hvw+oaQQGsgThg28MMSu/q3KwYRiqnkmS3slAI6+xM50pEBfluyBzAQgUOmllg1putCYJnb\nSTcVcJyKJ2l6qq3iusWOPuWzEZgezVi1U0EzHVdySN03EVLDihmMpCYhArL6YyZIn4Sx3jftalyN\nf+gho3nfXVzEW1okdc0+NNNE6DqTH3gfm37t18m+8Q4mP/yvyd1x53flGk5fUu/yP3rzDkb6E3z1\n2Rl+4Xef4LOPnccyNL6vp7x9NmVx3xs2UWt6/Lv/9SyfeFh5SLYNul9PBF6Tvalv8C/vfoFPPnSI\nqYUqXqtAafYrBF4NPbGLINR533Vn+Ik3LjDZV6cSbGNs7y/w+PQunp8Z58zMzXz9W7fz8CN38PTB\nN+Gk34njwe9+5gi/95mjaEJj4673kx29E5L34bQ0giDkB2/fQjJmcMuOQT79R89z/tRy57pattcB\nVNpMJdfxOxt2sZjBwHCKcrHZATja8rd23rTWrHt5ocrLh7qS8GK+wYXThU7p9d5oAxbFfH1Vxd71\nAMS3z0Bo+yklkiamqXPtgYl1IFEXVFrNpmnUnI5nztroMJUq65lK33zwJH/zmaOvem1X8lSCtaBS\n15C5nVdX1jCVPCfAtIzOAt11fOoRC2nPdWOdf9uyc4hEyuyASb2gUv0yoFL788QaQ+aRHrbe5NZ+\nBkdSxOJGB1RqA1TprLI+8NxglczRdYJOn7tSWDGjs5HT9LrJc7twTzvahXZGx9YzCGue6p+hDDub\nY+3n3z+UZMOk+k28qf6KKzuRXDFEuttvPKtF/2CKndE40T+YJJ2NU3HVNfZFG2BDiUFSZhI3ZhNq\nAV8tfJWKW8WJd+/zkdK3ODX8fIfJc6muqg16wqHpNSk0orxcBBTsIqYfI7c0ya/c/C8YjA9QjHKX\nrTmlvKjUGtSrTsd3aiWSv21Mj5Myk+SbBepGBauVJlMeQYSCoqe+sxZUsmIG1900yUJTERTKToX5\nhvrvjJlhttatjCgb0fpwcJ7AdDuV6toRixu0fIdHLj2JL30OLR1ZJQH1e5hKnlD9ob+wEYRkqnqR\nOyfeAMBT88+/4nP6bsZVUOl7KEo1h//5pePouuCnbsqSYPWE03j5KEGlTHx/tBi4qF7ej9U38n/+\n2UH+zUef4Tc//RItN+Afv3MPP/Z9u9kwkOSdt23G9UJ+85OHOX5hhf07hti9qZ9HDs9x/wNH8aLd\ngaePLdByA97xhk2MDaYYzFpMzxeYP/8lVnyDUaokdyrUWUpJs3SCpdMfYyw5y0+/Lcfv/dLd/ML7\nr+NXfvRG/MwIvh7j1MuLHY3o33a05W8T8XG0QL3M7Z2n3knzcxeWeaBSRUbrrVYkrWr0yOeaEULc\n/r0te7xPoh09X0Lc0EgaGo6U5AYSpNIx+iKj8/JKk3KxiaarE/XuQFVMB01XyUmgtweLSbRQvaKt\ngk0Y04hnY1w8v6Lkh5GcrDqnJpaNW/spyZAgYaAZQ3gxG2GqQSg01Dk1PyS3z2dqz7MEsZD0ghq4\npZVk046uqa7hxTANlVjGV1pRWd4GudggQsQ5svwMF8oLjE9dh5AaWn8cPxogddFg67ZBzLqHlzSZ\nCLWOBZ5GHT+YZ8UJmKn1yBP9EBmZ+poFNeG5GZN42SXQfYrb1fXfMT6gkpgldcRSI8B1fMw2Jdnz\ncJJ1SiPnCUJ1nMroHEJKNDtEAEMxg6LjI7WQav8SlpsgUU1Sp0mh5aG3fKTm44c+vrb6HbMTCgTw\nY5FcwGxRS1wgCLoAniMcLlZnlcRJgBcPkQHUZkNcw+Z0cBwZmYcbwqWtGQtlCxn5QBkNt8P8CC0d\n3XbQA0kuqrpmcxyE8s2q55aZ3vUCy3KeqaryUktbm8mEt2H4FjHrOgVaCchPnI36XoilR+BzfJHH\nLn0R31MJsPBCglSF0PCZb7aQMkD2tEMoHQVEYmP4FlqgrkkLI2Amuh8/UNrymYaPlA0MP47pxtEC\nAz9YivpKilyUXNT6ljqys0w2TmCsML3rIBc3PoQMPQQCLwKV3MBWoFYUQbiCpI7mG52kCE2Ssxxc\n7zSePwuySNNTYIuuZ9G1fqS0yVkWiC6oJGvdiV5a3fs2vN6kug3eReVuY9XouNHutwBhm0gpqTRV\nMiJEGolK4lqB+l2+uRJ9liUTMXzeu+P7uXXiRwgTJs3RBIGhIeQibuCQMOJsy27uXEVJrnRApaFc\nrgMahaE6btJIoEWmjX0xxeAyy/Xo/83oUqOFRNCiGaixx9ItNmaUuTkhbFjpmuUmzQSB6aH77UWY\nzkRqsvO5ED7/dv9Ofmbv9VyNq3E1VIQtm4WP/S/O/8ufp3H8GI1I+pa64YZV34tNTLLhn/wMyd17\nXt/xQ8nR8wUOn81zYrqI8wpSqdMzahF2464hPvzD+7n92lGyKYuWG3DvzRvJrlk4v/3WTdx5vfLP\nLFYdJoZTbB5R/kWvNXyvxvzpP2MiVyFh+lw3usD9DxylsPAiAAObfpDDR/Zy6KU9GFrI5vQFbM/g\n6Uu7eXmqyrdOj9CI3UmrNYHnmWzYOEypKNm/uZ+ErnFxvspQX5x//2M3sWvTMH3jb2FhvjtmX7+p\nnz/45bsJGooxPhN54IRhiNPyOwyUtuzHdYOeilM6A0MppOz6YraBh83bBhEC8j1MJbvp8jd//TKf\n/0S3ylSbVdJqrvfr6ZRGDyTllW5etNZ/5zuRv7XPsXYh2xuXK8fu+wFOyyeZXs9SAsWQ1zSxjqm0\nslznzPElZs4X17GJQBV3aTNm1sp/esMwtK78zQs7/9aWv1XWyO5cN8CKdeVbruPTjJ7BdTdPdEy9\nt+8ZJpWKdSq92as8lS7DVGquZ/dAtwIcwMYt/QghGN/YR63SolZpdcDHdC7WWSs4q3yy/FdnKvXI\n3+wgshJIGBTWMJVm5pY4tf9bnI8fX3eMtvwN6Pj+tO9l45aBThWzuN0GlV4bgGn3rJOCZLcNPdOh\nfyjJ6GSG+HaHoRt6PSRZZXYd1+OgSea2HqUUqPdSakGH9XTeO0OxfxaZcPFDn8WGyu19zeUbFx9H\nBoCQ6JqGG7rkvEHsukfOyvKOLV2Z65asApWW8iWEBvvuVszxlVYJTWjEjBjDiUGW7QKB8InbaWKt\nNIlmH0tNlfsX18jf2rHQ6G7MTlWmAchaaS7Ve8ZIIUnU+6j35ZlPTGMYq2WGVszgs2cfxA1VO54o\nnlnlqdRbAbLdHwYXt4CEY4VTbMluYjy1gaP5453qc3/XcRVU+h6KLz9xhLrt8a59oP3Rf2fxj/6f\nVZ9XnnwcADFiItE4ZY9xOLuT1tAk9xyY4PZrRzmwc4gP/9ANHNg5TCvyDrrr+jHips5c5Pfz/nu2\ns1xq8qmHz/LSuQLfOjSHlJJHDs+ha4K7rh/HqV9iNLFEwzWYLm4GBGNbPErzX6e69DTL5/6cwvQD\nhKHDwMYfoH/y7cQtkwM7h9kxketUPGjUnI7e9pUiCIOOxrQdrwZGNSJEfyLWXfC09da9k2bJ8fAE\nHXnWUkMNHnaP0Xc9Si5c1yfUAlph97N4wlDSJSGxNI2UoeMbgmS0C9DeUbk0VSQMJGOTajD1e8wQ\ny8JGj8yCQ80BCYNLmwlFSGNDAiklf3Byluq+AcIIjApMDaSkuVAnlVHgVTNU96VnIpZHxLSQ0cAk\nfMmKt0IjWyR/wMdoRN/X+tHTXbmX6cYwjI0gJan5pjLmFSF9+gAx63oC6XHo0vOk6gPIIRsvbXQ8\nnkqtSwyOpjDrHlIX+JUePx7qBL5C7j9/sbtjN99o4WfimBUbImqrGFKT3OK2JYzYFnKmxo5skonN\n/RgOyNCj3lJtEY8kQpo+CqGgbh4HTFJejll/hjCsYdiqLcZScWXcLeJU+xS4kailqOHSCkLMpnrG\nDc+lJVcnQ61kBQgILHXe8vASodbCD+aRMpIc6R7PLBwkjHxz/HgEQNkBS5tOcbp8rkNP1fWetpG2\nqrwWSnTHIZTdSSG0FxE7y0xEu4VCdFk6+WtO0sgVeHH5CF70/AcSW6htSaM7Ppa5GyFV8iA1ieYb\nSE0yYKZJLDXxDfXea0XFFDJsn527NvADW9/Ogm0jZbfql7pO5cUV0kLEQqQuMdwYORnJpVx1vLna\nFAldQ8oQKW1MJ6boxxK86P0JSdAXGZFLLVRSLsDOlvjDo39MvS8fGam3kEi8hLq/lt/qSNsEMYIg\nT9OvYDlJDC+GiMBYU9RpOc/RtL8K+JSdMgJTeSppSYRcIZB0mUoixC12p8Veg3LTSUTn0zrPOohA\nJc9S16JpWcLQVgB1KLCbHg2nFn2WREbPoS1/a+8eCi3ZYSoBbM9GCfN21Tb/L3vvHS3pdZb5/vaX\nKp86OZ+O6q5uhZZaWbYsB2FbtmXji8EBj00wGGbAXBZrFsy9M7Ng7rCGYRZcjMGADR7AM4CNI5YT\ntoKtZCu3utXhdA4n1zmnTuX64r5/7P19Vae7JdmXkRlA7z8t1an68rffdz/7eZ7XYhVPF+C7B7oA\nz2K4QCdXxQhNirk8brB5wiIQVNpqfJ3MKzCqmVPFz5AGlQwzpa+pS1szlRzTYTijmYsG2D3srbSZ\nJrQ8TF+Nb07KYosu1gC80KfoWFjG9y+NeTlejn+O0Tl/jnP/z29Q/86jRK0WCx/9CBvfvh+A3DXf\nP/gqpeRr3z3HgZNdI/6njpf58GcO8gefO8TvfOoAv/mXT9K4jJGvlJLZCxsMFFKM9GcY7c/ws2+9\nit/64K187N++hne8esclv0nZJj/95r387i+8kt/70O38+/fdQGXuayzN/tmLAktSSlobR1me/TOk\nv8qTF8aJsLn9ijUarQ4bKwcQRopssURlrcVKeYgnLuwGDB4+u5vD5zvc+5RaLHnd9dP4fogQsGWn\nGp8e/Mos10RwY8rm13/yZrb3sDTmznVry7q2JKhqA+UYRIilXrEBs9PDcOm2p7eS9t/r2g8qBov6\nBtL0D2VZXW4kBtcP33syqTGXNJM8BpVixlBvdHo+i9vSw2WYSt8DqOR7wSWyKLVfzbS5jIQtjlgS\n1nuMsdfQ8zGVDEOxhi72VDr4ZJelde7UpU0bPvrsJ/jos58AXoSpZPYwlcKYqWSSSluks3YCKkkp\n+eSRT9Nqd7AdM9mW2wloNtyE3XTF3lH6+tNMbR0gm3cI/IjFapl6o+uRFUuwKbGXAAAgAElEQVTi\neiO+JunLsLsmZopMbxtIJHcTW1SNP3+uQl0biecL6U3HBLH5snxRU2zHsQg0iOBGLtmcw+hEH/Wa\nm9xXgCPVWQKnw9HWsUu20egBGWJfpfh4Z3Yo71foyt+k9eLP2rnaBf7tg7/Os+XnAAhSXTAucDoM\nDGU5VjnBk0P38R35bUAxeYBkMVFKSVMb8VeHerrVWj4y6yGRiXdSLV9msblMGDPyLZ+Hzz+OJW0s\ny6Q/rbY5xChRJGm3fC70MIVMYWFIA1/6rEwf5wtLStK/3qmQt3P82kP/Cdvo3t9Uq4ARmRQ7w0RE\nmMJI2OUXR3xNQUngAApOYZNZeGB7TJ/ehxFanM/OMt9YTN5HIeBw9SiPLj6eyPROVc+giekEQZRY\ntMTXR0ihal7pMNeYRyJ5xeTNhDLk8aWnL3/TXuJ4GVT6JxJSSp45WSdlBVw18jDmdUWaRw9SP/o0\nbmuB+vwTdMLTpF69jSCssFTP88WRO9j5wZ/lt37uVt7/xhI/+9ar+NA79lHMp/h3f/Id/uOfPU6l\n7lJrevg9dMxypc1f33uCIIwwDcFXvnOOZ0+usbjWYt9AhDV3msrc1xkrqGT5zKxCe8eKLRrlx9lY\nuBe3cY5M324m9vxr8sOXGnHHtGKAYwcXL/n7xfF7T/8xv/3kRxLqZ2WtxZ/+zoObKMwXRytokzZT\nDND1hopXfnpBpXhFL8ypieWqZg+0w15QSQMGXkhkBJtMwFNpG4RqVZ8yDbKGQeSYpPQqQAwqxYl1\nauuAMnfsAcVquFiaqSRlh8LGKI6bozHUpDWaIbIMOlLSyHRR7cAQ2HWfyAuZ2a5WSNqBBr8KqgCS\n2hUqO6gSRkoIlk2V7KPUUOIxZJgDNGQjMYUzwzymMYpda2NrpgmAqJiYpqIQ+w11L+zJDm4nIMhq\namrtCK4b4uhtN5rdRBPJKgZqUF7teEkRdmi5CkJgbiwRRaoQzEz3Ud43iDuSRwib64dzGEIwMaMS\nttWJ8IXJzLYBjBmVTOzsVepe4JEx9tF/YQsSSUccx+qEpDMW4zkNzBg5Ar0aY4YGbU1Vs5oBkRnQ\n8D2aYZcNYzsG0gwxCJCaVdIYiIvWEN9XQFFkB6y215CGLoRS3cl1p6gSaky3NXtAC6mBFtMNETJE\nRnWCYJFG6x42UvdyaOBRbOLnvbsquK2oGGVnq135mGMaRLaJb6iOX469Td8AQV9FsWmGnAzDz1WI\ntB+a6Kwjggin5nHH9ddy55Y7qHth0ukr1dbPlOygpE4RfhRQSOX4+Yl/w94JBXZERFiGxanqGbbk\n7QSUsgKbwO4kYBtAK7CI6Or/444bK4YaE6bnriZfHVXMMdvFS7naQL9DJDsIkcE0R5CyQRD5ZMIc\nAoGlAY9eGVgQeax11rHNvALvAJuqAnc1UykyQtyemsEV3ets+2n2DV9J3s4T6WviplssbjtKaKn/\nN4yCelf0LV+er+FrVpLAIUS9I7H8LQbghEiT7+n+ckWfbpWbUXLQtLGBq7ezU8vSLGGyEF7ATTex\nO1kMYSTfibuYdEKXuq8mGNMFBfwYQnk9DWpQaWBEyWpbfidZIUuZDiOZrhw2CrrPsGNZSCGxAnWN\nbcdkS98U8UnH23g5Xo6XA2QQsPAHv4+/sszAXW9m8hc+hAxD/OVlUtu2YxX7X3wjF8WRsxU+861T\nfP7bp5LPzmumzJ3XT3PTnlHmV5t85HMHL2EsLay1qLd8Slv6L7E4sC3jRX2SijkHM6rSXFdMq9bG\n7CXfCbwq1aWHqcx/g5WT/5PVM58h8JrMLpT48pGdZAavwRZNfvr2MjnH5WRlHC/oSspa4hqm9/0q\nUWYvjbbPkbMVds/0MzOaJ/AiLNtkepuq62JPxcgNSfcwPnwvZHm+lnSgikGlWkX9G8uZOq3NDJ6Y\nNeJ7YeL14zgmQ6Nq3IzbrzdqLumsmsyOjBUI/IiN9TZnT6xy8shKwgJZnlc5KJYqtS/TXa3dU4+u\n9YJKbrcrHcBKq8ySltY8Xzz49yf47F88xcpibdPn7edh2vTG5eRvcSe0XOH5waiBoSztlp80uWm3\nPE4cWUFoZvH5U2ubvh/JiDO185ytXUBK2QMqXXpsz8dUAugfyFDbaCvfoqDNY4tPEfiRMlzvAQeb\ndY9c3kEIwevu3sN7PngLpmkk7Kv/99GPca6swIeB4RythnvJonUXlLv0GH/4x6/j7nd1weGtGvA8\n9NQ8Df3c5ftSCWgXG5PHoOX3xlRS+/dQpt8j4wWWpmf5/Wc+RhiFuB2fJUvVgAvNRTrBZrZV/TJM\npX03TfPqN+1mZvsgBW1snm7HoNJmQPNyERtmz1bUOOSa3UXYMOViOxYPzn0HgKXmMhtuNQGVYqbS\nSqtMJ3QTIGXGU75Gge3SStcUG1wzltbTywlgAxCZPp7rkzVyWJaZAEJDpvbVbLgcWZtNLAPKz/mI\nwMRIS5bHTrJUX8GPAqpuDcdUvzV6xr/4Wkx1dpC3c4QyYqW1yjfPfWvTdegEHdY6FUazar+xvDBv\n57jQmKdga+sL2yPVyTN9ah9SRHz80CeJ+jTIPbTOJ49+GsuwErZ/O+iwpDsph+FmT6XQ9LGlg0CQ\nFVnc0GOtXeGm8f1YwuSRhccvGWd+EPGSgkqlUumuUqk0WyqVTpZKpX93mb8XS6XSPaVS6dlSqXS4\nVCr9VM/fzpZKpUOlUulAqVR68qU8zn8KcW65zkZLsHtkHdMNsF85RPqD26l0vszy7J9RWfka9h1D\niKsNIOJkuY+3v2o7N+4Z3VQk1FoeH/7Ms7TcgLVah9/72wP85dePEUaS664YxjINPvalwxw8tcae\nLf287fbtNNo+H7tH0SmvfPbvmfvj38VrL7JFJ9nZphpk9+17G+Oln2F4x7sY2/3TjOx8N3Z66JJz\nAUVRBegfynLmxOqmJHau3uZTpxY3eQ4tNpeZbyzy8UOfxI8C1lYaRJG8xNiwN1p+m4yVIRN16am9\n8jeJpF4ss+p/hWbrqwQDBpEIk8528cQPoKGTZehLQjNImAagzP/QDB/HEKT19Ta1JCvfl8YwRVKg\nDA7nyOZThEgMva+WAaYGlSLZZmhZsQo2xjbw+p1EvtZEokkYdIQkva4SR9xVIdC+L17OxpBZIl8g\nDMGwBmJsJ6KSXWEmP0Vk9mGEEYYXYBoDbHSqybMinEGEEKTWXUwvJNJeNJ0V1UYcQLpqFUEWNVsj\nZ0EUsdI6T6W2gd1Q97TqdZOTG1ZwIgmRJPIjKvqaLOnVQ58lQrNNnzBYC0O8gRSGM4GUEdcPq3No\n9qki0upIIsfk7e+/AS+ru7QZfSAEQuSxoxGK6xM4OLjyJIbrgwljGZU8DZEHW7M1AgMM9Tzb7VAx\nlQKfZqT2Vd91hs6EWhU2RQi2xYWdz9DO15OOZ56vVoeaefU9YcVMJQOJJBIhrqWSbsxUMjYxldQz\nZbohZmAThlWa7a8ThkvYnkpKlc5JpJT4GvBMmQ7lltrfWqdCWrNOHEMxhBo8SsE4zkhhP6Y5DYZM\nZGpRXT1Icee/Tq7B+HeXGV6pUCimOblxGkQGNKCYauf0cbqJ8TZA1s6y99oJitl88lnBzuNHAXlz\nPfmuGPA4W3qy1y+aUDZ4eLlb/Axok+q1UJ3TgD+SdN/oZGt46TaW8ImIkLKDIfKYuoAAyEn1rses\nqmqnW8xKItzQI2t3x4OM2SKI5EWeSl1/rHgFze5kIDKU+WIqD2jZqIhwCxugwduUkUESEs9kTs2u\nEBpxdxWHQJtix/K3ePtCpDYxlQZTNulAPVgDkSBtCtxQgbBDafWuRzJS0jcBZmgRSZls9/bJW/X1\nDWn56v5OFbQxv37Oh3QBbzkKcK77baSWVDuGw1B6ACsG23qkuqYe68wgXtk36bNTGIaaHF/Mlno5\nXo5/SRE2GlQf/DZSL/A0DjxNUFmn/3V3MvKj7yS//wYmPvjzYJr03Xzr9739SKruvwBL6y1CXSct\naKb53a/cxs/98FXccuUYJ+eqfPTzh6j0eMQc19K30sz3D2bFUVt+iDjxtWsnNv0t9Bssn/hLqov3\nU1/5Lm7jDKn8Nr798PUcPjxOLm2z7QrlFTWVVb994GiR3/3kU8k2tozkMUyHXdPdY7zzBsU49/1Q\nGzVneeu7r+UdP3E9W6/QPno9tcbSfJUokkltlIBKutZIQKWLDJgdpyubSkCllMXweAHDECwv1BS7\nou4mbeuHx1Tu+9SfPs7XPvcchiF4849dg9At28MgSky+Lzbc7T0G2OzNFIMtcXv4v1v7O373qT+6\nBDCIo1l3OXlUTWgPP7OZQZaAZ5nvE1SKW68/D1MJYGBI5ZRYAnfkwCJhELE0dpxOpsbC+Y0EQAFo\n+E2CKMCPfNpBB9e9fPc30J5K4UVG3RpUKg5kkFLd25pXR0gDIQ1MWySgUrvt02p6yfELIRIJXPxZ\nGEbKFNk2GBjKIuVmORy8MCgnhNg0z+ofzLLrylEW11e5Z/krrI6dodCXvkT+1gtaglJkPLrweCIL\njMOwRbL4F5o++T4FKtX7V7jgXuDg6hHOz63SKKq6KZIR57QVQhx1v0HWyjCYHmBBS7XyhRRXXjuJ\nEEJ3ko2S+jA0XxxUqmoWdgx0xmbWlpvGszusttY4vHYsAYyOrZ+g6m72VDqlO+rO5KdItfNsX1fg\nXGC7dMwmRqF7HGVrIWEepWRaMe9DCwcHyzYSpn7OUs/j/Noqq511rhoqkasP4h8rYEkb0soOourW\nWWmWkcik1okbzUB3IXXAGOQ3X/nvmc6rzn6PLDy26Tos6vO/anAPeTuXSOQMIWj6LcZzyldqpF/V\nt30b41humtX2Go+Nfp257Qc5tf0xpIx42467Nm37REd52PV6KqXSFqHlY+g6t89W13KusUDeznHd\n6DUst1Y4WzvPDzpeMlCpVCqZwEeBNwFXAu8plUpXXvS1XwCOzM7OXgu8BvjdUqnUC4e/dnZ29rrZ\n2dkbX6rj/KcScaeMK8dquH87h1HNISo24fE6wTEX/+E15NMRh9du4lPP7KUqrmO97vJX3zyebCMI\nIz76+UOsVjvc/YptvPb6KebKTQ6frbB36wAfesc1vO9Ne3A1vfi9r9/NG7S+3vVChr0NJqI6xhbF\nLJjW6LEUAgPJ9OQoTnaSbLFEqsdj43IRt4fcu2+CKJRJFzaAg+sNDq43WNAMFyklnh4sTmyc5q+O\nfoaONsKuVtrMNTqU25ehqwZtsnaGdE/7+ngwb7c8zu1+knOlJwhYJgjn8QuSyAywdMt2P3KRujVj\nSyezKIDIDDbpiIUQ2Hn1cqdMg1Q8/9KrEoYhErYSKFO8fCFFJMEJJUgJzhBoFoPVisjXRmgW1unk\nW0S2SVTsJnRnJENoSOXhtKYnjFv78cIwYVwgBAVjF4ZrMzlTZEh3KHFz6yAkVw9dDUIQ2gKrFWAY\nBcrtWtdAULexd+oBRigTU2TLTVO4oK91pLqb+UFABARZC0cXdc/NP5WASm3HAD0pDWpNJp69Wv1N\nCM5WVaFV0fe6kTtPaPkUI+iEEcKPMM0BwnCewVQaP/T50sK9eDkTQydk3zLoaNBNsWIiTHOYQKxi\nRBZT3g4i2jSKZbxOyKgunEyjiLQ0qBQZCmQCrFZAZIa0A496VMO3Xc4NHOXC4FH1dxEisagOLSJl\nEzPKYJnThNEqrneU6uBZdXk0XdnAIMy3CQcaCaAyr6myhtXL4tIAg+9h+SkkDSAgnbqNYnmagpPj\nXPUYd4x3GWjb+raw2tEadCQ5WyVTxzCBkDBapWgt058aIeVcre5HVgGxjbUQicTThtpeqklj2OPc\n6DNU3TpH1mcRRo6UodkrnS5TSfbIP2Nj7ozVfcYH03oyEK1gaFApk7XpZDevnobhElO57u/G8wog\nWvFWyNlZCk6hB1Sq46VqOEZccEmEkU9kowBFU+039lBb7WxeIQUYSPXr8wjJmZ6WoWpQ0pKYkQUa\nnI0L1d0HX4NpKWZRwe52h0PIZF9gkrEzymhe3+izJ9aQohdU2ix/iyW6QqQp9KxWCiEYCdU2JoVF\nykwRyUj5fEn13EfI5Hlq9VXwIg839BAIcnZ3zAtkgCXMxMT7YqZSqD2V6l4H9LYd08Y0TN6y/Q3q\nWhndCYGw1LvcZSpZZCwzuQ+xoffL8XL8S4y1L32B5U/+Oetf/TIAG/ffB0D/a+9MvlO44Sau+Mgf\n0f/6N3zf23/y2ArnNCspCCVlPZFfWm+RTVn0ZW0MIfjAW/Zy9Y5Bnjuzzv/98e9yz6NnabT9xKR7\n50TfJYbM30v4nTWa64ew06Ok8tvx20vMLS0RhBFR5FM+/WlCb4PC6G2M7f4AE1f+IoXJd9FsZZFR\nxO6ZfvL9M0jdJTOI+ugf3MraapcVPKUNdHdpq4D+vMP+XSo3BH6IrVmd09sGGJ3oSwCEXrlYbKuw\nZ59i5l4qf1OMofZFYIudMFzCxC7BSVnYtsngSI7VpTqtpkcQRAmotHPPCFt2DDIxXWRipsjr7t7D\n6EQfE1N9lJfqrCzVNzFfLpbAxYCP7ZisrnRldF4PqCSRVIMNWkGbx5ee4nLx3DPzRJFECDh5dGXT\n9YjZUN8/U0nVKNkXBJXU/aptdAjDiMNPz2PZBmvD56n3lwlDyfz5rhSx0mN2vOFWk/N8IaNuKeUm\no26AovYrra63qbq1JBcHhp9sa0N7YMXAXG9k8w6+3cHNNlgZPEs25yTfu9hXqeup9PyMrTiklIR7\nypzY9yDl/nMsz8wiMmFyTLGvVpeppD5/ZuUgf3Xsszy58uym7cWmzKDAnnwhxch4PmEvPXDhIZ6Z\nP4w0IgpC1Uunta9PHHXd4n4qP07Nq1/iuSOJkgVGAN988cWhGCCKQZVG1AApyLT6kCLigbmHkUhe\nN/MqQIFKFzOV4uP88b0/yr7TdxLWBY5wCByX0PIJs534AHFFh6dXnsUQBqNCKSb6zSJEAsPsHo9v\nqnt3ell3gsvtYeaU6pzbl8/h97CpY7ZVzA6KWU0WFo6rnq9UxsI2LLZpo+9ye42a15WYxtK3yfw4\nM4WpHnsDNabtLG5jS2GKW7ddl/xGSMHbd74ZhxQbI3OYkc2HrvsgQ5mut61t2BxvziKRlP0Vqh11\nftmCQ2j6GL56bkY0eWNO+zfdueUOhjNDWMbzv+svVbyUTKWbgZOzs7OnZ2dnPeBTwA9f9B0JFEql\nkgDywDrw4vDov7CoPvIwjz9xCssIuSJdgWbI0NTbyO18H51vrhHcN89ieZynt7+bzzyZoiW28rZX\n7ebbBxa476m5hBb9rWfmOTFX5cY9o7z9Vdt57w/t5pYrx8hnbN7/xhJCCN58+1bMsbMMlc4xNZIn\n5Zi87SaFzt64cYyDw3fQuUrJL6K/u5ecbkU+ljOwre/9cfI91fEg7pzR28UgZii5+t9AhkQyYmdx\nOzOFKZ5YfoayNr/dqLT58+PzfObM5u5VYRTSCV2yVgbT775Y8crASmuVRn+ZAYYwhSpuvJQkMkNs\ns8eoFzX4tHVSk4EgMkM6gUuls8Ga9iux4lUu08DW8y/ZI1WLQSXDFFg5SaZggyGQvupIZhhq8BpO\n2/QvKSbF+ui5ZDIX9HcT2fquZU7vfxD8gFTVwxxIkck6LDbVNbG00bgttib7djVYVHUU7XVbugRA\nZMlEArfY6iRJLsyo43XqegIbqkSQNtL0zQcglaa3PrBCpxEgLYE0DdIdeNXUbYRuhOlHCC/Az9sI\nzU7a+tx+rFYa04uQpuDw4WWWF2p0LKEMotMtQssj62nzajvufHCGC/V5vnDqK6y7Tcr7Bqn1nVXP\ngOtR0+fnB2p1xjT68YwlnLRJfkEln9rAEmEgqSxsEMkOhjmAaSuWhhGaCH0PrFZAZAS0A59qUE2A\nkJapV2GMCBC8cdu7gYhU1I/jXAVAx30YmYCteiiTJgu7n2Xbq7rgSU13wJBGN7nFxtwicFWCk2CQ\nI+vuIMhXuWn6OmpendXGAXXMwmBncZv6b40uxGCCYxpIrTu3DZtICtKWbmefV/ey2WkTWl6yAgYR\n6zs9qsVlvj33CIfXTmGINGm9YuV0siDFpUyly4BKsfxqqXWBN0yrY8r3gjFJhGzVRtOGMHj1q67m\n7h+/ijV3nan8JIVcJjHebuU2CM06qR6AwxC5TUylQUetBAmpnpsYjO6NLQU1yQijVTJWiiCKEBpI\njuX08Wpd1auRjZSkzklZNPwmead7HlJEEGq/MpElbxuATFYtfS8k0vdYCAcvukj+5jdRzkcpCtbm\ngnqnYWLXPHanU6Q0NdsNPTxtpj2cHiRldgvlVtDGDV1SZoqsndm0rYyVwYlbR2vguc+2sA1BoM+1\nGfQwlfT+YqldYHuJH1agWxvH46qTMnEMgaVlsSuty3sOvBwvxz/3kFFE/SlFrF//6pepP/Uk7eOz\nZPdeiTMxuem7Rir1olKziyMII77w4GlMQ3DHtWp7C6tNgjBipdJmYjibbNMyDX75R6/lJ+4q4dgG\nX3jwNL/yhw/z9PFVijmH5x45x99+4onEr+ZyEYUu7dqZBOSQUrKxeD8gKU68mkxRNWb54jfv5wsP\nnmL93JfwWvM0xRV87IEBPDGCnRpMwA2TLkPKjfaq61Tdwq+8az/vvH17st+UbkgxOZzjTbdu4f1v\n3JN0YlVMpc1yoVQPEBTH/LkKhinYsnOIVNqiXu0QRZK6BuGiSNJp+5cYWCdMJa+XqaQ+G5vqIwwl\n57ScK68lQ/m+NG955z7e/q/28/b37mfXlcrTcmbbIFEkOaJZQ5fzLIIuiDM5U6TT8hMWVSyTyhdS\nREZAoMfnb809QiQ337fADznyzCKptMX1t20l8KNNi7UXy/wuF4lR9+U8lV5E/gaKBXbhzDrNhkdx\nh0FkBqyPKrZEr69SbwetqlfD7QQIcXkZmNVjVBwGEabZZQUlnZUrLWpeHTNuyCPaCdBYSUClS48/\nm3MINJASmL4ClTR41ryoA9zF8reqW+M/PPJfLutd89Wz9/Klua9gGgaFyijSiDhQOUgqbdPKVfj4\n6p9wdO34Jc9X3No+ZjDH0emx4oisgJZZw7VaSafoU9WzPN15YtNvT/WASmEU0vRbFJw8kzmVp3s9\ngEAZaHtOdz++cXk2XG/EdWzNq9P0W2y4VWw/he2p+/L40tNkrQx373gDfU6BY+snqHSqGMKgz1Hz\nnNPVc6TNFFP5cbI5R7HKjBy+7RLYHm5KnU++puq8ht9kLDuS1GjTuWnCIMLNNpIFtyrqWVtaU8yt\n4EQBy0tRnj5BLp3Bj7owQywFvJgd1m/3JwyrmGE2lOpaqZze6F7fmPkVg0pxnNw4DcCO/m382k3/\nJ7dtvSGR44a2x1BmkLfb72H8/B5urbyBnf3bWG9335PRzDD1sMHxa7/Fl6PP8uX1ewBIF0ylOtA1\n2FRB3dPYv2lLYZr/dNuvMVPYnHN+EPHC7mD/sJgCevl3c8AtF33nD4EvAQtAAXjX7OxsPFJK4N5S\nqRQCH5udnf34i+1wYCCLZb2wNvUfEiMjhRf/0v/ikGHI45//Misjb2DP6BrGcgVrdJSZ2/bz8S8e\n4sT47QxbIY+kdsDjC4wOZPjPP/9KHnyma5L3wIFFfuldY3z98fOkHZNffs/1FPXA+R8+cCt+ECWA\n0Ea7irP1GL5pMzycRwjBTeUDOOcfZOaVr+Rrc0NkB09jmXkG73wL2+fyPLcWsac09X1dnyCISGds\ntu8cBgFuOyDdJ5CAoQuGVC7FyEgh8R0ZzPcxnB3kQn2eUK/+1xsu7TBipe0xNJxP9LA1VwEAA7k+\nrE43iQrUfSxr45SbJ/Zzf/ksYVDGtwWhESRt2QGk9ECk8ZD0F7MIBJERIJH8xbG/pum1+I3X/Qqe\nrdtp51O0NEiT6ssk12Riqp+zJ9YYHsnz4YN/TKoxgRy/kSiIMGREqFcwrhrq43h5iMDyqA0sY6IG\nBa+vmxBXaOKaTYqVJkKCPZFnZKTAocYZAPpFwLprEjpDSJaZnO7nlL6mFWORdK3IiruClHkiS+LU\nfZpA1esOCEE2hZQ+lqu7x0mVPKKRJsbqIGZoE1oetf5l7No0ed1W1m4HfOj29/MHz32bylIN/Bph\nbhAiidUOMEOL6t4ynX41CK5dqHLUE4SDJmYo6XPyNGyXvCchpQzGpQzo+Kf47Sc/AsBwbh++4RB0\nmtjAXK1BzQuULMxXdHrD6MfzjzM8nWX+ZIC5NUWjb41ISE7MLhH1VTDNcUSmRGVXjhCBIQoYSEw3\nIiqGBFJSj5p4uqNF3JksbQnqAZQ9lYz60xM0zGkMkSGSba5M38CRzlNghBCBYULNqrAQdYdDN/QY\nGSlgpXzQ9UPMVJKRx+rEaRAwemEHtneK+s4Ot05fz/2nH+ExvUqZtlLs37qXr569V7FWgIyjzeFz\nGaip5zCfzbDmCTJ2joJRoK7BK89p4aU3G5H3p20smedb84/gh2kKeUjZajtWkMKRKQLZSXySQL2X\nIyMFxt1uwp0eGmesPsK52nlKo2qyMNI3yMnqGbJ2hlaPCb4r2ggEI9lBdu+eYHb1FJyEXSNbGW4M\nMHe8hhFYdHI1wsjHMrsFl2HkEaJA2krTCTpMD41RoWteCwqs6i3A901v4XBd0u6coi83TK6QJl5f\nMRNQySBCMYmmbMW6zBZtKn6Tkb4BiGt1ATKMV7iyDOVSzNX1BEz9GREbXgoHLzZMN3XDANnBNtMI\nIdg6XmQw033PX3n1FAt/8SQ3/+JVnD6bgzLk+m3aWm786h23cqG2wONzCmSMzIBA+mSdNOND3dUu\ngIFskbHh/KbPpkb7yJ2xCDSFOjDChJk5VFT3lOwkPK0kkgKhaOLZCDYUU8kwBOPjqutcX2Ynbfch\nVjvrpPqElgmq+MfInS/Hy/GDjvbJE4TVKs74BN7SIosf+yMAij0spX9IPH28zHKlzWv3T3HNziEe\nfHaB+dUmE0NZwkgyMbgZuDcMwauvm+KmPWN8+9l5Hj20xPxqk307h/0AEV4AACAASURBVKgtNWg2\nPObOVhL5WG947WXOH/0bHFHDyOxgYuf/wcbCvbQ3juJkJ8kU93Dk5GkKwO6RdU4vPU2rfxY7O8Of\nfXOaSqPGZ751ip9+897EDNsESltUnmh5O3jmwAYhY9z4Omj3mCP3ysOy5RZOfw/z0o8SplIcvf45\noMCY8lKDyZkitm1SKKbZWGvRqHU2MYZaDS9hjcRgQbwt3w03yd9AtWo/zAKnj6n68XLsl96Y3jbI\nYw+d4aT2/pzZPsiJIyubzJVBgThOymRkoo9zp9ZZXW6Qy6cSu4ZcIUXgdCf5y60yR9ePc9VQt0Pg\niSMrdNo++2/dwtXXT/LMd89z5JkFrtqv5E3fm1H38zOVXkj+1j8Yy9/aiUn4xugcuOA7baJMh/On\n1pKut5WeDlobbg23E5FKW5cFWS9uqW5aly7WVittvLF6wlRqykYC1LyQ0Xg27ySLJKHlkc7ZCfjU\nuBhUaqp7FAOaxyunqLgbHFg5xM3jXd/Y7y4+yVfPfJOh9AAf3P3T/N3/eJaj/ffz6MLjXDF6JYtb\nj9CRbU5snOZa1Hsn7IgNt0pVgzRuuPn5aAab67T717/F+VNHkUJi+jah7VO3K9h+Gt/uYBs2Z6rn\niWTEhfo8f3fqa0gkBVsxlQDmm4uUBq9ItllxN/BTbdDr+7GfpJSScnuV4cxQwuKJI2YGgQJnql6N\nVJDH9tR8oBW0uXPmDhzTYc/gLh5fehpDCPqcAoYwaPltllsr7BnYhSEMsjmHtZUmKZkhtCr4lktb\nN8XpX51K5H3T+SnObayAAcOpIZaCCDerF9WFSVmu0M8wlVqD8ZkxavMeCEl5/DQ7xNaElQSw0l5N\njhUgiALesv31+HVBLCCNGWYD6a4c92T1DNeNXgN0Abrx7BjrBQWYCuDplYPM5CfZM7ALUONxJufg\nuj6RGbLSWmU6V2J4aQcDu1XNttbzbgxlBplvLhLYLoY0qQTrDAB2Xh1/bPMw2TdGca0vAcj+MeOl\nBJW+l3gjcAB4HbAT+GapVHpodna2Btw+Ozs7XyqVRvXnx2ZnZx98oY1VLtO28n9VjIwUKJcv7ajw\nUkf79CmOGgqh3TO6RnS+Q+HG2zl8YoWvPXqWoem9/PIHbmbksfM8e2qND7xlL6Hr862nLiAEjPRn\neOjAPClLtYJ9061b8NreZeViAG4shQl9Ts0vkvMkS1/7OpO5PEuT1zLQOIJlhViF3eTfdDfbHjzN\nc4+eZaSY/r6uj9vxyeZsKpUW2ZzD+mqT37jvw0gpmR54JwDlSpOyZSV0SRkIfA1yVLRsKrRic1jJ\nyfkKA6muoSGAGdmsr3VpnrVqm3K5zpq/DjaM5IYRqwqAC4QgssNN7AYpdbvRIGRhQQ0WsdHwYn2F\nlt/mMwe+Rjk0gGHcpodXc8GCehAk18RJq0SUL6ZZbqwyYOSU908Ehu/jowZh50IdK7BZG5tDigiJ\n7naV7ZHGGIo2mllVxxYMqWt/YmkBGCVrW7TKLq2JLH7exrBho6lX56SLGWT53NlPkU2/nsgwEqbS\nRstgGJAC/IyF5x5FWjmEbyeAx7yzzI70DGZgE9g+oeUSrYIzoRk/NY9yuY7bVEWhL1awGARDyeyW\nZo4RjOZxHBMvkvgGHDy+AreN0Ze1CdwsVbvO8nPLcJta7fODOSDiFRM3k7Oz5NPXcv9iK7k2j80v\naLlRRBip2b5jFmjLFql+gUCQrw1RLS7g5pucP2kSXLuEZU0gjSHKo0+Qcq7CMDI4sqO2ZAS0/YCG\nu4aT3TwhNjToeE6bEw5kp2hikDH2E1kGNxVuYD46oVkoYJpaT14+nWwjlCErKzU6QRcgibuZhdSo\njF7AdjMMLU1xeu93SDmSq8dKZK1MkvzSRooBOZJM9gE22up5i7xQefsAkSdp+wEmghF7lLqrfKD8\ndDuRvsXhejXumHwFXz7zDYRQ9zTQ55EWGdJGhrpsJccKSmpWLtfprXsM32JrfobHG0/zzPwR9bxq\nQ+4+u5CASgLB/MYKd217HcVUkXK5zrmqeh8HTVXECATpdoFWXiXbU6tf7u5HKOB7R982TlfPMN4/\nzCFW8e1eWWEXUNrdv5Od6R3szD/DY9XDRP5rWdtoQY+nEoBB932bGh7lznfv44sbJ3HXPfA3F1aR\nBoiEyJCRRiJNtByD0ItAjxeWSBEvAG+01JhUbdcx9HV2ax3KPZT7XF+Kn/ylV6o7q/e5uLKeyMuC\njmRHbgePo0CljWadltcha2dwG5tXsQtmH43q5u48nVqbtBCUA72g0GyAVEW614ool+uEkameFaej\nvOYELMVja+AoucaqOpeMZk1JKfn8s9/gLdtfD7w0ufNlkOrl+McMKSX+ahlnZHTT540nHwdg5D3v\nZeP+e2k+ewBrcJD8tdddbjObIggjltaaycgjpeRv7jvBYCHNXbco2cV3D6v8ducN08lC4OJqk8Vh\nNamfGM5esl2AbNriTbds5a6bt1DeaFPMp/jsJxSz4dRs+RJQqbl+iLXz9+CIgJVGllFOs3D495Ey\nwMlOMrLzx5lfbfJHX57jZ27KcMVIle1DVUJpc6ZxG5XGAoYQPHxwkVftm8DX7CADwaSWLHleyHql\nH3BxO8Gm1vBxl7N2y+f8qXVkJLnyuknCMCKKZNKlNo7e9vHQ9fYZ0l5HhWKa1eUGywtqEmyagjCU\ntJpeIguLAZVkW16QeH8moNKUYs3GXp5xG/bnixltJh6FEtMUTG7pV+DPZZhK6YzNsPYpXVtpsHXn\n0CZPpUDntKuG9nB47RgPXHh4E6j03NPzCAFXXz9JNp9i264hTs+usrxQY3xKMaCEuLzELA7TNLAd\n8yJPJTVXiE2tLxcxU2m93GRlqU5ff5oDobILQEBzyyLGbJr11SZDI3nWeyTSVbeG66aftwNazFQK\nAsVUsnoUEcWBDJ10g3v4NHtrOzBCde/qYQ3DUOcSs+9jALDq1mgFbSZyY+TyqaQ1e2j5WKboyt96\nQE7fC1lfbTI22e0seKGhvH3O93QXO7lxhr869lmyVoZ/c+0HGM+N8mPvvpXPLixwuHqEB6r3JUzx\nDbeKL9SxPRl9h09/9wS7+lXnRe9iUMnfXKf5TpsNfQ1H3CkqQRk30yTX6WfDXsIUBp2ww2JzmSeX\nDyQenisdA4R6Ji9mKq13Kvg9TKW2aHJ47RhfPv0NztfneE/pR7h9arMHXLVHAna8cpJIRojITDrD\n7h3czRu2vRaAPQMKVKp5Dbb0qYW6uJYZ0QbX2Zz6neWlFDqRDuhoxn+6WWTIGmYtWGUiN8ph7zTY\ngBREoaSZqiXbWq4t0s+VmJ7DnVtew8GHG1h9EdKILgEuY9Zc7EfZDtq8efvrefrMURb06uHFoJJA\ncGrjTLKNheYSw+lB0laKLZqpJFELmu/d+05Mo1tPXrV/kmqzzrNAub3KrpyypYjZlmudLlMpa2f4\n17t/hgf+5izl/YdYlytqYS8PBGCGaswaLgwwXZjk8Noxxaa/rCrgBxMvpfxtHpjp+f9p/Vlv/BTw\n+dnZWTk7O3sSOAPsAZidnZ3X/64AX0DJ6f7FxdKBwzzTtxtDSHaPrCONAuu79vPp+04SRpJ3vGYn\nKdvkbbdv5z/+xI1MDudYr3U4NV+jNNPP2165jUhKvvHEBVKOyV03b3nB/TW97uC11llj45vfQHoe\nA296M2urLqMj6oEPInVrb7tqjD1b+rlxz8j3fE5RpFojxjriQl+aRt3lQm2e+cYibqgG2liyFaP2\nKdPBNjS91dPskZ4Es9rpDsTxxDtrZWg3VdKQIqLVVgNHJVLnca61SBdbDZFZO2GkqB95GF6IC3T0\n9sOYZRACIsN6u0Kku4gZoUS4AUOH1lk9sIQfRSy33cTIcWg8SyQj0nlTtR2XkpxG/4u2xYpeAasN\nVxCRidR0zsDsnqdJP0jIrvqEtkEwoJLgSlslq4FshlRVa7ZzFk7BoKOvpZRe4vXjB6eRRoStr48r\nVYHkZy0COU/Hf5SVyZOEZqA7bWUxRJ7oiowykrZ8ECAiA5nT8qGq6s7ltl0C08OLuvRrKULWxs/g\nRQ5Ffe/9jEWgZYJTuTQFJ09khERtF8OPJW0nAZN3ld7O2694M4ahpT0aLDhXC/W5BcSGydcMqMSR\n0Y9lbl2tAjQGV/DaIa73FPXmZzH8Z/H85/DXHsAP5im2FVU7MkO8MMDDvcQHyDHUvtbbixjGEMU+\nlaSNKI/j7MVyLMayI0nbU1ufn3+RFKviVvHCFiAQ0kyMun1DdY4bKE/T6a/QzlcxDRPLMNk3rGR2\nhjBIW2kyVppJvfIEXeNExzQh3r9pE0QS2xRMZTQdVkCQ6eCmFWB0y/gNALSDNV4xdYu+nlWiqEWk\nWUnv+8nbGS4WkbhEURcMy1xG/pa38+zQ0rzYLHAwra5T3EoWIGfnWGuvcfeON/IqXbCc3VBpYio/\nmdDx060+EODY1+CYXQaMMPI4huD9V76TX73xQ+y8Yoy7P1jCyzYS6nJvDGUGsQ2LSGvqbTOtjLqF\nwBAmmDHrqFsEDGcHmd42mCRqy9hc/MYgc9bOsqe/e2w5PeFQ8jebvG0TIbANW3evi2j6LQQpspaB\nZTy/FCaWuSn5m/bfMG2uHtpDykghELT9jpa/OWSszZOdocwATs/2Bcr/LWOZ+Ikkz71E/mYaJmmy\n+Kl2Apat+OqdtgJn00QgE/uTYfDtuUcuKYxfjpfjn0vUHnqQs//Xr9J4putto6RvT2HkcmRLexh9\n7/twpqYZeusPI8wXZ9D/z2/M8nO/dS8n5tQk5/DZde59co7PffsU67UOjbbPodNrbBnNMzmcY6iY\nxrEMFlabLK6p8fhiptLFIYRgdCCLYxlsnznCq1/5BBnxEPXVg0SRz+z5CmdPP8LauS/g+vA3T+/l\nTx7Zz8GVXUgZkC5sZ/SK9zG3GvLf/voZ2m5IqnAFlhGSskLuP7Wbex5bw7EMfuFH1GTpv3/lKJ/u\n9fbU/pS9HYDXVhpUexaFYyAg/jcGd2IG04szlfREVMtWCnosjsGgkQktqW54Xflb7Knk9HoqBfoz\ntb/iQIZU2krYTvkXYSr19WcSidzQaD4BZnrlb1JK2m2fdNZmaDSfXI/e88gXnARUunKoxBX92zm6\nfpxVLZXx3IDV5QYTM/0J0LXnGsUGP3tyLdlnOmO/qOQynbEvYSqls3bSGfj5fpPOWCxcqBL4EQN7\nDFpBO8nBa31qoeicPpZeT6Wqq+Rvl+v8BiT7VUylMGEugbrv7fEyLavO6eq5ZJJdjap4obcpP+X0\ntf/kkU/zO09+lDAKSWetxJcoNH2MdNcnq1f+tnB+g4CAmd1dpkosNaq4G4ma4oELDxPJiJ+5+n2J\nOfPoRB+v3fYKAB5bfwKhO+3MNxbxtAfpalSmE3YSQOESppIGlYxAe0Y5Li29irdn1wwzlb2ISBDq\n5h6Blned2ji76VqvuhbHqhaWMBP/nTgqnQ28VBdUWvIX+KNn/3viOXSyB0QBJamre42kOcyRdfWO\nt7Ib2CVVh27r25LUTXsGFVtHIhM/pdjXqeCo5z4BLlvqPHO5FB3NTLf9FFdkVYfhnJ0j0vVIw9VE\nCFMd+1R+Et9wiYyQXFhgd2oPgR+RHdANhmTcVEUdd+siwK6lrQlisBG6oNKQrmGzdoYL9QU6gTKI\nb/hNJvPqfctaXWD/DVtec4kE7cZXbuPVP7QXgaDcWk2AtJSWxq61K8l74wYu2/q2YIUO6TCDRCrW\nUlZ7iwU2UkQMZfuZ0vufr//jspVeSlDpCWBXqVTars23342SuvXGeeBOgFKpNAaUgNOlUilXKpUK\n+vMc8AbguZfwWP+3jFYn4E9noWbnufNKj6wT8InwJv7rV85x4OQq2yf6uLF0KZjz1KwCJm7aM8rN\ne8cY1Enth26YpvA81FcpJYdWj1Bt13hNxuE6x2JtbYGN++/FHCpi7EmRTx1gYrxMGBrUmmpla2Io\nx6/++PUMFzOX3e7lIi4UHMek5gU8tytPYzStQBQkBxf/lHrzs9S8LmsK4HwjSIzHOto3KOpZsSp3\nenTgmgnhmDatZgfpSLyUT1t/p8YGRIL5RjnxU5EyIMo6m0AlKV2sdkggoNLULBBDt3lN30UuezdV\nr4bOZRihJKh0yK606cw3eGBhnY88dx760/zYT93A9mtVUhoZHABDICQMaF+f8ZTN+mKDZn6dMG1g\nSCsxzu0N0+gn2xjA9KE9ko49sFnvqOMbLRQwdMEWpkzMXEQnjJAywjEKCUjiB+cJzRAjlMiwTWT0\nKZZSwU46mTUKq/iOD3gYIochcvTtGsLvK4BQA5uQFmUhEX6E3Qjw3JCquczxa7+FL89270mxo+RC\npBhOaY+YwVQi7Ss3DiWgYWZQkCm3MTsBQXABy7CTyXxsmh6zc0LdUSv2v7IMi606OVkDynQ+V1PP\nayO/igwERAZRVEFoIKeTXsH3z9KuL+l7HBLJkNA0cNNNrKinI4QJYbiMJMQyJxGxEU+kihArZTKa\ni99Lg4GZzcWSoYfds9XztIMWQqQxsJGyg++fRdoqiTqdHIvT6j7EIMcNY9eqc5WStKXe6/0j1ySe\nO51QnY9jmAlTyTIs/EhiC5HovU1p4Rsunax6Zt607YcQWHhhhYUeCm0QLuKHLSzDopDNJck/irqF\nSuzfsxlUyiV+TwBpM50UErv6dyZ08f5UH1WvvgmAOLcxhyEMxnOjSaGfbumubkY/r9v6s+zuV8XF\nZHaQW0aLFJw8YznV6XKooPYznt3MIoBuMRtpBqJtpLVRN1jCSphKZg/NOwbDYi8l46K0Geii9NbR\nMfYM9BN3RYonE5HwEcIhH8t6zRQNv803Ts+p7omkN3V+u1x0PZXc5Fo5ps1Aup/fvuPXydlZWkEL\nL/JJmSnSF4FKOTvb9VQC0qaBIQQZ0wChx9Swg5S+3nY3T+REXq1g6rnIkqtAJTOwE/+ReJtgk7Oz\nNP0W3138F9+09eX4Zxr1J1T3n7V7vpT4DSnp2wb5/TcgLAt7cIht/+k3Kb7q1Zf8fqXS4r/8j6f4\nzmGVb+bLDR46uEgk4a+/eYIoknzxITWJCyPJfU/N8dTsCmEkuUX79RhCMDGUY3G9lXR+mxjazFSS\nUl62rXRz/VmmJ+fI5dpMTSxSufBF5g59mOMHP4VRvY+mZ/OJx65hcmYf26eKfPHAOAO7PsTIzn/F\nyYU2/+1vnoa2z+un+tm2TRngljvTPHKqn/Wayx3XTbJ/1wh3XDvJcqVN0NP5qwsUdWubxbnqJmZI\nDB7F3dxicMePW8o7zwcq6QXJzuZuYoWiGg9j8+6JaQ0qNb1LvIZippLvqVrGsowE2BBCMNrDVonH\n+BeKcc1uGpkoJNKzXlDJ90KiUJLJ2BSKaZyUSXl5M6iUzacS9m3R6Uvy37oGIWIQamS8R3I8oXJm\nRXeda7f8F/RTikOBSkHy3LQaXgLIvFD09Xfz/+G0YsHtGlDMm4ao46fanD2xpo97I5k4b7SqhEH0\nvAwqU+fNMGYqXcRSc/u6zB+hG2yEps98Yym5l6CYSpGMOF09Syfs0AxaNMJGYk4dWQGB7XY9lTRr\nuOW3+crZb3Bs/318Nfhc8k7N9bS2v9CYR0rJyY3T9KeK7B7YuekYS4NXJHXE0NI2jMBScjP9vDa0\nJUHM/Ll4QSYGPmLj6MByk66uA7k+furNd/Oa9ltp9mkPIb2geLp6NlF6gGoIstDy2NG/nQv1+U1A\n0bq7sYmpVAvVsfzorrcBSu7XG3W/gURyhfZdPF/TliuGZGRAnWvVrSKl5KH577LWqSSG0kXtpxSb\nXffpujKjAZawru5bLpMmMH1EaGCEFq8ZvYMPXfezDGUGlJ8lJICea7axDIuZ/CQI8O0OmTBPpayu\n3cCIej7jzolxLeyG7iZZX9yEqddTKgY8i6k++pwCXqi6h5+pne+adOfUuBwDg0Wnj7u2/xCXC8uw\nGEwPUG6vMTZV5Kbbt3HV/imklKx3Koxk1HXqhG4CojqRVg7YLiKl7q8ZWkRGSNpKJ53p5i8CC3/Q\n8ZKBSrOzswHwi8DfA0eBv52dnT1cKpV+vlQq/bz+2n8GXlEqlQ4B9wG/Njs7uwqMAQ+XSqVngceB\nr8zOzn79pTrW/x0jiiR/+NkDLIk8N8glXltSg0XNzfGW27by1lds44NvvfKyqw5Pzq4ggOt3j2CZ\nBu963S72bOnnjS/AUjpWOcGfHPwLnjv/GLekHd6YS5N68gEi3yX1I1upLj/AtpkzZDMu5dUBqpX/\n/6vQvR01Dq7X6VgCtz+VTOgkEEUV1tpKhx479Vc9iaVphAlTaROodClT6cGF0xzc6TL/iknKN2/B\n9UPVDtaq4XgZWqFiEaj9BkSZ7nEAyMDF1H4Aay3teWOFGKIP0xzCNIrUvDZRYkYkaepBjEhyttpC\nAucbbYbHCvhSs65ydvKdop5QDuuE6KWbINIIaSEJNvEtrJYPpk2hpphi7dEMfqSS3IYG4fJpB0uv\npAUZEw+Xhu8h8QjJIg2JkCbg086qJC8662A6uP0pWv2SIFBtPjuZOp1cnbjT1nBmiog0wugWU7I4\ngiclqaqHkPCd+Sc5t+1xbQDtgT6WCC15EilGMw4pQ+DnbWrbVYI5ufEM53Ri2n3DEINHN+g/WgEC\nzJ79tXTBaYiuGTWA1OyZrJVJJtW+4TE4ksNxs1hehnamQkSUJJEo6iYOgzVqLV1gmAGSUJ2ngFG3\n280wbSiwBcCyJoiklk5pRo9tmz2AhkE+3S22elkkp6tnaQVNhMjg2CqZSiDSq0VGPqCV6ZpYg1ql\n/KXrfhaJTNgrd227k5+75ifpDcswEkDSNmz8KMIyDHYMqufG1CtezcIapjAZTPeTsgYJwg0eW+wa\nT4bhAp2gRcFWMrOcXoUJo43kmGKj7mwPkFFwcoznRhOgqZjqS0zEQxlQdFShHXeJi1dcIxlxvrrA\neHYU27ASY9PYrDuM1ik4KdzQwzIsfuma3bxpZjOwnrbSGMJgza1s+jxrZajoAiuIOvo6pQj082ka\nVgIo94JKMeU5b+uC/aIxN15Bzuj9TugVytK+CXZfNUYoPYRwyGkvCMdMUfVa/O1RVaRFwqFgvzCT\nwek16taMN0eDmbZhkbbSSfGYMlPJ6mH33NObmFAZfSxZy0TQBaziPhmpTaBSjt5BqKklm6bvYPcU\n7RnLRAgL23TI27lN3VFejpfjn0uErRat47MAuOfP0Tqq5L2x9K1w400v+PsgjPjYlw5zcr7Kn3/1\nKGeXanz+wdNICVvHC5xbrvPxew5zeqHGtTuH6Ms5fOvAPA8+q3LOzXvHkm1NDmfxg4jnzqxjmYLh\n/s1g8vr5e1g4/GHcRre1tN9ZpTL3NXzf5KHv3MJDj+6n0thLEIZcO1WmHaT43HP7SefGeOdrr+Ca\nHUNEUnL0gkfHC/noFw7h+RF3bBtiY75GZaPI2O6fYmLXOwCBaYiEEf/O1+7krlu28KqrumzauP7r\nNdWOPYqKgypfxOBRzFCKQaUXYyq5FzGVLgaV4g5w47obbqupmEqWbSTbNE0DwxAJU+liWVavBOqF\nJGFxTG7pT34X+zb1eir1MqWEEAyPFaiut/FcZRRuOyZOykryTJ9TSGqImFURg1DDY11ZcDbnkM7Y\nrK82CcMIzw2+p85l6aytWEG++o3vhS/opxRHn372shPw/7H33lGSned55++7sW7lqu7qPDn0zACY\nGQBEEAESYCZFUiQlBtGyvJKORGm9tmVJa+1Ze1dry7KlY8nnWLa0lrTSkY+SFSmRIsEoAiQBEgSR\nhsCEnpw6d1V15Zvv/vHde6u6ZwbAUstwjvH+M9PdVTff73u/532e573cl/njPZN3pS3awz1NVpfa\n9Luyyc24VUUVKu2+zFtvBSolBu2u5+Nvk79FUUTHHHa/FXqc9+oOz66d2LLNbN5gpbeWzp9dt8f6\noL6l49lA76BqChlLp9txaDltfvFrv8pJ5TlC1WfFXmOlv0bT2aTn99N59lpnkdX+Gl2vx/7ynhvW\nZYpQePfet3GocoDayl40z2Tg2ziOTygCenHuOojBIyfY6ufU2wYqoQzZTHk9R7mapbfvOqEasKsg\nczxLs7jYupzmPPI4LJquz1t3SWn6R899IrUHaNqxpxKyqOqFHkWjgBoXNDtuZwtA3Y5Z8bXsOGOZ\nyhafotm8HKM2nTbXu8v86cJH+b0X/ygtzCX5+RBUks9twtpRbHldLcsgUgJ0LyNtLLJZDlUP4PhO\nuk5LADdHGVAyiilYZOZU3H7IRvxuTEyWtuyzmilTyhTxQg9jpEtaAio5oUOgbAWnFaFw29ihVHVw\nsn6Gz1x5VJ5zzEhaH8g11Zt3vj4tkt8sJrLjtN0Obujwmgd3U6pY9Pw+duAwmauhCAXbd9Ln3fCH\noFIY+5yqvg5xN965eP/Xv8O+St9ST6WFhYVHgEe2/e63Rv6/hGQhbf/eReDYt/LYvtvj/GKLM9fb\n7O1d54P3lBjYl+jYBkd2T/ADD+275ffWmn3OX29xcEc5NeO+59AE9xy6sWoPclAOu13a7TpEEVZv\nOf19aY8H79tBaHRB389Xn8gyt7vKyRdcdh8Y3HR7rySSClXd26DRkAu1QPUh7pqVNY/QtZ/FjgfN\ngRd7B0VqOkE5vstY3qB7K/mb1wcUVOMNRKaKCCIiXWOga3TcLoHqkekWGfgeYRR7LkU+USZL2B0m\nO1OnTLKtJu2ez8aeeIFrGmjaUNk58COSxk3CD+i3hxPCYt8GBFdafVYfu8LMsXiwzBnQlqe829Qp\nz+SZ64VcBVyzj1CLiEAjigaUDI3N+JqJfgeyVbKDnQRqgF0xscKQttvBCwUacGnzfOrj41saawOb\nrhdC5KWT3URwhFXtBfrZVWAWpdsizM3Sn7To5s8CEYpvEGou7YoE9xSRo2xNUrc9hJDnoSpTqNOS\ndql1PQbZFn9x5RGUUEP1NXzDRun3CPP59DoLYVIxdcYzBov9DBhrRwAAIABJREFUEe+bsE3LlYP1\n2M4MpaqFTywJC7psOi3KZomen3jYZIiiPkIUUPFxQgkiFI38UC7kO0zO1qiv9bDsCTrFK2zWrhMK\nD03bhR/0IZJMGDu3yt6ClH6FSgBRwP7Kfs41n6XQG0fRrxFqPqaqxKCSQFOnCCIFCAlDGxVQDZUJ\nJQE6BJY6TPR35Gfp+X16fp/LnWs4gY2mjiGIW8sjiLQB+GAeGJomjoIcCc02OUchBBPZYQe0+GoO\nmUpCI4hAVwS1UpWdl4/TtRo0Jq8Saj61zDiqomLpVWx/jWfWnqdilmm7fXx/ib4YpFTuYat6H1PJ\nMAjsFFQaZcfk9TyKUNhT2smp+gLlEVBp1B9gLCNliRuDOjP5KTYGdRzfYXZMTpBJhSgzKACCIKyT\n0RQaTpOyWbrBOBLkxJ/Tsym1ek9xF3kjR8Nusj6QZqF+DCaq25hKKBHjE3mWTS3tQ5oAX4WYxn02\n9idIIlJlcmPEXSNrVpUNp8fu/WPMz9f46KMOqlpKmUqKMAjCzVTuKMT/N6aSN8JUSiKrZdiIkxlT\nNVAVFUM10oqnpWdRhEBXBF4YSYYSCRCkoAkNJ3BSue3otgtKAUYUwSCf04ya2eIpYqkKAg0/9Pml\nB/7Vlmf21fjOxPz8/NuBX0eahv3uwsLCr2z7ewn4I2AnMh/8tYWFhd+P/3YZOQIFgL+wsPCab9+R\nf/dG/8UXIAjIHb+T3vPP0fzUIwSdNpuPfgG1WCR76PDws7ZHxtTSBiIAH3v8EpeWO+ybLXJxsc1/\n+vMTtPseB+ZK/Ksfu4+f/OW/46nTsjD4Aw/v47lzG/z1ly5yabnNgbkSlRHJ1UzspdTpe8yO51BH\n2Ihuf4leQ/qtrZ7/Q6pz7yCKQlavPYEhPF44eRgyVbyey7PPwUp5jry4wo+85038H/cMgfqj+8b4\nmy9f4oULdZbrfTp9j/e9bg9WY8A6EqiZ3bWDmRx8+E0HyBgq1XhcyGZ0PviG/Tz2qYV0ewn7KPlX\n1RQ2YqbN+ESeVmMwwlTaCkAlP7+cp1IKKsVM1wRUSr6b2BH0u24qC0tCCIFhqrGn0s1AJbkAzmT1\nV9QY6NDRaayswe4D4wRx7pKwoxa7y3zsxBcQzKYsoonpAktXN1lf6eDYcv+6rqRG3SWzkBZrktbl\nycK5NpnnTOMcX1l6ih8+8iGq41mWrrVSKZf1CphK1ohZtx93Bnwl4FkhZipV9qnEpHF2F3cwZlVZ\n6a3SLC8xxQwXzq3R8brM5KcIopBub0CeW4NKqqZgWx1+6fQvM5c/zrh2MP1bw27iCBuzn8fJdvEy\nNoHi0S1u8Oi1dd6S+QAgu+5pmsq1Ef+jjttlY1DH14egUk+RCVe+YLLZ7PPF61+h43YZW9nNVH6C\nk/mnOFVfYDxmktw9eZwnlr7Gtc5iCvQlvkjb496pu7h36i7+7y98Hs0zcaMefbe/BdRKGq5sl791\nY6mbYSddoqtsxIyYvJ4jjEJON85SNKrYyhsx9GeYyLS40rm2xQZACHmPNHWCuyeO8czaCZ5efZ57\np+6iaW+iWHEB2PAICZnM1lLfo5CIjUE99T9KTMVLRpGp3OQWg+mp3CQZVRa6vr4qi5SbTisFYxKw\nZSh/2woq6bEnk6aqRCKSHksMwWM7cNIC4CAYYBJiiwGz5gQHK/v4kSMfZqOlcrnR4NpleZ12zNRg\nTTKsQOay49kKLbuNGecqSc7kBR62bxNoPqqrb3k2bx8/zFeXJRPv0WuPA3DH+BGOjh8BYKMv87Dk\nGblV1KwxTiM7kSceU424m/hY3NnXGWEq6TGo5BkOLnH+6uskuFXNGsNQ9Btkjd/ueDXr+y6NE+fk\ng/Ga6SVytx0m8jvU+5mU+nyz8PyA//qxk0TAQ3e+slaC9Y99lAs/80+p/NJv88/++zr7um1CN8T7\n9CqoAjGpYeb30Oy/lkazzMSOeYRisLbR4onFr92UWp2EE4Q8u9Em3PaZhPJ5enCZq71EvzpcaIbR\ncOAA6PkxKwkNQVK18CgUM2i54SS5PqID79oDhMghhEpgX6F2QaL1bsFgqR17gngmPb+PF0u9wsgh\nMM10sNKdDLlNExFB6XKHa0/IwbWQyaJpQ9aXG2qosZGu07QJ/JBIQKArePGAfrbe4uqFBosX5ECc\niSVvIooo5EzeNDuGF3epcM0BqBYSIvIIwmGlwSMG/TIZ+lUPS1fxwoiV3hpCGIRhl8cXHyVKdOim\nylLfxQtjP6V4IVu05xEixyC7QSgC1L4LvkO/lmGgngcUxlckeNkrxKCSkqMRRTRdPzVx1vWDdHWL\nuZyJABxLDtiT1w+SsSVgEPWvozgBYSjPXYgMVVOnNtLpKoxsdhen04rBcn+N93z4OFP3evHfPU7V\nY812CipZqYG4H3YI4+tUMctpBckOHKbiyqLpy2rpxpSk/JrGMdyoheaZTCxKvXfLkM9GwlRKpExG\nJ4/myW1qwicI1jC1cYQw8aJkGJVAq6arTGaTpDzCDYb3b2dxjonY6Gk9NjwWIoMXvyNBuEkYuWQ1\ni3xmKGUYTQxGPcaSKJnFtKIExAyuhNEl3yddEWiayo+/6/t46OiwW0ktKye/nD4WX+uQe6buxNKn\nCaM2XuhRiFk6Q1BJ7lNTtBTkUoRCRpUVpUQSl0jgSttApeQcEjAsAUSSKstc3CI19blQdRRRJAga\n6Egtf9Uc+htsj+R4Ae6bvpufOvojVMwybuAy8AcpqKQpGYIRppIf+Xzgx16zRdZVjveTVNlON4b+\nIKORSHOTOxWSVBwjBEOmkh2oQEA+7hipkCH/sqDSiKdSeKNEbVR6mMgiR8HMBPhLJHDZlKkUJyyq\nKatisXGoqQy3XdCGlfkkcnqW9/9Pd/Pgm4fdYzKqAkLDDTx0Rbsp4PdqfPtifn5eBX4TeAdwBPjw\n/Pz8kW0f+1+AUwsLC8eAh4H/GNsVJPGGhYWF468CSsPonngOgLHvey/WocP0T59k5Xd/ByWTYeaf\n/DQirjBtbA742d94gr/+0rBBw8LVJo989Qq1coaf/eBx3vna3bRjgOEHHtrHWMniXa/dBcA9hyeY\nq+V5w52zGDGIcttcmd/51S+m3jQJqARQySt89u/+jKVVOYZuLn8RgOLU6xBCo3HtEzSvP4IhWnz9\nyjTLqzWurHeZ3FnG7nssLw0YqAcpFLcyP3dOFihmdU5cqPPpp65SzBm85Z4duDFw0xkpor3lnh28\n7tiNueeoR487AhQpiqA2ORyrE7DH2wYqeW5AGEa3ZCqZN+n+BqNMpSEQVyxbqcwmYSqNgkogfZUk\nUylIt53EROzHVHgF0jeQzKe98zUURaDpKqqmpPK3z1/9IlfqMtdPjmEilq2tLXdiryENTVdvylRq\nO23+w9P/heuLG2i6Qqma5cuLX+WZtRNc61ynUpPPx/K11pZ9vFQk3i72wKP/Cjq/JTF/+yQHb5sk\nF3dUV4XKVG6CmhUXjsI1XLPP+QWZY1UyZcpmkUHcNOiWTCVNoVeoExLSKzRSIO9q5zqfvPQ5ua2N\nOVRPZ6B3WNz7ApEaEhHhZQZbjj/xBwIJLqwP6vj6EMDpCJmj5goGru/x+OKTmCLD5PV57pqSHmGn\nG2e5HoNTx2q3kdOyXO0scq4p3/NbgUoQG/xrDoYTd63z2lskZ0mMgkqfv/pFnlySIEbCVBr1dcwb\nORa7y3TcLjOFu7FDA9O4PWX/jDKIROxHuthzeM++d6ApGh+/8Gm8wKNhb1LNlhifzOONyeswka3R\nGAGLnlt/If1/0vmtaBSYzm1dl45bY5TNIk2nxTOrJ7C0DBk1kxYUk3w2AZWK2zyVNE8+3z2vD2L4\nc/Iu2v4IqBTa8h6KiJJZlB3Lp+6kVJTXam2pQ8bSqJXLW5hDWS3LePxsJmyt2ZxcI/R9m4FvE6jJ\nODJ8bw5V9qMJNd3WAzP38RO3/3B6TxKm0suBSjuLkphwvjWUICbA3FimQkY1sQMHRVEQAnRfPsO+\nbuPE6zg1MFKAXREKs/lplnurqWXMdyJezfq+S+PEuUVUEbLvLg9RMxAiomVnObpvDNcL+G+fOs2j\nzy1uAWz+5PPnuLLS4cE7prnv8K3Bp9HoPv00Qtfp75+lcTiPmtcIL/S40vN4vC/IVo5S2/tB6mty\n4BufzFMqW7SbNn9y5q94Zu3ELbf9tbUWf3lplbOtoanvxqDBUldOKn5p2PbaU4cDaxQT6JwYTOrF\n2mGBRhT3RglEgGlpqPEgZCqCtufjBCHL1za58pcKWijpjmHYwWjLl8wt6FxtyklcBCp+0CABDsKw\nha9LuiVAqS6TI7ti0J3JErnyc7lcAU2dTo+3WN/H9CmL4oU2rUVZ6dBzOl5uOIB1EUQCbDtZEMYL\nvnA4kHZaNku7XqSfbyI0K/Y6iVjvXY513CED47K8XlmNztiAoqHhRxGr/TUEBn37MYJwjV6ujvBD\nQkNhdRASRgphaBOGm5h+Cd830LU9hKpPr1hH8w3U1gae2iCMNjHDOcobM4hQIdCS65+jTcxQcoes\nCxDcNV4kNJShfCjQUYO4mhY+x+6riyPMDJOKqTFmDgfqMNjknsk7eWBGMoWeWnmGXMGkqQxR95We\nfG5GmUqJYbQf9ghjXXolU0kX1rbvsGv/GNZ0nqgogUDX6pFnDFUZw6OP5pkUNicxlDyrLBIRxh29\nAtYHq4hIIDYttLh6stq7DIRkdJk9JXYRhinvrVBEym6BiPX+tfQcdhRmU9ZPL+mAJixiH3V8/zpO\n4DJujW0BCkbp1Ak1OjlHeR+UkX1Kf7CEdZL4MekxoDA2kWf/jqGcL9FvF8zhJHjP5J0o6nBxkHgp\n5Ua6Sszkp/hPD/27dGIGyOtZ8kYuBRQSb4GaNZZ+V4JK8hwmY5lgUnVLWqImpoNJ9bZYttC0acDl\nM5f/FNja3nV7jHa/SI49+XzTaeHF3lOKMFJAT1c0vNjgMiREICgY+ZS1M5mdQCA7zQFYyvD+wNAc\nM2ElhFGUVpKFMMnFCyE71srePx2DUErmZeVvWz2VtsrfgC3G3AkANcocS6SJeiyBy8RgkhUbCAuh\nE0Qu+Vg+MApYFbWRTmvxdJPXc1TGclvkFBlNRaDdYEj/anzH4l7g/MLCwsWFhQUX+FPgPds+EwGF\n+fl5AeSBBilH79UAiIKApd/6TRqf+RRRENB74QW0ahVzx06q73gnAIqVZe7nfh5r75BF/vz5DVw/\n5LHnFvHiOetjj18iAn7i3bdhmRrvfXAPDx+f4R337eTgDjk+ve3enfyjt8/zQ2+RjIy8pfPWe3ZS\nzOpMaCpRBEvXZIFrFFQqiTMcqi7QufqHdDeexW6fw8ztpDT1MJMHf5T8+L185doR/vOX7mZm1/cC\nsuxwrS3HKBM4vn8741WOZ3fsHaM78HDcgHe/djcZQ8NJ/GBa9g3f2R6joNKop5JhqlskW4lRtb/N\nqDv5f+qp9DJG3fZgq/zNzOgpm6lYzqCqUuLU3hzge+ENDB7DUHEGHoEfpibdSWQsnYfecZD7Hro1\neHCz8EOff/u1XwM9wO67+KHPCxun0OK24EOmkgStVpfauI6PaWqoqoKvO6ihjqEa6di+3Fvl6uYi\nvabH2EQeRREs9aTHy8agQTV+Ppaubm7Zx0vFKFMpYTjlXsaQHKAyluNN7z7MhiutOqayEyhCoWJW\n0s9EMx3WrnURgUrVLFMyigg/9hq8lVG3puBYMs9zzX7K3Pjs5Uf52oo0yre6ZfKtCSIR0a4Ou5rZ\nsT9lcvxbQCW3y9pgg0AbDnfdqJ1+vjW2RM/vM9HbiRKqzO/bya7SLOc2L3KxJeV9c/lZdhRm2RjU\nOdM8R8HIM5G90ec2iZbbxldc9Bgc6vgdPPPG92fUU+nFjdOpR1ICKo0WbPJ6fuiNpMqcVFEKROJG\n034lZiot9h3GrCrfM30PTWeTs5sXsAObSqbMe3/oOOKIvIcVs7zF6HuhIVnam06Lc5sSRCuZxZuA\nSlXKZomBP2DTaXFn7Shv2vm69O+JyXYiRRtlKg2qJht3SsAlAWg030CIIUPRDpy0QYqLgx+zvUab\nwGRHgNBqLY+iKFRGCpEZzaRiyc87gRv7eMrzGPgDBr6NW1TpT1pbAM+MluFAZR9e6PP+A9/Hh+e/\nf0t3t41XCCodquzfck1h6Mc0ZslOck7s/6RqSsrW8nUHO2Ha+9qWsfBAZR9hFHJi/TtnQf0tlb+9\nGt9crK9fY6mpsrfaxDQjVq98DgWwcuMYusqXTixJff2JZb5+epV7Dk/y4sU6z53bYOdEnn/41oNb\nFqED32alt8ae0lZPJX9zE3dlmeztd3Dm3bcRrT/BLPCXMyrd3Xup200+uOOdKKrOxloXTVMoVbKU\nqhb19R6aZ/LnZ/+Gg5V9KSq+5TxiOVp9xED79178Q1a7dfaJhwgK40RRhBAQKLEfkJYjiIGLri0X\n3QlTCaHFUiPZyS2T0VGz8ucp0+DKwGHDdrlwao1QhBBPvEHYRXSqCD/EKxostlcggkHZRaZVBuAS\nhm08VSeMtbql+gyRCPEtle5sjvxSzKbKVgGFIKijqmNYzhTQo3S5QyO+7LVKlma8qI8iBxQTL6vh\n2gGUhgtCEUXpwqzeatGYvAqRwBAWxIvZKGoBEVHk4Ik6FuDlFXqZFjOKghdGXOssEqETBHIyWKo8\nSdGeJMzmWR2oCCEIo03AJx9N4kYRur4X13uRzfFF8oN59GaTTk4i5fneHgzXxOqW6RflQJcYmgOU\nrnr0d8ZG5gKOVwt8TlcgBpWUQEVFTmpursvqxAkUV94PRZiUDSl/SyKM2hwZu5dNu82j1x9nqbuC\nE7hbqJzLfQkq9beASs34GtmpvG7cqqRMJSdwyFg6O968h4XFOnQEiIjp6DaWY+ZUotcuKDPUw7MM\nci1CxScKQ9rOGlm3SOjF+mXgclt64WjqND5gBxFhZJMxpVV40ko0PjOud4eTxlx+BiWu0iQVJMn6\nSphKq0BEzRrD0m/erjgBZMxtvjljmWo6CctJO7lOQ6bS6GeTqFlyIVEyZTI0mZ2klp0gFEMfjISl\nM8pUKhqFG7wDPnDwPQTR0JNsb2k3P33nT7KzMIuuaBiqQc/rpd+bjjvXJcd9KU7WduRlcqTrKsfu\n3UG1luW0U6AZbrLYlZ+pvhSoZAwTqgRgSjp3LHaX8cIEVDLxY6d7TdHwY0DED32ymsWHDr4v3c5U\nboJffvD/JKdn+YWv/Ao5Jcf1wZBOn9yX5JJEDL3dhDDIxxVWEXu42X7sl4XJTPalk/atTKUb5W+j\nAGQCQI0CTdn4vqVMpRhMspJ2zaFGFPWwUinfCKikDxM1NZBdH0evb3oMMVMpCH3CKHyVqfSdj1ng\n2sjP14H7tn3mN5ANVJaAAvChhYWF5AWOgM/Pz88HwG8vLCz8zkvtrFLJviI50DcbtdqNOca3Izrn\nztN9+ut0n/464ZWLhP0eEw89yMREkah2P3njfyW3exfWzFaGztlFOb/0bJ+zy112TRU4c3WT4wdq\nfM/xIaj/cz+81YNpeqrEB6ZKW373ke8/yke+/yif/MtvAODaAVmjSTE4gaZG+IFgPNvjucUJ7pxd\no3HtEwDsOvy9FKpFoMjjzQyfPfU0D905x313zPD8Z86Ry+qcWW5zIHZWe+N9u6mN3/huP3B8jide\nXGGymuUH3jyPrimEsTTKHvgve298bzgnmHF3tcALyVgGe/aP8+KziwgBB+YngRdQFIVarcBFYz39\nXiFv0onZzZVKdss+S0U5/kVR/JzE4PfsbCUFFCpjOVaX2kzNlKjVChRLGdZW5KK2vG172bxJPTa4\nLhQzN5zfQ2+ef8nzHY3kuxu9Bqv9dQpKh9AJOTc4y8C3yfrynCYni9RqBcbHZZe4hF1UKMn9+4aD\nGcj/d1S5YHWwMfsFiAQ7dlUpVTPpXDpQety2f5wvf/YcK/GzWJsovOy9Gp+Qf9dUFdmiGKZnSy/7\nveTvp5+UUsd7dh6lViuwsz6Z9v1WZwaElyDfHmdnbQr0gIu+PN6x8dxN91EqWzgZmdu5Zp+calCr\nFRhEQ9ZLzRxHLCu0xhdRApX95T2c7ZynozeBacbG81THsix2lxFCSNsP3aXpNonUIag0oE+tVmBy\nusiGfxkiQebcFCIbcGB+gmPObVxpLXKmeY5ypsj+uRkO1vdwpnmOntfn/h13MTFxI7M3ieWV6wSa\nh+HGBVelnzKVVKGmHYN9vPRa+HgIBJrQ0sLmqK/UnulJnm0+C6hsODqKkDamNtvBLQHoCGDFdqnV\nCsy3dvHlxa+yGcbMx3KNmdkKwcmYuTNW48nVr5PTLXregOX+CmPjOX7tc/+FS005teyZmsb2q3Ba\n5r1FM8/s1BiTV8Y40zwHwFsOPcDeyk4evf44fW+Absr3exD2MTWTuSn5PEdRxGA6S5ixEB0l9YlU\nUCiWrfTaqssRCFACDV9xUxP72epEet2mZ4Zj6I5dFXlfi2OsDeQaqVzMoTiJj2/EuFWhVqrAMph5\nQaQFdPZUiXJVSpMFyiPKivt2HeN04yzVUv6G+91wm1StMrNTVV4qahSYzk9woXWJ6lgWVVHpX5XP\n+f7pOfJLWdYGG9RqBXRdxe7FLMysx6mWBE5VX2eiXEnP+V2Zh/nslUf5+sYzfO8dr9+6v2/T/Pkq\nqPRdGE9+5TFgln2dVYTQUfy4ej8lE5EvPr+EEHD7njFeuFjnTFyFmB7L8o+//w6MbVWcz1z+Ap+/\n+kX+9f0/T7EboNdqCCHoL5wGIHvoMLY34Khh0AkDroYhxwpz1O0mdbtJ1RxjkZDqziJeFGFWLQZj\nJma4k553jj9d+Gt+4vYfvmGBuRi3uV1q9mGqQhAGXO8uE0YhnWoTrB0EwSq6Vk1BpUqmQj2IqcnO\nQA4yMVor0LCDuPOXEneLMAUQ0WldB6PG+sDl6oU6/UITocbJUdiDMELverglg5XNJllnB33rGlo0\nhjAm8LzTEpQQgkALMPt5rEGRbmkTRcvg5XQiINBcnrY/j6YfixdhDxCaGvXdAcV1E72XtIA1IWaI\neN4lDOMQXsHAi820v3T9i0Tqu6hUs1RjivLaYB2KIDMik0gzIIDxTIZuJLubRTio+AwqoIY7WBs4\nBFHE6cY54BAgF8WBMqArvkSGt9MLbFTFgLhjV06fwVcFqjKBYedpV1bI9/ajdrt43nmEyJNrVwGH\nXKeagkpJWE6AMYgrg/5FXj9zSLIUTJVAjUGlUEUQU1ZVk6XeCpYu2ScVI4uqCMZGKlMZxWMiW8MP\n5fdDQl7cOLWlSrLSW8MLQ9wwafuewfYWmCvtZMUbEMVSuKJRxNSG8jeAMHTp259Pfbu0sEQYxayy\nmFaaY5I6Z+mWNmLGVUAY+eQ9CV6k7WrdFiBQ1Un8UIJcEtRKWC6MyEIj6oMVLE0CV5PZ2g0t6SXb\nK0m65fmPW2NbQIPRSLpX3AAqWVWImcphJD2hAETKVBq+n3k9l+rHE6ZSySiTMe7lbbuP0nEDFGUs\nNrJ2bip/uxmQfPv44Rt+N9oJJa/n6Hp9NEVHEQoFPUdOy1IfNHAClwubl9hT3rEFtHjtG+X3x150\nccO3M65+iYutiykYdrPIj8jfEqbS7eOH+ZsLj/DM6gmcwAZUItT0mZOG5n7suRRg6RZ3TtyxZbvJ\ntv7p8R/HdyJ+5elfjw3ph9XFln2dIAiJoj0jTCWDXCxxE0KOb2s9mdz82KF97C1u7dq0PUztZkyl\nUVDpRqbS6O+S/xux11oCJiWG3UEstdVjUHgUVCobQ/BODwwJKuk3Ljx3FxLJLnihv0We+Wp818bb\ngOeBNwL7gM/Nz89/eWFhoQ08uLCwsDg/Pz8R//7MwsLCl261oeZIS/j/v6NWK7C+/p0xfm8+JX2J\nhK7TeErKUNT524bHc+B2ukB35Pg8P+Ab59Yp5gzaPZdPfvkCExW5kHz90elbnsutzrO9+gS9xgus\nLx8HoLO5ypmn/hyikPHscVY6eb58aRcfeuvd/OWTj/K+O86RK+3BDiaw1zuEYcQffPIUihC8/d45\n1lYlyLB7psjF8xuAoGzqaFF40/3vmcxx/5FJXndshs2mnGf7sdl0Y6P7svdmtD17I84LbdujUMqQ\niRnd+WKGXtwoo9d1WF/v0GwMWe7Lyy3qdbnosl1vyz6T4mS3I7/XbsmFeqdn04+Lm1ZslaCbKuvr\nHYxRuZXClu2N4uFr/Q1+/lO/zD+/8yfRbzEn3ypG7+dSVy7cPc1hZXaB00/JyTrJPxzPTz9bm8xz\n5UJDFp4ErKxu4qsu2UGR9fUOg35srtxrY8VNLKyixotXLqa5x9X6MvfuijusNeS7GYTy/q711/nN\n53+PDx/6gbTVexKZ+Do9+9QVLjjn0BlLv/dy5/n8+ous9xsIBA9NvF5eZz/28REqG2KNKXaTa1fR\nvAxGaCFClV6+gesFN92HbXuprYJrDvAc+blGTwIOeSNHfkynXy+zY/k21FaOO942zdnOeda9NWpM\no+iCF69cxAlc9hR3cal9hdXNBsudtbSIDNC2e6yvdwimOjj1DuXNaUwnz+bUdVbXWhyfOszHz3wW\ngJmcfI/H1WEestPa+ZLX6czSZQLNQ48LrC2vLe0ukEzqhOUycG2evXiGolGg48h7l1EslDgHTbyu\nVKHSbrqst5po6gxeCHePWTy9sUndkR47iVxeYCKEYC6X4VrP5vLSJron7825NWnkn0PeRzcu5Ps2\nNAab7CzM0fOu0nI6/NVzn00BJYCgp2KKfAqKVc0q6+sdMpHMacpmiXEm6W56vH//9/EHp/+M9VZT\nvt/9FgUtt+WaeSUDIQSqsPCR73+zssTDD38o/VyjLccvLS5yJb5Ummemnxktblp5g/X1DjkxzFkb\n7Q7F/LAYVzJK4MoXf2mjwWavg59RUIHTi032l4Y52m5TshS/evl5jhWPD4899Kn3m+wr735F89W+\n0l4e7z7J0xdPs7e0i8WmLJyLgYkaagRhwNJqE6EICSoPIYiGAAAgAElEQVRFAifn4XhxU4FAx1D1\ndF8qFgfL+zi5dpaTVy6l9hLfivnzViDVq6XE77JoPP1pFhoyGV++/42cUh9I/7Z7bgdXVztcWm5z\ndO8Y//wDR/ln7z/KD7/1IP/+I/fzSz9+HxNl64Ztrg02iKKQ5sc/xuV/+fM0PvFxAPpnJKhkzR9i\n3F7HEhFnXEEE7Iid5DcGdZ691mT9jioLe3L8m2cv8NlsyMbxcbTx17OvtJcT6y+ysM28FmDTjw25\nezKhWB9spNrVzck6CIHnXyQIHQIhB49xYzKt5PuRx+9+7qOsx+ZlCI3TjZMAuPoA09IJNDlp9uoS\nub3a6NFpO/QKdZSE/hkvwo2OC0Iw6OXRIym7MdiFIuRCMZFnhVpIuSHPv11dJxR9UAVhBgZWmyBy\ncb1TEMv43GxIa1awdtc4WkyxNi0Nv6hDFBHF8ie3qOPGRtRrg1Vc7wWmZ4opGFf36vF5IrOaeNG4\nuzgZH58c6At6iKLk0LU5nDAiigKaziYRSeKmoKrTeOESQbhEGHaTOyLvtzFDpAqEEFTWdxEpEf3c\nMoN8C/Ax9MNo/djMrp1QOAUiiBekGzaFTGywHiyyM2ez3q8TahAqsQQo1FAMOQgn9zyKbAQm1Zih\nND4if5vLy8RoVF71d1e/nNJkhcjTsJs0neHCJWEqTWUuUbPG0vuX0YbdrxzfwfZtHr36R/j+ZRIc\nvRPWUw8mPYzlQsE4RNAtbsTUWnmvir6sOCjBEKzV1HHCuGtWPwgRkU0QT95RFKXnnMTDcw/ws3f/\nY1RFZTyztYJharlUqpbEdvnbqC4+Acq2d/ga3W4Q+iixP45gq/xNXjuRMncSmq6pqpjmMaZyO2h5\nPkIIalnJbszHYEr+ZUCll4ucnqXnS/mbqcokZ9waoz5ocLZ5Hj8KODa93fZFxpRlYqgGH7njR/nI\nHf+IuyZv3cthFJRKALHp3CQ7CrOcaizQcTelH1YY4cf3KuksGUQBfuTfAP6NxmRugulyDc0fAidO\n6BJGISfX/pqB/XgsfxsylRJPpQRUWu0mFPM8LxcJUOQG3hBUUl9G/jbyfCSeSskzkHZ/S1plJ8+y\n10MRCtqIP1cpM6z8G7GkNXcTUGnKMsjE4FfHfXlJzKvxLY9FYMfIz3OknIE0fhT46MLCQrSwsHAe\nuISsTrCwsLAY/7sG/DVSTvc/XAzOSw+1uZ/7efTJKdRCAWv+0Et+5+y1Fq4fcv+RSQ7tLHPm6iZf\neXGFWjnD0X03l0UMWue48Px/I9zW+cmz62wuP4pnr1EtyvynVjkNUUh59m3kC5Lt+T1HD3DPoQky\n5dv4j4/dw1Or93H+eouvnlzhV/74WZbrfR48OsVkJZt6E+2cKlCMvYGqLyGNMnWVj3zfbRzeNZQy\nJWbY3Y7zkt6aURRt9VRyJHDvOgGGoVEZz5HNG0zPlVIpR+qp5AUj3wtu6akkzbW1LUbdmq6gqsM5\nr1iKO5HG4F5iCgxDyVcShjEc++v+BpfbV7d4y3wzYceSa0+z6Rca+JFkw9Y0mYu6qvz7Y9eeYMNY\nZnNskVN3fwYn05XmwoLU1zEZzwfBIO2MStFluTeUftUHDayskYJpMPRUOtU4y4bd4OMXPn3DvZuY\nLjAxXeDq+QZeI54nYouG0/Wz/PSj/ztX2tfYHptOiz8+9ReA7AiWFMWqcZ5RMApsuBsEqofmmVL+\nZhZxrA6XjjzJcnT9hm0C+IqXmpRHakCgyTy4Hfvx6IqOPhZbVlzbRb49zp2x/1GbGHgqmKn07bYx\nyTJr2Jt4oUckQkSYyKrkPbjQldKuXfvHuHDkCa7PnuRC6zLz4/vSZkFTWVkkTbqNwUv7KQEs91a2\ngEoDegQZuc+kUCMQ2L7Drz39G/zRmb9IJVAlK8/7f1AOwUl+mdFkDtXxemia9GK7c7yC518hJENm\nRLqeNNfZV0wkcHZqCbAa+3vOxMzxfmzN0HW7hFHIuFVNi0R/df5v022qQpF+SZrJPzj0foDUDqFs\nSqbQayaPp6zlY7XbANh025Jc4A2I9Id4el3ep74f4MaeXqoY5pqB4eDlhgBzkgProSxyJX5j5RH5\nW64wfL/HJuS1HbVMsH0nLT6DfE6HBvgDbN9GUeQxrA62jsm17BhT2QkWGue2+Bc1Bo2Y9fTS0rck\n5mMJ3NnmeaIoYn1Qx9IssrqV2ls4voOiCpRQRfMNAnUQrwUVBmO5G+S5r52Rz0hiJv7tjldBpe+i\n6J18kcbCo1ysVyhlBeu6wpdXS0lHdszsOF88IeVADx2fRQjB8f3jvOGuOaaq2RuYQkm07Tb3v9Aj\n+rwsMjY//1lCx2Fw5gyKZdFXT7MvWmEzKvCUAxoaak8Oeht2g3pc9ZlAYW/BYoeuoPU8EAoPzb0B\ngHObFzl1Yol63MUjCCPs+Olqx3KlpdgTB6BvLRFFEWUjIop8CdwA+dZUKteJFJ/uaYO1fiy/QqPn\nyYHFyXbJWBquEiH8EBHr+q9vyu10K+spqKTGiYblxS3W3VncbGLUW0s/l4A2oRJSqs8QKD7t0ipO\nIBFeP6NgF+PJLepRXhuHMKJjnaXvfQ5P7aReAF/UPsUaf4c2CMitaRBGuAU9pewC2M4zDDyZqHiu\nT1dpD29a5KZV/7n8JEIoEB/fXWMmjnsCxz0ZfzjuDBclvlQhuirBgDBsSfkdEIVthMiiZUuEMVOh\nvD6DEqh0itfpltcQoYLrXiDy5Dlnu2VAQRFZFE/uX1lpUckPJ6ue1+f3T/0JtmqnnkoiUBGGHNQT\nn5ogtBHCpBqDSRlNRRPyb0cq00RRlHq/GIrOlc4wcVHiCeZqZy39XWIW3na6FA2TZOVrqgaaoqEp\nGoPA5sT6SVrOOrq2n5K5G4AO66lxeIYYvHFVsm6Jfn6TQPGJ8AGFaU9+R4TDwTurzxJGETGmiaBH\nkDCVItLqUBL3Tt2d6s4VRdkiDapZZbbbmNSs6hagYDTxu5lRN8B4djiR+VGAeAn5G8De0i5KRkEy\nnAA9ad0bhmkXvn1lCfDMxsnGKJhQNG9N875V5LQsbuDS83rp8Y9bVfwo4KuxGeWxqZuDSu/cWeN/\nPryDgmFyrHb7S7ZrTTq1qULdch3vnbyTMAoZ+H2EMPCjMDXqTpJFL/QJwmALsHKzUBQFLRgmJY7v\n0nY7hJFPGHWIGHbnEZhp97fkGV/rSRD5ZgDN9kjksk7gpPI3/RXK3xShpECTET8DWXUruCSE3Fbb\n7WAoxpa5xNT1dDFjhfJYb8ZUEkJQNeU+TzZbN/z91fi2x9eBA/Pz83ti8+0fRErdRuMq8CaA+fn5\nSWAeuDg/P5+bn58vxL/PIbv0fueMGr5DEUURg3Nn0SpVMvv2s+v/+kV2/eK/Q9FfmoX3wkX5bt+x\nd4zXH5cFKj+IeNNdcyjKLfK0ta+yuXaS7sbTW/bfvP5piEKEYjI7vcjU5DpTEytomQmM0t0s1m2K\nOYN3P7AHgPe9bg9OYPDRL1/l3//RM/w/f3uK84st7tg7xve/XrI+E68iw9R478P7CBma97+SCMMo\n3UYYRPS77i0/m5hsJ+CG5wbpd3VTRVUVfvDH7+Whd0jbBk1TUvDIdUdBJR/PvbmnUnIuo6DSdlPq\nO14zy2se2MXsLrmwHO1ott1rKPFfAujHDOgkj/lmI2Hce7l+7NkoF7nJmNoMG/ihz99e/DQng2/Q\nmLhKpIZsaMtp+3bV2Tq2276D1SsSipC2UWd5JMde62/wyKXPYYwPC1yZrJwzV3sSRLjSuZZ64yQh\nhODIXXK+z/bKREQEcVOJZ9dO4EcBz9/Es+WplWfpBzIHHfXYSRbyybzUz2+i+UZq1J1445zoPX/T\n69ZiK5hnaz2CMEgBIMd3CSvDvFpRBOWsbFwy0LocvmeC/YcnUlBpvnoARQylVTDsqhZGIbbv0IgZ\n8h23yyDfIlIDXtg4ha7qlDMyt7Z0KS+TBcAMOT2b+mXeKpZ7q4SajxaDSraQ8rfR+TSrWzihix8F\nXNy8zMAfEBGR07PMTFdRhJLK5JK8oOv20LWdZDWF3YUsRtLQRwxBYCEymArM5eS+l3oOlRj4Sa7F\nTG6KMApTwG4xBikrZjllhodRyL6SHGsMdZgrlMxCej0A7po8ysNzD/DmnQ+lx5DRMpiqQctpS+BK\nFPEZ54vLTaIo4npvWIyKlK3s7dX+Ol4Ycq1rp2x9HZNQDXDNfnwMMie90hmgx++/EFAZT0CloSRO\nFjaH7301U0lzs4E/oB946RpjZXDj+Hbb+CHc0OPc5oX0d4n0tPYKQaWDZTkeLzTO89z6C6z219lb\nkuCgmTYbshEqiEhFCy2iaIAmHIQwaR6u4m8DlY7XbierWTy5/DRBuK1t77chXgWVvkticOE8S7/5\nn7lSmsb2NSZq8uEfBILLvdshdxzHEzx5coVy3uCOfS+t1xyNiRcWue/FPmG1RPHB1xP2ejQe+QTe\n+hrmO3bSqz9DPSrwseBN9MIQYetcfV4OKvVBg17cAeKOQpYfPzTHw5NgbsqXbCwrJ48ra0t8/rEL\nfPkx+YJtul5qLtKPF/uJ0bIVZQnUAWFY52BlLxEeYdRD9XTc1QzETKVI+GQGBdzBcHHsxgi1m+lj\nZnScKELxQqxWBk1ENFyPQPEZWG1UUSKKAjRHHseYGfvWlMdxVQlUKeo4CsM26QC6m8FwsnQrTULN\nIUza2mc07Pxw0As0F8WxccLTQIDvXoJI6r6Xo0W8YAmt61KuT6H3fby8ziCW+U3lZoCAUxufkYh9\ny0nNCEGCVgkYkLTmTsCh+fIUtvMUnn81/n2c3EXd9PvEg2FiXh1FIW7QQ1UKhKZKcVb+feI1UG7O\n4us2nmlT2JxEVzppRwwVFct8kLHca9HWr9LtfwLhtBgvDQfnE+svcqV9DdVQ02RJCVWUjBwUE5aN\nHzoIkaEywlASUYcoCsnrET/92L/k15/7HcmU2AYYCCU2nOzKSc5zX8Bxn0NXdDpOF4XhoJ8wODKq\nNLo71ZAaf9M4xjv3vRsRKgyUjRRUsmLzwtDzyQ3GQYlSSZOuH0BLGDnKENgpZnYQRsMuFmq0RhAn\nnRFRatoMUDbHUhpqEqMskvfv2UXEVhAqSVSSGGUqJZWr0SoLwNHxIzw8J5mNfuijxKBSYm6vKVuH\n+w8efC+/cP/Pp+CMGS92vDCiHbd7Pl47xn943b9mZ0FKb7Mj4MU3y1QCaLmddNJMkpBvbJzCVA3m\nx25e7cvpKlMv4z2UxJBZldsCkNw9eWd6zxKmUmLUbajyOvihjx++NFMpCSMaHo8buqlcM4oG+GGQ\ngkoIHVNV+N4d49w5Xk33M9op76ViKH9zU5ndrYy6kwpXYuZqaZn0GhjbmUrbQKWu17tBdqlqSlpZ\nzYY3MtZGY9KSvz/Z2Lzp31+Nb18sLCz4wD8BPgOcBv58YWHh5Pz8/E/Nz8//VPyxfwu8dn5+/gXg\n74D/bWFhYQOYBB6fn58/ATwFfHJhYeHT3/6z+M6Gt7pK0OlgHZCAh2IYaIWXB9NPXmpgaAoHd5S4\n+2CNvKVj6ioPHp2+6efD0MPpySJKe+1Jonj+GLTPYncukCnsQcm/DSHgrmOnZTeg/Pfw5OlVBo7P\nw8dnUtuD6bEc//pH7+EfvvUgb79vJ+9+7W5+5Sfv52c+eIxizM5JDK91XeW+I1OUiia+88pBE8/d\n+tnOS5h1JyylQkmOIa4bpCynhBFkZrTUj0vT1fT4vG2g0q2YSiA7Q7mxebhjezd0bSuWLe553R6U\nxFduhKl0s+5v6bkhc6i/bwMCO54LChVjy+803yAiYs1b5WLrMnbg0M816RckmFJX1tP27YpjEEUR\nqqJKaVPgkelLts/yYCXNjyascVpum09e+hzLlaGKwNfkOaz2h8W5z1157IZjXS1extPl8fq6w7Wu\nBGQutC4DcDH+dzRGQZpR1nTRKKAKNW0q1Cs00APJOi4ZxTRnXOic3bKNdLuxb2bRlwBJX+3S84cg\nUs/vURfr6fEapvQQLRoFEKAf6mCYGmebF1AQzOVnyOu51CQaYEd1+F623Q5Np4lAsD7YoGQUMFWD\nFzeksmMyfyeaOstEdjcgizav3/E+Htrxvpf0EYyiiOXeGpZloIYaIlKwtT6OPqCaKePFjBdtxLvU\nDpzUpDunS+KApWWGoEqco3Q8A0XJcaicQxGCoj4gihxCMTwvoWTRlYjZnMwPrvdsLM3CVA363oCM\nmqFslmjamykYsdiRJIZKpsyuuFtZVrP40Px703NPIpHutf1Jfn9hkYxq8YGD70ktA5IomUXaToe2\n20FRZA5ZdzzWbY+r3djr0gthWyOUtf46X1nd5L+evkYn9jc1Y//dRB5ZMktc7Q747TPXeaLeJpsz\nqI7n0vFid3HoK+wE7haJXDVTxopzsb43wB4Z4rYzlQDuGJNWDyfWF3h2o00QM43g5U26k8gbOWbz\nc1wfTPBnZ7+Armi8/8C7AbY0GxJKhAgVhMgCIV7YIYNJpCusWVuLFLqqc8/UXbTdDmdHAK9vV7zq\nqfRdElf/5L/zham7eOakpMNdWOvAcpvQDfkDEsqeZBq9+e4dqMorwwOjKGLfwoBQKFz/oXczO1ak\n/cKTNB75BOodRaJpD92a5OOde3GwiPBQgyzOmgrTEnk1ey6YuvQJAmylh/CTTkcmY5kqS80Wzl2L\nNDsO74killvDbm6uKvDDMK2i7PUPc1J/Bs+/wpHqwzxXXyCMemTcLJsbPmKXXJhE+EQi1g+rAHq6\noPKMAYoZ4gwiCD3y3SoDx2Og6dR2D0CAUHNEkYMeV9knsxnOBCFOySDobqCIIugZdDtZIMkBxurK\npLFX7hIKlwA5gXlZAzduUUoEncoqeriQyq684CowQaecTNg+ar+H1S/Sb7t4+Ry2JSeOuybu5bPX\nnmPTviJNtltDM0Ig9vuRA+dqb5UomiKKXIpGIW5hrxOFWzWyQdhCCIMocmOfHgk06UIninpERJha\nkbbroyshUeQzvtegdn4vjXEJUBWbs0zty9KJ6aRWzuDHjjyMBvzx2QsEwTKhupvxchniwz0ZgzaV\nTJ51LQGVNLRMBoY4GREhumpyYESbHHhPI7C43K4RRAGX2tKEOTE4TkKJtdArvTWiqETf+ToQUtCL\ntJ0ugqGhZwJWmKrJwLc50zhHVi+gKBUyapaiX6Wl1xGhg+qZZDI6PhD6IfnuGOvlC2lfeEO/A9+U\n71rCwrI0i2pmlk4vICBCEwJNadCLPHRiplKQ6NgzHJ+4n+2R07PpOea2Vb01RaNkFtP2ovI+Die/\nW8nfNEXjgZn7eOz6ExKwELF0MAaVjG0V8oTNlYQRs1ecIKQVLxqKhkZuxDBcVVQszWLgD75JUGlY\nkctsA5UiIg5W9qOpf/9pKan83ZjQFDhUPcDpxlkEElS6kank4UcB6sswlQCyYY4EPnECl2aaFEey\nypnK30wU4MGpCk+vDgHZrGa9IkNrc0TOmVTMR5laW5lKW0GlUSBQTz2Vhs+EIkAVw2fQ2MaA03U1\nXrh0mQ524eTqzG/z4UiiYMh9Xul26Xg+Bf3VFOM7GQsLC48Aj2z73W+N/H8JyULa/r2LwK31pf+D\nxOCcnNusAzd/3m8WjbbN4kaPo/vG0OP37Gc+eIwgjMjeosOV070CUYCiZQj9Hr3GCczCHprXPgUo\nVObezrkFj/56hYlak+ZmgagwyxeeuYiqCB46Prtle7O1PLO1W8tqU6ZQXOXOFkzWlzuxN9HNmVRb\njtfeBiq1baYoveRni6UMa0sdPNfHSbrgmjeOsbo+ZCrd2P1N/px0gRoNw1Tx3IAgCHGd4JYt6pOw\nRuVv2a1j3uhxBYo81r8vUylh1gQZByJQQ4263cQJHQI1YKm/QS+QCdN0pZZ2Qt0IV1MARHNNgiBE\n01QyWoZ+30GJVOxsm8XugI1Bg7yW25I/9fTNtGzaDOtMUWW1v07FLDNmVTjVWGCxu5x2XPVCn89d\nfwxzcpLa9f14hs2l1lV2FuZSmdSV9rUbCi8tZ5iPji6qZQe4UjoX9gsNJtYl06VkFtO27SEhTyx+\njXfu3TocNYO4I5Y9TTvfpK900lb0SZzdPE+lICg1ZlJAcDo3SdPZ5MT6SULk+kMVCm7gktUsVvpr\ncXMOnz0Ts5y/Kk2l226Hpr1JwcjTdjvcPnYYTVF5fv1Fltor6NoMuez3pvYHAN/YLGEogne+xP1v\nuW2ZN+WyhIBuW9iZLoiIaqbC5da1+PrfHLxMmm1YmkV9IK+JGl9/O6qhAofL8p0vmwUavTpipIOv\nInJoIqSoa+Q0laW+gxCCsllitb/OjpxUv6z0h7l0wlSqZsopW/4tu96QFgdHGfQb8TFd6lp0/T4n\nmz2Ojd3EeN0ostbfYNNpyTVYHKc3uylTKbvcpz8p9yG9Lj1W+us4Ql4bO9DQFY1MXBB2rC6mMDFV\ng+s9mZGd3ezxgx86mnYLBNlV+Bfu+xf84td+FSdw6MUet7p2iIox7Bo98G3cUKR3eG3gEkZR2tkX\nZBMaS7N4sVXmxc4qgiGwtjwo8bdX1njXztrLjqe13DG6/RlcL8O79t6Zdg8cMpUcIiVECZXUJziM\nQrJaBh/w1Ru3/5adD9F1u0xYt+5E+K2KV5lK3wXhNhr8sb+frxsHKWUc9o71mN9RIXRDjILBa+Zr\n3HWwxqGdZQ7vqvDGu2ZffqNxtFaXqNU3WZnZhabVaa5/msw/mEN/8zjag2MoIkNmx/txyMeMFx8l\n0sFRMRWT+qBBvxfLbeLKT9vrpuydvh+wsziH7ynY3rM0rTN024405wYS7V7bDVjprWGqBlPdXRAp\neP4VxqwyB8ozgI/mZQiFSKvmET6hpshObmxlKiFgKVjCjwSuvolrDFA2PCJVsDy5AijSuyQYgkoT\n1RLqYEAYtQEXQ4wTaQqaN0z0cqog15YgXr9kg4iwTAWiSBpt6z1AwepVcTN9+uGJeF95gmiN9anz\ntCrL6fZEPNFqfTmh2jl53SqZcXRNMjLOb15ic7MnKZxh3I487KZMpS9c+wSu+w0iXHYXd9LzA4Qw\nCKM+UvKVfKeDrlTia6egCo0w7KIoxdRXKaeX6PkBTiDbzme1LCVRpbw+S7E+jeFa7CzMpRrlbM7g\nSCXPwUoeLYq7tig+lbEcysjwccf4YbKaSRB3jxKhim7eyMC4ozqe0m+DMKDlXGXM7LMc+1P9wv3/\ngoKRx1QNCkY+3UdS0diw1/H8a0hD6whV+X/Ze/NwSbO7vu9zzrvWXnX3Xm7v3dPL7IskJI12CQQI\n2UI2SAITmTWKbQixMQk8dhJjhyQkgZAHiLFxMF5kYxkhJCTbYmQktM2iGU1rpmem973vXnvVu578\ncc77VtW9t3taYhnpcf/+6b63blW99db7nvM73/NdbNphl3YwmggzBodve7TCNt2ox+7KAYQQxAqm\n1SwIhVJ93KCUN5BplOJ36ogx7yRLVgjN48pchz/1wI9Tdg27ScGhahFPytxTKVXkKWK2s497Zh/e\nch4ymq4t7AmjaNANmRRyAgwY31G5WfqbPl59rHE6kr+NmEq3ntwyeVyYKtpGKlpztzbmJaeo0/K2\nSQB7uRo3+s7o8LOF0a7m8akjX/drblc3A5UAXrXwIKC9jeJUEedMpSyVbXIX8FZ1eHgve1/U32+Q\nhBPG8q2wPZH+ljUX42Bgyb21QXdWtrCQQhIkIVEa4kpnolkp3sKou2iP3qPuOkig7mam4YJHZmvs\nG2NfbJZVWrZk4dJRDn3tUWbkLD/36p/OFyCbK7uGlYo5ud7d9m/u1J36VqnBab3QLBy+/XHp6dPa\nK+3E/tG4tn9HlUO7tgddAIZtLUHac/QvgbBoLf0Jy6d/myRqU9/5Fhx/luZan+deOMQwnOX5Fw5y\n9mKTKytdHjwyS+M2It/HKwNrMhlZqeySpopB//bYOJnMLGMf3R5TqWCemxCY5zuujVKKxy59No/W\ntl0rP75xUCl4GaaSa/rTblsfi1fYHsDrRwN+8fFf5no8shfbzFQa91TKQI8/PVNJzytt0USkgoc8\n7X2y5iyROhHXujd4bvUFHOlQsEzISb9MM21yo6c3Ku3II45SNlZ7RB2ddpc6Eclik0udK6wN13Et\nl66xiZBC0pWd/HMsDZYZxkOaQYv54ixv3/MmAB67/Ln8OJ9bPUUzaHH0vnlcXwNWX7h+jl94/J/p\nYxAWURpzuTNK5gVoh+2cBbxZ/tPw63SiLjOFKQbFNiKyDYApSe2YQreOb3n80eXPcq1zfeK56yYd\nrtbRqoiu6LJmLDEyVk836jGo6g0d1wCl2Rz1wsZp/s2LH9XnQKX8s+f+Fd1Ir08entMmyxW3nM/3\nzaDFRtDK582d5QXuntGS/CevnaRn7DwGSaYQUAzihCAZ9WjNoMU/evz/4oX10/nvsk31mVk917pB\nIQ+OaXj6/FjCynuG/dW9E+ehbI6naBdy5roUgiiJEdZeBBFHzIZtzauSqj5SVBF5D11CECGEYL7g\n0gxjgiTNe7I5I28bZ7Flm/gNr858SQMUURLmoN64zcPqYA0pG3QN9vr4yvYS+Kz3vdq9nvf1AM9v\n9LjcHVJ3LA5UC7nlRd2r4kqH5f4KHdOXhqmFZ3n45j5RMqVk6V7vRl/fZ9f6AYWpAvWpyR6rajws\ngySgZ64D1zmIbVXznrsX9YjUaEwIU8VGMHn/W9JiV/V1IDVw91Krb0AlyVNrCV9cbvHM2subYyeG\nTeY5i7xx18hDeTzBWskUkKRj/WzmGyq3Ac8bfp2/fvcHmC40tjz25113QKVvgnr8j5/hSmGeA/UN\nfuK1T/OuNz1E20yMtXun+ZHvOcHfeM89/Mz7H+TvvO8BauXbbyDOfelLAFw/eIDF8CzC8pF2GeuI\nNl+dOfj93Aj164nI0F09hUBQtWqsDtbylI8MpW2HHVKh/7bdC9lb2Z0nnaUy4vL1FsuGxui19HNX\n+gOW+isslOaJA4UXz5Gma3TCFhI9iNpxAWUJvPEmLPcAACAASURBVPWUXWfvRQYKZQnSbHGsrDwK\nHODZ5RcQSUoiBlw88gSVixuI4ZCV5DxS6gFSDofYkR54phplCFskiW76ylIPktaYXHbasSh26oTe\ngMTXt8fC1DQySkkKNonsIqkwfUNTQRUhjn0I2+wILO15KactAygTG66Md1JY0J+15k1hW1q3fqZ5\nnuutFZAKS2VgRTcH1wCC8Kuk6YB3H3wn/TgxjyUI0jzdC8C19SAiSKl4dVTaQYhKnnRWceva6yUR\nKCKKtk+h4LL7/H3sOfuANhGU9gSolJWTsZ+shPrUJMPinfvehmMJUsNUwnLwxha6WRXHQIWWMeub\n8htc692g7tWYL84y5TdIVMpPP/gh3rj7tfpzWSUsUaA5XCGKz4+9oiBKIjrhMobOlkuFxhfveyta\nu5woxYIzWhC7QRHXNJQqThGxND5SWaWs+vq6VyYlpOJWdHy6qWONEq7lkChtQKpQ+a6mwGI7LCcz\nr3Qth76hcm8GWcYlTemYNjoDlXx76ziQNVmxismS5MiZSrce7rPHh0nCpe4Qz5Lb+mw8MHsPD83f\n9w1Fxk+ASnbmqTRqQI9N3X5U861qyq9TtAvsLu/c8tj9s3dz/+x9OM4hojQlNsC3m+36mfObAXS3\nqqpbodKawxIWYRKyEYxApXbYyiUPQrgZ+S1nEAGU7NsD5oQQeJaXp79tZhP523kqWSP5W1ZvWGjw\nN07smZCgvnvvHMenRs3HuKwOdHyxlTq4QRFnG4bAeGXAnC0SmsGfbhF2p+7UK12D0y8hi0Xcnbe3\nkdfqBnz0c+fwHIuHjmzdJU6iHt21Z7aYIw875xDCpjF/L6Wp+0jCFknUwZ99K0PvAVKl2Fjr0+8X\n2OC7aLaqnHxJb6S89aHdX/fn2gzOlExPOZ7SdqvKZGYzc3qR02nf/HkZqFQ2huAT8jfP4omlp/nI\nmY/zifP/KT+m7ZhKWv62vafS9d4SodRjbQZw3YypdLlzlcvda3xx/Uv5727lqZR5/twuU2kYD7f1\nMsmk0K20id+vMh/pHnK9cAPL05/hRn+Zg7V9nG1doCGmqLT0NfTs6vMAOJFHFCac/MpViCxtWv2G\ny8zvrOWvP+61V7KLuY9nbGsj74xttFCa4/j0XTjS4Vp3BORkhuRH5vfxAz/xGoK7rtMOr9ENLwNF\nXr1Db6JslsC1gk7OXJouTNpzZP3OxrCJshICr0sUJvkmjBsUKLslgiTkwy99dOK5a+EaVuTitjUA\n0VVtLnQ0s37cVsCaMl6D3uQxJCrJN2B2l3fywsZpulHXHJfx13KKOUCx0teBQllwx67yDu6ePopA\n8NS1Z+maa3NgEtiCJEUBQZrm9/VXV57javc6j9/4Sn58GTC4b3GO+I3n6VbXRt+TUyRRCtsa9Z7f\ntuPhiY0tJTx++6WrOHKy7zvVbCJlmbK1ngdx1N0aSvWNXM4YgIsCGJuIuYKe41eHYd4z1nwN9iz1\nltlcU36dhaL2i1rqr9AKtBwzTuOcgbY6WMd39unvQArOdwasbONFlIFKlztXc1BpR9Hjcm/IIEnZ\nVynyjjccRBhPpapbNWEuG3QMgz5ObXzbp2CN2TEYU/Il854KONPemkia+1PGIb1wxCQvONWc8b0R\ntJCGBTVvztXSps+yNgxZj/dpNYxION3uszxYp+TuzROq//DyKoM4YRAnfO7GBuvDyZ4oTlOWBsbe\nBI+VYDRueGPyt1QkxEUbYY9ApUZJ/9+pTPZrr3TdAZVe4UpTxR+8NESolHeeOEcvmWXnbIOrN7q4\nDQ/Lt/NB7Fb11NJX+R/+5BcmdMIA4UltqDd7JMEhpr7jTey69ydx1xcpB/fjV/ZwzSC77qoBQMyk\nVErLhGlEJLOFkX7NdtAhEfpmbPVD9lZ35wtupSLOrXbZMDtR9VDfXGfXrpOohB3FeYIgxo91k3au\neZrUGCHacRFSxezJDRpru9lzaj+JrcYSwCCPXk8FZ9tnKF/qoVRAsWFzef8XOGxdQyeY6XQWGQTY\nkb7pBgUbhuskiZ5U664GdeRYr1DuD7FSm36tgxAevvd6rlcPIWJFbMUoAuykRLW5kLOKPPceLDnW\nQApwpN6VDC09aUbpCiJJiJw+QglcWUDKMkWnxtnmeW6Y3QG7cAKwjRfS2IRCQJJcp+yUNFMJHUOf\npgEaNDBeKU7GVIppFBomES7OvZVW+mvmcQEqpGAXKI1Rv5UdEyThtqCSm0lk7IRKzc9BxlcvPMTe\n6iKOkKRWjEgkqWvhWhbepgXqOKiQmSFWnDLNoMXOkv4+CpZPnMY0vFoul5r1iyDr9KImcXwJWxq2\nkzHHDpJubmyevWc2KAsEe2sjUGl3YbRAcIdFCmXThEUKEpi9djBvjhYKFquOIHFlHj3rSBt/DFS6\nq1bKGRqQkKqxXU1hsR0/aMF4kQkh8qSNDGzIQJZxSVOmqwc9GcL2TKXsuLWnkzFIV7fHVMpApa+s\nduhECQ/PVCfovln9pUPfyQdPvP+Wr3WzmmQq6eOveVU8y2WuMMNs8fa06C9Xvu3zP7/2Z3nXgW/f\n8phrufzQ8ffh2IvEShGnCinAMbK7rDm/HU+lSjUzlnc1qDQc7c51gg793KjbzcHFcZCnfJtMJdBg\nUZCEhOlWUGk7plIufxvzbHItua0vVdZQZ+8zXuMLuM1JI5vLNffB9+yd4u27/2y+yzt1p16JiptN\nopVlCocOIzYB8lGc8PipJYIx0EMpxT//Dy/SG8a8900Hmapu3VTZuPIp1i99jGFnZI4cRx2i4TJe\neS/ScqgtvB6vtIg/905+4SMpP/sbX+RD/8cfc+bcOhGK3/qMZvR0WkMW58oc3n1zBtTNKpORZfdz\nyTCdbmW4PV4ZKDRlUpW6m5hKZ04t87F//QxRmOSgkl9wtEQtiPPnx3bIv3tJe8d3TDiI7VgkiSJN\nFeGYd1MUjORvm8Htf3nq3/FcS3vetJu3BpU6pke+Go3SxrK/TVXKY5c/lyexASQGVIqTlwfJ4zTm\nf/zi/8avP/vPtiTAZvK3lJRCr4YzKDLlNehWV3ELMmeflN0SCsXdO+9idUFvoGWyGs1USui0hlix\nDQIqpSK7TO8ETHjiOZZDSspwbpXO1BLXe0s5qDRfnEMKSdkp0QlHPgUdw3Iqu2U832GqtJhbPNhW\nlbcsPgpMgkpKKe2RIwSCUbJsVlmqmG3mh365yaAf5TIukVi5fOpC+1IO4EVJxEbUxBuUiAYKO/To\nqDZXDAh2pHEwZ0cVGxa79tbZc0CDSVkKmWtV2GHOzwdPvE8zwc1zsnNxpp3gGIBi2fTI2fe3q7yD\nilvmcP0Ap1bO0N/EVMp+ThU56/ls87z5LKOwmQy4WyjN45WtCY/OILUpF/8yqcjWEZL50lwuhQJY\nHghebPUZJGPrAqVyNsysN2IG17wqaaoBlZKr52EpiqTGlzUDlbSsyyTJmb5hqb+Sn1PQ/UDBLlD3\napScIifXTvG1tRfyx1cH2mR7dbCG5+xDAN+xW4N9T2zDVqq7GlS60r2GFFU8CQ+MyeQWyz5118aV\nI6ZSySkyTIa0zf2f4OJb3gSbv+JUSJViaRBgm7717DagkiUtHGkTJCFdc90L4TJIRkFB68Om8S/S\nSgTYatb9qStrJEowGH4BqW7QjxOaoaDoaQubu2pFenHCh8/e4Fe+dpFPXl7lj66tTbzGuc6AIFU5\nc/xsa3S840ylRCQEdRcx5jOV+VoOk8lx5pWuO6DSK1yff+YyK8rnmH2N2fKAvXvv40vPa5qkv6Av\nmm788jskp5vnaIXt3KgPYNDpUr98kdaheQ67l2iqMuWZhxDCYuGtH2T6de8GyEElb90AUubG8gf6\n5o8cPTCMmErtHFTqDiIWK7tIje4cFXK1F9BOE0gVeyr6tS639YC6ozxPFCa4sR7kz7XO0Av1wO7G\nJdxOhEwUvcoGfr+EPUzBLKgdMbp5vGGJsNBD9jdQKuD49BEGpRYrrqab2pZG1X1S7MgjlYpraYLq\nXSOMXgAFc0UDKiWjwV1taKCjW9fpUI69mw4OIlUklh603bCITC2c0MeSU1jWlDaSVqPbyTKgVuDr\nQSss9aG/QUIHCzeDxpgtLtKL+5y3LuhjkTWkrKDSLuRR8D5C+CTpGqlKDTBkfH5UYJhKelAqOdlk\nnjBf1pNJmnZyUGmQjAYtpSIKjj8Rp5vaIUES6OMWMDU7YlJkC1mnJJBS5rs/77vrPfqcS0EqE2Rq\nkzoWjhQUneIEo+VK51rO3sh2xLLrakdZAy2ZWd4gGeZJVwvFIpbMdnESDjYeAFyCZLyZtRCIHAzI\nBuW91cUczEhSxVx5GmEMlv2gSMHsUookRcQCK3EoGarxiUYRJaC3UESJTKZm5/5DviWpunZ+bhTx\nJqaSvS0ws1jRDJphHORU9WyCzEAlz3KRQiIQE7ukw1z+tnWHwhmTv2VMpdRcK5vT3zaXa7TZK8MQ\nAXzbXP2Wf/+N1LinUgZ+SCH50H0/zI/c84N/pu9VsAs3ZRvZY6bksdK+WBkwmF2fL5f+BvDIo/v4\n3h96EM92CZJggqnUCtsMou3kb2NMpdtIfstqxFQKc/Amq3EAMmOwFXKj7pc3Ah9nvTmbjLrtMU+C\nlwOVHHNN2iLJd03v1J36Vqzu05plUDi01U/p41+4yG/8/nP8wu88ydJ6nzhJ+czTV3n69CpHFuu8\neRuLgiTq0jfAR9C9kP8+k775VS2Ht906s4f/K37nczbdQcSJfQ0Wpgo4Sksd3vDgLhCwu17gb33v\nvbflgbS5ck+lLJHSJKFloSyf+cMX+Ni/3sqoyiqXv1V9XM+i054Elb721FWuXmxy/UqL4UD/rV9w\ncFx7gqn0xeiP6cV9BCIHNrJjiqPkpvK3zUylteE6kaGcj5hK28vf2oalkloxSIXn21hmPn9h/TQf\nOf0HnAy+mv99xlQKb4OptDJYoxN1ObX+Ep+/9vjEY5n8DaDQqzHsRxyqHCS1Y9La6Pxl7J1XzT/A\n+I6UUAIrdokMqGQbeU7BKrCrMmLkOmNzQzaXFR7ukh5Z1Wwow0RZMHKmilvKexAgT1geLVpH43jJ\nqTJfnKXmVjnbupBfH4NoSJRGJGnKlF/fMu/urz/EfTt+nJ+450f135eaDPohyx29yJapRc2tYplk\nsxeNbGx5sKq9QIdlvW4IinTTTg4GHaztz9lKVa/C97zvfh56rZaNTRuzcCXmuN5bYa4wy0Jpnp99\n5Cd58+IbgBGodHIjIlZ6vlwb6mMaxENsYeWysHfsfTMCL49MyZhK/XjcmkCzlc4YUGmpv5wzec40\nz+NbHgvFuS1s4KfWHCxrKpfcSVklSZMJGWEr0t9DLx7z+0oVZ9t603jeH10sNa+KMgy1XZV7eN2u\nt2BZO0hSfSyzvn7/lWGYM98tYRsz8SVmS1N5397wGwghEELw/qPvJUoiPnf1iwC4zt28uLHOjf4y\nw0SRiml2l3wema1Rsi2eWm0TpZOgR8ZUWu6vImWZumtxrD7qgxbLesN6Z3kHtrWb+2fvM32SyKWH\nKR6e5VEa2zCruhWaYUyYKo7WS/iW5Eyrv+0YlvVSG8MMVPLoRQmO5WBLm/XhxhZQaak/ycZcHYb4\nlmSxFLMx0N57Qi6QigU8S/K+gzuY811Ot/v04gQptrKdTm3o9/92A8KNM6uyTcFhEhCLiKDu5ZJA\n0PenJUbX4TdL3en6XsGK4oQPf/olLJXw6N0aDCo3jvCl525gWQJ/zoBKt8FUunZDS7o22iOm0pnH\nn0CmKfKhEkLAF9L7uHS2mTcEWV3tDRFhhBzqC1pYZYSEdF0vzkMDKmVDVjvsgNnJ6QYxBbtAYn5W\nhKyphKElcIOEHUbjuzrUx7dQnCMOFZYoI0WFs83TnF7/PAD1zgwyUbT3lbl8/CStxgpWDFM3FhDY\nePbohvT7GtkOxXUcmbLP6I8vti8xV5wlivWkdPTwNHbkkbiKs90BqR0BEe6wyFRZ0wfFGKjU2VhH\noejXhgjhIYQe7JQUpMaZ2u0XiNwBkTckA360bjlLlfJpeBqwCgtdlIRBZYjq3wBibFHJkzDmijqN\nYM3XOxpheBLP8lGEufyt4EzhufcBKX/vi/8Ln730T0hSDTxmiXDZe1dcM5GqhJ2VLAK0myfDqbHG\nSBFTtAsUx5hKkdRxnWGhx7s+eJy7Hxo1xxkDyC1NNrGZBtzNQSWL1JHYUk6wMgCeXX2OLxtKcMZU\nygCTXSUtS8tkO4NokCdi7CqVkHK0+3WkcRzLmiEcB5WEmIg4zRbvx6aOYJnfxUpRqnjY6IaqJms5\n1V3EIFJBasX5gH6k5iEV9HcUc6aSLW0804TWjBFxvshXsWEqZed5e6ZStpOXqISXNnRCQyVjZZlG\nIkv6kEJOpMkFNzHqzo4NDFPJgLFhkoFKtyd/AzhWLzF1k6b8T1PlbTyVAA7V99/Up+fPo6QQWEJo\nT6VUYUuRU82zHeXbSn/zbOZ2VE2TEtIcNnMPg07YZpAMEVgIYefXQWEMwCndJEVtu/IszYbSTKXJ\n72YcFBrJKGewhJUzAG9V49fSdp5KWW0X5T1e2X0Q/in9R+7UnXola3jpIiu/+2Gk71N55NUTjyml\n+PKpJYSAqys9/qf/7wn+21/9E/7Ff3wJ15Z88DuPbruR0F17BgwrYNi5MHovw1ryK6PUy8eeusJz\nFza49+A0P/199/M3v/sEArjn6BwfeMddVGs+IkqZrm1lQ43XlQsbPPvElS2/38JUGpO/panizKll\nrl5scunc+ravm3kiuZ5NpebTaQ3zxVscJSxd1/KYlettgnGmkvFLCoKIXnmdM+FpDtT2cqi+n0E8\nIE5jHFfmxxiFSQ54aaZSSiJjnlj9Si4xS1VKJ+ySGu+jl5O/tTNDaQHDUnti4yxLb+qOhaAkdia5\nefkxbWnM5PijZz7Ban90/jKAB6A4qDPsRxz0NIO6XdbPmyvMcK51kX3VPeyr7cEWVh4YURDayzCO\nUjqtYQ4e+baf904wSuQa9+cp2UV2lObpxwNON/X1Nm/kTGWnTJRGBMY/J2NvlB3dH28EI3aFEDpZ\n7UB9H52wm7OLMoZurOJtk6+eWulyoZtytuchkQyLHYb9iJWuMZzG4kfv+UEWTcrs49d1j5gBYN7A\nSH0C7SeUMZzmS7PsNH1D1ZtMZdTSNkEUXyJVEf10luv9gILtM1/K/INWzOfysMRI+gR683yhNJ8D\nZEenDrOvcTB//YyhlP0LECaKteFGntYHcLF9hWbQYnmwyqH6fixp5dL/rCJVYTD8EscbM/psyBmC\nJKAxxvjaCPRxjDOVAlUlUhBGZ3KPHdDsntSASp49zQPzr0cISZjodcA4U6lt/JEG8YDlwSrdqMeh\n6f35JueUN9pcvH/2bt5z+Lvzn13nLh5b8vjohRs49m5AcKRWxJaCh2aqDJKUF5uTbKGaZ5iVooAQ\nNtO+y7TvslBw8SzJQkGPRTtLRUrFd7Kjctgk3416JSmKFGyP0vhndmt5StvOosfBaoGNMGZ9Gxl+\n1q+1hz302snJv8eC7ROlUS5/Wyz7eJbcAgj1ooSyY3Hf7Ani5ApKKcr+fcTK53C1iGtJvu/gAg/O\nVPjQ8T3sKHgsD0ISM04qpTjV7FGwJHdPlZkvuJzvDHIQzh8PZxERQd3FSkYgWskp4FtWzpj7Zqk7\noNIrWJ95+hqDVPLAzDV2LAwpNu7lucsp19f67FmsIU0zvxlUCoYx/+LXv8TJJ3WzkKaK1ZYeCNfW\n1ln7+MdY+cjvEn3m0+BJpqYVa2mRS+luPvHvT/L7//IZBsYnqRvFtKMEpxOS+vqGlLLKsNAlXTER\nsDlTCTbWenS6AxzfDKiR9pGJLT0RKRXSrzgkjqSoBPOm6Wkbs70dpXnSSJFKhW0vGpmQSV8KbZyS\nQ+tAFdtyuXbwOZSAynoDIWycsdh1N9CDSWx1KSQWu83EojB8FQMGVWyJHXngKS51B8S+/tzFYY2K\nYeiIdAQqtXsthsU2ytGvIcwEnbrSGHyD3/PYmLkCws2le5Bg27vNearRGzymv6tij2uvWyAqSJL4\nujm/9XxgKbs78+ejJEm6xJxZzGcyPceq4TrHAZeKW8G3Z/JzptTANHMRUtQo5JNVzM7KrLk+ujlT\nSTFKBVFKp2AUx5hKkTVizkxPVfMdPBh54FjZ2G5OW2Z2Z0uJEjEysUhciSN0XHqq0hwoAs1WghFT\nKXv+iKlkQKVkmDc6i+VKzlSyZJX54gK2tcmzQk0uiOu+nrzumTmW02ETpSiWPAryHjx5jBl7Ljfl\nzLy1EpnkA7otFLuEJCo7pI7CNk3VwYoe3BcMIJcbFKPvh0z+JoS17QJjvjTHm3e/HoBnVk6a4zzO\n63a+iiP1UePy5t2vp+ZVidM4b9qDJEAKuS3okTWgsUpQhqkUGJmm8zI72uOg0mvn/+xZSrA9U+mV\nKlvqVMqMqZSdz2xH+XbS37LyLIdhPKQddql5+jruhB0G8QBLZowsA3aOy9++LqaSq+XISYSzaadT\nColv+VjCGvO1aPCPXvfzvGH3t73sa4+DUpt3UYUQObC0nUHueGWMvfA2pCJ36k59M1bS7XL91/4f\nVBiy8MM/hjM7Oc9cWuqyvDHgkaNz/Oi7jiMEuI7F2x7azc/9tYeZb2wFipVSdNe+gpAOjj9H2L9G\nmgSoNGHYOYu0y3SjGr/50ZP80oef5t9+5gzlgsMH33kUIQQba3qOrE/r167UfPq9kDi+9YLi8c+d\n5/N/dGZLWtvIqFvf16WcqRTSXOvn3kVZjzleJ1ef53Rfb9p5vkWl6hNHaf4eS9fapGajbul6Z0z+\nZuN4FmGo5W+Z9+Rb97wxZy90o14OXGdMpezYMqZSa/4q/+rFf8eXbzwFQCfUybaZTK1pFrE3A5Uy\nw+SSXeTMkS+w9y2j/mc1B5XGknhzptJtgEoGBLlv5gTDJOCfPPXh/LGMYexIh4ZsMOhH7JSLWJHL\nknWFPeXd7KrsRKF4ZP4BpJBMjRntFs2iutMaEkdpnpLqWg7ThQa+5dPw6iRpghQyl38B2NJiR0nP\nS+dbF/EtP09vzcCIrgEXOlEPge7dBnFEL7hGlkScbeIdrO0DRhK4jcFI5jSzyU8J4EJX951PrHRp\nWNMMix1udAdc6OrN5kd2vZr9tb2cmNaeiifXTpGkSW4L4Q01qOQGWTK0vr4qbjmX/lU3hXLY0sa3\ny2DWDYlY4FOX9ftVDGCW92n4IPS8nDG1EpXmsj3Q8+Cj+96Q/5zJjsYX9cM0zaVvh+saJL7Qvpxv\nHB42oNTkxo3FvJ8QRidZrCxSLX8Qy5oiTMKJzxQpF0cKHUBkKhZ6zRHFZ/JNSdAJa8rI31K8kf9T\nrL/jOOniWYKlQZADYM2glR/7sZlDOajU8Cd7wXtnX43n3k/BnqY//AK+HHK5X6Tg6372qGEdHaga\nOeFwEoypm3tdSv3vjK97jx88vJMfO7o7Z5Fn4NKNfkDJKeYgD2h/KM/yqHhjoJJfZcmsa+cLLgcN\nw2g7XyXPchkmAd2wj0BvRGcsqIwtJmQRgaJgSeYLLqvDkNgAPqlS9OKEkm3xpt2v47++9wPsLLrE\nRgGRMa92FD3eu3+BHUWP+YJLrFTuq3StH9COYo7WS1hCcKhaJFaKS5kf8Vj629CRJAUbvylAmQRf\np0jBlneYSncKgsuXuPDz/z1PflInLjx4eJV1dR//+HO7+b8/8iwABw+N0P7N8rfWRp9Oa8gTf3KB\nMIi5enGDwBhnl7/8FdY++u/Z+OQnKN+4SnTvHJYQPB/XQUjigs3qcpeP/euv0u+FXM+kb+2EtKj/\nL0SBXi3AG1QQiSS01+kP/jOdfouP/PZXKJ/eS9Hc34M4pRN1USKbcFMSRw/4NctiulFERinDdAPX\ncmn4dVQsUFLlIAwACpIgxSm7IASOdElkQFoS+IMSXlDGEkH2FjjGXDyxQ6oDL58wAa73l6mahV1R\naUmUVxSESYtBSU9+jWiOYhZ3PUaPjOwh3doqCBvJaBBTtsyBmdn76yzvOo0UngF1UoT08dyHKbhv\nQMZ92lG206VIXQspSiRCM3NSa4rL7YsUbvR5+kZqDPSgmGbJDXqyj+OLAFiypkE1azc/96qfoVh4\nW35cqRqQpCtAimXN5F4/lkiZK2XytxZKZaDf2ACrInzbn0g+Sewol/BsZix4BhiyfGW+Bv1vBgo5\nUpCKOGcqOVLk0ptM0iaF5GpPg2sZqLQxbCIQuc9QZr43iEbyt4bnM+3vQMoatcK92FJO+lgBimTC\na+ati4/ydx7+G+ytLmKZiSpJFcWyi20tUJKvoVIu4Ng2qUiwQmPIbcW5H1Ocxhw0rJDEVbknQAYQ\n5NHsGXNEJaSM+y9sb9QN8B373opA5LuJe6q7ef/R907Ij965/23MFWZ002x2ZodxgGd528oeLKmv\ndw1CJYBgaHoex7r1cO/b2mlgR8Flf+XlJVPfSG2X/vZKlSMEkcqYSiOQ7uvxVMrKtVwD5ClKbgNw\n6URtBtEQS7gTE60UMr9Ov16mEuiGejNTCfQO22b2Wtkt3Zah+q08lWAkgXtZ+Zs5Z9l9e6fu1LdS\nqTTl+m/+BtHqClPvejflBx7c8jePn9KgxCNH5/m2Ewv86k+9gf/9Q6/l/W8/wuLc1rRJgGH7DEnY\npNi4m0LtCKAIupcYtF8ijfuUGif4p584xcc+d47nL2xQL3v82Pccz0NZMlCpMW02y8xmXfcWBtkA\nrQ09t2SytqziTUylYjnzVApYuaF7FyHg8vkNNtZ6E8/9ty/9Pp+NPo0izZlKMGIIXb00kgAvX29P\neiq5NmmiGPRChgX9PrtKO/JFfifs5sB1MIxJU4XnO9iOJApjoighNmEnz61pyUnbLIpTwyhqGkmJ\n7YttpS8tw0TpxX2wFE+sjsyUMwZMJ8424RS+q8faeBv5W5iktMIR2JQxX9598J3sr+7hK9dO0jf9\nUfbvYmUXxYJPrxPwlc9fpra+gyFDvvvgV5VWKAAAIABJREFUO1gbrCOF5KH5+wAthU8M47hk4sSz\n78OSGQPZQQrJj9z9A/zQ8e8jSiNc6UyYZScqZcH0yArFfGkUdZ5tbGSbib2oZxbxkseXzqGIKDhz\ngEUQt0mU4kBNKwMyUKk5HDFzNjOVmkFEM4yRQgMxnjODkikfC25wJdTXyucLBZpBxMHafn1e05CX\nNs7mMrhMmZCBSlmV7CJHpw5r38zqni3fjyNH7KW50h4udYekSlHeBEAJ4ZKaAPlsIxPYwp5eqIze\noxvp732z/C2Tvr11jwagLrYvc9qAStmGoQYM9FzpWBUWPN3r172S+V6058/43C2Ex2vn6ggxmuOF\n3EXNGZCmzQnWjk7o1fdjlFoM4qx3bJOkCb/89P9LP1xiLYjIVA7rQTM/9mOzh/JQnalNoNJLrT6+\n9wjvOfLDpOl1quIJ0nQNITzKtsUO49nYMPfN5sCOqvFUkkJ/p9NGitfwnPy5MGmQvZmplINK/jio\nVOOGYSrNFz0OG1DpT240+YOLK3zi0gr/5IUr/NKzF1DYBEnAIBrkIF32PWa+SlIU8aw0T8tLgRUD\nCA1ivfop2XoT78T0Ue6qG+ULcKS2dbNw3ny27BhPNfX9loFwmczujPFV8nOj7iG9in7t4kqAT5bq\nW6BgWQyT5KYy5Vei7oBKr0B1nnqC9vIa591ZZss9din4vWcWOHO1zX0Hp/m773+AmYXRRbmZqZTJ\n14JhzPPPXOeFZ2+QWBFTrZiFZ05hNxos/O2/y3/4rvcRPjhHqhSnY71IjAs2+w5Ps77S47GPnxqZ\ndLcjVCFjV3jM7tyJQOAPqsR2lyg+zYsrJ4nCBG9QolIxu/ppyo3uZFqAUnpQni64lCoechgSqxYL\nxTm+tNSivbuCkgpLzm05N8JIkfTCJiGu658rzVlQmemtwA71jRU7IYWeOxH3WnHK1P0FHClwQkMZ\nbVyn1/sIqYhAwb74cA7ACMhZN5ET0Kus6dQuo7GuOQbxT/XEWSm4IDDpBAqlBlhyDtueotrayWw0\nPqkqlEqRskbdDAzSbvDH1x4nmPZRjqTQ039fkvrxuqGHJqk5r8JMjEJTMBMqOMbELkm6hKE2Y3ed\nu/AtY8osEmZLJv0iWQIUlrBy8z4AKZRmOBTG4nPtiI1hC9c0KuNVcIzxtWtAJZVpyvVrWkKBSLGU\nQ9DwsKXMdzuyxqjh1bnevUGqUtaHTYpWgRv9ZWaL0/lCeZyplMnfXMtlb7VKpfRX2VG5H0sILGuU\n/KFrOLEg9m2ffabRGJe/+QUHZQlEqiiVPX1erBjbgErpGFMpVjF7XBeUIpVpDjSEaWbaLfPjA81U\nSpXK/Re07Gl7VKnslthTGQGr49Hv45W9ZyapC5JgW+nb+N9rUCkGrHxH7eWYSkXb4vsPLvC+Qzu+\nIZ+O2ynf8vLrarv0ur/IcqSRv6nUeCptlr99PUyl0WcpOlWkLNENOwySIVJuBQAz5t7Xx1QaYxNt\nA/y8auFBHl544LZf7+t57YzR8LJG3XeYSnfqW7hWf+8j9J/7GqV772P6Xe/e8rhSiideWMZ3Le49\nqOdXKcS2bNSswsEyrRt/DMCLa3v4zf+o+4gXz36N9ooGNJrpYU5d3ODeQzP8yt98PR94aJH9s6OF\nbzNjKk3p+XQzkAMaKHrmy5dyFlIYxAz7+j7cnOoWRQlCkDORXc/CdiS9bpiDSvc+otPJTj55deK5\nvahHLCKGxQ6uZ1OuTh7LdQMq7dxTZ9CLWF3uYtsS27Hy8aPdGhIUOzhCs2yyRX4n7OZMpX7PpHk5\nFq5nEwYJcZgQu/p9Xtw4TZImeRLVcGpD+xkOFJEz5Jcv/jIfO/upLd9HZ8w/SArJ2Y1Rmuzq0IBK\nZlMwtWJ2GLZKtM2Y9qkrq/zyyUt5nPxyf8WwhKZzn6OmOb6e2TxaKM5y+MQctiNZudGhvqr/7pPn\n/4hLnSscbRymYs7HuKdO2dKL8PUV08NZaf4ZAI5NH+Fw4yBhGuJYDhV3nnLp+wDt7ze+8ZolecFW\nUKkb9vLfPbPyEgCvWbifgl0jSdusDUJ2lXcgheRaT9t2NIetvJ+P1SRgk7GUHp1v4EhBFw1ShMka\ndtpFqgIIi+uDgH21PXm/9Plrj3O2dYGjjcM4JsF5HFTyLQ9LWuyv7eX/fOM/yFlO45WxYXaU5jlQ\nmSJIU5YHYQ5iAriWjxCSRG3tR/ZWFid+7o4Zx2826tafKeVs6zye5XJ86i7qXo0L7Uu8tHGWgu2z\n21wTnuUi5RSWtcDu8kE6kb5G6l4GTNgEaZhLJovODEJ43DNdoeGN94kOc662ziiPfSYhBDWviFIh\ng0TQN9dnooZ8dfU5VgdrJMk6IJCyhme5bAw3ONu6QMEusLu2Y8RU8jaDSvo6Od6oUXZKnGudptP7\nA2r2Bm/eOZWPhXWTwrdZfuZaDkW7kCe/jSfRjte8YSotD0NKTgkpTYqd+XyWLFEtjD7zVLHG0iDE\nlYK6azPlOSyWfNaCiC8uN/n8UpNznQHrQUSQWqQqZRj3cpBuJH8rmHNYoGBtOhYjgctYTaUx5nYG\nCi2W/YnfZ7WwKUXuTLuPgBz82l8pYAk42zGg0hhTKazUIFUUVocUjQqnaBco2JJEaW/Qb5a6Ayq9\nAjW8cJFTlb0kWNy7Y4XifT/E5eUeJ/Y1+Mm/ch937WnkMdewnfxt9PNXH7/M+ZdWSO2Qt365g0wV\ncx/4a8T7DjHcPc203eVirFCWfr2k7PDtf/kESydO8kTyBW70DKjUi1B+thD2mJ7Ti12/XwGhn9sx\nuxF26NOJm3R7f0CoUi43szhSfSMpQzdtFCWWJbHiHpBQ9vby8curtA5WEanCXRmZiiMgsSKEoSxn\ni5xwRv9cXp9CKWN6q7RJNqkgdkKcrsPz6y/lL/VDx7+fbqSoOjYEkkRGnC89jy0thCgilEvJL+ag\nkpISkRrKtTukX24appJB0J01RKpI0w4WPo5MzHnKJoBBLpOrXhqyICdjzHW0p0NmuyNFFWG/ltSR\n1M+2eftRvStVq+vBZbownpxh5waCYOUT9HTB0HLVDaL4HFLWsKyd+PaIqdTwa4AgSY2fVWkO1yL3\n2rFN+sS4oWViRURptK00addOkyLhTYJKPWNGLE3aWGVHnaDhGaaSkVCa3Z+GXyNMI1b6a6wPN6h5\nVfrxYML3ZdxTKaOcu5bLYkn/vmRrSZkQZTxrklFzM0nVuPxNSoGyJCJRlCoutrRIrBhhaKWJFeeA\nR5zGuI6FiBVKJGNMjCyG3kjLMlaL8VQa+S/cnKkEuhHMatx0cOLYM1DJNLVBEt6S5TMOKglGu1Qv\nZ9QNcM9UhRn/z49BJITI2TnfDPK3aMJTSd8H3yhTKauiXUWKIkEyJE5jpHC3wIqZBO7rM+oeS2KU\nWxux7zn4HfzVI1sXwrdT4z5P24JKhpH3sp5KVuapdIepdKe+tarz5BNsfPITOPPzLPzIj21JfAM4\nd73NamvIA4dncOxb3wtRsM7K+d/lxgu/Qdi/hlM+zL/8zy3OrpRJUoEYvMSwcxanuItPfUUvJN77\nlsOceuoqX3jsLM995Vr+Wu3mAClFDuBsByqdfXGFL37mHC88ez1/Tla9TaluUZjguFYOdgshKJU9\neh3NVBICHnn9XspVjxe/doPA7NAnaZIzOXqVdTzfplLzzPsNieOEpWttZubLLO7XfcygF+EZNrRr\nQKVWq0/gd/MEsophWWimkj7vOajkWriuRRjERFFK6OrPNYiHXGhf5rKR0wcMCQz7qVtbJVYxTy2P\nDLezyoyT91f3kKqUbtwjMvLyTP42TAISGZNYMbsyUGkbptLqMCJIU1qhfv6N/gqzhWksaVF39eZg\n0zCjsnml7tW4/9V7+Os/9Xre/+Ov5gPveQszhWnOtzVb5ZGxjYEZYzb9yPyDPFR5GID1VSPPsrIw\nkMkKE81Usq1ZLFlFCJe1wTrzxdkcsJkAlcy5f+LG0/yDL/0SvbiPa7kM4gGXO5ph87bFB5jyZ4CI\nC50NLGFRsH2udm+glGJj0Mo9QFeDyT7mQkd/7uONMg/OVEmEvi7qw1VC1cMxf78+jPAsNwdenjcJ\nY6571yh5N5g0Zs5quzkLIEH36IfrB1ks6+de7g3z6w3AMYlaCaPXmPLq/NDxH8tlfllloJK2OBCk\nSk2ASs2gz1J/hQO1fVjSYm91kXbYYXW4zqH6/hwA9CwX25qhXHwXb937zlxyV816QGERxGEuxZ+v\nvQfXspgvuOyt6OtKCJ8ofpFuoK//8ibW86sXHsKTik6U5D2gUgGfuawVMsemNAtLyjo1r8bacIPV\nwRoHa3uRQuapseNMpShNOd8ZMFdwqXvOGJMu4m07Xb5tzDbBkZKKY9EMt943Na+aA35TNwGVSo5F\n0ZasDELKTjFXdNTN1yRkkZJfQKQSFFS8KivDkPmCZ9YHgh8/tpufvW8//83xRX7i2G5+/oEDOFIQ\nptlaNUUaUKk35qkk8BDComTr+6VhLDJa5rN0I/1v2R71iPsqBR5daPCOXdun3mbA1NIgIEhSrvSG\n7C75+LnaQdLwnDw5PeuPW2FC6pfxNgJkrCgaQkHJGa1hB99ECXB3QKW/4FJKEVw8z9fmjwKKu/dP\ncfqGvoiO7RvRVcMxUGlpvccXHjuT/xyZC7tUcen3QtIo4O0vdtj7QAPxA3vZEP+Bzvnf5vXySQDO\nKxffpLNZdb1rvlq8wsb0FTYMLVqGKTgZWOLRc4z/R380cHdNFKuduGwMWiTpDfriKtfamg5uyRnz\nGXUz8EJfa949hoBgLdLUVqSguBJjrV+cODeROwDDVMoWOXEZ+qUmhXaVKDY7TKnEjj3sxCW2Q1RL\n8pyZgH7k7h/gUOMQvTih5tqkQ8HawgViEfHte9+MUAJheSRlO7+ZU0dqkEpBWghI7djE0xvPJdWi\n3AlIVYciVSDznjLMI/TnTZIWfjfirqlDE58rTbuApGOYTlKWEcKieLXD9HrIa/c8zANz9+ZpHLOF\nEQPHknViJbXETjic7+iGKDMmj5NlQOE5OgXGNQthSyRIKbGlT0bD2lPZxXxhJjfwywCRcaaSNjLf\nXgJz/G6j31YRSZrkuvaMqZRF2IuM1jsmf8t2XLJG4GzrPFEa5YvrcVCpOJb+FiShZqYJiz2mKSg5\nFpbQjXDdn6Qnb7fY1udxBCoppVBSp/6VKiOmUn4OZJLLgaI0xnYkMk5RpDnwkJnpudswlRSMpb9Z\nW1gq43Vs6oj5OzHhtTNeIyBLfzfDJMjleduVLS1iFZOSgLDzCeebJY0rA1JecfmblER/Vp5KYz5E\nBaeSe7oBSOFtARYz0HJzI3jL95hgE/3Zmqi/3GvftqeSOQ9R8vJJSXfqTn2zVBqGLP32byE8j50f\n+ltYxRIrzQG/8rtf5R/+zpP8/d96nH/4O0/yW5/Q6W2PHJu/6Ws12z0e//JHuPrcrzFongJnntkD\n389nLjzIIIh5z5vuwinuolEMkAKeujTLky8us2euzL6ZEs88rpkHzfURq7jdHFKp+UgzkFQMuNQe\nA5X6BjhaW9F9Umtj9NhmplIcpVvu5VLZZdCPWF3qMjVTwnFtjpyYJ47SnL00GDOb7lc2cD2bmfky\nQsCzT17h4pl1kkSxc7HO3I6R9CjrMVzDXlgerKCkYhAPWBusj+Rv0YipNOiOgUqeTWg8lUJnBJY9\nv/4ip9ZfHH3O6rr5d5TgNV5KqXyD631Hvzdf5LeCFt2oNyF9SkoDEjvMGT7bgUpDw1bpxQndqMcg\nHuQG2JlPVMZUCs28kjHRhRDUGgUWdtV4ZP5+fX6kw70zJ/LXz6Rku8oLzBd0f5gBiYHU/272eorS\nCMdycQyzSYoaq4N1LGkxW9SvN18aWQdkDJczzfO5h9GlzhX+9mf/PoPoGgV7hrpfZd4890JniUud\nK/SiPkES0Im6rA/ahhkNV/puHkQDmqnkSMHOosejCw3qnj4/veSyllAaoGjDrGsyICdIQ4TwuNCb\nY9jQc5IT+njGozBjc4VJyh9dXcuBk9F5SFFiHhA8OHcve8r6nrnUHeJZXt5X6R6ZCVmZZzf46GXF\n719aIRn3XM1BJX0/DJN0wtPmak/LH/dX9/Ppq2tI6xiWtROwJrwyPcvDkvp87ih69KMBBbuAb0AK\ngU2YhgySIeCwHih2l3zjvVPPjzcInmJ5sGq+x8kNqu8+8A52levm2hyBSudaF6l7NV67oJldD8y9\njoXiXL5JfLCu12n7qnu0wfuYr5Q2klbcZQKYpv3x8JzJdQ9oCVwzjCauB9CeT5qppKi5N9+8m/Fd\nNoII3y7kyWe1bJ2Kr5MbYwcrdumkilSNZHOgWaRV12ZXyWdPuUDRtthd8gmS0djnWj6+JUdMJadg\nVChQNmNR1Rxjx4BJ2zGVpBC8c3GGA9Xte7qKY1GwJDf6IRc6A1IFB6uTAGzR1pvAqVJ5f7xm2HnF\nFX2/77B2UXHKTPl1CkaZsvnafyXrm2OV8V9QxRvrrA7hatrgwFST4yfexKkL2lvm2N7RDRqPxTC2\nhjEnn7w6Muo1SOYDr9lDPVzl1Zc/xj1HalhHysiKheVWYXidXXKZoZI07RpFIwNLyg7DJEAJReJE\nNAdthFKIRKHMzSqFywCFkOAPRo1BLx6ZF2YJVAHnuN7VoJJr6cnCMbvUz7ee4CvLz+JaEa5zjGFa\nyHdV7FDQra5OSINCb4AyO1lDDgCSQEZ0GksIBGFPv79Qlh5IEpfYCYm7cGrpDDW3wv2z99Ax9O+a\na9MfDFldOI+NzZsXX4cgBOHQKdm4ZrGfWoJyewY7cok84yuFjTCLyjBuM91uA4qGqBIZeUxuHG2i\n5qP4Aq/6wSle+9Bx5ouzOQCRqi5CWAzSAXboUWkPCcMXabzQwi+5lN0SP3L3D+QpE1NePV/gWtYc\nKQ5K6Qn2ojFx21/VA71CIYSH4+gBPQMOpDFpduUIqDjSOMRccTY3F/fM3054KhlwZTvQIhvkgiSY\nkBtmnkrCgEqKbLKWuT45m1Iy4ORcSwOKGVV2fOLyJ9LfQlzLQQjB7pLHdy3O8OhCI3/eTGHRvFfJ\nvP72YEsufzMR8oiR/M2WNqk1GpRTK5lgKtm2hYwVigTHmGOGxojUsfTrZmCWUlr+lp8fYd9ykN1f\n3YNv+ZoOfBPvm6z5iZOYJE2I0/jW8jdhE6cJqWEq9b8OptJfRJXsby6mUqo06JjL374BptJ4mkvJ\nqSLlWFKJdLcAixkb7xtnKv3ZAnKWtPLxytvmtW9f/naHqXSnvvVqePYM6WBA/Q1vwtulE08/8/RV\nvnp2jfPXOqw0B5y/1uH6Wp9GxePu/VvNiEGHpnzhS7/HgvscnaHD7z5zF7/wycN88qsWn3n6GnP1\nAm95cBdl4x0TJRYff8ZHKfiO1+zh0x8/RWIWqZkfUhjEDAcR1fpoLs89lcZApSx8ZcMwWSaZSpvk\nb2GyhXVYrJg5L06Z3aEBiWLJzPnGhDvz/wPoV9aRUlCp+0zfI+h1Ah4zoNvOPXVmF0YbkllvmY0f\n64kGfdaG6zy19NUcIOiGvfxv+r0of47r2SSJIkxiQjlkT2UXUkhOrj7Ppc7ITLxbWUOh6FX0628e\nhwbxEIXexNlRmmfKLIqvdm7kSWZZxceWuLrva3RMGtpwE0AFI7PmXhTnfkrzRQ3YxKmeP5rDFqlK\niQzoUvOqfG29w2evj97vVQsPIoXkwbn7JnqYDFRaGaxt+b4GMkvtmjyujKlUdfXmpJQVEqVlgtnm\n3ThTKWPtdKMuDQN4TftTHKrfhW3v5cSs9gZarOjnXOut8vzaCMhb6i2zNghJVRshKnRjwVWjgOjH\nCcuDkMWSjyUFU57Dz95/FDv0aNt63eMM9efNZFIHxthBjn0EISyCurFeQPD2hbcA5JLJr653+KNr\n6zy5OvJ1AuiECba9gzfu/UkONw4wX3DxpORSd4AQIgfTrBxUGt1flgHknlhp889PX8vBw4yplKSt\n/PONM5WudvU1UPL28ti1dc716pSL30Wx8DYOj4EunuUirWkEilnf1T5WdmG08Se0508QB1gmkCZj\n6mdMpaItUQRsDJt4ljvhxZlVxVwzK8YsO0uLfnj+fuaL+vUKzvwEG+mQAZXevPh6/tdH//4EWHXa\n+P0cNp5BI9BzR34Pj1fDc0jViOGTVc2rIkUFT8Z5b75dzfrayyhK/RHQYza+E1xcz6ZgP4xX/Db+\n8LK+58dBpe1qT8nPWXWgN7FL9qhPLtqFnBVVcRzzr76XM1AxB5Vehq06XkII5ose60HEC8ZPaTMA\nVbItUrQ3V9aThSapurCix96HS6/iFx/9e/i2T8G+w1T6L74uP/08n96r03gOLEhst8bzFzYo+TZ7\n50eTcMZUKtqSyBakqcrNFcNA/1tvFHho7bNUDgjknMcLw4h/eq3AzmMf4trcD/NY8hp+r1+h7Fbx\n+0WsYULgytwwEKATryLjFFCkUt8wlnBRCvyanGAqDRJ9I6QiZZgG5jgvsRysICjiGebOXVMuh6sO\njlT8mxd/D2lF+N7DCFKOG6lVKhIG5SZTYoa37/1OACJvQGriZDvJPLa1k1CEtOsatAqG+qayUg+p\nLGTqoayYVCQkLYsT0zoppWmME2uuzfPhc6R2zJy1gGd5JEQI4ZK4MmcPKEdSXZ/HjnxCs/uDsEFI\n0rTPMOnhC/3Zp+x6zsyRhsY74ymC8Dni5AaRCpFS8lMP/kQ+cau0i1Ka4eMGRRavdog7TyAReKXR\nAJgxJIpOIR/kLWsWKWooAoTwCZIU35JM+aPByLHvQgizw2GAMGGALmdsIbq/uoe54myeCuHlfgqT\nnkr6sa0LfikkrnQIknBi1250PU2CSuNMpfHXALjW1RT9zPzyZkylMI3yBbQQgtctNHJ6K8CBxsP8\n0rf/PGVXeyfdjAZtsJ8J/bFIFNW6TsxK5DhTKc4/fzzGVIKR/G0zU8nJGVKJ/q4nmErbHpI+Lmnx\ngWPv5b1Hvuemf5P7OCVRDubeCpCxpUWSxqRpDMLKxxL7mwRUKufyt1eWqWQLkYOdjhxjKplz/PV4\nKo2DPCWnuoWptPnMv37Xa/jOw2/+hj2Vtmsg/7SVAZW3kr+9HFPJyZlKdzyV7tS3TvVf0GBI4dix\n/HdPn17FdSS//t+9gV/76Tfyj3/mTfzqTz3KL/74a7BvEnrwnx4/y6H6RYaxR2nPD/Poa95EwXP4\n+BcukqSK977pILYl8as6GcqvHcd1C8w1Cuwqupx69joLu6o0Zoq0mzrZtd00LN/GWJx0xUMI6LTH\nQSV9z62v9rQkaX3kHdRpj8Ag0J5KmwHiLGUNyAEh19gRZBuZgzFQKXZClgervLRxlv/sfQJvLslT\n43Ys1vB8G8fsSXbRC/6s1xj4JshEQDNs5wvS7TyVXNfCNQz22BmCgPniPAdr+7javZ7PtXW3Rr+6\nTuj1iTx9XsZTU0EzoUCPcVLIHAA62zrPmpG+Zb3IhrdMUu1zpnkOgOv9JTZXxlLpxUme/DZnXvPT\nVzWD5HM3rvC19VE6WsOv84eXV/nUlTVCsxicLczw3Qd/lDfteefE62dJamuD9Ynvy/UsBvTNMYyu\ngSx51pEOA4N1ZDKj1cEa333g23n/Xd/LfGmrp1KUxtSNf84jCw/wxj1/hVLhHRyfPgrAnvJc/jrj\ndhOXOldYHUiUGlBx9fE+39Tn+aKxa9g3FvwhpaQUjkAMLyjgWZINAyodrO/LH9tXXQAUQWME+PSM\nYiE77swXdm1TylgrykA8fV1LIdhd9lgZRvTjJAfTBFk662h+tcz8vbfsc7rd57GrxmvLgAppmkka\n0wlQ6WJniYZXp+bpa+DeKe1rZMt6LqMEsKWLJRsUrQgptN9WySlpBj7aizNIQoZJgG026xcN06rq\nlREIam62Yasm/JTGq2rAkNVhiO529PE/Mv8AddfGkYLlYZgnvNnSZnHM53PzZthLrR6uFOwzx/L/\ns/emUZKd533f77173Vq6unt6m33BzGAGy2AhAQIkwV0SKVIbJTGyZGtz5KOj5DhxnBOfnOQkShzF\ncew4zjk+iS3Fco4tS3FiOZZkSZathZIoCaRIgAQIYLDMvvRM77XX3d58eJe6Vd09mAEwJGTP82WA\n7upbVbdu3fd5/89/Mdfnielj7FTT+vu+MeGrVAumcJwqVe/WXkBzer/Yz30cESPICV11vecyIEPi\nTJ3Gj07YlLfF+NbDyoO1SO3vzGvxY2LPpZspw+u4xIpqarPxmu8igFZq5G+5/fmd1GIlQALPrbXw\nhOBQbVydYEAqA1pFbgPHWYDBJm6i7hVlcNkwlQb5OFNpbZDwc69cYXXwjR/u3QOVvoH1m89e4hd+\n7wJvsMix2Q2+7YNPsbLZZ6014P6D05baDFhPpSnXRfoOUoymRcao27l+HoZt/GfmSaXkdwYJqfYz\n2iwCXpVHuJqlNMI6XreC18/oC+zkBWAoN3DSgjQYkMqM2K/gCJcCyfTekMwd3QwGqL/L/PLUK6Mv\nujhO06aDHZ8K+NGTh/nWQx+jk3bpORlChMRFh1hvNrqNdaQj2ZMucUSbKSdBn7y0cDpOk/bgCwwr\nHTI/JS2GgIOb6cmCHCXAVXpTPKAXv7VhipQFV1pf4jXnRbwkoBk1LLVZ4JN6wt4wC8+h2p7BT0MK\nDcaEWznSESB79LM+SaTTV/wmPe0h5LrTgMOBxlHC4AEEI7+BRlC3YEIhuyD11G1YgdRhGiXbCqqj\nm5tpDipexEyoACvXmUUIYZlKoG6aBqyp+jUCfzQBEfYrbW5KJZ14NM18PJK/ha4BoARh5OG4Aqm1\n67tt+EM33M5U0o2m1O+xkCNQKZ7wCRqkQ2p+lRU9FVzurVD14jFDSsNU6qVK/rbTJte1XxWXg819\nVAI1Xd4NVPJK8jcDCB040KTRrOA5E/I3t2TUXeRqQ53lQI4rTEJYoV+rkb+VmErI0aZaeDi7GHWb\nemz+YZ5Y3J4yNHrtmqlUZBbwuDWzER9mAAAgAElEQVSopJhKucwRGOP2W5vJfiOrrtMN4zuQft2N\nKjO3XLGDp5K4A6aSvu58xyd0I9uUADgE2+Rvj84/xI889v13ZIge3EWmEoxYfjuDSrfLVLqX/nav\n/uxV75WXwXGoHFeSkOtrXW6s93jwyKz1TnKEoBr5u3opXVnpcOnCnxD5OdOLT3B0/x6eOLXAT//Y\nEzx2Yo73PbDA4yfVZjOsHmTP0c+xdPST/Pc//gT/5Z9/nNdfUqDE+z58lOZ0TDLMGfRTyzhqTJU3\n5oJKNbCSN8CacifDnG57yKVl5VlZiIJ2ewJ4SPId5G+jNcWASqHeFA77GV9afo4VDbz4qXrsG5vn\nFUNHQPDYJtV6wOL+Kct+HtYUmNTTQzlz/xjEbftcW8OW3RS305GnUl+DSl7gEmj5iQGLpqMpTs+M\njJkFgpMz95F7KWsLF8beV5ldtTlQYIBhUB+sq77hSueaNek28p920mauMmvf843uipUImfNomUpZ\nbplKi9U5kjxhKBWY00s7/NK5VRVbr57d+swYds7NQcLnbxT80Y3xpL3ADZgK6pqpNNquxfXRUKEM\n9BmALXB9uzFd1LK1VzdvslRd4P37nhx7jjIgYfwOa36VNe2jZdK55mLFfGoPb3Bh65L9m3Nbl+il\nah070dyLJwQv6wQ+46d0uDbeBzbyEajUcOrMBB7rwxQpJaFbw3UXcJ15fC5TdVPSeojUb/9CR8lD\n6xpUMgnWq4Nx4MKwSsryqoNV7avUGVimk9R9dZmpJLVf6g8f34sArnTV+2gnKYGD9Xft5/kYS0Ti\n8tGDH2So92+nmg1iVxB5tTEm+iAPEMKj4irf0KzIdMKZwHeEMurOlVG3N8FUqvlVfuqRH+fRuYfs\n8XYbThmGTS4h0k3zUnVBG60L9kQBq4PESjIPNw6M/EEnKisKVgYp+6oRnh6mPrznAR6fP8OH9r1/\nx78xJtwbE0yleqCAst38lEwZf89WKpQfLkOEZlul0mNZf+bTyz2+89AcH1hobrvWJutALUIwet5G\noJhKhYRhUShPJd2/TYcGcBTUfdeCSm+FqQQjFlVSSA7Wom2WFLEBlUw6p7+g/Hr7ozAsv3QfMB66\nZQkmYE3Jd/Kzutt1D1T6BlVRSH7tj85zIV6iFiR87okuM9OLvHRRS98OT4893qRLxXpDmgcOg+4W\nm9d/l6noj3ji8RfIB7+D/8kFRCD50iClLSWZk5ImmdV+FrKnfGzaAV5f+b3c6I9kbBkbOMOCJOqS\nFinVIMYRICWcenqO1x/6/dFjnZTCyZT3ETDlH7G/c90mta0IfxhZmvBpbUI80OCFkxeQqhtCp6YS\nRaa6c8xptDsNe6TBaJMl5RApOyAgCxNyRwErpqFxKCXA9aY4OXMcgHNbG3R7/5IvL/8uXhGw79wZ\nMi+xqU5CBCSOIJdSpXp5AoGDm4yaqupyAY7AFQN6aZ++r87ZbDhLN+3iCB8hXBxRYytx7Lk2TBLA\nRsAXRYdcaqpvEpKnMMWifu2jhsA0B6Eb8sF97+P07CM4jr4uyqBSJbAG2GmeICnfODRQJI3pnKP/\nreE6LgvxHHl+EykLpvzRlKDRjIgboxvtbvKq0A12ZSoZg/ZcalaD2M5U6qQd9tWWLNtrmA95Zv9T\nVvoHEOv0t0HeJ83TnT1eSsbbABX/EL47z+mZE9seO/b4Qlrp2lRDvUd3ElRyMiv/GzGVjDRUg0q5\nOb/GZM9sxLNtTKW3SxAyYEeSZxa0LMutJstzPFKZUUjFVFI/e3cASgDfcvDD/OD932enxN+s8koL\nureD/M29o/Q3beofTeEIB+GMmjzhBLsmAN5JlYHe8C4ylXYClONqgOMq8PlWZcCue+lv9+rPShWD\nAYML54kOHcatqPXq+dcVy+SR+yYTRneuQZLxs7/yIu89cA2Jy8ziE/Z3zVrIf/Q9D/ETn3lgzBg7\nnjqJ40ZM1UIacWC9cuYW65aVtLXRH4FKzfGpdlwNLDsJGPvv6zc2aG0NSP2BCh8pgU+Z3oB4k0wl\nLX9zHMHsnLp/GabSzdYa/+ilX+TZ68ons76pNoVvbF5gpafO1cDt8R/8xSf49OceBhRbcSVQJsIt\nNpFSWqPuYaWNm6l72NawpeQ7jr8LU8mzDCfTe06HTU7ptC9PeDSCOie1vGhjXoEORpJu0uEA6xlk\nmFHHm4oxttJbs/K3+7T8KpM501HTGm0nRWK9O9Xvpe0/umlhQaX5eI5XNs4jRBNwqJseTw+Ylkuk\nMQMqGQBnK9l+35ytzLIx3MQZTdIIaqO1q8xUMmB+4AZ00wwBPDqngLPnVq/ywnrbgi2mlDGx9td0\njddfldWhOtYevfGfjqYQOGT5FQoKK3262rlGrhGf+5pL3DcVc3OQ8Mc3Nnl+rYXDiGVjakaMhogN\nt8F06JMWkm6Wc703pBZ/B9XwBBdbl/HFOjgOw4ZaWy73dNpZUKOQkmUNKk2mjBnJlWHrACNfpe7A\n+ngVmF7Xt+chR5kgR57LbOSz3E+QUtJJMqqea2VkhqlkPhrPqfD00hN2k1/xHGaiGgXj37VWqoeR\nomf7ZzOAVcMujyRP6OdDXEfZhtRLIPCpmRPjyYDBLqBS6Tseex4Hanv5tkMftfehuUid93owj0Dw\n4OyIqdnPci60RxerOZ/TJWVDLajyYw/+oPXqmiwLKk18Nvvqaq92tHHr+6sBlVYHqZKkyb4F9JLc\n5ZoG++Ycjyfnm3zq4Jz1T92tar5H1R9dj9NhldiEA6QFFa9i7QtmorIxvEc7UWwmA/rslPJ2q1qs\njPZV9+3gvWSO17WWFRr4zEdgs+eVmUoGVBoxlfJC8rX1NlXP5Uj91gDb3ah7oNI3qF69vElvkCGF\nw3c/fJaZxceQUvKS9lM6fXhco58VEkdAkOmkrcClv/EHtJb/gEZ8gbk9GzAzwD0Yk4mAZzXNrXAz\net2kZCjXoxE0KFoezkDdCFf6oylRUWzgZAXDSJkU1gKFlhdAJYiQ7jgCmvpDG+k6Gx60CWheWqXy\nnGDp4gMWHNlXW8ITLonWp5JC3u9TFG164UXCfo1oY4aZqI4oXJKwT+aPeB1FsTk6H35K5iU4MsTN\nzWRBfSlXTwTI2RN2QXxp/WvkxSqPzj3Eoxc/QaXXIJEJw8wAPh6Z71jqceEZHXOJiqlvOr6T0M/6\ntFz1OS3GC3TSnmVlOe4059opDV+dy7LRY18/X1FskqWKQl3bnCdPJLVcfd4dfVxQspvADXAdl0fm\nH+LPn/p+myon5RBHT1KW4tCyCpIiQRajG3+hF3cDNLn67820aj6eI83eoNX5eabD0c3pW7/7QT78\nXeNGgjtV6Gmmkt40usKxUbTGqDEreSqVQSVHOLSSNvtqI3Ntz/H40P7xKUfohggEvXRAUiQ7sjKc\nCVBJOCELU9/DmbkHd3zd1lOpxFQyUwJPeNuYSmZjrYy6XYR+v0ZeaJsGd1z+ZphKNv1N3Nqo+3bK\nyLDSIrUSyTfzVErzVPlt6c8ieBeBSrOVGZ7e+95v9ssYYyqNGXXnbz39bTps4gjGmEqC7Ubdb6XK\nPmf+XZAOGr+znUDcpz92jO/9kcfHpLI7leu4OMK5Byrdqz8z1X/9VchzKvePNlTPv7aKEPDwfeOb\npdbNP2H1wr9ATjBW/s9/9TIxl5mJB9RmH8a9A1mrqfbWgFo9xPNdpgyotN4fyd+aE6au1YA0yW1w\ni/FUAvjiq1/HG0YUlSGpP2DYyyn0upfZKfho/U/yhFjL36b3xBbYMUyltbYCVrYS9W+lPY1XBLy+\ndZ4bfQWm9LI+QejZ457busjm1DUGlTZbtRtsDDfxA4/CyUjCHv5AvZ/N4RZCCOpBTae/jYNKfuDi\n614lDUZMpQP1vfzQ/d8PSKbCupXgSKcgKELrV2dAIcCyjqY0M2O/Ziq1khar/TUEgiMlT5+KG9lA\nEoDfvfyH6lxnfTpJma2dcbO3QtWPqflVvrZyHsdxEAQMsg6xC6CGVec7o37NACGGZdOaSHoGmKvM\nqpS6YjQMduPR9TfGVNL3Xd/x6GQ5sedyZlb1Wyu9q/xfr/whP/Pl37ebYkCzY9Q93wxSaoFiKnna\n6BjQKX1NjEPmxw9+SL32/hbosJrFeJ7TTXXef/XSCt005+P7Zq3Vgqk93mig1AyblrGyMcxY1pHr\n8xWPTtplva+kdsPpECkKMpMy7FfZGKZW3r+VZLa3U+dyO1PJgFuXO30LLOZyZK8Q6DThtIittGmh\nEjLIVcJfO8mo+Z4FlbpZziAvCLV9wny8SOSFdpMfuy6B62jvxtF1tJHo1GDRpqNBJXO9+o6j5W9D\nBtkAIQICx9nuy1hSAbwZU0kd3+OvPfGf8J5SuuCcBm0cZ4qffuq/4GMHn7G/+9dX1vjZV65YWaFh\nGxlJ2O2USU2bBJVWLQvu1seaDX0coczehXDIiy5Z0UXKgkEhrPTxUx/ZbhJ+q9pTsg+ZjWrE7gjM\nKXsqNfzR66v7HpmU9PPCgj7xW2QqARzbCVTSxzOSSkcPJ2UxAo7LjEUz1C6z5V5tdellBWdm67f0\nq7pbdQ9U+gbVl165gUDSjPpkQ5e/+vM3+am/8/s8/9oK0/WQhenxhiEtCnVzGeiLq5JTDF/FC6Z5\n+dzHefZfHmbwDy9y/rkmvzS8D9NO5G5Kr5PQTjN8kQMFVadK1hHIXIFJq4MR6pkXG4g0Zxh1yIpM\ngUqoRqkcM23W1aTatgv7VDRl9dqOnmpUW7P0ktGmbF99L9KdopAD0i4M1wcMk6+CkMysnqDXTghc\nBz+tkIZ9Eq0pLuSAvFi1T595CYWX4RQBXqZBJZ0CkTQk3aUGly9tMshyNvuKmvvZ458hbUMepCRa\nnwxq8zRA2sXIgkql5kFqUCl0MpIipeWuU8mrHD20QC/rUdFmw1HwIJmUHG9omniJqTTMh3jCpZAt\npEmMkw7psCBI1YK2IUc6/X42sAa+oCYTAmOwV2YqhVYiBlg5m4Px+nEppLkpqfdm9NwVL9IJbMWY\n/Kg+FTG/Z8SW240JM2IqmeYlsEbdhZa/ZYUBWoSlmatzGdBK2uwtgUrvW3zPNoM/Rzh6Ye6TFtkt\nmUommCOX8pYyM0cIHGHkb9K+PlCNVF4ClXDlyBxbZkr6o9OstjOVxuVvkFHIUSKLwH3bN1mbOJdn\nlkVT/vwny3PcUiNsmEr3bvWTNQYqlTyVDDDs3UH6WxlUUp4IFTv1dO4CU2m3lMO3d3wtf9sBxA0j\nn9m5nX0bJitwgnvyt3v1Z6Z6ryj2SaxBpVYv4fWrW9y3b4pGPPouyCJja/nz9DZeoLv2vP35r//R\nWaLkOb77QRW/Xp8blxdNVroDcFAUkk5ryNSMTji6DaZSRfsxGkPrQS+18rIbF9oIBAtz02oIKEeP\nSxMDKqk14cs3nuevfP6/phdu4TiCvQdH0iQDIre6qmfsJGqtdzOf2Xye1f4ay9pLqCwzA3h5/VXS\ncID8wGW6jXWudq4ThC6DimKf+4lOsEvaFLKg7tdopx2bNGlfZ+BacKvMVAI4M/cAmcxpBA2moyZR\nqg2E80XLeF4bjAyx1/tqiGdSq2JP3aczmXOxdZmpsEHFq5Xu3Sb4RBC5IWc3Xud/ePZ/4a/+/n/D\nL7zyS/a47SRhpb9mE9pe21JAG8Kln/VZjFKkHBC4VZvgCyWmku6fW8m4BxSoIQzAn9x81v7s7FAZ\nZQeOb5lK//bqGl9YXtU/D+imOTXfZTZq4gqXLL9Kf/C7tPu/yYvr18eew0izDHBR9WJWBwmzkT8m\nmzfJxL4TWcm+xCXXA+DF6jynmjVmQp8TUzH/8YMH+fDe7ab2e6I9iMLByT3qlapltKwPUytnO1xT\nEsz24DxIyXA6BGd0bmpBzYIKDqp7N3HsUGIqlUCl2HOZi3wud4aWqZTL0dDH133VsKhQ8wyopL5n\nFzsDCqmYLp4eQJvPb5Cp66oZqfNjNvkVz7GAWlLa+K8N9H/LLctUquoBbKBBpSRPGGRDBD6Bu71/\niL03B5XKLK3KDj5wM6XzPluZGZPoXWj3kcBNDfJt6ffavEVa22Q1Ax/BdlDpnP4OHHoTqZoxdzef\na5q3GeZDpOwzyJSfVuAI9ryJOfdkzVdGe5+5Ss0yhHpZri1gjAx09F4bJbPuTpoTe84dgzaR5zIT\n+lRch73V7YPhSU8lR4Nbhp0Fk55K6vMalK6t51fVPv/R2ZEf8jey7u00vgFVSMmfvrKCFA6LjR7P\nXjoMwExDpX48/eDiNhQ6KTI2O7/Kcu8FAA4uLCPISOvH6HRC5luXoJ/zlbmHWNeIvIODdAvanT6t\nNMcVemPbVV+4PG+x8OxN1lbVIqv8dzJk3mYYqaah6ldxhNqsj21cpXp9veoWSagX9mrT+uhgEtEK\nj+7qqGnaXz+E6zQItzIa54dsXOmQpGdxRJ14cIBBP6XIC/xhhdxLLZsoyy5TBnkGVR3znQe4uWaF\n6E2QlH2k6/DFV29wqdMjy69T9acJc+VLUISJRv0NqBSyNUyVxFAIpKcs7HI3tzHiSaTeT6yZWp2s\ny7H5g7iBUIwuDciouFA4rRs+syGVUtLPB9ZQV6A/Ay9FFhIdwMZyfsU2EYNsMBYrL4TAx0jLEoSI\nEKhFLnRHG1Vzwwldh35eIPCU9En9EoDvODYygJzX2vjKRIR94Ph2I70rU8kNKWRBTzcyoauSKwBy\nLfkqtF550qg78iK6aY+leBTHXJ6MlCtyI7YSbe65AyvDNAFm+lMU8Ga4iSuEkr9tYyqNy9/wpPXT\nyTRTyZHmmp5kKmmpn96IS6mMujMrD/TeAaaSMQdP7bmuBbv7EZUZNoZZ9W5Jfns3lVf6XDwhtnkJ\n3AlTyTDHpqMphBAI4RBbr4rtnkpvpcrfyd28w95O3cpT6U4qcP0xz7V7da/ezdV75WVwXSr3KUnG\n115fQ0p45Pi4NGPQPofUQ6ONq7/DF37761y6+DLH/X/OJ05cwHMyzr52GC/cXdb74pev8g//1z/k\nwmurYz/vdYYUhaSpwSQLKm0qplIU+9tYgrEFlRLSJCfLCuaX6jiOIGor0KU5HZNqD8yeToArgzUA\nX135OhLJSnaTz/3F9/Lkh47a5zBy197ApHlpP8nMZ1pHom8MFKBgvCZNvbLxGp5wLRP5Svs6fuAy\nrKhNj6dtDHKZ00171IMqWZFReCkSSGoekhFTSVJmKqn310rUsaa0T99MqnqLRbGPqqc22mv9ERvc\nMK1MDyTEyPcxKVKmwml+5vkLFlww67jreHbjvtJfJXQDXt14xfa/l9uXkEiSImGtv04rHQeGQkf7\nW1FnY5hxVMtSJuVvhglRLiNz+r3rf2h/tiHU9RN6oQWVvrC8yZ+uqN7ec3z6eUHVc1UYyP3fy8cO\nPsPRKWUP8OrG1bHnkKjn7OvPUIiYpJDMTnje7KvpJKrgAKEb6P4uo8g38ZyAml+l6rv81YcP8yMn\n9rFQ2bmPrMYRMzcPMn3zANVqUGIqpSz3hnhCcHLaGFsn+IOEYSMgmQ6pxp8hCp+k5lctAHVER7Mb\ncA7U5t8R231v5ishw6LgoblH+cTBj+N5B5nW7Jul2mnes/AeEC5VDSIsasDijZYBf1wiTy3ohsUz\n1KCSUQrYeHrPJXLGN/5SSm4MMoqiTVb06WaTTCUlfxvkQ4b5ECF8GwhTrsptgErj8rftQ7IRqDQu\nieynuU2MW9PXqGUqvYkPUrlcRzAVeGNgX1ZIzrf7zEXBGOC3Wxk2FUAuuxqE7tHJClb6CYtxyJ36\nhe6NRwOyehCPvIyyXHsqVUAOx2wj6vq1ttKMbpZR9W6/PyzXDxxb5EdO7NsRkJr0VJKoz7goyvK3\nsqeS2Y+oxw+ynJc3u8xFPnvfxLD8btU9UOkbUK//1u+z96aicDaiAee2Yu4/2OSv/8Un+fv/+Yf5\n7Ie2O+f30xZJdo0L4k/I8zWO7blEXgh+7sXfoXLlBeY6F6E5zcrCPnKp6dGuYg2ttdoM8wIHnYCx\npT7msCMIOimdltGYqy9rxhpJRVFrq0GMQFAgx+QWXqajZYMuWaQWnj2VaQpNyS1os/LwDN3FCoOV\n0ZeloVO5ok1J2M64OXMeKAiDR5Ca+thpDQkGOkY+a6nmIdOR89pTqLtXTxXiKsPDD9DZGyMj1Ujk\nuZoKvd4d8NzNc0DKkamjXNV+VXmzq5MURkbYWSFtkgNCIF3BoLqFq8GBVvUlpJTU/dF72V9bsqyc\neknDvDcOWaqqTb5hKg3zhEIWI5+SQE1BDSMm0f4JbWeDdd2UDbLBNqAndHSjJlPAYyb0CVxFhTUA\nVBlU6uU5iBGolOQqCaRaYiXN62napN+REMJSancHldT5MeBG6AakRUaSJ5apZJLoPEcxjgz4FXsV\nJNKmrDTDKdvcTVbsV+y53omVMemplEv5plMDV4gxplJQYiqVQSXhSis5y4oMxxGcfmiP+SWgmEqu\nGIE1gQYgJNqo23gqibfvqWRBpTzdRpXe+fHl5uEeqLRb+WOeSk4pwc/87PaZSidnjvP+vU/y1NJ7\n7ecd++r+JAhumQB4uzXGVLoLnkrv3/skz+x72ia6vNUKHP+e/O1evasra7Vof/lL9F5+ieHFC0RH\njiJ9nz/46jV++fcV42jST6m78RIATnwcWfQIst8lX/1lfLfgWuthfvvz7+P1cwcZDna/9q9f2aTI\nJf/mV15iZXlkQ2D8lJqaqVSthziuYHOtp2RxzWBbdLwFlTqJlb5VayFxc+QNMz1TtUBMt61BpXQc\nVDrfUszuzWGL5kw8JovzA5VeaoiHBix2c59YS4UMK7bMVOokXa60r3Fk6hBHpw4BcLWrQKVOQ63/\nXhZYtsjmsGWNkwcM6M9H3Hhyge5e9XrC0GPjVJPedIaLa1kaLT14agSq9z2SnqLaWeKYf59lQJv+\nyrwugMXqaLBlWE8Asd/U8Io6frkHcR2X/+6pv8b//MGf5kP7308uc7L8un6c6jWvdK7za+d/yzL4\npR5GDVPlLWXMu081q8Sesw1UUu9pfINvvIt817O+Sh8+8SQP73mAul+jnw3ICsmwKOiZMBrdpxgJ\n15NLj/M9932a9+99j3qd3ZHxbyELUs3ENr5Qw0KtL3ui8QHDgbpimediP1lRaMZXTiG3mA4btz1A\nq8Q+S5dOs3T5FJU4sEyl1WHCjX7CfCXgcGOUQlbtJ+AKbp5ZwHMXCfwHkSKyoNID07Udz2PD97YB\nDoZpkxYB79/3YYTwrAxrsf5ePnnkO8fOnQHGDLum6rvEmiFiAJe86OmgHg3O5aMgl0A/dqiHme00\np5cV5Pkaw3xorzHrqeQqplI76SKRSNxt8kGYAJV28VQKHcf2uZG7A6gUGVBpnFl8YatnR/rmGjWp\n2nfCVAIFQrXSzIZPXe4OSAvJscbt+f2Ur0FZ9FgbrCNlj1xCAW8JPFkogUqxVxmTnRlPJUeMn5OG\nvh62kox+Vtyxn5KpfdVom8eYqUn5mxQBUhZIyvK3HTyV9PX24kaHTEoemb397+I7XfdApbtc2dYm\n8v/5eRZ9tfi1NtQX8oNnFMNlN4Q10RpKKQrywW9T87usLEt+6JdXue/87+EVKb33fUABIlrfO60T\nw24ONNBT9Giu7uPlL6oFJG6rTU6S62mE1muuNv/YpmqUjbrN5F7g4SWq2UmDgXpsIQiFAPqAIPVa\nDOYq9BZj8pXRhkeiFtGgnbB+BNbnL+HIKr5/nLyivhxrN7sEQw3KZFsUMifPruCIBp6jzlOGbkQS\nnyII2Dg1DZE6dppdQBQFvemQl1cVeHdmzwmuXdLNxOzAUkkBqhosWytRMlf3XkLGC0SVP4cQNVJ3\njTR7g0YJ6d9bApWaYc0KWs7M1O2GzzCVDIC1r7ZELXqCKFBTUDx1s8jaCZmXI52CzeEWaZ6SyXyb\nV07FU685cCRHGjFnSpRG81gjf4tch15WIHDthC0pkm0b0NOzJwncYMzbyFTs3Tru3YBNnURdY4bd\n0Em7ZMUoXQ8UkKGkbOoGapq8Xzj7/wLwF059/47Pod7L6Ka7M1NpO6j0ZtMKVwjtqTQuf/OER+6U\nQCWvzA7S+v2aOYcjplLkjvyS7GuU2qjbbqrdty188i2olFn/qtotktPKqWUG4JtMmbhX4+blZfmb\n/dkdpr/9ufs/y2xlxm7oqjpP23HCN00AvN3nMHU35G8npo/xuZPfNUaBfyvlu37p+r9X9+rdVyv/\n9z/l+v/+97jyt/8mSEl08n7+5j99jp//jVfoDjK++5mjLM2ONmppmtBef5nNfsj/9OsLZHmVhfl1\nBAW//LXT3Lx+hCzTnmy93a/9zfU+QkCWFvzqP/sqGzp63YBKhqHkOIJGs8LazQ5FIbk2f5b/6gs/\nM8YGMh5I/W7CoK+esxL7VJqj7++emSnrgdltq/XZeCp5vsvWsMX6QAEim8nIe8iUEALHF7j5CKgC\ncDPP9gqmBtnApqOd3XgNieTUzAma4RSxV+Fy+wq/dOmfsbXnGv4wwsk9jk8rVtTWcMsCTAPZZ9hU\nfUX7UB0vcNgSku7eKoXoElKx6+7WUAFzStIPw8YC3tKnSSs1mhpoMsATjICvxVgZjf/B8gaRP0qR\nU55BgI6Uv9q5TuAEeP77FHOnMoPv+jYQRDHqIcuu2GN8cfkrFlQyXkMrfcUscvRxjzRiK+sZ5gVb\n6aj/aKXjoNKh+n4+cuAD/NSZH7cpeB858SR/6eEfJvYrCpjI9DWnh4lSeylOsnQO1BWYttpftSzv\nrWGLQkNpNzULa1PHl++Z8Lx5cvFxHl/6LK57jOu9hPl41EOWjaPfrEw6ICgZpzF/fn2rRyYlS3FI\n7Ffs0HFGs+u8YUaSvIwQDq9uFSz3Ehq+a2VUBlQqpKSdZjsyYQwospmk1sbAMLL6WTGKi9fnbjby\n8YSw4ErVc6l4415BkiGuKEagUpYTuQ6OEDYh2PzuRl/19IJNkjyxewozKDQgUD/TqgfcHT0xy9YS\nuw0ZhRDWVyn2tq/rNc/Fd7qV9roAACAASURBVMQ2ptL5zREzxpzTzeF2j6rbqZlAMQ6NCb1hfO1k\nVL1TzZWuQSl7SsIqRyDLWwGV4hJhIvZGTKVeluOIECFCImecMWiupeXeEMno+ngna9KoO5cBUg4I\nSzK8sqdS6KrOcqAf/4r+3M58k6RvcA9UuuvVffFFBHB1j44uHS5QjTzec3KcIp0X+ViiVlroG0/h\n0C+2+NIwpfHHywx9wWtzj/Pqmc9x4XFFKzagkpkwbyZq4XSWffafO0OnNWRmf4Rb6HhJvfl3RIPJ\nqgUxDkoOZmRZjlMhTBVglcUeaZDgpxGbWwqsElRI/S5S5qQVARsRvaTPP/r6L/Kb5/8+SXoWmbTZ\nnPs60iloDB9BCJc81tOJG238oVoUsqJDll9DkuK6+/A8RYHNc0Ufri3nTL9wkdqljpWUQUHD6ZLF\nHuuJWuQfnjvB1Uub+IGL11S+SH0N9NR8Q5UdNX/7zjTxvCWEcKhWvg0QpMnXqfujm8++MlPJj+3N\n+qGZmvUhMaCSaf6aUZOpymM4JrY00Pi/hDw0BoMt+9ommUp7gjaD4Zepuqv8xP37+fi+0cJtH6un\nh6HrKIRbuNYoOsnTbR4pj84/xN/50F/fkZFQ1edm9/Q33expcCPS0xIFKhlzagNkGIaSAZXq9hx9\n6vDHbVrfThX7twaVjMTceCopJ6lbl+cYptK4/E0xlUrGlZ7A05t2S38XZoEZGeNVSou0kTlKMgqp\nmEoCByGcO6bmbn/d2+Vvt2YqlRf9e0yl3WrSqHu7/O2tNQ3msI8tfJDPnfhuhKi+Q0yluyt/e6fq\nnqfSvXq31+DiBZwoYvpbP0njg8/Qe+h9vHZli+P7p/gff+J9fObpw0gpGfaukWYpv/Brv4UrUl66\nMUd3CF87f4pOp8KXnztN7Bzi+qURG6a/C6gkpWRzvcfMnioPfWCBQS/l13/3S8B2phIogMnY67TD\ndQb5YMwfqBL7SCS9bkJf+yVFsY/XGG2GFuamSX0NKk3K33yX81sX7WO3httBJfXAHDf3OFoysHZy\nf0yC7QgHibSef69vXgDg5Mx9CCHYW11ktb/OixsvE7dmOPrS00insOlrW0nLDp16RZ9EJ31lsce5\nYcJXBh2kzJByiCdH9z4jkTfyt16o1uQscJiKlBl3KxkZXCe6P2sEdc63+/zG5VVaxSH7+6qv/gah\njtdJu/juNHjHyJ3HbU98ZOoQvhOQZVco5IAsv8RivMAnD31MnTJ3WknPhJFJbejzVCVyHRYrSvKV\nS2kTtsx6NMlUch2X7z3+HRyfPqakgIFrpZAVTxmJbw6NTYJJfXb0+xlf04wnUppvWrPkm72RFLOQ\nBTW/ZoGE2Qmmkuu4PDJ3CiEEl7sDKv5e+7ulEvvrzaoSj4CCuOrjO46ObFfXppGcHWmoz2Zv4TP/\npRWOnd2gP3wWKVOevdliK81YikMr4zI9fSfNKYApfwdQST92M8ksI2Qq8PCEoJfldDJ1Dg1TyRGC\n+ZJnT813qXrqGLlp5+UQT8gSqFSU/DbHQSUDWrkkDPNk5Kmke28jy1chKy4g7DHK5bu+7VnquzCV\n1O/MtbK9nxFCeRatD9MxL6/zW137WkZMpYy6797xgLJZ8m0CBSoJuO1ksrL8rZA9MpnjllhEe6u7\n+4vuVuUBXcWLxuRvNzRAf2bP/rG/MZ5K17XH1CRg+05U4AhcISyolBYeUvaoBKP3WE5/c4SwticA\nNwcJFdexBunfjLoHKt3lWn3ueag4bBRVfDenk8BTDyziT1yQv3j2l/nrz/5tClkgpSTTi9/CyiFi\nIfhCP0He7PGHj9S4NPUQ+ewSV3uazSQHCBymY7UgnpMvUxRtgk2fvNbnB37iCWSqaMqFJyj0F9Jx\npkBPn3zH570Lj/LozMNUnl/BPb9lF2MhKgRDdewicMmcIX4Ssbmp2UNFE4SkKLbIQ5ck6PE3vvi/\n8aUbzwGS/uAPuLH0Aj0uErenqXbVl7XQTKWV5TbB0GhHNxkOv6JfXwPXU4tWIVXz4A88/B7Uz63w\nkaXQTtXnakOkzMiLm1T9ecTAY2u9z9KBKQJf3UDa+v3UA61nL1FlT86cwnHUlMp1p3HdJbLipp3g\nuMJlrjJLLzNU1ZiP7J3mW/bN0gwVNdp3PCt/M0yl2KvgOcKmuLnBaHeZB2ba1rLN2CSoVPF8hslX\ntjUHMEprMql1kevQSXNc4Vma+k5MpVuVoZWXpY/lMjdjQyM3gFE36ZEb+Zuekvn6PRua7oz2Qbiv\neYRvO/yxW76OyB0tOLcjfytuk6mUF1iD9hFTaVz+5vrgl+RvAC65fh4HKSWDLLd+SmOvUWZKvlmk\nliX09o26R0wlA2rupqGHUYILYOV690Cl7TXmqeQI66dmyr0DT6VymaNORws8s/8pCnhHjLrLQNKk\nVO/dVMpTKbOshXt1r95NVaQJ6Y0bhAcOMvd9n2Pxh3+Mixp3eOqBRWYaek1b+wo3zv4cl1/8P7hv\n6jUAPvHMx9gzFfHCuQqf/8J7ubk6i7vSoyjkiDm0C6jU7SRkacHUTIw82Oalx/81lwYXAGi3VN/Q\nLAW2TJX+e+Co+77pYaSU/IMr/4CrR7+mQCUtf6vEAdS0RC0Q1KsVXI39jORveqgSuJwrgUqbg51B\npdRJcDKfR+YeANS9zClcauFoDTKAQlcP0wxAtacyy43+0ALiJ6aPcfTsE/hpRBB4zMdquLo5HIFK\n7bRDWvNxhjlIyedXtjiX5BTSvPfRfbplmUpqQDrwjOOyYyX/ZhAD6A2p8mf6V5eU1EuWxlGxp3oU\nwygCyFG9r3AP8vnrChzyHI+5+BCFbDEc/ilQ8PDcI3zyyMd5Zt/TCFFlNvSp63NkEtpcp8p9jRhH\nb+YBXtPMjcOabbM1ASqV6/GnD/HUR45appbprTaGfX1uDKik1q5JNkXg+lT9KYpikys6jv1mf9zf\nqxZUWdUAwKSnEsABvYm/0hnQyUeDyYON/dseu1tFJQP8iv7vmdJzLWn2yXce+xQ/+fCPUotiwlai\nGUIprrxCR2+8l+KQ0FWglPFU2smk29SIqZTRM96Ynkvsqc25ZSqVeu5yalfVc6kFFTvMB7QJu7AS\nt36e20SxUPdeQ41AGW8l35Fj8jfjAWb9k4SHEP74zybK9Ou3GjLWNTi2k1E3qPM+zAt7LqSUnN/s\n0fBdluKQjSQlLyRbSXZHyW/l44M638O84HJ3wL5quCPItVNNyt8APGGGvYL56M4HbGZ/E3khruNa\nFlcvy7mqJZWHauPn1FxLRnL5VuVvtyohBFXPpZspKWWBQyH7xGEJVPLHP8eK59DPCvJCsj5M2RMF\n3zTpG9wDle5qyaIgeeUlbj52mrVeRKAvwmfO7N322Cuda6z21+ikXZJsgDQRnVHKqcAjBS4eWuLV\nw2qBC0KPmxpRVTc3QaTBk1XvVYbJi7hJTu0ADAcZGzcGtJs3yWsOuZOAVAwkcwnEXoVP7/k0/9/P\nfh1/uYd/qWOnYkJEBGkdNwvI83VA4mUVNltqOhemamEpik1wPS4f/RpryRrP7Hs/1cpnENKlV1dM\no6VLp/CG6uZV6GnLynIHP9FmienL5MVNFqonkTLHEZGNdwTw0gC/75P7CScakWXULEQZWb4MFDTS\nBa7qyeG+g01C45M01Pp7f9wkEaDAxXWaFEWXYfJVAk/5XF3rqHS2RlDHddwSVTXmyfnmWLJF6Ibb\nmEoVN9IAiE7hKt0Qcm2CuTVsWb+EaNJTSW8kd1o0DADlihxPwEMzdTpphu+oTZ2UkjRP70gqYxLh\n3kz+Zhq1uMRUSo38TS+ERl5kXueZuQf51OGP82MP/NA48LHj67g9+VvxljyV1PUXlJhKxuuqEDme\n61q2T1aYFAa94OKSFMrWssxU8sqeSpqp5GiQ4u0bdev0t2Ikf6veSv5WNuqeAPju1ajKiXieEDpa\nuUQzfstMJWOgL+2/74xRd9lT6d3LVDKsvTLz9l7dq3dLJdevg5QEe/fZn71+VYEg9+2bsj/rrH4F\nELjFBgen2+SyxszMQb7j/UfMHIdEQK43Y6ceVlIgI0WbrK11LZ2fqbA8uK5CVfxNikKO5G8TTCVQ\nw8Cu9q40oFI/67M6XGNr5jqtTpd+Sf6WVbX3y5Svf6buad2OWp8NU+mlzVf4k+U/xREOU0GDzeFI\nJmYqL3L6oodbeMxXlGTMlR4CQT0a9SSH6gcA7NCtlXRwhMPqQPB3X7zE5a7qJY9PHSXQG9NqHDEV\nKjBoa9iy8rfrvU2kK6isDqisDjTAIRB99fpyObqZThp1G1ApqAUWcOlnfaSUlhESugFfXm3Z5LBC\nH+/k9H00I9WbG2sIACmm8FmlKDr8m6trllk0FSkQJUlfAQTHpx/CdVw+cejbkQhmI5/paHQ9AXzX\n4Xm+85A6jxZU0qwQw9xop7vfN0+dWeKBR0fXremttoYGONOyGcY9lco1H88hZZ9zLXXN39Q+SqZq\nfpW1QULgCAtIlGs28olchwudPld6Pqa3PVDbt+2xu1WZqWRSDKd3AJWmwjoP7jllzYlDP2A6bLIQ\ntrY9djb02Rwq7x4jIdxJqmV+tjlMrcFx7LlUPJdelluT5DIgt1gyHK96LrEXT4BKQ0LHIS0kSV6Q\nFtL2h9GEp1LfgkowzFO62XhP57tlppIGlXZIf4MRqFi/BahkGDa7gTgzE0yiVpqxNUzZX42YjXwK\nCVe6A3Ip71j6Bowl+11o9ykkHKvfnvQNFHhTcR0UF9LYgajPaKESjNkY3G6ZfUxVsy0rnrKp6GYF\nVzXYum8ina3iqrQ3wzi7G0wldVyHXlrQ0dewlD0qGlRyHIE7AQ5WXJd+nrORpBRyXC74zah7O427\nWMNLl5BJQroUAYIkFeyZitg/vz2a2YAKG6vPcePFv8WMjppfmG5zUG9+XjlxkKanQIw8djEBE4Uc\nInHGqNGF7OIOcw4cmuHXXvodXnrst1ibv0gRFuSuXqRFhElYC5OYf/FPnmNro4/0BE4vZbW3po8W\n4SchbhYBumFyq7S3eoBH1FPSuLzYpCj6DGqbTA/neWz+Y3jeItO9pwCB5x6i4UxRNZsuDbAM+qlK\nApEqGNR15nl88ZMUUj2/EKMbkJv5OFKQeQmZzCyo1Ag8KkIBV+6VGq99XZ2/vQebdgO2pRuQZqib\njVLKxjB3cZw6ebHBYPglPnrgERzh8PK6im41zJAyqDRZoRvY9DrLPPINqKTeqx+WQCV9s91KWqVk\nuolENgMqedufz7x3z4nwHIcHpmskhRyBEHlKUqT4d7ABtUyl3eRvnmZ9TYAbnbRrqeWUPJVAJbV4\nwmU+nuXbj36LbQBvVZUxT6XtN8lR+pvatBdS3XBvVZ6zs6eSI1RqIkDh5viONwKV9OTPEUbj7NpG\npMxUcoSjzrtUtOusSC2o9HYBBcOaSvKUTtolcqNbJpP5Y15Amqm0S1Py73P5E55K6t/RtXYnnkrl\nMkeVpX/fibPviJGZ+N0w6n6nysht7/kq3at3YyVXVfJVuG+0CX7jaosocNm7R63zSf8GSf86XvUY\nP//Hj3Lt+hxXbyjZz1MPLhDrzXZtrwJFpvfELB1QAMKgt7P0c3NdgxEzsWXYDMOuNeOuVH1830VK\nyfM3X6DSUPefws1IpDpmO1WgkgFTpFNwTV62Pk5R7NMP2nQaqxw8qfqyWqVK5iZ0NFPJeCq90X2D\nbtpjMZ5nPt5DO+2UUktVXe1eJ3PVc9dQ67axUZiKVB/rOZ5lIRvPonbaoe5XudZLyPM1WsOb+udd\nHB1+0ohrTAUjUMkYdV9oqT4uaCfUryjwIHJSvC3V32aFsMMV09PVgzpZUdDTQL4IR2beuSx4eWOd\nqx113MiL+ddX1ggcwWzoW5BqPp6zcqYyU8l1mkRijf7wj5DA65pZ5Lvz+hESz92PcFQvZNgyM6HP\nbHV67HyeaM5bhoPZzK9oxrwBlVpJzu2WARVa6ThTKS20/G2Hje/BmmKVmfNclr+pv6myNkyZ3YXx\n4AjB/mrEZpKRSUEznCfywjsKeAgjz0rCjb+SOR9TgbcNAHE1QOP5Dn/tib/Mf/jgZ6yXjgWVogCJ\n8jmyTKUdGP5V7SM0xlRyHSqeyyAvLCBVBuQWSsyqqu8S+xWKCVAp8kYMKHVM9feT8jfjfxM4WKaS\nQFh/Uv8OmErNcIrIjbbtG8r1wHSNQ7WIg7uYQ0+CSpc7au9yoBbZ373RVtf8dPgWQCUNRH1ppcWv\nX1YA5rHb9FMy9b75Jscbo2sx1KDSJPBzu2UGdDbFW3tf9dKcq90hkeuMMedADYcbpWtiJ8D2najY\ndxkWhb2OiqJPPdSvcwdfrIqnwMxlve5Mmut/o+seqHQXq/viC7C/xvrQNNmS4/undnysASGS9jlA\nsl+oRI5mPGT/tSFCwvVai2lXLVL9SGvHszUgQQiPC63L9niy6CKSlPuPHuJqcoXCyxjUt8jDjNxL\ncaRBwTVqvtkgSws+9dmHKBZihISrm9ozSUR4iYcrSxrYsMGwm+E6M8RttQAXxSZZrgwLg5vT/Mvf\neAUAJ4XAP8NM9Sm+9wee5MPPKB194QkLBAgEHrMIUSOufIJ6EJFlV0jSc8hSnKKrU+gyPyEtMgu6\neMLD4wbgUL9R59K5dYLQZc/CyETbNGIz4XZU/6b2fSuKTTzh8KmDB7h/5niJQWQYOrcClUILrFiP\nJDfCdQTSyAyj0U05D9UxW8P2rp5KFlTa4fnMQuI5FbJCWmQ7cHUMsDbOvBOm0pOLj/Pk4uMcahzY\n8feT6W9VC7Z1LUtLTHgqfdexT/GfPv6T29LmblUmhQ7Y5gkF4/I346v0ZriJKyAvtnsqAQhPy+ic\nDE+4FlCwKW76e1LIkX65MnGD9x0PiWKIJSX529uVPpX9nbpp75YsJfX4slG3ZsjdYyptK2/CUwl4\nR5hKogR4qn/fXJp5u2W+fzt9J94tZQCve75K9+rdWMOrukfZp5gmnX7K8nqPY3sbth/prn8VgPOb\nB1hvV3nua6dYXVf+jq7jMD+l1t6PfvQYh4/P8p73H7ab4/5tMJVaGhxKoh43rm3R3hpQ17K7N7Yu\n8LMv/mO+3n8RgDwabV6N7Nz0MgDL3mUruavEAd28w4X7v8jjTx4GVEBGFgy3eSq1hWKqNMOpEmOo\nTS/t85sXn+UfvPAr/OOX/hmFq4+th3tOoe6LcaTTqoRn12vD0G4nbepBndVBwjBR70MgeGH1JSs1\nn67WqXgRgeMro27tC3OzryRmQSvF7fT4nsPz7A3PU6DOn3ACrmsGeWvYourF+I7HZpJZIH+YS5um\nBfCLr5/nWleDVe4UvSznqYUmzdDTfyPYHG6SasmuAYgAHLdJ5HYVE58Rk2hQBDhC9fO+f9wyXIyv\nz2zkM1MZJcvByM8TxuVeAthbDfEdsc2o+1Zl+sVO0tM/GQeVdtr4LlYVGHajt0JaFNzsr4z1naE/\nRVpI9twiOv5Aycfms8e/j//2I3/lTdnn5RJCEMU+Uezb75xhtCxWduj3dK/lug41v0rkhXzvkQW+\n+/C83UTPlnyVjC/VTswaIVTM/WaSqbRkFFPJJLoZr6myvGlhG1Npu/wt1qCSMaQ2/WE4ASqZ/jF0\n0X5YLap+bK08TN8shGeZ5uEuoNIP3P9Z/vJjP3HLcI3D9Qp/6dQB6xs0WZOg0pWuel/7q5E9p2+0\ntEfsW5C/TQUej8zWkVKyMkipuA6H6nfmg/SJ/bN8dO/oO1n1hnzm4BwfWXprSbWOcDg9c5IzSw+M\njum7bCYpa8OUfdVwR0C1LKe8e0wlddwb2rtJyh5VDSpNSt9glOpn5KzfbFDprro5nTx58tuAv4sa\nl//c2bNn/8bE76eAfwIc1K/lb509e/bnb+dv/yzUxle/inssZrk9AjGO729ue5yU0oIXeV8tfCYS\nPhQC7+UO8XuW6ATrxOIIKZKXxLPk+X2k2QUAAjfmaue6PWYhe2ThBkv1ebpai+56kIZDCjcjLOrW\n5wdA9HyEgAcf3cevvKjAqeWWAZUqeImDYHRjzasNnCTH92aJtjzAIy82IFNfxAcXT3Au10BM5SK+\ne4g9lT00Z2KcWgDLa0hUdK6hfjfcbyOLPYTwiD0XzxH0B789dq68TG+w/SFZkdlNYJInrPSXOVzf\nz1S1Sqc1ZOlAE8cRFpgx8reZqAKM+wcs9w0qvGnlG4/Pn+GlNcVUMsDMSP+8C1NJeyr105GczRMC\nqadhUehhlv8iCoi9CptJSf7mTsjfnN1BJdMI+G6FVEprchjq19/RlOg7kcocbOznL5z+3K6/H6W/\nqWMbH4RO2tOAmpL6CUbAz1TYsE3r7dabM5UUVJNLaX2V3ixhy8jfJj2VoAQquTlemalUGAqq+jeT\nzo5MJXU8n0GeUUj1dwJNWX3bTCV9jWcJnbTLvur21L5yjYMh6m93Sg/59712YiqVQSX3LTKVzHVY\n6O3KO8VUAvX966TddzVTybCpkntMpXv1LqzkmmYqafnbG1r6dkxL36TM6a6/gONW+K2veEQiAwnp\ncMQgCV2Hvu9wZN8URz77EACdllrDd0t/M0yl5kxM94paPws34+y5qxSFpK6BKgMY3ZTXcdwD+NMj\nRrWRvxmmE8BGdZme9mSqxD6dpIsrXNsf1P0qm8GAdCsnTXJS3Sd0hTqWIxymQ9WX/vPXfpWX1l8h\njD6DcI7Q7n6BpUYDViEfasgmU+vLUPTt3xtWUC/rk+TKgLge1LjSWSHNXifypvnI/sf5jQv/luls\ng5hpZutNtcEPG2wmLcsGT/OUsJD4nZQkTHh0tsafXL1O7uhNvBNyvXuD49NHaecz1tupbGcwzHP7\nmtT/d7jY0n2wowazx+oxN/R0P3BjNgZbNvbcMJUEDoEzRewmFIWR9qleoJumhMHDTHkX6YlD1lzX\neHXOhgFb+fgQueyF1wg8NeiSauPtOw4N37ulp9JkWVApNf6qJvXXGHXvBCopH6u82OR8a5PV/jr7\na3u51FZgq9ABPrO3kNEcqGlPGtfhzNw+FmcarKy0d338TvXEM0fGX5cGk0ySW7nKTCX7+DhksZT8\nNRMZ5ldifW928lQCBY6sDnr2XMeea0GXlUGCJ8QYkNPwXSLXQQglm4/9GClHewgph1S9AEhHTCV9\nvMn0t4EGsgwYsDncGmN5jYad5XThnUEj9XdvDVgxtY2p1B0gUKDSslDn0bCXmm+BqSSE4PuPLqqg\ngiTDFeItpRGX90AVL+Sphe176Tupn3rkx5mbq9vrNvZcCyjui3cGveol5tvd8FQyrwNUyhzAfKXG\nwwunWebCmEm3KeOVddmCSv+Oyt9OnjzpAn8P+CRwGviBkydPnp542E8BL509e/YM8GHgb588eTK4\nzb99V1fe75NfOod3JOLa1kjusxNTKSsycpkTCohlwsU0Y01Pg4KBJLnap6gcASHJSOlXN7lZvMgg\neZ4k/ToAVW/8uFL2CPbkOMKxJo+u69J1N0GAV4zHXOepZH5vQ03bqj6ZN+Rs9yy+4+EQ4yYS4ZS+\naEEdx58mZk4ZNzpNimKLLL8KeDzzwdPMHFM3u354DdeJ7BTJTE+kVKCSqSIMLLsjcp2dGSoaVMo9\nxVQym/8rnesUsuD49FEee0olRhw8qp5/xFQaGXVPskzWdcOYF5sWQDoz94A17zU39242Hv9ZrsAN\nyGROVmSWeRT7FVxh+GDjUap56NIIGrc06j7UOEDFq3CseXjb8xkAKtCm1ptDkwI3bkz+Tm5ALVNJ\nn4cRqKSYSkJ4CCG0Oflb30qPMZV2AcUcISikYh8Bt+WpJBkt7mVKsec5ZF5K5g/xtOk6jEAlw1jK\nb8lUCoCMgglPpdt5w7coc423ky5ZkVkd+G5VBkMsU+keqLStyj5ThqnkjTGV3qL8TZ9qE6YiJe8o\nU8kT7i0nk9/susdUulfv5hpevYI7NYVbV33ZG9fG/ZQGrTcosi5ZeJLLK30OG0lcabOfJjl+MN7g\nm7V9N0+lzY0eUcUjqviW0QNw7obazBtQqa9/d3Owwke//X4OPjzqHyflb2ERkfkJ59sXcV2BH7i0\nkw41P7brbz2okZUS4AyolLlq09JJOnbo89XVF6kHU3juLI6IeHrvB7i+51V61U3afbXmOxpU2swV\no6hAWgCnn/Zt35EVGS/f/EWgYK72GJ8++i183/HvtEmrczXVnzXDKTpJV7HVNds3GmYICYWTszbY\nYH2wSab7WJwK17vLrPX7eMEHke4jwCSoJMeY0YXssdxTn3NOU2+aQ7suNsMZNoabVhovZYoQMY4z\ny75ahcB1gRTfEbT1+RvkOb5/nB86/aMI4W1jKs2EPtPRaJjmCnesJ3KEsOwcs7FvBB7dLLc9zZtV\npQTmAQSOSR8T24ARU8YcPS+2+MK1r1DIgkfmHkQgEKLCuY76XG4V1X6gWiFwBA/N1N6079qtTp/Z\ny+mSv+y+asRPnjrA+xe3gwXGU2mnTbUpw6r5rSurvNbqsSfybwEqjZsuVzzHgkCDvKDmj39WQgg+\nsW+Wb79PDfTGmUoZvuMS60S4Df35W/mbM+GpVJLcgUrcMybdUGIq4YGVv929/q0ZegjU96ef5Vzu\nDNhXrxC6jgXqMt3MvBWmkimhr/fdPpM3q7g0yJ8cvr8TVWYe7SarK7/2mnd3ODmTTKXPnfgkx5qH\ncT1nR6aSuW6vaDDwVmDwN6LuZmf6BPD62bNnz509ezYBfgn4zonHSKB+8uRJAdSAdRR/83b+9l1d\n3Ze+zutLh/nV10+w3K4igDh0WdqzHYwwAMSSvgldzQou64Xb/foWFxZDQkfJkVpFiyRSi2uWXcd4\nHFX8Ku7Yx1kwvS8kL3ISTx1fIBgEOr0ii5CMJmCZl3DgsJrgiFrAzX2vk5JytHEYN/MQEoQzWqQd\nUSWvNWkMZsgqHq7TBHKk7OM40wzyIWvDBF9kQIbrRNZU2bBMCimp1ZUOWrgCWdIvRa5rGUP2OTMP\nod9j5idkRWZBn3NbU7x9CAAAIABJREFUFwCVLHb6kSW+6wcf4fQjagEwoIREWhZKYxdqr2IqqcdX\nvAof2f8BABINKhizx0nwB0YsniRP7CIfafmbghYEc42Rxj4PHZphg342asSiidS1vbVF/tYzPz0W\n52vPkUkwcMeNxyPje2SYSu9gUlQ04bXU8FXD202Up5Ijxv2U3mqNMZV2ef2uUNeQZSq9yVOaBnKg\nF/Wyz5ArXM6f/gpXjn4V11GbdoGwoFKm0/TSMlNposHxXQ8pM4pCKqaSUIv12zXqNgDXel/R72+V\n/Kbe5+h1CeOp9BYmQ/+ul7ejp9I7IH/TMKLZFhRW/Pr26+m9T/DM/qffoaPdnTLDgHtMpXv1bqu8\n3ydbWyPcO0qqev2KAhuOan+k9uqXAfjihVkA9jZ1iEiJqZSmOcHE5sjzXTzf2TH9Lc8L2psDa8Q9\nyEfSmb6vh10aVDK9w83eKsdOzSFqo+O1kw6X2lesl9AReQKA1eA6YUUNdDpp1/oTAdSCGmmgQaX2\nkDTJyd3UgjvXustMBSPg6jNHv8fesV5ZV6l3w0qb9kD1EyJTfjhrmfK8zIrUBnz0sr4Fvl7bPEch\ncyrRM1SC+wH48IH3c2hGMcQWp1S8/VTYsDIg5ZE4oJFqebubcbO3wvpgg8zvIkQF4QZc797gyyuK\nRZ4xhZTSbuZBbeBDN7D9opQ9lrvKUmIg9zAXBUTaWwegETTppj2GuRkurlCtfDtx5WMcrEaWYVT1\nBK1Eh6AUAkHKVGAGbSNQKXIdYs+hWRkNe/0dhhQGTDLsAmOMfSuz7nKZPsmAlFXNuN5IJdUJYMTU\nVNAgcAKKYpOX15/HEQ7vW3ovsT9DNf407czlqfkpTk9v9341VfVd/rOHD/Ppg3O39Tpvtw7Uoh17\nFctU2sFTxtRs5Gv2Ojw6W+cnTx3YFfAyjJutJMN3FHMmLh17J9ngUwtNvvWo8qOK/RKoJIc0ghqR\n/vtNy36aMOq2TKWCwBG2Tweoloao4/I3dV2EuzCV3okyDLn1Ycrza+3/n703D5Lkuu87P+/lWVln\n38dM95xAAYMBBidPkAJBiRQlkSJNyaRlra3LWlmyFVaEQwrtxsp/ODbsXa9jw7tyhDfCjnWsd9cO\nrWWvvRJ3JVGkJFIiROoACYJA454Bpmf6PqrryHv/eJlZWdXV10w3MODUNwKBnuqsrMys7Hy/933f\n7/dHEMe8dzbJY9O1HkKr9g62qk8X1GD3POk4kLcH7qVUSjOVBLsXlY8LqQJqqa3ur1QdNX9ulPlz\nu1VpKTnpRzE1S3/Ha/2TvENOAW/m/v0W8N6+bX4d+M/AIlAGPrewsBDV6/XDvHcXRkacfZns28XE\nxMEBwym+8ucv85v2k7AIlohwY8GFmSpTk7ttQH4Sgjab3KTemkvgxEpR9BdbrDw9x0w4wSY6q/E6\nxUzt0i1MIhFxafJenlt+MXvt3IVxfOlmLeeFEHR0VYzogYUXdwfhwHC5/IgqtMJSh/WJazhhmcuz\n97C0mgxwRioJFggKBFWD0T9bpXG6iJTd1QVdm0R3BJtuQNFQbKuhOQhNZtdQCJU5Ek4VWZzQGXur\n2V3iB2bGS5SsAluuIsFEJNECE8vRcVsBgeFiF/WssLvZWkYgeM+FyxRNh8ncdR7b6v7sGDYTE2Wq\nlsFSs3v9QElY47hNwRzJjvNnJz7PV37zGfzYZWKiTCfuUDQKTA+QXlaKRViFYs0gTnID5qYmcFZX\ngTb/5OO/xpQzyn//738HpCA2JFPVMV7ceJmNQIVQzo6PMTF2uPvsfcZDPLP0Daar51lbg3Zy+UaK\nDqx2LWqVUvFI9+5+aGi9wZOzU2M4RoFO3MHHR0uKL0vXbuszt+RY9vPEaHXgvjQpEZrMSCWnYOz7\nmc41A7YgSJ65MxMVCskD3DIMtuxtwMexbSYnK+iaDjJmYqKMuaK2C2OJlhSA06O919UxbWgGpMIq\nKXWkELd97bVWIqtPSKWJysi++6xtdEmnlCQZrRSO7R54O/B2HGsrpzQYGykyMVbGMbvFyvRk7ZaK\nl5GkY6BTNJmYKBPHYJqD/x6Oep5/deITRz6etxsjK2oy4pT17PzeTffeEN+9SK1v5qlThIly4PUb\nDU6NF3FsA3fnGp3tl9HsU/zeN12mRx0KyaTKcxWZIITA90IcZ7eCtlAwBpJKja0OURRTGymozL2w\nq+LzrMRKniqVEtWyH/lsdLbYdBXpJRBsdDb5777xP2WZhxfNOgv+t2nUltE6Aj/06YRu1kkNoGyU\n8E1FOmxttPG9kM3x65mE1ot8glg9szSh4ZiTwDJx7LLeuZkcYysjlfTAQDclr2+/kRxnkNn0m34r\nWyADKDmfQtPGs4UYgEevXOBqcS073zSs+8+XniXGII7bVMOIDsoe+NrWVfzIB81Hk9MUzBJv7TzP\njfbX0MzvJYwNNrygT6kUqdwerUQ73MbzXyWKNgCDWFSYS0KL0wl8OVFqbbrq2A22QVOK97mSTaOt\nxnxHF9xohWy428QYGDLOlAXNICSKY9Y7PlMFFXKdVyoNUlynpNJYkoOSZgBt+wG1fTKNUhSSLrmd\nsIOQYGmp5Vrv6V6WhxCC6eIk1xpv0fTgyvgDVK0ypvU+IlHjyojkh+YnDlwMKw8IwT4p6MnEeVBQ\ncQpb0/ixizOYUnBPdf+Ft7zixkkW8/NxBgfl5RR1J+tEFkZtRs1ypgpLSaXU3rY7UymkoGk9zXDy\nzoeugl7PlOZ7BXUfF0Ztgzcabf50ZQsp4P2nx/C2OwihwuxvtD0sTe7ZQe7tgBCCouGw5TX2bCR0\nO0hJpYIm9wwkT5VKjq4dm/p8r+NIYzpSovn7P3t54PZ27m9i4h3OU4ITzlQ6BD4OPAs8DVwAfq9e\nr3/lVne2sdE6eKNbRN57eRAi32fh9TWoTvHJS6/yne9c4FVgomgO3MfitlrxmU1upvf97hqvf7jK\nak0HJJ2L80xsSl42ZmiFbxINIB1aXpNToxd4DtXeFGK0yOCF165l2wRRQJiQStI1iONO9rvQdDEL\niZdz52sgY2bfug/qGpqXKJq0IoSKJRZeA7dWhbkSrUk7USop6PppXl3aIMaApBWuJiz8IOyef7KM\n/1XNJ9IknVrvH0On0cES6jWBYPrq/WihQbFsKlJJ99jY3sH3u2qr2dI0ra2QFr3X2Gt1tzGF+g4q\nyUNDI23CCiQeaRlrPd9TQbPZ7uywstJgu92goDuD74Vkde3G8jqbTXXeza2AMJFFi7ZNw3eV1cqU\nSCmxkvDzaxuLyXmHrESHu88KVPjVx3+J33x9CdjmRhKmp4XqIZMqlUKXI/vd90Kr2buC1th0cXSH\nrfY2bugjhSpmZXx7n9lpdovQ9k44cF8CcP2uVNx3B2+XIkgDShPf9NZ6k510VSiWpKq/0I9ZWWmg\nC42257Ky0mCzoa6lH0qWt9Rzxmt6PZ9nJd0UN5P2ycTKYHq71z7NS1hvKVJJBsa+++y0utdOEzoR\n0Gm5x3YPnDSO8qy9HTTaXVK5ud1hJQKi7gC9udZGk0e3cDWSv8NGw2V5eZsY1Xa8/5zervN8u+G3\n1fN2ZX2LFa1xIuc5JKmGuBWknd9+z5/hy//kD5kYKeD6IRdOVVjaaBEufRGA59cvEUZtPvbEHOvP\nqVDoKIoJwwhNkwPtbwC2Y7K+2szIpxSbSUh3ddShGbSI6dqbXDtZ/MlIpa41brm1wkZCKk05E6y0\nVa24knTs0jWN4toYO7UVfLuVdUUrmd1Jatks0SqpsWPxzU18P2B98hrEULHKbHsNXt54TR2DWc6i\nAIJgsXvd7BbNJAxa9y2kCc+tvYilWbihm51rK2hnJFjRqKJp48nrUXZNLt4/yXs/dJ61VVUj1awK\nhn6RL9900bQZguA1PN4EbCIZ8uKGUkshQMoajlFho9VBJyY9yzd3Oqy7QUYSpRN421CkUhSp62bo\nKscnDZpOV/XvG7mfZ5f+hJc330BoZygbHo3kK5or2ry8lkwmNfXNvb69jBAmthZgSmU1ayado4I4\nzrJ+anZXqeQMaFSSdi5LrWZpt7LD5iql+3TDDkVDoifVrEDfN/NlypnIMpQemXqMMI6JxSRhtMVT\nM2duW1193NCSudF+pBKoTmeHQV5xkyqKCj1Kpf2nxnmlUhy7VMxyRh6lMRTpfvu7v7WDiJqpZ1ES\n0JsXlFcqkUQZ7JWpdFwYtQxeb7RZbntcqhWpWgYrqLpz1Fak0sg7qFJKUTSKbHmNE1EqFZPva6+Q\nbugSqSeVp6SOo7tvXYgDVWp5MnTMeudJpZO8U68D+RZSp5PX8vhJ4D8sLCzECwsLrwCvA/cd8r13\nLFrfeZ4VJwm7K1gZxTFZHPyFpytTs5qG1wygHeGaAsuPeXNihNHKJNNCQ9eUbNi1t3ftww12ctak\nJHcoClneWcu28aMAw7gHANnRieMOesdGRILQcVWxFPpsd17FdCtYi+MYsYW1kzzkDHVObujR5gUQ\ngvDhSbyahUzCD3VtHE2OZr7yOFLHqgubQTZxARDHRH3MsKXJ7KFraSZjK2eorc8yOaWOwTfbiTWp\nu9OLtfMDr2/+4Z12TKtaqaJGZnkqIgk077dbOXqBVtAhjmOaQXtgSLf6HKt7fYI2UqhzSCW4qS/5\nkffPE12ooonuKl1aLO7XGnQvpNadVP5dTO1vXhrUfYyZSn0Pc0MalIyiCuqOvKxT2W3b33q6vx3O\n/nZQ97f0e24HEZogsSWm+8o/yNMcIp0gDehOA7uFlhV8hb6H/amSygfY7qhiXKDfdkg3dMM90+56\nR+n+lv78Tkti70T0ZCr12d8E4pZzi7r2t+7U8c4q0U8WqX3YH9rfhrjD4C6+RYjgmRWBpgk2knDt\ns7UV/q8v/BZB+y087Qz/zzd8iqWI1eJfstHq1lueGxIki0SDSKWCYxAGUbZNiq1cSHc+ZJsYvIRU\nKlV6lUqgFNib7hYFvcB4YZQwURS1gja2ZrEVr2O11US6aW1m1rMepZJZwi3sIMyIxWtbLHMDt6C2\ne3D8fgC+flNZ/kzNyFrcGyx1z9tq0/TUORhegbbRIIgCphxFGsXJGNwK2iwmndnKprIKSaFqnzSv\n6EuLa/zyl57L1EuWXsEpfAShXcC2HgPgmvZNYmIiLeDa9lvZcWiyipXkz0wUunk813Y6rLs+o5aB\nrclsAl/Qu4udmjaDYz8BkCmV0uf+XHmOz97zySyoe8JWY3fZ0FSIdlJD2YkS6GpjPWkooycKCo1m\nELKwpb7LelWN0RW7S36P2r0qb4BHxyv87fvnshbrqRKi4Yesdjy+cG0lO5c8VjseX7y+lkUf+GEH\nR9cQ2RLp3kolgClHdYATokjJPMP1ZocYHREtMWIP7lD9TsK0EsXOMREbeaVSqr7J25/2u3agYi1E\n4vSI4g5ls5QRP9t9Qd0qmFrghhFRHOOGEXZufqM+O08qJXVHzv520o1WRnKquMcnet00o1aviu6d\nRFr79sdwHAfS7392D+sbdEnfg+6P20GeVCqbgy2seeTJ0InC8c31bhUnOdP4BnBPvV4/V6/XTeDz\nKKtbHteAjwLU6/UpoA68dsj33rHY+bNvsGqN4Bg+Ip5hBzWpKA+Y2L24ucNK22VECgpSIBY7rI8W\n2CppBNLiK1fGmSiMU7INnJbyL8dxE+Lem9oLtzJiIu3qtults9ZM7GMIojhCS5QkuqsRRx1KjSm0\nwCJMbGqrrQ0gxgxGEQjipk5xVRAJQCsj5ThRHDFjd9BEl5U3tRrfN/8UY85jCOGw4aqBMAg3Keg2\nmtSyTKUUmoBPjdWQXkRg9l4bOycPNTWTUFMhyu976jwP/cAYvq1IpSjuDrgXa73dJFLkpZLpzxUz\nJT9k5nnWhSoI+rOcCnqBTtDBDVXHub0m9ekg4YYu7aBDQbMRQmTkRaqoefyDZwnPVJBCULFU0ZEW\ni4Oymg6CkTx0Un+4nXV/S4O6j4+9tvr2ZUidkuEQxiFRHKEnMvjbJTEO6v4G3W5uYdax5YCg7jRT\nKQx3HV8+PydtjasLnSCxMvlJppJA6wYx9q2azZWVfXSro/hvIQ4eEA4DXfT+rR+YqZQnyI6J5Ptu\nhN5DKvaSSpq89e8ufVsUkxHpd9PlT0lgLxqSSkPcGUhJD+/6da4Vpml6ER96aIZf+6FX+ftP/Snn\nrD/gU5deJI7hX/7RKM1OwL2XO3z5rT/itdFns/34XoDn7U0qpWHd/Ra4VKlUGy1kIdspPLuJ5ejZ\n/nYplTpbjFjVnpwkUOP6df8tTFfVIzvmdmZ571EqGSUQEI92aDZcXjNUFhEC7q1dSBZPEnVSFLDS\n9pAComgR0CnqVTyrlR2XFupsakqNm455ndDF1EzafosbTWWZKxgq0zJV4bQSEulqo8OWG2Rtyluh\nqoE67rM8PrLKY5NX2BRrNGrLGKbWo+qSskbJrPHA2H08OvVY9vpLW03cMGLEMjClxIvStu1FhFDE\ni2VeAeFgSsFUIa1T1IPZj2I+MvckowVFks04I3zmzCSfOjOJECLLQ7I0td/Fpvo+K4n9rKgnpNJm\nEyngYkIS6VLLxutBtZ0mREZwqf0l9jcv4DdfX+KrS5t89ebGrvd9bWmLLy2us9SJk+8tJZUUoSGE\nTnEftc3pcpI3atS53vKy7+KzF997rPXicWF2vsaHP34P9z00fSz7q5h6ttCT2Z7ypNIBShQhBGZq\nNUyVSklNmc5I8ouOppS4UYQXqp6wtq71XOeBSiV0hDj5TCXohpyXDW2XdTC1aB7GjnnSyEilW5gn\nHYSLFYf5os3D+8SP1CydqqH3/M0eN/IqqPIhwsDzSqXxO8D+dmJ36sLCQgD8HeB3gBeA31hYWHi+\nXq//XL1e/7lks38IfKBerz8H/D7wKwsLC6t7vfekjvU4EQcB69/8JhuRw2SpydJ2jRYqhdzr9BYa\nrSDk37x8g2fXBTPpA+1mh61zcyAEkdTYKbcZL4xi2jqlFTsZIEOIe1fCotjnxfWX1DEkBdSWu51J\npydNtTIRRqoY0AMTzQ0wnSvEVhFfesRxzEozIaakKmBaN8DaEbQnbKQ0sM1HAPjo/Hu5r1ailayi\nFA2NT1/8ASaKcwghWXMTyWewStEoJp26ekmlU0Wbx86OU9E1fD0Xmpt0D0sJDFMatM4vEt67SsEx\nmZxLWs9GAeEhSKX8w7uQKG1SpZIKzFPHashO8lrvH6ZjFIiJWetsJOe6B6mU7HvLbbDe2cgKu1Qh\nE+bOP4xjNCGoWd1VASlkT8vZwyIlSGLUYGhqfUqlYwzq7r82utR7/OApiXG73cYMzcgm+Pt3f8sr\nlQ7u/gbKq9xPsuQ7pnXVPfqu7m8IjU03VSr1Fh5zJaUk3PYSUaXQj+UBmwaHpygdSak0JJX2Qv6a\npD+nkwdd3PqqXNpVM4JsQiTuIq1SSsoPg7qHuBPgLi7y6i/+PC///M/SevEFFsZVaPQDUysE7etM\nTF3kjdYVVncKrEWXCeQolqlx9lTSVXV8kVZR2cc8N8Tfh1QqOLs7wEVRxOqyWuCpjnRJJUPqiuyR\nMecf7apDWkEne14sNpfohB1qVrVHfQSqa9Sb3ptYHTUeNPWtLM8ov/DgGAUEAq+6hWe22azcwG6r\niVPFKmfjlqWZbHkNltsuVd2l4a2hazNYWo3Q8NgOlGJLCwx8q8Xp0iyTiVKp6TcTRXc7q5OENo0U\n3ZX/tGtqGkL9apIluu4lHaaCq7x/5grff/ajAKzMvoJj9yoSNFlFYPDzV34KLWkco4nejmu2Jukk\nn2XoDro+z4g9hybVeZ4q2tkCVKpW9ZM6ctQeB2I+MvcBHpuoZnaqrBYRSb5hsmhatdQxFHUNP4q5\n3nI5Wypg5wiKtLPeYSbCqVLp2bVtriZt3P94abMnkyp/DTe9CEuziGIXR5fE2bxA21dN8cDYffzE\npb+BZT7MW80Or26r7+JSbbea6k6ApkkeeORUT/fk24EuRZZVky4OOjni5jD2poJ0CcMNgvA65Zz9\nLft97vpbiXou6xysyb5MpQFB3ehoMllYP2Gl+axjIYD3TtZ21dFzJTvplnhyRMphcZJKpZpl8HOX\n5jLr6iAYUvLLV87y8dPjx/75KRzt8OQm9CmV3uHOb3DCmUoLCwtfAL7Q99q/yP28CHzssO99N6D1\nwndYiSxAUC34PPOmWgGYQdDa6c3n2PQCYqAZQj1tL7nksvXkeeAvgBjfdJkojLFlx5TfauIUyzQr\nbQZhrbOBQBInqxVb7hbbvgsanCnPsbS2lJFKWmBgNTQoVhHSIQjXcEOXlVZCKulqILz+vBps2pPq\noRfFbUasGg9PXKZkdXh+QxUx6WCo0vEjNn1FBOx4y5wuTyKF6soAivSKUIymlILZsSKbm83sPMzE\nx5RXKv3MD34qsyjpuXbvaeDltDNJxRzMMOdJiUyplNjtTCmTAsPHFmoQ7yd2Ut96qgRzDlAq/dH1\nr+GGHo9PqVa3/fY3UNcib38DMmXTUZEncMp6V6q9k2QgGMe48qRJDUPq+FGQtTYv9ZFKPsdDYhQ0\nm0a0s4vISpHeU/mugvseu8iTCHsrlfL2Nz/JqEjJJYHGth+gid3nWLOrCFHEDbbSTzy2bAJd6tm9\nXjxC9zdzaH/bE4OUSkZGit66vDn9yuM4vsuVSkfPoxpiiONG89m/IGq3MSanCGN4yTlN1dYZif4Q\nqRcZnftBfveL32Jtu8qv/70P88CVmLYb8NXlP8z2cXPuBc69+D48t5t1MzhTSd37Ly9dZWL6Mtev\nbvLHv/8K6ytNJqbL6IbGlqvIGUuz8ZPMycn7u2NcO2hT0G0szWKpuQygSKU+pdKO30RoEtNV40Fb\n7mQLSem2a+0Ntr0GJbNIo7yKO7MOMmZ87Qxvnf42RaPI2cocr29fZcQe4WZziVbYpqypzzWNU4So\nY1xH5ThpoYFntvnA5JXMttMM2jh6gQ13MxsrW0GVUUvPJkapUqmRWANTIuNGKyKOfSYLGqdKMwgh\nOKdd4PXSq0R6N/dOExJbr/Zk0wCcKRV4raFq4lHL4EbLxY9iojhGlwUc+8PcV9niuY1NYJT53ORY\nzymVAIIoVp3AjN78IzNbnAkAnVZoIDUoJkqCvGWl3qf0qJhlrnPjUDkwaWbLth8iUfa4P1vd5k+W\nNvnoqW7zkpRUWu/42HoBz/NwdI2lyEckdcd+xIgUkiemL/OHy29wbadDEMXMONaJZsXcaaiaBtt+\nuIdS6eCpsWNorGz/ewAq1od7SCVN9FrWbE2y4wcZqaScGHmlUveeydvfdJHOgU62fpsomPzylXMZ\n0ZbHrGPxK1fOHYrgOGnULEW+9z8L306cdN6YJkVGjB8mDD912hhSZErHdxLDmcYxY/vrz7Aypmxq\nzaDEuhswCoxoklazt8huJN5bL4y5aOhEQYS/7rI2qSSesQwRQjBaGMWydEQMVishNPa4r6V0gBBD\nGmy52zSjHYgE50dUJwvVAUNJmEcXxwltPZMHb3s7GakUJ6srXiskBjqj6uE2ZhX4m5c+hyY1Llad\nbDBNAwdT320Q6xR1SRi7lPqUSim1kpIA/V7dbteERKmkmYzaI9mKT6om8KMAL/Ip6g4/8cCPDb4g\ngJWzT/VnKhma5GLV4WzJxk6kzf12q5RUWk1Ipb3tb+oavLb1BrrQePLU+4Cu7SrMCbWiOFb2txwR\ndquSzjy5UTK0jEjI7G/HqFSC7nmm5FWvdLdXVn47SDub7Gd/i3L2twMzlXLH1O9Rz2cqZfY3qeWU\nSunKs6bky9puwkgKsmBSUATUcT1gjZxyJm9tGIS8Uin9joZKpd3Qe0jGXvubPqD982GR7jXm7lQq\npST+UKk0xJ2A9stKwT33y79K52/9Mq1QcHl2B0nI6NwPsN3WuLHWoj43gq5JLEOjVlIB1ABmx6FV\n2aAxsoTndZVKg/JdUlLpCy99ia+/9i1++ze+xfpKk/uvzPADP/ogQKZUKiYKIoDlJHgbVKZSQS8w\n5UxkGUkj9m5SCSDWIuyoAJHA1X2+tDyNpk1TMkq8vPEq/+gb/yP/9M//OYYw2NCX2Zh4E6NToLYz\nmRyDw5On3sujkw9xNukoF0dNXF+pbU+Xz+HGarzZ0lT9IwMdz2rx6NRDmcKi5bdwjALtoIMfBUih\n045ixm0zm7S3AtVUo5mQS6sdn8WWy3LHZ9Yx+JnLfz0bU68UlCJ+x97MznWiME5B0+lE3S5aAPfm\nSJxRS88m914YoQlVQ2y6q4ShIsry1pW0DkizlPw47hkXUnQV2MkzTSTqo+Sz8mRMvbabVAIoaLuD\nund/jshq6icmq/zg/ASOLvnjpU06ObVSSsytuT6WZhPHLo6u4UUeUuwmuvbC6aJFJ4wI4pgL5YOP\n77sJNSsNX087rImshjxMZk4+eL1ilnpIpf760NQkXhRnirOC3qtUcnoyRLtKJZnU09bbUL9VTX3P\nhdnKPr97O/HU6Q/y05d/nPnEdvvdivRv91BKJU1DoKxvd0LA/pBUOkYEm5s0vv6nrIyogMKrawV0\nARdMA6do7iKVtpPVhvn4OlVNEn2nwVvjJttBEgAnQmpWFUPqWHaS9REmN1nPvZNjyKUitCpmmU1v\nm5ZoYgQ2k6V0lSNRdQQmhZ0ygSWRiYy44e2wnHausqvINIOmClFiT5st1rhn5IL6LCGyULezJbWP\nkVxL7mqSk5SRSsnr/av3u0kl9b68/S2PvFLJDV2KpsNceZa9kFcq2ZlSqWt/+75TY/zs/XNY+uDP\nS0Oj044rB2UqATw29XBWTKQqiDDabX8zNCML/r6VPCXoUyoZelYAnURQN3RJpfR7KPW0Q01IpVsM\nOc6jZBQxpN5D+OSRZSodWqnU/Xl/pVJKLBi77G8i+V1/nhIo4iD9+1MvHE9QtzqW7t/IXkHx2bY5\nAsrIzuWdH2zuNAghsglEV6mUPGf3uOcOg/Q+jOKYVJx4B4z1bxvS581QqTTEO404imi/8jLGxCR6\nrcbXX1TEwn14VhBBAAAgAElEQVTj19CMMoXqfXznDVXzXDrba/1JSaXpayrMem3yGr4b7G9/Kxj4\nRgfXavEXL71MFMW8/yMXeOoTdZykUUuqVHL0QvYsX26vZPtoB20c3WbS6Y4l15oav3t9Z9fnCQSl\nUgHTKxDJABDo2izXGm/x68/+S7zQRxOSTW+LTuQSy4iJGxeIpXowFY0i08UpfvryjzOR5AkF4SI3\ndhaomGXuH5lDClXHeJq6HlpocHnuHsYLY5nCouW3esKGbV0pCsYtI+uC1QoidoJU8avw+9cVUXV5\ndIzp4mT2/gfn6mihkRFZAFPFycxGBNBJlEr3VrufO2IZWbaNG0XZpHy1vYTrfZOHR2UPCaUn2/oJ\nUZUqlfqRLtRpqGealMXkPBNSKZkEjpj6LgtKWgcetmPVhG1gaZKPzo5iaZIPTY/QCSO+vqLumziO\nM6XShutjShvwsTWBH/poSf13mAnpXE61lYaF3y1Iw7rT+1MIkUUaHOba5ecB+Uyl/D5T2Mnv8k1e\nejKV9EFKJQ2ZdMA2Tlip9G6BYzg8OvnQHUGenCRSIv4wSiVNCj59dpJPnKAl7ygY3qnHiM0/+H0I\nQ5YMVZz4IZw1DcoFRSq1m16WdwSphDXminxRKS6e3eLatEnHTYpxETOSyP3MhFQK9d5Wo6dKMzjG\nCFKqLhdCTiFEkbJZZMdr4mpt7NChavV2dBCYRIYETWZKpYbXYLW5BgiELFEZSVahJrq3SdnsHTCf\nmhnlk/MTPDiqVtHG7O4gVdLTwsVB0rUp9duVKgeRSgPCoSEllbwexn8Q8r+3s0wlnQ9O1XjvRPe6\nZITInkolVXwe1P0N4Km5D2Y/pxPXoC9TKVNqZZ77W/MJ54sgpVTqJZX6w7VvF/1kXzGnnEknlMdB\nYnz2nk/ykw/82J4DiBSKoDxqphLsVu5oOdKmq1TSiYkJozAjlyCRSmu7i47dSiV5bINfes8X9EJ2\nfHuhx/6mdW2eQ+xGV6F0fEql9NaK79qg7rT72+HaYg8xxEnBW7xO0O7w5uwTtFoef/nSCrWSwany\nMlZxHiEE33lDqbcvnR3teW/abbPQrFKOq7RLm3RcHy+ZGA62v5msT16lXd7gxqbqgjYz11t7bXaU\nPbpkFKkmjTpSpVIYhbihp5RKxS6p9GpD0PB7x3GBYL5ymmLJUmHdIiCOPYRw+I+v/Da61Pn5Kz/F\n5+qfyRqaOKJIbfU0gfSxNDMbV4As37Hjfp0g9vnRe3+Yh8cqSNnbDcqMTf7mIz+i9pfURjtBq0dt\n4RhqYXXcNrOxsh2ENBJCrp6E4b6QxB6c71PJTM9UuTJzPzthl0ibciYyUimOY9phiC5U6LaTkTpG\nphhRuUrqmq23l4nibT4xN9VTm+SDutX/o4GqXj2rCQPyPWX6lUr31oq7xvyjZCoB/LWLM/zdB+Yz\nC9aVUXWtrrdUPIMbRdnxrrk+errIJ7xEuW/xnonqvtkwKVLVlhRw9i5TKs0V1fWZLHSvU0FXqg/n\niEollanU/d7768M00mMzeXbYer/9bY+gbgx0IQ6sbYf47kL6PBlkRxyEJyaqXKzeGaTwcKZxTIg8\nj80/+DKyWGTNtdGkGjyrUYxl6xSKJmEY93jyt72QeXGDMbHF1mKHuBGweMrBC7rbpN3BLFsnBrxC\nVwJbNBz+7sN/iydm/yqaVJNZTatiGvfh6I6yXogYhyK6zEunDSJbJ7TVDdtjf2uuY2olhJCMThQR\nUtAa794mVbO3sLE0yfunatmKz5jdfdAWtKTFfWZ/U6/3t9mumv0kTmp/S/3E/UqlbnchP/T3zNzp\nHuNupZIQgh+cn+D+ke51ST9nV1B3X6bSQfa3C9WzPfLMdJEhHJCpBDl59K0qlXIDTtnoZiqFSecy\n44Drc1R0lUrpilieVEqscccwiz5bmefKxOU9f7+7+9v++xtUTGa/y3dMyzKV1P/9KMjZ39SXOVip\nRK9SieMJ6lbHkkjaDwjpzm8LYCfqu5PuHvJuhS4FUnQJ7iyo+3YylZIn291rfxsqlYa4M9B++SXW\nnVM835zgmWeu0ewEXJxWY4VVOkMcx3zn6joVx+DURK9tqRMoZY4MNU5bp4m0gOXOMr6/f/c3z1ZZ\nQTIwEBLGJ0v87tUv89uv/S4AW4n9zTG6i31pbdHyVTZQwVD2NymVwlyKIkL01gefvvgD/MSlzzM5\nXcb21bgQRQ0gIIxDPjL3JPeN3sMHZt/DTFGRPI+NPYKMJb70dnURrWULjxFPTD3Go5MPMVkwOVfp\njmkiklQqTkacpOORUirlaj9ThWKP20aP/S1V2DwwXs5eN6Tg1IAQ4MtjSiGWKqAmnQlsTRLGaoGu\nHUQUdLVw8+FptUBoajJnf4shackexx6WZu46535SKYjigQtiadaeH/o4ufWGlDy4WHE4U7J7FilT\nPDp5hffPPMFD45d2/W4QyoaeddyC1HpE1iAkJeZAEWdxQpxJ4eFHPo5h8umzk4ciImYcC1uTnC8X\n7roa4YGREr/68LkeO+QTExXeN1k7lNUrzVa1NBNLM9GlzGr6/vowvbZbXtI5OBfUrYlegildJB4r\nTFAwSieepzTEnYeu/e2dz0g6KoZ36zFh+2t/QrSzQ/jk+2l6JlEkmRlzkH6EaemZ9DlvgWv4AQ/L\n7wBg/sk6W0WJGJkizHmnpxwlCbYsneasg5z9ODrqtYu185TNElWrjGk+gKFfQJNTmEa9Jx2/rJfZ\nCciKEiEsAlvDS+anMiGV1jubrLc3sZKQ7sc/cp7P/o1Hiexu8TRq7U985AkiU6qirGg4iapksFKp\n2veHk0pH91YqJStffpuYOLOt7QUpZDbR3m+1KJ0MGVrf8Rjp9dm/+9t8+RRPz32Iz9U/0/P6IPtb\nlNjfILeSpd1+plLZ0HpWH+Ek7G+pUim1v3WvR/pdvR3B0FIIYrp5CEdTKvUeX179k/6cWseCOEiC\nyfWsmB6kVBJCoEkbQ0tXdo8vqNsYYDXcC+m9LoXk6dlxPnN28lArb3cjDCl7SNksO+M2ur+lu4ty\nQd1300JjWixHuc6cQwzxTqD98sv4yZj01qpSxcyUFanTZoov/cV1tnY8Lp0d3TWRTO1vMtI5V1WZ\nlIveIr6bkEoDVpGFGeJZihgSkSAstdF0yZff/CpfevMrAFlOkq3bWU7STtK1rZmSSprNZGEcmdRs\nD4xO7iKV7h25wKQzwfs+cp7LD80DEEXbxEn494VcN9xHJ6+o/czcw4c+dg/LM6/sqmNGbaWw12SF\nz9c/nb3+/qnuZ8tQp1LNK5JSUqndU79oUpErVVPP6rl2GGVZQDXbyNRJ8yV7IJFzaayu9h0okm46\nUSoBSSetMMvf/PDMKD98NqmVk5m9G0ZEibI4xmO8MLZrPO63v/kDOsNCPsfTx5Ld51qqVBqxDP7L\n+wd3jqpaZX78/h891ILQIEghqJo6mwkhkRJzKdxQjVki9vBC/0gZmoaU/MKlOT53fuaWju3dDCHE\nLnvRk9MjfPLMxB7v6EVKouZzUVNFeKGv3rIzUinI/p3OW4qG03NfakItUI7YYwhhvi15SkPcWXhi\nosJ7JqrMFo+/y91JY0gqHRM2f/93QdN4Y0QphmIE9dNqYLXsLqnUbnbDS313g1mxwvq2g7bs8vz5\nIqOrZ+lqeWDTVVJp3dDwKwZCSJzCh3h86hE+fOr9gGK9dW0Sp/A0cbyDlEXaUXeArxoVNlwfIdSE\nVAiLrXmdtVklz9alGuxe336TmBg7IZUsx2BiuozIqTjG7f0HRlOTECvi7NrWC0A3UymGnhbwaRFX\nNvsfwIlSSU/bafa3sVeDZtNP7V0H/+FZaeDdPtum2/R/XiEZPFLVwV7FgSY1PnvPJzlV6h2g00lr\n2Gd/6yeVblWp1BvUre/qXneQkuuoSL+X9HvId66wkwL+7cjwSVeF0oLwKN3f+oO6e5VKvRaoIAoI\noqBHATRIqaSOAQp6mg2hHWOmUqoKO4RSSaQd33SmHYsnBqyeDqFQ0rWe1aAsU+k2lErpnZFXKsm7\nSKlUNkv82H2f5Xvnv+edPpQh7mLEcUz75ZeICmrSd3NL2YemCtcJYotf/Vcv8X/8ngrxfvie3XkU\nbugiI41iyeJCQirdDG9kSiXT2v2M2Io38U1FDIV6wKa9zFp7nYa3Qyd0aXjNjKwqaFZGKnVClzAK\naXmpUsmmYFSI4jYCnTG7mNRh3edIOpmVUnK2pkK2o2ibIFxBIDhXmc+2HbVVPMKmt8W9VybYKazv\n6iI6Yo/i2E9x7/hneqz4D4wU0ZJcJS0wKNfy7c91TM2kGbQI4i7ZoYl0QVAOVCpVLYOLSYbP+fLg\nMa1sljhTnsv+PZUjlTphRCdRKvUjzbbphBFRnJBKsSKV+pHWTkEUE8cxQRxnRFPvdt3mMKoDnIK9\nRx1w3KiZBg0/JIi6xFya3dQM1DlGcYcwDo/c7XfMNu+qrm/HhZRQLeeb7ST35y77W3JPpWqzgq5l\n843+btJCCAwp8KMYL4yGSqW7EPOlwqHVhnca3n3aqjsQkeviLS5SuHQ/q9trgCIVLkxX+NY3l3pI\npbxSyfFvAKC9skEo4MWzJc6+OkL8gJJCCwQ3mkvZ9mExCZTTR7k0Uee+UVUoWLkHWBRto2ljNIMu\nQTFaqLHu+khZIorWEMLErUIQRFjAfLnGc014s/EWALZewYvyXdokERDHPjXrYKWEoEOMyUsb32K+\nfJpLY3X+ckMFZMZxbr/J/w0pKepa1hWksEup1G9/U+e747d6ttsPaeGzX25ROhjvlamU4qgrTmn3\ntzRTKW01ngahV82jee77kS+CyoaGH/aRSiekVEqvk6OrLjYxcfd3bwOplJJIXnj07m+7lUqDM5UA\ngijEj/weBdggpRKov9mCOc22+wpI+9hsT+k93z8R2G/bfnJxiN34axdnCKLuyrNxDJlK6apjdJdm\nKgF8cPa97/QhDAHU6/XvB/4ZKgzuXy4sLPzjvt9Xgf8dmEfVg//DwsLC/3qY997pCNbXCDbWEZfe\nAx6sNl0EMFFY4vr2BFJKfvSpC9x3ZoT5qfKu93cCFxnqlMoWM5UptMBgVS5lQd16MhH/yvVnuFg7\nx0xxipvNZQJTkUaB4RLqHl+78WcZuXxj52a2f1u3exQKzaBFG1XTFPQC265LFDWQwspNLiSgPr9s\ndK376UJWGK0TRitMOzM99USqeF9qLtNM6qb+OuaPbmxgGPdwvlLreV2XkmJUZosVhLSoVHvrlKLu\n0PRbNJMMRwApul1HTako9TypVLEMHh2vEMQxj433Zjbl8cBYnauNNykZRRzDwdbUZ2x7ARFqQbUf\nmf0tivBjQRyHQMi4Pbpr27z9La3PjAETucz+FvlJWLeqc/aqA44btSR3dMsLsmt4plRgpePjx2m3\nzbTb73Ba93ZgkFIpvff2sr+lajNbU+6JqllmytmtjDKkxIsivCga5mEO8a7C8G49BgTrCQn0oM7q\njrqkUsD8WOK5TTKVAFo7ilSK4phapAgj+5UVXj/l0DENYk8SJyshI1aNm82lzEYQ2JI49hFxyBev\nr9NJWqoWNAlxjIi7K+NB3C0YxoojbLg+MqdUkgiEUEVJvToGCNxQreQVks4dXeuGBARx3D4UoTJq\nrOD73+bTF76Pv//YL2BpZja5joh32d+gtwNcyvbPlU9xabTOg31edCkkmtCOpFRKbVn7WcxSwsnu\n218+gFIKeWSbWr/9Lb2uKQkyYqvrfavy6F6lkrbLvndS3d/yqo50gB0vFLPwzJOGlpFKR1cq7cpU\n6un+1k8qBfhh0EM27qdUGik8yE9f/nFM7fSxkQnGgPyqvZAe95BUOhhVU2fM7t6rWVD3bXR/S7/y\nOHsa3132tyHuDNTrdQ3458AngEvAX6vX6/3BLr8AfGdhYeEK8BTwT+v1unnI997RaL+kVEiMjBMT\ns+kGTNU0LD3ipaUip8aLfOw98wMJJUhJJY1i2cK2DQo7NZqywXaSiWSaOjebS/y7hf/Ab732OwBc\na1zP3h8YHu3iFtd3bmSv3WwtZz8XdLtnQtr0W5n9zdELPHPzzwEfISu5caTbUTevphwvKMLED64C\nEeNOb8vttLPazdZythiXH0te3W7xpcV1aqbOU7O7yZdxEuJHsyj3kUqOUaDlt9j0trPX0i6phpBI\nISjoknaQs79ZBroUfGCqtm+Wz+VxlauUxUBkk/Ou4qMfeYucHwniRDk/UKmUdKn142749eBMpa79\nTdDJXrffJhVJLclY2vSC7BqeSeyDIlGFtXx1Xx5VqTTErSGt1ytmPpdV3Q/9cQN2RnSqeyzNAvvl\nJ36RH7/vR3ft25SCThARxl075xBDvBswJJWOAf76OrJeYq0Y8K1FxTqfn62qNGZUHpJTVINCqlRq\nBiHTYoUogHjV49sXS1iGhdQEkVRs9qQzjhf5bHQ2AQhMgyhqMOFt0AxCnllS1jgjjtE6IcIPsXYM\noqiJG3cfdFOVMda9oIdUKhsGUpYQxJytOD1+/UJif+t2qktvE/dQtpC/+9DT/IPHP8b3nXkq2z4d\np/dava/0kEpJhy29wC88/NNczGUDpNClhpeEJx9GqZRus59S6bHJK3zy/Me5f/TentfzSiVHLxw5\nJ0frs7/1dyu7PHY/n7n4g7xv5vEj7TdFaq8raBJdyl2e+mO3v2VqpO7nFE01wM44Rf7Boxe4r1Ya\n+N7jhOwjlY6UqdRXDOYzdPqVSn5ifzOknhEGg1ZIIVGpCI1HJx8iFuLYg7oPRyqlSqXhiuVRcTzd\n37pKpfQZejcFdQ9xx+A9wCsLCwuvLSwseMC/A364b5sYKNfrdQGUgHUgOOR772i0X14AIC7X6ABB\nDLM1pSJ6fa3M3OT+Y1Qn7CqVDFPHaajMoaVQqY0MU2MxUZIvJgqkm3llueniFnayxS/oBnKDIpXK\nuQnpjtekmdjfLM3kT2/8McpCXSHMGkWoZ3uejAK1aKYarqjtHHO25/cF3aZmVbnRXMqOJ50U7/gB\nv/HaTYSAz1+YHpi/N64lFmph7iKVirpDJ3RZbq1kr8VoaKKr0i5oWqZU0oXAOaTdaq58io/MPcn3\nzn84uS5qRN1wu4HH/ciTSl4YE5OSSrvJMj2vVEoK04GZSlqqVAqI4ySIfY9tTwIjSX286fqZUmmu\naCNQ9TzATkIqHSVTaYhbx5nKHB+YeYL3zz6RvZZaL/vvy7yFTdC1w9Wsas+idQpDSlqJc2OoVBri\n3YThrOMY4K+vYHxonC+9eJY4mUY+cG4Ut6Me/pZt4JTSTCU1wG21G4yKbcKbbbbLI1yb1hk1DC5d\nmeUl8QoAU8UJXtx4mRvNJYpGlVhqRMEOZ0SFZVQ70TCMeP73XoVTNiKMsRo6jfI2vkgyAmLBSrjM\n1YZBwSjR8cCQNmGsIUWRoi4Ytw2EKBDHqZ8/USol55e2Rpfx4br5mJq5K1y7O9HaQ6lk7FYq7QdD\nGlnL30NlKmVKpb23LZslvv/sRwd+liY0wjg8lP2oHynpE+wRVK5J7bbyR9LCJs2GyZM9UsjbyocZ\nBLtPqaQ+u8gyq5iakRWSJ43dmUoHbb93plL+Gul9HcCCKEiyFAyEJmmH0cAVUlCFZpSzOR53UPeh\nur+lq8THrFC7G5D+7dxWplLylcfEd639bYg7AqeAN3P/fgvo9yX+OvCfgUWgDHxuYWEhqtfrh3lv\nD0ZGHPQTbAgwMTFYUTQIcRxz9YXn0UsljEqFFmrCPVNeJRYGNxslPnF+bM99xnGMH3kUoiKT0xWm\npipU3DGWgXW5jMU8M7NV/uJ1tbC30l6jMmKx6W9m+4gsHwR4uNlrrXgn+3lqbITRQtdqpjkRraaq\nwbbjLba9DUzjPuLYx0jKFiE04hjGSrWeY/fCCCmrhKF6v22f2nVuZ0Zm+ebNF3D1JPh6RJ3/11+5\nQcMP+Sv1WR47N8kgzFWn+dN1EJrFuQvjFEvdOmqkVIHNXsJMajqmFmXHUC2YXN1qYYQRVdtACHHo\n7/NvT/717OfxtgtvQid5oI5VCrv2s6UndZWl40URIiHa6qfmmSj3bltMCBqpa5RrSUaOY+3ap9lJ\najYDZKroNySTk3tb91Ic5b7dC2dEDG8s4xladjfde2qE2iuLrDTVd9FKyK5K0TmWzzwq3onPfCeQ\nP8+/N/VTPb+rvGnCdovpsVLPdlNE8Kr6uWBoTB1w3xQsnaij5jeVAffj24G78fv8bsbbdZ5DUukY\n4O4scsMp89zNrjf2gbOjuCtqRcjsy1Raa6/z53/5r3mkAtFihz9739PE4o+xdJP3vf8c/+mZL0MA\np4rKJ3+jucS4cx6AOGwyY1+A2McLI776xVdYubpFfMZBeiHWjs5WtIauz2BEFviC53deJIqfIAhV\n+8qKNY0b6QhhUDV1SrqGLh28aB0hBAW9DDRzq+yK5DFktyvdUdGzep+8lp9n5e1ve1mL8sgrCQ6j\nVEoVSrcShi2EwNELNPwdigNWFQ5CSrKk9rewz/52u0jzgcpGr8IGjl+lBHsolRKyzTqBz9sLR1Uq\n5W+r/kylvN1JE72ZRCqoW2Uq6QmptBfxKYTIiIQoPj4yIf1Oi+bBpGa/0mqIwyPLVLqd7m/J/+NY\n2X3Va0NWaYg7Eh8HngWeBi4Av1ev179yKzva2Ggd53H1YGKizMpK49Dbu4uLuCurlB5/D41tl7YM\nIZJMFpZZ904RxYKaY+y5Tzf0iFHdzpCweHOdqj+mFum4yWnm2dpu89qK4t1iYr599VXWW4pkEpHE\nly4SyU6nne13udElXrxmjB92x5Ebq2t4utr2628+C4BpPki788dsNVMySj3bbZyeY9/yfKVo4iZS\nVFlrCVZWGnhhhBtFqk29oexfz76lGqjErmRlpcFLy8q2dq9t7Xk9JsQkJmfQjYs0Wy6tdneBUY92\nj/leEKMLsv0ZSYOWzY7P6aKqwY7yfabwW+pzb263knMIdu2n3VK0y2qjjRfFSEKV+dgyWOn0bpvm\n6TU7Pkur6neBt3ufnUDtc6fVxk0V8lIeeA5HvW/3guioz1zcaLLWdCloks31JjVDZzWxv6X3Vujd\n2rW9HRzXed7pOPA8A3U/eTsuK6K7XafRJZbtQ9w3It8p2g+H3+cJYXiet7fPQRjq6o4BQbjKF186\nm/1bk4KzM2W8JOnfMn00LUI3JK2mx7PfeYb73lDe+8a5J7h6+hyICNtQMutYqAFkvnwKUKTSSlKY\nSNenbKtB2YsiXn5+iWLFItIFIowxd2JEEvY4u/0ezt54mGW3qZRGAsI4pGBMEaEhhGTEMhMiSU1W\nRwu1bFKaPtdS9ZUluw+6oyKzv7G7+xv02t/289inOCqp9LEzT/Mj93xqV6eFwyKVqN5K7lHX/kby\n/93nfztwdI0J2+B8JfXYi2xyfNx5SpDLVMplN52rzFM2SlStt6/T2HFmKvUqlRJSJiEWOmGHIA7R\npd7t7rGPUilN0omJjzGo+/D2NykkF2vnBtpGh9gf/Sq1W0EW1E1M6iAeZioN8Q7gOjCX+/fp5LU8\nfhL4DwsLC/HCwsIrwOvAfYd87x2L1refA6B4+UHCsI1WagIx8zOzfPXaZYB97W9phzYZahQqOv/N\n1/4RQezjdCpsGeugR2ia5Gazm5F0tfEWXqRID7tVISamoNvZvoAsJBtUvmO+m+eO37W/LbVWmCze\ngyZrxHEbL1HHpFlFFav32JtBhJSqyC9Zs6x2fOI45v989Qb/7NtXCaKY6SSX6NXN19W1ScaSxZZL\nUdeyRalBKBZsat6H0exTWfZQ9ru+msiQBkEU9yzcpJa6GPb9nIOQZSq5B2cqbSe5S2XD4qGJBwYu\nsmhCjdB+FHXtb/sEdQdRQCfY6jmntwPpoutGYn8rJ6r0UctAoOqxLVeRg0P72zuH+ZJNSdeY6MsU\nzdvfDuXEyN2Dw0ylId5NOPDurtfrpw/a5m7HRuzy2toIlqYmt7PjRXRN4nZ8pIgQzX/L2tX/G6do\nsrXRZuv//QbWpEUUQefye5G+KkQKphocwkgVIZt+BUOrcqN5k9e3VwEwm4JC0sq25Qb4XsjUqaqa\ntQgwmz7ldKxrT3PGPMt64j//wMwDVM0KN5tvZMc+aqmHXynpJDJij2btr6M+TVHhNsZQLd1n3J1o\n5ef1PUqlQ3TUOCqpdL56ho/MPXnIo92NQpKrVNRv3f4W9tnfjqtdpC4Fv/TgWZ6e7QZRpkXQSRQY\ng5RKHzvzEf7xh35t38yq40Z6/3SVSvtvv29Q9wClUkosfPX6MwCcq57BSgrJvTKVpOBElEpTzgSG\nZjBR2N36ehB+6dG/zV+5+EPH8+F3EY4lUyn5/6BOl0MMcVTcRg32DeCeer1+rl6vm8DnUVa3PK4B\nH00+ZwqoA68d8r13LJrPp6TSZYr2Wyw3C9RMH2fkE1xbblMtmlScveuGVJkiI52WsUXD28GTLk5j\nhFhEBNUdojhiKZcj9PrWVUCpEuerKtPIkDp+locE7aAb8lzQbdXkQlO1xY7f7CGdSqbi9OK40yWV\nUGPuqDXSc7wtP0TXpgCYLl2kE0a82mjz0laLVhCx7QdMF9Xv1zob6toYRVpByKYXcKpo7WvVNi0d\n3VVK9e3EMpYinzlpSJ2yWcKP4h6LeZ78KRu3/my1+4O698lU2kq2uWdkjp998G8M3J8QAl0KgijG\nj/cO6pZCIhB4kU87aELwTT48PbJru5OCISUlXWOt49MOI0oJMTdqGblMJeWMGAZ1v3N4YqLKf/XI\n+T2Duvt/3gv5+nSYqTTEuwmHuVu/Xq/Xf7Nerz994kfzLsVrviJk3FA9CNIVMLcT4DhtiNu0t1+i\nVBb4XsjYziZiwmI1tJBajEwGvzQDJYo9pDD5t68tUSk8yc3mMm/tKK/+pCwze7qGIQXtpLXt+LQi\nOmIhEBGMRWqQ9x2d8dMOMerf86UaP3/lp5C57hVpV4mapVa5imY1W1XPcrqTF2z9dsJrSc5td6YQ\nDO7+tgso6d4AACAASURBVB+MHlLp5ImMtHAaFKp3EFL7W7BHUPdJICOVTqDAsPTdmUrHlR10FBxV\nqaTvM1BruXPpJxa+vfYiutR56vSTjFsGBU1S3GO1VQhBHMfqP47vujw99yH+1af/CVXr7vB/v1NI\nlX23E3Kef34OetYNMcQRcUs12MLCQgD8HeB3gBeA31hYWHi+Xq//XL1e/7lks38IfKBerz8H/D7w\nKwsLC6t7vfe4TugkEbku7YUXMU/PoddG8MU2XqhjRZLN7Q5r2+6BId1pXqOBwWqoiCNPuNjbKgOp\nXd5ko7PZQxi9vn0NAMdwOD+XkkoGQaTsV6Zm4oVd25idWPFHC4qc2PGatLyuVS5CKYDiuEMnUyqV\n+fiZp3vCgQFaQYiun+JH6r/IPbX7APjta13Ca8sLsg5wKUqGw2JiFZt19q+hShULLSGVUrImRapU\nMjWTT5z9Xr5v/in8XUql7s9l8/aVSmkNZQ9SKsleUsk+YJHSkCIJ6o6Sf++uP4UQGJpBEPm0/BZF\neY3Lo2/vWFyzdLYSQq+SVyoJiSa6C3wnoU4f4vaQd1/spXLPI1+fmoeYDw0xxJ2Cw1TOZ4HPAf9t\nvV6votrM/m8LCwvf/UbEQ8B3N3mtkXaV6CWVPDegWEyKhDji7LkmjU2bad9HaII3I4dTYQfN86EA\n652tJMjaQ0t80kjVAe5Gcxtklc98z+PYBQNDSjpeSAEYmSzB6jpxMmmuuQ7XDQgKOsZkgNxQoXCj\nlsHp8iwfnHmIP1XCJ2oJmTNmq21MrZJZdtKg7vS8HP3WB6veoO7ktdzv00FSisN11MhnnhxGqXS7\n6Nrfjq5UShU0/ZlKJxnemxKUJxHWPFOcpmQUmSu/syLGlFRK5fhH6v52KKVS9x5738zjVK0yPzhf\n5KOnxgYWnpAEdXP8ChUhBLZu0eBwYflD3BpOl2b52JmP8MTUI7e8D5FTemb2t+M4uCHuVpzlFmuw\nhYWFLwBf6HvtX+R+XgQ+dtj3vhvQfmmBOAgoXn4QgDcaagy0A50bN9UlO31Q57cgDWO2udFSHd08\n6eLsJARQcYObreWe96Tdz2pWJWszrkmNmBhTc9CllVmnBCKrW6acSd7aWWTD3cTILVYEcYGCJtgi\nxg1UHSmkxnumH9lV8zSTTlFjhSrt5OelXO7RputzrlyhbJZoeCqfqWgUeW5D/TxzAKlUHXF47OFT\nfHmrkdnKUqSk0lzpFB8/+zRRHPP/3XilZ4zNq89vR6nUH40wSKmkSYEuBO1ksemgRUpDSvw4yuqI\nvepPQ+q4oU876DBTnL6Vw78t1EyDt5qKBEyJudNFC00ILL1Ay/eT4xySSnca8iTRXir3PAxt7wXQ\nIYa4k3Hg3bqwsOAtLCz8m4WFhfcDPwP8CnC9Xq//z/V6fXCriLsI7bWXeW2thiO7A3heqVQqduXM\np2Y3+ZFPnca4oAiKN32T7aUmIll9uNa4xkZnG2IfmZBKQWwihIMbGRDHzJbUSpkpRabQqIwnK1pJ\n1wu7UUB6AYGj45a20bVZBDFTidy7PnIqO6aqqQagB8buQZNT1JyL3e5Fca933rkNa1O6zzAXXptf\nvTc1SUGT2Jp2KHXH261Uyuxvt5Wp9PYplcwTtL+NF0b5x0/+GlcmHjj2fR8F/UHdR+n+1k8KDcpU\nSu8xgeB751R3PkuTPaq6fqRKpWHXr3cnpJD88IVPMFu69UlDXpXZFXsOb4Qhbg3DGuxoaObylNY2\n1vmz65MUDZ9xBMtrqh6bm9ifVNreUVaiol3gxo4ilSItwPBsdM+mYa9xPXm9H5OFcUoJqZT+3cfY\neJFNFEcI1AKBFGoMmklsaVveNk2/jUzK8k5kZwRMJyWVkFktkkfafryoa4zZXcLp3qqT7FvVmGmu\nkiENTM1gsaXIs1POwQ1M5ieVMqff/lZLchTPV88ADCRnnB77260rlfoJor1UH9YR7EZGan+L9ra/\nqe0Mtr0GMfEt1YG3i1qu7kjvizHb5L9++By1XAOPYabSnQc9ITrhsE6M7jbDTKUh3k04FAVar9fP\n1Ov1fwT8W+CLwPcDSyhp9F2NN968xo5nMkkjW43uIZVKqdVMp739Mq3Xvo12b4lGO+ANT2f1dRcR\ndAmphY1XifEQolsYaNoEUhbRpZdNjE0pCYBy1UYkg3SUSIybi2A0Q0Jb463mFpo2wSlHZjLgfCh2\nOlCdqYxRKn6KIB7pmRQBiKTjyP2jF275Og1UKvU9Kx+fqPDI2OEkxUfNVLpdOLdBKul99rfjzlQa\n/JlpUPfJXJs7YZK8O1Pp8Pa3wyiV0hW/x6auMOGMcRikmUrxsOvXXYv0O4/J2d/eweMZ4t2PYQ12\nOMRxTPO5byEsi8LFe/hPf7RAEGlcqu4gEWxsKnLmIPvbRkMpmsqFAjeaNwFFKgkETmMET+vw22/8\nZbb92Uo303y2NE3FVHVMlC3MFRAiWfwjxta6JE5qS2v6LVpeCyEEmtDwIouSoWNrNp0wXZyUPRlG\nKVJSydE1xpNIA0uTfGRGqejTDKKUwCrlQrptTTJiHaweSuvGfvvbfPk0P3P5v+DjZ5U70x9gI+ux\nv92GUqnfCrSX6uNIpJJI7G/x3kHdoBaZ2gm5dysxCLeLNKoCyDKVQFkA812JT6rmG+L2kN67h7G/\nDTOVhni34sCne71e/y3gAeB/AR5dWFhIe6L+Sb1e//xJHty7AS8uKtJI22khS+NUiiblRBHkdgLK\nxRZxFBO/2kTeY9HUnkVogj9veggRsHU1RMx2ffnfXnsBNR3pDiC6nEKIIiU97L4GRFIwMV3qduOQ\nAr1s0tz00NsB7ojFGy012Fwe7XblqiaDuilFNuCWdA1TCpZaHS4U1XvyXawAyubBq1l7ISOV6BZa\n/cTEJ+YmDr2/HqXS2xAOfaF2luKiw/wtWL4ypVLUq1Q6yZyVkwzqvlOQZSpFhyOV9lcq5TOV1KB/\naexenjr9Qb53/nsOfUwCkdzj6t9DpdLdh7zSM7UQ3wEc7BDvUgxrsMOj8/rr+MtLlB5/D8sNjz95\nscWY02basrkJbG110KRgemz/xaGtRKlk2xZb7QZSSEJNkSnOTo3tsRsE4SLK2BpzeewSb2y/CSil\nUjkhbbLOb6KAEN36qaB3f550VPOFTuAmtVZM2awQC0HJ0KhYJVp+E2mCFPpAS3szRyqVDY1Hx8rM\nlwpMJl2otjxVY6Zh3UXDwQ0j1jo+Z8uFQy0SVfcglYQQPDL5YPbvQUql4wrq1oTIMpDSnwchTyod\n1E1YlxI/ijMyTN9jEp+3lRX1t1+pNDJAqZSiPyx9iDsPtiZpBeHRg7qHmUpDvItwmKfPvwb+48LC\nQtj/i4WFhcvHfkTvIkShxyuritCII0UajDkmX/qtF/ieT9Rx3YBioUm87RP8/+y9eZAk2X3f93kv\nrzr7nO459pxd7OZewC4OAiRBEqREyUvKEESJtAFRtCj54CHKIdqmI2QqHLIcDjIMhizZIYmUFRLs\noEyKCooWRYEgSB28TAogQRIEFkjuYu/Z2Znu6au668jr+Y+XWZVV3T1dPVXdndX9PhsbXVVdlf0y\nKyffL3/v+/v+XrhD+uhV9iohi3spn5UJbhKR9gQqHiSVgo1Ab0+4VC1JJ0lxnIcRQrJSHUxqKk7B\nEixfbgy1eK0tVdlphdhtPfHvJZdBwLuWFgbvsSWuFCx6Tj+YEEKwXHFZ2+vxjroOePJFtvznYZPt\nOOSfTAs3WpNcKotKJVee/MrM08tP8L9+/d+8p8/uL3/LX5/GyA4mN2s8z6tWeVIuGtOou5hUckeV\nSrKoVNLnVtWu8h2Pf+RYY7IEfaNuKIeiy3C65F95yqCE2Bh1GybgE5gYbCx2fvPXAZj/uq/jX3zm\nDVIl+KZ3vM4Xv/A4DfRC332rujvv3Wi1dVJJugo68Oj8w2y+lSeVBl2/bFklTts8Mv9g/7Xl6hLN\nTKmUd3MTsoZkkEiqFJJKeUfPRCW0I62EqTtNdoGGY9N0mmz0OtRdcA4p9W/Heg6s2RIpBN/+iC7f\nVUrhSdlPBF3NVFENp87b7R6Ko026c6qWxBZin6fSKOGBSiU9vwo4tMnFuFQsSZQmVC156PzqFeb3\nccrfEqWIkqM8lQbx91krlUaTSlWjVCo9+TlZtY9Z/maUSoYZYpyzdQvoa4V9318wneA0e60bvLo5\nzyWrxU62MiW7McEXbvHaS3dQSQfbTVCbETsbIZ/YafOPd9q8fCMkFQIpshgx7fW3mXf5AIf76h6X\nKg5SapXRldqgNExlnTjmVxv9SRxgfkWvoFhhVlInbCx2hiYkIQQfffQKH3lo2I5h2XMIU0WUt0TP\n/1Y+oglujIrlb9O40Trt8rdJGE0qnUb5Wx4AnYRRd1nIk3LjeioNl7+NKJVE0VNpks5fmVIpe27C\ngYuHzMvflBp4Kp3dcAyzj4nBxiANQ1qf+W2shQVqTz1D8PomjpVwtdIjzBYKLs9V+K7n/SO3tdvR\nyZ3I0rHZM5ee7CuVqu25viWAlA2kkDw8/1DfI2mpskTVrmALq98dTooaQg7ULUWlkms5+8yVq7aO\n9Rq2xZzXRKHjveIC2q1Or5/gaccJrhT75jUhBPOu3S9/u1a/iiMdVmqXuDFm57fRbY0qlUbJFzmL\nCze1LLFTs62J4x6vX0Z0+Ow6XP52dPc3gHaij/FhnkrFuKB2BkqloqfS3EhirqhUMt3fykm//O2I\n8xFGlUomejDMDuPc83wc2Ck83wF+7GSGM1u8/OYacWpxPb1Fy9ZJJSebcP/wd29Qq+rVrs5OxM/+\n8SW2M5XOrzSzpIKlbz0Vnf0bx2W54nJ/fRB8LBYSQ0k3ay26XB1SKi1nZopeZbDNRWdgFp7zxEKD\nh5vDqy3LFb39bialTkeMusfpynYYRZ+maZQGOYW270WT5TLS91QaKX872aRSXuJ4fgOMfvnbmJ5K\nd+3+doBR970gycqejFLpwpJ/5UVPJXMeGCbAxGBjsPt7nyPtdJj7mg+y10t4606b++dbbG/P8c7H\nVnA9iznP5tFr8wd+fv1Wi71dnWjZ6+n4aVdqb6XHFx5F2tm/ZSVxpC7VF6JJ02ngWS5XaqtULI85\nt4EQom/Wrd9XRYqDk0qw36vRs/VnG46VdZLLS7N0UkkpxT/80pv8zMva76kdJ0Nm2EUWPJtuktJL\nUhpunR9+/3/DRx79Fm7mSaX6+PYBc67Nbpz0Y5mDGJS/DZeg2VlSalJy5cbdvGncY3gq5Qr8vGve\nYZ1di8ma+hkolSqWxJP6OI6W9A2Xv53fmG+Wyc/Dccrfij5KxlPJMEuMc7aKIAj6M0gQBClQ7rv4\nU+LtO7od62pvgzCb7EVHJ3veen2Leq2DUop/uSjYakqe/aMO73i9y3ZlOKkUWzr55FmDYEcIl0ue\nM5RUWnAHk0W4p1fArKrTrwW/v+bx7idWeeY993HtHYNJ/6HGeDc0y1nSKm/F2i9/y35/2ArOOAwZ\ndefd3yZYv89XjcquUoLDlUon6ql0gcrfwrHL3waPR8/lvORNP773y5sQw12/TDhw8ciNus15YJgS\nJgYbg37p2we/npff2gbggYUdNjfneeBqk0rVoduJDvxsHCX83E/+Hp/6F18AoBNqxfhmbxOB4Ep9\nlXp1cOMuLe1NhJhn3psD4C8+9VG+79m/3E8gzxWSSlLUEGLw+cpIGVtu7J1jS71IqZNKTe2vwCCp\nlCodp7222yVOU/bihPohSZY8kbOV+Sqt1Jaxpcsfbe/hSclKZfwYId9WKzpcrTQw6h7MsUIIvv2R\ny3zrA5fG/luH0Vcq3eXmvHIMT6V8nJ2shPAwRf6QUukMur8JIXh8ocZj87V9ixRD5W+nYAdhOD55\norMyVvnb4Ps96vw1GMrEOGdry/f9D+RPssd7Jzek2eHOjg483E6IZUscS1fNu56e3Ov1DrtK8WbT\nwpUOf7r2bv7YV5xBdyCpJ+bI0atijr2El3UFEcJhperywFBSSU9qnXZI0tOfDQutUN+/Ok/Ftfn6\nP/kYD923TJLukKQ7PDo38FO6G3kr2nzFJmVUqTSBp5IY3GhNR6mUdzg5eZPuSRkklRj6eZKq1otg\n1N3v/pbmyq+7vz//Hhwp9iWgpqdUEigGHQ6NQOXiMVBlqn5i3pwHhgkwMdgRRBsbtL/0ApVH34F7\n5QovvqmTSvcv7LCxOYfr2lRqDp121C+/L7K32yOOUm6/1eLGa5tE6ATMWmedS9UlXMtlrqYTPakl\ncNyncZ1ncOxHmPd0Quj+5jXesXC9v81RpdJw+duw0mWxMhyj5Z3i6rbNnDsof7Oz+TxWA9Xz67td\nolQdqlQ6yGD7s2s7tKKED6zOH2txK2/ycrcSuIOMugHetdTkkbnJkzGVMbpoFX1oxvFUAugcUf5W\njKUO6sB3Gnzs0at812PX9r1uyt/Kz5MLda43q/3F+7vhDCmVTPBgmB3G0aL+98D/6/v+F7PnTwF/\n9uSGNDts7mZJoU5KkiiuLlYQmz2eee99/MFn3qRZ22UjL82RNpe/6y9xGXB/7W/RS/YI0w7tB2/S\na2bK9sjlavMhXt0OMqWSS9O1sIRORORJpe2NDiLLTERJemAL19XaCu32P0GhuFz/nrH25yClUjEA\nm6T8Lb/hT1FTUerkN/6zoVTSP4uBIJx097es/G0Gjs+9kieJxjXq1q2aDz6Pi+qkqSiVjEHzhSX/\nxhVMRZVpuPCYGOwIeq+/BkrRePY5AP7o9XVA4QmbMHJxPYtq1SFNFVGY4HrDoe/uzsDX8rf//cuk\ndR3bdeIujy88CsB8vcE2kHgWUtapVr4GpVKaztyBY5pzBuojKWuAi17HTanYFda7Ib+ztsPv39mB\nZHnosypLKjUci2ah/M0SOkZLCnHZl7d0fvHQ8rdM4Z4ngqI05ddubuBKwdddGW/Bsb9PWQx6N7Pu\nPB49qa5V4yiV8r8tD5nvi9iZF1Zudn7Y+4tKpdFyxbPGlL+Vn+eW53hu+eBrxSj5OSjFydpkGAzT\n5sikUhAEv+X7/lPA12Qv/VYQBJsnO6zZYHNXT+zr7gIKuNzw6LU32Jp7i0cev0SztseN3sCfaCBZ\nzTx2VMzGQy+joghSmPuKi/3OVSDAkgsseDZSCB5uVtnoRVSyoGG31UNmE2AvTQ9cGVqpXSJVLQSC\nS9XhgOUwmo6Fl7W9hCwBVPj9JDfIxZKQaag47BlSKulkhiDJPZX6ypqTmyzyVbXzvGqVHz818vyo\nzxykuMuTlJawJvK/kUJkyYTs+T1vyTCrCKGvdsoolQxTwMRgRxNva2WSvbhInKS88vYuq402e12t\n6nA9m0pNL7B0O9G+pNJea5BUun2zReIPGu1dretSt6XGHNsoEm+QvBFCUnMO9mhq9pVKAnCp2oqW\nqKHULomy+d+/8Hp/oUmqYeVLonKlUlb+xmBxEhjyNPrSEUmlQfmbTgT9ztoOO1HCN1xZpOEcz+Oo\nr3q6a/nb3buoTco4SqWif81R8/mg/C1XKh08axebnpyFUffdKJbjneeY76KQn5OuPPr8NRjKxFgz\nShbAfPKExzJTKJWy1bGp2z3erug68UVb8sJ9L/Lixlv8189+P95ayJ3NGCyIkpg3dru83emhsvr4\nVCV04x4iu/W0I4f0cw2az32UhcpCP4nznY9e7ZdMAezudBHZxB0m6sBJvGpXWK4s4lhuX7VyFEII\nVmoeN3c72T4WFBf3eqAyiiUh0+h+licBZkWJo9Vmo93fTu7v9T2VznF9/WjMOk4Ma0t5oPFh7qk0\nqel7PgRj0HyxEULfBp6Gf5rh/GNisLuTbG8BYM0v8MatFnEiuH++RS96AtjBdS0q1UyJ3Y6YWxhO\n4uzt6m65q1eb3L7ZIrViRFbKfLmuu+Reai7yChv9pJIlFIkSuPZwUilVijBJC0klRxt3213WRY1E\n7bLW1crlb75viS9v7fH6zvDiWJhWcKXCtaQuf1O5GlfPU0Wl0p2eLtWrHeLV0le49yLiVPGrNzdx\n7kGlBOMplXKPw0nsEu7GOEolr59UGr/TVie5u1Ipj6MFgopdrsXMXKkkEBMprQ3lIP+34xmTbsOM\ncWS2wff9dwE/ATwL9K+kQRBc6CtX1Ntmu+uxau+w7i4gBFTClKSug5Mt602uWrARp2AJUhL+6Ysv\nsBM3UXhAm0QlJEmCl934W4mD102QVrPvbwT0FUo5uzu9QflbOih/G71Z/ivP/ucIcbyL0mrd482W\nTirpBJB+fdJ7oqJR9zTabA88lWYjaWIJ0Q8E8wThSd5oVjJvrrIFP9NkNCk5TpLyWx64dOBEnScp\n7QkDsvw7zVeSTTn8xUQidFI+e25OA8O9YmKwo4mzpJI9v8CXX72hH1spVbcK7OB6NtVa1t22vd+s\nOy9/e98HH+YXf/YPSWWMJSxiFbNUWQRgtbkMbJB4ev5o2CHbkYcltNdSK4r5zO1tfnd9h+0w5psu\n5/6Y+iuryA5CViGF212FAL56dYEbez0QA+/MiuXRTgSNrG18o1D+dlBSKafuHHw6zBWUSi9s7rIT\nxXzw8sKxVUow7M/UjRN244RLI0bfZVAq5bHwOJ228nF2j0wq6fOnZleRx4yrT5paZtTtWo5ZyDoH\n5D5K7kmuPBsMJ8A4s8o/AP4G8LeB54G/ArROclCzwNb2HZJU0lQdblrzPHy5SXu7i1rSKzhfXH+J\nq8Ad3WMcBGx0d7DtJshLkA7U6yLVE9TSUpNuRwcgq/bhX81ua5BUCgvlb6MGg/kK23G4XBskIVKK\n3Ysmu7gNlErTWb2fJU8l0N9NnmhIpqDUOoqvuvJums0KTzQfO7G/cdaMnj/jnE/vvXRwTXuuUJpU\nqZSf5/l3LEw64UIiBMaw3TAtTAx2BP3yt/l5Xnzty4BgN6r3W+Q5RaXSAR3g8vK31WtNHnvqMoGd\n6MSBgkVPK5FWGkukIiGpZLGH3AO8fle3f/riTV7f6/a32Ut1LCWzhJEjWsjMK+lOV/DIQoWabVG3\nLUQhqdR0G+zFCfdnSTBH2tSsfFv6b+exhGAQox1W/uZISd222A5jPrOmj9P7Vw4u2TuKum0hBby4\n3eZH/uAV4lTxg+98aCixNEgqnUzi5cFGlbptDXVGHiVPJo3TOWt0nIfFZblSqeacjUn33ciVSudZ\nmX6RyM/Jg1T1BkOZGeeMrQRB8G8AGQTBzSAI/gbw7Sc8rtKztqknZxGnIASP3TfP3m5Iz9P17Tf3\nbhAqRcsRhTsKfbitvB1t/mqqJ6uVpXmuvlMngqxWeOjf3t3pYali+dv05MYr9UFSqVj+NulNUT5R\np4UbrYvS/Q2GlUqnURJTd2o8/9g3DvkAnDdGF3EmWdTJFUq2OP7qbZF8CAMz9ok2Z5hRBJlhuzHq\nNkyOicGOIN7aQtg2olbjlVsRNSfEqT1IFGqfHO2pdBelUquHZQkqVYdv/FYfbZmjEAgWsqRS1a6S\n2km//E2kG/pvK5ckVdxo97hcdfmO6zq+E5mCSUr9k3Qbx34Ex7oPaS3jz+vX67YFDLoCL3iLpIq+\nUgmg4eqkwahS6VphEfCwpBLoErjNXsTLrQ7Xm1VWqveWfJBCcMlz6WUxpwLebg/HqgPl/Mlc8x5u\nVvnhdz/C6l32wbWOr1QCsMX+zrCD92VKpZKZdIM2EXelc67jvYtE31PphMzuDYaTYpwzNncs3PB9\n/1nf95eBSyc4ppngzpZeKOwkepK/f1FP+qnQE+qdcIc7YTL0mTwgsEeSSkLpC8jS3BydSta1q3N4\nzfpuq0stM5osKpWmMYlfLiSVUjUw6p40AZKfaKmaTkekvBPHrCiVDip/M/PFZNyLUukwcgPUyZVK\nw+VvRql0MZFCoNR0EuiGC4+JwY4g2d7Gmp9nd/cO212XZiXkkWtLhD0dRzmu7v4G2qh7c32Pl750\nu//5vVaPetPTTTUsSZiEpCplzm0MzQnCVsSe7sgbJmsARMpmrRuSKMUD9QrLFf13hJznv3rnX8Rz\nnwEVESU7WNYKtdq3IoSLv5AllRzdHKJq6+cNd77/ek7TyRJTWSSVxxIPNqr9a0v9Lkmledfux3L3\nqlLK+a7HrvJ9Tz7QT55thsNJuvCElUrj4B0jqVRU+I+q/YsUy9/KyMNzD/JA876zHoZhCniWZKXi\n8mDjcDWewVBGxlmW/+ksiPkR4DcAC/gfT3RUM8D6VhuosKXqCAHLWZJHZUmlnlK8vNGDwjWh5tTp\npmBZS+TCZVe6pLFCpIKl+Tk+F0eQKqzt/atpAEmc0tmLmLumTSDDVPUn8btNiOOyWlj5UkCSrTpN\nbtQ98FSaRkmI008qzYZSyZaCdjxq1G3uNCehePwEE5ZTiul4KhmlkgEGZSnGU8kwBUwMdhdUmhLv\nbFN56GHeePNl/ZoSPHJtni+/tIFtSyxL9pVKN17b5Iu/d4Owl3D52hy1hkt7L+TqAzrZEqcxsUoQ\nwII3bGbteTaJZ1GVir3eBtjQihJutnX53NWax1zmVdQKE5594Gn+2St/gCKkm3RJ0xaWtcScY3Ml\nU9rkySDXqtKOd/EsHds1ChYIC5Uma+395W9VW3K16nGj3TvUqBtg3s0TIhZPL9YPfd84LFdclgFL\ni/LZ7A3HqgPl/Nld9VYqDo80qzy50DjyvcXk193GnMec9RIqlQD+6rv/S7OIdU6QQvDXnnnQ+GMZ\nZo675gp835fArwRBcCcIgk8BS8DlIAh+7FRGV2I2tnXtfEvWuLpU4/adO6jsv5wXGFYqedZghUNk\n2aYHm/eTECMTh7m5GmthjLMX0d7uchB7uzp4aWTtccPkcKPue2Hec4Y6WMX9BNCknkr684maTlJl\ntbaCZ7nc37g60bhOi2GlkkkqTYNiEmliJV1mvDktpVJiFCoXGiGELn8zXQANE2BisKNJ9nYhSbDn\nF3jzba0+2us5XL/aJOzFOFm5Wm7Ufftmi7CnY7P1Wy3aWee3RlMvUPUS/VwBi5VhVc+llTlSV5Kq\nEfPDGgAAIABJREFUPXbCdVAJrSgeSirlBtg7UaY2Fy4q7dGJOwjaAPgLtf41IS9bc6SOCW1LJ32a\n7mAu+lMP/3G9qZHyN1sIvuHqIl+1MtdPZh3EQrbo+d5Lc9hTUhAtefp47k8qnaxR9zg4UvJfPHE/\nzyyNk1QaU6lk5Ym5ciaVpDDt588T5rs0zCJ3VSoFQZD6vv+TwLuy5xFwsITmAHzffx74u+iVtX8U\nBMGPjvz+h4DvLIzlSWAlCIIN3/dfRZtRJkAcBMH7xv27p8HWnp44lZA8+fASn/nK56hJrUi3gRjY\nqAxP3qqwiiBlkyTtcLl2iVc23sCKXHpVh1gp5vYSdncOTirlXUqadRdI72rUfS8IIbClIEp1eixP\nWE266YFR93RKQi7XVvixb/hbpevCcRg6qaQfJ6fgqXQRKHooTdokQwiBKx1cOZknQf6VGqPui41k\nev5xhovLpDHYRSDZ0v6WcmGOt+60gDpupUGt4hCGCW7Wscz1bCxbopTiqWev8oXPvcX6rV2q2QJd\nPUsqdeNef9uLI0qlD33rs3z2C6/RCtfoJSF1EdEKE2529Geu1FxsKajZklYUZ/OATaq67MZ7WHYH\nBX0/JRiUuVkybwuvkxZ5pzWA1folJJv9Bbk4X5iSgncuNXnnUvOux+jZpSYbvYivv7Jw1/cdh4pt\nUbUkG71hq4ZpLnKeBnYhDnPuEk/afaVSOcvfDAaD4awZ56r/ku/7Dx93w77vW8DfA74FeAr4mO/7\nTxXfEwTBx4MgeC4IgueAvw78ahAEG4W3fFP2+1IllJRSbHVtRFalfv1aHdlxSSy9wvVAoba9WE4T\npwpPglIpUurJveE2SGSMldisJ3pynk8Uuzu9/ip3kTzZNJd5H4WpNuq+m8HgcXEKpWph1mZ1UlVN\nsfwtV3NNesM9Kwkl0MFfotSQz4rpFjoZ01QqAXzsiT/Hn370+Ym2kfuEJakpf7vISKEbHaRTutYZ\nLjT3FINdFOLtLQCspQprLX3j/8CqTrKEvRg3N9YWguf/7NN82194N+/52ocAuHN7j91WD4XiV61P\n8Uuv/lt6ySCptDCiVNrL4qEk1Z6arkxoxVqptOQ5VCz9t5qOTStK6GXvVypkJ2xRk6/zpx64xBML\nhaSSnSeVdDIpFXrso8ojWVQ7p8dTO8+5Nh95aLWvopoWi57DZi8ailWnacdwGoxb/rZU0TH7StXY\nmRkMBsNBjDPDNIHP+77/G8Bu/mIQBP/JEZ97P/BSEAQvA/i+/9PAR4AXDnn/x4CfGmM8Z04S77Ld\n9XBlQi+VLMxbeN067ZpeMZuXgsVIsekImm6DzZ5+PVEKz5LsRXewrGWiGLpxF4RCphZvZRLqS5bN\nepzS7UT9VbSc3az17cKcB1sdokypNE2psS0lJCmpGkiZp6ZU4mKWBtmFsihT/jYdisdvGsfy/Vfe\nM/E25KhSyXzHFxKB0J5KU/CPM1x47jUGuxDkSSW1kLD+0hyWSHlgtU6aKuIoxSkofh58ZFm/Vymq\nNYf127ssXquQWjFvxK/RfmuHxxcf7b9/0RtOKm1nptRpqsvYqpaiE0E7TrneHChYmo7NrU7ITqgX\nChU6bqvb8MEri0PbzJNKy/X38h3PvI/P3GgAe8y5w+F5Ue0cF8rfzpJFz+Gtdo9WlPTHO+1FzpNm\n3PK3h+ce5H/54A8z786dxrAMBoNh5hgnqfST2f/H5T7gjcLzN4EPHPRG3/drwPPADxReVsCv+L6f\nAD8RBME/POoPLi7WsO/SAWNSVlb0CtLG2hp7oYsntDLp/vur2LFHWNHxnisES7uwuQjNyiCppBBa\nsZK8jczaze4kOwBIJLd6MbYUXF+qs84atrT6fzMniXQw8fBDS4itLVIpSAV4zv733isVx2Iniqk3\nPGqZIsqxJ9v+20qv2FVrLnaWqFpcqLNyaTpjvhemdbzGofqq/qe2uFzHXdfnw6WlOitzJ1uff5r7\neNpErcHly7ZkKfa1+tYd/bOh/9006t5Ux1WGfTwNZn0/7cw0t56dBwtz1QP3adb3c1wuyn6eEPca\ng10Ikm09n4buLhvtVQRwbblBlCV0cqVSESEEly43eOOVTf7NF3+LuqXVJ3e6m6x17vTfN2rUvRNp\nL6aGI9iIoO5INrJixKuFJidzWUnb7W7mz6R0UumgzmGuJXGkIFJVvvbBp/mVV17AEvu7uVmSAzrI\nnm3iZinzatrsRYWk0nQXOU+a4liPGveCN1nnPIPBYDjPHJlUCoLg/zqFcXwY+M2R0revC4Lghu/7\nq8Av+77/5SAIfu1uG9ncbJ/YAFdWmqytacnzyy++BkCiLBpVh7dv3+HW+1ao8QoAbqRQ8Tywg104\nxIlSpGlCktzBcnQQ8/bmOgBCWLzZanO15uEkemJ747UNnMpwYLF2W48hTlIcKWj3YrpRgitlf3yT\n7qfMApftnQ4yr5dP1ETbb+109M/dXr9zSWunw9r+Cr9Tofh9ngZJFozeWmuxmynStrc6eL3kbh+b\niNPex9NmOwvYAYSa7PycFnkL663sfO+0w6mN67x/nznnYT9VqkhSxU5Llyvvtrqs2cP7dB72cxxO\nYj8vUpLqlGKwmSXe2gIBa502qdLJ3CvLtb4Zt+seHOYur+qkUmN7hcQezCXBxkv9x6NG3bny6InF\n+/n/2p9n0fN4Iws7i0mlZlZmdruTJ5X0z+oh7ejrtkU7Tvp/o+nY+5Q+lhD9src8hiqDUglgM4x4\nCL1vUapmxk8JGDIun5aJucFgMFxEjkwq+b7/z4F9t/5jSK9vAA8Unt+fvXYQH2Wk9C0IghvZz9u+\n7/8cupzurkml0+L227eAKjEWq4tVNnc72PMJTyVv8FobrC+32H343cxXbjHvDoISHQcolOohMlPG\nnVAH28JxSRTcV6/QyFZLWgeYde/t9LAdiVexcaXsG3U37OkFF/lqjfZrysrfJpxri55Kuc/IRZq+\n8xXFxHgqTQ1ryp5K06Bv1J1evHPcMEDAUC/QcpydhllkghjsQhBvbyGWXdb2MkNuwI7SfoL/IKUS\nwPKqVos7UYWet9d//cubL/Yfj5Y65Umlb37g/Tw2P4ewrvD5zTUArlYLSSV3NKmkF5KqTuXAsdRt\ni9vdkFQpWlHM/Y3977OE6Bt1D0roD9zcqbHo6qRS0aw7SlM8a3ZmviGlUkniCIPBYJhFxrny/wLw\nr7P//w2wCNwc43OfBR7zff+67/suOnH086Nv8n1/HvgQ8C8Lr9V932/mj4E/CXxhjL95KtzZ6Nsa\n8ODlBnfaId8kfxuRN2XZididW2G58R+TqEGgobu/pShCpNBJpbbKlrlc/fz+mkdzXn8m7/RWZLfV\npdH0dLcqSxImmVH3FFdYcuPCOE37nTysiU21s6QSFDoiXZwJPF9RjFNlur9NiaJS/ayD6xxZ8M7S\nz89wMIYzQwh9nVPGW8swOfcag10I4u1txJLL+m4WQyH4dz//Ar0s0WG5kl94+dOstdf58c//E/7V\nVz6lPzg3UCdRGzTU28rsCqpWFUsOElKpUrzV6SGA5UqF9195D/NZUqVqyaFubc28/G00qWQdklRy\nLKJUsd7ukQLzBxhqy2IH2ZKYYfeVSr3B8QtTNWR+XXaOU/5mMBgMhsM5dvmb7/v/BPj0GJ+Lfd//\nAeCXAAv4x0EQfNH3/e/Nfv/j2Vu/Dfh0EAR7hY9fBn7O9/18jP9PEASfGmN/ToWNvcGqzIOrTd7u\nbfOMfJuXEhfo0fEkQnrc6UXEsl74pEAplQUYrn6eL0A6+n331Ss0VKZU2h5WKkVRQrcTc+mylv67\nUrAbxSRqupNhvq2ooFSatHY/v+lPsw5ocLHMa61+skEZo+4pIads1D0N8lDaGHVfbGTWGzQ1yUXD\nhNxrDHZRSLa3sJ5osL6n/QlrwN5uyBuvaDeFtfA2n371V/jD9S/y5u5N/nD9S7x79V28kbxCKhKk\nsqg13X3bbbqNoeefWdvmdifkXUuN/iJeXuZ2teYNXevzzm13eiPlb4e0o69l/kmvZ2XToybdoGOo\n3j6l0lknlQaeSjlRms5UcqZYQnjWSTqDwWCYZe6lv6hCm3AfSRAEnwQ+OfLaj488/wTwiZHXXgae\nvYexnQqb3cHq1eWlCq/fbIMHe5mJdrsi+2L1KDOozm83FSlKhVkAopNQAMquYwErVRcJWJboK5V+\n5zdeZWN9j/d8jW6D22hqJZMuf1P9x9Mi31akFFGuqplQqZS31E5VUcVxcSbw4fI3025+GpSx/E0W\nkodgyt8uKkJolVJ+9S/H2Wk4J4wdg513lFLE29u4S9dYf7MKKLzsX1vwh7cA2Eq2wIY3d2+yVFlk\no7vJL7zyS8RpQq92idreAvYB/TLm3YFvVyuK+fSbd/AsyZ96cKX/+krF4fH5Gs8tD3t85cmmPNbp\nK5Xu4qkE8MaOVq4fnFQS/XklLklSyZGSpmP1k0pJqsv73RkKboQQ2EIQq9lSWBkMBkPZOK6nkgTe\nBfzySQ6q7Gz0XEAhKnv8/a98nMecp8GDuBeDAzs1CyFyQ8WRpJJK+wGGEO7gsaxSta1+kNCYq7C7\n06XXjfncb71GkijWb+myu/pcllQq1PyciFIpUURJVv42saeS/nlRPZWGy9/0a2cdEM46w0mlMxxI\ngXwYeXmCUSpdTCR60jTlb4ZJMTHY4aSdDioMYc5mfa+GZLAAttfSsdXN5C0AHGnz3773+/nHX/in\n/OH6l7CExSNzC7AHspLu2/Z8ZeCn9ItvrNNNUj784Eo/YQTa2Pm7H9+f32u6wz5Oirz72+GeSgBv\nZEqlg8rfikmlspS/gS6Be3O3S6JU3y5h1pIzjhTEiSrF8TQYDIZZZRyl0i8UHsfAx4Mg+A8nNJ7S\no1TKZreKRGE3W6SktJWWWcdhllRqWv0IMO/SUVQq6cOokKJKorRRtxI1avZgIm7Medx4rcNLX7pF\nkihcz2J7s9P/HQxP3NNMKrlZBilWampdRopG3eoCegoVy9/SkqwyzjrDnkrlOJb7lErlGJbhlBGZ\nqe7w1d9guCdMDHYIyfYWAHu2TTe2yR0sK1WHbkerZ9Z5G4DLtVUWvHk+/Mjz/J3f+3ESlXDpcpPW\nTcBNIQLPcuklulRtyVsAYDuM+P07La7VPD6wOl5LeUdKKpakmy3KHdn9zRlOKh2kVJIFo+6yKJUA\nllyH1+myHcb9OHGWyt9Af1+dZLbK9gwGg6FsHNtT6aLT3t2i1fMARbUREQIJ2vsoDSOoC7YbFjUG\nN5eV3XliTz9XSreNtYVCiEGAoUSNqj1Y3WrO6xWt3/8PbwDwZ77z3fz7Xwy4fbPF/IL+nDuUVJre\nbYtXNOqeUvAyUCpdTJ+RQflbefwQZh1ZwvK3fBh50C9M4dOFRAotLUmNUskwISYGO5x4exuqFrc7\nOibKk0rPvOcav/ObrwEQWTqhk1+LH1t8hAVvnq3eNo8/fZmuqPHG4h24DU8tPcHvrX0egKXKEgAv\nZ4me55abx5pnmo5VSCqNV/62lZWRHVj+JrVRtyqZL2PRrHshG/csKpUAbDFb4zYYDIYyceQV1Pf9\n3/B9f7HwfMn3/V872WGVl7fevJE9Enh1bdgdZwFDGuvnqpAtSWOLR7/0NVx940n9PEsqORKkGBTy\nC1HpmzWCLn8D2NnqsnqtyfJqgw9/9Fme/7PPcO1BvYJ2YuVv1qBUK1cqyYmNuvPub8Xyt7MPiE6L\nvlKpUP52kZJqJ0Hx4lWa7m+FZDKY7/iiIhAoBSr/t362wzHMMCYGO5x4ewux4HBjW3sa1bPmJ088\newUnK0FLLR2XJWrQYCW/Ti825nGeWyUUOvH0rpWn+u9ZrV8C4JWWTipdbx6cEDqMvExOq3d0cumo\n8recOcfa9x6rsDAXl6r8bWDWnTd2mTXFTz7eWRu3wWAwlIlxYt1GEASb+ZMgCDaA5l3ef665efNO\n/7Ht6WRSjF5dimBwF5HhdBoIJXFC3d0tzYILT0qkzA6jArCoFYyLmnNe//FjT14GwPVsrj9+qb/q\nfeJKJaX6iouJy98KRt0XUanU91RSijRVSGHUC5MihOgH2mVTKuWeFyaZcDERQifQVeG5wXCPmBjs\nEOKtLWQhqbRoCV59+rf5qVd/huuPXQIUqdQLeb1k0KEszRqoBFst/tXra9xqa7/KJxcfp5olflar\nOqn0cqtDxZJcrQ1isnHIO8BVC7YGR5W/gU4w2QfEc2XtIJsrlTYKSaVpNo45DWyTVDIYDIaJGefK\nL33f70tqfN9vAM7JDanc3N7Y7T9Wti5766UxcSwJbZAjfo9eV7eltWMdTKhstUyGKUJmAYYQCCEO\nVCoJAY8+ucJBuCflqVRIKuXGi9Mrf1MXsiQkT37kAWEZgsHzQJ5MKsvxzM/z+AKe44YBEr2+0O/0\neIFUmYapY2KwQ0i2t2HO4c3tBpZIqStBp7rNlzb+iK/95kfZeM8LIBWWsOjG3f7n4kwxfrO9A0A3\n0QuEdbfG8w//cZ5a9pn35tgOIzZ6EQ83qsdeuMjNuqu2RcXyEAgq9sGJqaJS6aDSNxhOKvUX+0qQ\nBFnql7/FhH2j7rMf13HIF2XLcDwNBoNhVhnHqPungF/2ff8fZM+/D/jJkxtSubm9M5BQJ7IHCXRV\nQthWhM7g1kFka9RCZbFf36g6QaSScCNEzOerVoPgI2duQSeVrj24QL1xcCByUuVvfaPuQvnb5Eml\nolF1/tpEm5wp+p5KqU4qlUVZM+tYQhBRnuM5KH/LnpdjWIZTRgg9AxilkmEKmBjsEKK1NVpLTXbv\neNgiRaUJqUwJ05Q78Rpv2dpXybUc2nGHVKVIIYky1dJ6twVcJkx6eJaLFJJvfvBDfPODHwLglZZe\nRLw+d7zSNxiUv1UtScWuIIRAHuLZ41kSiS6SO6jzGwzHUMmU4rJpMOfaSEbL32ZLqWQ8lQwGg2Fy\nxjHq/hHf998C/nT20k8EQfB/n+ywyst6Z/A4ztrEJkC4GxM6gjSb45Ua3FW25tfw0muATirNb1xF\nuQkyM9wWWVKp2P1tbqHKn/jIU6xePVzlXlQqudY0y9/2r4hNmrQqGnWrC+ipVCx/S1R5PIBmnYFS\n6YwHktEvfzNG3Rea/FtPjGLNMCEmBjuc8PYt3lpdBUAoQWol/d99fv2F/uOqVaETd+kloe7wlmoP\npa3eLpYDUdKlYu33O8pNuh85pp8SFJNKFk8vP0GYdZU7CJkp1XfjhDl3v58SDHsqJVOyJZgGlhDM\nezabYdRXts+aUmlWu9YZDAZDmRhHqZR3HzEdSICNrotAIeweUTqo0e/sdugti75Jt8hySomdsLV8\ng9X1+wBd/lbdm0OKdND9TWg1U3XErPEdT67edSxuYQKcZnDhZsmtZIqGkLKv1Cq02b5A87c1pNQy\n5W/TomyeSn2lUu6pVI5hGU6ZoqoAjLeWYTJMDLYflaZEa7e5ET8OwIJrk6rBqt9Xtl7tP667dTZ6\nW7SjDkk6SDyFSZeqA1EaMufuX8B7pdXBuwc/JdDd30B7Kn3HI3/uyPfXnTypdHT5W5k8lQAWXYeX\nWx3asT627oxNfLmyyiSVDAaD4d4Zp/vbz/q+v1R4vuz7/s+c7LDKSRQn7ESeLmvwuiRqEJzcpDnU\n9c2OdEARVrqElT1UdqRTlVBpzyHjFCFqSDGHbV0BoGYdvEJ1GI51MkbdlWxbSZpOLXjpK5Uo+IyU\nJCA6DSw5HBBepH0/ScrmqWSUSgYYXO8GSqUzHIxhpjEx2MHEW1soN+HGThNQvGO53u/0BrDWGTRV\naTra27ITd9iL9vqvK9VFKUWc9voG3TnbYcydXsTDjco9zdeXqx41W3J//eCOb6PknpqHlb8NeSql\nCkF5Fi1yX6XbHa3GmtXyt1kbt8FgMJSJca6gj2TdRgAIguAO8I6TG1J5WdvqogsbBNLbK/T2gTfs\n+aH3ijRvZ6sIvXb/riJJYyrtJjXX1t2rrEtYljbiLpa/jcNw+ds0jboHvjBTUyr1u79dTE8lu//9\nG6XSNMmPY1nOpX0KlZKMy3C65F97Vg1ikouGSTAx2AFEt2/BgsvNnQZ1N2K14fU7vQHsRe3+4wVP\nx2ftuMNePHhdqR6QoEipjCSVXm1p1dP1Zo17oe5Y/I13P8rXXF4Y7/1ZUmk8pZJ+Xpay2rwD3K08\nqVSWevQxyRN61WPG4AaDwWAYMM4V1PZ9vy+h8X3fAY6vBT4HvL3e6j8WFb3ald8stES2QpYlTEQm\nTUotRWJHJHYW7MQpduKyUHf1+4SHEPpwjpa/HUVRYjzNFRaZK5UKnkrHG9kB28yCH6V0q224WJ5K\neUDYSVJipUyyYUrkx7EsSbpRLx3zPV9M8pu92JwHhskxMdgBhLdvsdZcJkosKk6KnTLkqdTLOroB\nXKpqoVc77hSSTSJTKulEyGhS6e22/vz99dM51CsVFyngUsU98PeDBQt9XbFKdFFZ9HQibFaVSl93\nZYHvesfVQ4+9wWAwGI5mHE+lTwH/zPf9v5M9/0HgF09uSOXl53/3tf5jUdGBSUO6tNIeu5aeTIVy\nUCLqm28jBQhIPJ10skIdCCw2K0CCEG4/qVQ7blKpWP42xZvqfKvFLiNywiChWA5yETsi5QHgv31L\nLzgvuqYj9DQYKJXKcTL1z/PUlL9dZPKrZb/U15wHhnvHxGAHEN2+zVuVFYhgdaHG3k4X4ab93+eR\nRt2uMefNAdCJOv3AQwoP6CHQsVvVGk4e3erq11erp5No+NDVRb7xHZdxuvGBv89FNGmq47KyLKTA\noPxtd0Y9lRqOzZOLjbMehsFgMMw04ySV/ofs/7+N1uH8AvDvT3BMpSRKUtYH8QqNuqALLAtJC+g4\nXQCspEIsI3ITJZWvLmVJJa+tA5dLC1Vo7yLQSiXJ8SfiYvnbNOXG+Sp7scvIpDGCFPr2OkV3xhOU\nJxFwGlytelyteXhScLXm8dzy3FkP6VxQtu5vsq9QyZ+f4WAMZ8Y+by1zHhjunXuOwXzffx74u2ix\n8T8KguBHR37/Q8B3Zk9t4ElgJQiCDd/3XwVa6Aa3cRAE75t0R6ZJdOsWt6Uua3vykavc+sxtvKs6\nJlquLHGnqxdwlqpL1GzdFKUdd7JkkwRRAdXFzlTmo0ql252Qum3ROMTjaNq4lmSlWWWt2zrw98XS\n6lipUnR+y8nL33JmTalkMBgMhsk5crYMgiAC/iff9/9P4Luz//8z4LETHVnJuLXTIe4MpNXNhk03\ngVVSXgVCV9ff23GV2GkhhJ5Uc8VS6umbi8peA4hYvVSH13czpVKFinX8+vgTK38r3BDFfcPh6Ww3\nVQrUxbvJqjsWf/XpB896GOeOsnV/M63kDTBQqCV5OfQZjsUw29xrDJaVzP094E8AbwKf9X3/54Mg\neKGw7Y8DH8/e/2HgB4v+TcA3BUGwPr29mR7h7VtsXr4KwFMPXeaNX7+JU9P/0h6ee6CfVKo789Sy\nhFEn7pCoFCkXEMIhSbexhO7iWzTqDpOUjV7E9Wb1NHfprox2f7NKlLdp2BaOFESZQtd0UTMYDIaL\nx12TSr7v28BHgL8MfHX2/v8oCILfPoWxlYobm3sknUjfHSiYr3us7cCyAzKBVOrJ1I0qhI6NcrIZ\nP08uZcpqJ3JIZZeVxSq8rn8hhHdPBoFD5W9TnMTzLSkGRt3TuGkXCFMOYpgqZev+ZlrJG2B/97ey\nJD0Ns8WEMdj7gZeCIHg529ZPZ9t64ZD3fwz4qYkHfQqoNCVav83mUpWKHVHPFtUyQRLX5x/id2//\nAQAVe56ao822N3oJUQqWXEYRAgrUbva+QVLpdlb6dvmUSt/GYbT723Ebu5wkQggWXIe17mx6KhkM\nBoNhcg5NKvm+/7+hg4zPA58Avh144SImlABu7nRIwxQhwCKlInWwUbcFFWXRFlrF5MVVemEVPB0A\n5EolWcluKlJFVO9SyfyTpPAQ3GNSaUipNEVPpcINUJhOW6k0eGwwTErpPZVKMi7D6SIZTi6a08Bw\nXKYQg90HvFF4/ibwgUP+Vg14HviBwssK+BXf9xPgJ4Ig+Id3+2OLizXsY/pCHoeVlWb/cW/9DmlF\nsd31WKyFVCu6/MptSOjAY1cfhBdtIKZWXeL+1UuA4MW96yTKplp5lE7v17Ot6aTS6sJC/2+82NPJ\nkUdX5ob+7mlw2N+b29HNYRpzVRTgOfapj+1uXGlW+kmlq6tN6keUDZZp7CeJ2c/zhdnP84XZz+ly\nt6v+9wC/BfxIEAT/DsD3fXWX959rbu/1UKku3fIk9HZ1EqkqBF5q0866jnhhFVdUUfMjSSVP/xRK\nEde7OFIiSRGygRCCun38un1LCCTap8gRUyx/O+i1KdwVSVFQKpm7LMMUyMvfyuKpJDBKJcMgiWSU\nmYYJOM0Y7MPAb46Uvn1dEAQ3fN9fBX7Z9/0vB0Hwa4dtYHOzfdivJmZlpcna2sBrqP3lr9BamCNV\nkooD62s6MdTNOr61tntIOU+a3qEbVunsJAjRIFE2ki6xSvGsKlEEUbwNQNih/zdeur0DQC1RQ3/3\npBndzyLdtk7YbG61idIU0vRUx3YU9UJMt7OxR/suaqW77ed5wuzn+cLs5/nC7Odk2zyIu93zXAN+\nFvi47/sv+77/NxnP2Ptcst4JyduWuUqwu6uNuatC4MQDk8JqWMEJK7lPNwidTHIq+tCJFJKmruG3\npUIK/cXcS1JJCIFjSSzBVNvLHqSumI5SSZAqrVYyt1iGaSDLqlQyydMLjdinWDvDwRhmlUljsBvA\nA4Xn92evHcRHGSl9C4LgRvbzNvBz6HK6UhDevsVWQ5t01yoOYagX9VKpTbeFUNjWVbSufB7P8rAt\n/X5LvUbc+1n+2LVrAESJTiA5clDqdrujk1NlKn8bNIEoX/c3GJh1C8pTjm4wGAyG0+PQpFIQBFtB\nEPz9rOPHnwEWgIrv+7/m+/73nNoIS8JmOGjzasmUva425q4KgYwGrWhF28EJq6h+kkcfYscwNH1B\nAAAgAElEQVTKEk9KoZp6W45UfUPvunNvsnFPCuwp168flJ+aRoxgCUhRpChzs22YCmXzVBpVKpVk\nWIZTJlcm9bsAnuFYDLPJFGKwzwKP+b5/3fd9F504+vnRN/m+Pw98CPiXhdfqvu8388fAnwS+MPFO\nTYno1i22KrqDaqNWI8risyTr5Jakior3fpr1/5Td2EMIgWcv68/GG9TtGg1X+yz1sqSSLQdx3K1O\nSNOxqJ1gOd9xyee4OFWklGfOy1l0dYzryOM3nTEYDAbD7DNWrBsEweeDIPhr6Br9/wNt9nhhSJVi\nOx4kldxmSGxFCKWtk1SvPnhz16bSbvaTSiI7xHa2CqZUgmimgE4I5VStewteGo5NfcqBjzhARzSN\n8g3JQKlUlnIlw2wz6P52tuPIGSiVsudnNxTDGTJa/mZusgyTcC8xWBAEMdoj6ZeALwE/EwTBF33f\n/17f97+38NZvAz4dBMFe4bXLwG/4vv8HwGeAfx0EwaemtDsTE92+zZbdAGBxYYEoUyrFWSe3XqIQ\nwkLKOlthhFIKx1oEoBOtU3fq1B0dt8WpXiC0sm4qvSRlK4xZLZFKCQZq9DDV8aNdlkkvY8nTIjpj\n0m0wGAwXk2PVXGWtbf959v+FoRUl/TIGgNpcyp4dUhGCJLFJ4kb/d1ZiU91dZCuf8PPyN6FXcW4+\n+EXu83TJm1fIBVXvMTH0sUev9G9gp8VJKZW0UbdCYG6yDNOhbEql0X875jy/mOxTrJ3lYAznhuPG\nYEEQfBL45MhrPz7y/BNoI/Diay8Dz04w1BMlvH2LrfufAeC+lQWiHV2uFqMX/9pJCpmfZZQq9uJE\neywBUbJJ3XmYul0b2qbMYrTbHe1dtFrxKBP5AkqYBXxlmfNy8vI3t2TJLoPBYDCcDmZJYQw2epE2\n6c5ILy0QVRNcadHarSHTQStamVhYyhn6vMDCkjrACSstXEvn8jxrcPjvtT3scsWd+oraQSHBQeql\n45IbdaeqPMoSw2xTtu5vo/9OzHl+MdnvrXWGgzEYzhnx9h22oipSpDywWh8olYgQCDrx8ErbVi9G\nUSdVXRQ96k6NujOcVBKZmvxWCf2UYDDX5UqlsiWVqrbFnGPTOKLrm8FgMBjOJ+bqPwZ3uiGqEKNY\nzQqq06MjltlozREuLea/QSBJRyZ7S9hIqQ+1IsXN/JUqliR3/y5T7f5B6opp3BRJocuCpFDYJp9p\nmAJWX6l0xgPJ2KdUMhqVC0n+recqUqNYMximQxqGKDdmo12hunSLn7/5z3gm/FoAIhXhWg7tOAZc\nKjKlm0o2ehEJVdJ0HYCGU6fhFmwLkKSpjsFypVJpk0pJOcvfAP6Sfw3bXOsMBoPhQmLu7Mdgoxdp\nI6AMu6IARSrq/PryVxEv6q4iAp0sUnJUreAg+4c66SeVagUfpTIllWC/Wmk6SSWjVDJMl/w8Ko1S\nSRilkmFwPuaeSmaiNRimQ7KzTXe+Rjd2EIu3+crOV7iT6GRRqEI8y8uSSrDo6QTMq7sddOJoG0Ar\nlQrlb0I4RNm/1bVuVv5WsqRSfk3plVSpBHC56rFcKddxMxgMBsPpYGLdMbhTKH8TtkD33tCd37pO\nFSvOau/FIUkl6YDID3VKRWZJpUIiqWqV66sYDVemVv5G5qtUwoDIMHuUzlNp3/NyjMtwugyUSsao\n22CYJvH2NtvNbCHPzj2U2gCEaYhnuZmnEix7+or80o7+fZrqTm91p4YlLSqWti4QuP2ysr04wRIi\nU5KXh1FPJaMIMhgMBkOZKNesWVI2uhHEOuAQUqCU7jByXa4xt77J0sttQCCFh2UJ1MhRtaRN/qJS\nKZ6tV3JqzsB7qXRKpX2Gw5NvUwJKZe1wJ9+cwVDwVDrjgWSMJg9M3H8xyZOdiVJmkjUYpki8vc12\ndQ4AYeu4rJt2EALCRCuVupmn0uWqth1Y7+qYrahUKv5EuPSyZE07TqnZsnSJ4H2eSmWZ9AwGg8Fg\nwCSVxuJOL0JmQQlCkGRJpXqqSO58kVbty1S991Fx30djrnJA+Zs9rFTKyt+ajk4uCVKckgUIowqL\naSgudPmbUSoZpke+eltapVI5hmU4ZfKvPVVGpWQwTJNke4stR3fQzZNKHdXFdi16iVYqdTNl+aWK\nh1docT9QKtWznzqpJIRLlCVrOnFyz914T5J8juvlnkrmumIwGAyGEmGSSkfQjhO6SYrV0YkkIQVp\n2tWPQ4+txlusLwVIeRnbeYDGfGXfnaQUDkr1bzOoZEqlea8K6K4ZZbvx2N8afTrbTJVCGU8lw5SQ\nfaVSOU6o0XEYo+6LSfE0MNc6g2F6xNvbbCqdFMrL33qqi+0JFArP8siawTHvVlj0Bv1oiuVvxZ9C\nuPSSlFQpuklaOuU4lL/7m8FgMBguNiapdARbPZ1MktlPJCRpRz/uVujW9mj2VrCEXhl79KlVuvOt\noW1YwkZlN5dKJdSzpFLuo9QslMGVhdEboWnctBc9lcqSBDDMNs2sfXHTKcdNwOhpbRIKF5Pi9c2c\nAgbD9Ih3ttiK9IKckjouC0UX6ekYzLNcosxuYMGtsODq+MqVCkUPOCCphEOUKjpxigJqJfNTApAy\nTyrp/SzhEA0Gg8FwgTHT0hFcqri8q1almhk9CimIM1PIdM8jtWMeCB/nmUuPAfDwk6u0Hn1zaBsC\nq59UKiqVqln3tzJKrUcVFtO4McpLlVLMiWeYDh9Yned//oanuFz1znoowEHlbyalcBEpfutlU6Ea\nDLNMvLfJZqdCxY6IsiRRKHrIrNObZ3kkqYVSEXW3wkKmVJp3B1fnQflbpngSLr00pR1riVM5lUr6\nZ2jK3wwGg8FQQsy9/RG4luSr3Qoya1ErpCBJtFIp3dMrYKuVSzhZ3X6Upgihg5h80hfC6beWhgQn\n6/5WdywcKVgqyLPLwqgJ5DQUF8VElbnZNkwDSwiuNCpnPYw++4y6z2gchrOleB6YSdZgmB5xuMtO\nz8N1eyh0XBXbISJXKtkuCRZK9XCkzWKmVFrO4ixb2rj9GKzgqZSUPalkyt8MBoPBUF5ONJvh+/7z\nwN9FN/v6R0EQ/OjI738I+M7CWJ4EVoIg2Djqs6dJtxORZsGLsARhouvyVUcfvjm3TqcgTZbYJIBn\nSeI4QQqLOM8pofpJJc+SfP9TD/RLeMqEzFJA+bCn4Q1TTEyZsiDDeeQkykYNs0cxkWROAYNheuwl\nIUkqseyYzJSAxI4QaqBUSpWDYBeARU/HWysVvfhQt2v9pG8jVyrh0EsV7WQGkkpJXv5mLiwGg8Fg\nKA8ntojq+74F/D3gW4CngI/5vv9U8T1BEHw8CILngiB4DvjrwK9mCaUjP3ua9LoxaTZ/CykI0x2k\naNB1dAAyX2kMKZVkplSqZEXvUlgkqYIs6HHkIIl0ueqVMoARYlhlMY34pXiDbW62DeeRkygbNcwe\nxeulMWs3GKaDSlN2sn9PuUk3aKUSTh5fuSAcZJZyemKhzp+4b5kPXl4CBuokgJXqst6WbBKlKe1Y\nb6Nml09fmCeV4kz1bsrfDAaDwVAmTnLmfD/wUhAELwdBEAI/DXzkLu//GPBT9/jZE0UrlbJAxolJ\n6GLLebbrHcDimafvw82SSmGqEEInibwsqSSEnZW/ZUGPVT5j7lH2d7Ga7jZNOGQ4j+xXKp3NOAxn\ny3Cp7xkOxGA4R6R7e7RcbdJtZR5KoJVKKlvkyxf1pNDPbSn4pmtLzHkuX3X5Pbz38nP9zz259Dj/\n3Xt/ANd+hF6S0ilx+dvodcSUvxkMBoOhTJxk3dV9wBuF528CHzjojb7v14DngR847meLLC7WsE8g\nGBAIkmx1SHi6s5st59lcuIUUFa4/usqXXnobbkKt6WHbLiTQqDjQ7ukkkiUQWYe4K5cWWGk0pz7O\nabGy0sS2pJaIZ/u9vFRnZa52xCfvTu3N9f7jasVhZeVsj8FZ//3T4CLsI5RoP9u9oaerK3NTLVMo\nzX6eMLO+n83ddv+xbclD92fW93NcLsp+Gk6WeHuLXVuXrNnFtTmhCO2sKy/6F3aWVCry3U9/dOi5\nEILr8w/iWi8RparvqVTG5imjyiRT/mYwGAyGMlEWM58PA78ZBMHGJBvZ3Gwf/aZ72e5Ge9C9zdVJ\nJUfO06q+iBRzrK21CDshAGsbe6RZrZzMunSkqaAXJeRKpZ2tENFpnchYJ2VlpcnaWguVpgwclWBr\nq43X2x+kHYeoN5CrR2HM2trZHYN8P88zF2EfoVz7udWLhp7fWW9NrftXmfbzJDkP+9neC/uPVaoO\n3J/zsJ/jcBL7aZJUF5N4e5uWpZVKdiVTgiNQKDrWHiSQKH29dWR66HZG8SxJb8iou3zlb6PqcVP+\nZjAYDIYycZIz5w3ggcLz+7PXDuKjDErfjvvZE6fbicjTKZbUJt2OaJKqCCm1+aNr5Z5KCpHl6tyh\n8jcQWZKm6KlUVoQQSCHwstWwqRt1mwI4wzlEjJR4mnbyF5Pit25OAYNhcpRS/P7nN2gJrZj2qjq+\nmrPmANgT2pg7zBb13GNEt46UI55K5VMqjZa7mfI3g8FgMJSJk8xufBZ4zPf96+iE0EeBPz/6Jt/3\n54EPAX/huJ89LXqdiDQT7QhrGwBX6kDGknrVzCl0fxPCBgWOzJU+2qi7Yrl88NoHqNrlaYF+GBId\ntDy9UOd377SMUbfBMAZDBs3mFL+wCJNANximytrbLT7/WkprWcdclWoKKczJebaTbfaUVsNFWVKp\ncoy8kCcFe/Gg/K1mlS+pNBqD2ab8zWAwGAwl4sSUSkEQxGiPpF8CvgT8TBAEX/R9/3t93//ewlu/\nDfh0EAR7R332pMZ6FN3uwKhbOTtIUcPJzLhtkSmVCt3fRHZYnbxjnJDESlFzPP78E39uJtQLQght\npyRypdLkDCmVyn8IDIZjM9Qx0SQTLizF734GLvcGQ+npdiJAsRs5WCLF9nQCqCnmAdjdl1QaP7x1\nLEmUlb95liylX5EQAqswLKNUMhgMBkOZONE6rCAIPgl8cuS1Hx95/gngE+N89izY7u3wuUc/BTev\nw9p9JG4PV17tl7JZWflbUalElnCyMmNugUWq1EzVwEsgRWVd66ajLDJKJcN5Z6jDoTnFLyxDijWT\nXDQYJqbXjfHciFbPperECEt7NDaUVo13Um3U3cuSSscx2/akJAV2ooTaMZJRp40lBk1jZimeNBgM\nBsP5p7yzZ1lIBLEdEl15BVnTK2FSLvZvE5zMNLKvVEpSBJmKqW8UaZEoNVOJlLzxm8rL/qawTTni\nN2MwnDfk0GNzll9UhhRr5jQwGCYm7MU4bo+9nkvFSkilbopQSxpD74tSfRWuHyOplC8KtuOklH5K\nOcUYqoxqKoPBYDBcXExS6Qis2GFx7X7wutjXvgKAlAuITIWUJ5WcbHUrTBX5YZVZtzchdFJpluTK\nEqGVSpkiaxole0M33DN0LAyGcRFGqWRg+Dwwk6zBMDm9box0IhQCTypiegBU4+GkUi9LKjUcZ+xt\newV1UpmTSsUYcpbiSYPBYDCcf0y8ewTdTszy29dBgbWwDoCUS5AllVyZK5X0BB+lKWRKJSnynnGS\nRM3WytKoUmn6Rt2Tb89gKBvGN8wAo4bt5kQwGCYl7MUkThZTOR7bYZuK5WGH7tD7olS7OjTt8ZNK\njhyEwlW7vGFxMZFkyt8MBoPBUCbKO3uWBMuWeL06bK30XxPWIiqbz91cqSRzpdIgqSTQAZAi91g6\npUFPASm0a1RewDd9o+4ZOhgGw5gU/XOMl87FZfg8MBgMk9JthyRZR93dxQW2entU7SqEA2tQgUU7\nsVAqpuZ6Y2/bKwRn5VYqFR6bVQuDwWAwlAiTVDqCxeUaf+H7vhrefggAL5ZIUSHN0i2uNapUUqi8\n/E1oI8lBUml2goB8pCdm1D3x1gyG8mGUSgYYLn00CXSDYXJ6ux2ivBy/apOqkKpdIQkVMslUScKm\nkzgo1aViVcbedlGpVOak0pCnkrmsGAwGg6FEmHv7MWjOV1B7C0RvXefpbR2opJkKyRtRKkVpCiI/\nrLObVMqDl7zTyLSNus2NluE8MmTQbDQqF5bixGoudQbD5PTaIWHeOKRioQip2VXCMEGmugTOli6g\nSNUeFfsYSqUZSSoV1Um2WbUwGAwGQ4kwSaUxUUqQvunz3K4OOGKVADaupYMZS+iDGaYKpbLDqnR3\nklTNYlJJ/4zTKSqVDti+wXCeEGJQ+DRD/9wNU0aYTpcGw1TphQndzIRbuvmiXoUojLGUTiA1nAqP\nVL9Mu/Nv8azxk0rOUPlbecNiY9RtMBgMhrJS3tmzZCgEErAtrT5K0gQhKv2JXQiBY0miJEUhUCol\nzZNK2WGepSBgtPxt2kolY15rOK/kCVOjxru4mE6XBsN0CcOUTpw1QcmSSiJ1iMIEC60gd6ULahul\ndqna45e/zYxSqeipZK4rBoPBYCgR9tFvMYBWKknAcrSXUpRGOqlUkNy4UmilEhJISNIQKCiVZiiF\nNyh/y59PY5uFx5NvzmAoJVqrpIxC5QJjlEoGw3QJo4Rd5fD/t3f38VGVd97HP+fMTBIIMYA8CyoB\nelWeLYpd125dqVRcbKs3dalLRRQqbu12hVsqZl+WKm60PtW2PlUFLLuuVW5bW6sipcUuFaqiFtva\ny3XVqhA0QCCBkGQezv3HmZlMQkISyGSevu/XixczZ86cXNc8nWt+87t+VygQBdf/wY5I0A8qOX4A\nqThYTGO0yb/cnUyl1KBSIJuDSv6niesoWC0iItlF3+27wPO8eKZSDC/kx+EiXhTXKW71a1HIdQnH\n4plKRInGcj9TqaWmUg8X6s6hx0KkO5SpJFrpUqTneJ4HTjP1TUWUhGJ4nv+DXbQ54AeVXD+oVBIo\nojHShOu4hNyu/2aaK6u/JT5LgvpMERGRLKOgUhccaggTA1yixIr9AUcMf/pb6sk9mankueDFCMfC\neJ5H1PP3yaWgUmLwEkkElXo6Uyl3HgqRbnFSfk2WwuR0cFlEui8SiREINtMYCREKenien40UaQoQ\nicRw40GlPsFimqJNlASKuzXFvqjV9LfsHRYnxpu5NJYUEZHCkL1nzyxSu+cgMSBIOJmpFPO8w6a/\npWYqQZRwLAxEiXk5mKkUb2qiplJP/Nqe2n+tjCX5ym3zvxQep1VWZgYbIpIHGg+FiTh+6YFgCDz8\nTKWmQ/7tTkpQqTHa1K2pbwBF8doErgPFWVynIPFZopXfREQk22Tv2TOL7N3dAEAoFsYr8jOVPGKt\nCnUDhFyHqAdRz8XzooRjESCWrK6SS0GlRNAnGvN67EWi1d+kECQCCipGX7hSP+v0OhA5Nn5Qyb/s\nBt1kplLToXgwKNAy/e1A+CB9Q326dfyi+ICkTyCQ1e/XgDKVREQkSymo1AW79xwEoDjaRCyUmKef\nCCq17JdIoY54LZlKnhdN3p5LA4FE0CfqeT22NLpWf5NCkMxU0ku8YKlQt0jPaTwUxnPiq4YEA8ma\nSlAEgBsoB/wf+5qjzYwuP6lbx09kKmVzPSVQUElERLKXgkpdsHePn6nUN9JIrCgx6IjhOMWt0pBD\nyQiTX6i7OdoMpASVcujRTvQk4vVMkW5oW6i7Rw4pknUSr/Oeet9I7lFWpkjPaTwUxktcCQSSmUo4\nflApEBzEkOMuIhgvzj3x+E926/iJHwSzuZ4SpASV9KEiIiJZpuvLYxSw2tpGAEoiTXhBF2LgeTEC\nbuvpb6nFHolPf/OIJTfl0q9LiV/aY57XY1+KUo+TS4+FSHe0rP6W2XZI5qR+vCm4KHJsmg5FkplK\nXjCYzFRy3GIgSsSBsuJh/GXvFoJukE8MGNut4xe5Dp86vowT+3Vv2lxvSwSTtPqbiIhkGwWVOhGL\nxair84NKRUTwnNRMpcNrKiV4iULdeTD9raeWxHY1JUQKQOJlrimehSt1IQIFF0WOTWNjGI/Dg0q4\nJcBBIkDQ8dhxoJpTBn6C4kBRt47vOA5zKob1bKPTIPFZkktjSRERKQwKKnWifn8TkfgKaMVOlJjn\nP2T9SwZyKNa/40wlYjRHw5CrmUophboDgZ4KKqVezp3HQqQ7EgGF7J5IIemUz5lKc+ZcwEMPraV/\n//5d2mf58uX8+te/YcCAAaxd+3hynxtuWM777/8VgAMH6unXr4w1ax5Ne/sl9/g1lfzLsUCiULcD\ngRCeA1EHwlF/KbgJ3Zz6lksSY0it/iYiItlGQaUuSMzlD7kxYvEvCGP7T+SPtQda11RKPdF78ULd\nKTWVcimQkuhKjJ77pV2/3kshSLzN9RovXI7qxyVddNFF/MM/XMTKlTe02n7jjVXJyz/4wV3069ev\nt5smOaLxUDj581w0EMDzDuFQhBcMEIv/6HUoUgcURlApl36gFBGRwqCgUifKB/Rh6mdHYl/4gJAb\nJZbMP/BDTR1lKrVMf2vJVArm0Dgg9df1nivUnXI5z369F0lIvLbzLUNFui41Sy0bvv9VV+9k6dJv\nMGHCJN54YzunnDKe88+/gFWrHqC2tpYbbriJkSNHUVV1Izt37qC4uIRlyyoZO3Yc+/fvY8WKSmpq\napg4cRKelyyZzPr1z7Bu3WOEwxHGj5/A0qXXEQi0XkHr9NNPZ/t222HbPM/jN7/5FXfffV/a+i+5\nrfFQmFj8feQFQ8S8RlynBIpcv84lUNdcy5C+gxjSd1AGW5peyUylbPhQERERSaGgUheEo362USjQ\nkqmUqArUqqZSq2lifqHu1qu/5c5AoPVUtZ46ZkqgKnceCpFuUaFucToIoD/59tO89vEbgH8+iMa8\ntnfttlOHTOKisbM73W/Hjg+56aZbWb68goULL2XDhue4996H2bz5BdauXc2QIUMZN85QVXUH27a9\nzMqV32bNmkdZvfpBJk+eyoIFi3jxxc08/fRTALz33rts3LiB++5bRTAY5Pbbb+H5559l1qzO25Lq\nD394jQEDBjJq1IlH1X/pmDHmPOBuIAA8ZK29pc3t1wL/FL8aBE4BBltr93Z2397UeCiSrKmEGwGa\ncdzjiQUcYsHEVP1GJgzO3ywlSF39LcMNERERaUNBpS5obPSLQoaCsWSmUuKrQIeZSonpb4GWTKVc\nnP4GPZmp1HIcpW9LvkpMfVKh7sLVKtMzS14Gw4ePYMwYf1Ws0aMrOO206TiOQ0XFWKqrq9m1q5qV\nK78LwLRpp1NXt5+DBw/w+uuvcfPN/vYzzzyLsrLjANi27SWsfZOFCy8FoKmpkQEDBnS7Xb/61Xo+\n97nP90QXJYUxJgDcA5wLfAi8bIz5ubX2z4l9rLW3AbfF978AuCYeUOr0vr2p8VAzsURRJfeA/59T\nSixIMlPJ88KMHTAuE83rNSrULSIi2UpBpS5oamonqBSfAhBM+cWoVU0lojTHwjhuS6ZSLqUsO2mo\nf5RtU0JE0sFt878Uno6C8heNnZ3MKho8uIyamvpea1MoFGppn+smr7uuSzQaIRjs3nDA8zxmzZrN\n4sVXH3WbIpEIL7zwGx5+eO1RH0M6NB1421r7DoAx5jHgi0BHgaGvAP91lPdNq8ZDEWKJ1d8C/nvG\ndfoSDXqMnz6Sj6NNeF4zo487KRPN6zWa/iYiItlKQaUuONQUAfygkpdYFc3rpKaSFyUSCxPK0dXf\nWn0pSsP0N9VUknyVeJkrU6lw5eKiBFOmnMqGDc9x2WULefXVVygvL6e0tB9Tp7Zs37Lld9TX+wWR\np02bzvLlS/nHf7yEAQMGUle3n4aGBoYNG97lv/nKKy9x0kknM2TI0HR1q5CdAHyQcv1D4Iz2djTG\n9AXOAxIRwi7fN2HAgL4Eg4Ej7XLUUgt1e46fqRQKlhIjyJgJw9i0/a+UFRUxduSItPz93jR4cFmH\nt5XXNwDQr2/xEffLBbne/q5SP/OL+plf1M+epaBSFzSE/WyjopBHOJ5/EI2PcFrVVGqTqRSJRQi1\nWv0t7U3tMU4aAkDpqNMkkm0S7xdlKhUup1WmUm64/PKvUVV1I/Pnz6W4uITKyu8AsGDBIlasqGTe\nvIuZNGkyQ4cOA/wpdIsWXcU111yN58UIBIIsWfKtw4JKS5YsYevWrezbt48LLzyfK674GrNnfwmA\njRuf53Ofm9m7HZX2XAD8zlq792gPUFvb0IPNaa2xMUwsPlr1nHrwoCRwHGHH5bUP3wVcBpUc16uZ\nf+nQWfbioQY/a765KZzTfe3tLM1MUT/zi/qZX9TPYztmexRU6sTuxmbeioSAJkJFMZraZCoF3fYz\nlUj8rublaKZSyuW0ZCrl0GMh0h0tmUqZbYdkTupTnw2fdcOHj2Dt2seT1ysrV7R7W1XVHYfdt7y8\nP3fddU+7x50xYyYzZhweFFq37hfJy3feeWeHA5rUdkiP2wGMSrk+Mr6tPXNpmfrW3fumled5fqZS\nv/h1/Ey5sqL+7A3D/+7/CBjO8NLjM9G8XpVYC0bT30REJNsoqNSJhkgUL75CT6iopaZSpJ3pb6mr\nv3men6HkpWQqBXMoPSc1U6nnCnWnXs6dx0KkOxKvbb3GC1erlS4z2A4paC8D44wxo/EDQnOBS9ru\nZIwpBz4LzOvufXtDJBIjFvWIxgt1x7x6yoqOo7y4lL1h2N3YSCgEJ/Ybkonm9aqW1d/0qSIiItlF\nMzQ6EXCclKCSlwwqReMZSK2nv6U+nNE2/+fWl8zUnvTU+CXQakqdSH5KvMr1Gi9cjgLokmHW2gh+\njaT1wJvA49baPxljFhtjFqfseiHwvLX2YGf37b3Wt2hu9GtaRj3AieJxkIElx9O/uC8Ajuv/P7Lf\n4Ew0r1e5KtQtIiJZSplKnQi6Tnw0A6Fij1j8K2Mk1t70t5RMpUQwKVenv6V+KeqpY7YqXps7j4VI\ndyRe2yrUXbicDi6L9CZr7TPAM2223d/m+hpgTVfumwlNTYmgkoNT4tdtGtznePoX9QWacJ1SAPqm\nrG6Yr5KZSjq3iIhIltGP6Z0IOg5ePKhUVOwR8xLT3/zbO8xUamf6Wy4NBFpNf+uhdtpyu0UAACAA\nSURBVKdjRTmRbJN4nWuGQuFqNf1NrwORo/b23nd41/yesBvFLTkEwNC+g+gTX2nOcfxMpeJA/g9n\nE2PIXCqlICIihUGZSp1oNf2txCPq+QOZSKy96W/tZCqRmqmU5sb2oHT80q5C3VIInOT/eo0Xqmwr\n1C2Sqz4+tIeD5XsIHeqHGykBYHjpYKKOH0Ry4v8Xu/kfVBpd1odTjy/jk/1LM90UERGRVvL/LHyM\ngq6fqeQ4HoFil2g8DheOHV6o23WclrnuOZ6plI4AUDqm1Ilkm5ZC3RluiGSMCnWL9IyT3AqcaIDI\ngI+S09+G9B3UKjMp6DgFUby6NBTgyxXDGFRSlOmmiIiItJLWTCVjzHnA3UAAeMhae0s7+5wNfA8I\nAbuttZ+Nb38PqMevdB2x1p6WzrZ2JBjPVHJcIOT6098cCCcyldpER4oCDpGIRzJDyUsJKuXQoKdV\nplIPNVuZSlIIlKkkrT8/8+t1MGfOBTz00Fr69+/fpX2WL1/Or3/9GwYMGMDatY8n9/mf/7HcdlsV\nzc3NBAIBli79FuPHT+yNLkgOcZoDlO8dzr7BH+IGdgFwfMlADkYjyX36BPUzlYiISCal7UxsjAkA\n9wCzgPHAV4wx49vs0x+4F/iCtXYC8OU2h/l7a+3UTAWUwA8EedGYH1QKQMyJT3+L11RquwpHoq5S\n+9PfcufLRav6R2k4Zg7F10S6RZlKoqzMFhdddBF33PGDw7bfe+/3WbBgEWvWPMrChVdy773fz0Dr\nJNs1NUUYWDMKACcYJuj2pSRYTEnKL3qFUE9JREQkm6UzU2k68La19h0AY8xjwBeBP6fscwnwpLX2\nfQBr7cdpbM9RCcQLdbuugxN0iUUCrW5vm3GTrKuUmP7mafpb8jipq78pi0PylAp1i9NqpcsMNiSu\nunonS5d+gwkTJvHGG9s55ZTxnH/+Baxa9QC1tbXccMNNjBw5iqqqG9m5cwfFxSUsW1bJ2LHj2L9/\nHytWVFJTU8PEiZPwPC953PXrn2HduscIhyOMHz+BpUuvIxBofY48/fTT2b7dHtYmx3FoaPBXsT9w\n4ACDBuX/kvDSfQMHldLfGYh3qBSnz0FKgn6GXGogqRDqKYmIiGSzdAaVTgA+SLn+IXBGm30+AYSM\nMZuAMuBua+2P47d5wK+MMVHgAWvtjzr7gwMG9CUYDHS2W7d5UQ+3OH7ZCfgtwy+8PXTIca327VsU\nhMZwu5lKw4aUEcyBwc/gwWUc19iUvF5cFGTw4LJjPm6iuDnAoEH9OL5PZusC9ESfsl0h9BGyq599\nduwGoLRvcY+3K5v6mU653s9YSuClX7+SZH82/OLP/PkPO3v0b42fMoJzLxh/xH2amkrZseNDfvjD\nHzBu3DjmzJnDf//3Rp544nE2btzIT36yluHDhzN16mQeeuhHbNmyhVtu+Q5PPfUUDzxwN5/+9HSu\nvvpqNm3axNNPP8Xxx5dSW/sxmzf/hieeeJxQKMSKFSvYunUTX/rSlwgEXI4/vpSBA/1+DxxYSjAY\naPW8rlhxA1dccQX33fd9YrEYjz32WM4/79LzRn9iEGPK4ffPjCR0kqU05AeVlKkkIiKSPTK9+lsQ\nmAbMAPoAW4wxW621bwFnWWt3GGOGABuMMX+x1v72SAerrW1ISyP9mkr+z81Rr2Xw4joONTX1rfZ1\nYokvE4cX6t67+0DW19cYPLiMmpp6Dh5oCSpFwtHD+nk0Ur9o1e49SKyo6Qh7p1ein/msEPoI2dfP\n5ia/1kfjoeYebVe29TNd8q2fDQebkv1paGgmFvWD627ATV4+puM3dP4627v3IMOHj2DgwBHs2XOQ\nkSNPYuLEU9m9+wCDBp3AX//6Ae+//wErV36Xmpp6xo6dyN69tbz3XjVbtvyem2/2t0+YMI2ysuPY\ns+cgGzf+hu3b3+BLX7oIgKamRkpK+lFTU080GmPPnoNEoyEGDy5j796DRCKtzyOrVj3C17/+r5x9\n9gw2btzAtddex91339ulPiv4VFhqD9YT2T2S4PH7GTXcr7uVGkgqUVBJREQko9IZVNoBjEq5PjK+\nLdWHwB5r7UHgoDHmt8AU4C1r7Q7wp8QZY36KP53uiEGldIhEY+CBE/CDQamZSm3rKQEUJTKRvNaZ\nSq6TWwVbnQ4uHwvX8SeFeGTHlBCRdEhMF82l97v0vJbPupbXwZnnjOHMc8YAvR88C4VCycuu6yav\nu65LNBohGOzecMDzPGbNms3ixVcfVXueffZpvvnN/wvAOed8jltvXXlUx5H8t+fgfogOx/v404w+\nrQJIGWuhoJKIiEimpfNM/DIwzhgz2hhTBMwFft5mn6eAs4wxQWNMX/zpcW8aY0qNMWUAxphSYCbw\nxzS2tUPNYT8olMhUiqU8ZO3VSErWVGqz+lsu1VOCtjWVeu64icOqppLkq8QrW19zClviczNXPumm\nTDmVDRueA+DVV1+hvLyc0tJ+TJ3asn3Llt9RX18HwLRp09m0aSO1tXsBqKvbz65d1V3+e4MGDea1\n17YBsG3by4wcOaqTe0ih2t9wAPDHYeVFfvDTdZxkLSVNfxMREcmstGUqWWsjxpirgfVAAFhlrf2T\nMWZx/Pb7rbVvGmOeA7bjR2Eestb+0RhTAfzUGJNo46PW2ufS1dYjaY74QSEnPmiJpdZUaifaMuX4\nMlzH4fcHm4h54MWDS7kWVEptbk9mXLg4xPCUqSR5KxlMyLH3vPSsRF5mrrwMLr/8a1RV3cj8+XMp\nLi6hsvI7ACxYsIgVKyqZN+9iJk2azNChwwAYPbqCRYuu4pprrsbzYgQCQZYs+RbDhg1vddwlS5aw\ndetW9u3bx4UXns8VV3yN2bO/xLJl/8bdd99ONBqlqKiIZcsqe73PkhvqD/kF3UkJKoEfTGqKxRRU\nEhERybC01lSy1j4DPNNm2/1trt8G3NZm2zv40+AyrincOqjkOUfOVJo0sIxJA8t4ZYdLBHDjEahc\nCyq5HVw+5uPG54T01IpyItkmEUzS15zClvysy4JcpeHDR7B27ePJ65WVK9q9rarqjsPuW17en7vu\nuqfd486YMZMZM2Yetn3dul8kL995553tTvObMmUqq1b9R5f7IIXJ8zwaGv16mU47QSXCmv4mIiKS\naToTdyIx/Y1EphItq8sdKVDkOv5+jpOjQaWU9vZk0xPHVaaS5KvEh6pe44XNQZ91IsfqUKSRWMQf\nRzmB1kGlRDCpONDzq/6KiIhI1ymo1InmeKYSwZTpbyQ2dfxtIRDPaGrJVEpTA9Ok1fS3HvylPVnE\nOAt+vRdJBxXqFmj5DNWrQOTo1TXXEYj4466QEyWYUqA7Me1NmUoiIiKZpTNxJ8r7FeG4ECwrxvPa\nTn/r+H6BZKZS/HqO/VydOmWjJ5ueeMxy7OEQ6TIV6hZQbS2RnlDXXI/r+eOpIiKtbisOqFC3iIhI\nNkhrTaV8MKi8D8d/uohgn1JiuHidrP6W4MaDT4Ecnf6W2tyerAnixnOUVFNJ8pUylQQ0/U2kJ+xv\nqicQKwagyAu3uq1EQSUREZGsoDNxl/h1laK4eCkBliMFigKu/8taoAv7ZqPUQFLP1lTq2eOJZBtH\n2XhCSqaSJsCJHLW65nrwSgEoprnVbYNKQgQcGFCk30dFREQySWfiLvDw6ypFCRBLmf7WlZpKjgMe\nuRdUalVTqYcLdWfDakgi6ZJ4fSuYUNgSz3+OffSLZJWiQAi8EgBKYq0zlf526ACmDCyjf3EoE00T\nERGROGUqdUFLUMkl5nUxUyleUylRQyjnCnWnXO7R6W+OMjgkvylTSaDl+c+3k+ycORewb9++Lu+z\nfPlyZs8+l69+9eJW+/zP/7zFlVcu4NJL/5Fly67h4MEDaWuz5K6zRnyaE/qNBaDEa52pFHQdBZRE\nRESyQL6Nd9PDa8lU6vL0t8Tqb/FH+EhZTdkoteZRj2cq6ad7yWP5GkyQ7kkWbC/wz7uLLrqIO+74\nwWHbb711JYsXX82Pf/wT/u7vzubRR9dmoHWS7RzH4VDEfw/1jTV3sreIiIhkgqa/dUGrTKWUoFLw\niIW6E5lKDjNPOJ4RpcXpbWQPS42B9eQ0njOGlHMgHO2x44lkm+T0twIPJhQ6x8me6W/V1TtZuvQb\nTJgwiTfe2M4pp4zn/PMvYNWqB6itreWGG25i5MhRVFXdyM6dOyguLmHZskrGjh3H/v37WLGikpqa\nGiZOnITnecnjrl//DOvWPUY4HGH8+AksXXodgUCg1d8+/fTT2b7dHtamDz74K1Onfiq+zxksXfoN\nFi26Kr0PhOSkhrD/muujoJKIiEhWUlCpE/4A2l/GNkYgXrLbFzhSTSU3sfqby9kjBqaxhemRGkjq\nySSrTw/p33MHE8lCmv4m0H6h7mc/qOGNvf40r0DAJRqNtXfXbpk0sB+zRg3udL8dOz7kpptuZfny\nChYuvJQNG57j3nsfZvPmF1i7djVDhgxl3DhDVdUdbNv2MitXfps1ax5l9eoHmTx5KgsWLOLFFzfz\n9NNPAfDee++yceMG7rtvFcFgkNtvv4Xnn3+WWbNmd6ndo0eP4b//+wX+7u/O5je/+RUfffTRMT0O\nkr8SmUoKKomIiGQnzdDoRDQWxnP8oFIUF6/bNZVy8yFOV6aSSL5ToW6B1OlvGW1G0vDhIxgzZiyu\n6zJ6dAWnnTYdx3GoqBhLdXU127e/zuc/fz4A06adTl3dfg4ePMDrr7/GzJmzADjzzLMoKzsOgG3b\nXsLaN1m48FIuu+wStm17iZ07d3S5PcuX38BPf/oEl18+j4aGBkIh1caR9jXGk5tDWfJeEhERkdaU\nqdSJaDgCraa/tThSUMlN1FRyAh3uk81Sp+5ky5cikVzgKlNJSJn+lrJt1qjByayiwYPLqKmp77X2\npAZtXNdNXnddl2g0QjDYveGA53nMmjWbxYuvPqr2nHTSydx11z0AvP/+X9myZfNRHUfyW8zzaI75\n76Jcq00pIiJSKHIzjaYXNUea8fCXsY16gVarvwWP8OjlfKZSymUN40S67rgi/8v5cSHF7AtZ4jM0\nV2prTZlyKhs2PAfAq6++Qnl5OaWl/Zg6tWX7li2/o76+DoBp06azadNGamv3AlBXt59du6q7/PcS\n94vFYjzyyMN88Yv/pye7I3niYCRKLJ6pFHRzczwlIiKS7/StpxON4Uaco8hUStRUcnM0qJTatUJf\nvUikOyYO6Mf1U0fTT0GlgpYIJuXKGeDyy79GVdWNzJ8/l+LiEiorvwPAggWLWLGiknnzLmbSpMkM\nHToMgNGjK1i06CquueZqPC9GIBBkyZJvMWzY8FbHXbJkCVu3bmXfvn1ceOH5XHHF15g9+0ts2LCe\nJ598AoDPfvbv+Yd/+ELvdlhywv7mCF7ML9QddHMz81tERCTf6VtPJw41HcKNJWoqBYh5fuaOR2fT\n3xKZSrk5CHJT8pMUUxLpOsdxFFCSrMpUGj58BGvXPp68Xlm5ot3bqqruOOy+5eX9k9PU2poxYyYz\nZsw8bPu6db9IXr7zzjvbneZ38cVf4eKLv9LlPkhhqmuOQCKoFMiVEK2IiEhh0Rm6Ew3hRpyYn58U\nJUA0ZTnlI83vT0x7c3M0XTu1a64mwImIdEtyFcDMNkMkp+1LzVQK5uaPdCIiIvlO491OHGpuSslU\ncpOZSpDfq7+l/rqeBT+0i4jklOQqgPr8FDlqDiSDSkUBZYCKiIhko9yMePSixnATbqx1TaXEl4Su\nrP6Wu9PfWug7kYhI97RkKukTVORonTGknOPD+3HwCAZCnd9BREREep2CSp2I0Ac3Go1f9n8lc+Jf\nErqSqZS7hbpb+qZC3SIi3ZP4DNXHp8jRcx2HWMwjGIgRCBVlujkiIiLSjtyMePSiPkXB5PS3sBcP\nKsW/JByxppKb20GlVjWV9KVIRKRbEp/8CsqLHJtIzCHoxnCVqSQiIpKVcjPi0YumDB7FJ99qAiDs\n+QOaxIOWz9PfnA4ui4hI5xKnB31+ihybSMwhFIgRDBVnuikiIiLSDgWVOuFEI5y45wAA4cT0Nycx\n/a3j+wWSQaXcfIjdVoW69bVIRKQ78rVQ95w5F7Bv374u7dPU1MScOXOYP/8rzJt3MQ8//EBynwcf\nvI/58+dy2WWXcM01X2f37pp0N11yVDJTSdPfREREspKW0uhELBwhkKypFM9UShTqPtL0t0RNJTf3\nM5VyMywmIpI5KtQNRUVFPPLIIzQ0xIhEIlx11RWcccaZTJw4iUsu+SqLFl0FwBNPPMbq1Q9y7bXX\nZ7jFko0iMZdSN0ywSEElERGRbKSgUicizc0EovGaSvGHy+1SoW5lKomIFKpsylSqrt7J0qXfYMKE\nSbzxxnZOOWU8559/AatWPUBtbS033HATI0eOoqrqRnbu3EFxcQnLllUyduw49u/fx4oVldTU1DBx\n4iQ8z0sed/36Z1i37jHC4Qjjx09g6dLrCARafkhxHIfS0lIaGuqJRCJEo5Hk+aS0tF9yv8bGQzrP\nSIciMZeQEyUYUk0lERGRbKSgUiciTc3J1d/C8UylZKHuI9VUyvFC3aqpJCJy9NrLVHry7ad57eM3\nAD/TNRrz2rtrt5w6ZBIXjZ3d6X47dnzITTfdyvLlFSxceCkbNjzHvfc+zObNL7B27WqGDBnKuHGG\nqqo72LbtZVau/DZr1jzK6tUPMnnyVBYsWMSLL27m6aefAuC9995l48YN3HffKoLBILfffgvPP/8s\ns2a1bks0GuWyyy5hx44PuPDCLzNhwsTkbQ88cA/r1z9DaWkp3//+A4i05XkeUc8l6MYIFimoJCIi\nko1yM+LRiyLhSDJTKZLIVHK6k6mUm9PftPqbiMjRS3xsZksCzvDhIxgzZiyu6zJ6dAWnnTYdx3Go\nqBhLdXU127e/zuc/fz4A06adTl3dfg4ePMDrr7/GzJmzADjzzLMoKzsOgG3bXsLaN1m48FIuu+wS\ntm17iZ07dxz2dwOBAGvWPMqTTz7Dm2/+iXfeeTt525VXfp0nn/wlM2fO4sknH++FR0FyTTgSAyDo\nxAgWK6gkIiKSjZSp1InWmUr+w5UIE3WpplKuZiqlfBPSktgiIt2T+NxMPU1cNHZ2Mqto8OAyamrq\ne609oZSpQ67rJq+7rks0GiEY7N5wwPM8Zs2azeLFV3dp/7KyMj71qdPYunULFRVjW9127rmzuPba\nf+GKK67sVhsk/zUng0rRVq9hERERyR65GfHoTf0HEnH9KQpHl6mUmw9xaqsVUhIR6Z5EMClXgvJT\nppzKhg3PAfDqq69QXl5OaWk/pk5t2b5ly++or68DYNq06WzatJHa2r0A1NXtZ9eu6lbHrK2tpa7O\n37+pqZGXX/49J510MgAffPB+cr/Nmzclt0vPMcacZ4yxxpi3jTHXdbDP2caY140xfzLGvJCy/T1j\nzBvx217pvVa31jpTSb+DioiIZCOdoTsR61fOtgo/3T9MIvvIv+2INZXimUq5Ov3NcRwcwEOFukVE\nuuvMof0Z0qeIfsHcOAdcfvnXqKq6kfnz51JcXEJl5XcAWLBgEStWVDJv3sVMmjSZoUOHATB6dAWL\nFl3FNddcjefFCASCLFnyLYYNG5485p49u1m27F9obg4Ti8U455xz+du//QwA99//A95//6+4rsvQ\nocO59trlvd/pPGaMCQD3AOcCHwIvG2N+bq39c8o+/YF7gfOste8bY4a0OczfW2t391qj2xGO+Jni\nIaIEQ1r9TUREJBspqNSJSDRGMa2nv/UvCuEB5UUdP3wB18/1ydXpb+DXAvE8pbOJiHTXif36cGK/\nPpluBuDXU1q7tqVmUWXlinZvq6q647D7lpf356677mn3uDNmzGTGjJmHbV+37hcA9O/fn5/97Gft\nTvO7+ebbutUH6bbpwNvW2ncAjDGPAV8E/pyyzyXAk9ba9wGstR/3eis7kTr9jUBuBGhFREQKjYJK\nnYhGPYqI4RAjEs9UGlFazGXmhCPeL5GhFHBzdxDk4hDDy5pCsyIiItIlJwAfpFz/EDijzT6fAELG\nmE1AGXC3tfbH8ds84FfGmCjwgLX2R2lub7vCYX+hlCAxnG7W/RIREZHeoTN0J8LRGCHHI0CMCIns\no86jLIkMpVzPVMJrvSS2iIiI5IUgMA2YAfQBthhjtlpr3wLOstbuiE+J22CM+Yu19rcdHWjAgL4E\n0zDVs3rfwXhDowwe2p9gv9Ie/xvZZPDgskw3oVeon/lF/cwv6md+6a1+pjWoZIw5D7gbf8G0h6y1\nt7Szz9nA94AQsNta+9mu3rc3FIcCBB0IEE0W6j5Sge6EXF/9DRLBJGUqiYiI5JgdwKiU6yPj21J9\nCOyx1h4EDhpjfgtMAd6y1u4Af0qcMean+NPpOgwq1dY29GTbk2p2+4Xeg16UPfsO4R6KpeXvZIPe\nXhEyU9TP/KJ+5hf1M7+ko58dBanSFvFIKRI5CxgPfMUYM77NPokikV+w1k4AvtzV+/aWkYNLKQ05\nBGgZyAS6EGQZVjqEkkAJw/sOTWPr0isRTHKUqSQiIpJLXgbGGWNGG2OKgLnAz9vs8xRwljEmaIzp\niz897k1jTKkxpgzAGFMKzAT+2IttT2oOhwE/U8lRTSUREZGslM40mmSRSGttM5AoEpmqoyKRXblv\nr3Ach6ADbrxYN3Rt+tuoshO47e9WYAaOTWfz0qplSezMtkNERES6zlobAa4G1gNvAo9ba/9kjFls\njFkc3+dN4DlgO/ASflb4H4GhwGZjzB/i239prX0uE/1I1lTyVKhbREQkW6Vz+tuxFInsyn0Pk645\n/QG8VplKx5WV5PU8zETf/BXsYvQv75uX/c3HPrVVCH0E9TPfqJ/5pVD6mW2stc8Az7TZdn+b67cB\nt7XZ9g7+NLiMa44HlQLEcDQXX0REJCtlulB3u0Uij/Zg6ZrT73ixVkGlhoNNeTsPs9XcS88DoL7u\nEDVu7taGak8hzKUthD6C+plv1M/sNmfOBTz00Fr69+/f6T5NTY3ceuuNfPxxDeDwhS9cyMUXfwWA\ne+65m9/97reEQiFGjBjJ9dd/m7KyrgWfFKQqHE2J6W9etJM9RUREJFPSGSnoapHI9dbag9ba3fhF\nIKd08b69JhAv1N1yvTB+LUus+lYg3RURkR4UCAS57rrr+I//eIIf/Wg1Tz75BO+++w4Ap59+Bj/+\n8U945JHHGDXqRNauXZ3h1ko2amz2x14KKomIiGSvdGYqJYtE4geE5uLXUEr1FPBDY0wQKMKf4nYX\n8Jcu3LfXBKBNoe7CiLIkuumqULeISM6qrt7J0qXfYMKESbzxxnZOOWU8559/AatWPUBtbS033HAT\nI0eOoqrqRnbu3EFxcQnLllUyduw49u/fx4oVldTU1DBx4iS8eAYrwPr1z7Bu3WOEwxHGj5/A0qXX\nEUipezNo0KBkRlbfvqWcfPLJ7N79MaNHVzB9+qeT+02YMIlNmzb26mMiuaG52c9UCqS87kRERCS7\npC2oZK2NGGMSRSIDwKpEkcj47fdba980xiSKRMZoKRJJe/dNV1uPxPM8XDwCThTiY5pCKVydXP2t\nQPorIpJOL/76f3nnL/56FG7AJRY99uXRKz45hDPPGdPpfjt2fMhNN93K8uUVLFx4KRs2PMe99z7M\n5s0vsHbtaoYMGcq4cYaqqjvYtu1lVq78NmvWPMrq1Q8yefJUFixYxIsvbubpp58C4L333mXjxg3c\nd98qgsEgt99+C88//yyzZs1u9+9XV+/krbcs48dPPOy2X/7y58yYce6xPRCSl5ojfoZSQJlKIiIi\nWSutNZWOtkhkR/fNhJgXI+A4uCmZSl1Z/S0fJKe/ZbgdIiJybIYPH8GYMf5qpKNHV3DaadNxHIeK\nirFUV1eza1c1K1d+F4Bp006nrm4/Bw8e4PXXX+Pmm/3tZ555FmVlxwGwbdtLWPsmCxdeCkBTUyMD\nBgxo9283NDRQWbmMb35zKaWl/Vrd9sgjDxMIBJg5c1Za+i257ZSRQfbU7GLYwbpMN0VEREQ6kOlC\n3Vkv4kULtqZScvpbgfRXRCSdzjxnTDKrqLcLdYdCoeRl13WT113XJRqNEAx2bzjgeR6zZs1m8eKr\nj7hfOBzm3/5tGTNnnsdnP3tOq9ueeeYXvPjiZu6++z6t7CXtGnycxxcnvk3D7zX9TUREJFvl15Je\naRCNRXBdh2CrTKUMNqgXJTKVCqW/IiKFasqUU9mw4TkAXn31FcrLyykt7cfUqS3bt2z5HfX1fsbI\ntGnT2bRpI7W1ewGoq9vPrl3VrY7peR6VlZWcdNJo5s6d1+q2rVtf5NFHf8wtt9xJSUlJursnOSoW\njfj/xzQQERERyVbKVOqEn6nk4BZgplIimORoApyISF67/PKvUVV1I/Pnz6W4uITKyu8AsGDBIlas\nqGTevIuZNGkyQ4cOA/wpdIsWXcU111yN58UIBIIsWfIthg0bnjzm9u1/4KmnnmLMmLFcdpm/1saV\nV/4zf/M3Z3HXXd8lHA5zzTVfB2DChIlce+31vdxryXaRaNgfqHr6DVRERCRbKajUiWgsiuu2Xv2t\nUKaDJaYjFEh3RUTy0vDhI1i79vHk9crKFe3eVlV1x2H3LS/vz1133dPucWfMmMmMGTMP275u3S8A\n6N9/Ktbadqf5/eQnP+tWH6QwJTKVPAWVREREspbO0p2IxKK4rtOqplKhPGhum/9FREREekssGo5f\n0khEREQkW+ks3YmoF8F1WmcqFcr0t0Q3VUBVREREeltLUCmQ0XaIiIhIxxRU6kQkFsUJtMlUKpAg\niwp1i4iISKZEo/6qb15UQSUREZFspaBSJ6JefPqbk5qplMEG9SJHhbpFREQkQ+oiY2h+ZhexAwoq\niYiIZCsFlToRDjfjBBzcAizUneinMpVERESkt4WbHWLvNuAEQ5luioiIiHRAQaVOHB8qgwKd/ua0\n+V9ERESkt0Sb/dXf3KAylURERLKVgkqdOM7tGw8qFd70t5ZMpQLpsIiIHNGc/CFqcQAAFFJJREFU\nORewb9++Lu3z0Ue7+OpXv8q8eV9m3ryLefzx/0ru8+CD9zF//lwuu+wSrrnm6+zeXZPupksOioT9\nQt1OMJjhloiIiEhHdJbuhBcJH1aou1BWf3OTNZVERES6JxAIct111zFkyIk0NBzk8su/yumnn8Ho\n0RVccslXWbToKgCeeOIxVq9+kGuvvT7DLZZsE40HlVwFlURERLKWztKd8MIRP1PJK7yaSsnpbwXS\nXxGRfFRdvZOlS7/BhAmTeOON7ZxyynjOP/8CVq16gNraWm644SZGjhxFVdWN7Ny5g+LiEpYtq2Ts\n2HHs37+PFSsqqampYeLESXielzzu+vXPsG7dY4TDEcaPn8DSpdcRCLRMUxo0aBCDB5dRU1NP376l\nnHzyyeze/TGjR1dQWtovuV9j4yGdZ6RdbnzsFSxSTSUREZFspaBSJ7xIGIIOgZSBdKEUrnZUqFtE\npMc8+0ENb+w9AEAg4BKNxjq5R+cmDezHrFGDO91vx44PuemmW1m+vIKFCy9lw4bnuPfeh9m8+QXW\nrl3NkCFDGTfOUFV1B9u2vczKld9mzZpHWb36QSZPnsqCBYt48cXNPP30UwC89967bNy4gfvuW0Uw\nGOT222/h+eefZdas2e3+/erqnbz1lmX8+InJbQ88cA/r1z9DaWkp3//+A8f8WEj+OW3c8ewGjivr\nk+mmiIiISAdUU6kTXiTSTk2lwoiyJF4chdFbEZH8NXz4CMaMGYvruoweXcFpp03HcRwqKsZSXV3N\n9u2v8/nPnw/AtGmnU1e3n4MHD/D6668xc+YsAM488yzKyo4DYNu2l7D2TRYuvJTLLruEbdteYufO\nHe3+7YaGBiorl/HNby5tlaF05ZVf58knf8nMmbN48snH0/wISC7qE098U00lERGR7KWzdCdi8elv\nQVIzlQojzDKuvC+HojH6atUVEZFjNmvU4GRWUWJaWG8JhVqmD7mum7zuui7RaIRgN7+0e57HrFmz\nWbz46iPuFw6H+bd/W8bMmefx2c+e0+4+5547i2uv/ReuuOLKbrVB8p8X9etZOhqHiIiIZC1lKnUm\nFo0X6m4JKhXK6m9nDOnPok+OLJggmohIoZoy5VQ2bHgOgFdffYXy8nJKS/sxdWrL9i1bfkd9fR0A\n06ZNZ9OmjdTW7gWgrm4/u3ZVtzqm53lUVlZy0kmjmTt3XqvbPvjg/eTlzZs3cdJJJ6era5LLEkGl\ngH4DFRERyVY6S3ei+MSTcPYVUVxcAo3+NgVZREQkn1x++deoqrqR+fPnUlxcQmXldwBYsGARK1ZU\nMm/exUyaNJmhQ4cBMHp0BYsWXcU111yN58UIBIIsWfIthg0bnjzm9u1/4KmnnmLMmLFcdtklAFx5\n5T/zN39zFvff/wPef/+vuK7L0KHDufba5b3facl6XiQCgBNQppKIiEi2UlCpE25REU7QJVQUgka/\nvpCCSiIikiuGDx/B2rUtNYsqK1e0e1tV1R2H3be8vD933XVPu8edMWMmM2bMPGz7unW/AKB//6lY\na9ud5nfzzbd1qw9SmFqmv2m4KiIikq00/a0TnufheRFCrv9QKaAkIiIikn6JTCUUVBIREclaCip1\nyl/1LRgvpOQqpiQiIiKSdl5U099ERESynYJKnfBi/oAm5PoDmoAylURERETSzouoULeIiEi2U1Cp\nE57nD2hC8V/JNP1NREREJP2SmUpBZSqJiIhkKwWVOtGSqeQ/VAHFlERERETSL7n6mzKVREREspWC\nSp3wPH9AEwwqU0lERESkt7Ss/qZMJRERkWyloFInEplKQTeIg2oqiYhI4Zoz5wL27dvXpX2ampqY\nM2cO8+d/hXnzLubhhx9I7vPrX/+KefMu5jOfOZ2//OXP6W625KhETSWUqSQiIpK1dJbuTDxTyQ0E\nCTiOVn8TERHpgqKiIh555BEaGmJEIhGuuuoKzjjjTCZOnERFxRj+/d+/y3e/+++ZbqZkMa3+JiIi\nkv0UVOpEIlPJdUOEXIegMpVERCSHVFfvZOnSbzBhwiTeeGM7p5wynvPPv4BVqx6gtraWG264iZEj\nR1FVdSM7d+6guLiEZcsqGTt2HPv372PFikpqamqYOHESnuclj7t+/TOsW/cY4XCE8eMnsHTpdQRS\nvvw7jkNpaSkNDfVEIhGi0QhO/Bx68smje/1xkNzjJWoqBTVcFRERyVY6S3ciWDyQYPEg+vU/mfNG\nlVES0IxBERHpvifffprXPn4DgIDrEI15ndyjc6cOmcRFY2d3ut+OHR9y0023snx5BQsXXsqGDc9x\n770Ps3nzC6xdu5ohQ4YybpyhquoOtm17mZUrv82aNY+yevWDTJ48lQULFvHii5t5+umnAHjvvXfZ\nuHED9923imAwyO2338Lzzz/LrFmt2xKNRrnsskvYseMDLrzwy0yYMPGY+yyFo6Sigj4jT6Bo+IhM\nN0VEREQ6oKBSJwKhfowY/8+UDy7jdOoz3RwREZFuGz58BGPGjAVg9OgKTjttOo7jUFExlurqanbt\nqmblyu8CMG3a6dTV7efgwQO8/vpr3Hyzv/3MM8+irOw4ALZtewlr32ThwksBaGpqZMCAAYf93UAg\nwJo1j1JfX8/11/9f3nnnbSoqxvZGlyUP9Js8ldEzPkNNjcZfIiIi2UpBJRERkV5w0djZyayiwYPL\nevWLcigUSl52XTd53XVdotEIwW5OL/I8j1mzZrN48dVd2r+srIxPfeo0tm7doqCSiIiISB7RXC4R\nEZECN2XKqWzY8BwAr776CuXl5ZSW9mPq1JbtW7b8jvr6OgCmTZvOpk0bqa3dC0Bd3X527apudcza\n2lrq6vz9m5oaefnl33PSSSf3Uo9EREREpDekNVPJGHMecDcQAB6y1t7S5vazgaeAd+ObnrTW3hi/\n7T2gHogCEWvtaelsq4iISKG6/PKvUVV1I/Pnz6W4uITKyu8AsGDBIlasqGTevIuZNGkyQ4cOA/wp\ndIsWXcU111yN58UIBIIsWfIthg0bnjzmnj27WbbsX2huDhOLxTjnnHP527/9DAAvvPAbvve929i3\nr5Zrr/1Xxo37BHfe+cPe77iIiIiIHBMndSWXnmSMCQBvAecCHwIvA1+x1v45ZZ+zgf9rrT2symg8\nqHSatXZ3V/9mTU19ejpD709VyBT1M38UQh9B/cw36md+SUc/Bw8u0zKsWUbjr2OnfuYX9TO/qJ/5\nRf08pmO2OwZLZ6bSdOBta+07AMaYx4AvAn8+4r1ERERE5Jh0li0e3+ds4HtACNhtrf1sV+8rIiIi\nAukNKp0AfJBy/UPgjHb2O9MYsx3YgZ+19Kf4dg/4lTEmCjxgrf1RZ39wwIC+BIOBY2x2xwYPLkvb\nsbOJ+pk/CqGPoH7mG/UzvxRKP7NJPFv8HlKyxY0xP2+TLd4fuBc4z1r7vjFmSFfvKyIiIpKQ6dXf\nXgVOtNYeMMacD/wMGBe/7Sxr7Y74IGeDMeYv1trfHulgtbUNaWuo0uTySyH0sxD6COpnvlE/80ua\nUq979Hh5qivZ4pfg17J8H8Ba+3E37isiIiICpHf1tx3AqJTrI+Pbkqy1ddbaA/HLzwAhY8yg+PUd\n8f8/Bn6KP8gRERERkSNrL1v8hDb7fAIYYIzZZIzZZoy5tBv3FREREQHSm6n0MjDOGDMaP5g0F/9X\nsSRjzDDgI2utZ4yZjh/k2mOMKQVca219/PJM4MY0tlVERESkkASBacAMoA+wxRiz9WgOpPIDPUP9\nzC/qZ35RP/OL+tmz0hZUstZGjDFXA+vxCz2ustb+yRizOH77/cAc4CpjTAQ4BMyNB5iGAj81xiTa\n+Ki19rl0tVVEREQkj3SaLY6fgbTHWnsQOGiM+S0wJb69s/u2ovIDx079zC/qZ35RP/OL+nlsx2xP\nWmsqxae0PdNm2/0pl38I/LCd+72DP7ARERERke7pNFsceAr4oTEmCBThL6ZyF/CXLtxXREREBEhv\nTSURERER6WXW2giQyBZ/E3g8kS2ekjH+JvAcsB14CXjIWvvHju6biX6IiIhI9sv06m8iIiIi0sM6\nyxaPX78NuK0r9xURERFpjzKVRERERERERESk2xzP8zLdBhERERERERERyTHKVBIRERERERERkW5T\nUElERERERERERLpNQSUREREREREREek2BZVERERERERERKTbFFQSEREREREREZFuU1BJRERERERE\nRES6LZjpBmQ7Y8x5wN1AAHjIWntLhpvUI4wxo4AfA0MBD/iRtfZuY8wKYBFQE9/1emvtM5lpZc8w\nxrwH1ANRIGKtPc0YMxD4CXAy8B5wsbW2NkNNPGbGGIPfn4QK4AagPzn+fBpjVgGzgY+ttRPj2zp8\n/owxy4Er8J/vf7HWrs9As7utg37eBlwANAP/Cyyw1u4zxpwMvAnY+N23WmsX936ru6+Dfq6gg9dp\nnj2fPwFMfJf+wD5r7dRcfT6PcB7Ju/enZIbGYLl3zm5LY7Dcfj41BtMYjBx7Pgth/AXZNwZTptIR\nGGMCwD3ALGA88BVjzPjMtqrHRICl1trxwKeBr6f07S5r7dT4v5w6+R3B38f7c1r8+nXARmvtOGBj\n/HrOsr6p1tqpwDSgAfhp/OZcfz7XAOe12dbu8xd/Dc8FJsTvc2/8fZwL1nB4PzcAE621k4G3gOUp\nt/1vyvOaEyfAuDUc3k9o53Wab8+ntfYfU96n/w94MuXmXHw+OzqP5OP7U3qZxmA5e85uj8Zguft8\nrkFjMI3Bcuv5XEP+j78gy8ZgCiod2XTgbWvtO9baZuAx4IsZblOPsNZWW2tfjV+ux4/SnpDZVvWq\nLwKPxC8/Anwpg23paTPwPyD/mumG9ARr7W+BvW02d/T8fRF4zFrbZK19F3gb/32c9drrp7X2eWtt\nJH51KzCy1xvWwzp4PjuSV89ngjHGAS4G/qtXG9XDjnAeybv3p2SExmD5S2OwHKExmMZgufZ8FsL4\nC7JvDKag0pGdAHyQcv1D8vCkH0/9OxX4fXzTN4wx240xq4wxAzLXsh7jAb8yxmwzxnwtvm2otbY6\nfnkXfupgvphL6w/LfHs+oePnL5/fs5cDz6ZcH22Med0Y84Ix5jOZalQPau91mq/P52eAj6y1/5Oy\nLaefzzbnkUJ8f0rPK4jXi8ZgGoPloEL8jNcYLD+ez7wbf0F2jMEUVCpwxph++GmA/2qtrQPuw58L\nPhWoBu7IYPN6ylnxdMdZ+KmBf5d6o7XWwx/05DxjTBHwBeCJ+KZ8fD5byafnryPGmEr8NNf/jG+q\nBk6Mv66XAI8aY47LVPt6QN6/Ttv4Cq2/dOT089nOeSSpEN6fIkdLY7D8+ozQGCw/aQyWV/Jq/AXZ\nMwZTUOnIdgCjUq6PjG/LC8aYEP6L8D+ttU8CWGs/stZGrbUx4EFyIM2xM9baHfH/P8af4z4d+MgY\nMxwg/v/HmWthj5oFvGqt/Qjy8/mM6+j5y7v3rDHmMvyCg/8UPzkQT13dE7+8Db+A5Ccy1shjdITX\naT4+n0HgIlKKuuby89neeYQCen9KWuX160VjMI3BcljBfMZrDJY/z2e+jb8gu8ZgCiod2cvAOGPM\n6PivD3OBn2e4TT0iPqf0YeBNa+2dKduHp+x2IfDH3m5bTzLGlBpjyhKXgZn4ffo5MD++23zgqcy0\nsMe1isDn2/OZoqPn7+fAXGNMsTFmNDAOeCkD7esR8ZWPlgFfsNY2pGwfnCiuZ4ypwO/nO5lp5bE7\nwus0r57PuM8Bf7HWfpjYkKvPZ0fnEQrk/SlppzFYjp+zNQbLr+czRUF8xmsMll/PJ3k0/oLsG4M5\nnpfXGYvHzBhzPvA9/OVsV1lrb85wk3qEMeYs4L+BN4BYfPP1+CfEqfipcu8BV6bMy8w58Q+IxAoc\nQeBRa+3NxpjjgceBE4G/4i+32NXCdVkpPmB7H6iw1u6Pb1tLjj+fxpj/As4GBgEfAd8GfkYHz188\nTfly/FTlf7XWPtvOYbNOB/1cDhQDe+K7bbXWLjbG/B/gRiCM//79trX2F73e6KPQQT/PpoPXaT49\nn9bah40xa/Cfx/tT9s3J5/MI55Hfk2fvT8kMjcFy75ydSmMwjcFy5TNeY7D8GYMVwvgLsm8MpqCS\niIiIiIiIiIh0m6a/iYiIiIiIiIhItymoJCIiIiIiIiIi3aagkoiIiIiIiIiIdJuCSiIiIiIiIiIi\n0m0KKomIiIiIiIiISLcFM90AESlcxpj3gMb4v4QvWWvf68G/cTLwirV2UE8dU0RERCSXaQwmIj1F\nQSURybQ51to/ZroRIiIiIgVGYzAROWYKKolI1jHGeMCNwBeBPsD11tr/F7/tPKAKCAA1wJXW2rfj\nt10OfDN+mGZgdsoxbwbOB/oCV1hrN/dOb0RERERyg8ZgItJdqqkkIpm2zhjzevzfKynbo9baqcAX\ngB8ZY4YYY4YAa4F/stZOBh4F/hPAGHM2cD3weWvtFODvgf3xYx0PbLHWnoo/ULq1NzomIiIiksU0\nBhORY6ZMJRHJtI5Srx8GsNZaY8yrwKcBD/iDtfbP8X1WA/caY8qAfwB+bK3dFb/fAQBjDMABa+3T\n8ftsBe5IV2dEREREcoTGYCJyzJSpJCKFoCnlchQF1EVERER6g8ZgInlOQSURyVYLAIwx44BT8X/d\n2gpMMcZ8Mr7PfOA1a2098EvgUmPM0Pj9+hljSnq/2SIiIiI5TWMwEekyRYpFJNPWGWNSl7NdGP8/\naIx5Db+o45XW2o8BjDFfBR41xgTxi0TOA7DWbjLGVAG/MsbE8H8Zu6C3OiEiIiKSYzQGE5Fj5nie\nl+k2iIi0El95pCwxJ19ERERE0k9jMBHpLk1/ExERERERERGRblOmkoiIiIiIiIiIdJsylURERERE\nREREpNsUVBIRERERERERkW5TUElERERERERERLpNQSUREREREREREek2BZVERERERERERKTbFFQS\nEREREREREZFu+//aqgzaX3KoCwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3fcf6d8ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# all models\n", "models = {idx:i for idx,i in enumerate(hists)}\n", "# high validation accuracy variance models\n", "high_variance = {idx:i for idx,i in enumerate(hists) if np.var(i['history']['val_acc']) > 0.003}\n", "# low validation accuracy variance models\n", "low_variance = {idx:i for idx,i in enumerate(hists) if np.var(i['history']['val_acc']) < 0.0013}\n", "# highest mean validation accuracy models\n", "high_acc = {idx:i for idx,i in enumerate(hists) if np.mean(i['history']['val_acc']) > 0.83}\n", "# high validation accuracy and low validation accuracy variance\n", "high_acc_low_variance = {idx:i for idx, i in enumerate(hists) if np.mean(i['history']['val_acc']) > 0.80 and np.var(i['history']['val_acc']) < 0.0013}\n", "# low mean distance between training and validation accuracy\n", "low_overfitting = {idx:hist for idx,hist in enumerate(hists) if np.mean(np.array(hist['history']['acc']) - np.array(hist['history']['val_acc'])) < 0.05} \n", "\n", "plt.figure(1, figsize=(20, 14))\n", "\n", "plt.subplot(221)\n", "plt.title('Highest accuracy & low accuracy variance')\n", "for idx, i in high_acc_low_variance.items():\n", " hist = i['history']\n", "# plt.plot(hist['acc'])\n", " plt.plot(hist['val_acc'], label='model{}'.format(idx))\n", "# plt.plot(*fit(hist['val_acc'], grade=7))\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Accuracy')\n", " plt.legend()\n", "\n", "plt.subplot(222)\n", "plt.title('Lowest overfitting')\n", "for idx, i in low_overfitting.items():\n", " hist = i['history']\n", " plt.plot(hist['acc'])\n", " plt.plot(hist['val_acc'], label='model{}'.format(idx))\n", "# plt.plot(*fit(hist['val_acc'], grade=7))\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Accuracy')\n", " plt.legend()\n", "\n", "plt.subplot(223)\n", "plt.title('Highest accuracy')\n", "for idx,i in high_acc.items():\n", " hist = i['history']\n", " plt.plot(hist['acc'])\n", " plt.plot(hist['val_acc'], label='model{}'.format(idx))\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Accuracy')\n", " plt.legend()\n", " \n", "plt.subplot(224)\n", "plt.title('Lowest accuracy variance')\n", "for idx, i in low_variance.items():\n", " hist = i['history']\n", " plt.plot(hist['acc'])\n", " plt.plot(hist['val_acc'], label='model{}'.format(idx))\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Accuracy')\n", " plt.legend()\n", " " ] }, { "cell_type": "code", "execution_count": 252, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f3fcf2c0780>]" ] }, "execution_count": 252, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/data/installs/miniconda3/envs/dl-python35/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABIcAAAMYCAYAAABR5oeDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niAmHAAAAAEZMOAQAAAAwYv8P+Mj+McufyI0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3fcf436550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "component_models = [i['history']['val_acc'] for i in hists if np.mean(i['history']['val_acc']) > 0.83]\n", "ensemble = np.mean(np.array(component_models), axis = 0)\n", "\n", "plt.figure(1, figsize=(20, 14))\n", "plt.plot(ensemble)\n", "# plt.plot(*fit(ensemble, grade=4))\n" ] }, { "cell_type": "code", "execution_count": 253, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>learning_rate</th>\n", " <th>seq_size</th>\n", " <th>batch_size</th>\n", " <th>epochs</th>\n", " <th>val_acc_variance</th>\n", " <th>val_acc_max</th>\n", " <th>acc_max</th>\n", " <th>epoch_to_70</th>\n", " <th>epoch_to_75</th>\n", " <th>epoch_to_80</th>\n", " <th>mean_overfitting</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>18</th>\n", " <td>0.00010</td>\n", " <td>300.0</td>\n", " <td>30.0</td>\n", " <td>200.0</td>\n", " <td>0.001269</td>\n", " <td>0.900785</td>\n", " <td>0.936780</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>7.0</td>\n", " <td>0.047456</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>0.00010</td>\n", " <td>350.0</td>\n", " <td>30.0</td>\n", " <td>200.0</td>\n", " <td>0.002901</td>\n", " <td>0.900431</td>\n", " <td>0.936746</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>13.0</td>\n", " <td>0.067650</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>0.00001</td>\n", " <td>350.0</td>\n", " <td>30.0</td>\n", " <td>200.0</td>\n", " <td>0.006437</td>\n", " <td>0.906889</td>\n", " <td>0.944847</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>22.0</td>\n", " <td>0.072716</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " learning_rate seq_size batch_size epochs val_acc_variance \\\n", "18 0.00010 300.0 30.0 200.0 0.001269 \n", "19 0.00010 350.0 30.0 200.0 0.002901 \n", "24 0.00001 350.0 30.0 200.0 0.006437 \n", "\n", " val_acc_max acc_max epoch_to_70 epoch_to_75 epoch_to_80 \\\n", "18 0.900785 0.936780 1 4 7.0 \n", "19 0.900431 0.936746 0 6 13.0 \n", "24 0.906889 0.944847 1 6 22.0 \n", "\n", " mean_overfitting \n", "18 0.047456 \n", "19 0.067650 \n", "24 0.072716 " ] }, "execution_count": 253, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def gen_epoch_to(hists, acc):\n", " for hist in hists:\n", " where = np.where(np.array(hist['history']['val_acc']) > acc)\n", " if len(where[0]) > 0:\n", " yield where[0][0]\n", " else:\n", " yield None\n", " \n", " \n", "df = pd.DataFrame([hist['hyperparameters'] for hist in hists])\n", "df.columns = ['learning_rate', 'seq_size', 'batch_size', 'epochs']\n", "df['val_acc_variance'] = [np.var(hist['history']['val_acc']) for hist in hists]\n", "df['val_acc_max'] = [np.max(hist['history']['val_acc']) for hist in hists]\n", "df['acc_max'] = [np.max(hist['history']['acc']) for hist in hists]\n", "df['epoch_to_70'] = [i for i in gen_epoch_to(hists, 0.7)]\n", "df['epoch_to_75'] = [i for i in gen_epoch_to(hists, 0.75)]\n", "df['epoch_to_80'] = [i for i in gen_epoch_to(hists, 0.8)]\n", "df['mean_overfitting'] = \\\n", " [np.mean(np.array(hist['history']['acc']) - np.array(hist['history']['val_acc'])) for hist in hists]\n", "df[df.val_acc_max > 0.9]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "with open('model/model_simple.hist',\"rb\") as f:\n", " hist = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fc15883b240>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/data/installs/miniconda3/envs/dl-python35/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABIcAAAMYCAYAAABR5oeDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0DDhEAAAAEDD/j86Xxk/B1HJkAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc1587fa828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1, figsize=(20, 14))\n", "plt.plot(hist['history']['acc'])\n", "plt.plot(hist['history']['val_acc'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

