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the-stack_0_854 | #!/usr/bin/env python3
# Write a program that simulates random BAC coverage over a genome
# Command line arguments include
# Genome size (e.g. 1000)
# X coverage (e.g. 5)
# Use assert() to check parameter bounds
# Report min, max, and histogram of coverage
# Note that your output may vary due to random function
import sys
import random
assert(len(sys.argv) == 3)
bins = int(sys.argv[1])
x = float(sys.argv[2])
assert(bins > 0)
assert(x > 0)
bacs = int(bins * x)
genome = [0] * bins
#1st array
for i in range(bacs):
r = random.randint(0, bins -1)
genome[r] += 1
genome.sort()
min = genome[0]
max = genome[-1]
#2nd array
hist = [0] * (max + 1)
for v in genome:
hist[v] += 1
#output
print(f'Size: {bins}')
print(f'X: {x}')
print(f'BACs: {bacs}')
print(f'Min: {genome[0]}')
print(f'Max: {genome[-1]}')
print(f'Counts:')
for i in range(len(hist)):
print(i, hist[i])
"""
Size: 1000
X: 5.0
BACs: 5000
Min: 0
Max: 13
Counts:
0 5
1 39
2 88
3 144
4 175
5 150
6 151
7 116
8 59
9 40
10 20
11 5
12 6
13 2
"""
|
the-stack_0_855 | import logging
from typing import Any, Dict, List, TypedDict
from utility import Utility
log: logging.Logger = logging.getLogger(__name__)
class CamoIDs(TypedDict):
"""Structure of loot/camo_ids.csv"""
id: int
ref: str
rarity: int
price: int
salvage: int
license: int
premium: int # bool
class CamoTable(TypedDict):
"""Structure of mp/camotable.csv"""
index: int
ref: str
botValid: int # bool
category: str
unlockType: str
unlockString: str
hideInUI: int # bool
name: str
image: str
availableOffline: int # bool
platformExclusiveType: str
class Camos:
"""Camo XAssets."""
def Compile(self: Any) -> None:
"""Compile the Camo XAssets."""
camos: List[Dict[str, Any]] = []
camos = Camos.IDs(self, camos)
camos = Camos.Table(self, camos)
Utility.WriteFile(self, f"{self.eXAssets}/camos.json", camos)
log.info(f"Compiled {len(camos):,} Camos")
def IDs(self: Any, camos: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Compile the loot/camo_ids.csv XAsset."""
ids: List[Dict[str, Any]] = Utility.ReadCSV(
self, f"{self.iXAssets}/loot/camo_ids.csv", CamoIDs
)
if ids is None:
return camos
for entry in ids:
camos.append(
{
"id": entry.get("id"),
"altId": entry.get("ref"),
"name": None,
"category": None,
"type": self.ModernWarfare.GetLootType(entry.get("id")),
"rarity": self.ModernWarfare.GetLootRarity(entry.get("rarity")),
"season": self.ModernWarfare.GetLootSeason(entry.get("license")),
"exclusive": None,
"available": self.ModernWarfare.GetTitleAvailability(
entry.get("id")
),
"hidden": None,
"image": None,
}
)
return camos
def Table(self: Any, camos: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Compile the mp/camotable.csv XAsset."""
table: List[Dict[str, Any]] = Utility.ReadCSV(
self, f"{self.iXAssets}/mp/camotable.csv", CamoTable
)
if table is None:
return camos
for camo in camos:
for entry in table:
if camo.get("altId") != entry.get("ref"):
continue
camo["name"] = self.localize.get(entry.get("name"))
camo["category"] = self.ModernWarfare.GetCamoCategory(
entry.get("category")
)
camo["exclusive"] = self.ModernWarfare.GetPlatformExclusivity(
entry.get("platformExclusiveType")
)
camo["hidden"] = bool(entry.get("hidden"))
camo["image"] = entry.get("image")
return camos
|
the-stack_0_856 | class Pagelet(object):
def __init__(self, parent_request, target_element_id, route_view, params, method: str = 'GET', depends_on: str= None):
self.parent_request = parent_request
self.target = target_element_id
self.route_view = route_view
self.params = params
self.method = method
self.depends_on = depends_on
def render(self):
return self.route_view(self.parent_request, **self.params)
|
the-stack_0_857 | import numpy as np
import pandas as pd
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from sklearn.preprocessing import MultiLabelBinarizer
from tensorflow.keras.preprocessing.sequence import skipgrams
from keras.utils import np_utils
from keras.preprocessing.sequence import make_sampling_table
import scipy.io as sio
import os
def train(cleaned_tweets, tweets, hashtags, sentiment, source_idx, target_idx):
# Obtain skipgram embedding only
# Create feature representation: TFIDF-Variants and skipgram embedding with 1000 dimension and negative sampling
# Output will be saved to disk
# get_glove_embedding_matrix(cleaned_tweets)
# get_skipgram_gensim_embedding_matrix(cleaned_tweets)
# Sentence Skipgram is the base feature representation of the datatset
X = get_skipgram_sentence_embedding_matrix(cleaned_tweets)
# Create bytes file for the visualization
X.dtype=np.float32
X.tofile("data/skipgram_tensors.bytes")
create_domain_adaptation_dataset(X, tweets, source_idx, target_idx, sentiment)
def get_skipgram_sentence_embedding_matrix(text, dim=200, batch_size=256, window_size=5, epochs=1):
print("get_skipgram_sentence_embedding_matrix")
if os.path.isfile("data/sentqs_skipgram_sentence_embedding.npz"):
loaded_embedding = np.load("data/sentqs_skipgram_sentence_embedding.npz")
loaded_embedding = loaded_embedding["embedding"]
print('Loaded Skipgram embedding.')
return loaded_embedding
else:
text = [''.join(x) for x in text]
t = Tokenizer()
t.fit_on_texts(text)
corpus = t.texts_to_sequences(text)
# print(corpus)
V = len(t.word_index)
step_size = len(corpus) // batch_size
model = Sequential()
model.add(Dense(units=dim, input_dim=V, activation="softmax"))
model.add(Dense(units=V, input_dim=dim, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.summary()
model.fit(generate_data(corpus, window_size, V), epochs=epochs, steps_per_epoch=step_size)
# model.save("data/sentqs_full_skigram_arc.h5")
mlb = MultiLabelBinarizer()
enc = mlb.fit_transform(corpus)
emb = enc @ model.get_weights()[0]
np.savez_compressed("data/sentqs_skipgram_sentence_embedding", embedding=emb)
return emb
def create_domain_adaptation_dataset(X, tweets, source_idx, target_idx, sentiment):
Xs = X[source_idx]
Xt = X[target_idx]
Ys = sentiment[source_idx]
Yt = sentiment[target_idx]
data = [Xs, Ys, Xt, Yt]
np.savez('data/sentqs_dataset.npz', *data)
sio.savemat('data/sentqs_dataset.mat', {'Xs': Xs, 'Xt': Xt, 'Ys': Ys, 'Yt': Yt})
source_tweets = [tweets[i] for i in source_idx]
target_tweets = [tweets[i] for i in target_idx]
pd.DataFrame(source_tweets).to_csv("data/sentqs_source_tweets.csv")
pd.DataFrame(target_tweets).to_csv("data/sentqs_target_tweets.csv")
return Xs, Ys, Xt, Yt
def generate_data(corpus, window_size, V):
for words in corpus:
couples, labels = skipgrams(words, V, window_size, negative_samples=1, shuffle=True,
sampling_table=make_sampling_table(V, sampling_factor=1e-05))
if couples:
X, y = zip(*couples)
X = np_utils.to_categorical(X, V)
y = np_utils.to_categorical(y, V)
yield X, y
|
the-stack_0_858 | from commndata.models import TimeLinedTable
from django.db import models
from django.utils.translation import gettext_lazy as _
from enum import Enum
class SalaryTable(TimeLinedTable):
class SALARY_TABLE(models.IntegerChoices):
GS1 = (1010, '行（一）')
GS2 = (1020, '行（二）')
SGS = (1110, '専門行政')
ZM = (1210, '税務')
KA1 = (1310, '公安（一）')
KA2 = (1320, '公安（二）')
KJ1 = (1410, '海（一）')
KJ2 = (1420, '海（二）')
KI1 = (1510, '教（一）')
KI2 = (1520, '教（二）')
KK = (1610, '研究')
IR1 = (1710, '医（一）')
IR2 = (1720, '医（二）')
IR3 = (1730, '医（三）')
FS = (1810, '福祉')
NK1 = (1910, '任研（一）') # 任期付き研究員
NK2 = (1920, '任研（二）')
TNK = (1930, '特任研') # 特定任期付き研究員
SS = (2010, '専門スタッフ')
ST = (2110, '指定職') # 指定職
class STAFF_TYPE(models.IntegerChoices):
TY = (1, '定員')
SNY = (2, '再任用')
salary_table = models.IntegerField(verbose_name=_('salary table'), blank=False,
choices=SALARY_TABLE.choices, default=SALARY_TABLE.GS1) # 俸給表
salary_level = models.IntegerField(verbose_name=_('salary level')) # 級
salary_no = models.IntegerField(verbose_name=_('salary no')) # 号俸
salary_monthly = models.IntegerField(verbose_name=_('salary monthly')) # 俸給月額
salary_adjustment = models.IntegerField(verbose_name=_('salary adjustment')) # 俸給の調整額
@property
def sny_salary_no():
"""
再任用職員の号俸
"""
return 999
class Meta:
permissions = [
('import_salary_table', 'Can import salary_table'),
('export_salary_table', 'Can export salary_table'),
]
verbose_name = _('salary table')
verbose_name_plural = _('salary table')
constraints = [
models.UniqueConstraint(name='salary_table_unique', fields = ['start_date', 'salary_table', 'salary_level', 'salary_adjustment']),
]
ordering = ['-start_date', 'salary_table', 'salary_level', 'salary_no']
def __str__(self):
return self.salary_table
class SalaryTableExcel(TimeLinedTable):
salary_table = models.IntegerField(verbose_name=_('salary table'), blank=False,
choices=SalaryTable.SALARY_TABLE.choices, default=SalaryTable.SALARY_TABLE.GS1) # 俸給表
sheet_name = models.CharField(max_length=10, verbose_name=_('シート名'))
rows = models.IntegerField(verbose_name=_('級'), default=1)
cols = models.IntegerField(verbose_name=_('号俸'), default=1)
sny_flg = models.BooleanField(verbose_name=_('再任用有無'), default=True)
start_cell = models.CharField(max_length=10, verbose_name=_('データ開始セル'))
class Meta:
db_table = 'salary_table_excel'
verbose_name = _('俸給表取込エクセル設定')
verbose_name_plural = _('俸給表取込エクセル設定')
constraints = [
models.UniqueConstraint(name='salary_table_excel_unique', fields = ['start_date', 'salary_table',]),
]
ordering = ['-start_date', 'salary_table', ]
|
the-stack_0_860 | """
fitpack --- curve and surface fitting with splines
fitpack is based on a collection of Fortran routines DIERCKX
by P. Dierckx (see http://www.netlib.org/dierckx/) transformed
to double routines by Pearu Peterson.
"""
# Created by Pearu Peterson, June,August 2003
from __future__ import division, print_function, absolute_import
__all__ = [
'UnivariateSpline',
'InterpolatedUnivariateSpline',
'LSQUnivariateSpline',
'BivariateSpline',
'LSQBivariateSpline',
'SmoothBivariateSpline',
'LSQSphereBivariateSpline',
'SmoothSphereBivariateSpline',
'RectBivariateSpline',
'RectSphereBivariateSpline']
import warnings
from numpy import zeros, concatenate, alltrue, ravel, all, diff, array, ones
import numpy as np
from . import fitpack
from . import dfitpack
################ Univariate spline ####################
_curfit_messages = {1:"""
The required storage space exceeds the available storage space, as
specified by the parameter nest: nest too small. If nest is already
large (say nest > m/2), it may also indicate that s is too small.
The approximation returned is the weighted least-squares spline
according to the knots t[0],t[1],...,t[n-1]. (n=nest) the parameter fp
gives the corresponding weighted sum of squared residuals (fp>s).
""",
2:"""
A theoretically impossible result was found during the iteration
proces for finding a smoothing spline with fp = s: s too small.
There is an approximation returned but the corresponding weighted sum
of squared residuals does not satisfy the condition abs(fp-s)/s < tol.""",
3:"""
The maximal number of iterations maxit (set to 20 by the program)
allowed for finding a smoothing spline with fp=s has been reached: s
too small.
There is an approximation returned but the corresponding weighted sum
of squared residuals does not satisfy the condition abs(fp-s)/s < tol.""",
10:"""
Error on entry, no approximation returned. The following conditions
must hold:
xb<=x[0]<x[1]<...<x[m-1]<=xe, w[i]>0, i=0..m-1
if iopt=-1:
xb<t[k+1]<t[k+2]<...<t[n-k-2]<xe"""
}
# UnivariateSpline, ext parameter can be an int or a string
_extrap_modes = {0: 0, 'extrapolate': 0,
1: 1, 'zeros': 1,
2: 2, 'raise': 2,
3: 3, 'const': 3}
class UnivariateSpline(object):
"""
One-dimensional smoothing spline fit to a given set of data points.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. `s`
specifies the number of knots by specifying a smoothing condition.
Parameters
----------
x : (N,) array_like
1-D array of independent input data. Must be increasing.
y : (N,) array_like
1-D array of dependent input data, of the same length as `x`.
w : (N,) array_like, optional
Weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be <= 5.
Default is k=3, a cubic spline.
s : float or None, optional
Positive smoothing factor used to choose the number of knots. Number
of knots will be increased until the smoothing condition is satisfied::
sum((w[i] * (y[i]-spl(x[i])))**2, axis=0) <= s
If None (default), ``s = len(w)`` which should be a good value if
``1/w[i]`` is an estimate of the standard deviation of ``y[i]``.
If 0, spline will interpolate through all data points.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
check_finite : bool, optional
Whether to check that the input arrays contain only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination or non-sensical results) if the inputs
do contain infinities or NaNs.
Default is False.
See Also
--------
InterpolatedUnivariateSpline : Subclass with smoothing forced to 0
LSQUnivariateSpline : Subclass in which knots are user-selected instead of
being set by smoothing condition
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
**NaN handling**: If the input arrays contain ``nan`` values, the result
is not useful, since the underlying spline fitting routines cannot deal
with ``nan`` . A workaround is to use zero weights for not-a-number
data points:
>>> from scipy.interpolate import UnivariateSpline
>>> x, y = np.array([1, 2, 3, 4]), np.array([1, np.nan, 3, 4])
>>> w = np.isnan(y)
>>> y[w] = 0.
>>> spl = UnivariateSpline(x, y, w=~w)
Notice the need to replace a ``nan`` by a numerical value (precise value
does not matter as long as the corresponding weight is zero.)
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
>>> plt.plot(x, y, 'ro', ms=5)
Use the default value for the smoothing parameter:
>>> spl = UnivariateSpline(x, y)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(xs, spl(xs), 'g', lw=3)
Manually change the amount of smoothing:
>>> spl.set_smoothing_factor(0.5)
>>> plt.plot(xs, spl(xs), 'b', lw=3)
>>> plt.show()
"""
def __init__(self, x, y, w=None, bbox=[None]*2, k=3, s=None,
ext=0, check_finite=False):
if check_finite:
if not np.isfinite(x).all() or not np.isfinite(y).all():
raise ValueError("x and y array must not contain NaNs or infs.")
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
data = dfitpack.fpcurf0(x,y,k,w=w,
xb=bbox[0],xe=bbox[1],s=s)
if data[-1] == 1:
# nest too small, setting to maximum bound
data = self._reset_nest(data)
self._data = data
self._reset_class()
@classmethod
def _from_tck(cls, tck, ext=0):
"""Construct a spline object from given tck"""
self = cls.__new__(cls)
t, c, k = tck
self._eval_args = tck
#_data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
self._data = (None,None,None,None,None,k,None,len(t),t,
c,None,None,None,None)
self.ext = ext
return self
def _reset_class(self):
data = self._data
n,t,c,k,ier = data[7],data[8],data[9],data[5],data[-1]
self._eval_args = t[:n],c[:n],k
if ier == 0:
# the spline returned has a residual sum of squares fp
# such that abs(fp-s)/s <= tol with tol a relative
# tolerance set to 0.001 by the program
pass
elif ier == -1:
# the spline returned is an interpolating spline
self._set_class(InterpolatedUnivariateSpline)
elif ier == -2:
# the spline returned is the weighted least-squares
# polynomial of degree k. In this extreme case fp gives
# the upper bound fp0 for the smoothing factor s.
self._set_class(LSQUnivariateSpline)
else:
# error
if ier == 1:
self._set_class(LSQUnivariateSpline)
message = _curfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
def _set_class(self, cls):
self._spline_class = cls
if self.__class__ in (UnivariateSpline, InterpolatedUnivariateSpline,
LSQUnivariateSpline):
self.__class__ = cls
else:
# It's an unknown subclass -- don't change class. cf. #731
pass
def _reset_nest(self, data, nest=None):
n = data[10]
if nest is None:
k,m = data[5],len(data[0])
nest = m+k+1 # this is the maximum bound for nest
else:
if not n <= nest:
raise ValueError("`nest` can only be increased")
t, c, fpint, nrdata = [np.resize(data[j], nest) for j in [8,9,11,12]]
args = data[:8] + (t,c,n,fpint,nrdata,data[13])
data = dfitpack.fpcurf1(*args)
return data
def set_smoothing_factor(self, s):
""" Continue spline computation with the given smoothing
factor s and with the knots found at the last call.
This routine modifies the spline in place.
"""
data = self._data
if data[6] == -1:
warnings.warn('smoothing factor unchanged for'
'LSQ spline with fixed knots')
return
args = data[:6] + (s,) + data[7:]
data = dfitpack.fpcurf1(*args)
if data[-1] == 1:
# nest too small, setting to maximum bound
data = self._reset_nest(data)
self._data = data
self._reset_class()
def __call__(self, x, nu=0, ext=None):
"""
Evaluate spline (or its nu-th derivative) at positions x.
Parameters
----------
x : array_like
A 1-D array of points at which to return the value of the smoothed
spline or its derivatives. Note: x can be unordered but the
evaluation is more efficient if x is (partially) ordered.
nu : int
The order of derivative of the spline to compute.
ext : int
Controls the value returned for elements of ``x`` not in the
interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 or 'const', return the boundary value.
The default value is 0, passed from the initialization of
UnivariateSpline.
"""
x = np.asarray(x)
# empty input yields empty output
if x.size == 0:
return array([])
# if nu is None:
# return dfitpack.splev(*(self._eval_args+(x,)))
# return dfitpack.splder(nu=nu,*(self._eval_args+(x,)))
if ext is None:
ext = self.ext
else:
try:
ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
return fitpack.splev(x, self._eval_args, der=nu, ext=ext)
def get_knots(self):
""" Return positions of interior knots of the spline.
Internally, the knot vector contains ``2*k`` additional boundary knots.
"""
data = self._data
k,n = data[5],data[7]
return data[8][k:n-k]
def get_coeffs(self):
"""Return spline coefficients."""
data = self._data
k,n = data[5],data[7]
return data[9][:n-k-1]
def get_residual(self):
"""Return weighted sum of squared residuals of the spline approximation.
This is equivalent to::
sum((w[i] * (y[i]-spl(x[i])))**2, axis=0)
"""
return self._data[10]
def integral(self, a, b):
""" Return definite integral of the spline between two given points.
Parameters
----------
a : float
Lower limit of integration.
b : float
Upper limit of integration.
Returns
-------
integral : float
The value of the definite integral of the spline between limits.
Examples
--------
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(0, 3, 11)
>>> y = x**2
>>> spl = UnivariateSpline(x, y)
>>> spl.integral(0, 3)
9.0
which agrees with :math:`\int x^2 dx = x^3 / 3` between the limits
of 0 and 3.
A caveat is that this routine assumes the spline to be zero outside of
the data limits:
>>> spl.integral(-1, 4)
9.0
>>> spl.integral(-1, 0)
0.0
"""
return dfitpack.splint(*(self._eval_args+(a,b)))
def derivatives(self, x):
""" Return all derivatives of the spline at the point x.
Parameters
----------
x : float
The point to evaluate the derivatives at.
Returns
-------
der : ndarray, shape(k+1,)
Derivatives of the orders 0 to k.
Examples
--------
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(0, 3, 11)
>>> y = x**2
>>> spl = UnivariateSpline(x, y)
>>> spl.derivatives(1.5)
array([2.25, 3.0, 2.0, 0])
"""
d,ier = dfitpack.spalde(*(self._eval_args+(x,)))
if not ier == 0:
raise ValueError("Error code returned by spalde: %s" % ier)
return d
def roots(self):
""" Return the zeros of the spline.
Restriction: only cubic splines are supported by fitpack.
"""
k = self._data[5]
if k == 3:
z,m,ier = dfitpack.sproot(*self._eval_args[:2])
if not ier == 0:
raise ValueError("Error code returned by spalde: %s" % ier)
return z[:m]
raise NotImplementedError('finding roots unsupported for '
'non-cubic splines')
def derivative(self, n=1):
"""
Construct a new spline representing the derivative of this spline.
Parameters
----------
n : int, optional
Order of derivative to evaluate. Default: 1
Returns
-------
spline : UnivariateSpline
Spline of order k2=k-n representing the derivative of this
spline.
See Also
--------
splder, antiderivative
Notes
-----
.. versionadded:: 0.13.0
Examples
--------
This can be used for finding maxima of a curve:
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(0, 10, 70)
>>> y = np.sin(x)
>>> spl = UnivariateSpline(x, y, k=4, s=0)
Now, differentiate the spline and find the zeros of the
derivative. (NB: `sproot` only works for order 3 splines, so we
fit an order 4 spline):
>>> spl.derivative().roots() / np.pi
array([ 0.50000001, 1.5 , 2.49999998])
This agrees well with roots :math:`\pi/2 + n\pi` of `cos(x) = sin'(x)`.
"""
tck = fitpack.splder(self._eval_args, n)
return UnivariateSpline._from_tck(tck, self.ext)
def antiderivative(self, n=1):
"""
Construct a new spline representing the antiderivative of this spline.
Parameters
----------
n : int, optional
Order of antiderivative to evaluate. Default: 1
Returns
-------
spline : UnivariateSpline
Spline of order k2=k+n representing the antiderivative of this
spline.
Notes
-----
.. versionadded:: 0.13.0
See Also
--------
splantider, derivative
Examples
--------
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(0, np.pi/2, 70)
>>> y = 1 / np.sqrt(1 - 0.8*np.sin(x)**2)
>>> spl = UnivariateSpline(x, y, s=0)
The derivative is the inverse operation of the antiderivative,
although some floating point error accumulates:
>>> spl(1.7), spl.antiderivative().derivative()(1.7)
(array(2.1565429877197317), array(2.1565429877201865))
Antiderivative can be used to evaluate definite integrals:
>>> ispl = spl.antiderivative()
>>> ispl(np.pi/2) - ispl(0)
2.2572053588768486
This is indeed an approximation to the complete elliptic integral
:math:`K(m) = \\int_0^{\\pi/2} [1 - m\\sin^2 x]^{-1/2} dx`:
>>> from scipy.special import ellipk
>>> ellipk(0.8)
2.2572053268208538
"""
tck = fitpack.splantider(self._eval_args, n)
return UnivariateSpline._from_tck(tck, self.ext)
class InterpolatedUnivariateSpline(UnivariateSpline):
"""
One-dimensional interpolating spline for a given set of data points.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. Spline
function passes through all provided points. Equivalent to
`UnivariateSpline` with s=0.
Parameters
----------
x : (N,) array_like
Input dimension of data points -- must be increasing
y : (N,) array_like
input dimension of data points
w : (N,) array_like, optional
Weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be 1 <= `k` <= 5.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
check_finite : bool, optional
Whether to check that the input arrays contain only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination or non-sensical results) if the inputs
do contain infinities or NaNs.
Default is False.
See Also
--------
UnivariateSpline : Superclass -- allows knots to be selected by a
smoothing condition
LSQUnivariateSpline : spline for which knots are user-selected
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import InterpolatedUnivariateSpline
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
>>> spl = InterpolatedUnivariateSpline(x, y)
>>> plt.plot(x, y, 'ro', ms=5)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(xs, spl(xs), 'g', lw=3, alpha=0.7)
>>> plt.show()
Notice that the ``spl(x)`` interpolates `y`:
>>> spl.get_residual()
0.0
"""
def __init__(self, x, y, w=None, bbox=[None]*2, k=3,
ext=0, check_finite=False):
if check_finite:
if (not np.isfinite(x).all() or not np.isfinite(y).all() or
not np.isfinite(w).all()):
raise ValueError("Input must not contain NaNs or infs.")
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
self._data = dfitpack.fpcurf0(x,y,k,w=w,
xb=bbox[0],xe=bbox[1],s=0)
self._reset_class()
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
_fpchec_error_string = """The input parameters have been rejected by fpchec. \
This means that at least one of the following conditions is violated:
1) k+1 <= n-k-1 <= m
2) t(1) <= t(2) <= ... <= t(k+1)
t(n-k) <= t(n-k+1) <= ... <= t(n)
3) t(k+1) < t(k+2) < ... < t(n-k)
4) t(k+1) <= x(i) <= t(n-k)
5) The conditions specified by Schoenberg and Whitney must hold
for at least one subset of data points, i.e., there must be a
subset of data points y(j) such that
t(j) < y(j) < t(j+k+1), j=1,2,...,n-k-1
"""
class LSQUnivariateSpline(UnivariateSpline):
"""
One-dimensional spline with explicit internal knots.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. `t`
specifies the internal knots of the spline
Parameters
----------
x : (N,) array_like
Input dimension of data points -- must be increasing
y : (N,) array_like
Input dimension of data points
t : (M,) array_like
interior knots of the spline. Must be in ascending order and::
bbox[0] < t[0] < ... < t[-1] < bbox[-1]
w : (N,) array_like, optional
weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox = [x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be 1 <= `k` <= 5.
Default is k=3, a cubic spline.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
check_finite : bool, optional
Whether to check that the input arrays contain only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination or non-sensical results) if the inputs
do contain infinities or NaNs.
Default is False.
Raises
------
ValueError
If the interior knots do not satisfy the Schoenberg-Whitney conditions
See Also
--------
UnivariateSpline : Superclass -- knots are specified by setting a
smoothing condition
InterpolatedUnivariateSpline : spline passing through all points
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
Knots `t` must satisfy the Schoenberg-Whitney conditions,
i.e., there must be a subset of data points ``x[j]`` such that
``t[j] < x[j] < t[j+k+1]``, for ``j=0, 1,...,n-k-2``.
Examples
--------
>>> from scipy.interpolate import LSQUnivariateSpline, UnivariateSpline
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
Fit a smoothing spline with a pre-defined internal knots:
>>> t = [-1, 0, 1]
>>> spl = LSQUnivariateSpline(x, y, t)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(x, y, 'ro', ms=5)
>>> plt.plot(xs, spl(xs), 'g-', lw=3)
>>> plt.show()
Check the knot vector:
>>> spl.get_knots()
array([-3., -1., 0., 1., 3.])
Constructing lsq spline using the knots from another spline:
>>> x = np.arange(10)
>>> s = UnivariateSpline(x, x, s=0)
>>> s.get_knots()
array([ 0., 2., 3., 4., 5., 6., 7., 9.])
>>> knt = s.get_knots()
>>> s1 = LSQUnivariateSpline(x, x, knt[1:-1]) # Chop 1st and last knot
>>> s1.get_knots()
array([ 0., 2., 3., 4., 5., 6., 7., 9.])
"""
def __init__(self, x, y, t, w=None, bbox=[None]*2, k=3,
ext=0, check_finite=False):
if check_finite:
if (not np.isfinite(x).all() or not np.isfinite(y).all() or
not np.isfinite(w).all() or not np.isfinite(t).all()):
raise ValueError("Input(s) must not contain NaNs or infs.")
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
xb = bbox[0]
xe = bbox[1]
if xb is None:
xb = x[0]
if xe is None:
xe = x[-1]
t = concatenate(([xb]*(k+1), t, [xe]*(k+1)))
n = len(t)
if not alltrue(t[k+1:n-k]-t[k:n-k-1] > 0, axis=0):
raise ValueError('Interior knots t must satisfy '
'Schoenberg-Whitney conditions')
if not dfitpack.fpchec(x, t, k) == 0:
raise ValueError(_fpchec_error_string)
data = dfitpack.fpcurfm1(x, y, k, t, w=w, xb=xb, xe=xe)
self._data = data[:-3] + (None, None, data[-1])
self._reset_class()
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
################ Bivariate spline ####################
class _BivariateSplineBase(object):
""" Base class for Bivariate spline s(x,y) interpolation on the rectangle
[xb,xe] x [yb, ye] calculated from a given set of data points
(x,y,z).
See Also
--------
bisplrep, bisplev : an older wrapping of FITPACK
BivariateSpline :
implementation of bivariate spline interpolation on a plane grid
SphereBivariateSpline :
implementation of bivariate spline interpolation on a spherical grid
"""
def get_residual(self):
""" Return weighted sum of squared residuals of the spline
approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0)
"""
return self.fp
def get_knots(self):
""" Return a tuple (tx,ty) where tx,ty contain knots positions
of the spline with respect to x-, y-variable, respectively.
The position of interior and additional knots are given as
t[k+1:-k-1] and t[:k+1]=b, t[-k-1:]=e, respectively.
"""
return self.tck[:2]
def get_coeffs(self):
""" Return spline coefficients."""
return self.tck[2]
def __call__(self, x, y, mth=None, dx=0, dy=0, grid=True):
"""
Evaluate the spline or its derivatives at given positions.
Parameters
----------
x, y : array_like
Input coordinates.
If `grid` is False, evaluate the spline at points ``(x[i],
y[i]), i=0, ..., len(x)-1``. Standard Numpy broadcasting
is obeyed.
If `grid` is True: evaluate spline at the grid points
defined by the coordinate arrays x, y. The arrays must be
sorted to increasing order.
dx : int
Order of x-derivative
.. versionadded:: 0.14.0
dy : int
Order of y-derivative
.. versionadded:: 0.14.0
grid : bool
Whether to evaluate the results on a grid spanned by the
input arrays, or at points specified by the input arrays.
.. versionadded:: 0.14.0
mth : str
Deprecated argument. Has no effect.
"""
x = np.asarray(x)
y = np.asarray(y)
if mth is not None:
warnings.warn("The `mth` argument is deprecated and will be removed",
FutureWarning)
tx, ty, c = self.tck[:3]
kx, ky = self.degrees
if grid:
if x.size == 0 or y.size == 0:
return np.zeros((x.size, y.size), dtype=self.tck[2].dtype)
if dx or dy:
z,ier = dfitpack.parder(tx,ty,c,kx,ky,dx,dy,x,y)
if not ier == 0:
raise ValueError("Error code returned by parder: %s" % ier)
else:
z,ier = dfitpack.bispev(tx,ty,c,kx,ky,x,y)
if not ier == 0:
raise ValueError("Error code returned by bispev: %s" % ier)
else:
# standard Numpy broadcasting
if x.shape != y.shape:
x, y = np.broadcast_arrays(x, y)
shape = x.shape
x = x.ravel()
y = y.ravel()
if x.size == 0 or y.size == 0:
return np.zeros(shape, dtype=self.tck[2].dtype)
if dx or dy:
z,ier = dfitpack.pardeu(tx,ty,c,kx,ky,dx,dy,x,y)
if not ier == 0:
raise ValueError("Error code returned by pardeu: %s" % ier)
else:
z,ier = dfitpack.bispeu(tx,ty,c,kx,ky,x,y)
if not ier == 0:
raise ValueError("Error code returned by bispeu: %s" % ier)
z = z.reshape(shape)
return z
_surfit_messages = {1:"""
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.""",
2:"""
A theoretically impossible result was found during the iteration
process for finding a smoothing spline with fp = s: s too small or
badly chosen eps.
Weighted sum of squared residuals does not satisfy abs(fp-s)/s < tol.""",
3:"""
the maximal number of iterations maxit (set to 20 by the program)
allowed for finding a smoothing spline with fp=s has been reached:
s too small.
Weighted sum of squared residuals does not satisfy abs(fp-s)/s < tol.""",
4:"""
No more knots can be added because the number of b-spline coefficients
(nx-kx-1)*(ny-ky-1) already exceeds the number of data points m:
either s or m too small.
The weighted least-squares spline corresponds to the current set of
knots.""",
5:"""
No more knots can be added because the additional knot would (quasi)
coincide with an old one: s too small or too large a weight to an
inaccurate data point.
The weighted least-squares spline corresponds to the current set of
knots.""",
10:"""
Error on entry, no approximation returned. The following conditions
must hold:
xb<=x[i]<=xe, yb<=y[i]<=ye, w[i]>0, i=0..m-1
If iopt==-1, then
xb<tx[kx+1]<tx[kx+2]<...<tx[nx-kx-2]<xe
yb<ty[ky+1]<ty[ky+2]<...<ty[ny-ky-2]<ye""",
-3:"""
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=%i). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps."""
}
class BivariateSpline(_BivariateSplineBase):
"""
Base class for bivariate splines.
This describes a spline ``s(x, y)`` of degrees ``kx`` and ``ky`` on
the rectangle ``[xb, xe] * [yb, ye]`` calculated from a given set
of data points ``(x, y, z)``.
This class is meant to be subclassed, not instantiated directly.
To construct these splines, call either `SmoothBivariateSpline` or
`LSQBivariateSpline`.
See Also
--------
UnivariateSpline : a similar class for univariate spline interpolation
SmoothBivariateSpline :
to create a BivariateSpline through the given points
LSQBivariateSpline :
to create a BivariateSpline using weighted least-squares fitting
SphereBivariateSpline :
bivariate spline interpolation in spherical cooridinates
bisplrep : older wrapping of FITPACK
bisplev : older wrapping of FITPACK
"""
@classmethod
def _from_tck(cls, tck):
"""Construct a spline object from given tck and degree"""
self = cls.__new__(cls)
if len(tck) != 5:
raise ValueError("tck should be a 5 element tuple of tx, ty, c, kx, ky")
self.tck = tck[:3]
self.degrees = tck[3:]
return self
def ev(self, xi, yi, dx=0, dy=0):
"""
Evaluate the spline at points
Returns the interpolated value at ``(xi[i], yi[i]),
i=0,...,len(xi)-1``.
Parameters
----------
xi, yi : array_like
Input coordinates. Standard Numpy broadcasting is obeyed.
dx : int, optional
Order of x-derivative
.. versionadded:: 0.14.0
dy : int, optional
Order of y-derivative
.. versionadded:: 0.14.0
"""
return self.__call__(xi, yi, dx=dx, dy=dy, grid=False)
def integral(self, xa, xb, ya, yb):
"""
Evaluate the integral of the spline over area [xa,xb] x [ya,yb].
Parameters
----------
xa, xb : float
The end-points of the x integration interval.
ya, yb : float
The end-points of the y integration interval.
Returns
-------
integ : float
The value of the resulting integral.
"""
tx,ty,c = self.tck[:3]
kx,ky = self.degrees
return dfitpack.dblint(tx,ty,c,kx,ky,xa,xb,ya,yb)
class SmoothBivariateSpline(BivariateSpline):
"""
Smooth bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not important).
w : array_like, optional
Positive 1-D sequence of weights, of same length as `x`, `y` and `z`.
bbox : array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
Default ``s=len(w)`` which should be a good value if ``1/w[i]`` is an
estimate of the standard deviation of ``z[i]``.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
See Also
--------
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
LSQUnivariateSpline : to create a BivariateSpline using weighted
Notes
-----
The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
"""
def __init__(self, x, y, z, w=None, bbox=[None] * 4, kx=3, ky=3, s=None,
eps=None):
xb,xe,yb,ye = bbox
nx,tx,ny,ty,c,fp,wrk1,ier = dfitpack.surfit_smth(x,y,z,w,
xb,xe,yb,ye,
kx,ky,s=s,
eps=eps,lwrk2=1)
if ier > 10: # lwrk2 was to small, re-run
nx,tx,ny,ty,c,fp,wrk1,ier = dfitpack.surfit_smth(x,y,z,w,
xb,xe,yb,ye,
kx,ky,s=s,
eps=eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx],ty[:ny],c[:(nx-kx-1)*(ny-ky-1)]
self.degrees = kx,ky
class LSQBivariateSpline(BivariateSpline):
"""
Weighted least-squares bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not important).
tx, ty : array_like
Strictly ordered 1-D sequences of knots coordinates.
w : array_like, optional
Positive 1-D array of weights, of the same length as `x`, `y` and `z`.
bbox : (4,) array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
See Also
--------
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
SmoothBivariateSpline : create a smoothing BivariateSpline
Notes
-----
The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
"""
def __init__(self, x, y, z, tx, ty, w=None, bbox=[None]*4, kx=3, ky=3,
eps=None):
nx = 2*kx+2+len(tx)
ny = 2*ky+2+len(ty)
tx1 = zeros((nx,),float)
ty1 = zeros((ny,),float)
tx1[kx+1:nx-kx-1] = tx
ty1[ky+1:ny-ky-1] = ty
xb,xe,yb,ye = bbox
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,
xb,xe,yb,ye,
kx,ky,eps,lwrk2=1)
if ier > 10:
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,
xb,xe,yb,ye,
kx,ky,eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
if ier < -2:
deficiency = (nx-kx-1)*(ny-ky-1)+ier
message = _surfit_messages.get(-3) % (deficiency)
else:
message = _surfit_messages.get(ier, 'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx1, ty1, c
self.degrees = kx, ky
class RectBivariateSpline(BivariateSpline):
"""
Bivariate spline approximation over a rectangular mesh.
Can be used for both smoothing and interpolating data.
Parameters
----------
x,y : array_like
1-D arrays of coordinates in strictly ascending order.
z : array_like
2-D array of data with shape (x.size,y.size).
bbox : array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
Default is ``s=0``, which is for interpolation.
See Also
--------
SmoothBivariateSpline : a smoothing bivariate spline for scattered data
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
"""
def __init__(self, x, y, z, bbox=[None] * 4, kx=3, ky=3, s=0):
x, y = ravel(x), ravel(y)
if not all(diff(x) > 0.0):
raise TypeError('x must be strictly increasing')
if not all(diff(y) > 0.0):
raise TypeError('y must be strictly increasing')
if not ((x.min() == x[0]) and (x.max() == x[-1])):
raise TypeError('x must be strictly ascending')
if not ((y.min() == y[0]) and (y.max() == y[-1])):
raise TypeError('y must be strictly ascending')
if not x.size == z.shape[0]:
raise TypeError('x dimension of z must have same number of '
'elements as x')
if not y.size == z.shape[1]:
raise TypeError('y dimension of z must have same number of '
'elements as y')
z = ravel(z)
xb, xe, yb, ye = bbox
nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(x, y, z, xb, xe, yb,
ye, kx, ky, s)
if ier not in [0, -1, -2]:
msg = _surfit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(msg)
self.fp = fp
self.tck = tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)]
self.degrees = kx, ky
_spherefit_messages = _surfit_messages.copy()
_spherefit_messages[10] = """
ERROR. On entry, the input data are controlled on validity. The following
restrictions must be satisfied:
-1<=iopt<=1, m>=2, ntest>=8 ,npest >=8, 0<eps<1,
0<=teta(i)<=pi, 0<=phi(i)<=2*pi, w(i)>0, i=1,...,m
lwrk1 >= 185+52*v+10*u+14*u*v+8*(u-1)*v**2+8*m
kwrk >= m+(ntest-7)*(npest-7)
if iopt=-1: 8<=nt<=ntest , 9<=np<=npest
0<tt(5)<tt(6)<...<tt(nt-4)<pi
0<tp(5)<tp(6)<...<tp(np-4)<2*pi
if iopt>=0: s>=0
if one of these conditions is found to be violated,control
is immediately repassed to the calling program. in that
case there is no approximation returned."""
_spherefit_messages[-3] = """
WARNING. The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank
deficient system (deficiency=%i, rank=%i). Especially if the rank
deficiency, which is computed by 6+(nt-8)*(np-7)+ier, is large,
the results may be inaccurate. They could also seriously depend on
the value of eps."""
class SphereBivariateSpline(_BivariateSplineBase):
"""
Bivariate spline s(x,y) of degrees 3 on a sphere, calculated from a
given set of data points (theta,phi,r).
.. versionadded:: 0.11.0
See Also
--------
bisplrep, bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
SmoothUnivariateSpline :
to create a BivariateSpline through the given points
LSQUnivariateSpline :
to create a BivariateSpline using weighted least-squares fitting
"""
def __call__(self, theta, phi, dtheta=0, dphi=0, grid=True):
"""
Evaluate the spline or its derivatives at given positions.
Parameters
----------
theta, phi : array_like
Input coordinates.
If `grid` is False, evaluate the spline at points
``(theta[i], phi[i]), i=0, ..., len(x)-1``. Standard
Numpy broadcasting is obeyed.
If `grid` is True: evaluate spline at the grid points
defined by the coordinate arrays theta, phi. The arrays
must be sorted to increasing order.
dtheta : int, optional
Order of theta-derivative
.. versionadded:: 0.14.0
dphi : int
Order of phi-derivative
.. versionadded:: 0.14.0
grid : bool
Whether to evaluate the results on a grid spanned by the
input arrays, or at points specified by the input arrays.
.. versionadded:: 0.14.0
"""
theta = np.asarray(theta)
phi = np.asarray(phi)
if theta.size > 0 and (theta.min() < 0. or theta.max() > np.pi):
raise ValueError("requested theta out of bounds.")
if phi.size > 0 and (phi.min() < 0. or phi.max() > 2. * np.pi):
raise ValueError("requested phi out of bounds.")
return _BivariateSplineBase.__call__(self, theta, phi,
dx=dtheta, dy=dphi, grid=grid)
def ev(self, theta, phi, dtheta=0, dphi=0):
"""
Evaluate the spline at points
Returns the interpolated value at ``(theta[i], phi[i]),
i=0,...,len(theta)-1``.
Parameters
----------
theta, phi : array_like
Input coordinates. Standard Numpy broadcasting is obeyed.
dtheta : int, optional
Order of theta-derivative
.. versionadded:: 0.14.0
dphi : int, optional
Order of phi-derivative
.. versionadded:: 0.14.0
"""
return self.__call__(theta, phi, dtheta=dtheta, dphi=dphi, grid=False)
class SmoothSphereBivariateSpline(SphereBivariateSpline):
"""
Smooth bivariate spline approximation in spherical coordinates.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (order is not important). Coordinates
must be given in radians. Theta must lie within the interval (0, pi),
and phi must lie within the interval (0, 2pi).
w : array_like, optional
Positive 1-D sequence of weights.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w(i)*(r(i) - s(theta(i), phi(i))))**2, axis=0) <= s``
Default ``s=len(w)`` which should be a good value if 1/w[i] is an
estimate of the standard deviation of r[i].
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
Notes
-----
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/sphere.f
Examples
--------
Suppose we have global data on a coarse grid (the input data does not
have to be on a grid):
>>> theta = np.linspace(0., np.pi, 7)
>>> phi = np.linspace(0., 2*np.pi, 9)
>>> data = np.empty((theta.shape[0], phi.shape[0]))
>>> data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
>>> data[1:-1,1], data[1:-1,-1] = 1., 1.
>>> data[1,1:-1], data[-2,1:-1] = 1., 1.
>>> data[2:-2,2], data[2:-2,-2] = 2., 2.
>>> data[2,2:-2], data[-3,2:-2] = 2., 2.
>>> data[3,3:-2] = 3.
>>> data = np.roll(data, 4, 1)
We need to set up the interpolator object
>>> lats, lons = np.meshgrid(theta, phi)
>>> from scipy.interpolate import SmoothSphereBivariateSpline
>>> lut = SmoothSphereBivariateSpline(lats.ravel(), lons.ravel(),
... data.T.ravel(), s=3.5)
As a first test, we'll see what the algorithm returns when run on the
input coordinates
>>> data_orig = lut(theta, phi)
Finally we interpolate the data to a finer grid
>>> fine_lats = np.linspace(0., np.pi, 70)
>>> fine_lons = np.linspace(0., 2 * np.pi, 90)
>>> data_smth = lut(fine_lats, fine_lons)
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(131)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(132)
>>> ax2.imshow(data_orig, interpolation='nearest')
>>> ax3 = fig.add_subplot(133)
>>> ax3.imshow(data_smth, interpolation='nearest')
>>> plt.show()
"""
def __init__(self, theta, phi, r, w=None, s=0., eps=1E-16):
if np.issubclass_(w, float):
w = ones(len(theta)) * w
nt_, tt_, np_, tp_, c, fp, ier = dfitpack.spherfit_smth(theta, phi,
r, w=w, s=s,
eps=eps)
if ier not in [0, -1, -2]:
message = _spherefit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(message)
self.fp = fp
self.tck = tt_[:nt_], tp_[:np_], c[:(nt_ - 4) * (np_ - 4)]
self.degrees = (3, 3)
class LSQSphereBivariateSpline(SphereBivariateSpline):
"""
Weighted least-squares bivariate spline approximation in spherical
coordinates.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (order is not important). Coordinates
must be given in radians. Theta must lie within the interval (0, pi),
and phi must lie within the interval (0, 2pi).
tt, tp : array_like
Strictly ordered 1-D sequences of knots coordinates.
Coordinates must satisfy ``0 < tt[i] < pi``, ``0 < tp[i] < 2*pi``.
w : array_like, optional
Positive 1-D sequence of weights, of the same length as `theta`, `phi`
and `r`.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
Notes
-----
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/sphere.f
Examples
--------
Suppose we have global data on a coarse grid (the input data does not
have to be on a grid):
>>> theta = np.linspace(0., np.pi, 7)
>>> phi = np.linspace(0., 2*np.pi, 9)
>>> data = np.empty((theta.shape[0], phi.shape[0]))
>>> data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
>>> data[1:-1,1], data[1:-1,-1] = 1., 1.
>>> data[1,1:-1], data[-2,1:-1] = 1., 1.
>>> data[2:-2,2], data[2:-2,-2] = 2., 2.
>>> data[2,2:-2], data[-3,2:-2] = 2., 2.
>>> data[3,3:-2] = 3.
>>> data = np.roll(data, 4, 1)
We need to set up the interpolator object. Here, we must also specify the
coordinates of the knots to use.
>>> lats, lons = np.meshgrid(theta, phi)
>>> knotst, knotsp = theta.copy(), phi.copy()
>>> knotst[0] += .0001
>>> knotst[-1] -= .0001
>>> knotsp[0] += .0001
>>> knotsp[-1] -= .0001
>>> from scipy.interpolate import LSQSphereBivariateSpline
>>> lut = LSQSphereBivariateSpline(lats.ravel(), lons.ravel(),
... data.T.ravel(), knotst, knotsp)
As a first test, we'll see what the algorithm returns when run on the
input coordinates
>>> data_orig = lut(theta, phi)
Finally we interpolate the data to a finer grid
>>> fine_lats = np.linspace(0., np.pi, 70)
>>> fine_lons = np.linspace(0., 2*np.pi, 90)
>>> data_lsq = lut(fine_lats, fine_lons)
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(131)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(132)
>>> ax2.imshow(data_orig, interpolation='nearest')
>>> ax3 = fig.add_subplot(133)
>>> ax3.imshow(data_lsq, interpolation='nearest')
>>> plt.show()
"""
def __init__(self, theta, phi, r, tt, tp, w=None, eps=1E-16):
if np.issubclass_(w, float):
w = ones(len(theta)) * w
nt_, np_ = 8 + len(tt), 8 + len(tp)
tt_, tp_ = zeros((nt_,), float), zeros((np_,), float)
tt_[4:-4], tp_[4:-4] = tt, tp
tt_[-4:], tp_[-4:] = np.pi, 2. * np.pi
tt_, tp_, c, fp, ier = dfitpack.spherfit_lsq(theta, phi, r, tt_, tp_,
w=w, eps=eps)
if ier < -2:
deficiency = 6 + (nt_ - 8) * (np_ - 7) + ier
message = _spherefit_messages.get(-3) % (deficiency, -ier)
warnings.warn(message)
elif ier not in [0, -1, -2]:
message = _spherefit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(message)
self.fp = fp
self.tck = tt_, tp_, c
self.degrees = (3, 3)
_spfit_messages = _surfit_messages.copy()
_spfit_messages[10] = """
ERROR: on entry, the input data are controlled on validity
the following restrictions must be satisfied.
-1<=iopt(1)<=1, 0<=iopt(2)<=1, 0<=iopt(3)<=1,
-1<=ider(1)<=1, 0<=ider(2)<=1, ider(2)=0 if iopt(2)=0.
-1<=ider(3)<=1, 0<=ider(4)<=1, ider(4)=0 if iopt(3)=0.
mu >= mumin (see above), mv >= 4, nuest >=8, nvest >= 8,
kwrk>=5+mu+mv+nuest+nvest,
lwrk >= 12+nuest*(mv+nvest+3)+nvest*24+4*mu+8*mv+max(nuest,mv+nvest)
0< u(i-1)<u(i)< pi,i=2,..,mu,
-pi<=v(1)< pi, v(1)<v(i-1)<v(i)<v(1)+2*pi, i=3,...,mv
if iopt(1)=-1: 8<=nu<=min(nuest,mu+6+iopt(2)+iopt(3))
0<tu(5)<tu(6)<...<tu(nu-4)< pi
8<=nv<=min(nvest,mv+7)
v(1)<tv(5)<tv(6)<...<tv(nv-4)<v(1)+2*pi
the schoenberg-whitney conditions, i.e. there must be
subset of grid co-ordinates uu(p) and vv(q) such that
tu(p) < uu(p) < tu(p+4) ,p=1,...,nu-4
(iopt(2)=1 and iopt(3)=1 also count for a uu-value
tv(q) < vv(q) < tv(q+4) ,q=1,...,nv-4
(vv(q) is either a value v(j) or v(j)+2*pi)
if iopt(1)>=0: s>=0
if s=0: nuest>=mu+6+iopt(2)+iopt(3), nvest>=mv+7
if one of these conditions is found to be violated,control is
immediately repassed to the calling program. in that case there is no
approximation returned."""
class RectSphereBivariateSpline(SphereBivariateSpline):
"""
Bivariate spline approximation over a rectangular mesh on a sphere.