elespdn/coding-and-collation

workshopLausanne201904/02_PrepareEnvironment/JupyterNotebook.ipynb

1

10720

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prepare the environment: Jupyter Notebooks\n", "\n", "This notebook is about preparing everything we need for the next exercises.\n", "\n", "You should have already installed CollateX and Jupyter Notebooks. They are both installed in the computers in the classroom. For more information about the installation, see the [installation instructions](../00_Installation/Installation.ipynb).\n", "\n", "In the following exercises we'll focus on CollateX. Here we'll see how to work with Jupyter Notebooks, that is the environment we choose to use CollateX.\n", "\n", "**What is a Notebook?**\n", "\n", "The Notebook is a place where you can run code, document your program and see the results.\n", "\n", "The tutorials in this workshop, including the one you reading right now, are Jupyter Notebooks.\n", "\n", "The official home page of Jupyter Notebook is https://jupyter.org/.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Download the materials\n", "- Go to https://github.com/elespdn/CollateX_tutorial and download the entire repository, clicking on the Clone/Download green button on the right. This will download a zip folder.\n", "- Unzip the folder\n", "- You can leave the folder where it is or move it to a location of your choice, for example the Documents folder in your computer. In both cases, remember where it is.\n", "\n", "\n", "## Start Jupyter notebook\n", "\n", "To launch Jupyter notebook, you can\n", "- use the launcher (for example, in the Windows computers in the rooms, start writing 'Jupyter' in the bottom left launcher)\n", "- or use the terminal. This what you probably have to do on Linux. Open a terminal and type\n", "\n", " `jupyter notebook`\n", "\n", "\n", "In both cases, the terminal will show that Jupyter is starting and a window will open in your default web browser (Firefox, Chrome, Safari, etc.). You can ignore the terminal after that; your interaction with Jupyter will happen entirely in the browser window. \n", "\n", "## Open a Jupyter Notebook\n", "\n", "In the browser you can navigate into your local files. This might be confusing, but it is as simple as it appears: your file system is available in the web browser.\n", "\n", "Go to the materials folder that you downloaded before, just double-clicking on the icons of the folders as you would do in your computer.\n", "\n", "When you are in the materials directory (directory and folders are synonyms), choose '02_PrepareEnvironment' and inside this dir (abbreviation for directory), double-click on 'JupyterNotebook.ipynb'. Please note that 'ipynb' is the extension for a Jupyter Notebook file (as, for example, 'docx' is the extension for a Microsoft Word file). When you double-click on a Jupyter Notebook file, it will open in a new tab in the broswer.\n", "\n", "Done! Now you can continue read in this notebook that you have just opened.\n", "\n", "\n", "\n", "## Basic operations in Jupyter Notebook\n", "\n", "Now you are inside a Jupyter Notebook. It is a file, where you can write stuff, but it is special because you can also run code and see the output.\n", "\n", "First, a Jupyter Notenook is made of cells, just like a text is made of paragraphs. Each cell can be edited in two modes:\n", "\n", "1. **Markdown**: this is the styled text mode that we use when we’re not writing code, to add documentation and instructions. What you are reading is a markdown cell.\n", "1. **Code**: this is the default mode used for Python code. Python is a programming language.\n", "\n", "Let's see how they work.\n", "\n", "\n", "### Create a new cell\n", "\n", "Go the tha last cell, at the bottom of this file, the one entitles 'More about Jupyter Notebook'. Click in it and you will see the borders of the cell appearing.\n", "\n", "Now create a new cell after that, you can do it in multiple ways:\n", "- use the button with the sign '+' (second from the left in the toolbar)\n", "- go the the menu 'Insert' and choose 'Insert cell below'\n", "\n", "A new cell will appear below. Click on it and you are inside. Before start writing, we need to choose the editing mode.\n", "\n", "\n", "### Markdown Cells\n", "\n", "Select _Markdown_ mode from the dropdown menu in the toolbar.\n", "\n", "Then type the following text:\n", "\n", " # Getting started!\n", " This is _Hello World!, my first Jupyter Notebook.\n", "\n", "Now you can render the markdown, which is the same as run the code. You have multiple options for the command “run cell, select below”:\n", "- press the corresponding button on the toolbar (right-pointing triangle)\n", "- click on the Cell menu and then select *run cells and select below* \n", "- hit Shift+Enter\n", "\n", "Note that the Markdown language defines that\n", "- the sign \\# indicates a title\n", "- the sign \\_ indicates italic\n", "\n", "There is much more in the Markdown language, but we won't enter into details here. Markdown is used in a variety of cases, and not only in Jupyter Notebooks. Plenty of tutorials are available online if you are interested.\n", "\n", "The instructions here are Markdown themselves. Double-click here to see what's behind! And don't forget to run the cell afterwards, in order to have the nice rendition back.\n", "\n", "\n", "### Code Cells\n", "\n", "Now create a new cell, as we have seeen above.\n", "\n", "Select _Code_ mode from the dropdown menu in the toolbar.\n", "\n", "Our first bit of code is going to follow tradition: what better place to start than the classic [_Hello World!_](http://en.wikipedia.org/wiki/%22Hello,_world!%22_program_) program. It’s easy to describe what we want to do for our _Hello World!_ program: we want to write code that will display the string (or sequence of characters) “Hello World!”.\n", "Let’s use the `print` function and indicate that “Hello world” is a string using the double quotes. Write the following in your cell and than run the cell as we've seen for the Markwodn cell.\n", "\n", " print(\"Hello world!\")\n", "\n", "The result should appear below.\n", "\n", "Yay, our first program! That was easy, wasn’t it? :)\n", "\n", "Try to remove the double quotes around *Hello world* and run the code again:\n", "\n", " print(Hello world!)\n", "\n", "You will get an error message, because the syntax of your code is not correct.\n", "\n", "### Input and output sections\n", "\n", "The sequence above shows a key aspect of Jupyter code cells: there’s an **input section** and an **output section**: the input section is where you write the code and is indicated on the left of the editing box with the word “In” followed by a number in square brackets; the output section is where the result of running your code will appear, below the input section.\n", "\n", "### Save\n", "\n", "If we have been working on a notebook, we would of course want to save our work before quitting. For saving the notebook you are working on, you have again several options:\n", "- press the file icon, that is the first button on the left\n", "- select *Save and checkpoint* from the File menu\n", "- hit Ctrl+s\n", "\n", "### Download\n", "\n", "You can download the notebook in different formats (menu File > Download as).\n", "\n", "### Open a new Notebook\n", "\n", "Navigate to the folder in which you want to open a new file. Select the type of file you want to create from the 'New' dropdown menu on the right: for a Jupyter Notebook, select 'Python 3'. A new tab with the new file will open. Give it a name by double-clicking on the name 'Untitled' and typing your new name. That's it! The new file will be saved on your computer together with the others.\n", "\n", "\n", "## Quit Jupyter\n", "\n", "The browser window opened when Jupyter was launched is just a regular window. For quitting Jupyter we can close the browser window(s) opened by Jupyter, then switch to the terminal and follow the instruction:\n", "\n", " Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n", " \n", " \n", "## Where are my notebooks\n", "\n", "Everything that we do through the Jupyter interface is stored locally on our computer. Just remember that in order to open and edit the notebooks, we have to launch Jupyter again. It is the same as for a Microsoft Word file: you edit it through the program and you store it in your computer, the only difference is that the program runs in the broswer for a Jupyter Notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More about Jupyter notebook\n", "\n", "Resources about Jupyter Notebook are available through\n", "\n", "- the official [Jupyter](https://jupyter.org/) page\n", "- the Jupyter Notebook [documentation](https://jupyter-notebook.readthedocs.io/en/stable/index.html)\n", "- many other places on the web\n", "\n", "For a DH oriented introduction, have a look at the first chapters (*Getting setup* and *Getting started*) of [The Art of Literary Text Analysis](http://nbviewer.jupyter.org/github/sgsinclair/alta/blob/master/ipynb/ArtOfLiteraryTextAnalysis.ipynb) by Stéfan Sinclair & Geoffrey Rockwell." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started!\n", "\n", "This is _Hello World!_, my first Jupyter Notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ciao Roma!\n" ] } ], "source": [ "print(\"Ciao Roma!\")" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

jasonost/clinicaltrials

trial_criteria/Finding_criteria_concepts_and_terms.ipynb

1

33891

{ "metadata": { "name": "", "signature": "sha256:813b244b484648f04658fbf2b2cd59742ac35da9d9a59668fc715c2b4d4e0cb5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import nltk\n", "import codecs\n", "import unicodedata\n", "import re\n", "from copy import deepcopy\n", "from pyUtil import easyPickle as pickle\n", "from pyUtil import flattenList as flatten" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Data Input" ] }, { "cell_type": "code", "collapsed": false, "input": [ "criteria_text = codecs.open('data/ct_criteria_colin.txt',\n", " encoding=\"utf-8\")\n", "criteria_text = criteria_text.readlines()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Chunck sentences and tokens" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#break sentences on '-'\n", "criteria_text_sent = [re.split(' - ', line) for line in criteria_text]\n", "\n", "#get sentence tokenizer\n", "sent_tokenizer=nltk.data.load('tokenizers/punkt/english.pickle')\n", "\n", "#run the sentence tokenizer over all the documents\n", "def sent_token(text):\n", " sentence_groups = []\n", " for sent_group in text:\n", " group_holder = []\n", " for sent in sent_group:\n", " group_holder.append(sent_tokenizer.tokenize(sent))\n", " sentence_groups.append(group_holder)\n", " del group_holder\n", " return sentence_groups\n", "\n", "criteria_text_sent = sent_token(criteria_text_sent)\n", "\n", "\n", "#Flatten the documents to contain just a list of strings where each string is a sentence\n", "def flatten_docs(text):\n", " result = []\n", " for doc in text:\n", " result.append(flatten.flatten(doc))\n", " return result\n", "\n", "criteria_text_docs = flatten_docs(criteria_text_sent)\n", "\n", "#create a list of all sentences\n", "criteria_text_sents = flatten.flatten(criteria_text_docs)\n", "\n", "#CREATING TOKENS\n", "\n", "#patter for tokenizing\n", "pattern = r'''(?x) # set flag to allow verbose regexps\n", " ([A-Z]\\.)+ # abbreviations, e.g. U.S.A\n", " | \\w+([-\u2018]\\w+)* # words with optional internal hyphens\n", " | \\$?\\d+(\\.\\d+)?%? # currency and percentages, e.g. $12.40, 82%\n", " | \\.\\.\\. # ellipsis... \n", " | [][.,;\"'?():\\-_`]+ # these are separate tokens\n", " '''\n", "#create tokens for the sentence list\n", "criteria_text_sent_tokens = [nltk.regexp_tokenize(sent, pattern) for sent\n", " in criteria_text_sents]\n", "\n", "#use this for creating tokens for the documents\n", "def doc_token(text):\n", " result = []\n", " for doc in text:\n", " doc_text = []\n", " for sent in doc:\n", " doc_text.append(nltk.regexp_tokenize(sent, pattern))\n", " result.append(doc_text)\n", " return result\n", "#criteria_text_docs_token = doc_token(criteria_text_docs)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Tag tokens" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#tag document structured criteria text\n", "def doc_tagger_pos(text):\n", " result = []\n", " for doc in text:\n", " doc_text = []\n", " for sent in doc:\n", " doc_text.append(nltk.pos_tag(sent))\n", " result.append(doc_text)\n", " return result\n", "\n", "#criteria_text_docs_tagged_pos = doc_tagger_pos(criteria_text_docs_token)\n", "\n", "#tag sentence structured criteria text\n", "criteria_text_sent_tag = []\n", "for sent in criteria_text_sent_tokens:\n", " criteria_text_sent_tag.append(nltk.pos_tag(sent))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Save and load tagged corpus" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#save tagged corpus\n", "pickle.save_object(criteria_text_sent_tag,\n", " 'data/criteria_corpus_pos_tagged.pkl')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#load tagged corpus\n", "criteria_text_sent_tag = pickle.open_object('data/criteria_corpus_pos_tagged.pkl')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Keyphrases for criteria" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#imports\n", "from nltk.util import ngrams\n", "from nltk import FreqDist\n", "import string\n", "from nltk.corpus import stopwords" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "#remove stopwords and punctuation\n", "def remove_punct(text):\n", " return [[word for word in sent if word[0] not in string.punctuation] for sent in text]\n", "def remove_stop(text):\n", " return [[word for word in sent if word.lower() not in stopwords.words('english')] for sent in text]\n", "\n", "#create non-tagged corpus\n", "criteria_text_sent_tokens = [[w[0] for w in sent] for sent in criteria_text_sent_tag]\n", "criteria_sents_no_stop = remove_punct(criteria_text_sent_tokens)\n", "criteria_sents_no_stop = remove_stop(criteria_sents_no_stop)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Chunker Approach" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_specific_sent(text, spec_words):\n", "\n", " specific_sents = []\n", " spec_words = map(lambda x: x.lower(), spec_words)\n", " for sent in text:\n", " for word in sent:\n", " if word[0].lower() in spec_words:\n", " specific_sents.append(sent)\n", " break\n", " return specific_sents" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "#create subsection of sentences to run the chunker on that contain cerain phrases\n", "smoker_list = ['Non-smoker', 'smoker']\n", "smoker_sents = get_specific_sent(criteria_text_sent_tag, smoker_list)\n", "pregnancy_list = ['Pregnancy', 'pregnant']\n", "pregnancy_sents = get_specific_sent(criteria_text_sent_tag, pregnancy_list)\n", "birth_control_list = ['Birth control', 'contraception']\n", "birth_control_sents = get_specific_sent(criteria_text_sent_tag, birth_control_list)\n", "drug_list = ['Illicit drugs', 'Alcohol abuse', 'illegal', 'illicit']\n", "drug_sents = get_specific_sent(criteria_text_sent_tag, drug_list)\n", "heart_failure_list = ['Congestive Heart Failure', 'heart failure']\n", "heart_failure_sents = get_specific_sent(criteria_text_sent_tag, heart_failure_list)\n", "hiv_list = ['HIV', 'aids', 'human immunodeficiency virus']\n", "hiv_sents = get_specific_sent(criteria_text_sent_tag, hiv_list)\n", "allergy_list = ['Allergies', 'allergy', 'hypersensitivity']\n", "allergy_sents = get_specific_sent(criteria_text_sent_tag, allergy_list)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "term_sents_list = [smoker_sents, pregnancy_sents, birth_control_sents, drug_sents,\n", " heart_failure_sents, hiv_sents, allergy_sents]\n", "term_list = [smoker_list, pregnancy_list, birth_control_list, drug_list, heart_failure_list,\n", " hiv_list, allergy_list]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "#get chunks\n", "def chunker(tagged_corpus, chunk_reg):\n", " \n", " cp = nltk.RegexpParser(chunk_reg)\n", " \n", " results = []\n", " \n", " for sents in tagged_corpus:\n", " tree = cp.parse(sents)\n", " for subtree in tree.subtrees():\n", " if subtree.label() == 'CHUNK':\n", " results.append(subtree[:])\n", " return results\n", "\n", "chunk_reg = r\"\"\"\n", " CHUNK: {(<NN.*><POS>)?<RB>?<JJ.*>*<NN.*>+}\n", " \"\"\"\n", "\n", "\n", "def get_doc_desc(num, terms, text):\n", " print\n", " print 'For terms: ' + ', '.join(terms)\n", " for sent in [[word[0] for word in sent] for sent in text[:num]]:\n", " print ' '.join(sent)\n", " \n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "for idx, term in enumerate(term_sents_list):\n", " chunks_dict_criteria = chunker(term, chunk_reg)\n", " get_doc_desc(20, term_list[idx], chunks_dict_criteria)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "For terms: Non-smoker, smoker\n", "Non-smoker\n", "Current smoker\n", "nicotine patches\n", "gum\n", "Current cigarette smoker\n", "cigarettes day\n", "smoking\n", "year\n", "Current smoker\n", "subject\n", "smoker\n", "use\n", "tobacco\n", "nicotine\n", "products\n", "months\n", "Screening\n", "LDL\n", "risk factor\n", "LDL\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "For terms: Pregnancy, pregnant\n", "PRIOR CONCURRENT THERAPY\n", "Biologic therapy\n", "Pregnancy\n", "use\n", "double barrier method\n", "pregnancy\n", "e\n", "condom\n", "diaphragm\n", "cervical cap\n", "positive pregnancy test\n", "breast feeding\n", "screening\n", "Women\n", "negative pregnancy test\n", "Baseline Month\n", "Female subjects\n", "negative pregnancy\n", "test\n", "child\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "For terms: Birth control, contraception\n", "Must\n", "method\n", "contraception\n", "study\n", "Female subjects\n", "use\n", "effective nonhormonal birth control methods\n", "practicing\n", "birth control methods\n", "days\n", "end\n", "treatment period\n", "Note\n", "Estrogen-based hormonal contraception\n", "Prezista\n", "trial\n", "women\n", "Fertile patients\n", "effective contraception PRIOR CONCURRENT THERAPY\n", "Biologic therapy\n", "\n", "For terms: Illicit drugs, Alcohol abuse, illegal, illicit\n", "Known history\n", "alcohol abuse\n", "illicit drugs\n", "steroids\n", "alcoholic beverages day\n", "Alcohol abuse\n", "drug addiction\n", "use\n", "illegal drugs\n", "history\n", "drug abuse\n", "illicit drug use\n", "history\n", "alcohol abuse\n", "daily consumption\n", "alcoholic drinks\n", "day\n", "years\n", "alcohol\n", "drugs\n", "\n", "For terms: Congestive Heart Failure, heart failure\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "For terms: HIV, aids, human immunodeficiency virus\n", "HIV\n", "negative test\n", "Subjects\n", "laboratory abnormalities\n", "Division\n", "AIDS Table\n", "Grading\n", "Severity\n", "Adult\n", "Pediatric Adverse Events (\" DAIDS grading table\n", "accordance\n", "normal ranges\n", "trial\n", "clinical laboratory\n", "Subjects\n", "HIV\n", "signs\n", "active Hepatitis B\n", "Hepatitis C.\n", "multiple sclerosis\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "For terms: Allergies, allergy, hypersensitivity\n", "Known\n", "hypersensitivity\n", "vaccine components\n", "vaccine\n", "same substances\n", "eruptions\n", "drug allergies\n", "food allergy\n", "eczema\n", "psoriasis\n", "urticaria\n", "opinion\n", "investigator\n", "contraindication\n", "study enrollment\n", "clinically significant allergy\n", "hypersensitivity\n", "excipients\n", "medications\n", "trial\n" ] } ], "prompt_number": 41 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Ngram Approach" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#look at multigrams as well for the specific sentences\n", "def multinNgram(n, text):\n", " '''This funciton loops through ngrams of length 1 to n.'''\n", " text = remove_punct(text)\n", " text = remove_stop(text)\n", " result = {}\n", " flat_list = flatten.flatten(text)\n", " for num in range(n, 0, -1):\n", " result[num] = []\n", " ngram = ngrams(flat_list, num)\n", " result[num] = [' '.join(gram) for gram in ngram]\n", " return result\n", "\n", "def get_top_mulitgrams(multiGrams, terms, num):\n", " print 'For terms: ' + ', '.join(terms)\n", " for ngram in multiGrams:\n", " fd = FreqDist(multiGrams[ngram]).most_common(num)\n", " for key in fd:\n", " print key" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "for idx, term in enumerate(term_sents_list):\n", " multiGrams = multinNgram(4, [[word[0] for word in sent] for sent in term])\n", " get_top_mulitgrams(multiGrams, term_list[idx], 10)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "For terms: Non-smoker, smoker\n", "(u'smoker', 35)\n", "(u'Current', 12)\n", "(u'history', 11)\n", "(u'years', 9)\n", "(u'day', 8)\n", "(u'pack', 8)\n", "(u'smoking', 8)\n", "(u'cigarettes', 8)\n", "(u'10', 7)\n", "(u'Non-smoker', 6)\n", "(u'Current smoker', 9)\n", "(u'pack years', 5)\n", "(u'Exclusion Criteria', 4)\n", "(u'per day', 4)\n", "(u'1 year', 4)\n", "(u'cigarettes day', 4)\n", "(u'5 cigarettes', 3)\n", "(u'smoker defined', 3)\n", "(u'defined smoked', 3)\n", "(u'6 months', 3)\n", "(u'smoker defined smoked', 3)\n", "(u'smoker Current smoker', 3)\n", "(u'preceding 1 year', 2)\n", "(u'6 months pack', 2)\n", "(u'Current cigarette smoker', 2)\n", "(u'year Current smoker', 2)\n", "(u'5 cigarettes day', 2)\n", "(u'defined smoked preceding', 2)\n", "(u'non-smoker 18 years', 2)\n", "(u'10 pack years', 2)\n", "(u'defined smoked preceding 1', 2)\n", "(u'smoked preceding 1 year', 2)\n", "(u'Current smoker defined smoked', 2)\n", "(u'smoker defined smoked preceding', 2)\n", "(u'18 years age older', 2)\n", "(u'non-smoker 18 years age', 2)\n", "(u'1 year Current smoker', 2)\n", "(u'within last 2 years', 1)\n", "(u'containing products within 6', 1)\n", "(u'within past year prior', 1)\n", "For terms: Pregnancy, pregnant" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "(u'pregnant', 679)\n", "(u'pregnancy', 673)\n", "(u'test', 451)\n", "(u'study', 341)\n", "(u'potential', 336)\n", "(u'Pregnant', 314)\n", "(u'women', 314)\n", "(u'must', 267)\n", "(u'negative', 235)\n", "(u'lactating', 217)\n", "(u'pregnancy test', 430)\n", "(u'childbearing potential', 192)\n", "(u'pregnant nursing', 126)\n", "(u'must negative', 113)\n", "(u'become pregnant', 110)\n", "(u'urine pregnancy', 100)\n", "(u'pregnant lactating', 100)\n", "(u'breast feeding', 99)\n", "(u'negative pregnancy', 99)\n", "(u'potential must', 97)\n", "(u'urine pregnancy test', 95)\n", "(u'negative pregnancy test', 91)\n", "(u'Negative pregnancy test', 84)\n", "(u'potential must negative', 79)\n", "(u'childbearing potential must', 69)\n", "(u'serum pregnancy test', 63)\n", "(u'nursing Negative pregnancy', 59)\n", "(u'pregnant nursing Negative', 59)\n", "(u'Women childbearing potential', 50)\n", "(u'must negative pregnancy', 49)\n", "(u'nursing Negative pregnancy test', 59)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "(u'pregnant nursing Negative pregnancy', 59)\n", "(u'childbearing potential must negative', 58)\n", "(u'must negative pregnancy test', 48)\n", "(u'negative urine pregnancy test', 46)\n", "(u'potential must negative pregnancy', 39)\n", "(u'Women childbearing potential must', 33)\n", "(u'negative serum pregnancy test', 31)\n", "(u'potential must negative serum', 21)\n", "(u'must negative serum pregnancy', 21)\n", "For terms: Birth control, contraception" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "(u'contraception', 511)\n", "(u'use', 296)\n", "(u'must', 272)\n", "(u'study', 259)\n", "(u'potential', 203)\n", "(u'effective', 201)\n", "(u'patients', 135)\n", "(u'childbearing', 129)\n", "(u'method', 128)\n", "(u'least', 117)\n", "(u'effective contraception', 138)\n", "(u'use effective', 129)\n", "(u'childbearing potential', 118)\n", "(u'must use', 107)\n", "(u'patients must', 93)\n", "(u'method contraception', 83)\n", "(u'Fertile patients', 82)\n", "(u'adequate contraception', 72)\n", "(u'must agree', 63)\n", "(u'agree use', 59)\n", "(u'use effective contraception', 101)\n", "(u'must use effective', 89)\n", "(u'patients must use', 84)\n", "(u'Fertile patients must', 81)\n", "(u'must agree use', 51)\n", "(u'use adequate contraception', 37)\n", "(u'childbearing potential must', 36)\n", "(u'women childbearing potential', 30)\n", "(u'PRIOR CONCURRENT THERAPY', 28)\n", "(u'Women childbearing potential', 27)\n", "(u'patients must use effective', 81)\n", "(u'Fertile patients must use', 81)\n", "(u'must use effective contraception', 80)\n", "(u'PRIOR CONCURRENT THERAPY Biologic', 25)\n", "(u'must agree use adequate', 24)\n", "(u'CONCURRENT THERAPY Biologic therapy', 24)\n", "(u'agree use adequate contraception', 23)\n", "(u'contraception Fertile patients must', 18)\n", "(u'contraception PRIOR CONCURRENT THERAPY', 15)\n", "(u'use effective contraception PRIOR', 15)\n", "For terms: Illicit drugs, Alcohol abuse, illegal, illicit" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "(u'illicit', 39)\n", "(u'drug', 29)\n", "(u'drugs', 28)\n", "(u'alcohol', 27)\n", "(u'use', 26)\n", "(u'abuse', 20)\n", "(u'within', 16)\n", "(u'study', 12)\n", "(u'months', 11)\n", "(u'history', 10)\n", "(u'illicit drug', 19)\n", "(u'illicit drugs', 17)\n", "(u'alcohol illicit', 11)\n", "(u'drug use', 11)\n", "(u'drug abuse', 9)\n", "(u'prior first', 6)\n", "(u'illegal drugs', 6)\n", "(u'first dose', 5)\n", "(u'days prior', 5)\n", "(u'history alcohol', 5)\n", "(u'illicit drug use', 10)\n", "(u'alcohol illicit drug', 8)\n", "(u'illicit drug abuse', 6)\n", "(u'first dose study', 5)\n", "(u'days prior first', 5)\n", "(u'dose study medication', 5)\n", "(u'use illicit drugs', 5)\n", "(u'prior first dose', 5)\n", "(u'illicit drugs alcohol', 4)\n", "(u'drug abuse within', 3)\n", "(u'prior first dose study', 5)\n", "(u'first dose study medication', 5)\n", "(u'days prior first dose', 4)\n", "(u'alcohol illicit drug abuse', 4)\n", "(u'illicit drug abuse within', 3)\n", "(u'Current use illicit drugs', 3)\n", "(u'alcohol illicit drug use', 3)\n", "(u'illicit drug use within', 2)\n", "(u'abuse drug addiction use', 2)\n", "(u'90 days prior first', 2)\n", "For terms: Congestive Heart Failure, heart failure\n", "For terms: HIV, aids, human immunodeficiency virus" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "(u'HIV', 517)\n", "(u'hepatitis', 163)\n", "(u'infection', 146)\n", "(u'B', 127)\n", "(u'positive', 126)\n", "(u'C', 121)\n", "(u'virus', 118)\n", "(u'immunodeficiency', 97)\n", "(u'human', 84)\n", "(u'Hepatitis', 73)\n", "(u'HIV infection', 84)\n", "(u'immunodeficiency virus', 81)\n", "(u'virus HIV', 80)\n", "(u'human immunodeficiency', 79)\n", "(u'hepatitis B', 79)\n", "(u'hepatitis C', 60)\n", "(u'HIV positive', 56)\n", "(u'Hepatitis B', 41)\n", "(u'B C', 33)\n", "(u'HIV hepatitis', 28)\n", "(u'immunodeficiency virus HIV', 78)\n", "(u'human immunodeficiency virus', 78)\n", "(u'B hepatitis C', 25)\n", "(u'hepatitis B hepatitis', 23)\n", "(u'B surface antigen', 22)\n", "(u'HIV hepatitis B', 22)\n", "(u'hepatitis B C', 21)\n", "(u'virus HIV infection', 17)\n", "(u'HIV Hepatitis B', 16)\n", "(u'Hepatitis B surface', 13)\n", "(u'human immunodeficiency virus HIV', 75)\n", "(u'hepatitis B hepatitis C', 23)\n", "(u'immunodeficiency virus HIV infection', 17)\n", "(u'immunodeficiency virus HIV positive', 13)\n", "(u'hepatitis B surface antigen', 11)\n", "(u'Hepatitis B surface antigen', 11)\n", "(u'immunodeficiency virus HIV hepatitis', 9)\n", "(u'positive human immunodeficiency virus', 9)\n", "(u'B hepatitis C HIV', 9)\n", "(u'Known human immunodeficiency virus', 8)\n", "For terms: Allergies, allergy, hypersensitivity" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "(u'hypersensitivity', 331)\n", "(u'allergy', 272)\n", "(u'known', 157)\n", "(u'Known', 150)\n", "(u'history', 139)\n", "(u'study', 112)\n", "(u'History', 94)\n", "(u'drug', 88)\n", "(u'allergies', 76)\n", "(u'drugs', 70)\n", "(u'known hypersensitivity', 72)\n", "(u'Known hypersensitivity', 65)\n", "(u'Known allergy', 43)\n", "(u'History hypersensitivity', 35)\n", "(u'Patients known', 29)\n", "(u'known allergy', 28)\n", "(u'history allergy', 27)\n", "(u'allergy hypersensitivity', 23)\n", "(u'hypersensitivity reaction', 21)\n", "(u'study drug', 20)\n", "(u'Patients known hypersensitivity', 18)\n", "(u'PRIOR CONCURRENT THERAPY', 14)\n", "(u'CONCURRENT THERAPY Biologic', 11)\n", "(u'THERAPY Biologic therapy', 11)\n", "(u'history drug allergy', 10)\n", "(u'Known suspected allergy', 8)\n", "(u'history allergy hypersensitivity', 7)\n", "(u'Known suspected hypersensitivity', 7)\n", "(u'Patient known hypersensitivity', 7)\n", "(u'known suspected allergy', 7)\n", "(u'PRIOR CONCURRENT THERAPY Biologic', 11)\n", "(u'CONCURRENT THERAPY Biologic therapy', 11)\n", "(u'CHARACTERISTICS Age 18 Performance', 5)\n", "(u'PATIENT CHARACTERISTICS Age 18', 5)\n", "(u'drugs formulated polysorbate 80', 5)\n", "(u'Age 18 Performance status', 5)\n", "(u'times upper limit normal', 4)\n", "(u'sensitivity study medications components', 4)\n", "(u'least 100 000 mm3', 4)\n", "(u'study medications components thereof', 4)\n" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "multiGrams = multinNgram(4, [[word[0] for word in sent] for sent in fertile_sents])\n", "get_top_mulitgrams(multiGrams, fertile_terms, 10)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "For terms: fertile\n", "(u'contraception', 101)\n", "(u'use', 100)\n", "(u'patients', 95)\n", "(u'effective', 92)\n", "(u'must', 91)\n", "(u'Fertile', 88)\n", "(u'least', 63)\n", "(u'therapy', 55)\n", "(u'study', 53)\n", "(u'prior', 47)\n", "(u'use effective', 88)\n", "(u'patients must', 84)\n", "(u'must use', 84)\n", "(u'Fertile patients', 82)\n", "(u'effective contraception', 76)\n", "(u'PRIOR CONCURRENT', 29)\n", "(u'CONCURRENT THERAPY', 29)\n", "(u'contraception Fertile', 29)\n", "(u'Biologic therapy', 25)\n", "(u'THERAPY Biologic', 25)\n", "(u'patients must use', 83)\n", "(u'must use effective', 82)\n", "(u'Fertile patients must', 81)\n", "(u'use effective contraception', 75)\n", "(u'PRIOR CONCURRENT THERAPY', 29)\n", "(u'contraception Fertile patients', 28)\n", "(u'effective contraception Fertile', 26)\n", "(u'CONCURRENT THERAPY Biologic', 25)\n", "(u'THERAPY Biologic therapy', 24)\n", "(u'contraception PRIOR CONCURRENT', 15)\n", "(u'patients must use effective', 82)\n", "(u'Fertile patients must use', 81)\n", "(u'must use effective contraception', 74)\n", "(u'contraception Fertile patients must', 28)\n", "(u'use effective contraception Fertile', 26)\n", "(u'effective contraception Fertile patients', 25)\n", "(u'PRIOR CONCURRENT THERAPY Biologic', 25)\n", "(u'CONCURRENT THERAPY Biologic therapy', 24)\n", "(u'contraception PRIOR CONCURRENT THERAPY', 15)\n", "(u'use effective contraception PRIOR', 15)\n" ] } ], "prompt_number": 75 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Look at full sentences" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def check_sents(text):\n", " for sent in text:\n", " print ' '.join([word[0] for word in sent])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "check_sents(fertile_sents[:10])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fertile patients must use effective contraception PRIOR CONCURRENT THERAPY : Biologic therapy\n", "Fertile patients must use effective contraception PRIOR CONCURRENT THERAPY : Biologic therapy :\n", "Fertile patients must use effective contraception during and for 4 weeks after study participation\n", "Fertile patients must use effective contraception PRIOR CONCURRENT THERAPY : Biologic therapy :\n", "Agreement to use a condom , and with a fertile female partner , another form of contraception .\n", "DISEASE CHARACTERISTICS : Histologically proven epithelial adenocarcinoma of the ovary , fallopian tube , or peritoneum CA 125 greater than 35 U mL No conclusive radiological or clinical evidence of disease No disease recurrence Must have received only 1 prior platinum based chemotherapy regimen No tumors of low malignant potential or noninvasive disease PATIENT CHARACTERISTICS : Age : 18 and over Performance status : ECOG 0-2 Life expectancy : At least 6 months Hematopoietic : Hemoglobin at least 8 . 0 g dL Lymphocyte count at least 1 , 000 mm3 Neutrophil count at least 1 , 500 mm3 Platelet count at least 100 , 000 mm3 Hepatic : Bilirubin no greater than 1 . 5 times normal Renal : Creatinine no greater than 2 mg dL Cardiovascular : No uncontrolled hypertension No congestive heart failure No arrhythmias Other : Not pregnant or nursing Negative pregnancy test Fertile patients must use effective contraception No active autoimmune disease requiring chronic treatment No allergy to murine proteins No documented anaphylactic reaction to any drug No active infection causing fever No immunodeficiency disease No uncontrolled nonmalignant diseases No other malignancy ( except nonmelanomatous skin cancer or carcinoma in situ of the cervix ) unless curatively treated and free of disease for at least 5 years PRIOR CONCURRENT THERAPY : Biologic therapy : No prior murine monoclonal antibodies Chemotherapy : See Disease Characteristics At least 4 weeks since prior platinum based chemotherapy No concurrent chemotherapy Endocrine therapy : Not specified Radiotherapy : At least 6 months since prior limited field ( i . e ., abdominal or pelvic ) radiotherapy No prior whole abdominal radiotherapy Surgery : At least 4 weeks since prior surgery No prior splenectomy Other : At least 4 weeks since prior immunosuppressive drugs No concurrent immunosuppressive drugs At least 30 days since other prior investigational drugs\n", "Patients who are fertile must agree to use an effective method of contraception during participation in the study\n", "Fertile patients must use effective contraception PRIOR CONCURRENT THERAPY : Biologic therapy :\n", "Fertile patients must use effective contraception\n", "Fertile patients must use effective barrier contraception during and for 3 months after study\n" ] } ], "prompt_number": 79 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##chosen categories\n", "* Non-smoker\n", "* Pregnancy\n", "* Birth control\n", "* Illicit drugs/Alcohol abuse\n", "* Congestive Heart Failure\n", "* HIV\n", "* Allergies/hypersensitivity" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

keras-team/keras-io

examples/vision/ipynb/cct.ipynb

1

19791

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "# Compact Convolutional Transformers\n", "\n", "**Author:** [Sayak Paul](https://twitter.com/RisingSayak)<br>\n", "**Date created:** 2021/06/30<br>\n", "**Last modified:** 2021/06/30<br>\n", "**Description:** Compact Convolutional Transformers for efficient image classification." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "As discussed in the [Vision Transformers (ViT)](https://arxiv.org/abs/2010.11929) paper,\n", "a Transformer-based architecture for vision typically requires a larger dataset than\n", "usual, as well as a longer pre-training schedule. [ImageNet-1k](http://imagenet.org/)\n", "(which has about a million images) is considered to fall under the medium-sized data regime with\n", "respect to ViTs. This is primarily because, unlike CNNs, ViTs (or a typical\n", "Transformer-based architecture) do not have well-informed inductive biases (such as\n", "convolutions for processing images). This begs the question: can't we combine the\n", "benefits of convolution and the benefits of Transformers\n", "in a single network architecture? These benefits include parameter-efficiency, and\n", "self-attention to process long-range and global dependencies (interactions between\n", "different regions in an image).\n", "\n", "In [Escaping the Big Data Paradigm with Compact Transformers](https://arxiv.org/abs/2104.05704),\n", "Hassani et al. present an approach for doing exactly this. They proposed the\n", "**Compact Convolutional Transformer** (CCT) architecture. In this example, we will work on an\n", "implementation of CCT and we will see how well it performs on the CIFAR-10 dataset.\n", "\n", "If you are unfamiliar with the concept of self-attention or Transformers, you can read\n", "[this chapter](https://livebook.manning.com/book/deep-learning-with-python-second-edition/chapter-11/r-3/312)\n", "from Fran\u00e7ois Chollet's book *Deep Learning with Python*. This example uses\n", "code snippets from another example,\n", "[Image classification with Vision Transformer](https://keras.io/examples/vision/image_classification_with_vision_transformer/).\n", "\n", "This example requires TensorFlow 2.5 or higher, as well as TensorFlow Addons, which can\n", "be installed using the following command:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "!pip install -U -q tensorflow-addons" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "from tensorflow.keras import layers\n", "from tensorflow import keras\n", "\n", "import matplotlib.pyplot as plt\n", "import tensorflow_addons as tfa\n", "import tensorflow as tf\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Hyperparameters and constants" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "positional_emb = True\n", "conv_layers = 2\n", "projection_dim = 128\n", "\n", "num_heads = 2\n", "transformer_units = [\n", " projection_dim,\n", " projection_dim,\n", "]\n", "transformer_layers = 2\n", "stochastic_depth_rate = 0.1\n", "\n", "learning_rate = 0.001\n", "weight_decay = 0.0001\n", "batch_size = 128\n", "num_epochs = 30\n", "image_size = 32" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Load CIFAR-10 dataset" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "num_classes = 10\n", "input_shape = (32, 32, 3)\n", "\n", "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n", "\n", "y_train = keras.utils.to_categorical(y_train, num_classes)\n", "y_test = keras.utils.to_categorical(y_test, num_classes)\n", "\n", "print(f\"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}\")\n", "print(f\"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## The CCT tokenizer\n", "\n", "The first recipe introduced by the CCT authors is the tokenizer for processing the\n", "images. In a standard ViT, images are organized into uniform *non-overlapping* patches.\n", "This eliminates the boundary-level information present in between different patches. This\n", "is important for a neural network to effectively exploit the locality information. The\n", "figure below presents an illustration of how images are organized into patches.\n", "\n", "\n", "\n", "We already know that convolutions are quite good at exploiting locality information. So,\n", "based on this, the authors introduce an all-convolution mini-network to produce image\n", "patches." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "class CCTTokenizer(layers.Layer):\n", " def __init__(\n", " self,\n", " kernel_size=3,\n", " stride=1,\n", " padding=1,\n", " pooling_kernel_size=3,\n", " pooling_stride=2,\n", " num_conv_layers=conv_layers,\n", " num_output_channels=[64, 128],\n", " positional_emb=positional_emb,\n", " **kwargs,\n", " ):\n", " super(CCTTokenizer, self).__init__(**kwargs)\n", "\n", " # This is our tokenizer.\n", " self.conv_model = keras.Sequential()\n", " for i in range(num_conv_layers):\n", " self.conv_model.add(\n", " layers.Conv2D(\n", " num_output_channels[i],\n", " kernel_size,\n", " stride,\n", " padding=\"valid\",\n", " use_bias=False,\n", " activation=\"relu\",\n", " kernel_initializer=\"he_normal\",\n", " )\n", " )\n", " self.conv_model.add(layers.ZeroPadding2D(padding))\n", " self.conv_model.add(\n", " layers.MaxPool2D(pooling_kernel_size, pooling_stride, \"same\")\n", " )\n", "\n", " self.positional_emb = positional_emb\n", "\n", " def call(self, images):\n", " outputs = self.conv_model(images)\n", " # After passing the images through our mini-network the spatial dimensions\n", " # are flattened to form sequences.\n", " reshaped = tf.reshape(\n", " outputs,\n", " (-1, tf.shape(outputs)[1] * tf.shape(outputs)[2], tf.shape(outputs)[-1]),\n", " )\n", " return reshaped\n", "\n", " def positional_embedding(self, image_size):\n", " # Positional embeddings are optional in CCT. Here, we calculate\n", " # the number of sequences and initialize an `Embedding` layer to\n", " # compute the positional embeddings later.\n", " if self.positional_emb:\n", " dummy_inputs = tf.ones((1, image_size, image_size, 3))\n", " dummy_outputs = self.call(dummy_inputs)\n", " sequence_length = tf.shape(dummy_outputs)[1]\n", " projection_dim = tf.shape(dummy_outputs)[-1]\n", "\n", " embed_layer = layers.Embedding(\n", " input_dim=sequence_length, output_dim=projection_dim\n", " )\n", " return embed_layer, sequence_length\n", " else:\n", " return None\n", "" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Stochastic depth for regularization\n", "\n", "[Stochastic depth](https://arxiv.org/abs/1603.09382) is a regularization technique that\n", "randomly drops a set of layers. During inference, the layers are kept as they are. It is\n", "very much similar to [Dropout](https://jmlr.org/papers/v15/srivastava14a.html) but only\n", "that it operates on a block of layers rather than individual nodes present inside a\n", "layer. In CCT, stochastic depth is used just before the residual blocks of a Transformers\n", "encoder." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "# Referred from: github.com:rwightman/pytorch-image-models.\n", "class StochasticDepth(layers.Layer):\n", " def __init__(self, drop_prop, **kwargs):\n", " super(StochasticDepth, self).__init__(**kwargs)\n", " self.drop_prob = drop_prop\n", "\n", " def call(self, x, training=None):\n", " if training:\n", " keep_prob = 1 - self.drop_prob\n", " shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1)\n", " random_tensor = keep_prob + tf.random.uniform(shape, 0, 1)\n", " random_tensor = tf.floor(random_tensor)\n", " return (x / keep_prob) * random_tensor\n", " return x\n", "" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## MLP for the Transformers encoder" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "def mlp(x, hidden_units, dropout_rate):\n", " for units in hidden_units:\n", " x = layers.Dense(units, activation=tf.nn.gelu)(x)\n", " x = layers.Dropout(dropout_rate)(x)\n", " return x\n", "" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Data augmentation\n", "\n", "In the [original paper](https://arxiv.org/abs/2104.05704), the authors use\n", "[AutoAugment](https://arxiv.org/abs/1805.09501) to induce stronger regularization. For\n", "this example, we will be using the standard geometric augmentations like random cropping\n", "and flipping." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "# Note the rescaling layer. These layers have pre-defined inference behavior.\n", "data_augmentation = keras.Sequential(\n", " [\n", " layers.Rescaling(scale=1.0 / 255),\n", " layers.RandomCrop(image_size, image_size),\n", " layers.RandomFlip(\"horizontal\"),\n", " ],\n", " name=\"data_augmentation\",\n", ")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## The final CCT model\n", "\n", "Another recipe introduced in CCT is attention pooling or sequence pooling. In ViT, only\n", "the feature map corresponding to the class token is pooled and is then used for the\n", "subsequent classification task (or any other downstream task). In CCT, outputs from the\n", "Transformers encoder are weighted and then passed on to the final task-specific layer (in\n", "this example, we do classification)." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "def create_cct_model(\n", " image_size=image_size,\n", " input_shape=input_shape,\n", " num_heads=num_heads,\n", " projection_dim=projection_dim,\n", " transformer_units=transformer_units,\n", "):\n", "\n", " inputs = layers.Input(input_shape)\n", "\n", " # Augment data.\n", " augmented = data_augmentation(inputs)\n", "\n", " # Encode patches.\n", " cct_tokenizer = CCTTokenizer()\n", " encoded_patches = cct_tokenizer(augmented)\n", "\n", " # Apply positional embedding.\n", " if positional_emb:\n", " pos_embed, seq_length = cct_tokenizer.positional_embedding(image_size)\n", " positions = tf.range(start=0, limit=seq_length, delta=1)\n", " position_embeddings = pos_embed(positions)\n", " encoded_patches += position_embeddings\n", "\n", " # Calculate Stochastic Depth probabilities.\n", " dpr = [x for x in np.linspace(0, stochastic_depth_rate, transformer_layers)]\n", "\n", " # Create multiple layers of the Transformer block.\n", " for i in range(transformer_layers):\n", " # Layer normalization 1.\n", " x1 = layers.LayerNormalization(epsilon=1e-5)(encoded_patches)\n", "\n", " # Create a multi-head attention layer.\n", " attention_output = layers.MultiHeadAttention(\n", " num_heads=num_heads, key_dim=projection_dim, dropout=0.1\n", " )(x1, x1)\n", "\n", " # Skip connection 1.\n", " attention_output = StochasticDepth(dpr[i])(attention_output)\n", " x2 = layers.Add()([attention_output, encoded_patches])\n", "\n", " # Layer normalization 2.\n", " x3 = layers.LayerNormalization(epsilon=1e-5)(x2)\n", "\n", " # MLP.\n", " x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)\n", "\n", " # Skip connection 2.\n", " x3 = StochasticDepth(dpr[i])(x3)\n", " encoded_patches = layers.Add()([x3, x2])\n", "\n", " # Apply sequence pooling.\n", " representation = layers.LayerNormalization(epsilon=1e-5)(encoded_patches)\n", " attention_weights = tf.nn.softmax(layers.Dense(1)(representation), axis=1)\n", " weighted_representation = tf.matmul(\n", " attention_weights, representation, transpose_a=True\n", " )\n", " weighted_representation = tf.squeeze(weighted_representation, -2)\n", "\n", " # Classify outputs.\n", " logits = layers.Dense(num_classes)(weighted_representation)\n", " # Create the Keras model.\n", " model = keras.Model(inputs=inputs, outputs=logits)\n", " return model\n", "" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Model training and evaluation" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "def run_experiment(model):\n", " optimizer = tfa.optimizers.AdamW(learning_rate=0.001, weight_decay=0.0001)\n", "\n", " model.compile(\n", " optimizer=optimizer,\n", " loss=keras.losses.CategoricalCrossentropy(\n", " from_logits=True, label_smoothing=0.1\n", " ),\n", " metrics=[\n", " keras.metrics.CategoricalAccuracy(name=\"accuracy\"),\n", " keras.metrics.TopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),\n", " ],\n", " )\n", "\n", " checkpoint_filepath = \"/tmp/checkpoint\"\n", " checkpoint_callback = keras.callbacks.ModelCheckpoint(\n", " checkpoint_filepath,\n", " monitor=\"val_accuracy\",\n", " save_best_only=True,\n", " save_weights_only=True,\n", " )\n", "\n", " history = model.fit(\n", " x=x_train,\n", " y=y_train,\n", " batch_size=batch_size,\n", " epochs=num_epochs,\n", " validation_split=0.1,\n", " callbacks=[checkpoint_callback],\n", " )\n", "\n", " model.load_weights(checkpoint_filepath)\n", " _, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)\n", " print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n", " print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")\n", "\n", " return history\n", "\n", "\n", "cct_model = create_cct_model()\n", "history = run_experiment(cct_model)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "Let's now visualize the training progress of the model." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "plt.plot(history.history[\"loss\"], label=\"train_loss\")\n", "plt.plot(history.history[\"val_loss\"], label=\"val_loss\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Loss\")\n", "plt.title(\"Train and Validation Losses Over Epochs\", fontsize=14)\n", "plt.legend()\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "The CCT model we just trained has just **0.4 million** parameters, and it gets us to\n", "~78% top-1 accuracy within 30 epochs. The plot above shows no signs of overfitting as\n", "well. This means we can train this network for longer (perhaps with a bit more\n", "regularization) and may obtain even better performance. This performance can further be\n", "improved by additional recipes like cosine decay learning rate schedule, other data augmentation\n", "techniques like [AutoAugment](https://arxiv.org/abs/1805.09501),\n", "[MixUp](https://arxiv.org/abs/1710.09412) or\n", "[Cutmix](https://arxiv.org/abs/1905.04899). With these modifications, the authors present\n", "95.1% top-1 accuracy on the CIFAR-10 dataset. The authors also present a number of\n", "experiments to study how the number of convolution blocks, Transformers layers, etc.\n", "affect the final performance of CCTs.\n", "\n", "For a comparison, a ViT model takes about **4.7 million** parameters and **100\n", "epochs** of training to reach a top-1 accuracy of 78.22% on the CIFAR-10 dataset. You can\n", "refer to\n", "[this notebook](https://colab.research.google.com/gist/sayakpaul/1a80d9f582b044354a1a26c5cb3d69e5/image_classification_with_vision_transformer.ipynb)\n", "to know about the experimental setup.\n", "\n", "The authors also demonstrate the performance of Compact Convolutional Transformers on\n", "NLP tasks and they report competitive results there." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "cct", "private_outputs": false, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

dimazest/turing_machine

Turing machine.ipynb

1

24299

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Turing Machine as a Python Generator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a simulator of a Turing machine with a singly-infinite tape. You can run this notebook interactively using [IPython notebook](http://ipython.org/notebook.html). Refer to [this instructions](http://eecs.io/python-environment-for-scientific-computing.html) if you don't have Python or IPython installed." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import logging\n", "\n", "from itertools import islice" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class TuringMachine:\n", " \"\"\"Turing machine with a singly-infinite tape.\n", "\n", " A machine is instantiated with transitions, start, accept and reject states\n", " and a blank symbol. We assume that the input and the tape alphabet can be\n", " deducted from the transitions.\n", "\n", " :param dict transitions: a mapping from (state, symbol) tuples to (state,\n", " symbol, direction) tuple. Note that in theory δ is a transition *function*\n", " (in the sense of mathematical functions), but we expect a mapping, not a\n", " callable. Directions are either 'L' (for left) or 'R' (for right).\n", "\n", " :param start_state: the initial state of the machine.\n", "\n", " :param accpet_state: the accept state.\n", "\n", " :param reject_state: the reject state.\n", "\n", " :blank_symbol: the special symbold that marks the tape cell to be empty.\n", "\n", " We don't really care what the input alphabet Σ and the tape alphabet Γ are\n", " for the purpose of this implementation. For a particular run of the machine,\n", " the tape alphabet is the union of the input, symbols used in transitions and\n", " the blank symbol.\n", "\n", " The input on the tape is not part of a Turing machine, so it's not required\n", " on a Turing machine instantiation. To execute a particular machine use the\n", " .run() instance method.\n", "\n", " \"\"\"\n", "\n", " def __init__(self, transitions, start_state='q0', accept_state='qa', reject_state='qr', blank_symbol=''):\n", " self.blank_symbol = blank_symbol\n", " self.transitions = transitions\n", " self.start_state = start_state\n", " self.reject_state = reject_state\n", "\n", " self.states_to_actions = {\n", " accept_state: 'Accept',\n", " reject_state: 'Reject',\n", " }\n", "\n", " def run(self, input_):\n", " \"\"\"Exectute the Turing machine for a particular input.\n", "\n", " :param input_: the input that is written on the tape. It's can be a list\n", " of strings. Or just a string, in which case each letter is treated as a\n", " symbol.\n", "\n", " Given an input a machine can run forever or stop after a number of\n", " steps. So it would be great if we could write a function that\n", " potentially runs forever and it's up the the caller to decide how many\n", " steps are executed. Actually, we should not even bother with this. On\n", " the other side, ^C is not the best way for a user to tell us to stop.\n", " Instead we give the user control to execute us one step at a time. This\n", " is what Python generators are (partially) for. The yield expression\n", " suspends us and gives controll to the caller until he or she decides to\n", " resume our execution. Have a look to [1] to get familliar with the yield\n", " keyord and generators, and hopefully never, ever write something like::\n", "\n", " result = []\n", " for i in range(len(other_items)):\n", " item = other_items[i]\n", "\n", " result.append(item * 3)\n", "\n", " At each step the generator yields a (action, configuration) tuple.\n", "\n", " The action is either 'Accept', 'Reject' or None. 'Accept' and `Reject`\n", " are self explanatory and signal that the input is either accepted or\n", " rejected the machine stops in these states. None is returned in case the\n", " machine needs to continue running.\n", "\n", " Configuration is a dictionary with the following keys:\n", " - 'state' the current state,\n", " - 'left_hand_side': the symbols on the left hand side of the\n", " current position.\n", " - 'symbol': the current symbol,\n", " - 'right_hand_side': the symbols on the right hand side of the\n", " current position.\n", "\n", " [1] http://www.jeffknupp.com/blog/2013/04/07/improve-your-python-yield-and-generators-explained/\n", "\n", " \"\"\"\n", " state = self.start_state\n", "\n", " # Theory doesn't say how to implement the tape, so we store the symbols\n", " # on the tape the way that is most suitable for us. We get two lists to\n", " # store the symbols from left and right hand sides of the current\n", " # symbol.\n", " #\n", " # Initially, there is the blank symbol on the left. Note, that the\n", " # element at 0 position is the *closest* to the head.\n", " left_hand_side = [self.blank_symbol]\n", " if not input_:\n", " # In case input is empty, pretend that it consists of a blank.\n", " input_ = [self.blank_symbol]\n", " # Consume the right most symbol of the input and make it the current\n", " # symbol, everything else is stored in a list for the right side\n", " # symbols.\n", " symbol = input_[0]\n", " right_hand_side = list(input_[1:])\n", "\n", " while True:\n", " # Share our state with the caller.\n", " # Also give them a chance to control our execution.\n", " action = self.states_to_actions.get(state)\n", " yield (\n", " action,\n", " {\n", " 'state': state,\n", " 'left_hand_side': left_hand_side,\n", " 'symbol': symbol,\n", " 'right_hand_side': right_hand_side,\n", " }\n", " )\n", "\n", " # Check whether we need to stop the execution.\n", " if action is not None:\n", " break\n", "\n", " # Do the transition.\n", " state, symbol, direction = self.transitions.get(\n", " (state, symbol),\n", " (self.reject_state, symbol, 'R'), # All other transitions are to the reject state.\n", " )\n", "\n", " if direction == 'R':\n", " left_hand_side.insert(0, symbol)\n", "\n", " try:\n", " symbol = right_hand_side.pop(0)\n", " except IndexError:\n", " # Pretend that we always have a blank symbol on the right.\n", " symbol = self.blank_symbol\n", "\n", " elif left_hand_side:\n", " # Move to the left only if there is a symbold to move.\n", " right_hand_side.insert(0, symbol)\n", " symbol = left_hand_side.pop(0)\n", "\n", " else:\n", " assert direction in 'LR', 'L (left) and R (right) are the only correct directions to move.'\n", " logging.warning('An attempt to move left from the leftmost cell! The machine stays put.')\n", "\n", " def accepts(self, input_, step_limit=100):\n", " action = list(islice(self.run(input_), step_limit))[-1][0]\n", "\n", " if action is not None:\n", " return action == 'Accept'\n", " else:\n", " logging.warn(\n", " 'The step limit of %s steps is reached!',\n", " step_limit,\n", " )\n", "\n", " def rejects(self, input_, **kwargs):\n", " accepts = self.accepts(input_, **kwargs)\n", "\n", " if accepts is not None:\n", " return not accepts\n", "\n", " def debug(self, input_, step_limit=100, colored=False):\n", " for action, configuration in islice(self.run(input_), step_limit):\n", " print(\n", " '{state:<30} {left}{b}{symbol}{f}{right}'.format(\n", " left=''.join(reversed(configuration['left_hand_side'])),\n", " right=''.join(configuration['right_hand_side']),\n", " b='\\x1b[47;1m' if colored else '[',\n", " f='\\x1b[0m' if colored else ']',\n", " **configuration\n", " )\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# One hash" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "one_hash = TuringMachine(\n", " {\n", " ('q0', '#'): ('saw_#', '#', 'R'),\n", " ('saw_#', ''): ('qa', '', 'R'),\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execution = one_hash.run('#')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(None,\n", " {'left_hand_side': [''], 'right_hand_side': [], 'state': 'q0', 'symbol': '#'})" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "next(execution)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(None,\n", " {'left_hand_side': ['#', ''],\n", " 'right_hand_side': [],\n", " 'state': 'saw_#',\n", " 'symbol': ''})" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "next(execution)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "('Accept',\n", " {'left_hand_side': ['', '#', ''],\n", " 'right_hand_side': [],\n", " 'state': 'qa',\n", " 'symbol': ''})" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "next(execution)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_hash.accepts('#')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert one_hash.accepts('#')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert one_hash.rejects('##')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert one_hash.rejects('')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:The step limit of 1 steps is reached!\n" ] } ], "source": [ "assert one_hash.accepts('#', step_limit=1) is None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Two hashes" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "two_hashes = TuringMachine(\n", " {\n", " ('q0', '#'): ('saw_#', '#', 'R'),\n", " ('saw_#', '#'): ('saw_##', '#', 'R'),\n", " ('saw_##', ''): ('qa', '', 'R'),\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert two_hashes.accepts('##')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert two_hashes.rejects('#')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert two_hashes.rejects('###')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Two same words separated by # (w#w)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "w_hash_w = TuringMachine(\n", " {\n", " ('q0', '#'): ('End', '#', 'R'),\n", " ('End', ''): ('qa', '', 'R'),\n", "\n", " ('q0', '0'): ('FindDelimiter0', 'X', 'R'),\n", " ('FindDelimiter0', '#'): ('Check0', '#', 'R'),\n", " ('Check0', '0'): ('FindLeftmost', 'X', 'L'),\n", "\n", " ('q0', '1'): ('FindDelimiter1', 'X', 'R'),\n", " ('FindDelimiter1', '#'): ('Check1', '#', 'R'),\n", " ('Check1', '1'): ('FindLeftmost', 'X', 'L'),\n", "\n", " ('FindLeftmost', '0'): ('FindLeftmost', '0', 'L'),\n", " ('FindLeftmost', '1'): ('FindLeftmost', '1', 'L'),\n", " ('FindLeftmost', 'X'): ('FindLeftmost', 'X', 'L'),\n", " ('FindLeftmost', '#'): ('FindLeftmost', '#', 'L'),\n", " ('FindLeftmost', ''): ('FindNext', '', 'R'),\n", " \n", " ('FindNext', 'X'): ('FindNext', 'X', 'R'),\n", " ('FindNext', '0'): ('FindDelimiter0', 'X', 'R'),\n", " ('FindNext', '1'): ('FindDelimiter1', 'X', 'R'),\n", " ('FindNext', '#'): ('End', '#', 'R'),\n", " \n", " ('FindDelimiter0', '0'): ('FindDelimiter0', '0', 'R'),\n", " ('FindDelimiter0', '1'): ('FindDelimiter0', '1', 'R'),\n", " ('FindDelimiter1', '0'): ('FindDelimiter1', '0', 'R'),\n", " ('FindDelimiter1', '1'): ('FindDelimiter1', '1', 'R'),\n", " \n", " ('Check0', 'X'): ('Check0', 'X', 'R'),\n", " ('Check1', 'X'): ('Check1', 'X', 'R'),\n", " \n", " ('End', 'X'): ('End', 'X', 'R')\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert w_hash_w.accepts('#')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert w_hash_w.accepts('0#0')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert w_hash_w.accepts('1#1')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert w_hash_w.accepts('0000000000000#0000000000000', step_limit=10000)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert w_hash_w.accepts('1001#1001')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "assert w_hash_w.rejects('10#1')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "assert w_hash_w.rejects('1#01')" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "assert w_hash_w.rejects('1#1#')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "assert w_hash_w.rejects('1##1')" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "q0 \u001b[47;1m1\u001b[0m1#110\n", "FindDelimiter1 X\u001b[47;1m1\u001b[0m#110\n", "FindDelimiter1 X1\u001b[47;1m#\u001b[0m110\n", "Check1 X1#\u001b[47;1m1\u001b[0m10\n", "FindLeftmost X1\u001b[47;1m#\u001b[0mX10\n", "FindLeftmost X\u001b[47;1m1\u001b[0m#X10\n", "FindLeftmost \u001b[47;1mX\u001b[0m1#X10\n", "FindLeftmost \u001b[47;1m\u001b[0mX1#X10\n", "FindNext \u001b[47;1mX\u001b[0m1#X10\n", "FindNext X\u001b[47;1m1\u001b[0m#X10\n", "FindDelimiter1 XX\u001b[47;1m#\u001b[0mX10\n", "Check1 XX#\u001b[47;1mX\u001b[0m10\n", "Check1 XX#X\u001b[47;1m1\u001b[0m0\n", "FindLeftmost XX#\u001b[47;1mX\u001b[0mX0\n", "FindLeftmost XX\u001b[47;1m#\u001b[0mXX0\n", "FindLeftmost X\u001b[47;1mX\u001b[0m#XX0\n", "FindLeftmost \u001b[47;1mX\u001b[0mX#XX0\n", "FindLeftmost \u001b[47;1m\u001b[0mXX#XX0\n", "FindNext \u001b[47;1mX\u001b[0mX#XX0\n", "FindNext X\u001b[47;1mX\u001b[0m#XX0\n", "FindNext XX\u001b[47;1m#\u001b[0mXX0\n", "End XX#\u001b[47;1mX\u001b[0mX0\n", "End XX#X\u001b[47;1mX\u001b[0m0\n", "End XX#XX\u001b[47;1m0\u001b[0m\n", "qr XX#XX0\u001b[47;1m\u001b[0m\n" ] } ], "source": [ "w_hash_w.debug('11#110', colored=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Moving left" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [], "source": [ "move_left = TuringMachine(\n", " {\n", " ('q0', ''): ('q0', '', 'L'),\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execution = move_left.run('')" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert next(execution) == (\n", " None,\n", " {\n", " 'left_hand_side': [''],\n", " 'right_hand_side': [],\n", " 'state': 'q0',\n", " 'symbol': '',\n", " },\n", ")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:An attempt to move left from the leftmost cell! The machine stays put.\n", "WARNING:root:An attempt to move left from the leftmost cell! The machine stays put.\n" ] } ], "source": [ "for _ in range(3):\n", " assert next(execution) == (\n", " None,\n", " {\n", " 'left_hand_side': [],\n", " 'right_hand_side': [''],\n", " 'state': 'q0',\n", " 'symbol': '',\n", " },\n", ")" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <div id=\"disqus_thread\"></div>\n", " <script type=\"text/javascript\">\n", " /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */\n", " var disqus_shortname = 'notebookcomments'; // required: replace example with your forum shortname\n", "\n", " /* * * DON'T EDIT BELOW THIS LINE * * */\n", " (function() {\n", " var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;\n", " dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js';\n", " (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);\n", " })();\n", " </script>\n", " <noscript>Please enable JavaScript to view the <a href=\"http://disqus.com/?ref_noscript\">comments powered by Disqus.</a></noscript>\n", " <a href=\"http://disqus.com\" class=\"dsq-brlink\">comments powered by <span class=\"logo-disqus\">Disqus</span></a>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "\n", "HTML(\n", " \"\"\"\n", " <div id=\"disqus_thread\"></div>\n", " <script type=\"text/javascript\">\n", " /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */\n", " var disqus_shortname = 'notebookcomments'; // required: replace example with your forum shortname\n", "\n", " /* * * DON'T EDIT BELOW THIS LINE * * */\n", " (function() {\n", " var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;\n", " dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js';\n", " (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);\n", " })();\n", " </script>\n", " <noscript>Please enable JavaScript to view the <a href=\"http://disqus.com/?ref_noscript\">comments powered by Disqus.</a></noscript>\n", " <a href=\"http://disqus.com\" class=\"dsq-brlink\">comments powered by <span class=\"logo-disqus\">Disqus</span></a>\n", " \"\"\"\n", ")\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

BBN-Q/QGL

doc/ex4_update_in_place.ipynb

1

809440

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# In-Place Waveform Library Updates\n", "This example notebook shows how one can update pulses data in-place without recompiling.\n", "\n", "© Raytheon BBN Technologies 2020" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the `SAVE_WF_OFFSETS` flag in order that QGL will output a map of the waveform data within the compiled binary waveform library." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from QGL import *\n", "import QGL\n", "import os.path\n", "import pickle\n", "QGL.drivers.APS2Pattern.SAVE_WF_OFFSETS = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the usual channel library with a couple of AWGs." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating engine...\n" ] } ], "source": [ "cl = ChannelLibrary(\":memory:\")\n", "q1 = cl.new_qubit(\"q1\")\n", "aps2_1 = cl.new_APS2(\"BBNAPS1\", address=\"192.168.5.101\") \n", "aps2_2 = cl.new_APS2(\"BBNAPS2\", address=\"192.168.5.102\")\n", "dig_1 = cl.new_X6(\"X6_1\", address=0)\n", "h1 = cl.new_source(\"Holz1\", \"HolzworthHS9000\", \"HS9004A-009-1\", power=-30)\n", "h2 = cl.new_source(\"Holz2\", \"HolzworthHS9000\", \"HS9004A-009-2\", power=-30) \n", "cl.set_control(q1, aps2_1, generator=h1)\n", "cl.set_measure(q1, aps2_2, dig_1.ch(1), generator=h2)\n", "cl.set_master(aps2_1, aps2_1.ch(\"m2\"))\n", "cl[\"q1\"].measure_chan.frequency = 0e6\n", "cl.commit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compile a simple sequence." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compiled 11 sequences.\n", "<module 'QGL.drivers.APS2Pattern' from '/Users/growland/workspace/QGL/QGL/drivers/APS2Pattern.py'>\n", "<module 'QGL.drivers.APS2Pattern' from '/Users/growland/workspace/QGL/QGL/drivers/APS2Pattern.py'>\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cdea11751b284c579f0a1677fe55e9c8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(IntSlider(value=1, description='Segment', max=11, min=1), Figure(animation_duration=50, axes=[A…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mf = RabiAmp(cl[\"q1\"], np.linspace(-1, 1, 11))\n", "plot_pulse_files(mf, time=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open the offsets file (in the same directory as the `.aps2` files, one per AWG slice.)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Waveform(Utheta, 122815, 24)_0x2e0defb474f06986': ([0], 24),\n", " 'Waveform-TA(LOW, 96)_-0xf824eec18a1ee52': ([24], 4),\n", " 'Waveform(Utheta, 122815, 24)_-0x37af64bdef1017bf': ([28], 24),\n", " 'Waveform(Utheta, 122815, 24)_0x17bffe1abeb2c360': ([52], 24),\n", " 'Waveform(Utheta, 122815, 24)_0x30d81103120ed2bd': ([76], 24),\n", " 'Waveform(Utheta, 122815, 24)_0x455274f4ee41ba32': ([100], 24),\n", " 'Waveform(Utheta, 122815, 24)_-0x6218db19881971aa': ([124], 24),\n", " 'Waveform(Utheta, 122815, 24)_-0x274e23c9beffce37': ([148], 24),\n", " 'Waveform(Utheta, 122815, 24)_-0x1b783f0cedf12d43': ([172], 24),\n", " 'Waveform(Utheta, 122815, 24)_-0x51d0ae3b11be45ce': ([196], 24),\n", " 'Waveform(Utheta, 122815, 24)_-0x20b43f5ea9db1e21': ([220], 24)}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "offset_f = os.path.join(os.path.dirname(mf), \"Rabi-BBNAPS1.offsets\")\n", "with open(offset_f, \"rb\") as FID:\n", " offsets = pickle.load(FID)\n", "offsets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's replace every single pulse with a fixed amplitude `Utheta`" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "pulses = {l: Utheta(q1, amp=0.1, phase=0) for l in offsets}\n", "wfm_f = os.path.join(os.path.dirname(mf), \"Rabi-BBNAPS1.aps2\")\n", "QGL.drivers.APS2Pattern.update_wf_library(wfm_f, pulses, offsets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the data in the file has been updated." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<module 'QGL.drivers.APS2Pattern' from '/Users/growland/workspace/QGL/QGL/drivers/APS2Pattern.py'>\n", "<module 'QGL.drivers.APS2Pattern' from '/Users/growland/workspace/QGL/QGL/drivers/APS2Pattern.py'>\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7f942aa5bd3e4dd586750f7ab3b56b3a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(IntSlider(value=1, description='Segment', max=11, min=1), Figure(animation_duration=50, axes=[A…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_pulse_files(mf, time=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Profiling \n", "How long does this take?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compiled 100 sequences.\n", "Compiled 100 sequences.\n", "Compiled 100 sequences.\n", "Compiled 100 sequences.\n", "Compiled 100 sequences.\n", "Compiled 100 sequences.\n", "Compiled 100 sequences.\n", "Compiled 100 sequences.\n", "317 ms ± 6.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit mf = RabiAmp(cl[\"q1\"], np.linspace(-1, 1, 100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getting the offsets is fast, and only needs to be done once" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "61.1 µs ± 638 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "def get_offsets():\n", " offset_f = os.path.join(os.path.dirname(mf), \"Rabi-BBNAPS1.offsets\")\n", " with open(offset_f, \"rb\") as FID:\n", " offsets = pickle.load(FID)\n", " return offsets\n", "%timeit offsets = get_offsets()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "39.3 µs ± 1.17 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%timeit pulses = {l: Utheta(q1, amp=0.1, phase=0) for l in offsets}" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.25 ms ± 19.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "wfm_f = os.path.join(os.path.dirname(mf), \"Rabi-BBNAPS1.aps2\")\n", "%timeit QGL.drivers.APS2Pattern.update_wf_library(wfm_f, pulses, offsets)\n", "# %timeit QGL.drivers.APS2Pattern.update_wf_library(\"/Users/growland/workspace/AWG/Rabi/Rabi-BBNAPS1.aps2\", pulses, offsets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Moral of the story: 300 ms for initial compilation, and roughly 1.3 ms for update_in_place. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": false, "toc_window_display": false }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0490ccadaa934d6d9a6882708736e7ef": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "072cf286cd1d471bb1b8766a79641d91": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "AxisModel", "state": { "label": "Time (ns)", "scale": "IPY_MODEL_8e8ebdb406d4482ab5620aa6a4bff8ac", "side": "bottom", "tick_values": { "type": null, "values": null } } }, "0768a1a3181042fe8da6b4bbce422b65": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "AxisModel", "state": { "label": "Time (ns)", "scale": "IPY_MODEL_3f7099eb85e644d39be802793202d951", "side": "bottom", "tick_values": { "type": null, "values": null } } }, "0c7c37af1acc42ba9126e35a13d87988": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "display_legend": false, "fill_colors": [], "labels": [ "C1" ], "scales": { "x": "IPY_MODEL_f989834e0fd144cd95b580df0aaad5ab", "y": "IPY_MODEL_e8e3f7bb389f4168be4a3f68b16bf18a" }, "selected": [], "x": { "type": "float", "values": [ 0, 1, 2, 3, 4 ] }, "y": { "type": "float", "values": [ 1, 3, 2, 5, 4 ] } } }, "110f58f16ec44108a4310312eeb12166": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_f722402781aa40e5abfa266993ce0fa5", "IPY_MODEL_878486d7bb834d1891aed2a021fad962" ], "layout": "IPY_MODEL_182820199c354cb089f732d726e8b34d" } }, "12f876e9421b40429141e35e1eea7954": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#1f77b4" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS1_ch1" ], "scales": { "x": "IPY_MODEL_8e8ebdb406d4482ab5620aa6a4bff8ac", "y": "IPY_MODEL_a71a67a73d17471581bd9699c6803d86" }, "selected": [], "tooltip": "IPY_MODEL_1561859070ba41c48a54d5e58bddc084", "x": { "type": "float", "values": [ 0, 0.8333333333333334, 0.8333333333333334, 1.6666666666666667, 1.6666666666666667, 2.5, 2.5, 3.3333333333333335, 3.3333333333333335, 4.166666666666667, 4.166666666666667, 5, 5, 5.833333333333333, 5.833333333333333, 6.666666666666667, 6.666666666666667, 7.500000000000001, 7.500000000000001, 8.333333333333334, 8.333333333333334, 9.166666666666666, 9.166666666666666, 10, 10, 10.833333333333334, 10.833333333333334, 11.666666666666666, 11.666666666666666, 12.5, 12.5, 13.333333333333334, 13.333333333333334, 14.166666666666668, 14.166666666666668, 15.000000000000002, 15.000000000000002, 15.833333333333332, 15.833333333333332, 16.666666666666668, 16.666666666666668, 17.5, 17.5, 18.333333333333332, 18.333333333333332, 19.166666666666668, 19.166666666666668, 20, 20, 100, 100, 1100 ] }, "y": { "type": "float", "values": [ -0.04541570015871078, -0.04541570015871078, -0.10438285923574656, -0.10438285923574656, -0.17800024417043095, -0.17800024417043095, -0.2661457697472836, -0.2661457697472836, -0.36747649859602, -0.36747649859602, -0.4786961298986693, -0.4786961298986693, -0.5946770846050543, -0.5946770846050543, -0.7087046758637529, -0.7087046758637529, -0.8132096203149799, -0.8132096203149799, -0.9003784641679893, -0.9003784641679893, -0.9630081797094372, -0.9630081797094372, -0.9957270174581858, -0.9957270174581858, -0.9957270174581858, -0.9957270174581858, -0.9630081797094372, -0.9630081797094372, -0.9003784641679893, -0.9003784641679893, -0.8132096203149799, -0.8132096203149799, -0.7087046758637529, -0.7087046758637529, -0.5946770846050543, -0.5946770846050543, -0.4786961298986693, -0.4786961298986693, -0.36747649859602, -0.36747649859602, -0.2661457697472836, -0.2661457697472836, -0.17800024417043095, -0.17800024417043095, -0.10438285923574656, -0.10438285923574656, -0.04541570015871078, -0.04541570015871078, 0, 0, 0, 0 ] } } }, "1561859070ba41c48a54d5e58bddc084": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "TooltipModel", "state": { "fields": [ "name" ], "labels": [ "Channel" ], "layout": "IPY_MODEL_af69c3cbfaee4e319d2c44b463c2983f" } }, "182820199c354cb089f732d726e8b34d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "19e1aafd7f63482195d9af28dee252e4": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "24682eab19af4dfd8d318250d74bdc96": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntSliderModel", "state": { "description": "Segment", "layout": "IPY_MODEL_31eab17e8111403280522cadf13b3439", "max": 11, "min": 1, "style": "IPY_MODEL_651d6903727244588d4c9903e14b28cb", "value": 11 } }, "2b4e49a2d49b48e3bce000e83485de3a": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#9467bd" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_m1" ], "scales": { "x": "IPY_MODEL_3f7099eb85e644d39be802793202d951", "y": "IPY_MODEL_bb809aa69f6b4de692ddb7c47b2a8a8f" }, "selected": [], "tooltip": "IPY_MODEL_739989c6df8b44ac8e2e610ee68b5e36", "x": { "type": "float", "values": [ 0, 100, 100, 200, 200, 1100 ] }, "y": { "type": "float", "values": [ 8, 8, 9, 9, 8, 8 ] } } }, "2ed12d1557984347b7cb67660a9ebc16": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#ff7f0e" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS1_m2" ], "scales": { "x": "IPY_MODEL_8e8ebdb406d4482ab5620aa6a4bff8ac", "y": "IPY_MODEL_a71a67a73d17471581bd9699c6803d86" }, "selected": [], "tooltip": "IPY_MODEL_1561859070ba41c48a54d5e58bddc084", "x": { "type": "float", "values": [ 0, 100, 100, 1100 ] }, "y": { "type": "float", "values": [ 3, 3, 2, 2 ] } } }, "30d949e7ba5846e89f696eade5e275c3": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "max": 9, "min": -1, "stabilized": false } }, "31750defe61d492594401d2b55a58b40": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntSliderModel", "state": { "description": "Segment", "layout": "IPY_MODEL_f39a17cf33cc46afa2ee166ea8bca784", "max": 11, "min": 1, "style": "IPY_MODEL_9e46c1e4a1aa4731995efd0536d84f82", "value": 1 } }, "31eab17e8111403280522cadf13b3439": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "36d96bdd5b284556a2073db0e65f128f": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "385459ae10754d14b779ad18351d672f": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#2ca02c" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_ch1" ], "scales": { "x": "IPY_MODEL_8e8ebdb406d4482ab5620aa6a4bff8ac", "y": "IPY_MODEL_a71a67a73d17471581bd9699c6803d86" }, "selected": [], "tooltip": "IPY_MODEL_1561859070ba41c48a54d5e58bddc084", "x": { "type": "float", "values": [ 0, 100, 100, 100.83333333333333, 100.83333333333333, 101.66666666666666, 101.66666666666666, 102.5, 102.5, 103.33333333333333, 103.33333333333333, 104.16666666666667, 104.16666666666667, 105, 105, 105.83333333333333, 105.83333333333333, 106.66666666666667, 106.66666666666667, 107.5, 107.5, 108.33333333333334, 108.33333333333334, 109.16666666666667, 109.16666666666667, 110, 110, 110.83333333333334, 110.83333333333334, 111.66666666666667, 111.66666666666667, 112.50000000000001, 112.50000000000001, 113.33333333333334, 113.33333333333334, 114.16666666666666, 114.16666666666666, 115, 115, 115.83333333333333, 115.83333333333333, 116.66666666666667, 116.66666666666667, 117.5, 117.5, 118.33333333333333, 118.33333333333333, 119.16666666666667, 119.16666666666667, 120.00000000000001, 120.00000000000001, 120.83333333333333, 120.83333333333333, 121.66666666666666, 121.66666666666666, 122.49999999999999, 122.49999999999999, 123.33333333333333, 123.33333333333333, 124.16666666666666, 124.16666666666666, 125, 125, 125.83333333333333, 125.83333333333333, 126.66666666666666, 126.66666666666666, 127.5, 127.5, 128.33333333333334, 128.33333333333334, 129.16666666666666, 129.16666666666666, 130, 130, 130.83333333333334, 130.83333333333334, 131.66666666666666, 131.66666666666666, 132.5, 132.5, 133.33333333333334, 133.33333333333334, 134.16666666666666, 134.16666666666666, 135, 135, 135.83333333333334, 135.83333333333334, 136.66666666666669, 136.66666666666669, 137.5, 137.5, 138.33333333333334, 138.33333333333334, 139.16666666666669, 139.16666666666669, 140, 140, 140.83333333333334, 140.83333333333334, 141.66666666666669, 141.66666666666669, 142.5, 142.5, 143.33333333333334, 143.33333333333334, 144.16666666666669, 144.16666666666669, 145.00000000000003, 145.00000000000003, 145.83333333333334, 145.83333333333334, 146.66666666666666, 146.66666666666666, 147.5, 147.5, 148.33333333333331, 148.33333333333331, 149.16666666666666, 149.16666666666666, 150, 150, 150.83333333333331, 150.83333333333331, 151.66666666666666, 151.66666666666666, 152.5, 152.5, 153.33333333333334, 153.33333333333334, 154.16666666666666, 154.16666666666666, 155, 155, 155.83333333333334, 155.83333333333334, 156.66666666666666, 156.66666666666666, 157.5, 157.5, 158.33333333333334, 158.33333333333334, 159.16666666666666, 159.16666666666666, 160, 160, 160.83333333333334, 160.83333333333334, 161.66666666666669, 161.66666666666669, 162.5, 162.5, 163.33333333333334, 163.33333333333334, 164.16666666666669, 164.16666666666669, 165, 165, 165.83333333333334, 165.83333333333334, 166.66666666666669, 166.66666666666669, 167.5, 167.5, 168.33333333333334, 168.33333333333334, 169.16666666666669, 169.16666666666669, 170, 170, 170.83333333333334, 170.83333333333334, 171.66666666666669, 171.66666666666669, 172.50000000000003, 172.50000000000003, 173.33333333333334, 173.33333333333334, 174.16666666666666, 174.16666666666666, 175, 175, 175.83333333333331, 175.83333333333331, 176.66666666666666, 176.66666666666666, 177.5, 177.5, 178.33333333333334, 178.33333333333334, 179.16666666666666, 179.16666666666666, 180, 180, 180.83333333333334, 180.83333333333334, 181.66666666666666, 181.66666666666666, 182.5, 182.5, 183.33333333333334, 183.33333333333334, 184.16666666666666, 184.16666666666666, 185, 185, 185.83333333333334, 185.83333333333334, 186.66666666666666, 186.66666666666666, 187.5, 187.5, 188.33333333333334, 188.33333333333334, 189.16666666666669, 189.16666666666669, 190, 190, 190.83333333333334, 190.83333333333334, 191.66666666666669, 191.66666666666669, 192.5, 192.5, 193.33333333333334, 193.33333333333334, 194.16666666666669, 194.16666666666669, 195, 195, 195.83333333333334, 195.83333333333334, 196.66666666666669, 196.66666666666669, 197.50000000000003, 197.50000000000003, 198.33333333333334, 198.33333333333334, 199.16666666666669, 199.16666666666669, 200, 200, 200.83333333333331, 200.83333333333331, 201.66666666666666, 201.66666666666666, 202.5, 202.5, 203.33333333333331, 203.33333333333331, 204.16666666666666, 204.16666666666666, 205, 205, 205.83333333333334, 205.83333333333334, 206.66666666666666, 206.66666666666666, 207.5, 207.5, 208.33333333333334, 208.33333333333334, 209.16666666666666, 209.16666666666666, 210, 210, 210.83333333333334, 210.83333333333334, 211.66666666666666, 211.66666666666666, 212.5, 212.5, 213.33333333333334, 213.33333333333334, 214.16666666666669, 214.16666666666669, 215, 215, 215.83333333333334, 215.83333333333334, 216.66666666666669, 216.66666666666669, 217.5, 217.5, 218.33333333333334, 218.33333333333334, 219.16666666666669, 219.16666666666669, 220, 220, 220.83333333333334, 220.83333333333334, 221.66666666666669, 221.66666666666669, 222.5, 222.5, 223.33333333333334, 223.33333333333334, 224.16666666666669, 224.16666666666669, 225.00000000000003, 225.00000000000003, 225.83333333333334, 225.83333333333334, 226.66666666666669, 226.66666666666669, 227.5, 227.5, 228.33333333333331, 228.33333333333331, 229.16666666666666, 229.16666666666666, 230, 230, 230.83333333333334, 230.83333333333334, 231.66666666666666, 231.66666666666666, 232.5, 232.5, 233.33333333333334, 233.33333333333334, 234.16666666666666, 234.16666666666666, 235, 235, 235.83333333333334, 235.83333333333334, 236.66666666666666, 236.66666666666666, 237.5, 237.5, 238.33333333333334, 238.33333333333334, 239.16666666666666, 239.16666666666666, 240.00000000000003, 240.00000000000003, 240.83333333333334, 240.83333333333334, 241.66666666666666, 241.66666666666666, 242.5, 242.5, 243.33333333333331, 243.33333333333331, 244.16666666666669, 244.16666666666669, 244.99999999999997, 244.99999999999997, 245.83333333333334, 245.83333333333334, 246.66666666666666, 246.66666666666666, 247.5, 247.5, 248.33333333333331, 248.33333333333331, 249.16666666666669, 249.16666666666669, 250, 250, 250.83333333333334, 250.83333333333334, 251.66666666666666, 251.66666666666666, 252.50000000000003, 252.50000000000003, 253.33333333333331, 253.33333333333331, 254.16666666666669, 254.16666666666669, 255, 255, 255.83333333333334, 255.83333333333334, 256.6666666666667, 256.6666666666667, 257.5, 257.5, 258.3333333333333, 258.3333333333333, 259.1666666666667, 259.1666666666667, 260, 260, 260.83333333333337, 260.83333333333337, 261.6666666666667, 261.6666666666667, 262.5, 262.5, 263.3333333333333, 263.3333333333333, 264.1666666666667, 264.1666666666667, 265, 265, 265.83333333333337, 265.83333333333337, 266.6666666666667, 266.6666666666667, 267.5, 267.5, 268.3333333333333, 268.3333333333333, 269.16666666666663, 269.16666666666663, 270, 270, 270.8333333333333, 270.8333333333333, 271.6666666666667, 271.6666666666667, 272.5, 272.5, 273.33333333333337, 273.33333333333337, 274.16666666666663, 274.16666666666663, 275, 275, 275.8333333333333, 275.8333333333333, 276.6666666666667, 276.6666666666667, 277.5, 277.5, 278.33333333333337, 278.33333333333337, 279.1666666666667, 279.1666666666667, 280, 280, 280.8333333333333, 280.8333333333333, 281.6666666666667, 281.6666666666667, 282.5, 282.5, 283.33333333333337, 283.33333333333337, 284.1666666666667, 284.1666666666667, 285, 285, 285.8333333333333, 285.8333333333333, 286.6666666666667, 286.6666666666667, 287.5, 287.5, 288.33333333333337, 288.33333333333337, 289.1666666666667, 289.1666666666667, 290.00000000000006, 290.00000000000006, 290.8333333333333, 290.8333333333333, 291.6666666666667, 291.6666666666667, 292.5, 292.5, 293.3333333333333, 293.3333333333333, 294.1666666666667, 294.1666666666667, 295, 295, 295.8333333333333, 295.8333333333333, 296.66666666666663, 296.66666666666663, 297.5, 297.5, 298.3333333333333, 298.3333333333333, 299.1666666666667, 299.1666666666667, 300, 300, 300.83333333333337, 300.83333333333337, 301.66666666666663, 301.66666666666663, 302.5, 302.5, 303.3333333333333, 303.3333333333333, 304.1666666666667, 304.1666666666667, 305, 305, 305.83333333333337, 305.83333333333337, 306.6666666666667, 306.6666666666667, 307.5, 307.5, 308.3333333333333, 308.3333333333333, 309.1666666666667, 309.1666666666667, 310, 310, 310.83333333333337, 310.83333333333337, 311.6666666666667, 311.6666666666667, 312.5, 312.5, 313.3333333333333, 313.3333333333333, 314.1666666666667, 314.1666666666667, 315, 315, 315.83333333333337, 315.83333333333337, 316.6666666666667, 316.6666666666667, 317.50000000000006, 317.50000000000006, 318.3333333333333, 318.3333333333333, 319.1666666666667, 319.1666666666667, 320, 320, 320.8333333333333, 320.8333333333333, 321.6666666666667, 321.6666666666667, 322.5, 322.5, 323.33333333333337, 323.33333333333337, 324.16666666666663, 324.16666666666663, 325, 325, 325.8333333333333, 325.8333333333333, 326.6666666666667, 326.6666666666667, 327.5, 327.5, 328.33333333333337, 328.33333333333337, 329.16666666666663, 329.16666666666663, 330, 330, 330.8333333333333, 330.8333333333333, 331.6666666666667, 331.6666666666667, 332.5, 332.5, 333.33333333333337, 333.33333333333337, 334.1666666666667, 334.1666666666667, 335, 335, 335.8333333333333, 335.8333333333333, 336.6666666666667, 336.6666666666667, 337.5, 337.5, 338.33333333333337, 338.33333333333337, 339.1666666666667, 339.1666666666667, 340, 340, 340.8333333333333, 340.8333333333333, 341.6666666666667, 341.6666666666667, 342.5, 342.5, 343.33333333333337, 343.33333333333337, 344.1666666666667, 344.1666666666667, 345.00000000000006, 345.00000000000006, 345.8333333333333, 345.8333333333333, 346.6666666666667, 346.6666666666667, 347.5, 347.5, 348.3333333333333, 348.3333333333333, 349.1666666666667, 349.1666666666667, 350, 350, 350.83333333333337, 350.83333333333337, 351.66666666666663, 351.66666666666663, 352.5, 352.5, 353.3333333333333, 353.3333333333333, 354.1666666666667, 354.1666666666667, 355, 355, 355.83333333333337, 355.83333333333337, 356.6666666666667, 356.6666666666667, 357.5, 357.5, 358.3333333333333, 358.3333333333333, 359.1666666666667, 359.1666666666667, 360, 360, 360.83333333333337, 360.83333333333337, 361.6666666666667, 361.6666666666667, 362.5, 362.5, 363.3333333333333, 363.3333333333333, 364.1666666666667, 364.1666666666667, 365, 365, 365.83333333333337, 365.83333333333337, 366.6666666666667, 366.6666666666667, 367.5, 367.5, 368.3333333333333, 368.3333333333333, 369.1666666666667, 369.1666666666667, 370, 370, 370.83333333333337, 370.83333333333337, 371.6666666666667, 371.6666666666667, 372.50000000000006, 372.50000000000006, 373.3333333333333, 373.3333333333333, 374.16666666666663, 374.16666666666663, 375, 375, 375.8333333333333, 375.8333333333333, 376.6666666666667, 376.6666666666667, 377.5, 377.5, 378.33333333333337, 378.33333333333337, 379.16666666666663, 379.16666666666663, 380, 380, 380.8333333333333, 380.8333333333333, 381.6666666666667, 381.6666666666667, 382.5, 382.5, 383.33333333333337, 383.33333333333337, 384.1666666666667, 384.1666666666667, 385, 385, 385.8333333333333, 385.8333333333333, 386.6666666666667, 386.6666666666667, 387.5, 387.5, 388.33333333333337, 388.33333333333337, 389.1666666666667, 389.1666666666667, 390, 390, 390.8333333333333, 390.8333333333333, 391.6666666666667, 391.6666666666667, 392.5, 392.5, 393.33333333333337, 393.33333333333337, 394.1666666666667, 394.1666666666667, 395.00000000000006, 395.00000000000006, 395.8333333333333, 395.8333333333333, 396.6666666666667, 396.6666666666667, 397.5, 397.5, 398.33333333333337, 398.33333333333337, 399.1666666666667, 399.1666666666667, 400, 400, 400.8333333333333, 400.8333333333333, 401.66666666666663, 401.66666666666663, 402.5, 402.5, 403.3333333333333, 403.3333333333333, 404.1666666666667, 404.1666666666667, 405, 405, 405.83333333333337, 405.83333333333337, 406.66666666666663, 406.66666666666663, 407.5, 407.5, 408.3333333333333, 408.3333333333333, 409.1666666666667, 409.1666666666667, 410, 410, 410.83333333333337, 410.83333333333337, 411.6666666666667, 411.6666666666667, 412.5, 412.5, 413.3333333333333, 413.3333333333333, 414.1666666666667, 414.1666666666667, 415, 415, 415.83333333333337, 415.83333333333337, 416.6666666666667, 416.6666666666667, 417.5, 417.5, 418.3333333333333, 418.3333333333333, 419.1666666666667, 419.1666666666667, 420, 420, 420.83333333333337, 420.83333333333337, 421.6666666666667, 421.6666666666667, 422.50000000000006, 422.50000000000006, 423.3333333333333, 423.3333333333333, 424.1666666666667, 424.1666666666667, 425, 425, 425.83333333333337, 425.83333333333337, 426.6666666666667, 426.6666666666667, 427.5, 427.5, 428.33333333333337, 428.33333333333337, 429.16666666666663, 429.16666666666663, 430, 430, 430.8333333333333, 430.8333333333333, 431.6666666666667, 431.6666666666667, 432.5, 432.5, 433.33333333333337, 433.33333333333337, 434.16666666666663, 434.16666666666663, 435, 435, 435.8333333333333, 435.8333333333333, 436.6666666666667, 436.6666666666667, 437.5, 437.5, 438.33333333333337, 438.33333333333337, 439.1666666666667, 439.1666666666667, 440, 440, 440.8333333333333, 440.8333333333333, 441.6666666666667, 441.6666666666667, 442.5, 442.5, 443.33333333333337, 443.33333333333337, 444.1666666666667, 444.1666666666667, 445, 445, 445.8333333333333, 445.8333333333333, 446.6666666666667, 446.6666666666667, 447.5, 447.5, 448.33333333333337, 448.33333333333337, 449.1666666666667, 449.1666666666667, 450.00000000000006, 450.00000000000006, 450.8333333333333, 450.8333333333333, 451.6666666666667, 451.6666666666667, 452.5, 452.5, 453.33333333333337, 453.33333333333337, 454.1666666666667, 454.1666666666667, 455, 455, 455.83333333333337, 455.83333333333337, 456.66666666666663, 456.66666666666663, 457.5, 457.5, 458.3333333333333, 458.3333333333333, 459.1666666666667, 459.1666666666667, 460, 460, 460.83333333333337, 460.83333333333337, 461.6666666666667, 461.6666666666667, 462.5, 462.5, 463.3333333333333, 463.3333333333333, 464.1666666666667, 464.1666666666667, 465, 465, 465.83333333333337, 465.83333333333337, 466.6666666666667, 466.6666666666667, 467.5, 467.5, 468.3333333333333, 468.3333333333333, 469.1666666666667, 469.1666666666667, 470, 470, 470.83333333333337, 470.83333333333337, 471.6666666666667, 471.6666666666667, 472.50000000000006, 472.50000000000006, 473.3333333333333, 473.3333333333333, 474.1666666666667, 474.1666666666667, 475, 475, 475.83333333333337, 475.83333333333337, 476.6666666666667, 476.6666666666667, 477.5, 477.5, 478.3333333333333, 478.3333333333333, 479.1666666666667, 479.1666666666667, 480.00000000000006, 480.00000000000006, 480.8333333333333, 480.8333333333333, 481.6666666666667, 481.6666666666667, 482.50000000000006, 482.50000000000006, 483.3333333333333, 483.3333333333333, 484.16666666666663, 484.16666666666663, 485, 485, 485.83333333333337, 485.83333333333337, 486.66666666666663, 486.66666666666663, 487.5, 487.5, 488.33333333333337, 488.33333333333337, 489.1666666666667, 489.1666666666667, 489.99999999999994, 489.99999999999994, 490.8333333333333, 490.8333333333333, 491.6666666666667, 491.6666666666667, 492.50000000000006, 492.50000000000006, 493.3333333333333, 493.3333333333333, 494.1666666666667, 494.1666666666667, 495, 495, 495.83333333333337, 495.83333333333337, 496.66666666666663, 496.66666666666663, 497.5, 497.5, 498.33333333333337, 498.33333333333337, 499.16666666666674, 499.16666666666674, 500, 500, 500.8333333333333, 500.8333333333333, 501.6666666666667, 501.6666666666667, 502.50000000000006, 502.50000000000006, 503.3333333333333, 503.3333333333333, 504.1666666666667, 504.1666666666667, 505.00000000000006, 505.00000000000006, 505.83333333333337, 505.83333333333337, 506.66666666666663, 506.66666666666663, 507.5, 507.5, 508.33333333333337, 508.33333333333337, 509.16666666666663, 509.16666666666663, 510, 510, 510.83333333333337, 510.83333333333337, 511.6666666666667, 511.6666666666667, 512.5, 512.5, 513.3333333333334, 513.3333333333334, 514.1666666666666, 514.1666666666666, 515, 515, 515.8333333333333, 515.8333333333333, 516.6666666666666, 516.6666666666666, 517.5, 517.5, 518.3333333333334, 518.3333333333334, 519.1666666666666, 519.1666666666666, 520, 520, 520.8333333333334, 520.8333333333334, 521.6666666666667, 521.6666666666667, 522.5, 522.5, 523.3333333333334, 523.3333333333334, 524.1666666666667, 524.1666666666667, 525, 525, 525.8333333333333, 525.8333333333333, 526.6666666666666, 526.6666666666666, 527.5, 527.5, 528.3333333333334, 528.3333333333334, 529.1666666666666, 529.1666666666666, 530, 530, 530.8333333333334, 530.8333333333334, 531.6666666666667, 531.6666666666667, 532.5, 532.5, 533.3333333333334, 533.3333333333334, 534.1666666666667, 534.1666666666667, 535, 535, 535.8333333333334, 535.8333333333334, 536.6666666666666, 536.6666666666666, 537.5, 537.5, 538.3333333333333, 538.3333333333333, 539.1666666666666, 539.1666666666666, 540, 540, 540.8333333333334, 540.8333333333334, 541.6666666666666, 541.6666666666666, 542.5, 542.5, 543.3333333333334, 543.3333333333334, 544.1666666666667, 544.1666666666667, 545, 545, 545.8333333333334, 545.8333333333334, 546.6666666666667, 546.6666666666667, 547.5, 547.5, 548.3333333333333, 548.3333333333333, 549.1666666666666, 549.1666666666666, 550, 550, 550.8333333333334, 550.8333333333334, 551.6666666666666, 551.6666666666666, 552.5, 552.5, 553.3333333333334, 553.3333333333334, 554.1666666666667, 554.1666666666667, 555, 555, 555.8333333333334, 555.8333333333334, 556.6666666666667, 556.6666666666667, 557.5000000000001, 557.5000000000001, 558.3333333333334, 558.3333333333334, 559.1666666666666, 559.1666666666666, 560, 560, 560.8333333333333, 560.8333333333333, 561.6666666666666, 561.6666666666666, 562.5, 562.5, 563.3333333333334, 563.3333333333334, 564.1666666666666, 564.1666666666666, 565, 565, 565.8333333333334, 565.8333333333334, 566.6666666666667, 566.6666666666667, 567.5, 567.5, 568.3333333333334, 568.3333333333334, 569.1666666666667, 569.1666666666667, 570, 570, 570.8333333333333, 570.8333333333333, 571.6666666666666, 571.6666666666666, 572.5, 572.5, 573.3333333333334, 573.3333333333334, 574.1666666666666, 574.1666666666666, 575, 575, 575.8333333333334, 575.8333333333334, 576.6666666666667, 576.6666666666667, 577.5, 577.5, 578.3333333333334, 578.3333333333334, 579.1666666666667, 579.1666666666667, 580.0000000000001, 580.0000000000001, 580.8333333333333, 580.8333333333333, 581.6666666666666, 581.6666666666666, 582.5, 582.5, 583.3333333333334, 583.3333333333334, 584.1666666666666, 584.1666666666666, 585, 585, 585.8333333333334, 585.8333333333334, 586.6666666666666, 586.6666666666666, 587.5, 587.5, 588.3333333333334, 588.3333333333334, 589.1666666666667, 589.1666666666667, 590, 590, 590.8333333333334, 590.8333333333334, 591.6666666666666, 591.6666666666666, 592.5, 592.5, 593.3333333333333, 593.3333333333333, 594.1666666666666, 594.1666666666666, 595, 595, 595.8333333333334, 595.8333333333334, 596.6666666666666, 596.6666666666666, 597.5, 597.5, 598.3333333333334, 598.3333333333334, 599.1666666666667, 599.1666666666667, 600, 600, 600.8333333333334, 600.8333333333334, 601.6666666666667, 601.6666666666667, 602.5, 602.5, 603.3333333333333, 603.3333333333333, 604.1666666666666, 604.1666666666666, 605, 605, 605.8333333333334, 605.8333333333334, 606.6666666666666, 606.6666666666666, 607.5, 607.5, 608.3333333333334, 608.3333333333334, 609.1666666666667, 609.1666666666667, 610, 610, 610.8333333333334, 610.8333333333334, 611.6666666666667, 611.6666666666667, 612.5000000000001, 612.5000000000001, 613.3333333333334, 613.3333333333334, 614.1666666666666, 614.1666666666666, 615, 615, 615.8333333333333, 615.8333333333333, 616.6666666666666, 616.6666666666666, 617.5, 617.5, 618.3333333333334, 618.3333333333334, 619.1666666666666, 619.1666666666666, 620, 620, 620.8333333333334, 620.8333333333334, 621.6666666666667, 621.6666666666667, 622.5, 622.5, 623.3333333333334, 623.3333333333334, 624.1666666666667, 624.1666666666667, 625, 625, 625.8333333333333, 625.8333333333333, 626.6666666666666, 626.6666666666666, 627.5, 627.5, 628.3333333333334, 628.3333333333334, 629.1666666666666, 629.1666666666666, 630, 630, 630.8333333333334, 630.8333333333334, 631.6666666666667, 631.6666666666667, 632.5, 632.5, 633.3333333333334, 633.3333333333334, 634.1666666666667, 634.1666666666667, 635.0000000000001, 635.0000000000001, 635.8333333333334, 635.8333333333334, 636.6666666666666, 636.6666666666666, 637.5, 637.5, 638.3333333333334, 638.3333333333334, 639.1666666666666, 639.1666666666666, 640, 640, 640.8333333333334, 640.8333333333334, 641.6666666666666, 641.6666666666666, 642.5, 642.5, 643.3333333333334, 643.3333333333334, 644.1666666666667, 644.1666666666667, 645, 645, 645.8333333333334, 645.8333333333334, 646.6666666666667, 646.6666666666667, 647.5, 647.5, 648.3333333333333, 648.3333333333333, 649.1666666666666, 649.1666666666666, 650, 650, 650.8333333333334, 650.8333333333334, 651.6666666666666, 651.6666666666666, 652.5, 652.5, 653.3333333333334, 653.3333333333334, 654.1666666666667, 654.1666666666667, 655, 655, 655.8333333333334, 655.8333333333334, 656.6666666666667, 656.6666666666667, 657.5000000000001, 657.5000000000001, 658.3333333333333, 658.3333333333333, 659.1666666666666, 659.1666666666666, 660, 660, 660.8333333333334, 660.8333333333334, 661.6666666666666, 661.6666666666666, 662.5, 662.5, 663.3333333333334, 663.3333333333334, 664.1666666666667, 664.1666666666667, 665, 665, 665.8333333333334, 665.8333333333334, 666.6666666666667, 666.6666666666667, 667.5, 667.5, 668.3333333333334, 668.3333333333334, 669.1666666666666, 669.1666666666666, 670, 670, 670.8333333333333, 670.8333333333333, 671.6666666666666, 671.6666666666666, 672.5, 672.5, 673.3333333333334, 673.3333333333334, 674.1666666666666, 674.1666666666666, 675, 675, 675.8333333333334, 675.8333333333334, 676.6666666666667, 676.6666666666667, 677.5, 677.5, 678.3333333333334, 678.3333333333334, 679.1666666666667, 679.1666666666667, 680, 680, 680.8333333333333, 680.8333333333333, 681.6666666666666, 681.6666666666666, 682.5, 682.5, 683.3333333333334, 683.3333333333334, 684.1666666666666, 684.1666666666666, 685, 685, 685.8333333333334, 685.8333333333334, 686.6666666666667, 686.6666666666667, 687.5, 687.5, 688.3333333333334, 688.3333333333334, 689.1666666666667, 689.1666666666667, 690.0000000000001, 690.0000000000001, 690.8333333333334, 690.8333333333334, 691.6666666666666, 691.6666666666666, 692.5, 692.5, 693.3333333333334, 693.3333333333334, 694.1666666666666, 694.1666666666666, 695, 695, 695.8333333333334, 695.8333333333334, 696.6666666666666, 696.6666666666666, 697.5, 697.5, 698.3333333333334, 698.3333333333334, 699.1666666666667, 699.1666666666667, 700, 700, 700.8333333333334, 700.8333333333334, 701.6666666666667, 701.6666666666667, 702.5, 702.5, 703.3333333333333, 703.3333333333333, 704.1666666666666, 704.1666666666666, 705, 705, 705.8333333333334, 705.8333333333334, 706.6666666666666, 706.6666666666666, 707.5, 707.5, 708.3333333333334, 708.3333333333334, 709.1666666666667, 709.1666666666667, 710, 710, 710.8333333333334, 710.8333333333334, 711.6666666666667, 711.6666666666667, 712.5000000000001, 712.5000000000001, 713.3333333333334, 713.3333333333334, 714.1666666666666, 714.1666666666666, 715, 715, 715.8333333333334, 715.8333333333334, 716.6666666666666, 716.6666666666666, 717.5, 717.5, 718.3333333333334, 718.3333333333334, 719.1666666666667, 719.1666666666667, 720, 720, 720.8333333333334, 720.8333333333334, 721.6666666666667, 721.6666666666667, 722.5, 722.5, 723.3333333333334, 723.3333333333334, 724.1666666666667, 724.1666666666667, 725, 725, 725.8333333333333, 725.8333333333333, 726.6666666666666, 726.6666666666666, 727.5, 727.5, 728.3333333333334, 728.3333333333334, 729.1666666666666, 729.1666666666666, 730, 730, 730.8333333333334, 730.8333333333334, 731.6666666666667, 731.6666666666667, 732.5, 732.5, 733.3333333333334, 733.3333333333334, 734.1666666666667, 734.1666666666667, 735, 735, 735.8333333333333, 735.8333333333333, 736.6666666666666, 736.6666666666666, 737.5, 737.5, 738.3333333333334, 738.3333333333334, 739.1666666666666, 739.1666666666666, 740, 740, 740.8333333333334, 740.8333333333334, 741.6666666666667, 741.6666666666667, 742.5, 742.5, 743.3333333333334, 743.3333333333334, 744.1666666666667, 744.1666666666667, 745.0000000000001, 745.0000000000001, 745.8333333333334, 745.8333333333334, 746.6666666666666, 746.6666666666666, 747.5, 747.5, 748.3333333333333, 748.3333333333333, 749.1666666666666, 749.1666666666666, 750, 750, 750.8333333333334, 750.8333333333334, 751.6666666666666, 751.6666666666666, 752.5, 752.5, 753.3333333333334, 753.3333333333334, 754.1666666666667, 754.1666666666667, 755, 755, 755.8333333333334, 755.8333333333334, 756.6666666666667, 756.6666666666667, 757.5, 757.5, 758.3333333333333, 758.3333333333333, 759.1666666666666, 759.1666666666666, 760, 760, 760.8333333333334, 760.8333333333334, 761.6666666666666, 761.6666666666666, 762.5, 762.5, 763.3333333333334, 763.3333333333334, 764.1666666666667, 764.1666666666667, 765, 765, 765.8333333333334, 765.8333333333334, 766.6666666666667, 766.6666666666667, 767.5000000000001, 767.5000000000001, 768.3333333333334, 768.3333333333334, 769.1666666666666, 769.1666666666666, 770, 770, 770.8333333333334, 770.8333333333334, 771.6666666666666, 771.6666666666666, 772.5, 772.5, 773.3333333333334, 773.3333333333334, 774.1666666666666, 774.1666666666666, 775, 775, 775.8333333333334, 775.8333333333334, 776.6666666666667, 776.6666666666667, 777.5, 777.5, 778.3333333333334, 778.3333333333334, 779.1666666666667, 779.1666666666667, 780, 780, 780.8333333333333, 780.8333333333333, 781.6666666666666, 781.6666666666666, 782.5, 782.5, 783.3333333333334, 783.3333333333334, 784.1666666666666, 784.1666666666666, 785, 785, 785.8333333333334, 785.8333333333334, 786.6666666666667, 786.6666666666667, 787.5, 787.5, 788.3333333333334, 788.3333333333334, 789.1666666666667, 789.1666666666667, 790.0000000000001, 790.0000000000001, 790.8333333333333, 790.8333333333333, 791.6666666666666, 791.6666666666666, 792.5, 792.5, 793.3333333333334, 793.3333333333334, 794.1666666666666, 794.1666666666666, 795, 795, 795.8333333333334, 795.8333333333334, 796.6666666666667, 796.6666666666667, 797.5, 797.5, 798.3333333333334, 798.3333333333334, 799.1666666666667, 799.1666666666667, 800, 800, 800.8333333333334, 800.8333333333334, 801.6666666666666, 801.6666666666666, 802.5, 802.5, 803.3333333333333, 803.3333333333333, 804.1666666666666, 804.1666666666666, 805, 805, 805.8333333333334, 805.8333333333334, 806.6666666666666, 806.6666666666666, 807.5, 807.5, 808.3333333333334, 808.3333333333334, 809.1666666666667, 809.1666666666667, 810, 810, 810.8333333333334, 810.8333333333334, 811.6666666666667, 811.6666666666667, 812.5, 812.5, 813.3333333333333, 813.3333333333333, 814.1666666666666, 814.1666666666666, 815, 815, 815.8333333333334, 815.8333333333334, 816.6666666666666, 816.6666666666666, 817.5, 817.5, 818.3333333333334, 818.3333333333334, 819.1666666666667, 819.1666666666667, 820, 820, 820.8333333333334, 820.8333333333334, 821.6666666666667, 821.6666666666667, 822.5000000000001, 822.5000000000001, 823.3333333333334, 823.3333333333334, 824.1666666666666, 824.1666666666666, 825, 825, 825.8333333333334, 825.8333333333334, 826.6666666666666, 826.6666666666666, 827.5, 827.5, 828.3333333333334, 828.3333333333334, 829.1666666666666, 829.1666666666666, 830, 830, 830.8333333333334, 830.8333333333334, 831.6666666666667, 831.6666666666667, 832.5, 832.5, 833.3333333333334, 833.3333333333334, 834.1666666666667, 834.1666666666667, 835, 835, 835.8333333333333, 835.8333333333333, 836.6666666666666, 836.6666666666666, 837.5, 837.5, 838.3333333333334, 838.3333333333334, 839.1666666666666, 839.1666666666666, 840, 840, 840.8333333333334, 840.8333333333334, 841.6666666666667, 841.6666666666667, 842.5, 842.5, 843.3333333333334, 843.3333333333334, 844.1666666666667, 844.1666666666667, 845.0000000000001, 845.0000000000001, 845.8333333333334, 845.8333333333334, 846.6666666666666, 846.6666666666666, 847.5, 847.5, 848.3333333333334, 848.3333333333334, 849.1666666666666, 849.1666666666666, 850, 850, 850.8333333333334, 850.8333333333334, 851.6666666666667, 851.6666666666667, 852.5, 852.5, 853.3333333333334, 853.3333333333334, 854.1666666666667, 854.1666666666667, 855, 855, 855.8333333333334, 855.8333333333334, 856.6666666666667, 856.6666666666667, 857.5, 857.5, 858.3333333333333, 858.3333333333333, 859.1666666666666, 859.1666666666666, 860, 860, 860.8333333333334, 860.8333333333334, 861.6666666666666, 861.6666666666666, 862.5, 862.5, 863.3333333333334, 863.3333333333334, 864.1666666666667, 864.1666666666667, 865, 865, 865.8333333333334, 865.8333333333334, 866.6666666666667, 866.6666666666667, 867.5000000000001, 867.5000000000001, 868.3333333333333, 868.3333333333333, 869.1666666666666, 869.1666666666666, 870, 870, 870.8333333333334, 870.8333333333334, 871.6666666666666, 871.6666666666666, 872.5, 872.5, 873.3333333333334, 873.3333333333334, 874.1666666666667, 874.1666666666667, 875, 875, 875.8333333333334, 875.8333333333334, 876.6666666666667, 876.6666666666667, 877.5000000000001, 877.5000000000001, 878.3333333333334, 878.3333333333334, 879.1666666666666, 879.1666666666666, 880, 880, 880.8333333333333, 880.8333333333333, 881.6666666666666, 881.6666666666666, 882.5, 882.5, 883.3333333333334, 883.3333333333334, 884.1666666666666, 884.1666666666666, 885, 885, 885.8333333333334, 885.8333333333334, 886.6666666666667, 886.6666666666667, 887.5, 887.5, 888.3333333333334, 888.3333333333334, 889.1666666666667, 889.1666666666667, 890, 890, 890.8333333333333, 890.8333333333333, 891.6666666666666, 891.6666666666666, 892.5, 892.5, 893.3333333333334, 893.3333333333334, 894.1666666666666, 894.1666666666666, 895, 895, 895.8333333333334, 895.8333333333334, 896.6666666666667, 896.6666666666667, 897.5, 897.5, 898.3333333333334, 898.3333333333334, 899.1666666666667, 899.1666666666667, 900.0000000000001, 900.0000000000001, 900.8333333333334, 900.8333333333334, 901.6666666666666, 901.6666666666666, 902.5, 902.5, 903.3333333333334, 903.3333333333334, 904.1666666666666, 904.1666666666666, 905, 905, 905.8333333333334, 905.8333333333334, 906.6666666666667, 906.6666666666667, 907.5, 907.5, 908.3333333333334, 908.3333333333334, 909.1666666666667, 909.1666666666667, 910, 910, 910.8333333333334, 910.8333333333334, 911.6666666666667, 911.6666666666667, 912.5, 912.5, 913.3333333333333, 913.3333333333333, 914.1666666666666, 914.1666666666666, 915, 915, 915.8333333333334, 915.8333333333334, 916.6666666666666, 916.6666666666666, 917.5, 917.5, 918.3333333333334, 918.3333333333334, 919.1666666666667, 919.1666666666667, 920, 920, 920.8333333333334, 920.8333333333334, 921.6666666666667, 921.6666666666667, 922.5000000000001, 922.5000000000001, 923.3333333333334, 923.3333333333334, 924.1666666666666, 924.1666666666666, 925, 925, 925.8333333333334, 925.8333333333334, 926.6666666666666, 926.6666666666666, 927.5, 927.5, 928.3333333333334, 928.3333333333334, 929.1666666666667, 929.1666666666667, 930, 930, 930.8333333333334, 930.8333333333334, 931.6666666666667, 931.6666666666667, 932.5000000000001, 932.5000000000001, 933.3333333333334, 933.3333333333334, 934.1666666666667, 934.1666666666667, 935, 935, 935.8333333333333, 935.8333333333333, 936.6666666666666, 936.6666666666666, 937.5, 937.5, 938.3333333333334, 938.3333333333334, 939.1666666666666, 939.1666666666666, 940, 940, 940.8333333333334, 940.8333333333334, 941.6666666666667, 941.6666666666667, 942.5, 942.5, 943.3333333333334, 943.3333333333334, 944.1666666666667, 944.1666666666667, 945.0000000000001, 945.0000000000001, 945.8333333333333, 945.8333333333333, 946.6666666666666, 946.6666666666666, 947.5, 947.5, 948.3333333333334, 948.3333333333334, 949.1666666666666, 949.1666666666666, 950, 950, 950.8333333333334, 950.8333333333334, 951.6666666666667, 951.6666666666667, 952.5, 952.5, 953.3333333333334, 953.3333333333334, 954.1666666666667, 954.1666666666667, 955, 955, 955.8333333333335, 955.8333333333335, 956.6666666666666, 956.6666666666666, 957.4999999999999, 957.4999999999999, 958.3333333333334, 958.3333333333334, 959.1666666666666, 959.1666666666666, 960.0000000000001, 960.0000000000001, 960.8333333333334, 960.8333333333334, 961.6666666666666, 961.6666666666666, 962.5000000000001, 962.5000000000001, 963.3333333333334, 963.3333333333334, 964.1666666666666, 964.1666666666666, 965.0000000000001, 965.0000000000001, 965.8333333333334, 965.8333333333334, 966.6666666666666, 966.6666666666666, 967.5, 967.5, 968.3333333333333, 968.3333333333333, 969.1666666666667, 969.1666666666667, 970, 970, 970.8333333333333, 970.8333333333333, 971.6666666666667, 971.6666666666667, 972.5, 972.5, 973.3333333333333, 973.3333333333333, 974.1666666666667, 974.1666666666667, 975, 975, 975.8333333333335, 975.8333333333335, 976.6666666666667, 976.6666666666667, 977.5, 977.5, 978.3333333333334, 978.3333333333334, 979.1666666666666, 979.1666666666666, 979.9999999999999, 979.9999999999999, 980.8333333333334, 980.8333333333334, 981.6666666666666, 981.6666666666666, 982.5000000000001, 982.5000000000001, 983.3333333333334, 983.3333333333334, 984.1666666666666, 984.1666666666666, 985.0000000000001, 985.0000000000001, 985.8333333333334, 985.8333333333334, 986.6666666666666, 986.6666666666666, 987.5000000000001, 987.5000000000001, 988.3333333333334, 988.3333333333334, 989.1666666666666, 989.1666666666666, 990, 990, 990.8333333333333, 990.8333333333333, 991.6666666666667, 991.6666666666667, 992.5, 992.5, 993.3333333333333, 993.3333333333333, 994.1666666666667, 994.1666666666667, 995, 995, 995.8333333333333, 995.8333333333333, 996.6666666666667, 996.6666666666667, 997.5, 997.5, 998.3333333333335, 998.3333333333335, 999.1666666666667, 999.1666666666667, 1000, 1000, 1000.8333333333334, 1000.8333333333334, 1001.6666666666666, 1001.6666666666666, 1002.4999999999999, 1002.4999999999999, 1003.3333333333334, 1003.3333333333334, 1004.1666666666666, 1004.1666666666666, 1005.0000000000001, 1005.0000000000001, 1005.8333333333334, 1005.8333333333334, 1006.6666666666666, 1006.6666666666666, 1007.5000000000001, 1007.5000000000001, 1008.3333333333334, 1008.3333333333334, 1009.1666666666666, 1009.1666666666666, 1010.0000000000001, 1010.0000000000001, 1010.8333333333334, 1010.8333333333334, 1011.6666666666667, 1011.6666666666667, 1012.5, 1012.5, 1013.3333333333333, 1013.3333333333333, 1014.1666666666667, 1014.1666666666667, 1015, 1015, 1015.8333333333333, 1015.8333333333333, 1016.6666666666667, 1016.6666666666667, 1017.5, 1017.5, 1018.3333333333333, 1018.3333333333333, 1019.1666666666667, 1019.1666666666667, 1020, 1020, 1020.8333333333335, 1020.8333333333335, 1021.6666666666667, 1021.6666666666667, 1022.5, 1022.5, 1023.3333333333334, 1023.3333333333334, 1024.1666666666667, 1024.1666666666667, 1025, 1025, 1025.8333333333335, 1025.8333333333335, 1026.6666666666667, 1026.6666666666667, 1027.5000000000002, 1027.5000000000002, 1028.3333333333333, 1028.3333333333333, 1029.1666666666665, 1029.1666666666665, 1030, 1030, 1030.8333333333333, 1030.8333333333333, 1031.6666666666665, 1031.6666666666665, 1032.5, 1032.5, 1033.3333333333333, 1033.3333333333333, 1034.1666666666667, 1034.1666666666667, 1035, 1035, 1035.8333333333333, 1035.8333333333333, 1036.6666666666667, 1036.6666666666667, 1037.5, 1037.5, 1038.3333333333333, 1038.3333333333333, 1039.1666666666667, 1039.1666666666667, 1040, 1040, 1040.8333333333333, 1040.8333333333333, 1041.6666666666667, 1041.6666666666667, 1042.5, 1042.5, 1043.3333333333335, 1043.3333333333335, 1044.1666666666667, 1044.1666666666667, 1045, 1045, 1045.8333333333335, 1045.8333333333335, 1046.6666666666667, 1046.6666666666667, 1047.5, 1047.5, 1048.3333333333335, 1048.3333333333335, 1049.1666666666667, 1049.1666666666667, 1050, 1050, 1050.8333333333333, 1050.8333333333333, 1051.6666666666665, 1051.6666666666665, 1052.5, 1052.5, 1053.3333333333333, 1053.3333333333333, 1054.1666666666665, 1054.1666666666665, 1055, 1055, 1055.8333333333333, 1055.8333333333333, 1056.6666666666667, 1056.6666666666667, 1057.5, 1057.5, 1058.3333333333333, 1058.3333333333333, 1059.1666666666667, 1059.1666666666667, 1060, 1060, 1060.8333333333333, 1060.8333333333333, 1061.6666666666667, 1061.6666666666667, 1062.5, 1062.5, 1063.3333333333335, 1063.3333333333335, 1064.1666666666667, 1064.1666666666667, 1065, 1065, 1065.8333333333335, 1065.8333333333335, 1066.6666666666667, 1066.6666666666667, 1067.5, 1067.5, 1068.3333333333335, 1068.3333333333335, 1069.1666666666667, 1069.1666666666667, 1070, 1070, 1070.8333333333335, 1070.8333333333335, 1071.6666666666667, 1071.6666666666667, 1072.5, 1072.5, 1073.3333333333333, 1073.3333333333333, 1074.1666666666665, 1074.1666666666665, 1075, 1075, 1075.8333333333333, 1075.8333333333333, 1076.6666666666665, 1076.6666666666665, 1077.5, 1077.5, 1078.3333333333333, 1078.3333333333333, 1079.1666666666667, 1079.1666666666667, 1080, 1080, 1080.8333333333333, 1080.8333333333333, 1081.6666666666667, 1081.6666666666667, 1082.5, 1082.5, 1083.3333333333333, 1083.3333333333333, 1084.1666666666667, 1084.1666666666667, 1085, 1085, 1085.8333333333335, 1085.8333333333335, 1086.6666666666667, 1086.6666666666667, 1087.5, 1087.5, 1088.3333333333335, 1088.3333333333335, 1089.1666666666667, 1089.1666666666667, 1090, 1090, 1090.8333333333335, 1090.8333333333335, 1091.6666666666667, 1091.6666666666667, 1092.5000000000002, 1092.5000000000002, 1093.3333333333335, 1093.3333333333335, 1094.1666666666667, 1094.1666666666667, 1095, 1095, 1095.8333333333333, 1095.8333333333333, 1096.6666666666665, 1096.6666666666665, 1097.5, 1097.5, 1098.3333333333333, 1098.3333333333333, 1099.1666666666665, 1099.1666666666665, 1100 ] }, "y": { "type": "float", "values": [ 4, 4, 4.008973259578643, 4.008973259578643, 4.0228769159008335, 4.0228769159008335, 3.9999999164963085, 3.9999999164963085, 3.810601539422748, 3.810601539422748, 3.532352379871206, 3.532352379871206, 3.302043735269807, 3.302043735269807, 3.136194776613066, 3.136194776613066, 3.0345458086595984, 3.0345458086595984, 3.0001220852155135, 3.0001220852155135, 3.034192166109342, 3.034192166109342, 3.134080455785038, 3.134080455785038, 3.2929797322736354, 3.2929797322736354, 3.5000612723153353, 3.5000612723153353, 3.7412128109658083, 3.7412128109658083, 4.000000269144098, 4.000000269144098, 4.258787708980771, 4.258787708980771, 4.499939193855916, 4.499939193855916, 4.7070206483536765, 4.7070206483536765, 4.8659198133590555, 4.8659198133590555, 4.96580797320992, 4.96580797320992, 4.99987791478448, 4.99987791478448, 4.965807827833301, 4.965807827833301, 4.865919532513054, 4.865919532513054, 4.70702025117737, 4.70702025117737, 4.499938707416372, 4.499938707416372, 4.258818764372469, 4.258818764372469, 3.9999997074163773, 3.9999997074163773, 3.7411806703992663, 3.7411806703992663, 3.499999743236711, 3.499999743236711, 3.2928930077878276, 3.2928930077878276, 3.1339744460226218, 3.1339744460226218, 3.034074095460543, 3.034074095460543, 3.000000000000046, 3.000000000000046, 3.0340742529710916, 3.0340742529710916, 3.1339747503096915, 3.1339747503096915, 3.292893438114648, 3.292893438114648, 3.5000002702772766, 3.5000002702772766, 3.7411812582366326, 3.7411812582366326, 4.000000315990399, 4.000000315990399, 4.258819352209832, 4.258819352209832, 4.5000002770341485, 4.5000002770341485, 4.7071070087631774, 4.7071070087631774, 4.866025565680703, 4.866025565680703, 4.96592591059754, 4.96592591059754, 4.999999999999947, 4.999999999999947, 4.965925740970811, 4.965925740970811, 4.866025237986971, 4.866025237986971, 4.707106545334337, 4.707106545334337, 4.499999709451956, 4.499999709451956, 4.2588187191541556, 4.2588187191541556, 3.9999996606028247, 3.9999996606028247, 3.7411806251810704, 3.7411806251810704, 3.4999997026950913, 3.4999997026950913, 3.2928929746857385, 3.2928929746857385, 3.1339744226159154, 3.1339744226159154, 3.034074083344378, 3.034074083344378, 3.0000000000000617, 3.0000000000000617, 3.034074265087316, 3.034074265087316, 3.1339747737164245, 3.1339747737164245, 3.2928934712166784, 3.2928934712166784, 3.5000003108188134, 3.5000003108188134, 3.741181303454946, 3.741181303454946, 4.000000362803838, 4.000000362803838, 4.258819397428027, 4.258819397428027, 4.500000317575669, 4.500000317575669, 4.707107041865266, 4.707107041865266, 4.866025589087409, 4.866025589087409, 4.965925922713704, 4.965925922713704, 4.99999999999993, 4.99999999999993, 4.965925728854586, 4.965925728854586, 4.866025214580237, 4.866025214580237, 4.707106512232306, 4.707106512232306, 4.4999996689104185, 4.4999996689104185, 4.258818673935951, 4.258818673935951, 3.9999996137894995, 3.9999996137894995, 3.7411805799626556, 3.7411805799626556, 3.4999996621535714, 3.4999996621535714, 3.2928929415837307, 3.2928929415837307, 3.133974399209268, 3.133974399209268, 3.0340740712281558, 3.0340740712281558, 3.000000000000079, 3.000000000000079, 3.034074277203513, 3.034074277203513, 3.133974797123102, 3.133974797123102, 3.2928935043188714, 3.2928935043188714, 3.500000351360351, 3.500000351360351, 3.741181348673151, 3.741181348673151, 4.000000409617162, 4.000000409617162, 4.258819442646442, 4.258819442646442, 4.500000358117188, 4.500000358117188, 4.707107074967272, 4.707107074967272, 4.866025612494055, 4.866025612494055, 4.965925934829925, 4.965925934829925, 4.999999999999911, 4.999999999999911, 4.965925716738388, 4.965925716738388, 4.866025191173558, 4.866025191173558, 4.707106479130112, 4.707106479130112, 4.499999628368683, 4.499999628368683, 4.258818628717747, 4.258818628717747, 3.9999995669761748, 3.9999995669761748, 3.7411805347444607, 3.7411805347444607, 3.499999621611855, 3.499999621611855, 3.292892908481725, 3.292892908481725, 3.133974375802622, 3.133974375802622, 3.0340740591119943, 3.0340740591119943, 3.000000000000099, 3.000000000000099, 3.034074289319712, 3.034074289319712, 3.133974820529782, 3.133974820529782, 3.2928935374209054, 3.2928935374209054, 3.5000003919020863, 3.5000003919020863, 3.7411813938913556, 3.7411813938913556, 4.000000456430488, 4.000000456430488, 4.258819487864637, 4.258819487864637, 4.500000398658904, 4.500000398658904, 4.707107108069278, 4.707107108069278, 4.8660256359007, 4.8660256359007, 4.9659259469460855, 4.9659259469460855, 4.999999999999891, 4.999999999999891, 4.965925704622128, 4.965925704622128, 4.866025167766877, 4.866025167766877, 4.707106446028077, 4.707106446028077, 4.499999587827143, 4.499999587827143, 4.258818583499322, 4.258818583499322, 3.9999995201628495, 3.9999995201628495, 3.7411804895262666, 3.7411804895262666, 3.4999995810703375, 3.4999995810703375, 3.29289287537956, 3.29289287537956, 3.1339743523959784, 3.1339743523959784, 3.0340740469958356, 3.0340740469958356, 3.000000000000121, 3.000000000000121, 3.0340743014359726, 3.0340743014359726, 3.1339748439364636, 3.1339748439364636, 3.2928935705229403, 3.2928935705229403, 3.5000004324436262, 3.5000004324436262, 3.7411814391097806, 3.7411814391097806, 4.000000503243813, 4.000000503243813, 4.25881953308283, 4.25881953308283, 4.500000439200421, 4.500000439200421, 4.707107141171441, 4.707107141171441, 4.866025659307456, 4.866025659307456, 4.965925959062243, 4.965925959062243, 4.999999999999868, 4.999999999999868, 4.965925692505926, 4.965925692505926, 4.866025144360081, 4.866025144360081, 4.707106412926041, 4.707106412926041, 4.499999547285603, 4.499999547285603, 4.258818538281116, 4.258818538281116, 3.999999473349297, 3.999999473349297, 3.741180444308073, 3.741180444308073, 3.4999995405288207, 3.4999995405288207, 3.292892842277557, 3.292892842277557, 3.1339743289892232, 3.1339743289892232, 3.0340740348796786, 3.0340740348796786, 3.0000000000001448, 3.0000000000001448, 3.0340743135521757, 3.0340743135521757, 3.133974867343261, 3.133974867343261, 3.2928936036249774, 3.2928936036249774, 3.5000004729851675, 3.5000004729851675, 3.7411814843279867, 3.7411814843279867, 4.000000550057366, 4.000000550057366, 4.258819578301243, 4.258819578301243, 4.500000479741938, 4.500000479741938, 4.707107174273443, 4.707107174273443, 4.866025682714097, 4.866025682714097, 4.965925971178458, 4.965925971178458, 4.999999999999842, 4.999999999999842, 4.965925680389722, 4.965925680389722, 4.866025120953396, 4.866025120953396, 4.707106379823843, 4.707106379823843, 4.499999506744062, 4.499999506744062, 4.25881849306291, 4.25881849306291, 3.999999426535972, 3.999999426535972, 3.7411803990896604, 3.7411803990896604, 3.4999994999873048, 3.4999994999873048, 3.292892809175556, 3.292892809175556, 3.1339743055825835, 3.1339743055825835, 3.034074022763465, 3.034074022763465, 3.0000000000001714, 3.0000000000001714, 3.0340743256683815, 3.0340743256683815, 3.1339748907499465, 3.1339748907499465, 3.2928936367271766, 3.2928936367271766, 3.5000005135269063, 3.5000005135269063, 3.741181529546193, 3.741181529546193, 4.000000596870691, 4.000000596870691, 4.258819623519436, 4.258819623519436, 4.50000052028365, 4.50000052028365, 4.707107207375444, 4.707107207375444, 4.866025706120736, 4.866025706120736, 4.965925983294611, 4.965925983294611, 4.999999999999815, 4.999999999999815, 4.965925668273515, 4.965925668273515, 4.86602509754671, 4.86602509754671, 4.707106346721804, 4.707106346721804, 4.499999466202322, 4.499999466202322, 4.258818447844703, 4.258818447844703, 3.999999379722647, 3.999999379722647, 3.741180353871468, 3.741180353871468, 3.499999459445593, 3.499999459445593, 3.2928927760735562, 3.2928927760735562, 3.133974282175945, 3.133974282175945, 3.0340740106473123, 3.0340740106473123, 3.0000000000002, 3.0000000000002, 3.0340743377846477, 3.0340743377846477, 3.1339749141566338, 3.1339749141566338, 3.2928936698292164, 3.2928936698292164, 3.50000055406845, 3.50000055406845, 3.7411815747646204, 3.7411815747646204, 4.000000643684015, 4.000000643684015, 4.258819668737628, 4.258819668737628, 4.500000560825164, 4.500000560825164, 4.707107240477604, 4.707107240477604, 4.866025729527373, 4.866025729527373, 4.965925995410763, 4.965925995410763, 4.999999999999785, 4.999999999999785, 4.965925656157247, 4.965925656157247, 4.866025074140022, 4.866025074140022, 4.707106313619763, 4.707106313619763, 4.4999994256607785, 4.4999994256607785, 4.258818402626276, 4.258818402626276, 3.9999993329093217, 3.9999993329093217, 3.7411803086530564, 3.7411803086530564, 3.4999994189040793, 3.4999994189040793, 3.2928927429712367, 3.2928927429712367, 3.1339742587691957, 3.1339742587691957, 3.034073998531044, 3.034073998531044, 3.0000000000002305, 3.0000000000002305, 3.034074349900858, 3.034074349900858, 3.1339749375635506, 3.1339749375635506, 3.2928937029314187, 3.2928937029314187, 3.5000005946103876, 3.5000005946103876, 3.7411816199830477, 3.7411816199830477, 4.0000006904975685, 4.0000006904975685, 4.2588197139562585, 4.2588197139562585, 4.500000601366874, 4.500000601366874, 4.707107273579601, 4.707107273579601, 4.8660257529342354, 4.8660257529342354, 4.9659260075269716, 4.9659260075269716, 4.999999999999753, 4.999999999999753, 4.965925644040977, 4.965925644040977, 4.866025050733218, 4.866025050733218, 4.707106280517399, 4.707106280517399, 4.499999385119037, 4.499999385119037, 4.258818357408068, 4.258818357408068, 3.9999992860955422, 3.9999992860955422, 3.741180263434865, 3.741180263434865, 3.499999378362173, 3.499999378362173, 3.29289270986924, 3.29289270986924, 3.1339742353625613, 3.1339742353625613, 3.034073986414896, 3.034073986414896, 3.0000000000002633, 3.0000000000002633, 3.0340743620171873, 3.0340743620171873, 3.1339749609702414, 3.1339749609702414, 3.292893736033462, 3.292893736033462, 3.500000635151933, 3.500000635151933, 3.741181665201256, 3.741181665201256, 4.000000737310893, 4.000000737310893, 4.25881975917445, 4.25881975917445, 4.500000641908386, 4.500000641908386, 4.707107306681919, 4.707107306681919, 4.8660257763408685, 4.8660257763408685, 4.965926019643119, 4.965926019643119, 4.999999999999719, 4.999999999999719, 4.965925631924764, 4.965925631924764, 4.8660250273265255, 4.8660250273265255, 4.707106247415355, 4.707106247415355, 4.499999344577491, 4.499999344577491, 4.25881831218942, 4.25881831218942, 3.999999239282217, 3.999999239282217, 3.7411802182166745, 3.7411802182166745, 3.499999337820662, 3.499999337820662, 3.292892676767245, 3.292892676767245, 3.1339742119557013, 3.1339742119557013, 3.0340739742987495, 3.0340739742987495, 3.0000000000002984, 3.0000000000002984, 3.0340743741334015, 3.0340743741334015, 3.133974984376935, 3.133974984376935, 3.2928937691355062, 3.2928937691355062, 3.5000006756934794, 3.5000006756934794, 3.7411817104194647, 3.7411817104194647, 4.000000784124673, 4.000000784124673, 4.25881980439264, 4.25881980439264, 4.500000682449897, 4.500000682449897, 4.707107339783913, 4.707107339783913, 4.866025799747501, 4.866025799747501, 4.965926031759264, 4.965926031759264, 4.999999999999684, 4.999999999999684, 4.96592561980855, 4.96592561980855, 4.866025003919605, 4.866025003919605, 4.70710621431331, 4.70710621431331, 4.499999304035944, 4.499999304035944, 4.258818266971211, 4.258818266971211, 3.999999192468892, 3.999999192468892, 3.741180172998045, 3.741180172998045, 3.4999992972791514, 3.4999992972791514, 3.2928926436652515, 3.2928926436652515, 3.133974188549071, 3.133974188549071, 3.034073962182605, 3.034073962182605, 3.0000000000003357, 3.0000000000003357, 3.0340743862496176, 3.0340743862496176, 3.1339750077836297, 3.1339750077836297, 3.292893802237874, 3.292893802237874, 3.500000716235027, 3.500000716235027, 3.7411817556376743, 3.7411817556376743, 4.000000830937998, 4.000000830937998, 4.2588198496108305, 4.2588198496108305, 4.500000722991406, 4.500000722991406, 4.707107372885906, 4.707107372885906, 4.86602582315413, 4.86602582315413, 4.965926043875525, 4.965926043875525, 4.999999999999645, 4.999999999999645, 4.965925607692332, 4.965925607692332, 4.8660249805129085, 4.8660249805129085, 4.707106181211263, 4.707106181211263, 4.499999263494002, 4.499999263494002, 4.2588182217530015, 4.2588182217530015, 3.9999991456555666, 3.9999991456555666, 3.7411801277798555, 3.7411801277798555, 3.499999256737642, 3.499999256737642, 3.2928926105632597, 3.2928926105632597, 3.1339741651424426, 3.1339741651424426, 3.034073950066463, 3.034073950066463, 3.000000000000375, 3.000000000000375, 3.034074398365836, 3.034074398365836, 3.1339750311903263, 3.1339750311903263, 3.292893835339922, 3.292893835339922, 3.5000007567765756, 3.5000007567765756, 3.7411818008558844, 3.7411818008558844, 4.000000877751323, 4.000000877751323, 4.25881989482902, 4.25881989482902, 4.500000763533309, 4.500000763533309, 4.707107405987896, 4.707107405987896, 4.866025846560757, 4.866025846560757, 4.965926055991666, 4.965926055991666, 4.999999999999605, 4.999999999999605, 4.965925595575995, 4.965925595575995, 4.866024957106211, 4.866024957106211, 4.707106148109214, 4.707106148109214, 4.499999222952453, 4.499999222952453, 4.258818176534791, 4.258818176534791, 3.999999098842242, 3.999999098842242, 3.7411800825616663, 3.7411800825616663, 3.499999216196134, 3.499999216196134, 3.2928925774609477, 3.2928925774609477, 3.133974141735816, 3.133974141735816, 3.034073937950323, 3.034073937950323, 3.0000000000004166, 3.0000000000004166, 3.0340744104820563, 3.0340744104820563, 3.133975054597025, 3.133975054597025, 3.292893868441971, 3.292893868441971, 3.5000007973181253, 3.5000007973181253, 3.741181846074534, 3.741181846074534, 4.000000924564648, 4.000000924564648, 4.258819940047208, 4.258819940047208, 4.5000008040748165, 4.5000008040748165, 4.707107439089886, 4.707107439089886, 4.86602586996761, 4.86602586996761, 4.965926068107805, 4.965926068107805, 4.999999999999561, 4.999999999999561, 4.965925583459773, 4.965925583459773, 4.866024933699511, 4.866024933699511, 4.707106115007164, 4.707106115007164, 4.499999182410902, 4.499999182410902, 4.25881813131658, 4.25881813131658, 3.999999052028462, 3.999999052028462, 3.741180037343478, 3.741180037343478, 3.499999175654627, 3.499999175654627, 3.292892544358959, 3.292892544358959, 3.133974118329191, 3.133974118329191, 3.034073925834185, 3.034073925834185, 3.0000000000004605, 3.0000000000004605, 3.034074422598279, 3.034074422598279, 3.133975078003953, 3.133975078003953, 3.292893901544022, 3.292893901544022, 3.5000008378596763, 3.5000008378596763, 3.7411818912927455, 3.7411818912927455, 4.000000971377974, 4.000000971377974, 4.258819985265836, 4.258819985265836, 4.500000844616323, 4.500000844616323, 4.707107472191874, 4.707107472191874, 4.866025893374234, 4.866025893374234, 4.965926080223942, 4.965926080223942, 4.999999999999517, 4.999999999999517, 4.965925571343551, 4.965925571343551, 4.8660249102928095, 4.8660249102928095, 4.707106081904792, 4.707106081904792, 4.499999141869351, 4.499999141869351, 4.258818086098368, 4.258818086098368, 3.9999990052151366, 3.9999990052151366, 3.7411799921252897, 3.7411799921252897, 3.499999135113121, 3.499999135113121, 3.2928925112569716, 3.2928925112569716, 3.1339740949225683, 3.1339740949225683, 3.034073913717932, 3.034073913717932, 3.0000000000005063, 3.0000000000005063, 3.0340744347145034, 3.0340744347145034, 3.133975101410656, 3.133975101410656, 3.292893934646074, 3.292893934646074, 3.500000878401622, 3.500000878401622, 3.7411819365109573, 3.7411819365109573, 4.000001018191298, 4.000001018191298, 4.258820030484023, 4.258820030484023, 4.500000885157828, 4.500000885157828, 4.707107505293861, 4.707107505293861, 4.866025916780856, 4.866025916780856, 4.9659260923400765, 4.9659260923400765, 4.99999999999947, 4.99999999999947, 4.965925559227324, 4.965925559227324, 4.866024886886105, 4.866024886886105, 4.707106048802738, 4.707106048802738, 4.4999991013277985, 4.4999991013277985, 4.258818040880157, 4.258818040880157, 3.999998958401812, 3.999998958401812, 3.7411799469071028, 3.7411799469071028, 3.4999990945712227, 3.4999990945712227, 3.292892478154986, 3.292892478154986, 3.133974071515947, 3.133974071515947, 3.034073901601798, 3.034073901601798, 3.0000000000005547, 3.0000000000005547, 3.0340744468308483, 3.0340744468308483, 3.1339751248173604, 3.1339751248173604, 3.292893967748128, 3.292893967748128, 3.5000009189431753, 3.5000009189431753, 3.741181981729169, 3.741181981729169, 4.000001065004623, 4.000001065004623, 4.258820075702211, 4.258820075702211, 4.500000925699332, 4.500000925699332, 4.707107538396167, 4.707107538396167, 4.866025940187476, 4.866025940187476, 4.965926104456209, 4.965926104456209, 4.99999999999942, 4.99999999999942, 4.965925547111096, 4.965925547111096, 4.8660248634794, 4.8660248634794, 4.707106015700684, 4.707106015700684, 4.499999060786245, 4.499999060786245, 4.258817995661505, 4.258817995661505, 3.9999989115884866, 3.9999989115884866, 3.741179901688916, 3.741179901688916, 3.499999054029719, 3.499999054029719, 3.2928924450530017, 3.2928924450530017, 3.133974048109101, 3.133974048109101, 3.0340738894856667, 3.0340738894856667, 3.000000000000605, 3.000000000000605, 3.0340744589470767, 3.0340744589470767, 3.1339751482240668, 3.1339751482240668, 3.2928940008501835, 3.2928940008501835, 3.5000009594847294, 3.5000009594847294, 3.7411820269473823, 3.7411820269473823, 4.000001111818404, 4.000001111818404, 4.258820120920397, 4.258820120920397, 4.500000966240836, 4.500000966240836, 4.707107571498151, 4.707107571498151, 4.866025963594094, 4.866025963594094, 4.965926116572339, 4.965926116572339, 4.9999999999993685, 4.9999999999993685, 4.9659255349948666, 4.9659255349948666, 4.866024840072466, 4.866024840072466, 4.707105982598628, 4.707105982598628, 4.49999902024469, 4.49999902024469, 4.258817950443292, 4.258817950443292, 3.9999988647751614, 3.9999988647751614, 3.7411798564702905, 3.7411798564702905, 3.499999013488216, 3.499999013488216, 3.2928924119510192, 3.2928924119510192, 3.133974024702484, 3.133974024702484, 3.0340738773695373, 3.0340738773695373, 3.0000000000006577, 3.0000000000006577, 3.034074471063308, 3.034074471063308, 3.133975171630775, 3.133975171630775, 3.292894033952562, 3.292894033952562, 3.5000010000262844, 3.5000010000262844, 3.7411820721655955, 3.7411820721655955, 4.000001158631728, 4.000001158631728, 4.258820166138583, 4.258820166138583, 4.5000010067823375, 4.5000010067823375, 4.707107604600132, 4.707107604600132, 4.86602598700071, 4.86602598700071, 4.965926128688586, 4.965926128688586, 4.999999999999315, 4.999999999999315, 4.965925522878635, 4.965925522878635, 4.866024816665757, 4.866024816665757, 4.70710594949657, 4.70710594949657, 4.499998979702741, 4.499998979702741, 4.258817905225078, 4.258817905225078, 3.9999988179618367, 3.9999988179618367, 3.741179811252105, 3.741179811252105, 3.4999989729467145, 3.4999989729467145, 3.292892378849038, 3.292892378849038, 3.1339740012958686, 3.1339740012958686, 3.03407386525341, 3.03407386525341, 3.0000000000007123, 3.0000000000007123, 3.034074483179541, 3.034074483179541, 3.1339751950374852, 3.1339751950374852, 3.2928940670546205, 3.2928940670546205, 3.500001040567841, 3.500001040567841, 3.7411821173838096, 3.7411821173838096, 4.000001205445053, 4.000001205445053, 4.258820211356769, 4.258820211356769, 4.500001047324233, 4.500001047324233, 4.707107637702112, 4.707107637702112, 4.8660260104073245, 4.8660260104073245, 4.9659261408047115, 4.9659261408047115, 4.999999999999259, 4.999999999999259, 4.965925510762283, 4.965925510762283, 4.8660247932590455, 4.8660247932590455, 4.707105916394511, 4.707105916394511, 4.499998939161184, 4.499998939161184, 4.258817860006864, 4.258817860006864, 3.9999987711485114, 3.9999987711485114, 3.7411797660339197, 3.7411797660339197, 3.499998932405214, 3.499998932405214, 3.292892345746737, 3.292892345746737, 3.133973977889255, 3.133973977889255, 3.034073853137285, 3.034073853137285, 3.0000000000007696, 3.0000000000007696, 3.0340744952957763, 3.0340744952957763, 3.133975218444197, 3.133975218444197, 3.2928941001566807, 3.2928941001566807, 3.5000010811093984, 3.5000010811093984, 3.7411821626024637, 3.7411821626024637, 4.000001252258379, 4.000001252258379, 4.258820256574953, 4.258820256574953, 4.500001087865733, 4.500001087865733, 4.707107670804091, 4.707107670804091, 4.8660260338141645, 4.8660260338141645, 4.965926152920836, 4.965926152920836, 4.9999999999992015, 4.9999999999992015, 4.965925498646047, 4.965925498646047, 4.8660247698523325, 4.8660247698523325, 4.707105883292449, 4.707105883292449, 4.4999988986196255, 4.4999988986196255, 4.258817814788649, 4.258817814788649, 3.9999987243347315, 3.9999987243347315, 3.741179720815735, 3.741179720815735, 3.499998891863715, 3.499998891863715, 3.292892312644759, 3.292892312644759, 3.1339739544826437, 3.1339739544826437, 3.034073841021162, 3.034073841021162, 3.0000000000008287, 3.0000000000008287, 3.0340745074120132, 3.0340745074120132, 3.1339752418511386, 3.1339752418511386, 3.2928941332587423, 3.2928941332587423, 3.500001121650957, 3.500001121650957, 3.7411822078206787, 3.7411822078206787, 4.0000012990717035, 4.0000012990717035, 4.258820301793577, 4.258820301793577, 4.500001128407232, 4.500001128407232, 4.707107703906068, 4.707107703906068, 4.866026057220775, 4.866026057220775, 4.965926165036958, 4.965926165036958, 4.999999999999141, 4.999999999999141, 4.965925486529808, 4.965925486529808, 4.866024746445618, 4.866024746445618, 4.707105850190065, 4.707105850190065, 4.499998858078067, 4.499998858078067, 4.258817769570433, 4.258817769570433, 3.9999986775214067, 3.9999986775214067, 3.741179675597551, 3.741179675597551, 3.4999988513222164, 3.4999988513222164, 3.292892279542783, 3.292892279542783, 3.133973931076034, 3.133973931076034, 3.034073828905041, 3.034073828905041, 3.00000000000089, 3.00000000000089, 3.034074519528371, 3.034074519528371, 3.1339752652578543, 3.1339752652578543, 3.2928941663608056, 3.2928941663608056, 3.5000011621925164, 3.5000011621925164, 3.7411822530384553, 3.7411822530384553, 4.000001345885483, 4.000001345885483, 4.25882034701176, 4.25882034701176, 4.500001168948729, 4.500001168948729, 4.7071077370080445, 4.7071077370080445, 4.866026080627156, 4.866026080627156, 4.965926177153195, 4.965926177153195, 4.999999999999078, 4.999999999999078, 4.9659254744135675, 4.9659254744135675, 4.866024723038901, 4.866024723038901, 4.707105817088323, 4.707105817088323, 4.499998817536112, 4.499998817536112, 4.258817724351778, 4.258817724351778, 3.9999986307080815, 3.9999986307080815, 3.7411796303793676, 3.7411796303793676, 3.4999988107807196, 3.4999988107807196, 3.2928922464411294, 3.2928922464411294, 3.133973907669199, 3.133973907669199, 3.0340738167888044, 3.0340738167888044, 3.0000000000009535, 3.0000000000009535, 3.0340745316444946, 3.0340745316444946, 3.1339752886643444, 3.1339752886643444, 3.2928941994631917, 3.2928941994631917, 3.500001202734471, 3.500001202734471, 3.741182298257111, 3.741182298257111, 4.000001392698354, 4.000001392698354, 4.258820392229504, 4.258820392229504, 4.50000120949062, 4.50000120949062, 4.70710777011034, 4.70710777011034, 4.86602610403399, 4.86602610403399, 4.9659261892691955, 4.9659261892691955, 4.999999999999014, 4.999999999999014, 4.965925462297207, 4.965925462297207, 4.8660246996319545, 4.8660246996319545, 4.707105783985936, 4.707105783985936, 4.499998776994945, 4.499998776994945, 4.258817679134, 4.258817679134, 3.999998583895211, 3.999998583895211, 3.741179585160746, 3.741179585160746, 3.4999987702388298, 3.4999987702388298, 3.2928922133388343, 3.2928922133388343, 3.133973884262821, 3.133973884262821, 3.0340738046728055, 3.0340738046728055, 3.000000000001019, 3.000000000001019, 3.034074543760856, 3.034074543760856, 3.133975312071292, 3.133975312071292, 3.2928942325649366, 3.2928942325649366, 3.500001243275639, 3.500001243275639, 3.7411823434757667, 3.7411823434757667, 4.0000014395121335, 4.0000014395121335, 4.258820437448126, 4.258820437448126, 4.500001250031721, 4.500001250031721, 4.707107803211991, 4.707107803211991, 4.866026127440822, 4.866026127440822, 4.965926201385429, 4.965926201385429, 4.999999999998947, 4.999999999998947, 4.96592545018108, 4.96592545018108, 4.866024676225462, 4.866024676225462, 4.707105750884191, 4.707105750884191, 4.499998736452989, 4.499998736452989, 4.258817633915344, 4.258817633915344, 3.9999985370814315, 3.9999985370814315, 3.7411795399430026, 3.7411795399430026, 3.4999987296977286, 3.4999987296977286, 3.2928921802365414, 3.2928921802365414, 3.1339738608559893, 3.1339738608559893, 3.0340737925565735, 3.0340737925565735, 3.000000000001087, 3.000000000001087, 3.034074555876984, 3.034074555876984, 3.1339753354782403, 3.1339753354782403, 3.292894265667326, 3.292894265667326, 3.500001283817596, 3.500001283817596, 3.741182388693545, 3.741182388693545, 4.0000014863250035, 4.0000014863250035, 4.258820482666747, 4.258820482666747, 4.50000129057361, 4.50000129057361, 4.707107836314283, 4.707107836314283, 4.866026150847198, 4.866026150847198, 4.965926213501424, 4.965926213501424, 4.999999999998878, 4.999999999998878, 4.9659254380647155, 4.9659254380647155, 4.866024652818512, 4.866024652818512, 4.7071057177818005, 4.7071057177818005, 4.4999986959118194, 4.4999986959118194, 4.258817588697566, 4.258817588697566, 3.9999984902676515, 3.9999984902676515, 3.7411794947243817, 3.7411794947243817, 3.4999986891558406, 3.4999986891558406, 3.2928921471348924, 3.2928921471348924, 3.1339738374496147, 3.1339738374496147, 3.034073780440343, 3.034073780440343, 3.0000000000011573, 3.0000000000011573, 3.0340745679933496, 3.0340745679933496, 3.1339753588847366, 3.1339753588847366, 3.292894298769074, 3.292894298769074, 3.5000013243595536, 3.5000013243595536, 3.741182433912202, 3.741182433912202, 4.000001533138784, 4.000001533138784, 4.258820527884489, 4.258820527884489, 4.500001331114709, 4.500001331114709, 4.707107869415931, 4.707107869415931, 4.866026174254026, 4.866026174254026, 4.965926225617654, 4.965926225617654, 4.999999999998806, 4.999999999998806, 4.965925425948584, 4.965925425948584, 4.866024629412014, 4.866024629412014, 4.707105684679409, 4.707105684679409, 4.499998655369861, 4.499998655369861, 4.258817543478909, 4.258817543478909, 3.999998443454781, 3.999998443454781, 3.7411794495066397, 3.7411794495066397, 3.4999986486139543, 3.4999986486139543, 3.292892114032602, 3.292892114032602, 3.1339738140427875, 3.1339738140427875, 3.0340737683243506, 3.0340737683243506, 3.0000000000012297, 3.0000000000012297, 3.034074580109717, 3.034074580109717, 3.1339753822916894, 3.1339753822916894, 3.2928943318714663, 3.2928943318714663, 3.500001364900725, 3.500001364900725, 3.7411824791299813, 3.7411824791299813, 4.000001579951654, 4.000001579951654, 4.258820573103109, 4.258820573103109, 4.5000013716565945, 4.5000013716565945, 4.707107902518221, 4.707107902518221, 4.866026197660398, 4.866026197660398, 4.965926237733645, 4.965926237733645, 4.9999999999987335, 4.9999999999987335, 4.965925413832216, 4.965925413832216, 4.866024606005061, 4.866024606005061, 4.707105651577659, 4.707105651577659, 4.499998614828689, 4.499998614828689, 4.2588174982602505, 4.2588174982602505, 3.9999983966410015, 3.9999983966410015, 3.74117940428802, 3.74117940428802, 3.4999986080728562, 3.4999986080728562, 3.2928920809309568, 3.2928920809309568, 3.133973790635962, 3.133973790635962, 3.034073756208125, 3.034073756208125, 3.0000000000013043, 3.0000000000013043, 3.034074592225852, 3.034074592225852, 3.133975405698189, 3.133975405698189, 3.2928943649732174, 3.2928943649732174, 3.500001405442685, 3.500001405442685, 3.74118252434864, 3.74118252434864, 4.0000016267654335, 4.0000016267654335, 4.258820618320851, 4.258820618320851, 4.500001412197692, 4.500001412197692, 4.707107935620509, 4.707107935620509, 4.866026221067223, 4.866026221067223, 4.96592624984987, 4.96592624984987, 4.999999999998658, 4.999999999998658, 4.96592540171608, 4.96592540171608, 4.866024582598105, 4.866024582598105, 4.707105618475263, 4.707105618475263, 4.499998574286729, 4.499998574286729, 4.25881745304247, 4.25881745304247, 3.999998349828131, 3.999998349828131, 3.7411793590694007, 3.7411793590694007, 3.4999985675309717, 3.4999985675309717, 3.2928920478286696, 3.2928920478286696, 3.133973767229593, 3.133973767229593, 3.034073744092136, 3.034073744092136, 3.000000000001381, 3.000000000001381, 3.0340746043422238, 3.0340746043422238, 3.1339754291051456, 3.1339754291051456, 3.292894398075613, 3.292894398075613, 3.5000014459838584, 3.5000014459838584, 3.74118256956642, 3.74118256956642, 4.000001673579214, 4.000001673579214, 4.258820663539469, 4.258820663539469, 4.500001452739576, 4.500001452739576, 4.707107968722152, 4.707107968722152, 4.8660262444735904, 4.8660262444735904, 4.965926261966093, 4.965926261966093, 4.99999999999858, 4.99999999999858, 4.9659253895997075, 4.9659253895997075, 4.866024559191603, 4.866024559191603, 4.70710558537351, 4.70710558537351, 4.499998533744767, 4.499998533744767, 4.258817407823811, 4.258817407823811, 3.999998303014351, 3.999998303014351, 3.741179313851661, 3.741179313851661, 3.4999985269898763, 3.4999985269898763, 3.292892014727027, 3.292892014727027, 3.1339737438227715, 3.1339737438227715, 3.0340737319759143, 3.0340737319759143, 3.0000000000014597, 3.0000000000014597, 3.0340746164583625, 3.0340746164583625, 3.133975452511649, 3.133975452511649, 3.29289443117801, 3.29289443117801, 3.50000148652582, 3.50000148652582, 3.74118261478508, 3.74118261478508, 4.000001720392084, 4.000001720392084, 4.258820708757209, 4.258820708757209, 4.500001493281459, 4.500001493281459, 4.7071080018244364, 4.7071080018244364, 4.866026267880412, 4.866026267880412, 4.9659262740820775, 4.9659262740820775, 4.9999999999985, 4.9999999999985, 4.965925377483332, 4.965925377483332, 4.866024535784644, 4.866024535784644, 4.707105552271113, 4.707105552271113, 4.4999984932035915, 4.4999984932035915, 4.258817362606029, 4.258817362606029, 3.9999982562014806, 3.9999982562014806, 3.7411792686330427, 3.7411792686330427, 3.499998486447994, 3.499998486447994, 3.2928919816247433, 3.2928919816247433, 3.133973720416406, 3.133973720416406, 3.03407371985993, 3.03407371985993, 3.000000000001541, 3.000000000001541, 3.0340746285747384, 3.0340746285747384, 3.13397547591861, 3.13397547591861, 3.2928944642797657, 3.2928944642797657, 3.500001527066996, 3.500001527066996, 3.74118266000374, 3.74118266000374, 4.000001767205863, 4.000001767205863, 4.258820753975827, 4.258820753975827, 4.500001533822553, 4.500001533822553, 4.707108034926077, 4.707108034926077, 4.8660262912872305, 4.8660262912872305, 4.965926286198297, 4.965926286198297, 4.999999999998417, 4.999999999998417, 4.96592536536719, 4.96592536536719, 4.866024512378137, 4.866024512378137, 4.707105519169356, 4.707105519169356, 4.499998452661628, 4.499998452661628, 4.258817317387369, 4.258817317387369, 3.999998209387701, 3.999998209387701, 3.741179223415304, 3.741179223415304, 3.4999984459069005, 3.4999984459069005, 3.2928919485224606, 3.2928919485224606, 3.1339736970095884, 3.1339736970095884, 3.0340737077437128, 3.0340737077437128, 3.000000000001624, 3.000000000001624, 3.0340746406908816, 3.0340746406908816, 3.1339754993255715, 3.1339754993255715, 3.292894497382166, 3.292894497382166, 3.5000015676089604, 3.5000015676089604, 3.741182705221522, 3.741182705221522, 4.000001814018734, 4.000001814018734, 4.258820799194444, 4.258820799194444, 4.5000015743644335, 4.5000015743644335, 4.707108068028359, 4.707108068028359, 4.866026314693593, 4.866026314693593, 4.965926298314278, 4.965926298314278, 4.999999999998334, 4.999999999998334, 4.96592535325081, 4.96592535325081, 4.866024488971174, 4.866024488971174, 4.707105486066955, 4.707105486066955, 4.49999841212045, 4.49999841212045, 4.258817272169587, 4.258817272169587, 3.999998162573921, 3.999998162573921, 3.7411791781966865, 3.7411791781966865, 3.4999984053650204, 3.4999984053650204, 3.2928919154208227, 3.2928919154208227, 3.1339736736032267, 3.1339736736032267, 3.0340736956274976, 3.0340736956274976, 3.0000000000017097, 3.0000000000017097, 3.034074652807262, 3.034074652807262, 3.133975522732081, 3.133975522732081, 3.2928945304839243, 3.2928945304839243, 3.500001608150926, 3.500001608150926, 3.741182750440183, 3.741182750440183, 4.000001860832514, 4.000001860832514, 4.258820844412182, 4.258820844412182, 4.500001614905525, 4.500001614905525, 4.707108101129996, 4.707108101129996, 4.866026338100408, 4.866026338100408, 4.965926310430492, 4.965926310430492, 4.999999999998247, 4.999999999998247, 4.965925341134665, 4.965925341134665, 4.866024465564664, 4.866024465564664, 4.707105452964552, 4.707105452964552, 4.499998371578485, 4.499998371578485, 4.258817226950925, 4.258817226950925, 3.9999981157610507, 3.9999981157610507, 3.741179132978949, 3.741179132978949, 3.4999983648231416, 3.4999983648231416, 3.2928918823185436, 3.2928918823185436, 3.133973650196413, 3.133973650196413, 3.0340736835115196, 3.0340736835115196, 3.0000000000017972, 3.0000000000017972, 3.0340746649236445, 3.0340746649236445, 3.1339755461390473, 3.1339755461390473, 3.292894563586328, 3.292894563586328, 3.500001648692105, 3.500001648692105, 3.7411827956579664, 3.7411827956579664, 4.000001907645384, 4.000001907645384, 4.258820889630798, 4.258820889630798, 4.500001655447404, 4.500001655447404, 4.707108134232274, 4.707108134232274, 4.8660263615067665, 4.8660263615067665, 4.965926322546468, 4.965926322546468, 4.999999999998158, 4.999999999998158, 4.965925329018281, 4.965925329018281, 4.866024442157697, 4.866024442157697, 4.707105419862792, 4.707105419862792, 4.499998331037305, 4.499998331037305, 4.2588171817322635, 4.2588171817322635, 3.999998068947271, 3.999998068947271, 3.741179087760333, 3.741179087760333, 3.4999983242820516, 3.4999983242820516, 3.292891849216909, 3.292891849216909, 3.133973626789601, 3.133973626789601, 3.0340736713953085, 3.0340736713953085, 3.0000000000018874, 3.0000000000018874, 3.034074677039794, 3.034074677039794, 3.1339755695455604, 3.1339755695455604, 3.2928945966880896, 3.2928945966880896, 3.5000016892340726, 3.5000016892340726, 3.7411828408766286, 3.7411828408766286, 4.000001954459164, 4.000001954459164, 4.2588209348485355, 4.2588209348485355, 4.500001695988494, 4.500001695988494, 4.707108167334551, 4.707108167334551, 4.866026384913577, 4.866026384913577, 4.9659263346626785, 4.9659263346626785, 4.999999999998067, 4.999999999998067, 4.965925316902131, 4.965925316902131, 4.866024418750728, 4.866024418750728, 4.707105386760386, 4.707105386760386, 4.499998290495337, 4.499998290495337, 4.258817136514479, 4.258817136514479, 3.9999980221344007, 3.9999980221344007, 3.741179042541718, 3.741179042541718, 3.4999982837401746, 3.4999982837401746, 3.2928918161146323, 3.2928918161146323, 3.133973603383245, 3.133973603383245, 3.034073659279335, 3.034073659279335, 3.0000000000019793, 3.0000000000019793, 3.0340746891561805, 3.0340746891561805, 3.13397559295253, 3.13397559295253, 3.292894629790496, 3.292894629790496, 3.5000017297752537, 3.5000017297752537, 3.741182886094413, 3.741182886094413, 4.000002001272944, 4.000002001272944, 4.25882098006715, 4.25882098006715, 4.500001736530369, 4.500001736530369, 4.707108200436183, 4.707108200436183, 4.866026408319932, 4.866026408319932, 4.965926346778886, 4.965926346778886, 4.999999999997974, 4.999999999997974, 4.965925304785743, 4.965925304785743, 4.866024395344212, 4.866024395344212, 4.707105353658622, 4.707105353658622, 4.499998249953368, 4.499998249953368, 4.258817091295816, 4.258817091295816, 3.9999979753206207, 3.9999979753206207, 3.7411789973239817, 3.7411789973239817, 3.4999982431990864, 3.4999982431990864, 3.2928917830130007, 3.2928917830130007, 3.1339735799764363, 3.1339735799764363, 3.034073647163128, 3.034073647163128, 3.0000000000020735, 3.0000000000020735, 3.034074701272334, 3.034074701272334, 3.133975616359047, 3.133975616359047, 3.292894662892904, 3.292894662892904, 3.5000017703172235, 3.5000017703172235, 3.7411829313130767, 3.7411829313130767, 4.000002048085814, 4.000002048085814, 4.258821025284886, 4.258821025284886, 4.500001777072245, 4.500001777072245, 4.707108233538458, 4.707108233538458, 4.866026431726739, 4.866026431726739, 4.965926358894857, 4.965926358894857, 4.999999999997879, 4.999999999997879, 4.965925292669353, 4.965925292669353, 4.86602437193724, 4.86602437193724, 4.707105320556213, 4.707105320556213, 4.499998209412185, 4.499998209412185, 4.258817046078031, 4.258817046078031, 3.9999979285077507, 3.9999979285077507, 3.7411789521053676, 3.7411789521053676, 3.499998202657212, 3.499998202657212, 3.2928917499107278, 3.2928917499107278, 3.1339735565700844, 3.1339735565700844, 3.034073635047159, 3.034073635047159, 3.00000000000217, 3.00000000000217, 3.0340747133887254, 3.0340747133887254, 3.1339756397660206, 3.1339756397660206, 3.2928946959946703, 3.2928946959946703, 3.500001810858407, 3.500001810858407, 3.7411829765317406, 3.7411829765317406, 4.000002094899594, 4.000002094899594, 4.2588210705035, 4.2588210705035, 4.500001817613331, 4.500001817613331, 4.707108266640087, 4.707108266640087, 4.866026455133545, 4.866026455133545, 4.965926371011061, 4.965926371011061, 4.999999999997781, 4.999999999997781, 4.965925280553196, 4.965925280553196, 4.866024348530719, 4.866024348530719, 4.7071052874544455, 4.7071052874544455, 4.4999981688702135, 4.4999981688702135, 4.258817000859366, 4.258817000859366, 3.9999978816939707, 3.9999978816939707, 3.741178906887632, 3.741178906887632, 3.4999981621161265, 3.4999981621161265, 3.292891716808456, 3.292891716808456, 3.13397353316328, 3.13397353316328, 3.0340736229309564, 3.0340736229309564, 3.0000000000022684, 3.0000000000022684, 3.0340747255048828, 3.0340747255048828, 3.133975663172996, 3.133975663172996, 3.292894729097082, 3.292894729097082, 3.500001851400379, 3.500001851400379, 3.7411830217495265, 3.7411830217495265, 4.000002141712464, 4.000002141712464, 4.258821115722113, 4.258821115722113, 4.500001858155204, 4.500001858155204, 4.707108299742358, 4.707108299742358, 4.866026478539895, 4.866026478539895, 4.965926383127027, 4.965926383127027, 4.999999999997682, 4.999999999997682, 4.965925268436802, 4.965925268436802, 4.8660243251237425, 4.8660243251237425, 4.707105254352034, 4.707105254352034, 4.499998128329029, 4.499998128329029, 4.2588169556415805, 4.2588169556415805, 3.999997834880191, 3.999997834880191, 3.7411788616690194, 3.7411788616690194, 3.499998121574254, 3.499998121574254, 3.292891683706829, 3.292891683706829, 3.133973509756932, 3.133973509756932, 3.034073610814756, 3.034073610814756, 3.000000000002369, 3.000000000002369, 3.034074737621278, 3.034074737621278, 3.1339756865795185, 3.1339756865795185, 3.292894762198851, 3.292894762198851, 3.500001891942352, 3.500001891942352, 3.741183066968192, 3.741183066968192, 4.000002188526245, 4.000002188526245, 4.258821160939847, 4.258821160939847, 4.500001898696288, 4.500001898696288, 4.707108332843983, 4.707108332843983, 4.866026501946696, 4.866026501946696, 4.965926395243226, 4.965926395243226, 4.99999999999758, 4.99999999999758, 4.9659252563206415, 4.9659252563206415, 4.86602430171722, 4.86602430171722, 4.707105221249621, 4.707105221249621, 4.499998087787055, 4.499998087787055, 4.258816910422914, 4.258816910422914, 3.9999977880673208, 3.9999977880673208, 3.7411788164512854, 3.7411788164512854, 3.4999980810323827, 3.4999980810323827, 3.2928916506045605, 3.2928916506045605, 3.133973486350131, 3.133973486350131, 3.034073598698793, 3.034073598698793, 3.0000000000024722, 3.0000000000024722, 3.0340747497376754, 3.0340747497376754, 3.133975709986498, 3.133975709986498, 3.2928947953012653, 3.2928947953012653, 3.5000019324835385, 3.5000019324835385, 3.741183112185979, 3.741183112185979, 4.000002235339115, 4.000002235339115, 4.258821206158459, 4.258821206158459, 4.5000019392381585, 4.5000019392381585, 4.707108365946252, 4.707108365946252, 4.866026525353041, 4.866026525353041, 4.965926407359188, 4.965926407359188, 4.999999999997476, 4.999999999997476, 4.965925244204243, 4.965925244204243, 4.866024278310239, 4.866024278310239, 4.707105188147849, 4.707105188147849, 4.499998047245867, 4.499998047245867, 4.258816865204248, 4.258816865204248, 3.999997741253541, 3.999997741253541, 3.741178771232674, 3.741178771232674, 3.4999980404913003, 3.4999980404913003, 3.292891617502937, 3.292891617502937, 3.1339734629433322, 3.1339734629433322, 3.0340735865825965, 3.0340735865825965, 3.0000000000025775, 3.0000000000025775, 3.0340747618538395, 3.0340747618538395, 3.133975733393024, 3.133975733393024, 3.292894828403038, 3.292894828403038, 3.500001973025514, 3.500001973025514, 3.7411831574046452, 3.7411831574046452, 4.000002282152894, 4.000002282152894, 4.258821251376193, 4.258821251376193, 4.500001979779241, 4.500001979779241, 4.707108399048518, 4.707108399048518, 4.866026548759839, 4.866026548759839, 4.965926419475383, 4.965926419475383, 4.999999999997369, 4.999999999997369, 4.965925232088077, 4.965925232088077, 4.866024254903257, 4.866024254903257, 4.707105155045432, 4.707105155045432, 4.499998006703891, 4.499998006703891, 4.2588168199864604, 4.2588168199864604, 3.9999976944406703, 3.9999976944406703, 3.7411787260140623, 3.7411787260140623, 3.4999979999494313, 3.4999979999494313, 3.292891584400671, 3.292891584400671, 3.13397343953699, 3.13397343953699, 3.0340735744666376, 3.0340735744666376, 3.000000000002685, 3.000000000002685, 3.034074773970241, 3.034074773970241, 3.133975756800007, 3.133975756800007, 3.2928948615054554, 3.2928948615054554, 3.5000020135667027, 3.5000020135667027, 3.741183202622434, 3.741183202622434, 4.000002328966675, 4.000002328966675, 4.258821296594804, 4.258821296594804, 4.5000020203211095, 4.5000020203211095, 4.7071084321501395, 4.7071084321501395, 4.866026572166181, 4.866026572166181, 4.965926431591576, 4.965926431591576, 4.999999999997261, 4.999999999997261, 4.965925219971675, 4.965925219971675, 4.866024231496728, 4.866024231496728, 4.707105121943657, 4.707105121943657, 4.4999979661619145, 4.4999979661619145, 4.258816774767793, 4.258816774767793, 3.999997647626891, 3.999997647626891, 3.74117868079633, 3.74117868079633, 3.4999979594083506, 3.4999979594083506, 3.2928915512990504, 3.2928915512990504, 3.1339734161301944, 3.1339734161301944, 3.0340735623504456, 3.0340735623504456, 3.000000000002794, 3.000000000002794, 3.0340747860864097, 3.0340747860864097, 3.1339757802065376, 3.1339757802065376, 3.292894894607874, 3.292894894607874, 3.5000020541086805, 3.5000020541086805, 3.7411832478411013, 3.7411832478411013, 4.000002375779545, 4.000002375779545, 4.258821341812536, 4.258821341812536, 4.500002060862977, 4.500002060862977, 4.707108465252403, 4.707108465252403, 4.866026595572975, 4.866026595572975, 4.965926443707532, 4.965926443707532, 4.99999999999715, 4.99999999999715, 4.96592520785527, 4.96592520785527, 4.866024208089742, 4.866024208089742, 4.707105088841238, 4.707105088841238, 4.4999979256207245, 4.4999979256207245, 4.258816729550004, 4.258816729550004, 3.9999976008140203, 3.9999976008140203, 3.74117863557772, 3.74117863557772, 3.499997918866484, 3.499997918866484, 3.292891518196788, 3.292891518196788, 3.133973392723856, 3.133973392723856, 3.034073550234491, 3.034073550234491, 3.000000000002906, 3.000000000002906, 3.0340747982028153, 3.0340747982028153, 3.1339758036135246, 3.1339758036135246, 3.2928949277096513, 3.2928949277096513, 3.5000020946498713, 3.5000020946498713, 3.7411832930597693, 3.7411832930597693, 4.000002422593324, 4.000002422593324, 4.258821387031146, 4.258821387031146, 4.500002101404055, 4.500002101404055, 4.707108498354021, 4.707108498354021, 4.866026618979767, 4.866026618979767, 4.965926455823721, 4.965926455823721, 4.999999999997037, 4.999999999997037, 4.965925195739098, 4.965925195739098, 4.866024184683209, 4.866024184683209, 4.70710505573946, 4.70710505573946, 4.499997885078745, 4.499997885078745, 4.258816684331336, 4.258816684331336, 3.9999975540002404, 3.9999975540002404, 3.741178590359989, 3.741178590359989, 3.4999978783254058, 3.4999978783254058, 3.2928914850945272, 3.2928914850945272, 3.1339733693170646, 3.1339733693170646, 3.0340735381183035, 3.0340735381183035, 3.0000000000030203, 3.0000000000030203, 3.034074810318988, 3.034074810318988, 3.133975827020513, 3.133975827020513, 3.2928949608120734, 3.2928949608120734, 3.5000021351918513, 3.5000021351918513, 3.7411833382775592, 3.7411833382775592, 4.000002469406195, 4.000002469406195, 4.258821432249755, 4.258821432249755, 4.50000214194592, 4.50000214194592, 4.707108531456281, 4.707108531456281, 4.866026642386103, 4.866026642386103, 4.965926467939672, 4.965926467939672, 4.999999999996922, 4.999999999996922, 4.96592518362269, 4.96592518362269, 4.866024161276219, 4.866024161276219, 4.707105022637037, 4.707105022637037, 4.499997844537552, 4.499997844537552, 4.258816639113546, 4.258816639113546, 3.999997507186461, 3.999997507186461, 3.7411785451413797, 3.7411785451413797, 3.4999978377835412, 3.4999978377835412, 3.292891451992911, 3.292891451992911, 3.13397334591073, 3.13397334591073, 3.0340735260021177, 3.0340735260021177, 3.000000000003136, 3.000000000003136, 3.034074822435398, 3.034074822435398, 3.133975850427049, 3.133975850427049, 3.292894993913854, 3.292894993913854, 3.500002175733832, 3.500002175733832, 3.7411833834962285, 3.7411833834962285, 4.000002516219975, 4.000002516219975, 4.258821477467485, 4.258821477467485, 4.500002182486997, 4.500002182486997, 4.707108564557896, 4.707108564557896, 4.866026665792892, 4.866026665792892, 4.965926480055857, 4.965926480055857, 4.999999999996804, 4.999999999996804, 4.965925171506514, 4.965925171506514, 4.866024137869682, 4.866024137869682, 4.707018662469598, 4.707018662469598, 4.49993676165593, 4.49993676165593, 4.25878499621524, 4.25878499621524, 3.999997460683641, 3.999997460683641, 3.7412100982022576, 3.7412100982022576, 3.500058840118342, 3.500058840118342, 3.292977746394146, 3.292977746394146, 3.1340790515574763, 3.1340790515574763, 3.0341914392293647, 3.0341914392293647, 3.0001220852187345, 3.0001220852187345, 3.034192759733772, 3.034192759733772, 3.134081602576096, 3.134081602576096, 3.2929813540792727, 3.2929813540792727, 3.500063258612192, 3.500063258612192, 3.7412150263908948, 3.7412150263908948, 4.000002562719937, 4.000002562719937, 4.258789924404725, 4.258789924404725, 4.499758051512304, 4.499758051512304, 4.705295720044372, 4.705295720044372, 4.854819409373181, 4.854819409373181, 4.90342605988624, 4.90342605988624, 4.731778781587124, 4.731778781587124, 4.328185779341063, 4.328185779341063, 4.076547608212793, 4.076547608212793, 4.012690077963085, 4.012690077963085 ] } } }, "3a4cc4ad73e4436f9cb271dae2abec40": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#ff7f0e" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS1_m2" ], "scales": { "x": "IPY_MODEL_3f7099eb85e644d39be802793202d951", "y": "IPY_MODEL_bb809aa69f6b4de692ddb7c47b2a8a8f" }, "selected": [], "tooltip": "IPY_MODEL_739989c6df8b44ac8e2e610ee68b5e36", "x": { "type": "float", "values": [ 0, 100, 100, 1100 ] }, "y": { "type": "float", "values": [ 3, 3, 2, 2 ] } } }, "3f7099eb85e644d39be802793202d951": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "stabilized": false } }, "41194916d55a407aab9dd1bd9ece870f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "45c2edcf6a1b4345967c253305b43987": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": { "min_width": "125px" } }, "5122e806f7fb48a3b1d902506f58363c": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "53dca7485c1a4053b2efd051f21742fd": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "AxisModel", "state": { "label": "Amplitude", "orientation": "vertical", "scale": "IPY_MODEL_30d949e7ba5846e89f696eade5e275c3", "side": "left", "tick_values": { "type": null, "values": null } } }, "5ad23175c43b4d0db10f2e5232f30cb5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": { "min_width": "125px" } }, "651d6903727244588d4c9903e14b28cb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "68a3baf2f96b4169a405aa8ad5b8412c": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#d62728" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_ch2" ], "scales": { "x": "IPY_MODEL_3f7099eb85e644d39be802793202d951", "y": "IPY_MODEL_bb809aa69f6b4de692ddb7c47b2a8a8f" }, "selected": [], "tooltip": "IPY_MODEL_739989c6df8b44ac8e2e610ee68b5e36", "x": { "type": "float", "values": [ 0, 100, 100, 100.83333333333333, 100.83333333333333, 101.66666666666666, 101.66666666666666, 102.5, 102.5, 103.33333333333333, 103.33333333333333, 104.16666666666667, 104.16666666666667, 105, 105, 105.83333333333333, 105.83333333333333, 106.66666666666667, 106.66666666666667, 107.5, 107.5, 108.33333333333334, 108.33333333333334, 109.16666666666667, 109.16666666666667, 110, 110, 110.83333333333334, 110.83333333333334, 111.66666666666667, 111.66666666666667, 112.50000000000001, 112.50000000000001, 113.33333333333334, 113.33333333333334, 114.16666666666666, 114.16666666666666, 115, 115, 115.83333333333333, 115.83333333333333, 116.66666666666667, 116.66666666666667, 117.5, 117.5, 118.33333333333333, 118.33333333333333, 119.16666666666667, 119.16666666666667, 120.00000000000001, 120.00000000000001, 120.83333333333333, 120.83333333333333, 121.66666666666666, 121.66666666666666, 122.49999999999999, 122.49999999999999, 123.33333333333333, 123.33333333333333, 124.16666666666666, 124.16666666666666, 125, 125, 125.83333333333333, 125.83333333333333, 126.66666666666666, 126.66666666666666, 127.5, 127.5, 128.33333333333334, 128.33333333333334, 129.16666666666666, 129.16666666666666, 130, 130, 130.83333333333334, 130.83333333333334, 131.66666666666666, 131.66666666666666, 132.5, 132.5, 133.33333333333334, 133.33333333333334, 134.16666666666666, 134.16666666666666, 135, 135, 135.83333333333334, 135.83333333333334, 136.66666666666669, 136.66666666666669, 137.5, 137.5, 138.33333333333334, 138.33333333333334, 139.16666666666669, 139.16666666666669, 140, 140, 140.83333333333334, 140.83333333333334, 141.66666666666669, 141.66666666666669, 142.5, 142.5, 143.33333333333334, 143.33333333333334, 144.16666666666669, 144.16666666666669, 145.00000000000003, 145.00000000000003, 145.83333333333334, 145.83333333333334, 146.66666666666666, 146.66666666666666, 147.5, 147.5, 148.33333333333331, 148.33333333333331, 149.16666666666666, 149.16666666666666, 150, 150, 150.83333333333331, 150.83333333333331, 151.66666666666666, 151.66666666666666, 152.5, 152.5, 153.33333333333334, 153.33333333333334, 154.16666666666666, 154.16666666666666, 155, 155, 155.83333333333334, 155.83333333333334, 156.66666666666666, 156.66666666666666, 157.5, 157.5, 158.33333333333334, 158.33333333333334, 159.16666666666666, 159.16666666666666, 160, 160, 160.83333333333334, 160.83333333333334, 161.66666666666669, 161.66666666666669, 162.5, 162.5, 163.33333333333334, 163.33333333333334, 164.16666666666669, 164.16666666666669, 165, 165, 165.83333333333334, 165.83333333333334, 166.66666666666669, 166.66666666666669, 167.5, 167.5, 168.33333333333334, 168.33333333333334, 169.16666666666669, 169.16666666666669, 170, 170, 170.83333333333334, 170.83333333333334, 171.66666666666669, 171.66666666666669, 172.50000000000003, 172.50000000000003, 173.33333333333334, 173.33333333333334, 174.16666666666666, 174.16666666666666, 175, 175, 175.83333333333331, 175.83333333333331, 176.66666666666666, 176.66666666666666, 177.5, 177.5, 178.33333333333334, 178.33333333333334, 179.16666666666666, 179.16666666666666, 180, 180, 180.83333333333334, 180.83333333333334, 181.66666666666666, 181.66666666666666, 182.5, 182.5, 183.33333333333334, 183.33333333333334, 184.16666666666666, 184.16666666666666, 185, 185, 185.83333333333334, 185.83333333333334, 186.66666666666666, 186.66666666666666, 187.5, 187.5, 188.33333333333334, 188.33333333333334, 189.16666666666669, 189.16666666666669, 190, 190, 190.83333333333334, 190.83333333333334, 191.66666666666669, 191.66666666666669, 192.5, 192.5, 193.33333333333334, 193.33333333333334, 194.16666666666669, 194.16666666666669, 195, 195, 195.83333333333334, 195.83333333333334, 196.66666666666669, 196.66666666666669, 197.50000000000003, 197.50000000000003, 198.33333333333334, 198.33333333333334, 199.16666666666669, 199.16666666666669, 200, 200, 200.83333333333331, 200.83333333333331, 201.66666666666666, 201.66666666666666, 202.5, 202.5, 203.33333333333331, 203.33333333333331, 204.16666666666666, 204.16666666666666, 205, 205, 205.83333333333334, 205.83333333333334, 206.66666666666666, 206.66666666666666, 207.5, 207.5, 208.33333333333334, 208.33333333333334, 209.16666666666666, 209.16666666666666, 210, 210, 210.83333333333334, 210.83333333333334, 211.66666666666666, 211.66666666666666, 212.5, 212.5, 213.33333333333334, 213.33333333333334, 214.16666666666669, 214.16666666666669, 215, 215, 215.83333333333334, 215.83333333333334, 216.66666666666669, 216.66666666666669, 217.5, 217.5, 218.33333333333334, 218.33333333333334, 219.16666666666669, 219.16666666666669, 220, 220, 220.83333333333334, 220.83333333333334, 221.66666666666669, 221.66666666666669, 222.5, 222.5, 223.33333333333334, 223.33333333333334, 224.16666666666669, 224.16666666666669, 225.00000000000003, 225.00000000000003, 225.83333333333334, 225.83333333333334, 226.66666666666669, 226.66666666666669, 227.5, 227.5, 228.33333333333331, 228.33333333333331, 229.16666666666666, 229.16666666666666, 230, 230, 230.83333333333334, 230.83333333333334, 231.66666666666666, 231.66666666666666, 232.5, 232.5, 233.33333333333334, 233.33333333333334, 234.16666666666666, 234.16666666666666, 235, 235, 235.83333333333334, 235.83333333333334, 236.66666666666666, 236.66666666666666, 237.5, 237.5, 238.33333333333334, 238.33333333333334, 239.16666666666666, 239.16666666666666, 240.00000000000003, 240.00000000000003, 240.83333333333334, 240.83333333333334, 241.66666666666666, 241.66666666666666, 242.5, 242.5, 243.33333333333331, 243.33333333333331, 244.16666666666669, 244.16666666666669, 244.99999999999997, 244.99999999999997, 245.83333333333334, 245.83333333333334, 246.66666666666666, 246.66666666666666, 247.5, 247.5, 248.33333333333331, 248.33333333333331, 249.16666666666669, 249.16666666666669, 250, 250, 250.83333333333334, 250.83333333333334, 251.66666666666666, 251.66666666666666, 252.50000000000003, 252.50000000000003, 253.33333333333331, 253.33333333333331, 254.16666666666669, 254.16666666666669, 255, 255, 255.83333333333334, 255.83333333333334, 256.6666666666667, 256.6666666666667, 257.5, 257.5, 258.3333333333333, 258.3333333333333, 259.1666666666667, 259.1666666666667, 260, 260, 260.83333333333337, 260.83333333333337, 261.6666666666667, 261.6666666666667, 262.5, 262.5, 263.3333333333333, 263.3333333333333, 264.1666666666667, 264.1666666666667, 265, 265, 265.83333333333337, 265.83333333333337, 266.6666666666667, 266.6666666666667, 267.5, 267.5, 268.3333333333333, 268.3333333333333, 269.16666666666663, 269.16666666666663, 270, 270, 270.8333333333333, 270.8333333333333, 271.6666666666667, 271.6666666666667, 272.5, 272.5, 273.33333333333337, 273.33333333333337, 274.16666666666663, 274.16666666666663, 275, 275, 275.8333333333333, 275.8333333333333, 276.6666666666667, 276.6666666666667, 277.5, 277.5, 278.33333333333337, 278.33333333333337, 279.1666666666667, 279.1666666666667, 280, 280, 280.8333333333333, 280.8333333333333, 281.6666666666667, 281.6666666666667, 282.5, 282.5, 283.33333333333337, 283.33333333333337, 284.1666666666667, 284.1666666666667, 285, 285, 285.8333333333333, 285.8333333333333, 286.6666666666667, 286.6666666666667, 287.5, 287.5, 288.33333333333337, 288.33333333333337, 289.1666666666667, 289.1666666666667, 290.00000000000006, 290.00000000000006, 290.8333333333333, 290.8333333333333, 291.6666666666667, 291.6666666666667, 292.5, 292.5, 293.3333333333333, 293.3333333333333, 294.1666666666667, 294.1666666666667, 295, 295, 295.8333333333333, 295.8333333333333, 296.66666666666663, 296.66666666666663, 297.5, 297.5, 298.3333333333333, 298.3333333333333, 299.1666666666667, 299.1666666666667, 300, 300, 300.83333333333337, 300.83333333333337, 301.66666666666663, 301.66666666666663, 302.5, 302.5, 303.3333333333333, 303.3333333333333, 304.1666666666667, 304.1666666666667, 305, 305, 305.83333333333337, 305.83333333333337, 306.6666666666667, 306.6666666666667, 307.5, 307.5, 308.3333333333333, 308.3333333333333, 309.1666666666667, 309.1666666666667, 310, 310, 310.83333333333337, 310.83333333333337, 311.6666666666667, 311.6666666666667, 312.5, 312.5, 313.3333333333333, 313.3333333333333, 314.1666666666667, 314.1666666666667, 315, 315, 315.83333333333337, 315.83333333333337, 316.6666666666667, 316.6666666666667, 317.50000000000006, 317.50000000000006, 318.3333333333333, 318.3333333333333, 319.1666666666667, 319.1666666666667, 320, 320, 320.8333333333333, 320.8333333333333, 321.6666666666667, 321.6666666666667, 322.5, 322.5, 323.33333333333337, 323.33333333333337, 324.16666666666663, 324.16666666666663, 325, 325, 325.8333333333333, 325.8333333333333, 326.6666666666667, 326.6666666666667, 327.5, 327.5, 328.33333333333337, 328.33333333333337, 329.16666666666663, 329.16666666666663, 330, 330, 330.8333333333333, 330.8333333333333, 331.6666666666667, 331.6666666666667, 332.5, 332.5, 333.33333333333337, 333.33333333333337, 334.1666666666667, 334.1666666666667, 335, 335, 335.8333333333333, 335.8333333333333, 336.6666666666667, 336.6666666666667, 337.5, 337.5, 338.33333333333337, 338.33333333333337, 339.1666666666667, 339.1666666666667, 340, 340, 340.8333333333333, 340.8333333333333, 341.6666666666667, 341.6666666666667, 342.5, 342.5, 343.33333333333337, 343.33333333333337, 344.1666666666667, 344.1666666666667, 345.00000000000006, 345.00000000000006, 345.8333333333333, 345.8333333333333, 346.6666666666667, 346.6666666666667, 347.5, 347.5, 348.3333333333333, 348.3333333333333, 349.1666666666667, 349.1666666666667, 350, 350, 350.83333333333337, 350.83333333333337, 351.66666666666663, 351.66666666666663, 352.5, 352.5, 353.3333333333333, 353.3333333333333, 354.1666666666667, 354.1666666666667, 355, 355, 355.83333333333337, 355.83333333333337, 356.6666666666667, 356.6666666666667, 357.5, 357.5, 358.3333333333333, 358.3333333333333, 359.1666666666667, 359.1666666666667, 360, 360, 360.83333333333337, 360.83333333333337, 361.6666666666667, 361.6666666666667, 362.5, 362.5, 363.3333333333333, 363.3333333333333, 364.1666666666667, 364.1666666666667, 365, 365, 365.83333333333337, 365.83333333333337, 366.6666666666667, 366.6666666666667, 367.5, 367.5, 368.3333333333333, 368.3333333333333, 369.1666666666667, 369.1666666666667, 370, 370, 370.83333333333337, 370.83333333333337, 371.6666666666667, 371.6666666666667, 372.50000000000006, 372.50000000000006, 373.3333333333333, 373.3333333333333, 374.16666666666663, 374.16666666666663, 375, 375, 375.8333333333333, 375.8333333333333, 376.6666666666667, 376.6666666666667, 377.5, 377.5, 378.33333333333337, 378.33333333333337, 379.16666666666663, 379.16666666666663, 380, 380, 380.8333333333333, 380.8333333333333, 381.6666666666667, 381.6666666666667, 382.5, 382.5, 383.33333333333337, 383.33333333333337, 384.1666666666667, 384.1666666666667, 385, 385, 385.8333333333333, 385.8333333333333, 386.6666666666667, 386.6666666666667, 387.5, 387.5, 388.33333333333337, 388.33333333333337, 389.1666666666667, 389.1666666666667, 390, 390, 390.8333333333333, 390.8333333333333, 391.6666666666667, 391.6666666666667, 392.5, 392.5, 393.33333333333337, 393.33333333333337, 394.1666666666667, 394.1666666666667, 395.00000000000006, 395.00000000000006, 395.8333333333333, 395.8333333333333, 396.6666666666667, 396.6666666666667, 397.5, 397.5, 398.33333333333337, 398.33333333333337, 399.1666666666667, 399.1666666666667, 400, 400, 400.8333333333333, 400.8333333333333, 401.66666666666663, 401.66666666666663, 402.5, 402.5, 403.3333333333333, 403.3333333333333, 404.1666666666667, 404.1666666666667, 405, 405, 405.83333333333337, 405.83333333333337, 406.66666666666663, 406.66666666666663, 407.5, 407.5, 408.3333333333333, 408.3333333333333, 409.1666666666667, 409.1666666666667, 410, 410, 410.83333333333337, 410.83333333333337, 411.6666666666667, 411.6666666666667, 412.5, 412.5, 413.3333333333333, 413.3333333333333, 414.1666666666667, 414.1666666666667, 415, 415, 415.83333333333337, 415.83333333333337, 416.6666666666667, 416.6666666666667, 417.5, 417.5, 418.3333333333333, 418.3333333333333, 419.1666666666667, 419.1666666666667, 420, 420, 420.83333333333337, 420.83333333333337, 421.6666666666667, 421.6666666666667, 422.50000000000006, 422.50000000000006, 423.3333333333333, 423.3333333333333, 424.1666666666667, 424.1666666666667, 425, 425, 425.83333333333337, 425.83333333333337, 426.6666666666667, 426.6666666666667, 427.5, 427.5, 428.33333333333337, 428.33333333333337, 429.16666666666663, 429.16666666666663, 430, 430, 430.8333333333333, 430.8333333333333, 431.6666666666667, 431.6666666666667, 432.5, 432.5, 433.33333333333337, 433.33333333333337, 434.16666666666663, 434.16666666666663, 435, 435, 435.8333333333333, 435.8333333333333, 436.6666666666667, 436.6666666666667, 437.5, 437.5, 438.33333333333337, 438.33333333333337, 439.1666666666667, 439.1666666666667, 440, 440, 440.8333333333333, 440.8333333333333, 441.6666666666667, 441.6666666666667, 442.5, 442.5, 443.33333333333337, 443.33333333333337, 444.1666666666667, 444.1666666666667, 445, 445, 445.8333333333333, 445.8333333333333, 446.6666666666667, 446.6666666666667, 447.5, 447.5, 448.33333333333337, 448.33333333333337, 449.1666666666667, 449.1666666666667, 450.00000000000006, 450.00000000000006, 450.8333333333333, 450.8333333333333, 451.6666666666667, 451.6666666666667, 452.5, 452.5, 453.33333333333337, 453.33333333333337, 454.1666666666667, 454.1666666666667, 455, 455, 455.83333333333337, 455.83333333333337, 456.66666666666663, 456.66666666666663, 457.5, 457.5, 458.3333333333333, 458.3333333333333, 459.1666666666667, 459.1666666666667, 460, 460, 460.83333333333337, 460.83333333333337, 461.6666666666667, 461.6666666666667, 462.5, 462.5, 463.3333333333333, 463.3333333333333, 464.1666666666667, 464.1666666666667, 465, 465, 465.83333333333337, 465.83333333333337, 466.6666666666667, 466.6666666666667, 467.5, 467.5, 468.3333333333333, 468.3333333333333, 469.1666666666667, 469.1666666666667, 470, 470, 470.83333333333337, 470.83333333333337, 471.6666666666667, 471.6666666666667, 472.50000000000006, 472.50000000000006, 473.3333333333333, 473.3333333333333, 474.1666666666667, 474.1666666666667, 475, 475, 475.83333333333337, 475.83333333333337, 476.6666666666667, 476.6666666666667, 477.5, 477.5, 478.3333333333333, 478.3333333333333, 479.1666666666667, 479.1666666666667, 480.00000000000006, 480.00000000000006, 480.8333333333333, 480.8333333333333, 481.6666666666667, 481.6666666666667, 482.50000000000006, 482.50000000000006, 483.3333333333333, 483.3333333333333, 484.16666666666663, 484.16666666666663, 485, 485, 485.83333333333337, 485.83333333333337, 486.66666666666663, 486.66666666666663, 487.5, 487.5, 488.33333333333337, 488.33333333333337, 489.1666666666667, 489.1666666666667, 489.99999999999994, 489.99999999999994, 490.8333333333333, 490.8333333333333, 491.6666666666667, 491.6666666666667, 492.50000000000006, 492.50000000000006, 493.3333333333333, 493.3333333333333, 494.1666666666667, 494.1666666666667, 495, 495, 495.83333333333337, 495.83333333333337, 496.66666666666663, 496.66666666666663, 497.5, 497.5, 498.33333333333337, 498.33333333333337, 499.16666666666674, 499.16666666666674, 500, 500, 500.8333333333333, 500.8333333333333, 501.6666666666667, 501.6666666666667, 502.50000000000006, 502.50000000000006, 503.3333333333333, 503.3333333333333, 504.1666666666667, 504.1666666666667, 505.00000000000006, 505.00000000000006, 505.83333333333337, 505.83333333333337, 506.66666666666663, 506.66666666666663, 507.5, 507.5, 508.33333333333337, 508.33333333333337, 509.16666666666663, 509.16666666666663, 510, 510, 510.83333333333337, 510.83333333333337, 511.6666666666667, 511.6666666666667, 512.5, 512.5, 513.3333333333334, 513.3333333333334, 514.1666666666666, 514.1666666666666, 515, 515, 515.8333333333333, 515.8333333333333, 516.6666666666666, 516.6666666666666, 517.5, 517.5, 518.3333333333334, 518.3333333333334, 519.1666666666666, 519.1666666666666, 520, 520, 520.8333333333334, 520.8333333333334, 521.6666666666667, 521.6666666666667, 522.5, 522.5, 523.3333333333334, 523.3333333333334, 524.1666666666667, 524.1666666666667, 525, 525, 525.8333333333333, 525.8333333333333, 526.6666666666666, 526.6666666666666, 527.5, 527.5, 528.3333333333334, 528.3333333333334, 529.1666666666666, 529.1666666666666, 530, 530, 530.8333333333334, 530.8333333333334, 531.6666666666667, 531.6666666666667, 532.5, 532.5, 533.3333333333334, 533.3333333333334, 534.1666666666667, 534.1666666666667, 535, 535, 535.8333333333334, 535.8333333333334, 536.6666666666666, 536.6666666666666, 537.5, 537.5, 538.3333333333333, 538.3333333333333, 539.1666666666666, 539.1666666666666, 540, 540, 540.8333333333334, 540.8333333333334, 541.6666666666666, 541.6666666666666, 542.5, 542.5, 543.3333333333334, 543.3333333333334, 544.1666666666667, 544.1666666666667, 545, 545, 545.8333333333334, 545.8333333333334, 546.6666666666667, 546.6666666666667, 547.5, 547.5, 548.3333333333333, 548.3333333333333, 549.1666666666666, 549.1666666666666, 550, 550, 550.8333333333334, 550.8333333333334, 551.6666666666666, 551.6666666666666, 552.5, 552.5, 553.3333333333334, 553.3333333333334, 554.1666666666667, 554.1666666666667, 555, 555, 555.8333333333334, 555.8333333333334, 556.6666666666667, 556.6666666666667, 557.5000000000001, 557.5000000000001, 558.3333333333334, 558.3333333333334, 559.1666666666666, 559.1666666666666, 560, 560, 560.8333333333333, 560.8333333333333, 561.6666666666666, 561.6666666666666, 562.5, 562.5, 563.3333333333334, 563.3333333333334, 564.1666666666666, 564.1666666666666, 565, 565, 565.8333333333334, 565.8333333333334, 566.6666666666667, 566.6666666666667, 567.5, 567.5, 568.3333333333334, 568.3333333333334, 569.1666666666667, 569.1666666666667, 570, 570, 570.8333333333333, 570.8333333333333, 571.6666666666666, 571.6666666666666, 572.5, 572.5, 573.3333333333334, 573.3333333333334, 574.1666666666666, 574.1666666666666, 575, 575, 575.8333333333334, 575.8333333333334, 576.6666666666667, 576.6666666666667, 577.5, 577.5, 578.3333333333334, 578.3333333333334, 579.1666666666667, 579.1666666666667, 580.0000000000001, 580.0000000000001, 580.8333333333333, 580.8333333333333, 581.6666666666666, 581.6666666666666, 582.5, 582.5, 583.3333333333334, 583.3333333333334, 584.1666666666666, 584.1666666666666, 585, 585, 585.8333333333334, 585.8333333333334, 586.6666666666666, 586.6666666666666, 587.5, 587.5, 588.3333333333334, 588.3333333333334, 589.1666666666667, 589.1666666666667, 590, 590, 590.8333333333334, 590.8333333333334, 591.6666666666666, 591.6666666666666, 592.5, 592.5, 593.3333333333333, 593.3333333333333, 594.1666666666666, 594.1666666666666, 595, 595, 595.8333333333334, 595.8333333333334, 596.6666666666666, 596.6666666666666, 597.5, 597.5, 598.3333333333334, 598.3333333333334, 599.1666666666667, 599.1666666666667, 600, 600, 600.8333333333334, 600.8333333333334, 601.6666666666667, 601.6666666666667, 602.5, 602.5, 603.3333333333333, 603.3333333333333, 604.1666666666666, 604.1666666666666, 605, 605, 605.8333333333334, 605.8333333333334, 606.6666666666666, 606.6666666666666, 607.5, 607.5, 608.3333333333334, 608.3333333333334, 609.1666666666667, 609.1666666666667, 610, 610, 610.8333333333334, 610.8333333333334, 611.6666666666667, 611.6666666666667, 612.5000000000001, 612.5000000000001, 613.3333333333334, 613.3333333333334, 614.1666666666666, 614.1666666666666, 615, 615, 615.8333333333333, 615.8333333333333, 616.6666666666666, 616.6666666666666, 617.5, 617.5, 618.3333333333334, 618.3333333333334, 619.1666666666666, 619.1666666666666, 620, 620, 620.8333333333334, 620.8333333333334, 621.6666666666667, 621.6666666666667, 622.5, 622.5, 623.3333333333334, 623.3333333333334, 624.1666666666667, 624.1666666666667, 625, 625, 625.8333333333333, 625.8333333333333, 626.6666666666666, 626.6666666666666, 627.5, 627.5, 628.3333333333334, 628.3333333333334, 629.1666666666666, 629.1666666666666, 630, 630, 630.8333333333334, 630.8333333333334, 631.6666666666667, 631.6666666666667, 632.5, 632.5, 633.3333333333334, 633.3333333333334, 634.1666666666667, 634.1666666666667, 635.0000000000001, 635.0000000000001, 635.8333333333334, 635.8333333333334, 636.6666666666666, 636.6666666666666, 637.5, 637.5, 638.3333333333334, 638.3333333333334, 639.1666666666666, 639.1666666666666, 640, 640, 640.8333333333334, 640.8333333333334, 641.6666666666666, 641.6666666666666, 642.5, 642.5, 643.3333333333334, 643.3333333333334, 644.1666666666667, 644.1666666666667, 645, 645, 645.8333333333334, 645.8333333333334, 646.6666666666667, 646.6666666666667, 647.5, 647.5, 648.3333333333333, 648.3333333333333, 649.1666666666666, 649.1666666666666, 650, 650, 650.8333333333334, 650.8333333333334, 651.6666666666666, 651.6666666666666, 652.5, 652.5, 653.3333333333334, 653.3333333333334, 654.1666666666667, 654.1666666666667, 655, 655, 655.8333333333334, 655.8333333333334, 656.6666666666667, 656.6666666666667, 657.5000000000001, 657.5000000000001, 658.3333333333333, 658.3333333333333, 659.1666666666666, 659.1666666666666, 660, 660, 660.8333333333334, 660.8333333333334, 661.6666666666666, 661.6666666666666, 662.5, 662.5, 663.3333333333334, 663.3333333333334, 664.1666666666667, 664.1666666666667, 665, 665, 665.8333333333334, 665.8333333333334, 666.6666666666667, 666.6666666666667, 667.5, 667.5, 668.3333333333334, 668.3333333333334, 669.1666666666666, 669.1666666666666, 670, 670, 670.8333333333333, 670.8333333333333, 671.6666666666666, 671.6666666666666, 672.5, 672.5, 673.3333333333334, 673.3333333333334, 674.1666666666666, 674.1666666666666, 675, 675, 675.8333333333334, 675.8333333333334, 676.6666666666667, 676.6666666666667, 677.5, 677.5, 678.3333333333334, 678.3333333333334, 679.1666666666667, 679.1666666666667, 680, 680, 680.8333333333333, 680.8333333333333, 681.6666666666666, 681.6666666666666, 682.5, 682.5, 683.3333333333334, 683.3333333333334, 684.1666666666666, 684.1666666666666, 685, 685, 685.8333333333334, 685.8333333333334, 686.6666666666667, 686.6666666666667, 687.5, 687.5, 688.3333333333334, 688.3333333333334, 689.1666666666667, 689.1666666666667, 690.0000000000001, 690.0000000000001, 690.8333333333334, 690.8333333333334, 691.6666666666666, 691.6666666666666, 692.5, 692.5, 693.3333333333334, 693.3333333333334, 694.1666666666666, 694.1666666666666, 695, 695, 695.8333333333334, 695.8333333333334, 696.6666666666666, 696.6666666666666, 697.5, 697.5, 698.3333333333334, 698.3333333333334, 699.1666666666667, 699.1666666666667, 700, 700, 700.8333333333334, 700.8333333333334, 701.6666666666667, 701.6666666666667, 702.5, 702.5, 703.3333333333333, 703.3333333333333, 704.1666666666666, 704.1666666666666, 705, 705, 705.8333333333334, 705.8333333333334, 706.6666666666666, 706.6666666666666, 707.5, 707.5, 708.3333333333334, 708.3333333333334, 709.1666666666667, 709.1666666666667, 710, 710, 710.8333333333334, 710.8333333333334, 711.6666666666667, 711.6666666666667, 712.5000000000001, 712.5000000000001, 713.3333333333334, 713.3333333333334, 714.1666666666666, 714.1666666666666, 715, 715, 715.8333333333334, 715.8333333333334, 716.6666666666666, 716.6666666666666, 717.5, 717.5, 718.3333333333334, 718.3333333333334, 719.1666666666667, 719.1666666666667, 720, 720, 720.8333333333334, 720.8333333333334, 721.6666666666667, 721.6666666666667, 722.5, 722.5, 723.3333333333334, 723.3333333333334, 724.1666666666667, 724.1666666666667, 725, 725, 725.8333333333333, 725.8333333333333, 726.6666666666666, 726.6666666666666, 727.5, 727.5, 728.3333333333334, 728.3333333333334, 729.1666666666666, 729.1666666666666, 730, 730, 730.8333333333334, 730.8333333333334, 731.6666666666667, 731.6666666666667, 732.5, 732.5, 733.3333333333334, 733.3333333333334, 734.1666666666667, 734.1666666666667, 735, 735, 735.8333333333333, 735.8333333333333, 736.6666666666666, 736.6666666666666, 737.5, 737.5, 738.3333333333334, 738.3333333333334, 739.1666666666666, 739.1666666666666, 740, 740, 740.8333333333334, 740.8333333333334, 741.6666666666667, 741.6666666666667, 742.5, 742.5, 743.3333333333334, 743.3333333333334, 744.1666666666667, 744.1666666666667, 745.0000000000001, 745.0000000000001, 745.8333333333334, 745.8333333333334, 746.6666666666666, 746.6666666666666, 747.5, 747.5, 748.3333333333333, 748.3333333333333, 749.1666666666666, 749.1666666666666, 750, 750, 750.8333333333334, 750.8333333333334, 751.6666666666666, 751.6666666666666, 752.5, 752.5, 753.3333333333334, 753.3333333333334, 754.1666666666667, 754.1666666666667, 755, 755, 755.8333333333334, 755.8333333333334, 756.6666666666667, 756.6666666666667, 757.5, 757.5, 758.3333333333333, 758.3333333333333, 759.1666666666666, 759.1666666666666, 760, 760, 760.8333333333334, 760.8333333333334, 761.6666666666666, 761.6666666666666, 762.5, 762.5, 763.3333333333334, 763.3333333333334, 764.1666666666667, 764.1666666666667, 765, 765, 765.8333333333334, 765.8333333333334, 766.6666666666667, 766.6666666666667, 767.5000000000001, 767.5000000000001, 768.3333333333334, 768.3333333333334, 769.1666666666666, 769.1666666666666, 770, 770, 770.8333333333334, 770.8333333333334, 771.6666666666666, 771.6666666666666, 772.5, 772.5, 773.3333333333334, 773.3333333333334, 774.1666666666666, 774.1666666666666, 775, 775, 775.8333333333334, 775.8333333333334, 776.6666666666667, 776.6666666666667, 777.5, 777.5, 778.3333333333334, 778.3333333333334, 779.1666666666667, 779.1666666666667, 780, 780, 780.8333333333333, 780.8333333333333, 781.6666666666666, 781.6666666666666, 782.5, 782.5, 783.3333333333334, 783.3333333333334, 784.1666666666666, 784.1666666666666, 785, 785, 785.8333333333334, 785.8333333333334, 786.6666666666667, 786.6666666666667, 787.5, 787.5, 788.3333333333334, 788.3333333333334, 789.1666666666667, 789.1666666666667, 790.0000000000001, 790.0000000000001, 790.8333333333333, 790.8333333333333, 791.6666666666666, 791.6666666666666, 792.5, 792.5, 793.3333333333334, 793.3333333333334, 794.1666666666666, 794.1666666666666, 795, 795, 795.8333333333334, 795.8333333333334, 796.6666666666667, 796.6666666666667, 797.5, 797.5, 798.3333333333334, 798.3333333333334, 799.1666666666667, 799.1666666666667, 800, 800, 800.8333333333334, 800.8333333333334, 801.6666666666666, 801.6666666666666, 802.5, 802.5, 803.3333333333333, 803.3333333333333, 804.1666666666666, 804.1666666666666, 805, 805, 805.8333333333334, 805.8333333333334, 806.6666666666666, 806.6666666666666, 807.5, 807.5, 808.3333333333334, 808.3333333333334, 809.1666666666667, 809.1666666666667, 810, 810, 810.8333333333334, 810.8333333333334, 811.6666666666667, 811.6666666666667, 812.5, 812.5, 813.3333333333333, 813.3333333333333, 814.1666666666666, 814.1666666666666, 815, 815, 815.8333333333334, 815.8333333333334, 816.6666666666666, 816.6666666666666, 817.5, 817.5, 818.3333333333334, 818.3333333333334, 819.1666666666667, 819.1666666666667, 820, 820, 820.8333333333334, 820.8333333333334, 821.6666666666667, 821.6666666666667, 822.5000000000001, 822.5000000000001, 823.3333333333334, 823.3333333333334, 824.1666666666666, 824.1666666666666, 825, 825, 825.8333333333334, 825.8333333333334, 826.6666666666666, 826.6666666666666, 827.5, 827.5, 828.3333333333334, 828.3333333333334, 829.1666666666666, 829.1666666666666, 830, 830, 830.8333333333334, 830.8333333333334, 831.6666666666667, 831.6666666666667, 832.5, 832.5, 833.3333333333334, 833.3333333333334, 834.1666666666667, 834.1666666666667, 835, 835, 835.8333333333333, 835.8333333333333, 836.6666666666666, 836.6666666666666, 837.5, 837.5, 838.3333333333334, 838.3333333333334, 839.1666666666666, 839.1666666666666, 840, 840, 840.8333333333334, 840.8333333333334, 841.6666666666667, 841.6666666666667, 842.5, 842.5, 843.3333333333334, 843.3333333333334, 844.1666666666667, 844.1666666666667, 845.0000000000001, 845.0000000000001, 845.8333333333334, 845.8333333333334, 846.6666666666666, 846.6666666666666, 847.5, 847.5, 848.3333333333334, 848.3333333333334, 849.1666666666666, 849.1666666666666, 850, 850, 850.8333333333334, 850.8333333333334, 851.6666666666667, 851.6666666666667, 852.5, 852.5, 853.3333333333334, 853.3333333333334, 854.1666666666667, 854.1666666666667, 855, 855, 855.8333333333334, 855.8333333333334, 856.6666666666667, 856.6666666666667, 857.5, 857.5, 858.3333333333333, 858.3333333333333, 859.1666666666666, 859.1666666666666, 860, 860, 860.8333333333334, 860.8333333333334, 861.6666666666666, 861.6666666666666, 862.5, 862.5, 863.3333333333334, 863.3333333333334, 864.1666666666667, 864.1666666666667, 865, 865, 865.8333333333334, 865.8333333333334, 866.6666666666667, 866.6666666666667, 867.5000000000001, 867.5000000000001, 868.3333333333333, 868.3333333333333, 869.1666666666666, 869.1666666666666, 870, 870, 870.8333333333334, 870.8333333333334, 871.6666666666666, 871.6666666666666, 872.5, 872.5, 873.3333333333334, 873.3333333333334, 874.1666666666667, 874.1666666666667, 875, 875, 875.8333333333334, 875.8333333333334, 876.6666666666667, 876.6666666666667, 877.5000000000001, 877.5000000000001, 878.3333333333334, 878.3333333333334, 879.1666666666666, 879.1666666666666, 880, 880, 880.8333333333333, 880.8333333333333, 881.6666666666666, 881.6666666666666, 882.5, 882.5, 883.3333333333334, 883.3333333333334, 884.1666666666666, 884.1666666666666, 885, 885, 885.8333333333334, 885.8333333333334, 886.6666666666667, 886.6666666666667, 887.5, 887.5, 888.3333333333334, 888.3333333333334, 889.1666666666667, 889.1666666666667, 890, 890, 890.8333333333333, 890.8333333333333, 891.6666666666666, 891.6666666666666, 892.5, 892.5, 893.3333333333334, 893.3333333333334, 894.1666666666666, 894.1666666666666, 895, 895, 895.8333333333334, 895.8333333333334, 896.6666666666667, 896.6666666666667, 897.5, 897.5, 898.3333333333334, 898.3333333333334, 899.1666666666667, 899.1666666666667, 900.0000000000001, 900.0000000000001, 900.8333333333334, 900.8333333333334, 901.6666666666666, 901.6666666666666, 902.5, 902.5, 903.3333333333334, 903.3333333333334, 904.1666666666666, 904.1666666666666, 905, 905, 905.8333333333334, 905.8333333333334, 906.6666666666667, 906.6666666666667, 907.5, 907.5, 908.3333333333334, 908.3333333333334, 909.1666666666667, 909.1666666666667, 910, 910, 910.8333333333334, 910.8333333333334, 911.6666666666667, 911.6666666666667, 912.5, 912.5, 913.3333333333333, 913.3333333333333, 914.1666666666666, 914.1666666666666, 915, 915, 915.8333333333334, 915.8333333333334, 916.6666666666666, 916.6666666666666, 917.5, 917.5, 918.3333333333334, 918.3333333333334, 919.1666666666667, 919.1666666666667, 920, 920, 920.8333333333334, 920.8333333333334, 921.6666666666667, 921.6666666666667, 922.5000000000001, 922.5000000000001, 923.3333333333334, 923.3333333333334, 924.1666666666666, 924.1666666666666, 925, 925, 925.8333333333334, 925.8333333333334, 926.6666666666666, 926.6666666666666, 927.5, 927.5, 928.3333333333334, 928.3333333333334, 929.1666666666667, 929.1666666666667, 930, 930, 930.8333333333334, 930.8333333333334, 931.6666666666667, 931.6666666666667, 932.5000000000001, 932.5000000000001, 933.3333333333334, 933.3333333333334, 934.1666666666667, 934.1666666666667, 935, 935, 935.8333333333333, 935.8333333333333, 936.6666666666666, 936.6666666666666, 937.5, 937.5, 938.3333333333334, 938.3333333333334, 939.1666666666666, 939.1666666666666, 940, 940, 940.8333333333334, 940.8333333333334, 941.6666666666667, 941.6666666666667, 942.5, 942.5, 943.3333333333334, 943.3333333333334, 944.1666666666667, 944.1666666666667, 945.0000000000001, 945.0000000000001, 945.8333333333333, 945.8333333333333, 946.6666666666666, 946.6666666666666, 947.5, 947.5, 948.3333333333334, 948.3333333333334, 949.1666666666666, 949.1666666666666, 950, 950, 950.8333333333334, 950.8333333333334, 951.6666666666667, 951.6666666666667, 952.5, 952.5, 953.3333333333334, 953.3333333333334, 954.1666666666667, 954.1666666666667, 955, 955, 955.8333333333335, 955.8333333333335, 956.6666666666666, 956.6666666666666, 957.4999999999999, 957.4999999999999, 958.3333333333334, 958.3333333333334, 959.1666666666666, 959.1666666666666, 960.0000000000001, 960.0000000000001, 960.8333333333334, 960.8333333333334, 961.6666666666666, 961.6666666666666, 962.5000000000001, 962.5000000000001, 963.3333333333334, 963.3333333333334, 964.1666666666666, 964.1666666666666, 965.0000000000001, 965.0000000000001, 965.8333333333334, 965.8333333333334, 966.6666666666666, 966.6666666666666, 967.5, 967.5, 968.3333333333333, 968.3333333333333, 969.1666666666667, 969.1666666666667, 970, 970, 970.8333333333333, 970.8333333333333, 971.6666666666667, 971.6666666666667, 972.5, 972.5, 973.3333333333333, 973.3333333333333, 974.1666666666667, 974.1666666666667, 975, 975, 975.8333333333335, 975.8333333333335, 976.6666666666667, 976.6666666666667, 977.5, 977.5, 978.3333333333334, 978.3333333333334, 979.1666666666666, 979.1666666666666, 979.9999999999999, 979.9999999999999, 980.8333333333334, 980.8333333333334, 981.6666666666666, 981.6666666666666, 982.5000000000001, 982.5000000000001, 983.3333333333334, 983.3333333333334, 984.1666666666666, 984.1666666666666, 985.0000000000001, 985.0000000000001, 985.8333333333334, 985.8333333333334, 986.6666666666666, 986.6666666666666, 987.5000000000001, 987.5000000000001, 988.3333333333334, 988.3333333333334, 989.1666666666666, 989.1666666666666, 990, 990, 990.8333333333333, 990.8333333333333, 991.6666666666667, 991.6666666666667, 992.5, 992.5, 993.3333333333333, 993.3333333333333, 994.1666666666667, 994.1666666666667, 995, 995, 995.8333333333333, 995.8333333333333, 996.6666666666667, 996.6666666666667, 997.5, 997.5, 998.3333333333335, 998.3333333333335, 999.1666666666667, 999.1666666666667, 1000, 1000, 1000.8333333333334, 1000.8333333333334, 1001.6666666666666, 1001.6666666666666, 1002.4999999999999, 1002.4999999999999, 1003.3333333333334, 1003.3333333333334, 1004.1666666666666, 1004.1666666666666, 1005.0000000000001, 1005.0000000000001, 1005.8333333333334, 1005.8333333333334, 1006.6666666666666, 1006.6666666666666, 1007.5000000000001, 1007.5000000000001, 1008.3333333333334, 1008.3333333333334, 1009.1666666666666, 1009.1666666666666, 1010.0000000000001, 1010.0000000000001, 1010.8333333333334, 1010.8333333333334, 1011.6666666666667, 1011.6666666666667, 1012.5, 1012.5, 1013.3333333333333, 1013.3333333333333, 1014.1666666666667, 1014.1666666666667, 1015, 1015, 1015.8333333333333, 1015.8333333333333, 1016.6666666666667, 1016.6666666666667, 1017.5, 1017.5, 1018.3333333333333, 1018.3333333333333, 1019.1666666666667, 1019.1666666666667, 1020, 1020, 1020.8333333333335, 1020.8333333333335, 1021.6666666666667, 1021.6666666666667, 1022.5, 1022.5, 1023.3333333333334, 1023.3333333333334, 1024.1666666666667, 1024.1666666666667, 1025, 1025, 1025.8333333333335, 1025.8333333333335, 1026.6666666666667, 1026.6666666666667, 1027.5000000000002, 1027.5000000000002, 1028.3333333333333, 1028.3333333333333, 1029.1666666666665, 1029.1666666666665, 1030, 1030, 1030.8333333333333, 1030.8333333333333, 1031.6666666666665, 1031.6666666666665, 1032.5, 1032.5, 1033.3333333333333, 1033.3333333333333, 1034.1666666666667, 1034.1666666666667, 1035, 1035, 1035.8333333333333, 1035.8333333333333, 1036.6666666666667, 1036.6666666666667, 1037.5, 1037.5, 1038.3333333333333, 1038.3333333333333, 1039.1666666666667, 1039.1666666666667, 1040, 1040, 1040.8333333333333, 1040.8333333333333, 1041.6666666666667, 1041.6666666666667, 1042.5, 1042.5, 1043.3333333333335, 1043.3333333333335, 1044.1666666666667, 1044.1666666666667, 1045, 1045, 1045.8333333333335, 1045.8333333333335, 1046.6666666666667, 1046.6666666666667, 1047.5, 1047.5, 1048.3333333333335, 1048.3333333333335, 1049.1666666666667, 1049.1666666666667, 1050, 1050, 1050.8333333333333, 1050.8333333333333, 1051.6666666666665, 1051.6666666666665, 1052.5, 1052.5, 1053.3333333333333, 1053.3333333333333, 1054.1666666666665, 1054.1666666666665, 1055, 1055, 1055.8333333333333, 1055.8333333333333, 1056.6666666666667, 1056.6666666666667, 1057.5, 1057.5, 1058.3333333333333, 1058.3333333333333, 1059.1666666666667, 1059.1666666666667, 1060, 1060, 1060.8333333333333, 1060.8333333333333, 1061.6666666666667, 1061.6666666666667, 1062.5, 1062.5, 1063.3333333333335, 1063.3333333333335, 1064.1666666666667, 1064.1666666666667, 1065, 1065, 1065.8333333333335, 1065.8333333333335, 1066.6666666666667, 1066.6666666666667, 1067.5, 1067.5, 1068.3333333333335, 1068.3333333333335, 1069.1666666666667, 1069.1666666666667, 1070, 1070, 1070.8333333333335, 1070.8333333333335, 1071.6666666666667, 1071.6666666666667, 1072.5, 1072.5, 1073.3333333333333, 1073.3333333333333, 1074.1666666666665, 1074.1666666666665, 1075, 1075, 1075.8333333333333, 1075.8333333333333, 1076.6666666666665, 1076.6666666666665, 1077.5, 1077.5, 1078.3333333333333, 1078.3333333333333, 1079.1666666666667, 1079.1666666666667, 1080, 1080, 1080.8333333333333, 1080.8333333333333, 1081.6666666666667, 1081.6666666666667, 1082.5, 1082.5, 1083.3333333333333, 1083.3333333333333, 1084.1666666666667, 1084.1666666666667, 1085, 1085, 1085.8333333333335, 1085.8333333333335, 1086.6666666666667, 1086.6666666666667, 1087.5, 1087.5, 1088.3333333333335, 1088.3333333333335, 1089.1666666666667, 1089.1666666666667, 1090, 1090, 1090.8333333333335, 1090.8333333333335, 1091.6666666666667, 1091.6666666666667, 1092.5000000000002, 1092.5000000000002, 1093.3333333333335, 1093.3333333333335, 1094.1666666666667, 1094.1666666666667, 1095, 1095, 1095.8333333333333, 1095.8333333333333, 1096.6666666666665, 1096.6666666666665, 1097.5, 1097.5, 1098.3333333333333, 1098.3333333333333, 1099.1666666666665, 1099.1666666666665, 1100 ] }, "y": { "type": "float", "values": [ 6, 6, 5.984457849818863, 5.984457849818863, 5.914622104270368, 5.914622104270368, 5.6602368453180425, 5.6602368453180425, 5.293156022650223, 5.293156022650223, 5.190011028931016, 5.190011028931016, 5.3020440865118905, 5.3020440865118905, 5.501282113799794, 5.501282113799794, 5.741307593508806, 5.741307593508806, 6.000000257442196, 6.000000257442196, 6.258787697677602, 6.258787697677602, 6.499939183721772, 6.499939183721772, 6.707020640079183, 6.707020640079183, 6.865919807508107, 6.865919807508107, 6.965807970181247, 6.965807970181247, 6.999877914784483, 6.999877914784483, 6.96580783086198, 6.96580783086198, 6.8659195383640075, 6.8659195383640075, 6.707020259451868, 6.707020259451868, 6.499938717550518, 6.499938717550518, 6.258787177731021, 6.258787177731021, 5.999999719154, 5.999999719154, 5.74121227971606, 5.74121227971606, 5.500060796009942, 5.500060796009942, 5.292979343371831, 5.292979343371831, 5.134080180789995, 5.134080180789995, 5.034074098489585, 5.034074098489585, 5.000000000000043, 5.000000000000043, 5.034074249942043, 5.034074249942043, 5.133974744457966, 5.133974744457966, 5.292893429839141, 5.292893429839141, 5.500000260141892, 5.500000260141892, 5.741181246932082, 5.741181246932082, 6.0000003042870675, 6.0000003042870675, 6.258819340905283, 6.258819340905283, 6.500000266898768, 6.500000266898768, 6.7071070004876745, 6.7071070004876745, 6.8660255598290405, 6.8660255598290405, 6.965925907568499, 6.965925907568499, 6.99999999999995, 6.99999999999995, 6.96592574399986, 6.96592574399986, 6.8660252438386395, 6.8660252438386395, 6.707106553609845, 6.707106553609845, 6.49999971958734, 6.49999971958734, 6.258818730458817, 6.258818730458817, 5.99999967230627, 5.99999967230627, 5.74118063648562, 5.74118063648562, 5.499999712830472, 5.499999712830472, 5.2928929829613205, 5.2928929829613205, 5.133974428467635, 5.133974428467635, 5.03407408637339, 5.03407408637339, 5.000000000000058, 5.000000000000058, 5.034074262058238, 5.034074262058238, 5.133974767864698, 5.133974767864698, 5.292893462941251, 5.292893462941251, 5.500000300683527, 5.500000300683527, 5.741181292150285, 5.741181292150285, 6.000000351100393, 6.000000351100393, 6.258819386123588, 6.258819386123588, 6.500000307440387, 6.500000307440387, 6.707107033589684, 6.707107033589684, 6.86602558323569, 6.86602558323569, 6.965925919684692, 6.965925919684692, 6.999999999999934, 6.999999999999934, 6.965925731883664, 6.965925731883664, 6.866025220431963, 6.866025220431963, 6.707106520507733, 6.707106520507733, 6.499999679045704, 6.499999679045704, 6.2588186852406125, 6.2588186852406125, 5.999999625492944, 5.999999625492944, 5.741180591267314, 5.741180591267314, 5.499999672288853, 5.499999672288853, 5.292892949859313, 5.292892949859313, 5.133974405060987, 5.133974405060987, 5.034074074257226, 5.034074074257226, 5.000000000000075, 5.000000000000075, 5.034074274174493, 5.034074274174493, 5.133974791271376, 5.133974791271376, 5.292893496043283, 5.292893496043283, 5.500000341225065, 5.500000341225065, 5.741181337368709, 5.741181337368709, 6.000000397913718, 6.000000397913718, 6.2588194313417835, 6.2588194313417835, 6.500000347981907, 6.500000347981907, 6.707107066691851, 6.707107066691851, 6.866025606642337, 6.866025606642337, 6.9659259318008555, 6.9659259318008555, 6.9999999999999165, 6.9999999999999165, 6.965925719767408, 6.965925719767408, 6.866025197025285, 6.866025197025285, 6.707106487405701, 6.707106487405701, 6.499999638504166, 6.499999638504166, 6.258818640022188, 6.258818640022188, 5.99999957867962, 5.99999957867962, 5.74118054604912, 5.74118054604912, 5.499999631747333, 5.499999631747333, 5.292892916757146, 5.292892916757146, 5.133974381654227, 5.133974381654227, 5.034074062141064, 5.034074062141064, 5.000000000000094, 5.000000000000094, 5.034074286290692, 5.034074286290692, 5.133974814678169, 5.133974814678169, 5.292893529145316, 5.292893529145316, 5.500000381766603, 5.500000381766603, 5.741181382586914, 5.741181382586914, 6.00000044472727, 6.00000044472727, 6.258819476559978, 6.258819476559978, 6.500000388523426, 6.500000388523426, 6.707107099793856, 6.707107099793856, 6.866025630049096, 6.866025630049096, 6.965925943917016, 6.965925943917016, 6.999999999999896, 6.999999999999896, 6.965925707651208, 6.965925707651208, 6.866025173618491, 6.866025173618491, 6.707106454303666, 6.707106454303666, 6.499999597962627, 6.499999597962627, 6.258818594803984, 6.258818594803984, 5.9999995318660675, 5.9999995318660675, 5.741180500830706, 5.741180500830706, 5.499999591205815, 5.499999591205815, 5.292892883655141, 5.292892883655141, 5.133974358247583, 5.133974358247583, 5.034074050024846, 5.034074050024846, 5.0000000000001155, 5.0000000000001155, 5.0340742984068925, 5.0340742984068925, 5.13397483808485, 5.13397483808485, 5.292893562247512, 5.292893562247512, 5.500000422308143, 5.500000422308143, 5.741181427805119, 5.741181427805119, 6.000000491540596, 6.000000491540596, 6.258819521778392, 6.258819521778392, 6.500000429064944, 6.500000429064944, 6.70710713289586, 6.70710713289586, 6.866025653455739, 6.866025653455739, 6.965925956033233, 6.965925956033233, 6.999999999999873, 6.999999999999873, 6.965925695535006, 6.965925695535006, 6.866025150211809, 6.866025150211809, 6.70710642120147, 6.70710642120147, 6.49999955742089, 6.49999955742089, 6.2588185495857775, 6.2588185495857775, 5.999999485052742, 5.999999485052742, 5.741180455612511, 5.741180455612511, 5.499999550664101, 5.499999550664101, 5.292892850553138, 5.292892850553138, 5.13397433484094, 5.13397433484094, 5.034074037908688, 5.034074037908688, 5.0000000000001386, 5.0000000000001386, 5.034074310523096, 5.034074310523096, 5.133974861491533, 5.133974861491533, 5.292893595349549, 5.292893595349549, 5.50000046284988, 5.50000046284988, 5.7411814730233255, 5.7411814730233255, 6.0000005383539206, 6.0000005383539206, 6.258819566996585, 6.258819566996585, 6.500000469606658, 6.500000469606658, 6.707107165997863, 6.707107165997863, 6.86602567686238, 6.86602567686238, 6.9659259681493895, 6.9659259681493895, 6.999999999999849, 6.999999999999849, 6.965925683418743, 6.965925683418743, 6.866025126805125, 6.866025126805125, 6.707106388099433, 6.707106388099433, 6.499999516879349, 6.499999516879349, 6.258818504367352, 6.258818504367352, 5.999999438239417, 5.999999438239417, 5.7411804103943185, 5.7411804103943185, 5.499999510122585, 5.499999510122585, 5.2928928174509755, 5.2928928174509755, 5.1339743114343, 5.1339743114343, 5.034074025792533, 5.034074025792533, 5.000000000000164, 5.000000000000164, 5.034074322639359, 5.034074322639359, 5.133974884898218, 5.133974884898218, 5.292893628451586, 5.292893628451586, 5.500000503391423, 5.500000503391423, 5.741181518241752, 5.741181518241752, 6.000000585167245, 6.000000585167245, 6.2588196122147775, 6.2588196122147775, 6.500000510148173, 6.500000510148173, 6.707107199100024, 6.707107199100024, 6.866025700269134, 6.866025700269134, 6.965925980265544, 6.965925980265544, 6.999999999999822, 6.999999999999822, 6.9659256713025375, 6.9659256713025375, 6.866025103398325, 6.866025103398325, 6.707106354997394, 6.707106354997394, 6.499999476337806, 6.499999476337806, 6.258818459149145, 6.258818459149145, 5.999999391425865, 5.999999391425865, 5.741180365176126, 5.741180365176126, 5.49999946958107, 5.49999946958107, 5.292892784348975, 5.292892784348975, 5.133974288027548, 5.133974288027548, 5.0340740136763795, 5.0340740136763795, 5.000000000000193, 5.000000000000193, 5.034074334755567, 5.034074334755567, 5.133974908305019, 5.133974908305019, 5.2928936615536255, 5.2928936615536255, 5.500000543932965, 5.500000543932965, 5.741181563459959, 5.741181563459959, 6.000000631980798, 6.000000631980798, 6.25881965743319, 6.25881965743319, 6.500000550689687, 6.500000550689687, 6.7071072322020235, 6.7071072322020235, 6.8660257236757705, 6.8660257236757705, 6.965925992381755, 6.965925992381755, 6.999999999999793, 6.999999999999793, 6.965925659186329, 6.965925659186329, 6.866025079991637, 6.866025079991637, 6.707106321895193, 6.707106321895193, 6.499999435796263, 6.499999435796263, 6.258818413930937, 6.258818413930937, 5.999999344612539, 5.999999344612539, 5.7411803199577145, 5.7411803199577145, 5.499999429039557, 5.499999429039557, 5.292892751246978, 5.292892751246978, 5.133974264620911, 5.133974264620911, 5.034074001560169, 5.034074001560169, 5.000000000000222, 5.000000000000222, 5.034074346871835, 5.034074346871835, 5.133974931711707, 5.133974931711707, 5.292893694655989, 5.292893694655989, 5.500000584474706, 5.500000584474706, 5.741181608678605, 5.741181608678605, 6.0000006787943505, 6.0000006787943505, 6.258819702651381, 6.258819702651381, 6.500000591231594, 6.500000591231594, 6.707107265304183, 6.707107265304183, 6.866025747082634, 6.866025747082634, 6.965926004497964, 6.965926004497964, 6.999999999999762, 6.999999999999762, 6.9659256470700015, 6.9659256470700015, 6.866025056584833, 6.866025056584833, 6.707106288793151, 6.707106288793151, 6.499999395254324, 6.499999395254324, 6.25881836871251, 6.25881836871251, 5.99999929779876, 5.99999929779876, 5.741180274739303, 5.741180274739303, 5.499999388497846, 5.499999388497846, 5.292892718144659, 5.292892718144659, 5.133974241214163, 5.133974241214163, 5.034073989444021, 5.034073989444021, 5.000000000000255, 5.000000000000255, 5.034074358988046, 5.034074358988046, 5.133974955118625, 5.133974955118625, 5.292893727758031, 5.292893727758031, 5.500000625016251, 5.500000625016251, 5.741181653896813, 5.741181653896813, 6.000000725607675, 6.000000725607675, 6.258819747870012, 6.258819747870012, 6.500000631773107, 6.500000631773107, 6.707107298406179, 6.707107298406179, 6.8660257704892675, 6.8660257704892675, 6.9659260166141115, 6.9659260166141115, 6.999999999999728, 6.999999999999728, 6.965925634953789, 6.965925634953789, 6.866025033178142, 6.866025033178142, 6.707106255690786, 6.707106255690786, 6.499999354712779, 6.499999354712779, 6.258818323494301, 6.258818323494301, 5.999999250985435, 5.999999250985435, 5.741180229521112, 5.741180229521112, 5.499999347956335, 5.499999347956335, 5.292892685042663, 5.292892685042663, 5.13397421780753, 5.13397421780753, 5.034073977327756, 5.034073977327756, 5.0000000000002895, 5.0000000000002895, 5.03407437110426, 5.03407437110426, 5.133974978525318, 5.133974978525318, 5.292893760860076, 5.292893760860076, 5.500000665558192, 5.500000665558192, 5.741181699115023, 5.741181699115023, 6.000000772421001, 6.000000772421001, 6.258819793088203, 6.258819793088203, 6.500000672314617, 6.500000672314617, 6.707107331508173, 6.707107331508173, 6.8660257938959, 6.8660257938959, 6.965926028730258, 6.965926028730258, 6.999999999999693, 6.999999999999693, 6.965925622837574, 6.965925622837574, 6.866025009771448, 6.866025009771448, 6.707106222588741, 6.707106222588741, 6.499999314171232, 6.499999314171232, 6.258818278276093, 6.258818278276093, 5.999999204172109, 5.999999204172109, 5.741180184302922, 5.741180184302922, 5.499999307414431, 5.499999307414431, 5.29289265194067, 5.29289265194067, 5.133974194400899, 5.133974194400899, 5.034073965211611, 5.034073965211611, 5.000000000000326, 5.000000000000326, 5.0340743832205925, 5.0340743832205925, 5.133975001932012, 5.133975001932012, 5.292893793962121, 5.292893793962121, 5.500000706099739, 5.500000706099739, 5.741181744333232, 5.741181744333232, 6.000000819234326, 6.000000819234326, 6.258819838306392, 6.258819838306392, 6.500000712856128, 6.500000712856128, 6.707107364610488, 6.707107364610488, 6.866025817302529, 6.866025817302529, 6.965926040846401, 6.965926040846401, 6.9999999999996545, 6.9999999999996545, 6.965925610721357, 6.965925610721357, 6.866024986364753, 6.866024986364753, 6.707106189486694, 6.707106189486694, 6.499999273629684, 6.499999273629684, 6.258818233057443, 6.258818233057443, 5.9999991573587845, 5.9999991573587845, 5.741180139084732, 5.741180139084732, 5.499999266872921, 5.499999266872921, 5.292892618838677, 5.292892618838677, 5.133974170994042, 5.133974170994042, 5.034073953095469, 5.034073953095469, 5.000000000000365, 5.000000000000365, 5.034074395336811, 5.034074395336811, 5.133975025338708, 5.133975025338708, 5.2928938270641686, 5.2928938270641686, 5.500000746641287, 5.500000746641287, 5.741181789551441, 5.741181789551441, 6.000000866048105, 6.000000866048105, 6.258819883524582, 6.258819883524582, 6.500000753397637, 6.500000753397637, 6.7071073977124795, 6.7071073977124795, 6.866025840709158, 6.866025840709158, 6.965926052962542, 6.965926052962542, 6.9999999999996145, 6.9999999999996145, 6.965925598605138, 6.965925598605138, 6.8660249629578285, 6.8660249629578285, 6.707106156384646, 6.707106156384646, 6.499999233088135, 6.499999233088135, 6.258818187839234, 6.258818187839234, 5.999999110545459, 5.999999110545459, 5.741180093866103, 5.741180093866103, 5.499999226331413, 5.499999226331413, 5.292892585736686, 5.292892585736686, 5.133974147587415, 5.133974147587415, 5.0340739409793285, 5.0340739409793285, 5.000000000000406, 5.000000000000406, 5.034074407453031, 5.034074407453031, 5.133975048745407, 5.133975048745407, 5.292893860166539, 5.292893860166539, 5.500000787182836, 5.500000787182836, 5.741181834769652, 5.741181834769652, 6.000000912861431, 6.000000912861431, 6.258819928742771, 6.258819928742771, 6.500000793939145, 6.500000793939145, 6.707107430814469, 6.707107430814469, 6.866025864115784, 6.866025864115784, 6.9659260650788, 6.9659260650788, 6.999999999999573, 6.999999999999573, 6.965925586488917, 6.965925586488917, 6.86602493955113, 6.86602493955113, 6.707106123282597, 6.707106123282597, 6.499999192546191, 6.499999192546191, 6.258818142621023, 6.258818142621023, 5.999999063732134, 5.999999063732134, 5.741180048647915, 5.741180048647915, 5.499999185789905, 5.499999185789905, 5.292892552634697, 5.292892552634697, 5.13397412418079, 5.13397412418079, 5.03407392886319, 5.03407392886319, 5.000000000000449, 5.000000000000449, 5.034074419569253, 5.034074419569253, 5.133975072152108, 5.133975072152108, 5.292893893268589, 5.292893893268589, 5.500000827724387, 5.500000827724387, 5.741181879987863, 5.741181879987863, 6.000000959674756, 6.000000959674756, 6.25881997396096, 6.25881997396096, 6.500000834481045, 6.500000834481045, 6.707107463916458, 6.707107463916458, 6.866025887522408, 6.866025887522408, 6.965926077194937, 6.965926077194937, 6.999999999999528, 6.999999999999528, 6.9659255743725765, 6.9659255743725765, 6.866024916144428, 6.866024916144428, 6.707106090180545, 6.707106090180545, 6.499999152004641, 6.499999152004641, 6.258818097402812, 6.258818097402812, 5.999999016918809, 5.999999016918809, 5.741180003429727, 5.741180003429727, 5.499999145248399, 5.499999145248399, 5.292892519532388, 5.292892519532388, 5.133974100774167, 5.133974100774167, 5.034073916747054, 5.034073916747054, 5.000000000000495, 5.000000000000495, 5.034074431685477, 5.034074431685477, 5.133975095558809, 5.133975095558809, 5.292893926370641, 5.292893926370641, 5.500000868265939, 5.500000868265939, 5.741181925206514, 5.741181925206514, 6.00000100648808, 6.00000100648808, 6.258820019179147, 6.258820019179147, 6.50000087502255, 6.50000087502255, 6.707107497018445, 6.707107497018445, 6.866025910929258, 6.866025910929258, 6.965926089311072, 6.965926089311072, 6.999999999999481, 6.999999999999481, 6.965925562256352, 6.965925562256352, 6.866024892737725, 6.866024892737725, 6.707106057078493, 6.707106057078493, 6.499999111463088, 6.499999111463088, 6.2588180521845995, 6.2588180521845995, 5.999998970105029, 5.999998970105029, 5.741179958211539, 5.741179958211539, 5.499999104706894, 5.499999104706894, 5.292892486430402, 5.292892486430402, 5.1339740773675455, 5.1339740773675455, 5.03407390463092, 5.03407390463092, 5.000000000000543, 5.000000000000543, 5.034074443801702, 5.034074443801702, 5.133975118965741, 5.133975118965741, 5.2928939594726945, 5.2928939594726945, 5.5000009088074915, 5.5000009088074915, 5.741181970424726, 5.741181970424726, 6.000001053301406, 6.000001053301406, 6.258820064397773, 6.258820064397773, 6.500000915564055, 6.500000915564055, 6.70710753012043, 6.70710753012043, 6.8660259343358785, 6.8660259343358785, 6.965926101427206, 6.965926101427206, 6.999999999999433, 6.999999999999433, 6.965925550140124, 6.965925550140124, 6.86602486933102, 6.86602486933102, 6.707106023976117, 6.707106023976117, 6.499999070921535, 6.499999070921535, 6.258818006966387, 6.258818006966387, 5.999998923291704, 5.999998923291704, 5.741179912993353, 5.741179912993353, 5.49999906416539, 5.49999906416539, 5.292892453328417, 5.292892453328417, 5.133974053960926, 5.133974053960926, 5.0340738925146695, 5.0340738925146695, 5.000000000000592, 5.000000000000592, 5.034074455917931, 5.034074455917931, 5.133975142372447, 5.133975142372447, 5.29289399257475, 5.29289399257475, 5.500000949349439, 5.500000949349439, 5.741182015642939, 5.741182015642939, 6.000001100114731, 6.000001100114731, 6.25882010961596, 6.25882010961596, 6.500000956105558, 6.500000956105558, 6.707107563222413, 6.707107563222413, 6.8660259577424965, 6.8660259577424965, 6.965926113543336, 6.965926113543336, 6.999999999999382, 6.999999999999382, 6.965925538023895, 6.965925538023895, 6.866024845924313, 6.866024845924313, 6.707105990874061, 6.707105990874061, 6.49999903037998, 6.49999903037998, 6.258817961748174, 6.258817961748174, 5.999998876478379, 5.999998876478379, 5.741179867775166, 5.741179867775166, 5.499999023623493, 5.499999023623493, 5.292892420226434, 5.292892420226434, 5.133974030554309, 5.133974030554309, 5.03407388039854, 5.03407388039854, 5.000000000000645, 5.000000000000645, 5.034074468034279, 5.034074468034279, 5.133975165779154, 5.133975165779154, 5.292894025676807, 5.292894025676807, 5.500000989890994, 5.500000989890994, 5.741182060861152, 5.741182060861152, 6.000001146928056, 6.000001146928056, 6.258820154834146, 6.258820154834146, 6.500000996647061, 6.500000996647061, 6.707107596324717, 6.707107596324717, 6.8660259811491136, 6.8660259811491136, 6.965926125659466, 6.965926125659466, 6.9999999999993285, 6.9999999999993285, 6.965925525907664, 6.965925525907664, 6.866024822517605, 6.866024822517605, 6.707105957772004, 6.707105957772004, 6.499998989838425, 6.499998989838425, 6.258817916529521, 6.258817916529521, 5.999998829665054, 5.999998829665054, 5.741179822556981, 5.741179822556981, 5.4999989830819915, 5.4999989830819915, 5.292892387124453, 5.292892387124453, 5.133974007147465, 5.133974007147465, 5.034073868282412, 5.034073868282412, 5.000000000000698, 5.000000000000698, 5.034074480150512, 5.034074480150512, 5.133975189185864, 5.133975189185864, 5.292894058778865, 5.292894058778865, 5.50000103043255, 5.50000103043255, 5.741182106079366, 5.741182106079366, 6.000001193741836, 6.000001193741836, 6.258820200052332, 6.258820200052332, 6.500001037188563, 6.500001037188563, 6.707107629426698, 6.707107629426698, 6.866026004555728, 6.866026004555728, 6.965926137775592, 6.965926137775592, 6.9999999999992735, 6.9999999999992735, 6.96592551379143, 6.96592551379143, 6.866024799110667, 6.866024799110667, 6.7071059246699445, 6.7071059246699445, 6.499998949296868, 6.499998949296868, 6.258817871311307, 6.258817871311307, 5.999998782851729, 5.999998782851729, 5.741179777338356, 5.741179777338356, 5.499998942540491, 5.499998942540491, 5.292892354022473, 5.292892354022473, 5.133973983740852, 5.133973983740852, 5.0340738561662866, 5.0340738561662866, 5.000000000000755, 5.000000000000755, 5.034074492266747, 5.034074492266747, 5.133975212592576, 5.133975212592576, 5.292894091881246, 5.292894091881246, 5.500001070974108, 5.500001070974108, 5.74118215129758, 5.74118215129758, 6.000001240555161, 6.000001240555161, 6.258820245270517, 6.258820245270517, 6.5000010777300625, 6.5000010777300625, 6.707107662528677, 6.707107662528677, 6.8660260279623415, 6.8660260279623415, 6.965926149891835, 6.965926149891835, 6.999999999999216, 6.999999999999216, 6.965925501675194, 6.965925501675194, 6.866024775703954, 6.866024775703954, 6.707105891567885, 6.707105891567885, 6.499998908754916, 6.499998908754916, 6.258817826093092, 6.258817826093092, 5.999998736038404, 5.999998736038404, 5.741179732120171, 5.741179732120171, 5.499998901998991, 5.499998901998991, 5.292892320920495, 5.292892320920495, 5.13397396033424, 5.13397396033424, 5.034073844050163, 5.034073844050163, 5.000000000000814, 5.000000000000814, 5.034074504382984, 5.034074504382984, 5.133975235999289, 5.133975235999289, 5.292894124983307, 5.292894124983307, 5.500001111515665, 5.500001111515665, 5.741182196515796, 5.741182196515796, 6.000001287368486, 6.000001287368486, 6.258820290488701, 6.258820290488701, 6.500001118271955, 6.500001118271955, 6.707107695630655, 6.707107695630655, 6.866026051368952, 6.866026051368952, 6.965926162007957, 6.965926162007957, 6.999999999999156, 6.999999999999156, 6.965925489558838, 6.965925489558838, 6.86602475229724, 6.86602475229724, 6.707105858465822, 6.707105858465822, 6.499998868213358, 6.499998868213358, 6.258817780874877, 6.258817780874877, 5.9999986892250785, 5.9999986892250785, 5.7411796869019875, 5.7411796869019875, 5.499998861457493, 5.499998861457493, 5.292892287818196, 5.292892287818196, 5.13397393692763, 5.13397393692763, 5.034073831934042, 5.034073831934042, 5.000000000000875, 5.000000000000875, 5.034074516499222, 5.034074516499222, 5.133975259406005, 5.133975259406005, 5.29289415808537, 5.29289415808537, 5.500001152057225, 5.500001152057225, 5.741182241734011, 5.741182241734011, 6.0000013341813565, 6.0000013341813565, 6.258820335707324, 6.258820335707324, 6.500001158813453, 6.500001158813453, 6.70710772873263, 6.70710772873263, 6.8660260747755615, 6.8660260747755615, 6.965926174123959, 6.965926174123959, 6.999999999999094, 6.999999999999094, 6.965925477442599, 6.965925477442599, 6.866024728890523, 6.866024728890523, 6.707105825363759, 6.707105825363759, 6.499998827672192, 6.499998827672192, 6.258817735656222, 6.258817735656222, 5.999998642411299, 5.999998642411299, 5.741179641683804, 5.741179641683804, 5.499998820915995, 5.499998820915995, 5.292892254716542, 5.292892254716542, 5.133973913520794, 5.133973913520794, 5.034073819817804, 5.034073819817804, 5.000000000000938, 5.000000000000938, 5.034074528615463, 5.034074528615463, 5.133975282812722, 5.133975282812722, 5.292894191187113, 5.292894191187113, 5.500001192599179, 5.500001192599179, 5.741182286952666, 5.741182286952666, 6.000001380995136, 6.000001380995136, 6.258820380925068, 6.258820380925068, 6.500001199354556, 6.500001199354556, 6.707107761834926, 6.707107761834926, 6.866026098182395, 6.866026098182395, 6.965926186240195, 6.965926186240195, 6.99999999999903, 6.99999999999903, 6.965925465326475, 6.965925465326475, 6.866024705483578, 6.866024705483578, 6.707105792261372, 6.707105792261372, 6.499998787130237, 6.499998787130237, 6.258817690438445, 6.258817690438445, 5.999998595598429, 5.999998595598429, 5.741179596465182, 5.741179596465182, 5.499998780374105, 5.499998780374105, 5.292892221614247, 5.292892221614247, 5.133973890114415, 5.133973890114415, 5.034073807701805, 5.034073807701805, 5.000000000001003, 5.000000000001003, 5.034074540731824, 5.034074540731824, 5.133975306219669, 5.133975306219669, 5.2928942242895, 5.2928942242895, 5.500001233140347, 5.500001233140347, 5.741182332170444, 5.741182332170444, 6.0000014278089155, 6.0000014278089155, 6.25882042614369, 6.25882042614369, 6.500001239896446, 6.500001239896446, 6.7071077949365785, 6.7071077949365785, 6.8660261215887735, 6.8660261215887735, 6.96592619835643, 6.96592619835643, 6.999999999998964, 6.999999999998964, 6.9659254532101125, 6.9659254532101125, 6.866024682077085, 6.866024682077085, 6.707105759159627, 6.707105759159627, 6.499998746588282, 6.499998746588282, 6.258817645219788, 6.258817645219788, 5.9999985487846486, 5.9999985487846486, 5.741179551247439, 5.741179551247439, 5.4999987398330035, 5.4999987398330035, 5.292892188512597, 5.292892188512597, 5.133973866707583, 5.133973866707583, 5.034073795585572, 5.034073795585572, 5.00000000000107, 5.00000000000107, 5.034074552847952, 5.034074552847952, 5.133975329626162, 5.133975329626162, 5.29289425739189, 5.29289425739189, 5.500001273682304, 5.500001273682304, 5.741182377389101, 5.741182377389101, 6.0000014746217865, 6.0000014746217865, 6.258820471361433, 6.258820471361433, 6.500001280438334, 6.500001280438334, 6.707107828038871, 6.707107828038871, 6.866026144995605, 6.866026144995605, 6.965926210472426, 6.965926210472426, 6.999999999998895, 6.999999999998895, 6.965925441093749, 6.965925441093749, 6.866024658670136, 6.866024658670136, 6.707105726057237, 6.707105726057237, 6.499998706047112, 6.499998706047112, 6.25881760000201, 6.25881760000201, 5.9999985019717785, 5.9999985019717785, 5.741179506028818, 5.741179506028818, 5.499998699291115, 5.499998699291115, 5.292892155410304, 5.292892155410304, 5.1339738433012085, 5.1339738433012085, 5.034073783469577, 5.034073783469577, 5.0000000000011395, 5.0000000000011395, 5.034074564964317, 5.034074564964317, 5.1339753530331125, 5.1339753530331125, 5.292894290493637, 5.292894290493637, 5.500001314223473, 5.500001314223473, 5.741182422607757, 5.741182422607757, 6.000001521435566, 6.000001521435566, 6.2588205165800535, 6.2588205165800535, 6.500001320979434, 6.500001320979434, 6.7071078611405195, 6.7071078611405195, 6.866026168402433, 6.866026168402433, 6.965926222588656, 6.965926222588656, 6.999999999998825, 6.999999999998825, 6.965925428977617, 6.965925428977617, 6.866024635263639, 6.866024635263639, 6.707105692955489, 6.707105692955489, 6.499998665505154, 6.499998665505154, 6.258817554783353, 6.258817554783353, 5.999998455157999, 5.999998455157999, 5.741179460811075, 5.741179460811075, 5.499998658750016, 5.499998658750016, 5.292892122308014, 5.292892122308014, 5.133973819894381, 5.133973819894381, 5.0340737713533485, 5.0340737713533485, 5.0000000000012115, 5.0000000000012115, 5.034074577080449, 5.034074577080449, 5.133975376440064, 5.133975376440064, 5.292894323596029, 5.292894323596029, 5.500001354765431, 5.500001354765431, 5.741182467825537, 5.741182467825537, 6.000001568248436, 6.000001568248436, 6.258820561798674, 6.258820561798674, 6.500001361521321, 6.500001361521321, 6.707107894242809, 6.707107894242809, 6.866026191808805, 6.866026191808805, 6.965926234704647, 6.965926234704647, 6.999999999998752, 6.999999999998752, 6.965925416861249, 6.965925416861249, 6.866024611856686, 6.866024611856686, 6.707105659853096, 6.707105659853096, 6.4999986249639825, 6.4999986249639825, 6.258817509565573, 6.258817509565573, 5.999998408344219, 5.999998408344219, 5.741179415592455, 5.741179415592455, 5.499998618208131, 5.499998618208131, 5.292892089206368, 5.292892089206368, 5.133973796488009, 5.133973796488009, 5.034073759237122, 5.034073759237122, 5.000000000001285, 5.000000000001285, 5.034074589196818, 5.034074589196818, 5.133975399846564, 5.133975399846564, 5.292894356697779, 5.292894356697779, 5.500001395307391, 5.500001395307391, 5.741182513044195, 5.741182513044195, 6.000001615062216, 6.000001615062216, 6.258820607016415, 6.258820607016415, 6.500001402062418, 6.500001402062418, 6.707107927344454, 6.707107927344454, 6.86602621521563, 6.86602621521563, 6.965926246820873, 6.965926246820873, 6.999999999998677, 6.999999999998677, 6.965925404745114, 6.965925404745114, 6.866024588450186, 6.866024588450186, 6.707105626750701, 6.707105626750701, 6.499998584422022, 6.499998584422022, 6.258817464346915, 6.258817464346915, 5.999998361531349, 5.999998361531349, 5.741179370374715, 5.741179370374715, 5.499998577666246, 5.499998577666246, 5.292892056104081, 5.292892056104081, 5.133973773081185, 5.133973773081185, 5.034073747121133, 5.034073747121133, 5.000000000001362, 5.000000000001362, 5.03407460131319, 5.03407460131319, 5.13397542325352, 5.13397542325352, 5.292894389800175, 5.292894389800175, 5.5000014358485645, 5.5000014358485645, 5.741182558261975, 5.741182558261975, 6.000001661875086, 6.000001661875086, 6.258820652235034, 6.258820652235034, 6.500001442604303, 6.500001442604303, 6.707107960446741, 6.707107960446741, 6.866026238621999, 6.866026238621999, 6.965926258936861, 6.965926258936861, 6.999999999998599, 6.999999999998599, 6.965925392628741, 6.965925392628741, 6.8660245650432286, 6.8660245650432286, 6.707105593648949, 6.707105593648949, 6.4999985438808485, 6.4999985438808485, 6.258817419128256, 6.258817419128256, 5.999998314717569, 5.999998314717569, 5.741179325156096, 5.741179325156096, 5.49999853712515, 5.49999853712515, 5.292892023002437, 5.292892023002437, 5.133973749674363, 5.133973749674363, 5.0340737350049105, 5.0340737350049105, 5.00000000000144, 5.00000000000144, 5.034074613429327, 5.034074613429327, 5.133975446660023, 5.133975446660023, 5.292894422901928, 5.292894422901928, 5.500001476390526, 5.500001476390526, 5.741182603480635, 5.741182603480635, 6.000001708688867, 6.000001708688867, 6.258820697452774, 6.258820697452774, 6.500001483145398, 6.500001483145398, 6.707107993549027, 6.707107993549027, 6.86602626202882, 6.86602626202882, 6.965926271053082, 6.965926271053082, 6.99999999999852, 6.99999999999852, 6.965925380512602, 6.965925380512602, 6.8660245416362695, 6.8660245416362695, 6.707105560546552, 6.707105560546552, 6.499998503338886, 6.499998503338886, 6.258817373910475, 6.258817373910475, 5.999998267904698, 5.999998267904698, 5.7411792799374775, 5.7411792799374775, 5.499998496583268, 5.499998496583268, 5.292891989900153, 5.292891989900153, 5.133973726267997, 5.133973726267997, 5.034073722888926, 5.034073722888926, 5.000000000001521, 5.000000000001521, 5.034074625545704, 5.034074625545704, 5.133975470066983, 5.133975470066983, 5.292894456004326, 5.292894456004326, 5.500001516931702, 5.500001516931702, 5.741182648698416, 5.741182648698416, 6.000001755502646, 6.000001755502646, 6.258820742671392, 6.258820742671392, 6.5000015236872795, 6.5000015236872795, 6.707108026650667, 6.707108026650667, 6.866026285435185, 6.866026285435185, 6.965926283169301, 6.965926283169301, 6.999999999998439, 6.999999999998439, 6.965925368396226, 6.965925368396226, 6.8660245182297635, 6.8660245182297635, 6.707105527444796, 6.707105527444796, 6.499998462796922, 6.499998462796922, 6.258817328691815, 6.258817328691815, 5.999998221090919, 5.999998221090919, 5.741179234719739, 5.741179234719739, 5.4999984560421735, 5.4999984560421735, 5.292891956798513, 5.292891956798513, 5.133973702861179, 5.133973702861179, 5.0340737107727085, 5.0340737107727085, 5.000000000001603, 5.000000000001603, 5.0340746376618455, 5.0340746376618455, 5.13397549347349, 5.13397549347349, 5.292894489106726, 5.292894489106726, 5.5000015574736665, 5.5000015574736665, 5.741182693917076, 5.741182693917076, 6.000001802315516, 6.000001802315516, 6.258820787889131, 6.258820787889131, 6.50000156422916, 6.50000156422916, 6.707108059752949, 6.707108059752949, 6.866026308842002, 6.866026308842002, 6.965926295285282, 6.965926295285282, 6.999999999998355, 6.999999999998355, 6.965925356279847, 6.965925356279847, 6.866024494822801, 6.866024494822801, 6.707105494342395, 6.707105494342395, 6.499998422255745, 6.499998422255745, 6.258817283474032, 6.258817283474032, 5.999998174278049, 5.999998174278049, 5.741179189501121, 5.741179189501121, 5.4999984155002934, 5.4999984155002934, 5.292891923696232, 5.292891923696232, 5.133973679454817, 5.133973679454817, 5.034073698656727, 5.034073698656727, 5.000000000001688, 5.000000000001688, 5.034074649778225, 5.034074649778225, 5.1339755168804535, 5.1339755168804535, 5.292894522208485, 5.292894522208485, 5.500001598014844, 5.500001598014844, 5.741182739135738, 5.741182739135738, 6.000001849129297, 6.000001849129297, 6.258820833107748, 6.258820833107748, 6.500001604770253, 6.500001604770253, 6.707108092854586, 6.707108092854586, 6.8660263322488175, 6.8660263322488175, 6.965926307401497, 6.965926307401497, 6.999999999998269, 6.999999999998269, 6.9659253441637015, 6.9659253441637015, 6.866024471416291, 6.866024471416291, 6.707105461240635, 6.707105461240635, 6.499998381713779, 6.499998381713779, 6.2588172382553715, 6.2588172382553715, 5.999998127464268, 5.999998127464268, 5.741179144283383, 5.741179144283383, 5.499998374959202, 5.499998374959202, 5.292891890593952, 5.292891890593952, 5.133973656048003, 5.133973656048003, 5.0340736865405145, 5.0340736865405145, 5.0000000000017755, 5.0000000000017755, 5.034074661894373, 5.034074661894373, 5.133975540287419, 5.133975540287419, 5.2928945553108875, 5.2928945553108875, 5.50000163855681, 5.50000163855681, 5.74118278435352, 5.74118278435352, 6.000001895942167, 6.000001895942167, 6.258820878326364, 6.258820878326364, 6.500001645312131, 6.500001645312131, 6.707108125956865, 6.707108125956865, 6.866026355655177, 6.866026355655177, 6.965926319517474, 6.965926319517474, 6.99999999999818, 6.99999999999818, 6.965925332047318, 6.965925332047318, 6.866024448009325, 6.866024448009325, 6.707105428138232, 6.707105428138232, 6.4999983411726, 6.4999983411726, 6.258817193037588, 6.258817193037588, 5.999998080650489, 5.999998080650489, 5.741179099064768, 5.741179099064768, 5.499998334417324, 5.499998334417324, 5.292891857492317, 5.292891857492317, 5.133973632641645, 5.133973632641645, 5.034073674424302, 5.034073674424302, 5.000000000001864, 5.000000000001864, 5.034074674010756, 5.034074674010756, 5.133975563693932, 5.133975563693932, 5.292894588412649, 5.292894588412649, 5.500001679098777, 5.500001679098777, 5.741182829572183, 5.741182829572183, 6.000001942755946, 6.000001942755946, 6.258820923544102, 6.258820923544102, 6.500001685853221, 6.500001685853221, 6.7071081590584996, 6.7071081590584996, 6.866026379061989, 6.866026379061989, 6.965926331633685, 6.965926331633685, 6.99999999999809, 6.99999999999809, 6.965925319931168, 6.965925319931168, 6.866024424602811, 6.866024424602811, 6.707105395035827, 6.707105395035827, 6.499998300630632, 6.499998300630632, 6.258817147818926, 6.258817147818926, 5.999998033837619, 5.999998033837619, 5.741179053847031, 5.741179053847031, 5.499998293875446, 5.499998293875446, 5.292891824390041, 5.292891824390041, 5.133973609234833, 5.133973609234833, 5.034073662308328, 5.034073662308328, 5.000000000001956, 5.000000000001956, 5.034074686127143, 5.034074686127143, 5.133975587100901, 5.133975587100901, 5.292894621515055, 5.292894621515055, 5.500001719639958, 5.500001719639958, 5.741182874789967, 5.741182874789967, 6.000001989568817, 6.000001989568817, 6.258820968762716, 6.258820968762716, 6.500001726395098, 6.500001726395098, 6.707108192160776, 6.707108192160776, 6.866026402468344, 6.866026402468344, 6.965926343749658, 6.965926343749658, 6.999999999997997, 6.999999999997997, 6.965925307814781, 6.965925307814781, 6.8660244011958405, 6.8660244011958405, 6.707105361934063, 6.707105361934063, 6.499998260089451, 6.499998260089451, 6.258817102600262, 6.258817102600262, 5.999997987023838, 5.999997987023838, 5.741179008628416, 5.741179008628416, 5.4999982533343585, 5.4999982533343585, 5.292891791288408, 5.292891791288408, 5.133973585828024, 5.133973585828024, 5.034073650192121, 5.034073650192121, 5.00000000000205, 5.00000000000205, 5.034074698243296, 5.034074698243296, 5.1339756105074175, 5.1339756105074175, 5.29289465461682, 5.29289465461682, 5.500001760181928, 5.500001760181928, 5.74118292000863, 5.74118292000863, 6.000002036382597, 6.000002036382597, 6.2588210139804525, 6.2588210139804525, 6.500001766936185, 6.500001766936185, 6.70710822526305, 6.70710822526305, 6.866026425875152, 6.866026425875152, 6.965926355865864, 6.965926355865864, 6.999999999997903, 6.999999999997903, 6.965925295698627, 6.965925295698627, 6.866024377788869, 6.866024377788869, 6.707105328831655, 6.707105328831655, 6.49999821954748, 6.49999821954748, 6.258817057382477, 6.258817057382477, 5.999997940210968, 5.999997940210968, 5.741178963409801, 5.741178963409801, 5.499998212792484, 5.499998212792484, 5.292891758186135, 5.292891758186135, 5.133973562421673, 5.133973562421673, 5.034073638076151, 5.034073638076151, 5.000000000002146, 5.000000000002146, 5.034074710359686, 5.034074710359686, 5.133975633914391, 5.133975633914391, 5.292894687719229, 5.292894687719229, 5.500001800723111, 5.500001800723111, 5.7411829652264155, 5.7411829652264155, 6.000002083196376, 6.000002083196376, 6.258821059199066, 6.258821059199066, 6.50000180747806, 6.50000180747806, 6.70710825836468, 6.70710825836468, 6.866026449281503, 6.866026449281503, 6.965926367982068, 6.965926367982068, 6.999999999997805, 6.999999999997805, 6.965925283582235, 6.965925283582235, 6.86602435438235, 6.86602435438235, 6.707105295729888, 6.707105295729888, 6.49999817900551, 6.49999817900551, 6.258817012163813, 6.258817012163813, 5.999997893397189, 5.999997893397189, 5.741178918192066, 5.741178918192066, 5.499998172251398, 5.499998172251398, 5.292891725084506, 5.292891725084506, 5.1339735390148675, 5.1339735390148675, 5.034073625959948, 5.034073625959948, 5.0000000000022435, 5.0000000000022435, 5.034074722475843, 5.034074722475843, 5.133975657320911, 5.133975657320911, 5.292894720821639, 5.292894720821639, 5.500001841265083, 5.500001841265083, 5.74118301044508, 5.74118301044508, 6.000002130009247, 6.000002130009247, 6.258821104416802, 6.258821104416802, 6.500001848019933, 6.500001848019933, 6.707108291466951, 6.707108291466951, 6.866026472688307, 6.866026472688307, 6.965926380098035, 6.965926380098035, 6.999999999997707, 6.999999999997707, 6.965925271465842, 6.965925271465842, 6.866024330975374, 6.866024330975374, 6.7071052626274765, 6.7071052626274765, 6.499998138464325, 6.499998138464325, 6.258816966946027, 6.258816966946027, 5.999997846584318, 5.999997846584318, 5.741178872973453, 5.741178872973453, 5.499998131709525, 5.499998131709525, 5.292891691982236, 5.292891691982236, 5.133973515608519, 5.133973515608519, 5.034073613843982, 5.034073613843982, 5.000000000002344, 5.000000000002344, 5.034074734592238, 5.034074734592238, 5.133975680727888, 5.133975680727888, 5.292894753923409, 5.292894753923409, 5.500001881806268, 5.500001881806268, 5.741183055663745, 5.741183055663745, 6.000002176823027, 6.000002176823027, 6.2588211496354145, 6.2588211496354145, 6.500001888561017, 6.500001888561017, 6.707108324568577, 6.707108324568577, 6.86602649609511, 6.86602649609511, 6.965926392214235, 6.965926392214235, 6.9999999999976055, 6.9999999999976055, 6.965925259349682, 6.965925259349682, 6.866024307568851, 6.866024307568851, 6.707105229525706, 6.707105229525706, 6.499998097922352, 6.499998097922352, 6.2588169217273615, 6.2588169217273615, 5.999997799770538, 5.999997799770538, 5.741178827755719, 5.741178827755719, 5.499998091168441, 5.499998091168441, 5.292891658879967, 5.292891658879967, 5.133973492201718, 5.133973492201718, 5.034073601727783, 5.034073601727783, 5.000000000002446, 5.000000000002446, 5.0340747467083995, 5.0340747467083995, 5.133975704134866, 5.133975704134866, 5.292894787025823, 5.292894787025823, 5.500001922348242, 5.500001922348242, 5.741183100881532, 5.741183100881532, 6.000002223635897, 6.000002223635897, 6.258821194854026, 6.258821194854026, 6.500001929102888, 6.500001929102888, 6.707108357670846, 6.707108357670846, 6.866026519501455, 6.866026519501455, 6.965926404330197, 6.965926404330197, 6.9999999999975016, 6.9999999999975016, 6.965925247233284, 6.965925247233284, 6.86602428416187, 6.86602428416187, 6.707105196423292, 6.707105196423292, 6.499998057381164, 6.499998057381164, 6.258816876509574, 6.258816876509574, 5.999997752956759, 5.999997752956759, 5.741178782537107, 5.741178782537107, 5.499998050626571, 5.499998050626571, 5.292891625778342, 5.292891625778342, 5.133973468795372, 5.133973468795372, 5.0340735896115865, 5.0340735896115865, 5.000000000002551, 5.000000000002551, 5.034074758824798, 5.034074758824798, 5.133975727541393, 5.133975727541393, 5.292894820127595, 5.292894820127595, 5.5000019628902175, 5.5000019628902175, 5.741183146100198, 5.741183146100198, 6.000002270449677, 6.000002270449677, 6.258821240071759, 6.258821240071759, 6.500001969643971, 6.500001969643971, 6.707108390772469, 6.707108390772469, 6.866026542908253, 6.866026542908253, 6.965926416446393, 6.965926416446393, 6.999999999997396, 6.999999999997396, 6.965925235117119, 6.965925235117119, 6.866024260755344, 6.866024260755344, 6.707105163320875, 6.707105163320875, 6.499998016839188, 6.499998016839188, 6.258816831290908, 6.258816831290908, 5.999997706143888, 5.999997706143888, 5.741178737319374, 5.741178737319374, 5.499998010084701, 5.499998010084701, 5.292891592676076, 5.292891592676076, 5.133973445388575, 5.133973445388575, 5.034073577495628, 5.034073577495628, 5.000000000002657, 5.000000000002657, 5.034074770941199, 5.034074770941199, 5.133975750948375, 5.133975750948375, 5.2928948532300115, 5.2928948532300115, 5.500002003431406, 5.500002003431406, 5.741183191317987, 5.741183191317987, 6.000002317262547, 6.000002317262547, 6.258821285290371, 6.258821285290371, 6.500002010185839, 6.500002010185839, 6.707108423874734, 6.707108423874734, 6.866026566314596, 6.866026566314596, 6.9659264285623514, 6.9659264285623514, 6.9999999999972875, 6.9999999999972875, 6.965925223000717, 6.965925223000717, 6.86602423734836, 6.86602423734836, 6.707105130219101, 6.707105130219101, 6.499997976297999, 6.499997976297999, 6.258816786072241, 6.258816786072241, 5.999997659330108, 5.999997659330108, 5.741178692100763, 5.741178692100763, 5.499997969543621, 5.499997969543621, 5.292891559574455, 5.292891559574455, 5.133973421981779, 5.133973421981779, 5.034073565379435, 5.034073565379435, 5.000000000002767, 5.000000000002767, 5.034074783057367, 5.034074783057367, 5.133975774354905, 5.133975774354905, 5.292894886331787, 5.292894886331787, 5.5000020439733825, 5.5000020439733825, 5.741183236536654, 5.741183236536654, 6.000002364076327, 6.000002364076327, 6.258821330508103, 6.258821330508103, 6.500002050726919, 6.500002050726919, 6.707108456976997, 6.707108456976997, 6.866026589721391, 6.866026589721391, 6.965926440678543, 6.965926440678543, 6.999999999997177, 6.999999999997177, 6.9659252108845475, 6.9659252108845475, 6.866024213941375, 6.866024213941375, 6.707105097116682, 6.707105097116682, 6.499997935756022, 6.499997935756022, 6.258816740854451, 6.258816740854451, 5.999997612517238, 5.999997612517238, 5.741178646882153, 5.741178646882153, 5.499997929001753, 5.499997929001753, 5.292891526472193, 5.292891526472193, 5.13397339857544, 5.13397339857544, 5.03407355326348, 5.03407355326348, 5.000000000002878, 5.000000000002878, 5.0340747951737725, 5.0340747951737725, 5.133975797761892, 5.133975797761892, 5.292894919434207, 5.292894919434207, 5.500002084514573, 5.500002084514573, 5.741183281754443, 5.741183281754443, 6.000002410890107, 6.000002410890107, 6.258821375726713, 6.258821375726713, 6.500002091268786, 6.500002091268786, 6.707108490078617, 6.707108490078617, 6.866026613127728, 6.866026613127728, 6.965926452794733, 6.965926452794733, 6.9999999999970655, 6.9999999999970655, 6.965925198768142, 6.965925198768142, 6.8660241905348425, 6.8660241905348425, 6.707105064014904, 6.707105064014904, 6.499997895214043, 6.499997895214043, 6.258816695635783, 6.258816695635783, 5.999997565703458, 5.999997565703458, 5.741178601664422, 5.741178601664422, 5.499997888460675, 5.499997888460675, 5.292891493370575, 5.292891493370575, 5.133973375168649, 5.133973375168649, 5.034073541147292, 5.034073541147292, 5.000000000002991, 5.000000000002991, 5.034074807289945, 5.034074807289945, 5.133975821168425, 5.133975821168425, 5.292894952536629, 5.292894952536629, 5.500002125056553, 5.500002125056553, 5.741183326973112, 5.741183326973112, 6.000002457702977, 6.000002457702977, 6.258821420944444, 6.258821420944444, 6.500002131810652, 6.500002131810652, 6.707108523180877, 6.707108523180877, 6.866026636534519, 6.866026636534519, 6.965926464910685, 6.965926464910685, 6.999999999996951, 6.999999999996951, 6.965925186651733, 6.965925186651733, 6.866024167127853, 6.866024167127853, 6.707105030912482, 6.707105030912482, 6.49999785467285, 6.49999785467285, 6.258816650417993, 6.258816650417993, 5.999997518890588, 5.999997518890588, 5.741178556445813, 5.741178556445813, 5.49999784791881, 5.49999784791881, 5.292891460268315, 5.292891460268315, 5.133973351762314, 5.133973351762314, 5.034073529031341, 5.034073529031341, 5.000000000003107, 5.000000000003107, 5.034074819406354, 5.034074819406354, 5.133975844575415, 5.133975844575415, 5.292894985638409, 5.292894985638409, 5.500002165597746, 5.500002165597746, 5.741183372191781, 5.741183372191781, 6.000002504516757, 6.000002504516757, 6.258821466163052, 6.258821466163052, 6.500002172351728, 6.500002172351728, 6.707108556282493, 6.707108556282493, 6.8660266599413085, 6.8660266599413085, 6.965926477026869, 6.965926477026869, 6.9999999999968345, 6.9999999999968345, 6.965925174535558, 6.965925174535558, 6.866024143721317, 6.866024143721317, 6.707104997810701, 6.707104997810701, 6.49999781413087, 6.49999781413087, 6.258816605199324, 6.258816605199324, 5.999997472076808, 5.999997472076808, 5.741178511228083, 5.741178511228083, 5.499997807377734, 5.499997807377734, 5.29297775466854, 5.29297775466854, 5.134079057408345, 5.134079057408345, 5.034191442257982, 5.034191442257982, 5.000122085218705, 5.000122085218705, 5.034192756704862, 5.034192756704862, 5.134081596725176, 5.134081596725176, 5.292981345804837, 5.292981345804837, 5.50006324847813, 5.50006324847813, 5.741215015087827, 5.741215015087827, 6.000002551018148, 6.000002551018148, 6.258789913101673, 6.258789913101673, 6.499941170015689, 6.499941170015689, 6.707022261880247, 6.707022261880247, 6.865920954293392, 6.865920954293392, 6.965808563799252, 6.965808563799252, 6.999877914781235, 6.999877914781235, 6.965807237237553, 6.965807237237553, 6.865601205348964, 6.865601205348964, 6.705292096406619, 6.705292096406619, 6.493527285981263, 6.493527285981263, 6.242069791969054, 6.242069791969054, 5.999998115862116, 5.999998115862116, 5.912061979111753, 5.912061979111753, 5.955804954607395, 5.955804954607395, 5.987309856541017, 5.987309856541017 ] } } }, "739989c6df8b44ac8e2e610ee68b5e36": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "TooltipModel", "state": { "fields": [ "name" ], "labels": [ "Channel" ], "layout": "IPY_MODEL_ac135538e5624fb58eda02fa58dd9333" } }, "7a061f2853d949568f68550f3c362e1b": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#d62728" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_ch2" ], "scales": { "x": "IPY_MODEL_8342a76ecdda4fb9a4e600d8ab8fc329", "y": "IPY_MODEL_30d949e7ba5846e89f696eade5e275c3" }, "selected": [], "tooltip": "IPY_MODEL_de89493a5a54462aa3afafdeebcc5adf", "x": { "type": "float", "values": [ 0, 100, 100, 100.83333333333333, 100.83333333333333, 101.66666666666666, 101.66666666666666, 102.5, 102.5, 103.33333333333333, 103.33333333333333, 104.16666666666667, 104.16666666666667, 105, 105, 105.83333333333333, 105.83333333333333, 106.66666666666667, 106.66666666666667, 107.5, 107.5, 108.33333333333334, 108.33333333333334, 109.16666666666667, 109.16666666666667, 110, 110, 110.83333333333334, 110.83333333333334, 111.66666666666667, 111.66666666666667, 112.50000000000001, 112.50000000000001, 113.33333333333334, 113.33333333333334, 114.16666666666666, 114.16666666666666, 115, 115, 115.83333333333333, 115.83333333333333, 116.66666666666667, 116.66666666666667, 117.5, 117.5, 118.33333333333333, 118.33333333333333, 119.16666666666667, 119.16666666666667, 120.00000000000001, 120.00000000000001, 120.83333333333333, 120.83333333333333, 121.66666666666666, 121.66666666666666, 122.49999999999999, 122.49999999999999, 123.33333333333333, 123.33333333333333, 124.16666666666666, 124.16666666666666, 125, 125, 125.83333333333333, 125.83333333333333, 126.66666666666666, 126.66666666666666, 127.5, 127.5, 128.33333333333334, 128.33333333333334, 129.16666666666666, 129.16666666666666, 130, 130, 130.83333333333334, 130.83333333333334, 131.66666666666666, 131.66666666666666, 132.5, 132.5, 133.33333333333334, 133.33333333333334, 134.16666666666666, 134.16666666666666, 135, 135, 135.83333333333334, 135.83333333333334, 136.66666666666669, 136.66666666666669, 137.5, 137.5, 138.33333333333334, 138.33333333333334, 139.16666666666669, 139.16666666666669, 140, 140, 140.83333333333334, 140.83333333333334, 141.66666666666669, 141.66666666666669, 142.5, 142.5, 143.33333333333334, 143.33333333333334, 144.16666666666669, 144.16666666666669, 145.00000000000003, 145.00000000000003, 145.83333333333334, 145.83333333333334, 146.66666666666666, 146.66666666666666, 147.5, 147.5, 148.33333333333331, 148.33333333333331, 149.16666666666666, 149.16666666666666, 150, 150, 150.83333333333331, 150.83333333333331, 151.66666666666666, 151.66666666666666, 152.5, 152.5, 153.33333333333334, 153.33333333333334, 154.16666666666666, 154.16666666666666, 155, 155, 155.83333333333334, 155.83333333333334, 156.66666666666666, 156.66666666666666, 157.5, 157.5, 158.33333333333334, 158.33333333333334, 159.16666666666666, 159.16666666666666, 160, 160, 160.83333333333334, 160.83333333333334, 161.66666666666669, 161.66666666666669, 162.5, 162.5, 163.33333333333334, 163.33333333333334, 164.16666666666669, 164.16666666666669, 165, 165, 165.83333333333334, 165.83333333333334, 166.66666666666669, 166.66666666666669, 167.5, 167.5, 168.33333333333334, 168.33333333333334, 169.16666666666669, 169.16666666666669, 170, 170, 170.83333333333334, 170.83333333333334, 171.66666666666669, 171.66666666666669, 172.50000000000003, 172.50000000000003, 173.33333333333334, 173.33333333333334, 174.16666666666666, 174.16666666666666, 175, 175, 175.83333333333331, 175.83333333333331, 176.66666666666666, 176.66666666666666, 177.5, 177.5, 178.33333333333334, 178.33333333333334, 179.16666666666666, 179.16666666666666, 180, 180, 180.83333333333334, 180.83333333333334, 181.66666666666666, 181.66666666666666, 182.5, 182.5, 183.33333333333334, 183.33333333333334, 184.16666666666666, 184.16666666666666, 185, 185, 185.83333333333334, 185.83333333333334, 186.66666666666666, 186.66666666666666, 187.5, 187.5, 188.33333333333334, 188.33333333333334, 189.16666666666669, 189.16666666666669, 190, 190, 190.83333333333334, 190.83333333333334, 191.66666666666669, 191.66666666666669, 192.5, 192.5, 193.33333333333334, 193.33333333333334, 194.16666666666669, 194.16666666666669, 195, 195, 195.83333333333334, 195.83333333333334, 196.66666666666669, 196.66666666666669, 197.50000000000003, 197.50000000000003, 198.33333333333334, 198.33333333333334, 199.16666666666669, 199.16666666666669, 200, 200, 200.83333333333331, 200.83333333333331, 201.66666666666666, 201.66666666666666, 202.5, 202.5, 203.33333333333331, 203.33333333333331, 204.16666666666666, 204.16666666666666, 205, 205, 205.83333333333334, 205.83333333333334, 206.66666666666666, 206.66666666666666, 207.5, 207.5, 208.33333333333334, 208.33333333333334, 209.16666666666666, 209.16666666666666, 210, 210, 210.83333333333334, 210.83333333333334, 211.66666666666666, 211.66666666666666, 212.5, 212.5, 213.33333333333334, 213.33333333333334, 214.16666666666669, 214.16666666666669, 215, 215, 215.83333333333334, 215.83333333333334, 216.66666666666669, 216.66666666666669, 217.5, 217.5, 218.33333333333334, 218.33333333333334, 219.16666666666669, 219.16666666666669, 220, 220, 220.83333333333334, 220.83333333333334, 221.66666666666669, 221.66666666666669, 222.5, 222.5, 223.33333333333334, 223.33333333333334, 224.16666666666669, 224.16666666666669, 225.00000000000003, 225.00000000000003, 225.83333333333334, 225.83333333333334, 226.66666666666669, 226.66666666666669, 227.5, 227.5, 228.33333333333331, 228.33333333333331, 229.16666666666666, 229.16666666666666, 230, 230, 230.83333333333334, 230.83333333333334, 231.66666666666666, 231.66666666666666, 232.5, 232.5, 233.33333333333334, 233.33333333333334, 234.16666666666666, 234.16666666666666, 235, 235, 235.83333333333334, 235.83333333333334, 236.66666666666666, 236.66666666666666, 237.5, 237.5, 238.33333333333334, 238.33333333333334, 239.16666666666666, 239.16666666666666, 240.00000000000003, 240.00000000000003, 240.83333333333334, 240.83333333333334, 241.66666666666666, 241.66666666666666, 242.5, 242.5, 243.33333333333331, 243.33333333333331, 244.16666666666669, 244.16666666666669, 244.99999999999997, 244.99999999999997, 245.83333333333334, 245.83333333333334, 246.66666666666666, 246.66666666666666, 247.5, 247.5, 248.33333333333331, 248.33333333333331, 249.16666666666669, 249.16666666666669, 250, 250, 250.83333333333334, 250.83333333333334, 251.66666666666666, 251.66666666666666, 252.50000000000003, 252.50000000000003, 253.33333333333331, 253.33333333333331, 254.16666666666669, 254.16666666666669, 255, 255, 255.83333333333334, 255.83333333333334, 256.6666666666667, 256.6666666666667, 257.5, 257.5, 258.3333333333333, 258.3333333333333, 259.1666666666667, 259.1666666666667, 260, 260, 260.83333333333337, 260.83333333333337, 261.6666666666667, 261.6666666666667, 262.5, 262.5, 263.3333333333333, 263.3333333333333, 264.1666666666667, 264.1666666666667, 265, 265, 265.83333333333337, 265.83333333333337, 266.6666666666667, 266.6666666666667, 267.5, 267.5, 268.3333333333333, 268.3333333333333, 269.16666666666663, 269.16666666666663, 270, 270, 270.8333333333333, 270.8333333333333, 271.6666666666667, 271.6666666666667, 272.5, 272.5, 273.33333333333337, 273.33333333333337, 274.16666666666663, 274.16666666666663, 275, 275, 275.8333333333333, 275.8333333333333, 276.6666666666667, 276.6666666666667, 277.5, 277.5, 278.33333333333337, 278.33333333333337, 279.1666666666667, 279.1666666666667, 280, 280, 280.8333333333333, 280.8333333333333, 281.6666666666667, 281.6666666666667, 282.5, 282.5, 283.33333333333337, 283.33333333333337, 284.1666666666667, 284.1666666666667, 285, 285, 285.8333333333333, 285.8333333333333, 286.6666666666667, 286.6666666666667, 287.5, 287.5, 288.33333333333337, 288.33333333333337, 289.1666666666667, 289.1666666666667, 290.00000000000006, 290.00000000000006, 290.8333333333333, 290.8333333333333, 291.6666666666667, 291.6666666666667, 292.5, 292.5, 293.3333333333333, 293.3333333333333, 294.1666666666667, 294.1666666666667, 295, 295, 295.8333333333333, 295.8333333333333, 296.66666666666663, 296.66666666666663, 297.5, 297.5, 298.3333333333333, 298.3333333333333, 299.1666666666667, 299.1666666666667, 300, 300, 300.83333333333337, 300.83333333333337, 301.66666666666663, 301.66666666666663, 302.5, 302.5, 303.3333333333333, 303.3333333333333, 304.1666666666667, 304.1666666666667, 305, 305, 305.83333333333337, 305.83333333333337, 306.6666666666667, 306.6666666666667, 307.5, 307.5, 308.3333333333333, 308.3333333333333, 309.1666666666667, 309.1666666666667, 310, 310, 310.83333333333337, 310.83333333333337, 311.6666666666667, 311.6666666666667, 312.5, 312.5, 313.3333333333333, 313.3333333333333, 314.1666666666667, 314.1666666666667, 315, 315, 315.83333333333337, 315.83333333333337, 316.6666666666667, 316.6666666666667, 317.50000000000006, 317.50000000000006, 318.3333333333333, 318.3333333333333, 319.1666666666667, 319.1666666666667, 320, 320, 320.8333333333333, 320.8333333333333, 321.6666666666667, 321.6666666666667, 322.5, 322.5, 323.33333333333337, 323.33333333333337, 324.16666666666663, 324.16666666666663, 325, 325, 325.8333333333333, 325.8333333333333, 326.6666666666667, 326.6666666666667, 327.5, 327.5, 328.33333333333337, 328.33333333333337, 329.16666666666663, 329.16666666666663, 330, 330, 330.8333333333333, 330.8333333333333, 331.6666666666667, 331.6666666666667, 332.5, 332.5, 333.33333333333337, 333.33333333333337, 334.1666666666667, 334.1666666666667, 335, 335, 335.8333333333333, 335.8333333333333, 336.6666666666667, 336.6666666666667, 337.5, 337.5, 338.33333333333337, 338.33333333333337, 339.1666666666667, 339.1666666666667, 340, 340, 340.8333333333333, 340.8333333333333, 341.6666666666667, 341.6666666666667, 342.5, 342.5, 343.33333333333337, 343.33333333333337, 344.1666666666667, 344.1666666666667, 345.00000000000006, 345.00000000000006, 345.8333333333333, 345.8333333333333, 346.6666666666667, 346.6666666666667, 347.5, 347.5, 348.3333333333333, 348.3333333333333, 349.1666666666667, 349.1666666666667, 350, 350, 350.83333333333337, 350.83333333333337, 351.66666666666663, 351.66666666666663, 352.5, 352.5, 353.3333333333333, 353.3333333333333, 354.1666666666667, 354.1666666666667, 355, 355, 355.83333333333337, 355.83333333333337, 356.6666666666667, 356.6666666666667, 357.5, 357.5, 358.3333333333333, 358.3333333333333, 359.1666666666667, 359.1666666666667, 360, 360, 360.83333333333337, 360.83333333333337, 361.6666666666667, 361.6666666666667, 362.5, 362.5, 363.3333333333333, 363.3333333333333, 364.1666666666667, 364.1666666666667, 365, 365, 365.83333333333337, 365.83333333333337, 366.6666666666667, 366.6666666666667, 367.5, 367.5, 368.3333333333333, 368.3333333333333, 369.1666666666667, 369.1666666666667, 370, 370, 370.83333333333337, 370.83333333333337, 371.6666666666667, 371.6666666666667, 372.50000000000006, 372.50000000000006, 373.3333333333333, 373.3333333333333, 374.16666666666663, 374.16666666666663, 375, 375, 375.8333333333333, 375.8333333333333, 376.6666666666667, 376.6666666666667, 377.5, 377.5, 378.33333333333337, 378.33333333333337, 379.16666666666663, 379.16666666666663, 380, 380, 380.8333333333333, 380.8333333333333, 381.6666666666667, 381.6666666666667, 382.5, 382.5, 383.33333333333337, 383.33333333333337, 384.1666666666667, 384.1666666666667, 385, 385, 385.8333333333333, 385.8333333333333, 386.6666666666667, 386.6666666666667, 387.5, 387.5, 388.33333333333337, 388.33333333333337, 389.1666666666667, 389.1666666666667, 390, 390, 390.8333333333333, 390.8333333333333, 391.6666666666667, 391.6666666666667, 392.5, 392.5, 393.33333333333337, 393.33333333333337, 394.1666666666667, 394.1666666666667, 395.00000000000006, 395.00000000000006, 395.8333333333333, 395.8333333333333, 396.6666666666667, 396.6666666666667, 397.5, 397.5, 398.33333333333337, 398.33333333333337, 399.1666666666667, 399.1666666666667, 400, 400, 400.8333333333333, 400.8333333333333, 401.66666666666663, 401.66666666666663, 402.5, 402.5, 403.3333333333333, 403.3333333333333, 404.1666666666667, 404.1666666666667, 405, 405, 405.83333333333337, 405.83333333333337, 406.66666666666663, 406.66666666666663, 407.5, 407.5, 408.3333333333333, 408.3333333333333, 409.1666666666667, 409.1666666666667, 410, 410, 410.83333333333337, 410.83333333333337, 411.6666666666667, 411.6666666666667, 412.5, 412.5, 413.3333333333333, 413.3333333333333, 414.1666666666667, 414.1666666666667, 415, 415, 415.83333333333337, 415.83333333333337, 416.6666666666667, 416.6666666666667, 417.5, 417.5, 418.3333333333333, 418.3333333333333, 419.1666666666667, 419.1666666666667, 420, 420, 420.83333333333337, 420.83333333333337, 421.6666666666667, 421.6666666666667, 422.50000000000006, 422.50000000000006, 423.3333333333333, 423.3333333333333, 424.1666666666667, 424.1666666666667, 425, 425, 425.83333333333337, 425.83333333333337, 426.6666666666667, 426.6666666666667, 427.5, 427.5, 428.33333333333337, 428.33333333333337, 429.16666666666663, 429.16666666666663, 430, 430, 430.8333333333333, 430.8333333333333, 431.6666666666667, 431.6666666666667, 432.5, 432.5, 433.33333333333337, 433.33333333333337, 434.16666666666663, 434.16666666666663, 435, 435, 435.8333333333333, 435.8333333333333, 436.6666666666667, 436.6666666666667, 437.5, 437.5, 438.33333333333337, 438.33333333333337, 439.1666666666667, 439.1666666666667, 440, 440, 440.8333333333333, 440.8333333333333, 441.6666666666667, 441.6666666666667, 442.5, 442.5, 443.33333333333337, 443.33333333333337, 444.1666666666667, 444.1666666666667, 445, 445, 445.8333333333333, 445.8333333333333, 446.6666666666667, 446.6666666666667, 447.5, 447.5, 448.33333333333337, 448.33333333333337, 449.1666666666667, 449.1666666666667, 450.00000000000006, 450.00000000000006, 450.8333333333333, 450.8333333333333, 451.6666666666667, 451.6666666666667, 452.5, 452.5, 453.33333333333337, 453.33333333333337, 454.1666666666667, 454.1666666666667, 455, 455, 455.83333333333337, 455.83333333333337, 456.66666666666663, 456.66666666666663, 457.5, 457.5, 458.3333333333333, 458.3333333333333, 459.1666666666667, 459.1666666666667, 460, 460, 460.83333333333337, 460.83333333333337, 461.6666666666667, 461.6666666666667, 462.5, 462.5, 463.3333333333333, 463.3333333333333, 464.1666666666667, 464.1666666666667, 465, 465, 465.83333333333337, 465.83333333333337, 466.6666666666667, 466.6666666666667, 467.5, 467.5, 468.3333333333333, 468.3333333333333, 469.1666666666667, 469.1666666666667, 470, 470, 470.83333333333337, 470.83333333333337, 471.6666666666667, 471.6666666666667, 472.50000000000006, 472.50000000000006, 473.3333333333333, 473.3333333333333, 474.1666666666667, 474.1666666666667, 475, 475, 475.83333333333337, 475.83333333333337, 476.6666666666667, 476.6666666666667, 477.5, 477.5, 478.3333333333333, 478.3333333333333, 479.1666666666667, 479.1666666666667, 480.00000000000006, 480.00000000000006, 480.8333333333333, 480.8333333333333, 481.6666666666667, 481.6666666666667, 482.50000000000006, 482.50000000000006, 483.3333333333333, 483.3333333333333, 484.16666666666663, 484.16666666666663, 485, 485, 485.83333333333337, 485.83333333333337, 486.66666666666663, 486.66666666666663, 487.5, 487.5, 488.33333333333337, 488.33333333333337, 489.1666666666667, 489.1666666666667, 489.99999999999994, 489.99999999999994, 490.8333333333333, 490.8333333333333, 491.6666666666667, 491.6666666666667, 492.50000000000006, 492.50000000000006, 493.3333333333333, 493.3333333333333, 494.1666666666667, 494.1666666666667, 495, 495, 495.83333333333337, 495.83333333333337, 496.66666666666663, 496.66666666666663, 497.5, 497.5, 498.33333333333337, 498.33333333333337, 499.16666666666674, 499.16666666666674, 500, 500, 500.8333333333333, 500.8333333333333, 501.6666666666667, 501.6666666666667, 502.50000000000006, 502.50000000000006, 503.3333333333333, 503.3333333333333, 504.1666666666667, 504.1666666666667, 505.00000000000006, 505.00000000000006, 505.83333333333337, 505.83333333333337, 506.66666666666663, 506.66666666666663, 507.5, 507.5, 508.33333333333337, 508.33333333333337, 509.16666666666663, 509.16666666666663, 510, 510, 510.83333333333337, 510.83333333333337, 511.6666666666667, 511.6666666666667, 512.5, 512.5, 513.3333333333334, 513.3333333333334, 514.1666666666666, 514.1666666666666, 515, 515, 515.8333333333333, 515.8333333333333, 516.6666666666666, 516.6666666666666, 517.5, 517.5, 518.3333333333334, 518.3333333333334, 519.1666666666666, 519.1666666666666, 520, 520, 520.8333333333334, 520.8333333333334, 521.6666666666667, 521.6666666666667, 522.5, 522.5, 523.3333333333334, 523.3333333333334, 524.1666666666667, 524.1666666666667, 525, 525, 525.8333333333333, 525.8333333333333, 526.6666666666666, 526.6666666666666, 527.5, 527.5, 528.3333333333334, 528.3333333333334, 529.1666666666666, 529.1666666666666, 530, 530, 530.8333333333334, 530.8333333333334, 531.6666666666667, 531.6666666666667, 532.5, 532.5, 533.3333333333334, 533.3333333333334, 534.1666666666667, 534.1666666666667, 535, 535, 535.8333333333334, 535.8333333333334, 536.6666666666666, 536.6666666666666, 537.5, 537.5, 538.3333333333333, 538.3333333333333, 539.1666666666666, 539.1666666666666, 540, 540, 540.8333333333334, 540.8333333333334, 541.6666666666666, 541.6666666666666, 542.5, 542.5, 543.3333333333334, 543.3333333333334, 544.1666666666667, 544.1666666666667, 545, 545, 545.8333333333334, 545.8333333333334, 546.6666666666667, 546.6666666666667, 547.5, 547.5, 548.3333333333333, 548.3333333333333, 549.1666666666666, 549.1666666666666, 550, 550, 550.8333333333334, 550.8333333333334, 551.6666666666666, 551.6666666666666, 552.5, 552.5, 553.3333333333334, 553.3333333333334, 554.1666666666667, 554.1666666666667, 555, 555, 555.8333333333334, 555.8333333333334, 556.6666666666667, 556.6666666666667, 557.5000000000001, 557.5000000000001, 558.3333333333334, 558.3333333333334, 559.1666666666666, 559.1666666666666, 560, 560, 560.8333333333333, 560.8333333333333, 561.6666666666666, 561.6666666666666, 562.5, 562.5, 563.3333333333334, 563.3333333333334, 564.1666666666666, 564.1666666666666, 565, 565, 565.8333333333334, 565.8333333333334, 566.6666666666667, 566.6666666666667, 567.5, 567.5, 568.3333333333334, 568.3333333333334, 569.1666666666667, 569.1666666666667, 570, 570, 570.8333333333333, 570.8333333333333, 571.6666666666666, 571.6666666666666, 572.5, 572.5, 573.3333333333334, 573.3333333333334, 574.1666666666666, 574.1666666666666, 575, 575, 575.8333333333334, 575.8333333333334, 576.6666666666667, 576.6666666666667, 577.5, 577.5, 578.3333333333334, 578.3333333333334, 579.1666666666667, 579.1666666666667, 580.0000000000001, 580.0000000000001, 580.8333333333333, 580.8333333333333, 581.6666666666666, 581.6666666666666, 582.5, 582.5, 583.3333333333334, 583.3333333333334, 584.1666666666666, 584.1666666666666, 585, 585, 585.8333333333334, 585.8333333333334, 586.6666666666666, 586.6666666666666, 587.5, 587.5, 588.3333333333334, 588.3333333333334, 589.1666666666667, 589.1666666666667, 590, 590, 590.8333333333334, 590.8333333333334, 591.6666666666666, 591.6666666666666, 592.5, 592.5, 593.3333333333333, 593.3333333333333, 594.1666666666666, 594.1666666666666, 595, 595, 595.8333333333334, 595.8333333333334, 596.6666666666666, 596.6666666666666, 597.5, 597.5, 598.3333333333334, 598.3333333333334, 599.1666666666667, 599.1666666666667, 600, 600, 600.8333333333334, 600.8333333333334, 601.6666666666667, 601.6666666666667, 602.5, 602.5, 603.3333333333333, 603.3333333333333, 604.1666666666666, 604.1666666666666, 605, 605, 605.8333333333334, 605.8333333333334, 606.6666666666666, 606.6666666666666, 607.5, 607.5, 608.3333333333334, 608.3333333333334, 609.1666666666667, 609.1666666666667, 610, 610, 610.8333333333334, 610.8333333333334, 611.6666666666667, 611.6666666666667, 612.5000000000001, 612.5000000000001, 613.3333333333334, 613.3333333333334, 614.1666666666666, 614.1666666666666, 615, 615, 615.8333333333333, 615.8333333333333, 616.6666666666666, 616.6666666666666, 617.5, 617.5, 618.3333333333334, 618.3333333333334, 619.1666666666666, 619.1666666666666, 620, 620, 620.8333333333334, 620.8333333333334, 621.6666666666667, 621.6666666666667, 622.5, 622.5, 623.3333333333334, 623.3333333333334, 624.1666666666667, 624.1666666666667, 625, 625, 625.8333333333333, 625.8333333333333, 626.6666666666666, 626.6666666666666, 627.5, 627.5, 628.3333333333334, 628.3333333333334, 629.1666666666666, 629.1666666666666, 630, 630, 630.8333333333334, 630.8333333333334, 631.6666666666667, 631.6666666666667, 632.5, 632.5, 633.3333333333334, 633.3333333333334, 634.1666666666667, 634.1666666666667, 635.0000000000001, 635.0000000000001, 635.8333333333334, 635.8333333333334, 636.6666666666666, 636.6666666666666, 637.5, 637.5, 638.3333333333334, 638.3333333333334, 639.1666666666666, 639.1666666666666, 640, 640, 640.8333333333334, 640.8333333333334, 641.6666666666666, 641.6666666666666, 642.5, 642.5, 643.3333333333334, 643.3333333333334, 644.1666666666667, 644.1666666666667, 645, 645, 645.8333333333334, 645.8333333333334, 646.6666666666667, 646.6666666666667, 647.5, 647.5, 648.3333333333333, 648.3333333333333, 649.1666666666666, 649.1666666666666, 650, 650, 650.8333333333334, 650.8333333333334, 651.6666666666666, 651.6666666666666, 652.5, 652.5, 653.3333333333334, 653.3333333333334, 654.1666666666667, 654.1666666666667, 655, 655, 655.8333333333334, 655.8333333333334, 656.6666666666667, 656.6666666666667, 657.5000000000001, 657.5000000000001, 658.3333333333333, 658.3333333333333, 659.1666666666666, 659.1666666666666, 660, 660, 660.8333333333334, 660.8333333333334, 661.6666666666666, 661.6666666666666, 662.5, 662.5, 663.3333333333334, 663.3333333333334, 664.1666666666667, 664.1666666666667, 665, 665, 665.8333333333334, 665.8333333333334, 666.6666666666667, 666.6666666666667, 667.5, 667.5, 668.3333333333334, 668.3333333333334, 669.1666666666666, 669.1666666666666, 670, 670, 670.8333333333333, 670.8333333333333, 671.6666666666666, 671.6666666666666, 672.5, 672.5, 673.3333333333334, 673.3333333333334, 674.1666666666666, 674.1666666666666, 675, 675, 675.8333333333334, 675.8333333333334, 676.6666666666667, 676.6666666666667, 677.5, 677.5, 678.3333333333334, 678.3333333333334, 679.1666666666667, 679.1666666666667, 680, 680, 680.8333333333333, 680.8333333333333, 681.6666666666666, 681.6666666666666, 682.5, 682.5, 683.3333333333334, 683.3333333333334, 684.1666666666666, 684.1666666666666, 685, 685, 685.8333333333334, 685.8333333333334, 686.6666666666667, 686.6666666666667, 687.5, 687.5, 688.3333333333334, 688.3333333333334, 689.1666666666667, 689.1666666666667, 690.0000000000001, 690.0000000000001, 690.8333333333334, 690.8333333333334, 691.6666666666666, 691.6666666666666, 692.5, 692.5, 693.3333333333334, 693.3333333333334, 694.1666666666666, 694.1666666666666, 695, 695, 695.8333333333334, 695.8333333333334, 696.6666666666666, 696.6666666666666, 697.5, 697.5, 698.3333333333334, 698.3333333333334, 699.1666666666667, 699.1666666666667, 700, 700, 700.8333333333334, 700.8333333333334, 701.6666666666667, 701.6666666666667, 702.5, 702.5, 703.3333333333333, 703.3333333333333, 704.1666666666666, 704.1666666666666, 705, 705, 705.8333333333334, 705.8333333333334, 706.6666666666666, 706.6666666666666, 707.5, 707.5, 708.3333333333334, 708.3333333333334, 709.1666666666667, 709.1666666666667, 710, 710, 710.8333333333334, 710.8333333333334, 711.6666666666667, 711.6666666666667, 712.5000000000001, 712.5000000000001, 713.3333333333334, 713.3333333333334, 714.1666666666666, 714.1666666666666, 715, 715, 715.8333333333334, 715.8333333333334, 716.6666666666666, 716.6666666666666, 717.5, 717.5, 718.3333333333334, 718.3333333333334, 719.1666666666667, 719.1666666666667, 720, 720, 720.8333333333334, 720.8333333333334, 721.6666666666667, 721.6666666666667, 722.5, 722.5, 723.3333333333334, 723.3333333333334, 724.1666666666667, 724.1666666666667, 725, 725, 725.8333333333333, 725.8333333333333, 726.6666666666666, 726.6666666666666, 727.5, 727.5, 728.3333333333334, 728.3333333333334, 729.1666666666666, 729.1666666666666, 730, 730, 730.8333333333334, 730.8333333333334, 731.6666666666667, 731.6666666666667, 732.5, 732.5, 733.3333333333334, 733.3333333333334, 734.1666666666667, 734.1666666666667, 735, 735, 735.8333333333333, 735.8333333333333, 736.6666666666666, 736.6666666666666, 737.5, 737.5, 738.3333333333334, 738.3333333333334, 739.1666666666666, 739.1666666666666, 740, 740, 740.8333333333334, 740.8333333333334, 741.6666666666667, 741.6666666666667, 742.5, 742.5, 743.3333333333334, 743.3333333333334, 744.1666666666667, 744.1666666666667, 745.0000000000001, 745.0000000000001, 745.8333333333334, 745.8333333333334, 746.6666666666666, 746.6666666666666, 747.5, 747.5, 748.3333333333333, 748.3333333333333, 749.1666666666666, 749.1666666666666, 750, 750, 750.8333333333334, 750.8333333333334, 751.6666666666666, 751.6666666666666, 752.5, 752.5, 753.3333333333334, 753.3333333333334, 754.1666666666667, 754.1666666666667, 755, 755, 755.8333333333334, 755.8333333333334, 756.6666666666667, 756.6666666666667, 757.5, 757.5, 758.3333333333333, 758.3333333333333, 759.1666666666666, 759.1666666666666, 760, 760, 760.8333333333334, 760.8333333333334, 761.6666666666666, 761.6666666666666, 762.5, 762.5, 763.3333333333334, 763.3333333333334, 764.1666666666667, 764.1666666666667, 765, 765, 765.8333333333334, 765.8333333333334, 766.6666666666667, 766.6666666666667, 767.5000000000001, 767.5000000000001, 768.3333333333334, 768.3333333333334, 769.1666666666666, 769.1666666666666, 770, 770, 770.8333333333334, 770.8333333333334, 771.6666666666666, 771.6666666666666, 772.5, 772.5, 773.3333333333334, 773.3333333333334, 774.1666666666666, 774.1666666666666, 775, 775, 775.8333333333334, 775.8333333333334, 776.6666666666667, 776.6666666666667, 777.5, 777.5, 778.3333333333334, 778.3333333333334, 779.1666666666667, 779.1666666666667, 780, 780, 780.8333333333333, 780.8333333333333, 781.6666666666666, 781.6666666666666, 782.5, 782.5, 783.3333333333334, 783.3333333333334, 784.1666666666666, 784.1666666666666, 785, 785, 785.8333333333334, 785.8333333333334, 786.6666666666667, 786.6666666666667, 787.5, 787.5, 788.3333333333334, 788.3333333333334, 789.1666666666667, 789.1666666666667, 790.0000000000001, 790.0000000000001, 790.8333333333333, 790.8333333333333, 791.6666666666666, 791.6666666666666, 792.5, 792.5, 793.3333333333334, 793.3333333333334, 794.1666666666666, 794.1666666666666, 795, 795, 795.8333333333334, 795.8333333333334, 796.6666666666667, 796.6666666666667, 797.5, 797.5, 798.3333333333334, 798.3333333333334, 799.1666666666667, 799.1666666666667, 800, 800, 800.8333333333334, 800.8333333333334, 801.6666666666666, 801.6666666666666, 802.5, 802.5, 803.3333333333333, 803.3333333333333, 804.1666666666666, 804.1666666666666, 805, 805, 805.8333333333334, 805.8333333333334, 806.6666666666666, 806.6666666666666, 807.5, 807.5, 808.3333333333334, 808.3333333333334, 809.1666666666667, 809.1666666666667, 810, 810, 810.8333333333334, 810.8333333333334, 811.6666666666667, 811.6666666666667, 812.5, 812.5, 813.3333333333333, 813.3333333333333, 814.1666666666666, 814.1666666666666, 815, 815, 815.8333333333334, 815.8333333333334, 816.6666666666666, 816.6666666666666, 817.5, 817.5, 818.3333333333334, 818.3333333333334, 819.1666666666667, 819.1666666666667, 820, 820, 820.8333333333334, 820.8333333333334, 821.6666666666667, 821.6666666666667, 822.5000000000001, 822.5000000000001, 823.3333333333334, 823.3333333333334, 824.1666666666666, 824.1666666666666, 825, 825, 825.8333333333334, 825.8333333333334, 826.6666666666666, 826.6666666666666, 827.5, 827.5, 828.3333333333334, 828.3333333333334, 829.1666666666666, 829.1666666666666, 830, 830, 830.8333333333334, 830.8333333333334, 831.6666666666667, 831.6666666666667, 832.5, 832.5, 833.3333333333334, 833.3333333333334, 834.1666666666667, 834.1666666666667, 835, 835, 835.8333333333333, 835.8333333333333, 836.6666666666666, 836.6666666666666, 837.5, 837.5, 838.3333333333334, 838.3333333333334, 839.1666666666666, 839.1666666666666, 840, 840, 840.8333333333334, 840.8333333333334, 841.6666666666667, 841.6666666666667, 842.5, 842.5, 843.3333333333334, 843.3333333333334, 844.1666666666667, 844.1666666666667, 845.0000000000001, 845.0000000000001, 845.8333333333334, 845.8333333333334, 846.6666666666666, 846.6666666666666, 847.5, 847.5, 848.3333333333334, 848.3333333333334, 849.1666666666666, 849.1666666666666, 850, 850, 850.8333333333334, 850.8333333333334, 851.6666666666667, 851.6666666666667, 852.5, 852.5, 853.3333333333334, 853.3333333333334, 854.1666666666667, 854.1666666666667, 855, 855, 855.8333333333334, 855.8333333333334, 856.6666666666667, 856.6666666666667, 857.5, 857.5, 858.3333333333333, 858.3333333333333, 859.1666666666666, 859.1666666666666, 860, 860, 860.8333333333334, 860.8333333333334, 861.6666666666666, 861.6666666666666, 862.5, 862.5, 863.3333333333334, 863.3333333333334, 864.1666666666667, 864.1666666666667, 865, 865, 865.8333333333334, 865.8333333333334, 866.6666666666667, 866.6666666666667, 867.5000000000001, 867.5000000000001, 868.3333333333333, 868.3333333333333, 869.1666666666666, 869.1666666666666, 870, 870, 870.8333333333334, 870.8333333333334, 871.6666666666666, 871.6666666666666, 872.5, 872.5, 873.3333333333334, 873.3333333333334, 874.1666666666667, 874.1666666666667, 875, 875, 875.8333333333334, 875.8333333333334, 876.6666666666667, 876.6666666666667, 877.5000000000001, 877.5000000000001, 878.3333333333334, 878.3333333333334, 879.1666666666666, 879.1666666666666, 880, 880, 880.8333333333333, 880.8333333333333, 881.6666666666666, 881.6666666666666, 882.5, 882.5, 883.3333333333334, 883.3333333333334, 884.1666666666666, 884.1666666666666, 885, 885, 885.8333333333334, 885.8333333333334, 886.6666666666667, 886.6666666666667, 887.5, 887.5, 888.3333333333334, 888.3333333333334, 889.1666666666667, 889.1666666666667, 890, 890, 890.8333333333333, 890.8333333333333, 891.6666666666666, 891.6666666666666, 892.5, 892.5, 893.3333333333334, 893.3333333333334, 894.1666666666666, 894.1666666666666, 895, 895, 895.8333333333334, 895.8333333333334, 896.6666666666667, 896.6666666666667, 897.5, 897.5, 898.3333333333334, 898.3333333333334, 899.1666666666667, 899.1666666666667, 900.0000000000001, 900.0000000000001, 900.8333333333334, 900.8333333333334, 901.6666666666666, 901.6666666666666, 902.5, 902.5, 903.3333333333334, 903.3333333333334, 904.1666666666666, 904.1666666666666, 905, 905, 905.8333333333334, 905.8333333333334, 906.6666666666667, 906.6666666666667, 907.5, 907.5, 908.3333333333334, 908.3333333333334, 909.1666666666667, 909.1666666666667, 910, 910, 910.8333333333334, 910.8333333333334, 911.6666666666667, 911.6666666666667, 912.5, 912.5, 913.3333333333333, 913.3333333333333, 914.1666666666666, 914.1666666666666, 915, 915, 915.8333333333334, 915.8333333333334, 916.6666666666666, 916.6666666666666, 917.5, 917.5, 918.3333333333334, 918.3333333333334, 919.1666666666667, 919.1666666666667, 920, 920, 920.8333333333334, 920.8333333333334, 921.6666666666667, 921.6666666666667, 922.5000000000001, 922.5000000000001, 923.3333333333334, 923.3333333333334, 924.1666666666666, 924.1666666666666, 925, 925, 925.8333333333334, 925.8333333333334, 926.6666666666666, 926.6666666666666, 927.5, 927.5, 928.3333333333334, 928.3333333333334, 929.1666666666667, 929.1666666666667, 930, 930, 930.8333333333334, 930.8333333333334, 931.6666666666667, 931.6666666666667, 932.5000000000001, 932.5000000000001, 933.3333333333334, 933.3333333333334, 934.1666666666667, 934.1666666666667, 935, 935, 935.8333333333333, 935.8333333333333, 936.6666666666666, 936.6666666666666, 937.5, 937.5, 938.3333333333334, 938.3333333333334, 939.1666666666666, 939.1666666666666, 940, 940, 940.8333333333334, 940.8333333333334, 941.6666666666667, 941.6666666666667, 942.5, 942.5, 943.3333333333334, 943.3333333333334, 944.1666666666667, 944.1666666666667, 945.0000000000001, 945.0000000000001, 945.8333333333333, 945.8333333333333, 946.6666666666666, 946.6666666666666, 947.5, 947.5, 948.3333333333334, 948.3333333333334, 949.1666666666666, 949.1666666666666, 950, 950, 950.8333333333334, 950.8333333333334, 951.6666666666667, 951.6666666666667, 952.5, 952.5, 953.3333333333334, 953.3333333333334, 954.1666666666667, 954.1666666666667, 955, 955, 955.8333333333335, 955.8333333333335, 956.6666666666666, 956.6666666666666, 957.4999999999999, 957.4999999999999, 958.3333333333334, 958.3333333333334, 959.1666666666666, 959.1666666666666, 960.0000000000001, 960.0000000000001, 960.8333333333334, 960.8333333333334, 961.6666666666666, 961.6666666666666, 962.5000000000001, 962.5000000000001, 963.3333333333334, 963.3333333333334, 964.1666666666666, 964.1666666666666, 965.0000000000001, 965.0000000000001, 965.8333333333334, 965.8333333333334, 966.6666666666666, 966.6666666666666, 967.5, 967.5, 968.3333333333333, 968.3333333333333, 969.1666666666667, 969.1666666666667, 970, 970, 970.8333333333333, 970.8333333333333, 971.6666666666667, 971.6666666666667, 972.5, 972.5, 973.3333333333333, 973.3333333333333, 974.1666666666667, 974.1666666666667, 975, 975, 975.8333333333335, 975.8333333333335, 976.6666666666667, 976.6666666666667, 977.5, 977.5, 978.3333333333334, 978.3333333333334, 979.1666666666666, 979.1666666666666, 979.9999999999999, 979.9999999999999, 980.8333333333334, 980.8333333333334, 981.6666666666666, 981.6666666666666, 982.5000000000001, 982.5000000000001, 983.3333333333334, 983.3333333333334, 984.1666666666666, 984.1666666666666, 985.0000000000001, 985.0000000000001, 985.8333333333334, 985.8333333333334, 986.6666666666666, 986.6666666666666, 987.5000000000001, 987.5000000000001, 988.3333333333334, 988.3333333333334, 989.1666666666666, 989.1666666666666, 990, 990, 990.8333333333333, 990.8333333333333, 991.6666666666667, 991.6666666666667, 992.5, 992.5, 993.3333333333333, 993.3333333333333, 994.1666666666667, 994.1666666666667, 995, 995, 995.8333333333333, 995.8333333333333, 996.6666666666667, 996.6666666666667, 997.5, 997.5, 998.3333333333335, 998.3333333333335, 999.1666666666667, 999.1666666666667, 1000, 1000, 1000.8333333333334, 1000.8333333333334, 1001.6666666666666, 1001.6666666666666, 1002.4999999999999, 1002.4999999999999, 1003.3333333333334, 1003.3333333333334, 1004.1666666666666, 1004.1666666666666, 1005.0000000000001, 1005.0000000000001, 1005.8333333333334, 1005.8333333333334, 1006.6666666666666, 1006.6666666666666, 1007.5000000000001, 1007.5000000000001, 1008.3333333333334, 1008.3333333333334, 1009.1666666666666, 1009.1666666666666, 1010.0000000000001, 1010.0000000000001, 1010.8333333333334, 1010.8333333333334, 1011.6666666666667, 1011.6666666666667, 1012.5, 1012.5, 1013.3333333333333, 1013.3333333333333, 1014.1666666666667, 1014.1666666666667, 1015, 1015, 1015.8333333333333, 1015.8333333333333, 1016.6666666666667, 1016.6666666666667, 1017.5, 1017.5, 1018.3333333333333, 1018.3333333333333, 1019.1666666666667, 1019.1666666666667, 1020, 1020, 1020.8333333333335, 1020.8333333333335, 1021.6666666666667, 1021.6666666666667, 1022.5, 1022.5, 1023.3333333333334, 1023.3333333333334, 1024.1666666666667, 1024.1666666666667, 1025, 1025, 1025.8333333333335, 1025.8333333333335, 1026.6666666666667, 1026.6666666666667, 1027.5000000000002, 1027.5000000000002, 1028.3333333333333, 1028.3333333333333, 1029.1666666666665, 1029.1666666666665, 1030, 1030, 1030.8333333333333, 1030.8333333333333, 1031.6666666666665, 1031.6666666666665, 1032.5, 1032.5, 1033.3333333333333, 1033.3333333333333, 1034.1666666666667, 1034.1666666666667, 1035, 1035, 1035.8333333333333, 1035.8333333333333, 1036.6666666666667, 1036.6666666666667, 1037.5, 1037.5, 1038.3333333333333, 1038.3333333333333, 1039.1666666666667, 1039.1666666666667, 1040, 1040, 1040.8333333333333, 1040.8333333333333, 1041.6666666666667, 1041.6666666666667, 1042.5, 1042.5, 1043.3333333333335, 1043.3333333333335, 1044.1666666666667, 1044.1666666666667, 1045, 1045, 1045.8333333333335, 1045.8333333333335, 1046.6666666666667, 1046.6666666666667, 1047.5, 1047.5, 1048.3333333333335, 1048.3333333333335, 1049.1666666666667, 1049.1666666666667, 1050, 1050, 1050.8333333333333, 1050.8333333333333, 1051.6666666666665, 1051.6666666666665, 1052.5, 1052.5, 1053.3333333333333, 1053.3333333333333, 1054.1666666666665, 1054.1666666666665, 1055, 1055, 1055.8333333333333, 1055.8333333333333, 1056.6666666666667, 1056.6666666666667, 1057.5, 1057.5, 1058.3333333333333, 1058.3333333333333, 1059.1666666666667, 1059.1666666666667, 1060, 1060, 1060.8333333333333, 1060.8333333333333, 1061.6666666666667, 1061.6666666666667, 1062.5, 1062.5, 1063.3333333333335, 1063.3333333333335, 1064.1666666666667, 1064.1666666666667, 1065, 1065, 1065.8333333333335, 1065.8333333333335, 1066.6666666666667, 1066.6666666666667, 1067.5, 1067.5, 1068.3333333333335, 1068.3333333333335, 1069.1666666666667, 1069.1666666666667, 1070, 1070, 1070.8333333333335, 1070.8333333333335, 1071.6666666666667, 1071.6666666666667, 1072.5, 1072.5, 1073.3333333333333, 1073.3333333333333, 1074.1666666666665, 1074.1666666666665, 1075, 1075, 1075.8333333333333, 1075.8333333333333, 1076.6666666666665, 1076.6666666666665, 1077.5, 1077.5, 1078.3333333333333, 1078.3333333333333, 1079.1666666666667, 1079.1666666666667, 1080, 1080, 1080.8333333333333, 1080.8333333333333, 1081.6666666666667, 1081.6666666666667, 1082.5, 1082.5, 1083.3333333333333, 1083.3333333333333, 1084.1666666666667, 1084.1666666666667, 1085, 1085, 1085.8333333333335, 1085.8333333333335, 1086.6666666666667, 1086.6666666666667, 1087.5, 1087.5, 1088.3333333333335, 1088.3333333333335, 1089.1666666666667, 1089.1666666666667, 1090, 1090, 1090.8333333333335, 1090.8333333333335, 1091.6666666666667, 1091.6666666666667, 1092.5000000000002, 1092.5000000000002, 1093.3333333333335, 1093.3333333333335, 1094.1666666666667, 1094.1666666666667, 1095, 1095, 1095.8333333333333, 1095.8333333333333, 1096.6666666666665, 1096.6666666666665, 1097.5, 1097.5, 1098.3333333333333, 1098.3333333333333, 1099.1666666666665, 1099.1666666666665, 1100 ] }, "y": { "type": "float", "values": [ 6, 6, 5.984457849818863, 5.984457849818863, 5.914622104270368, 5.914622104270368, 5.6602368453180425, 5.6602368453180425, 5.293156022650223, 5.293156022650223, 5.190011028931016, 5.190011028931016, 5.3020440865118905, 5.3020440865118905, 5.501282113799794, 5.501282113799794, 5.741307593508806, 5.741307593508806, 6.000000257442196, 6.000000257442196, 6.258787697677602, 6.258787697677602, 6.499939183721772, 6.499939183721772, 6.707020640079183, 6.707020640079183, 6.865919807508107, 6.865919807508107, 6.965807970181247, 6.965807970181247, 6.999877914784483, 6.999877914784483, 6.96580783086198, 6.96580783086198, 6.8659195383640075, 6.8659195383640075, 6.707020259451868, 6.707020259451868, 6.499938717550518, 6.499938717550518, 6.258787177731021, 6.258787177731021, 5.999999719154, 5.999999719154, 5.74121227971606, 5.74121227971606, 5.500060796009942, 5.500060796009942, 5.292979343371831, 5.292979343371831, 5.134080180789995, 5.134080180789995, 5.034074098489585, 5.034074098489585, 5.000000000000043, 5.000000000000043, 5.034074249942043, 5.034074249942043, 5.133974744457966, 5.133974744457966, 5.292893429839141, 5.292893429839141, 5.500000260141892, 5.500000260141892, 5.741181246932082, 5.741181246932082, 6.0000003042870675, 6.0000003042870675, 6.258819340905283, 6.258819340905283, 6.500000266898768, 6.500000266898768, 6.7071070004876745, 6.7071070004876745, 6.8660255598290405, 6.8660255598290405, 6.965925907568499, 6.965925907568499, 6.99999999999995, 6.99999999999995, 6.96592574399986, 6.96592574399986, 6.8660252438386395, 6.8660252438386395, 6.707106553609845, 6.707106553609845, 6.49999971958734, 6.49999971958734, 6.258818730458817, 6.258818730458817, 5.99999967230627, 5.99999967230627, 5.74118063648562, 5.74118063648562, 5.499999712830472, 5.499999712830472, 5.2928929829613205, 5.2928929829613205, 5.133974428467635, 5.133974428467635, 5.03407408637339, 5.03407408637339, 5.000000000000058, 5.000000000000058, 5.034074262058238, 5.034074262058238, 5.133974767864698, 5.133974767864698, 5.292893462941251, 5.292893462941251, 5.500000300683527, 5.500000300683527, 5.741181292150285, 5.741181292150285, 6.000000351100393, 6.000000351100393, 6.258819386123588, 6.258819386123588, 6.500000307440387, 6.500000307440387, 6.707107033589684, 6.707107033589684, 6.86602558323569, 6.86602558323569, 6.965925919684692, 6.965925919684692, 6.999999999999934, 6.999999999999934, 6.965925731883664, 6.965925731883664, 6.866025220431963, 6.866025220431963, 6.707106520507733, 6.707106520507733, 6.499999679045704, 6.499999679045704, 6.2588186852406125, 6.2588186852406125, 5.999999625492944, 5.999999625492944, 5.741180591267314, 5.741180591267314, 5.499999672288853, 5.499999672288853, 5.292892949859313, 5.292892949859313, 5.133974405060987, 5.133974405060987, 5.034074074257226, 5.034074074257226, 5.000000000000075, 5.000000000000075, 5.034074274174493, 5.034074274174493, 5.133974791271376, 5.133974791271376, 5.292893496043283, 5.292893496043283, 5.500000341225065, 5.500000341225065, 5.741181337368709, 5.741181337368709, 6.000000397913718, 6.000000397913718, 6.2588194313417835, 6.2588194313417835, 6.500000347981907, 6.500000347981907, 6.707107066691851, 6.707107066691851, 6.866025606642337, 6.866025606642337, 6.9659259318008555, 6.9659259318008555, 6.9999999999999165, 6.9999999999999165, 6.965925719767408, 6.965925719767408, 6.866025197025285, 6.866025197025285, 6.707106487405701, 6.707106487405701, 6.499999638504166, 6.499999638504166, 6.258818640022188, 6.258818640022188, 5.99999957867962, 5.99999957867962, 5.74118054604912, 5.74118054604912, 5.499999631747333, 5.499999631747333, 5.292892916757146, 5.292892916757146, 5.133974381654227, 5.133974381654227, 5.034074062141064, 5.034074062141064, 5.000000000000094, 5.000000000000094, 5.034074286290692, 5.034074286290692, 5.133974814678169, 5.133974814678169, 5.292893529145316, 5.292893529145316, 5.500000381766603, 5.500000381766603, 5.741181382586914, 5.741181382586914, 6.00000044472727, 6.00000044472727, 6.258819476559978, 6.258819476559978, 6.500000388523426, 6.500000388523426, 6.707107099793856, 6.707107099793856, 6.866025630049096, 6.866025630049096, 6.965925943917016, 6.965925943917016, 6.999999999999896, 6.999999999999896, 6.965925707651208, 6.965925707651208, 6.866025173618491, 6.866025173618491, 6.707106454303666, 6.707106454303666, 6.499999597962627, 6.499999597962627, 6.258818594803984, 6.258818594803984, 5.9999995318660675, 5.9999995318660675, 5.741180500830706, 5.741180500830706, 5.499999591205815, 5.499999591205815, 5.292892883655141, 5.292892883655141, 5.133974358247583, 5.133974358247583, 5.034074050024846, 5.034074050024846, 5.0000000000001155, 5.0000000000001155, 5.0340742984068925, 5.0340742984068925, 5.13397483808485, 5.13397483808485, 5.292893562247512, 5.292893562247512, 5.500000422308143, 5.500000422308143, 5.741181427805119, 5.741181427805119, 6.000000491540596, 6.000000491540596, 6.258819521778392, 6.258819521778392, 6.500000429064944, 6.500000429064944, 6.70710713289586, 6.70710713289586, 6.866025653455739, 6.866025653455739, 6.965925956033233, 6.965925956033233, 6.999999999999873, 6.999999999999873, 6.965925695535006, 6.965925695535006, 6.866025150211809, 6.866025150211809, 6.70710642120147, 6.70710642120147, 6.49999955742089, 6.49999955742089, 6.2588185495857775, 6.2588185495857775, 5.999999485052742, 5.999999485052742, 5.741180455612511, 5.741180455612511, 5.499999550664101, 5.499999550664101, 5.292892850553138, 5.292892850553138, 5.13397433484094, 5.13397433484094, 5.034074037908688, 5.034074037908688, 5.0000000000001386, 5.0000000000001386, 5.034074310523096, 5.034074310523096, 5.133974861491533, 5.133974861491533, 5.292893595349549, 5.292893595349549, 5.50000046284988, 5.50000046284988, 5.7411814730233255, 5.7411814730233255, 6.0000005383539206, 6.0000005383539206, 6.258819566996585, 6.258819566996585, 6.500000469606658, 6.500000469606658, 6.707107165997863, 6.707107165997863, 6.86602567686238, 6.86602567686238, 6.9659259681493895, 6.9659259681493895, 6.999999999999849, 6.999999999999849, 6.965925683418743, 6.965925683418743, 6.866025126805125, 6.866025126805125, 6.707106388099433, 6.707106388099433, 6.499999516879349, 6.499999516879349, 6.258818504367352, 6.258818504367352, 5.999999438239417, 5.999999438239417, 5.7411804103943185, 5.7411804103943185, 5.499999510122585, 5.499999510122585, 5.2928928174509755, 5.2928928174509755, 5.1339743114343, 5.1339743114343, 5.034074025792533, 5.034074025792533, 5.000000000000164, 5.000000000000164, 5.034074322639359, 5.034074322639359, 5.133974884898218, 5.133974884898218, 5.292893628451586, 5.292893628451586, 5.500000503391423, 5.500000503391423, 5.741181518241752, 5.741181518241752, 6.000000585167245, 6.000000585167245, 6.2588196122147775, 6.2588196122147775, 6.500000510148173, 6.500000510148173, 6.707107199100024, 6.707107199100024, 6.866025700269134, 6.866025700269134, 6.965925980265544, 6.965925980265544, 6.999999999999822, 6.999999999999822, 6.9659256713025375, 6.9659256713025375, 6.866025103398325, 6.866025103398325, 6.707106354997394, 6.707106354997394, 6.499999476337806, 6.499999476337806, 6.258818459149145, 6.258818459149145, 5.999999391425865, 5.999999391425865, 5.741180365176126, 5.741180365176126, 5.49999946958107, 5.49999946958107, 5.292892784348975, 5.292892784348975, 5.133974288027548, 5.133974288027548, 5.0340740136763795, 5.0340740136763795, 5.000000000000193, 5.000000000000193, 5.034074334755567, 5.034074334755567, 5.133974908305019, 5.133974908305019, 5.2928936615536255, 5.2928936615536255, 5.500000543932965, 5.500000543932965, 5.741181563459959, 5.741181563459959, 6.000000631980798, 6.000000631980798, 6.25881965743319, 6.25881965743319, 6.500000550689687, 6.500000550689687, 6.7071072322020235, 6.7071072322020235, 6.8660257236757705, 6.8660257236757705, 6.965925992381755, 6.965925992381755, 6.999999999999793, 6.999999999999793, 6.965925659186329, 6.965925659186329, 6.866025079991637, 6.866025079991637, 6.707106321895193, 6.707106321895193, 6.499999435796263, 6.499999435796263, 6.258818413930937, 6.258818413930937, 5.999999344612539, 5.999999344612539, 5.7411803199577145, 5.7411803199577145, 5.499999429039557, 5.499999429039557, 5.292892751246978, 5.292892751246978, 5.133974264620911, 5.133974264620911, 5.034074001560169, 5.034074001560169, 5.000000000000222, 5.000000000000222, 5.034074346871835, 5.034074346871835, 5.133974931711707, 5.133974931711707, 5.292893694655989, 5.292893694655989, 5.500000584474706, 5.500000584474706, 5.741181608678605, 5.741181608678605, 6.0000006787943505, 6.0000006787943505, 6.258819702651381, 6.258819702651381, 6.500000591231594, 6.500000591231594, 6.707107265304183, 6.707107265304183, 6.866025747082634, 6.866025747082634, 6.965926004497964, 6.965926004497964, 6.999999999999762, 6.999999999999762, 6.9659256470700015, 6.9659256470700015, 6.866025056584833, 6.866025056584833, 6.707106288793151, 6.707106288793151, 6.499999395254324, 6.499999395254324, 6.25881836871251, 6.25881836871251, 5.99999929779876, 5.99999929779876, 5.741180274739303, 5.741180274739303, 5.499999388497846, 5.499999388497846, 5.292892718144659, 5.292892718144659, 5.133974241214163, 5.133974241214163, 5.034073989444021, 5.034073989444021, 5.000000000000255, 5.000000000000255, 5.034074358988046, 5.034074358988046, 5.133974955118625, 5.133974955118625, 5.292893727758031, 5.292893727758031, 5.500000625016251, 5.500000625016251, 5.741181653896813, 5.741181653896813, 6.000000725607675, 6.000000725607675, 6.258819747870012, 6.258819747870012, 6.500000631773107, 6.500000631773107, 6.707107298406179, 6.707107298406179, 6.8660257704892675, 6.8660257704892675, 6.9659260166141115, 6.9659260166141115, 6.999999999999728, 6.999999999999728, 6.965925634953789, 6.965925634953789, 6.866025033178142, 6.866025033178142, 6.707106255690786, 6.707106255690786, 6.499999354712779, 6.499999354712779, 6.258818323494301, 6.258818323494301, 5.999999250985435, 5.999999250985435, 5.741180229521112, 5.741180229521112, 5.499999347956335, 5.499999347956335, 5.292892685042663, 5.292892685042663, 5.13397421780753, 5.13397421780753, 5.034073977327756, 5.034073977327756, 5.0000000000002895, 5.0000000000002895, 5.03407437110426, 5.03407437110426, 5.133974978525318, 5.133974978525318, 5.292893760860076, 5.292893760860076, 5.500000665558192, 5.500000665558192, 5.741181699115023, 5.741181699115023, 6.000000772421001, 6.000000772421001, 6.258819793088203, 6.258819793088203, 6.500000672314617, 6.500000672314617, 6.707107331508173, 6.707107331508173, 6.8660257938959, 6.8660257938959, 6.965926028730258, 6.965926028730258, 6.999999999999693, 6.999999999999693, 6.965925622837574, 6.965925622837574, 6.866025009771448, 6.866025009771448, 6.707106222588741, 6.707106222588741, 6.499999314171232, 6.499999314171232, 6.258818278276093, 6.258818278276093, 5.999999204172109, 5.999999204172109, 5.741180184302922, 5.741180184302922, 5.499999307414431, 5.499999307414431, 5.29289265194067, 5.29289265194067, 5.133974194400899, 5.133974194400899, 5.034073965211611, 5.034073965211611, 5.000000000000326, 5.000000000000326, 5.0340743832205925, 5.0340743832205925, 5.133975001932012, 5.133975001932012, 5.292893793962121, 5.292893793962121, 5.500000706099739, 5.500000706099739, 5.741181744333232, 5.741181744333232, 6.000000819234326, 6.000000819234326, 6.258819838306392, 6.258819838306392, 6.500000712856128, 6.500000712856128, 6.707107364610488, 6.707107364610488, 6.866025817302529, 6.866025817302529, 6.965926040846401, 6.965926040846401, 6.9999999999996545, 6.9999999999996545, 6.965925610721357, 6.965925610721357, 6.866024986364753, 6.866024986364753, 6.707106189486694, 6.707106189486694, 6.499999273629684, 6.499999273629684, 6.258818233057443, 6.258818233057443, 5.9999991573587845, 5.9999991573587845, 5.741180139084732, 5.741180139084732, 5.499999266872921, 5.499999266872921, 5.292892618838677, 5.292892618838677, 5.133974170994042, 5.133974170994042, 5.034073953095469, 5.034073953095469, 5.000000000000365, 5.000000000000365, 5.034074395336811, 5.034074395336811, 5.133975025338708, 5.133975025338708, 5.2928938270641686, 5.2928938270641686, 5.500000746641287, 5.500000746641287, 5.741181789551441, 5.741181789551441, 6.000000866048105, 6.000000866048105, 6.258819883524582, 6.258819883524582, 6.500000753397637, 6.500000753397637, 6.7071073977124795, 6.7071073977124795, 6.866025840709158, 6.866025840709158, 6.965926052962542, 6.965926052962542, 6.9999999999996145, 6.9999999999996145, 6.965925598605138, 6.965925598605138, 6.8660249629578285, 6.8660249629578285, 6.707106156384646, 6.707106156384646, 6.499999233088135, 6.499999233088135, 6.258818187839234, 6.258818187839234, 5.999999110545459, 5.999999110545459, 5.741180093866103, 5.741180093866103, 5.499999226331413, 5.499999226331413, 5.292892585736686, 5.292892585736686, 5.133974147587415, 5.133974147587415, 5.0340739409793285, 5.0340739409793285, 5.000000000000406, 5.000000000000406, 5.034074407453031, 5.034074407453031, 5.133975048745407, 5.133975048745407, 5.292893860166539, 5.292893860166539, 5.500000787182836, 5.500000787182836, 5.741181834769652, 5.741181834769652, 6.000000912861431, 6.000000912861431, 6.258819928742771, 6.258819928742771, 6.500000793939145, 6.500000793939145, 6.707107430814469, 6.707107430814469, 6.866025864115784, 6.866025864115784, 6.9659260650788, 6.9659260650788, 6.999999999999573, 6.999999999999573, 6.965925586488917, 6.965925586488917, 6.86602493955113, 6.86602493955113, 6.707106123282597, 6.707106123282597, 6.499999192546191, 6.499999192546191, 6.258818142621023, 6.258818142621023, 5.999999063732134, 5.999999063732134, 5.741180048647915, 5.741180048647915, 5.499999185789905, 5.499999185789905, 5.292892552634697, 5.292892552634697, 5.13397412418079, 5.13397412418079, 5.03407392886319, 5.03407392886319, 5.000000000000449, 5.000000000000449, 5.034074419569253, 5.034074419569253, 5.133975072152108, 5.133975072152108, 5.292893893268589, 5.292893893268589, 5.500000827724387, 5.500000827724387, 5.741181879987863, 5.741181879987863, 6.000000959674756, 6.000000959674756, 6.25881997396096, 6.25881997396096, 6.500000834481045, 6.500000834481045, 6.707107463916458, 6.707107463916458, 6.866025887522408, 6.866025887522408, 6.965926077194937, 6.965926077194937, 6.999999999999528, 6.999999999999528, 6.9659255743725765, 6.9659255743725765, 6.866024916144428, 6.866024916144428, 6.707106090180545, 6.707106090180545, 6.499999152004641, 6.499999152004641, 6.258818097402812, 6.258818097402812, 5.999999016918809, 5.999999016918809, 5.741180003429727, 5.741180003429727, 5.499999145248399, 5.499999145248399, 5.292892519532388, 5.292892519532388, 5.133974100774167, 5.133974100774167, 5.034073916747054, 5.034073916747054, 5.000000000000495, 5.000000000000495, 5.034074431685477, 5.034074431685477, 5.133975095558809, 5.133975095558809, 5.292893926370641, 5.292893926370641, 5.500000868265939, 5.500000868265939, 5.741181925206514, 5.741181925206514, 6.00000100648808, 6.00000100648808, 6.258820019179147, 6.258820019179147, 6.50000087502255, 6.50000087502255, 6.707107497018445, 6.707107497018445, 6.866025910929258, 6.866025910929258, 6.965926089311072, 6.965926089311072, 6.999999999999481, 6.999999999999481, 6.965925562256352, 6.965925562256352, 6.866024892737725, 6.866024892737725, 6.707106057078493, 6.707106057078493, 6.499999111463088, 6.499999111463088, 6.2588180521845995, 6.2588180521845995, 5.999998970105029, 5.999998970105029, 5.741179958211539, 5.741179958211539, 5.499999104706894, 5.499999104706894, 5.292892486430402, 5.292892486430402, 5.1339740773675455, 5.1339740773675455, 5.03407390463092, 5.03407390463092, 5.000000000000543, 5.000000000000543, 5.034074443801702, 5.034074443801702, 5.133975118965741, 5.133975118965741, 5.2928939594726945, 5.2928939594726945, 5.5000009088074915, 5.5000009088074915, 5.741181970424726, 5.741181970424726, 6.000001053301406, 6.000001053301406, 6.258820064397773, 6.258820064397773, 6.500000915564055, 6.500000915564055, 6.70710753012043, 6.70710753012043, 6.8660259343358785, 6.8660259343358785, 6.965926101427206, 6.965926101427206, 6.999999999999433, 6.999999999999433, 6.965925550140124, 6.965925550140124, 6.86602486933102, 6.86602486933102, 6.707106023976117, 6.707106023976117, 6.499999070921535, 6.499999070921535, 6.258818006966387, 6.258818006966387, 5.999998923291704, 5.999998923291704, 5.741179912993353, 5.741179912993353, 5.49999906416539, 5.49999906416539, 5.292892453328417, 5.292892453328417, 5.133974053960926, 5.133974053960926, 5.0340738925146695, 5.0340738925146695, 5.000000000000592, 5.000000000000592, 5.034074455917931, 5.034074455917931, 5.133975142372447, 5.133975142372447, 5.29289399257475, 5.29289399257475, 5.500000949349439, 5.500000949349439, 5.741182015642939, 5.741182015642939, 6.000001100114731, 6.000001100114731, 6.25882010961596, 6.25882010961596, 6.500000956105558, 6.500000956105558, 6.707107563222413, 6.707107563222413, 6.8660259577424965, 6.8660259577424965, 6.965926113543336, 6.965926113543336, 6.999999999999382, 6.999999999999382, 6.965925538023895, 6.965925538023895, 6.866024845924313, 6.866024845924313, 6.707105990874061, 6.707105990874061, 6.49999903037998, 6.49999903037998, 6.258817961748174, 6.258817961748174, 5.999998876478379, 5.999998876478379, 5.741179867775166, 5.741179867775166, 5.499999023623493, 5.499999023623493, 5.292892420226434, 5.292892420226434, 5.133974030554309, 5.133974030554309, 5.03407388039854, 5.03407388039854, 5.000000000000645, 5.000000000000645, 5.034074468034279, 5.034074468034279, 5.133975165779154, 5.133975165779154, 5.292894025676807, 5.292894025676807, 5.500000989890994, 5.500000989890994, 5.741182060861152, 5.741182060861152, 6.000001146928056, 6.000001146928056, 6.258820154834146, 6.258820154834146, 6.500000996647061, 6.500000996647061, 6.707107596324717, 6.707107596324717, 6.8660259811491136, 6.8660259811491136, 6.965926125659466, 6.965926125659466, 6.9999999999993285, 6.9999999999993285, 6.965925525907664, 6.965925525907664, 6.866024822517605, 6.866024822517605, 6.707105957772004, 6.707105957772004, 6.499998989838425, 6.499998989838425, 6.258817916529521, 6.258817916529521, 5.999998829665054, 5.999998829665054, 5.741179822556981, 5.741179822556981, 5.4999989830819915, 5.4999989830819915, 5.292892387124453, 5.292892387124453, 5.133974007147465, 5.133974007147465, 5.034073868282412, 5.034073868282412, 5.000000000000698, 5.000000000000698, 5.034074480150512, 5.034074480150512, 5.133975189185864, 5.133975189185864, 5.292894058778865, 5.292894058778865, 5.50000103043255, 5.50000103043255, 5.741182106079366, 5.741182106079366, 6.000001193741836, 6.000001193741836, 6.258820200052332, 6.258820200052332, 6.500001037188563, 6.500001037188563, 6.707107629426698, 6.707107629426698, 6.866026004555728, 6.866026004555728, 6.965926137775592, 6.965926137775592, 6.9999999999992735, 6.9999999999992735, 6.96592551379143, 6.96592551379143, 6.866024799110667, 6.866024799110667, 6.7071059246699445, 6.7071059246699445, 6.499998949296868, 6.499998949296868, 6.258817871311307, 6.258817871311307, 5.999998782851729, 5.999998782851729, 5.741179777338356, 5.741179777338356, 5.499998942540491, 5.499998942540491, 5.292892354022473, 5.292892354022473, 5.133973983740852, 5.133973983740852, 5.0340738561662866, 5.0340738561662866, 5.000000000000755, 5.000000000000755, 5.034074492266747, 5.034074492266747, 5.133975212592576, 5.133975212592576, 5.292894091881246, 5.292894091881246, 5.500001070974108, 5.500001070974108, 5.74118215129758, 5.74118215129758, 6.000001240555161, 6.000001240555161, 6.258820245270517, 6.258820245270517, 6.5000010777300625, 6.5000010777300625, 6.707107662528677, 6.707107662528677, 6.8660260279623415, 6.8660260279623415, 6.965926149891835, 6.965926149891835, 6.999999999999216, 6.999999999999216, 6.965925501675194, 6.965925501675194, 6.866024775703954, 6.866024775703954, 6.707105891567885, 6.707105891567885, 6.499998908754916, 6.499998908754916, 6.258817826093092, 6.258817826093092, 5.999998736038404, 5.999998736038404, 5.741179732120171, 5.741179732120171, 5.499998901998991, 5.499998901998991, 5.292892320920495, 5.292892320920495, 5.13397396033424, 5.13397396033424, 5.034073844050163, 5.034073844050163, 5.000000000000814, 5.000000000000814, 5.034074504382984, 5.034074504382984, 5.133975235999289, 5.133975235999289, 5.292894124983307, 5.292894124983307, 5.500001111515665, 5.500001111515665, 5.741182196515796, 5.741182196515796, 6.000001287368486, 6.000001287368486, 6.258820290488701, 6.258820290488701, 6.500001118271955, 6.500001118271955, 6.707107695630655, 6.707107695630655, 6.866026051368952, 6.866026051368952, 6.965926162007957, 6.965926162007957, 6.999999999999156, 6.999999999999156, 6.965925489558838, 6.965925489558838, 6.86602475229724, 6.86602475229724, 6.707105858465822, 6.707105858465822, 6.499998868213358, 6.499998868213358, 6.258817780874877, 6.258817780874877, 5.9999986892250785, 5.9999986892250785, 5.7411796869019875, 5.7411796869019875, 5.499998861457493, 5.499998861457493, 5.292892287818196, 5.292892287818196, 5.13397393692763, 5.13397393692763, 5.034073831934042, 5.034073831934042, 5.000000000000875, 5.000000000000875, 5.034074516499222, 5.034074516499222, 5.133975259406005, 5.133975259406005, 5.29289415808537, 5.29289415808537, 5.500001152057225, 5.500001152057225, 5.741182241734011, 5.741182241734011, 6.0000013341813565, 6.0000013341813565, 6.258820335707324, 6.258820335707324, 6.500001158813453, 6.500001158813453, 6.70710772873263, 6.70710772873263, 6.8660260747755615, 6.8660260747755615, 6.965926174123959, 6.965926174123959, 6.999999999999094, 6.999999999999094, 6.965925477442599, 6.965925477442599, 6.866024728890523, 6.866024728890523, 6.707105825363759, 6.707105825363759, 6.499998827672192, 6.499998827672192, 6.258817735656222, 6.258817735656222, 5.999998642411299, 5.999998642411299, 5.741179641683804, 5.741179641683804, 5.499998820915995, 5.499998820915995, 5.292892254716542, 5.292892254716542, 5.133973913520794, 5.133973913520794, 5.034073819817804, 5.034073819817804, 5.000000000000938, 5.000000000000938, 5.034074528615463, 5.034074528615463, 5.133975282812722, 5.133975282812722, 5.292894191187113, 5.292894191187113, 5.500001192599179, 5.500001192599179, 5.741182286952666, 5.741182286952666, 6.000001380995136, 6.000001380995136, 6.258820380925068, 6.258820380925068, 6.500001199354556, 6.500001199354556, 6.707107761834926, 6.707107761834926, 6.866026098182395, 6.866026098182395, 6.965926186240195, 6.965926186240195, 6.99999999999903, 6.99999999999903, 6.965925465326475, 6.965925465326475, 6.866024705483578, 6.866024705483578, 6.707105792261372, 6.707105792261372, 6.499998787130237, 6.499998787130237, 6.258817690438445, 6.258817690438445, 5.999998595598429, 5.999998595598429, 5.741179596465182, 5.741179596465182, 5.499998780374105, 5.499998780374105, 5.292892221614247, 5.292892221614247, 5.133973890114415, 5.133973890114415, 5.034073807701805, 5.034073807701805, 5.000000000001003, 5.000000000001003, 5.034074540731824, 5.034074540731824, 5.133975306219669, 5.133975306219669, 5.2928942242895, 5.2928942242895, 5.500001233140347, 5.500001233140347, 5.741182332170444, 5.741182332170444, 6.0000014278089155, 6.0000014278089155, 6.25882042614369, 6.25882042614369, 6.500001239896446, 6.500001239896446, 6.7071077949365785, 6.7071077949365785, 6.8660261215887735, 6.8660261215887735, 6.96592619835643, 6.96592619835643, 6.999999999998964, 6.999999999998964, 6.9659254532101125, 6.9659254532101125, 6.866024682077085, 6.866024682077085, 6.707105759159627, 6.707105759159627, 6.499998746588282, 6.499998746588282, 6.258817645219788, 6.258817645219788, 5.9999985487846486, 5.9999985487846486, 5.741179551247439, 5.741179551247439, 5.4999987398330035, 5.4999987398330035, 5.292892188512597, 5.292892188512597, 5.133973866707583, 5.133973866707583, 5.034073795585572, 5.034073795585572, 5.00000000000107, 5.00000000000107, 5.034074552847952, 5.034074552847952, 5.133975329626162, 5.133975329626162, 5.29289425739189, 5.29289425739189, 5.500001273682304, 5.500001273682304, 5.741182377389101, 5.741182377389101, 6.0000014746217865, 6.0000014746217865, 6.258820471361433, 6.258820471361433, 6.500001280438334, 6.500001280438334, 6.707107828038871, 6.707107828038871, 6.866026144995605, 6.866026144995605, 6.965926210472426, 6.965926210472426, 6.999999999998895, 6.999999999998895, 6.965925441093749, 6.965925441093749, 6.866024658670136, 6.866024658670136, 6.707105726057237, 6.707105726057237, 6.499998706047112, 6.499998706047112, 6.25881760000201, 6.25881760000201, 5.9999985019717785, 5.9999985019717785, 5.741179506028818, 5.741179506028818, 5.499998699291115, 5.499998699291115, 5.292892155410304, 5.292892155410304, 5.1339738433012085, 5.1339738433012085, 5.034073783469577, 5.034073783469577, 5.0000000000011395, 5.0000000000011395, 5.034074564964317, 5.034074564964317, 5.1339753530331125, 5.1339753530331125, 5.292894290493637, 5.292894290493637, 5.500001314223473, 5.500001314223473, 5.741182422607757, 5.741182422607757, 6.000001521435566, 6.000001521435566, 6.2588205165800535, 6.2588205165800535, 6.500001320979434, 6.500001320979434, 6.7071078611405195, 6.7071078611405195, 6.866026168402433, 6.866026168402433, 6.965926222588656, 6.965926222588656, 6.999999999998825, 6.999999999998825, 6.965925428977617, 6.965925428977617, 6.866024635263639, 6.866024635263639, 6.707105692955489, 6.707105692955489, 6.499998665505154, 6.499998665505154, 6.258817554783353, 6.258817554783353, 5.999998455157999, 5.999998455157999, 5.741179460811075, 5.741179460811075, 5.499998658750016, 5.499998658750016, 5.292892122308014, 5.292892122308014, 5.133973819894381, 5.133973819894381, 5.0340737713533485, 5.0340737713533485, 5.0000000000012115, 5.0000000000012115, 5.034074577080449, 5.034074577080449, 5.133975376440064, 5.133975376440064, 5.292894323596029, 5.292894323596029, 5.500001354765431, 5.500001354765431, 5.741182467825537, 5.741182467825537, 6.000001568248436, 6.000001568248436, 6.258820561798674, 6.258820561798674, 6.500001361521321, 6.500001361521321, 6.707107894242809, 6.707107894242809, 6.866026191808805, 6.866026191808805, 6.965926234704647, 6.965926234704647, 6.999999999998752, 6.999999999998752, 6.965925416861249, 6.965925416861249, 6.866024611856686, 6.866024611856686, 6.707105659853096, 6.707105659853096, 6.4999986249639825, 6.4999986249639825, 6.258817509565573, 6.258817509565573, 5.999998408344219, 5.999998408344219, 5.741179415592455, 5.741179415592455, 5.499998618208131, 5.499998618208131, 5.292892089206368, 5.292892089206368, 5.133973796488009, 5.133973796488009, 5.034073759237122, 5.034073759237122, 5.000000000001285, 5.000000000001285, 5.034074589196818, 5.034074589196818, 5.133975399846564, 5.133975399846564, 5.292894356697779, 5.292894356697779, 5.500001395307391, 5.500001395307391, 5.741182513044195, 5.741182513044195, 6.000001615062216, 6.000001615062216, 6.258820607016415, 6.258820607016415, 6.500001402062418, 6.500001402062418, 6.707107927344454, 6.707107927344454, 6.86602621521563, 6.86602621521563, 6.965926246820873, 6.965926246820873, 6.999999999998677, 6.999999999998677, 6.965925404745114, 6.965925404745114, 6.866024588450186, 6.866024588450186, 6.707105626750701, 6.707105626750701, 6.499998584422022, 6.499998584422022, 6.258817464346915, 6.258817464346915, 5.999998361531349, 5.999998361531349, 5.741179370374715, 5.741179370374715, 5.499998577666246, 5.499998577666246, 5.292892056104081, 5.292892056104081, 5.133973773081185, 5.133973773081185, 5.034073747121133, 5.034073747121133, 5.000000000001362, 5.000000000001362, 5.03407460131319, 5.03407460131319, 5.13397542325352, 5.13397542325352, 5.292894389800175, 5.292894389800175, 5.5000014358485645, 5.5000014358485645, 5.741182558261975, 5.741182558261975, 6.000001661875086, 6.000001661875086, 6.258820652235034, 6.258820652235034, 6.500001442604303, 6.500001442604303, 6.707107960446741, 6.707107960446741, 6.866026238621999, 6.866026238621999, 6.965926258936861, 6.965926258936861, 6.999999999998599, 6.999999999998599, 6.965925392628741, 6.965925392628741, 6.8660245650432286, 6.8660245650432286, 6.707105593648949, 6.707105593648949, 6.4999985438808485, 6.4999985438808485, 6.258817419128256, 6.258817419128256, 5.999998314717569, 5.999998314717569, 5.741179325156096, 5.741179325156096, 5.49999853712515, 5.49999853712515, 5.292892023002437, 5.292892023002437, 5.133973749674363, 5.133973749674363, 5.0340737350049105, 5.0340737350049105, 5.00000000000144, 5.00000000000144, 5.034074613429327, 5.034074613429327, 5.133975446660023, 5.133975446660023, 5.292894422901928, 5.292894422901928, 5.500001476390526, 5.500001476390526, 5.741182603480635, 5.741182603480635, 6.000001708688867, 6.000001708688867, 6.258820697452774, 6.258820697452774, 6.500001483145398, 6.500001483145398, 6.707107993549027, 6.707107993549027, 6.86602626202882, 6.86602626202882, 6.965926271053082, 6.965926271053082, 6.99999999999852, 6.99999999999852, 6.965925380512602, 6.965925380512602, 6.8660245416362695, 6.8660245416362695, 6.707105560546552, 6.707105560546552, 6.499998503338886, 6.499998503338886, 6.258817373910475, 6.258817373910475, 5.999998267904698, 5.999998267904698, 5.7411792799374775, 5.7411792799374775, 5.499998496583268, 5.499998496583268, 5.292891989900153, 5.292891989900153, 5.133973726267997, 5.133973726267997, 5.034073722888926, 5.034073722888926, 5.000000000001521, 5.000000000001521, 5.034074625545704, 5.034074625545704, 5.133975470066983, 5.133975470066983, 5.292894456004326, 5.292894456004326, 5.500001516931702, 5.500001516931702, 5.741182648698416, 5.741182648698416, 6.000001755502646, 6.000001755502646, 6.258820742671392, 6.258820742671392, 6.5000015236872795, 6.5000015236872795, 6.707108026650667, 6.707108026650667, 6.866026285435185, 6.866026285435185, 6.965926283169301, 6.965926283169301, 6.999999999998439, 6.999999999998439, 6.965925368396226, 6.965925368396226, 6.8660245182297635, 6.8660245182297635, 6.707105527444796, 6.707105527444796, 6.499998462796922, 6.499998462796922, 6.258817328691815, 6.258817328691815, 5.999998221090919, 5.999998221090919, 5.741179234719739, 5.741179234719739, 5.4999984560421735, 5.4999984560421735, 5.292891956798513, 5.292891956798513, 5.133973702861179, 5.133973702861179, 5.0340737107727085, 5.0340737107727085, 5.000000000001603, 5.000000000001603, 5.0340746376618455, 5.0340746376618455, 5.13397549347349, 5.13397549347349, 5.292894489106726, 5.292894489106726, 5.5000015574736665, 5.5000015574736665, 5.741182693917076, 5.741182693917076, 6.000001802315516, 6.000001802315516, 6.258820787889131, 6.258820787889131, 6.50000156422916, 6.50000156422916, 6.707108059752949, 6.707108059752949, 6.866026308842002, 6.866026308842002, 6.965926295285282, 6.965926295285282, 6.999999999998355, 6.999999999998355, 6.965925356279847, 6.965925356279847, 6.866024494822801, 6.866024494822801, 6.707105494342395, 6.707105494342395, 6.499998422255745, 6.499998422255745, 6.258817283474032, 6.258817283474032, 5.999998174278049, 5.999998174278049, 5.741179189501121, 5.741179189501121, 5.4999984155002934, 5.4999984155002934, 5.292891923696232, 5.292891923696232, 5.133973679454817, 5.133973679454817, 5.034073698656727, 5.034073698656727, 5.000000000001688, 5.000000000001688, 5.034074649778225, 5.034074649778225, 5.1339755168804535, 5.1339755168804535, 5.292894522208485, 5.292894522208485, 5.500001598014844, 5.500001598014844, 5.741182739135738, 5.741182739135738, 6.000001849129297, 6.000001849129297, 6.258820833107748, 6.258820833107748, 6.500001604770253, 6.500001604770253, 6.707108092854586, 6.707108092854586, 6.8660263322488175, 6.8660263322488175, 6.965926307401497, 6.965926307401497, 6.999999999998269, 6.999999999998269, 6.9659253441637015, 6.9659253441637015, 6.866024471416291, 6.866024471416291, 6.707105461240635, 6.707105461240635, 6.499998381713779, 6.499998381713779, 6.2588172382553715, 6.2588172382553715, 5.999998127464268, 5.999998127464268, 5.741179144283383, 5.741179144283383, 5.499998374959202, 5.499998374959202, 5.292891890593952, 5.292891890593952, 5.133973656048003, 5.133973656048003, 5.0340736865405145, 5.0340736865405145, 5.0000000000017755, 5.0000000000017755, 5.034074661894373, 5.034074661894373, 5.133975540287419, 5.133975540287419, 5.2928945553108875, 5.2928945553108875, 5.50000163855681, 5.50000163855681, 5.74118278435352, 5.74118278435352, 6.000001895942167, 6.000001895942167, 6.258820878326364, 6.258820878326364, 6.500001645312131, 6.500001645312131, 6.707108125956865, 6.707108125956865, 6.866026355655177, 6.866026355655177, 6.965926319517474, 6.965926319517474, 6.99999999999818, 6.99999999999818, 6.965925332047318, 6.965925332047318, 6.866024448009325, 6.866024448009325, 6.707105428138232, 6.707105428138232, 6.4999983411726, 6.4999983411726, 6.258817193037588, 6.258817193037588, 5.999998080650489, 5.999998080650489, 5.741179099064768, 5.741179099064768, 5.499998334417324, 5.499998334417324, 5.292891857492317, 5.292891857492317, 5.133973632641645, 5.133973632641645, 5.034073674424302, 5.034073674424302, 5.000000000001864, 5.000000000001864, 5.034074674010756, 5.034074674010756, 5.133975563693932, 5.133975563693932, 5.292894588412649, 5.292894588412649, 5.500001679098777, 5.500001679098777, 5.741182829572183, 5.741182829572183, 6.000001942755946, 6.000001942755946, 6.258820923544102, 6.258820923544102, 6.500001685853221, 6.500001685853221, 6.7071081590584996, 6.7071081590584996, 6.866026379061989, 6.866026379061989, 6.965926331633685, 6.965926331633685, 6.99999999999809, 6.99999999999809, 6.965925319931168, 6.965925319931168, 6.866024424602811, 6.866024424602811, 6.707105395035827, 6.707105395035827, 6.499998300630632, 6.499998300630632, 6.258817147818926, 6.258817147818926, 5.999998033837619, 5.999998033837619, 5.741179053847031, 5.741179053847031, 5.499998293875446, 5.499998293875446, 5.292891824390041, 5.292891824390041, 5.133973609234833, 5.133973609234833, 5.034073662308328, 5.034073662308328, 5.000000000001956, 5.000000000001956, 5.034074686127143, 5.034074686127143, 5.133975587100901, 5.133975587100901, 5.292894621515055, 5.292894621515055, 5.500001719639958, 5.500001719639958, 5.741182874789967, 5.741182874789967, 6.000001989568817, 6.000001989568817, 6.258820968762716, 6.258820968762716, 6.500001726395098, 6.500001726395098, 6.707108192160776, 6.707108192160776, 6.866026402468344, 6.866026402468344, 6.965926343749658, 6.965926343749658, 6.999999999997997, 6.999999999997997, 6.965925307814781, 6.965925307814781, 6.8660244011958405, 6.8660244011958405, 6.707105361934063, 6.707105361934063, 6.499998260089451, 6.499998260089451, 6.258817102600262, 6.258817102600262, 5.999997987023838, 5.999997987023838, 5.741179008628416, 5.741179008628416, 5.4999982533343585, 5.4999982533343585, 5.292891791288408, 5.292891791288408, 5.133973585828024, 5.133973585828024, 5.034073650192121, 5.034073650192121, 5.00000000000205, 5.00000000000205, 5.034074698243296, 5.034074698243296, 5.1339756105074175, 5.1339756105074175, 5.29289465461682, 5.29289465461682, 5.500001760181928, 5.500001760181928, 5.74118292000863, 5.74118292000863, 6.000002036382597, 6.000002036382597, 6.2588210139804525, 6.2588210139804525, 6.500001766936185, 6.500001766936185, 6.70710822526305, 6.70710822526305, 6.866026425875152, 6.866026425875152, 6.965926355865864, 6.965926355865864, 6.999999999997903, 6.999999999997903, 6.965925295698627, 6.965925295698627, 6.866024377788869, 6.866024377788869, 6.707105328831655, 6.707105328831655, 6.49999821954748, 6.49999821954748, 6.258817057382477, 6.258817057382477, 5.999997940210968, 5.999997940210968, 5.741178963409801, 5.741178963409801, 5.499998212792484, 5.499998212792484, 5.292891758186135, 5.292891758186135, 5.133973562421673, 5.133973562421673, 5.034073638076151, 5.034073638076151, 5.000000000002146, 5.000000000002146, 5.034074710359686, 5.034074710359686, 5.133975633914391, 5.133975633914391, 5.292894687719229, 5.292894687719229, 5.500001800723111, 5.500001800723111, 5.7411829652264155, 5.7411829652264155, 6.000002083196376, 6.000002083196376, 6.258821059199066, 6.258821059199066, 6.50000180747806, 6.50000180747806, 6.70710825836468, 6.70710825836468, 6.866026449281503, 6.866026449281503, 6.965926367982068, 6.965926367982068, 6.999999999997805, 6.999999999997805, 6.965925283582235, 6.965925283582235, 6.86602435438235, 6.86602435438235, 6.707105295729888, 6.707105295729888, 6.49999817900551, 6.49999817900551, 6.258817012163813, 6.258817012163813, 5.999997893397189, 5.999997893397189, 5.741178918192066, 5.741178918192066, 5.499998172251398, 5.499998172251398, 5.292891725084506, 5.292891725084506, 5.1339735390148675, 5.1339735390148675, 5.034073625959948, 5.034073625959948, 5.0000000000022435, 5.0000000000022435, 5.034074722475843, 5.034074722475843, 5.133975657320911, 5.133975657320911, 5.292894720821639, 5.292894720821639, 5.500001841265083, 5.500001841265083, 5.74118301044508, 5.74118301044508, 6.000002130009247, 6.000002130009247, 6.258821104416802, 6.258821104416802, 6.500001848019933, 6.500001848019933, 6.707108291466951, 6.707108291466951, 6.866026472688307, 6.866026472688307, 6.965926380098035, 6.965926380098035, 6.999999999997707, 6.999999999997707, 6.965925271465842, 6.965925271465842, 6.866024330975374, 6.866024330975374, 6.7071052626274765, 6.7071052626274765, 6.499998138464325, 6.499998138464325, 6.258816966946027, 6.258816966946027, 5.999997846584318, 5.999997846584318, 5.741178872973453, 5.741178872973453, 5.499998131709525, 5.499998131709525, 5.292891691982236, 5.292891691982236, 5.133973515608519, 5.133973515608519, 5.034073613843982, 5.034073613843982, 5.000000000002344, 5.000000000002344, 5.034074734592238, 5.034074734592238, 5.133975680727888, 5.133975680727888, 5.292894753923409, 5.292894753923409, 5.500001881806268, 5.500001881806268, 5.741183055663745, 5.741183055663745, 6.000002176823027, 6.000002176823027, 6.2588211496354145, 6.2588211496354145, 6.500001888561017, 6.500001888561017, 6.707108324568577, 6.707108324568577, 6.86602649609511, 6.86602649609511, 6.965926392214235, 6.965926392214235, 6.9999999999976055, 6.9999999999976055, 6.965925259349682, 6.965925259349682, 6.866024307568851, 6.866024307568851, 6.707105229525706, 6.707105229525706, 6.499998097922352, 6.499998097922352, 6.2588169217273615, 6.2588169217273615, 5.999997799770538, 5.999997799770538, 5.741178827755719, 5.741178827755719, 5.499998091168441, 5.499998091168441, 5.292891658879967, 5.292891658879967, 5.133973492201718, 5.133973492201718, 5.034073601727783, 5.034073601727783, 5.000000000002446, 5.000000000002446, 5.0340747467083995, 5.0340747467083995, 5.133975704134866, 5.133975704134866, 5.292894787025823, 5.292894787025823, 5.500001922348242, 5.500001922348242, 5.741183100881532, 5.741183100881532, 6.000002223635897, 6.000002223635897, 6.258821194854026, 6.258821194854026, 6.500001929102888, 6.500001929102888, 6.707108357670846, 6.707108357670846, 6.866026519501455, 6.866026519501455, 6.965926404330197, 6.965926404330197, 6.9999999999975016, 6.9999999999975016, 6.965925247233284, 6.965925247233284, 6.86602428416187, 6.86602428416187, 6.707105196423292, 6.707105196423292, 6.499998057381164, 6.499998057381164, 6.258816876509574, 6.258816876509574, 5.999997752956759, 5.999997752956759, 5.741178782537107, 5.741178782537107, 5.499998050626571, 5.499998050626571, 5.292891625778342, 5.292891625778342, 5.133973468795372, 5.133973468795372, 5.0340735896115865, 5.0340735896115865, 5.000000000002551, 5.000000000002551, 5.034074758824798, 5.034074758824798, 5.133975727541393, 5.133975727541393, 5.292894820127595, 5.292894820127595, 5.5000019628902175, 5.5000019628902175, 5.741183146100198, 5.741183146100198, 6.000002270449677, 6.000002270449677, 6.258821240071759, 6.258821240071759, 6.500001969643971, 6.500001969643971, 6.707108390772469, 6.707108390772469, 6.866026542908253, 6.866026542908253, 6.965926416446393, 6.965926416446393, 6.999999999997396, 6.999999999997396, 6.965925235117119, 6.965925235117119, 6.866024260755344, 6.866024260755344, 6.707105163320875, 6.707105163320875, 6.499998016839188, 6.499998016839188, 6.258816831290908, 6.258816831290908, 5.999997706143888, 5.999997706143888, 5.741178737319374, 5.741178737319374, 5.499998010084701, 5.499998010084701, 5.292891592676076, 5.292891592676076, 5.133973445388575, 5.133973445388575, 5.034073577495628, 5.034073577495628, 5.000000000002657, 5.000000000002657, 5.034074770941199, 5.034074770941199, 5.133975750948375, 5.133975750948375, 5.2928948532300115, 5.2928948532300115, 5.500002003431406, 5.500002003431406, 5.741183191317987, 5.741183191317987, 6.000002317262547, 6.000002317262547, 6.258821285290371, 6.258821285290371, 6.500002010185839, 6.500002010185839, 6.707108423874734, 6.707108423874734, 6.866026566314596, 6.866026566314596, 6.9659264285623514, 6.9659264285623514, 6.9999999999972875, 6.9999999999972875, 6.965925223000717, 6.965925223000717, 6.86602423734836, 6.86602423734836, 6.707105130219101, 6.707105130219101, 6.499997976297999, 6.499997976297999, 6.258816786072241, 6.258816786072241, 5.999997659330108, 5.999997659330108, 5.741178692100763, 5.741178692100763, 5.499997969543621, 5.499997969543621, 5.292891559574455, 5.292891559574455, 5.133973421981779, 5.133973421981779, 5.034073565379435, 5.034073565379435, 5.000000000002767, 5.000000000002767, 5.034074783057367, 5.034074783057367, 5.133975774354905, 5.133975774354905, 5.292894886331787, 5.292894886331787, 5.5000020439733825, 5.5000020439733825, 5.741183236536654, 5.741183236536654, 6.000002364076327, 6.000002364076327, 6.258821330508103, 6.258821330508103, 6.500002050726919, 6.500002050726919, 6.707108456976997, 6.707108456976997, 6.866026589721391, 6.866026589721391, 6.965926440678543, 6.965926440678543, 6.999999999997177, 6.999999999997177, 6.9659252108845475, 6.9659252108845475, 6.866024213941375, 6.866024213941375, 6.707105097116682, 6.707105097116682, 6.499997935756022, 6.499997935756022, 6.258816740854451, 6.258816740854451, 5.999997612517238, 5.999997612517238, 5.741178646882153, 5.741178646882153, 5.499997929001753, 5.499997929001753, 5.292891526472193, 5.292891526472193, 5.13397339857544, 5.13397339857544, 5.03407355326348, 5.03407355326348, 5.000000000002878, 5.000000000002878, 5.0340747951737725, 5.0340747951737725, 5.133975797761892, 5.133975797761892, 5.292894919434207, 5.292894919434207, 5.500002084514573, 5.500002084514573, 5.741183281754443, 5.741183281754443, 6.000002410890107, 6.000002410890107, 6.258821375726713, 6.258821375726713, 6.500002091268786, 6.500002091268786, 6.707108490078617, 6.707108490078617, 6.866026613127728, 6.866026613127728, 6.965926452794733, 6.965926452794733, 6.9999999999970655, 6.9999999999970655, 6.965925198768142, 6.965925198768142, 6.8660241905348425, 6.8660241905348425, 6.707105064014904, 6.707105064014904, 6.499997895214043, 6.499997895214043, 6.258816695635783, 6.258816695635783, 5.999997565703458, 5.999997565703458, 5.741178601664422, 5.741178601664422, 5.499997888460675, 5.499997888460675, 5.292891493370575, 5.292891493370575, 5.133973375168649, 5.133973375168649, 5.034073541147292, 5.034073541147292, 5.000000000002991, 5.000000000002991, 5.034074807289945, 5.034074807289945, 5.133975821168425, 5.133975821168425, 5.292894952536629, 5.292894952536629, 5.500002125056553, 5.500002125056553, 5.741183326973112, 5.741183326973112, 6.000002457702977, 6.000002457702977, 6.258821420944444, 6.258821420944444, 6.500002131810652, 6.500002131810652, 6.707108523180877, 6.707108523180877, 6.866026636534519, 6.866026636534519, 6.965926464910685, 6.965926464910685, 6.999999999996951, 6.999999999996951, 6.965925186651733, 6.965925186651733, 6.866024167127853, 6.866024167127853, 6.707105030912482, 6.707105030912482, 6.49999785467285, 6.49999785467285, 6.258816650417993, 6.258816650417993, 5.999997518890588, 5.999997518890588, 5.741178556445813, 5.741178556445813, 5.49999784791881, 5.49999784791881, 5.292891460268315, 5.292891460268315, 5.133973351762314, 5.133973351762314, 5.034073529031341, 5.034073529031341, 5.000000000003107, 5.000000000003107, 5.034074819406354, 5.034074819406354, 5.133975844575415, 5.133975844575415, 5.292894985638409, 5.292894985638409, 5.500002165597746, 5.500002165597746, 5.741183372191781, 5.741183372191781, 6.000002504516757, 6.000002504516757, 6.258821466163052, 6.258821466163052, 6.500002172351728, 6.500002172351728, 6.707108556282493, 6.707108556282493, 6.8660266599413085, 6.8660266599413085, 6.965926477026869, 6.965926477026869, 6.9999999999968345, 6.9999999999968345, 6.965925174535558, 6.965925174535558, 6.866024143721317, 6.866024143721317, 6.707104997810701, 6.707104997810701, 6.49999781413087, 6.49999781413087, 6.258816605199324, 6.258816605199324, 5.999997472076808, 5.999997472076808, 5.741178511228083, 5.741178511228083, 5.499997807377734, 5.499997807377734, 5.29297775466854, 5.29297775466854, 5.134079057408345, 5.134079057408345, 5.034191442257982, 5.034191442257982, 5.000122085218705, 5.000122085218705, 5.034192756704862, 5.034192756704862, 5.134081596725176, 5.134081596725176, 5.292981345804837, 5.292981345804837, 5.50006324847813, 5.50006324847813, 5.741215015087827, 5.741215015087827, 6.000002551018148, 6.000002551018148, 6.258789913101673, 6.258789913101673, 6.499941170015689, 6.499941170015689, 6.707022261880247, 6.707022261880247, 6.865920954293392, 6.865920954293392, 6.965808563799252, 6.965808563799252, 6.999877914781235, 6.999877914781235, 6.965807237237553, 6.965807237237553, 6.865601205348964, 6.865601205348964, 6.705292096406619, 6.705292096406619, 6.493527285981263, 6.493527285981263, 6.242069791969054, 6.242069791969054, 5.999998115862116, 5.999998115862116, 5.912061979111753, 5.912061979111753, 5.955804954607395, 5.955804954607395, 5.987309856541017, 5.987309856541017 ] } } }, "7a62c17a55544d42920816325e1a1959": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "7c73001ae93544b19f05d18a3b674848": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#ff7f0e" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS1_m2" ], "scales": { "x": "IPY_MODEL_8342a76ecdda4fb9a4e600d8ab8fc329", "y": "IPY_MODEL_30d949e7ba5846e89f696eade5e275c3" }, "selected": [], "tooltip": "IPY_MODEL_de89493a5a54462aa3afafdeebcc5adf", "x": { "type": "float", "values": [ 0, 100, 100, 1100 ] }, "y": { "type": "float", "values": [ 3, 3, 2, 2 ] } } }, "7dbdbb3b142f43269fa741441883ff11": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "ToolbarModel", "state": { "figure": "IPY_MODEL_ddd19c8b9e194e08a9942e939bec829a", "layout": "IPY_MODEL_92263012278b492a9f1b6c4f0ff56c42" } }, "8342a76ecdda4fb9a4e600d8ab8fc329": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "stabilized": false } }, "878486d7bb834d1891aed2a021fad962": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "FigureModel", "state": { "_dom_classes": [], "animation_duration": 50, "axes": [ "IPY_MODEL_072cf286cd1d471bb1b8766a79641d91", "IPY_MODEL_d7ab204b03e24cfa8d4159f02ba14287" ], "layout": "IPY_MODEL_5ad23175c43b4d0db10f2e5232f30cb5", "marks": [ "IPY_MODEL_12f876e9421b40429141e35e1eea7954", "IPY_MODEL_2ed12d1557984347b7cb67660a9ebc16", "IPY_MODEL_385459ae10754d14b779ad18351d672f", "IPY_MODEL_b584ff40c57945878ce435747b67111c", "IPY_MODEL_aafad6bc42b041b699d43cb2d1fe5c2c" ], "scale_x": "IPY_MODEL_19e1aafd7f63482195d9af28dee252e4", "scale_y": "IPY_MODEL_36d96bdd5b284556a2073db0e65f128f", "title": "Waveform Plotter" } }, "893ac633d30d4e9ca175cc0aaac5e3ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "8d324a24ca1f4a449af7987e58c3e72d": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "8d3d28f582504308b011c6e283382a08": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "FigureModel", "state": { "_dom_classes": [], "animation_duration": 50, "axes": [ "IPY_MODEL_d829e29c146e4ece8f6f5b8c4f12b6b9", "IPY_MODEL_53dca7485c1a4053b2efd051f21742fd" ], "layout": "IPY_MODEL_a44c381dd7c54036aae5f46845e47ba3", "marks": [ "IPY_MODEL_918842bceca14fc9b748793a392f622b", "IPY_MODEL_7c73001ae93544b19f05d18a3b674848", "IPY_MODEL_d638ae4f6976431795a33ba1b24ec4d6", "IPY_MODEL_7a061f2853d949568f68550f3c362e1b", "IPY_MODEL_db86edeb23c44827acf5b0a480f1dd2e" ], "scale_x": "IPY_MODEL_8d324a24ca1f4a449af7987e58c3e72d", "scale_y": "IPY_MODEL_b94d787734a54d0887d66a9c8b5e1214", "title": "Waveform Plotter" } }, "8e8ebdb406d4482ab5620aa6a4bff8ac": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "stabilized": false } }, "918842bceca14fc9b748793a392f622b": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#1f77b4" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS1_ch1" ], "scales": { "x": "IPY_MODEL_8342a76ecdda4fb9a4e600d8ab8fc329", "y": "IPY_MODEL_30d949e7ba5846e89f696eade5e275c3" }, "selected": [], "tooltip": "IPY_MODEL_de89493a5a54462aa3afafdeebcc5adf", "x": { "type": "float", "values": [ 0, 0.8333333333333334, 0.8333333333333334, 1.6666666666666667, 1.6666666666666667, 2.5, 2.5, 3.3333333333333335, 3.3333333333333335, 4.166666666666667, 4.166666666666667, 5, 5, 5.833333333333333, 5.833333333333333, 6.666666666666667, 6.666666666666667, 7.500000000000001, 7.500000000000001, 8.333333333333334, 8.333333333333334, 9.166666666666666, 9.166666666666666, 10, 10, 10.833333333333334, 10.833333333333334, 11.666666666666666, 11.666666666666666, 12.5, 12.5, 13.333333333333334, 13.333333333333334, 14.166666666666668, 14.166666666666668, 15.000000000000002, 15.000000000000002, 15.833333333333332, 15.833333333333332, 16.666666666666668, 16.666666666666668, 17.5, 17.5, 18.333333333333332, 18.333333333333332, 19.166666666666668, 19.166666666666668, 20, 20, 100, 100, 1100 ] }, "y": { "type": "float", "values": [ 0.04541570015871078, 0.04541570015871078, 0.10438285923574656, 0.10438285923574656, 0.17800024417043095, 0.17800024417043095, 0.2661457697472836, 0.2661457697472836, 0.36747649859602, 0.36747649859602, 0.4786961298986693, 0.4786961298986693, 0.5946770846050543, 0.5946770846050543, 0.7087046758637529, 0.7087046758637529, 0.8132096203149799, 0.8132096203149799, 0.9003784641679893, 0.9003784641679893, 0.9630081797094372, 0.9630081797094372, 0.9957270174581858, 0.9957270174581858, 0.9957270174581858, 0.9957270174581858, 0.9630081797094372, 0.9630081797094372, 0.9003784641679893, 0.9003784641679893, 0.8132096203149799, 0.8132096203149799, 0.7087046758637529, 0.7087046758637529, 0.5946770846050543, 0.5946770846050543, 0.4786961298986693, 0.4786961298986693, 0.36747649859602, 0.36747649859602, 0.2661457697472836, 0.2661457697472836, 0.17800024417043095, 0.17800024417043095, 0.10438285923574656, 0.10438285923574656, 0.04541570015871078, 0.04541570015871078, 0, 0, 0, 0 ] } } }, "92263012278b492a9f1b6c4f0ff56c42": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "94472b136d2c40a9805b6ad94b3ebaaa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_31750defe61d492594401d2b55a58b40", "IPY_MODEL_d6a9f12103894fce9f96e7f361f02fd6" ], "layout": "IPY_MODEL_41194916d55a407aab9dd1bd9ece870f" } }, "96231612b10340089312f47857d3890c": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "9cf9e49671af4768939f815ee6f2f6de": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "AxisModel", "state": { "scale": "IPY_MODEL_f989834e0fd144cd95b580df0aaad5ab", "side": "bottom", "tick_values": { "type": null, "values": null } } }, "9e46c1e4a1aa4731995efd0536d84f82": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "a44c381dd7c54036aae5f46845e47ba3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": { "min_width": "125px" } }, "a71a67a73d17471581bd9699c6803d86": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "max": 9, "min": -1, "stabilized": false } }, "aafad6bc42b041b699d43cb2d1fe5c2c": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#9467bd" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_m1" ], "scales": { "x": "IPY_MODEL_8e8ebdb406d4482ab5620aa6a4bff8ac", "y": "IPY_MODEL_a71a67a73d17471581bd9699c6803d86" }, "selected": [], "tooltip": "IPY_MODEL_1561859070ba41c48a54d5e58bddc084", "x": { "type": "float", "values": [ 0, 100, 100, 200, 200, 1100 ] }, "y": { "type": "float", "values": [ 8, 8, 9, 9, 8, 8 ] } } }, "ac135538e5624fb58eda02fa58dd9333": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "add375242ff64f41ab228c922b92d020": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "AxisModel", "state": { "orientation": "vertical", "scale": "IPY_MODEL_e8e3f7bb389f4168be4a3f68b16bf18a", "side": "left", "tick_values": { "type": null, "values": null } } }, "af69c3cbfaee4e319d2c44b463c2983f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "b584ff40c57945878ce435747b67111c": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#d62728" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_ch2" ], "scales": { "x": "IPY_MODEL_8e8ebdb406d4482ab5620aa6a4bff8ac", "y": "IPY_MODEL_a71a67a73d17471581bd9699c6803d86" }, "selected": [], "tooltip": "IPY_MODEL_1561859070ba41c48a54d5e58bddc084", "x": { "type": "float", "values": [ 0, 100, 100, 100.83333333333333, 100.83333333333333, 101.66666666666666, 101.66666666666666, 102.5, 102.5, 103.33333333333333, 103.33333333333333, 104.16666666666667, 104.16666666666667, 105, 105, 105.83333333333333, 105.83333333333333, 106.66666666666667, 106.66666666666667, 107.5, 107.5, 108.33333333333334, 108.33333333333334, 109.16666666666667, 109.16666666666667, 110, 110, 110.83333333333334, 110.83333333333334, 111.66666666666667, 111.66666666666667, 112.50000000000001, 112.50000000000001, 113.33333333333334, 113.33333333333334, 114.16666666666666, 114.16666666666666, 115, 115, 115.83333333333333, 115.83333333333333, 116.66666666666667, 116.66666666666667, 117.5, 117.5, 118.33333333333333, 118.33333333333333, 119.16666666666667, 119.16666666666667, 120.00000000000001, 120.00000000000001, 120.83333333333333, 120.83333333333333, 121.66666666666666, 121.66666666666666, 122.49999999999999, 122.49999999999999, 123.33333333333333, 123.33333333333333, 124.16666666666666, 124.16666666666666, 125, 125, 125.83333333333333, 125.83333333333333, 126.66666666666666, 126.66666666666666, 127.5, 127.5, 128.33333333333334, 128.33333333333334, 129.16666666666666, 129.16666666666666, 130, 130, 130.83333333333334, 130.83333333333334, 131.66666666666666, 131.66666666666666, 132.5, 132.5, 133.33333333333334, 133.33333333333334, 134.16666666666666, 134.16666666666666, 135, 135, 135.83333333333334, 135.83333333333334, 136.66666666666669, 136.66666666666669, 137.5, 137.5, 138.33333333333334, 138.33333333333334, 139.16666666666669, 139.16666666666669, 140, 140, 140.83333333333334, 140.83333333333334, 141.66666666666669, 141.66666666666669, 142.5, 142.5, 143.33333333333334, 143.33333333333334, 144.16666666666669, 144.16666666666669, 145.00000000000003, 145.00000000000003, 145.83333333333334, 145.83333333333334, 146.66666666666666, 146.66666666666666, 147.5, 147.5, 148.33333333333331, 148.33333333333331, 149.16666666666666, 149.16666666666666, 150, 150, 150.83333333333331, 150.83333333333331, 151.66666666666666, 151.66666666666666, 152.5, 152.5, 153.33333333333334, 153.33333333333334, 154.16666666666666, 154.16666666666666, 155, 155, 155.83333333333334, 155.83333333333334, 156.66666666666666, 156.66666666666666, 157.5, 157.5, 158.33333333333334, 158.33333333333334, 159.16666666666666, 159.16666666666666, 160, 160, 160.83333333333334, 160.83333333333334, 161.66666666666669, 161.66666666666669, 162.5, 162.5, 163.33333333333334, 163.33333333333334, 164.16666666666669, 164.16666666666669, 165, 165, 165.83333333333334, 165.83333333333334, 166.66666666666669, 166.66666666666669, 167.5, 167.5, 168.33333333333334, 168.33333333333334, 169.16666666666669, 169.16666666666669, 170, 170, 170.83333333333334, 170.83333333333334, 171.66666666666669, 171.66666666666669, 172.50000000000003, 172.50000000000003, 173.33333333333334, 173.33333333333334, 174.16666666666666, 174.16666666666666, 175, 175, 175.83333333333331, 175.83333333333331, 176.66666666666666, 176.66666666666666, 177.5, 177.5, 178.33333333333334, 178.33333333333334, 179.16666666666666, 179.16666666666666, 180, 180, 180.83333333333334, 180.83333333333334, 181.66666666666666, 181.66666666666666, 182.5, 182.5, 183.33333333333334, 183.33333333333334, 184.16666666666666, 184.16666666666666, 185, 185, 185.83333333333334, 185.83333333333334, 186.66666666666666, 186.66666666666666, 187.5, 187.5, 188.33333333333334, 188.33333333333334, 189.16666666666669, 189.16666666666669, 190, 190, 190.83333333333334, 190.83333333333334, 191.66666666666669, 191.66666666666669, 192.5, 192.5, 193.33333333333334, 193.33333333333334, 194.16666666666669, 194.16666666666669, 195, 195, 195.83333333333334, 195.83333333333334, 196.66666666666669, 196.66666666666669, 197.50000000000003, 197.50000000000003, 198.33333333333334, 198.33333333333334, 199.16666666666669, 199.16666666666669, 200, 200, 200.83333333333331, 200.83333333333331, 201.66666666666666, 201.66666666666666, 202.5, 202.5, 203.33333333333331, 203.33333333333331, 204.16666666666666, 204.16666666666666, 205, 205, 205.83333333333334, 205.83333333333334, 206.66666666666666, 206.66666666666666, 207.5, 207.5, 208.33333333333334, 208.33333333333334, 209.16666666666666, 209.16666666666666, 210, 210, 210.83333333333334, 210.83333333333334, 211.66666666666666, 211.66666666666666, 212.5, 212.5, 213.33333333333334, 213.33333333333334, 214.16666666666669, 214.16666666666669, 215, 215, 215.83333333333334, 215.83333333333334, 216.66666666666669, 216.66666666666669, 217.5, 217.5, 218.33333333333334, 218.33333333333334, 219.16666666666669, 219.16666666666669, 220, 220, 220.83333333333334, 220.83333333333334, 221.66666666666669, 221.66666666666669, 222.5, 222.5, 223.33333333333334, 223.33333333333334, 224.16666666666669, 224.16666666666669, 225.00000000000003, 225.00000000000003, 225.83333333333334, 225.83333333333334, 226.66666666666669, 226.66666666666669, 227.5, 227.5, 228.33333333333331, 228.33333333333331, 229.16666666666666, 229.16666666666666, 230, 230, 230.83333333333334, 230.83333333333334, 231.66666666666666, 231.66666666666666, 232.5, 232.5, 233.33333333333334, 233.33333333333334, 234.16666666666666, 234.16666666666666, 235, 235, 235.83333333333334, 235.83333333333334, 236.66666666666666, 236.66666666666666, 237.5, 237.5, 238.33333333333334, 238.33333333333334, 239.16666666666666, 239.16666666666666, 240.00000000000003, 240.00000000000003, 240.83333333333334, 240.83333333333334, 241.66666666666666, 241.66666666666666, 242.5, 242.5, 243.33333333333331, 243.33333333333331, 244.16666666666669, 244.16666666666669, 244.99999999999997, 244.99999999999997, 245.83333333333334, 245.83333333333334, 246.66666666666666, 246.66666666666666, 247.5, 247.5, 248.33333333333331, 248.33333333333331, 249.16666666666669, 249.16666666666669, 250, 250, 250.83333333333334, 250.83333333333334, 251.66666666666666, 251.66666666666666, 252.50000000000003, 252.50000000000003, 253.33333333333331, 253.33333333333331, 254.16666666666669, 254.16666666666669, 255, 255, 255.83333333333334, 255.83333333333334, 256.6666666666667, 256.6666666666667, 257.5, 257.5, 258.3333333333333, 258.3333333333333, 259.1666666666667, 259.1666666666667, 260, 260, 260.83333333333337, 260.83333333333337, 261.6666666666667, 261.6666666666667, 262.5, 262.5, 263.3333333333333, 263.3333333333333, 264.1666666666667, 264.1666666666667, 265, 265, 265.83333333333337, 265.83333333333337, 266.6666666666667, 266.6666666666667, 267.5, 267.5, 268.3333333333333, 268.3333333333333, 269.16666666666663, 269.16666666666663, 270, 270, 270.8333333333333, 270.8333333333333, 271.6666666666667, 271.6666666666667, 272.5, 272.5, 273.33333333333337, 273.33333333333337, 274.16666666666663, 274.16666666666663, 275, 275, 275.8333333333333, 275.8333333333333, 276.6666666666667, 276.6666666666667, 277.5, 277.5, 278.33333333333337, 278.33333333333337, 279.1666666666667, 279.1666666666667, 280, 280, 280.8333333333333, 280.8333333333333, 281.6666666666667, 281.6666666666667, 282.5, 282.5, 283.33333333333337, 283.33333333333337, 284.1666666666667, 284.1666666666667, 285, 285, 285.8333333333333, 285.8333333333333, 286.6666666666667, 286.6666666666667, 287.5, 287.5, 288.33333333333337, 288.33333333333337, 289.1666666666667, 289.1666666666667, 290.00000000000006, 290.00000000000006, 290.8333333333333, 290.8333333333333, 291.6666666666667, 291.6666666666667, 292.5, 292.5, 293.3333333333333, 293.3333333333333, 294.1666666666667, 294.1666666666667, 295, 295, 295.8333333333333, 295.8333333333333, 296.66666666666663, 296.66666666666663, 297.5, 297.5, 298.3333333333333, 298.3333333333333, 299.1666666666667, 299.1666666666667, 300, 300, 300.83333333333337, 300.83333333333337, 301.66666666666663, 301.66666666666663, 302.5, 302.5, 303.3333333333333, 303.3333333333333, 304.1666666666667, 304.1666666666667, 305, 305, 305.83333333333337, 305.83333333333337, 306.6666666666667, 306.6666666666667, 307.5, 307.5, 308.3333333333333, 308.3333333333333, 309.1666666666667, 309.1666666666667, 310, 310, 310.83333333333337, 310.83333333333337, 311.6666666666667, 311.6666666666667, 312.5, 312.5, 313.3333333333333, 313.3333333333333, 314.1666666666667, 314.1666666666667, 315, 315, 315.83333333333337, 315.83333333333337, 316.6666666666667, 316.6666666666667, 317.50000000000006, 317.50000000000006, 318.3333333333333, 318.3333333333333, 319.1666666666667, 319.1666666666667, 320, 320, 320.8333333333333, 320.8333333333333, 321.6666666666667, 321.6666666666667, 322.5, 322.5, 323.33333333333337, 323.33333333333337, 324.16666666666663, 324.16666666666663, 325, 325, 325.8333333333333, 325.8333333333333, 326.6666666666667, 326.6666666666667, 327.5, 327.5, 328.33333333333337, 328.33333333333337, 329.16666666666663, 329.16666666666663, 330, 330, 330.8333333333333, 330.8333333333333, 331.6666666666667, 331.6666666666667, 332.5, 332.5, 333.33333333333337, 333.33333333333337, 334.1666666666667, 334.1666666666667, 335, 335, 335.8333333333333, 335.8333333333333, 336.6666666666667, 336.6666666666667, 337.5, 337.5, 338.33333333333337, 338.33333333333337, 339.1666666666667, 339.1666666666667, 340, 340, 340.8333333333333, 340.8333333333333, 341.6666666666667, 341.6666666666667, 342.5, 342.5, 343.33333333333337, 343.33333333333337, 344.1666666666667, 344.1666666666667, 345.00000000000006, 345.00000000000006, 345.8333333333333, 345.8333333333333, 346.6666666666667, 346.6666666666667, 347.5, 347.5, 348.3333333333333, 348.3333333333333, 349.1666666666667, 349.1666666666667, 350, 350, 350.83333333333337, 350.83333333333337, 351.66666666666663, 351.66666666666663, 352.5, 352.5, 353.3333333333333, 353.3333333333333, 354.1666666666667, 354.1666666666667, 355, 355, 355.83333333333337, 355.83333333333337, 356.6666666666667, 356.6666666666667, 357.5, 357.5, 358.3333333333333, 358.3333333333333, 359.1666666666667, 359.1666666666667, 360, 360, 360.83333333333337, 360.83333333333337, 361.6666666666667, 361.6666666666667, 362.5, 362.5, 363.3333333333333, 363.3333333333333, 364.1666666666667, 364.1666666666667, 365, 365, 365.83333333333337, 365.83333333333337, 366.6666666666667, 366.6666666666667, 367.5, 367.5, 368.3333333333333, 368.3333333333333, 369.1666666666667, 369.1666666666667, 370, 370, 370.83333333333337, 370.83333333333337, 371.6666666666667, 371.6666666666667, 372.50000000000006, 372.50000000000006, 373.3333333333333, 373.3333333333333, 374.16666666666663, 374.16666666666663, 375, 375, 375.8333333333333, 375.8333333333333, 376.6666666666667, 376.6666666666667, 377.5, 377.5, 378.33333333333337, 378.33333333333337, 379.16666666666663, 379.16666666666663, 380, 380, 380.8333333333333, 380.8333333333333, 381.6666666666667, 381.6666666666667, 382.5, 382.5, 383.33333333333337, 383.33333333333337, 384.1666666666667, 384.1666666666667, 385, 385, 385.8333333333333, 385.8333333333333, 386.6666666666667, 386.6666666666667, 387.5, 387.5, 388.33333333333337, 388.33333333333337, 389.1666666666667, 389.1666666666667, 390, 390, 390.8333333333333, 390.8333333333333, 391.6666666666667, 391.6666666666667, 392.5, 392.5, 393.33333333333337, 393.33333333333337, 394.1666666666667, 394.1666666666667, 395.00000000000006, 395.00000000000006, 395.8333333333333, 395.8333333333333, 396.6666666666667, 396.6666666666667, 397.5, 397.5, 398.33333333333337, 398.33333333333337, 399.1666666666667, 399.1666666666667, 400, 400, 400.8333333333333, 400.8333333333333, 401.66666666666663, 401.66666666666663, 402.5, 402.5, 403.3333333333333, 403.3333333333333, 404.1666666666667, 404.1666666666667, 405, 405, 405.83333333333337, 405.83333333333337, 406.66666666666663, 406.66666666666663, 407.5, 407.5, 408.3333333333333, 408.3333333333333, 409.1666666666667, 409.1666666666667, 410, 410, 410.83333333333337, 410.83333333333337, 411.6666666666667, 411.6666666666667, 412.5, 412.5, 413.3333333333333, 413.3333333333333, 414.1666666666667, 414.1666666666667, 415, 415, 415.83333333333337, 415.83333333333337, 416.6666666666667, 416.6666666666667, 417.5, 417.5, 418.3333333333333, 418.3333333333333, 419.1666666666667, 419.1666666666667, 420, 420, 420.83333333333337, 420.83333333333337, 421.6666666666667, 421.6666666666667, 422.50000000000006, 422.50000000000006, 423.3333333333333, 423.3333333333333, 424.1666666666667, 424.1666666666667, 425, 425, 425.83333333333337, 425.83333333333337, 426.6666666666667, 426.6666666666667, 427.5, 427.5, 428.33333333333337, 428.33333333333337, 429.16666666666663, 429.16666666666663, 430, 430, 430.8333333333333, 430.8333333333333, 431.6666666666667, 431.6666666666667, 432.5, 432.5, 433.33333333333337, 433.33333333333337, 434.16666666666663, 434.16666666666663, 435, 435, 435.8333333333333, 435.8333333333333, 436.6666666666667, 436.6666666666667, 437.5, 437.5, 438.33333333333337, 438.33333333333337, 439.1666666666667, 439.1666666666667, 440, 440, 440.8333333333333, 440.8333333333333, 441.6666666666667, 441.6666666666667, 442.5, 442.5, 443.33333333333337, 443.33333333333337, 444.1666666666667, 444.1666666666667, 445, 445, 445.8333333333333, 445.8333333333333, 446.6666666666667, 446.6666666666667, 447.5, 447.5, 448.33333333333337, 448.33333333333337, 449.1666666666667, 449.1666666666667, 450.00000000000006, 450.00000000000006, 450.8333333333333, 450.8333333333333, 451.6666666666667, 451.6666666666667, 452.5, 452.5, 453.33333333333337, 453.33333333333337, 454.1666666666667, 454.1666666666667, 455, 455, 455.83333333333337, 455.83333333333337, 456.66666666666663, 456.66666666666663, 457.5, 457.5, 458.3333333333333, 458.3333333333333, 459.1666666666667, 459.1666666666667, 460, 460, 460.83333333333337, 460.83333333333337, 461.6666666666667, 461.6666666666667, 462.5, 462.5, 463.3333333333333, 463.3333333333333, 464.1666666666667, 464.1666666666667, 465, 465, 465.83333333333337, 465.83333333333337, 466.6666666666667, 466.6666666666667, 467.5, 467.5, 468.3333333333333, 468.3333333333333, 469.1666666666667, 469.1666666666667, 470, 470, 470.83333333333337, 470.83333333333337, 471.6666666666667, 471.6666666666667, 472.50000000000006, 472.50000000000006, 473.3333333333333, 473.3333333333333, 474.1666666666667, 474.1666666666667, 475, 475, 475.83333333333337, 475.83333333333337, 476.6666666666667, 476.6666666666667, 477.5, 477.5, 478.3333333333333, 478.3333333333333, 479.1666666666667, 479.1666666666667, 480.00000000000006, 480.00000000000006, 480.8333333333333, 480.8333333333333, 481.6666666666667, 481.6666666666667, 482.50000000000006, 482.50000000000006, 483.3333333333333, 483.3333333333333, 484.16666666666663, 484.16666666666663, 485, 485, 485.83333333333337, 485.83333333333337, 486.66666666666663, 486.66666666666663, 487.5, 487.5, 488.33333333333337, 488.33333333333337, 489.1666666666667, 489.1666666666667, 489.99999999999994, 489.99999999999994, 490.8333333333333, 490.8333333333333, 491.6666666666667, 491.6666666666667, 492.50000000000006, 492.50000000000006, 493.3333333333333, 493.3333333333333, 494.1666666666667, 494.1666666666667, 495, 495, 495.83333333333337, 495.83333333333337, 496.66666666666663, 496.66666666666663, 497.5, 497.5, 498.33333333333337, 498.33333333333337, 499.16666666666674, 499.16666666666674, 500, 500, 500.8333333333333, 500.8333333333333, 501.6666666666667, 501.6666666666667, 502.50000000000006, 502.50000000000006, 503.3333333333333, 503.3333333333333, 504.1666666666667, 504.1666666666667, 505.00000000000006, 505.00000000000006, 505.83333333333337, 505.83333333333337, 506.66666666666663, 506.66666666666663, 507.5, 507.5, 508.33333333333337, 508.33333333333337, 509.16666666666663, 509.16666666666663, 510, 510, 510.83333333333337, 510.83333333333337, 511.6666666666667, 511.6666666666667, 512.5, 512.5, 513.3333333333334, 513.3333333333334, 514.1666666666666, 514.1666666666666, 515, 515, 515.8333333333333, 515.8333333333333, 516.6666666666666, 516.6666666666666, 517.5, 517.5, 518.3333333333334, 518.3333333333334, 519.1666666666666, 519.1666666666666, 520, 520, 520.8333333333334, 520.8333333333334, 521.6666666666667, 521.6666666666667, 522.5, 522.5, 523.3333333333334, 523.3333333333334, 524.1666666666667, 524.1666666666667, 525, 525, 525.8333333333333, 525.8333333333333, 526.6666666666666, 526.6666666666666, 527.5, 527.5, 528.3333333333334, 528.3333333333334, 529.1666666666666, 529.1666666666666, 530, 530, 530.8333333333334, 530.8333333333334, 531.6666666666667, 531.6666666666667, 532.5, 532.5, 533.3333333333334, 533.3333333333334, 534.1666666666667, 534.1666666666667, 535, 535, 535.8333333333334, 535.8333333333334, 536.6666666666666, 536.6666666666666, 537.5, 537.5, 538.3333333333333, 538.3333333333333, 539.1666666666666, 539.1666666666666, 540, 540, 540.8333333333334, 540.8333333333334, 541.6666666666666, 541.6666666666666, 542.5, 542.5, 543.3333333333334, 543.3333333333334, 544.1666666666667, 544.1666666666667, 545, 545, 545.8333333333334, 545.8333333333334, 546.6666666666667, 546.6666666666667, 547.5, 547.5, 548.3333333333333, 548.3333333333333, 549.1666666666666, 549.1666666666666, 550, 550, 550.8333333333334, 550.8333333333334, 551.6666666666666, 551.6666666666666, 552.5, 552.5, 553.3333333333334, 553.3333333333334, 554.1666666666667, 554.1666666666667, 555, 555, 555.8333333333334, 555.8333333333334, 556.6666666666667, 556.6666666666667, 557.5000000000001, 557.5000000000001, 558.3333333333334, 558.3333333333334, 559.1666666666666, 559.1666666666666, 560, 560, 560.8333333333333, 560.8333333333333, 561.6666666666666, 561.6666666666666, 562.5, 562.5, 563.3333333333334, 563.3333333333334, 564.1666666666666, 564.1666666666666, 565, 565, 565.8333333333334, 565.8333333333334, 566.6666666666667, 566.6666666666667, 567.5, 567.5, 568.3333333333334, 568.3333333333334, 569.1666666666667, 569.1666666666667, 570, 570, 570.8333333333333, 570.8333333333333, 571.6666666666666, 571.6666666666666, 572.5, 572.5, 573.3333333333334, 573.3333333333334, 574.1666666666666, 574.1666666666666, 575, 575, 575.8333333333334, 575.8333333333334, 576.6666666666667, 576.6666666666667, 577.5, 577.5, 578.3333333333334, 578.3333333333334, 579.1666666666667, 579.1666666666667, 580.0000000000001, 580.0000000000001, 580.8333333333333, 580.8333333333333, 581.6666666666666, 581.6666666666666, 582.5, 582.5, 583.3333333333334, 583.3333333333334, 584.1666666666666, 584.1666666666666, 585, 585, 585.8333333333334, 585.8333333333334, 586.6666666666666, 586.6666666666666, 587.5, 587.5, 588.3333333333334, 588.3333333333334, 589.1666666666667, 589.1666666666667, 590, 590, 590.8333333333334, 590.8333333333334, 591.6666666666666, 591.6666666666666, 592.5, 592.5, 593.3333333333333, 593.3333333333333, 594.1666666666666, 594.1666666666666, 595, 595, 595.8333333333334, 595.8333333333334, 596.6666666666666, 596.6666666666666, 597.5, 597.5, 598.3333333333334, 598.3333333333334, 599.1666666666667, 599.1666666666667, 600, 600, 600.8333333333334, 600.8333333333334, 601.6666666666667, 601.6666666666667, 602.5, 602.5, 603.3333333333333, 603.3333333333333, 604.1666666666666, 604.1666666666666, 605, 605, 605.8333333333334, 605.8333333333334, 606.6666666666666, 606.6666666666666, 607.5, 607.5, 608.3333333333334, 608.3333333333334, 609.1666666666667, 609.1666666666667, 610, 610, 610.8333333333334, 610.8333333333334, 611.6666666666667, 611.6666666666667, 612.5000000000001, 612.5000000000001, 613.3333333333334, 613.3333333333334, 614.1666666666666, 614.1666666666666, 615, 615, 615.8333333333333, 615.8333333333333, 616.6666666666666, 616.6666666666666, 617.5, 617.5, 618.3333333333334, 618.3333333333334, 619.1666666666666, 619.1666666666666, 620, 620, 620.8333333333334, 620.8333333333334, 621.6666666666667, 621.6666666666667, 622.5, 622.5, 623.3333333333334, 623.3333333333334, 624.1666666666667, 624.1666666666667, 625, 625, 625.8333333333333, 625.8333333333333, 626.6666666666666, 626.6666666666666, 627.5, 627.5, 628.3333333333334, 628.3333333333334, 629.1666666666666, 629.1666666666666, 630, 630, 630.8333333333334, 630.8333333333334, 631.6666666666667, 631.6666666666667, 632.5, 632.5, 633.3333333333334, 633.3333333333334, 634.1666666666667, 634.1666666666667, 635.0000000000001, 635.0000000000001, 635.8333333333334, 635.8333333333334, 636.6666666666666, 636.6666666666666, 637.5, 637.5, 638.3333333333334, 638.3333333333334, 639.1666666666666, 639.1666666666666, 640, 640, 640.8333333333334, 640.8333333333334, 641.6666666666666, 641.6666666666666, 642.5, 642.5, 643.3333333333334, 643.3333333333334, 644.1666666666667, 644.1666666666667, 645, 645, 645.8333333333334, 645.8333333333334, 646.6666666666667, 646.6666666666667, 647.5, 647.5, 648.3333333333333, 648.3333333333333, 649.1666666666666, 649.1666666666666, 650, 650, 650.8333333333334, 650.8333333333334, 651.6666666666666, 651.6666666666666, 652.5, 652.5, 653.3333333333334, 653.3333333333334, 654.1666666666667, 654.1666666666667, 655, 655, 655.8333333333334, 655.8333333333334, 656.6666666666667, 656.6666666666667, 657.5000000000001, 657.5000000000001, 658.3333333333333, 658.3333333333333, 659.1666666666666, 659.1666666666666, 660, 660, 660.8333333333334, 660.8333333333334, 661.6666666666666, 661.6666666666666, 662.5, 662.5, 663.3333333333334, 663.3333333333334, 664.1666666666667, 664.1666666666667, 665, 665, 665.8333333333334, 665.8333333333334, 666.6666666666667, 666.6666666666667, 667.5, 667.5, 668.3333333333334, 668.3333333333334, 669.1666666666666, 669.1666666666666, 670, 670, 670.8333333333333, 670.8333333333333, 671.6666666666666, 671.6666666666666, 672.5, 672.5, 673.3333333333334, 673.3333333333334, 674.1666666666666, 674.1666666666666, 675, 675, 675.8333333333334, 675.8333333333334, 676.6666666666667, 676.6666666666667, 677.5, 677.5, 678.3333333333334, 678.3333333333334, 679.1666666666667, 679.1666666666667, 680, 680, 680.8333333333333, 680.8333333333333, 681.6666666666666, 681.6666666666666, 682.5, 682.5, 683.3333333333334, 683.3333333333334, 684.1666666666666, 684.1666666666666, 685, 685, 685.8333333333334, 685.8333333333334, 686.6666666666667, 686.6666666666667, 687.5, 687.5, 688.3333333333334, 688.3333333333334, 689.1666666666667, 689.1666666666667, 690.0000000000001, 690.0000000000001, 690.8333333333334, 690.8333333333334, 691.6666666666666, 691.6666666666666, 692.5, 692.5, 693.3333333333334, 693.3333333333334, 694.1666666666666, 694.1666666666666, 695, 695, 695.8333333333334, 695.8333333333334, 696.6666666666666, 696.6666666666666, 697.5, 697.5, 698.3333333333334, 698.3333333333334, 699.1666666666667, 699.1666666666667, 700, 700, 700.8333333333334, 700.8333333333334, 701.6666666666667, 701.6666666666667, 702.5, 702.5, 703.3333333333333, 703.3333333333333, 704.1666666666666, 704.1666666666666, 705, 705, 705.8333333333334, 705.8333333333334, 706.6666666666666, 706.6666666666666, 707.5, 707.5, 708.3333333333334, 708.3333333333334, 709.1666666666667, 709.1666666666667, 710, 710, 710.8333333333334, 710.8333333333334, 711.6666666666667, 711.6666666666667, 712.5000000000001, 712.5000000000001, 713.3333333333334, 713.3333333333334, 714.1666666666666, 714.1666666666666, 715, 715, 715.8333333333334, 715.8333333333334, 716.6666666666666, 716.6666666666666, 717.5, 717.5, 718.3333333333334, 718.3333333333334, 719.1666666666667, 719.1666666666667, 720, 720, 720.8333333333334, 720.8333333333334, 721.6666666666667, 721.6666666666667, 722.5, 722.5, 723.3333333333334, 723.3333333333334, 724.1666666666667, 724.1666666666667, 725, 725, 725.8333333333333, 725.8333333333333, 726.6666666666666, 726.6666666666666, 727.5, 727.5, 728.3333333333334, 728.3333333333334, 729.1666666666666, 729.1666666666666, 730, 730, 730.8333333333334, 730.8333333333334, 731.6666666666667, 731.6666666666667, 732.5, 732.5, 733.3333333333334, 733.3333333333334, 734.1666666666667, 734.1666666666667, 735, 735, 735.8333333333333, 735.8333333333333, 736.6666666666666, 736.6666666666666, 737.5, 737.5, 738.3333333333334, 738.3333333333334, 739.1666666666666, 739.1666666666666, 740, 740, 740.8333333333334, 740.8333333333334, 741.6666666666667, 741.6666666666667, 742.5, 742.5, 743.3333333333334, 743.3333333333334, 744.1666666666667, 744.1666666666667, 745.0000000000001, 745.0000000000001, 745.8333333333334, 745.8333333333334, 746.6666666666666, 746.6666666666666, 747.5, 747.5, 748.3333333333333, 748.3333333333333, 749.1666666666666, 749.1666666666666, 750, 750, 750.8333333333334, 750.8333333333334, 751.6666666666666, 751.6666666666666, 752.5, 752.5, 753.3333333333334, 753.3333333333334, 754.1666666666667, 754.1666666666667, 755, 755, 755.8333333333334, 755.8333333333334, 756.6666666666667, 756.6666666666667, 757.5, 757.5, 758.3333333333333, 758.3333333333333, 759.1666666666666, 759.1666666666666, 760, 760, 760.8333333333334, 760.8333333333334, 761.6666666666666, 761.6666666666666, 762.5, 762.5, 763.3333333333334, 763.3333333333334, 764.1666666666667, 764.1666666666667, 765, 765, 765.8333333333334, 765.8333333333334, 766.6666666666667, 766.6666666666667, 767.5000000000001, 767.5000000000001, 768.3333333333334, 768.3333333333334, 769.1666666666666, 769.1666666666666, 770, 770, 770.8333333333334, 770.8333333333334, 771.6666666666666, 771.6666666666666, 772.5, 772.5, 773.3333333333334, 773.3333333333334, 774.1666666666666, 774.1666666666666, 775, 775, 775.8333333333334, 775.8333333333334, 776.6666666666667, 776.6666666666667, 777.5, 777.5, 778.3333333333334, 778.3333333333334, 779.1666666666667, 779.1666666666667, 780, 780, 780.8333333333333, 780.8333333333333, 781.6666666666666, 781.6666666666666, 782.5, 782.5, 783.3333333333334, 783.3333333333334, 784.1666666666666, 784.1666666666666, 785, 785, 785.8333333333334, 785.8333333333334, 786.6666666666667, 786.6666666666667, 787.5, 787.5, 788.3333333333334, 788.3333333333334, 789.1666666666667, 789.1666666666667, 790.0000000000001, 790.0000000000001, 790.8333333333333, 790.8333333333333, 791.6666666666666, 791.6666666666666, 792.5, 792.5, 793.3333333333334, 793.3333333333334, 794.1666666666666, 794.1666666666666, 795, 795, 795.8333333333334, 795.8333333333334, 796.6666666666667, 796.6666666666667, 797.5, 797.5, 798.3333333333334, 798.3333333333334, 799.1666666666667, 799.1666666666667, 800, 800, 800.8333333333334, 800.8333333333334, 801.6666666666666, 801.6666666666666, 802.5, 802.5, 803.3333333333333, 803.3333333333333, 804.1666666666666, 804.1666666666666, 805, 805, 805.8333333333334, 805.8333333333334, 806.6666666666666, 806.6666666666666, 807.5, 807.5, 808.3333333333334, 808.3333333333334, 809.1666666666667, 809.1666666666667, 810, 810, 810.8333333333334, 810.8333333333334, 811.6666666666667, 811.6666666666667, 812.5, 812.5, 813.3333333333333, 813.3333333333333, 814.1666666666666, 814.1666666666666, 815, 815, 815.8333333333334, 815.8333333333334, 816.6666666666666, 816.6666666666666, 817.5, 817.5, 818.3333333333334, 818.3333333333334, 819.1666666666667, 819.1666666666667, 820, 820, 820.8333333333334, 820.8333333333334, 821.6666666666667, 821.6666666666667, 822.5000000000001, 822.5000000000001, 823.3333333333334, 823.3333333333334, 824.1666666666666, 824.1666666666666, 825, 825, 825.8333333333334, 825.8333333333334, 826.6666666666666, 826.6666666666666, 827.5, 827.5, 828.3333333333334, 828.3333333333334, 829.1666666666666, 829.1666666666666, 830, 830, 830.8333333333334, 830.8333333333334, 831.6666666666667, 831.6666666666667, 832.5, 832.5, 833.3333333333334, 833.3333333333334, 834.1666666666667, 834.1666666666667, 835, 835, 835.8333333333333, 835.8333333333333, 836.6666666666666, 836.6666666666666, 837.5, 837.5, 838.3333333333334, 838.3333333333334, 839.1666666666666, 839.1666666666666, 840, 840, 840.8333333333334, 840.8333333333334, 841.6666666666667, 841.6666666666667, 842.5, 842.5, 843.3333333333334, 843.3333333333334, 844.1666666666667, 844.1666666666667, 845.0000000000001, 845.0000000000001, 845.8333333333334, 845.8333333333334, 846.6666666666666, 846.6666666666666, 847.5, 847.5, 848.3333333333334, 848.3333333333334, 849.1666666666666, 849.1666666666666, 850, 850, 850.8333333333334, 850.8333333333334, 851.6666666666667, 851.6666666666667, 852.5, 852.5, 853.3333333333334, 853.3333333333334, 854.1666666666667, 854.1666666666667, 855, 855, 855.8333333333334, 855.8333333333334, 856.6666666666667, 856.6666666666667, 857.5, 857.5, 858.3333333333333, 858.3333333333333, 859.1666666666666, 859.1666666666666, 860, 860, 860.8333333333334, 860.8333333333334, 861.6666666666666, 861.6666666666666, 862.5, 862.5, 863.3333333333334, 863.3333333333334, 864.1666666666667, 864.1666666666667, 865, 865, 865.8333333333334, 865.8333333333334, 866.6666666666667, 866.6666666666667, 867.5000000000001, 867.5000000000001, 868.3333333333333, 868.3333333333333, 869.1666666666666, 869.1666666666666, 870, 870, 870.8333333333334, 870.8333333333334, 871.6666666666666, 871.6666666666666, 872.5, 872.5, 873.3333333333334, 873.3333333333334, 874.1666666666667, 874.1666666666667, 875, 875, 875.8333333333334, 875.8333333333334, 876.6666666666667, 876.6666666666667, 877.5000000000001, 877.5000000000001, 878.3333333333334, 878.3333333333334, 879.1666666666666, 879.1666666666666, 880, 880, 880.8333333333333, 880.8333333333333, 881.6666666666666, 881.6666666666666, 882.5, 882.5, 883.3333333333334, 883.3333333333334, 884.1666666666666, 884.1666666666666, 885, 885, 885.8333333333334, 885.8333333333334, 886.6666666666667, 886.6666666666667, 887.5, 887.5, 888.3333333333334, 888.3333333333334, 889.1666666666667, 889.1666666666667, 890, 890, 890.8333333333333, 890.8333333333333, 891.6666666666666, 891.6666666666666, 892.5, 892.5, 893.3333333333334, 893.3333333333334, 894.1666666666666, 894.1666666666666, 895, 895, 895.8333333333334, 895.8333333333334, 896.6666666666667, 896.6666666666667, 897.5, 897.5, 898.3333333333334, 898.3333333333334, 899.1666666666667, 899.1666666666667, 900.0000000000001, 900.0000000000001, 900.8333333333334, 900.8333333333334, 901.6666666666666, 901.6666666666666, 902.5, 902.5, 903.3333333333334, 903.3333333333334, 904.1666666666666, 904.1666666666666, 905, 905, 905.8333333333334, 905.8333333333334, 906.6666666666667, 906.6666666666667, 907.5, 907.5, 908.3333333333334, 908.3333333333334, 909.1666666666667, 909.1666666666667, 910, 910, 910.8333333333334, 910.8333333333334, 911.6666666666667, 911.6666666666667, 912.5, 912.5, 913.3333333333333, 913.3333333333333, 914.1666666666666, 914.1666666666666, 915, 915, 915.8333333333334, 915.8333333333334, 916.6666666666666, 916.6666666666666, 917.5, 917.5, 918.3333333333334, 918.3333333333334, 919.1666666666667, 919.1666666666667, 920, 920, 920.8333333333334, 920.8333333333334, 921.6666666666667, 921.6666666666667, 922.5000000000001, 922.5000000000001, 923.3333333333334, 923.3333333333334, 924.1666666666666, 924.1666666666666, 925, 925, 925.8333333333334, 925.8333333333334, 926.6666666666666, 926.6666666666666, 927.5, 927.5, 928.3333333333334, 928.3333333333334, 929.1666666666667, 929.1666666666667, 930, 930, 930.8333333333334, 930.8333333333334, 931.6666666666667, 931.6666666666667, 932.5000000000001, 932.5000000000001, 933.3333333333334, 933.3333333333334, 934.1666666666667, 934.1666666666667, 935, 935, 935.8333333333333, 935.8333333333333, 936.6666666666666, 936.6666666666666, 937.5, 937.5, 938.3333333333334, 938.3333333333334, 939.1666666666666, 939.1666666666666, 940, 940, 940.8333333333334, 940.8333333333334, 941.6666666666667, 941.6666666666667, 942.5, 942.5, 943.3333333333334, 943.3333333333334, 944.1666666666667, 944.1666666666667, 945.0000000000001, 945.0000000000001, 945.8333333333333, 945.8333333333333, 946.6666666666666, 946.6666666666666, 947.5, 947.5, 948.3333333333334, 948.3333333333334, 949.1666666666666, 949.1666666666666, 950, 950, 950.8333333333334, 950.8333333333334, 951.6666666666667, 951.6666666666667, 952.5, 952.5, 953.3333333333334, 953.3333333333334, 954.1666666666667, 954.1666666666667, 955, 955, 955.8333333333335, 955.8333333333335, 956.6666666666666, 956.6666666666666, 957.4999999999999, 957.4999999999999, 958.3333333333334, 958.3333333333334, 959.1666666666666, 959.1666666666666, 960.0000000000001, 960.0000000000001, 960.8333333333334, 960.8333333333334, 961.6666666666666, 961.6666666666666, 962.5000000000001, 962.5000000000001, 963.3333333333334, 963.3333333333334, 964.1666666666666, 964.1666666666666, 965.0000000000001, 965.0000000000001, 965.8333333333334, 965.8333333333334, 966.6666666666666, 966.6666666666666, 967.5, 967.5, 968.3333333333333, 968.3333333333333, 969.1666666666667, 969.1666666666667, 970, 970, 970.8333333333333, 970.8333333333333, 971.6666666666667, 971.6666666666667, 972.5, 972.5, 973.3333333333333, 973.3333333333333, 974.1666666666667, 974.1666666666667, 975, 975, 975.8333333333335, 975.8333333333335, 976.6666666666667, 976.6666666666667, 977.5, 977.5, 978.3333333333334, 978.3333333333334, 979.1666666666666, 979.1666666666666, 979.9999999999999, 979.9999999999999, 980.8333333333334, 980.8333333333334, 981.6666666666666, 981.6666666666666, 982.5000000000001, 982.5000000000001, 983.3333333333334, 983.3333333333334, 984.1666666666666, 984.1666666666666, 985.0000000000001, 985.0000000000001, 985.8333333333334, 985.8333333333334, 986.6666666666666, 986.6666666666666, 987.5000000000001, 987.5000000000001, 988.3333333333334, 988.3333333333334, 989.1666666666666, 989.1666666666666, 990, 990, 990.8333333333333, 990.8333333333333, 991.6666666666667, 991.6666666666667, 992.5, 992.5, 993.3333333333333, 993.3333333333333, 994.1666666666667, 994.1666666666667, 995, 995, 995.8333333333333, 995.8333333333333, 996.6666666666667, 996.6666666666667, 997.5, 997.5, 998.3333333333335, 998.3333333333335, 999.1666666666667, 999.1666666666667, 1000, 1000, 1000.8333333333334, 1000.8333333333334, 1001.6666666666666, 1001.6666666666666, 1002.4999999999999, 1002.4999999999999, 1003.3333333333334, 1003.3333333333334, 1004.1666666666666, 1004.1666666666666, 1005.0000000000001, 1005.0000000000001, 1005.8333333333334, 1005.8333333333334, 1006.6666666666666, 1006.6666666666666, 1007.5000000000001, 1007.5000000000001, 1008.3333333333334, 1008.3333333333334, 1009.1666666666666, 1009.1666666666666, 1010.0000000000001, 1010.0000000000001, 1010.8333333333334, 1010.8333333333334, 1011.6666666666667, 1011.6666666666667, 1012.5, 1012.5, 1013.3333333333333, 1013.3333333333333, 1014.1666666666667, 1014.1666666666667, 1015, 1015, 1015.8333333333333, 1015.8333333333333, 1016.6666666666667, 1016.6666666666667, 1017.5, 1017.5, 1018.3333333333333, 1018.3333333333333, 1019.1666666666667, 1019.1666666666667, 1020, 1020, 1020.8333333333335, 1020.8333333333335, 1021.6666666666667, 1021.6666666666667, 1022.5, 1022.5, 1023.3333333333334, 1023.3333333333334, 1024.1666666666667, 1024.1666666666667, 1025, 1025, 1025.8333333333335, 1025.8333333333335, 1026.6666666666667, 1026.6666666666667, 1027.5000000000002, 1027.5000000000002, 1028.3333333333333, 1028.3333333333333, 1029.1666666666665, 1029.1666666666665, 1030, 1030, 1030.8333333333333, 1030.8333333333333, 1031.6666666666665, 1031.6666666666665, 1032.5, 1032.5, 1033.3333333333333, 1033.3333333333333, 1034.1666666666667, 1034.1666666666667, 1035, 1035, 1035.8333333333333, 1035.8333333333333, 1036.6666666666667, 1036.6666666666667, 1037.5, 1037.5, 1038.3333333333333, 1038.3333333333333, 1039.1666666666667, 1039.1666666666667, 1040, 1040, 1040.8333333333333, 1040.8333333333333, 1041.6666666666667, 1041.6666666666667, 1042.5, 1042.5, 1043.3333333333335, 1043.3333333333335, 1044.1666666666667, 1044.1666666666667, 1045, 1045, 1045.8333333333335, 1045.8333333333335, 1046.6666666666667, 1046.6666666666667, 1047.5, 1047.5, 1048.3333333333335, 1048.3333333333335, 1049.1666666666667, 1049.1666666666667, 1050, 1050, 1050.8333333333333, 1050.8333333333333, 1051.6666666666665, 1051.6666666666665, 1052.5, 1052.5, 1053.3333333333333, 1053.3333333333333, 1054.1666666666665, 1054.1666666666665, 1055, 1055, 1055.8333333333333, 1055.8333333333333, 1056.6666666666667, 1056.6666666666667, 1057.5, 1057.5, 1058.3333333333333, 1058.3333333333333, 1059.1666666666667, 1059.1666666666667, 1060, 1060, 1060.8333333333333, 1060.8333333333333, 1061.6666666666667, 1061.6666666666667, 1062.5, 1062.5, 1063.3333333333335, 1063.3333333333335, 1064.1666666666667, 1064.1666666666667, 1065, 1065, 1065.8333333333335, 1065.8333333333335, 1066.6666666666667, 1066.6666666666667, 1067.5, 1067.5, 1068.3333333333335, 1068.3333333333335, 1069.1666666666667, 1069.1666666666667, 1070, 1070, 1070.8333333333335, 1070.8333333333335, 1071.6666666666667, 1071.6666666666667, 1072.5, 1072.5, 1073.3333333333333, 1073.3333333333333, 1074.1666666666665, 1074.1666666666665, 1075, 1075, 1075.8333333333333, 1075.8333333333333, 1076.6666666666665, 1076.6666666666665, 1077.5, 1077.5, 1078.3333333333333, 1078.3333333333333, 1079.1666666666667, 1079.1666666666667, 1080, 1080, 1080.8333333333333, 1080.8333333333333, 1081.6666666666667, 1081.6666666666667, 1082.5, 1082.5, 1083.3333333333333, 1083.3333333333333, 1084.1666666666667, 1084.1666666666667, 1085, 1085, 1085.8333333333335, 1085.8333333333335, 1086.6666666666667, 1086.6666666666667, 1087.5, 1087.5, 1088.3333333333335, 1088.3333333333335, 1089.1666666666667, 1089.1666666666667, 1090, 1090, 1090.8333333333335, 1090.8333333333335, 1091.6666666666667, 1091.6666666666667, 1092.5000000000002, 1092.5000000000002, 1093.3333333333335, 1093.3333333333335, 1094.1666666666667, 1094.1666666666667, 1095, 1095, 1095.8333333333333, 1095.8333333333333, 1096.6666666666665, 1096.6666666666665, 1097.5, 1097.5, 1098.3333333333333, 1098.3333333333333, 1099.1666666666665, 1099.1666666666665, 1100 ] }, "y": { "type": "float", "values": [ 6, 6, 5.984457849818863, 5.984457849818863, 5.914622104270368, 5.914622104270368, 5.6602368453180425, 5.6602368453180425, 5.293156022650223, 5.293156022650223, 5.190011028931016, 5.190011028931016, 5.3020440865118905, 5.3020440865118905, 5.501282113799794, 5.501282113799794, 5.741307593508806, 5.741307593508806, 6.000000257442196, 6.000000257442196, 6.258787697677602, 6.258787697677602, 6.499939183721772, 6.499939183721772, 6.707020640079183, 6.707020640079183, 6.865919807508107, 6.865919807508107, 6.965807970181247, 6.965807970181247, 6.999877914784483, 6.999877914784483, 6.96580783086198, 6.96580783086198, 6.8659195383640075, 6.8659195383640075, 6.707020259451868, 6.707020259451868, 6.499938717550518, 6.499938717550518, 6.258787177731021, 6.258787177731021, 5.999999719154, 5.999999719154, 5.74121227971606, 5.74121227971606, 5.500060796009942, 5.500060796009942, 5.292979343371831, 5.292979343371831, 5.134080180789995, 5.134080180789995, 5.034074098489585, 5.034074098489585, 5.000000000000043, 5.000000000000043, 5.034074249942043, 5.034074249942043, 5.133974744457966, 5.133974744457966, 5.292893429839141, 5.292893429839141, 5.500000260141892, 5.500000260141892, 5.741181246932082, 5.741181246932082, 6.0000003042870675, 6.0000003042870675, 6.258819340905283, 6.258819340905283, 6.500000266898768, 6.500000266898768, 6.7071070004876745, 6.7071070004876745, 6.8660255598290405, 6.8660255598290405, 6.965925907568499, 6.965925907568499, 6.99999999999995, 6.99999999999995, 6.96592574399986, 6.96592574399986, 6.8660252438386395, 6.8660252438386395, 6.707106553609845, 6.707106553609845, 6.49999971958734, 6.49999971958734, 6.258818730458817, 6.258818730458817, 5.99999967230627, 5.99999967230627, 5.74118063648562, 5.74118063648562, 5.499999712830472, 5.499999712830472, 5.2928929829613205, 5.2928929829613205, 5.133974428467635, 5.133974428467635, 5.03407408637339, 5.03407408637339, 5.000000000000058, 5.000000000000058, 5.034074262058238, 5.034074262058238, 5.133974767864698, 5.133974767864698, 5.292893462941251, 5.292893462941251, 5.500000300683527, 5.500000300683527, 5.741181292150285, 5.741181292150285, 6.000000351100393, 6.000000351100393, 6.258819386123588, 6.258819386123588, 6.500000307440387, 6.500000307440387, 6.707107033589684, 6.707107033589684, 6.86602558323569, 6.86602558323569, 6.965925919684692, 6.965925919684692, 6.999999999999934, 6.999999999999934, 6.965925731883664, 6.965925731883664, 6.866025220431963, 6.866025220431963, 6.707106520507733, 6.707106520507733, 6.499999679045704, 6.499999679045704, 6.2588186852406125, 6.2588186852406125, 5.999999625492944, 5.999999625492944, 5.741180591267314, 5.741180591267314, 5.499999672288853, 5.499999672288853, 5.292892949859313, 5.292892949859313, 5.133974405060987, 5.133974405060987, 5.034074074257226, 5.034074074257226, 5.000000000000075, 5.000000000000075, 5.034074274174493, 5.034074274174493, 5.133974791271376, 5.133974791271376, 5.292893496043283, 5.292893496043283, 5.500000341225065, 5.500000341225065, 5.741181337368709, 5.741181337368709, 6.000000397913718, 6.000000397913718, 6.2588194313417835, 6.2588194313417835, 6.500000347981907, 6.500000347981907, 6.707107066691851, 6.707107066691851, 6.866025606642337, 6.866025606642337, 6.9659259318008555, 6.9659259318008555, 6.9999999999999165, 6.9999999999999165, 6.965925719767408, 6.965925719767408, 6.866025197025285, 6.866025197025285, 6.707106487405701, 6.707106487405701, 6.499999638504166, 6.499999638504166, 6.258818640022188, 6.258818640022188, 5.99999957867962, 5.99999957867962, 5.74118054604912, 5.74118054604912, 5.499999631747333, 5.499999631747333, 5.292892916757146, 5.292892916757146, 5.133974381654227, 5.133974381654227, 5.034074062141064, 5.034074062141064, 5.000000000000094, 5.000000000000094, 5.034074286290692, 5.034074286290692, 5.133974814678169, 5.133974814678169, 5.292893529145316, 5.292893529145316, 5.500000381766603, 5.500000381766603, 5.741181382586914, 5.741181382586914, 6.00000044472727, 6.00000044472727, 6.258819476559978, 6.258819476559978, 6.500000388523426, 6.500000388523426, 6.707107099793856, 6.707107099793856, 6.866025630049096, 6.866025630049096, 6.965925943917016, 6.965925943917016, 6.999999999999896, 6.999999999999896, 6.965925707651208, 6.965925707651208, 6.866025173618491, 6.866025173618491, 6.707106454303666, 6.707106454303666, 6.499999597962627, 6.499999597962627, 6.258818594803984, 6.258818594803984, 5.9999995318660675, 5.9999995318660675, 5.741180500830706, 5.741180500830706, 5.499999591205815, 5.499999591205815, 5.292892883655141, 5.292892883655141, 5.133974358247583, 5.133974358247583, 5.034074050024846, 5.034074050024846, 5.0000000000001155, 5.0000000000001155, 5.0340742984068925, 5.0340742984068925, 5.13397483808485, 5.13397483808485, 5.292893562247512, 5.292893562247512, 5.500000422308143, 5.500000422308143, 5.741181427805119, 5.741181427805119, 6.000000491540596, 6.000000491540596, 6.258819521778392, 6.258819521778392, 6.500000429064944, 6.500000429064944, 6.70710713289586, 6.70710713289586, 6.866025653455739, 6.866025653455739, 6.965925956033233, 6.965925956033233, 6.999999999999873, 6.999999999999873, 6.965925695535006, 6.965925695535006, 6.866025150211809, 6.866025150211809, 6.70710642120147, 6.70710642120147, 6.49999955742089, 6.49999955742089, 6.2588185495857775, 6.2588185495857775, 5.999999485052742, 5.999999485052742, 5.741180455612511, 5.741180455612511, 5.499999550664101, 5.499999550664101, 5.292892850553138, 5.292892850553138, 5.13397433484094, 5.13397433484094, 5.034074037908688, 5.034074037908688, 5.0000000000001386, 5.0000000000001386, 5.034074310523096, 5.034074310523096, 5.133974861491533, 5.133974861491533, 5.292893595349549, 5.292893595349549, 5.50000046284988, 5.50000046284988, 5.7411814730233255, 5.7411814730233255, 6.0000005383539206, 6.0000005383539206, 6.258819566996585, 6.258819566996585, 6.500000469606658, 6.500000469606658, 6.707107165997863, 6.707107165997863, 6.86602567686238, 6.86602567686238, 6.9659259681493895, 6.9659259681493895, 6.999999999999849, 6.999999999999849, 6.965925683418743, 6.965925683418743, 6.866025126805125, 6.866025126805125, 6.707106388099433, 6.707106388099433, 6.499999516879349, 6.499999516879349, 6.258818504367352, 6.258818504367352, 5.999999438239417, 5.999999438239417, 5.7411804103943185, 5.7411804103943185, 5.499999510122585, 5.499999510122585, 5.2928928174509755, 5.2928928174509755, 5.1339743114343, 5.1339743114343, 5.034074025792533, 5.034074025792533, 5.000000000000164, 5.000000000000164, 5.034074322639359, 5.034074322639359, 5.133974884898218, 5.133974884898218, 5.292893628451586, 5.292893628451586, 5.500000503391423, 5.500000503391423, 5.741181518241752, 5.741181518241752, 6.000000585167245, 6.000000585167245, 6.2588196122147775, 6.2588196122147775, 6.500000510148173, 6.500000510148173, 6.707107199100024, 6.707107199100024, 6.866025700269134, 6.866025700269134, 6.965925980265544, 6.965925980265544, 6.999999999999822, 6.999999999999822, 6.9659256713025375, 6.9659256713025375, 6.866025103398325, 6.866025103398325, 6.707106354997394, 6.707106354997394, 6.499999476337806, 6.499999476337806, 6.258818459149145, 6.258818459149145, 5.999999391425865, 5.999999391425865, 5.741180365176126, 5.741180365176126, 5.49999946958107, 5.49999946958107, 5.292892784348975, 5.292892784348975, 5.133974288027548, 5.133974288027548, 5.0340740136763795, 5.0340740136763795, 5.000000000000193, 5.000000000000193, 5.034074334755567, 5.034074334755567, 5.133974908305019, 5.133974908305019, 5.2928936615536255, 5.2928936615536255, 5.500000543932965, 5.500000543932965, 5.741181563459959, 5.741181563459959, 6.000000631980798, 6.000000631980798, 6.25881965743319, 6.25881965743319, 6.500000550689687, 6.500000550689687, 6.7071072322020235, 6.7071072322020235, 6.8660257236757705, 6.8660257236757705, 6.965925992381755, 6.965925992381755, 6.999999999999793, 6.999999999999793, 6.965925659186329, 6.965925659186329, 6.866025079991637, 6.866025079991637, 6.707106321895193, 6.707106321895193, 6.499999435796263, 6.499999435796263, 6.258818413930937, 6.258818413930937, 5.999999344612539, 5.999999344612539, 5.7411803199577145, 5.7411803199577145, 5.499999429039557, 5.499999429039557, 5.292892751246978, 5.292892751246978, 5.133974264620911, 5.133974264620911, 5.034074001560169, 5.034074001560169, 5.000000000000222, 5.000000000000222, 5.034074346871835, 5.034074346871835, 5.133974931711707, 5.133974931711707, 5.292893694655989, 5.292893694655989, 5.500000584474706, 5.500000584474706, 5.741181608678605, 5.741181608678605, 6.0000006787943505, 6.0000006787943505, 6.258819702651381, 6.258819702651381, 6.500000591231594, 6.500000591231594, 6.707107265304183, 6.707107265304183, 6.866025747082634, 6.866025747082634, 6.965926004497964, 6.965926004497964, 6.999999999999762, 6.999999999999762, 6.9659256470700015, 6.9659256470700015, 6.866025056584833, 6.866025056584833, 6.707106288793151, 6.707106288793151, 6.499999395254324, 6.499999395254324, 6.25881836871251, 6.25881836871251, 5.99999929779876, 5.99999929779876, 5.741180274739303, 5.741180274739303, 5.499999388497846, 5.499999388497846, 5.292892718144659, 5.292892718144659, 5.133974241214163, 5.133974241214163, 5.034073989444021, 5.034073989444021, 5.000000000000255, 5.000000000000255, 5.034074358988046, 5.034074358988046, 5.133974955118625, 5.133974955118625, 5.292893727758031, 5.292893727758031, 5.500000625016251, 5.500000625016251, 5.741181653896813, 5.741181653896813, 6.000000725607675, 6.000000725607675, 6.258819747870012, 6.258819747870012, 6.500000631773107, 6.500000631773107, 6.707107298406179, 6.707107298406179, 6.8660257704892675, 6.8660257704892675, 6.9659260166141115, 6.9659260166141115, 6.999999999999728, 6.999999999999728, 6.965925634953789, 6.965925634953789, 6.866025033178142, 6.866025033178142, 6.707106255690786, 6.707106255690786, 6.499999354712779, 6.499999354712779, 6.258818323494301, 6.258818323494301, 5.999999250985435, 5.999999250985435, 5.741180229521112, 5.741180229521112, 5.499999347956335, 5.499999347956335, 5.292892685042663, 5.292892685042663, 5.13397421780753, 5.13397421780753, 5.034073977327756, 5.034073977327756, 5.0000000000002895, 5.0000000000002895, 5.03407437110426, 5.03407437110426, 5.133974978525318, 5.133974978525318, 5.292893760860076, 5.292893760860076, 5.500000665558192, 5.500000665558192, 5.741181699115023, 5.741181699115023, 6.000000772421001, 6.000000772421001, 6.258819793088203, 6.258819793088203, 6.500000672314617, 6.500000672314617, 6.707107331508173, 6.707107331508173, 6.8660257938959, 6.8660257938959, 6.965926028730258, 6.965926028730258, 6.999999999999693, 6.999999999999693, 6.965925622837574, 6.965925622837574, 6.866025009771448, 6.866025009771448, 6.707106222588741, 6.707106222588741, 6.499999314171232, 6.499999314171232, 6.258818278276093, 6.258818278276093, 5.999999204172109, 5.999999204172109, 5.741180184302922, 5.741180184302922, 5.499999307414431, 5.499999307414431, 5.29289265194067, 5.29289265194067, 5.133974194400899, 5.133974194400899, 5.034073965211611, 5.034073965211611, 5.000000000000326, 5.000000000000326, 5.0340743832205925, 5.0340743832205925, 5.133975001932012, 5.133975001932012, 5.292893793962121, 5.292893793962121, 5.500000706099739, 5.500000706099739, 5.741181744333232, 5.741181744333232, 6.000000819234326, 6.000000819234326, 6.258819838306392, 6.258819838306392, 6.500000712856128, 6.500000712856128, 6.707107364610488, 6.707107364610488, 6.866025817302529, 6.866025817302529, 6.965926040846401, 6.965926040846401, 6.9999999999996545, 6.9999999999996545, 6.965925610721357, 6.965925610721357, 6.866024986364753, 6.866024986364753, 6.707106189486694, 6.707106189486694, 6.499999273629684, 6.499999273629684, 6.258818233057443, 6.258818233057443, 5.9999991573587845, 5.9999991573587845, 5.741180139084732, 5.741180139084732, 5.499999266872921, 5.499999266872921, 5.292892618838677, 5.292892618838677, 5.133974170994042, 5.133974170994042, 5.034073953095469, 5.034073953095469, 5.000000000000365, 5.000000000000365, 5.034074395336811, 5.034074395336811, 5.133975025338708, 5.133975025338708, 5.2928938270641686, 5.2928938270641686, 5.500000746641287, 5.500000746641287, 5.741181789551441, 5.741181789551441, 6.000000866048105, 6.000000866048105, 6.258819883524582, 6.258819883524582, 6.500000753397637, 6.500000753397637, 6.7071073977124795, 6.7071073977124795, 6.866025840709158, 6.866025840709158, 6.965926052962542, 6.965926052962542, 6.9999999999996145, 6.9999999999996145, 6.965925598605138, 6.965925598605138, 6.8660249629578285, 6.8660249629578285, 6.707106156384646, 6.707106156384646, 6.499999233088135, 6.499999233088135, 6.258818187839234, 6.258818187839234, 5.999999110545459, 5.999999110545459, 5.741180093866103, 5.741180093866103, 5.499999226331413, 5.499999226331413, 5.292892585736686, 5.292892585736686, 5.133974147587415, 5.133974147587415, 5.0340739409793285, 5.0340739409793285, 5.000000000000406, 5.000000000000406, 5.034074407453031, 5.034074407453031, 5.133975048745407, 5.133975048745407, 5.292893860166539, 5.292893860166539, 5.500000787182836, 5.500000787182836, 5.741181834769652, 5.741181834769652, 6.000000912861431, 6.000000912861431, 6.258819928742771, 6.258819928742771, 6.500000793939145, 6.500000793939145, 6.707107430814469, 6.707107430814469, 6.866025864115784, 6.866025864115784, 6.9659260650788, 6.9659260650788, 6.999999999999573, 6.999999999999573, 6.965925586488917, 6.965925586488917, 6.86602493955113, 6.86602493955113, 6.707106123282597, 6.707106123282597, 6.499999192546191, 6.499999192546191, 6.258818142621023, 6.258818142621023, 5.999999063732134, 5.999999063732134, 5.741180048647915, 5.741180048647915, 5.499999185789905, 5.499999185789905, 5.292892552634697, 5.292892552634697, 5.13397412418079, 5.13397412418079, 5.03407392886319, 5.03407392886319, 5.000000000000449, 5.000000000000449, 5.034074419569253, 5.034074419569253, 5.133975072152108, 5.133975072152108, 5.292893893268589, 5.292893893268589, 5.500000827724387, 5.500000827724387, 5.741181879987863, 5.741181879987863, 6.000000959674756, 6.000000959674756, 6.25881997396096, 6.25881997396096, 6.500000834481045, 6.500000834481045, 6.707107463916458, 6.707107463916458, 6.866025887522408, 6.866025887522408, 6.965926077194937, 6.965926077194937, 6.999999999999528, 6.999999999999528, 6.9659255743725765, 6.9659255743725765, 6.866024916144428, 6.866024916144428, 6.707106090180545, 6.707106090180545, 6.499999152004641, 6.499999152004641, 6.258818097402812, 6.258818097402812, 5.999999016918809, 5.999999016918809, 5.741180003429727, 5.741180003429727, 5.499999145248399, 5.499999145248399, 5.292892519532388, 5.292892519532388, 5.133974100774167, 5.133974100774167, 5.034073916747054, 5.034073916747054, 5.000000000000495, 5.000000000000495, 5.034074431685477, 5.034074431685477, 5.133975095558809, 5.133975095558809, 5.292893926370641, 5.292893926370641, 5.500000868265939, 5.500000868265939, 5.741181925206514, 5.741181925206514, 6.00000100648808, 6.00000100648808, 6.258820019179147, 6.258820019179147, 6.50000087502255, 6.50000087502255, 6.707107497018445, 6.707107497018445, 6.866025910929258, 6.866025910929258, 6.965926089311072, 6.965926089311072, 6.999999999999481, 6.999999999999481, 6.965925562256352, 6.965925562256352, 6.866024892737725, 6.866024892737725, 6.707106057078493, 6.707106057078493, 6.499999111463088, 6.499999111463088, 6.2588180521845995, 6.2588180521845995, 5.999998970105029, 5.999998970105029, 5.741179958211539, 5.741179958211539, 5.499999104706894, 5.499999104706894, 5.292892486430402, 5.292892486430402, 5.1339740773675455, 5.1339740773675455, 5.03407390463092, 5.03407390463092, 5.000000000000543, 5.000000000000543, 5.034074443801702, 5.034074443801702, 5.133975118965741, 5.133975118965741, 5.2928939594726945, 5.2928939594726945, 5.5000009088074915, 5.5000009088074915, 5.741181970424726, 5.741181970424726, 6.000001053301406, 6.000001053301406, 6.258820064397773, 6.258820064397773, 6.500000915564055, 6.500000915564055, 6.70710753012043, 6.70710753012043, 6.8660259343358785, 6.8660259343358785, 6.965926101427206, 6.965926101427206, 6.999999999999433, 6.999999999999433, 6.965925550140124, 6.965925550140124, 6.86602486933102, 6.86602486933102, 6.707106023976117, 6.707106023976117, 6.499999070921535, 6.499999070921535, 6.258818006966387, 6.258818006966387, 5.999998923291704, 5.999998923291704, 5.741179912993353, 5.741179912993353, 5.49999906416539, 5.49999906416539, 5.292892453328417, 5.292892453328417, 5.133974053960926, 5.133974053960926, 5.0340738925146695, 5.0340738925146695, 5.000000000000592, 5.000000000000592, 5.034074455917931, 5.034074455917931, 5.133975142372447, 5.133975142372447, 5.29289399257475, 5.29289399257475, 5.500000949349439, 5.500000949349439, 5.741182015642939, 5.741182015642939, 6.000001100114731, 6.000001100114731, 6.25882010961596, 6.25882010961596, 6.500000956105558, 6.500000956105558, 6.707107563222413, 6.707107563222413, 6.8660259577424965, 6.8660259577424965, 6.965926113543336, 6.965926113543336, 6.999999999999382, 6.999999999999382, 6.965925538023895, 6.965925538023895, 6.866024845924313, 6.866024845924313, 6.707105990874061, 6.707105990874061, 6.49999903037998, 6.49999903037998, 6.258817961748174, 6.258817961748174, 5.999998876478379, 5.999998876478379, 5.741179867775166, 5.741179867775166, 5.499999023623493, 5.499999023623493, 5.292892420226434, 5.292892420226434, 5.133974030554309, 5.133974030554309, 5.03407388039854, 5.03407388039854, 5.000000000000645, 5.000000000000645, 5.034074468034279, 5.034074468034279, 5.133975165779154, 5.133975165779154, 5.292894025676807, 5.292894025676807, 5.500000989890994, 5.500000989890994, 5.741182060861152, 5.741182060861152, 6.000001146928056, 6.000001146928056, 6.258820154834146, 6.258820154834146, 6.500000996647061, 6.500000996647061, 6.707107596324717, 6.707107596324717, 6.8660259811491136, 6.8660259811491136, 6.965926125659466, 6.965926125659466, 6.9999999999993285, 6.9999999999993285, 6.965925525907664, 6.965925525907664, 6.866024822517605, 6.866024822517605, 6.707105957772004, 6.707105957772004, 6.499998989838425, 6.499998989838425, 6.258817916529521, 6.258817916529521, 5.999998829665054, 5.999998829665054, 5.741179822556981, 5.741179822556981, 5.4999989830819915, 5.4999989830819915, 5.292892387124453, 5.292892387124453, 5.133974007147465, 5.133974007147465, 5.034073868282412, 5.034073868282412, 5.000000000000698, 5.000000000000698, 5.034074480150512, 5.034074480150512, 5.133975189185864, 5.133975189185864, 5.292894058778865, 5.292894058778865, 5.50000103043255, 5.50000103043255, 5.741182106079366, 5.741182106079366, 6.000001193741836, 6.000001193741836, 6.258820200052332, 6.258820200052332, 6.500001037188563, 6.500001037188563, 6.707107629426698, 6.707107629426698, 6.866026004555728, 6.866026004555728, 6.965926137775592, 6.965926137775592, 6.9999999999992735, 6.9999999999992735, 6.96592551379143, 6.96592551379143, 6.866024799110667, 6.866024799110667, 6.7071059246699445, 6.7071059246699445, 6.499998949296868, 6.499998949296868, 6.258817871311307, 6.258817871311307, 5.999998782851729, 5.999998782851729, 5.741179777338356, 5.741179777338356, 5.499998942540491, 5.499998942540491, 5.292892354022473, 5.292892354022473, 5.133973983740852, 5.133973983740852, 5.0340738561662866, 5.0340738561662866, 5.000000000000755, 5.000000000000755, 5.034074492266747, 5.034074492266747, 5.133975212592576, 5.133975212592576, 5.292894091881246, 5.292894091881246, 5.500001070974108, 5.500001070974108, 5.74118215129758, 5.74118215129758, 6.000001240555161, 6.000001240555161, 6.258820245270517, 6.258820245270517, 6.5000010777300625, 6.5000010777300625, 6.707107662528677, 6.707107662528677, 6.8660260279623415, 6.8660260279623415, 6.965926149891835, 6.965926149891835, 6.999999999999216, 6.999999999999216, 6.965925501675194, 6.965925501675194, 6.866024775703954, 6.866024775703954, 6.707105891567885, 6.707105891567885, 6.499998908754916, 6.499998908754916, 6.258817826093092, 6.258817826093092, 5.999998736038404, 5.999998736038404, 5.741179732120171, 5.741179732120171, 5.499998901998991, 5.499998901998991, 5.292892320920495, 5.292892320920495, 5.13397396033424, 5.13397396033424, 5.034073844050163, 5.034073844050163, 5.000000000000814, 5.000000000000814, 5.034074504382984, 5.034074504382984, 5.133975235999289, 5.133975235999289, 5.292894124983307, 5.292894124983307, 5.500001111515665, 5.500001111515665, 5.741182196515796, 5.741182196515796, 6.000001287368486, 6.000001287368486, 6.258820290488701, 6.258820290488701, 6.500001118271955, 6.500001118271955, 6.707107695630655, 6.707107695630655, 6.866026051368952, 6.866026051368952, 6.965926162007957, 6.965926162007957, 6.999999999999156, 6.999999999999156, 6.965925489558838, 6.965925489558838, 6.86602475229724, 6.86602475229724, 6.707105858465822, 6.707105858465822, 6.499998868213358, 6.499998868213358, 6.258817780874877, 6.258817780874877, 5.9999986892250785, 5.9999986892250785, 5.7411796869019875, 5.7411796869019875, 5.499998861457493, 5.499998861457493, 5.292892287818196, 5.292892287818196, 5.13397393692763, 5.13397393692763, 5.034073831934042, 5.034073831934042, 5.000000000000875, 5.000000000000875, 5.034074516499222, 5.034074516499222, 5.133975259406005, 5.133975259406005, 5.29289415808537, 5.29289415808537, 5.500001152057225, 5.500001152057225, 5.741182241734011, 5.741182241734011, 6.0000013341813565, 6.0000013341813565, 6.258820335707324, 6.258820335707324, 6.500001158813453, 6.500001158813453, 6.70710772873263, 6.70710772873263, 6.8660260747755615, 6.8660260747755615, 6.965926174123959, 6.965926174123959, 6.999999999999094, 6.999999999999094, 6.965925477442599, 6.965925477442599, 6.866024728890523, 6.866024728890523, 6.707105825363759, 6.707105825363759, 6.499998827672192, 6.499998827672192, 6.258817735656222, 6.258817735656222, 5.999998642411299, 5.999998642411299, 5.741179641683804, 5.741179641683804, 5.499998820915995, 5.499998820915995, 5.292892254716542, 5.292892254716542, 5.133973913520794, 5.133973913520794, 5.034073819817804, 5.034073819817804, 5.000000000000938, 5.000000000000938, 5.034074528615463, 5.034074528615463, 5.133975282812722, 5.133975282812722, 5.292894191187113, 5.292894191187113, 5.500001192599179, 5.500001192599179, 5.741182286952666, 5.741182286952666, 6.000001380995136, 6.000001380995136, 6.258820380925068, 6.258820380925068, 6.500001199354556, 6.500001199354556, 6.707107761834926, 6.707107761834926, 6.866026098182395, 6.866026098182395, 6.965926186240195, 6.965926186240195, 6.99999999999903, 6.99999999999903, 6.965925465326475, 6.965925465326475, 6.866024705483578, 6.866024705483578, 6.707105792261372, 6.707105792261372, 6.499998787130237, 6.499998787130237, 6.258817690438445, 6.258817690438445, 5.999998595598429, 5.999998595598429, 5.741179596465182, 5.741179596465182, 5.499998780374105, 5.499998780374105, 5.292892221614247, 5.292892221614247, 5.133973890114415, 5.133973890114415, 5.034073807701805, 5.034073807701805, 5.000000000001003, 5.000000000001003, 5.034074540731824, 5.034074540731824, 5.133975306219669, 5.133975306219669, 5.2928942242895, 5.2928942242895, 5.500001233140347, 5.500001233140347, 5.741182332170444, 5.741182332170444, 6.0000014278089155, 6.0000014278089155, 6.25882042614369, 6.25882042614369, 6.500001239896446, 6.500001239896446, 6.7071077949365785, 6.7071077949365785, 6.8660261215887735, 6.8660261215887735, 6.96592619835643, 6.96592619835643, 6.999999999998964, 6.999999999998964, 6.9659254532101125, 6.9659254532101125, 6.866024682077085, 6.866024682077085, 6.707105759159627, 6.707105759159627, 6.499998746588282, 6.499998746588282, 6.258817645219788, 6.258817645219788, 5.9999985487846486, 5.9999985487846486, 5.741179551247439, 5.741179551247439, 5.4999987398330035, 5.4999987398330035, 5.292892188512597, 5.292892188512597, 5.133973866707583, 5.133973866707583, 5.034073795585572, 5.034073795585572, 5.00000000000107, 5.00000000000107, 5.034074552847952, 5.034074552847952, 5.133975329626162, 5.133975329626162, 5.29289425739189, 5.29289425739189, 5.500001273682304, 5.500001273682304, 5.741182377389101, 5.741182377389101, 6.0000014746217865, 6.0000014746217865, 6.258820471361433, 6.258820471361433, 6.500001280438334, 6.500001280438334, 6.707107828038871, 6.707107828038871, 6.866026144995605, 6.866026144995605, 6.965926210472426, 6.965926210472426, 6.999999999998895, 6.999999999998895, 6.965925441093749, 6.965925441093749, 6.866024658670136, 6.866024658670136, 6.707105726057237, 6.707105726057237, 6.499998706047112, 6.499998706047112, 6.25881760000201, 6.25881760000201, 5.9999985019717785, 5.9999985019717785, 5.741179506028818, 5.741179506028818, 5.499998699291115, 5.499998699291115, 5.292892155410304, 5.292892155410304, 5.1339738433012085, 5.1339738433012085, 5.034073783469577, 5.034073783469577, 5.0000000000011395, 5.0000000000011395, 5.034074564964317, 5.034074564964317, 5.1339753530331125, 5.1339753530331125, 5.292894290493637, 5.292894290493637, 5.500001314223473, 5.500001314223473, 5.741182422607757, 5.741182422607757, 6.000001521435566, 6.000001521435566, 6.2588205165800535, 6.2588205165800535, 6.500001320979434, 6.500001320979434, 6.7071078611405195, 6.7071078611405195, 6.866026168402433, 6.866026168402433, 6.965926222588656, 6.965926222588656, 6.999999999998825, 6.999999999998825, 6.965925428977617, 6.965925428977617, 6.866024635263639, 6.866024635263639, 6.707105692955489, 6.707105692955489, 6.499998665505154, 6.499998665505154, 6.258817554783353, 6.258817554783353, 5.999998455157999, 5.999998455157999, 5.741179460811075, 5.741179460811075, 5.499998658750016, 5.499998658750016, 5.292892122308014, 5.292892122308014, 5.133973819894381, 5.133973819894381, 5.0340737713533485, 5.0340737713533485, 5.0000000000012115, 5.0000000000012115, 5.034074577080449, 5.034074577080449, 5.133975376440064, 5.133975376440064, 5.292894323596029, 5.292894323596029, 5.500001354765431, 5.500001354765431, 5.741182467825537, 5.741182467825537, 6.000001568248436, 6.000001568248436, 6.258820561798674, 6.258820561798674, 6.500001361521321, 6.500001361521321, 6.707107894242809, 6.707107894242809, 6.866026191808805, 6.866026191808805, 6.965926234704647, 6.965926234704647, 6.999999999998752, 6.999999999998752, 6.965925416861249, 6.965925416861249, 6.866024611856686, 6.866024611856686, 6.707105659853096, 6.707105659853096, 6.4999986249639825, 6.4999986249639825, 6.258817509565573, 6.258817509565573, 5.999998408344219, 5.999998408344219, 5.741179415592455, 5.741179415592455, 5.499998618208131, 5.499998618208131, 5.292892089206368, 5.292892089206368, 5.133973796488009, 5.133973796488009, 5.034073759237122, 5.034073759237122, 5.000000000001285, 5.000000000001285, 5.034074589196818, 5.034074589196818, 5.133975399846564, 5.133975399846564, 5.292894356697779, 5.292894356697779, 5.500001395307391, 5.500001395307391, 5.741182513044195, 5.741182513044195, 6.000001615062216, 6.000001615062216, 6.258820607016415, 6.258820607016415, 6.500001402062418, 6.500001402062418, 6.707107927344454, 6.707107927344454, 6.86602621521563, 6.86602621521563, 6.965926246820873, 6.965926246820873, 6.999999999998677, 6.999999999998677, 6.965925404745114, 6.965925404745114, 6.866024588450186, 6.866024588450186, 6.707105626750701, 6.707105626750701, 6.499998584422022, 6.499998584422022, 6.258817464346915, 6.258817464346915, 5.999998361531349, 5.999998361531349, 5.741179370374715, 5.741179370374715, 5.499998577666246, 5.499998577666246, 5.292892056104081, 5.292892056104081, 5.133973773081185, 5.133973773081185, 5.034073747121133, 5.034073747121133, 5.000000000001362, 5.000000000001362, 5.03407460131319, 5.03407460131319, 5.13397542325352, 5.13397542325352, 5.292894389800175, 5.292894389800175, 5.5000014358485645, 5.5000014358485645, 5.741182558261975, 5.741182558261975, 6.000001661875086, 6.000001661875086, 6.258820652235034, 6.258820652235034, 6.500001442604303, 6.500001442604303, 6.707107960446741, 6.707107960446741, 6.866026238621999, 6.866026238621999, 6.965926258936861, 6.965926258936861, 6.999999999998599, 6.999999999998599, 6.965925392628741, 6.965925392628741, 6.8660245650432286, 6.8660245650432286, 6.707105593648949, 6.707105593648949, 6.4999985438808485, 6.4999985438808485, 6.258817419128256, 6.258817419128256, 5.999998314717569, 5.999998314717569, 5.741179325156096, 5.741179325156096, 5.49999853712515, 5.49999853712515, 5.292892023002437, 5.292892023002437, 5.133973749674363, 5.133973749674363, 5.0340737350049105, 5.0340737350049105, 5.00000000000144, 5.00000000000144, 5.034074613429327, 5.034074613429327, 5.133975446660023, 5.133975446660023, 5.292894422901928, 5.292894422901928, 5.500001476390526, 5.500001476390526, 5.741182603480635, 5.741182603480635, 6.000001708688867, 6.000001708688867, 6.258820697452774, 6.258820697452774, 6.500001483145398, 6.500001483145398, 6.707107993549027, 6.707107993549027, 6.86602626202882, 6.86602626202882, 6.965926271053082, 6.965926271053082, 6.99999999999852, 6.99999999999852, 6.965925380512602, 6.965925380512602, 6.8660245416362695, 6.8660245416362695, 6.707105560546552, 6.707105560546552, 6.499998503338886, 6.499998503338886, 6.258817373910475, 6.258817373910475, 5.999998267904698, 5.999998267904698, 5.7411792799374775, 5.7411792799374775, 5.499998496583268, 5.499998496583268, 5.292891989900153, 5.292891989900153, 5.133973726267997, 5.133973726267997, 5.034073722888926, 5.034073722888926, 5.000000000001521, 5.000000000001521, 5.034074625545704, 5.034074625545704, 5.133975470066983, 5.133975470066983, 5.292894456004326, 5.292894456004326, 5.500001516931702, 5.500001516931702, 5.741182648698416, 5.741182648698416, 6.000001755502646, 6.000001755502646, 6.258820742671392, 6.258820742671392, 6.5000015236872795, 6.5000015236872795, 6.707108026650667, 6.707108026650667, 6.866026285435185, 6.866026285435185, 6.965926283169301, 6.965926283169301, 6.999999999998439, 6.999999999998439, 6.965925368396226, 6.965925368396226, 6.8660245182297635, 6.8660245182297635, 6.707105527444796, 6.707105527444796, 6.499998462796922, 6.499998462796922, 6.258817328691815, 6.258817328691815, 5.999998221090919, 5.999998221090919, 5.741179234719739, 5.741179234719739, 5.4999984560421735, 5.4999984560421735, 5.292891956798513, 5.292891956798513, 5.133973702861179, 5.133973702861179, 5.0340737107727085, 5.0340737107727085, 5.000000000001603, 5.000000000001603, 5.0340746376618455, 5.0340746376618455, 5.13397549347349, 5.13397549347349, 5.292894489106726, 5.292894489106726, 5.5000015574736665, 5.5000015574736665, 5.741182693917076, 5.741182693917076, 6.000001802315516, 6.000001802315516, 6.258820787889131, 6.258820787889131, 6.50000156422916, 6.50000156422916, 6.707108059752949, 6.707108059752949, 6.866026308842002, 6.866026308842002, 6.965926295285282, 6.965926295285282, 6.999999999998355, 6.999999999998355, 6.965925356279847, 6.965925356279847, 6.866024494822801, 6.866024494822801, 6.707105494342395, 6.707105494342395, 6.499998422255745, 6.499998422255745, 6.258817283474032, 6.258817283474032, 5.999998174278049, 5.999998174278049, 5.741179189501121, 5.741179189501121, 5.4999984155002934, 5.4999984155002934, 5.292891923696232, 5.292891923696232, 5.133973679454817, 5.133973679454817, 5.034073698656727, 5.034073698656727, 5.000000000001688, 5.000000000001688, 5.034074649778225, 5.034074649778225, 5.1339755168804535, 5.1339755168804535, 5.292894522208485, 5.292894522208485, 5.500001598014844, 5.500001598014844, 5.741182739135738, 5.741182739135738, 6.000001849129297, 6.000001849129297, 6.258820833107748, 6.258820833107748, 6.500001604770253, 6.500001604770253, 6.707108092854586, 6.707108092854586, 6.8660263322488175, 6.8660263322488175, 6.965926307401497, 6.965926307401497, 6.999999999998269, 6.999999999998269, 6.9659253441637015, 6.9659253441637015, 6.866024471416291, 6.866024471416291, 6.707105461240635, 6.707105461240635, 6.499998381713779, 6.499998381713779, 6.2588172382553715, 6.2588172382553715, 5.999998127464268, 5.999998127464268, 5.741179144283383, 5.741179144283383, 5.499998374959202, 5.499998374959202, 5.292891890593952, 5.292891890593952, 5.133973656048003, 5.133973656048003, 5.0340736865405145, 5.0340736865405145, 5.0000000000017755, 5.0000000000017755, 5.034074661894373, 5.034074661894373, 5.133975540287419, 5.133975540287419, 5.2928945553108875, 5.2928945553108875, 5.50000163855681, 5.50000163855681, 5.74118278435352, 5.74118278435352, 6.000001895942167, 6.000001895942167, 6.258820878326364, 6.258820878326364, 6.500001645312131, 6.500001645312131, 6.707108125956865, 6.707108125956865, 6.866026355655177, 6.866026355655177, 6.965926319517474, 6.965926319517474, 6.99999999999818, 6.99999999999818, 6.965925332047318, 6.965925332047318, 6.866024448009325, 6.866024448009325, 6.707105428138232, 6.707105428138232, 6.4999983411726, 6.4999983411726, 6.258817193037588, 6.258817193037588, 5.999998080650489, 5.999998080650489, 5.741179099064768, 5.741179099064768, 5.499998334417324, 5.499998334417324, 5.292891857492317, 5.292891857492317, 5.133973632641645, 5.133973632641645, 5.034073674424302, 5.034073674424302, 5.000000000001864, 5.000000000001864, 5.034074674010756, 5.034074674010756, 5.133975563693932, 5.133975563693932, 5.292894588412649, 5.292894588412649, 5.500001679098777, 5.500001679098777, 5.741182829572183, 5.741182829572183, 6.000001942755946, 6.000001942755946, 6.258820923544102, 6.258820923544102, 6.500001685853221, 6.500001685853221, 6.7071081590584996, 6.7071081590584996, 6.866026379061989, 6.866026379061989, 6.965926331633685, 6.965926331633685, 6.99999999999809, 6.99999999999809, 6.965925319931168, 6.965925319931168, 6.866024424602811, 6.866024424602811, 6.707105395035827, 6.707105395035827, 6.499998300630632, 6.499998300630632, 6.258817147818926, 6.258817147818926, 5.999998033837619, 5.999998033837619, 5.741179053847031, 5.741179053847031, 5.499998293875446, 5.499998293875446, 5.292891824390041, 5.292891824390041, 5.133973609234833, 5.133973609234833, 5.034073662308328, 5.034073662308328, 5.000000000001956, 5.000000000001956, 5.034074686127143, 5.034074686127143, 5.133975587100901, 5.133975587100901, 5.292894621515055, 5.292894621515055, 5.500001719639958, 5.500001719639958, 5.741182874789967, 5.741182874789967, 6.000001989568817, 6.000001989568817, 6.258820968762716, 6.258820968762716, 6.500001726395098, 6.500001726395098, 6.707108192160776, 6.707108192160776, 6.866026402468344, 6.866026402468344, 6.965926343749658, 6.965926343749658, 6.999999999997997, 6.999999999997997, 6.965925307814781, 6.965925307814781, 6.8660244011958405, 6.8660244011958405, 6.707105361934063, 6.707105361934063, 6.499998260089451, 6.499998260089451, 6.258817102600262, 6.258817102600262, 5.999997987023838, 5.999997987023838, 5.741179008628416, 5.741179008628416, 5.4999982533343585, 5.4999982533343585, 5.292891791288408, 5.292891791288408, 5.133973585828024, 5.133973585828024, 5.034073650192121, 5.034073650192121, 5.00000000000205, 5.00000000000205, 5.034074698243296, 5.034074698243296, 5.1339756105074175, 5.1339756105074175, 5.29289465461682, 5.29289465461682, 5.500001760181928, 5.500001760181928, 5.74118292000863, 5.74118292000863, 6.000002036382597, 6.000002036382597, 6.2588210139804525, 6.2588210139804525, 6.500001766936185, 6.500001766936185, 6.70710822526305, 6.70710822526305, 6.866026425875152, 6.866026425875152, 6.965926355865864, 6.965926355865864, 6.999999999997903, 6.999999999997903, 6.965925295698627, 6.965925295698627, 6.866024377788869, 6.866024377788869, 6.707105328831655, 6.707105328831655, 6.49999821954748, 6.49999821954748, 6.258817057382477, 6.258817057382477, 5.999997940210968, 5.999997940210968, 5.741178963409801, 5.741178963409801, 5.499998212792484, 5.499998212792484, 5.292891758186135, 5.292891758186135, 5.133973562421673, 5.133973562421673, 5.034073638076151, 5.034073638076151, 5.000000000002146, 5.000000000002146, 5.034074710359686, 5.034074710359686, 5.133975633914391, 5.133975633914391, 5.292894687719229, 5.292894687719229, 5.500001800723111, 5.500001800723111, 5.7411829652264155, 5.7411829652264155, 6.000002083196376, 6.000002083196376, 6.258821059199066, 6.258821059199066, 6.50000180747806, 6.50000180747806, 6.70710825836468, 6.70710825836468, 6.866026449281503, 6.866026449281503, 6.965926367982068, 6.965926367982068, 6.999999999997805, 6.999999999997805, 6.965925283582235, 6.965925283582235, 6.86602435438235, 6.86602435438235, 6.707105295729888, 6.707105295729888, 6.49999817900551, 6.49999817900551, 6.258817012163813, 6.258817012163813, 5.999997893397189, 5.999997893397189, 5.741178918192066, 5.741178918192066, 5.499998172251398, 5.499998172251398, 5.292891725084506, 5.292891725084506, 5.1339735390148675, 5.1339735390148675, 5.034073625959948, 5.034073625959948, 5.0000000000022435, 5.0000000000022435, 5.034074722475843, 5.034074722475843, 5.133975657320911, 5.133975657320911, 5.292894720821639, 5.292894720821639, 5.500001841265083, 5.500001841265083, 5.74118301044508, 5.74118301044508, 6.000002130009247, 6.000002130009247, 6.258821104416802, 6.258821104416802, 6.500001848019933, 6.500001848019933, 6.707108291466951, 6.707108291466951, 6.866026472688307, 6.866026472688307, 6.965926380098035, 6.965926380098035, 6.999999999997707, 6.999999999997707, 6.965925271465842, 6.965925271465842, 6.866024330975374, 6.866024330975374, 6.7071052626274765, 6.7071052626274765, 6.499998138464325, 6.499998138464325, 6.258816966946027, 6.258816966946027, 5.999997846584318, 5.999997846584318, 5.741178872973453, 5.741178872973453, 5.499998131709525, 5.499998131709525, 5.292891691982236, 5.292891691982236, 5.133973515608519, 5.133973515608519, 5.034073613843982, 5.034073613843982, 5.000000000002344, 5.000000000002344, 5.034074734592238, 5.034074734592238, 5.133975680727888, 5.133975680727888, 5.292894753923409, 5.292894753923409, 5.500001881806268, 5.500001881806268, 5.741183055663745, 5.741183055663745, 6.000002176823027, 6.000002176823027, 6.2588211496354145, 6.2588211496354145, 6.500001888561017, 6.500001888561017, 6.707108324568577, 6.707108324568577, 6.86602649609511, 6.86602649609511, 6.965926392214235, 6.965926392214235, 6.9999999999976055, 6.9999999999976055, 6.965925259349682, 6.965925259349682, 6.866024307568851, 6.866024307568851, 6.707105229525706, 6.707105229525706, 6.499998097922352, 6.499998097922352, 6.2588169217273615, 6.2588169217273615, 5.999997799770538, 5.999997799770538, 5.741178827755719, 5.741178827755719, 5.499998091168441, 5.499998091168441, 5.292891658879967, 5.292891658879967, 5.133973492201718, 5.133973492201718, 5.034073601727783, 5.034073601727783, 5.000000000002446, 5.000000000002446, 5.0340747467083995, 5.0340747467083995, 5.133975704134866, 5.133975704134866, 5.292894787025823, 5.292894787025823, 5.500001922348242, 5.500001922348242, 5.741183100881532, 5.741183100881532, 6.000002223635897, 6.000002223635897, 6.258821194854026, 6.258821194854026, 6.500001929102888, 6.500001929102888, 6.707108357670846, 6.707108357670846, 6.866026519501455, 6.866026519501455, 6.965926404330197, 6.965926404330197, 6.9999999999975016, 6.9999999999975016, 6.965925247233284, 6.965925247233284, 6.86602428416187, 6.86602428416187, 6.707105196423292, 6.707105196423292, 6.499998057381164, 6.499998057381164, 6.258816876509574, 6.258816876509574, 5.999997752956759, 5.999997752956759, 5.741178782537107, 5.741178782537107, 5.499998050626571, 5.499998050626571, 5.292891625778342, 5.292891625778342, 5.133973468795372, 5.133973468795372, 5.0340735896115865, 5.0340735896115865, 5.000000000002551, 5.000000000002551, 5.034074758824798, 5.034074758824798, 5.133975727541393, 5.133975727541393, 5.292894820127595, 5.292894820127595, 5.5000019628902175, 5.5000019628902175, 5.741183146100198, 5.741183146100198, 6.000002270449677, 6.000002270449677, 6.258821240071759, 6.258821240071759, 6.500001969643971, 6.500001969643971, 6.707108390772469, 6.707108390772469, 6.866026542908253, 6.866026542908253, 6.965926416446393, 6.965926416446393, 6.999999999997396, 6.999999999997396, 6.965925235117119, 6.965925235117119, 6.866024260755344, 6.866024260755344, 6.707105163320875, 6.707105163320875, 6.499998016839188, 6.499998016839188, 6.258816831290908, 6.258816831290908, 5.999997706143888, 5.999997706143888, 5.741178737319374, 5.741178737319374, 5.499998010084701, 5.499998010084701, 5.292891592676076, 5.292891592676076, 5.133973445388575, 5.133973445388575, 5.034073577495628, 5.034073577495628, 5.000000000002657, 5.000000000002657, 5.034074770941199, 5.034074770941199, 5.133975750948375, 5.133975750948375, 5.2928948532300115, 5.2928948532300115, 5.500002003431406, 5.500002003431406, 5.741183191317987, 5.741183191317987, 6.000002317262547, 6.000002317262547, 6.258821285290371, 6.258821285290371, 6.500002010185839, 6.500002010185839, 6.707108423874734, 6.707108423874734, 6.866026566314596, 6.866026566314596, 6.9659264285623514, 6.9659264285623514, 6.9999999999972875, 6.9999999999972875, 6.965925223000717, 6.965925223000717, 6.86602423734836, 6.86602423734836, 6.707105130219101, 6.707105130219101, 6.499997976297999, 6.499997976297999, 6.258816786072241, 6.258816786072241, 5.999997659330108, 5.999997659330108, 5.741178692100763, 5.741178692100763, 5.499997969543621, 5.499997969543621, 5.292891559574455, 5.292891559574455, 5.133973421981779, 5.133973421981779, 5.034073565379435, 5.034073565379435, 5.000000000002767, 5.000000000002767, 5.034074783057367, 5.034074783057367, 5.133975774354905, 5.133975774354905, 5.292894886331787, 5.292894886331787, 5.5000020439733825, 5.5000020439733825, 5.741183236536654, 5.741183236536654, 6.000002364076327, 6.000002364076327, 6.258821330508103, 6.258821330508103, 6.500002050726919, 6.500002050726919, 6.707108456976997, 6.707108456976997, 6.866026589721391, 6.866026589721391, 6.965926440678543, 6.965926440678543, 6.999999999997177, 6.999999999997177, 6.9659252108845475, 6.9659252108845475, 6.866024213941375, 6.866024213941375, 6.707105097116682, 6.707105097116682, 6.499997935756022, 6.499997935756022, 6.258816740854451, 6.258816740854451, 5.999997612517238, 5.999997612517238, 5.741178646882153, 5.741178646882153, 5.499997929001753, 5.499997929001753, 5.292891526472193, 5.292891526472193, 5.13397339857544, 5.13397339857544, 5.03407355326348, 5.03407355326348, 5.000000000002878, 5.000000000002878, 5.0340747951737725, 5.0340747951737725, 5.133975797761892, 5.133975797761892, 5.292894919434207, 5.292894919434207, 5.500002084514573, 5.500002084514573, 5.741183281754443, 5.741183281754443, 6.000002410890107, 6.000002410890107, 6.258821375726713, 6.258821375726713, 6.500002091268786, 6.500002091268786, 6.707108490078617, 6.707108490078617, 6.866026613127728, 6.866026613127728, 6.965926452794733, 6.965926452794733, 6.9999999999970655, 6.9999999999970655, 6.965925198768142, 6.965925198768142, 6.8660241905348425, 6.8660241905348425, 6.707105064014904, 6.707105064014904, 6.499997895214043, 6.499997895214043, 6.258816695635783, 6.258816695635783, 5.999997565703458, 5.999997565703458, 5.741178601664422, 5.741178601664422, 5.499997888460675, 5.499997888460675, 5.292891493370575, 5.292891493370575, 5.133973375168649, 5.133973375168649, 5.034073541147292, 5.034073541147292, 5.000000000002991, 5.000000000002991, 5.034074807289945, 5.034074807289945, 5.133975821168425, 5.133975821168425, 5.292894952536629, 5.292894952536629, 5.500002125056553, 5.500002125056553, 5.741183326973112, 5.741183326973112, 6.000002457702977, 6.000002457702977, 6.258821420944444, 6.258821420944444, 6.500002131810652, 6.500002131810652, 6.707108523180877, 6.707108523180877, 6.866026636534519, 6.866026636534519, 6.965926464910685, 6.965926464910685, 6.999999999996951, 6.999999999996951, 6.965925186651733, 6.965925186651733, 6.866024167127853, 6.866024167127853, 6.707105030912482, 6.707105030912482, 6.49999785467285, 6.49999785467285, 6.258816650417993, 6.258816650417993, 5.999997518890588, 5.999997518890588, 5.741178556445813, 5.741178556445813, 5.49999784791881, 5.49999784791881, 5.292891460268315, 5.292891460268315, 5.133973351762314, 5.133973351762314, 5.034073529031341, 5.034073529031341, 5.000000000003107, 5.000000000003107, 5.034074819406354, 5.034074819406354, 5.133975844575415, 5.133975844575415, 5.292894985638409, 5.292894985638409, 5.500002165597746, 5.500002165597746, 5.741183372191781, 5.741183372191781, 6.000002504516757, 6.000002504516757, 6.258821466163052, 6.258821466163052, 6.500002172351728, 6.500002172351728, 6.707108556282493, 6.707108556282493, 6.8660266599413085, 6.8660266599413085, 6.965926477026869, 6.965926477026869, 6.9999999999968345, 6.9999999999968345, 6.965925174535558, 6.965925174535558, 6.866024143721317, 6.866024143721317, 6.707104997810701, 6.707104997810701, 6.49999781413087, 6.49999781413087, 6.258816605199324, 6.258816605199324, 5.999997472076808, 5.999997472076808, 5.741178511228083, 5.741178511228083, 5.499997807377734, 5.499997807377734, 5.29297775466854, 5.29297775466854, 5.134079057408345, 5.134079057408345, 5.034191442257982, 5.034191442257982, 5.000122085218705, 5.000122085218705, 5.034192756704862, 5.034192756704862, 5.134081596725176, 5.134081596725176, 5.292981345804837, 5.292981345804837, 5.50006324847813, 5.50006324847813, 5.741215015087827, 5.741215015087827, 6.000002551018148, 6.000002551018148, 6.258789913101673, 6.258789913101673, 6.499941170015689, 6.499941170015689, 6.707022261880247, 6.707022261880247, 6.865920954293392, 6.865920954293392, 6.965808563799252, 6.965808563799252, 6.999877914781235, 6.999877914781235, 6.965807237237553, 6.965807237237553, 6.865601205348964, 6.865601205348964, 6.705292096406619, 6.705292096406619, 6.493527285981263, 6.493527285981263, 6.242069791969054, 6.242069791969054, 5.999998115862116, 5.999998115862116, 5.912061979111753, 5.912061979111753, 5.955804954607395, 5.955804954607395, 5.987309856541017, 5.987309856541017 ] } } }, "b94d787734a54d0887d66a9c8b5e1214": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "bb809aa69f6b4de692ddb7c47b2a8a8f": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "max": 9, "min": -1, "stabilized": false } }, "bebbd0a9c0534c7b94b4b45f4e66a568": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "c0d1530a498a43cc9e83cebe4159ffc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "cacd7716181840f9a418289dbbd4eef4": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#2ca02c" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_ch1" ], "scales": { "x": "IPY_MODEL_3f7099eb85e644d39be802793202d951", "y": "IPY_MODEL_bb809aa69f6b4de692ddb7c47b2a8a8f" }, "selected": [], "tooltip": "IPY_MODEL_739989c6df8b44ac8e2e610ee68b5e36", "x": { "type": "float", "values": [ 0, 100, 100, 100.83333333333333, 100.83333333333333, 101.66666666666666, 101.66666666666666, 102.5, 102.5, 103.33333333333333, 103.33333333333333, 104.16666666666667, 104.16666666666667, 105, 105, 105.83333333333333, 105.83333333333333, 106.66666666666667, 106.66666666666667, 107.5, 107.5, 108.33333333333334, 108.33333333333334, 109.16666666666667, 109.16666666666667, 110, 110, 110.83333333333334, 110.83333333333334, 111.66666666666667, 111.66666666666667, 112.50000000000001, 112.50000000000001, 113.33333333333334, 113.33333333333334, 114.16666666666666, 114.16666666666666, 115, 115, 115.83333333333333, 115.83333333333333, 116.66666666666667, 116.66666666666667, 117.5, 117.5, 118.33333333333333, 118.33333333333333, 119.16666666666667, 119.16666666666667, 120.00000000000001, 120.00000000000001, 120.83333333333333, 120.83333333333333, 121.66666666666666, 121.66666666666666, 122.49999999999999, 122.49999999999999, 123.33333333333333, 123.33333333333333, 124.16666666666666, 124.16666666666666, 125, 125, 125.83333333333333, 125.83333333333333, 126.66666666666666, 126.66666666666666, 127.5, 127.5, 128.33333333333334, 128.33333333333334, 129.16666666666666, 129.16666666666666, 130, 130, 130.83333333333334, 130.83333333333334, 131.66666666666666, 131.66666666666666, 132.5, 132.5, 133.33333333333334, 133.33333333333334, 134.16666666666666, 134.16666666666666, 135, 135, 135.83333333333334, 135.83333333333334, 136.66666666666669, 136.66666666666669, 137.5, 137.5, 138.33333333333334, 138.33333333333334, 139.16666666666669, 139.16666666666669, 140, 140, 140.83333333333334, 140.83333333333334, 141.66666666666669, 141.66666666666669, 142.5, 142.5, 143.33333333333334, 143.33333333333334, 144.16666666666669, 144.16666666666669, 145.00000000000003, 145.00000000000003, 145.83333333333334, 145.83333333333334, 146.66666666666666, 146.66666666666666, 147.5, 147.5, 148.33333333333331, 148.33333333333331, 149.16666666666666, 149.16666666666666, 150, 150, 150.83333333333331, 150.83333333333331, 151.66666666666666, 151.66666666666666, 152.5, 152.5, 153.33333333333334, 153.33333333333334, 154.16666666666666, 154.16666666666666, 155, 155, 155.83333333333334, 155.83333333333334, 156.66666666666666, 156.66666666666666, 157.5, 157.5, 158.33333333333334, 158.33333333333334, 159.16666666666666, 159.16666666666666, 160, 160, 160.83333333333334, 160.83333333333334, 161.66666666666669, 161.66666666666669, 162.5, 162.5, 163.33333333333334, 163.33333333333334, 164.16666666666669, 164.16666666666669, 165, 165, 165.83333333333334, 165.83333333333334, 166.66666666666669, 166.66666666666669, 167.5, 167.5, 168.33333333333334, 168.33333333333334, 169.16666666666669, 169.16666666666669, 170, 170, 170.83333333333334, 170.83333333333334, 171.66666666666669, 171.66666666666669, 172.50000000000003, 172.50000000000003, 173.33333333333334, 173.33333333333334, 174.16666666666666, 174.16666666666666, 175, 175, 175.83333333333331, 175.83333333333331, 176.66666666666666, 176.66666666666666, 177.5, 177.5, 178.33333333333334, 178.33333333333334, 179.16666666666666, 179.16666666666666, 180, 180, 180.83333333333334, 180.83333333333334, 181.66666666666666, 181.66666666666666, 182.5, 182.5, 183.33333333333334, 183.33333333333334, 184.16666666666666, 184.16666666666666, 185, 185, 185.83333333333334, 185.83333333333334, 186.66666666666666, 186.66666666666666, 187.5, 187.5, 188.33333333333334, 188.33333333333334, 189.16666666666669, 189.16666666666669, 190, 190, 190.83333333333334, 190.83333333333334, 191.66666666666669, 191.66666666666669, 192.5, 192.5, 193.33333333333334, 193.33333333333334, 194.16666666666669, 194.16666666666669, 195, 195, 195.83333333333334, 195.83333333333334, 196.66666666666669, 196.66666666666669, 197.50000000000003, 197.50000000000003, 198.33333333333334, 198.33333333333334, 199.16666666666669, 199.16666666666669, 200, 200, 200.83333333333331, 200.83333333333331, 201.66666666666666, 201.66666666666666, 202.5, 202.5, 203.33333333333331, 203.33333333333331, 204.16666666666666, 204.16666666666666, 205, 205, 205.83333333333334, 205.83333333333334, 206.66666666666666, 206.66666666666666, 207.5, 207.5, 208.33333333333334, 208.33333333333334, 209.16666666666666, 209.16666666666666, 210, 210, 210.83333333333334, 210.83333333333334, 211.66666666666666, 211.66666666666666, 212.5, 212.5, 213.33333333333334, 213.33333333333334, 214.16666666666669, 214.16666666666669, 215, 215, 215.83333333333334, 215.83333333333334, 216.66666666666669, 216.66666666666669, 217.5, 217.5, 218.33333333333334, 218.33333333333334, 219.16666666666669, 219.16666666666669, 220, 220, 220.83333333333334, 220.83333333333334, 221.66666666666669, 221.66666666666669, 222.5, 222.5, 223.33333333333334, 223.33333333333334, 224.16666666666669, 224.16666666666669, 225.00000000000003, 225.00000000000003, 225.83333333333334, 225.83333333333334, 226.66666666666669, 226.66666666666669, 227.5, 227.5, 228.33333333333331, 228.33333333333331, 229.16666666666666, 229.16666666666666, 230, 230, 230.83333333333334, 230.83333333333334, 231.66666666666666, 231.66666666666666, 232.5, 232.5, 233.33333333333334, 233.33333333333334, 234.16666666666666, 234.16666666666666, 235, 235, 235.83333333333334, 235.83333333333334, 236.66666666666666, 236.66666666666666, 237.5, 237.5, 238.33333333333334, 238.33333333333334, 239.16666666666666, 239.16666666666666, 240.00000000000003, 240.00000000000003, 240.83333333333334, 240.83333333333334, 241.66666666666666, 241.66666666666666, 242.5, 242.5, 243.33333333333331, 243.33333333333331, 244.16666666666669, 244.16666666666669, 244.99999999999997, 244.99999999999997, 245.83333333333334, 245.83333333333334, 246.66666666666666, 246.66666666666666, 247.5, 247.5, 248.33333333333331, 248.33333333333331, 249.16666666666669, 249.16666666666669, 250, 250, 250.83333333333334, 250.83333333333334, 251.66666666666666, 251.66666666666666, 252.50000000000003, 252.50000000000003, 253.33333333333331, 253.33333333333331, 254.16666666666669, 254.16666666666669, 255, 255, 255.83333333333334, 255.83333333333334, 256.6666666666667, 256.6666666666667, 257.5, 257.5, 258.3333333333333, 258.3333333333333, 259.1666666666667, 259.1666666666667, 260, 260, 260.83333333333337, 260.83333333333337, 261.6666666666667, 261.6666666666667, 262.5, 262.5, 263.3333333333333, 263.3333333333333, 264.1666666666667, 264.1666666666667, 265, 265, 265.83333333333337, 265.83333333333337, 266.6666666666667, 266.6666666666667, 267.5, 267.5, 268.3333333333333, 268.3333333333333, 269.16666666666663, 269.16666666666663, 270, 270, 270.8333333333333, 270.8333333333333, 271.6666666666667, 271.6666666666667, 272.5, 272.5, 273.33333333333337, 273.33333333333337, 274.16666666666663, 274.16666666666663, 275, 275, 275.8333333333333, 275.8333333333333, 276.6666666666667, 276.6666666666667, 277.5, 277.5, 278.33333333333337, 278.33333333333337, 279.1666666666667, 279.1666666666667, 280, 280, 280.8333333333333, 280.8333333333333, 281.6666666666667, 281.6666666666667, 282.5, 282.5, 283.33333333333337, 283.33333333333337, 284.1666666666667, 284.1666666666667, 285, 285, 285.8333333333333, 285.8333333333333, 286.6666666666667, 286.6666666666667, 287.5, 287.5, 288.33333333333337, 288.33333333333337, 289.1666666666667, 289.1666666666667, 290.00000000000006, 290.00000000000006, 290.8333333333333, 290.8333333333333, 291.6666666666667, 291.6666666666667, 292.5, 292.5, 293.3333333333333, 293.3333333333333, 294.1666666666667, 294.1666666666667, 295, 295, 295.8333333333333, 295.8333333333333, 296.66666666666663, 296.66666666666663, 297.5, 297.5, 298.3333333333333, 298.3333333333333, 299.1666666666667, 299.1666666666667, 300, 300, 300.83333333333337, 300.83333333333337, 301.66666666666663, 301.66666666666663, 302.5, 302.5, 303.3333333333333, 303.3333333333333, 304.1666666666667, 304.1666666666667, 305, 305, 305.83333333333337, 305.83333333333337, 306.6666666666667, 306.6666666666667, 307.5, 307.5, 308.3333333333333, 308.3333333333333, 309.1666666666667, 309.1666666666667, 310, 310, 310.83333333333337, 310.83333333333337, 311.6666666666667, 311.6666666666667, 312.5, 312.5, 313.3333333333333, 313.3333333333333, 314.1666666666667, 314.1666666666667, 315, 315, 315.83333333333337, 315.83333333333337, 316.6666666666667, 316.6666666666667, 317.50000000000006, 317.50000000000006, 318.3333333333333, 318.3333333333333, 319.1666666666667, 319.1666666666667, 320, 320, 320.8333333333333, 320.8333333333333, 321.6666666666667, 321.6666666666667, 322.5, 322.5, 323.33333333333337, 323.33333333333337, 324.16666666666663, 324.16666666666663, 325, 325, 325.8333333333333, 325.8333333333333, 326.6666666666667, 326.6666666666667, 327.5, 327.5, 328.33333333333337, 328.33333333333337, 329.16666666666663, 329.16666666666663, 330, 330, 330.8333333333333, 330.8333333333333, 331.6666666666667, 331.6666666666667, 332.5, 332.5, 333.33333333333337, 333.33333333333337, 334.1666666666667, 334.1666666666667, 335, 335, 335.8333333333333, 335.8333333333333, 336.6666666666667, 336.6666666666667, 337.5, 337.5, 338.33333333333337, 338.33333333333337, 339.1666666666667, 339.1666666666667, 340, 340, 340.8333333333333, 340.8333333333333, 341.6666666666667, 341.6666666666667, 342.5, 342.5, 343.33333333333337, 343.33333333333337, 344.1666666666667, 344.1666666666667, 345.00000000000006, 345.00000000000006, 345.8333333333333, 345.8333333333333, 346.6666666666667, 346.6666666666667, 347.5, 347.5, 348.3333333333333, 348.3333333333333, 349.1666666666667, 349.1666666666667, 350, 350, 350.83333333333337, 350.83333333333337, 351.66666666666663, 351.66666666666663, 352.5, 352.5, 353.3333333333333, 353.3333333333333, 354.1666666666667, 354.1666666666667, 355, 355, 355.83333333333337, 355.83333333333337, 356.6666666666667, 356.6666666666667, 357.5, 357.5, 358.3333333333333, 358.3333333333333, 359.1666666666667, 359.1666666666667, 360, 360, 360.83333333333337, 360.83333333333337, 361.6666666666667, 361.6666666666667, 362.5, 362.5, 363.3333333333333, 363.3333333333333, 364.1666666666667, 364.1666666666667, 365, 365, 365.83333333333337, 365.83333333333337, 366.6666666666667, 366.6666666666667, 367.5, 367.5, 368.3333333333333, 368.3333333333333, 369.1666666666667, 369.1666666666667, 370, 370, 370.83333333333337, 370.83333333333337, 371.6666666666667, 371.6666666666667, 372.50000000000006, 372.50000000000006, 373.3333333333333, 373.3333333333333, 374.16666666666663, 374.16666666666663, 375, 375, 375.8333333333333, 375.8333333333333, 376.6666666666667, 376.6666666666667, 377.5, 377.5, 378.33333333333337, 378.33333333333337, 379.16666666666663, 379.16666666666663, 380, 380, 380.8333333333333, 380.8333333333333, 381.6666666666667, 381.6666666666667, 382.5, 382.5, 383.33333333333337, 383.33333333333337, 384.1666666666667, 384.1666666666667, 385, 385, 385.8333333333333, 385.8333333333333, 386.6666666666667, 386.6666666666667, 387.5, 387.5, 388.33333333333337, 388.33333333333337, 389.1666666666667, 389.1666666666667, 390, 390, 390.8333333333333, 390.8333333333333, 391.6666666666667, 391.6666666666667, 392.5, 392.5, 393.33333333333337, 393.33333333333337, 394.1666666666667, 394.1666666666667, 395.00000000000006, 395.00000000000006, 395.8333333333333, 395.8333333333333, 396.6666666666667, 396.6666666666667, 397.5, 397.5, 398.33333333333337, 398.33333333333337, 399.1666666666667, 399.1666666666667, 400, 400, 400.8333333333333, 400.8333333333333, 401.66666666666663, 401.66666666666663, 402.5, 402.5, 403.3333333333333, 403.3333333333333, 404.1666666666667, 404.1666666666667, 405, 405, 405.83333333333337, 405.83333333333337, 406.66666666666663, 406.66666666666663, 407.5, 407.5, 408.3333333333333, 408.3333333333333, 409.1666666666667, 409.1666666666667, 410, 410, 410.83333333333337, 410.83333333333337, 411.6666666666667, 411.6666666666667, 412.5, 412.5, 413.3333333333333, 413.3333333333333, 414.1666666666667, 414.1666666666667, 415, 415, 415.83333333333337, 415.83333333333337, 416.6666666666667, 416.6666666666667, 417.5, 417.5, 418.3333333333333, 418.3333333333333, 419.1666666666667, 419.1666666666667, 420, 420, 420.83333333333337, 420.83333333333337, 421.6666666666667, 421.6666666666667, 422.50000000000006, 422.50000000000006, 423.3333333333333, 423.3333333333333, 424.1666666666667, 424.1666666666667, 425, 425, 425.83333333333337, 425.83333333333337, 426.6666666666667, 426.6666666666667, 427.5, 427.5, 428.33333333333337, 428.33333333333337, 429.16666666666663, 429.16666666666663, 430, 430, 430.8333333333333, 430.8333333333333, 431.6666666666667, 431.6666666666667, 432.5, 432.5, 433.33333333333337, 433.33333333333337, 434.16666666666663, 434.16666666666663, 435, 435, 435.8333333333333, 435.8333333333333, 436.6666666666667, 436.6666666666667, 437.5, 437.5, 438.33333333333337, 438.33333333333337, 439.1666666666667, 439.1666666666667, 440, 440, 440.8333333333333, 440.8333333333333, 441.6666666666667, 441.6666666666667, 442.5, 442.5, 443.33333333333337, 443.33333333333337, 444.1666666666667, 444.1666666666667, 445, 445, 445.8333333333333, 445.8333333333333, 446.6666666666667, 446.6666666666667, 447.5, 447.5, 448.33333333333337, 448.33333333333337, 449.1666666666667, 449.1666666666667, 450.00000000000006, 450.00000000000006, 450.8333333333333, 450.8333333333333, 451.6666666666667, 451.6666666666667, 452.5, 452.5, 453.33333333333337, 453.33333333333337, 454.1666666666667, 454.1666666666667, 455, 455, 455.83333333333337, 455.83333333333337, 456.66666666666663, 456.66666666666663, 457.5, 457.5, 458.3333333333333, 458.3333333333333, 459.1666666666667, 459.1666666666667, 460, 460, 460.83333333333337, 460.83333333333337, 461.6666666666667, 461.6666666666667, 462.5, 462.5, 463.3333333333333, 463.3333333333333, 464.1666666666667, 464.1666666666667, 465, 465, 465.83333333333337, 465.83333333333337, 466.6666666666667, 466.6666666666667, 467.5, 467.5, 468.3333333333333, 468.3333333333333, 469.1666666666667, 469.1666666666667, 470, 470, 470.83333333333337, 470.83333333333337, 471.6666666666667, 471.6666666666667, 472.50000000000006, 472.50000000000006, 473.3333333333333, 473.3333333333333, 474.1666666666667, 474.1666666666667, 475, 475, 475.83333333333337, 475.83333333333337, 476.6666666666667, 476.6666666666667, 477.5, 477.5, 478.3333333333333, 478.3333333333333, 479.1666666666667, 479.1666666666667, 480.00000000000006, 480.00000000000006, 480.8333333333333, 480.8333333333333, 481.6666666666667, 481.6666666666667, 482.50000000000006, 482.50000000000006, 483.3333333333333, 483.3333333333333, 484.16666666666663, 484.16666666666663, 485, 485, 485.83333333333337, 485.83333333333337, 486.66666666666663, 486.66666666666663, 487.5, 487.5, 488.33333333333337, 488.33333333333337, 489.1666666666667, 489.1666666666667, 489.99999999999994, 489.99999999999994, 490.8333333333333, 490.8333333333333, 491.6666666666667, 491.6666666666667, 492.50000000000006, 492.50000000000006, 493.3333333333333, 493.3333333333333, 494.1666666666667, 494.1666666666667, 495, 495, 495.83333333333337, 495.83333333333337, 496.66666666666663, 496.66666666666663, 497.5, 497.5, 498.33333333333337, 498.33333333333337, 499.16666666666674, 499.16666666666674, 500, 500, 500.8333333333333, 500.8333333333333, 501.6666666666667, 501.6666666666667, 502.50000000000006, 502.50000000000006, 503.3333333333333, 503.3333333333333, 504.1666666666667, 504.1666666666667, 505.00000000000006, 505.00000000000006, 505.83333333333337, 505.83333333333337, 506.66666666666663, 506.66666666666663, 507.5, 507.5, 508.33333333333337, 508.33333333333337, 509.16666666666663, 509.16666666666663, 510, 510, 510.83333333333337, 510.83333333333337, 511.6666666666667, 511.6666666666667, 512.5, 512.5, 513.3333333333334, 513.3333333333334, 514.1666666666666, 514.1666666666666, 515, 515, 515.8333333333333, 515.8333333333333, 516.6666666666666, 516.6666666666666, 517.5, 517.5, 518.3333333333334, 518.3333333333334, 519.1666666666666, 519.1666666666666, 520, 520, 520.8333333333334, 520.8333333333334, 521.6666666666667, 521.6666666666667, 522.5, 522.5, 523.3333333333334, 523.3333333333334, 524.1666666666667, 524.1666666666667, 525, 525, 525.8333333333333, 525.8333333333333, 526.6666666666666, 526.6666666666666, 527.5, 527.5, 528.3333333333334, 528.3333333333334, 529.1666666666666, 529.1666666666666, 530, 530, 530.8333333333334, 530.8333333333334, 531.6666666666667, 531.6666666666667, 532.5, 532.5, 533.3333333333334, 533.3333333333334, 534.1666666666667, 534.1666666666667, 535, 535, 535.8333333333334, 535.8333333333334, 536.6666666666666, 536.6666666666666, 537.5, 537.5, 538.3333333333333, 538.3333333333333, 539.1666666666666, 539.1666666666666, 540, 540, 540.8333333333334, 540.8333333333334, 541.6666666666666, 541.6666666666666, 542.5, 542.5, 543.3333333333334, 543.3333333333334, 544.1666666666667, 544.1666666666667, 545, 545, 545.8333333333334, 545.8333333333334, 546.6666666666667, 546.6666666666667, 547.5, 547.5, 548.3333333333333, 548.3333333333333, 549.1666666666666, 549.1666666666666, 550, 550, 550.8333333333334, 550.8333333333334, 551.6666666666666, 551.6666666666666, 552.5, 552.5, 553.3333333333334, 553.3333333333334, 554.1666666666667, 554.1666666666667, 555, 555, 555.8333333333334, 555.8333333333334, 556.6666666666667, 556.6666666666667, 557.5000000000001, 557.5000000000001, 558.3333333333334, 558.3333333333334, 559.1666666666666, 559.1666666666666, 560, 560, 560.8333333333333, 560.8333333333333, 561.6666666666666, 561.6666666666666, 562.5, 562.5, 563.3333333333334, 563.3333333333334, 564.1666666666666, 564.1666666666666, 565, 565, 565.8333333333334, 565.8333333333334, 566.6666666666667, 566.6666666666667, 567.5, 567.5, 568.3333333333334, 568.3333333333334, 569.1666666666667, 569.1666666666667, 570, 570, 570.8333333333333, 570.8333333333333, 571.6666666666666, 571.6666666666666, 572.5, 572.5, 573.3333333333334, 573.3333333333334, 574.1666666666666, 574.1666666666666, 575, 575, 575.8333333333334, 575.8333333333334, 576.6666666666667, 576.6666666666667, 577.5, 577.5, 578.3333333333334, 578.3333333333334, 579.1666666666667, 579.1666666666667, 580.0000000000001, 580.0000000000001, 580.8333333333333, 580.8333333333333, 581.6666666666666, 581.6666666666666, 582.5, 582.5, 583.3333333333334, 583.3333333333334, 584.1666666666666, 584.1666666666666, 585, 585, 585.8333333333334, 585.8333333333334, 586.6666666666666, 586.6666666666666, 587.5, 587.5, 588.3333333333334, 588.3333333333334, 589.1666666666667, 589.1666666666667, 590, 590, 590.8333333333334, 590.8333333333334, 591.6666666666666, 591.6666666666666, 592.5, 592.5, 593.3333333333333, 593.3333333333333, 594.1666666666666, 594.1666666666666, 595, 595, 595.8333333333334, 595.8333333333334, 596.6666666666666, 596.6666666666666, 597.5, 597.5, 598.3333333333334, 598.3333333333334, 599.1666666666667, 599.1666666666667, 600, 600, 600.8333333333334, 600.8333333333334, 601.6666666666667, 601.6666666666667, 602.5, 602.5, 603.3333333333333, 603.3333333333333, 604.1666666666666, 604.1666666666666, 605, 605, 605.8333333333334, 605.8333333333334, 606.6666666666666, 606.6666666666666, 607.5, 607.5, 608.3333333333334, 608.3333333333334, 609.1666666666667, 609.1666666666667, 610, 610, 610.8333333333334, 610.8333333333334, 611.6666666666667, 611.6666666666667, 612.5000000000001, 612.5000000000001, 613.3333333333334, 613.3333333333334, 614.1666666666666, 614.1666666666666, 615, 615, 615.8333333333333, 615.8333333333333, 616.6666666666666, 616.6666666666666, 617.5, 617.5, 618.3333333333334, 618.3333333333334, 619.1666666666666, 619.1666666666666, 620, 620, 620.8333333333334, 620.8333333333334, 621.6666666666667, 621.6666666666667, 622.5, 622.5, 623.3333333333334, 623.3333333333334, 624.1666666666667, 624.1666666666667, 625, 625, 625.8333333333333, 625.8333333333333, 626.6666666666666, 626.6666666666666, 627.5, 627.5, 628.3333333333334, 628.3333333333334, 629.1666666666666, 629.1666666666666, 630, 630, 630.8333333333334, 630.8333333333334, 631.6666666666667, 631.6666666666667, 632.5, 632.5, 633.3333333333334, 633.3333333333334, 634.1666666666667, 634.1666666666667, 635.0000000000001, 635.0000000000001, 635.8333333333334, 635.8333333333334, 636.6666666666666, 636.6666666666666, 637.5, 637.5, 638.3333333333334, 638.3333333333334, 639.1666666666666, 639.1666666666666, 640, 640, 640.8333333333334, 640.8333333333334, 641.6666666666666, 641.6666666666666, 642.5, 642.5, 643.3333333333334, 643.3333333333334, 644.1666666666667, 644.1666666666667, 645, 645, 645.8333333333334, 645.8333333333334, 646.6666666666667, 646.6666666666667, 647.5, 647.5, 648.3333333333333, 648.3333333333333, 649.1666666666666, 649.1666666666666, 650, 650, 650.8333333333334, 650.8333333333334, 651.6666666666666, 651.6666666666666, 652.5, 652.5, 653.3333333333334, 653.3333333333334, 654.1666666666667, 654.1666666666667, 655, 655, 655.8333333333334, 655.8333333333334, 656.6666666666667, 656.6666666666667, 657.5000000000001, 657.5000000000001, 658.3333333333333, 658.3333333333333, 659.1666666666666, 659.1666666666666, 660, 660, 660.8333333333334, 660.8333333333334, 661.6666666666666, 661.6666666666666, 662.5, 662.5, 663.3333333333334, 663.3333333333334, 664.1666666666667, 664.1666666666667, 665, 665, 665.8333333333334, 665.8333333333334, 666.6666666666667, 666.6666666666667, 667.5, 667.5, 668.3333333333334, 668.3333333333334, 669.1666666666666, 669.1666666666666, 670, 670, 670.8333333333333, 670.8333333333333, 671.6666666666666, 671.6666666666666, 672.5, 672.5, 673.3333333333334, 673.3333333333334, 674.1666666666666, 674.1666666666666, 675, 675, 675.8333333333334, 675.8333333333334, 676.6666666666667, 676.6666666666667, 677.5, 677.5, 678.3333333333334, 678.3333333333334, 679.1666666666667, 679.1666666666667, 680, 680, 680.8333333333333, 680.8333333333333, 681.6666666666666, 681.6666666666666, 682.5, 682.5, 683.3333333333334, 683.3333333333334, 684.1666666666666, 684.1666666666666, 685, 685, 685.8333333333334, 685.8333333333334, 686.6666666666667, 686.6666666666667, 687.5, 687.5, 688.3333333333334, 688.3333333333334, 689.1666666666667, 689.1666666666667, 690.0000000000001, 690.0000000000001, 690.8333333333334, 690.8333333333334, 691.6666666666666, 691.6666666666666, 692.5, 692.5, 693.3333333333334, 693.3333333333334, 694.1666666666666, 694.1666666666666, 695, 695, 695.8333333333334, 695.8333333333334, 696.6666666666666, 696.6666666666666, 697.5, 697.5, 698.3333333333334, 698.3333333333334, 699.1666666666667, 699.1666666666667, 700, 700, 700.8333333333334, 700.8333333333334, 701.6666666666667, 701.6666666666667, 702.5, 702.5, 703.3333333333333, 703.3333333333333, 704.1666666666666, 704.1666666666666, 705, 705, 705.8333333333334, 705.8333333333334, 706.6666666666666, 706.6666666666666, 707.5, 707.5, 708.3333333333334, 708.3333333333334, 709.1666666666667, 709.1666666666667, 710, 710, 710.8333333333334, 710.8333333333334, 711.6666666666667, 711.6666666666667, 712.5000000000001, 712.5000000000001, 713.3333333333334, 713.3333333333334, 714.1666666666666, 714.1666666666666, 715, 715, 715.8333333333334, 715.8333333333334, 716.6666666666666, 716.6666666666666, 717.5, 717.5, 718.3333333333334, 718.3333333333334, 719.1666666666667, 719.1666666666667, 720, 720, 720.8333333333334, 720.8333333333334, 721.6666666666667, 721.6666666666667, 722.5, 722.5, 723.3333333333334, 723.3333333333334, 724.1666666666667, 724.1666666666667, 725, 725, 725.8333333333333, 725.8333333333333, 726.6666666666666, 726.6666666666666, 727.5, 727.5, 728.3333333333334, 728.3333333333334, 729.1666666666666, 729.1666666666666, 730, 730, 730.8333333333334, 730.8333333333334, 731.6666666666667, 731.6666666666667, 732.5, 732.5, 733.3333333333334, 733.3333333333334, 734.1666666666667, 734.1666666666667, 735, 735, 735.8333333333333, 735.8333333333333, 736.6666666666666, 736.6666666666666, 737.5, 737.5, 738.3333333333334, 738.3333333333334, 739.1666666666666, 739.1666666666666, 740, 740, 740.8333333333334, 740.8333333333334, 741.6666666666667, 741.6666666666667, 742.5, 742.5, 743.3333333333334, 743.3333333333334, 744.1666666666667, 744.1666666666667, 745.0000000000001, 745.0000000000001, 745.8333333333334, 745.8333333333334, 746.6666666666666, 746.6666666666666, 747.5, 747.5, 748.3333333333333, 748.3333333333333, 749.1666666666666, 749.1666666666666, 750, 750, 750.8333333333334, 750.8333333333334, 751.6666666666666, 751.6666666666666, 752.5, 752.5, 753.3333333333334, 753.3333333333334, 754.1666666666667, 754.1666666666667, 755, 755, 755.8333333333334, 755.8333333333334, 756.6666666666667, 756.6666666666667, 757.5, 757.5, 758.3333333333333, 758.3333333333333, 759.1666666666666, 759.1666666666666, 760, 760, 760.8333333333334, 760.8333333333334, 761.6666666666666, 761.6666666666666, 762.5, 762.5, 763.3333333333334, 763.3333333333334, 764.1666666666667, 764.1666666666667, 765, 765, 765.8333333333334, 765.8333333333334, 766.6666666666667, 766.6666666666667, 767.5000000000001, 767.5000000000001, 768.3333333333334, 768.3333333333334, 769.1666666666666, 769.1666666666666, 770, 770, 770.8333333333334, 770.8333333333334, 771.6666666666666, 771.6666666666666, 772.5, 772.5, 773.3333333333334, 773.3333333333334, 774.1666666666666, 774.1666666666666, 775, 775, 775.8333333333334, 775.8333333333334, 776.6666666666667, 776.6666666666667, 777.5, 777.5, 778.3333333333334, 778.3333333333334, 779.1666666666667, 779.1666666666667, 780, 780, 780.8333333333333, 780.8333333333333, 781.6666666666666, 781.6666666666666, 782.5, 782.5, 783.3333333333334, 783.3333333333334, 784.1666666666666, 784.1666666666666, 785, 785, 785.8333333333334, 785.8333333333334, 786.6666666666667, 786.6666666666667, 787.5, 787.5, 788.3333333333334, 788.3333333333334, 789.1666666666667, 789.1666666666667, 790.0000000000001, 790.0000000000001, 790.8333333333333, 790.8333333333333, 791.6666666666666, 791.6666666666666, 792.5, 792.5, 793.3333333333334, 793.3333333333334, 794.1666666666666, 794.1666666666666, 795, 795, 795.8333333333334, 795.8333333333334, 796.6666666666667, 796.6666666666667, 797.5, 797.5, 798.3333333333334, 798.3333333333334, 799.1666666666667, 799.1666666666667, 800, 800, 800.8333333333334, 800.8333333333334, 801.6666666666666, 801.6666666666666, 802.5, 802.5, 803.3333333333333, 803.3333333333333, 804.1666666666666, 804.1666666666666, 805, 805, 805.8333333333334, 805.8333333333334, 806.6666666666666, 806.6666666666666, 807.5, 807.5, 808.3333333333334, 808.3333333333334, 809.1666666666667, 809.1666666666667, 810, 810, 810.8333333333334, 810.8333333333334, 811.6666666666667, 811.6666666666667, 812.5, 812.5, 813.3333333333333, 813.3333333333333, 814.1666666666666, 814.1666666666666, 815, 815, 815.8333333333334, 815.8333333333334, 816.6666666666666, 816.6666666666666, 817.5, 817.5, 818.3333333333334, 818.3333333333334, 819.1666666666667, 819.1666666666667, 820, 820, 820.8333333333334, 820.8333333333334, 821.6666666666667, 821.6666666666667, 822.5000000000001, 822.5000000000001, 823.3333333333334, 823.3333333333334, 824.1666666666666, 824.1666666666666, 825, 825, 825.8333333333334, 825.8333333333334, 826.6666666666666, 826.6666666666666, 827.5, 827.5, 828.3333333333334, 828.3333333333334, 829.1666666666666, 829.1666666666666, 830, 830, 830.8333333333334, 830.8333333333334, 831.6666666666667, 831.6666666666667, 832.5, 832.5, 833.3333333333334, 833.3333333333334, 834.1666666666667, 834.1666666666667, 835, 835, 835.8333333333333, 835.8333333333333, 836.6666666666666, 836.6666666666666, 837.5, 837.5, 838.3333333333334, 838.3333333333334, 839.1666666666666, 839.1666666666666, 840, 840, 840.8333333333334, 840.8333333333334, 841.6666666666667, 841.6666666666667, 842.5, 842.5, 843.3333333333334, 843.3333333333334, 844.1666666666667, 844.1666666666667, 845.0000000000001, 845.0000000000001, 845.8333333333334, 845.8333333333334, 846.6666666666666, 846.6666666666666, 847.5, 847.5, 848.3333333333334, 848.3333333333334, 849.1666666666666, 849.1666666666666, 850, 850, 850.8333333333334, 850.8333333333334, 851.6666666666667, 851.6666666666667, 852.5, 852.5, 853.3333333333334, 853.3333333333334, 854.1666666666667, 854.1666666666667, 855, 855, 855.8333333333334, 855.8333333333334, 856.6666666666667, 856.6666666666667, 857.5, 857.5, 858.3333333333333, 858.3333333333333, 859.1666666666666, 859.1666666666666, 860, 860, 860.8333333333334, 860.8333333333334, 861.6666666666666, 861.6666666666666, 862.5, 862.5, 863.3333333333334, 863.3333333333334, 864.1666666666667, 864.1666666666667, 865, 865, 865.8333333333334, 865.8333333333334, 866.6666666666667, 866.6666666666667, 867.5000000000001, 867.5000000000001, 868.3333333333333, 868.3333333333333, 869.1666666666666, 869.1666666666666, 870, 870, 870.8333333333334, 870.8333333333334, 871.6666666666666, 871.6666666666666, 872.5, 872.5, 873.3333333333334, 873.3333333333334, 874.1666666666667, 874.1666666666667, 875, 875, 875.8333333333334, 875.8333333333334, 876.6666666666667, 876.6666666666667, 877.5000000000001, 877.5000000000001, 878.3333333333334, 878.3333333333334, 879.1666666666666, 879.1666666666666, 880, 880, 880.8333333333333, 880.8333333333333, 881.6666666666666, 881.6666666666666, 882.5, 882.5, 883.3333333333334, 883.3333333333334, 884.1666666666666, 884.1666666666666, 885, 885, 885.8333333333334, 885.8333333333334, 886.6666666666667, 886.6666666666667, 887.5, 887.5, 888.3333333333334, 888.3333333333334, 889.1666666666667, 889.1666666666667, 890, 890, 890.8333333333333, 890.8333333333333, 891.6666666666666, 891.6666666666666, 892.5, 892.5, 893.3333333333334, 893.3333333333334, 894.1666666666666, 894.1666666666666, 895, 895, 895.8333333333334, 895.8333333333334, 896.6666666666667, 896.6666666666667, 897.5, 897.5, 898.3333333333334, 898.3333333333334, 899.1666666666667, 899.1666666666667, 900.0000000000001, 900.0000000000001, 900.8333333333334, 900.8333333333334, 901.6666666666666, 901.6666666666666, 902.5, 902.5, 903.3333333333334, 903.3333333333334, 904.1666666666666, 904.1666666666666, 905, 905, 905.8333333333334, 905.8333333333334, 906.6666666666667, 906.6666666666667, 907.5, 907.5, 908.3333333333334, 908.3333333333334, 909.1666666666667, 909.1666666666667, 910, 910, 910.8333333333334, 910.8333333333334, 911.6666666666667, 911.6666666666667, 912.5, 912.5, 913.3333333333333, 913.3333333333333, 914.1666666666666, 914.1666666666666, 915, 915, 915.8333333333334, 915.8333333333334, 916.6666666666666, 916.6666666666666, 917.5, 917.5, 918.3333333333334, 918.3333333333334, 919.1666666666667, 919.1666666666667, 920, 920, 920.8333333333334, 920.8333333333334, 921.6666666666667, 921.6666666666667, 922.5000000000001, 922.5000000000001, 923.3333333333334, 923.3333333333334, 924.1666666666666, 924.1666666666666, 925, 925, 925.8333333333334, 925.8333333333334, 926.6666666666666, 926.6666666666666, 927.5, 927.5, 928.3333333333334, 928.3333333333334, 929.1666666666667, 929.1666666666667, 930, 930, 930.8333333333334, 930.8333333333334, 931.6666666666667, 931.6666666666667, 932.5000000000001, 932.5000000000001, 933.3333333333334, 933.3333333333334, 934.1666666666667, 934.1666666666667, 935, 935, 935.8333333333333, 935.8333333333333, 936.6666666666666, 936.6666666666666, 937.5, 937.5, 938.3333333333334, 938.3333333333334, 939.1666666666666, 939.1666666666666, 940, 940, 940.8333333333334, 940.8333333333334, 941.6666666666667, 941.6666666666667, 942.5, 942.5, 943.3333333333334, 943.3333333333334, 944.1666666666667, 944.1666666666667, 945.0000000000001, 945.0000000000001, 945.8333333333333, 945.8333333333333, 946.6666666666666, 946.6666666666666, 947.5, 947.5, 948.3333333333334, 948.3333333333334, 949.1666666666666, 949.1666666666666, 950, 950, 950.8333333333334, 950.8333333333334, 951.6666666666667, 951.6666666666667, 952.5, 952.5, 953.3333333333334, 953.3333333333334, 954.1666666666667, 954.1666666666667, 955, 955, 955.8333333333335, 955.8333333333335, 956.6666666666666, 956.6666666666666, 957.4999999999999, 957.4999999999999, 958.3333333333334, 958.3333333333334, 959.1666666666666, 959.1666666666666, 960.0000000000001, 960.0000000000001, 960.8333333333334, 960.8333333333334, 961.6666666666666, 961.6666666666666, 962.5000000000001, 962.5000000000001, 963.3333333333334, 963.3333333333334, 964.1666666666666, 964.1666666666666, 965.0000000000001, 965.0000000000001, 965.8333333333334, 965.8333333333334, 966.6666666666666, 966.6666666666666, 967.5, 967.5, 968.3333333333333, 968.3333333333333, 969.1666666666667, 969.1666666666667, 970, 970, 970.8333333333333, 970.8333333333333, 971.6666666666667, 971.6666666666667, 972.5, 972.5, 973.3333333333333, 973.3333333333333, 974.1666666666667, 974.1666666666667, 975, 975, 975.8333333333335, 975.8333333333335, 976.6666666666667, 976.6666666666667, 977.5, 977.5, 978.3333333333334, 978.3333333333334, 979.1666666666666, 979.1666666666666, 979.9999999999999, 979.9999999999999, 980.8333333333334, 980.8333333333334, 981.6666666666666, 981.6666666666666, 982.5000000000001, 982.5000000000001, 983.3333333333334, 983.3333333333334, 984.1666666666666, 984.1666666666666, 985.0000000000001, 985.0000000000001, 985.8333333333334, 985.8333333333334, 986.6666666666666, 986.6666666666666, 987.5000000000001, 987.5000000000001, 988.3333333333334, 988.3333333333334, 989.1666666666666, 989.1666666666666, 990, 990, 990.8333333333333, 990.8333333333333, 991.6666666666667, 991.6666666666667, 992.5, 992.5, 993.3333333333333, 993.3333333333333, 994.1666666666667, 994.1666666666667, 995, 995, 995.8333333333333, 995.8333333333333, 996.6666666666667, 996.6666666666667, 997.5, 997.5, 998.3333333333335, 998.3333333333335, 999.1666666666667, 999.1666666666667, 1000, 1000, 1000.8333333333334, 1000.8333333333334, 1001.6666666666666, 1001.6666666666666, 1002.4999999999999, 1002.4999999999999, 1003.3333333333334, 1003.3333333333334, 1004.1666666666666, 1004.1666666666666, 1005.0000000000001, 1005.0000000000001, 1005.8333333333334, 1005.8333333333334, 1006.6666666666666, 1006.6666666666666, 1007.5000000000001, 1007.5000000000001, 1008.3333333333334, 1008.3333333333334, 1009.1666666666666, 1009.1666666666666, 1010.0000000000001, 1010.0000000000001, 1010.8333333333334, 1010.8333333333334, 1011.6666666666667, 1011.6666666666667, 1012.5, 1012.5, 1013.3333333333333, 1013.3333333333333, 1014.1666666666667, 1014.1666666666667, 1015, 1015, 1015.8333333333333, 1015.8333333333333, 1016.6666666666667, 1016.6666666666667, 1017.5, 1017.5, 1018.3333333333333, 1018.3333333333333, 1019.1666666666667, 1019.1666666666667, 1020, 1020, 1020.8333333333335, 1020.8333333333335, 1021.6666666666667, 1021.6666666666667, 1022.5, 1022.5, 1023.3333333333334, 1023.3333333333334, 1024.1666666666667, 1024.1666666666667, 1025, 1025, 1025.8333333333335, 1025.8333333333335, 1026.6666666666667, 1026.6666666666667, 1027.5000000000002, 1027.5000000000002, 1028.3333333333333, 1028.3333333333333, 1029.1666666666665, 1029.1666666666665, 1030, 1030, 1030.8333333333333, 1030.8333333333333, 1031.6666666666665, 1031.6666666666665, 1032.5, 1032.5, 1033.3333333333333, 1033.3333333333333, 1034.1666666666667, 1034.1666666666667, 1035, 1035, 1035.8333333333333, 1035.8333333333333, 1036.6666666666667, 1036.6666666666667, 1037.5, 1037.5, 1038.3333333333333, 1038.3333333333333, 1039.1666666666667, 1039.1666666666667, 1040, 1040, 1040.8333333333333, 1040.8333333333333, 1041.6666666666667, 1041.6666666666667, 1042.5, 1042.5, 1043.3333333333335, 1043.3333333333335, 1044.1666666666667, 1044.1666666666667, 1045, 1045, 1045.8333333333335, 1045.8333333333335, 1046.6666666666667, 1046.6666666666667, 1047.5, 1047.5, 1048.3333333333335, 1048.3333333333335, 1049.1666666666667, 1049.1666666666667, 1050, 1050, 1050.8333333333333, 1050.8333333333333, 1051.6666666666665, 1051.6666666666665, 1052.5, 1052.5, 1053.3333333333333, 1053.3333333333333, 1054.1666666666665, 1054.1666666666665, 1055, 1055, 1055.8333333333333, 1055.8333333333333, 1056.6666666666667, 1056.6666666666667, 1057.5, 1057.5, 1058.3333333333333, 1058.3333333333333, 1059.1666666666667, 1059.1666666666667, 1060, 1060, 1060.8333333333333, 1060.8333333333333, 1061.6666666666667, 1061.6666666666667, 1062.5, 1062.5, 1063.3333333333335, 1063.3333333333335, 1064.1666666666667, 1064.1666666666667, 1065, 1065, 1065.8333333333335, 1065.8333333333335, 1066.6666666666667, 1066.6666666666667, 1067.5, 1067.5, 1068.3333333333335, 1068.3333333333335, 1069.1666666666667, 1069.1666666666667, 1070, 1070, 1070.8333333333335, 1070.8333333333335, 1071.6666666666667, 1071.6666666666667, 1072.5, 1072.5, 1073.3333333333333, 1073.3333333333333, 1074.1666666666665, 1074.1666666666665, 1075, 1075, 1075.8333333333333, 1075.8333333333333, 1076.6666666666665, 1076.6666666666665, 1077.5, 1077.5, 1078.3333333333333, 1078.3333333333333, 1079.1666666666667, 1079.1666666666667, 1080, 1080, 1080.8333333333333, 1080.8333333333333, 1081.6666666666667, 1081.6666666666667, 1082.5, 1082.5, 1083.3333333333333, 1083.3333333333333, 1084.1666666666667, 1084.1666666666667, 1085, 1085, 1085.8333333333335, 1085.8333333333335, 1086.6666666666667, 1086.6666666666667, 1087.5, 1087.5, 1088.3333333333335, 1088.3333333333335, 1089.1666666666667, 1089.1666666666667, 1090, 1090, 1090.8333333333335, 1090.8333333333335, 1091.6666666666667, 1091.6666666666667, 1092.5000000000002, 1092.5000000000002, 1093.3333333333335, 1093.3333333333335, 1094.1666666666667, 1094.1666666666667, 1095, 1095, 1095.8333333333333, 1095.8333333333333, 1096.6666666666665, 1096.6666666666665, 1097.5, 1097.5, 1098.3333333333333, 1098.3333333333333, 1099.1666666666665, 1099.1666666666665, 1100 ] }, "y": { "type": "float", "values": [ 4, 4, 4.008973259578643, 4.008973259578643, 4.0228769159008335, 4.0228769159008335, 3.9999999164963085, 3.9999999164963085, 3.810601539422748, 3.810601539422748, 3.532352379871206, 3.532352379871206, 3.302043735269807, 3.302043735269807, 3.136194776613066, 3.136194776613066, 3.0345458086595984, 3.0345458086595984, 3.0001220852155135, 3.0001220852155135, 3.034192166109342, 3.034192166109342, 3.134080455785038, 3.134080455785038, 3.2929797322736354, 3.2929797322736354, 3.5000612723153353, 3.5000612723153353, 3.7412128109658083, 3.7412128109658083, 4.000000269144098, 4.000000269144098, 4.258787708980771, 4.258787708980771, 4.499939193855916, 4.499939193855916, 4.7070206483536765, 4.7070206483536765, 4.8659198133590555, 4.8659198133590555, 4.96580797320992, 4.96580797320992, 4.99987791478448, 4.99987791478448, 4.965807827833301, 4.965807827833301, 4.865919532513054, 4.865919532513054, 4.70702025117737, 4.70702025117737, 4.499938707416372, 4.499938707416372, 4.258818764372469, 4.258818764372469, 3.9999997074163773, 3.9999997074163773, 3.7411806703992663, 3.7411806703992663, 3.499999743236711, 3.499999743236711, 3.2928930077878276, 3.2928930077878276, 3.1339744460226218, 3.1339744460226218, 3.034074095460543, 3.034074095460543, 3.000000000000046, 3.000000000000046, 3.0340742529710916, 3.0340742529710916, 3.1339747503096915, 3.1339747503096915, 3.292893438114648, 3.292893438114648, 3.5000002702772766, 3.5000002702772766, 3.7411812582366326, 3.7411812582366326, 4.000000315990399, 4.000000315990399, 4.258819352209832, 4.258819352209832, 4.5000002770341485, 4.5000002770341485, 4.7071070087631774, 4.7071070087631774, 4.866025565680703, 4.866025565680703, 4.96592591059754, 4.96592591059754, 4.999999999999947, 4.999999999999947, 4.965925740970811, 4.965925740970811, 4.866025237986971, 4.866025237986971, 4.707106545334337, 4.707106545334337, 4.499999709451956, 4.499999709451956, 4.2588187191541556, 4.2588187191541556, 3.9999996606028247, 3.9999996606028247, 3.7411806251810704, 3.7411806251810704, 3.4999997026950913, 3.4999997026950913, 3.2928929746857385, 3.2928929746857385, 3.1339744226159154, 3.1339744226159154, 3.034074083344378, 3.034074083344378, 3.0000000000000617, 3.0000000000000617, 3.034074265087316, 3.034074265087316, 3.1339747737164245, 3.1339747737164245, 3.2928934712166784, 3.2928934712166784, 3.5000003108188134, 3.5000003108188134, 3.741181303454946, 3.741181303454946, 4.000000362803838, 4.000000362803838, 4.258819397428027, 4.258819397428027, 4.500000317575669, 4.500000317575669, 4.707107041865266, 4.707107041865266, 4.866025589087409, 4.866025589087409, 4.965925922713704, 4.965925922713704, 4.99999999999993, 4.99999999999993, 4.965925728854586, 4.965925728854586, 4.866025214580237, 4.866025214580237, 4.707106512232306, 4.707106512232306, 4.4999996689104185, 4.4999996689104185, 4.258818673935951, 4.258818673935951, 3.9999996137894995, 3.9999996137894995, 3.7411805799626556, 3.7411805799626556, 3.4999996621535714, 3.4999996621535714, 3.2928929415837307, 3.2928929415837307, 3.133974399209268, 3.133974399209268, 3.0340740712281558, 3.0340740712281558, 3.000000000000079, 3.000000000000079, 3.034074277203513, 3.034074277203513, 3.133974797123102, 3.133974797123102, 3.2928935043188714, 3.2928935043188714, 3.500000351360351, 3.500000351360351, 3.741181348673151, 3.741181348673151, 4.000000409617162, 4.000000409617162, 4.258819442646442, 4.258819442646442, 4.500000358117188, 4.500000358117188, 4.707107074967272, 4.707107074967272, 4.866025612494055, 4.866025612494055, 4.965925934829925, 4.965925934829925, 4.999999999999911, 4.999999999999911, 4.965925716738388, 4.965925716738388, 4.866025191173558, 4.866025191173558, 4.707106479130112, 4.707106479130112, 4.499999628368683, 4.499999628368683, 4.258818628717747, 4.258818628717747, 3.9999995669761748, 3.9999995669761748, 3.7411805347444607, 3.7411805347444607, 3.499999621611855, 3.499999621611855, 3.292892908481725, 3.292892908481725, 3.133974375802622, 3.133974375802622, 3.0340740591119943, 3.0340740591119943, 3.000000000000099, 3.000000000000099, 3.034074289319712, 3.034074289319712, 3.133974820529782, 3.133974820529782, 3.2928935374209054, 3.2928935374209054, 3.5000003919020863, 3.5000003919020863, 3.7411813938913556, 3.7411813938913556, 4.000000456430488, 4.000000456430488, 4.258819487864637, 4.258819487864637, 4.500000398658904, 4.500000398658904, 4.707107108069278, 4.707107108069278, 4.8660256359007, 4.8660256359007, 4.9659259469460855, 4.9659259469460855, 4.999999999999891, 4.999999999999891, 4.965925704622128, 4.965925704622128, 4.866025167766877, 4.866025167766877, 4.707106446028077, 4.707106446028077, 4.499999587827143, 4.499999587827143, 4.258818583499322, 4.258818583499322, 3.9999995201628495, 3.9999995201628495, 3.7411804895262666, 3.7411804895262666, 3.4999995810703375, 3.4999995810703375, 3.29289287537956, 3.29289287537956, 3.1339743523959784, 3.1339743523959784, 3.0340740469958356, 3.0340740469958356, 3.000000000000121, 3.000000000000121, 3.0340743014359726, 3.0340743014359726, 3.1339748439364636, 3.1339748439364636, 3.2928935705229403, 3.2928935705229403, 3.5000004324436262, 3.5000004324436262, 3.7411814391097806, 3.7411814391097806, 4.000000503243813, 4.000000503243813, 4.25881953308283, 4.25881953308283, 4.500000439200421, 4.500000439200421, 4.707107141171441, 4.707107141171441, 4.866025659307456, 4.866025659307456, 4.965925959062243, 4.965925959062243, 4.999999999999868, 4.999999999999868, 4.965925692505926, 4.965925692505926, 4.866025144360081, 4.866025144360081, 4.707106412926041, 4.707106412926041, 4.499999547285603, 4.499999547285603, 4.258818538281116, 4.258818538281116, 3.999999473349297, 3.999999473349297, 3.741180444308073, 3.741180444308073, 3.4999995405288207, 3.4999995405288207, 3.292892842277557, 3.292892842277557, 3.1339743289892232, 3.1339743289892232, 3.0340740348796786, 3.0340740348796786, 3.0000000000001448, 3.0000000000001448, 3.0340743135521757, 3.0340743135521757, 3.133974867343261, 3.133974867343261, 3.2928936036249774, 3.2928936036249774, 3.5000004729851675, 3.5000004729851675, 3.7411814843279867, 3.7411814843279867, 4.000000550057366, 4.000000550057366, 4.258819578301243, 4.258819578301243, 4.500000479741938, 4.500000479741938, 4.707107174273443, 4.707107174273443, 4.866025682714097, 4.866025682714097, 4.965925971178458, 4.965925971178458, 4.999999999999842, 4.999999999999842, 4.965925680389722, 4.965925680389722, 4.866025120953396, 4.866025120953396, 4.707106379823843, 4.707106379823843, 4.499999506744062, 4.499999506744062, 4.25881849306291, 4.25881849306291, 3.999999426535972, 3.999999426535972, 3.7411803990896604, 3.7411803990896604, 3.4999994999873048, 3.4999994999873048, 3.292892809175556, 3.292892809175556, 3.1339743055825835, 3.1339743055825835, 3.034074022763465, 3.034074022763465, 3.0000000000001714, 3.0000000000001714, 3.0340743256683815, 3.0340743256683815, 3.1339748907499465, 3.1339748907499465, 3.2928936367271766, 3.2928936367271766, 3.5000005135269063, 3.5000005135269063, 3.741181529546193, 3.741181529546193, 4.000000596870691, 4.000000596870691, 4.258819623519436, 4.258819623519436, 4.50000052028365, 4.50000052028365, 4.707107207375444, 4.707107207375444, 4.866025706120736, 4.866025706120736, 4.965925983294611, 4.965925983294611, 4.999999999999815, 4.999999999999815, 4.965925668273515, 4.965925668273515, 4.86602509754671, 4.86602509754671, 4.707106346721804, 4.707106346721804, 4.499999466202322, 4.499999466202322, 4.258818447844703, 4.258818447844703, 3.999999379722647, 3.999999379722647, 3.741180353871468, 3.741180353871468, 3.499999459445593, 3.499999459445593, 3.2928927760735562, 3.2928927760735562, 3.133974282175945, 3.133974282175945, 3.0340740106473123, 3.0340740106473123, 3.0000000000002, 3.0000000000002, 3.0340743377846477, 3.0340743377846477, 3.1339749141566338, 3.1339749141566338, 3.2928936698292164, 3.2928936698292164, 3.50000055406845, 3.50000055406845, 3.7411815747646204, 3.7411815747646204, 4.000000643684015, 4.000000643684015, 4.258819668737628, 4.258819668737628, 4.500000560825164, 4.500000560825164, 4.707107240477604, 4.707107240477604, 4.866025729527373, 4.866025729527373, 4.965925995410763, 4.965925995410763, 4.999999999999785, 4.999999999999785, 4.965925656157247, 4.965925656157247, 4.866025074140022, 4.866025074140022, 4.707106313619763, 4.707106313619763, 4.4999994256607785, 4.4999994256607785, 4.258818402626276, 4.258818402626276, 3.9999993329093217, 3.9999993329093217, 3.7411803086530564, 3.7411803086530564, 3.4999994189040793, 3.4999994189040793, 3.2928927429712367, 3.2928927429712367, 3.1339742587691957, 3.1339742587691957, 3.034073998531044, 3.034073998531044, 3.0000000000002305, 3.0000000000002305, 3.034074349900858, 3.034074349900858, 3.1339749375635506, 3.1339749375635506, 3.2928937029314187, 3.2928937029314187, 3.5000005946103876, 3.5000005946103876, 3.7411816199830477, 3.7411816199830477, 4.0000006904975685, 4.0000006904975685, 4.2588197139562585, 4.2588197139562585, 4.500000601366874, 4.500000601366874, 4.707107273579601, 4.707107273579601, 4.8660257529342354, 4.8660257529342354, 4.9659260075269716, 4.9659260075269716, 4.999999999999753, 4.999999999999753, 4.965925644040977, 4.965925644040977, 4.866025050733218, 4.866025050733218, 4.707106280517399, 4.707106280517399, 4.499999385119037, 4.499999385119037, 4.258818357408068, 4.258818357408068, 3.9999992860955422, 3.9999992860955422, 3.741180263434865, 3.741180263434865, 3.499999378362173, 3.499999378362173, 3.29289270986924, 3.29289270986924, 3.1339742353625613, 3.1339742353625613, 3.034073986414896, 3.034073986414896, 3.0000000000002633, 3.0000000000002633, 3.0340743620171873, 3.0340743620171873, 3.1339749609702414, 3.1339749609702414, 3.292893736033462, 3.292893736033462, 3.500000635151933, 3.500000635151933, 3.741181665201256, 3.741181665201256, 4.000000737310893, 4.000000737310893, 4.25881975917445, 4.25881975917445, 4.500000641908386, 4.500000641908386, 4.707107306681919, 4.707107306681919, 4.8660257763408685, 4.8660257763408685, 4.965926019643119, 4.965926019643119, 4.999999999999719, 4.999999999999719, 4.965925631924764, 4.965925631924764, 4.8660250273265255, 4.8660250273265255, 4.707106247415355, 4.707106247415355, 4.499999344577491, 4.499999344577491, 4.25881831218942, 4.25881831218942, 3.999999239282217, 3.999999239282217, 3.7411802182166745, 3.7411802182166745, 3.499999337820662, 3.499999337820662, 3.292892676767245, 3.292892676767245, 3.1339742119557013, 3.1339742119557013, 3.0340739742987495, 3.0340739742987495, 3.0000000000002984, 3.0000000000002984, 3.0340743741334015, 3.0340743741334015, 3.133974984376935, 3.133974984376935, 3.2928937691355062, 3.2928937691355062, 3.5000006756934794, 3.5000006756934794, 3.7411817104194647, 3.7411817104194647, 4.000000784124673, 4.000000784124673, 4.25881980439264, 4.25881980439264, 4.500000682449897, 4.500000682449897, 4.707107339783913, 4.707107339783913, 4.866025799747501, 4.866025799747501, 4.965926031759264, 4.965926031759264, 4.999999999999684, 4.999999999999684, 4.96592561980855, 4.96592561980855, 4.866025003919605, 4.866025003919605, 4.70710621431331, 4.70710621431331, 4.499999304035944, 4.499999304035944, 4.258818266971211, 4.258818266971211, 3.999999192468892, 3.999999192468892, 3.741180172998045, 3.741180172998045, 3.4999992972791514, 3.4999992972791514, 3.2928926436652515, 3.2928926436652515, 3.133974188549071, 3.133974188549071, 3.034073962182605, 3.034073962182605, 3.0000000000003357, 3.0000000000003357, 3.0340743862496176, 3.0340743862496176, 3.1339750077836297, 3.1339750077836297, 3.292893802237874, 3.292893802237874, 3.500000716235027, 3.500000716235027, 3.7411817556376743, 3.7411817556376743, 4.000000830937998, 4.000000830937998, 4.2588198496108305, 4.2588198496108305, 4.500000722991406, 4.500000722991406, 4.707107372885906, 4.707107372885906, 4.86602582315413, 4.86602582315413, 4.965926043875525, 4.965926043875525, 4.999999999999645, 4.999999999999645, 4.965925607692332, 4.965925607692332, 4.8660249805129085, 4.8660249805129085, 4.707106181211263, 4.707106181211263, 4.499999263494002, 4.499999263494002, 4.2588182217530015, 4.2588182217530015, 3.9999991456555666, 3.9999991456555666, 3.7411801277798555, 3.7411801277798555, 3.499999256737642, 3.499999256737642, 3.2928926105632597, 3.2928926105632597, 3.1339741651424426, 3.1339741651424426, 3.034073950066463, 3.034073950066463, 3.000000000000375, 3.000000000000375, 3.034074398365836, 3.034074398365836, 3.1339750311903263, 3.1339750311903263, 3.292893835339922, 3.292893835339922, 3.5000007567765756, 3.5000007567765756, 3.7411818008558844, 3.7411818008558844, 4.000000877751323, 4.000000877751323, 4.25881989482902, 4.25881989482902, 4.500000763533309, 4.500000763533309, 4.707107405987896, 4.707107405987896, 4.866025846560757, 4.866025846560757, 4.965926055991666, 4.965926055991666, 4.999999999999605, 4.999999999999605, 4.965925595575995, 4.965925595575995, 4.866024957106211, 4.866024957106211, 4.707106148109214, 4.707106148109214, 4.499999222952453, 4.499999222952453, 4.258818176534791, 4.258818176534791, 3.999999098842242, 3.999999098842242, 3.7411800825616663, 3.7411800825616663, 3.499999216196134, 3.499999216196134, 3.2928925774609477, 3.2928925774609477, 3.133974141735816, 3.133974141735816, 3.034073937950323, 3.034073937950323, 3.0000000000004166, 3.0000000000004166, 3.0340744104820563, 3.0340744104820563, 3.133975054597025, 3.133975054597025, 3.292893868441971, 3.292893868441971, 3.5000007973181253, 3.5000007973181253, 3.741181846074534, 3.741181846074534, 4.000000924564648, 4.000000924564648, 4.258819940047208, 4.258819940047208, 4.5000008040748165, 4.5000008040748165, 4.707107439089886, 4.707107439089886, 4.86602586996761, 4.86602586996761, 4.965926068107805, 4.965926068107805, 4.999999999999561, 4.999999999999561, 4.965925583459773, 4.965925583459773, 4.866024933699511, 4.866024933699511, 4.707106115007164, 4.707106115007164, 4.499999182410902, 4.499999182410902, 4.25881813131658, 4.25881813131658, 3.999999052028462, 3.999999052028462, 3.741180037343478, 3.741180037343478, 3.499999175654627, 3.499999175654627, 3.292892544358959, 3.292892544358959, 3.133974118329191, 3.133974118329191, 3.034073925834185, 3.034073925834185, 3.0000000000004605, 3.0000000000004605, 3.034074422598279, 3.034074422598279, 3.133975078003953, 3.133975078003953, 3.292893901544022, 3.292893901544022, 3.5000008378596763, 3.5000008378596763, 3.7411818912927455, 3.7411818912927455, 4.000000971377974, 4.000000971377974, 4.258819985265836, 4.258819985265836, 4.500000844616323, 4.500000844616323, 4.707107472191874, 4.707107472191874, 4.866025893374234, 4.866025893374234, 4.965926080223942, 4.965926080223942, 4.999999999999517, 4.999999999999517, 4.965925571343551, 4.965925571343551, 4.8660249102928095, 4.8660249102928095, 4.707106081904792, 4.707106081904792, 4.499999141869351, 4.499999141869351, 4.258818086098368, 4.258818086098368, 3.9999990052151366, 3.9999990052151366, 3.7411799921252897, 3.7411799921252897, 3.499999135113121, 3.499999135113121, 3.2928925112569716, 3.2928925112569716, 3.1339740949225683, 3.1339740949225683, 3.034073913717932, 3.034073913717932, 3.0000000000005063, 3.0000000000005063, 3.0340744347145034, 3.0340744347145034, 3.133975101410656, 3.133975101410656, 3.292893934646074, 3.292893934646074, 3.500000878401622, 3.500000878401622, 3.7411819365109573, 3.7411819365109573, 4.000001018191298, 4.000001018191298, 4.258820030484023, 4.258820030484023, 4.500000885157828, 4.500000885157828, 4.707107505293861, 4.707107505293861, 4.866025916780856, 4.866025916780856, 4.9659260923400765, 4.9659260923400765, 4.99999999999947, 4.99999999999947, 4.965925559227324, 4.965925559227324, 4.866024886886105, 4.866024886886105, 4.707106048802738, 4.707106048802738, 4.4999991013277985, 4.4999991013277985, 4.258818040880157, 4.258818040880157, 3.999998958401812, 3.999998958401812, 3.7411799469071028, 3.7411799469071028, 3.4999990945712227, 3.4999990945712227, 3.292892478154986, 3.292892478154986, 3.133974071515947, 3.133974071515947, 3.034073901601798, 3.034073901601798, 3.0000000000005547, 3.0000000000005547, 3.0340744468308483, 3.0340744468308483, 3.1339751248173604, 3.1339751248173604, 3.292893967748128, 3.292893967748128, 3.5000009189431753, 3.5000009189431753, 3.741181981729169, 3.741181981729169, 4.000001065004623, 4.000001065004623, 4.258820075702211, 4.258820075702211, 4.500000925699332, 4.500000925699332, 4.707107538396167, 4.707107538396167, 4.866025940187476, 4.866025940187476, 4.965926104456209, 4.965926104456209, 4.99999999999942, 4.99999999999942, 4.965925547111096, 4.965925547111096, 4.8660248634794, 4.8660248634794, 4.707106015700684, 4.707106015700684, 4.499999060786245, 4.499999060786245, 4.258817995661505, 4.258817995661505, 3.9999989115884866, 3.9999989115884866, 3.741179901688916, 3.741179901688916, 3.499999054029719, 3.499999054029719, 3.2928924450530017, 3.2928924450530017, 3.133974048109101, 3.133974048109101, 3.0340738894856667, 3.0340738894856667, 3.000000000000605, 3.000000000000605, 3.0340744589470767, 3.0340744589470767, 3.1339751482240668, 3.1339751482240668, 3.2928940008501835, 3.2928940008501835, 3.5000009594847294, 3.5000009594847294, 3.7411820269473823, 3.7411820269473823, 4.000001111818404, 4.000001111818404, 4.258820120920397, 4.258820120920397, 4.500000966240836, 4.500000966240836, 4.707107571498151, 4.707107571498151, 4.866025963594094, 4.866025963594094, 4.965926116572339, 4.965926116572339, 4.9999999999993685, 4.9999999999993685, 4.9659255349948666, 4.9659255349948666, 4.866024840072466, 4.866024840072466, 4.707105982598628, 4.707105982598628, 4.49999902024469, 4.49999902024469, 4.258817950443292, 4.258817950443292, 3.9999988647751614, 3.9999988647751614, 3.7411798564702905, 3.7411798564702905, 3.499999013488216, 3.499999013488216, 3.2928924119510192, 3.2928924119510192, 3.133974024702484, 3.133974024702484, 3.0340738773695373, 3.0340738773695373, 3.0000000000006577, 3.0000000000006577, 3.034074471063308, 3.034074471063308, 3.133975171630775, 3.133975171630775, 3.292894033952562, 3.292894033952562, 3.5000010000262844, 3.5000010000262844, 3.7411820721655955, 3.7411820721655955, 4.000001158631728, 4.000001158631728, 4.258820166138583, 4.258820166138583, 4.5000010067823375, 4.5000010067823375, 4.707107604600132, 4.707107604600132, 4.86602598700071, 4.86602598700071, 4.965926128688586, 4.965926128688586, 4.999999999999315, 4.999999999999315, 4.965925522878635, 4.965925522878635, 4.866024816665757, 4.866024816665757, 4.70710594949657, 4.70710594949657, 4.499998979702741, 4.499998979702741, 4.258817905225078, 4.258817905225078, 3.9999988179618367, 3.9999988179618367, 3.741179811252105, 3.741179811252105, 3.4999989729467145, 3.4999989729467145, 3.292892378849038, 3.292892378849038, 3.1339740012958686, 3.1339740012958686, 3.03407386525341, 3.03407386525341, 3.0000000000007123, 3.0000000000007123, 3.034074483179541, 3.034074483179541, 3.1339751950374852, 3.1339751950374852, 3.2928940670546205, 3.2928940670546205, 3.500001040567841, 3.500001040567841, 3.7411821173838096, 3.7411821173838096, 4.000001205445053, 4.000001205445053, 4.258820211356769, 4.258820211356769, 4.500001047324233, 4.500001047324233, 4.707107637702112, 4.707107637702112, 4.8660260104073245, 4.8660260104073245, 4.9659261408047115, 4.9659261408047115, 4.999999999999259, 4.999999999999259, 4.965925510762283, 4.965925510762283, 4.8660247932590455, 4.8660247932590455, 4.707105916394511, 4.707105916394511, 4.499998939161184, 4.499998939161184, 4.258817860006864, 4.258817860006864, 3.9999987711485114, 3.9999987711485114, 3.7411797660339197, 3.7411797660339197, 3.499998932405214, 3.499998932405214, 3.292892345746737, 3.292892345746737, 3.133973977889255, 3.133973977889255, 3.034073853137285, 3.034073853137285, 3.0000000000007696, 3.0000000000007696, 3.0340744952957763, 3.0340744952957763, 3.133975218444197, 3.133975218444197, 3.2928941001566807, 3.2928941001566807, 3.5000010811093984, 3.5000010811093984, 3.7411821626024637, 3.7411821626024637, 4.000001252258379, 4.000001252258379, 4.258820256574953, 4.258820256574953, 4.500001087865733, 4.500001087865733, 4.707107670804091, 4.707107670804091, 4.8660260338141645, 4.8660260338141645, 4.965926152920836, 4.965926152920836, 4.9999999999992015, 4.9999999999992015, 4.965925498646047, 4.965925498646047, 4.8660247698523325, 4.8660247698523325, 4.707105883292449, 4.707105883292449, 4.4999988986196255, 4.4999988986196255, 4.258817814788649, 4.258817814788649, 3.9999987243347315, 3.9999987243347315, 3.741179720815735, 3.741179720815735, 3.499998891863715, 3.499998891863715, 3.292892312644759, 3.292892312644759, 3.1339739544826437, 3.1339739544826437, 3.034073841021162, 3.034073841021162, 3.0000000000008287, 3.0000000000008287, 3.0340745074120132, 3.0340745074120132, 3.1339752418511386, 3.1339752418511386, 3.2928941332587423, 3.2928941332587423, 3.500001121650957, 3.500001121650957, 3.7411822078206787, 3.7411822078206787, 4.0000012990717035, 4.0000012990717035, 4.258820301793577, 4.258820301793577, 4.500001128407232, 4.500001128407232, 4.707107703906068, 4.707107703906068, 4.866026057220775, 4.866026057220775, 4.965926165036958, 4.965926165036958, 4.999999999999141, 4.999999999999141, 4.965925486529808, 4.965925486529808, 4.866024746445618, 4.866024746445618, 4.707105850190065, 4.707105850190065, 4.499998858078067, 4.499998858078067, 4.258817769570433, 4.258817769570433, 3.9999986775214067, 3.9999986775214067, 3.741179675597551, 3.741179675597551, 3.4999988513222164, 3.4999988513222164, 3.292892279542783, 3.292892279542783, 3.133973931076034, 3.133973931076034, 3.034073828905041, 3.034073828905041, 3.00000000000089, 3.00000000000089, 3.034074519528371, 3.034074519528371, 3.1339752652578543, 3.1339752652578543, 3.2928941663608056, 3.2928941663608056, 3.5000011621925164, 3.5000011621925164, 3.7411822530384553, 3.7411822530384553, 4.000001345885483, 4.000001345885483, 4.25882034701176, 4.25882034701176, 4.500001168948729, 4.500001168948729, 4.7071077370080445, 4.7071077370080445, 4.866026080627156, 4.866026080627156, 4.965926177153195, 4.965926177153195, 4.999999999999078, 4.999999999999078, 4.9659254744135675, 4.9659254744135675, 4.866024723038901, 4.866024723038901, 4.707105817088323, 4.707105817088323, 4.499998817536112, 4.499998817536112, 4.258817724351778, 4.258817724351778, 3.9999986307080815, 3.9999986307080815, 3.7411796303793676, 3.7411796303793676, 3.4999988107807196, 3.4999988107807196, 3.2928922464411294, 3.2928922464411294, 3.133973907669199, 3.133973907669199, 3.0340738167888044, 3.0340738167888044, 3.0000000000009535, 3.0000000000009535, 3.0340745316444946, 3.0340745316444946, 3.1339752886643444, 3.1339752886643444, 3.2928941994631917, 3.2928941994631917, 3.500001202734471, 3.500001202734471, 3.741182298257111, 3.741182298257111, 4.000001392698354, 4.000001392698354, 4.258820392229504, 4.258820392229504, 4.50000120949062, 4.50000120949062, 4.70710777011034, 4.70710777011034, 4.86602610403399, 4.86602610403399, 4.9659261892691955, 4.9659261892691955, 4.999999999999014, 4.999999999999014, 4.965925462297207, 4.965925462297207, 4.8660246996319545, 4.8660246996319545, 4.707105783985936, 4.707105783985936, 4.499998776994945, 4.499998776994945, 4.258817679134, 4.258817679134, 3.999998583895211, 3.999998583895211, 3.741179585160746, 3.741179585160746, 3.4999987702388298, 3.4999987702388298, 3.2928922133388343, 3.2928922133388343, 3.133973884262821, 3.133973884262821, 3.0340738046728055, 3.0340738046728055, 3.000000000001019, 3.000000000001019, 3.034074543760856, 3.034074543760856, 3.133975312071292, 3.133975312071292, 3.2928942325649366, 3.2928942325649366, 3.500001243275639, 3.500001243275639, 3.7411823434757667, 3.7411823434757667, 4.0000014395121335, 4.0000014395121335, 4.258820437448126, 4.258820437448126, 4.500001250031721, 4.500001250031721, 4.707107803211991, 4.707107803211991, 4.866026127440822, 4.866026127440822, 4.965926201385429, 4.965926201385429, 4.999999999998947, 4.999999999998947, 4.96592545018108, 4.96592545018108, 4.866024676225462, 4.866024676225462, 4.707105750884191, 4.707105750884191, 4.499998736452989, 4.499998736452989, 4.258817633915344, 4.258817633915344, 3.9999985370814315, 3.9999985370814315, 3.7411795399430026, 3.7411795399430026, 3.4999987296977286, 3.4999987296977286, 3.2928921802365414, 3.2928921802365414, 3.1339738608559893, 3.1339738608559893, 3.0340737925565735, 3.0340737925565735, 3.000000000001087, 3.000000000001087, 3.034074555876984, 3.034074555876984, 3.1339753354782403, 3.1339753354782403, 3.292894265667326, 3.292894265667326, 3.500001283817596, 3.500001283817596, 3.741182388693545, 3.741182388693545, 4.0000014863250035, 4.0000014863250035, 4.258820482666747, 4.258820482666747, 4.50000129057361, 4.50000129057361, 4.707107836314283, 4.707107836314283, 4.866026150847198, 4.866026150847198, 4.965926213501424, 4.965926213501424, 4.999999999998878, 4.999999999998878, 4.9659254380647155, 4.9659254380647155, 4.866024652818512, 4.866024652818512, 4.7071057177818005, 4.7071057177818005, 4.4999986959118194, 4.4999986959118194, 4.258817588697566, 4.258817588697566, 3.9999984902676515, 3.9999984902676515, 3.7411794947243817, 3.7411794947243817, 3.4999986891558406, 3.4999986891558406, 3.2928921471348924, 3.2928921471348924, 3.1339738374496147, 3.1339738374496147, 3.034073780440343, 3.034073780440343, 3.0000000000011573, 3.0000000000011573, 3.0340745679933496, 3.0340745679933496, 3.1339753588847366, 3.1339753588847366, 3.292894298769074, 3.292894298769074, 3.5000013243595536, 3.5000013243595536, 3.741182433912202, 3.741182433912202, 4.000001533138784, 4.000001533138784, 4.258820527884489, 4.258820527884489, 4.500001331114709, 4.500001331114709, 4.707107869415931, 4.707107869415931, 4.866026174254026, 4.866026174254026, 4.965926225617654, 4.965926225617654, 4.999999999998806, 4.999999999998806, 4.965925425948584, 4.965925425948584, 4.866024629412014, 4.866024629412014, 4.707105684679409, 4.707105684679409, 4.499998655369861, 4.499998655369861, 4.258817543478909, 4.258817543478909, 3.999998443454781, 3.999998443454781, 3.7411794495066397, 3.7411794495066397, 3.4999986486139543, 3.4999986486139543, 3.292892114032602, 3.292892114032602, 3.1339738140427875, 3.1339738140427875, 3.0340737683243506, 3.0340737683243506, 3.0000000000012297, 3.0000000000012297, 3.034074580109717, 3.034074580109717, 3.1339753822916894, 3.1339753822916894, 3.2928943318714663, 3.2928943318714663, 3.500001364900725, 3.500001364900725, 3.7411824791299813, 3.7411824791299813, 4.000001579951654, 4.000001579951654, 4.258820573103109, 4.258820573103109, 4.5000013716565945, 4.5000013716565945, 4.707107902518221, 4.707107902518221, 4.866026197660398, 4.866026197660398, 4.965926237733645, 4.965926237733645, 4.9999999999987335, 4.9999999999987335, 4.965925413832216, 4.965925413832216, 4.866024606005061, 4.866024606005061, 4.707105651577659, 4.707105651577659, 4.499998614828689, 4.499998614828689, 4.2588174982602505, 4.2588174982602505, 3.9999983966410015, 3.9999983966410015, 3.74117940428802, 3.74117940428802, 3.4999986080728562, 3.4999986080728562, 3.2928920809309568, 3.2928920809309568, 3.133973790635962, 3.133973790635962, 3.034073756208125, 3.034073756208125, 3.0000000000013043, 3.0000000000013043, 3.034074592225852, 3.034074592225852, 3.133975405698189, 3.133975405698189, 3.2928943649732174, 3.2928943649732174, 3.500001405442685, 3.500001405442685, 3.74118252434864, 3.74118252434864, 4.0000016267654335, 4.0000016267654335, 4.258820618320851, 4.258820618320851, 4.500001412197692, 4.500001412197692, 4.707107935620509, 4.707107935620509, 4.866026221067223, 4.866026221067223, 4.96592624984987, 4.96592624984987, 4.999999999998658, 4.999999999998658, 4.96592540171608, 4.96592540171608, 4.866024582598105, 4.866024582598105, 4.707105618475263, 4.707105618475263, 4.499998574286729, 4.499998574286729, 4.25881745304247, 4.25881745304247, 3.999998349828131, 3.999998349828131, 3.7411793590694007, 3.7411793590694007, 3.4999985675309717, 3.4999985675309717, 3.2928920478286696, 3.2928920478286696, 3.133973767229593, 3.133973767229593, 3.034073744092136, 3.034073744092136, 3.000000000001381, 3.000000000001381, 3.0340746043422238, 3.0340746043422238, 3.1339754291051456, 3.1339754291051456, 3.292894398075613, 3.292894398075613, 3.5000014459838584, 3.5000014459838584, 3.74118256956642, 3.74118256956642, 4.000001673579214, 4.000001673579214, 4.258820663539469, 4.258820663539469, 4.500001452739576, 4.500001452739576, 4.707107968722152, 4.707107968722152, 4.8660262444735904, 4.8660262444735904, 4.965926261966093, 4.965926261966093, 4.99999999999858, 4.99999999999858, 4.9659253895997075, 4.9659253895997075, 4.866024559191603, 4.866024559191603, 4.70710558537351, 4.70710558537351, 4.499998533744767, 4.499998533744767, 4.258817407823811, 4.258817407823811, 3.999998303014351, 3.999998303014351, 3.741179313851661, 3.741179313851661, 3.4999985269898763, 3.4999985269898763, 3.292892014727027, 3.292892014727027, 3.1339737438227715, 3.1339737438227715, 3.0340737319759143, 3.0340737319759143, 3.0000000000014597, 3.0000000000014597, 3.0340746164583625, 3.0340746164583625, 3.133975452511649, 3.133975452511649, 3.29289443117801, 3.29289443117801, 3.50000148652582, 3.50000148652582, 3.74118261478508, 3.74118261478508, 4.000001720392084, 4.000001720392084, 4.258820708757209, 4.258820708757209, 4.500001493281459, 4.500001493281459, 4.7071080018244364, 4.7071080018244364, 4.866026267880412, 4.866026267880412, 4.9659262740820775, 4.9659262740820775, 4.9999999999985, 4.9999999999985, 4.965925377483332, 4.965925377483332, 4.866024535784644, 4.866024535784644, 4.707105552271113, 4.707105552271113, 4.4999984932035915, 4.4999984932035915, 4.258817362606029, 4.258817362606029, 3.9999982562014806, 3.9999982562014806, 3.7411792686330427, 3.7411792686330427, 3.499998486447994, 3.499998486447994, 3.2928919816247433, 3.2928919816247433, 3.133973720416406, 3.133973720416406, 3.03407371985993, 3.03407371985993, 3.000000000001541, 3.000000000001541, 3.0340746285747384, 3.0340746285747384, 3.13397547591861, 3.13397547591861, 3.2928944642797657, 3.2928944642797657, 3.500001527066996, 3.500001527066996, 3.74118266000374, 3.74118266000374, 4.000001767205863, 4.000001767205863, 4.258820753975827, 4.258820753975827, 4.500001533822553, 4.500001533822553, 4.707108034926077, 4.707108034926077, 4.8660262912872305, 4.8660262912872305, 4.965926286198297, 4.965926286198297, 4.999999999998417, 4.999999999998417, 4.96592536536719, 4.96592536536719, 4.866024512378137, 4.866024512378137, 4.707105519169356, 4.707105519169356, 4.499998452661628, 4.499998452661628, 4.258817317387369, 4.258817317387369, 3.999998209387701, 3.999998209387701, 3.741179223415304, 3.741179223415304, 3.4999984459069005, 3.4999984459069005, 3.2928919485224606, 3.2928919485224606, 3.1339736970095884, 3.1339736970095884, 3.0340737077437128, 3.0340737077437128, 3.000000000001624, 3.000000000001624, 3.0340746406908816, 3.0340746406908816, 3.1339754993255715, 3.1339754993255715, 3.292894497382166, 3.292894497382166, 3.5000015676089604, 3.5000015676089604, 3.741182705221522, 3.741182705221522, 4.000001814018734, 4.000001814018734, 4.258820799194444, 4.258820799194444, 4.5000015743644335, 4.5000015743644335, 4.707108068028359, 4.707108068028359, 4.866026314693593, 4.866026314693593, 4.965926298314278, 4.965926298314278, 4.999999999998334, 4.999999999998334, 4.96592535325081, 4.96592535325081, 4.866024488971174, 4.866024488971174, 4.707105486066955, 4.707105486066955, 4.49999841212045, 4.49999841212045, 4.258817272169587, 4.258817272169587, 3.999998162573921, 3.999998162573921, 3.7411791781966865, 3.7411791781966865, 3.4999984053650204, 3.4999984053650204, 3.2928919154208227, 3.2928919154208227, 3.1339736736032267, 3.1339736736032267, 3.0340736956274976, 3.0340736956274976, 3.0000000000017097, 3.0000000000017097, 3.034074652807262, 3.034074652807262, 3.133975522732081, 3.133975522732081, 3.2928945304839243, 3.2928945304839243, 3.500001608150926, 3.500001608150926, 3.741182750440183, 3.741182750440183, 4.000001860832514, 4.000001860832514, 4.258820844412182, 4.258820844412182, 4.500001614905525, 4.500001614905525, 4.707108101129996, 4.707108101129996, 4.866026338100408, 4.866026338100408, 4.965926310430492, 4.965926310430492, 4.999999999998247, 4.999999999998247, 4.965925341134665, 4.965925341134665, 4.866024465564664, 4.866024465564664, 4.707105452964552, 4.707105452964552, 4.499998371578485, 4.499998371578485, 4.258817226950925, 4.258817226950925, 3.9999981157610507, 3.9999981157610507, 3.741179132978949, 3.741179132978949, 3.4999983648231416, 3.4999983648231416, 3.2928918823185436, 3.2928918823185436, 3.133973650196413, 3.133973650196413, 3.0340736835115196, 3.0340736835115196, 3.0000000000017972, 3.0000000000017972, 3.0340746649236445, 3.0340746649236445, 3.1339755461390473, 3.1339755461390473, 3.292894563586328, 3.292894563586328, 3.500001648692105, 3.500001648692105, 3.7411827956579664, 3.7411827956579664, 4.000001907645384, 4.000001907645384, 4.258820889630798, 4.258820889630798, 4.500001655447404, 4.500001655447404, 4.707108134232274, 4.707108134232274, 4.8660263615067665, 4.8660263615067665, 4.965926322546468, 4.965926322546468, 4.999999999998158, 4.999999999998158, 4.965925329018281, 4.965925329018281, 4.866024442157697, 4.866024442157697, 4.707105419862792, 4.707105419862792, 4.499998331037305, 4.499998331037305, 4.2588171817322635, 4.2588171817322635, 3.999998068947271, 3.999998068947271, 3.741179087760333, 3.741179087760333, 3.4999983242820516, 3.4999983242820516, 3.292891849216909, 3.292891849216909, 3.133973626789601, 3.133973626789601, 3.0340736713953085, 3.0340736713953085, 3.0000000000018874, 3.0000000000018874, 3.034074677039794, 3.034074677039794, 3.1339755695455604, 3.1339755695455604, 3.2928945966880896, 3.2928945966880896, 3.5000016892340726, 3.5000016892340726, 3.7411828408766286, 3.7411828408766286, 4.000001954459164, 4.000001954459164, 4.2588209348485355, 4.2588209348485355, 4.500001695988494, 4.500001695988494, 4.707108167334551, 4.707108167334551, 4.866026384913577, 4.866026384913577, 4.9659263346626785, 4.9659263346626785, 4.999999999998067, 4.999999999998067, 4.965925316902131, 4.965925316902131, 4.866024418750728, 4.866024418750728, 4.707105386760386, 4.707105386760386, 4.499998290495337, 4.499998290495337, 4.258817136514479, 4.258817136514479, 3.9999980221344007, 3.9999980221344007, 3.741179042541718, 3.741179042541718, 3.4999982837401746, 3.4999982837401746, 3.2928918161146323, 3.2928918161146323, 3.133973603383245, 3.133973603383245, 3.034073659279335, 3.034073659279335, 3.0000000000019793, 3.0000000000019793, 3.0340746891561805, 3.0340746891561805, 3.13397559295253, 3.13397559295253, 3.292894629790496, 3.292894629790496, 3.5000017297752537, 3.5000017297752537, 3.741182886094413, 3.741182886094413, 4.000002001272944, 4.000002001272944, 4.25882098006715, 4.25882098006715, 4.500001736530369, 4.500001736530369, 4.707108200436183, 4.707108200436183, 4.866026408319932, 4.866026408319932, 4.965926346778886, 4.965926346778886, 4.999999999997974, 4.999999999997974, 4.965925304785743, 4.965925304785743, 4.866024395344212, 4.866024395344212, 4.707105353658622, 4.707105353658622, 4.499998249953368, 4.499998249953368, 4.258817091295816, 4.258817091295816, 3.9999979753206207, 3.9999979753206207, 3.7411789973239817, 3.7411789973239817, 3.4999982431990864, 3.4999982431990864, 3.2928917830130007, 3.2928917830130007, 3.1339735799764363, 3.1339735799764363, 3.034073647163128, 3.034073647163128, 3.0000000000020735, 3.0000000000020735, 3.034074701272334, 3.034074701272334, 3.133975616359047, 3.133975616359047, 3.292894662892904, 3.292894662892904, 3.5000017703172235, 3.5000017703172235, 3.7411829313130767, 3.7411829313130767, 4.000002048085814, 4.000002048085814, 4.258821025284886, 4.258821025284886, 4.500001777072245, 4.500001777072245, 4.707108233538458, 4.707108233538458, 4.866026431726739, 4.866026431726739, 4.965926358894857, 4.965926358894857, 4.999999999997879, 4.999999999997879, 4.965925292669353, 4.965925292669353, 4.86602437193724, 4.86602437193724, 4.707105320556213, 4.707105320556213, 4.499998209412185, 4.499998209412185, 4.258817046078031, 4.258817046078031, 3.9999979285077507, 3.9999979285077507, 3.7411789521053676, 3.7411789521053676, 3.499998202657212, 3.499998202657212, 3.2928917499107278, 3.2928917499107278, 3.1339735565700844, 3.1339735565700844, 3.034073635047159, 3.034073635047159, 3.00000000000217, 3.00000000000217, 3.0340747133887254, 3.0340747133887254, 3.1339756397660206, 3.1339756397660206, 3.2928946959946703, 3.2928946959946703, 3.500001810858407, 3.500001810858407, 3.7411829765317406, 3.7411829765317406, 4.000002094899594, 4.000002094899594, 4.2588210705035, 4.2588210705035, 4.500001817613331, 4.500001817613331, 4.707108266640087, 4.707108266640087, 4.866026455133545, 4.866026455133545, 4.965926371011061, 4.965926371011061, 4.999999999997781, 4.999999999997781, 4.965925280553196, 4.965925280553196, 4.866024348530719, 4.866024348530719, 4.7071052874544455, 4.7071052874544455, 4.4999981688702135, 4.4999981688702135, 4.258817000859366, 4.258817000859366, 3.9999978816939707, 3.9999978816939707, 3.741178906887632, 3.741178906887632, 3.4999981621161265, 3.4999981621161265, 3.292891716808456, 3.292891716808456, 3.13397353316328, 3.13397353316328, 3.0340736229309564, 3.0340736229309564, 3.0000000000022684, 3.0000000000022684, 3.0340747255048828, 3.0340747255048828, 3.133975663172996, 3.133975663172996, 3.292894729097082, 3.292894729097082, 3.500001851400379, 3.500001851400379, 3.7411830217495265, 3.7411830217495265, 4.000002141712464, 4.000002141712464, 4.258821115722113, 4.258821115722113, 4.500001858155204, 4.500001858155204, 4.707108299742358, 4.707108299742358, 4.866026478539895, 4.866026478539895, 4.965926383127027, 4.965926383127027, 4.999999999997682, 4.999999999997682, 4.965925268436802, 4.965925268436802, 4.8660243251237425, 4.8660243251237425, 4.707105254352034, 4.707105254352034, 4.499998128329029, 4.499998128329029, 4.2588169556415805, 4.2588169556415805, 3.999997834880191, 3.999997834880191, 3.7411788616690194, 3.7411788616690194, 3.499998121574254, 3.499998121574254, 3.292891683706829, 3.292891683706829, 3.133973509756932, 3.133973509756932, 3.034073610814756, 3.034073610814756, 3.000000000002369, 3.000000000002369, 3.034074737621278, 3.034074737621278, 3.1339756865795185, 3.1339756865795185, 3.292894762198851, 3.292894762198851, 3.500001891942352, 3.500001891942352, 3.741183066968192, 3.741183066968192, 4.000002188526245, 4.000002188526245, 4.258821160939847, 4.258821160939847, 4.500001898696288, 4.500001898696288, 4.707108332843983, 4.707108332843983, 4.866026501946696, 4.866026501946696, 4.965926395243226, 4.965926395243226, 4.99999999999758, 4.99999999999758, 4.9659252563206415, 4.9659252563206415, 4.86602430171722, 4.86602430171722, 4.707105221249621, 4.707105221249621, 4.499998087787055, 4.499998087787055, 4.258816910422914, 4.258816910422914, 3.9999977880673208, 3.9999977880673208, 3.7411788164512854, 3.7411788164512854, 3.4999980810323827, 3.4999980810323827, 3.2928916506045605, 3.2928916506045605, 3.133973486350131, 3.133973486350131, 3.034073598698793, 3.034073598698793, 3.0000000000024722, 3.0000000000024722, 3.0340747497376754, 3.0340747497376754, 3.133975709986498, 3.133975709986498, 3.2928947953012653, 3.2928947953012653, 3.5000019324835385, 3.5000019324835385, 3.741183112185979, 3.741183112185979, 4.000002235339115, 4.000002235339115, 4.258821206158459, 4.258821206158459, 4.5000019392381585, 4.5000019392381585, 4.707108365946252, 4.707108365946252, 4.866026525353041, 4.866026525353041, 4.965926407359188, 4.965926407359188, 4.999999999997476, 4.999999999997476, 4.965925244204243, 4.965925244204243, 4.866024278310239, 4.866024278310239, 4.707105188147849, 4.707105188147849, 4.499998047245867, 4.499998047245867, 4.258816865204248, 4.258816865204248, 3.999997741253541, 3.999997741253541, 3.741178771232674, 3.741178771232674, 3.4999980404913003, 3.4999980404913003, 3.292891617502937, 3.292891617502937, 3.1339734629433322, 3.1339734629433322, 3.0340735865825965, 3.0340735865825965, 3.0000000000025775, 3.0000000000025775, 3.0340747618538395, 3.0340747618538395, 3.133975733393024, 3.133975733393024, 3.292894828403038, 3.292894828403038, 3.500001973025514, 3.500001973025514, 3.7411831574046452, 3.7411831574046452, 4.000002282152894, 4.000002282152894, 4.258821251376193, 4.258821251376193, 4.500001979779241, 4.500001979779241, 4.707108399048518, 4.707108399048518, 4.866026548759839, 4.866026548759839, 4.965926419475383, 4.965926419475383, 4.999999999997369, 4.999999999997369, 4.965925232088077, 4.965925232088077, 4.866024254903257, 4.866024254903257, 4.707105155045432, 4.707105155045432, 4.499998006703891, 4.499998006703891, 4.2588168199864604, 4.2588168199864604, 3.9999976944406703, 3.9999976944406703, 3.7411787260140623, 3.7411787260140623, 3.4999979999494313, 3.4999979999494313, 3.292891584400671, 3.292891584400671, 3.13397343953699, 3.13397343953699, 3.0340735744666376, 3.0340735744666376, 3.000000000002685, 3.000000000002685, 3.034074773970241, 3.034074773970241, 3.133975756800007, 3.133975756800007, 3.2928948615054554, 3.2928948615054554, 3.5000020135667027, 3.5000020135667027, 3.741183202622434, 3.741183202622434, 4.000002328966675, 4.000002328966675, 4.258821296594804, 4.258821296594804, 4.5000020203211095, 4.5000020203211095, 4.7071084321501395, 4.7071084321501395, 4.866026572166181, 4.866026572166181, 4.965926431591576, 4.965926431591576, 4.999999999997261, 4.999999999997261, 4.965925219971675, 4.965925219971675, 4.866024231496728, 4.866024231496728, 4.707105121943657, 4.707105121943657, 4.4999979661619145, 4.4999979661619145, 4.258816774767793, 4.258816774767793, 3.999997647626891, 3.999997647626891, 3.74117868079633, 3.74117868079633, 3.4999979594083506, 3.4999979594083506, 3.2928915512990504, 3.2928915512990504, 3.1339734161301944, 3.1339734161301944, 3.0340735623504456, 3.0340735623504456, 3.000000000002794, 3.000000000002794, 3.0340747860864097, 3.0340747860864097, 3.1339757802065376, 3.1339757802065376, 3.292894894607874, 3.292894894607874, 3.5000020541086805, 3.5000020541086805, 3.7411832478411013, 3.7411832478411013, 4.000002375779545, 4.000002375779545, 4.258821341812536, 4.258821341812536, 4.500002060862977, 4.500002060862977, 4.707108465252403, 4.707108465252403, 4.866026595572975, 4.866026595572975, 4.965926443707532, 4.965926443707532, 4.99999999999715, 4.99999999999715, 4.96592520785527, 4.96592520785527, 4.866024208089742, 4.866024208089742, 4.707105088841238, 4.707105088841238, 4.4999979256207245, 4.4999979256207245, 4.258816729550004, 4.258816729550004, 3.9999976008140203, 3.9999976008140203, 3.74117863557772, 3.74117863557772, 3.499997918866484, 3.499997918866484, 3.292891518196788, 3.292891518196788, 3.133973392723856, 3.133973392723856, 3.034073550234491, 3.034073550234491, 3.000000000002906, 3.000000000002906, 3.0340747982028153, 3.0340747982028153, 3.1339758036135246, 3.1339758036135246, 3.2928949277096513, 3.2928949277096513, 3.5000020946498713, 3.5000020946498713, 3.7411832930597693, 3.7411832930597693, 4.000002422593324, 4.000002422593324, 4.258821387031146, 4.258821387031146, 4.500002101404055, 4.500002101404055, 4.707108498354021, 4.707108498354021, 4.866026618979767, 4.866026618979767, 4.965926455823721, 4.965926455823721, 4.999999999997037, 4.999999999997037, 4.965925195739098, 4.965925195739098, 4.866024184683209, 4.866024184683209, 4.70710505573946, 4.70710505573946, 4.499997885078745, 4.499997885078745, 4.258816684331336, 4.258816684331336, 3.9999975540002404, 3.9999975540002404, 3.741178590359989, 3.741178590359989, 3.4999978783254058, 3.4999978783254058, 3.2928914850945272, 3.2928914850945272, 3.1339733693170646, 3.1339733693170646, 3.0340735381183035, 3.0340735381183035, 3.0000000000030203, 3.0000000000030203, 3.034074810318988, 3.034074810318988, 3.133975827020513, 3.133975827020513, 3.2928949608120734, 3.2928949608120734, 3.5000021351918513, 3.5000021351918513, 3.7411833382775592, 3.7411833382775592, 4.000002469406195, 4.000002469406195, 4.258821432249755, 4.258821432249755, 4.50000214194592, 4.50000214194592, 4.707108531456281, 4.707108531456281, 4.866026642386103, 4.866026642386103, 4.965926467939672, 4.965926467939672, 4.999999999996922, 4.999999999996922, 4.96592518362269, 4.96592518362269, 4.866024161276219, 4.866024161276219, 4.707105022637037, 4.707105022637037, 4.499997844537552, 4.499997844537552, 4.258816639113546, 4.258816639113546, 3.999997507186461, 3.999997507186461, 3.7411785451413797, 3.7411785451413797, 3.4999978377835412, 3.4999978377835412, 3.292891451992911, 3.292891451992911, 3.13397334591073, 3.13397334591073, 3.0340735260021177, 3.0340735260021177, 3.000000000003136, 3.000000000003136, 3.034074822435398, 3.034074822435398, 3.133975850427049, 3.133975850427049, 3.292894993913854, 3.292894993913854, 3.500002175733832, 3.500002175733832, 3.7411833834962285, 3.7411833834962285, 4.000002516219975, 4.000002516219975, 4.258821477467485, 4.258821477467485, 4.500002182486997, 4.500002182486997, 4.707108564557896, 4.707108564557896, 4.866026665792892, 4.866026665792892, 4.965926480055857, 4.965926480055857, 4.999999999996804, 4.999999999996804, 4.965925171506514, 4.965925171506514, 4.866024137869682, 4.866024137869682, 4.707018662469598, 4.707018662469598, 4.49993676165593, 4.49993676165593, 4.25878499621524, 4.25878499621524, 3.999997460683641, 3.999997460683641, 3.7412100982022576, 3.7412100982022576, 3.500058840118342, 3.500058840118342, 3.292977746394146, 3.292977746394146, 3.1340790515574763, 3.1340790515574763, 3.0341914392293647, 3.0341914392293647, 3.0001220852187345, 3.0001220852187345, 3.034192759733772, 3.034192759733772, 3.134081602576096, 3.134081602576096, 3.2929813540792727, 3.2929813540792727, 3.500063258612192, 3.500063258612192, 3.7412150263908948, 3.7412150263908948, 4.000002562719937, 4.000002562719937, 4.258789924404725, 4.258789924404725, 4.499758051512304, 4.499758051512304, 4.705295720044372, 4.705295720044372, 4.854819409373181, 4.854819409373181, 4.90342605988624, 4.90342605988624, 4.731778781587124, 4.731778781587124, 4.328185779341063, 4.328185779341063, 4.076547608212793, 4.076547608212793, 4.012690077963085, 4.012690077963085 ] } } }, "cf77b58bb11047bd8b52e3f2f5a3d800": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "AxisModel", "state": { "label": "Amplitude", "orientation": "vertical", "scale": "IPY_MODEL_bb809aa69f6b4de692ddb7c47b2a8a8f", "side": "left", "tick_values": { "type": null, "values": null } } }, "d638ae4f6976431795a33ba1b24ec4d6": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#2ca02c" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_ch1" ], "scales": { "x": "IPY_MODEL_8342a76ecdda4fb9a4e600d8ab8fc329", "y": "IPY_MODEL_30d949e7ba5846e89f696eade5e275c3" }, "selected": [], "tooltip": "IPY_MODEL_de89493a5a54462aa3afafdeebcc5adf", "x": { "type": "float", "values": [ 0, 100, 100, 100.83333333333333, 100.83333333333333, 101.66666666666666, 101.66666666666666, 102.5, 102.5, 103.33333333333333, 103.33333333333333, 104.16666666666667, 104.16666666666667, 105, 105, 105.83333333333333, 105.83333333333333, 106.66666666666667, 106.66666666666667, 107.5, 107.5, 108.33333333333334, 108.33333333333334, 109.16666666666667, 109.16666666666667, 110, 110, 110.83333333333334, 110.83333333333334, 111.66666666666667, 111.66666666666667, 112.50000000000001, 112.50000000000001, 113.33333333333334, 113.33333333333334, 114.16666666666666, 114.16666666666666, 115, 115, 115.83333333333333, 115.83333333333333, 116.66666666666667, 116.66666666666667, 117.5, 117.5, 118.33333333333333, 118.33333333333333, 119.16666666666667, 119.16666666666667, 120.00000000000001, 120.00000000000001, 120.83333333333333, 120.83333333333333, 121.66666666666666, 121.66666666666666, 122.49999999999999, 122.49999999999999, 123.33333333333333, 123.33333333333333, 124.16666666666666, 124.16666666666666, 125, 125, 125.83333333333333, 125.83333333333333, 126.66666666666666, 126.66666666666666, 127.5, 127.5, 128.33333333333334, 128.33333333333334, 129.16666666666666, 129.16666666666666, 130, 130, 130.83333333333334, 130.83333333333334, 131.66666666666666, 131.66666666666666, 132.5, 132.5, 133.33333333333334, 133.33333333333334, 134.16666666666666, 134.16666666666666, 135, 135, 135.83333333333334, 135.83333333333334, 136.66666666666669, 136.66666666666669, 137.5, 137.5, 138.33333333333334, 138.33333333333334, 139.16666666666669, 139.16666666666669, 140, 140, 140.83333333333334, 140.83333333333334, 141.66666666666669, 141.66666666666669, 142.5, 142.5, 143.33333333333334, 143.33333333333334, 144.16666666666669, 144.16666666666669, 145.00000000000003, 145.00000000000003, 145.83333333333334, 145.83333333333334, 146.66666666666666, 146.66666666666666, 147.5, 147.5, 148.33333333333331, 148.33333333333331, 149.16666666666666, 149.16666666666666, 150, 150, 150.83333333333331, 150.83333333333331, 151.66666666666666, 151.66666666666666, 152.5, 152.5, 153.33333333333334, 153.33333333333334, 154.16666666666666, 154.16666666666666, 155, 155, 155.83333333333334, 155.83333333333334, 156.66666666666666, 156.66666666666666, 157.5, 157.5, 158.33333333333334, 158.33333333333334, 159.16666666666666, 159.16666666666666, 160, 160, 160.83333333333334, 160.83333333333334, 161.66666666666669, 161.66666666666669, 162.5, 162.5, 163.33333333333334, 163.33333333333334, 164.16666666666669, 164.16666666666669, 165, 165, 165.83333333333334, 165.83333333333334, 166.66666666666669, 166.66666666666669, 167.5, 167.5, 168.33333333333334, 168.33333333333334, 169.16666666666669, 169.16666666666669, 170, 170, 170.83333333333334, 170.83333333333334, 171.66666666666669, 171.66666666666669, 172.50000000000003, 172.50000000000003, 173.33333333333334, 173.33333333333334, 174.16666666666666, 174.16666666666666, 175, 175, 175.83333333333331, 175.83333333333331, 176.66666666666666, 176.66666666666666, 177.5, 177.5, 178.33333333333334, 178.33333333333334, 179.16666666666666, 179.16666666666666, 180, 180, 180.83333333333334, 180.83333333333334, 181.66666666666666, 181.66666666666666, 182.5, 182.5, 183.33333333333334, 183.33333333333334, 184.16666666666666, 184.16666666666666, 185, 185, 185.83333333333334, 185.83333333333334, 186.66666666666666, 186.66666666666666, 187.5, 187.5, 188.33333333333334, 188.33333333333334, 189.16666666666669, 189.16666666666669, 190, 190, 190.83333333333334, 190.83333333333334, 191.66666666666669, 191.66666666666669, 192.5, 192.5, 193.33333333333334, 193.33333333333334, 194.16666666666669, 194.16666666666669, 195, 195, 195.83333333333334, 195.83333333333334, 196.66666666666669, 196.66666666666669, 197.50000000000003, 197.50000000000003, 198.33333333333334, 198.33333333333334, 199.16666666666669, 199.16666666666669, 200, 200, 200.83333333333331, 200.83333333333331, 201.66666666666666, 201.66666666666666, 202.5, 202.5, 203.33333333333331, 203.33333333333331, 204.16666666666666, 204.16666666666666, 205, 205, 205.83333333333334, 205.83333333333334, 206.66666666666666, 206.66666666666666, 207.5, 207.5, 208.33333333333334, 208.33333333333334, 209.16666666666666, 209.16666666666666, 210, 210, 210.83333333333334, 210.83333333333334, 211.66666666666666, 211.66666666666666, 212.5, 212.5, 213.33333333333334, 213.33333333333334, 214.16666666666669, 214.16666666666669, 215, 215, 215.83333333333334, 215.83333333333334, 216.66666666666669, 216.66666666666669, 217.5, 217.5, 218.33333333333334, 218.33333333333334, 219.16666666666669, 219.16666666666669, 220, 220, 220.83333333333334, 220.83333333333334, 221.66666666666669, 221.66666666666669, 222.5, 222.5, 223.33333333333334, 223.33333333333334, 224.16666666666669, 224.16666666666669, 225.00000000000003, 225.00000000000003, 225.83333333333334, 225.83333333333334, 226.66666666666669, 226.66666666666669, 227.5, 227.5, 228.33333333333331, 228.33333333333331, 229.16666666666666, 229.16666666666666, 230, 230, 230.83333333333334, 230.83333333333334, 231.66666666666666, 231.66666666666666, 232.5, 232.5, 233.33333333333334, 233.33333333333334, 234.16666666666666, 234.16666666666666, 235, 235, 235.83333333333334, 235.83333333333334, 236.66666666666666, 236.66666666666666, 237.5, 237.5, 238.33333333333334, 238.33333333333334, 239.16666666666666, 239.16666666666666, 240.00000000000003, 240.00000000000003, 240.83333333333334, 240.83333333333334, 241.66666666666666, 241.66666666666666, 242.5, 242.5, 243.33333333333331, 243.33333333333331, 244.16666666666669, 244.16666666666669, 244.99999999999997, 244.99999999999997, 245.83333333333334, 245.83333333333334, 246.66666666666666, 246.66666666666666, 247.5, 247.5, 248.33333333333331, 248.33333333333331, 249.16666666666669, 249.16666666666669, 250, 250, 250.83333333333334, 250.83333333333334, 251.66666666666666, 251.66666666666666, 252.50000000000003, 252.50000000000003, 253.33333333333331, 253.33333333333331, 254.16666666666669, 254.16666666666669, 255, 255, 255.83333333333334, 255.83333333333334, 256.6666666666667, 256.6666666666667, 257.5, 257.5, 258.3333333333333, 258.3333333333333, 259.1666666666667, 259.1666666666667, 260, 260, 260.83333333333337, 260.83333333333337, 261.6666666666667, 261.6666666666667, 262.5, 262.5, 263.3333333333333, 263.3333333333333, 264.1666666666667, 264.1666666666667, 265, 265, 265.83333333333337, 265.83333333333337, 266.6666666666667, 266.6666666666667, 267.5, 267.5, 268.3333333333333, 268.3333333333333, 269.16666666666663, 269.16666666666663, 270, 270, 270.8333333333333, 270.8333333333333, 271.6666666666667, 271.6666666666667, 272.5, 272.5, 273.33333333333337, 273.33333333333337, 274.16666666666663, 274.16666666666663, 275, 275, 275.8333333333333, 275.8333333333333, 276.6666666666667, 276.6666666666667, 277.5, 277.5, 278.33333333333337, 278.33333333333337, 279.1666666666667, 279.1666666666667, 280, 280, 280.8333333333333, 280.8333333333333, 281.6666666666667, 281.6666666666667, 282.5, 282.5, 283.33333333333337, 283.33333333333337, 284.1666666666667, 284.1666666666667, 285, 285, 285.8333333333333, 285.8333333333333, 286.6666666666667, 286.6666666666667, 287.5, 287.5, 288.33333333333337, 288.33333333333337, 289.1666666666667, 289.1666666666667, 290.00000000000006, 290.00000000000006, 290.8333333333333, 290.8333333333333, 291.6666666666667, 291.6666666666667, 292.5, 292.5, 293.3333333333333, 293.3333333333333, 294.1666666666667, 294.1666666666667, 295, 295, 295.8333333333333, 295.8333333333333, 296.66666666666663, 296.66666666666663, 297.5, 297.5, 298.3333333333333, 298.3333333333333, 299.1666666666667, 299.1666666666667, 300, 300, 300.83333333333337, 300.83333333333337, 301.66666666666663, 301.66666666666663, 302.5, 302.5, 303.3333333333333, 303.3333333333333, 304.1666666666667, 304.1666666666667, 305, 305, 305.83333333333337, 305.83333333333337, 306.6666666666667, 306.6666666666667, 307.5, 307.5, 308.3333333333333, 308.3333333333333, 309.1666666666667, 309.1666666666667, 310, 310, 310.83333333333337, 310.83333333333337, 311.6666666666667, 311.6666666666667, 312.5, 312.5, 313.3333333333333, 313.3333333333333, 314.1666666666667, 314.1666666666667, 315, 315, 315.83333333333337, 315.83333333333337, 316.6666666666667, 316.6666666666667, 317.50000000000006, 317.50000000000006, 318.3333333333333, 318.3333333333333, 319.1666666666667, 319.1666666666667, 320, 320, 320.8333333333333, 320.8333333333333, 321.6666666666667, 321.6666666666667, 322.5, 322.5, 323.33333333333337, 323.33333333333337, 324.16666666666663, 324.16666666666663, 325, 325, 325.8333333333333, 325.8333333333333, 326.6666666666667, 326.6666666666667, 327.5, 327.5, 328.33333333333337, 328.33333333333337, 329.16666666666663, 329.16666666666663, 330, 330, 330.8333333333333, 330.8333333333333, 331.6666666666667, 331.6666666666667, 332.5, 332.5, 333.33333333333337, 333.33333333333337, 334.1666666666667, 334.1666666666667, 335, 335, 335.8333333333333, 335.8333333333333, 336.6666666666667, 336.6666666666667, 337.5, 337.5, 338.33333333333337, 338.33333333333337, 339.1666666666667, 339.1666666666667, 340, 340, 340.8333333333333, 340.8333333333333, 341.6666666666667, 341.6666666666667, 342.5, 342.5, 343.33333333333337, 343.33333333333337, 344.1666666666667, 344.1666666666667, 345.00000000000006, 345.00000000000006, 345.8333333333333, 345.8333333333333, 346.6666666666667, 346.6666666666667, 347.5, 347.5, 348.3333333333333, 348.3333333333333, 349.1666666666667, 349.1666666666667, 350, 350, 350.83333333333337, 350.83333333333337, 351.66666666666663, 351.66666666666663, 352.5, 352.5, 353.3333333333333, 353.3333333333333, 354.1666666666667, 354.1666666666667, 355, 355, 355.83333333333337, 355.83333333333337, 356.6666666666667, 356.6666666666667, 357.5, 357.5, 358.3333333333333, 358.3333333333333, 359.1666666666667, 359.1666666666667, 360, 360, 360.83333333333337, 360.83333333333337, 361.6666666666667, 361.6666666666667, 362.5, 362.5, 363.3333333333333, 363.3333333333333, 364.1666666666667, 364.1666666666667, 365, 365, 365.83333333333337, 365.83333333333337, 366.6666666666667, 366.6666666666667, 367.5, 367.5, 368.3333333333333, 368.3333333333333, 369.1666666666667, 369.1666666666667, 370, 370, 370.83333333333337, 370.83333333333337, 371.6666666666667, 371.6666666666667, 372.50000000000006, 372.50000000000006, 373.3333333333333, 373.3333333333333, 374.16666666666663, 374.16666666666663, 375, 375, 375.8333333333333, 375.8333333333333, 376.6666666666667, 376.6666666666667, 377.5, 377.5, 378.33333333333337, 378.33333333333337, 379.16666666666663, 379.16666666666663, 380, 380, 380.8333333333333, 380.8333333333333, 381.6666666666667, 381.6666666666667, 382.5, 382.5, 383.33333333333337, 383.33333333333337, 384.1666666666667, 384.1666666666667, 385, 385, 385.8333333333333, 385.8333333333333, 386.6666666666667, 386.6666666666667, 387.5, 387.5, 388.33333333333337, 388.33333333333337, 389.1666666666667, 389.1666666666667, 390, 390, 390.8333333333333, 390.8333333333333, 391.6666666666667, 391.6666666666667, 392.5, 392.5, 393.33333333333337, 393.33333333333337, 394.1666666666667, 394.1666666666667, 395.00000000000006, 395.00000000000006, 395.8333333333333, 395.8333333333333, 396.6666666666667, 396.6666666666667, 397.5, 397.5, 398.33333333333337, 398.33333333333337, 399.1666666666667, 399.1666666666667, 400, 400, 400.8333333333333, 400.8333333333333, 401.66666666666663, 401.66666666666663, 402.5, 402.5, 403.3333333333333, 403.3333333333333, 404.1666666666667, 404.1666666666667, 405, 405, 405.83333333333337, 405.83333333333337, 406.66666666666663, 406.66666666666663, 407.5, 407.5, 408.3333333333333, 408.3333333333333, 409.1666666666667, 409.1666666666667, 410, 410, 410.83333333333337, 410.83333333333337, 411.6666666666667, 411.6666666666667, 412.5, 412.5, 413.3333333333333, 413.3333333333333, 414.1666666666667, 414.1666666666667, 415, 415, 415.83333333333337, 415.83333333333337, 416.6666666666667, 416.6666666666667, 417.5, 417.5, 418.3333333333333, 418.3333333333333, 419.1666666666667, 419.1666666666667, 420, 420, 420.83333333333337, 420.83333333333337, 421.6666666666667, 421.6666666666667, 422.50000000000006, 422.50000000000006, 423.3333333333333, 423.3333333333333, 424.1666666666667, 424.1666666666667, 425, 425, 425.83333333333337, 425.83333333333337, 426.6666666666667, 426.6666666666667, 427.5, 427.5, 428.33333333333337, 428.33333333333337, 429.16666666666663, 429.16666666666663, 430, 430, 430.8333333333333, 430.8333333333333, 431.6666666666667, 431.6666666666667, 432.5, 432.5, 433.33333333333337, 433.33333333333337, 434.16666666666663, 434.16666666666663, 435, 435, 435.8333333333333, 435.8333333333333, 436.6666666666667, 436.6666666666667, 437.5, 437.5, 438.33333333333337, 438.33333333333337, 439.1666666666667, 439.1666666666667, 440, 440, 440.8333333333333, 440.8333333333333, 441.6666666666667, 441.6666666666667, 442.5, 442.5, 443.33333333333337, 443.33333333333337, 444.1666666666667, 444.1666666666667, 445, 445, 445.8333333333333, 445.8333333333333, 446.6666666666667, 446.6666666666667, 447.5, 447.5, 448.33333333333337, 448.33333333333337, 449.1666666666667, 449.1666666666667, 450.00000000000006, 450.00000000000006, 450.8333333333333, 450.8333333333333, 451.6666666666667, 451.6666666666667, 452.5, 452.5, 453.33333333333337, 453.33333333333337, 454.1666666666667, 454.1666666666667, 455, 455, 455.83333333333337, 455.83333333333337, 456.66666666666663, 456.66666666666663, 457.5, 457.5, 458.3333333333333, 458.3333333333333, 459.1666666666667, 459.1666666666667, 460, 460, 460.83333333333337, 460.83333333333337, 461.6666666666667, 461.6666666666667, 462.5, 462.5, 463.3333333333333, 463.3333333333333, 464.1666666666667, 464.1666666666667, 465, 465, 465.83333333333337, 465.83333333333337, 466.6666666666667, 466.6666666666667, 467.5, 467.5, 468.3333333333333, 468.3333333333333, 469.1666666666667, 469.1666666666667, 470, 470, 470.83333333333337, 470.83333333333337, 471.6666666666667, 471.6666666666667, 472.50000000000006, 472.50000000000006, 473.3333333333333, 473.3333333333333, 474.1666666666667, 474.1666666666667, 475, 475, 475.83333333333337, 475.83333333333337, 476.6666666666667, 476.6666666666667, 477.5, 477.5, 478.3333333333333, 478.3333333333333, 479.1666666666667, 479.1666666666667, 480.00000000000006, 480.00000000000006, 480.8333333333333, 480.8333333333333, 481.6666666666667, 481.6666666666667, 482.50000000000006, 482.50000000000006, 483.3333333333333, 483.3333333333333, 484.16666666666663, 484.16666666666663, 485, 485, 485.83333333333337, 485.83333333333337, 486.66666666666663, 486.66666666666663, 487.5, 487.5, 488.33333333333337, 488.33333333333337, 489.1666666666667, 489.1666666666667, 489.99999999999994, 489.99999999999994, 490.8333333333333, 490.8333333333333, 491.6666666666667, 491.6666666666667, 492.50000000000006, 492.50000000000006, 493.3333333333333, 493.3333333333333, 494.1666666666667, 494.1666666666667, 495, 495, 495.83333333333337, 495.83333333333337, 496.66666666666663, 496.66666666666663, 497.5, 497.5, 498.33333333333337, 498.33333333333337, 499.16666666666674, 499.16666666666674, 500, 500, 500.8333333333333, 500.8333333333333, 501.6666666666667, 501.6666666666667, 502.50000000000006, 502.50000000000006, 503.3333333333333, 503.3333333333333, 504.1666666666667, 504.1666666666667, 505.00000000000006, 505.00000000000006, 505.83333333333337, 505.83333333333337, 506.66666666666663, 506.66666666666663, 507.5, 507.5, 508.33333333333337, 508.33333333333337, 509.16666666666663, 509.16666666666663, 510, 510, 510.83333333333337, 510.83333333333337, 511.6666666666667, 511.6666666666667, 512.5, 512.5, 513.3333333333334, 513.3333333333334, 514.1666666666666, 514.1666666666666, 515, 515, 515.8333333333333, 515.8333333333333, 516.6666666666666, 516.6666666666666, 517.5, 517.5, 518.3333333333334, 518.3333333333334, 519.1666666666666, 519.1666666666666, 520, 520, 520.8333333333334, 520.8333333333334, 521.6666666666667, 521.6666666666667, 522.5, 522.5, 523.3333333333334, 523.3333333333334, 524.1666666666667, 524.1666666666667, 525, 525, 525.8333333333333, 525.8333333333333, 526.6666666666666, 526.6666666666666, 527.5, 527.5, 528.3333333333334, 528.3333333333334, 529.1666666666666, 529.1666666666666, 530, 530, 530.8333333333334, 530.8333333333334, 531.6666666666667, 531.6666666666667, 532.5, 532.5, 533.3333333333334, 533.3333333333334, 534.1666666666667, 534.1666666666667, 535, 535, 535.8333333333334, 535.8333333333334, 536.6666666666666, 536.6666666666666, 537.5, 537.5, 538.3333333333333, 538.3333333333333, 539.1666666666666, 539.1666666666666, 540, 540, 540.8333333333334, 540.8333333333334, 541.6666666666666, 541.6666666666666, 542.5, 542.5, 543.3333333333334, 543.3333333333334, 544.1666666666667, 544.1666666666667, 545, 545, 545.8333333333334, 545.8333333333334, 546.6666666666667, 546.6666666666667, 547.5, 547.5, 548.3333333333333, 548.3333333333333, 549.1666666666666, 549.1666666666666, 550, 550, 550.8333333333334, 550.8333333333334, 551.6666666666666, 551.6666666666666, 552.5, 552.5, 553.3333333333334, 553.3333333333334, 554.1666666666667, 554.1666666666667, 555, 555, 555.8333333333334, 555.8333333333334, 556.6666666666667, 556.6666666666667, 557.5000000000001, 557.5000000000001, 558.3333333333334, 558.3333333333334, 559.1666666666666, 559.1666666666666, 560, 560, 560.8333333333333, 560.8333333333333, 561.6666666666666, 561.6666666666666, 562.5, 562.5, 563.3333333333334, 563.3333333333334, 564.1666666666666, 564.1666666666666, 565, 565, 565.8333333333334, 565.8333333333334, 566.6666666666667, 566.6666666666667, 567.5, 567.5, 568.3333333333334, 568.3333333333334, 569.1666666666667, 569.1666666666667, 570, 570, 570.8333333333333, 570.8333333333333, 571.6666666666666, 571.6666666666666, 572.5, 572.5, 573.3333333333334, 573.3333333333334, 574.1666666666666, 574.1666666666666, 575, 575, 575.8333333333334, 575.8333333333334, 576.6666666666667, 576.6666666666667, 577.5, 577.5, 578.3333333333334, 578.3333333333334, 579.1666666666667, 579.1666666666667, 580.0000000000001, 580.0000000000001, 580.8333333333333, 580.8333333333333, 581.6666666666666, 581.6666666666666, 582.5, 582.5, 583.3333333333334, 583.3333333333334, 584.1666666666666, 584.1666666666666, 585, 585, 585.8333333333334, 585.8333333333334, 586.6666666666666, 586.6666666666666, 587.5, 587.5, 588.3333333333334, 588.3333333333334, 589.1666666666667, 589.1666666666667, 590, 590, 590.8333333333334, 590.8333333333334, 591.6666666666666, 591.6666666666666, 592.5, 592.5, 593.3333333333333, 593.3333333333333, 594.1666666666666, 594.1666666666666, 595, 595, 595.8333333333334, 595.8333333333334, 596.6666666666666, 596.6666666666666, 597.5, 597.5, 598.3333333333334, 598.3333333333334, 599.1666666666667, 599.1666666666667, 600, 600, 600.8333333333334, 600.8333333333334, 601.6666666666667, 601.6666666666667, 602.5, 602.5, 603.3333333333333, 603.3333333333333, 604.1666666666666, 604.1666666666666, 605, 605, 605.8333333333334, 605.8333333333334, 606.6666666666666, 606.6666666666666, 607.5, 607.5, 608.3333333333334, 608.3333333333334, 609.1666666666667, 609.1666666666667, 610, 610, 610.8333333333334, 610.8333333333334, 611.6666666666667, 611.6666666666667, 612.5000000000001, 612.5000000000001, 613.3333333333334, 613.3333333333334, 614.1666666666666, 614.1666666666666, 615, 615, 615.8333333333333, 615.8333333333333, 616.6666666666666, 616.6666666666666, 617.5, 617.5, 618.3333333333334, 618.3333333333334, 619.1666666666666, 619.1666666666666, 620, 620, 620.8333333333334, 620.8333333333334, 621.6666666666667, 621.6666666666667, 622.5, 622.5, 623.3333333333334, 623.3333333333334, 624.1666666666667, 624.1666666666667, 625, 625, 625.8333333333333, 625.8333333333333, 626.6666666666666, 626.6666666666666, 627.5, 627.5, 628.3333333333334, 628.3333333333334, 629.1666666666666, 629.1666666666666, 630, 630, 630.8333333333334, 630.8333333333334, 631.6666666666667, 631.6666666666667, 632.5, 632.5, 633.3333333333334, 633.3333333333334, 634.1666666666667, 634.1666666666667, 635.0000000000001, 635.0000000000001, 635.8333333333334, 635.8333333333334, 636.6666666666666, 636.6666666666666, 637.5, 637.5, 638.3333333333334, 638.3333333333334, 639.1666666666666, 639.1666666666666, 640, 640, 640.8333333333334, 640.8333333333334, 641.6666666666666, 641.6666666666666, 642.5, 642.5, 643.3333333333334, 643.3333333333334, 644.1666666666667, 644.1666666666667, 645, 645, 645.8333333333334, 645.8333333333334, 646.6666666666667, 646.6666666666667, 647.5, 647.5, 648.3333333333333, 648.3333333333333, 649.1666666666666, 649.1666666666666, 650, 650, 650.8333333333334, 650.8333333333334, 651.6666666666666, 651.6666666666666, 652.5, 652.5, 653.3333333333334, 653.3333333333334, 654.1666666666667, 654.1666666666667, 655, 655, 655.8333333333334, 655.8333333333334, 656.6666666666667, 656.6666666666667, 657.5000000000001, 657.5000000000001, 658.3333333333333, 658.3333333333333, 659.1666666666666, 659.1666666666666, 660, 660, 660.8333333333334, 660.8333333333334, 661.6666666666666, 661.6666666666666, 662.5, 662.5, 663.3333333333334, 663.3333333333334, 664.1666666666667, 664.1666666666667, 665, 665, 665.8333333333334, 665.8333333333334, 666.6666666666667, 666.6666666666667, 667.5, 667.5, 668.3333333333334, 668.3333333333334, 669.1666666666666, 669.1666666666666, 670, 670, 670.8333333333333, 670.8333333333333, 671.6666666666666, 671.6666666666666, 672.5, 672.5, 673.3333333333334, 673.3333333333334, 674.1666666666666, 674.1666666666666, 675, 675, 675.8333333333334, 675.8333333333334, 676.6666666666667, 676.6666666666667, 677.5, 677.5, 678.3333333333334, 678.3333333333334, 679.1666666666667, 679.1666666666667, 680, 680, 680.8333333333333, 680.8333333333333, 681.6666666666666, 681.6666666666666, 682.5, 682.5, 683.3333333333334, 683.3333333333334, 684.1666666666666, 684.1666666666666, 685, 685, 685.8333333333334, 685.8333333333334, 686.6666666666667, 686.6666666666667, 687.5, 687.5, 688.3333333333334, 688.3333333333334, 689.1666666666667, 689.1666666666667, 690.0000000000001, 690.0000000000001, 690.8333333333334, 690.8333333333334, 691.6666666666666, 691.6666666666666, 692.5, 692.5, 693.3333333333334, 693.3333333333334, 694.1666666666666, 694.1666666666666, 695, 695, 695.8333333333334, 695.8333333333334, 696.6666666666666, 696.6666666666666, 697.5, 697.5, 698.3333333333334, 698.3333333333334, 699.1666666666667, 699.1666666666667, 700, 700, 700.8333333333334, 700.8333333333334, 701.6666666666667, 701.6666666666667, 702.5, 702.5, 703.3333333333333, 703.3333333333333, 704.1666666666666, 704.1666666666666, 705, 705, 705.8333333333334, 705.8333333333334, 706.6666666666666, 706.6666666666666, 707.5, 707.5, 708.3333333333334, 708.3333333333334, 709.1666666666667, 709.1666666666667, 710, 710, 710.8333333333334, 710.8333333333334, 711.6666666666667, 711.6666666666667, 712.5000000000001, 712.5000000000001, 713.3333333333334, 713.3333333333334, 714.1666666666666, 714.1666666666666, 715, 715, 715.8333333333334, 715.8333333333334, 716.6666666666666, 716.6666666666666, 717.5, 717.5, 718.3333333333334, 718.3333333333334, 719.1666666666667, 719.1666666666667, 720, 720, 720.8333333333334, 720.8333333333334, 721.6666666666667, 721.6666666666667, 722.5, 722.5, 723.3333333333334, 723.3333333333334, 724.1666666666667, 724.1666666666667, 725, 725, 725.8333333333333, 725.8333333333333, 726.6666666666666, 726.6666666666666, 727.5, 727.5, 728.3333333333334, 728.3333333333334, 729.1666666666666, 729.1666666666666, 730, 730, 730.8333333333334, 730.8333333333334, 731.6666666666667, 731.6666666666667, 732.5, 732.5, 733.3333333333334, 733.3333333333334, 734.1666666666667, 734.1666666666667, 735, 735, 735.8333333333333, 735.8333333333333, 736.6666666666666, 736.6666666666666, 737.5, 737.5, 738.3333333333334, 738.3333333333334, 739.1666666666666, 739.1666666666666, 740, 740, 740.8333333333334, 740.8333333333334, 741.6666666666667, 741.6666666666667, 742.5, 742.5, 743.3333333333334, 743.3333333333334, 744.1666666666667, 744.1666666666667, 745.0000000000001, 745.0000000000001, 745.8333333333334, 745.8333333333334, 746.6666666666666, 746.6666666666666, 747.5, 747.5, 748.3333333333333, 748.3333333333333, 749.1666666666666, 749.1666666666666, 750, 750, 750.8333333333334, 750.8333333333334, 751.6666666666666, 751.6666666666666, 752.5, 752.5, 753.3333333333334, 753.3333333333334, 754.1666666666667, 754.1666666666667, 755, 755, 755.8333333333334, 755.8333333333334, 756.6666666666667, 756.6666666666667, 757.5, 757.5, 758.3333333333333, 758.3333333333333, 759.1666666666666, 759.1666666666666, 760, 760, 760.8333333333334, 760.8333333333334, 761.6666666666666, 761.6666666666666, 762.5, 762.5, 763.3333333333334, 763.3333333333334, 764.1666666666667, 764.1666666666667, 765, 765, 765.8333333333334, 765.8333333333334, 766.6666666666667, 766.6666666666667, 767.5000000000001, 767.5000000000001, 768.3333333333334, 768.3333333333334, 769.1666666666666, 769.1666666666666, 770, 770, 770.8333333333334, 770.8333333333334, 771.6666666666666, 771.6666666666666, 772.5, 772.5, 773.3333333333334, 773.3333333333334, 774.1666666666666, 774.1666666666666, 775, 775, 775.8333333333334, 775.8333333333334, 776.6666666666667, 776.6666666666667, 777.5, 777.5, 778.3333333333334, 778.3333333333334, 779.1666666666667, 779.1666666666667, 780, 780, 780.8333333333333, 780.8333333333333, 781.6666666666666, 781.6666666666666, 782.5, 782.5, 783.3333333333334, 783.3333333333334, 784.1666666666666, 784.1666666666666, 785, 785, 785.8333333333334, 785.8333333333334, 786.6666666666667, 786.6666666666667, 787.5, 787.5, 788.3333333333334, 788.3333333333334, 789.1666666666667, 789.1666666666667, 790.0000000000001, 790.0000000000001, 790.8333333333333, 790.8333333333333, 791.6666666666666, 791.6666666666666, 792.5, 792.5, 793.3333333333334, 793.3333333333334, 794.1666666666666, 794.1666666666666, 795, 795, 795.8333333333334, 795.8333333333334, 796.6666666666667, 796.6666666666667, 797.5, 797.5, 798.3333333333334, 798.3333333333334, 799.1666666666667, 799.1666666666667, 800, 800, 800.8333333333334, 800.8333333333334, 801.6666666666666, 801.6666666666666, 802.5, 802.5, 803.3333333333333, 803.3333333333333, 804.1666666666666, 804.1666666666666, 805, 805, 805.8333333333334, 805.8333333333334, 806.6666666666666, 806.6666666666666, 807.5, 807.5, 808.3333333333334, 808.3333333333334, 809.1666666666667, 809.1666666666667, 810, 810, 810.8333333333334, 810.8333333333334, 811.6666666666667, 811.6666666666667, 812.5, 812.5, 813.3333333333333, 813.3333333333333, 814.1666666666666, 814.1666666666666, 815, 815, 815.8333333333334, 815.8333333333334, 816.6666666666666, 816.6666666666666, 817.5, 817.5, 818.3333333333334, 818.3333333333334, 819.1666666666667, 819.1666666666667, 820, 820, 820.8333333333334, 820.8333333333334, 821.6666666666667, 821.6666666666667, 822.5000000000001, 822.5000000000001, 823.3333333333334, 823.3333333333334, 824.1666666666666, 824.1666666666666, 825, 825, 825.8333333333334, 825.8333333333334, 826.6666666666666, 826.6666666666666, 827.5, 827.5, 828.3333333333334, 828.3333333333334, 829.1666666666666, 829.1666666666666, 830, 830, 830.8333333333334, 830.8333333333334, 831.6666666666667, 831.6666666666667, 832.5, 832.5, 833.3333333333334, 833.3333333333334, 834.1666666666667, 834.1666666666667, 835, 835, 835.8333333333333, 835.8333333333333, 836.6666666666666, 836.6666666666666, 837.5, 837.5, 838.3333333333334, 838.3333333333334, 839.1666666666666, 839.1666666666666, 840, 840, 840.8333333333334, 840.8333333333334, 841.6666666666667, 841.6666666666667, 842.5, 842.5, 843.3333333333334, 843.3333333333334, 844.1666666666667, 844.1666666666667, 845.0000000000001, 845.0000000000001, 845.8333333333334, 845.8333333333334, 846.6666666666666, 846.6666666666666, 847.5, 847.5, 848.3333333333334, 848.3333333333334, 849.1666666666666, 849.1666666666666, 850, 850, 850.8333333333334, 850.8333333333334, 851.6666666666667, 851.6666666666667, 852.5, 852.5, 853.3333333333334, 853.3333333333334, 854.1666666666667, 854.1666666666667, 855, 855, 855.8333333333334, 855.8333333333334, 856.6666666666667, 856.6666666666667, 857.5, 857.5, 858.3333333333333, 858.3333333333333, 859.1666666666666, 859.1666666666666, 860, 860, 860.8333333333334, 860.8333333333334, 861.6666666666666, 861.6666666666666, 862.5, 862.5, 863.3333333333334, 863.3333333333334, 864.1666666666667, 864.1666666666667, 865, 865, 865.8333333333334, 865.8333333333334, 866.6666666666667, 866.6666666666667, 867.5000000000001, 867.5000000000001, 868.3333333333333, 868.3333333333333, 869.1666666666666, 869.1666666666666, 870, 870, 870.8333333333334, 870.8333333333334, 871.6666666666666, 871.6666666666666, 872.5, 872.5, 873.3333333333334, 873.3333333333334, 874.1666666666667, 874.1666666666667, 875, 875, 875.8333333333334, 875.8333333333334, 876.6666666666667, 876.6666666666667, 877.5000000000001, 877.5000000000001, 878.3333333333334, 878.3333333333334, 879.1666666666666, 879.1666666666666, 880, 880, 880.8333333333333, 880.8333333333333, 881.6666666666666, 881.6666666666666, 882.5, 882.5, 883.3333333333334, 883.3333333333334, 884.1666666666666, 884.1666666666666, 885, 885, 885.8333333333334, 885.8333333333334, 886.6666666666667, 886.6666666666667, 887.5, 887.5, 888.3333333333334, 888.3333333333334, 889.1666666666667, 889.1666666666667, 890, 890, 890.8333333333333, 890.8333333333333, 891.6666666666666, 891.6666666666666, 892.5, 892.5, 893.3333333333334, 893.3333333333334, 894.1666666666666, 894.1666666666666, 895, 895, 895.8333333333334, 895.8333333333334, 896.6666666666667, 896.6666666666667, 897.5, 897.5, 898.3333333333334, 898.3333333333334, 899.1666666666667, 899.1666666666667, 900.0000000000001, 900.0000000000001, 900.8333333333334, 900.8333333333334, 901.6666666666666, 901.6666666666666, 902.5, 902.5, 903.3333333333334, 903.3333333333334, 904.1666666666666, 904.1666666666666, 905, 905, 905.8333333333334, 905.8333333333334, 906.6666666666667, 906.6666666666667, 907.5, 907.5, 908.3333333333334, 908.3333333333334, 909.1666666666667, 909.1666666666667, 910, 910, 910.8333333333334, 910.8333333333334, 911.6666666666667, 911.6666666666667, 912.5, 912.5, 913.3333333333333, 913.3333333333333, 914.1666666666666, 914.1666666666666, 915, 915, 915.8333333333334, 915.8333333333334, 916.6666666666666, 916.6666666666666, 917.5, 917.5, 918.3333333333334, 918.3333333333334, 919.1666666666667, 919.1666666666667, 920, 920, 920.8333333333334, 920.8333333333334, 921.6666666666667, 921.6666666666667, 922.5000000000001, 922.5000000000001, 923.3333333333334, 923.3333333333334, 924.1666666666666, 924.1666666666666, 925, 925, 925.8333333333334, 925.8333333333334, 926.6666666666666, 926.6666666666666, 927.5, 927.5, 928.3333333333334, 928.3333333333334, 929.1666666666667, 929.1666666666667, 930, 930, 930.8333333333334, 930.8333333333334, 931.6666666666667, 931.6666666666667, 932.5000000000001, 932.5000000000001, 933.3333333333334, 933.3333333333334, 934.1666666666667, 934.1666666666667, 935, 935, 935.8333333333333, 935.8333333333333, 936.6666666666666, 936.6666666666666, 937.5, 937.5, 938.3333333333334, 938.3333333333334, 939.1666666666666, 939.1666666666666, 940, 940, 940.8333333333334, 940.8333333333334, 941.6666666666667, 941.6666666666667, 942.5, 942.5, 943.3333333333334, 943.3333333333334, 944.1666666666667, 944.1666666666667, 945.0000000000001, 945.0000000000001, 945.8333333333333, 945.8333333333333, 946.6666666666666, 946.6666666666666, 947.5, 947.5, 948.3333333333334, 948.3333333333334, 949.1666666666666, 949.1666666666666, 950, 950, 950.8333333333334, 950.8333333333334, 951.6666666666667, 951.6666666666667, 952.5, 952.5, 953.3333333333334, 953.3333333333334, 954.1666666666667, 954.1666666666667, 955, 955, 955.8333333333335, 955.8333333333335, 956.6666666666666, 956.6666666666666, 957.4999999999999, 957.4999999999999, 958.3333333333334, 958.3333333333334, 959.1666666666666, 959.1666666666666, 960.0000000000001, 960.0000000000001, 960.8333333333334, 960.8333333333334, 961.6666666666666, 961.6666666666666, 962.5000000000001, 962.5000000000001, 963.3333333333334, 963.3333333333334, 964.1666666666666, 964.1666666666666, 965.0000000000001, 965.0000000000001, 965.8333333333334, 965.8333333333334, 966.6666666666666, 966.6666666666666, 967.5, 967.5, 968.3333333333333, 968.3333333333333, 969.1666666666667, 969.1666666666667, 970, 970, 970.8333333333333, 970.8333333333333, 971.6666666666667, 971.6666666666667, 972.5, 972.5, 973.3333333333333, 973.3333333333333, 974.1666666666667, 974.1666666666667, 975, 975, 975.8333333333335, 975.8333333333335, 976.6666666666667, 976.6666666666667, 977.5, 977.5, 978.3333333333334, 978.3333333333334, 979.1666666666666, 979.1666666666666, 979.9999999999999, 979.9999999999999, 980.8333333333334, 980.8333333333334, 981.6666666666666, 981.6666666666666, 982.5000000000001, 982.5000000000001, 983.3333333333334, 983.3333333333334, 984.1666666666666, 984.1666666666666, 985.0000000000001, 985.0000000000001, 985.8333333333334, 985.8333333333334, 986.6666666666666, 986.6666666666666, 987.5000000000001, 987.5000000000001, 988.3333333333334, 988.3333333333334, 989.1666666666666, 989.1666666666666, 990, 990, 990.8333333333333, 990.8333333333333, 991.6666666666667, 991.6666666666667, 992.5, 992.5, 993.3333333333333, 993.3333333333333, 994.1666666666667, 994.1666666666667, 995, 995, 995.8333333333333, 995.8333333333333, 996.6666666666667, 996.6666666666667, 997.5, 997.5, 998.3333333333335, 998.3333333333335, 999.1666666666667, 999.1666666666667, 1000, 1000, 1000.8333333333334, 1000.8333333333334, 1001.6666666666666, 1001.6666666666666, 1002.4999999999999, 1002.4999999999999, 1003.3333333333334, 1003.3333333333334, 1004.1666666666666, 1004.1666666666666, 1005.0000000000001, 1005.0000000000001, 1005.8333333333334, 1005.8333333333334, 1006.6666666666666, 1006.6666666666666, 1007.5000000000001, 1007.5000000000001, 1008.3333333333334, 1008.3333333333334, 1009.1666666666666, 1009.1666666666666, 1010.0000000000001, 1010.0000000000001, 1010.8333333333334, 1010.8333333333334, 1011.6666666666667, 1011.6666666666667, 1012.5, 1012.5, 1013.3333333333333, 1013.3333333333333, 1014.1666666666667, 1014.1666666666667, 1015, 1015, 1015.8333333333333, 1015.8333333333333, 1016.6666666666667, 1016.6666666666667, 1017.5, 1017.5, 1018.3333333333333, 1018.3333333333333, 1019.1666666666667, 1019.1666666666667, 1020, 1020, 1020.8333333333335, 1020.8333333333335, 1021.6666666666667, 1021.6666666666667, 1022.5, 1022.5, 1023.3333333333334, 1023.3333333333334, 1024.1666666666667, 1024.1666666666667, 1025, 1025, 1025.8333333333335, 1025.8333333333335, 1026.6666666666667, 1026.6666666666667, 1027.5000000000002, 1027.5000000000002, 1028.3333333333333, 1028.3333333333333, 1029.1666666666665, 1029.1666666666665, 1030, 1030, 1030.8333333333333, 1030.8333333333333, 1031.6666666666665, 1031.6666666666665, 1032.5, 1032.5, 1033.3333333333333, 1033.3333333333333, 1034.1666666666667, 1034.1666666666667, 1035, 1035, 1035.8333333333333, 1035.8333333333333, 1036.6666666666667, 1036.6666666666667, 1037.5, 1037.5, 1038.3333333333333, 1038.3333333333333, 1039.1666666666667, 1039.1666666666667, 1040, 1040, 1040.8333333333333, 1040.8333333333333, 1041.6666666666667, 1041.6666666666667, 1042.5, 1042.5, 1043.3333333333335, 1043.3333333333335, 1044.1666666666667, 1044.1666666666667, 1045, 1045, 1045.8333333333335, 1045.8333333333335, 1046.6666666666667, 1046.6666666666667, 1047.5, 1047.5, 1048.3333333333335, 1048.3333333333335, 1049.1666666666667, 1049.1666666666667, 1050, 1050, 1050.8333333333333, 1050.8333333333333, 1051.6666666666665, 1051.6666666666665, 1052.5, 1052.5, 1053.3333333333333, 1053.3333333333333, 1054.1666666666665, 1054.1666666666665, 1055, 1055, 1055.8333333333333, 1055.8333333333333, 1056.6666666666667, 1056.6666666666667, 1057.5, 1057.5, 1058.3333333333333, 1058.3333333333333, 1059.1666666666667, 1059.1666666666667, 1060, 1060, 1060.8333333333333, 1060.8333333333333, 1061.6666666666667, 1061.6666666666667, 1062.5, 1062.5, 1063.3333333333335, 1063.3333333333335, 1064.1666666666667, 1064.1666666666667, 1065, 1065, 1065.8333333333335, 1065.8333333333335, 1066.6666666666667, 1066.6666666666667, 1067.5, 1067.5, 1068.3333333333335, 1068.3333333333335, 1069.1666666666667, 1069.1666666666667, 1070, 1070, 1070.8333333333335, 1070.8333333333335, 1071.6666666666667, 1071.6666666666667, 1072.5, 1072.5, 1073.3333333333333, 1073.3333333333333, 1074.1666666666665, 1074.1666666666665, 1075, 1075, 1075.8333333333333, 1075.8333333333333, 1076.6666666666665, 1076.6666666666665, 1077.5, 1077.5, 1078.3333333333333, 1078.3333333333333, 1079.1666666666667, 1079.1666666666667, 1080, 1080, 1080.8333333333333, 1080.8333333333333, 1081.6666666666667, 1081.6666666666667, 1082.5, 1082.5, 1083.3333333333333, 1083.3333333333333, 1084.1666666666667, 1084.1666666666667, 1085, 1085, 1085.8333333333335, 1085.8333333333335, 1086.6666666666667, 1086.6666666666667, 1087.5, 1087.5, 1088.3333333333335, 1088.3333333333335, 1089.1666666666667, 1089.1666666666667, 1090, 1090, 1090.8333333333335, 1090.8333333333335, 1091.6666666666667, 1091.6666666666667, 1092.5000000000002, 1092.5000000000002, 1093.3333333333335, 1093.3333333333335, 1094.1666666666667, 1094.1666666666667, 1095, 1095, 1095.8333333333333, 1095.8333333333333, 1096.6666666666665, 1096.6666666666665, 1097.5, 1097.5, 1098.3333333333333, 1098.3333333333333, 1099.1666666666665, 1099.1666666666665, 1100 ] }, "y": { "type": "float", "values": [ 4, 4, 4.008973259578643, 4.008973259578643, 4.0228769159008335, 4.0228769159008335, 3.9999999164963085, 3.9999999164963085, 3.810601539422748, 3.810601539422748, 3.532352379871206, 3.532352379871206, 3.302043735269807, 3.302043735269807, 3.136194776613066, 3.136194776613066, 3.0345458086595984, 3.0345458086595984, 3.0001220852155135, 3.0001220852155135, 3.034192166109342, 3.034192166109342, 3.134080455785038, 3.134080455785038, 3.2929797322736354, 3.2929797322736354, 3.5000612723153353, 3.5000612723153353, 3.7412128109658083, 3.7412128109658083, 4.000000269144098, 4.000000269144098, 4.258787708980771, 4.258787708980771, 4.499939193855916, 4.499939193855916, 4.7070206483536765, 4.7070206483536765, 4.8659198133590555, 4.8659198133590555, 4.96580797320992, 4.96580797320992, 4.99987791478448, 4.99987791478448, 4.965807827833301, 4.965807827833301, 4.865919532513054, 4.865919532513054, 4.70702025117737, 4.70702025117737, 4.499938707416372, 4.499938707416372, 4.258818764372469, 4.258818764372469, 3.9999997074163773, 3.9999997074163773, 3.7411806703992663, 3.7411806703992663, 3.499999743236711, 3.499999743236711, 3.2928930077878276, 3.2928930077878276, 3.1339744460226218, 3.1339744460226218, 3.034074095460543, 3.034074095460543, 3.000000000000046, 3.000000000000046, 3.0340742529710916, 3.0340742529710916, 3.1339747503096915, 3.1339747503096915, 3.292893438114648, 3.292893438114648, 3.5000002702772766, 3.5000002702772766, 3.7411812582366326, 3.7411812582366326, 4.000000315990399, 4.000000315990399, 4.258819352209832, 4.258819352209832, 4.5000002770341485, 4.5000002770341485, 4.7071070087631774, 4.7071070087631774, 4.866025565680703, 4.866025565680703, 4.96592591059754, 4.96592591059754, 4.999999999999947, 4.999999999999947, 4.965925740970811, 4.965925740970811, 4.866025237986971, 4.866025237986971, 4.707106545334337, 4.707106545334337, 4.499999709451956, 4.499999709451956, 4.2588187191541556, 4.2588187191541556, 3.9999996606028247, 3.9999996606028247, 3.7411806251810704, 3.7411806251810704, 3.4999997026950913, 3.4999997026950913, 3.2928929746857385, 3.2928929746857385, 3.1339744226159154, 3.1339744226159154, 3.034074083344378, 3.034074083344378, 3.0000000000000617, 3.0000000000000617, 3.034074265087316, 3.034074265087316, 3.1339747737164245, 3.1339747737164245, 3.2928934712166784, 3.2928934712166784, 3.5000003108188134, 3.5000003108188134, 3.741181303454946, 3.741181303454946, 4.000000362803838, 4.000000362803838, 4.258819397428027, 4.258819397428027, 4.500000317575669, 4.500000317575669, 4.707107041865266, 4.707107041865266, 4.866025589087409, 4.866025589087409, 4.965925922713704, 4.965925922713704, 4.99999999999993, 4.99999999999993, 4.965925728854586, 4.965925728854586, 4.866025214580237, 4.866025214580237, 4.707106512232306, 4.707106512232306, 4.4999996689104185, 4.4999996689104185, 4.258818673935951, 4.258818673935951, 3.9999996137894995, 3.9999996137894995, 3.7411805799626556, 3.7411805799626556, 3.4999996621535714, 3.4999996621535714, 3.2928929415837307, 3.2928929415837307, 3.133974399209268, 3.133974399209268, 3.0340740712281558, 3.0340740712281558, 3.000000000000079, 3.000000000000079, 3.034074277203513, 3.034074277203513, 3.133974797123102, 3.133974797123102, 3.2928935043188714, 3.2928935043188714, 3.500000351360351, 3.500000351360351, 3.741181348673151, 3.741181348673151, 4.000000409617162, 4.000000409617162, 4.258819442646442, 4.258819442646442, 4.500000358117188, 4.500000358117188, 4.707107074967272, 4.707107074967272, 4.866025612494055, 4.866025612494055, 4.965925934829925, 4.965925934829925, 4.999999999999911, 4.999999999999911, 4.965925716738388, 4.965925716738388, 4.866025191173558, 4.866025191173558, 4.707106479130112, 4.707106479130112, 4.499999628368683, 4.499999628368683, 4.258818628717747, 4.258818628717747, 3.9999995669761748, 3.9999995669761748, 3.7411805347444607, 3.7411805347444607, 3.499999621611855, 3.499999621611855, 3.292892908481725, 3.292892908481725, 3.133974375802622, 3.133974375802622, 3.0340740591119943, 3.0340740591119943, 3.000000000000099, 3.000000000000099, 3.034074289319712, 3.034074289319712, 3.133974820529782, 3.133974820529782, 3.2928935374209054, 3.2928935374209054, 3.5000003919020863, 3.5000003919020863, 3.7411813938913556, 3.7411813938913556, 4.000000456430488, 4.000000456430488, 4.258819487864637, 4.258819487864637, 4.500000398658904, 4.500000398658904, 4.707107108069278, 4.707107108069278, 4.8660256359007, 4.8660256359007, 4.9659259469460855, 4.9659259469460855, 4.999999999999891, 4.999999999999891, 4.965925704622128, 4.965925704622128, 4.866025167766877, 4.866025167766877, 4.707106446028077, 4.707106446028077, 4.499999587827143, 4.499999587827143, 4.258818583499322, 4.258818583499322, 3.9999995201628495, 3.9999995201628495, 3.7411804895262666, 3.7411804895262666, 3.4999995810703375, 3.4999995810703375, 3.29289287537956, 3.29289287537956, 3.1339743523959784, 3.1339743523959784, 3.0340740469958356, 3.0340740469958356, 3.000000000000121, 3.000000000000121, 3.0340743014359726, 3.0340743014359726, 3.1339748439364636, 3.1339748439364636, 3.2928935705229403, 3.2928935705229403, 3.5000004324436262, 3.5000004324436262, 3.7411814391097806, 3.7411814391097806, 4.000000503243813, 4.000000503243813, 4.25881953308283, 4.25881953308283, 4.500000439200421, 4.500000439200421, 4.707107141171441, 4.707107141171441, 4.866025659307456, 4.866025659307456, 4.965925959062243, 4.965925959062243, 4.999999999999868, 4.999999999999868, 4.965925692505926, 4.965925692505926, 4.866025144360081, 4.866025144360081, 4.707106412926041, 4.707106412926041, 4.499999547285603, 4.499999547285603, 4.258818538281116, 4.258818538281116, 3.999999473349297, 3.999999473349297, 3.741180444308073, 3.741180444308073, 3.4999995405288207, 3.4999995405288207, 3.292892842277557, 3.292892842277557, 3.1339743289892232, 3.1339743289892232, 3.0340740348796786, 3.0340740348796786, 3.0000000000001448, 3.0000000000001448, 3.0340743135521757, 3.0340743135521757, 3.133974867343261, 3.133974867343261, 3.2928936036249774, 3.2928936036249774, 3.5000004729851675, 3.5000004729851675, 3.7411814843279867, 3.7411814843279867, 4.000000550057366, 4.000000550057366, 4.258819578301243, 4.258819578301243, 4.500000479741938, 4.500000479741938, 4.707107174273443, 4.707107174273443, 4.866025682714097, 4.866025682714097, 4.965925971178458, 4.965925971178458, 4.999999999999842, 4.999999999999842, 4.965925680389722, 4.965925680389722, 4.866025120953396, 4.866025120953396, 4.707106379823843, 4.707106379823843, 4.499999506744062, 4.499999506744062, 4.25881849306291, 4.25881849306291, 3.999999426535972, 3.999999426535972, 3.7411803990896604, 3.7411803990896604, 3.4999994999873048, 3.4999994999873048, 3.292892809175556, 3.292892809175556, 3.1339743055825835, 3.1339743055825835, 3.034074022763465, 3.034074022763465, 3.0000000000001714, 3.0000000000001714, 3.0340743256683815, 3.0340743256683815, 3.1339748907499465, 3.1339748907499465, 3.2928936367271766, 3.2928936367271766, 3.5000005135269063, 3.5000005135269063, 3.741181529546193, 3.741181529546193, 4.000000596870691, 4.000000596870691, 4.258819623519436, 4.258819623519436, 4.50000052028365, 4.50000052028365, 4.707107207375444, 4.707107207375444, 4.866025706120736, 4.866025706120736, 4.965925983294611, 4.965925983294611, 4.999999999999815, 4.999999999999815, 4.965925668273515, 4.965925668273515, 4.86602509754671, 4.86602509754671, 4.707106346721804, 4.707106346721804, 4.499999466202322, 4.499999466202322, 4.258818447844703, 4.258818447844703, 3.999999379722647, 3.999999379722647, 3.741180353871468, 3.741180353871468, 3.499999459445593, 3.499999459445593, 3.2928927760735562, 3.2928927760735562, 3.133974282175945, 3.133974282175945, 3.0340740106473123, 3.0340740106473123, 3.0000000000002, 3.0000000000002, 3.0340743377846477, 3.0340743377846477, 3.1339749141566338, 3.1339749141566338, 3.2928936698292164, 3.2928936698292164, 3.50000055406845, 3.50000055406845, 3.7411815747646204, 3.7411815747646204, 4.000000643684015, 4.000000643684015, 4.258819668737628, 4.258819668737628, 4.500000560825164, 4.500000560825164, 4.707107240477604, 4.707107240477604, 4.866025729527373, 4.866025729527373, 4.965925995410763, 4.965925995410763, 4.999999999999785, 4.999999999999785, 4.965925656157247, 4.965925656157247, 4.866025074140022, 4.866025074140022, 4.707106313619763, 4.707106313619763, 4.4999994256607785, 4.4999994256607785, 4.258818402626276, 4.258818402626276, 3.9999993329093217, 3.9999993329093217, 3.7411803086530564, 3.7411803086530564, 3.4999994189040793, 3.4999994189040793, 3.2928927429712367, 3.2928927429712367, 3.1339742587691957, 3.1339742587691957, 3.034073998531044, 3.034073998531044, 3.0000000000002305, 3.0000000000002305, 3.034074349900858, 3.034074349900858, 3.1339749375635506, 3.1339749375635506, 3.2928937029314187, 3.2928937029314187, 3.5000005946103876, 3.5000005946103876, 3.7411816199830477, 3.7411816199830477, 4.0000006904975685, 4.0000006904975685, 4.2588197139562585, 4.2588197139562585, 4.500000601366874, 4.500000601366874, 4.707107273579601, 4.707107273579601, 4.8660257529342354, 4.8660257529342354, 4.9659260075269716, 4.9659260075269716, 4.999999999999753, 4.999999999999753, 4.965925644040977, 4.965925644040977, 4.866025050733218, 4.866025050733218, 4.707106280517399, 4.707106280517399, 4.499999385119037, 4.499999385119037, 4.258818357408068, 4.258818357408068, 3.9999992860955422, 3.9999992860955422, 3.741180263434865, 3.741180263434865, 3.499999378362173, 3.499999378362173, 3.29289270986924, 3.29289270986924, 3.1339742353625613, 3.1339742353625613, 3.034073986414896, 3.034073986414896, 3.0000000000002633, 3.0000000000002633, 3.0340743620171873, 3.0340743620171873, 3.1339749609702414, 3.1339749609702414, 3.292893736033462, 3.292893736033462, 3.500000635151933, 3.500000635151933, 3.741181665201256, 3.741181665201256, 4.000000737310893, 4.000000737310893, 4.25881975917445, 4.25881975917445, 4.500000641908386, 4.500000641908386, 4.707107306681919, 4.707107306681919, 4.8660257763408685, 4.8660257763408685, 4.965926019643119, 4.965926019643119, 4.999999999999719, 4.999999999999719, 4.965925631924764, 4.965925631924764, 4.8660250273265255, 4.8660250273265255, 4.707106247415355, 4.707106247415355, 4.499999344577491, 4.499999344577491, 4.25881831218942, 4.25881831218942, 3.999999239282217, 3.999999239282217, 3.7411802182166745, 3.7411802182166745, 3.499999337820662, 3.499999337820662, 3.292892676767245, 3.292892676767245, 3.1339742119557013, 3.1339742119557013, 3.0340739742987495, 3.0340739742987495, 3.0000000000002984, 3.0000000000002984, 3.0340743741334015, 3.0340743741334015, 3.133974984376935, 3.133974984376935, 3.2928937691355062, 3.2928937691355062, 3.5000006756934794, 3.5000006756934794, 3.7411817104194647, 3.7411817104194647, 4.000000784124673, 4.000000784124673, 4.25881980439264, 4.25881980439264, 4.500000682449897, 4.500000682449897, 4.707107339783913, 4.707107339783913, 4.866025799747501, 4.866025799747501, 4.965926031759264, 4.965926031759264, 4.999999999999684, 4.999999999999684, 4.96592561980855, 4.96592561980855, 4.866025003919605, 4.866025003919605, 4.70710621431331, 4.70710621431331, 4.499999304035944, 4.499999304035944, 4.258818266971211, 4.258818266971211, 3.999999192468892, 3.999999192468892, 3.741180172998045, 3.741180172998045, 3.4999992972791514, 3.4999992972791514, 3.2928926436652515, 3.2928926436652515, 3.133974188549071, 3.133974188549071, 3.034073962182605, 3.034073962182605, 3.0000000000003357, 3.0000000000003357, 3.0340743862496176, 3.0340743862496176, 3.1339750077836297, 3.1339750077836297, 3.292893802237874, 3.292893802237874, 3.500000716235027, 3.500000716235027, 3.7411817556376743, 3.7411817556376743, 4.000000830937998, 4.000000830937998, 4.2588198496108305, 4.2588198496108305, 4.500000722991406, 4.500000722991406, 4.707107372885906, 4.707107372885906, 4.86602582315413, 4.86602582315413, 4.965926043875525, 4.965926043875525, 4.999999999999645, 4.999999999999645, 4.965925607692332, 4.965925607692332, 4.8660249805129085, 4.8660249805129085, 4.707106181211263, 4.707106181211263, 4.499999263494002, 4.499999263494002, 4.2588182217530015, 4.2588182217530015, 3.9999991456555666, 3.9999991456555666, 3.7411801277798555, 3.7411801277798555, 3.499999256737642, 3.499999256737642, 3.2928926105632597, 3.2928926105632597, 3.1339741651424426, 3.1339741651424426, 3.034073950066463, 3.034073950066463, 3.000000000000375, 3.000000000000375, 3.034074398365836, 3.034074398365836, 3.1339750311903263, 3.1339750311903263, 3.292893835339922, 3.292893835339922, 3.5000007567765756, 3.5000007567765756, 3.7411818008558844, 3.7411818008558844, 4.000000877751323, 4.000000877751323, 4.25881989482902, 4.25881989482902, 4.500000763533309, 4.500000763533309, 4.707107405987896, 4.707107405987896, 4.866025846560757, 4.866025846560757, 4.965926055991666, 4.965926055991666, 4.999999999999605, 4.999999999999605, 4.965925595575995, 4.965925595575995, 4.866024957106211, 4.866024957106211, 4.707106148109214, 4.707106148109214, 4.499999222952453, 4.499999222952453, 4.258818176534791, 4.258818176534791, 3.999999098842242, 3.999999098842242, 3.7411800825616663, 3.7411800825616663, 3.499999216196134, 3.499999216196134, 3.2928925774609477, 3.2928925774609477, 3.133974141735816, 3.133974141735816, 3.034073937950323, 3.034073937950323, 3.0000000000004166, 3.0000000000004166, 3.0340744104820563, 3.0340744104820563, 3.133975054597025, 3.133975054597025, 3.292893868441971, 3.292893868441971, 3.5000007973181253, 3.5000007973181253, 3.741181846074534, 3.741181846074534, 4.000000924564648, 4.000000924564648, 4.258819940047208, 4.258819940047208, 4.5000008040748165, 4.5000008040748165, 4.707107439089886, 4.707107439089886, 4.86602586996761, 4.86602586996761, 4.965926068107805, 4.965926068107805, 4.999999999999561, 4.999999999999561, 4.965925583459773, 4.965925583459773, 4.866024933699511, 4.866024933699511, 4.707106115007164, 4.707106115007164, 4.499999182410902, 4.499999182410902, 4.25881813131658, 4.25881813131658, 3.999999052028462, 3.999999052028462, 3.741180037343478, 3.741180037343478, 3.499999175654627, 3.499999175654627, 3.292892544358959, 3.292892544358959, 3.133974118329191, 3.133974118329191, 3.034073925834185, 3.034073925834185, 3.0000000000004605, 3.0000000000004605, 3.034074422598279, 3.034074422598279, 3.133975078003953, 3.133975078003953, 3.292893901544022, 3.292893901544022, 3.5000008378596763, 3.5000008378596763, 3.7411818912927455, 3.7411818912927455, 4.000000971377974, 4.000000971377974, 4.258819985265836, 4.258819985265836, 4.500000844616323, 4.500000844616323, 4.707107472191874, 4.707107472191874, 4.866025893374234, 4.866025893374234, 4.965926080223942, 4.965926080223942, 4.999999999999517, 4.999999999999517, 4.965925571343551, 4.965925571343551, 4.8660249102928095, 4.8660249102928095, 4.707106081904792, 4.707106081904792, 4.499999141869351, 4.499999141869351, 4.258818086098368, 4.258818086098368, 3.9999990052151366, 3.9999990052151366, 3.7411799921252897, 3.7411799921252897, 3.499999135113121, 3.499999135113121, 3.2928925112569716, 3.2928925112569716, 3.1339740949225683, 3.1339740949225683, 3.034073913717932, 3.034073913717932, 3.0000000000005063, 3.0000000000005063, 3.0340744347145034, 3.0340744347145034, 3.133975101410656, 3.133975101410656, 3.292893934646074, 3.292893934646074, 3.500000878401622, 3.500000878401622, 3.7411819365109573, 3.7411819365109573, 4.000001018191298, 4.000001018191298, 4.258820030484023, 4.258820030484023, 4.500000885157828, 4.500000885157828, 4.707107505293861, 4.707107505293861, 4.866025916780856, 4.866025916780856, 4.9659260923400765, 4.9659260923400765, 4.99999999999947, 4.99999999999947, 4.965925559227324, 4.965925559227324, 4.866024886886105, 4.866024886886105, 4.707106048802738, 4.707106048802738, 4.4999991013277985, 4.4999991013277985, 4.258818040880157, 4.258818040880157, 3.999998958401812, 3.999998958401812, 3.7411799469071028, 3.7411799469071028, 3.4999990945712227, 3.4999990945712227, 3.292892478154986, 3.292892478154986, 3.133974071515947, 3.133974071515947, 3.034073901601798, 3.034073901601798, 3.0000000000005547, 3.0000000000005547, 3.0340744468308483, 3.0340744468308483, 3.1339751248173604, 3.1339751248173604, 3.292893967748128, 3.292893967748128, 3.5000009189431753, 3.5000009189431753, 3.741181981729169, 3.741181981729169, 4.000001065004623, 4.000001065004623, 4.258820075702211, 4.258820075702211, 4.500000925699332, 4.500000925699332, 4.707107538396167, 4.707107538396167, 4.866025940187476, 4.866025940187476, 4.965926104456209, 4.965926104456209, 4.99999999999942, 4.99999999999942, 4.965925547111096, 4.965925547111096, 4.8660248634794, 4.8660248634794, 4.707106015700684, 4.707106015700684, 4.499999060786245, 4.499999060786245, 4.258817995661505, 4.258817995661505, 3.9999989115884866, 3.9999989115884866, 3.741179901688916, 3.741179901688916, 3.499999054029719, 3.499999054029719, 3.2928924450530017, 3.2928924450530017, 3.133974048109101, 3.133974048109101, 3.0340738894856667, 3.0340738894856667, 3.000000000000605, 3.000000000000605, 3.0340744589470767, 3.0340744589470767, 3.1339751482240668, 3.1339751482240668, 3.2928940008501835, 3.2928940008501835, 3.5000009594847294, 3.5000009594847294, 3.7411820269473823, 3.7411820269473823, 4.000001111818404, 4.000001111818404, 4.258820120920397, 4.258820120920397, 4.500000966240836, 4.500000966240836, 4.707107571498151, 4.707107571498151, 4.866025963594094, 4.866025963594094, 4.965926116572339, 4.965926116572339, 4.9999999999993685, 4.9999999999993685, 4.9659255349948666, 4.9659255349948666, 4.866024840072466, 4.866024840072466, 4.707105982598628, 4.707105982598628, 4.49999902024469, 4.49999902024469, 4.258817950443292, 4.258817950443292, 3.9999988647751614, 3.9999988647751614, 3.7411798564702905, 3.7411798564702905, 3.499999013488216, 3.499999013488216, 3.2928924119510192, 3.2928924119510192, 3.133974024702484, 3.133974024702484, 3.0340738773695373, 3.0340738773695373, 3.0000000000006577, 3.0000000000006577, 3.034074471063308, 3.034074471063308, 3.133975171630775, 3.133975171630775, 3.292894033952562, 3.292894033952562, 3.5000010000262844, 3.5000010000262844, 3.7411820721655955, 3.7411820721655955, 4.000001158631728, 4.000001158631728, 4.258820166138583, 4.258820166138583, 4.5000010067823375, 4.5000010067823375, 4.707107604600132, 4.707107604600132, 4.86602598700071, 4.86602598700071, 4.965926128688586, 4.965926128688586, 4.999999999999315, 4.999999999999315, 4.965925522878635, 4.965925522878635, 4.866024816665757, 4.866024816665757, 4.70710594949657, 4.70710594949657, 4.499998979702741, 4.499998979702741, 4.258817905225078, 4.258817905225078, 3.9999988179618367, 3.9999988179618367, 3.741179811252105, 3.741179811252105, 3.4999989729467145, 3.4999989729467145, 3.292892378849038, 3.292892378849038, 3.1339740012958686, 3.1339740012958686, 3.03407386525341, 3.03407386525341, 3.0000000000007123, 3.0000000000007123, 3.034074483179541, 3.034074483179541, 3.1339751950374852, 3.1339751950374852, 3.2928940670546205, 3.2928940670546205, 3.500001040567841, 3.500001040567841, 3.7411821173838096, 3.7411821173838096, 4.000001205445053, 4.000001205445053, 4.258820211356769, 4.258820211356769, 4.500001047324233, 4.500001047324233, 4.707107637702112, 4.707107637702112, 4.8660260104073245, 4.8660260104073245, 4.9659261408047115, 4.9659261408047115, 4.999999999999259, 4.999999999999259, 4.965925510762283, 4.965925510762283, 4.8660247932590455, 4.8660247932590455, 4.707105916394511, 4.707105916394511, 4.499998939161184, 4.499998939161184, 4.258817860006864, 4.258817860006864, 3.9999987711485114, 3.9999987711485114, 3.7411797660339197, 3.7411797660339197, 3.499998932405214, 3.499998932405214, 3.292892345746737, 3.292892345746737, 3.133973977889255, 3.133973977889255, 3.034073853137285, 3.034073853137285, 3.0000000000007696, 3.0000000000007696, 3.0340744952957763, 3.0340744952957763, 3.133975218444197, 3.133975218444197, 3.2928941001566807, 3.2928941001566807, 3.5000010811093984, 3.5000010811093984, 3.7411821626024637, 3.7411821626024637, 4.000001252258379, 4.000001252258379, 4.258820256574953, 4.258820256574953, 4.500001087865733, 4.500001087865733, 4.707107670804091, 4.707107670804091, 4.8660260338141645, 4.8660260338141645, 4.965926152920836, 4.965926152920836, 4.9999999999992015, 4.9999999999992015, 4.965925498646047, 4.965925498646047, 4.8660247698523325, 4.8660247698523325, 4.707105883292449, 4.707105883292449, 4.4999988986196255, 4.4999988986196255, 4.258817814788649, 4.258817814788649, 3.9999987243347315, 3.9999987243347315, 3.741179720815735, 3.741179720815735, 3.499998891863715, 3.499998891863715, 3.292892312644759, 3.292892312644759, 3.1339739544826437, 3.1339739544826437, 3.034073841021162, 3.034073841021162, 3.0000000000008287, 3.0000000000008287, 3.0340745074120132, 3.0340745074120132, 3.1339752418511386, 3.1339752418511386, 3.2928941332587423, 3.2928941332587423, 3.500001121650957, 3.500001121650957, 3.7411822078206787, 3.7411822078206787, 4.0000012990717035, 4.0000012990717035, 4.258820301793577, 4.258820301793577, 4.500001128407232, 4.500001128407232, 4.707107703906068, 4.707107703906068, 4.866026057220775, 4.866026057220775, 4.965926165036958, 4.965926165036958, 4.999999999999141, 4.999999999999141, 4.965925486529808, 4.965925486529808, 4.866024746445618, 4.866024746445618, 4.707105850190065, 4.707105850190065, 4.499998858078067, 4.499998858078067, 4.258817769570433, 4.258817769570433, 3.9999986775214067, 3.9999986775214067, 3.741179675597551, 3.741179675597551, 3.4999988513222164, 3.4999988513222164, 3.292892279542783, 3.292892279542783, 3.133973931076034, 3.133973931076034, 3.034073828905041, 3.034073828905041, 3.00000000000089, 3.00000000000089, 3.034074519528371, 3.034074519528371, 3.1339752652578543, 3.1339752652578543, 3.2928941663608056, 3.2928941663608056, 3.5000011621925164, 3.5000011621925164, 3.7411822530384553, 3.7411822530384553, 4.000001345885483, 4.000001345885483, 4.25882034701176, 4.25882034701176, 4.500001168948729, 4.500001168948729, 4.7071077370080445, 4.7071077370080445, 4.866026080627156, 4.866026080627156, 4.965926177153195, 4.965926177153195, 4.999999999999078, 4.999999999999078, 4.9659254744135675, 4.9659254744135675, 4.866024723038901, 4.866024723038901, 4.707105817088323, 4.707105817088323, 4.499998817536112, 4.499998817536112, 4.258817724351778, 4.258817724351778, 3.9999986307080815, 3.9999986307080815, 3.7411796303793676, 3.7411796303793676, 3.4999988107807196, 3.4999988107807196, 3.2928922464411294, 3.2928922464411294, 3.133973907669199, 3.133973907669199, 3.0340738167888044, 3.0340738167888044, 3.0000000000009535, 3.0000000000009535, 3.0340745316444946, 3.0340745316444946, 3.1339752886643444, 3.1339752886643444, 3.2928941994631917, 3.2928941994631917, 3.500001202734471, 3.500001202734471, 3.741182298257111, 3.741182298257111, 4.000001392698354, 4.000001392698354, 4.258820392229504, 4.258820392229504, 4.50000120949062, 4.50000120949062, 4.70710777011034, 4.70710777011034, 4.86602610403399, 4.86602610403399, 4.9659261892691955, 4.9659261892691955, 4.999999999999014, 4.999999999999014, 4.965925462297207, 4.965925462297207, 4.8660246996319545, 4.8660246996319545, 4.707105783985936, 4.707105783985936, 4.499998776994945, 4.499998776994945, 4.258817679134, 4.258817679134, 3.999998583895211, 3.999998583895211, 3.741179585160746, 3.741179585160746, 3.4999987702388298, 3.4999987702388298, 3.2928922133388343, 3.2928922133388343, 3.133973884262821, 3.133973884262821, 3.0340738046728055, 3.0340738046728055, 3.000000000001019, 3.000000000001019, 3.034074543760856, 3.034074543760856, 3.133975312071292, 3.133975312071292, 3.2928942325649366, 3.2928942325649366, 3.500001243275639, 3.500001243275639, 3.7411823434757667, 3.7411823434757667, 4.0000014395121335, 4.0000014395121335, 4.258820437448126, 4.258820437448126, 4.500001250031721, 4.500001250031721, 4.707107803211991, 4.707107803211991, 4.866026127440822, 4.866026127440822, 4.965926201385429, 4.965926201385429, 4.999999999998947, 4.999999999998947, 4.96592545018108, 4.96592545018108, 4.866024676225462, 4.866024676225462, 4.707105750884191, 4.707105750884191, 4.499998736452989, 4.499998736452989, 4.258817633915344, 4.258817633915344, 3.9999985370814315, 3.9999985370814315, 3.7411795399430026, 3.7411795399430026, 3.4999987296977286, 3.4999987296977286, 3.2928921802365414, 3.2928921802365414, 3.1339738608559893, 3.1339738608559893, 3.0340737925565735, 3.0340737925565735, 3.000000000001087, 3.000000000001087, 3.034074555876984, 3.034074555876984, 3.1339753354782403, 3.1339753354782403, 3.292894265667326, 3.292894265667326, 3.500001283817596, 3.500001283817596, 3.741182388693545, 3.741182388693545, 4.0000014863250035, 4.0000014863250035, 4.258820482666747, 4.258820482666747, 4.50000129057361, 4.50000129057361, 4.707107836314283, 4.707107836314283, 4.866026150847198, 4.866026150847198, 4.965926213501424, 4.965926213501424, 4.999999999998878, 4.999999999998878, 4.9659254380647155, 4.9659254380647155, 4.866024652818512, 4.866024652818512, 4.7071057177818005, 4.7071057177818005, 4.4999986959118194, 4.4999986959118194, 4.258817588697566, 4.258817588697566, 3.9999984902676515, 3.9999984902676515, 3.7411794947243817, 3.7411794947243817, 3.4999986891558406, 3.4999986891558406, 3.2928921471348924, 3.2928921471348924, 3.1339738374496147, 3.1339738374496147, 3.034073780440343, 3.034073780440343, 3.0000000000011573, 3.0000000000011573, 3.0340745679933496, 3.0340745679933496, 3.1339753588847366, 3.1339753588847366, 3.292894298769074, 3.292894298769074, 3.5000013243595536, 3.5000013243595536, 3.741182433912202, 3.741182433912202, 4.000001533138784, 4.000001533138784, 4.258820527884489, 4.258820527884489, 4.500001331114709, 4.500001331114709, 4.707107869415931, 4.707107869415931, 4.866026174254026, 4.866026174254026, 4.965926225617654, 4.965926225617654, 4.999999999998806, 4.999999999998806, 4.965925425948584, 4.965925425948584, 4.866024629412014, 4.866024629412014, 4.707105684679409, 4.707105684679409, 4.499998655369861, 4.499998655369861, 4.258817543478909, 4.258817543478909, 3.999998443454781, 3.999998443454781, 3.7411794495066397, 3.7411794495066397, 3.4999986486139543, 3.4999986486139543, 3.292892114032602, 3.292892114032602, 3.1339738140427875, 3.1339738140427875, 3.0340737683243506, 3.0340737683243506, 3.0000000000012297, 3.0000000000012297, 3.034074580109717, 3.034074580109717, 3.1339753822916894, 3.1339753822916894, 3.2928943318714663, 3.2928943318714663, 3.500001364900725, 3.500001364900725, 3.7411824791299813, 3.7411824791299813, 4.000001579951654, 4.000001579951654, 4.258820573103109, 4.258820573103109, 4.5000013716565945, 4.5000013716565945, 4.707107902518221, 4.707107902518221, 4.866026197660398, 4.866026197660398, 4.965926237733645, 4.965926237733645, 4.9999999999987335, 4.9999999999987335, 4.965925413832216, 4.965925413832216, 4.866024606005061, 4.866024606005061, 4.707105651577659, 4.707105651577659, 4.499998614828689, 4.499998614828689, 4.2588174982602505, 4.2588174982602505, 3.9999983966410015, 3.9999983966410015, 3.74117940428802, 3.74117940428802, 3.4999986080728562, 3.4999986080728562, 3.2928920809309568, 3.2928920809309568, 3.133973790635962, 3.133973790635962, 3.034073756208125, 3.034073756208125, 3.0000000000013043, 3.0000000000013043, 3.034074592225852, 3.034074592225852, 3.133975405698189, 3.133975405698189, 3.2928943649732174, 3.2928943649732174, 3.500001405442685, 3.500001405442685, 3.74118252434864, 3.74118252434864, 4.0000016267654335, 4.0000016267654335, 4.258820618320851, 4.258820618320851, 4.500001412197692, 4.500001412197692, 4.707107935620509, 4.707107935620509, 4.866026221067223, 4.866026221067223, 4.96592624984987, 4.96592624984987, 4.999999999998658, 4.999999999998658, 4.96592540171608, 4.96592540171608, 4.866024582598105, 4.866024582598105, 4.707105618475263, 4.707105618475263, 4.499998574286729, 4.499998574286729, 4.25881745304247, 4.25881745304247, 3.999998349828131, 3.999998349828131, 3.7411793590694007, 3.7411793590694007, 3.4999985675309717, 3.4999985675309717, 3.2928920478286696, 3.2928920478286696, 3.133973767229593, 3.133973767229593, 3.034073744092136, 3.034073744092136, 3.000000000001381, 3.000000000001381, 3.0340746043422238, 3.0340746043422238, 3.1339754291051456, 3.1339754291051456, 3.292894398075613, 3.292894398075613, 3.5000014459838584, 3.5000014459838584, 3.74118256956642, 3.74118256956642, 4.000001673579214, 4.000001673579214, 4.258820663539469, 4.258820663539469, 4.500001452739576, 4.500001452739576, 4.707107968722152, 4.707107968722152, 4.8660262444735904, 4.8660262444735904, 4.965926261966093, 4.965926261966093, 4.99999999999858, 4.99999999999858, 4.9659253895997075, 4.9659253895997075, 4.866024559191603, 4.866024559191603, 4.70710558537351, 4.70710558537351, 4.499998533744767, 4.499998533744767, 4.258817407823811, 4.258817407823811, 3.999998303014351, 3.999998303014351, 3.741179313851661, 3.741179313851661, 3.4999985269898763, 3.4999985269898763, 3.292892014727027, 3.292892014727027, 3.1339737438227715, 3.1339737438227715, 3.0340737319759143, 3.0340737319759143, 3.0000000000014597, 3.0000000000014597, 3.0340746164583625, 3.0340746164583625, 3.133975452511649, 3.133975452511649, 3.29289443117801, 3.29289443117801, 3.50000148652582, 3.50000148652582, 3.74118261478508, 3.74118261478508, 4.000001720392084, 4.000001720392084, 4.258820708757209, 4.258820708757209, 4.500001493281459, 4.500001493281459, 4.7071080018244364, 4.7071080018244364, 4.866026267880412, 4.866026267880412, 4.9659262740820775, 4.9659262740820775, 4.9999999999985, 4.9999999999985, 4.965925377483332, 4.965925377483332, 4.866024535784644, 4.866024535784644, 4.707105552271113, 4.707105552271113, 4.4999984932035915, 4.4999984932035915, 4.258817362606029, 4.258817362606029, 3.9999982562014806, 3.9999982562014806, 3.7411792686330427, 3.7411792686330427, 3.499998486447994, 3.499998486447994, 3.2928919816247433, 3.2928919816247433, 3.133973720416406, 3.133973720416406, 3.03407371985993, 3.03407371985993, 3.000000000001541, 3.000000000001541, 3.0340746285747384, 3.0340746285747384, 3.13397547591861, 3.13397547591861, 3.2928944642797657, 3.2928944642797657, 3.500001527066996, 3.500001527066996, 3.74118266000374, 3.74118266000374, 4.000001767205863, 4.000001767205863, 4.258820753975827, 4.258820753975827, 4.500001533822553, 4.500001533822553, 4.707108034926077, 4.707108034926077, 4.8660262912872305, 4.8660262912872305, 4.965926286198297, 4.965926286198297, 4.999999999998417, 4.999999999998417, 4.96592536536719, 4.96592536536719, 4.866024512378137, 4.866024512378137, 4.707105519169356, 4.707105519169356, 4.499998452661628, 4.499998452661628, 4.258817317387369, 4.258817317387369, 3.999998209387701, 3.999998209387701, 3.741179223415304, 3.741179223415304, 3.4999984459069005, 3.4999984459069005, 3.2928919485224606, 3.2928919485224606, 3.1339736970095884, 3.1339736970095884, 3.0340737077437128, 3.0340737077437128, 3.000000000001624, 3.000000000001624, 3.0340746406908816, 3.0340746406908816, 3.1339754993255715, 3.1339754993255715, 3.292894497382166, 3.292894497382166, 3.5000015676089604, 3.5000015676089604, 3.741182705221522, 3.741182705221522, 4.000001814018734, 4.000001814018734, 4.258820799194444, 4.258820799194444, 4.5000015743644335, 4.5000015743644335, 4.707108068028359, 4.707108068028359, 4.866026314693593, 4.866026314693593, 4.965926298314278, 4.965926298314278, 4.999999999998334, 4.999999999998334, 4.96592535325081, 4.96592535325081, 4.866024488971174, 4.866024488971174, 4.707105486066955, 4.707105486066955, 4.49999841212045, 4.49999841212045, 4.258817272169587, 4.258817272169587, 3.999998162573921, 3.999998162573921, 3.7411791781966865, 3.7411791781966865, 3.4999984053650204, 3.4999984053650204, 3.2928919154208227, 3.2928919154208227, 3.1339736736032267, 3.1339736736032267, 3.0340736956274976, 3.0340736956274976, 3.0000000000017097, 3.0000000000017097, 3.034074652807262, 3.034074652807262, 3.133975522732081, 3.133975522732081, 3.2928945304839243, 3.2928945304839243, 3.500001608150926, 3.500001608150926, 3.741182750440183, 3.741182750440183, 4.000001860832514, 4.000001860832514, 4.258820844412182, 4.258820844412182, 4.500001614905525, 4.500001614905525, 4.707108101129996, 4.707108101129996, 4.866026338100408, 4.866026338100408, 4.965926310430492, 4.965926310430492, 4.999999999998247, 4.999999999998247, 4.965925341134665, 4.965925341134665, 4.866024465564664, 4.866024465564664, 4.707105452964552, 4.707105452964552, 4.499998371578485, 4.499998371578485, 4.258817226950925, 4.258817226950925, 3.9999981157610507, 3.9999981157610507, 3.741179132978949, 3.741179132978949, 3.4999983648231416, 3.4999983648231416, 3.2928918823185436, 3.2928918823185436, 3.133973650196413, 3.133973650196413, 3.0340736835115196, 3.0340736835115196, 3.0000000000017972, 3.0000000000017972, 3.0340746649236445, 3.0340746649236445, 3.1339755461390473, 3.1339755461390473, 3.292894563586328, 3.292894563586328, 3.500001648692105, 3.500001648692105, 3.7411827956579664, 3.7411827956579664, 4.000001907645384, 4.000001907645384, 4.258820889630798, 4.258820889630798, 4.500001655447404, 4.500001655447404, 4.707108134232274, 4.707108134232274, 4.8660263615067665, 4.8660263615067665, 4.965926322546468, 4.965926322546468, 4.999999999998158, 4.999999999998158, 4.965925329018281, 4.965925329018281, 4.866024442157697, 4.866024442157697, 4.707105419862792, 4.707105419862792, 4.499998331037305, 4.499998331037305, 4.2588171817322635, 4.2588171817322635, 3.999998068947271, 3.999998068947271, 3.741179087760333, 3.741179087760333, 3.4999983242820516, 3.4999983242820516, 3.292891849216909, 3.292891849216909, 3.133973626789601, 3.133973626789601, 3.0340736713953085, 3.0340736713953085, 3.0000000000018874, 3.0000000000018874, 3.034074677039794, 3.034074677039794, 3.1339755695455604, 3.1339755695455604, 3.2928945966880896, 3.2928945966880896, 3.5000016892340726, 3.5000016892340726, 3.7411828408766286, 3.7411828408766286, 4.000001954459164, 4.000001954459164, 4.2588209348485355, 4.2588209348485355, 4.500001695988494, 4.500001695988494, 4.707108167334551, 4.707108167334551, 4.866026384913577, 4.866026384913577, 4.9659263346626785, 4.9659263346626785, 4.999999999998067, 4.999999999998067, 4.965925316902131, 4.965925316902131, 4.866024418750728, 4.866024418750728, 4.707105386760386, 4.707105386760386, 4.499998290495337, 4.499998290495337, 4.258817136514479, 4.258817136514479, 3.9999980221344007, 3.9999980221344007, 3.741179042541718, 3.741179042541718, 3.4999982837401746, 3.4999982837401746, 3.2928918161146323, 3.2928918161146323, 3.133973603383245, 3.133973603383245, 3.034073659279335, 3.034073659279335, 3.0000000000019793, 3.0000000000019793, 3.0340746891561805, 3.0340746891561805, 3.13397559295253, 3.13397559295253, 3.292894629790496, 3.292894629790496, 3.5000017297752537, 3.5000017297752537, 3.741182886094413, 3.741182886094413, 4.000002001272944, 4.000002001272944, 4.25882098006715, 4.25882098006715, 4.500001736530369, 4.500001736530369, 4.707108200436183, 4.707108200436183, 4.866026408319932, 4.866026408319932, 4.965926346778886, 4.965926346778886, 4.999999999997974, 4.999999999997974, 4.965925304785743, 4.965925304785743, 4.866024395344212, 4.866024395344212, 4.707105353658622, 4.707105353658622, 4.499998249953368, 4.499998249953368, 4.258817091295816, 4.258817091295816, 3.9999979753206207, 3.9999979753206207, 3.7411789973239817, 3.7411789973239817, 3.4999982431990864, 3.4999982431990864, 3.2928917830130007, 3.2928917830130007, 3.1339735799764363, 3.1339735799764363, 3.034073647163128, 3.034073647163128, 3.0000000000020735, 3.0000000000020735, 3.034074701272334, 3.034074701272334, 3.133975616359047, 3.133975616359047, 3.292894662892904, 3.292894662892904, 3.5000017703172235, 3.5000017703172235, 3.7411829313130767, 3.7411829313130767, 4.000002048085814, 4.000002048085814, 4.258821025284886, 4.258821025284886, 4.500001777072245, 4.500001777072245, 4.707108233538458, 4.707108233538458, 4.866026431726739, 4.866026431726739, 4.965926358894857, 4.965926358894857, 4.999999999997879, 4.999999999997879, 4.965925292669353, 4.965925292669353, 4.86602437193724, 4.86602437193724, 4.707105320556213, 4.707105320556213, 4.499998209412185, 4.499998209412185, 4.258817046078031, 4.258817046078031, 3.9999979285077507, 3.9999979285077507, 3.7411789521053676, 3.7411789521053676, 3.499998202657212, 3.499998202657212, 3.2928917499107278, 3.2928917499107278, 3.1339735565700844, 3.1339735565700844, 3.034073635047159, 3.034073635047159, 3.00000000000217, 3.00000000000217, 3.0340747133887254, 3.0340747133887254, 3.1339756397660206, 3.1339756397660206, 3.2928946959946703, 3.2928946959946703, 3.500001810858407, 3.500001810858407, 3.7411829765317406, 3.7411829765317406, 4.000002094899594, 4.000002094899594, 4.2588210705035, 4.2588210705035, 4.500001817613331, 4.500001817613331, 4.707108266640087, 4.707108266640087, 4.866026455133545, 4.866026455133545, 4.965926371011061, 4.965926371011061, 4.999999999997781, 4.999999999997781, 4.965925280553196, 4.965925280553196, 4.866024348530719, 4.866024348530719, 4.7071052874544455, 4.7071052874544455, 4.4999981688702135, 4.4999981688702135, 4.258817000859366, 4.258817000859366, 3.9999978816939707, 3.9999978816939707, 3.741178906887632, 3.741178906887632, 3.4999981621161265, 3.4999981621161265, 3.292891716808456, 3.292891716808456, 3.13397353316328, 3.13397353316328, 3.0340736229309564, 3.0340736229309564, 3.0000000000022684, 3.0000000000022684, 3.0340747255048828, 3.0340747255048828, 3.133975663172996, 3.133975663172996, 3.292894729097082, 3.292894729097082, 3.500001851400379, 3.500001851400379, 3.7411830217495265, 3.7411830217495265, 4.000002141712464, 4.000002141712464, 4.258821115722113, 4.258821115722113, 4.500001858155204, 4.500001858155204, 4.707108299742358, 4.707108299742358, 4.866026478539895, 4.866026478539895, 4.965926383127027, 4.965926383127027, 4.999999999997682, 4.999999999997682, 4.965925268436802, 4.965925268436802, 4.8660243251237425, 4.8660243251237425, 4.707105254352034, 4.707105254352034, 4.499998128329029, 4.499998128329029, 4.2588169556415805, 4.2588169556415805, 3.999997834880191, 3.999997834880191, 3.7411788616690194, 3.7411788616690194, 3.499998121574254, 3.499998121574254, 3.292891683706829, 3.292891683706829, 3.133973509756932, 3.133973509756932, 3.034073610814756, 3.034073610814756, 3.000000000002369, 3.000000000002369, 3.034074737621278, 3.034074737621278, 3.1339756865795185, 3.1339756865795185, 3.292894762198851, 3.292894762198851, 3.500001891942352, 3.500001891942352, 3.741183066968192, 3.741183066968192, 4.000002188526245, 4.000002188526245, 4.258821160939847, 4.258821160939847, 4.500001898696288, 4.500001898696288, 4.707108332843983, 4.707108332843983, 4.866026501946696, 4.866026501946696, 4.965926395243226, 4.965926395243226, 4.99999999999758, 4.99999999999758, 4.9659252563206415, 4.9659252563206415, 4.86602430171722, 4.86602430171722, 4.707105221249621, 4.707105221249621, 4.499998087787055, 4.499998087787055, 4.258816910422914, 4.258816910422914, 3.9999977880673208, 3.9999977880673208, 3.7411788164512854, 3.7411788164512854, 3.4999980810323827, 3.4999980810323827, 3.2928916506045605, 3.2928916506045605, 3.133973486350131, 3.133973486350131, 3.034073598698793, 3.034073598698793, 3.0000000000024722, 3.0000000000024722, 3.0340747497376754, 3.0340747497376754, 3.133975709986498, 3.133975709986498, 3.2928947953012653, 3.2928947953012653, 3.5000019324835385, 3.5000019324835385, 3.741183112185979, 3.741183112185979, 4.000002235339115, 4.000002235339115, 4.258821206158459, 4.258821206158459, 4.5000019392381585, 4.5000019392381585, 4.707108365946252, 4.707108365946252, 4.866026525353041, 4.866026525353041, 4.965926407359188, 4.965926407359188, 4.999999999997476, 4.999999999997476, 4.965925244204243, 4.965925244204243, 4.866024278310239, 4.866024278310239, 4.707105188147849, 4.707105188147849, 4.499998047245867, 4.499998047245867, 4.258816865204248, 4.258816865204248, 3.999997741253541, 3.999997741253541, 3.741178771232674, 3.741178771232674, 3.4999980404913003, 3.4999980404913003, 3.292891617502937, 3.292891617502937, 3.1339734629433322, 3.1339734629433322, 3.0340735865825965, 3.0340735865825965, 3.0000000000025775, 3.0000000000025775, 3.0340747618538395, 3.0340747618538395, 3.133975733393024, 3.133975733393024, 3.292894828403038, 3.292894828403038, 3.500001973025514, 3.500001973025514, 3.7411831574046452, 3.7411831574046452, 4.000002282152894, 4.000002282152894, 4.258821251376193, 4.258821251376193, 4.500001979779241, 4.500001979779241, 4.707108399048518, 4.707108399048518, 4.866026548759839, 4.866026548759839, 4.965926419475383, 4.965926419475383, 4.999999999997369, 4.999999999997369, 4.965925232088077, 4.965925232088077, 4.866024254903257, 4.866024254903257, 4.707105155045432, 4.707105155045432, 4.499998006703891, 4.499998006703891, 4.2588168199864604, 4.2588168199864604, 3.9999976944406703, 3.9999976944406703, 3.7411787260140623, 3.7411787260140623, 3.4999979999494313, 3.4999979999494313, 3.292891584400671, 3.292891584400671, 3.13397343953699, 3.13397343953699, 3.0340735744666376, 3.0340735744666376, 3.000000000002685, 3.000000000002685, 3.034074773970241, 3.034074773970241, 3.133975756800007, 3.133975756800007, 3.2928948615054554, 3.2928948615054554, 3.5000020135667027, 3.5000020135667027, 3.741183202622434, 3.741183202622434, 4.000002328966675, 4.000002328966675, 4.258821296594804, 4.258821296594804, 4.5000020203211095, 4.5000020203211095, 4.7071084321501395, 4.7071084321501395, 4.866026572166181, 4.866026572166181, 4.965926431591576, 4.965926431591576, 4.999999999997261, 4.999999999997261, 4.965925219971675, 4.965925219971675, 4.866024231496728, 4.866024231496728, 4.707105121943657, 4.707105121943657, 4.4999979661619145, 4.4999979661619145, 4.258816774767793, 4.258816774767793, 3.999997647626891, 3.999997647626891, 3.74117868079633, 3.74117868079633, 3.4999979594083506, 3.4999979594083506, 3.2928915512990504, 3.2928915512990504, 3.1339734161301944, 3.1339734161301944, 3.0340735623504456, 3.0340735623504456, 3.000000000002794, 3.000000000002794, 3.0340747860864097, 3.0340747860864097, 3.1339757802065376, 3.1339757802065376, 3.292894894607874, 3.292894894607874, 3.5000020541086805, 3.5000020541086805, 3.7411832478411013, 3.7411832478411013, 4.000002375779545, 4.000002375779545, 4.258821341812536, 4.258821341812536, 4.500002060862977, 4.500002060862977, 4.707108465252403, 4.707108465252403, 4.866026595572975, 4.866026595572975, 4.965926443707532, 4.965926443707532, 4.99999999999715, 4.99999999999715, 4.96592520785527, 4.96592520785527, 4.866024208089742, 4.866024208089742, 4.707105088841238, 4.707105088841238, 4.4999979256207245, 4.4999979256207245, 4.258816729550004, 4.258816729550004, 3.9999976008140203, 3.9999976008140203, 3.74117863557772, 3.74117863557772, 3.499997918866484, 3.499997918866484, 3.292891518196788, 3.292891518196788, 3.133973392723856, 3.133973392723856, 3.034073550234491, 3.034073550234491, 3.000000000002906, 3.000000000002906, 3.0340747982028153, 3.0340747982028153, 3.1339758036135246, 3.1339758036135246, 3.2928949277096513, 3.2928949277096513, 3.5000020946498713, 3.5000020946498713, 3.7411832930597693, 3.7411832930597693, 4.000002422593324, 4.000002422593324, 4.258821387031146, 4.258821387031146, 4.500002101404055, 4.500002101404055, 4.707108498354021, 4.707108498354021, 4.866026618979767, 4.866026618979767, 4.965926455823721, 4.965926455823721, 4.999999999997037, 4.999999999997037, 4.965925195739098, 4.965925195739098, 4.866024184683209, 4.866024184683209, 4.70710505573946, 4.70710505573946, 4.499997885078745, 4.499997885078745, 4.258816684331336, 4.258816684331336, 3.9999975540002404, 3.9999975540002404, 3.741178590359989, 3.741178590359989, 3.4999978783254058, 3.4999978783254058, 3.2928914850945272, 3.2928914850945272, 3.1339733693170646, 3.1339733693170646, 3.0340735381183035, 3.0340735381183035, 3.0000000000030203, 3.0000000000030203, 3.034074810318988, 3.034074810318988, 3.133975827020513, 3.133975827020513, 3.2928949608120734, 3.2928949608120734, 3.5000021351918513, 3.5000021351918513, 3.7411833382775592, 3.7411833382775592, 4.000002469406195, 4.000002469406195, 4.258821432249755, 4.258821432249755, 4.50000214194592, 4.50000214194592, 4.707108531456281, 4.707108531456281, 4.866026642386103, 4.866026642386103, 4.965926467939672, 4.965926467939672, 4.999999999996922, 4.999999999996922, 4.96592518362269, 4.96592518362269, 4.866024161276219, 4.866024161276219, 4.707105022637037, 4.707105022637037, 4.499997844537552, 4.499997844537552, 4.258816639113546, 4.258816639113546, 3.999997507186461, 3.999997507186461, 3.7411785451413797, 3.7411785451413797, 3.4999978377835412, 3.4999978377835412, 3.292891451992911, 3.292891451992911, 3.13397334591073, 3.13397334591073, 3.0340735260021177, 3.0340735260021177, 3.000000000003136, 3.000000000003136, 3.034074822435398, 3.034074822435398, 3.133975850427049, 3.133975850427049, 3.292894993913854, 3.292894993913854, 3.500002175733832, 3.500002175733832, 3.7411833834962285, 3.7411833834962285, 4.000002516219975, 4.000002516219975, 4.258821477467485, 4.258821477467485, 4.500002182486997, 4.500002182486997, 4.707108564557896, 4.707108564557896, 4.866026665792892, 4.866026665792892, 4.965926480055857, 4.965926480055857, 4.999999999996804, 4.999999999996804, 4.965925171506514, 4.965925171506514, 4.866024137869682, 4.866024137869682, 4.707018662469598, 4.707018662469598, 4.49993676165593, 4.49993676165593, 4.25878499621524, 4.25878499621524, 3.999997460683641, 3.999997460683641, 3.7412100982022576, 3.7412100982022576, 3.500058840118342, 3.500058840118342, 3.292977746394146, 3.292977746394146, 3.1340790515574763, 3.1340790515574763, 3.0341914392293647, 3.0341914392293647, 3.0001220852187345, 3.0001220852187345, 3.034192759733772, 3.034192759733772, 3.134081602576096, 3.134081602576096, 3.2929813540792727, 3.2929813540792727, 3.500063258612192, 3.500063258612192, 3.7412150263908948, 3.7412150263908948, 4.000002562719937, 4.000002562719937, 4.258789924404725, 4.258789924404725, 4.499758051512304, 4.499758051512304, 4.705295720044372, 4.705295720044372, 4.854819409373181, 4.854819409373181, 4.90342605988624, 4.90342605988624, 4.731778781587124, 4.731778781587124, 4.328185779341063, 4.328185779341063, 4.076547608212793, 4.076547608212793, 4.012690077963085, 4.012690077963085 ] } } }, "d6a9f12103894fce9f96e7f361f02fd6": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "FigureModel", "state": { "_dom_classes": [], "animation_duration": 50, "axes": [ "IPY_MODEL_0768a1a3181042fe8da6b4bbce422b65", "IPY_MODEL_cf77b58bb11047bd8b52e3f2f5a3d800" ], "layout": "IPY_MODEL_45c2edcf6a1b4345967c253305b43987", "marks": [ "IPY_MODEL_fce5b8ea17d24b178bd55a3ebd2afa00", "IPY_MODEL_3a4cc4ad73e4436f9cb271dae2abec40", "IPY_MODEL_cacd7716181840f9a418289dbbd4eef4", "IPY_MODEL_68a3baf2f96b4169a405aa8ad5b8412c", "IPY_MODEL_2b4e49a2d49b48e3bce000e83485de3a" ], "scale_x": "IPY_MODEL_f38f74461f534092845c7df194e683d8", "scale_y": "IPY_MODEL_5122e806f7fb48a3b1d902506f58363c", "title": "Waveform Plotter" } }, "d7ab204b03e24cfa8d4159f02ba14287": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "AxisModel", "state": { "label": "Amplitude", "orientation": "vertical", "scale": "IPY_MODEL_a71a67a73d17471581bd9699c6803d86", "side": "left", "tick_values": { "type": null, "values": null } } }, "d829e29c146e4ece8f6f5b8c4f12b6b9": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "AxisModel", "state": { "label": "Time (ns)", "scale": "IPY_MODEL_8342a76ecdda4fb9a4e600d8ab8fc329", "side": "bottom", "tick_values": { "type": null, "values": null } } }, "da91096284d444d693d75d54a5f347ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": { "min_width": "125px" } }, "db86edeb23c44827acf5b0a480f1dd2e": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#9467bd" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS2_m1" ], "scales": { "x": "IPY_MODEL_8342a76ecdda4fb9a4e600d8ab8fc329", "y": "IPY_MODEL_30d949e7ba5846e89f696eade5e275c3" }, "selected": [], "tooltip": "IPY_MODEL_de89493a5a54462aa3afafdeebcc5adf", "x": { "type": "float", "values": [ 0, 100, 100, 200, 200, 1100 ] }, "y": { "type": "float", "values": [ 8, 8, 9, 9, 8, 8 ] } } }, "ddd19c8b9e194e08a9942e939bec829a": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "FigureModel", "state": { "_dom_classes": [], "axes": [ "IPY_MODEL_9cf9e49671af4768939f815ee6f2f6de", "IPY_MODEL_add375242ff64f41ab228c922b92d020" ], "layout": "IPY_MODEL_da91096284d444d693d75d54a5f347ca", "marks": [ "IPY_MODEL_e33fcee4a35d4da99148870babdbd871", "IPY_MODEL_0c7c37af1acc42ba9126e35a13d87988" ], "scale_x": "IPY_MODEL_0490ccadaa934d6d9a6882708736e7ef", "scale_y": "IPY_MODEL_96231612b10340089312f47857d3890c" } }, "de89493a5a54462aa3afafdeebcc5adf": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "TooltipModel", "state": { "fields": [ "name" ], "labels": [ "Channel" ], "layout": "IPY_MODEL_7a62c17a55544d42920816325e1a1959" } }, "e33fcee4a35d4da99148870babdbd871": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "display_legend": false, "fill_colors": [], "labels": [ "C1" ], "scales": { "x": "IPY_MODEL_f989834e0fd144cd95b580df0aaad5ab", "y": "IPY_MODEL_e8e3f7bb389f4168be4a3f68b16bf18a" }, "selected": [], "x": { "type": "float", "values": [ 0, 1, 2, 3, 4 ] }, "y": { "type": "float", "values": [ 1, 3, 2, 5, 4 ] } } }, "e7fa8d927d004fc9be47e78c4a10cd5f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "e8d5c1f27a3d4c5d8da78f80cc35ca25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_24682eab19af4dfd8d318250d74bdc96", "IPY_MODEL_8d3d28f582504308b011c6e283382a08" ], "layout": "IPY_MODEL_bebbd0a9c0534c7b94b4b45f4e66a568" } }, "e8e3f7bb389f4168be4a3f68b16bf18a": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "stabilized": false } }, "eb501ddbfb024a14a0c77f7d7e6e218d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_ddd19c8b9e194e08a9942e939bec829a", "IPY_MODEL_7dbdbb3b142f43269fa741441883ff11" ], "layout": "IPY_MODEL_893ac633d30d4e9ca175cc0aaac5e3ca" } }, "f38f74461f534092845c7df194e683d8": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "allow_padding": false, "max": 1, "min": 0, "stabilized": false } }, "f39a17cf33cc46afa2ee166ea8bca784": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "f722402781aa40e5abfa266993ce0fa5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntSliderModel", "state": { "description": "Segment", "layout": "IPY_MODEL_e7fa8d927d004fc9be47e78c4a10cd5f", "max": 1, "min": 1, "style": "IPY_MODEL_c0d1530a498a43cc9e83cebe4159ffc5", "value": 1 } }, "f989834e0fd144cd95b580df0aaad5ab": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinearScaleModel", "state": { "stabilized": false } }, "fce5b8ea17d24b178bd55a3ebd2afa00": { "model_module": "bqplot", "model_module_version": "^0.4.5", "model_name": "LinesModel", "state": { "color": { "type": null, "values": null }, "colors": [ "#1f77b4" ], "display_legend": false, "fill_colors": [], "labels": [ "BBNAPS1_ch1" ], "scales": { "x": "IPY_MODEL_3f7099eb85e644d39be802793202d951", "y": "IPY_MODEL_bb809aa69f6b4de692ddb7c47b2a8a8f" }, "selected": [], "tooltip": "IPY_MODEL_739989c6df8b44ac8e2e610ee68b5e36", "x": { "type": "float", "values": [ 0, 0.8333333333333334, 0.8333333333333334, 1.6666666666666667, 1.6666666666666667, 2.5, 2.5, 3.3333333333333335, 3.3333333333333335, 4.166666666666667, 4.166666666666667, 5, 5, 5.833333333333333, 5.833333333333333, 6.666666666666667, 6.666666666666667, 7.500000000000001, 7.500000000000001, 8.333333333333334, 8.333333333333334, 9.166666666666666, 9.166666666666666, 10, 10, 10.833333333333334, 10.833333333333334, 11.666666666666666, 11.666666666666666, 12.5, 12.5, 13.333333333333334, 13.333333333333334, 14.166666666666668, 14.166666666666668, 15.000000000000002, 15.000000000000002, 15.833333333333332, 15.833333333333332, 16.666666666666668, 16.666666666666668, 17.5, 17.5, 18.333333333333332, 18.333333333333332, 19.166666666666668, 19.166666666666668, 20, 20, 100, 100, 1100 ] }, "y": { "type": "float", "values": [ -0.04541570015871078, -0.04541570015871078, -0.10438285923574656, -0.10438285923574656, -0.17800024417043095, -0.17800024417043095, -0.2661457697472836, -0.2661457697472836, -0.36747649859602, -0.36747649859602, -0.4786961298986693, -0.4786961298986693, -0.5946770846050543, -0.5946770846050543, -0.7087046758637529, -0.7087046758637529, -0.8132096203149799, -0.8132096203149799, -0.9003784641679893, -0.9003784641679893, -0.9630081797094372, -0.9630081797094372, -0.9957270174581858, -0.9957270174581858, -0.9957270174581858, -0.9957270174581858, -0.9630081797094372, -0.9630081797094372, -0.9003784641679893, -0.9003784641679893, -0.8132096203149799, -0.8132096203149799, -0.7087046758637529, -0.7087046758637529, -0.5946770846050543, -0.5946770846050543, -0.4786961298986693, -0.4786961298986693, -0.36747649859602, -0.36747649859602, -0.2661457697472836, -0.2661457697472836, -0.17800024417043095, -0.17800024417043095, -0.10438285923574656, -0.10438285923574656, -0.04541570015871078, -0.04541570015871078, 0, 0, 0, 0 ] } } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }

apache-2.0

OpenBookProjects/ipynb

_graph_polt_gallery/mpld3_demo.ipynb

1

828414

{ "metadata": { "name": "", "signature": "sha256:12f5cc9c134806b43f4afa326f4de0284af463657e408fd49ae28784ee44ce99" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Demo of mpld3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook gives a brief overview of [mpld3](http://mpld3.github.io), a package which allows seamless visualization of matplotlib plots using HTML, Javascript, and the [D3js](http://d3js.org/) package.\n", "\n", "One of the nicest parts of the IPython notebook is the ability to embed figures inline with code. For example, we can quickly create a scatter plot as so:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Scatter points\n", "fig, ax = plt.subplots()\n", "np.random.seed(0)\n", "x, y = np.random.normal(size=(2, 200))\n", "color, size = np.random.random((2, 200))\n", "\n", "ax.scatter(x, y, c=color, s=500 * size, alpha=0.3)\n", "ax.grid(color='lightgray', alpha=0.7)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUwIHmBI4wJTAAaYEDjAlcICpAHrHmB6NaClTAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x1060939d0>" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A weakness quickly becomes clear, however: the embedded figure is a simple static PNG image.\n", "\n", "This is where mpld3 comes in. Using the simple ``mpld3.display()`` command, we can create a fully interactive visualization of the same plot:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import mpld3\n", "mpld3.display(fig)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div id=\"fig_el949843962352165673190064\"></div>\n", "<script>\n", "function mpld3_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n", " // already loaded: just create the figure\n", " !function(mpld3){\n", " \n", " mpld3.draw_figure(\"fig_el949843962352165673190064\", {\"axes\": [{\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984396237008\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984396371664\", \"pathtransforms\": [[16.348522912120725, 0.0, 0.0, 16.348522912120725, 0.0, 0.0], [18.057420231551603, 0.0, 0.0, 18.057420231551603, 0.0, 0.0], [14.686860930116316, 0.0, 0.0, 14.686860930116316, 0.0, 0.0], [21.963488223860693, 0.0, 0.0, 21.963488223860693, 0.0, 0.0], [21.53122408051117, 0.0, 0.0, 21.53122408051117, 0.0, 0.0], [23.923899504768666, 0.0, 0.0, 23.923899504768666, 0.0, 0.0], [4.2275221872632205, 0.0, 0.0, 4.2275221872632205, 0.0, 0.0], [23.513737660604505, 0.0, 0.0, 23.513737660604505, 0.0, 0.0], [15.566836950098674, 0.0, 0.0, 15.566836950098674, 0.0, 0.0], [23.285301030086014, 0.0, 0.0, 23.285301030086014, 0.0, 0.0], [20.649705945900994, 0.0, 0.0, 20.649705945900994, 0.0, 0.0], [24.687538180298255, 0.0, 0.0, 24.687538180298255, 0.0, 0.0], [21.649315726600737, 0.0, 0.0, 21.649315726600737, 0.0, 0.0], [15.000918202283376, 0.0, 0.0, 15.000918202283376, 0.0, 0.0], [17.58687734750734, 0.0, 0.0, 17.58687734750734, 0.0, 0.0], [15.242669867443535, 0.0, 0.0, 15.242669867443535, 0.0, 0.0], [15.008471599939693, 0.0, 0.0, 15.008471599939693, 0.0, 0.0], [12.690632770345427, 0.0, 0.0, 12.690632770345427, 0.0, 0.0], [17.49727131060602, 0.0, 0.0, 17.49727131060602, 0.0, 0.0], [20.514071428802847, 0.0, 0.0, 20.514071428802847, 0.0, 0.0], [13.08425383186103, 0.0, 0.0, 13.08425383186103, 0.0, 0.0], [17.991421328113333, 0.0, 0.0, 17.991421328113333, 0.0, 0.0], [8.512166099778627, 0.0, 0.0, 8.512166099778627, 0.0, 0.0], [9.933273889151966, 0.0, 0.0, 9.933273889151966, 0.0, 0.0], [5.375203396219885, 0.0, 0.0, 5.375203396219885, 0.0, 0.0], [24.478908063511984, 0.0, 0.0, 24.478908063511984, 0.0, 0.0], [1.543675170178418, 0.0, 0.0, 1.543675170178418, 0.0, 0.0], [10.499264173442748, 0.0, 0.0, 10.499264173442748, 0.0, 0.0], [19.45026505130572, 0.0, 0.0, 19.45026505130572, 0.0, 0.0], [7.0871819110498295, 0.0, 0.0, 7.0871819110498295, 0.0, 0.0], [23.331964286514335, 0.0, 0.0, 23.331964286514335, 0.0, 0.0], [21.076289378747887, 0.0, 0.0, 21.076289378747887, 0.0, 0.0], [24.424107341029007, 0.0, 0.0, 24.424107341029007, 0.0, 0.0], [17.701843860867893, 0.0, 0.0, 17.701843860867893, 0.0, 0.0], [13.617428990821058, 0.0, 0.0, 13.617428990821058, 0.0, 0.0], [18.417325659322138, 0.0, 0.0, 18.417325659322138, 0.0, 0.0], [23.970387045732192, 0.0, 0.0, 23.970387045732192, 0.0, 0.0], [17.9292408458861, 0.0, 0.0, 17.9292408458861, 0.0, 0.0], [12.84299856967564, 0.0, 0.0, 12.84299856967564, 0.0, 0.0], [23.27239117169145, 0.0, 0.0, 23.27239117169145, 0.0, 0.0], [15.151880226242822, 0.0, 0.0, 15.151880226242822, 0.0, 0.0], [0.9240777848582903, 0.0, 0.0, 0.9240777848582903, 0.0, 0.0], [12.364950027913077, 0.0, 0.0, 12.364950027913077, 0.0, 0.0], [14.015721093244498, 0.0, 0.0, 14.015721093244498, 0.0, 0.0], [23.024107980360448, 0.0, 0.0, 23.024107980360448, 0.0, 0.0], [16.823395802789392, 0.0, 0.0, 16.823395802789392, 0.0, 0.0], [16.566128015151374, 0.0, 0.0, 16.566128015151374, 0.0, 0.0], [14.403837503066512, 0.0, 0.0, 14.403837503066512, 0.0, 0.0], [23.315841632176088, 0.0, 0.0, 23.315841632176088, 0.0, 0.0], [24.152636756861217, 0.0, 0.0, 24.152636756861217, 0.0, 0.0], [24.744251473011822, 0.0, 0.0, 24.744251473011822, 0.0, 0.0], [15.249797953751347, 0.0, 0.0, 15.249797953751347, 0.0, 0.0], [24.421042404359767, 0.0, 0.0, 24.421042404359767, 0.0, 0.0], [22.10915080130572, 0.0, 0.0, 22.10915080130572, 0.0, 0.0], [20.422831988905905, 0.0, 0.0, 20.422831988905905, 0.0, 0.0], [12.294972359507321, 0.0, 0.0, 12.294972359507321, 0.0, 0.0], [11.559221131868624, 0.0, 0.0, 11.559221131868624, 0.0, 0.0], [10.124162044924017, 0.0, 0.0, 10.124162044924017, 0.0, 0.0], [23.86635384423282, 0.0, 0.0, 23.86635384423282, 0.0, 0.0], [13.473262557384754, 0.0, 0.0, 13.473262557384754, 0.0, 0.0], [16.723869343924, 0.0, 0.0, 16.723869343924, 0.0, 0.0], [17.46173654600223, 0.0, 0.0, 17.46173654600223, 0.0, 0.0], [21.916953180077012, 0.0, 0.0, 21.916953180077012, 0.0, 0.0], [22.82833091394729, 0.0, 0.0, 22.82833091394729, 0.0, 0.0], [9.26538430870693, 0.0, 0.0, 9.26538430870693, 0.0, 0.0], [16.233336379402587, 0.0, 0.0, 16.233336379402587, 0.0, 0.0], [22.809664507838747, 0.0, 0.0, 22.809664507838747, 0.0, 0.0], [22.47128903097196, 0.0, 0.0, 22.47128903097196, 0.0, 0.0], [7.950998013776123, 0.0, 0.0, 7.950998013776123, 0.0, 0.0], [9.825117373343545, 0.0, 0.0, 9.825117373343545, 0.0, 0.0], [13.703173724473146, 0.0, 0.0, 13.703173724473146, 0.0, 0.0], [6.820406430602588, 0.0, 0.0, 6.820406430602588, 0.0, 0.0], [16.190665708192483, 0.0, 0.0, 16.190665708192483, 0.0, 0.0], [8.150501959456237, 0.0, 0.0, 8.150501959456237, 0.0, 0.0], [18.72836354385424, 0.0, 0.0, 18.72836354385424, 0.0, 0.0], [12.336759779566853, 0.0, 0.0, 12.336759779566853, 0.0, 0.0], [19.187718959214298, 0.0, 0.0, 19.187718959214298, 0.0, 0.0], [8.51743465782941, 0.0, 0.0, 8.51743465782941, 0.0, 0.0], [24.54378637539591, 0.0, 0.0, 24.54378637539591, 0.0, 0.0], [23.992812759438387, 0.0, 0.0, 23.992812759438387, 0.0, 0.0], [15.551526036918505, 0.0, 0.0, 15.551526036918505, 0.0, 0.0], [12.226731343130503, 0.0, 0.0, 12.226731343130503, 0.0, 0.0], [12.432489616233788, 0.0, 0.0, 12.432489616233788, 0.0, 0.0], [17.273999858532697, 0.0, 0.0, 17.273999858532697, 0.0, 0.0], [4.968592834347467, 0.0, 0.0, 4.968592834347467, 0.0, 0.0], [19.871580085843778, 0.0, 0.0, 19.871580085843778, 0.0, 0.0], [15.875730922023227, 0.0, 0.0, 15.875730922023227, 0.0, 0.0], [15.263257192244291, 0.0, 0.0, 15.263257192244291, 0.0, 0.0], [22.351912828482885, 0.0, 0.0, 22.351912828482885, 0.0, 0.0], [20.920712463720857, 0.0, 0.0, 20.920712463720857, 0.0, 0.0], [24.27127824761742, 0.0, 0.0, 24.27127824761742, 0.0, 0.0], [14.739219548489482, 0.0, 0.0, 14.739219548489482, 0.0, 0.0], [23.538027606635694, 0.0, 0.0, 23.538027606635694, 0.0, 0.0], [21.801109755563566, 0.0, 0.0, 21.801109755563566, 0.0, 0.0], [14.853703275089035, 0.0, 0.0, 14.853703275089035, 0.0, 0.0], [19.58938734560336, 0.0, 0.0, 19.58938734560336, 0.0, 0.0], [13.34652027641291, 0.0, 0.0, 13.34652027641291, 0.0, 0.0], [23.23258563378997, 0.0, 0.0, 23.23258563378997, 0.0, 0.0], [8.330640943295903, 0.0, 0.0, 8.330640943295903, 0.0, 0.0], [11.451302187369132, 0.0, 0.0, 11.451302187369132, 0.0, 0.0], [10.629370330813623, 0.0, 0.0, 10.629370330813623, 0.0, 0.0], [15.772808341117006, 0.0, 0.0, 15.772808341117006, 0.0, 0.0], [21.44808489583266, 0.0, 0.0, 21.44808489583266, 0.0, 0.0], [18.034730709400826, 0.0, 0.0, 18.034730709400826, 0.0, 0.0], [17.35035353000559, 0.0, 0.0, 17.35035353000559, 0.0, 0.0], [0.58053024771827, 0.0, 0.0, 0.58053024771827, 0.0, 0.0], [16.204741825360163, 0.0, 0.0, 16.204741825360163, 0.0, 0.0], [6.263443558259721, 0.0, 0.0, 6.263443558259721, 0.0, 0.0], [11.33805054665249, 0.0, 0.0, 11.33805054665249, 0.0, 0.0], [23.990660978246197, 0.0, 0.0, 23.990660978246197, 0.0, 0.0], [11.530908660329553, 0.0, 0.0, 11.530908660329553, 0.0, 0.0], [23.018211236750528, 0.0, 0.0, 23.018211236750528, 0.0, 0.0], [22.262371668845418, 0.0, 0.0, 22.262371668845418, 0.0, 0.0], [9.91152954218379, 0.0, 0.0, 9.91152954218379, 0.0, 0.0], [19.33639754245669, 0.0, 0.0, 19.33639754245669, 0.0, 0.0], [8.44962823000615, 0.0, 0.0, 8.44962823000615, 0.0, 0.0], [21.197020498312288, 0.0, 0.0, 21.197020498312288, 0.0, 0.0], [19.836714267887377, 0.0, 0.0, 19.836714267887377, 0.0, 0.0], [22.387421535745197, 0.0, 0.0, 22.387421535745197, 0.0, 0.0], [17.202220452948637, 0.0, 0.0, 17.202220452948637, 0.0, 0.0], [23.76405481250449, 0.0, 0.0, 23.76405481250449, 0.0, 0.0], [5.5192674180882175, 0.0, 0.0, 5.5192674180882175, 0.0, 0.0], [13.446018388746271, 0.0, 0.0, 13.446018388746271, 0.0, 0.0], [21.009295377581058, 0.0, 0.0, 21.009295377581058, 0.0, 0.0], [16.065245283446018, 0.0, 0.0, 16.065245283446018, 0.0, 0.0], [10.332477690714308, 0.0, 0.0, 10.332477690714308, 0.0, 0.0], [8.135076673686413, 0.0, 0.0, 8.135076673686413, 0.0, 0.0], [22.461752285096775, 0.0, 0.0, 22.461752285096775, 0.0, 0.0], [17.08986737672652, 0.0, 0.0, 17.08986737672652, 0.0, 0.0], [23.33708530573924, 0.0, 0.0, 23.33708530573924, 0.0, 0.0], [21.27551676871227, 0.0, 0.0, 21.27551676871227, 0.0, 0.0], [15.903377342035718, 0.0, 0.0, 15.903377342035718, 0.0, 0.0], [15.184279845191494, 0.0, 0.0, 15.184279845191494, 0.0, 0.0], [17.84083169896093, 0.0, 0.0, 17.84083169896093, 0.0, 0.0], [23.426532825848838, 0.0, 0.0, 23.426532825848838, 0.0, 0.0], [21.33331278520665, 0.0, 0.0, 21.33331278520665, 0.0, 0.0], [1.7834915590975935, 0.0, 0.0, 1.7834915590975935, 0.0, 0.0], [20.70006040679928, 0.0, 0.0, 20.70006040679928, 0.0, 0.0], [23.82429778097729, 0.0, 0.0, 23.82429778097729, 0.0, 0.0], [20.94165556888317, 0.0, 0.0, 20.94165556888317, 0.0, 0.0], [10.4528861147572, 0.0, 0.0, 10.4528861147572, 0.0, 0.0], [17.27622585790927, 0.0, 0.0, 17.27622585790927, 0.0, 0.0], [9.306708649068904, 0.0, 0.0, 9.306708649068904, 0.0, 0.0], [14.886303223649568, 0.0, 0.0, 14.886303223649568, 0.0, 0.0], [24.051347360175598, 0.0, 0.0, 24.051347360175598, 0.0, 0.0], [23.873448596207165, 0.0, 0.0, 23.873448596207165, 0.0, 0.0], [13.213275501986978, 0.0, 0.0, 13.213275501986978, 0.0, 0.0], [14.479253872185012, 0.0, 0.0, 14.479253872185012, 0.0, 0.0], [19.248422544494808, 0.0, 0.0, 19.248422544494808, 0.0, 0.0], [24.38372882973828, 0.0, 0.0, 24.38372882973828, 0.0, 0.0], [9.551721907509092, 0.0, 0.0, 9.551721907509092, 0.0, 0.0], [12.593272838870766, 0.0, 0.0, 12.593272838870766, 0.0, 0.0], [23.22138259102476, 0.0, 0.0, 23.22138259102476, 0.0, 0.0], [17.425188092725982, 0.0, 0.0, 17.425188092725982, 0.0, 0.0], [23.556618060772873, 0.0, 0.0, 23.556618060772873, 0.0, 0.0], [10.701271919417742, 0.0, 0.0, 10.701271919417742, 0.0, 0.0], [18.13305738269335, 0.0, 0.0, 18.13305738269335, 0.0, 0.0], [14.19158228698458, 0.0, 0.0, 14.19158228698458, 0.0, 0.0], [13.978434887673792, 0.0, 0.0, 13.978434887673792, 0.0, 0.0], [16.608731972807778, 0.0, 0.0, 16.608731972807778, 0.0, 0.0], [16.350283138358392, 0.0, 0.0, 16.350283138358392, 0.0, 0.0], [14.852087180876866, 0.0, 0.0, 14.852087180876866, 0.0, 0.0], [23.76545332619945, 0.0, 0.0, 23.76545332619945, 0.0, 0.0], [21.25309251909572, 0.0, 0.0, 21.25309251909572, 0.0, 0.0], [21.19205230903429, 0.0, 0.0, 21.19205230903429, 0.0, 0.0], [13.377552821843278, 0.0, 0.0, 13.377552821843278, 0.0, 0.0], [18.884140323327827, 0.0, 0.0, 18.884140323327827, 0.0, 0.0], [21.931141302456027, 0.0, 0.0, 21.931141302456027, 0.0, 0.0], [22.160892711427838, 0.0, 0.0, 22.160892711427838, 0.0, 0.0], [14.58331662971133, 0.0, 0.0, 14.58331662971133, 0.0, 0.0], [21.813926299452167, 0.0, 0.0, 21.813926299452167, 0.0, 0.0], [21.313270322890975, 0.0, 0.0, 21.313270322890975, 0.0, 0.0], [9.346105203915398, 0.0, 0.0, 9.346105203915398, 0.0, 0.0], [23.1199965366747, 0.0, 0.0, 23.1199965366747, 0.0, 0.0], [16.505170723297347, 0.0, 0.0, 16.505170723297347, 0.0, 0.0], [17.327820506674286, 0.0, 0.0, 17.327820506674286, 0.0, 0.0], [16.63643886428774, 0.0, 0.0, 16.63643886428774, 0.0, 0.0], [18.722238731900198, 0.0, 0.0, 18.722238731900198, 0.0, 0.0], [19.581568042848367, 0.0, 0.0, 19.581568042848367, 0.0, 0.0], [17.536198290135545, 0.0, 0.0, 17.536198290135545, 0.0, 0.0], [23.1312484819297, 0.0, 0.0, 23.1312484819297, 0.0, 0.0], [19.684780675537198, 0.0, 0.0, 19.684780675537198, 0.0, 0.0], [15.74150661263102, 0.0, 0.0, 15.74150661263102, 0.0, 0.0], [16.037990332154024, 0.0, 0.0, 16.037990332154024, 0.0, 0.0], [22.372252091817813, 0.0, 0.0, 22.372252091817813, 0.0, 0.0], [14.660603602569678, 0.0, 0.0, 14.660603602569678, 0.0, 0.0], [11.424869877488288, 0.0, 0.0, 11.424869877488288, 0.0, 0.0], [6.054443731050978, 0.0, 0.0, 6.054443731050978, 0.0, 0.0], [23.254189160906442, 0.0, 0.0, 23.254189160906442, 0.0, 0.0], [23.81184541811628, 0.0, 0.0, 23.81184541811628, 0.0, 0.0], [8.61093842706695, 0.0, 0.0, 8.61093842706695, 0.0, 0.0], [14.368899489121146, 0.0, 0.0, 14.368899489121146, 0.0, 0.0], [10.404535736797616, 0.0, 0.0, 10.404535736797616, 0.0, 0.0], [8.458266057965615, 0.0, 0.0, 8.458266057965615, 0.0, 0.0], [23.568481031839255, 0.0, 0.0, 23.568481031839255, 0.0, 0.0], [5.925320179157247, 0.0, 0.0, 5.925320179157247, 0.0, 0.0], [24.601586612854003, 0.0, 0.0, 24.601586612854003, 0.0, 0.0], [7.716059119247248, 0.0, 0.0, 7.716059119247248, 0.0, 0.0], [23.08693512738942, 0.0, 0.0, 23.08693512738942, 0.0, 0.0], [18.700137105676205, 0.0, 0.0, 18.700137105676205, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [0.3], \"facecolors\": [\"#004CFF\", \"#00DCFE\", \"#FFDA00\", \"#FF1A00\", \"#9DFF59\", \"#009CFF\", \"#00009F\", \"#FFBD00\", \"#000084\", \"#12FCE4\", \"#93FF63\", \"#D50000\", \"#0000E3\", \"#2CFFCA\", \"#000096\", \"#00DCFE\", \"#E0FF15\", \"#0098FF\", \"#0054FF\", \"#0000FF\", \"#BAFF3C\", \"#FFCB00\", \"#FFE200\", \"#B60000\", \"#00007F\", \"#F4F802\", \"#CDFF29\", \"#C3FF32\", \"#A80000\", \"#00008D\", \"#FFC800\", \"#FF5900\", \"#83FF73\", \"#00D4FF\", \"#FF6F00\", \"#0000EC\", \"#49FFAD\", \"#89FF6C\", \"#FFCB00\", \"#0000E3\", \"#0064FF\", \"#2FFFC7\", \"#E0FF15\", \"#FE1200\", \"#DDFF18\", \"#0004FF\", \"#910000\", \"#FF2100\", \"#7CFF79\", \"#D50000\", \"#9DFF59\", \"#D50000\", \"#FF4A00\", \"#9F0000\", \"#DA0000\", \"#0000A3\", \"#0030FF\", \"#1FFFD7\", \"#B60000\", \"#00B0FF\", \"#0020FF\", \"#FF1600\", \"#4CFFAA\", \"#E80000\", \"#0020FF\", \"#FFE900\", \"#49FFAD\", \"#0000D1\", \"#FFC800\", \"#0078FF\", \"#0000A8\", \"#0000BF\", \"#0000BF\", \"#E80000\", \"#FF9F00\", \"#F50B00\", \"#FFDE00\", \"#93FF63\", \"#00B4FF\", \"#7F0000\", \"#08F0ED\", \"#63FF93\", \"#15FFE1\", \"#960000\", \"#0030FF\", \"#00CCFF\", \"#FFD700\", \"#0000C3\", \"#D4FF22\", \"#69FF8D\", \"#00A0FF\", \"#0070FF\", \"#86FF70\", \"#0CF4EA\", \"#56FFA0\", \"#00D4FF\", \"#9F0000\", \"#0004FF\", \"#0000EC\", \"#00DCFE\", \"#C7FF2F\", \"#FEED00\", \"#25FFD0\", \"#7F0000\", \"#00E4F7\", \"#FFB100\", \"#EDFF08\", \"#FF5900\", \"#960000\", \"#FE1200\", \"#FF8900\", \"#FFC800\", \"#00D4FF\", \"#0014FF\", \"#0000C3\", \"#0074FF\", \"#42FFB3\", \"#8DFF69\", \"#FF8100\", \"#AC0000\", \"#0000FF\", \"#0000F5\", \"#C3FF32\", \"#FF9B00\", \"#FF3700\", \"#C80000\", \"#910000\", \"#29FFCD\", \"#18FFDD\", \"#0014FF\", \"#FFD300\", \"#FEED00\", \"#FF2C00\", \"#0000EC\", \"#79FF7D\", \"#BDFF39\", \"#0074FF\", \"#0028FF\", \"#FF2C00\", \"#0000BF\", \"#63FF93\", \"#0000FF\", \"#56FFA0\", \"#910000\", \"#3CFFBA\", \"#FF3000\", \"#0000FF\", \"#0090FF\", \"#2CFFCA\", \"#29FFCD\", \"#FFE200\", \"#00DCFE\", \"#FFB900\", \"#EDFF08\", \"#25FFD0\", \"#42FFB3\", \"#DAFF1C\", \"#0000CC\", \"#FF5100\", \"#FAF000\", \"#FFAE00\", \"#9AFF5C\", \"#0000FA\", \"#2CFFCA\", \"#2CFFCA\", \"#00C4FF\", \"#00009A\", \"#FFA300\", \"#0000FA\", \"#D4FF22\", \"#FFC400\", \"#EAFF0C\", \"#AC0000\", \"#0000F1\", \"#FF2500\", \"#00009A\", \"#96FF5F\", \"#2CFFCA\", \"#8DFF69\", \"#0CF4EA\", \"#0040FF\", \"#000091\", \"#89FF6C\", \"#FF3F00\", \"#12FCE4\", \"#0060FF\", \"#0000D5\", \"#0000DE\", \"#0060FF\", \"#0000EC\", \"#008CFF\", \"#0000C8\", \"#0000C8\", \"#FF3000\", \"#0020FF\", \"#ADFF49\", \"#FF8100\", \"#56FFA0\", \"#0018FF\", \"#0048FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[1.764052345967664, -0.3691818379424436], [0.4001572083672233, -0.2393791775759264], [0.9787379841057392, 1.0996595958871132], [2.240893199201458, 0.6552637307225978], [1.8675579901499675, 0.640131526097592], [-0.977277879876411, -1.6169560443108344], [0.9500884175255894, -0.024326124398935636], [-0.1513572082976979, -0.7380309092056887], [-0.10321885179355784, 0.27992459904323824], [0.41059850193837233, -0.09815038964295794], [0.144043571160878, 0.9101789080925919], [1.454273506962975, 0.31721821519130206], [0.7610377251469934, 0.7863279621089762], [0.12167501649282841, -0.46641909673594306], [0.44386323274542566, -0.9444462559182504], [0.33367432737426683, -0.41004969320254847], [1.4940790731576061, -0.017020413861440594], [-0.20515826376580087, 0.3791517355550818], [0.31306770165090136, 2.259308950690852], [-0.8540957393017248, -0.04225715166064269], [-2.5529898158340787, -0.955945000492777], [0.6536185954403606, -0.34598177569938643], [0.8644361988595057, -0.4635959746460942], [-0.7421650204064419, 0.4814814737734622], [2.2697546239876076, -1.5407970144446248], [-1.4543656745987648, 0.06326199420033171], [0.04575851730144607, 0.1565065379653756], [-0.1871838500258336, 0.23218103620027578], [1.5327792143584575, -0.5973160689653627], [1.469358769900285, -0.237921729736007], [0.1549474256969163, -1.4240609089825316], [0.37816251960217356, -0.49331988336219407], [-0.8877857476301128, -0.5428614760167177], [-1.980796468223927, 0.4160500462614255], [-0.3479121493261526, -1.1561824318219127], [0.15634896910398005, 0.7811981017099934], [1.2302906807277207, 1.4944845444913688], [1.2023798487844113, -2.0699850250135325], [-0.3873268174079523, 0.42625873077810095], [-0.30230275057533557, 0.6769080350302455], [-1.0485529650670926, -0.637437025552229], [-1.4200179371789752, -0.39727181432879766], [-1.7062701906250126, -0.13288057758695562], [1.9507753952317897, -0.2977908794017283], [-0.5096521817516535, -0.3090129690471222], [-0.4380743016111864, -1.6760038063299767], [-1.2527953600499262, 1.15233156478312], [0.7774903558319101, 1.079618592036821], [-1.6138978475579515, -0.8133642592042029], [-0.2127402802139687, -1.466424327802514], [-0.8954665611936756, 0.5210648764527586], [0.386902497859262, -0.5757879698130661], [-0.510805137568873, 0.14195316332077967], [-1.180632184122412, -0.3193284171450952], [-0.028182228338654868, 0.6915387510701866], [0.42833187053041766, 0.6947491436560059], [0.06651722238316789, -0.7255973784635843], [0.3024718977397814, -1.3833639553950554], [-0.6343220936809636, -1.582938397335082], [-0.3627411659871381, 0.6103793791072052], [-0.672460447775951, -1.188859257784029], [-0.3595531615405413, -0.5068163542986875], [-0.813146282044454, -0.5963140384505081], [-1.7262826023316769, -0.05256729626954629], [0.17742614225375283, -1.936279805846507], [-0.4017809362082619, 0.18877859679382855], [-1.6301983469660446, 0.5238910238342056], [0.4627822555257742, 0.08842208704466141], [-0.9072983643832422, -0.3108861716984717], [0.05194539579613895, 0.09740016626878341], [0.7290905621775369, 0.3990463456401302], [0.12898291075741067, -2.77259275642665], [1.1394006845433007, 1.9559123082506942], [-1.2348258203536526, 0.39009332268792646], [0.402341641177549, -0.65240858238702], [-0.6848100909403132, -0.3909533751876011], [-0.8707971491818818, 0.49374177734918845], [-0.5788496647644155, -0.11610393903436653], [-0.31155253212737266, -2.0306844677814944], [0.05616534222974544, 2.0644928613593194], [-1.1651498407833565, -0.11054065723247261], [0.9008264869541871, 1.0201727117157997], [0.46566243973045984, -0.6920498477843912], [-1.5362436862772237, 1.5363770542457977], [1.4882521937955997, 0.28634368889227957], [1.8958891760305832, 0.6088438344754508], [1.1787795711596507, -1.0452533661469547], [-0.17992483581235091, 1.2111452896827009], [-1.0707526215105425, 0.6898181645347884], [1.0544517269311366, 1.3018462295649984], [-0.40317694697317963, -0.6280875596415789], [1.2224450703824274, -0.4810271184607877], [0.2082749780768603, 2.303916697683942], [0.9766390364837128, -1.0600158227215473], [0.3563663971744019, -0.13594970067832082], [0.7065731681919482, 1.1368913626026953], [0.010500020720820478, 0.0977249677148556], [1.7858704939058352, 0.5829536797532936], [0.12691209270361992, -0.3994490292628752], [0.40198936344470165, 0.37005588784751875], [1.8831506970562544, -1.3065268517353166], [-1.3477590611424464, 1.658130679618188], [-1.2704849984857336, -0.11816404512856976], [0.9693967081580112, -0.6801782039968504], [-1.17312340511416, 0.6663830820319143], [1.9436211856492926, -0.4607197873885533], [-0.41361898075974735, -1.3342584714027534], [-0.7474548114407578, -1.3467175057975553], [1.9229420264803847, 0.6937731526901325], [1.4805147914344243, -0.1595734381462669], [1.8675589604265699, -0.13370155966843916], [0.9060446582753853, 1.0777438059762627], [-0.8612256850547025, -1.1268258087567435], [1.9100649530990337, -0.7306777528648248], [-0.2680033709513804, -0.38487980918127546], [0.8024563957963952, 0.094351589317074], [0.947251967773748, -0.042171451290578935], [-0.1550100930908342, -0.2868871923899076], [0.6140793703460803, -0.0616264020956474], [0.9222066715665268, -0.10730527629117469], [0.37642553115562943, -0.7196043885517929], [-1.0994007905841945, -0.8129929885540773], [0.298238174206056, 0.2745163577239395], [1.3263858966870303, -0.8909150829955279], [-0.6945678597313655, -1.1573552591908536], [-0.14963454032767076, -0.3122922511256933], [-0.43515355172163744, -0.1576670161638159], [1.8492637284793418, 2.2567234972982093], [0.6722947570124355, -0.7047002758562337], [0.40746183624111043, 0.9432607249694948], [-0.7699160744453164, 0.7471883342046318], [0.5392491912918173, -1.188944955203736], [-0.6743326606573761, 0.7732529774025997], [0.03183055827435118, -1.1838806401933177], [-0.635846078378881, -2.659172237996741], [0.6764332949464997, 0.6063195243593807], [0.5765908166149409, -1.7558905834377194], [-0.20829875557799488, 0.45093446180591484], [0.3960067126616453, -0.6840108977372166], [-1.0930615087305058, 1.6595507961898721], [-1.4912575927056055, 1.068509399316009], [0.4393917012645369, -0.45338580385138766], [0.16667349537252904, -0.6878376110286823], [0.6350314368921064, -1.2140774030941206], [2.383144774863942, -0.4409226322925914], [0.9444794869904138, -0.2803554951845091], [-0.9128222254441586, -0.3646935443916854], [1.117016288095853, 0.15670385527236397], [-1.3159074105115212, 0.5785214977288784], [-0.461584604814709, 0.349654456993174], [-0.06824160532463124, -0.764143923906443], [1.7133427216493666, -1.4377914738015785], [-0.7447548220484399, 1.3645318481024713], [-0.8264385386590144, -0.6894491845499376], [-0.0984525244254323, -0.6522935999350191], [-0.6634782863621074, -0.5211893123011109], [1.126635922106507, -1.8430695501566485], [-1.0799315083634233, -0.4779740040404867], [-1.1474686524111024, -0.47965581400794766], [-0.43782004474443403, 0.6203582983435125], [-0.4980324506923049, 0.698457149107336], [1.9295320538169858, 0.00377088908626934], [0.9494208069257608, 0.9318483741143037], [0.0875512413851909, 0.339964983801262], [-1.225435518830168, -0.01568211160255477], [0.8443629764015471, 0.16092816829822298], [-1.0002153473895647, -0.19065349358139935], [-1.5447710967776116, -0.3948495140334503], [1.1880297923523018, -0.26773353689396645], [0.3169426119248496, -1.1280113314700069], [0.920858823780819, 0.280441705316296], [0.3187276529430212, -0.9931236109295807], [0.8568306119026912, 0.8416312640736364], [-0.6510255933001469, -0.24945858016094885], [-1.0342428417844647, 0.04949498165009074], [0.681594518281627, 0.49383677628095635], [-0.8034096641738411, 0.6433144650629279], [-0.6895497777502005, -1.5706234086334527], [-0.45553250351734315, -0.20690367616397173], [0.01747915902505673, 0.8801789120807822], [-0.35399391125348395, -1.6981058194322545], [-1.3749512934180188, 0.3872804753950634], [-0.6436184028328905, -2.2555642294021894], [-2.2234031522244266, -1.0225068436356035], [0.6252314510271875, 0.0386305518401881], [-1.6020576556067476, -1.6567151023219537], [-1.1043833394284506, -0.9855107376841507], [0.052165079260974405, -1.4718350074635869], [-0.7395629963913133, 1.6481349322075596], [1.5430145954067358, 0.16422775548733395], [-1.2928569097234486, 0.5672902778526694], [0.26705086934918293, -0.2226751005151545], [-0.0392828182274956, -0.35343174875719907], [-1.1680934977411974, -1.6164741886510325], [0.5232766605317537, -0.2918373627478628], [-0.1715463312222481, -0.7614922118116233], [0.7717905512136674, 0.8579239242923363], [0.8235041539637314, 1.1411018666575734], [2.16323594928069, 1.4665787155741776], [1.336527949436392, 0.852551939461232]]}, \"id\": \"el94984396235216\"});\n", " }(mpld3);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/mpld3\n", " require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949843962352165673190064\", {\"axes\": [{\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984396237008\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984396371664\", \"pathtransforms\": [[16.348522912120725, 0.0, 0.0, 16.348522912120725, 0.0, 0.0], [18.057420231551603, 0.0, 0.0, 18.057420231551603, 0.0, 0.0], [14.686860930116316, 0.0, 0.0, 14.686860930116316, 0.0, 0.0], [21.963488223860693, 0.0, 0.0, 21.963488223860693, 0.0, 0.0], [21.53122408051117, 0.0, 0.0, 21.53122408051117, 0.0, 0.0], [23.923899504768666, 0.0, 0.0, 23.923899504768666, 0.0, 0.0], [4.2275221872632205, 0.0, 0.0, 4.2275221872632205, 0.0, 0.0], [23.513737660604505, 0.0, 0.0, 23.513737660604505, 0.0, 0.0], [15.566836950098674, 0.0, 0.0, 15.566836950098674, 0.0, 0.0], [23.285301030086014, 0.0, 0.0, 23.285301030086014, 0.0, 0.0], [20.649705945900994, 0.0, 0.0, 20.649705945900994, 0.0, 0.0], [24.687538180298255, 0.0, 0.0, 24.687538180298255, 0.0, 0.0], [21.649315726600737, 0.0, 0.0, 21.649315726600737, 0.0, 0.0], [15.000918202283376, 0.0, 0.0, 15.000918202283376, 0.0, 0.0], [17.58687734750734, 0.0, 0.0, 17.58687734750734, 0.0, 0.0], [15.242669867443535, 0.0, 0.0, 15.242669867443535, 0.0, 0.0], [15.008471599939693, 0.0, 0.0, 15.008471599939693, 0.0, 0.0], [12.690632770345427, 0.0, 0.0, 12.690632770345427, 0.0, 0.0], [17.49727131060602, 0.0, 0.0, 17.49727131060602, 0.0, 0.0], [20.514071428802847, 0.0, 0.0, 20.514071428802847, 0.0, 0.0], [13.08425383186103, 0.0, 0.0, 13.08425383186103, 0.0, 0.0], [17.991421328113333, 0.0, 0.0, 17.991421328113333, 0.0, 0.0], [8.512166099778627, 0.0, 0.0, 8.512166099778627, 0.0, 0.0], [9.933273889151966, 0.0, 0.0, 9.933273889151966, 0.0, 0.0], [5.375203396219885, 0.0, 0.0, 5.375203396219885, 0.0, 0.0], [24.478908063511984, 0.0, 0.0, 24.478908063511984, 0.0, 0.0], [1.543675170178418, 0.0, 0.0, 1.543675170178418, 0.0, 0.0], [10.499264173442748, 0.0, 0.0, 10.499264173442748, 0.0, 0.0], [19.45026505130572, 0.0, 0.0, 19.45026505130572, 0.0, 0.0], [7.0871819110498295, 0.0, 0.0, 7.0871819110498295, 0.0, 0.0], [23.331964286514335, 0.0, 0.0, 23.331964286514335, 0.0, 0.0], [21.076289378747887, 0.0, 0.0, 21.076289378747887, 0.0, 0.0], [24.424107341029007, 0.0, 0.0, 24.424107341029007, 0.0, 0.0], [17.701843860867893, 0.0, 0.0, 17.701843860867893, 0.0, 0.0], [13.617428990821058, 0.0, 0.0, 13.617428990821058, 0.0, 0.0], [18.417325659322138, 0.0, 0.0, 18.417325659322138, 0.0, 0.0], [23.970387045732192, 0.0, 0.0, 23.970387045732192, 0.0, 0.0], [17.9292408458861, 0.0, 0.0, 17.9292408458861, 0.0, 0.0], [12.84299856967564, 0.0, 0.0, 12.84299856967564, 0.0, 0.0], [23.27239117169145, 0.0, 0.0, 23.27239117169145, 0.0, 0.0], [15.151880226242822, 0.0, 0.0, 15.151880226242822, 0.0, 0.0], [0.9240777848582903, 0.0, 0.0, 0.9240777848582903, 0.0, 0.0], [12.364950027913077, 0.0, 0.0, 12.364950027913077, 0.0, 0.0], [14.015721093244498, 0.0, 0.0, 14.015721093244498, 0.0, 0.0], [23.024107980360448, 0.0, 0.0, 23.024107980360448, 0.0, 0.0], [16.823395802789392, 0.0, 0.0, 16.823395802789392, 0.0, 0.0], [16.566128015151374, 0.0, 0.0, 16.566128015151374, 0.0, 0.0], [14.403837503066512, 0.0, 0.0, 14.403837503066512, 0.0, 0.0], [23.315841632176088, 0.0, 0.0, 23.315841632176088, 0.0, 0.0], [24.152636756861217, 0.0, 0.0, 24.152636756861217, 0.0, 0.0], [24.744251473011822, 0.0, 0.0, 24.744251473011822, 0.0, 0.0], [15.249797953751347, 0.0, 0.0, 15.249797953751347, 0.0, 0.0], [24.421042404359767, 0.0, 0.0, 24.421042404359767, 0.0, 0.0], [22.10915080130572, 0.0, 0.0, 22.10915080130572, 0.0, 0.0], [20.422831988905905, 0.0, 0.0, 20.422831988905905, 0.0, 0.0], [12.294972359507321, 0.0, 0.0, 12.294972359507321, 0.0, 0.0], [11.559221131868624, 0.0, 0.0, 11.559221131868624, 0.0, 0.0], [10.124162044924017, 0.0, 0.0, 10.124162044924017, 0.0, 0.0], [23.86635384423282, 0.0, 0.0, 23.86635384423282, 0.0, 0.0], [13.473262557384754, 0.0, 0.0, 13.473262557384754, 0.0, 0.0], [16.723869343924, 0.0, 0.0, 16.723869343924, 0.0, 0.0], [17.46173654600223, 0.0, 0.0, 17.46173654600223, 0.0, 0.0], [21.916953180077012, 0.0, 0.0, 21.916953180077012, 0.0, 0.0], [22.82833091394729, 0.0, 0.0, 22.82833091394729, 0.0, 0.0], [9.26538430870693, 0.0, 0.0, 9.26538430870693, 0.0, 0.0], [16.233336379402587, 0.0, 0.0, 16.233336379402587, 0.0, 0.0], [22.809664507838747, 0.0, 0.0, 22.809664507838747, 0.0, 0.0], [22.47128903097196, 0.0, 0.0, 22.47128903097196, 0.0, 0.0], [7.950998013776123, 0.0, 0.0, 7.950998013776123, 0.0, 0.0], [9.825117373343545, 0.0, 0.0, 9.825117373343545, 0.0, 0.0], [13.703173724473146, 0.0, 0.0, 13.703173724473146, 0.0, 0.0], [6.820406430602588, 0.0, 0.0, 6.820406430602588, 0.0, 0.0], [16.190665708192483, 0.0, 0.0, 16.190665708192483, 0.0, 0.0], [8.150501959456237, 0.0, 0.0, 8.150501959456237, 0.0, 0.0], [18.72836354385424, 0.0, 0.0, 18.72836354385424, 0.0, 0.0], [12.336759779566853, 0.0, 0.0, 12.336759779566853, 0.0, 0.0], [19.187718959214298, 0.0, 0.0, 19.187718959214298, 0.0, 0.0], [8.51743465782941, 0.0, 0.0, 8.51743465782941, 0.0, 0.0], [24.54378637539591, 0.0, 0.0, 24.54378637539591, 0.0, 0.0], [23.992812759438387, 0.0, 0.0, 23.992812759438387, 0.0, 0.0], [15.551526036918505, 0.0, 0.0, 15.551526036918505, 0.0, 0.0], [12.226731343130503, 0.0, 0.0, 12.226731343130503, 0.0, 0.0], [12.432489616233788, 0.0, 0.0, 12.432489616233788, 0.0, 0.0], [17.273999858532697, 0.0, 0.0, 17.273999858532697, 0.0, 0.0], [4.968592834347467, 0.0, 0.0, 4.968592834347467, 0.0, 0.0], [19.871580085843778, 0.0, 0.0, 19.871580085843778, 0.0, 0.0], [15.875730922023227, 0.0, 0.0, 15.875730922023227, 0.0, 0.0], [15.263257192244291, 0.0, 0.0, 15.263257192244291, 0.0, 0.0], [22.351912828482885, 0.0, 0.0, 22.351912828482885, 0.0, 0.0], [20.920712463720857, 0.0, 0.0, 20.920712463720857, 0.0, 0.0], [24.27127824761742, 0.0, 0.0, 24.27127824761742, 0.0, 0.0], [14.739219548489482, 0.0, 0.0, 14.739219548489482, 0.0, 0.0], [23.538027606635694, 0.0, 0.0, 23.538027606635694, 0.0, 0.0], [21.801109755563566, 0.0, 0.0, 21.801109755563566, 0.0, 0.0], [14.853703275089035, 0.0, 0.0, 14.853703275089035, 0.0, 0.0], [19.58938734560336, 0.0, 0.0, 19.58938734560336, 0.0, 0.0], [13.34652027641291, 0.0, 0.0, 13.34652027641291, 0.0, 0.0], [23.23258563378997, 0.0, 0.0, 23.23258563378997, 0.0, 0.0], [8.330640943295903, 0.0, 0.0, 8.330640943295903, 0.0, 0.0], [11.451302187369132, 0.0, 0.0, 11.451302187369132, 0.0, 0.0], [10.629370330813623, 0.0, 0.0, 10.629370330813623, 0.0, 0.0], [15.772808341117006, 0.0, 0.0, 15.772808341117006, 0.0, 0.0], [21.44808489583266, 0.0, 0.0, 21.44808489583266, 0.0, 0.0], [18.034730709400826, 0.0, 0.0, 18.034730709400826, 0.0, 0.0], [17.35035353000559, 0.0, 0.0, 17.35035353000559, 0.0, 0.0], [0.58053024771827, 0.0, 0.0, 0.58053024771827, 0.0, 0.0], [16.204741825360163, 0.0, 0.0, 16.204741825360163, 0.0, 0.0], [6.263443558259721, 0.0, 0.0, 6.263443558259721, 0.0, 0.0], [11.33805054665249, 0.0, 0.0, 11.33805054665249, 0.0, 0.0], [23.990660978246197, 0.0, 0.0, 23.990660978246197, 0.0, 0.0], [11.530908660329553, 0.0, 0.0, 11.530908660329553, 0.0, 0.0], [23.018211236750528, 0.0, 0.0, 23.018211236750528, 0.0, 0.0], [22.262371668845418, 0.0, 0.0, 22.262371668845418, 0.0, 0.0], [9.91152954218379, 0.0, 0.0, 9.91152954218379, 0.0, 0.0], [19.33639754245669, 0.0, 0.0, 19.33639754245669, 0.0, 0.0], [8.44962823000615, 0.0, 0.0, 8.44962823000615, 0.0, 0.0], [21.197020498312288, 0.0, 0.0, 21.197020498312288, 0.0, 0.0], [19.836714267887377, 0.0, 0.0, 19.836714267887377, 0.0, 0.0], [22.387421535745197, 0.0, 0.0, 22.387421535745197, 0.0, 0.0], [17.202220452948637, 0.0, 0.0, 17.202220452948637, 0.0, 0.0], [23.76405481250449, 0.0, 0.0, 23.76405481250449, 0.0, 0.0], [5.5192674180882175, 0.0, 0.0, 5.5192674180882175, 0.0, 0.0], [13.446018388746271, 0.0, 0.0, 13.446018388746271, 0.0, 0.0], [21.009295377581058, 0.0, 0.0, 21.009295377581058, 0.0, 0.0], [16.065245283446018, 0.0, 0.0, 16.065245283446018, 0.0, 0.0], [10.332477690714308, 0.0, 0.0, 10.332477690714308, 0.0, 0.0], [8.135076673686413, 0.0, 0.0, 8.135076673686413, 0.0, 0.0], [22.461752285096775, 0.0, 0.0, 22.461752285096775, 0.0, 0.0], [17.08986737672652, 0.0, 0.0, 17.08986737672652, 0.0, 0.0], [23.33708530573924, 0.0, 0.0, 23.33708530573924, 0.0, 0.0], [21.27551676871227, 0.0, 0.0, 21.27551676871227, 0.0, 0.0], [15.903377342035718, 0.0, 0.0, 15.903377342035718, 0.0, 0.0], [15.184279845191494, 0.0, 0.0, 15.184279845191494, 0.0, 0.0], [17.84083169896093, 0.0, 0.0, 17.84083169896093, 0.0, 0.0], [23.426532825848838, 0.0, 0.0, 23.426532825848838, 0.0, 0.0], [21.33331278520665, 0.0, 0.0, 21.33331278520665, 0.0, 0.0], [1.7834915590975935, 0.0, 0.0, 1.7834915590975935, 0.0, 0.0], [20.70006040679928, 0.0, 0.0, 20.70006040679928, 0.0, 0.0], [23.82429778097729, 0.0, 0.0, 23.82429778097729, 0.0, 0.0], [20.94165556888317, 0.0, 0.0, 20.94165556888317, 0.0, 0.0], [10.4528861147572, 0.0, 0.0, 10.4528861147572, 0.0, 0.0], [17.27622585790927, 0.0, 0.0, 17.27622585790927, 0.0, 0.0], [9.306708649068904, 0.0, 0.0, 9.306708649068904, 0.0, 0.0], [14.886303223649568, 0.0, 0.0, 14.886303223649568, 0.0, 0.0], [24.051347360175598, 0.0, 0.0, 24.051347360175598, 0.0, 0.0], [23.873448596207165, 0.0, 0.0, 23.873448596207165, 0.0, 0.0], [13.213275501986978, 0.0, 0.0, 13.213275501986978, 0.0, 0.0], [14.479253872185012, 0.0, 0.0, 14.479253872185012, 0.0, 0.0], [19.248422544494808, 0.0, 0.0, 19.248422544494808, 0.0, 0.0], [24.38372882973828, 0.0, 0.0, 24.38372882973828, 0.0, 0.0], [9.551721907509092, 0.0, 0.0, 9.551721907509092, 0.0, 0.0], [12.593272838870766, 0.0, 0.0, 12.593272838870766, 0.0, 0.0], [23.22138259102476, 0.0, 0.0, 23.22138259102476, 0.0, 0.0], [17.425188092725982, 0.0, 0.0, 17.425188092725982, 0.0, 0.0], [23.556618060772873, 0.0, 0.0, 23.556618060772873, 0.0, 0.0], [10.701271919417742, 0.0, 0.0, 10.701271919417742, 0.0, 0.0], [18.13305738269335, 0.0, 0.0, 18.13305738269335, 0.0, 0.0], [14.19158228698458, 0.0, 0.0, 14.19158228698458, 0.0, 0.0], [13.978434887673792, 0.0, 0.0, 13.978434887673792, 0.0, 0.0], [16.608731972807778, 0.0, 0.0, 16.608731972807778, 0.0, 0.0], [16.350283138358392, 0.0, 0.0, 16.350283138358392, 0.0, 0.0], [14.852087180876866, 0.0, 0.0, 14.852087180876866, 0.0, 0.0], [23.76545332619945, 0.0, 0.0, 23.76545332619945, 0.0, 0.0], [21.25309251909572, 0.0, 0.0, 21.25309251909572, 0.0, 0.0], [21.19205230903429, 0.0, 0.0, 21.19205230903429, 0.0, 0.0], [13.377552821843278, 0.0, 0.0, 13.377552821843278, 0.0, 0.0], [18.884140323327827, 0.0, 0.0, 18.884140323327827, 0.0, 0.0], [21.931141302456027, 0.0, 0.0, 21.931141302456027, 0.0, 0.0], [22.160892711427838, 0.0, 0.0, 22.160892711427838, 0.0, 0.0], [14.58331662971133, 0.0, 0.0, 14.58331662971133, 0.0, 0.0], [21.813926299452167, 0.0, 0.0, 21.813926299452167, 0.0, 0.0], [21.313270322890975, 0.0, 0.0, 21.313270322890975, 0.0, 0.0], [9.346105203915398, 0.0, 0.0, 9.346105203915398, 0.0, 0.0], [23.1199965366747, 0.0, 0.0, 23.1199965366747, 0.0, 0.0], [16.505170723297347, 0.0, 0.0, 16.505170723297347, 0.0, 0.0], [17.327820506674286, 0.0, 0.0, 17.327820506674286, 0.0, 0.0], [16.63643886428774, 0.0, 0.0, 16.63643886428774, 0.0, 0.0], [18.722238731900198, 0.0, 0.0, 18.722238731900198, 0.0, 0.0], [19.581568042848367, 0.0, 0.0, 19.581568042848367, 0.0, 0.0], [17.536198290135545, 0.0, 0.0, 17.536198290135545, 0.0, 0.0], [23.1312484819297, 0.0, 0.0, 23.1312484819297, 0.0, 0.0], [19.684780675537198, 0.0, 0.0, 19.684780675537198, 0.0, 0.0], [15.74150661263102, 0.0, 0.0, 15.74150661263102, 0.0, 0.0], [16.037990332154024, 0.0, 0.0, 16.037990332154024, 0.0, 0.0], [22.372252091817813, 0.0, 0.0, 22.372252091817813, 0.0, 0.0], [14.660603602569678, 0.0, 0.0, 14.660603602569678, 0.0, 0.0], [11.424869877488288, 0.0, 0.0, 11.424869877488288, 0.0, 0.0], [6.054443731050978, 0.0, 0.0, 6.054443731050978, 0.0, 0.0], [23.254189160906442, 0.0, 0.0, 23.254189160906442, 0.0, 0.0], [23.81184541811628, 0.0, 0.0, 23.81184541811628, 0.0, 0.0], [8.61093842706695, 0.0, 0.0, 8.61093842706695, 0.0, 0.0], [14.368899489121146, 0.0, 0.0, 14.368899489121146, 0.0, 0.0], [10.404535736797616, 0.0, 0.0, 10.404535736797616, 0.0, 0.0], [8.458266057965615, 0.0, 0.0, 8.458266057965615, 0.0, 0.0], [23.568481031839255, 0.0, 0.0, 23.568481031839255, 0.0, 0.0], [5.925320179157247, 0.0, 0.0, 5.925320179157247, 0.0, 0.0], [24.601586612854003, 0.0, 0.0, 24.601586612854003, 0.0, 0.0], [7.716059119247248, 0.0, 0.0, 7.716059119247248, 0.0, 0.0], [23.08693512738942, 0.0, 0.0, 23.08693512738942, 0.0, 0.0], [18.700137105676205, 0.0, 0.0, 18.700137105676205, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [0.3], \"facecolors\": [\"#004CFF\", \"#00DCFE\", \"#FFDA00\", \"#FF1A00\", \"#9DFF59\", \"#009CFF\", \"#00009F\", \"#FFBD00\", \"#000084\", \"#12FCE4\", \"#93FF63\", \"#D50000\", \"#0000E3\", \"#2CFFCA\", \"#000096\", \"#00DCFE\", \"#E0FF15\", \"#0098FF\", \"#0054FF\", \"#0000FF\", \"#BAFF3C\", \"#FFCB00\", \"#FFE200\", \"#B60000\", \"#00007F\", \"#F4F802\", \"#CDFF29\", \"#C3FF32\", \"#A80000\", \"#00008D\", \"#FFC800\", \"#FF5900\", \"#83FF73\", \"#00D4FF\", \"#FF6F00\", \"#0000EC\", \"#49FFAD\", \"#89FF6C\", \"#FFCB00\", \"#0000E3\", \"#0064FF\", \"#2FFFC7\", \"#E0FF15\", \"#FE1200\", \"#DDFF18\", \"#0004FF\", \"#910000\", \"#FF2100\", \"#7CFF79\", \"#D50000\", \"#9DFF59\", \"#D50000\", \"#FF4A00\", \"#9F0000\", \"#DA0000\", \"#0000A3\", \"#0030FF\", \"#1FFFD7\", \"#B60000\", \"#00B0FF\", \"#0020FF\", \"#FF1600\", \"#4CFFAA\", \"#E80000\", \"#0020FF\", \"#FFE900\", \"#49FFAD\", \"#0000D1\", \"#FFC800\", \"#0078FF\", \"#0000A8\", \"#0000BF\", \"#0000BF\", \"#E80000\", \"#FF9F00\", \"#F50B00\", \"#FFDE00\", \"#93FF63\", \"#00B4FF\", \"#7F0000\", \"#08F0ED\", \"#63FF93\", \"#15FFE1\", \"#960000\", \"#0030FF\", \"#00CCFF\", \"#FFD700\", \"#0000C3\", \"#D4FF22\", \"#69FF8D\", \"#00A0FF\", \"#0070FF\", \"#86FF70\", \"#0CF4EA\", \"#56FFA0\", \"#00D4FF\", \"#9F0000\", \"#0004FF\", \"#0000EC\", \"#00DCFE\", \"#C7FF2F\", \"#FEED00\", \"#25FFD0\", \"#7F0000\", \"#00E4F7\", \"#FFB100\", \"#EDFF08\", \"#FF5900\", \"#960000\", \"#FE1200\", \"#FF8900\", \"#FFC800\", \"#00D4FF\", \"#0014FF\", \"#0000C3\", \"#0074FF\", \"#42FFB3\", \"#8DFF69\", \"#FF8100\", \"#AC0000\", \"#0000FF\", \"#0000F5\", \"#C3FF32\", \"#FF9B00\", \"#FF3700\", \"#C80000\", \"#910000\", \"#29FFCD\", \"#18FFDD\", \"#0014FF\", \"#FFD300\", \"#FEED00\", \"#FF2C00\", \"#0000EC\", \"#79FF7D\", \"#BDFF39\", \"#0074FF\", \"#0028FF\", \"#FF2C00\", \"#0000BF\", \"#63FF93\", \"#0000FF\", \"#56FFA0\", \"#910000\", \"#3CFFBA\", \"#FF3000\", \"#0000FF\", \"#0090FF\", \"#2CFFCA\", \"#29FFCD\", \"#FFE200\", \"#00DCFE\", \"#FFB900\", \"#EDFF08\", \"#25FFD0\", \"#42FFB3\", \"#DAFF1C\", \"#0000CC\", \"#FF5100\", \"#FAF000\", \"#FFAE00\", \"#9AFF5C\", \"#0000FA\", \"#2CFFCA\", \"#2CFFCA\", \"#00C4FF\", \"#00009A\", \"#FFA300\", \"#0000FA\", \"#D4FF22\", \"#FFC400\", \"#EAFF0C\", \"#AC0000\", \"#0000F1\", \"#FF2500\", \"#00009A\", \"#96FF5F\", \"#2CFFCA\", \"#8DFF69\", \"#0CF4EA\", \"#0040FF\", \"#000091\", \"#89FF6C\", \"#FF3F00\", \"#12FCE4\", \"#0060FF\", \"#0000D5\", \"#0000DE\", \"#0060FF\", \"#0000EC\", \"#008CFF\", \"#0000C8\", \"#0000C8\", \"#FF3000\", \"#0020FF\", \"#ADFF49\", \"#FF8100\", \"#56FFA0\", \"#0018FF\", \"#0048FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[1.764052345967664, -0.3691818379424436], [0.4001572083672233, -0.2393791775759264], [0.9787379841057392, 1.0996595958871132], [2.240893199201458, 0.6552637307225978], [1.8675579901499675, 0.640131526097592], [-0.977277879876411, -1.6169560443108344], [0.9500884175255894, -0.024326124398935636], [-0.1513572082976979, -0.7380309092056887], [-0.10321885179355784, 0.27992459904323824], [0.41059850193837233, -0.09815038964295794], [0.144043571160878, 0.9101789080925919], [1.454273506962975, 0.31721821519130206], [0.7610377251469934, 0.7863279621089762], [0.12167501649282841, -0.46641909673594306], [0.44386323274542566, -0.9444462559182504], [0.33367432737426683, -0.41004969320254847], [1.4940790731576061, -0.017020413861440594], [-0.20515826376580087, 0.3791517355550818], [0.31306770165090136, 2.259308950690852], [-0.8540957393017248, -0.04225715166064269], [-2.5529898158340787, -0.955945000492777], [0.6536185954403606, -0.34598177569938643], [0.8644361988595057, -0.4635959746460942], [-0.7421650204064419, 0.4814814737734622], [2.2697546239876076, -1.5407970144446248], [-1.4543656745987648, 0.06326199420033171], [0.04575851730144607, 0.1565065379653756], [-0.1871838500258336, 0.23218103620027578], [1.5327792143584575, -0.5973160689653627], [1.469358769900285, -0.237921729736007], [0.1549474256969163, -1.4240609089825316], [0.37816251960217356, -0.49331988336219407], [-0.8877857476301128, -0.5428614760167177], [-1.980796468223927, 0.4160500462614255], [-0.3479121493261526, -1.1561824318219127], [0.15634896910398005, 0.7811981017099934], [1.2302906807277207, 1.4944845444913688], [1.2023798487844113, -2.0699850250135325], [-0.3873268174079523, 0.42625873077810095], [-0.30230275057533557, 0.6769080350302455], [-1.0485529650670926, -0.637437025552229], [-1.4200179371789752, -0.39727181432879766], [-1.7062701906250126, -0.13288057758695562], [1.9507753952317897, -0.2977908794017283], [-0.5096521817516535, -0.3090129690471222], [-0.4380743016111864, -1.6760038063299767], [-1.2527953600499262, 1.15233156478312], [0.7774903558319101, 1.079618592036821], [-1.6138978475579515, -0.8133642592042029], [-0.2127402802139687, -1.466424327802514], [-0.8954665611936756, 0.5210648764527586], [0.386902497859262, -0.5757879698130661], [-0.510805137568873, 0.14195316332077967], [-1.180632184122412, -0.3193284171450952], [-0.028182228338654868, 0.6915387510701866], [0.42833187053041766, 0.6947491436560059], [0.06651722238316789, -0.7255973784635843], [0.3024718977397814, -1.3833639553950554], [-0.6343220936809636, -1.582938397335082], [-0.3627411659871381, 0.6103793791072052], [-0.672460447775951, -1.188859257784029], [-0.3595531615405413, -0.5068163542986875], [-0.813146282044454, -0.5963140384505081], [-1.7262826023316769, -0.05256729626954629], [0.17742614225375283, -1.936279805846507], [-0.4017809362082619, 0.18877859679382855], [-1.6301983469660446, 0.5238910238342056], [0.4627822555257742, 0.08842208704466141], [-0.9072983643832422, -0.3108861716984717], [0.05194539579613895, 0.09740016626878341], [0.7290905621775369, 0.3990463456401302], [0.12898291075741067, -2.77259275642665], [1.1394006845433007, 1.9559123082506942], [-1.2348258203536526, 0.39009332268792646], [0.402341641177549, -0.65240858238702], [-0.6848100909403132, -0.3909533751876011], [-0.8707971491818818, 0.49374177734918845], [-0.5788496647644155, -0.11610393903436653], [-0.31155253212737266, -2.0306844677814944], [0.05616534222974544, 2.0644928613593194], [-1.1651498407833565, -0.11054065723247261], [0.9008264869541871, 1.0201727117157997], [0.46566243973045984, -0.6920498477843912], [-1.5362436862772237, 1.5363770542457977], [1.4882521937955997, 0.28634368889227957], [1.8958891760305832, 0.6088438344754508], [1.1787795711596507, -1.0452533661469547], [-0.17992483581235091, 1.2111452896827009], [-1.0707526215105425, 0.6898181645347884], [1.0544517269311366, 1.3018462295649984], [-0.40317694697317963, -0.6280875596415789], [1.2224450703824274, -0.4810271184607877], [0.2082749780768603, 2.303916697683942], [0.9766390364837128, -1.0600158227215473], [0.3563663971744019, -0.13594970067832082], [0.7065731681919482, 1.1368913626026953], [0.010500020720820478, 0.0977249677148556], [1.7858704939058352, 0.5829536797532936], [0.12691209270361992, -0.3994490292628752], [0.40198936344470165, 0.37005588784751875], [1.8831506970562544, -1.3065268517353166], [-1.3477590611424464, 1.658130679618188], [-1.2704849984857336, -0.11816404512856976], [0.9693967081580112, -0.6801782039968504], [-1.17312340511416, 0.6663830820319143], [1.9436211856492926, -0.4607197873885533], [-0.41361898075974735, -1.3342584714027534], [-0.7474548114407578, -1.3467175057975553], [1.9229420264803847, 0.6937731526901325], [1.4805147914344243, -0.1595734381462669], [1.8675589604265699, -0.13370155966843916], [0.9060446582753853, 1.0777438059762627], [-0.8612256850547025, -1.1268258087567435], [1.9100649530990337, -0.7306777528648248], [-0.2680033709513804, -0.38487980918127546], [0.8024563957963952, 0.094351589317074], [0.947251967773748, -0.042171451290578935], [-0.1550100930908342, -0.2868871923899076], [0.6140793703460803, -0.0616264020956474], [0.9222066715665268, -0.10730527629117469], [0.37642553115562943, -0.7196043885517929], [-1.0994007905841945, -0.8129929885540773], [0.298238174206056, 0.2745163577239395], [1.3263858966870303, -0.8909150829955279], [-0.6945678597313655, -1.1573552591908536], [-0.14963454032767076, -0.3122922511256933], [-0.43515355172163744, -0.1576670161638159], [1.8492637284793418, 2.2567234972982093], [0.6722947570124355, -0.7047002758562337], [0.40746183624111043, 0.9432607249694948], [-0.7699160744453164, 0.7471883342046318], [0.5392491912918173, -1.188944955203736], [-0.6743326606573761, 0.7732529774025997], [0.03183055827435118, -1.1838806401933177], [-0.635846078378881, -2.659172237996741], [0.6764332949464997, 0.6063195243593807], [0.5765908166149409, -1.7558905834377194], [-0.20829875557799488, 0.45093446180591484], [0.3960067126616453, -0.6840108977372166], [-1.0930615087305058, 1.6595507961898721], [-1.4912575927056055, 1.068509399316009], [0.4393917012645369, -0.45338580385138766], [0.16667349537252904, -0.6878376110286823], [0.6350314368921064, -1.2140774030941206], [2.383144774863942, -0.4409226322925914], [0.9444794869904138, -0.2803554951845091], [-0.9128222254441586, -0.3646935443916854], [1.117016288095853, 0.15670385527236397], [-1.3159074105115212, 0.5785214977288784], [-0.461584604814709, 0.349654456993174], [-0.06824160532463124, -0.764143923906443], [1.7133427216493666, -1.4377914738015785], [-0.7447548220484399, 1.3645318481024713], [-0.8264385386590144, -0.6894491845499376], [-0.0984525244254323, -0.6522935999350191], [-0.6634782863621074, -0.5211893123011109], [1.126635922106507, -1.8430695501566485], [-1.0799315083634233, -0.4779740040404867], [-1.1474686524111024, -0.47965581400794766], [-0.43782004474443403, 0.6203582983435125], [-0.4980324506923049, 0.698457149107336], [1.9295320538169858, 0.00377088908626934], [0.9494208069257608, 0.9318483741143037], [0.0875512413851909, 0.339964983801262], [-1.225435518830168, -0.01568211160255477], [0.8443629764015471, 0.16092816829822298], [-1.0002153473895647, -0.19065349358139935], [-1.5447710967776116, -0.3948495140334503], [1.1880297923523018, -0.26773353689396645], [0.3169426119248496, -1.1280113314700069], [0.920858823780819, 0.280441705316296], [0.3187276529430212, -0.9931236109295807], [0.8568306119026912, 0.8416312640736364], [-0.6510255933001469, -0.24945858016094885], [-1.0342428417844647, 0.04949498165009074], [0.681594518281627, 0.49383677628095635], [-0.8034096641738411, 0.6433144650629279], [-0.6895497777502005, -1.5706234086334527], [-0.45553250351734315, -0.20690367616397173], [0.01747915902505673, 0.8801789120807822], [-0.35399391125348395, -1.6981058194322545], [-1.3749512934180188, 0.3872804753950634], [-0.6436184028328905, -2.2555642294021894], [-2.2234031522244266, -1.0225068436356035], [0.6252314510271875, 0.0386305518401881], [-1.6020576556067476, -1.6567151023219537], [-1.1043833394284506, -0.9855107376841507], [0.052165079260974405, -1.4718350074635869], [-0.7395629963913133, 1.6481349322075596], [1.5430145954067358, 0.16422775548733395], [-1.2928569097234486, 0.5672902778526694], [0.26705086934918293, -0.2226751005151545], [-0.0392828182274956, -0.35343174875719907], [-1.1680934977411974, -1.6164741886510325], [0.5232766605317537, -0.2918373627478628], [-0.1715463312222481, -0.7614922118116233], [0.7717905512136674, 0.8579239242923363], [0.8235041539637314, 1.1411018666575734], [2.16323594928069, 1.4665787155741776], [1.336527949436392, 0.852551939461232]]}, \"id\": \"el94984396235216\"});\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & mpld3\n", " mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949843962352165673190064\", {\"axes\": [{\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984396237008\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984396371664\", \"pathtransforms\": [[16.348522912120725, 0.0, 0.0, 16.348522912120725, 0.0, 0.0], [18.057420231551603, 0.0, 0.0, 18.057420231551603, 0.0, 0.0], [14.686860930116316, 0.0, 0.0, 14.686860930116316, 0.0, 0.0], [21.963488223860693, 0.0, 0.0, 21.963488223860693, 0.0, 0.0], [21.53122408051117, 0.0, 0.0, 21.53122408051117, 0.0, 0.0], [23.923899504768666, 0.0, 0.0, 23.923899504768666, 0.0, 0.0], [4.2275221872632205, 0.0, 0.0, 4.2275221872632205, 0.0, 0.0], [23.513737660604505, 0.0, 0.0, 23.513737660604505, 0.0, 0.0], [15.566836950098674, 0.0, 0.0, 15.566836950098674, 0.0, 0.0], [23.285301030086014, 0.0, 0.0, 23.285301030086014, 0.0, 0.0], [20.649705945900994, 0.0, 0.0, 20.649705945900994, 0.0, 0.0], [24.687538180298255, 0.0, 0.0, 24.687538180298255, 0.0, 0.0], [21.649315726600737, 0.0, 0.0, 21.649315726600737, 0.0, 0.0], [15.000918202283376, 0.0, 0.0, 15.000918202283376, 0.0, 0.0], [17.58687734750734, 0.0, 0.0, 17.58687734750734, 0.0, 0.0], [15.242669867443535, 0.0, 0.0, 15.242669867443535, 0.0, 0.0], [15.008471599939693, 0.0, 0.0, 15.008471599939693, 0.0, 0.0], [12.690632770345427, 0.0, 0.0, 12.690632770345427, 0.0, 0.0], [17.49727131060602, 0.0, 0.0, 17.49727131060602, 0.0, 0.0], [20.514071428802847, 0.0, 0.0, 20.514071428802847, 0.0, 0.0], [13.08425383186103, 0.0, 0.0, 13.08425383186103, 0.0, 0.0], [17.991421328113333, 0.0, 0.0, 17.991421328113333, 0.0, 0.0], [8.512166099778627, 0.0, 0.0, 8.512166099778627, 0.0, 0.0], [9.933273889151966, 0.0, 0.0, 9.933273889151966, 0.0, 0.0], [5.375203396219885, 0.0, 0.0, 5.375203396219885, 0.0, 0.0], [24.478908063511984, 0.0, 0.0, 24.478908063511984, 0.0, 0.0], [1.543675170178418, 0.0, 0.0, 1.543675170178418, 0.0, 0.0], [10.499264173442748, 0.0, 0.0, 10.499264173442748, 0.0, 0.0], [19.45026505130572, 0.0, 0.0, 19.45026505130572, 0.0, 0.0], [7.0871819110498295, 0.0, 0.0, 7.0871819110498295, 0.0, 0.0], [23.331964286514335, 0.0, 0.0, 23.331964286514335, 0.0, 0.0], [21.076289378747887, 0.0, 0.0, 21.076289378747887, 0.0, 0.0], [24.424107341029007, 0.0, 0.0, 24.424107341029007, 0.0, 0.0], [17.701843860867893, 0.0, 0.0, 17.701843860867893, 0.0, 0.0], [13.617428990821058, 0.0, 0.0, 13.617428990821058, 0.0, 0.0], [18.417325659322138, 0.0, 0.0, 18.417325659322138, 0.0, 0.0], [23.970387045732192, 0.0, 0.0, 23.970387045732192, 0.0, 0.0], [17.9292408458861, 0.0, 0.0, 17.9292408458861, 0.0, 0.0], [12.84299856967564, 0.0, 0.0, 12.84299856967564, 0.0, 0.0], [23.27239117169145, 0.0, 0.0, 23.27239117169145, 0.0, 0.0], [15.151880226242822, 0.0, 0.0, 15.151880226242822, 0.0, 0.0], [0.9240777848582903, 0.0, 0.0, 0.9240777848582903, 0.0, 0.0], [12.364950027913077, 0.0, 0.0, 12.364950027913077, 0.0, 0.0], [14.015721093244498, 0.0, 0.0, 14.015721093244498, 0.0, 0.0], [23.024107980360448, 0.0, 0.0, 23.024107980360448, 0.0, 0.0], [16.823395802789392, 0.0, 0.0, 16.823395802789392, 0.0, 0.0], [16.566128015151374, 0.0, 0.0, 16.566128015151374, 0.0, 0.0], [14.403837503066512, 0.0, 0.0, 14.403837503066512, 0.0, 0.0], [23.315841632176088, 0.0, 0.0, 23.315841632176088, 0.0, 0.0], [24.152636756861217, 0.0, 0.0, 24.152636756861217, 0.0, 0.0], [24.744251473011822, 0.0, 0.0, 24.744251473011822, 0.0, 0.0], [15.249797953751347, 0.0, 0.0, 15.249797953751347, 0.0, 0.0], [24.421042404359767, 0.0, 0.0, 24.421042404359767, 0.0, 0.0], [22.10915080130572, 0.0, 0.0, 22.10915080130572, 0.0, 0.0], [20.422831988905905, 0.0, 0.0, 20.422831988905905, 0.0, 0.0], [12.294972359507321, 0.0, 0.0, 12.294972359507321, 0.0, 0.0], [11.559221131868624, 0.0, 0.0, 11.559221131868624, 0.0, 0.0], [10.124162044924017, 0.0, 0.0, 10.124162044924017, 0.0, 0.0], [23.86635384423282, 0.0, 0.0, 23.86635384423282, 0.0, 0.0], [13.473262557384754, 0.0, 0.0, 13.473262557384754, 0.0, 0.0], [16.723869343924, 0.0, 0.0, 16.723869343924, 0.0, 0.0], [17.46173654600223, 0.0, 0.0, 17.46173654600223, 0.0, 0.0], [21.916953180077012, 0.0, 0.0, 21.916953180077012, 0.0, 0.0], [22.82833091394729, 0.0, 0.0, 22.82833091394729, 0.0, 0.0], [9.26538430870693, 0.0, 0.0, 9.26538430870693, 0.0, 0.0], [16.233336379402587, 0.0, 0.0, 16.233336379402587, 0.0, 0.0], [22.809664507838747, 0.0, 0.0, 22.809664507838747, 0.0, 0.0], [22.47128903097196, 0.0, 0.0, 22.47128903097196, 0.0, 0.0], [7.950998013776123, 0.0, 0.0, 7.950998013776123, 0.0, 0.0], [9.825117373343545, 0.0, 0.0, 9.825117373343545, 0.0, 0.0], [13.703173724473146, 0.0, 0.0, 13.703173724473146, 0.0, 0.0], [6.820406430602588, 0.0, 0.0, 6.820406430602588, 0.0, 0.0], [16.190665708192483, 0.0, 0.0, 16.190665708192483, 0.0, 0.0], [8.150501959456237, 0.0, 0.0, 8.150501959456237, 0.0, 0.0], [18.72836354385424, 0.0, 0.0, 18.72836354385424, 0.0, 0.0], [12.336759779566853, 0.0, 0.0, 12.336759779566853, 0.0, 0.0], [19.187718959214298, 0.0, 0.0, 19.187718959214298, 0.0, 0.0], [8.51743465782941, 0.0, 0.0, 8.51743465782941, 0.0, 0.0], [24.54378637539591, 0.0, 0.0, 24.54378637539591, 0.0, 0.0], [23.992812759438387, 0.0, 0.0, 23.992812759438387, 0.0, 0.0], [15.551526036918505, 0.0, 0.0, 15.551526036918505, 0.0, 0.0], [12.226731343130503, 0.0, 0.0, 12.226731343130503, 0.0, 0.0], [12.432489616233788, 0.0, 0.0, 12.432489616233788, 0.0, 0.0], [17.273999858532697, 0.0, 0.0, 17.273999858532697, 0.0, 0.0], [4.968592834347467, 0.0, 0.0, 4.968592834347467, 0.0, 0.0], [19.871580085843778, 0.0, 0.0, 19.871580085843778, 0.0, 0.0], [15.875730922023227, 0.0, 0.0, 15.875730922023227, 0.0, 0.0], [15.263257192244291, 0.0, 0.0, 15.263257192244291, 0.0, 0.0], [22.351912828482885, 0.0, 0.0, 22.351912828482885, 0.0, 0.0], [20.920712463720857, 0.0, 0.0, 20.920712463720857, 0.0, 0.0], [24.27127824761742, 0.0, 0.0, 24.27127824761742, 0.0, 0.0], [14.739219548489482, 0.0, 0.0, 14.739219548489482, 0.0, 0.0], [23.538027606635694, 0.0, 0.0, 23.538027606635694, 0.0, 0.0], [21.801109755563566, 0.0, 0.0, 21.801109755563566, 0.0, 0.0], [14.853703275089035, 0.0, 0.0, 14.853703275089035, 0.0, 0.0], [19.58938734560336, 0.0, 0.0, 19.58938734560336, 0.0, 0.0], [13.34652027641291, 0.0, 0.0, 13.34652027641291, 0.0, 0.0], [23.23258563378997, 0.0, 0.0, 23.23258563378997, 0.0, 0.0], [8.330640943295903, 0.0, 0.0, 8.330640943295903, 0.0, 0.0], [11.451302187369132, 0.0, 0.0, 11.451302187369132, 0.0, 0.0], [10.629370330813623, 0.0, 0.0, 10.629370330813623, 0.0, 0.0], [15.772808341117006, 0.0, 0.0, 15.772808341117006, 0.0, 0.0], [21.44808489583266, 0.0, 0.0, 21.44808489583266, 0.0, 0.0], [18.034730709400826, 0.0, 0.0, 18.034730709400826, 0.0, 0.0], [17.35035353000559, 0.0, 0.0, 17.35035353000559, 0.0, 0.0], [0.58053024771827, 0.0, 0.0, 0.58053024771827, 0.0, 0.0], [16.204741825360163, 0.0, 0.0, 16.204741825360163, 0.0, 0.0], [6.263443558259721, 0.0, 0.0, 6.263443558259721, 0.0, 0.0], [11.33805054665249, 0.0, 0.0, 11.33805054665249, 0.0, 0.0], [23.990660978246197, 0.0, 0.0, 23.990660978246197, 0.0, 0.0], [11.530908660329553, 0.0, 0.0, 11.530908660329553, 0.0, 0.0], [23.018211236750528, 0.0, 0.0, 23.018211236750528, 0.0, 0.0], [22.262371668845418, 0.0, 0.0, 22.262371668845418, 0.0, 0.0], [9.91152954218379, 0.0, 0.0, 9.91152954218379, 0.0, 0.0], [19.33639754245669, 0.0, 0.0, 19.33639754245669, 0.0, 0.0], [8.44962823000615, 0.0, 0.0, 8.44962823000615, 0.0, 0.0], [21.197020498312288, 0.0, 0.0, 21.197020498312288, 0.0, 0.0], [19.836714267887377, 0.0, 0.0, 19.836714267887377, 0.0, 0.0], [22.387421535745197, 0.0, 0.0, 22.387421535745197, 0.0, 0.0], [17.202220452948637, 0.0, 0.0, 17.202220452948637, 0.0, 0.0], [23.76405481250449, 0.0, 0.0, 23.76405481250449, 0.0, 0.0], [5.5192674180882175, 0.0, 0.0, 5.5192674180882175, 0.0, 0.0], [13.446018388746271, 0.0, 0.0, 13.446018388746271, 0.0, 0.0], [21.009295377581058, 0.0, 0.0, 21.009295377581058, 0.0, 0.0], [16.065245283446018, 0.0, 0.0, 16.065245283446018, 0.0, 0.0], [10.332477690714308, 0.0, 0.0, 10.332477690714308, 0.0, 0.0], [8.135076673686413, 0.0, 0.0, 8.135076673686413, 0.0, 0.0], [22.461752285096775, 0.0, 0.0, 22.461752285096775, 0.0, 0.0], [17.08986737672652, 0.0, 0.0, 17.08986737672652, 0.0, 0.0], [23.33708530573924, 0.0, 0.0, 23.33708530573924, 0.0, 0.0], [21.27551676871227, 0.0, 0.0, 21.27551676871227, 0.0, 0.0], [15.903377342035718, 0.0, 0.0, 15.903377342035718, 0.0, 0.0], [15.184279845191494, 0.0, 0.0, 15.184279845191494, 0.0, 0.0], [17.84083169896093, 0.0, 0.0, 17.84083169896093, 0.0, 0.0], [23.426532825848838, 0.0, 0.0, 23.426532825848838, 0.0, 0.0], [21.33331278520665, 0.0, 0.0, 21.33331278520665, 0.0, 0.0], [1.7834915590975935, 0.0, 0.0, 1.7834915590975935, 0.0, 0.0], [20.70006040679928, 0.0, 0.0, 20.70006040679928, 0.0, 0.0], [23.82429778097729, 0.0, 0.0, 23.82429778097729, 0.0, 0.0], [20.94165556888317, 0.0, 0.0, 20.94165556888317, 0.0, 0.0], [10.4528861147572, 0.0, 0.0, 10.4528861147572, 0.0, 0.0], [17.27622585790927, 0.0, 0.0, 17.27622585790927, 0.0, 0.0], [9.306708649068904, 0.0, 0.0, 9.306708649068904, 0.0, 0.0], [14.886303223649568, 0.0, 0.0, 14.886303223649568, 0.0, 0.0], [24.051347360175598, 0.0, 0.0, 24.051347360175598, 0.0, 0.0], [23.873448596207165, 0.0, 0.0, 23.873448596207165, 0.0, 0.0], [13.213275501986978, 0.0, 0.0, 13.213275501986978, 0.0, 0.0], [14.479253872185012, 0.0, 0.0, 14.479253872185012, 0.0, 0.0], [19.248422544494808, 0.0, 0.0, 19.248422544494808, 0.0, 0.0], [24.38372882973828, 0.0, 0.0, 24.38372882973828, 0.0, 0.0], [9.551721907509092, 0.0, 0.0, 9.551721907509092, 0.0, 0.0], [12.593272838870766, 0.0, 0.0, 12.593272838870766, 0.0, 0.0], [23.22138259102476, 0.0, 0.0, 23.22138259102476, 0.0, 0.0], [17.425188092725982, 0.0, 0.0, 17.425188092725982, 0.0, 0.0], [23.556618060772873, 0.0, 0.0, 23.556618060772873, 0.0, 0.0], [10.701271919417742, 0.0, 0.0, 10.701271919417742, 0.0, 0.0], [18.13305738269335, 0.0, 0.0, 18.13305738269335, 0.0, 0.0], [14.19158228698458, 0.0, 0.0, 14.19158228698458, 0.0, 0.0], [13.978434887673792, 0.0, 0.0, 13.978434887673792, 0.0, 0.0], [16.608731972807778, 0.0, 0.0, 16.608731972807778, 0.0, 0.0], [16.350283138358392, 0.0, 0.0, 16.350283138358392, 0.0, 0.0], [14.852087180876866, 0.0, 0.0, 14.852087180876866, 0.0, 0.0], [23.76545332619945, 0.0, 0.0, 23.76545332619945, 0.0, 0.0], [21.25309251909572, 0.0, 0.0, 21.25309251909572, 0.0, 0.0], [21.19205230903429, 0.0, 0.0, 21.19205230903429, 0.0, 0.0], [13.377552821843278, 0.0, 0.0, 13.377552821843278, 0.0, 0.0], [18.884140323327827, 0.0, 0.0, 18.884140323327827, 0.0, 0.0], [21.931141302456027, 0.0, 0.0, 21.931141302456027, 0.0, 0.0], [22.160892711427838, 0.0, 0.0, 22.160892711427838, 0.0, 0.0], [14.58331662971133, 0.0, 0.0, 14.58331662971133, 0.0, 0.0], [21.813926299452167, 0.0, 0.0, 21.813926299452167, 0.0, 0.0], [21.313270322890975, 0.0, 0.0, 21.313270322890975, 0.0, 0.0], [9.346105203915398, 0.0, 0.0, 9.346105203915398, 0.0, 0.0], [23.1199965366747, 0.0, 0.0, 23.1199965366747, 0.0, 0.0], [16.505170723297347, 0.0, 0.0, 16.505170723297347, 0.0, 0.0], [17.327820506674286, 0.0, 0.0, 17.327820506674286, 0.0, 0.0], [16.63643886428774, 0.0, 0.0, 16.63643886428774, 0.0, 0.0], [18.722238731900198, 0.0, 0.0, 18.722238731900198, 0.0, 0.0], [19.581568042848367, 0.0, 0.0, 19.581568042848367, 0.0, 0.0], [17.536198290135545, 0.0, 0.0, 17.536198290135545, 0.0, 0.0], [23.1312484819297, 0.0, 0.0, 23.1312484819297, 0.0, 0.0], [19.684780675537198, 0.0, 0.0, 19.684780675537198, 0.0, 0.0], [15.74150661263102, 0.0, 0.0, 15.74150661263102, 0.0, 0.0], [16.037990332154024, 0.0, 0.0, 16.037990332154024, 0.0, 0.0], [22.372252091817813, 0.0, 0.0, 22.372252091817813, 0.0, 0.0], [14.660603602569678, 0.0, 0.0, 14.660603602569678, 0.0, 0.0], [11.424869877488288, 0.0, 0.0, 11.424869877488288, 0.0, 0.0], [6.054443731050978, 0.0, 0.0, 6.054443731050978, 0.0, 0.0], [23.254189160906442, 0.0, 0.0, 23.254189160906442, 0.0, 0.0], [23.81184541811628, 0.0, 0.0, 23.81184541811628, 0.0, 0.0], [8.61093842706695, 0.0, 0.0, 8.61093842706695, 0.0, 0.0], [14.368899489121146, 0.0, 0.0, 14.368899489121146, 0.0, 0.0], [10.404535736797616, 0.0, 0.0, 10.404535736797616, 0.0, 0.0], [8.458266057965615, 0.0, 0.0, 8.458266057965615, 0.0, 0.0], [23.568481031839255, 0.0, 0.0, 23.568481031839255, 0.0, 0.0], [5.925320179157247, 0.0, 0.0, 5.925320179157247, 0.0, 0.0], [24.601586612854003, 0.0, 0.0, 24.601586612854003, 0.0, 0.0], [7.716059119247248, 0.0, 0.0, 7.716059119247248, 0.0, 0.0], [23.08693512738942, 0.0, 0.0, 23.08693512738942, 0.0, 0.0], [18.700137105676205, 0.0, 0.0, 18.700137105676205, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [0.3], \"facecolors\": [\"#004CFF\", \"#00DCFE\", \"#FFDA00\", \"#FF1A00\", \"#9DFF59\", \"#009CFF\", \"#00009F\", \"#FFBD00\", \"#000084\", \"#12FCE4\", \"#93FF63\", \"#D50000\", \"#0000E3\", \"#2CFFCA\", \"#000096\", \"#00DCFE\", \"#E0FF15\", \"#0098FF\", \"#0054FF\", \"#0000FF\", \"#BAFF3C\", \"#FFCB00\", \"#FFE200\", \"#B60000\", \"#00007F\", \"#F4F802\", \"#CDFF29\", \"#C3FF32\", \"#A80000\", \"#00008D\", \"#FFC800\", \"#FF5900\", \"#83FF73\", \"#00D4FF\", \"#FF6F00\", \"#0000EC\", \"#49FFAD\", \"#89FF6C\", \"#FFCB00\", \"#0000E3\", \"#0064FF\", \"#2FFFC7\", \"#E0FF15\", \"#FE1200\", \"#DDFF18\", \"#0004FF\", \"#910000\", \"#FF2100\", \"#7CFF79\", \"#D50000\", \"#9DFF59\", \"#D50000\", \"#FF4A00\", \"#9F0000\", \"#DA0000\", \"#0000A3\", \"#0030FF\", \"#1FFFD7\", \"#B60000\", \"#00B0FF\", \"#0020FF\", \"#FF1600\", \"#4CFFAA\", \"#E80000\", \"#0020FF\", \"#FFE900\", \"#49FFAD\", \"#0000D1\", \"#FFC800\", \"#0078FF\", \"#0000A8\", \"#0000BF\", \"#0000BF\", \"#E80000\", \"#FF9F00\", \"#F50B00\", \"#FFDE00\", \"#93FF63\", \"#00B4FF\", \"#7F0000\", \"#08F0ED\", \"#63FF93\", \"#15FFE1\", \"#960000\", \"#0030FF\", \"#00CCFF\", \"#FFD700\", \"#0000C3\", \"#D4FF22\", \"#69FF8D\", \"#00A0FF\", \"#0070FF\", \"#86FF70\", \"#0CF4EA\", \"#56FFA0\", \"#00D4FF\", \"#9F0000\", \"#0004FF\", \"#0000EC\", \"#00DCFE\", \"#C7FF2F\", \"#FEED00\", \"#25FFD0\", \"#7F0000\", \"#00E4F7\", \"#FFB100\", \"#EDFF08\", \"#FF5900\", \"#960000\", \"#FE1200\", \"#FF8900\", \"#FFC800\", \"#00D4FF\", \"#0014FF\", \"#0000C3\", \"#0074FF\", \"#42FFB3\", \"#8DFF69\", \"#FF8100\", \"#AC0000\", \"#0000FF\", \"#0000F5\", \"#C3FF32\", \"#FF9B00\", \"#FF3700\", \"#C80000\", \"#910000\", \"#29FFCD\", \"#18FFDD\", \"#0014FF\", \"#FFD300\", \"#FEED00\", \"#FF2C00\", \"#0000EC\", \"#79FF7D\", \"#BDFF39\", \"#0074FF\", \"#0028FF\", \"#FF2C00\", \"#0000BF\", \"#63FF93\", \"#0000FF\", \"#56FFA0\", \"#910000\", \"#3CFFBA\", \"#FF3000\", \"#0000FF\", \"#0090FF\", \"#2CFFCA\", \"#29FFCD\", \"#FFE200\", \"#00DCFE\", \"#FFB900\", \"#EDFF08\", \"#25FFD0\", \"#42FFB3\", \"#DAFF1C\", \"#0000CC\", \"#FF5100\", \"#FAF000\", \"#FFAE00\", \"#9AFF5C\", \"#0000FA\", \"#2CFFCA\", \"#2CFFCA\", \"#00C4FF\", \"#00009A\", \"#FFA300\", \"#0000FA\", \"#D4FF22\", \"#FFC400\", \"#EAFF0C\", \"#AC0000\", \"#0000F1\", \"#FF2500\", \"#00009A\", \"#96FF5F\", \"#2CFFCA\", \"#8DFF69\", \"#0CF4EA\", \"#0040FF\", \"#000091\", \"#89FF6C\", \"#FF3F00\", \"#12FCE4\", \"#0060FF\", \"#0000D5\", \"#0000DE\", \"#0060FF\", \"#0000EC\", \"#008CFF\", \"#0000C8\", \"#0000C8\", \"#FF3000\", \"#0020FF\", \"#ADFF49\", \"#FF8100\", \"#56FFA0\", \"#0018FF\", \"#0048FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[1.764052345967664, -0.3691818379424436], [0.4001572083672233, -0.2393791775759264], [0.9787379841057392, 1.0996595958871132], [2.240893199201458, 0.6552637307225978], [1.8675579901499675, 0.640131526097592], [-0.977277879876411, -1.6169560443108344], [0.9500884175255894, -0.024326124398935636], [-0.1513572082976979, -0.7380309092056887], [-0.10321885179355784, 0.27992459904323824], [0.41059850193837233, -0.09815038964295794], [0.144043571160878, 0.9101789080925919], [1.454273506962975, 0.31721821519130206], [0.7610377251469934, 0.7863279621089762], [0.12167501649282841, -0.46641909673594306], [0.44386323274542566, -0.9444462559182504], [0.33367432737426683, -0.41004969320254847], [1.4940790731576061, -0.017020413861440594], [-0.20515826376580087, 0.3791517355550818], [0.31306770165090136, 2.259308950690852], [-0.8540957393017248, -0.04225715166064269], [-2.5529898158340787, -0.955945000492777], [0.6536185954403606, -0.34598177569938643], [0.8644361988595057, -0.4635959746460942], [-0.7421650204064419, 0.4814814737734622], [2.2697546239876076, -1.5407970144446248], [-1.4543656745987648, 0.06326199420033171], [0.04575851730144607, 0.1565065379653756], [-0.1871838500258336, 0.23218103620027578], [1.5327792143584575, -0.5973160689653627], [1.469358769900285, -0.237921729736007], [0.1549474256969163, -1.4240609089825316], [0.37816251960217356, -0.49331988336219407], [-0.8877857476301128, -0.5428614760167177], [-1.980796468223927, 0.4160500462614255], [-0.3479121493261526, -1.1561824318219127], [0.15634896910398005, 0.7811981017099934], [1.2302906807277207, 1.4944845444913688], [1.2023798487844113, -2.0699850250135325], [-0.3873268174079523, 0.42625873077810095], [-0.30230275057533557, 0.6769080350302455], [-1.0485529650670926, -0.637437025552229], [-1.4200179371789752, -0.39727181432879766], [-1.7062701906250126, -0.13288057758695562], [1.9507753952317897, -0.2977908794017283], [-0.5096521817516535, -0.3090129690471222], [-0.4380743016111864, -1.6760038063299767], [-1.2527953600499262, 1.15233156478312], [0.7774903558319101, 1.079618592036821], [-1.6138978475579515, -0.8133642592042029], [-0.2127402802139687, -1.466424327802514], [-0.8954665611936756, 0.5210648764527586], [0.386902497859262, -0.5757879698130661], [-0.510805137568873, 0.14195316332077967], [-1.180632184122412, -0.3193284171450952], [-0.028182228338654868, 0.6915387510701866], [0.42833187053041766, 0.6947491436560059], [0.06651722238316789, -0.7255973784635843], [0.3024718977397814, -1.3833639553950554], [-0.6343220936809636, -1.582938397335082], [-0.3627411659871381, 0.6103793791072052], [-0.672460447775951, -1.188859257784029], [-0.3595531615405413, -0.5068163542986875], [-0.813146282044454, -0.5963140384505081], [-1.7262826023316769, -0.05256729626954629], [0.17742614225375283, -1.936279805846507], [-0.4017809362082619, 0.18877859679382855], [-1.6301983469660446, 0.5238910238342056], [0.4627822555257742, 0.08842208704466141], [-0.9072983643832422, -0.3108861716984717], [0.05194539579613895, 0.09740016626878341], [0.7290905621775369, 0.3990463456401302], [0.12898291075741067, -2.77259275642665], [1.1394006845433007, 1.9559123082506942], [-1.2348258203536526, 0.39009332268792646], [0.402341641177549, -0.65240858238702], [-0.6848100909403132, -0.3909533751876011], [-0.8707971491818818, 0.49374177734918845], [-0.5788496647644155, -0.11610393903436653], [-0.31155253212737266, -2.0306844677814944], [0.05616534222974544, 2.0644928613593194], [-1.1651498407833565, -0.11054065723247261], [0.9008264869541871, 1.0201727117157997], [0.46566243973045984, -0.6920498477843912], [-1.5362436862772237, 1.5363770542457977], [1.4882521937955997, 0.28634368889227957], [1.8958891760305832, 0.6088438344754508], [1.1787795711596507, -1.0452533661469547], [-0.17992483581235091, 1.2111452896827009], [-1.0707526215105425, 0.6898181645347884], [1.0544517269311366, 1.3018462295649984], [-0.40317694697317963, -0.6280875596415789], [1.2224450703824274, -0.4810271184607877], [0.2082749780768603, 2.303916697683942], [0.9766390364837128, -1.0600158227215473], [0.3563663971744019, -0.13594970067832082], [0.7065731681919482, 1.1368913626026953], [0.010500020720820478, 0.0977249677148556], [1.7858704939058352, 0.5829536797532936], [0.12691209270361992, -0.3994490292628752], [0.40198936344470165, 0.37005588784751875], [1.8831506970562544, -1.3065268517353166], [-1.3477590611424464, 1.658130679618188], [-1.2704849984857336, -0.11816404512856976], [0.9693967081580112, -0.6801782039968504], [-1.17312340511416, 0.6663830820319143], [1.9436211856492926, -0.4607197873885533], [-0.41361898075974735, -1.3342584714027534], [-0.7474548114407578, -1.3467175057975553], [1.9229420264803847, 0.6937731526901325], [1.4805147914344243, -0.1595734381462669], [1.8675589604265699, -0.13370155966843916], [0.9060446582753853, 1.0777438059762627], [-0.8612256850547025, -1.1268258087567435], [1.9100649530990337, -0.7306777528648248], [-0.2680033709513804, -0.38487980918127546], [0.8024563957963952, 0.094351589317074], [0.947251967773748, -0.042171451290578935], [-0.1550100930908342, -0.2868871923899076], [0.6140793703460803, -0.0616264020956474], [0.9222066715665268, -0.10730527629117469], [0.37642553115562943, -0.7196043885517929], [-1.0994007905841945, -0.8129929885540773], [0.298238174206056, 0.2745163577239395], [1.3263858966870303, -0.8909150829955279], [-0.6945678597313655, -1.1573552591908536], [-0.14963454032767076, -0.3122922511256933], [-0.43515355172163744, -0.1576670161638159], [1.8492637284793418, 2.2567234972982093], [0.6722947570124355, -0.7047002758562337], [0.40746183624111043, 0.9432607249694948], [-0.7699160744453164, 0.7471883342046318], [0.5392491912918173, -1.188944955203736], [-0.6743326606573761, 0.7732529774025997], [0.03183055827435118, -1.1838806401933177], [-0.635846078378881, -2.659172237996741], [0.6764332949464997, 0.6063195243593807], [0.5765908166149409, -1.7558905834377194], [-0.20829875557799488, 0.45093446180591484], [0.3960067126616453, -0.6840108977372166], [-1.0930615087305058, 1.6595507961898721], [-1.4912575927056055, 1.068509399316009], [0.4393917012645369, -0.45338580385138766], [0.16667349537252904, -0.6878376110286823], [0.6350314368921064, -1.2140774030941206], [2.383144774863942, -0.4409226322925914], [0.9444794869904138, -0.2803554951845091], [-0.9128222254441586, -0.3646935443916854], [1.117016288095853, 0.15670385527236397], [-1.3159074105115212, 0.5785214977288784], [-0.461584604814709, 0.349654456993174], [-0.06824160532463124, -0.764143923906443], [1.7133427216493666, -1.4377914738015785], [-0.7447548220484399, 1.3645318481024713], [-0.8264385386590144, -0.6894491845499376], [-0.0984525244254323, -0.6522935999350191], [-0.6634782863621074, -0.5211893123011109], [1.126635922106507, -1.8430695501566485], [-1.0799315083634233, -0.4779740040404867], [-1.1474686524111024, -0.47965581400794766], [-0.43782004474443403, 0.6203582983435125], [-0.4980324506923049, 0.698457149107336], [1.9295320538169858, 0.00377088908626934], [0.9494208069257608, 0.9318483741143037], [0.0875512413851909, 0.339964983801262], [-1.225435518830168, -0.01568211160255477], [0.8443629764015471, 0.16092816829822298], [-1.0002153473895647, -0.19065349358139935], [-1.5447710967776116, -0.3948495140334503], [1.1880297923523018, -0.26773353689396645], [0.3169426119248496, -1.1280113314700069], [0.920858823780819, 0.280441705316296], [0.3187276529430212, -0.9931236109295807], [0.8568306119026912, 0.8416312640736364], [-0.6510255933001469, -0.24945858016094885], [-1.0342428417844647, 0.04949498165009074], [0.681594518281627, 0.49383677628095635], [-0.8034096641738411, 0.6433144650629279], [-0.6895497777502005, -1.5706234086334527], [-0.45553250351734315, -0.20690367616397173], [0.01747915902505673, 0.8801789120807822], [-0.35399391125348395, -1.6981058194322545], [-1.3749512934180188, 0.3872804753950634], [-0.6436184028328905, -2.2555642294021894], [-2.2234031522244266, -1.0225068436356035], [0.6252314510271875, 0.0386305518401881], [-1.6020576556067476, -1.6567151023219537], [-1.1043833394284506, -0.9855107376841507], [0.052165079260974405, -1.4718350074635869], [-0.7395629963913133, 1.6481349322075596], [1.5430145954067358, 0.16422775548733395], [-1.2928569097234486, 0.5672902778526694], [0.26705086934918293, -0.2226751005151545], [-0.0392828182274956, -0.35343174875719907], [-1.1680934977411974, -1.6164741886510325], [0.5232766605317537, -0.2918373627478628], [-0.1715463312222481, -0.7614922118116233], [0.7717905512136674, 0.8579239242923363], [0.8235041539637314, 1.1411018666575734], [2.16323594928069, 1.4665787155741776], [1.336527949436392, 0.852551939461232]]}, \"id\": \"el94984396235216\"});\n", " })\n", " });\n", "}\n", "</script>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "<IPython.core.display.HTML at 0x10656dad0>" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that as you hover over the plot, a toolbar appears in the lower left. This has tools to enable panning and zooming, and a button to reset the view once you've explored the plot.\n", "\n", "If you'd like to use mpld3 by default for every figure you generate, you can call the following command:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mpld3.enable_notebook()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Other Plot Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nearly any type of plot matplotlib can do, mpld3 can visualize as well. Below are some examples:" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "A Histogram" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Histogram with modified axes/grid\n", "fig = plt.figure()\n", "\n", "ax = fig.add_subplot(111, axisbg='#EEEEEE')\n", "ax.grid(color='white', linestyle='solid')\n", "\n", "x = np.random.normal(size=1000)\n", "ax.hist(x, 30, histtype='stepfilled', fc='lightblue', alpha=0.5);" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div id=\"fig_el949844002837923260127407\"></div>\n", "<script>\n", "function mpld3_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n", " // already loaded: just create the figure\n", " !function(mpld3){\n", " \n", " mpld3.draw_figure(\"fig_el949844002837923260127407\", {\"axes\": [{\"xlim\": [-3.0, 4.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 4.0], \"ylim\": [0.0, 100.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el94984401567632\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984400283728\", \"ydomain\": [0.0, 100.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[-2.994612860227619, 0.0], [-2.994612860227619, 2.0], [-2.7890932724436923, 2.0], [-2.7890932724436923, 1.0], [-2.5835736846597657, 1.0], [-2.5835736846597657, 3.0], [-2.378054096875839, 3.0], [-2.378054096875839, 1.0], [-2.1725345090919124, 1.0], [-2.1725345090919124, 6.0], [-1.967014921307986, 6.0], [-1.967014921307986, 13.0], [-1.7614953335240593, 13.0], [-1.7614953335240593, 20.0], [-1.5559757457401326, 20.0], [-1.5559757457401326, 33.0], [-1.350456157956206, 33.0], [-1.350456157956206, 32.0], [-1.1449365701722793, 32.0], [-1.1449365701722793, 48.0], [-0.9394169823883529, 48.0], [-0.9394169823883529, 71.0], [-0.7338973946044263, 71.0], [-0.7338973946044263, 53.0], [-0.5283778068204996, 53.0], [-0.5283778068204996, 81.0], [-0.32285821903657297, 81.0], [-0.32285821903657297, 68.0], [-0.11733863125264632, 68.0], [-0.11733863125264632, 94.0], [0.08818095653128033, 94.0], [0.08818095653128033, 92.0], [0.293700544315207, 92.0], [0.293700544315207, 78.0], [0.49922013209913363, 78.0], [0.49922013209913363, 71.0], [0.7047397198830603, 71.0], [0.7047397198830603, 54.0], [0.9102593076669869, 54.0], [0.9102593076669869, 54.0], [1.1157788954509131, 54.0], [1.1157788954509131, 27.0], [1.3212984832348402, 27.0], [1.3212984832348402, 32.0], [1.5268180710187664, 32.0], [1.5268180710187664, 23.0], [1.7323376588026935, 23.0], [1.7323376588026935, 16.0], [1.9378572465866197, 16.0], [1.9378572465866197, 6.0], [2.143376834370547, 6.0], [2.143376834370547, 12.0], [2.348896422154473, 12.0], [2.348896422154473, 4.0], [2.5544160099384, 4.0], [2.5544160099384, 4.0], [2.7599355977223263, 4.0], [2.7599355977223263, 0.0], [2.9654551855062534, 0.0], [2.9654551855062534, 1.0], [3.1709747732901796, 1.0], [3.1709747732901796, 0.0], [2.9654551855062534, 0.0], [2.9654551855062534, 0.0], [2.7599355977223263, 0.0], [2.7599355977223263, 0.0], [2.5544160099384, 0.0], [2.5544160099384, 0.0], [2.348896422154473, 0.0], [2.348896422154473, 0.0], [2.143376834370547, 0.0], [2.143376834370547, 0.0], [1.9378572465866197, 0.0], [1.9378572465866197, 0.0], [1.7323376588026935, 0.0], [1.7323376588026935, 0.0], [1.5268180710187664, 0.0], [1.5268180710187664, 0.0], [1.3212984832348402, 0.0], [1.3212984832348402, 0.0], [1.1157788954509131, 0.0], [1.1157788954509131, 0.0], [0.9102593076669869, 0.0], [0.9102593076669869, 0.0], [0.7047397198830603, 0.0], [0.7047397198830603, 0.0], [0.49922013209913363, 0.0], [0.49922013209913363, 0.0], [0.293700544315207, 0.0], [0.293700544315207, 0.0], [0.08818095653128033, 0.0], [0.08818095653128033, 0.0], [-0.11733863125264632, 0.0], [-0.11733863125264632, 0.0], [-0.32285821903657297, 0.0], [-0.32285821903657297, 0.0], [-0.5283778068204996, 0.0], [-0.5283778068204996, 0.0], [-0.7338973946044263, 0.0], [-0.7338973946044263, 0.0], [-0.9394169823883529, 0.0], [-0.9394169823883529, 0.0], [-1.1449365701722793, 0.0], [-1.1449365701722793, 0.0], [-1.350456157956206, 0.0], [-1.350456157956206, 0.0], [-1.5559757457401326, 0.0], [-1.5559757457401326, 0.0], [-1.7614953335240593, 0.0], [-1.7614953335240593, 0.0], [-1.967014921307986, 0.0], [-1.967014921307986, 0.0], [-2.1725345090919124, 0.0], [-2.1725345090919124, 0.0], [-2.378054096875839, 0.0], [-2.378054096875839, 0.0], [-2.5835736846597657, 0.0], [-2.5835736846597657, 0.0], [-2.7890932724436923, 0.0], [-2.7890932724436923, 0.0]]}, \"id\": \"el94984400283792\"});\n", " }(mpld3);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/mpld3\n", " require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949844002837923260127407\", {\"axes\": [{\"xlim\": [-3.0, 4.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 4.0], \"ylim\": [0.0, 100.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el94984401567632\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984400283728\", \"ydomain\": [0.0, 100.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[-2.994612860227619, 0.0], [-2.994612860227619, 2.0], [-2.7890932724436923, 2.0], [-2.7890932724436923, 1.0], [-2.5835736846597657, 1.0], [-2.5835736846597657, 3.0], [-2.378054096875839, 3.0], [-2.378054096875839, 1.0], [-2.1725345090919124, 1.0], [-2.1725345090919124, 6.0], [-1.967014921307986, 6.0], [-1.967014921307986, 13.0], [-1.7614953335240593, 13.0], [-1.7614953335240593, 20.0], [-1.5559757457401326, 20.0], [-1.5559757457401326, 33.0], [-1.350456157956206, 33.0], [-1.350456157956206, 32.0], [-1.1449365701722793, 32.0], [-1.1449365701722793, 48.0], [-0.9394169823883529, 48.0], [-0.9394169823883529, 71.0], [-0.7338973946044263, 71.0], [-0.7338973946044263, 53.0], [-0.5283778068204996, 53.0], [-0.5283778068204996, 81.0], [-0.32285821903657297, 81.0], [-0.32285821903657297, 68.0], [-0.11733863125264632, 68.0], [-0.11733863125264632, 94.0], [0.08818095653128033, 94.0], [0.08818095653128033, 92.0], [0.293700544315207, 92.0], [0.293700544315207, 78.0], [0.49922013209913363, 78.0], [0.49922013209913363, 71.0], [0.7047397198830603, 71.0], [0.7047397198830603, 54.0], [0.9102593076669869, 54.0], [0.9102593076669869, 54.0], [1.1157788954509131, 54.0], [1.1157788954509131, 27.0], [1.3212984832348402, 27.0], [1.3212984832348402, 32.0], [1.5268180710187664, 32.0], [1.5268180710187664, 23.0], [1.7323376588026935, 23.0], [1.7323376588026935, 16.0], [1.9378572465866197, 16.0], [1.9378572465866197, 6.0], [2.143376834370547, 6.0], [2.143376834370547, 12.0], [2.348896422154473, 12.0], [2.348896422154473, 4.0], [2.5544160099384, 4.0], [2.5544160099384, 4.0], [2.7599355977223263, 4.0], [2.7599355977223263, 0.0], [2.9654551855062534, 0.0], [2.9654551855062534, 1.0], [3.1709747732901796, 1.0], [3.1709747732901796, 0.0], [2.9654551855062534, 0.0], [2.9654551855062534, 0.0], [2.7599355977223263, 0.0], [2.7599355977223263, 0.0], [2.5544160099384, 0.0], [2.5544160099384, 0.0], [2.348896422154473, 0.0], [2.348896422154473, 0.0], [2.143376834370547, 0.0], [2.143376834370547, 0.0], [1.9378572465866197, 0.0], [1.9378572465866197, 0.0], [1.7323376588026935, 0.0], [1.7323376588026935, 0.0], [1.5268180710187664, 0.0], [1.5268180710187664, 0.0], [1.3212984832348402, 0.0], [1.3212984832348402, 0.0], [1.1157788954509131, 0.0], [1.1157788954509131, 0.0], [0.9102593076669869, 0.0], [0.9102593076669869, 0.0], [0.7047397198830603, 0.0], [0.7047397198830603, 0.0], [0.49922013209913363, 0.0], [0.49922013209913363, 0.0], [0.293700544315207, 0.0], [0.293700544315207, 0.0], [0.08818095653128033, 0.0], [0.08818095653128033, 0.0], [-0.11733863125264632, 0.0], [-0.11733863125264632, 0.0], [-0.32285821903657297, 0.0], [-0.32285821903657297, 0.0], [-0.5283778068204996, 0.0], [-0.5283778068204996, 0.0], [-0.7338973946044263, 0.0], [-0.7338973946044263, 0.0], [-0.9394169823883529, 0.0], [-0.9394169823883529, 0.0], [-1.1449365701722793, 0.0], [-1.1449365701722793, 0.0], [-1.350456157956206, 0.0], [-1.350456157956206, 0.0], [-1.5559757457401326, 0.0], [-1.5559757457401326, 0.0], [-1.7614953335240593, 0.0], [-1.7614953335240593, 0.0], [-1.967014921307986, 0.0], [-1.967014921307986, 0.0], [-2.1725345090919124, 0.0], [-2.1725345090919124, 0.0], [-2.378054096875839, 0.0], [-2.378054096875839, 0.0], [-2.5835736846597657, 0.0], [-2.5835736846597657, 0.0], [-2.7890932724436923, 0.0], [-2.7890932724436923, 0.0]]}, \"id\": \"el94984400283792\"});\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & mpld3\n", " mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949844002837923260127407\", {\"axes\": [{\"xlim\": [-3.0, 4.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 4.0], \"ylim\": [0.0, 100.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.5, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el94984401567632\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984400283728\", \"ydomain\": [0.0, 100.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[-2.994612860227619, 0.0], [-2.994612860227619, 2.0], [-2.7890932724436923, 2.0], [-2.7890932724436923, 1.0], [-2.5835736846597657, 1.0], [-2.5835736846597657, 3.0], [-2.378054096875839, 3.0], [-2.378054096875839, 1.0], [-2.1725345090919124, 1.0], [-2.1725345090919124, 6.0], [-1.967014921307986, 6.0], [-1.967014921307986, 13.0], [-1.7614953335240593, 13.0], [-1.7614953335240593, 20.0], [-1.5559757457401326, 20.0], [-1.5559757457401326, 33.0], [-1.350456157956206, 33.0], [-1.350456157956206, 32.0], [-1.1449365701722793, 32.0], [-1.1449365701722793, 48.0], [-0.9394169823883529, 48.0], [-0.9394169823883529, 71.0], [-0.7338973946044263, 71.0], [-0.7338973946044263, 53.0], [-0.5283778068204996, 53.0], [-0.5283778068204996, 81.0], [-0.32285821903657297, 81.0], [-0.32285821903657297, 68.0], [-0.11733863125264632, 68.0], [-0.11733863125264632, 94.0], [0.08818095653128033, 94.0], [0.08818095653128033, 92.0], [0.293700544315207, 92.0], [0.293700544315207, 78.0], [0.49922013209913363, 78.0], [0.49922013209913363, 71.0], [0.7047397198830603, 71.0], [0.7047397198830603, 54.0], [0.9102593076669869, 54.0], [0.9102593076669869, 54.0], [1.1157788954509131, 54.0], [1.1157788954509131, 27.0], [1.3212984832348402, 27.0], [1.3212984832348402, 32.0], [1.5268180710187664, 32.0], [1.5268180710187664, 23.0], [1.7323376588026935, 23.0], [1.7323376588026935, 16.0], [1.9378572465866197, 16.0], [1.9378572465866197, 6.0], [2.143376834370547, 6.0], [2.143376834370547, 12.0], [2.348896422154473, 12.0], [2.348896422154473, 4.0], [2.5544160099384, 4.0], [2.5544160099384, 4.0], [2.7599355977223263, 4.0], [2.7599355977223263, 0.0], [2.9654551855062534, 0.0], [2.9654551855062534, 1.0], [3.1709747732901796, 1.0], [3.1709747732901796, 0.0], [2.9654551855062534, 0.0], [2.9654551855062534, 0.0], [2.7599355977223263, 0.0], [2.7599355977223263, 0.0], [2.5544160099384, 0.0], [2.5544160099384, 0.0], [2.348896422154473, 0.0], [2.348896422154473, 0.0], [2.143376834370547, 0.0], [2.143376834370547, 0.0], [1.9378572465866197, 0.0], [1.9378572465866197, 0.0], [1.7323376588026935, 0.0], [1.7323376588026935, 0.0], [1.5268180710187664, 0.0], [1.5268180710187664, 0.0], [1.3212984832348402, 0.0], [1.3212984832348402, 0.0], [1.1157788954509131, 0.0], [1.1157788954509131, 0.0], [0.9102593076669869, 0.0], [0.9102593076669869, 0.0], [0.7047397198830603, 0.0], [0.7047397198830603, 0.0], [0.49922013209913363, 0.0], [0.49922013209913363, 0.0], [0.293700544315207, 0.0], [0.293700544315207, 0.0], [0.08818095653128033, 0.0], [0.08818095653128033, 0.0], [-0.11733863125264632, 0.0], [-0.11733863125264632, 0.0], [-0.32285821903657297, 0.0], [-0.32285821903657297, 0.0], [-0.5283778068204996, 0.0], [-0.5283778068204996, 0.0], [-0.7338973946044263, 0.0], [-0.7338973946044263, 0.0], [-0.9394169823883529, 0.0], [-0.9394169823883529, 0.0], [-1.1449365701722793, 0.0], [-1.1449365701722793, 0.0], [-1.350456157956206, 0.0], [-1.350456157956206, 0.0], [-1.5559757457401326, 0.0], [-1.5559757457401326, 0.0], [-1.7614953335240593, 0.0], [-1.7614953335240593, 0.0], [-1.967014921307986, 0.0], [-1.967014921307986, 0.0], [-2.1725345090919124, 0.0], [-2.1725345090919124, 0.0], [-2.378054096875839, 0.0], [-2.378054096875839, 0.0], [-2.5835736846597657, 0.0], [-2.5835736846597657, 0.0], [-2.7890932724436923, 0.0], [-2.7890932724436923, 0.0]]}, \"id\": \"el94984400283792\"});\n", " })\n", " });\n", "}\n", "</script>" ], "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x106470090>" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Line Plots with Legend" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Draw lines\n", "fig, ax = plt.subplots()\n", "x = np.linspace(-5, 15, 1000)\n", "for offset in np.linspace(0, 3, 4):\n", " ax.plot(x, 0.9 * np.sin(x - offset), lw=5, alpha=0.4,\n", " label=\"Offset: {0}\".format(offset))\n", "ax.set_xlim(0, 10)\n", "ax.set_ylim(-1.2, 1.0)\n", "ax.text(5, -1.1, \"Here are some curves\", size=18, ha='center')\n", "ax.grid(color='lightgray', alpha=0.7)\n", "ax.legend()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "<matplotlib.legend.Legend at 0x1060a8450>" ] }, { "html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div id=\"fig_el949844002360481884642452\"></div>\n", "<script>\n", "function mpld3_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n", " // already loaded: just create the figure\n", " !function(mpld3){\n", " \n", " mpld3.draw_figure(\"fig_el949844002360481884642452\", {\"axes\": [{\"xlim\": [0.0, 10.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"Here are some curves\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [5.0, -1.1], \"rotation\": -0.0, \"id\": \"el94984400236304\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 0.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el94984396322128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 1.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.83198924731182788], \"rotation\": -0.0, \"id\": \"el94984396238864\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 2.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.75268817204301075], \"rotation\": -0.0, \"id\": \"el94984396237136\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 3.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.67338709677419351], \"rotation\": -0.0, \"id\": \"el94984396236112\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 10.0], \"ylim\": [-1.2, 1.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el94984396322064\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396386960\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396384592\"}, {\"color\": \"#FF0000\", \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396285392\"}, {\"color\": \"#00BFBF\", \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396283344\"}, {\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396320656\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396238608\"}, {\"color\": \"#FF0000\", \"yindex\": 3, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984401323920\"}, {\"color\": \"#00BFBF\", \"yindex\": 4, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396235920\"}], \"markers\": [], \"id\": \"el94984400229904\", \"ydomain\": [-1.2, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data02\": [[0.6903001792114694, 0.9301075268817204, 0.8508064516129031, 0.771505376344086, 0.6922043010752688], [0.7404793906810034, 0.9301075268817204, 0.8508064516129031, 0.771505376344086, 0.6922043010752688]], \"data03\": [[0.6652105734767024, 0.6397849462365592], [0.9820788530465949, 0.6397849462365592], [0.9820788530465949, 0.9731182795698926], [0.6652105734767024, 0.9731182795698926], [0.6652105734767024, 0.6397849462365592]], \"data01\": [[-5.0, 0.8630318471968246, 0.2514739483790333, -0.5912879388469102, -0.8904224219610436], [-4.97997997997998, 0.8679695898303729, 0.26872276441239507, -0.577586531327817, -0.8928654338419951], [-4.95995995995996, 0.8725594608285433, 0.28586387963617244, -0.5636536341648707, -0.8949505961366454], [-4.93993993993994, 0.8767996206269677, 0.30289042410024863, -0.5494948314934817, -0.8966770731373546], [-4.91991991991992, 0.8806883698212653, 0.3197955737730711, -0.535115797989235, -0.8980441728931777], [-4.8998998998999, 0.8842241498481432, 0.3365725532766406, -0.5205222965935509, -0.8990513474871911], [-4.87987987987988, 0.8874055436100506, 0.35321463860199986, -0.5057201762039664, -0.8996981932560897], [-4.85985985985986, 0.8902312760431328, 0.3697151598041348, -0.49071536932996934, -0.8999844509519712], [-4.83983983983984, 0.8927002146282623, 0.3860675036752065, -0.4755138897153212, -0.8999100058462391], [-4.81981981981982, 0.8948113698449383, 0.4022651163950453, -0.46012182992782114, -0.8994748877755848], [-4.7997997997998, 0.8965638955678743, 0.4183015061578412, -0.4445453589174795, -0.8986792711300291], [-4.77977977977978, 0.8979570894061152, 0.434170245773981, -0.4287907195440775, -0.8975234747830286], [-4.75975975975976, 0.8989903929845467, 0.44986497524598745, -0.4128642260751055, -0.8960079619636763], [-4.73973973973974, 0.8996633921676853, 0.4653794043175288, -0.39677226165508117, -0.8941333400710433], [-4.71971971971972, 0.8999758172256591, 0.4807073149944767, -0.38052127574726324, -0.8919003604307418], [-4.6996996996997, 0.8999275429423117, 0.4958425640370029, -0.36411778154878494, -0.889309917993803], [-4.67967967967968, 0.8995185886653875, 0.5107790854217142, -0.3475683533802441, -0.8863630509779908], [-4.65965965965966, 0.8987491182987778, 0.5255108927728417, -0.33087962405079563, -0.8830609404516978], [-4.63963963963964, 0.8976194402368293, 0.5400320817615062, -0.31405828219980225, -0.8794049098605863], [-4.61961961961962, 0.8961300072407442, 0.5543368324721021, -0.29711106961610945, -0.8753964244971686], [-4.5995995995996, 0.8942814162571185, 0.5684194117348481, -0.2800447785360186, -0.8710370909135349], [-4.57957957957958, 0.8920744081786935, 0.5822741754235725, -0.26286624892104166, -0.8663286562774669], [-4.55955955955956, 0.8895098675474145, 0.5958955707178093, -0.24558236571652747, -0.8612730076721944], [-4.53953953953954, 0.8865888221999173, 0.6092781383283015, -0.22820005609225996, -0.8558721713400752], [-4.51951951951952, 0.8833124428555845, 0.6224165146850176, -0.21072628666613283, -0.850128311870502], [-4.4994994994995, 0.8796820426473345, 0.6353054340868053, -0.1931680607120138, -0.844043731332361], [-4.47947947947948, 0.8756990765953342, 0.6479397308118201, -0.17553241535291797, -0.8376208683513913], [-4.45945945945946, 0.8713651410238447, 0.6603143411878836, -0.1578264187406146, -0.8308622971328129], [-4.43943943943944, 0.866681972921434, 0.6724243056219421, -0.14005716722279796, -0.8237707264296168], [-4.41941941941942, 0.8616514492448131, 0.6842647705878105, -0.1222317824989577, -0.8163489984569301], [-4.3993993993994, 0.8562755861665744, 0.695830990571407, -0.10435740876608852, -0.8086000877528902], [-4.37937937937938, 0.850556538267135, 0.7071183299726964, -0.08644120985538295, -0.8005270999864869], [-4.35935935935936, 0.8444965976712059, 0.7181222649635836, -0.06849036636105515, -0.792133270712847], [-4.33933933933934, 0.8380981931291377, 0.7288383853010069, -0.0505120727624463, -0.7834219640764648], [-4.31931931931932, 0.831363889043506, 0.7392623960945115, -0.03251353454056483, -0.7743966714628926], [-4.2992992992992995, 0.8242963844413308, 0.749390119527588, -0.014501965290217559, -0.7650610100994366], [-4.2792792792792795, 0.8168985118923391, 0.7592174965320911, 0.0035154161711113013, -0.7554187216054159], [-4.2592592592592595, 0.8091732363737051, 0.7687405884150641, 0.021531388696474196, -0.7454736704925676], [-4.2392392392392395, 0.8011236540817241, 0.7779555784373193, 0.039538731703607936, -0.7352298426161972], [-4.2192192192192195, 0.7927529911908945, 0.7868587733431406, 0.057530228068855084, -0.7246913435776958], [-4.1991991991991995, 0.784064602560906, 0.7954466048404955, 0.07549866701969918, -0.7138623970790667], [-4.1791791791791795, 0.7750619703920537, 0.8037156310311625, 0.0934368470247545, -0.7027473432301156], [-4.1591591591591595, 0.7657487028296133, 0.8116625377902029, 0.11133757868005209, -0.6913506368089878], [-4.1391391391391394, 0.7561285325177417, 0.8192841400942208, 0.1291936875904653, -0.6796768454767479], [-4.119119119119119, 0.7462053151034778, 0.8265773832978821, 0.14699801724511982, -0.6677306479467164], [-4.099099099099099, 0.7359830276914466, 0.8335393443581789, 0.1647434318856362, -0.6555168321092989], [-4.079079079079079, 0.7254657672498843, 0.8401672330059502, 0.1824228193660547, -0.6430402931130572], [-4.059059059059059, 0.7146577489686227, 0.8464583928641868, 0.20002909400329694, -0.6303060314027944], [-4.039039039039039, 0.703563304569693, 0.8524103025126752, 0.21755519941702098, -0.6173191507154364], [-4.019019019019019, 0.6921868805712241, 0.8580205764985528, 0.23499411135773307, -0.6040848560345177], [-3.998998998998999, 0.6805330365053327, 0.863286966292367, 0.2523388405220207, -0.5906084515040859], [-3.978978978978979, 0.6686064430907186, 0.8682073611892598, 0.2695824353537809, -0.5768953383028668], [-3.958958958958959, 0.6564118803606961, 0.8727797891549115, 0.28671798483031846, -0.5629510124795367], [-3.938938938938939, 0.6439542357474166, 0.8770024176159085, 0.30373862123220025, -0.5487810627499738], [-3.918918918918919, 0.6312385021230444, 0.8808735541942165, 0.32063752289575315, -0.5343911682573689], [-3.898898898898899, 0.6182697757986748, 0.8843916473854649, 0.33740791694710387, -0.519787096296093], [-3.878878878878879, 0.6050532544817941, 0.8875552871807707, 0.35404308201666423, -0.5049747000002375], [-3.858858858858859, 0.5915942351931028, 0.8903632056318527, 0.37053635093297416, -0.48995991599774835], [-3.838838838838839, 0.5778981121435335, 0.8928142773592105, 0.38688111339482273, -0.4747487620310985], [-3.818818818818819, 0.5639703745723166, 0.894907520003162, 0.4030708186205766, -0.45934733454544946], [-3.798798798798799, 0.5498166045469611, 0.8966420946175617, 0.4190989779736531, -0.4437618062452708], [-3.7787787787787788, 0.5354424747260293, 0.89801730600604, 0.43495916756308683, -0.4279984236203952], [-3.7587587587587588, 0.5208537460856043, 0.8990326030006295, 0.4506450308181473, -0.4120635044425017], [-3.7387387387387387, 0.5060562656103611, 0.8996875786826659, 0.4661502810359743, -0.39596343523302946], [-3.7187187187187187, 0.49105596395016665, 0.899981970545877, 0.48146870390121227, -0.379704668703538], [-3.6986986986986987, 0.47585885304314746, 0.8999156606015912, 0.49659415997663203, -0.36329372116953884], [-3.6786786786786787, 0.460471023706178, 0.899488675426026, 0.5115205871637428, -0.346737169938836], [-3.6586586586586587, 0.4448986431937554, 0.898701186149637, 0.5262420031324067, -0.33004165067542085], [-3.6386386386386387, 0.42914795272623935, 0.8975535083885307, 0.5407525077184839, -0.3132138547399789], [-3.6186186186186187, 0.4132252649884468, 0.8960461021179691, 0.5550462852885475, -0.29626052650807405], [-3.5985985985985987, 0.3971369615996047, 0.8941795714880179, 0.5691176070707172, -0.2791884606670851], [-3.5785785785785786, 0.3808894905556751, 0.8919546645814096, 0.5829608334506821, -0.2620044994929775], [-3.5585585585585586, 0.3644893636450766, 0.889372273113722, 0.5965704162319889, -0.24471553010800312], [-3.5385385385385386, 0.34794315383883917, 0.8864334320759889, 0.6099409008596921, -0.22732848172042486], [-3.5185185185185186, 0.3312574926562372, 0.8831393193198884, 0.6230669286064724, -0.20985032284737504], [-3.4984984984984986, 0.31443906750695766, 0.8794912550856742, 0.6359432387203501, -0.19228805852195827], [-3.4784784784784786, 0.2974946190108674, 0.8754907014730395, 0.6485646705331292, -0.17464872748571958], [-3.4584584584584586, 0.28043093829645527, 0.8711392618551258, 0.6609261655287316, -0.15693939936760282], [-3.4384384384384385, 0.2632548642790304, 0.8664386802359094, 0.6730227693705885, -0.1391671718505291], [-3.4184184184184185, 0.2459732809197681, 0.8613908405512265, 0.684849633887277, -0.12133916782673242], [-3.3983983983983985, 0.22859311446670239, 0.8559977659137148, 0.6964020190156096, -0.10346253254299087], [-3.378378378378378, 0.21112133067876954, 0.850261617801974, 0.7076752947003935, -0.08554443073689828], [-3.358358358358358, 0.1935649320340186, 0.8441846951942729, 0.718664942750098, -0.06759204376532768], [-3.338338338338338, 0.17593095492310082, 0.8377694336471446, 0.7293665586476946, -0.0496125667262263], [-3.318318318318318, 0.15822646682917196, 0.8310184043192475, 0.7397758533159309, -0.03161320557491114], [-3.298298298298298, 0.1404585634953306, 0.8239343129408745, 0.7498886548363414, -0.013601174236009072], [-3.278278278278278, 0.12263436608073057, 0.8165199987295305, 0.7597009101213011, 0.00441630828779621], [-3.258258258258258, 0.10476101830650755, 0.8087784332520078, 0.7692086865384546, 0.022432020809052548], [-3.238238238238238, 0.08684568359266231, 0.8007127192334185, 0.7784081734868665, 0.040438742849703224], [-3.218218218218218, 0.06889554218704948, 0.7923260893136606, 0.7872956839242633, 0.05842925753496662], [-3.198198198198198, 0.05091778828762159, 0.7836219047518144, 0.7958676558447566, 0.07639635448577162], [-3.178178178178178, 0.032919627159082275, 0.7746036540789918, 0.80412065370645, 0.09433283270858979], [-3.158158158158158, 0.014908272245103896, 0.765274951700175, 0.8120513698083638, 0.11223150348150575], [-3.138138138138138, -0.0031090577227327423, 0.755639536445607, 0.8196566256161201, 0.13008519323536938], [-3.118118118118118, -0.021125141618118036, 0.7457012700723139, 0.8269333730358612, 0.1478867464288749], [-3.098098098098098, -0.03913275881415305, 0.7354641357163603, 0.833878695635889, 0.16562902841641455], [-3.078078078078078, -0.05712469207728877, 0.7249322362964564, 0.8404898098155339, 0.18330492830755787], [-3.058058058058058, -0.0750937304599054, 0.7141097928695582, 0.8467640659207893, 0.2009073618170096], [-3.038038038038038, -0.09303267219037227, 0.7030011429391195, 0.8526989493062603, 0.21842927410390522], [-3.018018018018018, -0.11093432755943015, 0.6916107387166736, 0.8582920813430026, 0.2358636425993049], [-2.997997997997998, -0.12879152180173914, 0.6799431453374402, 0.8635412203718483, 0.25320347982075386], [-2.977977977977978, -0.1465970979714372, 0.668003039030676, 0.8684442626018356, 0.27044183617278], [-2.957957957957958, -0.1643439198105568, 0.6557952052454983, 0.8729992429533822, 0.2875718027322071], [-2.937937937937938, -0.18202487460915015, 0.6433245367329355, 0.8772043358458658, 0.30458651401716713], [-2.9179179179179178, -0.1996328760559768, 0.6305960315849722, 0.8810578559292943, 0.3214791507387017], [-2.8978978978978978, -0.21716086707861068, 0.6176147912313744, 0.8845582587597733, 0.33824294253384984], [-2.8778778778778777, -0.23460182267182886, 0.6043860183950994, 0.8877041414185011, 0.3548711706791272], [-2.8578578578578577, -0.2519487527131479, 0.590915015007107, 0.8904942430740405, 0.3713571707833086], [-2.8378378378378377, -0.26919470476437996, 0.5772071800814106, 0.8929274454876455, 0.3876943354584343], [-2.8178178178178177, -0.28633276685808523, 0.5632680075512183, 0.8950027734614372, 0.40387611696797127], [-2.7977977977977977, -0.30335607026780403, 0.5491030840670311, 0.8967193952292518, 0.4198960298510658], [-2.7777777777777777, -0.32025779226095913, 0.5347180867575816, 0.8980766227900018, 0.43574765352183736], [-2.7577577577577577, -0.3370311588333234, 0.5201187809545099, 0.8990739121834186, 0.45142463484267087], [-2.7377377377377377, -0.35366944742395856, 0.5053110178816891, 0.8997108637080654, 0.4669206906704775], [-2.7177177177177176, -0.37016598960953523, 0.4903007323101259, 0.8999872220815323, 0.48222961037490103], [-2.6976976976976976, -0.38651417377695624, 0.4750939401793767, 0.8999028765427508, 0.49734525832746346], [-2.6776776776776776, -0.4027074477732101, 0.4596967361864325, 0.8994578608963848, 0.51226157636065], [-2.6576576576576576, -0.41873932153139454, 0.4441152913430384, 0.8986523534992832, 0.5269725861959482], [-2.6376376376376376, -0.434603369671856, 0.4283558505024275, 0.8974866771889954, 0.5414723918398704], [-2.6176176176176176, -0.4502932340774034, 0.4124247298564596, 0.8959612991543829, 0.5557551819469949], [-2.5975975975975976, -0.46580262644156445, 0.39632831440416855, 0.8940768307483751, 0.5698152321490826], [-2.5775775775775776, -0.48112533078886166, 0.38007305539273256, 0.8918340272429469, 0.5836469073493346], [-2.5575575575575575, -0.49625520596610034, 0.3636654677318927, 0.8892337875264147, 0.59724466398087], [-2.5375375375375375, -0.511186188103667, 0.34711212738285663, 0.8862771537431728, 0.6106030522285195], [-2.5175175175175175, -0.5259122930458555, 0.3304196687227336, 0.882965310876015, 0.6237167182130459], [-2.4974974974974975, -0.5404276187492426, 0.3135947818855565, 0.8792995862712075, 0.636580406136913], [-2.4774774774774775, -0.554726347648156, 0.2966442100809582, 0.8752814491065052, 0.6491889603907449], [-2.4574574574574575, -0.5688027489862835, 0.27957474689157497, 0.8709125098023224, 0.6615373276196324], [-2.4374374374374375, -0.5826511811134897, 0.26239323355026123, 0.8661945193762967, 0.673620558748455], [-2.4174174174174174, -0.5962660937469216, 0.24510655619820726, 0.8611293687415019, 0.6854338109654109], [-2.3973973973973974, -0.6096420301954936, 0.22772164312505716, 0.8557190879485936, 0.6969723496629561], [-2.3773773773773774, -0.6227736295468633, 0.2102454619921352, 0.8499658453721893, 0.708231550335378], [-2.3573573573573574, -0.6356556288160194, 0.19268501703989183, 0.8438719468418101, 0.7192069004322407], [-2.3373373373373374, -0.6482828650546222, 0.17504734628068955, 0.8374398347177328, 0.7298940011669601], [-2.3173173173173174, -0.6606502774202505, 0.15733951867805326, 0.8306720869121211, 0.7402885692797845], [-2.2972972972972974, -0.672752909204725, 0.1395686313135159, 0.8235714158558308, 0.7503864387544721], [-2.2772772772772774, -0.6845859098206977, 0.12174180654219457, 0.816140667411299, 0.7601835624879802], [-2.2572572572572573, -0.6961445367457068, 0.1038661891382374, 0.8083828197319587, 0.7696760139124947], [-2.2372372372372373, -0.7074241574229229, 0.08594894343128517, 0.8003009820686302, 0.7788599885691501], [-2.2172172172172173, -0.718420251117821, 0.06799725043509522, 0.7918983935233717, 0.7877318056328112], [-2.1971971971971973, -0.7291284107300358, 0.05001830496947869, 0.783178421751286, 0.7962879093873019], [-2.1771771771771773, -0.7395443445596738, 0.032019312776704424, 0.774144561610807, 0.8045248706504939], [-2.1571571571571573, -0.7496638780273746, 0.014007487633525202, 0.7648004337630007, 0.8124393881486803], [-2.1371371371371373, -0.7594829553474308, -0.004009951540016031, 0.7551497832204505, 0.8200282898396865], [-2.1171171171171173, -0.7689976411532984, -0.022025783573841363, 0.7451964778463002, 0.8272885341841856], [-2.0970970970970972, -0.7782041220748429, -0.0400327879419949, 0.7349445068040626, 0.8342172113647105], [-2.0770770770770772, -0.7870987082666921, -0.05802374765654159, 0.7243979789588111, 0.8408115444518732], [-2.057057057057057, -0.7956778348870811, -0.07599145216004802, 0.7135611212303964, 0.8470688905173247], [-2.037037037037037, -0.8039380635265972, -0.09392870021548604, 0.7024382768993486, 0.8529867416930078], [-2.017017017017017, -0.8118760835862529, -0.1118283027924007, 0.6910339038661436, 0.8585627261762803], [-1.9969969969969972, -0.819488713604333, -0.1296830859481862, 0.6793525728645305, 0.8637946091805048], [-1.9769769769769772, -0.8267729025314863, -0.14748589370331464, 0.6673989656296377, 0.8686802938307239], [-1.9569569569569571, -0.8337257309535488, -0.16522959090936523, 0.6551778730215899, 0.8732178220040618], [-1.9369369369369371, -0.8403444122616095, -0.18290706610870514, 0.6426941931053901, 0.8774053751145168], [-1.9169169169169171, -0.8466262937688486, -0.2005112343846748, 0.6299529291878351, 0.8812412748418272], [-1.8968968968968971, -0.8525688577737031, -0.2180350402011364, 0.61695918781225, 0.8847239838041231], [-1.876876876876877, -0.8581697225689299, -0.23547146023024657, 0.6037181767118495, 0.8878521061740893], [-1.856856856856857, -0.8634266433961658, -0.2528135061673208, 0.5902352027225406, 0.890624388238396], [-1.836836836836837, -0.8683375133455989, -0.27005422753166064, 0.576515669656008, 0.8930397189001723], [-1.816816816816817, -0.8729003642003926, -0.28718671445222177, 0.5625650761339318, 0.8950971301243197], [-1.796796796796797, -0.8771133672255228, -0.30420410043700613, 0.5483890133842075, 0.8967957973254891], [-1.7767767767767766, -0.8809748339007124, -0.3210995651250686, 0.5339931630000494, 0.8981350396985648], [-1.7567567567567566, -0.8844832165971699, -0.33786633702003305, 0.5193832946628781, 0.8991143204915222], [-1.7367367367367366, -0.8876371091978604, -0.35449769620402766, 0.5045652638299016, 0.8997332472205519], [-1.7167167167167166, -0.8904352476610599, -0.3709869770309451, 0.4895450093873195, 0.8999915718273627], [-1.6966966966966965, -0.8928765105269676, -0.387327570797952, 0.4743285512700904, 0.8998891907785999], [-1.6766766766766765, -0.8949599193671738, -0.4035129283941771, 0.45892198804921497, 0.8994261451073404], [-1.6566566566566565, -0.8966846391768014, -0.41953656292551456, 0.4433314944875026, 0.8986026203966468], [-1.6366366366366365, -0.8980499787091669, -0.4353920523144928, 0.4275633190648026, 0.8974189467051887], [-1.6166166166166165, -0.8990553907528221, -0.45107304187416386, 0.4116237814736888, 0.8958755984349583], [-1.5965965965965965, -0.8997004723508709, -0.46657324685498563, 0.395519270086604, 0.893973194141137], [-1.5765765765765765, -0.8999849649624694, -0.481886454963672, 0.37925623939547853, 0.8917124962841845], [-1.5565565565565564, -0.899908754566445, -0.49700652885300467, 0.362841207424848, 0.8890944109242553], [-1.5365365365365364, -0.8994718717069963, -0.5119274085816072, 0.3462807531195088, 0.8861199873580604], [-1.5165165165165164, -0.8986744914814494, -0.5266431140426958, 0.3295815137077569, 0.8827904176983216], [-1.4964964964964964, -0.8975169334700829, -0.5411477473608326, 0.31275018204126814, 0.8791070363959878], [-1.4764764764764764, -0.8959996616080429, -0.555435495255723, 0.2957935039126843, 0.8750713197054028], [-1.4564564564564564, -0.8941232839994037, -0.5695006313721072, 0.2787182753519818, 0.8706848850926407], [-1.4364364364364364, -0.891888552673447, -0.5833375185748136, 0.26153133990270583, 0.8659494905872459], [-1.4164164164164164, -0.8892963632832582, -0.5969406112080544, 0.24423958587916134, 0.8608670340776351], [-1.3963963963963963, -0.8863477547467589, -0.6103044573180557, 0.22684994360566055, 0.8554395525504473], [-1.3763763763763763, -0.8830439088303217, -0.6234237008381358, 0.2093693826389336, 0.849669221274145], [-1.3563563563563563, -0.8793861496751325, -0.6362930837353499, 0.19180490897481492, 0.8435583529271921], [-1.3363363363363363, -0.8753759432664909, -0.6489074481178464, 0.1741635622403257, 0.8371093966711606], [-1.3163163163163163, -0.8710148968462609, -0.661261738302087, 0.15645241287227693, 0.8303249371691378], [-1.2962962962962963, -0.8663047582687083, -0.6733510028391033, 0.13867855928352468, 0.8232076935498229], [-1.2762762762762763, -0.8612474152999819, -0.6851703964989768, 0.12084912501801248, 0.8157605183177351], [-1.2562562562562563, -0.8558448948615198, -0.6967151822127481, 0.10297125589574202, 0.8079863962099633], [-1.2362362362362362, -0.8500993622176839, -0.7079807329709764, 0.08505211714881532, 0.7998884429999203], [-1.2162162162162162, -0.8440131201079484, -0.7189625336781891, 0.06709889054969705, 0.7914699042485793], [-1.1961961961961962, -0.8375886078239898, -0.7296561829624759, 0.049118771532847744, 0.7827341540036916], [-1.1761761761761762, -0.8308284002320487, -0.7400573949395062, 0.031118966310881267, 0.7736846934475101], [-1.1561561561561562, -0.8237352067409549, -0.7501620009302584, 0.013106688986402595, 0.7643251494935579], [-1.1361361361361362, -0.8163118702162289, -0.7599659511317763, -0.004910841339316566, 0.7546592733330054], [-1.1161161161161162, -0.808561365840696, -0.7694653162402805, -0.022926403459665626, 0.7446909389312389], [-1.0960960960960962, -0.8004867999220704, -0.7786562890259854, -0.04093277695686673, 0.7344241414752224], [-1.0760760760760761, -0.792091408647983, -0.7875351858589906, -0.05892274509583019, 0.723862995772275], [-1.0560560560560561, -0.7833785567889587, -0.7960984481856351, -0.07688909771653404, 0.7130117346009067], [-1.0360360360360361, -0.7743517363498564, -0.8043426439547219, -0.09482463412376826, 0.7018747070143723], [-1.016016016016016, -0.765014565170316, -0.8122644689930423, -0.11272216597308565, 0.6904563765976234], [-0.9959959959959956, -0.7553707854747724, -0.8198607483296488, -0.13057452015180326, 0.6787613196783585], [-0.9759759759759756, -0.7454242623726185, -0.8271284374683437, -0.14837454165389666, 0.6667942234928864], [-0.9559559559559556, -0.7351789823091147, -0.8340646236078776, -0.16611509644764186, 0.6545598843075389], [-0.9359359359359356, -0.7246390514676716, -0.8406665268093629, -0.1837890743348465, 0.6420632054963858], [-0.9159159159159156, -0.7138086941241412, -0.846931501110441, -0.2013893918005312, 0.6293091955760225], [-0.8958958958958956, -0.7026922509537785, -0.8528570355857508, -0.2189089948519159, 0.616302966198217], [-0.8758758758758756, -0.6912941772915525, -0.8584407553532774, -0.23634086184557426, 0.603049730101223], [-0.8558558558558556, -0.6796190413465009, -0.8636804225261756, -0.25367800630162296, 0.589554799020577], [-0.8358358358358355, -0.6676715223708487, -0.8685739371096881, -0.27091347970381735, 0.5758235815602202], [-0.8158158158158155, -0.6554564087846209, -0.8731193378427973, -0.28804037428443247, 0.5618615810247956], [-0.7957957957957955, -0.6429785962565012, -0.8773148029842752, -0.305051825792812, 0.5476743932139905], [-0.7757757757757755, -0.6302430857417087, -0.881158651042815, -0.32194101624647614, 0.5332677041798084], [-0.7557557557557555, -0.617254981477675, -0.884649341450952, -0.33870117666368627, 0.5186472879476685], [-0.7357357357357355, -0.604019488938329, -0.8877854751825048, -0.35532558977636997, 0.5038190042022461], [-0.7157157157157155, -0.5905419127478059, -0.8905657953132878, -0.3718075927223205, 0.48878879593898406], [-0.6956956956956954, -0.576827654554418, -0.8929891875248709, -0.3881405797155907, 0.4735626870822106], [-0.6756756756756754, -0.5628822108657409, -0.8950546805511844, -0.40431800469401136, 0.45814678007082504], [-0.6556556556556554, -0.5487111708456796, -0.8967614465677918, -0.4203333839427734, 0.4425472534125146], [-0.6356356356356354, -0.5343202140743993, -0.8981088015236703, -0.43618029869302166, 0.4267703592074833], [-0.6156156156156154, -0.5197151082720205, -0.8990962054153714, -0.45185239769441915, 0.4108224206426867], [-0.5955955955955954, -0.5049017069869848, -0.8997232625034468, -0.4673433997606509, 0.3947098294575757], [-0.5755755755755754, -0.4898859472500258, -0.8999897214710562, -0.48264709628684704, 0.37843904338236506], [-0.5555555555555554, -0.4746738471946788, -0.8998954755246922, -0.4977573537379157, 0.3620165835498539], [-0.5355355355355353, -0.4592715036452863, -0.8994405624369817, -0.5126681161067891, 0.345449031881835], [-0.5155155155155153, -0.44368508967346487, -0.8986251645315472, -0.527373407341598, 0.32874302845114106], [-0.4954954954954953, -0.42792085212401376, -0.8974496086099335, -0.5418673337407994, 0.31190526882038405], [-0.4754754754754753, -0.41198510911125524, -0.89591436582063, -0.5561440863153017, 0.29494250135845557], [-0.4554554554554553, -0.3958842474868115, -0.8940200514702393, -0.5701979431166352, 0.27786152453586277], [-0.4354354354354353, -0.3796247202798323, -0.8917674247768713, -0.5840232715302405, 0.260669184199984], [-0.41541541541541527, -0.3632130441106995, -0.8891573885658559, -0.5976145305329514, 0.24337237083133706], [-0.39539539539539525, -0.3466557965792451, -0.8861909889079035, -0.6109662729137695, 0.22597801678195795], [-0.37537537537537524, -0.3299596136285294, -0.8828694146998511, -0.6240731474570395, 0.2084930934969991], [-0.35535535535535523, -0.31313118688523567, -0.879193997188168, -0.6369299010871522, 0.19092460872065906], [-0.3353353353353352, -0.29617726097774794, -0.8751662094354078, -0.6495313809739125, 0.1732796036875639], [-0.3153153153153152, -0.279104630832986, -0.8707876657298232, -0.6618725365977308, 0.15556515030072604], [-0.2952952952952952, -0.2619201389530815, -0.8660601209383787, -0.6739484217738104, 0.13778834829721204], [-0.2752752752752752, -0.24463067267298666, -0.8609854698034214, -0.6857541966345176, 0.11995632240265423], [-0.25525525525525516, -0.22724316140011394, -0.8555657461832916, -0.6972851295691421, 0.10207621947574753], [-0.23523523523523515, -0.20976457383711442, -0.8498031222371768, -0.7085365991202681, 0.08415520564387569], [-0.21521521521521514, -0.19220191518890636, -0.8436999075545369, -0.7195040958359988, 0.06620046343101459], [-0.19519519519519513, -0.17456222435507468, -0.8372585482294489, -0.73018322407729, 0.048219188879064276], [-0.1751751751751751, -0.15685257110876555, -0.8304816258802421, -0.7405697037796676, 0.030218588663762983], [-0.1551551551551551, -0.13908005326320766, -0.8233718566148173, -0.7506593721686265, 0.012205877206339349], [-0.1351351351351351, -0.12125179382699504, -0.8159320899420635, -0.7604481854280194, -0.005811726217939738], [-0.11511511511511507, -0.10337493814927201, -0.808165307629809, -0.7699322203207686, -0.023827000373166672], [-0.09509509509509506, -0.0854566510559644, -0.8000746225097666, -0.7791076757612523, -0.04183272495697651], [-0.07507507507507505, -0.0675041139782045, -0.791663277229947, -0.787970874338733, -0.05982168349435614], [-0.055055055055055035, -0.04952452207410106, -0.7829346429550461, -0.7965182637912191, -0.07778666622991962], [-0.03503503503503502, -0.031525081345007595, -0.7738922180153227, -0.8047464184291683, -0.09572047301749019], [-0.01501501501501501, -0.013513005747444783, -0.7645396265045104, -0.8126520405084618, -0.11361591620583114], [0.005005005005005003, 0.00450448569816537, -0.7548806168273252, -0.8202319615521003, -0.13146582351936897], [0.025025025025025016, 0.022520171800794946, -0.7449190601971507, -0.8274831436200912, -0.14926304093275386], [0.04504504504504503, 0.040526832092975494, -0.7346589490845025, -0.8344026805270174, -0.16700043553810592], [0.06506506506506504, 0.058517249724673405, -0.7241043956168947, -0.8409877990068011, -0.18467089840379788], [0.08508508508508505, 0.07648421435571551, -0.7132596299307497, -0.8472358598241956, -0.20226734742362806], [0.10510510510510507, 0.09442052504560546, -0.702128998476011, -0.853144358832558, -0.21978273015524244], [0.12512512512512508, 0.11231899313957308, -0.6907169622741385, -0.8587109279774808, -0.23721002664666743], [0.1451451451451451, 0.13017244514969945, -0.679028095130186, -0.8639333362458795, -0.2545422522498212], [0.1651651651651651, 0.1479737256299636, -0.667067081799676, -0.8688094905601557, -0.2717724604198755], [0.18518518518518512, 0.165715700044058, -0.6548387161110061, -0.8733374366170755, -0.28889374549934627], [0.20520520520520513, 0.18339125762482375, -0.6423478990441412, -0.877515359671033, -0.30589924548579683], [0.22522522522522515, 0.2009933142241595, -0.6295996367663613, -0.881341585261376, -0.3227821447820448], [0.24524524524524516, 0.21851481515226165, -0.6165990386258491, -0.8848145798835104, -0.3395356769277709], [0.26526526526526517, 0.23594873800505806, -0.6033513151039269, -0.8879329516035106, -0.3561531273114332], [0.2852852852852852, 0.25328809547870196, -0.5898617757267586, -0.8906954506159882, -0.37262783586140186], [0.3053053053053052, 0.27052593816999837, -0.5761358269373561, -0.8931009697450004, -0.3889531997152347], [0.3253253253253252, 0.2876553573616401, -0.5621789699287434, -0.8951485448877913, -0.40512267586602385], [0.3453453453453452, 0.30466948779113734, -0.547996798439145, -0.8968373554011937, -0.42112978378475363], [0.36536536536536524, 0.3215615104023315, -0.5335949965100837, -0.8981667244305318, -0.43696810801761704], [0.38538538538538525, 0.3383246550783897, -0.5189793362082866, -0.8991361191808972, -0.45263130075725194], [0.40540540540540526, 0.35495220335518496, -0.5041556753123104, -0.899745151130686, -0.46811308438686444], [0.4254254254254253, 0.37143749111397495, -0.48912995496481587, -0.8999935761873137, -0.4834072539962218], [0.4454454454454453, 0.38777391125229954, -0.47390819729142947, -0.8998812947850447, -0.49850767986850425], [0.4654654654654653, 0.4039549163320268, -0.45849650298714917, -0.899408351924897, -0.5134083099370211], [0.4854854854854853, 0.4199740212034867, -0.44290104887126036, -0.8985749371566059, -0.5281031722108055], [0.5055055055055053, 0.4358248056046398, -0.42712808541174185, -0.8973813845026556, -0.5425863771681151], [0.5255255255255253, 0.45150091673424037, -0.41118393422015437, -0.8958281723244063, -0.5568521201168813], [0.5455455455455454, 0.4669960717979612, -0.39507498551801573, -0.8939159231303727, -0.5708946835211586], [0.5655655655655654, 0.48230406052646163, -0.3788076955756777, -0.8916454033267317, -0.5847084392926434], [0.5855855855855854, 0.4974187476643875, -0.36238858412473224, -0.8890175229101538, -0.5982878510463434], [0.6056056056056054, 0.5123340754293065, -0.3458242317449814, -0.8860333351030892, -0.6116274763194931], [0.6256256256256254, 0.5270440659395942, -0.32912127722702217, -0.8826940359316487, -0.6247219687528266], [0.6456456456456454, 0.5415428236102953, -0.3122864149114995, -0.8790009637462508, -0.6375660802333334], [0.6656656656656654, 0.5558245375160032, -0.29532639200609634, -0.8749555986852273, -0.6501546629976376], [0.6856856856856854, 0.5698834837198077, -0.27824800588133497, -0.8705595620816017, -0.6624826716951596], [0.7057057057057055, 0.5837140275673783, -0.2610581013462737, -0.8658146158132789, -0.6745451654102304], [0.7257257257257255, 0.597310625945267, -0.2437635679051909, -0.8607226615969059, -0.6863373096423507], [0.7457457457457455, 0.6106678295025182, -0.22637133699635548, -0.8552857402256874, -0.6978543782437998], [0.7657657657657655, 0.6237802848347043, -0.2088883792139911, -0.8495060307514597, -0.7090917553138181], [0.7857857857857855, 0.6366427366295033, -0.19132170151454664, -0.8433858496113544, -0.7200449370486032], [0.8058058058058055, 0.6492500297729655, -0.17367834440839383, -0.8369276496993978, -0.7307095335463808], [0.8258258258258255, 0.6615971114156188, -0.15596537913807618, -0.8301340193834212, -0.7410812705668228], [0.8458458458458455, 0.6736790329975905, -0.1381899048442411, -0.823007681467675, -0.7511559912441114], [0.8658658658658656, 0.6854909522319287, -0.12035904572039115, -0.8155514921015611, -0.7609296577529597], [0.8858858858858856, 0.697028135045333, -0.10247994815759376, -0.8077684396349237, -0.7703983529269233], [0.9059059059059056, 0.7082859574755125, -0.08455977788029499, -0.7996616434203547, -0.7795582818283526], [0.9259259259259256, 0.719259907524414, -0.06660571707438431, -0.7912343525629955, -0.7884057732693579], [0.9459459459459456, 0.7299455869665756, -0.04862496150866218, -0.782489944618335, -0.7969372812831765], [0.9659659659659656, 0.7403387131118822, -0.030624717650863484, -0.7734319242385267, -0.8051493865453532], [0.9859859859859856, 0.7504351205220151, -0.012612199779393094, -0.7640639217677665, -0.8130387977441632], [1.0060060060060056, 0.7602307626799093, 0.005405372908068972, -0.7543896917872955, -0.820602352899731], [1.0260260260260257, 0.769721713611548, 0.02342077918793406, -0.7444131116106093, -0.8278370206313123], [1.0460460460460457, 0.7789041694594452, 0.04142679870488327, -0.7341381797294777, -0.8347399013722345], [1.0660660660660657, 0.7877744500071854, 0.05941621486569792, -0.7235690142113986, -0.8413082285320087], [1.0860860860860857, 0.7963290001544099, 0.07738181773158212, -0.712709851049126, -0.8475393696051444], [1.1061061061061057, 0.8045643913416567, 0.09531640690781845, -0.7015650424629363, -0.8534308272262261], [1.1261261261261257, 0.8124773229244865, 0.1132127944295984, -0.6901390551563111, -0.8589802401708267], [1.1461461461461457, 0.8200646234963401, 0.13106380764287107, -0.6784364685257369, -0.8641853843018578], [1.1661661661661658, 0.8273232521595998, 0.14886229207905552, -0.6664619728253394, -0.8690441734609763], [1.1861861861861858, 0.8342502997443438, 0.1666011143224646, -0.6542203672870855, -0.8735546603046918], [1.2062062062062058, 0.8408429899743064, 0.18427316486929105, -0.6417165581973108, -0.8777150370848376], [1.2262262262262258, 0.8470986805795755, 0.20187136097700997, -0.6289555569303392, -0.8815236363730946], [1.2462462462462458, 0.853014864355582, 0.21938864950305578, -0.6159424779399858, -0.884978931729276], [1.2662662662662658, 0.8585891701679577, 0.2368180097316357, -0.602682536709745, -0.8880795383131056], [1.2862862862862858, 0.8638193639028555, 0.2541524561875472, -0.5891810476624894, -0.8908242134392458], [1.3063063063063058, 0.8687033493623569, 0.2713850414358714, -0.5754434220305131, -0.89321185707535], [1.3263263263263259, 0.8732391691046012, 0.2885088588664201, -0.5614751656867762, -0.8952415122829421], [1.3463463463463459, 0.8774250052283045, 0.3055170454618212, -0.547281876938218, -0.8969123656009464], [1.3663663663663659, 0.881259180101353, 0.32240278454813226, -0.5328692442820232, -0.8982237473717126], [1.386386386386386, 0.884740157033177, 0.3391593085268807, -0.5182430441257422, -0.8991751320094058], [1.406406406406406, 0.8878665408906382, 0.3557799015874343, -0.5034091384721772, -0.8997661382106562], [1.4264264264264268, 0.8906370786571817, 0.37225790239861734, -0.48837347256996194, -0.8999965291073796], [1.4464464464464468, 0.8930506599350293, 0.3885867067784881, -0.4731420725307816, -0.8998662123617119], [1.4664664664664668, 0.8951063173902144, 0.4047597703412186, -0.45772104291417637, -0.899375240203017], [1.4864864864864868, 0.8968032271402775, 0.42077061112000197, -0.44211656428091084, -0.8985238094069534], [1.5065065065065069, 0.8981407090844675, 0.4366128121649465, -0.4263348907158804, -0.8973122612166097], [1.5265265265265269, 0.8991182271763185, 0.45228002411491103, -0.41038234732155, -0.8957410812057375], [1.5465465465465469, 0.8997353896384902, 0.4677659677422511, -0.39426532768293, -0.8938108990841406], [1.566566566566567, 0.899991949119788, 0.48306443646945746, -0.3779902913051053, -0.8915224884452939], [1.586586586586587, 0.899887802794298, 0.49816929885667643, -0.3615637610243446, -0.8888767664562972], [1.606606606606607, 0.8994229924025984, 0.5130745010591161, -0.3449923203938268, -0.8858747934902855], [1.626626626626627, 0.8985977042350304, 0.5277740692533542, -0.3282826110450333, -0.8825177727014444], [1.646646646646647, 0.8974122690570355, 0.5422621120315728, -0.3114413300258629, -0.8788070495428009], [1.666666666666667, 0.8958671619765884, 0.5565328227627635, -0.2944752271165368, -0.8747441112269815], [1.686686686686687, 0.89396300225378, 0.5705804819199541, -0.27739110212436874, -0.8703305861301563], [1.706706706706707, 0.8917005530526247, 0.5843994593725255, -0.2601958021584852, -0.8655682431394052], [1.726726726726727, 0.8890807211351945, 0.5979842166426992, -0.24289621888558743, -0.8604589909437697], [1.746746746746747, 0.8861045564981992, 0.6113293091252917, -0.2254992857678555, -0.8550048772692734], [1.766766766766767, 0.8827732519521607, 0.6244293882698463, -0.20801197528410095, -0.8492080880582187], [1.786786786786787, 0.8790881426433489, 0.6372792037242666, -0.19044129613528252, -0.8430709465930872], [1.806806806806807, 0.8750507055186717, 0.6498736054390936, -0.17279429043550393, -0.8365959125653972], [1.826826826826827, 0.8706625587337326, 0.6622075457315831, -0.1550780308896206, -0.8297855810898883], [1.846846846846847, 0.865925461004293, 0.6742760813087553, -0.13729961795858553, -0.8226426816644321], [1.866866866866867, 0.8608413109014016, 0.686074375248606, -0.11946617701367114, -0.8151700770760818], [1.886886886886887, 0.8554121460904689, 0.6975976989386867, -0.10158485548070739, -0.8073707622537026], [1.9069069069069071, 0.8496401425145969, 0.7088414339712743, -0.08366281997548039, -0.7992478630676406], [1.9269269269269271, 0.8435276135224866, 0.7198010739943728, -0.06570725343144032, -0.7908046350769118], [1.9469469469469471, 0.8370770089412768, 0.7304722265178044, -0.04772535222086907, -0.7820444622244139], [1.9669669669669672, 0.8302909140946829, 0.740850614673666, -0.02972432327066205, -0.7729708554806834], [1.9869869869869872, 0.8231720487668309, 0.7509320789304456, -0.0117113811738796, -0.7635874514367412], [2.007007007007007, 0.8157232661122005, 0.7607125787601117, 0.006306254701773933, -0.7538980108465911], [2.027027027027027, 0.8079475515121162, 0.7701881942575083, 0.024321363107384832, -0.7439064171199556], [2.047047047047047, 0.7998480213782424, 0.7793551277114047, 0.04232672380701857, -0.7336166747658526], [2.067067067067067, 0.7914279219035637, 0.7882097051265716, 0.0603151204715024, -0.7230329077876368], [2.0870870870870872, 0.782690627761351, 0.7967483776962744, 0.07827934357064219, -0.7121593580301497], [2.1071071071071072, 0.7736396407526318, 0.804967723224591, 0.09621219326271417, -0.7010003834796398], [2.1271271271271273, 0.7642785884027131, 0.8128644474979865, 0.11410648228007389, -0.6895604565171346], [2.1471471471471473, 0.7546112225073119, 0.8204353856055938, 0.13195503880972548, -0.6778441621259648], [2.1671671671671673, 0.7446414176288823, 0.8276775032076715, 0.14975070936769683, -0.6658561960541591], [2.1871871871871873, 0.7343731695437371, 0.8345878977517301, 0.16748636166606892, -0.6536013629324456], [2.2072072072072073, 0.7238105936405902, 0.8411637996358396, 0.1851548874715098, -0.6410845743486142], [2.2272272272272273, 0.7129579232711583, 0.8474025733186522, 0.202749205454168, -0.6283108468790118], [2.2472472472472473, 0.7018195080534854, 0.8533017183756955, 0.22026226402578317, -0.6152853000779602], [2.2672672672672673, 0.6903998121286689, 0.8588588705015119, 0.2376870441658768, -0.6020131544259006], [2.2872872872872874, 0.6787034123716865, 0.8640718024572432, 0.25501656223488994, -0.5884997292370892], [2.3073073073073074, 0.6667349965570397, 0.8689384249632813, 0.27224387277314116, -0.5747504405276801], [2.3273273273273274, 0.6544993614799518, 0.8734567875366258, 0.28936207128448177, -0.5607707988450528], [2.3473473473473474, 0.64200141103387, 0.8776250792726135, 0.30636429700353396, -0.5465664070592514], [2.3673673673673674, 0.6292461542450456, 0.8814416295707074, 0.32324373564540215, -0.5321429581174231], [2.3873873873873874, 0.6162387032649764, 0.8849049088040527, 0.33999362213675477, -0.5175062327621536], [2.4074074074074074, 0.6029842713215191, 0.8880135289325328, 0.3566072433271839, -0.5026620972146163], [2.4274274274274275, 0.5894881706294912, 0.8907662440590789, 0.37307794067975364, -0.48761650082346303], [2.4474474474474475, 0.5757558102615999, 0.8931619509290101, 0.3893991129396609, -0.47237547368039623], [2.4674674674674675, 0.5617926939805514, 0.8951996893722046, 0.40556421877993776, -0.4569451242033825], [2.4874874874874875, 0.5476044180332104, 0.8968786426879244, 0.4215667794231354, -0.44133163668847336], [2.5075075075075075, 0.5331966689076921, 0.8981981379721385, 0.43740038123793973, -0.425541268831214], [2.5275275275275275, 0.5185752210542875, 0.8991576463872152, 0.453058678309676, -0.4095803492186358], [2.5475475475475475, 0.5037459345711334, 0.8997567833738732, 0.4685353949836745, -0.39345527479283526], [2.5675675675675675, 0.48871475285555716, 0.8999953088053088, 0.4838243283804765, -0.37717250828715787], [2.5875875875875876, 0.4734877002220352, 0.8998731270834357, 0.4989193508818724, -0.3607385756360133], [2.6076076076076076, 0.45807087948772107, 0.8993902871771989, 0.5138144125867768, -0.34416006335936045], [2.6276276276276276, 0.44247046952651076, 0.8985469826029497, 0.5285035437359551, -0.32744361592291016], [2.6476476476476476, 0.42669272279262555, 0.8973435513468856, 0.54298085710463, -0.3105959330751048], [2.6676676676676676, 0.4107439628147042, 0.8957804757295904, 0.5572405503620095, -0.2936237671619404], [2.6876876876876876, 0.3946305816614098, 0.8938583822127251, 0.5712769083967909, -0.27653392042070885], [2.7077077077077076, 0.37835903737956683, 0.8915780411479508, 0.5850843056077071, -0.2593332422537438], [2.7277277277277276, 0.3619358514058544, 0.8889403664681801, 0.5986572081581993, -0.24202862648326412], [2.7477477477477477, 0.3453676059530938, 0.8859464153212846, 0.6119901761943115, -0.2246270085884134], [2.7677677677677677, 0.3286609413721783, 0.8825973876464026, 0.6250778660249172, -0.20713536292560492], [2.7877877877877877, 0.31182255349070065, 0.8788946256930182, 0.637915032263406, -0.18956069993328428], [2.8078078078078077, 0.29485919092934787, 0.8748396134830041, 0.6504965299299706, -0.1719100633222312], [2.8278278278278277, 0.27777765239713553, 0.8704339762158438, 0.6628173165136523, -0.154190527252526], [2.8478478478478477, 0.2605847839665686, 0.8656794796172711, 0.674872453993317, -0.1364091934983126], [2.8678678678678677, 0.2432874763298184, 0.8605780292315889, 0.6866571108167552, -0.1185731886014937], [2.8878878878878878, 0.2258926620370179, 0.8551316696579508, 0.6981665638371084, -0.10068966101549995], [2.9079079079079078, 0.20840731271777943, 0.8493425837309098, 0.7093962002058486, -0.08276577824027667], [2.9279279279279278, 0.19083843628705083, 0.8432130916455652, 0.7203415192215531, -0.06480872394963728], [2.947947947947948, 0.17319307413642843, 0.8367456500276571, 0.7309981341337287, -0.04682569511213446], [2.967967967967968, 0.15547829831205312, 0.8299428509489799, 0.74136177390097, -0.028823899106602692], [2.987987987987988, 0.1377012086802204, 0.822807420888512, 0.7514282849027395, -0.010810550833528764], [3.0080080080080087, 0.11986893008183978, 0.8153422196396761, 0.7611936326040887, 0.007207130176593267], [3.0280280280280287, 0.1019886094768884, 0.8075502391641698, 0.7706539031726488, 0.025221922656758742], [3.0480480480480487, 0.08406741307999185, 0.7994346023928217, 0.7798053050472501, 0.043226606497653], [3.0680680680680688, 0.06611252348829592, 0.7909985619739608, 0.7886441704575313, 0.06121396564138005], [3.0880880880880888, 0.04813113680276825, 0.7822454989697936, 0.7971669568939377, 0.07917679097357139], [3.108108108108109, 0.03013045974408887, 0.7731789215013157, 0.8053702485275157, 0.09710788321271126], [3.128128128128129, 0.01211770676428429, 0.7638024633423006, 0.8132507575789362, 0.1150000557955209], [3.148148148148149, -0.0058999028447369204, 0.7541198824629255, 0.8208053256361952, 0.13284613775724563], [3.168168168168169, -0.02391514784458839, 0.7441350595236247, 0.8280309249204679, 0.15063897660569003], [3.188188188188189, -0.04192080794459029, 0.7338519963197669, 0.8349246594996057, 0.1683714411878497], [3.208208208208209, -0.05990966669557385, 0.7232748141777852, 0.841483766448789, 0.18603642454799058], [3.228228228228229, -0.07787451438214628, 0.7124077523033986, 0.8477056169578757, 0.20362684677603005], [3.248248248248249, -0.09580815091225683, 0.7012551660825911, 0.8535877173849952, 0.22113565784507877], [3.268268268268269, -0.11370338870290593, 0.6898215253360254, 0.8591277102559697, 0.23855584043700548], [3.288288288288289, -0.13155305556084088, 0.6781114125275938, 0.8643233752091612, 0.2558804127548926], [3.308308308308309, -0.14934999755708359, 0.6661295209278218, 0.8691726298853663, 0.27310243132125533], [3.328328328328329, -0.16708708189413804, 0.6538806527328631, 0.8736735307623985, 0.29021499376090293], [3.348348348348349, -0.18475719976472865, 0.6413697171398376, 0.8778242739340302, 0.3072112415673265], [3.368368368368369, -0.20235326920092364, 0.6286017283792847, 0.8816231958329749, 0.3240843628515048], [3.388388388388389, -0.2198682379125014, 0.6155818037055218, 0.8850687738976255, 0.3408275950720266], [3.408408408408409, -0.23729508611342262, 0.6023151613457101, 0.8881596271822781, 0.35743422774543426], [3.428428428428429, -0.25462682933527475, 0.5888071184084528, 0.8908945169105983, 0.3738976051357046], [3.448448448448449, -0.2718565212265622, 0.5750630887527636, 0.8932723469721079, 0.39021112892178617], [3.468468468468469, -0.288977256336719, 0.5610885808182572, 0.8952921643614918, 0.40636826084212596], [3.488488488488489, -0.30598217288372953, 0.5468891954174341, 0.8969531595605521, 0.4223625253151255], [3.508508508508509, -0.322864455504247, 0.5324706234909431, 0.8982546668626522, 0.4381875120344742], [3.528528528528529, -0.339617337985108, 0.5178386438267218, 0.8991961646395243, 0.45383687853832294], [3.548548548548549, -0.3562341059751485, 0.5029991207439287, 0.8997772755503316, 0.4693043527512646], [3.568568568568569, -0.37270809967623364, 0.4879580017425956, 0.899997766692902, 0.4845837354981063], [3.588588588588589, -0.38903271651242394, 0.4727213151199417, 0.899857549697072, 0.4996689029884225], [3.608608608608609, -0.4052014137762072, 0.45729516755430627, 0.8993566807601052, 0.5145538092708962], [3.628628628628629, -0.4212077112507362, 0.44168574165766544, 0.8984953606241682, 0.5292324886564624], [3.648648648648649, -0.43704519380701995, 0.42589929349771755, 0.8972739344958767, 0.5436990581092831], [3.668668668668669, -0.45270751397503, 0.4099421500905269, 0.8956928919079394, 0.5579477196045973], [3.688688688688689, -0.4681883944876888, 0.39382070686473314, 0.8937528665229608, 0.5719727624524977], [3.708708708708709, -0.48348163079672146, 0.37754142509834093, 0.8914546358794758, 0.5857685655867064], [3.728728728728729, -0.498581093559364, 0.3611108293291188, 0.8887991210803223, 0.5993295998174293], [3.748748748748749, -0.5134807310949282, 0.34453550473964417, 0.8857873864234744, 0.6126504300473882], [3.768768768768769, -0.5281745718102422, 0.3278220945180416, 0.8824206389754845, 0.6257257174501415], [3.788788788788789, -0.5426567265929921, 0.31097729719547484, 0.8787002280877058, 0.6385502216098208], [3.8088088088088092, -0.5569213911720063, 0.2940078639614565, 0.8746276448554888, 0.6511188026214255], [3.8288288288288292, -0.5709628484435367, 0.27692059595805396, 0.8702045215205688, 0.6634264231508351], [3.8488488488488493, -0.5847754707626044, 0.2597223415540743, 0.8654326308168822, 0.6754681504537094], [3.8688688688688693, -0.5983537221984909, 0.2424199936003215, 0.8603138852600776, 0.6872391583524747], [3.8888888888888893, -0.6116921607534719, 0.225020486667026, 0.8548503363810022, 0.6987347291705946], [3.9089089089089093, -0.6247854405439037, 0.20753079426455343, 0.8490441739034732, 0.7099502556233592], [3.9289289289289293, -0.6376283139427884, 0.18995792604850634, 0.8428977248666634, 0.7208812426644278], [3.9489489489489493, -0.650215633682958, 0.17230892501034, 0.8364134526924523, 0.7315233092873882], [3.9689689689689693, -0.6625423549200377, 0.15459086465461716, 0.829593956198116, 0.741872190281612], [3.9889889889889893, -0.6746035372543571, 0.13681084616403344, 0.8224419685547542, 0.7519237379416981], [4.009009009009009, -0.686394346711002, 0.11897599555334996, 0.8149603561918669, 0.7616739237298225], [4.029029029029029, -0.6979100576772135, 0.10109346081337353, 0.8071521176485258, 0.7711188398903274], [4.049049049049049, -0.7091460547963555, 0.08317040904612888, 0.7990203823715952, 0.7802547010159023], [4.069069069069069, -0.7200978348176937, 0.06521402359237159, 0.7905684094614891, 0.7890778455647288], [4.089089089089089, -0.7307610084012433, 0.04723150115259262, 0.7817995863659623, 0.7975847373279819], [4.109109109109109, -0.7411313018769624, 0.029230048902668455, 0.772717427522463, 0.8057719668470993], [4.129129129129129, -0.7512045589575886, 0.011216881605312803, 0.7633255729495896, 0.8136362527802514], [4.1491491491491495, -0.7609767424044268, -0.006800781281512331, 0.753627786788215, 0.8211744432174628], [4.1691691691691695, -0.7704439356454257, -0.024815718498067487, 0.7436279557928647, 0.8283835169438591], [4.1891891891891895, -0.7796023443448923, -0.04282070987702863, 0.7333300877739538, 0.8352605846505338], [4.2092092092092095, -0.788448297924215, -0.06080853923724224, 0.7227383099915046, 0.8418028900925468], [4.2292292292292295, -0.7969782510329876, -0.0787719972758828, 0.7118568675009911, 0.8480078111935954], [4.2492492492492495, -0.8051887849699416, -0.0967038844578535, 0.7006901214519728, 0.8538728610969091], [4.2692692692692695, -0.8130766090531206, -0.11459701390127221, 0.6892425473401994, 0.8593956891619526], [4.2892892892892895, -0.8206385619387455, -0.13244421425788613, 0.6775187332138859, 0.8645740819065331], [4.3093093093093096, -0.8278716128882421, -0.1502383325872608, 0.6655233778348791, 0.8694059638939375], [4.32932932932933, -0.8347728629829247, -0.16797223722359148, 0.6532612887954514, 0.8738893985647418], [4.34934934934935, -0.8413395462858474, -0.18563882063398796, 0.6407373805914761, 0.8780225890129606], [4.36936936936937, -0.8475690309503573, -0.20323100226708693, 0.627956672652757, 0.8818038787062253], [4.38938938938939, -0.8534588202749076, -0.22074173139085096, 0.614924287331303, 0.885231752149702], [4.40940940940941, -0.8590065537037038, -0.23816398991841584, 0.6016454478483508, 0.888304835493483], [4.42942942942943, -0.8642100077727872, -0.25549079522085427, 0.5881254762009628, 0.8910218970832101], [4.44944944944945, -0.869067097001171, -0.2727152029257285, 0.5743697910290335, 0.8933818479537052], [4.46946946946947, -0.8735758747266762, -0.28983030970031004, 0.560383905443567, 0.8953837422654157], [4.48948948948949, -0.87773453388613, -0.30682925601835176, 0.5461734248170875, 0.8970267776834945], [4.50950950950951, -0.8815414077396143, -0.3237052289093017, 0.531744044537075, 0.8983102956993664], [4.52952952952953, -0.884994970538476, -0.34045146468885934, 0.5171015477233235, 0.8992337818946505], [4.54954954954955, -0.8880938381368267, -0.35706125166977737, 0.5022518029101369, 0.8997968661473323], [4.56956956956957, -0.8908367685462928, -0.37352793285182445, 0.48720076169429205, 0.8999993227801044], [4.58958958958959, -0.8932226624337889, -0.3898449085898296, 0.4719544563497108, 0.8998410706508156], [4.60960960960961, -0.8952505635621171, -0.4060056392387398, 0.4565189974097987, 0.899322173184991], [4.62962962962963, -0.896919659173215, -0.4220036477746296, 0.4409005712184177, 0.8984428383504117], [4.64964964964965, -0.8982292803138998, -0.43783252239061404, 0.42510543745047435, 0.8972034185737652], [4.66966966966967, -0.899178902103976, -0.45348591906662217, 0.4091399266031189, 0.8956044105993953], [4.68968968968969, -0.8997681439466012, -0.468957564112003, 0.39301043745855885, 0.8936464552902142], [4.70970970970971, -0.899996769680825, -0.48424125667994483, 0.3767234345195052, 0.8913303373708524], [4.72972972972973, -0.8998646876762388, -0.4993308712526984, 0.3602854454182785, 0.8886569851131498], [4.74974974974975, -0.8993719508697008, -0.5142203600966104, 0.3437030583006133, 0.8856274699641165], [4.76976976976977, -0.8985187567441187, -0.5289037556859806, 0.32698291918520966, 0.8822430061165093], [4.78978978978979, -0.8973054472493022, -0.5433751730947745, 0.3101317293000898, 0.8785049500221989], [4.80980980980981, -0.8957325086649124, -0.557628812355229, 0.29315624239682764, 0.8744147998485194], [4.82982982982983, -0.8938005714055677, -0.5716589607824094, 0.27606326204372733, 0.869974194877822], [4.84984984984985, -0.8915104097681811, -0.5854599952637838, 0.258859638899036, 0.8651849148504701], [4.86986986986987, -0.8888629416216309, -0.5990263845128978, 0.24155226796528378, 0.860048879251541], [4.88988988988989, -0.8858592280388911, -0.6123526912862473, 0.22414808582585014, 0.8545681465415202], [4.90990990990991, -0.8825004728717651, -0.6254335745624585, 0.20665406786486626, 0.8487449133312951], [4.92992992992993, -0.8787880222683976, -0.6382637916829054, 0.18907722547156597, 0.8425815135017799], [4.94994994994995, -0.874723364133754, -0.6508382004529025, 0.17142460323020633, 0.8360804172685254], [4.96996996996997, -0.8703081275332867, -0.6631517612026343, 0.15370327609668416, 0.8292442301916858], [4.98998998998999, -0.8655440820400255, -0.675199538806993, 0.13592034656297983, 0.8220756921317425], [5.01001001001001, -0.8604331370253542, -0.6869767046635175, 0.11808294181056511, 0.8145776761514009], [5.03003003003003, -0.8549773408937575, -0.6984785386276372, 0.1001982108539157, 0.8067531873641027], [5.05005005005005, -0.8491788802618462, -0.7097004309044497, 0.08227332167527315, 0.7986053617296123], [5.07007007007007, -0.843040079081987, -0.7206378838962691, 0.06431545835180517, 0.7901374647971624], [5.09009009009009, -0.8365633977108915, -0.7312865140052095, 0.046331818176314904, 0.7813528903966622], [5.11011011011011, -0.8297514319235347, -0.7416420533900769, 0.028329608772653676, 0.7722551592784911], [5.13013013013013, -0.8226069118728004, -0.7517003516768686, 0.010316045206993193, 0.7628479177024253], [5.15015015015015, -0.8151327009952686, -0.7614573776221913, -0.007701652903885035, 0.7531349359762611], [5.17017017017017, -0.8073317948635856, -0.7709092207289362, -0.02571626428612418, 0.7431201069447214], [5.19019019019019, -0.799207319985875, -0.7800520928135566, -0.04372056890299062, 0.7328074444292513], [5.21021021021021, -0.7907625325526707, -0.7888823295243274, -0.061707350848576724, 0.7222010816193268], [5.23023023023023, -0.7820008171318761, -0.7973963918099711, -0.07966940123984799, 0.7113052694159234], [5.25025025025025, -0.7729256853122702, -0.8055908673380682, -0.09759952110587553, 0.7001243747278068], [5.27027027027027, -0.7635407742961057, -0.8134624718626764, -0.11549052427309589, 0.6886628787213281], [5.29029029029029, -0.7538498454413624, -0.821008050540618, -0.13333524024544188, 0.6769253750244287], [5.31031031031031, -0.7438567827542412, -0.8282245791959021, -0.15112651707819, 0.6649165678855684], [5.33033033033033, -0.7335655913325013, -0.8351091655317768, -0.1688572242443727, 0.6526412702883208], [5.35035035035035, -0.7229803957602665, -0.841659050289928, -0.1865202554926068, 0.640104402022388], [5.37037037037037, -0.7121054384549412, -0.8478716083563548, -0.2041085316951924, 0.6273109877118093], [5.39039039039039, -0.7009450779669016, -0.8537443498134845, -0.22161500368534104, 0.6142661548011523], [5.41041041041041, -0.6895037872326424, -0.8592749209381003, -0.23903265508239568, 0.6009751315004961], [5.43043043043043, -0.6777861517820772, -0.8644611051446857, -0.2563545051039109, 0.587443244690028], [5.45045045045045, -0.6657968679007154, -0.8693008238738045, -0.273573611363465, 0.5736759177850956], [5.47047047047047, -0.6535407407474466, -0.873792137425162, -0.2906830726530844, 0.5596786685625671], [5.49049049049049, -0.6410226824286928, -0.8779332457350137, -0.30767603170916336, 0.5454571069493741], [5.51051051051051, -0.6282477100296955, -0.8817224890976086, -0.3245456779607717, 0.5310169327741213], [5.53053053053053, -0.6152209436037288, -0.8851583488303788, -0.3412852502592488, 0.5163639334826643], [5.55055055055055, -0.6019476041200449, -0.8882394478826093, -0.35788803958798937, 0.5015039818185719], [5.57057057057057, -0.5884330113713729, -0.8909645513873434, -0.37434739175133575, 0.4864430334694024], [5.59059059059059, -0.574682581841811, -0.8933325671563019, -0.39065671004149843, 0.4711871246797363], [5.61061061061061, -0.5607018265359653, -0.8953425461176189, -0.4068094578824361, 0.4557423698319226], [5.63063063063063, -0.5464963487702066, -0.8969936826962182, -0.42279916144963564, 0.4401149589955101], [5.65065065065065, -0.5320718419269294, -0.8982853151366782, -0.43861941226474216, 0.4243111554463414], [5.67067067067067, -0.5174340871727137, -0.8992169257684555, -0.45426386976399896, 0.4083372931563095], [5.6906906906906904, -0.5025889511413034, -0.8997881412133605, -0.4697262638394685, 0.3921997742547785], [5.7107107107107105, -0.48754238358233193, -0.8999987325352031, -0.4850003973520148, 0.3759050664626879], [5.7307307307307305, -0.4723004149767341, -0.8998486153315477, -0.5000801486150417, 0.3594597005003697], [5.7507507507507505, -0.4568691541198033, -0.8993378497675403, -0.51495947384799, 0.34287026747011434], [5.7707707707707705, -0.44125478567286075, -0.8984666405517951, -0.5296324095986111, 0.3261434162145378], [5.7907907907907905, -0.42546356768451865, -0.8972353368543503, -0.5440930751330465, 0.3092858506518063], [5.8108108108108105, -0.40950182908253047, -0.8956444321667243, -0.558335674792753, 0.29230432708878773], [5.8308308308308305, -0.3933759671372337, -0.8936945641041306, -0.5723545003173339, 0.27520565151320586], [5.8508508508508505, -0.37709244489760213, -0.8913865141499296, -0.5861439331323381, 0.2579966768658837], [5.870870870870871, -0.36065778860093445, -0.8887212073424193, -0.599698446601118, 0.2406843002941682], [5.890890890890891, -0.34407858505721756, -0.8856997119040914, -0.6130126082398376, 0.22327546038763765], [5.910910910910911, -0.32736147900921403, -0.8823232388135005, -0.6260810818947452, 0.20577713439719958], [5.930930930930931, -0.31051317046932964, -0.8785931413199188, -0.6388986298808395, 0.18819633543869352], [5.950950950950951, -0.29354041203433007, -0.8745109144009707, -0.6514601150810696, 0.1705401096821196], [5.970970970970971, -0.27645000617898274, -0.8700781941634631, -0.6637605030052294, 0.15281553352761965], [5.990990990990991, -0.25924880252970767, -0.8652967571876545, -0.6757948638077195, 0.13502971076934206], [6.011011011011011, -0.24194369511933034, -0.8601685198152229, -0.6875583742633689, 0.11718976974832775], [6.031031031031031, -0.22454161962403776, -0.8546955373812195, -0.6990463197005257, 0.09930286049555796], [6.051051051051051, -0.20704955058364397, -0.8488800033903153, -0.7102540958906394, 0.08137615186630882], [6.071071071071071, -0.18947449860627896, -0.8427242486376718, -0.7211772108935802, 0.06341682866696166], [6.091091091091091, -0.171823507558623, -0.8362307402747856, -0.731811286857953, 0.04543208877542007], [6.111111111111111, -0.15410365174281038, -0.8294020808206849, -0.7421520617756882, 0.02742914025628817], [6.131131131131131, -0.13632203306113516, -0.8222410071188719, -0.7521953911902007, 0.00941519847196616], [6.151151151151151, -0.1184857781696957, -0.8147503892404282, -0.7619372498574397, -0.008602516809179039], [6.171171171171171, -0.10060203562211731, -0.8069332293337264, -0.771373733359156, -0.026616784306408958], [6.191191191191191, -0.08267797300449935, -0.7987926604212044, -0.7805010596677461, -0.04462038412081489], [6.211211211211211, -0.06472077406273388, -0.79033194514369, -0.7893155706620417, -0.06260610062896546], [6.231231231231231, -0.04673763582334795, -0.7815544744527719, -0.7978137335934417, -0.08056672537484048], [6.251251251251251, -0.028735765709023038, -0.7724637662517497, -0.8059921425017931, -0.09849505995889239], [6.271271271271271, -0.010722378649947805, -0.7630634639856997, -0.8138475195804596, -0.11638391892307699], [6.291291291291291, 0.007295305807837932, -0.7533573351812274, -0.8213767164900262, -0.13422613263069755], [6.311311311311311, 0.025310066395949328, -0.7433492699364882, -0.8285767156201149, -0.15201455013990772], [6.331331331331331, 0.043314683017852885, -0.7330432793620837, -0.8354446312988064, -0.1697420420697221], [6.351351351351351, 0.061301939642593124, -0.7224434939734576, -0.8419777109491823, -0.18740150345738496], [6.371371371371371, 0.07926462719688995, -0.7115541620354348, -0.8481733361925247, -0.20498585660595312], [6.391391391391391, 0.09719554645444728, -0.7003796478595696, -0.8540290238977296, -0.22248805392095022], [6.411411411411411, 0.11508751092131529, -0.6889244300549818, -0.8595424271765165, -0.23990108073495708], [6.431431431431431, 0.13293334971614962, -0.6771930997333856, -0.8647113363240323, -0.25721795811900466], [6.451451451451451, 0.15072591044421313, -0.6651903586690272, -0.8695336797044738, -0.27443174567964396], [6.471471471471471, 0.16845806206396877, -0.6529210174142718, -0.8740075245813744, -0.29153554434057105], [6.491491491491491, 0.1861226977451139, -0.6403899933715921, -0.8781310778922216, -0.30852249910769275], [6.511511511511511, 0.20371273771691145, -0.6276023088227334, -0.8819026869670938, -0.32538580181652466], [6.531531531531531, 0.2212211321056758, -0.614563088915845, -0.885320840191031, -0.3421186938608208], [6.551551551551551, 0.23864086376027593, -0.6012775596113822, -0.8883841676098713, -0.3587144689013405], [6.571571571571571, 0.25596495106452455, -0.5877510455876059, -0.8910914414793117, -0.3751664755536673], [6.591591591591591, 0.2731864507353244, -0.5739889681065161, -0.8934415767569739, -0.39146812005400294], [6.611611611611611, 0.2902984606054517, -0.5599968428410759, -0.8954336315372747, -0.40761286890186765], [6.631631631631631, 0.3072941223898607, -0.5457802776645974, -0.8970668074289317, -0.42359425147864715], [6.651651651651651, 0.3241666244344007, -0.5313449704031741, -0.8983404498749475, -0.4394058626409382], [6.671671671671671, 0.34090920444584405, -0.516696706552062, -0.8992540484149494, -0.45504136528765177], [6.691691691691691, 0.35751515220213076, -0.5018413569569233, -0.8998072368897746, -0.4704944928998459], [6.711711711711711, 0.3739778122417439, -0.48678487546086296, -0.8999997935882229, -0.48575905205227027], [6.731731731731731, 0.3902905865311375, -0.4715332965182011, -0.8998316413359162, -0.5008289248956157], [6.751751751751751, 0.4064469371091478, -0.4560927327759371, -0.8993028475262279, -0.515698071608473], [6.771771771771771, 0.42244038870732853, -0.44046937262387525, -0.8984136240932739, -0.5303605328180206], [6.791791791791791, 0.43826453134515975, -0.4246694777143937, -0.8971643274269707, -0.5448104319884682], [6.811811811811811, 0.4539130228990896, -0.40869938045285, -0.8955554582301996, -0.5590419777763006], [6.831831831831831, 0.46937959164438053, -0.39256548145963066, -0.8935876613181303, -0.5730494663513775], [6.851851851851851, 0.48465803876873986, -0.37627424700486034, -0.8912617253597879, -0.5868272836829598], [6.871871871871871, 0.4997422408567282, -0.35983220641679947, -0.8885785825619626, -0.6003699077897436], [6.891891891891891, 0.5146261523439501, -0.3432459494649692, -0.8855393082955938, -0.6136719109530043], [6.911911911911911, 0.5293038079400423, -0.32652212371905187, -0.8821451206647738, -0.6267279618919593], [6.931931931931931, 0.5437693250194893, -0.3096674318846264, -0.8783973800185467, -0.6395328279004803], [6.951951951951951, 0.5580169059793082, -0.2926886291168053, -0.8742975884056974, -0.6520813769442987], [6.971971971971971, 0.5720408405626574, -0.2755925203128507, -0.8698473889727482, -0.6643685797178622], [6.991991991991991, 0.5858355081474378, -0.258385957384855, -0.865048565305406, -0.6763895116600187], [7.012012012012011, 0.5993953799989699, -0.24107583651357736, -0.8599030407137229, -0.6881393549277213], [7.032032032032031, 0.6127150214858427, -0.2236690953845388, -0.854412877461257, -0.6996134003269602], [7.052052052052051, 0.6257890942580483, -0.2061727104074824, -0.8485802759385422, -0.7108070492001509], [7.072072072072071, 0.6386123583865277, -0.18859369392031314, -0.8424075737811983, -0.7217158152692198], [7.092092092092091, 0.6511796744632696, -0.17093909137863847, -0.8358972449330356, -0.7323353264336503], [7.112112112112111, 0.6634860056611237, -0.15321597853203536, -0.8290518986545279, -0.7426613265227661], [7.132132132132131, 0.6755264197524988, -0.1354314585881763, -0.821874278477053, -0.7526896770015544], [7.152152152152151, 0.6872960910861394, -0.11759265936595002, -0.8143672611033186, -0.7624163586293388], [7.172172172172171, 0.6987903025211891, -0.09970673043871875, -0.8065338552544147, -0.7718374730706425], [7.192192192192191, 0.7100044473177617, -0.08178084026885607, -0.7983772004639555, -0.7809492444575943], [7.212212212212211, 0.7209340309832671, -0.06382217333471475, -0.7899005658197922, -0.7897480209032491], [7.232232232232231, 0.7315746730737498, -0.0458379272511753, -0.7811073486538019, -0.7982302759652209], [7.2522522522522515, 0.741922108949516, -0.027835309884929625, -0.7720010731802794, -0.8063926100590374], [7.2722722722722715, 0.7519721914843507, -0.009821536465655855, -0.7625853890834745, -0.8142317518206517], [7.2922922922922915, 0.7617208927276341, 0.008196173305757738, -0.7528640700548425, -0.8217445594175659], [7.3123123123123115, 0.7711643055186962, 0.02621059815078091, -0.742841012280596, -0.828928021808039], [7.3323323323323315, 0.7802986450527584, 0.044214518107441904, -0.7325202328801594, -0.8357792599478756], [7.3523523523523515, 0.7891202503978375, 0.062200717424000185, -0.7219058682961578, -0.8422955279443114], [7.3723723723723715, 0.7976255859620028, 0.08016198745093188, -0.71100217263658, -0.8484742141565347], [7.3923923923923915, 0.8058112429103979, 0.09809112953006863, -0.699813515969785, -0.8543128422423987], [7.4124124124124116, 0.81367394053146, 0.11598095787973203, -0.6883443825730304, -0.8598090721509105], [7.432432432432432, 0.8212105275517899, 0.13382430247470747, -0.6765993691352303, -0.8649607010600946], [7.452452452452452, 0.8284179833991433, 0.15161401191990276, -0.6645831829146571, -0.8697656642598566], [7.472472472472472, 0.8352934194130396, 0.16934295631654026, -0.6523006398523309, -0.8742220359794949], [7.492492492492492, 0.8418340800025016, 0.18700403011973327, -0.6397566626418477, -0.878328030159525], [7.512512512512512, 0.8480373437504631, 0.20459015498630204, -0.626956278756425, -0.8820820011675112], [7.532532532532532, 0.853900724464401, 0.22209428261168732, -0.6139046184339512, -0.8854824444576159], [7.552552552552552, 0.8594218721727706, 0.239509397554825, -0.6006069126208488, -0.8885279971736028], [7.572572572572572, 0.8645985740668455, 0.2568285200498493, -0.5870684908755748, -0.8912174386950533], [7.592592592592592, 0.8694287553875848, 0.2740447088034982, -0.5732947792325989, -0.8935496911265767], [7.612612612612612, 0.873910480257171, 0.29115106377709865, -0.5592912980277145, -0.8955238197298169], [7.632632632632632, 0.8780419524548863, 0.3081407289520187, -0.5450636596855559, -0.8971390332980844], [7.652652652652652, 0.8818215161370159, 0.32500689507747554, -0.5306175664702067, -0.8983946844734627], [7.672672672672672, 0.8852476565004901, 0.34174280239960064, -0.5159588081998017, -0.8992902700062607], [7.692692692692692, 0.8883190003899988, 0.3583417433706666, -0.5010932599260381, -0.8998254309567096], [7.712712712712712, 0.8910343168483355, 0.37479706533739077, -0.48602687957952734, -0.8999999528388214], [7.732732732732732, 0.8933925176097498, 0.3911021732072372, -0.47076570558192804, -0.8998137657063522], [7.752752752752752, 0.8953926575361112, 0.4072505320916498, -0.45531585442582073, -0.8992669441808362], [7.772772772772772, 0.8970339349957092, 0.42323566992515566, -0.4396835182232922, -0.8983597074216777], [7.792792792792792, 0.8983156921845367, 0.4390511800592904, -0.4238749622242119, -0.897092419038315], [7.812812812812812, 0.8992374153899308, 0.45469072383030457, -0.4078965223051968, -0.8954655869444903], [7.832832832832834, 0.8997987351964621, 0.47014803309962416, -0.39175460243026733, -0.8934798631546836], [7.852852852852854, 0.8999994266339917, 0.4854169127660389, -0.37545567208422276, -0.8911360435227943], [7.872872872872874, 0.8998394092678368, 0.5004912432486316, -0.3590062636797415, -0.8884350674231709], [7.892892892892894, 0.899318747231008, 0.5153649829394257, -0.34241296993927556, -0.885378017374123], [7.912912912912914, 0.8984376491985054, 0.53003217062479, -0.32568244125276674, -0.8819661186040597], [7.932932932932934, 0.8971964683036844, 0.5444869278746204, -0.3088213830122507, -0.8782007385604343], [7.952952952952954, 0.8955957019967234, 0.5587234613983422, -0.29183655292441785, -0.8740833863616868], [7.972972972972974, 0.8936359918452534, 0.5727360653667918, -0.27473475830220695, -0.8696157121924087], [7.992992992992994, 0.8913181232772236, 0.5865191236990425, -0.2575228533365169, -0.8647995066419683], [8.013013013013014, 0.8886430252661129, 0.6000671123132615, -0.24020773634913062, -0.8596366999868643], [8.033033033033034, 0.8856117699586081, 0.6133746013406948, -0.22279634702795148, -0.8541293614170957], [8.053053053053054, 0.8822255722449006, 0.6264362573018922, -0.20529566364566135, -0.8482796982068539], [8.073073073073074, 0.878485789271773, 0.639246845244301, -0.1877127002629137, -0.8420900548298754], [8.093093093093094, 0.8743939198986717, 0.6518012308403709, -0.17005450391718355, -0.8355629120198047], [8.113113113113114, 0.8699516040969818, 0.6640943824453308, -0.15232815179840087, -0.8287008857759468], [8.133133133133134, 0.8651606222927472, 0.6761213731138096, -0.13454074841249883, -0.8215067263148063], [8.153153153153154, 0.8600228946530966, 0.6878773825744964, -0.11669942273401471, -0.8139833169678342], [8.173173173173174, 0.8545404803166645, 0.6993576991620466, -0.09881132534888352, -0.806133673025824], [8.193193193193194, 0.848715576568314, 0.7105577217054597, -0.08088362558857043, -0.7979609405304194], [8.213213213213214, 0.8425505179584907, 0.721472961372173, -0.06292350865668997, -0.7894683950132199], [8.233233233233234, 0.836047775367564, 0.7320990434671303, -0.044938172749264094, -0.7806594401829867], [8.253253253253254, 0.8292099550155284, 0.742431709186107, -0.026934826169772823, -0.7715376065614786], [8.273273273273274, 0.8220397974174619, 0.7524668173225868, -0.008920684440154085, -0.7621065500684615], [8.293293293293294, 0.8145401762851616, 0.7622003459275066, 0.009097032591089554, -0.7523700505564599], [8.313313313313314, 0.8067140973753938, 0.7716283939212045, 0.027111103642518203, -0.7423320102958391], [8.333333333333334, 0.7985646972852252, 0.7807471826569258, 0.045114308893956255, -0.7319964524108216], [8.353353353353354, 0.7900952421949107, 0.7895530574352577, 0.06309943288010833, -0.7213675192670689], [8.373373373373374, 0.7813091265588504, 0.798042488968889, 0.08105926738242988, -0.7104494708114711], [8.393393393393394, 0.7722098717451323, 0.8062120747971047, 0.0989866143180933, -0.6992466828648127], [8.413413413413414, 0.762801124624211, 0.8140585406494516, 0.11687428862489184, -0.6877636453679965], [8.433433433433434, 0.7530866561072869, 0.8215787417580253, 0.13471512114092501, -0.6760049605825301], [8.453453453453454, 0.7430703596349704, 0.8287696641178554, 0.15250196147791126, -0.663975341245996], [8.473473473473474, 0.732756249616839, 0.8356284256948812, 0.17022768088697654, -0.6516796086832427], [8.493493493493494, 0.7221484598225104, 0.8421522775810373, 0.1878851751157699, -0.6391226908740579], [8.513513513513514, 0.7112512417248779, 0.8483386050959808, 0.20546736725576137, -0.6263096204780932], [8.533533533533534, 0.7000689627961721, 0.8541849288350242, 0.2229672105785806, -0.6132455328178359], [8.553553553553554, 0.6886061047575318, 0.8596889056628504, 0.24037769136025972, -0.5999356638204337], [8.573573573573574, 0.6768672617827832, 0.8648483296526129, 0.25769183169224846, -0.5863853479192002], [8.593593593593594, 0.6648571386571529, 0.8696611329700444, 0.27490269227807496, -0.5726000159156375], [8.613613613613614, 0.6525805488916459, 0.8741253867022194, 0.29200337521453107, -0.5585851928028379], [8.633633633633634, 0.6400424127938498, 0.8782393016306388, 0.30898702675626794, -0.5443464955511346], [8.653653653653654, 0.6272477554959351, 0.8820012289483277, 0.3258468400626938, -0.5298896308568869], [8.673673673673674, 0.6142017049406437, 0.8854096609206562, 0.34257605792607265, -0.5152203928553072], [8.693693693693694, 0.600909489826072, 0.8884632314896215, 0.3591679754797307, -0.5003446607982416], [8.713713713713714, 0.5873764375100716, 0.8911607168213476, 0.3756159428852851, -0.4852683966978375], [8.733733733733734, 0.5736079718751105, 0.8935010357965822, 0.3919133679978187, -0.4699976429370417], [8.753753753753754, 0.5596096111544462, 0.8954832504439971, 0.4080537190079308, -0.4545385198478872], [8.773773773773774, 0.5453869657204852, 0.8971065663161149, 0.4240305270596069, -0.43889722325853836], [8.793793793793794, 0.5309457358362141, 0.8983703328077141, 0.43983738884285783, -0.42308002201007866], [8.813813813813814, 0.516291709370604, 0.8992740434165838, 0.45546796916008764, -0.40709325544403563], [8.833833833833834, 0.5014307594789027, 0.8998173359465239, 0.4709160034651642, -0.3909433308616498], [8.853853853853854, 0.4863688422487457, 0.8999999926525084, 0.48617530037417245, -0.3746367209559067], [8.873873873873874, 0.47111199431302925, 0.899821940327955, 0.5012397441468464, -0.35817996121736023], [8.893893893893894, 0.4556663304305009, 0.8992832503340662, 0.5161032971376821, -0.3415796473147876], [8.913913913913914, 0.44003804103503913, 0.8983841385712272, 0.5307600022157537, -0.32484243245172584], [8.933933933933934, 0.4242333897546026, 0.8971249653924771, 0.5452039851522583, -0.3079750246999488], [8.953953953953954, 0.40825871090084453, 0.8955062354590828, 0.5594294569748349, -0.29098418431095363], [8.973973973973974, 0.3921204069303976, 0.8935285975382773, 0.5734307162877149, -0.27387672100653465], [8.993993993993994, 0.37582494587884785, 0.8911928442432419, 0.5872021515567711, -0.25665949124952936], [9.014014014014014, 0.3593788587684241, 0.8884999117154359, 0.6007382433585531, -0.2393393954958321], [9.034034034034034, 0.3427887369904444, 0.8854508792494029, 0.6140335665924039, -0.22192337542877538], [9.054054054054054, 0.3260612296635662, 0.882046968860203, 0.6270827926547756, -0.2044184111769874], [9.074074074074074, 0.30920304096890133, 0.8782895447936434, 0.6398806915748673, -0.186831518516842], [9.094094094094094, 0.2922209274630612, 0.8741801129795056, 0.6524221341107344, -0.1691697460606207], [9.114114114114114, 0.2751216953702116, 0.8697203204279873, 0.6647020938050262, -0.15144017243151497], [9.134134134134134, 0.25791219785422076, 0.8649119545696005, 0.676715648999529, -0.13364990342660013], [9.154154154154154, 0.24059933227199504, 0.8597569425387911, 0.6884579848077059, -0.11580606916891857], [9.174174174174174, 0.22319003740910232, 0.8542573504015671, 0.699924395044446, -0.09791582124981332], [9.194194194194194, 0.20569129069879089, 0.8484153823274447, 0.7111102841122445, -0.07998632986265733], [9.214214214214214, 0.1881101054255198, 0.8422333797060434, 0.7220111688430636, -0.06202478092912736], [9.234234234234235, 0.17045352791411947, 0.8357138202086859, 0.7326226802951303, -0.04403837321917415], [9.254254254254255, 0.1527286347057107, 0.8288593167953779, 0.7429405655039564, -0.02603431546584314], [9.274274274274275, 0.13494252972151324, 0.8216726166675652, 0.7529606891868738, -0.008019823476102052], [9.294294294294295, 0.11710234141568075, 0.8141566001670889, 0.7626790354004057, 0.00999788276116655], [9.314314314314315, 0.09921521991830316, 0.8063142796217797, 0.7720917091498078, 0.028011581968848866], [9.334334334334335, 0.08128833416972171, 0.7981487981381532, 0.7811949379501353, 0.046014054475799746], [9.354354354354355, 0.06332886904730496, 0.7896634283416916, 0.7899850733382078, 0.06399808511039887], [9.374374374374375, 0.04534402248583755, 0.7808615710652146, 0.7984585923348698, 0.08195646609230366], [9.394394394394395, 0.027341002592675606, 0.7717467539858682, 0.8066120988569575, 0.09988199992123978], [9.414414414414415, 0.009327024758825166, 0.7623226302112736, 0.8144423250784074, 0.11776750226167157], [9.434434434434435, -0.008690691232898635, 0.7525929768154063, 0.8219461327399614, 0.1356058048221962], [9.454454454454455, -0.02670492410147253, 0.7425616933247914, 0.8291205144069428, 0.15338975822850745], [9.474474474474475, -0.044708453961866575, 0.7322328001556204, 0.8359625946745995, 0.1711122348887779], [9.494494494494495, -0.06269406521868609, 0.7216104370024181, 0.8424696313205311, 0.1887661318503108], [9.514514514514515, -0.08065454945809436, 0.7106988611789025, 0.8486390164037382, 0.20634437364631714], [9.534534534534535, -0.09858270833685706, 0.6995024459117056, 0.8544682773098535, 0.2238399151316762], [9.554554554554555, -0.11647135646735057, 0.6880256785876383, 0.8599550777421356, 0.24124574430654422], [9.574574574574575, -0.1343133242973777, 0.6762731589551993, 0.8650972186578281, 0.2585548851266782], [9.594594594594595, -0.15210146098363692, 0.6642495972810527, 0.8698926391495089, 0.2757604002993498], [9.614614614614615, -0.16982863725769315, 0.65195981246221, 0.8743394172710771, 0.2928553940637271], [9.634634634634635, -0.18748774828330173, 0.6394087300946756, 0.878435770808046, 0.30983301495461246], [9.654654654654655, -0.2050717165039402, 0.6266013804993268, 0.882180057991832, 0.32668645854842526], [9.674674674674675, -0.22257349447940677, 0.613542896705824, 0.885570778157756, 0.34340897019033273], [9.694694694694695, -0.23998606771034833, 0.6002385123953536, 0.8886065723464914, 0.35999384770143283], [9.714714714714715, -0.2573024574495865, 0.5866935598030331, 0.8912862238487187, 0.3764344440649062], [9.734734734734735, -0.2745157234991143, 0.5729134675808158, 0.8936086586927673, 0.39272417009005944], [9.754754754754755, -0.291618966991643, 0.558903758621754, 0.8955729460750508, 0.4088564970531922], [9.774774774774775, -0.30860533315558436, 0.5446700478464896, 0.8971782987331212, 0.4248249593142304], [9.794794794794795, -0.3254680140623589, 0.5302180399528637, 0.8984240732611943, 0.4406231569080758], [9.814814814814815, -0.3422002513549313, 0.5155535271295416, 0.8993097703680191, 0.4562447581096342], [9.834834834834835, -0.3587953389564773, 0.5006823867345758, 0.8998350350769874, 0.47168350197149356], [9.854854854854855, -0.37524662575809775, 0.48561057893983134, 0.8999996568684042, 0.4869332008332352], [9.874874874874875, -0.39154751828450185, 0.47034414434222216, 0.8998035697638614, 0.5019877428013736], [9.894894894894895, -0.40769148333659155, 0.45488920154271306, 0.8992468523526813, 0.5168410941989274], [9.914914914914915, -0.4236720506098886, 0.43925194469405954, 0.8983297277604186, 0.5314873019836451], [9.934934934934935, -0.43948281528775346, 0.42343864101826606, 0.8970525635594346, 0.5459204961339109], [9.954954954954955, -0.45511744060835757, 0.4074556282947605, 0.895415871621579, 0.5601348920013781], [9.974974974974975, -0.47056966040438064, 0.3913093123202892, 0.8934203079130373, 0.5741247926293856], [9.994994994994995, -0.48583328161441414, 0.37500616434155143, 0.8910666722314277, 0.5878845910362291], [10.015015015015015, -0.5009021867650646, 0.35855271846160297, 0.8883559078852524, 0.6014087724623702], [10.035035035035035, -0.5157703364227624, 0.34195556902106694, 0.8852891013158309, 0.6146919165806862], [10.055055055055055, -0.5304317716142936, 0.32522136795520257, 0.8818674816618667, 0.6277286996688696], [10.075075075075075, -0.5448806162150831, 0.30835682212789034, 0.878092420266825, 0.640513896743112], [10.095095095095095, -0.5591110793042745, 0.2913686906436032, 0.8739654301293135, 0.653042383652214], [10.115115115115115, -0.5731174574856602, 0.2742637821384392, 0.8694881652966918, 0.6653091391312824], [10.135135135135135, -0.586894137173533, 0.2570489520513037, 0.8646624202021495, 0.6773092468141917], [10.155155155155155, -0.6004355968425431, 0.2397310998763324, 0.8594901289455185, 0.6890378972040042], [10.175175175175175, -0.6137364092406581, 0.2223171663976588, 0.8539733645181099, 0.7004903896005575], [10.195195195195195, -0.6267912435643395, 0.20481413090763206, 0.8481143379718847, 0.711662133984448], [10.215215215215215, -0.6395948675950637, 0.1872290084096017, 0.8419153975332899, 0.7225486528566544], [10.235235235235235, -0.6521421497963317, 0.16956884680638984, 0.8353790276621194, 0.7331455830330653], [10.255255255255255, -0.6644280613703262, 0.1518407240755777, 0.8285078480557714, 0.7434486773931895], [10.275275275275275, -0.6764476782733922, 0.1340517454327382, 0.821304612599307, 0.7534538065823512], [10.295295295295295, -0.6881961831895345, 0.11620904048375243, 0.8137722082617254, 0.763156960666684], [10.315315315315315, -0.6996688674611381, 0.09831976036735014, 0.8059136539389026, 0.7725542507402638], [10.335335335335335, -0.7108611329761421, 0.08039107488902082, 0.7977320992436546, 0.7816419104837357], [10.355355355355355, -0.7217684940109059, 0.0624301696474428, 0.789230823243411, 0.7904162976738074], [10.375375375375375, -0.7323865790280333, 0.044444243154582924, 0.7804132331460043, 0.7988738956430095], [10.395395395395395, -0.742711132428431, 0.026440503950620645, 0.771282862934103, 0.8070113146891317], [10.415415415415415, -0.7527380162569008, 0.008426167714853012, 0.7618433719488344, 0.8148252934337751], [10.435435435435435, -0.7624632118605823, -0.00959154562626172, 0.752098543423165, 0.8223127001294722], [10.455455455455455, -0.7718828214995792, -0.027605414792762614, 0.742052282965627, 0.8294705339148537], [10.475475475475475, -0.7809930699091261, -0.04560822004538708, 0.7317086169949978, 0.8362959260173566], [10.495495495495495, -0.7897903058126691, -0.06359274607915442, 0.7210716911265607, 0.842786140902994], [10.515515515515515, -0.7982710033852527, -0.08155178491517205, 0.7101457685105926, 0.8489385773727227], [10.535535535535535, -0.8064317636666272, -0.09947813878950579, 0.6989352281237455, 0.8547507696049736], [10.555555555555555, -0.8142693159235107, -0.11736462303795575, 0.6874445630140058, 0.8602203881439217], [10.575575575575575, -0.8217805189604596, -0.1352040689755823, 0.6756783784999354, 0.8653452408331037], [10.595595595595595, -0.8289623623788206, -0.15298932676982763, 0.6636413903249163, 0.8701232736940084], [10.615615615615615, -0.8358119677832627, -0.17071326830608152, 0.6513384227671383, 0.8745525717492856], [10.635635635635635, -0.8423265899354024, -0.18836879004454285, 0.6387744067060865, 0.8786313597902456], [10.655655655655655, -0.8485036178540618, -0.20594881586723168, 0.6259543776463053, 0.8823580030883419], [10.675675675675675, -0.8543405758617184, -0.22344629991401121, 0.61288347369923, 0.8857310080503499], [10.695695695695695, -0.8598351245767261, -0.24085422940648277, 0.5995669335238929, 0.8887490228169808], [10.715715715715715, -0.8649850618509111, -0.25816562745862165, 0.5860100942273342, 0.8914108378046907], [10.735735735735735, -0.869788323652166, -0.2753735558730283, 0.5722183892255536, 0.8937153861904669], [10.755755755755755, -0.8742429848916887, -0.29247111792167313, 0.5581973460658635, 0.8956617443393972], [10.775775775775776, -0.8783472601955347, -0.30945146111002125, 0.5439525842115158, 0.897249132174852], [10.795795795795796, -0.8820995046201726, -0.3263077799234285, 0.5294898127894888, 0.8984769134911293], [10.815815815815816, -0.8854982143117583, -0.3430333185547091, 0.5148148283023395, 0.899344596208438], [10.835835835835836, -0.8885420271088611, -0.35962137361178065, 0.4999335123050353, 0.8998518325701178], [10.855855855855856, -0.8912297230884003, -0.376065296804302, 0.4848518290476988, 0.8999984192820154], [10.875875875875876, -0.8935602250545771, -0.3923584976082271, 0.46957582308520884, 0.8997842975939631], [10.895895895895896, -0.8955325989706008, -0.40849444590720674, 0.45411161685461693, 0.8992095533233243], [10.915915915915916, -0.8971460543330391, -0.42446667460977955, 0.4384654082213486, 0.8982744168205993], [10.935935935935936, -0.8983999444886429, -0.44026878224130345, 0.42264346799517466, 0.8969792628771038], [10.955955955955956, -0.8992937668935164, -0.45589443550958864, 0.4066521374169471, 0.8953246105747569], [10.975975975975976, -0.8998271633145309, -0.47133737184320423, 0.390497825617107, 0.8933111230780394], [10.995995995995996, -0.8999999199729005, -0.48659140190143996, 0.3741870070469832, 0.8909396073682057], [11.016016016016017, -0.8998119676298615, -0.5016504120549207, 0.3577262188839094, 0.8882110139198549], [11.036036036036037, -0.8992633816144227, -0.5165083668358684, 0.3411220584112093, 0.8851264363199937], [11.056056056056057, -0.8983543817931745, -0.5311593113570529, 0.3243811803740769, 0.8816871108297383], [11.076076076076077, -0.8970853324821693, -0.5455973736984319, 0.30751029431244153, 0.8778944158888369], [11.096096096096097, -0.8954567423009073, -0.5598167672605472, 0.29051616187186474, 0.8737498715632076], [11.116116116116117, -0.893469263968489, -0.5738117930837227, 0.2734055940935547, 0.869255138935715], [11.136136136136138, -0.8911236940420125, -0.587576842132138, 0.2561854486845844, 0.8644120194404279], [11.156156156156158, -0.8884209725973234, -0.601106397541862, 0.23886262726940605, 0.8592224541406261], [11.176176176176178, -0.8853621828522442, -0.6143950368319444, 0.22144407262376384, 0.8536885229508471], [11.196196196196198, -0.8819485507324332, -0.6274374340776799, 0.20393676589211435, 0.8478124438032807], [11.216216216216218, -0.8781814443800487, -0.6402283620451745, 0.18634772378966877, 0.8415965717588489], [11.236236236236238, -0.8740623736054146, -0.6527626942863564, 0.16868399579017893, 0.8350433980633268], [11.256256256256258, -0.8695929892819066, -0.6650354071935943, 0.15095266130059415, 0.8281555491488809], [11.276276276276278, -0.8647750826843019, -0.6770415820130985, 0.13316082682372118, 0.8209357855814278], [11.296296296296298, -0.8596105847708584, -0.6887764068162966, 0.11531562311002454, 0.8133870009542319], [11.316316316316318, -0.8541015654094105, -0.7002351784283968, 0.09742420229970851, 0.80551222072819], [11.336336336336338, -0.8482502325477905, -0.7114133043133619, 0.07949373505622655, 0.7973146010192623], [11.356356356356358, -0.8420589313289111, -0.7223063044145432, 0.06153140769236676, 0.78879742733354], [11.376376376376378, -0.8355301431508595, -0.7329098129502315, 0.04354441929006527, 0.7799641132504543], [11.396396396396398, -0.8286664846723839, -0.7432195801634097, 0.025539978815101914, 0.7708181990546558], [11.416416416416418, -0.8214707067641689, -0.7532314740250033, 0.0075253022278345995, 0.7613633503171108], [11.436436436436438, -0.8139456934063194, -0.762941481889948, -0.010492390408869675, 0.751603356425986], [11.456456456456458, -0.8060944605324966, -0.7723457121054079, -0.028505877823348052, 0.7415421290679072], [11.476476476476478, -0.7979201548211691, -0.7814403955705035, -0.04650794042933951, 0.731183700660203], [11.496496496496498, -0.7894260524344618, -0.7902218872469208, -0.06449136321950706, 0.720532222734762], [11.516516516516518, -0.7806155577051106, -0.7986866676197985, -0.08244893865712472, 0.709591964274148], [11.536536536536538, -0.7714922017720468, -0.8068313441083068, -0.10037346956477053, 0.6983673100006448], [11.556556556556558, -0.7620596411651603, -0.8146526524253521, -0.11825777200886746, 0.6868627586189124], [11.576576576576578, -0.7523216563398065, -0.8221474578858637, -0.1360946781789165, 0.6750829210129617], [11.596596596596598, -0.7422821501616454, -0.8293127566631374, -0.1538770392602677, 0.6630325183981676], [11.616616616616618, -0.7319451463424221, -0.8361456769927326, -0.1715977282992781, 0.6507163804290635], [11.636636636636638, -0.7213147878273106, -0.8426434803234403, -0.18924964305970782, 0.6381394432636753], [11.656656656656658, -0.7103953351344725, -0.8488035624148615, -0.20682570886920978, 0.6253067475851684], [11.676676676676678, -0.6991911646474935, -0.854623454381155, -0.2243188814547722, 0.6122234365816043], [11.696696696696698, -0.6877067668613818, -0.8601008236805356, -0.2417221497659775, 0.5988947538846127], [11.716716716716718, -0.6759467445828323, -0.8652334750501304, -0.2590285387849457, 0.5853260414678083], [11.736736736736738, -0.6639158110854783, -0.8700193513858118, -0.2762311123218366, 0.5715227375057929], [11.756756756756758, -0.651618788220868, -0.8744565345666605, -0.2933229757947905, 0.5574903741946008], [11.776776776776778, -0.6390606044859267, -0.8785432462237246, -0.3102972789931919, 0.5432345755344626], [11.796796796796798, -0.6262462930476731, -0.8822778484527689, -0.32714721882315123, 0.528761055075774], [11.816816816816818, -0.6131809897259892, -0.8856588444707261, -0.3438660420341015, 0.5140756136291746], [11.836836836836838, -0.5998699309352457, -0.8886848792155895, -0.3604470479254188, 0.4991841369406541], [11.856856856856858, -0.5863184515856098, -0.8913547398895058, -0.376883591031982, 0.4840925933326162], [11.876876876876878, -0.5725319829448802, -0.8936673564448492, -0.39316908378759335, 0.46880703131184825], [11.896896896896898, -0.5585160504616996, -0.8956218020130837, -0.40929699916519563, 0.45333357714535316], [11.916916916916918, -0.5442762715510229, -0.8972172932762403, -0.4252608732928243, 0.43767843240501675], [11.936936936936938, -0.5298183533427255, -0.8984531907808619, -0.44105430804424933, 0.4218478714820927], [11.956956956956958, -0.5151480903942552, -0.8993289991942883, -0.4566709736032655, 0.405848239072503], [11.976976976976978, -0.5002713623682444, -0.8998443675031788, -0.472104611000606, 0.38968594763396175], [11.996996996996998, -0.4851941316760128, -0.8999990891541951, -0.4873490346224615, 0.3733674748159396], [12.017017017017018, -0.4699224410879052, -0.8997931021367849, -0.5023981346895976, 0.3568993608635012], [12.037037037037038, -0.4544624113114237, -0.8992264890080346, -0.5172458797060813, 0.3402882059960544], [12.057057057057058, -0.43882023853812185, -0.8982994768595822, -0.5318863188766298, 0.32354066776206275], [12.077077077077078, -0.42300219196024713, -0.897012437226601, -0.5463135844916177, 0.30666345837078135], [12.097097097097098, -0.40701461125812466, -0.8953658859388933, -0.560521894278783, 0.2896633420020851], [12.117117117117118, -0.39086390405929083, -0.8933604829141519, -0.5745055537206916, 0.27254713209546794], [12.137137137137138, -0.3745565433703943, -0.8909970318934736, -0.5882589583370301, 0.255321688619299], [12.157157157157158, -0.3580990649828936, -0.8882764801192283, -0.601776595930814, 0.23799391532143113], [12.177177177177178, -0.3414980648535919, -0.8851999179554174, -0.6150530487976085, 0.22057075696226294], [12.197197197197198, -0.32476019646105747, -0.8817685784506683, -0.6280829958968781, 0.20305919653136323], [12.217217217217218, -0.3078921681389907, -0.8779838368440434, -0.6408612149845957, 0.18546625244877402], [12.237237237237238, -0.2909007403876058, -0.8738472100138613, -0.6533825847062539, 0.1677989757521132], [12.257257257257258, -0.2737927231641048, -0.8693603558697511, -0.6656420866494412, 0.15006444727060472], [12.277277277277278, -0.2565749731533293, -0.864525072688181, -0.6776348073551614, 0.1322697747871684], [12.297297297297298, -0.23925439101968493, -0.8593432983917324, -0.6893559402870867, 0.11442209018970713], [12.317317317317318, -0.22183791864143926, -0.853817109772403, -0.7008007877579605, 0.09652854661273325], [12.337337337337338, -0.20433253632850146, -0.847948721659255, -0.7119647628123722, 0.07859631557047919], [12.357357357357358, -0.18674526002479913, -0.8417404860307376, -0.7228433910651532, 0.0606325840826421], [12.377377377377378, -0.1690831384963735, -0.8351948910720431, -0.733432312494656, 0.04264455179391392], [12.397397397397398, -0.15135325050631981, -0.8283145601778731, -0.7437272831901978, 0.02463942808845142], [12.417417417417418, -0.13356270197770548, -0.8211022509010127, -0.7537241770529685, 0.006624429200442979], [12.437437437437438, -0.11571862314560262, -0.813560853847138, -0.76341898744972, -0.0113932246780702], [12.457457457457458, -0.09782816569937665, -0.8056933915162963, -0.7728078288185769, -0.029406312290959073], [12.477477477477478, -0.0798984999163765, -0.7975030170915244, -0.7818869382263216, -0.04740761421219821], [12.497497497497498, -0.061936811788174624, -0.7889930131750921, -0.790652676876533, -0.06538991573932382], [12.517517517517518, -0.04395030014050931, -0.7801667904728743, -0.7991015315679703, -0.08334600978499848], [12.537537537537538, -0.025946173748082935, -0.7710278864273824, -0.8072301161026227, -0.10126869976552406], [12.557557557557558, -0.00793164844537296, -0.7615799638000003, -0.8150351726428561, -0.11915080248514472], [12.577577577577578, 0.010086055765386627, -0.7518268092029937, -0.8225135730171135, -0.13698515101498412], [12.597597597597598, 0.028099717607894235, -0.7417723315818828, -0.8296623199736495, -0.15476459756546349], [12.617617617617618, 0.0461021174259802, -0.7314205606487842, -0.8364785483817915, -0.17248201635104823], [12.637637637637638, 0.06408604007715703, -0.7207756452673514, -0.8429595263802495, -0.19013030644617618], [12.657657657657658, 0.08204427782436059, -0.7098418517899605, -0.8491026564720124, -0.207702394631222], [12.677677677677679, 0.09996963322472326, -0.698623562347809, -0.8549054765653938, -0.2251912382273573], [12.697697697697699, 0.11785492201422135, -0.6871252730946102, -0.8603656609608077, -0.2425898279191707], [12.717717717717719, 0.1356929759870407, -0.6753515924045899, -0.8654810212828805, -0.25989119056391585], [12.737737737737739, 0.15347664586850596, -0.663307239025506, -0.8702495073575265, -0.27708839198626223], [12.757757757757759, 0.17119880418042305, -0.6509970401874324, -0.8746692080336306, -0.29417453975742797], [12.777777777777779, 0.1888523480976854, -0.6384259296680631, -0.8787383519490161, -0.3111427859575814], [12.797797797797799, 0.20643020229499967, -0.6255989458153153, -0.8824553082403842, -0.32798632992040383], [12.817817817817819, 0.22392532178259014, -0.61252122952802, -0.8858185871969448, -0.34469842095871406], [12.837837837837839, 0.2413306947297446, -0.599198022195513, -0.8888268408574735, -0.36127236107006094], [12.857857857857859, 0.25863934527507054, -0.5856346635969495, -0.8914788635505588, -0.37770150762120164], [12.877877877877879, 0.2758443363223356, -0.5718365897611861, -0.8937735923778203, -0.39397927601038807], [12.897897897897899, 0.29293877232077137, -0.5578093307880864, -0.8957101076399053, -0.41009914230639455], [12.917917917917919, 0.3099158020287256, -0.5435585086321243, -0.8972876332050934, -0.4260546458632308], [12.937937937937939, 0.32676862125955675, -0.5290898348491735, -0.89850553682036, -0.44183939190948907], [12.957957957957959, 0.3434904756086691, -0.514409108307385, -0.8993633303647766, -0.45744705411129166], [12.977977977977979, 0.36007466316059555, -0.49952221286307186, -0.8998606700451426, -0.47287137710780763], [12.997997997997999, 0.3765145371750441, -0.48443511500253117, -0.8999973565337741, -0.4881061790183257], [13.018018018018019, 0.3928035087508297, -0.4691538614507487, -0.8997733350483923, -0.5031453539198765], [13.038038038038039, 0.4089350494666264, -0.45368457674794566, -0.8991886953740785, -0.5179828742944121], [13.058058058058059, 0.42490269399747843, -0.4380334607949374, -0.8982436718272906, -0.5326127934445619], [13.078078078078079, 0.44070004270602386, -0.42220678636828884, -0.8969386431619508, -0.5470292478769964], [13.098098098098099, 0.4563207642073913, -0.4062108966062617, -0.8952741324176465, -0.5612264596524438], [13.118118118118119, 0.4717585979067417, -0.3900522024665624, -0.893250806710002, -0.5751987387014177], [13.138138138138139, 0.4870073565084388, -0.3737371801569082, -0.8908694769633065, -0.588940485104728], [13.158158158158159, 0.5020609284958415, -0.35727236853944194, -0.888131097585506, -0.6024461913378595], [13.178178178178179, 0.516913280580725, -0.3406643665100358, -0.8850367660856879, -0.6157104444783214], [13.198198198198199, 0.5315584601213503, -0.32391983035353433, -0.8815877226342128, -0.6287279283750801], [13.218218218218219, 0.5459905975082091, -0.3070454710759966, -0.8777853495656693, -0.6414934257792079], [13.238238238238239, 0.5602039085164926, -0.2900480517150067, -0.8736311708248516, -0.6540018204348934], [13.258258258258259, 0.5741926966243383, -0.2729343846291305, -0.8691268513559801, -0.6662480991299735], [13.278278278278279, 0.587951355295926, -0.2557113287676057, -0.8642741964354126, -0.6782273537051682], [13.298298298298299, 0.6014743702285098, -0.2383857869213584, -0.8590751509481117, -0.6899347830212114], [13.318318318318319, 0.6147563215624828, -0.2209647029564484, -0.8535317986081582, -0.7013656948830885], [13.338338338338339, 0.6277918860535915, -0.2034550590310527, -0.847646361123624, -0.712515507920611], [13.358358358358359, 0.640575839206426, -0.1858638727971012, -0.8414211973061387, -0.7233797534245744], [13.378378378378379, 0.6531030573683357, -0.16819819458768753, -0.834858802125507, -0.7339540771377613], [13.398398398398399, 0.6653685197829258, -0.15046510459138135, -0.827961805709754, -0.7442342410000763], [13.418418418418419, 0.677367310602315, -0.1326717100145752, -0.8207329722910032, -0.754216124847107], [13.438438438438439, 0.6890946208573486, -0.11482514223300253, -0.8131751990976055, -0.7638957280614371], [13.458458458458459, 0.700545750384974, -0.09693255393356948, -0.8052915151929648, -0.7732691711760445], [13.478478478478479, 0.7117161097120087, -0.0790011162476449, -0.7970850802615278, -0.7823326974291444], [13.498498498498499, 0.7226012218945455, -0.06103801587695842, -0.788559183342419, -0.7910826742698539], [13.518518518518519, 0.7331967243122568, -0.04305045221325785, -0.7797172415112356, -0.7995155948140736], [13.538538538538539, 0.74349837041688, -0.025045634452880803, -0.7705627985105241, -0.807628079250005], [13.558558558558559, 0.7535020314341824, -0.007030778707396588, -0.7610995233294923, -0.8154168761927368], [13.578578578578579, 0.7632036980187227, 0.010986894888523379, -0.7513312087335225, -0.822878863987361], [13.598598598598599, 0.772599481860749, 0.029000165070847548, -0.7412617697440778, -0.8300110519600941], [13.618618618618619, 0.7816856172445852, 0.0470018123403789, -0.7308952420696087, -0.8368105816169022], [13.63863863863864, 0.7904584625578842, 0.06498462185624222, -0.7202357804880907, -0.8432747277891517], [13.65865865865866, 0.7989145017511416, 0.08294138632750446, -0.7092876571818401, -0.849400899725822], [13.67867867867868, 0.8070503457468862, 0.10086490890176902, -0.6980552600252748, -0.8551866421318487], [13.6986986986987, 0.8148627337979792, 0.1187480060495866, -0.6865430908263084, -0.8606296361521748], [13.71871871871872, 0.8223485347944844, 0.1365835104435263, -0.6747557635220797, -0.86572770030112], [13.73873873873874, 0.8295047485185768, 0.15436427383075296, -0.6626980023297434, -0.8704787913366933], [13.75875875875876, 0.8363285068469946, 0.1720831698979599, -0.6503746398530613, -0.8748810050794997], [13.77877877877878, 0.8428170749005469, 0.18973309712750855, -0.6377906151455538, -0.8789325771759126], [13.7987987987988, 0.8489678521402217, 0.20730698164362993, -0.6249509717309869, -0.8826318838052036], [13.81881881881882, 0.8547783734094484, 0.22479778004754816, -0.61186085558199, -0.8859774423303522], [13.83883883883884, 0.8602463099221036, 0.24219848224038865, -0.5985255130576123, -0.8889679118922673], [13.85885885885886, 0.8653694701958601, 0.2595021142327404, -0.584950288800646, -0.891602093947187], [13.87887887887888, 0.8701458009305059, 0.2767017409397459, -0.5711406235955592, -0.8938789327470417], [13.8988988988989, 0.8745733878308822, 0.2937904689605989, -0.5571020521878948, -0.8957975157625834], [13.91891891891892, 0.8786504563741097, 0.3107614493413351, -0.5428402010660126, -0.8973570740491176], [13.93893893893894, 0.8823753725207951, 0.32760788031980953, -0.5283607862060619, -0.8985569825546864], [13.95895895895896, 0.8857466433699354, 0.3443230100517602, -0.513669610781086, -0.8993967603705815], [13.97897897897898, 0.888762917757253, 0.36090013931686465, -0.49877256283518334, -0.8998760709240871], [13.998998998999, 0.891422986796727, 0.3773326242037061, -0.48367561292364963, -0.8999947221133736], [14.01901901901902, 0.8937257843650992, 0.39361387877257215, -0.4683848117200529, -0.8997526663844903], [14.03903903903904, 0.8956703875291638, 0.4097373776950188, -0.45290628759119683, -0.8991500007504237], [14.05905905905906, 0.897256016915668, 0.4256966588691432, -0.43724624414094604, -0.8981869667522167], [14.07907907907908, 0.8984820370236742, 0.441485326009515, -0.4214109577238976, -0.896863950362161], [14.0990990990991, 0.8993479564792626, 0.4570970512107294, -0.4054067749298941, -0.8951814818291047], [14.11911911911912, 0.8998534282324663, 0.47252557748355495, -0.3892401100403881, -0.8931402354659352], [14.13913913913914, 0.8999982496963658, 0.4877647212626585, -0.3729174424576762, -0.890741029379322], [14.15915915915916, 0.8997823628282832, 0.5028083748849025, -0.3564453141080336, -0.8879848251418303], [14.17917917917918, 0.8992058541530447, 0.5176505090372224, -0.3398303268197896, -0.884872727406534], [14.1991991991992, 0.8982689547283024, 0.5322851751731018, -0.32307913967739527, -0.8814059834642847], [14.21921921921922, 0.8969720400519297, 0.546706507896677, -0.3061984663525439, -0.8775859827438118], [14.23923923923924, 0.8953156299115258, 0.5609087273135166, -0.28919507241341263, -0.8734142562548575], [14.25925925925926, 0.8933003881760918, 0.5748861413471327, -0.27207577261310567, -0.8688924759745659], [14.27927927927928, 0.8909271225299593, 0.5886331480202955, -0.2548474281583839, -0.8640224541773753], [14.2992992992993, 0.8881967841490811, 0.6021442377002383, -0.23751694395977715, -0.8588061427086809], [14.31931931931932, 0.885110467319811, 0.61541399530685, -0.2200912658641799, -0.8532456322025583], [14.33933933933934, 0.8816694090003274, 0.6284371024829742, -0.20257737787104066, -0.8473431512438637], [14.35935935935936, 0.8778749883248754, 0.6412083397259416, -0.18498229933325994, -0.8411010654750432], [14.37937937937938, 0.8737287260510265, 0.6537225884794828, -0.16731308214391938, -0.8345218766480131], [14.3993993993994, 0.8692322839501778, 0.6659748331851839, -0.1495768079099687, -0.8276082216214877], [14.41941941941942, 0.8643874641415342, 0.6779601632926607, -0.131780585114004, -0.8203628713041587], [14.43943943943944, 0.8591962083698407, 0.6896737752276475, -0.1139315462652746, -0.8127887295441485], [14.45945945945946, 0.853660597227155, 0.7011109743172114, -0.09603684504106016, -0.8048888319651835], [14.47947947947948, 0.8477828493189717, 0.7122671766713198, -0.07810365341956413, -0.7966663447499529], [14.4994994994995, 0.8415653203750318, 0.7231379110200078, -0.06013915880547205, -0.7881245633711413], [14.51951951951952, 0.8350105023051753, 0.7337188205054084, -0.04215056114932755, -0.7792669112706437], [14.53953953953954, 0.8281210222006141, 0.7440056644279285, -0.02414507006187981, -0.7700969384874914], [14.55955955955956, 0.8208996412810255, 0.7539943199458693, -0.006129901924559417, -0.7606183202350394], [14.57957957957958, 0.8133492537878898, 0.7636807837278113, 0.011887723002759365, -0.7508348554279861], [14.5995995995996, 0.8054728858245122, 0.7730611735571005, 0.029900583475550777, -0.7407504651598147], [14.61961961961962, 0.7972736941431989, 0.7821317298877932, 0.047901460158824384, -0.7303691911312659], [14.63963963963964, 0.7887549648800685, 0.7908888173514372, 0.06588313852054656, -0.7196951940304745], [14.65965965965966, 0.7799201122380087, 0.7993289262140825, 0.083838411723137, -0.7087327518654157], [14.67967967967968, 0.7707726771183057, 0.8074486737829395, 0.10176008351188151, -0.6974862582493314], [14.6996996996997, 0.7613163257014937, 0.8152448057621222, 0.11964097109910302, -0.6859602206398245], [14.71971971971972, 0.7515548479779945, 0.8227141975569292, 0.13747390804293558, -0.6741592585323238], [14.73973973973974, 0.7414921562291364, 0.8298538555261439, 0.15525174711954678, -0.662088101608647], [14.75975975975976, 0.731132283459158, 0.836660918181849, 0.172967363187658, -0.6497515878414019], [14.77977977977978, 0.7204793817788302, 0.8431326573362764, 0.1906136560442143, -0.6371546615549855], [14.7997997997998, 0.7095377207413406, 0.8492664791952311, 0.2081835532700592, -0.6243023714439598], [14.81981981981982, 0.6983116856311088, 0.855059925397653, 0.22567001306447404, -0.6111998685495958], [14.83983983983984, 0.6868057757062176, 0.8605106740008973, 0.24306602706744618, -0.5978524041953995], [14.85985985985986, 0.6750246023951649, 0.8656165404113413, 0.2603646231685339, -0.5842653278824449], [14.87987987987988, 0.6629728874486603, 0.8703754782599429, 0.27755886830120374, -0.5704440851453594], [14.8998998998999, 0.6506554610472036, 0.8747855802223998, 0.29464187122151864, -0.5563942153698198], [14.91991991991992, 0.6380772598652088, 0.878845078783583, 0.3116067852700648, -0.5421213495724343], [14.93993993993994, 0.6252433250924437, 0.882552346945934, 0.3284468111160095, -0.5276312081438994], [14.95995995995996, 0.6121588004135837, 0.8859058988815466, 0.34515519948218965, -0.5129295985563369], [14.97997997997998, 0.5988289299466847, 0.8889043905276681, 0.3617252538501402, -0.4980224130357302], [15.0, 0.5852590561414053, 0.8915466201253833, 0.37815033314397684, -0.48291562620039147]]}, \"id\": \"el94984400236048\"});\n", " }(mpld3);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/mpld3\n", " require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949844002360481884642452\", {\"axes\": [{\"xlim\": [0.0, 10.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"Here are some curves\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [5.0, -1.1], \"rotation\": -0.0, \"id\": \"el94984400236304\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 0.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el94984396322128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 1.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.83198924731182788], \"rotation\": -0.0, \"id\": \"el94984396238864\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 2.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.75268817204301075], \"rotation\": -0.0, \"id\": \"el94984396237136\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 3.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.67338709677419351], \"rotation\": -0.0, \"id\": \"el94984396236112\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 10.0], \"ylim\": [-1.2, 1.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el94984396322064\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396386960\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396384592\"}, {\"color\": \"#FF0000\", \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396285392\"}, {\"color\": \"#00BFBF\", \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396283344\"}, {\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396320656\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396238608\"}, {\"color\": \"#FF0000\", \"yindex\": 3, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984401323920\"}, {\"color\": \"#00BFBF\", \"yindex\": 4, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396235920\"}], \"markers\": [], \"id\": \"el94984400229904\", \"ydomain\": [-1.2, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data02\": [[0.6903001792114694, 0.9301075268817204, 0.8508064516129031, 0.771505376344086, 0.6922043010752688], [0.7404793906810034, 0.9301075268817204, 0.8508064516129031, 0.771505376344086, 0.6922043010752688]], \"data03\": [[0.6652105734767024, 0.6397849462365592], [0.9820788530465949, 0.6397849462365592], [0.9820788530465949, 0.9731182795698926], [0.6652105734767024, 0.9731182795698926], [0.6652105734767024, 0.6397849462365592]], \"data01\": [[-5.0, 0.8630318471968246, 0.2514739483790333, -0.5912879388469102, -0.8904224219610436], [-4.97997997997998, 0.8679695898303729, 0.26872276441239507, -0.577586531327817, -0.8928654338419951], [-4.95995995995996, 0.8725594608285433, 0.28586387963617244, -0.5636536341648707, -0.8949505961366454], [-4.93993993993994, 0.8767996206269677, 0.30289042410024863, -0.5494948314934817, -0.8966770731373546], [-4.91991991991992, 0.8806883698212653, 0.3197955737730711, -0.535115797989235, -0.8980441728931777], [-4.8998998998999, 0.8842241498481432, 0.3365725532766406, -0.5205222965935509, -0.8990513474871911], [-4.87987987987988, 0.8874055436100506, 0.35321463860199986, -0.5057201762039664, -0.8996981932560897], [-4.85985985985986, 0.8902312760431328, 0.3697151598041348, -0.49071536932996934, -0.8999844509519712], [-4.83983983983984, 0.8927002146282623, 0.3860675036752065, -0.4755138897153212, -0.8999100058462391], [-4.81981981981982, 0.8948113698449383, 0.4022651163950453, -0.46012182992782114, -0.8994748877755848], [-4.7997997997998, 0.8965638955678743, 0.4183015061578412, -0.4445453589174795, -0.8986792711300291], [-4.77977977977978, 0.8979570894061152, 0.434170245773981, -0.4287907195440775, -0.8975234747830286], [-4.75975975975976, 0.8989903929845467, 0.44986497524598745, -0.4128642260751055, -0.8960079619636763], [-4.73973973973974, 0.8996633921676853, 0.4653794043175288, -0.39677226165508117, -0.8941333400710433], [-4.71971971971972, 0.8999758172256591, 0.4807073149944767, -0.38052127574726324, -0.8919003604307418], [-4.6996996996997, 0.8999275429423117, 0.4958425640370029, -0.36411778154878494, -0.889309917993803], [-4.67967967967968, 0.8995185886653875, 0.5107790854217142, -0.3475683533802441, -0.8863630509779908], [-4.65965965965966, 0.8987491182987778, 0.5255108927728417, -0.33087962405079563, -0.8830609404516978], [-4.63963963963964, 0.8976194402368293, 0.5400320817615062, -0.31405828219980225, -0.8794049098605863], [-4.61961961961962, 0.8961300072407442, 0.5543368324721021, -0.29711106961610945, -0.8753964244971686], [-4.5995995995996, 0.8942814162571185, 0.5684194117348481, -0.2800447785360186, -0.8710370909135349], [-4.57957957957958, 0.8920744081786935, 0.5822741754235725, -0.26286624892104166, -0.8663286562774669], [-4.55955955955956, 0.8895098675474145, 0.5958955707178093, -0.24558236571652747, -0.8612730076721944], [-4.53953953953954, 0.8865888221999173, 0.6092781383283015, -0.22820005609225996, -0.8558721713400752], [-4.51951951951952, 0.8833124428555845, 0.6224165146850176, -0.21072628666613283, -0.850128311870502], [-4.4994994994995, 0.8796820426473345, 0.6353054340868053, -0.1931680607120138, -0.844043731332361], [-4.47947947947948, 0.8756990765953342, 0.6479397308118201, -0.17553241535291797, -0.8376208683513913], [-4.45945945945946, 0.8713651410238447, 0.6603143411878836, -0.1578264187406146, -0.8308622971328129], [-4.43943943943944, 0.866681972921434, 0.6724243056219421, -0.14005716722279796, -0.8237707264296168], [-4.41941941941942, 0.8616514492448131, 0.6842647705878105, -0.1222317824989577, -0.8163489984569301], [-4.3993993993994, 0.8562755861665744, 0.695830990571407, -0.10435740876608852, -0.8086000877528902], [-4.37937937937938, 0.850556538267135, 0.7071183299726964, -0.08644120985538295, -0.8005270999864869], [-4.35935935935936, 0.8444965976712059, 0.7181222649635836, -0.06849036636105515, -0.792133270712847], [-4.33933933933934, 0.8380981931291377, 0.7288383853010069, -0.0505120727624463, -0.7834219640764648], [-4.31931931931932, 0.831363889043506, 0.7392623960945115, -0.03251353454056483, -0.7743966714628926], [-4.2992992992992995, 0.8242963844413308, 0.749390119527588, -0.014501965290217559, -0.7650610100994366], [-4.2792792792792795, 0.8168985118923391, 0.7592174965320911, 0.0035154161711113013, -0.7554187216054159], [-4.2592592592592595, 0.8091732363737051, 0.7687405884150641, 0.021531388696474196, -0.7454736704925676], [-4.2392392392392395, 0.8011236540817241, 0.7779555784373193, 0.039538731703607936, -0.7352298426161972], [-4.2192192192192195, 0.7927529911908945, 0.7868587733431406, 0.057530228068855084, -0.7246913435776958], [-4.1991991991991995, 0.784064602560906, 0.7954466048404955, 0.07549866701969918, -0.7138623970790667], [-4.1791791791791795, 0.7750619703920537, 0.8037156310311625, 0.0934368470247545, -0.7027473432301156], [-4.1591591591591595, 0.7657487028296133, 0.8116625377902029, 0.11133757868005209, -0.6913506368089878], [-4.1391391391391394, 0.7561285325177417, 0.8192841400942208, 0.1291936875904653, -0.6796768454767479], [-4.119119119119119, 0.7462053151034778, 0.8265773832978821, 0.14699801724511982, -0.6677306479467164], [-4.099099099099099, 0.7359830276914466, 0.8335393443581789, 0.1647434318856362, -0.6555168321092989], [-4.079079079079079, 0.7254657672498843, 0.8401672330059502, 0.1824228193660547, -0.6430402931130572], [-4.059059059059059, 0.7146577489686227, 0.8464583928641868, 0.20002909400329694, -0.6303060314027944], [-4.039039039039039, 0.703563304569693, 0.8524103025126752, 0.21755519941702098, -0.6173191507154364], [-4.019019019019019, 0.6921868805712241, 0.8580205764985528, 0.23499411135773307, -0.6040848560345177], [-3.998998998998999, 0.6805330365053327, 0.863286966292367, 0.2523388405220207, -0.5906084515040859], [-3.978978978978979, 0.6686064430907186, 0.8682073611892598, 0.2695824353537809, -0.5768953383028668], [-3.958958958958959, 0.6564118803606961, 0.8727797891549115, 0.28671798483031846, -0.5629510124795367], [-3.938938938938939, 0.6439542357474166, 0.8770024176159085, 0.30373862123220025, -0.5487810627499738], [-3.918918918918919, 0.6312385021230444, 0.8808735541942165, 0.32063752289575315, -0.5343911682573689], [-3.898898898898899, 0.6182697757986748, 0.8843916473854649, 0.33740791694710387, -0.519787096296093], [-3.878878878878879, 0.6050532544817941, 0.8875552871807707, 0.35404308201666423, -0.5049747000002375], [-3.858858858858859, 0.5915942351931028, 0.8903632056318527, 0.37053635093297416, -0.48995991599774835], [-3.838838838838839, 0.5778981121435335, 0.8928142773592105, 0.38688111339482273, -0.4747487620310985], [-3.818818818818819, 0.5639703745723166, 0.894907520003162, 0.4030708186205766, -0.45934733454544946], [-3.798798798798799, 0.5498166045469611, 0.8966420946175617, 0.4190989779736531, -0.4437618062452708], [-3.7787787787787788, 0.5354424747260293, 0.89801730600604, 0.43495916756308683, -0.4279984236203952], [-3.7587587587587588, 0.5208537460856043, 0.8990326030006295, 0.4506450308181473, -0.4120635044425017], [-3.7387387387387387, 0.5060562656103611, 0.8996875786826659, 0.4661502810359743, -0.39596343523302946], [-3.7187187187187187, 0.49105596395016665, 0.899981970545877, 0.48146870390121227, -0.379704668703538], [-3.6986986986986987, 0.47585885304314746, 0.8999156606015912, 0.49659415997663203, -0.36329372116953884], [-3.6786786786786787, 0.460471023706178, 0.899488675426026, 0.5115205871637428, -0.346737169938836], [-3.6586586586586587, 0.4448986431937554, 0.898701186149637, 0.5262420031324067, -0.33004165067542085], [-3.6386386386386387, 0.42914795272623935, 0.8975535083885307, 0.5407525077184839, -0.3132138547399789], [-3.6186186186186187, 0.4132252649884468, 0.8960461021179691, 0.5550462852885475, -0.29626052650807405], [-3.5985985985985987, 0.3971369615996047, 0.8941795714880179, 0.5691176070707172, -0.2791884606670851], [-3.5785785785785786, 0.3808894905556751, 0.8919546645814096, 0.5829608334506821, -0.2620044994929775], [-3.5585585585585586, 0.3644893636450766, 0.889372273113722, 0.5965704162319889, -0.24471553010800312], [-3.5385385385385386, 0.34794315383883917, 0.8864334320759889, 0.6099409008596921, -0.22732848172042486], [-3.5185185185185186, 0.3312574926562372, 0.8831393193198884, 0.6230669286064724, -0.20985032284737504], [-3.4984984984984986, 0.31443906750695766, 0.8794912550856742, 0.6359432387203501, -0.19228805852195827], [-3.4784784784784786, 0.2974946190108674, 0.8754907014730395, 0.6485646705331292, -0.17464872748571958], [-3.4584584584584586, 0.28043093829645527, 0.8711392618551258, 0.6609261655287316, -0.15693939936760282], [-3.4384384384384385, 0.2632548642790304, 0.8664386802359094, 0.6730227693705885, -0.1391671718505291], [-3.4184184184184185, 0.2459732809197681, 0.8613908405512265, 0.684849633887277, -0.12133916782673242], [-3.3983983983983985, 0.22859311446670239, 0.8559977659137148, 0.6964020190156096, -0.10346253254299087], [-3.378378378378378, 0.21112133067876954, 0.850261617801974, 0.7076752947003935, -0.08554443073689828], [-3.358358358358358, 0.1935649320340186, 0.8441846951942729, 0.718664942750098, -0.06759204376532768], [-3.338338338338338, 0.17593095492310082, 0.8377694336471446, 0.7293665586476946, -0.0496125667262263], [-3.318318318318318, 0.15822646682917196, 0.8310184043192475, 0.7397758533159309, -0.03161320557491114], [-3.298298298298298, 0.1404585634953306, 0.8239343129408745, 0.7498886548363414, -0.013601174236009072], [-3.278278278278278, 0.12263436608073057, 0.8165199987295305, 0.7597009101213011, 0.00441630828779621], [-3.258258258258258, 0.10476101830650755, 0.8087784332520078, 0.7692086865384546, 0.022432020809052548], [-3.238238238238238, 0.08684568359266231, 0.8007127192334185, 0.7784081734868665, 0.040438742849703224], [-3.218218218218218, 0.06889554218704948, 0.7923260893136606, 0.7872956839242633, 0.05842925753496662], [-3.198198198198198, 0.05091778828762159, 0.7836219047518144, 0.7958676558447566, 0.07639635448577162], [-3.178178178178178, 0.032919627159082275, 0.7746036540789918, 0.80412065370645, 0.09433283270858979], [-3.158158158158158, 0.014908272245103896, 0.765274951700175, 0.8120513698083638, 0.11223150348150575], [-3.138138138138138, -0.0031090577227327423, 0.755639536445607, 0.8196566256161201, 0.13008519323536938], [-3.118118118118118, -0.021125141618118036, 0.7457012700723139, 0.8269333730358612, 0.1478867464288749], [-3.098098098098098, -0.03913275881415305, 0.7354641357163603, 0.833878695635889, 0.16562902841641455], [-3.078078078078078, -0.05712469207728877, 0.7249322362964564, 0.8404898098155339, 0.18330492830755787], [-3.058058058058058, -0.0750937304599054, 0.7141097928695582, 0.8467640659207893, 0.2009073618170096], [-3.038038038038038, -0.09303267219037227, 0.7030011429391195, 0.8526989493062603, 0.21842927410390522], [-3.018018018018018, -0.11093432755943015, 0.6916107387166736, 0.8582920813430026, 0.2358636425993049], [-2.997997997997998, -0.12879152180173914, 0.6799431453374402, 0.8635412203718483, 0.25320347982075386], [-2.977977977977978, -0.1465970979714372, 0.668003039030676, 0.8684442626018356, 0.27044183617278], [-2.957957957957958, -0.1643439198105568, 0.6557952052454983, 0.8729992429533822, 0.2875718027322071], [-2.937937937937938, -0.18202487460915015, 0.6433245367329355, 0.8772043358458658, 0.30458651401716713], [-2.9179179179179178, -0.1996328760559768, 0.6305960315849722, 0.8810578559292943, 0.3214791507387017], [-2.8978978978978978, -0.21716086707861068, 0.6176147912313744, 0.8845582587597733, 0.33824294253384984], [-2.8778778778778777, -0.23460182267182886, 0.6043860183950994, 0.8877041414185011, 0.3548711706791272], [-2.8578578578578577, -0.2519487527131479, 0.590915015007107, 0.8904942430740405, 0.3713571707833086], [-2.8378378378378377, -0.26919470476437996, 0.5772071800814106, 0.8929274454876455, 0.3876943354584343], [-2.8178178178178177, -0.28633276685808523, 0.5632680075512183, 0.8950027734614372, 0.40387611696797127], [-2.7977977977977977, -0.30335607026780403, 0.5491030840670311, 0.8967193952292518, 0.4198960298510658], [-2.7777777777777777, -0.32025779226095913, 0.5347180867575816, 0.8980766227900018, 0.43574765352183736], [-2.7577577577577577, -0.3370311588333234, 0.5201187809545099, 0.8990739121834186, 0.45142463484267087], [-2.7377377377377377, -0.35366944742395856, 0.5053110178816891, 0.8997108637080654, 0.4669206906704775], [-2.7177177177177176, -0.37016598960953523, 0.4903007323101259, 0.8999872220815323, 0.48222961037490103], [-2.6976976976976976, -0.38651417377695624, 0.4750939401793767, 0.8999028765427508, 0.49734525832746346], [-2.6776776776776776, -0.4027074477732101, 0.4596967361864325, 0.8994578608963848, 0.51226157636065], [-2.6576576576576576, -0.41873932153139454, 0.4441152913430384, 0.8986523534992832, 0.5269725861959482], [-2.6376376376376376, -0.434603369671856, 0.4283558505024275, 0.8974866771889954, 0.5414723918398704], [-2.6176176176176176, -0.4502932340774034, 0.4124247298564596, 0.8959612991543829, 0.5557551819469949], [-2.5975975975975976, -0.46580262644156445, 0.39632831440416855, 0.8940768307483751, 0.5698152321490826], [-2.5775775775775776, -0.48112533078886166, 0.38007305539273256, 0.8918340272429469, 0.5836469073493346], [-2.5575575575575575, -0.49625520596610034, 0.3636654677318927, 0.8892337875264147, 0.59724466398087], [-2.5375375375375375, -0.511186188103667, 0.34711212738285663, 0.8862771537431728, 0.6106030522285195], [-2.5175175175175175, -0.5259122930458555, 0.3304196687227336, 0.882965310876015, 0.6237167182130459], [-2.4974974974974975, -0.5404276187492426, 0.3135947818855565, 0.8792995862712075, 0.636580406136913], [-2.4774774774774775, -0.554726347648156, 0.2966442100809582, 0.8752814491065052, 0.6491889603907449], [-2.4574574574574575, -0.5688027489862835, 0.27957474689157497, 0.8709125098023224, 0.6615373276196324], [-2.4374374374374375, -0.5826511811134897, 0.26239323355026123, 0.8661945193762967, 0.673620558748455], [-2.4174174174174174, -0.5962660937469216, 0.24510655619820726, 0.8611293687415019, 0.6854338109654109], [-2.3973973973973974, -0.6096420301954936, 0.22772164312505716, 0.8557190879485936, 0.6969723496629561], [-2.3773773773773774, -0.6227736295468633, 0.2102454619921352, 0.8499658453721893, 0.708231550335378], [-2.3573573573573574, -0.6356556288160194, 0.19268501703989183, 0.8438719468418101, 0.7192069004322407], [-2.3373373373373374, -0.6482828650546222, 0.17504734628068955, 0.8374398347177328, 0.7298940011669601], [-2.3173173173173174, -0.6606502774202505, 0.15733951867805326, 0.8306720869121211, 0.7402885692797845], [-2.2972972972972974, -0.672752909204725, 0.1395686313135159, 0.8235714158558308, 0.7503864387544721], [-2.2772772772772774, -0.6845859098206977, 0.12174180654219457, 0.816140667411299, 0.7601835624879802], [-2.2572572572572573, -0.6961445367457068, 0.1038661891382374, 0.8083828197319587, 0.7696760139124947], [-2.2372372372372373, -0.7074241574229229, 0.08594894343128517, 0.8003009820686302, 0.7788599885691501], [-2.2172172172172173, -0.718420251117821, 0.06799725043509522, 0.7918983935233717, 0.7877318056328112], [-2.1971971971971973, -0.7291284107300358, 0.05001830496947869, 0.783178421751286, 0.7962879093873019], [-2.1771771771771773, -0.7395443445596738, 0.032019312776704424, 0.774144561610807, 0.8045248706504939], [-2.1571571571571573, -0.7496638780273746, 0.014007487633525202, 0.7648004337630007, 0.8124393881486803], [-2.1371371371371373, -0.7594829553474308, -0.004009951540016031, 0.7551497832204505, 0.8200282898396865], [-2.1171171171171173, -0.7689976411532984, -0.022025783573841363, 0.7451964778463002, 0.8272885341841856], [-2.0970970970970972, -0.7782041220748429, -0.0400327879419949, 0.7349445068040626, 0.8342172113647105], [-2.0770770770770772, -0.7870987082666921, -0.05802374765654159, 0.7243979789588111, 0.8408115444518732], [-2.057057057057057, -0.7956778348870811, -0.07599145216004802, 0.7135611212303964, 0.8470688905173247], [-2.037037037037037, -0.8039380635265972, -0.09392870021548604, 0.7024382768993486, 0.8529867416930078], [-2.017017017017017, -0.8118760835862529, -0.1118283027924007, 0.6910339038661436, 0.8585627261762803], [-1.9969969969969972, -0.819488713604333, -0.1296830859481862, 0.6793525728645305, 0.8637946091805048], [-1.9769769769769772, -0.8267729025314863, -0.14748589370331464, 0.6673989656296377, 0.8686802938307239], [-1.9569569569569571, -0.8337257309535488, -0.16522959090936523, 0.6551778730215899, 0.8732178220040618], [-1.9369369369369371, -0.8403444122616095, -0.18290706610870514, 0.6426941931053901, 0.8774053751145168], [-1.9169169169169171, -0.8466262937688486, -0.2005112343846748, 0.6299529291878351, 0.8812412748418272], [-1.8968968968968971, -0.8525688577737031, -0.2180350402011364, 0.61695918781225, 0.8847239838041231], [-1.876876876876877, -0.8581697225689299, -0.23547146023024657, 0.6037181767118495, 0.8878521061740893], [-1.856856856856857, -0.8634266433961658, -0.2528135061673208, 0.5902352027225406, 0.890624388238396], [-1.836836836836837, -0.8683375133455989, -0.27005422753166064, 0.576515669656008, 0.8930397189001723], [-1.816816816816817, -0.8729003642003926, -0.28718671445222177, 0.5625650761339318, 0.8950971301243197], [-1.796796796796797, -0.8771133672255228, -0.30420410043700613, 0.5483890133842075, 0.8967957973254891], [-1.7767767767767766, -0.8809748339007124, -0.3210995651250686, 0.5339931630000494, 0.8981350396985648], [-1.7567567567567566, -0.8844832165971699, -0.33786633702003305, 0.5193832946628781, 0.8991143204915222], [-1.7367367367367366, -0.8876371091978604, -0.35449769620402766, 0.5045652638299016, 0.8997332472205519], [-1.7167167167167166, -0.8904352476610599, -0.3709869770309451, 0.4895450093873195, 0.8999915718273627], [-1.6966966966966965, -0.8928765105269676, -0.387327570797952, 0.4743285512700904, 0.8998891907785999], [-1.6766766766766765, -0.8949599193671738, -0.4035129283941771, 0.45892198804921497, 0.8994261451073404], [-1.6566566566566565, -0.8966846391768014, -0.41953656292551456, 0.4433314944875026, 0.8986026203966468], [-1.6366366366366365, -0.8980499787091669, -0.4353920523144928, 0.4275633190648026, 0.8974189467051887], [-1.6166166166166165, -0.8990553907528221, -0.45107304187416386, 0.4116237814736888, 0.8958755984349583], [-1.5965965965965965, -0.8997004723508709, -0.46657324685498563, 0.395519270086604, 0.893973194141137], [-1.5765765765765765, -0.8999849649624694, -0.481886454963672, 0.37925623939547853, 0.8917124962841845], [-1.5565565565565564, -0.899908754566445, -0.49700652885300467, 0.362841207424848, 0.8890944109242553], [-1.5365365365365364, -0.8994718717069963, -0.5119274085816072, 0.3462807531195088, 0.8861199873580604], [-1.5165165165165164, -0.8986744914814494, -0.5266431140426958, 0.3295815137077569, 0.8827904176983216], [-1.4964964964964964, -0.8975169334700829, -0.5411477473608326, 0.31275018204126814, 0.8791070363959878], [-1.4764764764764764, -0.8959996616080429, -0.555435495255723, 0.2957935039126843, 0.8750713197054028], [-1.4564564564564564, -0.8941232839994037, -0.5695006313721072, 0.2787182753519818, 0.8706848850926407], [-1.4364364364364364, -0.891888552673447, -0.5833375185748136, 0.26153133990270583, 0.8659494905872459], [-1.4164164164164164, -0.8892963632832582, -0.5969406112080544, 0.24423958587916134, 0.8608670340776351], [-1.3963963963963963, -0.8863477547467589, -0.6103044573180557, 0.22684994360566055, 0.8554395525504473], [-1.3763763763763763, -0.8830439088303217, -0.6234237008381358, 0.2093693826389336, 0.849669221274145], [-1.3563563563563563, -0.8793861496751325, -0.6362930837353499, 0.19180490897481492, 0.8435583529271921], [-1.3363363363363363, -0.8753759432664909, -0.6489074481178464, 0.1741635622403257, 0.8371093966711606], [-1.3163163163163163, -0.8710148968462609, -0.661261738302087, 0.15645241287227693, 0.8303249371691378], [-1.2962962962962963, -0.8663047582687083, -0.6733510028391033, 0.13867855928352468, 0.8232076935498229], [-1.2762762762762763, -0.8612474152999819, -0.6851703964989768, 0.12084912501801248, 0.8157605183177351], [-1.2562562562562563, -0.8558448948615198, -0.6967151822127481, 0.10297125589574202, 0.8079863962099633], [-1.2362362362362362, -0.8500993622176839, -0.7079807329709764, 0.08505211714881532, 0.7998884429999203], [-1.2162162162162162, -0.8440131201079484, -0.7189625336781891, 0.06709889054969705, 0.7914699042485793], [-1.1961961961961962, -0.8375886078239898, -0.7296561829624759, 0.049118771532847744, 0.7827341540036916], [-1.1761761761761762, -0.8308284002320487, -0.7400573949395062, 0.031118966310881267, 0.7736846934475101], [-1.1561561561561562, -0.8237352067409549, -0.7501620009302584, 0.013106688986402595, 0.7643251494935579], [-1.1361361361361362, -0.8163118702162289, -0.7599659511317763, -0.004910841339316566, 0.7546592733330054], [-1.1161161161161162, -0.808561365840696, -0.7694653162402805, -0.022926403459665626, 0.7446909389312389], [-1.0960960960960962, -0.8004867999220704, -0.7786562890259854, -0.04093277695686673, 0.7344241414752224], [-1.0760760760760761, -0.792091408647983, -0.7875351858589906, -0.05892274509583019, 0.723862995772275], [-1.0560560560560561, -0.7833785567889587, -0.7960984481856351, -0.07688909771653404, 0.7130117346009067], [-1.0360360360360361, -0.7743517363498564, -0.8043426439547219, -0.09482463412376826, 0.7018747070143723], [-1.016016016016016, -0.765014565170316, -0.8122644689930423, -0.11272216597308565, 0.6904563765976234], [-0.9959959959959956, -0.7553707854747724, -0.8198607483296488, -0.13057452015180326, 0.6787613196783585], [-0.9759759759759756, -0.7454242623726185, -0.8271284374683437, -0.14837454165389666, 0.6667942234928864], [-0.9559559559559556, -0.7351789823091147, -0.8340646236078776, -0.16611509644764186, 0.6545598843075389], [-0.9359359359359356, -0.7246390514676716, -0.8406665268093629, -0.1837890743348465, 0.6420632054963858], [-0.9159159159159156, -0.7138086941241412, -0.846931501110441, -0.2013893918005312, 0.6293091955760225], [-0.8958958958958956, -0.7026922509537785, -0.8528570355857508, -0.2189089948519159, 0.616302966198217], [-0.8758758758758756, -0.6912941772915525, -0.8584407553532774, -0.23634086184557426, 0.603049730101223], [-0.8558558558558556, -0.6796190413465009, -0.8636804225261756, -0.25367800630162296, 0.589554799020577], [-0.8358358358358355, -0.6676715223708487, -0.8685739371096881, -0.27091347970381735, 0.5758235815602202], [-0.8158158158158155, -0.6554564087846209, -0.8731193378427973, -0.28804037428443247, 0.5618615810247956], [-0.7957957957957955, -0.6429785962565012, -0.8773148029842752, -0.305051825792812, 0.5476743932139905], [-0.7757757757757755, -0.6302430857417087, -0.881158651042815, -0.32194101624647614, 0.5332677041798084], [-0.7557557557557555, -0.617254981477675, -0.884649341450952, -0.33870117666368627, 0.5186472879476685], [-0.7357357357357355, -0.604019488938329, -0.8877854751825048, -0.35532558977636997, 0.5038190042022461], [-0.7157157157157155, -0.5905419127478059, -0.8905657953132878, -0.3718075927223205, 0.48878879593898406], [-0.6956956956956954, -0.576827654554418, -0.8929891875248709, -0.3881405797155907, 0.4735626870822106], [-0.6756756756756754, -0.5628822108657409, -0.8950546805511844, -0.40431800469401136, 0.45814678007082504], [-0.6556556556556554, -0.5487111708456796, -0.8967614465677918, -0.4203333839427734, 0.4425472534125146], [-0.6356356356356354, -0.5343202140743993, -0.8981088015236703, -0.43618029869302166, 0.4267703592074833], [-0.6156156156156154, -0.5197151082720205, -0.8990962054153714, -0.45185239769441915, 0.4108224206426867], [-0.5955955955955954, -0.5049017069869848, -0.8997232625034468, -0.4673433997606509, 0.3947098294575757], [-0.5755755755755754, -0.4898859472500258, -0.8999897214710562, -0.48264709628684704, 0.37843904338236506], [-0.5555555555555554, -0.4746738471946788, -0.8998954755246922, -0.4977573537379157, 0.3620165835498539], [-0.5355355355355353, -0.4592715036452863, -0.8994405624369817, -0.5126681161067891, 0.345449031881835], [-0.5155155155155153, -0.44368508967346487, -0.8986251645315472, -0.527373407341598, 0.32874302845114106], [-0.4954954954954953, -0.42792085212401376, -0.8974496086099335, -0.5418673337407994, 0.31190526882038405], [-0.4754754754754753, -0.41198510911125524, -0.89591436582063, -0.5561440863153017, 0.29494250135845557], [-0.4554554554554553, -0.3958842474868115, -0.8940200514702393, -0.5701979431166352, 0.27786152453586277], [-0.4354354354354353, -0.3796247202798323, -0.8917674247768713, -0.5840232715302405, 0.260669184199984], [-0.41541541541541527, -0.3632130441106995, -0.8891573885658559, -0.5976145305329514, 0.24337237083133706], [-0.39539539539539525, -0.3466557965792451, -0.8861909889079035, -0.6109662729137695, 0.22597801678195795], [-0.37537537537537524, -0.3299596136285294, -0.8828694146998511, -0.6240731474570395, 0.2084930934969991], [-0.35535535535535523, -0.31313118688523567, -0.879193997188168, -0.6369299010871522, 0.19092460872065906], [-0.3353353353353352, -0.29617726097774794, -0.8751662094354078, -0.6495313809739125, 0.1732796036875639], [-0.3153153153153152, -0.279104630832986, -0.8707876657298232, -0.6618725365977308, 0.15556515030072604], [-0.2952952952952952, -0.2619201389530815, -0.8660601209383787, -0.6739484217738104, 0.13778834829721204], [-0.2752752752752752, -0.24463067267298666, -0.8609854698034214, -0.6857541966345176, 0.11995632240265423], [-0.25525525525525516, -0.22724316140011394, -0.8555657461832916, -0.6972851295691421, 0.10207621947574753], [-0.23523523523523515, -0.20976457383711442, -0.8498031222371768, -0.7085365991202681, 0.08415520564387569], [-0.21521521521521514, -0.19220191518890636, -0.8436999075545369, -0.7195040958359988, 0.06620046343101459], [-0.19519519519519513, -0.17456222435507468, -0.8372585482294489, -0.73018322407729, 0.048219188879064276], [-0.1751751751751751, -0.15685257110876555, -0.8304816258802421, -0.7405697037796676, 0.030218588663762983], [-0.1551551551551551, -0.13908005326320766, -0.8233718566148173, -0.7506593721686265, 0.012205877206339349], [-0.1351351351351351, -0.12125179382699504, -0.8159320899420635, -0.7604481854280194, -0.005811726217939738], [-0.11511511511511507, -0.10337493814927201, -0.808165307629809, -0.7699322203207686, -0.023827000373166672], [-0.09509509509509506, -0.0854566510559644, -0.8000746225097666, -0.7791076757612523, -0.04183272495697651], [-0.07507507507507505, -0.0675041139782045, -0.791663277229947, -0.787970874338733, -0.05982168349435614], [-0.055055055055055035, -0.04952452207410106, -0.7829346429550461, -0.7965182637912191, -0.07778666622991962], [-0.03503503503503502, -0.031525081345007595, -0.7738922180153227, -0.8047464184291683, -0.09572047301749019], [-0.01501501501501501, -0.013513005747444783, -0.7645396265045104, -0.8126520405084618, -0.11361591620583114], [0.005005005005005003, 0.00450448569816537, -0.7548806168273252, -0.8202319615521003, -0.13146582351936897], [0.025025025025025016, 0.022520171800794946, -0.7449190601971507, -0.8274831436200912, -0.14926304093275386], [0.04504504504504503, 0.040526832092975494, -0.7346589490845025, -0.8344026805270174, -0.16700043553810592], [0.06506506506506504, 0.058517249724673405, -0.7241043956168947, -0.8409877990068011, -0.18467089840379788], [0.08508508508508505, 0.07648421435571551, -0.7132596299307497, -0.8472358598241956, -0.20226734742362806], [0.10510510510510507, 0.09442052504560546, -0.702128998476011, -0.853144358832558, -0.21978273015524244], [0.12512512512512508, 0.11231899313957308, -0.6907169622741385, -0.8587109279774808, -0.23721002664666743], [0.1451451451451451, 0.13017244514969945, -0.679028095130186, -0.8639333362458795, -0.2545422522498212], [0.1651651651651651, 0.1479737256299636, -0.667067081799676, -0.8688094905601557, -0.2717724604198755], [0.18518518518518512, 0.165715700044058, -0.6548387161110061, -0.8733374366170755, -0.28889374549934627], [0.20520520520520513, 0.18339125762482375, -0.6423478990441412, -0.877515359671033, -0.30589924548579683], [0.22522522522522515, 0.2009933142241595, -0.6295996367663613, -0.881341585261376, -0.3227821447820448], [0.24524524524524516, 0.21851481515226165, -0.6165990386258491, -0.8848145798835104, -0.3395356769277709], [0.26526526526526517, 0.23594873800505806, -0.6033513151039269, -0.8879329516035106, -0.3561531273114332], [0.2852852852852852, 0.25328809547870196, -0.5898617757267586, -0.8906954506159882, -0.37262783586140186], [0.3053053053053052, 0.27052593816999837, -0.5761358269373561, -0.8931009697450004, -0.3889531997152347], [0.3253253253253252, 0.2876553573616401, -0.5621789699287434, -0.8951485448877913, -0.40512267586602385], [0.3453453453453452, 0.30466948779113734, -0.547996798439145, -0.8968373554011937, -0.42112978378475363], [0.36536536536536524, 0.3215615104023315, -0.5335949965100837, -0.8981667244305318, -0.43696810801761704], [0.38538538538538525, 0.3383246550783897, -0.5189793362082866, -0.8991361191808972, -0.45263130075725194], [0.40540540540540526, 0.35495220335518496, -0.5041556753123104, -0.899745151130686, -0.46811308438686444], [0.4254254254254253, 0.37143749111397495, -0.48912995496481587, -0.8999935761873137, -0.4834072539962218], [0.4454454454454453, 0.38777391125229954, -0.47390819729142947, -0.8998812947850447, -0.49850767986850425], [0.4654654654654653, 0.4039549163320268, -0.45849650298714917, -0.899408351924897, -0.5134083099370211], [0.4854854854854853, 0.4199740212034867, -0.44290104887126036, -0.8985749371566059, -0.5281031722108055], [0.5055055055055053, 0.4358248056046398, -0.42712808541174185, -0.8973813845026556, -0.5425863771681151], [0.5255255255255253, 0.45150091673424037, -0.41118393422015437, -0.8958281723244063, -0.5568521201168813], [0.5455455455455454, 0.4669960717979612, -0.39507498551801573, -0.8939159231303727, -0.5708946835211586], [0.5655655655655654, 0.48230406052646163, -0.3788076955756777, -0.8916454033267317, -0.5847084392926434], [0.5855855855855854, 0.4974187476643875, -0.36238858412473224, -0.8890175229101538, -0.5982878510463434], [0.6056056056056054, 0.5123340754293065, -0.3458242317449814, -0.8860333351030892, -0.6116274763194931], [0.6256256256256254, 0.5270440659395942, -0.32912127722702217, -0.8826940359316487, -0.6247219687528266], [0.6456456456456454, 0.5415428236102953, -0.3122864149114995, -0.8790009637462508, -0.6375660802333334], [0.6656656656656654, 0.5558245375160032, -0.29532639200609634, -0.8749555986852273, -0.6501546629976376], [0.6856856856856854, 0.5698834837198077, -0.27824800588133497, -0.8705595620816017, -0.6624826716951596], [0.7057057057057055, 0.5837140275673783, -0.2610581013462737, -0.8658146158132789, -0.6745451654102304], [0.7257257257257255, 0.597310625945267, -0.2437635679051909, -0.8607226615969059, -0.6863373096423507], [0.7457457457457455, 0.6106678295025182, -0.22637133699635548, -0.8552857402256874, -0.6978543782437998], [0.7657657657657655, 0.6237802848347043, -0.2088883792139911, -0.8495060307514597, -0.7090917553138181], [0.7857857857857855, 0.6366427366295033, -0.19132170151454664, -0.8433858496113544, -0.7200449370486032], [0.8058058058058055, 0.6492500297729655, -0.17367834440839383, -0.8369276496993978, -0.7307095335463808], [0.8258258258258255, 0.6615971114156188, -0.15596537913807618, -0.8301340193834212, -0.7410812705668228], [0.8458458458458455, 0.6736790329975905, -0.1381899048442411, -0.823007681467675, -0.7511559912441114], [0.8658658658658656, 0.6854909522319287, -0.12035904572039115, -0.8155514921015611, -0.7609296577529597], [0.8858858858858856, 0.697028135045333, -0.10247994815759376, -0.8077684396349237, -0.7703983529269233], [0.9059059059059056, 0.7082859574755125, -0.08455977788029499, -0.7996616434203547, -0.7795582818283526], [0.9259259259259256, 0.719259907524414, -0.06660571707438431, -0.7912343525629955, -0.7884057732693579], [0.9459459459459456, 0.7299455869665756, -0.04862496150866218, -0.782489944618335, -0.7969372812831765], [0.9659659659659656, 0.7403387131118822, -0.030624717650863484, -0.7734319242385267, -0.8051493865453532], [0.9859859859859856, 0.7504351205220151, -0.012612199779393094, -0.7640639217677665, -0.8130387977441632], [1.0060060060060056, 0.7602307626799093, 0.005405372908068972, -0.7543896917872955, -0.820602352899731], [1.0260260260260257, 0.769721713611548, 0.02342077918793406, -0.7444131116106093, -0.8278370206313123], [1.0460460460460457, 0.7789041694594452, 0.04142679870488327, -0.7341381797294777, -0.8347399013722345], [1.0660660660660657, 0.7877744500071854, 0.05941621486569792, -0.7235690142113986, -0.8413082285320087], [1.0860860860860857, 0.7963290001544099, 0.07738181773158212, -0.712709851049126, -0.8475393696051444], [1.1061061061061057, 0.8045643913416567, 0.09531640690781845, -0.7015650424629363, -0.8534308272262261], [1.1261261261261257, 0.8124773229244865, 0.1132127944295984, -0.6901390551563111, -0.8589802401708267], [1.1461461461461457, 0.8200646234963401, 0.13106380764287107, -0.6784364685257369, -0.8641853843018578], [1.1661661661661658, 0.8273232521595998, 0.14886229207905552, -0.6664619728253394, -0.8690441734609763], [1.1861861861861858, 0.8342502997443438, 0.1666011143224646, -0.6542203672870855, -0.8735546603046918], [1.2062062062062058, 0.8408429899743064, 0.18427316486929105, -0.6417165581973108, -0.8777150370848376], [1.2262262262262258, 0.8470986805795755, 0.20187136097700997, -0.6289555569303392, -0.8815236363730946], [1.2462462462462458, 0.853014864355582, 0.21938864950305578, -0.6159424779399858, -0.884978931729276], [1.2662662662662658, 0.8585891701679577, 0.2368180097316357, -0.602682536709745, -0.8880795383131056], [1.2862862862862858, 0.8638193639028555, 0.2541524561875472, -0.5891810476624894, -0.8908242134392458], [1.3063063063063058, 0.8687033493623569, 0.2713850414358714, -0.5754434220305131, -0.89321185707535], [1.3263263263263259, 0.8732391691046012, 0.2885088588664201, -0.5614751656867762, -0.8952415122829421], [1.3463463463463459, 0.8774250052283045, 0.3055170454618212, -0.547281876938218, -0.8969123656009464], [1.3663663663663659, 0.881259180101353, 0.32240278454813226, -0.5328692442820232, -0.8982237473717126], [1.386386386386386, 0.884740157033177, 0.3391593085268807, -0.5182430441257422, -0.8991751320094058], [1.406406406406406, 0.8878665408906382, 0.3557799015874343, -0.5034091384721772, -0.8997661382106562], [1.4264264264264268, 0.8906370786571817, 0.37225790239861734, -0.48837347256996194, -0.8999965291073796], [1.4464464464464468, 0.8930506599350293, 0.3885867067784881, -0.4731420725307816, -0.8998662123617119], [1.4664664664664668, 0.8951063173902144, 0.4047597703412186, -0.45772104291417637, -0.899375240203017], [1.4864864864864868, 0.8968032271402775, 0.42077061112000197, -0.44211656428091084, -0.8985238094069534], [1.5065065065065069, 0.8981407090844675, 0.4366128121649465, -0.4263348907158804, -0.8973122612166097], [1.5265265265265269, 0.8991182271763185, 0.45228002411491103, -0.41038234732155, -0.8957410812057375], [1.5465465465465469, 0.8997353896384902, 0.4677659677422511, -0.39426532768293, -0.8938108990841406], [1.566566566566567, 0.899991949119788, 0.48306443646945746, -0.3779902913051053, -0.8915224884452939], [1.586586586586587, 0.899887802794298, 0.49816929885667643, -0.3615637610243446, -0.8888767664562972], [1.606606606606607, 0.8994229924025984, 0.5130745010591161, -0.3449923203938268, -0.8858747934902855], [1.626626626626627, 0.8985977042350304, 0.5277740692533542, -0.3282826110450333, -0.8825177727014444], [1.646646646646647, 0.8974122690570355, 0.5422621120315728, -0.3114413300258629, -0.8788070495428009], [1.666666666666667, 0.8958671619765884, 0.5565328227627635, -0.2944752271165368, -0.8747441112269815], [1.686686686686687, 0.89396300225378, 0.5705804819199541, -0.27739110212436874, -0.8703305861301563], [1.706706706706707, 0.8917005530526247, 0.5843994593725255, -0.2601958021584852, -0.8655682431394052], [1.726726726726727, 0.8890807211351945, 0.5979842166426992, -0.24289621888558743, -0.8604589909437697], [1.746746746746747, 0.8861045564981992, 0.6113293091252917, -0.2254992857678555, -0.8550048772692734], [1.766766766766767, 0.8827732519521607, 0.6244293882698463, -0.20801197528410095, -0.8492080880582187], [1.786786786786787, 0.8790881426433489, 0.6372792037242666, -0.19044129613528252, -0.8430709465930872], [1.806806806806807, 0.8750507055186717, 0.6498736054390936, -0.17279429043550393, -0.8365959125653972], [1.826826826826827, 0.8706625587337326, 0.6622075457315831, -0.1550780308896206, -0.8297855810898883], [1.846846846846847, 0.865925461004293, 0.6742760813087553, -0.13729961795858553, -0.8226426816644321], [1.866866866866867, 0.8608413109014016, 0.686074375248606, -0.11946617701367114, -0.8151700770760818], [1.886886886886887, 0.8554121460904689, 0.6975976989386867, -0.10158485548070739, -0.8073707622537026], [1.9069069069069071, 0.8496401425145969, 0.7088414339712743, -0.08366281997548039, -0.7992478630676406], [1.9269269269269271, 0.8435276135224866, 0.7198010739943728, -0.06570725343144032, -0.7908046350769118], [1.9469469469469471, 0.8370770089412768, 0.7304722265178044, -0.04772535222086907, -0.7820444622244139], [1.9669669669669672, 0.8302909140946829, 0.740850614673666, -0.02972432327066205, -0.7729708554806834], [1.9869869869869872, 0.8231720487668309, 0.7509320789304456, -0.0117113811738796, -0.7635874514367412], [2.007007007007007, 0.8157232661122005, 0.7607125787601117, 0.006306254701773933, -0.7538980108465911], [2.027027027027027, 0.8079475515121162, 0.7701881942575083, 0.024321363107384832, -0.7439064171199556], [2.047047047047047, 0.7998480213782424, 0.7793551277114047, 0.04232672380701857, -0.7336166747658526], [2.067067067067067, 0.7914279219035637, 0.7882097051265716, 0.0603151204715024, -0.7230329077876368], [2.0870870870870872, 0.782690627761351, 0.7967483776962744, 0.07827934357064219, -0.7121593580301497], [2.1071071071071072, 0.7736396407526318, 0.804967723224591, 0.09621219326271417, -0.7010003834796398], [2.1271271271271273, 0.7642785884027131, 0.8128644474979865, 0.11410648228007389, -0.6895604565171346], [2.1471471471471473, 0.7546112225073119, 0.8204353856055938, 0.13195503880972548, -0.6778441621259648], [2.1671671671671673, 0.7446414176288823, 0.8276775032076715, 0.14975070936769683, -0.6658561960541591], [2.1871871871871873, 0.7343731695437371, 0.8345878977517301, 0.16748636166606892, -0.6536013629324456], [2.2072072072072073, 0.7238105936405902, 0.8411637996358396, 0.1851548874715098, -0.6410845743486142], [2.2272272272272273, 0.7129579232711583, 0.8474025733186522, 0.202749205454168, -0.6283108468790118], [2.2472472472472473, 0.7018195080534854, 0.8533017183756955, 0.22026226402578317, -0.6152853000779602], [2.2672672672672673, 0.6903998121286689, 0.8588588705015119, 0.2376870441658768, -0.6020131544259006], [2.2872872872872874, 0.6787034123716865, 0.8640718024572432, 0.25501656223488994, -0.5884997292370892], [2.3073073073073074, 0.6667349965570397, 0.8689384249632813, 0.27224387277314116, -0.5747504405276801], [2.3273273273273274, 0.6544993614799518, 0.8734567875366258, 0.28936207128448177, -0.5607707988450528], [2.3473473473473474, 0.64200141103387, 0.8776250792726135, 0.30636429700353396, -0.5465664070592514], [2.3673673673673674, 0.6292461542450456, 0.8814416295707074, 0.32324373564540215, -0.5321429581174231], [2.3873873873873874, 0.6162387032649764, 0.8849049088040527, 0.33999362213675477, -0.5175062327621536], [2.4074074074074074, 0.6029842713215191, 0.8880135289325328, 0.3566072433271839, -0.5026620972146163], [2.4274274274274275, 0.5894881706294912, 0.8907662440590789, 0.37307794067975364, -0.48761650082346303], [2.4474474474474475, 0.5757558102615999, 0.8931619509290101, 0.3893991129396609, -0.47237547368039623], [2.4674674674674675, 0.5617926939805514, 0.8951996893722046, 0.40556421877993776, -0.4569451242033825], [2.4874874874874875, 0.5476044180332104, 0.8968786426879244, 0.4215667794231354, -0.44133163668847336], [2.5075075075075075, 0.5331966689076921, 0.8981981379721385, 0.43740038123793973, -0.425541268831214], [2.5275275275275275, 0.5185752210542875, 0.8991576463872152, 0.453058678309676, -0.4095803492186358], [2.5475475475475475, 0.5037459345711334, 0.8997567833738732, 0.4685353949836745, -0.39345527479283526], [2.5675675675675675, 0.48871475285555716, 0.8999953088053088, 0.4838243283804765, -0.37717250828715787], [2.5875875875875876, 0.4734877002220352, 0.8998731270834357, 0.4989193508818724, -0.3607385756360133], [2.6076076076076076, 0.45807087948772107, 0.8993902871771989, 0.5138144125867768, -0.34416006335936045], [2.6276276276276276, 0.44247046952651076, 0.8985469826029497, 0.5285035437359551, -0.32744361592291016], [2.6476476476476476, 0.42669272279262555, 0.8973435513468856, 0.54298085710463, -0.3105959330751048], [2.6676676676676676, 0.4107439628147042, 0.8957804757295904, 0.5572405503620095, -0.2936237671619404], [2.6876876876876876, 0.3946305816614098, 0.8938583822127251, 0.5712769083967909, -0.27653392042070885], [2.7077077077077076, 0.37835903737956683, 0.8915780411479508, 0.5850843056077071, -0.2593332422537438], [2.7277277277277276, 0.3619358514058544, 0.8889403664681801, 0.5986572081581993, -0.24202862648326412], [2.7477477477477477, 0.3453676059530938, 0.8859464153212846, 0.6119901761943115, -0.2246270085884134], [2.7677677677677677, 0.3286609413721783, 0.8825973876464026, 0.6250778660249172, -0.20713536292560492], [2.7877877877877877, 0.31182255349070065, 0.8788946256930182, 0.637915032263406, -0.18956069993328428], [2.8078078078078077, 0.29485919092934787, 0.8748396134830041, 0.6504965299299706, -0.1719100633222312], [2.8278278278278277, 0.27777765239713553, 0.8704339762158438, 0.6628173165136523, -0.154190527252526], [2.8478478478478477, 0.2605847839665686, 0.8656794796172711, 0.674872453993317, -0.1364091934983126], [2.8678678678678677, 0.2432874763298184, 0.8605780292315889, 0.6866571108167552, -0.1185731886014937], [2.8878878878878878, 0.2258926620370179, 0.8551316696579508, 0.6981665638371084, -0.10068966101549995], [2.9079079079079078, 0.20840731271777943, 0.8493425837309098, 0.7093962002058486, -0.08276577824027667], [2.9279279279279278, 0.19083843628705083, 0.8432130916455652, 0.7203415192215531, -0.06480872394963728], [2.947947947947948, 0.17319307413642843, 0.8367456500276571, 0.7309981341337287, -0.04682569511213446], [2.967967967967968, 0.15547829831205312, 0.8299428509489799, 0.74136177390097, -0.028823899106602692], [2.987987987987988, 0.1377012086802204, 0.822807420888512, 0.7514282849027395, -0.010810550833528764], [3.0080080080080087, 0.11986893008183978, 0.8153422196396761, 0.7611936326040887, 0.007207130176593267], [3.0280280280280287, 0.1019886094768884, 0.8075502391641698, 0.7706539031726488, 0.025221922656758742], [3.0480480480480487, 0.08406741307999185, 0.7994346023928217, 0.7798053050472501, 0.043226606497653], [3.0680680680680688, 0.06611252348829592, 0.7909985619739608, 0.7886441704575313, 0.06121396564138005], [3.0880880880880888, 0.04813113680276825, 0.7822454989697936, 0.7971669568939377, 0.07917679097357139], [3.108108108108109, 0.03013045974408887, 0.7731789215013157, 0.8053702485275157, 0.09710788321271126], [3.128128128128129, 0.01211770676428429, 0.7638024633423006, 0.8132507575789362, 0.1150000557955209], [3.148148148148149, -0.0058999028447369204, 0.7541198824629255, 0.8208053256361952, 0.13284613775724563], [3.168168168168169, -0.02391514784458839, 0.7441350595236247, 0.8280309249204679, 0.15063897660569003], [3.188188188188189, -0.04192080794459029, 0.7338519963197669, 0.8349246594996057, 0.1683714411878497], [3.208208208208209, -0.05990966669557385, 0.7232748141777852, 0.841483766448789, 0.18603642454799058], [3.228228228228229, -0.07787451438214628, 0.7124077523033986, 0.8477056169578757, 0.20362684677603005], [3.248248248248249, -0.09580815091225683, 0.7012551660825911, 0.8535877173849952, 0.22113565784507877], [3.268268268268269, -0.11370338870290593, 0.6898215253360254, 0.8591277102559697, 0.23855584043700548], [3.288288288288289, -0.13155305556084088, 0.6781114125275938, 0.8643233752091612, 0.2558804127548926], [3.308308308308309, -0.14934999755708359, 0.6661295209278218, 0.8691726298853663, 0.27310243132125533], [3.328328328328329, -0.16708708189413804, 0.6538806527328631, 0.8736735307623985, 0.29021499376090293], [3.348348348348349, -0.18475719976472865, 0.6413697171398376, 0.8778242739340302, 0.3072112415673265], [3.368368368368369, -0.20235326920092364, 0.6286017283792847, 0.8816231958329749, 0.3240843628515048], [3.388388388388389, -0.2198682379125014, 0.6155818037055218, 0.8850687738976255, 0.3408275950720266], [3.408408408408409, -0.23729508611342262, 0.6023151613457101, 0.8881596271822781, 0.35743422774543426], [3.428428428428429, -0.25462682933527475, 0.5888071184084528, 0.8908945169105983, 0.3738976051357046], [3.448448448448449, -0.2718565212265622, 0.5750630887527636, 0.8932723469721079, 0.39021112892178617], [3.468468468468469, -0.288977256336719, 0.5610885808182572, 0.8952921643614918, 0.40636826084212596], [3.488488488488489, -0.30598217288372953, 0.5468891954174341, 0.8969531595605521, 0.4223625253151255], [3.508508508508509, -0.322864455504247, 0.5324706234909431, 0.8982546668626522, 0.4381875120344742], [3.528528528528529, -0.339617337985108, 0.5178386438267218, 0.8991961646395243, 0.45383687853832294], [3.548548548548549, -0.3562341059751485, 0.5029991207439287, 0.8997772755503316, 0.4693043527512646], [3.568568568568569, -0.37270809967623364, 0.4879580017425956, 0.899997766692902, 0.4845837354981063], [3.588588588588589, -0.38903271651242394, 0.4727213151199417, 0.899857549697072, 0.4996689029884225], [3.608608608608609, -0.4052014137762072, 0.45729516755430627, 0.8993566807601052, 0.5145538092708962], [3.628628628628629, -0.4212077112507362, 0.44168574165766544, 0.8984953606241682, 0.5292324886564624], [3.648648648648649, -0.43704519380701995, 0.42589929349771755, 0.8972739344958767, 0.5436990581092831], [3.668668668668669, -0.45270751397503, 0.4099421500905269, 0.8956928919079394, 0.5579477196045973], [3.688688688688689, -0.4681883944876888, 0.39382070686473314, 0.8937528665229608, 0.5719727624524977], [3.708708708708709, -0.48348163079672146, 0.37754142509834093, 0.8914546358794758, 0.5857685655867064], [3.728728728728729, -0.498581093559364, 0.3611108293291188, 0.8887991210803223, 0.5993295998174293], [3.748748748748749, -0.5134807310949282, 0.34453550473964417, 0.8857873864234744, 0.6126504300473882], [3.768768768768769, -0.5281745718102422, 0.3278220945180416, 0.8824206389754845, 0.6257257174501415], [3.788788788788789, -0.5426567265929921, 0.31097729719547484, 0.8787002280877058, 0.6385502216098208], [3.8088088088088092, -0.5569213911720063, 0.2940078639614565, 0.8746276448554888, 0.6511188026214255], [3.8288288288288292, -0.5709628484435367, 0.27692059595805396, 0.8702045215205688, 0.6634264231508351], [3.8488488488488493, -0.5847754707626044, 0.2597223415540743, 0.8654326308168822, 0.6754681504537094], [3.8688688688688693, -0.5983537221984909, 0.2424199936003215, 0.8603138852600776, 0.6872391583524747], [3.8888888888888893, -0.6116921607534719, 0.225020486667026, 0.8548503363810022, 0.6987347291705946], [3.9089089089089093, -0.6247854405439037, 0.20753079426455343, 0.8490441739034732, 0.7099502556233592], [3.9289289289289293, -0.6376283139427884, 0.18995792604850634, 0.8428977248666634, 0.7208812426644278], [3.9489489489489493, -0.650215633682958, 0.17230892501034, 0.8364134526924523, 0.7315233092873882], [3.9689689689689693, -0.6625423549200377, 0.15459086465461716, 0.829593956198116, 0.741872190281612], [3.9889889889889893, -0.6746035372543571, 0.13681084616403344, 0.8224419685547542, 0.7519237379416981], [4.009009009009009, -0.686394346711002, 0.11897599555334996, 0.8149603561918669, 0.7616739237298225], [4.029029029029029, -0.6979100576772135, 0.10109346081337353, 0.8071521176485258, 0.7711188398903274], [4.049049049049049, -0.7091460547963555, 0.08317040904612888, 0.7990203823715952, 0.7802547010159023], [4.069069069069069, -0.7200978348176937, 0.06521402359237159, 0.7905684094614891, 0.7890778455647288], [4.089089089089089, -0.7307610084012433, 0.04723150115259262, 0.7817995863659623, 0.7975847373279819], [4.109109109109109, -0.7411313018769624, 0.029230048902668455, 0.772717427522463, 0.8057719668470993], [4.129129129129129, -0.7512045589575886, 0.011216881605312803, 0.7633255729495896, 0.8136362527802514], [4.1491491491491495, -0.7609767424044268, -0.006800781281512331, 0.753627786788215, 0.8211744432174628], [4.1691691691691695, -0.7704439356454257, -0.024815718498067487, 0.7436279557928647, 0.8283835169438591], [4.1891891891891895, -0.7796023443448923, -0.04282070987702863, 0.7333300877739538, 0.8352605846505338], [4.2092092092092095, -0.788448297924215, -0.06080853923724224, 0.7227383099915046, 0.8418028900925468], [4.2292292292292295, -0.7969782510329876, -0.0787719972758828, 0.7118568675009911, 0.8480078111935954], [4.2492492492492495, -0.8051887849699416, -0.0967038844578535, 0.7006901214519728, 0.8538728610969091], [4.2692692692692695, -0.8130766090531206, -0.11459701390127221, 0.6892425473401994, 0.8593956891619526], [4.2892892892892895, -0.8206385619387455, -0.13244421425788613, 0.6775187332138859, 0.8645740819065331], [4.3093093093093096, -0.8278716128882421, -0.1502383325872608, 0.6655233778348791, 0.8694059638939375], [4.32932932932933, -0.8347728629829247, -0.16797223722359148, 0.6532612887954514, 0.8738893985647418], [4.34934934934935, -0.8413395462858474, -0.18563882063398796, 0.6407373805914761, 0.8780225890129606], [4.36936936936937, -0.8475690309503573, -0.20323100226708693, 0.627956672652757, 0.8818038787062253], [4.38938938938939, -0.8534588202749076, -0.22074173139085096, 0.614924287331303, 0.885231752149702], [4.40940940940941, -0.8590065537037038, -0.23816398991841584, 0.6016454478483508, 0.888304835493483], [4.42942942942943, -0.8642100077727872, -0.25549079522085427, 0.5881254762009628, 0.8910218970832101], [4.44944944944945, -0.869067097001171, -0.2727152029257285, 0.5743697910290335, 0.8933818479537052], [4.46946946946947, -0.8735758747266762, -0.28983030970031004, 0.560383905443567, 0.8953837422654157], [4.48948948948949, -0.87773453388613, -0.30682925601835176, 0.5461734248170875, 0.8970267776834945], [4.50950950950951, -0.8815414077396143, -0.3237052289093017, 0.531744044537075, 0.8983102956993664], [4.52952952952953, -0.884994970538476, -0.34045146468885934, 0.5171015477233235, 0.8992337818946505], [4.54954954954955, -0.8880938381368267, -0.35706125166977737, 0.5022518029101369, 0.8997968661473323], [4.56956956956957, -0.8908367685462928, -0.37352793285182445, 0.48720076169429205, 0.8999993227801044], [4.58958958958959, -0.8932226624337889, -0.3898449085898296, 0.4719544563497108, 0.8998410706508156], [4.60960960960961, -0.8952505635621171, -0.4060056392387398, 0.4565189974097987, 0.899322173184991], [4.62962962962963, -0.896919659173215, -0.4220036477746296, 0.4409005712184177, 0.8984428383504117], [4.64964964964965, -0.8982292803138998, -0.43783252239061404, 0.42510543745047435, 0.8972034185737652], [4.66966966966967, -0.899178902103976, -0.45348591906662217, 0.4091399266031189, 0.8956044105993953], [4.68968968968969, -0.8997681439466012, -0.468957564112003, 0.39301043745855885, 0.8936464552902142], [4.70970970970971, -0.899996769680825, -0.48424125667994483, 0.3767234345195052, 0.8913303373708524], [4.72972972972973, -0.8998646876762388, -0.4993308712526984, 0.3602854454182785, 0.8886569851131498], [4.74974974974975, -0.8993719508697008, -0.5142203600966104, 0.3437030583006133, 0.8856274699641165], [4.76976976976977, -0.8985187567441187, -0.5289037556859806, 0.32698291918520966, 0.8822430061165093], [4.78978978978979, -0.8973054472493022, -0.5433751730947745, 0.3101317293000898, 0.8785049500221989], [4.80980980980981, -0.8957325086649124, -0.557628812355229, 0.29315624239682764, 0.8744147998485194], [4.82982982982983, -0.8938005714055677, -0.5716589607824094, 0.27606326204372733, 0.869974194877822], [4.84984984984985, -0.8915104097681811, -0.5854599952637838, 0.258859638899036, 0.8651849148504701], [4.86986986986987, -0.8888629416216309, -0.5990263845128978, 0.24155226796528378, 0.860048879251541], [4.88988988988989, -0.8858592280388911, -0.6123526912862473, 0.22414808582585014, 0.8545681465415202], [4.90990990990991, -0.8825004728717651, -0.6254335745624585, 0.20665406786486626, 0.8487449133312951], [4.92992992992993, -0.8787880222683976, -0.6382637916829054, 0.18907722547156597, 0.8425815135017799], [4.94994994994995, -0.874723364133754, -0.6508382004529025, 0.17142460323020633, 0.8360804172685254], [4.96996996996997, -0.8703081275332867, -0.6631517612026343, 0.15370327609668416, 0.8292442301916858], [4.98998998998999, -0.8655440820400255, -0.675199538806993, 0.13592034656297983, 0.8220756921317425], [5.01001001001001, -0.8604331370253542, -0.6869767046635175, 0.11808294181056511, 0.8145776761514009], [5.03003003003003, -0.8549773408937575, -0.6984785386276372, 0.1001982108539157, 0.8067531873641027], [5.05005005005005, -0.8491788802618462, -0.7097004309044497, 0.08227332167527315, 0.7986053617296123], [5.07007007007007, -0.843040079081987, -0.7206378838962691, 0.06431545835180517, 0.7901374647971624], [5.09009009009009, -0.8365633977108915, -0.7312865140052095, 0.046331818176314904, 0.7813528903966622], [5.11011011011011, -0.8297514319235347, -0.7416420533900769, 0.028329608772653676, 0.7722551592784911], [5.13013013013013, -0.8226069118728004, -0.7517003516768686, 0.010316045206993193, 0.7628479177024253], [5.15015015015015, -0.8151327009952686, -0.7614573776221913, -0.007701652903885035, 0.7531349359762611], [5.17017017017017, -0.8073317948635856, -0.7709092207289362, -0.02571626428612418, 0.7431201069447214], [5.19019019019019, -0.799207319985875, -0.7800520928135566, -0.04372056890299062, 0.7328074444292513], [5.21021021021021, -0.7907625325526707, -0.7888823295243274, -0.061707350848576724, 0.7222010816193268], [5.23023023023023, -0.7820008171318761, -0.7973963918099711, -0.07966940123984799, 0.7113052694159234], [5.25025025025025, -0.7729256853122702, -0.8055908673380682, -0.09759952110587553, 0.7001243747278068], [5.27027027027027, -0.7635407742961057, -0.8134624718626764, -0.11549052427309589, 0.6886628787213281], [5.29029029029029, -0.7538498454413624, -0.821008050540618, -0.13333524024544188, 0.6769253750244287], [5.31031031031031, -0.7438567827542412, -0.8282245791959021, -0.15112651707819, 0.6649165678855684], [5.33033033033033, -0.7335655913325013, -0.8351091655317768, -0.1688572242443727, 0.6526412702883208], [5.35035035035035, -0.7229803957602665, -0.841659050289928, -0.1865202554926068, 0.640104402022388], [5.37037037037037, -0.7121054384549412, -0.8478716083563548, -0.2041085316951924, 0.6273109877118093], [5.39039039039039, -0.7009450779669016, -0.8537443498134845, -0.22161500368534104, 0.6142661548011523], [5.41041041041041, -0.6895037872326424, -0.8592749209381003, -0.23903265508239568, 0.6009751315004961], [5.43043043043043, -0.6777861517820772, -0.8644611051446857, -0.2563545051039109, 0.587443244690028], [5.45045045045045, -0.6657968679007154, -0.8693008238738045, -0.273573611363465, 0.5736759177850956], [5.47047047047047, -0.6535407407474466, -0.873792137425162, -0.2906830726530844, 0.5596786685625671], [5.49049049049049, -0.6410226824286928, -0.8779332457350137, -0.30767603170916336, 0.5454571069493741], [5.51051051051051, -0.6282477100296955, -0.8817224890976086, -0.3245456779607717, 0.5310169327741213], [5.53053053053053, -0.6152209436037288, -0.8851583488303788, -0.3412852502592488, 0.5163639334826643], [5.55055055055055, -0.6019476041200449, -0.8882394478826093, -0.35788803958798937, 0.5015039818185719], [5.57057057057057, -0.5884330113713729, -0.8909645513873434, -0.37434739175133575, 0.4864430334694024], [5.59059059059059, -0.574682581841811, -0.8933325671563019, -0.39065671004149843, 0.4711871246797363], [5.61061061061061, -0.5607018265359653, -0.8953425461176189, -0.4068094578824361, 0.4557423698319226], [5.63063063063063, -0.5464963487702066, -0.8969936826962182, -0.42279916144963564, 0.4401149589955101], [5.65065065065065, -0.5320718419269294, -0.8982853151366782, -0.43861941226474216, 0.4243111554463414], [5.67067067067067, -0.5174340871727137, -0.8992169257684555, -0.45426386976399896, 0.4083372931563095], [5.6906906906906904, -0.5025889511413034, -0.8997881412133605, -0.4697262638394685, 0.3921997742547785], [5.7107107107107105, -0.48754238358233193, -0.8999987325352031, -0.4850003973520148, 0.3759050664626879], [5.7307307307307305, -0.4723004149767341, -0.8998486153315477, -0.5000801486150417, 0.3594597005003697], [5.7507507507507505, -0.4568691541198033, -0.8993378497675403, -0.51495947384799, 0.34287026747011434], [5.7707707707707705, -0.44125478567286075, -0.8984666405517951, -0.5296324095986111, 0.3261434162145378], [5.7907907907907905, -0.42546356768451865, -0.8972353368543503, -0.5440930751330465, 0.3092858506518063], [5.8108108108108105, -0.40950182908253047, -0.8956444321667243, -0.558335674792753, 0.29230432708878773], [5.8308308308308305, -0.3933759671372337, -0.8936945641041306, -0.5723545003173339, 0.27520565151320586], [5.8508508508508505, -0.37709244489760213, -0.8913865141499296, -0.5861439331323381, 0.2579966768658837], [5.870870870870871, -0.36065778860093445, -0.8887212073424193, -0.599698446601118, 0.2406843002941682], [5.890890890890891, -0.34407858505721756, -0.8856997119040914, -0.6130126082398376, 0.22327546038763765], [5.910910910910911, -0.32736147900921403, -0.8823232388135005, -0.6260810818947452, 0.20577713439719958], [5.930930930930931, -0.31051317046932964, -0.8785931413199188, -0.6388986298808395, 0.18819633543869352], [5.950950950950951, -0.29354041203433007, -0.8745109144009707, -0.6514601150810696, 0.1705401096821196], [5.970970970970971, -0.27645000617898274, -0.8700781941634631, -0.6637605030052294, 0.15281553352761965], [5.990990990990991, -0.25924880252970767, -0.8652967571876545, -0.6757948638077195, 0.13502971076934206], [6.011011011011011, -0.24194369511933034, -0.8601685198152229, -0.6875583742633689, 0.11718976974832775], [6.031031031031031, -0.22454161962403776, -0.8546955373812195, -0.6990463197005257, 0.09930286049555796], [6.051051051051051, -0.20704955058364397, -0.8488800033903153, -0.7102540958906394, 0.08137615186630882], [6.071071071071071, -0.18947449860627896, -0.8427242486376718, -0.7211772108935802, 0.06341682866696166], [6.091091091091091, -0.171823507558623, -0.8362307402747856, -0.731811286857953, 0.04543208877542007], [6.111111111111111, -0.15410365174281038, -0.8294020808206849, -0.7421520617756882, 0.02742914025628817], [6.131131131131131, -0.13632203306113516, -0.8222410071188719, -0.7521953911902007, 0.00941519847196616], [6.151151151151151, -0.1184857781696957, -0.8147503892404282, -0.7619372498574397, -0.008602516809179039], [6.171171171171171, -0.10060203562211731, -0.8069332293337264, -0.771373733359156, -0.026616784306408958], [6.191191191191191, -0.08267797300449935, -0.7987926604212044, -0.7805010596677461, -0.04462038412081489], [6.211211211211211, -0.06472077406273388, -0.79033194514369, -0.7893155706620417, -0.06260610062896546], [6.231231231231231, -0.04673763582334795, -0.7815544744527719, -0.7978137335934417, -0.08056672537484048], [6.251251251251251, -0.028735765709023038, -0.7724637662517497, -0.8059921425017931, -0.09849505995889239], [6.271271271271271, -0.010722378649947805, -0.7630634639856997, -0.8138475195804596, -0.11638391892307699], [6.291291291291291, 0.007295305807837932, -0.7533573351812274, -0.8213767164900262, -0.13422613263069755], [6.311311311311311, 0.025310066395949328, -0.7433492699364882, -0.8285767156201149, -0.15201455013990772], [6.331331331331331, 0.043314683017852885, -0.7330432793620837, -0.8354446312988064, -0.1697420420697221], [6.351351351351351, 0.061301939642593124, -0.7224434939734576, -0.8419777109491823, -0.18740150345738496], [6.371371371371371, 0.07926462719688995, -0.7115541620354348, -0.8481733361925247, -0.20498585660595312], [6.391391391391391, 0.09719554645444728, -0.7003796478595696, -0.8540290238977296, -0.22248805392095022], [6.411411411411411, 0.11508751092131529, -0.6889244300549818, -0.8595424271765165, -0.23990108073495708], [6.431431431431431, 0.13293334971614962, -0.6771930997333856, -0.8647113363240323, -0.25721795811900466], [6.451451451451451, 0.15072591044421313, -0.6651903586690272, -0.8695336797044738, -0.27443174567964396], [6.471471471471471, 0.16845806206396877, -0.6529210174142718, -0.8740075245813744, -0.29153554434057105], [6.491491491491491, 0.1861226977451139, -0.6403899933715921, -0.8781310778922216, -0.30852249910769275], [6.511511511511511, 0.20371273771691145, -0.6276023088227334, -0.8819026869670938, -0.32538580181652466], [6.531531531531531, 0.2212211321056758, -0.614563088915845, -0.885320840191031, -0.3421186938608208], [6.551551551551551, 0.23864086376027593, -0.6012775596113822, -0.8883841676098713, -0.3587144689013405], [6.571571571571571, 0.25596495106452455, -0.5877510455876059, -0.8910914414793117, -0.3751664755536673], [6.591591591591591, 0.2731864507353244, -0.5739889681065161, -0.8934415767569739, -0.39146812005400294], [6.611611611611611, 0.2902984606054517, -0.5599968428410759, -0.8954336315372747, -0.40761286890186765], [6.631631631631631, 0.3072941223898607, -0.5457802776645974, -0.8970668074289317, -0.42359425147864715], [6.651651651651651, 0.3241666244344007, -0.5313449704031741, -0.8983404498749475, -0.4394058626409382], [6.671671671671671, 0.34090920444584405, -0.516696706552062, -0.8992540484149494, -0.45504136528765177], [6.691691691691691, 0.35751515220213076, -0.5018413569569233, -0.8998072368897746, -0.4704944928998459], [6.711711711711711, 0.3739778122417439, -0.48678487546086296, -0.8999997935882229, -0.48575905205227027], [6.731731731731731, 0.3902905865311375, -0.4715332965182011, -0.8998316413359162, -0.5008289248956157], [6.751751751751751, 0.4064469371091478, -0.4560927327759371, -0.8993028475262279, -0.515698071608473], [6.771771771771771, 0.42244038870732853, -0.44046937262387525, -0.8984136240932739, -0.5303605328180206], [6.791791791791791, 0.43826453134515975, -0.4246694777143937, -0.8971643274269707, -0.5448104319884682], [6.811811811811811, 0.4539130228990896, -0.40869938045285, -0.8955554582301996, -0.5590419777763006], [6.831831831831831, 0.46937959164438053, -0.39256548145963066, -0.8935876613181303, -0.5730494663513775], [6.851851851851851, 0.48465803876873986, -0.37627424700486034, -0.8912617253597879, -0.5868272836829598], [6.871871871871871, 0.4997422408567282, -0.35983220641679947, -0.8885785825619626, -0.6003699077897436], [6.891891891891891, 0.5146261523439501, -0.3432459494649692, -0.8855393082955938, -0.6136719109530043], [6.911911911911911, 0.5293038079400423, -0.32652212371905187, -0.8821451206647738, -0.6267279618919593], [6.931931931931931, 0.5437693250194893, -0.3096674318846264, -0.8783973800185467, -0.6395328279004803], [6.951951951951951, 0.5580169059793082, -0.2926886291168053, -0.8742975884056974, -0.6520813769442987], [6.971971971971971, 0.5720408405626574, -0.2755925203128507, -0.8698473889727482, -0.6643685797178622], [6.991991991991991, 0.5858355081474378, -0.258385957384855, -0.865048565305406, -0.6763895116600187], [7.012012012012011, 0.5993953799989699, -0.24107583651357736, -0.8599030407137229, -0.6881393549277213], [7.032032032032031, 0.6127150214858427, -0.2236690953845388, -0.854412877461257, -0.6996134003269602], [7.052052052052051, 0.6257890942580483, -0.2061727104074824, -0.8485802759385422, -0.7108070492001509], [7.072072072072071, 0.6386123583865277, -0.18859369392031314, -0.8424075737811983, -0.7217158152692198], [7.092092092092091, 0.6511796744632696, -0.17093909137863847, -0.8358972449330356, -0.7323353264336503], [7.112112112112111, 0.6634860056611237, -0.15321597853203536, -0.8290518986545279, -0.7426613265227661], [7.132132132132131, 0.6755264197524988, -0.1354314585881763, -0.821874278477053, -0.7526896770015544], [7.152152152152151, 0.6872960910861394, -0.11759265936595002, -0.8143672611033186, -0.7624163586293388], [7.172172172172171, 0.6987903025211891, -0.09970673043871875, -0.8065338552544147, -0.7718374730706425], [7.192192192192191, 0.7100044473177617, -0.08178084026885607, -0.7983772004639555, -0.7809492444575943], [7.212212212212211, 0.7209340309832671, -0.06382217333471475, -0.7899005658197922, -0.7897480209032491], [7.232232232232231, 0.7315746730737498, -0.0458379272511753, -0.7811073486538019, -0.7982302759652209], [7.2522522522522515, 0.741922108949516, -0.027835309884929625, -0.7720010731802794, -0.8063926100590374], [7.2722722722722715, 0.7519721914843507, -0.009821536465655855, -0.7625853890834745, -0.8142317518206517], [7.2922922922922915, 0.7617208927276341, 0.008196173305757738, -0.7528640700548425, -0.8217445594175659], [7.3123123123123115, 0.7711643055186962, 0.02621059815078091, -0.742841012280596, -0.828928021808039], [7.3323323323323315, 0.7802986450527584, 0.044214518107441904, -0.7325202328801594, -0.8357792599478756], [7.3523523523523515, 0.7891202503978375, 0.062200717424000185, -0.7219058682961578, -0.8422955279443114], [7.3723723723723715, 0.7976255859620028, 0.08016198745093188, -0.71100217263658, -0.8484742141565347], [7.3923923923923915, 0.8058112429103979, 0.09809112953006863, -0.699813515969785, -0.8543128422423987], [7.4124124124124116, 0.81367394053146, 0.11598095787973203, -0.6883443825730304, -0.8598090721509105], [7.432432432432432, 0.8212105275517899, 0.13382430247470747, -0.6765993691352303, -0.8649607010600946], [7.452452452452452, 0.8284179833991433, 0.15161401191990276, -0.6645831829146571, -0.8697656642598566], [7.472472472472472, 0.8352934194130396, 0.16934295631654026, -0.6523006398523309, -0.8742220359794949], [7.492492492492492, 0.8418340800025016, 0.18700403011973327, -0.6397566626418477, -0.878328030159525], [7.512512512512512, 0.8480373437504631, 0.20459015498630204, -0.626956278756425, -0.8820820011675112], [7.532532532532532, 0.853900724464401, 0.22209428261168732, -0.6139046184339512, -0.8854824444576159], [7.552552552552552, 0.8594218721727706, 0.239509397554825, -0.6006069126208488, -0.8885279971736028], [7.572572572572572, 0.8645985740668455, 0.2568285200498493, -0.5870684908755748, -0.8912174386950533], [7.592592592592592, 0.8694287553875848, 0.2740447088034982, -0.5732947792325989, -0.8935496911265767], [7.612612612612612, 0.873910480257171, 0.29115106377709865, -0.5592912980277145, -0.8955238197298169], [7.632632632632632, 0.8780419524548863, 0.3081407289520187, -0.5450636596855559, -0.8971390332980844], [7.652652652652652, 0.8818215161370159, 0.32500689507747554, -0.5306175664702067, -0.8983946844734627], [7.672672672672672, 0.8852476565004901, 0.34174280239960064, -0.5159588081998017, -0.8992902700062607], [7.692692692692692, 0.8883190003899988, 0.3583417433706666, -0.5010932599260381, -0.8998254309567096], [7.712712712712712, 0.8910343168483355, 0.37479706533739077, -0.48602687957952734, -0.8999999528388214], [7.732732732732732, 0.8933925176097498, 0.3911021732072372, -0.47076570558192804, -0.8998137657063522], [7.752752752752752, 0.8953926575361112, 0.4072505320916498, -0.45531585442582073, -0.8992669441808362], [7.772772772772772, 0.8970339349957092, 0.42323566992515566, -0.4396835182232922, -0.8983597074216777], [7.792792792792792, 0.8983156921845367, 0.4390511800592904, -0.4238749622242119, -0.897092419038315], [7.812812812812812, 0.8992374153899308, 0.45469072383030457, -0.4078965223051968, -0.8954655869444903], [7.832832832832834, 0.8997987351964621, 0.47014803309962416, -0.39175460243026733, -0.8934798631546836], [7.852852852852854, 0.8999994266339917, 0.4854169127660389, -0.37545567208422276, -0.8911360435227943], [7.872872872872874, 0.8998394092678368, 0.5004912432486316, -0.3590062636797415, -0.8884350674231709], [7.892892892892894, 0.899318747231008, 0.5153649829394257, -0.34241296993927556, -0.885378017374123], [7.912912912912914, 0.8984376491985054, 0.53003217062479, -0.32568244125276674, -0.8819661186040597], [7.932932932932934, 0.8971964683036844, 0.5444869278746204, -0.3088213830122507, -0.8782007385604343], [7.952952952952954, 0.8955957019967234, 0.5587234613983422, -0.29183655292441785, -0.8740833863616868], [7.972972972972974, 0.8936359918452534, 0.5727360653667918, -0.27473475830220695, -0.8696157121924087], [7.992992992992994, 0.8913181232772236, 0.5865191236990425, -0.2575228533365169, -0.8647995066419683], [8.013013013013014, 0.8886430252661129, 0.6000671123132615, -0.24020773634913062, -0.8596366999868643], [8.033033033033034, 0.8856117699586081, 0.6133746013406948, -0.22279634702795148, -0.8541293614170957], [8.053053053053054, 0.8822255722449006, 0.6264362573018922, -0.20529566364566135, -0.8482796982068539], [8.073073073073074, 0.878485789271773, 0.639246845244301, -0.1877127002629137, -0.8420900548298754], [8.093093093093094, 0.8743939198986717, 0.6518012308403709, -0.17005450391718355, -0.8355629120198047], [8.113113113113114, 0.8699516040969818, 0.6640943824453308, -0.15232815179840087, -0.8287008857759468], [8.133133133133134, 0.8651606222927472, 0.6761213731138096, -0.13454074841249883, -0.8215067263148063], [8.153153153153154, 0.8600228946530966, 0.6878773825744964, -0.11669942273401471, -0.8139833169678342], [8.173173173173174, 0.8545404803166645, 0.6993576991620466, -0.09881132534888352, -0.806133673025824], [8.193193193193194, 0.848715576568314, 0.7105577217054597, -0.08088362558857043, -0.7979609405304194], [8.213213213213214, 0.8425505179584907, 0.721472961372173, -0.06292350865668997, -0.7894683950132199], [8.233233233233234, 0.836047775367564, 0.7320990434671303, -0.044938172749264094, -0.7806594401829867], [8.253253253253254, 0.8292099550155284, 0.742431709186107, -0.026934826169772823, -0.7715376065614786], [8.273273273273274, 0.8220397974174619, 0.7524668173225868, -0.008920684440154085, -0.7621065500684615], [8.293293293293294, 0.8145401762851616, 0.7622003459275066, 0.009097032591089554, -0.7523700505564599], [8.313313313313314, 0.8067140973753938, 0.7716283939212045, 0.027111103642518203, -0.7423320102958391], [8.333333333333334, 0.7985646972852252, 0.7807471826569258, 0.045114308893956255, -0.7319964524108216], [8.353353353353354, 0.7900952421949107, 0.7895530574352577, 0.06309943288010833, -0.7213675192670689], [8.373373373373374, 0.7813091265588504, 0.798042488968889, 0.08105926738242988, -0.7104494708114711], [8.393393393393394, 0.7722098717451323, 0.8062120747971047, 0.0989866143180933, -0.6992466828648127], [8.413413413413414, 0.762801124624211, 0.8140585406494516, 0.11687428862489184, -0.6877636453679965], [8.433433433433434, 0.7530866561072869, 0.8215787417580253, 0.13471512114092501, -0.6760049605825301], [8.453453453453454, 0.7430703596349704, 0.8287696641178554, 0.15250196147791126, -0.663975341245996], [8.473473473473474, 0.732756249616839, 0.8356284256948812, 0.17022768088697654, -0.6516796086832427], [8.493493493493494, 0.7221484598225104, 0.8421522775810373, 0.1878851751157699, -0.6391226908740579], [8.513513513513514, 0.7112512417248779, 0.8483386050959808, 0.20546736725576137, -0.6263096204780932], [8.533533533533534, 0.7000689627961721, 0.8541849288350242, 0.2229672105785806, -0.6132455328178359], [8.553553553553554, 0.6886061047575318, 0.8596889056628504, 0.24037769136025972, -0.5999356638204337], [8.573573573573574, 0.6768672617827832, 0.8648483296526129, 0.25769183169224846, -0.5863853479192002], [8.593593593593594, 0.6648571386571529, 0.8696611329700444, 0.27490269227807496, -0.5726000159156375], [8.613613613613614, 0.6525805488916459, 0.8741253867022194, 0.29200337521453107, -0.5585851928028379], [8.633633633633634, 0.6400424127938498, 0.8782393016306388, 0.30898702675626794, -0.5443464955511346], [8.653653653653654, 0.6272477554959351, 0.8820012289483277, 0.3258468400626938, -0.5298896308568869], [8.673673673673674, 0.6142017049406437, 0.8854096609206562, 0.34257605792607265, -0.5152203928553072], [8.693693693693694, 0.600909489826072, 0.8884632314896215, 0.3591679754797307, -0.5003446607982416], [8.713713713713714, 0.5873764375100716, 0.8911607168213476, 0.3756159428852851, -0.4852683966978375], [8.733733733733734, 0.5736079718751105, 0.8935010357965822, 0.3919133679978187, -0.4699976429370417], [8.753753753753754, 0.5596096111544462, 0.8954832504439971, 0.4080537190079308, -0.4545385198478872], [8.773773773773774, 0.5453869657204852, 0.8971065663161149, 0.4240305270596069, -0.43889722325853836], [8.793793793793794, 0.5309457358362141, 0.8983703328077141, 0.43983738884285783, -0.42308002201007866], [8.813813813813814, 0.516291709370604, 0.8992740434165838, 0.45546796916008764, -0.40709325544403563], [8.833833833833834, 0.5014307594789027, 0.8998173359465239, 0.4709160034651642, -0.3909433308616498], [8.853853853853854, 0.4863688422487457, 0.8999999926525084, 0.48617530037417245, -0.3746367209559067], [8.873873873873874, 0.47111199431302925, 0.899821940327955, 0.5012397441468464, -0.35817996121736023], [8.893893893893894, 0.4556663304305009, 0.8992832503340662, 0.5161032971376821, -0.3415796473147876], [8.913913913913914, 0.44003804103503913, 0.8983841385712272, 0.5307600022157537, -0.32484243245172584], [8.933933933933934, 0.4242333897546026, 0.8971249653924771, 0.5452039851522583, -0.3079750246999488], [8.953953953953954, 0.40825871090084453, 0.8955062354590828, 0.5594294569748349, -0.29098418431095363], [8.973973973973974, 0.3921204069303976, 0.8935285975382773, 0.5734307162877149, -0.27387672100653465], [8.993993993993994, 0.37582494587884785, 0.8911928442432419, 0.5872021515567711, -0.25665949124952936], [9.014014014014014, 0.3593788587684241, 0.8884999117154359, 0.6007382433585531, -0.2393393954958321], [9.034034034034034, 0.3427887369904444, 0.8854508792494029, 0.6140335665924039, -0.22192337542877538], [9.054054054054054, 0.3260612296635662, 0.882046968860203, 0.6270827926547756, -0.2044184111769874], [9.074074074074074, 0.30920304096890133, 0.8782895447936434, 0.6398806915748673, -0.186831518516842], [9.094094094094094, 0.2922209274630612, 0.8741801129795056, 0.6524221341107344, -0.1691697460606207], [9.114114114114114, 0.2751216953702116, 0.8697203204279873, 0.6647020938050262, -0.15144017243151497], [9.134134134134134, 0.25791219785422076, 0.8649119545696005, 0.676715648999529, -0.13364990342660013], [9.154154154154154, 0.24059933227199504, 0.8597569425387911, 0.6884579848077059, -0.11580606916891857], [9.174174174174174, 0.22319003740910232, 0.8542573504015671, 0.699924395044446, -0.09791582124981332], [9.194194194194194, 0.20569129069879089, 0.8484153823274447, 0.7111102841122445, -0.07998632986265733], [9.214214214214214, 0.1881101054255198, 0.8422333797060434, 0.7220111688430636, -0.06202478092912736], [9.234234234234235, 0.17045352791411947, 0.8357138202086859, 0.7326226802951303, -0.04403837321917415], [9.254254254254255, 0.1527286347057107, 0.8288593167953779, 0.7429405655039564, -0.02603431546584314], [9.274274274274275, 0.13494252972151324, 0.8216726166675652, 0.7529606891868738, -0.008019823476102052], [9.294294294294295, 0.11710234141568075, 0.8141566001670889, 0.7626790354004057, 0.00999788276116655], [9.314314314314315, 0.09921521991830316, 0.8063142796217797, 0.7720917091498078, 0.028011581968848866], [9.334334334334335, 0.08128833416972171, 0.7981487981381532, 0.7811949379501353, 0.046014054475799746], [9.354354354354355, 0.06332886904730496, 0.7896634283416916, 0.7899850733382078, 0.06399808511039887], [9.374374374374375, 0.04534402248583755, 0.7808615710652146, 0.7984585923348698, 0.08195646609230366], [9.394394394394395, 0.027341002592675606, 0.7717467539858682, 0.8066120988569575, 0.09988199992123978], [9.414414414414415, 0.009327024758825166, 0.7623226302112736, 0.8144423250784074, 0.11776750226167157], [9.434434434434435, -0.008690691232898635, 0.7525929768154063, 0.8219461327399614, 0.1356058048221962], [9.454454454454455, -0.02670492410147253, 0.7425616933247914, 0.8291205144069428, 0.15338975822850745], [9.474474474474475, -0.044708453961866575, 0.7322328001556204, 0.8359625946745995, 0.1711122348887779], [9.494494494494495, -0.06269406521868609, 0.7216104370024181, 0.8424696313205311, 0.1887661318503108], [9.514514514514515, -0.08065454945809436, 0.7106988611789025, 0.8486390164037382, 0.20634437364631714], [9.534534534534535, -0.09858270833685706, 0.6995024459117056, 0.8544682773098535, 0.2238399151316762], [9.554554554554555, -0.11647135646735057, 0.6880256785876383, 0.8599550777421356, 0.24124574430654422], [9.574574574574575, -0.1343133242973777, 0.6762731589551993, 0.8650972186578281, 0.2585548851266782], [9.594594594594595, -0.15210146098363692, 0.6642495972810527, 0.8698926391495089, 0.2757604002993498], [9.614614614614615, -0.16982863725769315, 0.65195981246221, 0.8743394172710771, 0.2928553940637271], [9.634634634634635, -0.18748774828330173, 0.6394087300946756, 0.878435770808046, 0.30983301495461246], [9.654654654654655, -0.2050717165039402, 0.6266013804993268, 0.882180057991832, 0.32668645854842526], [9.674674674674675, -0.22257349447940677, 0.613542896705824, 0.885570778157756, 0.34340897019033273], [9.694694694694695, -0.23998606771034833, 0.6002385123953536, 0.8886065723464914, 0.35999384770143283], [9.714714714714715, -0.2573024574495865, 0.5866935598030331, 0.8912862238487187, 0.3764344440649062], [9.734734734734735, -0.2745157234991143, 0.5729134675808158, 0.8936086586927673, 0.39272417009005944], [9.754754754754755, -0.291618966991643, 0.558903758621754, 0.8955729460750508, 0.4088564970531922], [9.774774774774775, -0.30860533315558436, 0.5446700478464896, 0.8971782987331212, 0.4248249593142304], [9.794794794794795, -0.3254680140623589, 0.5302180399528637, 0.8984240732611943, 0.4406231569080758], [9.814814814814815, -0.3422002513549313, 0.5155535271295416, 0.8993097703680191, 0.4562447581096342], [9.834834834834835, -0.3587953389564773, 0.5006823867345758, 0.8998350350769874, 0.47168350197149356], [9.854854854854855, -0.37524662575809775, 0.48561057893983134, 0.8999996568684042, 0.4869332008332352], [9.874874874874875, -0.39154751828450185, 0.47034414434222216, 0.8998035697638614, 0.5019877428013736], [9.894894894894895, -0.40769148333659155, 0.45488920154271306, 0.8992468523526813, 0.5168410941989274], [9.914914914914915, -0.4236720506098886, 0.43925194469405954, 0.8983297277604186, 0.5314873019836451], [9.934934934934935, -0.43948281528775346, 0.42343864101826606, 0.8970525635594346, 0.5459204961339109], [9.954954954954955, -0.45511744060835757, 0.4074556282947605, 0.895415871621579, 0.5601348920013781], [9.974974974974975, -0.47056966040438064, 0.3913093123202892, 0.8934203079130373, 0.5741247926293856], [9.994994994994995, -0.48583328161441414, 0.37500616434155143, 0.8910666722314277, 0.5878845910362291], [10.015015015015015, -0.5009021867650646, 0.35855271846160297, 0.8883559078852524, 0.6014087724623702], [10.035035035035035, -0.5157703364227624, 0.34195556902106694, 0.8852891013158309, 0.6146919165806862], [10.055055055055055, -0.5304317716142936, 0.32522136795520257, 0.8818674816618667, 0.6277286996688696], [10.075075075075075, -0.5448806162150831, 0.30835682212789034, 0.878092420266825, 0.640513896743112], [10.095095095095095, -0.5591110793042745, 0.2913686906436032, 0.8739654301293135, 0.653042383652214], [10.115115115115115, -0.5731174574856602, 0.2742637821384392, 0.8694881652966918, 0.6653091391312824], [10.135135135135135, -0.586894137173533, 0.2570489520513037, 0.8646624202021495, 0.6773092468141917], [10.155155155155155, -0.6004355968425431, 0.2397310998763324, 0.8594901289455185, 0.6890378972040042], [10.175175175175175, -0.6137364092406581, 0.2223171663976588, 0.8539733645181099, 0.7004903896005575], [10.195195195195195, -0.6267912435643395, 0.20481413090763206, 0.8481143379718847, 0.711662133984448], [10.215215215215215, -0.6395948675950637, 0.1872290084096017, 0.8419153975332899, 0.7225486528566544], [10.235235235235235, -0.6521421497963317, 0.16956884680638984, 0.8353790276621194, 0.7331455830330653], [10.255255255255255, -0.6644280613703262, 0.1518407240755777, 0.8285078480557714, 0.7434486773931895], [10.275275275275275, -0.6764476782733922, 0.1340517454327382, 0.821304612599307, 0.7534538065823512], [10.295295295295295, -0.6881961831895345, 0.11620904048375243, 0.8137722082617254, 0.763156960666684], [10.315315315315315, -0.6996688674611381, 0.09831976036735014, 0.8059136539389026, 0.7725542507402638], [10.335335335335335, -0.7108611329761421, 0.08039107488902082, 0.7977320992436546, 0.7816419104837357], [10.355355355355355, -0.7217684940109059, 0.0624301696474428, 0.789230823243411, 0.7904162976738074], [10.375375375375375, -0.7323865790280333, 0.044444243154582924, 0.7804132331460043, 0.7988738956430095], [10.395395395395395, -0.742711132428431, 0.026440503950620645, 0.771282862934103, 0.8070113146891317], [10.415415415415415, -0.7527380162569008, 0.008426167714853012, 0.7618433719488344, 0.8148252934337751], [10.435435435435435, -0.7624632118605823, -0.00959154562626172, 0.752098543423165, 0.8223127001294722], [10.455455455455455, -0.7718828214995792, -0.027605414792762614, 0.742052282965627, 0.8294705339148537], [10.475475475475475, -0.7809930699091261, -0.04560822004538708, 0.7317086169949978, 0.8362959260173566], [10.495495495495495, -0.7897903058126691, -0.06359274607915442, 0.7210716911265607, 0.842786140902994], [10.515515515515515, -0.7982710033852527, -0.08155178491517205, 0.7101457685105926, 0.8489385773727227], [10.535535535535535, -0.8064317636666272, -0.09947813878950579, 0.6989352281237455, 0.8547507696049736], [10.555555555555555, -0.8142693159235107, -0.11736462303795575, 0.6874445630140058, 0.8602203881439217], [10.575575575575575, -0.8217805189604596, -0.1352040689755823, 0.6756783784999354, 0.8653452408331037], [10.595595595595595, -0.8289623623788206, -0.15298932676982763, 0.6636413903249163, 0.8701232736940084], [10.615615615615615, -0.8358119677832627, -0.17071326830608152, 0.6513384227671383, 0.8745525717492856], [10.635635635635635, -0.8423265899354024, -0.18836879004454285, 0.6387744067060865, 0.8786313597902456], [10.655655655655655, -0.8485036178540618, -0.20594881586723168, 0.6259543776463053, 0.8823580030883419], [10.675675675675675, -0.8543405758617184, -0.22344629991401121, 0.61288347369923, 0.8857310080503499], [10.695695695695695, -0.8598351245767261, -0.24085422940648277, 0.5995669335238929, 0.8887490228169808], [10.715715715715715, -0.8649850618509111, -0.25816562745862165, 0.5860100942273342, 0.8914108378046907], [10.735735735735735, -0.869788323652166, -0.2753735558730283, 0.5722183892255536, 0.8937153861904669], [10.755755755755755, -0.8742429848916887, -0.29247111792167313, 0.5581973460658635, 0.8956617443393972], [10.775775775775776, -0.8783472601955347, -0.30945146111002125, 0.5439525842115158, 0.897249132174852], [10.795795795795796, -0.8820995046201726, -0.3263077799234285, 0.5294898127894888, 0.8984769134911293], [10.815815815815816, -0.8854982143117583, -0.3430333185547091, 0.5148148283023395, 0.899344596208438], [10.835835835835836, -0.8885420271088611, -0.35962137361178065, 0.4999335123050353, 0.8998518325701178], [10.855855855855856, -0.8912297230884003, -0.376065296804302, 0.4848518290476988, 0.8999984192820154], [10.875875875875876, -0.8935602250545771, -0.3923584976082271, 0.46957582308520884, 0.8997842975939631], [10.895895895895896, -0.8955325989706008, -0.40849444590720674, 0.45411161685461693, 0.8992095533233243], [10.915915915915916, -0.8971460543330391, -0.42446667460977955, 0.4384654082213486, 0.8982744168205993], [10.935935935935936, -0.8983999444886429, -0.44026878224130345, 0.42264346799517466, 0.8969792628771038], [10.955955955955956, -0.8992937668935164, -0.45589443550958864, 0.4066521374169471, 0.8953246105747569], [10.975975975975976, -0.8998271633145309, -0.47133737184320423, 0.390497825617107, 0.8933111230780394], [10.995995995995996, -0.8999999199729005, -0.48659140190143996, 0.3741870070469832, 0.8909396073682057], [11.016016016016017, -0.8998119676298615, -0.5016504120549207, 0.3577262188839094, 0.8882110139198549], [11.036036036036037, -0.8992633816144227, -0.5165083668358684, 0.3411220584112093, 0.8851264363199937], [11.056056056056057, -0.8983543817931745, -0.5311593113570529, 0.3243811803740769, 0.8816871108297383], [11.076076076076077, -0.8970853324821693, -0.5455973736984319, 0.30751029431244153, 0.8778944158888369], [11.096096096096097, -0.8954567423009073, -0.5598167672605472, 0.29051616187186474, 0.8737498715632076], [11.116116116116117, -0.893469263968489, -0.5738117930837227, 0.2734055940935547, 0.869255138935715], [11.136136136136138, -0.8911236940420125, -0.587576842132138, 0.2561854486845844, 0.8644120194404279], [11.156156156156158, -0.8884209725973234, -0.601106397541862, 0.23886262726940605, 0.8592224541406261], [11.176176176176178, -0.8853621828522442, -0.6143950368319444, 0.22144407262376384, 0.8536885229508471], [11.196196196196198, -0.8819485507324332, -0.6274374340776799, 0.20393676589211435, 0.8478124438032807], [11.216216216216218, -0.8781814443800487, -0.6402283620451745, 0.18634772378966877, 0.8415965717588489], [11.236236236236238, -0.8740623736054146, -0.6527626942863564, 0.16868399579017893, 0.8350433980633268], [11.256256256256258, -0.8695929892819066, -0.6650354071935943, 0.15095266130059415, 0.8281555491488809], [11.276276276276278, -0.8647750826843019, -0.6770415820130985, 0.13316082682372118, 0.8209357855814278], [11.296296296296298, -0.8596105847708584, -0.6887764068162966, 0.11531562311002454, 0.8133870009542319], [11.316316316316318, -0.8541015654094105, -0.7002351784283968, 0.09742420229970851, 0.80551222072819], [11.336336336336338, -0.8482502325477905, -0.7114133043133619, 0.07949373505622655, 0.7973146010192623], [11.356356356356358, -0.8420589313289111, -0.7223063044145432, 0.06153140769236676, 0.78879742733354], [11.376376376376378, -0.8355301431508595, -0.7329098129502315, 0.04354441929006527, 0.7799641132504543], [11.396396396396398, -0.8286664846723839, -0.7432195801634097, 0.025539978815101914, 0.7708181990546558], [11.416416416416418, -0.8214707067641689, -0.7532314740250033, 0.0075253022278345995, 0.7613633503171108], [11.436436436436438, -0.8139456934063194, -0.762941481889948, -0.010492390408869675, 0.751603356425986], [11.456456456456458, -0.8060944605324966, -0.7723457121054079, -0.028505877823348052, 0.7415421290679072], [11.476476476476478, -0.7979201548211691, -0.7814403955705035, -0.04650794042933951, 0.731183700660203], [11.496496496496498, -0.7894260524344618, -0.7902218872469208, -0.06449136321950706, 0.720532222734762], [11.516516516516518, -0.7806155577051106, -0.7986866676197985, -0.08244893865712472, 0.709591964274148], [11.536536536536538, -0.7714922017720468, -0.8068313441083068, -0.10037346956477053, 0.6983673100006448], [11.556556556556558, -0.7620596411651603, -0.8146526524253521, -0.11825777200886746, 0.6868627586189124], [11.576576576576578, -0.7523216563398065, -0.8221474578858637, -0.1360946781789165, 0.6750829210129617], [11.596596596596598, -0.7422821501616454, -0.8293127566631374, -0.1538770392602677, 0.6630325183981676], [11.616616616616618, -0.7319451463424221, -0.8361456769927326, -0.1715977282992781, 0.6507163804290635], [11.636636636636638, -0.7213147878273106, -0.8426434803234403, -0.18924964305970782, 0.6381394432636753], [11.656656656656658, -0.7103953351344725, -0.8488035624148615, -0.20682570886920978, 0.6253067475851684], [11.676676676676678, -0.6991911646474935, -0.854623454381155, -0.2243188814547722, 0.6122234365816043], [11.696696696696698, -0.6877067668613818, -0.8601008236805356, -0.2417221497659775, 0.5988947538846127], [11.716716716716718, -0.6759467445828323, -0.8652334750501304, -0.2590285387849457, 0.5853260414678083], [11.736736736736738, -0.6639158110854783, -0.8700193513858118, -0.2762311123218366, 0.5715227375057929], [11.756756756756758, -0.651618788220868, -0.8744565345666605, -0.2933229757947905, 0.5574903741946008], [11.776776776776778, -0.6390606044859267, -0.8785432462237246, -0.3102972789931919, 0.5432345755344626], [11.796796796796798, -0.6262462930476731, -0.8822778484527689, -0.32714721882315123, 0.528761055075774], [11.816816816816818, -0.6131809897259892, -0.8856588444707261, -0.3438660420341015, 0.5140756136291746], [11.836836836836838, -0.5998699309352457, -0.8886848792155895, -0.3604470479254188, 0.4991841369406541], [11.856856856856858, -0.5863184515856098, -0.8913547398895058, -0.376883591031982, 0.4840925933326162], [11.876876876876878, -0.5725319829448802, -0.8936673564448492, -0.39316908378759335, 0.46880703131184825], [11.896896896896898, -0.5585160504616996, -0.8956218020130837, -0.40929699916519563, 0.45333357714535316], [11.916916916916918, -0.5442762715510229, -0.8972172932762403, -0.4252608732928243, 0.43767843240501675], [11.936936936936938, -0.5298183533427255, -0.8984531907808619, -0.44105430804424933, 0.4218478714820927], [11.956956956956958, -0.5151480903942552, -0.8993289991942883, -0.4566709736032655, 0.405848239072503], [11.976976976976978, -0.5002713623682444, -0.8998443675031788, -0.472104611000606, 0.38968594763396175], [11.996996996996998, -0.4851941316760128, -0.8999990891541951, -0.4873490346224615, 0.3733674748159396], [12.017017017017018, -0.4699224410879052, -0.8997931021367849, -0.5023981346895976, 0.3568993608635012], [12.037037037037038, -0.4544624113114237, -0.8992264890080346, -0.5172458797060813, 0.3402882059960544], [12.057057057057058, -0.43882023853812185, -0.8982994768595822, -0.5318863188766298, 0.32354066776206275], [12.077077077077078, -0.42300219196024713, -0.897012437226601, -0.5463135844916177, 0.30666345837078135], [12.097097097097098, -0.40701461125812466, -0.8953658859388933, -0.560521894278783, 0.2896633420020851], [12.117117117117118, -0.39086390405929083, -0.8933604829141519, -0.5745055537206916, 0.27254713209546794], [12.137137137137138, -0.3745565433703943, -0.8909970318934736, -0.5882589583370301, 0.255321688619299], [12.157157157157158, -0.3580990649828936, -0.8882764801192283, -0.601776595930814, 0.23799391532143113], [12.177177177177178, -0.3414980648535919, -0.8851999179554174, -0.6150530487976085, 0.22057075696226294], [12.197197197197198, -0.32476019646105747, -0.8817685784506683, -0.6280829958968781, 0.20305919653136323], [12.217217217217218, -0.3078921681389907, -0.8779838368440434, -0.6408612149845957, 0.18546625244877402], [12.237237237237238, -0.2909007403876058, -0.8738472100138613, -0.6533825847062539, 0.1677989757521132], [12.257257257257258, -0.2737927231641048, -0.8693603558697511, -0.6656420866494412, 0.15006444727060472], [12.277277277277278, -0.2565749731533293, -0.864525072688181, -0.6776348073551614, 0.1322697747871684], [12.297297297297298, -0.23925439101968493, -0.8593432983917324, -0.6893559402870867, 0.11442209018970713], [12.317317317317318, -0.22183791864143926, -0.853817109772403, -0.7008007877579605, 0.09652854661273325], [12.337337337337338, -0.20433253632850146, -0.847948721659255, -0.7119647628123722, 0.07859631557047919], [12.357357357357358, -0.18674526002479913, -0.8417404860307376, -0.7228433910651532, 0.0606325840826421], [12.377377377377378, -0.1690831384963735, -0.8351948910720431, -0.733432312494656, 0.04264455179391392], [12.397397397397398, -0.15135325050631981, -0.8283145601778731, -0.7437272831901978, 0.02463942808845142], [12.417417417417418, -0.13356270197770548, -0.8211022509010127, -0.7537241770529685, 0.006624429200442979], [12.437437437437438, -0.11571862314560262, -0.813560853847138, -0.76341898744972, -0.0113932246780702], [12.457457457457458, -0.09782816569937665, -0.8056933915162963, -0.7728078288185769, -0.029406312290959073], [12.477477477477478, -0.0798984999163765, -0.7975030170915244, -0.7818869382263216, -0.04740761421219821], [12.497497497497498, -0.061936811788174624, -0.7889930131750921, -0.790652676876533, -0.06538991573932382], [12.517517517517518, -0.04395030014050931, -0.7801667904728743, -0.7991015315679703, -0.08334600978499848], [12.537537537537538, -0.025946173748082935, -0.7710278864273824, -0.8072301161026227, -0.10126869976552406], [12.557557557557558, -0.00793164844537296, -0.7615799638000003, -0.8150351726428561, -0.11915080248514472], [12.577577577577578, 0.010086055765386627, -0.7518268092029937, -0.8225135730171135, -0.13698515101498412], [12.597597597597598, 0.028099717607894235, -0.7417723315818828, -0.8296623199736495, -0.15476459756546349], [12.617617617617618, 0.0461021174259802, -0.7314205606487842, -0.8364785483817915, -0.17248201635104823], [12.637637637637638, 0.06408604007715703, -0.7207756452673514, -0.8429595263802495, -0.19013030644617618], [12.657657657657658, 0.08204427782436059, -0.7098418517899605, -0.8491026564720124, -0.207702394631222], [12.677677677677679, 0.09996963322472326, -0.698623562347809, -0.8549054765653938, -0.2251912382273573], [12.697697697697699, 0.11785492201422135, -0.6871252730946102, -0.8603656609608077, -0.2425898279191707], [12.717717717717719, 0.1356929759870407, -0.6753515924045899, -0.8654810212828805, -0.25989119056391585], [12.737737737737739, 0.15347664586850596, -0.663307239025506, -0.8702495073575265, -0.27708839198626223], [12.757757757757759, 0.17119880418042305, -0.6509970401874324, -0.8746692080336306, -0.29417453975742797], [12.777777777777779, 0.1888523480976854, -0.6384259296680631, -0.8787383519490161, -0.3111427859575814], [12.797797797797799, 0.20643020229499967, -0.6255989458153153, -0.8824553082403842, -0.32798632992040383], [12.817817817817819, 0.22392532178259014, -0.61252122952802, -0.8858185871969448, -0.34469842095871406], [12.837837837837839, 0.2413306947297446, -0.599198022195513, -0.8888268408574735, -0.36127236107006094], [12.857857857857859, 0.25863934527507054, -0.5856346635969495, -0.8914788635505588, -0.37770150762120164], [12.877877877877879, 0.2758443363223356, -0.5718365897611861, -0.8937735923778203, -0.39397927601038807], [12.897897897897899, 0.29293877232077137, -0.5578093307880864, -0.8957101076399053, -0.41009914230639455], [12.917917917917919, 0.3099158020287256, -0.5435585086321243, -0.8972876332050934, -0.4260546458632308], [12.937937937937939, 0.32676862125955675, -0.5290898348491735, -0.89850553682036, -0.44183939190948907], [12.957957957957959, 0.3434904756086691, -0.514409108307385, -0.8993633303647766, -0.45744705411129166], [12.977977977977979, 0.36007466316059555, -0.49952221286307186, -0.8998606700451426, -0.47287137710780763], [12.997997997997999, 0.3765145371750441, -0.48443511500253117, -0.8999973565337741, -0.4881061790183257], [13.018018018018019, 0.3928035087508297, -0.4691538614507487, -0.8997733350483923, -0.5031453539198765], [13.038038038038039, 0.4089350494666264, -0.45368457674794566, -0.8991886953740785, -0.5179828742944121], [13.058058058058059, 0.42490269399747843, -0.4380334607949374, -0.8982436718272906, -0.5326127934445619], [13.078078078078079, 0.44070004270602386, -0.42220678636828884, -0.8969386431619508, -0.5470292478769964], [13.098098098098099, 0.4563207642073913, -0.4062108966062617, -0.8952741324176465, -0.5612264596524438], [13.118118118118119, 0.4717585979067417, -0.3900522024665624, -0.893250806710002, -0.5751987387014177], [13.138138138138139, 0.4870073565084388, -0.3737371801569082, -0.8908694769633065, -0.588940485104728], [13.158158158158159, 0.5020609284958415, -0.35727236853944194, -0.888131097585506, -0.6024461913378595], [13.178178178178179, 0.516913280580725, -0.3406643665100358, -0.8850367660856879, -0.6157104444783214], [13.198198198198199, 0.5315584601213503, -0.32391983035353433, -0.8815877226342128, -0.6287279283750801], [13.218218218218219, 0.5459905975082091, -0.3070454710759966, -0.8777853495656693, -0.6414934257792079], [13.238238238238239, 0.5602039085164926, -0.2900480517150067, -0.8736311708248516, -0.6540018204348934], [13.258258258258259, 0.5741926966243383, -0.2729343846291305, -0.8691268513559801, -0.6662480991299735], [13.278278278278279, 0.587951355295926, -0.2557113287676057, -0.8642741964354126, -0.6782273537051682], [13.298298298298299, 0.6014743702285098, -0.2383857869213584, -0.8590751509481117, -0.6899347830212114], [13.318318318318319, 0.6147563215624828, -0.2209647029564484, -0.8535317986081582, -0.7013656948830885], [13.338338338338339, 0.6277918860535915, -0.2034550590310527, -0.847646361123624, -0.712515507920611], [13.358358358358359, 0.640575839206426, -0.1858638727971012, -0.8414211973061387, -0.7233797534245744], [13.378378378378379, 0.6531030573683357, -0.16819819458768753, -0.834858802125507, -0.7339540771377613], [13.398398398398399, 0.6653685197829258, -0.15046510459138135, -0.827961805709754, -0.7442342410000763], [13.418418418418419, 0.677367310602315, -0.1326717100145752, -0.8207329722910032, -0.754216124847107], [13.438438438438439, 0.6890946208573486, -0.11482514223300253, -0.8131751990976055, -0.7638957280614371], [13.458458458458459, 0.700545750384974, -0.09693255393356948, -0.8052915151929648, -0.7732691711760445], [13.478478478478479, 0.7117161097120087, -0.0790011162476449, -0.7970850802615278, -0.7823326974291444], [13.498498498498499, 0.7226012218945455, -0.06103801587695842, -0.788559183342419, -0.7910826742698539], [13.518518518518519, 0.7331967243122568, -0.04305045221325785, -0.7797172415112356, -0.7995155948140736], [13.538538538538539, 0.74349837041688, -0.025045634452880803, -0.7705627985105241, -0.807628079250005], [13.558558558558559, 0.7535020314341824, -0.007030778707396588, -0.7610995233294923, -0.8154168761927368], [13.578578578578579, 0.7632036980187227, 0.010986894888523379, -0.7513312087335225, -0.822878863987361], [13.598598598598599, 0.772599481860749, 0.029000165070847548, -0.7412617697440778, -0.8300110519600941], [13.618618618618619, 0.7816856172445852, 0.0470018123403789, -0.7308952420696087, -0.8368105816169022], [13.63863863863864, 0.7904584625578842, 0.06498462185624222, -0.7202357804880907, -0.8432747277891517], [13.65865865865866, 0.7989145017511416, 0.08294138632750446, -0.7092876571818401, -0.849400899725822], [13.67867867867868, 0.8070503457468862, 0.10086490890176902, -0.6980552600252748, -0.8551866421318487], [13.6986986986987, 0.8148627337979792, 0.1187480060495866, -0.6865430908263084, -0.8606296361521748], [13.71871871871872, 0.8223485347944844, 0.1365835104435263, -0.6747557635220797, -0.86572770030112], [13.73873873873874, 0.8295047485185768, 0.15436427383075296, -0.6626980023297434, -0.8704787913366933], [13.75875875875876, 0.8363285068469946, 0.1720831698979599, -0.6503746398530613, -0.8748810050794997], [13.77877877877878, 0.8428170749005469, 0.18973309712750855, -0.6377906151455538, -0.8789325771759126], [13.7987987987988, 0.8489678521402217, 0.20730698164362993, -0.6249509717309869, -0.8826318838052036], [13.81881881881882, 0.8547783734094484, 0.22479778004754816, -0.61186085558199, -0.8859774423303522], [13.83883883883884, 0.8602463099221036, 0.24219848224038865, -0.5985255130576123, -0.8889679118922673], [13.85885885885886, 0.8653694701958601, 0.2595021142327404, -0.584950288800646, -0.891602093947187], [13.87887887887888, 0.8701458009305059, 0.2767017409397459, -0.5711406235955592, -0.8938789327470417], [13.8988988988989, 0.8745733878308822, 0.2937904689605989, -0.5571020521878948, -0.8957975157625834], [13.91891891891892, 0.8786504563741097, 0.3107614493413351, -0.5428402010660126, -0.8973570740491176], [13.93893893893894, 0.8823753725207951, 0.32760788031980953, -0.5283607862060619, -0.8985569825546864], [13.95895895895896, 0.8857466433699354, 0.3443230100517602, -0.513669610781086, -0.8993967603705815], [13.97897897897898, 0.888762917757253, 0.36090013931686465, -0.49877256283518334, -0.8998760709240871], [13.998998998999, 0.891422986796727, 0.3773326242037061, -0.48367561292364963, -0.8999947221133736], [14.01901901901902, 0.8937257843650992, 0.39361387877257215, -0.4683848117200529, -0.8997526663844903], [14.03903903903904, 0.8956703875291638, 0.4097373776950188, -0.45290628759119683, -0.8991500007504237], [14.05905905905906, 0.897256016915668, 0.4256966588691432, -0.43724624414094604, -0.8981869667522167], [14.07907907907908, 0.8984820370236742, 0.441485326009515, -0.4214109577238976, -0.896863950362161], [14.0990990990991, 0.8993479564792626, 0.4570970512107294, -0.4054067749298941, -0.8951814818291047], [14.11911911911912, 0.8998534282324663, 0.47252557748355495, -0.3892401100403881, -0.8931402354659352], [14.13913913913914, 0.8999982496963658, 0.4877647212626585, -0.3729174424576762, -0.890741029379322], [14.15915915915916, 0.8997823628282832, 0.5028083748849025, -0.3564453141080336, -0.8879848251418303], [14.17917917917918, 0.8992058541530447, 0.5176505090372224, -0.3398303268197896, -0.884872727406534], [14.1991991991992, 0.8982689547283024, 0.5322851751731018, -0.32307913967739527, -0.8814059834642847], [14.21921921921922, 0.8969720400519297, 0.546706507896677, -0.3061984663525439, -0.8775859827438118], [14.23923923923924, 0.8953156299115258, 0.5609087273135166, -0.28919507241341263, -0.8734142562548575], [14.25925925925926, 0.8933003881760918, 0.5748861413471327, -0.27207577261310567, -0.8688924759745659], [14.27927927927928, 0.8909271225299593, 0.5886331480202955, -0.2548474281583839, -0.8640224541773753], [14.2992992992993, 0.8881967841490811, 0.6021442377002383, -0.23751694395977715, -0.8588061427086809], [14.31931931931932, 0.885110467319811, 0.61541399530685, -0.2200912658641799, -0.8532456322025583], [14.33933933933934, 0.8816694090003274, 0.6284371024829742, -0.20257737787104066, -0.8473431512438637], [14.35935935935936, 0.8778749883248754, 0.6412083397259416, -0.18498229933325994, -0.8411010654750432], [14.37937937937938, 0.8737287260510265, 0.6537225884794828, -0.16731308214391938, -0.8345218766480131], [14.3993993993994, 0.8692322839501778, 0.6659748331851839, -0.1495768079099687, -0.8276082216214877], [14.41941941941942, 0.8643874641415342, 0.6779601632926607, -0.131780585114004, -0.8203628713041587], [14.43943943943944, 0.8591962083698407, 0.6896737752276475, -0.1139315462652746, -0.8127887295441485], [14.45945945945946, 0.853660597227155, 0.7011109743172114, -0.09603684504106016, -0.8048888319651835], [14.47947947947948, 0.8477828493189717, 0.7122671766713198, -0.07810365341956413, -0.7966663447499529], [14.4994994994995, 0.8415653203750318, 0.7231379110200078, -0.06013915880547205, -0.7881245633711413], [14.51951951951952, 0.8350105023051753, 0.7337188205054084, -0.04215056114932755, -0.7792669112706437], [14.53953953953954, 0.8281210222006141, 0.7440056644279285, -0.02414507006187981, -0.7700969384874914], [14.55955955955956, 0.8208996412810255, 0.7539943199458693, -0.006129901924559417, -0.7606183202350394], [14.57957957957958, 0.8133492537878898, 0.7636807837278113, 0.011887723002759365, -0.7508348554279861], [14.5995995995996, 0.8054728858245122, 0.7730611735571005, 0.029900583475550777, -0.7407504651598147], [14.61961961961962, 0.7972736941431989, 0.7821317298877932, 0.047901460158824384, -0.7303691911312659], [14.63963963963964, 0.7887549648800685, 0.7908888173514372, 0.06588313852054656, -0.7196951940304745], [14.65965965965966, 0.7799201122380087, 0.7993289262140825, 0.083838411723137, -0.7087327518654157], [14.67967967967968, 0.7707726771183057, 0.8074486737829395, 0.10176008351188151, -0.6974862582493314], [14.6996996996997, 0.7613163257014937, 0.8152448057621222, 0.11964097109910302, -0.6859602206398245], [14.71971971971972, 0.7515548479779945, 0.8227141975569292, 0.13747390804293558, -0.6741592585323238], [14.73973973973974, 0.7414921562291364, 0.8298538555261439, 0.15525174711954678, -0.662088101608647], [14.75975975975976, 0.731132283459158, 0.836660918181849, 0.172967363187658, -0.6497515878414019], [14.77977977977978, 0.7204793817788302, 0.8431326573362764, 0.1906136560442143, -0.6371546615549855], [14.7997997997998, 0.7095377207413406, 0.8492664791952311, 0.2081835532700592, -0.6243023714439598], [14.81981981981982, 0.6983116856311088, 0.855059925397653, 0.22567001306447404, -0.6111998685495958], [14.83983983983984, 0.6868057757062176, 0.8605106740008973, 0.24306602706744618, -0.5978524041953995], [14.85985985985986, 0.6750246023951649, 0.8656165404113413, 0.2603646231685339, -0.5842653278824449], [14.87987987987988, 0.6629728874486603, 0.8703754782599429, 0.27755886830120374, -0.5704440851453594], [14.8998998998999, 0.6506554610472036, 0.8747855802223998, 0.29464187122151864, -0.5563942153698198], [14.91991991991992, 0.6380772598652088, 0.878845078783583, 0.3116067852700648, -0.5421213495724343], [14.93993993993994, 0.6252433250924437, 0.882552346945934, 0.3284468111160095, -0.5276312081438994], [14.95995995995996, 0.6121588004135837, 0.8859058988815466, 0.34515519948218965, -0.5129295985563369], [14.97997997997998, 0.5988289299466847, 0.8889043905276681, 0.3617252538501402, -0.4980224130357302], [15.0, 0.5852590561414053, 0.8915466201253833, 0.37815033314397684, -0.48291562620039147]]}, \"id\": \"el94984400236048\"});\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & mpld3\n", " mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949844002360481884642452\", {\"axes\": [{\"xlim\": [0.0, 10.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"Here are some curves\", \"coordinates\": \"data\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [5.0, -1.1], \"rotation\": -0.0, \"id\": \"el94984400236304\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 0.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.91129032258064513], \"rotation\": -0.0, \"id\": \"el94984396322128\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 1.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.83198924731182788], \"rotation\": -0.0, \"id\": \"el94984396238864\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 2.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.75268817204301075], \"rotation\": -0.0, \"id\": \"el94984396237136\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Offset: 3.0\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.77990591397849451, 0.67338709677419351], \"rotation\": -0.0, \"id\": \"el94984396236112\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 10.0], \"ylim\": [-1.2, 1.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el94984396322064\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396386960\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396384592\"}, {\"color\": \"#FF0000\", \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396285392\"}, {\"color\": \"#00BFBF\", \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data01\", \"id\": \"el94984396283344\"}, {\"color\": \"#0000FF\", \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396320656\"}, {\"color\": \"#007F00\", \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396238608\"}, {\"color\": \"#FF0000\", \"yindex\": 3, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984401323920\"}, {\"color\": \"#00BFBF\", \"yindex\": 4, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000002.0, \"alpha\": 0.4, \"xindex\": 0, \"linewidth\": 5, \"data\": \"data02\", \"id\": \"el94984396235920\"}], \"markers\": [], \"id\": \"el94984400229904\", \"ydomain\": [-1.2, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 320.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data02\": [[0.6903001792114694, 0.9301075268817204, 0.8508064516129031, 0.771505376344086, 0.6922043010752688], [0.7404793906810034, 0.9301075268817204, 0.8508064516129031, 0.771505376344086, 0.6922043010752688]], \"data03\": [[0.6652105734767024, 0.6397849462365592], [0.9820788530465949, 0.6397849462365592], [0.9820788530465949, 0.9731182795698926], [0.6652105734767024, 0.9731182795698926], [0.6652105734767024, 0.6397849462365592]], \"data01\": [[-5.0, 0.8630318471968246, 0.2514739483790333, -0.5912879388469102, -0.8904224219610436], [-4.97997997997998, 0.8679695898303729, 0.26872276441239507, -0.577586531327817, -0.8928654338419951], [-4.95995995995996, 0.8725594608285433, 0.28586387963617244, -0.5636536341648707, -0.8949505961366454], [-4.93993993993994, 0.8767996206269677, 0.30289042410024863, -0.5494948314934817, -0.8966770731373546], [-4.91991991991992, 0.8806883698212653, 0.3197955737730711, -0.535115797989235, -0.8980441728931777], [-4.8998998998999, 0.8842241498481432, 0.3365725532766406, -0.5205222965935509, -0.8990513474871911], [-4.87987987987988, 0.8874055436100506, 0.35321463860199986, -0.5057201762039664, -0.8996981932560897], [-4.85985985985986, 0.8902312760431328, 0.3697151598041348, -0.49071536932996934, -0.8999844509519712], [-4.83983983983984, 0.8927002146282623, 0.3860675036752065, -0.4755138897153212, -0.8999100058462391], [-4.81981981981982, 0.8948113698449383, 0.4022651163950453, -0.46012182992782114, -0.8994748877755848], [-4.7997997997998, 0.8965638955678743, 0.4183015061578412, -0.4445453589174795, -0.8986792711300291], [-4.77977977977978, 0.8979570894061152, 0.434170245773981, -0.4287907195440775, -0.8975234747830286], [-4.75975975975976, 0.8989903929845467, 0.44986497524598745, -0.4128642260751055, -0.8960079619636763], [-4.73973973973974, 0.8996633921676853, 0.4653794043175288, -0.39677226165508117, -0.8941333400710433], [-4.71971971971972, 0.8999758172256591, 0.4807073149944767, -0.38052127574726324, -0.8919003604307418], [-4.6996996996997, 0.8999275429423117, 0.4958425640370029, -0.36411778154878494, -0.889309917993803], [-4.67967967967968, 0.8995185886653875, 0.5107790854217142, -0.3475683533802441, -0.8863630509779908], [-4.65965965965966, 0.8987491182987778, 0.5255108927728417, -0.33087962405079563, -0.8830609404516978], [-4.63963963963964, 0.8976194402368293, 0.5400320817615062, -0.31405828219980225, -0.8794049098605863], [-4.61961961961962, 0.8961300072407442, 0.5543368324721021, -0.29711106961610945, -0.8753964244971686], [-4.5995995995996, 0.8942814162571185, 0.5684194117348481, -0.2800447785360186, -0.8710370909135349], [-4.57957957957958, 0.8920744081786935, 0.5822741754235725, -0.26286624892104166, -0.8663286562774669], [-4.55955955955956, 0.8895098675474145, 0.5958955707178093, -0.24558236571652747, -0.8612730076721944], [-4.53953953953954, 0.8865888221999173, 0.6092781383283015, -0.22820005609225996, -0.8558721713400752], [-4.51951951951952, 0.8833124428555845, 0.6224165146850176, -0.21072628666613283, -0.850128311870502], [-4.4994994994995, 0.8796820426473345, 0.6353054340868053, -0.1931680607120138, -0.844043731332361], [-4.47947947947948, 0.8756990765953342, 0.6479397308118201, -0.17553241535291797, -0.8376208683513913], [-4.45945945945946, 0.8713651410238447, 0.6603143411878836, -0.1578264187406146, -0.8308622971328129], [-4.43943943943944, 0.866681972921434, 0.6724243056219421, -0.14005716722279796, -0.8237707264296168], [-4.41941941941942, 0.8616514492448131, 0.6842647705878105, -0.1222317824989577, -0.8163489984569301], [-4.3993993993994, 0.8562755861665744, 0.695830990571407, -0.10435740876608852, -0.8086000877528902], [-4.37937937937938, 0.850556538267135, 0.7071183299726964, -0.08644120985538295, -0.8005270999864869], [-4.35935935935936, 0.8444965976712059, 0.7181222649635836, -0.06849036636105515, -0.792133270712847], [-4.33933933933934, 0.8380981931291377, 0.7288383853010069, -0.0505120727624463, -0.7834219640764648], [-4.31931931931932, 0.831363889043506, 0.7392623960945115, -0.03251353454056483, -0.7743966714628926], [-4.2992992992992995, 0.8242963844413308, 0.749390119527588, -0.014501965290217559, -0.7650610100994366], [-4.2792792792792795, 0.8168985118923391, 0.7592174965320911, 0.0035154161711113013, -0.7554187216054159], [-4.2592592592592595, 0.8091732363737051, 0.7687405884150641, 0.021531388696474196, -0.7454736704925676], [-4.2392392392392395, 0.8011236540817241, 0.7779555784373193, 0.039538731703607936, -0.7352298426161972], [-4.2192192192192195, 0.7927529911908945, 0.7868587733431406, 0.057530228068855084, -0.7246913435776958], [-4.1991991991991995, 0.784064602560906, 0.7954466048404955, 0.07549866701969918, -0.7138623970790667], [-4.1791791791791795, 0.7750619703920537, 0.8037156310311625, 0.0934368470247545, -0.7027473432301156], [-4.1591591591591595, 0.7657487028296133, 0.8116625377902029, 0.11133757868005209, -0.6913506368089878], [-4.1391391391391394, 0.7561285325177417, 0.8192841400942208, 0.1291936875904653, -0.6796768454767479], [-4.119119119119119, 0.7462053151034778, 0.8265773832978821, 0.14699801724511982, -0.6677306479467164], [-4.099099099099099, 0.7359830276914466, 0.8335393443581789, 0.1647434318856362, -0.6555168321092989], [-4.079079079079079, 0.7254657672498843, 0.8401672330059502, 0.1824228193660547, -0.6430402931130572], [-4.059059059059059, 0.7146577489686227, 0.8464583928641868, 0.20002909400329694, -0.6303060314027944], [-4.039039039039039, 0.703563304569693, 0.8524103025126752, 0.21755519941702098, -0.6173191507154364], [-4.019019019019019, 0.6921868805712241, 0.8580205764985528, 0.23499411135773307, -0.6040848560345177], [-3.998998998998999, 0.6805330365053327, 0.863286966292367, 0.2523388405220207, -0.5906084515040859], [-3.978978978978979, 0.6686064430907186, 0.8682073611892598, 0.2695824353537809, -0.5768953383028668], [-3.958958958958959, 0.6564118803606961, 0.8727797891549115, 0.28671798483031846, -0.5629510124795367], [-3.938938938938939, 0.6439542357474166, 0.8770024176159085, 0.30373862123220025, -0.5487810627499738], [-3.918918918918919, 0.6312385021230444, 0.8808735541942165, 0.32063752289575315, -0.5343911682573689], [-3.898898898898899, 0.6182697757986748, 0.8843916473854649, 0.33740791694710387, -0.519787096296093], [-3.878878878878879, 0.6050532544817941, 0.8875552871807707, 0.35404308201666423, -0.5049747000002375], [-3.858858858858859, 0.5915942351931028, 0.8903632056318527, 0.37053635093297416, -0.48995991599774835], [-3.838838838838839, 0.5778981121435335, 0.8928142773592105, 0.38688111339482273, -0.4747487620310985], [-3.818818818818819, 0.5639703745723166, 0.894907520003162, 0.4030708186205766, -0.45934733454544946], [-3.798798798798799, 0.5498166045469611, 0.8966420946175617, 0.4190989779736531, -0.4437618062452708], [-3.7787787787787788, 0.5354424747260293, 0.89801730600604, 0.43495916756308683, -0.4279984236203952], [-3.7587587587587588, 0.5208537460856043, 0.8990326030006295, 0.4506450308181473, -0.4120635044425017], [-3.7387387387387387, 0.5060562656103611, 0.8996875786826659, 0.4661502810359743, -0.39596343523302946], [-3.7187187187187187, 0.49105596395016665, 0.899981970545877, 0.48146870390121227, -0.379704668703538], [-3.6986986986986987, 0.47585885304314746, 0.8999156606015912, 0.49659415997663203, -0.36329372116953884], [-3.6786786786786787, 0.460471023706178, 0.899488675426026, 0.5115205871637428, -0.346737169938836], [-3.6586586586586587, 0.4448986431937554, 0.898701186149637, 0.5262420031324067, -0.33004165067542085], [-3.6386386386386387, 0.42914795272623935, 0.8975535083885307, 0.5407525077184839, -0.3132138547399789], [-3.6186186186186187, 0.4132252649884468, 0.8960461021179691, 0.5550462852885475, -0.29626052650807405], [-3.5985985985985987, 0.3971369615996047, 0.8941795714880179, 0.5691176070707172, -0.2791884606670851], [-3.5785785785785786, 0.3808894905556751, 0.8919546645814096, 0.5829608334506821, -0.2620044994929775], [-3.5585585585585586, 0.3644893636450766, 0.889372273113722, 0.5965704162319889, -0.24471553010800312], [-3.5385385385385386, 0.34794315383883917, 0.8864334320759889, 0.6099409008596921, -0.22732848172042486], [-3.5185185185185186, 0.3312574926562372, 0.8831393193198884, 0.6230669286064724, -0.20985032284737504], [-3.4984984984984986, 0.31443906750695766, 0.8794912550856742, 0.6359432387203501, -0.19228805852195827], [-3.4784784784784786, 0.2974946190108674, 0.8754907014730395, 0.6485646705331292, -0.17464872748571958], [-3.4584584584584586, 0.28043093829645527, 0.8711392618551258, 0.6609261655287316, -0.15693939936760282], [-3.4384384384384385, 0.2632548642790304, 0.8664386802359094, 0.6730227693705885, -0.1391671718505291], [-3.4184184184184185, 0.2459732809197681, 0.8613908405512265, 0.684849633887277, -0.12133916782673242], [-3.3983983983983985, 0.22859311446670239, 0.8559977659137148, 0.6964020190156096, -0.10346253254299087], [-3.378378378378378, 0.21112133067876954, 0.850261617801974, 0.7076752947003935, -0.08554443073689828], [-3.358358358358358, 0.1935649320340186, 0.8441846951942729, 0.718664942750098, -0.06759204376532768], [-3.338338338338338, 0.17593095492310082, 0.8377694336471446, 0.7293665586476946, -0.0496125667262263], [-3.318318318318318, 0.15822646682917196, 0.8310184043192475, 0.7397758533159309, -0.03161320557491114], [-3.298298298298298, 0.1404585634953306, 0.8239343129408745, 0.7498886548363414, -0.013601174236009072], [-3.278278278278278, 0.12263436608073057, 0.8165199987295305, 0.7597009101213011, 0.00441630828779621], [-3.258258258258258, 0.10476101830650755, 0.8087784332520078, 0.7692086865384546, 0.022432020809052548], [-3.238238238238238, 0.08684568359266231, 0.8007127192334185, 0.7784081734868665, 0.040438742849703224], [-3.218218218218218, 0.06889554218704948, 0.7923260893136606, 0.7872956839242633, 0.05842925753496662], [-3.198198198198198, 0.05091778828762159, 0.7836219047518144, 0.7958676558447566, 0.07639635448577162], [-3.178178178178178, 0.032919627159082275, 0.7746036540789918, 0.80412065370645, 0.09433283270858979], [-3.158158158158158, 0.014908272245103896, 0.765274951700175, 0.8120513698083638, 0.11223150348150575], [-3.138138138138138, -0.0031090577227327423, 0.755639536445607, 0.8196566256161201, 0.13008519323536938], [-3.118118118118118, -0.021125141618118036, 0.7457012700723139, 0.8269333730358612, 0.1478867464288749], [-3.098098098098098, -0.03913275881415305, 0.7354641357163603, 0.833878695635889, 0.16562902841641455], [-3.078078078078078, -0.05712469207728877, 0.7249322362964564, 0.8404898098155339, 0.18330492830755787], [-3.058058058058058, -0.0750937304599054, 0.7141097928695582, 0.8467640659207893, 0.2009073618170096], [-3.038038038038038, -0.09303267219037227, 0.7030011429391195, 0.8526989493062603, 0.21842927410390522], [-3.018018018018018, -0.11093432755943015, 0.6916107387166736, 0.8582920813430026, 0.2358636425993049], [-2.997997997997998, -0.12879152180173914, 0.6799431453374402, 0.8635412203718483, 0.25320347982075386], [-2.977977977977978, -0.1465970979714372, 0.668003039030676, 0.8684442626018356, 0.27044183617278], [-2.957957957957958, -0.1643439198105568, 0.6557952052454983, 0.8729992429533822, 0.2875718027322071], [-2.937937937937938, -0.18202487460915015, 0.6433245367329355, 0.8772043358458658, 0.30458651401716713], [-2.9179179179179178, -0.1996328760559768, 0.6305960315849722, 0.8810578559292943, 0.3214791507387017], [-2.8978978978978978, -0.21716086707861068, 0.6176147912313744, 0.8845582587597733, 0.33824294253384984], [-2.8778778778778777, -0.23460182267182886, 0.6043860183950994, 0.8877041414185011, 0.3548711706791272], [-2.8578578578578577, -0.2519487527131479, 0.590915015007107, 0.8904942430740405, 0.3713571707833086], [-2.8378378378378377, -0.26919470476437996, 0.5772071800814106, 0.8929274454876455, 0.3876943354584343], [-2.8178178178178177, -0.28633276685808523, 0.5632680075512183, 0.8950027734614372, 0.40387611696797127], [-2.7977977977977977, -0.30335607026780403, 0.5491030840670311, 0.8967193952292518, 0.4198960298510658], [-2.7777777777777777, -0.32025779226095913, 0.5347180867575816, 0.8980766227900018, 0.43574765352183736], [-2.7577577577577577, -0.3370311588333234, 0.5201187809545099, 0.8990739121834186, 0.45142463484267087], [-2.7377377377377377, -0.35366944742395856, 0.5053110178816891, 0.8997108637080654, 0.4669206906704775], [-2.7177177177177176, -0.37016598960953523, 0.4903007323101259, 0.8999872220815323, 0.48222961037490103], [-2.6976976976976976, -0.38651417377695624, 0.4750939401793767, 0.8999028765427508, 0.49734525832746346], [-2.6776776776776776, -0.4027074477732101, 0.4596967361864325, 0.8994578608963848, 0.51226157636065], [-2.6576576576576576, -0.41873932153139454, 0.4441152913430384, 0.8986523534992832, 0.5269725861959482], [-2.6376376376376376, -0.434603369671856, 0.4283558505024275, 0.8974866771889954, 0.5414723918398704], [-2.6176176176176176, -0.4502932340774034, 0.4124247298564596, 0.8959612991543829, 0.5557551819469949], [-2.5975975975975976, -0.46580262644156445, 0.39632831440416855, 0.8940768307483751, 0.5698152321490826], [-2.5775775775775776, -0.48112533078886166, 0.38007305539273256, 0.8918340272429469, 0.5836469073493346], [-2.5575575575575575, -0.49625520596610034, 0.3636654677318927, 0.8892337875264147, 0.59724466398087], [-2.5375375375375375, -0.511186188103667, 0.34711212738285663, 0.8862771537431728, 0.6106030522285195], [-2.5175175175175175, -0.5259122930458555, 0.3304196687227336, 0.882965310876015, 0.6237167182130459], [-2.4974974974974975, -0.5404276187492426, 0.3135947818855565, 0.8792995862712075, 0.636580406136913], [-2.4774774774774775, -0.554726347648156, 0.2966442100809582, 0.8752814491065052, 0.6491889603907449], [-2.4574574574574575, -0.5688027489862835, 0.27957474689157497, 0.8709125098023224, 0.6615373276196324], [-2.4374374374374375, -0.5826511811134897, 0.26239323355026123, 0.8661945193762967, 0.673620558748455], [-2.4174174174174174, -0.5962660937469216, 0.24510655619820726, 0.8611293687415019, 0.6854338109654109], [-2.3973973973973974, -0.6096420301954936, 0.22772164312505716, 0.8557190879485936, 0.6969723496629561], [-2.3773773773773774, -0.6227736295468633, 0.2102454619921352, 0.8499658453721893, 0.708231550335378], [-2.3573573573573574, -0.6356556288160194, 0.19268501703989183, 0.8438719468418101, 0.7192069004322407], [-2.3373373373373374, -0.6482828650546222, 0.17504734628068955, 0.8374398347177328, 0.7298940011669601], [-2.3173173173173174, -0.6606502774202505, 0.15733951867805326, 0.8306720869121211, 0.7402885692797845], [-2.2972972972972974, -0.672752909204725, 0.1395686313135159, 0.8235714158558308, 0.7503864387544721], [-2.2772772772772774, -0.6845859098206977, 0.12174180654219457, 0.816140667411299, 0.7601835624879802], [-2.2572572572572573, -0.6961445367457068, 0.1038661891382374, 0.8083828197319587, 0.7696760139124947], [-2.2372372372372373, -0.7074241574229229, 0.08594894343128517, 0.8003009820686302, 0.7788599885691501], [-2.2172172172172173, -0.718420251117821, 0.06799725043509522, 0.7918983935233717, 0.7877318056328112], [-2.1971971971971973, -0.7291284107300358, 0.05001830496947869, 0.783178421751286, 0.7962879093873019], [-2.1771771771771773, -0.7395443445596738, 0.032019312776704424, 0.774144561610807, 0.8045248706504939], [-2.1571571571571573, -0.7496638780273746, 0.014007487633525202, 0.7648004337630007, 0.8124393881486803], [-2.1371371371371373, -0.7594829553474308, -0.004009951540016031, 0.7551497832204505, 0.8200282898396865], [-2.1171171171171173, -0.7689976411532984, -0.022025783573841363, 0.7451964778463002, 0.8272885341841856], [-2.0970970970970972, -0.7782041220748429, -0.0400327879419949, 0.7349445068040626, 0.8342172113647105], [-2.0770770770770772, -0.7870987082666921, -0.05802374765654159, 0.7243979789588111, 0.8408115444518732], [-2.057057057057057, -0.7956778348870811, -0.07599145216004802, 0.7135611212303964, 0.8470688905173247], [-2.037037037037037, -0.8039380635265972, -0.09392870021548604, 0.7024382768993486, 0.8529867416930078], [-2.017017017017017, -0.8118760835862529, -0.1118283027924007, 0.6910339038661436, 0.8585627261762803], [-1.9969969969969972, -0.819488713604333, -0.1296830859481862, 0.6793525728645305, 0.8637946091805048], [-1.9769769769769772, -0.8267729025314863, -0.14748589370331464, 0.6673989656296377, 0.8686802938307239], [-1.9569569569569571, -0.8337257309535488, -0.16522959090936523, 0.6551778730215899, 0.8732178220040618], [-1.9369369369369371, -0.8403444122616095, -0.18290706610870514, 0.6426941931053901, 0.8774053751145168], [-1.9169169169169171, -0.8466262937688486, -0.2005112343846748, 0.6299529291878351, 0.8812412748418272], [-1.8968968968968971, -0.8525688577737031, -0.2180350402011364, 0.61695918781225, 0.8847239838041231], [-1.876876876876877, -0.8581697225689299, -0.23547146023024657, 0.6037181767118495, 0.8878521061740893], [-1.856856856856857, -0.8634266433961658, -0.2528135061673208, 0.5902352027225406, 0.890624388238396], [-1.836836836836837, -0.8683375133455989, -0.27005422753166064, 0.576515669656008, 0.8930397189001723], [-1.816816816816817, -0.8729003642003926, -0.28718671445222177, 0.5625650761339318, 0.8950971301243197], [-1.796796796796797, -0.8771133672255228, -0.30420410043700613, 0.5483890133842075, 0.8967957973254891], [-1.7767767767767766, -0.8809748339007124, -0.3210995651250686, 0.5339931630000494, 0.8981350396985648], [-1.7567567567567566, -0.8844832165971699, -0.33786633702003305, 0.5193832946628781, 0.8991143204915222], [-1.7367367367367366, -0.8876371091978604, -0.35449769620402766, 0.5045652638299016, 0.8997332472205519], [-1.7167167167167166, -0.8904352476610599, -0.3709869770309451, 0.4895450093873195, 0.8999915718273627], [-1.6966966966966965, -0.8928765105269676, -0.387327570797952, 0.4743285512700904, 0.8998891907785999], [-1.6766766766766765, -0.8949599193671738, -0.4035129283941771, 0.45892198804921497, 0.8994261451073404], [-1.6566566566566565, -0.8966846391768014, -0.41953656292551456, 0.4433314944875026, 0.8986026203966468], [-1.6366366366366365, -0.8980499787091669, -0.4353920523144928, 0.4275633190648026, 0.8974189467051887], [-1.6166166166166165, -0.8990553907528221, -0.45107304187416386, 0.4116237814736888, 0.8958755984349583], [-1.5965965965965965, -0.8997004723508709, -0.46657324685498563, 0.395519270086604, 0.893973194141137], [-1.5765765765765765, -0.8999849649624694, -0.481886454963672, 0.37925623939547853, 0.8917124962841845], [-1.5565565565565564, -0.899908754566445, -0.49700652885300467, 0.362841207424848, 0.8890944109242553], [-1.5365365365365364, -0.8994718717069963, -0.5119274085816072, 0.3462807531195088, 0.8861199873580604], [-1.5165165165165164, -0.8986744914814494, -0.5266431140426958, 0.3295815137077569, 0.8827904176983216], [-1.4964964964964964, -0.8975169334700829, -0.5411477473608326, 0.31275018204126814, 0.8791070363959878], [-1.4764764764764764, -0.8959996616080429, -0.555435495255723, 0.2957935039126843, 0.8750713197054028], [-1.4564564564564564, -0.8941232839994037, -0.5695006313721072, 0.2787182753519818, 0.8706848850926407], [-1.4364364364364364, -0.891888552673447, -0.5833375185748136, 0.26153133990270583, 0.8659494905872459], [-1.4164164164164164, -0.8892963632832582, -0.5969406112080544, 0.24423958587916134, 0.8608670340776351], [-1.3963963963963963, -0.8863477547467589, -0.6103044573180557, 0.22684994360566055, 0.8554395525504473], [-1.3763763763763763, -0.8830439088303217, -0.6234237008381358, 0.2093693826389336, 0.849669221274145], [-1.3563563563563563, -0.8793861496751325, -0.6362930837353499, 0.19180490897481492, 0.8435583529271921], [-1.3363363363363363, -0.8753759432664909, -0.6489074481178464, 0.1741635622403257, 0.8371093966711606], [-1.3163163163163163, -0.8710148968462609, -0.661261738302087, 0.15645241287227693, 0.8303249371691378], [-1.2962962962962963, -0.8663047582687083, -0.6733510028391033, 0.13867855928352468, 0.8232076935498229], [-1.2762762762762763, -0.8612474152999819, -0.6851703964989768, 0.12084912501801248, 0.8157605183177351], [-1.2562562562562563, -0.8558448948615198, -0.6967151822127481, 0.10297125589574202, 0.8079863962099633], [-1.2362362362362362, -0.8500993622176839, -0.7079807329709764, 0.08505211714881532, 0.7998884429999203], [-1.2162162162162162, -0.8440131201079484, -0.7189625336781891, 0.06709889054969705, 0.7914699042485793], [-1.1961961961961962, -0.8375886078239898, -0.7296561829624759, 0.049118771532847744, 0.7827341540036916], [-1.1761761761761762, -0.8308284002320487, -0.7400573949395062, 0.031118966310881267, 0.7736846934475101], [-1.1561561561561562, -0.8237352067409549, -0.7501620009302584, 0.013106688986402595, 0.7643251494935579], [-1.1361361361361362, -0.8163118702162289, -0.7599659511317763, -0.004910841339316566, 0.7546592733330054], [-1.1161161161161162, -0.808561365840696, -0.7694653162402805, -0.022926403459665626, 0.7446909389312389], [-1.0960960960960962, -0.8004867999220704, -0.7786562890259854, -0.04093277695686673, 0.7344241414752224], [-1.0760760760760761, -0.792091408647983, -0.7875351858589906, -0.05892274509583019, 0.723862995772275], [-1.0560560560560561, -0.7833785567889587, -0.7960984481856351, -0.07688909771653404, 0.7130117346009067], [-1.0360360360360361, -0.7743517363498564, -0.8043426439547219, -0.09482463412376826, 0.7018747070143723], [-1.016016016016016, -0.765014565170316, -0.8122644689930423, -0.11272216597308565, 0.6904563765976234], [-0.9959959959959956, -0.7553707854747724, -0.8198607483296488, -0.13057452015180326, 0.6787613196783585], [-0.9759759759759756, -0.7454242623726185, -0.8271284374683437, -0.14837454165389666, 0.6667942234928864], [-0.9559559559559556, -0.7351789823091147, -0.8340646236078776, -0.16611509644764186, 0.6545598843075389], [-0.9359359359359356, -0.7246390514676716, -0.8406665268093629, -0.1837890743348465, 0.6420632054963858], [-0.9159159159159156, -0.7138086941241412, -0.846931501110441, -0.2013893918005312, 0.6293091955760225], [-0.8958958958958956, -0.7026922509537785, -0.8528570355857508, -0.2189089948519159, 0.616302966198217], [-0.8758758758758756, -0.6912941772915525, -0.8584407553532774, -0.23634086184557426, 0.603049730101223], [-0.8558558558558556, -0.6796190413465009, -0.8636804225261756, -0.25367800630162296, 0.589554799020577], [-0.8358358358358355, -0.6676715223708487, -0.8685739371096881, -0.27091347970381735, 0.5758235815602202], [-0.8158158158158155, -0.6554564087846209, -0.8731193378427973, -0.28804037428443247, 0.5618615810247956], [-0.7957957957957955, -0.6429785962565012, -0.8773148029842752, -0.305051825792812, 0.5476743932139905], [-0.7757757757757755, -0.6302430857417087, -0.881158651042815, -0.32194101624647614, 0.5332677041798084], [-0.7557557557557555, -0.617254981477675, -0.884649341450952, -0.33870117666368627, 0.5186472879476685], [-0.7357357357357355, -0.604019488938329, -0.8877854751825048, -0.35532558977636997, 0.5038190042022461], [-0.7157157157157155, -0.5905419127478059, -0.8905657953132878, -0.3718075927223205, 0.48878879593898406], [-0.6956956956956954, -0.576827654554418, -0.8929891875248709, -0.3881405797155907, 0.4735626870822106], [-0.6756756756756754, -0.5628822108657409, -0.8950546805511844, -0.40431800469401136, 0.45814678007082504], [-0.6556556556556554, -0.5487111708456796, -0.8967614465677918, -0.4203333839427734, 0.4425472534125146], [-0.6356356356356354, -0.5343202140743993, -0.8981088015236703, -0.43618029869302166, 0.4267703592074833], [-0.6156156156156154, -0.5197151082720205, -0.8990962054153714, -0.45185239769441915, 0.4108224206426867], [-0.5955955955955954, -0.5049017069869848, -0.8997232625034468, -0.4673433997606509, 0.3947098294575757], [-0.5755755755755754, -0.4898859472500258, -0.8999897214710562, -0.48264709628684704, 0.37843904338236506], [-0.5555555555555554, -0.4746738471946788, -0.8998954755246922, -0.4977573537379157, 0.3620165835498539], [-0.5355355355355353, -0.4592715036452863, -0.8994405624369817, -0.5126681161067891, 0.345449031881835], [-0.5155155155155153, -0.44368508967346487, -0.8986251645315472, -0.527373407341598, 0.32874302845114106], [-0.4954954954954953, -0.42792085212401376, -0.8974496086099335, -0.5418673337407994, 0.31190526882038405], [-0.4754754754754753, -0.41198510911125524, -0.89591436582063, -0.5561440863153017, 0.29494250135845557], [-0.4554554554554553, -0.3958842474868115, -0.8940200514702393, -0.5701979431166352, 0.27786152453586277], [-0.4354354354354353, -0.3796247202798323, -0.8917674247768713, -0.5840232715302405, 0.260669184199984], [-0.41541541541541527, -0.3632130441106995, -0.8891573885658559, -0.5976145305329514, 0.24337237083133706], [-0.39539539539539525, -0.3466557965792451, -0.8861909889079035, -0.6109662729137695, 0.22597801678195795], [-0.37537537537537524, -0.3299596136285294, -0.8828694146998511, -0.6240731474570395, 0.2084930934969991], [-0.35535535535535523, -0.31313118688523567, -0.879193997188168, -0.6369299010871522, 0.19092460872065906], [-0.3353353353353352, -0.29617726097774794, -0.8751662094354078, -0.6495313809739125, 0.1732796036875639], [-0.3153153153153152, -0.279104630832986, -0.8707876657298232, -0.6618725365977308, 0.15556515030072604], [-0.2952952952952952, -0.2619201389530815, -0.8660601209383787, -0.6739484217738104, 0.13778834829721204], [-0.2752752752752752, -0.24463067267298666, -0.8609854698034214, -0.6857541966345176, 0.11995632240265423], [-0.25525525525525516, -0.22724316140011394, -0.8555657461832916, -0.6972851295691421, 0.10207621947574753], [-0.23523523523523515, -0.20976457383711442, -0.8498031222371768, -0.7085365991202681, 0.08415520564387569], [-0.21521521521521514, -0.19220191518890636, -0.8436999075545369, -0.7195040958359988, 0.06620046343101459], [-0.19519519519519513, -0.17456222435507468, -0.8372585482294489, -0.73018322407729, 0.048219188879064276], [-0.1751751751751751, -0.15685257110876555, -0.8304816258802421, -0.7405697037796676, 0.030218588663762983], [-0.1551551551551551, -0.13908005326320766, -0.8233718566148173, -0.7506593721686265, 0.012205877206339349], [-0.1351351351351351, -0.12125179382699504, -0.8159320899420635, -0.7604481854280194, -0.005811726217939738], [-0.11511511511511507, -0.10337493814927201, -0.808165307629809, -0.7699322203207686, -0.023827000373166672], [-0.09509509509509506, -0.0854566510559644, -0.8000746225097666, -0.7791076757612523, -0.04183272495697651], [-0.07507507507507505, -0.0675041139782045, -0.791663277229947, -0.787970874338733, -0.05982168349435614], [-0.055055055055055035, -0.04952452207410106, -0.7829346429550461, -0.7965182637912191, -0.07778666622991962], [-0.03503503503503502, -0.031525081345007595, -0.7738922180153227, -0.8047464184291683, -0.09572047301749019], [-0.01501501501501501, -0.013513005747444783, -0.7645396265045104, -0.8126520405084618, -0.11361591620583114], [0.005005005005005003, 0.00450448569816537, -0.7548806168273252, -0.8202319615521003, -0.13146582351936897], [0.025025025025025016, 0.022520171800794946, -0.7449190601971507, -0.8274831436200912, -0.14926304093275386], [0.04504504504504503, 0.040526832092975494, -0.7346589490845025, -0.8344026805270174, -0.16700043553810592], [0.06506506506506504, 0.058517249724673405, -0.7241043956168947, -0.8409877990068011, -0.18467089840379788], [0.08508508508508505, 0.07648421435571551, -0.7132596299307497, -0.8472358598241956, -0.20226734742362806], [0.10510510510510507, 0.09442052504560546, -0.702128998476011, -0.853144358832558, -0.21978273015524244], [0.12512512512512508, 0.11231899313957308, -0.6907169622741385, -0.8587109279774808, -0.23721002664666743], [0.1451451451451451, 0.13017244514969945, -0.679028095130186, -0.8639333362458795, -0.2545422522498212], [0.1651651651651651, 0.1479737256299636, -0.667067081799676, -0.8688094905601557, -0.2717724604198755], [0.18518518518518512, 0.165715700044058, -0.6548387161110061, -0.8733374366170755, -0.28889374549934627], [0.20520520520520513, 0.18339125762482375, -0.6423478990441412, -0.877515359671033, -0.30589924548579683], [0.22522522522522515, 0.2009933142241595, -0.6295996367663613, -0.881341585261376, -0.3227821447820448], [0.24524524524524516, 0.21851481515226165, -0.6165990386258491, -0.8848145798835104, -0.3395356769277709], [0.26526526526526517, 0.23594873800505806, -0.6033513151039269, -0.8879329516035106, -0.3561531273114332], [0.2852852852852852, 0.25328809547870196, -0.5898617757267586, -0.8906954506159882, -0.37262783586140186], [0.3053053053053052, 0.27052593816999837, -0.5761358269373561, -0.8931009697450004, -0.3889531997152347], [0.3253253253253252, 0.2876553573616401, -0.5621789699287434, -0.8951485448877913, -0.40512267586602385], [0.3453453453453452, 0.30466948779113734, -0.547996798439145, -0.8968373554011937, -0.42112978378475363], [0.36536536536536524, 0.3215615104023315, -0.5335949965100837, -0.8981667244305318, -0.43696810801761704], [0.38538538538538525, 0.3383246550783897, -0.5189793362082866, -0.8991361191808972, -0.45263130075725194], [0.40540540540540526, 0.35495220335518496, -0.5041556753123104, -0.899745151130686, -0.46811308438686444], [0.4254254254254253, 0.37143749111397495, -0.48912995496481587, -0.8999935761873137, -0.4834072539962218], [0.4454454454454453, 0.38777391125229954, -0.47390819729142947, -0.8998812947850447, -0.49850767986850425], [0.4654654654654653, 0.4039549163320268, -0.45849650298714917, -0.899408351924897, -0.5134083099370211], [0.4854854854854853, 0.4199740212034867, -0.44290104887126036, -0.8985749371566059, -0.5281031722108055], [0.5055055055055053, 0.4358248056046398, -0.42712808541174185, -0.8973813845026556, -0.5425863771681151], [0.5255255255255253, 0.45150091673424037, -0.41118393422015437, -0.8958281723244063, -0.5568521201168813], [0.5455455455455454, 0.4669960717979612, -0.39507498551801573, -0.8939159231303727, -0.5708946835211586], [0.5655655655655654, 0.48230406052646163, -0.3788076955756777, -0.8916454033267317, -0.5847084392926434], [0.5855855855855854, 0.4974187476643875, -0.36238858412473224, -0.8890175229101538, -0.5982878510463434], [0.6056056056056054, 0.5123340754293065, -0.3458242317449814, -0.8860333351030892, -0.6116274763194931], [0.6256256256256254, 0.5270440659395942, -0.32912127722702217, -0.8826940359316487, -0.6247219687528266], [0.6456456456456454, 0.5415428236102953, -0.3122864149114995, -0.8790009637462508, -0.6375660802333334], [0.6656656656656654, 0.5558245375160032, -0.29532639200609634, -0.8749555986852273, -0.6501546629976376], [0.6856856856856854, 0.5698834837198077, -0.27824800588133497, -0.8705595620816017, -0.6624826716951596], [0.7057057057057055, 0.5837140275673783, -0.2610581013462737, -0.8658146158132789, -0.6745451654102304], [0.7257257257257255, 0.597310625945267, -0.2437635679051909, -0.8607226615969059, -0.6863373096423507], [0.7457457457457455, 0.6106678295025182, -0.22637133699635548, -0.8552857402256874, -0.6978543782437998], [0.7657657657657655, 0.6237802848347043, -0.2088883792139911, -0.8495060307514597, -0.7090917553138181], [0.7857857857857855, 0.6366427366295033, -0.19132170151454664, -0.8433858496113544, -0.7200449370486032], [0.8058058058058055, 0.6492500297729655, -0.17367834440839383, -0.8369276496993978, -0.7307095335463808], [0.8258258258258255, 0.6615971114156188, -0.15596537913807618, -0.8301340193834212, -0.7410812705668228], [0.8458458458458455, 0.6736790329975905, -0.1381899048442411, -0.823007681467675, -0.7511559912441114], [0.8658658658658656, 0.6854909522319287, -0.12035904572039115, -0.8155514921015611, -0.7609296577529597], [0.8858858858858856, 0.697028135045333, -0.10247994815759376, -0.8077684396349237, -0.7703983529269233], [0.9059059059059056, 0.7082859574755125, -0.08455977788029499, -0.7996616434203547, -0.7795582818283526], [0.9259259259259256, 0.719259907524414, -0.06660571707438431, -0.7912343525629955, -0.7884057732693579], [0.9459459459459456, 0.7299455869665756, -0.04862496150866218, -0.782489944618335, -0.7969372812831765], [0.9659659659659656, 0.7403387131118822, -0.030624717650863484, -0.7734319242385267, -0.8051493865453532], [0.9859859859859856, 0.7504351205220151, -0.012612199779393094, -0.7640639217677665, -0.8130387977441632], [1.0060060060060056, 0.7602307626799093, 0.005405372908068972, -0.7543896917872955, -0.820602352899731], [1.0260260260260257, 0.769721713611548, 0.02342077918793406, -0.7444131116106093, -0.8278370206313123], [1.0460460460460457, 0.7789041694594452, 0.04142679870488327, -0.7341381797294777, -0.8347399013722345], [1.0660660660660657, 0.7877744500071854, 0.05941621486569792, -0.7235690142113986, -0.8413082285320087], [1.0860860860860857, 0.7963290001544099, 0.07738181773158212, -0.712709851049126, -0.8475393696051444], [1.1061061061061057, 0.8045643913416567, 0.09531640690781845, -0.7015650424629363, -0.8534308272262261], [1.1261261261261257, 0.8124773229244865, 0.1132127944295984, -0.6901390551563111, -0.8589802401708267], [1.1461461461461457, 0.8200646234963401, 0.13106380764287107, -0.6784364685257369, -0.8641853843018578], [1.1661661661661658, 0.8273232521595998, 0.14886229207905552, -0.6664619728253394, -0.8690441734609763], [1.1861861861861858, 0.8342502997443438, 0.1666011143224646, -0.6542203672870855, -0.8735546603046918], [1.2062062062062058, 0.8408429899743064, 0.18427316486929105, -0.6417165581973108, -0.8777150370848376], [1.2262262262262258, 0.8470986805795755, 0.20187136097700997, -0.6289555569303392, -0.8815236363730946], [1.2462462462462458, 0.853014864355582, 0.21938864950305578, -0.6159424779399858, -0.884978931729276], [1.2662662662662658, 0.8585891701679577, 0.2368180097316357, -0.602682536709745, -0.8880795383131056], [1.2862862862862858, 0.8638193639028555, 0.2541524561875472, -0.5891810476624894, -0.8908242134392458], [1.3063063063063058, 0.8687033493623569, 0.2713850414358714, -0.5754434220305131, -0.89321185707535], [1.3263263263263259, 0.8732391691046012, 0.2885088588664201, -0.5614751656867762, -0.8952415122829421], [1.3463463463463459, 0.8774250052283045, 0.3055170454618212, -0.547281876938218, -0.8969123656009464], [1.3663663663663659, 0.881259180101353, 0.32240278454813226, -0.5328692442820232, -0.8982237473717126], [1.386386386386386, 0.884740157033177, 0.3391593085268807, -0.5182430441257422, -0.8991751320094058], [1.406406406406406, 0.8878665408906382, 0.3557799015874343, -0.5034091384721772, -0.8997661382106562], [1.4264264264264268, 0.8906370786571817, 0.37225790239861734, -0.48837347256996194, -0.8999965291073796], [1.4464464464464468, 0.8930506599350293, 0.3885867067784881, -0.4731420725307816, -0.8998662123617119], [1.4664664664664668, 0.8951063173902144, 0.4047597703412186, -0.45772104291417637, -0.899375240203017], [1.4864864864864868, 0.8968032271402775, 0.42077061112000197, -0.44211656428091084, -0.8985238094069534], [1.5065065065065069, 0.8981407090844675, 0.4366128121649465, -0.4263348907158804, -0.8973122612166097], [1.5265265265265269, 0.8991182271763185, 0.45228002411491103, -0.41038234732155, -0.8957410812057375], [1.5465465465465469, 0.8997353896384902, 0.4677659677422511, -0.39426532768293, -0.8938108990841406], [1.566566566566567, 0.899991949119788, 0.48306443646945746, -0.3779902913051053, -0.8915224884452939], [1.586586586586587, 0.899887802794298, 0.49816929885667643, -0.3615637610243446, -0.8888767664562972], [1.606606606606607, 0.8994229924025984, 0.5130745010591161, -0.3449923203938268, -0.8858747934902855], [1.626626626626627, 0.8985977042350304, 0.5277740692533542, -0.3282826110450333, -0.8825177727014444], [1.646646646646647, 0.8974122690570355, 0.5422621120315728, -0.3114413300258629, -0.8788070495428009], [1.666666666666667, 0.8958671619765884, 0.5565328227627635, -0.2944752271165368, -0.8747441112269815], [1.686686686686687, 0.89396300225378, 0.5705804819199541, -0.27739110212436874, -0.8703305861301563], [1.706706706706707, 0.8917005530526247, 0.5843994593725255, -0.2601958021584852, -0.8655682431394052], [1.726726726726727, 0.8890807211351945, 0.5979842166426992, -0.24289621888558743, -0.8604589909437697], [1.746746746746747, 0.8861045564981992, 0.6113293091252917, -0.2254992857678555, -0.8550048772692734], [1.766766766766767, 0.8827732519521607, 0.6244293882698463, -0.20801197528410095, -0.8492080880582187], [1.786786786786787, 0.8790881426433489, 0.6372792037242666, -0.19044129613528252, -0.8430709465930872], [1.806806806806807, 0.8750507055186717, 0.6498736054390936, -0.17279429043550393, -0.8365959125653972], [1.826826826826827, 0.8706625587337326, 0.6622075457315831, -0.1550780308896206, -0.8297855810898883], [1.846846846846847, 0.865925461004293, 0.6742760813087553, -0.13729961795858553, -0.8226426816644321], [1.866866866866867, 0.8608413109014016, 0.686074375248606, -0.11946617701367114, -0.8151700770760818], [1.886886886886887, 0.8554121460904689, 0.6975976989386867, -0.10158485548070739, -0.8073707622537026], [1.9069069069069071, 0.8496401425145969, 0.7088414339712743, -0.08366281997548039, -0.7992478630676406], [1.9269269269269271, 0.8435276135224866, 0.7198010739943728, -0.06570725343144032, -0.7908046350769118], [1.9469469469469471, 0.8370770089412768, 0.7304722265178044, -0.04772535222086907, -0.7820444622244139], [1.9669669669669672, 0.8302909140946829, 0.740850614673666, -0.02972432327066205, -0.7729708554806834], [1.9869869869869872, 0.8231720487668309, 0.7509320789304456, -0.0117113811738796, -0.7635874514367412], [2.007007007007007, 0.8157232661122005, 0.7607125787601117, 0.006306254701773933, -0.7538980108465911], [2.027027027027027, 0.8079475515121162, 0.7701881942575083, 0.024321363107384832, -0.7439064171199556], [2.047047047047047, 0.7998480213782424, 0.7793551277114047, 0.04232672380701857, -0.7336166747658526], [2.067067067067067, 0.7914279219035637, 0.7882097051265716, 0.0603151204715024, -0.7230329077876368], [2.0870870870870872, 0.782690627761351, 0.7967483776962744, 0.07827934357064219, -0.7121593580301497], [2.1071071071071072, 0.7736396407526318, 0.804967723224591, 0.09621219326271417, -0.7010003834796398], [2.1271271271271273, 0.7642785884027131, 0.8128644474979865, 0.11410648228007389, -0.6895604565171346], [2.1471471471471473, 0.7546112225073119, 0.8204353856055938, 0.13195503880972548, -0.6778441621259648], [2.1671671671671673, 0.7446414176288823, 0.8276775032076715, 0.14975070936769683, -0.6658561960541591], [2.1871871871871873, 0.7343731695437371, 0.8345878977517301, 0.16748636166606892, -0.6536013629324456], [2.2072072072072073, 0.7238105936405902, 0.8411637996358396, 0.1851548874715098, -0.6410845743486142], [2.2272272272272273, 0.7129579232711583, 0.8474025733186522, 0.202749205454168, -0.6283108468790118], [2.2472472472472473, 0.7018195080534854, 0.8533017183756955, 0.22026226402578317, -0.6152853000779602], [2.2672672672672673, 0.6903998121286689, 0.8588588705015119, 0.2376870441658768, -0.6020131544259006], [2.2872872872872874, 0.6787034123716865, 0.8640718024572432, 0.25501656223488994, -0.5884997292370892], [2.3073073073073074, 0.6667349965570397, 0.8689384249632813, 0.27224387277314116, -0.5747504405276801], [2.3273273273273274, 0.6544993614799518, 0.8734567875366258, 0.28936207128448177, -0.5607707988450528], [2.3473473473473474, 0.64200141103387, 0.8776250792726135, 0.30636429700353396, -0.5465664070592514], [2.3673673673673674, 0.6292461542450456, 0.8814416295707074, 0.32324373564540215, -0.5321429581174231], [2.3873873873873874, 0.6162387032649764, 0.8849049088040527, 0.33999362213675477, -0.5175062327621536], [2.4074074074074074, 0.6029842713215191, 0.8880135289325328, 0.3566072433271839, -0.5026620972146163], [2.4274274274274275, 0.5894881706294912, 0.8907662440590789, 0.37307794067975364, -0.48761650082346303], [2.4474474474474475, 0.5757558102615999, 0.8931619509290101, 0.3893991129396609, -0.47237547368039623], [2.4674674674674675, 0.5617926939805514, 0.8951996893722046, 0.40556421877993776, -0.4569451242033825], [2.4874874874874875, 0.5476044180332104, 0.8968786426879244, 0.4215667794231354, -0.44133163668847336], [2.5075075075075075, 0.5331966689076921, 0.8981981379721385, 0.43740038123793973, -0.425541268831214], [2.5275275275275275, 0.5185752210542875, 0.8991576463872152, 0.453058678309676, -0.4095803492186358], [2.5475475475475475, 0.5037459345711334, 0.8997567833738732, 0.4685353949836745, -0.39345527479283526], [2.5675675675675675, 0.48871475285555716, 0.8999953088053088, 0.4838243283804765, -0.37717250828715787], [2.5875875875875876, 0.4734877002220352, 0.8998731270834357, 0.4989193508818724, -0.3607385756360133], [2.6076076076076076, 0.45807087948772107, 0.8993902871771989, 0.5138144125867768, -0.34416006335936045], [2.6276276276276276, 0.44247046952651076, 0.8985469826029497, 0.5285035437359551, -0.32744361592291016], [2.6476476476476476, 0.42669272279262555, 0.8973435513468856, 0.54298085710463, -0.3105959330751048], [2.6676676676676676, 0.4107439628147042, 0.8957804757295904, 0.5572405503620095, -0.2936237671619404], [2.6876876876876876, 0.3946305816614098, 0.8938583822127251, 0.5712769083967909, -0.27653392042070885], [2.7077077077077076, 0.37835903737956683, 0.8915780411479508, 0.5850843056077071, -0.2593332422537438], [2.7277277277277276, 0.3619358514058544, 0.8889403664681801, 0.5986572081581993, -0.24202862648326412], [2.7477477477477477, 0.3453676059530938, 0.8859464153212846, 0.6119901761943115, -0.2246270085884134], [2.7677677677677677, 0.3286609413721783, 0.8825973876464026, 0.6250778660249172, -0.20713536292560492], [2.7877877877877877, 0.31182255349070065, 0.8788946256930182, 0.637915032263406, -0.18956069993328428], [2.8078078078078077, 0.29485919092934787, 0.8748396134830041, 0.6504965299299706, -0.1719100633222312], [2.8278278278278277, 0.27777765239713553, 0.8704339762158438, 0.6628173165136523, -0.154190527252526], [2.8478478478478477, 0.2605847839665686, 0.8656794796172711, 0.674872453993317, -0.1364091934983126], [2.8678678678678677, 0.2432874763298184, 0.8605780292315889, 0.6866571108167552, -0.1185731886014937], [2.8878878878878878, 0.2258926620370179, 0.8551316696579508, 0.6981665638371084, -0.10068966101549995], [2.9079079079079078, 0.20840731271777943, 0.8493425837309098, 0.7093962002058486, -0.08276577824027667], [2.9279279279279278, 0.19083843628705083, 0.8432130916455652, 0.7203415192215531, -0.06480872394963728], [2.947947947947948, 0.17319307413642843, 0.8367456500276571, 0.7309981341337287, -0.04682569511213446], [2.967967967967968, 0.15547829831205312, 0.8299428509489799, 0.74136177390097, -0.028823899106602692], [2.987987987987988, 0.1377012086802204, 0.822807420888512, 0.7514282849027395, -0.010810550833528764], [3.0080080080080087, 0.11986893008183978, 0.8153422196396761, 0.7611936326040887, 0.007207130176593267], [3.0280280280280287, 0.1019886094768884, 0.8075502391641698, 0.7706539031726488, 0.025221922656758742], [3.0480480480480487, 0.08406741307999185, 0.7994346023928217, 0.7798053050472501, 0.043226606497653], [3.0680680680680688, 0.06611252348829592, 0.7909985619739608, 0.7886441704575313, 0.06121396564138005], [3.0880880880880888, 0.04813113680276825, 0.7822454989697936, 0.7971669568939377, 0.07917679097357139], [3.108108108108109, 0.03013045974408887, 0.7731789215013157, 0.8053702485275157, 0.09710788321271126], [3.128128128128129, 0.01211770676428429, 0.7638024633423006, 0.8132507575789362, 0.1150000557955209], [3.148148148148149, -0.0058999028447369204, 0.7541198824629255, 0.8208053256361952, 0.13284613775724563], [3.168168168168169, -0.02391514784458839, 0.7441350595236247, 0.8280309249204679, 0.15063897660569003], [3.188188188188189, -0.04192080794459029, 0.7338519963197669, 0.8349246594996057, 0.1683714411878497], [3.208208208208209, -0.05990966669557385, 0.7232748141777852, 0.841483766448789, 0.18603642454799058], [3.228228228228229, -0.07787451438214628, 0.7124077523033986, 0.8477056169578757, 0.20362684677603005], [3.248248248248249, -0.09580815091225683, 0.7012551660825911, 0.8535877173849952, 0.22113565784507877], [3.268268268268269, -0.11370338870290593, 0.6898215253360254, 0.8591277102559697, 0.23855584043700548], [3.288288288288289, -0.13155305556084088, 0.6781114125275938, 0.8643233752091612, 0.2558804127548926], [3.308308308308309, -0.14934999755708359, 0.6661295209278218, 0.8691726298853663, 0.27310243132125533], [3.328328328328329, -0.16708708189413804, 0.6538806527328631, 0.8736735307623985, 0.29021499376090293], [3.348348348348349, -0.18475719976472865, 0.6413697171398376, 0.8778242739340302, 0.3072112415673265], [3.368368368368369, -0.20235326920092364, 0.6286017283792847, 0.8816231958329749, 0.3240843628515048], [3.388388388388389, -0.2198682379125014, 0.6155818037055218, 0.8850687738976255, 0.3408275950720266], [3.408408408408409, -0.23729508611342262, 0.6023151613457101, 0.8881596271822781, 0.35743422774543426], [3.428428428428429, -0.25462682933527475, 0.5888071184084528, 0.8908945169105983, 0.3738976051357046], [3.448448448448449, -0.2718565212265622, 0.5750630887527636, 0.8932723469721079, 0.39021112892178617], [3.468468468468469, -0.288977256336719, 0.5610885808182572, 0.8952921643614918, 0.40636826084212596], [3.488488488488489, -0.30598217288372953, 0.5468891954174341, 0.8969531595605521, 0.4223625253151255], [3.508508508508509, -0.322864455504247, 0.5324706234909431, 0.8982546668626522, 0.4381875120344742], [3.528528528528529, -0.339617337985108, 0.5178386438267218, 0.8991961646395243, 0.45383687853832294], [3.548548548548549, -0.3562341059751485, 0.5029991207439287, 0.8997772755503316, 0.4693043527512646], [3.568568568568569, -0.37270809967623364, 0.4879580017425956, 0.899997766692902, 0.4845837354981063], [3.588588588588589, -0.38903271651242394, 0.4727213151199417, 0.899857549697072, 0.4996689029884225], [3.608608608608609, -0.4052014137762072, 0.45729516755430627, 0.8993566807601052, 0.5145538092708962], [3.628628628628629, -0.4212077112507362, 0.44168574165766544, 0.8984953606241682, 0.5292324886564624], [3.648648648648649, -0.43704519380701995, 0.42589929349771755, 0.8972739344958767, 0.5436990581092831], [3.668668668668669, -0.45270751397503, 0.4099421500905269, 0.8956928919079394, 0.5579477196045973], [3.688688688688689, -0.4681883944876888, 0.39382070686473314, 0.8937528665229608, 0.5719727624524977], [3.708708708708709, -0.48348163079672146, 0.37754142509834093, 0.8914546358794758, 0.5857685655867064], [3.728728728728729, -0.498581093559364, 0.3611108293291188, 0.8887991210803223, 0.5993295998174293], [3.748748748748749, -0.5134807310949282, 0.34453550473964417, 0.8857873864234744, 0.6126504300473882], [3.768768768768769, -0.5281745718102422, 0.3278220945180416, 0.8824206389754845, 0.6257257174501415], [3.788788788788789, -0.5426567265929921, 0.31097729719547484, 0.8787002280877058, 0.6385502216098208], [3.8088088088088092, -0.5569213911720063, 0.2940078639614565, 0.8746276448554888, 0.6511188026214255], [3.8288288288288292, -0.5709628484435367, 0.27692059595805396, 0.8702045215205688, 0.6634264231508351], [3.8488488488488493, -0.5847754707626044, 0.2597223415540743, 0.8654326308168822, 0.6754681504537094], [3.8688688688688693, -0.5983537221984909, 0.2424199936003215, 0.8603138852600776, 0.6872391583524747], [3.8888888888888893, -0.6116921607534719, 0.225020486667026, 0.8548503363810022, 0.6987347291705946], [3.9089089089089093, -0.6247854405439037, 0.20753079426455343, 0.8490441739034732, 0.7099502556233592], [3.9289289289289293, -0.6376283139427884, 0.18995792604850634, 0.8428977248666634, 0.7208812426644278], [3.9489489489489493, -0.650215633682958, 0.17230892501034, 0.8364134526924523, 0.7315233092873882], [3.9689689689689693, -0.6625423549200377, 0.15459086465461716, 0.829593956198116, 0.741872190281612], [3.9889889889889893, -0.6746035372543571, 0.13681084616403344, 0.8224419685547542, 0.7519237379416981], [4.009009009009009, -0.686394346711002, 0.11897599555334996, 0.8149603561918669, 0.7616739237298225], [4.029029029029029, -0.6979100576772135, 0.10109346081337353, 0.8071521176485258, 0.7711188398903274], [4.049049049049049, -0.7091460547963555, 0.08317040904612888, 0.7990203823715952, 0.7802547010159023], [4.069069069069069, -0.7200978348176937, 0.06521402359237159, 0.7905684094614891, 0.7890778455647288], [4.089089089089089, -0.7307610084012433, 0.04723150115259262, 0.7817995863659623, 0.7975847373279819], [4.109109109109109, -0.7411313018769624, 0.029230048902668455, 0.772717427522463, 0.8057719668470993], [4.129129129129129, -0.7512045589575886, 0.011216881605312803, 0.7633255729495896, 0.8136362527802514], [4.1491491491491495, -0.7609767424044268, -0.006800781281512331, 0.753627786788215, 0.8211744432174628], [4.1691691691691695, -0.7704439356454257, -0.024815718498067487, 0.7436279557928647, 0.8283835169438591], [4.1891891891891895, -0.7796023443448923, -0.04282070987702863, 0.7333300877739538, 0.8352605846505338], [4.2092092092092095, -0.788448297924215, -0.06080853923724224, 0.7227383099915046, 0.8418028900925468], [4.2292292292292295, -0.7969782510329876, -0.0787719972758828, 0.7118568675009911, 0.8480078111935954], [4.2492492492492495, -0.8051887849699416, -0.0967038844578535, 0.7006901214519728, 0.8538728610969091], [4.2692692692692695, -0.8130766090531206, -0.11459701390127221, 0.6892425473401994, 0.8593956891619526], [4.2892892892892895, -0.8206385619387455, -0.13244421425788613, 0.6775187332138859, 0.8645740819065331], [4.3093093093093096, -0.8278716128882421, -0.1502383325872608, 0.6655233778348791, 0.8694059638939375], [4.32932932932933, -0.8347728629829247, -0.16797223722359148, 0.6532612887954514, 0.8738893985647418], [4.34934934934935, -0.8413395462858474, -0.18563882063398796, 0.6407373805914761, 0.8780225890129606], [4.36936936936937, -0.8475690309503573, -0.20323100226708693, 0.627956672652757, 0.8818038787062253], [4.38938938938939, -0.8534588202749076, -0.22074173139085096, 0.614924287331303, 0.885231752149702], [4.40940940940941, -0.8590065537037038, -0.23816398991841584, 0.6016454478483508, 0.888304835493483], [4.42942942942943, -0.8642100077727872, -0.25549079522085427, 0.5881254762009628, 0.8910218970832101], [4.44944944944945, -0.869067097001171, -0.2727152029257285, 0.5743697910290335, 0.8933818479537052], [4.46946946946947, -0.8735758747266762, -0.28983030970031004, 0.560383905443567, 0.8953837422654157], [4.48948948948949, -0.87773453388613, -0.30682925601835176, 0.5461734248170875, 0.8970267776834945], [4.50950950950951, -0.8815414077396143, -0.3237052289093017, 0.531744044537075, 0.8983102956993664], [4.52952952952953, -0.884994970538476, -0.34045146468885934, 0.5171015477233235, 0.8992337818946505], [4.54954954954955, -0.8880938381368267, -0.35706125166977737, 0.5022518029101369, 0.8997968661473323], [4.56956956956957, -0.8908367685462928, -0.37352793285182445, 0.48720076169429205, 0.8999993227801044], [4.58958958958959, -0.8932226624337889, -0.3898449085898296, 0.4719544563497108, 0.8998410706508156], [4.60960960960961, -0.8952505635621171, -0.4060056392387398, 0.4565189974097987, 0.899322173184991], [4.62962962962963, -0.896919659173215, -0.4220036477746296, 0.4409005712184177, 0.8984428383504117], [4.64964964964965, -0.8982292803138998, -0.43783252239061404, 0.42510543745047435, 0.8972034185737652], [4.66966966966967, -0.899178902103976, -0.45348591906662217, 0.4091399266031189, 0.8956044105993953], [4.68968968968969, -0.8997681439466012, -0.468957564112003, 0.39301043745855885, 0.8936464552902142], [4.70970970970971, -0.899996769680825, -0.48424125667994483, 0.3767234345195052, 0.8913303373708524], [4.72972972972973, -0.8998646876762388, -0.4993308712526984, 0.3602854454182785, 0.8886569851131498], [4.74974974974975, -0.8993719508697008, -0.5142203600966104, 0.3437030583006133, 0.8856274699641165], [4.76976976976977, -0.8985187567441187, -0.5289037556859806, 0.32698291918520966, 0.8822430061165093], [4.78978978978979, -0.8973054472493022, -0.5433751730947745, 0.3101317293000898, 0.8785049500221989], [4.80980980980981, -0.8957325086649124, -0.557628812355229, 0.29315624239682764, 0.8744147998485194], [4.82982982982983, -0.8938005714055677, -0.5716589607824094, 0.27606326204372733, 0.869974194877822], [4.84984984984985, -0.8915104097681811, -0.5854599952637838, 0.258859638899036, 0.8651849148504701], [4.86986986986987, -0.8888629416216309, -0.5990263845128978, 0.24155226796528378, 0.860048879251541], [4.88988988988989, -0.8858592280388911, -0.6123526912862473, 0.22414808582585014, 0.8545681465415202], [4.90990990990991, -0.8825004728717651, -0.6254335745624585, 0.20665406786486626, 0.8487449133312951], [4.92992992992993, -0.8787880222683976, -0.6382637916829054, 0.18907722547156597, 0.8425815135017799], [4.94994994994995, -0.874723364133754, -0.6508382004529025, 0.17142460323020633, 0.8360804172685254], [4.96996996996997, -0.8703081275332867, -0.6631517612026343, 0.15370327609668416, 0.8292442301916858], [4.98998998998999, -0.8655440820400255, -0.675199538806993, 0.13592034656297983, 0.8220756921317425], [5.01001001001001, -0.8604331370253542, -0.6869767046635175, 0.11808294181056511, 0.8145776761514009], [5.03003003003003, -0.8549773408937575, -0.6984785386276372, 0.1001982108539157, 0.8067531873641027], [5.05005005005005, -0.8491788802618462, -0.7097004309044497, 0.08227332167527315, 0.7986053617296123], [5.07007007007007, -0.843040079081987, -0.7206378838962691, 0.06431545835180517, 0.7901374647971624], [5.09009009009009, -0.8365633977108915, -0.7312865140052095, 0.046331818176314904, 0.7813528903966622], [5.11011011011011, -0.8297514319235347, -0.7416420533900769, 0.028329608772653676, 0.7722551592784911], [5.13013013013013, -0.8226069118728004, -0.7517003516768686, 0.010316045206993193, 0.7628479177024253], [5.15015015015015, -0.8151327009952686, -0.7614573776221913, -0.007701652903885035, 0.7531349359762611], [5.17017017017017, -0.8073317948635856, -0.7709092207289362, -0.02571626428612418, 0.7431201069447214], [5.19019019019019, -0.799207319985875, -0.7800520928135566, -0.04372056890299062, 0.7328074444292513], [5.21021021021021, -0.7907625325526707, -0.7888823295243274, -0.061707350848576724, 0.7222010816193268], [5.23023023023023, -0.7820008171318761, -0.7973963918099711, -0.07966940123984799, 0.7113052694159234], [5.25025025025025, -0.7729256853122702, -0.8055908673380682, -0.09759952110587553, 0.7001243747278068], [5.27027027027027, -0.7635407742961057, -0.8134624718626764, -0.11549052427309589, 0.6886628787213281], [5.29029029029029, -0.7538498454413624, -0.821008050540618, -0.13333524024544188, 0.6769253750244287], [5.31031031031031, -0.7438567827542412, -0.8282245791959021, -0.15112651707819, 0.6649165678855684], [5.33033033033033, -0.7335655913325013, -0.8351091655317768, -0.1688572242443727, 0.6526412702883208], [5.35035035035035, -0.7229803957602665, -0.841659050289928, -0.1865202554926068, 0.640104402022388], [5.37037037037037, -0.7121054384549412, -0.8478716083563548, -0.2041085316951924, 0.6273109877118093], [5.39039039039039, -0.7009450779669016, -0.8537443498134845, -0.22161500368534104, 0.6142661548011523], [5.41041041041041, -0.6895037872326424, -0.8592749209381003, -0.23903265508239568, 0.6009751315004961], [5.43043043043043, -0.6777861517820772, -0.8644611051446857, -0.2563545051039109, 0.587443244690028], [5.45045045045045, -0.6657968679007154, -0.8693008238738045, -0.273573611363465, 0.5736759177850956], [5.47047047047047, -0.6535407407474466, -0.873792137425162, -0.2906830726530844, 0.5596786685625671], [5.49049049049049, -0.6410226824286928, -0.8779332457350137, -0.30767603170916336, 0.5454571069493741], [5.51051051051051, -0.6282477100296955, -0.8817224890976086, -0.3245456779607717, 0.5310169327741213], [5.53053053053053, -0.6152209436037288, -0.8851583488303788, -0.3412852502592488, 0.5163639334826643], [5.55055055055055, -0.6019476041200449, -0.8882394478826093, -0.35788803958798937, 0.5015039818185719], [5.57057057057057, -0.5884330113713729, -0.8909645513873434, -0.37434739175133575, 0.4864430334694024], [5.59059059059059, -0.574682581841811, -0.8933325671563019, -0.39065671004149843, 0.4711871246797363], [5.61061061061061, -0.5607018265359653, -0.8953425461176189, -0.4068094578824361, 0.4557423698319226], [5.63063063063063, -0.5464963487702066, -0.8969936826962182, -0.42279916144963564, 0.4401149589955101], [5.65065065065065, -0.5320718419269294, -0.8982853151366782, -0.43861941226474216, 0.4243111554463414], [5.67067067067067, -0.5174340871727137, -0.8992169257684555, -0.45426386976399896, 0.4083372931563095], [5.6906906906906904, -0.5025889511413034, -0.8997881412133605, -0.4697262638394685, 0.3921997742547785], [5.7107107107107105, -0.48754238358233193, -0.8999987325352031, -0.4850003973520148, 0.3759050664626879], [5.7307307307307305, -0.4723004149767341, -0.8998486153315477, -0.5000801486150417, 0.3594597005003697], [5.7507507507507505, -0.4568691541198033, -0.8993378497675403, -0.51495947384799, 0.34287026747011434], [5.7707707707707705, -0.44125478567286075, -0.8984666405517951, -0.5296324095986111, 0.3261434162145378], [5.7907907907907905, -0.42546356768451865, -0.8972353368543503, -0.5440930751330465, 0.3092858506518063], [5.8108108108108105, -0.40950182908253047, -0.8956444321667243, -0.558335674792753, 0.29230432708878773], [5.8308308308308305, -0.3933759671372337, -0.8936945641041306, -0.5723545003173339, 0.27520565151320586], [5.8508508508508505, -0.37709244489760213, -0.8913865141499296, -0.5861439331323381, 0.2579966768658837], [5.870870870870871, -0.36065778860093445, -0.8887212073424193, -0.599698446601118, 0.2406843002941682], [5.890890890890891, -0.34407858505721756, -0.8856997119040914, -0.6130126082398376, 0.22327546038763765], [5.910910910910911, -0.32736147900921403, -0.8823232388135005, -0.6260810818947452, 0.20577713439719958], [5.930930930930931, -0.31051317046932964, -0.8785931413199188, -0.6388986298808395, 0.18819633543869352], [5.950950950950951, -0.29354041203433007, -0.8745109144009707, -0.6514601150810696, 0.1705401096821196], [5.970970970970971, -0.27645000617898274, -0.8700781941634631, -0.6637605030052294, 0.15281553352761965], [5.990990990990991, -0.25924880252970767, -0.8652967571876545, -0.6757948638077195, 0.13502971076934206], [6.011011011011011, -0.24194369511933034, -0.8601685198152229, -0.6875583742633689, 0.11718976974832775], [6.031031031031031, -0.22454161962403776, -0.8546955373812195, -0.6990463197005257, 0.09930286049555796], [6.051051051051051, -0.20704955058364397, -0.8488800033903153, -0.7102540958906394, 0.08137615186630882], [6.071071071071071, -0.18947449860627896, -0.8427242486376718, -0.7211772108935802, 0.06341682866696166], [6.091091091091091, -0.171823507558623, -0.8362307402747856, -0.731811286857953, 0.04543208877542007], [6.111111111111111, -0.15410365174281038, -0.8294020808206849, -0.7421520617756882, 0.02742914025628817], [6.131131131131131, -0.13632203306113516, -0.8222410071188719, -0.7521953911902007, 0.00941519847196616], [6.151151151151151, -0.1184857781696957, -0.8147503892404282, -0.7619372498574397, -0.008602516809179039], [6.171171171171171, -0.10060203562211731, -0.8069332293337264, -0.771373733359156, -0.026616784306408958], [6.191191191191191, -0.08267797300449935, -0.7987926604212044, -0.7805010596677461, -0.04462038412081489], [6.211211211211211, -0.06472077406273388, -0.79033194514369, -0.7893155706620417, -0.06260610062896546], [6.231231231231231, -0.04673763582334795, -0.7815544744527719, -0.7978137335934417, -0.08056672537484048], [6.251251251251251, -0.028735765709023038, -0.7724637662517497, -0.8059921425017931, -0.09849505995889239], [6.271271271271271, -0.010722378649947805, -0.7630634639856997, -0.8138475195804596, -0.11638391892307699], [6.291291291291291, 0.007295305807837932, -0.7533573351812274, -0.8213767164900262, -0.13422613263069755], [6.311311311311311, 0.025310066395949328, -0.7433492699364882, -0.8285767156201149, -0.15201455013990772], [6.331331331331331, 0.043314683017852885, -0.7330432793620837, -0.8354446312988064, -0.1697420420697221], [6.351351351351351, 0.061301939642593124, -0.7224434939734576, -0.8419777109491823, -0.18740150345738496], [6.371371371371371, 0.07926462719688995, -0.7115541620354348, -0.8481733361925247, -0.20498585660595312], [6.391391391391391, 0.09719554645444728, -0.7003796478595696, -0.8540290238977296, -0.22248805392095022], [6.411411411411411, 0.11508751092131529, -0.6889244300549818, -0.8595424271765165, -0.23990108073495708], [6.431431431431431, 0.13293334971614962, -0.6771930997333856, -0.8647113363240323, -0.25721795811900466], [6.451451451451451, 0.15072591044421313, -0.6651903586690272, -0.8695336797044738, -0.27443174567964396], [6.471471471471471, 0.16845806206396877, -0.6529210174142718, -0.8740075245813744, -0.29153554434057105], [6.491491491491491, 0.1861226977451139, -0.6403899933715921, -0.8781310778922216, -0.30852249910769275], [6.511511511511511, 0.20371273771691145, -0.6276023088227334, -0.8819026869670938, -0.32538580181652466], [6.531531531531531, 0.2212211321056758, -0.614563088915845, -0.885320840191031, -0.3421186938608208], [6.551551551551551, 0.23864086376027593, -0.6012775596113822, -0.8883841676098713, -0.3587144689013405], [6.571571571571571, 0.25596495106452455, -0.5877510455876059, -0.8910914414793117, -0.3751664755536673], [6.591591591591591, 0.2731864507353244, -0.5739889681065161, -0.8934415767569739, -0.39146812005400294], [6.611611611611611, 0.2902984606054517, -0.5599968428410759, -0.8954336315372747, -0.40761286890186765], [6.631631631631631, 0.3072941223898607, -0.5457802776645974, -0.8970668074289317, -0.42359425147864715], [6.651651651651651, 0.3241666244344007, -0.5313449704031741, -0.8983404498749475, -0.4394058626409382], [6.671671671671671, 0.34090920444584405, -0.516696706552062, -0.8992540484149494, -0.45504136528765177], [6.691691691691691, 0.35751515220213076, -0.5018413569569233, -0.8998072368897746, -0.4704944928998459], [6.711711711711711, 0.3739778122417439, -0.48678487546086296, -0.8999997935882229, -0.48575905205227027], [6.731731731731731, 0.3902905865311375, -0.4715332965182011, -0.8998316413359162, -0.5008289248956157], [6.751751751751751, 0.4064469371091478, -0.4560927327759371, -0.8993028475262279, -0.515698071608473], [6.771771771771771, 0.42244038870732853, -0.44046937262387525, -0.8984136240932739, -0.5303605328180206], [6.791791791791791, 0.43826453134515975, -0.4246694777143937, -0.8971643274269707, -0.5448104319884682], [6.811811811811811, 0.4539130228990896, -0.40869938045285, -0.8955554582301996, -0.5590419777763006], [6.831831831831831, 0.46937959164438053, -0.39256548145963066, -0.8935876613181303, -0.5730494663513775], [6.851851851851851, 0.48465803876873986, -0.37627424700486034, -0.8912617253597879, -0.5868272836829598], [6.871871871871871, 0.4997422408567282, -0.35983220641679947, -0.8885785825619626, -0.6003699077897436], [6.891891891891891, 0.5146261523439501, -0.3432459494649692, -0.8855393082955938, -0.6136719109530043], [6.911911911911911, 0.5293038079400423, -0.32652212371905187, -0.8821451206647738, -0.6267279618919593], [6.931931931931931, 0.5437693250194893, -0.3096674318846264, -0.8783973800185467, -0.6395328279004803], [6.951951951951951, 0.5580169059793082, -0.2926886291168053, -0.8742975884056974, -0.6520813769442987], [6.971971971971971, 0.5720408405626574, -0.2755925203128507, -0.8698473889727482, -0.6643685797178622], [6.991991991991991, 0.5858355081474378, -0.258385957384855, -0.865048565305406, -0.6763895116600187], [7.012012012012011, 0.5993953799989699, -0.24107583651357736, -0.8599030407137229, -0.6881393549277213], [7.032032032032031, 0.6127150214858427, -0.2236690953845388, -0.854412877461257, -0.6996134003269602], [7.052052052052051, 0.6257890942580483, -0.2061727104074824, -0.8485802759385422, -0.7108070492001509], [7.072072072072071, 0.6386123583865277, -0.18859369392031314, -0.8424075737811983, -0.7217158152692198], [7.092092092092091, 0.6511796744632696, -0.17093909137863847, -0.8358972449330356, -0.7323353264336503], [7.112112112112111, 0.6634860056611237, -0.15321597853203536, -0.8290518986545279, -0.7426613265227661], [7.132132132132131, 0.6755264197524988, -0.1354314585881763, -0.821874278477053, -0.7526896770015544], [7.152152152152151, 0.6872960910861394, -0.11759265936595002, -0.8143672611033186, -0.7624163586293388], [7.172172172172171, 0.6987903025211891, -0.09970673043871875, -0.8065338552544147, -0.7718374730706425], [7.192192192192191, 0.7100044473177617, -0.08178084026885607, -0.7983772004639555, -0.7809492444575943], [7.212212212212211, 0.7209340309832671, -0.06382217333471475, -0.7899005658197922, -0.7897480209032491], [7.232232232232231, 0.7315746730737498, -0.0458379272511753, -0.7811073486538019, -0.7982302759652209], [7.2522522522522515, 0.741922108949516, -0.027835309884929625, -0.7720010731802794, -0.8063926100590374], [7.2722722722722715, 0.7519721914843507, -0.009821536465655855, -0.7625853890834745, -0.8142317518206517], [7.2922922922922915, 0.7617208927276341, 0.008196173305757738, -0.7528640700548425, -0.8217445594175659], [7.3123123123123115, 0.7711643055186962, 0.02621059815078091, -0.742841012280596, -0.828928021808039], [7.3323323323323315, 0.7802986450527584, 0.044214518107441904, -0.7325202328801594, -0.8357792599478756], [7.3523523523523515, 0.7891202503978375, 0.062200717424000185, -0.7219058682961578, -0.8422955279443114], [7.3723723723723715, 0.7976255859620028, 0.08016198745093188, -0.71100217263658, -0.8484742141565347], [7.3923923923923915, 0.8058112429103979, 0.09809112953006863, -0.699813515969785, -0.8543128422423987], [7.4124124124124116, 0.81367394053146, 0.11598095787973203, -0.6883443825730304, -0.8598090721509105], [7.432432432432432, 0.8212105275517899, 0.13382430247470747, -0.6765993691352303, -0.8649607010600946], [7.452452452452452, 0.8284179833991433, 0.15161401191990276, -0.6645831829146571, -0.8697656642598566], [7.472472472472472, 0.8352934194130396, 0.16934295631654026, -0.6523006398523309, -0.8742220359794949], [7.492492492492492, 0.8418340800025016, 0.18700403011973327, -0.6397566626418477, -0.878328030159525], [7.512512512512512, 0.8480373437504631, 0.20459015498630204, -0.626956278756425, -0.8820820011675112], [7.532532532532532, 0.853900724464401, 0.22209428261168732, -0.6139046184339512, -0.8854824444576159], [7.552552552552552, 0.8594218721727706, 0.239509397554825, -0.6006069126208488, -0.8885279971736028], [7.572572572572572, 0.8645985740668455, 0.2568285200498493, -0.5870684908755748, -0.8912174386950533], [7.592592592592592, 0.8694287553875848, 0.2740447088034982, -0.5732947792325989, -0.8935496911265767], [7.612612612612612, 0.873910480257171, 0.29115106377709865, -0.5592912980277145, -0.8955238197298169], [7.632632632632632, 0.8780419524548863, 0.3081407289520187, -0.5450636596855559, -0.8971390332980844], [7.652652652652652, 0.8818215161370159, 0.32500689507747554, -0.5306175664702067, -0.8983946844734627], [7.672672672672672, 0.8852476565004901, 0.34174280239960064, -0.5159588081998017, -0.8992902700062607], [7.692692692692692, 0.8883190003899988, 0.3583417433706666, -0.5010932599260381, -0.8998254309567096], [7.712712712712712, 0.8910343168483355, 0.37479706533739077, -0.48602687957952734, -0.8999999528388214], [7.732732732732732, 0.8933925176097498, 0.3911021732072372, -0.47076570558192804, -0.8998137657063522], [7.752752752752752, 0.8953926575361112, 0.4072505320916498, -0.45531585442582073, -0.8992669441808362], [7.772772772772772, 0.8970339349957092, 0.42323566992515566, -0.4396835182232922, -0.8983597074216777], [7.792792792792792, 0.8983156921845367, 0.4390511800592904, -0.4238749622242119, -0.897092419038315], [7.812812812812812, 0.8992374153899308, 0.45469072383030457, -0.4078965223051968, -0.8954655869444903], [7.832832832832834, 0.8997987351964621, 0.47014803309962416, -0.39175460243026733, -0.8934798631546836], [7.852852852852854, 0.8999994266339917, 0.4854169127660389, -0.37545567208422276, -0.8911360435227943], [7.872872872872874, 0.8998394092678368, 0.5004912432486316, -0.3590062636797415, -0.8884350674231709], [7.892892892892894, 0.899318747231008, 0.5153649829394257, -0.34241296993927556, -0.885378017374123], [7.912912912912914, 0.8984376491985054, 0.53003217062479, -0.32568244125276674, -0.8819661186040597], [7.932932932932934, 0.8971964683036844, 0.5444869278746204, -0.3088213830122507, -0.8782007385604343], [7.952952952952954, 0.8955957019967234, 0.5587234613983422, -0.29183655292441785, -0.8740833863616868], [7.972972972972974, 0.8936359918452534, 0.5727360653667918, -0.27473475830220695, -0.8696157121924087], [7.992992992992994, 0.8913181232772236, 0.5865191236990425, -0.2575228533365169, -0.8647995066419683], [8.013013013013014, 0.8886430252661129, 0.6000671123132615, -0.24020773634913062, -0.8596366999868643], [8.033033033033034, 0.8856117699586081, 0.6133746013406948, -0.22279634702795148, -0.8541293614170957], [8.053053053053054, 0.8822255722449006, 0.6264362573018922, -0.20529566364566135, -0.8482796982068539], [8.073073073073074, 0.878485789271773, 0.639246845244301, -0.1877127002629137, -0.8420900548298754], [8.093093093093094, 0.8743939198986717, 0.6518012308403709, -0.17005450391718355, -0.8355629120198047], [8.113113113113114, 0.8699516040969818, 0.6640943824453308, -0.15232815179840087, -0.8287008857759468], [8.133133133133134, 0.8651606222927472, 0.6761213731138096, -0.13454074841249883, -0.8215067263148063], [8.153153153153154, 0.8600228946530966, 0.6878773825744964, -0.11669942273401471, -0.8139833169678342], [8.173173173173174, 0.8545404803166645, 0.6993576991620466, -0.09881132534888352, -0.806133673025824], [8.193193193193194, 0.848715576568314, 0.7105577217054597, -0.08088362558857043, -0.7979609405304194], [8.213213213213214, 0.8425505179584907, 0.721472961372173, -0.06292350865668997, -0.7894683950132199], [8.233233233233234, 0.836047775367564, 0.7320990434671303, -0.044938172749264094, -0.7806594401829867], [8.253253253253254, 0.8292099550155284, 0.742431709186107, -0.026934826169772823, -0.7715376065614786], [8.273273273273274, 0.8220397974174619, 0.7524668173225868, -0.008920684440154085, -0.7621065500684615], [8.293293293293294, 0.8145401762851616, 0.7622003459275066, 0.009097032591089554, -0.7523700505564599], [8.313313313313314, 0.8067140973753938, 0.7716283939212045, 0.027111103642518203, -0.7423320102958391], [8.333333333333334, 0.7985646972852252, 0.7807471826569258, 0.045114308893956255, -0.7319964524108216], [8.353353353353354, 0.7900952421949107, 0.7895530574352577, 0.06309943288010833, -0.7213675192670689], [8.373373373373374, 0.7813091265588504, 0.798042488968889, 0.08105926738242988, -0.7104494708114711], [8.393393393393394, 0.7722098717451323, 0.8062120747971047, 0.0989866143180933, -0.6992466828648127], [8.413413413413414, 0.762801124624211, 0.8140585406494516, 0.11687428862489184, -0.6877636453679965], [8.433433433433434, 0.7530866561072869, 0.8215787417580253, 0.13471512114092501, -0.6760049605825301], [8.453453453453454, 0.7430703596349704, 0.8287696641178554, 0.15250196147791126, -0.663975341245996], [8.473473473473474, 0.732756249616839, 0.8356284256948812, 0.17022768088697654, -0.6516796086832427], [8.493493493493494, 0.7221484598225104, 0.8421522775810373, 0.1878851751157699, -0.6391226908740579], [8.513513513513514, 0.7112512417248779, 0.8483386050959808, 0.20546736725576137, -0.6263096204780932], [8.533533533533534, 0.7000689627961721, 0.8541849288350242, 0.2229672105785806, -0.6132455328178359], [8.553553553553554, 0.6886061047575318, 0.8596889056628504, 0.24037769136025972, -0.5999356638204337], [8.573573573573574, 0.6768672617827832, 0.8648483296526129, 0.25769183169224846, -0.5863853479192002], [8.593593593593594, 0.6648571386571529, 0.8696611329700444, 0.27490269227807496, -0.5726000159156375], [8.613613613613614, 0.6525805488916459, 0.8741253867022194, 0.29200337521453107, -0.5585851928028379], [8.633633633633634, 0.6400424127938498, 0.8782393016306388, 0.30898702675626794, -0.5443464955511346], [8.653653653653654, 0.6272477554959351, 0.8820012289483277, 0.3258468400626938, -0.5298896308568869], [8.673673673673674, 0.6142017049406437, 0.8854096609206562, 0.34257605792607265, -0.5152203928553072], [8.693693693693694, 0.600909489826072, 0.8884632314896215, 0.3591679754797307, -0.5003446607982416], [8.713713713713714, 0.5873764375100716, 0.8911607168213476, 0.3756159428852851, -0.4852683966978375], [8.733733733733734, 0.5736079718751105, 0.8935010357965822, 0.3919133679978187, -0.4699976429370417], [8.753753753753754, 0.5596096111544462, 0.8954832504439971, 0.4080537190079308, -0.4545385198478872], [8.773773773773774, 0.5453869657204852, 0.8971065663161149, 0.4240305270596069, -0.43889722325853836], [8.793793793793794, 0.5309457358362141, 0.8983703328077141, 0.43983738884285783, -0.42308002201007866], [8.813813813813814, 0.516291709370604, 0.8992740434165838, 0.45546796916008764, -0.40709325544403563], [8.833833833833834, 0.5014307594789027, 0.8998173359465239, 0.4709160034651642, -0.3909433308616498], [8.853853853853854, 0.4863688422487457, 0.8999999926525084, 0.48617530037417245, -0.3746367209559067], [8.873873873873874, 0.47111199431302925, 0.899821940327955, 0.5012397441468464, -0.35817996121736023], [8.893893893893894, 0.4556663304305009, 0.8992832503340662, 0.5161032971376821, -0.3415796473147876], [8.913913913913914, 0.44003804103503913, 0.8983841385712272, 0.5307600022157537, -0.32484243245172584], [8.933933933933934, 0.4242333897546026, 0.8971249653924771, 0.5452039851522583, -0.3079750246999488], [8.953953953953954, 0.40825871090084453, 0.8955062354590828, 0.5594294569748349, -0.29098418431095363], [8.973973973973974, 0.3921204069303976, 0.8935285975382773, 0.5734307162877149, -0.27387672100653465], [8.993993993993994, 0.37582494587884785, 0.8911928442432419, 0.5872021515567711, -0.25665949124952936], [9.014014014014014, 0.3593788587684241, 0.8884999117154359, 0.6007382433585531, -0.2393393954958321], [9.034034034034034, 0.3427887369904444, 0.8854508792494029, 0.6140335665924039, -0.22192337542877538], [9.054054054054054, 0.3260612296635662, 0.882046968860203, 0.6270827926547756, -0.2044184111769874], [9.074074074074074, 0.30920304096890133, 0.8782895447936434, 0.6398806915748673, -0.186831518516842], [9.094094094094094, 0.2922209274630612, 0.8741801129795056, 0.6524221341107344, -0.1691697460606207], [9.114114114114114, 0.2751216953702116, 0.8697203204279873, 0.6647020938050262, -0.15144017243151497], [9.134134134134134, 0.25791219785422076, 0.8649119545696005, 0.676715648999529, -0.13364990342660013], [9.154154154154154, 0.24059933227199504, 0.8597569425387911, 0.6884579848077059, -0.11580606916891857], [9.174174174174174, 0.22319003740910232, 0.8542573504015671, 0.699924395044446, -0.09791582124981332], [9.194194194194194, 0.20569129069879089, 0.8484153823274447, 0.7111102841122445, -0.07998632986265733], [9.214214214214214, 0.1881101054255198, 0.8422333797060434, 0.7220111688430636, -0.06202478092912736], [9.234234234234235, 0.17045352791411947, 0.8357138202086859, 0.7326226802951303, -0.04403837321917415], [9.254254254254255, 0.1527286347057107, 0.8288593167953779, 0.7429405655039564, -0.02603431546584314], [9.274274274274275, 0.13494252972151324, 0.8216726166675652, 0.7529606891868738, -0.008019823476102052], [9.294294294294295, 0.11710234141568075, 0.8141566001670889, 0.7626790354004057, 0.00999788276116655], [9.314314314314315, 0.09921521991830316, 0.8063142796217797, 0.7720917091498078, 0.028011581968848866], [9.334334334334335, 0.08128833416972171, 0.7981487981381532, 0.7811949379501353, 0.046014054475799746], [9.354354354354355, 0.06332886904730496, 0.7896634283416916, 0.7899850733382078, 0.06399808511039887], [9.374374374374375, 0.04534402248583755, 0.7808615710652146, 0.7984585923348698, 0.08195646609230366], [9.394394394394395, 0.027341002592675606, 0.7717467539858682, 0.8066120988569575, 0.09988199992123978], [9.414414414414415, 0.009327024758825166, 0.7623226302112736, 0.8144423250784074, 0.11776750226167157], [9.434434434434435, -0.008690691232898635, 0.7525929768154063, 0.8219461327399614, 0.1356058048221962], [9.454454454454455, -0.02670492410147253, 0.7425616933247914, 0.8291205144069428, 0.15338975822850745], [9.474474474474475, -0.044708453961866575, 0.7322328001556204, 0.8359625946745995, 0.1711122348887779], [9.494494494494495, -0.06269406521868609, 0.7216104370024181, 0.8424696313205311, 0.1887661318503108], [9.514514514514515, -0.08065454945809436, 0.7106988611789025, 0.8486390164037382, 0.20634437364631714], [9.534534534534535, -0.09858270833685706, 0.6995024459117056, 0.8544682773098535, 0.2238399151316762], [9.554554554554555, -0.11647135646735057, 0.6880256785876383, 0.8599550777421356, 0.24124574430654422], [9.574574574574575, -0.1343133242973777, 0.6762731589551993, 0.8650972186578281, 0.2585548851266782], [9.594594594594595, -0.15210146098363692, 0.6642495972810527, 0.8698926391495089, 0.2757604002993498], [9.614614614614615, -0.16982863725769315, 0.65195981246221, 0.8743394172710771, 0.2928553940637271], [9.634634634634635, -0.18748774828330173, 0.6394087300946756, 0.878435770808046, 0.30983301495461246], [9.654654654654655, -0.2050717165039402, 0.6266013804993268, 0.882180057991832, 0.32668645854842526], [9.674674674674675, -0.22257349447940677, 0.613542896705824, 0.885570778157756, 0.34340897019033273], [9.694694694694695, -0.23998606771034833, 0.6002385123953536, 0.8886065723464914, 0.35999384770143283], [9.714714714714715, -0.2573024574495865, 0.5866935598030331, 0.8912862238487187, 0.3764344440649062], [9.734734734734735, -0.2745157234991143, 0.5729134675808158, 0.8936086586927673, 0.39272417009005944], [9.754754754754755, -0.291618966991643, 0.558903758621754, 0.8955729460750508, 0.4088564970531922], [9.774774774774775, -0.30860533315558436, 0.5446700478464896, 0.8971782987331212, 0.4248249593142304], [9.794794794794795, -0.3254680140623589, 0.5302180399528637, 0.8984240732611943, 0.4406231569080758], [9.814814814814815, -0.3422002513549313, 0.5155535271295416, 0.8993097703680191, 0.4562447581096342], [9.834834834834835, -0.3587953389564773, 0.5006823867345758, 0.8998350350769874, 0.47168350197149356], [9.854854854854855, -0.37524662575809775, 0.48561057893983134, 0.8999996568684042, 0.4869332008332352], [9.874874874874875, -0.39154751828450185, 0.47034414434222216, 0.8998035697638614, 0.5019877428013736], [9.894894894894895, -0.40769148333659155, 0.45488920154271306, 0.8992468523526813, 0.5168410941989274], [9.914914914914915, -0.4236720506098886, 0.43925194469405954, 0.8983297277604186, 0.5314873019836451], [9.934934934934935, -0.43948281528775346, 0.42343864101826606, 0.8970525635594346, 0.5459204961339109], [9.954954954954955, -0.45511744060835757, 0.4074556282947605, 0.895415871621579, 0.5601348920013781], [9.974974974974975, -0.47056966040438064, 0.3913093123202892, 0.8934203079130373, 0.5741247926293856], [9.994994994994995, -0.48583328161441414, 0.37500616434155143, 0.8910666722314277, 0.5878845910362291], [10.015015015015015, -0.5009021867650646, 0.35855271846160297, 0.8883559078852524, 0.6014087724623702], [10.035035035035035, -0.5157703364227624, 0.34195556902106694, 0.8852891013158309, 0.6146919165806862], [10.055055055055055, -0.5304317716142936, 0.32522136795520257, 0.8818674816618667, 0.6277286996688696], [10.075075075075075, -0.5448806162150831, 0.30835682212789034, 0.878092420266825, 0.640513896743112], [10.095095095095095, -0.5591110793042745, 0.2913686906436032, 0.8739654301293135, 0.653042383652214], [10.115115115115115, -0.5731174574856602, 0.2742637821384392, 0.8694881652966918, 0.6653091391312824], [10.135135135135135, -0.586894137173533, 0.2570489520513037, 0.8646624202021495, 0.6773092468141917], [10.155155155155155, -0.6004355968425431, 0.2397310998763324, 0.8594901289455185, 0.6890378972040042], [10.175175175175175, -0.6137364092406581, 0.2223171663976588, 0.8539733645181099, 0.7004903896005575], [10.195195195195195, -0.6267912435643395, 0.20481413090763206, 0.8481143379718847, 0.711662133984448], [10.215215215215215, -0.6395948675950637, 0.1872290084096017, 0.8419153975332899, 0.7225486528566544], [10.235235235235235, -0.6521421497963317, 0.16956884680638984, 0.8353790276621194, 0.7331455830330653], [10.255255255255255, -0.6644280613703262, 0.1518407240755777, 0.8285078480557714, 0.7434486773931895], [10.275275275275275, -0.6764476782733922, 0.1340517454327382, 0.821304612599307, 0.7534538065823512], [10.295295295295295, -0.6881961831895345, 0.11620904048375243, 0.8137722082617254, 0.763156960666684], [10.315315315315315, -0.6996688674611381, 0.09831976036735014, 0.8059136539389026, 0.7725542507402638], [10.335335335335335, -0.7108611329761421, 0.08039107488902082, 0.7977320992436546, 0.7816419104837357], [10.355355355355355, -0.7217684940109059, 0.0624301696474428, 0.789230823243411, 0.7904162976738074], [10.375375375375375, -0.7323865790280333, 0.044444243154582924, 0.7804132331460043, 0.7988738956430095], [10.395395395395395, -0.742711132428431, 0.026440503950620645, 0.771282862934103, 0.8070113146891317], [10.415415415415415, -0.7527380162569008, 0.008426167714853012, 0.7618433719488344, 0.8148252934337751], [10.435435435435435, -0.7624632118605823, -0.00959154562626172, 0.752098543423165, 0.8223127001294722], [10.455455455455455, -0.7718828214995792, -0.027605414792762614, 0.742052282965627, 0.8294705339148537], [10.475475475475475, -0.7809930699091261, -0.04560822004538708, 0.7317086169949978, 0.8362959260173566], [10.495495495495495, -0.7897903058126691, -0.06359274607915442, 0.7210716911265607, 0.842786140902994], [10.515515515515515, -0.7982710033852527, -0.08155178491517205, 0.7101457685105926, 0.8489385773727227], [10.535535535535535, -0.8064317636666272, -0.09947813878950579, 0.6989352281237455, 0.8547507696049736], [10.555555555555555, -0.8142693159235107, -0.11736462303795575, 0.6874445630140058, 0.8602203881439217], [10.575575575575575, -0.8217805189604596, -0.1352040689755823, 0.6756783784999354, 0.8653452408331037], [10.595595595595595, -0.8289623623788206, -0.15298932676982763, 0.6636413903249163, 0.8701232736940084], [10.615615615615615, -0.8358119677832627, -0.17071326830608152, 0.6513384227671383, 0.8745525717492856], [10.635635635635635, -0.8423265899354024, -0.18836879004454285, 0.6387744067060865, 0.8786313597902456], [10.655655655655655, -0.8485036178540618, -0.20594881586723168, 0.6259543776463053, 0.8823580030883419], [10.675675675675675, -0.8543405758617184, -0.22344629991401121, 0.61288347369923, 0.8857310080503499], [10.695695695695695, -0.8598351245767261, -0.24085422940648277, 0.5995669335238929, 0.8887490228169808], [10.715715715715715, -0.8649850618509111, -0.25816562745862165, 0.5860100942273342, 0.8914108378046907], [10.735735735735735, -0.869788323652166, -0.2753735558730283, 0.5722183892255536, 0.8937153861904669], [10.755755755755755, -0.8742429848916887, -0.29247111792167313, 0.5581973460658635, 0.8956617443393972], [10.775775775775776, -0.8783472601955347, -0.30945146111002125, 0.5439525842115158, 0.897249132174852], [10.795795795795796, -0.8820995046201726, -0.3263077799234285, 0.5294898127894888, 0.8984769134911293], [10.815815815815816, -0.8854982143117583, -0.3430333185547091, 0.5148148283023395, 0.899344596208438], [10.835835835835836, -0.8885420271088611, -0.35962137361178065, 0.4999335123050353, 0.8998518325701178], [10.855855855855856, -0.8912297230884003, -0.376065296804302, 0.4848518290476988, 0.8999984192820154], [10.875875875875876, -0.8935602250545771, -0.3923584976082271, 0.46957582308520884, 0.8997842975939631], [10.895895895895896, -0.8955325989706008, -0.40849444590720674, 0.45411161685461693, 0.8992095533233243], [10.915915915915916, -0.8971460543330391, -0.42446667460977955, 0.4384654082213486, 0.8982744168205993], [10.935935935935936, -0.8983999444886429, -0.44026878224130345, 0.42264346799517466, 0.8969792628771038], [10.955955955955956, -0.8992937668935164, -0.45589443550958864, 0.4066521374169471, 0.8953246105747569], [10.975975975975976, -0.8998271633145309, -0.47133737184320423, 0.390497825617107, 0.8933111230780394], [10.995995995995996, -0.8999999199729005, -0.48659140190143996, 0.3741870070469832, 0.8909396073682057], [11.016016016016017, -0.8998119676298615, -0.5016504120549207, 0.3577262188839094, 0.8882110139198549], [11.036036036036037, -0.8992633816144227, -0.5165083668358684, 0.3411220584112093, 0.8851264363199937], [11.056056056056057, -0.8983543817931745, -0.5311593113570529, 0.3243811803740769, 0.8816871108297383], [11.076076076076077, -0.8970853324821693, -0.5455973736984319, 0.30751029431244153, 0.8778944158888369], [11.096096096096097, -0.8954567423009073, -0.5598167672605472, 0.29051616187186474, 0.8737498715632076], [11.116116116116117, -0.893469263968489, -0.5738117930837227, 0.2734055940935547, 0.869255138935715], [11.136136136136138, -0.8911236940420125, -0.587576842132138, 0.2561854486845844, 0.8644120194404279], [11.156156156156158, -0.8884209725973234, -0.601106397541862, 0.23886262726940605, 0.8592224541406261], [11.176176176176178, -0.8853621828522442, -0.6143950368319444, 0.22144407262376384, 0.8536885229508471], [11.196196196196198, -0.8819485507324332, -0.6274374340776799, 0.20393676589211435, 0.8478124438032807], [11.216216216216218, -0.8781814443800487, -0.6402283620451745, 0.18634772378966877, 0.8415965717588489], [11.236236236236238, -0.8740623736054146, -0.6527626942863564, 0.16868399579017893, 0.8350433980633268], [11.256256256256258, -0.8695929892819066, -0.6650354071935943, 0.15095266130059415, 0.8281555491488809], [11.276276276276278, -0.8647750826843019, -0.6770415820130985, 0.13316082682372118, 0.8209357855814278], [11.296296296296298, -0.8596105847708584, -0.6887764068162966, 0.11531562311002454, 0.8133870009542319], [11.316316316316318, -0.8541015654094105, -0.7002351784283968, 0.09742420229970851, 0.80551222072819], [11.336336336336338, -0.8482502325477905, -0.7114133043133619, 0.07949373505622655, 0.7973146010192623], [11.356356356356358, -0.8420589313289111, -0.7223063044145432, 0.06153140769236676, 0.78879742733354], [11.376376376376378, -0.8355301431508595, -0.7329098129502315, 0.04354441929006527, 0.7799641132504543], [11.396396396396398, -0.8286664846723839, -0.7432195801634097, 0.025539978815101914, 0.7708181990546558], [11.416416416416418, -0.8214707067641689, -0.7532314740250033, 0.0075253022278345995, 0.7613633503171108], [11.436436436436438, -0.8139456934063194, -0.762941481889948, -0.010492390408869675, 0.751603356425986], [11.456456456456458, -0.8060944605324966, -0.7723457121054079, -0.028505877823348052, 0.7415421290679072], [11.476476476476478, -0.7979201548211691, -0.7814403955705035, -0.04650794042933951, 0.731183700660203], [11.496496496496498, -0.7894260524344618, -0.7902218872469208, -0.06449136321950706, 0.720532222734762], [11.516516516516518, -0.7806155577051106, -0.7986866676197985, -0.08244893865712472, 0.709591964274148], [11.536536536536538, -0.7714922017720468, -0.8068313441083068, -0.10037346956477053, 0.6983673100006448], [11.556556556556558, -0.7620596411651603, -0.8146526524253521, -0.11825777200886746, 0.6868627586189124], [11.576576576576578, -0.7523216563398065, -0.8221474578858637, -0.1360946781789165, 0.6750829210129617], [11.596596596596598, -0.7422821501616454, -0.8293127566631374, -0.1538770392602677, 0.6630325183981676], [11.616616616616618, -0.7319451463424221, -0.8361456769927326, -0.1715977282992781, 0.6507163804290635], [11.636636636636638, -0.7213147878273106, -0.8426434803234403, -0.18924964305970782, 0.6381394432636753], [11.656656656656658, -0.7103953351344725, -0.8488035624148615, -0.20682570886920978, 0.6253067475851684], [11.676676676676678, -0.6991911646474935, -0.854623454381155, -0.2243188814547722, 0.6122234365816043], [11.696696696696698, -0.6877067668613818, -0.8601008236805356, -0.2417221497659775, 0.5988947538846127], [11.716716716716718, -0.6759467445828323, -0.8652334750501304, -0.2590285387849457, 0.5853260414678083], [11.736736736736738, -0.6639158110854783, -0.8700193513858118, -0.2762311123218366, 0.5715227375057929], [11.756756756756758, -0.651618788220868, -0.8744565345666605, -0.2933229757947905, 0.5574903741946008], [11.776776776776778, -0.6390606044859267, -0.8785432462237246, -0.3102972789931919, 0.5432345755344626], [11.796796796796798, -0.6262462930476731, -0.8822778484527689, -0.32714721882315123, 0.528761055075774], [11.816816816816818, -0.6131809897259892, -0.8856588444707261, -0.3438660420341015, 0.5140756136291746], [11.836836836836838, -0.5998699309352457, -0.8886848792155895, -0.3604470479254188, 0.4991841369406541], [11.856856856856858, -0.5863184515856098, -0.8913547398895058, -0.376883591031982, 0.4840925933326162], [11.876876876876878, -0.5725319829448802, -0.8936673564448492, -0.39316908378759335, 0.46880703131184825], [11.896896896896898, -0.5585160504616996, -0.8956218020130837, -0.40929699916519563, 0.45333357714535316], [11.916916916916918, -0.5442762715510229, -0.8972172932762403, -0.4252608732928243, 0.43767843240501675], [11.936936936936938, -0.5298183533427255, -0.8984531907808619, -0.44105430804424933, 0.4218478714820927], [11.956956956956958, -0.5151480903942552, -0.8993289991942883, -0.4566709736032655, 0.405848239072503], [11.976976976976978, -0.5002713623682444, -0.8998443675031788, -0.472104611000606, 0.38968594763396175], [11.996996996996998, -0.4851941316760128, -0.8999990891541951, -0.4873490346224615, 0.3733674748159396], [12.017017017017018, -0.4699224410879052, -0.8997931021367849, -0.5023981346895976, 0.3568993608635012], [12.037037037037038, -0.4544624113114237, -0.8992264890080346, -0.5172458797060813, 0.3402882059960544], [12.057057057057058, -0.43882023853812185, -0.8982994768595822, -0.5318863188766298, 0.32354066776206275], [12.077077077077078, -0.42300219196024713, -0.897012437226601, -0.5463135844916177, 0.30666345837078135], [12.097097097097098, -0.40701461125812466, -0.8953658859388933, -0.560521894278783, 0.2896633420020851], [12.117117117117118, -0.39086390405929083, -0.8933604829141519, -0.5745055537206916, 0.27254713209546794], [12.137137137137138, -0.3745565433703943, -0.8909970318934736, -0.5882589583370301, 0.255321688619299], [12.157157157157158, -0.3580990649828936, -0.8882764801192283, -0.601776595930814, 0.23799391532143113], [12.177177177177178, -0.3414980648535919, -0.8851999179554174, -0.6150530487976085, 0.22057075696226294], [12.197197197197198, -0.32476019646105747, -0.8817685784506683, -0.6280829958968781, 0.20305919653136323], [12.217217217217218, -0.3078921681389907, -0.8779838368440434, -0.6408612149845957, 0.18546625244877402], [12.237237237237238, -0.2909007403876058, -0.8738472100138613, -0.6533825847062539, 0.1677989757521132], [12.257257257257258, -0.2737927231641048, -0.8693603558697511, -0.6656420866494412, 0.15006444727060472], [12.277277277277278, -0.2565749731533293, -0.864525072688181, -0.6776348073551614, 0.1322697747871684], [12.297297297297298, -0.23925439101968493, -0.8593432983917324, -0.6893559402870867, 0.11442209018970713], [12.317317317317318, -0.22183791864143926, -0.853817109772403, -0.7008007877579605, 0.09652854661273325], [12.337337337337338, -0.20433253632850146, -0.847948721659255, -0.7119647628123722, 0.07859631557047919], [12.357357357357358, -0.18674526002479913, -0.8417404860307376, -0.7228433910651532, 0.0606325840826421], [12.377377377377378, -0.1690831384963735, -0.8351948910720431, -0.733432312494656, 0.04264455179391392], [12.397397397397398, -0.15135325050631981, -0.8283145601778731, -0.7437272831901978, 0.02463942808845142], [12.417417417417418, -0.13356270197770548, -0.8211022509010127, -0.7537241770529685, 0.006624429200442979], [12.437437437437438, -0.11571862314560262, -0.813560853847138, -0.76341898744972, -0.0113932246780702], [12.457457457457458, -0.09782816569937665, -0.8056933915162963, -0.7728078288185769, -0.029406312290959073], [12.477477477477478, -0.0798984999163765, -0.7975030170915244, -0.7818869382263216, -0.04740761421219821], [12.497497497497498, -0.061936811788174624, -0.7889930131750921, -0.790652676876533, -0.06538991573932382], [12.517517517517518, -0.04395030014050931, -0.7801667904728743, -0.7991015315679703, -0.08334600978499848], [12.537537537537538, -0.025946173748082935, -0.7710278864273824, -0.8072301161026227, -0.10126869976552406], [12.557557557557558, -0.00793164844537296, -0.7615799638000003, -0.8150351726428561, -0.11915080248514472], [12.577577577577578, 0.010086055765386627, -0.7518268092029937, -0.8225135730171135, -0.13698515101498412], [12.597597597597598, 0.028099717607894235, -0.7417723315818828, -0.8296623199736495, -0.15476459756546349], [12.617617617617618, 0.0461021174259802, -0.7314205606487842, -0.8364785483817915, -0.17248201635104823], [12.637637637637638, 0.06408604007715703, -0.7207756452673514, -0.8429595263802495, -0.19013030644617618], [12.657657657657658, 0.08204427782436059, -0.7098418517899605, -0.8491026564720124, -0.207702394631222], [12.677677677677679, 0.09996963322472326, -0.698623562347809, -0.8549054765653938, -0.2251912382273573], [12.697697697697699, 0.11785492201422135, -0.6871252730946102, -0.8603656609608077, -0.2425898279191707], [12.717717717717719, 0.1356929759870407, -0.6753515924045899, -0.8654810212828805, -0.25989119056391585], [12.737737737737739, 0.15347664586850596, -0.663307239025506, -0.8702495073575265, -0.27708839198626223], [12.757757757757759, 0.17119880418042305, -0.6509970401874324, -0.8746692080336306, -0.29417453975742797], [12.777777777777779, 0.1888523480976854, -0.6384259296680631, -0.8787383519490161, -0.3111427859575814], [12.797797797797799, 0.20643020229499967, -0.6255989458153153, -0.8824553082403842, -0.32798632992040383], [12.817817817817819, 0.22392532178259014, -0.61252122952802, -0.8858185871969448, -0.34469842095871406], [12.837837837837839, 0.2413306947297446, -0.599198022195513, -0.8888268408574735, -0.36127236107006094], [12.857857857857859, 0.25863934527507054, -0.5856346635969495, -0.8914788635505588, -0.37770150762120164], [12.877877877877879, 0.2758443363223356, -0.5718365897611861, -0.8937735923778203, -0.39397927601038807], [12.897897897897899, 0.29293877232077137, -0.5578093307880864, -0.8957101076399053, -0.41009914230639455], [12.917917917917919, 0.3099158020287256, -0.5435585086321243, -0.8972876332050934, -0.4260546458632308], [12.937937937937939, 0.32676862125955675, -0.5290898348491735, -0.89850553682036, -0.44183939190948907], [12.957957957957959, 0.3434904756086691, -0.514409108307385, -0.8993633303647766, -0.45744705411129166], [12.977977977977979, 0.36007466316059555, -0.49952221286307186, -0.8998606700451426, -0.47287137710780763], [12.997997997997999, 0.3765145371750441, -0.48443511500253117, -0.8999973565337741, -0.4881061790183257], [13.018018018018019, 0.3928035087508297, -0.4691538614507487, -0.8997733350483923, -0.5031453539198765], [13.038038038038039, 0.4089350494666264, -0.45368457674794566, -0.8991886953740785, -0.5179828742944121], [13.058058058058059, 0.42490269399747843, -0.4380334607949374, -0.8982436718272906, -0.5326127934445619], [13.078078078078079, 0.44070004270602386, -0.42220678636828884, -0.8969386431619508, -0.5470292478769964], [13.098098098098099, 0.4563207642073913, -0.4062108966062617, -0.8952741324176465, -0.5612264596524438], [13.118118118118119, 0.4717585979067417, -0.3900522024665624, -0.893250806710002, -0.5751987387014177], [13.138138138138139, 0.4870073565084388, -0.3737371801569082, -0.8908694769633065, -0.588940485104728], [13.158158158158159, 0.5020609284958415, -0.35727236853944194, -0.888131097585506, -0.6024461913378595], [13.178178178178179, 0.516913280580725, -0.3406643665100358, -0.8850367660856879, -0.6157104444783214], [13.198198198198199, 0.5315584601213503, -0.32391983035353433, -0.8815877226342128, -0.6287279283750801], [13.218218218218219, 0.5459905975082091, -0.3070454710759966, -0.8777853495656693, -0.6414934257792079], [13.238238238238239, 0.5602039085164926, -0.2900480517150067, -0.8736311708248516, -0.6540018204348934], [13.258258258258259, 0.5741926966243383, -0.2729343846291305, -0.8691268513559801, -0.6662480991299735], [13.278278278278279, 0.587951355295926, -0.2557113287676057, -0.8642741964354126, -0.6782273537051682], [13.298298298298299, 0.6014743702285098, -0.2383857869213584, -0.8590751509481117, -0.6899347830212114], [13.318318318318319, 0.6147563215624828, -0.2209647029564484, -0.8535317986081582, -0.7013656948830885], [13.338338338338339, 0.6277918860535915, -0.2034550590310527, -0.847646361123624, -0.712515507920611], [13.358358358358359, 0.640575839206426, -0.1858638727971012, -0.8414211973061387, -0.7233797534245744], [13.378378378378379, 0.6531030573683357, -0.16819819458768753, -0.834858802125507, -0.7339540771377613], [13.398398398398399, 0.6653685197829258, -0.15046510459138135, -0.827961805709754, -0.7442342410000763], [13.418418418418419, 0.677367310602315, -0.1326717100145752, -0.8207329722910032, -0.754216124847107], [13.438438438438439, 0.6890946208573486, -0.11482514223300253, -0.8131751990976055, -0.7638957280614371], [13.458458458458459, 0.700545750384974, -0.09693255393356948, -0.8052915151929648, -0.7732691711760445], [13.478478478478479, 0.7117161097120087, -0.0790011162476449, -0.7970850802615278, -0.7823326974291444], [13.498498498498499, 0.7226012218945455, -0.06103801587695842, -0.788559183342419, -0.7910826742698539], [13.518518518518519, 0.7331967243122568, -0.04305045221325785, -0.7797172415112356, -0.7995155948140736], [13.538538538538539, 0.74349837041688, -0.025045634452880803, -0.7705627985105241, -0.807628079250005], [13.558558558558559, 0.7535020314341824, -0.007030778707396588, -0.7610995233294923, -0.8154168761927368], [13.578578578578579, 0.7632036980187227, 0.010986894888523379, -0.7513312087335225, -0.822878863987361], [13.598598598598599, 0.772599481860749, 0.029000165070847548, -0.7412617697440778, -0.8300110519600941], [13.618618618618619, 0.7816856172445852, 0.0470018123403789, -0.7308952420696087, -0.8368105816169022], [13.63863863863864, 0.7904584625578842, 0.06498462185624222, -0.7202357804880907, -0.8432747277891517], [13.65865865865866, 0.7989145017511416, 0.08294138632750446, -0.7092876571818401, -0.849400899725822], [13.67867867867868, 0.8070503457468862, 0.10086490890176902, -0.6980552600252748, -0.8551866421318487], [13.6986986986987, 0.8148627337979792, 0.1187480060495866, -0.6865430908263084, -0.8606296361521748], [13.71871871871872, 0.8223485347944844, 0.1365835104435263, -0.6747557635220797, -0.86572770030112], [13.73873873873874, 0.8295047485185768, 0.15436427383075296, -0.6626980023297434, -0.8704787913366933], [13.75875875875876, 0.8363285068469946, 0.1720831698979599, -0.6503746398530613, -0.8748810050794997], [13.77877877877878, 0.8428170749005469, 0.18973309712750855, -0.6377906151455538, -0.8789325771759126], [13.7987987987988, 0.8489678521402217, 0.20730698164362993, -0.6249509717309869, -0.8826318838052036], [13.81881881881882, 0.8547783734094484, 0.22479778004754816, -0.61186085558199, -0.8859774423303522], [13.83883883883884, 0.8602463099221036, 0.24219848224038865, -0.5985255130576123, -0.8889679118922673], [13.85885885885886, 0.8653694701958601, 0.2595021142327404, -0.584950288800646, -0.891602093947187], [13.87887887887888, 0.8701458009305059, 0.2767017409397459, -0.5711406235955592, -0.8938789327470417], [13.8988988988989, 0.8745733878308822, 0.2937904689605989, -0.5571020521878948, -0.8957975157625834], [13.91891891891892, 0.8786504563741097, 0.3107614493413351, -0.5428402010660126, -0.8973570740491176], [13.93893893893894, 0.8823753725207951, 0.32760788031980953, -0.5283607862060619, -0.8985569825546864], [13.95895895895896, 0.8857466433699354, 0.3443230100517602, -0.513669610781086, -0.8993967603705815], [13.97897897897898, 0.888762917757253, 0.36090013931686465, -0.49877256283518334, -0.8998760709240871], [13.998998998999, 0.891422986796727, 0.3773326242037061, -0.48367561292364963, -0.8999947221133736], [14.01901901901902, 0.8937257843650992, 0.39361387877257215, -0.4683848117200529, -0.8997526663844903], [14.03903903903904, 0.8956703875291638, 0.4097373776950188, -0.45290628759119683, -0.8991500007504237], [14.05905905905906, 0.897256016915668, 0.4256966588691432, -0.43724624414094604, -0.8981869667522167], [14.07907907907908, 0.8984820370236742, 0.441485326009515, -0.4214109577238976, -0.896863950362161], [14.0990990990991, 0.8993479564792626, 0.4570970512107294, -0.4054067749298941, -0.8951814818291047], [14.11911911911912, 0.8998534282324663, 0.47252557748355495, -0.3892401100403881, -0.8931402354659352], [14.13913913913914, 0.8999982496963658, 0.4877647212626585, -0.3729174424576762, -0.890741029379322], [14.15915915915916, 0.8997823628282832, 0.5028083748849025, -0.3564453141080336, -0.8879848251418303], [14.17917917917918, 0.8992058541530447, 0.5176505090372224, -0.3398303268197896, -0.884872727406534], [14.1991991991992, 0.8982689547283024, 0.5322851751731018, -0.32307913967739527, -0.8814059834642847], [14.21921921921922, 0.8969720400519297, 0.546706507896677, -0.3061984663525439, -0.8775859827438118], [14.23923923923924, 0.8953156299115258, 0.5609087273135166, -0.28919507241341263, -0.8734142562548575], [14.25925925925926, 0.8933003881760918, 0.5748861413471327, -0.27207577261310567, -0.8688924759745659], [14.27927927927928, 0.8909271225299593, 0.5886331480202955, -0.2548474281583839, -0.8640224541773753], [14.2992992992993, 0.8881967841490811, 0.6021442377002383, -0.23751694395977715, -0.8588061427086809], [14.31931931931932, 0.885110467319811, 0.61541399530685, -0.2200912658641799, -0.8532456322025583], [14.33933933933934, 0.8816694090003274, 0.6284371024829742, -0.20257737787104066, -0.8473431512438637], [14.35935935935936, 0.8778749883248754, 0.6412083397259416, -0.18498229933325994, -0.8411010654750432], [14.37937937937938, 0.8737287260510265, 0.6537225884794828, -0.16731308214391938, -0.8345218766480131], [14.3993993993994, 0.8692322839501778, 0.6659748331851839, -0.1495768079099687, -0.8276082216214877], [14.41941941941942, 0.8643874641415342, 0.6779601632926607, -0.131780585114004, -0.8203628713041587], [14.43943943943944, 0.8591962083698407, 0.6896737752276475, -0.1139315462652746, -0.8127887295441485], [14.45945945945946, 0.853660597227155, 0.7011109743172114, -0.09603684504106016, -0.8048888319651835], [14.47947947947948, 0.8477828493189717, 0.7122671766713198, -0.07810365341956413, -0.7966663447499529], [14.4994994994995, 0.8415653203750318, 0.7231379110200078, -0.06013915880547205, -0.7881245633711413], [14.51951951951952, 0.8350105023051753, 0.7337188205054084, -0.04215056114932755, -0.7792669112706437], [14.53953953953954, 0.8281210222006141, 0.7440056644279285, -0.02414507006187981, -0.7700969384874914], [14.55955955955956, 0.8208996412810255, 0.7539943199458693, -0.006129901924559417, -0.7606183202350394], [14.57957957957958, 0.8133492537878898, 0.7636807837278113, 0.011887723002759365, -0.7508348554279861], [14.5995995995996, 0.8054728858245122, 0.7730611735571005, 0.029900583475550777, -0.7407504651598147], [14.61961961961962, 0.7972736941431989, 0.7821317298877932, 0.047901460158824384, -0.7303691911312659], [14.63963963963964, 0.7887549648800685, 0.7908888173514372, 0.06588313852054656, -0.7196951940304745], [14.65965965965966, 0.7799201122380087, 0.7993289262140825, 0.083838411723137, -0.7087327518654157], [14.67967967967968, 0.7707726771183057, 0.8074486737829395, 0.10176008351188151, -0.6974862582493314], [14.6996996996997, 0.7613163257014937, 0.8152448057621222, 0.11964097109910302, -0.6859602206398245], [14.71971971971972, 0.7515548479779945, 0.8227141975569292, 0.13747390804293558, -0.6741592585323238], [14.73973973973974, 0.7414921562291364, 0.8298538555261439, 0.15525174711954678, -0.662088101608647], [14.75975975975976, 0.731132283459158, 0.836660918181849, 0.172967363187658, -0.6497515878414019], [14.77977977977978, 0.7204793817788302, 0.8431326573362764, 0.1906136560442143, -0.6371546615549855], [14.7997997997998, 0.7095377207413406, 0.8492664791952311, 0.2081835532700592, -0.6243023714439598], [14.81981981981982, 0.6983116856311088, 0.855059925397653, 0.22567001306447404, -0.6111998685495958], [14.83983983983984, 0.6868057757062176, 0.8605106740008973, 0.24306602706744618, -0.5978524041953995], [14.85985985985986, 0.6750246023951649, 0.8656165404113413, 0.2603646231685339, -0.5842653278824449], [14.87987987987988, 0.6629728874486603, 0.8703754782599429, 0.27755886830120374, -0.5704440851453594], [14.8998998998999, 0.6506554610472036, 0.8747855802223998, 0.29464187122151864, -0.5563942153698198], [14.91991991991992, 0.6380772598652088, 0.878845078783583, 0.3116067852700648, -0.5421213495724343], [14.93993993993994, 0.6252433250924437, 0.882552346945934, 0.3284468111160095, -0.5276312081438994], [14.95995995995996, 0.6121588004135837, 0.8859058988815466, 0.34515519948218965, -0.5129295985563369], [14.97997997997998, 0.5988289299466847, 0.8889043905276681, 0.3617252538501402, -0.4980224130357302], [15.0, 0.5852590561414053, 0.8915466201253833, 0.37815033314397684, -0.48291562620039147]]}, \"id\": \"el94984400236048\"});\n", " })\n", " });\n", "}\n", "</script>" ], "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x106464610>" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Multiple Subplots with linked axes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# multiple subplots, shared axes\n", "fig, ax = plt.subplots(2, 2, figsize=(8, 6),sharex='col', sharey='row')\n", "fig.subplots_adjust(hspace=0.3)\n", "\n", "np.random.seed(0)\n", "\n", "for axi in ax.flat:\n", " color = np.random.random(3)\n", " axi.plot(np.random.random(30), lw=2, c=color)\n", " axi.set_title(\"RGB = ({0:.2f}, {1:.2f}, {2:.2f})\".format(*color),\n", " size=14)\n", " axi.grid(color='lightgray', alpha=0.7)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div id=\"fig_el949843963138723132322169\"></div>\n", "<script>\n", "function mpld3_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n", " // already loaded: just create the figure\n", " !function(mpld3){\n", " \n", " mpld3.draw_figure(\"fig_el949843963138723132322169\", {\"axes\": [{\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.55, 0.72, 0.60)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.49999999999999994, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984401611792\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984401568336\"], \"sharex\": [\"el94984399836432\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#8BB699\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984396313232\"}], \"markers\": [], \"id\": \"el94984396311056\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.56304347826086953, 0.35227272727272724, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.57, 0.02, 0.62)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.5, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984396235792\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984396311056\"], \"sharex\": [\"el94984399973584\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#90049D\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403559056\"}], \"markers\": [], \"id\": \"el94984401568336\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.54772727272727262, 0.56304347826086953, 0.35227272727272729, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.82, 0.10, 0.84)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.49999999999999994, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984400229072\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984399973584\"], \"sharex\": [\"el94984396311056\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#D118D5\", \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403559632\"}], \"markers\": [], \"id\": \"el94984399836432\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.12499999999999989, 0.35227272727272724, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.00, 0.68, 0.27)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.5, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984399998160\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984399836432\"], \"sharex\": [\"el94984401568336\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#01AC44\", \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403570960\"}], \"markers\": [], \"id\": \"el94984399973584\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.54772727272727262, 0.12499999999999989, 0.35227272727272729, 0.33695652173913049]}], \"height\": 480.0, \"width\": 640.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[0.0, 0.5448831829968969, 0.6120957227224214, 0.09609840789396307, 0.7351940221225949], [1.0, 0.4236547993389047, 0.6169339968747569, 0.9764594650133958, 0.9621885451174382], [2.0, 0.6458941130666561, 0.9437480785146242, 0.4686512016477016, 0.24875314351995803], [3.0, 0.4375872112626925, 0.6818202991034834, 0.9767610881903371, 0.5761573344178369], [4.0, 0.8917730007820798, 0.359507900573786, 0.604845519745046, 0.592041931271839], [5.0, 0.9636627605010293, 0.43703195379934145, 0.7392635793983017, 0.5722519057908734], [6.0, 0.3834415188257777, 0.6976311959272649, 0.039187792254320675, 0.2230816326406183], [7.0, 0.7917250380826646, 0.06022547162926983, 0.2828069625764096, 0.952749011516985], [8.0, 0.5288949197529045, 0.6667667154456677, 0.1201965612131689, 0.44712537861762736], [9.0, 0.5680445610939323, 0.6706378696181594, 0.29614019752214493, 0.8464086724711278], [10.0, 0.925596638292661, 0.2103825610738409, 0.11872771895424405, 0.6994792753175043], [11.0, 0.07103605819788694, 0.1289262976548533, 0.317983179393976, 0.29743695085513366], [12.0, 0.08712929970154071, 0.31542835092418386, 0.41426299451466997, 0.8137978197024772], [13.0, 0.02021839744032572, 0.3637107709426226, 0.06414749634878436, 0.39650574084698464], [14.0, 0.832619845547938, 0.5701967704178796, 0.6924721193700198, 0.8811031971111616], [15.0, 0.7781567509498505, 0.43860151346232035, 0.5666014542065752, 0.5812728726358587], [16.0, 0.8700121482468192, 0.9883738380592262, 0.2653894909394454, 0.8817353618548528], [17.0, 0.978618342232764, 0.10204481074802807, 0.5232480534666997, 0.6925315900777659], [18.0, 0.7991585642167236, 0.2088767560948347, 0.09394051075844168, 0.7252542798196405], [19.0, 0.46147936225293185, 0.16130951788499626, 0.5759464955561793, 0.5013243819267023], [20.0, 0.7805291762864555, 0.6531083254653984, 0.9292961975762141, 0.9560836347232239], [21.0, 0.11827442586893322, 0.2532916025397821, 0.31856895245132366, 0.6439901992296374], [22.0, 0.6399210213275238, 0.4663107728563063, 0.6674103799636817, 0.4238550485581797], [23.0, 0.1433532874090464, 0.24442559200160274, 0.13179786240439217, 0.6063932141279244], [24.0, 0.9446689170495839, 0.15896958364551972, 0.7163272041185655, 0.019193198309333526], [25.0, 0.5218483217500717, 0.11037514116430513, 0.2894060929472011, 0.30157481667454933], [26.0, 0.4146619399905236, 0.6563295894652734, 0.18319136200711683, 0.660173537492685], [27.0, 0.26455561210462697, 0.1381829513486138, 0.5865129348100832, 0.29007760721044407], [28.0, 0.7742336894342167, 0.1965823616800535, 0.020107546187493552, 0.6180154289988415], [29.0, 0.45615033221654855, 0.3687251706609641, 0.8289400292173631, 0.42876870094576613]]}, \"id\": \"el94984396313872\"});\n", " }(mpld3);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/mpld3\n", " require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949843963138723132322169\", {\"axes\": [{\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.55, 0.72, 0.60)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.49999999999999994, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984401611792\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984401568336\"], \"sharex\": [\"el94984399836432\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#8BB699\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984396313232\"}], \"markers\": [], \"id\": \"el94984396311056\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.56304347826086953, 0.35227272727272724, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.57, 0.02, 0.62)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.5, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984396235792\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984396311056\"], \"sharex\": [\"el94984399973584\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#90049D\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403559056\"}], \"markers\": [], \"id\": \"el94984401568336\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.54772727272727262, 0.56304347826086953, 0.35227272727272729, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.82, 0.10, 0.84)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.49999999999999994, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984400229072\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984399973584\"], \"sharex\": [\"el94984396311056\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#D118D5\", \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403559632\"}], \"markers\": [], \"id\": \"el94984399836432\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.12499999999999989, 0.35227272727272724, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.00, 0.68, 0.27)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.5, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984399998160\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984399836432\"], \"sharex\": [\"el94984401568336\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#01AC44\", \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403570960\"}], \"markers\": [], \"id\": \"el94984399973584\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.54772727272727262, 0.12499999999999989, 0.35227272727272729, 0.33695652173913049]}], \"height\": 480.0, \"width\": 640.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[0.0, 0.5448831829968969, 0.6120957227224214, 0.09609840789396307, 0.7351940221225949], [1.0, 0.4236547993389047, 0.6169339968747569, 0.9764594650133958, 0.9621885451174382], [2.0, 0.6458941130666561, 0.9437480785146242, 0.4686512016477016, 0.24875314351995803], [3.0, 0.4375872112626925, 0.6818202991034834, 0.9767610881903371, 0.5761573344178369], [4.0, 0.8917730007820798, 0.359507900573786, 0.604845519745046, 0.592041931271839], [5.0, 0.9636627605010293, 0.43703195379934145, 0.7392635793983017, 0.5722519057908734], [6.0, 0.3834415188257777, 0.6976311959272649, 0.039187792254320675, 0.2230816326406183], [7.0, 0.7917250380826646, 0.06022547162926983, 0.2828069625764096, 0.952749011516985], [8.0, 0.5288949197529045, 0.6667667154456677, 0.1201965612131689, 0.44712537861762736], [9.0, 0.5680445610939323, 0.6706378696181594, 0.29614019752214493, 0.8464086724711278], [10.0, 0.925596638292661, 0.2103825610738409, 0.11872771895424405, 0.6994792753175043], [11.0, 0.07103605819788694, 0.1289262976548533, 0.317983179393976, 0.29743695085513366], [12.0, 0.08712929970154071, 0.31542835092418386, 0.41426299451466997, 0.8137978197024772], [13.0, 0.02021839744032572, 0.3637107709426226, 0.06414749634878436, 0.39650574084698464], [14.0, 0.832619845547938, 0.5701967704178796, 0.6924721193700198, 0.8811031971111616], [15.0, 0.7781567509498505, 0.43860151346232035, 0.5666014542065752, 0.5812728726358587], [16.0, 0.8700121482468192, 0.9883738380592262, 0.2653894909394454, 0.8817353618548528], [17.0, 0.978618342232764, 0.10204481074802807, 0.5232480534666997, 0.6925315900777659], [18.0, 0.7991585642167236, 0.2088767560948347, 0.09394051075844168, 0.7252542798196405], [19.0, 0.46147936225293185, 0.16130951788499626, 0.5759464955561793, 0.5013243819267023], [20.0, 0.7805291762864555, 0.6531083254653984, 0.9292961975762141, 0.9560836347232239], [21.0, 0.11827442586893322, 0.2532916025397821, 0.31856895245132366, 0.6439901992296374], [22.0, 0.6399210213275238, 0.4663107728563063, 0.6674103799636817, 0.4238550485581797], [23.0, 0.1433532874090464, 0.24442559200160274, 0.13179786240439217, 0.6063932141279244], [24.0, 0.9446689170495839, 0.15896958364551972, 0.7163272041185655, 0.019193198309333526], [25.0, 0.5218483217500717, 0.11037514116430513, 0.2894060929472011, 0.30157481667454933], [26.0, 0.4146619399905236, 0.6563295894652734, 0.18319136200711683, 0.660173537492685], [27.0, 0.26455561210462697, 0.1381829513486138, 0.5865129348100832, 0.29007760721044407], [28.0, 0.7742336894342167, 0.1965823616800535, 0.020107546187493552, 0.6180154289988415], [29.0, 0.45615033221654855, 0.3687251706609641, 0.8289400292173631, 0.42876870094576613]]}, \"id\": \"el94984396313872\"});\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & mpld3\n", " mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949843963138723132322169\", {\"axes\": [{\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.55, 0.72, 0.60)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.49999999999999994, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984401611792\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984401568336\"], \"sharex\": [\"el94984399836432\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#8BB699\", \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984396313232\"}], \"markers\": [], \"id\": \"el94984396311056\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.56304347826086953, 0.35227272727272724, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.57, 0.02, 0.62)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.5, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984396235792\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984396311056\"], \"sharex\": [\"el94984399973584\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#90049D\", \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403559056\"}], \"markers\": [], \"id\": \"el94984401568336\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.54772727272727262, 0.56304347826086953, 0.35227272727272729, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.82, 0.10, 0.84)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.49999999999999994, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984400229072\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984399973584\"], \"sharex\": [\"el94984396311056\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#D118D5\", \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403559632\"}], \"markers\": [], \"id\": \"el94984399836432\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.12499999999999989, 0.35227272727272724, 0.33695652173913049]}, {\"xlim\": [0.0, 30.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"RGB = (0.00, 0.68, 0.27)\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 14.0, \"position\": [0.5, 1.0343488649940262], \"rotation\": -0.0, \"id\": \"el94984399998160\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [0.0, 30.0], \"ylim\": [0.0, 1.0], \"paths\": [], \"sharey\": [\"el94984399836432\"], \"sharex\": [\"el94984401568336\"], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"color\": \"#D3D3D3\", \"alpha\": 0.7, \"dasharray\": \"2,2\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 6, \"tickvalues\": null}], \"lines\": [{\"color\": \"#01AC44\", \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 2, \"alpha\": 1, \"xindex\": 0, \"linewidth\": 2, \"data\": \"data01\", \"id\": \"el94984403570960\"}], \"markers\": [], \"id\": \"el94984399973584\", \"ydomain\": [0.0, 1.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.54772727272727262, 0.12499999999999989, 0.35227272727272729, 0.33695652173913049]}], \"height\": 480.0, \"width\": 640.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data01\": [[0.0, 0.5448831829968969, 0.6120957227224214, 0.09609840789396307, 0.7351940221225949], [1.0, 0.4236547993389047, 0.6169339968747569, 0.9764594650133958, 0.9621885451174382], [2.0, 0.6458941130666561, 0.9437480785146242, 0.4686512016477016, 0.24875314351995803], [3.0, 0.4375872112626925, 0.6818202991034834, 0.9767610881903371, 0.5761573344178369], [4.0, 0.8917730007820798, 0.359507900573786, 0.604845519745046, 0.592041931271839], [5.0, 0.9636627605010293, 0.43703195379934145, 0.7392635793983017, 0.5722519057908734], [6.0, 0.3834415188257777, 0.6976311959272649, 0.039187792254320675, 0.2230816326406183], [7.0, 0.7917250380826646, 0.06022547162926983, 0.2828069625764096, 0.952749011516985], [8.0, 0.5288949197529045, 0.6667667154456677, 0.1201965612131689, 0.44712537861762736], [9.0, 0.5680445610939323, 0.6706378696181594, 0.29614019752214493, 0.8464086724711278], [10.0, 0.925596638292661, 0.2103825610738409, 0.11872771895424405, 0.6994792753175043], [11.0, 0.07103605819788694, 0.1289262976548533, 0.317983179393976, 0.29743695085513366], [12.0, 0.08712929970154071, 0.31542835092418386, 0.41426299451466997, 0.8137978197024772], [13.0, 0.02021839744032572, 0.3637107709426226, 0.06414749634878436, 0.39650574084698464], [14.0, 0.832619845547938, 0.5701967704178796, 0.6924721193700198, 0.8811031971111616], [15.0, 0.7781567509498505, 0.43860151346232035, 0.5666014542065752, 0.5812728726358587], [16.0, 0.8700121482468192, 0.9883738380592262, 0.2653894909394454, 0.8817353618548528], [17.0, 0.978618342232764, 0.10204481074802807, 0.5232480534666997, 0.6925315900777659], [18.0, 0.7991585642167236, 0.2088767560948347, 0.09394051075844168, 0.7252542798196405], [19.0, 0.46147936225293185, 0.16130951788499626, 0.5759464955561793, 0.5013243819267023], [20.0, 0.7805291762864555, 0.6531083254653984, 0.9292961975762141, 0.9560836347232239], [21.0, 0.11827442586893322, 0.2532916025397821, 0.31856895245132366, 0.6439901992296374], [22.0, 0.6399210213275238, 0.4663107728563063, 0.6674103799636817, 0.4238550485581797], [23.0, 0.1433532874090464, 0.24442559200160274, 0.13179786240439217, 0.6063932141279244], [24.0, 0.9446689170495839, 0.15896958364551972, 0.7163272041185655, 0.019193198309333526], [25.0, 0.5218483217500717, 0.11037514116430513, 0.2894060929472011, 0.30157481667454933], [26.0, 0.4146619399905236, 0.6563295894652734, 0.18319136200711683, 0.660173537492685], [27.0, 0.26455561210462697, 0.1381829513486138, 0.5865129348100832, 0.29007760721044407], [28.0, 0.7742336894342167, 0.1965823616800535, 0.020107546187493552, 0.6180154289988415], [29.0, 0.45615033221654855, 0.3687251706609641, 0.8289400292173631, 0.42876870094576613]]}, \"id\": \"el94984396313872\"});\n", " })\n", " });\n", "}\n", "</script>" ], "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAeUAAAF7CAYAAADsXJNRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x1060a6d10>" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Plugins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the key features of mpld3 is the plugin framework. Plugins are a way to specify additional interactivity for your plots. A number of plugins are built-in, and it is also possible to define new, custom plugins for nearly limitless interactive behaviors. For example, here is the built-in Linked Brushing plugin that allows exploration of multi-dimensional datasets:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from mpld3 import plugins\n", "\n", "fig, ax = plt.subplots(3, 3, figsize=(6, 6))\n", "fig.subplots_adjust(hspace=0.1, wspace=0.1)\n", "ax = ax[::-1]\n", "\n", "X = np.random.normal(size=(3, 100))\n", "for i in range(3):\n", " for j in range(3):\n", " ax[i, j].xaxis.set_major_formatter(plt.NullFormatter())\n", " ax[i, j].yaxis.set_major_formatter(plt.NullFormatter())\n", " points = ax[i, j].scatter(X[j], X[i])\n", " \n", "plugins.connect(fig, plugins.LinkedBrush(points))" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div id=\"fig_el949844037015846005097298\"></div>\n", "<script>\n", "function mpld3_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n", " // already loaded: just create the figure\n", " !function(mpld3){\n", " \n", " mpld3.draw_figure(\"fig_el949844037015846005097298\", {\"axes\": [{\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984403660432\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984407891344\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.65781250000000002, 0.2421875, 0.2421875]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302708816\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984303204048\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.65781250000000002, 0.24218749999999994, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302861456\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984407898832\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.65781250000000002, 0.2421875, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302976656\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407864592\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.39140625000000007, 0.2421875, 0.24218749999999989]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984303126096\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407874896\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.39140625000000007, 0.24218749999999994, 0.24218749999999989]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984303203600\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407876944\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.39140625000000007, 0.2421875, 0.24218749999999989]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407407056\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407842320\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.12500000000000011, 0.2421875, 0.2421875]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407561872\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407844240\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.12500000000000011, 0.24218749999999994, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407693712\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407862544\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.12500000000000011, 0.2421875, 0.2421875]}], \"height\": 480.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}, {\"enabled\": true, \"button\": true, \"type\": \"linkedbrush\", \"id\": \"el94984407898832\"}], \"data\": {\"data01\": [[-0.41361898075974735, -1.3342584714027534, -0.024326124398935636], [-0.7474548114407578, -1.3467175057975553, -0.7380309092056887], [1.9229420264803847, 0.6937731526901325, 0.27992459904323824], [1.4805147914344243, -0.1595734381462669, -0.09815038964295794], [1.8675589604265699, -0.13370155966843916, 0.9101789080925919], [0.9060446582753853, 1.0777438059762627, 0.31721821519130206], [-0.8612256850547025, -1.1268258087567435, 0.7863279621089762], [1.9100649530990337, -0.7306777528648248, -0.46641909673594306], [-0.2680033709513804, -0.38487980918127546, -0.9444462559182504], [0.8024563957963952, 0.094351589317074, -0.41004969320254847], [0.947251967773748, -0.042171451290578935, -0.017020413861440594], [-0.1550100930908342, -0.2868871923899076, 0.3791517355550818], [0.6140793703460803, -0.0616264020956474, 2.259308950690852], [0.9222066715665268, -0.10730527629117469, -0.04225715166064269], [0.37642553115562943, -0.7196043885517929, -0.955945000492777], [-1.0994007905841945, -0.8129929885540773, -0.34598177569938643], [0.298238174206056, 0.2745163577239395, -0.4635959746460942], [1.3263858966870303, -0.8909150829955279, 0.4814814737734622], [-0.6945678597313655, -1.1573552591908536, -1.5407970144446248], [-0.14963454032767076, -0.3122922511256933, 0.06326199420033171], [-0.43515355172163744, -0.1576670161638159, 0.1565065379653756], [1.8492637284793418, 2.2567234972982093, 0.23218103620027578], [0.6722947570124355, -0.7047002758562337, -0.5973160689653627], [0.40746183624111043, 0.9432607249694948, -0.237921729736007], [-0.7699160744453164, 0.7471883342046318, -1.4240609089825316], [0.5392491912918173, -1.188944955203736, -0.49331988336219407], [-0.6743326606573761, 0.7732529774025997, -0.5428614760167177], [0.03183055827435118, -1.1838806401933177, 0.4160500462614255], [-0.635846078378881, -2.659172237996741, -1.1561824318219127], [0.6764332949464997, 0.6063195243593807, 0.7811981017099934], [0.5765908166149409, -1.7558905834377194, 1.4944845444913688], [-0.20829875557799488, 0.45093446180591484, -2.0699850250135325], [0.3960067126616453, -0.6840108977372166, 0.42625873077810095], [-1.0930615087305058, 1.6595507961898721, 0.6769080350302455], [-1.4912575927056055, 1.068509399316009, -0.637437025552229], [0.4393917012645369, -0.45338580385138766, -0.39727181432879766], [0.16667349537252904, -0.6878376110286823, -0.13288057758695562], [0.6350314368921064, -1.2140774030941206, -0.2977908794017283], [2.383144774863942, -0.4409226322925914, -0.3090129690471222], [0.9444794869904138, -0.2803554951845091, -1.6760038063299767], [-0.9128222254441586, -0.3646935443916854, 1.15233156478312], [1.117016288095853, 0.15670385527236397, 1.079618592036821], [-1.3159074105115212, 0.5785214977288784, -0.8133642592042029], [-0.461584604814709, 0.349654456993174, -1.466424327802514], [-0.06824160532463124, -0.764143923906443, 0.5210648764527586], [1.7133427216493666, -1.4377914738015785, -0.5757879698130661], [-0.7447548220484399, 1.3645318481024713, 0.14195316332077967], [-0.8264385386590144, -0.6894491845499376, -0.3193284171450952], [-0.0984525244254323, -0.6522935999350191, 0.6915387510701866], [-0.6634782863621074, -0.5211893123011109, 0.6947491436560059], [1.126635922106507, -1.8430695501566485, -0.7255973784635843], [-1.0799315083634233, -0.4779740040404867, -1.3833639553950554], [-1.1474686524111024, -0.47965581400794766, -1.582938397335082], [-0.43782004474443403, 0.6203582983435125, 0.6103793791072052], [-0.4980324506923049, 0.698457149107336, -1.188859257784029], [1.9295320538169858, 0.00377088908626934, -0.5068163542986875], [0.9494208069257608, 0.9318483741143037, -0.5963140384505081], [0.0875512413851909, 0.339964983801262, -0.05256729626954629], [-1.225435518830168, -0.01568211160255477, -1.936279805846507], [0.8443629764015471, 0.16092816829822298, 0.18877859679382855], [-1.0002153473895647, -0.19065349358139935, 0.5238910238342056], [-1.5447710967776116, -0.3948495140334503, 0.08842208704466141], [1.1880297923523018, -0.26773353689396645, -0.3108861716984717], [0.3169426119248496, -1.1280113314700069, 0.09740016626878341], [0.920858823780819, 0.280441705316296, 0.3990463456401302], [0.3187276529430212, -0.9931236109295807, -2.77259275642665], [0.8568306119026912, 0.8416312640736364, 1.9559123082506942], [-0.6510255933001469, -0.24945858016094885, 0.39009332268792646], [-1.0342428417844647, 0.04949498165009074, -0.65240858238702], [0.681594518281627, 0.49383677628095635, -0.3909533751876011], [-0.8034096641738411, 0.6433144650629279, 0.49374177734918845], [-0.6895497777502005, -1.5706234086334527, -0.11610393903436653], [-0.45553250351734315, -0.20690367616397173, -2.0306844677814944], [0.01747915902505673, 0.8801789120807822, 2.0644928613593194], [-0.35399391125348395, -1.6981058194322545, -0.11054065723247261], [-1.3749512934180188, 0.3872804753950634, 1.0201727117157997], [-0.6436184028328905, -2.2555642294021894, -0.6920498477843912], [-2.2234031522244266, -1.0225068436356035, 1.5363770542457977], [0.6252314510271875, 0.0386305518401881, 0.28634368889227957], [-1.6020576556067476, -1.6567151023219537, 0.6088438344754508], [-1.1043833394284506, -0.9855107376841507, -1.0452533661469547], [0.052165079260974405, -1.4718350074635869, 1.2111452896827009], [-0.7395629963913133, 1.6481349322075596, 0.6898181645347884], [1.5430145954067358, 0.16422775548733395, 1.3018462295649984], [-1.2928569097234486, 0.5672902778526694, -0.6280875596415789], [0.26705086934918293, -0.2226751005151545, -0.4810271184607877], [-0.0392828182274956, -0.35343174875719907, 2.303916697683942], [-1.1680934977411974, -1.6164741886510325, -1.0600158227215473], [0.5232766605317537, -0.2918373627478628, -0.13594970067832082], [-0.1715463312222481, -0.7614922118116233, 1.1368913626026953], [0.7717905512136674, 0.8579239242923363, 0.0977249677148556], [0.8235041539637314, 1.1411018666575734, 0.5829536797532936], [2.16323594928069, 1.4665787155741776, -0.3994490292628752], [1.336527949436392, 0.852551939461232, 0.37005588784751875], [-0.3691818379424436, -0.5986539369229861, -1.3065268517353166], [-0.2393791775759264, -1.1158969859603944, 1.658130679618188], [1.0996595958871132, 0.7666631816450861, -0.11816404512856976], [0.6552637307225978, 0.3562928174722889, -0.6801782039968504], [0.640131526097592, -1.7685384506770307, 0.6663830820319143], [-1.6169560443108344, 0.35548179274376907, -0.4607197873885533]]}, \"id\": \"el94984403701584\"});\n", " }(mpld3);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/mpld3\n", " require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949844037015846005097298\", {\"axes\": [{\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984403660432\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984407891344\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.65781250000000002, 0.2421875, 0.2421875]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302708816\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984303204048\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.65781250000000002, 0.24218749999999994, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302861456\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984407898832\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.65781250000000002, 0.2421875, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302976656\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407864592\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.39140625000000007, 0.2421875, 0.24218749999999989]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984303126096\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407874896\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.39140625000000007, 0.24218749999999994, 0.24218749999999989]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984303203600\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407876944\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.39140625000000007, 0.2421875, 0.24218749999999989]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407407056\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407842320\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.12500000000000011, 0.2421875, 0.2421875]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407561872\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407844240\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.12500000000000011, 0.24218749999999994, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407693712\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407862544\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.12500000000000011, 0.2421875, 0.2421875]}], \"height\": 480.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}, {\"enabled\": true, \"button\": true, \"type\": \"linkedbrush\", \"id\": \"el94984407898832\"}], \"data\": {\"data01\": [[-0.41361898075974735, -1.3342584714027534, -0.024326124398935636], [-0.7474548114407578, -1.3467175057975553, -0.7380309092056887], [1.9229420264803847, 0.6937731526901325, 0.27992459904323824], [1.4805147914344243, -0.1595734381462669, -0.09815038964295794], [1.8675589604265699, -0.13370155966843916, 0.9101789080925919], [0.9060446582753853, 1.0777438059762627, 0.31721821519130206], [-0.8612256850547025, -1.1268258087567435, 0.7863279621089762], [1.9100649530990337, -0.7306777528648248, -0.46641909673594306], [-0.2680033709513804, -0.38487980918127546, -0.9444462559182504], [0.8024563957963952, 0.094351589317074, -0.41004969320254847], [0.947251967773748, -0.042171451290578935, -0.017020413861440594], [-0.1550100930908342, -0.2868871923899076, 0.3791517355550818], [0.6140793703460803, -0.0616264020956474, 2.259308950690852], [0.9222066715665268, -0.10730527629117469, -0.04225715166064269], [0.37642553115562943, -0.7196043885517929, -0.955945000492777], [-1.0994007905841945, -0.8129929885540773, -0.34598177569938643], [0.298238174206056, 0.2745163577239395, -0.4635959746460942], [1.3263858966870303, -0.8909150829955279, 0.4814814737734622], [-0.6945678597313655, -1.1573552591908536, -1.5407970144446248], [-0.14963454032767076, -0.3122922511256933, 0.06326199420033171], [-0.43515355172163744, -0.1576670161638159, 0.1565065379653756], [1.8492637284793418, 2.2567234972982093, 0.23218103620027578], [0.6722947570124355, -0.7047002758562337, -0.5973160689653627], [0.40746183624111043, 0.9432607249694948, -0.237921729736007], [-0.7699160744453164, 0.7471883342046318, -1.4240609089825316], [0.5392491912918173, -1.188944955203736, -0.49331988336219407], [-0.6743326606573761, 0.7732529774025997, -0.5428614760167177], [0.03183055827435118, -1.1838806401933177, 0.4160500462614255], [-0.635846078378881, -2.659172237996741, -1.1561824318219127], [0.6764332949464997, 0.6063195243593807, 0.7811981017099934], [0.5765908166149409, -1.7558905834377194, 1.4944845444913688], [-0.20829875557799488, 0.45093446180591484, -2.0699850250135325], [0.3960067126616453, -0.6840108977372166, 0.42625873077810095], [-1.0930615087305058, 1.6595507961898721, 0.6769080350302455], [-1.4912575927056055, 1.068509399316009, -0.637437025552229], [0.4393917012645369, -0.45338580385138766, -0.39727181432879766], [0.16667349537252904, -0.6878376110286823, -0.13288057758695562], [0.6350314368921064, -1.2140774030941206, -0.2977908794017283], [2.383144774863942, -0.4409226322925914, -0.3090129690471222], [0.9444794869904138, -0.2803554951845091, -1.6760038063299767], [-0.9128222254441586, -0.3646935443916854, 1.15233156478312], [1.117016288095853, 0.15670385527236397, 1.079618592036821], [-1.3159074105115212, 0.5785214977288784, -0.8133642592042029], [-0.461584604814709, 0.349654456993174, -1.466424327802514], [-0.06824160532463124, -0.764143923906443, 0.5210648764527586], [1.7133427216493666, -1.4377914738015785, -0.5757879698130661], [-0.7447548220484399, 1.3645318481024713, 0.14195316332077967], [-0.8264385386590144, -0.6894491845499376, -0.3193284171450952], [-0.0984525244254323, -0.6522935999350191, 0.6915387510701866], [-0.6634782863621074, -0.5211893123011109, 0.6947491436560059], [1.126635922106507, -1.8430695501566485, -0.7255973784635843], [-1.0799315083634233, -0.4779740040404867, -1.3833639553950554], [-1.1474686524111024, -0.47965581400794766, -1.582938397335082], [-0.43782004474443403, 0.6203582983435125, 0.6103793791072052], [-0.4980324506923049, 0.698457149107336, -1.188859257784029], [1.9295320538169858, 0.00377088908626934, -0.5068163542986875], [0.9494208069257608, 0.9318483741143037, -0.5963140384505081], [0.0875512413851909, 0.339964983801262, -0.05256729626954629], [-1.225435518830168, -0.01568211160255477, -1.936279805846507], [0.8443629764015471, 0.16092816829822298, 0.18877859679382855], [-1.0002153473895647, -0.19065349358139935, 0.5238910238342056], [-1.5447710967776116, -0.3948495140334503, 0.08842208704466141], [1.1880297923523018, -0.26773353689396645, -0.3108861716984717], [0.3169426119248496, -1.1280113314700069, 0.09740016626878341], [0.920858823780819, 0.280441705316296, 0.3990463456401302], [0.3187276529430212, -0.9931236109295807, -2.77259275642665], [0.8568306119026912, 0.8416312640736364, 1.9559123082506942], [-0.6510255933001469, -0.24945858016094885, 0.39009332268792646], [-1.0342428417844647, 0.04949498165009074, -0.65240858238702], [0.681594518281627, 0.49383677628095635, -0.3909533751876011], [-0.8034096641738411, 0.6433144650629279, 0.49374177734918845], [-0.6895497777502005, -1.5706234086334527, -0.11610393903436653], [-0.45553250351734315, -0.20690367616397173, -2.0306844677814944], [0.01747915902505673, 0.8801789120807822, 2.0644928613593194], [-0.35399391125348395, -1.6981058194322545, -0.11054065723247261], [-1.3749512934180188, 0.3872804753950634, 1.0201727117157997], [-0.6436184028328905, -2.2555642294021894, -0.6920498477843912], [-2.2234031522244266, -1.0225068436356035, 1.5363770542457977], [0.6252314510271875, 0.0386305518401881, 0.28634368889227957], [-1.6020576556067476, -1.6567151023219537, 0.6088438344754508], [-1.1043833394284506, -0.9855107376841507, -1.0452533661469547], [0.052165079260974405, -1.4718350074635869, 1.2111452896827009], [-0.7395629963913133, 1.6481349322075596, 0.6898181645347884], [1.5430145954067358, 0.16422775548733395, 1.3018462295649984], [-1.2928569097234486, 0.5672902778526694, -0.6280875596415789], [0.26705086934918293, -0.2226751005151545, -0.4810271184607877], [-0.0392828182274956, -0.35343174875719907, 2.303916697683942], [-1.1680934977411974, -1.6164741886510325, -1.0600158227215473], [0.5232766605317537, -0.2918373627478628, -0.13594970067832082], [-0.1715463312222481, -0.7614922118116233, 1.1368913626026953], [0.7717905512136674, 0.8579239242923363, 0.0977249677148556], [0.8235041539637314, 1.1411018666575734, 0.5829536797532936], [2.16323594928069, 1.4665787155741776, -0.3994490292628752], [1.336527949436392, 0.852551939461232, 0.37005588784751875], [-0.3691818379424436, -0.5986539369229861, -1.3065268517353166], [-0.2393791775759264, -1.1158969859603944, 1.658130679618188], [1.0996595958871132, 0.7666631816450861, -0.11816404512856976], [0.6552637307225978, 0.3562928174722889, -0.6801782039968504], [0.640131526097592, -1.7685384506770307, 0.6663830820319143], [-1.6169560443108344, 0.35548179274376907, -0.4607197873885533]]}, \"id\": \"el94984403701584\"});\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & mpld3\n", " mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el949844037015846005097298\", {\"axes\": [{\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984403660432\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984407891344\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.65781250000000002, 0.2421875, 0.2421875]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302708816\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984303204048\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.65781250000000002, 0.24218749999999994, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302861456\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el94984407898832\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.65781250000000002, 0.2421875, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984302976656\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407864592\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.39140625000000007, 0.2421875, 0.24218749999999989]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984303126096\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407874896\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.39140625000000007, 0.24218749999999994, 0.24218749999999989]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-4.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984303203600\", \"ydomain\": [-4.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 2, \"id\": \"el94984407876944\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.39140625000000007, 0.2421875, 0.24218749999999989]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407407056\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407842320\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.12500000000000011, 0.2421875, 0.2421875]}, {\"xlim\": [-4.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-4.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407561872\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407844240\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 2, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.39140625000000001, 0.12500000000000011, 0.24218749999999994, 0.2421875]}, {\"xlim\": [-3.0, 3.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [-3.0, 3.0], \"ylim\": [-3.0, 3.0], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el94984407693712\", \"ydomain\": [-3.0, 3.0], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#000000\"], \"edgewidths\": [1.0], \"offsets\": \"data01\", \"yindex\": 0, \"id\": \"el94984407862544\", \"pathtransforms\": [[4.969039949999533, 0.0, 0.0, 4.969039949999533, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 1, \"alphas\": [null], \"facecolors\": [\"#0000FF\"]}], \"xscale\": \"linear\", \"bbox\": [0.65781249999999991, 0.12500000000000011, 0.2421875, 0.2421875]}], \"height\": 480.0, \"width\": 480.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}, {\"enabled\": true, \"button\": true, \"type\": \"linkedbrush\", \"id\": \"el94984407898832\"}], \"data\": {\"data01\": [[-0.41361898075974735, -1.3342584714027534, -0.024326124398935636], [-0.7474548114407578, -1.3467175057975553, -0.7380309092056887], [1.9229420264803847, 0.6937731526901325, 0.27992459904323824], [1.4805147914344243, -0.1595734381462669, -0.09815038964295794], [1.8675589604265699, -0.13370155966843916, 0.9101789080925919], [0.9060446582753853, 1.0777438059762627, 0.31721821519130206], [-0.8612256850547025, -1.1268258087567435, 0.7863279621089762], [1.9100649530990337, -0.7306777528648248, -0.46641909673594306], [-0.2680033709513804, -0.38487980918127546, -0.9444462559182504], [0.8024563957963952, 0.094351589317074, -0.41004969320254847], [0.947251967773748, -0.042171451290578935, -0.017020413861440594], [-0.1550100930908342, -0.2868871923899076, 0.3791517355550818], [0.6140793703460803, -0.0616264020956474, 2.259308950690852], [0.9222066715665268, -0.10730527629117469, -0.04225715166064269], [0.37642553115562943, -0.7196043885517929, -0.955945000492777], [-1.0994007905841945, -0.8129929885540773, -0.34598177569938643], [0.298238174206056, 0.2745163577239395, -0.4635959746460942], [1.3263858966870303, -0.8909150829955279, 0.4814814737734622], [-0.6945678597313655, -1.1573552591908536, -1.5407970144446248], [-0.14963454032767076, -0.3122922511256933, 0.06326199420033171], [-0.43515355172163744, -0.1576670161638159, 0.1565065379653756], [1.8492637284793418, 2.2567234972982093, 0.23218103620027578], [0.6722947570124355, -0.7047002758562337, -0.5973160689653627], [0.40746183624111043, 0.9432607249694948, -0.237921729736007], [-0.7699160744453164, 0.7471883342046318, -1.4240609089825316], [0.5392491912918173, -1.188944955203736, -0.49331988336219407], [-0.6743326606573761, 0.7732529774025997, -0.5428614760167177], [0.03183055827435118, -1.1838806401933177, 0.4160500462614255], [-0.635846078378881, -2.659172237996741, -1.1561824318219127], [0.6764332949464997, 0.6063195243593807, 0.7811981017099934], [0.5765908166149409, -1.7558905834377194, 1.4944845444913688], [-0.20829875557799488, 0.45093446180591484, -2.0699850250135325], [0.3960067126616453, -0.6840108977372166, 0.42625873077810095], [-1.0930615087305058, 1.6595507961898721, 0.6769080350302455], [-1.4912575927056055, 1.068509399316009, -0.637437025552229], [0.4393917012645369, -0.45338580385138766, -0.39727181432879766], [0.16667349537252904, -0.6878376110286823, -0.13288057758695562], [0.6350314368921064, -1.2140774030941206, -0.2977908794017283], [2.383144774863942, -0.4409226322925914, -0.3090129690471222], [0.9444794869904138, -0.2803554951845091, -1.6760038063299767], [-0.9128222254441586, -0.3646935443916854, 1.15233156478312], [1.117016288095853, 0.15670385527236397, 1.079618592036821], [-1.3159074105115212, 0.5785214977288784, -0.8133642592042029], [-0.461584604814709, 0.349654456993174, -1.466424327802514], [-0.06824160532463124, -0.764143923906443, 0.5210648764527586], [1.7133427216493666, -1.4377914738015785, -0.5757879698130661], [-0.7447548220484399, 1.3645318481024713, 0.14195316332077967], [-0.8264385386590144, -0.6894491845499376, -0.3193284171450952], [-0.0984525244254323, -0.6522935999350191, 0.6915387510701866], [-0.6634782863621074, -0.5211893123011109, 0.6947491436560059], [1.126635922106507, -1.8430695501566485, -0.7255973784635843], [-1.0799315083634233, -0.4779740040404867, -1.3833639553950554], [-1.1474686524111024, -0.47965581400794766, -1.582938397335082], [-0.43782004474443403, 0.6203582983435125, 0.6103793791072052], [-0.4980324506923049, 0.698457149107336, -1.188859257784029], [1.9295320538169858, 0.00377088908626934, -0.5068163542986875], [0.9494208069257608, 0.9318483741143037, -0.5963140384505081], [0.0875512413851909, 0.339964983801262, -0.05256729626954629], [-1.225435518830168, -0.01568211160255477, -1.936279805846507], [0.8443629764015471, 0.16092816829822298, 0.18877859679382855], [-1.0002153473895647, -0.19065349358139935, 0.5238910238342056], [-1.5447710967776116, -0.3948495140334503, 0.08842208704466141], [1.1880297923523018, -0.26773353689396645, -0.3108861716984717], [0.3169426119248496, -1.1280113314700069, 0.09740016626878341], [0.920858823780819, 0.280441705316296, 0.3990463456401302], [0.3187276529430212, -0.9931236109295807, -2.77259275642665], [0.8568306119026912, 0.8416312640736364, 1.9559123082506942], [-0.6510255933001469, -0.24945858016094885, 0.39009332268792646], [-1.0342428417844647, 0.04949498165009074, -0.65240858238702], [0.681594518281627, 0.49383677628095635, -0.3909533751876011], [-0.8034096641738411, 0.6433144650629279, 0.49374177734918845], [-0.6895497777502005, -1.5706234086334527, -0.11610393903436653], [-0.45553250351734315, -0.20690367616397173, -2.0306844677814944], [0.01747915902505673, 0.8801789120807822, 2.0644928613593194], [-0.35399391125348395, -1.6981058194322545, -0.11054065723247261], [-1.3749512934180188, 0.3872804753950634, 1.0201727117157997], [-0.6436184028328905, -2.2555642294021894, -0.6920498477843912], [-2.2234031522244266, -1.0225068436356035, 1.5363770542457977], [0.6252314510271875, 0.0386305518401881, 0.28634368889227957], [-1.6020576556067476, -1.6567151023219537, 0.6088438344754508], [-1.1043833394284506, -0.9855107376841507, -1.0452533661469547], [0.052165079260974405, -1.4718350074635869, 1.2111452896827009], [-0.7395629963913133, 1.6481349322075596, 0.6898181645347884], [1.5430145954067358, 0.16422775548733395, 1.3018462295649984], [-1.2928569097234486, 0.5672902778526694, -0.6280875596415789], [0.26705086934918293, -0.2226751005151545, -0.4810271184607877], [-0.0392828182274956, -0.35343174875719907, 2.303916697683942], [-1.1680934977411974, -1.6164741886510325, -1.0600158227215473], [0.5232766605317537, -0.2918373627478628, -0.13594970067832082], [-0.1715463312222481, -0.7614922118116233, 1.1368913626026953], [0.7717905512136674, 0.8579239242923363, 0.0977249677148556], [0.8235041539637314, 1.1411018666575734, 0.5829536797532936], [2.16323594928069, 1.4665787155741776, -0.3994490292628752], [1.336527949436392, 0.852551939461232, 0.37005588784751875], [-0.3691818379424436, -0.5986539369229861, -1.3065268517353166], [-0.2393791775759264, -1.1158969859603944, 1.658130679618188], [1.0996595958871132, 0.7666631816450861, -0.11816404512856976], [0.6552637307225978, 0.3562928174722889, -0.6801782039968504], [0.640131526097592, -1.7685384506770307, 0.6663830820319143], [-1.6169560443108344, 0.35548179274376907, -0.4607197873885533]]}, \"id\": \"el94984403701584\"});\n", " })\n", " });\n", "}\n", "</script>" ], "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAFdCAYAAACgiL63AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x1067b2750>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Selecting points with the brush lets you quickly explore the relationships between the points." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "For More Information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More examples, documentation, and information can be found on the [mpld3 website](http://mpld3.github.io). See especially the [Example Gallery](http://mpld3.github.io/examples/index.html) and the [Notebook Gallery](http://mpld3.github.io/notebooks/index.html). If you are interested in contributing, the source of mpld3 can be found on [GitHub](http://github.com/jakevdp/mpld3)." ] } ], "metadata": {} } ] }