Can be used for smoothing data.
.. versionadded:: 0.11.0
Parameters
----------
u : array_like
1-D array of latitude coordinates in strictly ascending order.
Coordinates must be given in radians and lie within the interval
(0, pi).
v : array_like
1-D array of longitude coordinates in strictly ascending order.
Coordinates must be given in radians, and must lie within (0, 2pi).
r : array_like
2-D array of data with shape ``(u.size, v.size)``.
s : float, optional
Positive smoothing factor defined for estimation condition
(``s=0`` is for interpolation).
pole_continuity : bool or (bool, bool), optional
Order of continuity at the poles ``u=0`` (``pole_continuity[0]``) and
``u=pi`` (``pole_continuity[1]``). The order of continuity at the pole
will be 1 or 0 when this is True or False, respectively.
Defaults to False.
pole_values : float or (float, float), optional
Data values at the poles ``u=0`` and ``u=pi``. Either the whole
parameter or each individual element can be None. Defaults to None.
pole_exact : bool or (bool, bool), optional
Data value exactness at the poles ``u=0`` and ``u=pi``. If True, the
value is considered to be the right function value, and it will be
fitted exactly. If False, the value will be considered to be a data
value just like the other data values. Defaults to False.
pole_flat : bool or (bool, bool), optional
For the poles at ``u=0`` and ``u=pi``, specify whether or not the
approximation has vanishing derivatives. Defaults to False.
See Also
--------
RectBivariateSpline : bivariate spline approximation over a rectangular
mesh
Notes
-----
Currently, only the smoothing spline approximation (``iopt[0] = 0`` and
``iopt[0] = 1`` in the FITPACK routine) is supported. The exact
least-squares spline approximation is not implemented yet.
When actually performing the interpolation, the requested `v` values must
lie within the same length 2pi interval that the original `v` values were
chosen from.
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/spgrid.f
Examples
--------
Suppose we have global data on a coarse grid
>>> lats = np.linspace(10, 170, 9) * np.pi / 180.
>>> lons = np.linspace(0, 350, 18) * np.pi / 180.
>>> data = np.dot(np.atleast_2d(90. - np.linspace(-80., 80., 18)).T,
... np.atleast_2d(180. - np.abs(np.linspace(0., 350., 9)))).T
We want to interpolate it to a global one-degree grid
>>> new_lats = np.linspace(1, 180, 180) * np.pi / 180
>>> new_lons = np.linspace(1, 360, 360) * np.pi / 180
>>> new_lats, new_lons = np.meshgrid(new_lats, new_lons)
We need to set up the interpolator object
>>> from scipy.interpolate import RectSphereBivariateSpline
>>> lut = RectSphereBivariateSpline(lats, lons, data)
Finally we interpolate the data. The `RectSphereBivariateSpline` object
only takes 1-D arrays as input, therefore we need to do some reshaping.
>>> data_interp = lut.ev(new_lats.ravel(),
... new_lons.ravel()).reshape((360, 180)).T
Looking at the original and the interpolated data, one can see that the
interpolant reproduces the original data very well:
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(211)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(212)
>>> ax2.imshow(data_interp, interpolation='nearest')
>>> plt.show()
Chosing the optimal value of ``s`` can be a delicate task. Recommended
values for ``s`` depend on the accuracy of the data values. If the user
has an idea of the statistical errors on the data, she can also find a
proper estimate for ``s``. By assuming that, if she specifies the
right ``s``, the interpolator will use a spline ``f(u,v)`` which exactly
reproduces the function underlying the data, she can evaluate
``sum((r(i,j)-s(u(i),v(j)))**2)`` to find a good estimate for this ``s``.
For example, if she knows that the statistical errors on her
``r(i,j)``-values are not greater than 0.1, she may expect that a good
``s`` should have a value not larger than ``u.size * v.size * (0.1)**2``.
If nothing is known about the statistical error in ``r(i,j)``, ``s`` must
be determined by trial and error. The best is then to start with a very
large value of ``s`` (to determine the least-squares polynomial and the
corresponding upper bound ``fp0`` for ``s``) and then to progressively
decrease the value of ``s`` (say by a factor 10 in the beginning, i.e.
``s = fp0 / 10, fp0 / 100, ...`` and more carefully as the approximation
shows more detail) to obtain closer fits.
The interpolation results for different values of ``s`` give some insight
into this process:
>>> fig2 = plt.figure()
>>> s = [3e9, 2e9, 1e9, 1e8]
>>> for ii in xrange(len(s)):
... lut = RectSphereBivariateSpline(lats, lons, data, s=s[ii])
... data_interp = lut.ev(new_lats.ravel(),
... new_lons.ravel()).reshape((360, 180)).T
... ax = fig2.add_subplot(2, 2, ii+1)
... ax.imshow(data_interp, interpolation='nearest')
... ax.set_title("s = %g" % s[ii])
>>> plt.show()
"""
def __init__(self, u, v, r, s=0., pole_continuity=False, pole_values=None,
pole_exact=False, pole_flat=False):
iopt = np.array([0, 0, 0], dtype=int)
ider = np.array([-1, 0, -1, 0], dtype=int)
if pole_values is None:
pole_values = (None, None)
elif isinstance(pole_values, (float, np.float32, np.float64)):
pole_values = (pole_values, pole_values)
if isinstance(pole_continuity, bool):
pole_continuity = (pole_continuity, pole_continuity)
if isinstance(pole_exact, bool):
pole_exact = (pole_exact, pole_exact)
if isinstance(pole_flat, bool):
pole_flat = (pole_flat, pole_flat)
r0, r1 = pole_values
iopt[1:] = pole_continuity
if r0 is None:
ider[0] = -1
else:
ider[0] = pole_exact[0]
if r1 is None:
ider[2] = -1
else:
ider[2] = pole_exact[1]
ider[1], ider[3] = pole_flat
u, v = np.ravel(u), np.ravel(v)
if not np.all(np.diff(u) > 0.0):
raise TypeError('u must be strictly increasing')
if not np.all(np.diff(v) > 0.0):
raise TypeError('v must be strictly increasing')
if not u.size == r.shape[0]:
raise TypeError('u dimension of r must have same number of '
'elements as u')
if not v.size == r.shape[1]:
raise TypeError('v dimension of r must have same number of '
'elements as v')
if pole_continuity[1] is False and pole_flat[1] is True:
raise TypeError('if pole_continuity is False, so must be '
'pole_flat')
if pole_continuity[0] is False and pole_flat[0] is True:
raise TypeError('if pole_continuity is False, so must be '
'pole_flat')
r = np.ravel(r)
nu, tu, nv, tv, c, fp, ier = dfitpack.regrid_smth_spher(iopt, ider,
u.copy(), v.copy(), r.copy(), r0, r1, s)
if ier not in [0, -1, -2]:
msg = _spfit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(msg)
self.fp = fp
self.tck = tu[:nu], tv[:nv], c[:(nu - 4) * (nv-4)]
self.degrees = (3, 3)
|
the-stack_0_866 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
from __future__ import print_function
import functools
import os
import pprint
import re
import sys
import subprocess
perr = functools.partial(print, file=sys.stderr)
def dump_env_vars(prefix, pattern=None):
if pattern is not None:
match = lambda s: re.search(pattern, s)
else:
match = lambda s: True
for name in sorted(os.environ):
if name.startswith(prefix) and match(name):
perr("- {0}: {1!r}".format(name, os.environ[name]))
def run_cmd(cmdline):
proc = subprocess.Popen(cmdline,
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = proc.communicate()
if proc.returncode != 0:
raise RuntimeError("Command {cmdline} failed with code {returncode}, "
"stderr was:\n{stderr}\n"
.format(cmdline=cmdline, returncode=proc.returncode,
stderr=err.decode()))
return out
def get_commit_description(commit):
"""
Return the textual description (title + body) of the given git commit.
"""
out = run_cmd(["git", "show", "--no-patch", "--pretty=format:%B",
commit])
return out.decode('utf-8', 'ignore')
def list_affected_files(commit_range):
"""
Return a list of files changed by the given git commit range.
"""
perr("Getting affected files from", repr(commit_range))
out = run_cmd(["git", "diff", "--name-only", commit_range])
return list(filter(None, (s.strip() for s in out.decode().splitlines())))
def get_travis_head_commit():
return os.environ['TRAVIS_COMMIT']
def get_travis_commit_range():
if os.environ['TRAVIS_EVENT_TYPE'] == 'pull_request':
# TRAVIS_COMMIT_RANGE is too pessimistic for PRs, as it may contain
# unrelated changes. Instead, use the same strategy as on AppVeyor
# below.
run_cmd(["git", "fetch", "-q", "origin",
"+refs/heads/{0}".format(os.environ['TRAVIS_BRANCH'])])
merge_base = run_cmd(["git", "merge-base",
"HEAD", "FETCH_HEAD"]).decode().strip()
return "{0}..HEAD".format(merge_base)
else:
cr = os.environ['TRAVIS_COMMIT_RANGE']
# See
# https://github.com/travis-ci/travis-ci/issues/4596#issuecomment-139811122
return cr.replace('...', '..')
def get_travis_commit_description():
# Prefer this to get_commit_description(get_travis_head_commit()),
# as rebasing or other repository events may make TRAVIS_COMMIT invalid
# at the time we inspect it
return os.environ['TRAVIS_COMMIT_MESSAGE']
def list_travis_affected_files():
"""
Return a list of files affected in the current Travis build.
"""
commit_range = get_travis_commit_range()
try:
return list_affected_files(commit_range)
except RuntimeError:
# TRAVIS_COMMIT_RANGE can contain invalid revisions when
# building a branch (not a PR) after rebasing:
# https://github.com/travis-ci/travis-ci/issues/2668
if os.environ['TRAVIS_EVENT_TYPE'] == 'pull_request':
raise
# If it's a rebase, it's probably enough to use the last commit only
commit_range = '{0}^..'.format(get_travis_head_commit())
return list_affected_files(commit_range)
def list_appveyor_affected_files():
"""
Return a list of files affected in the current AppVeyor build.
This only works for PR builds.
"""
# Re-fetch PR base branch (e.g. origin/master), pointing FETCH_HEAD to it
run_cmd(["git", "fetch", "-q", "origin",
"+refs/heads/{0}".format(os.environ['APPVEYOR_REPO_BRANCH'])])
# Compute base changeset between FETCH_HEAD (PR base) and HEAD (PR head)
merge_base = run_cmd(["git", "merge-base",
"HEAD", "FETCH_HEAD"]).decode().strip()
# Compute changes files between base changeset and HEAD
return list_affected_files("{0}..HEAD".format(merge_base))
LANGUAGE_TOPICS = ['c_glib', 'cpp', 'docs', 'go', 'java', 'js', 'python',
'r', 'ruby', 'rust', 'csharp']
ALL_TOPICS = LANGUAGE_TOPICS + ['integration', 'site', 'dev']
AFFECTED_DEPENDENCIES = {
'java': ['integration', 'python'],
'js': ['integration'],
'ci': ALL_TOPICS,
'cpp': ['python', 'c_glib', 'r', 'ruby', 'integration'],
'format': LANGUAGE_TOPICS,
'.travis.yml': ALL_TOPICS,
'c_glib': ['ruby']
}
COMPONENTS = {'cpp', 'java', 'c_glib', 'r', 'ruby', 'integration', 'js',
'rust', 'csharp', 'site', 'go', 'docs', 'python', 'dev'}
def get_affected_topics(affected_files):
"""
Return a dict of topics affected by the given files.
Each dict value is True if affected, False otherwise.
"""
affected = dict.fromkeys(ALL_TOPICS, False)
for path in affected_files:
parts = []
head = path
while head:
head, tail = os.path.split(head)
parts.append(tail)
parts.reverse()
assert parts
p = parts[0]
fn = parts[-1]
if fn.startswith('README'):
continue
if p in COMPONENTS:
affected[p] = True
_path_already_affected = {}
def _affect_dependencies(component):
if component in _path_already_affected:
# For circular dependencies, terminate
return
for topic in AFFECTED_DEPENDENCIES.get(component, ()):
affected[topic] = True
_affect_dependencies(topic)
_path_already_affected[topic] = True
_affect_dependencies(p)
return affected
def make_env_for_topics(affected):
return {'ARROW_CI_{0}_AFFECTED'.format(k.upper()): '1' if v else '0'
for k, v in affected.items()}
def get_unix_shell_eval(env):
"""
Return a shell-evalable string to setup some environment variables.
"""
return "; ".join(("export {0}='{1}'".format(k, v)
for k, v in env.items()))
def get_windows_shell_eval(env):
"""
Return a shell-evalable string to setup some environment variables.
"""
return "\n".join(('set "{0}={1}"'.format(k, v)
for k, v in env.items()))
def run_from_travis():
perr("Environment variables (excerpt):")
dump_env_vars('TRAVIS_', '(BRANCH|COMMIT|PULL)')
if (os.environ['TRAVIS_REPO_SLUG'] == 'apache/arrow' and
os.environ['TRAVIS_BRANCH'] == 'master' and
os.environ['TRAVIS_EVENT_TYPE'] != 'pull_request'):
# Never skip anything on master builds in the official repository
affected = dict.fromkeys(ALL_TOPICS, True)
else:
desc = get_travis_commit_description()
if '[skip travis]' in desc:
# Skip everything
affected = dict.fromkeys(ALL_TOPICS, False)
elif '[force ci]' in desc or '[force travis]' in desc:
# Test everything
affected = dict.fromkeys(ALL_TOPICS, True)
else:
# Test affected topics
affected_files = list_travis_affected_files()
perr("Affected files:", affected_files)
affected = get_affected_topics(affected_files)
assert set(affected) <= set(ALL_TOPICS), affected
perr("Affected topics:")
perr(pprint.pformat(affected))
return get_unix_shell_eval(make_env_for_topics(affected))
def run_from_appveyor():
perr("Environment variables (excerpt):")
dump_env_vars('APPVEYOR_', '(PULL|REPO)')
if not os.environ.get('APPVEYOR_PULL_REQUEST_HEAD_COMMIT'):
# Not a PR build, test everything
affected = dict.fromkeys(ALL_TOPICS, True)
else:
affected_files = list_appveyor_affected_files()
perr("Affected files:", affected_files)
affected = get_affected_topics(affected_files)
assert set(affected) <= set(ALL_TOPICS), affected
perr("Affected topics:")
perr(pprint.pformat(affected))
return get_windows_shell_eval(make_env_for_topics(affected))
def test_get_affected_topics():
affected_topics = get_affected_topics(['cpp/CMakeLists.txt'])
assert affected_topics == {
'c_glib': True,
'cpp': True,
'docs': False,
'go': False,
'java': False,
'js': False,
'python': True,
'r': True,
'ruby': True,
'rust': False,
'csharp': False,
'integration': True,
'site': False,
'dev': False
}
affected_topics = get_affected_topics(['format/Schema.fbs'])
assert affected_topics == {
'c_glib': True,
'cpp': True,
'docs': True,
'go': True,
'java': True,
'js': True,
'python': True,
'r': True,
'ruby': True,
'rust': True,
'csharp': True,
'integration': True,
'site': False,
'dev': False
}
if __name__ == "__main__":
# This script should have its output evaluated by a shell,
# e.g. "eval `python ci/detect-changes.py`"
if os.environ.get('TRAVIS'):
try:
print(run_from_travis())
except Exception:
# Make sure the enclosing eval will return an error
print("exit 1")
raise
elif os.environ.get('APPVEYOR'):
try:
print(run_from_appveyor())
except Exception:
print("exit 1")
raise
else:
sys.exit("Script must be run under Travis-CI or AppVeyor")
|
the-stack_0_867 | # coding: utf-8
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from biocontainers_flask.server.models.base_model_ import Model
from biocontainers_flask.server import util
class Checksum(Model):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, checksum: str=None, type: str=None): # noqa: E501
"""Checksum - a model defined in Swagger
:param checksum: The checksum of this Checksum. # noqa: E501
:type checksum: str
:param type: The type of this Checksum. # noqa: E501
:type type: str
"""
self.swagger_types = {
'checksum': str,
'type': str
}
self.attribute_map = {
'checksum': 'checksum',
'type': 'type'
}
self._checksum = checksum
self._type = type
@classmethod
def from_dict(cls, dikt) -> 'Checksum':
"""Returns the dict as a model
:param dikt: A dict.
:type: dict
:return: The Checksum of this Checksum. # noqa: E501
:rtype: Checksum
"""
return util.deserialize_model(dikt, cls)
@property
def checksum(self) -> str:
"""Gets the checksum of this Checksum.
The hex-string encoded checksum for the data. # noqa: E501
:return: The checksum of this Checksum.
:rtype: str
"""
return self._checksum
@checksum.setter
def checksum(self, checksum: str):
"""Sets the checksum of this Checksum.
The hex-string encoded checksum for the data. # noqa: E501
:param checksum: The checksum of this Checksum.
:type checksum: str
"""
if checksum is None:
raise ValueError("Invalid value for `checksum`, must not be `None`") # noqa: E501
self._checksum = checksum
@property
def type(self) -> str:
"""Gets the type of this Checksum.
The digest method used to create the checksum. The value (e.g. `sha-256`) SHOULD be listed as `Hash Name String` in the https://github.com/ga4gh-discovery/ga4gh-checksum/blob/master/hash-alg.csv[GA4GH Checksum Hash Algorithm Registry]. Other values MAY be used, as long as implementors are aware of the issues discussed in https://tools.ietf.org/html/rfc6920#section-9.4[RFC6920]. GA4GH may provide more explicit guidance for use of non-IANA-registered algorithms in the future. # noqa: E501
:return: The type of this Checksum.
:rtype: str
"""
return self._type
@type.setter
def type(self, type: str):
"""Sets the type of this Checksum.
The digest method used to create the checksum. The value (e.g. `sha-256`) SHOULD be listed as `Hash Name String` in the https://github.com/ga4gh-discovery/ga4gh-checksum/blob/master/hash-alg.csv[GA4GH Checksum Hash Algorithm Registry]. Other values MAY be used, as long as implementors are aware of the issues discussed in https://tools.ietf.org/html/rfc6920#section-9.4[RFC6920]. GA4GH may provide more explicit guidance for use of non-IANA-registered algorithms in the future. # noqa: E501
:param type: The type of this Checksum.
:type type: str
"""
if type is None:
raise ValueError("Invalid value for `type`, must not be `None`") # noqa: E501
self._type = type
|
the-stack_0_868 | from .config import UTILS1_LOGLEVEL
import logging
from log_utils.utils import get_logger_with_file_handler
formatter = 'logger name : %(name)s ,%(levelname)s , func : %(funcName)s , %(message)s , module : %(module)s ,line : %(lineno)d , %(asctime)s'
logger = get_logger_with_file_handler(__name__,UTILS1_LOGLEVEL,formatter)
stream_handler = logging.StreamHandler()
logger.addHandler(stream_handler)
def add(num1 : float,num2 : float)->float:
logger.warning(f'args : {num1} , {num2}')
return num1+num2 |
the-stack_0_869 | import asyncio
import contextlib
from types import TracebackType
from typing import Optional, Type, Dict, Any
import aiojobs
from aiojobs import Scheduler
from .client import ChaosIQClient
from .log import logger
from .types import Config
__all__ = ["Heartbeat"]
class Heartbeat:
def __init__(self, config: Config) -> None:
self.sched: Scheduler = None
self.config = config
self._running = False
self.aiojob = None
async def __aenter__(self) -> 'Heartbeat':
await self.setup()
return self
async def __aexit__(self, exc_type: Optional[Type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType]) -> None:
await self.cleanup()
@property
def running(self) -> bool:
"""
Flag that is set when the heartbeat is active.
"""
return self._running
async def setup(self) -> None:
"""
Create the underlying scheduler to periodically send the heartbeat.
"""
logger.info("Creating heartbeat loop")
self.sched = await asyncio.wait_for(
aiojobs.create_scheduler(
exception_handler=self.aiojobs_exception), None)
period = self.config.heartbeat_interval
if not period:
logger.critical(f"Heartbeat is not properly configured; "
f"interval '{period}' is not valid")
return
logger.info("Spawning the heartbeat...")
self.aiojob = await self.sched.spawn(self.send_pulse())
async def cleanup(self) -> None:
"""
Gracefully terminate the scheduler.
"""
if self.aiojob:
logger.info("Stopping heartbeat pulse...")
await self.aiojob.close()
if not self.sched.closed:
logger.info("Closing heartbeat loop")
await asyncio.wait_for(self.sched.close(), None)
self._running = False
async def send_pulse(self) -> None:
"""
Sends its heartbeat periodically to the console
This must be interrupted instantly and not until wait is complete !!
We can NOT wait for end of iddle before leaving the loop
"""
self._running = True
wait = self.config.heartbeat_interval
logger.info(f"Sending heartbeat every {wait} seconds")
while self._running and not self.sched.closed:
await asyncio.sleep(wait)
with contextlib.suppress(Exception):
async with ChaosIQClient(self.config) as client:
await client.post(
"/agent/actions", json={"action": "heartbeat"})
@staticmethod
def aiojobs_exception(
scheduler: Scheduler,
context: Dict[str, Any]) -> None: # pragma: no cover
logger.error(context)
|
the-stack_0_874 | # Copyright 2018-2019 The glTF-Blender-IO authors.
#
# 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.
from ..com.gltf2_io import gltf_from_dict
from ..com.gltf2_io_debug import Log
import logging
import json
import struct
import base64
from os.path import dirname, join, isfile, basename
from urllib.parse import unquote
class glTFImporter():
"""glTF Importer class."""
def __init__(self, filename, import_settings):
"""initialization."""
self.filename = filename
self.import_settings = import_settings
self.glb_buffer = None
self.buffers = {}
self.accessor_cache = {}
if 'loglevel' not in self.import_settings.keys():
self.import_settings['loglevel'] = logging.ERROR
log = Log(import_settings['loglevel'])
self.log = log.logger
self.log_handler = log.hdlr
self.SIMPLE = 1
self.TEXTURE = 2
self.TEXTURE_FACTOR = 3
# TODO: move to a com place?
self.extensions_managed = [
'KHR_materials_pbrSpecularGlossiness',
'KHR_lights_punctual',
'KHR_materials_unlit',
'KHR_texture_transform'
]
# TODO : merge with io_constants
self.fmt_char_dict = {}
self.fmt_char_dict[5120] = 'b' # Byte
self.fmt_char_dict[5121] = 'B' # Unsigned Byte
self.fmt_char_dict[5122] = 'h' # Short
self.fmt_char_dict[5123] = 'H' # Unsigned Short
self.fmt_char_dict[5125] = 'I' # Unsigned Int
self.fmt_char_dict[5126] = 'f' # Float
self.component_nb_dict = {}
self.component_nb_dict['SCALAR'] = 1
self.component_nb_dict['VEC2'] = 2
self.component_nb_dict['VEC3'] = 3
self.component_nb_dict['VEC4'] = 4
self.component_nb_dict['MAT2'] = 4
self.component_nb_dict['MAT3'] = 9
self.component_nb_dict['MAT4'] = 16
@staticmethod
def bad_json_value(val):
"""Bad Json value."""
raise ValueError('Json contains some unauthorized values')
def checks(self):
"""Some checks."""
if self.data.asset.version != "2.0":
return False, "glTF version must be 2"
if self.data.extensions_required is not None:
for extension in self.data.extensions_required:
if extension not in self.data.extensions_used:
return False, "Extension required must be in Extension Used too"
if extension not in self.extensions_managed:
return False, "Extension " + extension + " is not available on this addon version"
if self.data.extensions_used is not None:
for extension in self.data.extensions_used:
if extension not in self.extensions_managed:
# Non blocking error #TODO log
pass
return True, None
def load_glb(self):
"""Load binary glb."""
header = struct.unpack_from('<4sII', self.content)
self.format = header[0]
self.version = header[1]
self.file_size = header[2]
if self.format != b'glTF':
return False, "This file is not a glTF/glb file"
if self.version != 2:
return False, "GLB version %d unsupported" % self.version
if self.file_size != len(self.content):
return False, "Bad GLB: file size doesn't match"
offset = 12 # header size = 12
# JSON chunk is first
type_, len_, json_bytes, offset = self.load_chunk(offset)
if type_ != b"JSON":
return False, "Bad GLB: first chunk not JSON"
if len_ != len(json_bytes):
return False, "Bad GLB: length of json chunk doesn't match"
try:
json_str = str(json_bytes, encoding='utf-8')
json_ = json.loads(json_str, parse_constant=glTFImporter.bad_json_value)
self.data = gltf_from_dict(json_)
except ValueError as e:
return False, e.args[0]
# BIN chunk is second (if it exists)
if offset < len(self.content):
type_, len_, data, offset = self.load_chunk(offset)
if type_ == b"BIN\0":
if len_ != len(data):
return False, "Bad GLB: length of BIN chunk doesn't match"
self.glb_buffer = data
return True, None
def load_chunk(self, offset):
"""Load chunk."""
chunk_header = struct.unpack_from('<I4s', self.content, offset)
data_length = chunk_header[0]
data_type = chunk_header[1]
data = self.content[offset + 8: offset + 8 + data_length]
return data_type, data_length, data, offset + 8 + data_length
def read(self):
"""Read file."""
# Check this is a file
if not isfile(self.filename):
return False, "Please select a file"
# Check if file is gltf or glb
with open(self.filename, 'rb') as f:
self.content = memoryview(f.read())
self.is_glb_format = self.content[:4] == b'glTF'
# glTF file
if not self.is_glb_format:
content = str(self.content, encoding='utf-8')
self.content = None
try:
self.data = gltf_from_dict(json.loads(content, parse_constant=glTFImporter.bad_json_value))
return True, None
except ValueError as e:
return False, e.args[0]
# glb file
else:
# Parsing glb file
success, txt = self.load_glb()
self.content = None
return success, txt
def is_node_joint(self, node_idx):
"""Check if node is a joint."""
if not self.data.skins: # if no skin in gltf file
return False, None
for skin_idx, skin in enumerate(self.data.skins):
if node_idx in skin.joints:
return True, skin_idx
return False, None
def load_buffer(self, buffer_idx):
"""Load buffer."""
buffer = self.data.buffers[buffer_idx]
if buffer.uri:
data, _file_name = self.load_uri(buffer.uri)
if data is not None:
self.buffers[buffer_idx] = data
else:
# GLB-stored buffer
if buffer_idx == 0 and self.glb_buffer is not None:
self.buffers[buffer_idx] = self.glb_buffer
def load_uri(self, uri):
"""Loads a URI.
Returns the data and the filename of the resource, if there is one.
"""
sep = ';base64,'
if uri.startswith('data:'):
idx = uri.find(sep)
if idx != -1:
data = uri[idx + len(sep):]
return memoryview(base64.b64decode(data)), None
path = join(dirname(self.filename), unquote(uri))
try:
with open(path, 'rb') as f_:
return memoryview(f_.read()), basename(path)
except Exception:
self.log.error("Couldn't read file: " + path)
return None, None
|
the-stack_0_876 | # Copyright (C) 2021-present MongoDB, Inc.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the Server Side Public License, version 1,
# as published by MongoDB, Inc.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# Server Side Public License for more details.
#
# You should have received a copy of the Server Side Public License
# along with this program. If not, see
# <http://www.mongodb.com/licensing/server-side-public-license>.
#
# As a special exception, the copyright holders give permission to link the
# code of portions of this program with the OpenSSL library under certain
# conditions as described in each individual source file and distribute
# linked combinations including the program with the OpenSSL library. You
# must comply with the Server Side Public License in all respects for
# all of the code used other than as permitted herein. If you modify file(s)
# with this exception, you may extend this exception to your version of the
# file(s), but you are not obligated to do so. If you do not wish to do so,
# delete this exception statement from your version. If you delete this
# exception statement from all source files in the program, then also delete
# it in the license file.
#
# pylint: disable=too-many-lines
"""Checks compatibility of old and new IDL files.
In order to support user-selectable API versions for the server, server commands are now
defined using IDL files. This script checks that old and new commands are compatible with each
other, which allows commands to be updated without breaking the API specifications within a
specific API version.
This script accepts two directories as arguments, the "old" and the "new" IDL directory.
Before running this script, run checkout_idl_files_from_past_releases.py to find and create
directories containing the old IDL files from previous releases.
"""
import argparse
import os
import sys
from dataclasses import dataclass
from enum import Enum
from typing import Dict, List, Set, Optional, Tuple, Union
from idl import parser, syntax, errors, common
from idl.compiler import CompilerImportResolver
from idl_compatibility_errors import IDLCompatibilityContext, IDLCompatibilityErrorCollection
ALLOW_ANY_TYPE_LIST: List[str] = [
# This list if only used in unit-tests.
"commandAllowedAnyTypes",
"commandAllowedAnyTypes-param-anyTypeParam",
"commandAllowedAnyTypes-reply-anyTypeField",
"oldTypeBsonAnyAllowList",
"newTypeBsonAnyAllowList",
"oldReplyFieldTypeBsonAnyAllowList-reply-oldBsonSerializationTypeAnyReplyField",
"newReplyFieldTypeBsonAnyAllowList-reply-newBsonSerializationTypeAnyReplyField",
"oldParamTypeBsonAnyAllowList-param-bsonTypeAnyParam",
"newParamTypeBsonAnyAllowList-param-bsonTypeAnyParam",
"commandAllowedAnyTypesWithVariant-reply-anyTypeField",
"replyFieldTypeBsonAnyWithVariant-reply-bsonSerializationTypeAnyStructField",
"replyFieldTypeBsonAnyWithVariantWithArray-reply-bsonSerializationTypeAnyStructField",
"parameterFieldTypeBsonAnyWithVariant-param-bsonSerializationTypeAnyStructField",
"parameterFieldTypeBsonAnyWithVariantWithArray-param-bsonSerializationTypeAnyStructField",
"commandTypeBsonAnyWithVariant",
"commandTypeBsonAnyWithVariantWithArray",
"replyFieldCppTypeNotEqual-reply-cppTypeNotEqualReplyField",
"commandCppTypeNotEqual",
"commandParameterCppTypeNotEqual-param-cppTypeNotEqualParam",
"replyFieldSerializerNotEqual-reply-serializerNotEqualReplyField",
"commandSerializerNotEqual",
"commandParameterSerializerNotEqual-param-serializerNotEqualParam",
"replyFieldDeserializerNotEqual-reply-deserializerNotEqualReplyField",
"commandDeserializerNotEqual",
"commandParameterDeserializerNotEqual-param-deserializerNotEqualParam",
"newlyAddedReplyFieldTypeBsonAnyAllowed-reply-newlyAddedBsonSerializationTypeAnyReplyField",
"replyFieldTypeBsonAnyWithVariantUnstable-reply-bsonSerializationTypeWithVariantAnyUnstableReplyField",
"newlyAddedParamBsonAnyAllowList-param-newlyAddedBsonAnyAllowListParam",
"newlyAddedTypeFieldBsonAnyAllowList",
"parameterFieldTypeBsonAnyWithVariantUnstable-param-bsonSerializationTypeAnyStructField",
"commandTypeBsonAnyWithVariantUnstable",
"commandParameterCppTypeNotEqualUnstable-param-cppTypeNotEqualParam",
"replyFieldCppTypeNotEqualUnstable-reply-cppTypeNotEqualReplyUnstableField",
"commandCppTypeNotEqualUnstable",
"commandParameterSerializerNotEqualUnstable-param-serializerNotEqualParam",
"replyFieldSerializerNotEqualUnstable-reply-serializerNotEqualReplyUnstableField",
"commandSerializerNotEqualUnstable",
"commandParameterDeserializerNotEqualUnstable-param-deserializerNotEqualParam",
"replyFieldDeserializerNotEqualUnstable-reply-deserializerNotEqualReplyUnstableField",
"commandDeserializerNotEqualUnstable",
'create-param-backwards',
'saslStart-param-payload',
'saslStart-param-payload',
'saslStart-reply-payload',
'saslContinue-param-payload',
'saslContinue-reply-payload',
# These commands (aggregate, find, update, delete, findAndModify, explain) might contain some
# fields with type `any`. Currently, it's not possible to avoid the `any` type in those cases.
# Instead, here are the preventive measures in-place to catch unintentional breaking changes:
# 1- Added comments on top of custom serializers/deserializers (related to these fields) to
# let the future developers know that their modifications to these methods might lead to
# a breaking change in the API.
# 2- Added proper unit-tests to catch accidental changes to the custom serializers/deserializers
# by over-fitting on the current implementation of these custom serializers/deserializers.
# 3- Added further checks to the current script (idl_check_compatibility.py) to check for
# changing a custom serializer/deserializer and considering it as a potential breaking
# change.
'aggregate-param-pipeline',
'aggregate-param-explain',
'aggregate-param-allowDiskUse',
'aggregate-param-cursor',
'aggregate-param-hint',
'aggregate-param-needsMerge',
'aggregate-param-fromMongos',
'aggregate-param-$_requestReshardingResumeToken',
'aggregate-param-isMapReduceCommand',
'count-param-hint',
'count-param-limit',
'count-param-maxTimeMS',
'find-param-filter',
'find-param-projection',
'find-param-sort',
'find-param-hint',
'find-param-collation',
'find-param-singleBatch',
'find-param-allowDiskUse',
'find-param-min',
'find-param-max',
'find-param-returnKey',
'find-param-showRecordId',
'find-param-$queryOptions',
'find-param-tailable',
'find-param-oplogReplay',
'find-param-noCursorTimeout',
'find-param-awaitData',
'find-param-allowPartialResults',
'find-param-readOnce',
'find-param-allowSpeculativeMajorityRead',
'find-param-$_requestResumeToken',
'find-param-$_resumeAfter',
'find-param-maxTimeMS',
'update-param-u',
'update-param-hint',
'update-param-upsertSupplied',
'update-reply-_id',
'delete-param-limit',
'delete-param-hint',
'findAndModify-param-hint',
'findAndModify-param-update',
'findAndModify-reply-upserted',
'insert-reply-opTime',
'update-reply-opTime',
'delete-reply-opTime',
'aggregate-reply-partialResultsReturned',
'aggregate-reply-invalidated',
'find-reply-partialResultsReturned',
'find-reply-invalidated',
'getMore-reply-partialResultsReturned',
'getMore-reply-invalidated',
]
# Do not add user visible fields already released in earlier versions.
IGNORE_UNSTABLE_LIST: List[str] = [
# The 'originalSpec' field was introduced in v5.1 behind a disabled feature flag and is not user
# visible. This is part of the listIndexes output when executed against system.bucket.*
# collections, which users should avoid doing.
'listIndexes-reply-originalSpec',
# The 'vars' field was introduced to facilitate communication between mongot and mongod and is
# not user visible.
'find-reply-vars',
'aggregate-reply-vars',
# The 'cursor' field is now optional in a reply, as inter-node communication in aggregation
# can return one or more cursors. Multiple cursors are covered under the 'cursors' field.
'find-reply-cursor',
'aggregate-reply-cursor',
# The 'recordPreImages' field is only used by Realm and is not documented to users.
'collMod-param-recordPreImages',
# The 'ignoreUnknownIndexOptions' field is for internal use only and is not documented to users.
'createIndexes-param-ignoreUnknownIndexOptions',
# The 'runtimeConstants' field is a legacy field for internal use only and is not documented to
# users.
'delete-param-runtimeConstants',
]
SKIPPED_FILES = [
"unittest.idl", "mozILocalization.idl", "mozILocaleService.idl", "mozIOSPreferences.idl",
"nsICollation.idl", "nsIStringBundle.idl", "nsIScriptableUConv.idl", "nsITextToSubURI.idl"
]
# Do not add commands that were visible to users in previously released versions.
IGNORE_COMMANDS_LIST: List[str] = [
# The following commands were released behind a feature flag in 5.3 but were shelved in
# favor of getClusterParameter and setClusterParameter. Since the feature flag was not enabled
# in 5.3, they were effectively unusable and so can be safely removed from the strict API.
'getChangeStreamOptions',
'setChangeStreamOptions',
]
class FieldCompatibility:
"""Information about a Field to check compatibility."""
def __init__(self, field_type: Optional[Union[syntax.Enum, syntax.Struct, syntax.Type]],
idl_file: syntax.IDLParsedSpec, idl_file_path: str, unstable: Optional[bool],
optional: bool) -> None:
"""Initialize data members and hand special cases, such as optionalBool type."""
self.field_type = field_type
self.idl_file = idl_file
self.idl_file_path = idl_file_path
self.unstable = unstable
self.optional = optional
if isinstance(self.field_type, syntax.Type) and self.field_type.name == "optionalBool":
# special case for optionalBool type, because it is compatible
# with bool type, but has bson_serialization_type == 'any'
# which is not supported by many checks
self.field_type = syntax.Type(field_type.file_name, field_type.line, field_type.column)
self.field_type.name = "bool"
self.field_type.bson_serialization_type = ["bool"]
self.optional = True
@dataclass
class FieldCompatibilityPair:
"""Information about an old and new Field pair to check compatibility."""
old: FieldCompatibility
new: FieldCompatibility
cmd_name: str
field_name: str
class ArrayTypeCheckResult(Enum):
"""Enumeration representing different return values of check_array_type."""
INVALID = 0
TRUE = 1
FALSE = 2
def get_new_commands(
ctxt: IDLCompatibilityContext, new_idl_dir: str, import_directories: List[str]
) -> Tuple[Dict[str, syntax.Command], Dict[str, syntax.IDLParsedSpec], Dict[str, str]]:
"""Get new IDL commands and check validity."""
new_commands: Dict[str, syntax.Command] = dict()
new_command_file: Dict[str, syntax.IDLParsedSpec] = dict()
new_command_file_path: Dict[str, str] = dict()
for dirpath, _, filenames in os.walk(new_idl_dir):
for new_filename in filenames:
if not new_filename.endswith('.idl') or new_filename in SKIPPED_FILES:
continue
new_idl_file_path = os.path.join(dirpath, new_filename)
with open(new_idl_file_path) as new_file:
new_idl_file = parser.parse(
new_file, new_idl_file_path,
CompilerImportResolver(import_directories + [new_idl_dir]))
if new_idl_file.errors:
new_idl_file.errors.dump_errors()
raise ValueError(f"Cannot parse {new_idl_file_path}")
for new_cmd in new_idl_file.spec.symbols.commands:
# Ignore imported commands as they will be processed in their own file.
if new_cmd.api_version == "" or new_cmd.imported:
continue
if new_cmd.api_version != "1":
# We're not ready to handle future API versions yet.
ctxt.add_command_invalid_api_version_error(
new_cmd.command_name, new_cmd.api_version, new_idl_file_path)
continue
if new_cmd.command_name in new_commands:
ctxt.add_duplicate_command_name_error(new_cmd.command_name, new_idl_dir,
new_idl_file_path)
continue
new_commands[new_cmd.command_name] = new_cmd
new_command_file[new_cmd.command_name] = new_idl_file
new_command_file_path[new_cmd.command_name] = new_idl_file_path
return new_commands, new_command_file, new_command_file_path
def get_chained_type_or_struct(
chained_type_or_struct: Union[syntax.ChainedType, syntax.ChainedStruct],
idl_file: syntax.IDLParsedSpec,
idl_file_path: str) -> Optional[Union[syntax.Enum, syntax.Struct, syntax.Type]]:
"""Resolve and get chained type or struct from the IDL file."""
parser_ctxt = errors.ParserContext(idl_file_path, errors.ParserErrorCollection())
resolved = idl_file.spec.symbols.resolve_type_from_name(parser_ctxt, chained_type_or_struct,
chained_type_or_struct.name,
chained_type_or_struct.name)
if parser_ctxt.errors.has_errors():
parser_ctxt.errors.dump_errors()
return resolved
def get_field_type(field: Union[syntax.Field, syntax.Command], idl_file: syntax.IDLParsedSpec,
idl_file_path: str) -> Optional[Union[syntax.Enum, syntax.Struct, syntax.Type]]:
"""Resolve and get field type of a field from the IDL file."""
parser_ctxt = errors.ParserContext(idl_file_path, errors.ParserErrorCollection())
field_type = idl_file.spec.symbols.resolve_field_type(parser_ctxt, field, field.name,
field.type)
if parser_ctxt.errors.has_errors():
parser_ctxt.errors.dump_errors()
return field_type
def check_subset(ctxt: IDLCompatibilityContext, cmd_name: str, field_name: str, type_name: str,
sub_list: List[Union[str, syntax.EnumValue]],
super_list: List[Union[str, syntax.EnumValue]], file_path: str):
# pylint: disable=too-many-arguments
"""Check if sub_list is a subset of the super_list and log an error if not."""
if not set(sub_list).issubset(super_list):
ctxt.add_reply_field_not_subset_error(cmd_name, field_name, type_name, file_path)
def check_superset(ctxt: IDLCompatibilityContext, cmd_name: str, type_name: str,
super_list: List[Union[str, syntax.EnumValue]],
sub_list: List[Union[str, syntax.EnumValue]], file_path: str,
param_name: Optional[str], is_command_parameter: bool):
# pylint: disable=too-many-arguments
"""Check if super_list is a superset of the sub_list and log an error if not."""
if not set(super_list).issuperset(sub_list):
ctxt.add_command_or_param_type_not_superset_error(cmd_name, type_name, file_path,
param_name, is_command_parameter)
def check_reply_field_type_recursive(ctxt: IDLCompatibilityContext,
field_pair: FieldCompatibilityPair) -> None:
# pylint: disable=too-many-branches
"""Check compatibility between old and new reply field type if old field type is a syntax.Type instance."""
old_field = field_pair.old
new_field = field_pair.new
old_field_type = old_field.field_type
new_field_type = new_field.field_type
cmd_name = field_pair.cmd_name
field_name = field_pair.field_name
# If the old field is unstable, we only add errors related to the use of 'any' as the
# bson_serialization_type. For all other errors, we check that the old field is stable
# before adding an error.
if not isinstance(new_field_type, syntax.Type):
if not old_field.unstable:
ctxt.add_new_reply_field_type_enum_or_struct_error(
cmd_name, field_name, new_field_type.name, old_field_type.name,
new_field.idl_file_path)
return
# If bson_serialization_type switches from 'any' to non-any type.
if "any" in old_field_type.bson_serialization_type and "any" not in new_field_type.bson_serialization_type:
ctxt.add_old_reply_field_bson_any_error(cmd_name, field_name, old_field_type.name,
new_field_type.name, old_field.idl_file_path)
return
# If bson_serialization_type switches from non-any to 'any' type.
if "any" not in old_field_type.bson_serialization_type and "any" in new_field_type.bson_serialization_type:
ctxt.add_new_reply_field_bson_any_error(cmd_name, field_name, old_field_type.name,
new_field_type.name, new_field.idl_file_path)
return
allow_name: str = cmd_name + "-reply-" + field_name
if "any" in old_field_type.bson_serialization_type:
# If 'any' is not explicitly allowed as the bson_serialization_type.
if allow_name not in ALLOW_ANY_TYPE_LIST:
ctxt.add_old_reply_field_bson_any_not_allowed_error(
cmd_name, field_name, old_field_type.name, old_field.idl_file_path)
return
# If cpp_type is changed, it's a potential breaking change.
if old_field_type.cpp_type != new_field_type.cpp_type:
ctxt.add_reply_field_cpp_type_not_equal_error(cmd_name, field_name, new_field_type.name,
new_field.idl_file_path)
# If serializer is changed, it's a potential breaking change.
if (not old_field.unstable) and old_field_type.serializer != new_field_type.serializer:
ctxt.add_reply_field_serializer_not_equal_error(
cmd_name, field_name, new_field_type.name, new_field.idl_file_path)
# If deserializer is changed, it's a potential breaking change.
if (not old_field.unstable) and old_field_type.deserializer != new_field_type.deserializer:
ctxt.add_reply_field_deserializer_not_equal_error(
cmd_name, field_name, new_field_type.name, new_field.idl_file_path)
if isinstance(old_field_type, syntax.VariantType):
# If the new type is not variant just check the single type.
new_variant_types = new_field_type.variant_types if isinstance(
new_field_type, syntax.VariantType) else [new_field_type]
old_variant_types = old_field_type.variant_types
# Check that new variant types are a subset of old variant types.
for new_variant_type in new_variant_types:
for old_variant_type in old_variant_types:
if old_variant_type.name == new_variant_type.name:
# Check that the old and new version of each variant type is also compatible.
old = FieldCompatibility(old_variant_type, old_field.idl_file,
old_field.idl_file_path, old_field.unstable,
old_field.optional)
new = FieldCompatibility(new_variant_type, new_field.idl_file,
new_field.idl_file_path, new_field.unstable,
new_field.optional)
check_reply_field_type(ctxt,
FieldCompatibilityPair(old, new, cmd_name, field_name))
break
else:
# new_variant_type was not found in old_variant_types.
if not old_field.unstable:
ctxt.add_new_reply_field_variant_type_not_subset_error(
cmd_name, field_name, new_variant_type.name, new_field.idl_file_path)
# If new type is variant and has a struct as a variant type, compare old and new variant_struct_type.
# Since enums can't be part of variant types, we don't explicitly check for enums.
if isinstance(new_field_type,
syntax.VariantType) and new_field_type.variant_struct_type is not None:
if old_field_type.variant_struct_type is None and not old_field.unstable:
ctxt.add_new_reply_field_variant_type_not_subset_error(
cmd_name, field_name, new_field_type.variant_struct_type.name,
new_field.idl_file_path)
else:
check_reply_fields(ctxt, old_field_type.variant_struct_type,
new_field_type.variant_struct_type, cmd_name, old_field.idl_file,
new_field.idl_file, old_field.idl_file_path,
new_field.idl_file_path)
elif not old_field.unstable:
if isinstance(new_field_type, syntax.VariantType):
ctxt.add_new_reply_field_variant_type_error(cmd_name, field_name, old_field_type.name,
new_field.idl_file_path)
else:
check_subset(ctxt, cmd_name, field_name, new_field_type.name,
new_field_type.bson_serialization_type,
old_field_type.bson_serialization_type, new_field.idl_file_path)
def check_reply_field_type(ctxt: IDLCompatibilityContext, field_pair: FieldCompatibilityPair):
"""Check compatibility between old and new reply field type."""
# pylint: disable=too-many-branches
old_field = field_pair.old
new_field = field_pair.new
array_check = check_array_type(ctxt, "reply_field", old_field.field_type, new_field.field_type,
field_pair.cmd_name, 'type', old_field.idl_file_path,
new_field.idl_file_path, old_field.unstable)
if array_check == ArrayTypeCheckResult.INVALID:
return
if array_check == ArrayTypeCheckResult.TRUE:
old_field.field_type = old_field.field_type.element_type
new_field.field_type = new_field.field_type.element_type
old_field_type = old_field.field_type
new_field_type = new_field.field_type
cmd_name = field_pair.cmd_name
field_name = field_pair.field_name
if old_field_type is None:
ctxt.add_reply_field_type_invalid_error(cmd_name, field_name, old_field.idl_file_path)
ctxt.errors.dump_errors()
sys.exit(1)
if new_field_type is None:
ctxt.add_reply_field_type_invalid_error(cmd_name, field_name, new_field.idl_file_path)
ctxt.errors.dump_errors()
sys.exit(1)
if isinstance(old_field_type, syntax.Type):
check_reply_field_type_recursive(ctxt, field_pair)
elif isinstance(old_field_type, syntax.Enum) and not old_field.unstable:
if isinstance(new_field_type, syntax.Enum):
check_subset(ctxt, cmd_name, field_name, new_field_type.name, new_field_type.values,
old_field_type.values, new_field.idl_file_path)
else:
ctxt.add_new_reply_field_type_not_enum_error(cmd_name, field_name, new_field_type.name,
old_field_type.name,
new_field.idl_file_path)
elif isinstance(old_field_type, syntax.Struct):
if isinstance(new_field_type, syntax.Struct):
check_reply_fields(ctxt, old_field_type, new_field_type, cmd_name, old_field.idl_file,
new_field.idl_file, old_field.idl_file_path, new_field.idl_file_path)
else:
if not old_field.unstable:
ctxt.add_new_reply_field_type_not_struct_error(
cmd_name, field_name, new_field_type.name, old_field_type.name,
new_field.idl_file_path)
def check_array_type(ctxt: IDLCompatibilityContext, symbol: str,
old_type: Optional[Union[syntax.Enum, syntax.Struct, syntax.Type]],
new_type: Optional[Union[syntax.Enum, syntax.Struct, syntax.Type]],
cmd_name: str, symbol_name: str, old_idl_file_path: str,
new_idl_file_path: str, old_field_unstable: bool) -> ArrayTypeCheckResult:
"""
Check compatibility between old and new ArrayTypes.
:returns:
- ArrayTypeCheckResult.TRUE : when the old type and new type are of array type.
- ArrayTypeCheckResult.FALSE : when the old type and new type aren't of array type.
- ArrayTypeCheckResult.INVALID : when one of the types is not of array type while the other one is.
"""
# pylint: disable=too-many-arguments,too-many-branches
old_is_array = isinstance(old_type, syntax.ArrayType)
new_is_array = isinstance(new_type, syntax.ArrayType)
if not old_is_array and not new_is_array:
return ArrayTypeCheckResult.FALSE
if (not old_is_array or not new_is_array) and not old_field_unstable:
ctxt.add_type_not_array_error(symbol, cmd_name, symbol_name, new_type.name, old_type.name,
new_idl_file_path if old_is_array else old_idl_file_path)
return ArrayTypeCheckResult.INVALID
return ArrayTypeCheckResult.TRUE
def check_reply_field(ctxt: IDLCompatibilityContext, old_field: syntax.Field,
new_field: syntax.Field, cmd_name: str, old_idl_file: syntax.IDLParsedSpec,
new_idl_file: syntax.IDLParsedSpec, old_idl_file_path: str,
new_idl_file_path: str):
"""Check compatibility between old and new reply field."""