mit

mgupta011235/TweetSafe

notebook/xgboost gridsearch eda.ipynb

1

56921

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sb\n", "from operator import itemgetter\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results = {'eval': {'auc': [0.499306, 0.494721, 0.488865, 0.478342, 0.5674]},\n", " 'train': {'auc': [0.557143, 0.632473, 0.715275, 0.74478, 0.780549]}}\n", "\n", "labels = ['max_depth','eta','num_rounds','eval_reslts']\n", "data = [[3,0.1,100,results],[3,0.1,100,results],[3,0.1,100,results]]\n", "df = pd.DataFrame(data=data,columns=labels)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>max_depth</th>\n", " <th>eta</th>\n", " <th>num_rounds</th>\n", " <th>eval_reslts</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>3</td>\n", " <td>0.1</td>\n", " <td>100</td>\n", " <td>{u'train': {u'auc': [0.557143, 0.632473, 0.715...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>0.1</td>\n", " <td>100</td>\n", " <td>{u'train': {u'auc': [0.557143, 0.632473, 0.715...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>0.1</td>\n", " <td>100</td>\n", " <td>{u'train': {u'auc': [0.557143, 0.632473, 0.715...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " max_depth eta num_rounds \\\n", "0 3 0.1 100 \n", "1 3 0.1 100 \n", "2 3 0.1 100 \n", "\n", " eval_reslts \n", "0 {u'train': {u'auc': [0.557143, 0.632473, 0.715... \n", "1 {u'train': {u'auc': [0.557143, 0.632473, 0.715... \n", "2 {u'train': {u'auc': [0.557143, 0.632473, 0.715... " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def myfunc(x):\n", " \n", " val = np.array(x['eval']['auc'])\n", " maxInd = np.argmax(val)\n", " maxVal = val[maxInd]\n", " \n", " return (maxInd,maxVal)\n", " " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<built-in method values of dict object at 0x7fc0bbb6d168>\n" ] } ], "source": [ "test = df['eval_reslts'].loc[0]\n", "print test['eval'].values" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4, 0.56740000000000002)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test = df['eval_reslts'].loc[0]\n", "myfunc(test)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['eval_auc'] = df['eval_reslts'].map(lambda x: myfunc(x))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>max_depth</th>\n", " <th>eta</th>\n", " <th>num_rounds</th>\n", " <th>eval_reslts</th>\n", " <th>eval_auc</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>3</td>\n", " <td>0.1</td>\n", " <td>100</td>\n", " <td>{u'train': {u'auc': [0.557143, 0.632473, 0.715...</td>\n", " <td>(4, 0.5674)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>0.1</td>\n", " <td>100</td>\n", " <td>{u'train': {u'auc': [0.557143, 0.632473, 0.715...</td>\n", " <td>(4, 0.5674)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>0.1</td>\n", " <td>100</td>\n", " <td>{u'train': {u'auc': [0.557143, 0.632473, 0.715...</td>\n", " <td>(4, 0.5674)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " max_depth eta num_rounds \\\n", "0 3 0.1 100 \n", "1 3 0.1 100 \n", "2 3 0.1 100 \n", "\n", " eval_reslts eval_auc \n", "0 {u'train': {u'auc': [0.557143, 0.632473, 0.715... (4, 0.5674) \n", "1 {u'train': {u'auc': [0.557143, 0.632473, 0.715... (4, 0.5674) \n", "2 {u'train': {u'auc': [0.557143, 0.632473, 0.715... (4, 0.5674) " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def build_roc(df):\n", " \n", " df['TPR'] = df['recall']\n", " df['FPR'] = df['FP']/(df['FP'] + df['TN'])\n", " \n", "# plt.plot([0,1],[0,1],'k',linewidth=0.5)\n", " plt.figure()\n", " plt.plot(df.FPR.values,df.TPR.values,'r*',markersize=7)\n", " plt.xlabel('FPR')\n", " plt.xlim([0,1])\n", " plt.ylabel('TPR')\n", " plt.ylim([0,1])\n", " titlestr = \"AUC: {} k = {}\".format(np.trapz(df.TPR.values[::-1],x=df.FPR.values[::-1]),int(df.k.unique()))\n", " plt.title(titlestr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for k in df.k.unique():\n", " build_roc(df[df['k']==k])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "path = '../../data/gridsearch_xgb.csv'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv(path)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unnamed: 0</th>\n", " <th>num_rounds</th>\n", " <th>max_depth</th>\n", " <th>eta</th>\n", " <th>eval_results</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>100</td>\n", " <td>3</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>900</td>\n", " <td>3</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>100</td>\n", " <td>4</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.564531', '0.564540', '0....</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 num_rounds max_depth eta \\\n", "0 0 100 3 0.03 \n", "1 1 300 3 0.03 \n", "2 2 600 3 0.03 \n", "3 3 900 3 0.03 \n", "4 4 100 4 0.03 \n", "\n", " eval_results \n", "0 {'train': {'auc': ['0.552844', '0.552849', '0.... \n", "1 {'train': {'auc': ['0.552844', '0.552849', '0.... \n", "2 {'train': {'auc': ['0.552844', '0.552849', '0.... \n", "3 {'train': {'auc': ['0.552844', '0.552849', '0.... \n", "4 {'train': {'auc': ['0.564531', '0.564540', '0.... " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"{'train': {'auc': ['0.552844', '0.552849', '0.552853', '0.552854', '0.564506', '0.564508', '0.564585', '0.570304', '0.570369', '0.570379', '0.574745', '0.574746', '0.574748', '0.574748', '0.574755', '0.574814', '0.577942', '0.577889', '0.577944', '0.591977', '0.591976', '0.591979', '0.595014', '0.605616', '0.609023', '0.609023', '0.609026', '0.609065', '0.615975', '0.616123', '0.616305', '0.616303', '0.616452', '0.616523', '0.616298', '0.616303', '0.648427', '0.648950', '0.648568', '0.652931', '0.652968', '0.652983', '0.653288', '0.653320', '0.657873', '0.657791', '0.657966', '0.657924', '0.658021', '0.658081', '0.658085', '0.658091', '0.663862', '0.663871', '0.663874', '0.663901', '0.665859', '0.668477', '0.668617', '0.668639', '0.668658', '0.668621', '0.668623', '0.670125', '0.670147', '0.670208', '0.669043', '0.671257', '0.671260', '0.677831', '0.680031', '0.680049', '0.680268', '0.680302', '0.680307', '0.680937', '0.680934', '0.682168', '0.682185', '0.682234', '0.682249', '0.683827', '0.685565', '0.686696', '0.686442', '0.687807', '0.687826', '0.687395', '0.686994', '0.687051', '0.687506', '0.690304', '0.690345', '0.690778', '0.690929', '0.691567', '0.691241', '0.696854', '0.696816', '0.697191']}, 'eval': {'auc': ['0.58177', '0.58177', '0.58177', '0.58177', '0.57943', '0.57943', '0.57943', '0.57936', '0.57936', '0.57936', '0.57965', '0.57965', '0.57965', '0.57965', '0.57965', '0.57966', '0.57966', '0.57965', '0.57966', '0.67961', '0.67961', '0.67961', '0.67961', '0.68028', '0.68028', '0.68027', '0.68027', '0.68028', '0.68180', '0.68180', '0.68180', '0.68180', '0.68220', '0.68220', '0.68180', '0.68180', '0.70203', '0.70203', '0.70203', '0.70193', '0.70204', '0.70204', '0.70204', '0.70204', '0.70215', '0.70214', '0.70215', '0.70203', '0.70193', '0.70204', '0.70204', '0.70204', '0.69895', '0.69895', '0.69894', '0.69894', '0.69894', '0.69910', '0.69943', '0.69944', '0.69944', '0.69944', '0.69944', '0.69927', '0.69928', '0.69929', '0.69931', '0.69916', '0.69916', '0.69586', '0.69586', '0.69588', '0.69584', '0.69584', '0.69584', '0.69512', '0.69513', '0.69506', '0.69509', '0.69509', '0.69509', '0.69585', '0.69584', '0.69583', '0.69561', '0.69560', '0.69560', '0.69548', '0.69547', '0.69547', '0.69557', '0.70430', '0.70409', '0.70395', '0.70395', '0.70396', '0.70396', '0.70401', '0.70427', '0.70424']}}\"" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['eval_results'].ix[0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def findtrain(x):\n", " badset = {\"{\",\"}\",\"'\",\",\",\"t\",\"r\",\"a\",\"i\",\"n\",\"u\",\"c\",\":\",\"[\",\"]\"}\n", " outstr = \"\"\n", " results = x.split(\"eval\")\n", " \n", " for char in results[0]:\n", " if char not in badset:\n", " outstr = outstr + char\n", " \n", " numbers = outstr.split(\" \")\n", " aucVals = np.array([float(number) for number in numbers[2:-1]])\n", " maxInd = np.argmax(aucVals)\n", " maxVal = aucVals[maxInd]\n", " \n", " return (maxInd,maxVal)\n", " \n", "def findbest(x):\n", " numvals = len(x)\n", " bestrow = None\n", " bestVal = None\n", " \n", " for row in xrange(numvals):\n", " if x[row][1] > bestVal:\n", " bestrow = row\n", " bestVal = x[row][1]\n", " \n", " return bestrow, x[bestrow]\n", "\n", "\n", "def findeval(x):\n", " badset = {\"{\",\"}\",\"'\",\",\",\"t\",\"r\",\"a\",\"i\",\"n\",\"u\",\"c\",\":\",\"[\",\"]\"}\n", " outstr = \"\"\n", " results = x.split(\"eval\")\n", " \n", " for char in results[1]:\n", " if char not in badset:\n", " outstr = outstr + char\n", " \n", " numbers = outstr.split(\" \")\n", " aucVals = np.array([float(number) for number in numbers[2:]])\n", " maxInd = np.argmax(aucVals)\n", " maxVal = aucVals[maxInd]\n", " \n", " return (maxInd,maxVal)\n", " " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['eval_auc'] = df['eval_results'].map(lambda x: findeval(x))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['train_auc'] = df['eval_results'].map(lambda x: findtrain(x))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unnamed: 0</th>\n", " <th>num_rounds</th>\n", " <th>max_depth</th>\n", " <th>eta</th>\n", " <th>eval_results</th>\n", " <th>eval_auc</th>\n", " <th>train_auc</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>100</td>\n", " <td>3</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " <td>(91, 0.7043)</td>\n", " <td>(99, 0.697191)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " <td>(284, 0.74588)</td>\n", " <td>(299, 0.743419)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " <td>(596, 0.80778)</td>\n", " <td>(599, 0.767899)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>900</td>\n", " <td>3</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " <td>(661, 0.80968)</td>\n", " <td>(897, 0.782626)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>100</td>\n", " <td>4</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.564531', '0.564540', '0....</td>\n", " <td>(97, 0.70845)</td>\n", " <td>(98, 0.708373)</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5</td>\n", " <td>300</td>\n", " <td>4</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.564531', '0.564540', '0....</td>\n", " <td>(292, 0.77796)</td>\n", " <td>(299, 0.753988)</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6</td>\n", " <td>600</td>\n", " <td>4</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.564531', '0.564540', '0....</td>\n", " <td>(522, 0.81082)</td>\n", " <td>(599, 0.77964)</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7</td>\n", " <td>900</td>\n", " <td>4</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.564531', '0.564540', '0....</td>\n", " <td>(899, 0.81823)</td>\n", " <td>(899, 0.792982)</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8</td>\n", " <td>100</td>\n", " <td>5</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.570415', '0.570409', '0....</td>\n", " <td>(91, 0.71407)</td>\n", " <td>(99, 0.718837)</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9</td>\n", " <td>300</td>\n", " <td>5</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.570415', '0.570409', '0....</td>\n", " <td>(280, 0.79814)</td>\n", " <td>(299, 0.76341)</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10</td>\n", " <td>600</td>\n", " <td>5</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.570415', '0.570409', '0....</td>\n", " <td>(388, 0.81023)</td>\n", " <td>(599, 0.788445)</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>11</td>\n", " <td>900</td>\n", " <td>5</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.570415', '0.570409', '0....</td>\n", " <td>(881, 0.82387)</td>\n", " <td>(899, 0.802094)</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>12</td>\n", " <td>100</td>\n", " <td>6</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.574951', '0.574936', '0....</td>\n", " <td>(99, 0.72097)</td>\n", " <td>(97, 0.727374)</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>13</td>\n", " <td>300</td>\n", " <td>6</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.574951', '0.574936', '0....</td>\n", " <td>(290, 0.80857)</td>\n", " <td>(299, 0.771393)</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>14</td>\n", " <td>600</td>\n", " <td>6</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.574951', '0.574936', '0....</td>\n", " <td>(596, 0.81735)</td>\n", " <td>(599, 0.795562)</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>15</td>\n", " <td>900</td>\n", " <td>6</td>\n", " <td>0.03</td>\n", " <td>{'train': {'auc': ['0.574951', '0.574936', '0....</td>\n", " <td>(837, 0.82755)</td>\n", " <td>(899, 0.809408)</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>16</td>\n", " <td>100</td>\n", " <td>3</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " <td>(98, 0.73037)</td>\n", " <td>(99, 0.725216)</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>17</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " <td>(286, 0.80978)</td>\n", " <td>(299, 0.76855)</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>18</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " <td>(583, 0.81891)</td>\n", " <td>(599, 0.792096)</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>19</td>\n", " <td>900</td>\n", " <td>3</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.552844', '0.552849', '0....</td>\n", " <td>(886, 0.82937)</td>\n", " <td>(899, 0.805351)</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>20</td>\n", " <td>100</td>\n", " <td>4</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.564531', '0.570346', '0....</td>\n", " <td>(95, 0.74017)</td>\n", " <td>(99, 0.739443)</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>21</td>\n", " <td>300</td>\n", " <td>4</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.564531', '0.570346', '0....</td>\n", " <td>(257, 0.81035)</td>\n", " <td>(299, 0.780313)</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>22</td>\n", " <td>600</td>\n", " <td>4</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.564531', '0.570346', '0....</td>\n", " <td>(534, 0.8261)</td>\n", " <td>(599, 0.803442)</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>23</td>\n", " <td>900</td>\n", " <td>4</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.564531', '0.570346', '0....</td>\n", " <td>(839, 0.84644)</td>\n", " <td>(899, 0.815612)</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>24</td>\n", " <td>100</td>\n", " <td>5</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.570415', '0.570412', '0....</td>\n", " <td>(92, 0.74551)</td>\n", " <td>(99, 0.748918)</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>25</td>\n", " <td>300</td>\n", " <td>5</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.570415', '0.570412', '0....</td>\n", " <td>(205, 0.81014)</td>\n", " <td>(299, 0.789367)</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>26</td>\n", " <td>600</td>\n", " <td>5</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.570415', '0.570412', '0....</td>\n", " <td>(599, 0.8387)</td>\n", " <td>(599, 0.812053)</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>27</td>\n", " <td>900</td>\n", " <td>5</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.570415', '0.570412', '0....</td>\n", " <td>(892, 0.84947)</td>\n", " <td>(899, 0.823539)</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>28</td>\n", " <td>100</td>\n", " <td>6</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.574951', '0.613902', '0....</td>\n", " <td>(78, 0.74694)</td>\n", " <td>(99, 0.755981)</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>29</td>\n", " <td>300</td>\n", " <td>6</td>\n", " <td>0.06</td>\n", " <td>{'train': {'auc': ['0.574951', '0.613902', '0....</td>\n", " <td>(299, 0.82282)</td>\n", " <td>(299, 0.795567)</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>66</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.552844', '0.570280', '0....</td>\n", " <td>(152, 0.85972)</td>\n", " <td>(599, 0.851604)</td>\n", " </tr>\n", " <tr>\n", " <th>67</th>\n", " <td>67</td>\n", " <td>900</td>\n", " <td>3</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.552844', '0.570280', '0....</td>\n", " <td>(152, 0.85972)</td>\n", " <td>(899, 0.86043)</td>\n", " </tr>\n", " <tr>\n", " <th>68</th>\n", " <td>68</td>\n", " <td>100</td>\n", " <td>4</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.564531', '0.613752', '0....</td>\n", " <td>(71, 0.83969)</td>\n", " <td>(99, 0.81354)</td>\n", " </tr>\n", " <tr>\n", " <th>69</th>\n", " <td>69</td>\n", " <td>300</td>\n", " <td>4</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.564531', '0.613752', '0....</td>\n", " <td>(235, 0.85638)</td>\n", " <td>(299, 0.844328)</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td>70</td>\n", " <td>600</td>\n", " <td>4</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.564531', '0.613752', '0....</td>\n", " <td>(235, 0.85638)</td>\n", " <td>(599, 0.86115)</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>71</td>\n", " <td>900</td>\n", " <td>4</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.564531', '0.613752', '0....</td>\n", " <td>(235, 0.85638)</td>\n", " <td>(899, 0.870383)</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td>72</td>\n", " <td>100</td>\n", " <td>5</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.570415', '0.603725', '0....</td>\n", " <td>(93, 0.85893)</td>\n", " <td>(99, 0.820764)</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td>73</td>\n", " <td>300</td>\n", " <td>5</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.570415', '0.603725', '0....</td>\n", " <td>(93, 0.85893)</td>\n", " <td>(299, 0.852038)</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td>74</td>\n", " <td>600</td>\n", " <td>5</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.570415', '0.603725', '0....</td>\n", " <td>(93, 0.85893)</td>\n", " <td>(599, 0.869036)</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>75</td>\n", " <td>900</td>\n", " <td>5</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.570415', '0.603725', '0....</td>\n", " <td>(93, 0.85893)</td>\n", " <td>(899, 0.879071)</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td>76</td>\n", " <td>100</td>\n", " <td>6</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.574951', '0.644546', '0....</td>\n", " <td>(74, 0.84766)</td>\n", " <td>(99, 0.828738)</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td>77</td>\n", " <td>300</td>\n", " <td>6</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.574951', '0.644546', '0....</td>\n", " <td>(131, 0.85167)</td>\n", " <td>(299, 0.859913)</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td>78</td>\n", " <td>600</td>\n", " <td>6</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.574951', '0.644546', '0....</td>\n", " <td>(131, 0.85167)</td>\n", " <td>(599, 0.877316)</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td>79</td>\n", " <td>900</td>\n", " <td>6</td>\n", " <td>0.60</td>\n", " <td>{'train': {'auc': ['0.574951', '0.644546', '0....</td>\n", " <td>(131, 0.85167)</td>\n", " <td>(899, 0.88754)</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td>80</td>\n", " <td>100</td>\n", " <td>3</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.552844', '0.574873', '0....</td>\n", " <td>(94, 0.8327)</td>\n", " <td>(99, 0.805588)</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>81</td>\n", " <td>300</td>\n", " <td>3</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.552844', '0.574873', '0....</td>\n", " <td>(138, 0.85149)</td>\n", " <td>(299, 0.839067)</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td>82</td>\n", " <td>600</td>\n", " <td>3</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.552844', '0.574873', '0....</td>\n", " <td>(138, 0.85149)</td>\n", " <td>(599, 0.856192)</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td>83</td>\n", " <td>900</td>\n", " <td>3</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.552844', '0.574873', '0....</td>\n", " <td>(138, 0.85149)</td>\n", " <td>(899, 0.865057)</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td>84</td>\n", " <td>100</td>\n", " <td>4</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.564531', '0.626056', '0....</td>\n", " <td>(95, 0.83787)</td>\n", " <td>(99, 0.816703)</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td>85</td>\n", " <td>300</td>\n", " <td>4</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.564531', '0.626056', '0....</td>\n", " <td>(134, 0.83875)</td>\n", " <td>(299, 0.849048)</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td>86</td>\n", " <td>600</td>\n", " <td>4</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.564531', '0.626056', '0....</td>\n", " <td>(134, 0.83875)</td>\n", " <td>(599, 0.866013)</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td>87</td>\n", " <td>900</td>\n", " <td>4</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.564531', '0.626056', '0....</td>\n", " <td>(134, 0.83875)</td>\n", " <td>(899, 0.875472)</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>88</td>\n", " <td>100</td>\n", " <td>5</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.570415', '0.637539', '0....</td>\n", " <td>(74, 0.84632)</td>\n", " <td>(99, 0.825646)</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td>89</td>\n", " <td>300</td>\n", " <td>5</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.570415', '0.637539', '0....</td>\n", " <td>(113, 0.84751)</td>\n", " <td>(299, 0.857447)</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>90</td>\n", " <td>600</td>\n", " <td>5</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.570415', '0.637539', '0....</td>\n", " <td>(113, 0.84751)</td>\n", " <td>(599, 0.874347)</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>91</td>\n", " <td>900</td>\n", " <td>5</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.570415', '0.637539', '0....</td>\n", " <td>(113, 0.84751)</td>\n", " <td>(899, 0.884148)</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td>92</td>\n", " <td>100</td>\n", " <td>6</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.574951', '0.645773', '0....</td>\n", " <td>(85, 0.84649)</td>\n", " <td>(99, 0.833498)</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>93</td>\n", " <td>300</td>\n", " <td>6</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.574951', '0.645773', '0....</td>\n", " <td>(85, 0.84649)</td>\n", " <td>(299, 0.864634)</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td>94</td>\n", " <td>600</td>\n", " <td>6</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.574951', '0.645773', '0....</td>\n", " <td>(85, 0.84649)</td>\n", " <td>(599, 0.882257)</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td>95</td>\n", " <td>900</td>\n", " <td>6</td>\n", " <td>0.90</td>\n", " <td>{'train': {'auc': ['0.574951', '0.645773', '0....</td>\n", " <td>(85, 0.84649)</td>\n", " <td>(899, 0.892832)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>96 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " Unnamed: 0 num_rounds max_depth eta \\\n", "0 0 100 3 0.03 \n", "1 1 300 3 0.03 \n", "2 2 600 3 0.03 \n", "3 3 900 3 0.03 \n", "4 4 100 4 0.03 \n", "5 5 300 4 0.03 \n", "6 6 600 4 0.03 \n", "7 7 900 4 0.03 \n", "8 8 100 5 0.03 \n", "9 9 300 5 0.03 \n", "10 10 600 5 0.03 \n", "11 11 900 5 0.03 \n", "12 12 100 6 0.03 \n", "13 13 300 6 0.03 \n", "14 14 600 6 0.03 \n", "15 15 900 6 0.03 \n", "16 16 100 3 0.06 \n", "17 17 300 3 0.06 \n", "18 18 600 3 0.06 \n", "19 19 900 3 0.06 \n", "20 20 100 4 0.06 \n", "21 21 300 4 0.06 \n", "22 22 600 4 0.06 \n", "23 23 900 4 0.06 \n", "24 24 100 5 0.06 \n", "25 25 300 5 0.06 \n", "26 26 600 5 0.06 \n", "27 27 900 5 0.06 \n", "28 28 100 6 0.06 \n", "29 29 300 6 0.06 \n", ".. ... ... ... ... \n", "66 66 600 3 0.60 \n", "67 67 900 3 0.60 \n", "68 68 100 4 0.60 \n", "69 69 300 4 0.60 \n", "70 70 600 4 0.60 \n", "71 71 900 4 0.60 \n", "72 72 100 5 0.60 \n", "73 73 300 5 0.60 \n", "74 74 600 5 0.60 \n", "75 75 900 5 0.60 \n", "76 76 100 6 0.60 \n", "77 77 300 6 0.60 \n", "78 78 600 6 0.60 \n", "79 79 900 6 0.60 \n", "80 80 100 3 0.90 \n", "81 81 300 3 0.90 \n", "82 82 600 3 0.90 \n", "83 83 900 3 0.90 \n", "84 84 100 4 0.90 \n", "85 85 300 4 0.90 \n", "86 86 600 4 0.90 \n", "87 87 900 4 0.90 \n", "88 88 100 5 0.90 \n", "89 89 300 5 0.90 \n", "90 90 600 5 0.90 \n", "91 91 900 5 0.90 \n", "92 92 100 6 0.90 \n", "93 93 300 6 0.90 \n", "94 94 600 6 0.90 \n", "95 95 900 6 0.90 \n", "\n", " eval_results eval_auc \\\n", "0 {'train': {'auc': ['0.552844', '0.552849', '0.... (91, 0.7043) \n", "1 {'train': {'auc': ['0.552844', '0.552849', '0.... (284, 0.74588) \n", "2 {'train': {'auc': ['0.552844', '0.552849', '0.... (596, 0.80778) \n", "3 {'train': {'auc': ['0.552844', '0.552849', '0.... (661, 0.80968) \n", "4 {'train': {'auc': ['0.564531', '0.564540', '0.... (97, 0.70845) \n", "5 {'train': {'auc': ['0.564531', '0.564540', '0.... (292, 0.77796) \n", "6 {'train': {'auc': ['0.564531', '0.564540', '0.... (522, 0.81082) \n", "7 {'train': {'auc': ['0.564531', '0.564540', '0.... (899, 0.81823) \n", "8 {'train': {'auc': ['0.570415', '0.570409', '0.... (91, 0.71407) \n", "9 {'train': {'auc': ['0.570415', '0.570409', '0.... (280, 0.79814) \n", "10 {'train': {'auc': ['0.570415', '0.570409', '0.... (388, 0.81023) \n", "11 {'train': {'auc': ['0.570415', '0.570409', '0.... (881, 0.82387) \n", "12 {'train': {'auc': ['0.574951', '0.574936', '0.... (99, 0.72097) \n", "13 {'train': {'auc': ['0.574951', '0.574936', '0.... (290, 0.80857) \n", "14 {'train': {'auc': ['0.574951', '0.574936', '0.... (596, 0.81735) \n", "15 {'train': {'auc': ['0.574951', '0.574936', '0.... (837, 0.82755) \n", "16 {'train': {'auc': ['0.552844', '0.552849', '0.... (98, 0.73037) \n", "17 {'train': {'auc': ['0.552844', '0.552849', '0.... (286, 0.80978) \n", "18 {'train': {'auc': ['0.552844', '0.552849', '0.... (583, 0.81891) \n", "19 {'train': {'auc': ['0.552844', '0.552849', '0.... (886, 0.82937) \n", "20 {'train': {'auc': ['0.564531', '0.570346', '0.... (95, 0.74017) \n", "21 {'train': {'auc': ['0.564531', '0.570346', '0.... (257, 0.81035) \n", "22 {'train': {'auc': ['0.564531', '0.570346', '0.... (534, 0.8261) \n", "23 {'train': {'auc': ['0.564531', '0.570346', '0.... (839, 0.84644) \n", "24 {'train': {'auc': ['0.570415', '0.570412', '0.... (92, 0.74551) \n", "25 {'train': {'auc': ['0.570415', '0.570412', '0.... (205, 0.81014) \n", "26 {'train': {'auc': ['0.570415', '0.570412', '0.... (599, 0.8387) \n", "27 {'train': {'auc': ['0.570415', '0.570412', '0.... (892, 0.84947) \n", "28 {'train': {'auc': ['0.574951', '0.613902', '0.... (78, 0.74694) \n", "29 {'train': {'auc': ['0.574951', '0.613902', '0.... (299, 0.82282) \n", ".. ... ... \n", "66 {'train': {'auc': ['0.552844', '0.570280', '0.... (152, 0.85972) \n", "67 {'train': {'auc': ['0.552844', '0.570280', '0.... (152, 0.85972) \n", "68 {'train': {'auc': ['0.564531', '0.613752', '0.... (71, 0.83969) \n", "69 {'train': {'auc': ['0.564531', '0.613752', '0.... (235, 0.85638) \n", "70 {'train': {'auc': ['0.564531', '0.613752', '0.... (235, 0.85638) \n", "71 {'train': {'auc': ['0.564531', '0.613752', '0.... (235, 0.85638) \n", "72 {'train': {'auc': ['0.570415', '0.603725', '0.... (93, 0.85893) \n", "73 {'train': {'auc': ['0.570415', '0.603725', '0.... (93, 0.85893) \n", "74 {'train': {'auc': ['0.570415', '0.603725', '0.... (93, 0.85893) \n", "75 {'train': {'auc': ['0.570415', '0.603725', '0.... (93, 0.85893) \n", "76 {'train': {'auc': ['0.574951', '0.644546', '0.... (74, 0.84766) \n", "77 {'train': {'auc': ['0.574951', '0.644546', '0.... (131, 0.85167) \n", "78 {'train': {'auc': ['0.574951', '0.644546', '0.... (131, 0.85167) \n", "79 {'train': {'auc': ['0.574951', '0.644546', '0.... (131, 0.85167) \n", "80 {'train': {'auc': ['0.552844', '0.574873', '0.... (94, 0.8327) \n", "81 {'train': {'auc': ['0.552844', '0.574873', '0.... (138, 0.85149) \n", "82 {'train': {'auc': ['0.552844', '0.574873', '0.... (138, 0.85149) \n", "83 {'train': {'auc': ['0.552844', '0.574873', '0.... (138, 0.85149) \n", "84 {'train': {'auc': ['0.564531', '0.626056', '0.... (95, 0.83787) \n", "85 {'train': {'auc': ['0.564531', '0.626056', '0.... (134, 0.83875) \n", "86 {'train': {'auc': ['0.564531', '0.626056', '0.... (134, 0.83875) \n", "87 {'train': {'auc': ['0.564531', '0.626056', '0.... (134, 0.83875) \n", "88 {'train': {'auc': ['0.570415', '0.637539', '0.... (74, 0.84632) \n", "89 {'train': {'auc': ['0.570415', '0.637539', '0.... (113, 0.84751) \n", "90 {'train': {'auc': ['0.570415', '0.637539', '0.... (113, 0.84751) \n", "91 {'train': {'auc': ['0.570415', '0.637539', '0.... (113, 0.84751) \n", "92 {'train': {'auc': ['0.574951', '0.645773', '0.... (85, 0.84649) \n", "93 {'train': {'auc': ['0.574951', '0.645773', '0.... (85, 0.84649) \n", "94 {'train': {'auc': ['0.574951', '0.645773', '0.... (85, 0.84649) \n", "95 {'train': {'auc': ['0.574951', '0.645773', '0.... (85, 0.84649) \n", "\n", " train_auc \n", "0 (99, 0.697191) \n", "1 (299, 0.743419) \n", "2 (599, 0.767899) \n", "3 (897, 0.782626) \n", "4 (98, 0.708373) \n", "5 (299, 0.753988) \n", "6 (599, 0.77964) \n", "7 (899, 0.792982) \n", "8 (99, 0.718837) \n", "9 (299, 0.76341) \n", "10 (599, 0.788445) \n", "11 (899, 0.802094) \n", "12 (97, 0.727374) \n", "13 (299, 0.771393) \n", "14 (599, 0.795562) \n", "15 (899, 0.809408) \n", "16 (99, 0.725216) \n", "17 (299, 0.76855) \n", "18 (599, 0.792096) \n", "19 (899, 0.805351) \n", "20 (99, 0.739443) \n", "21 (299, 0.780313) \n", "22 (599, 0.803442) \n", "23 (899, 0.815612) \n", "24 (99, 0.748918) \n", "25 (299, 0.789367) \n", "26 (599, 0.812053) \n", "27 (899, 0.823539) \n", "28 (99, 0.755981) \n", "29 (299, 0.795567) \n", ".. ... \n", "66 (599, 0.851604) \n", "67 (899, 0.86043) \n", "68 (99, 0.81354) \n", "69 (299, 0.844328) \n", "70 (599, 0.86115) \n", "71 (899, 0.870383) \n", "72 (99, 0.820764) \n", "73 (299, 0.852038) \n", "74 (599, 0.869036) \n", "75 (899, 0.879071) \n", "76 (99, 0.828738) \n", "77 (299, 0.859913) \n", "78 (599, 0.877316) \n", "79 (899, 0.88754) \n", "80 (99, 0.805588) \n", "81 (299, 0.839067) \n", "82 (599, 0.856192) \n", "83 (899, 0.865057) \n", "84 (99, 0.816703) \n", "85 (299, 0.849048) \n", "86 (599, 0.866013) \n", "87 (899, 0.875472) \n", "88 (99, 0.825646) \n", "89 (299, 0.857447) \n", "90 (599, 0.874347) \n", "91 (899, 0.884148) \n", "92 (99, 0.833498) \n", "93 (299, 0.864634) \n", "94 (599, 0.882257) \n", "95 (899, 0.892832) \n", "\n", "[96 rows x 7 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(163, 0.86024999999999996)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max(df['eval_auc'].values,key=itemgetter(1)) " ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(53, (163, 0.86024999999999996))" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "findbest(df['eval_auc'].values)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Unnamed: 0 53\n", "num_rounds 300\n", "max_depth 4\n", "eta 0.3\n", "eval_results {'train': {'auc': ['0.564531', '0.574791', '0....\n", "eval_auc (163, 0.86025)\n", "train_auc (299, 0.829813)\n", "Name: 53, dtype: object" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ix[53]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results = x.split(\"eval\")" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"{'train': {'auc': ['0.552844', '0.552849', '0.552853', '0.552854', '0.564506', '0.564508', '0.564585', '0.570304', '0.570369', '0.570379', '0.574745', '0.574746', '0.574748', '0.574748', '0.574755', '0.574814', '0.577942', '0.577889', '0.577944', '0.591977', '0.591976', '0.591979', '0.595014', '0.605616', '0.609023', '0.609023', '0.609026', '0.609065', '0.615975', '0.616123', '0.616305', '0.616303', '0.616452', '0.616523', '0.616298', '0.616303', '0.648427', '0.648950', '0.648568', '0.652931', '0.652968', '0.652983', '0.653288', '0.653320', '0.657873', '0.657791', '0.657966', '0.657924', '0.658021', '0.658081', '0.658085', '0.658091', '0.663862', '0.663871', '0.663874', '0.663901', '0.665859', '0.668477', '0.668617', '0.668639', '0.668658', '0.668621', '0.668623', '0.670125', '0.670147', '0.670208', '0.669043', '0.671257', '0.671260', '0.677831', '0.680031', '0.680049', '0.680268', '0.680302', '0.680307', '0.680937', '0.680934', '0.682168', '0.682185', '0.682234', '0.682249', '0.683827', '0.685565', '0.686696', '0.686442', '0.687807', '0.687826', '0.687395', '0.686994', '0.687051', '0.687506', '0.690304', '0.690345', '0.690778', '0.690929', '0.691567', '0.691241', '0.696854', '0.696816', '0.697191']}, '\"" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[0]" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "outstr = \"\"\n", "badset = {\"{\",\"}\",\"'\",\",\",\"t\",\"r\",\"a\",\"i\",\"n\",\"u\",\"c\",\":\",\"[\",\"]\"}\n", "for char in results[0]:\n", " if char not in badset:\n", " outstr = outstr + char" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['',\n", " '',\n", " '0.552844',\n", " '0.552849',\n", " '0.552853',\n", " '0.552854',\n", " '0.564506',\n", " '0.564508',\n", " '0.564585',\n", " '0.570304',\n", " '0.570369',\n", " '0.570379',\n", " '0.574745',\n", " '0.574746',\n", " '0.574748',\n", " '0.574748',\n", " '0.574755',\n", " '0.574814',\n", " '0.577942',\n", " '0.577889',\n", " '0.577944',\n", " '0.591977',\n", " '0.591976',\n", " '0.591979',\n", " '0.595014',\n", " '0.605616',\n", " '0.609023',\n", " '0.609023',\n", " '0.609026',\n", " '0.609065',\n", " '0.615975',\n", " '0.616123',\n", " '0.616305',\n", " '0.616303',\n", " '0.616452',\n", " '0.616523',\n", " '0.616298',\n", " '0.616303',\n", " '0.648427',\n", " '0.648950',\n", " '0.648568',\n", " '0.652931',\n", " '0.652968',\n", " '0.652983',\n", " '0.653288',\n", " '0.653320',\n", " '0.657873',\n", " '0.657791',\n", " '0.657966',\n", " '0.657924',\n", " '0.658021',\n", " '0.658081',\n", " '0.658085',\n", " '0.658091',\n", " '0.663862',\n", " '0.663871',\n", " '0.663874',\n", " '0.663901',\n", " '0.665859',\n", " '0.668477',\n", " '0.668617',\n", " '0.668639',\n", " '0.668658',\n", " '0.668621',\n", " '0.668623',\n", " '0.670125',\n", " '0.670147',\n", " '0.670208',\n", " '0.669043',\n", " '0.671257',\n", " '0.671260',\n", " '0.677831',\n", " '0.680031',\n", " '0.680049',\n", " '0.680268',\n", " '0.680302',\n", " '0.680307',\n", " '0.680937',\n", " '0.680934',\n", " '0.682168',\n", " '0.682185',\n", " '0.682234',\n", " '0.682249',\n", " '0.683827',\n", " '0.685565',\n", " '0.686696',\n", " '0.686442',\n", " '0.687807',\n", " '0.687826',\n", " '0.687395',\n", " '0.686994',\n", " '0.687051',\n", " '0.687506',\n", " '0.690304',\n", " '0.690345',\n", " '0.690778',\n", " '0.690929',\n", " '0.691567',\n", " '0.691241',\n", " '0.696854',\n", " '0.696816',\n", " '0.697191',\n", " '']" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "outstr.split(\" \")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = \"ljklj{}\".format(5)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'ljklj5'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

WarwickEPR/qudi

notebooks/fit_testing_N15.ipynb

4

9291

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from lmfit import Parameters\n", "import matplotlib.pyplot as plt\n", "from scipy.interpolate import InterpolatedUnivariateSpline\n", "from scipy.signal import wiener\n", "from scipy.ndimage import filters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Set this flag to True if you want to plot the results\n", "plot_results = False\n", "# This is the number of repetitions for each test function\n", "repetitions = 100" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def N15_testing():\n", " \"\"\" Test function to implement the estimator for the N15 fit with offset. \"\"\"\n", " x_axis = np.linspace(2850, 2860, 101)*1e6\n", "\n", " mod,params = fitlogic.make_multiplelorentzian_model(no_of_functions=2)\n", "# print('Parameters of the model',mod.param_names)\n", "\n", " p = Parameters()\n", "\n", " p.add('l0_amplitude',value=-1e4)\n", " p.add('l0_center',value=2850*1e6+abs(np.random.random(1)*8)*1e6)\n", "# p.add('lorentz0_sigma',value=abs(np.random.random(1)*1)*1e6+0.5*1e6)\n", " p.add('l0_sigma',value=0.5*1e6)\n", " p.add('l1_amplitude',value=p['l0_amplitude'].value)\n", " p.add('l1_center',value=p['l0_center'].value+3.03*1e6)\n", " p.add('l1_sigma',value=p['l0_sigma'].value)\n", " p.add('offset',value=100000.)\n", "\n", " data_nice = mod.eval(x=x_axis, params=p)\n", "\n", " data_noisy= data_nice + 6000*np.random.normal(size=x_axis.shape)\n", "\n", " data_smooth_lorentz, offset = fitlogic.find_offset_parameter(x_axis, data_noisy)\n", "\n", " x_offset = np.array([offset]*len(x_axis))\n", "\n", " if plot_results:\n", " plt.figure()\n", " plt.plot(x_axis, data_noisy, label='noisy data')\n", " plt.plot(x_axis, data_smooth_lorentz, label='smoothed data')\n", " plt.plot(x_axis, x_offset, label='offset estimation')\n", " plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0.)\n", " plt.show()\n", "\n", " hf_splitting = 3.03 * 1e6 # Hz\n", "\n", " # filter should always have a length of approx linewidth 1MHz\n", " points_within_1MHz = len(x_axis) / (x_axis.max() - x_axis.min()) * 1e6\n", "\n", " # filter should have a width of 4 MHz\n", " x_filter = np.linspace(0, 4 * points_within_1MHz, 4 * points_within_1MHz)\n", " lorentz = np.piecewise(\n", " x_filter,\n", " [(x_filter >= 0)*(x_filter < len(x_filter)/4),\n", " (x_filter >= len(x_filter)/4)*(x_filter < len(x_filter)*3/4),\n", " (x_filter >= len(x_filter)*3/4)],\n", " [1, 0, 1])\n", "\n", " # if the filter is smaller than 3 points a convolution does not make sense\n", " if len(lorentz) >= 3:\n", " data_convolved = filters.convolve1d(\n", " data_smooth_lorentz,\n", " lorentz / lorentz.sum(),\n", " mode='constant',\n", " cval=data_smooth_lorentz.max())\n", " x_axis_min = x_axis[data_convolved.argmin()]-hf_splitting/2.\n", " else:\n", " x_axis_min = x_axis[data_smooth_lorentz.argmin()]\n", "\n", " # data_level = data_smooth_lorentz - data_smooth_lorentz.max()\n", " data_level = data_smooth_lorentz - offset\n", "\n", " # multiply\n", " minimum_level = data_level.min()\n", "\n", " x_min_level = np.array([minimum_level] * len(x_axis))\n", "\n", " if plot_results:\n", " plt.figure()\n", " plt.plot(x_axis, data_noisy-offset, label='leveled noisy data')\n", " plt.plot(x_axis, data_level, label='leveled smoothed data')\n", " plt.plot(x_axis, x_min_level, label='minimum level estimation')\n", " plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0.)\n", " plt.show()\n", "\n", " # integral of data:\n", " function = InterpolatedUnivariateSpline(x_axis, data_level, k=1)\n", " Integral = function.integral(x_axis[0], x_axis[-1])\n", "\n", " # assume both peaks contribute to the linewidth, so devive by 2:\n", " sigma = abs(Integral /(np.pi * minimum_level) )/2\n", "\n", " # amplitude = -1*abs(minimum_level*np.pi*sigma)\n", " amplitude = -abs(minimum_level)\n", "\n", " minimal_sigma = x_axis[1]-x_axis[0]\n", " maximal_sigma = x_axis[-1]-x_axis[0]\n", "\n", " mod, params = fitlogic.make_multiplelorentzian_model(no_of_functions=2)\n", "\n", " params['l0_amplitude'].set(value=amplitude, max=-1e-6)\n", " params['l0_center'].set(value=x_axis_min)\n", " params['l0_sigma'].set(value=sigma, min=minimal_sigma,\n", " max=maximal_sigma)\n", " params['l1_amplitude'].set(value=params['l0_amplitude'].value,\n", " max=-1e-6)\n", " params['l1_center'].set(value=params['l0_center'].value+hf_splitting,\n", " expr='l0_center+3.03*1e6')\n", " params['l1_sigma'].set(value=params['l0_sigma'].value,\n", " min=minimal_sigma, max=maximal_sigma,\n", " expr='l0_sigma')\n", " params['offset'].set(value=offset)\n", "\n", " result = mod.fit(data_noisy, x=x_axis, params=params)\n", "\n", " if plot_results:\n", " plt.figure()\n", " plt.plot(x_axis, data_noisy, label='original data')\n", " plt.plot(x_axis, result.init_fit,'-y', label='initial values')\n", " plt.plot(x_axis, result.best_fit,'-r', label='actual fit')\n", " plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0.)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(repetitions):\n", " N15_testing()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def N15_testing2():\n", " \"\"\" Test direkt the implemented fit method with simulated data.\"\"\"\n", "\n", " x_axis = np.linspace(2850, 2860, 101)*1e6\n", "\n", " mod, params = fitlogic.make_multiplelorentzian_model(no_of_functions=2)\n", "# print('Parameters of the model',mod.param_names)\n", "\n", " p = Parameters()\n", "\n", " p.add('l0_amplitude', value=-3e4)\n", " p.add('l0_center', value=2850*1e6+abs(np.random.random(1)*8)*1e6)\n", "# p.add('lorentz0_sigma',value=abs(np.random.random(1)*1)*1e6+0.5*1e6)\n", " p.add('l0_sigma', value=0.5*1e6)\n", " p.add('l1_amplitude', value=p['l0_amplitude'].value)\n", " p.add('l1_center', value=p['l0_center'].value+3.03*1e6)\n", " p.add('l1_sigma', value=p['l0_sigma'].value)\n", " p.add('offset', value=100.)\n", "\n", " data_nice = mod.eval(x=x_axis, params=p)\n", "\n", " data_noisy = (data_nice + 14000 * np.random.normal(size=x_axis.shape))\n", "\n", " result = fitlogic.make_lorentziandouble_fit(x_axis, data_noisy, estimator=fitlogic.estimate_lorentziandouble_N15)\n", "\n", " if plot_results:\n", " plt.figure()\n", " plt.plot(x_axis, data_noisy,'-b', label='data')\n", " plt.plot(x_axis, result.init_fit,'-y', label='initial values')\n", " plt.plot(x_axis, result.best_fit,'-r', label='actual fit')\n", " plt.plot(x_axis, data_nice,'-g', label='actual fit')\n", " plt.xlabel('Frequency (Hz)')\n", " plt.ylabel('Counts (#)')\n", " plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0.)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(repetitions):\n", " N15_testing2()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Qudi", "language": "python", "name": "qudi" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": "3.6.0" }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

KIPAC/StatisticalMethods

tutorials/probability_essentials.ipynb

1

22201

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial: Probability Essentials\n", "\n", "### Analytic and numerical manipulations of probability distributions\n", "\n", "In this notebook we will work through some basic manipulations of probability distributions, e.g. marginalization and conditioning. We'll use a bivariate Gaussian distribution because so many manipulations one might do are analytic.\n", "\n", "By the end of the notebook, you should be able to:\n", "\n", "* Compute marginal and conditional probabilities, both analytically and numerically \n", "* Use normalization as a check of your calculations\n", "\n", "Here is some information about multivariate Gaussians, and in particular identities for conditional and marginal distributions: https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Joint_normality\n", "\n", "Potentially useful properties of probability distributions:\n", "* marginalization (continuous variables): $p(x) = \\int p(x,y)\\,dy$\n", "* conditioning: $p(x|y) = p(x,y)/p(y)$\n", "\n", "**Note:** Equations in markdown cells are coded in LaTeX, between $'s. Examine this cell in edit mode (double click it) to see. If you're not yet a LaTeX magician, don't worry; you'll probably see everything you need for the moment as we go. When in doubt, the appendices of [this guide](http://www.math.hkbu.edu.hk/TeX/essential.pdf) list many helpful math commands." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 0. Setup\n", "\n", "Import packages" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "exec(open('tbc.py').read()) # define TBC and TBC_above\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.use('TkAgg')\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Uncorrelated distributions\n", "\n", "We start with a simple 2D _uncorrelated_ Gaussian probability distribution: $x$ and $y$ are independent. The function below defines this distribution (you can also find a 2D gaussian in `scipy.stats`, but for now we'll write our own). When $x$ and $y$ are uncorrelated, their joint 2D Gaussian distribution is nothing more that the product of a 1D Gaussian over $x$ and a 1D Gaussian over $y$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# dict of parameter values: the means and standard deviations for x and y\n", "unc = {'mx':1.0, 'my':2.3, 'sx':1.0, 'sy':0.5}\n", "\n", "def unc_p_xy(x, y, mx, my, sx, sy):\n", " '''returns pdf of 2D uncorrelated gaussian distribution evaluated at (x,y)'''\n", " return np.exp(-0.5 * ( ((x-mx)/sx)**2 + ((y-my)/sy)**2 )) / (2*np.pi * sx * sy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1a. Analytics: marginalizing and conditioning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with analytic manipulations. First write down the marginal probability distributions for $p(x)$ and $p(y)$:\n", "\n", "> $p(x)$ =\n", "> \n", "> $p(y)$ =" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, using properties of conditional distributions, work out $p(x|y)$ and $p(y|x)$:\n", "\n", "> $p(x|y)$ =\n", "> \n", "> $p(y|x)$ =" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you have the equations, code them up as functions in the cell below. Replace the TBC() calls with your solution, and delete the TBC_above() command outright. We will use these functions to compare our analytic and numerical results in later sections of this notebook.\n", "\n", "**Aside:** If the `**kwargs` syntax below is unfamiliar, have a look [here](https://book.pythontips.com/en/latest/args_and_kwargs.html). In brief, in an argument list this is shorthand for any number of keyword arguments the user might pass. If we called `unc_p_x_given_y(1, 2, mx=3)` later on, then within the `unc_p_x_given_y` function scope we would have access to a dictionary called `kwargs` which would be `{'mx':3}`. We can (and will) also make calls like `unc_p_x(1, **unc)`; this is executed as if it were `unc_p_x(1, mx=1.0, my=2.3, sx=1.0, sy=0.5)` (see the definition of `unc` above). You're obviously **not required** to use this syntax in your own code, but it's convenient enough that you'll be seeing a lot of it in these notebooks. (Incidentally, the fact that we used `**kwargs` in some of the prototypes below is a pretty heavy hint, once you're fluent with the syntax.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def unc_p_x(x, mx, my, sx, sy):\n", " '''returns p(x) for uncorrelated 2D Gaussian distribution'''\n", " TBC()\n", "\n", "def unc_p_y(y, mx, my, sx, sy):\n", " '''returns p(y) for uncorrelated 2D Gaussian distribution'''\n", " TBC()\n", "\n", "def unc_p_x_given_y(x, y, **kwargs):\n", " '''returns p(x|y) for uncorrelated 2D Gaussian distribution'''\n", " TBC()\n", "\n", "def unc_p_y_given_x(y, x, **kwargs):\n", " '''returns p(x|y) for uncorrelated 2D Gaussian distribution'''\n", " TBC()\n", "\n", "TBC_above()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1b. Numerics: probabilities on a grid\n", "\n", "For our numerical caluations we will use a grid. This has been implemented for you in the next cell using the handy np.meshgrid() function. \n", "\n", "The grid resolution is deliberately chosen to be different with respect to to the distribution widths in $x$ and $y$. That way, if we even get confused about which index corresponds to $x$ and which to $y$, we just need to look at the shape of the grid. The bounds are chosen to contain most of $p(x,y)$. Of course, you can play with all of these things and see what changes (or doesn't) as a result!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# x bounds\n", "xmin = -4.0\n", "xmax = 6.0\n", "dx = 0.1\n", "\n", "# y bounds\n", "ymin = -0.2\n", "ymax = 4.8\n", "dy = 0.1\n", "\n", "# defind the x and y values and the meshgrid\n", "xvalues = np.arange(xmin, xmax+dx, dx)\n", "yvalues = np.arange(ymin, ymax+dy, dy)\n", "grid_y, grid_x = np.meshgrid(yvalues, xvalues)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE the intentional swapping of x and y in the meshgrid call.**\n", "\n", "This is done so that we get arrays where the first index corresponds to $x$ and the second to $y$ instead of vice versa.\n", "The result is a much better convention in terms of generalizing to higher dimensions, but it means we\n", "will need to transpose arrays before plotting them if we want $x$ to appear on the horizontal axis and $y$ to appear on the vertical axis." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# plot the x and y grids as a sanity check\n", "plt.rcParams['figure.figsize'] = (14.0, 5.0)\n", "fig, ax = plt.subplots(1,2);\n", "ax[0].imshow(grid_x.T, cmap='gray', origin='lower', extent=[xmin, xmax, ymin, ymax]);\n", "ax[1].imshow(grid_y.T, cmap='gray', origin='lower', extent=[xmin, xmax, ymin, ymax]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's evaluate $p(x,y)$ on this grid and visualize the probability distribution:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# evaluate p(x,y)\n", "ugrid_p_xy = unc_p_xy(grid_x, grid_y, **unc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (7.0, 5.0)\n", "plt.imshow(ugrid_p_xy.T, origin='lower', extent=[xmin, xmax, ymin, ymax]);\n", "plt.xlabel('x');\n", "plt.ylabel('y');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1c. Comparing analytic and numerical results of marginal distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### First, $p(x)$\n", "The above plot shows $p(x,y)$; we want to marginalize over $y$ to find $p(x)$. It may help to write the equation down before jumping straight into code. How would you do this marginalization on a discrete grid?\n", "\n", "> $p(x)$ = " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Marginalize over y. The result should be a 1D array, since it is still a function of x.\n", "# As you might guess, the comment below indicates that the remaining notebook cells assume your answer is stored\n", "# in a variable named ugrid_p_x.\n", "\n", "TBC() \n", "# ugrid_p_x = ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a look. The grid calculation above should match the analytic result very closely." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (7.0, 5.0)\n", "plt.plot(xvalues, ugrid_p_x, 'bo', label='grid calculation');\n", "plt.plot(xvalues, unc_p_x(xvalues, **unc), 'r-', label='analytic expression');\n", "plt.xlabel('x');\n", "plt.ylabel('p(x)');\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the defining features of a probability distribution is that it integrates to 1. Verify by a quick calculation that our discrete approximation for $p(x)$, `ugrid_p_x` , is indeed normalized:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# verify that it's normalized (within reasonable numerical error)\n", "\n", "TBC()\n", "\n", "# print(...)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Next, $p(y)$\n", "We'll repeat the above steps to perform the marginalization over $x$ instead of $y$. Make sure to verify the distribution is properly normalized." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# marginalize p(x,y) over x and verify that it's normalized\n", "\n", "TBC()\n", "\n", "# ugrid_p_y = ...\n", "# print(...)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (7.0, 5.0)\n", "plt.plot(yvalues, ugrid_p_y, 'bo', label='grid calculation');\n", "plt.plot(yvalues, unc_p_y(yvalues, **unc), 'r-', label='analytic expression');\n", "plt.xlabel('y');\n", "plt.xlabel('p(y)');\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1d. Comparing analytic and numerical results of conditional distributions\n", "\n", "Now we would like to get a conditional distribution: given some value of $x$ (or $y$), what is the probability distribution for $y$ (or $x$)? In our example, this is most straightforward if we condition on a grid value. Otherwise, we would probably do some interpolation. Here we choose a couple particular values for fixing; feel free to play around with them." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xi = 30 # index into the grid of x's\n", "fixed_x = xvalues[xi]\n", "print(fixed_x)\n", "yi = 40 # similarly for y\n", "fixed_y = yvalues[yi]\n", "print(fixed_y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### First, $p(x|y)$\n", "Calculate the conditional probability $p(x|y=$ fixed_y$)$, and verify its normalization. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# p(x,y) = p(x|y) p(y)\n", "# ugrid_p_x_given_y = ...\n", "\n", "# verify that it's normalized\n", "# print(...)\n", "\n", "TBC()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (7.0, 5.0)\n", "plt.plot(xvalues, ugrid_p_x_given_y, 'bo', label='grid calculation');\n", "plt.plot(xvalues, unc_p_x_given_y(xvalues, fixed_y, **unc), 'r-', label='analytic expression');\n", "plt.xlabel('x');\n", "plt.ylabel('p(x|y=' + str(fixed_y) + ')');\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Next, $p(y|x)$\n", "Now condition on $x=$ fixed_x instead." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ugrid_p_y_given_x = ...\n", "\n", "# verify that it's normalized\n", "# print(...)\n", "\n", "TBC()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (7.0, 5.0)\n", "plt.plot(yvalues, ugrid_p_y_given_x, 'bo', label='grid calculation');\n", "plt.plot(yvalues, unc_p_y_given_x(yvalues, fixed_x, **unc), 'r-', label='analytic expression');\n", "plt.xlabel('y');\n", "plt.ylabel('p(y|x=' + str(fixed_x) + ')');\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Correlated distributions\n", "\n", "Now that we've gotten the hang of some of these manipulations, we'll add a bit of complexity. For the second half of the notebook, we'll go through the same exercises as above, but we've removed our assumption of the independence of $x$ and $y$.\n", "\n", "The new parameter below, $r$, is the _correlation coefficient_ of $x$ and $y$ (cf the [expression for a bivariate normal density on wikipedia](https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Bivariate_case))." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cor = {'mx':1.0, 'my':2.3, 'sx':1.0, 'sy':0.5, 'r':-0.5} # parameter values\n", "\n", "def cor_p_xy(x, y, mx, my, sx, sy, r):\n", " return np.exp(-0.5/(1.0-r**2)*( ((x-mx)/sx)**2 + ((y-my)/sy)**2 -2.0*r*(x-mx)/sx*(y-my)/sy )) / (2*np.pi*sx*sy*np.sqrt(1.0-r**2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2a. analytics\n", "As before, work out the analytic solutions first. Then fill in the functions below.\n", "\n", "Marginal distributions:\n", "> $p(x)$ = \n", ">\n", "> $p(y)$ =\n", "\n", "Conditional distributions:\n", "> $p(x|y)$ =\n", ">\n", "> $p(y|x)$ =" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# marginal\n", "def cor_p_x(x, mx, my, sx, sy, r):\n", " TBC()\n", "\n", "def cor_p_y(y, mx, my, sx, sy, r):\n", " TBC()\n", "\n", "# conditional distributions\n", "def cor_p_x_given_y(x, y, mx, my, sx, sy, r):\n", " TBC()\n", " \n", "def cor_p_y_given_x(y, x, mx, my, sx, sy, r):\n", " TBC()\n", " \n", "TBC_above()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2b. numerics\n", "We'll use the same grid definition as in 1b." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cgrid_p_xy = cor_p_xy(grid_x, grid_y, **cor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the correlated pdf:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (7.0, 5.0)\n", "plt.imshow(cgrid_p_xy.T, origin='lower', extent=[xmin, xmax, ymin, ymax]);\n", "plt.xlabel('x');\n", "plt.ylabel('y');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2c. Marginal distributions\n", "\n", "#### First, $p(x)$\n", "Marginalize over $y$ (remember to check the normalization)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# cgrid_p_x = ...\n", "# print(...)\n", "\n", "TBC()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we'll compare the marginal $p(x)$ of the correlated and uncorrelated distributions $p(x,y)$. Do the similarities or differences make sense?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (14.0, 5.0)\n", "fig, ax = plt.subplots(1,2);\n", "ax[0].plot(xvalues, cgrid_p_x, 'bo');\n", "ax[0].plot(xvalues, cor_p_x(xvalues, **cor), 'r-');\n", "ax[0].set_xlabel('x');\n", "ax[0].set_ylabel('p(x)');\n", "ax[0].set_title('correlated p(x,y)');\n", "ax[1].plot(xvalues, ugrid_p_x, 'bo');\n", "ax[1].plot(xvalues, unc_p_x(xvalues, **unc), 'r-');\n", "ax[1].set_xlabel('x');\n", "ax[1].set_ylabel('p(x)');\n", "ax[1].set_title('uncorrelated p(x,y)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Next, $p(y)$\n", "Marginalize over $x$ (remember to check the normalization)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# cgrid_p_y = ...\n", "# print(...)\n", "\n", "TBC()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (14.0, 5.0)\n", "fig, ax = plt.subplots(1,2);\n", "ax[0].plot(yvalues, cgrid_p_y, 'bo');\n", "ax[0].plot(yvalues, cor_p_y(yvalues, **cor), 'r-');\n", "ax[0].set_xlabel('y');\n", "ax[0].set_ylabel('p(y)');\n", "ax[0].set_title('correlated p(x,y)');\n", "ax[1].plot(yvalues, ugrid_p_y, 'bo');\n", "ax[1].plot(yvalues, unc_p_y(yvalues, **unc), 'r-');\n", "ax[1].set_xlabel('y');\n", "ax[1].set_ylabel('p(y)');\n", "ax[1].set_title('uncorrelated p(x,y)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2d. Conditional distributions\n", "\n", "#### First, $p(x|y)$\n", "As before, condition on $y=$ fixed_y:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# cgrid_p_x_given_y = ...\n", "# print(...)\n", "\n", "TBC()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again we'll compare this calculation with what we got for the uncorrelated case. Do the similarities or differences make sense?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (14.0, 5.0)\n", "fig, ax = plt.subplots(1,2);\n", "ax[0].plot(xvalues, cgrid_p_x_given_y, 'bo');\n", "ax[0].plot(xvalues, cor_p_x_given_y(xvalues, fixed_y, **cor), 'r-');\n", "ax[0].set_xlabel('x');\n", "ax[0].set_ylabel('p(x|y=' + str(fixed_y) + ')');\n", "ax[0].set_title('correlated p(x,y)');\n", "ax[1].plot(xvalues, ugrid_p_x_given_y, 'bo');\n", "ax[1].plot(xvalues, unc_p_x_given_y(xvalues, fixed_y, **unc), 'r-');\n", "ax[1].set_xlabel('x');\n", "ax[1].set_ylabel('p(x|y=' + str(fixed_y) + ')');\n", "ax[1].set_title('uncorrelated p(x,y)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Next, $p(y|x)$\n", "Condition on $x=$ fixed_x:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# cgrid_p_y_given_x = ...\n", "# print(...)\n", "\n", "TBC()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (14.0, 5.0)\n", "fig, ax = plt.subplots(1,2);\n", "ax[0].plot(yvalues, cgrid_p_y_given_x, 'bo');\n", "ax[0].plot(yvalues, cor_p_y_given_x(yvalues, fixed_x, **cor), 'r-');\n", "ax[0].set_xlabel('y');\n", "ax[0].set_ylabel('p(y|x=' + str(fixed_x) + ')');\n", "ax[0].set_title('correlated p(x,y)');\n", "ax[1].plot(yvalues, ugrid_p_y_given_x, 'bo');\n", "ax[1].plot(yvalues, unc_p_y_given_x(yvalues, fixed_x, **unc), 'r-');\n", "ax[1].set_xlabel('y');\n", "ax[1].set_ylabel('p(y|x=' + str(fixed_x) + ')');\n", "ax[1].set_title('uncorrelated p(x,y)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fin\n", "\n", "You reached the end! We'll be making frequent use of these basic probability manipulations, not to mention many of the python operations above, as we go on." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-2.0

asimihsan/pydata-ldn2014-writeup

21 - Winning Ways for Your Visualization Plays.ipynb

1

4449

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%autosave 10" ], "language": "python", "metadata": {}, "outputs": [ { "javascript": [ "IPython.notebook.set_autosave_interval(10000)" ], "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": [ "Autosaving every 10 seconds\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://www.functionalelegance.com\n", "\n", "## History\n", "\n", "- John Snow's cholera investigation; plot dots on a map\n", "\n", "## \"Hockey stick\" chart\n", "\n", "- Famous climate change temperature change chart; controversial\n", " - All down to the axis scale!\n", " - Such a simple choice is a frame; we choose to present a bias, honestly or not.\n", "\n", "## Aspect ratio\n", "\n", "- Another simple question! How do you choose it?\n", "- 1:1 - fair?\n", "- 3:2 - landscape?\n", "- golden ratio - beautiful?\n", "- average slope 45 degrees - perceptually optimal for orientation discrimination.\n", " - easiest angle to see deviations in trend (rather than horizontal / vertical)\n", "- but graphic designers know - all choices depend on what story you want to tell\n", " - choices inevitably affect this.\n", "\n", "##\u00a0Map projections\n", "\n", "- Mercator - preserve angles, not areas.\n", " - used for shipping / navigation\n", "- Choices are frames. You understand \"3D projections onto 2D are necessarily imperfect\", but ordinary people just blindly accept the frame.\n", "- Robinson - almost preserve area, not angle\n", "- But why not plot, scaling for GDP and population, not area? Surely more informative.\n", "\n", "## Choices\n", "\n", "- Representing numeric values without misleading users - our goal.\n", "- Difficult to judge area of quadrangles\n", " - Yet we use quadrangular heat maps!\n", "- **Ebbinghaus Illusion**: very difficult to perceive areas of circles based on context\n", " - But we use bubble charts!\n", "- Even if you use numeric labels, it's too late. Your users have made judgements visually.\n", "- Can't distinguish colours if they're next to other colours\n", " - Yet we use heat maps!\n", "\n", "## Context\n", "\n", "- Add context!\n", " - Textual callouts\n", " - Don't use many colour graduations, few\n", " - Different charts on same page for different views - allows different perspectives, robust.\n", "- The more people are paid, the less able they are to read charts.\n", "- Use familiar idioms\n", " - Next to a vertical bar chart, add traffic lights! Green is up, red is down, yellow is OK.\n", "- \"Familiar visual metaphors make interpretation easier\"\n", "\n", "## Colour gradients\n", "\n", "- Don't use rainbow palettes for continuous numeric values.\n", "- We always see edges between hues (turns categorical).\n", "- Yellow stands out too much\n", " - Use iso-luminate palette, yellow turns brown.\n", "- Brain can distinguish brightness much better than hue.\n", "\n", "## Transparency layering\n", "\n", "- !!AI see presenter's website for info; different ways of mixing layers.\n", "\n", "## Sphere of Influence graphs\n", "\n", "http://demonstrations.wolfram.com/SphereOfInfluenceGraphs/\n", "\n", "## Graphs\n", "\n", "http://visualization.geblogs.com/visualization/network/\n", "\n", "## Summary\n", "\n", "- Since framing is inevitable, start with the user and their context and objecives.\n", "\n", "## Questions\n", "\n", "- Given interaction *or* offer multiple simultaneous views, which to do?\n", " - Interaction always wins.\n", " - Multiple charts will often leave users confused." ] } ], "metadata": {} } ] }

mit

probml/pyprobml

internal/py_to_notebook_book1.ipynb

1

58124

{ "cells": [ { "cell_type": "markdown", "id": "28e6898c", "metadata": {}, "source": [ "# Main Workflow" ] }, { "cell_type": "code", "execution_count": 1, "id": "d2982c90", "metadata": {}, "outputs": [], "source": [ "from time import time\n", "\n", "init = time()\n", "\n", "import re\n", "import os\n", "import sys\n", "import json\n", "import yaml\n", "from functools import reduce\n", "from collections import ChainMap\n", "import subprocess\n", "\n", "import pandas as pd\n", "from glob import glob\n", "import nbformat\n", "\n", "import jax" ] }, { "cell_type": "markdown", "id": "8187838b", "metadata": {}, "source": [ "## Load old colab notebook names" ] }, { "cell_type": "code", "execution_count": 2, "id": "f5604532", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "['../../pml-book/pml1/figure_notebooks/chapter10_logistic_regression_figures.ipynb',\n", " '../../pml-book/pml1/figure_notebooks/chapter4_statistics_figures.ipynb']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "old_nb_files = glob(\"../../pml-book/pml1/figure_notebooks/*\")\n", "old_nb_files[:2]" ] }, { "cell_type": "markdown", "id": "8ce3a5fd", "metadata": {}, "source": [ "## Parse script names from colab notebooks" ] }, { "cell_type": "code", "execution_count": 3, "id": "0e413f1e", "metadata": {}, "outputs": [], "source": [ "new_nb_path = \"../notebooks/book1/\"\n", "scripts_path = \"../scripts/\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "f2d752b2", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 164 unique scripts\n" ] } ], "source": [ "def get_fig_wise_scripts(cells):\n", " prev_cell, cell = cells\n", " scripts = re.findall(\"\\[(\\S*?\\.py)\\]\\(http\", cell[\"source\"])\n", "\n", " if scripts:\n", " fig_num = re.findall(\"## Figure (.*?):\", prev_cell[\"source\"])[0]\n", " fig_num = \".\".join([fig_num.split(\".\")[0].zfill(2), fig_num.split(\".\")[1].zfill(2)])\n", " return {fig_num: scripts}\n", "\n", "\n", "def process_notebook(file_name):\n", " chap_num, chap_name = file_name.split(\"/\")[-1].split(\".\")[0].split(\"_\", 1)\n", " chap_num = chap_num.replace(\"chapter\", \"\").zfill(2)\n", " chap_name = chap_name.replace(\"_figures\", \"\")\n", " nb = nbformat.read(file_name, as_version=4)\n", "\n", " scripts = map(get_fig_wise_scripts, zip(nb[\"cells\"], nb[\"cells\"][1:]))\n", " scripts = filter(None, scripts)\n", " # https://stackoverflow.com/a/15714097\n", " scripts = reduce(lambda x, y: x.update(y) or x, scripts, {})\n", " return {f\"{chap_num}_{chap_name}\": scripts}\n", "\n", "\n", "master_metadata = map(process_notebook, old_nb_files)\n", "master_metadata = reduce(lambda x, y: x.update(y) or x, master_metadata, {})\n", "\n", "scripts = list(set(jax.tree_leaves(master_metadata)))\n", "print(f\"Found {len(set(scripts))} unique scripts\")\n", "\n", "# Check appendix to see full output mapping" ] }, { "cell_type": "markdown", "id": "f39b0c02", "metadata": {}, "source": [ "## Process the code" ] }, { "cell_type": "markdown", "id": "89b71369", "metadata": {}, "source": [ "Ways to import modules in python\n", "* `import foo`\n", "* `import foo as bar`\n", "* `import foo.bar`\n", "* `import foo.bar as bar`\n", "* `from foo import bar`\n", "* `from foo import *`\n", "* `from foo.bar import baz`\n", "* `from foo.bar import baz as qux`" ] }, { "cell_type": "code", "execution_count": 5, "id": "e282f527", "metadata": {}, "outputs": [], "source": [ "def get_module(line):\n", " line = line.rstrip()\n", " import_kw = None\n", "\n", " if line.lstrip().startswith(\"import \"):\n", " import_kw = \"import \"\n", " elif line.lstrip().startswith(\"from \"):\n", " import_kw = \"from \"\n", "\n", " if import_kw:\n", " module = line.lstrip()[len(import_kw) :].split(\" \")[0].split(\".\")[0]\n", " return module, import_kw\n", " return (None, None)\n", "\n", "\n", "def get_modules_from_script(file_name):\n", " try:\n", " with open(os.path.join(scripts_path, file_name)) as f:\n", " code = f.read()\n", " codelines = code.split(\"\\n\")\n", " modules = set(filter(None, map(lambda x: get_module(x)[0], codelines)))\n", " return modules\n", " except FileNotFoundError:\n", " print(f\"{file_name} not found\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "3ea79a05", "metadata": {}, "outputs": [], "source": [ "INBUILT_MODULES = [\n", " \"__future__\",\n", " \"collections\",\n", " \"functools\",\n", " \"io\",\n", " \"itertools\",\n", " \"math\",\n", " \"os\",\n", " \"pathlib\",\n", " \"pprint\",\n", " \"random\",\n", " \"sys\",\n", " \"time\",\n", " \"timeit\",\n", " \"warnings\",\n", " \"mpl_toolkits\",\n", "]\n", "REMOVE_MODULES = [\"superimport\"]\n", "SCRIPT_MODULES = [\n", " \"rvm_regressor\",\n", " \"gmm_lib\",\n", " \"rvm_classifier\",\n", " \"gauss_utils\",\n", " \"prefit_voting_classifier\",\n", " \"mix_bernoulli_lib\",\n", " \"fisher_lda_fit\",\n", "]\n", "TRANSFORM_MODULES = {\"PIL\": \"pillow\", \"tensorflow_probability\": \"tensorflow-probability\", \"sklearn\": \"scikit-learn\"}\n", "with open(\"../requirements.txt\") as f:\n", " REQ_MODULES = f.read().strip().split(\"\\n\")\n", "# TODO: Replace import pyprobml_utils with probml_utils" ] }, { "cell_type": "markdown", "id": "3a486cee", "metadata": {}, "source": [ "#### Corrected scripts:\n", "* Figure 2.5: typo_fix: changed anscobmes_quartet.py to anscombes_quartet.py\n", "* Figure 3.13: name_change: changed mix_ber_em_mnist.py to mix_bernoulli_em_mnist.py\n", "* Figure 4.17: missing: gaussInferParamsMean2d.py is not present in scripts folder (changed to gauss_infer_2d.py)\n", "* Figure 9.5: name_change: changed fisher_vowel_demo.py to fisher_discrim_vowel.py" ] }, { "cell_type": "code", "execution_count": 7, "id": "49509d2f", "metadata": {}, "outputs": [], "source": [ "all_modules = reduce(\n", " lambda x, y: x.union(y) or x, filter(None, map(get_modules_from_script, jax.tree_leaves(master_metadata)))\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "248a64c6", "metadata": {}, "outputs": [], "source": [ "check_modules = all_modules - set(INBUILT_MODULES) - set(SCRIPT_MODULES) - set(REQ_MODULES) - set(REMOVE_MODULES)" ] }, { "cell_type": "code", "execution_count": 9, "id": "4e2aaad8", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No module named 'pyprobml_utils'\n", "pyprobml_utils failed\n" ] } ], "source": [ "for module in check_modules:\n", " try:\n", " if module in TRANSFORM_MODULES:\n", " module_install = TRANSFORM_MODULES[module]\n", " else:\n", " module_install = module\n", " exec(f\"import {module}\")\n", " except Exception as e:\n", " print(e)\n", " print(module, \"failed\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "a917bebb", "metadata": {}, "outputs": [], "source": [ "def get_white_space(line):\n", " space = 0\n", " while line[0] == \" \":\n", " line = line[1:]\n", " space += 1\n", " return space * \" \"\n", "\n", "\n", "def convert_py_to_ipynb(file_name, chapter, fig_num, prev=\"\"):\n", " chap_num, _ = chapter.split(\"_\", 1)\n", " current_modules = set()\n", " new_lines = []\n", " notebook = nbformat.v4.new_notebook()\n", "\n", " with open(os.path.join(scripts_path, file_name)) as f:\n", " code = f.read().strip()\n", " codelines = code.split(\"\\n\")\n", " for line in codelines:\n", " # Ignore superimport\n", " if line.strip().startswith(\"import superimport\"):\n", " continue\n", "\n", " # consistently use savefig only\n", " line = line.replace(\"save_fig\", \"savefig\")\n", "\n", " # change folder path\n", " line = line.replace(\"../figures\", \"figures\")\n", "\n", " # Change pyprobml_utils to probml_utils\n", " if \"pyprobml_utils\" in line:\n", " line = line.replace(\"pyprobml_utils\", \"probml_utils\")\n", " current_modules.add(\"probml_utils\")\n", "\n", " # Check if the line is an import command\n", " module, import_kw = get_module(line)\n", " if module:\n", " if module in SCRIPT_MODULES:\n", " if import_kw == \"import \":\n", " if \" as \" in line:\n", " line = line.replace(f\"{module}\", f\"probml_utils.{module}\", 1)\n", " else:\n", " line = line.replace(f\"{module}\", f\"probml_utils.{module} as {module}\", 1)\n", " elif import_kw == \"from \":\n", " line = line.replace(f\"{module}\", f\"probml_utils.{module}\", 1)\n", " else:\n", " raise NameError()\n", " elif module not in INBUILT_MODULES + REQ_MODULES + list(current_modules):\n", " current_modules.add(module)\n", " module_install = TRANSFORM_MODULES[module] if module in TRANSFORM_MODULES else module\n", " space = get_white_space(line)\n", " line = f\"{space}try:\\n {space}{line}\\n{space}except ModuleNotFoundError:\\n {space}%pip install {module_install}\\n {space}{line}\"\n", "\n", " new_lines.append(line)\n", " new_code = \"\\n\".join(new_lines) + \"\\n\"\n", " if len(prev) == 0:\n", " notebook[\"cells\"].append(nbformat.v4.new_code_cell(new_code))\n", " else:\n", " notebook[\"cells\"].append(nbformat.v4.new_markdown_cell(prev))\n", "\n", " save_path = f\"../notebooks/book1/{chap_num}\"\n", " if not os.path.exists(save_path):\n", " os.makedirs(save_path)\n", " nbformat.write(notebook, os.path.join(save_path, f\"{file_name.replace('.py', '.ipynb')}\"))\n", " print(f\"{file_name} saved\")" ] }, { "cell_type": "markdown", "id": "b343ee99", "metadata": {}, "source": [ "## Convert" ] }, { "cell_type": "code", "execution_count": 11, "id": "e4a0a423", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing: chapter 01_introduction, figure 01.03, script_name iris_plot.py\n", "iris_plot.py saved\n", "Processing: chapter 01_introduction, figure 01.05, script_name linreg_residuals_plot.py\n", "linreg_residuals_plot.py saved\n", "Processing: chapter 01_introduction, figure 01.06, script_name linreg_2d_surface_demo.py\n", "linreg_2d_surface_demo.py saved\n", "Processing: chapter 01_introduction, figure 01.07, script_name linreg_poly_vs_degree.py\n", "linreg_poly_vs_degree.py saved\n", "Processing: chapter 01_introduction, figure 01.08, script_name iris_kmeans.py\n", "iris_kmeans.py saved\n", "Processing: chapter 01_introduction, figure 01.09, script_name iris_pca.py\n", "iris_pca.py saved\n", "Processing: chapter 01_introduction, figure 01.12, script_name mnist_viz_tf.py\n", "mnist_viz_tf.py saved\n", "Processing: chapter 01_introduction, figure 01.12, script_name emnist_viz_pytorch.py\n", "emnist_viz_pytorch.py saved\n", "Processing: chapter 01_introduction, figure 01.13, script_name fashion_viz_tf.py\n", "fashion_viz_tf.py saved\n", "Processing: chapter 01_introduction, figure 01.13, script_name cifar_viz_tf.py\n", "cifar_viz_tf.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.01, script_name discrete_prob_dist_plot.py\n", "discrete_prob_dist_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.02, script_name gauss_plot.py\n", "gauss_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.02, script_name quantile_plot.py\n", "quantile_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.04, script_name bimodal_dist_plot.py\n", "bimodal_dist_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.05, script_name anscombes_quartet.py\n", "anscombes_quartet.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.06, script_name datasaurus_dozen.py\n", "datasaurus_dozen.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.09, script_name binom_dist_plot.py\n", "binom_dist_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.10, script_name activation_fun_plot.py\n", "activation_fun_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.11, script_name iris_logreg.py\n", "iris_logreg.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.12, script_name softmax_plot.py\n", "softmax_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.13, script_name iris_logreg.py\n", "iris_logreg.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.14, script_name linreg_1d_hetero_tfp.py\n", "linreg_1d_hetero_tfp.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.15, script_name student_laplace_pdf_plot.py\n", "student_laplace_pdf_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.16, script_name robust_pdf_plot.py\n", "robust_pdf_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.17, script_name beta_dist_plot.py\n", "beta_dist_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.17, script_name gamma_dist_plot.py\n", "gamma_dist_plot.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.23, script_name centralLimitDemo.py\n", "centralLimitDemo.py saved\n", "Processing: chapter 02_probability_univariate_models, figure 02.24, script_name change_of_vars_demo1d.py\n", "change_of_vars_demo1d.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.05, script_name gauss_plot_2d.py\n", "gauss_plot_2d.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.06, script_name gauss_plot_2d.py\n", "gauss_plot_2d.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.07, script_name gauss_imputation_known_params_demo.py\n", "gauss_imputation_known_params_demo.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.08, script_name gauss_infer_1d.py\n", "gauss_infer_1d.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.09, script_name gauss_infer_2d.py\n", "gauss_infer_2d.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.10, script_name sensor_fusion_2d.py\n", "sensor_fusion_2d.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.11, script_name gmm_plot_demo.py\n", "gmm_plot_demo.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.12, script_name gmm_2d.py\n", "gmm_2d.py saved\n", "Processing: chapter 03_probability_multivariate_models, figure 03.13, script_name mix_bernoulli_em_mnist.py\n", "mix_bernoulli_em_mnist.py saved\n", "Processing: chapter 04_statistics, figure 04.01, script_name iris_cov_mat.py\n", "iris_cov_mat.py saved\n", "Processing: chapter 04_statistics, figure 04.02, script_name hinge_loss_plot.py\n", "hinge_loss_plot.py saved\n", "Processing: chapter 04_statistics, figure 04.03, script_name ema_demo.py\n", "ema_demo.py saved\n", "Processing: chapter 04_statistics, figure 04.04, script_name shrinkcov_plots.py\n", "shrinkcov_plots.py saved\n", "Processing: chapter 04_statistics, figure 04.05, script_name linreg_poly_ridge.py\n", "linreg_poly_ridge.py saved\n", "Processing: chapter 04_statistics, figure 04.07, script_name polyfitRidgeCV.py\n", "polyfitRidgeCV.py saved\n", "Processing: chapter 04_statistics, figure 04.08, script_name imdb_mlp_bow_tf.py\n", "imdb_mlp_bow_tf.py saved\n", "Processing: chapter 04_statistics, figure 04.09, script_name linreg_poly_vs_n.py\n", "linreg_poly_vs_n.py saved\n", "Processing: chapter 04_statistics, figure 04.10, script_name beta_binom_post_plot.py\n", "beta_binom_post_plot.py saved\n", "Processing: chapter 04_statistics, figure 04.12, script_name beta_binom_post_pred_plot.py\n", "beta_binom_post_pred_plot.py saved\n", "Processing: chapter 04_statistics, figure 04.13, script_name mixbetademo.py\n", "mixbetademo.py saved\n", "Processing: chapter 04_statistics, figure 04.14, script_name dirichlet_3d_triangle_plot.py\n", "dirichlet_3d_triangle_plot.py saved\n", "Processing: chapter 04_statistics, figure 04.14, script_name dirichlet_3d_spiky_plot.py\n", "dirichlet_3d_spiky_plot.py saved\n", "Processing: chapter 04_statistics, figure 04.15, script_name dirichlet_samples_plot.py\n", "dirichlet_samples_plot.py saved\n", "Processing: chapter 04_statistics, figure 04.16, script_name gauss_infer_1d.py\n", "##### PREV triggered. duplicate of 03 gauss_infer_1d.py\n", "gauss_infer_1d.py saved\n", "Processing: chapter 04_statistics, figure 04.17, script_name gauss_infer_2d.py\n", "##### PREV triggered. duplicate of 03 gauss_infer_2d.py\n", "gauss_infer_2d.py saved\n", "Processing: chapter 04_statistics, figure 04.18, script_name betaHPD.py\n", "betaHPD.py saved\n", "Processing: chapter 04_statistics, figure 04.19, script_name postDensityIntervals.py\n", "postDensityIntervals.py saved\n", "Processing: chapter 04_statistics, figure 04.20, script_name logreg_iris_1d.py\n", "logreg_iris_1d.py saved\n", "Processing: chapter 04_statistics, figure 04.20, script_name logreg_iris_bayes_1d_pymc3.py\n", "logreg_iris_bayes_1d_pymc3.py saved\n", "Processing: chapter 04_statistics, figure 04.22, script_name beta_binom_approx_post_pymc3.py\n", "beta_binom_approx_post_pymc3.py saved\n", "Processing: chapter 04_statistics, figure 04.23, script_name bootstrapDemoBer.py\n", "bootstrapDemoBer.py saved\n", "Processing: chapter 04_statistics, figure 04.24, script_name samplingDistributionGaussianShrinkage.py\n", "samplingDistributionGaussianShrinkage.py saved\n", "Processing: chapter 04_statistics, figure 04.25, script_name biasVarModelComplexity3.py\n", "biasVarModelComplexity3.py saved\n", "Processing: chapter 05_decision_theory, figure 05.02, script_name roc_plot.py\n", "roc_plot.py saved\n", "Processing: chapter 05_decision_theory, figure 05.02, script_name pr_plot.py\n", "pr_plot.py saved\n", "Processing: chapter 05_decision_theory, figure 05.03, script_name huberLossPlot.py\n", "huberLossPlot.py saved\n", "Processing: chapter 05_decision_theory, figure 05.04, script_name coins_model_sel_demo.py\n", "coins_model_sel_demo.py saved\n", "Processing: chapter 05_decision_theory, figure 05.05, script_name linreg_eb_modelsel_vs_n.py\n", "linreg_eb_modelsel_vs_n.py saved\n", "Processing: chapter 05_decision_theory, figure 05.06, script_name linreg_eb_modelsel_vs_n.py\n", "linreg_eb_modelsel_vs_n.py saved\n", "Processing: chapter 05_decision_theory, figure 05.08, script_name riskFnGauss.py\n", "riskFnGauss.py saved\n", "Processing: chapter 05_decision_theory, figure 05.10, script_name neymanPearson2.py\n", "neymanPearson2.py saved\n", "Processing: chapter 05_decision_theory, figure 05.10, script_name twoPowerCurves.py\n", "twoPowerCurves.py saved\n", "Processing: chapter 06_information_theory, figure 06.01, script_name bernoulli_entropy_fig.py\n", "bernoulli_entropy_fig.py saved\n", "Processing: chapter 06_information_theory, figure 06.02, script_name seq_logo_demo.py\n", "seq_logo_demo.py saved\n", "Processing: chapter 06_information_theory, figure 06.03, script_name KLfwdReverseMixGauss.py\n", "KLfwdReverseMixGauss.py saved\n", "Processing: chapter 07_linear_algebra, figure 07.06, script_name gaussEvec.py\n", "gaussEvec.py saved\n", "Processing: chapter 07_linear_algebra, figure 07.07, script_name height_weight_whiten_plot.py\n", "height_weight_whiten_plot.py saved\n", "Processing: chapter 07_linear_algebra, figure 07.09, script_name svd_image_demo.py\n", "svd_image_demo.py saved\n", "Processing: chapter 07_linear_algebra, figure 07.10, script_name svd_image_demo.py\n", "svd_image_demo.py saved\n", "Processing: chapter 08_optimization, figure 08.01, script_name extrema_fig_1d.py\n", "extrema_fig_1d.py saved\n", "Processing: chapter 08_optimization, figure 08.01, script_name saddle.py\n", "saddle.py saved\n", "Processing: chapter 08_optimization, figure 08.07, script_name smooth-vs-nonsmooth-1d.py\n", "smooth-vs-nonsmooth-1d.py saved\n", "Processing: chapter 08_optimization, figure 08.11, script_name steepestDescentDemo.py\n", "steepestDescentDemo.py saved\n", "Processing: chapter 08_optimization, figure 08.12, script_name lineSearchConditionNum.py\n", "lineSearchConditionNum.py saved\n", "Processing: chapter 08_optimization, figure 08.14, script_name newtonsMethodMinQuad.py\n", "newtonsMethodMinQuad.py saved\n", "Processing: chapter 08_optimization, figure 08.14, script_name newtonsMethodNonConvex.py\n", "newtonsMethodNonConvex.py saved\n", "Processing: chapter 08_optimization, figure 08.16, script_name lms_demo.py\n", "lms_demo.py saved\n", "Processing: chapter 08_optimization, figure 08.18, script_name learning_rate_plot.py\n", "learning_rate_plot.py saved\n", "Processing: chapter 08_optimization, figure 08.23, script_name emLogLikelihoodMax.py\n", "emLogLikelihoodMax.py saved\n", "Processing: chapter 08_optimization, figure 08.25, script_name mix_gauss_demo_faithful.py\n", "mix_gauss_demo_faithful.py saved\n", "Processing: chapter 08_optimization, figure 08.26, script_name mix_gauss_singularity.py\n", "mix_gauss_singularity.py saved\n", "Processing: chapter 08_optimization, figure 08.26, script_name mix_gauss_mle_vs_map.py\n", "mix_gauss_mle_vs_map.py saved\n", "Processing: chapter 08_optimization, figure 08.27, script_name gmm_lik_surface_plot.py\n", "gmm_lik_surface_plot.py saved\n", "Processing: chapter 09_linear_discriminant_analysis, figure 09.01, script_name discrim_analysis_dboundaries_plot2.py\n", "discrim_analysis_dboundaries_plot2.py saved\n", "Processing: chapter 09_linear_discriminant_analysis, figure 09.02, script_name discrim_analysis_dboundaries_plot2.py\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "discrim_analysis_dboundaries_plot2.py saved\n", "Processing: chapter 09_linear_discriminant_analysis, figure 09.04, script_name fisher_lda_demo.py\n", "fisher_lda_demo.py saved\n", "Processing: chapter 09_linear_discriminant_analysis, figure 09.05, script_name fisher_discrim_vowel.py\n", "fisher_discrim_vowel.py saved\n", "Processing: chapter 09_linear_discriminant_analysis, figure 09.08, script_name generativeVsDiscrim.py\n", "generativeVsDiscrim.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.01, script_name iris_logreg.py\n", "##### PREV triggered. duplicate of 02 iris_logreg.py\n", "iris_logreg.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.02, script_name sigmoid_2d_plot.py\n", "sigmoid_2d_plot.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.04, script_name logreg_poly_demo.py\n", "logreg_poly_demo.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.05, script_name iris_logreg_loss_surface.py\n", "iris_logreg_loss_surface.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.06, script_name logreg_poly_demo.py\n", "logreg_poly_demo.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.07, script_name logreg_multiclass_demo.py\n", "logreg_multiclass_demo.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.10, script_name logreg_iris_bayes_robust_1d_pymc3.py\n", "logreg_iris_bayes_robust_1d_pymc3.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.13, script_name logreg_laplace_demo.py\n", "logreg_laplace_demo.py saved\n", "Processing: chapter 10_logistic_regression, figure 10.14, script_name logreg_laplace_demo.py\n", "logreg_laplace_demo.py saved\n", "Processing: chapter 11_linear_regression, figure 11.01, script_name linreg_poly_vs_degree.py\n", "##### PREV triggered. duplicate of 01 linreg_poly_vs_degree.py\n", "linreg_poly_vs_degree.py saved\n", "Processing: chapter 11_linear_regression, figure 11.02, script_name linreg_contours_sse_plot.py\n", "linreg_contours_sse_plot.py saved\n", "Processing: chapter 11_linear_regression, figure 11.04, script_name linregOnlineDemo.py\n", "linregOnlineDemo.py saved\n", "Processing: chapter 11_linear_regression, figure 11.05, script_name linreg_poly_vs_degree.py\n", "##### PREV triggered. duplicate of 01 linreg_poly_vs_degree.py\n", "linreg_poly_vs_degree.py saved\n", "Processing: chapter 11_linear_regression, figure 11.06, script_name linreg_poly_vs_degree.py\n", "##### PREV triggered. duplicate of 01 linreg_poly_vs_degree.py\n", "linreg_poly_vs_degree.py saved\n", "Processing: chapter 11_linear_regression, figure 11.07, script_name geom_ridge.py\n", "geom_ridge.py saved\n", "Processing: chapter 11_linear_regression, figure 11.10, script_name ridgePathProstate.py\n", "ridgePathProstate.py saved\n", "Processing: chapter 11_linear_regression, figure 11.10, script_name lassoPathProstate.py\n", "lassoPathProstate.py saved\n", "Processing: chapter 11_linear_regression, figure 11.11, script_name prostate_comparison.py\n", "prostate_comparison.py saved\n", "Processing: chapter 11_linear_regression, figure 11.12, script_name prostate_comparison.py\n", "prostate_comparison.py saved\n", "Processing: chapter 11_linear_regression, figure 11.13, script_name sparse_sensing_demo.py\n", "sparse_sensing_demo.py saved\n", "Processing: chapter 11_linear_regression, figure 11.14, script_name groupLassoDemo.py\n", "groupLassoDemo.py saved\n", "Processing: chapter 11_linear_regression, figure 11.15, script_name groupLassoDemo.py\n", "groupLassoDemo.py saved\n", "Processing: chapter 11_linear_regression, figure 11.16, script_name splines_basis_weighted.py\n", "splines_basis_weighted.py saved\n", "Processing: chapter 11_linear_regression, figure 11.17, script_name splines_basis_heatmap.py\n", "splines_basis_heatmap.py saved\n", "Processing: chapter 11_linear_regression, figure 11.18, script_name splines_cherry_blossoms.py\n", "splines_cherry_blossoms.py saved\n", "Processing: chapter 11_linear_regression, figure 11.19, script_name linregRobustDemoCombined.py\n", "linregRobustDemoCombined.py saved\n", "Processing: chapter 11_linear_regression, figure 11.19, script_name huberLossPlot.py\n", "##### PREV triggered. duplicate of 05 huberLossPlot.py\n", "huberLossPlot.py saved\n", "Processing: chapter 11_linear_regression, figure 11.20, script_name linreg_2d_bayes_demo.py\n", "linreg_2d_bayes_demo.py saved\n", "Processing: chapter 11_linear_regression, figure 11.21, script_name linreg_post_pred_plot.py\n", "linreg_post_pred_plot.py saved\n", "Processing: chapter 11_linear_regression, figure 11.22, script_name linreg_2d_bayes_centering_pymc3.py\n", "linreg_2d_bayes_centering_pymc3.py saved\n", "Processing: chapter 11_linear_regression, figure 11.23, script_name multi_collinear_legs_numpyro.py\n", "multi_collinear_legs_numpyro.py saved\n", "Processing: chapter 11_linear_regression, figure 11.24, script_name multi_collinear_legs_numpyro.py\n", "multi_collinear_legs_numpyro.py saved\n", "Processing: chapter 13_neural_networks_for_structured_data, figure 13.01, script_name xor_heaviside.py\n", "xor_heaviside.py saved\n", "Processing: chapter 13_neural_networks_for_structured_data, figure 13.02, script_name activation_fun_plot.py\n", "##### PREV triggered. duplicate of 02 activation_fun_plot.py\n", "activation_fun_plot.py saved\n", "Processing: chapter 13_neural_networks_for_structured_data, figure 13.21, script_name logregXorDemo.py\n", "logregXorDemo.py saved\n", "Processing: chapter 13_neural_networks_for_structured_data, figure 13.22, script_name linregRbfDemo.py\n", "linregRbfDemo.py saved\n", "Processing: chapter 13_neural_networks_for_structured_data, figure 13.23, script_name mixexpDemoOneToMany.py\n", "mixexpDemoOneToMany.py saved\n", "Processing: chapter 16_exemplar-based_methods, figure 16.01, script_name knn_voronoi_plot.py\n", "knn_voronoi_plot.py saved\n", "Processing: chapter 16_exemplar-based_methods, figure 16.02, script_name knn_classify_demo.py\n", "knn_classify_demo.py saved\n", "Processing: chapter 16_exemplar-based_methods, figure 16.03, script_name curse_dimensionality_plot.py\n", "curse_dimensionality_plot.py saved\n", "Processing: chapter 16_exemplar-based_methods, figure 16.08, script_name smoothingKernelPlot.py\n", "smoothingKernelPlot.py saved\n", "Processing: chapter 16_exemplar-based_methods, figure 16.09, script_name parzen_window_demo2.py\n", "parzen_window_demo2.py saved\n", "Processing: chapter 16_exemplar-based_methods, figure 16.10, script_name kernelRegressionDemo.py\n", "kernelRegressionDemo.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.01, script_name gprDemoArd.py\n", "gprDemoArd.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.02, script_name gpKernelPlot.py\n", "gpKernelPlot.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.03, script_name gpKernelPlot.py\n", "gpKernelPlot.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.07, script_name gprDemoNoiseFree.py\n", "gprDemoNoiseFree.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.08, script_name gprDemoChangeHparams.py\n", "gprDemoChangeHparams.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.09, script_name gpr_demo_marglik.py\n", "gpr_demo_marglik.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.10, script_name gp_classify_iris_1d_pymc3.py\n", "gp_classify_iris_1d_pymc3.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.11, script_name gp_classify_spaceflu_1d_pymc3.py\n", "gp_classify_spaceflu_1d_pymc3.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.14, script_name svm_classifier_feature_scaling.py\n", "svm_classifier_feature_scaling.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.17, script_name svm_classifier_2d.py\n", "svm_classifier_2d.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.18, script_name svmCgammaDemo.py\n", "svmCgammaDemo.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.19, script_name huberLossPlot.py\n", "##### PREV triggered. duplicate of 05 huberLossPlot.py\n", "huberLossPlot.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.20, script_name svm_regression_1d.py\n", "svm_regression_1d.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.21, script_name kernelBinaryClassifDemo.py\n", "kernelBinaryClassifDemo.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.22, script_name rvm_regression_1d.py\n", "rvm_regression_1d.py saved\n", "Processing: chapter 17_kernel_methods, figure 17.23, script_name rvm_regression_1d.py\n", "rvm_regression_1d.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.01, script_name regtreeSurfaceDemo.py\n", "regtreeSurfaceDemo.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.03, script_name dtree_sensitivity.py\n", "dtree_sensitivity.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.04, script_name bagging_trees.py\n", "bagging_trees.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.04, script_name rf_demo_2d.py\n", "rf_demo_2d.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.05, script_name spam_tree_ensemble_compare.py\n", "spam_tree_ensemble_compare.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.06, script_name boosted_regr_trees.py\n", "boosted_regr_trees.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.07, script_name hinge_loss_plot.py\n", "##### PREV triggered. duplicate of 04 hinge_loss_plot.py\n", "hinge_loss_plot.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.08, script_name rf_feature_importance_mnist.py\n", "rf_feature_importance_mnist.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.09, script_name spam_tree_ensemble_interpret.py\n", "spam_tree_ensemble_interpret.py saved\n", "Processing: chapter 18_trees_forests_bagging_and_boosting, figure 18.10, script_name spam_tree_ensemble_interpret.py\n", "spam_tree_ensemble_interpret.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.01, script_name pcaDemo2d.py\n", "pcaDemo2d.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.02, script_name pca_digits.py\n", "pca_digits.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.03, script_name pcaImageDemo.py\n", "pcaImageDemo.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.04, script_name pca_projected_variance.py\n", "pca_projected_variance.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.05, script_name pcaStandardization.py\n", "pcaStandardization.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.06, script_name pcaOverfitDemo.py\n", "pcaOverfitDemo.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.07, script_name pcaOverfitDemo.py\n", "pcaOverfitDemo.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.08, script_name pcaOverfitDemo.py\n", "pcaOverfitDemo.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.10, script_name pcaEmStepByStep.py\n", "pcaEmStepByStep.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.12, script_name mixPpcaDemo.py\n", "mixPpcaDemo.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.13, script_name binary_fa_demo.py\n", "binary_fa_demo.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.30, script_name manifold_swiss_sklearn.py\n", "manifold_swiss_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.30, script_name manifold_digits_sklearn.py\n", "manifold_digits_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.31, script_name manifold_swiss_sklearn.py\n", "manifold_swiss_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.31, script_name manifold_digits_sklearn.py\n", "manifold_digits_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.33, script_name manifold_swiss_sklearn.py\n", "manifold_swiss_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.33, script_name manifold_digits_sklearn.py\n", "manifold_digits_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.34, script_name manifold_swiss_sklearn.py\n", "manifold_swiss_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.35, script_name kpcaScholkopf.py\n", "kpcaScholkopf.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.36, script_name manifold_swiss_sklearn.py\n", "manifold_swiss_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.36, script_name manifold_digits_sklearn.py\n", "manifold_digits_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.37, script_name manifold_swiss_sklearn.py\n", "manifold_swiss_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.37, script_name manifold_digits_sklearn.py\n", "manifold_digits_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.38, script_name manifold_swiss_sklearn.py\n", "manifold_swiss_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.38, script_name manifold_digits_sklearn.py\n", "manifold_digits_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.41, script_name manifold_swiss_sklearn.py\n", "manifold_swiss_sklearn.py saved\n", "Processing: chapter 20_dimensionality_reduction, figure 20.41, script_name manifold_digits_sklearn.py\n", "manifold_digits_sklearn.py saved\n", "Processing: chapter 21_clustering, figure 21.02, script_name agglomDemo.py\n", "agglomDemo.py saved\n", "Processing: chapter 21_clustering, figure 21.04, script_name hclust_yeast_demo.py\n", "hclust_yeast_demo.py saved\n", "Processing: chapter 21_clustering, figure 21.05, script_name yeast_data_viz.py\n", "yeast_data_viz.py saved\n", "Processing: chapter 21_clustering, figure 21.06, script_name hclust_yeast_demo.py\n", "hclust_yeast_demo.py saved\n", "Processing: chapter 21_clustering, figure 21.07, script_name kmeans_voronoi.py\n", "kmeans_voronoi.py saved\n", "Processing: chapter 21_clustering, figure 21.08, script_name kmeans_yeast_demo.py\n", "kmeans_yeast_demo.py saved\n", "Processing: chapter 21_clustering, figure 21.09, script_name vqDemo.py\n", "vqDemo.py saved\n", "Processing: chapter 21_clustering, figure 21.10, script_name kmeans_minibatch.py\n", "kmeans_minibatch.py saved\n", "Processing: chapter 21_clustering, figure 21.11, script_name kmeans_silhouette.py\n", "kmeans_silhouette.py saved\n", "Processing: chapter 21_clustering, figure 21.11, script_name gmm_2d.py\n", "##### PREV triggered. duplicate of 03 gmm_2d.py\n", "gmm_2d.py saved\n", "Processing: chapter 21_clustering, figure 21.11, script_name kmeans_silhouette.py\n", "kmeans_silhouette.py saved\n", "Processing: chapter 21_clustering, figure 21.12, script_name kmeans_silhouette.py\n", "kmeans_silhouette.py saved\n", "Processing: chapter 21_clustering, figure 21.13, script_name kmeans_silhouette.py\n", "kmeans_silhouette.py saved\n", "Processing: chapter 21_clustering, figure 21.14, script_name gmm_2d.py\n", "##### PREV triggered. duplicate of 03 gmm_2d.py\n", "gmm_2d.py saved\n", "Processing: chapter 21_clustering, figure 21.15, script_name gmm_identifiability_pymc3.py\n", "gmm_identifiability_pymc3.py saved\n", "Processing: chapter 21_clustering, figure 21.16, script_name gmm_identifiability_pymc3.py\n", "gmm_identifiability_pymc3.py saved\n", "Processing: chapter 21_clustering, figure 21.19, script_name spectral_clustering_demo.py\n", "spectral_clustering_demo.py saved\n" ] } ], "source": [ "global_store = []\n", "global_chap = []\n", "repo_path = \"https://github.com/probml/pyprobml/tree/master/notebooks/book1\"\n", "for chapter in sorted(master_metadata):\n", " local_store = []\n", " for fig_num, script_names in master_metadata[chapter].items():\n", " for script_name in script_names:\n", " print(f\"Processing: chapter {chapter}, figure {fig_num}, script_name {script_name}\")\n", " prev = \"\"\n", " if script_name in global_store:\n", " if script_name not in local_store:\n", " idx = global_store.index(script_name)\n", " chap_num = global_chap[idx]\n", " prev = f\"Source of this notebook is here: {repo_path}/{chap_num}/{script_name.replace('.py', '.ipynb')}\"\n", " print(\"##### PREV triggered. duplicate of\", chap_num, script_name)\n", " else:\n", " global_store.append(script_name)\n", " global_chap.append(chapter.split(\"_\", 1)[0])\n", " local_store.append(script_name)\n", "\n", " convert_py_to_ipynb(script_name, chapter, fig_num, prev)" ] }, { "cell_type": "code", "execution_count": 12, "id": "be6fb119", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total notebooks: 174\n" ] } ], "source": [ "print(\"Total notebooks:\", len(glob(\"../notebooks/book1/*/*.ipynb\")))" ] }, { "cell_type": "markdown", "id": "e435022f", "metadata": {}, "source": [ "### Save metadata" ] }, { "cell_type": "code", "execution_count": 13, "id": "d1c85347", "metadata": {}, "outputs": [], "source": [ "pd.to_pickle(master_metadata, \"metadata_book1.pkl\")" ] }, { "cell_type": "code", "execution_count": 14, "id": "eb928e30", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Everything is done in 8.67478609085083 seconds\n" ] } ], "source": [ "print(\"Everything is done in\", time() - init, \"seconds\")" ] }, { "cell_type": "markdown", "id": "311308a5", "metadata": {}, "source": [ "# Appendix" ] }, { "cell_type": "markdown", "id": "3636eb7f", "metadata": {}, "source": [ "## Chapter wise figure number map with scripts" ] }, { "cell_type": "code", "execution_count": 15, "id": "8738d280", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chapter_01_introduction\n", "'01.03':\n", "- iris_plot.py\n", "'01.05':\n", "- linreg_residuals_plot.py\n", "'01.06':\n", "- linreg_2d_surface_demo.py\n", "'01.07':\n", "- linreg_poly_vs_degree.py\n", "'01.08':\n", "- iris_kmeans.py\n", "'01.09':\n", "- iris_pca.py\n", "'01.12':\n", "- mnist_viz_tf.py\n", "- emnist_viz_pytorch.py\n", "'01.13':\n", "- fashion_viz_tf.py\n", "- cifar_viz_tf.py\n", "\n", "Chapter_02_probability_univariate_models\n", "'02.01':\n", "- discrete_prob_dist_plot.py\n", "'02.02':\n", "- gauss_plot.py\n", "- quantile_plot.py\n", "'02.04':\n", "- bimodal_dist_plot.py\n", "'02.05':\n", "- anscombes_quartet.py\n", "'02.06':\n", "- datasaurus_dozen.py\n", "'02.09':\n", "- binom_dist_plot.py\n", "'02.10':\n", "- activation_fun_plot.py\n", "'02.11':\n", "- iris_logreg.py\n", "'02.12':\n", "- softmax_plot.py\n", "'02.13':\n", "- iris_logreg.py\n", "'02.14':\n", "- linreg_1d_hetero_tfp.py\n", "'02.15':\n", "- student_laplace_pdf_plot.py\n", "'02.16':\n", "- robust_pdf_plot.py\n", "'02.17':\n", "- beta_dist_plot.py\n", "- gamma_dist_plot.py\n", "'02.23':\n", "- centralLimitDemo.py\n", "'02.24':\n", "- change_of_vars_demo1d.py\n", "\n", "Chapter_03_probability_multivariate_models\n", "'03.05':\n", "- gauss_plot_2d.py\n", "'03.06':\n", "- gauss_plot_2d.py\n", "'03.07':\n", "- gauss_imputation_known_params_demo.py\n", "'03.08':\n", "- gauss_infer_1d.py\n", "'03.09':\n", "- gauss_infer_2d.py\n", "'03.10':\n", "- sensor_fusion_2d.py\n", "'03.11':\n", "- gmm_plot_demo.py\n", "'03.12':\n", "- gmm_2d.py\n", "'03.13':\n", "- mix_bernoulli_em_mnist.py\n", "\n", "Chapter_04_statistics\n", "'04.01':\n", "- iris_cov_mat.py\n", "'04.02':\n", "- hinge_loss_plot.py\n", "'04.03':\n", "- ema_demo.py\n", "'04.04':\n", "- shrinkcov_plots.py\n", "'04.05':\n", "- linreg_poly_ridge.py\n", "'04.07':\n", "- polyfitRidgeCV.py\n", "'04.08':\n", "- imdb_mlp_bow_tf.py\n", "'04.09':\n", "- linreg_poly_vs_n.py\n", "'04.10':\n", "- beta_binom_post_plot.py\n", "'04.12':\n", "- beta_binom_post_pred_plot.py\n", "'04.13':\n", "- mixbetademo.py\n", "'04.14':\n", "- dirichlet_3d_triangle_plot.py\n", "- dirichlet_3d_spiky_plot.py\n", "'04.15':\n", "- dirichlet_samples_plot.py\n", "'04.16':\n", "- gauss_infer_1d.py\n", "'04.17':\n", "- gauss_infer_2d.py\n", "'04.18':\n", "- betaHPD.py\n", "'04.19':\n", "- postDensityIntervals.py\n", "'04.20':\n", "- logreg_iris_1d.py\n", "- logreg_iris_bayes_1d_pymc3.py\n", "'04.22':\n", "- beta_binom_approx_post_pymc3.py\n", "'04.23':\n", "- bootstrapDemoBer.py\n", "'04.24':\n", "- samplingDistributionGaussianShrinkage.py\n", "'04.25':\n", "- biasVarModelComplexity3.py\n", "\n", "Chapter_05_decision_theory\n", "'05.02':\n", "- roc_plot.py\n", "- pr_plot.py\n", "'05.03':\n", "- huberLossPlot.py\n", "'05.04':\n", "- coins_model_sel_demo.py\n", "'05.05':\n", "- linreg_eb_modelsel_vs_n.py\n", "'05.06':\n", "- linreg_eb_modelsel_vs_n.py\n", "'05.08':\n", "- riskFnGauss.py\n", "'05.10':\n", "- neymanPearson2.py\n", "- twoPowerCurves.py\n", "\n", "Chapter_06_information_theory\n", "'06.01':\n", "- bernoulli_entropy_fig.py\n", "'06.02':\n", "- seq_logo_demo.py\n", "'06.03':\n", "- KLfwdReverseMixGauss.py\n", "\n", "Chapter_07_linear_algebra\n", "'07.06':\n", "- gaussEvec.py\n", "'07.07':\n", "- height_weight_whiten_plot.py\n", "'07.09':\n", "- svd_image_demo.py\n", "'07.10':\n", "- svd_image_demo.py\n", "\n", "Chapter_08_optimization\n", "'08.01':\n", "- extrema_fig_1d.py\n", "- saddle.py\n", "'08.07':\n", "- smooth-vs-nonsmooth-1d.py\n", "'08.11':\n", "- steepestDescentDemo.py\n", "'08.12':\n", "- lineSearchConditionNum.py\n", "'08.14':\n", "- newtonsMethodMinQuad.py\n", "- newtonsMethodNonConvex.py\n", "'08.16':\n", "- lms_demo.py\n", "'08.18':\n", "- learning_rate_plot.py\n", "'08.23':\n", "- emLogLikelihoodMax.py\n", "'08.25':\n", "- mix_gauss_demo_faithful.py\n", "'08.26':\n", "- mix_gauss_singularity.py\n", "- mix_gauss_mle_vs_map.py\n", "'08.27':\n", "- gmm_lik_surface_plot.py\n", "\n", "Chapter_09_linear_discriminant_analysis\n", "'09.01':\n", "- discrim_analysis_dboundaries_plot2.py\n", "'09.02':\n", "- discrim_analysis_dboundaries_plot2.py\n", "'09.04':\n", "- fisher_lda_demo.py\n", "'09.05':\n", "- fisher_discrim_vowel.py\n", "'09.08':\n", "- generativeVsDiscrim.py\n", "\n", "Chapter_10_logistic_regression\n", "'10.01':\n", "- iris_logreg.py\n", "'10.02':\n", "- sigmoid_2d_plot.py\n", "'10.04':\n", "- logreg_poly_demo.py\n", "'10.05':\n", "- iris_logreg_loss_surface.py\n", "'10.06':\n", "- logreg_poly_demo.py\n", "'10.07':\n", "- logreg_multiclass_demo.py\n", "'10.10':\n", "- logreg_iris_bayes_robust_1d_pymc3.py\n", "'10.13':\n", "- logreg_laplace_demo.py\n", "'10.14':\n", "- logreg_laplace_demo.py\n", "\n", "Chapter_11_linear_regression\n", "'11.01':\n", "- linreg_poly_vs_degree.py\n", "'11.02':\n", "- linreg_contours_sse_plot.py\n", "'11.04':\n", "- linregOnlineDemo.py\n", "'11.05':\n", "- linreg_poly_vs_degree.py\n", "'11.06':\n", "- linreg_poly_vs_degree.py\n", "'11.07':\n", "- geom_ridge.py\n", "'11.10':\n", "- ridgePathProstate.py\n", "- lassoPathProstate.py\n", "'11.11':\n", "- prostate_comparison.py\n", "'11.12':\n", "- prostate_comparison.py\n", "'11.13':\n", "- sparse_sensing_demo.py\n", "'11.14':\n", "- groupLassoDemo.py\n", "'11.15':\n", "- groupLassoDemo.py\n", "'11.16':\n", "- splines_basis_weighted.py\n", "'11.17':\n", "- splines_basis_heatmap.py\n", "'11.18':\n", "- splines_cherry_blossoms.py\n", "'11.19':\n", "- linregRobustDemoCombined.py\n", "- huberLossPlot.py\n", "'11.20':\n", "- linreg_2d_bayes_demo.py\n", "'11.21':\n", "- linreg_post_pred_plot.py\n", "'11.22':\n", "- linreg_2d_bayes_centering_pymc3.py\n", "'11.23':\n", "- multi_collinear_legs_numpyro.py\n", "'11.24':\n", "- multi_collinear_legs_numpyro.py\n", "\n", "Chapter_12_generalized_linear_models\n", "{}\n", "\n", "Chapter_13_neural_networks_for_structured_data\n", "'13.01':\n", "- xor_heaviside.py\n", "'13.02':\n", "- activation_fun_plot.py\n", "'13.21':\n", "- logregXorDemo.py\n", "'13.22':\n", "- linregRbfDemo.py\n", "'13.23':\n", "- mixexpDemoOneToMany.py\n", "\n", "Chapter_14_neural_networks_for_images\n", "{}\n", "\n", "Chapter_15_neural_networks_for_sequences\n", "{}\n", "\n", "Chapter_16_exemplar-based_methods\n", "'16.01':\n", "- knn_voronoi_plot.py\n", "'16.02':\n", "- knn_classify_demo.py\n", "'16.03':\n", "- curse_dimensionality_plot.py\n", "'16.08':\n", "- smoothingKernelPlot.py\n", "'16.09':\n", "- parzen_window_demo2.py\n", "'16.10':\n", "- kernelRegressionDemo.py\n", "\n", "Chapter_17_kernel_methods\n", "'17.01':\n", "- gprDemoArd.py\n", "'17.02':\n", "- gpKernelPlot.py\n", "'17.03':\n", "- gpKernelPlot.py\n", "'17.07':\n", "- gprDemoNoiseFree.py\n", "'17.08':\n", "- gprDemoChangeHparams.py\n", "'17.09':\n", "- gpr_demo_marglik.py\n", "'17.10':\n", "- gp_classify_iris_1d_pymc3.py\n", "'17.11':\n", "- gp_classify_spaceflu_1d_pymc3.py\n", "'17.14':\n", "- svm_classifier_feature_scaling.py\n", "'17.17':\n", "- svm_classifier_2d.py\n", "'17.18':\n", "- svmCgammaDemo.py\n", "'17.19':\n", "- huberLossPlot.py\n", "'17.20':\n", "- svm_regression_1d.py\n", "'17.21':\n", "- kernelBinaryClassifDemo.py\n", "'17.22':\n", "- rvm_regression_1d.py\n", "'17.23':\n", "- rvm_regression_1d.py\n", "\n", "Chapter_18_trees_forests_bagging_and_boosting\n", "'18.01':\n", "- regtreeSurfaceDemo.py\n", "'18.03':\n", "- dtree_sensitivity.py\n", "'18.04':\n", "- bagging_trees.py\n", "- rf_demo_2d.py\n", "'18.05':\n", "- spam_tree_ensemble_compare.py\n", "'18.06':\n", "- boosted_regr_trees.py\n", "'18.07':\n", "- hinge_loss_plot.py\n", "'18.08':\n", "- rf_feature_importance_mnist.py\n", "'18.09':\n", "- spam_tree_ensemble_interpret.py\n", "'18.10':\n", "- spam_tree_ensemble_interpret.py\n", "\n", "Chapter_19_learning_with_fewer_labeled_examples\n", "{}\n", "\n", "Chapter_20_dimensionality_reduction\n", "'20.01':\n", "- pcaDemo2d.py\n", "'20.02':\n", "- pca_digits.py\n", "'20.03':\n", "- pcaImageDemo.py\n", "'20.04':\n", "- pca_projected_variance.py\n", "'20.05':\n", "- pcaStandardization.py\n", "'20.06':\n", "- pcaOverfitDemo.py\n", "'20.07':\n", "- pcaOverfitDemo.py\n", "'20.08':\n", "- pcaOverfitDemo.py\n", "'20.10':\n", "- pcaEmStepByStep.py\n", "'20.12':\n", "- mixPpcaDemo.py\n", "'20.13':\n", "- binary_fa_demo.py\n", "'20.30':\n", "- manifold_swiss_sklearn.py\n", "- manifold_digits_sklearn.py\n", "'20.31':\n", "- manifold_swiss_sklearn.py\n", "- manifold_digits_sklearn.py\n", "'20.33':\n", "- manifold_swiss_sklearn.py\n", "- manifold_digits_sklearn.py\n", "'20.34':\n", "- manifold_swiss_sklearn.py\n", "'20.35':\n", "- kpcaScholkopf.py\n", "'20.36':\n", "- manifold_swiss_sklearn.py\n", "- manifold_digits_sklearn.py\n", "'20.37':\n", "- manifold_swiss_sklearn.py\n", "- manifold_digits_sklearn.py\n", "'20.38':\n", "- manifold_swiss_sklearn.py\n", "- manifold_digits_sklearn.py\n", "'20.41':\n", "- manifold_swiss_sklearn.py\n", "- manifold_digits_sklearn.py\n", "\n", "Chapter_21_clustering\n", "'21.02':\n", "- agglomDemo.py\n", "'21.04':\n", "- hclust_yeast_demo.py\n", "'21.05':\n", "- yeast_data_viz.py\n", "'21.06':\n", "- hclust_yeast_demo.py\n", "'21.07':\n", "- kmeans_voronoi.py\n", "'21.08':\n", "- kmeans_yeast_demo.py\n", "'21.09':\n", "- vqDemo.py\n", "'21.10':\n", "- kmeans_minibatch.py\n", "'21.11':\n", "- kmeans_silhouette.py\n", "- gmm_2d.py\n", "- kmeans_silhouette.py\n", "'21.12':\n", "- kmeans_silhouette.py\n", "'21.13':\n", "- kmeans_silhouette.py\n", "'21.14':\n", "- gmm_2d.py\n", "'21.15':\n", "- gmm_identifiability_pymc3.py\n", "'21.16':\n", "- gmm_identifiability_pymc3.py\n", "'21.19':\n", "- spectral_clustering_demo.py\n", "\n", "Chapter_22_recommender_systems\n", "{}\n", "\n", "Chapter_23_graph_embeddings\n", "{}\n", "\n" ] } ], "source": [ "def print_names(key):\n", " print(f\"Chapter_{key}\")\n", " print(yaml.dump(master_metadata[key]))\n", "\n", "\n", "list(map(print_names, sorted(master_metadata)));" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:jax_cpu]", "language": "python", "name": "conda-env-jax_cpu-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 5 }

mit

MPIBGC-TEE/CompartmentalSystems

notebooks/nonl_gcm_3p_many_params/nonl_gcm_3p_many_params-Copy1.ipynb

1

7943583

mit

Hyperparticle/deep-learning-foundation

first-neural-network/Your_first_neural_network.ipynb

1

313542

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Your first neural network\n", "\n", "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Load and prepare the data\n", "\n", "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "data_path = 'Bike-Sharing-Dataset/hour.csv'\n", "\n", "rides = pd.read_csv(data_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>instant</th>\n", " <th>dteday</th>\n", " <th>season</th>\n", " <th>yr</th>\n", " <th>mnth</th>\n", " <th>hr</th>\n", " <th>holiday</th>\n", " <th>weekday</th>\n", " <th>workingday</th>\n", " <th>weathersit</th>\n", " <th>temp</th>\n", " <th>atemp</th>\n", " <th>hum</th>\n", " <th>windspeed</th>\n", " <th>casual</th>\n", " <th>registered</th>\n", " <th>cnt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.81</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.22</td>\n", " <td>0.2727</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>8</td>\n", " <td>32</td>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.22</td>\n", " <td>0.2727</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " <td>27</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>2011-01-01</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.24</td>\n", " <td>0.2879</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " instant dteday season yr mnth hr holiday weekday workingday \\\n", "0 1 2011-01-01 1 0 1 0 0 6 0 \n", "1 2 2011-01-01 1 0 1 1 0 6 0 \n", "2 3 2011-01-01 1 0 1 2 0 6 0 \n", "3 4 2011-01-01 1 0 1 3 0 6 0 \n", "4 5 2011-01-01 1 0 1 4 0 6 0 \n", "\n", " weathersit temp atemp hum windspeed casual registered cnt \n", "0 1 0.24 0.2879 0.81 0.0 3 13 16 \n", "1 1 0.22 0.2727 0.80 0.0 8 32 40 \n", "2 1 0.22 0.2727 0.80 0.0 5 27 32 \n", "3 1 0.24 0.2879 0.75 0.0 3 10 13 \n", "4 1 0.24 0.2879 0.75 0.0 0 1 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rides.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Checking out the data\n", "\n", "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n", "\n", "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1c66d238a58>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvgAAAIPCAYAAAAGtapCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c66d238eb8>" ] }, "metadata": { "image/png": { "height": 263, "width": 380 } }, "output_type": "display_data" } ], "source": [ "rides[:24*10].plot(x='dteday', y='cnt')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Dummy variables\n", "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>yr</th>\n", " <th>holiday</th>\n", " <th>temp</th>\n", " <th>hum</th>\n", " <th>windspeed</th>\n", " <th>casual</th>\n", " <th>registered</th>\n", " <th>cnt</th>\n", " <th>season_1</th>\n", " <th>season_2</th>\n", " <th>...</th>\n", " <th>hr_21</th>\n", " <th>hr_22</th>\n", " <th>hr_23</th>\n", " <th>weekday_0</th>\n", " <th>weekday_1</th>\n", " <th>weekday_2</th>\n", " <th>weekday_3</th>\n", " <th>weekday_4</th>\n", " <th>weekday_5</th>\n", " <th>weekday_6</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.81</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.22</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>8</td>\n", " <td>32</td>\n", " <td>40</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.22</td>\n", " <td>0.80</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " <td>27</td>\n", " <td>32</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>13</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.24</td>\n", " <td>0.75</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 59 columns</p>\n", "</div>" ], "text/plain": [ " yr holiday temp hum windspeed casual registered cnt season_1 \\\n", "0 0 0 0.24 0.81 0.0 3 13 16 1 \n", "1 0 0 0.22 0.80 0.0 8 32 40 1 \n", "2 0 0 0.22 0.80 0.0 5 27 32 1 \n", "3 0 0 0.24 0.75 0.0 3 10 13 1 \n", "4 0 0 0.24 0.75 0.0 0 1 1 1 \n", "\n", " season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \\\n", "0 0 ... 0 0 0 0 0 0 \n", "1 0 ... 0 0 0 0 0 0 \n", "2 0 ... 0 0 0 0 0 0 \n", "3 0 ... 0 0 0 0 0 0 \n", "4 0 ... 0 0 0 0 0 0 \n", "\n", " weekday_3 weekday_4 weekday_5 weekday_6 \n", "0 0 0 0 1 \n", "1 0 0 0 1 \n", "2 0 0 0 1 \n", "3 0 0 0 1 \n", "4 0 0 0 1 \n", "\n", "[5 rows x 59 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n", "for each in dummy_fields:\n", " dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n", " rides = pd.concat([rides, dummies], axis=1)\n", "\n", "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n", " 'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n", "data = rides.drop(fields_to_drop, axis=1)\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Scaling target variables\n", "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n", "\n", "The scaling factors are saved so we can go backwards when we use the network for predictions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n", "# Store scalings in a dictionary so we can convert back later\n", "scaled_features = {}\n", "for each in quant_features:\n", " mean, std = data[each].mean(), data[each].std()\n", " scaled_features[each] = [mean, std]\n", " data.loc[:, each] = (data[each] - mean)/std" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Splitting the data into training, testing, and validation sets\n", "\n", "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Save data for approximately the last 21 days \n", "test_data = data[-21*24:]\n", "\n", "# Now remove the test data from the data set \n", "data = data[:-21*24]\n", "\n", "# Separate the data into features and targets\n", "target_fields = ['cnt', 'casual', 'registered']\n", "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n", "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Hold out the last 60 days or so of the remaining data as a validation set\n", "train_features, train_targets = features[:-60*24], targets[:-60*24]\n", "val_features, val_targets = features[-60*24:], targets[-60*24:]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Time to build the network\n", "\n", "Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n", "\n", "<img src=\"assets/neural_network.png\" width=300px>\n", "\n", "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n", "\n", "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n", "\n", "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n", "\n", "Below, you have these tasks:\n", "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n", "2. Implement the forward pass in the `train` method.\n", "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n", "4. Implement the forward pass in the `run` method.\n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "class NeuralNetwork(object):\n", " def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n", " (self.input_nodes, self.hidden_nodes))\n", "\n", " self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " self.lr = learning_rate\n", " \n", " #### Set self.activation_function to the sigmoid function ####\n", " self.activation_function = lambda x : 1 / (1 + np.exp(-x))\n", "\n", " def train(self, features, targets):\n", " ''' Train the network on batch of features and targets. \n", " \n", " Arguments\n", " ---------\n", " \n", " features: 2D array, each row is one data record, each column is a feature\n", " targets: 1D array of target values\n", " \n", " '''\n", " n_records = features.shape[0]\n", " delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n", " delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n", " for X, y in zip(features, targets):\n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", " # Hidden layer\n", " hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer\n", " hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n", "\n", " # Output layer\n", " final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n", " final_outputs = final_inputs # signals from final output layer\n", " \n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # Output error\n", " error = y - final_outputs # Output layer error is the difference between desired target and actual output.\n", " \n", " # Calculate the hidden layer's contribution to the error\n", " hidden_error = error * self.weights_hidden_to_output\n", " \n", " # Backpropagated error terms\n", " output_error_term = error\n", " hidden_error_term = hidden_error.T * hidden_outputs * (1 - hidden_outputs)\n", "\n", " # Weight step (input to hidden)\n", " delta_weights_i_h += hidden_error_term * X[:, None]\n", " # Weight step (hidden to output)\n", " delta_weights_h_o += output_error_term * hidden_outputs[:, None]\n", "\n", " # Update the weights\n", " self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step\n", " self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step\n", " \n", " def run(self, features):\n", " ''' Run a forward pass through the network with input features \n", " \n", " Arguments\n", " ---------\n", " features: 1D array of feature values\n", " '''\n", " \n", " #### Implement the forward pass here ####\n", " # Hidden layer\n", " hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer\n", " hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n", " \n", " # Output layer\n", " final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n", " final_outputs = final_inputs # signals from final output layer \n", " \n", " return final_outputs" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def MSE(y, Y):\n", " return np.mean((y-Y)**2)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Unit tests\n", "\n", "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ".....\n", "----------------------------------------------------------------------\n", "Ran 5 tests in 0.016s\n", "\n", "OK\n" ] }, { "data": { "text/plain": [ "<unittest.runner.TextTestResult run=5 errors=0 failures=0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import unittest\n", "\n", "inputs = np.array([[0.5, -0.2, 0.1]])\n", "targets = np.array([[0.4]])\n", "test_w_i_h = np.array([[0.1, -0.2],\n", " [0.4, 0.5],\n", " [-0.3, 0.2]])\n", "test_w_h_o = np.array([[0.3],\n", " [-0.1]])\n", "\n", "class TestMethods(unittest.TestCase):\n", " \n", " ##########\n", " # Unit tests for data loading\n", " ##########\n", " \n", " def test_data_path(self):\n", " # Test that file path to dataset has been unaltered\n", " self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n", " \n", " def test_data_loaded(self):\n", " # Test that data frame loaded\n", " self.assertTrue(isinstance(rides, pd.DataFrame))\n", " \n", " ##########\n", " # Unit tests for network functionality\n", " ##########\n", "\n", " def test_activation(self):\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " # Test that the activation function is a sigmoid\n", " self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n", "\n", " def test_train(self):\n", " # Test that weights are updated correctly on training\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " network.weights_input_to_hidden = test_w_i_h.copy()\n", " network.weights_hidden_to_output = test_w_h_o.copy()\n", " \n", " network.train(inputs, targets)\n", " self.assertTrue(np.allclose(network.weights_hidden_to_output, \n", " np.array([[ 0.37275328], \n", " [-0.03172939]])))\n", " self.assertTrue(np.allclose(network.weights_input_to_hidden,\n", " np.array([[ 0.10562014, -0.20185996], \n", " [0.39775194, 0.50074398], \n", " [-0.29887597, 0.19962801]])))\n", "\n", " def test_run(self):\n", " # Test correctness of run method\n", " network = NeuralNetwork(3, 2, 1, 0.5)\n", " network.weights_input_to_hidden = test_w_i_h.copy()\n", " network.weights_hidden_to_output = test_w_h_o.copy()\n", "\n", " self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n", "\n", "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n", "unittest.TextTestRunner().run(suite)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Training the network\n", "\n", "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n", "\n", "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n", "\n", "### Choose the number of iterations\n", "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.\n", "\n", "### Choose the learning rate\n", "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n", "\n", "### Choose the number of hidden nodes\n", "The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress: 100.0% ... Training loss: 0.057 ... Validation loss: 0.137" ] } ], "source": [ "import sys\n", "\n", "### Set the hyperparameters here ###\n", "iterations = 6000\n", "learning_rate = 1.2\n", "hidden_nodes = 12\n", "output_nodes = 1\n", "\n", "N_i = train_features.shape[1]\n", "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n", "\n", "losses = {'train':[], 'validation':[]}\n", "for ii in range(iterations):\n", " # Go through a random batch of 128 records from the training data set\n", " batch = np.random.choice(train_features.index, size=128)\n", " X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n", " \n", " network.train(X, y)\n", " \n", " # Printing out the training progress\n", " train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n", " val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n", " sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n", " + \"% ... Training loss: \" + str(train_loss)[:5] \\\n", " + \" ... Validation loss: \" + str(val_loss)[:5])\n", " sys.stdout.flush()\n", " \n", " losses['train'].append(train_loss)\n", " losses['validation'].append(val_loss)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvgAAAH0CAYAAABICFkFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c66d35d5c0>" ] }, "metadata": { "image/png": { "height": 250, "width": 380 } }, "output_type": "display_data" } ], "source": [ "plt.plot(losses['train'], label='Training loss')\n", "plt.plot(losses['validation'], label='Validation loss')\n", "plt.legend()\n", "_ = plt.ylim()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Check out your predictions\n", "\n", "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+0AAAIgCAYAAADwRojNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAGAoox0AAACG+gX6SU6PkGow+AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c66ec87390>" ] }, "metadata": { "image/png": { "height": 272, "width": 502 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,4))\n", "\n", "mean, std = scaled_features['cnt']\n", "predictions = network.run(test_features).T*std + mean\n", "ax.plot(predictions[0], label='Prediction')\n", "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n", "ax.set_xlim(right=len(predictions))\n", "ax.legend()\n", "\n", "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n", "dates = dates.apply(lambda d: d.strftime('%b %d'))\n", "ax.set_xticks(np.arange(len(dates))[12::24])\n", "_ = ax.set_xticklabels(dates[12::24], rotation=45)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n", " \n", "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n", "\n", "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n", "\n", "#### Your answer below\n", "\n", "The model predicts the data relatively well. We can see that the shape of the prediction corresponds closely to the shape of the data, as there are regular intervals where the count of the riders spike accordingly.\n", "\n", "The model fails after December 21, where the variance of the data reduced to produce softer peaks. The model still predicts that the data peaks much higher, as before.\n", "\n", "The model fails where it does because it has reached a local minimum, where a small change in the model will increase the loss." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

kmunve/APS

aps/satskred/import_skreddb/create_test_data2.ipynb

1

40359

{ "cells": [ { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": true, "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "from datetime import datetime, timedelta\n", "import geopandas as gp\n", "import pandas as pd\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import seaborn as sb\n", "from shapely.geometry import Polygon\n", "import os\n", "\n", "plt.rcParams['figure.figsize'] = (10, 20)\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 109, "outputs": [ { "data": { "text/plain": " area aspect det_count east length north raster_val \\\n0 10394.8943 None 1 20.944515 793.557988 69.714452 11.0 \n\n refdate sat_geom \\\n0 2019-03-13 15:50:19.000220 87 \n\n source \\\n0 AvalDet_20190424_155109_ref_20190313_trno_087_... \n\n ... vh1_max vv0_mean \\\n0 ... -15.749162 -10.2966 \n\n vv0_median vv0_min vv0_max vh0_mean vh0_median vh0_min \\\n0 -10.15564 -13.197838 -7.651885 -19.937243 -20.155186 -22.938601 \n\n vh0_max geometry \n0 -16.017008 POLYGON ((20.94465714165575 69.71382402696912,... \n\n[1 rows x 45 columns]", "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>area</th>\n <th>aspect</th>\n <th>det_count</th>\n <th>east</th>\n <th>length</th>\n <th>north</th>\n <th>raster_val</th>\n <th>refdate</th>\n <th>sat_geom</th>\n <th>source</th>\n <th>...</th>\n <th>vh1_max</th>\n <th>vv0_mean</th>\n <th>vv0_median</th>\n <th>vv0_min</th>\n <th>vv0_max</th>\n <th>vh0_mean</th>\n <th>vh0_median</th>\n <th>vh0_min</th>\n <th>vh0_max</th>\n <th>geometry</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>10394.8943</td>\n <td>None</td>\n <td>1</td>\n <td>20.944515</td>\n <td>793.557988</td>\n <td>69.714452</td>\n <td>11.0</td>\n <td>2019-03-13 15:50:19.000220</td>\n <td>87</td>\n <td>AvalDet_20190424_155109_ref_20190313_trno_087_...</td>\n <td>...</td>\n <td>-15.749162</td>\n <td>-10.2966</td>\n <td>-10.15564</td>\n <td>-13.197838</td>\n <td>-7.651885</td>\n <td>-19.937243</td>\n <td>-20.155186</td>\n <td>-22.938601</td>\n <td>-16.017008</td>\n <td>POLYGON ((20.94465714165575 69.71382402696912,...</td>\n </tr>\n </tbody>\n</table>\n<p>1 rows × 45 columns</p>\n</div>" }, "metadata": {}, "output_type": "execute_result", "execution_count": 109 } ], "source": [ "base_file = \"../data/AvalDet_20190424_155109_ref_20190313_trno_087_VV/AvalDet_20190424_155109_ref_20190313_trno_087_VV.shp\"\n", "gdf = gp.read_file(base_file)\n", "gdf.drop(index=1, inplace=True)\n", "gdf.head()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 110, "outputs": [ { "data": { "text/plain": "Index(['area', 'aspect', 'det_count', 'east', 'length', 'north', 'raster_val',\n 'refdate', 'sat_geom', 'source', 't_0', 't_1', 'time', 'track_id',\n 'uuid', 'width', 'dem_mean', 'dem_median', 'dem_min', 'dem_max',\n 'slp_mean', 'slp_median', 'slp_min', 'slp_max', 'asp_mean',\n 'asp_median', 'asp_min', 'asp_max', 'vv1_mean', 'vv1_median', 'vv1_min',\n 'vv1_max', 'vh1_mean', 'vh1_median', 'vh1_min', 'vh1_max', 'vv0_mean',\n 'vv0_median', 'vv0_min', 'vv0_max', 'vh0_mean', 'vh0_median', 'vh0_min',\n 'vh0_max', 'geometry'],\n dtype='object')" }, "metadata": {}, "output_type": "execute_result", "execution_count": 110 } ], "source": [ "gdf.columns" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "markdown", "source": [ "The following columns are used in the import routine\n", "- time -> skredTidspunkt (should probably be t_1)\n", "- time -> registrertDato (should probably be t_1)\n", "- geometry -> SHAPE\n", "\n", "- dem_min -> hoydeStoppSkred_moh\n", "- asp_median -> eksposisjonUtlopsomr \n", "- slp_mean -> snittHelningUtlopsomr_gr \n", "- slp_max -> maksHelningUtlopsomr_gr \n", "- slp_min -> minHelningUtlopsomr_gr\n", "- area -> arealUtlopsomr_m2 \n", "\n", "Important: skredID needs to be the same in all tables." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 111, "outputs": [], "source": [ "import_list = ['time', 'area', 'dem_min', 'asp_median', 'slp_mean', 'slp_max', 'slp_min', 'geometry']" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 112, "outputs": [], "source": [ "# Add a name to the polygon\n", "gdf['_name'] = \"Original\"\n", "\n", "# convert t_0 (the time the reference image was taken) into a datetime object and give it a descriptive name\n", "gdf['_reference_date'] = pd.to_datetime(gdf['t_0']) # this actually overwrites the existing column \"refdate\", but since it is a duplicate of t_0 we don't care. \n", "\n", "# convert t_1 (the time the activity image was taken) into a datetime object and give it a descriptive name\n", "gdf['_detection_date'] = pd.to_datetime(gdf['t_1'])\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 113, "outputs": [ { "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/png": "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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "aval_map = gdf.plot(column=\"area\", linewidth=0.3, edgecolor='black', cmap=\"OrRd\", alpha=0.9)\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 114, "outputs": [ { "name": "stdout", "text": [ "<class 'shapely.geometry.polygon.Polygon'> POLYGON ((20.94465714165575 69.71382402696912, 20.94470735316531 69.71400241245273, 20.94419325473413 69.71401983461618, 20.9442434628668 69.7141982202462, 20.94372935925285 69.71421564107656, 20.94270114931648 69.71425047823899, 20.94285174061144 69.71478563650619, 20.94387997622828 69.71475079834599, 20.94393018369257 69.71492918406263, 20.94547254318632 69.71487691507734, 20.94598666121134 69.71485948908331, 20.94573554731867 69.71396756362762, 20.94522145069351 69.71398498878987, 20.94517123490418 69.71380660347262, 20.94465714165575 69.71382402696912))\n" ], "output_type": "stream" } ], "source": [ "plg = gdf['geometry'][0]\n", "print(type(plg), plg)\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 115, "outputs": [ { "name": "stdout", "text": [ "2019-04-21T15:51:09.025897\n2019-04-15T15:50:09.025897\nAvalDet_20190421_155109_ref_20190415_trno_087_ZZ\n" ], "output_type": "stream" } ], "source": [ "print(dt_base.strftime(dt_fmt))\n", "dt_1 = dt_base + timedelta(days=-6, minutes=-1)\n", "print(dt_1.strftime(dt_fmt))\n", "print(filename_base.format(dt_base.strftime(dt_fmt_act), dt_1.strftime(dt_fmt_ref)))" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 116, "outputs": [], "source": [ "scns = []\n", "\n", "scn_dict = {\n", " \"scn_0\": {\n", " \"le\": 0.005,\n", " \"re\": 0.002,\n", " \"bn\": 0.005,\n", " \"tn\": 0.002,\n", " \"dem_min\": 800,\n", " \"asp_median\": 180,\n", " \"slp_mean\": 9.0,\n", " \"slp_max\": 12.0,\n", " \"slp_min\": 4.0,\n", " \"days\": 1,\n", " \"seconds\": 15,\n", " \"_dis_id\": 1\n", " },\n", " \"scn_1\": {\n", " \"le\": 0.006,\n", " \"re\": 0.002,\n", " \"bn\": 0.004,\n", " \"tn\": 0.002,\n", " \"dem_min\": 850,\n", " \"asp_median\": 190,\n", " \"slp_mean\": 8.0,\n", " \"slp_max\": 11.0,\n", " \"slp_min\": 3.0,\n", " \"days\": 3,\n", " \"seconds\": 65,\n", " \"_dis_id\": 1\n", " },\n", " \"scn_2\": {\n", " \"le\": 0.0055,\n", " \"re\": 0.002,\n", " \"bn\": 0.0055,\n", " \"tn\": 0.002,\n", " \"dem_min\": 850,\n", " \"asp_median\": 210,\n", " \"slp_mean\": 7.0,\n", " \"slp_max\": 10.0,\n", " \"slp_min\": 2.0,\n", " \"days\": 8,\n", " \"seconds\": 22,\n", " \"_dis_id\": 1\n", " },\n", " \"scn_3\": {\n", " \"le\": 0.007,\n", " \"re\": 0.003,\n", " \"bn\": 0.008,\n", " \"tn\": 0.003,\n", " \"dem_min\": 650,\n", " \"asp_median\": 110,\n", " \"slp_mean\": 9.5,\n", " \"slp_max\": 15.0,\n", " \"slp_min\": 5.2,\n", " \"days\": 5,\n", " \"seconds\": 8,\n", " \"_dis_id\": 1\n", " },\n", " \"scn_4\": {\n", " \"le\": 0.006,\n", " \"re\": 0.002,\n", " \"bn\": 0.006,\n", " \"tn\": 0.005,\n", " \"dem_min\": 770,\n", " \"asp_median\": 140,\n", " \"slp_mean\": 8.5,\n", " \"slp_max\": 14.3,\n", " \"slp_min\": 4.2,\n", " \"days\": 4,\n", " \"seconds\": 78,\n", " \"_dis_id\": 1\n", " }\n", "}" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 117, "outputs": [], "source": [ "def create_scenario(sd):\n", " # Input is an element from scn_dict\n", " \n", " # Define the base coordinates for the test polygons\n", " e_base = 20.94\n", " n_base= 69.71\n", " dt_fmt = '%Y-%m-%dT%H:%M:%S.%f'\n", " dt_fmt_act = '%Y%m%d_%H%M%S'\n", " dt_fmt_ref = '%Y%m%d'\n", " dt_base = datetime.strptime('2019-08-21T15:51:09.025897', dt_fmt)\n", " filename_base =\"AvalDet_{0}_ref_{1}_trno_087_ZZ\"\n", " \n", " le = e_base + sd[\"le\"] # left_easting\n", " re = le + sd[\"re\"] # right_easting\n", " bn = n_base + sd[\"bn\"] # bottom_northing\n", " tn = bn + sd[\"tn\"] # top_northing\n", " p = Polygon([(le, bn), (re, bn), (re, tn), (le, tn)])\n", " act_date = dt_base + timedelta(days=sd[\"days\"], seconds=sd[\"seconds\"])\n", " ref_date = act_date + timedelta(days=-6, seconds=-60)\n", " \n", " # Create a copy of the original polygon and alter its properties\n", " scn = gdf.copy(deep=True)\n", " scn['geometry'] = p\n", " print(scn.crs)\n", " scn.to_crs({'init': 'epsg:32633'}, inplace=True) \n", " print(scn.crs)\n", " scn['area'] = scn['geometry'].area\n", " scn['length'] = scn['geometry'].length\n", " scn.to_crs(gdf.crs, inplace=True)\n", " \n", " scn['_name'] = filename_base.format(act_date.strftime(dt_fmt_act), ref_date.strftime(dt_fmt_ref))\n", " print(scn['_name'][0])\n", " scn['east'] = scn['geometry'].centroid.x\n", " scn['north'] = scn['geometry'].centroid.y\n", " scn['dem_min'] = sd['dem_min']\n", " scn['asp_median'] = sd['asp_median']\n", " scn['slp_mean'] = sd['slp_mean']\n", " scn['slp_max'] = sd['slp_max']\n", " scn['slp_min'] = sd['slp_min']\n", " scn['time'] = act_date.strftime(dt_fmt)\n", " scn['t_1'] = act_date.strftime(dt_fmt)\n", " scn['refdate'] = ref_date.strftime(dt_fmt)\n", " scn['t_0'] = ref_date.strftime(dt_fmt)\n", " scn['_dis_id'] = 1 # used only to merge (dissolve) polygons for verification needs to be same for all scns\n", " \n", " new_dir = '../data/{0}'.format(scn['_name'][0])\n", " if not os.path.exists(new_dir):\n", " os.mkdir(new_dir)\n", " scn.drop(['_detection_date', '_reference_date', '_dis_id'], axis=1).to_file(filename='../data/{0}/{0}.shp'.format(scn['_name'][0], scn['_name'][0]))\n", "\n", " return scn\n", " " ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 118, "outputs": [ { "name": "stdout", "text": [ "{'init': 'epsg:4326'}\n{'init': 'epsg:32633'}\nAvalDet_20190822_155124_ref_20190816_trno_087_ZZ\n", "{'init': 'epsg:4326'}\n{'init': 'epsg:32633'}\nAvalDet_20190824_155214_ref_20190818_trno_087_ZZ\n{'init': 'epsg:4326'}\n{'init': 'epsg:32633'}\nAvalDet_20190829_155131_ref_20190823_trno_087_ZZ\n", "{'init': 'epsg:4326'}\n{'init': 'epsg:32633'}\nAvalDet_20190826_155117_ref_20190820_trno_087_ZZ\n", "{'init': 'epsg:4326'}\n{'init': 'epsg:32633'}\nAvalDet_20190825_155227_ref_20190819_trno_087_ZZ\n" ], "output_type": "stream" }, { "name": "stderr", "text": [ "C:\\Anaconda3\\envs\\APS\\lib\\site-packages\\geopandas\\io\\file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n with fiona.drivers():\n", "C:\\Anaconda3\\envs\\APS\\lib\\site-packages\\geopandas\\io\\file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n with fiona.drivers():\nC:\\Anaconda3\\envs\\APS\\lib\\site-packages\\geopandas\\io\\file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n with fiona.drivers():\n", "C:\\Anaconda3\\envs\\APS\\lib\\site-packages\\geopandas\\io\\file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n with fiona.drivers():\n", "C:\\Anaconda3\\envs\\APS\\lib\\site-packages\\geopandas\\io\\file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n with fiona.drivers():\n" ], "output_type": "stream" } ], "source": [ "for sd in scn_dict.keys():\n", " scns.append(create_scenario(scn_dict[sd]))\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 119, "outputs": [ { "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/png": "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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = gdf.plot()\n", "for scn in scns:\n", " scn.plot(ax=ax, edgecolor='black', alpha=0.6)\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 120, "outputs": [ { "name": "stderr", "text": [ "C:\\Anaconda3\\envs\\APS\\lib\\site-packages\\geopandas\\io\\file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n with fiona.drivers():\n" ], "output_type": "stream" } ], "source": [ "test_scns = pd.concat(scns)\n", "new_dir = '../data/scns'\n", "if not os.path.exists(new_dir):\n", " os.mkdir(new_dir)\n", "test_scns.drop(['_detection_date', '_reference_date', '_dis_id'], axis=1).to_file(filename='../data/scns/test_scns.shp')\n", "\n", "dissolved_scns = test_scns.dissolve(by='_dis_id')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } }, { "cell_type": "code", "execution_count": 121, "outputs": [ { "data": { "text/plain": " time area dem_min asp_median slp_mean \\\n0 2019-08-22T15:51:24.025897 17280.273674 800 180 9.0 \n0 2019-08-24T15:52:14.025897 17281.096651 850 190 8.0 \n0 2019-08-29T15:51:31.025897 17279.869669 850 210 7.0 \n0 2019-08-26T15:51:17.025897 38874.235753 650 110 9.5 \n0 2019-08-25T15:52:27.025897 43195.605978 770 140 8.5 \n\n slp_max slp_min geometry \n0 12.0 4.0 POLYGON ((20.945 69.71499999999999, 20.947 69.... \n0 11.0 3.0 POLYGON ((20.946 69.714, 20.948 69.714, 20.948... \n0 10.0 2.0 POLYGON ((20.9455 69.71549999999999, 20.9475 6... \n0 15.0 5.2 POLYGON ((20.947 69.71799999999999, 20.95 69.7... \n0 14.3 4.2 POLYGON ((20.94600000000001 69.71599999999999,... ", "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>time</th>\n <th>area</th>\n <th>dem_min</th>\n <th>asp_median</th>\n <th>slp_mean</th>\n <th>slp_max</th>\n <th>slp_min</th>\n <th>geometry</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2019-08-22T15:51:24.025897</td>\n <td>17280.273674</td>\n <td>800</td>\n <td>180</td>\n <td>9.0</td>\n <td>12.0</td>\n <td>4.0</td>\n <td>POLYGON ((20.945 69.71499999999999, 20.947 69....</td>\n </tr>\n <tr>\n <th>0</th>\n <td>2019-08-24T15:52:14.025897</td>\n <td>17281.096651</td>\n <td>850</td>\n <td>190</td>\n <td>8.0</td>\n <td>11.0</td>\n <td>3.0</td>\n <td>POLYGON ((20.946 69.714, 20.948 69.714, 20.948...</td>\n </tr>\n <tr>\n <th>0</th>\n <td>2019-08-29T15:51:31.025897</td>\n <td>17279.869669</td>\n <td>850</td>\n <td>210</td>\n <td>7.0</td>\n <td>10.0</td>\n <td>2.0</td>\n <td>POLYGON ((20.9455 69.71549999999999, 20.9475 6...</td>\n </tr>\n <tr>\n <th>0</th>\n <td>2019-08-26T15:51:17.025897</td>\n <td>38874.235753</td>\n <td>650</td>\n <td>110</td>\n <td>9.5</td>\n <td>15.0</td>\n <td>5.2</td>\n <td>POLYGON ((20.947 69.71799999999999, 20.95 69.7...</td>\n </tr>\n <tr>\n <th>0</th>\n <td>2019-08-25T15:52:27.025897</td>\n <td>43195.605978</td>\n <td>770</td>\n <td>140</td>\n <td>8.5</td>\n <td>14.3</td>\n <td>4.2</td>\n <td>POLYGON ((20.94600000000001 69.71599999999999,...</td>\n </tr>\n </tbody>\n</table>\n</div>" }, "metadata": {}, "output_type": "execute_result", "execution_count": 121 } ], "source": [ "test_scns.to_csv('../data/scns/scns.csv')\n", "test_scns.filter(import_list).head()\n", "\n", "\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n", "is_executing": false } } } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" }, "kernelspec": { "name": "pycharm-26af264e", "language": "python", "display_name": "PyCharm (APS)" }, "pycharm": { "stem_cell": { "cell_type": "raw", "source": [], "metadata": { "collapsed": false } } } }, "nbformat": 4, "nbformat_minor": 0 }

mit

awhite40/pymks

notebooks/stress_homogenization_2D.ipynb

1

287102

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#Effective Stiffness \n", "\n", "##Introduction\n", "\n", "This example uses the `MKSHomogenizationModel` to create a homogenization linkage for the effective stiffness. This example starts with a brief background of the homogenization theory on the components of the effective elastic stiffness tensor for a composite material. Then the example generates random microstructures and their average stress values that will be used to show how to calibrate and use our model. We will also show how to use tools from [sklearn](http://scikit-learn.org/stable/) to optimize fit parameters for the `MKSHomogenizationModel`. Lastly, the data is used to evaluate the `MKSHomogenizationModel` for effective stiffness values for a new set of microstructures.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Elasticity and Effective Elastic Modulus\n", "\n", "For this example we are looking to create a homogenization linkage that predicts the effective isotropic stiffness components for two-phase microstructures. The specific stiffness component we are looking to predict in this example is $C_{xxxx}$ which is easily accessed by applying an uniaxial macroscal strain tensor (the only non-zero component is $\\varepsilon_{xx}$). \n", "\n", "$$ u(L, y) = u(0, y) + L\\bar{\\varepsilon}_{xx}$$\n", "\n", "$$ u(0, L) = u(0, 0) = 0 $$\n", "\n", "$$ u(x, 0) = u(x, L) $$\n", "\n", "More details about these boundary conditions can be found in [1]. Using these boundary conditions, $C_{xxxx}$ can be estimated calculating the ratio of the averaged stress over the applied averaged strain.\n", "\n", "$$ C_{xxxx}^* \\cong \\bar{\\sigma}_{xx} / \\bar{\\varepsilon}_{xx}$$ \n", "\n", "In this example, $C_{xxxx}$ for 6 different types of microstructures will be estimated, using the `MKSHomogenizationModel` from `pymks`, and provides a method to compute $\\bar{\\sigma}_{xx}$ for a new microstructure with an applied strain of $\\bar{\\varepsilon}_{xx}$.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Generation\n", "\n", "A set of periodic microstructures and their volume averaged elastic stress values $\\bar{\\sigma}_{xx}$ can be generated by importing the `make_elastic_stress_random` function from `pymks.datasets`. This function has several arguments. `n_samples` is the number of samples that will be generated, `size` specifies the dimensions of the microstructures, `grain_size` controls the effective microstructure feature size, `elastic_modulus` and `poissons_ratio` are used to indicate the material property for each of the\n", "phases, `macro_strain` is the value of the applied uniaxial strain, and the `seed` can be used to change the the random number generator seed.\n", "\n", "Let's go ahead and create 6 different types of microstructures each with 200 samples with dimensions 21 x 21. Each of the 6 samples will have a different microstructure feature size. The function will return and the microstructures and their associated volume averaged stress values.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymks.datasets import make_elastic_stress_random\n", "sample_size = 200\n", "grain_size = [(15, 2), (2, 15), (7, 7), (8, 3), (3, 9), (2, 2)]\n", "n_samples = [sample_size] * 6\n", "elastic_modulus = (410, 200)\n", "poissons_ratio = (0.28, 0.3)\n", "macro_strain = 0.001\n", "size = (21, 21)\n", "\n", "X, y = make_elastic_stress_random(n_samples=n_samples, size=size, grain_size=grain_size, \n", " elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio, \n", " macro_strain=macro_strain, seed=0)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The array `X` contains the microstructure information and has the dimensions \n", "of `(n_samples, Nx, Ny)`. The array `y` contains the average stress value for \n", "each of the microstructures and has dimensions of `(n_samples,)`.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1200, 21, 21)\n", "(1200,)\n" ] } ], "source": [ "print(X.shape)\n", "print(y.shape)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets take a look at the 6 types the microstructures to get an idea of what they \n", "look like. We can do this by importing `draw_microstructures`. \n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA54AAAEaCAYAAAB5MYgAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAHNNJREFUeJzt3V9oZGf9B+DvZNuYTrZW9mLVkgk7jMTiGKKtClFEU/Si\n", "qFRBg11R4th6ZS96I+JqY2QtouBN612NQVeQiLWIIBIo4p8LKxVLxIuQP+1OUKTQSmEyZJvu/C6E\n", "+e2xm2TP+M4kM3keCMzMO+c97zlzznvOJ++ZOYVWq9UKAAAA6JKho24AAAAAg03wBAAAoKsETwAA\n", "ALpK8AQAAKCrBE8AAAC6SvAEAACgq2466gYAAABwPL300kvx7W9/O7a3t+PHP/5xDA39/9jliy++\n", "GI8++mjs7e3F7OxsTE5O7luPEU8AAACu6/Tp0/Hwww/HxMTEa8qefPLJuO++++LChQvxxBNPHFjP\n", "oSOe//jHPzpvZZe0Wq3c04yNjXWhJf+7QqGQe5rt7e0utAQ6d/vttyerq9t9znHtPzrpC+r1eq73\n", "l0ql3PPopL/Ju447aVfeZe90PnmXv5PtqxfL36t13It9pZN1vJ+8/U3eeR/Xc49eyduvnfTzm+N6\n", "fDquenEOnfL8pp/dfPPNcfPNN1+3rF6vtwPpyMhINJvNuOWWW677XiOeAAAA5Hb16tX242KxGI1G\n", "Y9/3+o4nAADACba8vNx+XK1Wo1qt3tB0137fs9lsxunTp/d9r+AJAACQ2HH8yuL13H777TE7O9vR\n", "tOPj47G2thbj4+PRbDZjZGRk3/cKngAAAIml/E76UXr11VfjkUceieeeey6+9a1vxX333Re/+93v\n", "olarxb333huPPfZYXLly5dDwKngCAAAkNijB89SpU/H1r38989pb3vKWiIg4c+ZMPPzwwzdUj+AJ\n", "AACQ2KAEz1QETwAAgMQEzyy3UwEAAKCrjHgCAAAkZsQzS/AEAABITPDM6svgWSgUck9Tr9dzTzM+\n", "Pt71eZBPJztwqVTqQkv6Q6/2FQaXfe7k6kX/YVvpH51sD5cvX871/rGxsdzz6JW8y3/Sj6XW138I\n", "nll9GTwBAACOM8EzS/AEAABITPDMEjwBAAASEzyzBE8AAIDEBM8swRMAACAxwTNL8AQAAEhM8Mwa\n", "OuoGAAAAMNiMeAIAACRmxDNL8AQAAEhM8MwSPAEAABITPLMETwAAgMQEzyzBEwAAIDHBM+vQ4Jl3\n", "hZVKpY4bc9yc5GUfJMd1py8UCrmnqdfrXWgJpNWLfe649rcnfb8eGsr3Y/nb29tdagmpdbJfj4+P\n", "d30eg6ST/mNQ9qFOlr0fDNI2vbS0FFtbW1Eul2Nubq79+nPPPRc/+MEPYmhoKO67776444479q3D\n", "7VQAAAASa7VaffF3mM3Nzdjd3Y2FhYXY29uLjY2Ndtny8nI89NBDceHChXjiiScOrMeltgAAAIkN\n", "yojn+vp6TE1NRUTE5ORkrK2tRaVSiYiIRqMRZ86ciYiI3d3duHLlSgwPD1+3HsETAAAgsUEJno1G\n", "I86ePRsREcViMfMVkVtvvTXq9Xrcdtttcfny5djZ2RE8AQAAeK3l5eX242q1GtVqtf28WCxGs9mM\n", "iIidnZ0YHR1tl33mM5+JxcXFGBkZiXPnzsXrX//6fecheAIAACTWTyOes7Oz+5ZNTEzEyspKTE9P\n", "x+rqaszMzLTL3vzmN8eFCxfi5Zdfjh/96EcH/sic4AkAAJBYPwXPg5TL5RgeHo75+fk4d+5cVCqV\n", "WFxcjFqtFk899VT8/ve/j+Hh4bj//vsPrEfwBAAASGxQgmdEZG6hEhFRq9UiIuLuu++Ou++++4bq\n", "EDwBAAASG6TgmYLgCQAAkJjgmSV4AgAAJCZ4ZgmeAAAAiQmeWYInAABAYoJn1qHBs1Ao5KqwXq93\n", "3BjgYHn3RxhUnRzMj+v+00m7BuVk5qg/k7GxsSOd/6AblO30ODvqfYiD2Qey9r/DJwAAACTgUlsA\n", "AIDEjHhmCZ4AAACJCZ5ZgicAAEBigmeW4AkAAJCY4JkleAIAACQmeGYJngAAAIkJnlmCJwAAQGKC\n", "Z5bgCQAAkJjgmSV4AgAAJCZ4Zg0ddQMAAAAYbIeOeI6NjfWiHXBDCoVC7mnq9Xqu93fy36lSqZR7\n", "mkFx/vz5uHTp0lE3A/qWPufoHMfRCMc5GBzHsY85Si61BQAASGyQgufS0lJsbW1FuVyOubm59utr\n", "a2vxox/9KFqtVnzwgx+MD3/4w/vW4VJbAACAxFqtVl/8HWZzczN2d3djYWEh9vb2YmNjo132y1/+\n", "Mh566KG4ePFi/Pa3vz2wHsETAAAgsaMOlKmC5/r6ekxNTUVExOTkZKytrbXLTp8+HY1GI1555ZV4\n", "3eted2A9LrUFAABIbFAutW00GnH27NmIiCgWi5nvld9zzz3xyCOPxKlTp+JTn/rUgfUIngAAAIn1\n", "U/BcXl5uP65Wq1GtVtvPi8ViNJvNiIjY2dmJ0dHRdtmlS5fikUceide//vVx8eLFeO973xvDw8PX\n", "nYfgCQAAkFg/Bc/Z2dl9yyYmJmJlZSWmp6djdXU1ZmZm2mVXrlyJYrEYN910UxQKhXj11Vf3rUfw\n", "BAAASKyfgudByuVyDA8Px/z8fJw7dy4qlUosLi5GrVaLe++9N775zW/G0NBQvPOd74xbbrll33oE\n", "TwAAAPZ17S1UIiJqtVpERNx5551x55133lAdgicAAEBigzLimYrgCQAAkJjgmSV4AgAAJCZ4Zgme\n", "AAAAiQmeWYcGz2tvEHojOlnBpVIp1/sLhULueeRdDk4u2xf/Le82YXug2/Ieazvp106CvPtq3vOV\n", "XvH5cqM6OU8fHx/PPY3j4H8InllGPAEAABITPLMETwAAgMQEzyzBEwAAIDHBM0vwBAAASEzwzBo6\n", "6gYAAAAw2Ix4AgAAJGbEM0vwBAAASEzwzBI8AQAAEhM8swRPAACAxATPLMETAAAgMcEzS/AEAABI\n", "TPDMEjwBAAASEzyzkgfP8fHx3NMUCoVc76/X67nn0YlSqdST+XRb3vUb0bt1PCg6WccMrl5tDw5o\n", "8L8ZGsp3O/O8x8ZBOY/g+Mp7HLBN9pbjdJYRTwAAAPa1tLQUW1tbUS6XY25uLvP6888/HxERzz33\n", "XPzwhz/ctw7BEwAAILFBGfHc3NyM3d3dWFhYiMcffzw2NjaiUqlERLRD6HPPPRe/+tWvDqwn3zUm\n", "AAAAnBjr6+sxNTUVERGTk5Oxtrb2mvf86U9/ive85z0H1iN4AgAAJNZqtfri7zCNRiNGRkYiIqJY\n", "LEaj0XjNe5599tl4xzvecWA9LrUFAABIrJ8utV1eXm4/rlarUa1W28+LxWI0m82IiNjZ2YnR0dHM\n", "tP/85z/jzJkzMTw8fOA8BE8AAIDE+il4zs7O7ls2MTERKysrMT09HaurqzEzM5Mpf/rppw+9zDbC\n", "pbYAAADJHfUltKkutS2XyzE8PBzz8/Nx6tSpqFQqsbi42C7/y1/+Eu9617sOrceIJwAAQGL9NOJ5\n", "mGtvoRIRUavV2o8XFhZuqA7BEwAAILFBCp4pCJ4AAACJCZ5ZgicAAEBigmfWocGzUCjkqvDy5cu5\n", "G1Eqlbr6/k7l3VjyrquIiHq9nnsa4Hjr5EAzPj6ee5pO+tvjqJO+sxfHmk6mOel9et5tv5PP5OrV\n", "q7mnSWVoKN9vMm5vb3epJVm9WO8nXS/WcS/6tU6OT5300fyH4JnlV20BAADoKpfaAgAAJGbEM0vw\n", "BAAASEzwzBI8AQAAEhM8swRPAACAxATPLMETAAAgMcEzS/AEAABITPDMEjwBAAASEzyzBE8AAIDE\n", "BM8swRMAACAxwTNr6KgbAAAAwGAz4gkAAJCYEc+s5MFzaCj/IOr29nbqZrxGJx98qVQ6dvPgZCsU\n", "CrneX6/Xu9SS7smzT/Tj8u3HwSmfvPtCp3wu3TXo67dX22leveo7836+g3ROdPXq1dzTdLL8g7IP\n", "dbIc4+Pjuafp9XnDoHw+qRjxBAAASEzwzBI8AQAAEhuk4Lm0tBRbW1tRLpdjbm6u/fqVK1fiBz/4\n", "QbzwwgtRKpXi85///L51CJ4AAACJDUrw3NzcjN3d3VhYWIjHH388NjY2olKpRETEr3/963j/+98f\n", "b3/72w+tR/AEAABIbFCC5/r6ekxNTUVExOTkZKytrbWD59///vd46aWX4uc//3l85CMfiXe96137\n", "1uN2KgAAAIm1Wq2++DtMo9GIkZGRiIgoFovRaDTaZf/617/izjvvjK985Svx85///MAf1jLiCQAA\n", "kFg/jXguLy+3H1er1ahWq+3nxWIxms1mRETs7OzE6Ohopuxtb3tb3HTTTfGmN70p/v3vf8eZM2eu\n", "Ow/BEwAA4ASbnZ3dt2xiYiJWVlZieno6VldXY2ZmJlP2/PPPR7lcjhdeeCFuu+22fesRPAEAABLr\n", "pxHPg5TL5RgeHo75+fk4d+5cVCqVWFxcjFqtFh//+Mfj+9//fuzs7MSHPvShOHXq1L71CJ4AAACJ\n", "DUrwjIjMLVQiImq1WkREvOENb4gLFy7cUB2CJwAAQGKDFDxTEDwBAAASEzyzBE8AAIDEBM+svgye\n", "pVLpqJuQjA0yn0KhkHuaer3ehZbQLfaJG5d3f7AvdF8n2+8gHdPoD50cS3sxn076qEHaf47r8a+T\n", "do2NjXWhJVm9aNdB96S8Ecf1Mz0qfRk8AQAAjjPBM0vwBAAASEzwzBI8AQAAEhM8s4aOugEAAAAM\n", "NiOeAAAAiRnxzBI8AQAAEhM8swRPAACAxATPLMETAAAgMcEzS/AEAABITPDMEjwBAAASEzyzBE8A\n", "AIDEBM+sQ4Pn2NhYrgoLhULuRtTr9a6+n97Iu3OVSqWuz6PT+XDjzp8/H5cuXTrqZnADOumfT/pB\n", "87j2a71wEvrbvO11/pFP3m1ofHy8Sy2h1wapX+un+R13RjwBAAASEzyzho66AQAAAAw2I54AAACJ\n", "DdKI59LSUmxtbUW5XI65ubn268vLy/HnP/85Tp8+HXfddVd89KMf3bcOwRMAACCxQQmem5ubsbu7\n", "GwsLC/H444/HxsZGVCqViPjP70d87nOfi8nJyUPrETwBAAASG5Tgub6+HlNTUxERMTk5GWtra+3g\n", "GRHxk5/8JEZHR+Ozn/1snDt3bt96fMcTAAAgsVar1Rd/h2k0GjEyMhIREcViMRqNRrvsnnvuiW9/\n", "+9vxwAMPxA9/+MMD6zHiCQAAkFg/jXguLy+3H1er1ahWq+3nxWIxms1mRETs7OzE6Ohou+z06dMR\n", "EfGmN73p0HkIngAAAIn1U/CcnZ3dt2xiYiJWVlZieno6VldXY2Zmpl3WbDbjlltuiZdffjleffXV\n", "A+cheAIAACTWT8HzIOVyOYaHh2N+fj7OnTsXlUolFhcXo1arxY9//OOo1+vRarXiM5/5zIH1CJ4A\n", "AACJDUrwjIjMLVQiImq1WkREfPGLX7zhOgRPAACAxAYpeKbgV20BAADoqkNHPOv1ei/akUuhUDjq\n", "JiTTyX9CSqVSF1oyuE7yf5s62VeO4z6/n17tP3nnYx89uU5yfxPRf8t/9erVXO8fGxvrUkv+N3n7\n", "+k76+V70t/22/cBhbNNZLrUFAABITPDMEjwBAAASEzyzBE8AAIDEBM8swRMAACAxwTNL8AQAAEhM\n", "8MwSPAEAABITPLMETwAAgMQEzyzBEwAAIDHBM2voqBsAAADAYDPiCQAAkJgRzyzBEwAAIDHBM0vw\n", "BAAASEzwzDo0eJZKpV60gxzybsSFQiH3POr1eu5pjqu862uQtvlOOry8y3/+/Pm4dOlS7vkclV4c\n", "BDqZRyf7Kfl0so4vX76c6/296j/yLssg9elHyUlkPtbX8dOrc8KTfO51LftAlhFPAACAxATPLMET\n", "AAAgMcEzS/AEAABIbJCC59LSUmxtbUW5XI65ublMWavVii9/+ctxzz33xN13371vHe7jCQAAkFir\n", "1eqLv8Nsbm7G7u5uLCwsxN7eXmxsbGTKn3nmmbjtttsOrceIJwAAQGKDMuK5vr4eU1NTERExOTkZ\n", "a2trUalU2uV/+MMf4r3vfe+h9RjxBAAA4LoajUaMjIxERESxWIxGo9Eue/bZZ6NarcbQ0OGx0ogn\n", "AADACba8vNx+XK1Wo1qttp8Xi8VoNpsREbGzsxOjo6Ptsqeeeiq+9KUvxR//+MdD5yF4AgAAJNZP\n", "l9rOzs7uWzYxMRErKysxPT0dq6urMTMz0y775z//Gd/97nfjxRdfjFarFXfccUfcfvvt161H8AQA\n", "AEisn4LnQcrlcgwPD8f8/HycO3cuKpVKLC4uRq1Wi+985zsREfHb3/42rl69um/ojBA8AQAAkhuU\n", "4BkRr7mFSq1Wyzz/4Ac/eGgdgicAAEBigxQ8Uzg0eFph/a+Tz7BUKnWhJfSDftrnC4VC7mnq9Xru\n", "aewPJ1cn29hx1Mly9FNfAHn04tgxSOdeg9IPHgX9aJYRTwAAgMQEzyzBEwAAIDHBM0vwBAAASEzw\n", "zBo66gYAAAAw2Ix4AgAAJGbEM0vwBAAASEzwzBI8AQAAEhM8swRPAACAxATPLMETAAAgMcEzS/AE\n", "AABITPDMEjwBAAASEzyzBE8AAIDEBM8swfMAhUIh1/vr9XqXWgL/kbcDK5VKXWpJ9+RZxk6W7/Ll\n", "y7mnOcny9oMR+fvC8fHx3PPolV4cB/pxP+Xk6UVf0Kt9oZMwMCj7aSfLPjY21oWWnAyCZ9bQUTcA\n", "AACAwWbEEwAAIDEjnlmCJwAAQGKCZ5bgCQAAkNggBc+lpaXY2tqKcrkcc3Nz7deffPLJ+Otf/xpX\n", "rlyJT37yk3HnnXfuW4fveAIAACTWarX64u8wm5ubsbu7GwsLC7G3txcbGxvtso997GPxjW98Ix5+\n", "+OH4xS9+cWA9RjwBAAASG5QRz/X19ZiamoqIiMnJyVhbW4tKpRIREadOnYqIiCtXrsTo6OiB9Qie\n", "AAAAiQ1K8Gw0GnH27NmIiCgWi6+5VdLjjz8eTz/9dDz44IMH1iN4AgAAJNZPwXN5ebn9uFqtRrVa\n", "bT8vFovRbDYjImJnZ+c1I5v3339/nD9/Pi5evBiTk5P7zkPwBAAASKyfgufs7Oy+ZRMTE7GyshLT\n", "09OxuroaMzMz7bJXXnklbr755hgeHj50eQVPAACAxPopeB6kXC7H8PBwzM/Px7lz56JSqcTi4mLU\n", "arVYWlqKf/zjH/HKK6/Exz72sQPrETwBAADY17W3UImIqNVqERHxwAMP3HAdgicAAEBigzLimcqx\n", "CJ6FQiHX+//7l5SOi7zLEdHZBlkqlXJPA/0iz/49Pj7exZbQqU76wkFxkpcd/tsg7Q8nOUCc5GX/\n", "X1l3WccieAIAAAwSwTNL8AQAAEhM8MwSPAEAABITPLMETwAAgMQEzyzBEwAAIDHBM0vwBAAASEzw\n", "zBI8AQAAEhM8s4aOugEAAAAMNiOeAAAAiRnxzBI8AQAAEhM8swRPAACAxATPrGMRPPN+KKVSqUst\n", "6Q82YgZZoVA46ibAsdeL4+bly5dzTwPXytuf1+v13PM46eeEHG/O2bOORfAEAAAYJIJnluAJAACQ\n", "mOCZJXgCAAAkJnhmCZ4AAACJCZ5ZgicAAEBigxQ8l5aWYmtrK8rlcszNzbVf/9nPfhbPPvtsRER8\n", "+tOfjre//e371jHU7UYCAADQnzY3N2N3dzcWFhZib28vNjY22mUf+MAH4uLFi/HVr341fvaznx1Y\n", "jxFPAACAxAZlxHN9fT2mpqYiImJycjLW1taiUqlERMTZs2cjIuKmm2469BZKgicAAEBi/RQ8l5eX\n", "24+r1WpUq9X280aj0Q6YxWLxuvfcXV5ejg9/+MMHzkPwBAAASKyfgufs7Oy+ZcViMZrNZkRE7Ozs\n", "xOjoaKb86aefjkajEe973/sOnIfveAIAACTWarX64u8wExMTsbq6GhERq6urMTEx0S57/vnn4ze/\n", "+U184QtfOLQewRMAACCxow6UqYJnuVyO4eHhmJ+fj1OnTkWlUonFxcWIiLh06VK8/PLL8a1vfSu+\n", "853vHFiPS20BAAAS66dLbQ9z7S1UIiJqtVpERFy4cOGG6+jL4NnJh3jYryxdz/W+ONuPOllfpVKp\n", "Cy353/Xicxyk9dWP8nzGx3UfPcn9Dfl1sr1cvnw51/vHx8dzz+Mk2N7ezvX+vMcHx4Z8hobyX4jX\n", "Sd/pc6FXBil4ptCXwRMAAOA4EzyzfMcTAACArjLiCQAAkJgRzyzBEwAAIDHBM0vwBAAASEzwzBI8\n", "AQAAEhM8swRPAACAxATPLMETAAAgMcEzS/AEAABITPDMEjwBAAASEzyzBE8AAIDEBM+soaNuAAAA\n", "AIPt0BHP7e3tXrTjWCoUCkfdBP5LJ/85KpVKXWhJf+hkG67X611oSXd0snzH9b+P+hvysL2kcRzX\n", "46D326kNDeUfQ+nFua3zFSKO7znHUXGpLQAAQGKCZ5bgCQAAkJjgmSV4AgAAJCZ4ZgmeAAAAiQ1S\n", "8FxaWoqtra0ol8sxNzfXfv2pp56KJ554It761rfGgw8+eGAdftUWAAAgsVar1Rd/h9nc3Izd3d1Y\n", "WFiIvb292NjYaJe9+93vjq997Ws3tD4ETwAAgMSOOlCmCp7r6+sxNTUVERGTk5OxtrbWLrv11ltv\n", "+NelXWoLAACQ2KBcattoNOLs2bMREVEsFju+hZPgCQAAkFg/Bc/l5eX242q1GtVqtf28WCxGs9mM\n", "iIidnZ0YHR3NTHuj9x8WPAEAAE6w2dnZfcsmJiZiZWUlpqenY3V1NWZmZjLlNxqwfccTAAAgsaP+\n", "7maq73iWy+UYHh6O+fn5OHXqVFQqlVhcXIyIiGeeeSYee+yx+Nvf/hbf+973DqzHiCcAAAD7uvYW\n", "KhERtVotIiLuuuuuuOuuu26oDsETAAAgsX76jmcvHBo8x8fHc1XY6a8c0T03+oXfa21vb3ehJVmd\n", "7IylUqkn8xkUvVjH58+fj0uXLuWeDwBpdHKcP8lO8vrqZNkvX76ce5pOztcG0Uk+B70eI54AAACJ\n", "CZ5ZgicAAEBigmeW4AkAAJCY4JkleAIAACQmeGYJngAAAIkJnlmCJwAAQGKCZ9bQUTcAAACAwWbE\n", "EwAAIDEjnlmCJwAAQGKCZ5bgCQAAkJjgmSV4AgAAJCZ4Zh0aPK9evZqrwrGxsY4bcxIVCoXc09Tr\n", "9S60JKuTdvViHtvb211oydHopDMqlUpdaEmWTrJ/5P2sxsfHc8+jk/4m777diz4N+kUnffBxPfc6\n", "yX1Br87v8p4XdLJ9dXLs6IXjeg59LedUWUY8AQAAEhM8swRPAACAxATPLMETAAAgMcEzS/AEAABI\n", "TPDMEjwBAADY19LSUmxtbUW5XI65ubn26y+++GI8+uijsbe3F7OzszE5OblvHUM9aCcAAMCJ0mq1\n", "+uLvMJubm7G7uxsLCwuxt7cXGxsb7bInn3wy7rvvvrhw4UI88cQTB9ZjxBMAACCxQbnUdn19Paam\n", "piIiYnJyMtbW1qJSqUTEf25RMzExERERIyMj0Ww245ZbbrluPUY8AQAAEjvqkcxUI56NRiNGRkYi\n", "IqJYLEaj0WiXXb16tf34v8v+mxFPAACAxK4NZcfd8vJy+3G1Wo1qtdp+XiwWo9lsRkTEzs5OjI6O\n", "tsuGhv5/HLPZbMbp06f3nYfgCQAAcILNzs7uWzYxMRErKysxPT0dq6urMTMz0y4bHx+PtbW1GB8f\n", "j2az2R4ZvR6X2gIAAHBd5XI5hoeHY35+Pk6dOhWVSiUWFxcjIuLee++Nn/70p3Hx4sX4xCc+cWA9\n", "h454vvGNb8zVsEKhkOv9wI3Luz/2wq233nrUTei6QekHz549e9RNOFLHcf+J6E27erXsx3UdH5Ve\n", "rY/j2ufQfb3YxjrZvnrxozq2+9669hYqERG1Wi0iIs6cORMPP/zwDdVRaA3Kzy0BAABwLLnUFgAA\n", "gK4SPAEAAOgqwRMAAICuEjwBAADoKsETAACArhI8AQAA6CrBEwAAgK76PwtqwvVMBpGIAAAAAElF\n", "TkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f3729337450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_microstructures\n", "X_examples = X[::sample_size]\n", "draw_microstructures((X_examples[:3]))\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA6EAAAEdCAYAAAD9zGENAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3V1opNd9P/DfaG11PdrY9V4saaoZaVBRQydCrd0WlFIa\n", "mebCtMEttKJuaFFF0qvmIjd929aqysaEFHqT9M5RRJJCUYhrSqEUgQl9uahLSo1KL4RerJGaUEKc\n", "YpCE1rL0vwiev5/YkvY5q/NoNPv5gEDSM+c85znPec4zX53RTO3k5OQkAAAAoAIDl90AAAAAHhxC\n", "KAAAAJURQgEAAKiMEAoAAEBlhFAAAAAqI4QCAABQGSEUAACAyjx03gO+9a1vVdGO0lI+3nR4eDhD\n", "S+5frVYrXWZ3dzdDS3gvZcdar46zqjz++OPx+uuvJ5cvO+f08lyQcm3v7OyULtNoNEqXKTuHpPRz\n", "Srt69fgj+qsPUvaRct1UdX/7wAc+ULrM2wYGyv093hjt3fGjz6q75qrot16+1u5nzrnqvve978Vn\n", "P/vZ2N3dja985SuFOfT111+Pz3/+83F0dBQzMzMxMTFxaj1WQgEAADjXjRs34rnnnovx8fF3bXvp\n", "pZfi2Wefjdu3b8eLL754Zj3nroQCAADAww8/HA8//PB7btvZ2emG0+vXr8fBwUE88sgj7/lYK6EA\n", "AADcl+Pj4+739Xo99vb2Tn2sEAoAAMB9eef/hx4cHMSNGzdOfayX4wIAABAREcvLy93v2+12tNvt\n", "eyrXbDZjbW0tms1mHBwcxPXr1099rBAKAACQ0f/8z/9cdhPuyY/+6I/GzMzMqdvfeuuteP755+O1\n", "116Lz3zmM/Hss8/GP/3TP8Xc3Fw888wz8YUvfCHu3r17Zh0RQigAAEBWKR9r04uuXbsWf/qnf1r4\n", "3Y/92I9FRMTNmzfjueeeu6d6hFAAAICM+iWEXhQhFAAAICMhtMi74wIAAFAZK6EAAAAZWQktuvAQ\n", "mtLBjUbjoptxpaT02fDwcIaWcJXUarXSZXZ2djK0hKuo7LyTMk+7H/RuH1Q1fzSbzdJlqlb2uKrq\n", "u5Sxk9LfVd0Xqui3qvqs0+lUsp/d3d3SZVJUNU/16n0n5Tn18fFx6TJCaJGVUAAAgIyE0CIhFAAA\n", "ICMhtEgIBQAAyEgILRJCAQAAMhJCi4RQAACAjITQIp8TCgAAQGWshAIAAGRkJbRICAUAAMhICC3y\n", "clwAAAAqYyUUAAAgIyuhRUIoAABARkJokRAKAACQkRBadOEhtFarlS6zs7Nz0c3gPaQM/kajkaEl\n", "72bcVCOlnyGiuptnyn6qGtcp+9nd3S1dJqUPms1m6TJlDQyUfxuJqzBPlz2vvXwvrWrsdDqd0mWq\n", "kNLPKX02MjJSukxVfdbLY60KKXPOg95nl8VKKAAAQEb9FEKXlpZia2srWq1WzM7Odn//2muvxRe/\n", "+MUYGBiIZ599Nj74wQ+eWod3xwUAAMjo5OTkSnydZ3NzMw4PD2NhYSGOjo5iY2Oju215eTk+/elP\n", "x+3bt+PFF188sx4roQAAABn1y0ro+vp6TE5ORkTExMRErK2txdjYWERE7O3txc2bNyMi4vDwMO7e\n", "vRuDg4PvWY8QCgAAkFG/hNC9vb24detWRETU6/XC/+G+733vi52dnXjsscei0+nE/v6+EAoAAHAZ\n", "+iWE1uv1ODg4iIiI/f39GBoa6m77+Mc/HouLi3H9+vUYHR2NRx999NR6hFAAAAAi4vv/2/m2drsd\n", "7Xa7+/P4+HisrKzE1NRUrK6uxvT0dHfbj/zIj8Tt27fjjTfeiC9/+ctnvqu6EAoAAJDRVVoJnZmZ\n", "OXVbq9WKwcHBmJ+fj9HR0RgbG4vFxcWYm5uLl19+Of75n/85BgcH4xOf+MSZ+xBCAQAAMrpKIfQ8\n", "7/xYloiIubm5iIh46qmn4qmnnrqnOoRQAACAjPophF4EIRQAACAjIbRICAUAAMhICC0SQgEAADIS\n", "QovODaHDw8NVtIMHXMqF2Ww2S5d55wfq9pKU4280Ghlacv8ef/zx+O53v5tcvmxf9Go/QERErVa7\n", "7CZcqqtw/GXnkJT7SEqZqu6LnU6ndJmU85rSB2X3k3IsKX22vb1dyX5S+qyqPtjd3S1dpgopY7Oq\n", "YxFCi6yEAgAAZCSEFgmhAAAAGQmhRQOX3QAAAAAeHFZCAQAAMrISWiSEAgAAZCSEFgmhAAAAGQmh\n", "RUIoAABARkJokRAKAACQkRBaJIQCAABkJIQWCaEAAAAZCaFFQigAAEBGQmiREAoAAJCREFp0bgjd\n", "2dkpVWFKBzcajdJlUtRqtdJlyh5/L+u3c9PpdDK05HI86GPzflQ1qffyOari2q7qGq1qzoG3lb1+\n", "Uq63ZrNZukzK/OGaK6+q81nVc7CUcVPVvaqfxtrx8fFlN+HKsxIKAACQUT+thC4tLcXW1la0Wq2Y\n", "nZ3t/n5tbS2+/OUvx8nJSXzkIx+Jj370o6fWMVBBOwEAAB5YJycnV+LrPJubm3F4eBgLCwtxdHQU\n", "Gxsb3W1/93d/F5/+9Kfjzp078Y1vfOPMeoRQAACAjC47XF5UCF1fX4/JycmIiJiYmIi1tbXuths3\n", "bsTe3l68+eab8UM/9ENn1uPluAAAABn1y8tx9/b24tatWxERUa/XC/9T/PTTT8fzzz8f165di1//\n", "9V8/sx4hFAAAIKN+CaH1ej0ODg4iImJ/fz+Ghoa627761a/G888/H48++mjcuXMnPvzhD8fg4OB7\n", "1iOEAgAAZHSVQujy8nL3+3a7He12u/vz+Ph4rKysxNTUVKyursb09HR32927d6Ner8dDDz0UtVot\n", "3nrrrVP3IYQCAABkdJVC6MzMzKnbWq1WDA4Oxvz8fIyOjsbY2FgsLi7G3NxcPPPMM/Hnf/7nMTAw\n", "ED/1Uz8VjzzyyKn1CKEAAAAZXaUQep53fixLRMTc3FxERDzxxBPxxBNP3FMd3h0XAACAylgJBQAA\n", "yKifVkIvghAKAACQkRBaJIQCAABkJIQWnRtCa7VaFe0oLaVd7/ww1Vz7SRlgjUajdJmqVNXPKXp1\n", "bKaMgWazWbpMVf1MupQxWtVNqux+Uo6lV6/RKvXbPaEfdTqdUo9Pma+3t7dLlxkeHi5dppfv2SnK\n", "Xj9V3UtTrtGUc1N2bKbq1XtVL4/NFEJokZVQAACAjITQIiEUAAAgIyG0SAgFAADISAgtEkIBAAAy\n", "EkKLhFAAAICMhNCigctuAAAAAA8OK6EAAAAZWQktEkIBAAAyEkKLhFAAAICMhNAiIRQAACAjIbRI\n", "CAUAAMhICC268BBaq9VKl9nZ2SldptFoVFKmV6X0WVVSxsCDzsT0/5UdPynXQrPZLF2ml6+5KqSM\n", "0X6ac6uU0tfm3XQjIyOlHr+9vV16HylzTopOp1PJfvrp2h4YKP9BEVXdd6q6rnt1fu+3ec1zvSIr\n", "oQAAABkJoUVCKAAAQEb9FEKXlpZia2srWq1WzM7OFn7/9qtFXnvttfjSl750ah1CKAAAQEb9EkI3\n", "Nzfj8PAwFhYW4oUXXoiNjY0YGxuLiOgG0tdeey3+/u///sx6yr8QHgAAgAfO+vp6TE5ORkTExMRE\n", "rK2tvesx//Zv/xY/+7M/e2Y9QigAAEBGJycnV+LrPHt7e3H9+vWIiKjX67G3t/eux7z66qvxkz/5\n", "k2fW4+W4AAAAGfXLy3Hr9XocHBxERMT+/n4MDQ0Vtn/729+OmzdvxuDg4Jn1CKEAAAAZXaUQury8\n", "3P2+3W5Hu93u/jw+Ph4rKysxNTUVq6urMT09XSj7yiuvnPtS3AghFAAAIKurFEJnZmZO3dZqtWJw\n", "cDDm5+djdHQ0xsbGYnFxMebm5iIi4j/+4z/iD/7gD87dhxAKAACQ0VUKoed558eyREQ3gEZELCws\n", "3FMdQigAAEBG/RRCL4IQCgAAkJEQWiSEAgAAZCSEFvVECB0YKP9xpbu7uxlacv9SBlij0aikDPSj\n", "Wq3WV/vpJ1XdcKuad3d2dkqX6Scp/dxsNkuXqbqft7e3Sz1+ZGSk9D46nU7pMlU9N0iZ21KOp4qx\n", "kDJGh4eHS5ehmvk9ZTxXda0dHx+XLiOEFpVPfwAAAJCoJ1ZCAQAA+pWV0CIhFAAAICMhtEgIBQAA\n", "yEgILRJCAQAAMhJCi4RQAACAjITQIiEUAAAgIyG0SAgFAADISAgtEkIBAAAyEkKLBi67AQAAADw4\n", "rIQCAABkZCW0yEooAAAAlXmgVkIbjUb2fdRqtdJldnd3S5dJ+WtKFcdP2hjY2dnJ0JIHQ1X9nXLN\n", "NZvN0mU6nU7pMil9UHY/KfNHFe2KSGtbyvk075Z3fHxcuszw8HAl+3nbyMhIqcenjNEU/XYv6dXr\n", "p5dXp1Kuharm3RRl21ZVu6oaA7081i7DAxVCAQAAqiaEFgmhAAAAGfVTCF1aWoqtra1otVoxOzvb\n", "/f3du3fji1/8YnznO9+JRqMRv/M7v3NqHUIoAABARv0SQjc3N+Pw8DAWFhbihRdeiI2NjRgbG4uI\n", "iH/4h3+In//5n48PfehD59YjhAIAAGTULyF0fX09JicnIyJiYmIi1tbWuiH0v//7v+N73/tefP3r\n", "X49f+qVfip/+6Z8+tR7vjgsAAJDRycnJlfg6z97eXly/fj0iIur1euzt7XW3/e///m888cQT8Yd/\n", "+Ifx9a9//cw3jbMSCgAAkFG/rITW6/U4ODiIiIj9/f0YGhoqbPuJn/iJeOihh+L9739//N///V/c\n", "vHnzPesRQgEAADK6SiF0eXm5+3273Y52u939eXx8PFZWVmJqaipWV1djenq6sG17eztarVZ85zvf\n", "iccee+zUfQihAAAARETEzMzMqdtarVYMDg7G/Px8jI6OxtjYWCwuLsbc3Fz8yq/8SvzVX/1V7O/v\n", "xy/+4i/GtWvXTq1HCAUAAMjoKq2EnuedH8sSETE3NxcRET/8wz8ct2/fvqc6hFAAAICM+imEXgQh\n", "FAAAICMhtEgIBQAAyEgILTo3hA4PD2dvRK1WK11mZ2enkjJVSDn+lDK7u7uly1Ql5cJsNBoZWnL/\n", "+ulYUjz++OPx3e9+97KbcaaU6yflvKaUqWo+LLuflH00m83SZVKOP0VV16knHeVV3WedTif7PlKu\n", "hZR2VXUvqeo5SNmxkHL8Vc25KeM6Zdxsb2+XLlNVv5Ud01VdN1VxPyiyEgoAAJCREFokhAIAAGQk\n", "hBYJoQAAABkJoUVCKAAAQEZCaNHAZTcAAACAB4eVUAAAgIyshBYJoQAAABkJoUVCKAAAQEZCaJEQ\n", "CgAAkJEQWiSEAgAAZCSEFgmhAAAAGQmhRUIoAABARkJo0YWH0J2dnYuu8sLUarXLbsKlSjn+lAum\n", "0WiULvOg66eJqZ+O5aqo4tpuNpul99Fv+mlsm9v/vyqOK6W/R0ZGSpfpt+dgZcukHH/K3NbLzyer\n", "eq6XouyY3t7eLr2PlPO5u7tbukyKfrqHXAQroQAAABkJoUVCKAAAAPdkaWkptra2otVqxezsbPf3\n", "y8vL8e///u9x48aNePLJJ+OXf/mXT61DCAUAAMioX1ZCNzc34/DwMBYWFuKFF16IjY2NGBsbi4jv\n", "vxz8t3/7t2NiYuLceoRQAACAjPolhK6vr8fk5GRERExMTMTa2lo3hEZE/PVf/3UMDQ3Fb/3Wb8Xo\n", "6Oip9QzkbigAAMCD7OTk5Ep8nWdvby+uX78eERH1ej329va6255++un47Gc/G5/85CfjS1/60pn1\n", "WAkFAADI6CqthC4vL3e/b7fb0W63uz/X6/U4ODiIiIj9/f0YGhrqbrtx40ZERLz//e8/dx9CKAAA\n", "QEZXKYTOzMycum18fDxWVlZiamoqVldXY3p6urvt4OAgHnnkkXjjjTfirbfeOnMfQigAAEBGVymE\n", "nqXVasXg4GDMz8/H6OhojI2NxeLiYszNzcVXvvKV2NnZiZOTk/j4xz9+Zj1CKAAAQEb9EkIjovCx\n", "LBERc3NzERHxu7/7u/dchxAKAACQUT+F0IsghAIAAGQkhBb5iBYAAAAqc+5KaNnU3mg0khuTW61W\n", "K11mZ2cnQ0uKUv4yUlU/92qfRfRuv1XVZ716/Pern+acqlQxFlL2kXIt0Lvzrr/if19V97iqpMyh\n", "vTpGq5pzqrr/9vJ9PqWvt7e3Sz1+ZGQk+z4i0vrs+Pi4dBlzaJGX4wIAAGQkhBYJoQAAABkJoUVC\n", "KAAAQEZCaJEQCgAAkJEQWiSEAgAAZCSEFgmhAAAAGQmhRUIoAABARkJokRAKAACQkRBaNHDZDQAA\n", "AODBYSUUAAAgIyuhRUIoAABARkJokRAKAACQkRBadOEhtJc7OKVtjUaj1OM7nU72fURU189V9Blp\n", "ms3mZTehJ/TynFOr1S67Cafq5X4rK6Wfq5qrq1K2D/rp/N+vnZ2d7PtIGTtVjesUKfvpp+cTKecm\n", "ZZxV1WdVjbWRkZFSj9/e3i69j5TnRr38nLqfWQkFAADISAgtEkIBAAAy6qcQurS0FFtbW9FqtWJ2\n", "draw7eTkJH7/938/nn766XjqqadOrcNHtAAAAGR0cnJyJb7Os7m5GYeHh7GwsBBHR0exsbFR2P7N\n", "b34zHnvssXPrsRIKAACQUb+shK6vr8fk5GRERExMTMTa2lqMjY11t//Lv/xLfPjDHz63HiEUAAAg\n", "o34JoXt7e3Hr1q2IiKjX64U33Xr11Vej3W7HwMBAHB8fn1mPEAoAAEBERCwvL3e/b7fb0W63uz/X\n", "6/U4ODiIiIj9/f0YGhrqbnv55Zfj937v9+Jf//Vfz92HEAoAAJDRVVoJnZmZOXXb+Ph4rKysxNTU\n", "VKyursb09HR327e//e34i7/4i3j99dfj5OQkPvjBD8YHPvCB96xHCAUAAMjoKoXQs7RarRgcHIz5\n", "+fkYHR2NsbGxWFxcjLm5ufjc5z4XERHf+MY34vj4+NQAGiGEAgAAZNUvITQi3vWxLHNzc4WfP/KR\n", "j5xbhxAKAACQUT+F0IsghAIAAGQkhBYJoQAAABkJoUVC6DnKDpharVZ6H+/8fB3ySpkAms1mqcen\n", "nM+UcdPpdEqXaTQapctU7Sq0katvYGCgdJmUa9t47n1VzPFVzddljyWiuntWyv23iudgKedmeHi4\n", "dJmq2laVlDm0iuPp5aDXy227DEIoAABARkJoUfk/YwAAAEAiK6EAAAAZWQktEkIBAAAyEkKLhFAA\n", "AICMhNAiIRQAACAjIbRICAUAAMhICC0SQgEAADISQouEUAAAgIyE0CIhFAAAICMhtEgIBQAAyEgI\n", "LRq47AYAAADw4HigVkJrtVrpMjs7OxlaUpTSrpS/pjQajdJl+k1KX3c6nVKP18/3x18Ky6tibjOu\n", "0/qZ3ld2jk+Zo5rNZukyKY6Pj0uXSWlb2T5L3c/u7m6px6ecm5GRkdJlqnhuGFHd89aU+T2lTNm2\n", "eT7Q3x6oEAoAAFA1obpICAUAAMion0Lo0tJSbG1tRavVitnZ2e7vX3rppfjP//zPuHv3bvzar/1a\n", "PPHEE6fW4X9CAQAAMjo5ObkSX+fZ3NyMw8PDWFhYiKOjo9jY2Ohu+9jHPhZ/9md/Fs8991z87d/+\n", "7Zn1WAkFAADIqF9WQtfX12NycjIiIiYmJmJtbS3GxsYiIuLatWsREXH37t0YGho6sx4hFAAAIKN+\n", "CaF7e3tx69atiIio1+vvesOpF154IV555ZX41Kc+dWY9QigAAEBGVymELi8vd79vt9vRbre7P9fr\n", "9Tg4OIiIiP39/XeteH7iE5+I3/zN34w7d+7ExMTEqfsQQgEAADK6SiF0Zmbm1G3j4+OxsrISU1NT\n", "sbq6GtPT091tb775Zjz88MMxODh47vEKoQAAABldpRB6llarFYODgzE/Px+jo6MxNjYWi4uLMTc3\n", "F0tLS/Gtb30r3nzzzfjYxz52Zj1CKAAAQEb9EkIjovCxLBERc3NzERHxyU9+8p7r8BEtAAAAVMZK\n", "KAAAQEb9tBJ6ER6oEJpy8huNRqnH12q10vvodDqly5RtV0Ta8acczw++VfO9qOLcpO6n2Wxm30eK\n", "qs5NSj/fj7LHVdX1028GBsq9ECZl7JS9dlJVNX+kjLUUVRxPVcfSj1LGdVX3xZS2pYyFlPvP9vZ2\n", "6TJljyflWI6Pj0uX6eV7SFXPDVLGWtkx3cv9nEIILXqgQigAAEDVhNAiIRQAACAjIbRICAUAAMhI\n", "CC0SQgEAADISQouEUAAAgIyE0CIhFAAAICMhtEgIBQAAyEgILSr3QXEAAABwH6yEAgAAZGQltEgI\n", "BQAAyEgILRJCAQAAMhJCi4RQAACAjITQIiH0gnU6ndJlarVa6TI7Ozuly6RIuWAajUaGllyMXu3r\n", "qvq52WyWLtPrUs4p5fVbPx8fH5cu08tzW9nj6eVjqVqv9kXKfJ1yL0nZT8pznZGRkdJltre3Sz0+\n", "5Vz26vOCiN5+blDFWEvp515+3iqEFgmhAAAAGQmhRUIoAABARkJokRAKAACQUT+F0KWlpdja2opW\n", "qxWzs7Pd33/ta1+LV199NSIifuM3fiM+9KEPnVrHQO5GAgAAPMhOTk6uxNd5Njc34/DwMBYWFuLo\n", "6Cg2Nja6237hF34h7ty5E3/8x38cX/va186sRwgFAADgXOvr6zE5ORkRERMTE7G2ttbdduvWrYiI\n", "eOihh8590y8vxwUAAMioX16Ou7e31w2b9Xr9Pd/FeHl5OT760Y+eWY8QCgAAkNFVCqHLy8vd79vt\n", "drTb7e7P9Xo9Dg4OIiJif38/hoaGCmVfeeWV2Nvbi5/7uZ87cx9CKAAAQEZXKYTOzMycum18fDxW\n", "VlZiamoqVldXY3p6urtte3s7/vEf/zH+6I/+6Nx9+J9QAACAjC77DYcu6o2JWq1WDA4Oxvz8fFy7\n", "di3GxsZicXExIiK++tWvxhtvvBGf+cxn4nOf+9yZ9VgJBQAAyOgqrYSe550fyxIRMTc3FxERt2/f\n", "vuc6hFAAAICM+imEXgQhFAAAICMhtOjcEHreZ7z8oPd6m97zpJyURqNRukzZY4kofzwp+0hR1X5S\n", "9PJFVtVY61W9fG7eljKHVKGK+YM0Keem0+mULmMu6E9lx0Kz2czUkqKUc1TVtZAi5XgGBsq9dUnK\n", "sVR1Pqt63rq7u1u6TIqU81lFX1d1PlOYd4u8MREAAACV8XJcAACAjKyEFgmhAAAAGQmhRUIoAABA\n", "RkJokRAKAACQkRBaJIQCAABkJIQWCaEAAAAZCaFFQigAAEBGQmiREAoAAJCREFokhAIAAGQkhBYN\n", "XHYDAAAAeHBYCQUAAMjISmjRuSF0Z2enVIW1Wi25Mb2o346nrJTj393dzdCSd0u5mBuNRiX7IV3Z\n", "MdfL56ef5o+UYyl7/6hSVeemqn4rex2kzIX9amRkpNTje3lcp8yHzWazdJmUPqii3wYGyr/Ar9Pp\n", "lC5T1XOJqp7npEiZ28r2dcrYrOp8pujl5yuXwUooAABARkJokRAKAACQkRBaJIQCAABk1E8hdGlp\n", "Kba2tqLVasXs7Gz39y+//HK8+OKL8eM//uPxqU996sw6vDsuAABARicnJ1fi6zybm5txeHgYCwsL\n", "cXR0FBsbG91tP/MzPxN/8id/ck/9IYQCAABkdNnh8qJC6Pr6ekxOTkZExMTERKytrXW3ve9977vn\n", "NwjzclwAAICM+uXluHt7e3Hr1q2IiKjX68nvfC2EAgAAZHSVQujy8nL3+3a7He12u/tzvV6Pg4OD\n", "iIjY39+PoaGhQtl7/fgeIRQAAICIiJiZmTl12/j4eKysrMTU1FSsrq7G9PR0Yfu9hm3/EwoAAJDR\n", "Zf+v50X9T2ir1YrBwcGYn5+Pa9euxdjYWCwuLkZExDe/+c34whe+EP/1X/8Vf/mXf3lmPVZCAQAA\n", "MrpKL8c9zzs/liUiYm5uLiIinnzyyXjyySfvqQ4hFAAAIKN+CqEXQQgFAADISAgtOjeENpvNUhV2\n", "Op3SjWg0GqXL9KqUAVbV8d/ru1W9U8rbLqfsJ0XKfnZ3dzO05HL08liDiOrmgtS3h+8nIyMjpR6f\n", "0mcp80dV9537UfZ5S8oxVfXkM6VtKc/bUlQxH6T0c9lrJyKtz8o+n07dT1XXaRXj5vj4uHSZlH6u\n", "ihBaZCUUAAAgIyG0SAgFAADISAgtEkIBAAAyEkKLhFAAAICMhNCigctuAAAAAA8OK6EAAAAZWQkt\n", "EkIBAAAyEkKLhFAAAICMhNAiIRQAACAjIbRICAUAAMhICC0SQgEAADISQouEUAAAgIyE0KJzQ2in\n", "0ylVYa1WK92InZ2d0mVSpJz84eHhDC0pSmlXSj+XPZe9LqXfms1m6TJVjU+IMK5TpMyHKVL6LOXc\n", "pCg7bqrqsxRVt63sOUq5lzYajdJlelnK8aT0W9lzk3KN9vJzo4GBgdJlqpqnqrhOU46ll681IbTI\n", "SigAAEBGQmhR+T+xAAAAQCIroQAAABn100ro0tJSbG1tRavVitnZ2e7vX3/99fj85z8fR0dHMTMz\n", "ExMTE6fWYSUUAACAc21ubsbh4WEsLCzE0dFRbGxsdLe99NJL8eyzz8bt27fjxRdfPLMeK6EAAAAZ\n", "9ctK6Pr6ekxOTkZExMTERKytrcXY2FhEfP/NpMbHxyMi4vr163FwcBCPPPLIe9ZjJRQAACCjk5OT\n", "K/F1nr29vbh+/XpERNTr9djb2+tuOz4+7n7/g9t+kJVQAACAjN4Z0Hrd8vJy9/t2ux3tdrv7c71e\n", "j4ODg4iI2N/fj6Ghoe62d36s0MHBQdy4cePUfQihAAAARETEzMzMqdvGx8djZWUlpqamYnV1Naan\n", "p7vbms1mrK2tRbPZjIODg+6K6XvxclwAAADO1Wq1YnBwMObn5+PatWsxNjYWi4uLERHxzDPPxN/8\n", "zd/EnTt34ld/9VfPrMdKKAAAAPfknR/LEhExNzcXERE3b96M55577p7qsBIKAABAZayEnuOsD1m9\n", "KClv2Vyr1TK0pP9VcT57WRXH/+ijj2bfx/1K6YdevuYe9HHdy6o6N1Xsp9+um7f1at/18nODfhrX\n", "VenlY+nltpXVT8fS72on/fKhNQAAAPQ8L8cFAACgMkIoAAAAlRFCAQAAqIwQCgAAQGWEUAAAACoj\n", "hAIAAFCaMFPiAAAAB0lEQVSZ/wfCY7pgoK/riQAAAABJRU5ErkJggg==\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f374817ab90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_microstructures((X_examples[3:]))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this dataset 4 of the 6 microstructure types have grains that are elongated in either\n", "the x or y directions. The remaining 2 types of samples have equiaxed grains with\n", "different average sizes.\n", "\n", "Let's look at the stress values for each of the microstructures shown above.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Stress Values [ 0.30279774 0.27063703 0.30712908 0.29559632 0.28195039 0.28474614]\n" ] } ], "source": [ "print('Stress Values'), (y[::200])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have a dataset to work with, we can look at how to use the `MKSHomogenizationModel`to predict stress values for new microstructures.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MKSHomogenizationModel Work Flow\n", "\n", "The default instance of the `MKSHomogenizationModel` takes in a dataset and \n", " - calculates the 2-point statistics \n", " - performs [dimensionality reduction](http://en.wikipedia.org/wiki/Dimensionality_reduction) using [Singular Valued Decomposition](http://en.wikipedia.org/wiki/Singular_value_decomposition) (SVD) \n", " - and fits a [polynomial regression model](http://en.wikipedia.org/wiki/Polynomial_regression) model to the low-dimensional representation. \n", "\n", "This work flow has been shown to accurately predict effective properties in several examples [2][3], and requires that we specify the number of components used in dimensionality reduction and the order of the polynomial we will be using for the polynomial regression. In this example we will show how we can use tools from [sklearn](http://scikit-learn.org/stable/) to try and optimize our selection for these two parameters.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Modeling with MKSHomogenizationModel\n", "\n", "In order to make an instance of the `MKSHomogenizationModel`, we need to pass an instance of a basis (used to compute the 2-point statistics). For this particular example, there are only 2 discrete phases, so we will use the `PrimitiveBasis` from `pymks`. We only have two phases denoted by 0 and 1, therefore we have two local states and our domain is 0 to 1.\n", "\n", "Let's make an instance of the `MKSHomgenizationModel`.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymks import MKSHomogenizationModel\n", "from pymks import PrimitiveBasis\n", "\n", "prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", "model = MKSHomogenizationModel(basis=prim_basis, \n", " correlations=[(0, 0), (1, 1), (0, 1)])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the default values for the number of components and the order of the polynomial." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Default Number of Components 2\n", "Default Polynomail Order 1\n" ] } ], "source": [ "print('Default Number of Components'), (model.n_components)\n", "print('Default Polynomail Order'), (model.degree)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These default parameters may not be the best model for a given problem; we will now show one method that can be used to optimize them.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizing the Number of Components and Polynomial Order\n", "\n", "To start with, we can look at how the variance changes as a function of the number of components.\n", "In general for SVD as well as PCA, the amount of variance captured in each component decreases\n", "as the component number increases.\n", "This means that as the number of components used in the dimensionality reduction increases, the percentage of the variance will asymptotically approach 100%. Let's see if this is true for our dataset.\n", "\n", "In order to do this we will change the number of components to 40 and then\n", "fit the data we have using the `fit` function. This function performs the dimensionality reduction and \n", "also fits the regression model. Because our microstructures are periodic, we need to \n", "use the `periodic_axes` argument when we `fit` the data.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model.n_components = 40\n", "model.fit(X, y, periodic_axes=[0, 1])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now look at how the cumlative variance changes as a function of the number of components using `draw_component_variance` \n", "from `pymks.tools`.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlcVPX+x/HXDDAsMsMioCESi+KCuKCpiaaZ5q6VXczS\n", "yrqtVtfSdguVq/Wra1pki22mZkpqGS6VmvtuatLgjguIioiArMMyvz+ISZxBR4WZYebzfDx6PJg5\n", "c868Odh8zny3o9Dr9XqEEEI4LKW1AwghhLAuKQRCCOHgpBAIIYSDk0IghBAOTgqBEEI4OCkEQgjh\n", "4Jwt+Wbl5eUkJCSQm5tLeHg4o0aN4pNPPiEzMxMnJyfGjRuHWq22ZCQhhHB4Fv1GsHPnTkJCQoiL\n", "i0On03Hy5EkUCgWTJk3izjvvZNOmTdc8hlartUDSmyMZa4dkrB22ntHW84H9Z7RoIcjMzCQ4OBiA\n", "kJAQdu3aRXFxMQD5+floNJprHsPe/yCWIhlrh2S8ebaeD+w/o0ULQWBgICkpKUBlaGdnZ1xcXHjx\n", "xRdZs2YNnTt3tmQcIYQQWLgQdOzYEZ1OR3x8PC4uLpSVleHu7s6MGTO4//77+fnnny0ZRwghBKCw\n", "1lpDs2fPpkWLFpw9e5YRI0ag1Wr5448/ePjhh6u9TqvVVvvKExsba+moQghhFxITEw0/R0ZGEhkZ\n", "CVi4EGRnZ5OQkIBCoaBnz57ExMQwc+ZM8vPzAXj22WcJCAi45nEyMjLqOupNUavVXLp0ydoxrkoy\n", "1g7JePNsPR/YR8bAwMAat1l0+Kivry9xcXHVnpswYYIlIwghhLiCTCgTQggHJ4VACCEcnBQCIYRw\n", "cFIIhBDCwUkhEEIIByeFQAghHJwUAiGEcHBSCIQQwsFZdEKZEEKI2rV203rmrvyeCidQlsPDA0dy\n", "V49e13UMKQRCCFFPrd20nmmJM8nr42l4blriTIDrKgZSCIQQwoZVXfGXUoYLzjw88AEiO7XlaO4p\n", "3vnho2pFACCvjyfzVi2UQiCEEPWF8Qf9P007v2z4jamJH1J8t8/fry5h7JyJqHb44Nrcm7yic2gI\n", "NTqmTl96XRmkEAghhJUYN+2U8Np302h15GcKb3XirwVbUA8Nq7aPemgoRctP0en2bhxwzaPAxHFV\n", "CpfryiGFQAgh6pCppp0W0a05mHOCqYkzyO+jrr7DgAB2JG1H0zAMlAqTx4xq2JxPY95gbYVxH4Fm\n", "TT6jY/99XRmlEAghxA26WrMOwOpNvxO/aAZFfb3+fuaKpp3iLDSojY57qzqQhJ7/5b/bpnGQQqPt\n", "rsrKK/6q95q3aiHlSj1OFQpGx/5bRg0JIYQlmGrWmbxoOruztLg08+Jg7nE2zfkVz6Eh1fZTDw2l\n", "eEUaMd17st8tH1O3kgl096OFdwiPDnrwmlf8d/XoxV09et3UzXOkEAghRA1qGqOv1+uZ/fO3RiN2\n", "ivp68XXSd2iGVLbrVyhN3wAyqmEzZnZ9mbWlt131g/7yK36dvhSVwuWGrvivRQqBEEKYYGqM/mvf\n", "TSXs4GIuBJWRlnPY5Igdb1c1T7a8n5beocza+hFHKDF6TVVnrjkf9FVX/HVJCoEQwmGZauPv3q0b\n", "B3NP8H+LE4yu+BnQiH1Je9AEhOGsdzJ5zFZeoTze4h4AigePNrtpx5qkEAghHJKpNv7/fPs2zju8\n", "cGnuRV7hWZNX/GGaIL7qO4MDvn8xLfHDa37IQ9037dwsKQRCCLt15RX/yP7DCYgMZn/2YT5bMIvy\n", "/v7VXu8+JJi8pFRaRkdyWnWRYhPHbOTmS6CHP4E97gQU1/yQt4Ur/muRQiCEqLeuNnxz7ab1xC/6\n", "gIK+mr9fXcJ/vp2Ea2vfyqGbZflo8Dc6ZvuGLVjY+13Wulx7jH59+JA3hxQCIUS9ZKppZ9LC99lw\n", "djelIa4sn7sYt8FNq+2jGRZG+coM7u97Pxs9V5Fp4rjuTq5A7Y3Rrw+kEAghbNbVrvi/Xj7fqDO3\n", "+G4fFiUtRTMkDB1luJk4ZkvfUF5t9yid7guxyBj9+sCihaC8vJyEhARyc3MJDw9n1KhR7N+/n59+\n", "+gm9Xs/o0aMJCwu79oGEEHbP1BX/xAXv8sPx1eQ2rWBP9gGTnbkN3bx5pf3jfLf5W05QZrT9eoZu\n", "OgqLFoKdO3cSEhLCPffcw9dff83JkydZs2YNEydORKmUm6UJ4UiudkMVvV7P5z/PMbriL+vvx7qk\n", "dWiGhKGoMH3cCE0w99x6J+qhCodp479ZFi0EmZmZBAcHAxASEsLBgwdRKBS88847eHl58cQTT+Dq\n", "6mrJSEIIKzA1WWvywv+x+dweim91Zs+FQ5zIOWLyir9JgwA+6P4WZ71P8f7iWVaflWsPLFoIAgMD\n", "SUlJITo6Gq1WS9OmTcnJyWHSpEmsXr2a1atXM3jwYEtGEkLUkau1789d8b3x8gx3e7MgabFheQYX\n", "TE/YCm7QmPYNW0DPFrgoXaw+K9ceWLQQdOzYkeTkZOLj4/H398fDw4MWLVqgUCho06YNSUlJRvto\n", "tVq0Wq3hcWxsLGq18Wp9tkSlUknGWiAZa4c1Mv66bg3vLv6InN4efz9TQnziDHZdTKEoWMkfFw/g\n", "TrDRfg3dvZnY5VluC4jkSJMDxM1777JjgPfaQp58+HnD73PPwCHcM3BInf8+9vJ3TkxMNPwcGRlJ\n", "ZGQkYOFCoFQqeeyxxwCYPXs20dHRfPPNNwCcOHGCRo0aGe1zedgqtt57Xx9GGEjG2iEZTft8ydfV\n", "PsABCvqo+ebn+WiGhFFaVoa7if0i1MEMbNwNAP9OXXmt6IXqV/z/+jfdOnWx+O9jD39ntVpNbGys\n", "yW0WLQTZ2dkkJCSgUCjo2bMnfn5+tG7dmri4ONzc3HjhhRcsGUcIcROubPr5V7978Gzhz87zWvZd\n", "PIyKJkb7+LhqeLntGHTuOXyZNO+aN1SRph3LsGgh8PX1JS4urtpzgwYNYtCgQZaMIYS4SWs3rWdq\n", "4kwuXTa0c/zceMOs3eLSElQm9mvpFcLw0LsgFALcGzrEZK36QCaUCSFMMnWLxdB2EWw/n8yHCz6g\n", "on9AtddrhoXBqnM8NWQ4uBTw/arFMlmrnpBCIIQwYmoy1/Nz3sK5lfff6/QUoDGxX3OfYP7d4l5o\n", "AaHqIBm6WU9IIRDCQZka3tkzpjv7s48yNXEGl664qXqDoSEUrTjFgDsHsdtTx3kTx6yatQvSvl+f\n", "SCEQwgGZuuIfP28Sql0+6MMb1HhT9Sjf5kzp+AxrC1tdc9auqD+kEAhhp2pawqGkXMdHP842mtDl\n", "MiiI3KRU2rXrTqZbHgUmjumqlHV67JEUAiHskKklHN5Y8A6hhxaT3riQ85dOmly+Icq3GYl3vcda\n", "leOsxS+kEAhhl+asWGB0xV/R35+9SXvQDAmjgdL0ml6ezpWTwOSK37FIIRCiHjLV0dumUxRbzv3J\n", "lnP72J2dgichRvvdqg5kQb+P+dN3n1zxCwMpBELUMzXddF25Q4Nrc28AKspNr9Ec6O6Hn5u3XPGL\n", "aqQQCGGDalq5s6C0iJk/fmbU7OM+JJiCpBPc3eMuYhq1p8Izn09++kqu+IVZpBAIYWNMXfG/seAd\n", "bj24mPTG+Vy4lGayo7etXwT/6/Ji5YMQ8FapZQkHYRYpBELYGFNr9Vf09+fPpD14DQlH7WRq3U5w\n", "U1Zf3UeWcBDmkkIghBVc2fQzauAI/FoHsv7MH+y9eAhXgoz2uVUdyML+H7PXd69M5hK1SgqBEBZm\n", "qunnhTlv4dLKB9fm3pSU6TA1uDPQ3Q9fVy/p6BW1zuxCkJOTw/Llyzl27BjZ2dlMmDCBpk2bsmLF\n", "Cpo3b05ERERd5hSiXqmpsze/tJAPln5q1PTjOTQU3cp0Rg14kAbDK8xeuVOI2mBWITh69Cjx8fFo\n", "NBpatWpFSkoKpaWlAFy8eJGkpCTGjx9fp0GFqC9MXfG/ueAdPj+4mLRGl8jOTzfZ2dvaJ5z/RD4I\n", "kbJyp7AsswrBt99+S2RkJBMmTECv17NhwwbDtmbNmrFly5Y6CyhEfTN3pXFnb/nfnb3eQ8LROHmY\n", "3K9qHR+QK35hWWYVgtTUVF5++WWUSiXl5eXVtqnVanJzc+sknBC2ytSCbh07d+T3Mzv5K/cYShob\n", "7XOr+hYW9Z/FHt890tkrbIpZhcDDw4O8vDyT2zIzM/Hy8qrVUELYMlMLur00dxKKHZ64NPcmv6TQ\n", "5E1bAt398XHVSGevsDlmFYJOnTrxww8/EBERQUDAP7eny8vLIykpiS5dutRZQCFszZwV3xk1/agG\n", "B3Ep6Tg9Y3pyy6Cu/Lp6Nfl9/1nPXzp7hS0zqxA89NBDxMfH89JLLxEWFgbAF198wdmzZwkICCA2\n", "NrZOQwphaVeO+nloQCyalgH8kr6VP7IP0MDEgm5tGzbnw9tfBuA2/zZyxS/qDbMKgaenJ//973/Z\n", "tGkT+/fvx9XVFbVazV133UXPnj1xcXG59kGEqCdML+oWh8vf9+str2FBNw8nN8PPcsUv6hOz5xG4\n", "uLjQu3dvevfuXZd5hLC62T9/a2Kcfwi6lad5cvBjeD6g58uf50lnr7AbZhWC/fv3k52dTa9evYy2\n", "rVu3Dn9/f9q0aVPb2YSoE6Yme3Xp2pm1GTtZfmoj+3IO1zDOP4wnWt4HLSHAraEs6CbshlmFYOHC\n", "hXTu3NnktkuXLrFmzRqmTp16zeOUl5eTkJBAbm4u4eHhjBo1CoAdO3YwZ84cPv300+uILsT1M9Xs\n", "M2HeZJS7NCjDKzt3laZbfkyO85cF3YQ9UJrzovT0dMLDw01uCwkJIT093aw327lzJyEhIcTFxaHT\n", "6Th58iQA27dvx8/Pz8zIQtw4U5O9nAc1IT/lPNENW/JW+yeYPvptNGvyq71Gsyaf0QMesGRUISzG\n", "rG8ETk5ONV715Ofnm3zelMzMTIKDg4HKAnLo0CGys7Np27Yt69atM/s4QlyNqaafHt1iWH9mN9rc\n", "VBQ0MtqnjW8zPu8+sfLBreDq5CqjfoTDMKsQtGjRgqSkJG677bZqI4RKS0tZvnw5LVu2NOvNAgMD\n", "SUlJITo6Gq1WS9OmTdmwYQPPPfecFAJRK0w1/bwy/7+odntTFubGpZICk5O91M7Vl32QUT/CkZhV\n", "CEaOHMlbb73FCy+8QLdu3fDx8SE7O5tt27ZRWFhIfHy8WW/WsWNHkpOTiY+Px9/fHy8vLyIiInB2\n", "rjmGVqtFq9UaHsfGxqJWq2t8vS1QqVSSsRbcSMYFv/5g1PSjHNiY7KRUunS8i1ZDu7Jm7Vry7mpg\n", "2O69tpAnH37+hs6HvZ5HS7L1fGA/GRMTEw0/R0ZGEhkZCYBCr9frzXmT06dP88MPP6DVasnPz8fT\n", "05OoqCjuv/9+AgMDrzv07Nmz8fX1JSUlBWdnZ44cOUL//v0ZMWLENffNyMi47vezpPrQgWiPGU9c\n", "ymD0G09S3MfbaFvgZj0/vTcfhULB2k3rqzf7DHjghq/+7fE8Wpqt5wP7yHi1z2mz5xE0adKEcePG\n", "XV+yK2RnZ5OQkIBCoaBnz5707NnTsC0uLs6sIiDE5X0Aznon2nbtwFH/bP7IOkBeYTYajAuBj4sa\n", "hUIBSLOPEFcy+xuBLZFvBDevvmY0teBb3rJUXFv74t2yEa0uBHBgZzJFd/9TDDRr8nkjdlydfPjX\n", "1/NoS2w9H9hHxpv+RqDX69m+fTs7duwgOzvbcFOay73zzjvmHEqIG1aur+DDHz836gPQDAuj4bpi\n", "Fv8nAU8XD9aGrZcRP0JcB7MKwQ8//MCSJUu49dZbadKkiVHnbtVXbiFqw5Vr/d9791AuNinnxxO/\n", "c+jSKZOzfn3dvfB0qRz5I00/QlwfswrBunXrGDZsGA8++GBd5xEOzlTTz/Z5U3Ft7Ytrc29cMb3A\n", "oUohCx8KcaPMmllcVFREVFRUXWcRgjnLjdf61wwLwyO1jA+7vswHD8usXyFqm1nfCLp168a+ffuk\n", "GIg6k1F4nsXH1/DHxYM04Faj7WFeTejWqB00AgVK6QMQohaZVQiioqKYP38+eXl5tGvXDg8P45tv\n", "R0dH13o4YZ+q+gB0+jKKS4vxaO3PUb8LVKA3uid2lcubfqQPQIjaZVYhmDlzJgAbN25k48aNJl+z\n", "aNGi2ksl7NbaTeuZumgGlwy3cVSQt2wn7q0bMvTOgYTeP4j5KxfJWv9CWJBZhSAhIaGucwgHkFmU\n", "zdTEy4tAJc2wMJpvdWNKx2cACPa8Rdb6F8KCzCoEl9+wXohruXL1z549epIacJE1GTu5WJyFBhPr\n", "oSj/mdcoa/0LYVlmLzEBlTeWycrKMjmhLCgoqNZCifrL1Oqf2xd/jGtrXzwifPF10VBmYj8Z/imE\n", "9ZhVCMrKyvj666/ZsGEDZWWm/jeWPgJR6Zvl800O//Rcc4nvn/0Arc9fRvMEpA9ACOsyqxAsXryY\n", "PXv28Mwzz5CQkMDjjz+OSqVi8+bNnDt3jjFjxtR1TmHjsktyWZT6G3suHjI5/PMWTz8ae/jR+O+2\n", "fhn+KYTtMKsQbNu2jX/961/cfvvtJCQk0KxZM8LCwujVqxcff/wxu3btkuGjDuLK9v9Bdw0g1f8i\n", "y09tpKSiVIZ/ClEPmTWz+MKFCwQGBuLk5ISLi0u121N2796dHTt21FlAYTuq2v9TuhZwpGsJKV0L\n", "eGvBeyxYvYSSilLuaBzNy8OflZm/QtQzZn0j8PHxMYze8Pf3JyUlhbZt2wKV9yEWjsHUjd81w8Jw\n", "/e0ic5/8P8I0TQAIVQdJ048Q9YhZhaBVq1YcPHiQzp0706dPH+bPn8+5c+dwdnZm69atxMTE1HVO\n", "YWXai8c4kHccMB5KHKxpbCgCIE0/QtQ3ZhWCBx98kLy8PAAGDRpkuD9BaWkpAwYM4P7776/TkMJy\n", "ruwD6HVHL/70SWfLuT/JK85HY6IQyNBPIeo3swqBt7c33t7/3PFp8ODBDB48uM5CCeswOQfghwTD\n", "3b9ievfjz9V/kN9XY9hHhn4KUf9d14QyYd9q6gPwXHOJpc/PwMdVw9pgufuXEPamxkLw+uuvM3bs\n", "WIKCgnj99deveSC5VWX9VlhWzImCM4DGaNstnn74uFY+L+3/QtifGgtBUFAQLi4uhp+vRm5VWX9V\n", "6Cv4JX0rCdqFnC3IQmOiEEgfgBD2rcZCMHbsWJM/i/rt8vsBFxYXUhruxpnAIgCad2pN7m9nKL7b\n", "x/B66QMQwv5ds49Ap9Px6KOPMm7cODp37myJTKKOmLofcN4yLX7tgnjj3ucZMDSGdZs3Sh+AEA7m\n", "moVApVKh0WhwcnKyRB5Rh+auMN0ZHLbNg0HBPQDpAxDCEZk1aqhPnz6sWrWKdu3a4ex84wONysvL\n", "SUhIIDc3l/DwcO6++25mzZoFQMOGDXnuuedQKs1a9UJcp3NFF0ipYUJYucL0+kBCCMdg1qd6YWEh\n", "aWlpjB07lqioKLy8vIw6iEeNGnXN4+zcuZOQkBDuuecevv76a7Kysnjttddwd3dn4cKF7N27l44d\n", "O97YbyJM0uv1/HxqAzP++o68EpkQJoQwZlYh2LFjh+GbwIEDB0y+xpxCkJmZSXBwMAAhISGkp6fT\n", "unVrAJycnKT5qRZcPjO4olxPRTMPjjfKAaBjt9vIWH2cApkQJoS4jFmFoKr55mYFBgaSkpJCdHQ0\n", "f/31l6EoZGdns3//foYPH14r7+OoTHcGH8S3bSCT7n+Ju5vczu+bN8j9gIUQ1Sj0er3+2i+rHRUV\n", "FcyZM4fTp0/j7+9Py5YtiYmJ4f/+7/8YM2YMTZo0MdpHq9Wi1WoNj2NjY23+PrYqlQqdTmfx9439\n", "z8Psvy3X6PlWOxrwY8KCas9ZK+P1kIy1w9Yz2no+sI+MarWaxMREw+PIyEgiIyOB61hiQq/Xc/Dg\n", "Qc6cOWPynsX9+vW75jGUSiWPPfYYALNnz6Zt27Z8/vnn9OvXz2QRuDJsFVsvBNa46bperye9IBNw\n", "NdpWpi8zylMfbgwvGWuHrWe09XxgHxnVajWxsbEmt5lVCHJycpgyZQqnT5+u8TXmFILs7GwSEhJQ\n", "KBT07NmTrKwsdu3axYULF1i5ciUDBgyQuQo3IFeXz7t/fsOJvNNoCDPaLp3BQoirMasQzJ07Fw8P\n", "Dz799FOeeeYZpk6dipeXF5s2bWLjxo289tprZr2Zr68vcXFx1Z779ttvrz+1MNiWuZ8pe2aTVZKD\n", "JrIRTr9kUd7fz7BdOoOFENdiViE4cOAAjz76aLWlqP39/bnvvvuoqKjgyy+/ZOLEiXUWUvyjalRQ\n", "ib6UjPzz5ITocW3uTXvfCOL6PM2hPSkyM1gIcV3MKgQFBQWo1WqUSiXu7u7k5v7TIdmiRQuWLVtW\n", "ZwHFP4xHBXlRsiyVwcE9iO/+Mk4KJUE9AuSDXwhxXcyaxhsQEEB2djZQuRLppk2bDNv++OMPPD09\n", "a9pV1KKa7heQse84TgqZkS2EuDFmfSPo0KED+/fvp3v37gwfPpz33nuPp59+GicnJ7KysnjooYfq\n", "OqfD0+v1nCo8BxgXXZ3eeBSXEEKYq8ZCsG3bNjp27IhKpar2Qd+hQwfi4+PZuXMnOp2Odu3a0aFD\n", "B4uEdVTl+gre3/8tGfmZaEwUAhkVJIS4GTUWgpkzZ+Lm5kanTp2IiYmhffv2hgXhmjVrRrNmzSwW\n", "0pEVl5Uw8Y9P2HD2Dxq09sfl1wuU9mto2C6jgoQQN6vGQjBt2jS2bNnCtm3b2Lx5M56ennTp0oXu\n", "3bsb1gcSdStHd4mXtn9A8sUjaFwaMP3Rt7iQckZGBQkhatU1l5jQ6/UcOnSILVu2sH37dvLy8vDx\n", "8aFbt27ExMQQHh5uqawGGRkZFn/P63EzsxCrhofmlxdxNCcNfUQDgtuG81HXVwjTmJ59bemMliIZ\n", "a4etZ7T1fGAfGQMDA2vcds3OYoVCQcuWLWnZsiVjxoxBq9WyZcsW1q9fz4oVK2jcuDHdunVjxIgR\n", "N5ZeGFw5PFRFE4qSTjGmQ59aLQJCCHG56xpzqFQqiYqK4umnn+azzz6jb9++nD17lqVLl9ZVPodi\n", "anio+5BgktausFIiIYQjuK7bjen1erRaLVu3bmXHjh3k5+dzyy23EBMTU1f5HEpOaT6gMHpehocK\n", "IeqSWYWgqo9gx44d5OTk0LBhQ+68805iYmIIDQ2t64wO4UjuKY7mnMKDW422yfBQIURdqrEQpKam\n", "snXrVrZt20ZWVhZqtZquXbvSvXt3WrZsacmMdu/EpQye2/YuTi29KF2RjsugIMM2GR4qhKhrNRaC\n", "119/HTc3Nzp37kxMTAxRUVFyK8k6cLogk7Fb3yG7JI87unVn8G0dWfjrEhkeKoSwmBoLwYsvvkjH\n", "jh1xcZFmibqSWZTNs1vfIbP4Ih0atuB/nV/EzdmV/j37WjuaEMKB1FgIunbtaskcDie7JJexW98l\n", "o/A8rbxD+aDLeNycje8uJoQQde26Rg2Jm1M1WayoQseRnJOUN/cgsmMUCbe/iqeLh7XjCSEclBQC\n", "C7lyspgzgeiSTjIiujteKlnGWwhhPbKIvYWYmizmMeRWflqTZKVEQghRyaxCkJKSQlFRkcltxcXF\n", "pKSk1Gooe6TTl9XwvEwWE0JYl1mFYPLkyZw+fdrkttOnTzN58uRaDWVv9Ho9aZfOmtwmk8WEENZ2\n", "001DJSUlqFSq2shitxJSFpIXquDSstRqz2vW5DN6wANWSiWEEJVq7CxOSUkhJSWFqlWq165dy759\n", "+6q9RqfTsWfPHoKDg+s2ZT327ZEk5h1dgUdEQ55scR+7t+2UyWJCCJtSYyE4cuQIq1atMjzevn27\n", "4Q5lhp2dnWnSpAmjRo2qu4T12I8n1vFxyiIUKJgc/RT9grrBkMesHUsIIaqpsRAMGzaMYcOGATB2\n", "7FhefvllQkJCburNysvLSUhIIDc3l/DwcEaNGsXPP//M7t278fPzY+zYsXazjMXvGTt598+vAXi5\n", "7cOVRUAIIWyQWfMIZs2aVStvtnPnTkJCQrjnnnv4+uuvSUlJQavVMmXKFJYtW8auXbvq9Yzmqglj\n", "F8vzOXzhBKpWPjw/5HH+FSpLRgghbJfZE8p0Oh0pKSlkZ2dTWmo85LFfv37XPEZmZqahPyEkJIS0\n", "tDQiIyMBiIqKYvPmzfW2EFSfMKZATSgVK88QlukNLaydTgghamZWITh48CD/+9//rno/THMKQWBg\n", "ICkpKURHR/PXX38RFBSEWq0GwMPDg4KCAjNj2x5TE8aUA29h3qpF3NXjTiulEkKIazOrEHzzzTc0\n", "atSIiRMnEhQUhLPzja1M0bFjR5KTk4mPj8ff358GDRoYJqoVFhbSoEEDo320Wi1ardbwODY21lA8\n", "bEmZssLk8+VKvU3mValUNpnrcpKxdth6RlvPB/aTMTEx0fBzZGSkoUXGrE/0jIwMxo8ff9OdxUql\n", "ksceqxw1M3v2bDp27MiXX37J0KFDSU5OJiIiwmify8NWudo3E2vQ6/WczMkAfIy2OVUobC4vgFqt\n", "tslcl5OMtcPWM9p6PrCPjGq1mtjYWJPbzCoEwcHB5OTk3Fi6y2RnZ5OQkIBCoaBnz574+fnRqlUr\n", "3n77bfz8/Bg8ePBNv4c1/HB8DXmhCnTLUlEPCzM8L3cXE0LUBwp91Yyxqzhx4gSzZs3i0UcfNbo6\n", "t4aMjAxrRzD4I+sAY7e+S7m+nFh9N7Q791Ou1ONUoWD0gAdsdsKYPVzh2ALJePNsPR/YR8bAwMAa\n", "t5n1jSA+Ph6dTseUKVNwdnbGzc2t2naFQsGXX35pZlz7cbYwi9d3JVCuL2dUs4H8J/JBuKd+/KMR\n", "QogqZhWCa40IUigUtRKmPiku1/HKzg+5qMuji38bxrYaYe1IQghxQ8wqBDV1MDgqvV7PtH1fcSD3\n", "OIEe/kzt9BzOSvuYES2EcDzXNQ40Pz+ftLQ0Lly4QPv27fH09ESn0+Hs7Gy0DpE9qpo5nF6USfql\n", "c3hGBvC/x6fJHcaEEPWaWYWgvLycBQsW8OuvvxpmFb/zzjt4enoyffp0wsLCGDHCvptGqs8cboCG\n", "MFx+vcCp/ak07yGrrwoh6i+zLuO///57fv/9dx5//HESEhKqbbvtttvYs2dPnYSzJaZmDpf2a8i8\n", "VQutlEj0OHOVAAAeUklEQVQIIWqHWd8INm7cyMiRI7nzzjspLy+vti0gIICzZ03ffcuelCK3mhRC\n", "2CezvhEUFBTQuHFjk9vKysqoqDC9vIJdKTf9tNxqUghR35lVCJo2bcquXbtMbtu3bx9hYWEmt9kT\n", "rzaNyZNbTQoh7JBZTUPDhw9n+vTp6HQ6br/9dqBytvHOnTtZs2YNr7zySp2GtLaT+WfY652GW2tf\n", "Qre44OyklFtNCiHshlmF4LbbbuOFF15g/vz5rF+/HoDPP/8cX19fnnvuOdq3b1+XGa3uk5REyvUV\n", "/OuuYUzs8IS14wghRK0yex5Bt27duP322zlz5gx5eXl4enoSGBho9/MHkrOP8vuZXbg6qXiy5XBr\n", "xxFCiFp3XRPKFAoFgYGBV128yJ7o9Xo+0i4A4MGw/gS4+1o5kRBC1D6zLuc/+eQTZs6caXLbzJkz\n", "+eyzz2o1lK3YeHYP+7IP46Xy5OHm9XOJbCGEuBazCkFycjKdO3c2ua1r1678+eeftRrKFpRVlDMr\n", "ZREAj0fcg6eLh5UTCSFE3TCrEOTl5dV4CzQPDw9yc3NrNZQtWJ62keP5GTTxCOD+0D7WjiOEEHXG\n", "rELg5+dHSkqKyW0HDx6kYcOGtRrK2orKivn8wBIAnm31L1yUN3aPZiGEqA/MKgS9evVi2bJl/PLL\n", "LxQXFwNQXFzML7/8wrJly+jdu3edhrS074/9SlZJDq28QunTpIu14wghRJ0y61J32LBhnDt3jm++\n", "+YZvvvkGV1dXSkpKALjrrrsYNmxYnYa0lLWb1vPV8nnsu3iI8vIKut3XF6XCvofHCiGEWYVAqVTy\n", "9NNPM2TIELRaLZcuXUKtVtOmTRu7GUp6+TLTDQgB4IdffqS55laZPSyEsGvXLAQ6nY5HHnmEF198\n", "kc6dO9OkSRNL5LI4U8tM5/XxZN6qhVIIhBB27ZrtHiqVCi8vL5yc7PtWjLLMtBDCUZnVAN6nTx9W\n", "rVpFWZnpD0t74FLDlyNZZloIYe/M6iMoLCwkLS2NsWPHEhUVhZeXFwqFotprRo0aVScBLeXuXn3Z\n", "vugDNMP+WVJbsyaf0bH/tmIqIYSoe2YVgh07duDsXPnSAwcOmHyNOYWgtLSUGTNmUFRUhLu7Oy+9\n", "9BKzZ88mMzMTJycnxo0bV+PEtbp2sWk5rq19cVudQ1N1I1lmWgjhMMwqBLNmzaqVN9u3bx/h4eEM\n", "Hz6cpUuX8ttvv6FQKJg0aRKbN29m06ZNDBw4sFbe63pU6Cv4JX0rrs29+fDRiUT7tbR4BiGEsBaL\n", "DpJXq9UUFBQAlc1NISEhhglq+fn5aDQaS8Yx2HvhIOeKLtDYvSHtG0ZYJYMQQliL2YXgxIkTfPDB\n", "Bzz33HOMHDmS1NTK2zYuWLCAvXv3mnWMiIgIjh8/zvjx40lNTaVVq1a4uLjw4osvsmbNmhoXtqtr\n", "K9O2ADAgKEYmkAkhHI5ZTUN79+7lvffeIyIigp49e7J48WLDNhcXF3755Rc6dOhwzeNs3LiR6Oho\n", "hgwZQlJSEkuWLMHd3Z0ZM2awfft2fv75Z+6///5q+2i1WrRareFxbGxsrfYjFJeV8PuZyvsx/6tl\n", "v1o5tkqlslpfh7kkY+2QjDfP1vOB/WRMTEw0/BwZGUlkZCRgZiFYsGABPXv25Omnn6a8vLxaIQgJ\n", "CWH16tVmBS0qKqJBgwZAZTORk5MT5eXlhseFhYVG+1wetsqlS5fMej9z/HZ6O/mlhbTyDsVf6VUr\n", "x1ar1bWasS5IxtohGW+erecD+8ioVquJjY01uc2sdpCMjAy6detmcpu7uzv5+fnmHIYePXqwdetW\n", "Jk+ezJYtW+jevTtpaWlMmjSJH374gf79+5t1nNq06u9moYFB3S3+3kIIYQvM+kag0Wg4d+6cyW3p\n", "6en4+fmZ9Waenp5MnDix2nMTJkwwa9+6cLEkj22Z+3FSKLk7qKvVcgghhDWZ9Y0gJiaGxMREDh48\n", "WG0iWUZGBsuWLaN79/p5Nf3b6e2U68vpGhCFr6uXteMIIYRVmPWNIDY2lvT0dOLi4vD29gbg/fff\n", "Jycnh3bt2nHffffVaci6sjJtMyDNQkIIx2ZWIVCpVLz22mskJyeTnJxsuHVlVFQUbdu2reuMdeLE\n", "pQxSclJp4OzGHY2jrR1HCCGs5qqFoKSkhL1793L+/Hm8vb2JiooiKirKUtnq1Kr0yk7iO2+5DTdn\n", "VyunEUII66mxEJw7d44pU6aQlZVleM7d3Z1x48bRvn17i4SrK1VLSgAMbCrNQkIIx1ZjZ/H8+fNR\n", "KpVMmTKFefPmMX36dEJCQvjiiy8sma9O/Jl9mIzC8wS4+dLRr5W14wghhFXVWAgOHz7MiBEjaNGi\n", "BSqViqCgIJ588kmysrK4ePGiJTPWuqolJfoHdZMlJYQQDq/GT8GcnBwaN25c7blGjRoZttVXJeU6\n", "1pzeAUizkBBCgJmjhqpUzSHQ6/V1Eqaurd20npk/fkbGpTQ8lK6c8D1KeI8ga8cSQgirumohmDp1\n", "Kkql8ZeG+Pj4as8rFAq+/PLL2k9Xi9ZuWs+0xJnk9fFEQygA0xJnAsjNZ4QQDq3GQjB8+HCzD3Ll\n", "bStt0dyV35PXx7Pac3l9PJm3aqEUAiGEQ6uxENS0Sl19VUqZyed1+lILJxFCCNviMENmXGqoeSqF\n", "i4WTCCGEbXGYQvDwwJGULE+r9pxmTT6jBzxgpURCCGEbrmvUUH3WM6Y7Lju9yUtKpV3DCDyc3Bgd\n", "+2/pHxBCODyHKQRH89JQNlMT2a4ZiX2mWzuOEELYDIdpGvrr4jEAIn3CrZxECCFsi8MVgiifZlZO\n", "IoQQtsWBCsFRQL4RCCHElRyiEOTpCjiZfwaV0oUIr1utHUcIIWyKQxSClJxUAFp43YqL0mH6x4UQ\n", "wiwOUQiqmoXaSP+AEEIYcbBCIP0DQghxJbsvBHq9XoaOCiHEVVi0wby0tJQZM2ZQVFSEu7s7L730\n", "EikpKfz000/o9XpGjx5NWFhYrb7n6cJMcnX5+Kg0BHr41+qxhRDCHli0EOzbt4/w8HCGDx/O0qVL\n", "2bdvHxs3bmTixIkm73tQG6q+DbTxDa8Xy2ULIYSlWbRpSK1WU1BQAEBBQQEnTpxAoVDwzjvv8PHH\n", "H1NSUlLr7/lX9t/9A97SUSyEEKZYtBBERERw/Phxxo8fT2pqKo0bNyYnJ4c33niDiIgIVq9eXevv\n", "Kf0DQghxdRZtGtq4cSPR0dEMGTKEpKQkKioqaNGiBQqFgjZt2pCUlGS0j1arRavVGh7HxsaiVqvN\n", "ej9deSmH806iQEGXpm1RqxrU2u9yNSqVyuyM1iIZa4dkvHm2ng/sJ2NiYqLh58jISCIjIwELF4Ki\n", "oiIaNKj8MFar1Zw/f57Tp08DcOLECRo1amS0z+Vhq1y6dMms9/sr+yilFWWEqptASQWXSszb72ap\n", "1WqzM1qLZKwdkvHm2Xo+sI+MarW6xjtPWrQQ9OjRg5kzZ7Jp0yacnZ0ZN24c69evJy4uDjc3N154\n", "4YVafb9kmT8ghBDXZNFC4OnpycSJE6s9N2jQIAYNGlQn76etGjEkhUAIIWpk1xPK/ukolhFDQghR\n", "E7stBBdL8jhdmImbkyvh6iBrxxFCCJtlt4Wg6ttAK+9QnJVOVk4jhBC2y44LgXQUCyGEOey2EEhH\n", "sRBCmMcuC0GFvgLtxcqb0cg9CIQQ4ursshCczD9DflkhAW4+BLj7WjuOEELYNLssBDJsVAghzGeX\n", "hUD6B4QQwnx2WQiqRgzJiqNCCHFtdlcIistKOJqXhhIFrb1DrR1HCCFsnt0VggO5xynXVxCuaYq7\n", "s5u14wghhM2zu0Lwl/QPCCHEdbG7QvBPR7GMGBJCCHNYdBnqurZ203pWzF2CjjLmbf6GBkP13NWj\n", "l7VjCSGETbObQrB203riF32A2+CmuAEnKGNa4kwAKQZCCHEVdtM0NHfl9xT01VR7Lq+PJ/NWLbRS\n", "IiGEqB/sphCUUmbyeZ2+1MJJhBCifrGbQuBSQyuXSuFi4SRCCFG/2E0heHjgSDRr8qs9p1mTz+gB\n", "D1gpkRBC1A9201lc1SE8b9VCdPpSVAoXRsf+WzqKhRDiGuymEEBlMZAPfiGEuD520zQkhBDixlj0\n", "G0FpaSkzZsygqKgId3d3XnrpJZydndmxYwdz5szh008/tWQcIYQQWPgbwb59+wgPDycuLo5mzZqx\n", "b98+ALZv346fn58lowghhPibRQuBWq2moKAAgMLCQtRqNXv27KFt27YoFApLRhFCCPE3ixaCiIgI\n", "jh8/zvjx40lNTSUiIoKNGzfSo0cPS8YQQghxGYv2EWzcuJHo6GiGDBlCUlISmzZtIiIiAmdnuxq8\n", "JIQQ9YpCr9frLfVmq1atwtXVld69e7N+/XoKCwvZvXs3zs7OHDlyhP79+zNixIhq+2i1WrRareFx\n", "bGyspeIKIYRdSUxMNPwcGRlJZGRk5QO9BV26dEkfHx+vnzRpkv6///2vPj8/37Dt7bffNusYixYt\n", "qqt4tUYy1g7JWDtsPaOt59Pr7T+jRdtkPD09mThxosltkydPtmQUIYQQf5MJZUII4eCcJk2aNMna\n", "Ia5XQECAtSNck2SsHZKxdth6RlvPB/ad0aKdxUIIIWyPNA0JIYSDk0IghBAOrl7N5JozZw7Hjx8n\n", "NDSURx991NpxjGRmZvLmm28SFBSEs7Mzb775prUjGVy8eJF3332X9PR05s2bh1Kp5Oeff2b37t34\n", "+fkxduxYnJycbC7jI488QlhYGADjx4/H09PTavmOHDnC3LlzUSgUhIeH88gjj9jcOTSV0ZbOIUBa\n", "WhqzZ89GqVQSFBTEE088YXPn0VRGWzuPAMuXL2fnzp1MmTLl5s5hrQ1irWPHjh3Tf/bZZ3q9Xq//\n", "4osv9EePHrVyImPnzp3Tf/TRR9aOYZJOp9Pn5+frJ02apC8vL9fn5OTop02bptfr9fqffvpJv23b\n", "NisnNM6o1+v1b731lpVT/ePixYv60tJSvV6v13/44Yd6rVZrc+fwyownT560qXOo1+v1ZWVlhp9n\n", "zZqlP3LkiM2dxyszpqam2tx51Ol0+o8//lj/9ttv63Nzc2/qHNabpqGjR4/Srl07AKKiojh8+LCV\n", "E5mm1WqJi4tjxYoV1o5SjYuLCw0aNDA8PnbsmGFWoa2czyszApw+fZq4uDgWLFhgpVT/8Pb2NiyH\n", "4uzsTHp6us2dwyszKpVKmzqHQLUrVZ1OZ5P/Fq/M6OHhYXPn8ffff6dnz57o9fqbPof1phAUFBTg\n", "5uYGgIeHh2EVU1vi6+vLRx99RFxcHMnJyZw6dcrakWpUWFiIu7s7YLvnE+Cjjz5i8uTJ5Ofns3v3\n", "bmvHAeDkyZPk5eXh4eFhs+ewKmNQUJBNnsPdu3czfvx4Q/G3xfNYlVGlUtGoUSObOo9lZWWkpKTQ\n", "pk0boPLz8WbOYb0pBB4eHhQVFQGVH2JXXjnaAmdnZ1QqFUqlkujoaJsuBPXhfAKGXJ07dyYtLc3K\n", "aSA/P5+vv/6aZ555xmbP4eUZwfbOIUCnTp2YPn067u7uuLm52eR5rMro5ubG/v37beo8bty4ke7d\n", "uxse3+y/xXpTCCIiIkhOTgYgOTmZiIgIKycyVlxcbPj50KFDNG7c2Ippri48PJyUlBTAds9nSUkJ\n", "FRUVABw8eNDq57O8vJyEhARGjx6Nl5eXTZ7DKzPa2jmEyqvZKlVXr7Z2Hq/MqNPpbOo8njlzht9+\n", "+41p06aRlpZGamrqTZ3DejWhrGrUUEhICGPGjLF2HCN79+5l0aJFuLi40KpVKx588EFrRzIoLy9n\n", "2rRppKamEhYWxsiRI9Fqtfzxxx82M1LDVMYvvvgCNzc3GjVqxDPPPGPVGxht3ryZOXPm0LRpUwBG\n", "jhzJgQMHbOocmsr41Vdf2cw5hMoml+XLl6PX6wkICOCZZ54hKSnJps7jlRkHDBjA559/blPnsUpc\n", "XByTJ09m2bJlN3wO61UhEEIIUfvqTdOQEEKIuiGFQAghHJwUAiGEcHBSCIQQwsFJIRBCCAcnhUAI\n", "IRxcvVp9VNycxMRElixZQtu2bY1WRp0+fTr5+fnExcVZJItWq2XKlClMnz6doKAgi7zn9UhPT2f2\n", "7NkcP34cnU7HrFmz8PPzM/nawsJCkpKS2L59O+fPn8fJyYmQkBB69uxJr169UCrleutqMjIy2Lx5\n", "M4MHD8bDw8PacRySFAIHtH//fo4dO0Z4eLi1o9is+fPnU1RUxKuvvoqbmxve3t4mX5ebm8ukSZMo\n", "Kipi8ODBhIWFUVpaSnJyMt9++y0ajYZOnTpZOH39cubMGZYsWULv3r2lEFiJFAIH4+npia+vL0uX\n", "LuXll1+2dpw6U1paiouLyw3vf/r0aW677TbDol41+eKLLygsLOTdd9/Fx8fH8Hy7du0YMGCAzSyg\n", "Vh/I3FbrkULggO69914+/PBDTp06RXBwsMnXJCYm8uuvv/LVV19Ve37EiBGMGTOG/v37AzB27Fi6\n", "du2KWq1m5cqV6HQ6evfuzcMPP8zu3bv57rvvyM7OJioqimeeecZoMazs7Gzmz5+PVqtFrVZz7733\n", "0rdv32qvOXDgAAsXLiQ1NRWVSkXnzp155JFHDKvRrl+/nk8//ZSpU6cyf/58jh49yn333cd9991n\n", "8nc7ceIEc+fO5ciRIzg7O9OhQwceeeQRvLy8yMzM5PnnnwdgxYoVrFixgtatW5tsMsvMzGTXrl2M\n", "GTOmWhGo0rBhQxo2bGh4/Ndff7FgwQJOnjyJh4cHXbp0YdSoUYbfo6q57K233mLlypUkJyfj6+vL\n", "Y489RmRkJN999x0bN27ExcWFIUOGMGjQIMOxZ82aRXp6Ovfeey8LFizg/PnzhIeH8+STT1Zreisp\n", "KeG7775j27ZtFBYWEhwczMiRI2nbtq3hNZMmTUKj0dC5c2cWLVpEXl4eLVu25KmnnsLX19fwOp1O\n", "R2JiIlu2bCEvL4/AwEAefPBBOnToYHhN1b8PHx8fli9fTklJCe3atePJJ5/Ew8MDrVbLe++9B8Bz\n", "zz0HgJ+fH7NmzaKgoIB58+axd+9e8vPz8fLyol27djz11FMm/67ixjlNmjRpkrVDCMvQarWkpqby\n", "7LPPsmXLFs6cOUPXrl0B2LZtGzqdjl69elV77dChQ6sdY/HixURHR9OsWTMAVq5cyalTp1AoFDzw\n", "wAMEBATw448/kpOTw+bNm4mNjaV9+/b8+uuv5OTk0LFjRwDOnz/Phg0b0Gq1tG/fnnvuuYfy8nIW\n", "L15MeHg4t9xyC1C5wFd8fDzNmjXjwQcfpFWrVqxevZrU1FRuv/12oPKDfffu3fz111/ccccdDB06\n", "lODgYJPNOXl5ebz66qt4enryyCOPEBUVxbp169i+fTu9e/fG3d2d6Oho9uzZQ6dOnXjqqafo0qUL\n", "Go3G6Fh//PGHoRBc625VaWlpvP3229x66608/PDDhIaGsnLlSg4fPkyPHj2qnZNDhw7RpUsXBg4c\n", "SFpaGitWrODcuXMoFAqGDx+Ok5MTS5YsoUOHDoYP5t27d3Ps2DFSUlKIjY0lJiaGffv2sXbtWvr1\n", "62dYd+aTTz5h27ZtxMbG0q9fP86ePUtiYiJt2rQx9IFs2LCBU6dOcebMGUaMGEGHDh1Yt24dqamp\n", "hqwA77//Pnv37uX++++nX79+FBQUsGDBAjp16mQ49ytXriQtLQ2dTscDDzxAs2bN+OWXX8jLyyM6\n", "Ohq1Wo1arWb//v1MmDCBQYMG0aNHD7y9vfnyyy85fPgwDz30EHfffTfh4eFkZWUZ/g2J2iPfCByM\n", "Xq9HoVBwzz338Nlnn3HmzBnDh66p15pDpVLx0ksvoVAoaNeuHbt37+b333/no48+wt/fH6j8sN6w\n", "YQNPPPFEtX07dOjAAw88AEDbtm05d+4cS5YsITo6GoAFCxbQsmVLxo0bZ9jH19eX+Ph40tPTq13t\n", "Dhw4kAEDBlw1a1JSEgqFgjfffNNwJX7LLbfw5ptvsmPHDmJiYmjevDnOzs74+PgYCp4p2dnZADV2\n", "Il9uyZIlBAQE8OqrrxoWK/P09GTmzJkcPny42mqRd9xxB0OGDDH8ruPHj+fChQu89dZbQOWNR7Zu\n", "3cqOHTsM+fR6PZcuXeKVV14xHCssLIznn3+e9evX07dvX9LT09myZQtjx47ljjvuACqbsCZMmMCS\n", "JUsMAwj0ej3FxcW8/vrrhjb7nJwcvv32W0OTW3JyMnv37mXy5Mm0bNkSqPz7nTlzhqVLl/LSSy8Z\n", "fh9nZ2defvllQ6d5eno6W7du5d///jfu7u6Gf3+hoaHVzuWxY8fo16+foeAD1QqRqD0ynMFB9ejR\n", "Az8/P3788cebPlbr1q2rrcTYqFEjAgICDEUAoHHjxuTl5VFeXl5t386dOxs9Tk1NRa/XU1JSwpEj\n", "R+jatSvl5eWG/1q2bImTkxOpqanV9q0qHldTdae7qiIA0KxZM/z9/Tl48OB1/d5VzFmF8ujRo3Tu\n", "3Lnaa7t06YJSqeTQoUPVXhsVFWX4uWq546q7T1W9X6NGjbh48WK1/by8vKoVFD8/P8LCwjh69ChQ\n", "+cEKGL4FVh2ra9euRr97eHh4tY7bJk2aAP8Uv+TkZLy9vYmIiKj2t2nTpo3R3yUyMrLayKmgoCBy\n", "c3MNyzrXJCQkhJ9//pnffvuNjIyMq75W3Bz5RuCgnJycGDp0KN988w2xsbE3dawr2/2dnZ2NRn9U\n", "3T6xrKys2vK4Xl5e1V6n0WioqKjg0qVLlJWVUVFRwVdffWXUVwGQlZVV7fGVxzIlJyfHZL+Il5fX\n", "dXfsVjXLZGVl0ahRo2u+75X5lEolarWa/Pz8as9ffj6rztuV59jJyQmdTlftOVPNV2q1mpycHAAu\n", "XryIm5sbKpWq2mu8vLzQ6XSUlZUZ3q+mv19paSlQ2cSWk5PDyJEjjd7zyuGypv59VB3L1dXVaP8q\n", "jz/+OIsWLWLx4sV89dVXNG7cmBEjRtCtW7ca9xE3RgqBA+vduzdLly7lp59+MrqqValU1W7OARh9\n", "YNWG3Nzcao/z8vIMH5AlJSUAxMbGVuuArHJ5xyWYd2Xu4+Nj9J5Q+UF9vcNpW7VqBcC+ffvo16/f\n", "db9vVcG7Vv+CuUz9Xnl5eYbC5+PjQ3FxMTqdrloxyM3NRaVSGT6gzVE1+qwuR555eHgwZswYxowZ\n", "w6lTp1i2bBkfffQRwcHBNjn3pD6TpiEH5uzszJAhQ1i3bp1RM4Ovry/FxcWGpgConH9Q23bu3Gn0\n", "ODw8HIVCgZubGxEREZw+fZqwsDCj/2oa2381zZo1488//6x2N7mjR4+SlZVlaOs2l7+/P507dzZ0\n", "jl8pKyvLcLvSZs2asXPnzmrNITt27KCiouK63xdMF728vLxqNy3Pysri+PHjhn6EqkK3fft2w2v0\n", "ej3bt283FLWajn2ltm3bkpOTg5ubm8m/zfWoKkBXfsO5XHBwMKNGjUKv10szUR2QbwQOrm/fvvz4\n", "448cPnyY1q1bG57v0KEDKpWKTz/9lMGDB5OZmcmaNWtq/f337dvHwoULadWqFTt27CA5OZlXXnnF\n", "sP2hhx4iPj6ejz/+mC5duuDu7k5WVhZ79uxh5MiRNXZ012Tw4MGsXr2aqVOnMmzYMIqKiliwYAHB\n", "wcF06dLluvM/8cQTxMXF8dprrzF48GBCQ0MpLS0lJSWF3377jeeee47g4GCGDx/OK6+8wvvvv0/f\n", "vn3Jzs7mu+++o3379jRv3vy639dUR75arSYhIYERI0agUqlITEzE29vbMBIsKCiImJgYvvrqK4qK\n", "imjUqBFr1qzhzJkzPPnkk1c99pXatm1Lu3btiI+PZ9iwYQQFBVFUVMSJEycoLS29rrvzBQYGArB6\n", "9Wq6deuGq6srwcHBvPXWW3Tp0oWgoCAUCgVr167Fzc3tqh344sZIIXAgCoXCZBPQoEGDWLhwYbXn\n", "1Wo148ePZ968efzvf/8jLCyMF154odpokKu9j7mefvppw3h9T09PHn/88WrDA1u2bMnkyZNJTEzk\n", "448/pqKiAn9/f9q3b29Wn8CVNBoNcXFxzJ07lw8//NAwj+DRRx+9odsjajQapk6dSlJSEmvXriUz\n", "MxNnZ2dCQ0N59NFHDR3YQUFBvPHGG3z//fdMnz4dDw8PunfvzqhRo677PcH0Ofb39+fee+/lu+++\n", "Iysri/DwcMaNG1etyefpp59m/vz5LF682DCP4LXXXqNFixZXPbYpEyZMYOnSpaxcuZKsrCw8PT0J\n", "DQ01zDExl7+/P6NHj2bVqlX88ssvNGzYkI8//pgWLVqwfv16zp8/j1KpJDQ0lNdff92oSVDcPLlV\n", "pRB2oGpC2TvvvGPtKKIekj4CIYRwcFIIhLAD19McJ8SVpGlICCEcnHwjEEIIByeFQAghHJwUAiGE\n", "cHBSCIQQwsFJIRBCCAcnhUAIIRzc/wOxZGVy2pMUhAAAAABJRU5ErkJggg==\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f3729432890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_component_variance\n", "\n", "draw_component_variance(model.dimension_reducer.explained_variance_ratio_)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Roughly 90 percent of the variance is captured with the first 5 components. This means our model may only need a few components to predict the average stress.\n", "\n", "Next we need to optimize the number of components and the polynomial order. To do this we are going to split the data into test and training sets. This can be done using the [train_test_spilt](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html) function from `sklearn`.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(960, 441)\n", "(240, 441)\n" ] } ], "source": [ "from sklearn.cross_validation import train_test_split\n", "\n", "flat_shape = (X.shape[0],) + (np.prod(X.shape[1:]),)\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X.reshape(flat_shape), y,\n", " test_size=0.2, random_state=3)\n", "print(X_train.shape)\n", "print(X_test.shape)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use cross validation with the testing data to fit a number \n", "of models, each with a different number \n", "of components and a different polynomial order.\n", "Then we will use the testing data to verify the best model. \n", "This can be done using [GridSeachCV](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html) \n", "from sklearn.\n", "\n", "We will pass a dictionary `params_to_tune` with the range of\n", "polynomial order `degree` and components `n_components` we want to try.\n", "A dictionary `fit_params` can be used to pass the `periodic_axes` variable to \n", "calculate periodic 2-point statistics. The argument `cv` can be used to specify \n", "the number of folds used in cross validation and `n_jobs` can be used to specify \n", "the number of jobs that are ran in parallel.\n", "\n", "Let's vary `n_components` from 1 to 7 and `degree` from 1 to 3.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.grid_search import GridSearchCV\n", "\n", "params_to_tune = {'degree': np.arange(1, 4), 'n_components': np.arange(1, 8)}\n", "fit_params = {'size': X[0].shape, 'periodic_axes': [0, 1]}\n", "gs = GridSearchCV(model, params_to_tune, cv=12, n_jobs=6, fit_params=fit_params).fit(X_train, y_train)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default `score` method for the `MKSHomogenizationModel` is the [R-squared](http://en.wikipedia.org/wiki/Coefficient_of_determination) value. Let's look at the how the mean R-squared values and their \n", "standard deviations change, as we varied the number of `n_components` and `degree`, using\n", "`draw_gridscores_matrix` from `pymks.tools`.\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA10AAADTCAYAAAB6KXlqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtcVHX++PEX9xFwuIgmCIqIV1LJW7NpaPFtN3dNS9PU\n", "spTAvGSZaf2qNbOvrVqutN7YTczVzQyKTc2yvJSQl7Twgte8DQqC4g1xhGFgZn5/+JjzdRwGBwJn\n", "yPfz8eDxcM58zud8zoC8eX/O+3yOm9lsNiOEEEIIIYQQol64O3sAQgghhBBCCPF7JkmXEEIIIYQQ\n", "QtQjSbqEEEIIIYQQoh5J0iWEEEIIIYQQ9UiSLiGEEEIIIYSoR5J0CSGEEEIIIUQ98nT2AETDNXHi\n", "RC5evGi1zdPTk8DAQNq1a8ef//xn2rZtW+N+DQYD3333Hbt37yY/Px+9Xo+/vz9qtZrIyEg6dOhA\n", "XFwcPj4+dXUqv3tPPfUUAGlpaU4eiRDC1Z09e5avv/6aQ4cOcenSJQDUajXBwcG0a9eO2NhYunTp\n", "4uRR/jaW+LV48WJCQkKcPRwWL15MVlYW48ePp1+/fg7tk56eTkZGhtU2T09PfH19CQ4OJjIykm7d\n", "utGjRw88PDzqYdSOKyoqYtKkSYSEhLB48eI7csx33nmHI0eOMGPGDDp16nRHjilEdSTpEr9ZbGws\n", "AQEBAFy/fh2tVsuOHTvYuXMnY8eO5eGHH3a4rytXrvDuu+9SUFCAl5cXbdu2JSgoCIPBwNmzZ9m2\n", "bRvbtm2jY8eOhIeH19cpCSHEXWnHjh0sWrQIo9FIcHAw9957L35+fpSUlHDq1CmOHz/OkSNHbJIu\n", "V0tiGio3N7ca79O8eXPat28PgMlk4vr16+Tn57N161a2bt1KcHAw48ePd4lEuTbnV5WtW7eSkpJC\n", "3759mTBhQr0eS4i6IkmX+M0GDRpkNYtkNBpZvnw5mzZtYvny5fTs2ZPGjRs71NeyZcsoKCjg3nvv\n", "ZfLkyTb7Xbx4kaysLLnKJYQQday4uJiUlBSMRiPPPfcc/fv3t/rD1Ww2c/ToUX799VcnjlLcqn37\n", "9lUmHgUFBaSnp7Nz507+9re/MXXqVHr06OGEEUKTJk1ITk7G0/PO/dk5ceJEDAaDTAIIlyH3dIk6\n", "5+HhwbPPPotKpcJgMLB//36H9jMYDGRnZwOQlJRUZaIWEhLC4MGDadq0aZ2OWQgh7nbZ2dkYDAal\n", "PPzWKwVubm507NiRxx9/3G4fZrO5vocpHBQWFsbkyZP5y1/+gtlsZvHixZSWljplLB4eHoSFhdGs\n", "WbM7dsyQkBDCwsLw9va+Y8cUojpypUvUC29vb0JDQ9Fqtco9Abej0+kwmUzAjfsHakqv1/Pll1+y\n", "Y8cOLl26REBAAN27d+epp55i5cqVVdbL367m216dfUlJCdu2bWPv3r0UFBRQXFyMl5cXLVq0IC4u\n", "jkceeQR3d+s5jZtr2hcuXMj69ev58ccfOXfuHJ6enixfvlxpe/z4cb7++muOHj3K1atX8fPzo127\n", "dgwcOJAOHTpUef5nzpwhLS2Nw4cPU1lZSVhYGH/6059qVN4phLh7Xb16FUApF3eEpczL4sUXX7R6\n", "31JuaDQa2b59O3v27EGr1XLlyhVMJhNNmzalR48eDBo0CH9/f5v+LWWLixYtorCwkDVr1nDy5EmM\n", "RiOtWrXiiSeesHv15sKFC6SlpbF//37Kyspo1qwZ/fr1Y8CAAXbPJz8/n+3bt3PgwAEuXLhASUkJ\n", "vr6+REdH079/f2JjY+1+Bn379mXUqFF8/vnnZGdnc+XKFe677z6mTZsGVB+j6tPTTz/Njh07uHLl\n", "Clu2bOGxxx6zet9sNrNjxw5++OEHtFoter2egIAAunbtajPJuXHjRpYtW0bPnj2ZOnVqlcc7c+YM\n", "06ZNIzAwkJSUFNzd3au9p+v48eP89NNPyj2EOp0OtVpN+/bteeyxx2zuDb/5fvLMzEwyMzOV924u\n", "N6wuvldWVrJ582aysrI4e/YsRqORpk2b0rNnTwYOHGjzs3jr+L/77js2b95MYWEhXl5edOzYkREj\n", "RhAREeHIt0TcpSTpEvXGMqPWqFEjh9qr1Wq8vb0xGAx88803PPnkkw4fS6/XM3PmTE6dOoWvry/d\n", "unXDzc2NHTt2kJOTo9z/Vdsa71v327dvHytWrKBJkyaEhobSvn17rly5wrFjxzhx4gQ5OTlKoK3K\n", "vHnz2L9/P506dSIiIsJqQZKvvvqKTz75BDc3N1q3bk379u25dOkSe/bsYc+ePSQlJREfH2/V3+HD\n", "h/nb3/5GRUUFYWFhtG7dmitXrvDRRx+Rl5dXq3MWQtxdLH9cHzhwgLy8PIf+gGzevDl9+/blp59+\n", "ory8nPvvvx+VSqW8bykFLy4uZvHixfj7+yu/o8rKyjh58iTr1q3jp59+4m9/+5vdUvQtW7bw5Zdf\n", "Eh0dTffu3SkoKODEiRN88MEHvPLKK2g0Gqv2+fn5zJgxA51OR0hICPfeey86nY60tDSOHz9u93zW\n", "r1/PDz/8QHh4OJGRkTRq1Ijz58+zb98+9u3bx6hRo+wmbSUlJbzxxhuUlZXRsWNHoqOjlfNxNEbV\n", "Bw8PD/7whz/wzTffkJOTY5V0VVZW8uGHH/Lzzz/j7e1NmzZtCAgI4MyZM3z//ffs2rWLv/71r0RF\n", "RQHQp08fVq5cyd69e7l27VqV3y9LEvTggw/aTD5WFYM/++wzDh8+TEREBG3btsXT05OCggJ27drF\n", "zz//zMsvv2z1/dVoNBw/fpxff/3V6n42wO6k5M0MBgOzZ8/m8OHD+Pj4EBMTg4+PD0eOHGHt2rVs\n", "376dGTNm2L0qt2jRInbu3EmnTp0ICwvjxIkTZGdnc/jwYd5///07ejVPNCySdIl6kZeXR1FREYDy\n", "y/p2PD09efjhh/n222/5/PPP2blzJ/fddx9t2rQhKiqKe+65x+6+6enpnDp1ipYtWzJ9+nTlSllp\n", "aSmzZ89WyhbrSps2bXjvvfeIjo622l5cXMzs2bP55Zdf2LFjBw888IDNvpYEa/78+TbntHfvXj75\n", "5BOCg4N59dVXrfr/9ddfmT17NsuWLaNTp06EhoYCNwLIggULqKio4IknnmD48OHKPocPH2b27Nl1\n", "dt5CiN+vnj17EhQUxJUrV3jttdfo2rUrnTp1onXr1rRp0wZfX1+bfTp06ECHDh04dOgQ5eXlPPvs\n", "s1XeQ+Pn58frr79ObGys1R/iBoOBZcuWsXXrVtLS0khMTKxybF999RVvvvkmXbt2Vbb997//JS0t\n", "jdWrV9skXQsXLkSn0xEXF8e4ceOU1fvy8/OZOXMmJSUlVR4nLi6OJ5980uYcTpw4waxZs/j00095\n", "4IEHCA4Ottl37969dO3alSlTplglnnDnY9St2rRpA9w4/5ulpaXx888/06lTJyZNmmR1Xt9++y3L\n", "ly/nww8/5MMPP8Td3R1fX1969uzJjh072LZtG/3797fqz2QysW3bNgCHV2F87LHHePnll20qXLKz\n", "s/n73//O0qVL6datm1ImOGrUKLZu3cqvv/5q93626qSnp3P48GFatGjB9OnTCQoKAm78LC5atIhd\n", "u3axYMECZs2aZbPvxYsX+fXXX0lOTlaSq8rKSubNm8fevXv58ssveeGFF2o0HnH3kHu6RJ3S6XTs\n", "3buXefPmYTabiY2NtUlMqjNq1CgeffRR3N3dyc/P56uvvuLDDz/kpZdeYvz48axevZrr169b7WMw\n", "GNi8eTMAY8aMsfrF7evrS1JSUt2c3E1atGhR5XkFBgby9NNPA/DTTz/Z3X/kyJFVJpGff/45AC+8\n", "8IJN/+3bt2fIkCEYjUY2bdqkbP/pp5+4cuUKzZs3tylT6dSpE4888ojjJyaEuGupVCqmT59OVFQU\n", "JpOJvXv3smrVKmbNmsWYMWOYPn06O3bsqHXf3bp1s7ny4e3tTUJCAu7u7uzatcvu/v3797dKuAAG\n", "DhxIo0aNOHfunFW1wJEjR8jNzcXX15eEhASr5dLDw8MZMmSI3eN06tSpyqQxOjqaP/3pTxiNRn7+\n", "+ecq9/X09GTs2LE2CZczYtStLOVyOp1O2abT6diwYQMqlYpXXnnFJpF89NFHue+++zh//jx79+5V\n", "tluSqa1bt9ocZ9++fRQXFxMVFeXw1bvY2Ngqbyno3r07Go0GnU7HwYMHHerrdgwGAxs3bgRufC8s\n", "CRfc+FlMSkpCpVIpV9KqMmbMGKurWZ6enkplTl2NU/w+yZUu8ZvNnDnTZpuHhwf/8z//w7PPPluj\n", "vjw9PRkzZgyPP/44u3bt4ujRo2i1Ws6dO8fly5dZs2YN27Zt45133lFKYU6dOkV5eTnBwcFV3pfV\n", "smVLWrZsyZkzZ2p3gnYYjUYOHjzIsWPHKC4upqKiArPZTFlZGQDnzp2zu2+vXr1stpWUlHDy5El8\n", "fX3tLu3bsWNHAKvymMOHDwPwwAMPVFm6ERcXx9dff+34iQkh7lotWrRg9uzZHDt2jD179nD8+HFy\n", "c3PR6XQcO3aMY8eOsW/fvhpfXbDQarXK/VLl5eXKwhuenp6UlJRQWlpa5RW1bt262Wzz9PTknnvu\n", "ITc3l+LiYiVZsvxO7N69e5Xl7XFxcVb30N6qrKyMPXv2KOddWVkJ/N/v9MLCwir3a926dZUJm7Ni\n", "1M0sn/PNMeLgwYNUVFTQuXNnu/dRd+rUib1793L8+HG6d+8OQJcuXQgKCiI3N5czZ87QsmVLpb2l\n", "tNDRq1wWJSUl7NmzhzNnzlBaWorRaARQyuOri6c1cfP3onPnzjbvN27cmO7du7N9+3YOHTpkVboI\n", "N/62qeq+vrCwMODGY2+EsEeSLvGb3fycruLiYg4fPkxFRQUGg8FqhnH37t1VzhA+8cQTyi8si6Cg\n", "IB599FEeffRRAC5dusT333/PmjVruHjxIsuWLeP//b//p7wHVFtH3bRp0zoNaAUFBXzwwQcUFBTY\n", "bWNJvm4VEBCAl5eXzXZLOWZpaSkjRoyo9vg3l8bc7vxlpUchRE21a9eOdu3aATf+YD9+/Diff/45\n", "OTk5ZGZm0q1bN5uSvuro9XoWLFhw2zI6e0mXvWW/LUmVwWBQtt3ud6Kvry+NGjWq8nf0zz//TEpK\n", "ik1Fxc3s/W63N0ZnxKhbXbt2DcBqgQhLzNmzZ89tF/O4Oea4ubnRt29f1qxZw9atW5XJ1evXr/PL\n", "L7/g6elJnz59HB7bpk2bWLlypdX38FZ1teri5cuXgeq/F5b3qkqggoKCbK7WAsrPbEVFRV0MU/xO\n", "SdIlfrNbn9NVXFzMe++9R1ZWFiqViueffx6A06dPk5WVZbP/Qw89ZJN03apJkyYMHToUX19fVq5c\n", "SU5ODhUVFVUmL3XJ3vLH8+fPp6CgQFl1q0WLFvj6+uLm5kZhYSGTJ0+2u6+95WstKzdaauarU5vV\n", "HYUQojbc3Nxo164db7zxBm+++SZarZbdu3fXKOn69NNPyc7OJjw8nKeffpqoqCjUarXyB+wLL7xA\n", "cXGx3f2r+kO3rl26dIl//OMfyv2xvXv3pmnTpkq54ObNm1m6dGmNf7e7glOnTgFYXZWyxJywsDCb\n", "FQJvdWu5e79+/ZTKk2eeeQZ3d3d27NhBZWUl999/P35+fg6N68SJE6SmpuLp6cmoUaPo3r07TZo0\n", "UT7L1atXs2bNGofPs77JA5fFbyFJl6hzgYGBvPLKK0ydOpWNGzfy4IMP0q5dO4YOHcrQoUN/U9+W\n", "mn6j0cj169cJDAykSZMmwI3lge2x957lQY16vd7h/c6ePUteXh4BAQFMnTrV5pewvdKT27HMknp6\n", "etaodMdSh2+ZtbxVdZ+LEEI4yt3dnZiYGLRarXLlxFE7d+4E4JVXXrG510ev11ebcNWUJSbY+514\n", "/fr1Kq9WZWdnU1FRwf3332+1IJFFbUvcfkuMqguVlZXK539z6bol5rRs2bLG5aKhoaG0a9eOY8eO\n", "sXfvXrp3767c41WT0kLLfXz9+/evclXI2sZTe24XLwHOnz9v1VaIuiILaYh6ERYWxh//+EcA/vOf\n", "/9RZv5bA5OXlpSxVGxUVhbe3N5cuXeLIkSM2+5w5c8Zu2Ybll+rZs2dt3isuLkar1dpst9yIHBQU\n", "VOWsl2XlppoKDg6mZcuWlJSUKPckOCImJgaAHTt2KDOXN/vxxx9rNR4hhLiVZcEKSyJhYZnAstyL\n", "cyvL782q/pCt7e9MeyyVF9nZ2VUmV/Z+J1rGWFWZYEVFRbULfVTnt8SouvDpp59SXFyMv7+/1XMb\n", "O3fujIeHBzk5ObUq37MkV5mZmcoS/oGBgVXe82RPdT8XJSUlHDhwoMr9LD9vVcW86kRFRaFSqbh8\n", "+XKVi15cu3ZNKYG1xFYh6ookXaLeDB48GJVKxbFjx/jll19u2/769eu8/vrrbNu2rcra7jNnzvDv\n", "f/8buLEQheV+MW9vb+W5Vf/+97+tas9LS0tZtmyZ3WNabqT97rvvrGZadTodixcvpry83Gaf0NBQ\n", "3NzcOHPmjE0A/eGHH9i+ffttz9UeS139woULycnJsXnfZDJx8OBBq4U0NBoNgYGBnDt3Tln90OLo\n", "0aNWKx0KIYQ93377LUuWLOHEiRM27xmNRjZv3qysynrr4zAsfzTfuiS5heXqlmXlOIuTJ0+yevXq\n", "3zz2m3Xs2JHIyEhKS0tZvny5VSKYn59PRkZGtWP86aeflAdFw40rRR9//HG1V0eq81ti1G9RUFDA\n", "hx9+yNdff427uzsTJ060WlkxICCAP/3pT5SWljJ37twq71HW6/Vs27bN6vOweOCBB/D29iY7O5v1\n", "69cDVT+bqzotWrQAICsry6ripKysjCVLlthNBm/382aPt7e3sqLv8uXLreK+wWBg6dKllJeXW93T\n", "KERdkfJCUW/UajWPPfYYn3/+OatXr6Z79+63rYfOzc1l4cKFeHl50bp1a4KDgzEajVy4cIHc3Fzg\n", "RinE6NGjrfYbPny4stLhSy+9RExMDO7u7hw6dAh/f3+6d+9e5Q3cf/jDH1i/fj25ublMmTKFdu3a\n", "YTQaOXnyJMHBwfTs2dNm8Q+1Ws0f//hHvvvuO2bOnEnHjh0JDAzkzJkz5Ofn88QTT/Dll1/W6jPr\n", "0aMHo0aNYtWqVbz33nuEhoYSGhqKSqWiuLiY3NxcSktLSUpKUmrwvb29mTRpEnPmzOG///0vP/30\n", "E5GRkRQXF3PkyBH+/Oc/y+qFQojbMplMZGZmkpmZSWBgIK1atcLf3x+dTsfp06eVP1AHDRpks8Jq\n", "r169OHz4MAsWLKBLly7KPT3PPPMM/v7+DBkyhOTkZFavXs327dtp0aIFV65c4ddff6V3794cPXrU\n", "atn33+rFF1/knXfeITMzk0OHDtG2bVuuX7/O4cOH6d69OydPnrQ5Xo8ePYiMjCQ3N5eXXnqJTp06\n", "4eXlxa+//oper6d///5s2LChVuOpbYxyxNGjR1m8eDFw43t4/fp1zp49qySJISEhjB8/nnvvvddm\n", "32eeeYYrV66wc+dOXn31VVq1akWzZs1wc3PjwoULnD59msrKSpKTk5UFsywaNWpEz5492b59O1u2\n", "bAFqvmrhQw89xDfffINWq2XSpEm0b98es9nMkSNH8PLy4qGHHuKHH36w2a9du3YEBgai1Wp54403\n", "aNGiBZ6ennTo0OG2Y3jqqac4efIkhw8fVr4X3t7eHD16VFkFc9KkSTU6DyEcIUmXqFcDBgxg48aN\n", "5Ofn8+OPPxIXF2e3rZ+fH++99x45OTkcOXKEoqIiTp8+jdFoxN/fny5dunD//ffz0EMPWa2KCDee\n", "AfPOO+/w3//+lx07drBv3z7UajV/+MMfGDFiBCtWrKjymJ6enkyfPp3PPvuM7OxsDhw4QGBgIP36\n", "9WPYsGF8/PHHVe43ZswYWrVqxcaNGzl16hQeHh5ERUXx7LPPEhYWVuuky/KZde7cmQ0bNnD48GEO\n", "HjyIu7s7QUFBdOrUie7du9ssOX/vvffy3nvvkZaWxtGjR/nll18ICwsjMTGR//mf/5GkSwhxWw8/\n", "/DBNmzblwIEDnDx5kry8PK5evYqnpydNmjSha9euxMfH2yyjDTee6VRWVsaPP/7Inj17lGXWn3zy\n", "Sfz9/dFoNMyYMYMvvviC06dPU1RURPPmzRk9ejR/+tOfePHFF2s1ZnsTeREREcyePZv09HT279/P\n", "L7/8QtOmTRk6dCgDBw6s8o9qd3d3Zs6cSUZGBj///DMHDhzAz8+PmJgYhg4dave5TY64XYxauXJl\n", "jfu0nPv58+eV+5A8PT3x9fWlSZMmPPzww9x333306NHD7tUnDw8PJk+ezIMPPsj333/PiRMnyMvL\n", "Q6VSERQURJ8+fejZs2eVz5WEG0mTpbqjJs/msvDz82POnDl89tlnHDhwgL179xIQEIBGo2HYsGF2\n", "KzU8PT158803Wb16NcePH0er1WI2mzGZTErSZe9nw8vLi7/+9a9s2rSJrKwsDh8+jNFopGnTpvTt\n", "25eBAwdarfIoRF1xM9tbhkeI35HFixeTlZXFhAkT6Nu3r7OHI8Tvhk6nIyUlhZycHNRqNSNGjKhy\n", "ueiKigpWrVrFzp07MRgM9O7dmzFjxigTKJcuXWLp0qUcO3YMT09PNBoNo0ePviOr1gkhhBD17a6O\n", "ZocOHXL2EGpMxnxnyJjvnIY47oY45vqSmpqKl5cXqampTJo0idTU1Crvs1izZg1arZb58+fzj3/8\n", "A61Wa3VvzfLly1Gr1Xz00Ue8//77HD58mO++++5OnopLaIg/WzLmO6Mhjhka5rhlzKI+SNLVwMiY\n", "7wwZ853TEMfdEMdcH/R6Pbt372b48OH4+PjQoUMHevToUeXz+Pbs2UP//v3x8/NDrVbTv39/q3s1\n", "8vLyeOCBB/D09FRWQMvLy7uTp+MSGuLPloz5zmiIY4aGOW4Zs6gPck+XuCvIAw2FqHuFhYV4eHjQ\n", "vHlzZVtkZKTd4H9zNbvZbOby5cuUlZXRqFEjunbtyrZt2+jUqRM6nY69e/dW+awkIYQQojqOlr0D\n", "rF+/nnXr1lFeXo5GoyEpKUl5JIFFYWEhU6dORaPRKPeDFhUVMWnSJHx8fJR2jz/+OIMHD7Y7Lkm6\n", "xF1hwoQJNX74oxCienq9nkaNGlltU6lUVT5svGvXrnzzzTfExMRgMpmUVeDKy8tp1KgRw4YN43//\n", "93957rnnMJlM9O3bl549e96R8xBCCPH7cXPZu1arZc6cOURGRtos9LJv3z7Wrl3LjBkzCAoKYt68\n", "eaSnpzNy5EirdsuWLSM6OrrKCfwVK1Y4PLFvdyGNmjwE8P7773e4rRBCiN8HrVbL22+/bfUA9HXr\n", "1nHkyBFef/11q7YGg4FPPvmE3bt34+XlRXx8POnp6Xz66aeYzWbefPNNevXqxWOPPYZer2fJkiWE\n", "hYXxzDPPKH1IXBJCCFEdvV5PQkIC8+fPV6owFi1aRHBwsE0y9Y9//IN77rlHqao4ePAgCxYs4KOP\n", "PlLabN++nd27dxMeHs65c+dsrnStXr3a4QWf7F7pmj9/vsMnmJaW5nDb2mhoq1ft37/f2UOosU6d\n", "Ojl7CHeNhljq2BDH3BAVFhYSFhZmtc2ZC8y6ubmRnp6uvI6JiSEmJkZ5HRoaitFo5Ny5c0pwO336\n", "NBERETZ9eXt7k5CQQEJCAgCbN2+mTZs2AFy7do1Tp07x9ttv4+npib+/P/369SMtLc0q6XKluNTQ\n", "Fv5t1aqVs4dQY4WFhc4eQq3cWprUEDS0n2dAeSxBQ9IQY2m/fv1slu53dlyqTk3K3vPz860ewdOq\n", "VSuuXr2KTqfD39+f0tJS0tPTmTFjBps3b67yeBMmTMDNzY3OnTszatQoGjdubHdsdn8z1HfAEkII\n", "cXvODm7Dhg2z+75KpaJXr16kpaUxbtw4tFot2dnZzJo1y6bt5cuXAQgKCuL48eNkZGQwfvx4ABo3\n", "bkxgYCAbN27kscceo6ysjMzMTJtEQeKSEEI4n7PjUnVqUvau1+vx9fVVXlv20+v1+Pv7k5aWRnx8\n", "PMHBwTbHVavVzJ49m8jISK5du8ayZctYsGABb731lt2xNbzpGCGEuIuYTCanHduRKoPExERSUlJI\n", "TExErVaTlJREeHg4Fy9eZMqUKSQnJ9OkSRPOnz/PokWLKCkpISQkhKeffpouXboAN4Lo1KlT+c9/\n", "/sOaNWtwd3enc+fOjB49up7PUAghRE05Oy5VV4GhUqkoKyuz2qe0tBSVSmXT161tS0tLle25ubkc\n", "PHiQuXPnAraJpkqlIioqCoCAgAASEhJ44YUX0Ov1VR4LapB0VVZWcuLECS5dumRzSVceNiuEEPXD\n", "mcHNEf7+/kybNs1me0hICCtXrlRed+zYkcWLF9vtp23btrz77rs1OrbEJSGEuPOcHZeqq8CoSdl7\n", "REQEubm5aDQapV1AQAD+/v5kZmZSVFSkLMKm1+sxmUycPXuWOXPm2D1+dVcBHUq6zp49y9y5cykq\n", "KsJsNuPu7o7JZMLd3R0vLy8JbkIIUU8a4r0Wd4LEJSGEcA5Xjks1KXuPi4tjyZIl9OnTh8DAQDIy\n", "MujXrx8AjzzyiLLMvNls5quvvuLChQskJSUBcOLECXx9fWnevDnXr19n+fLlxMTE2JQ23syhpOvf\n", "//43rVu35v3332fs2LG8//77lJaWsnTpUnmOihBC1CNnzyi6KolLQgjhHK4elxwte4+NjWXgwIHM\n", "nDkTg8GARqNRrqJ5e3vj7e2t9KlSqfD29lYWyjh//jyrV6/m6tWr+Pr60qVLF15++eVqx+VQ0nXy\n", "5EneeecdVCoVbm5umEwmoqKiGDVqFB9//DFdu3at7ecihBCiGq48o+hMEpeEEMI5XD0uOVr2DjBg\n", "wAAGDBhw2z6HDh1q9bp379707t27RuNyKOkym81KtqdWq7l8+TJhYWEEBwc32CVdhRCiIXD1GUVn\n", "kbgkhBDOIXGpdhxKuiIiIjhz5gzNmzcnOjqatWvX4u7uzubNm63WwRdCCFG3XH1G0VkkLgkhhHNI\n", "XKodh546PHjwYOUDfuqpp7h48SIzZ84kJyeHMWPG1OsAhRDibmYymZz25cokLgkhhHNIXKodh650\n", "xcbGKv9B9Y3VAAAgAElEQVRu3rw5ycnJXLt2DT8/P4ee4yKEEKJ2XH1GUafTkZKSQk5ODmq1mhEj\n", "RigrPt2soqKCVatWsXPnTgwGA71792bMmDF4eHgobbZv384XX3zBxYsXCQwMZOLEiXTo0KHK40pc\n", "EkII53D1uOSqav1wZMvqHUIIIeqPq8/spaam4uXlRWpqKlqtljlz5hAZGUl4eLhVuzVr1qDVapk/\n", "fz5Go5G5c+eSkZGhrBSVk5PDp59+yiuvvEJ0dDRXrlypcWCXuCSEEPXP1eOSq7KbdM2ZM4eXXnoJ\n", "X19f5syZg5ubW5UB0M3Njddff71eBymEEHcrVw5uer2e3bt3M3/+fHx8fOjQoQM9evQgKyuLkSNH\n", "WrXds2cPgwYNws/PD4D+/fuzatUqJelKT0/nySefJDo6GoCgoCCb40lcEkII53PluOTK7CZdjRs3\n", "xs3Nzerf9oKbEEKI+uHKZRyFhYV4eHhYLVwRGRnJoUOHqmx/87mYzWYuX75MWVkZPj4+nDp1ih49\n", "evDSSy9RUVFBz549eeaZZ6yekyJxSQghnM+V4xI4XvYOsH79etatW0d5eTkajYakpCQ8Pa3To8LC\n", "QqZOnYpGo2HSpEnK9gMHDrBs2TIuXbpEdHQ0EydOJCQkxO647CZdEydOrPLfQggh7hxXnlHU6/U0\n", "atTIaptKpUKv19u07dq1K9988w0xMTGYTCY2bNgAQHl5OWVlZRiNRnbt2sW7776Lh4cH77//Pv/9\n", "73+tHnQscUkIIZzPleMSOF72vm/fPtauXcuMGTMICgpi3rx5pKen21RqLFu2jOjoaKsJvZKSEv7+\n", "978zbtw4evTowWeffUZycjLvvfee3XHJ3cZCCOHCzGaz075uR6VSUVZWZrWttLQUlUpl03bw4MG0\n", "bt2a1157jenTp9OrVy88PDwIDAxUrmb179+fwMBAGjduzIABA9i7d2/dfIhCCCHqjCvHJUvZ+/Dh\n", "w23K3m+VmZlJfHw84eHh+Pn5MWTIELZu3WrVZvv27fj5+XHvvfdaHX/37t1ERESg0Wjw9PRk6NCh\n", "nD59moKCArtjc2ghjYqKCjZu3MiBAwcoKSmxOenZs2c70g3ffvstW7duJS8vj969ezNhwgSH9hNC\n", "iLuVs2cU09PTlX/HxMQQExOjvA4NDcVoNHLu3DmlxPD06dNERETY9OPt7U1CQgIJCQkAbN68mTZt\n", "2gDg7+9PcHBwjcYlcUkIIZzD2XGpOjUpe8/Pz6dXr17K61atWnH16lV0Oh3+/v6UlpaSnp7OjBkz\n", "2Lx5s9W+eXl5tGrVSnnt4+ND8+bNycvLIywsrMqxOZR0ffTRR/zyyy/07NmT8PBwq8trNamdDw4O\n", "ZsiQIezfvx+DweDwfkIIcbdydu28ZaGLqqhUKnr16kVaWhrjxo1Dq9WSnZ3NrFmzbNpevnwZuLFA\n", "xvHjx8nIyGD8+PHK+w899BAbNmwgNjYWd3d3vv76a7p372732BKXhBDCOZwdl6pTk7J3vV6Pr6+v\n", "8tqyn16vx9/fn7S0NOLj4wkODraJK+Xl5ajVaqttjRo1qvI4Fg4lXT///DPTpk2zmuGsDUs2efLk\n", "SSUACyGEsM+VZxQBEhMTSUlJITExEbVaTVJSEuHh4Vy8eJEpU6aQnJxMkyZNOH/+PIsWLaKkpISQ\n", "kBCefvppunTpovQzZMgQSkpKePnll/Hy8uKBBx5g8ODBdo8rcUkIIZzD2XGpugqMmpS939q2tLRU\n", "2Z6bm8vBgweZO3cuYJtoqlQqpf3N+9+a8N3MoaQrICBAnn8ihBBO4MozinCjNHDatGk220NCQli5\n", "cqXyumPHjixevNhuPx4eHiQmJpKYmOjQcSUuCSGEczg7LlVXgVGTsveIiAhyc3PRaDRKu4CAAPz9\n", "/cnMzKSoqEgpOdfr9ZhMJs6ePcucOXMIDw8nMzNT6Uuv13P+/HmbxTpu5tBCGk899RSrV69Gp9M5\n", "0lwIIUQdMZlMTvtyZRKXhBDCOVw5Lt1c9l5eXs7Ro0fJzs4mLi7Opm1cXBzff/89+fn56HQ6MjIy\n", "6NevHwCPPPIIixYt4oMPPuD999/nkUceoVu3brz11lvAjSqJvLw8du3ahcFg4IsvviAyMtLu/Vzg\n", "4JWu2NhYtmzZQmJiIoGBgXh4eCjvubm5sWjRIke6EUIIUUOunvw4i8QlIYRwDlePS46WvcfGxjJw\n", "4EBmzpyJwWBAo9EoV9G8vb2tnhOpUqnw9vZWKizUajWvvvoqH3/8MQsXLqRt27ZMnjy52nE5lHQt\n", "WrSI/Px8/vKXvxAQEGD1Xl0/hPLQoUNWK4xUdwlRCCF+jyz16sOGDXN6GYerkrgkhBB3TkOKS46W\n", "vQMMGDCAAQMG3LbPoUOH2mzr3LkzycnJDo/LoaTrwIEDTJ8+nXbt2jnccVVMJhOVlZXKJcKKigo8\n", "PDxwd/+/Ksdbb4gTQoi7zc1/1Lv6jKKzSFwSQog7R+LSb+dQ0tWkSRO8vLx+88G++OILMjIylNc/\n", "/vgjQ4cO5cknn/zNfQshxO+Rq88oOovEJSGEcA6JS7XjUNI1evRoVq1axfPPP09oaGitDzZs2DAp\n", "yxBCiBqQGcWqSVwSQgjnkLhUOw4lXfPnz6eiooLJkyfj6elpc8PyihUr6m2AQghxN3P1GUWdTkdK\n", "Sgo5OTmo1WpGjBhBnz59bNpVVFSwatUqdu7cicFgoHfv3owZM8YqngAUFhYydepUNBoNkyZNsntc\n", "iUtCCOEcrh6XXJVDSVdCQkJ9j0MIIUQVXH1GMTU1FS8vL1JTU9FqtcyZM4fIyEibZ5WsWbMGrVbL\n", "/PnzMRqNzJ07l4yMDJurTMuWLSM6Ovq2i2FIXBJCCOdw9bjkqhxKuixr1gshhLizXHlGUa/Xs3v3\n", "bubPn4+Pjw8dOnSgR48eZGVlMXLkSKu2e/bsYdCgQfj5+QHQv39/Vq1aZZV0bd++HT8/P8LDwzl3\n", "7ly1x5a4JIQQzuHKccmVOZR0ARgMBrZt20Z+fj5ubm6Eh4fTp0+fOrmRWQghRNVceUaxsLAQDw8P\n", "mjdvrmyLjIy0Wl79ZjcHarPZzOXLlykrK6NRo0aUlpaSnp7OjBkz2Lx5s0PHl7gkhBB3nivHJXC8\n", "7B1g/fr1rFu3jvLycjQaDUlJSXh63kiPFixYwMGDBykvL6dx48Y8/PDDDB48GICioiImTZqEj4+P\n", "0tfjjz+uvF8Vh5Ku/Px83nvvPcrKymjZsiVms5ktW7bw+eef8+abb9qUkQghhKgbrhzc9Ho9jRo1\n", "stqmUqnQ6/U2bbt27co333xDTEwMJpOJDRs2AFBeXk6jRo1IS0sjPj6e4OBgh56zJXFJCCGcw5Xj\n", "Ejhe9r5v3z7Wrl3LjBkzCAoKYt68eaSnpyuVGk888QTjxo3D29ubgoICZsyYQVRUFLGxsUofK1as\n", "cPjZkA4lXcuXL6d169a8+OKL+Pr6AlBaWsrChQv597//zV//+leHDiaEEKJmXLmMQ6VSUVZWZrWt\n", "tLQUlUpl03bw4MGUlpby2muv4eXlRXx8PLm5uQQGBpKbm8vBgweZO3cu4Ng5S1wSQgjncOW4VJOy\n", "98zMTOLj45VkbMiQISxYsEBpFxERYdXew8MDtVpttc1sNtdt0vXrr7/yt7/9TQlsAL6+vowYMYI3\n", "33zToQMJIYSoOWfPKKanpyv/vvUhwaGhoRiNRs6dO6eUGJ4+fdomUAF4e3uTkJCgLICxefNm2rRp\n", "A8ChQ4coKipiwoQJwI2gaTKZOHv2LHPmzKlyXBKXhBDCOZwdl6pTk7L3/Px8evXqpbxu1aoVV69e\n", "RafT4e/vD9y4apaZmUlFRQUJCQlERUVZ9TFhwgTc3Nzo3Lkzo0aNonHjxnbH5lDS5eXlRWlpqc32\n", "0tJSqZ0XQoh65OwZxeqeYaVSqejVqxdpaWmMGzcOrVZLdnY2s2bNsml7+fJlAIKCgjh+/DgZGRmM\n", "Hz8egEceeUSptzebzXz11VdcuHCBpKQku8eWuCSEEM7h7LhUnZqUvev1equJO8t+er1eSboSExN5\n", "/vnnOXz4MPPnzycqKoro6GjUajWzZ88mMjKSa9eusWzZMhYsWMBbb71ld2zujpxA9+7d+de//sXR\n", "o0cxmUyYTCaOHDnCv/71L3r06OFIF0IIIWrB8jvXGV+OSExMxGAwkJiYyMKFC0lKSiI8PJyLFy/y\n", "7LPPcunSJQDOnz/P9OnTefbZZ0lJSeHpp5+mS5cuwI2rYAEBAQQEBBAYGIhKpcLb27vaGUOJS0II\n", "4RyuHJdqUvZ+a1vLRN6tbd3c3IiJiUGj0bBt2zalTVRUFO7u7gQEBJCQkEBOTk6VyZ2FQ1e6Ro8e\n", "zZIlS5gxY4ZSt2g2m+nRowejR492pAshhBC14MozigD+/v5MmzbNZntISAgrV65UXnfs2JHFixc7\n", "1OfQoUNv20bikhBCOIez41Jdlb1HRESQm5uLRqNR2gUEBChXuW5lNBqrnQyE6j8bh5Iuf39/Xnvt\n", "NQoLCzl79iwALVq0IDQ01JHdhRBC1JIr1847k8QlIYRwDmfHpboqe4+Li2PJkiX06dOHwMBAMjIy\n", "lGdAlpSUcODAAbp37463tzc5OTns3LmT6dOnA3DixAl8fX1p3rw5169fZ/ny5cTExNiUNt7M4ed0\n", "wY3sUQKaEELcOc6eUXR1EpeEEOLOcvW4lJiYSEpKComJiajVaquy9ylTppCcnEyTJk2IjY1l4MCB\n", "zJw5E4PBgEajsUroNm3aRGpqKmazmdDQUCZNmkR0dDRwo2R+9erVXL16FV9fX7p06cLLL79c7bjc\n", "zA58cmazmR07dnDgwAFKSkqUD9uyTOLrr7/+Wz6b23J3d+jWM5exf/9+Zw+hxjp16uTsIdw1HF1a\n", "1JU0xDE3RIWFhYSFhVlts9SPO4O9h0m6AmfHJVf/o+NWrVq1cvYQaqywsNDZQ6gVy4NVG5KG9vMM\n", "UFlZ6ewh1FhDjKX9+vVj06ZNVtskLtWOQ78ZPvnkE+WhloGBgVY/NA3xB0gIIRoKZ5dxuCqJS0II\n", "4RwSl2rHoaQrKyuLl156iT/84Q/1PR4hhBA3aYgz0HeCxCUhhHAOiUu141DSZTKZaN26dX2PRQgh\n", "xC1kRrFqEpeEEMI5JC7VjkM3S8XHx5OVlVXfYxFCCHELs9nstC9XJnFJCCGcQ+JS7Th0pau0tJRt\n", "27Zx4MABWrZsiYeHh9X7CQkJ9TI4IYS427n6jKJOpyMlJYWcnBzUajUjRoyo8kbniooKVq1axc6d\n", "OzEYDPTu3ZsxY8bg4eFBZWUlS5cu5eDBg+h0Ou655x5GjhxJbGys3eNKXBJCCOdw9bjkqhxKuvLz\n", "84mMjASgoKBA2W5ZJaq+NbTVC9u3b+/sIdRYQ/uMLRrirIfRaHT2EGrs1j9oG4Lfy2IKrv4znpqa\n", "ipeXF6mpqWi1WubMmUNkZCTh4eFW7dasWYNWq2X+/PkYjUbmzp1LRkYGw4YNw2g0EhISwsyZMwkJ\n", "CWHPnj0kJyczb948mjZtWuVxnR2XGtr/4wsXLjh7CDXm6j/79pSXlzt7CDV26//XhuDKlSvOHkKN\n", "6fV6Zw+hTjTU/5vO5lDS9c4779TzMIQQQlTFlWcU9Xo9u3fvZv78+fj4+NChQwd69OhBVlYWI0eO\n", "tGq7Z88eBg0ahJ+fHwD9+/dn1apVDBs2DB8fH4YOHaq07datG82aNUOr1dpNuiQuCSGEc7hyXALH\n", "KzAA1q9fz7p16ygvL0ej0ZCUlKQ89mHBggUcPHiQ8vJyGjduzMMPP8zgwYOVfQ8cOMCyZcu4dOkS\n", "0dHRTJw4kZCQELvjcujyxtdff821a9dqcr5CCCHqgCvXzhcWFuLh4UHz5s2VbZGRkeTl5dk9l5v/\n", "ffnyZcrKymzaFRcXU1BQUO3su8QlIYRwDleOS2BdgTFp0iRSU1PJz8+3abdv3z7Wrl3L22+/zZIl\n", "SygqKiI9PV15/4knnmDRokWsWLGCN998kw0bNrBv3z4ASkpK+Pvf/87w4cNZvnw5bdq0ITk5udpx\n", "OZR0rV+/nhdeeIH58+crBxNCCFH/TCaT075uR6/X06hRI6ttKpWqyhKarl278s0331BSUkJxcTEb\n", "NmwAbEuxKisrWbhwIf369bN5UPTNJC4JIYRzuHpc2r17N8OHD7epwLhVZmYm8fHxhIeH4+fnx5Ah\n", "Q9i6davyfkREBN7e3sprDw8P1Go1ALt37yYiIgKNRoOnpydDhw7l9OnTVuXut3KovHDx4sXk5OTw\n", "ww8/8MEHHxAQEEDfvn156KGHaNasmSNdCCGEqAVnl3HcPOsXExNDTEyM8lqlUtlcqSotLUWlUtn0\n", "M3jwYEpLS3nttdfw8vIiPj6e3NxcAgMDlTYmk4lFixbh5eXF888/X+24JC4JIYRzODsuVcdeBcah\n", "Q4ds2ubn59OrVy/ldatWrbh69So6nQ5/f3/gxlWzzMxMKioqSEhIICoqCoC8vDxatWql7Ovj40Pz\n", "5s3Jy8uzO2HoUNLl7u5ObGwssbGx6HQ6fvzxR7Zu3cqXX35JTEwMDz30EA888ECDXYxBCCFclbNv\n", "WB42bJjd90JDQzEajZw7d04JcKdPnyYiIsKmrbe3NwkJCcqqgps3b6ZNmzbK+2azmX/+85+UlJTw\n", "xhtv3DaeSFwSQgjncHZcqk5NKjD0ej2+vr7Ka8t+er1eSboSExN5/vnnOXz4MPPnzycqKoro6GjK\n", "y8uVq14371/dYik1jkb+/v60bt2aVq1a4e7uTlFRER9//DETJ04kJyenpt0JIYSohivXzqtUKnr1\n", "6kVaWhrl5eUcPXqU7Oxs4uLibNpevnyZy5cvYzabOXbsGBkZGVaLZyxdupSzZ88qV8JqQuKSEELc\n", "Oa4elxytwLi1bWlpqbL9Zm5ubsTExKDRaNi2bZvSxtL+5v1vTfhu5tCVLrhxY/PWrVvZunUrFy5c\n", "4P777+ett94iJiYGg8FARkYG//znP1myZImjXQohhLgNVy7jgBuzgCkpKSQmJqJWq0lKSiI8PJyL\n", "Fy8yZcoUkpOTadKkCefPn2fRokWUlJQQEhLC008/TZcuXYAby5lv2bIFLy8vxo4dq/Q9duxYuytO\n", "gcQlIYRwBmfHperK3mtSgREREUFubi4ajUZpFxAQoFzlupXRaKRx48bAjccsZGZmKu/p9XrOnz9f\n", "7QJQDiVdc+bMYf/+/YSFhfHHP/6RuLg4qwF5e3vz5z//mTVr1jjSnRBCCAc5O7jdjr+/P9OmTbPZ\n", "HhISwsqVK5XXHTt2ZPHixVX20bRpU9LS0mp0XIlLQgjhHM6OS9WVvd9cgTFu3Di0Wi3Z2dnMmjXL\n", "pm1cXBxLliyhT58+BAYGkpGRQb9+/YAbqxMeOHCA7t274+3tTU5ODjt37mT69OkA9OrVi08++YRd\n", "u3Zx33338cUXXxAZGVntAlAOJV1qtZqZM2fSrl27atssXLjQke6EEEI4yJVr551J4pIQQjiHq8cl\n", "RyswYmNjGThwIDNnzsRgMKDRaKwSuk2bNpGamorZbCY0NJRJkyYRHR0N3Igvr776Kh9//DELFy6k\n", "bdu2TJ48udpxuZld/ZMD5SFlDcWtNZ4NQU3voXAVDeDH14bRaHT2EGrMw8PD2UOoMTc3N2cPocYK\n", "CwttZsk+//xzJ40Gq3uuhLXKykpnD6FGLCUxDUlFRYWzh1Arzr4KUBvVlUS5qitXrjh7CDVW3SIL\n", "rqpfv35s2rTJapvEpdqxm8189dVXDv/RMmDAgDobkBBCiP/TECcW6ovEJSGEcD6JS7VjN+n69ttv\n", "He5EgpsQQtSPhjhrXl8kLgkhhPNJXKodu0mXvRuehRBC3DkS3P6PxCUhhHA+iUu107BulhJCiLuM\n", "lHEIIYRwJRKXasfhpCs7O5u1a9eSn5+Pm5sb4eHhDBo0iG7dutXn+IQQ4q7m6sFNp9ORkpJCTk4O\n", "arWaESNGVPlsrYqKClatWsXOnTsxGAz07t2bMWPGKIu0ONrPzSQuCSHEnefqcclVOZR0bdmyhdTU\n", "VB588EH69u0LwNGjR/nggw9ISkri4YcfrtdBCiHE3crVyzhSU1Px8vIiNTUVrVbLnDlziIyMtFkN\n", "bc2aNWi1WubPn4/RaGTu3LlkZGQoy/M62o+FxCUhhHAOV49LrsqhpGvt2rU899xzPProo8q2+Ph4\n", "oqKiWLt2rQQ3IYSoJ64c3PR6Pbt372b+/Pn4+PjQoUMHevToQVZWFiNHjrRqu2fPHgYNGoSfnx8A\n", "/fv3Z9WqVQwbNqxG/VhIXBJCCOdw5bjkyhxKui5evEhsbKzN9tjYWFauXOnQgSorK1m6dCkHDx5E\n", "p9Nxzz33MHLkyCr7FUIIcYMrl3EUFhbi4eFB8+bNlW2RkZEcOnSoyvY3n4vZbOby5cuUlZVx7ty5\n", "GvUDdROXQGKTEELUlCvHJahZufr69etZt24d5eXlaDQakpKS8PT0vG1sKCoqYtKkSfj4+Ch9Pf74\n", "4wwePNjuuBxKupo0acL+/futAiJATk4OTZs2daQLjEYjISEhzJw5k5CQEPbs2UNycjLz5s1zuA8h\n", "hLjbuPKMol6vp1GjRlbbVCpVlQ8A7dq1K9988w0xMTGYTCY2bNgAQHl5eY36saiLuAQSm4QQoqZc\n", "OS6B4+Xq+/btY+3atcyYMYOgoCDmzZtHeno6I0eOdDg2rFixwuHnRzqUdA0cOJCPP/4YrVZL+/bt\n", "gRu181lZWSQkJDh0IB8fH6unSHfr1o1mzZqh1WolsAkhhB3OnlFMT09X/h0TE0NMTIzyWqVSUVZW\n", "ZtW+tLQUlUpl08/gwYMpLS3ltddew8vLi/j4eHJzcwkMDOTKlSsO92NRF3EJJDYJIURNOTsuVacm\n", "5eqZmZnEx8crydiQIUNYsGABI0eOdDg2mM3muk26HnnkEQICAvjqq6/YvXs3AC1atGDKlCn07NnT\n", "oQPdqri4mIKCArs3SQshhHD+jKJloYuqhIaGYjQaOXfunHLF6fTp00RERNi09fb2JiEhQUmINm/e\n", "TJs2bWrcj0V9xCWQ2CSEELfj7LhUnZqUvefn59OrVy/ldatWrbh69So6nQ5/f3+rtvZiw4QJE3Bz\n", "c6Nz586MGjWKxo0b2x3bbZMuk8lEUVERoaGhzJgxA0/P3/5or8rKShYuXEi/fv0ICwv7zf0JIcTv\n", "lSsHN5VKRa9evUhLS2PcuHFotVqys7OZNWuWTdvLly8DEBQUxPHjx8nIyGD8+PE17gfqJy6BxCYh\n", "hHCEK8elmpSr6/V6fH19ldeW/fR6vVXSVVVsUKvVzJ49m8jISK5du8ayZctYsGABb731lt2xVRup\n", "ioqKeP/998nLywNu1NBPnTqVqKio252zXSaTiUWLFuHl5cXzzz9v8/6hQ4esstHqZlmFEOL3yFLS\n", "N2zYMJcObgCJiYmkpKSQmJiIWq0mKSmJ8PBwLl68yJQpU0hOTqZJkyacP3+eRYsWUVJSQkhICE8/\n", "/TRdunS5bT+3qo+4BNXHJolLQoi7nSvFpboqe7+1bWlpqbLdwl5sUKlUStwJCAggISGBF154Ab1e\n", "b7c0vtqka9WqVVRUVDBp0iS8vLxYt24dS5cuZfbs2dXtZpfZbOaf//wnJSUlvPHGG7i7u9u0ufXD\n", "E0KIu83Nf9S7cu08gL+/P9OmTbPZHhISYrWKYMeOHVm8eHGN+7lVXccluH1skrgkhLjbuVJcqquy\n", "94iICHJzc9FoNEq7gIAA5SqXI3nLrar7bKrd++jRo4wdO5Y+ffpw//33M2XKFE6dOoXBYLjtQauy\n", "dOlSzp49q9xILYQQonomk8lpX66oruMSSGwSQoiacOW4dHO5enl5OUePHiU7O5u4uDibtnFxcXz/\n", "/ffk5+ej0+nIyMigX79+yvvVxYYTJ05QUFCAyWTi2rVrLF++nJiYGJvSxptVe6WruLiYFi1aKK+b\n", "NGmCt7c3xcXFNGvW7LYnfrMLFy6wZcsWvLy8GDt2rLLdEjyFEELYctXkx1nqMi6BxCYhhKgpV49L\n", "jpa9x8bGMnDgQGbOnInBYECj0ShX0W4XG86fP8/q1au5evUqvr6+dOnShZdffrnacd327uNbl0F0\n", "c3Or1WXFpk2bkpaWVuP9hBDibmY0Gp09BJdTV3EJJDYJIURNuXpccrTsHWDAgAEMGDDApu3tYkPv\n", "3r3p3bt3jcZ126TrxRdftApw5eXlTJ06Vdnm5ubGihUranRQIYQQjnH1GUVnkLgkhBDOI3GpdqpN\n", "uizL+QohhHAOV59RvNMkLgkhhHNJXKqdapOum28mE0IIcefJjKI1iUtCCOFcEpdqp26eKCmEEKJe\n", "uHpw0+l0pKSkkJOTg1qtZsSIEXYXoPjiiy/YsmULZWVltG7dmueff155FtelS5dYunQpx44dw9PT\n", "E41Gw+jRox1aolcIIcSd4+pxyVVJ0iWEEC7M1cs4UlNT8fLyIjU1Fa1Wy5w5c4iMjLR5sPEvv/zC\n", "pk2b+N///V9CQkL47LPPWLhwIXPnzgVg+fLlqNVqPvroI3Q6HbNmzeK7776jf//+zjgtIYQQdrh6\n", "XHJVMoUohBAuzGw2O+3rdvR6Pbt372b48OH4+PjQoUMHevToQVZWlk3bvLw8OnToQLNmzXB3d+fB\n", "Bx8kPz/f6v0HHngAT09PAgMDiY2NJS8vr04/SyGEEL+dK8clVyZXuoQQwoW5chlHYWEhHh4eNG/e\n", "XNkWGRnJoUOHbNp27tyZjRs3UlhYSNOmTcnMzOS+++5T3u/atSvbtm2jU6dO6HQ69u7dy/Dhw+/I\n", "eQghhHCcK8clqFnZ+/r161m3bh3l5eVoNBqSkpLw9PSksrKSpUuXcvDgQXQ6Hffccw8jR44kNjZW\n", "2ffAgQMsW7aMS5cuER0dzcSJEwkJCbE7LrtXup566imuXr0KwJIlSygtLa3tuQshhKglk8nktK/b\n", "0ev1NGrUyGqbSqVCr9fbtI2OjqZv375MnjyZUaNGsWvXLp599lnl/WHDhpGXl8dzzz3H+PHjadOm\n", "DazyfzUAACAASURBVD179rTqQ+KSEEI4nyvHJbAue580aRKpqalWlRUW+/btY+3atbz99tssWbKE\n", "oqIi0tPTgRsllCEhIcycOZMVK1YwfPhwkpOTuXDhAgAlJSX8/e9/Z/jw4Sxfvpw2bdqQnJxc7bjs\n", "Jl3e3t6UlZUBkJmZSUVFhUMnKoQQou4YjUanfQGkp6crX7dewVKpVEqcsCgtLUWlUtmcx7fffsvB\n", "gwdJSUlh1apVDBkyhHfffReDwYDZbOa9995Do9Hwn//8h2XLlqHT6fjkk0+s+pC4JIQQzufsuFSd\n", "mpS9Z2ZmEh8fT3h4OH5+fgwZMoStW7cC4OPjw9ChQ5UrV926daNZs2ZotVoAdu/eTUREBBqNBk9P\n", "T4YOHcrp06cpKCiwOza75YXt27dn3rx5tG7dGrhxk7O3t3eVbSdMmHDbD0EIIUTNObuMY9iwYXbf\n", "Cw0NxWg0cu7cOaXE8PTp00RERNi03bdvH7179yY4OBi4sfT7ihUryM/PJyQkhFOnTvH222/j6emJ\n", "v78//fr1Iy0tjWeeeUbpQ+KSEEI4n7PjUnVqUvaen59Pr169lNetWrXi6tWr6HQ6/P39rdoWFxdT\n", "UFCgLBKVl5dHq1atlPd9fHxo3rw5eXl5hIWFVTk2u1e6XnzxRbp06cL169eBG/WRJSUlVX4JIYSo\n", "H648o6hSqejVqxdpaWmUl5dz9OhRsrOziYuLs2nbsmVLdu7cydWrVzGZTGRlZWE0GmnevDmNGzcm\n", "MDCQjRs3YjKZuH79OpmZmVYBDSQuCSGEK3DluFSTsne9Xo+vr6/y2rLfrW0rKytZuHAh/fr1UxKq\n", "8vJyq30t+1d1HAu7V7oCAwOVevuJEyfy0ksvoVar7XYkhBCi7rnyjCJAYmIiKSkpJCYmolarSUpK\n", "Ijw8nIsXLzJlyhSSk5Np0qQJQ4YMYfny5UydOhWDwUBoaCivvvqqErSmTp3Kf/7zH9asWYO7uzud\n", "O3dm9OjRVseSuCSEEM7n7Lhkue8KICYmhpiYGOV1Tcreb21ruU/45rYmk4lFixbh5eXF888/b7Xv\n", "rfcVl5aW2iR8N3No9cLFixc70kwIIUQdc3Zwux1/f3+mTZtmsz0kJISVK1cqr318fBg3bpzdftq2\n", "bcu7777r8HElLgkhhHM4Oy7VVdl7REQEubm5aDQapV1AQIBSWmg2m/nnP/9JSUkJb7zxBu7u/1cg\n", "GB4eTmZmpvJar9dz/vx5m2dU3szhJeOzs7NZu3Yt+fn5uLm5ER4ezqBBg+jWrZujXQghhKghR8op\n", "7lYSl4QQ4s5z5bh0c9n7uHHj0Gq1ZGdnM2vWLJu2cXFxLFmyhD59+hAYGEhGRgb9+vVT3l+6dCln\n", "z55l+vTpeHl5We3bq1cvPvnkE3bt2sV9993HF198QWRkpN37ucDBpGvLli2kpqby4IMP0rdvXwCO\n", "Hj3KBx98QFJSEg8//LAj3QghhKihhv4wyPoicUkIIZzD1eOSo2XvsbGxDBw4kJkzZ2IwGNBoNMpV\n", "tAsXLrBlyxa8vLwYO3as0vfYsWPp06cParWaV199lY8//piFCxfStm1bJk+eXO24HEq61q5dy3PP\n", "Pcejjz6qbIuPjycqKoq1a9dKcBNCiHri7DIOVyVxSQghnMPV45KjZe8AAwYMYMCAATZtmzZtSlpa\n", "WrXH6dy5822fzXUzu6sX3uzixYtWT2C2iI2NpaioyOGDCSGEqBlXfwils0hcEkII55C4VDsOXelq\n", "0qTJ/2/vzqOiuu/+gb+HdUA2cUDQYRuxUqnGFWlQsCFJm8ckTYIaxXhaWZSiZnFpkzYGNa6NlVM1\n", "kkcxRlMXCKTHLTUufYRofbSiiLiboDIsglUhiMPAML8/eLg/h2GZQYd7B96vczjx3rlz72cmnHnz\n", "vfO534vz588bzHkPAAUFBfDy8rJIYdZMJpOJXYLZrLFmwDrrbm86UaKWpNw7Lyaxc+nxC6qtga2t\n", "rdglmK2urk7sEjpF6q1XrXF1dRW7BLM1NDSIXYLZrO1zA2iaBKkl5lLnmDToevXVV/H555+jqKgI\n", "gwYNAtDUO5+bm4u4uDiLFkhE1JNZ+5k9S2EuERGJg7nUOSYNul544QW4u7tj3759OH36NACgf//+\n", "mDdvHkaPHm3RAomIejKpn1GsqalBWloaCgoK4ObmhqlTp2Ls2LGtbpuVlYWjR4/i0aNHCAoKQnx8\n", "vMH0uidOnEBWVhbu3r0LDw8PzJ49GyEhIa3ui7lERCQOqeeSVJk8ZXxYWBjCwsIsWQsREbUg9TOK\n", "6enpsLe3R3p6OoqKirBq1SoEBgYa3avkzJkzOHz4MD7++GMoFArs3r0b69evx+rVqwE0tQXu3LkT\n", "7733HoKDg3H//v0O27SYS0REXU/quSRV1tdcSkTUg0j5gmWNRoPTp09jypQpcHR0REhICEaNGoXc\n", "3FyjbYuLixESEgJvb2/Y2Nhg3LhxUKvVwuOZmZmYOHEigoODAQC9e/eGp6fn03sjiYjoqZByLkmZ\n", "yd90ERFR15NyG0dZWRlsbW0NJrMIDAzExYsXjbYdMmQIDh06hLKyMnh5eSEnJwfDhw8H0BTgP/zw\n", "A0aNGoW3334b9fX1GD16NN566y04ODh02eshIqKOSTmXAPPa3vfv34+9e/eirq4O4eHhSExMhJ1d\n", "0/Do4MGDOHbsGIqLixEREYHk5GTheRUVFZg7d67BRCOvvfYa3njjjTbr4qCLiEjCpDwTmkajgZOT\n", "k8E6uVze6gydwcHBiIqKwrvvvgsbGxsoFAosWrQIAPDgwQPodDqcOnUKS5cuha2tLf785z/j66+/\n", "xpQpU7rktRARkWmknEuA6W3v+fn52LNnD1JSUtC7d2+sWbMGmZmZiI2NBQB4enoiJiYG58+fh1ar\n", "bfVY27ZtM3kmbbYXEhFJmNhtHJmZmcJPy2+w5HI5Hj16ZLCutrYWcrnc6HUcPHgQhYWFSEtLw44d\n", "OxATE4OlS5dCq9UK32a99NJL8PDwgKurK15++WWcO3fOQu8qERF1lti51B5z2t5zcnIQHR0NpVKJ\n", "Xr16ISYmBseOHRMeDwsLw+jRo+Hi4tLm8cwZgHb4TVdDQwM++ugjzJkzB/369TN5x0RE9OTE7mGf\n", "PHlym4/5+vpCp9OhvLxcaDG8desW/Pz8jLbNz89HRESEcJ3W+PHjsW3bNqjVaqhUKrOu32IuERGJ\n", "R+xcao85be9qtdpgMqaAgABUVVWhpqam3YHW45KTkyGTyTBkyBBMnz693XvedfhNl52dHSoqKkw6\n", "MBERPV06nU60n47I5XKEhYUhIyMDdXV1uHLlCvLy8hAZGWm0rb+/P06ePImqqio0NjYiNzcXOp1O\n", "CMZf/OIX+Mc//oHq6mrU1NTgwIEDGDlyZKvHZS4REYlHyrlkTtu7RqOBs7OzsNz8vNa2bcnNzQ0r\n", "V67Exo0bsWrVKmg0Gqxbt67d55h0TVdkZCSOHj2K6dOnm7I5ERE9JVI+owgACQkJSEtLQ0JCAtzc\n", "3JCYmAilUom7d+9i3rx5SE1NRZ8+fRATE4OtW7diwYIF0Gq18PX1xfz584XAi4mJQXV1Nd555x3Y\n", "29vj2WefbfeCZOYSEZE4xM6lzMxM4d+hoaEIDQ0Vls1pe2+5bW1trbC+I3K5HCqVCgDg7u6OuLg4\n", "zJo1CxqNps3nmzTo0mq1+O6771BQUACVSmUwUwcAxMXFmbIbIiIyk9RniXJxccHChQuN1isUCmzf\n", "vl1YdnR0RFJSUpv7sbW1RUJCAhISEkw6LnOJiEgcYufS02p79/Pzw82bNxEeHi5s5+7ubnJrYWva\n", "u8bLpEGXWq1GUFAQAODOnTvCLB16vd7kGTuIiMh8Yp9RlCrmEhGROKScS4+3vSclJaGoqAh5eXlY\n", "tmyZ0baRkZHYuHEjxo4dCw8PD2RnZ2P8+PHC442NjWhoaBAm8aivr4etrS1sbGxw48YNODs7w8fH\n", "Bw8fPsTWrVsRGhpq1Nr4OJMGXYsXLzb7RRMR0ZOTcriJiblERCQOqeeSqW3vw4YNw6uvvoolS5ZA\n", "q9UiPDzc4Fu0rKwsZGdnC8vfffcdJk2ahIkTJ+LOnTvYtWsXqqqq4OzsjKFDh+Kdd95pty6Z3oy5\n", "Dqurq3Hnzh0EBAR06Q0rm29SZi1a9pJaA3t7e7FL6DGae4atCX8/ukZlZaXRbHy/+tWvRKqmaZp3\n", "qRMrl6T+R0dLbm5uYpdgNmv8rASkfw+j1gwePFjsEsx2//59sUsw28OHD8UuwWzjxo3D/v37DdYx\n", "lzrHpNHMo0ePkJaWhlOnTgEA1q1bh759+2LTpk3w8PBot7eSiIg6T+zeealiLhERiYO51Dkm3Rx5\n", "x44duHfvHlavXm1wJnHkyJE4ffq0xYojIurppHwTSjExl4iIxMFc6hyTvuk6c+YMFixYgMDAQIML\n", "lPv37487d+5YrDgiop7O2kPGUphLRETiYC51jkmDrocPH7Y6feKjR49gY2PSl2UAmto/CgsLUVdX\n", "B1dXVzz33HPt3oeFiKink3obR01NDdLS0lBQUAA3NzdMnToVY8eObXXbrKwsHD16FI8ePUJQUBDi\n", "4+OhVCoNtikrK8OCBQsQHh6OuXPntnlc5hIRkTiknktSZdKgS6VS4cyZM3j55ZcN1h85cgSDBg0y\n", "+WCvv/46kpKS4ODggNLSUqSkpEClUmHYsGHmVU1E1ENI/Yxieno67O3tkZ6ejqKiIqxatQqBgYFG\n", "g6kzZ87g8OHD+Pjjj6FQKLB7926sX78eq1evNthuy5YtCA4O7nDad+YSEZE4pJ5LUmXSoCs2NhbL\n", "ly+HWq2GTqfDgQMHUFxcjBs3bmDJkiUmH6zljclsbW2tckYlIqKuIuUzihqNBqdPn8batWvh6OiI\n", "kJAQjBo1Crm5uYiNjTXYtri4GCEhIfD29gbQNCPWgQMHDLY5ceIEevXqBaVSifLy8naPzVwiIhKH\n", "lHNJykwadA0aNAjLli3D3r170bdvX1y4cAFBQUFYvnw5/P39zTpgeno6cnJyUF9fj7i4OKhUqk4V\n", "TkTUE0j5jGJZWRlsbW3h4+MjrAsMDMTFixeNth0yZAgOHTqEsrIyeHl5IScnB8OHDxcer62tRWZm\n", "JlJSUnDkyJEOj81cIiISh5RzCTCv7X3//v3Yu3cv6urqEB4ejsTEROFWVQcPHsSxY8dQXFyMiIgI\n", "JCcnGzz3woUL2LJlC/7zn/8gODgYs2fPhkKhaLMuk2+A5e/vjzlz5pi6eZsSEhIQHx+PS5cuYe3a\n", "tVCpVAgODn7i/RIRdUdSDjeNRgMnJyeDdXK5HBqNxmjb4OBgREVF4d1334WNjQ0UCgUWLVokPJ6R\n", "kYHo6Gh4enp22FrYjLlERNT1pJxLgOlt7/n5+dizZw9SUlLQu3dvrFmzBpmZmUKnhqenJ2JiYnD+\n", "/HlotVqD51ZXV+Mvf/kLkpKSMGrUKOzevRupqalYvnx5m3W1Oei6e/euyS+uvVFda2QyGUJDQxEe\n", "Ho7jx48bhNvFixcNzpLyXitE1NNkZmYCaPr8E/tGq821AEBoaChCQ0OFZblcbnQz+NraWsjlcqP9\n", "HDx4EIWFhUhLS4OHhwdyc3OxdOlSrF27FqWlpSgsLBSu72rrNTOXiIjEIaVcao85be85OTmIjo4W\n", "BmMxMTFYt26dsF1YWBgA4Pvvv8e9e/cMnnv69Gn4+fkhPDwcADBp0iTEx8ejtLQU/fr1a7W2Ngdd\n", "s2fPNvkFZmRkmLzt43Q6HVxdXQ3WtQx1IqKe5vE/6sXunW9vgOHr6wudTofy8nKhxfDWrVtG10kB\n", "TWcUIyIi4OnpCQAYP348tm3bBrVajStXrqCiokJo3dBoNGhsbERJSQlWrVol7IO5REQkDinlUnvM\n", "aXtXq9XCwAoAAgICUFVVhZqamlZnx31ccXExAgIChGVHR0f4+PiguLjY/EHXihUrDF7A3/72N7z4\n", "4osYOHAgAOD69es4fPgwpk2b1m5Rzaqrq3HhwgWMHDkSDg4OKCgowMmTJw3aS4iIyJCU2zjkcjnC\n", "wsKQkZGBpKQkFBUVIS8vD8uWLTPa1t/fHydPnsSzzz4LV1dXHD9+HDqdDj4+PlAqlYiIiADQ9C3X\n", "vn37UFlZicTERIN9MJeIiMQn5Vwyp+1do9HA2dlZWG5+nkaj6XDQVVdXZzTpkpOTU6vHadbmoGvA\n", "gAHCv7dv347f/OY3+PnPfy6sGzJkCPr164dvvvmmzYvTWjp8+DDS09Oh1+vh6+uLuXPnsm+eiKgd\n", "Ug43oOl6qLS0NCQkJMDNzQ2JiYlQKpW4e/cu5s2bh9TUVPTp0wcxMTHYunUrFixYAK1WC19fX8yf\n", "P18IPAcHB2GfcrkcDg4ORt84MZeIiMQn5Vwyp+295ba1tbXCelOO07z9489vOeB7nEkTady4ccPg\n", "K7Rm/v7++P77703ZBdzc3LB48WKTtiUioiZSbuMAABcXFyxcuNBovUKhwPbt24VlR0dHJCUlmbTP\n", "SZMmdbgNc4mISBxi51J71xqb0/bu5+eHmzdvCtdl3bp1C+7u7h1+ywUASqUSOTk5wrJGo8GdO3eM\n", "Jut4nE3HLw3w8vLCt99+a7T+0KFD8PLyMmUXRETUCY2NjaL9SBlziYhIHGLn0uTJk4WfltfbPt72\n", "XldXhytXriAvLw+RkZFGryMyMhL//Oc/oVarUVNTg+zsbIwfP97gdWq1WuHY9fX1Qg1hYWEoLi7G\n", "qVOnoNVqkZWVhcDAwDav5wIAmd6EKUjy8/PxySefwMvLCwMHDoRer8eNGzdQWVmJ+fPnY8SIESb9\n", "T+qs5vnyrUXLrzWtgb29vdgl9Bgtv462Bvz96BqVlZVGH9jN1yuJ4fr166IduyNi55LUB6UtWeMN\n", "n63xsxJoe/ZNKRs8eLDYJZjt/v37YpdgtocPH4pdgtnGjRuH/fv3G6yTei61vE9XbGwsIiIijNre\n", "gab7dO3ZswdardboPl2ZmZnIzs422PekSZMwceJEAE336fr8889RWVmJgQMHdnifLpMGXUDTVL2H\n", "Dh1CSUkJZDIZ+vfvjxdeeMHsaXk7g4Muy+Mf1V3HGv+Q4O9H12ht0PX4dUxdzdQ2PbGImUscdFme\n", "NX5WAhx0dRUOurpGa4Mu5lLndDiaaWhowEcffYQ5c+YYzW9PRESWZW1/3HcF5hIRkXiYS53T4aDL\n", "zs4OFRUVXVELERG1YI1nzS2NuUREJB7mUueYNJFGZGQkjh49aulaiIioBZ1OJ9qPlDGXiIjEwVzq\n", "HJMultJqtfjuu+9QUFAAlUoFR0dHg8fj4uIsUhwRUU8n9TaOlhcsT506tc17ZGVlZeHo0aN49OgR\n", "goKCEB8fD6VSiYaGBmzevBmFhYWoqalB3759ERsbi2HDhrV5XOYSEZE4pJ5LUmXSoEutViMoKAgA\n", "cOfOHchkMgBNXy82/5uIiJ4+qYdbeno67O3tkZ6ejqKiIqxatQqBgYFG9yo5c+YMDh8+jI8//hgK\n", "hQK7d+/G+vXrsXr1auh0OigUCixZsgQKhQJnz55Famoq1qxZ0+b078wlIiJxSD2XpMqkQRdvHklE\n", "JA4pt1NoNBqcPn0aa9euhaOjI0JCQjBq1Cjk5uYaTXBRXFyMkJAQeHt7A2iaEevAgQMAmm6c/PgN\n", "kUeMGAFvb28UFRW1OehiLhERiUPKuSRlJs/FXltbi7KyMgCAj48PevXqZbGiiIioiZTPKJaVlcHW\n", "1hY+Pj7CusDAQFy8eNFo2yFDhuDQoUMoKyuDl5cXcnJyMHz48Fb3++DBA5SWlhp9W9YSc4mIqOtJ\n", "OZcA89re9+/fj71796Kurs7oPl3t7aeiogJz5841aG1/7bXX8MYbb7RZV4eDrsrKSmzZsgXnzp0z\n", "WD98+HDEx8e3eRaSiIienJTPKGo0Gjg5ORmsk8vl0Gg0RtsGBwcjKioK7777LmxsbKBQKLBo0SKj\n", "7RoaGrB+/XqMHz/e6J5lzZhLRETikXIuAaa3vefn52PPnj1ISUlB7969sWbNGmRmZgqdGqbsZ9u2\n", "bSa3tLc76Lp37x4+/PBDyGQyvPnmm8JB1Go1vv32W3z44YdYuXIlPD09zXoziIjINGKfUczMzBT+\n", "HRoaitDQUGFZLpcb3Qy+trYWcrncaD8HDx5EYWEh0tLS4OHhgdzcXCxduhRr166Fg4MDgKbXumHD\n", "Btjb2yM+Pr7VephLRETiEjuX2mNO23tOTg6io6OFHImJicG6desQGxtr8n7MuY643UHXV199BW9v\n", "byxatEgIRQAICwvDhAkTsGzZMnz11VeYNWuWyW8GERGZTuxwmzx5cpuP+fr6QqfToby8XGgxvHXr\n", "Fvz8/Iy2zc/PR0REhDAYGj9+PLZt2wa1Wg2VSgW9Xo/PPvsM1dXV+OCDD2Bj0/odTZhLRETiEjuX\n", "2mNO27tarUZYWJiwHBAQgKqqKtTU1KCystKk/SQnJ0Mmk2HIkCGYPn06XF1d26yt3ft0nTt3DlOm\n", "TDEItmaOjo6YMmUKzp49294uiIjoCej1etF+OiKXyxEWFoaMjAzU1dXhypUryMvLQ2RkpNG2/v7+\n", "OHnyJKqqqtDY2Ijc3FzodDoh0DZv3oySkhL8/ve/h729fZvHZC4REYlLyrlkTtu7RqOBs7OzsNz8\n", "PI1G0+F+3NzcsHLlSmzcuBGrVq2CRqPBunXr2q2t3W+6qqurDUZ4LfXt2xfV1dXtHoCIiDpP6r3z\n", "CQkJSEtLQ0JCAtzc3JCYmAilUom7d+9i3rx5SE1NRZ8+fRATE4OtW7diwYIF0Gq18PX1xfz58+Hs\n", "7IzKykocPXoU9vb2mDlzprDvmTNnGl38zFwiIhKXlHPJnLb3ltvW1tYK6zvaj1wuh0qlAgC4u7sj\n", "Li4Os2bNgkajafVYQAeDLnd3d5SVlaFPnz6tPl5eXg53d/f2dkFERE9Aym0cAODi4oKFCxcarVco\n", "FNi+fbuw7OjoiKSkpFb34eXlhYyMDJOOx1wiIhKX2LnU3rXG5rS9+/n54ebNmwgPDxe2c3d3h4uL\n", "C+zs7Ezez+Pa+zau3fbCYcOGISMjA1qt1ugxrVaLjIyMNqf8JSKiJ9fY2CjajxQxl4iIxCV2Lk2e\n", "PFn4eXzABZjX9h4ZGYl//vOfUKvVqKmpQXZ2NsaPH2/Sfm7cuIHS0lI0Njbixx9/xNatWxEaGmrU\n", "kvg4mb6dIdm9e/fw/vvvw9bWFr/85S/Rv39/AE03uTx06BB0Oh1WrVrV5hnHp2XYsGEW3f/T9q9/\n", "/UvsEszWfE8CsrzW+oqljr8fXePBgwdG06S3NaFEV5DiwEsquVRXV2fR/T9trf3BIXXW+FkJtH+m\n", "W6qa26SsiTW2EbdsV7MGI0aMwKeffmqwTuq51PL+WrGxsYiIiDBqewea7tO1Z88eaLXaDu/T1bwf\n", "ADhx4gR27dqFqqoqODs7Y+jQoXjrrbfa7bRod9AFNN38a8uWLcjPzzdYP2zYMMTFxaFv374dvngi\n", "IqKnhblERERWR2+iH3/8UX/t2jX9tWvX9NXV1aY+TdIyMjLELsFsrLlrsOauY411W2PN3RFzSRpY\n", "c9ewxpr1euusmzWTJZjcM+Ti4oKBAwdacvxHRERkMuYSERFZC/GaMomIiIiIiHoA28WLFy8Wuwgx\n", "eXt7i12C2Vhz12DNXcca67bGmsk6WOPvFmvuGtZYM2CddbNmeto6nEiDiIiIiIiIOo/thURERERE\n", "RBbEQRcREREREZEFcdBFRERERERkQSZPGd9dHDx4EMeOHUNxcTEiIiKQnJwsdkkdamhowObNm1FY\n", "WIiamhr07dsXsbGxGDZsmNiltWvdunUoLCxEXV0dXF1d8dxzz+GNN94QuyyTlJWVYcGCBQgPD8fc\n", "uXPFLqdDixcvxvXr12FrawsA6NOnD1JTU0WuqmMnTpxAVlYW7t69Cw8PD8yePRshISFil9Wq6dOn\n", "QyaTCctarRYvvvgi4uLiRKyKugPmUtdhLnUd5pLlMZesS48bdHl6eiImJgbnz5+HVqsVuxyT6HQ6\n", "KBQKLFmyBAqFAmfPnkVqairWrFkDLy8vsctr0+uvv46kpCQ4ODigtLQUKSkpUKlUkg9lANiyZQuC\n", "g4MNPsykTCaTIT4+Hs8995zYpZisoKAAO3fuxHvvvYfg4GDcv38fUp7X58svvxT+rdFoMHPmTDz7\n", "7LMiVkTdBXOp6zCXug5zyfKYS9alxw26wsLCAADff/897t27J3I1pnF0dMSkSZOE5REjRsDb2xtF\n", "RUWSDjc/Pz+DZVtbW7i5uYlUjelOnDiBXr16QalUory8XOxyuq3MzExMnDgRwcHBAIDevXuLXJHp\n", "/vd//xfu7u6SPftJ1oW51HWYS9Qe5hJZUo8bdHUHDx48QGlpKZRKpdildCg9PR05OTmor69HXFwc\n", "VCqV2CW1q7a2FpmZmUhJScGRI0fELscsO3fuxI4dO9CvXz9MnToVgwcPFrukNjU2NuKHH37AqFGj\n", "8Pbbb6O+vh6jR4/GW2+9BQcHB7HL61BOTg6ioqLELoNIMphLlsNc6hrMJbI0TqRhZRoaGrB+/XqM\n", "Hz8e/fr1E7ucDiUkJGD79u1YtGgRMjIycOPGDbFLaldGRgaio6Ph6elpNS0cADBt2jRs2LAB//3f\n", "/43nn38eq1evxp07d8Quq00PHjyATqfDqVOnsHTpUvz5z39GUVERvv76a7FL61BlZSUuX77McCP6\n", "P8wly2IudQ3mElkaB11WpLGxERs2bIC9vT3i4+PFLsdkMpkMoaGhCA8Px/Hjx8Uup003b95EYWEh\n", "/uu//gsAJN3H3VJwcDDkcjns7OwQFRWFQYMG4dy5c2KX1abms4YvvfQSPDw84OrqipdfflnSNTfL\n", "zc3FT3/6U0m3UBF1FeaSZTGXug5ziSyN7YVWQq/X47PPPkN1dTU++OAD2NhY33hZp9PB1dVV7DLa\n", "dOnSJVRUVAgzh2k0GjQ2NqKkpASrVq0SubruxcXFBZ6enmKX0Sm5ubl4/fXXxS6DSHTMJctj60yk\n", "bQAADChJREFULnUd5hJZWo8bdDU2NqKhoQGNjY1obGxEfX09bG1tJR8WmzdvRklJCRYtWgR7e3ux\n", "y+lQdXU1Lly4gJEjR8LBwQEFBQU4efIkFi1aJHZpbXr++ecREREBoOmPiX379qGyshKJiYkiV9a+\n", "2tpaXLt2DYMHD4atrS3+9a9/4fLly5KfMvYXv/gF/vGPf2DYsGGwsbHBgQMHMHLkSLHLatfVq1dx\n", "7949hIeHi10KdSPMpa7BXOo6zKWuw1yyHj1u0JWVlYXs7Gxh+bvvvsOkSZMwceJEEatqX2VlJY4e\n", "PQp7e3vMnDlTWD9z5kyMHTtWxMrad/jwYaSnp0Ov18PX1xdz584VZgSSIgcHB4OLZeVyORwcHCR9\n", "FhRoup4iIyMDpaWlsLGxQf/+/fH73/8ePj4+YpfWrpiYGFRXV+Odd96Bvb09nn32WcnfLycnJwdj\n", "xoyBXC4XuxTqRphLXYe51DWYS12HuWQ9ZHprahAmIiIiIiKyMtLuXSAiIiIiIrJyHHQRERERERFZ\n", "EAddREREREREFsRBFxERERERkQVx0EVERERERGRBHHQRERERERFZEAddREREREREFtTjbo5Mlvfp\n", "p5/ixx9/xPvvvy92KYJ///vf+PLLL1FZWYlx48YhOTlZ7JKIiKiLMJeISGwcdHUzn376KXJzczF5\n", "8mTExMQI6y9evIilS5diy5YtcHFxsWgNMpkMMpnMoscw12effYbo6Gi89NJL7d61vby8HH//+99R\n", "UFCA6upqeHh4YMCAAXj55Zfxk5/8pAsrlrau/H0iIuvGXGodc+npYi6R1LG9sJuRyWSwt7fH3r17\n", "UV1dLUoNer3eIvvV6XSdel5NTQ1qamrwzDPPoHfv3nBycmp1u++//x5/+MMfUFJSgsTERKSmpuIP\n", "f/gDVCoVPv/88ycpvduy1P9rIuo+mEvGmEuWw1wiqeI3Xd1QaGgo7t27h+zsbMyYMaPVbVo7I1RR\n", "UYG5c+di5cqVUKlUwjYffPABdu3ahZKSEgwYMADvvPMOSktL8cUXX6CiogKhoaGYPXu2sB+ZTAa9\n", "Xo/s7GwcPHgQdXV1CA8PR0JCAhwcHIQa9uzZgyNHjuD+/fvw8fHBr3/9a4wbN86glrfffhtHjhzB\n", "9evXMX36dPzyl780ei01NTX44osvkJeXh/r6egwaNAgzZsyAUqkUXgMA4b8pKSkYPHiwwT70ej02\n", "btwIHx8ffPzxxwZnRP39/fHiiy8Ky7dv38a2bdtw9epVODg4YNSoUfjtb38LZ2dnAP+/jSUkJATf\n", "fPMNtFotXnzxRUyZMgWZmZk4cuQIbGxs8Morr+CVV14R9vvmm29ixowZOHfuHC5dugQ3NzdMmTJF\n", "eE/MOfbQoUOxd+9e1NXVYfTo0Z167+fNm4dDhw7h2rVr8PLywm9/+1sMHToUFRUVwnuZkJAAAIiK\n", "ikJycjIuXbqEHTt2oLi4GDY2NujXrx9+97vfwc/Pr9XfQyLqGZhLzCXmEvV0/Karm9Hr9ZDJZIiN\n", "jcXhw4dx586dJ97nV199hRkzZmDFihWoqalBamoqsrOzkZSUhMWLF6O4uBhZWVkGNVy+fBm3b99G\n", "SkoK5s+fj4KCAuzYsUPYZteuXTh27BgSEhKQmpqK1157DZs2bcLZs2cNjr1z50786le/QmpqKkaP\n", "Ht1qfRs3bhTOBq5YsQKOjo5Yvnw5tFotBg0ahL/85S8AgPnz52PTpk2ttmPcvHkTarUar776aqst\n", "KM3hodFosHz5cjg5OWHlypVYsGABrl69irS0NIPtL1++jLt372Lx4sVITEzEnj17sGLFCuj1eixb\n", "tgyTJk3C3/72N9y8edPovR49ejQ++eQTREdHY8OGDfjhhx/MOvaVK1egVqvx0Ucf4b333sO///1v\n", "fPPNN2a/97t378aECRPwySefYMCAAfjrX/8KjUYDhUKB+fPnAwDWrl2LTZs2YcaMGdDpdPjkk0/w\n", "05/+FGvWrMGKFSswYcIE2NjwY4aoJ2MuMZeYS0QcdHVLMpkMw4cPx6BBg7Br164n3t+bb76JkJAQ\n", "+Pv744UXXsC1a9fwm9/8BsHBwVCpVIiKisKFCxcMnmNjY4Pk5GQolUo888wzmDZtGo4cOQKtVguN\n", "RoMDBw5g1qxZeOaZZ+Dl5YWxY8ciOjoa3377rcF+XnrpJYwZMwZeXl7w9PQ0qq2srAx5eXmYNWuW\n", "UOOcOXPw6NEjHD9+HHZ2dnBzcwMAuLi4wN3dHXZ2xl/wlpWVAQD69+/f7ntx/Phx1NXVYc6cOfDz\n", "88PgwYMxa9YsnD592uAPiV69eiE+Ph79+vVDREQEVCoVqqurMXXqVPj4+OCFF16AQqEwet/GjBmD\n", "559/Hj4+PnjjjTfws5/9DAcOHDDr2M7OzkhMTES/fv0wdOhQhIeHC8cx572fMGECRowYAR8fH0yd\n", "OhU1NTW4desWbGxs0KtXLwCAu7s73N3d4eTkhEePHqG2thYjRoyAt7e38No7ek+JqPtjLjGXmEvU\n", "07G9sBtq7meeNm0aPvzwQ+GMVGf5+/sL/3Z3d291Xcs+/YCAADg6OgrLAwcORENDA8rLy6HValFf\n", "X4/ly5cbnL3T6XTw9vY22M+AAQPara2kpAQymczgLKGzszP8/f2hVqvNeJWmKSkpQUBAgMFFzz/5\n", "yU8gk8mgVqvRt29fAIBSqTR4be7u7kIgNPPw8DB631qe7Rw4cCDOnTv3RMfu3bs3bty4AQBQq9Um\n", "v/cBAQEG+wCAqqqqNt8bFxcXREVFYfny5RgyZAh+9rOfITw8HAqFos3nEFHPwFxiLjGXqKfjoKsb\n", "Cw4OxpgxY7Bjxw6DGaMACB9sj19w2tYFwY+fgWt+Xsuv5hsbGw2W27uQtfmx999/3+iDz9bW1mD5\n", "8YA0R3M7i6l8fX0BNH34BwYGduqYjx+vtdaFlq8NMO2CX1NeR0fHbv7/Y857//hya78vrUlOTsaE\n", "CROQn5+PvLw87N69GwsXLsQzzzzT4Wsgou6PucRcasZcop6G7YXd3NSpU3H58mXk5+cbrG9ubbh/\n", "/76wrmUf95O4ffs26urqhOXr16/Dzs4OPj4+UCqVsLOzQ2VlJfr27WvwY+7Zp/79+0Ov1+Pq1avC\n", "utraWhQXF0OpVJq8n6CgICiVSuzbt88oqAHg4cOHAJrO1t2+fRsajUZ47OrVq9Dr9QbtCp2dmvja\n", "tWsGy9evXxf2279//yc+9tN675v/4GntvQoICMCvf/1rpKSkIDQ0FDk5OSbvl4i6P+aSaZhLzCXq\n", "Xjjo6uZ8fHzw/PPPC/3Xj6/v06cPMjMzUVZWhvPnz+Prr79+asdtbGxEWloa1Go1CgoKsHPnTkRH\n", "R8PBwQFOTk545ZVX8OWXX+J//ud/UF5ejps3b+LQoUM4cuSIWcfx9fXFqFGjsGnTJly5cgW3b9/G\n", "+vXr4ezsjLFjx5q1r9/97ncoLy/HRx99hLNnz6K8vBy3b9/Gnj17sGzZMgDAuHHj4ODggA0bNuD2\n", "7du4dOkSNm3ahDFjxghtFIDpU9a23O706dM4evQoysrK8Pe//x2FhYWYMGECACAyMvKJj/203nsv\n", "Ly8AQF5eHqqrq6HRaFBRUYEdO3bg2rVrqKysRGFhIW7dumXWHxlE1P0xl0zHXGIuUffB9sJuprUb\n", "QE6cOBE5OTloaGgQ1tnZ2eHdd99Feno6Fi5ciKCgIEydOhWrV69+4mPKZDIMHjwYSqUSS5YsEabm\n", "feutt4RtpkyZAg8PD+zbtw/p6elwcnJCUFAQXn31VbOPn5ycjC+++AKrV69GfX09QkJC8Mc//hH2\n", "9vZm7Sc4OBirV6/G119/jc2bN6Oqqgq9e/dGYGAgpk+fDgBwcHDAn/70J2zbtk04xujRow2mQDbn\n", "Jpwtt5s0aRJOnTqFrVu3wt3dHbNnz4ZKpXqiY7dc9zTee09PT0yePBm7d+/GZ599hqioKEybNg1l\n", "ZWVYu3YtfvzxR7i7u2PcuHF47bXXTN4vEXU/zCXmEnOJCJDpeRc5Ikl48803MW/ePIwZM0bsUoiI\n", "iJhLRE8R2wuJiIiIiIgsiIMuIiIiIiIiC2J7IRERERERkQXxmy4iIiIiIiIL4qCLiIiIiIjIgjjo\n", "IiIiIiIisiAOuoiIiIiIiCyIgy4iIiIiIiIL4qCLiIiIiIjIgv4fxCxGFfEChuEAAAAASUVORK5C\n", "YII=\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f3729060e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_gridscores_matrix\n", "\n", "draw_gridscores_matrix(gs, ['n_components', 'degree'], score_label='R-Squared',\n", " param_labels=['Number of Components', 'Order of Polynomial'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we get a poor fit, when only the first and second component are used, and when we increase\n", "the polynomial order and the components together. The models have a high standard deviation and \n", "poor R-squared values for both of these cases.\n", "\n", "There seems to be several potential models that use 3 to 6 components. It's difficult to see which model \n", "is the best. Let's use our test data `X_test` to see which model performs the best.\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Order of Polynomial 3\n", "Number of Components 3\n", "R-squared Value 0.982073916103\n" ] } ], "source": [ "print('Order of Polynomial'), (gs.best_estimator_.degree)\n", "print('Number of Components'), (gs.best_estimator_.n_components)\n", "print('R-squared Value'), (gs.score(X_test, y_test))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the parameter range that we searched, we have found that a model with 3rd order polynomial \n", "and 3 components had the best R-squared value. It's difficult to see the differences in the score\n", "values and the standard deviation when we have 3 or more components. Let's take a closer look at those values, using `draw_grid_scores`.\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAiYAAAEWCAYAAABSXFx2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXl4HNWZt31XVXerW7slS7a1WKv33dgQg83rJSwOYJwF\n", "g/EwEAOTycJ8IUwymRDgIpCQ701IJpOMSTJgliQshrAHYxsb8L5ghBdZXiR5kWzZlm3Zknrvqnr/\n", "qO5St7ola1/PzWW66tSpU+e0urt+9TzPeY6k67qOQCAQCAQCQR9A7u0OCAQCgUAgEIQQwkQgEAgE\n", "AkGfYVAKk9LS0t7uQrcixtd/GchjAzG+/s5AH5+gbyCEyQBEjK//MpDHBmJ8/Z2BPj5B32BQChOB\n", "QCAQCAR9EyFMBAKBQCAQ9BkkMV1YIBAIBD1FIBBAVdXe7oagl1EUBYvFEvNY7NJBwKlTp3q7C91G\n", "UlISDQ0Nvd2NbmMgj28gjw3E+Po7WVlZnW5DVVXOnz/fBb0R9GfS09NbFCbClSMQCAQCgaDPIISJ\n", "QCAQCASCPoMQJgKBQCAQCPoMQpgIBAKBQCDoM/R68OsLL7zA0aNHKSgo4J577jHLjx07xnPPPYcs\n", "yyxdupSxY8dSU1PDM888g67rTJw4kdtvv53S0lJWrFhBZmYmQ4cO5bvf/W7vDUYgEAgEA57a2lo2\n", "bNjAsWPHOH36NEVFRXzve99rVxtnz55l9+7dzJ07F4fD0aZzduzYwZYtWzhz5gySJJGdnc28efOY\n", "OHFiR4YBGEnznn32WR599FGGDBnS4Xa6kl61mFRWVuL1enn88ccJBAJUVFSYx1atWsWDDz7Iww8/\n", "zJtvvgnAmjVruPPOO3niiSc4cuQILpcLSZKYM2cOjz32mBAlAoFAIOh2zpw5Q1lZGcOGDSMzMxNJ\n", "ktrdRm1tLWvXrsXj8bSp/qpVq3jttdfIz8/n3nvv5e677yYtLY3nnnuO9evXt/v6fZletZiUl5cz\n", "ZcoUACZNmsThw4cpKioCwOl0kpaWBoDX68Xn85GcnIzT6UTTNACsVisAW7ZsoaysjOuvv55rrrmm\n", "F0YiEAgEgsHChAkTTCvF888/j8vl6nBbbUkltnfvXrZt28Ztt93G1VdfbZaPGzeO5ORk/vGPfzBm\n", "zBhycnJinq+qKpIkIcvdZ4vw+XzYbLYuaatXhYnT6SQzMxOA+Ph4qqqqzGNJSUlUVVWRkpLCiRMn\n", "cLlczJs3j0ceeYQXX3yR2bNnY7VaKSws5He/+x1+v58nnniCSZMmkZycfNlre51eJElCUoJ/LElC\n", "kjDK5ParX4FAIBB0nu0bP2Hzm69g0VQCssLsry3lS9fO7VNtttVCsm7dOnbs2MGlS5ew2+1kZ2ez\n", "bNkyTp8+zbPPPgvAE088AUBaWhqPPPJIzHY2btzI0KFDmTVrVtSx6667jq1bt7Jp0yaWLl0KwO9/\n", "/3sSExMZM2YM69evp66ujkcffZTU1FRWr17Nli1b8Pv9TJ48mbFjx0a16ff7Wb16NZ9//jmNjY1k\n", "ZmZy8803M378eLPO448/ztSpU7Hb7WzdupXGxkaefvrpNr0vl6NXhUl8fDxutxsAl8tFQkKCeWzZ\n", "smWsXLkSu91Ofn4+SUlJ/OlPf+IHP/gBBQUFPP3009TW1pKRkQFAXFwc48aN4/Tp01HCpLS0NGLx\n", "qSVLlmCT4tB1Hd0fKtXRddAlY1sCkECSDcESVC2GcJGMY7JsiBpJkZFkjG1JCjuvdwSOzWYjKSmp\n", "V67dEwzk8Q3ksYEYX0+j6zo6uvlUroO5Hzqm6RqarqOhowe3Ca8XbCtUvmrVKrP9CRMmMGHChC7r\n", "7/aNn7Bl5e94YmzTe/joyt8BdFhIdEebbWHnzp189NFHLFq0iOHDh+N0Ojly5Aher5fc3FwWLVrE\n", "u+++y/Lly0lOTm4x2Ziqqhw7dow5c+bEvKfY7XZGjRoVEQohSRJHjx7l/PnzLFq0CJvNht1u59NP\n", "P2Xt2rVcd911FBUVsWfPHt59992oNp9//nlOnDjBwoULGTp0KCUlJTz77LM89NBDZGdnm9fYvXs3\n", "I0aMYMmSJV2azbdXhcno0aNZt24ds2bNYt++fcybN888NmLECB5++GHq6+t56aWXUBTFFC+SJBEf\n", "H4/H48HtduNwONA0jYqKCm666aao68T68jg7YXoLoes66E2vxlcZJIxdKahuJNnYkQiKFqlJ7Egy\n", "yIqhdGRFQpJlUwyZIqedAmegZ58cyOMbyGODwT2+kBCAoEDQQzLB2NZ0LSgO9KAIMMSCpmvBM0I/\n", "M8Fzoto0Hq7MbXSk0E+TZDaBUWj8Hhk/Ucbvi/Er1PIDlV8LwLCRLFmypKNvz2XZ/OYrEQIC4Gdj\n", "k/jZbx7mhq1FHWpz+7YKfjYr8tyfjU3i0bde7VZhcuLECcaOHRsRXjB58mRzO+QtyMnJaTXo1Ol0\n", "oqpqq3VSU1MpKysz93Vdx+Px8KMf/YjExEQANE1j/fr1XHPNNXzlK18BYMyYMTzzzDPU19eb5x4+\n", "fJiysjIeeOABCgsLzXq1tbWsW7fOnKSi6zqSJHH//fe3KKo6Sq8Kk4KCAmw2G4899hj5+fkUFRWx\n", "cuVKli9fzoYNG9i0aRM2m4377rsPgMWLF/P73/8eRVHIzs4mNzeX9evXs379eiRJYvbs2aSmpvZY\n", "/03hQDuEg07YU4jx/0Dw6SV0TMKw3EihH5nmYoZIYQMhl5Txz2/1o/pV0yUl3FMCQTSmNYFWRAKg\n", "a8ZWk0jArNtcJLhkH43uxiaxoF9OJBi7hlAIlUmXFQnhhM6UzMYiDl5+uw9h0WI/dSudsD5bWvjt\n", "U9RAh9tsCzk5Ofz9739n9erVjB8/ntzc3G6N8Yh1/ZAoAairq6OhoSFqBk8ovjPEoUOHSEpKIj8/\n", "P8IKMmrUKHbt2mXuS5LEqFGjulyUQB+YLhw+RRhg+fLlAMyfP5/58+dHHCsuLubJJ5+MKFuwYAEL\n", "Fizo1j52N5LUfreP8TuqY/xO6hCAgGb8AMqqBafTha4bD0O6TtOTUdDNZF6X4H5I/ChGX7rKeiMQ\n", "tIXWRIKu62hoaIREgmFLCFkXWhIJTU6IJmtCSyJBQkKXgmc3sya0RySEHlRMmdFWYSC+VgAEZCVm\n", "uTd/Ipd+/McOten5/rcAd1S5qnTv7e+qq67C4/Gwbds21q5dS3x8PNdccw033nhjuwRKQkICFouF\n", "urq6FuvU1dVFPZQ3dymGLHnhYiVWPafTSUNDAw899FDUdZr3u7vclr0uTHqLS74GFElGlmQUSUaR\n", "FfPHR+6neeck2fhBVCwyshL7Cx5B8MdZV3WzQPfFcE9JElJQ5ejoUdabltxTIrh48KCHxSiomkZA\n", "V1F1FVXXzFgFTddxyl7DohBDJBg3fkMY6OgxXQ4dsSSY20IkdApN1S5fqZPM/tpSHl35O34W5s55\n", "pKyB2fcu71NttgVJkpg7dy5z587l4sWLfPbZZ3zwwQekpKS0a/aooijk5+dTWlrKokWLoj77Ho8n\n", "YoZr+PXDCYmIxsbGiPLmrsf4+HhSUlK499572zTG7mDQCpOApuLXA4SetoznsWhfq4SELDVtWyQF\n", "SZaxIKPIMkgycvgTUj+ny9xTQeuN8bYGfd+m2ZrLWm+au6cIvkqy1KbpdYLOYQZE6kGRoWmoqOi6\n", "FhQhoGlqsJ7WZJYLvki6jkzwc6Tr5j9JAdnjxrz7h37YJIhSCxJhx2h2rNn5sYg6JrWyK9RJCE3V\n", "0VQV1auhqRq6qqOpOv6AD6IncHQpoZiPR996FUUNoCoWZt+7vFOxIN3RZntJTU3ly1/+Mjt37uTM\n", "mTOAITjAmAFzOa699lpWrlzJ9u3bo2bmfPTRR3i9XmbPnm2WxRIMQ4YMISkpiX379kXMxNm7d29E\n", "vTFjxvDJJ59gs9kYNmxY2wfZhQxaYQKhP55xQ2zNSqKFflgBvx5oMjmHiRnAECiDUMzEImS9abPx\n", "KZb1xh/beqO7JVxupzkjSrHKWOIsppVmUKE3vV+h96gpAtLY1nUVVdXQ0VBVFVUPoGrGa8hVoupa\n", "2IyMplkc6LrxWQZkSTYsZ8FryZLxmTeEZsjsQZOPpClookl4qFakQPMfYj3mZruONf+zh9eVpMgC\n", "PayyFFW56f2LJYjMJlr4nGlecLkiT25VPLVS0Nqxy2FeM4ZpKHhM13U0FVRfANVvuIVDVhFJAsK+\n", "TzISkt51sy5a40vXzu1y0dDVbfp8Pg4cOADAxYsX8Xq9fPHFFwCMHz8em83Ga6+9RkJCAnl5edjt\n", "dsrLy6mtrWXRokVAU/Drli1bmDZtGjabjaysrJjXmzRpEldffTVvvPEGp0+fZvz48WiaRklJCbt2\n", "7eLmm2+OyGES+l6HI8sy8+fP59133yUhIYHCwkL27NnD2bNnI+qNGTOGsWPH8swzz7BgwQKGDx+O\n", "x+Ph5MmTBAIBbr75ZvMa3cWgFiYdISQ2DDonZgwBY2zJkiGNDJeIjIKCLBuiRpakQSFmmtOS9Uax\n", "yEiSbNx3AxoBXwBvoxddx4yNkRUJxapgsSnIitz97qPQDRtaEAjBmRUagBZUYVqzegAqOBtjtxFy\n", "iaCDZkRdBFQVFRVVC0VdGNH3uqQHp4CGEYp/CLoww0WcHKygRFgwYtAVb2Nrlox+8vFusZuy8dls\n", "Mx0VYu1A13Xje+LX0HwqmqYbAiQUeyZLpsVSCb9OKDY0pNN83g5dfyDS0NDAiy++GFEW2n/00Uex\n", "2WwUFBSwbds2tm7dSiAQYOjQodxxxx1m8GlaWhqLFi1i48aNbNq0iSFDhrSYxwTgtttuIy8vjy1b\n", "trBt2zYkSSI3N5f77rsvatZpS3GLc+fOxeVysXXrVj799FMmTZrELbfcwl//+teIesuXL2fdunV8\n", "+umn1NXVkZCQQHZ2NnPmzIm4Rnch6YPULr5t52e93YVW0fXgTSbsxygkZ2KJGVlSkJFRZInkxBTc\n", "wXT9/TVepjUSExNobHS2cLTpiV7XNHRVM7wMcjDvjAKKIqFYZRQlJHlCwiGsiVgWCIgQIFJza4UE\n", "5nSq0L75BB5mNTBfJdNloqOjahr2BAf1DQ0EdBUIiQtjDKGwz9BVm2Ki+o+lKD4hHpez81P1+yq9\n", "PT4toKEGNFRf0AWjaeZHu7PxXT6vh2tvWNjpPnq9Xs6fP9/pdgT9m/T0dOLi4mIeExaTPookyUEX\n", "U8vouo5qbACaKWYCHh2nx/hxbE3MKJJi7EsSFtkSdqPrbjGjRd70QxaF0LamB+u04KbQfeB00iQQ\n", "wuqEbtA6SFECwdANfk3HZ97dJWSLjCzLyBYJxSajKEowt0wMQtn1aHoJoes6WjCeRpM0ApoR/BnQ\n", "1bBkVqFX0KVIc6ssSSQEdLx6mKtDAgkZqR1eMcHAR1M1tKAVRA8YMSDG10qPECCSJCO1IQ5eIOhL\n", "CGEygAiJGUVWsLTwaxQuZgJhYqYpNRyAHhb8qyPrxo1R0g1TryxJKLqMEiyT0I1Yg+C5UaIjXEA0\n", "zV1uVj9oSQizJrQyUDM+iPCm2vo+ydGnaJqG6tXxu5oMHLqsgwyyRUJXdLAYU0rVoAsmljXDeDeC\n", "Q4llzQi6Ak3d06wjcnvcAIIBj6bphhXEp6IFdMMKogddgsFp/RA0qyvRbk+BoD8ihMmAImiJUDXQ\n", "1SZLRCiuwbREhOrrprAwxYQpIjTT2qAHb7agG7Ntgt4JI+9DSESEbsLB2BkkU9yEpmVbJBlZUoJP\n", "/z3rfmiazgqarhIIziZRg7NMQgm0zHwXkg4BQ0Tp7uD7owctTnIw34tFwmKRka0ylrZMzxYIWkDX\n", "dDRNR/WqqIGQG0YPWv5oEuOSEVgvzGeCgYwQJr1GpECIFhGhwMjwOs1eY4gI407qR3K7wlwbYXEN\n", "rT5RhVshYpgVAElpm4FCCxuXX1dpWncjPKQpUswowQmmxuxgI7+MRVbCXBlNYsaIzTCuEXKZGELD\n", "eF+MOSiamS+j6V0MtwY1DyYOy6obejsg5k1A10D36XjcgaaqwTgWyWrkklEsspmwTiCAYCCqqjcL\n", "RNWbAlFNN0xwmrxAMAgRwuSyNAVTmvttFRHhMRItiojgL1JXighZAcnSZ2Y4mALgMv1Rg++TqoMe\n", "EjOBSCcTgEPy4vI2xdBIpsUmUkGEBE130jwWxQiB0Qm4jRw56Jj5WCRFQrbJWIRgGRRoqobqbxaI\n", "qhI0MgoBIhC0xOAVJp7QrI5eFhEtxBoMdloTM0YMTd/+6IYWZgxhTG3WUX0BfMHPmiSDFEwcJ9sU\n", "FKssksf1Q8IDUSW/grPe03Igat/+2AoEfYJB+zWRtJbSKwsRIeg+zMRzIXSMJFfOAD5NB4+M2+NG\n", "DmW9tcooNsXMeivoPSICUVXDmtc8EFWz6CIQVSDoJINWmAgEfQkpKDxCU5dNweJX8TsDZrLSkEtI\n", "sRiCJeQmEnQdIhBVIOhdhDARCPowoTiEcOmhq+APqPhdAXOmtSwZs4RkRTIsLBZZCJbLIAJRBYK+\n", "iRAmAkE/xHAXRAuWQEAj4FGNbJ9yULCEpjZbZUOwKIPvET9mIGpURlQhQASCvoAQJgLBACLkYgjP\n", "06ZroHk1Am4jm64UdD/IQcGiWIyFEAeCYGktIyqEJyQTGVEFHSe0eF51dTVer5fMzEzmzZvH9OnT\n", "u6R9r9fLj3/8Y5YuXcqVV17Zat26ujo+/PBDDh48iNPpJDk5mYkTJ3LDDTeQkJDQ4T48/fTTjBgx\n", "gjvvvLPDbXQUIUwEgkGAJEnBG3HYTCEtcmpzaDVoM47F1ndzsYQCUTWfZqycHCMQFURGVEH38Omn\n", "n5Kens7XvvY1EhISOHDgAH/5y19wOp0RC911NzU1NfzhD38gOTmZm266ifT0dM6cOcO6des4cOAA\n", "DzzwACkpKT3Wn65CCBOBYJDT2tRmXQ/lYjGWTZcUY6aQxdozgkUEogr6Ivfff3+ENWLUqFHU19fz\n", "ySef9Jgw0XWdv/71ryQkJPD973/fXBCvqKiICRMm8H//7//ljTfe4N57722xDb/fj9Vq7dZ+BgIB\n", "LJb2SQ0hTAQCQUyaT22+bC6WTkxtFoGoghDrN33CSx+8gp8AViz881eWsmDO3D7VZiwXSXZ2Nnv2\n", "7DH3z58/z5NPPsndd9/NoUOHKCkpwW63c9VVV3HjjTdGiPo9e/bw/vvvc/HiRUaOHMmtt9562T5U\n", "VFRw6tQpli1bFrVKb0pKCnPmzGHNmjVcuHCBtLQ0jhw5wooVK/jWt77Fpk2bKC8vZ9q0adxxxx3U\n", "1NSwatUqqqurSUtL45Zbbmnxmh988AFVVVVYrVYmT57M4sWLzevv2LGDV199lQcffJB33nmHEydO\n", "cN1113H99de36X0NIYSJQCBoFy3mYglNbQZjppBsuFTk4NTmkHtFBKIKWmL9pk/4xar/ov7LicES\n", "L79Y9V8AHRYS3dFmLI4dO0ZmZmZU+bvvvsuUKVNYvnw5hw4dYu3atYwYMYKpU6cCUFVVxYsvvsjk\n", "yZP52te+Rk1NDS+++OJlr1dRUQHApEmTYh6fNGkSa9as4ejRo6SlpZnlr776KldddRVz587FarXi\n", "8/n44x//SGJiInfddRd+v5+33noLr9fLiBEjzPMqKytZsWIFkydP5pvf/CZOp5P33nsPl8vFN7/5\n", "zYhrv/TSS8yePZuFCxdit9sv/+Y1QwgTgUDQJcSa2mwKluDUZs0t4XZ5EYGogli89MErYQLCoP7L\n", "iXznxZ+QfKGwQ23Wv1tJ8qLIc+u/nMhfVr/aZcLk8OHD7Nu3j6VLl0YdKyoqMi0go0eP5uDBg+zZ\n", "s8cUJuvXryczM5N77rkHgHHjxqGqKh988EGr17x06RIOhyPKWhJiyJAhZr1wpk6dysKFC839zZs3\n", "09jYyA9+8AMzHiUtLY3//u//jjjv/fffp7CwkLvvvtssS0lJYcWKFZw+fZrhw4eb5ddeey3XXntt\n", "q/1vDeGRFQgE3YokS0iKMeun6Z+whgii8ROIfaAzsUwtWN58ur/jbYZx/vx5XnrpJSZNmhRzBs3Y\n", "sWMj9ocNGxYhFo4fP87EiRMj6rRkBekKxo8fH7F//PhxcnNzI4JkCwoKSExsEog+n49jx44xdepU\n", "VFU1/xUUFCDLMlVVVa1eo70Ii4lAIBAI+gRWLIA3qnxWxiReuPWPHWrz7u3f4gDOqHKb1PmgT6fT\n", "yZ/+9CfS09O56667YtZxOBwR+4qi4Pc3iaLGxsYIEQCQlJR02WunpKTgdrvxer0xrSZ1dXVmvdba\n", "bmhoiLo+EFHmcrnQdZ033niDN954I6ruxYsX293/1hDCRCAQCAR9gn/+ytJm8SCQ/FEjdy25r0+1\n", "CYYV4X//93/RNI3777+/w7NbkpKSaGhoiChrvh+L4uJiAPbt28eMGTOiju/fvx+AwsJIN1bzmXTJ\n", "ycmcOXMm6vzwPoTE1cKFCxk3blxU3ebip7Oz9YQwEQgEAkGfIBTz8ZfVr+LT/dgkK3ctua9TsSDd\n", "0aaqqrzwwgucO3eO73//+zEtDm1l5MiRlJaWRsyE2bt372XPKyoqIjs7m7Vr1zJp0qQIq8mlS5fY\n", "uHEjkyZNMmNNWrv+7t27uXjxIqmpqYAR6Op0NlmZ4uLiyM/P58yZM+2eYdMRhDARCAQCQZ9hwZy5\n", "XTpbpjvafOONNygrK+OrX/0qjY2NNDY2msdycnLalbdjwYIF/Pa3v+WFF17gqquuoqamhh07drTp\n", "3GXLlvE///M//Nd//Rfz588nLS3NTLDmcDj4xje+cdk2rrzyStauXcuf//xnbrzxRvx+P6tXr46a\n", "En3LLbewYsUKJEliypQpxMXFUVdXR1lZGTfddBMZGRltHvPlEMJEIBAIBIJ2cOjQIQDeeuutqGOP\n", "Pvpoq1YKSYpMTJibm8s///M/8/7771NaWkpubi533303v/3tby/bjxEjRvDQQw+xZs0a3n//fRob\n", "G0lJSWHy5Mlcf/31bUpJb7PZ+Nd//Vdef/11XnrpJdLS0li8eDFr1qyJqFdYWMgDDzzA6tWr+dvf\n", "/oamaaSlpTFu3LhOx5Q0R9L1YKakQcb2Tz7u7S50G454B26Xu7e70W0M5PEN5LGBGF9/xuf1cO0N\n", "Cy9f8TJ4vV7Onz/fBT0S9GfS09NbnOospgsLBAKBQCDoMwhhIhAIBAKBoM8waIXJLx/9Hbt2ft7b\n", "3RAIBAKBQBDGoBUmY87P4J3n1wpxIhAIBAJBH2JQz8q5Qp7DOyvXkXwhAyVOwWKTm15tCpY4BUtc\n", "aNt4Vaxyu1dOFQgEAoFA0DYGtTABcJ/3ceC9E+06R7FFihVT0MTJWGwKSug1XNCYZXJQ8DRtK7am\n", "Y0L0CAQCgWAwM+iFSXxGHKOvzybg1VB9KgGfhuoNe/WqqD6NgE9F9TYt1676NHyNl2+/vShWOUrE\n", "xLbmtCSOFOKTvah6oEnwBNsTokcgEAgEfZ1BLUx2a5u49ZvXM+HK/Dafo2t6k1DxaU3CJZaI8alh\n", "gscoC3ibiZ+wOqovKHz8Gr6WVtnsBLJVji1smrmvoiw5QWFjiKRmoin42hOiZ9fOz1n//iYkXUaX\n", "NBbcPIeZV07v9usKBAKBoOcYtMLkUPpubr35+nbf2CRZwmJXsNiVLu+TrulBi4whVsJFT0xrjils\n", "gqInKH60gI7fE2h2vobm1/D5NXB2g+ixSFEWHUO4NLP2xLD6RLjBmgkexaYgKxK7dn7OO8+v5Qp5\n", "jnnNd55fCyDEiUAgEAwgel2YvPDCCxw9epSCggLuueces/zYsWM899xzyLLM0qVLGTt2LDU1NTzz\n", "zDPous7EiRO5/fbbUVWVFStWUFtby/Tp01m8eHGbrvvjn/1bN42o40iyZMafxHUiw2+s7JPNRU+k\n", "NSfafdXcmtO8boSVyG+IIS0QwB+9uninkS0SG09/xOyhCyLKr5DnsP79zUKYCASCHuWLL77gk08+\n", "oba2Fp/Px5AhQ5gxYwYLFixAUTr20Pr888/jdDr53ve+12o9VVXZtGkTO3fupLa2FqvVSn5+Pl/+\n", "8pejVhJuD5s2beLNN99sUyr87qZXhUllZSVer5fHH3+cZ599loqKCoqKigBYtWoVDz74IImJifz6\n", "17/mJz/5CWvWrOHOO+9k7NixPPnkk7hcLvbt20dOTg4PPPAAv/zlLyNWSBQ00VWiJxa6HhQ9YVYb\n", "w9XVTNg0t/KEu8LCXF3NrURaQActtqtIDwzKFRUEAkEv4nK5GD16NAsWLMDhcHD8+HE+/PBDGhoa\n", "+PrXv97hdsPX0ImFpmk899xzlJeXM2/ePEaNGoXH42Hbtm384Q9/YNmyZVxxxRUdvn5foVeFSXl5\n", "OVOmTAFg0qRJHD582BQmTqeTtLQ0wFhbwefzkZycjNPpRNM0ACwWC0eOHGHWrFkATJw4kfLycmbM\n", "mHHZa//wD0/w1Wtu5OppM7tjaIMKSZKMGBSbQhzWLm1b13U0v0bZ4zvhYvTxxnNu/O4AVkevG/8E\n", "AsEg4eqrr47YLy4uxuPxsHnz5laFic/nw2azdfi6GzdupKysjG9961uMHTvWLJ84cSIvvvgiq1at\n", "ori4mJSUlG65flvoimv06q+50+kkMzMTgPj4eKqqqsxjSUlJVFVVkZKSwokTJ3C5XMybN49HHnmE\n", "F198kdmzZ2Oz2XC5XDgcDrMNl8sVdZ3S0lJKS0vN/SVLllC5AH73wV+o1eu5ZsaVxMt2EhQ7cbL1\n", "sqq1r2O1WCDe0dvd6FJuuu06/v6//2Aas82yjWfWUZA4ivU//4KZd40lZ1rXLbvdWwzEv104Ynz9\n", "FyUY4L5q1SqzbMKECUyYMKFLr7Nxwybe/tu76H4dySqxeNkirp0/5/In9nCbzYmPj0dVVXP//Pnz\n", "PPnkk/zTP/0TZWVl5srB3/nOd6irq2PVqlWUl5eTlJTE9ddfDxgPYq2OY+NGRo0aFSFKQtx0003s\n", "2bOH7du3c8MNNwDw+OOPM3XqVOx2O1u3bqWxsZGnn36aQCDA22+/zWeffYYsy8ycOTPmishOp5P3\n", "33+f/fv34/F4yMnJYfHixeTl5Zl1HnzwQW699VYuXLjA7t27cTgc/PSnP+3QexiiV4VJfHw8brcR\n", "C+FyuSI/hUclAAAgAElEQVSWaF62bBkrV67EbreTn59PUlISf/rTn/jBD35AQUEBTz/9NLW1tRFi\n", "xOVyMXz48KjrtPTlCXwlgz+891decmw1y2QkHHIcDjmOeMVGvGw3tx1yHPHmsbBtOQ5HcD+0bZd6\n", "UeAMwBVOp0ydiO9uH+vf34ykS+iSzq03XQ/7E6g73sjG/95D9rR0Jt9WiD2le58IupUB+LeLQIyv\n", "3+LzegDjwa672LhhEy/86q9MCVxllr3wq78CdFhIdEebITRNIxAIUF1dzaZNm7jmmmui6rzzzjtM\n", "mTKFb37zm+Y94bnnnsPpdLJ06VIsFgurV6/G5XKZD+qxqKuro66ujnnz5sU8PnToULKysqioqDDL\n", "JEli9+7djBgxgiVLlpjC6b333mPHjh3cdNNNDBs2jG3btvHFF19EtBcIBHjmmWfweDzceuutJCQk\n", "sGXLFlasWMFPf/pTkpKaYgI2bNhAUVERd91112XFVVvoVWEyevRo1q1bx6xZs9i3b1/EGz5ixAge\n", "fvhh6uvreemll1AUxRQvkiSZomb06NHs37+f4uJiSktLmT17ditXjMahxJFlS8OlenFpXnx6AKfm\n", "wal56MyMXRkJu2wzxIoS17KoMY8FRZBibIfqx8k25H5uwekqZl45nZlXTo8I7tVv1Kn8tIbSd49z\n", "suQ8Zw9eZOLXCsibldnvLV8CQV9hz+5dlHz0AdfesLBbr/P2396NEBAAUwJX8d8PPkNJ5pEOtbnp\n", "7EfMyfxyVJvv/O29TguTH/3oR+bNfvr06dxyyy1RdfLz8yPcOwcOHODkyZM8+OCDjBw5EoDc3Fye\n", "eOKJVoXJpUuXAGJaNkKkpqZSW1tr7uu6jiRJ3H///Vgsxu3e6XSydetWFi5cyNy5cwEYO3YsTz31\n", "VERbn332GTU1Nfznf/4nQ4cOBWDMmDH84he/4OOPP2bRokVm3ZSUFO6+++4W+9VeelWYFBQUYLPZ\n", "eOyxx8jPz6eoqIiVK1eyfPlyNmzYwKZNm7DZbNx3330ALF68mN///vcoikJ2djYjR44kOzub7du3\n", "8+ijjzJ9+vR2B76Osefwq6LvmvsBXcWt+XCrXlyaB5e57cWtGa8utWnbrXlxqT5cmsfcdmtevLrf\n", "qKt5OyVwJIiyyoT2w0WNI3g8QY4jNZCM4m86byALHEmWKJqXxYjJaXzxaiVnDtRR8rdyqnaeZdqd\n", "xSRmDkyzukDQYXQNSQ0gayqSGkDSAsiqiqQFkDQVOVgWqrN7334Obt7I01eM6P6u+WM/bUudWNat\n", "pXM1v9bhNkN8//vfx+/3c/z4cdasWcPrr7/O7bffHlFn/PjxEfvHjx8nKSnJFCVgiI3c3NxO9wci\n", "A2glSWLUqFGmKAE4deoUgUCAiRMnRtSbOHEiH3/8sVl26NAhcnNzSUtLi3BRFRYWRoRdAIwbN65L\n", "+h6i1yMGw6cIAyxfvhyA+fPnM3/+/IhjxcXFPPnkkxFliqLwb//Wsam/8R/U8dX5SyPKLJJCkuIg\n", "SencDU3VtaBQiRQ1IZHTtO1rUfC4VS+eLhY4xj9b0GpjN7ZjuKKaC6DQvr2PCpz4dDuzvjOO6t3n\n", "2Pv6Uc4dqWf9z0sYe9NIRi3IQlYG7XqVgr6CriGpKnLwph8hAiIEQbBOUDjIwTJDQATLw7cj2mq5\n", "fbOtdpran99WwZOzirrpTYlEssb+bRk5I4v/77nlHWrz+L1lEGPVEdna+d+EnJwcwHjITkhI4OWX\n", "X2bBggWmhQGIcHkANDQ0kJiYGNVWYmIiPp+vxWuFAlrr6uparFNXVxcV+Brr+qHrtVbP6XRy/Phx\n", "HnrooajrhI8v1rmdpdeFSW9RuEHiq/OXdtusHEWSSVQcJHaVwAkKFbfmC4oYT4SoCRc8XgI4/a6g\n", "wPHhUj2RAqeTOMJcTa26qeQ4HErQnSXbze2QwFGk9v0wbC3ZxVtbPkS1gBIgalaVJEnkzsggc2wq\n", "+988xokdZznwznFOfnaOacuKGJLXxfOkBW1mz+5d7Pl4DXGSjleXmDLvBqZc0UMz4oKCIPxmHn3z\n", "DrvBm9aC2IKgJeEgawFkXYOAP0o4yJqKpHf+Cb0r0AFdsaDLFjRZQVcsaLIFXVHQZQu6rKApxquu\n", "WMBxusf6tnjZoqh4kD3Kdu5ZdlefajMWIZFy4cKFiBt3c5dyUlISjY3R65k0NDQQFxfXYvtDhgwh\n", "LS2Nffv2MWdOtAvq/PnznD592gx8be36AI2NjcTHx0dcP5yEhARyc3O57bbboq4VboGJdY3OMmiF\n", "ya++27mo4Z4iQuC0cSZurARrqq7hCYqaCKtNM0tNpNXGF7T4eExB5NF8hqtL83GBhhZ60MZ+ytEB\n", "xY5grE3TtnHs+IFK1m77FPWmJh/sM6tfAYgSl3GJVq7451Hkzsyg5JVyLp108smv9lI0L4vxN4/E\n", "Etf1WXsFLaDr7Nm1gwP/eI1fTRmKYbuDh9/+K3GXzjJjwrjIm/1lhINsioNApDiIsCQ0c0v0U0Gg\n", "yc22g/XMOrIFLepcS7C+EryWEnUukgztuJE0birrvjelGaGYj3f+9h6aX0O2ytyz7K5OxYJ0R5ux\n", "qKysBCA9Pb3Venl5eaxdu5bjx4+bs1vq6uqorq4202W0xLXXXsvbb7/NoUOHGDNmTMSxf/zjH1gs\n", "Fq666qoWzjbIysrCYrGwb98+FiwwklZqmsb+/fsj6o0ePZr33nuPIUOGxLTwdCeDVpgMNhRJJkEx\n", "pkR3JtVISODEdlP5gnE5TaKmRTdVOwVO/eZKkhdFZjV0LRzCc2veIG98AVm29CjVnjkulQUPT6Ps\n", "Hyco33CKig2nqNlznql3FDFsfMsBZIMSXUMO+JFVP3LA12zb17St+o1jUdu+ZuVNx9/ceoRfNHMF\n", "/HxaJj/78O/cdrH7XQSdFgSh7TYIAlt8PG6/GiUIdMWC3k5B0FeYMu8G7nzlWXwOH6t74HrXzp/T\n", "5aKhq9v84x//yJgxYxg2bBiyLHP06FE++eQTpk2bdllhMn78eLKysnjhhRe45ZZbUBSFDz/8kKSk\n", "pMvOaJkzZw6HDx/mueeeY968eRQXF+P1etm+fTsHDhzgn/7pnyJcObHaS0hIYNasWXz44YfIsszw\n", "4cPZtm1blBtp5syZbNmyhT/84Q/MmzePtLQ0XC4Xx48fJzk52Qyc7Q5aFCbnzp3rcKPN/U+CgUNX\n", "CRxN14MCJVzUeFuMy1lvPROznRP+Wv6t8hmSFAejHTmMdeQwxpFLsSOLONmKJU5h0tcKyJmRQcnL\n", "5VyqcrL1fw6QOzODSd8oIC6xaxPCdSu6blgBwm/+AX+TKAgviykWWqir+pDVrl8/KYSlpQUerXYa\n", "MgsuLwjMp//2Wwh6WhDo8Q68A2y6sFOGz9NltMUdT3c+0Bg5ciQ7d+7kwoULyLLM0KFDufnmm2NO\n", "F47Ffffdx2uvvcYrr7xCUlIS1113HQcPHoyZhyscWZa599572bhxIzt37mTDhg1mSvoHHniAgoKC\n", "iPotuVgWLVqEpmmsWbMGWZaZMWMGhYWFvPPOO2Ydi8XC9773PT744ANWr15NQ0MDSUlJ5OXlMWnS\n", "pDaNs6NIegsSrXlkcXt47bXXOnxuT7H9k48vX6mfEsuV09/54R+eoHJBdLn8wRmSbyniohrps1WQ\n", "ybcPY4wjl7HxOYxx5JAmJ1O+4SRl/6hC82vYEixM+kYBuTMzutZHGrI+tCQgosqbyqy6iu71xBYV\n", "AR8S3ZOCXwc0xYpmsaFZrGhK8NViayqPON6WulZ0i40Xf/MUvyqMfn9/dFTnrof6h0u1rQyE755L\n", "9VLhOUW5+xRHPKf45OW1OG4xZpCc/PaGTrfv9Xo5f/58p9sR9G/S09NbjKlp0WJy7bXXRpXV1tZS\n", "VlaGw+EgPz+f1NRULl68yLFjx3C73YwbN67Vedh9iZd+/WTPBuAJOsVXr7mR/3r7BdTFTQn0lLdq\n", "+P7CbzJr1Axq/Zc45K7ioLuaQ65qjnvPUOGpocJTwwd1OwFIsyQxZlwOY/JzcLxvx1nuYfeLRzi5\n", "rYYZi4aSnEIL7ohWLBOxxIamtjSMTqPJSsSN3xQFUUKhPQLCZgQ5dpNVYcq8G/jJe6/wi8lNJu7/\n", "3HOeyYuWtnKWoCfwawGOe89yxH2S8qAYOek7FyF//ZKKmHQv6ElaFCbf/e53I/ZPnjzJT3/6U77y\n", "la9w2223RUTzulwuVq1axaeffsq//Mu/dF9vu5BfFUr85D0jeLJHxEmEYUqn6ZtvbEhRdaLrmbeN\n", "5m2FvYCOrOgoPk+L9SKeulu5phRe1sq1Jb1ZnWb1pBjjjVmn2TjC6w25dIHptV58rxzFq0CcCnGN\n", "Epmnj5JeKZGh+pkY8CEHrMhqNp5AOmXUs092sldxsdfq5UKggW0NZWyjDG6A0SOHc8XmUZw+3MgH\n", "v73ErKKjzMs+iSx3ziqhQwwLQ3NREFsgWOIT8Ki0UNcGcv+b9hz6fv3okzXY0PEhMXnRUvFQ0MNo\n", "us4p3zmOuE+ZIuSY9wwBPVJIWySF/LhhFDuyGGXP4u9xb3Cql/osGJy06Mppzq9+9SsaGxt5/PHH\n", "W6zz6KOPkpSUxA9/+MMu62B34fw3IxPg4zuO8tNrIqOb0fXIm3IrN9WW6vW/ELe+zePbKngsRi6F\n", "n22v4NEvXT6AUkOn0gYlDiiJ1/ncAZVxYHdZmbFpNPlHhgHQkNGAPucE49MUJurJ5CnJSLEsDa2I\n", "DV3uuPVhILgCWkOMr2fQdZ3zgXrTHVPuPkWF5xRuLTLAUQKybUMpdmRRbM9ilCObvLhMrHLTM+vW\n", "kl088/EruBYOEa4cQZfRIVdOc8rKyrjuuutarTN27Fg++uij9vWul7GgowRaTmrT1YTbCJrUi9R0\n", "LOqGFlnPPD+inhTxgiQ1GSNaqBdTOsWoqzfrY+v1mrUphcu26PFGt9W8XlNd2RqZadCsb0/kfMH0\n", "NsdATFSsjLfYWKpYadA8HHaf5GBuFRX7axixZghJtUlob43jw2kn+NWVh4mzWRnlyA4G1Q5llCOb\n", "eKXlXAMCQW/QoLqpCLOEHHGfioq7AhhqSaYoaAkpdmRTZB9x2c9zaDr+mx+thm93S/cFggjaLEz8\n", "fn+rGecALl68iN/v73SnepJLQ/MoveUhYt1UWxMRoTrQutgIr9dT9JWntq6kviR2kidnSianpnVs\n", "/Y4kOZ4rkkZxRdIomA/eWX52vn2Qc1vqmfh5PgWVw9k69wB7cirZ4zRyFEjAyLhMxjhyGRMMqh1u\n", "HSLW5RH0GF7Nz1HP6QgRctp/Iapegmw33TEhi8gQa8eSDF49bSYzxnfvTAyBIESbhUlBQQHbtm3j\n", "xhtvpLAwetpYZWUlW7dujXmsrxIKwNOs9t7uiuAy9EQAZZzDypylk7jwpQY+/1s51MB1b0/HMcPG\n", "6Xl1HNSrOeqp4bj3LMe9Z1l7cTcAyUo8Yxw5jI3PZYwjh0L7COLkfjQNWdBnUXWNKm+tKULK3Sc5\n", "7j2L1iymyyZZKLAPD7pjDGuIEMyC/kqbY0z27t3Lz3/+cxRFYfbs2YwfP56UlBQuXbpEaWkpmzdv\n", "Rtd1fvKTnzB58uTu7nen+e4tC5g8d2DOyhmIFhMw0prvDQ+g7Ma/nxbQOLzuJIc+rEIL6MQlWZmy\n", "pJD0Kckc9Z7mUHD2z0F3FfVqZO4BCzIF9hGMiW/Kq5LWxifVgfq3CyHG1zK6rnPWf5Fyj2EFKXef\n", "otJTg1ePtELLSOTEZZjumGLHCEbGZWKRujejsc/r6ZLVhUWMiQBajzFpszAB2L59O3/+859xOp1R\n", "xxISEviXf/kXvvSlL3W8pz2IyGPSf+nJ8TWcdlHycgXnK+oBGD5pCFNvL8IxxPhC6brOGX+dOU35\n", "kLuKE96zUdlGMqwpEQng8uyxbyTib9e/ac/4LgWchhUkKEQqPKeiRC5ApjXVsIIEhUihfTh22dbV\n", "Xb8sQpgIupIuEyYAHo+HXbt2cfToUVwuF/Hx8RQWFjJjxgzs9v7jEhHCpP/S0+PTNZ1jW86w/+1j\n", "BDwqFrvChEV5FMwZjhQjq6lL9XLEfdLMq3LEfTJq8cQ4yUqxI8twATlyGe3IJskSL/52/ZyWxufW\n", "fFR6aoIxISep8NRw1n8xql6yEh/hjimyjyDFktATXb8sXSVMAoEAqtp9uX4E/QNFUaIWAwzRbmEy\n", "UBDCpP/SW+NzX/SyZ1UlNXuMQMO0wiSm3VlM8oj4Vs9TdY1qby2HgmLlkLuaGl90sGK2LZ0JSfkU\n", "WQ03ULZtKPIAixEYDJ/NBmcjJzxnzWm65Z6TVHvPRcWF2CUrhY4RFNsNd8woezYZ1pQ+GxfSVcJE\n", "ILgcHRYmjY2NeDyefrsujhAm/ZfeHt/JL86zd1UFnkt+JEVizA05jL4+B8Xa9uRnlwJODrurTRdQ\n", "hecUPj1yvZoE2W64f4Kzf4od2Th6wYTflfT2366r0XSd074Lpjum0ldDpasm6m+pIJNnzzTdMaPs\n", "WWTHDUWR+k/CPCFMBD1Fu4SJ2+1m1apVbN68mfp6w+ceWhfnyJEjvPHGG9x+++39YmaOECb9l74w\n", "Pp8rQOnbxzi2xVhcMGm4g2l3FpNelNyh9vy6yjHPaSoDZ9h3qZJD7mouBCJXXZaRyIsbFhFU25ef\n", "sGPRF/52naHO38ART5M7ptx9Cqfmiao3wpbWFJxqzyLfPqzfz9QSwkTQU7R5urDL5eKRRx6hurqa\n", "vLw8kpKSOHnypHk8NzeXsrIyNm/e3C+EiUDQGWzxFqbdWUzuTGPV4obTbjb+Zh8Fc4Yz4dY8rI42\n", "f7UAsEoKoxzZTI4v5oak6QCc81/ikLuag8Gg2qOe0xz1Gv8+rPsMgFQlMUKoFNqHR2TtFHQcp+ox\n", "xUe55yRH3KeixCIYfwMjJiSLCakF5ErpJCpidRmBoKO0+RfszTffpLq6mm9/+9vMnTuXVatW8fe/\n", "/908brfbGTduHKWlpd3SUYGgLzJ0VArzfzKNQx9WcXjtSY5uOk3NvgtMWVJI1pT0yzfQWtvWFIZa\n", "U7gmeQIAHs1HhbumabFCdzUX1UZ2NBxkR8NBwFjnpMg+wlhV2ZHD6PgchlgSOz3OgY5fC3DMeya4\n", "jszJ4GJ20TNH4uU4iuwjgonLsil2ZJFmSTKtVv3dIiQQ9AXaLEx27NjB5MmTmTt3bot1MjIyqKys\n", "7Ip+CQT9BsUqM/6WPLKnD6Xk5XLqjjWy488HyZqWzpTbCrGndE1ciF22MSEhjwkJeYAxVbnGdyEo\n", "Uoyg2ipvrZFjxV3Nu8HzhlmHMMZhxKmMic9hZFxmv4pt6GpUXeOU77w5RfeI+yTHPWcIoEXUs0gK\n", "BXHDgu6YERQ7ssmypQ+4gGSBoK/RZmFy4cIFrrrqqlbr2O32mDlOBILBQEp2Av/noclUflpD6bvH\n", "OVVyntqDF5n41Xzyrh7W5bEgkiSRFZdOVlw681OnANCoujniPmkG1R7xnOSMv44z/jo21u8DDIEz\n", "yp5tuoBGO3JIUPrPVP/2oOs654KL2YXcMRWeGjwxFrPLCS5mF7KENF/MTiAQ9Axt/tbZ7XYz4LUl\n", "zp49S1JSx9ZiEAgGApIsUTQvixGT0/jitUrOlNZR8nIFVbtqmXZnMYmZ3Rt7kKg4mJZYzLTEYsCw\n", "DpzwnjWTvx1yG0Jln+so+1xHzfNy4zKCVpVcxgQtA/0pqDZEg+o2U7eH0rhfVKMfloZakg0R4sim\n", "yJ7VpsXsBAJBz9BmYVJcXMzu3bvNpGrNqauro6SkhOnTp3dpBwWC/kh8up1Z3x5H9e5z7H39KOeO\n", "1LP+5yWM/cpIRn05C1npGVeKIskU2IdTYB/OjcwAoC7QyGFXtekCqvDUUOWtpcpby0cXSwBIUhwR\n", "mWqLHVl9blZJaDG7I2Ei5LQ/eqHRRNke4Y4pdmSJuJt2smvn56x791MxK0fQI7RZmCxcuJCnnnqK\n", "p556im9961sRT1PV1dX86U9/wufzsXCh+OAKBGC4WnJnZJA5NpX9bx3jxPazHHj3ONW7a5m+rJgh\n", "eb1jXRxiSeSq5LFclTwWMAI/Kz2nm4JqXUZQ7e7GI+xuPAIYeTjy7cOMoNpgXpWh1pQe67OxmN1Z\n", "yt01HAkGp55oZTG7UcFpusWOLLGYXSfZtfNz3nl+LVfIc3q7K4JBQrvymLz++uu88cYbgJFOVlVV\n", "EhMTaWxsBODOO+/k1ltv7Z6edjEij0n/pb+O7+zBi5S8XI7rvBckKJo7gvE352GxN62Z0xfGFlpM\n", "LjwB3HHvmSgRkG5JNgNqxzhyyLcPx3qZheTaMj5j/aGLlLtPcsRziopgXEjzpGUyErlxGaY7ZpQj\n", "i9y4jG5fzK41+sLfrytxnvfwy0d+x3RpNgD/f+lPe7lHgsFAuzO/7t+/n9WrV3P48GEaGxuJj49n\n", "1KhR3HzzzUycOLG7+tnlbP1oPZJE2E+tRMRDlUS/fcoaaD+OzenP4wv4VA7+o4oj60+CDvFpcUy9\n", "o4hhE4YAfXdsbs1HufukmVflsLs6KrGYTbJQZM+KCKoNrfOytWQXb235ENUCSgC+es2NXD3NWBn6\n", "YqDRXMwu9NqgRr8Hw6xDKA6mcB/lyKKglxaza42++vdrD95GPyc/P0fVrlouVDaw+ewGZmfOB4Qw\n", "EfQMbRYmBw4cID4+nvz8/G7uUs/wxeebDVGig65poGNMFtQBTQ/uhwoBXTfrEzrP+F/YvrER8Y7q\n", "wX9S8F/4fnh7kvG/rhBHA+HHsTUGwvgunmjk85fLuVRlBGbmzsxg0tcLSB2W3C/Gpuk6J33nwoJq\n", "q2Pm/RhhSyPpmMbhkoNw03CzXH+/hsKpo2gsUKj1X4o6L1mJj3DHFNuzSLa0viZRX6C/fjYDPpXT\n", "+y5QtbOWMwcuomvGD5Nik9lW/zFX2v8PIISJoGdoszC5/fbbue6667jvvvu6u089wt69W3v0erqu\n", "E65jaLava7ohfoyXpuPBMkJlwU1T4BAmkIIv8fEOXC5XhECSdMnoQ1DnSKHmgmKpP1mP+uuPf3M0\n", "Vad8w0nK/lGF5tewJVi44s4xDJvSv9LMh2gIuDjsPsnBoFApd5/Cq/upf7eS5EXR2aDr36sk+ZZC\n", "7JKVIkeW6Y4pdmSRYemf70F/+mxqqk7t4YtU7aql5ovzBLzGU5gkQ+bYVHJmZpA1OZ2SvXvMGBMh\n", "TAQ9QZuDX5OSkrDZ+pbZtD8hSZJxg28q6bZrxSc4kJzRetPUoCFBFLU/8KxHfRlZkRh9XQ5ZU9P5\n", "4pUKag9dYtv/lpI5LpWpdxSRMLR/5RZJssRzRdIorkgaBUBAVznuOcvj9l8TK7tRdtxQflbwrX63\n", "mF1/Rtd1Lp5opGpXLdWfncPb4DePDclPJHdmBtnTh2JPbvqtn3mlMdPyo3c/7fH+CgYnbRYmEyZM\n", "4NChQ93ZF0E3Y97YTYEU60bf/TeIVq1Heuh4M+uRZogfTQVN1Vpsuz+SmOHgmgcmcGL7Wfa/dYyz\n", "ZRdZ//MSxt08kqK5WchK/xRkFkmhyDGCYUoqsfJBZ1hSGGnP7PF+DUYaa91U76qlalctjWebYoMS\n", "Mu3kzsggd2ZGqzl2Zl45nSlTxvdEVwWCtrtyTp06xcMPP8wNN9zAN77xDSyW/p0RsaddOT1JfEI8\n", "Lqert7vRbSiahfoLDUjywHvKlgIKO/9ygOrPzgGQOjKBaXcWk5rbf/NubC3ZxTMfv4Jr4RCzLP6D\n", "Or49f6kZADtQ6EuuHG+Dj+rdRhBr3bFGszwuyUrOjKHkzswgdWRimy2RYnVhQU/RZmGyYsUKzpw5\n", "w8GDB0lNTSUvL4/U1NSYdb/zne90aSe7AyFM+i/xCfFcOFWHFggGxwwgQje20/sv8MWrFbjrfEgy\n", "FC/IZtxXclFsvTcVtjNsLdnFW1vXoCo6iirx1atvGHCiBHpfmAQ8Kqf2nqd6Vy1nD15EDxoXLXEy\n", "I6akk3tlBhmjUztkhRPCRNBTtCv4ta289tprHe5QTyGESf8lND5XnRdd1ZHkgSNOwm9sfk+AsvdO\n", "UPFpDeiQkGFn2tIiMsbEfiDoD/T2jbu76Y3xaarG2bJgEOveC6i+UBCrxLDxqeTOzGD45DQsnRS1\n", "QpgIeoo2+2N+//vfd2c/BIJ240i14brgRdf1ARcYC2C1W5h8WyE5MzIoebmc+lMuNv93KXmzMpn4\n", "1XxsCX0rRbyg59B1nQtHG6jeVUv15+fwNTYln0srTDKDWOMSxWdE0P9oszDJzBRBaoK+hSRJxA+J\n", "w1Xn7e2udCtpBUnM+48pHF53kkMfVnF821lO769j8m2FZE/vn4vtCTpGw2mXOaPGea4piDVpuIPc\n", "mRnkzMjod7O5BILm9O8IVsGgR5IlHEPicF3wIA/AYNgQskVm7MJcsqelU/JyBecr6tm18hBVu4Yw\n", "5fYi4oeIlXEHKp5LPqp3GzNqLp5omnhtT7GZQawpOQlCoAoGDB0SJpqmUV9fTyAQiHl86NChbW7r\n", "hRde4OjRoxQUFHDPPfeY5ceOHeO5555DlmWWLl3K2LFjefvtt9mzZw8A5eXl/PGPf+TYsWOsWLGC\n", "zMxMhg4dyne/+92ODEnQj5FlCUeKDXedD9kycMUJQNLweOZ8fyLHtpxh/9vHOL2vjnOHP2fCrfkU\n", "zBk+oOJtBjN+d4BTX5yn6rNaag9dMufWW+wKWVPTyZ2ZQcboFPH3FgxI2iVMjh8/zssvv8z+/ftb\n", "FCXQ9uDXyspKvF4vjz/+OM8++ywVFRUUFRUBsGrVKh588EESExP59a9/zU9+8hMWL17M4sWLaWho\n", "4De/+Q0JCcY6HHPmzOGOO+5oz1BQNQ099G0PJvvSaR4HLCEFSyUz4ZcUzA8miSeUPoRiVbCn2vDU\n", "+wa05QQMK1HBnOEMnzSEva8f5dQX59mzqpKqXbVMu7OY5Ky+n7pdEI0W0DhzoM4IYt1Xh+YPBrEq\n", "EsMnDiF3RgbDJw7ptzOzBIK20mZhUl1dzSOPPALA5MmT+fzzz8nLyyMlJYXKykoaGxuZMGFCu6wl\n", "5bKXG1IAACAASURBVOXlTJkyBYBJkyZx+PBhU5g4nU7S0tIA8Hq9+Hw+M/Psrl27mDFjBmDEGWzZ\n", "soWysjKuv/56rrnmmjZde7hjSMS+ruumMNFpypKqB//TtOArulnXyPulmdvhSd1DZaHzjWvQ1L4p\n", "eEKvhO01ES6IQv8Xoig2FpuCLdGKryHQb5OStQdHahxX3T82KEwquHC0gQ2//IIx1+cw+oYcFOvA\n", "FmgDAV3TOV9ZT9WuWk6WnMfvbHrgSy9OJvfKDLKnDcUWL7zugsFDmz/tb775JoFAgKeeeoq8vDxu\n", "v/12rrzySr7xjW/g8Xh4/vnnKSkpaVcOE6fTaQbVxsfHU1VVZR5LSkqiqqqKlJQUTpw4gcvlihAm\n", "y5cvB6CwsJDf/e53+P1+nnjiCSZNmkRycnKb+xBCkqSgLSRU0KxCN/7Gm0KHsLTxBDOd6lqYAAIt\n", "mJhA03W0YI74cFGkoxvjCBNWoTp62P7lrESmEOpnViKb3QKqjt8dGJAJ2GKRNTWdoaNTKH3nGMc2\n", "n+Hg6iqqPz/H9GXFpBe1/7sg6H7qTznNIFbXhabg7eSseDOINT5NxA0JBidtFialpaVMnz6dvLw8\n", "syx0E7Xb7dx///388Ic/5NVXX+V73/tem9qMj4/H7Tbm/LtcLtM1A7Bs2TJWrlyJ3W4nPz/fFBtu\n", "t5uGhgYyMjLMawPExcUxbtw4Tp8+HSVMSktLKS0tNfeXLFlCYmL/zaR5OaxWK3775XNdxLISmSW6\n", "jqY3WYlCAsmUQ8H1cvSgBclsI9SC3mQVolnbUkhISVJwMUFDThlaSDcFYkgENRdFVouV+ISW3RXx\n", "CeCp9xHw9j9xYrVYIL7l1OAt4YiHq++dRPHsHHa+UEb9aRcbf7OPUfNymPKN4j7zxN3R8fUXWhuf\n", "64KHYztOc2zbaS5WNWVijU+LI/9Lw8n/0og+neFXCX6VVq1aZZZNmDCBCRMm9FKPBAOVNv9aNTQ0\n", "kJWVZe7LsozX26T0LRYLEyZMYNeuXW2++OjRo1m3bh2zZs1i3759zJs3zzw2YsQIHn74Yerr63np\n", "pZfMuIGSkhKmTZtm1nO73TgcDjRNo6KigptuuinqOrG+PI2NjVH1BgqJiYldPr7QunsAilkS/to+\n", "2mMlCtVVg1aigF3F43Ijt7bwmwI+v7f/ZYftZIKupFw7c388hUMfVnF47UmOfFxN1ednmXJ7IVlT\n", "0ruwox1kgCdYaz4+nyvAqRIjLfy58nrT22t1KGRPN2bUpBclm0GsPfPeaE0mVCnSYqpLMua3XSL4\n", "3TG2fUEv05IlS3qgj4LBTJuFSUJCAh5P2Lz5pCTOnTsX2ZjFgtMZax3R2BQUFGCz2XjsscfIz8+n\n", "qKiIlStXsnz5cjZs2MCmTZuw2Wzcd9995jm7du3i61//urm/bds2PvroIyRJYvbs2S2myRf0LSJc\n", "Z+3UDQmOBE55zuJSPSiS0qJ7yZ4SN6ATsLWEYpUZf0se2dOHUvJyOXXHGtnx54NkTU1nypJC7Cli\n", "lfDuRPVrnC6to2rnWc6UBpdOAGSLxPBJaeTOzGDY+CGdjAEyV7gM7obcr4YQ10OCwliWu0mcSxLI\n", "VpBkkOUw4RH+6NECsr/14wJBF9HmlPSPPPIICQkJ/PjHPwbgl7/8JUeOHOHpp58mNTUVj8fDv//7\n", "v2Oz2fjNb37TrZ3uCmoOlV6+Uj+lOywmfYnQ+DRd46LPiUfzYWlBoOi6juu8N/jb2/fFSVenNNc1\n", "ncqNNZS+cxzVp2F1KEz8aj55Vw/rlfdjoKak1zWdc+WXqPm8jhOfncHvVo0DEmSMTiF3ZgZZU9Ox\n", "Opo/CwYFhq6HWS+CAoOQwJAjrRfQJCxkCWSFpiXD2yAwOojP7+f/XDOrW9oWCMJps8VkypQpvPPO\n", "O3g8Hux2O9dffz0lJSX8x3/8B2PGjKGiooJz585x1113dWd/BQITWZJJi0tC1TUu+hrxan6scuRH\n", "WpIk4tOMBGz9yqXTRUiyRNHcLEZMTueLVys4U1pHycsVVO2qZerSYpKGDdx4j+5G13UuVQeDWHef\n", "w3PRZx5LyYknd+ZQcq5Ixz7ETkgw6M3cI0hKk+VClpvKuzPaXiDo47TZYnLhwgXKysoiZr188MEH\n", "vP766+aMmYULF3LHHXf0izwSwmLSf2lpfAFN5aK/Eb8ewCJFChRN1XDVefv8Z7M7LQq6rnNy9zn2\n", "vH4UX6Mf2SIxdmEuo67LRlZ65n3p2xaTcOsFLbpHXOe9VH9mxI001DQtlhk/1E7B7JEMn5FOUlZS\n", "29wj/QhhMRH0FG0WJi2hqioNDQ0kJyf3+R/9cIQw6b9cbnw+1c9FvxNV17DITcmoVL+K+6Kvx27C\n", "HaEnbtzeRj/73zrGie1nAWOK6rRlxaTlJ3XrdaGnhIkWmmbWQnAntNc94mv0c3LXaaq31XD+SJ3Z\n", "ni3RSvaVI8j50gjSilNJSEwYsCt7C2Ei6Ck6PYdQURQRcCroU9gUK5lKKu6Al/qAC13XUWTFyA6b\n", "bMNb70Pqw+Kku4lLtHLFXaPInZnBF69UUH/Kxae/3kvR3BGMvzkPi70vZBYNBnbqzYI7g1PKI2eP\n", "hMVXSBJI1i5xj6g+ldNfnKXq/7V35/FRlffixz/nzHpmyZ6QhEBZQ0hAQBZBUcReW3BBrICKL5Xr\n", "rfdVr62vq95XvS0vBfRnW6+1u9prb7VWpQWRahW3CrKKyL6ErQKBQAghLFlmJsucc35/hAwZEkLI\n", "Nku+7z80nHNmzvPMk8l85znf53vWl3BiZzmm3tAWi10lc1QGfcZnkzEsLe5vgyBEd4uO4gZCdAHN\n", "6kCzOvAFa6iq96MoClaHBcPbc6rDtiYjL4kb5o5k77Jivl5xjAOfH+f49tOMuGsgmQXJl36CS2qy\n", "cqRxaappgKETfnnk3Ad7s9UjliazF9Dm1SMdabFhcnLPKYrXl3B88wmCNeeTWDOGpZEzIYusUb1a\n", "SGIVQnSWNr+7FixY0OYnnTdvXrsaI0RXcFuduK1Oqur9+IK1WB0qBC3U1wZRWquF0gNY7Q2rdHJG\n", "p7Fl4ddUFPtY/9JucsakccUd38DhsZ27HKJcUPfiwiWpXHBZBKDJpRHl3AyG5sZUrERTcqdpmpwt\n", "quTolyUc3VBKbcX5+kxJ/RPpMz6L3ldl4UyUSqxCdIc2Bya7d+/uynZ0u4ap4Auniht+VkyjcbUe\n", "mMq5b3o0/GENVWu/YPpYRD2vzYXHqlFVH8DnrkHVVYx6M47u0Nrk8kdohgIafkdbDygS+yUw6YlR\n", "HFhxjL3vH+bopnLK9lYw7M7B9Lk6+3wA19EZC4tKtAQlvjI/xetLOPrlcapLz9dfcme4yJmQRZ/x\n", "2Xgy3a08gxCiK3Q4+dXn83HgwAHeeustsrKyeOSRR2IiCbakpOTSB5lNp6KbBDCmTkNddgOMxv3G\n", "uf3n/mPSZFvToKfxZyX8w6OxdEGziouX/yHQ05Nf28IwDSrqfZw+XYVFVyNcuv7875imaQQC/rYF\n", "FE1/Py42QxGawWh7QOEr87Pt9UJO7j4FQHpBKiPvL8Cd3vG7Frvcrogmh9ZW1nHsq+MUf1nCmQMV\n", "oe2OBDu9x2XSZ0I2Sf0T213jJdL960qS/Cq6S4cDk0bV1dU8/vjjTJ06lenTp3fGU3apNgUmXanp\n", "tfemyxRNo8nPjf9uIehpYbanMejxuD1U+6rPzfY0PjB+Zns6M/AKGjrHT5w6VwPFdpmPbucMRSsB\n", "hcvtwR8I0J6AojOZpknxuhJ2/nUv9b56LHYLebcPYuCN3+jQqqZIfHAHa4Mc31LG0fUllBWewjTO\n", "JbE6LGRd2Ys+E7JIz0/tlNVaEpgI0XGdlsHl8XgYOXIkn3/+eUwEJhHXtMhSJwlFmB4PpsXRPIAx\n", "DBpmeWgS8DQNcGh5hqjpbE+oxkPTS1w0CYJiIOhp0kerCTkZyVScrKbKqKXerMeiNCwVbXNA0Zi0\n", "2bgCBNofUFisoER+VYyiKPSd2JteV6Sxc+Fejm44TuGifRzbcJyRc4aR9I3ovmuxoRucLDyXxLql\n", "DL2uIYlVURV6jUinz/gsMkdlYHVIEqsQ0aZT35WapnHy5MnOfErRHkrjVP4F2zv4eRcKfJrO9jS9\n", "XNV0tsdoDHpame1p6RJXYy5P07M2vcSF0rCqQ9fDc39CrWsSUIRWeTRZUtq4CiQUUDT87HUno5QH\n", "0FWDyno/QVPHGgUBQqQ5EhyM+d4IciZks/3PhZwtqmTV0+sZ9O1+DLltEFZH9LxGpmly5mAFxetL\n", "OPZVKXVV5yuxpgxMImdCFr3HZuFIkHsFCRHNOi0wqaurY+vWrSQmJnbWU4poFTbb0zm5GS0GPRfO\n", "9jQGPpoH02jhJmQdmKFRLeDJsFBV7iPVmUSdXkdlXTUmtH4X4x4ic0Q6qf9vInuW/pODyw/zz48O\n", "UbL5BCPvLyA9P7J3La4u9YWSWH1l5y+jeLLc9BmfTc74LNwZHc+PEUJ0jzYHJitXrmwxIUzXdcrL\n", "y1m3bh2lpaXceuutndpA0cNc7BJX0y/mDu38Pdg7kWpRcSdr+E4HsFvtpGkpBPQafHX+c03r2QGK\n", "TbNyxT1DyRmfxbbXdlF5rJp1z2+k78TeDLtzCHZP981E1FTUcnTDcY6uL+FsUWVouzPJEUpiTfxG\n", "QkzcuFEIEa7NgcnLL7/c6n5FUbj22mu56667OtwoISLFarfiSnISqKhBtVjQLE40zYmvPoA/GDh3\n", "RahnBygpA5O4fv7V/POjQ+z7+wGOrD3GiR0nGT57KL3HZXZZMFAfCHJ88wmKvyxpWDF0bjLN6rSQ\n", "PSaTnPFZpA9NjaPl39HDNE3M8/OaQnSpNq/KWblyZctPoCi43W4GDRoUU6XpI74qpwt5vV6qqqoi\n", "3Ywu0x39q/XVUVtdi2o5P1VjYuKvD1AdDGBB6ZIZlFhb1VF1vJptrxdyal/D/WN6jUhnxL35uFJb\n", "vmvx5fbPCBqc2FXO0S9KOL6tDKO+IWdJsSj0Gp5On6uzyRyRjsUeHbkusTZ+l2KcyxFzWOw4FTvD\n", "RudHuEWiJ+i05cKxRgKT2NVd/QtU1RIM1DercWJgUF3np0avQ0Xp1BmCWPxgMw2Tw6uPsmvxPoKB\n", "IFanhfw7cul/Q99msxdt6Z9pmJz++izFXzYksdb76kP7UnOTyRmfRe+xmd166aitYnH8LmSaBjom\n", "NsWC26bhsDRUvNV1naGjhkS4daInkLVyQlyE5nXg1030+vDS9SoqCXYPbgyq66qp0et79AoeRVXo\n", "d30feo1IZ8dbezi++QQ73tpD8ZcljPrXYST0bttdiyuPVXF0fUPxs8CpmtB2b28PfSZkk3NVFq60\n", "lmdiRMcFTR0LCprViWZzovbwS5YicmTGJA7JjEnn8p32YwQvXrpeN3Uqa6upM4MdDlDi4Rt3yeYT\n", "7HhzNzVna1EsCrk3D6Ay7TSff/A5qqFiqAbfvO0Gxl49jsCZGo5+eZyjX5ZQceT8mGrJTnqPz6LP\n", "+CwS+nhjJok11sbPNA0MTOwWGx6bC6ty8e+qMmMiukubA5M777yz3SdZtGhRux/bVSQwiV3d3T/T\n", "NKk+1VAmvrUPSN0MUlFbTX0HaqDE2gfbxdT76ylcsp+iz4sp9hVxuOYAE1O/Gdq/IbCKIdkFJJ1N\n", "O5/EqlnpPTaTnAlZpOWmxGQSayyMn2maGBioqLhsTjSrE6UNxQAlMBHdpc2XcoYOHYrP5+PIkSMA\n", "pKWlkZSUxNmzZykvLwegb9++uN3hN72KlW86QlyMoih4UlxUlfta/X22KFZSQjVQfBiYWHroEmOb\n", "y8bI+wrIGZ/Fc098HhaUAFylTWLNvs+YlP0tMkemkzM+m15XpGOx9czXqzs0TWR127RzFY6FiD5t\n", "DkweeeQRnnzyScaNG8e9995LRkZGaN+JEyd44403KCoqYu7cuTG1OkeItlBUBU+qi+py/yXvqWK3\n", "2EnT7NToNVT38BooabkppPRLgrPN93kzPUz59WTsrsu9R5Foq6aJrAl2dyiRVYho1ua/lgsXLsTt\n", "dvPYY4+FBSUAvXr14rHHHkPTNN58881Ob6QQ0UC1qLhTXRi63qbjnRYnaVoKLpuGYRqYGF3cwuik\n", "2FueZdLSnBKUdJGgqWOaBprVSbqWTIozSYISETPaHJhs376dkSNHXnQqW1VVRowYwfbt2zutcUJE\n", "G4tVxZWstTk4AXBZNdK0ZDSLhm4amGbPClC+edsNbGZd2LbN5jpuuO2GCLUoPpmmgW7qWFSVVGci\n", "aVoKbptLVteImNPmSzmBQACfz3fJY/z+6E78EqKjrHYrzgQHgco6LJe4rNNIQcFt03DZnFTX+wkE\n", "a1BRe0QO1tirxwGw4r0VKIaKqRpMu+3W0HbRfqZpomNgQcVl09qcyCpENGtzYNK7d2/Wr1/P7bff\n", "TlpaWrP9J0+e5IsvviAnJ6dTGyhENLJrdkwDan21qGrbkwgVFLw2N26bRlUPqoEy9upxjL16XEys\n", "WokFksgq4lmbA5Np06bxm9/8hieeeIIpU6aQn59PYmIiFRUVFBYW8vHHH+P3+5k2bVpXtleIqOFw\n", "2zF0g2BNsFl12EtRUUm0J+DBoLK2mnqjXj5cRKskkVX0FG0OTK655hrOnDnDW2+9xZIlS5rtt1gs\n", "3HvvvVxzzTWd2kAhopmW4MRv+NHrjXatvLGgkuxICNVA0Y22566InkEqsoqe5rJK0t9yyy2MGzeO\n", "tWvXcvDgQQKBAJqmMWDAAK699lrS09O7qp1CRC1XkovqU35M/eLVYS+lsQaK3WmnxH+iR9dAEeEV\n", "WRNtnlYrsgoRby77tz0jI4PvfOc7XdEWIWKWO0WjutyPaZodSmi1W2ykacmhGigmoEqA0iNIIqsQ\n", "DSQMF6ITKEpjATYfdMJKG6fFiVNz4tdr8NX5URRQZAo/LummjoIiiaxCnNNqYFJbW8vZs2fxer24\n", "XK6wfWVlZbz++usUFhZimiZDhw7lvvvuIzs7u0sbLES0UlQFdxurw7aVy+LEpTnx1QfwBQOo9Nwq\n", "svGkaSJrot0jiaxCNNHqX7hPPvmERx55hKNHj4ZtDwQCLFiwgE2bNhEIBKipqWHr1q3Mnz8/rm8e\n", "J8SlqBYVV4qGEezcJFa3TSNdS8ZpdaCbOj30puAxTyqyCnFprQYmu3fvJjU1ldzc3LDtn376KeXl\n", "5eTm5vKb3/yGV155hSlTplBRUcGHH37YpQ0WItpZbRa0pM4PThQUPDY3aVoyNov13IecBCjRzjQN\n", "DNOQiqxCtFGr74xjx46Rl5fXbPuGDRsAeOihh+jVqxeJiYnMmTOHjIwMtm3b1jUtFSKG2JwN1WF1\n", "vfPLzzfUQPGSpiVjtVgImrLEONqYphkKHF22hlsSJNkTZHWNEG3QamBSWVnZbAlwMBjk0KFDZGdn\n", "h+WTKIpCQUEBpaWlXdNSIWKM3WXH6bFhdFFtEgsqSfYE0pxJqCjoPewePNFIN3QM02hYXeVMIk1L\n", "xmXVZHWNEJeh1fA9GAxSV1cXtu3o0aMYhsGgQYOaHZ+YmEhNTU3ntlCIGOZwOzCCJsHay68O21YW\n", "xUKyM5F6o57KOh9BU+8RZe6jRdNE1mRnAh5Ti3SThIhprQYmiYmJFBcXh23bt28fAAMGDGh2fCAQ\n", "wOPxdGLzhIh9WqIT/xk/RtDslKXEF2NTbaQ6k6jVa6mq80kNlC7WUkVWh8VOPfWRbpoQMa3VwCQv\n", "L48vvviCXbt2MWzYMGpra1m+fDkAV1xxRbPjjx49SkpKymU14E9/+hOHDh2if//+zJkzJ7S9qKiI\n", "P/7xj6iqyt13301eXh7vvvsu27dvB+Drr7/m97//PU6nk5deeomTJ09y5ZVXMn369Ms6vxDdwZXc\n", "UOOkowXY2sJhceDQHATO1UABWWLcWUzTwARsFqtUZBWii7T61+qmm27CNE2effZZnnjiCb7//e9z\n", "+PBh8vPz6d27d9ixfr+fffv2MXjw4Daf/ODBg9TW1rJgwQKCwSAHDhwI7Vu8eDGPPvooc+fOZenS\n", "pQBMnz6defPm8dhjjzFo0CDcbjebNm0iJyeHp59+mr1793L27NnL6b8Q3cad6urWVTSaxUmaloJm\n", "1TDOrQwRly8skdXukkRWIbpYq4HJoEGDePjhh7Hb7RQVFVFZWcnAgQN5+OGHmx27cuVKgsEgI0aM\n", "aPPJv/7669Dxw4cPZ//+/aF9Pp+PlJQU7HY7tbW1YbkuGzduZMyYMQD885//DM3eDBs2jK+//rrN\n", "5xeiOymKgjfNjWl0b4DgPrcqRAvVQJEApS10s4VEVouUiReiq10y5L/uuuu46qqrKC4uxuv10qtX\n", "rxaPGzNmDPn5+eTk5LT55D6fj4yMDABcLldYPovX66W4uJjExESOHDmC3+/HbrcDDYHJAw88ADTM\n", "1GiaFnoOv9/f7DyFhYUUFhaG/j1r1iy8Xm+b2xlr7Ha79C+Kedweqk5Wo1qbfy+w2e10VZqWFw+G\n", "aVBV56cmWIOiqF1+WelCje/haGWYBqZpYrPY8Ng0HJbLa29Xjl+k6fUNq8sWL14c2lZQUEBBQUGk\n", "miTiVJvmIh0OR4urcJpqDDAuh8vlIhAIAA0BhtvtDu275557ePXVV3E6nfTr14+EhASgIcG2qqoq\n", "tIy5aTDi9/vJzMxsdp6W3jzxXKHW6/VK/6KcYdepPuNrVrre44Hqal+XntuCghMH1bXV1Oj13b6C\n", "x+9r/uUh0s4nsmq4bA7UoEp9sP6yE1m7Y/wiRdcbApNZs2ZFuCUi3rU7I66oqIhVq1Z16OS5ubns\n", "3LkTgJ07d4ZVmM3KymLu3Lk8+OCDpKamop5barl161ZGjRoV9hy7du0CGmZGLhVACRENrHYrWqID\n", "Q49McTQLKolNaqD0xCJtjRVZrapKWqgiqyYVWYWIsHa/A7/66iteeumlDp28f//+2O125s2bh8Vi\n", "YeDAgbz66qsArFixggULFvDiiy9y5513hh6zceNGrrrqqtC/R48ezZEjR3jqqacYMmQISUlJHWqT\n", "EN3F5rTh8DgwujnnpKnGGiipjoYZyXgPUFqqyJpoT8AiiaxCRA3FbOcygcWLF/POO++waNGizm5T\n", "tygpKYl0E7pMPFzqaE289S9QVUswUI+iqng87oheCqjT66is82FgYumCJcYutysil3J0U0dVFOyq\n", "HbdNw9JFl68iPX5dSdd1ho4aEulmiB5AviYIEWGa14FfN9Hrg5FuCnaLnTTNHhc1UEzTwABsigWP\n", "3SN38RUiRkhgIkQUcCU1VIc1jei4W7BmcaJpTvzBAL76AIoCSozkXjRNZNVsDskZESLGtDswcbvd\n", "pKWldWZbhOjRtCQNpYZuqQ7bVi6rhmZ14q8P4AvWoBKdMyiNFVntFitJNo/kjAgRw9r97r355pu5\n", "+eabm22vrKwMLe0VQrSdoih40jxUVlVHTWACoKDgtrlw2TSq6/0EgjWodH8NlAuZpomOgVWx4LK7\n", "0CwOKX4mRBzotK8+Pp+PhQsX8v3vf7+znlKIHkdRFTypLgw9+qqzKih4bW7StGRsFkvEVvDopo6J\n", "gcNqJ82ZRKozSSqydjHTNCIeiIqeo00zJmVlZRw8eBCbzcbgwYPDZkTq6ur44IMPeP/998Oqswoh\n", "2ke1qHjSGm76p1q6t/hZW6jnaqB4TJ3KOh/1Rn2XrXJp1DSR1Wv3Yr/MiqyifUzDxDRN7G4bDre8\n", "5qJ7XDIw+eMf/8inn34a+rfdbuff/u3fuP766yksLOTFF1/k1KlTWK1Wpk6dyu23396lDRaiJ1At\n", "Kq5kDd/pABZr9AUncK4GiiMB3QxSUVtN0DQ6fYmxJLJGhmmamIaJTbPi9DhQVJktEd2n1cBk5cqV\n", "fPrppyiKQnZ2NgDHjh3jlVdewWaz8dJLL2EYBjfeeCPf+c53SElJ6ZZGC9ETWO1WXElOAhU1UTlz\n", "0siiWElxJlFv1FNRW93hGiiSyBpZRlDH6rDiTHA0u2WCEN2h1Xf8qlWrsFgszJs3jyFDGgrr7N69\n", "m2eeeYbf/va3pKam8sQTT9C3b99uaawQPY3NacPQTWqra6M6OAGwqTbStGRq9BqqL7MGiiSyRp6u\n", "G1itCp50twQkIqJa/e07fPgw48aNCwUlAPn5+YwbNw7TNPne974nQYkQXczhtmNz2TGjMCG2JU6L\n", "kzQtBZdNa7hbLxdvtySyRp5h6ICJO9mJO1WCEhF5rc6YXOxuvY3bmgYsQoiu01AdVkevN6KyjkhL\n", "ztdAqcEXDIS+BTUmstpVC16bJLJGimkYoICW4MTmtEW6OUKEtBqYmKaJ1dr8EMu5KWVZgSNE93El\n", "ufCd9mMEzZhJRmyogaLhsjmprvdjYkoia4SZpoFpNszEyUobEY3alVUm69mFiAxXskZ1uT+qqsO2\n", "RWMNFI/mplqPz5vcRbuGpb8G9nMBSSz9/oiepdW7C995553tetJYuOOw3F04dsVz/9rSN9MwqT7l\n", "i5lLOk3F8913ITr7Z5omhm5gc9rQEjq29LdxdaYQXSn2/rIJ0cMpqoI7xYURjI1kWBE5RlDHYlXx\n", "prtxJTlj5hKg6NlavZQTCzMfQvREqkXFlarhP+VHjdICbCJyGpf+utPcWKzy/VPEFvmNFSJGWW0W\n", "tGQNQ4/MPWtE9Llw6a8EJSIWSUlFIWKYzWHF9DoIVNVhkfoTPZYs/RXxRAITIWKc3WXHNE1qfXWo\n", "qlzW6UlMw8TElKW/Iq5IYCJEHHC4HRhBk2BtEEWVmZN413CTPQOby47TI0t/RXyRv2BCxAkt0YnF\n", "rmKaslonnulBHYvNgjfdg+Z1SFAi4o4EJkLEEVeSC0VRMI2LlicSMcrQdVQLsvRXxD0JTISIM+5U\n", "FygN0/0i9hm6jqKYuFPduJJdcpM9EffkN1yIOKMoCp5UF0hgEtMa7iZtoiVpsvRX9CiS/CpEHFJU\n", "BXeqi+pyv3zDjjGNS3+dCQ5smiz9FT2PBCZCxCnVouJK0fCfDkhwEgNk6a8QDSQwESKOWW0WVY0s\n", "vgAAHC1JREFUtEQngbMBKV0fpUzTxNQNbC4bTlllI4QEJkLEO5vTipkg1WGjkR7UsTmtaKkuWWUj\n", "xDkSmAjRA9hddgzDpN5fLwXYooCu61htFrzpbrnMJsQFJDARoodwehqqw+p1Uh02UgxdR7WquFNc\n", "WG1yaU2IlkhgIkQP4kpy4j/jxwiaILkM3cbUDVBBS9KwOeTPrhCtkXeIED2MlqThO+XHNE1JtOxi\n", "pmmACQ6vHbtLVtoI0RYynytED6MoDTVOpDJs1zENE0M3sLtseDM8EpQIcRkkMBGiB2qsDmvocsO/\n", "zmSaJkZQx+q04M1w43A7It0kIWKOBCZC9FCqRcWT5kLX9Ug3JS4YjXf9zfCgJTjlMpkQ7SSBiRA9\n", "mGpRcSdr6EEJTtpL13UUFTzpbtzJUo9EiI6KePLrn/70Jw4dOkT//v2ZM2dOaHtRURF//OMfUVWV\n", "u+++m7y8PAzD4M033+Tw4cN4PB4effRRCgsLeemll8jIyCAtLY2HH344cp0RIgZZ7VZcSU4CFTWo\n", "FlnC2lay9FeIrhHRwOTgwYPU1tayYMEC/u///o8DBw4wcOBAABYvXsyjjz6Kx+Ph5z//OT/+8Y/5\n", "8ssvycnJ4b777gs9h6IoXHvttdx1112R6oYQMc/mtGHoJrW+OlSpcdKq0NLfRCc2p9xkT4jOFtG/\n", "QF9//TUjRowAYPjw4ezfvz+0z+fzkZKSgt1up7a2lrq6OrZs2cLRo0dZsGABy5cvDx27bt065s2b\n", "x7p167q9D0LEC4fbjk2zNdzdVjRjmgamYeDw2vGmeyQoEaKLRDQw8fl8OJ1OAFwuFz6fL7TP6/VS\n", "XFxMZWUlR44cwe/3U1FRQe/evXnyySdZu3YtFRUVDBgwgF//+tf8+Mc/5qOPPqKysjJS3REi5mle\n", "Bxa7taH+hgDCl/560t2y9FeILhbRSzkul4tAIACA3+/H7XaH9t1zzz28+uqrOJ1O+vXrh9frxeVy\n", "kZ+fj6qqDB48mNLSUoYMGQKAw+Fg6NChlJaWkpCQEHaewsJCCgsLQ/+eNWsWXq+3G3oYGXa7XfoX\n", "o6Khb16vl+pTPoyg0emJnDa7HY+nU5+yy5imiWmY2DUbzjausomG8etqixcvDv1cUFBAQUFBBFsj\n", "4lFEA5Pc3Fz+8Y9/MGHCBHbu3MnkyZND+7Kyspg7dy6VlZX8+c9/xmKxMGTIEA4fPkxWVhZHjhxh\n", "ypQpBAIBNE3DMAwOHDjAzTff3Ow8Lb15qqqqurx/keL1eqV/MSpa+mbaTKor/WDSqctePR6orvZd\n", "+sAIa6hFYsXpdRBUg1RXV7fpcdEyfl3F6/Uya9asSDdDxLmIBib9+/fHbrczb948+vXrx8CBA3n1\n", "1Vd54IEHWLFiBWvWrMFut/Pd734XgBtuuIHf/e53LFu2jJEjR5KSksKKFSv47LPPUBSFiRMnkpSU\n", "FMkuCREXFEXBk+KiqtzXo+px6LqB1abikbv+ChExitlD61KXlJREugldpid8a4vX/kVb3wzdoLrc\n", "32kf0h6POypnTAxDR1VVnAkOrPb2f1+LtvHrbNnZ2ZFugugB5CuBEOKiGqvDGnFaHdY0DEzTQEtw\n", "4klzdygoEUJ0DnkXCiFapVpUXMka/jOBuCnAZpoGptmwRNrhllU2QkQTCUyEEJdktVvREp34K2qx\n", "xHDuhWmYGKYRCkh6Uv6MELFCAhMhRJvYnDacukmtrxZVja2ZE9NsqEVic9rwJMj9bISIZhKYCCHa\n", "zOG2Y+gGwZogSoyUrjeCOlaHFXeKJitthIgBEpgIIS6LluDEb/jR6w0UJXo/6HXdwGpVZOmvEDFG\n", "3q1CiMvmSnKhWlRMI/qqDRiGDpi4k524UyUoESLWyDtWCNEurmQNlIb8jWggS3+FiA/yzhVCtIui\n", "KHhSXVSf8gGRSyY1zt0NWZb+ChEfZMZECNFuiqrgTnFh6N1/N+KGlTY6Ns2GN90tQYkQcUICEyFE\n", "h6gWFVeKhhHsnuqwpmmiB3UsNgvedA+a1yH1SISIIxKYCCE6zGqzoCV1fXBi6DoWq4I33Y0rySn1\n", "SISIQ5JjIoToFDanFTPBQaCqrtOrwzYEJCruVDcWq3yfEiKeSWAihOg0dpcd0zSp9dV1SnVY0zBQ\n", "VAUtScPmkD9XQvQE8k4XQnQqh9uBETQJ1ra/OqxpGKCA0+vAptk6uYVCiGgmgYkQotNpiU78Zy+/\n", "OqxpmJiYsvRXiB5MLtYKIbqEK8mFoihtKsDWuPTXqlll6a8QPZwEJkKILuNOdWGaZqvBiSz9FUI0\n", "JYGJEKLLKIqCN80NLQQmuq6jqMjSXyFEGAlMhBBdSlEV3Knnq8Maug6KiSfVjTvFJTfZE0KEkeRX\n", "IUSXa6wOS9CUpb9CiFbJVxUhRLew2ix4M7wSlAghWiWBiRBCCCGihgQmQgghhIgaEpgIIYQQImpI\n", "YCKEEEKIqCGBiRBCCCGihgQmQgghhIgaEpgIIYQQImpIYCKEEEKIqCGBiRBCCCGihmK25Z7kQggh\n", "hBDdoEfOmCxevDjSTehS0r/YFc99A+lfrIv3/ono0CMDEyGEEEJEJwlMhBBCCBE1emRgUlBQEOkm\n", "dCnpX+yK576B9C/WxXv/RHSQ5FchhBBCRI0eOWMihBBCiOgkgYkQQgghooY10g3oSsXFxbzyyiuo\n", "qkpOTg4PPvhgaN/p06f57W9/SzAYZNasWQwfPjyCLW2f1vq3ePFiNm7ciMfjYfTo0dxyyy0RbGnH\n", "fPDBB3z11Vc8/fTToW3xMH6NWupfPIxfWVkZc+fOJScnB6vVyty5c0P7Yn38WutbPIwdwKpVq1i9\n", "ejWGYfCDH/yAlJQUIPbHTkS/uA5MsrOzeeaZZwB46aWXOHToEP379wfg3Xff5e6776Zv374899xz\n", "Mfnmaq1/iqJw3333xWS/mqqvr+fw4cMoihK2PR7GDy7ev3gZvyuuuIIf/OAHzbbHw/hdrG/xMHan\n", "T59mz549PPnkk832xcPYiegW15dyLBZL6Oe6ujrcbnfo38XFxeTm5uJ0OnE6nQQCgUg0sUNa6x/A\n", "W2+9xTPPPENRUVE3t6zzrFixgkmTJnFhjnY8jB9cvH8QH+NXWFjIvHnzWLZsWdj2eBi/i/UNYn/s\n", "tm3bhmEYPPPMM7z66qsYhhHaFw9jJ6JbXAcmAJs2beLxxx/HbreTkZER2t70jeZyufD5fJFoXodd\n", "rH9Tp07lZz/7GQ8++CCvvfZaBFvYfsFgkN27dzNs2LBm++Jh/FrrXzyMX0pKCr/5zW+YN28eO3fu\n", "5MiRI6F9sT5+rfUtHsauoqKCYDDIk08+icPhYNOmTaF9sT52IvrFfWAyZswYXnjhBZxOJzt27Aht\n", "V9XzXQ8EAng8nkg0r8Mu1r/G/mRmZkaqaR22evVqJk6c2OK+eBi/1voXD+NntVqx2+2oqsqVV14Z\n", "9uEd6+PXWt/iYezcbjf5+fkADBs2jKNHj4b2xfrYiegX14FJMBgM/exyucL+3bdvX/bv309NTQ2B\n", "QACn0xmJJnZIa/1rnF6trKxE1/Vub1tnOH78OJ9++ik/+clPKC4u5uOPPw7ti4fxa61/8TB+NTU1\n", "oZ/37dsX9kEd6+PXWt/iYexyc3M5fPgwAIcOHaJXr16hfbE+diL6xXXy67Zt2/jggw8wTZOMjAxG\n", "jhzJq6++ygMPPMBtt93G7373O+rq6pg1a1akm9ourfXvjTfeoLi4GNM0ueeeeyLd1HZp2u558+Yx\n", "ZcqUuBq/1voXD+O3Z88eFi1ahM1mY+jQoQwaNChuxq+1vsXD2PXr1w+73c6CBQvwer3ccsstcTN2\n", "IvpJ5VchhBBCRI24vpQjhBBCiNgigYkQQgghooYEJkIIIYSIGhKYCCGEECJqSGAihBBCiKghgYkQ\n", "QgghokZc1zER0Wv+/PmhWhDx4vjx47z55pvs37+fyspKXC5XzJYkF0KISJHAJIbdeeedAKSlpfGr\n", "X/0Km83W7JiHH36Y8vJy/vKXv4SVkhadyzAMnn/+eU6cOMF1111Hampqi+NxMceOHeOTTz6hsLCQ\n", "8vJy6uvr8Xq99O/fn3HjxnHddddhtcrbNdJWrlzJyy+/zEMPPcT1118f6eYIEZfkL10cKC8vZ9my\n", "ZUyfPj3STemxysrKOHbsGN/85jf593//98t67JIlS3j77beBhlLgkydPxul0cvbsWfbs2cP//u//\n", "8o9//IOf/vSnXdF00Q6KokS6CULELQlMYpzb7UZRFN577z2++c1v4vV6I92kHun06dMAJCcnX9bj\n", "li5dyttvv01aWhqPPvoogwYNanbMtm3beP/99zulnaJzSMFsIbqOBCYxzuFwcOutt/L666/z9ttv\n", "88ADD1zyMYWFhTz99NPMmDGDmTNnNtv/8MMPA/Diiy+GtjWdwk5JSWHJkiUUFRVhs9kYPXo0c+bM\n", "weVycfDgQRYtWsT+/fvRdZ1hw4bxr//6r6Snp7fYlmAwyJIlS1izZg1nz54lJSWFSZMmMX369BYv\n", "XRw7dox3332XXbt2UVFRgdvtZvjw4cyYMYPs7OywY1988UVWr17Nb3/7WzZv3szy5cspLS1l8ODB\n", "zJs375Kv08GDB1m6dCl79+4lEAiQlJTEqFGjmDFjBklJSaHjGi+pQcPsx5IlSwAu+vo2Kisr4+23\n", "38ZqtfKjH/2InJycFo8bOXIkw4YNa7b9iy++4JNPPqGoqAhd18nMzGTixInccsstzV67xjF94YUX\n", "+Otf/8qGDRuoqqoiKyuLmTNnMm7cOHRd591332XVqlWcOnWKlJQUbr75ZqZMmRL2XE1/f0aMGMGi\n", "RYs4cOAApmmSm5vL3XffzYABA5q11+/38+6777JhwwbKy8ux2+0MGjSIadOmMXz48IueY+zYsfzl\n", "L39h37596LrOwIEDmT17Nrm5uc3Ooes6n332GatXr+bo0aMYhkF2djaTJ0/m29/+dthMR1lZGT/4\n", "wQ+YNGkSM2bMYOHChezcuZOamhr69u3LzJkzufLKK0PHN+ZFAbz88su8/PLLoX0vvvgiaWlpBAIB\n", "li1bxvr16ykvLwcgISGBgQMHMm3atBZfFyFEOMv8+fPnR7oRon2WLFmCpmk88sgjrF27ll27dnHN\n", "NdeE3Yb8ww8/xO/3M2PGjNAf5ZMnT7Jq1SoKCgpCtzZv6sMPP0RRFG666abQtqKiIjZt2oSiKLz9\n", "9tv079+fESNGUFdXx5YtW9i3bx/Z2dksWLAgdENBq9XKtm3b2LFjB9/61rfCPhRWrlxJeXk5hw8f\n", "ZsuWLVx11VXk5uZSUlLCV199RVFRERMnTgxr17Zt23j66ac5cuQIw4YN48orr8Tr9bJhwwZWrlzJ\n", "iBEjwmYsNm7cyOHDhzlx4gSrVq1i6NChjBgxgvT09BY/6JvavHkzzz77LKWlpYwePZpRo0YRDAZZ\n", "v34969atY9y4cbjd7tDx6enpHD58mPz8fK6//noKCgooKCi4aEAGsGzZMnbv3s2ECRO48cYbW23P\n", "hflBCxcu5PXXX6euro6rr76avLw8jh8/zhdffMHevXuZOHFi2GM+/PBDdF1n06ZNFBcXM2rUKPr0\n", "6cPevXtZu3Ytubm5vPHGG2zZsoURI0YwaNAgioqK2LBhA71796ZPnz6h52r8/bHb7bz99ttkZWUx\n", "ZswYPB4PW7duZdWqVeTn55OWlhZ6jM/n48knn2Tjxo1kZmZyzTXXkJ6ezvbt21mxYgXJyclhH9qN\n", "53A4HCxatIjk5GRGjx5NYmIi27dvZ82aNYwfPz5shjAYDPLcc8/x8ccf43Q6GTNmDIMHDw6N/4kT\n", "Jxg3blxYmz766CPcbjfvvvsuqqoyevRoevXqxa5du1i7di1Dhw4lIyMj7LUvKSlh7NixXHPNNaFx\n", "zs/Px2q18vTTT7NmzRoyMzND59c0jd27d9OrV68WZ8SEEOFkxiQOWCwWZs+ezS9/+UvefPNN/uu/\n", "/qvLzrV582aeeuophg4dCjRMaT/77LPs3LmTn/70p3zve98LCyh+//vf8/nnn7N582bGjBnT7PlK\n", "Skr45S9/icvlAuCuu+5iwYIFbNmyhdWrV3PdddcBUF1dza9//WucTicLFiygd+/eoecoLi5m7ty5\n", "/P73v+e5555rdo6ioiL+53/+p9UgoamamhpefPFFTNPkqaeeIi8vL7TvvffeY+HChfzhD39g7ty5\n", "AMycOZPCwsJQsDdjxow2nWfv3r0AlwySLrR//37ee+890tLS+MlPfkJiYiIAs2fP5vnnn2fLli28\n", "//773H777WGPO3PmDAMGDGD+/PmhGZXrrruOefPm8Ytf/ILs7GxeeOGF0Fjccsst/Od//ifvvfce\n", "V199dbN2bNu2jQceeIBvf/vboW2bNm3i+eef5+WXX+ZXv/pVKBh96623OHbsGP/yL//Cgw8+GDp+\n", "+vTp/Pd//zevvfZaKGhsauvWrfzHf/wHkyZNCm377LPP+MMf/sCHH37Id7/73dD2pUuXsmPHDqZM\n", "mcKcOXNC5zYMg1deeYXPP/+c8ePHN/s93L17NzNnzgwbt4kTJ/KTn/yEv//97xQUFACEkl03bdrE\n", "2LFjw9oEcOTIEfbv38/YsWNbfA/6fL5m24QQzckyjTgxfvx4cnNz2bhxY+gDrytcc801oaAEGpIA\n", "G4OHfv36NZvlaNxXVFTU4vPdcccdoQ9CAJvNxuzZswH4/PPPQ9tXr16N3+9n1qxZYUEJQJ8+fbjh\n", "hhsoKiri6NGjzc4xbdq0Ngcl0DDT4vP5mDBhQlhQAnDrrbeSlpbGjh07QlP17XXmzBkAUlNTL+tx\n", "K1asAOA73/lOKCiBhlmV++67D0VRQsdcaM6cOWGXefLy8khPTycQCHDPPfeEjUVGRgZDhgyhuLi4\n", "xZyKzMzMsKAEYMyYMeTn51NaWhq67BEMBlmzZg1OpzM0tk2fY+rUqQSDQVatWtXsHHl5ec0CgMmT\n", "J6OqKgcOHAhtMwyDjz/+mKSkJO6///6w2TlVVbn33nsBWLNmTbNzpKenc8cdd4RtGzFiBKmpqWHn\n", "aCu73d7i9qYzbEKIi5MZkzhy77338uSTT/LGG2/w7LPPdsk5Bg4c2GxbY75FS9fPU1JSgPPJoRdq\n", "6VLSkCFDUBQlLJjZv38/0BDgLF68uNljjh8/DjTkoFyYq3G50+eHDh0CWp7JUFWVoUOHsmbNGoqK\n", "isIuV3SX1tqXlZVFSkoKZWVlBAIBNE0L7XO73c0uS0BDwu7JkydbHL/k5GR0Xefs2bPNEnubBqgX\n", "bt+9ezdFRUXk5+dTUlJCXV0deXl5LX44Dxs2jKVLl7YYvLbUJovFQmJiYtgMxPHjx/H5fGRmZoZy\n", "fC5kt9s5duxYs+39+vVrcZVNamoqX3/9dYvP1ZKcnBz69evHunXrOHnyJGPHjiUvL48BAwbIUm8h\n", "LoO8W+JIbm4uV111FRs2bOCLL75ocfq9o5p+o25ksVguuq8xzyEYDLb4fE2/8Td9Pq/XS1VVVWhb\n", "48/Lly9vtX21tbXNtjVNVG0Lv98PXHyFTeP2xuPaKzk5mZKSEk6dOtXp7Tt16hQ+ny8sMGlpfOD8\n", "+DU99sJ9uq4329fS2MH517uxnY3/v9g4XHh8UxebZbBYLBiGEfp34+9HaWkp77zzTouPgZZ/P1p7\n", "XZqe41JUVeWpp55iyZIlfPnll7z11lsAOJ1OJk2axOzZs3E6nW1+PiF6KglM4szs2bPZtGkTf/nL\n", "X8IS/Zpq/HbY0ocNNFwLb5pA25UqKiqaXcrQdZ2qqqoWP1Sff/55+vbte1nnuNyaE43nOnv2bIv7\n", "Gy/BXOwDra3y8vIoLCxk165d3HDDDZfdvjNnztCrV68ua9+lVFRUtLi98XVrPH93vJ6Njx03bhyP\n", "P/54u5+no9xuN/fffz/3338/paWl7N69m88++4xPPvkEv9/P97///Yi1TYhYITkmcSYzM5Nvfetb\n", "lJWV8fHHH7d4TGPQ0VKORGlpKYFAoEvb2FRhYWGzbXv37sU0Tfr37x/a1rg0tDFvoSs1nreltum6\n", "Hsrhadq+9pg8eTIWi4UNGza0mBvTVNMZp9baV1payqlTp8jIyOjywGTPnj0t5p7s3r07rJ3Z2dnY\n", "7XYOHz7c4qxIYz868nrm5OTgcrlCy9S7SuMMYFtmUjIzM7nhhhuYP38+DoeDTZs2dVm7hIgnEpjE\n", "oRkzZuByuVi6dCk1NTXN9vfu3RtN09i0aROVlZWh7XV1dd1+b5d33nknLFegrq6OhQsXAoSV/J48\n", "eTIul4slS5a0eN3fMIwWP6jbY+zYsXg8HtatW8c///nPsH3Lli3j5MmTXHHFFZedtHqh9PR0Zs6c\n", "STAY5Gc/+xkHDx5s8bitW7eG5Qw1zq4sXbo0bPwMw+DPf/5z2DFdqbS0lE8++SRs28aNG9mzZw+Z\n", "mZmhHBSr1cq1115LIBDgr3/9a7Pn+Oijj7BaraFE6fZQVZWpU6dy9uxZXnvtNerq6podc+bMmUsG\n", "gJfSGNSfPHmy2b6ysjJOnDjRbHt1dTX19fUXTYoVQoSTSzlxyOPxcPvtt4eucV/IYrFw00038c47\n", "7/DDH/6QsWPHous6O3fuJCUlheTk5G6rbJmTk8Njjz3G+PHjsVgsbNy4kbKyMq688sqwDyqPx8Pj\n", "jz/O888/z9y5cxk+fHgoyfXUqVPs378fn8/Hm2++2eE2OZ1OHnroIX7xi18wf/58xo8fT2pqKocO\n", "HWLHjh0kJSWFLXntiNtvvx1d11myZAk/+tGPyM3NZcCAATidTioqKtizZw+lpaVhSce5ublMmzaN\n", "v//97zz++OOMHz8eh8PB1q1bOXr0KHl5eUybNq1T2teakSNH8sYbb7Bt2zb69u1LaWkpX331FXa7\n", "nYceeijs2NmzZ7Nnzx4++eQTDhw4QEFBAZWVlaxfv57a2loeeOCBy1o51ZI77riDoqIi/vGPf7B5\n", "82YKCgpISUmhoqKC0tJS9u3bx913333RQnZtMWTIEOx2Ox9++CHV1dWhPJupU6dSVFTECy+8wKBB\n", "g8jOziY5OZnKyko2bdqEYRjcdtttHeqfED2FBCZx6qabbuLTTz9t8ZsdwKxZs7Db7Sxfvpzly5eT\n", "nJzM1VdfzcyZM3n00Ue7/F4gjc//6KOPsmTJEtauXcuZM2dISUlh5syZLd73Z9iwYfz85z/n/fff\n", "Z/v27ezZswebzUZycjLDhw9n/PjxLZ6jPcaMGcMzzzzD3/72N7Zv347f7yc5OZkbb7yxWeXXjpox\n", "YwYTJkwI3cRv5cqVoZv49evXj+nTp3PttdeGPeaee+6hf//+fPzxx6xevZpgMEhmZiZ33XUXt956\n", "ayhptS1ae51a2zd48GDuuOMOFi1aFJo5GT58eIuVXz0eD88++yx/+9vf+Oqrr1i2bBl2u53c3Fxu\n", "vfVWrrjiija392IsFgs//OEPWb16NatWrWLLli3U1NSQmJhIRkYGd911V7PXsTUt9d3tdvP444+z\n", "ZMkSVq5cGUqmnTRpEoMGDWL69Ons3r2b7du34/P5QlVfp06dysiRIzvcRyF6AsWUmz4IIS5DY7n4\n", "C4uSCSFEZ5AcEyGEEEJEDQlMhBBCCBE1JDARQgghRNSQHBMhhBBCRA2ZMRFCCCFE1JDARAghhBBR\n", "QwITIYQQQkQNCUyEEEIIETUkMBFCCCFE1JDARAghhBBR4/8DiIl6BlDitMYAAAAASUVORK5CYII=\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f372938a350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_gridscores\n", "\n", "gs_deg_1 = [x for x in gs.grid_scores_ \\\n", " if x.parameters['degree'] == 1][2:-1]\n", "gs_deg_2 = [x for x in gs.grid_scores_ \\\n", " if x.parameters['degree'] == 2][2:-1]\n", "gs_deg_3 = [x for x in gs.grid_scores_ \\\n", " if x.parameters['degree'] == 3][2:-1]\n", "\n", "draw_gridscores([gs_deg_1, gs_deg_2, gs_deg_3], 'n_components', \n", " data_labels=['1st Order', '2nd Order', '3rd Order'], \n", " colors=['#f46d43', '#1a9641', '#762a83'],\n", " param_label='Number of Components', score_label='R-Squared')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we said, a model with a 3rd order polynomial and 3 components will give us the best result,\n", "but there are several other models that will likely provide comparable results. Let's make the\n", "best model from our grid scores.\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = gs.best_estimator_\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Prediction using MKSHomogenizationModel\n", "\n", "Now that we have selected values for `n_components` and `degree`, lets fit the model with the data. Again, because\n", "our microstructures are periodic, we need to use the `periodic_axes` argument.\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model.fit(X, y, periodic_axes=[0, 1])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's generate some more data that can be used to try and validate our model's prediction accuracy. We are going to\n", "generate 20 samples of all six different types of microstructures using the same \n", "`make_elastic_stress_random` function.\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_sample_size = 20\n", "n_samples = [test_sample_size] * 6\n", "X_new, y_new = make_elastic_stress_random(n_samples=n_samples, size=size, grain_size=grain_size, \n", " elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio, \n", " macro_strain=macro_strain, seed=1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's predict the stress values for the new microstructures. \n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y_predict = model.predict(X_new, periodic_axes=[0, 1])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look to see, if the low-dimensional representation of the \n", "new data is similar to the low-dimensional representation of the data \n", "we used to fit the model using `draw_components` from `pymks.tools`.\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAjUAAAEmCAYAAACav2EwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VGXev+9pyaRNeoHQIQkkIKGIIAmigAJKUaS6VMXG\n", "7qtu88eqKLq66+tr2XURbEhZRIMiEOkikRqpgZACISGUQHqZ1Mm03x/DnORkZuiBgM99XVxXOOU5\n", "z5xz5pzPfKvCarVaEQgEAoFAILjNUd7qCQgEAoFAIBDcCISoEQgEAoFAcEcgRI1AIBAIBII7AiFq\n", "BAKBQCAQ3BEIUSMQCAQCgeCOQIgagUAgEAgEdwRC1NzhTJw4kfnz59/qadw0kpKSmDhxIklJSbd6\n", "KpclISGBiRMnkp6efqunImhBLFiwgIkTJ1JcXHyrpyIQ3Haob/UEbiYTJ04E4Ntvv73FM7ly0tLS\n", "ePPNN2XL3Nzc8PT0JCwsjIiICOLi4ujQocOtmWALRaFQ3Oop3HASEhL4/vvvZcvUajWBgYF0796d\n", "Rx99lODg4Fs0uzuXN954g4yMjBv23LBfx9dff53o6GiH9XfivSsQ3Cx+U6LmdiY4OJjBgwcDYDKZ\n", "0Ov15OTkkJiYSGJiIgMHDuTpp59Gq9XK9vvwww9xd3e/BTO+NfTr14/IyEj8/Pxu9VSajejoaGJi\n", "YgCorKzk2LFjbNu2jeTkZN5++21atWp1i2couB6mTJnC2LFj8ff3v9VTEQhuO4SouU0IDg7m8ccf\n", "d1iem5vLggUL2L17N9XV1cydO1e2vnXr1jdrii0CT09PPD09b/U0mpWYmBjZvWC1Wnn33Xc5fPgw\n", "q1evZs6cObdwdoLrxc/P744W5QJBcyJEjQuMRiPr169n586dFBYWolQq6dChA8OHD2fAgAHSdnV1\n", "dcycOZMuXbrw1ltvScvr6+uZOXMmJpOJOXPmMGjQIGndli1b+PLLL3nuueck68u10qFDB1577TX+\n", "9Kc/kZKSwv79+7n77rul9RMnTiQ6OprXX39dWtbY/F1aWsqPP/5IXl4enp6eDBw4kClTpqBWqzl6\n", "9Cjff/89ubm5KJVKevfuzcyZM/H29naYR0lJCWvWrOHw4cOUlZWh1WqJiopi3LhxdO7cWbZt4+Pr\n", "9XrWrl3LuXPn0Gg03HXXXUybNo2AgADZPgUFBaxZs4Zjx45RVlaGm5sbAQEBREVFMXnyZGlOSUlJ\n", "LFy40Om5zcnJYfXq1WRmZlJbW4ufnx+9evXi8ccfd3iJLFiwgB07dvCf//yHlJQUNm3aRH5+Pp6e\n", "ntx999387ne/cxBPx44dY/fu3WRmZlJaWorZbCY0NJQBAwYwZswYNBrNlV/Yq0ChUDB48GAOHz5M\n", "Tk6Ow3qDwcCGDRvYs2cP+fn5KBQK2rVrx4gRIxg4cKBsW7u78/HHH6dnz558++23ZGdnY7VaiYyM\n", "ZPLkyXTq1Em2T9P7acOGDZw9exadTseCBQuueg5gu44//fQTFy5coK6uDp1OR5s2bbj//vu59957\n", "Zds2171XWFjIH/7wB2lfu/sakH2nrua6z5kzR4qVaRrrZndv2e+9BQsWEBQUJNtmz549bN68mdzc\n", "XMxmM2FhYcTFxfHII4+gVssf53Zx+8EHH5CQkMCePXvQ6/UEBgYyZMgQxowZ43DeBYLbHSFqnGAy\n", "mXj77bfJyMggPDychx56CIPBQHJyMh999BG5ublMnjwZAK1WS0REBCdPnqSurk5y/2RmZmIymQDb\n", "Q6+xqElNTQWge/fuN2S+Op2OoUOHsnr1anbu3CkTNZdi48aNpKSkcPfddxMTE8ORI0dYv349er2e\n", "vn378vHHH9O7d2+GDRvG8ePH2bVrF1VVVQ7WoJycHN5++22qqqqIjY2lf//+6PV69u/fz7x58/jz\n", "n/9Mr169HI6/efNmDhw4IB0/KyuLvXv3cvr0ad577z3pIV1WVsbcuXOpra2ld+/eDBgwgPr6egoL\n", "C9m5cycjRoxwEFpN4xIOHjzI+++/j0Kh4J577iE4OJicnBy2bt3KgQMHePPNNwkJCXGY4/Llyzl6\n", "9Ch9+/YlNjZWcvXk5+czb9482bbr1q3j/PnzREZG0qdPH4xGI5mZmaxatYq0tDRee+01lMrmic23\n", "t3BrKpyqq6t58803yc3NpVOnTjzwwANYrVZSUlL497//zdmzZ5k0aZLDeCdPnmTNmjX06NGD4cOH\n", "c+HCBfbt28e8efN49dVX6dq1q8M+iYmJ0rnq3r07NTU11zSHr7/+mrVr1xISEsLAgQPx9PSktLSU\n", "7OxskpOTZaKmOe89b29vHn/8cZKSkiguLmb8+PHS/o1jl67muj/88MPs37+f9PR07rvvPqf3nCvs\n", "50Wn0xEfH49Wq+Xw4cOsXLmSI0eO8MorrzgIG7PZzN///nfKy8vp3bs3KpWKffv28fXXX2M0Gp1a\n", "fwWC2xkhapyQmJhIRkYGvXr14q9//av0QBo/fjxz585lzZo19OnTh8jISMAmTo4fP056ejq9e/cG\n", "bEJGqVQSHR0tiRgAi8VCWloaoaGhDr/CroeYmBhWr15Ndnb2Fe9z7Ngx3n33XclFNXnyZF5++WV2\n", "7tzJwYMHefXVV+nWrRtge2m+/fbbpKSkkJubKwUmm81mPvzwQwwGA2+88Ya0PTSIkUWLFrFgwQKH\n", "B+6RI0f45z//Sdu2baVl//73v9m9ezf79++XLGLJyclUV1czY8YMRowYIRujvr7+sp+zrq6OBQsW\n", "YLVamTdvnuyFvHbtWr7++ms+//xzXnnlFYd9s7Ozef/99wkMDARs12/+/PmkpaVx8uRJunTpIm37\n", "1FNPOX1Jffvtt6xevdrhhXyjsFgs/PzzzwAOgadLliwhNzeXJ554gtGjR0vLjUYj7733Hj/88AP9\n", "+/d3CDRPSUlh1qxZPPTQQ9KyAwcO8N5777Fw4UI++ugjB+GYlpbG22+/7TDW1c7hp59+IiAggPff\n", "fx83NzfZWJWVldLfzX3veXp6Mn78eNLS0iguLnYpAK7muo8cOZKqqirS09MZPHiw00BhZ5w4cYK1\n", "a9cSFBTEO++8g6+vL2CLv3nvvfc4dOgQiYmJPProo7L9ysrK6NChA/PmzZME7+OPP84LL7zA+vXr\n", "efTRR1GpVFc0B4HgdkCkdDth+/btKBQKpk2bJvtlrdPpGDduHADbtm2TltstLseOHZOWpaam0qlT\n", "J/r160dpaSkXLlwAbDEw1dXVN8xKY8ceVKjX6694nxEjRshibtRqtSQk+vbtK3tJKBQK4uPjAThz\n", "5oy0/NChQxQWFjJ8+HDZ9vY5jR49mvLycpmwa3z8xi8VgCFDhgDIxJn95enMfePm5ubw4mvK/v37\n", "qa6uZsCAAQ4WhlGjRhEUFMTRo0edptCOGzdOEjQASqWS+++/32GOgMtf3Q8//DAAR48eveQ8r5S0\n", "tDQSEhJISEhg8eLF/OlPf+Lo0aN07txZuj/BJgB27txJ586dZWICbOdyypQpAOzatcvhGGFhYTJB\n", "A7Z7Ijo6mvz8fDIyMhz2GTp0qIOguZY5KBQK1Gq1U6uWj4+P9PfNuPeuhJtx3e2i9bHHHpMEDdju\n", "x2nTpqFQKKRtmjJz5kzZd0en09GnTx9qamqk55JAcKcgLDVNqK2tpaCggICAAKdBtnYxkpubKy2L\n", "jIzEzc1NenjW1NSQm5vLmDFjZIKnVatWkvC50aLG7n64mnTQprER0CCOLrWupKREWnbixAkAioqK\n", "SEhIcNgnPz8fgLy8PAc3QNN4B0ASEFVVVdKyvn37snLlSr788kuOHDlCz5496dq1K23atLn0B7zI\n", "qVOnAOfnXKlU0q1bN3bu3Elubq6D9czZHO0xF9XV1bLldXV1bNiwgf3793P+/Hnq6upk60tLS69o\n", "vpcjPT3dobZN586def3112WZbvZYGMDptTGbzYDt2jSlqUhovDw9PZ3c3FwHK0Njq9X1zCEuLo5N\n", "mzbx0ksvMWDAAKKjo4mMjHSIYboZ996VcDOu+6Xu4VatWhEQEEBhYSG1tbV4eHhI6zw9PQkNDXXY\n", "x36fX+1nFQhaOkLUNMEeB+AqndIeUGrfDmwWjqioKFJTU9Hr9Rw/fhyLxUL37t0JDw/Hz8+P1NRU\n", "hg0bxrFjx1AoFDdc1JSVlQG2X2FXirMsIbsp+lLr7C8iaHAHJCcnX/JYTR/0ro5h/3VusVikZXaT\n", "+6pVq0hJSWHfvn2A7SU0atQoB5dUUy53Te3LG19TO15eXg7L7Oeh8RxNJhNvvvkm2dnZtGvXjoED\n", "B6LT6VCr1VitVr777juMRuMl53mljB8/XnKFlJSUkJiYyMaNG3n//feZO3euJGzt1yY7O/uS1geD\n", "weCwrLE1oDHO7v+m6xpzLXOYPn06oaGhbN++nbVr17J27VqUSiW9evVi2rRphIWFycZuznvvctys\n", "634l93BJSQnV1dUyUePs/oVr+6wCwe2AEDVNsD/sysvLna63L2/6UOzevTupqakcO3aMzMxMNBqN\n", "5Oro3r07KSkpmEwmMjIyaNu27VWJjyshLS0NcP5ruTmxn4e//vWv9OnTp9mOEx4ezosvvojFYiE3\n", "N5fU1FQ2bdrEkiVLcHd354EHHrjsHF1dU7sgvJ5U8AMHDpCdnc3gwYN57rnnHMb/7rvvrnnsSxEY\n", "GMiMGTMoLS3l119/ZfPmzQwfPhxo+DwPP/ww06ZNu6pxKyoqnC53df+74lrmoFQqGTlyJCNHjkSv\n", "15OZmcnu3btJTk7m3LlzfPDBB6jV6pt2712Km3Xd7Z+1rKzMqeXlRtzDAsGdgIipaYKHhwehoaGU\n", "lJRI5uvG2N1HHTt2lC3v0aMHYIulSUtLIyoqSgpO7NGjB1VVVWzevJn6+vobbqWpqKhg69atAFLc\n", "y83CHiztLMaiOVAqlXTq1IkxY8bwwgsvALYXy6WwXyu78GuM2WwmMzNTtt21YL9X7rnnHod1N6MN\n", "wrRp01Cr1axatYra2loAIiIiUCgU13RtMjIyJLdRY+yf5UrP1fXMAWyWx379+vHSSy8RExNDQUEB\n", "Z8+eBW7evWe3ajg7H9dy3a/FSnKpezg/P5+SkhJCQkKEqBH85hGixgn2QNDly5fLHjx6vV4qU2/f\n", "xk7Hjh3x9PTkwIEDnDt3ThI50OAHX7Nmjez/N4Lc3Fz+/ve/U1VVRa9evW76L9a7776b0NBQNm/e\n", "zOHDh51uc+LEiSvKUnJFTk6OU3eH3WpwuUDhu+++G29vb3bv3k1WVpZs3fr16ykqKuKuu+6SBQRf\n", "LfZg0cbB4mCrr7NixYprHvdKCQoKYsiQIVRVVZGYmAjYBEFcXBw5OTl8//33Tl+i+fn5FBYWOl2+\n", "efNm2bL9+/eTkZFBWFiYy5ibplztHEwmkyQyG2MymaT4D3vc0M249wCpXEBRUZHDumu57vZg56vp\n", "7WS3RK5evVqWDGCxWFi2bJlsG4Hgt8xv0v1kLwjWFIVCwVNPPcWoUaNISUnhwIED/OUvf6FXr15S\n", "nRq9Xs+YMWOIioqS7WtP37ZbDRoLl6CgIEJDQykoKJC2u1oKCwulYEiz2UxlZSU5OTlSAGF8fDxP\n", "P/30VY97vahUKv785z/z9ttv889//pPIyEjat2+Pu7s7JSUlZGdnU1hYyGeffXZZ8eGKHTt28NNP\n", "P9G1a1dCQkLw9vYmPz+fgwcPotFopCwTV2i1Wp577jk++OAD3njjDfr3709gYCCnTp3i6NGj+Pn5\n", "MXv27Guam50+ffoQFhbG+vXrOXv2LO3bt6e4uJjDhw/Tu3dv9uzZc13jXwmPPfYY27dvZ/369YwY\n", "MQIfHx+efPJJ8vPzSUhIYMeOHURFReHn50dpaSl5eXnk5OTwwgsvOGTwxMbGsnz5clJSUmjXrh35\n", "+fns27cPNzc3BzfL5biaORgMBl5//XXCwsLo2LEjQUFBGI1Gjh49yvnz5+nbt68UwH8z7j2Au+66\n", "i19//ZX333+f2NhY3NzcCA4OZtCgQdd03bt3745CoeDrr7/mzJkzUtxL48y1pkRGRjJ69GjWrVvH\n", "n/70J/r374+7uzuHDx/m3LlzdO3a1SG7TCD4LfKbFDU7duxwuW7GjBl4enry6quv8uOPP7Jr1y42\n", "bdqESqWiQ4cOzJw502WtkR49enDgwAE8PT0dsiu6d+9OQUEBnTp1kgXyXQ570GdxcbFkJdJoNHh5\n", "edGqVStGjRpFfHw87du3v+oxbxTt2rXjvffe48cff+TgwYP88ssvKBQK/P396dixIxMnTpSl4l7t\n", "8ePi4jCZTBw/fpycnBzq6+sJDAwkLi6OUaNGXVEWVN++fXnrrbf44YcfOHLkCDU1Nfj7+zNs2DCn\n", "FYWvdo7u7u7MmzePFStWkJ6eTkZGBqGhoYwbN45HHnnE6cvtao9xue39/PwYNmwY69evZ82aNUyd\n", "OhUPDw/eeOMNfvrpJ3bv3s2+ffswGo34+fkRFhbG9OnTueuuuxzGioiIYNy4cXz77beSxaZHjx5O\n", "Kwpfbl5XMwetVssTTzxBWloaJ06cYP/+/VIGz+zZsx0spM1974HNAlJUVMSePXtYt24dFouF6Oho\n", "Bg0adE3XPTw8nDlz5pCYmMiWLVukQGK7qHE1xyeeeIKOHTuyadMmduzYgclkIiwsjEmTJjFq1Kir\n", "qjcjmmYK7lQUVmeOYoFA8JvE3iahcYaVQCAQ3C6ImJo7EGfBhIJbi7gmLRNxXQSCOwshau5AxIO6\n", "5SGuSctEXBeB4M5CiBqBQCAQCAR3BL/JQGGBQOCcmJgYvv3221s9DYFAILgmRKCwQCAQCG4YJpNJ\n", "1kpFILjRqFQqqbhtU+5YS8358+dv9RRuGT4+PlJfHEHLQFyTlom4LnKcNfG9Wsxms6zprUBwowkM\n", "DHQpakRMjUAgEAgEgjsCIWoEAoFAIBDcEQhRIxAIBAKB4I5AiBqBQCAQCAR3BHdsoLBAIBAIBFfL\n", "Sy+9dNltfv/73zv097sSSkpK+Pvf/87s2bOvqrFxVlYWn3zyCS+//DJhYWFXfdxrYf78+ZSXlwO2\n", "bCMvLy/Cw8Pp3bs3ffr0uer+YYWFhRw8eJDBgwdfVf/Dq0WIGoGgmdm2M4mvt6ym1mRAg4ppIycw\n", "JH7wrZ6WQCBwwosvvij9XV9fzyeffMKDDz4oEyGhoaHXNLavry8vvvgiISEhV7Vf27ZtefHFFwkM\n", "DLym414LCoWCPn36EB8fj8ViQa/Xk5mZycqVKzlw4ACzZ8++qiaqRUVFbNmyhf79+wtRIxC0VLbt\n", "TGLZhgSMmJ0Klm07k3jl039Qpq0HpQIsVo5/+g8AIWwEghZI+/btpb8NBgNgSyFuvLwxFosFq9V6\n", "RS94tVrtcpxLodVqr2m/60Wn08mO27NnT2JjY/n000/ZunUrw4cPv+oxm7s0nhA1AsE1sm1nEu+s\n", "WoB+WAj2r9I7qxYADYLlw6WfUOZej25Mw6+8sjXpfLhsoRA1gt8kl/sh0FLGdMWKFSvIz89n2LBh\n", "bNiwgaKiIubMmUNQUBA//vgj2dnZ6PV6/Pz86NWrFw899JAkeJy5n+bPn09sbCy+vr5s374do9FI\n", "VFQUEyZMkCwaztxPL730EmPHjqWyspLk5GQAYmNjGTt2rKyGS1ZWFqtXr6a4uJhWrVoxbtw4Pvvs\n", "M+Lj469JlERFRdGzZ0/27Nkj7V9QUMCmTZs4deoUNTU1BAQEMGDAAAYNGoRCoSArK4svvvgCgLfe\n", "eguAgIAAXnvtNSoqKli/fv0lz9vVIESN4DfL9T4Il21IuChoGtAPC2H5xlXSOHnlheimx8i20Y2N\n", "Jm+paKQo+O1xJT8EWsKYl0KhUFBaWkpiYiLDhw/Hx8eHgIAAqqur8fT0ZMyYMXh5eVFYWMimTZuo\n", "qqpiwoQJlxwvJSWF1q1bM2nSJMrLy1mzZg3r16/n8ccfv+RckpKSiIiIYOrUqeTl5fHjjz8SEBDA\n", "Aw88AEB5eTmfffYZnTp1YtSoUej1ev773/9iNBqv6xxERUWRkpJCWVkZ/v7+VFRUEBISQp8+ffDw\n", "8ODcuXNs3LgRo9HI0KFDadu2LaNHj2bdunXMmjULnU4nCa+ampprOm+uEKJG8JvkRjwIjZhx9hWq\n", "t5oa/qN2nmCocLFcILiTuZIfAi1hzEthtVqpqalhzpw5sgrMfn5+jB07Vvp/hw4dcHNz45tvvmHc\n", "uHEurQ5219WTTz6JUml7LuTn53P48OHLipqAgACmTJkC2ITGqVOnOHLkiCRqfvnlF9zd3Zk9e7Yk\n", "IrRaLUuXLr32E4AtNgigsrISf39/IiMjiYyMlD5Phw4dqK+vZ+/evQwdOhStVivFEbVp0wZ/f39p\n", "rFatWl3TeXOFEDWC3yQ34kGowfmXzU1h+1pt25mEqbaemsQMsFhxjw7BPSIIgHD/aws0FAhuZ67o\n", "h0ALGPNy+Pr6Om0pkZSUxN69eyktLcVkajh+WVkZQUFBTsdSKBRERERIggZsgciVlZVYLBbZ8qZ0\n", "7dpV9v/Q0FDOnj0r/f/MmTNERUXJ3FExMXLL8Y3AaDTy008/cfDgQcrKyrBYLNK6y30GuLbz5goh\n", "agR3NNt2JjH/43+SX1kCbircUTNzxMQb8iCcNnJCI2uPDd2WQqZOmCNZgrRP9UR7cZ1+TToAXhnV\n", "vDjtz9fzsQSC25LL/RBoKWNeDh8fH4dlSUlJrFu3jqFDh9K5c2c8PT05ffo033//vexF7Yym2UB2\n", "64TJZMLNze2q9mvsWqqsrCQ8PFy2jUajueSYV0JFRQXQcB4SExNJTk5m+PDhtGnTBg8PD1JTU9m6\n", "detlP8P1nDdnCFEjuGPZtjOJP/3fa1R7WPB7vp+0/NMV39JK7Qc4ZhNczYPQbtFZvnEV9VYTbgo1\n", "UyfMYUj8YKbPfd7BEqQbG41laTp/f+F1ESQs+E1yqR8CLWnMayElJYXY2FhGjhwpLbtw4cJNnUNT\n", "dDodVVVVsmVGo5H6+vrrGjczMxOdTie5kVJSUhg0aJDk9gJIS7uyuMEbfd6EqBHcsSzbkECltY6A\n", "J/rJluue6EnJwkOEbPW47gfhkPjBTgWKK0uQRa3gyw0rWbYhQdSrEfzmuNQPgZY05uVwVnjOZDI5\n", "xH8cPHiw2eZwJbRr145ff/0Vo9GIRqMB4NixY9c15vHjxzl69Kgsc6rpZ7dYLBw6dEi2n3190yDl\n", "G33ehKgR3LEYMaPQODdNq3Ra/jZ+TrM9CPVFZejXFkm1aezxNDU+VrLubf4MDYGgpeLqh0BLG/NS\n", "OKu1EhkZyc6dO2nfvj2BgYEcPHiQ4uLiaxrrRnHfffexa9cuPv/8c+677z4qKyvZtm0bGo3msnEu\n", "VquViooKcnNzsVgsVFZWkpmZyb59+4iKimLo0KHStpGRkezatYvg4GA8PDzYtWsXZrNZNp49UHj3\n", "7t306tULNzc3Wrdufc3nzRVC1AhaPNeaeq1BhdVodrpOZWoQE8s2JFBvNbFsQwInjx2l8ngaaosZ\n", "k1JF3GOT6T/o8sdqOt9SRQ260Q21afRr0qnZewbPAe0aljVjhoZAIGgeFAqFU0vNQw89RFVVFRs2\n", "bABsheoee+wxqT7LpcZrrrn5+voye/ZsfvjhB7766ivCwsKYPHkyCxcuxN3d/bJjHTp0iEOHDqFU\n", "KvHy8qJNmzZMnjyZvn37yrYdN24cq1at4rvvvkOj0dCvXz/uuusuEhISpG0CAgIYPXo0O3bsYOfO\n", "nfj7+/Paa69d83lzOW9rc5f3u0WcP3/+Vk/hluHj40NlZeWtnsYNQZ56bUO3tZC/jb+8VUUWU/NE\n", "rLRcv+IIzwyeSM+YHrKx1ZlF3Jt0li/iI6RtZ/+SRZqPH/6d212xoJo+93nS73V8UJWvPILf5J6y\n", "ZRF7THz9j08vOZ6g+biTvis3AmcZPVeLwWCgpKTkBsxG0Bzk5OTw8ccfM2fOHLp06XKrp3NNBAYG\n", "uhRlwlIjaNFcT+r1kPjBvM9bzP/4XfIX7gONCq1CzTPDJ/LHZ//gEMwbnpIvEzQAn98Xweitx1Bo\n", "aqjGyjsLT0pjg3Mrkqt4GqW3YwbAlQYm38yKqQKB4M5h3bp1tGnTBh8fHwoLC9myZQutW7e+bQXN\n", "5RCiRtCiudLUa1cv/Uv52puO7YlzM/A9Ok9e6WHLlJp1MIfPvvqEIfGDXRbw86hXAY6N59RFBtn/\n", "rzQw2dlxXlv2f3y49BN0wf5C5AgEApeYzWbWrVtHZWUlWq2Wrl27yord3WkIUSNo0VxJDYqrqQ7c\n", "WPykpqehCA6XCuLV4NwTe7ysml15ZcSF+7O4TyfG7MwF4MMVi9A/7GhF8lhXjG5rocwK5PdTMZMf\n", "msTRvZkUlBdTWFiIxi+QZRsSnM6zMc6OYxzdnqx16ejuDb7k5xUIBL9tHn30UR599NFbPY2bhhA1\n", "ghaNsxoUqm9PUuztz5S5z6BBRUlRMfoxl3dRNRU/2nt7Ur4iBQD3iCDyYsOY8XMmS+5rqNL5xp6T\n", "PN2jDT+dKQUgLtwfT6WCbTuTyCnJw8uJRUYX5MdLIyfLMqv69nqA/cePUFBUSF5tMdrpMZQAJdgE\n", "ydb168hPPYC7EgwWGPDI4zzz3P9c8jg0Cgi8EUHHyTuS2LV65XUFSQsEAsGtRIgaQYumaQ0KfXE5\n", "pR4aCh4OpODiNrXfFKLMUkgWFzv1VpPsRZ2SfYKawWGybfyeiKX0830YMovAauWXYC2PJKYwIESH\n", "2WplaPtA4sL9iQv3583kbOLC/amor+ev/5pPvdKEl5M5H886wZcbVqJBxZMjJwPwz+8XUj4kCP3a\n", "EnST5GXKy1WV1Kbs4ofBDWJq+g/LGbRpNdUaBUaNBUNWMYb0QlmKOE1i/K+nLHzyjiR2L/4Xb3Vt\n", "qJQ6b/G/AISwEQgEtw1C1AhaPI1Tr/Mqi6jxtuKeVSyJGI9JMejXpTuImrKs0/yQ+iqL4jrZFrTu\n", "wKx9OSQDpq7B0nZKXy26Ud0wZBVTm1aI1ceNV+7p5DAPlULBzAM55A5ti7JrMIrlhyn9bB8BTzcU\n", "9ytbdgiUcLAkCyxWjn/6D4K0vpSPuXg8pWPcTttjRSwZIhc6Sx/oxrDU05yeFIPxw51Y957Bf1pv\n", "aX358sMWBNc1AAAgAElEQVSYa+rRr02XauBcT9Dx4dUrZYIG4M2uPsz74RshagQCwW2DEDWCFk9j\n", "t5H63m7oaOijZBcyqnKblcKQVUxN8hksJbWEWxQseqiHbKzFfToxLOU0pxuJGnNJjW3f9EJ0Y6Op\n", "W2yrZrkrr4xtZ0pRKxWYLFZ2FenJe7SbJIj8pvaifOUR9OvSMZfUotCqMVfUEvSHgdLYtUsOojlT\n", "QLdvCqjBSlZdQ3lyu/Wli8V5LI/nxW1U3u4yQWM/tn5dOrrR0ejXpON5qJypz8y9qnPZOP4ovsIK\n", "YZ4O21eWFjN97vMi60ogENwWCFEjaPE4S+vWjY2WWWdURit1n6dQW1eHqq0OlU6LrtbibDgav7r1\n", "a9JRGCxE7DGRWmYTRhfubce4zWn08NLyxoDO0raz9maR12QspbcbulHdAChZmIzP8ChpnTqziDil\n", "msUj75KWzdybxfYlB7EObI8hzSaijN+kOp1nDTahpQ5zbJ4HSDE1urHRBG8ovWSaeWNrl7MU+dxF\n", "qUAHh0NkFpwj+/FQLheALRAIBC0BIWoELYqrqftif6nr16TjPsiWxVS3IgX3mBAMmUUus5n0hVXo\n", "EzPAasU9JgSvagVf/+NT+jx+P2BzTVXuPsMb/TvL9ls8IIIhe7JIO97Q/sCsr2vYQNkQ12PIKiZ8\n", "SzaLm7iVvhoQweCtqaRuyiLwDwMAyIsNY9a+HBb3aXB5zTyQQ949rSGrGFxYchrH1Pj46aTzd6lM\n", "MFfnsjI0mHmZlbzZyAX1zK4cTg9pJ9tOVEEWCAQtGSFqBC2Gq637Ys2rsllrYkLwMlsJ/yaVbio1\n", "+o0nyXZXkHdfJwexMD0pk4IHO6Nr5H7yOmxzPwX7BXJqTTq6sdF4eDmvVqmtMaKb1eAKMnxxgLaL\n", "D+Lt6U6F3khhZhHVKoXNChPi7XQM3zAdGq+GVHVT12CSgWEpp3ErqqLGCgUPdra5uY4XofTVUr4i\n", "RVYVuWzFYTz7tZX+b4+ncWaJKeqg5Om3/ojKww1zbT1u9aF4D5bHDPmEhTFw+Hjm/fANKrMJs0pN\n", "dkioLPbIzvUEJAsEAkFzIkSNoMWwbEMCRR2UGNamS5YQQ3QI7TKsDnVfdFsKCWzVjpLRoagzi+i/\n", "73wT8ZLBL8lnSO7fjmEpp/HEZqE5UVePV6MXddmyQ5RV1NPr8cGY6o24D26Dfl06+qIqp3OsD2rI\n", "d1JnFhHnpmk4bo/2TP85k188lehm9KHGhVupqqYei0WJOrOI8JR8PFFQg5W82DBKTyhx7xaCIaMQ\n", "057TmEuqUYf64DmgHfp16aBQYDxXgSrEW7IKqdedZuq0PwOOBQUNWcUY0goJuGgVAluQcVVSDppw\n", "HYb0QlQVJooDw6lWwJ8+XCRtd3Tu807nf6UByQLB7chLL7102W1+//vf07lz58tu54o9e/bg4+ND\n", "jx7ymL/58+fTq1cvRo8efc1jXw0bN25ky5Yt0v89PDwICgoiKiqKQYMG4ePjwvV9CbZt20b79u1v\n", "WcVi8XQStBgKigoxFJagGytvBGlSBPK38b936Ki9bEMCJdjaGzQWNABLB3djyK9ZpJ0ootRLZXM1\n", "jeiC8fB5ypYfwlJjRIECpY8bHsO7oI4IonLZIVS/5qH7XU8KMoscrDxP7cgi7/4G64jT497XlcEb\n", "j3Ae126lrLp63Kst9Ps5V1YTZ/q2TLYrzNSU1KIK9EChUaIJ98Vvis1C0zi7q/jfuylPOIpSq8a/\n", "tqH9gr6sgsZWLXvwsx11ZhE9NWo0By9Ql1LAmbtCqAKyLIX8zwev8GTaJP747B8A5zWCrrQKskBw\n", "u/Liiy9Kf9fX1/PJJ5/w4IMPEh3d8D0KDQ29rmPs3buXVq1aOYiap556Ci8vZ4Uimg+tVsuzzz4L\n", "QF1dHWfPnmX37t3s3buXZ555hrZt215mBDk///wz8fHxQtQALFmyhFOnTtGxY0dmzJghLU9ISGD/\n", "/v14e3vTp08fHnnkkVs3SUGzUVRegm56tGyZbmw0RUvTXbY7eGfVApftDbw93WSdsgFqks+g8tHi\n", "P9XmQjJkFVOdlEPt4fOofLUY8/VYlqbTMSKS8z4wJ7MON6uF3OJC0i0WrI2sPK6O66W0uZYau5U8\n", "gYpz5eSGeFKjUdJTq2HJPZGy/ZYO7sqw1NOkeqlw7xpMddIprC5cPSqdFqVWjW50NGaQ4lz05RXo\n", "1xQ0CJlGKeQyi9bFZ+n07Rn8olNT56tFFd+Gr3aupmdMD9n5biomRTyN4E6mffv20t8Gg621SWBg\n", "oGx5cxEeHt7sx2iKUqmUfbaoqCgGDhzIxx9/zLJly5g7dy5KpfKmz+taaTGiJicnB4PBwPz58/ni\n", "iy/Izs6WzHsKhYJp06Y5qFrBnUVISAjFLpY7w/5y/fIt56nM+kK5C0m/PAVLVb1M0BjSCgmY3VBn\n", "pnxFClqLxtaeIdSfo2UVFFdVYH62h237izE34LqtQp2fO/qL25m6BnO6azBl/z2M55iuuEcEEQBo\n", "//Or0301F/QY6urBbCVg9t2Ufr7f6XZWk0VWUdge51KJAfeYECnN3GoyS9s4tSzdf7Eezhhbarh7\n", "nxD++q/5RF0sHjht5ASWvLPA6RwEgmuhOSpX3+xq2Hv37uWXX36huLgYHx8f4uPjeeCBB6T1Fy5c\n", "YO3atZw5cwaTyYS/vz/x8fHExcXx8ccfc+7cOc6dO8f+/bbv9+TJk+nXrx/z588nNjaWMWPGALBi\n", "xQry8/N55JFHWLNmDSUlJbRp04YJEyYQFtZQSLSmpoZVq1aRlpaGh4cHgwYNoqqqiiNHjjBv3ryr\n", "/nweHh6MGjWKzz77jOPHj9Otmy3DMzExkfT0dEpLS/Hw8KBz586MHTtWclPNnz+fmpoaNm/ezObN\n", "m4EGV9327ds5dOgQRUVFaDQa2rVrx6OPPkpQUJDLeVwLLUbUnDx5kp49ewLQo0cPTpw4IfNZrlix\n", "Ai8vL6ZOnUqHDh1u0SwFzYE94ym/tIiatYVSMTk7oX4Nfzt7eP3Pq//gLwv+yXu9GsTPzAM5nO0e\n", "TNW6dDRlJvpG3sVdD0zi840rpW2aumYAPPq1oWjvWervVWD7egSiX1OAe1YxXmYrXUpr0S74FUO9\n", "lUqtF08mneDLwZGy4164ty3K/EpKFv1qy1AyWbCarVT9dBJjnh7vwZ2oD3KsCQNgbKVDbahH6acF\n", "QKFVoV+TTkDXYCn+puJ8BTVWM3UWKyULk7GazNT5+PDIs5OoKCwlMKIb7hFB6BMzcO8aLAksV5Yl\n", "+0zsafKGAAVZ99oeDa98+g8+XLEInb8v+qIyUCvR+fuKmjWCa6I5Klff7GrYP//8M+vXr2fIkCF0\n", "6dKFM2fOsGHDBjQaDfHx8QB88cUXhIWFMXXqVNRqNQUFBZLVZ/z48Xz11VcEBQXx4IMPAkgvdoVC\n", "gaLRjxWFQkFZWRnr1q3joYceQq1Ws3btWpYuXcrLL78sbff1119z6tQpHnvsMXx8fPjll18oLCxE\n", "pXLeO+9K6NKlC0qlktOnT0uiprKykqFDh+Ln50dVVRXbt29nwYIFvPzyyygUCp588kkWLFhAbGws\n", "/fv3BxpcdeXl5cTFxREQEEB9fT27d+/mo48+4tVXX0Wr1V7zPJvSYkRNdXW19Ivc09OTs2fPSutG\n", "jBjB+PHjyc/PZ+HChcyfP/9WTVNwg7lcYb3GMRyuHl4DZ73A0Dn/j0ffnYfFT0ENkHdPa5Rdg9EB\n", "MXuRrA1fbfy24eBOqvsa0gvxndZLtkw3Npraf+2mn8ZNFgMze8dJTnj68PDPWVhNdRhb6ci7pzXV\n", "KgXmomoCn71H2rZs2SEUaiX1J0soO1WGpb0f03/JZGmj8WbszSIvri3+XYNtrRvCdXj2b4d160n6\n", "na9uOHYPW7fw5H6tMV0ULYqYEAoiAlGVeVH84U5U/p5YquulGjqXCn6uafS3pc6EUmt7LBiyijG4\n", "12N+OJAzWcUYCovQPRwttacQNWsEV8uuZqhc3RxjuqKuro5Nmzbx4IMP8tBDDwEQGRmJ0Whk69at\n", "xMXFUV1dTWlpKU899RStWrUCICIiQhojLCwMNzc3vLy8LuvSslqt1NTU8OKLL0rCx2q1snjxYgoL\n", "CwkJCeHChQukpaUxY8YMyTAQGRnJG2+8cV2iRqPR4OXlRVVVw3NjypQp0t8Wi4X27dszf/58cnJy\n", "6Ny5M23atEGpVOLr6+vw2Ro31bRYLERERPDaa6+RmprK3Xfffc3zbEqLETWenp7U1tYCNlNa42Ap\n", "b29bamxjc1tj0tLSSEtLk/4/YcKEa4ravlNwc3O7bT7/11tWOy2sZ16WTs+yMGL7DOXrLatZsjkB\n", "dUoaq+PkX5Q3u/owP/E7Xlu0FIuXB68v/4DyIQ2WHb+finl62h+l82G1WCTLhdP6L06EDkAXpUom\n", "aAA+H9SFYamnyZrZm9LP9xEwqTsAhrXp+I5vcJUasoptxQCbBEBvr6zl/k1H8dSqqfPQcCGurZRC\n", "rQ7zwZBRiG50NG13nnaIv2lcGblxIUK/38VKlYarknIoX34Yv6m9cI8IoiCziOnbMlg6uJs0jlQP\n", "5yLm0lrUF+fQ2JLlzKqlHxbCyi0/MHbkKHb//BPbvl2GxmLCqFQzZOI0Bj4w1Om5bEncTt+Vm0VC\n", "QoL0d0xMDDExMZfY+upQW8xOl6vM114moDnGdMWpU6cwGo307NkTs7nhuF26dGHLli2Ul5fj6+uL\n", "n58fCQkJDBo0iC5dulzXPRYYGChz0TS2fISEhHDmzBkA2XXSaDRERkZK664Va5P+cunp6WzZsoX8\n", "/HzJ8gRQVFR02Wyw3NxcNmzYQF5eHjU1DT+lioqKrmuOTWkxoiYyMpKtW7cyYMAAUlNTuf/++6V1\n", "tbW1eHh4oNfrZTeSHWdfvMrKymafc0vFx8fntvn8tSYDzm7Drl0imfzgo42ybxR0O+ViEEMdlZWV\n", "3NvnHv5fzXPywNbHn+PePvdI50Pj7obSHnNSaXCo/2LOc37ePF2IHbvrxmtwJ+oW/UqUnyduRdXU\n", "f5NKXmwYpq7BTgWBbmw0tZ+ncNbNDWWT4GjA5ra6aIb29nReM0fmwGpksrb/bamow6N/WykVHKuV\n", "3d0CGbY5je7tOnL0XA4XhneRhFTZskOodO4Yc8vQr7WdHwkXn7/GVMfW9YnsXvwvWeG+eQv+SU1t\n", "bYvvG3U7fVduBj4+PkyYMKHZxjcpnVsOzKprfxU1x5iuqK6uBuDdd991ur68vBx/f3+effZZNmzY\n", "wMqVKzEajXTs2JHHHnuMNm3aXPUxPTw8ZP+3W19MpotxdJWVuLu7o1bLP6+3t7eDKLkajEYjNTU1\n", "klHhzJkzfPHFF/Ts2ZNhw4ZJyz/66CNpLq4oKytj4cKFdOjQgQkTJuDr64tKpeKzzz677L5XS4sR\n", "NR07dsTNzY3XX3+dDh060LlzZxYvXsysWbNYvnw5Z8+exWq18sQTT9zqqQpuIBqcP5Aqy/X89V/z\n", "ZS98V4G5jR9errKk7IT7hZCV1sgCkVVM6ef78NRo6RN1FzlegRQ0CgYGm1WlzsP5V8X+e8PLbOVu\n", "s4KlPTpI6+zNM10JAjdPd54Y/CgLl32NX6PeTnZXlUVvoPTz/VRU1kvZSs6ODcg7dtv/vljhuGmj\n", "z2qDL2988i3Dn3qc0hNFkFWMubwWpYdGZmEqW3YIg71xqIuqxm4K9U01/wtub+Iem8y8JgL4tYxK\n", "4p6c1aLGdIWnp+2nxOzZs51aX+whFKGhocycOROLxUJ2djaJiYl8/vnn1xQ6cTlh4uPjg8FgwGQy\n", "yYRNVVWVLD7nasnKysJisdCxY0cAjh49io+PD9OnT5e2KS0tvaKxMjIyMBqNPPnkk7i52UpQmM1m\n", "mcXmRtFiRA0gS+MGmDXLdlM+/fTTt2A2ghvFpXoROauFol53mqJaI1UBCnSNxnFW9+VSDy9nQcUv\n", "TX+eVz79B2WNrBfBugDefmYuQ+IHs21nkrTefLFejHtMCBfMVtetDLBlFjV260CDi6jUw3k6ZLh/\n", "KH989g8kH/yVgwuTUbfxdSouTn1xgOlJrt1G5atS8Yi1+e71a2wVlgGXQsQeeB0aHELJvTZTtn5t\n", "OroxcouR/7TelC761SaMokMa3HYXsRf9S131X6fHaQ7zv+D2xi5yG1eujnty1nWJ3+YY0xUdOnRA\n", "o9FQUVEhq1vjCqVSSUREBPfddx///e9/qampwdPTE7VajdFovKJjXk6YtGtna2Vy7NgxYmNtVuf6\n", "+nqOHz/uYOW5UmpqakhMTCQoKIjISJvr22g0OqR2Hzx40GFfZ5/NaDSiUChk+6ekpGCxOO/Pdz20\n", "KFEjuPO4XC8iZ7VQiq2eFEwMhrXpsrHsdV/u35Jma0FQbuX5l990+vC6VFDx28/MlY6nLy4HbwVf\n", "bljJsg0JTBs5QVqfby0gr6YE94ggTCAd28tNRXW9uaGVAeBW5PwXh+aCnnqLibKlB/Gf3kdarl53\n", "mhcvVgF+ZtpTzF/wLvl5JVjNZnynyjtyuz/Vl1+WHuT+bbbPXVVTT7bKSl1WMZwownJOT7h3K0r3\n", "ZGKqq5KEh9JXS/myQzIrUOPAa5mgdGFNUgZ4YFmaTnhICDl5VZSvPAIqBZaKOlRmJR+uWESPWgu0\n", "dsxeaA7zv+D2p/+gwTdccDTHmM7w9PRk+PDh/PDDD5SVldGpUyesViuFhYVkZ2cza9Yszp8/z9q1\n", "a+nVqxeBgYHU1NSwbds2wsPDJUtPSEgImZmZZGZm4unpSWBgIF5eXk6tMpez1LRq1YqYmBhWrVpF\n", "XV0dPj4+JCUl4ebmdkWWGovFQm5uLmCry2Mvvmc0Gnn22WelMaKiotixYwc//PADMTExnDp1yqmo\n", "CQkJIT09nW7duuHm5kZoaCiRkZFYrVZWrlzJPffcw4ULF0hKSsLDw+O6XGTOEE8dQbPiqit046aI\n", "TV1GU+Y+QwE4tQ6UZhZRPcJWqVKzp5B/b1zJwo0JDunFrlwicxZ/wjtLEiSrjP2l3jij52/j50jZ\n", "Utt2JkkCqLJchalDF6xBfpiKy3E/WkPFvqOY/dTUeTn/Khlb6fDpE0b9z6epW5qKGiXh/qG8NO3P\n", "sjmU99OhSK9DUVbrdJy6AE8yApCymdwu/gOI2GPi6398CsAHiz5mxdLV1BrqMHso8WjUXkFdZGDy\n", "Q5MAmD73eYyY8ahX4bGumNoig5OjAmYrURGRaFBRPCdIqu3jd7G2TwFQuzKLvxxWylLqm8v8LxDc\n", "ah544AF0Oh2//PIL27dvR6PREBwcTK9etqxJnU6Hj48PW7duRa/X4+HhQUREBKNGjZLGePDBBykr\n", "K2PJkiUYDAapTk1TEdI0xdsVU6ZMYdWqVaxevRqtVktcXBxBQUGXDRRWKBTU1dXxr3/ZUuC1Wi3B\n", "wcHcfffdxMfHy1xs0dHRjBo1ih07drB37146duzI7Nmzeeedd2Rjjh49mu+++47PPvsMo9Eo1amZ\n", "MmUKmzZt4ujRo4SHhzNjxgyWLl16XS4yp5/JeqNlUgvh/Pnzt3oKt4yWFPw4Ze4zUs2TxjR+ETdl\n", "+tznSb/XdqMbsoqpPZCHtc5kcwV1s704jfsv4D2lwUWj21rI38Y3VLv96H+e4vXW9Q5jP7Yji9/N\n", "/QdD4gfLjtOYxingTWnqSuvZvhtHz2RSmZ9Pl8ICFsU1uKee2ZVDirsnF7RGtJMahFnjuU6f+zz7\n", "6k9Rn1WMOswHU34lXoM7OcTB6NelgxUHF5Gr+do/myGrGEN6odRLK1SvRRPmLW99sLUQY34lBT4G\n", "h1gic2UdcV16U281kXWv2qmbCqDTD0X08w2QzP8DH510W8TTtKTvSkugdevWl9/oMhgMBkpKSm7A\n", "bATXitls5t1336VDhw6yNOw7hcDAQNzdnSdQCEuNoFlxFQh8qaaIjd0i9kBX1bcnCVYH4FOk43jW\n", "CdynyyNnm1p/XGVEVPq7Sds1bf5ox1UXameutHNbd0gCJXlHksyv/9hf/07BxgTKmggn/bCGqr1H\n", "jhzB5K2UVTUuW3ZIKtIH8jiZppYr3ZZC7ooZxCNPTSCvvBDUSsL9QjCZTFQlVWPK08uyu84v2of/\n", "MHlVYf2wEExLi3CPCZVlSrnHhGDae56pI8azbMPFNF8Xbiq9VsFRrRIjKjQoib2xP74EAsElSElJ\n", "oaKiglatWlFXV0dycjLFxcX87ne/u9VTu+kIUSNoVq6lKaLTnkMXA3nhovXHyX6NxUjcY5N55n9f\n", "5dM4x8DejqW27a5WcF3OldbYr79tZxILNyRw5FQGmnsdLRtVF6v2moo88HMSoFuy6Ffqjl5ArVSh\n", "9faUWW5qFqfQrlUbQv2C8PFpx2cbV2AN8kA33VbWoACoXH4Ew5kqgv4wUDa2KtxFvQy10mmmlP/+\n", "SpZtSKCwooTKT3Opr3fupjpz4RzFI4JwFjclEAiaFzc3N/bt20dxcTEWi4XWrVsze/ZsKYj4t4QQ\n", "NYJm5VqbIjaOs7G7fL682I+oaSdqO43FSP9Bg/lgSWuGpdqaSdqrDJu6BuO217bN5QRXU1dTQVEh\n", "4Nidt6llp7FFp7YINM4+YKO0a2downWY8ivxm92P0n/vxfzpQdy8PdCYYMZDtk7a23Ym8T8fvII1\n", "2MPBJeQztSfGL5z0jXKREdXGP5SarYWyc6H69iRGb81FF10QPgRhWZVK2bJD+DcKPq5YdhhtfEMn\n", "X0NWMedqivnLgjfpscEx3kkgENxYoqOjrygb67eAEDWCZudytWMuhTOXj+rbMjTrqjCObqgu7Mz6\n", "88zM53ln1QJOuxAtlxJczo5b900xiiyVZM2wx6tkVimYPvd56eXd2KKj9NVS+vk+1GE+YLHiHh2C\n", "Ia3wsmnXWK0ofbUYsopRt/PFq5HLaf3WHfTc2YNlGxIwuFmh1HlwsULlmEruHh3iUHCw9ps0Xnz2\n", "DYdzUeztT8HDcvHoO74H5SuPSG4qU34lCg+N/Jw0qgOUjrDaCASCm4cIFL4DuZOCH10F84auKyYo\n", "OOhiVpIeq9GMLtjfoQ5O4+wlN4WaqSPGy9Z9uGKRLRbFZCHcL4QH+sZz5HQGR7PSUU93LA9fszgF\n", "z1mxDi9vgLpv0pgZP44Dp4+Rda/a6TYVy1NQtdVJ8TJVSTkYz5TLLB/2GBpDRuElg4PzCwvIrrqA\n", "0tvN6TblK4+g9NDIjl+24jCacF8s+jopdiaCEH78/FuH/Uc8P/GiS0mOPjGjoadUYgZYrNLxXQUS\n", "Xyr4+lZyJ31XbgQiUFhwOyAChQW3La6CeXVBfix5Z0GDRWVkiNNGi66sRNt2JvHnj+dTowN0CrAo\n", "yKw4y/H1/8Xj/g4YSpSywn92lEol5hUZ1FVU4vt8P9k67aQYvlz8DR0DwwHn7RF8p8ZSszgFLk7J\n", "UlGHpp0fxR/vRql1Q6FRonBTUbU9G+/7O2PIdN4Xpd5qIq8oH7+nbAKraQBx2X8P2yxDfcMlq4oi\n", "vxZvhRrN4IY4I92WQl6a8JzTrK4zF87hidwqhdJmnZEqDVutstR7i8F5kHVBebHT5QKBQHAjEaJG\n", "0KKxB/M2TU3WK2zumyupg+OMD5d+Qo2P3AqiX5OOWVFHTfIZrHXOX871AWq0o7uhSMxwWGfIKqbO\n", "bOR0eT6WxXlYvJ1XEjaYjNR/nIzG3Q2r2Qy+WjRhOlkV4fLlh21/XKI9ASqbBcvu+rGLF+N5PUpP\n", "DZ4D2tmsPQoF3qVW/vcv/wAc3W2Ag6vt0DffY+mka7AaNbE4la9IoWrbSbyHdJGOX/bJr7joZEFh\n", "YaHzFQKBQHADEaJG0KKZNnICr3z6Dwzu9fIifOtOs21nEjnnzwDtHfbLzjt9yXHzygulbCE7urHR\n", "lPxnLwqtGoVWTcWqVJnQuFQLArurKWD23dKyuoW/Oj22KswL3ehoKlaloo1tR9VPJwl8rr9sG7+p\n", "vShbkIyH0g3L91kox0U0zPNiXNCREw2d6RtnLtnFjX2Zbkshf3uhITi7qdibPvd5B2GonRRj6/wd\n", "E0J1Uo4s5RzA74lYSj/d1/D50wrRRAdjPFZI2VcHUQV44B5tS8nXr0mno58tNsdZ64rboZ6N4MpR\n", "qVQEBjoG8gsENwp7U09nCFEjuOXYXR8FRYUUlZcQEhJCiG+gFBvz4YpFmJsErBpHt2f5xlUUFBbg\n", "60TUnM8/LwvedUDt3Iqi9HbDf4atnUHZskOULTkIVrBUGbBixVJvwpBeiNJXK3P5OHM1eQ3tTPnS\n", "Q/hNd4yXgYagW6VWnh8lWaXc1bRv1Z4HYgdydG8m9VYT5oIyOpkV7PziP3QsquDEv3Zj7OAvExDu\n", "MSFo9hQSsefKss1cufjswsiVC0zhpqJ6ew7VSTkoAzyhvA7/3zeIs/IVKdQkn8GzfzvCioNctq4A\n", "hLC5g1Cr1Q4dowWCm4W48wS3FHtMTFEHJYbCEnTToykGimmIjdH5+0rxMo2pt9q60jaNJ9GvScdi\n", "tZJclMH+f79GpxXhvPTEs7IXe7hfiNMxFe4NXwn/ab0p/XwfXoM7Obhf9GvSUfppKVn0K0p3NVaj\n", "2WEs94ggKjceR78uHVNBFepQb9xjQmS1YKx1JlQBHrbKyfvPYak1YjWYUfq44dvJH23heY6s+S+t\n", "Q9vT9Z44ii9s581uF0VBtx7MOphDclQwxfvOSQLCK6Oav7/wuksh0zR+xlWKvNreOsGFC0wV6IFu\n", "tO2clH6+T2qdYMfviVj069IJPmVh6oTx7PpedPMWCATNixA1gptO45fq8awTGO8NcWrpsMfGXKpI\n", "noe7FmNMiEMlXGN+pRQvU4BjWvFLTzzLX798R+bWKV+VikffcNkx1GE+srmpM4sIT8nHEwUV+8+T\n", "46VGO7MP+ibNN+0o1Cp0o6MpW34IrGDILMKQXihZVqxmC0pfLTV7z8gyoOqXHGRgRomsM/fU1StY\n", "PjRKNr69E7jpiVgqFu3Hf38lnmYrS//5Gqv+V4F/QBhjnnxeVhTwSlPkJz80iaN7M8knkOwmjTFl\n", "rlK6YLUAACAASURBVLiL58kZXpUK/jbNZikS3bwFAkFzI0SNoFloag2wu4GavlSV90ZjWJOOucp5\n", "pdp6q4m+7btzYPE3mILdpVovtl//c1hDIhu37bBVyr24rmbvGbyHdJaN46yJZquln5BlD67NLcN7\n", "eKRDRV2sVqlAnjqziP77zrO4z8XsoR7tmbU3i5xPDxDlpqLq38myzt36NelYzWYMWcUotRoCooIl\n", "QaTfeJKTm06g8NNiOq+XCRqACK0bS+/uIFvW1cdpGT/cSqrRJ2bgplJjqqqkq9bK4viGDKe/LPgn\n", "YHPxOAusNk/sQtC6YoL24rJA4sAJD5J/sd6OqaAKr/s6ys+VC2vOXZ26Sdc9JfsEtO7gsE1K9gmm\n", "zH3GIR1fIBAIrhYhagQ3nG07k3ht2f9d/OVvu8VeW/Z/gPNsJd3YaEo/39d0GAD0xeWsr96B56yG\n", "YnHVK1Ix4sV7S/9DXm0x/s/K+yaZ9QZHcYItrdjenVqDChMWdKOjMWQVU59TSu2+c7L9ypYexPPe\n", "9tQkn0G/Np1uZ/QsfkAeXLx4QARvJmczr59NRE3flsm2jcepdVNhrTOCm4rqpBxC4jtKgmhXXhnb\n", "qky0qaihpMhEqsLoMFdPHGvzmFwIh/pAL6luTPC/k1k8UD7H93qFSC4eV/EzZs2l68h06dyFyuAS\n", "qXZO0/PrHh1C3Tfpssadtd+kUewRwgeLPmZ9+g5qBocxa19OgygEntyZxfHBbTF1Fe0VBALB9SNE\n", "jeCG8+GKRTJXBtgCez/6+lN8/HSA2iFFG6XSabNGVCoHEeT1RA8K1qWDtQ7dJPkL3B4H44ys3Gxy\n", "WtuCfC0VdZiKqlF8dQ5UCoJeGGir97IuHXNJLZY6I+7dQqjenoPK39aGQPfNMafjqhQNAmTp4K4M\n", "Sz3N6UndgYtp4lUGwlPyJUHz05lS3hjQYEma8XMG+zKLJAsPQI2T3Ogh7QJ4OimTzwZ3lZbZ+1lJ\n", "5yzE2/kczZfud3Xmwjm27UxyKSamjZzAuVUL0F8UgU2vVfApCw/HP8b2DXvILj6H2U+Ne59QCiKC\n", "+Oqb71H0CcU9IohkYFjKxdYV56vIG9lZ9rmvJB1fIBAIXOE8BUQguA7yyp3XJMkrK0CDqqHS7pho\n", "dKO6oRsTjcpNTVClBsvSdII2FhOzF/42YQ5mpYvCJwoFKBW2F+zadPSJGejXpmPIKsZTo6X2mzTZ\n", "5vo16XgO7Yx7dAimcxXoxkTjdV9HVAEeWKrqKVn0K7WHz4MVPOPagxW8B3dCoVFJad3OhAaAuUlR\n", "bs9Gf+vGRmOtNUqWl21NBA3Akge60SoxU7Ysq66e6T/La+F8eaGMvVH+DEs9zcjNqQxef4RfL/az\n", "suNqjoV6PWATJ3VOzo0qvg3LN65yui/YLCd/Gz+HmL3QvciPCGUIYRtKidhjkq7VH5/9AwF+/nhN\n", "uwvd6GjJmqOdFGOz8ACmrsGcntSdjEndORmqk83djqsu6QKBQHA5hKVGcOMxWZwutpos9Gzfjd2J\n", "y2QuI7BV2o1wUkr/r/+ajxJHVxJWK+aKOqmPkt3qU52UQ7g2iNeffZnlG1exL/0w5mB3KetIvzYd\n", "v9/1koSVe0yIrRVBk8JyVrMZ/dp0FF4NcSx5sY7ukzf2nGRoe3nmUE2TqSp9tejzqwBQu2hg2Vqt\n", "JuvTfdS6q7BU1aP0dWd3TCD3/5yGLthb1pDzNLb2BKb8SgKaiIK82DBm/JzJkvsarDkz9maRH2hr\n", "ODkkfjDhS4M41SSw2j0iiPqiS4uJK+nhdan08KaoXBzOVZd0gUAguBzi6SG44fgotVxo0jSx7L+H\n", "aa305cjpDBQBWtn2dldUShUOtWWC/QLJXpGCR782sjL9bhFBWKrqnVa7Lf46FYCpI8az+9h+/Eb3\n", "ajjYRVFhz2jSr5W7UQxZxSi93VBdTNE2FVTKXGU/W0wM+TUL94q6/8/emcdHUd///zl7ZHeTzeY+\n", "JNwQhCCHpQqkoEFQW2zR2op4gWBb0Fatbe2j1q8UtdXvt7W1ahW8EFAQta2IPy8wGAUhBsEAZhMJ\n", "BBISSDaba5PsvTu/PzY72cnOhgABQef5j2Z2js8Ou5lX3sfrjUcUSTHqWZaTIh0fTgdFHhNsc3P4\n", "vAQWflTBUKNysW+uxUR9go7qeRfIZiuVt3sUZymFB172TAPZS46whQAztpQRrxFwm/QcmzaIYc3d\n", "a8zKyKQpP3raeH+IiVjpLak9vAvLJhtXzbqWdzZ/EnNKuoqKisqJoooalX7HkppM66gkWZt1/ORB\n", "JFVq8BFAE+EFc7ypzlkZmXxVfQBXSa1MJLWuLSXQ5qazqIqEiFlGAAk3juPxNcs51mIjKIo43rJK\n", "LdTB9q6HazhiEhE58VTacZcek48reKWUzi0HSV08Wdq2b20pgXgt+uxEUs/PkGpE2pudHNCICFoh\n", "eqzAy19Q2OZkjC+B2z+0snxW92vhaM/Ouq4hgBEFwUpTtSMHXhrGZNL6ZDEBLWgS9ARdPoTLR1LX\n", "o5A38GYjj/16MbpggAFtDmpfbcVxQ7RLcSQn4/47f/bciO627nOH28OjuqtWwNrV/yWgC0Vurpp1\n", "rVpPo6KictKookal37GkJGHI1UV1yCQ2+tGjlQ1A7M2fZub0AubPnsvWz3fIHurQbexmmZOHY0PI\n", "IyZ8PU+lncPOBkwLxpIaPucGK746B6I/GNo/rGUiBIRrZy3JN/a4zs0TaX11T9S1m58vIcUdh7Df\n", "KRUFA3T8cxu8v5/0O/Plx9xyIc5/bMUT9FDjCjD//X1kx8dhjtMxa0ga03JSeKDiKJ5KO+ZEAwP+\n", "8SlJA5JwInIgTkvT8mL0A5OkdJGUOgNIjiMtoiW8dW2p7H5Y1lUyLqjh4VFdEbIBRn7+SQdlqw+S\n", "kjtEsYX7ZN1/w+foOV8q1lDRd6yfoFmQJxX3vbT+PwD8ZsmdMa+hoqKiEgtV1Kj0O7FSEA57K2gF\n", "/AeOEhD8tL66BzHGVOdwsejM6QVoTXHKF+qq07BckxeaUxQWNVZbVFeU5Zo8mp7eQdovp+KptOMs\n", "rqG1K60VFliiJ9oVGEKuv9JU6i7i9Ub+svg+IPQAb2i1c6CmisSrRiuOFdBVNDIzzsDqGd1mest2\n", "HOSyQalMy0lhwccVNFwxAmF7Nd/rCLD6yvHSfgs+Kucjkw5fvQs04MEm1cG0righeYnCXKbnS9CU\n", "2BiUkk2i3cHfZo6S7fP8JSP5QdF+bpv9B0XBse2/fXf/VfIk6q09PIxSe3940vmEsePUiI2KisoJ\n", "02v3kyiKWK1Wtm/fzqFDhxT3aW5u5t///vdpWZzKucn82XOxbJZ3QGlfO0AznTRclUbC/PGk3PId\n", "DIIOvVv5HO2tDun/BZ9y4TGRXUcRhaj+xk7F3YU4LbqKRkbtOka+Po5xngDC9mp8Rx20LS8h2OlV\n", "PE6bZpK6d8JMOn+8VDi76pGnyUxKw3TZcDxWG/6uouBIckrrZYIGYNnUEfxtTw0zCsv4SAjgH53B\n", "0EaXzEUYYPWMMeS2+/nF7Bt5+rePkksm2u0NBNaWy9Jnsveq1xJwemlsb0aTqPw1N5g0MTuedEFl\n", "gdfT/TdspmjNF6jM12HNF3jkjacp3FqkeHwkoaLiaPwZhl47sVRUVFRiETNS43Q6+fOf/8zBgwel\n", "bXl5edx+++1kZnb/dWW323njjTf46U9/enpXqnLOoJSCsJtTaOgxlNI0byzu5bsVZzdlabo/Y9mJ\n", "aVGFxz1t+v317TjeLgdRVJzDBGDq9DF52xFemtpdS7JwRyWfBAT+9sDfuOvRP8SuX4mIvlg22Rg/\n", "9hKZkd+Bgwfw2HyhlJqCj4vepiy0fHotVT8YibOoCiOQYFIuJDZnm3n5w/+w5fOtNAtOdF0TxjUx\n", "xjNoU00Y5+RhW1FMVrtyNCyu1UVzfb3ia36NcrQtoJX/ylCKtvTVayZWRA9RVNu6VVRUToqYoub1\n", "11+nqamJ+++/n6FDh7J//37Wrl3Lfffdx7333svo0aNjHaqiEtX+e+N9ixUHSGotRgwKs5ssjcnS\n", "Pn+68w/89rEHQqZ6goDoD2KeOUJKBzk2WEkoGN49pXpMpqJQGi1oZIIG4KWpuVy39Qgzpxdg+qsB\n", "Z2Nb1FoMuel0bKqkc81eRqQPZMaES3jH+olsflLbwU6MYwcC3bUsjo1W/PUd6LLNOP3KQsulCRUV\n", "h80H3Sblr6QTcKZqqAw2SkMkAVl9UuR7NYzNxFNpR5toxFaQwy8K5aZ9y7Yf4K4LBvLgl4cVRxRM\n", "u/YGlq58gociUlAPlLcz7bZFsnXFauHuiyiZP3suv16xDGNEqjC89ji78n2INX5DRUVFBXoRNbt2\n", "7WLevHmMHx/K7X/3u99l3LhxLF++nIcffpg777yTKVOmnLGFqpzbxPqrXOsHfW56VFFxnL37/2dO\n", "L+DvPCxFfhz2VoRKDbUl5XRovIj+AJ2fHKbj/f1os8zQ5kaTbJTEib++nYSC4STUtSuuQacJpbc8\n", "gh/jxPPw1znk7egvf4EYFAkk6RD9QfZUl0dFJ5JumSir6zF0vaeWVbuwzMmjdstBbv1Y7h+z4OMK\n", "jlyQgeWyETQ9tR3DJUM5FhCjfGbCbeJxHx9ihDeIZf2XOBGpm5gNXT41bSt2gk6DkGlCk2zEY7UR\n", "aHahTTXRqRWo1gk8VHwQrSAQEEWpODm+2UF5fvSIgk4BPtPouXrrYeI1kJqWzZzb7o6qp4n17xqZ\n", "PozFzOkFLCz7CS+G53p1icjwVO+eKA3jVMcqqKioRBJT1LS2tpKVJfeyMBgM3H333axdu5bHH3+c\n", "BQsWkJubG+MMKirdxGr17atXiZLxW/ghd1TfERIit31Xei38F7+r9BiCIdSJ5Xz/gOLanF0dUNr4\n", "OIwFw6VxCeFoTaDVRWLXsMvD68sItAFKhoAKBnPhVJjmshFsA2YUlhGv1dDp8lL7nfPQXBZyFzaY\n", "TGi3N9Du8fKxx8+M9/cSb9SFfGbyB+EsrmGGH1ZHzJ5aVFJFMcDoDPTbbQR1Au1tLkRPQNaW7thg\n", "xa8XWDpZ7mQMcqPAcNoIQmLB8eNMICSaLJttzFQo35k/e27EnK/u62k9cb2OXQjzmyV3MmHsuO5U\n", "pV3HLXOvUzzuVFJdKioq3w5iiprU1FRqamrIy5O32wqCwM0330xycjKrV69mwoQJp32RKuc+vbX6\n", "Ttg6rk8twLHOefsj95J8h7wDKNztZL4iF1+dg7ZnSvAEglHRkoWfV+HJCs1OCjvcGnpEjpqWF0s/\n", "m+aNxbbaquhy3NNgzvFyKWIwKNXpaC4bQd1lIyTBFXmNkQOG8OsbF0vCr65re+vaUsSdRxgfJKqA\n", "eOWk4VxeWs2+XUcx5OdgyE1H+5Y1yqzPck0eB1fvYtEuuRuyklHgnuYgdlsjjqujxcM9f7sf7RN6\n", "0GnISc7knpuWMHN6AY9HTDsPR1sCueknJDbErqJvscfIiUhOJdWloqLy7SCmqLngggvYsmUL3//+\n", "9xVf/+EPf0hSUhLPPPPMaVucyjeLWDb7fbHf7+2c8UnKQxw1CaFW8GCrm6Q7LsZTaafo02oK3t9L\n", "gl6L2xxHmzGRPy68A4CbZl3L8+vewHxjd5Sj5ZUvMIyRP+AzktPwbbYd12Bu/GXz2FtTQb2tAfua\n", "cuK9QVKaWxip1+BzB6gLiPhHZ0iF0UqRiLAfj9mp3AFmPNaB4YcjuwVSjG4od2o8xbnpzHy/jOEp\n", "mdhdnXxVMIBOBaPA/S+XYqwUolKCTl2AlAWhtFwD3ZPXLRkpWPJPbobTiaSUYqW61LEKKioqYWL+\n", "NrjqqqvYu3cvHR0dmM3KD43p06eTlpZGWVmZ4usqKmeCWDOERF8AjzXk6dK6rhTRE0CbauLQ94Zg\n", "yE3Hvd7Kwuk/kh6eYcO3sMNtZ2s7+rEZmCMciz2Vdhpbm8iMyyS42kpGchrZmVnHjS7t3fkZm5/+\n", "Xx6aOljadutHFRR9VoMhfwiWxmQpEqGraCSntJ54BJyIVHp9OOOUu6JcIBcfQeVIR6DWwfnpY7hl\n", "2R3MnF4giYlmZ6NM0OgqGhmv1xK36SDeXceom5gtDZ3Upplk5/TNGcLL772hKDZ0FY3E7a3nn3f9\n", "rFc34hNJKcVKYapjFVRUVMLEFDUDBgxgwIABxz1BXl5eVIpKRUWJ09W5ctOsa1nxynqSbo4s7t2N\n", "GBTx17SBiMwpOOxAbJyXx9rV/2XT5x+HhEpmJplJafz17j9JD/77n32UlresoBEItLrQCloMCyZg\n", "BzSk49ts45YfhIpaI1u858+eC8Dja1dQ12pjeEMbH14pNwRcdcloZmwpo9xqwyFAWnoauopGppQc\n", "laWJbv24gl2jLNxaVMGqiA6mB8rbORIn/wrH6oYanTNCZogXvu/3Pv2QtE3p2uG6ncZddcRfPDDq\n", "3ntFP7fNvkEmNnQVjeQXHeGF6blAyPsnlhvxiaSUTsStWEVF5duJGrdVOSlOVKCczs6V3yy5kw1b\n", "3qHu+RIEvRbRFyAuNx1zwXDanimJGsMQ6UDcpvXQITZhWZCHHbBHrAtAY9JjmTMSQJoh5egSOQRF\n", "PHmZ/PGJhzEPSKVxqAaP1UbQ42frn7YjmPRoLHEEXT7iUE4fWTLMWK7Oo3ljNZcNmYZnw1pWFsjd\n", "f1ddOpoZhWVsuyCDy/dVk9ji5bzMQcy57W6K1jxDZYSIMeSm07HlIK2v7kFjjgNRJMUTxz2Lb4+6\n", "9szpBYx793XCTjc5pfUyQQNddTvvltGYEReVjoJQ6idKbOyt7xI03cRyIz7RlNKppCpVVFS++aii\n", "RuWEORmB0luaIfz6qURw/nTnHxRTE8bMLBRNi7s6lUSXD8vNF8peahyq4fZH7kXQayHThKFrREKg\n", "3YOnzEbq6AwpPeR47wD7PV4aO1vRHIsjpWsGU8vLu9EY9RgnnofHasPlUm5xDncf+eYMYe+OCkZn\n", "RUdDACyZZjSXjaC662dxRyjqcY9AKJoUUahr0ZoYlDSQxGRLKJrxA+VuIpCndOJRrse5YPAwagPt\n", "tPSIAOk2VnPL/N8BcrHxz7t+RjhCE0lPN+Ke15fea4+UkupNo6Ki0ldUUaNywpxMa22sNENDq71f\n", "IjixUhNrIiIRMkQRxwYrmiSjbHN4anhSRDdV2xv7cBbXEHR4yPjuQKZsO8LKSFfiD8s40OHD4oPO\n", "J3ZwOCmOYKeP+CmDpSLchopGFpXIu49u3VFJ3bRB0s97qsoJtvuAYVHLdfb4OXI2VtT7nh9bxPRE\n", "EiLrnqX9aDuMi94nMTWdv1z7Kx5fs5y61WUIOg05KVn8ev7vFK/TVzfimOuPSCmp3jQqKiongiD2\n", "1kN5DnP06NGvewlfG4mJibS3KxvN9Qc33reYyvzoB1Tudj/rHn1W8ZgF992BNT86EhBcbUWzILom\n", "a+wOuOUH153yX+jyh2II1/oyhBYPwqzBoeGXES3QDoWWaAi5Awfdfia2+9g0ZVTU6/cUVfB4V73L\n", "rVu/otDtwTcshdTzu6M6rk4PXr9IvNuHSxA4KIi400yYLhoYGk756h7iPX4KXEFWXSJvO/9s8gCp\n", "YDd8f/oyNPJEeHb5k1S98zorpnULr5CLcLTpXm+EJ3xHuxGf2Hkg9ucm+91mUpNTTjl6c7q/K+ca\n", "famjVFE5m+lTpMZqtTJs2DBMJlPUa263m6qqKrVY+FtEuA4i0t+EoIhDyIx5TKw0gz45jSaF/ett\n", "DVF/of96xTJyVqeTlZHZ54eYYiRgyTL2lO3jxQ/WEzRraH6+hLjcdIJtbvxNPWMiXQgCGoOO5HqX\n", "4sudEfOmVk0/nxmFZRxqc0cV3i7cUUlhnIDx9ikYASOhSFBn0SEIBolbPJmSikYuL60mrqGDeIOF\n", "Q0nxMkFzujp+Ft9+F8Vjx7P0zfVoA34CWh3Tblt0wkIkvP+pngdiR/gO2mupn52KGr1RUVGJpE+i\n", "5sEHH+Qvf/kLI0eOjHqtrq6OBx98kNdee63fF6dydjJ/9lzuf/ZRPAavrMaieWN1TBfZ3tJDSqKm\n", "sbUJTY+IiXHeWA5ttNKUn3VCD7GexaWFW4v4964PiF80kXhC4sy5o4aU+d+hdV0pAB1FVXgr7VLh\n", "ccDhRptoxONSnuTtDcgLgbOCYK7rYOUceb3OS1NzuXxftVQbA5B03Tjs/9xG+q+nAeAfnUF1l4hp\n", "f343v7jkWpn/jVLHT3/VnUy5pOCkxIfiGq6dd8oiI1YhcSBZ/qtLdRZWUVGBfqip8Xg8xMXF9cda\n", "VM4RZk4v4PG1Kwj0mLod9i2J9WCJ1bmiFMGJy8zEHrUnUoHviT7EIh+4+6xlCJfmYOh6zbWzVirw\n", "FQNBmlYUo000kvrz7rqa1pe/QDcoiZZ9DSzbcZBlU7tHDizbfoAEfejhu62uhcKaZiyAIU7HtroW\n", "puWkyNaSbuuE9ftkHjCCUS91V0V2GYnZJlZsXEOCJVHm4tvzvfWMaj2w5jEeX/0MloyUM1Jce7pq\n", "X5QifK71ZRgmZUXtqzoLq6ioxBQ1VqsVq9Uq2ZYXFhZSWloq28fr9bJ7924GDx6sdAqVbzCWlCTF\n", "qdsn+mDpLYKjKGoiSsB6u1bxJ0Vs+++r6IIBGtoclPhacdyQC+gw5k+QvGoMuemInu7UkTbZRLDT\n", "R/qkHHLW75MM8HQXDcS29RD2y0fy5ZZDsuGQX7V08suJg3l2zxHKmjt4ckb3OINlOw4CyITNmAQD\n", "S8cNlTxg/KMzQADL1XmydQEEWl2IOgGnBSr9Dfzif3+H4a96TAYjN826lt8suVOxcNs3ZwiVG62S\n", "0+/JCIzIe9ibgR6cvrlMSp8PuymThhjt5b2hFM26ZvaPTnptKioqZx8xfwtUVlby3nvvST8XFxej\n", "0WjkB+t05OTkcPPNN5++FaqclfSnZX1fIzjhmUnHu1a4UPXhcKHqACOLdjkprmgMjQWw2kAr0FlU\n", "BYDoj0gdBUUS/EFFE7oPPQH8ozOwAm2lDeiPOXCKIrVakftKqxkgCrz2Q/kstGVTR/BQ8UFJ1Ny1\n", "pYK554eiDNLspopGRJcv5H/TtS5fnQPPlw0I8Xp0mQkYRmdgyE3HscGKu8FBINnIM++/wsZtH2Cy\n", "JHC8AZsnKjCi7iGw5K//w99WZWHOyoqK/JyOuUw9Rchts2+QuSGfiLNwrEiSKd5E/qTJJ71GFRWV\n", "s4uYT6Crr76aq6++GoBf/vKX3HvvvQwdOvRMrUvlLOd0W9ZH/oXe0Gqn5lgthukDpQhGb9fa9t9X\n", "eXh0opQK0mkEhgRFDmw+SPMgS5TbrhgMSC68hrxMhn3VxMoZ0SZ0Be/t4SjdNS/2pz5FQCDtzuno\n", "nipmfEJ0IT1AZUsnf/msioAoEhCDsqhNnK0DrxhAEx9HsNOLGAgimPT4alpJ+9VU2Tr1R9oY5w6g\n", "7wzisNqJ0wgY24/irPajGzZaVkwMyKJacGICI3wPI1kxbTiX76vGmp8dFfnp77lMfUlnnYizcKxI\n", "0soN61RRo6LyDaJPv3Gefrp/W0dVzn3OhGV9ZASncGtR6FqNx79WR0sT2wIePqxpltW+HC20Yh+d\n", "QeSj3XJNHk3Li/E3tOPoMrAzC8omdGazQfp/xwYrBABT6GEerxHwx5i7lG6K4/7Jw1m2/QA3jD5P\n", "9lqn20fij8dIYs2xwUrQ6SW5hyFghjmOqV/aeW5mHttSW6Le24LCcnaCJGxa39iHaaL8WiciMHTB\n", "gOL2+K7/9oz89LfIPV4660SdhWNFkjyi76TWp6KicnbS599yXq8Xq9VKc3MzPl/0L4Irr7yyXxem\n", "cvZzJi3rT+Rah+02ClsDsoc+wLMz87i8tFrqLAqjMenRD03Bf6wdo92JSVQWNZ3eAI63y0EUMYzN\n", "xFfbhugNPfzdJh0zB6ZGFREv+dCKXiPw4/f3oTXpWBYRpVn4eRW2OaNxlx5Df6SNYTYn8Qi0HevE\n", "VtEoCRRdRSOj99p47sqQM15hD0EDsLpgDAX/bw8VlXYQRTQ2l6zg2LLJxvixl0TNp4p1T2MZ6EU2\n", "vEdGfvpb5PZ3OitWJMkgKA8KVVFROTfpk6ipqKjgscce69WkShU15x5nq/38qa6rPSuDgzWHFV+L\n", "V9imTTVhLhiO4y0r52eYuTs1KUqc/OxDKw1XjsDSJTRaXt6N6PFBnBbHBivH8gfzfOEhfp6bLRUR\n", "f9XcyfcGJLN4wiDmbP6SA6NSKXh/L0k5STiBui5DvTTg4i2HWXXp6FDKrMPPwQ8OUv9pDbXfG0xO\n", "aT0TkxOkteg0yqIrXqeFoIghL5McfSZZO5AExvixl/CO9ZNe0zmR993vaOHeYw7+dmF3tGTh51XU\n", "Te42Z+sZ+elPkRtLhDjsrX0WZpHEiiQtuvV3/bJeFRWVs4M+iZqXXnqJrKws/ud//oeBAwei06nT\n", "Fc51zlb7+f5Ylzkri/rmOsXXvE0e2c+tL3+BaUrXqAKNQDyCVPMS2eF0OOjHtvUQQnENwQ4v2vMS\n", "IU6LoNViGJtJc7mNjzRBjnxRzUCjnpFmI78YP5BpOSks236A3+flsPJoC5t1YJp3gWwNOaX1kqCJ\n", "TCttq2vhiXcr0Wk0fOUPSu3hsdJcvuxEqYNKq8mUOQ4vuO+OXtM50fc9g2OrbVgLKzHpBJpdLmou\n", "zELTJer6Uph7KsJUSYRoXztAs0lPQ346J/rZiBVJurJgluoorKLyDaJP6uTo0aP89re/VQuFv0Gc\n", "rhbcU6U/1jV/9lweqT7Aol3yWUu/221jxtU3YtzxlfRgSxw5le3bd+MrseFvbsOZYQZCLdiRBb0z\n", "CsvQZSeGim+1Ask/uYCOoircu+uk+U4AFUVVHLDaaGntRCsIbDnSzKwhadL5Ln2nlGM91hseJFnY\n", "Q9B8WNPMG1d2D2O666NyAGYOjk5zRUZRLNfk0fF6jewax0vnKN1394Kx7NpoxTIn9N68663kvNVA\n", "dmZWr6ml/hCmiq3c5hQaengjnchnQ53wraLyzadPombw4MG0trae7rWonEFORwtuf9Af6wo/WU+B\n", "owAAIABJREFUuJ576Rmu3nqYeI1AaloWc371B6ZcUsDiGMd9/2c/pbKlNkoMLfy8ioYrQqknxwYr\n", "8VNCvkzmguH4m5z4jjmkIuNghxe3P0DQqOP+ycOjrmHJMFO+tpTkmyZK29qOtsE4eVpJqW7myRlj\n", "WPD+PkalJLCz0cGst3YTZ9LjO88ipbLC2NytMnfn43UnxbrvkW3hxnl5ZPdh5tSpCtNIf5wLIvxx\n", "brxvcb94I6moqHxz6ZOo+fnPf87TTz9NRkYGY8eOPd1rUjkD9HcLbn/RX+s6mb/KdTod4veGsGV7\n", "NTO2lBGvEXAJAvs9XvyVdnwf7EcwxmG5JsITptNLXKdIsK6DgCAiBoIgQmen8jgFt0mPaVI2TU9t\n", "B70GAiJOMcitH1cwLK67aDVW3YxXr6UoXkvdj8fQXGnHX99Oao90FoBPL8pSS9VHamh5sRFterzk\n", "Wtyxbi/jL50LxL7vJ9MWfirCVMkfZ+nKJ3pd49f9mVVRUTl76NNvg4cffhiv18tDDz2ETqfDaDTK\n", "XhcEgRdeeOG0LFDl9HC6fWZOlvmz5/LI8kdIEdySm29L0Mgtd/zxtFwvsvajuqYar8+IZcEkwhU5\n", "jg1WAu1uUn40BsdGK95DLbT8awcaYxy6gIDeGyQwMomkHt43tRaDYsSnbvIADLnpeCoaMYzOwFNu\n", "w1XroOSyoRz+uJpjRRWsKBgds27GnplAdZeI8W89RFxuOi1rdktjHsLXD7p81NsapFRQ5/VDSGEI\n", "AK1rS3EW1xA/ZTB7ayoA5c9DT7ND6JuAOBXxoeSP89DoRJa+uZ751847Kz+zKioqZw99EjXH62wS\n", "Yvh6qJy9nAmfmZMhQYQZAQ1/u3CotO3nH1fyzEtPsebd15kwZAx7qsv7pWOrZ+2Ht8mEYXSGlEoK\n", "t257KhqlB7xlTh7Nz5eQ9LNJOFeW4s82YukxeNNyTR5N/9rBJq9fsdsJIHDEgdPuRJtmwpCXSXNF\n", "I/7F38XWNaFbbG7nF1vKee6y7pELkXUzLat3Eez04t53DI1BH7VmS246jautiqmg5Jsm4thoxZCb\n", "jrcxFD3p+Xlob3Wg9cQR6NEW3hcBcSqCOZY/jjbgP2s/syoqKmcPfRI1c+fOPd3rUPkaOBsLJ7f9\n", "91VZGzHA85fmSk62JeveQH/ReZIHy6l0bEU98IMihtx0mb8LQGdRFQkFw6XtQtfwSp0uSG6NA8v6\n", "L3EiygZU6ock4w+I7Kt3oNOKJF3XXfBr2WTjhqsXsGXXNva31WKek4en0o5jo5VAk4uGTi8k6dkv\n", "isz8rBKjy0enJ8CBoB/3dh9iURWix09Cghm3GOrEsvxoDD3JzMw8bq1MZPREaZp5XwVEz26nq/Iu\n", "6XWyeKzuqFj+OAGtTnGNKioqKpGcUDK6o6ODI0eO0NTUxMSJEzGbzXi9XnQ6XdRcqJNh1apVHDp0\n", "iGHDhnHrrbdK25ubm3nqqafw+/3MnTuXcePGxT6JyjnN8ZxszTeOk6IMcGodWz0f+Ia8TNre2CcT\n", "II4NVpmgARB9AXQVjeR3BFh1WXeNmWxApShiuSYPx0YrujoX4ppyMjIyyEpOlx7wv+FO/rHiKV5c\n", "uR5/RsitWD8shUBNG0nzL8RTaaes3EagSURINRLs8JIwfais28qzrhRipKqyktOlgbRRiOJxoyd9\n", "FRBK3U61mz/hj9cpi6DeuqOmXXsDS1c+wUMRKagHytuZdtui465DRUVFpU+iJhAIsG7dOj744APJ\n", "TfjRRx/FbDbz97//neHDh3P99def0kKqqqrweDw8+OCDvPDCCxw8eJARI0LdHxs2bOCGG25g8ODB\n", "/N///Z8qar7B9MXJlh7pzv5ymTXkpuMsrpFSOYEmJ6I3IJsV1fLKF8TlpkveMpGEB1Tu2VffPaJA\n", "EJgwYQJv/2udzA8lMlIxLGsQBEQs6cl8VbkfzYILpfWExVTz8yUIBh2unbUk39jdOWW6aCDOHTXS\n", "7KowHa/sxZ54Hpd9dzq1mz+RRaRc68vIjc/inrm390vU40S7nXrbP9xZtfTN9WgDfgJaHdNuWxRz\n", "OriKiopKJH0SNa+++ipbtmzhtttuY+zYsdx5553SaxdddBGbN28+ZVFz4MABJkwITTgeN24c+/fv\n", "l0TNkSNHGDVqFABGoxGXy4XJpDw8UOXcJPyQ73DYWXKggRXTogtsJXpEH062+0Wp9iNO1JEwJ0Ic\n", "FFXh+uenjPCH5ju5jTrqsxOJr3cqnZI4WwemH4zsjuyIYtT6oiMVabjXl5HTGCCoE1CKeWqSjCTP\n", "HU/zcyWy7eHrBD6sxv3iHtxBL5jjME3OoSE3nXc2fxKdClqy7KTETKyU0Yl2Ox1v/ymXFKgiRkVF\n", "5aTo09Pgk08+4YYbbmDGjBkEAvL0QGZmJvX19ae8kM7OTjIzQw+X+Ph4jhw5Ir0WDAal/4+Pj6ez\n", "s1MVNd8g5A/5bI5WaPlB4X4GmlOo7WiheuZgqValY91eDBd1C5xT6X5RNHhLy5F5oSRnJzIltV3W\n", "xbT44yqqnT5QCBh6M82y4ZQpnjhu+cF1sn2UIhXGeWM5tNEKZrAorFVjDH1VtYHoonxDbjpj7aFU\n", "kzVf/rrj8kz27qg4rrfM8egtZXSi3U5qa7aKisrpok+/RTo7O8nOzlZ8ze/3y0THyRIfH4/L5QLA\n", "6XSSkNA96yayXsflcmE2m2XHlpWVUVZWJv08d+5cEhPlbaHfJuLi4s6p9//CmhWkOBvIWW+TCm4P\n", "/nIS5hIdd11zIys3rMPzmQ+DoGfilTdTetAq/bzo1t9xZcGsPl3ng6IPeXHDWknA3HbNTVwz+0dc\n", "M/tHsn3+9PI/aJ0ZEiY5pfUyQQPw7LTh3LPfx5JPD7Hie8Ok7Qs+KucrAjifKwGNgM4LN18/j2tm\n", "/0j2bxKMYQmDIGAYkxGVSopsrR4zaATOQru0PoC4t2toFswcaWtAlx9dMNzoaOa2B+6Sve8rC2Yp\n", "3o9Y93Ldpv8qpoxe3fQmi3+6QHbPAJI/tPOL+b9R/Bye6P5hPt3yIYWvrUEf9OPT6Jh5/Xy+d1nf\n", "/u1jca59V84Er7/+uvT/Y8eOVb3JVM4p+iRqBg0axM6dOxk/fnzUa6WlpQwfHu2ceqKMGjWKzZs3\n", "M3XqVPbt28eMGTOk1wYPHsz+/fsZPHgwLpcryidH6Yv3bZ7nkpiYeM68/+JPihh0rJZnI9JN4YJb\n", "pz+F/EmTyZ80uddz9OW9yiMNAhDggdWP4XK6ZKmY/EmT+YPzdl5+7w32VJUT16qcZko2J+DOvIjL\n", "NhWReF5iqGV71jDcu+qIy7Ggz7HgsdpY+f5r7PxqD4t/ukB6HxrlWuhQO3a4hubpYoR4fajle2zI\n", "LE+3sZq75ocGMD6+Zjl1LQ34CdLu9tB+WTweq6gY5fnqYCUHBjVIwy4Pr36M4p0lEQMuY9+PMC6/\n", "B6VfF06/W3bPpBTXT28nf9JkxX+bE90fuk35IguIlz79vzhdrlNKVZ1L35UzQWJiotrtqnJOI4gx\n", "2yO62blzJ3//+9+59NJLmTp1Ko8++iiLFy/GZrOxceNGfv/73zNx4sTjnea4hLufhg4dysKFC1m5\n", "ciWLFi2iubmZf/3rX3i9XubOnasornpy9OjRU17Pucq59Iv6sV8v5uFsV9T2y/dVYx5ywSmnTcIs\n", "uO8O9qfaySmtl0z96iZmc35LRsxr3HjfYrw15WweNzTqtaUN8ew1aqLSPQCtr+5BY9LLoi3JhXb+\n", "8JPbFYZHhghHY8KiJvvdZkR/kLqWBgSdhkTBSGKSBUtKEo7GFpoFJ745ITM9XUUjWZsOkhCnpdMb\n", "oOGKEVK6LjywMzIlZhibiX67Dc0Cub8OwNgYYxAW3HeH4nuNtX9/E+tzsrQhnt8+vuKkz3sufVfO\n", "BAMGDDj+TioqZzF9itRcdNFF3HXXXbzyyisUFRUB8Oyzz5KamsqvfvWrfhE0gKyNG2DRolAbZ2pq\n", "KkuXLu2Xa6j0P5GzevwRs3r6QqwW7sQWLzcvuU7xtZNZQ0dDA1MONMhSSYtKqqhJjBU2CdV+HJqY\n", "zaISuTPw4m1VXPv7P7PrvVdR+gqJbj+WGybItrXOTJe6gSJreRpa7dQcq8UwfaAkPCybbPz6xu52\n", "6LAIarg8jQbA8VajNGRSV9HIlJKjrJzZHam8dUsFWz6oREhIwHTpIFk7erjNXKNDsSC5odWueC++\n", "bgfq3kz5VFRUVML0uTIvPz+fqVOncuzYMRwOB2azmQEDBvSLP43KuUtvs3r6ImxitXCflzlIimoo\n", "ddyc6BoSGxpZOV2eJl05aTg/3lodc23hB3nxxQO4vLSaeMDb5GHG1Tcy5ZIClr/3uuJxYkC5xiyy\n", "GyhS3Egmd42hVMz4sZew5t3XefHdV9GjpfpIDZ3XD+k+UcRcKKWan1WXjmZGYRk1pji0PYwEAQIO\n", "D06HjySFNVYeOsgP7riezKQ02b3+ut18j2fKp6KiogKgXbZs2bK+7iwIAomJiaSnp2OxWM7q8Qjf\n", "5pCywWDA61UeqNjfrH/8UR4eKhe2M9INrPn8K/K//8PjHq+JT2TN5k+YkW6Qtj1Q3s7lt93JVzWH\n", "eeSNp6ktMNM8SEPjIIGSt4sYaM5k+JCh0v7PLv09fx2TIDtvzzWUbnqH72fo6clnzjgKfqxcQzB8\n", "yFAGmjOp23cYklLRJWezcNHdXPeTkH2BxZBAydtFeEZ0X9u93oq2M4D+ougwfvLnTVR/9CEl777F\n", "tg/eQROfyMAhQxk+ZCjXzLyKn8z6EYlxCaz8+N/Se651N9FUeRTTd7rPF54bBTDgy0ZuyEqOutYb\n", "hxo5TABvbStoBXRpIftCT6WdwBEHxkuH4tx6WDoPhDx44i8ZindyetS97ou4PJ3E+pxMu/FnDIz4\n", "LJwoZ/K7ci6gFk2rnOv0+c+c5uZmdu3aRXNzs2TAF8nNN9/crwtTOTc41bRAOJKiZLa24L47jmvq\n", "VvxJER11h2GM3Aiv5xrMKWlAdE1GYmp0JCOS3lx1FaMXS/4EEJWqSVp/kHF+eHhUd5F7ZDQpLBr2\n", "HqrAaRbRFHUQbHMTaHYh9ugu1CQZaV1bSvJNE3GiXBLnzTST1DX40rHBCoRavzvf+QrdsBQ8FY0E\n", "2t2h+h9zHL7DLZi/P0qWqgrfa/n7kbdznylh09vnREVFRSVMn0RNSUkJ//znPxFFEYvFgk4XfZgq\n", "ar6d9EdaIJbZWl9M3d568RmGJERHYHqu4XTZ7/cmeiLFzkBDMn8bFyd7PTx9ulPoFg26/DFYCE3S\n", "Nl0cqrPpKKqi9eUvSL4lNDoh2OrGdPFAHButVHp93PpROatmKA++hFAdjX91GVnl0J5uJvHqHu3i\n", "ozMINDmjZl5B6F7HcgC++2/3M+ndCWcsanM6Tfm+7kiUiopK/9BnR+EJEybwy1/+MsojRuXbzemc\n", "1dMXk7aW5npuHJrKsh0HWTZ1hLR9yZYKfvw/f5V+PtN/6fcUO//6zWKUIkXaQEg0NA7V4HnLGqqX\n", "CYqYLh6Ip9yGITedYJsb05RBODZa8Td0kvrzi4BuN+GSikZ++OEBTBpodHZi++H5UvdTmDG55+No\n", "aSPxZnkBc7hwONDqpuXl3YjuAIJOg2DQohtgYd+BdrQmPfr86E4pty7AFxlN1J7hqE1/05ux4Ln6\n", "nlRUvq30SdTY7XYWLlyoChqVKE6nWOhLx02b28u0nBQAHio+iFYQCIgiB1z+qDV8nfb7Po3yVy2g\n", "1dHQaMNja8IwNhOP1QYagc6iKsTwoEqNIAkYf8MhHG+XS54zhtx0/KMzsO6y4/f66BS1pPUQNBAS\n", "gnWtNnSkRb3mt3UCIqLbjy47UTp3R+EBvBoNYqtT4SjQppnwlNtwzMk76aGiZwMnOrtKRUXl7KVP\n", "ombUqFEcPXq0T/4wKt8+TpdYOF7HTeHWIsp8bm79pIJVl4yWxM3Cz6uoTYiLcdavh5nXz2fp0/+r\n", "GNFa9+SDGPIzET6tZpwxLuSjk2jiq+ZOPJV2CIp4Ku14ymxSlAbktTLORBHLnPEIlXap3iZM25ov\n", "uOWuR9jzxDLFtQU7vOgyzLJjWtbsRojToUuNxzA2M8rluOWVLwAQXX48lXa8YnSx8rnCic6uUlFR\n", "OXvpk6hZsGABTz75JEajkfHjx8tGGIQxGAwKR6qonBqxalbCKQPDb79HSUUjl5dWE2fvxB2v51j+\n", "IFIPnf7OvBOpw+jQwGcaPVdvPUy8BlLTsplz291MuaSAzPXLOfppNZdpdKyMMPpbtKuKLdurMeQP\n", "wVlYRcqSi2XnDKeOnJ8dQT8w1KAdOdVbl50YGqjpDt3HnNWZVPYUJ2t2I2gFNBYDjresUvRHmxQq\n", "aLZE1t9stBJ0+wk0uzDPHCEz9HOcw84O6iwqFZVvDn361t57770ALF++POY+r732Wv+sSEWlDzy+\n", "+hlqscPbTRAU6ZyUjSE3HcdGK5bRGWS1RB/Tn8WgJ1KHUbi1iP/9z3Jaf5wBhFJDls02Znbprsyk\n", "NAwHDrDyslGy41ZOGs6sTWUk2tM5mN6JW2EdflsnCZcOw1Nuk7YZctPxVDRi+VGoeDj73WYA7llw\n", "B/c/+ygtG60EmlyI/iAao460O/OlY1vXluKrc8i8cMLnNOSm43jLSvIS+dgKyzV5CF3XOBf5uo0F\n", "VVRU+o8+iZrbb7/9dK9DRaXPFG4t4rDLhmVet4tuOBWDIGDZZGP82EtYcN8dkoCZMGRMxKyjUysG\n", "LdxaxO+feJCOVAHeskvRjVh1GGvefV02vBHkNRsThoxhZ/EOxWtlJJhZ8cjToTEFSjuEp5z08IwK\n", "doS8V3Qbq/l117yo8Lp+/8SDtOk1iG4/ybdcKDsu+aaJND9fgmDUobXIZ6wBUWInjF849aG2Xxdf\n", "t7GgiopK/9EnUVNQUHCal6GicnwkL5dKK6YF8gGm4VSMuQWumnVJlID5fOV64hfJx3k4Ls/kd08s\n", "Y/y7eX2O2oQjNJoFedLwyMjaFqU6jOPVbOypLsdtVP4qpqZlUbi1iKZGO20v15F0S/d7cGywklAw\n", "XBalCW8POjw4l+9i2IDBstdmTi/gr4QEXU1cveI1ddmJ+Gta0XqEqFoaoV55wGfNsVoKtxbJ7uG5\n", "1CbdW2u+iorKucMJJY2bm5vZv38/HR0dmM1mRo0aRWpq6ulam8o3kJN90EWme5xNGsVp1NpWP3+9\n", "+2HFbhZ/hnLNlzNVwxcZTXz+j/sZ/OrAqPEAPVE6d1hQGXLTFeswjlez4SNA/WXDWLitkpem5kqv\n", "3/ZxJefPuSH0vq/OJPhKLY6N1lBURhSlAZjOjw+DQRPqiuraHl5Tw5wMKSIVXr+PACavFmNbjOiK\n", "KDIgawDLfva70DTw1WX4CSJ4A6QZLTS+XBolrgzTB8qiVGqbtIqKytdBn0RNMBjkxRdfpLCwkMih\n", "3oIgMGvWLBYtWqTOgFI5LqfyoJOJiaCouE+mMZk1777OnkPluBqR0kK9HRPs8OLcUYM2zUiVv5Gq\n", "RhtfPftozDXFirogCOg2VnNLV6onkvmz54ZqaiJSUJE1G3q0+Edn8BlIM6acgPa8QTTVVEjvW5to\n", "kAZZRmIUdZhuURgq25WSclyeyeNrluMyBSPufRoJr7XQvOYLkuZ3p6DCU7yd221S9CLy380LBF/5\n", "QlFc7Skpp3BrEQC/fvQ+vAOMUs2TIS9TbZNWUVE57fRJ1Lz++usUFRVx4403MnXqVJKSkmhra2PH\n", "jh289tprmM1m5s2bd7rXqnIOoTQ1e817J+8HYmtrAkKiwJDX3WKsq2gkp7SehCYPrR1unO2NjE8w\n", "4HSJfFV4kJbPjqBNNKBJMuJeb8U4Ly/UHm214W9yEmz3oE0xkXp+Bjml9cQj4LDZeeyJ/1VcU6yo\n", "i7++nbSsjJhDOB+85Tc89581ijUb82fP5f5nH6XZ6MWuCRJsc6MNaBg1SENbow3IinrfYSybbGRl\n", "5tCgtKiIP0DqWhrQzZGn7ALXj0R48jNZp5RhbCaeMhsDkrudaXpGp2KJK2eiyP3PPorH68Vt0ZDc\n", "w7kYYNdXR6PSVCoqKir9RZ9Ezccff8z111/PnDlzpG0ZGRnSz++9954qas5yzmR9Q6yp2R0aDZAd\n", "tX9f/EBsNhuasKjpir54V+8ivz3Aqhlj2FbXwoc1zSyb0u0qvGhXFVsCfuKuzqNj3T4Khl3EoY21\n", "7G+rlaVPvKt2MXnbEVnqZ0GhlXEzJ9KpFRDdfgYOG8LwAYOZMGQMu9b/B1NEkXLrG/sQjDpq2238\n", "cMk87B0tBK4fSWQ06uEFv2PVI91poJ5oTHosc0ZKPzs2WKkZJSLusiNUaqXuIwi1Vie0C4wfPkaK\n", "9vTs3glHXMIIOuVIanySmUB+VqguRxBCLsZjM8m2d0eVwtGpsBgMtHuivHDC12ux2sAIpryBOCIc\n", "kg1jM0PXSNXxu6ceJGftCiwpSVIR957q8nOi9kZFReXspk+ixuFwMGTIEMXXBg8eTFtbW78uSqV/\n", "OdP1Ddv++6pM0EBoztGPtx5GSdRE1qFEii9HYwvoNFhSkvB4PbjX7CZl/neAkLDJee8Aq2aGxEVh\n", "TbNsTAKEWqJnbCmjDjDfOI5DG2s51mIjaYE8VZNrjOOlCH8YgNUz87h8XzXV8y6gdW0p9UI7tsZy\n", "tpfvwt/qxLm8GI1JT9DtB0TSlkwBoAFwbGjAUGnvFiGXZ7Jywzo0na6o6NWUSwp4fO0KfHPk369w\n", "TYxl3licK0sht1vQZRwK8sf50d05L7/3Bg2tdmqO1WKYPlC6vmWTDVNyZlQ0x1Npp7OlFW2xD+OU\n", "gZKbceCTWsZ/f5q0nx6tZP4XjhJ5Ku00PV2MfnCSLAXlqWgk0OaW7Qsh0eM76sCQl4kz6KbhqjQa\n", "us6z/Z1X0GTFSwKot/SfioqKSm/0SdRkZ2fz6aefMmHChKjXtm/fzoABAxSOUjlbONM28LEmdw9N\n", "z+TIZltMP5BI8eWpbMVja8RyVR4NgCn/Qjwvf0HTM8UIei2CUcv5id3Fv7oYrcbxXds9lXb2tx1B\n", "SDVGFRnHE+PYrv8m3zSRpqd3kPzLqdJr4chEQkAka9NBzC99gduk41BmPAjg/LQaj9Um1fW0HTum\n", "GL3aU7aXqqY6EpQGEXTVxAw+byBZO+i13Tiye6dwa1GoPbmxe38g5FFj9IJGINjuIdjuIfXX36Oj\n", "qApXSa0s8vLO5k+YsHUcM6cXMH/2XD7/x/1YIrrHDLnpeKw2yQtHIigiunxYbpa3iluuyaNpxWcE\n", "29wyseMsrkGbGS8z+WvZYOXxNctVUaOionLC9EnU/OQnP+GJJ57AbrczZcoUkpOTpZqasrIy7r77\n", "7tO9TpVT4EzbwMea3J2Yms4fr50X0w8kUnx5rPK/9AGSb7kwFL2Yk4djgxXP4fbua8YoBHab9NL5\n", "km65MJQS6YET+bHb6loorGkmrs3JoJW7OJY/GEGvlc1cslyTh3f1LqYIOlbO7E5F3fpxBSWXDZUG\n", "SoZrScSDB3noyrGy8xs0AltfeQHBIODpiuyEUzxoBHx1DjyVdrKS02OmrmKlFZUMAJVSXJ5KO8E2\n", "t0zQQLfohdC/i6DVyByHIVTj41pfJkvFBdqULAJDaFNNUT43wQ4vKbd8R7bNck0edavLYp5HRUVF\n", "JRZ9EjX5+fkkJCTw+uuvs2rVKgKBAFqtluHDh3P//ferM6HOcs60DXxvk7un9OIHIhNfCpEXT6Ud\n", "f0NnSFwIUIuWRR/vZ+Wlo5g5OHpS94KPyjk2axgAgebQhGylYtuvWjq5detXrJp+fndtTsR5Fm6r\n", "5EOnD1/Xz84dNQAMd/iiXIBXXTqay0urqe4SNZZr8mh6phi9JiSclM6/aFcVHxYepKPOQbBVHsno\n", "WLeP8ZdOQ4kTSSuueff1mCkuIqJZYUFFUORAp1E6vyl/PCbknjyG3HSyyiF9B5RWWXElQvzUwbh2\n", "1iquN9jmRuz0SeIIQNArfzZj1QCpqKio9Eafn2oTJkxgwoQJBINBHA4HFotFbeM+RzgTNvA9Iwb5\n", "k2ewdL/1hCZ3y8RXj8iL0kBH+5rdfNjpZUZhGeZEA+2BAFcUV6J3+/CmJVDh9+EuroGdRwh6QlGp\n", "yGJbBAFfTSva7EQKj7RR8P5ekgIib10lT7O+NDWXgg/2cmh0Rsg5eIMVZ3GNlNrqSYLTJ/tZPygJ\n", "X2fo+rFqfy7fV83usgbSIlJcEKoFenn1f9hTXc782XOBkEBpaLRRVVdN0h3yeVCx0oqRgjFSvPgb\n", "OhGM0TUzALaXSzEOtRDp8BPpyWPZZOOertqeBffdgTU/dD/0R9rIebIYS6YZJyJ1E7NpLDlCXG46\n", "wTa3NIUcEXRZZsV7mJOSpbj9XDL0U1FROfMIYqTxTB8QRZH29nYSExMRhNM/NPBkOXr06Ne9hK+N\n", "xMRE2tvbZdukOotw2ucH1/Xbw0AeMQhh2Wzjj9eFRFPkQ6i3Thd5TY0dd+kxkq4bB4Q6lHLDE6y7\n", "HpT+0RmydFS4WNXxdjm+Y+1o9FqCXj8ERNCCLjVBOh+EupZ8NS3oMxJJ7vJqGbP+S94aF10Uf/W+\n", "aj4zaqRrND1TzESjnsLJo6L2/eHGL/BmJmDQaOho93BQI6IxxzHDLzBSo+X+ycOjjplbaKUjGMSX\n", "bZbeWxjH2+VYfjQG/cZqgi4fzu8k4ymzgVaQaloihYre5mHR9+fJ7nNTo52GqzMUxUvbG/vw1TtI\n", "v/N7UesK399I2l7YxfkDhjE0ZQDb9+8moIOAy4cWAdO086I6yRZ+VE5RlgmXXiu7buua3egGJ0dF\n", "p9pf2cPTv/qzYgot1ufsZD/LSt+VbzNqfaTKuU6fIzW7d+/mP//5D1VVVQSDQTQaDSNGjODHP/4x\n", "kyZNOp1rVOkHTqcNfKxC5H+uexan3i8V/rp21vJp+S606fFokowE29zsfPIBhq/N4Z6blgBg8mpp\n", "WVuO1h9E09KJY6MVY6ubSzsCrL5oqHT+RSVVFINUTBsZQfAesKNNiSfl1u7PpWODFV9fOEUgAAAg\n", "AElEQVS9g9ZX96Axx+E93ILGoMMwMl1WpNqzvqZ7u/waaGB/u5tffGjluVndx9++uYxso55Bohad\n", "KOCPN5Lm9WKdMpiPimtoPOZQPP8FCQaWdrWjh9+bJGy6/u7wzRkSijB11RuF64OUhMqza1/DcHGO\n", "FJkK/qeVjqcP4Y8TZNEugKTrxtH0tPLsqZ4zpQCETBNHjtayv+UIlgUT0AB6wLF2DyPfPsBLsy+Q\n", "7f/SjDEUbNrH0XvyZduT538ndD/HZsrM/GjzKC7lTBe8q6ionHv0SdRs3ryZF154gXHjxrFw4UIs\n", "FgsOh4OSkhL++te/ctttt3HFFVec7rWqnKXEKkSubWlAd9MY6aGbfGOoGNVTaZd12zQAD6x5jKDL\n", "R+D6kWi7OoF068vQjMkkd9cxVs/IlZ175aThXF5aTXN8RApUCM0q0mUlStcKI9WPAIbRGfhqWkn9\n", "xcWh+pwI6iZms6ikipWTuqMpt+6opG7aIOkaAKInwNBh5+OxtfBQ8UG0gkBAFHH6AwxLTuBPESmm\n", "ZTsOYv+4mqOLv0vp49tYsKWc1Zd1dw0t236AWUO6u5/C7616dEaU3wyCQLhZK1wfhEBUUbXlpgnd\n", "AgzQ/CQXNlrRxYiuauLjFLfrGuUCI7ye9oYqUm/qTn15Ku1g1qPXKovC+BjXDTQ6McyJ8ODZYMVw\n", "xfDjptAiOV0F7yoqKucefRI1b775JrNmzeLnP/+5bPsVV1zBc889x5tvvqmKmm8phVuL+KpyPx1N\n", "gtQZJI0m8IdmC/XsZPJYbVHdNuEoRGS7tWneWBwbrTFbruPsnRiu7BYP/vr20IDHikblxQoCQZcP\n", "T5kN/eDk0LYetTv+0RkUAwVvl5IQH4fbpOfYtEFS1CTY4aVpeTFx8UbS0tNI8IosHdPtvbPg/X1R\n", "NTPLpo7gms1fchRwJxr4dHgqBR/sJV7QoPf6uffCIUzLSZEdY7I5pSiGdD8hFMkQu9NNQa+fgN0p\n", "dU/1fL9RP8foEov369BvrJYVE1s22bjhynm8vPo/OFM1kh8NgOgLSt1gmiSjlEJyPVWseH6nT7nN\n", "X3T5FEcueBujhcqZLnhXUVE59+jTb4P29nYmT56s+NrkyZPZunVrvy5KpX9RGllwvKLdvtDbxOqM\n", "Q8Fuw7eeBbUxCmyVUh3aVj/OOOX93fF66UHesraUhILhkn+KIqJIsM2NqWA4nUVVON4uJ+Bw0/bG\n", "PlmtTeOuWuzDU9BoNaSODo1PMO6opd3eiUsv4BuWgiEvE2uuwJfPVnPXZwk8OXkgAHExiufD20Vf\n", "gA6Hh6O/CdWvDFm/L0rQAAzKGUalq40Wqy0k0oIi3to2BI2Axmyg48MDmGeNlN5/2xv7AKIFUAT+\n", "+nY0qfFRbsDu9Vb+ee+fARTb7fdUl0tFwB1FVfjrHKTdMUU6vnVtKaaLQ+//UKKeRbvkka6Fn1fR\n", "ZkmWxlR0X7cMIUl55IKSUDkTBe8qKirnNn0SNWPHjsVqtSq2bpeXl5OXF/1LSeXsINbIAuCUhU2s\n", "idXB1Vb+ePefgC77/p7RgRjRgp4PYYAR6QPRegIsLKrgpYLR0vZbd1RyUC/S8eJOEEEwaGX+KT2F\n", "imODFewu6PB1dVF1p05a1uym9dU9BDu9aBLiiL94UGj69ePbuKiuk9UR1120q4ri8zNoLgsJJ8Pi\n", "7/LOU58RqHCTabHQHFAWYK0JelrW7A4Z00VErZTSXQ+UtxM8bxjNZdUI8QlSNEQ45kB/ngU0AlqL\n", "QWotN+Smk3TdOFpf3dMt8l75gvjJoZSZp9JOZ1EVglFPwNaJYUyGFB3x17eTbUyRUj1KtSlhMXFU\n", "34GnvJG02+V/4CTfNFFKdYnfG8KWT6u5fF/3YM4WfTwP3P1HQC6a6k3pHJ2kjWqxd60v45Yly6LW\n", "EV5bLJ8jFRUVlT51P+3du5fly5dz4YUXcvHFF0sDLUtKSigtLWXJkiWkpqZK+w8cOPC0LrovqN1P\n", "oY6Ox369mIezXVH7LG2I57ePrzjpaxRuLeJ3Tz+E7qYxUa/lbvez7tFnpf0eX7Ocw84GyaTNU2nH\n", "/VktSTd3Rwt0G6sRu2pqIrelifFYMlJo2X+YhLYm4rMScAJ1E7Normgk4HAj6DToBljw1zmkCISn\n", "0k7Hpkq0eh1mQzw5KVncM/92fv/Eg2gWRIvwpqe2YxiXTdDhJtDkQptmIq+2gy2XRr+/8PiEcGdQ\n", "83MlmNAzNGcwDQeq+J5W5KUZ3cfd+lE5W/TgMunQppiiXHh1FY2c904l44eMIDE1HfOoPJ745N8k\n", "3Ngtypqf/QyNxUjyDd3t5uGupbihqaFIzqEWNCY9GosB3XmJeCvtCCY9giBgurh7DIK/vp243HTM\n", "BSEhFVxtZde/P4r9jw38Y8VTLH97DZqcxGgXYbo7tML33lNu655PFaPTLtwGHt4/nILKJZP/9/xr\n", "va6nv1C7n+So3U8q5zp9itT85S9/AaCwsJDCwsKYr4d57bUz8wtJ5fjEGlmgDZx8cWU47eQ0i1Ej\n", "B0CeOgh3XclbytMZP2Mae3dUdP/FPf93QPdf4e2tDhpdPhquzwilsPJHcuiFNgJiAI05DvY3okk2\n", "ggC++nYC5TYs6QnkPFlMvFZDp9NLVaqRSXnflbnxZr66HLvCmjVxOvwHm9Gkx4Neg2VOHonrv1R8\n", "/+HxCeF0mS7bjC8IBxPa8KVo+eziQVxeGopUOOrbOTI+E2+7h5Sr8xQdjf2jM9iz9RDWjqOMNOlg\n", "z3aZoAldI1HWpQVdXUvLiyUx0bJmN4ZOkfgu4eNo90h1Tj27o1rXlkp1OJmZ8mibEnuqy9HkJMaM\n", "skUWFPc2nyqSyHRS5Jyqe+beftz1xEL1sVFR+XbTJ1GzdOnS070OldNErJEFAe3x/+lj1eKE006G\n", "Sk1U6sCyrpLz4pL5510/kx0Tq6U8/BB68d1XZQ+hBffdQf3sVNm+iT+bFIqO/GhMVBuzrqKRi7cc\n", "lgZcQihCkjfofNk5MpPSFEWN9jyzVNvRtuYLWl7ejaPVq3hfHLaOkLtxfTsta3ajH5xMsM2N56tG\n", "9NmJdGoFqud1tzU7utrIQdnRuGXNbgSTHjHRQM0oEc/WGiw9Z0HFqEMStN01PCnzv0Pn8s+7XwyK\n", "oBGUR05EpIyyknsUGCvgIyAJpJ7rd6+3ctuV8+QitQ9pofDrj69ZTl1LA4JOgyn5+AIrFmd6cKuK\n", "isrZR59ralTOTXobWdAbvdXihFtre7rzJtV0MiMphb+NMQJe2TFK9Tu9PYRite+GoyOunbWytu2c\n", "0npWXTpatuuqGWNYul8eGZkwZAx7nn2FkUkGycjvq5ZONGO6H6ZJ80MzphouGhRV77Lw8yoarhiB\n", "q/gImtR4gm1uAo2d0fU7dBftih4/xOtl28L3zHuoGW2KCW2SEYIizh01CEaFYuMYEZKe2eOgSYv2\n", "tQM4v5OMv6ED0eNHk2hAV9FITmm9zLww0OzC+WIpiXlTWHDfHb1GN/RopYhPpK9M8Gg7t/9wPr9Z\n", "cqfi+o7HnrJ9VDsbMC4I/Y5p4OSFiOpjo6KicsK9kIFAAL8/OnVhMBgU9lb5ugmLiaVvrj+hkQXb\n", "/vuqTNAAPDQ6kaVvrkdv7I7+hGcAAeSt2MffLsxUPEbper09hGK17+oaPXgq7YgeeVotVtt3zzRb\n", "cclWCrQaXho3VNq2cEclH1W3yA8UBKm9e8amsi7Lf6ibPAD/6AySR2fQ/HwJmoQ4maCBHiZ9gOgP\n", "yiIc4XvW9Oxn6M+zRAkiX4MjqkPJV9+uWPys6RJLYcRUA0Z3HK0f16DLNIcGb67axZSSozJxtqik\n", "ii0CiJcM5MOdOzHfOI7wr4MH1jzG46ufwZKRIomc+bPnUvvG0zSOzZTqX3SNHm47BUFTuLWIFz9Y\n", "T/wi5WGaJypEVB8bFRWVPomazs5O1q1bR0lJCQ6HsiOqWkdz9jLlkoIT7nTqrRZn/uybFVtrh6Yr\n", "pw5i1e/09hC6bfYNite44cp5rP3wv6GJzxHEcgLumWYzNhzjpelyI7+XpuYyY0sZdZEbuyIg/tEZ\n", "lFfaFYtjddmJBJqcitcNR5QcG6wEXV5cXxzF39AuORr7atvAH1QURM0v7EQMBmla8Rn6HEvIv+X8\n", "kGFgpKeLr8GB+TL51G3D2EzaShoRM4xSDc6IICwakMKDOw6i0wj4gyKLBqdS9eURviw6FOUw7Jsz\n", "hMqNViz5IW+eR954mj9e90v+eN0vQzVP6cmhFNMt10n1UidTx7Lm3dfxZyj/MXQyQkT1sVFRUenT\n", "t3358uWUlZUxc+ZMsrOz0enUXxLfdHqrxQk/sJ576RkMDceI12hISc3CGQjGPCZM+AFoa2vi4KEq\n", "jBnDo0zj4gRdr+27n1d/yZcZrbLajrqJ2SwoLGd1Qbf4UEqzxRpCGbk9ysW3lxZ00af8nv0NHTS/\n", "sJNEQwK5Q84nJSuNFmMTgk6DXwhSi4tOvU/xWIIigiuIJkEPAVF6j+HWbE1QQHNeQmhbeZePTYRx\n", "nX97A4F2b6goWSMgtLj4MCgfpLlsx0EMCQZ02f+/vTsPa+pM+wf+PVkhQAhbQMENxSpUpepYpbVS\n", "l5lWR8e2I7VasX1t6/bacdqZzrQdi23nre9v2plqN+107CjWpdrW7YWpVVus1n3BUoKKolIXCFsI\n", "gZD1/P4IOeSQEwibhHB/rsvrknBy8oRAzp3nuZ/7DhIcgq3SyHXTds6cbHjrwxb6MbUuj8WZpyOk\n", "LYFIc3VsKIGYkJ7Bq3eOvLw8PPvss7j//vs7ezzER7SUixPEAsNq9Xh7/ADu+8tP3MCi4zqsGxsr\n", "eB/+BTASoYiEbnMuAPB2vziLqXlKLpZC7JabApbFDyY7ljbUi/G0zBYWHuN2PgCoNVmh31sAu8EM\n", "1mLjBVqWkhroNp2Dat493G3OwMd69SJqN+chaC5/WShowgDU7iuE0aDH9XozWLkIkZERvItp6pO/\n", "Rq3AWPqoYjDjvl/i4+zNkI/l90UKSo2H6KQW0hmJLsnSjYGcYcuPCDKJwAQx3ExN4OpjglWOZ+z/\n", "yWNQIQ4P5BqFAkCpTvCwNuexOCtRy1OEE4/nLcrgHesMSPRVjhkuZVQY7//OQIWbTXIJhAF4DLxm\n", "Tp3ucYyEkO7Hq6AmPDwcMplwbxhy53RWZWAhLeXi7F7/ET5qkj+z+t44PH26HK+VKgTvI3QBVM1N\n", "RuUnJ2G6UIbgShav/C6jxU/QQluB9bs0sEwfhBPX7Hhl1pMez/GbBUvw/LtvcBWAAWD+dwXQTh0M\n", "ZUMrhKrMs9B9cByifqGwltQgeOJAWG7qUfnJSUhiQrhZkbqTP0PMiBFpV0D7yVmwMYGN3zvxM0S9\n", "QgCxCAEzE6EFoAV/FmPFwj9gReY7vNYEkj3XseK5PzRW8k1g3Gay7EcbCv81Cey4NhE5N3j5OGJl\n", "gODPQqQMENzN5DpT5cwPKi434+DhHN7P9eDhHPxYqEFdhcitRUaJttRj8rEzuLWkqB0FBBmg8l+n\n", "wIhFkJhZPPfrJ92ObQxIIhzji2IgT4ls/H9CJFZkvoPwhrpGUoi5+jjzX17iMfCioIYQ/+JVUDN3\n", "7lzs2LEDAwYMQFRUVGePiQjozMrAnjSXi1NVWQKgv9vt9aZarqjfwcM5WJu9He/9x7FdW1tdAaDx\n", "Au3ckZNgBsy1VrDKKK+WBCaNT8X5/DysW5sJJi6Et/SiTwBvlkBo2eFiWCSv4u3VxAgYLpXBdrwY\n", "4vBAKMb1dcyGTB8K/d4CLrHXFKvkkmRrD11F0IQBMBVoYZwxEAH/+hH1JUawMYEwFWihuLcPanOK\n", "oHKpXAzwZzEEl9jS/8Dd7mk5ZdrkR5G1/3suqJMnOC7uzjYRlpP8NhG1CuE/81qXNhP6PRpYSwyQ\n", "xAS79ZuyamshnzDA7ef61o4PIZmf5NYiAwBuGitQkRIN51vM8nUroXxfhnq7BWa7FeZwCUQ3A8Ba\n", "7ZBEKBxb1u0spFVWfHv6ME6//BOkEKOiqhL6ae5Vq52J2K7/F8oFAiiBmJCexKugZvTo0Th37hye\n", "f/55qNVqKBQKt2NWrVrV4YMjjZrbjdRZQU1z6jwsWzhvF8q1qPnkOkIaghrJhTK3HTmLjhTh+Pc5\n", "LT6fg4dzkKX5HkyccHVb58XKOYZbUoOjuq5UjB/OnUSoIgSipxvryIgAKMGviuucDXFdnnHd6VW1\n", "6SwMBy6DkYpRsfY42HoLIn8/njcO04UyrvGk86ItT1TzlnKcQUJm9naYWSsys7dztzeXVzTi8DC8\n", "tOZ1GML5jSABQNzkWi3UimHB4ULcTO3DPS/LqVtgbXbBPkxsvQUmjRa5Bi3mv7wE6VPTPLbI0O/R\n", "wHJTj4jFY3nfC5idhJJPTiL82TEIABAAoPz9HyDr4+ij5fwZ1VnqoSm5guDEQTBptLAUV0O6O5T3\n", "85MnRPL7hHn4v36KGu9mrsXtKq1gw1VKICbE/3j1V52ZmYkDBw5g4MCBiI6OdksUZgQaEZKO1RmV\n", "gdujPrqXYONCU7SjzLrQRU+W2o/bqhybW8K7LwCsuz8eSz/9CGv/w59ZcZ7PeVtFVSXKBotgzREu\n", "b++8WGVmb0exXgvUWdx6PSmEulo37HhynQ0xCSzPVPzzJERyMe/CXb/uBPp8egbBCnljHRid0a2S\n", "r36XBhevVeEf697HC4uW4R/r3se/D3+JgNlJEEq09ZRXNGl8Kv4GtDiTAzh2cP14Vof00+WwmAyo\n", "swNlAUpEFokRUukIloZPSMOub7NR8lkur31FVeZZMAoZl5+jaXhMmVUE11k3J2uJAeII9w89gGO3\n", "mCtRgAzyJPdqx1WZZ1F3rBiKcX1hN5h5lZS52SCX+jzWkhquY7hNX8/dbiosd7TnEJhNirpqp0aY\n", "hPghr4Kab7/9Fo8//jgeffTRzh4P8aA9lYE7w8Knl+CttW+5NS585eklAISn/OUJkag78TP0ezSQ\n", "lQmlyAI3bl1DwWNR3H1f/XgVRIHShrwTx20V71+C1BKKoNR4t4BD/PlllAeH4eElj+PS5UKwYBGx\n", "LIX3GGHpI1Gx9jgvqKnadBbiKguSjoE3G7LpPztQwthwa/15sGIGEkYMpt6KsOcagyTJhTJMVgS4\n", "1YE5YLQgYN5I3mMrZyZCt/U8/n34KwBoV52WlmZyXG8fPmE6sjTfQz+lMbFbuV+L5VOf4M7jDLI2\n", "rP8cdawFLGsHWCD8OfclNPtGDUQCQY0kJhhCu+udFZidwYc8UQ1GIhKsdhyWPhL6PRqYNFpebpDz\n", "51fx0XGIVQEwFZaj7sTP3LIb4OiH5Wz/YNJooZyd5HZ/Z8NV2v1EiP/x6oook8kwcODAlg8knaat\n", "lYE7S9MLajAjwWKXxoWeaoaIlXIoZyTCvC1P8PvVVgsMOUVcs8WqADOUMxy1WAw5RY5lJLkY1jID\n", "RDcDeNVtzYXl6DWoL0qnOVoMhCMSlZ+cFHwcRixCxdrjYKRiSNRBUIztC/HRUq5PlGsuTnSUGn+c\n", "/99cTZYX//oi+m3L46rzWqpN+PR+fjuGT0fFI/Xr8xBqq8rWW2GOlGHd7kwwfUIEjvA+36O5mRzX\n", "23/9TBpuoBzYW9HY7kAgeHph0TK8sGgZ16srt8i9VxUARKkiYNmv5c0SVW3OhWKMIwHbNdg0FZbD\n", "ePxn3myZfpcGrMXmsf0DGAYe6ilC2icUyulDUbXxLKT9VLzgNHTWMC7HRlwt/DO8K2EwBTSE+Cmv\n", "gpqpU6fiwIEDGD58OC01dZG2VgbuTJ4uqIBwkqvrrpqbyTF46tsLvNYGT58ugvbhBJgPX4UpVum4\n", "WDVc9Aw5RbDe1CP82TFcnor5cgVM+VqIlDIoxvaF7Wc9bydRcyQxwQAAyw095EMdeRa2k2UA3POB\n", "TIXlOPq/f4T8XTlElQakyiTY6FKReFHOBRy5WYX7Y8N4jxEkdf/zMhWWg7U6lhLtgWKI21inpTV1\n", "Vw4ezsGV2hIo57i3cvC0Vdv52s5/eQmEwpoYdTTmPTwLv3/nLzBFyxzLQXY7L8BwBpuWazpELBvH\n", "u79yZiJqPj4N28/CxTzBsoIzPtz3AITNd8zoNBVYAyQdA8ojYh3NUJugXBpC/JdXf901NTW4fPky\n", "li9fjsTERAQFuRfsevLJJzt8cISvLZWBu0rTmZwanR5ikwy2houedUgUvt1/GZNOFCLAaEGdncXV\n", "EKljiScmBKYCreMCaWcb6rGUImLpOLdGloDjAu3cGtyULCESVZlnEZbeuAxU9dk5SONCYa8xQaSQ\n", "wpTvSAruFxYNgJ8P5Hw81SLHLEO/bXm8gAYA1qUOwRvHr7gFNfXBMvcZi5M3uBkLrrhdK+q0SCHG\n", "iH5DG5aSvCt4l5m9vaENQiNnUq+2yu1wnuYK2k0an4q7s5NQmOIYgyGnqHHbe8NsUNRVO8y9FKgX\n", "OHdURBQMxlq356/bcAZ2kw1BE92XF90KIwp8yEqOT8SGtz5sEpzyx04I8U9eBTXHjx+HSCSC1WrF\n", "jz/+KHgMBTX+z7UasFarRZQqAhKIAIkIyrBQ7oJ7/nqBcKKvxYKyjRqo1WpotVrUycXIDw+AcqYj\n", "4JDBcdGy1dRDrHK0QQgss6LuxE2Ighx1koRyMJx5Kja4d9UOTo1H2duHeDVmFPf2gSlfC1tNPRi5\n", "BMqZiahaewIPzpgPgJ8P1PTxPPWYKtTx2yU8fboIt1P6QC5moNt6Hmy9FfY6MyKWNs5YCDWIlJSZ\n", "sOBXs3lbp1/9eBWqAszcDqATmrOQTegHuOys0ttZvJu51q2OTGb2dpy/WgBpivuuJluFEUYLw+1o\n", "8rSMBbjn7QSxwDvLF0J5pQD9ioGragXsehNvial+mwaJ/Ubi6O2zEGjRibraWkjmJ0JeWM4rMMiK\n", "GQRNjIepQAu2pA61H5+FhbFBFBPE2+VlKix3tI5wydOpP34D85b9T7Njp6UnQvyXV0HNhx9+2Nnj\n", "ID6uaTVgESJRtCMPdqMFYekjuWn+k1t2QPqLXtyFh5/oGw1LoRjFh28gXBkGnb7aY4CiqLQ7lhB6\n", "xaJ0WkRjboyHHAy23orgyYPcZkYMBy5DFCjjXWwBR9JyxUfHEDzF0QeKiQhEluZ7jDg8jJ8P1OTx\n", "PPWYKlUFcEnT1bf1KDRbYSuUAXYWgaNjHd23xfxLu/NnVH/wKkYPv4fXT8np3Y0foUruvgOoNucq\n", "JFFBvJ/ftW35XIE819fLWAbw21462E1WmAMkOF5WgIsfO0oyeJOfw6uZFNMfAPDUoQs4ObE/XLNY\n", "AmYn4ttPj0M8Pg6mJjMuym+0kKoiUAH+VnkAvNpAgGMpad7DsxzPxyWgMZ68wQsSdZtzoTJKeWN1\n", "HbszyFufvZULuKn4HiH+hRaXfcidrBjcWkJbtJ1Jma6C5wxD1aazMJ66AdZkg91ogShEBkVhQ6+i\n", "fC2U/5WMegCSvcLNIMV1Nrzz4l8xaXwq5ry8EMWF5WAkYkejR6ud293iirXZeYXkbHoTGIZBxOKx\n", "jk/yAhipGHVHrqM25yrs9WaU3dMbm/6zA+lT07D4/VcROi/ZrY2AUM2Xp08X4cZ9fWAd0liY0rZH\n", "w9W80W3OhbSvCpJC9/wReUIk+hWKsGXVx4JjvKnTQjnffQdPxUfH3QLCwNlJXOLvu5vXcUXr5Ilq\n", "tw7fVZ+dQ/CkgY11d3Zp3GZ6PBGqmbRhwhBMyb2O60P4xTmtUXIomlQ+dlaOzszejgqhB2D5P3Mz\n", "a3WbdcnT3ITqGf7OKNXcZMRkVwqO2dPW+UBFIIx1RuoLRYif8DqoKSkpwZ49e3Dx4kUYDAYEBwdj\n", "yJAhmDFjBqKjoztzjD1CV1QMbg1PVVmb5jQYcooAOwvVvMYLTvWOPNR8fRGMTIKIhfc2HuwpSRZi\n", "7qKiL6uCSVuGsKdHcd9v2i9Kt/U87EYzd5s8IRL63ZrG2Q0PjyOJDuaKzel3aVB7+BoO115G3iUN\n", "oKt3VNktr0PVxrMIm+9YIrMOicKBb68gdd+PUIjFMIqA0ikDeQFN05+Lam4yjJ+ex9NTZ+OLPft4\n", "ycy1n/2IBx9MExyfY5BCCzcAIxaesTKzVhw8nIOiipsIQoTbz4kxWGAzWRD0qwReYKicmYibG/M9\n", "j8N1SB5qJglWp2kIUFxnXhKONgYpzSWTOzkTe10LFYoDheaegBCV0u22g4dzPG6d/9u/30eNyNSm\n", "hpyEEN/jVVBTVFSE119/HVKpFCNHjkRoaCiqq6tx4sQJHDlyBBkZGYiPj2/5RMQjX6sY3JQUYsHq\n", "uK6fqk2F5TBfLEf4Qv5ST+isYaj85CQYKX+bt1CSrG7recjMVsx5eSGkEMNQXwfl4/wZCdd+UWBZ\n", "WEtrIOmj4p/LZdmopf5GgEs13DodLClq2P5TDbHegqAH+qP22yu8nA9bsAxXp/RF3bFi2KqMiGwa\n", "0ABusw39YvvghUXLgHXA+k+3wRold1QCvrc3t+wldBGNVakFd/DIWYng6yFjIpGZvR22UPcaQfKE\n", "SCQdA84XFUDStPAgAMZDANWUp5pJ5goT7+v6bfmQj3L/wNM0SPGUTA7wE3u9WVKr0bnPhmVmb3f8\n", "vAUUV9yGeM4Q3m3e1gkihPger4KaTZs2oX///njllVcglze+OZhMJqxatQqbNm1CRkZGM2cgLemI\n", "isHOnAG7GBDZ0KHT6CP6DcXJQzt424J1n52DzdB4ITNptJD0Fq674twR0/RCLFIFcEm8doMZ9hoT\n", "FIvHoLDhfrWZ1XDfa+c4n3N5R7+3AOZrlRAHB6DynycBhuG2TQP8xo+2SiNYqx1BEwa4VxRmGEj7\n", "hcGUr0XQwwkwZ11G/f4ihC+5F03p92gQlj4S5asPe+zg7cqZp3P+eoH7jEGTflWufj93Edf00vmz\n", "Y8uMENntMOwrhLRPKORDoiBPiIRhSx6GT7gfp6//JBjIGbflY96ilSjfvE4wUIoN827G1VPNpAd/\n", "MwcBxy42Fvwb/5hjl1ZC432b7j5qmq/jrI8jlNjrugTqKVAVm2RujTctsHmcrRDQzZEAACAASURB\n", "VIPVLngz9YUipHvyKqhxbud2DWgAQC6XY/r06Xj33Xc7ZXA9SXsrBgttX+3IafTz1wt424JNheUQ\n", "hchhrzVD9+EJqJXhCBIxqA32cPFgWYhCA2A8ye8grducC1lCJIJT46HfrYHqiRG8uzWdcXA9n5Pd\n", "YIYkTAHVk42BRVXmWV4eiTwhEqZ8LRT39YNJo3UPaFzO6Zy1CYxSInHAYC7A4mlYXhIp5Agc24eb\n", "ybEbzGAtNsgTIrmGnbLyOlisDD5e+16rmys6X7t3M9fiWl0pr0KufpeG20EFOPKZNm/8Cr0ioyFP\n", "4eexgGWRoIjmzifUHXx5+h8Ex9BUczWTFjY5tmllY0+7j5puW1/gUunYyfVn1zRQFYcHQp6khi0h\n", "0i1AlELscev8gPAY3BZ4jlTLhpDuyeuKwjU1wn12DAYDpFLh9W3ivfZWDBZK5O3IaXTeNmeBWjFl\n", "n+WCqTDBYmChyzwHVTp/5kKkCoC5sNxtF5JqbjJ0W887vhDY2SRPVKN6U64jadflfM6ZEP0uDVir\n", "DWFN2hGEpY9E1aazjiWl2zWQ9grhbQcWml2x6euhGNfXcQPDQGxlPVZGdgZArNXutnvHVFiOur8f\n", "xmRFADY84FJccPdnuAIRTFH93IKq5i6ik8anIjN7O0pT+PdxBl+unaoN4QzKDVWQ7jEAMxofR/mN\n", "Fr9PW8ydD/DcHdwb3tZMaq5Ao5NQ81OhgLzpa8HlT+3R8BpxNg0Q06em4caOD1HWsHXeXm8FW2ZE\n", "TJhj2VC65zovwKNaNoR0X14FNffccw+2bt2K6OhoDB3a2BW5oKAAW7ZswahRo5q5N/FGeysGezsD\n", "0JpKtK5cLyhCtWJCn0xGxdrjkEYreXVXLDeqIVYHA7p6t4aGTmy9FabCcjAl/N1QzuUWW1UdKj44\n", "BrE6CGydBbaaekDMOAr0JalR991VwfOKVYGOcvobzsBeY+Yu8PKESNT+pxC6j07ALmEAEQNGLoZi\n", "XF/uGNtNPeZOn48RScM8JrPqd2nA2tyXDeUJkejNMryABgD+nToEU/KuI//Ube44wLuLqKfXN0BX\n", "j37b8iCrMMK8LQ+FJjNs80chck85Io/B4wyJN8HGneJtQN5SlWqnpgGiaxBXwtpwTX8TbFwIykQm\n", "lNlKEFhmhWp7MaottYDVjkAV/3yEkO7Dq6AmPT0db7/9NlauXInQ0FAolUpUV1dDr9dj8ODBSE9P\n", "7+xx9gjtqRjsaUbB9Q3e20/EQngXFA+1YhixiAt2XGciKj44CtV/p0C/W7iPkDgiEIZ9hRjdNxEF\n", "2/IRMDtJcDZIt/EsQs1y9OrXD4xYhBCVEvoCHS4zHhJcWRb6XRoo7usHy009Kj85CTEjRog0EEsf\n", "exojkoZxyzqBLss61Znn8PCICY7E3gYvrXkd1SIT7NVGxySNCJAnqSFSBUC36RwCx/bhcoUsN6qR\n", "ECA8e6mAY5nIvlGDhDKV1wXhhF5fyYUypBhs2DChMWnl6WOFOHGhDMrIMK6PVUdra2DsibcBeWsT\n", "i5ved9L4VPz6mTSwkQFudX+MN0ugXOqYRSwF7YAipLvyKqhRKpV48803kZubi8uXL6OqqgphYWFI\n", "SEjAiBEjWj5BC4xGI9asWYPa2lpMmTIFDzzwAO/7K1euBAAwDIPHHnsMd999d7sf0980V87eqa1L\n", "VM6LmMwqgn2jBtYq4YZBrIekSzTsepInqh0F0uYKLCUxDPLLrkI8Ps6xlbq0FuHP/oJ3GtX8kdB9\n", "fBJ/S1/MjXf+y0tQPBRu+RJVG8+ANdsRlDqgcXkoNR4JR628mjDOQnW8pZjn33Kb1Zibn4dPDu1A\n", "8H+nOAKuAi3qvr+GuBA11KHxyD1+lbdEZnz3mOCPwjkXdVfCYI+1aZo6eDgHFVWVqNlQDDZMDnmi\n", "Yxkt+psr2DCJX8Pm3+MSMCX3OmT9BHZkdYD2BMaeeBOQO7UmsVjItfKbUC5w7/xd+a9TvNtoBxQh\n", "3VOrsuGSk5ORnJzc8oGtdPDgQdx///1ISUnB66+/jpSUFEgkjUNjGAYrVqyASOTdltOeyPVTrE3E\n", "Qmxn3N7g27JEpS+rQiVT15Bz4KgkrPqyELp/nobqudHcfao2nQVr9rBjpCHYkSdEou54MS951Znn\n", "YirQcoXa5AmRHgvmiXqH8C42Fth4SaPO80IsgiQq0O3+3lwohbgmSrvm0PQ/BrAsi9AZ/L+LkocH\n", "4ekDF/DvB/kNO2/e29vjOIRwQcQ0NUIa6s7oPjuHmq8vYhArHAyEVJnx5KJZXp27tTMuLQXGbSkg\n", "6U1A3hzWmd/UZBu9EIvdBvffCvft7KbCcpy/VMqVFqCCfIR0Dx7fWauqqrB+/XpMnjzZYyCTm5uL\n", "AwcO4Nlnn0VoaGibB1FYWIgFCxZAJBKhX79+uHXrFvr27ct9n2EYvPnmmwgNDcUzzzyD4ODgNj+W\n", "P3NenENCQgQTu9uyRKXfXcZLwgQA0WMJiN1ejMr152GoqwVEDETKAEjUwaj850mEP9eYDFy7OQ9S\n", "K8PN0CjG9m1YVmrMzarKPAvFuL4wFWgbH8TTFlyW5QVhzufkVmq/IXnUmUALtC8B1C1RumGp6Xyl\n", "HTHqaAD8JF7rkCj8XGDD0gv1uK39GTVhMty8tzesQ6JaNQ6hIEL15D0NNXWEywD0Uvdpd3Kup+Ck\n", "ucC4rQUk29qjqS2zRlLBLlQAI298TqbCctTn3kbo/GHczrcVme80e15CiG/wGNTs3bsXpaWlGD58\n", "uMc7Dx8+HFu2bMHevXvb1dCyrq4OCoWjHqlCoUBtbS3v+y+88AKCg4Nx5MgRfPXVV5TD00ZCn4jF\n", "n19GeXAY94m0oqwc+t+4XEQ95M/0juuN4LJAFLJlvGWf6h15MHxwCveMbOhltPQNAMDr7/8/3Hj/\n", "KEQhcthq6lG14QzEEQpHMbvqerBnSnmF2ppbqpKVN/7atpQ8GlTDIOFo+5sZOoMnoVyf4k9zoYD7\n", "FvGQmBi81dAteus3OyGrrIfsGFo1juYqOQu1bFhRUIMZC37X4nmbm3EJYoGd/3gTvdh6SBrqCe38\n", "x5sAmg+M21NAsi2Jy0LPoay/CC+teR13ufR3cj1vf3UcLjVpGaHbeh5MjYX72njqBlRz+B/kLDP6\n", "YfWWjymoIcTHeQxqzpw5g2nTpjW75CMSiTBlyhRkZWV5FdTodDqsWbOGd1toaCgCAwNRV1cHpVIJ\n", "o9GIoCB+uTXnzMyYMWNw6NAht/Pm5+cjP7+xxHtaWhpCQoR32vQEMplM8PnPnDodgYpAfLprC0ys\n", "BfqyKpQHyVE6LYIrxmbcpoWo0BHImDRaWLW1bucBgEuXC6GvNSB0MT/vJXTWMNgyNdj7wRa3x35w\n", "zq9x0VqCsKcad8vpd2kQIJIhwqZA+ZlSwGWHkv3bYlStOwlxbAi3VBV9HXguPZ17fs7n9Pz/voya\n", "MPCWtABgZMIwfL7601b/DJta+Nv5yNj0DxQb3Hd+icfHof5zDQJcKh+rDpTjufQXEBISgplTpyNt\n", "5mMwm927iLckUCIHIDAjw7KwDolCXq4eywotiAlVwiqW4pf/vRT3TZzc4nntHnaq20Qsdq3/CDFs\n", "PTLGDeRuX3nsCnavX4uFz7+AjE3/gG5SYxDnfK5nNwv/nOUM2yl/j02fgzPglM9P5GZY/vfLtSi4\n", "chHnruTDzFohlkuhqGD4S6B6G8xWO6o2nAFrsYO1C+eG3dJpe8T7yvbt27n/JyUlISkpqZmjCfEt\n", "HoOa8vJy9OnTp8UTxMbGQqvVtngcAKhUKsHKw//3f/+HvLw8jBs3DteuXUNsbCzv+86ZnAsXLgj2\n", "mRL6w/NUV6cn8LT8BADGOiMsVgtssOFGRQksKWq4llQMnJ2Eio+PQxwcANXcZJgKy92TcBuWi5gL\n", "wo9vsVu5x3fN27DbbFDU8HNfAmuAgEgVah8fAFFhOfR7NBDrrBgYGYflf3J0juaWJcolmPfbWUgZ\n", "dS/v+aWMuhf/7/nXBPMynkhb2iG/Cymj7sWf6xbjjx++4fY9eUIkIjQ2xLhuof7tYt44m3tNmjPn\n", "l4/iWpPnVb9NgwGBEYg5Bsxb9Ge32QNvHkckvHIFsZ1BbektrEwdyLt9ct9wrM4txMmNn2B0NYui\n", "nWUQR4fxnuvRTcJBjYllOuXvselzECo1UNoX+Hj/NgTMTgTAAFBBsacacQiCMkKF2ko9CpgiMHHB\n", "EDfMSlmKhRPh7Rab37+vhISEIC2tmV5khPg4j0GNTCaD0Whs8QT19fWQyWTtGsSkSZOwZs0afP31\n", "15g8eTLEYjGuXbuGoqIiTJw4EW+++SZkMhlkMhmWLFnSrsfqaTwn/UogSkmEaZdjm7VzZsNUWA7Y\n", "wS37uLUYsNgQlBrvSO7VCAez9bVGzH95CUb0G+ook8/lPERB/nk14iThCFEpIWMkKGfLUPqbKO6x\n", "nI8Xcawxf8GbKf+25mW0xqTxqRiWvR1CG9Nj1NGdsoVa8Hktymj382ouOXfb8dN4/dgVSEQMrHYW\n", "MQoZbteZ8cW0uwGYgd4BeO1CDe57mJ8E3N4Cku1+DgJLpSaNFsrZ/EDHMqMfIo8BG976EDMWPgFG\n", "reBt8a7850m3rub6XRokeNlGghDSdTwGNf3798epU6cwcuRIT4cAAE6fPo0BAwa0axCBgYH485//\n", "7Pb4/fv3BwCsWrWqXefvqbxJ+lXOTGxsDmlnYdPXQxoXKtgs0XShDEBjoOOp/w4bKoMmhcHZbV+C\n", "GRXNmwmyPT4IEQ0XFACY8/JCwT5Ebem9cycKyrV3p05bdMbz8hQEBrGAWi7iLT09/10B0gbH8O4v\n", "lCvT3gKS7X0OFysFkss95IQ5f79uVJZAmc7/mwh/bgzKV//Am1EMM8nw+4WLO3T8hJCO5zGo+dWv\n", "foXVq1fjrrvuQmpqquAxhw4dwnfffYfly5d31vhIO7gmUpoKy2HV1jq2SjcEKc7gxLU5pC7zHKy3\n", "awAb6xas2GrqIVYGcLd56r9Tm1PkeJxABrbjxW4tAYR2LzXVEb13OrpIHHBnZoTuFKFg6Z3lC/GB\n", "SzE/AHjvwaF44/gV3B8bxrtdqNlqewpItoXrcxDqfybRmgTvx/1+eehMLg6QYrAkhptRnPfwLK9q\n", "OXXk7xohpPU8XjnGjh2LqVOnYu3atdi3bx9GjBiByMhIMAyD8vJy5ObmoqioCNOmTcO997p3MSZd\n", "4+DhHGz55isYrSYUXC2EJGUol0DpWsxO77rs5FLfQ5V+Dyo+POaWm6CcmQjd1vNuu5JcG0XKEyJR\n", "9dk5bnkKcDSsNBWW8wIb14Cls2Y+OqNInJMvtRjoaB67xTPuMx65Vy75VB0XoYBz+EOzkbX/e4+/\n", "X33CY3BL4Fxx4THYu3Yr7zZPgUtn/q4RQlqn2Y/D6enpSExMRFZWFvbu3Qur1fHJTCKRYMiQIXjp\n", "pZeo75MPafrmWlfGQgnhBEpnE0RTvtatdw4jFZ49EQXLIE+IhOWm3tHnKS7U0ZXaaoPx+2swHLyC\n", "4EkDeQGMam5ys7ViOmvmo7MbfPorT93iL7hseQaABYcLcTG1D6xDGi7ia9/CgX99hOhQpddF9zqD\n", "UMDZXKfwP/3X8/jDujfdOpaveI7fsby5wIV+1wjxHS3O8Y8ePRqjR4+G1WqFwWAA4Nhi7Vrxl/iG\n", "pm+uzpwXiIXzCqwlBgQ9GO+2PAThw2Ev1sOyWQOFFZicnIoa1MMc0fCJuO8Q/OvgdvdzoeVaMZ0x\n", "8+Ft9WTC5ynZ9xePzsVrhRqIbVacvJCPq1P6wDrEkeAtuVCG4ZY6fDQ8BoBj27o3RfcAtKkCcWs1\n", "9/v1q9TJMNYZW+xY3lzgQr9rhPgOryMTiUQClUrVmWMhrSA0Fd70zdUZYNTvLxI8hyhEDuPJG/xK\n", "vLs0kCVEuhW+s39RiHWvvtNs8PFt7g+CSb/D44d2WnNFTzozV8eftZTse/BwDv753goEDWnsLRWb\n", "W8IrAAh4V3SvrRWIO5o3QXVzgYu3v2uUd0NI56NmSt2Qcypck8KgMEUCTQrjmBqvqnbUldmtgX5v\n", "AdcVe3BcPJT7+duvDVt+RODoWLB2O/R7Go7f46jEG5wajwC9HdaN+bBtLkBMdiX+9swrLb4B/37u\n", "IrfHUX6jxbyHW+5D1NHSp6b5zFi6E+fMidhmhVUkxn2PzOYFGJnZ22EL5V+sFR6m9oQSiV0d+Wor\n", "b0YIcARDP+zc1rbBd6LmAhdvftc8/c0ePJzTmcMmpMehj63dUGb2dpT1F8G0W8NtuTYlqqE6Xg3L\n", "qVoo5zTW1zBsycPECbMwIqkxr6BGp0e1LQg1R0shNzFgxTYEudxH+Y0W//vyqlZ/ivSlnUG+NJbu\n", "wpuZEwtsblv56yDcp8smbv7txWNScgvBUFdoLqHdm981yrsh5M6goKYbKi3TwqStcNtyXWEwIngx\n", "P3E7eM4w/HjsAl5YtIy3U8M4byAkcPwCiD+/jKjsysbtq+24+PvSziBfGkt34E3vJinEbl3RC01m\n", "PHuoEJ+4bAX3puiep6TkloKhrhJYx6ByYz4gESEuLBrL5zT+nbT0u0Z5N4TcGb757kGaVaargHK+\n", "+26m6nWnBI93feMU+sTYtCAe6Zm8mTlxnbFw3dGWkDger13StKro3p2uQNxW3M6n36ghgSOXqK5h\n", "ucnbPBnK8SLkzqC/qG5IrVajXOB2eYBc4FbgYmFjPRFtdQUg0FGaPjESb2ZOOnJZ705XIG4rT0tH\n", "q7d8jDqp1av6NF1RiZqQnoiCmm5IHRohGNT0V8ehbr+W98Zp2PIjpCm9UZjgeKnrPr0BhUBQQ58Y\n", "ibczJx25rHenKxC3haeloxtVpZDMHcq7zVOeDOV4EXJn0JWsG/L0qW/5HMenvq3f7ESdtR4XCy9B\n", "mtKbt2VbPD4O9ds0DV2LG+9LnxhJV8+c3ImaNd5yXVa6WHgJopRE94OsdsH7epr1pBwvQjofBTU+\n", "xps1+pY+9c2cOh01NTWY8/JCbobGSZ4QiQiNDTHHQJ8YiZuumjnxlZo1gHv1YEuUGpYteQieM4xr\n", "9CqutkJstsPWpAUIQLOehHQl+uvzIa3pIePNpz5PyYkx6mhKCiY+xZudV3eKW2VuZxHLtWdhlrJA\n", "VCBsYVLY7Czs313jHUOznoR0LQpqfEhH17Kg5ETiazwtMflSzRqhHBp5QiTqDlwHIgOh/A2/lAJ7\n", "oBgJZSqa9STEB1BQ40M6upYFJScSX9LcEtOtyiq8fr0EEhEDq53FpL7huD82rEtq1nia4bTAjlCB\n", "xrDWjfnYsurjFs/rXFrWVldAq9UiShWB6Cg1tUsgpANRUONDOqOWBSUnEl/haYnp+U8/Qqi5Fhnj\n", "BnK3rzx2BZuK6/Doiyvu9DA9znDWBwqXTGAkLXeb4S8tR0KESFzdpcGtKDFueFhiJoS0HvV+8iHU\n", "r4j4M09LTHVlJfjbSP6y68pxAyEPi+iSpOVJ41PxyqylSDoGJBy1IukY8EraUvSLihU8PjYsusVz\n", "Ci0tK2cmwlSg5ZaYCSHtRzM1PoSWi4g/81Tcz2wT7h0VoVR25nCa5WmGc0XmO7DM6Md9LdlzHcvT\n", "/9Di+SywwVSog0mjBUQMbDojGLEIrNkG/W4NShDRkcMnpMeioMbHtLRc5G1ZdkJ8jafifoGRasHj\n", "fa0HlOCHjvQ/ePX3py+rgklb5pidKSyHKV/L6912c5sGBw/n0N8yIe3kW+8apFmt2fJNiK/xVNwP\n", "QLfoAQW0I0dNIoJymiOIMWn4AQ0ABMxOpI7dhHQACmq6kY7e8k3IndZccT9f7wHVHsqwUJQ6vxAx\n", "gsdQ/zVC2o+Cmm6ko7d8E+IrukMPqPbg7Wy0C+cQUSViQtqPdj91I52x5ZsQ0vlcdzbKE9XQ79Lw\n", "vi/Zcx3l2jLMeXkh5r+8BAcP53TBKAnp/uhq2I1QhWBCuid+krEKehHAZFciRKVEjU6PMqMFpY9H\n", "cUtUlCtHSNswLMsKz4V2c7du3erqIXSKg4dz+LsvHp7l9sYXEhKCmpqarhkgEUSviW/yhddl/stL\n", "oElxz7NJOoY73qOtd+/ed/TxCOloNFPTzVCFYEJax1O/KV9BuXKEdBwKagghfqu5flO+EthQrhwh\n", "HYcShQkhfuvIV1t59W8AR7+pH3Zu66IRuaP2KIR0HPooQAjxW576TYltvrO0Q+1RCOk4FNQQQvyW\n", "p35TvtiCgYIYQtqPlp8IIX7n+Pc5eGf5QlRotXj+cBHveysKanDfI7O7aGSEkM7kWx9XCCGknXjJ\n", "wTEhOHLTinkHLkId2wdB4ZF+14KBENKIghpCiF858tVW3m6n+2PDcH9sGF4rVeDFd9d14cg618HD\n", "OcjM3g4LbJBCjPSpabSkRXocCmoIIX6lOyQHd7SDh3Ncqo073tapKjHpiSioIYT4lbYmB/t6kb7m\n", "ZGZv57VPAQD9FDU2/WcHBTWkR6FEYUKIX7n/0Sfw2gV+64OWkoO5PJwYIzJ6m/FmjBE/fLoGx7/P\n", "6eTRdgxHVWJ3VJWY9DQ0U0MI8SvO2ZXXdm6D2GaFTSxpMTm4aR4O4CjS99rObd1itoaqEhPiQL/x\n", "hBC/M/aB1FYFI909Dyd9appLTo2D8hst5qUt7cJREXLnUVBDCOnxXPNwjtyswsHiSkhEDC4bGRz/\n", "PsfnZ2uoKjEhDhTUEEJ6vPsffQKvfboGvwyx4kBxJVaOG8h9z9caYHpCVYkJoURhQgjB2AdScd9/\n", "/Q4fF+p4AQ3gew0wCSGeUVBDCCFwBDaDBt8l+L3ukltDSE9HQQ0hhDToLg0wCSHCKKghhJAGbalx\n", "QwjxHfTxowH1TSGEtKXGDSHEd1BQA+qbQghp1NoaN12FPogR4o6Wn9B83xRCCPE1zg9imhQGhSkS\n", "aFIYvLXjQxw8nNPVQyOkS9FMDZx9U9x/FNQ3hRDSlC80vvT0QewPa1ZieHYizdqQHouCGlDfFEKI\n", "d7jGly59orqiOJ+nD2J14SJu1gag5XPS89DyExx9U5T7tbzblN9oMe/hWV00IkKILzry1Va8IdD4\n", "8k4X5/P0QQwsC4CWz0nP5RNTEefOnUNmZiZCQkLwxhtvuH3/p59+wrZt2yCVSrFs2TKEh4d36ONT\n", "3xRCiDfuVOPLlpKAhRpY6ndpIE9q/JqWz0lP5BNBzeDBg/H2228LBjQA8OWXX+Ivf/kLbty4gZ07\n", "d2LBggUdPgbqm0IIacmdKM7nzW5M1w9i54sKUBfCQp6khjwhkjsPLZ+Tnsgnlp+CgoIgkQj/AZpM\n", "JshkMgQEBGDQoEG4cePGHR4dIYQ43InifN7uxpw0PhUb3voQ7yx9DXGBkbyAhpbPSU/l86F8bW0t\n", "AgMDua/tdnsXjoYQ0pPdieJ8rd2NScvnhDS6o0GNTqfDmjVreLeFhoZi+fLlHu+jUChgNBq5r0Ui\n", "98ml/Px85Ofnc1+npaUhJCTE7bieQiaT9ejn74voNfFNbXldpkybjinTpnfSiIBAiRyAe+6OQhLg\n", "cawzp07HzKkdM6bt27dz/09KSkJSUlKHnJeQO+GOBjUqlQoZGRmtuk9AQADMZjPq6+tx48YNxMXF\n", "uR0j9IdXU1PjdlxPERIS0qOfvy+i18Q3+eLrMueXj+JakyRg5TdaPJG2tNPHGhISgrS0tE59DEI6\n", "E8OyDXsAu1BRURE2b96MoqIiDBw4EH/6059w8+ZNFBUVYeLEicjLy8Pnn38OmUyGpUuXIiIiosVz\n", "3rp16w6M3Df54ht1T0eviW/y1dfl4OEc/nLSw7PuyHJS7969O/0xCOlMPhHUdAYKanzvjbono9fE\n", "N9HrwkdBDenufGL3EyGEEEJIe1FQQwghhBC/QEENIYQQQvwCBTWEEEII8QsU1BBCCCHEL/jt7idC\n", "CCGE9Cw0U+OHXCuCEt9Ar4lvoteFEP9CQQ0hhBBC/AIFNYQQQgjxCxTU+CFqQOd76DXxTfS6EOJf\n", "KFGYEEIIIX6BZmoIIYQQ4hcoqCGEEEKIX5B09QBI58jJycGuXbsQFhaGQYMGYe7cuV09pB5rw4YN\n", "uHr1KgYMGICnnnqqq4dDAGi1Wrz66quIi4uDRCLBq6++2tVDIoR0AApq/NiMGTMwceLErh5Gj1ZU\n", "VASTyYTXX38d//rXv3DlyhUMHDiwq4dFAAwfPhzLli3r6mEQQjoQLT/5saysLGRkZOCnn37q6qH0\n", "WJcvX8aIESMAAMOGDcOlS5e6eETEKT8/HxkZGcjKyurqoRBCOggFNX5qzJgx+Pvf/44XX3wRmzZt\n", "Am1y6xq1tbUICAgAACgUCtTW1nbxiAgAhIeH47333kNGRgby8vJQXFzc1UMihHQAWn7q5nQ6Hdas\n", "WcO7LTQ0FMuXLwcAKJVK9OrVCzqdDmFhYV0xxB5NoVDAaDQCAOrq6hAUFNTFIyIAIJE0vvWNHDkS\n", "xcXF6Nu3bxeOiBDSESio6eZUKhUyMjLcbjcajQgMDITZbMbt27cRGhraBaMjgwcPxv79+zFu3Djk\n", "5eXhwQcf7OohEQD19fXcDNrFixfx8MMPd/GICCEdgYIaP5WVlYXc3FywLItHHnkEIhGtNHaFAQMG\n", "QCaTISMjA/3796ckYR9RUFCAzz//HFKpFEOHDsWgQYO6ekiEkA5AFYUJIYQQ4hfo4zshhBBC/AIF\n", "NYQQQgjxCxTUEEIIIcQvUFBDCCGEEL9AQQ0hhBBC/AIFNYQQQgjxC1SnhnQ7x48fx759+3Dt2jWY\n", "zWZERkZi1KhRmD59OlVN9sLu3buRkJCAxMTEFo/94osvUFBQgMuXL6O+vh4ffvghIiMj78AoCSGk\n", "9WimhnQrmZmZWL16NWJiYrBs2TL85S9/wbRp05CXl4f169d39fC6hT179kCj0Xh17MGDB2G325GU\n", "lNTJoyKEkPajmRrSbZw+fRpZWVlYvHgxUlNTuduHDh2KyZMn48cff+y6wXUz3tbcXLt2LQDgzJkz\n", "OHPmTGcOiRBC2o2CGtJtZGVlIT4+nhfQOIlEIiQnJ3Nf6/V6ZGZm4ty5ww3CigAABtNJREFUczCb\n", "zRg0aBDmzZuH+Ph47pilS5di7NixCAkJQXZ2NsxmMyZOnIj09HScPn0amzdvRmVlJYYNG4bFixdz\n", "zSjz8/Pxxhtv4NVXX0V2djby8/MREhKCRx55BFOmTOGN6+jRo/jyyy9RUlICpVKJCRMmIC0tjWtb\n", "kZOTg7Vr1+Ltt9/Gxo0bcenSJURGRuKJJ57AmDFjeOc6deoUvvzyS/z8888ICgrCAw88gCeeeAJi\n", "sRgAsH37duzbtw8rVqzAJ598guLiYvTu3RtPP/00hgwZwj1ng8GAL774Al988QUAICMjw6ulKEII\n", "8XXilStXruzqQRDSEqvVivXr1+OBBx7A3Xff3eLxf/3rX3H16lXMnTsXEyZMwIULF7B7926MGzcO\n", "wcHBAIDs7GwUFxeDYRjMnj0barUaO3fuhE6nw5EjR5CWlobk5GTs27cPOp0Oo0aNAgCUlZXh0KFD\n", "yM/PR3JyMmbOnAmbzYYvvvgCAwcORK9evQAA58+fxzvvvIN77rkHTzzxBKKiovDVV1+hqqqKO9e1\n", "a9dw+vRpXLhwAePHj8dDDz2EkpIS7N69Gw8++CACAwMBOIKj1atX4xe/+AXS0tLQt29f7NmzBwaD\n", "ASNGjADgCLYKCgpQUFCAhx56CBMnTsSFCxewf/9+PPTQQxCLxUhKSsLRo0cxfvx4PPPMM5g0aRL6\n", "9u0LqVTa7M/z9u3b+OGHHzBt2jQoFIq2vYiEENLJaKaGdAsGgwFWq9WrJNXc3FxcunQJK1euxNCh\n", "QwEAd999N5YuXYo9e/bgueee446VyWR44YUXwDAMRowYgdOnT+Pbb7/Fe++9h6ioKACOwOPQoUN4\n", "9tlneY9zzz33YPbs2QCA4cOHo7S0FF9++SVGjhwJwDFzkpSUhCVLlgAAF3xs2bIFjz32GMLDw7lz\n", "/frXv+ZmoOLj4/Hss8/izJkzmDJlCliWxWeffYYJEyZgwYIF3ONJpVKsX78ejzzyCBeomc1mPPXU\n", "U1wOjEqlwp/+9CdoNBokJyejf//+EIlEiIiIoCaOhBC/Q4nCpFthGKbFYy5fvozQ0FAuoAEAuVyO\n", "kSNH4uLFi7xjExMTeeeMjo6GWq3mAhoAiImJgV6vh81m49236fLQmDFjUFRUBJZlYbfbcfXqVYwd\n", "O5Z3zLhx48CyLC5dusS7ffjw4dz/g4ODoVQqUVlZCcAxS1JRUYFx48bBZrNx/5KSkmCxWPDzzz9z\n", "95VIJLyk3ri4OADgzkUIIf6MZmpItxAcHAyJRILy8vIWj62qqoJSqXS7PTQ0FAaDgXebM0/GSSKR\n", "uC2vSCSOPxOr1crlrzjP50qpVMJut6OmpgZ2ux02mw0qlcptDAC8GofFYgHgyA8CgFWrVgk8W6Ci\n", "ooL7f0BAgODYnecihBB/RkEN6RYkEgmGDBmC3NxcPP74480eGxYWhurqarfbq6uruWWajtD0MfR6\n", "PUQiEUJCQsCyLMRisdsxzq9bMw7nsQsXLkT//v3dvq9Wq1s5ckII8U+0/ES6jalTp6KoqAiHDh1y\n", "+57dbkdubi4AICEhAXq9HgUFBdz3TSYTzp49y+0C6ggnT550+3rgwIFgGAYikQjx8fE4duwY75hj\n", "x46BYRgMHjzY68fp3bs3wsPDodVqER8f7/avtYGaRCKB2Wxu1X0IIaQ7oJka0m2MGjUK06ZNw7p1\n", "63Dx4kWMHj0aAQEBuHnzJvbv3w+1Wo3k5GSMGDECgwcPxurVqzFnzhwEBwdj7969sFgsmDFjRoeN\n", "Jzc3F9u2bcPQoUNx4sQJ5OXl4aWXXuK+n5aWhv/5n//BRx99hJSUFBQXF+Pzzz/H5MmTeUnCLRGJ\n", "RJg3bx4++OAD1NXVITk5GRKJBFqtFqdOncKLL74ImUzm9fliY2Nx9uxZJCcnQy6XIzY21m3Zykmj\n", "0UCv16OoqAgAcPbsWSiVSsTFxXH5OoQQ4isoqCHdSnp6Ou666y58/fXXeO+992A2m6FWqzF69GhM\n", "nz6dO+6Pf/wjMjMzsWHDBlgsFiQkJCAjIwPR0dHNnt+bRGSnRYsWISsrC1lZWQgODsaCBQu4rdqA\n", "I/n3d7/7Hb766iscOXIEoaGhmD59OtLS0lr9vFNSUqBQKLBz50589913EIlEiImJwciRI7m8GYZh\n", "vBr/k08+ifXr12PVqlUwm83N1qnZsWMHr/qws2rzrFmz8Nvf/rbVz4MQQjoTw3pbWpQQAqCx+N7f\n", "//53mq0ghBAfQjk1hBBCCPELFNQQQgghxC/Q8hMhhBBC/ALN1BBCCCHEL1BQQwghhBC/QEENIYQQ\n", "QvwCBTWEEEII8QsU1BBCCCHEL1BQQwghhBC/8P8BecVOO7NwoL0AAAAASUVORK5CYII=\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f3729025650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_components\n", "\n", "draw_components([model.reduced_fit_data[:, :2], \n", " model.reduced_predict_data[:, :2]],\n", " ['Training Data', 'Test Data'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The predicted data seems to be reasonably similar to the data we used to fit the model\n", "with. Now let's look at the score value for the predicted data.\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R-squared 0.99834842402\n" ] } ], "source": [ "from sklearn.metrics import r2_score\n", "print('R-squared'), (model.score(X_new, y_new, periodic_axes=[0, 1]))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks pretty good. Let's print out one actual and predicted stress value for each of the 6 microstructure types to see how they compare.\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Actual Stress [ 0.28985647 0.2831434 0.25138814 0.29399186 0.26338502 0.27548337]\n", "Predicted Stress [ 0.29038894 0.28375754 0.25230674 0.29388488 0.26327469 0.27586485]\n" ] } ], "source": [ "print('Actual Stress '), (y_new[::20])\n", "print('Predicted Stress'), (y_predict[::20])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, we can also evaluate our prediction by looking at a goodness-of-fit plot. We\n", "can do this by importing `draw_goodness_of_fit` from `pymks.tools`.\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEpCAYAAABbU781AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xlc1HX+wPHXHAww3MgpqCACKuKdeeCNR5mmqaSWVpZb\n", "bbXb3VoZUmZb/bIt86hcU8vcvI9UPFAMb1NRBPEAPBCR+xwY5vr9QTM6MigiCOjn+Xj0WPie7+93\n", "cd7zuSUGg8GAIAiCINQBaUMHIAiCINw/RFIRBEEQ6oxIKoIgCEKdEUlFEARBqDMiqQiCIAh1RiQV\n", "QRAEoc7IGzoAQWgsXnnlFQDmzZvXwJE0PidOnGDVqlWkp6dTVlZG9+7deeedd+76urGxsSxYsICX\n", "X36ZAQMG3H2gQoMTSUWoFxkZGezYsYOkpCSysrIoLy/H1tYWb29v2rZtS58+fWjdunVDh1mFRCJp\n", "6BAanaysLL744gvs7e0ZNGgQSqWS5s2b3/KcxMREPv7441se891335l+vvm9iwTfdImkItS5VatW\n", "sXr1agBat25Nnz59sLe3p7y8nIsXLxIdHc3vv//O1KlTGTZsWANHK9xOQkICWq2WKVOm0KdPnzs6\n", "193dvdoSiJ2dHT169CAoKAhnZ+cq+0WCb5pEUhHqlDGhuLm58c9//pOgoKAqxxQVFbF582bKysoa\n", "IELhTuXn5wPg4uJyx+e6u7szbty4Wx6jVCprFZfQOImkItSZa9eusXbtWuRyOdOnT8fX19ficY6O\n", "jkycOBG9Xl9lX35+PmvWrOH48ePk5+ejVCpp27YtTzzxhMXqMo1Gw+bNm4mLiyMrKwupVIqfnx/D\n", "hw+nV69eFu8fHR3N9u3buXbtGg4ODvTo0YMJEyZYPPbGOn83NzdWr15NWloaAO3atWPy5Mn4+PhU\n", "OU+tVrNlyxb2799PZmYmEomEli1b8sgjj1j8th8bG8vOnTu5evUq5eXlODo64uvry8CBA+ndu7fp\n", "uIsXL7Ju3TrOnTtHQUEBtra2NGvWzBSLTCaz+Bw3279/P9u2bePChQvodDq8vLwICwvjscceQy6v\n", "/Fi4uQorKirK9HNkZCTt27ev0b1u5eY2lZvv+eSTT5p+7t+/P3//+9/v+p5C/RJJRagzu3fvRq/X\n", "07t372oTyo2kUvPOh1lZWcyYMYOCggI6dOhAWFgYOTk5HDx4kOPHj/PWW2/RtWtX0/FarZZPP/2U\n", "06dP4+Pjw7Bhw1Cr1Rw8eJD//Oc/XLhwgYkTJ5rd46effiI6OhoXFxeGDBmCVCrlzz//5Ny5c2i1\n", "WqysrCzGeuzYMY4cOUKXLl0YMmQI6enpHD9+nJSUFObMmYODg4Pp2NLSUj7++GMuXLhA69atGTRo\n", "EAaDgfj4eL799lsuX75slsR+/fVXNmzYgIeHB3369EGpVJKXl0dKSgoHDx40JZWLFy/y/vvvI5VK\n", "6d69Ox4eHpSVlXH16lV27NjBxIkTa5RUjPdzdHSkb9++2NjYcPz4cVasWMGJEyf44IMPkMvleHh4\n", "MG7cOJKSkkhKSqJ///54eHgAlSWQumSs6jLec8uWLQCMGDHCdIyfn1+d3lOoHyKpCHXmzJkzAHTo\n", "0KFW5//4448UFBQwYcIExowZY9o+bNgwIiMjmTdvHvPmzcPGxgaATZs2cfr0abp06cK7775rSlLj\n", "x49n+vTprF+/nm7dupmq4M6cOUN0dDReXl7Mnj0bOzs7ACZOnEhUVBQFBQXVflgeOXKEDz74wOzZ\n", "jB/Ou3fvZtSoUabtS5Ys4cKFCzz11FNm2zUaDV9++SXr1q2jZ8+epg/JnTt34urqyldffYVCoTC7\n", "b3FxsennPXv2oNVqeeedd+jevbvZcSqVqsq5lpw9e5YNGzbg5ubG7NmzcXJyAmDSpEl8+eWXHDt2\n", "jE2bNjFmzBjc3d0ZP348K1euJCkpiQEDBtxx6SQrK4uVK1dW2d6hQweL1zLeMzY2FolEctuqM6Hx\n", "EeNUhDpTUFAAgKura5V9xg+XG/8zfhsFyM3N5eTJk7i5ufH444+bnRsUFESfPn0oKSnh8OHDpu27\n", "d+9GIpEwZcoUs1KPo6MjY8eOBSAmJsbseIAxY8aYEgqAlZUVkyZNuuWz9enTp0qyDA8PB+D8+fOm\n", "bcXFxcTFxREQEGCWUG6+z969e03bJRIJcrm8SskNMCsBGVlKHkqlskYN27t27QLgiSeeMCUUqCw1\n", "TpkyBYlEYjqmLuTk5LBmzZoq/yUlJdXZPYTGRZRUhHsiOzubNWvWmG1zc3Pj0UcfBTBrp7D04dqh\n", "Qwfi4uK4cOEC/fr1o6ysjGvXruHq6mqxe6sxAVy4cMG0zXgPS9+Qg4ODb/mhbKk9p1mzZkBldZdR\n", "SkoKxtUkLH1D1+l0AFy5csW0LSwsjOjoaN544w169epF+/btCQoKqtKA3bt3b7Zu3cqXX37Jww8/\n", "TGhoKMHBwXh5eVUb982M78BSadLb2xtXV1eysrIoKyvD1ta2xtetTvv27YmMjLzr6whNh0gqQp1x\n", "dnYmIyODvLy8KvtCQkL47bffANDr9UycONHsQ1ylUpmuUd214foHuPH46nokGY83Hne7e8hkMoul\n", "AqMbSzY3nmN8HiNjdVVKSgopKSnVXk+tVpt+fuaZZ/D09GT37t1s2LCBDRs2IJVK6dKlC1OmTDEl\n", "jTZt2hAVFcXatWs5dOgQcXFxADRv3pxx48bVqLvv7d6bi4sLubm5lJaW1klSER48IqkIdaZt27Yk\n", "JSWRkJDAwIEDqz3O0rpwxm/lxiq0mxm7tRqPu93xxu03ftu/8Rxjg7ORTqejuLgYa2vrauOuCeM9\n", "RowYwZQpU2p0jlQq5dFHH+XRRx+lqKiI5ORk9u3bx8GDB0lPT2fOnDmmHllBQUH861//QqvVkpqa\n", "Snx8PFu3buXbb7/F0dGR0NDQGsWXn5+Pp6dnlf03v2dBuFOiTUWoMwMGDEAqlXLo0CGz6p2aMFYv\n", "nTlzxmJX48TERAD8/f0BsLW1xdPTk9zcXDIzM6scf+rUKbPjb7yHpfr85ORki8nuTgUGBiKRSDh9\n", "+nStznd0dKRHjx688cYbhISEcO3aNS5fvlzlOLlcTlBQEBERETz33HMA/Pnnn7e9vvF9GN/njTIz\n", "M8nNzcXDw6PBk4pUKrX4dyA0fiKpCHXG09OTsWPHotVqmT17NmfPnrV43I1tEEaurq507NiRrKws\n", "Nm/ebLbv3Llz7N27F3t7e3r06GHabiwN/fzzz2YfQEVFRab2mxtLTMaR3WvXrqWkpMS0vaKigl9/\n", "/fUOn9YyR0dHwsLCSE1NZc2aNRY/GDMzM8nKygIqu0UnJydXOUar1ZpiNJaezpw5Q0VFRZVjjaWy\n", "mpSyBg0aBFS+g6KiItN2vV7PsmXLzI5pSPb29hQWFlp8XqFxE9VfQp0aN24cBoOBNWvWMGPGDFq3\n", "bk1AQAD29vaUlpaSnZ1NQkICUNkof6Np06YxY8YMfvnlF06ePIm/vz+5ubkcPHgQmUzGyy+/bOpO\n", "DDBy5Eji4+P5888/eeedd+jSpYtpnEpRURGPP/44wcHBpuODg4MZPnw40dHRvPXWWzz88MPI5XKO\n", "HDmCg4MDzs7OdVJaef7558nMzGTlypX88ccfBAcH4+zsTF5eHleuXCE1NZV//vOfeHh4oFariYyM\n", "xMvLC39/f9zc3NBoNJw8eZKMjAy6d+9u6oiwYcMGEhMTadeuHe7u7tjY2HD58mVOnDiBvb29qTfa\n", "rQQFBTFq1Cg2btzIW2+9Rc+ePbG2tub48eOkp6fTtm3bKr3WGkJoaCipqanMnj2btm3bYmVlhZ+f\n", "H926dWvo0ITbEElFqHPjx4+nT58+7Nixg8TERPbt20d5eTlKpRJPT0+GDRtGv379qgxm8/Dw4LPP\n", "PmPt2rUcP36cxMRElEolXbp0sTiiXi6X8+GHH/L777+zd+9eoqOjkclk+Pn58dxzz5mNRDd67rnn\n", "8Pb2Ztu2bcTExJiNqH/nnXfqZL4pW1tbZs6cyc6dO9m3bx+HDx9Go9Hg7OyMl5cXzzzzDB07dgTA\n", "xsaGp556isTERM6ePcuRI0dM72natGlmJa3hw4djb2/P+fPnSU5ORqfT4ebmxrBhw3jsscdwc3Or\n", "UXxPPfUU/v7+REdH88cff6DVavHy8mLChAmMHDmyygDK2ryTu32PY8eORaVScfToUVOVaP/+/UVS\n", "aQIkhrr4aiYIgiAINHCbypIlS4iMjGTJkiVm29evX8/MmTN5//33OXbsGHC9zveTTz7h66+/rtH1\n", "LTVGNiUi/obVlONvyrGDiL+h3U38DZZUUlNTUavVREVFodVqzfr0jxw5kpkzZ/LRRx+xbt06AA4e\n", "PIivry8zZszgjTfeqNE9HuT/YxsDEX/Dacqxg4i/oTXJpHL+/Hk6deoEVDbK3dhTyFinW1FRYRp0\n", "duzYMdLT04mKijKbekMQBEFoPBosqZSWlpp68iiVyirdTBctWsTbb79tmqW0sLAQHx8fZsyYwd69\n", "eyksLLznMQuCIAi31mAN9du2bcPR0ZFevXpx6NAh8vLyeOSRR8yOUalUzJo1i9mzZ/P1118zYcIE\n", "vL29+fXXX+nWrZtZd1GoLLLdWGyLiIi4J88iCIJwv7lx7rqQkBBCQkJqdF6DdSkOCgpix44d9OrV\n", "q8q0HhqNBisrKxQKhWncQHBwMBcvXsTb25tLly4xfPjwKte09OAZGRn1+yD1yMHBwWzq86ZGxN9w\n", "mnLsIOJvaM2bN6/1l/IGSyr+/v4oFAoiIyPx8/MjICCAxYsXM3XqVJYsWUJGRgYajYaRI0cClaN8\n", "v/vuOzZv3kznzp0tTq8uCIIgNKz7fpyKKKk0HBF/w2nKsYOIv6FZWk6ipsTcX4IgCEKdEUlFEARB\n", "qDMiqQiCIAh15oGeUPJWK/01BrdbjbCxE/FXasp164Jwpx7opALiH7xQv5pyUhWE2hDVX4IgCEKd\n", "EUlFEARBqDMiqQiCIAh1RiQVQRAEoc6IpCIIgiDUmQe+91dTVpPFyl599VUCAgLu+Nq5ubnMmjWL\n", "adOm0b59+xqfd+7cOebPn897772Hl5fXHd+3NqKioigoKAAquwHb2dnh4+ND165d6dat2x2vl56V\n", "lcXRo0cZMGAAtra29RGyINy3RFJpwl5//XXTzxUVFcyfP5+hQ4eaJQFPT89aXdvJyYnXX38dDw+P\n", "OzqvRYsWvP766zRr1qxW960NiURCt27d6Nu3L3q9nqKiIpKTk1mxYgV//vkn06ZNMy38VhPZ2dls\n", "376dnj17iqQiCHdIJJUmrFWrVqaf1Wo1AM2aNTPbfiO9Xo/BYKjRB6xcLq/2OrdiY2NTq/PulqOj\n", "o9l9O3XqROfOnfn+++/ZsWOHxaUSbuc+n2tVEOqFSCq3EBMXy7ItK9GgwwoZUx6NYHDfAY3umtVZ\n", "vnw5mZmZDBkyhC1btpCdnc0rr7yCm5sbv//+OykpKRQVFeHs7EyXLl0YNmyYKeFYqv6Kioqic+fO\n", "ODk5sXv3bjQaDcHBwURERJi+0Vuq/nrjjTcYPXo0xcXFHDx4EIDOnTszevRo5PLrf4Lnzp1j7dq1\n", "5OTk4O3tzdixY/nhhx/o27dvrZJCcHAwnTp1Yv/+/abzr127RnR0NGlpaahUKlxdXenVqxf9+vVD\n", "IpFw7tw5Fi1aBMAnn3wCgKurKzNmzKCwsJDNmzff8r0JwoNOJJVqxMTFMnvVPIqGeGB8TbNXzQOo\n", "dRKoj2veikQiIS8vj02bNjF8+HAcHBxwdXWltLQUpVLJ448/jp2dHVlZWURHR1NSUnLLhXkkEgnx\n", "8fE0b96cCRMmUFBQwPr169m8eTPjxo27ZSyxsbEEBgYyefJkrly5wu+//46rqyuDBg0CoKCggB9+\n", "+IHWrVszcuRIioqK+OWXX9BoNHf1DoKDg4mPjyc/Px8XFxcKCwvx8PCgW7du2Nrakp6eztatW9Fo\n", "NISHh9OiRQtGjRrFxo0bmTp1Ko6OjqbEp1KpavXeBOFBIpJKNZZtWfnXh/91RUM8+HnrqlongPq4\n", "5q0YDAZUKhWvvPKK2foIzs7OjB492vS7n58fCoWC//3vf4wdO7bab93GqrPnn38eqbSy42BmZibH\n", "jx+/bVJxdXVl0qRJQOUHfVpaGidOnDAllT179mBtbc20adNMH+I2NjYsXbq09i+AyrYhqJyOx8XF\n", "haCgIIKCgkzP4+fnR0VFBQcOHCA8PBwbGxtTO5Kvry8uLi6ma3l7e9fqvQnCg0QklWpo0GHp9VQY\n", "tI3qmrfj5ORkccGd2NhYDhw4QF5eHlrt9fvn5+fj5uZm8VoSiYTAwEBTQoHKjgDFxcXo9Xqz7Tdr\n", "27at2e+enp5cvnzZ9PulS5cIDg42qw6r6ZrYd0Kj0bBz506OHj1Kfn4+er3etO92zwC1e2+CcCv3\n", "skr8XhBJpRpWWP7WqZDU/pXVxzVvx9KEhrGxsWzcuJHw8HACAgJQKpVcvHiRNWvWmH1QWnJzbyjj\n", "t3OtVotCobij826s2iouLsbHx8fsGCsrq1tesyYKCwuB6+9h06ZNHDx4kOHDh+Pr64utrS0JCQns\n", "2LHjts9wN+9NECyJiYtl1m9zKRnmxb2oEr8XxODHakx5NALHHVlm2xy3ZzH5kfGN6pq1ER8fT+fO\n", "nXn00UcJDg6mRYsWd/3hfbccHR0pKSkx26bRaKioqLir6yYnJ+Po6GiqxoqPj6dfv34MGjSIoKAg\n", "WrRocdvSiVFjfG9C0zZn2fy/Esp1xirxpkqUVKph/Jbw89ZVVBi0KCRyJke8clffHurjmrdjaeCf\n", "VqutUv9/9OjReouhJlq2bMmhQ4fQaDRYWVkBcOrUqbu65pkzZzh58qRZz7Gbn12v13Ps2DGz84z7\n", "b+4k0Bjfm9A05eXl8dFHH5F0Lhk33Kvsr88q8fomksotDO47oM4/8OvjmrdiaaxFUFAQcXFxtGrV\n", "imbNmnH06FFycnJqda260r9/f/bu3cuPP/5I//79KS4uJiYmBisrq9uWJAwGA4WFhVy4cAG9Xk9x\n", "cTHJyckcPnyY4OBgwsPDTccGBQWxd+9e3N3dsbW1Ze/eveh0OrPrGRvq9+3bR5cuXVAoFDRv3rzW\n", "700QjGLiYpmzbD7nLqRQUVKOQW/531R9VonXt6YbuXBbEonEYkll2LBhlJSUsGXLFqByoOATTzxh\n", "Gp9xq+vVV2xOTk5MmzaNdevW8dNPP+Hl5cXEiRNZsGAB1tbWt73WsWPHOHbsGFKpFDs7O3x9fZk4\n", "cSLdu3c3O3bs2LGsWrWK1atXY2VlRY8ePejYsSMrV640HePq6sqoUaP4448/iIuLw8XFhRkzZtT6\n", "vQkCwPqtm4hcNgfFpPY4/VU6yVt0hNIfj2E3ravpOMftWUyOeKWhwrxrEsN9Pmw4IyOj2n0ODg5i\n", "5cdGLDU1lblz5/LKK6/Qpk2bhg6nVhrqb6yp/23fb/Fv27aN12b/C6e3e1c51mtLHs1cXK9XiT8y\n", "vsEb6S31GK0pUVIRGo2NGzfi6+uLg4MDWVlZbN++nebNmzfZhCIIxraTdevWoQi0PB+eg7MjS2bP\n", "u8eR1Z8GTSpLliwhLS0Nf39/nn32WdP29evXEx8fT0VFBePGjaNr1+tFwy+++IKWLVsyYcKEBohY\n", "qE86nY6NGzdSXFyMjY0Nbdu2NRtsKAiNnXHMiV4GeddySDtymsLsPAAMap3Fc5py+4klDfY0qamp\n", "qNVqoqKiWLRoESkpKaYp2keOHMno0aMpLy/n008/NSWVixcvotFo6rRuX2g8xowZw5gxYxo6DEGo\n", "FfNpmADcUV9LgyIZqHX0COhI9rZMsy7ETb39xJIGG6dy/vx5OnXqBEBoaChnz5417TN226yoqMDO\n", "zs60fevWrQwdOlTMHisIQqNjaRom12k9sGvZjJ9++omVv6zgwydfI+QABO7XEnIA3q/nIQUNocFK\n", "KqWlpaaum0ql0mzKDoBFixZx+PBhXnvtNQCuXLmCk5OTWZK5WWJiIomJiabfIyIiLI4oNxJzNQn1\n", "TSaT3fJvsL4oFIoGuW9daWrx5+bmcvr8Gejdtsq+bj0fYuzYsQCMfnQkox8dea/Dq5Ube0SGhITU\n", "eNqkBksqSqWSsrIyoHL215uTxQsvvMCkSZOYNWsWoaGh/P777zz55JNcuXKl2mtaevBb9SBpSn+0\n", "QtOk0+lE769aaErxb9u2jffee48CWw3uVE0q1lKrJvMsRg4ODrWeebvBqr+CgoJISEgAICEhwTRz\n", "LFwfyaxQKExVXTk5OcyfP5/ly5ezb98+Tp8+fe+DFgRB+Et+fj6vvvoqU6dOJTs7G821YvJ+PGx2\n", "TENMw9TQGqyk4u/vj0KhIDIyEj8/PwICAli8eDFTp05lyZIlZGRkoNFoGDmysqj4wQcfAJCUlERC\n", "QgLt2rVrqNAFQXjA3DyTcAevNiz/aRnZ2dmmYzycmvFU+EROHUhBJzUg00vqfRqmxkgMfmxixVKh\n", "aRGDH2unscQfExfL10vnc6EsC9sJ16vW8348TPmpa/BXN+GxY8cSFRVlmri0scRfW2Lw4wPqjTfe\n", "uO0xr776qqmrdm3s378fBwcHQkNDzbZHRUXRpUsXRo0aVetr34mtW7eyfft20++2tra4ubkRHBxM\n", "v379atU+FhMTQ6tWrcTgSsEiYxfhdHJwnGDeVus6rQdZn8XiWqbg888/Z+jQoQ0UZeMjkkoT9vrr\n", "r5t+rqioYP78+QwdOtS0pjxULoZ1Nw4cOIC3t3eVpPLCCy/csidefbCxseGll14CoLy8nMuXL7Nv\n", "3z4OHDjAiy++SIsWLe7oert27aJv374iqQhmYuJi+Xr5Qs5eSsXp5YfQ/XKFog1JIJWA3oB1ew+s\n", "A93w8PFi+w+rzVYHFURSadJatWpl+lmtVgPQrFkzs+315eYFte4FqVRq9mzBwcH06dOHuXPnsmzZ\n", "MqZPn17jtVEEwZKYuFhmLPs/NKNaIdmUhfpcDhKJBMfHr39RK1qfBEDXwLYioVggksotHPwjlr1r\n", "VyDX69BKZYQ9MZGe/QY0umveyoEDB9izZw85OTk4ODjQt29f07rwAFevXmXDhg1cunQJrVaLi4sL\n", "ffv2JSwsjLlz55Kenk56ejpHjhwBYOLEifTo0YOoqCg6d+7M448/DsDy5cvJzMzkscceY/369eTm\n", "5uLr60tERAReXtdHEKtUKlatWkViYiK2trb069ePkpISTpw4wUcffXTHz2dra8vIkSP54YcfOHPm\n", "jKkDx6ZNm0hKSiIvLw9bW1sCAgIYPXq0qZosKioKlUrFtm3b2LZtG3C9qnD37t0cO3aM7OxsrKys\n", "aNmyJWPGjBHLBd/HjA3xx84mUCbTIvk1H4NaR2lsKq7Tepgd6zi6ParF8Ux+8+UGirZxE0mlGgf/\n", "iGXf4m/4pO31uvqPFn8DUOskUB/XvJVdu3axefNmBg8eTJs2bbh06RJbtmzBysqKvn37ApWDTL28\n", "vJg8eTJyuZxr166ZSj3jx4/np59+ws3NzVRnbPxgvXnqeolEQn5+Phs3bmTYsGHI5XI2bNjA0qVL\n", "ee+990zH/frrr6SlpfHEE0/g4ODAnj17yMrKuquBqG3atEEqlXLx4kVTUikuLiY8PBxnZ2dKSkrY\n", "vXs38+bN47333kMikfD8888zb948OnfuTM+ePYHrVYUFBQWEhYXh6upKRUUF+/bt4z//+Q8ffvgh\n", "NjY2tY5TaJxunF5F4t4cWWIWjqMrSyZFmywPXWjp7fvA9eqqKZFUqrF37QqzD3+Aj9s68NG6/9U6\n", "AdTHNatTXl5OdHQ0Q4cOZdiwYUDl2CCNRsOOHTsICwujtLSUvLw8XnjhBby9vQEIDAw0XcPLywuF\n", "QoGdnd1tq9QMBgMqlYrXX3/dlHgMBgOLFy8mKysLDw8Prl69SmJiIs8++6xpip6goCBmzpx5V0nF\n", "ysoKOzs7s+WIJ02aZPpZr9fTqlUroqKiSE1NJSAgAF9fX6RSKU5OTlWe7cb5x/R6PYGBgcyYMYOE\n", "hAQeeuihWscpNE43Tq+iTrqeUACoZhEtT2dRaq2OSCrVkOstzygq09V+mc/6uGZ10tLS0Gg0dOrU\n", "yWxlwzZt2rB9+3YKCgpwcnLC2dmZlStX0q9fP9q0aXNXsww0a9bMrIroxm/+Hh4eXLp0CcBs1gMr\n", "KyuCgoJM+2rr5p7xSUlJbN++nczMTFPJCyA7O/u2veEuXLjAli1buHLlCiqVyuxc4f6jQYfpo1Bq\n", "PlmtdXsPitYnmSWa+3ESyLokkko1tFLL35x1stq/svq4ZnVKS0sB+Pzzzy3uLygowMXFhZdeeokt\n", "W7awYsUKNBoN/v7+PPHEE/j6+t7xPW1tbc1+N5Y+tNrKpFlcXIy1tTVyufnz2tvb39UkoRqNBpVK\n", "hb29PQCXLl1i0aJFdOrUiSFDhpi2/+c//zHFUp38/HwWLFiAn58fERERODk5IZPJ+OGHH257rtD0\n", "5Ofnk3L6HPT+azD1TSUT68DKL0nq/56gQ7uQykW0HsABjXdCJJVqhD0xkY8Wf8PHN1RXzThdTNjz\n", "UxvVNaujVCoBmDZtmsXSh3EyT09PT5577jn0ej0pKSls2rSJH3/8kaioqDu+5+0Sg4ODA2q1Gq1W\n", "a5ZYSkpK7mo5g3PnzqHX6/H39wfg5MmTODg48Mwzz5iOycvLq9G1Tp8+jUaj4fnnn0ehUACV83fd\n", "WGIR7g/btm3jjffeptRah9XSMlye6WqxZGJ3upRZb80SiaSGRFKphrGN46N1/0Om06KTyQl7fupd\n", "tX3UxzWr4+fnh5WVFYWFhWbjVqojlUoJDAykf//+/PLLL6hUKpRKJXK53DQX2+3cLjG0bNkSgFOn\n", "TtG5c2egcnzNmTNnqpRyakqlUrFp0ybc3NxM88dpNJoqXYuPHj1a5VxLz2Zcr+fG8+Pj49Hr9bWK\n", "T2h88vPz+eijj1i7eQM2HTzxmNYD9bkcijYmocstw6DVU7DiBKi0+Di4M+Nvb4uEcgdEUrmFnv0G\n", "1PkHfn1c0xKlUsnw4cNZt24d+fn5tG7dGoPBQFZWFikpKUydOpWMjAw2bNhAly5daNasGSqVipiY\n", "GHx8fExJ6xQjAAAgAElEQVQlHQ8PD5KTk0lOTkapVNKsWTPs7OwslkpuV1Lx9vYmJCSEVatWUV5e\n", "joODA7GxsSgUihqVVPR6PRcuXAAqx+UYBz9qNBpeeukl0zWCg4P5448/WLduHSEhIaSlpVlMKh4e\n", "HiQlJdGuXTsUCgWenp4EBQVhMBhYsWIFDz/8MFevXiU2NhZbW1uxjs99wDijcHZ2NlYtnU3dha0D\n", "3UxVXdqliXTyC2kUa8U3RSKp3McGDRqEo6Mje/bsYffu3VhZWeHu7k6XLl0AcHR0xMHBgR07dlBU\n", "VIStrS2BgYGmSTwBhg4dSn5+PkuWLEGtVpvGqdycBG7uYlydSZMmsWrVKtauXYuNjQ1hYWG4ubnd\n", "tqFeIpFQXl7ON99UdsG2sbHB3d2dhx56iL59+5pV8bVv356RI0fyxx9/cODAAfz9/Zk2bRqzZ882\n", "u+aoUaNYvXo1P/zwAxqNxjROZdKkSURHR3Py5El8fHx49tlnWbp0qVhxtAm7ca14I4m15TbOdoHB\n", "99Wa8feamFCyCU/6dj/Q6XR8/vnn+Pn5mXUDvl+ICSVrpy7iNw5ovJaXTWryeUov55omgPTw8MDn\n", "oSCujazaNTjkAHedVJr6+xcTSgpNRnx8PIWFhXh7e1NeXs7BgwfJycnh6aefbujQhPtITFws7y+c\n", "TYGtBqQSJIHOKKwNVJzPY+xjo4mKiuLYqRM3rSkvugvXBZFUhHtKoVBw+PBhcnJy0Ov1NG/enGnT\n", "ppka8QXhbsXExfLKJ+9gaGFfZc6uFq7efPvttwCm9pKft66iwqAV3YXriKj+asJFVKHxE9VftVPb\n", "+Ndv3cT782dTrlFj1dLZbFZhqGyEP746to6jraqpv39R/SUIwgOn6mqMASxcuQR5Wzeaje5qOs44\n", "q7B1oBsSuZjFur6JpCIIQpNiWu/k6gV0Bh0SWytkDtbExR9CbwWuo83HZTmObk/RxiSsA93wcbm7\n", "9YWE2xNJRRCEJsM0o/AID5xoBkDB8nh0xWokVlIoq+ZEiQT5xou8PuXtexfsA0qUBQVBaPRi4mJ5\n", "Zvrf+edXH5j11gJwfqozMkdrXKf1QO5uj/pcTpXzrbMqmDVFjIy/F0RJRRCERismLpavl84npTQT\n", "+0mhaHKtsTihz18DU12mdCXvx8OmhnkA+caL/FvM3XXPPPBJ5W6meq9vMpnMbNr6pkbEL9yNOQvn\n", "snDzL+itJNhJoMXio9iUaan4XwJXOnuhbet+/eAbOrFKnWzI+/EwDkp7Orduz2RRQrmnHuik0ti7\n", "/DX1bokifqG2Ppv7fyyI+RXnv3VHnpxNz8MZLH6otWn/1MOpHAS0bd0pWp+Edcj1KjF9YTl2A1rT\n", "OcdNTLfSABo0qSxZsoS0tDT8/f159tlnTdvXr19PfHw8FRUVjBs3jq5du7Jz5052794NwCOPPEJY\n", "WFgDRS0IQn04+Ecse9euoCQ/l/jUc7iNCkYL+MRnsrhba7NjF3drzYAtCSTEpmI/OMBU3ZW/7BiK\n", "QDfc0/RMjhjfAE8hNFhSSU1NRa1WExUVxaJFi0hJSTGtyDdy5EhGjx5NeXk5n376KV27dqVTp06E\n", "h4ej0+n44IMPRFIRhPvIwT9i2bf4G4Y5aIkpyCPczYH4LedITi9EieWJPJ2aO2LfzQv16SzUydlg\n", "MKAohx7WrZk8Wsww3FAaLKmcP3/etE55aGgoZ8+eNSUV44qBFRUV2NnZAeDuXll/KpVK72o9c0EQ\n", "GpeYuFj+O2s604Pd2Hkpj5m9ri/3/LfYZM7KLScVFeZT1kPdTAYp3J0G61JcWlqKjY0NULn2h3H5\n", "W6NFixbx9ttvM2LECLPtO3bs4KGHHrpncQqCUH9i4mKZsez/sLKTsObsNbOEAvDDgLYo5FKmHk01\n", "2/7sgXOcK1ebbXPcnsXkR0SVV0NrsJKKUqmkrKxypJJKpTKVSIxeeOEFJk2axKxZswgNDQUql42N\n", "j4/nnXfesXjNxMREEhMTTb9HREQ06t5dt6NQKET8Dagpx99UYv91+1oMQUo0iRcpsVFYPEZeVM4u\n", "V2v6bzqOs58rKuBKWAsMMglFG5OwL5HSpU0Hpj77NsMGhN/bB6hGU3n/t7Jy5UrTzyEhIYSEhNTo\n", "vAZLKkFBQezYsYNevXqRkJDAwIEDTfs0Gg1WVlYoFArTant5eXn8/PPPvPvuu9UulmTpwZty752m\n", "3vtIxN9wGmvs3y/4lgO/r0ZXpqJIU0GKQkpbL0eCHGxxtrGyeI5Kr0clkWDTvhPHVVewmVA5DYs1\n", "4J6m5/0p12cWbizP3Fjff005ODgQERFRq3MbLKn4+/ujUCiIjIzEz8+PgIAAFi9ezNSpU1myZAkZ\n", "GRloNBrTKoSrV6+msLCQr776CoDp06ejUFj+ZiMIQuPz/YJvObthOesGBJu2Pbf7NBmFatyUCvr7\n", "ujDzQIp5m0pMEucqtAwP7M3c2V8RExcrpqpv5B7oqe8bu/vh246Iv2E0ltiNkz9ezLpCm8wCdo3q\n", "UuWYERvj6eHuQGSvAPZeyWfX5TxkEgk6g4Ht1woZ/MyLvPnSaw0Qfe01lvdfW2Lqe0EQGh1jI7xm\n", "VCtsaIbbt4csHmdnLeNkYamplBLm4wLA1JjTjJryN15sYgnlQSeSiiAIdcpYOrl87iz+Ugl2C7Mo\n", "rdBRWKS2eHyFVMLZ4YFcW53I8S0nsJZIkDm68uyML+nZb8C9DV64ayKpCIJQZ2LiYnn3v7NRhDgz\n", "6Ko1i3sFmvZNi0ni5Z1JLAi/vt7JzP3nKbC1IvvwZdTurrh37MiER8Yz+tGRTbr66EEmkoogCHXm\n", "6+UL0XR0odW6ZBaP6GS278fB7Xni9xN8fDDF1GZyskDFeYOORzoOZe7srxooaqEuiaQiCEKdmLNw\n", "LolnTyO9osBVZnlctUwhI85OjiJXRUUzJVf6BKE4lCUSyn2k2qTy5JNP1uqCv/32W62DEQSh6YmJ\n", "iyVq3ufkF2bRESl2GgNareUlAzQSuDihA0Ubk3AcVVkNJjladVEtoemqNqn069evyra0tDQuX76M\n", "t7c3Pj4+AFy5coWrV6/SokULWrduXeUcQRDuT8YG+dTcK3All3B7JUuHVs5+sfdKPv/YfZpvB7Yz\n", "HW9sP7l5qnqxbvz9pdqk8sorr5j9fuLECQ4dOsTbb79dZe6tw4cP89133zFlypT6iVIQhEYjJi6W\n", "j2fPwKW8BAephDblGuQyK5YOut4Ab+wW/Ex0AkEudugMBk7kl5JiJcE6xM80CaRYN/7+U+M2ld9+\n", "+43w8HCLkzn26NGD8PBwfvvtNzp27FinAQqC0HjExMXy0RczeKhCxU/DQk3bp2w7VeXYMB8Xvk2+\n", "ymGljMKMQlLt5Wglcuz2Z+F2Hjyd3cSqjPehGieVS5cuMWDAgGr3e3p6sn379rqISRCERiQmLpZl\n", "W1aiQcexw38SolLz0+NdzY4JcLS4cjzZEgMnc4vBRoanrzezRBK579V46ns7Ozvi4+Or3X/ixAmU\n", "SmWdBCUIQuMQExfL7FXzSOot4XjFJXTuNrgpqk78OLilKy/vTDLb9syeZK4NCQADtPdtIxLKA6LG\n", "SSUsLIyjR48yf/580tPT0ev16PV60tPTmT9/PkePHhWrMQrCfSZq3uekq3Io2nQadeI1nJ/qjEan\n", "r3JcmI8LF4vLGZJwkccTLjIwJpF9bV3JO5zOq2Oe4/cffxMJ5QFR4+qvJ598kszMTPbs2cOePXuQ\n", "SivzkV5f+QfWrVs3JkyYUD9RCoJwT81ZOJf5//svuNvi8ldVV96iIwAUoa8ym/DM/eepAI4XlmIo\n", "14JUgkNKCfPfmS2SyQOmxklFoVDwzjvvcOLECY4cOcK1a9eAyraUhx56yLQ0sCAITZOx7eTPpHhK\n", "C4uRyGXIbeTk/XgYpFIMZRoAMrs158yJa2Yj48/kq0hFj8RaRrMXH8Zxexbvi2npH0h3PKK+U6dO\n", "IoEIwn3G2HZSNMQDmXsLrBOzcBx9vYtw0fokpAGuFPxyHOenu3AKKD2VjVJnoKRCx2l0lDvbE+Tp\n", "h+cBxDonD7BaTdOSmZlJQUEBLVq0qLIMsCAITc8n3/8fpU+2AkCdZJ5QABxHt6doYxK2D7cgd+Eh\n", "rHwcyW7thHU7D0q2nWNEtyFiqhUBuMOk8ueff7JkyRKys7MBmDFjBh06dKCgoIAZM2YwadIkevXq\n", "VS+BCoJQd4xVXdeys7iUmY4aHdJf83G8Wkx3DThcPESJXs+FDu6ow9tUniSRYB3ohmrfRRxHVo6U\n", "z196lFfHPNfkFtES6k+Nk0piYiJfffUVfn5+9O/fn9WrV5v2OTs74+npyf79+0VSEYRGzljVle0n\n", "RZ2Vi+OL3bADrHeep29uBfOHX59a5e+xycRxvjKx/LVIrF6loWjTaTSXCvB2dBMJRTBT4y7Fq1ev\n", "pmXLlnz66acMGzasyv6goCDS0tLqNDhBEOre18sXku0npTQ2FcfR7VGfy6FoQxKtjmUyf1A7s2Pn\n", "D2iL/6nsyvm62nlUtq04KNDll2Ed4klgm8Bq7iI8qGpcUklJSSEiIsLUlfhmrq6u5Ofn11lggiDU\n", "re8XfMuO/y3FQaIj4JSeCr0Bl5+OU1Ks5trQABwuWl4US6nVoytRoz6dhSazCIlMht0Af9zT9Ewe\n", "Pf4eP4XQ2NU4qRgMBqysqo6kNSouLkYuF8uzCEJj9P2Cbzn063/poLSmRAO5eh0GAzQvrsBDaUPC\n", "rjRKK7QWzy3RG6Bci7POFvfmbXB0c0aRI2dyxHjRw0uoosZZoHnz5pw+fdpi1RfAsWPH8PPzq6u4\n", "BEGoA6+9/xb7d23FXwvdPB35d98g9l7JZ+elPPPBiwdS2F5Uzt9jk5k/oK1p+8sxp1G5e/H9G9NF\n", "AhFqpMZtKoMHD+bgwYPs2rXLbHt5eTmLFy/m7NmzDB48uM4DFAShdiKmPc3+PdsY6miHi7UV/+4b\n", "BEDMTQkFYGavADxsrIgLdmHUjlNM2HqS8dtO027MZNat3SYSilBjNS6pDBkyhOTkZL7//nuWLl0K\n", "wDfffENxcTEGg4EBAwZYXNhLEIR7KyYulk/mfcHFzMv0lsiYP6gdk7eeNO2XSyUWz1NIpajD23Dg\n", "SiF/D39K9OoSaqXGSUUikfCPf/yDnj178scff3DlyhUA2rRpQ//+/enZs+cd33zJkiWkpaXh7+/P\n", "s88+a9q+fv164uPjqaioYNy4cXTt2pWysjK++eYbSktLGTJkiEhggnCTmLhYZkT9C3e1Ch+pFB8N\n", "yKicm0+jM5iO0+oNFs/P1WrJ/W4/I7oNEglFqLU7blnv0aMHPXr0uOsbp6amolariYqKYtGiRaSk\n", "pBAQUFkkHzlyJKNHj6a8vJxPP/2Url27EhMTQ1hYGL179yYqKorevXuLjgGC8Jc5C+fy068/MlBp\n", "zdLwzqbtL+9MYu+VfAa2qJyafkF4ewa3dK0yIeTU/ec4p5AyottAMTJeuCs1blOJiooiISGh2v2n\n", "Tp0iKiqqxjc+f/68aQ6x0NBQzp49a9onk8kAqKioME0Dc+7cOTp27IhUKqVVq1ZkZGTU+F6CcD8b\n", "EvEY321cgr8elg4wH2eyILw98+Iv8WKnFnR2d2DC5hPMj7/M4cwCRmw4zmNbTzJg20m255fw3Jjn\n", "REIR7lqNv+onJSXdsiG+sLCQpKSkavffrLS0FA8PDwCUSiWXL182279o0SIOHz7Ma69VFsNVKpVp\n", "ETClUklpaWmN7yUI95OYuFi+XjqflKuXKNeqQa0FGyusDAaiDqQgl0rQ6g0MbulKmI8LrjZWPBOd\n", "gBYoAZLlekqCPNAVlaPs1RKrE/l8+8L7ojFeqBN1Vn+kUqnuqDpKqVRSVlZmOvfmiSlfeOEFJk2a\n", "xKxZswgNDcXW1haVSoWjoyNlZWUWJ7JMTEwkMTHR9HtERAQODg61fKKGp1AoRPwNqDHGvy12J2/N\n", "iaRYo0Lu44CsQIIuuxTXCh0BSmsib+omDOBpZ828ngEMjEkkdVQQJdFnMaTkIJHJaJ1qxbv/+IRh\n", "A8Ib6pEsaozv/k409fgBVq5cafo5JCSEkJCQGp13yyxw4cIFLl68iOGvOX9Onz6NTqerclxxcTHb\n", "t2/H19e3xgEHBQWxY8cOevXqRUJCAgMHDjTt02g0WFlZoVAoTPcOCgoiISGBXr16ceHCBXx8fKpc\n", "09KDFxdbHiXcFDg4OIj4G1BjjH/2gq/QVZTSSSHHJq2QUo2OTFUF7a0UfD/SfGbhmb0CmLI1gb91\n", "9OW5P1O5NjQAdWIWEjsFdvb2/OeNT0ylk8b2nI3x3d+J+yH+iIiIWp17y6Ry+PBh1qxZY/p9586d\n", "7Ny50+KxNjY2PPfcczW+sb+/PwqFgsjISPz8/AgICGDx4sVMnTqVJUuWkJGRgUajYeTIkUDlOJlv\n", "vvmG6OhowsPDTe0ugvAgMFZ5pZ09zWBra5b+NeYEKhvjy7RVv+wBlOv1zEjOIM1ZQfHO8xgMBuzl\n", "Nvxn+ieiukuoFxKDsShgQVZWlmma+48//pgxY8YQGhpqfgGJBBsbG3x9fVEoFPUbbS005Qb9++Hb\n", "joi/doxT02vQUZRfSFr6RQwlJXRVQ08vJ7M2E4ApWxNY9kholesM2nOa9L9X9tYsWHEC54mdCNyv\n", "5dfPvr+nz3OnxN9Ow2revHmtz71lScXDw8PUmP7yyy/Tvn170++CINSPG1dhBDnqcwb0xwsY6mTH\n", "kuHXq7iMbSZhPi44WcurdBN+ISaJzKGtAShYlYBt98oqY4VEdMUX6k+N/7rCwsKoqKiodr9KpUKh\n", "UIixI4Jwl5ZtWUnREA/U53Io3pyMRCYl1FrBkkFV20w+PphCmI8L7koFg1q48vHBFNKLy8kp0xCv\n", "01K4OwWrs9nYdvbGOtANx+1ZTI54pYGeTHgQ1Hicys8//8z06dOr3T99+nSWL19eJ0EJwoMs5UIa\n", "qq/j8Ft7mp4GGR0NUpxsLH9ZK6nQMnP/eQa1qKwK+6hnAAVqLfE6LT4dQ/lx+lf0cm9Ph2xnQg7A\n", "+2LteKGe1bhYceLEiVuOpH/44Yc5cuQIzzzzTJ0EJggPotfef4u8y5d5WCanUzM705iT+Kwii8ef\n", "LyjjzW6tTG0rL+xM4pjcgI1/K96Y8jKD+w4QSUS4p2qcVHJzc/Hy8qp2v4eHBzk5OXUSlCA8CG5s\n", "jM+4fIUrVzOQF5YSIpHRsbmd2ZiTiE3xVdpMZu4/j1Zv4KNjF7BJTEdlMHBBo6dFu3a8/9Kb9O72\n", "cEM8lvCAq3FSkcvlt1zZsbCwsNpVIQVBMGdsjFe1kOC9/xLNskrx1YNUrkAmAZVGS9SBFFMPrxA3\n", "Bwa3rGwzkUkk6AwGwls1Y59GQ5KDgkAnX1PJBJp+7yOh6apxUmnVqhUHDhxg9OjRVRrjtVot+/fv\n", "p2XLlnUeoCDcj5ZtWUmBrJiw7Tn0cnNgj7QcXydrMksr+FtHX1N1lrGH1+CWrvxy+ioLw6831k/Z\n", "mUiG3Jr5r30qqriERqPGRYvhw4eTnp7OZ599xvnz59FqtWi1Ws6fP89nn31Geno6w4cPr89YBeG+\n", "kZZwCv99l1Ea4Hh2Eb+O6MgX/YJZ9kgoOy/lsfdKZa3AzF4B7LqcR5iPC5eLyxm04RiPbj3JgE3x\n", "2HXvz/7o/SKhCI3KLQc/3mzFihWsX7++8kSJBIlEgl5fuV7D448/zqRJk+onyrsgBj82HBG/uZi4\n", "WKLm/pucK+n0lEjxtbPB1cbKrO3E6OODKXzUs3L7p4dS0ej0hLdqxgfHL2IIDWHTghX3NPZ7TcTf\n", "sOpt8OPNJk6cyEMPPURcXByZmZkAeHt7ExYWRps2bWodhCDcz2LiYvl6+ULSEk4RhISWWpDIwVom\n", "4UJhmcVzZJLrqzOezS9lWmhllZjdycu8MOnFexW6INyxOx6p2KZNG5FABKGG5iycy7w1i7Eu0zLE\n", "1ppe7o4k5pXw7cDKdU+i/mozuZnurwqEmfvPmxIKgJ29i6juEho1MfxdEOrYnIVzWb5zLWXqclSF\n", "xSg1erpI5ThIpcSm57FiRCfTsZZWYXxpRyJXSyt4fMNx3unuZ0oorx28zAvvRd7z5xGEO1FtUlm1\n", "ahUSiYQnnngCqVRq+v12xo0bV6cBCkJTMmfhXBZs/gWpp5KyhAIcrOX0sLKik4s96cXlKGRS9l7J\n", "NyUK4/8+G51AoIsdOoOBKyVqErVa9C5KPkvOIajIGgdXNx57M5Ke/QY04NMJwu1Vm1RWr14NwOjR\n", "o5FKpabfb0ckFeFBZBzIuPfwfiSutsjO5NBFaYO1WktHd3uLi2fdmFh2Xc7jg4crJ38csOUEPh1D\n", "zMadCEJTUW1SmTt3buUBf41JMf4uCIK5mLhYPvj+M7IKcjBIwPlyIZ2kMtyspOQiMavaAvOJIAH+\n", "ses0EcGVs1VMiUmiXe/B/Pvfc+75cwhCXag2qdw8xb2Y8l4QLIua9znXsit7QyoNMEhpw6KhHYDK\n", "7sCWpBeX8+mhVE7kFJNepiE19Rolpy7j4t+BuSKhCE2YmFdFEGopJi6W7iP7cznnKgatHnkzO0Ik\n", "MlNCAdDqLQ8Dy9bp2ZFTxD69jtNKGQkyBf2ffJ4li3+5V+ELQr24bUP9nRJtKsKDYM7CuXz780Ks\n", "mjvh1S8Ij9/P4KwB2wrzZX0t9e6auv8cR60lFBZoaB7YkrkvThdtJ8J947YN9XdKJBXhfvevf73J\n", "6UO76CmTo75agvPvZ1n7SGU34YjfT5gda2w3eSY6AbXOQKbEwDm9HpXUgF8rPyJffE8kFOG+ctuG\n", "eqPy8nLmzZuHTCZjxIgR+PhULk2anp7O5s2b0ev1vPrqq/UbrSA0oJi4WGa+/Rq97BXEDuto2v7P\n", "3cn8fWcSk9p5o9Hp+VfcWf7dN8i0f+fFXNRaHX/KIVutxdvHm7n/nCGSiXBfqvHcX4sXLyYlJYWo\n", "qCiLsxRHRkYSEBDA1KlT6yXQ2hJzfzWc+yn+IRGPUZJ8hnb2tqwZ1bnKsR8fTEFvAI1Ox6Xictxs\n", "FZRqdFTo9OSWaUjVasmwlfGPp1/izZdeu6exN0Ui/oZ1N3N/1bih/sCBA/Tp08fiGvRyuZzevXtz\n", "8ODBWgciCI3RnIVz8Q/rQPbZs/TzcKSrp6PF42SSyq7D1nIZYc1dSC8uB6BIoyPVQ4m6Q0v++9m8\n", "e5JQBKEh1XialrKyMlQqVbX7VSoVpaWldRKUIDS0mLhYPpn3BVkXLhAqkeJpb4tKo+dgRgFRB1JM\n", "y/waF9EyztUlk0j4M7+UfIOBLLWGAhdngkJDmfzIeFHdJTwQapxU/P392bZtG2FhYVWWFb569Srb\n", "tm2jdevWdR6gINxrcxbOZf7aJShUasIdlUwL9ibmUh45ZRUo5dIqo+OXJ2XwVPvK6oKDucWcsJNT\n", "qKnAx7M5B37f3VCPIQgNosZtKsnJyXzyyScAdO/e3ayh/s8//0QikfDhhx/Srl27Gt98yZIlpKWl\n", "4e/vz7PPPmvavmrVKk6cqOxFM2HCBDp06MDVq1dZsGABBoOBDh068OSTT9boHqJNpeE0pfiNk0Dq\n", "84toWaFFLgM7tR5fe2uKK3T8raMvMZfyLK598kZsMl8PaMuUnYnEVFSgwoCzwoH4nQca4EkqNaV3\n", "b4mIv2Hdk/VU2rZty8yZM1m6dGmVtpPAwECmTJlCUFBQNWdXlZqailqtJioqikWLFpGSkkJAQOU/\n", "2P79+zN+/HhUKhWff/45HTp0YNu2bUyaNIm2bdsya9YsVCoVSqWyxvcThOrMWTiX79YsxkatJVyh\n", "YFrnluy8lGc2tmTmgRRUGq3F87PLKhi07ihnZAZkzk78Y9Qk0XYiPLDuaOr7wMBAZs2aRWFhIdeu\n", "XQMqp29xdna+4xufP3+eTp0q+/aHhoZy9uxZU1IxTgkjl8tNAzAdHR0pLS01rTRpZWV1x/cUhJvN\n", "WTiXb5d/j9TBmrblUpaGhxB102BFqJyva8rWBIvXyFBrOe1qz7z3PxftJsIDr1brqTg5OeHk5HRX\n", "Ny4tLTUlD6VSyeXLl6scs3LlSoYMGQLAwIEDmTFjBkuXLiUsLEwkFaHWjDMKJyQkUKTQILW3xqZI\n", "TXN55d+UXGp5Jgkna3mV0fHP7kwkz8WVee9/LBKKIHCHSUWn0xEXF8fJkycpLCzk6aefxt/fn5KS\n", "Eo4ePUpoaCiurq41upZSqaSsrHIpVZVKhZ2dndn+w4cPU1paSp8+fQBYsWIFb775Jv7+/nz11Vdk\n", "Z2fj7u5udk5iYiKJiYmm3yMiInBwcLiTR2xUFAqFiL+ObYvdyYc/fk6OpgidQY0UOfpSNYEKK9o4\n", "VFanVjdfl7tSwaAWrjz5+wm0BgP5egMhQx7l2Nfz7+Uj1EhjfPd3QsTf8FauXGn6OSQkhJCQkBqd\n", "V+OkolarmTVrFmfPnkWhUFBRUWHqQmxra8uvv/7KgAEDmDhxYo2uFxQUxI4dO+jVqxcJCQkMHDjQ\n", "tO/ixYts27aN6dOnm7YZE49EIkGpVFJeXl7lmpYevCk3ljX1xr7GGP/sBV+RmXEVibUciUyCvkQN\n", "gJO3I4NdHXklJomJbb2rlEhe3pmEXCph1+U8MtQaJN078fqkFxncd0Cje0ZonO/+Toj4G5aDgwMR\n", "ERG1OrfGgx9XrVpFamoqb731FvPmzTPbJ5PJeOihhzh58mSNb+zv749CoSAyMhKZTEZAQACLFy8G\n", "4JdffqGoqIhPP/2UL7/8EqhcLGzu3LlERkYil8tp0aJFje8lCFDZfpKQdAp5MyXN2zSjV6mOoQYZ\n", "fXRSstPyCPNxIaNEza7LeeSWVfBMdALv/nGGjw+mIJXA3EHtyNRK+fCr+WxasEJUdwmCBTUuqRw4\n", "cIDBgwfTo0cPioqKquz38vLiwIE760J5YzdiwDTFywcffFDl2DZt2jBr1qw7ur4gGEVMe5ojqQnI\n", "7K3x9HKg75l85g+5PkX932OSeDT6JI/7uZGYV8LcQde7xr+4I5G0EjXj4y7zwnufiCV9BeEWapxU\n", "8p+ZEz0AACAASURBVPPz8fPzq3a/tbW1qY1EEBqLmLhYPvn+/8hKSaWjjQKlARRHrzL/MfP5u+YP\n", "bs+o6JOslurRarSM3hiPjUJGiVZHMgaeeu5F0U1YEGqgxknF3t6evLy8avenp6fj4uJSJ0EJwt36\n", "fsG3bFu+GGs52BZXMMLFlh/CK9vbqluNUSmRcNhOhsZail6vRSLV07KFL1/8/V1R1SUINVTjNpXQ\n", "0FB2795tsYE8KyuL3bt307lz1dlbBeFe+9e/3iRx7TKiurTATy6nlVJhSihQfe+ukgodmsuF6ArL\n", "cXdoxvIvf2DPii0ioQjCHahxUhk3bhwlJSVMnz6d7du3AxAfH8/y5ct59913kcvljBkzpt4CFYSa\n", "iImLJWnPDqa19WbnpTw87azp4GZvdoxxNcYbvRyTRJK6ApmnPf4BrTmyKZZhA8LvZeiCcF+ocfWX\n", "t7c3kZGRLFiwgFWrVgGwadMmAFq0aMGrr76Km5tb/UQpCLcxZ+Fclq9aio+qHG9rK2L+mmbl00Op\n", "VUomN67GqNMbKNDqSbaTUdLOHTcrB2Y8/1ZDPIIg3BfuaPBj69at+fLLL7l06RLp6elAZbLx9/ev\n", "l+AEoSYipj3N6UtJDLa35qch7U1T0wOm6en/sfs03w683qNr58VcpoX68u/4SySMa4d1oBuOS5OY\n", "9be3RXWXINyFGiWVsrIy3nnnHR555BFGjBhBy5YtadmyZX3HJggWxcTF8vXS+Zy7lIqsUIUfErrL\n", "ZMg1Et7YnUxbVzv2ZRQAlVVdOy/lEeJqz5ToBIJd7NAZDIS3asbys9e42NWrMqFsz+L9f0aKhCII\n", "d6lGScXW1paSkhJsbGzqOx5BuKWYuFg++P4zruVkY1umoae1FaEu9maj39+MTaaFgzWvxCQxb3B7\n", "AHZdzqNEreV4VhFyqYTDWUWck0AzjT9tDsDkiFdEQhGEOlDj6q/AwEBSUlIYPHhwfcYjCLf03heR\n", "5JYVgsFAsA46uttXWeNkzoC2vBGbjMFQuXa87K+Zrt/s7keYjwuDohPo+9QLLBfjTgShztU4qUya\n", "NImPP/6YNm3aMHDgQNOU9IJwr0RMe5qy4nw6SmTYyWU4yLTVzihcqtGRo9dRVibhpxvaUl7cm8rw\n", "J5/jRZFQBKFe1DipLFu27P/bu/e4KOu8/+OvGYYBxgGFFDkkSSzYCoqbZZmuaIqr1rbtL0Xu20dp\n", "1m57d9i1bXMzQ8NS071tM8ukXLJW1wOVu6XlCUTFVFy9VQRaOeiooIKSHGZgYJjr94cxOYHmKMxB\n", "Ps+/9DrMvGf04sP3ur4H9Ho9aWlprFq1ipCQELRabavjZs+e3a4BhQBITHqQ8rMGRvr58tF3RSJ1\n", "T8kVx5ycb2hiv68axWxm+MZDRPcMJ7TXbfy/6a/LNCtCdKBrLioVFRUAtm7DFy9e7JhEQlzmzWVL\n", "WPJxGl6hen4W1IWP7v1+ddGREUGsKjzTakbh323NpyrQjwY/FdYKE7+f97Y8LxHCSa6pqFRXV/OH\n", "P/yBgIAAQkJCOjqT6MRaFtA6V1lByfHjNGsUVP5aetwbQchm+wGLLeNN/nrgBFM25aH1UtPF24sT\n", "tQ0UNalprlPx3H/9RgqKEE501aJitVpZvnw5mZmZtm0xMTG8+OKLBAQEdHg40blk7spmXsa7VPZW\n", "Y/zmFJreAXR7uC81XxQSfugsUV1a9z4cGh5I1qkqZt17qaXy5NZ8jtFMj/DbmCVzdgnhdFedpmXT\n", "pk1kZmYSGBjIoEGDiIiI4NixY6SlpTkrn+hE/vrRUk6bzlO3uQjNLToCHr7UHRirgg5Vm9OrPJNZ\n", "yDmjmbn7Snlw42G2aCEg6naZs0sIF7lqS2Xnzp2EhYUxb948/Pz8UBSFtLQ0duzYgdFobLUEsBDX\n", "681lS8g//h90CtxhgS6n62hck0fZgBB8+gZTs7GIof1uA77vJtysKIDCuyP7Mnl7IUd7+uGl9mPW\n", "09Nd+2GE6MSu2lIpLy9n+PDh+Pn5AaBSqRg7dixWq5UzZ844JaC4+b25bAkfL1vCnTVN/FLtTfa4\n", "eDYO/ylb+/Xm3txyujQrnOofzG8yCxgaHsise6OYec/tnDc1olapeOiLQ5SFR/CzmIEsek5GxQvh\n", "SldtqZjNZoKCguy2tayZ0tYU+EJcq707s/nX35Zy+lQpldV1jO0RQE+dT6uBjOkDbyfxkAFDchzb\n", "886RtOkI8YF6mhWFpD4hrCg+zzPz/yLdhIVwEz869b0MchTtbe/ObD6d/zJLf+rL56P7Eq33Y+nI\n", "vlccyOh9tpaLqw/TNC6Gow/EsPVCDfvVCnOLKol56L+koAjhRn60S/HBgwftxqS0tFD27NnDiRMn\n", "Wh3/4IMPtl86cVNpWdo3oOQEO351p227r+bS7zZXGsjYFOJPt+RL68lXrjiAOiSEwJ9E8/jYCXKr\n", "Swg386NFZffu3ezevbvV9m3btrV5vBQV0ZbMXdlMeysFo8nEz9Qq2/T0FqtCjdkCfL941uUDGR/b\n", "lk+hpYn6tH1oLPD0w5NlrXgh3NhVi8qsWbOclUPcxN5ctoQlK9NQBfgQZFWI8rN/dvLklqN2MwrP\n", "2VtCyUUT5U0WClQKPiHdiQzsyfOP/Y+0TIRwcypFUdq+53CTKC8vd3WE6+bv709tba2rY1y3rw/s\n", "46W/vMrJ8lOo1CoCbunC3VVmMn7Rr9WxY/95EG9fDV28NdTWN1LQZOGCSqF/v/5s+GCtC9J79vfv\n", "ydlB8rtaWFjYdZ/r0MqPQlyLlkW0vjl7gmZjA5qe/ijmZqJ9tQzo1vZ/OS9fDXtu8aW5ugHFS0Gx\n", "gl4fwPOP/Y+T0wshboRLi8qKFSs4fvw4kZGRTJkyxbY9IyODw4cPA5CcnExcXBxWq5WVK1diMBjQ\n", "6/U8//zzLkotriZzVzavLn6VwLNVDPLzxqTx5tygXlTuOYkO1RUfxpvDu4LZTLOxEW2AL2E9b2X2\n", "c3+W211CeBiXFZXS0lLMZjOpqaksX76ckpISoqIu3WdPSEhgwoQJmEwmFixYQFxcHHv37uXWW2/l\n", "sccec1VkcQ3mvDGLn5mNfPTgANu2qbmlbGu0YPLTtvkw/slt+RSYG+nWNYgFC5dKIRHCg/3oOJWO\n", "UlxcTHx8PAD9+vXj2LFjtn3BwcEAaDQa2ziZgwcPcvr0aVJTU+0muBTuIXNXNv1HD6ZbzUU+GhVr\n", "ty994O1Ee3tzrM5M+tlvGRURxJy9JczdV8qEjYf52tzMknlL+PeGHVJQhPBwLisqRqPRtua9TqfD\n", "aDS2OmbdunUkJiYCl6bfDw8PJyUlhZycHKqrq52aV1zZm8uWMC3l90Sa6wnReJG6p4Scsm/tjtHr\n", "tJDQmyzFwit5J9l70cjWyhrKeoby+ry3pJgIcZNw2e0vnU5HfX09ACaTqdXklLm5uRiNRoYMGWI7\n", "vm/fvqjVaqKjozl79ixdu3a1Oyc/P5/8/Hzb35OSkvD39+/gT9JxtFqtW+ffnL2NaakvUd9Yw6ge\n", "AaQPvN22r2U24ZY1T2oq6jBml2JtaOIIVl6Y8iwznvuTS3JfK3f//q/Gk7OD5HcH69ats/05NjaW\n", "2NjYqxz9PZcVlZiYGLZu3crgwYPJy8tjxIgRtn0Gg4HNmzczY8YM27Y+ffpgMBgIDQ3l5MmTjBkz\n", "ptVrtvXBPblbnzt3S3xz2RKWbVyJpdnCnbd0Ib1fb7v9rw6OYs7eEoaGBzJ55zcUNTWi+GjR9gok\n", "Wh3Ms1OectvP1sKdv/8f48nZQfK7mr+/P0lJSdd1rstuf0VGRqLVapk9ezZeXl5ERUWRnp4OwMqV\n", "K6mpqWHu3LksXLgQgPvvv5+cnBxSUlKIjo5uNdGl6HiZu7K568FhxA7rx45/LGdgjZl+pmbU1eY2\n", "jy80mhmRmc92pRmTtxdqnZZAs1a6CQtxE5PBj27MnX7bydyVzXOv/RkvLwuj9H52t7p+m/0Nj0X3\n", "tN3qajF88xEOK80AeHl70/cnfZj23095zPMTd/r+HeXJ2UHyu5oMfhQdInNXNn9dtYySMydpMJpQ\n", "mpq569bAVre63h9+B0mb8+yKyuSsQorMjQSGBRMuU6wI0WlIURFtSnvvbbZ9tpKgYD989RrKhsZQ\n", "mXsK33pLm8fXe6kYkZWPTq3CWN/E2W7+LJ//DvcNvMfJyYUQriRFRbSyd2c2RZ+vZsvIO2zbHs8p\n", "Yt/QXtRtKWnznIae/pQlx/HtRwfwVtS886fX+cXwUR59C0AI4TgpKsKm5XaXd+F/yBprP+njh4Oj\n", "Gfl1EUUqhakHSu2eqUzeXkhRgIa6D3LxsWpY8spCudUlRCclRUXYFs+6cPY0USovglWQuqeEkRFB\n", "ds9JfOubaOiuI6vRYrvVVVdrpsRHRX1tM919uzL/RVkjXojOTIpKJ5f23tts+fRjgn29uFuj5f3h\n", "39/y+uEARmNDE353RaGN7k4Z8O2KAzTXNZBw7zAelVUYhRBIUenU9u7MpuiLNWwb1ZfUPSXMHhpl\n", "t//yAYyP/7uUUh8vjNuKqdtajGJpxrd/KEP7DGTFvHdd9AmEEO7GZYMfhev9629L+WDYTwDQqFVt\n", "HlNoNJOYZyDTWE99Vx8AVDoN3X8/hLBGPY+OneC0vEII9yctlU4o7b23yfpkJd0sjaRe7MLIiKAr\n", "rnNyxtrMNzo1jWWN0NSMtb4R31tDsH5UwMt/kOcnQgh7UlQ6mbT33uabz//BpkT7ZyehOm2rdU6m\n", "7CmiYkw0zbmnUAC/+FD0w28nYEsFL//hGSkoQohWpKh0Im8uW8KOf/yN7Afi7ba3PDsZFRHEhC8P\n", "U69AnRpKNCqsO04xtv991EY30KhY0O6BR5OkoAgh2iZFpZN4c9kSlmWu5t6Ibm3uLzSa2VVVQ9kv\n", "+3BuaxGoVTxw5wiWzFvk5KRCCE8mReUmlrkrm9Qlb1Becx6rtZlbnrsP05q8No89H9wFw3cj4q01\n", "Zn4/5X/44++ec3JiIYSnk6Jyk8rclc0L/5uC0c9K4DP3UPNFIQBlA0KYmms/Iv6xrAK+sVowLt5N\n", "iG8ge/budVVsIYSHk6JyE8rclc0zr75Ao5eVnkOjCV+Th/aCicY1eZQNCGHvoDASDxnQAdWnL3L6\n", "zlAaTlcTpvJn97otro4vhPBgUlRuMm8uW8I7a5bjFdwFf0Xh3txyu1bJ1NxS9g4Ku3Sra9X/0axV\n", "oTlXh25wBNHnu7swuRDiZiBF5SaQuSubj79cx7nKCorOnABfL9TdfOn9nwukj7Xv6ZU+8HaGf3mE\n", "b45VYjU24tv/+27CjybJQEYhxI2RouLh3ly2hLR/foxFq0KxWkFR0I+Oxie6O/7n/6/Nc3SKitAG\n", "PfqQYAK03aSbsBCi3UhR8VCZu7L560dLKTx7HE24nqCH+9r2VWdc6uHV4Nf2P29k72hWr1jnlJxC\n", "iM5F5v7yQC+99EfeSXke/9Ji+pmtBN3Rw25/1wn9MBdWXOrpdaDUbl9KYS0PTX3amXGFEJ2ItFQ8\n", "SOaubP53yQKiq8+TNeb7RbSm5payF7BcXlxUKix39GAvMPzzQ/TqdguRP+nD0Cemcu+w4c6OLoTo\n", "JKSoeIjMXdnMy3iXQG8jH434qd2+9IG3k3jIgOHyoqJcmiCy+sBZJkz6jQxkFEI4hRQVD5C5K5tp\n", "82fQGOZLzwv1bR6ju+zP3358EG0DxO6BR5+ZIw/ghRBOI0XFzWXuymZm2nwaAtR0+1VfGq8wzUr1\n", "qW+p+tt+1DotusER3Hm+uyyeJYRwOpcWlRUrVnD8+HEiIyOZMmWKbXtGRgaHDx8GIDk5mbi4ONu+\n", "hQsXEhERQXJysrPjOk3LuBOrFxwtyKfOx0K3SQOAtqdZmby9EEPvbgRNuPScRcacCCFcxWVFpbS0\n", "FLPZTGpqKsuXL6ekpISoqEtreSQkJDBhwgRMJhMLFiywFRWDwUBTUxMqVdurFN4MWp6d1CQGA6C5\n", "Jw7lwwO2/S0P3xMPGdCerQW1DxEDf84AtZnGry1oVRoZcyKEcBmXFZXi4mLi4y+N9u7Xrx/Hjh2z\n", "FZXg4O9+oGo0dgXkq6++YvTo0ZSUlDg/cAdqaZk00cx/io7RdF8wPpft9wryszveckcPDHf0IOTL\n", "Kr54b7VzwwohxFW4bJyK0WjE19cXAJ1Oh9FobHXMunXrSExMBKCsrIyuXbvSpUsXp+bsaC0tk4L7\n", "VBTdp0E9uS/m/ArMRedtx/j0DebiqkN252k+NzDtv59ydlwhhLgql7VUdDod9fWXejKZTKZWxSI3\n", "Nxej0ciQIUMA2LhxI0lJSZSVlV3xNfPz88nPz7f9PSkpCX9//w5I337+seUz262uFgEP96Xm8wJ8\n", "oi9N8OgT3R0l8xTNHxeg0qjpdUso03+Xwi+Gj3JF5Gum1Wrd/vu/Gk/O78nZQfK7g3Xrvp91IzY2\n", "ltjY2Gs6z2VFJSYmhq1btzJ48GDy8vIYMWKEbZ/BYGDz5s3MmDHDtq2yspKlS5dSV1dHbW0t8fHx\n", "/PSn9uM12vrgtbW1HftBblC9xUyb/wyX3fYL2FLBGy/Na/WcxN0/m7+/v9tnvBpPzu/J2UHyu5q/\n", "vz9JSUnXda7LikpkZCRarZbZs2fTu3dvoqKiSE9PZ+rUqaxcuZKamhrmzp2Ln58f06dPZ+bMmQAU\n", "FBSQl5fXqqB4oqqqKooLj8F9fVvt012wcsc+BS+rSh68CyE8hkpRvht6fZMqLy93dYQ2bdq0iZde\n", "eonKmip843oS9JtBtn0BWyp4OekZHh73S4//bUfyu4YnZwfJ72phYWHXfa4MfnSyqqoqZs2axfr1\n", "623bGo6eQ51eyG0/iUTn7SstEyGEx5Ki4kS21kllpW1bz549eeONNxg9erQLkwkhRPuQouIEbbVO\n", "AMaPH09qairdunVzUTIhhGhfUlQ6mLROhBCdiRSVDiKtEyFEZyRFpQNI60QI0VlJUWlH0joRQnR2\n", "UlTaibROhBBCisp1a5lZ2GQxYygqpexICZibbfsfeeQR5syZI60TIUSnIkXlOtiveeILw/ri+0Ed\n", "DUfPEdz1FhYsWCCtEyFEpyRF5Tr87V+rWs0sHPSbQajTC9mU/imBgYEuSiaEEK7lsvVUPNWmTZv4\n", "9+GDbe6LuiNaCooQolOTonKNqqqqePbZZ3niiSdorGto8xitShp+QojOTYrKNThz5gz333+/ratw\n", "07laqtPtWysBWyp4dOwEV8QTQgi3Ib9aX4OQkBAGDhzIpk2bABj/y18zcuxoPtv5JY2KBa1KIzML\n", "CyEEUlSuiUqlYv78+RgMBqZPn27r2fXQmAdcnEwIIdyLFJVrFBwczNatW1FdtsyvEEIIe/JMxQFS\n", "UIQQ4uqkqAghhGg3UlSEEEK0GykqQggh2o0UFSGEEO1GiooQQoh2I0VFCCFEu3HpOJUVK1Zw/Phx\n", "IiMjmTJlim17RkYGhw8fBiA5OZm4uDi2bdvG9u3bARg7dixDhw51RWQhhBBX4bKiUlpaitlsJjU1\n", "leXLl1NSUkJUVBQACQkJTJgwAZPJxIIFC4iLiyM+Pp5Ro0bR3NzMzJkzpagIIYQbctntr+LiYuLj\n", "4wHo168fx44ds+0LDr60VolGo7ENOOzRowcAarUaLy8vJ6cVQghxLVxWVIxGI76+vgDodDqMRmOr\n", "Y9atW0diYqLdtq1bt3L33Xc7JaMQQgjHuOz2l06no76+HgCTyUSXLl3s9ufm5mI0GhkyZIhtW1FR\n", "EYcOHeLFF19s8zXz8/PJz8+3/T0pKYmwsLAOSO88/v7+ro5wQyS/63hydpD8rrZu3Trbn2NjY4mN\n", "jb22ExUXKS0tVdLS0hRFUZQPPvhAKS4utu07ceKEMmfOHKWpqcm27cKFC0pKSopSW1t7ze+xdu3a\n", "9gvsApLftTw5vydnVxTJ72o3kt9lt78iIyPRarXMnj0bLy8voqKiSE9PB2DlypXU1NQwd+5c/vKX\n", "vwDwySefUF1dzaJFi0hNTaWxsdFV0YUQQlyBS7sUX96NGGDq1KkAzJw5s9Wxv/3tb50RSQghxA24\n", "qQc/XvM9QDcl+V3Lk/N7cnaQ/K52I/lViqIo7ZhFCCFEJ3ZTt1SEEEI4lxQVIYQQ7cbj16j35PnD\n", "HMneYuHChURERJCcnOzsuK04kt9qtbJy5UoMBgN6vZ7nn3/eRam/50j+M2fO8N5776EoCnFxcUyc\n", "ONFFqb93pfz//Oc/OXToEI2NjYwfP54777yT+vp6Fi9ejNFoJDExkWHDhrku+Hccye9u1y44lr+F\n", "u1y/jmR3+Nptr37NrlBSUqIsW7ZMUZTWY13OnTunKIqiGI1GZdasWYqiKEpFRYWiKIpisViUP//5\n", "z05Oa8/R7IpyafzO66+/rqxZs8a5YdvgaP7du3crmZmZzg96BY7m//DDD5XCwkJFURTltddeU4xG\n", "o5MT27tafovFoiiKotTX1yuvvPKKoiiK8sUXXyi7du1SmpublVmzZtmNAXMFR/O707WrKI7nVxT3\n", "uX4dze7otevRt788ef4wR7MDfPXVV4wePRrFDfpWOJr/4MGDnD59mtTUVDIzM50f+AcczR8QEIDR\n", "aMRqtQLg7e3t5MT2rpa/5f92Y2OjbaaKoqIi+vfvj1qt5rbbbqO8vNz5oS/jaH53unbB8fzgPtev\n", "o9kdvXY9uqh48vxhjmYvKyuja9euraazcRVH81dXVxMeHk5KSgo5OTlUV1c7Ne8POZp/xIgRfPjh\n", "h0ybNo2YmBiXF5Ufy798+XL+9Kc/8cADDwCXpkLS6XRXPN7ZHM3fwh2uXXA8vztdv45md/Ta9eii\n", "ciPzh/3qV79yatYfcjT7xo0bGTt2rMt/y2nhaH6dTkffvn1Rq9VER0dz9uxZp2e+nKP5V69ezR//\n", "+EcWL17MyZMnqaysdHrmy/1Y/ieffJK33nqL1atXA+Dn54fJZAKgvr7e5T/cHM0P7nPtguP5N2zY\n", "4DbXr6PZHb12PbqoxMTEkJeXB0BeXh4xMTG2fQaDgc2bN/PEE0/YtlVVVfH3v/+dp59+2u62kis4\n", "mr2yspKlS5eyatUqdu/eTWFhodMzX87R/H369MFgMABw8uRJ2+0MV3E0f8vFp1Kp0Ol0NDQ0OD3z\n", "5a6Wv6mpCQCtVmv7IdZyvNVq5cSJE4SHhzs/9GUcze9O1y44nv/8+fNuc/06mt3Ra9fjBz+29GLo\n", "3bs3jz/+OOnp6UydOpW5c+dy8eJF9Ho9Op2OF198kffff5/8/HyCgoIAmDFjBlqt1q2z+/n5MX36\n", "dNs5BQUF5OXluVXvo2vJ39DQwDvvvEN1dTUDBgzgkUcecXV8h/7vFBcXs2LFCry8vAgPD3eLaYOu\n", "lP+DDz6gvLycpqYmxo0bx3333WfX+2vUqFEkJCS4Or5D+d3t2nU0fwt3uX4dye7otevxRUUIIYT7\n", "8OjbX0IIIdyLFBUhhBDtRoqKEEKIdiNFRQghRLuRoiKEEKLdSFERQgjRbqSoCHGTeuaZZ0hNTXV1\n", "DNHJSFER4grq6uqYNGkSEydOZOfOndf9Ovn5+WRkZNimSRHiZiZFRYgryMnJwWKx4OPjY1vL43rk\n", "5+fzySefSFERnYIUFSGuICsri4iICMaNG0dBQQEVFRU39HoyeYXoDDx+5UchOkJpaSkGg4HJkydz\n", "1113sX79erKyslqt2GexWNi4cSM5OTmcPXsWLy8vQkNDSUhIYMyYMbz77ru2W2fPPvus7bwJEyYw\n", "fvx42/61a9e2yjBx4kQSEhJ4+umnbds2b97M/v37OX36NDU1Nfj7+xMXF0dycrLLJ+kUAqSoCNGm\n", "rKwsNBoNw4YNQ6/XExcXx44dO5g4caJtllyLxcLcuXMpKCggPj6ehIQEvL29MRgM7N+/nzFjxpCY\n", "mEh9fT379+9n8uTJBAQEABAREXFduTZs2EB0dDTjxo1Dr9dz8uRJMjMzOXr0KIsWLUKv17fbdyDE\n", "9ZCiIsQPNDY2snv3bu6++27bD+mRI0eyePFiDh8+zIABA4BLa9wUFBTw61//ulUL5vIp5yMiIti/\n", "fz+DBg2ie/fuN5Rt0aJFrWbnveuuu3jttdfIysrioYceuqHXF+JGyTMVIX4gNzcXk8nE/fffb9s2\n", "aNAg9Ho9WVlZtm05OTno9XrGjx/f6jU6as2PloJitVoxmUzU1NQQERGBTqejuLi4Q95TCEdIS0WI\n", "H8jKysLf358ePXrYrXIXHx/Pvn37qKurQ6/Xc+bMGSIjI9FonHcZHT16lE8++YTi4mLbgkotXL1E\n", "sBAgRUUIOxUVFeTn5wMwbdq0No/ZuXMn48aNa5f3u1KLprm5udW24uJiXn/9dUJDQ5k0aRLBwcG2\n", "lstbb72F1Wptl0xC3AgpKkJcpmU8ylNPPdVq7W5FUVi7di3bt29n3LhxhIaGUlZWhsViuWpr5Wq3\n", "wlqe2RiNRrv3O3fuXKtjc3JyUBSFl19+2a6nV0NDA3V1ddf2AYXoYFJUhPiO1WolOzubiIgIu+cp\n", "lzt9+jQZGRmUlJTw85//nFWrVvHpp5+2Wh5WURRbMfH19QWgtra21YP6sLAwAI4cOcLgwYNt2zds\n", "2NDqvdVqte21L7d+/XpHPqYQHUqKihDfOXLkCFVVVYwcOfKKx9xzzz1kZGSQlZXF448/zoEDB/js\n", "s88oKSmhf//+eHt7c+rUKc6cOUNKSgpwqQcYwKpVqxg6dCje3t5ERETQq1cvhgwZwurVq3n//fcp\n", "KytDr9dz6NAhamtr23zvL7/8kvnz5zNy5Eg0Gg1Hjhzh5MmT+Pv7d8yXIoSDpPeXEN9p6dl1zz33\n", "XPGYXr16ERoaytdff42iKLzyyitMnDiRCxcusHr1atasWUNpaanda/Tp04dJkyZx7tw50tLSePvt\n", "t9m3bx8Afn5+zJgxg1tvvZX169eTkZFBUFAQM2fObPXeffr04YUXXsDHx4e1a9eSkZGBj48Pr776\n", "Kj4+Pu38bQhxfVSKzB0hhBCinUhLRQghRLuRoiKEEKLdSFERQgjRbqSoCCGEaDdSVIQQQrQbOZjO\n", "MQAAAChJREFUKSpCCCHajRQVIYQQ7UaKihBCiHYjRUUIIUS7kaIihBCi3fx/xBLcBDODUNoAAAAA\n", "SUVORK5CYII=\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f3729025cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymks.tools import draw_goodness_of_fit\n", "\n", "fit_data = np.array([y, model.predict(X, periodic_axes=[0, 1])])\n", "pred_data = np.array([y_new, y_predict])\n", "draw_goodness_of_fit(fit_data, pred_data, ['Training Data', 'Test Data'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the `MKSHomogenizationModel` has created a homogenization linkage for the effective stiffness for the 6 different microstructures and has predicted the average stress values for our new microstructures reasonably well.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##References\n", "\n", "[1] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", "\n", "[2] Çeçen, A., et al. \"A data-driven approach to establishing microstructure–property relationships in porous transport layers of polymer electrolyte fuel cells.\" Journal of Power Sources 245 (2014): 144-153. [doi:10.1016/j.jpowsour.2013.06.100](http://dx.doi.org/10.1016/j.jpowsour.2013.06.100)\n", "\n", "[3] Deshpande, P. D., et al. \"Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels.\" 2 World Congress on Integrated Computational Materials Engineering. John Wiley & Sons, Inc. [doi:10.1002/9781118767061.ch25](http://dx.doi.org/10.1002/9781118767061.ch25)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ledeprogram/algorithms