# pylint: disable=too-many-arguments
old_field_type = get_field_type(old_field, old_idl_file, old_idl_file_path)
new_field_type = get_field_type(new_field, new_idl_file, new_idl_file_path)
old_field_optional = old_field.optional or (old_field_type
and old_field_type.name == "optionalBool")
new_field_optional = new_field.optional or (new_field_type
and new_field_type.name == "optionalBool")
field_name: str = cmd_name + "-reply-" + new_field.name
if not old_field.unstable and field_name not in IGNORE_UNSTABLE_LIST:
if new_field.unstable and field_name not in IGNORE_UNSTABLE_LIST:
ctxt.add_new_reply_field_unstable_error(cmd_name, new_field.name, new_idl_file_path)
if new_field_optional and not old_field_optional:
ctxt.add_new_reply_field_optional_error(cmd_name, new_field.name, new_idl_file_path)
if new_field.validator:
if old_field.validator:
if new_field.validator != old_field.validator:
ctxt.add_reply_field_validators_not_equal_error(cmd_name, new_field.name,
new_idl_file_path)
else:
ctxt.add_reply_field_contains_validator_error(cmd_name, new_field.name,
new_idl_file_path)
old_field_compatibility = FieldCompatibility(old_field_type, old_idl_file, old_idl_file_path,
old_field.unstable, old_field.optional)
new_field_compatibility = FieldCompatibility(new_field_type, new_idl_file, new_idl_file_path,
new_field.unstable, new_field.optional)
field_pair = FieldCompatibilityPair(old_field_compatibility, new_field_compatibility, cmd_name,
old_field.name)
check_reply_field_type(ctxt, field_pair)
def check_reply_fields(ctxt: IDLCompatibilityContext, old_reply: syntax.Struct,
new_reply: syntax.Struct, cmd_name: str, old_idl_file: syntax.IDLParsedSpec,
new_idl_file: syntax.IDLParsedSpec, old_idl_file_path: str,
new_idl_file_path: str):
"""Check compatibility between old and new reply fields."""
# pylint: disable=too-many-arguments,too-many-branches
for new_chained_type in new_reply.chained_types or []:
resolved_new_chained_type = get_chained_type_or_struct(new_chained_type, new_idl_file,
new_idl_file_path)
if resolved_new_chained_type is not None:
for old_chained_type in old_reply.chained_types or []:
resolved_old_chained_type = get_chained_type_or_struct(
old_chained_type, old_idl_file, old_idl_file_path)
if (resolved_old_chained_type is not None
and resolved_old_chained_type.name == resolved_new_chained_type.name):
# Check that the old and new version of each chained type is also compatible.
old = FieldCompatibility(resolved_old_chained_type, old_idl_file,
old_idl_file_path, unstable=False, optional=False)
new = FieldCompatibility(resolved_new_chained_type, new_idl_file,
new_idl_file_path, unstable=False, optional=False)
check_reply_field_type(
ctxt, FieldCompatibilityPair(old, new, cmd_name, old_reply.name))
break
else:
# new chained type was not found in old chained types.
ctxt.add_new_reply_chained_type_not_subset_error(
cmd_name, new_reply.name, resolved_new_chained_type.name, new_idl_file_path)
old_reply_fields = get_all_struct_fields(old_reply, old_idl_file, old_idl_file_path)
new_reply_fields = get_all_struct_fields(new_reply, new_idl_file, new_idl_file_path)
for old_field in old_reply_fields or []:
new_field_exists = False
for new_field in new_reply_fields or []:
if new_field.name == old_field.name:
new_field_exists = True
check_reply_field(ctxt, old_field, new_field, cmd_name, old_idl_file, new_idl_file,
old_idl_file_path, new_idl_file_path)
break
if not new_field_exists and not old_field.unstable:
ctxt.add_new_reply_field_missing_error(cmd_name, old_field.name, old_idl_file_path)
for new_field in new_reply_fields or []:
# Check that all fields in the new IDL have specified the 'unstable' field.
if new_field.unstable is None:
ctxt.add_new_reply_field_requires_unstable_error(cmd_name, new_field.name,
new_idl_file_path)
# Check that newly added fields do not have an unallowed use of 'any' as the
# bson_serialization_type.
newly_added = True
for old_field in old_reply_fields or []:
if new_field.name == old_field.name:
newly_added = False
if newly_added:
allow_name: str = cmd_name + "-reply-" + new_field.name
new_field_type = get_field_type(new_field, new_idl_file, new_idl_file_path)
# If we encounter a bson_serialization_type of None, we skip checking if 'any' is used.
if isinstance(
new_field_type, syntax.Type
) and new_field_type.bson_serialization_type is not None and "any" in new_field_type.bson_serialization_type:
# If 'any' is not explicitly allowed as the bson_serialization_type.
any_allow = allow_name in ALLOW_ANY_TYPE_LIST or new_field_type.name == 'optionalBool'
if not any_allow:
ctxt.add_new_reply_field_bson_any_not_allowed_error(
cmd_name, new_field.name, new_field_type.name, new_idl_file_path)
def check_param_or_command_type_recursive(ctxt: IDLCompatibilityContext,
field_pair: FieldCompatibilityPair,
is_command_parameter: bool):
# pylint: disable=too-many-branches,too-many-locals
"""
Check compatibility between old and new command or param type recursively.
If the old type is a syntax.Type instance, check the compatibility between the old and new
command type or parameter type recursively.
"""
old_field = field_pair.old
new_field = field_pair.new
old_type = old_field.field_type
new_type = new_field.field_type
cmd_name = field_pair.cmd_name
param_name = field_pair.field_name
# If the old field is unstable, we only add errors related to the use of 'any' as the
# bson_serialization_type. For all other errors, we check that the old field is stable
# before adding an error.
if not isinstance(new_type, syntax.Type):
if not old_field.unstable:
ctxt.add_new_command_or_param_type_enum_or_struct_error(
cmd_name, new_type.name, old_type.name, new_field.idl_file_path, param_name,
is_command_parameter)
return
allow_name: str = cmd_name + "-param-" + param_name if is_command_parameter else cmd_name
# If bson_serialization_type switches from 'any' to non-any type.
if "any" in old_type.bson_serialization_type and "any" not in new_type.bson_serialization_type:
ctxt.add_old_command_or_param_type_bson_any_error(cmd_name, old_type.name, new_type.name,
old_field.idl_file_path, param_name,
is_command_parameter)
return
# If bson_serialization_type switches from non-any to 'any' type.
if "any" not in old_type.bson_serialization_type and "any" in new_type.bson_serialization_type:
ctxt.add_new_command_or_param_type_bson_any_error(cmd_name, old_type.name, new_type.name,
new_field.idl_file_path, param_name,
is_command_parameter)
return
if "any" in old_type.bson_serialization_type:
# If 'any' is not explicitly allowed as the bson_serialization_type.
if allow_name not in ALLOW_ANY_TYPE_LIST:
ctxt.add_old_command_or_param_type_bson_any_not_allowed_error(
cmd_name, old_type.name, old_field.idl_file_path, param_name, is_command_parameter)
return
# If cpp_type is changed, it's a potential breaking change.
if old_type.cpp_type != new_type.cpp_type:
ctxt.add_command_or_param_cpp_type_not_equal_error(
cmd_name, new_type.name, new_field.idl_file_path, param_name, is_command_parameter)
# If serializer is changed, it's a potential breaking change.
if (not old_field.unstable) and old_type.serializer != new_type.serializer:
ctxt.add_command_or_param_serializer_not_equal_error(
cmd_name, new_type.name, new_field.idl_file_path, param_name, is_command_parameter)
# If deserializer is changed, it's a potential breaking change.
if (not old_field.unstable) and old_type.deserializer != new_type.deserializer:
ctxt.add_command_or_param_deserializer_not_equal_error(
cmd_name, new_type.name, new_field.idl_file_path, param_name, is_command_parameter)
if isinstance(old_type, syntax.VariantType):
if not isinstance(new_type, syntax.VariantType):
if not old_field.unstable:
ctxt.add_new_command_or_param_type_not_variant_type_error(
cmd_name, new_type.name, new_field.idl_file_path, param_name,
is_command_parameter)
else:
new_variant_types = new_type.variant_types
old_variant_types = old_type.variant_types
# Check that new variant types are a superset of old variant types.
for old_variant_type in old_variant_types:
for new_variant_type in new_variant_types:
# object->object_owned serialize to the same bson type. object_owned->object is
# not always safe so we only limit this special case to object->object_owned.
if (old_variant_type.name == "object" and new_variant_type.name == "object_owned") or \
old_variant_type.name == new_variant_type.name:
# Check that the old and new version of each variant type is also compatible.
old = FieldCompatibility(old_variant_type, old_field.idl_file,
old_field.idl_file_path, old_field.unstable,
old_field.optional)
new = FieldCompatibility(new_variant_type, new_field.idl_file,
new_field.idl_file_path, new_field.unstable,
new_field.optional)
check_param_or_command_type(
ctxt, FieldCompatibilityPair(old, new, cmd_name, param_name),
is_command_parameter)
break
else:
if not old_field.unstable:
# old_variant_type was not found in new_variant_types.
ctxt.add_new_command_or_param_variant_type_not_superset_error(
cmd_name, old_variant_type.name, new_field.idl_file_path, param_name,
is_command_parameter)
# If old and new types both have a struct as a variant type, compare old and new variant_struct_type.
# Since enums can't be part of variant types, we don't explicitly check for enums.
if old_type.variant_struct_type is not None:
if new_type.variant_struct_type is not None:
check_command_params_or_type_struct_fields(
ctxt, old_type.variant_struct_type, new_type.variant_struct_type, cmd_name,
old_field.idl_file, new_field.idl_file, old_field.idl_file_path,
new_field.idl_file_path, is_command_parameter)
# If old type has a variant struct type and new type does not have a variant struct type.
elif not old_field.unstable:
ctxt.add_new_command_or_param_variant_type_not_superset_error(
cmd_name, old_type.variant_struct_type.name, new_field.idl_file_path,
param_name, is_command_parameter)
elif not old_field.unstable:
check_superset(ctxt, cmd_name, new_type.name, new_type.bson_serialization_type,
old_type.bson_serialization_type, new_field.idl_file_path, param_name,
is_command_parameter)
def check_param_or_command_type(ctxt: IDLCompatibilityContext, field_pair: FieldCompatibilityPair,
is_command_parameter: bool):
"""Check compatibility between old and new command parameter type or command type."""
# pylint: disable=too-many-branches
old_field = field_pair.old
new_field = field_pair.new
array_check = check_array_type(
ctxt, "command_parameter" if is_command_parameter else "command_namespace",
old_field.field_type, new_field.field_type, field_pair.cmd_name,
field_pair.field_name if is_command_parameter else "type", old_field.idl_file_path,
new_field.idl_file_path, old_field.unstable)
if array_check == ArrayTypeCheckResult.INVALID:
return
if array_check == ArrayTypeCheckResult.TRUE:
old_field.field_type = old_field.field_type.element_type
new_field.field_type = new_field.field_type.element_type
old_type = old_field.field_type
new_type = new_field.field_type
if old_type is None:
ctxt.add_command_or_param_type_invalid_error(field_pair.cmd_name, old_field.idl_file_path,
field_pair.field_name, is_command_parameter)
ctxt.errors.dump_errors()
sys.exit(1)
if new_type is None:
ctxt.add_command_or_param_type_invalid_error(field_pair.cmd_name, new_field.idl_file_path,
field_pair.field_name, is_command_parameter)
ctxt.errors.dump_errors()
sys.exit(1)
if isinstance(old_type, syntax.Type):
check_param_or_command_type_recursive(ctxt, field_pair, is_command_parameter)
# Only add type errors if the old field is stable.
elif isinstance(old_type, syntax.Enum) and not old_field.unstable:
if isinstance(new_type, syntax.Enum):
check_superset(ctxt, field_pair.cmd_name, new_type.name, new_type.values,
old_type.values, new_field.idl_file_path, field_pair.field_name,
is_command_parameter)
else:
ctxt.add_new_command_or_param_type_not_enum_error(
field_pair.cmd_name, new_type.name, old_type.name, new_field.idl_file_path,
field_pair.field_name, is_command_parameter)
elif isinstance(old_type, syntax.Struct):
if isinstance(new_type, syntax.Struct):
check_command_params_or_type_struct_fields(
ctxt, old_type, new_type, field_pair.cmd_name, old_field.idl_file,
new_field.idl_file, old_field.idl_file_path, new_field.idl_file_path,
is_command_parameter)
else:
if not old_field.unstable:
ctxt.add_new_command_or_param_type_not_struct_error(
field_pair.cmd_name, new_type.name, old_type.name, new_field.idl_file_path,
field_pair.field_name, is_command_parameter)
def check_param_or_type_validator(ctxt: IDLCompatibilityContext, old_field: syntax.Field,
new_field: syntax.Field, cmd_name: str, new_idl_file_path: str,
type_name: Optional[str], is_command_parameter: bool):
"""
Check compatibility between old and new validators.
Check compatibility between old and new validators in command parameter type and command type
struct fields.
"""
# pylint: disable=too-many-arguments
if new_field.validator:
if old_field.validator:
if new_field.validator != old_field.validator:
ctxt.add_command_or_param_type_validators_not_equal_error(
cmd_name, new_field.name, new_idl_file_path, type_name, is_command_parameter)
else:
ctxt.add_command_or_param_type_contains_validator_error(
cmd_name, new_field.name, new_idl_file_path, type_name, is_command_parameter)
def get_all_struct_fields(struct: syntax.Struct, idl_file: syntax.IDLParsedSpec,
idl_file_path: str):
"""Get all the fields of a struct, including the chained struct fields."""
all_fields = struct.fields or []
for chained_struct in struct.chained_structs or []:
resolved_chained_struct = get_chained_type_or_struct(chained_struct, idl_file,
idl_file_path)
if resolved_chained_struct is not None:
for field in resolved_chained_struct.fields:
all_fields.append(field)
return all_fields
def check_command_params_or_type_struct_fields(
ctxt: IDLCompatibilityContext, old_struct: syntax.Struct, new_struct: syntax.Struct,
cmd_name: str, old_idl_file: syntax.IDLParsedSpec, new_idl_file: syntax.IDLParsedSpec,
old_idl_file_path: str, new_idl_file_path: str, is_command_parameter: bool):
"""Check compatibility between old and new parameters or command type fields."""
# pylint: disable=too-many-arguments,too-many-branches
# Check chained types.
for old_chained_type in old_struct.chained_types or []:
resolved_old_chained_type = get_chained_type_or_struct(old_chained_type, old_idl_file,
old_idl_file_path)
if resolved_old_chained_type is not None:
for new_chained_type in new_struct.chained_types or []:
resolved_new_chained_type = get_chained_type_or_struct(
new_chained_type, new_idl_file, new_idl_file_path)
if (resolved_new_chained_type is not None
and resolved_old_chained_type.name == resolved_new_chained_type.name):
# Check that the old and new version of each chained type is also compatible.
old = FieldCompatibility(resolved_old_chained_type, old_idl_file,
old_idl_file_path, unstable=False, optional=False)
new = FieldCompatibility(resolved_new_chained_type, new_idl_file,
new_idl_file_path, unstable=False, optional=False)
check_param_or_command_type(
ctxt, FieldCompatibilityPair(old, new, cmd_name, old_struct.name),
is_command_parameter=False)
break
else:
# old chained type was not found in new chained types.
ctxt.add_new_command_or_param_chained_type_not_superset_error(
cmd_name, old_chained_type.name, new_idl_file_path, old_struct.name,
is_command_parameter)
old_struct_fields = get_all_struct_fields(old_struct, old_idl_file, old_idl_file_path)
new_struct_fields = get_all_struct_fields(new_struct, new_idl_file, new_idl_file_path)
# We need to special-case the stmtId parameter because it was removed. However, it's not a
# breaking change to the API because it was added and removed behind a feature flag, so it was
# never officially released.
allow_list = ["endSessions-param-stmtId", "refreshSessions-param-stmtId"]
for old_field in old_struct_fields or []:
new_field_exists = False
for new_field in new_struct_fields or []:
if new_field.name == old_field.name:
new_field_exists = True
check_command_param_or_type_struct_field(
ctxt, old_field, new_field, cmd_name, old_idl_file, new_idl_file,
old_idl_file_path, new_idl_file_path, old_struct.name, is_command_parameter)
break
allow_name: str = cmd_name + "-param-" + old_field.name
if not new_field_exists and not old_field.unstable and allow_name not in allow_list:
ctxt.add_new_param_or_command_type_field_missing_error(
cmd_name, old_field.name, old_idl_file_path, old_struct.name, is_command_parameter)
# Check if a new field has been added to the parameters or type struct.
# If so, it must be optional.
for new_field in new_struct_fields or []:
# Check that all fields in the new IDL have specified the 'unstable' field.
if new_field.unstable is None:
ctxt.add_new_param_or_command_type_field_requires_unstable_error(
cmd_name, new_field.name, new_idl_file_path, is_command_parameter)
newly_added = True
for old_field in old_struct_fields or []:
if new_field.name == old_field.name:
newly_added = False
if newly_added:
new_field_type = get_field_type(new_field, new_idl_file, new_idl_file_path)
new_field_optional = new_field.optional or (new_field_type
and new_field_type.name == 'optionalBool')
if not new_field_optional and not new_field.unstable:
ctxt.add_new_param_or_command_type_field_added_required_error(
cmd_name, new_field.name, new_idl_file_path, new_struct.name,
is_command_parameter)
# Check that a new field does not have an unallowed use of 'any' as the bson_serialization_type.
any_allow_name: str = (cmd_name + "-param-" + new_field.name
if is_command_parameter else cmd_name)
# If we encounter a bson_serialization_type of None, we skip checking if 'any' is used.
if isinstance(
new_field_type, syntax.Type
) and new_field_type.bson_serialization_type is not None and "any" in new_field_type.bson_serialization_type:
# If 'any' is not explicitly allowed as the bson_serialization_type.
any_allow = any_allow_name in ALLOW_ANY_TYPE_LIST or new_field_type.name == 'optionalBool'
if not any_allow:
ctxt.add_new_command_or_param_type_bson_any_not_allowed_error(
cmd_name, new_field_type.name, old_idl_file_path, new_field.name,
is_command_parameter)
def check_command_param_or_type_struct_field(
ctxt: IDLCompatibilityContext, old_field: syntax.Field, new_field: syntax.Field,
cmd_name: str, old_idl_file: syntax.IDLParsedSpec, new_idl_file: syntax.IDLParsedSpec,
old_idl_file_path: str, new_idl_file_path: str, type_name: Optional[str],
is_command_parameter: bool):
"""Check compatibility between the old and new command parameter or command type struct field."""
# pylint: disable=too-many-arguments
field_name: str = cmd_name + "-param-" + new_field.name
if not old_field.unstable and new_field.unstable and field_name not in IGNORE_UNSTABLE_LIST:
ctxt.add_new_param_or_command_type_field_unstable_error(
cmd_name, old_field.name, old_idl_file_path, type_name, is_command_parameter)
# If old field is unstable and new field is stable, the new field should either be optional or
# have a default value.
old_field_type = get_field_type(old_field, old_idl_file, old_idl_file_path)
new_field_type = get_field_type(new_field, new_idl_file, new_idl_file_path)
old_field_optional = old_field.optional or (old_field_type
and old_field_type.name == "optionalBool")
new_field_optional = new_field.optional or (new_field_type
and new_field_type.name == "optionalBool")
if old_field.unstable and not new_field.unstable and not new_field_optional and new_field.default is None:
ctxt.add_new_param_or_command_type_field_stable_required_no_default_error(
cmd_name, old_field.name, old_idl_file_path, type_name, is_command_parameter)
if old_field_optional and not new_field_optional:
ctxt.add_new_param_or_command_type_field_required_error(
cmd_name, old_field.name, old_idl_file_path, type_name, is_command_parameter)
if not old_field.unstable:
check_param_or_type_validator(ctxt, old_field, new_field, cmd_name, new_idl_file_path,
type_name, is_command_parameter)
old_field_compatibility = FieldCompatibility(old_field_type, old_idl_file, old_idl_file_path,
old_field.unstable, old_field.optional)
new_field_compatibility = FieldCompatibility(new_field_type, new_idl_file, new_idl_file_path,
new_field.unstable, new_field.optional)
field_pair = FieldCompatibilityPair(old_field_compatibility, new_field_compatibility, cmd_name,
old_field.name)
check_param_or_command_type(ctxt, field_pair, is_command_parameter)
def check_namespace(ctxt: IDLCompatibilityContext, old_cmd: syntax.Command, new_cmd: syntax.Command,
old_idl_file: syntax.IDLParsedSpec, new_idl_file: syntax.IDLParsedSpec,
old_idl_file_path: str, new_idl_file_path: str):
"""Check compatibility between old and new namespace."""
# pylint: disable=too-many-arguments
old_namespace = old_cmd.namespace
new_namespace = new_cmd.namespace
# IDL parser already checks that namespace must be one of these 4 types.
if old_namespace == common.COMMAND_NAMESPACE_IGNORED:
if new_namespace != common.COMMAND_NAMESPACE_IGNORED:
ctxt.add_new_namespace_incompatible_error(old_cmd.command_name, old_namespace,
new_namespace, new_idl_file_path)
elif old_namespace == common.COMMAND_NAMESPACE_CONCATENATE_WITH_DB_OR_UUID:
if new_namespace not in (common.COMMAND_NAMESPACE_IGNORED,
common.COMMAND_NAMESPACE_CONCATENATE_WITH_DB_OR_UUID):
ctxt.add_new_namespace_incompatible_error(old_cmd.command_name, old_namespace,
new_namespace, new_idl_file_path)
elif old_namespace == common.COMMAND_NAMESPACE_CONCATENATE_WITH_DB:
if new_namespace == common.COMMAND_NAMESPACE_TYPE:
ctxt.add_new_namespace_incompatible_error(old_cmd.command_name, old_namespace,
new_namespace, new_idl_file_path)
elif old_namespace == common.COMMAND_NAMESPACE_TYPE:
old_type = get_field_type(old_cmd, old_idl_file, old_idl_file_path)
if new_namespace == common.COMMAND_NAMESPACE_TYPE:
new_type = get_field_type(new_cmd, new_idl_file, new_idl_file_path)
old = FieldCompatibility(old_type, old_idl_file, old_idl_file_path, unstable=False,
optional=False)
new = FieldCompatibility(new_type, new_idl_file, new_idl_file_path, unstable=False,
optional=False)
check_param_or_command_type(ctxt,
FieldCompatibilityPair(old, new, old_cmd.command_name, ""),
is_command_parameter=False)
# If old type is "namespacestring", the new namespace can be changed to any
# of the other namespace types.
elif old_type.name != "namespacestring":
# Otherwise, the new namespace can only be changed to "ignored".
if new_namespace != common.COMMAND_NAMESPACE_IGNORED:
ctxt.add_new_namespace_incompatible_error(old_cmd.command_name, old_namespace,
new_namespace, new_idl_file_path)
else:
assert False, 'unrecognized namespace option'
def check_error_reply(old_basic_types_path: str, new_basic_types_path: str,
old_import_directories: List[str],
new_import_directories: List[str]) -> IDLCompatibilityErrorCollection:
"""Check IDL compatibility between old and new ErrorReply."""
old_idl_dir = os.path.dirname(old_basic_types_path)
new_idl_dir = os.path.dirname(new_basic_types_path)
ctxt = IDLCompatibilityContext(old_idl_dir, new_idl_dir, IDLCompatibilityErrorCollection())
with open(old_basic_types_path) as old_file:
old_idl_file = parser.parse(old_file, old_basic_types_path,
CompilerImportResolver(old_import_directories))
if old_idl_file.errors:
old_idl_file.errors.dump_errors()
raise ValueError(f"Cannot parse {old_basic_types_path}")
old_error_reply_struct = old_idl_file.spec.symbols.get_struct("ErrorReply")
if old_error_reply_struct is None:
ctxt.add_missing_error_reply_struct_error(old_basic_types_path)
else:
with open(new_basic_types_path) as new_file:
new_idl_file = parser.parse(new_file, new_basic_types_path,
CompilerImportResolver(new_import_directories))
if new_idl_file.errors:
new_idl_file.errors.dump_errors()
raise ValueError(f"Cannot parse {new_basic_types_path}")
new_error_reply_struct = new_idl_file.spec.symbols.get_struct("ErrorReply")
if new_error_reply_struct is None:
ctxt.add_missing_error_reply_struct_error(new_basic_types_path)
else:
check_reply_fields(ctxt, old_error_reply_struct, new_error_reply_struct, "n/a",
old_idl_file, new_idl_file, old_basic_types_path,
new_basic_types_path)
ctxt.errors.dump_errors()
return ctxt.errors
def split_complex_checks(
complex_checks: List[syntax.AccessCheck]) -> Tuple[List[str], List[syntax.Privilege]]:
"""Split a list of AccessCheck into checks and privileges."""
checks = [x.check for x in complex_checks if x.check is not None]
privileges = [x.privilege for x in complex_checks if x.privilege is not None]
# Sort the list of privileges by the length of the action_type list, in decreasing order
# so that two lists of privileges can be compared later.
return checks, sorted(privileges, key=lambda x: len(x.action_type), reverse=True)
def check_complex_checks(ctxt: IDLCompatibilityContext,
old_complex_checks: List[syntax.AccessCheck],
new_complex_checks: List[syntax.AccessCheck], cmd: syntax.Command,
new_idl_file_path: str) -> None:
"""Check the compatibility between complex access checks of the old and new command."""
cmd_name = cmd.command_name
if len(new_complex_checks) > len(old_complex_checks):
ctxt.add_new_additional_complex_access_check_error(cmd_name, new_idl_file_path)
else:
old_checks, old_privileges = split_complex_checks(old_complex_checks)
new_checks, new_privileges = split_complex_checks(new_complex_checks)
if not set(new_checks).issubset(old_checks):
ctxt.add_new_complex_checks_not_subset_error(cmd_name, new_idl_file_path)
if len(new_privileges) > len(old_privileges):
ctxt.add_new_complex_privileges_not_subset_error(cmd_name, new_idl_file_path)
else:
# Check that each new_privilege matches an old_privilege (the resource_pattern is
# equal and the action_types are a subset of the old action_types).
for new_privilege in new_privileges:
for old_privilege in old_privileges:
if (new_privilege.resource_pattern == old_privilege.resource_pattern
and set(new_privilege.action_type).issubset(old_privilege.action_type)):
old_privileges.remove(old_privilege)
break
else:
ctxt.add_new_complex_privileges_not_subset_error(cmd_name, new_idl_file_path)
def split_complex_checks_agg_stages(
complex_checks: List[syntax.AccessCheck]) -> Dict[str, List[syntax.AccessCheck]]:
"""Split a list of AccessChecks into a map keyed by aggregation stage (defaults to None)."""
complex_checks_agg_stages: Dict[str, List[syntax.AccessCheck]] = dict()
for access_check in complex_checks:
agg_stage = None
if access_check.privilege is not None:
# x.privilege.agg_stage can still be None.
agg_stage = access_check.privilege.agg_stage
if agg_stage not in complex_checks_agg_stages:
complex_checks_agg_stages[agg_stage] = []
complex_checks_agg_stages[agg_stage].append(access_check)
return complex_checks_agg_stages
def check_complex_checks_agg_stages(ctxt: IDLCompatibilityContext,
old_complex_checks: List[syntax.AccessCheck],
new_complex_checks: List[syntax.AccessCheck],
cmd: syntax.Command, new_idl_file_path: str) -> None:
"""Check the compatibility between complex access checks of the old and new agggreation stages."""
new_complex_checks_agg_stages = split_complex_checks_agg_stages(new_complex_checks)
old_complex_checks_agg_stages = split_complex_checks_agg_stages(old_complex_checks)
for agg_stage in new_complex_checks_agg_stages:
# Aggregation stages are considered separate commands in the context of validating the
# Stable API. Therefore, it is okay to skip recently added aggregation stages that are
# are not present in the previous release.
if agg_stage not in old_complex_checks_agg_stages:
continue
check_complex_checks(ctxt, old_complex_checks_agg_stages[agg_stage],
new_complex_checks_agg_stages[agg_stage], cmd, new_idl_file_path)
def check_security_access_checks(ctxt: IDLCompatibilityContext,
old_access_checks: syntax.AccessChecks,
new_access_checks: syntax.AccessChecks, cmd: syntax.Command,
new_idl_file_path: str) -> None:
"""Check the compatibility between security access checks of the old and new command."""
# pylint:disable=too-many-locals,too-many-branches,too-many-nested-blocks
cmd_name = cmd.command_name
if old_access_checks is not None and new_access_checks is not None:
old_access_check_type = old_access_checks.get_access_check_type()
new_access_check_type = new_access_checks.get_access_check_type()
if old_access_check_type != new_access_check_type:
ctxt.add_access_check_type_not_equal_error(cmd_name, old_access_check_type,
new_access_check_type, new_idl_file_path)
else:
old_simple_check = old_access_checks.simple
new_simple_check = new_access_checks.simple
if old_simple_check is not None and new_simple_check is not None:
if old_simple_check.check != new_simple_check.check:
ctxt.add_check_not_equal_error(cmd_name, old_simple_check.check,
new_simple_check.check, new_idl_file_path)
else:
old_privilege = old_simple_check.privilege
new_privilege = new_simple_check.privilege
if old_privilege is not None and new_privilege is not None:
if old_privilege.resource_pattern != new_privilege.resource_pattern:
ctxt.add_resource_pattern_not_equal_error(
cmd_name, old_privilege.resource_pattern,
new_privilege.resource_pattern, new_idl_file_path)
if not set(new_privilege.action_type).issubset(old_privilege.action_type):
ctxt.add_new_action_types_not_subset_error(cmd_name, new_idl_file_path)
old_complex_checks = old_access_checks.complex
new_complex_checks = new_access_checks.complex
if old_complex_checks is not None and new_complex_checks is not None:
check_complex_checks_agg_stages(ctxt, old_complex_checks, new_complex_checks, cmd,
new_idl_file_path)
elif new_access_checks is None and old_access_checks is not None:
ctxt.add_removed_access_check_field_error(cmd_name, new_idl_file_path)
elif old_access_checks is None and new_access_checks is not None and cmd.api_version == '1':
ctxt.add_added_access_check_field_error(cmd_name, new_idl_file_path)
def check_compatibility(old_idl_dir: str, new_idl_dir: str, old_import_directories: List[str],
new_import_directories: List[str]) -> IDLCompatibilityErrorCollection:
"""Check IDL compatibility between old and new IDL commands."""
# pylint: disable=too-many-locals
ctxt = IDLCompatibilityContext(old_idl_dir, new_idl_dir, IDLCompatibilityErrorCollection())
new_commands, new_command_file, new_command_file_path = get_new_commands(
ctxt, new_idl_dir, new_import_directories)
# Check new commands' compatibility with old ones.
# Note, a command can be added to V1 at any time, it's ok if a
# new command has no corresponding old command.
old_commands: Dict[str, syntax.Command] = dict()
for dirpath, _, filenames in os.walk(old_idl_dir):
for old_filename in filenames:
if not old_filename.endswith('.idl') or old_filename in SKIPPED_FILES:
continue
old_idl_file_path = os.path.join(dirpath, old_filename)
with open(old_idl_file_path) as old_file:
old_idl_file = parser.parse(
old_file, old_idl_file_path,
CompilerImportResolver(old_import_directories + [old_idl_dir]))
if old_idl_file.errors:
old_idl_file.errors.dump_errors()
raise ValueError(f"Cannot parse {old_idl_file_path}")
for old_cmd in old_idl_file.spec.symbols.commands:
# Ignore imported commands as they will be processed in their own file.
if old_cmd.api_version == "" or old_cmd.imported:
continue
# Ignore select commands that were removed after being added to the strict API.
# Only commands that were never visible to the end-user in previous releases
# (i.e., hidden behind a feature flag) should be allowed here.
if old_cmd.command_name in IGNORE_COMMANDS_LIST:
continue
if old_cmd.api_version != "1":
# We're not ready to handle future API versions yet.
ctxt.add_command_invalid_api_version_error(
old_cmd.command_name, old_cmd.api_version, old_idl_file_path)
continue
if old_cmd.command_name in old_commands:
ctxt.add_duplicate_command_name_error(old_cmd.command_name, old_idl_dir,
old_idl_file_path)
continue
old_commands[old_cmd.command_name] = old_cmd
if old_cmd.command_name not in new_commands:
# Can't remove a command from V1
ctxt.add_command_removed_error(old_cmd.command_name, old_idl_file_path)
continue
new_cmd = new_commands[old_cmd.command_name]
new_idl_file = new_command_file[old_cmd.command_name]
new_idl_file_path = new_command_file_path[old_cmd.command_name]
if not old_cmd.strict and new_cmd.strict:
ctxt.add_command_strict_true_error(new_cmd.command_name, new_idl_file_path)
# Check compatibility of command's parameters.
check_command_params_or_type_struct_fields(
ctxt, old_cmd, new_cmd, old_cmd.command_name, old_idl_file, new_idl_file,
old_idl_file_path, new_idl_file_path, is_command_parameter=True)
check_namespace(ctxt, old_cmd, new_cmd, old_idl_file, new_idl_file,
old_idl_file_path, new_idl_file_path)
old_reply = old_idl_file.spec.symbols.get_struct(old_cmd.reply_type)
new_reply = new_idl_file.spec.symbols.get_struct(new_cmd.reply_type)
check_reply_fields(ctxt, old_reply, new_reply, old_cmd.command_name,
old_idl_file, new_idl_file, old_idl_file_path,
new_idl_file_path)
check_security_access_checks(ctxt, old_cmd.access_check, new_cmd.access_check,
old_cmd, new_idl_file_path)
ctxt.errors.dump_errors()
return ctxt.errors
def get_generic_arguments(gen_args_file_path: str) -> Tuple[Set[str], Set[str]]:
"""Get arguments and reply fields from generic_argument.idl and check validity."""
arguments: Set[str] = set()
reply_fields: Set[str] = set()
with open(gen_args_file_path) as gen_args_file:
parsed_idl_file = parser.parse(gen_args_file, gen_args_file_path,
CompilerImportResolver([]))
if parsed_idl_file.errors:
parsed_idl_file.errors.dump_errors()
raise ValueError(f"Cannot parse {gen_args_file_path}")
for argument in parsed_idl_file.spec.symbols.get_generic_argument_list(
"generic_args_api_v1").fields:
arguments.add(argument.name)
for reply_field in parsed_idl_file.spec.symbols.get_generic_reply_field_list(
"generic_reply_fields_api_v1").fields:
reply_fields.add(reply_field.name)
return arguments, reply_fields
def check_generic_arguments_compatibility(old_gen_args_file_path: str, new_gen_args_file_path: str
) -> IDLCompatibilityErrorCollection:
"""Check IDL compatibility between old and new generic_argument.idl files."""
# IDLCompatibilityContext takes in both 'old_idl_dir' and 'new_idl_dir',
# but for generic_argument.idl, the parent directories aren't helpful for logging purposes.
# Instead, we pass in "old generic_argument.idl" and "new generic_argument.idl"
# to make error messages clearer.
ctxt = IDLCompatibilityContext("old generic_argument.idl", "new generic_argument.idl",
IDLCompatibilityErrorCollection())
old_arguments, old_reply_fields = get_generic_arguments(old_gen_args_file_path)
new_arguments, new_reply_fields = get_generic_arguments(new_gen_args_file_path)
for old_argument in old_arguments:
if old_argument not in new_arguments:
ctxt.add_generic_argument_removed(old_argument, new_gen_args_file_path)
for old_reply_field in old_reply_fields:
if old_reply_field not in new_reply_fields:
ctxt.add_generic_argument_removed_reply_field(old_reply_field, new_gen_args_file_path)
return ctxt.errors
def main():
"""Run the script."""
arg_parser = argparse.ArgumentParser(description=__doc__)
arg_parser.add_argument("-v", "--verbose", action="count", help="Enable verbose logging")
arg_parser.add_argument("--old-include", dest="old_include", type=str, action="append",
default=[], help="Directory to search for old IDL import files")
arg_parser.add_argument("--new-include", dest="new_include", type=str, action="append",
default=[], help="Directory to search for new IDL import files")
arg_parser.add_argument("old_idl_dir", metavar="OLD_IDL_DIR",
help="Directory where old IDL files are located")
arg_parser.add_argument("new_idl_dir", metavar="NEW_IDL_DIR",
help="Directory where new IDL files are located")
args = arg_parser.parse_args()
error_coll = check_compatibility(args.old_idl_dir, args.new_idl_dir, args.old_include,
args.new_include)
if error_coll.has_errors():
sys.exit(1)
old_basic_types_path = os.path.join(args.old_idl_dir, "mongo/idl/basic_types.idl")
new_basic_types_path = os.path.join(args.new_idl_dir, "mongo/idl/basic_types.idl")
error_reply_coll = check_error_reply(old_basic_types_path, new_basic_types_path,
args.old_include, args.new_include)
if error_reply_coll.has_errors():
sys.exit(1)
old_generic_args_path = os.path.join(args.old_idl_dir, "mongo/idl/generic_argument.idl")
new_generic_args_path = os.path.join(args.new_idl_dir, "mongo/idl/generic_argument.idl")
error_gen_args_coll = check_generic_arguments_compatibility(old_generic_args_path,
new_generic_args_path)
if error_gen_args_coll.has_errors():
sys.exit(1)
if __name__ == "__main__":
main()
|
the-stack_0_878 | # Copyright 2014-2017 Insight Software Consortium.
# Copyright 2004-2009 Roman Yakovenko.
# Distributed under the Boost Software License, Version 1.0.
# See http://www.boost.org/LICENSE_1_0.txt
import unittest
import logging
from . import parser_test_case
from pygccxml import utils
class Test(parser_test_case.parser_test_case_t):
mock_logger = logging.getLogger("Test")
def test_old_xml_generators(self):
"""
Tests for the xml_generators class.
This is for gccxml and for castxml using the gccxml xml file format
"""
self._test_impl("0.6", False, "is_gccxml_06")
self._test_impl("1.114", False, "is_gccxml_07")
self._test_impl("1.115", False, "is_gccxml_09_buggy")
self._test_impl("1.126", False, "is_gccxml_09_buggy")
self._test_impl("1.127", False, "is_gccxml_09")
self._test_impl("1.136", True, "is_castxml")
def test_casxtml_epic_version_1(self):
"""
Test with the castxml epic version set to 1
"""
gen = utils.xml_generators(
self.mock_logger, castxml_format="1.1.0")
self.assertFalse(gen.is_gccxml)
self.assertTrue(gen.is_castxml)
self.assertTrue(gen.is_castxml1)
self.assertEqual(gen.xml_output_version, "1.1.0")
self.assertRaises(RuntimeError, lambda: utils.xml_generators(
self.mock_logger, "1.136", "1.1.0"))
self.assertRaises(RuntimeError, lambda: utils.xml_generators(
self.mock_logger, None, None))
def _test_impl(
self, gccxml_cvs_revision, is_castxml,
expected_gccxml_cvs_revision):
"""
Implementation detail for the test
Args:
gccxml_cvs_revision (str|None) : a known cvs revision
is_castxml (bool): check for castxml
expected_gccxml_cvs_revision (str): will be used to check if the
attribute is set to True.
"""
gen = utils.xml_generators(
self.mock_logger, gccxml_cvs_revision)
if is_castxml:
self.assertFalse(gen.is_gccxml)
self.assertTrue(gen.is_castxml)
else:
self.assertTrue(gen.is_gccxml)
self.assertFalse(gen.is_castxml)
self.assertTrue(getattr(gen, expected_gccxml_cvs_revision))
self.assertEqual(gen.xml_output_version, gccxml_cvs_revision)
def create_suite():
suite = unittest.TestSuite()
suite.addTest(unittest.makeSuite(Test))
return suite
def run_suite():
unittest.TextTestRunner(verbosity=2).run(create_suite())
if __name__ == "__main__":
run_suite()
|
the-stack_0_879 | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# File: transformer.py
import inspect
import numpy as np
import pprint
import sys
from abc import ABCMeta, abstractmethod
from fvcore.transforms.transform import (
BlendTransform,
CropTransform,
HFlipTransform,
NoOpTransform,
Transform,
TransformList,
VFlipTransform,
)
from PIL import Image
from .transform import ExtentTransform, ResizeTransform, RotationTransform
__all__ = [
"RandomApply",
"RandomBrightness",
"RandomContrast",
"RandomCrop",
"RandomExtent",
"RandomFlip",
"RandomSaturation",
"RandomLighting",
"RandomRotation",
"Resize",
"ResizeShortestEdge",
"TransformGen",
"apply_transform_gens",
]
def check_dtype(img):
assert isinstance(img, np.ndarray), "[TransformGen] Needs an numpy array, but got a {}!".format(
type(img)
)
assert not isinstance(img.dtype, np.integer) or (
img.dtype == np.uint8
), "[TransformGen] Got image of type {}, use uint8 or floating points instead!".format(
img.dtype
)
assert img.ndim in [2, 3], img.ndim
class TransformGen(metaclass=ABCMeta):
"""
TransformGen takes an image of type uint8 in range [0, 255], or
floating point in range [0, 1] or [0, 255] as input.
It creates a :class:`Transform` based on the given image, sometimes with randomness.
The transform can then be used to transform images
or other data (boxes, points, annotations, etc.) associated with it.
The assumption made in this class
is that the image itself is sufficient to instantiate a transform.
When this assumption is not true, you need to create the transforms by your own.
A list of `TransformGen` can be applied with :func:`apply_transform_gens`.
"""
def _init(self, params=None):
if params:
for k, v in params.items():
if k != "self" and not k.startswith("_"):
setattr(self, k, v)
@abstractmethod
def get_transform(self, img):
pass
def _rand_range(self, low=1.0, high=None, size=None):
"""
Uniform float random number between low and high.
"""
if high is None:
low, high = 0, low
if size is None:
size = []
return np.random.uniform(low, high, size)
def __repr__(self):
"""
Produce something like:
"MyTransformGen(field1={self.field1}, field2={self.field2})"
"""
try:
sig = inspect.signature(self.__init__)
classname = type(self).__name__
argstr = []
for name, param in sig.parameters.items():
assert (
param.kind != param.VAR_POSITIONAL and param.kind != param.VAR_KEYWORD
), "The default __repr__ doesn't support *args or **kwargs"
assert hasattr(self, name), (
"Attribute {} not found! "
"Default __repr__ only works if attributes match the constructor.".format(name)
)
attr = getattr(self, name)
default = param.default
if default is attr:
continue
argstr.append("{}={}".format(name, pprint.pformat(attr)))
return "{}({})".format(classname, ", ".join(argstr))
except AssertionError:
return super().__repr__()
__str__ = __repr__
class RandomApply(TransformGen):
"""
Randomly apply the wrapper transformation with a given probability.
"""
def __init__(self, transform, prob=0.5):
"""
Args:
transform (Transform, TransformGen): the transform to be wrapped
by the `RandomApply`. The `transform` can either be a
`Transform` or `TransformGen` instance.
prob (float): probability between 0.0 and 1.0 that
the wrapper transformation is applied
"""
super().__init__()
assert isinstance(transform, (Transform, TransformGen)), (
f"The given transform must either be a Transform or TransformGen instance. "
f"Not {type(transform)}"
)
assert 0.0 <= prob <= 1.0, f"Probablity must be between 0.0 and 1.0 (given: {prob})"
self.prob = prob
self.transform = transform
def get_transform(self, img):
do = self._rand_range() < self.prob
if do:
if isinstance(self.transform, TransformGen):
return self.transform.get_transform(img)
else:
return self.transform
else:
return NoOpTransform()
class RandomFlip(TransformGen):
"""
Flip the image horizontally or vertically with the given probability.
"""
def __init__(self, prob=0.5, *, horizontal=True, vertical=False):
"""
Args:
prob (float): probability of flip.
horizontal (boolean): whether to apply horizontal flipping
vertical (boolean): whether to apply vertical flipping
"""
super().__init__()
if horizontal and vertical:
raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.")
if not horizontal and not vertical:
raise ValueError("At least one of horiz or vert has to be True!")
self._init(locals())
def get_transform(self, img):
h, w = img.shape[:2]
do = self._rand_range() < self.prob
if do:
if self.horizontal:
return HFlipTransform(w)
elif self.vertical:
return VFlipTransform(h)
else:
return NoOpTransform()
class Resize(TransformGen):
""" Resize image to a target size"""
def __init__(self, shape, interp=Image.BILINEAR):
"""
Args:
shape: (h, w) tuple or a int
interp: PIL interpolation method
"""
if isinstance(shape, int):
shape = (shape, shape)
shape = tuple(shape)
self._init(locals())
def get_transform(self, img):
return ResizeTransform(
img.shape[0], img.shape[1], self.shape[0], self.shape[1], self.interp
)
class ResizeShortestEdge(TransformGen):
"""
Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge.
If `max_size` is reached, then downscale so that the longer edge does not exceed max_size.
"""
def __init__(
self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR
):
"""
Args:
short_edge_length (list[int]): If ``sample_style=="range"``,
a [min, max] interval from which to sample the shortest edge length.
If ``sample_style=="choice"``, a list of shortest edge lengths to sample from.
max_size (int): maximum allowed longest edge length.
sample_style (str): either "range" or "choice".
"""
super().__init__()
assert sample_style in ["range", "choice"], sample_style
self.is_range = sample_style == "range"
if isinstance(short_edge_length, int):
short_edge_length = (short_edge_length, short_edge_length)
self._init(locals())
def get_transform(self, img):
h, w = img.shape[:2]
if self.is_range:
size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1)
else:
size = np.random.choice(self.short_edge_length)
if size == 0:
return NoOpTransform()
scale = size * 1.0 / min(h, w)
if h < w:
newh, neww = size, scale * w
else:
newh, neww = scale * h, size
if max(newh, neww) > self.max_size:
scale = self.max_size * 1.0 / max(newh, neww)
newh = newh * scale
neww = neww * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
return ResizeTransform(h, w, newh, neww, self.interp)
class RandomRotation(TransformGen):
"""
This method returns a copy of this image, rotated the given
number of degrees counter clockwise around the given center.
"""
def __init__(self, angle, expand=True, center=None, sample_style="range", interp=None):
"""
Args:
angle (list[float]): If ``sample_style=="range"``,
a [min, max] interval from which to sample the angle (in degrees).
If ``sample_style=="choice"``, a list of angles to sample from
expand (bool): choose if the image should be resized to fit the whole
rotated image (default), or simply cropped
center (list[[float, float]]): If ``sample_style=="range"``,
a [[minx, miny], [maxx, maxy]] relative interval from which to sample the center,
[0, 0] being the top left of the image and [1, 1] the bottom right.
If ``sample_style=="choice"``, a list of centers to sample from
Default: None, which means that the center of rotation is the center of the image
center has no effect if expand=True because it only affects shifting
"""
super().__init__()
assert sample_style in ["range", "choice"], sample_style
self.is_range = sample_style == "range"
if isinstance(angle, (float, int)):
angle = (angle, angle)
if center is not None and isinstance(center[0], (float, int)):
center = (center, center)
self._init(locals())
def get_transform(self, img):
h, w = img.shape[:2]
center = None
if self.is_range:
angle = np.random.uniform(self.angle[0], self.angle[1])
if self.center is not None:
center = (
np.random.uniform(self.center[0][0], self.center[1][0]),
np.random.uniform(self.center[0][1], self.center[1][1]),
)
else:
angle = np.random.choice(self.angle)
if self.center is not None:
center = np.random.choice(self.center)
if center is not None:
center = (w * center[0], h * center[1]) # Convert to absolute coordinates
return RotationTransform(h, w, angle, expand=self.expand, center=center, interp=self.interp)
class RandomCrop(TransformGen):
"""
Randomly crop a subimage out of an image.
"""
def __init__(self, crop_type: str, crop_size):
"""
Args:
crop_type (str): one of "relative_range", "relative", "absolute".
See `config/defaults.py` for explanation.
crop_size (tuple[float]): the relative ratio or absolute pixels of
height and width
"""
super().__init__()
assert crop_type in ["relative_range", "relative", "absolute"]
self._init(locals())
def get_transform(self, img):
h, w = img.shape[:2]
croph, cropw = self.get_crop_size((h, w))
assert h >= croph and w >= cropw, "Shape computation in {} has bugs.".format(self)
h0 = np.random.randint(h - croph + 1)
w0 = np.random.randint(w - cropw + 1)
return CropTransform(w0, h0, cropw, croph)
def get_crop_size(self, image_size):
"""
Args:
image_size (tuple): height, width
Returns:
crop_size (tuple): height, width in absolute pixels
"""
h, w = image_size
if self.crop_type == "relative":
ch, cw = self.crop_size
return int(h * ch + 0.5), int(w * cw + 0.5)
elif self.crop_type == "relative_range":
crop_size = np.asarray(self.crop_size, dtype=np.float32)
ch, cw = crop_size + np.random.rand(2) * (1 - crop_size)
return int(h * ch + 0.5), int(w * cw + 0.5)
elif self.crop_type == "absolute":
return (min(self.crop_size[0], h), min(self.crop_size[1], w))
else:
NotImplementedError("Unknown crop type {}".format(self.crop_type))
class RandomExtent(TransformGen):
"""
Outputs an image by cropping a random "subrect" of the source image.
The subrect can be parameterized to include pixels outside the source image,
in which case they will be set to zeros (i.e. black). The size of the output
image will vary with the size of the random subrect.
"""
def __init__(self, scale_range, shift_range):
"""
Args:
output_size (h, w): Dimensions of output image
scale_range (l, h): Range of input-to-output size scaling factor
shift_range (x, y): Range of shifts of the cropped subrect. The rect
is shifted by [w / 2 * Uniform(-x, x), h / 2 * Uniform(-y, y)],
where (w, h) is the (width, height) of the input image. Set each
component to zero to crop at the image's center.