class10/donow/DoNow_10.ipynb

1

2938

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a classifier to predict the wine color from wine quality attributes using this dataset: http://archive.ics.uci.edu/ml/datasets/Wine+Quality" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The data is in the database we've been using\n", "+ host='training.c1erymiua9dx.us-east-1.rds.amazonaws.com'\n", "+ database='training'\n", "+ port=5432\n", "+ user='dot_student'\n", "+ password='qgis'\n", "+ table name = 'winequality'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Query for the data and create a numpy array" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split the data into features (x) and target (y, the last column in the table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remember you can cast the results into an numpy array and then slice out what you want" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a decision tree with the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run 10-fold cross validation on the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## If you have time, calculate the feature importance and graph based on the code in the [slides from last class](http://ledeprogram.github.io/algorithms/class9/#21)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use [this tip for getting the column names from your cursor object](http://stackoverflow.com/questions/10252247/how-do-i-get-a-list-of-column-names-from-a-psycopg2-cursor)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

CDNoyes/EDL-Py

GradientCovariance.ipynb

1

346926

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "By expanding a trajectory to second order, the gradient of the covariance matrix (or any function thereof) with respect to nominal design parameters can be computed. This requires expanding in both initial states (and/or parametric uncertainties) and the design parameters. Due to the quadratic nature of the covariance matrix, a second order expansion of the trajectory is required, but this has the additional benefit of enabling Newton-type methods rather than being limited to gradient-based solutions or quasi-Newton approximations. \n", "\n", "Much more care must be taken in a closed loop setting; first the nominal trajectory must be expanded, then any reference variables and gains must be constructed with DA variables. Then, the closed loop trajectory must be expanded in terms of initial conditions and other uncertain terms. \n", "\n", "\\begin{align}\n", "\\Phi = \\frac{\\partial}{\\partial {x_0}}x_f({x_0}) \\\\\n", "P_f = \\Phi P_0 \\Phi^T \n", "\\end{align}\n", "\n", "Goals:\n", "- Expand an open loop trajectory and compute the gradient of the covariance matrix \n", "- Expand a closed loop trajectory \n", "- A good question to answer is how closely the linear covariance analysis matches the true covariance from a monte carlo " ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"./EntryGuidance\")\n", "from scipy.interpolate import interp1d\n", "from scipy.integrate import odeint, cumtrapz \n", "\n", "from pyaudi import gdual_double, gdual_vdouble\n", "import pyaudi as pa \n", "import numpy as np\n", "# import seaborn\n", "import matplotlib.pyplot as plt \n", "# from mpl_toolkits.mplot3d import Axes3D\n", "# print(plt.style.available)\n", "# plt.style.use(\"seaborn-whitegrid\")\n", "\n", "# from Utils.boxgrid import boxgrid\n", "import Utils.DA as da\n", "from Utils.RK4 import RK4\n", "from Utils.submatrix import submatrix \n", "from Utils.gpops import srp \n", "\n", "from EntryGuidance.EntryEquations import Entry\n", "from EntryGuidance.Simulation import Simulation, Cycle, EntrySim\n", "from EntryGuidance.InitialState import InitialState\n", "# from ParametrizedPlanner import profile\n", "from EntryGuidance.Apollo import gains " ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "m0 = 8500.0 kg\n", "Resetting simulation states.\n", "\n", "L/D: 0.24\n", "BC : 368.599+0.0433646*dm kg/m^2\n", "current simulation time = 0 s\n", "current simulation time = 10 s\n", "current simulation time = 20 s\n", "current simulation time = 30 s\n", "current simulation time = 40 s\n", "current simulation time = 50 s\n", "current simulation time = 60 s\n", "current simulation time = 70 s\n", "current simulation time = 80 s\n", "current simulation time = 90 s\n", "current simulation time = 100 s\n", "current simulation time = 110 s\n", "current simulation time = 120 s\n", "current simulation time = 130 s\n", "current simulation time = 140 s\n", "current simulation time = 150 s\n", "current simulation time = 160 s\n", "current simulation time = 170 s\n", "current simulation time = 180 s\n", "current simulation time = 190 s\n", "current simulation time = 200 s\n", "current simulation time = 210 s\n", "current simulation time = 220 s\n", "current simulation time = 230 s\n", "current simulation time = 240 s\n", "current simulation time = 250 s\n", "current simulation time = 260 s\n", "current simulation time = 270 s\n", "current simulation time = 280 s\n", "current simulation time = 290 s\n", "current simulation time = 300 s\n", "current simulation time = 310 s\n", "current simulation time = 320 s\n", "current simulation time = 330 s\n", "current simulation time = 340 s\n", "current simulation time = 350 s\n", "current simulation time = 360 s\n", "current simulation time = 370 s\n", "current simulation time = 380 s\n", "current simulation time = 390 s\n", "current simulation time = 400 s\n", "current simulation time = 410 s\n", "current simulation time = 420 s\n", "current simulation time = 430 s\n", "current simulation time = 440 s\n", "current simulation time = 450 s\n", "current simulation time = 460 s\n", "current simulation time = 470 s\n", "current simulation time = 480 s\n", "current simulation time = 490 s\n", "current simulation time = 500 s\n", "current simulation time = 510 s\n", "current simulation time = 520 s\n", "current simulation time = 530 s\n", "current simulation time = 540 s\n", "Transitioning from state Entry to Complete because the following condition was met:\n", "Velocity <= 530 m/s\n", "None\n", "time : 548.99\n", "\n", "altitude : 1.60499e+07*dfpa**2-4.73946*dV*du+27.0238*dV+0.000769835*dV*dr+2.36668e-05*dm**2+928.881*dV*dfpa-68737*dfpa*du+26.5679*dfpa*dr+1.00096e+06*dfpa+0.00883239*dV**2-0.200551*dm-0.0428237*dm*du+2.16797e-05*dm*dr-45377.6*du**2+9.61616e-06*dr**2-1783.41+0.000866069*dV*dm-0.0358336*dr*du-13510.2*du+29.7599*dfpa*dm+...\n", "\n", "longitude : 0.0147308*dfpa*dpsi+6.08141e-11*dr**2+6.77713e-08*dV**2+0.00890017*dpsi+0.0583944*dpsi*du+0.000185608*dV+0.173281*dphi*dpsi-4.8025e-05*dV*du-6.71009e-07*dm*du+6.77426e-06*dm-6.36452e-08*dphi*dr+2.25766e-06*dV*dpsi+5.46059e-09*dV*dr+0.189308*dphi**2-7.39074e-08*dm*dphi+108.625*dfpa**2+0.00665149*dV*dfpa+1.39681e-10*dm*dr+0.000199852*dfpa*dm+0.000175893*dfpa*dr+...\n", "\n", "latitude : 0.0139302*du**2+6.1592e-06*dm*dpsi-0.00370554*dphi*dpsi+2.30031*dfpa**2-0.0639088*du-0.00978863-2.48623e-06*dphi*dr+6.10979*dfpa*dpsi-2.33448e-05*dV*du-2.7978*dfpa*dphi-2.82301e-07*dm*du+7.37064e-11*dV*dm-7.7277e-05*dV*dphi+3.46741e-12*dm*dr+0.909207*dphi+0.0288639*dphi*du-3.26988e-08*dm-2.82043e-06*dm*dphi-3.31479e-06*dV+0.416345*dpsi+...\n", "\n", "velocity : 3.09956e+06*dfpa**2-1.92774*dV*du+1.29594*dV+0.000143192*dV*dr-7.91754e-08*dm**2+173.244*dV*dfpa-64947.8*dfpa*du+5.1219*dfpa*dr+47976.5*dfpa+0.002201*dV**2+0.0434943*dm-0.0578784*dm*du+4.51755e-06*dm*dr-1676.11*du**2+2.04894e-06*dr**2+525.047+0.000157896*dV*dm-0.0527354*dr*du-620.038*du+5.64105*dfpa*dm+...\n", "\n", "fpa : -1435.67*dfpa**2+0.00115247*dV*du+0.000637361*dV-6.2675e-08*dV*dr-2.41722e-09*dm**2-0.07524*dV*dfpa+45.5702*dfpa*du-0.00239564*dfpa*dr+22.9578*dfpa-1.09297e-06*dV**2+2.05303e-05*dm+4.16927e-05*dm*du-2.2271e-09*dm*dr-1.3994*du**2-1.03189e-09*dr**2-0.229688-6.75213e-08*dV*dm+3.83932e-05*dr*du-0.32523*du-0.0025861*dfpa*dm+...\n", "\n", "heading : -0.615627*du**2-2.82027e-06*dm*dpsi-0.00639556*dphi*dpsi+29.3423*dfpa**2-0.745133*du-0.102627-5.42975e-06*dphi*dr-2.79753*dfpa*dpsi+0.00088216*dV*du-6.11027*dfpa*dphi+3.11715e-05*dm*du+2.06899e-09*dV*dm-0.000168778*dV*dphi+3.18586e-11*dm*dr-0.416365*dphi+0.062775*dphi*du+4.57298e-06*dm-6.15963e-06*dm*dphi+0.000132289*dV+0.90925*dpsi+...\n", "\n", "rangeToGo : 22.9968*dm-2.53933*dm*du+ds+2.37889e+06+0.000212855*dr**2-3.5262e+06*dfpa*du+2.31692e+07*dfpa+0.0190099*dV*dr-2.22754*dr*du+0.0214323*dV*dm+0.000482042*dm*dr+3.78455e+08*dfpa**2+689.004*dfpa*dm+20.6929*dr+23164.8*dV*dfpa+0.237406*dV**2-0.00108402*dm**2+613.13*dfpa*dr-172.244*dV*du-228978*du+...\n", "\n", "mass : 8500+dm\n", "\n", "drag : 25744.5*dfpa**2-0.0160506*dV*du+0.0126911*dV+1.24084e-06*dV*dr-4.55297e-09*dm**2+1.49286*dV*dfpa-510.535*dfpa*du+0.0427962*dfpa*dr+469.434*dfpa+1.9431e-05*dV**2+0.000431722*dm-0.000458636*dm*du+3.78507e-08*dm*dr-15.7969*du**2+1.71168e-08*dr**2+7.06613+1.37125e-06*dV*dm-0.000417388*dr*du-5.633*du+0.0472587*dfpa*dm+...\n", "\n", "lift : 7068.97*dfpa**2-0.00429304*dV*du+0.00413323*dV+3.44884e-07*dV*dr-2.54236e-09*dm**2+0.414523*dV*dfpa-131.959*dfpa*du+0.0117635*dfpa*dr+152.891*dfpa+5.35191e-06*dV**2+0.000140477*dm-0.000118366*dm*du+1.03638e-08*dm*dr-5.38836*du**2+4.67626e-09*dr**2+2.206+3.81864e-07*dV*dm-0.00010756*dr*du-1.8405*du+0.0130151*dfpa*dm+...\n", "\n", "aero_ratios : (1, 1)\n", "\n", "bank : 0.15+du\n", "\n", "energy : 2.83682e+09*dfpa**2-1832.5*dV*du+780.835*dV+0.129406*dV*dr+0.000992197*dm**2+156528*dV*dfpa-6.40737e+07*dfpa*du+4689.43*dfpa*dr+2.89089e+07*dfpa+2.02737*dV**2+22.0914*dm-57.5221*dm*du+0.0041783*dm*dr-856613*du**2+0.00189768*dr**2+131214+0.142498*dV*dm-52.395*dr*du-375746*du+5159.53*dfpa*dm+...\n", "\n", "disturbance : 0\n", "\n" ] } ], "source": [ "x0 = InitialState(vehicle='heavy')\n", "print(\"m0 = {} kg\".format(x0[-1]))\n", "Vf = 530 \n", "dasim = Simulation(cycle=Cycle(1), output=True, use_da=True, **EntrySim(Vf=Vf), )\n", "\n", "names = ['r', 'theta', 'phi', 'V', 'fpa', 'psi', 's', 'm']\n", "da_names = names + ['u']\n", "order = 2\n", "x0d = da.make(x0, names, order)\n", "u = gdual_double(0.15, 'u', order)\n", "ref_profile = lambda **args: u\n", "\n", "res = dasim.run(x0d, [ref_profile])" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\[ 3.09956e+06{dfpa}^{2}-1.92774{dV}{du}+1.29594{dV}+0.000143192{dV}{dr}-7.91754e-08{dm}^{2}+173.244{dV}{dfpa}-64947.8{dfpa}{du}+5.1219{dfpa}{dr}+47976.5{dfpa}+0.002201{dV}^{2}+0.0434943{dm}-0.0578784{dm}{du}+4.51755e-06{dm}{dr}-1676.11{du}^{2}+2.04894e-06{dr}^{2}+525.047+0.000157896{dV}{dm}-0.0527354{dr}{du}-620.038{du}+5.64105{dfpa}{dm}+\\ldots+\\mathcal{O}\\left(3\\right) \\]" ], "text/plain": [ "3.09956e+06*dfpa**2-1.92774*dV*du+1.29594*dV+0.000143192*dV*dr-7.91754e-08*dm**2+173.244*dV*dfpa-64947.8*dfpa*du+5.1219*dfpa*dr+47976.5*dfpa+0.002201*dV**2+0.0434943*dm-0.0578784*dm*du+4.51755e-06*dm*dr-1676.11*du**2+2.04894e-06*dr**2+525.047+0.000157896*dV*dm-0.0527354*dr*du-620.038*du+5.64105*dfpa*dm+..." ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dasim.history[-1, 3]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 4.09787297e-06 0.00000000e+00 0.00000000e+00 1.43192406e-04\n", " 5.12189746e+00 0.00000000e+00 0.00000000e+00 4.51755163e-06\n", " -5.27353554e-02]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00]\n", " [ 1.43192406e-04 0.00000000e+00 0.00000000e+00 4.40199229e-03\n", " 1.73243599e+02 0.00000000e+00 0.00000000e+00 1.57895795e-04\n", " -1.92773974e+00]\n", " [ 5.12189746e+00 0.00000000e+00 0.00000000e+00 1.73243599e+02\n", " 6.19911260e+06 0.00000000e+00 0.00000000e+00 5.64104628e+00\n", " -6.49478020e+04]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00]\n", " [ 4.51755163e-06 0.00000000e+00 0.00000000e+00 1.57895795e-04\n", " 5.64104628e+00 0.00000000e+00 0.00000000e+00 -1.58350790e-07\n", " -5.78784221e-02]\n", " [-5.27353554e-02 0.00000000e+00 0.00000000e+00 -1.92773974e+00\n", " -6.49478020e+04 0.00000000e+00 0.00000000e+00 -5.78784221e-02\n", " -3.35222827e+03]]\n" ] } ], "source": [ "print(da.hessian(dasim.history[-1,3], da_names))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "da_names =['r', 'theta', 'phi', 'V', 'fpa', 'psi', 'u']\n", "stmf = np.array([da.differentiate(x, da_names) for x in dasim.history[-1][0:6]])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-6.00423277e+07, -6.05656839e+02, -1.64336249e+02,\n", " -2.98050583e+07, 1.92143312e+04, 1.49531862e+04],\n", " [-6.05656839e+02, -5.42558476e-03, -1.08797681e-03,\n", " -2.09804316e+02, 1.24348829e-01, 9.93773483e-02],\n", " [-1.64336249e+02, -1.08797681e-03, 2.26555463e-06,\n", " -6.63063740e+00, -4.72205476e-03, -1.55299250e-03],\n", " [-2.98050583e+07, -2.09804316e+02, -6.63063740e+00,\n", " -2.71920227e+06, 3.03355972e+02, 5.79783951e+02],\n", " [ 1.92143312e+04, 1.24348829e-01, -4.72205476e-03,\n", " 3.03355972e+02, 9.12975051e-01, 4.47673003e-01],\n", " [ 1.49531862e+04, 9.93773483e-02, -1.55299250e-03,\n", " 5.79783951e+02, 4.47673003e-01, 1.53644447e-01]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "P0 = np.diag([100, 0.0001, 0.0001, 1.5, np.radians(0.025), np.radians(0.02), 0])\n", "Pf = stmf.dot(P0).dot(stmf.T)\n", "# W = np.diag(np.ones()\n", "# C = np.trace(Pf)\n", "# dCdu = da.gradient(C, ['u'])\n", "# dCdu\n", "# dPfdu = np.array([da.gradient(x, ['u']) for x in np.diag(Pf)])\n", "dPfdu = np.array([da.jacobian(x, ['u']) for x in Pf[:6]])\n", "\n", "print(dPfdu)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3.31572e+11*dfpa**2-9877.4*dV*du+717507*dV+16.0713*dV*dr+1.95601e+07*dV*dfpa+0.285436*dm**2-3.34874e+08*dfpa*du+544862*dfpa*dr+2.43255e+10*dfpa+288.474*dV**2+22569.8*dm-310.704*dm*du+0.505536*dm*dr+84565.5*du**2+0.223839*dr**2+4.46155e+08+18.1484*dV*dm-275.144*dr*du-1.22839e+07*du+615280*dfpa*dm+...,\n", " 0.0038449*dfpa*dpsi+1.52619e-11*dr**2+2.15553e-08*dV**2+0.000111546*dpsi-3.43142e-05*dpsi*du-3.4299e-05*dphi*dpsi+4.19015e-05*dV-6.52479e-06*dV*du-1.96953e-07*dm*du+1.26479e-06*dm-1.35973e-08*dphi*dr+1.16626e-07*dV*dpsi+1.14713e-09*dV*dr+2.80037e-05*dphi**2-1.54244e-08*dm*dphi+0.00141521*dV*dfpa+23.229*dfpa**2+3.4626e-11*dm*dr+4.27181e-05*dfpa*dm+3.76574e-05*dfpa*dr+...,\n", " 7.75732e-05*du**2+2.12788e-08*dm*dpsi-0.015447*dphi*dpsi+0.022716*dfpa**2+5.60755e-06*du+0.000144576-8.98019e-09*dphi*dr+0.0238787*dfpa*dpsi-3.64052e-08*dV*du-0.0109675*dfpa*dphi-1.72621e-09*dm*du-1.93322e-07*dV*dphi+1.05805e-12*dV*dm+0.000128967*dphi+3.78158e-14*dm*dr+0.00103891*dphi*du+1.12063e-09*dm-9.7734e-09*dm*dphi+3.21112e-08*dV-0.000280789*dpsi+...,\n", " 1.49338e+10*dfpa**2-8809.04*dV*du+6209.38*dV+0.687369*dV*dr+832247*dV*dfpa+0.0123458*dm**2-3.16139e+08*dfpa*du+24668.3*dfpa*dr+2.22842e+08*dfpa+11.5951*dV**2+202.615*dm-287.443*dm*du+0.0224292*dm*dr+1.67311e+06*du**2+0.010187*dr**2+831311+0.756705*dV*dm-261.105*dr*du-2.35871e+06*du+27156.6*dfpa*dm+...,\n", " 3957.48*dfpa**2-0.00332341*dV*du-0.00186421*dV+1.71638e-07*dV*dr+0.205719*dV*dfpa+3.20315e-09*dm**2-127.867*dfpa*du+0.00660372*dfpa*dr-71.7249*dfpa+2.67344e-06*dV**2-6.45281e-05*dm-0.000115037*dm*du+5.94112e-09*dm*dr+1.03285*du**2+2.75486e-09*dr**2+0.324983+1.85077e-07*dV*dm-0.000106684*dr*du+1.15872*du+0.00712078*dfpa*dm+...,\n", " 0.556085*du**2-1.29773e-07*dm*dpsi+0.0154465*dphi*dpsi+1.25631*dfpa**2+0.163153*du+0.0122708-2.41782e-07*dphi*dr-0.133122*dfpa*dpsi+6.19047e-05*dV*du-0.28994*dfpa*dphi+1.62513e-06*dm*du-1.07664e-05*dV*dphi+9.0842e-11*dV*dm-0.0283762*dphi+2.04366e-12*dm*dr-0.193417*dphi*du+2.37105e-07*dm-2.82647e-07*dm*dphi+9.04548e-06*dV-0.0130327*dpsi+...],\n", " dtype=object)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.diag(Pf)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "H = np.array([da.hessian(x, ['u']) for x in np.diag(Pf)])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-7.26292176e+01, -6.40469477e+00, 3.61436418e-02, -7.04887641e-01,\n", " 5.60933320e-01, 1.46697884e-01])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dPfdu/H.squeeze() # Newton step update " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Timing, Finite Differencing\n", "A good question to address is how much accuracy we lose for different integration cycles: compare 0.1, 1, 5,?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import time \n", "\n", "def da_integration_timer(order, cycle=1, steps=10):\n", " x0 = InitialState(vehicle='heavy')\n", " P0 = np.diag([100, 0.0001, 0.0001, 1.5, np.radians(0.025), np.radians(0.02)])\n", "\n", " Vf = 530 \n", " dasim = Simulation(cycle=Cycle(cycle), output=False, use_da=True, **EntrySim(Vf=Vf), )\n", "\n", " names = ['r', 'theta', 'phi', 'V', 'fpa', 'psi', 's', 'm']\n", " da_names = names + ['u']\n", " x0d = da.make(x0, names, order)\n", " u = gdual_double(0.15, 'u', order) \n", " ref_profile = lambda **args: u\n", "\n", " t0 = time.time()\n", " dasim.run(x0d, [ref_profile], StepsPerCycle=steps)\n", " t = time.time() - t0\n", " print(\"order {} DA integration: {:.2f} s\".format(order, t))\n", "\n", "# stmf = np.array([da.differentiate(x, da_names[0:6]) for x in dasim.history[-1][0:6]])\n", "# Pf = stmf.dot(P0).dot(stmf.T)\n", " return t, dasim" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "times = []\n", "for i in range(1, 5):\n", " print(f\"Order: {i}\")\n", " t,sim = da_integration_timer(i)\n", " times.append(t)\n", "times = np.array(times)\n" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "plt.plot(list(range(1,5)), times, 'o-')\n", "plt.xlabel('DA Order, 9 variables')\n", "plt.ylabel('Time, seconds')\n", "plt.savefig('DA_time_vs_order.png')\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dt: 0.1\n", "order 2 DA integration: 110.18 s\n", "dt: 0.5\n", "order 2 DA integration: 21.98 s\n", "dt: 1\n", "order 2 DA integration: 11.04 s\n", "dt: 5\n", "order 2 DA integration: 2.24 s\n", "dt: 10\n", "order 2 DA integration: 1.13 s\n" ] } ], "source": [ "times = []\n", "xf = []\n", "# dts = [1, 5]\n", "dts = [0.1, 0.5, 1, 5, 10]\n", "for dt in dts:\n", " print(f\"dt: {dt}\")\n", " t,sim = da_integration_timer(order=2, cycle=dt, steps=2)\n", " xf.append(sim.history)\n", " times.append(t)\n", "times = np.array(times)\n", "# xf = np.array(xf)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Step size (s)')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(dts, times, 'o-')\n", "plt.ylabel('Time')\n", "plt.xlabel('Step size (s)')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error in Constant Term of final radius: -0.04128614207729697 m\n", "Error in Constant Term of final lon: -1.4585660457200333e-10 rad\n", "Error in Constant Term of final lat: -2.9286213332896383e-09 rad\n", "Error in Constant Term of final fpa: -0.0001773126082769952 deg\n", "Error in Constant Term of final heading: -1.637340795302116e-05 deg\n", "Maximum % error over the terminal jacobian = 0.002%\n", "\n", "\n", "Error in Constant Term of final radius: -0.1105618136934936 m\n", "Error in Constant Term of final lon: -2.3513928582019616e-09 rad\n", "Error in Constant Term of final lat: -7.715533874991243e-09 rad\n", "Error in Constant Term of final fpa: -0.00047432987801853283 deg\n", "Error in Constant Term of final heading: -4.371230690341263e-05 deg\n", "Maximum % error over the terminal jacobian = 0.005%\n", "\n", "\n", "Error in Constant Term of final radius: -3.9427922600880265 m\n", "Error in Constant Term of final lon: -1.4786077787154461e-06 rad\n", "Error in Constant Term of final lat: -2.0982508194475336e-07 rad\n", "Error in Constant Term of final fpa: -0.016922466776177765 deg\n", "Error in Constant Term of final heading: -0.0015611216764006989 deg\n", "Maximum % error over the terminal jacobian = 0.163%\n", "\n", "\n", "Error in Constant Term of final radius: -9.96956771146506 m\n", "Error in Constant Term of final lon: -2.066442625836551e-05 rad\n", "Error in Constant Term of final lat: 3.9589680461867005e-07 rad\n", "Error in Constant Term of final fpa: -0.04262742728136633 deg\n", "Error in Constant Term of final heading: -0.003860007871752537 deg\n", "Maximum % error over the terminal jacobian = 0.428%\n", "\n", "\n", "Somewhat surprisingly, large steps can be take with minimal loss, at least for a constant bank angle\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "E:\\Anaconda3\\envs\\research\\lib\\site-packages\\ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in true_divide\n" ] } ], "source": [ "# print(da.const(xf[:,3]))\n", "names = ['r', 'theta', 'phi', 'V', 'fpa', 'psi', 'u']\n", "for xfi in xf[1:]:\n", "# print(da.const(xfi[:,3], True)[-1])\n", " # interpolate the coarser integrations onto the same final velocity \n", " \n", " xfv = da.interp([xf[0][-1,3].constant_cf], da.const(xfi[::-1,3], True), xfi[::-1])[0]\n", "# for state in xfv:\n", "# display(state.constant_cf)\n", "# for state in xf[0][-1]:\n", "# display(state.constant_cf)\n", " \n", " \n", " delta = xfv-xf[0][-1]\n", " h,lon,lat,v,fpa,psi,s,m = delta\n", " for d,state,unit in zip(delta, ['radius','lon','lat'], ['m','rad','rad']):\n", " print(f\"Error in Constant Term of final {state}: {d.constant_cf} {unit}\")\n", " for d,state,unit in zip(delta[4:], ['fpa','heading'], ['deg']*2):\n", " print(f\"Error in Constant Term of final {state}: {np.degrees(d.constant_cf)} {unit}\")\n", "\n", " \n", "# display(da.jacobian(delta, names))\n", " pdiff = da.jacobian(delta, names)/da.jacobian(xf[0][-1], names) * 100 \n", " pdiff[np.isnan(pdiff)] = 0\n", " pdiff_max = np.max(np.abs(pdiff))\n", " print(\"Maximum % error over the terminal jacobian = {:.3f}%\".format(pdiff_max))\n", "# display() # Percent diff, lots of nans anywhere with zero gradient \n", "\n", "# for d in delta:\n", "# display(da.gradient(delta[0], names))\n", "# display(da.gradient(delta[0], names)/da.gradient(xf[0][-1]))\n", "\n", " print(\"\\n\")\n", "\n", "print(\"Somewhat surprisingly, large steps can be take with minimal loss, at least for a constant bank angle\")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3.39522e+06-13510.2*du -0.0693269*du+0.429422 -0.0639088*du-0.00978863\n", " 525.047-620.038*du -0.229688-0.32523*du -0.745133*du-0.102627\n", " -228978*du+2.37889e+06 8500]\n" ] } ], "source": [ "print(dasim.history[-1])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Central difference:\n", "[[-6.00414e+07 -605.655 -164.337 -2.98049e+07 19214.4 14953.2]\n", " [-605.655 -0.00542561 -0.00108798 -209.804 0.124349 0.0993773]\n", " [-164.337 -0.00108798 2.26584e-06 -6.63062 -0.00472211 -0.00155301]\n", " [-2.98049e+07 -209.804 -6.63062 -2.71918e+06 303.351 579.782]\n", " [19214.4 0.124349 -0.00472211 303.351 0.912982 0.447676]\n", " [14953.2 0.0993773 -0.00155301 579.782 0.447676 0.153645]]\n" ] } ], "source": [ "gradient = []\n", "for unew in [0.149, 0.151,]:\n", " ref_profile = lambda **args: unew\n", " t0 = time.time()\n", " res = dasim.run(x0d, [ref_profile])\n", " print(\"\\nFirst order DA integration: {:.2f} s\".format(time.time()-t0))\n", " stmf = np.array([da.differentiate(x, da_names[0:6]) for x in dasim.history[-1][0:6]])\n", " Pf_new = stmf.dot(P0).dot(stmf.T)\n", "\n", " dPf_du = ( Pf_new - Pf ) / (unew-u)\n", " gradient.append(dPf_du)\n", " print(dPf_du)\n", " \n", "dPf_du_central = np.mean(gradient, axis=0)\n", "print(\"\\nCentral difference:\")\n", "print(dPf_du_central)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using differential algebra, dPf/du = \n", "[[-6.00423277e+07 -6.05656839e+02 -1.64336249e+02 -2.98050583e+07\n", " 1.92143312e+04 1.49531862e+04]\n", " [-6.05656839e+02 -5.42558476e-03 -1.08797681e-03 -2.09804316e+02\n", " 1.24348829e-01 9.93773483e-02]\n", " [-1.64336249e+02 -1.08797681e-03 2.26555463e-06 -6.63063740e+00\n", " -4.72205476e-03 -1.55299250e-03]\n", " [-2.98050583e+07 -2.09804316e+02 -6.63063740e+00 -2.71920227e+06\n", " 3.03355972e+02 5.79783951e+02]\n", " [ 1.92143312e+04 1.24348829e-01 -4.72205476e-03 3.03355972e+02\n", " 9.12975051e-01 4.47673003e-01]\n", " [ 1.49531862e+04 9.93773483e-02 -1.55299250e-03 5.79783951e+02\n", " 4.47673003e-01 1.53644447e-01]]\n" ] } ], "source": [ "print(\"Using differential algebra, dPf/du = \")\n", "print(dPfdu)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1.61789547e-03, -2.51784184e-04, -3.22957267e-04,\n", " -3.91395948e-04, 1.36092009e-04, 8.38860768e-05],\n", " [-2.51784184e-04, -4.00525751e-04, -2.34335869e-04,\n", " -3.27111019e-04, 4.18164361e-05, 2.01169914e-06],\n", " [-3.22957267e-04, -2.34335870e-04, 1.25666668e-02,\n", " -3.13368293e-04, -1.13042521e-03, -9.37532641e-04],\n", " [-3.91395948e-04, -3.27111033e-04, -3.13368292e-04,\n", " -6.98790388e-04, 1.76064514e-03, 3.58027993e-04],\n", " [ 1.36092004e-04, 4.18164416e-05, -1.13042521e-03,\n", " 1.76064516e-03, 8.05666254e-04, 6.52190259e-04],\n", " [ 8.38860753e-05, 2.01169826e-06, -9.37532643e-04,\n", " 3.58027994e-04, 6.52190259e-04, 2.03767466e-04]])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.abs(dPfdu-da.const(dPf_du_central))/dPfdu * 100 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That weird saturation used in desensitizing the minimum fuel powered descent paper" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "umin = 0\n", "umax = 1\n", "\n", "u = np.linspace(0,1,300)\n", "\n", "def sat(u, umin, umax):\n", " return 4*(u-umin)*(umax-u)/(umax-umin)**2\n", "\n", "plt.figure(figsize=(10,10))\n", "plt.plot(u, sat(u, umin, umax)**0.5)\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\[ -0.494398{dV}-2.22188e-08{dm}+11627.6{dfpa}+3.53701e+06+0.999998{dr}+\\mathcal{O}\\left(2\\right) \\]" ], "text/plain": [ "-0.494398*dV-2.22188e-08*dm+11627.6*dfpa+3.53701e+06+0.999998*dr" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "\\[ -0.736817{dV}-5.27794e-08{dm}+17448.7{dfpa}+3.53552e+06+0.999997{dr}+\\mathcal{O}\\left(2\\right) \\]" ], "text/plain": [ "-0.736817*dV-5.27794e-08*dm+17448.7*dfpa+3.53552e+06+0.999997*dr" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[None, None]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[display(x) for x in dasim.history[2:4, 0]]" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\[ 3.53626e+06-2.32903e-05{du}+\\mathcal{O}\\left(2\\right) \\]" ], "text/plain": [ "3.53626e+06-2.32903e-05*du" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da.interp([0.5],np.array([0,1]),dasim.history[2:4, 0])[0]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\[ -0.615607{dV}-3.74991e-08{dm}+14538.2{dfpa}+3.53626e+06+0.999997{dr}+\\mathcal{O}\\left(2\\right) \\]" ], "text/plain": [ "-0.615607*dV-3.74991e-08*dm+14538.2*dfpa+3.53626e+06+0.999997*dr" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(dasim.history[2:4, 0])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\[ -478999{dfpa}^{2}-1.56486e-06{dr}^{2}-0.000179553{dV}^{2}+3.89323e-05{dV}{dr}-1.72145{dfpa}{dr}+25.3655{dV}{dfpa}+\\mathcal{O}\\left(3\\right) \\]" ], "text/plain": [ "-478999*dfpa**2-1.56486e-06*dr**2-0.000179553*dV**2+3.89323e-05*dV*dr-1.72145*dfpa*dr+25.3655*dV*dfpa" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dasim.history[100, 3].extract_terms(2).subs('dm', 0).subs('du', 0)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "m0 = 8500.0 kg\n", "Resetting simulation states.\n", "\n", "L/D: 0.24\n", "BC : 368.598738682043 kg/m^2\n", "current simulation time = 0 s\n", "current simulation time = 10 s\n", "current simulation time = 20 s\n", "current simulation time = 30 s\n", "current simulation time = 40 s\n", "current simulation time = 50 s\n", "current simulation time = 60 s\n", "current simulation time = 70 s\n", "current simulation time = 80 s\n", "current simulation time = 90 s\n", "current simulation time = 100 s\n", "current simulation time = 110 s\n", "current simulation time = 120 s\n", "current simulation time = 130 s\n", "current simulation time = 140 s\n", "current simulation time = 150 s\n", "current simulation time = 160 s\n", "current simulation time = 170 s\n", "current simulation time = 180 s\n", "current simulation time = 190 s\n", "current simulation time = 200 s\n", "current simulation time = 210 s\n", "current simulation time = 220 s\n", "current simulation time = 230 s\n", "current simulation time = 240 s\n", "current simulation time = 250 s\n", "Transitioning from state Entry to Complete because the following condition was met:\n", "Altitude <= 3 km\n", "None\n", "time : 253.99\n", "\n", "altitude : 2880.994489639066\n", "\n", "longitude : 0.267357529206153\n", "\n", "latitude : 0.002440517874850808\n", "\n", "velocity : 754.7774984250073\n", "\n", "fpa : -0.18909434945815326\n", "\n", "heading : -0.22137360932300368\n", "\n", "rangeToGo : 1823991.2224802056\n", "\n", "mass : 8500.0\n", "\n", "drag : 8.542003007006642\n", "\n", "lift : 2.650334978745434\n", "\n", "aero_ratios : (1, 1)\n", "\n", "bank : 1.0362572304539612\n", "\n", "energy : 295528.471119266\n", "\n", "disturbance : 0\n", "\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from Triggers import AltitudeTrigger\n", "x0 = InitialState(vehicle='heavy')\n", "print(\"m0 = {} kg\".format(x0[-1]))\n", "Vf = 330 \n", "\n", "sim_conditions = EntrySim(Vf=Vf)\n", "sim_conditions['conditions'] = [AltitudeTrigger(3)]\n", "\n", "sim = Simulation(cycle=Cycle(1), output=True, **sim_conditions, )\n", "\n", "ref_profile = lambda **args: np.sin(args['velocity']/1000.)*1.5\n", "\n", "# v = np.linspace(5000, Vf, 1000)\n", "# plt.plot(v, np.degrees(np.sin(v/500.)))\n", "# plt.show()\n", "\n", "res = sim.run(x0, [ref_profile])\n", "sim.plot()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "# target = (h, lon, lat)\n", "target = [0e3, np.radians(15.4), 0.0]\n", "def spherical_to_cartesian(x, target):\n", " r,th,ph,v,fpa,azi,s,m = x\n", " x,y = sim.edlModel.planet.range(th,ph,azi, target[1], target[2], km=False)\n", " z = sim.edlModel.altitude(r, km=False) - target[0] \n", " vx = v*np.cos(fpa)*np.cos(azi) \n", " vy = v*np.cos(fpa)*np.sin(azi)\n", " vz = v*np.sin(fpa)\n", " return [x, y, z, vx, vy, vz]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "def solve_srp(x_cart, m0, display=False):\n", " x_cart.append(m0)\n", " x_cart = [float(x) for x in x_cart]\n", " srp_sol = srp((x_cart, 0)) # srp takes cartesian states, with the target at the origin \n", " srp_x = np.array(srp_sol['state'])\n", " srp_u = np.array(srp_sol['control'])\n", " srp_t = np.array(srp_sol['time']).squeeze()\n", " if display:\n", " print(\"Optimal ToF = {:.1f} s\".format(srp_t[-1]))\n", " print(\"Optimal Prop = {:.1f} kg\".format(srp_x[0,-1]-srp_x[-1,-1]))\n", " return srp_t, srp_x, srp_u" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(255, 8)\n", "[17406.05106322574, 7524.15838050389, 4794.664208166767, 845.9025483899766, -153.2269714106775, -130.80575186438452]\n", "No shared Matlab instance found, creating a new instance...\n", "Matlab instance created successfully.\n", "Optimal ToF = 32.1 s\n", "Optimal Prop = 5636.4 s\n", "[15750.909620771628, 7425.976596966812, 4531.120231649838, 827.7300778539958, -155.0061102312109, -132.72254676640884]\n", "No shared Matlab instance found, creating a new instance...\n", "Matlab instance created successfully.\n", "Optimal ToF = 34.7 s\n", "Optimal Prop = 5211.5 s\n", "[14130.192149365475, 7336.938171303556, 4263.856624688022, 809.7752429488609, -156.64705668886782, -134.52550196984186]\n", "No shared Matlab instance found, creating a new instance...\n", "Matlab instance created successfully.\n", "Optimal ToF = 34.7 s\n", "Optimal Prop = 5207.4 s\n", "[12543.510881601911, 7256.976804329072, 3993.10000132164, 792.0336579914417, -158.14971790164012, -136.21569586348735]\n", "No shared Matlab instance found, creating a new instance...\n", "Matlab instance created successfully.\n", "Optimal ToF = 30.3 s\n", "Optimal Prop = 6333.4 s\n", "[10990.486399894324, 7186.022750632883, 3719.074676584918, 774.5020241119756, -159.51400569878922, -137.79451294205987]\n", "No shared Matlab instance found, creating a new instance...\n", "Matlab instance created successfully.\n", "Optimal ToF = 38.2 s\n", "Optimal Prop = 5449.0 s\n", "[9470.746497199438, 7124.00061877625, 3442.001879502088, 757.1780562851403, -160.7398782662102, -139.26364217673455]\n", "No shared Matlab instance found, creating a new instance...\n", "Matlab instance created successfully.\n", "Optimal ToF = 36.9 s\n", "Optimal Prop = 5262.3 s\n", "[7983.925918143393, 7070.8265594964505, 3162.0988486222923, 740.060405005075, -161.82737678443053, -140.62506155686762]\n", "No shared Matlab instance found, creating a new instance...\n", "Matlab instance created successfully.\n", "Optimal ToF = 35.7 s\n", "Optimal Prop = 5065.9 s\n" ] } ], "source": [ "print(sim.history.shape)\n", "mprop = []\n", "sol = []\n", "for x in sim.history[240:-1:2]:\n", " x_cart = spherical_to_cartesian(x, target)\n", " print(x_cart)\n", " t,x,u = solve_srp(x_cart, 8500, display=True)\n", " sol.append((t,x,u))\n", " mprop.append(x[-1,-1]) \n" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x25ee1aa1c18>]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(mprop)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x25edfbb3b00>]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAI/CAYAAAA2mq62AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3iUVcL+8fuk9wIkQAIhdASlg6igiysgIISq2Ht7dVdXd91VV/dnXXV1V31XYG0rigoKiQQpioqKAkqR3hI6SUgjkB6SzPn9kZEXFREh4ZmZfD/XNddMzjyT3LZwe57znMdYawUAAABn+TkdAAAAAJQyAAAAj0ApAwAA8ACUMgAAAA9AKQMAAPAAlDIAAAAPEOB0gFPVrFkzm5yc7HQMAACAX7Rq1aoCa23csd7z+lKWnJyslStXOh0DAADgFxljdv/ce5y+BAAA8ACUMgAAAA9AKQMAAPAAlDIAAAAPQCkDAADwAJQyAAAAD0ApAwAA8ACUMgAAAA9AKQMAAPAAlDIAAAAPQCkDAADwAJQyAAAAD0ApAwAA8ACUMgAAAA9AKQMAAPAAlDIAAAAPQCkDAADwAJQyAAAAD0ApAwAA8ACnXMqMMa2NMYuNMZuNMRuNMXe5x5sYYxYZYzLcz7FHfeZ+Y0ymMWarMWbYUeN9jDHr3e+9aIwxp5oPAADAG9THTFmNpHuttWdIGiDpDmNMV0l/kfSptbajpE/dX8v93iRJ3SRdLGmyMcbf/b2mSLpFUkf34+J6yAcAAODxTrmUWWtzrLWr3a9LJG2WlCgpRdI092HTJI1xv06RNMNaW2Wt3SkpU1J/Y0xLSVHW2mXWWivpzaM+46hqV7XTEQAAgI+r1zVlxphkSb0kfSOpubU2R6orbpLi3YclStp71Mf2uccS3a9/PO6o9fnrNTJ1pL7K+srpKAAAwIfVWykzxkRImi3pbmtt8fEOPcaYPc74sX7WLcaYlcaYlfn5+b8+7K8Q6B+osIAw3f7J7Xpk2SMqqy5r0J8HAAAap3opZcaYQNUVsrettanu4Vz3KUm5n/Pc4/sktT7q460kZbvHWx1j/CestS9ba/taa/vGxcXVx1/Cz+rSpItmjpqp68+8XrO3zdb49PFasX9Fg/5MAADQ+NTH1ZdG0muSNltr/3nUW+mSrnW/vlbSnKPGJxljgo0xbVW3oP9b9ynOEmPMAPf3vOaozzgq2D9Y9/S5R28Of1P+xl83fHSDnv72aVXUVDgdDQAA+Ij6mCk7T9LVki40xqxxP0ZIekrSEGNMhqQh7q9lrd0o6T1JmyQtlHSHtbbW/b1ul/Sq6hb/b5e0oB7y1Zue8T31/qj3dUWXKzR983RdOvdSrc1f63QsAADgA0zdhY7eq2/fvnblypWn/ed+k/ONHvr6IeWW5+r6btfrf3r+j4L8g057DgAA4D2MMaustX2P9R47+p+ks1uerdTRqRrTYYxe2/CaLvvwMm0u3Ox0LAAA4KUoZacgIihCj5z7iF767Us6VHVIV8y7QlPWTmFfMwAA8KtRyurB+a3OV1pKmoYmD9XkNZN11fyrlFmU6XQsAADgRShl9SQ6OFpPn/+0/vmbfyqnNEeXfnip/rvhv6p11f7yhwEAQKNHKatnQ9oMUVpKmgYlDtI/V/1T1y28TnuK9zgdCwAAeDhKWQNoGtpUzw9+Xk8OfFLbD23XhLkT9O6Wd+WyLqejAQAAD0UpayDGGI1qP0ppo9PUu3lvPfnNk7pl0S3KKc1xOhoAAPBAlLIG1jy8uab8dor+ds7ftD5/vcamj1VaRpq8fX84AABQvyhlp4ExRhM6TVBqSqq6Nu2qh5c+rDs+vUN55Xm//GEAANAoUMpOo8SIRL069FX9pf9f9O3+bzV2zljN3zGfWTMAAEApO938jJ+uPONKvT/qfSVHJ+vPS/6se7+4VwcqDzgdDQAAOIhS5pC20W315sVv6u7ed+vzvZ9r7Jyx+nTPp07HAgAADqGUOcjfz183nnWjZlwyQ83DmuvuxXfrgSUP6FDVIaejAQCA04xS5gE6xXbS2yPe1m09btP8nfM1Ln2cvs762ulYAADgNKKUeYhA/0Dd0fMOvT3ibUUGRuq2T27TI8seUVl1mdPRAADAaUAp8zDdmnXTzFEzdf2Z12v2ttkanz5eK/avcDoWAABoYJQyDxTsH6x7+tyjacOnyd/464aPbtDT3z6tipoKp6MBAIAGQinzYL3ie+n9Ue/r8i6Xa/rm6bp07qVam7/W6VgAAKABUMo8XFhgmB44+wG9MvQVVdVW6ZoF1+iF1S/ocO1hp6MBAIB6RCnzEgNaDlDq6FSN6TBGr65/VZPmTdLmws1OxwIAAPWEUuZFIoIi9Mi5j+il376kg5UHdcW8KzR17VRVu6qdjgYAAE4RpcwLnd/qfKWlpGlo8lC9tOYlXT3/am0/uN3pWAAA4BRQyrxUdHC0nj7/aT13wXPKLs3WpXMv1Rsb3lCtq9bpaAAA4CRQyrzc0OShSk1J1cDEgXpu1XO6/qPrtad4j9OxAADAr0Qp8wHNQpvp+cHP68mBTyqzKFMT5k7Qu1velcu6nI4GAABOEKXMRxhjNKr9KKWmpKp3fG89+c2TumXRLcopzXE6GgAAOAGUMh/TIryFplw0RX87529an79e49LHKS0jTdZap6MBAIDjoJT5IGOMJnSaoNmjZ6tLky56eOnDuvOzO5Vfnu90NAAA8DMoZT6sVWQrvTbsNf2535/1Tc43GjNnjBbsXMCsGQAAHohS5uP8jJ+u6nqV3h/1vpKjk3Xfl/fpj1/8UUWVRU5HAwAAR6GUNRJto9tq2sXTdFfvu/TZ3s80Zs4YfbrnU6djAQAAN0pZIxLgF6CbzrpJMy+ZqfiweN29+G49sOQBFR8udjoaAACNHqWsEeoU20nvjHhHt/W4TfN3ztfYOWP1ddbXTscCAKBRo5Q1UoH+gbqj5x16e8TbigiM0G2f3KZHlz2qsuoyp6MBANAoUcoauW7Nuum9Ue/p+m7Xa9a2WRqfPl4r9q9wOhYAAI0OpQwK9g/WPX3v0bTh0+Rn/HTjRzfq6W+fVmVNpdPRAABoNChlOKJXfC/NGjVLl3W+TNM3T9fEuRO1Ln+d07EAAGgUKGX4gbDAMD044EG9MvQVVdZW6uoFV+uF1S/ocO1hp6MBAODTKGU4pgEtByh1dKpS2qfo1fWvatK8SdpyYIvTsQAA8FmUMvysyKBIPXreo3rpty+pqLJIl394uaaunapqV7XT0QAA8DmUMvyi81udr7TRaRqSPEQvrXlJV8+/WtsPbnc6FgAAPoVShhMSExKjZ85/Rs9e8KyySrN06dxLNW3jNNW6ap2OBgCAT6CU4VcZljxMaSlpGpg4UM+ufFbXf3S99hTvcToWAABej1KGX61ZaDM9P/h5PTnwSWUWZWrC3AmasWWGXNbldDQAALwWpQwnxRijUe1HKTUlVb3je+uJb57QrYtuVU5pjtPRAADwSpQynJIW4S005aIpevich7U2f63GpY9TWkaarLVORwMAwKtQynDKjDGa2GmiZo+erc5NOuvhpQ/rd5/9Tvnl+U5HAwDAa1DKUG9aR7bW68Ne13397tPynOUamz5WC3cudDoWAABegVKGeuVn/HR116v1/qj31Sayjf705Z907+f3qqiyyOloAAB4NEoZGkTb6LaaNnya7up9lz7b+5nGzBmjz/Z85nQsAAA8FqUMDSbAL0A3nXWTZoycofiweN21+C49+NWDKj5c7HQ0AAA8DqUMDa5zk856Z8Q7urX7rZq3Y57GzhmrpVlLnY4FAIBHoZThtAj0D9Sdve7U2yPeVkRghG795FY9tuwxlVeXOx0NAACPUC+lzBjzujEmzxiz4aix/2eMyTLGrHE/Rhz13v3GmExjzFZjzLCjxvsYY9a733vRGGPqIx88R7dm3fTeqPd0Xbfr9P629zUufZxW7F/hdCwAABxXXzNlb0i6+Bjj/7LW9nQ/5kuSMaarpEmSurk/M9kY4+8+foqkWyR1dD+O9T3h5YL9g3Vv33v1xsVvyM/46caPbtTT3z6typpKp6MBAOCYeill1tovJR04wcNTJM2w1lZZa3dKypTU3xjTUlKUtXaZrdsO/k1JY+ojHzxT7+a9NWvULF3W+TJN3zxdE+dO1Lr8dU7HAgDAEQ29puxOY8w69+nNWPdYoqS9Rx2zzz2W6H7943H4sLDAMD044EG9PORlVdZW6uoFV+vF1S/qcO1hp6MBAHBaNWQpmyKpvaSeknIkPeceP9Y6MXuc8Z8wxtxijFlpjFmZn8+tfHzBOQnnKHV0qlLap+iV9a/o8nmXa+uBrU7HAgDgtGmwUmatzbXW1lprXZJekdTf/dY+Sa2POrSVpGz3eKtjjB/re79sre1rre0bFxdX/+HhiMigSD163qP694X/1oHKA5o0b5L+s/Y/qnHVOB0NAIAG12ClzL1G7HtjJX1/ZWa6pEnGmGBjTFvVLej/1lqbI6nEGDPAfdXlNZLmNFQ+eK4LWl+gtNFpGpI0RP9e829dPf9q7Ti4w+lYAAA0qPraEuNdScskdTbG7DPG3CjpGff2FuskDZb0B0my1m6U9J6kTZIWSrrDWlvr/la3S3pVdYv/t0taUB/54H1iQmL0zAXP6NkLntW+0n2aOHeipm2cplpX7S9/GAAAL2TqLnT0Xn379rUrV650OgYaUEFFgR5d9qgW712s3vG99fh5j6t1VOtf/iAAAB7GGLPKWtv3WO+xoz88XrPQZnph8At6cuCTyijK0Pi54zVzy0x5+/9QAABwNEoZvIIxRqPaj1JqSqp6xffS4988rpsX3azs0mNeCwIAgNehlMGrtAhvoakXTdVDAx7S+vz1GjtnrN7b+h6zZgAAr0cpg9cxxujSzpcqLSVN3eO667HljzFrBgDwepQyeK2EiAS9PORlZs0AAD6BUgavxqwZAMBXUMrgE5g1AwB4O0oZfAazZgAAb0Ypg89h1gwA4I0oZfBJPzdrllWa5XQ0AACOiVIGn/bjWbNxc8YxawYA8EiUMvi8Y86afcysGQDAs1DK0Gj8YNasgFkzAIBnoZShUfm5WbO9JXudjgYAaOQoZWiUvp81e/ich7WhcIPGp4/X25vflsu6nI4GAGikKGVotIwxmthpoj5I+UB9mvfRU98+pesWXqedh3Y6HQ0A0AhRytDotQhvocm/nawnBj6hzIOZmpA+Qa9veF01rhqnowEAGhFKGaC6WbPR7UdrTsocDUwcqH+t+peumn+VthVtczoaAKCRoJQBR4kLi9Pzg5/XPy74h7JLs3XZh5dpytopqq6tdjoaAMDHUcqAHzHG6OLki/XBmA80pM0QTV4zWZPmTdLGwo1ORwMA+DBKGfAzmoQ00TPnP6MXBr+gosoiXTnvSr2w+gVV1VY5HQ0A4IMoZcAvuDDpQqWlpGlU+1F6df2rmjh3otbkrXE6FgDAx1DKgBMQHRytx857TFMvmqqKmgpds+AaPbPiGVXUVDgdDQDgIyhlwK9wXuJ5Shudpks7X6q3Nr2l8enjtWL/CqdjAQB8AKUM+JUigiL01wF/1evDXpe1Vjd8dIMeX/64yqrLnI4GAPBilDLgJPVr0U+zR8/W1V2v1ntb39PYOWO1NGup07EAAF6KUgacgrDAMN3X7z69OfxNhQSE6NZPbtVDXz+kQ1WHnI4GAPAylDKgHvSM76n3R72vm866SXO3z9XYOWO1eM9ip2MBALwIpQyoJ8H+wbqr9116Z+Q7ig2J1e8X/173fXmfiiqLnI4GAPAClDKgnnVt2lUzRs7Q//T8Hy3avUhj5ozRwl0LZa11OhoAwINRyoAGEOgfqNt73K6Zl8xUy/CW+tMXf9IfPv+DCioKnI4GAPBQlDKgAXWK7aTpI6brD33+oCX7lmj0B6OVlpHGrBkA4CcoZUADC/AL0A1n3qDZo2erY0xHPbz0Yd286GbtLdnrdDQAgAehlAGnSXJ0sv578X/10ICHtLFgo8bNGadpG6epxlXjdDQAgAeglAGnkZ/x06WdL9UHKR9oQMIAPbvyWV01/yptPbDV6WgAAIdRygAHNA9vrhcHv6h/XPAP5ZTlaNKHk/Ti6hdVVVvldDQAgEMoZYBDjDG6OPlizUmZoxHtRuiV9a9oQvoErc5d7XQ0AIADKGWAw2JCYvTEwCf0n4v+o2pXta5deK0eX/64Sg+XOh0NAHAaUcoAD3Fu4rlKHZ2qq864Su9tfU9j5ozRF3u/cDoWAOA0oZQBHiQsMEx/7v9nTR8xXZFBkbrzszt13xf3qbCi0OloAIAGRikDPFD3uO5675L36m7VtKfuVk3p29PZdBYAfBilDPBQ39+qadaoWUqOStaDXz2omxfdrD3Fe5yOBgBoAJQywMO1j2mvacOn6a9n/7Vu09n0cXp1/auqdlU7HQ0AUI8oZYAX8DN+uqzLZZozZo7Ob3W+Xlj9gi778DKty1/ndDQAQD2hlAFeJD4sXv/8zT/1wuAXdKjqkK6af5X+/s3fVVZd5nQ0AMApopQBXujCpAs1J2WOLu9yud7d8q5SPkjR4j2LnY4FADgFlDLAS0UERej+s+/X9BHTFRUcpd8v/r3+sPgPyivPczoaAOAkUMoAL9c9rrtmXjJTd/W+S0uylijlgxTN3DJTLutyOhoA4FeglAE+INAvUDeddZNSR6eqW7Nuevybx3XtgmuVWZTpdDQAwAmilAE+JCkqSa8MeUVPDHxCu4p3aeKHE/W/3/2vqmqrnI4GAPgFlDLAxxhjNLr9aKWPSdeItiP08rqXNT59vFbsX+F0NADAcVDKAB8VGxKrJwY+oZeHvKxaV61u+OgGPfz1wzpUdcjpaACAY6iXUmaMed0Yk2eM2XDUWBNjzCJjTIb7Ofao9+43xmQaY7YaY4YdNd7HGLPe/d6LxhhTH/mAxuychHOUmpKqG8+8Uenb0zX6g9Gau30u99EEAA9TXzNlb0i6+Edjf5H0qbW2o6RP3V/LGNNV0iRJ3dyfmWyM8Xd/ZoqkWyR1dD9+/D0BnITQgFDd3eduzbxkplpFtNIDXz2gmz6+STsO7XA6GgDArV5KmbX2S0kHfjScImma+/U0SWOOGp9hra2y1u6UlCmpvzGmpaQoa+0yW/e/8G8e9RkA9aBzk856a8RbemjAQ9p8YLPGp4/Xi6tfVGVNpdPRAKDRa8g1Zc2ttTmS5H6Od48nStp71HH73GOJ7tc/HgdQj/yMny7tfKnSx6RrePJwvbL+FY2dM1ZfZX3ldDQAaNScWOh/rHVi9jjjP/0GxtxijFlpjFmZn59fr+GAxqJZaDM9OehJvTb0NQX4Bej2T27XPZ/fo9yyXKejAUCj1JClLNd9SlLu5+/v/bJPUuujjmslKds93uoY4z9hrX3ZWtvXWts3Li6u3oMDjUn/lv01e/Rs/a7X7/Tlvi81+oPRmr5pumpcNU5HA4BGpSFLWbqka92vr5U056jxScaYYGNMW9Ut6P/WfYqzxBgzwH3V5TVHfQZAAwryD9It3W9R2ug09WreS0+veFpXzLtC6/PXOx0NABqN+toS411JyyR1NsbsM8bcKOkpSUOMMRmShri/lrV2o6T3JG2StFDSHdbaWve3ul3Sq6pb/L9d0oL6yAfgxLSOaq0pv52iZy94VoUVhbpy/pV6fPnjKj5c7HQ0APB5xtv3Kurbt69duXKl0zEAn1N6uFQvrXlJ72x5R7HBsfpjvz9qZNuRYvtAADh5xphV1tq+x3qPHf0BHFNEUIT+3P/Penfku2oZ3lL3L7lfN398s3Ye2ul0NADwSZQyAMfVtWlXTR8xXX89+6/aVLhJ49PH66U1L3GTcwCoZ5QyAL/I389fl3W5TOlj0zU0eaimrp2qsXPGamnWUqejAYDPoJQBOGHNQpvpqUFP6ZWhr8jf+OvWT27Vn774k/LK8375wwCA46KUAfjVBrQcoNmjZ+uOnnfosz2fafQHo/X25rfZ2wwATgGlDMBJCfIP0m09blNaSpp6xPXQU98+pUkfTtJ3ed85HQ0AvBKlDMApSYpK0tSLpuq5C57TwaqDumbBNfrrV39VYUWh09EAwKtQygCcMmOMhiYPVfqYdN145o2at3OeRqWN0jub3+GUJgCcIEoZgHoTFhimu/vcrdTRqerWrJv+/u3fdfm8y7Umb43T0QDA41HKANS7ttFt9fKQl/XcBc+pqLJIVy+4mlOaAPALKGUAGgSnNAHg16GUAWhQ35/SnD16Nqc0AeA4KGUATot20e1+ckrzwa8eVEFFgdPRAMAjUMoAnDZHn9K84cwbNH/nfI1KG6VpG6ep2lXtdDwAcBSlDMBpFxYYpj/0+YPSRqepZ3xPPbvyWU1In6Cl2dxLE0DjRSkD4Jjk6GRN/u1k/fvCf6vaVa1bF92quxffrazSLKejAcBpRykD4ChjjC5ofYHSUtL0+16/19LspUr5IEUvrXlJFTUVTscDgNOGUgbAIwT7B+vm7jcrfUy6Lmx9oaaunaqUD1K0aPciWWudjgcADY5SBsCjtAhvoWcueEavD3tdEUERuufze3TzopuVWZTpdDQAaFCUMgAeqV+Lfnrvkvf04NkPanPhZo2fO16PL39cRZVFTkcDgAZBKQPgsQL8AjSpyyTNGztPl3W+TLO2zdLI1JF6c+Obqq5lCw0AvoVSBsDjxYTE6IGzH9Ds0bPVPa67/rHyHxqXPk5f7P2C9WYAfAalDIDXaB/TXlOHTNXk306WMUZ3fnanbl10qzKKMpyOBgCnjFIGwOsMajVIs0fP1l/6/0UbCzdqwtwJrDcD4PUoZQC8UqBfoK4840rWmwHwGZQyAF7tWOvNxqaP1ed7P2e9GQCvQikD4BOOXm/mZ/z0u89+x3ozAF6FUgbAp/zcerMDlQecjgYAx0UpA+Bzjl5vNqnzJM3aNkuXpF6iaRunsd4MgMeilAHwWTEhMbr/7PuVOjpV3eO769mVz7LeDIDHopQB8HntYtpp6kU/XG92y6JbtPXAVqejAcARlDIAjcbR6802FW7SxLkT9eBXD2p/2X6nowEApQxA4/L9erP54+brum7XaeHOhbok7RI9v+p5lRwucToegEaMUgagUYoOjtY9fe/R3LFzNaTNEL224TWNSB2htze/zcUAABxBKQPQqCVEJOjvg/6umZfMVOfYznrq26eUMidFH+36iIsBAJxWlDIAkNS1aVe9MvQVTf7tZAX7B+uPX/xRV82/SqtzVzsdDUAjQSkDADdjjAa1GqRZo2bp0XMf1f6y/bp24bX6/We/145DO5yOB8DHUcoA4Ef8/fw1tuNYfTjuQ/2+1+/17f5vNW7OOD227DEVVBQ4HQ+Aj6KUAcDPCA0I1c3db9a8sfN0aedLlZqRqpGpIzVl7RSVV5c7HQ+Aj6GUAcAvaBraVA+c/YDSUtJ0XuJ5mrxmskamjdT7295XjavG6XgAfASlDABOUHJ0sv75m3/qreFvqVVEKz267FGNmTNGC3culMu6nI4HwMtRygDgV+oZ31NvDn9TLw5+UYF+gfrTl3/SZR9epi/3fck2GgBOGqUMAE6CMUaDkwZr1qhZ+vugv6v0cKnu+PQOXbfwOq3KXeV0PABeiFIGAKfA389fl7S7ROlj0vXQgIe0t2Svrlt4nW7/5HZtLtzsdDwAXsR4+1R737597cqVK52OAQCSpIqaCr275V29tv41FR8u1rDkYbqz551Kjk52OhoAD2CMWWWt7XvM9yhlAFD/ig8Xa9rGaXpr01s6XHtYYzqM0W09blOL8BZORwPgIEoZADikoKJAr61/TTO3zpSR0WVdLtNNZ92kJiFNnI4GwAGUMgBwWHZptqaunao52+coxD9E13S7Rtd2vVYRQRFORwNwGlHKAMBD7Di0Q//+7t9atHuRooOjddOZN2lSl0kKCQhxOhqA04BSBgAeZmPhRv3v6v/V19lfKz40Xrd0v0VjO45VkH+Q09EANCBKGQB4qBX7V+jF1S9qTf4atQhvoZvPulljOoyhnAE+ilIGAB7MWqtl2cs0ee1krc1fe6Scje0wVoH+gU7HA1CPKGUA4AW+L2cvrX1J6/LXUc4AH3S8UtbgO/obY3YZY9YbY9YYY1a6x5oYYxYZYzLcz7FHHX+/MSbTGLPVGDOsofMBgKcwxujcxHM1ffh0/eei/yg+LF6PLX9MI9NG6r2t76m6ttrpiAAaUIPPlBljdknqa60tOGrsGUkHrLVPGWP+IinWWvtnY0xXSe9K6i8pQdInkjpZa2t/7vszUwbAV/145qxleEvd3P1mjWk/hpkzwEs5OlP2M1IkTXO/niZpzFHjM6y1VdbanZIyVVfQAKDROXrmbOpFUxUXGqdHlz2qkWkj9f6295k5A3zM6ShlVtLHxphVxphb3GPNrbU5kuR+jnePJ0rae9Rn97nHAKDRMsbovMTzNH0E5QzwZaejlJ1nre0tabikO4wx5x/nWHOMsZ+cXzXG3GKMWWmMWZmfn19fOQHAo/1cObsk7RLN2jaLcgZ4uQYvZdbabPdznqQ01Z2OzDXGtJQk93Oe+/B9klof9fFWkrKP8T1fttb2tdb2jYuLa8j4AOBxji5nUy6aomahzfTIskc0Im2E3tn8jiprKp2OCOAkNGgpM8aEG2Miv38taaikDZLSJV3rPuxaSXPcr9MlTTLGBBtj2krqKOnbhswIAN7KGKOBiQOPlLOE8AT9/du/a9jsYXp1/asqOVzidEQAv0KDXn1pjGmnutkxSQqQ9I619gljTFNJ70lKkrRH0kRr7QH3Zx6UdIOkGkl3W2sXHO9ncPUlAPyfVbmr9Mr6V/R11teKDIzUpC6TdFXXq9QkpInT0QCIzWMBoNHZVLhJr65/VZ/s/kTB/sGa0GmCru12rVqEt3A6GtCoUcoAoJHacWiHXl//uubtmCcZaXT70brhzBvUJqqN09GARolSBgCNXHZptt7Y+IZSM1JV7arW0DZDddNZN6lzk85ORwMaFUoZAECSVFBRoOmbpmvG1hkqqy7T+a3O181n3aye8T2djgY0CpQyAMAPFB8u1owtMzR903QVVRWpd3xv3XDmDRrUapD8jFM3ewF8H6UMAHBM5YJHj4MAACAASURBVNXlSs1I1Zub3lROWY7aRbfTdd2u08h2IxXkH+R0PMDnUMoAAMdV7arWR7s+0hsb3tDWoq2KC43TlWdcqYmdJyoqKMrpeIDPoJQBAE6ItVbLcpbpvxv+q+U5yxUeGK4JHSfoqq5XsZ0GUA8oZQCAX21z4Wa9sfENfbTrIxkZDWs7TFd3vVrdmnZzOhrgtShlAICTll2arbc2vaXUjFSV15SrT/M+urrr1fpNq9/I38/f6XiAV6GUAQBOWcnhEqVmpOqdze8ouyxbrSNb68ozrtTYDmMVFhjmdDzAK1DKAAD1psZVo8/2fKY3N72ptflrFRkYqfGdxuuKLleoZURLp+MBHo1SBgBoEOvy1+mtTW9p0e5FkqQhbYboyjOuVI+4HjLGOJwO8DyUMgBAg8opzdE7W97R7G2zVVJdoi5NuujyLpdreNvhCg0IdToe4DEoZQCA06K8ulzzds7Tu1veVUZRhqKCojSmwxhN6jxJraNaOx0PcBylDABwWllrtTpvtd7d8q4+3f2pam2tzks8T5d3uVwDEwdyKyc0WpQyAIBj8srzNGvbLL2/7X0VVBSoVUQrTeoySWM6jFF0cLTT8YDTilIGAHBcdW21Pt3zqd7d8q5W561WiH+IRrQboUmdJ+mMpmc4HQ84LShlAACPsvXAVs3YOkPzdsxTRU2Fesb11KQukzS0zVAF+gc6HQ9oMJQyAIBHKj5crDmZczRjywztKdmjpiFNNb7TeE3sNJF7bcInUcoAAB7NZV1amr1UM7bM0Jf7vpQxRgMTB2p8x/E6v9X5CvALcDoiUC8oZQAAr7GvZJ9SM1L1QeYHyq/IV3xovMZ0HKNxHccpMSLR6XjAKaGUAQC8To2rRl/u+1Kzts3SV1lfSZLOTThXEzpN0AWtL1CgH2vP4H0oZQAAr5ZTmqO0zDSlZqQqtzxXTUOaanT70RrTcYzaRbdzOh5wwihlAACfUOuq1dfZX2vWtllasm+JamyNesT10LiO4zQseZjCA8OdjggcF6UMAOBzCioK9OH2D5WWmaYdh3YoNCBUQ9sMVUqHFPVp3oe7BsAjUcoAAD7LWqt1BeuUlpGmhbsWqqy6TIkRibqk3SUa3X60kqKSnI4IHEEpAwA0ChU1Ffp0z6dKz0zX8pzlsrLqGddTozuM1rDkYYoKinI6Iho5ShkAoNHJLcvVhzs+VPr2dO04tENBfkEanDRYo9uP1jkJ53D1JhxBKQMANFrWWm0q3KT07emav3O+DlYdVGxwrIYmD9XIdiPVI64H689w2lDKAABQ3U3Rl2Qt0YKdC/T53s9VWVuphPAEjWg3QiPajlDH2I5OR4SPo5QBAPAjZdVl+mzPZ5q3c56WZy9Xra1Vx9iOGtF2hIa3Hc7dA9AgKGUAABxHYUWhPt79sebvmK81+WskST3jempY8jANaTNEzcObO5wQvoJSBgDACdpXsk8Ldi7Qgl0LlFGUIUnqFd9Lw5KH6aKkiyhoOCWUMgAATsKOQzv08a6P9fHujyloqBeUMgAATtHPFbSLki7S4KTBah3Z2uGE8AaUMgAA6tGxClrH2I4a3HqwLky6UF2bdJUxxuGU8ESUMgAAGsjekr1avGexPtv7mb7L+04u61LzsOZHClrfFn3ZqBZHUMoAADgNiiqL9MW+L7R4z2ItzV6qytpKRQZGalCrQRqcNFgDEwYqIijC6ZhwEKUMAIDTrKKmQsuzl+uzvZ/pi71fqKiqSIF+gerfsr8ubH2hBiYOVEJEgtMxcZpRygAAcFCtq1Zr8tccOc25t2SvJKlDTAcNTByoQYmD1Cu+lwL9Oc3p6yhlAAB4CGutdh7aqSVZS7Qka4lW5a5SjatG4YHhGtBygAYlDtLAxIFst+GjKGUAAHiosuoyfZPzjZZkLdFXWV9pf9l+SXVXc57b8lydk3COejfvrdCAUIeToj5QygAA8ALWWmUezNRXWV/pq6yv9F3ed6p2VSvQL1C943trQMIAnZNwjs5ocob8jJ/TcXESKGUAAHih8upyrc5brWXZy7QsZ9mRPdFigmN0dsuzNaDlAPVr0U9JkUnsi+YlKGUAAPiAgooCLctepuU5y7Use5nyK/IlSfGh8erboq/6tehHSfNwlDIAAHyMtVa7indpxf4VWrl/pVbkrlBBRYEkSpono5QBAODjflDScldqxf7/K2nNQpupZ1xP9Yyve3Rt0pXtNxxCKQMAoJGx1mp38W59u/9bfZf3ndbkrdG+0n2SpCC/IJ3Z7My6kuYua7EhsQ4nbhwoZQAAQPnl+Vqbv/ZISdt0YJNqXDWSpOSoZHVr1k3dmtY9ujTporDAMIcT+x5KGQAA+InKmkptLNyoNXlrtCZ/jTYVbFJeRZ4kyc/4qW1UW3Vr1k1dm3ZVt6bd1LlJZ/ZLO0WUMgAAcELyy/O1qXCTNhZurHsUbFRhZaEkyd/4q11MuyOzad2adlOnJp0U7B/scGrvQSkDAAAnxVqr3PLcHxS1TQWbVFRVJEkKMAFqF9NOHWM7qmNMxyPPLcJbcMXnMVDKAABAvbHWKqcsR5sKN2lDwQZtLdqqjKIM5ZbnHjkmIjBCHWI6qGNsxyPPHWM6KiYkxsHkzvOqUmaMuVjSC5L8Jb1qrX3qeMdTygAA8AzFh4uVWZSpzIOZ2la0TZkHM5VRlKHiw8VHjmkS0kRtotr85JEUmaSQgBAH058eXlPKjDH+krZJGiJpn6QVki631m76uc9QygAA8FzWWuVX5CujKEMZRRnaVbxLu4t3a3fx7iN3JPhei/AWah3ZWi3DW6pFeAu1DG/5g9e+cDXo8UpZwOkO8wv6S8q01u6QJGPMDEkpkn62lAEAAM9ljFF8WLziw+J1XuJ5P3ivrLpMe4r3aHfx7iNlLas0S9/u/1Z55XlyWdcPjo8MilTTkKZqEtJETUPrnr9/xIbEKjIoUhGBEYoIjFB4YLgigiIUGhDqNTdv97RSlihp71Ff75N0tkNZ6iz4i7R/vaMRAADwReGSznA/fihINUpUvmqVrRpl27rH/poaHSjfrwMmS9vkUpFxqcRY2eNcT2CsFGKlIGsUICnAGgVY/eC1Ud1jeHAnXX/tew3y13oiPK2UHetv60/OrxpjbpF0iyQlJSU1dCYAAHAKrKxqXVY1LquaWqtql0s1tVY1ta4jYzXusWqXS7WuuuNrrZW1UpzqHj1kVLfk3P/I966VVZm/VOxvVelnVeFnVeUnVflLFX5SlZ9VhZ9UY6xqjP7vIataI1UbKysdt9idLp5WyvZJan3U160kZf/4IGvty5JelurWlDVoouHHvc4AAIBGq6yqRvklVcovrap7LqlSXknlkdffjxeUHlat69h/XBsjRYcGKjYsSDGRdc9RIQGKCAlQeHCAIoLcz8F1z+HB/ooIrns/NNBfIYH+CvL3U3Cgn4L8/RTg7x2nKo/F00rZCkkdjTFtJWVJmiTpCmcjAQDQuNTUupRbUqXsgxXKPlihrIMVyj1Uqbwfla3yw7U/+ay/n1GziCDFRQYrLiJYXVtGqVlEsJqEBykmLEixYYFHnmPDghQVGih/Pw+YpvIAHlXKrLU1xpg7JX2kurnJ1621Gx2OBQCAz7DWqriiRlnuwpV9qML9uvJICcstrtSPJ7aiQgIUHxWiuIhg9WgVU1e63MXr+9fxkcGKDQuSHyXrpHhUKZMka+18SfOdzgEAgLc6WH5YuwvLtedA3WNfUbmyjipdP57hCvL3U8uYECVEh+rc9s2UGBOihJhQtYwJVWJMiFpGhyo82OMqg8/h7zAAAF7GWqvc4irtLCjTngNl2l1Yrt0HyrWnsFy7C8tUXFnzg+ObhgcpMTZUHeIidH7HOCXEhCgxJtRdvELULDyY2S0PQCkDAMBDVVbXaldhmbbnlWlHfqm255dqR0GZtueVquyo2a4AP6PE2FAlNQlTj9YJatMkXElNw9SmaZiSmoQpLIg/7r0B/5QAAHCQtVb5pVXakV9WV7rcz9vzS7WvqEJH33gnMSZU7eLCNbFva7WLC1fbZuFKbhqultEhXn3VIepQygAAOE0KS6u0NbdE2/aXaGtuibbuL1FGXqlKjjrdGBLop3bNItSzdazG9Wql9vERau8uYMx4+Tb+6QIAUM+qamq1bX+pNuUc0pb9JdqWW6Kt+0tVUFp15JiYsEB1bh6plJ4J6hAXoXZxEWofH6GWUSGs72qkKGUAAJyC8sM12pxTrA1ZxdqQdUgbs4u1LbdENe49JUID/dWpeYQGd45T5xaRdY/mkYqLDJYxlC/8H0oZAAAn6FB5tTZmH9KG7LrytSHrkHYUlB1Z99UkPEhnJkbrgs5xOjMhWt0SopTUJIyZL5wQShkAAMeQV1KpjVnFdSUsq1gbsg9pX1HFkfdbRoeoW0K0RvVIULeEaJ2ZGKUWUSHMfuGkUcoAAI1exeFabcg+pO/2FGnN3oP6bs9B5RyqPPJ+ctMw9WgVoyvOTjoyA9Y0ItjBxPBFlDIAQKPiclntLCzTmj0H9d3euhK2OafkyA2zW8WGqk+bWPVsHaMzE6PVNSFKUSGBDqdGY0ApAwD4tMrqWq3de1Ardxdp5a4DWr3noA5VVEuSIoID1L1VtG67oJ16tq4rYnGRzIDBGZQyAIBPKSyt0qrdRUdK2PqsQ6qurZsF6xAfoeFntlCvpBj1bB2rDvER8mcRPjwEpQwA4NVyiyu1fEehlu84oG92FmpHfpmkuptsd28VrRsGtlW/Nk3Up02sYsODHE4L/DxKGQDAq+QcqtA3Ow5o+Y5CfbPzgHYW1JWwyOAA9WvbRBP7tFa/5FidmRitkEB/h9MCJ45SBgDwaAWlVfo6s0BLMwu1fGehdheWS5KiQgLUv20TXXl2ks5u21RdE6I4FQmvRikDAHiUyupafbvzgL7KLNBXGQXalFMsqa6End2uqa45J1lnt22iM1pSwuBbKGUAAEe5XFYbs4u1JDNfX2UUaOXuIh2ucSnQ36hPm1j9aVhnndehmc5KjKaEwadRygAAp93eA+VHZsKWbi9QUXndFhVdWkTqmgFtdF7HZjq7bROFBfHHFBoP/m0HADS4qpq6U5KLt+Tr86152uFenN88KlgXdmmuQR2b6dwOTRUfGeJwUsA5lDIAQIPIPlihz7fma/HWPH2dWaDyw7UKCvDTOe2a6qoBbTSoYzN1iI/gXpGAG6UMAFAvampdWr3noBZvzdPiLXnasr9EkpQYE6pxvRN1YZd4ndOumUKD2KYCOBZKGQDgpJVV1WhJRr4+3pSrxVvyVFRerQA/o37JTfTAiC4a3Dme2TDgBFHKAAC/Sl5JpT7dnKdFm3L1VWaBDte4FB0aqAu7xOuiM5prUKdm3MAbOAmUMgDAL8rMK9FHG3O1aFOu1uw9KElqFRuqK89O0pCuzdUvuYkC/f0cTgl4N0oZAOAnrLXalFOshRv2a8GG/crMK5UkdW8VrXuHdNKQbs3VuXkkpyWBekQpAwBIqitia/cd0oINOVq4Yb92F5bLz0hnt22qa85po6FdW6hFNFtWAA2FUgYAjZjLZbV6T5Hmr9+vjzbuV9bBCgX4GZ3boZluv6C9hnRtrqYRwU7HBBoFShkANDLWWm3IKtbcddn6cG22sg9VKijAT+d3bKY/DOmkIWc0V3QYC/WB041SBgCNxNb9JZq7NlsfrsvWrsJyBfobnd8xTn+6uLMuOqO5IrliEnAUpQwAfNiugjLNXZutueuytS23VH5GOrd9M93+m/Ya1q2FYsKCnI4IwI1SBgA+5mD5Yc1dl6O01fu0ek/d9hX9k5vo0ZRuGn5mS8VFskYM8ESUMgDwAYdrXPp8a55SV2fpsy15OlzrUqfmEfrL8C4a3SNBCTGhTkcE8AsoZQDgpb7fwiJ19T7NXZutovJqNYsI0tXntNHYXonqlhDFPmKAF6GUAYCX2VdUrg++y1Lqd1nakV+m4AA/DenaXON7t9Kgjs0UwM76gFeilAGAFyitqtH89TlKXb1Py3cckCT1b9tEt57fTsPPasm9JgEfQCkDAA9lbd3GrjNX7NWH63JUfrhWbZuF694hnTSmV6JaNwlzOiKAekQpAwAPU1hapdTVWZq5cq8y80oVFuSvUd0TdGm/1uqdFMM6McBHUcoAwAPUuqyWZOTrvZV7tWhTrqprrXolxejp8WdpZPcERQTz6xrwdfxXDgAOyjlUoRnf7tX7K/cq+1ClYsMCdc05ybqsX2t1ah7pdDwApxGlDABOM5fL6qvMAk1fvlufbsmTy1oN7NBMD47sqou6xis4wN/piAAcQCkDgNPkQNlhzVq1V29/s0e7C8vVJDxINw9qpyv6JympKYv2gcaOUgYADej7KyinL9+jeetzdLjGpX7JsbpnSCddfGYLZsUAHEEpA4AGUH64RmnfZemtZbu1ZX+JIoIDdFnf1rpyQJK6tIhyOh4AD0QpA4B6tPdAud5ctkszV+xVcWWNuraM0pNjz1JKzwSFcwUlgOPgNwQAnCJrrZZtL9R/l+7SJ5tz5WeMLj6zha4/N1l92sSyrxiAE0IpA4CT9P0pymlLd2lbbqmahAfpjt900JUDktQyOtTpeAC8DKUMAH6lnEMVemPpLr37zR4VV9aoW0KU/jGhu0b1SFBIIAv3AZwcShkAnKCN2Yf06pKdmrs2Wy5rNfzMlrr+PE5RAqgflDIAOA6Xy+qLbfl6ZckOLd1eqPAgf11zTrKuPy+ZG4IDqFeUMgA4hsrqWs1Zk6VXluxUZl6pWkSF6P7hXTSpf5KiQwOdjgfAB1HKAOAoB8oOa/ry3Xpz2S4VlB5W15ZR+tdlPTTyrAQFBfg5HQ+AD6OUAYCk7IMVevnLHZqxYo8qq10a3DlONw9qp3PaN2W9GIDTglIGoFHbkV+qqV9sV9p3WbJWGtMrUbee304dm0c6HQ1AI9NgpcwY8/8k3Swp3z30gLV2vvu9+yXdKKlW0u+ttR+5x/tIekNSqKT5ku6y1tqGygig8dqUXazJn2dq/vocBfr76Yr+SbrlgvZKjGF/MQDOaOiZsn9Za589esAY01XSJEndJCVI+sQY08laWytpiqRbJC1XXSm7WNKCBs4IoBFZtbtIkxdn6tMteYoIDtCtF7TXDee1VVxksNPRADRyTpy+TJE0w1pbJWmnMSZTUn9jzC5JUdbaZZJkjHlT0hhRygCcImutvs4s1L8XZ2j5jgOKDQvUvUM66ZpzkhUdxpWUADxDQ5eyO40x10haKelea22RpETVzYR9b597rNr9+sfjAHBSXC6rRZtzNXlxptbuO6TmUcH668gzdHn/JG4ODsDjnNJvJWPMJ5JaHOOtB1V3KvIxSdb9/JykGyQd6zIme5zxY/3cW1R3mlNJSUm/OjcA31ZT69KH63I0+fNMbcstVVKTMD059iyN75Oo4ABugwTAM51SKbPWXnQixxljXpH0ofvLfZJaH/V2K0nZ7vFWxxg/1s99WdLLktS3b18uBAAgSaqudWnWqn2a/Hmm9h6oUKfmEXphUk+NPKulAvzZYwyAZ2vIqy9bWmtz3F+OlbTB/Tpd0jvGmH+qbqF/R0nfWmtrjTElxpgBkr6RdI2k/22ofAB8R02tS6mrs/TiZxnaV1ShHq2i9dDIrrrojOby82OPMQDeoSEXVTxjjOmpulOQuyTdKknW2o3GmPckbZJUI+kO95WXknS7/m9LjAVikT+A46ipdSl9bbZe+DRDuwvLdVZitB5LOVO/6RzHhq8AvI7x9m3A+vbta1euXOl0DACnUa3L6sN12XrhkwztKCjTGS2jdM+QTrrojHjKGACPZoxZZa3te6z3uPwIgNdwuazmb8jR859kKDOvVJ2bR2rqVb01tGsLTlMC8HqUMgAez+Wy+njTfv1rUYa25paoQ3yEXrqit4afSRkD4DsoZQA8lrVWX2zL1z8+2qqN2cVq1yxcL0zqqUu6J8ifMgbAx1DKAHikVbuL9MzCLfpm5wG1bhKq5yb2UErPBLa2AOCzKGUAPMq23BL946OtWrQpV80igvVoSjdN6pekoADKGADfRikD4BH2FZXr+U8ylLp6n8KDAvTHoZ10/XltuR0SgEaD33YAHFVYWqWXFm/X9OW7JSPdOLCt/uc3HRQbHuR0NAA4rShlABxRWlWj15bs1CtLdqj8cI0m9mmtuy7qqISYUKejAYAjKGUATquaWpdmrtyrfy3apoLSw7q4Wwv9cVgndYiPdDoaADiKUgbgtLDW6vNt+Xpy3mZl5JWqX3KsXrmmr3olxTodDQA8AqUMQIPblF2sJ+dv1leZBUpuGqapV/XWsG4tuCUSAByFUgagweQWV+q5j7fq/VX7FB0aqIcv6aqrBrRhewsAOAZKGYB6V364Rv/5Yode/nKHal1WNw1sqzsHd1R0WKDT0QDAY1HKANSbWpfV7FX79OzHW5VXUqWR3Vvqz8O6KKlpmNPRAMDjUcoA1IulmQV69MNN2rK/RL2TYjTlqj7q04ZF/ABwoihlAE7JvqJyPTFvsxZs2K9WsaF66YreGnEWi/gB4NeilAE4KZXVtZr6xXZN+Xy7/IzRH4d20k2D2ikk0N/paADglShlAH4Va60Wbtivx+dtVtbBCo3qkaD7h3dhJ34AOEWUMgAnbOv+Ej0yd6OWbi9UlxaRmnnLAJ3drqnTsQDAJ1DKAPyiQ+XV+tcn2/TW8t2KCA7QYynddHn/JAX4s98YANQXShmAn1Xrsnpv5V7946OtOlh+WFee3Ub3DOmk2PAgp6MBgM+hlAE4plW7D+hv6Ru1IatY/ds20f8b1U1dE6KcjgUAPotSBuAH8kuq9Pf5m5X6XZZaRIXoxct7aVT3lmxxAQANjFIGQFLdqcp3vtmtZz7aqqpql+4Y3F53DO6gsCB+TQDA6cBvWwBav++Q/vrBeq3dd0jndWiqR1POVPu4CKdjAUCjQikDGrHiymo999FWvbV8t5pGBOuFST01ukcCpyoBwAGUMqARstYqfW22Hp+3WYWlVbp6QBvdO6yzokICnY4GAI0WpQxoZLbnl+rhORv0dWahureK1uvX9tNZraKdjgUAjR6lDGgkqmpqNXlx3b0qgwP99FhKN11xdhv5+3GqEgA8AaUMaARW7Dqgv8xep+35ZRrdI0F/veQMxUeGOB0LAHAUShngww5VVOvphVv0zjd7lBgTqv9e30+DO8c7HQsAcAyUMsAHWWu1cMN+/S19owpKq3TTwLa6Z2gn9hwDAA/Gb2jAx+QcqtDDczZq0aZcdUuI0mss5AcAr0ApA3yEy2U1/ZvdembhVtW4XLp/eBfdOLCtAvz9nI4GADgBlDLAB2TklujPs9dp9Z6DGtSxmZ4Yc5aSmoY5HQsA8CtQygAvVl3r0stf7tALn2QoPNhf/7y0h8b2SmRHfgDwQpQywEttyi7WfbPXakNWsUae1VKPpHRTs4hgp2MBAE4SpQzwModrXPr34kxNXpypmLAgTbmyt4af1dLpWACAU0QpA7zI2r0Hdd+sddqaW6JxvRL10CVdFRse5HQsAEA9oJQBXqCyulbPf5Khl7/crvjIEL1+XV9d2KW507EAAPWIUgZ4uO/2FOne99dqR36ZJvVrrQdGnqGokECnYwEA6hmlDPBQVTW1euGTDE39YrtaRIXorRv7a1DHOKdjAQAaCKUM8EAbsg7pj++v1Zb9Jbq0byv99ZKuzI4BgI+jlAEepLrWpSmfb9eLn2YoNjyItWP/v737DrOqvvc9/v5SRRSMoiBNsAsWyoCI1xxr1HOMJMaCjaaxRJOYY5oxOWn3eOIx12g01tBExYIavbEkGhOTaChDUUBAQVGQ3oY6MOV3/pjtuXM9I6hT1p4979fz8LDmt2bNfJ7FPLM//NZvrS1JTYilTMoTb6/cxPWPv84bS0s4+5jO/PTs3t5ZKUlNiKVMylhFZWLM39/llj8uYI/WLXzumCQ1UZYyKUNL1m3lXx+bxbTF6/lCr47cdM5RPpVfkpooS5mUgZQST8z4gJ88M5cIfM9KSZKlTGpoG7bu4AdPzea52Ss4tufe3HpBH7rs1SbrWJKkjFnKpAb097fXcP3js1i3ZQffP/NwvnrCgTRv5uyYJMlSJjWI0rIKbvnDAkb//V0O2rcto4cP4Mgu7bOOJUnKI5YyqZ7NX7GR6x6ZxfwVmxh23AHccOYRtGnVPOtYkqQ8YymT6kllZWLsa4u5+YX5tNutJWNHDOCkw/fLOpYkKU81q83BEXFeRMyNiMqIKPrIvhsiYmFELIiI06uN94+I2bl9v47c7WYR0ToiHs2NT4mIHrXJJmVp5cZSho+dys9//yafP6QDL1x3goVMkrRTtZ0pmwOcA9xbfTAiegFDgd5AZ+CliDg0pVQB3A1cAUwGngPOAJ4HLgPWp5QOjoihwM3ABbXMJzW452cv54anZrO9rJKbvnwUFw7s5qMuJEm7VKtSllKaB9T0gjMEeCSltB14NyIWAgMjYjHQLqX0j9xxDwBfoqqUDQF+kjt+EnBnRERKKdUmo9RQNm8v5yfPzGXS9KUc3bU9t13QhwP33SPrWJKkRqK+1pR1oWom7ENLc2Nlue2Pjn94zBKAlFJ5RJQA+wBr6imjVGfeWLqBr0+cyZJ1W/n6yQfzjVMOoWXzWq0OkCQ1MbssZRHxEtCphl03ppSe/rjDahhLOxnf2TE1ZbqCqkugdO/e/WMiSPWvsjIx5tV3ufmF+XTYozWPXHEcA3vunXUsSVIjtMtSllI69TN83aVAt2ofdwWW5ca71jBe/ZilEdECaA+s+5hM9wH3ARQVFXl5U5lYu3k73378df68YDWn9erILecezV67t8o6liSpkaqv6yvPAENzd1T2BA4BpqaUlgObImJQ7q7LYcDT1Y4Znts+F3jZ9WTKV68tWsOZt/+NVxet5WdDenPfpf0tZJKkWqnVmrKI+DJwB7Av8GxEzEopnZ5SmhsRjwFvXhZZ2AAAFQxJREFUAuXANbk7LwGuBsYBbaha4P98bnw0MCF3U8A6qu7elPJKeUUlt//pbe7880J6dmjLuJED6dW5XdaxJEkFIBr7ZFRRUVEqLi7OOoaagA82bOObE2dS/N56zuvflZ8O6c3urXz+siTpk4uI6Smlopr2+YoifQIvzFnBdye9TmWC24f2YUifLrs+SJKkT8FSJu3EjvJKbnpuHuNeW8xRXdpzx4V96dGhbdaxJEkFyFImfYwl67Zy7cSZvL5kAyOP78ENZx5BqxY+e0ySVD8sZVINXnxzJdc/NouU4O6L+3HmUftnHUmSVOAsZVI1ZRWV3PKHBdz313c4sks7fnNRPw7Yx8uVkqT6ZymTcpaXbOPah2cy/b31XDKoOz/8l17s1rJ51rEkSU2EpUwC/rJgFd96dBY7yiv59YV9OfuYzllHkiQ1MZYyNWnlFZX86qW3+M2fF3F4pz35zcX9OGjfPbKOJUlqgixlarJWbSrlGxNnMvmddQwd0I2fnN3by5WSpMxYytQkFS9ex9cemsHG0jL+z3nH8JX+XbOOJElq4ixlalJSSox9dTE3PTePrp9rw/hRAzlif9+7UpKUPUuZmowt28v5/pOz+b+vL+O0Xh355XnH0L5Ny6xjSZIEWMrURCxctZmrH5zOotWb+c7ph3H1Px1Es2aRdSxJkv6bpUwF7/nZy/n246/TumVzJlx2LMcf3CHrSJIk/Q+WMhWs8opK/jP3dP4+3fbirov70XmvNlnHkiSpRpYyFaQ1m7dz7cMzmPzOOi4ddAA/POsIWrfwcReSpPxlKVPBmb20hCsnFLN2yw4fdyFJajQsZSooT0xfyg1PzaZD21ZMumowR3Vtn3UkSZI+EUuZCkJZRSU3PTePsa8uZtCBe/Obi/qxzx6ts44lSdInZilTo7d283auya0fG3l8D37wz0fQsnmzrGNJkvSpWMrUqFVfP3br+cdwTj/Xj0mSGidLmRqtJ2cs5YYnZ9Nhj9Y8cfVgjuzi+jFJUuNlKVOjU15RyU3PzWfMq++6fkySVDAsZWpUSraWce3EGfzt7TWMPL4HN/7zEbRw/ZgkqQBYytRovLN6M5ePL2bJ+q3851eO5vwB3bKOJElSnbGUqVH461urufbhGbRo3oyHvzqIAT32zjqSJEl1ylKmvJZSYtxri/n579/k0I57cv+wIrrtvXvWsSRJqnOWMuWtHeWV/PiZOUycuoTTenXktgv60La1P7KSpMLkK5zy0trN27n6oRlMfXcd15x0ENefdhjNmkXWsSRJqjeWMuWd+Ss2cvn4YlZt2s7tQ/swpE+XrCNJklTvLGXKKy++uZLrHplJ29YteOzK4+jTba+sI0mS1CAsZcoLKSXufmURt/xhAUd1ac99lxbRqf1uWceSJKnBWMqUudKyCr7/xBv8btYyvnhMZ24592h2a9k861iSJDUoS5kytWpTKV99YDqvL9nAt79wKNecdDARLuiXJDU9ljJlZv6KjYwaO431W8u455L+nHFkp6wjSZKUGUuZMvGXBau49uGZtG3dnMevOo4ju7TPOpIkSZmylKnBTfjHYn78zFwO69SOMSOK2L99m6wjSZKUOUuZGkxFZeLfn53HmFff5eTD9+OOC/v6hH5JknJ8RVSD2LK9nG8+MouX5q1kxOAe/OisXjT3Cf2SJP03S5nq3YqSUi4bP415yzfy07N7M3xwj6wjSZKUdyxlqldzl5Vw2bhiNpWWMXr4AE46fL+sI0mSlJcsZao3L89fybUPz6R9m5Y8ftVgenVul3UkSZLylqVM9eLBye/xb0/PoVfndowePoCO7XzLJEmSdsZSpjpVWZm45Y8LuPsvizj58P2486K+7N7KHzNJknbFV0vVme3lFXx30hs8PWsZFx3bnZ+d3ZsWzZtlHUuSpEbBUqY6UbKtjCsnFDP5nXV85/TD+NqJB/kelpIkfQqWMtXaBxu2MWLMVBav3cJtF/ThS327ZB1JkqRGx1KmWpm7rISRY6exrayC8aMGMvigDllHkiSpUbKU6TN75a3VfO3B6bRv05JJVw3msE57Zh1JkqRGy1Kmz+Sx4iXc8ORsDu24J2NHDKBTex95IUlSbVjK9KmklLj9T29z20tvc8IhHbjr4n7suVvLrGNJktToWcr0iVVUJn74uzlMnPo+X+nXlV985Sha+sgLSZLqRK1eUSPivIiYGxGVEVFUbbxHRGyLiFm5P/dU29c/ImZHxMKI+HXknpsQEa0j4tHc+JSI6FGbbKpbpWUVXP3gdCZOfZ+vnXgQvzzvaAuZJEl1qLYzZXOAc4B7a9i3KKXUp4bxu4ErgMnAc8AZwPPAZcD6lNLBETEUuBm4oJb5VAdKtpZx+QPTKH5vPT/5Yi9GHN8z60iSJBWcWk11pJTmpZQWfNLPj4j9gXYppX+klBLwAPCl3O4hwPjc9iTglPDpo5lbXrKN8+59jdeXlHDHhX0tZJIk1ZP6vP7UMyJmRsQrEXFCbqwLsLTa5yzNjX24bwlASqkcKAH2qcd82oW3V27inLteY9mGUsaNGsBZR3fOOpIkSQVrl5cvI+IloFMNu25MKT39MYctB7qnlNZGRH/gdxHRG6hp5it9+K12su+jma6g6hIo3bt331l8fUbFi9dx2fhiWrVoxqNXDqJ35/ZZR5IkqaDtspSllE79tF80pbQd2J7bnh4Ri4BDqZoZ61rtU7sCy3LbS4FuwNKIaAG0B9Z9zNe/D7gPoKioqMbips/uxTdXcu3DM+i8VxseGDWQbnvvnnUkSZIKXr1cvoyIfSOieW77QOAQ4J2U0nJgU0QMyq0XGwZ8ONv2DDA8t30u8HJu3Zka0GPTlnDlhGIO77Qnk646zkImSVIDqdXdlxHxZeAOYF/g2YiYlVI6Hfg88LOIKAcqgKtSSh/Oel0NjAPaUHXX5fO58dHAhIhYSNUM2dDaZNOnd88ri/jF8/M54ZAO3HNJf9q29jF2kiQ1lGjsk1FFRUWpuLg46xiNWkqJXzw/n3v/+g5nHb0/t57fh1YtfAaZJEl1LSKmp5SKatrnVEgTV15RyY1PzeHR4iVcMqg7Pz37SJo380kkkiQ1NEtZE1ZaVsF1j8zihbkr+MbJB/Ot0w7FR8NJkpQNS1kTtXl7OVc8UMxri9byb2f1YtT/8qGwkiRlyVLWBK3bsoMRY6cyd9lGbj3/GM7p13XXB0mSpHplKWtilm3YxqWjp7B0/TbuvaQ/p/bqmHUkSZKEpaxJWbhqM8NGT2FTaTkPjBrIsQf6LlaSJOULS1kTMeeDEoaNmUqzgIlXDOLILr5tkiRJ+cRS1gRMf28dI8ZMo12bljx4+bH07NA260iSJOkjLGUF7tWFa7h8fDGd2u/GQ5cfS+e92mQdSZIk1cBSVsD+NG8lVz80g577tGXC5QPZb8/dso4kSZI+hqWsQD37xnK++chMenVux/iRA/lc21ZZR5IkSTthKStAjxcv4XtPvEH/Az7H6BEDaLdby6wjSZKkXbCUFZgJ/1jMj56eywmHdODeS/uzeyv/iSVJagx8xS4g97yyiF88P59Tj+jInRf1ZbeWzbOOJEmSPiFLWQFIKfGrF9/i1y8v5IvHdObW84+hZfNmWceSJEmfgqWskUsp8b+fncfov7/L+UVd+Y9zjqZ5s8g6liRJ+pQsZY1YZWXixt/NYeLU9xkxuAf/dlYvmlnIJElqlCxljVR5RSXfmfQGT838gK+deBDfOf0wIixkkiQ1VpayRmh7eQXfmDiTP8xdyXdOP4xrTjo460iSJKmWLGWNzLYdFVz14HReeWs1P/5iL0Ye3zPrSJIkqQ5YyhqRzdvLuWzcNKYuXsfNXzmKCwZ0zzqSJEmqI5ayRmJTaRkjxk5j1pIN3HZBH4b06ZJ1JEmSVIcsZY3AxtIyRoyZyhtLS7jzwr6cedT+WUeSJEl1zFKW5zaWljFs9FTmfFDCnRf144wjO2UdSZIk1QNLWR4r2VbGsDFTeXNZCXdd3I8v9LaQSZJUqCxleapkaxmXjpnCvOUbuevi/pzWq2PWkSRJUj2ylOWhDVt3cMnoKby1YjP3XNKfU46wkEmSVOgsZXlm/ZaqQvb2ys3ce2l/Tjp8v6wjSZKkBmApyyPrtuzg4t9OYdHqzdw3rD8nHmYhkySpqbCU5Ym1m7dz8W+n8M6aLdw/rIh/OnTfrCNJkqQGZCnLA2s2b+fi+6eweO0WRg8v4oRDLGSSJDU1lrKMrdm8nYvun8z767YyZsQAjj+4Q9aRJElSBixlGVq9qaqQLVm/lTHDBzDYQiZJUpNlKcvIqo2lXHj/ZJZtKGXsiIEcd9A+WUeSJEkZspRlYNXGUobeP5kVJaWMGzmAYw+0kEmS1NRZyhrYipJSLrp/Mis2ljJu5EAG9tw760iSJCkPWMoa0MrcJctVG0t5YNRAinpYyCRJUhVLWQP5cFH/qo2lPHDZQPofYCGTJEn/j6WsAVQ9GLZqUf+4kQMsZJIk6X9olnWAQrc+99ZJ763dyujhRS7qlyRJNXKmrB6VbCvj0jFVb53022FFPodMkiR9LGfK6snG0jKGjZnKghWbuPeS/nze97KUJEk7YSmrB5u3lzNy7DTmflDCXRf356TD98s6kiRJynNevqxjW3eUM2rcNGYt2cCdF/bltF4ds44kSZIaAWfK6lBpWQVXTphO8eJ1/OqCPpx51P5ZR5IkSY2EM2V1pKyikmsfnsnf3l7DLecezdnHdM46kiRJakScKasDFZWJ6x97nZfmreRnQ3pzXlG3rCNJkqRGxlJWS5WViR88OZtnXl/G9844nGHH9cg6kiRJaoQsZbWQUuLnz77Jo8VL+PrJB3P1iQdlHUmSJDVSlrJauPXFtxj76mJGHd+Tfz3t0KzjSJKkRsxS9hnd+8oi7nh5IUMHdONHZx1BRGQdSZIkNWKWss9g4tT3+Y/n53PW0fvz718+ykImSZJqzVL2KT37xnJ+8NRsTjxsX249vw/Nm1nIJElS7dWqlEXELRExPyLeiIinImKvavtuiIiFEbEgIk6vNt4/Imbn9v06ctNMEdE6Ih7NjU+JiB61yVYf/vrWaq57dCb9u3+Ouy/uT6sWdlpJklQ3atsqXgSOTCkdDbwF3AAQEb2AoUBv4AzgrohonjvmbuAK4JDcnzNy45cB61NKBwO/Am6uZbY6Nf29dVw5YToH77cno0cMoE2r5rs+SJIk6ROqVSlLKf0xpVSe+3Ay0DW3PQR4JKW0PaX0LrAQGBgR+wPtUkr/SCkl4AHgS9WOGZ/bngScEnmyWGve8o2MHDuNju1a88CogbRv0zLrSJIkqcDU5fW3UcDzue0uwJJq+5bmxrrktj86/v8dkyt6JcA+dZjvM1m8ZguXjp7K7q1aMOGyY9l3z9ZZR5IkSQVol+99GREvAZ1q2HVjSunp3OfcCJQDD314WA2fn3YyvrNjasp0BVWXQOnevfvHZq8Ly0q20bpFM8aNHEC3vXev1+8lSZKarl2WspTSqTvbHxHDgbOAU3KXJKFqBqz6G0B2BZblxrvWMF79mKUR0QJoD6z7mEz3AfcBFBUV1Vjc6srggzrw52+f6KJ+SZJUr2p79+UZwPeAs1NKW6vtegYYmrujsidVC/qnppSWA5siYlBuvdgw4OlqxwzPbZ8LvFyt5GXKQiZJkurbLmfKduFOoDXwYm5N/uSU0lUppbkR8RjwJlWXNa9JKVXkjrkaGAe0oWoN2ofr0EYDEyJiIVUzZENrmU2SJKnRiDyZjPrMioqKUnFxcdYxJEmSdikipqeUimra53U5SZKkPGApkyRJygOWMkmSpDxgKZMkScoDljJJkqQ8YCmTJEnKA5YySZKkPGApkyRJygOWMkmSpDxgKZMkScoDljJJkqQ8YCmTJEnKA5YySZKkPGApkyRJygOWMkmSpDxgKZMkScoDljJJkqQ8YCmTJEnKA5YySZKkPGApkyRJygORUso6Q61ExGrgvXr+Nh2ANfX8PVTFc91wPNcNx3PdcDzXDcdz/dkckFLat6Ydjb6UNYSIKE4pFWWdoynwXDccz3XD8Vw3HM91w/Fc1z0vX0qSJOUBS5kkSVIesJR9MvdlHaAJ8Vw3HM91w/FcNxzPdcPxXNcx15RJkiTlAWfKJEmS8oClbBci4oyIWBARCyPi+1nnKVQR0S0i/hwR8yJibkR8M+tMhSwimkfEzIj4fdZZCl1E7BURkyJifu7n+7isMxWiiPhW7nfHnIiYGBG7ZZ2pUETEmIhYFRFzqo3tHREvRsTbub8/l2XGQmEp24mIaA78BjgT6AVcGBG9sk1VsMqB61NKRwCDgGs81/Xqm8C8rEM0EbcDL6SUDgeOwfNe5yKiC/ANoCildCTQHBiabaqCMg444yNj3wf+lFI6BPhT7mPVkqVs5wYCC1NK76SUdgCPAEMyzlSQUkrLU0ozctubqHrh6pJtqsIUEV2BfwF+m3WWQhcR7YDPA6MBUko7Ukobsk1VsFoAbSKiBbA7sCzjPAUjpfRXYN1HhocA43Pb44EvNWioAmUp27kuwJJqHy/FolDvIqIH0BeYkm2SgnUb8F2gMusgTcCBwGpgbO5y8W8jom3WoQpNSukD4JfA+8ByoCSl9MdsUxW8jiml5VD1n2pgv4zzFARL2c5FDWPerlqPImIP4AngupTSxqzzFJqIOAtYlVKannWWJqIF0A+4O6XUF9iCl3nqXG490xCgJ9AZaBsRl2SbSvr0LGU7txToVu3jrjglXm8ioiVVheyhlNKTWecpUMcDZ0fEYqoux58cEQ9mG6mgLQWWppQ+nPWdRFVJU906FXg3pbQ6pVQGPAkMzjhToVsZEfsD5P5elXGegmAp27lpwCER0TMiWlG1cPSZjDMVpIgIqtbdzEsp3Zp1nkKVUrohpdQ1pdSDqp/nl1NKzijUk5TSCmBJRByWGzoFeDPDSIXqfWBQROye+11yCt5QUd+eAYbntocDT2eYpWC0yDpAPksplUfEtcAfqLqbZ0xKaW7GsQrV8cClwOyImJUb+0FK6bkMM0l14evAQ7n/2L0DjMw4T8FJKU2JiEnADKru5J6JT5uvMxExETgR6BARS4EfA78AHouIy6gqxedll7Bw+ER/SZKkPODlS0mSpDxgKZMkScoDljJJkqQ8YCmTJEnKA5YySZKkPGApkyRJygOWMkmSpDxgKZMkScoD/wWC4Ouoi6jCvAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def srp_plot(srp_t, srp_x, srp_u):\n", "\n", " figsize = (10,10)\n", " plt.figure(figsize=figsize)\n", " plt.plot(srp_t, srp_x[:,:3])\n", " plt.xlabel('Time since ignition (s)')\n", " plt.figure(figsize=figsize)\n", " plt.plot(srp_t, srp_x[:,3:6])\n", " plt.xlabel('Time since ignition (s)')\n", " plt.figure(figsize=figsize)\n", " plt.plot(srp_t, srp_x[:,-1])\n", " plt.xlabel('Time since ignition (s)')\n", "\n", " plt.figure(figsize=figsize)\n", " plt.plot(srp_t, srp_u[:,0]/(70*8500))\n", " plt.xlabel('Time since ignition (s)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi-phase adjoint/STM computations\n", "\n", "\\begin{align}\n", "J(x_f) = m_f \\\\ \n", "\\frac{\\partial J}\\frac{\\partial p} = \\frac{\\partial J}\\frac{\\partial x}\n", "\\end{align}\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

hwNumPDE/lectNumPDE_F10ND_F11ND_F71NT

Lecture1_Introduction.ipynb

1

68006

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 1 and 2: Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Structure of the course\n", "\n", "This course consists of the following three Sections:\n", "\n", "1. Parabolic PDEs\n", "2. Hyperblic PDEs\n", "3. Elliptic PDEs\n", "\n", "\n", "## What is a Partial Differential Equation (PDE)?\n", "We give the term/acronym PDE a meaning by the following" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Definition. <em> (Partial Differential Equation)</em>\n", "<em>Equations which contain the partial derivatives of a function \n", "$ u(x,y)\\,:\\,\\mathbb{R}^2\\to\\mathbb{R}$ are called $\\texttt{Partial Differential Equations}$ (PDEs):</em>\n", "\n", "$$\n", "F\\Bigl(\n", "\t\tx,y,u,\\frac{\\partial u}{\\partial x},\\frac{\\partial u}{\\partial y},\\frac{\\partial u}{\\partial x\\partial y}\n", "\t\\Bigr)\n", "\t= 0\n", "\t\\,.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Examples:\n", "1. Laplace equation in 1D\n", "$$\\Delta u = u_{xx} = 0\\,.$$\n", "2. Laplace equation in 2D\n", "$$\\Delta u = u_{xx}+u_{yy} = 0\\,.$$\n", "3. Heat equation\n", "$$u_t-a\\Delta u = f\\,,$$\n", "where $a$ is the material's heat conductivity and $f$ is a heat source/sink.\n", "4. Linear transport equation in 1D\n", "$$u_t+cu_x=0$$\n", "5. Wave equation in 1D\n", "$$u_{tt}-c^2u_{xx}=0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification of PDEs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Definition. <em>(Classification of PDEs)</em>\n", "<em> A linear PDE in two variables $(x,y)$ of the form \n", "\n", "$\n", "a(x,y)u_{xx}+2b(x,y)u_{x,y}+c(x,y)u_{yy}+d(x,y)u_x\n", "\t+e(x,y)u_y+f(x,y)u=g\\,,\n", "$\n", "\n", "is called \n", "\n", "i) $\\texttt{elliptic}$ in $(x,y)\\in\\Omega$, if $ac-b^2>0$, \n", "\n", "ii) $\\texttt{hyperbolic}$ in $ (x,y)\\in\\Omega$, if $ac-b^2<0$,\n", "\n", "iii) $\\texttt{parabolic}$ in $ (x,y)\\in\\Omega$, if $ac-b^2=0$.\n", "\n", "The above linear PDE is $\\texttt{elliptic (hyperbolic, parabolic)}$ if it is \n", "$\\texttt{elliptic (hyperbolic, parabolic)}$ for all $(x,y)\\in\\Omega$.\n", "</em>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise: <em>(Classify PDEs)</em>\n", "Look at the examples 1.-5. above and decide to which class of PDE each example belongs. \n", "[Note that the heat equation (Example 3.) is to be considered in one space dimension such that it becomes an equation for the two variables $(t,x)$.]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finite Difference Method for parabolic PDEs" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Let us discretize both time and space as follows:\n", "\n", "$$t_n = n \\Delta t,~ n = 0, \\ldots, N-1,$$\n", "\n", "$$x_j = j \\Delta x,~ j = 0, \\ldots, J-1,$$\n", "\n", "where $N$ and $J$ are the number of discrete time and space points in our grid respectively.\n", "$\\Delta t(=k)$ and $\\Delta x(=h)$ are the time step and space step respectively and defined as follows:\n", "\n", "$$\\Delta t = T / N,$$\n", "\n", "$$\\Delta x = L / J,$$\n", "\n", "where $T$ is the point in time up to which we will integrate $u$ numerically.\n", "\n", "<!-- PDF and SVG files could not be loaded via a direct download link \n", "from Google Drive but PNG files -->\n", "\n", "\n", "\n", "<!-- Not working PDF and SVG loads:\n", "\n", "\n", "\n", "\n", "\n", "-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we show how to define a grid in Python using Python libraries \n", "such as [NumPy](http://www.numpy.org/) \n", "and [pyplot](http://matplotlib.org/api/pyplot_api.html)." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot\n", "%matplotlib inline\n", "from matplotlib import rcParams\n", "rcParams['font.family'] = 'serif'\n", "rcParams['font.size'] = 16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Physical parameters:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a=0.3 # diffusion constant" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Specify spatial grid in Python:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "L = 1. # length of domain\n", "J = 41 # number of grid points\n", "dx = float(L)/float(J-1) # mesh size h = dx\n", "#x_grid = np.array([j*dx for j in range(J)]) # spatial grid points\n", "x_grid = np.linspace(0,1.0,J) # " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Specify temporal grid in Python:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "T = 1. # length of time\n", "N = 20 # number of time steps\n", "#dt = float(T)/float(N-1) # time step size\n", "sigma = .4 # stability if a dt/dx <= 1/2 ==> dt <= dx**2/(2*a)\n", " # hence, sigma < 0.5\n", "dt = sigma*dx**2/a # stability: dt <= dx**2/(2*a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Goal: <em>(Compute discrete approximations $U^n_j$)</em>\n", "To construct a numerical method, which gives a unique discrete solutions $U^n_j:=U(x_j,t_n)$, that reliably approximates \n", "the unknonwn analytic solution $u(x,t)$ solution of the general \n", "heat equation\n", "\n", "$$\n", "u_t(x,t) - au_{xx}(x,t) = f(x,t)\n", "$$\n", "\n", "in the discrete grid points $(x_j,t_n)$, $n=0,\\ldots,N-1$, \n", "$j=0,\\ldots,J-1$. The function $f(x,t)$ is an external heat \n", "source/sink and $a=const.$ the heat conductivity.\n", "\n", "Mathematically, one is generally interested in quantifying the error \n", "pointwise error $u(x_j,t_n) - U^n_j$ or with respect to a suitable \n", "norm $\\|\\cdot\\|_e$, i.e.,\n", "\n", "$$\n", "\\| u(x_j,t_n) - U^n_j \\|_e\\,.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 1: Approximation of the differential operators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Definition. <em>(Three fundamental finite differences)</em> \n", "<em> One defines a\n", "\n", "(a) <strong>''forward difference''</strong> operator by \n", "$$\n", "D^+_x U^n_j :=\\frac{F U^n_j}{h} :=\\frac{ U_{j+1}^n - U_j^n}{h}\\,,\n", "$$\n", "(b) <strong>''backward difference''</strong> operator by \n", "$$\n", "D_x^-U_j^n := \\frac{B U^n_j}{h} :=\\frac{U_{j}^n-U_{j-1}^n}{h}\\,,\n", "$$\n", "(c) <strong>''central difference''</strong> operator by\n", "$$\n", "D_x^0 U_j^n := \\frac{D U^n_j}{{\\color{red} 2}h} :=\\frac{U_{j+1}^n-U_{j-1}^n}{{\\color{red} 2}h}\\,,\n", "$$\n", "(d) and a <strong>''second central difference''</strong> operator by\n", "$$\n", "D_x^2 U_j^n := \\frac{D^+_x U_j^n - D^-_x U_j^n}{h} := \\frac{\\delta_{x_j}^2 U^n_j}{{\\color{red} h^2}}\n", ":= \\frac{U_{j+1}^n-2U_j^n+U_{j-1}^n}{{\\color{red} h^2}}\\,.\n", "$$\n", "</em>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Approximate $u_t$:\n", "Applying the forward difference $D^+_t$ to approximate $u_t$ in grid point $(j,n)$, we use the values of $U$ in two specific grid points:\n", "\n", "$$u_t\\Bigg|_{x = j \\Delta x, t = n \\Delta t}=\\frac{\\partial u}{\\partial t}\\Bigg|_{x = j \\Delta x, t = n \\Delta t} \\approx \n", "D^+_t f(t) = \\frac{U_j^{n+1} - U_j^n}{\\Delta t}.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Quantifying approximation $D_t^+ U^n_j$ with exact solution $u(x_j,t_n)$:\n", "After applying the Taylor expansion to a funtion $f(t)$ around $t\\in\\mathbb{R}$, i.e., for $k>0$ small it holds that\n", "\n", "$$\n", "f(t+k) = f(t) + f'(t)k + \\frac{1}{2}f''(t)k^2 + \\text{h.o.t.}\\,,\n", "$$\n", "\n", "where $\\text{h.o.t.}$ stands for <em>higher order terms</em>, we can write \n", "the forward difference $D_t^+f(t)$ as follows\n", "\n", "$$\n", "D^+_t f(t) \\approx f'(t) + \\frac{1}{2}f''(t)k + {\\cal O}(k^2)\\,.\n", "$$\n", "\n", "Similarly, it holds that \n", "\n", "$$\n", "D^-_t f(t) \\approx f'(t) - \\frac{1}{2}f''(t)k + {\\cal O}(k^2)\\,.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Approximate $u_{xx}$:\n", "Applying the second central difference $D^2_x$ to approximate $u_xx$ \n", "in grid point $(j,n)$, we use the values of $U$ as follows\n", "\n", "$$\\frac{\\partial^2 u}{\\partial x^2}\\Bigg|_{x = x_j, t = t_n} \n", "\\approx D^2_x U^n_j\n", "= \\frac{U_{j+1}^n-2U_j^n+U_{j-1}^n}{{h^2}}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Quantifying approximation $D_x^2 U^n_j$ with exact solution $u(x_j,t_n)$:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "D^2_x U^n_j = \\frac{D^+_x U_j^n - D^-_x U_j^n}{h} \n", "\\approx \\frac{[u_x(x_j,t_n)+\\frac{1}{2}u_{xx}(x_j,t_n)h+\\ldots] - [u_x(x_j,t_n)-\\frac{1}{2}u_{xx}(x_j,t_n)h+\\ldots]}{h} \n", "= u_{xx} + {\\cal O}(h^2)\\,.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: Putting the discrete operators $D_t^+$ and $D^2_x$ together" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the discrete differential operators $D_t^+$ and $D^2_x$, \n", "the 1D diffusion equation turns into the following numerical scheme:\n", " \n", "$$\n", "U_{j}^{n+1}=U_{j}^{n}+\\frac{ak}{h^2}(U_{j+1}^{n}-2U_{j}^{n}+U_{j-1}^{n})\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Initial and boundary conditions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We choose an initial condition defined as follows,\n", "\n", "\\begin{equation}\n", "u(x,0)=\\begin{cases}2 & \\text{where } 0.125\\leq x \\leq 0.25,\\\\\n", "1 & \\text{everywhere else in } (0, 1),\n", "\\end{cases}\n", "\\end{equation}\n", "\n", "We set homogeneous Dirichlet boundary conditions, i.e.,\n", "\n", "$$\n", "u(0,t_n)=u(1,t_n) = 1.0\n", "$$" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "u = np.ones(J) #numpy function ones()\n", "lbound = np.where(x_grid >= 0.125)\n", "ubound = np.where(x_grid <= 0.25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That leaves us with two vectors: \n", "\n", "'lbound', which has the indices for $x\\geq 0.125$ and \n", "'ubound', which has the indices for $ x\\leq 0.5$. \n", "\n", "To combine these two, we can use an intersection with np.intersect1d()." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bounds = np.intersect1d(lbound, ubound)\n", "u[bounds]=2 #setting u = 2 between 0.5 and 1 as per our I.C.s\n", "\n", "u0 = np.ones(J)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAFwCAYAAAAYFxnDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQhk+JElSoQwfkiSpUIYPSZJUqP8P4Bqb7iBzifYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e4971ee50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.plot(x_grid, u, color='#003366', ls='--', lw=3)\n", "pyplot.ylim(0,2.5);" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAFwCAYAAAAYFxnDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYQxtTQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e499600d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for n in range(N): \n", " u0 = u.copy() \n", " u[1:-1] = u0[1:-1] + a*dt/dx**2*(u0[2:] -2*u0[1:-1] +u0[0:-2])\n", "\n", " # Set Dirichlet boundary conditions\n", " u[0] = 1; u[N] = 1\n", "\n", " if n%3 == 0:\n", " pyplot.plot(x_grid, u, color='#003366', ls='--', lw=3)\n", " pyplot.ylim(0,2.5);\n", " \n", "pyplot.plot(x_grid, u, color='#003366', ls='--', lw=3)\n", "pyplot.ylim(0.8,2.2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise. \n", "Investigate computationally the threshold $\\texttt{sigma}<=0.5$, e.g. \n", "\n", "(a) what happens for $\\texttt{sigma}=0.5$\n", "\n", "or \n", "\n", "(b) for $\\texttt{sigma}>0.5$ ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will see later in the lecture how to analyse stability and \n", "convergence of finite differences schemes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

afronski/playground-notes

scalable-machine-learning/lab-3/ML_lab3_linear_reg_student.ipynb

3

58801

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# **Linear Regression Lab**\n", "#### This lab covers a common supervised learning pipeline, using a subset of the [Million Song Dataset](http://labrosa.ee.columbia.edu/millionsong/) from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD). Our goal is to train a linear regression model to predict the release year of a song given a set of audio features.\n", "#### ** This lab will cover: **\n", "+ ####*Part 1:* Read and parse the initial dataset\n", " + #### *Visualization 1:* Features\n", " + #### *Visualization 2:* Shifting labels\n", "+ ####*Part 2:* Create and evaluate a baseline model\n", " + #### *Visualization 3:* Predicted vs. actual\n", "+ ####*Part 3:* Train (via gradient descent) and evaluate a linear regression model\n", " + #### *Visualization 4:* Training error\n", "+ ####*Part 4:* Train using MLlib and tune hyperparameters via grid search\n", " + #### *Visualization 5:* Best model's predictions\n", " + #### *Visualization 6:* Hyperparameter heat map\n", "+ ####*Part 5:* Add interactions between features\n", " \n", "#### Note that, for reference, you can look up the details of the relevant Spark methods in [Spark's Python API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD) and the relevant NumPy methods in the [NumPy Reference](http://docs.scipy.org/doc/numpy/reference/index.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "labVersion = 'cs190_week3_v_1_2'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 1: Read and parse the initial dataset **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1a) Load and check the data **\n", "#### The raw data is currently stored in text file. We will start by storing this raw data in as an RDD, with each element of the RDD representing a data point as a comma-delimited string. Each string starts with the label (a year) followed by numerical audio features. Use the [count method](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.count) to check how many data points we have. Then use the [take method](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.take) to create and print out a list of the first 5 data points in their initial string format." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load testing library\n", "from test_helper import Test\n", "import os.path\n", "baseDir = os.path.join('data')\n", "inputPath = os.path.join('cs190', 'millionsong.txt')\n", "fileName = os.path.join(baseDir, inputPath)\n", "\n", "numPartitions = 2\n", "rawData = sc.textFile(fileName, numPartitions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "numPoints = <FILL IN>\n", "print numPoints\n", "samplePoints = <FILL IN>\n", "print samplePoints" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Load and check the data (1a)\n", "Test.assertEquals(numPoints, 6724, 'incorrect value for numPoints')\n", "Test.assertEquals(len(samplePoints), 5, 'incorrect length for samplePoints')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1b) Using `LabeledPoint` **\n", "#### In MLlib, labeled training instances are stored using the [LabeledPoint](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint) object. Write the parsePoint function that takes as input a raw data point, parses it using Python's [unicode.split](https://docs.python.org/2/library/string.html#string.split) method, and returns a `LabeledPoint`. Use this function to parse samplePoints (from the previous question). Then print out the features and label for the first training point, using the `LabeledPoint.features` and `LabeledPoint.label` attributes. Finally, calculate the number features for this dataset.\n", "#### Note that `split()` can be called directly on a `unicode` or `str` object. For example, `u'split,me'.split(',')` returns `[u'split', u'me']`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark.mllib.regression import LabeledPoint\n", "import numpy as np\n", "\n", "# Here is a sample raw data point:\n", "# '2001.0,0.884,0.610,0.600,0.474,0.247,0.357,0.344,0.33,0.600,0.425,0.60,0.419'\n", "# In this raw data point, 2001.0 is the label, and the remaining values are features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "def parsePoint(line):\n", " \"\"\"Converts a comma separated unicode string into a `LabeledPoint`.\n", "\n", " Args:\n", " line (unicode): Comma separated unicode string where the first element is the label and the\n", " remaining elements are features.\n", "\n", " Returns:\n", " LabeledPoint: The line is converted into a `LabeledPoint`, which consists of a label and\n", " features.\n", " \"\"\"\n", " <FILL IN>\n", "\n", "parsedSamplePoints = <FILL IN>\n", "firstPointFeatures = <FILL IN>\n", "firstPointLabel = <FILL IN>\n", "print firstPointFeatures, firstPointLabel\n", "\n", "d = len(firstPointFeatures)\n", "print d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Using LabeledPoint (1b)\n", "Test.assertTrue(isinstance(firstPointLabel, float), 'label must be a float')\n", "expectedX0 = [0.8841,0.6105,0.6005,0.4747,0.2472,0.3573,0.3441,0.3396,0.6009,0.4257,0.6049,0.4192]\n", "Test.assertTrue(np.allclose(expectedX0, firstPointFeatures, 1e-4, 1e-4),\n", " 'incorrect features for firstPointFeatures')\n", "Test.assertTrue(np.allclose(2001.0, firstPointLabel), 'incorrect label for firstPointLabel')\n", "Test.assertTrue(d == 12, 'incorrect number of features')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **Visualization 1: Features**\n", "#### First we will load and setup the visualization library. Then we will look at the raw features for 50 data points by generating a heatmap that visualizes each feature on a grey-scale and shows the variation of each feature across the 50 sample data points. The features are all between 0 and 1, with values closer to 1 represented via darker shades of grey." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "\n", "sampleMorePoints = rawData.take(50)\n", "# You can uncomment the line below to see randomly selected features. These will be randomly\n", "# selected each time you run the cell. Note that you should run this cell with the line commented\n", "# out when answering the lab quiz questions.\n", "# sampleMorePoints = rawData.takeSample(False, 50)\n", "\n", "parsedSampleMorePoints = map(parsePoint, sampleMorePoints)\n", "dataValues = map(lambda lp: lp.features.toArray(), parsedSampleMorePoints)\n", "\n", "def preparePlot(xticks, yticks, figsize=(10.5, 6), hideLabels=False, gridColor='#999999',\n", " gridWidth=1.0):\n", " \"\"\"Template for generating the plot layout.\"\"\"\n", " plt.close()\n", " fig, ax = plt.subplots(figsize=figsize, facecolor='white', edgecolor='white')\n", " ax.axes.tick_params(labelcolor='#999999', labelsize='10')\n", " for axis, ticks in [(ax.get_xaxis(), xticks), (ax.get_yaxis(), yticks)]:\n", " axis.set_ticks_position('none')\n", " axis.set_ticks(ticks)\n", " axis.label.set_color('#999999')\n", " if hideLabels: axis.set_ticklabels([])\n", " plt.grid(color=gridColor, linewidth=gridWidth, linestyle='-')\n", " map(lambda position: ax.spines[position].set_visible(False), ['bottom', 'top', 'left', 'right'])\n", " return fig, ax\n", "\n", "# generate layout and plot\n", "fig, ax = preparePlot(np.arange(.5, 11, 1), np.arange(.5, 49, 1), figsize=(8,7), hideLabels=True,\n", " gridColor='#eeeeee', gridWidth=1.1)\n", "image = plt.imshow(dataValues,interpolation='nearest', aspect='auto', cmap=cm.Greys)\n", "for x, y, s in zip(np.arange(-.125, 12, 1), np.repeat(-.75, 12), [str(x) for x in range(12)]):\n", " plt.text(x, y, s, color='#999999', size='10')\n", "plt.text(4.7, -3, 'Feature', color='#999999', size='11'), ax.set_ylabel('Observation')\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **(1c) Find the range **\n", "#### Now let's examine the labels to find the range of song years. To do this, first parse each element of the `rawData` RDD, and then find the smallest and largest labels." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "parsedDataInit = rawData.map(<FILL IN>)\n", "onlyLabels = parsedDataInit.map(<FILL IN>)\n", "minYear = <FILL IN>\n", "maxYear = <FILL IN>\n", "print maxYear, minYear" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Find the range (1c)\n", "Test.assertEquals(len(parsedDataInit.take(1)[0].features), 12,\n", " 'unexpected number of features in sample point')\n", "sumFeatTwo = parsedDataInit.map(lambda lp: lp.features[2]).sum()\n", "Test.assertTrue(np.allclose(sumFeatTwo, 3158.96224351), 'parsedDataInit has unexpected values')\n", "yearRange = maxYear - minYear\n", "Test.assertTrue(yearRange == 89, 'incorrect range for minYear to maxYear')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **(1d) Shift labels **\n", "#### As we just saw, the labels are years in the 1900s and 2000s. In learning problems, it is often natural to shift labels such that they start from zero. Starting with `parsedDataInit`, create a new RDD consisting of `LabeledPoint` objects in which the labels are shifted such that smallest label equals zero." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "parsedData = parsedDataInit.<FILL IN>\n", "\n", "# Should be a LabeledPoint\n", "print type(parsedData.take(1)[0])\n", "# View the first point\n", "print '\\n{0}'.format(parsedData.take(1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Shift labels (1d)\n", "oldSampleFeatures = parsedDataInit.take(1)[0].features\n", "newSampleFeatures = parsedData.take(1)[0].features\n", "Test.assertTrue(np.allclose(oldSampleFeatures, newSampleFeatures),\n", " 'new features do not match old features')\n", "sumFeatTwo = parsedData.map(lambda lp: lp.features[2]).sum()\n", "Test.assertTrue(np.allclose(sumFeatTwo, 3158.96224351), 'parsedData has unexpected values')\n", "minYearNew = parsedData.map(lambda lp: lp.label).min()\n", "maxYearNew = parsedData.map(lambda lp: lp.label).max()\n", "Test.assertTrue(minYearNew == 0, 'incorrect min year in shifted data')\n", "Test.assertTrue(maxYearNew == 89, 'incorrect max year in shifted data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** Visualization 2: Shifting labels **\n", "#### We will look at the labels before and after shifting them. Both scatter plots below visualize tuples storing i) a label value and ii) the number of training points with this label. The first scatter plot uses the initial labels, while the second one uses the shifted labels. Note that the two plots look the same except for the labels on the x-axis." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# get data for plot\n", "oldData = (parsedDataInit\n", " .map(lambda lp: (lp.label, 1))\n", " .reduceByKey(lambda x, y: x + y)\n", " .collect())\n", "x, y = zip(*oldData)\n", "\n", "# generate layout and plot data\n", "fig, ax = preparePlot(np.arange(1920, 2050, 20), np.arange(0, 150, 20))\n", "plt.scatter(x, y, s=14**2, c='#d6ebf2', edgecolors='#8cbfd0', alpha=0.75)\n", "ax.set_xlabel('Year'), ax.set_ylabel('Count')\n", "pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# get data for plot\n", "newData = (parsedData\n", " .map(lambda lp: (lp.label, 1))\n", " .reduceByKey(lambda x, y: x + y)\n", " .collect())\n", "x, y = zip(*newData)\n", "\n", "# generate layout and plot data\n", "fig, ax = preparePlot(np.arange(0, 120, 20), np.arange(0, 120, 20))\n", "plt.scatter(x, y, s=14**2, c='#d6ebf2', edgecolors='#8cbfd0', alpha=0.75)\n", "ax.set_xlabel('Year (shifted)'), ax.set_ylabel('Count')\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1e) Training, validation, and test sets **\n", "#### We're almost done parsing our dataset, and our final task involves split it into training, validation and test sets. Use the [randomSplit method](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.randomSplit) with the specified weights and seed to create RDDs storing each of these datasets. Next, cache each of these RDDs, as we will be accessing them multiple times in the remainder of this lab. Finally, compute the size of each dataset and verify that the sum of their sizes equals the value computed in Part (1a)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "weights = [.8, .1, .1]\n", "seed = 42\n", "parsedTrainData, parsedValData, parsedTestData = parsedData.<FILL IN>\n", "parsedTrainData.<FILL IN>\n", "parsedValData.<FILL IN>\n", "parsedTestData.<FILL IN>\n", "nTrain = parsedTrainData.<FILL IN>\n", "nVal = parsedValData.<FILL IN>\n", "nTest = parsedTestData.<FILL IN>\n", "\n", "print nTrain, nVal, nTest, nTrain + nVal + nTest\n", "print parsedData.count()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Training, validation, and test sets (1e)\n", "Test.assertEquals(parsedTrainData.getNumPartitions(), numPartitions,\n", " 'parsedTrainData has wrong number of partitions')\n", "Test.assertEquals(parsedValData.getNumPartitions(), numPartitions,\n", " 'parsedValData has wrong number of partitions')\n", "Test.assertEquals(parsedTestData.getNumPartitions(), numPartitions,\n", " 'parsedTestData has wrong number of partitions')\n", "Test.assertEquals(len(parsedTrainData.take(1)[0].features), 12,\n", " 'parsedTrainData has wrong number of features')\n", "sumFeatTwo = (parsedTrainData\n", " .map(lambda lp: lp.features[2])\n", " .sum())\n", "sumFeatThree = (parsedValData\n", " .map(lambda lp: lp.features[3])\n", " .reduce(lambda x, y: x + y))\n", "sumFeatFour = (parsedTestData\n", " .map(lambda lp: lp.features[4])\n", " .reduce(lambda x, y: x + y))\n", "Test.assertTrue(np.allclose([sumFeatTwo, sumFeatThree, sumFeatFour],\n", " 2526.87757656, 297.340394298, 184.235876654),\n", " 'parsed Train, Val, Test data has unexpected values')\n", "Test.assertTrue(nTrain + nVal + nTest == 6724, 'unexpected Train, Val, Test data set size')\n", "Test.assertEquals(nTrain, 5371, 'unexpected value for nTrain')\n", "Test.assertEquals(nVal, 682, 'unexpected value for nVal')\n", "Test.assertEquals(nTest, 671, 'unexpected value for nTest')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 2: Create and evaluate a baseline model **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **(2a) Average label **\n", "#### A very simple yet natural baseline model is one where we always make the same prediction independent of the given data point, using the average label in the training set as the constant prediction value. Compute this value, which is the average (shifted) song year for the training set. Use an appropriate method in the [RDD API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "averageTrainYear = (parsedTrainData\n", " <FILL IN>)\n", "print averageTrainYear" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Average label (2a)\n", "Test.assertTrue(np.allclose(averageTrainYear, 53.9316700801),\n", " 'incorrect value for averageTrainYear')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **(2b) Root mean squared error **\n", "#### We naturally would like to see how well this naive baseline performs. We will use root mean squared error ([RMSE](http://en.wikipedia.org/wiki/Root-mean-square_deviation)) for evaluation purposes. Implement a function to compute RMSE given an RDD of (label, prediction) tuples, and test out this function on an example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "def squaredError(label, prediction):\n", " \"\"\"Calculates the the squared error for a single prediction.\n", "\n", " Args:\n", " label (float): The correct value for this observation.\n", " prediction (float): The predicted value for this observation.\n", "\n", " Returns:\n", " float: The difference between the `label` and `prediction` squared.\n", " \"\"\"\n", " <FILL IN>\n", "\n", "def calcRMSE(labelsAndPreds):\n", " \"\"\"Calculates the root mean squared error for an `RDD` of (label, prediction) tuples.\n", "\n", " Args:\n", " labelsAndPred (RDD of (float, float)): An `RDD` consisting of (label, prediction) tuples.\n", "\n", " Returns:\n", " float: The square root of the mean of the squared errors.\n", " \"\"\"\n", " <FILL IN>\n", "\n", "labelsAndPreds = sc.parallelize([(3., 1.), (1., 2.), (2., 2.)])\n", "# RMSE = sqrt[((3-1)^2 + (1-2)^2 + (2-2)^2) / 3] = 1.291\n", "exampleRMSE = calcRMSE(labelsAndPreds)\n", "print exampleRMSE" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Root mean squared error (2b)\n", "Test.assertTrue(np.allclose(squaredError(3, 1), 4.), 'incorrect definition of squaredError')\n", "Test.assertTrue(np.allclose(exampleRMSE, 1.29099444874), 'incorrect value for exampleRMSE')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **(2c) Training, validation and test RMSE **\n", "#### Now let's calculate the training, validation and test RMSE of our baseline model. To do this, first create RDDs of (label, prediction) tuples for each dataset, and then call calcRMSE. Note that each RMSE can be interpreted as the average prediction error for the given dataset (in terms of number of years)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "labelsAndPredsTrain = parsedTrainData.<FILL IN>\n", "rmseTrainBase = <FILL IN>\n", "\n", "labelsAndPredsVal = parsedValData.<FILL IN>\n", "rmseValBase = <FILL IN>\n", "\n", "labelsAndPredsTest = parsedTestData.<FILL IN>\n", "rmseTestBase = <FILL IN>\n", "\n", "print 'Baseline Train RMSE = {0:.3f}'.format(rmseTrainBase)\n", "print 'Baseline Validation RMSE = {0:.3f}'.format(rmseValBase)\n", "print 'Baseline Test RMSE = {0:.3f}'.format(rmseTestBase)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Training, validation and test RMSE (2c)\n", "Test.assertTrue(np.allclose([rmseTrainBase, rmseValBase, rmseTestBase],\n", " [21.305869, 21.586452, 22.136957]), 'incorrect RMSE value')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** Visualization 3: Predicted vs. actual **\n", "#### We will visualize predictions on the validation dataset. The scatter plots below visualize tuples storing i) the predicted value and ii) true label. The first scatter plot represents the ideal situation where the predicted value exactly equals the true label, while the second plot uses the baseline predictor (i.e., `averageTrainYear`) for all predicted values. Further note that the points in the scatter plots are color-coded, ranging from light yellow when the true and predicted values are equal to bright red when they drastically differ." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib.colors import ListedColormap, Normalize\n", "from matplotlib.cm import get_cmap\n", "cmap = get_cmap('YlOrRd')\n", "norm = Normalize()\n", "\n", "actual = np.asarray(parsedValData\n", " .map(lambda lp: lp.label)\n", " .collect())\n", "error = np.asarray(parsedValData\n", " .map(lambda lp: (lp.label, lp.label))\n", " .map(lambda (l, p): squaredError(l, p))\n", " .collect())\n", "clrs = cmap(np.asarray(norm(error)))[:,0:3]\n", "\n", "fig, ax = preparePlot(np.arange(0, 100, 20), np.arange(0, 100, 20))\n", "plt.scatter(actual, actual, s=14**2, c=clrs, edgecolors='#888888', alpha=0.75, linewidths=0.5)\n", "ax.set_xlabel('Predicted'), ax.set_ylabel('Actual')\n", "pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "predictions = np.asarray(parsedValData\n", " .map(lambda lp: averageTrainYear)\n", " .collect())\n", "error = np.asarray(parsedValData\n", " .map(lambda lp: (lp.label, averageTrainYear))\n", " .map(lambda (l, p): squaredError(l, p))\n", " .collect())\n", "norm = Normalize()\n", "clrs = cmap(np.asarray(norm(error)))[:,0:3]\n", "\n", "fig, ax = preparePlot(np.arange(53.0, 55.0, 0.5), np.arange(0, 100, 20))\n", "ax.set_xlim(53, 55)\n", "plt.scatter(predictions, actual, s=14**2, c=clrs, edgecolors='#888888', alpha=0.75, linewidths=0.3)\n", "ax.set_xlabel('Predicted'), ax.set_ylabel('Actual')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 3: Train (via gradient descent) and evaluate a linear regression model **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (3a) Gradient summand **\n", "#### Now let's see if we can do better via linear regression, training a model via gradient descent (we'll omit the intercept for now). Recall that the gradient descent update for linear regression is: $$ \\scriptsize \\mathbf{w}_{i+1} = \\mathbf{w}_i - \\alpha_i \\sum_j (\\mathbf{w}_i^\\top\\mathbf{x}_j - y_j) \\mathbf{x}_j \\,.$$ where $ \\scriptsize i $ is the iteration number of the gradient descent algorithm, and $ \\scriptsize j $ identifies the observation.\n", "#### First, implement a function that computes the summand for this update, i.e., the summand equals $ \\scriptsize (\\mathbf{w}^\\top \\mathbf{x} - y) \\mathbf{x} \\, ,$ and test out this function on two examples. Use the `DenseVector` [dot](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.DenseVector.dot) method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark.mllib.linalg import DenseVector" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "def gradientSummand(weights, lp):\n", " \"\"\"Calculates the gradient summand for a given weight and `LabeledPoint`.\n", "\n", " Note:\n", " `DenseVector` behaves similarly to a `numpy.ndarray` and they can be used interchangably\n", " within this function. For example, they both implement the `dot` method.\n", "\n", " Args:\n", " weights (DenseVector): An array of model weights (betas).\n", " lp (LabeledPoint): The `LabeledPoint` for a single observation.\n", "\n", " Returns:\n", " DenseVector: An array of values the same length as `weights`. The gradient summand.\n", " \"\"\"\n", " <FILL IN>\n", "\n", "exampleW = DenseVector([1, 1, 1])\n", "exampleLP = LabeledPoint(2.0, [3, 1, 4])\n", "# gradientSummand = (dot([1 1 1], [3 1 4]) - 2) * [3 1 4] = (8 - 2) * [3 1 4] = [18 6 24]\n", "summandOne = gradientSummand(exampleW, exampleLP)\n", "print summandOne\n", "\n", "exampleW = DenseVector([.24, 1.2, -1.4])\n", "exampleLP = LabeledPoint(3.0, [-1.4, 4.2, 2.1])\n", "summandTwo = gradientSummand(exampleW, exampleLP)\n", "print summandTwo" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Gradient summand (3a)\n", "Test.assertTrue(np.allclose(summandOne, [18., 6., 24.]), 'incorrect value for summandOne')\n", "Test.assertTrue(np.allclose(summandTwo, [1.7304,-5.1912,-2.5956]), 'incorrect value for summandTwo')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (3b) Use weights to make predictions **\n", "#### Next, implement a `getLabeledPredictions` function that takes in weights and an observation's `LabeledPoint` and returns a (label, prediction) tuple. Note that we can predict by computing the dot product between weights and an observation's features." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "def getLabeledPrediction(weights, observation):\n", " \"\"\"Calculates predictions and returns a (label, prediction) tuple.\n", "\n", " Note:\n", " The labels should remain unchanged as we'll use this information to calculate prediction\n", " error later.\n", "\n", " Args:\n", " weights (np.ndarray): An array with one weight for each features in `trainData`.\n", " observation (LabeledPoint): A `LabeledPoint` that contain the correct label and the\n", " features for the data point.\n", "\n", " Returns:\n", " RDD of tuple: An RDD containing (label, prediction) tuples.\n", " \"\"\"\n", " return <FILL IN>\n", "\n", "weights = np.array([1.0, 1.5])\n", "predictionExample = sc.parallelize([LabeledPoint(2, np.array([1.0, .5])),\n", " LabeledPoint(1.5, np.array([.5, .5]))])\n", "labelsAndPredsExample = predictionExample.map(lambda lp: getLabeledPrediction(weights, lp))\n", "print labelsAndPredsExample.collect()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Use weights to make predictions (3b)\n", "Test.assertEquals(labelsAndPredsExample.collect(), [(2.0, 1.75), (1.5, 1.25)],\n", " 'incorrect definition for getLabeledPredictions')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (3c) Gradient descent **\n", "#### Next, implement a gradient descent function for linear regression and test out this function on an example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "def linregGradientDescent(trainData, numIters):\n", " \"\"\"Calculates the weights and error for a linear regression model trained with gradient descent.\n", "\n", " Note:\n", " `DenseVector` behaves similarly to a `numpy.ndarray` and they can be used interchangably\n", " within this function. For example, they both implement the `dot` method.\n", "\n", " Args:\n", " trainData (RDD of LabeledPoint): The labeled data for use in training the model.\n", " numIters (int): The number of iterations of gradient descent to perform.\n", "\n", " Returns:\n", " (np.ndarray, np.ndarray): A tuple of (weights, training errors). Weights will be the\n", " final weights (one weight per feature) for the model, and training errors will contain\n", " an error (RMSE) for each iteration of the algorithm.\n", " \"\"\"\n", " # The length of the training data\n", " n = trainData.count()\n", " # The number of features in the training data\n", " d = len(trainData.take(1)[0].features)\n", " w = np.zeros(d)\n", " alpha = 1.0\n", " # We will compute and store the training error after each iteration\n", " errorTrain = np.zeros(numIters)\n", " for i in range(numIters):\n", " # Use getLabeledPrediction from (3b) with trainData to obtain an RDD of (label, prediction)\n", " # tuples. Note that the weights all equal 0 for the first iteration, so the predictions will\n", " # have large errors to start.\n", " labelsAndPredsTrain = trainData.<FILL IN>\n", " errorTrain[i] = calcRMSE(labelsAndPredsTrain)\n", "\n", " # Calculate the `gradient`. Make use of the `gradientSummand` function you wrote in (3a).\n", " # Note that `gradient` sould be a `DenseVector` of length `d`.\n", " gradient = <FILL IN>\n", "\n", " # Update the weights\n", " alpha_i = alpha / (n * np.sqrt(i+1))\n", " w -= <FILL IN>\n", " return w, errorTrain\n", "\n", "# create a toy dataset with n = 10, d = 3, and then run 5 iterations of gradient descent\n", "# note: the resulting model will not be useful; the goal here is to verify that\n", "# linregGradientDescent is working properly\n", "exampleN = 10\n", "exampleD = 3\n", "exampleData = (sc\n", " .parallelize(parsedTrainData.take(exampleN))\n", " .map(lambda lp: LabeledPoint(lp.label, lp.features[0:exampleD])))\n", "print exampleData.take(2)\n", "exampleNumIters = 5\n", "exampleWeights, exampleErrorTrain = linregGradientDescent(exampleData, exampleNumIters)\n", "print exampleWeights" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Gradient descent (3c)\n", "expectedOutput = [48.88110449, 36.01144093, 30.25350092]\n", "Test.assertTrue(np.allclose(exampleWeights, expectedOutput), 'value of exampleWeights is incorrect')\n", "expectedError = [79.72013547, 30.27835699, 9.27842641, 9.20967856, 9.19446483]\n", "Test.assertTrue(np.allclose(exampleErrorTrain, expectedError),\n", " 'value of exampleErrorTrain is incorrect')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (3d) Train the model **\n", "#### Now let's train a linear regression model on all of our training data and evaluate its accuracy on the validation set. Note that the test set will not be used here. If we evaluated the model on the test set, we would bias our final results.\n", "#### We've already done much of the required work: we computed the number of features in Part (1b); we created the training and validation datasets and computed their sizes in Part (1e); and, we wrote a function to compute RMSE in Part (2b)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "numIters = 50\n", "weightsLR0, errorTrainLR0 = linregGradientDescent(<FILL IN>)\n", "\n", "labelsAndPreds = parsedValData.<FILL IN>\n", "rmseValLR0 = calcRMSE(labelsAndPreds)\n", "\n", "print 'Validation RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLR0 = {1:.3f}'.format(rmseValBase,\n", " rmseValLR0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Train the model (3d)\n", "expectedOutput = [22.64535883, 20.064699, -0.05341901, 8.2931319, 5.79155768, -4.51008084,\n", " 15.23075467, 3.8465554, 9.91992022, 5.97465933, 11.36849033, 3.86452361]\n", "Test.assertTrue(np.allclose(weightsLR0, expectedOutput), 'incorrect value for weightsLR0')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** Visualization 4: Training error **\n", "#### We will look at the log of the training error as a function of iteration. The first scatter plot visualizes the logarithm of the training error for all 50 iterations. The second plot shows the training error itself, focusing on the final 44 iterations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "norm = Normalize()\n", "clrs = cmap(np.asarray(norm(np.log(errorTrainLR0))))[:,0:3]\n", "\n", "fig, ax = preparePlot(np.arange(0, 60, 10), np.arange(2, 6, 1))\n", "ax.set_ylim(2, 6)\n", "plt.scatter(range(0, numIters), np.log(errorTrainLR0), s=14**2, c=clrs, edgecolors='#888888', alpha=0.75)\n", "ax.set_xlabel('Iteration'), ax.set_ylabel(r'$\\log_e(errorTrainLR0)$')\n", "pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "norm = Normalize()\n", "clrs = cmap(np.asarray(norm(errorTrainLR0[6:])))[:,0:3]\n", "\n", "fig, ax = preparePlot(np.arange(0, 60, 10), np.arange(17, 22, 1))\n", "ax.set_ylim(17.8, 21.2)\n", "plt.scatter(range(0, numIters-6), errorTrainLR0[6:], s=14**2, c=clrs, edgecolors='#888888', alpha=0.75)\n", "ax.set_xticklabels(map(str, range(6, 66, 10)))\n", "ax.set_xlabel('Iteration'), ax.set_ylabel(r'Training Error')\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 4: Train using MLlib and perform grid search **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **(4a) `LinearRegressionWithSGD` **\n", "#### We're already doing better than the baseline model, but let's see if we can do better by adding an intercept, using regularization, and (based on the previous visualization) training for more iterations. MLlib's [LinearRegressionWithSGD](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionWithSGD) essentially implements the same algorithm that we implemented in Part (3b), albeit more efficiently and with various additional functionality, such as stochastic gradient approximation, including an intercept in the model and also allowing L1 or L2 regularization. First use LinearRegressionWithSGD to train a model with L2 regularization and with an intercept. This method returns a [LinearRegressionModel](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel). Next, use the model's [weights](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel.weights) and [intercept](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel.intercept) attributes to print out the model's parameters." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark.mllib.regression import LinearRegressionWithSGD\n", "# Values to use when training the linear regression model\n", "numIters = 500 # iterations\n", "alpha = 1.0 # step\n", "miniBatchFrac = 1.0 # miniBatchFraction\n", "reg = 1e-1 # regParam\n", "regType = 'l2' # regType\n", "useIntercept = True # intercept" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "firstModel = LinearRegressionWithSGD.<FILL IN>\n", "\n", "# weightsLR1 stores the model weights; interceptLR1 stores the model intercept\n", "weightsLR1 = <FILL IN>\n", "interceptLR1 = <FILL IN>\n", "print weightsLR1, interceptLR1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST LinearRegressionWithSGD (4a)\n", "expectedIntercept = 13.3335907631\n", "expectedWeights = [16.682292427, 14.7439059559, -0.0935105608897, 6.22080088829, 4.01454261926, -3.30214858535,\n", " 11.0403027232, 2.67190962854, 7.18925791279, 4.46093254586, 8.14950409475, 2.75135810882]\n", "Test.assertTrue(np.allclose(interceptLR1, expectedIntercept), 'incorrect value for interceptLR1')\n", "Test.assertTrue(np.allclose(weightsLR1, expectedWeights), 'incorrect value for weightsLR1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **(4b) Predict**\n", "#### Now use the [LinearRegressionModel.predict()](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel.predict) method to make a prediction on a sample point. Pass the `features` from a `LabeledPoint` into the `predict()` method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "samplePoint = parsedTrainData.take(1)[0]\n", "samplePrediction = <FILL IN>\n", "print samplePrediction" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Predict (4b)\n", "Test.assertTrue(np.allclose(samplePrediction, 56.8013380112),\n", " 'incorrect value for samplePrediction')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4c) Evaluate RMSE **\n", "#### Next evaluate the accuracy of this model on the validation set. Use the `predict()` method to create a `labelsAndPreds` RDD, and then use the `calcRMSE()` function from Part (2b)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "labelsAndPreds = <FILL IN>\n", "rmseValLR1 = <FILL IN>\n", "\n", "print ('Validation RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLR0 = {1:.3f}' +\n", " '\\n\\tLR1 = {2:.3f}').format(rmseValBase, rmseValLR0, rmseValLR1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Evaluate RMSE (4c)\n", "Test.assertTrue(np.allclose(rmseValLR1, 19.691247), 'incorrect value for rmseValLR1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4d) Grid search **\n", "#### We're already outperforming the baseline on the validation set by almost 2 years on average, but let's see if we can do better. Perform grid search to find a good regularization parameter. Try `regParam` values `1e-10`, `1e-5`, and `1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "bestRMSE = rmseValLR1\n", "bestRegParam = reg\n", "bestModel = firstModel\n", "\n", "numIters = 500\n", "alpha = 1.0\n", "miniBatchFrac = 1.0\n", "for reg in <FILL IN>:\n", " model = LinearRegressionWithSGD.train(parsedTrainData, numIters, alpha,\n", " miniBatchFrac, regParam=reg,\n", " regType='l2', intercept=True)\n", " labelsAndPreds = parsedValData.map(lambda lp: (lp.label, model.predict(lp.features)))\n", " rmseValGrid = calcRMSE(labelsAndPreds)\n", " print rmseValGrid\n", "\n", " if rmseValGrid < bestRMSE:\n", " bestRMSE = rmseValGrid\n", " bestRegParam = reg\n", " bestModel = model\n", "rmseValLRGrid = bestRMSE\n", "\n", "print ('Validation RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLR0 = {1:.3f}\\n\\tLR1 = {2:.3f}\\n' +\n", " '\\tLRGrid = {3:.3f}').format(rmseValBase, rmseValLR0, rmseValLR1, rmseValLRGrid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Grid search (4d)\n", "Test.assertTrue(np.allclose(17.017170, rmseValLRGrid), 'incorrect value for rmseValLRGrid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** Visualization 5: Best model's predictions**\n", "#### Next, we create a visualization similar to 'Visualization 3: Predicted vs. actual' from Part 2 using the predictions from the best model from Part (4d) on the validation dataset. Specifically, we create a color-coded scatter plot visualizing tuples storing i) the predicted value from this model and ii) true label." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "predictions = np.asarray(parsedValData\n", " .map(lambda lp: bestModel.predict(lp.features))\n", " .collect())\n", "actual = np.asarray(parsedValData\n", " .map(lambda lp: lp.label)\n", " .collect())\n", "error = np.asarray(parsedValData\n", " .map(lambda lp: (lp.label, bestModel.predict(lp.features)))\n", " .map(lambda (l, p): squaredError(l, p))\n", " .collect())\n", "\n", "norm = Normalize()\n", "clrs = cmap(np.asarray(norm(error)))[:,0:3]\n", "\n", "fig, ax = preparePlot(np.arange(0, 120, 20), np.arange(0, 120, 20))\n", "ax.set_xlim(15, 82), ax.set_ylim(-5, 105)\n", "plt.scatter(predictions, actual, s=14**2, c=clrs, edgecolors='#888888', alpha=0.75, linewidths=.5)\n", "ax.set_xlabel('Predicted'), ax.set_ylabel(r'Actual')\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4e) Vary alpha and the number of iterations **\n", "#### In the previous grid search, we set `alpha = 1` for all experiments. Now let's see what happens when we vary `alpha`. Specifically, try `1e-5` and `10` as values for `alpha` and also try training models for 500 iterations (as before) but also for 5 iterations. Evaluate all models on the validation set. Note that if we set `alpha` too small the gradient descent will require a huge number of steps to converge to the solution, and if we use too large of an `alpha` it can cause numerical problems, like you'll see below for `alpha = 10`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "reg = bestRegParam\n", "modelRMSEs = []\n", "\n", "for alpha in <FILL IN>:\n", " for numIters in <FILL IN>:\n", " model = LinearRegressionWithSGD.train(parsedTrainData, numIters, alpha,\n", " miniBatchFrac, regParam=reg,\n", " regType='l2', intercept=True)\n", " labelsAndPreds = parsedValData.map(lambda lp: (lp.label, model.predict(lp.features)))\n", " rmseVal = calcRMSE(labelsAndPreds)\n", " print 'alpha = {0:.0e}, numIters = {1}, RMSE = {2:.3f}'.format(alpha, numIters, rmseVal)\n", " modelRMSEs.append(rmseVal)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Vary alpha and the number of iterations (4e)\n", "expectedResults = sorted([56.969705, 56.892949, 355124752.221221])\n", "Test.assertTrue(np.allclose(sorted(modelRMSEs)[:3], expectedResults), 'incorrect value for modelRMSEs')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **Visualization 6: Hyperparameter heat map **\n", "#### Next, we perform a visualization of hyperparameter search using a larger set of hyperparameters (with precomputed results). Specifically, we create a heat map where the brighter colors correspond to lower RMSE values. The first plot has a large area with brighter colors. In order to differentiate within the bright region, we generate a second plot corresponding to the hyperparameters found within that region." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib.colors import LinearSegmentedColormap\n", "\n", "# Saved parameters and results, to save the time required to run 36 models\n", "numItersParams = [10, 50, 100, 250, 500, 1000]\n", "regParams = [1e-8, 1e-6, 1e-4, 1e-2, 1e-1, 1]\n", "rmseVal = np.array([[ 20.36769649, 20.36770128, 20.36818057, 20.41795354, 21.09778437, 301.54258421],\n", " [ 19.04948826, 19.0495 , 19.05067418, 19.16517726, 19.97967727, 23.80077467],\n", " [ 18.40149024, 18.40150998, 18.40348326, 18.59457491, 19.82155716, 23.80077467],\n", " [ 17.5609346 , 17.56096749, 17.56425511, 17.88442127, 19.71577117, 23.80077467],\n", " [ 17.0171705 , 17.01721288, 17.02145207, 17.44510574, 19.69124734, 23.80077467],\n", " [ 16.58074813, 16.58079874, 16.58586512, 17.11466904, 19.6860931 , 23.80077467]])\n", "\n", "numRows, numCols = len(numItersParams), len(regParams)\n", "rmseVal = np.array(rmseVal)\n", "rmseVal.shape = (numRows, numCols)\n", "\n", "fig, ax = preparePlot(np.arange(0, numCols, 1), np.arange(0, numRows, 1), figsize=(8, 7), hideLabels=True,\n", " gridWidth=0.)\n", "ax.set_xticklabels(regParams), ax.set_yticklabels(numItersParams)\n", "ax.set_xlabel('Regularization Parameter'), ax.set_ylabel('Number of Iterations')\n", "\n", "colors = LinearSegmentedColormap.from_list('blue', ['#0022ff', '#000055'], gamma=.2)\n", "image = plt.imshow(rmseVal,interpolation='nearest', aspect='auto',\n", " cmap = colors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Zoom into the bottom left\n", "numItersParamsZoom, regParamsZoom = numItersParams[-3:], regParams[:4]\n", "rmseValZoom = rmseVal[-3:, :4]\n", "\n", "numRows, numCols = len(numItersParamsZoom), len(regParamsZoom)\n", "\n", "fig, ax = preparePlot(np.arange(0, numCols, 1), np.arange(0, numRows, 1), figsize=(8, 7), hideLabels=True,\n", " gridWidth=0.)\n", "ax.set_xticklabels(regParamsZoom), ax.set_yticklabels(numItersParamsZoom)\n", "ax.set_xlabel('Regularization Parameter'), ax.set_ylabel('Number of Iterations')\n", "\n", "colors = LinearSegmentedColormap.from_list('blue', ['#0022ff', '#000055'], gamma=.2)\n", "image = plt.imshow(rmseValZoom,interpolation='nearest', aspect='auto',\n", " cmap = colors)\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 5: Add interactions between features **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (5a) Add 2-way interactions **\n", "#### So far, we've used the features as they were provided. Now, we will add features that capture the two-way interactions between our existing features. Write a function `twoWayInteractions` that takes in a `LabeledPoint` and generates a new `LabeledPoint` that contains the old features and the two-way interactions between them. Note that a dataset with three features would have nine ( $ \\scriptsize 3^2 $ ) two-way interactions.\n", "#### You might want to use [itertools.product](https://docs.python.org/2/library/itertools.html#itertools.product) to generate tuples for each of the possible 2-way interactions. Remember that you can combine two `DenseVector` or `ndarray` objects using [np.hstack](http://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html#numpy.hstack)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "import itertools\n", "\n", "def twoWayInteractions(lp):\n", " \"\"\"Creates a new `LabeledPoint` that includes two-way interactions.\n", "\n", " Note:\n", " For features [x, y] the two-way interactions would be [x^2, x*y, y*x, y^2] and these\n", " would be appended to the original [x, y] feature list.\n", "\n", " Args:\n", " lp (LabeledPoint): The label and features for this observation.\n", "\n", " Returns:\n", " LabeledPoint: The new `LabeledPoint` should have the same label as `lp`. Its features\n", " should include the features from `lp` followed by the two-way interaction features.\n", " \"\"\"\n", " <FILL IN>\n", "\n", "print twoWayInteractions(LabeledPoint(0.0, [2, 3]))\n", "\n", "# Transform the existing train, validation, and test sets to include two-way interactions.\n", "trainDataInteract = <FILL IN>\n", "valDataInteract = <FILL IN>\n", "testDataInteract = <FILL IN>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Add two-way interactions (5a)\n", "twoWayExample = twoWayInteractions(LabeledPoint(0.0, [2, 3]))\n", "Test.assertTrue(np.allclose(sorted(twoWayExample.features),\n", " sorted([2.0, 3.0, 4.0, 6.0, 6.0, 9.0])),\n", " 'incorrect features generatedBy twoWayInteractions')\n", "twoWayPoint = twoWayInteractions(LabeledPoint(1.0, [1, 2, 3]))\n", "Test.assertTrue(np.allclose(sorted(twoWayPoint.features),\n", " sorted([1.0,2.0,3.0,1.0,2.0,3.0,2.0,4.0,6.0,3.0,6.0,9.0])),\n", " 'incorrect features generated by twoWayInteractions')\n", "Test.assertEquals(twoWayPoint.label, 1.0, 'incorrect label generated by twoWayInteractions')\n", "Test.assertTrue(np.allclose(sum(trainDataInteract.take(1)[0].features), 40.821870576035529),\n", " 'incorrect features in trainDataInteract')\n", "Test.assertTrue(np.allclose(sum(valDataInteract.take(1)[0].features), 45.457719932695696),\n", " 'incorrect features in valDataInteract')\n", "Test.assertTrue(np.allclose(sum(testDataInteract.take(1)[0].features), 35.109111632783168),\n", " 'incorrect features in testDataInteract')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (5b) Build interaction model **\n", "#### Now, let's build the new model. We've done this several times now. To implement this for the new features, we need to change a few variable names. Remember that we should build our model from the training data and evaluate it on the validation data.\n", "#### Note that you should re-run your hyperparameter search after changing features, as using the best hyperparameters from your prior model will not necessary lead to the best model. For this exercise, we have already preset the hyperparameters to reasonable values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "numIters = 500\n", "alpha = 1.0\n", "miniBatchFrac = 1.0\n", "reg = 1e-10\n", "\n", "modelInteract = LinearRegressionWithSGD.train(<FILL IN>, numIters, alpha,\n", " miniBatchFrac, regParam=reg,\n", " regType='l2', intercept=True)\n", "labelsAndPredsInteract = <FILL IN>.map(lambda lp: (lp.label, <FILL IN>.predict(lp.features)))\n", "rmseValInteract = calcRMSE(labelsAndPredsInteract)\n", "\n", "print ('Validation RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLR0 = {1:.3f}\\n\\tLR1 = {2:.3f}\\n\\tLRGrid = ' +\n", " '{3:.3f}\\n\\tLRInteract = {4:.3f}').format(rmseValBase, rmseValLR0, rmseValLR1,\n", " rmseValLRGrid, rmseValInteract)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Build interaction model (5b)\n", "Test.assertTrue(np.allclose(rmseValInteract, 15.6894664683), 'incorrect value for rmseValInteract')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (5c) Evaluate interaction model on test data **\n", "#### Our final step is to evaluate the new model on the test dataset. Note that we haven't used the test set to evaluate any of our models. Because of this, our evaluation provides us with an unbiased estimate for how our model will perform on new data. If we had changed our model based on viewing its performance on the test set, our estimate of RMSE would likely be overly optimistic.\n", "#### We'll also print the RMSE for both the baseline model and our new model. With this information, we can see how much better our model performs than the baseline model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "labelsAndPredsTest = <FILL IN>\n", "rmseTestInteract = <FILL IN>\n", "\n", "print ('Test RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLRInteract = {1:.3f}'\n", " .format(rmseTestBase, rmseTestInteract))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Evaluate interaction model on test data (5c)\n", "Test.assertTrue(np.allclose(rmseTestInteract, 16.3272040537),\n", " 'incorrect value for rmseTestInteract')" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }

mit