"""
super().__init__()
self._init(locals())
def get_transform(self, img):
img_h, img_w = img.shape[:2]
# Initialize src_rect to fit the input image.
src_rect = np.array([-0.5 * img_w, -0.5 * img_h, 0.5 * img_w, 0.5 * img_h])
# Apply a random scaling to the src_rect.
src_rect *= np.random.uniform(self.scale_range[0], self.scale_range[1])
# Apply a random shift to the coordinates origin.
src_rect[0::2] += self.shift_range[0] * img_w * (np.random.rand() - 0.5)
src_rect[1::2] += self.shift_range[1] * img_h * (np.random.rand() - 0.5)
# Map src_rect coordinates into image coordinates (center at corner).
src_rect[0::2] += 0.5 * img_w
src_rect[1::2] += 0.5 * img_h
return ExtentTransform(
src_rect=(src_rect[0], src_rect[1], src_rect[2], src_rect[3]),
output_size=(int(src_rect[3] - src_rect[1]), int(src_rect[2] - src_rect[0])),
)
class RandomContrast(TransformGen):
"""
Randomly transforms image contrast.
Contrast intensity is uniformly sampled in (intensity_min, intensity_max).
- intensity < 1 will reduce contrast
- intensity = 1 will preserve the input image
- intensity > 1 will increase contrast
See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html
"""
def __init__(self, intensity_min, intensity_max):
"""
Args:
intensity_min (float): Minimum augmentation
intensity_max (float): Maximum augmentation
"""
super().__init__()
self._init(locals())
def get_transform(self, img):
w = np.random.uniform(self.intensity_min, self.intensity_max)
return BlendTransform(src_image=img.mean(), src_weight=1 - w, dst_weight=w)
class RandomBrightness(TransformGen):
"""
Randomly transforms image brightness.
Brightness intensity is uniformly sampled in (intensity_min, intensity_max).
- intensity < 1 will reduce brightness
- intensity = 1 will preserve the input image
- intensity > 1 will increase brightness
See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html
"""
def __init__(self, intensity_min, intensity_max):
"""
Args:
intensity_min (float): Minimum augmentation
intensity_max (float): Maximum augmentation
"""
super().__init__()
self._init(locals())
def get_transform(self, img):
w = np.random.uniform(self.intensity_min, self.intensity_max)
return BlendTransform(src_image=0, src_weight=1 - w, dst_weight=w)
class RandomSaturation(TransformGen):
"""
Randomly transforms image saturation.
Saturation intensity is uniformly sampled in (intensity_min, intensity_max).
- intensity < 1 will reduce saturation (make the image more grayscale)
- intensity = 1 will preserve the input image
- intensity > 1 will increase saturation
See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html
"""
def __init__(self, intensity_min, intensity_max):
"""
Args:
intensity_min (float): Minimum augmentation (1 preserves input).
intensity_max (float): Maximum augmentation (1 preserves input).
"""
super().__init__()
self._init(locals())
def get_transform(self, img):
assert img.shape[-1] == 3, "Saturation only works on RGB images"
w = np.random.uniform(self.intensity_min, self.intensity_max)
grayscale = img.dot([0.299, 0.587, 0.114])[:, :, np.newaxis]
return BlendTransform(src_image=grayscale, src_weight=1 - w, dst_weight=w)
class RandomLighting(TransformGen):
"""
Randomly transforms image color using fixed PCA over ImageNet.
The degree of color jittering is randomly sampled via a normal distribution,
with standard deviation given by the scale parameter.
"""
def __init__(self, scale):
"""
Args:
scale (float): Standard deviation of principal component weighting.
"""
super().__init__()
self._init(locals())
self.eigen_vecs = np.array(
[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203]]
)
self.eigen_vals = np.array([0.2175, 0.0188, 0.0045])
def get_transform(self, img):
assert img.shape[-1] == 3, "Saturation only works on RGB images"
weights = np.random.normal(scale=self.scale, size=3)
return BlendTransform(
src_image=self.eigen_vecs.dot(weights * self.eigen_vals), src_weight=1.0, dst_weight=1.0
)
def apply_transform_gens(transform_gens, img):
"""
Apply a list of :class:`TransformGen` on the input image, and
returns the transformed image and a list of transforms.
We cannot simply create and return all transforms without
applying it to the image, because a subsequent transform may
need the output of the previous one.
Args:
transform_gens (list): list of :class:`TransformGen` instance to
be applied.
img (ndarray): uint8 or floating point images with 1 or 3 channels.
Returns:
ndarray: the transformed image
TransformList: contain the transforms that's used.
"""
for g in transform_gens:
assert isinstance(g, TransformGen), g
check_dtype(img)
tfms = []
for g in transform_gens:
tfm = g.get_transform(img)
assert isinstance(
tfm, Transform
), "TransformGen {} must return an instance of Transform! Got {} instead".format(g, tfm)
img = tfm.apply_image(img)
tfms.append(tfm)
return img, TransformList(tfms)
|
the-stack_0_882 | # pylint: disable=C0302
"""
@file
@brief Implements a class able to compute the predictions
from on an :epkg:`ONNX` model.
"""
from collections import OrderedDict
from io import BytesIO
from time import perf_counter
import warnings
import textwrap
import pprint
import numpy
from scipy.sparse import coo_matrix
from onnx import load, load_model, checker, shape_inference
from onnx import onnx_pb as onnx_proto
from onnx.helper import make_model
from ..tools.code_helper import make_callable, print_code
from ..onnx_tools.onnx2py_helper import (
_var_as_dict, numpy_min, numpy_max, guess_numpy_type_from_string)
from ..onnx_tools.onnx_manipulations import (
select_model_inputs_outputs, enumerate_model_node_outputs,
overwrite_opset, insert_results_into_onnx)
from ..onnx_tools.optim import onnx_remove_node_unused
from .onnx_inference_node import OnnxInferenceNode
from .onnx_inference_exports import OnnxInferenceExport
from .shape_object import ShapeObject
from .type_object import SequenceType
class OnnxInference:
"""
Loads an :epkg:`ONNX` file or object or stream.
Computes the output of the :epkg:`ONNX` graph.
Several runtimes are available.
* ``'python'``: the runtime implements every onnx operator
needed to run a :epkg:`scikit-learn` model by using :epkg:`numpy`
or C++ code.
* ``'python_compiled'``: it is the same runtime than the previous
one except every operator is called from a compiled function
(@see me _build_compile_run) instead for a method going through
the list of operator
* ``'onnxruntime1'``: uses :epkg:`onnxruntime`
* ``'onnxruntime2'``: this mode is mostly used to debug as
python handles calling every operator but :epkg:`onnxruntime`
is called for every of them, this process may fail due to
wrong inference type specially of the graph includes
custom nodes, in that case, it is better to compute the output
of intermediates nodes. It is much slower as fo every output, every
node is computed but more robust.
:param onnx_or_bytes_or_stream: :epkg:`onnx` object,
bytes, or filename or stream
:param runtime: runtime options
:param skip_run: do not build the runtime
:param inplace: use inplace computation as much as possible
:param input_inplace: the computation is allowed
to overwrite the input, see :meth:`_guess_inplace
<mlprodict.onnxrt.onnx_inference.OnnxInference._guess_inplace>`
:param ir_version: if not None, overwrite the default version
:param target_opset: used to overwrite *target_opset*
:param runtime_options: specific options for the runtime
:param inside_loop: tells the runtime the graph is meant to
be repeated multiple times (in that case, inputs and
outputs may share the same name)
:param static_inputs: Loop can use static variables,
variables from the graph which runs the loop
(enumerate of strings)
:param new_outputs: if the loading fails, it might worth
cutting the graph, if not None, the graph will
be cut to have these new_outputs as the final outputs
:param new_opset: overwrite the main opset and replaces
by this new one
:param device: device, a string `cpu`, `cuda`, `cuda:0`...,
this option is only available with runtime *onnxruntime1*
Among the possible runtime_options, there are:
* *enable_profiling*: enables profiling for :epkg:`onnxruntime`
* *session_options*: an instance of *SessionOptions* from
:epkg:`onnxruntime`
* *ir_version*: change ir_version
.. versionchanged:: 0.7
Parameters *new_outputs*, *new_opset* were added.
.. versionchanged:: 0.8
Parameters *static_inputs*, *device* were added.
"""
def __init__(self, onnx_or_bytes_or_stream, runtime=None,
skip_run=False, inplace=True,
input_inplace=False, ir_version=None,
target_opset=None, runtime_options=None,
session_options=None, inside_loop=False,
static_inputs=None, new_outputs=None, new_opset=None,
device=None):
if isinstance(onnx_or_bytes_or_stream, bytes):
self.obj = load_model(BytesIO(onnx_or_bytes_or_stream))
elif isinstance(onnx_or_bytes_or_stream, BytesIO):
self.obj = load_model(onnx_or_bytes_or_stream)
elif isinstance(onnx_or_bytes_or_stream, str):
self.obj = load(onnx_or_bytes_or_stream)
elif hasattr(onnx_or_bytes_or_stream, 'graph'):
self.obj = onnx_or_bytes_or_stream
elif isinstance(onnx_or_bytes_or_stream, onnx_proto.GraphProto):
self.obj = make_model(onnx_or_bytes_or_stream,
producer_name='mlprodict')
else:
raise TypeError("Unable to handle type {}.".format( # pragma: no cover
type(onnx_or_bytes_or_stream)))
if ir_version is not None:
self.obj.ir_version = ir_version
if new_outputs is not None:
self.obj = select_model_inputs_outputs(
self.obj, outputs=new_outputs, infer_shapes=True)
if new_opset is not None:
self.obj = overwrite_opset(self.obj, new_opset)
if device is not None and runtime != 'onnxruntime1':
raise ValueError(
"Incompatible values, device can be specified with "
"runtime 'onnxruntime1', not %r." % runtime)
self.runtime = runtime
self.skip_run = skip_run
self.input_inplace = input_inplace
self.inplace = inplace
self.force_target_opset = target_opset
self.runtime_options = runtime_options
self.inside_loop = inside_loop
self.static_inputs = static_inputs
self.device = device
self._init()
def __getstate__(self):
"""
To pickle the object.
"""
return {'onnx': self.obj.SerializeToString(),
'runtime': self.runtime,
'runtime_options': self.runtime_options,
'skip_run': self.skip_run,
'input_inplace': self.input_inplace,
'inplace': self.inplace,
'force_target_opset': self.force_target_opset,
'static_inputs': self.static_inputs,
'inside_loop': self.inside_loop,
'device': self.device}
def __setstate__(self, state):
"""
To unpickle the object.
"""
onx = state['onnx']
self.obj = load_model(BytesIO(onx))
self.runtime = state['runtime']
self.runtime_options = state['runtime_options']
self.skip_run = state['skip_run']
self.input_inplace = state['input_inplace']
self.inplace = state['inplace']
self.force_target_opset = state['force_target_opset']
self.static_inputs = state['static_inputs']
self.inside_loop = state['inside_loop']
self.device = state['device']
self._init()
def _init(self):
"""
Prepares the instance to deliver predictions.
"""
self.graph_ = self.to_sequence()
if len(self.graph_['sequence']) == 0:
raise RuntimeError( # pragma: no cover
"No runnable nodes was found in the ONNX graph.")
self.outputs_ = self.graph_['outputs']
self.inputs_ = self.graph_['inputs']
for ino in [self.obj.graph.input, self.obj.graph.output]:
for xy in ino:
shape = xy.type.tensor_type.shape
for d in shape.dim:
if d.dim_value == 0 and "0" in str(d) and 'dim_param' not in str(d):
# d.dim_value returns 0 whether is is 0 or empty.
# it may be a parameter as well
raise RuntimeError( # pragma: no cover
"Wrong ONNX file, one input or output has an empty shape: "
"{}.".format(xy))
self.target_opset_ = self.graph_['targets']
if self.force_target_opset is not None:
if isinstance(self.force_target_opset, dict):
self.target_opset_ = self.force_target_opset # pragma: no cover
else:
self.target_opset_ = {'': self.force_target_opset}
self.ir_version_ = self.graph_['ir_version']
if not self.skip_run:
if self.runtime == 'onnxruntime1':
# Loads the onnx with onnxruntime as a single file.
del self.graph_
from .ops_whole.session import OnnxWholeSession
self._whole = OnnxWholeSession(
self.obj, self.runtime, self.runtime_options,
self.device)
self._run = self._run_whole_runtime
else:
self.sequence_ = self.graph_['sequence']
self.inits_ = self.graph_['inits']
self.statics_ = self.graph_['statics']
dtype = self._guess_input_dtype()
variables = self.inits_.copy()
for node in self.sequence_:
domain = node.onnx_node.domain
target_opset = self.target_opset_.get(domain, None)
if self.runtime in ('onnxruntime2', 'empty'):
node.setup_runtime(self.runtime, variables, self.__class__,
target_opset=target_opset, dtype=dtype,
domain=domain, ir_version=self.ir_version_,
runtime_options=self.runtime_options)
else:
node.setup_runtime(self.runtime, variables, self.__class__,
target_opset=target_opset, domain=domain,
ir_version=self.ir_version_,
runtime_options=self.runtime_options)
if hasattr(node, 'ops_') and hasattr(node.ops_, 'typed_outputs_'):
for k, v in node.ops_.typed_outputs_:
variables[k] = v
self._run = self._run_sequence_runtime
if not self.skip_run and self.runtime in ('python', None):
self.shapes_ = self._set_shape_inference_runtime()
if self.inplace:
self.inplaces_ = self._guess_inplace(self.input_inplace)
self.exporters_ = OnnxInferenceExport(self)
self.to_json = self.exporters_.to_json
self.to_dot = self.exporters_.to_dot
self.to_python = self.exporters_.to_python
self.to_text = self.exporters_.to_text
self.to_onnx_code = self.exporters_.to_onnx_code
if self.runtime in ('python_compiled', 'python_compiled_debug'):
# switch the inference method to the compiled one
_, fct, code = self._build_compile_run('debug' in self.runtime)
setattr(self, '_run_compiled', fct)
setattr(self, '_run_compiled_code', code)
self._run = self._run_sequence_runtime_compiled
def _run_sequence_runtime_compiled(
self, inputs, clean_right_away=False, intermediate=False,
verbose=0, node_time=False, yield_ops=None, fLOG=None):
"""
Executes a compiled version of @see me _run_sequence_runtime,
compiled with method @see me _build_compile_run.
Every parameter with a default value is ignored.
Switch to ``runtime='python'`` to enable those.
"""
try:
return self._run_compiled( # pylint: disable=E1101
inputs, yield_ops=yield_ops)
except NameError as e:
raise RuntimeError( # pragma: no cover
"Unable to compute prediction due to %r. Code:\n%s"
"" % (e, print_code(
self._run_compiled_code))) from e # pylint: disable=E1101
def _guess_input_dtype(self):
for _, v in self.graph_['inputs'].items():
if 'type' not in v:
continue # pragma: no cover
t = v['type']
if 'elem' not in t:
continue
if t['elem'] == 'double':
return numpy.float64
return numpy.float32
def __str__(self):
"""
usual
"""
rows = ['OnnxInference(...)']
if hasattr(self, '_run_compiled_code'):
rows.append(
textwrap.indent(
self._run_compiled_code, ' ')) # pylint: disable=E1101
else:
rows.append(textwrap.indent(str(self.obj), ' '))
return "\n".join(rows)
def __repr__(self):
"""
usual
"""
return "OnnxInference(...)" # pragma: no cover
def check_model(self):
"""
Checks the model follow :epkg:`ONNX` conventions.
"""
checker.check_model(self.obj)
def shape_inference(self):
"""
Infers the shape of the outputs
with :epkg:`onnx` package.
@return A new :epkg:`ONNX` graph which defined outputs.
"""
return shape_inference.infer_shapes(self.obj)
@property
def input_names(self):
"""
Returns the names of all inputs.
It does not include the optional inputs.
.. versionchanged:: 0.6
The list does not include optional inputs anymore.
"""
inits = set(_.name for _ in self.obj.graph.initializer)
return [_.name for _ in self.obj.graph.input if _.name not in inits]
@property
def input_names_shapes(self):
"""
Returns the names and shapes of all inputs.
This method assumes all inputs are tensors.
It does not include the optional inputs.
.. versionchanged:: 0.6
The list does not include optional inputs anymore.
"""
names = set(self.input_names)
return [(_.name, _var_as_dict(_)['type']['shape'])
for _ in self.obj.graph.input if _.name in names]
@staticmethod
def _get_type_property(info, prop):
if prop in info:
return info[prop]
if 'kind' in info and info['kind'] == 'sequence':
if prop == 'shape':
return ('?', )
raise NotImplementedError(
"Unable to retrieve property %r from %r."
"" % (prop, info))
@property
def input_names_shapes_types(self):
"""
Returns the names, shapes, types of all inputs.
This method assumes all inputs are tensors.
It does not include the optional inputs.
.. versionchanged:: 0.6
The list does not include optional inputs anymore.
"""
f = OnnxInference._get_type_property
names = set(self.input_names)
return [(_.name, f(_var_as_dict(_)['type'], 'shape'),
'tensor(%s)' % f(_var_as_dict(_)['type'], 'elem'))
for _ in self.obj.graph.input if _.name in names]
@property
def output_names(self):
"""
Returns the names of all outputs.
"""
return [_.name for _ in self.obj.graph.output]
@property
def output_names_shapes(self):
"""
Returns the names and shapes of all outputs.
This method assumes all inputs are tensors.
"""
f = OnnxInference._get_type_property
return [(_.name, f(_var_as_dict(_)['type'], 'shape'))
for _ in self.obj.graph.output]
@property
def output_names_shapes_types(self):
"""
Returns the names, shapes, types of all outputs.
This method assumes all inputs are tensors.
It does not include the optional outputs.
.. versionadd:: 0.7
"""
names = set(self.output_names)
f = OnnxInference._get_type_property
return [(_.name, f(_var_as_dict(_)['type'], 'shape'),
'tensor(%s)' % f(_var_as_dict(_)['type'], 'elem'))
for _ in self.obj.graph.output if _.name in names]
def global_index(self, name):
"""
Maps every name to one integer to avoid using dictionaries
when running the predictions.
@param name outputs name
@return integer
"""
if not hasattr(self, '_global_index'):
self._global_index = {}
if name in self._global_index:
return self._global_index[name]
self._global_index[name] = len(self._global_index)
return self._global_index[name]
def to_sequence(self):
"""
Produces a graph to facilitate the execution.
One example:
.. exref::
:title: Convert ONNX into graph
An example on how to convert an :epkg:`ONNX`
graph into a graph.
.. runpython::
:showcode:
:warningout: DeprecationWarning
import pprint
import numpy
from skl2onnx.algebra.onnx_ops import OnnxLinearRegressor
from skl2onnx.common.data_types import FloatTensorType
from mlprodict.onnxrt import OnnxInference
pars = dict(coefficients=numpy.array([1., 2.]),
intercepts=numpy.array([1.]),
post_transform='NONE')
onx = OnnxLinearRegressor('X', output_names=['Y'], **pars)
model_def = onx.to_onnx({'X': pars['coefficients'].astype(numpy.float32)},
outputs=[('Y', FloatTensorType([1]))],
target_opset=12)
oinf = OnnxInference(model_def)
pprint.pprint(oinf.to_sequence())
See an example of representation in notebook
:ref:`onnxvisualizationrst`.
"""
inits = {}
variables = {}
outputs = {}
nodes = {}
statics = {}
targets = {}
for o in self.obj.opset_import:
targets[o.domain] = o.version
# static variables
if self.static_inputs is not None:
for n in self.static_inputs:
statics[n] = {'name': n}
self.global_index(n)
# inputs
for obj in self.obj.graph.input:
variables[obj.name] = _var_as_dict(obj)
self.global_index(obj.name)
# outputs
for obj in self.obj.graph.output:
if hasattr(obj, 'type') and str(obj.type) != '':
outputs[obj.name] = _var_as_dict(obj)
else:
outputs[obj.name] = {'name': obj.name}
self.global_index(obj.name)
# initializer
for obj in self.obj.graph.initializer:
init_obj = _var_as_dict(obj)
if init_obj is None:
raise RuntimeError( # pragma: no cover
"Unable to convert an initializer\n{}".format(obj))
inits[obj.name] = init_obj
self.global_index(obj.name)
if 'value' not in inits[obj.name]:
raise RuntimeError( # pragma: no cover
"One initializer has no value: '{}'\n{}\n{}".format(
obj.name, inits[obj.name], obj))
# nodes
for node in self.obj.graph.node:
dobj = _var_as_dict(node)
if dobj is None:
raise RuntimeError( # pragma: no cover
"Unable to convert a node\n{}".format(node))
if 'atts' in dobj:
atts = dobj['atts']
for k, v in atts.items():
if not isinstance(v, dict) or 'value' not in v:
raise RuntimeError( # pragma: no cover
"A parameter has no (sparse) value '{}' "
"for node '{}'\nv={}\ndobj=[{}]".format(
k, node.name, v, node))
if node.name in nodes: # pragma: no cover
i = 2
while True:
new_name = "%s_n%i" % (node.name, i)
if new_name not in nodes:
break
i += 1
else:
new_name = node.name
nodes[new_name] = OnnxInferenceNode(node, dobj, self.global_index)
# names
names = {}
for k, v in statics.items():
if (k, 0) in names:
raise RuntimeError( # pragma: no cover
"Static variables '{}' already exists (tag='{}').".format(
k, names[k, 0][0]))
names[k, 0] = ('S', v)
for k, v in inits.items():
if (k, 0) in names:
raise RuntimeError( # pragma: no cover
"Initializer '{}' already exists (tag='{}').".format(
k, names[k, 0][0]))
names[k, 0] = ('C', v)
for k, v in variables.items():
if (k, 0) in names:
if k in inits:
# Kind of default value for an input
continue
raise RuntimeError( # pragma: no cover
"Variable '{}' already exists (tag='{}').".format(
k, names[k, 0][0]))
names[k, 0] = ('I', v)
for k, v in outputs.items():
if (k, 0) in names and self.runtime != 'empty':
if not self.inside_loop or names[k, 0][0] != 'I':
raise RuntimeError( # pragma: no cover
"Output '{}' already exists (tag='{}').".format(
k, names[k, 0][0]))
else:
# For input, output sharing the same name, we marked the name
# as an input.
continue
names[k, 0] = ('O', v)
for k, v in nodes.items():
if (k, 1) in names:
raise RuntimeError( # pragma: no cover
"Node '{}' already exists (tag='{}'). "
"Use inside_loop=True to bypass this exception.".format(
k, names[k, 0][0]))
names[k, 1] = ('N', v)
# ordering
order = {}
modif = 1
intermediate = {}
while modif > 0:
modif = 0
for (k, _), v in names.items():
if (k, 1) in order:
# The operator node is already processed.
continue
if v[0] in {'I', 'C', 'S'}:
if (k, 0) not in order:
order[k, 0] = len(order) # A data node.
modif += 1
continue
if v[0] == 'O':
continue
if all((inp, 0) in order for inp in v[1].inputs):
# If all inputs are available,
# We tell the operator node is processed.
order[k, 1] = len(order)
modif += 1
for o in v[1].outputs:
if (o, 0) in order:
raise RuntimeError( # pragma: no cover
"Two nodes share the same output '{}' "
"or an operator and an output "
"share the same name. "
"(node: {}).".format(o, v[1]))
# We add a data node.
order[o, 0] = len(order)
intermediate[o] = None
modif += 1
# compute
rev = [(v, k[0], k[1]) for k, v in order.items()]
rev.sort()
sequence = []
for _, name, node_kind in rev:
if name not in nodes:
continue
if node_kind == 0:
# It is an output which shares the same name
# as a node.
continue
node = nodes[name]
node.set_order(len(sequence))
sequence.append(node)
if len(sequence) == 0:
raise RuntimeError( # pragma: no cover
"No runnable nodes was found in the ONNX graph"
"\n--rev--\n{}"
"\n--order--\n{}"
"\n--nodes--\n{}"
"\n---".format(
"\n".join([str(_) for _ in names.items()]),
"\n".join([str(_) for _ in order.items()]),
"\n".join([str(_) for _ in nodes.items()])))
# defines where an intermediare output is not needed
last_used = {}
for node in sequence:
for inp in node.inputs:
last_used[inp] = node.order
for k, ord in last_used.items():
sequence[ord].add_variable_to_clean(k)
results = dict(inits=inits, inputs=variables, outputs=outputs,
nodes=nodes, sequence=sequence,
intermediate=intermediate,
targets=targets, ir_version=self.obj.ir_version,
statics=statics)
if len(sequence) < len(nodes):
# Not all node will be executed.
raise RuntimeError( # pragma: no cover
"Unable to run all nodes.\n--Nodes--\n%s\n--Sequence--\n%s"
"\n--Inputs--\n%s\n--Inits--\n%s\n--Statics\n%s"
"" % (pprint.pformat(nodes), pprint.pformat(sequence),
pprint.pformat(list(variables)),
pprint.pformat(list(inits)),
pprint.pformat(list(statics))))
return results
def run(self, inputs, clean_right_away=False,
intermediate=False, verbose=0, node_time=False,
overwrite_types=None, yield_ops=None, fLOG=None):
"""
Computes the predictions for this :epkg:`onnx` graph.
:param inputs: inputs as dictionary or a dataframe
:param clean_right_away: clean the intermediate outputs
as soon as they are not needed
:param intermediate: returns a dictionary of intermediate
variables instead of the results only
:param verbose: display information while predicting
:param node_time: measure time of each node
:param overwrite_types: shape inference does not work all the time,
this allows to force types when building intermediate
results, see @see fn select_model_inputs_outputs
:param yield_ops: dictionary to overwrite the output of
operator *YieldOp*
:param fLOG: logging function if *verbose > 0*
:return: outputs as dictionary
and a second dictionary of the time spent
in each node if *node_time* is True
.. exref::
:title: Computes predictions with any runtime
The following example compares predictions
between :epkg:`scikit-learn` and this runtime
for the python runtime.
.. runpython::
:showcode:
:warningout: DeprecationWarning
import numpy
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from mlprodict.onnxrt import OnnxInference
from mlprodict.onnx_conv import to_onnx
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y)
clr = LinearRegression()
clr.fit(X_train, y_train)
exp = clr.predict(X_test[:5])
print(exp)
model_def = to_onnx(clr, X_train.astype(numpy.float32),
target_opset=12)
oinf = OnnxInference(model_def)
y = oinf.run({'X': X_test[:5]})
print(y)
The function returns all intermediate outputs
if *intermediate* is True. In case of runtime
*onnxruntime1*, if intermediate is True,
the first class builds all :epkg:`ONNX` cut out
to keep the one output and converted into
*OnnxInference*.
.. versionchanged:: 0.8
Parameter *yield_ops* was added.
"""
def retype(col_array):
if (hasattr(col_array, 'categories') and
hasattr(col_array, 'from_codes')):
# isinstance(col_array, pandas.Categorical):
return col_array.astype(numpy.int64)
return col_array
if hasattr(inputs, 'columns') and hasattr(inputs, 'iloc'):
# == isinstance(inputs, pandas.DataFrame)
inputs = OrderedDict((
name, retype(numpy.expand_dims(inputs[name].values, axis=1)))
for name in inputs.columns)
if intermediate:
if self.inplace:
raise RuntimeError( # pragma: no cover
"inplace must be False if intermediate is True, a container "
"might be used by several nodes.")
return self._run(inputs, clean_right_away=False,
intermediate=intermediate,
verbose=verbose, node_time=node_time,
overwrite_types=overwrite_types,
yield_ops=yield_ops, fLOG=fLOG)
if overwrite_types is not None:
raise RuntimeError( # pragma: no cover
"overwrite_types is not used if intermediate is False.")
return self._run(inputs, clean_right_away=False,
intermediate=intermediate,
verbose=verbose, node_time=node_time,
yield_ops=yield_ops, fLOG=fLOG)
def run2onnx(self, inputs, verbose=0, fLOG=None,
as_parameter=True, suffix='_DBG',
param_name=None, node_type='DEBUG',
domain='DEBUG', domain_opset=1):
"""
Executes the graphs with the given inputs, then adds the intermediate
results into ONNX nodes in the original graph. Once saved, it can be
looked with a tool such as :epkg:`netron`.
:param inputs: inputs as dictionary or a dataframe
:param verbose: display information while predicting
:param fLOG: logging function if *verbose > 0*
:param as_parameter: add new nodes with results as one parameter
(True) or as initializer (False)
:param suffix: suffix to add to new results
:param param_name: name of the parameter to add
(by default the result name), it can be a function
`param_name(reult_name) -> parameter_name`
:param node_type: type of the new node
:param domain: domain the new node
:param domain_opset: opset for *domain*
:return: outputs as dictionary
and the onnx graph with new nodes
The following example shows how to use it.
.. gdot::
:script: DOT-SECTION
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_iris
from mlprodict.onnxrt import OnnxInference
import numpy
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
lr = LinearRegression()
lr.fit(X, y)
from mlprodict.onnx_conv import to_onnx
model_onnx = to_onnx(lr, X.astype(numpy.float32))
oinf = OnnxInference(model_onnx, inplace=False)
model_onnx_debug = oinf.run2onnx({'X': X[:3].astype(numpy.float32)})
oinf_debug = OnnxInference(model_onnx_debug[1])
print("DOT-SECTION", oinf_debug.to_dot())
.. versionadded:: 0.7
"""
intermediate = self.run(inputs, verbose=verbose, fLOG=fLOG,
intermediate=True)
for name in self.input_names:
del intermediate[name]
new_onx = insert_results_into_onnx(
self.obj, intermediate, as_parameter=as_parameter,
suffix=suffix, param_name=param_name, node_type=node_type,
domain=domain, domain_opset=domain_opset)
return intermediate, new_onx
def display_sequence(self, verbose=1):
"""
Shows the sequence of nodes to run if ``runtime=='python'``.
"""
rows = []
rows.append("#node: {}".format(len(self.sequence_)))
for i, node in enumerate(self.sequence_):
if verbose >= 1:
rows.append("{}: {}".format(i, str(node)))
return "\n".join(rows)
def _run_sequence_runtime(self, inputs, clean_right_away=False,
intermediate=False, verbose=0, node_time=False,
overwrite_types=None, yield_ops=None,
fLOG=None):
if overwrite_types is not None:
raise NotImplementedError( # pragma: no cover
"overwrite_types != None not implemented.")
if clean_right_away:
raise NotImplementedError( # pragma: no cover
"clean_right_away=true not implemented.")
if node_time:
mtime = []
if verbose >= 1 and fLOG is not None:
printed = set()
if hasattr(self, "_values_init"):
values = self._values_init.copy() # pylint: disable=E0203
else:
values = [None] * len(self._global_index)
if verbose >= 1 and fLOG is not None:
for k, v in self.inits_.items():
values[self._global_index[k]] = v['value']
if verbose < 3:
fLOG("+ki='{}': {} (dtype={} min={} max={})".format(
k, v['value'].shape, v['value'].dtype,
numpy_min(v['value']), numpy_max(v['value'])))
else:
fLOG("+ki='{}': {} (dtype={} min={} max={}\n{}".format(
k, v['value'].shape, v['value'].dtype,
numpy_min(v['value']), numpy_max(v['value']),
v['value']))
printed.add(k)
else:
for k, v in self.inits_.items():
values[self._global_index[k]] = v['value']
# stores the array to skip initialing a second time
if verbose == 0 or fLOG is None:
self._values_init = values.copy()
for name, value in inputs.items():
values[self._global_index[name]] = value
if verbose == 0 or fLOG is None:
if node_time:
for i, node in enumerate(self.sequence_):
if yield_ops is not None and node.onnx_node.op_type == 'YieldOp':
out = node.onnx_node.output[0]
if out in yield_ops:
values[out] = yield_ops[out]
continue
raise RuntimeError( # pragma: no cover
"YieldOp output %r could not be found in "
"yield_ops: %r (node=%r)." % (
out, list(sorted(yield_ops)), node.onnx_node))
t = perf_counter()
node.run(values)
t2 = perf_counter()
mtime.append(dict(i=i, name=node.onnx_node.name,
op_type=node.onnx_node.op_type,
time=t2 - t))
else:
for node in self.sequence_:
node.run(values)
else:
def dispsimple(arr):
if hasattr(arr, 'shape'):
if len(arr.shape) <= 1:
threshold = 8
else:
threshold = min(
50, min(50 // max(arr.shape[1], 1), 8) * arr.shape[1])
if hasattr(arr, 'todense'):
fLOG( # pragma: no cover
numpy.array2string(arr.todense(), max_line_width=120,
suppress_small=True, threshold=threshold))
else:
fLOG(numpy.array2string(arr, max_line_width=120,
suppress_small=True,
threshold=threshold))
else: # pragma: no cover
s = str(arr)
if len(s) > 50:
s = s[:50] + "..."
fLOG(s)
if verbose >= 2:
for k in sorted(self._global_index):
if values[self._global_index[k]] is None:
continue
obj = values[self._global_index[k]]
if k not in printed:
printed.add(k)
if hasattr(obj, 'shape'):
fLOG("-kv='{}' shape={} dtype={} min={} max={}{}".format(
k, obj.shape, obj.dtype, numpy_min(obj),
numpy_max(obj),
' (sparse)' if isinstance(obj, coo_matrix) else ''))
elif (isinstance(obj, list) and len(obj) > 0 and
not isinstance(obj[0], dict)): # pragma: no cover
fLOG("-kv='{}' list len={}".format(k, len(obj)))
if verbose >= 3 and len(obj) > 0:
fLOG("first={} last={}".format(
obj[0], obj[-1]))
else: # pragma: no cover
fLOG("-kv='{}' type={}".format(k, type(obj)))
keys = set(k for k in range(len(values)) if values[k] is not None)
if verbose >= 1:
fLOG("-- OnnxInference: run {} nodes".format(len(self.sequence_)))
for i, node in enumerate(self.sequence_):
if verbose >= 1:
fLOG(node)
if yield_ops is not None and node.onnx_node.op_type == 'YieldOp':
out = node.onnx_node.output[0]
if out in yield_ops:
fLOG("+yo=%r" % out)
values[node.outputs_indices[0]] = yield_ops[out]
else:
raise RuntimeError( # pragma: no cover
"YieldOp output %r could not be found in "
"yield_ops: %r (node=%r)." % (
out, list(sorted(yield_ops)), node.onnx_node))
elif node_time:
t = perf_counter()
node.run(values)
t2 = perf_counter()
mtime.append(dict(i=i, name=node.onnx_node.name,
op_type=node.onnx_node.op_type,
time=t2 - t))
else:
node.run(values)
added = 0
for k in range(len(values)): # pylint: disable=C0200
if values[k] is None:
continue
if k not in keys and k not in printed:
added += 1
printed.add(k)
name = list(
name for name in self._global_index # pylint: disable=C0206
if self._global_index[name] == k)
if isinstance(values[k], (numpy.ndarray, coo_matrix)):
name = name[0]
mini = numpy_min(values[k])
maxi = numpy_max(values[k])
fLOG("+kr{}'{}': {} (dtype={} min={} max={}{})".format(
"=" if len(values[k].shape) == 0 or min(
values[k].shape) > 0 else "*",
name, values[k].shape, values[k].dtype,
mini, maxi,
' sparse' if isinstance(values[k], coo_matrix) else ''))
if verbose >= 3:
dispsimple(values[k])
else:
fLOG("+kr='{}': {}".format(
name, type(values[k])))
if verbose >= 3: # pragma: no cover
dispsimple(values[k])
if added == 0:
fLOG("? no new result") # pragma: no cover
if intermediate:
values = [(v, k, values[v]) for k, v in self._global_index.items()]
values.sort()
values = OrderedDict((k, v) for _, k, v in values)
return (values, mtime) if node_time else values
try:
res = {k: values[self._global_index[k]] for k in self.outputs_}
except KeyError as e: # pragma: no cover
raise RuntimeError("Unable to find one output [{}]\n in [{}]"
".".format(", ".join(sorted(self.outputs_)),
", ".join(sorted(values)))) from e
return (res, mtime) if node_time else res
def build_intermediate(self, outputs=None, verbose=0, overwrite_types=None,
fLOG=None):
"""
Builds every possible :epkg:`ONNX` file
which computes one specific intermediate output
from the inputs.
:param outputs: subsets of outputs to get,
None to get all outputs,
:param overwrite_types: shape inference does not work all the time,
this allows to force types when building intermediate
results, see @see fn select_model_inputs_outputs
:param verbose: displays intermediate information
:param fLOG: logging function
:return: :epkg:`*py:collections:OrderedDict`
.. versionchanged: 0.6
"""
if verbose > 0:
fLOG('[build_intermediate] BEGIN.')
if outputs is not None:
if isinstance(outputs, str):
outputs = [outputs]
if not isinstance(outputs, set):
outputs = set(outputs)
ord = OrderedDict()
for output in enumerate_model_node_outputs(self.obj, order=True):
if outputs is not None and output not in outputs:
continue
subonx = select_model_inputs_outputs(
self.obj, outputs=output, infer_shapes=True,
overwrite=overwrite_types)
subonx = onnx_remove_node_unused(subonx)
if verbose > 0:
fLOG( # pragma: no cover
'[build_intermediate] + {}'.format(output))
ord[output] = OnnxInference(subonx, runtime=self.runtime,
skip_run=self.skip_run,
runtime_options=self.runtime_options,
inplace=self.inplace,
input_inplace=self.input_inplace)
if verbose > 0:
fLOG( # pragma: no cover
'[build_intermediate] END.')
return ord
def _run_whole_runtime(self, inputs, clean_right_away=False,
intermediate=False, verbose=0, node_time=False,
overwrite_types=None, yield_ops=None, fLOG=None):
# node_time is unused
if clean_right_away:
raise RuntimeError( # pragma: no cover
"clean_right_away=true does not work with this runtime.")
if intermediate:
if hasattr(self, "intermediate_onnx_inference_"):
inter_run = self.intermediate_onnx_inference_ # pylint: disable=E0203
else:
if verbose > 0:
fLOG( # pragma: no cover
"-- OnnxInference: build intermediate")
inter_run = self.build_intermediate(
verbose=verbose, fLOG=fLOG, overwrite_types=overwrite_types)
self.intermediate_onnx_inference_ = inter_run
graph = self.to_sequence()
self.inits_ = graph['inits']
if verbose >= 1:
fLOG( # pragma: no cover
"-- OnnxInference: run {} nodes".format(
len(self.intermediate_onnx_inference_)))
values = OrderedDict(inputs)
for k, v in self.inits_.items():
values[k] = v['value']
if verbose >= 2: # pragma: no cover
for k in sorted(values):
fLOG("-k='{}' shape={} dtype={}".format(
k, values[k].shape, values[k].dtype))
for node, oinf in self.intermediate_onnx_inference_.items():
if verbose >= 4: # pragma: no cover
fLOG('[intermediate] %r' % node)
if verbose >= 5: # pragma: no cover
fLOG(oinf.obj)
if yield_ops is not None and node.onnx_node.op_type == 'YieldOp':
out = node.onnx_node.output[0]
if out in yield_ops:
values[out] = yield_ops[out]
continue
raise RuntimeError( # pragma: no cover
"YieldOp output %r could not be found in "
"yield_ops: %r (node=%r)." % (
out, list(sorted(yield_ops)), node.onnx_node))
output = oinf.run(inputs)[node]
values[node] = output
if verbose >= 1:
if verbose >= 4: # pragma: no cover
for k, v in inputs.items():
if isinstance(output, numpy.ndarray):
fLOG("-i='{}': {} (dtype={}) {}".format(
k, v.shape, v.dtype, v.ravel().tolist()))
else:
fLOG("-i='{}': {} (dtype={}) - ?".format(
k, v.shape, v.dtype))
if isinstance(output, numpy.ndarray):
fLOG("+k='{}': {} (dtype={})".format(
node, output.shape, output.dtype))
if verbose >= 2: # pragma: no cover
fLOG(output)
else:
fLOG("+k='{}': {}".format( # pragma: no cover
node, type(output)))
if verbose >= 2: # pragma: no cover
fLOG(output)
return values
if verbose != 0:
warnings.warn(
"verbose option not implemented if runtime is 'onnxruntime1'")
res = self._whole.run(inputs)
return {k: v for k, v in zip(self.outputs_, res)}
def __getitem__(self, item):
"""
Returns the ONNX verions of a node.
"""
if isinstance(item, tuple):
node_name, att_name = item
else:
node_name = item
att_name = None
node_ = None
for node in self.obj.graph.node:
if node.name == node_name:
node_ = node
break
if node_ is None:
raise IndexError( # pragma: no cover
"Unable to get node name '{}'.\n{}".format(
node_name, "\n".join(node.name for node in self.obj.graph.node)))
if att_name is None:
return node_
for att in node_.attribute:
if att.name == att_name:
return att
raise IndexError( # pragma: no cover
"Unable to find attribute '{}' from node "
"'{}'.".format(att_name, node_name))
def switch_initializers_dtype(self, model=None,
dtype_in=numpy.float32,
dtype_out=numpy.float64):
"""
Switches all initializers to ``numpy.float64``. If *model*
is None, a simple cast is done. Otherwise, the function assumes
the model is a :epkg:`scikit-learn` pipeline.
This only works if the runtime is ``'python'``.
@param model :epkg:`scikit-learn` model or None
@param dtype_in previous type
@param dtype_out next type
@return done operations
"""
from ..onnx_tools.optim.sklearn_helper import enumerate_fitted_arrays, pairwise_array_distances
if self.runtime != 'python': # pragma: no cover
raise RuntimeError("Initializers can be casted only if the "
"runtime is 'python' not '{}'.".format(self.runtime))
if hasattr(self, '_values_init'):
del self._values_init
# first pass: simple cast
done = []
initializer = self.inits_
for k, v in initializer.items():
if isinstance(v['value'], numpy.ndarray):
if v['value'].dtype == dtype_in:
v['value'] = v['value'].astype(dtype_out)
done.append(("pass1", "+", "init", k, v['value']))
else:
done.append(("pass1", "-", "init", k,
v['value'])) # pragma: no cover
for k, v in self.graph_['nodes'].items():
res = v.switch_initializers_dtype(dtype_in=dtype_in,
dtype_out=dtype_out)
for r in res:
done.append(("pass1", "node", k) + r)
for k, v in self.graph_['intermediate'].items():
if v is None:
continue
res = v.switch_initializers_dtype(dtype_in=dtype_in,
dtype_out=dtype_out)
for r in res:
done.append(("pass1", "sub", k) + r)
if model is not None:
# Second pass, we compare all arrays from the model
# to the arrays in the converted models.
def dist(a):
cast = a.astype(dtype_in).astype(dtype_out)
d = pairwise_array_distances([cast], [a])[0, 0]
return d
done_ = [(c, c[-1]) for c in done]
moda_ = [(a, a[-2][-1]) for a in enumerate_fitted_arrays(model)
if dist(a[-2][-1]) > 0]
aconv = [_[-1] for _ in done_]
amoda = [_[-1] for _ in moda_]
distances = pairwise_array_distances(aconv, amoda)
for i in range(distances.shape[0]):
j = numpy.argmin(distances[i])
d = distances[i, j]
if d < 0.1:
numpy.copyto(aconv[i], amoda[j])
done.append(("pass2", d) + done_[i][0])
return done
def _set_shape_inference_runtime(self):
"""
Set shapes based on shape inference
relying on the runtime.
The values are stored in every node.
"""
if not hasattr(self, 'sequence_') or not hasattr(self, 'inputs_'):
raise RuntimeError( # pragma: no cover
"This method only works if the runtime is 'python' not "
"'{}'.".format(self.runtime))
values = OrderedDict()
for k, v in self.inputs_.items():
# The function assumes the first dimension is unknown
# and is the batch size.
try:
values[k] = ShapeObject(v, use_n1=True, name=k)
except TypeError as e: # pragma: no cover
raise TypeError(
"Unable to guess shape for %r (shape=%r)." % (k, v)) from e
impossible = False
for k, v in self.statics_.items():
# static inputs should be known.
if k not in values:
try:
values[k] = ShapeObject(v)
except TypeError:
# default value is wrong
impossible = True
values[k] = None
for k, v in self.inits_.items():
values[k] = ShapeObject(v['value'], name=k)
last = None
for i, node in enumerate(self.sequence_):
try:
s = node._set_shape_inference_runtime(values)
last = s
except (IndexError, TypeError, KeyError,
AttributeError) as e: # pragma: no cover
rows = []
if last is not None:
for k, v in last.items():
rows.append("{}: {}".format(k, v))
for k in range(i + 1):
rows.append("{} --> {}".format(k, self.sequence_[k]))
if not impossible:
raise RuntimeError("Unable to infer shape of node {}\n{}".format(
i, '\n'.join(rows))) from e
return values
def infer_shapes(self):
"""
Computes expected shapes.
:return: dictionary of shapes
"""
return self._set_shape_inference_runtime()
def _set_type_inference_runtime(self):
"""
Set types based on type inference
relying on the runtime.
The values are stored in every node.
"""
if not hasattr(self, 'sequence_') or not hasattr(self, 'inputs_'):
raise RuntimeError( # pragma: no cover
"This method only works if the runtime is 'python' not "
"'{}'.".format(self.runtime))
values = OrderedDict()
for k, v in self.statics_.items():
values[k] = None
for k, v in self.inputs_.items():
# The function assumes the first dimension is unknown
# and is the batch size.
if isinstance(v['type']['elem'], dict):
# sequence
values[k] = SequenceType()
else:
values[k] = guess_numpy_type_from_string(v['type']['elem'])
for k, v in self.inits_.items():
values[k] = v['value'].dtype
last = None
for i, node in enumerate(self.sequence_):
try:
s = node._set_type_inference_runtime(values)
last = s
except IndexError as e: # pragma: no cover
rows = []
if last is not None:
for k, v in last.items():
rows.append("{}: {}".format(k, v))
for k in range(i + 1):
rows.append("{} --> {}".format(k, self.sequence_[k]))
raise RuntimeError("Unable to infer type of node {}\n{}".format(
i, '\n'.join(rows))) from e
return values
def infer_types(self):
"""
Computes expected shapes.
:return: dictionary of types
"""
return self._set_type_inference_runtime()
def _set_size_inference_runtime(self, inputs, context=None):
"""
Set sizes allocated during inference
relying on the runtime.
The values are stored in every node.
"""
if not hasattr(self, 'sequence_') or not hasattr(self, 'inputs_'):
raise RuntimeError( # pragma: no cover
"This method only works if the runtime is 'python' not "
"'{}'.".format(self.runtime))
values = OrderedDict()
for k, v in self.statics_.items():
if context is None:
raise RuntimeError( # pragma: no cover
"static variable but context is None.")
values[k] = context[k]
for k, v in self.inits_.items():
values[k] = v['value']
for k, v in self.inputs_.items():
if k in inputs:
values[k] = inputs[k]
last = None
for i, node in enumerate(self.sequence_):
try:
s = node._set_size_inference_runtime(values)
last = s
except IndexError as e: # pragma: no cover
rows = []
if last is not None:
for k, v in last.items():
rows.append("{}: {}".format(k, v))
for k in range(i + 1):
rows.append("{} --> {}".format(k, self.sequence_[k]))
raise RuntimeError("Unable to infer size of node {}\n{}".format(
i, '\n'.join(rows))) from e
return values
def infer_sizes(self, inputs, context=None):
"""
Computes expected sizes.
:param inputs: inputs as a dictionary
:return: dictionary of dictionary of sizes
"""
res = self._set_size_inference_runtime(inputs, context=context)
return {k: v for k, v in res.items() if k.startswith('#')}
def _guess_inplace(self, input_inplace=False):
"""
Looks into every node of the graph to see
if there is a way to do the computation
inplace. By default (*input_inplace=False*),
the function assumes inputs cannot be modified
so the first node cannot do inplace computation.
This function only works with the python runtime.
@param input_inplace the computation is allowed
to overwrite the input
This function checks that one node is used only
once and then can be modified by the next node.
Nodes `A`, `C` can be overwritten by the computation.
Node `B` cannot as it is used by two nodes.
.. blockdiag::
diagram {
A -> B -> C -> E;
B -> D;
}
It does not handle specific case such node `B` being
overwritten by node `C` but without changing its shape
and node `D` only needs the shape of `B`. Then `B` could
be overwritten as well.
"""
forbid = {}
values = OrderedDict()
for k in self.statics_:
values[k] = dict(inplace=False, to=[], fr=[])
for k in self.inputs_:
values[k] = dict(inplace=input_inplace, to=[], fr=[])
for k in self.inits_:
values[k] = dict(inplace=False, to=[], fr=[])
for node in self.sequence_:
for n in node.inputs:
values[n]['to'].append(node)
for n in node.outputs:
if node.op_type == 'Constant':
# We cannot modify constant.
forbid[n] = node
if n not in values:
values[n] = dict(inplace=None, to=[], fr=[])
values[n]['fr'].append(node)
# checks the number of outputs
outputs = set(self.output_names)
modif = 1
while modif > 0:
modif = 0
for n, v in values.items():
if v['inplace'] is not None:
continue
if n in forbid:
continue
if len(v['to']) == 1:
v['inplace'] = True
modif += 1
# convey the information to every node
inplaces = {}
for n, v in values.items():
if v['inplace']:
inplaces[n] = v
for node in v['to']:
if n in outputs:
continue
node.enable_inplace_compute(n)
return inplaces
def _build_compile_run(self, debug=False):
"""
Rewrite the run function in python,
compiles it, and adds it as a method.
@param debug insert debugging code
@return method name, callable object
.. exref::
:title: Run a model with runtime 'python_compiled'
The following code trains a model and compute
the predictions with runtime ``'python_compiled'``.
It converts the onnx graph into a python function
which calls every operator. Its code is printed
below.
.. runpython::
:showcode:
:warningout: DeprecationWarning
import numpy
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from skl2onnx import to_onnx
from mlprodict.onnxrt import OnnxInference
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, __ = train_test_split(X, y, random_state=11)
y_train = y_train.astype(numpy.float32)
clr = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(max_depth=3),
n_estimators=3)
clr.fit(X_train, y_train)
model_def = to_onnx(clr, X_train.astype(numpy.float32),
target_opset=12)
oinf2 = OnnxInference(model_def, runtime='python_compiled')
print(oinf2.run({'X': X_test[:5]}))
# prints out the python function equivalent
# to the onnx graph
print(oinf2)
"""
def clean_name(name):
return name.replace(":", "_").replace('.', '_').replace('/', '_')
# inits
inputs = self.input_names
code = ['def compiled_run(dict_inputs, yield_ops=None):']
code.append(" if yield_ops is not None:")
code.append(
" raise NotImplementedError('yields_ops should be None.')")
if debug:
code.append(" printed = {}")
context = {}
# static variables
for k in sorted(self.statics_):
code.append(" # static: {0}".format(k))
code.append(" {0} = dict_inputs['{1}']".format(
clean_name(k), k))
if debug:
code.append(
" debug_print('i.{0}', {1}, printed)".format(
clean_name(k), k))
# initializers
for k, v in sorted(self.inits_.items()):
if k.startswith("_OPT_"):
raise RuntimeError( # pragma: no cover
"The runtime cannot handle any constant name "
"starting with '_OPT_': '{}'.".format(k))
if k in inputs:
context["_OPT_" + clean_name(k)] = v['value']
code.append(" # init: _OPT_{0} ({1})".format(
clean_name(k), k))
if debug:
code.append(
" debug_print('c.[_OPT_{0}]', _OPT_{1}, printed)".format(
clean_name(k), k))
else:
context[clean_name(k)] = v['value']
code.append(" # init: {0} ({1})".format(
clean_name(k), k))
if debug:
code.append(
" debug_print('c.[{0}]', {1}, printed)".format(
clean_name(k), k))
# method signature
code.append(" # inputs")
for inp in inputs:
if '_OPT_' + inp in context:
# optional inputs
code.append(
" {0} = dict_inputs.get('{1}', _OPT_{0})".format(
clean_name(inp), inp))
else:
code.append(" {0} = dict_inputs['{1}']".format(
clean_name(inp), inp))
if debug:
code.append(
" debug_print('i.{0}', {1}, printed)".format(
clean_name(inp), inp))
# code
for i, node in enumerate(self.sequence_):
name = "n{}_{}".format(i, node.ops_.__class__.__name__.lower())
context[name] = node.ops_._run
if (node.ops_.__class__.__name__ == 'Loop' and
node.ops_.need_context()):
# Adding context.
ctx = "{%s}" % ", ".join(
"'%s': %s" % (n, n) for n in node.ops_.additional_inputs)
code.append(' ({1}, ) = {2}({0}, context={3})'.format(
', '.join(map(clean_name, node.inputs)),
', '.join(map(clean_name, node.outputs)),
name, ctx))
else:
code.append(' ({1}, ) = {2}({0})'.format(
', '.join(map(clean_name, node.inputs)),
', '.join(map(clean_name, node.outputs)),
name))
if debug:
code.append(" print('''# {}''')".format(code[-1][4:]))
for o in node.outputs:
code.append(
" debug_print('o.{0}', {1}, printed)".format(
clean_name(o), o))
# return
code.append(' return {')
for out in self.output_names:
code.append(" '{1}': {0},".format(
clean_name(out), out))
code.append(' }')
final_code = '\n'.join(code)
# compile the outcome
context['self'] = self
try:
obj = compile(final_code, "<string>", 'exec')
except SyntaxError as e: # pragma: no cover
raise SyntaxError(
"Unable to compile\n#####\n{}".format(final_code)) from e
fcts_obj = [_ for _ in obj.co_consts
if _ is not None and not isinstance(_, (bool, str, int))]
fct = make_callable(
"compiled_run", fcts_obj[0], final_code, context, debug)
# end
return "compiled_run", fct, final_code
def reduce_size(self, pickable=False):
"""
Reduces the memory footprint as much as possible.
@param pickable keeps a pickle object?
"""
import gc
del self.graph_
if not pickable:
del self.obj
if self.runtime in ('python_compiled', 'python_compiled_debug'):
del self.sequence_
gc.collect()
def get_profiling(self, as_df=False):
"""
Returns the profiling after a couple of execution.
:param as_df: return the results as a dataframe (True)
:return: dataframe or list of dictionaries
.. versionadded:: 0.6
"""
if (self.runtime_options is None or
not self.runtime_options.get('enable_profiling', False)):
raise RuntimeError(
"Profiling is available if options 'enable_profiling' "
"is set to true in 'runtime_options' but is %r." % self.runtime_options)
prof = None
if hasattr(self, '_whole'):
prof = self._whole.get_profiling()
if prof is None:
raise NotImplementedError( # pragma: no cover
"profiling is only implemented for runtime 'onnxruntime1'.")
if as_df:
import pandas
return pandas.DataFrame(prof)
return prof
def get_execution_order(self):
"""
This function returns a dictionary `{(kind, name): (order, op)}`,
*name* can be a node name or a result name. In that case,
it gets the execution order than the node which created it.
The function returns None if the order is not available
(the selected runtime does not return it). *kind* is either
`'node'` or `'node'`. If two nodes have the same name,
returned order is the last one. Initializers gets an execution
order equal to -1, inputs to 0, all others results are >= 1.
.. versionadded:: 0.7
"""
if not hasattr(self, "sequence_"):
return None
res = {}
for k, v in self.inits_.items():
res['res', k] = (-1, v)
for name, shape in self.input_names_shapes:
res['res', name] = (0, shape)
for i, node in enumerate(self.sequence_):
key = ('node', node.onnx_node.name)
res[key] = (i + 1, node)
for out in node.onnx_node.output:
key = ('res', out)
if key in res:
raise RuntimeError( # pragma: no cover
"Output %r of node name %r already registered."
"" % (out, node.onnx_node.name))
res[key] = (i + 1, None)
return res
|
the-stack_0_883 | from typing import Iterable
import re
from dbt.clients.jinja import get_rendered
from dbt.contracts.graph.parsed import ParsedDocumentation
from dbt.node_types import NodeType
from dbt.parser.base import Parser
from dbt.parser.search import (
BlockContents, FileBlock, BlockSearcher
)
SHOULD_PARSE_RE = re.compile(r'{[{%]')
class DocumentationParser(Parser[ParsedDocumentation]):
@property
def resource_type(self) -> NodeType:
return NodeType.Documentation
@classmethod
def get_compiled_path(cls, block: FileBlock):
return block.path.relative_path
def generate_unique_id(self, resource_name: str) -> str:
# because docs are in their own graph namespace, node type doesn't
# need to be part of the unique ID.
return '{}.{}'.format(self.project.project_name, resource_name)
def parse_block(
self, block: BlockContents
) -> Iterable[ParsedDocumentation]:
unique_id = self.generate_unique_id(block.name)
contents = get_rendered(block.contents, {}).strip()
doc = ParsedDocumentation(
root_path=self.project.project_root,
path=block.file.path.relative_path,
original_file_path=block.path.original_file_path,
package_name=self.project.project_name,
unique_id=unique_id,
name=block.name,
block_contents=contents,
)
return [doc]
def parse_file(self, file_block: FileBlock):
searcher: Iterable[BlockContents] = BlockSearcher(
source=[file_block],
allowed_blocks={'docs'},
source_tag_factory=BlockContents,
)
for block in searcher:
for parsed in self.parse_block(block):
self.manifest.add_doc(file_block.file, parsed)
|
the-stack_0_887 | #!/usr/bin/env python3
# Copyright (c) 2020-2021 The Eleccoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Functionality to build scripts, as well as signature hash functions.
This file is modified from python-eleccoinlib.
"""
from collections import namedtuple
import hashlib
import struct
import unittest
from typing import List, Dict
from .key import TaggedHash, tweak_add_pubkey
from .messages import (
CTransaction,
CTxOut,
hash256,
ser_string,
ser_uint256,
sha256,
uint256_from_str,
)
MAX_SCRIPT_ELEMENT_SIZE = 520
LOCKTIME_THRESHOLD = 500000000
ANNEX_TAG = 0x50
OPCODE_NAMES = {} # type: Dict[CScriptOp, str]
LEAF_VERSION_TAPSCRIPT = 0xc0
def hash160(s):
return hashlib.new('ripemd160', sha256(s)).digest()
def bn2vch(v):
"""Convert number to eleccoin-specific little endian format."""
# We need v.bit_length() bits, plus a sign bit for every nonzero number.
n_bits = v.bit_length() + (v != 0)
# The number of bytes for that is:
n_bytes = (n_bits + 7) // 8
# Convert number to absolute value + sign in top bit.
encoded_v = 0 if v == 0 else abs(v) | ((v < 0) << (n_bytes * 8 - 1))
# Serialize to bytes
return encoded_v.to_bytes(n_bytes, 'little')
_opcode_instances = [] # type: List[CScriptOp]
class CScriptOp(int):
"""A single script opcode"""
__slots__ = ()
@staticmethod
def encode_op_pushdata(d):
"""Encode a PUSHDATA op, returning bytes"""
if len(d) < 0x4c:
return b'' + bytes([len(d)]) + d # OP_PUSHDATA
elif len(d) <= 0xff:
return b'\x4c' + bytes([len(d)]) + d # OP_PUSHDATA1
elif len(d) <= 0xffff:
return b'\x4d' + struct.pack(b'<H', len(d)) + d # OP_PUSHDATA2
elif len(d) <= 0xffffffff:
return b'\x4e' + struct.pack(b'<I', len(d)) + d # OP_PUSHDATA4
else:
raise ValueError("Data too long to encode in a PUSHDATA op")
@staticmethod
def encode_op_n(n):
"""Encode a small integer op, returning an opcode"""
if not (0 <= n <= 16):
raise ValueError('Integer must be in range 0 <= n <= 16, got %d' % n)
if n == 0:
return OP_0
else:
return CScriptOp(OP_1 + n - 1)
def decode_op_n(self):
"""Decode a small integer opcode, returning an integer"""
if self == OP_0:
return 0
if not (self == OP_0 or OP_1 <= self <= OP_16):
raise ValueError('op %r is not an OP_N' % self)
return int(self - OP_1 + 1)
def is_small_int(self):
"""Return true if the op pushes a small integer to the stack"""
if 0x51 <= self <= 0x60 or self == 0:
return True
else:
return False
def __str__(self):
return repr(self)
def __repr__(self):
if self in OPCODE_NAMES:
return OPCODE_NAMES[self]
else:
return 'CScriptOp(0x%x)' % self
def __new__(cls, n):
try:
return _opcode_instances[n]
except IndexError:
assert len(_opcode_instances) == n
_opcode_instances.append(super().__new__(cls, n))
return _opcode_instances[n]
# Populate opcode instance table
for n in range(0xff + 1):
CScriptOp(n)
# push value
OP_0 = CScriptOp(0x00)
OP_FALSE = OP_0
OP_PUSHDATA1 = CScriptOp(0x4c)
OP_PUSHDATA2 = CScriptOp(0x4d)
OP_PUSHDATA4 = CScriptOp(0x4e)
OP_1NEGATE = CScriptOp(0x4f)
OP_RESERVED = CScriptOp(0x50)
OP_1 = CScriptOp(0x51)
OP_TRUE = OP_1
OP_2 = CScriptOp(0x52)
OP_3 = CScriptOp(0x53)
OP_4 = CScriptOp(0x54)
OP_5 = CScriptOp(0x55)
OP_6 = CScriptOp(0x56)
OP_7 = CScriptOp(0x57)
OP_8 = CScriptOp(0x58)
OP_9 = CScriptOp(0x59)
OP_10 = CScriptOp(0x5a)
OP_11 = CScriptOp(0x5b)
OP_12 = CScriptOp(0x5c)
OP_13 = CScriptOp(0x5d)
OP_14 = CScriptOp(0x5e)
OP_15 = CScriptOp(0x5f)
OP_16 = CScriptOp(0x60)
# control
OP_NOP = CScriptOp(0x61)
OP_VER = CScriptOp(0x62)
OP_IF = CScriptOp(0x63)
OP_NOTIF = CScriptOp(0x64)
OP_VERIF = CScriptOp(0x65)
OP_VERNOTIF = CScriptOp(0x66)
OP_ELSE = CScriptOp(0x67)
OP_ENDIF = CScriptOp(0x68)
OP_VERIFY = CScriptOp(0x69)
OP_RETURN = CScriptOp(0x6a)
# stack ops
OP_TOALTSTACK = CScriptOp(0x6b)
OP_FROMALTSTACK = CScriptOp(0x6c)
OP_2DROP = CScriptOp(0x6d)
OP_2DUP = CScriptOp(0x6e)
OP_3DUP = CScriptOp(0x6f)
OP_2OVER = CScriptOp(0x70)
OP_2ROT = CScriptOp(0x71)
OP_2SWAP = CScriptOp(0x72)
OP_IFDUP = CScriptOp(0x73)
OP_DEPTH = CScriptOp(0x74)
OP_DROP = CScriptOp(0x75)
OP_DUP = CScriptOp(0x76)
OP_NIP = CScriptOp(0x77)
OP_OVER = CScriptOp(0x78)
OP_PICK = CScriptOp(0x79)
OP_ROLL = CScriptOp(0x7a)
OP_ROT = CScriptOp(0x7b)
OP_SWAP = CScriptOp(0x7c)
OP_TUCK = CScriptOp(0x7d)
# splice ops
OP_CAT = CScriptOp(0x7e)
OP_SUBSTR = CScriptOp(0x7f)
OP_LEFT = CScriptOp(0x80)
OP_RIGHT = CScriptOp(0x81)
OP_SIZE = CScriptOp(0x82)
# bit logic
OP_INVERT = CScriptOp(0x83)
OP_AND = CScriptOp(0x84)
OP_OR = CScriptOp(0x85)
OP_XOR = CScriptOp(0x86)
OP_EQUAL = CScriptOp(0x87)
OP_EQUALVERIFY = CScriptOp(0x88)
OP_RESERVED1 = CScriptOp(0x89)
OP_RESERVED2 = CScriptOp(0x8a)
# numeric
OP_1ADD = CScriptOp(0x8b)
OP_1SUB = CScriptOp(0x8c)
OP_2MUL = CScriptOp(0x8d)
OP_2DIV = CScriptOp(0x8e)
OP_NEGATE = CScriptOp(0x8f)
OP_ABS = CScriptOp(0x90)
OP_NOT = CScriptOp(0x91)
OP_0NOTEQUAL = CScriptOp(0x92)
OP_ADD = CScriptOp(0x93)
OP_SUB = CScriptOp(0x94)
OP_MUL = CScriptOp(0x95)
OP_DIV = CScriptOp(0x96)
OP_MOD = CScriptOp(0x97)
OP_LSHIFT = CScriptOp(0x98)
OP_RSHIFT = CScriptOp(0x99)
OP_BOOLAND = CScriptOp(0x9a)
OP_BOOLOR = CScriptOp(0x9b)
OP_NUMEQUAL = CScriptOp(0x9c)
OP_NUMEQUALVERIFY = CScriptOp(0x9d)
OP_NUMNOTEQUAL = CScriptOp(0x9e)
OP_LESSTHAN = CScriptOp(0x9f)
OP_GREATERTHAN = CScriptOp(0xa0)
OP_LESSTHANOREQUAL = CScriptOp(0xa1)
OP_GREATERTHANOREQUAL = CScriptOp(0xa2)
OP_MIN = CScriptOp(0xa3)
OP_MAX = CScriptOp(0xa4)
OP_WITHIN = CScriptOp(0xa5)
# crypto
OP_RIPEMD160 = CScriptOp(0xa6)
OP_SHA1 = CScriptOp(0xa7)
OP_SHA256 = CScriptOp(0xa8)
OP_HASH160 = CScriptOp(0xa9)
OP_HASH256 = CScriptOp(0xaa)
OP_CODESEPARATOR = CScriptOp(0xab)
OP_CHECKSIG = CScriptOp(0xac)
OP_CHECKSIGVERIFY = CScriptOp(0xad)
OP_CHECKMULTISIG = CScriptOp(0xae)
OP_CHECKMULTISIGVERIFY = CScriptOp(0xaf)
# expansion
OP_NOP1 = CScriptOp(0xb0)
OP_CHECKLOCKTIMEVERIFY = CScriptOp(0xb1)
OP_CHECKSEQUENCEVERIFY = CScriptOp(0xb2)
OP_NOP4 = CScriptOp(0xb3)
OP_NOP5 = CScriptOp(0xb4)
OP_NOP6 = CScriptOp(0xb5)
OP_NOP7 = CScriptOp(0xb6)
OP_NOP8 = CScriptOp(0xb7)
OP_NOP9 = CScriptOp(0xb8)
OP_NOP10 = CScriptOp(0xb9)
# BIP 342 opcodes (Tapscript)
OP_CHECKSIGADD = CScriptOp(0xba)
OP_INVALIDOPCODE = CScriptOp(0xff)
OPCODE_NAMES.update({
OP_0: 'OP_0',
OP_PUSHDATA1: 'OP_PUSHDATA1',
OP_PUSHDATA2: 'OP_PUSHDATA2',
OP_PUSHDATA4: 'OP_PUSHDATA4',
OP_1NEGATE: 'OP_1NEGATE',
OP_RESERVED: 'OP_RESERVED',
OP_1: 'OP_1',
OP_2: 'OP_2',
OP_3: 'OP_3',
OP_4: 'OP_4',
OP_5: 'OP_5',
OP_6: 'OP_6',
OP_7: 'OP_7',
OP_8: 'OP_8',
OP_9: 'OP_9',
OP_10: 'OP_10',
OP_11: 'OP_11',
OP_12: 'OP_12',
OP_13: 'OP_13',
OP_14: 'OP_14',
OP_15: 'OP_15',
OP_16: 'OP_16',
OP_NOP: 'OP_NOP',
OP_VER: 'OP_VER',
OP_IF: 'OP_IF',
OP_NOTIF: 'OP_NOTIF',
OP_VERIF: 'OP_VERIF',
OP_VERNOTIF: 'OP_VERNOTIF',
OP_ELSE: 'OP_ELSE',
OP_ENDIF: 'OP_ENDIF',
OP_VERIFY: 'OP_VERIFY',
OP_RETURN: 'OP_RETURN',
OP_TOALTSTACK: 'OP_TOALTSTACK',
OP_FROMALTSTACK: 'OP_FROMALTSTACK',
OP_2DROP: 'OP_2DROP',
OP_2DUP: 'OP_2DUP',
OP_3DUP: 'OP_3DUP',
OP_2OVER: 'OP_2OVER',
OP_2ROT: 'OP_2ROT',
OP_2SWAP: 'OP_2SWAP',
OP_IFDUP: 'OP_IFDUP',
OP_DEPTH: 'OP_DEPTH',
OP_DROP: 'OP_DROP',
OP_DUP: 'OP_DUP',
OP_NIP: 'OP_NIP',
OP_OVER: 'OP_OVER',
OP_PICK: 'OP_PICK',
OP_ROLL: 'OP_ROLL',
OP_ROT: 'OP_ROT',
OP_SWAP: 'OP_SWAP',
OP_TUCK: 'OP_TUCK',
OP_CAT: 'OP_CAT',
OP_SUBSTR: 'OP_SUBSTR',
OP_LEFT: 'OP_LEFT',
OP_RIGHT: 'OP_RIGHT',
OP_SIZE: 'OP_SIZE',
OP_INVERT: 'OP_INVERT',
OP_AND: 'OP_AND',
OP_OR: 'OP_OR',
OP_XOR: 'OP_XOR',
OP_EQUAL: 'OP_EQUAL',
OP_EQUALVERIFY: 'OP_EQUALVERIFY',
OP_RESERVED1: 'OP_RESERVED1',
OP_RESERVED2: 'OP_RESERVED2',
OP_1ADD: 'OP_1ADD',
OP_1SUB: 'OP_1SUB',
OP_2MUL: 'OP_2MUL',
OP_2DIV: 'OP_2DIV',
OP_NEGATE: 'OP_NEGATE',
OP_ABS: 'OP_ABS',
OP_NOT: 'OP_NOT',
OP_0NOTEQUAL: 'OP_0NOTEQUAL',
OP_ADD: 'OP_ADD',
OP_SUB: 'OP_SUB',
OP_MUL: 'OP_MUL',
OP_DIV: 'OP_DIV',
OP_MOD: 'OP_MOD',
OP_LSHIFT: 'OP_LSHIFT',
OP_RSHIFT: 'OP_RSHIFT',
OP_BOOLAND: 'OP_BOOLAND',
OP_BOOLOR: 'OP_BOOLOR',
OP_NUMEQUAL: 'OP_NUMEQUAL',
OP_NUMEQUALVERIFY: 'OP_NUMEQUALVERIFY',
OP_NUMNOTEQUAL: 'OP_NUMNOTEQUAL',
OP_LESSTHAN: 'OP_LESSTHAN',
OP_GREATERTHAN: 'OP_GREATERTHAN',
OP_LESSTHANOREQUAL: 'OP_LESSTHANOREQUAL',
OP_GREATERTHANOREQUAL: 'OP_GREATERTHANOREQUAL',
OP_MIN: 'OP_MIN',
OP_MAX: 'OP_MAX',
OP_WITHIN: 'OP_WITHIN',
OP_RIPEMD160: 'OP_RIPEMD160',
OP_SHA1: 'OP_SHA1',
OP_SHA256: 'OP_SHA256',
OP_HASH160: 'OP_HASH160',
OP_HASH256: 'OP_HASH256',
OP_CODESEPARATOR: 'OP_CODESEPARATOR',
OP_CHECKSIG: 'OP_CHECKSIG',
OP_CHECKSIGVERIFY: 'OP_CHECKSIGVERIFY',
OP_CHECKMULTISIG: 'OP_CHECKMULTISIG',
OP_CHECKMULTISIGVERIFY: 'OP_CHECKMULTISIGVERIFY',
OP_NOP1: 'OP_NOP1',
OP_CHECKLOCKTIMEVERIFY: 'OP_CHECKLOCKTIMEVERIFY',
OP_CHECKSEQUENCEVERIFY: 'OP_CHECKSEQUENCEVERIFY',
OP_NOP4: 'OP_NOP4',
OP_NOP5: 'OP_NOP5',
OP_NOP6: 'OP_NOP6',
OP_NOP7: 'OP_NOP7',
OP_NOP8: 'OP_NOP8',
OP_NOP9: 'OP_NOP9',
OP_NOP10: 'OP_NOP10',
OP_CHECKSIGADD: 'OP_CHECKSIGADD',
OP_INVALIDOPCODE: 'OP_INVALIDOPCODE',
})
class CScriptInvalidError(Exception):
"""Base class for CScript exceptions"""
pass
class CScriptTruncatedPushDataError(CScriptInvalidError):
"""Invalid pushdata due to truncation"""
def __init__(self, msg, data):
self.data = data
super().__init__(msg)
# This is used, eg, for blockchain heights in coinbase scripts (bip34)
class CScriptNum:
__slots__ = ("value",)
def __init__(self, d=0):
self.value = d
@staticmethod
def encode(obj):
r = bytearray(0)
if obj.value == 0:
return bytes(r)
neg = obj.value < 0
absvalue = -obj.value if neg else obj.value
while (absvalue):
r.append(absvalue & 0xff)
absvalue >>= 8
if r[-1] & 0x80:
r.append(0x80 if neg else 0)
elif neg:
r[-1] |= 0x80
return bytes([len(r)]) + r
@staticmethod
def decode(vch):
result = 0
# We assume valid push_size and minimal encoding
value = vch[1:]
if len(value) == 0:
return result
for i, byte in enumerate(value):
result |= int(byte) << 8 * i
if value[-1] >= 0x80:
# Mask for all but the highest result bit
num_mask = (2**(len(value) * 8) - 1) >> 1
result &= num_mask
result *= -1
return result
class CScript(bytes):
"""Serialized script
A bytes subclass, so you can use this directly whenever bytes are accepted.
Note that this means that indexing does *not* work - you'll get an index by
byte rather than opcode. This format was chosen for efficiency so that the
general case would not require creating a lot of little CScriptOP objects.
iter(script) however does iterate by opcode.
"""
__slots__ = ()
@classmethod
def __coerce_instance(cls, other):
# Coerce other into bytes
if isinstance(other, CScriptOp):
other = bytes([other])
elif isinstance(other, CScriptNum):
if (other.value == 0):
other = bytes([CScriptOp(OP_0)])
else:
other = CScriptNum.encode(other)
elif isinstance(other, int):
if 0 <= other <= 16:
other = bytes([CScriptOp.encode_op_n(other)])
elif other == -1:
other = bytes([OP_1NEGATE])
else:
other = CScriptOp.encode_op_pushdata(bn2vch(other))
elif isinstance(other, (bytes, bytearray)):
other = CScriptOp.encode_op_pushdata(other)
return other
def __add__(self, other):
# add makes no sense for a CScript()
raise NotImplementedError
def join(self, iterable):
# join makes no sense for a CScript()
raise NotImplementedError
def __new__(cls, value=b''):
if isinstance(value, bytes) or isinstance(value, bytearray):
return super().__new__(cls, value)
else:
def coerce_iterable(iterable):
for instance in iterable:
yield cls.__coerce_instance(instance)
# Annoyingly on both python2 and python3 bytes.join() always
# returns a bytes instance even when subclassed.
return super().__new__(cls, b''.join(coerce_iterable(value)))
def raw_iter(self):
"""Raw iteration
Yields tuples of (opcode, data, sop_idx) so that the different possible
PUSHDATA encodings can be accurately distinguished, as well as
determining the exact opcode byte indexes. (sop_idx)
"""
i = 0
while i < len(self):
sop_idx = i
opcode = self[i]
i += 1
if opcode > OP_PUSHDATA4:
yield (opcode, None, sop_idx)
else:
datasize = None
pushdata_type = None
if opcode < OP_PUSHDATA1:
pushdata_type = 'PUSHDATA(%d)' % opcode
datasize = opcode
elif opcode == OP_PUSHDATA1:
pushdata_type = 'PUSHDATA1'
if i >= len(self):
raise CScriptInvalidError('PUSHDATA1: missing data length')
datasize = self[i]
i += 1
elif opcode == OP_PUSHDATA2:
pushdata_type = 'PUSHDATA2'
if i + 1 >= len(self):
raise CScriptInvalidError('PUSHDATA2: missing data length')
datasize = self[i] + (self[i + 1] << 8)
i += 2
elif opcode == OP_PUSHDATA4:
pushdata_type = 'PUSHDATA4'
if i + 3 >= len(self):
raise CScriptInvalidError('PUSHDATA4: missing data length')
datasize = self[i] + (self[i + 1] << 8) + (self[i + 2] << 16) + (self[i + 3] << 24)
i += 4
else:
assert False # shouldn't happen
data = bytes(self[i:i + datasize])
# Check for truncation
if len(data) < datasize:
raise CScriptTruncatedPushDataError('%s: truncated data' % pushdata_type, data)
i += datasize
yield (opcode, data, sop_idx)
def __iter__(self):
"""'Cooked' iteration
Returns either a CScriptOP instance, an integer, or bytes, as
appropriate.
See raw_iter() if you need to distinguish the different possible
PUSHDATA encodings.
"""
for (opcode, data, sop_idx) in self.raw_iter():
if data is not None:
yield data
else:
opcode = CScriptOp(opcode)
if opcode.is_small_int():
yield opcode.decode_op_n()
else:
yield CScriptOp(opcode)
def __repr__(self):
def _repr(o):
if isinstance(o, bytes):
return "x('%s')" % o.hex()
else:
return repr(o)
ops = []
i = iter(self)
while True:
op = None
try:
op = _repr(next(i))
except CScriptTruncatedPushDataError as err:
op = '%s...<ERROR: %s>' % (_repr(err.data), err)
break
except CScriptInvalidError as err:
op = '<ERROR: %s>' % err
break
except StopIteration:
break
finally:
if op is not None:
ops.append(op)
return "CScript([%s])" % ', '.join(ops)
def GetSigOpCount(self, fAccurate):
"""Get the SigOp count.
fAccurate - Accurately count CHECKMULTISIG, see BIP16 for details.
Note that this is consensus-critical.
"""
n = 0
lastOpcode = OP_INVALIDOPCODE
for (opcode, data, sop_idx) in self.raw_iter():
if opcode in (OP_CHECKSIG, OP_CHECKSIGVERIFY):
n += 1
elif opcode in (OP_CHECKMULTISIG, OP_CHECKMULTISIGVERIFY):
if fAccurate and (OP_1 <= lastOpcode <= OP_16):
n += opcode.decode_op_n()
else:
n += 20
lastOpcode = opcode
return n
SIGHASH_DEFAULT = 0 # Taproot-only default, semantics same as SIGHASH_ALL
SIGHASH_ALL = 1
SIGHASH_NONE = 2
SIGHASH_SINGLE = 3
SIGHASH_ANYONECANPAY = 0x80
def FindAndDelete(script, sig):
"""Consensus critical, see FindAndDelete() in Electron codebase"""
r = b''
last_sop_idx = sop_idx = 0
skip = True
for (opcode, data, sop_idx) in script.raw_iter():
if not skip:
r += script[last_sop_idx:sop_idx]
last_sop_idx = sop_idx
if script[sop_idx:sop_idx + len(sig)] == sig:
skip = True
else:
skip = False
if not skip:
r += script[last_sop_idx:]
return CScript(r)
def LegacySignatureHash(script, txTo, inIdx, hashtype):
"""Consensus-correct SignatureHash
Returns (hash, err) to precisely match the consensus-critical behavior of
the SIGHASH_SINGLE bug. (inIdx is *not* checked for validity)
"""
HASH_ONE = b'\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
if inIdx >= len(txTo.vin):
return (HASH_ONE, "inIdx %d out of range (%d)" % (inIdx, len(txTo.vin)))
txtmp = CTransaction(txTo)
for txin in txtmp.vin:
txin.scriptSig = b''
txtmp.vin[inIdx].scriptSig = FindAndDelete(script, CScript([OP_CODESEPARATOR]))
if (hashtype & 0x1f) == SIGHASH_NONE:
txtmp.vout = []
for i in range(len(txtmp.vin)):
if i != inIdx:
txtmp.vin[i].nSequence = 0
elif (hashtype & 0x1f) == SIGHASH_SINGLE:
outIdx = inIdx
if outIdx >= len(txtmp.vout):
return (HASH_ONE, "outIdx %d out of range (%d)" % (outIdx, len(txtmp.vout)))
tmp = txtmp.vout[outIdx]
txtmp.vout = []
for _ in range(outIdx):
txtmp.vout.append(CTxOut(-1))
txtmp.vout.append(tmp)
for i in range(len(txtmp.vin)):
if i != inIdx:
txtmp.vin[i].nSequence = 0
if hashtype & SIGHASH_ANYONECANPAY:
tmp = txtmp.vin[inIdx]
txtmp.vin = []
txtmp.vin.append(tmp)
s = txtmp.serialize_without_witness()
s += struct.pack(b"<I", hashtype)
hash = hash256(s)
return (hash, None)
# TODO: Allow cached hashPrevouts/hashSequence/hashOutputs to be provided.
# Performance optimization probably not necessary for python tests, however.
# Note that this corresponds to sigversion == 1 in EvalScript, which is used
# for version 0 witnesses.
def SegwitV0SignatureHash(script, txTo, inIdx, hashtype, amount):
hashPrevouts = 0
hashSequence = 0
hashOutputs = 0
if not (hashtype & SIGHASH_ANYONECANPAY):
serialize_prevouts = bytes()
for i in txTo.vin:
serialize_prevouts += i.prevout.serialize()
hashPrevouts = uint256_from_str(hash256(serialize_prevouts))
if (not (hashtype & SIGHASH_ANYONECANPAY) and (hashtype & 0x1f) != SIGHASH_SINGLE and (hashtype & 0x1f) != SIGHASH_NONE):
serialize_sequence = bytes()
for i in txTo.vin:
serialize_sequence += struct.pack("<I", i.nSequence)
hashSequence = uint256_from_str(hash256(serialize_sequence))
if ((hashtype & 0x1f) != SIGHASH_SINGLE and (hashtype & 0x1f) != SIGHASH_NONE):
serialize_outputs = bytes()
for o in txTo.vout:
serialize_outputs += o.serialize()
hashOutputs = uint256_from_str(hash256(serialize_outputs))
elif ((hashtype & 0x1f) == SIGHASH_SINGLE and inIdx < len(txTo.vout)):
serialize_outputs = txTo.vout[inIdx].serialize()
hashOutputs = uint256_from_str(hash256(serialize_outputs))
ss = bytes()
ss += struct.pack("<i", txTo.nVersion)
ss += ser_uint256(hashPrevouts)
ss += ser_uint256(hashSequence)
ss += txTo.vin[inIdx].prevout.serialize()
ss += ser_string(script)
ss += struct.pack("<q", amount)
ss += struct.pack("<I", txTo.vin[inIdx].nSequence)
ss += ser_uint256(hashOutputs)
ss += struct.pack("<i", txTo.nLockTime)
ss += struct.pack("<I", hashtype)
return hash256(ss)
class TestFrameworkScript(unittest.TestCase):
def test_bn2vch(self):
self.assertEqual(bn2vch(0), bytes([]))
self.assertEqual(bn2vch(1), bytes([0x01]))
self.assertEqual(bn2vch(-1), bytes([0x81]))
self.assertEqual(bn2vch(0x7F), bytes([0x7F]))
self.assertEqual(bn2vch(-0x7F), bytes([0xFF]))
self.assertEqual(bn2vch(0x80), bytes([0x80, 0x00]))
self.assertEqual(bn2vch(-0x80), bytes([0x80, 0x80]))
self.assertEqual(bn2vch(0xFF), bytes([0xFF, 0x00]))
self.assertEqual(bn2vch(-0xFF), bytes([0xFF, 0x80]))
self.assertEqual(bn2vch(0x100), bytes([0x00, 0x01]))
self.assertEqual(bn2vch(-0x100), bytes([0x00, 0x81]))
self.assertEqual(bn2vch(0x7FFF), bytes([0xFF, 0x7F]))
self.assertEqual(bn2vch(-0x8000), bytes([0x00, 0x80, 0x80]))
self.assertEqual(bn2vch(-0x7FFFFF), bytes([0xFF, 0xFF, 0xFF]))
self.assertEqual(bn2vch(0x80000000), bytes([0x00, 0x00, 0x00, 0x80, 0x00]))
self.assertEqual(bn2vch(-0x80000000), bytes([0x00, 0x00, 0x00, 0x80, 0x80]))
self.assertEqual(bn2vch(0xFFFFFFFF), bytes([0xFF, 0xFF, 0xFF, 0xFF, 0x00]))
self.assertEqual(bn2vch(123456789), bytes([0x15, 0xCD, 0x5B, 0x07]))
self.assertEqual(bn2vch(-54321), bytes([0x31, 0xD4, 0x80]))
def test_cscriptnum_encoding(self):
# round-trip negative and multi-byte CScriptNums
values = [0, 1, -1, -2, 127, 128, -255, 256, (1 << 15) - 1, -(1 << 16), (1 << 24) - 1, (1 << 31), 1 - (1 << 32), 1 << 40, 1500, -1500]
for value in values:
self.assertEqual(CScriptNum.decode(CScriptNum.encode(CScriptNum(value))), value)
def TaprootSignatureHash(txTo, spent_utxos, hash_type, input_index = 0, scriptpath = False, script = CScript(), codeseparator_pos = -1, annex = None, leaf_ver = LEAF_VERSION_TAPSCRIPT):
assert (len(txTo.vin) == len(spent_utxos))
assert (input_index < len(txTo.vin))
out_type = SIGHASH_ALL if hash_type == 0 else hash_type & 3
in_type = hash_type & SIGHASH_ANYONECANPAY
spk = spent_utxos[input_index].scriptPubKey
ss = bytes([0, hash_type]) # epoch, hash_type
ss += struct.pack("<i", txTo.nVersion)
ss += struct.pack("<I", txTo.nLockTime)
if in_type != SIGHASH_ANYONECANPAY:
ss += sha256(b"".join(i.prevout.serialize() for i in txTo.vin))
ss += sha256(b"".join(struct.pack("<q", u.nValue) for u in spent_utxos))
ss += sha256(b"".join(ser_string(u.scriptPubKey) for u in spent_utxos))
ss += sha256(b"".join(struct.pack("<I", i.nSequence) for i in txTo.vin))
if out_type == SIGHASH_ALL:
ss += sha256(b"".join(o.serialize() for o in txTo.vout))
spend_type = 0
if annex is not None:
spend_type |= 1
if (scriptpath):
spend_type |= 2
ss += bytes([spend_type])
if in_type == SIGHASH_ANYONECANPAY:
ss += txTo.vin[input_index].prevout.serialize()
ss += struct.pack("<q", spent_utxos[input_index].nValue)
ss += ser_string(spk)
ss += struct.pack("<I", txTo.vin[input_index].nSequence)
else:
ss += struct.pack("<I", input_index)
if (spend_type & 1):
ss += sha256(ser_string(annex))
if out_type == SIGHASH_SINGLE:
if input_index < len(txTo.vout):
ss += sha256(txTo.vout[input_index].serialize())
else:
ss += bytes(0 for _ in range(32))
if (scriptpath):
ss += TaggedHash("TapLeaf", bytes([leaf_ver]) + ser_string(script))
ss += bytes([0])
ss += struct.pack("<i", codeseparator_pos)
assert len(ss) == 175 - (in_type == SIGHASH_ANYONECANPAY) * 49 - (out_type != SIGHASH_ALL and out_type != SIGHASH_SINGLE) * 32 + (annex is not None) * 32 + scriptpath * 37
return TaggedHash("TapSighash", ss)
def taproot_tree_helper(scripts):
if len(scripts) == 0:
return ([], bytes(0 for _ in range(32)))
if len(scripts) == 1:
# One entry: treat as a leaf
script = scripts[0]
assert(not callable(script))
if isinstance(script, list):
return taproot_tree_helper(script)
assert(isinstance(script, tuple))
version = LEAF_VERSION_TAPSCRIPT
name = script[0]
code = script[1]
if len(script) == 3:
version = script[2]
assert version & 1 == 0
assert isinstance(code, bytes)
h = TaggedHash("TapLeaf", bytes([version]) + ser_string(code))
if name is None:
return ([], h)
return ([(name, version, code, bytes())], h)
elif len(scripts) == 2 and callable(scripts[1]):
# Two entries, and the right one is a function
left, left_h = taproot_tree_helper(scripts[0:1])
right_h = scripts[1](left_h)
left = [(name, version, script, control + right_h) for name, version, script, control in left]
right = []
else:
# Two or more entries: descend into each side
split_pos = len(scripts) // 2
left, left_h = taproot_tree_helper(scripts[0:split_pos])
right, right_h = taproot_tree_helper(scripts[split_pos:])
left = [(name, version, script, control + right_h) for name, version, script, control in left]
right = [(name, version, script, control + left_h) for name, version, script, control in right]
if right_h < left_h:
right_h, left_h = left_h, right_h
h = TaggedHash("TapBranch", left_h + right_h)
return (left + right, h)
TaprootInfo = namedtuple("TaprootInfo", "scriptPubKey,inner_pubkey,negflag,tweak,leaves")
TaprootLeafInfo = namedtuple("TaprootLeafInfo", "script,version,merklebranch")
def taproot_construct(pubkey, scripts=None):
"""Construct a tree of Taproot spending conditions
pubkey: an ECPubKey object for the internal pubkey
scripts: a list of items; each item is either:
- a (name, CScript) tuple
- a (name, CScript, leaf version) tuple
- another list of items (with the same structure)
- a function, which specifies how to compute the hashing partner
in function of the hash of whatever it is combined with
Returns: script (sPK or redeemScript), tweak, {name:(script, leaf version, negation flag, innerkey, merklepath), ...}
"""
if scripts is None:
scripts = []
ret, h = taproot_tree_helper(scripts)
tweak = TaggedHash("TapTweak", pubkey + h)
tweaked, negated = tweak_add_pubkey(pubkey, tweak)
leaves = dict((name, TaprootLeafInfo(script, version, merklebranch)) for name, version, script, merklebranch in ret)
return TaprootInfo(CScript([OP_1, tweaked]), pubkey, negated + 0, tweak, leaves)
def is_op_success(o):
return o == 0x50 or o == 0x62 or o == 0x89 or o == 0x8a or o == 0x8d or o == 0x8e or (o >= 0x7e and o <= 0x81) or (o >= 0x83 and o <= 0x86) or (o >= 0x95 and o <= 0x99) or (o >= 0xbb and o <= 0xfe)
|
the-stack_0_890 | from aiohttp import web
from utils.utils import *
def get_members(request, client):
try:
role_ids = request.query["ids"].split(",")
guild = client.get_guild(client.config["main_guild_id"])
roles = [get(guild.roles, id=int(role_id)) for role_id in role_ids]
members = [role.members for role in roles]
except KeyError:
return web.json_response({"error": "You did not provide the correct query param ids"})
except AttributeError:
return web.json_response({"error": "Invalid Guild ID or Role ID provided"})
member_data = [
{
"name": member.name,
"id": member.id,
"discriminator": member.discriminator,
} for member in [m for m in members][0]
]
return web.json_response({"member_data": member_data}) |
the-stack_0_891 |
import os
import cv2
cascPath = "./haarcascades/haarcascade_frontalface_alt.xml"
input_dir = './lfw'
output_dir = './other_faces'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# classifiers
faceCascade = cv2.CascadeClassifier(cascPath)
index = 1
for (path,dirnames,filenames) in os.walk(input_dir):
for filename in filenames:
if filename.endswith('.jpg'):
print('处理picture %s'%index)
image = cv2.imread(path + '/' + filename)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.3,
minNeighbors=5,
minSize=(30, 30)
)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
image = image[y:y+h,x:x+w]
image = cv2.resize(image,(64,64))
cv2.imshow('image',image)
cv2.imwrite(output_dir+'/'+str(index)+'.jpg',image)
index +=1
if cv2.waitKey(30) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
|
the-stack_0_892 | #######################
# Dennis MUD #
# remake_item.py #
# Copyright 2018-2020 #
# Michael D. Reiley #
#######################
# **********
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
# **********
NAME = "remake item"
CATEGORIES = ["items"]
USAGE = "remake item <item_id>"
DESCRIPTION = """Resets the item <item_id> in your inventory.
Name and ID are untouched, you must be the primary owner of the item.
Owners are reset to the primary owner only. Wizards can remake any item.
Ex. `remake item 3`"""
def COMMAND(console, args):
# Perform initial checks.
if not COMMON.check(NAME, console, args, argmin=1, argmax=1):
return False
# Perform argument type checks and casts.
itemid = COMMON.check_argtypes(NAME, console, args, checks=[[0, int]], retargs=0)
if itemid is None:
return False
# Lookup the target item and perform item checks.
thisitem = COMMON.check_item(NAME, console, itemid, owner=True, primary=True, holding=True)
if not thisitem:
return False
# remake the item.
if len(thisitem["container"]["inventory"])>0:
console.msg("{0} is not empty, please empty it before remaking.".format(thisitem["name"]))
return False
if thisitem["duplified"]:
console.msg("Please unduplify this item before remaking.")
return False
thisitem["desc"] = ""
thisitem["action"] = ""
thisitem["message"] = ""
thisitem["mlang"] = None
thisitem["lang"] = None
thisitem["owners"] = [console.user["name"]]
thisitem["glued"] = console.database.defaults["items"]["glued"]
thisitem["hidden"] = False
thisitem["truehide"] = False
thisitem["chance"] = 1
thisitem["container"]["enabled"] = False
thisitem["container"]["inventory"] = []
thisitem["telekey"] = None
console.database.upsert_item(thisitem)
# Finished.
console.msg("{0}: Done.".format(NAME))
return True
|
the-stack_0_894 | # -*- coding: utf-8 -*-
"""
mslib.msui.performance_settings
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This module defines the performance settings dialog
This file is part of mss.
:copyright: Copyright 2017 Joern Ungermann
:copyright: Copyright 2017-2022 by the mss team, see AUTHORS.
:license: APACHE-2.0, see LICENSE for details.
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.
"""
import json
from PyQt5 import QtCore, QtWidgets
from mslib.utils import FatalUserError
from mslib.msui import aircrafts, constants
from mslib.msui.mss_qt import get_open_filename
from mslib.msui.mss_qt import ui_performance_dockwidget as ui_dw
DEFAULT_PERFORMANCE = {
"aircraft": aircrafts.SimpleAircraft(aircrafts.AIRCRAFT_DUMMY),
"visible": False,
"takeoff_weight": 0,
"takeoff_time": QtCore.QDateTime.currentDateTimeUtc(),
"empty_weight": 0,
"ceiling_alt": [410],
}
class MSS_PerformanceSettingsWidget(QtWidgets.QWidget, ui_dw.Ui_PerformanceDockWidget):
"""
This class implements setting the performance settings as a dockable widget.
"""
def __init__(self, parent=None, view=None, settings_dict=None):
"""
Arguments:
parent -- Qt widget that is parent to this widget.
view -- reference to mpl canvas class
settings_dict -- dictionary containing topview options
"""
super(MSS_PerformanceSettingsWidget, self).__init__(parent)
self.setupUi(self)
self.view = view
self.parent = parent
if not settings_dict:
settings_dict = DEFAULT_PERFORMANCE
self.aircraft = settings_dict["aircraft"]
self.lbAircraftName.setText(self.aircraft.name)
self.cbShowPerformance.setChecked(settings_dict["visible"])
self.dsbTakeoffWeight.setValue(settings_dict["takeoff_weight"])
self.dsbEmptyWeight.setValue(
settings_dict.get("empty_weight", settings_dict["takeoff_weight"] - settings_dict.get("fuel", 0)))
self.dteTakeoffTime.setDateTime(settings_dict["takeoff_time"])
# Connecting signals
self.pbLoadPerformance.clicked.connect(self.load_performance)
self.cbShowPerformance.stateChanged.connect(self.update_parent_performance)
self.dteTakeoffTime.dateTimeChanged.connect(self.update_parent_performance)
self.dsbTakeoffWeight.valueChanged.connect(self.update_parent_performance)
self.dsbEmptyWeight.valueChanged.connect(self.update_parent_performance)
def get_settings(self):
"""
Encapsulates GUI selections in a python dictionary.
:return:
Dictionary of all setting informations
"""
settings_dict = {
"aircraft": self.aircraft,
"visible": self.cbShowPerformance.isChecked(),
"takeoff_time": self.dteTakeoffTime.dateTime(),
"takeoff_weight": self.dsbTakeoffWeight.value(),
"empty_weight": self.dsbEmptyWeight.value()
}
return settings_dict
def update_parent_performance(self):
self.parent.setPerformance(self.get_settings())
def load_performance(self):
"""
Gets a filename for a JSON file specifying aircraft performance and initializes an SimpleAircraft model.
"""
filename = get_open_filename(
self, "Open Aircraft Performance JSON File", constants.MSS_CONFIG_PATH,
"Performance File (*.json)", pickertag="filepicker_default")
if filename is not None:
try:
with open(filename) as tf:
performance = json.load(tf)
self.aircraft = aircrafts.SimpleAircraft(performance)
self.lbAircraftName.setText(self.aircraft.name)
self.dsbTakeoffWeight.setValue(self.aircraft.takeoff_weight)
if not any(hasattr(self.aircraft, _x) for _x in ("fuel", "empty_weight")):
raise KeyError("empty_weight")
if hasattr(self.aircraft, "empty_weight"):
self.dsbEmptyWeight.setValue(self.aircraft.empty_weight)
else:
self.dsbEmptyWeight.setValue(self.aircraft.takeoff_weight - self.aircraft.fuel)
self.update_parent_performance()
except KeyError as ex:
QtWidgets.QMessageBox.critical(self, self.tr("Performance JSON Load"),
self.tr(f"JSON File missing '{ex}' entry"))
except (FatalUserError, ValueError) as ex:
QtWidgets.QMessageBox.critical(self, self.tr("Performance JSON Load"),
self.tr(f"JSON File has Syntax Problems:\n{ex}"))
|
the-stack_0_895 | from decimal import Decimal
import graphene
from django_filters import FilterSet, OrderingFilter
from graphene import relay
from graphene_django.filter import DjangoFilterConnectionField
from graphene_django.types import DjangoObjectType
from graphene_file_upload.scalars import Upload
from graphql import GraphQLError
from graphql_jwt.decorators import login_required
from graphql_relay.node.node import from_global_id, to_global_id
from .models import BilbyJob, Label, FileDownloadToken, BilbyJobUploadToken
from .status import JobStatus
from .types import JobStatusType, BilbyJobCreationResult, JobParameterInput, JobParameterOutput, JobIniInput, \
JobDetailsInput
from .utils.db_search.db_search import perform_db_search
from .utils.derive_job_status import derive_job_status
from .utils.gen_parameter_output import generate_parameter_output
from .utils.jobs.request_file_download_id import request_file_download_ids
from .utils.jobs.request_job_filter import request_job_filter
from .views import create_bilby_job, update_bilby_job, create_bilby_job_from_ini_string, upload_bilby_job
class LabelType(DjangoObjectType):
class Meta:
model = Label
interfaces = (relay.Node,)
class UserBilbyJobFilter(FilterSet):
class Meta:
model = BilbyJob
fields = '__all__'
order_by = OrderingFilter(
fields=(
('last_updated', 'lastUpdated'),
('name', 'name'),
)
)
@property
def qs(self):
return BilbyJob.user_bilby_job_filter(super(UserBilbyJobFilter, self).qs, self)
class PublicBilbyJobFilter(FilterSet):
class Meta:
model = BilbyJob
fields = '__all__'
order_by = OrderingFilter(
fields=(
('last_updated', 'last_updated'),
('name', 'name'),
)
)
@property
def qs(self):
return BilbyJob.public_bilby_job_filter(super(PublicBilbyJobFilter, self).qs, self)
class BilbyJobNode(DjangoObjectType):
class Meta:
model = BilbyJob
convert_choices_to_enum = False
interfaces = (relay.Node,)
job_status = graphene.Field(JobStatusType)
last_updated = graphene.String()
params = graphene.Field(JobParameterOutput)
labels = graphene.List(LabelType)
@classmethod
def get_queryset(parent, queryset, info):
return BilbyJob.bilby_job_filter(queryset, info)
def resolve_last_updated(parent, info):
return parent.last_updated.strftime("%Y-%m-%d %H:%M:%S UTC")
def resolve_params(parent, info):
return generate_parameter_output(parent)
def resolve_labels(parent, info):
return parent.labels.all()
def resolve_job_status(parent, info):
# Uploaded jobs are always complete
if parent.is_uploaded_job:
return {
"name": JobStatus.display_name(JobStatus.COMPLETED),
"number": JobStatus.COMPLETED,
"date": parent.creation_time
}
try:
# Get job details from the job controller
_, jc_jobs = request_job_filter(
info.context.user.user_id,
ids=[parent.job_controller_id]
)
status_number, status_name, status_date = derive_job_status(jc_jobs[0]["history"])
return {
"name": status_name,
"number": status_number,
"date": status_date.strftime("%Y-%m-%d %H:%M:%S UTC")
}
except Exception:
return {
"name": "Unknown",
"number": 0,
"data": "Unknown"
}
class UserDetails(graphene.ObjectType):
username = graphene.String()
def resolve_username(parent, info):
return "Todo"
class BilbyResultFile(graphene.ObjectType):
path = graphene.String()
is_dir = graphene.Boolean()
file_size = graphene.Decimal()
download_token = graphene.String()
class BilbyResultFiles(graphene.ObjectType):
class Meta:
interfaces = (relay.Node,)
class Input:
job_id = graphene.ID()
files = graphene.List(BilbyResultFile)
is_uploaded_job = graphene.Boolean()
class BilbyPublicJobNode(graphene.ObjectType):
user = graphene.String()
name = graphene.String()
job_status = graphene.Field(JobStatusType)
labels = graphene.List(LabelType)
description = graphene.String()
timestamp = graphene.String()
id = graphene.ID()
class BilbyPublicJobConnection(relay.Connection):
class Meta:
node = BilbyPublicJobNode
class GenerateBilbyJobUploadToken(graphene.ObjectType):
token = graphene.String()
class Query(object):
bilby_job = relay.Node.Field(BilbyJobNode)
bilby_jobs = DjangoFilterConnectionField(BilbyJobNode, filterset_class=UserBilbyJobFilter)
public_bilby_jobs = relay.ConnectionField(
BilbyPublicJobConnection,
search=graphene.String(),
time_range=graphene.String()
)
all_labels = graphene.List(LabelType)
bilby_result_files = graphene.Field(BilbyResultFiles, job_id=graphene.ID(required=True))
gwclouduser = graphene.Field(UserDetails)
generate_bilby_job_upload_token = graphene.Field(GenerateBilbyJobUploadToken)
@login_required
def resolve_generate_bilby_job_upload_token(self, info, **kwargs):
user = info.context.user
# Create a job upload token
token = BilbyJobUploadToken.create(user)
# Return the generated token
return GenerateBilbyJobUploadToken(token=str(token.token))
@login_required
def resolve_all_labels(self, info, **kwargs):
return Label.all()
@login_required
def resolve_public_bilby_jobs(self, info, **kwargs):
# Perform the database search
success, jobs = perform_db_search(info.context.user, kwargs)
if not success:
return []
# Parse the result in to graphql objects
result = []
for job in jobs:
bilby_job = BilbyJob.get_by_id(job['job']['id'], info.context.user)
result.append(
BilbyPublicJobNode(
user=f"{job['user']['firstName']} {job['user']['lastName']}",
name=job['job']['name'],
description=job['job']['description'],
job_status=JobStatusType(
name=JobStatus.display_name(
JobStatus.COMPLETED if bilby_job.is_uploaded_job else job['history'][0]['state']
),
number=JobStatus.COMPLETED if bilby_job.is_uploaded_job else job['history'][0]['state'],
date=bilby_job.creation_time if bilby_job.is_uploaded_job else job['history'][0]['timestamp']
),
labels=bilby_job.labels.all(),
timestamp=bilby_job.creation_time if bilby_job.is_uploaded_job else job['history'][0]['timestamp'],
id=to_global_id("BilbyJobNode", job['job']['id'])
)
)
# Nb. The perform_db_search function currently requests one extra record than kwargs['first'].
# This triggers the ArrayConnection used by returning the result array to correctly set
# hasNextPage correctly, such that infinite scroll works as expected.
return result
@login_required
def resolve_gwclouduser(self, info, **kwargs):
return info.context.user
@login_required
def resolve_bilby_result_files(self, info, **kwargs):
# Get the model id of the bilby job
_, job_id = from_global_id(kwargs.get("job_id"))
# Try to look up the job with the id provided
job = BilbyJob.get_by_id(job_id, info.context.user)
# Fetch the file list from the job controller
success, files = job.get_file_list()
if not success:
raise Exception("Error getting file list. " + str(files))
# Generate download tokens for the list of files
paths = [f['path'] for f in filter(lambda x: not x['isDir'], files)]
tokens = FileDownloadToken.create(job, paths)
# Generate a dict that can be used to query the generated tokens
token_dict = {tk.path: tk.token for tk in tokens}
# Build the resulting file list and send it back to the client
result = [
BilbyResultFile(
path=f["path"],
is_dir=f["isDir"],
file_size=Decimal(f["fileSize"]),
download_token=token_dict[f["path"]] if f["path"] in token_dict else None
)
for f in files
]
return BilbyResultFiles(
files=result,
is_uploaded_job=job.is_uploaded_job
)
class BilbyJobMutation(relay.ClientIDMutation):
class Input:
params = JobParameterInput()
result = graphene.Field(BilbyJobCreationResult)
@classmethod
@login_required
def mutate_and_get_payload(cls, root, info, params):
user = info.context.user
# Create the bilby job
bilby_job = create_bilby_job(user, params)
# Convert the bilby job id to a global id
job_id = to_global_id("BilbyJobNode", bilby_job.id)
# Return the bilby job id to the client
return BilbyJobMutation(
result=BilbyJobCreationResult(job_id=job_id)
)
class BilbyJobFromIniStringMutation(relay.ClientIDMutation):
class Input:
params = JobIniInput()
result = graphene.Field(BilbyJobCreationResult)
@classmethod
@login_required
def mutate_and_get_payload(cls, root, info, params):
user = info.context.user
# Create the bilby job
bilby_job = create_bilby_job_from_ini_string(user, params)
# Convert the bilby job id to a global id
job_id = to_global_id("BilbyJobNode", bilby_job.id)
# Return the bilby job id to the client
return BilbyJobFromIniStringMutation(
result=BilbyJobCreationResult(job_id=job_id)
)
class UpdateBilbyJobMutation(relay.ClientIDMutation):
class Input:
job_id = graphene.ID(required=True)
private = graphene.Boolean(required=False)
labels = graphene.List(graphene.String, required=False)
result = graphene.String()
@classmethod
@login_required
def mutate_and_get_payload(cls, root, info, **kwargs):
user = info.context.user
job_id = kwargs.pop("job_id")
# Update privacy of bilby job
message = update_bilby_job(from_global_id(job_id)[1], user, **kwargs)
# Return the bilby job id to the client
return UpdateBilbyJobMutation(
result=message
)
class GenerateFileDownloadIds(relay.ClientIDMutation):
class Input:
job_id = graphene.ID(required=True)
download_tokens = graphene.List(graphene.String, required=True)
result = graphene.List(graphene.String)
@classmethod
@login_required
def mutate_and_get_payload(cls, root, info, job_id, download_tokens):
user = info.context.user
# Get the job these file downloads are for
job = BilbyJob.get_by_id(from_global_id(job_id)[1], user)
# Verify the download tokens and get the paths
paths = FileDownloadToken.get_paths(job, download_tokens)
# Check that all tokens were found
if None in paths:
raise GraphQLError("At least one token was invalid or expired.")
# For uploaded jobs, we can just return the exact some download tokens - this function is basically a no-op
# for uploaded jobs
if job.is_uploaded_job:
return GenerateFileDownloadIds(
result=download_tokens
)
# Request the list of file download ids from the list of paths
# Only the original job author may generate a file download id
success, result = request_file_download_ids(
job,
paths
)
# Report the error if there is one
if not success:
raise GraphQLError(result)
# Return the list of file download ids
return GenerateFileDownloadIds(
result=result
)
class UploadBilbyJobMutation(relay.ClientIDMutation):
class Input:
upload_token = graphene.String()
details = JobDetailsInput()
job_file = Upload(required=True)
result = graphene.Field(BilbyJobCreationResult)
@classmethod
def mutate_and_get_payload(cls, root, info, upload_token, details, job_file):
# Get the token being used to perform the upload - this will return None if the token doesn't exist or
# is expired
token = BilbyJobUploadToken.get_by_token(upload_token)
if not token:
raise GraphQLError("Job upload token is invalid or expired.")
# Try uploading the bilby job
bilby_job = upload_bilby_job(token, details, job_file)
# Convert the bilby job id to a global id
job_id = to_global_id("BilbyJobNode", bilby_job.id)
# Return the bilby job id to the client
return BilbyJobMutation(
result=BilbyJobCreationResult(job_id=job_id)
)
class Mutation(graphene.ObjectType):
new_bilby_job = BilbyJobMutation.Field()
new_bilby_job_from_ini_string = BilbyJobFromIniStringMutation.Field()
update_bilby_job = UpdateBilbyJobMutation.Field()
generate_file_download_ids = GenerateFileDownloadIds.Field()
upload_bilby_job = UploadBilbyJobMutation.Field()
|
the-stack_0_896 | from grpc.beta import implementations
import numpy
import traceback
import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
from flask_restplus import Resource, abort
from monocker_api.api.restplus import restplus_api
from monocker_api.api.models import getModel
from monocker_api.db.data_models import PredictionRequest
from monocker_api import settings
#==============================================================================
# helper functions
#==============================================================================
def getMonockerModelStub(host, port):
try:
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
except Exception as e:
print("===========================================================")
print("Encountered error while requesting gRPC connection.")
print("Error: ")
print(e)
traceback.print_exc()
print("===========================================================")
stub = None
return stub
def getServingRequest(model, payload):
request = predict_pb2.PredictRequest()
request.model_spec.name = model['model_name']
request.model_spec.signature_name = model['model_signature']
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(
payload['model_input'],
shape=payload['model_input_shape']
)
)
return request
#==============================================================================
#==============================================================================
# Models API
#==============================================================================
# define namespace
api = restplus_api.namespace(
'predict',
description="Operations related to requesting model evaluations"
)
# Define /models route handlers
@api.route('/')
class Models(Resource):
@api.response(501, 'Error in model computation')
@api.response(403, 'Could not connect to tf serving server')
@api.response(404, 'Model not found.')
@api.response(201, 'Successfully retrieved model evaluation.')
@api.expect(PredictionRequest, validate=False, required=True)
def post(self):
# get inputs
payload = restplus_api.payload
# get model
model = getModel(payload['model_name'])
if model is None:
return 'Model not found.', 404
# get request
model['model_signature'] = payload['model_signature']
serving_request = getServingRequest(model, payload)
# get stub
stub = getMonockerModelStub(model['ip_address'], model['port'])
if stub is None:
return 'Could not connect to tf serving server', 403
# make grpc prediction request then return results
try:
prediction = stub.Predict(serving_request, 5.0)
model_response = list(prediction.outputs['scores'].float_val)
return {'model_response': model_response}, 201
except Exception as e:
return str(e), 501
#============================================================================== |
the-stack_0_899 | # Copyright Contributors to the Rez project
#
# 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.
'''
Bundle a context and its packages into a relocatable dir.
'''
from __future__ import print_function
import os
import os.path
import sys
def setup_parser(parser, completions=False):
group = parser.add_mutually_exclusive_group()
group.add_argument(
"-s", "--skip-non-relocatable", action="store_true",
help="leave non-relocatable packages non-bundled, rather than raise an error")
group.add_argument(
"-f", "--force", action="store_true",
help="bundle package even if it isn't relocatable (use at your own risk)")
group.add_argument(
"-n", "--no-lib-patch", action="store_true",
help="don't apply library patching within the bundle")
parser.add_argument(
"RXT",
help="context to bundle")
parser.add_argument(
"DEST_DIR",
help="directory to create bundle in; must not exist")
def command(opts, parser, extra_arg_groups=None):
from rez.utils.logging_ import print_error
from rez.bundle_context import bundle_context
from rez.resolved_context import ResolvedContext
rxt_filepath = os.path.abspath(os.path.expanduser(opts.RXT))
dest_dir = os.path.abspath(os.path.expanduser(opts.DEST_DIR))
# sanity checks
if not os.path.exists(rxt_filepath):
print_error("File does not exist: %s", rxt_filepath)
sys.exit(1)
context = ResolvedContext.load(rxt_filepath)
bundle_context(
context=context,
dest_dir=dest_dir,
force=opts.force,
skip_non_relocatable=opts.skip_non_relocatable,
verbose=opts.verbose,
patch_libs=(not opts.no_lib_patch)
)
|
the-stack_0_901 | #!/usr/bin/python2.7
# -*- coding: UTF-8 -*-
'''
Created on 2018年6月15日
@author: zhaohongxing
'''
import os
from PyQt5.Qt import Qt
from PyQt5.Qt import QIcon,QStandardItemModel,QStandardItem
'''
from PyQt5 import QtGui
'''
from PyQt5.QtWidgets import QTableView,QVBoxLayout,QDialog,QPushButton
from PyQt5.QtCore import QSize, pyqtSignal
import wechatutil
class ContactListWindow(QDialog):
WIDTH = 600
membersConfirmed = pyqtSignal(str)
def __init__(self,member_list,parent = None):
super(ContactListWindow,self).__init__(parent)
self.setModal(True)
self.user_home = os.path.expanduser('~')
self.app_home = self.user_home + '/.wechat/'
self.head_home = ("%s/heads"%(self.app_home))
self.cache_home = ("%s/cache/"%(self.app_home))
self.cache_image_home = "%s/image/"%(self.cache_home)
self.contact_head_home = ("%s/contact/"%(self.head_home))
self.default_head_icon = './resource/images/default.png'
self.members = member_list
self.membersTable = QTableView()
self.membersTable.horizontalHeader().setStretchLastSection(True)
self.membersTable.verticalHeader().setDefaultSectionSize(60)
#self.membersTable.horizontalHeader().setDefaultSectionSize(60)
self.membersTable.setColumnWidth(0, 10);
self.membersTable.setColumnWidth(1, 60);
self.membersTable.verticalHeader().setVisible(False)
self.membersTable.horizontalHeader().setVisible(False)
#confirm
self.confirm = QPushButton(wechatutil.unicode("確定"),self)
self.membersTableModel = QStandardItemModel(0,2)
self.membersTableModel.itemChanged.connect(self.itemChanged)
self.initinal_member_list_widget()
mainLayout=QVBoxLayout()
mainLayout.addWidget(self.membersTable)
mainLayout.addWidget(self.confirm)
self.setLayout(mainLayout)
#self.membersTable.clicked.connect(self.contact_item_clicked)
self.confirm.clicked.connect(self.do_confirm)
self.selectedRowCount = 0
def itemChanged(self,item):
if item.checkState() == Qt.Checked:
self.selectedRowCount += 1
else:
self.selectedRowCount -= 1
if self.selectedRowCount > 0:
self.confirm.setText(wechatutil.unicode("確定(%d)"%(self.selectedRowCount)))
else:
self.confirm.setText(wechatutil.unicode("確定"))
def initinal_member_list_widget(self):
self.append_row(self.members, self.membersTableModel)
self.membersTable.setModel(self.membersTableModel)
self.membersTable.setIconSize(QSize(40,40))
def append_row(self,members,data_model):
for (i,member) in enumerate(members):
cells = []
user_name = member['UserName']
user_name_cell = QStandardItem(user_name)
user_name_cell.setCheckable(True)
cells.append(user_name_cell)
user_avatar = self.contact_head_home + member['UserName']+".jpg"
if not os.path.exists(user_avatar):
user_avatar = self.default_head_icon
dn = member['DisplayName'] or member['NickName']
if not dn:
dn = member['NickName']
item = QStandardItem(QIcon(user_avatar),wechatutil.unicode(dn))
cells.append(item)
data_model.appendRow(cells)
def do_confirm(self):
rowCount = self.membersTableModel.rowCount()
selected_user_names = ""
for row in range(rowCount):
item = self.membersTableModel.item(row,0)
if item.checkState() == Qt.Checked:
index = self.membersTableModel.index(row,0)
user_name_obj = self.membersTableModel.data(index)
if user_name_obj:
user_name = user_name_obj
user = {}
user['UserName']=user_name
selected_user_names=selected_user_names+(user_name)
selected_user_names=selected_user_names+(";")
if len(selected_user_names) > 0:
dictt = {}
dictt['UserNames']=selected_user_names
self.membersConfirmed.emit(selected_user_names)
self.close() |
the-stack_0_902 | """
This module contains pdsolve() and different helper functions that it
uses. It is heavily inspired by the ode module and hence the basic
infrastructure remains the same.
**Functions in this module**
These are the user functions in this module:
- pdsolve() - Solves PDE's
- classify_pde() - Classifies PDEs into possible hints for dsolve().
- pde_separate() - Separate variables in partial differential equation either by
additive or multiplicative separation approach.
These are the helper functions in this module:
- pde_separate_add() - Helper function for searching additive separable solutions.
- pde_separate_mul() - Helper function for searching multiplicative
separable solutions.
**Currently implemented solver methods**
The following methods are implemented for solving partial differential
equations. See the docstrings of the various pde_hint() functions for
more information on each (run help(pde)):
- 1st order linear homogeneous partial differential equations
with constant coefficients.
- 1st order linear general partial differential equations
with constant coefficients.
- 1st order linear partial differential equations with
variable coefficients.
"""
from functools import reduce
from itertools import combinations_with_replacement
from sympy.simplify import simplify # type: ignore
from sympy.core import Add, S
from sympy.core.function import Function, expand, AppliedUndef, Subs
from sympy.core.relational import Equality, Eq
from sympy.core.symbol import Symbol, Wild, symbols
from sympy.functions import exp
from sympy.integrals.integrals import Integral, integrate
from sympy.utilities.iterables import has_dups, is_sequence
from sympy.utilities.misc import filldedent
from sympy.solvers.deutils import _preprocess, ode_order, _desolve
from sympy.solvers.solvers import solve
from sympy.simplify.radsimp import collect
import operator
allhints = (
"1st_linear_constant_coeff_homogeneous",
"1st_linear_constant_coeff",
"1st_linear_constant_coeff_Integral",
"1st_linear_variable_coeff"
)
def pdsolve(eq, func=None, hint='default', dict=False, solvefun=None, **kwargs):
"""
Solves any (supported) kind of partial differential equation.
**Usage**
pdsolve(eq, f(x,y), hint) -> Solve partial differential equation
eq for function f(x,y), using method hint.
**Details**
``eq`` can be any supported partial differential equation (see
the pde docstring for supported methods). This can either
be an Equality, or an expression, which is assumed to be
equal to 0.
``f(x,y)`` is a function of two variables whose derivatives in that
variable make up the partial differential equation. In many
cases it is not necessary to provide this; it will be autodetected
(and an error raised if it couldn't be detected).
``hint`` is the solving method that you want pdsolve to use. Use
classify_pde(eq, f(x,y)) to get all of the possible hints for
a PDE. The default hint, 'default', will use whatever hint
is returned first by classify_pde(). See Hints below for
more options that you can use for hint.
``solvefun`` is the convention used for arbitrary functions returned
by the PDE solver. If not set by the user, it is set by default
to be F.
**Hints**
Aside from the various solving methods, there are also some
meta-hints that you can pass to pdsolve():
"default":
This uses whatever hint is returned first by
classify_pde(). This is the default argument to
pdsolve().
"all":
To make pdsolve apply all relevant classification hints,
use pdsolve(PDE, func, hint="all"). This will return a
dictionary of hint:solution terms. If a hint causes
pdsolve to raise the NotImplementedError, value of that
hint's key will be the exception object raised. The
dictionary will also include some special keys:
- order: The order of the PDE. See also ode_order() in
deutils.py
- default: The solution that would be returned by
default. This is the one produced by the hint that
appears first in the tuple returned by classify_pde().
"all_Integral":
This is the same as "all", except if a hint also has a
corresponding "_Integral" hint, it only returns the
"_Integral" hint. This is useful if "all" causes
pdsolve() to hang because of a difficult or impossible
integral. This meta-hint will also be much faster than
"all", because integrate() is an expensive routine.
See also the classify_pde() docstring for more info on hints,
and the pde docstring for a list of all supported hints.
**Tips**
- You can declare the derivative of an unknown function this way:
>>> from sympy import Function, Derivative
>>> from sympy.abc import x, y # x and y are the independent variables
>>> f = Function("f")(x, y) # f is a function of x and y
>>> # fx will be the partial derivative of f with respect to x
>>> fx = Derivative(f, x)
>>> # fy will be the partial derivative of f with respect to y
>>> fy = Derivative(f, y)
- See test_pde.py for many tests, which serves also as a set of
examples for how to use pdsolve().
- pdsolve always returns an Equality class (except for the case
when the hint is "all" or "all_Integral"). Note that it is not possible
to get an explicit solution for f(x, y) as in the case of ODE's
- Do help(pde.pde_hintname) to get help more information on a
specific hint
Examples
========
>>> from sympy.solvers.pde import pdsolve
>>> from sympy import Function, Eq
>>> from sympy.abc import x, y
>>> f = Function('f')
>>> u = f(x, y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> eq = Eq(1 + (2*(ux/u)) + (3*(uy/u)), 0)
>>> pdsolve(eq)
Eq(f(x, y), F(3*x - 2*y)*exp(-2*x/13 - 3*y/13))
"""
if not solvefun:
solvefun = Function('F')
# See the docstring of _desolve for more details.
hints = _desolve(eq, func=func, hint=hint, simplify=True,
type='pde', **kwargs)
eq = hints.pop('eq', False)
all_ = hints.pop('all', False)
if all_:
# TODO : 'best' hint should be implemented when adequate
# number of hints are added.
pdedict = {}
failed_hints = {}
gethints = classify_pde(eq, dict=True)
pdedict.update({'order': gethints['order'],
'default': gethints['default']})
for hint in hints:
try:
rv = _helper_simplify(eq, hint, hints[hint]['func'],
hints[hint]['order'], hints[hint][hint], solvefun)
except NotImplementedError as detail:
failed_hints[hint] = detail
else:
pdedict[hint] = rv
pdedict.update(failed_hints)
return pdedict
else:
return _helper_simplify(eq, hints['hint'], hints['func'],
hints['order'], hints[hints['hint']], solvefun)
def _helper_simplify(eq, hint, func, order, match, solvefun):
"""Helper function of pdsolve that calls the respective
pde functions to solve for the partial differential
equations. This minimizes the computation in
calling _desolve multiple times.
"""
if hint.endswith("_Integral"):
solvefunc = globals()[
"pde_" + hint[:-len("_Integral")]]
else:
solvefunc = globals()["pde_" + hint]
return _handle_Integral(solvefunc(eq, func, order,
match, solvefun), func, order, hint)
def _handle_Integral(expr, func, order, hint):
r"""
Converts a solution with integrals in it into an actual solution.
Simplifies the integral mainly using doit()
"""
if hint.endswith("_Integral"):
return expr
elif hint == "1st_linear_constant_coeff":
return simplify(expr.doit())
else:
return expr
def classify_pde(eq, func=None, dict=False, *, prep=True, **kwargs):
"""
Returns a tuple of possible pdsolve() classifications for a PDE.
The tuple is ordered so that first item is the classification that
pdsolve() uses to solve the PDE by default. In general,
classifications near the beginning of the list will produce
better solutions faster than those near the end, though there are
always exceptions. To make pdsolve use a different classification,
use pdsolve(PDE, func, hint=<classification>). See also the pdsolve()
docstring for different meta-hints you can use.
If ``dict`` is true, classify_pde() will return a dictionary of
hint:match expression terms. This is intended for internal use by
pdsolve(). Note that because dictionaries are ordered arbitrarily,
this will most likely not be in the same order as the tuple.
You can get help on different hints by doing help(pde.pde_hintname),
where hintname is the name of the hint without "_Integral".
See sympy.pde.allhints or the sympy.pde docstring for a list of all
supported hints that can be returned from classify_pde.
Examples
========
>>> from sympy.solvers.pde import classify_pde
>>> from sympy import Function, Eq
>>> from sympy.abc import x, y
>>> f = Function('f')
>>> u = f(x, y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> eq = Eq(1 + (2*(ux/u)) + (3*(uy/u)), 0)
>>> classify_pde(eq)
('1st_linear_constant_coeff_homogeneous',)
"""
if func and len(func.args) != 2:
raise NotImplementedError("Right now only partial "
"differential equations of two variables are supported")
if prep or func is None:
prep, func_ = _preprocess(eq, func)
if func is None:
func = func_
if isinstance(eq, Equality):
if eq.rhs != 0:
return classify_pde(eq.lhs - eq.rhs, func)
eq = eq.lhs
f = func.func
x = func.args[0]
y = func.args[1]
fx = f(x,y).diff(x)
fy = f(x,y).diff(y)
# TODO : For now pde.py uses support offered by the ode_order function
# to find the order with respect to a multi-variable function. An
# improvement could be to classify the order of the PDE on the basis of
# individual variables.
order = ode_order(eq, f(x,y))
# hint:matchdict or hint:(tuple of matchdicts)
# Also will contain "default":<default hint> and "order":order items.
matching_hints = {'order': order}
if not order:
if dict:
matching_hints["default"] = None
return matching_hints
else:
return ()
eq = expand(eq)
a = Wild('a', exclude = [f(x,y)])
b = Wild('b', exclude = [f(x,y), fx, fy, x, y])
c = Wild('c', exclude = [f(x,y), fx, fy, x, y])
d = Wild('d', exclude = [f(x,y), fx, fy, x, y])
e = Wild('e', exclude = [f(x,y), fx, fy])
n = Wild('n', exclude = [x, y])
# Try removing the smallest power of f(x,y)
# from the highest partial derivatives of f(x,y)
reduced_eq = None
if eq.is_Add:
var = set(combinations_with_replacement((x,y), order))
dummyvar = var.copy()
power = None
for i in var:
coeff = eq.coeff(f(x,y).diff(*i))
if coeff != 1:
match = coeff.match(a*f(x,y)**n)
if match and match[a]:
power = match[n]
dummyvar.remove(i)
break
dummyvar.remove(i)
for i in dummyvar:
coeff = eq.coeff(f(x,y).diff(*i))
if coeff != 1:
match = coeff.match(a*f(x,y)**n)
if match and match[a] and match[n] < power:
power = match[n]
if power:
den = f(x,y)**power
reduced_eq = Add(*[arg/den for arg in eq.args])
if not reduced_eq:
reduced_eq = eq
if order == 1:
reduced_eq = collect(reduced_eq, f(x, y))
r = reduced_eq.match(b*fx + c*fy + d*f(x,y) + e)
if r:
if not r[e]:
## Linear first-order homogeneous partial-differential
## equation with constant coefficients
r.update({'b': b, 'c': c, 'd': d})
matching_hints["1st_linear_constant_coeff_homogeneous"] = r
else:
if r[b]**2 + r[c]**2 != 0:
## Linear first-order general partial-differential
## equation with constant coefficients
r.update({'b': b, 'c': c, 'd': d, 'e': e})
matching_hints["1st_linear_constant_coeff"] = r
matching_hints[
"1st_linear_constant_coeff_Integral"] = r
else:
b = Wild('b', exclude=[f(x, y), fx, fy])
c = Wild('c', exclude=[f(x, y), fx, fy])
d = Wild('d', exclude=[f(x, y), fx, fy])
r = reduced_eq.match(b*fx + c*fy + d*f(x,y) + e)
if r:
r.update({'b': b, 'c': c, 'd': d, 'e': e})
matching_hints["1st_linear_variable_coeff"] = r
# Order keys based on allhints.
retlist = []
for i in allhints:
if i in matching_hints:
retlist.append(i)
if dict:
# Dictionaries are ordered arbitrarily, so make note of which
# hint would come first for pdsolve(). Use an ordered dict in Py 3.
matching_hints["default"] = None
matching_hints["ordered_hints"] = tuple(retlist)
for i in allhints:
if i in matching_hints:
matching_hints["default"] = i
break
return matching_hints
else:
return tuple(retlist)
def checkpdesol(pde, sol, func=None, solve_for_func=True):
"""
Checks if the given solution satisfies the partial differential
equation.
pde is the partial differential equation which can be given in the
form of an equation or an expression. sol is the solution for which
the pde is to be checked. This can also be given in an equation or
an expression form. If the function is not provided, the helper
function _preprocess from deutils is used to identify the function.
If a sequence of solutions is passed, the same sort of container will be
used to return the result for each solution.
The following methods are currently being implemented to check if the
solution satisfies the PDE:
1. Directly substitute the solution in the PDE and check. If the
solution hasn't been solved for f, then it will solve for f
provided solve_for_func hasn't been set to False.
If the solution satisfies the PDE, then a tuple (True, 0) is returned.
Otherwise a tuple (False, expr) where expr is the value obtained
after substituting the solution in the PDE. However if a known solution
returns False, it may be due to the inability of doit() to simplify it to zero.
Examples
========
>>> from sympy import Function, symbols
>>> from sympy.solvers.pde import checkpdesol, pdsolve
>>> x, y = symbols('x y')
>>> f = Function('f')
>>> eq = 2*f(x,y) + 3*f(x,y).diff(x) + 4*f(x,y).diff(y)
>>> sol = pdsolve(eq)
>>> assert checkpdesol(eq, sol)[0]
>>> eq = x*f(x,y) + f(x,y).diff(x)
>>> checkpdesol(eq, sol)
(False, (x*F(4*x - 3*y) - 6*F(4*x - 3*y)/25 + 4*Subs(Derivative(F(_xi_1), _xi_1), _xi_1, 4*x - 3*y))*exp(-6*x/25 - 8*y/25))
"""
# Converting the pde into an equation
if not isinstance(pde, Equality):
pde = Eq(pde, 0)
# If no function is given, try finding the function present.
if func is None:
try:
_, func = _preprocess(pde.lhs)
except ValueError:
funcs = [s.atoms(AppliedUndef) for s in (
sol if is_sequence(sol, set) else [sol])]
funcs = set().union(funcs)
if len(funcs) != 1:
raise ValueError(
'must pass func arg to checkpdesol for this case.')
func = funcs.pop()
# If the given solution is in the form of a list or a set
# then return a list or set of tuples.
if is_sequence(sol, set):
return type(sol)([checkpdesol(
pde, i, func=func,
solve_for_func=solve_for_func) for i in sol])
# Convert solution into an equation
if not isinstance(sol, Equality):
sol = Eq(func, sol)
elif sol.rhs == func:
sol = sol.reversed
# Try solving for the function
solved = sol.lhs == func and not sol.rhs.has(func)
if solve_for_func and not solved:
solved = solve(sol, func)
if solved:
if len(solved) == 1:
return checkpdesol(pde, Eq(func, solved[0]),
func=func, solve_for_func=False)
else:
return checkpdesol(pde, [Eq(func, t) for t in solved],
func=func, solve_for_func=False)
# try direct substitution of the solution into the PDE and simplify
if sol.lhs == func:
pde = pde.lhs - pde.rhs
s = simplify(pde.subs(func, sol.rhs).doit())
return s is S.Zero, s
raise NotImplementedError(filldedent('''
Unable to test if %s is a solution to %s.''' % (sol, pde)))
def pde_1st_linear_constant_coeff_homogeneous(eq, func, order, match, solvefun):
r"""
Solves a first order linear homogeneous
partial differential equation with constant coefficients.
The general form of this partial differential equation is
.. math:: a \frac{\partial f(x,y)}{\partial x}
+ b \frac{\partial f(x,y)}{\partial y} + c f(x,y) = 0
where `a`, `b` and `c` are constants.
The general solution is of the form:
.. math::
f(x, y) = F(- a y + b x ) e^{- \frac{c (a x + b y)}{a^2 + b^2}}
and can be found in SymPy with ``pdsolve``::
>>> from sympy.solvers import pdsolve
>>> from sympy.abc import x, y, a, b, c
>>> from sympy import Function, pprint
>>> f = Function('f')
>>> u = f(x,y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> genform = a*ux + b*uy + c*u
>>> pprint(genform)
d d
a*--(f(x, y)) + b*--(f(x, y)) + c*f(x, y)
dx dy
>>> pprint(pdsolve(genform))
-c*(a*x + b*y)
---------------
2 2
a + b
f(x, y) = F(-a*y + b*x)*e
Examples
========
>>> from sympy import pdsolve
>>> from sympy import Function, pprint
>>> from sympy.abc import x,y
>>> f = Function('f')
>>> pdsolve(f(x,y) + f(x,y).diff(x) + f(x,y).diff(y))
Eq(f(x, y), F(x - y)*exp(-x/2 - y/2))
>>> pprint(pdsolve(f(x,y) + f(x,y).diff(x) + f(x,y).diff(y)))
x y
- - - -
2 2
f(x, y) = F(x - y)*e
References
==========
- Viktor Grigoryan, "Partial Differential Equations"
Math 124A - Fall 2010, pp.7
"""
# TODO : For now homogeneous first order linear PDE's having
# two variables are implemented. Once there is support for
# solving systems of ODE's, this can be extended to n variables.
f = func.func
x = func.args[0]
y = func.args[1]
b = match[match['b']]
c = match[match['c']]
d = match[match['d']]
return Eq(f(x,y), exp(-S(d)/(b**2 + c**2)*(b*x + c*y))*solvefun(c*x - b*y))
def pde_1st_linear_constant_coeff(eq, func, order, match, solvefun):
r"""
Solves a first order linear partial differential equation
with constant coefficients.
The general form of this partial differential equation is
.. math:: a \frac{\partial f(x,y)}{\partial x}
+ b \frac{\partial f(x,y)}{\partial y}
+ c f(x,y) = G(x,y)
where `a`, `b` and `c` are constants and `G(x, y)` can be an arbitrary
function in `x` and `y`.
The general solution of the PDE is:
.. math::
f(x, y) = \left. \left[F(\eta) + \frac{1}{a^2 + b^2}
\int\limits^{a x + b y} G\left(\frac{a \xi + b \eta}{a^2 + b^2},
\frac{- a \eta + b \xi}{a^2 + b^2} \right)
e^{\frac{c \xi}{a^2 + b^2}}\, d\xi\right]
e^{- \frac{c \xi}{a^2 + b^2}}
\right|_{\substack{\eta=- a y + b x\\ \xi=a x + b y }}\, ,
where `F(\eta)` is an arbitrary single-valued function. The solution
can be found in SymPy with ``pdsolve``::
>>> from sympy.solvers import pdsolve
>>> from sympy.abc import x, y, a, b, c
>>> from sympy import Function, pprint
>>> f = Function('f')
>>> G = Function('G')
>>> u = f(x,y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> genform = a*ux + b*uy + c*u - G(x,y)
>>> pprint(genform)
d d
a*--(f(x, y)) + b*--(f(x, y)) + c*f(x, y) - G(x, y)
dx dy
>>> pprint(pdsolve(genform, hint='1st_linear_constant_coeff_Integral'))
// a*x + b*y \
|| / |
|| | |
|| | c*xi |
|| | ------- |
|| | 2 2 |
|| | /a*xi + b*eta -a*eta + b*xi\ a + b |
|| | G|------------, -------------|*e d(xi)|
|| | | 2 2 2 2 | |
|| | \ a + b a + b / |
|| | |
|| / |
|| |
f(x, y) = ||F(eta) + -------------------------------------------------------|*
|| 2 2 |
\\ a + b /
<BLANKLINE>
\|
||
||
||
||
||
||
||
||
-c*xi ||
-------||
2 2||
a + b ||
e ||
||
/|eta=-a*y + b*x, xi=a*x + b*y
Examples
========
>>> from sympy.solvers.pde import pdsolve
>>> from sympy import Function, pprint, exp
>>> from sympy.abc import x,y
>>> f = Function('f')
>>> eq = -2*f(x,y).diff(x) + 4*f(x,y).diff(y) + 5*f(x,y) - exp(x + 3*y)
>>> pdsolve(eq)
Eq(f(x, y), (F(4*x + 2*y)*exp(x/2) + exp(x + 4*y)/15)*exp(-y))
References
==========
- Viktor Grigoryan, "Partial Differential Equations"
Math 124A - Fall 2010, pp.7
"""
# TODO : For now homogeneous first order linear PDE's having
# two variables are implemented. Once there is support for
# solving systems of ODE's, this can be extended to n variables.
xi, eta = symbols("xi eta")
f = func.func
x = func.args[0]
y = func.args[1]
b = match[match['b']]
c = match[match['c']]
d = match[match['d']]
e = -match[match['e']]
expterm = exp(-S(d)/(b**2 + c**2)*xi)
functerm = solvefun(eta)
solvedict = solve((b*x + c*y - xi, c*x - b*y - eta), x, y)
# Integral should remain as it is in terms of xi,
# doit() should be done in _handle_Integral.
genterm = (1/S(b**2 + c**2))*Integral(
(1/expterm*e).subs(solvedict), (xi, b*x + c*y))
return Eq(f(x,y), Subs(expterm*(functerm + genterm),
(eta, xi), (c*x - b*y, b*x + c*y)))
def pde_1st_linear_variable_coeff(eq, func, order, match, solvefun):
r"""
Solves a first order linear partial differential equation
with variable coefficients. The general form of this partial
differential equation is
.. math:: a(x, y) \frac{\partial f(x, y)}{\partial x}
+ b(x, y) \frac{\partial f(x, y)}{\partial y}
+ c(x, y) f(x, y) = G(x, y)
where `a(x, y)`, `b(x, y)`, `c(x, y)` and `G(x, y)` are arbitrary
functions in `x` and `y`. This PDE is converted into an ODE by
making the following transformation:
1. `\xi` as `x`
2. `\eta` as the constant in the solution to the differential
equation `\frac{dy}{dx} = -\frac{b}{a}`
Making the previous substitutions reduces it to the linear ODE
.. math:: a(\xi, \eta)\frac{du}{d\xi} + c(\xi, \eta)u - G(\xi, \eta) = 0
which can be solved using ``dsolve``.
>>> from sympy.abc import x, y
>>> from sympy import Function, pprint
>>> a, b, c, G, f= [Function(i) for i in ['a', 'b', 'c', 'G', 'f']]
>>> u = f(x,y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> genform = a(x, y)*u + b(x, y)*ux + c(x, y)*uy - G(x,y)
>>> pprint(genform)
d d
-G(x, y) + a(x, y)*f(x, y) + b(x, y)*--(f(x, y)) + c(x, y)*--(f(x, y))
dx dy
Examples
========
>>> from sympy.solvers.pde import pdsolve
>>> from sympy import Function, pprint
>>> from sympy.abc import x,y
>>> f = Function('f')
>>> eq = x*(u.diff(x)) - y*(u.diff(y)) + y**2*u - y**2
>>> pdsolve(eq)
Eq(f(x, y), F(x*y)*exp(y**2/2) + 1)
References
==========
- Viktor Grigoryan, "Partial Differential Equations"
Math 124A - Fall 2010, pp.7
"""
from sympy.solvers.ode import dsolve
xi, eta = symbols("xi eta")
f = func.func
x = func.args[0]
y = func.args[1]
b = match[match['b']]
c = match[match['c']]
d = match[match['d']]
e = -match[match['e']]
if not d:
# To deal with cases like b*ux = e or c*uy = e
if not (b and c):
if c:
try:
tsol = integrate(e/c, y)
except NotImplementedError:
raise NotImplementedError("Unable to find a solution"
" due to inability of integrate")
else:
return Eq(f(x,y), solvefun(x) + tsol)
if b:
try:
tsol = integrate(e/b, x)
except NotImplementedError:
raise NotImplementedError("Unable to find a solution"
" due to inability of integrate")
else:
return Eq(f(x,y), solvefun(y) + tsol)
if not c:
# To deal with cases when c is 0, a simpler method is used.
# The PDE reduces to b*(u.diff(x)) + d*u = e, which is a linear ODE in x
plode = f(x).diff(x)*b + d*f(x) - e
sol = dsolve(plode, f(x))
syms = sol.free_symbols - plode.free_symbols - {x, y}
rhs = _simplify_variable_coeff(sol.rhs, syms, solvefun, y)
return Eq(f(x, y), rhs)
if not b:
# To deal with cases when b is 0, a simpler method is used.
# The PDE reduces to c*(u.diff(y)) + d*u = e, which is a linear ODE in y
plode = f(y).diff(y)*c + d*f(y) - e
sol = dsolve(plode, f(y))
syms = sol.free_symbols - plode.free_symbols - {x, y}
rhs = _simplify_variable_coeff(sol.rhs, syms, solvefun, x)
return Eq(f(x, y), rhs)
dummy = Function('d')
h = (c/b).subs(y, dummy(x))
sol = dsolve(dummy(x).diff(x) - h, dummy(x))
if isinstance(sol, list):
sol = sol[0]
solsym = sol.free_symbols - h.free_symbols - {x, y}
if len(solsym) == 1:
solsym = solsym.pop()
etat = (solve(sol, solsym)[0]).subs(dummy(x), y)
ysub = solve(eta - etat, y)[0]
deq = (b*(f(x).diff(x)) + d*f(x) - e).subs(y, ysub)
final = (dsolve(deq, f(x), hint='1st_linear')).rhs
if isinstance(final, list):
final = final[0]
finsyms = final.free_symbols - deq.free_symbols - {x, y}
rhs = _simplify_variable_coeff(final, finsyms, solvefun, etat)
return Eq(f(x, y), rhs)
else:
raise NotImplementedError("Cannot solve the partial differential equation due"
" to inability of constantsimp")
def _simplify_variable_coeff(sol, syms, func, funcarg):
r"""
Helper function to replace constants by functions in 1st_linear_variable_coeff
"""
eta = Symbol("eta")
if len(syms) == 1:
sym = syms.pop()
final = sol.subs(sym, func(funcarg))
else:
for key, sym in enumerate(syms):
final = sol.subs(sym, func(funcarg))
return simplify(final.subs(eta, funcarg))
def pde_separate(eq, fun, sep, strategy='mul'):
"""Separate variables in partial differential equation either by additive
or multiplicative separation approach. It tries to rewrite an equation so
that one of the specified variables occurs on a different side of the
equation than the others.
:param eq: Partial differential equation
:param fun: Original function F(x, y, z)
:param sep: List of separated functions [X(x), u(y, z)]
:param strategy: Separation strategy. You can choose between additive
separation ('add') and multiplicative separation ('mul') which is
default.
Examples
========
>>> from sympy import E, Eq, Function, pde_separate, Derivative as D
>>> from sympy.abc import x, t
>>> u, X, T = map(Function, 'uXT')
>>> eq = Eq(D(u(x, t), x), E**(u(x, t))*D(u(x, t), t))
>>> pde_separate(eq, u(x, t), [X(x), T(t)], strategy='add')
[exp(-X(x))*Derivative(X(x), x), exp(T(t))*Derivative(T(t), t)]
>>> eq = Eq(D(u(x, t), x, 2), D(u(x, t), t, 2))
>>> pde_separate(eq, u(x, t), [X(x), T(t)], strategy='mul')
[Derivative(X(x), (x, 2))/X(x), Derivative(T(t), (t, 2))/T(t)]
See Also
========
pde_separate_add, pde_separate_mul
"""
do_add = False
if strategy == 'add':
do_add = True
elif strategy == 'mul':
do_add = False
else:
raise ValueError('Unknown strategy: %s' % strategy)
if isinstance(eq, Equality):
if eq.rhs != 0:
return pde_separate(Eq(eq.lhs - eq.rhs, 0), fun, sep, strategy)
else:
return pde_separate(Eq(eq, 0), fun, sep, strategy)
if eq.rhs != 0:
raise ValueError("Value should be 0")
# Handle arguments
orig_args = list(fun.args)
subs_args = []
for s in sep:
for j in range(0, len(s.args)):
subs_args.append(s.args[j])
if do_add:
functions = reduce(operator.add, sep)
else:
functions = reduce(operator.mul, sep)
# Check whether variables match
if len(subs_args) != len(orig_args):
raise ValueError("Variable counts do not match")
# Check for duplicate arguments like [X(x), u(x, y)]
if has_dups(subs_args):
raise ValueError("Duplicate substitution arguments detected")
# Check whether the variables match
if set(orig_args) != set(subs_args):
raise ValueError("Arguments do not match")
# Substitute original function with separated...
result = eq.lhs.subs(fun, functions).doit()
# Divide by terms when doing multiplicative separation
if not do_add:
eq = 0
for i in result.args:
eq += i/functions
result = eq
svar = subs_args[0]
dvar = subs_args[1:]
return _separate(result, svar, dvar)
def pde_separate_add(eq, fun, sep):
"""
Helper function for searching additive separable solutions.
Consider an equation of two independent variables x, y and a dependent
variable w, we look for the product of two functions depending on different
arguments:
`w(x, y, z) = X(x) + y(y, z)`
Examples
========
>>> from sympy import E, Eq, Function, pde_separate_add, Derivative as D
>>> from sympy.abc import x, t
>>> u, X, T = map(Function, 'uXT')
>>> eq = Eq(D(u(x, t), x), E**(u(x, t))*D(u(x, t), t))
>>> pde_separate_add(eq, u(x, t), [X(x), T(t)])
[exp(-X(x))*Derivative(X(x), x), exp(T(t))*Derivative(T(t), t)]
"""
return pde_separate(eq, fun, sep, strategy='add')
def pde_separate_mul(eq, fun, sep):
"""
Helper function for searching multiplicative separable solutions.
Consider an equation of two independent variables x, y and a dependent
variable w, we look for the product of two functions depending on different
arguments:
`w(x, y, z) = X(x)*u(y, z)`
Examples
========
>>> from sympy import Function, Eq, pde_separate_mul, Derivative as D
>>> from sympy.abc import x, y
>>> u, X, Y = map(Function, 'uXY')
>>> eq = Eq(D(u(x, y), x, 2), D(u(x, y), y, 2))
>>> pde_separate_mul(eq, u(x, y), [X(x), Y(y)])
[Derivative(X(x), (x, 2))/X(x), Derivative(Y(y), (y, 2))/Y(y)]
"""
return pde_separate(eq, fun, sep, strategy='mul')
def _separate(eq, dep, others):
"""Separate expression into two parts based on dependencies of variables."""
# FIRST PASS
# Extract derivatives depending our separable variable...
terms = set()
for term in eq.args:
if term.is_Mul:
for i in term.args:
if i.is_Derivative and not i.has(*others):
terms.add(term)
continue
elif term.is_Derivative and not term.has(*others):
terms.add(term)
# Find the factor that we need to divide by
div = set()
for term in terms:
ext, sep = term.expand().as_independent(dep)
# Failed?
if sep.has(*others):
return None
div.add(ext)
# FIXME: Find lcm() of all the divisors and divide with it, instead of
# current hack :(
# https://github.com/sympy/sympy/issues/4597
if len(div) > 0:
final = 0
for term in eq.args:
eqn = 0
for i in div:
eqn += term / i
final += simplify(eqn)
eq = final
# SECOND PASS - separate the derivatives
div = set()
lhs = rhs = 0
for term in eq.args:
# Check, whether we have already term with independent variable...
if not term.has(*others):
lhs += term
continue
# ...otherwise, try to separate
temp, sep = term.expand().as_independent(dep)
# Failed?
if sep.has(*others):
return None
# Extract the divisors
div.add(sep)
rhs -= term.expand()
# Do the division
fulldiv = reduce(operator.add, div)
lhs = simplify(lhs/fulldiv).expand()
rhs = simplify(rhs/fulldiv).expand()
# ...and check whether we were successful :)
if lhs.has(*others) or rhs.has(dep):
return None
return [lhs, rhs]
|
the-stack_0_903 | from notifications.signals import notify
def notify_answer(request, topico, resposta):
recipient = resposta.parent.user if resposta.parent else topico.user
verb = 'responder'
description = f'{recipient} respondeu seu post em {topico.titulo}.'
url = topico.get_absolute_url() + f'#post{resposta.pk}'
if request.user.pk != recipient.pk:
notify.send(sender=request.user, recipient=recipient, target=topico,
action_object=resposta, verb=verb, description=description, url=url) |
the-stack_0_904 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torchvision
from torch.autograd import Variable
import itertools
import operator
from itertools import islice
from collections import OrderedDict
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted from: Adrien Ecoffet https://github.com/AdrienLE
def parameters(self):
for name, param in self.named_params(self):
yield param
def named_parameters(self):
for name, param in self.named_params(self):
yield name, param
def named_leaves(self):
return []
def named_submodules(self):
return []
def named_params(self, curr_module=None, memo=None, prefix=''):
if memo is None:
memo = set()
if hasattr(curr_module, 'named_leaves'):
for name, p in curr_module.named_leaves():
if p is not None and p not in memo:
memo.add(p)
yield prefix + ('.' if prefix else '') + name, p
else:
for name, p in curr_module._parameters.items():
if p is not None and p not in memo:
memo.add(p)
yield prefix + ('.' if prefix else '') + name, p
for mname, module in curr_module.named_children():
submodule_prefix = prefix + ('.' if prefix else '') + mname
for name, p in self.named_params(module, memo, submodule_prefix):
yield name, p
def update_params(self, lr_inner, first_order=False, source_params=None, detach=False):
if source_params is not None:
for tgt, src in zip(self.named_params(self), source_params):
name_t, param_t = tgt
# name_s, param_s = src
# grad = param_s.grad
# name_s, param_s = src
grad = src
if first_order:
grad = to_var(grad.detach().data)
tmp = param_t - lr_inner * grad
self.set_param(self, name_t, tmp)
else:
for name, param in self.named_params(self):
if not detach:
grad = param.grad
if first_order:
grad = to_var(grad.detach().data)
tmp = param - lr_inner * grad
self.set_param(self, name, tmp)
else:
param = param.detach_()
self.set_param(self, name, param)
def set_param(self,curr_mod, name, param):
if '.' in name:
n = name.split('.')
module_name = n[0]
rest = '.'.join(n[1:])
for name, mod in curr_mod.named_children():
if module_name == name:
self.set_param(mod, rest, param)
break
else:
setattr(curr_mod, name, param)
def detach_params(self):
for name, param in self.named_params(self):
self.set_param(self, name, param.detach())
def copy(self, other, same_var=False):
for name, param in other.named_params():
if not same_var:
param = to_var(param.data.clone(), requires_grad=True)
self.set_param(name, param)
class MetaLinear(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.Linear(*args, **kwargs)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
self.in_features = ignore.weight.size(1)
self.out_features = ignore.weight.size(0)
def forward(self, x):
return F.linear(x, self.weight, self.bias)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
class MetaSequential(MetaModule):
r"""A sequential container.
Modules will be added to it in the order they are passed in the constructor.
Alternatively, an ordered dict of modules can also be passed in.
To make it easier to understand, here is a small example::
# Example of using Sequential
model = MetaSequential(
MetaConv2d(1,20,5),
nn.ReLU(),
MetaConv2d(20,64,5),
nn.ReLU()
)
# Example of using Sequential with OrderedDict
model = MetaSequential(OrderedDict([
('conv1', MetaConv2d(1,20,5)),
('relu1', nn.ReLU()),
('conv2', MetaConv2d(20,64,5)),
('relu2', nn.ReLU())
]))
"""
def __init__(self, *args):
super(MetaSequential, self).__init__()
if len(args) == 1 and isinstance(args[0], OrderedDict):
for key, module in args[0].items():
self.add_module(key, module)
else:
for idx, module in enumerate(args):
self.add_module(str(idx), module)
def _get_item_by_idx(self, iterator, idx):
"""Get the idx-th item of the iterator"""
size = len(self)
idx = operator.index(idx)
if not -size <= idx < size:
raise IndexError('index {} is out of range'.format(idx))
idx %= size
return next(islice(iterator, idx, None))
def __getitem__(self, idx):
if isinstance(idx, slice):
return self.__class__(OrderedDict(list(self._modules.items())[idx]))
else:
return self._get_item_by_idx(self._modules.values(), idx)
def __setitem__(self, idx, module):
key = self._get_item_by_idx(self._modules.keys(), idx)
return setattr(self, key, module)
def __delitem__(self, idx):
if isinstance(idx, slice):
for key in list(self._modules.keys())[idx]:
delattr(self, key)
else:
key = self._get_item_by_idx(self._modules.keys(), idx)
delattr(self, key)
def __len__(self):
return len(self._modules)
def __dir__(self):
keys = super(Sequential, self).__dir__()
keys = [key for key in keys if not key.isdigit()]
return keys
def forward(self, input):
for module in self._modules.values():
input = module(input)
return input
class MetaModuleList(MetaModule):
r"""Holds submodules in a list.
:class:`~MetaModuleList` can be indexed like a regular Python list, but
modules it contains are properly registered, and will be visible by all
:class:`~MetaModule` methods.
Arguments:
modules (iterable, optional): an iterable of modules to add
Example::
class MyModule(MetaModule):
def __init__(self):
super(MyModule, self).__init__()
self.linears = MetaModuleList([MetaLinear(10, 10) for i in range(10)])
def forward(self, x):
# ModuleList can act as an iterable, or be indexed using ints
for i, l in enumerate(self.linears):
x = self.linears[i // 2](x) + l(x)
return x
"""
def __init__(self, modules=None):
super(MetaModuleList, self).__init__()
if modules is not None:
self += modules
def _get_abs_string_index(self, idx):
"""Get the absolute index for the list of modules"""
idx = operator.index(idx)
if not (-len(self) <= idx < len(self)):
raise IndexError('index {} is out of range'.format(idx))
if idx < 0:
idx += len(self)
return str(idx)
def __getitem__(self, idx):
if isinstance(idx, slice):
return self.__class__(list(self._modules.values())[idx])
else:
return self._modules[self._get_abs_string_index(idx)]
def __setitem__(self, idx, module):
idx = self._get_abs_string_index(idx)
return setattr(self, str(idx), module)
def __delitem__(self, idx):
if isinstance(idx, slice):
for k in range(len(self._modules))[idx]:
delattr(self, str(k))
else:
delattr(self, self._get_abs_string_index(idx))
# To preserve numbering, self._modules is being reconstructed with modules after deletion
str_indices = [str(i) for i in range(len(self._modules))]
self._modules = OrderedDict(list(zip(str_indices, self._modules.values())))
def __len__(self):
return len(self._modules)
def __iter__(self):
return iter(self._modules.values())
def __iadd__(self, modules):
return self.extend(modules)
def __dir__(self):
keys = super(ModuleList, self).__dir__()
keys = [key for key in keys if not key.isdigit()]
return keys
def insert(self, index, module):
r"""Insert a given module before a given index in the list.
Arguments:
index (int): index to insert.
module (MetaModule): module to insert
"""
for i in range(len(self._modules), index, -1):
self._modules[str(i)] = self._modules[str(i - 1)]
self._modules[str(index)] = module
def append(self, module):
r"""Appends a given module to the end of the list.
Arguments:
module (MetaModule): module to append
"""
self.add_module(str(len(self)), module)
return self
def extend(self, modules):
r"""Appends modules from a Python iterable to the end of the list.
Arguments:
modules (iterable): iterable of modules to append
"""
if not isinstance(modules, container_abcs.Iterable):
raise TypeError("ModuleList.extend should be called with an "
"iterable, but got " + type(modules).__name__)
offset = len(self)
for i, module in enumerate(modules):
self.add_module(str(offset + i), module)
return self
class ModuleDict(MetaModule):
r"""Holds submodules in a dictionary.
:class:`~MetaModuleDict` can be indexed like a regular Python dictionary,
but modules it contains are properly registered, and will be visible by all
:class:`~MetaModule` methods.
:class:`~MetaModuleDict` is an **ordered** dictionary that respects
* the order of insertion, and
* in :meth:`~MetaModuleDict.update`, the order of the merged ``OrderedDict``
or another :class:`~MetaModuleDict` (the argument to :meth:`~MetaModuleDict.update`).
Note that :meth:`~MetaModuleDict.update` with other unordered mapping
types (e.g., Python's plain ``dict``) does not preserve the order of the
merged mapping.
Arguments:
modules (iterable, optional): a mapping (dictionary) of (string: module)
or an iterable of key-value pairs of type (string, module)
Example::
class MyModule(MetaModule):
def __init__(self):
super(MyModule, self).__init__()
self.choices = MetaModuleDict({
'conv': MetaConv2d(10, 10, 3),
'pool': nn.MaxPool2d(3)
})
self.activations = MetaModuleDict([
['lrelu', nn.LeakyReLU()],
['prelu', nn.PReLU()]
])
def forward(self, x, choice, act):
x = self.choices[choice](x)
x = self.activations[act](x)
return x
"""
def __init__(self, modules=None):
super(MetaModuleDict, self).__init__()
if modules is not None:
self.update(modules)
def __getitem__(self, key):
return self._modules[key]
def __setitem__(self, key, module):
self.add_module(key, module)
def __delitem__(self, key):
del self._modules[key]
def __len__(self):
return len(self._modules)
def __iter__(self):
return iter(self._modules)
def __contains__(self, key):
return key in self._modules
def clear(self):
"""Remove all items from the ModuleDict.
"""
self._modules.clear()
def pop(self, key):
r"""Remove key from the ModuleDict and return its module.
Arguments:
key (string): key to pop from the ModuleDict
"""
v = self[key]
del self[key]
return v
def keys(self):
r"""Return an iterable of the ModuleDict keys.
"""
return self._modules.keys()
def items(self):
r"""Return an iterable of the ModuleDict key/value pairs.
"""
return self._modules.items()
def values(self):
r"""Return an iterable of the ModuleDict values.
"""
return self._modules.values()
def update(self, modules):
r"""Update the :class:`~MetaModuleDict` with the key-value pairs from a
mapping or an iterable, overwriting existing keys.
.. note::
If :attr:`modules` is an ``OrderedDict``, a :class:`~MetaModuleDict`, or
an iterable of key-value pairs, the order of new elements in it is preserved.
Arguments:
modules (iterable): a mapping (dictionary) from string to :class:`~MetaModule`,
or an iterable of key-value pairs of type (string, :class:`~MetaModule`)
"""
if not isinstance(modules, container_abcs.Iterable):
raise TypeError("ModuleDict.update should be called with an "
"iterable of key/value pairs, but got " +
type(modules).__name__)
if isinstance(modules, container_abcs.Mapping):
if isinstance(modules, (OrderedDict, ModuleDict)):
for key, module in modules.items():
self[key] = module
else:
for key, module in sorted(modules.items()):
self[key] = module
else:
for j, m in enumerate(modules):
if not isinstance(m, container_abcs.Iterable):
raise TypeError("ModuleDict update sequence element "
"#" + str(j) + " should be Iterable; is" +
type(m).__name__)
if not len(m) == 2:
raise ValueError("ModuleDict update sequence element "
"#" + str(j) + " has length " + str(len(m)) +
"; 2 is required")
self[m[0]] = m[1]
def forward(self):
raise NotImplementedError()
class LeNet(MetaModule):
def __init__(self, n_out):
super(LeNet, self).__init__()
layers = []
layers.append(MetaConv2d(1, 6, kernel_size=5))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
layers.append(MetaConv2d(6, 16, kernel_size=5))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
layers.append(MetaConv2d(16, 120, kernel_size=5))
layers.append(nn.ReLU(inplace=True))
self.main = nn.Sequential(*layers)
layers = []
layers.append(MetaLinear(120, 84))
layers.append(nn.ReLU(inplace=True))
layers.append(MetaLinear(84, n_out))
self.fc_layers = nn.Sequential(*layers)
def forward(self, x):
x = self.main(x)
x = x.view(-1, 120)
return self.fc_layers(x).squeeze()
|
the-stack_0_905 | #!/usr/bin/env python
# Software License Agreement (Apache License 2.0)
#
# Copyright 2017 Florian Kromer
#
# 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.
import rospy
class ParameterContractViolation(Exception):
"""
Basic exception for contract violations raised by parameters.
"""
def __init__(self, name, msg=None):
if msg is None:
# default error message
msg = "Contract violation of parameter %s" % name
super(ParameterContractViolation, self).__init__(msg)
# make name accessible for exception handling
self.name = name
class ParameterValueViolation(ParameterContractViolation):
"""
Exception for value contract violations raised by parameters.
"""
def __init__(self, name, value):
super(ParameterValueViolation, self).__init__(
name, msg="Parameter %s violated contract with value %s" % (name, value))
self.value = value
def _check_parameter_exists(name):
"""
Checks if a parameter exists.
Args:
name (string): Name of the parameter.
Returns:
bool: True if existing, False if not existing.
"""
if rospy.has_param(name):
return True
return False
def assert_parameter_exists(name):
"""
Indicates a contract violation if the parameter is expected to exist but if
it does not exist by raising an exception.
Args:
name (string): Name of the parameter.
Raises:
ParameterContractViolation: Raised if parameter is not existing.
"""
if not _check_parameter_exists(name):
raise ParameterContractViolation(name, "Parameter %s not existing" % (name))
def enforce_parameter_exists(name):
"""
Indicates a contract violation if the parameter is expected to exist but if
it does not exist by logging or diagnostics.
Args:
name (string): Name of the parameter.
"""
if not _check_parameter_exists(name):
rospy.logwarn("Parameter %s not existing" % (name))
def assert_parameter_not_exists(name):
"""
Indicates a contract violation if the parameter is expected to not exist but
if it does exist.
Args:
name (string): Name of the parameter.
Raises:
ParameterContractViolation: Raised if parameter is existing.
"""
if rospy.has_param(name):
raise ParameterContractViolation(name, "Parameter %s existing" % (name))
def assert_parameter_has_value(name, value):
"""
Indicates a contract violation if it is expected that the parameter has a
specific value but if it has not.
Args:
name (string): Name of the parameter.
value (depends on the parameter type): Value of the parameter.
Raises:
ParameterValueViolation: Raised if parameter value is not like expected.
"""
if rospy.has_param(name):
observed_value = rospy.get_param(name)
if value != observed_value:
ParameterValueViolation(name, value)
else:
raise ParameterContractViolation(name, "Parameter %s not existing" % (name))
def assert_parameter_in_range(name, lower_bound, upper_bound):
"""
Indicates a contract violation if it is expected that a parameter value of
type 32-bit integers has a value within a defined range but if it has not.
Args:
name (string): Name of the parameter.
Raises:
ParameterValueViolation: Raised if parameter value is not in the range.
ParameterContractViolation: Raised if parameter does not exist.
"""
if rospy.has_param(name):
value = rospy.get_param(name)
if lower_bound > value > upper_bound:
raise ParameterValueViolation(name, value)
else:
raise ParameterContractViolation(name, "Parameter %s not existing" % (name))
def assert_parameter_out_range(name, lower_bound, upper_bound):
"""
Indicates a contract violation if it is expected that a parameter value of
type 32-bit integers has a value outside a defined range but if it has not.
Args:
name (string): Name of the parameter.
Raises:
ParameterValueViolation: Raised if parameter value is not outside the range.
ParameterContractViolation: Raised if parameter does not exist.
"""
if rospy.has_param(name):
value = rospy.get_param(name)
if lower_bound > value > upper_bound:
raise ParameterValueViolation(name, value)
else:
raise ParameterContractViolation(name, "Parameter %s not existing" % (name))
|
the-stack_0_913 | """
===============
Subplots Adjust
===============
Adjusting the spacing of margins and subplots using
:func:`~matplotlib.pyplot.subplots_adjust`.
"""
import matplotlib.pyplot as plt
import numpy as np
# Fixing random state for reproducibility
np.random.seed(19680801)
plt.subplot(211)
plt.imshow(np.random.random((100, 100)), cmap=plt.cm.BuPu_r)
plt.subplot(212)
plt.imshow(np.random.random((100, 100)), cmap=plt.cm.BuPu_r)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.075, 0.8])
plt.colorbar(cax=cax)
plt.show()
|
the-stack_0_914 | #! /usr/bin/python2
#
# Copyright (c) 2017 Intel Corporation
#
# SPDX-License-Identifier: Apache-2.0
#
import codecs
import os
import shutil
import socket
import string
import subprocess
import sys
import telnetlib
import tempfile
import time
import serial
import commonl
import ttbl
import ttbl.cm_loopback
import ttbl.cm_serial
import ttbl.config
import ttbl.pc_ykush
import ttbl.tt_qemu
class tt_serial(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial):
"""A generic test target, power switched with a pluggable power
control implementation and with one or more serial ports.
Example configuration::
>>> ttbl.config.target_add(
>>> tt_serial(
>>> "minnow-01",
>>> power_control = ttbl.pc.dlwps7("http://URL"),
>>> serial_ports = [
>>> { "port": "/dev/tty-minnow-01", "baudrate": 115200 }
>>> ]),
>>> tags = {
>>> 'build_only': True,
>>> 'bsp_models': { 'x86': None },
>>> 'bsps': {
>>> 'x86': dict(board = 'minnowboard',
>>> console = "")
>>> }
>>> },
>>> target_type = "minnow_max")
With a udev configuration that generated the ``/dev/tty-minnow-01``
name such as ``/etc/udev/rules.d/SOMETHING.rules``::
SUBSYSTEM == "tty", ENV{ID_SERIAL_SHORT} == "SERIALNUMBER", \
GROUP = "SOMEGROUP", MODE = "0660", \
SYMLINK += "tty-minnow-01"
:param power_control: an instance of an implementation
of the power_control_mixin used to implement power control for
the target. Use ttbl.pc.manual() for manual power control that
requires user interaction.
:param serial_ports: list of serial port dictionaries, specified
as for :func:`serial.serial_for_url` with a couple of extras as
specified in :class:`ttbl.cm_serial`.
"""
def __init__(self, id, power_control, serial_ports,
_tags = None, target_type = None):
ttbl.test_target.__init__(self, id, _tags = _tags, _type = target_type)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.cm_serial.cm_serial.__init__(self, self.state_dir, serial_ports)
class tt_power(
ttbl.test_target,
ttbl.tt_power_control_mixin):
def __init__(self, id, power_control, power = None):
"""
A generic test target for just power control
>>> ttbl.config.target_add(
>>> ttbl.tt.tt_power(name, ttbl.pc.dlwps7(URL), power = None),
>>> tags = dict(idle_poweroff = 0))
:param bool power: if specified, switch the power of the target
upon initialization; *True* powers it on, *False* powers it
off, *None* does nothing.
"""
assert isinstance(id, basestring)
ttbl.test_target.__init__(self, id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
if power == True:
self.log.info("Powering on per configuration")
self._power_on_do()
elif power == False:
self.log.info("Powering off per configuration")
self._power_off_do()
class tt_power_lc(
ttbl.test_target,
ttbl.cm_loopback.cm_loopback,
ttbl.tt_power_control_mixin):
def __init__(self, id, power_control, power = None, consoles = None):
"""
A generic test target for just power control and fake loopback consoles
>>> ttbl.config.target_add(
>>> ttbl.tt.tt_power(name, ttbl.pc.dlwps7(URL), power = None))
:param bool power: if specified, switch the power of the target
upon initialization; *True* powers it on, *False* powers it
off, *None* does nothing.
:param consoles: see :class:`ttbl.cm_loopback.cm_loopback`.
"""
ttbl.test_target.__init__(self, id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.cm_loopback.cm_loopback.__init__(self, self.state_dir, consoles)
if power == True:
self.log.info("Powering on per configuration")
self._power_on_do()
elif power == False:
self.log.info("Powering off per configuration")
self._power_off_do()
class tt_arduino2(
ttbl.test_target,
ttbl.test_target_images_mixin,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial):
#: Command to call to execute the BOSSA command line flasher
bossac_cmd = "bossac"
def __init__(self, _id, serial_port,
power_control = None,
bossac_cmd = "bossac"):
"""Test target for a target flashable with the bossac tool (mostly
Arduino Due)
*Requirements*
- Needs a connection to the USB programming port
- Uses the bossac utility built on the *arduino* branch from
https://github.com/shumatech/BOSSA/tree/arduino; requires it
to be installed in the path ``bossac_cmd`` (defaults to sytem
path). Supports ``kernel{,-arm}`` images::
$ git clone https://github.com/shumatech/BOSSA.git bossac.git
$ cd bossac.git
$ make -k
$ sudo install -o root -g root bin/bossac /usr/local/bin
- TTY devices need to be properly configured permission wise for
bossac and serial console to work; for such, choose a Unix group
which can get access to said devices and add udev rules such as::
# Arduino2 boards: allow reading USB descriptors
SUBSYSTEM=="usb", ATTR{idVendor}=="2a03", ATTR{idProduct}=="003d", \
GROUP="GROUPNAME", MODE = "660"
# Arduino2 boards: allow reading serial port
SUBSYSTEM == "tty", ENV{ID_SERIAL_SHORT} == "SERIALNUMBER", \
GROUP = "GROUPNAME", MODE = "0660", \
SYMLINK += "tty-TARGETNAME"
The theory of operation is quite simple. According to
https://www.arduino.cc/en/Guide/ArduinoDue#toc4, the Due will
erase the flash if you open the programming port at 1200bps
and then start a reset process and launch the flash when you
open the port at 115200. This is not so clear in the URL
above, but this is what expermientation found.
So for flashing, we'll take over the console, set the serial
port to 1200bps, wait a wee bit and then call bossac.
We need power control to fully reset the Arduino Due when it
gets in a tight spot (and to save power when not using it).
There is no reset, we just power cycle -- found no way to do a
reset in SW without erasing the flash.
:param str _id: name identifying the target
:param str serial_port: File name of the device node
representing the serial port this device is connected to.
:param ttbl.tt_power_control_impl power_control: power controller
(if any)
:param bossac_cmd: Path and file where to find the `bossac`
utility.
"""
self.serial_port = serial_port
self.serial_port_basename = os.path.basename(serial_port)
#:param power_url: http://USER:PASSWORD@HOST:PORT/OUTLETNUMBER
ttbl.test_target.__init__(self, _id)
ttbl.test_target_images_mixin.__init__(self)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.cm_serial.cm_serial.__init__(
self, self.state_dir,
[
"pc",
{ 'port': serial_port, 'baudrate': 115200 }
])
self.bossac_cmd = bossac_cmd
def image_do_set(self, image_type, image_name):
"""Just validates the image types are ok. The flashing happens in
images_do_set().
:param str image_type: Type of the image supported
:param str image_name: Name of image file in the daemon
storage space for the user
:raises: Any exception on failure
"""
if image_type != "kernel" and image_type != "kernel-arm":
raise self.unsupported_image_e("%s: image type not supported "
"(only kernel or kernel-arm)"
% image_type)
self.power_on(self.owner_get())
with self.console_takeover():
# erase the flash by opening the serial port at 1200bps
self.log.info("Erasing the flash")
eo = serial.Serial(port = self.serial_port, baudrate = 1200)
time.sleep(0.25)
eo.close()
self.log.debug("Erased the flash")
# now write it
cmdline = [ self.bossac_cmd,
"-p", self.serial_port_basename,
"-e", # Erase current
"-w", # Write a new one
"-v", # Verify,
"-b", # Boot from Flash
image_name ]
self.log.info("flashing image with: %s" % " ".join(cmdline))
so = commonl.logfile_open("bossac", type(self), True, 0)
s = subprocess.Popen(
cmdline, stdin = None, cwd = "/tmp",
stdout = so, stderr = subprocess.STDOUT)
self.log.info("running %s" % (" ".join(cmdline)))
r = s.wait()
del s
so.seek(0)
# Say what happened
if r != 0:
self.log.error("flashing failed")
m = ""
with codecs.open(so.name, "r", encoding = 'utf-8') as so_r:
for line in so_r:
line = line.decode('utf-8').strip()
self.log.error("flashing output: " + line)
m += "flashing output: " + line + "\n"
raise Exception("Flashing failed\n" + m)
# Check the log, if it does not say "Verify succesful", it didn't work
with codecs.open(so.name, "r", encoding = 'utf-8') as so_r:
m = ""
for line in so_r:
line = line.decode('utf-8').strip()
if line.endswith("Verify successful"):
break
m += "flashing output: " + line + "\n"
else:
raise Exception(
"Flashing failed (can't find 'Verify syccessful')\n" + m)
self.log.info("flashing succeeded")
with codecs.open(so.name, "r", encoding = 'utf-8') as so_r:
for line in so_r:
line = line.strip()
self.log.debug("flashing: " + line)
def images_do_set(self, images):
pass
class tt_esp32(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial,
ttbl.test_target_images_mixin):
esptool_path = "__unconfigured__tt_esp32.esptool_path__"
def __init__(self, _id, serial_number,
power_control, serial_port):
"""\
Test target ESP32 Tensilica based MCUs that use the ESP-IDF framework
:param str _id: name identifying the target
:param str serial_number: Unique USB serial number of the device (can
be updated with http://cp210x-program.sourceforge.net/)
:param power_control: Power control implementation or rail
(:class:`ttbl.tt_power_control_impl` or list of such)
:param str serial_port: Device name of the serial port where
the console will be found. This can be set with udev to be a
constant name.
The base code will convert the *ELF* image to the required
*bin* image using the ``esptool.py`` script. Then it will
flash it via the serial port.
*Requirements*
- The ESP-IDK framework, of which ``esptool.py`` is used to
flash the target; to install::
$ cd /opt
$ git clone --recursive https://github.com/espressif/esp-idf.git
(note the ``--recursive``!! it is needed so all the
submodules are picked up)
configure path to it globally by setting
:attr:`esptool_path` in a /etc/ttbd-production/conf_*.py file:
.. code-block:: python
import ttbl.tt
ttbl.tt.tt_esp32.esptool_path = "/opt/esp-idf/components/esptool_py/esptool/esptool.py"
Note you will also most likely need this in the client to
compile code for the board.
- Permissions to use USB devices in */dev/bus/usb* are needed;
*ttbd* usually roots with group *root*, which shall be
enough.
- Needs power control for proper operation; FIXME: pending to
make it operate without power control, using ``esptool.py``.
"""
assert isinstance(_id, basestring)
assert isinstance(serial_number, basestring)
assert isinstance(power_control, ttbl.tt_power_control_impl) \
or isinstance(power_control, list)
self.serial_number = serial_number
ttbl.test_target.__init__(self, _id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.test_target_images_mixin.__init__(self)
self.serial_port = serial_port
ttbl.cm_serial.cm_serial.__init__(
self, self.state_dir,
[
"pc",
{ 'port': serial_port, 'baudrate': 115200 }
])
def images_do_set(self, images):
# We implement image_do_set(), as there is only one image to set
pass
def image_do_set(self, image_type, image_name):
"""Just validates the image types are ok. The flashing happens in
images_do_set().
:param str image_type: Type of the image supported
:param str image_name: Name of image file in the daemon
storage space for the user
:raises: Any exception on failure
"""
cmdline_convert = [
self.esptool_path,
"--chip", "esp32",
"elf2image",
]
cmdline_flash = [
self.esptool_path,
"--chip", "esp32",
"--port", self.serial_port,
"--baud", "921600",
"--before", "default_reset",
"write_flash", "-u",
"--flash_mode", "dio",
"--flash_freq", "40m",
"--flash_size", "detect",
"0x1000",
]
if image_type == "kernel":
image_type = "kernel-xternsa"
if not image_type.startswith("kernel-"):
raise RuntimeError(
"Unknown image type '%s' (valid: kernel-{%s})"
% (image_type, ",".join(self.tags['bsps'].keys())))
image_name_bin = image_name + ".bin"
try:
cmdline = cmdline_convert + [ image_name,
"--output", image_name_bin ]
self.log.info("converting with %s" % " ".join(cmdline))
s = subprocess.check_output(cmdline, cwd = "/tmp",
stderr = subprocess.STDOUT)
except subprocess.CalledProcessError as e:
self.log.error("converting image with %s failed: (%d) %s"
% (" ".join(cmdline), e.returncode, e.output))
raise
self._power_cycle_do()
with self.console_takeover(): # give up the serial port
try:
cmdline = cmdline_flash + [ image_name_bin ]
self.log.info("flashing with %s" % " ".join(cmdline))
s = subprocess.check_output(cmdline, cwd = "/tmp",
stderr = subprocess.STDOUT)
self.log.info("flashed with %s: %s" % (" ".join(cmdline), s))
except subprocess.CalledProcessError as e:
self.log.error("flashing with %s failed: (%d) %s"
% (" ".join(cmdline), e.returncode, e.output))
raise
self._power_off_do()
self.log.info("flashing succeeded")
class tt_flasher(
ttbl.test_target,
ttbl.test_target_images_mixin,
ttbl.tt_power_control_mixin,
ttbl.tt_debug_mixin,
ttbl.cm_serial.cm_serial):
class error(RuntimeError):
pass
def __init__(self, _id, serial_ports,
flasher, power_control):
"""Test target flashable, power switchable with debuggin
Any target which supports the :class:`ttbl.flasher.flasher_c`
interface can be used, mostly OpenOCD targets.
How we use this, is for example:
>>> flasher_openocd = ttbl.flasher.openocd_c("frdm_k64f", FRDM_SERIAL,
>>> openocd10_path, openocd10_scripts)
>>> ttbl.config.target_add(
>>> ttbl.tt.tt_flasher(
>>> NAME,
>>> serial_ports = [
>>> "pc",
>>> dict(port = "/dev/tty-NAME", baudrate = 115200)
>>> ],
>>> flasher = flasher_obj,
>>> power_control = [
>>> ttbl.pc_ykush.ykush(YKUSH_SERIAL, YKUSH_PORT)
>>> # delay until device comes up
>>> ttbl.pc.delay_til_usb_device(FRDM_SERIAL),
>>> ttbl.cm_serial.pc(), # Connect serial ports
>>> flasher_openocd, # Start / stop OpenOCD
>>> ]
>>> ),
>>> tags = {
>>> 'bsp_models' : { 'arm': None },
>>> 'bsps' : {
>>> "arm": dict(board = "frdm_k64f", kernelname = 'zephyr.bin',
>>> kernel = [ "micro", "nano" ],
>>> console = "", quark_se_stub = "no"),
>>> },
>>> 'slow_flash_factor': 5, # Flash verification slow
>>> 'flash_verify': 'False', # Or disable it ...
>>> },
>>> target_type = "frdm_k64f")
.. note: the power for this target is a normal power control
implementation, HOWEVER, the power rail also contains
the OpenOCD flasher to start/stop the daemon once the
board is powered up.
:param str _id: target name
:param serial_ports: list of serial port dictionaries,
specified as for :func:`serial.serial_for_url` with a couple
of extras as specified in :class:`ttbl.cm_serial`.
:param ttbl.flasher.flasher_c flasher: flashing object that
provides access to deploy images and debug control
:param power_control: an instance of an implementation
of the power_control_mixin used to implement power control for
the target. Use ttbl.pc.manual() for manual power control that
requires user interaction.
"""
ttbl.test_target.__init__(self, _id)
ttbl.test_target_images_mixin.__init__(self)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.tt_debug_mixin.__init__(self)
ttbl.cm_serial.cm_serial.__init__(self, self.state_dir, serial_ports)
self.flasher = flasher
self.flasher.test_target_link(self)
self.power_on_post_fns.append(self.power_on_do_post)
self.power_off_pre_fns.append(self.power_off_do_pre)
# Debugging interface
#
# We don't do much other than resuming the target if we stop
# debugging
def debug_do_start(self, tt_ignored):
pass
def debug_do_halt(self, _):
if self.flasher:
self.flasher.target_halt(for_what = "debug_halt")
def debug_do_reset(self, _):
if self.flasher:
self.flasher.target_reset_halt(for_what = "debug_reset")
def debug_do_reset_halt(self, _):
if self.flasher:
self.flasher.target_reset_halt(for_what = "debug_reset_halt")
def debug_do_resume(self, _):
if self.flasher:
self.flasher.target_resume(for_what = "debug_resume")
def debug_do_stop(self, _):
if self.flasher:
self.flasher.target_resume()
def debug_do_info(self, _):
# FIXME: self.flasher should be providing this information, this
# is breaking segmentation
count = 2 # port #0 is for telnet, #1 for TCL
tcp_port_base_s = self.fsdb.get("openocd.port")
if tcp_port_base_s == None:
return "Debugging information not available, power on?"
tcp_port_base = int(tcp_port_base_s)
s = "OpenOCD telnet server: %s %d\n" \
% (socket.getfqdn('0.0.0.0'), tcp_port_base)
for target in self.flasher.board['targets']:
s += "GDB server: %s: tcp:%s:%d\n" % (target,
socket.getfqdn('0.0.0.0'),
tcp_port_base + count)
count +=1
if self.fsdb.get('powered') != None:
s += "Debugging available as target is ON"
else:
s += "Debugging not available as target is OFF"
return s
def debug_do_openocd(self, _, command):
return self.flasher.openocd_cmd(command)
# Wrap actual reset with retries
def target_reset_halt(self, for_what = ""):
tries = 1
tries_max = 2
# FIXME: current limitation, can't access the tags from the
# constructor as the ones we add in target_add() aren't there
# yet.
wait = \
float(self.tags.get('hard_recover_rest_time', 2))
while tries <= tries_max:
# The Arduino101 get's so stuck sometimes
try:
self.flasher.target_reset_halt(for_what)
break
except self.flasher.error:
pass
try_s = "%d/%d" % (tries, tries_max)
time.sleep(2)
try:
self.flasher.target_reset("[recover reset #1 %s] " % try_s
+ for_what)
except self.flasher.error:
pass
try:
self.flasher.target_reset_halt("[retry %s] " % try_s
+ for_what)
break
except self.flasher.error:
pass
# In some targets, this fails because maybe we just
# power-cycled and the JTAG said it was ready but it
# is really not ready...when that happens, just
# power-cycle again.
# well, that didn't work either; bring the big guns,
# power cycle it and try the whole thing again
wait_s = (1 + 2.0 * tries/tries_max) * wait
self.log.info("Failed to reset/halt, power-cycle (%.2fs) "
"and retrying (try %d/%d)"
% (wait_s, tries, tries_max))
self.power_cycle(self.owner_get(), wait_s)
tries += 1
else:
# FIXME: pass the exception we get or the log or something
raise self.error("Can't reset/halt the target")
def target_reset(self, for_what = ""):
tries = 1
tries_max = 5
# FIXME: current limitation, can't access the tags from the
# constructor as the ones we add in target_add() aren't there
# yet.
wait = \
float(self.tags.get('hard_recover_rest_time', 10))
while tries <= tries_max:
# The Arduino101 get's so stuck sometimes
try:
self.flasher.target_reset(for_what)
break
except self.flasher.error:
pass
# Try again
try:
self.flasher.target_reset(for_what)
break
except self.flasher.error:
pass
# Bring the big guns, power cycle it
if wait != None:
wait_s = tries * wait
self.log.info("Failed to reset/run, power-cycle (%.2fs) "
"and retrying (try %d/%d)"
% (wait_s, tries, tries_max))
self.power_cycle(self.owner_get(), wait_s)
tries += 1
else:
# FIXME: pass the exception we get or the log or something
raise self.error("Can't reset/run the target")
# Power interface
#
# Fire up the flasher when we power the target up, so it can
# access the JTAG
def power_on_do_post(self):
self.flasher.start()
def power_off_do_pre(self):
self.flasher.stop()
def reset_do(self, _):
# We halt first so we can stop recording from the serial ports
# and then restart wihout getting any trash; we use reset_halt
# because it is a single command for all targets (halt needs
# to select each target).
self.flasher.target_reset_halt()
self.consoles_reset()
# When we reset, if we are debugging we need to halt the target as
# soon as it starts. Otherwise, we reset it normally. These
# are atomic (they act on all the targets at the same time..in
# theory)
if self.fsdb.get("debug") != None:
self.flasher.target_reset_halt()
else:
self.flasher.target_reset()
# Flashing interface -- quite simple, we need the target on and
# then just flash the image in.
def image_do_set(self, image_type, image_name):
pass
def images_do_set(self, images):
# FIXME: current limitation, can't access the tags from the
# constructor as the ones we add in target_add() aren't there
# yet.
wait = \
float(self.tags.get('hard_recover_rest_time', 10))
if self.fsdb.get("disable_power_cycle_before_flash") != 'True':
# Make sure the target is really fresh before flashing it
try:
# See the documentation for this on class flasher_c
# for why we have to do it.
self.flasher.hack_reset_after_power_on = True
self.power_cycle(self.owner_get(), wait = wait)
finally:
self.flasher.hack_reset_after_power_on = False
self.log.info("sleeping 2s after power cycle")
# HACK: For whatever the reason, we need to sleep before
# resetting/halt, seems some of the targets are not ready
# inmediately after
time.sleep(2)
self.target_reset_halt(for_what = "for image flashing")
timeout_factor = self.tags.get('slow_flash_factor', 1)
verify = self.tags.get('flash_verify', 'True') == 'True'
# FIXME: replace this check for verifying which image types
# the flasher supports
for t, n in images.iteritems():
if t == "kernel-x86":
it = "x86"
elif t == "kernel":
it = "x86"
elif t == "kernel-arc":
it = "arc"
elif t == "kernel-arm":
it = "arm"
elif t == "rom":
it = "rom"
elif t == "bootloader":
it = "bootloader"
else:
raise self.unsupported_image_e(
"%s: Unknown image type (expected "
"kernel|kernel-(x86,arc,arm), rom)"
% t)
try:
self.flasher.image_write(it, n, timeout_factor, verify)
except ValueError as e:
self.log.exception("flashing got exception: %s", e)
raise self.unsupported_image_e(e.message)
class tt_dfu(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial,
ttbl.test_target_images_mixin):
def __init__(self, _id, serial_number,
power_control, power_control_board,
serial_ports = None):
"""Test target for a flashable with DFU Utils
*Requirements*
- Needs a connection to the USB port that exposes a DFU
interface upon boot
- Uses the dfu-utils utility, available for most (if not all)
Linux distributions
- Permissions to use USB devices in */dev/bus/usb* are needed;
*ttbd* usually roots with group *root*, which shall be
enough.
- Needs power control for proper operation
:param str _id: name identifying the target
:param power_control: Power control implementation or rail
(:class:`ttbl.tt_power_control_impl` or list of such)
:param ttbl.tt_power_control_impl power_control: power controller
*just* for the board--this is the component in the power
control rail that controls the board only (versus other
parts such as serial ports or pseudo-power-controllers that
wait for the USB device to pop up.
Note the tags to the target must include, on each supported
BSP, a tag named *dfu_interface_name* listing the name of the
*altsetting* of the DFU interface to which the image for said
BSP needs to be flashed.
This can be found, when the device exposes the DFU interfaces
with the *lsusb -v* command; for example, for a tinyTILE
(output summarized for clarity)::
$ lsusb -v
...
Bus 002 Device 110: ID 8087:0aba Intel Corp.
Device Descriptor:
bLength 18
bDescriptorType 1
...
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update...
iInterface 4 x86_rom
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update...
iInterface 5 x86_boot
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 6 x86_app
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 7 config
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 8 panic
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 9 events
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 10 logs
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 11 sensor_core
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 12 ble_core
In this case, the three cores available are x86 (x86_app), arc
(sensor_core) and ARM (ble_core).
*Example*
A Tiny Tile can be connected, without exposing a serial console:
>>> pc_board = ttbl.pc_ykush.ykush("YK22909", 1)
>>>
>>> ttbl.config.target_add(
>>> tt_dfu("ti-01",
>>> serial_number = "5614010001031629",
>>> power_control = [
>>> pc_board,
>>> ttbl.pc.delay_til_usb_device("5614010001031629"),
>>> ],
>>> power_control_board = pc_board),
>>> tags = {
>>> 'bsp_models': { 'x86+arc': ['x86', 'arc'], 'x86': None, 'arc': None},
>>> 'bsps' : {
>>> "x86": dict(zephyr_board = "tinytile",
>>> zephyr_kernelname = 'zephyr.bin',
>>> dfu_interface_name = "x86_app",
>>> console = ""),
>>> "arm": dict(zephyr_board = "arduino_101_ble",
>>> zephyr_kernelname = 'zephyr.bin',
>>> dfu_interface_name = "ble_core",
>>> console = ""),
>>> "arc": dict(zephyr_board = "arduino_101_sss",
>>> zephyr_kernelname = 'zephyr.bin',
>>> dfu_interface_name = 'sensor_core',
>>> console = "")
>>> },
>>>
>>> },
>>> target_type = "tile"
>>> )
"""
assert isinstance(_id, basestring)
assert isinstance(serial_number, basestring)
assert isinstance(power_control, ttbl.tt_power_control_impl) \
or isinstance(power_control, list)
self.serial_number = serial_number
self.pc_board = power_control_board
self.pc_usb = ttbl.pc.delay_til_usb_device(serial_number)
ttbl.test_target.__init__(self, _id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.test_target_images_mixin.__init__(self)
ttbl.cm_serial.cm_serial.__init__(self, self.state_dir, serial_ports)
def images_do_set(self, images):
"""Just validates the image types are ok. The flashing happens in
images_do_set().
:param str image_type: Type of the image supported
:param str image_name: Name of image file in the daemon
storage space for the user
:raises: Any exception on failure
"""
# Power cycle the board so it goes into DFU mode; it then
# stays there for five seconds
self.pc_board.power_cycle_raw(self, 5)
self.pc_usb.power_on_do(self)
cmdline = [
"/usr/bin/dfu-util",
"-S", self.serial_number
]
for image_type, image_name in images.iteritems():
if image_type == "kernel":
image_type = "kernel-x86"
if not image_type.startswith("kernel-"):
raise RuntimeError(
"Unknown image type '%s' (valid: kernel-{%s})"
% (image_type, ",".join(self.tags['bsps'].keys())))
bsp = image_type[len("kernel-"):]
tags_bsp = self.tags.get('bsps', {}).get(bsp, None)
if tags_bsp == None:
raise RuntimeError(
"Unknown BSP %s from image type '%s' (valid: %s)"
% (bsp, image_type, " ".join(self.tags['bsps'].keys())))
dfu_if_name = tags_bsp.get('dfu_interface_name', None)
if dfu_if_name == None:
raise RuntimeError(
"Misconfigured target: image type %s (BSP %s) has "
"no 'dfu_interface_name' key to indicate which DFU "
"interface shall it flash"
% (image_type, bsp))
# now write it
cmdline += [
"-a", dfu_if_name,
"-D", image_name,
]
try:
self.log.info("flashing with %s" % (" ".join(cmdline)))
s = subprocess.check_output(cmdline, cwd = "/tmp",
stderr = subprocess.STDOUT)
self.log.info("flashed with %s: %s" % (" ".join(cmdline), s))
except subprocess.CalledProcessError as e:
self.log.error("flashing with %s failed: (%d) %s" %
(" ".join(cmdline), e.returncode, e.output))
raise
self.log.info("flashing succeeded")
self.pc_board.power_off_do(self)
def image_do_set(self, t, n):
pass
class tt_max10(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial,
ttbl.test_target_images_mixin):
"""
Test target for an Altera MAX10
This allows to flash images to an Altera MAX10, using the Quartus
tools, freely downloadable from http://dl.altera.com.
Exports the following interfaces:
- power control (using any AC power switch, such as the
:class:`Digital Web Power Switch 7 <ttbl.pc.dlwps7>`)
- serial console
- image (in hex format) flashing (using the Quartus Prime tools
package)
Multiple instances at the same time are supported; however, due to
the JTAG interface not exporting a serial number, addressing has
to be done by USB path, which is risky (as it will change when the
cable is plugged to another port or might be enumerated in a
different number).
Note that:
- when flashing LED1 blinks green/blue
- the blue power switch must be pressed, to ensure the board is
*ON* when we switch the AC power to the power brick on
- SW2 DIP bank on the back of the board has to be all OFF (down)
except for 3, that has to be ON (this comes from the Zephyr
Altera MAX10 configuration)
- J7 (at the front of the board, next to the coaxial connectors)
has to be open
Pending:
- CPU design hardcoded to use Zephyr's -- it shall be possible to
flash it
"""
#: Path where the Quartus Programmer binaries have been installed
#:
#: 1. Download Quartus Prime Programmer and Tools from
#: http://dl.altera.com/17.1/?edition=lite&platform=linux&download_manager=direct
#: 2. Install to e.g `/opt/intelFPGA/17.1/qprogrammer/bin`.
#: 3. Configure in /etc/ttbd-production/conf_00_max10.py::
#:
#: .. code-block: python
#:
#: import ttbl.tt
#: ttbl.tt.tt_max10.quartus_path = "/opt/intelFPGA/17.1/qprogrammer/bin"
quartus_path = "__unconfigured__tt_max10.quartus_path__"
#: Path to where the NIOS Zephyr CPU image has been installed
#:
#: 1. Download the CPU image to `/var/lib/ttbd`::
#:
#: $ wget -O /var/lib/ttbd/ghrd_10m50da.sof \
#: https://github.com/zephyrproject-rtos/zephyr/raw/master/arch/nios2/soc/nios2f-zephyr/cpu/ghrd_10m50da.sof
#:
#: 3. Configure in /etc/ttbd-production/conf_00_max10.py:
#:
#: .. code-block: python
#:
#: import ttbl.tt
#: ttbl.tt.tt_max10.input_sof = "/var/lib/ttbd/ghrd_10m50da.sof"
input_sof = "__unconfigured__tt_max10.input_sof__"
def __init__(self, _id, device_id,
power_control, serial_port = None):
assert isinstance(_id, basestring)
assert isinstance(device_id, basestring)
assert isinstance(power_control, ttbl.tt_power_control_impl) \
or isinstance(power_control, list)
self.device_id = device_id
ttbl.test_target.__init__(self, _id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.test_target_images_mixin.__init__(self)
self.serial_port = serial_port
if serial_port:
ttbl.cm_serial.cm_serial.__init__(
self, self.state_dir,
[
"pc",
{ 'port': serial_port, 'baudrate': 115200 }
])
else:
ttbl.cm_serial.cm_serial.__init__(self, self.state_dir, [])
quartus_cpf_template = """\
<?xml version="1.0" encoding="US-ASCII" standalone="yes"?>
<cof>
<output_filename>${OUTPUT_FILENAME}</output_filename>
<n_pages>1</n_pages>
<width>1</width>
<mode>14</mode>
<sof_data>
<user_name>Page_0</user_name>
<page_flags>1</page_flags>
<bit0>
<sof_filename>${SOF_FILENAME}<compress_bitstream>1</compress_bitstream></sof_filename>
</bit0>
</sof_data>
<version>10</version>
<create_cvp_file>0</create_cvp_file>
<create_hps_iocsr>0</create_hps_iocsr>
<auto_create_rpd>0</auto_create_rpd>
<rpd_little_endian>1</rpd_little_endian>
<options>
<map_file>1</map_file>
</options>
<MAX10_device_options>
<por>0</por>
<io_pullup>1</io_pullup>
<config_from_cfm0_only>0</config_from_cfm0_only>
<isp_source>0</isp_source>
<verify_protect>0</verify_protect>
<epof>0</epof>
<ufm_source>2</ufm_source>
<ufm_filepath>${KERNEL_FILENAME}</ufm_filepath>
</MAX10_device_options>
<advanced_options>
<ignore_epcs_id_check>2</ignore_epcs_id_check>
<ignore_condone_check>2</ignore_condone_check>
<plc_adjustment>0</plc_adjustment>
<post_chain_bitstream_pad_bytes>-1</post_chain_bitstream_pad_bytes>
<post_device_bitstream_pad_bytes>-1</post_device_bitstream_pad_bytes>
<bitslice_pre_padding>1</bitslice_pre_padding>
</advanced_options>
</cof>
"""
# XXX Do we care about FileRevision, DefaultMfr, PartName? Do they need
# to be parameters? So far seems to work across 2 different boards, leave
# this alone for now.
quartus_pgm_template = """\
/* Quartus Prime Version 16.0.0 Build 211 04/27/2016 SJ Lite Edition */
JedecChain;
FileRevision(JESD32A);
DefaultMfr(6E);
P ActionCode(Cfg)
Device PartName(10M50DAF484ES) Path("${POF_DIR}/") File("${POF_FILE}") MfrSpec(OpMask(1));
ChainEnd;
AlteraBegin;
ChainType(JTAG);
AlteraEnd;"""
def _create_pof(self, output_pof, input_sof, kernel_hex):
t = string.Template(self.quartus_cpf_template)
input_sof = os.path.abspath(input_sof)
kernel_hex = os.path.abspath(kernel_hex)
# These tools are very stupid and freak out if the desired filename
# extensions are used. The kernel image must have extension .hex
with tempfile.NamedTemporaryFile(dir = self.state_dir,
suffix = ".cof") as temp_xml:
xml = t.substitute(SOF_FILENAME = input_sof,
OUTPUT_FILENAME = output_pof.name,
KERNEL_FILENAME = kernel_hex)
temp_xml.write(xml)
temp_xml.flush()
try:
cmd = [
os.path.join(self.quartus_path, "quartus_cpf"),
"-c", temp_xml.name
]
subprocess.check_output(cmd)
except OSError as e:
raise RuntimeError("Failed to create POF file w/ %s: %s"
% (" ".join(cmd), e))
except subprocess.CalledProcessError as cpe:
raise RuntimeError("Failed to create POF file: %s"
% cpe.output.decode("UTF-8"))
return output_pof
def images_do_set(self, images):
# We implement image_do_set(), as there is only one image to set
pass
# FIXME: limitation: SOF image is fixed, should be possible to
# upload it and default to built-in? Problem is we need to fixup
# the build instructions so they understand they need to upload
# the SOF too
# FIXME: also, the SOF is kinda big, 3M
def image_do_set(self, image_type, image_name):
if image_type == "kernel":
image_type = "kernel-max10"
if not image_type.startswith("kernel-"):
raise RuntimeError(
"Unknown image type '%s' (valid: kernel-{%s})"
% (image_type, ",".join(self.tags['bsps'].keys())))
self._power_cycle_do()
# This code snippet lifted from Zephyr's
# scripts/support/quartus-flash.py -- thx
# Minimum changes to place files in directories and wipe them
# upon context exit, match local style .
# def _flash_kernel(device_id, input_sof, kernel_hex):
self.log.info("Flashing %s:%s" % (image_type, image_name))
with tempfile.NamedTemporaryFile(dir = self.state_dir,
suffix = ".pof") as output_pof, \
tempfile.NamedTemporaryFile(dir = self.state_dir,
suffix = ".hex") as kernel_hex, \
tempfile.NamedTemporaryFile(dir = self.state_dir,
suffix = ".cdf") as temp_cdf:
# Apparently, the tools get freaked out by our largish
# file names, so just make it a temp with a short sweet name
shutil.copyfile(image_name, kernel_hex.name)
pof_file = self._create_pof(output_pof, self.input_sof, kernel_hex.name)
dname, fname = os.path.split(pof_file.name)
t = string.Template(self.quartus_pgm_template)
cdf = t.substitute(POF_DIR = dname, POF_FILE = fname)
temp_cdf.write(cdf)
temp_cdf.flush()
try:
output = subprocess.check_output([
os.path.join(self.quartus_path, "quartus_pgm"),
"--quiet",
"-c", self.device_id,
temp_cdf.name
])
except subprocess.CalledProcessError as cpe:
raise RuntimeError("Failed to flash image: %s"
% cpe.output.decode("UTF-8"))
self.log.info("Flashed %s:%s; output:\n%s"
% (image_type, image_name, output))
self._power_off_do()
self.log.info("flashing succeeded")
class grub2elf(tt_serial, ttbl.test_target_images_mixin):
"""Boot anything that can take an ELF image with grub2
**Overview**
A platform that can EFI boot off a multiplexed boot USB drive;
this drive:
- when connected to the target, acts as boot drive which boots
into grub2 which multiboots into whatever ELF binary we gave it
- when connected to the server, we partition, format, install
grub2 and the ELF kernel to be booted.
An eight-port USBRLY8 relay bank acting as a USB switcher, each
relay switching one of the four USB lines from target to server,
using :class:`ttbl.usbrly08b.plugger`:
- the USB-A female cable is connected to the C relay terminals
- the USB-A male cable for the server is connected to the NC relay
terminals
- the USB-A male cable for the client is connected to the NO relay
terminal
- a target that EFI/boots and can boot off a USB drive
Limitations:
- kinda hardcoded x86-64, shall be easy to fix
**Methodology**
The power rail for the target ensures that when the target is
powered on, the USB boot drive is connected to the target by the
USB multiplexor. When the target is off, the USB boot drive is
connected to the server.
The imaging process in :meth:`image_do_set` will make sure the USB
drive is connected to the server (by powering off the target) and
then use the helper script ``/usr/share/tcf/setup-efi-grub2-elf.sh``
to flash the ELF kernel to the drive (as well, will create the
grub2 boot structure)--for this we need the drive's USB serial
number and the ELF file to boot.
Upon boot, the boot drive will be detected and booted by default,
as the grub configuration is set to just boot that ELF kernel.
For cases where BIOS interaction with the console might be
necessary, a boot coercer can be implemented in the form of a
power control implementation that in its `power_on_do()` method
talks to the serial port to do whatever is needed. See for example
:class:`conf_00_lib.minnowboard_EFI_boot_grub_pc` which does so
for Minnowboards.
**Setup**
- the helper script ``/usr/share/tcf/setup-efi-grub2-elf.sh`` is
used to partition, configure and setup the USB drive--it
is run with *sudo* (via the sudo configurations script
:download:`/etc/sudoers.d/ttbd_sudo <../ttbd/ttbd_sudo>`)
- The daemon will require specific capabilities for being able to
run *sudo* (*CAP_SETGID*, *CAP_SETUID*, *CAP_SYS_ADMIN*,
*CAP_FOWNER*, *CAP_DAC_OVERRIDE*) setup in
:download:`/etc/systemd/system/[email protected]
<../ttbd/[email protected]>`.
- Ensure the following packages are available in the system:
* parted
* dosfstools
* grub2-efi-x64-cdboot and grub2-efi-x64-modules
* util-linux
- Identify the serial number for the USB drive; plug it to a
machine and issue::
$ lsblk -o "NAME,SERIAL,VENDOR,MODEL"
NAME SERIAL VENDOR MODEL
sdb AOJROZB8 JetFlash Transcend 8GB
sdj 76508A8E JetFlash Transcend 8GB
...
(for this example, ours is *76508A8E*, `/dev/sdj`)
blank the USB drive (**NOTE!!!** This will destroy the drive's
contents)::
$ dd if=/dev/zero of=/dev/sdj
- Create a power controller
- Setup the target's BIOS to boot by default off the USB drive
See :func:`conf_00_lib.minnowboard_add` for an example instantiation.
"""
def __init__(self, _id,
power_controller,
usb_drive_serial,
usbrly08b_serial, usbrly08b_bank,
serial_port,
boot_coercer = None):
power_control = [
# Ensure the USB dongle is / has been connected to the server
ttbl.pc.delay_til_usb_device(usb_drive_serial,
when_powering_on = False,
want_connected = True),
ttbl.usbrly08b.plugger(usbrly08b_serial, usbrly08b_bank),
# let the dongle power up, otherwise it won't be seen
ttbl.pc.delay(2),
ttbl.pc.delay_til_usb_device(usb_drive_serial,
when_powering_on = True,
want_connected = False),
ttbl.pc.delay(2), # let USB dongle settle to the target
ttbl.cm_serial.pc(), # Let it open and close ports
power_controller,
ttbl.pc.delay(2), # board powers up...
]
# A boot coercer is a PCI that talks to the target to get it to
# boot right, so it only implements power_on_do() to do that,
# power_off_do() has only a pass and power_get_do() returns
# True.
# This is eg needed if we need to tell the bios to do this, do
# that -- in the case of Minnowboard, tell the EFI shell to
# run grub (sometimes).
if boot_coercer:
assert isinstance(boot_coercer, ttbl.tt_power_control_impl)
power_control.append(boot_coercer)
self.usb_drive_serial = usb_drive_serial
tt_serial.__init__(
self,
_id,
power_control,
serial_ports = [
"pc",
{ "port": serial_port, "baudrate": 115200 }
])
ttbl.test_target_images_mixin.__init__(self)
image_types_valid = ("kernel", "kernel-x86")
def image_do_set(self, image_type, image_name):
if image_type not in self.image_types_valid:
raise self.unsupported_image_e(
"%s: image type not supported (valid: %s)"
% (image_type, ", ".join(self.image_types_valid)))
# power off the board to flash, this will redirect the USB
# drive to be connected to the server
self.power_off(self.owner_get())
# We don't verify image_name is an ELF file so that we can
# also use this to flash other stuff and it's up to the Grub
# bootloader to interpret it.
# We need an image with a bootloader, we use grub2 and we
# share the setup-efi-grub2-elf.sh implementation from
# simics and others
cmd_path = commonl.ttbd_locate_helper("setup-efi-grub2-elf.sh",
log = self.log)
# Yeah, sudo ... it kinda sucks, but it is the best way to
# isolate it -- could run from the daemon, then it'd have too
# many permissions--nope. file ./ttbd.sudo contains the config
# to put in /etc/sudoers.d for this to work.
cmdline = [ "sudo", "-n", cmd_path, self.usb_drive_serial,
image_name, "x86_64" ]
try:
self.log.debug("flashing with command '%s'" % " ".join(cmdline))
output = subprocess.check_output(cmdline,
stderr = subprocess.STDOUT)
except subprocess.CalledProcessError as cpe:
msg = "flashing with command '%s' failed: %s" \
% (" ".join(cpe.cmd), cpe.output)
self.log.error(msg)
raise RuntimeError(msg)
self.log.debug("flashed with command '%s': %s"
% (" ".join(cmdline), output))
def images_do_set(self, images):
# No need to set multiple images at the same time
pass
class simics(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.tt_power_control_impl,
ttbl.test_target_images_mixin,
ttbl.test_target_console_mixin):
"""
Driver for a target based on Simics simulation of a platform
Currently this driver is quite basic and supports only the image
and console management interfaces:
- images are only supported as an ELF file that is booted by
*grub2* when simics boots from a hard disk image generated on
the fly.
- the only supported console is a serial output (no input)
**System setup**
1. In a configuration file (e.g. */etc/environment*), set the base
package for Simics::
SIMICS_BASE_PACKAGE=/opt/simics/5.0/simics-5.0.136
note that all the packages and extensions installed in there
must have been registered with the global Simics configuration,
as it will execute under the user as which the daemon is run
(usually *ttbd*).
Note that the installation of Simics and any extra packages
needed can be done automagically with::
$ destdir=/opt/simics/5.0
$ mkdir -p $destdir
# --batch: no questions asked, just proceed
# -a: auto select packages and register them
$ ./install-simics.pl --batch -a --prefix $destdir \\
package-1000-5.0.136-linux64.tar.gz.aes KEY-1000 \\
package-1001-5.0.54-linux64.tar.gz.aes KEY-1001 \\
package-1010-5.0.59-linux64.tar.gz.aes KEY-1010 \\
package-1012-5.0.24-linux64.tar.gz.aes KEY-1012 \\
package-2018-5.0.31-linux64.tar.gz.aes KEY-2018 \\
package-2075-5.0.50-linux64.tar.gz.aes KEY-2075
"""
class error_e(Exception): # pylint: disable = missing-docstring
pass
class simics_start_e(error_e): # pylint: disable = missing-docstring
pass
#: location of the base Simics installation in the file system; by
#: default this taken from the *SIMICS_BASE_PACKAGE* environment
#: variable, if it exists; it can also be set in a configuration
#: file as:
#:
#: >>> ttbl.tt.simics.base_package = "/some/path/simics-5.0.136"
base_package = os.environ.get('SIMICS_BASE_PACKAGE', None)
def __init__(self, _id, simics_cmds, _tags = None,
image_size_mb = 100):
assert isinstance(_id, basestring)
assert isinstance(simics_cmds, basestring)
assert image_size_mb > 0
if self.base_package == None:
raise RuntimeError(
"Simics not yet configured, either define environment "
"variable SIMICS_BASE_PACKAGE or configuration "
"ttbl.tt.simics.base_package")
ttbl.test_target.__init__(self, _id, _tags = _tags)
ttbl.tt_power_control_mixin.__init__(self)
ttbl.tt_power_control_impl.__init__(self)
ttbl.test_target_images_mixin.__init__(self)
ttbl.test_target_console_mixin.__init__(self)
self.simics_path = os.path.join(self.base_package, "bin/simics")
self.simics_check_path = os.path.join(self.base_package,
"linux64/bin/simics-common")
self.simics_cmds = simics_cmds
#: Variables that can be expanded in the Simics configuration
#: script passed as an argument
self.simics_vars = dict(
simics_workspace = os.path.join(self.state_dir,
"simics.workspace"),
simics_pidfile = os.path.join(self.state_dir, "simics.pid"),
simics_console = os.path.join(self.state_dir,
"simics-console.read"),
simics_hd0 = os.path.join(self.state_dir, "simics-hd0.img"),
simics_hd0_size = image_size_mb,
)
self.logfile_name = os.path.join(self.state_dir, "simics.log")
self.telnet = None
# FIXME: verify the BSP is kosher? generate command line from it?
image_types_valid = ( "kernel", "kernel-x86" )
# Image management interface
def image_do_set(self, image_type, image_name):
if image_type not in self.image_types_valid:
raise self.unsupported_image_e(
"%s: image type not supported (valid: %s)"
% (image_type, ", ".join(self.image_types_valid)))
# power off the target to flash, so in case simics is running
# on the image/files, it is stopped and we won't conflict /
# corrupt anything.
self.power_off(self.owner_get())
# Remove old image and create a new one, just writing one byte
# at the end to create a shallow file.
commonl.rm_f(self.simics_vars['simics_hd0'])
with open(self.simics_vars['simics_hd0'], "w") as f:
f.seek(self.simics_vars['simics_hd0_size'] * 1024 * 1024 - 1)
f.write('0')
# We don't verify image_name is an ELF file so that we can
# also use this to flash other stuff and it's up to the Grub
# bootloader to interpret it.
# Simics needs an image with a bootloader, we use grub2 and we
# share the setup-efi-grub2-elf.sh implementation from
# grub2elf.
cmd_path = commonl.ttbd_locate_helper("setup-efi-grub2-elf.sh",
log = self.log)
# Yeah, sudo ... it kinda sucks, but it is the best way to
# isolate it -- could run from the daemon, then it'd have too
# many permissions--nope. file ./ttbd_sudo contains the config
# to put in /etc/sudoers.d for this to work. Also note the
# systemd configuration requires us to have permission to
# regain certain capabilities.
cmdline = [ "sudo", "-n", cmd_path, self.simics_vars['simics_hd0'],
image_name, "i386" ]
try:
self.log.debug("flashing with '%s'" % " ".join(cmdline))
output = subprocess.check_output(cmdline,
stderr = subprocess.STDOUT)
except subprocess.CalledProcessError as cpe:
msg = "flashing with command '%s' failed: %s" \
% (" ".join(cpe.cmd), cpe.output)
self.log.error(msg)
raise RuntimeError(msg)
self.log.debug("flashed with command '%s': %s"
% (" ".join(cmdline), output))
def images_do_set(self, images):
pass
# power control interface
def _simics_launch(self, _target):
# Note this function will be called again if there is a
# resource conflict because simics will fail to start and
# _power_on_do() will detect it.
cmd_file_name = os.path.join(self.state_dir, "commands")
# clean up old state, but NOT the hd, as we probably created
# the image with images_do_set() before
commonl.rm_f(cmd_file_name)
if self.fsdb.get("debug") != None: # if debugging, keep log
commonl.rm_f(self.logfile_name)
commonl.rm_f(self.simics_vars['simics_console'])
commonl.rm_f(self.simics_vars['simics_pidfile'])
try:
# Create a fresh Simics workspace
shutil.rmtree(self.simics_vars['simics_workspace'],
ignore_errors = True)
cmdline = [
os.path.join(self.base_package, "bin/project-setup"),
"--ignore-existing-files",
self.simics_vars['simics_workspace'] ]
self.log.info("creating workspace with %s" % " ".join(cmdline))
subprocess.check_output(cmdline, shell = False,
stderr = subprocess.STDOUT)
except subprocess.CalledProcessError as e:
self.log.error("failed to create workspace: %s" % e.output)
except OSError as e:
self.log.error("failed to create workspace: %s" % e)
# Write the command script here, in case anything changes in
# the interpretation of the fields
simics_console_port = commonl.tcp_port_assigner(1)
with open(cmd_file_name, "w") as cmd_file:
simics_vars = dict(self.simics_vars)
simics_vars['simics_console_port'] = simics_console_port
cmd_file.write(self.simics_cmds % simics_vars)
cmdline = [ self.simics_path, "-no-gui" ]
if self.fsdb.get("debug"): # if debugging, be verbose
cmdline += [ "-verbose", "-verbose" ]
cmdline += [
"-project", self.simics_vars['simics_workspace'], cmd_file_name
]
# Fire up simics, redirecting all the output (stdout, stderr,
# traces) to a log file
logfile = open(self.logfile_name, "ab")
try:
env = dict(os.environ)
env['SIMICS_BASE_PACKAGE'] = self.base_package
self.log.info("Starting simics with: %s" % " ".join(cmdline))
p = subprocess.Popen(
cmdline, shell = False, cwd = self.state_dir, env = env,
close_fds = True, stdout = logfile, stderr = subprocess.STDOUT)
except OSError as e:
raise self.simics_start_e("Simics failed to start: %s" % e)
with open(self.simics_vars['simics_pidfile'], "w") as pidfilef:
pidfilef.write("%d" % p.pid)
pid = commonl.process_started( # Verify it started
self.simics_vars['simics_pidfile'],
self.simics_check_path,
verification_f = os.path.exists,
verification_f_args = (self.simics_vars['simics_console'],),
timeout = 20, tag = "simics", log = self.log)
if pid == None:
raise self.simics_start_e("Simics failed to start after 5s")
self.fsdb.set('simics_console_port', "%d" %
simics_console_port)
def power_on_do(self, target):
# try to start qemu, retrying if we have to
for cnt in range(5):
try:
self._simics_launch(target)
break
except self.error_e:
with open(self.logfile_name) as logfile:
for line in logfile:
if 'Address already in use' in line:
# Ops, port we took for the console is
# taken, try again with another port
self.log.info("%d/5: port conflict, trying again"
% cnt)
self.power_off_do(target)
continue
else:
raise RuntimeError("simis: did not start after 5 tries")
def power_off_do(self, _target):
self.fsdb.set('simics_console_port', None)
commonl.process_terminate(self.simics_vars['simics_pidfile'],
tag = "simics",
path = self.simics_check_path)
def power_get_do(self, _target):
pid = commonl.process_alive(self.simics_vars['simics_pidfile'],
self.simics_check_path)
return pid != None
# Console mixin
# Any file SOMETHING-console.read describes a console that is available.
def console_do_list(self):
consoles = []
for filename in os.listdir(self.state_dir):
if filename.endswith("-console.read"):
console_name = filename[:-len("-console.read")]
consoles.append(console_name)
return consoles
def console_do_read(self, console_id = None, offset = 0):
if console_id == None:
console_id = 'simics'
if console_id != 'simics':
raise RuntimeError("console ID '%s' not found" % console_id)
# Reading is simple -- simics pipes all the output to a file
# called simics-console.read
consolefname = os.path.join(self.state_dir,
"%s-console.read" % console_id)
if os.path.isfile(consolefname):
# don't open codecs.open() UTF-8, as that will trip Flask
# when passing the generator up to serve to the client
ifd = open(consolefname, "rb")
if offset > 0:
ifd.seek(offset)
return ifd
else:
return iter(())
def console_do_write(self, _data, _console_id = None):
_simics_console_port = self.fsdb.get('simics_console_port')
if _simics_console_port == None:
raise RuntimeError("target is off, cannot write to it")
simics_console_port = int(_simics_console_port)
# re-create it for every write -- yeah, overkill, but this
# runs across multiple servers, so we don't know if it was
# power cycled and thus the port is still valid/open..
# FIXME: hack, should cache
telnet = telnetlib.Telnet('127.0.0.1', simics_console_port)
# KLUDGE, workaround
# So this C-like loop (because I want it to be clearer
# than hidden iterator pythonic stuff) it is chunking
# the data to be sent to the VM's serial console
# and doing a short sleep in between. Why?
# Because by observation we've seen data being lost
# when sending it to the sock that represents the
# input. Chunking it up and giving it a breather
# alleviated it.
chunk_size = 8
count = 0
l = len(_data)
while l > 0:
if l >= chunk_size:
chunk_data = _data[count:count + chunk_size]
else:
chunk_data = _data[count:count + l]
# FIXME: I seriously don't have any idea of what am I doing
# here; this Python2 string decoding/encoding stuff is
# utterly confusing -- but this is how it works :/
telnet.write(chunk_data.decode('latin1').encode('utf-8'))
time.sleep(0.15)
l -= chunk_size
count += chunk_size
|
the-stack_0_915 | #!/usr/bin/python
# -*- coding: utf-8 -*-
def warn(*args, **kwargs):
pass
from django.shortcuts import render
from django.core.files.storage import FileSystemStorage
from django.http import HttpResponse, JsonResponse
from django.db.models import Q
from .models import *
def search(request):
try:
query = request.GET['search']
query = str(query).lower()
mydict = {
"urls" : Url.objects.all().filter(Q(link__contains=query) | Q(result__contains=query) | Q(created_at__contains=query) |
Q(rank__contains=query) | Q(dom__contains=query) | Q(country__contains=query) | Q(state__contains=query) | Q(emails__contains=query) |
Q(add__contains=query) | Q(org__contains=query) | Q(city__contains=query)
).order_by('-created_at')
}
return render(request,'list.html',context=mydict)
except:
return render(request,'404.html')
def error_404_view(request, exception):
return render(request,'404.html')
def index(request):
try:
return render(request, '404.html')
except:
return render(request, '404.html')
from requests import get
import json
from dateutil import parser as dateparser
from django.http import HttpResponse
from django.shortcuts import render
def result(request):
#text=request.GET['nm'].strip()
#http://127.0.0.1:8000/result?uniqueid=12&nm=hi&phonenumber=12321&time=21%2F12%2F1998+12%3A12%3A12
#result="booked"
lat=request.GET['LAT']
lon=request.GET['LON']
import reverse_geocoder as rg
coordinates = (lat,lon)
#print (rg.search(coordinates)[0]['name'], rg.search(coordinates)[0]['admin1'])
mydict = {
"query" : f"{lat},{lon}",
"city" : rg.search(coordinates)[0]['name'],
"state" : rg.search(coordinates)[0]['admin1']
}
response = JsonResponse(mydict)
return response
|
the-stack_0_916 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import time
import yaml
import cv2
import re
import numpy as np
from collections import defaultdict
import paddle
from paddle.inference import Config
from paddle.inference import create_predictor
from picodet_postprocess import PicoDetPostProcess
from utils import argsparser, Timer, get_current_memory_mb, _is_valid_video, video2frames
from det_infer import Detector, DetectorPicoDet, get_test_images, print_arguments, PredictConfig
from det_infer import load_predictor
from benchmark_utils import PaddleInferBenchmark
from visualize import plot_tracking
from mot.tracker import DeepSORTTracker
from mot.utils import MOTTimer, write_mot_results, flow_statistic, scale_coords, clip_box, preprocess_reid
from mot.mtmct.utils import parse_bias
from mot.mtmct.postprocess import trajectory_fusion, sub_cluster, gen_res, print_mtmct_result
from mot.mtmct.postprocess import get_mtmct_matching_results, save_mtmct_crops, save_mtmct_vis_results
# Global dictionary
MOT_SUPPORT_MODELS = {'DeepSORT'}
def bench_log(detector, img_list, model_info, batch_size=1, name=None):
mems = {
'cpu_rss_mb': detector.cpu_mem / len(img_list),
'gpu_rss_mb': detector.gpu_mem / len(img_list),
'gpu_util': detector.gpu_util * 100 / len(img_list)
}
perf_info = detector.det_times.report(average=True)
data_info = {
'batch_size': batch_size,
'shape': "dynamic_shape",
'data_num': perf_info['img_num']
}
log = PaddleInferBenchmark(detector.config, model_info, data_info,
perf_info, mems)
log(name)
class SDE_Detector(Detector):
"""
Detector of SDE methods
Args:
pred_config (object): config of model, defined by `Config(model_dir)`
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
batch_size (int): size of per batch in inference, default is 1 in tracking models
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
"""
def __init__(self,
pred_config,
model_dir,
device='CPU',
run_mode='fluid',
batch_size=1,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False):
super(SDE_Detector, self).__init__(
pred_config=pred_config,
model_dir=model_dir,
device=device,
run_mode=run_mode,
batch_size=batch_size,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn)
assert batch_size == 1, "The detector of tracking models only supports batch_size=1 now"
self.pred_config = pred_config
def postprocess(self,
boxes,
ori_image_shape,
threshold,
inputs,
scaled=False):
over_thres_idx = np.nonzero(boxes[:, 1:2] >= threshold)[0]
if len(over_thres_idx) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
return pred_dets, pred_xyxys
else:
boxes = boxes[over_thres_idx]
if not scaled:
# scaled means whether the coords after detector outputs
# have been scaled back to the original image, set True
# in general detector, set False in JDE YOLOv3.
input_shape = inputs['image'].shape[2:]
im_shape = inputs['im_shape'][0]
scale_factor = inputs['scale_factor'][0]
pred_bboxes = scale_coords(boxes[:, 2:], input_shape, im_shape,
scale_factor)
else:
pred_bboxes = boxes[:, 2:]
pred_xyxys, keep_idx = clip_box(pred_bboxes, ori_image_shape)
if len(keep_idx[0]) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
return pred_dets, pred_xyxys
pred_scores = boxes[:, 1:2][keep_idx[0]]
pred_cls_ids = boxes[:, 0:1][keep_idx[0]]
pred_tlwhs = np.concatenate(
(pred_xyxys[:, 0:2], pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1),
axis=1)
pred_dets = np.concatenate(
(pred_tlwhs, pred_scores, pred_cls_ids), axis=1)
return pred_dets, pred_xyxys
def predict(self,
image_path,
ori_image_shape,
threshold=0.5,
scaled=False,
repeats=1,
add_timer=True):
'''
Args:
image_path (list[str]): path of images, only support one image path
(batch_size=1) in tracking model
ori_image_shape (list[int]: original image shape
threshold (float): threshold of predicted box' score
scaled (bool): whether the coords after detector outputs are scaled,
default False in jde yolov3, set True in general detector.
repeats (int): repeat number for prediction
add_timer (bool): whether add timer during prediction
Returns:
pred_dets (np.ndarray, [N, 6]): 'x,y,w,h,score,cls_id'
pred_xyxys (np.ndarray, [N, 4]): 'x1,y1,x2,y2'
'''
# preprocess
if add_timer:
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(image_path)
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[input_names[i]])
if add_timer:
self.det_times.preprocess_time_s.end()
self.det_times.inference_time_s.start()
# model prediction
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
boxes = boxes_tensor.copy_to_cpu()
if add_timer:
self.det_times.inference_time_s.end(repeats=repeats)
self.det_times.postprocess_time_s.start()
# postprocess
if len(boxes) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
else:
pred_dets, pred_xyxys = self.postprocess(
boxes, ori_image_shape, threshold, inputs, scaled=scaled)
if add_timer:
self.det_times.postprocess_time_s.end()
self.det_times.img_num += 1
return pred_dets, pred_xyxys
class SDE_DetectorPicoDet(DetectorPicoDet):
"""
PicoDet of SDE methods, the postprocess of PicoDet has not been exported as
other detectors, so do postprocess here.
Args:
pred_config (object): config of model, defined by `Config(model_dir)`
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
batch_size (int): size of per batch in inference, default is 1 in tracking models
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
"""
def __init__(self,
pred_config,
model_dir,
device='CPU',
run_mode='fluid',
batch_size=1,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False):
super(SDE_DetectorPicoDet, self).__init__(
pred_config=pred_config,
model_dir=model_dir,
device=device,
run_mode=run_mode,
batch_size=batch_size,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn)
assert batch_size == 1, "The detector of tracking models only supports batch_size=1 now"
self.pred_config = pred_config
def postprocess(self, boxes, ori_image_shape, threshold):
over_thres_idx = np.nonzero(boxes[:, 1:2] >= threshold)[0]
if len(over_thres_idx) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
return pred_dets, pred_xyxys
else:
boxes = boxes[over_thres_idx]
pred_bboxes = boxes[:, 2:]
pred_xyxys, keep_idx = clip_box(pred_bboxes, ori_image_shape)
if len(keep_idx[0]) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
return pred_dets, pred_xyxys
pred_scores = boxes[:, 1:2][keep_idx[0]]
pred_cls_ids = boxes[:, 0:1][keep_idx[0]]
pred_tlwhs = np.concatenate(
(pred_xyxys[:, 0:2], pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1),
axis=1)
pred_dets = np.concatenate(
(pred_tlwhs, pred_scores, pred_cls_ids), axis=1)
return pred_dets, pred_xyxys
def predict(self,
image_path,
ori_image_shape,
threshold=0.5,
scaled=False,
repeats=1,
add_timer=True):
'''
Args:
image_path (list[str]): path of images, only support one image path
(batch_size=1) in tracking model
ori_image_shape (list[int]: original image shape
threshold (float): threshold of predicted box' score
scaled (bool): whether the coords after detector outputs are scaled,
default False in jde yolov3, set True in general detector.
repeats (int): repeat number for prediction
add_timer (bool): whether add timer during prediction
Returns:
pred_dets (np.ndarray, [N, 6]): 'x,y,w,h,score,cls_id'
pred_xyxys (np.ndarray, [N, 4]): 'x1,y1,x2,y2'
'''
# preprocess
if add_timer:
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(image_path)
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[input_names[i]])
if add_timer:
self.det_times.preprocess_time_s.end()
self.det_times.inference_time_s.start()
# model prediction
for i in range(repeats):
self.predictor.run()
np_score_list.clear()
np_boxes_list.clear()
output_names = self.predictor.get_output_names()
num_outs = int(len(output_names) / 2)
for out_idx in range(num_outs):
np_score_list.append(
self.predictor.get_output_handle(output_names[out_idx])
.copy_to_cpu())
np_boxes_list.append(
self.predictor.get_output_handle(output_names[
out_idx + num_outs]).copy_to_cpu())
if add_timer:
self.det_times.inference_time_s.end(repeats=repeats)
self.det_times.postprocess_time_s.start()
# postprocess
self.picodet_postprocess = PicoDetPostProcess(
inputs['image'].shape[2:],
inputs['im_shape'],
inputs['scale_factor'],
strides=self.pred_config.fpn_stride,
nms_threshold=self.pred_config.nms['nms_threshold'])
boxes, boxes_num = self.picodet_postprocess(np_score_list,
np_boxes_list)
if len(boxes) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
else:
pred_dets, pred_xyxys = self.postprocess(boxes, ori_image_shape,
threshold)
if add_timer:
self.det_times.postprocess_time_s.end()
self.det_times.img_num += 1
return pred_dets, pred_xyxys
class SDE_ReID(object):
"""
ReID of SDE methods
Args:
pred_config (object): config of model, defined by `Config(model_dir)`
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
batch_size (int): size of per batch in inference, default 50 means at most
50 sub images can be made a batch and send into ReID model
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
"""
def __init__(self,
pred_config,
model_dir,
device='CPU',
run_mode='fluid',
batch_size=50,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False):
self.pred_config = pred_config
self.predictor, self.config = load_predictor(
model_dir,
run_mode=run_mode,
batch_size=batch_size,
min_subgraph_size=self.pred_config.min_subgraph_size,
device=device,
use_dynamic_shape=self.pred_config.use_dynamic_shape,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn)
self.det_times = Timer()
self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
self.batch_size = batch_size
assert pred_config.tracker, "Tracking model should have tracker"
pt = pred_config.tracker
max_age = pt['max_age'] if 'max_age' in pt else 30
max_iou_distance = pt[
'max_iou_distance'] if 'max_iou_distance' in pt else 0.7
self.tracker = DeepSORTTracker(
max_age=max_age, max_iou_distance=max_iou_distance)
def get_crops(self, xyxy, ori_img):
w, h = self.tracker.input_size
self.det_times.preprocess_time_s.start()
crops = []
xyxy = xyxy.astype(np.int64)
ori_img = ori_img.transpose(1, 0, 2) # [h,w,3]->[w,h,3]
for i, bbox in enumerate(xyxy):
crop = ori_img[bbox[0]:bbox[2], bbox[1]:bbox[3], :]
crops.append(crop)
crops = preprocess_reid(crops, w, h)
self.det_times.preprocess_time_s.end()
return crops
def preprocess(self, crops):
# to keep fast speed, only use topk crops
crops = crops[:self.batch_size]
inputs = {}
inputs['crops'] = np.array(crops).astype('float32')
return inputs
def postprocess(self, pred_dets, pred_embs):
tracker = self.tracker
tracker.predict()
online_targets = tracker.update(pred_dets, pred_embs)
online_tlwhs, online_scores, online_ids = [], [], []
for t in online_targets:
if not t.is_confirmed() or t.time_since_update > 1:
continue
tlwh = t.to_tlwh()
tscore = t.score
tid = t.track_id
if tlwh[2] * tlwh[3] <= tracker.min_box_area:
continue
if tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
3] > tracker.vertical_ratio:
continue
online_tlwhs.append(tlwh)
online_scores.append(tscore)
online_ids.append(tid)
tracking_outs = {
'online_tlwhs': online_tlwhs,
'online_scores': online_scores,
'online_ids': online_ids,
}
return tracking_outs
def postprocess_mtmct(self, pred_dets, pred_embs, frame_id, seq_name):
tracker = self.tracker
tracker.predict()
online_targets = tracker.update(pred_dets, pred_embs)
online_tlwhs, online_scores, online_ids = [], [], []
online_tlbrs, online_feats = [], []
for t in online_targets:
if not t.is_confirmed() or t.time_since_update > 1:
continue
tlwh = t.to_tlwh()
tscore = t.score
tid = t.track_id
if tlwh[2] * tlwh[3] <= tracker.min_box_area:
continue
if tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
3] > tracker.vertical_ratio:
continue
online_tlwhs.append(tlwh)
online_scores.append(tscore)
online_ids.append(tid)
online_tlbrs.append(t.to_tlbr())
online_feats.append(t.feat)
tracking_outs = {
'online_tlwhs': online_tlwhs,
'online_scores': online_scores,
'online_ids': online_ids,
'feat_data': {},
}
for _tlbr, _id, _feat in zip(online_tlbrs, online_ids, online_feats):
feat_data = {}
feat_data['bbox'] = _tlbr
feat_data['frame'] = f"{frame_id:06d}"
feat_data['id'] = _id
_imgname = f'{seq_name}_{_id}_{frame_id}.jpg'
feat_data['imgname'] = _imgname
feat_data['feat'] = _feat
tracking_outs['feat_data'].update({_imgname: feat_data})
return tracking_outs
def predict(self,
crops,
pred_dets,
repeats=1,
add_timer=True,
MTMCT=False,
frame_id=0,
seq_name=''):
# preprocess
if add_timer:
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(crops)
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[input_names[i]])
if add_timer:
self.det_times.preprocess_time_s.end()
self.det_times.inference_time_s.start()
# model prediction
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
feature_tensor = self.predictor.get_output_handle(output_names[0])
pred_embs = feature_tensor.copy_to_cpu()
if add_timer:
self.det_times.inference_time_s.end(repeats=repeats)
self.det_times.postprocess_time_s.start()
# postprocess
if MTMCT == False:
tracking_outs = self.postprocess(pred_dets, pred_embs)
else:
tracking_outs = self.postprocess_mtmct(pred_dets, pred_embs,
frame_id, seq_name)
if add_timer:
self.det_times.postprocess_time_s.end()
self.det_times.img_num += 1
return tracking_outs
def predict_image(detector, reid_model, image_list):
image_list.sort()
for i, img_file in enumerate(image_list):
frame = cv2.imread(img_file)
ori_image_shape = list(frame.shape[:2])
if FLAGS.run_benchmark:
# warmup
pred_dets, pred_xyxys = detector.predict(
[img_file],
ori_image_shape,
FLAGS.threshold,
FLAGS.scaled,
repeats=10,
add_timer=False)
# run benchmark
pred_dets, pred_xyxys = detector.predict(
[img_file],
ori_image_shape,
FLAGS.threshold,
FLAGS.scaled,
repeats=10,
add_timer=True)
cm, gm, gu = get_current_memory_mb()
detector.cpu_mem += cm
detector.gpu_mem += gm
detector.gpu_util += gu
print('Test iter {}, file name:{}'.format(i, img_file))
else:
pred_dets, pred_xyxys = detector.predict(
[img_file], ori_image_shape, FLAGS.threshold, FLAGS.scaled)
if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
print('Frame {} has no object, try to modify score threshold.'.
format(i))
online_im = frame
else:
# reid process
crops = reid_model.get_crops(pred_xyxys, frame)
if FLAGS.run_benchmark:
# warmup
tracking_outs = reid_model.predict(
crops, pred_dets, repeats=10, add_timer=False)
# run benchmark
tracking_outs = reid_model.predict(
crops, pred_dets, repeats=10, add_timer=True)
else:
tracking_outs = reid_model.predict(crops, pred_dets)
online_tlwhs = tracking_outs['online_tlwhs']
online_scores = tracking_outs['online_scores']
online_ids = tracking_outs['online_ids']
online_im = plot_tracking(
frame, online_tlwhs, online_ids, online_scores, frame_id=i)
if FLAGS.save_images:
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
img_name = os.path.split(img_file)[-1]
out_path = os.path.join(FLAGS.output_dir, img_name)
cv2.imwrite(out_path, online_im)
print("save result to: " + out_path)
def predict_video(detector, reid_model, camera_id):
if camera_id != -1:
capture = cv2.VideoCapture(camera_id)
video_name = 'mot_output.mp4'
else:
capture = cv2.VideoCapture(FLAGS.video_file)
video_name = os.path.split(FLAGS.video_file)[-1]
# Get Video info : resolution, fps, frame count
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(capture.get(cv2.CAP_PROP_FPS))
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
print("fps: %d, frame_count: %d" % (fps, frame_count))
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
out_path = os.path.join(FLAGS.output_dir, video_name)
if not FLAGS.save_images:
video_format = 'mp4v'
fourcc = cv2.VideoWriter_fourcc(*video_format)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
frame_id = 0
timer = MOTTimer()
results = defaultdict(list)
id_set = set()
interval_id_set = set()
in_id_list = list()
out_id_list = list()
prev_center = dict()
records = list()
entrance = [0, height / 2., width, height / 2.]
video_fps = fps
while (1):
ret, frame = capture.read()
if not ret:
break
timer.tic()
ori_image_shape = list(frame.shape[:2])
pred_dets, pred_xyxys = detector.predict([frame], ori_image_shape,
FLAGS.threshold, FLAGS.scaled)
if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
print('Frame {} has no object, try to modify score threshold.'.
format(frame_id))
timer.toc()
im = frame
else:
# reid process
crops = reid_model.get_crops(pred_xyxys, frame)
tracking_outs = reid_model.predict(crops, pred_dets)
online_tlwhs = tracking_outs['online_tlwhs']
online_scores = tracking_outs['online_scores']
online_ids = tracking_outs['online_ids']
results[0].append(
(frame_id + 1, online_tlwhs, online_scores, online_ids))
# NOTE: just implement flow statistic for one class
result = (frame_id + 1, online_tlwhs, online_scores, online_ids)
statistic = flow_statistic(
result, FLAGS.secs_interval, FLAGS.do_entrance_counting,
video_fps, entrance, id_set, interval_id_set, in_id_list,
out_id_list, prev_center, records)
id_set = statistic['id_set']
interval_id_set = statistic['interval_id_set']
in_id_list = statistic['in_id_list']
out_id_list = statistic['out_id_list']
prev_center = statistic['prev_center']
records = statistic['records']
timer.toc()
fps = 1. / timer.duration
im = plot_tracking(
frame,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
fps=fps,
do_entrance_counting=FLAGS.do_entrance_counting,
entrance=entrance)
if FLAGS.save_images:
save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2])
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(
os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im)
else:
writer.write(im)
frame_id += 1
print('detect frame:%d, fps: %f' % (frame_id, fps))
if camera_id != -1:
cv2.imshow('Tracking Detection', im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if FLAGS.save_mot_txts:
result_filename = os.path.join(FLAGS.output_dir,
video_name.split('.')[-2] + '.txt')
write_mot_results(result_filename, results)
result_filename = os.path.join(
FLAGS.output_dir, video_name.split('.')[-2] + '_flow_statistic.txt')
f = open(result_filename, 'w')
for line in records:
f.write(line)
print('Flow statistic save in {}'.format(result_filename))
f.close()
if FLAGS.save_images:
save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2])
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format(save_dir,
out_path)
os.system(cmd_str)
print('Save video in {}.'.format(out_path))
else:
writer.release()
def predict_mtmct_seq(detector, reid_model, seq_name, output_dir):
fpath = os.path.join(FLAGS.mtmct_dir, seq_name)
if os.path.exists(os.path.join(fpath, 'img1')):
fpath = os.path.join(fpath, 'img1')
assert os.path.isdir(fpath), '{} should be a directory'.format(fpath)
image_list = os.listdir(fpath)
image_list.sort()
assert len(image_list) > 0, '{} has no images.'.format(fpath)
results = defaultdict(list)
mot_features_dict = {} # cid_tid_fid feats
print('Totally {} frames found in seq {}.'.format(
len(image_list), seq_name))
for frame_id, img_file in enumerate(image_list):
if frame_id % 40 == 0:
print('Processing frame {} of seq {}.'.format(frame_id, seq_name))
frame = cv2.imread(os.path.join(fpath, img_file))
ori_image_shape = list(frame.shape[:2])
frame_path = os.path.join(fpath, img_file)
pred_dets, pred_xyxys = detector.predict([frame_path], ori_image_shape,
FLAGS.threshold, FLAGS.scaled)
if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
print('Frame {} has no object, try to modify score threshold.'.
format(frame_id))
online_im = frame
else:
# reid process
crops = reid_model.get_crops(pred_xyxys, frame)
tracking_outs = reid_model.predict(
crops,
pred_dets,
MTMCT=True,
frame_id=frame_id,
seq_name=seq_name)
feat_data_dict = tracking_outs['feat_data']
mot_features_dict = dict(mot_features_dict, **feat_data_dict)
online_tlwhs = tracking_outs['online_tlwhs']
online_scores = tracking_outs['online_scores']
online_ids = tracking_outs['online_ids']
online_im = plot_tracking(frame, online_tlwhs, online_ids,
online_scores, frame_id)
results[0].append(
(frame_id + 1, online_tlwhs, online_scores, online_ids))
if FLAGS.save_images:
save_dir = os.path.join(output_dir, seq_name)
if not os.path.exists(save_dir): os.makedirs(save_dir)
img_name = os.path.split(img_file)[-1]
out_path = os.path.join(save_dir, img_name)
cv2.imwrite(out_path, online_im)
if FLAGS.save_mot_txts:
result_filename = os.path.join(output_dir, seq_name + '.txt')
write_mot_results(result_filename, results)
return mot_features_dict
def predict_mtmct(detector, reid_model, mtmct_dir, mtmct_cfg):
MTMCT = mtmct_cfg['MTMCT']
assert MTMCT == True, 'predict_mtmct should be used for MTMCT.'
cameras_bias = mtmct_cfg['cameras_bias']
cid_bias = parse_bias(cameras_bias)
scene_cluster = list(cid_bias.keys())
# 1.zone releated parameters
use_zone = mtmct_cfg['use_zone']
zone_path = mtmct_cfg['zone_path']
# 2.tricks parameters, can be used for other mtmct dataset
use_ff = mtmct_cfg['use_ff']
use_rerank = mtmct_cfg['use_rerank']
# 3.camera releated parameters
use_camera = mtmct_cfg['use_camera']
use_st_filter = mtmct_cfg['use_st_filter']
# 4.zone releated parameters
use_roi = mtmct_cfg['use_roi']
roi_dir = mtmct_cfg['roi_dir']
mot_list_breaks = []
cid_tid_dict = dict()
output_dir = FLAGS.output_dir
if not os.path.exists(output_dir): os.makedirs(output_dir)
seqs = os.listdir(mtmct_dir)
seqs.sort()
for seq in seqs:
fpath = os.path.join(mtmct_dir, seq)
if os.path.isfile(fpath) and _is_valid_video(fpath):
ext = seq.split('.')[-1]
seq = seq.split('.')[-2]
print('ffmpeg processing of video {}'.format(fpath))
frames_path = video2frames(
video_path=fpath, outpath=mtmct_dir, frame_rate=25)
fpath = os.path.join(mtmct_dir, seq)
if os.path.isdir(fpath) == False:
print('{} is not a image folder.'.format(fpath))
continue
mot_features_dict = predict_mtmct_seq(detector, reid_model, seq,
output_dir)
cid = int(re.sub('[a-z,A-Z]', "", seq))
tid_data, mot_list_break = trajectory_fusion(
mot_features_dict,
cid,
cid_bias,
use_zone=use_zone,
zone_path=zone_path)
mot_list_breaks.append(mot_list_break)
# single seq process
for line in tid_data:
tracklet = tid_data[line]
tid = tracklet['tid']
if (cid, tid) not in cid_tid_dict:
cid_tid_dict[(cid, tid)] = tracklet
map_tid = sub_cluster(
cid_tid_dict,
scene_cluster,
use_ff=use_ff,
use_rerank=use_rerank,
use_camera=use_camera,
use_st_filter=use_st_filter)
pred_mtmct_file = os.path.join(output_dir, 'mtmct_result.txt')
if use_camera:
gen_res(pred_mtmct_file, scene_cluster, map_tid, mot_list_breaks)
else:
gen_res(
pred_mtmct_file,
scene_cluster,
map_tid,
mot_list_breaks,
use_roi=use_roi,
roi_dir=roi_dir)
if FLAGS.save_images:
camera_results, cid_tid_fid_res = get_mtmct_matching_results(
pred_mtmct_file)
crops_dir = os.path.join(output_dir, 'mtmct_crops')
save_mtmct_crops(
cid_tid_fid_res, images_dir=mtmct_dir, crops_dir=crops_dir)
save_dir = os.path.join(output_dir, 'mtmct_vis')
save_mtmct_vis_results(
camera_results,
images_dir=mtmct_dir,
save_dir=save_dir,
save_videos=FLAGS.save_images)
# evalution metrics
data_root_gt = os.path.join(mtmct_dir, '..', 'gt', 'gt.txt')
if os.path.exists(data_root_gt):
print_mtmct_result(data_root_gt, pred_mtmct_file)
def main():
pred_config = PredictConfig(FLAGS.model_dir)
detector_func = 'SDE_Detector'
if pred_config.arch == 'PicoDet':
detector_func = 'SDE_DetectorPicoDet'
detector = eval(detector_func)(pred_config,
FLAGS.model_dir,
device=FLAGS.device,
run_mode=FLAGS.run_mode,
batch_size=FLAGS.batch_size,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape,
trt_calib_mode=FLAGS.trt_calib_mode,
cpu_threads=FLAGS.cpu_threads,
enable_mkldnn=FLAGS.enable_mkldnn)
pred_config = PredictConfig(FLAGS.reid_model_dir)
reid_model = SDE_ReID(
pred_config,
FLAGS.reid_model_dir,
device=FLAGS.device,
run_mode=FLAGS.run_mode,
batch_size=FLAGS.reid_batch_size,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape,
trt_calib_mode=FLAGS.trt_calib_mode,
cpu_threads=FLAGS.cpu_threads,
enable_mkldnn=FLAGS.enable_mkldnn)
# predict from video file or camera video stream
if FLAGS.video_file is not None or FLAGS.camera_id != -1:
predict_video(detector, reid_model, FLAGS.camera_id)
elif FLAGS.mtmct_dir is not None:
mtmct_cfg_file = FLAGS.mtmct_cfg
with open(mtmct_cfg_file) as f:
mtmct_cfg = yaml.safe_load(f)
predict_mtmct(detector, reid_model, FLAGS.mtmct_dir, mtmct_cfg)
else:
# predict from image
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
predict_image(detector, reid_model, img_list)
if not FLAGS.run_benchmark:
detector.det_times.info(average=True)
reid_model.det_times.info(average=True)
else:
mode = FLAGS.run_mode
det_model_dir = FLAGS.model_dir
det_model_info = {
'model_name': det_model_dir.strip('/').split('/')[-1],
'precision': mode.split('_')[-1]
}
bench_log(detector, img_list, det_model_info, name='Det')
reid_model_dir = FLAGS.reid_model_dir
reid_model_info = {
'model_name': reid_model_dir.strip('/').split('/')[-1],
'precision': mode.split('_')[-1]
}
bench_log(reid_model, img_list, reid_model_info, name='ReID')
if __name__ == '__main__':
paddle.enable_static()
parser = argsparser()
FLAGS = parser.parse_args()
print_arguments(FLAGS)
FLAGS.device = FLAGS.device.upper()
assert FLAGS.device in ['CPU', 'GPU', 'XPU'
], "device should be CPU, GPU or XPU"
main()
|
the-stack_0_918 | # coding: utf-8
import os
import sys
import re
import time
import pickle
import shutil
import random
import argparse
from darknet_util import *
from darknet import Darknet
from preprocess import prep_image, process_img, inp_to_image
from dataset import color_attrs, direction_attrs, type_attrs
import torch
import torchvision
import paramiko
import cv2
import numpy as np
import PIL
from PIL import Image
from matplotlib import pyplot as plt
from matplotlib.widgets import Cursor
from matplotlib.image import AxesImage
from scipy.spatial.distance import cityblock
from tqdm import tqdm
# -------------------------------------
# for matplotlib to displacy chinese characters correctly
from pylab import *
mpl.rcParams['font.sans-serif'] = ['SimHei']
use_cuda = True # True
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
device = torch.device(
'cuda: 0' if torch.cuda.is_available() and use_cuda else 'cpu')
if use_cuda:
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
print('=> device: ', device)
local_model_path = './checkpoints/epoch_39.pth'
local_car_cfg_path = './car.cfg'
local_car_det_weights_path = './car_detect.weights'
class Cls_Net(torch.nn.Module):
"""
vehicle multilabel classification model
"""
def __init__(self, num_cls, input_size):
"""
network definition
:param is_freeze:
"""
torch.nn.Module.__init__(self)
# output channels
self._num_cls = num_cls
# input image size
self.input_size = input_size
# delete original FC and add custom FC
self.features = torchvision.models.resnet18(pretrained=True)
del self.features.fc
# print('feature extractor:\n', self.features)
self.features = torch.nn.Sequential(
*list(self.features.children()))
self.fc = torch.nn.Linear(512 ** 2, num_cls) # 输出类别数
# print('=> fc layer:\n', self.fc)
def forward(self, X):
"""
:param X:
:return:
"""
N = X.size()[0]
X = self.features(X) # extract features
X = X.view(N, 512, 1 ** 2)
X = torch.bmm(X, torch.transpose(X, 1, 2)) / (1 ** 2) # Bi-linear CNN
X = X.view(N, 512 ** 2)
X = torch.sqrt(X + 1e-5)
X = torch.nn.functional.normalize(X)
X = self.fc(X)
assert X.size() == (N, self._num_cls)
return X
# ------------------------------------- vehicle detection model
class Car_Classifier(object):
"""
vehicle detection model mabager
"""
def __init__(self,
num_cls,
model_path=local_model_path):
"""
load model and initialize
"""
# define model and load weights
self.net = Cls_Net(num_cls=num_cls, input_size=224).to(device)
# self.net = torch.nn.DataParallel(Net(num_cls=20, input_size=224),
# device_ids=[0]).to(device)
self.net.load_state_dict(torch.load(model_path))
print('=> vehicle classifier loaded from %s' % model_path)
# set model to eval mode
self.net.eval()
# test data transforms
self.transforms = torchvision.transforms.Compose([
torchvision.transforms.Resize(size=224),
torchvision.transforms.CenterCrop(size=224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
# split each label
self.color_attrs = color_attrs
print('=> color_attrs:\n', self.color_attrs)
self.direction_attrs = direction_attrs
print('=> direction attrs:\n', self.direction_attrs)
self.type_attrs = type_attrs
print('=> type_attrs:\n', self.type_attrs)
def get_predict(self, output):
"""
get prediction from output
"""
# get each label's prediction from output
output = output.cpu() # fetch data from gpu
pred_color = output[:, :9]
pred_direction = output[:, 9:11]
pred_type = output[:, 11:]
color_idx = pred_color.max(1, keepdim=True)[1]
direction_idx = pred_direction.max(1, keepdim=True)[1]
type_idx = pred_type.max(1, keepdim=True)[1]
pred = torch.cat((color_idx, direction_idx, type_idx), dim=1)
return pred
def pre_process(self, image):
"""
image formatting
:rtype: PIL.JpegImagePlugin.JpegImageFile
"""
# image data formatting
if type(image) == np.ndarray:
if image.shape[2] == 3: # turn all 3 channels to RGB format
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
elif image.shape[2] == 1: # turn 1 channel to RGB
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
# turn numpy.ndarray into PIL.Image
image = Image.fromarray(image)
elif type(image) == PIL.JpegImagePlugin.JpegImageFile:
if image.mode == 'L' or image.mode == 'I': # turn 8bits or 32bits into 3 channels RGB
image = image.convert('RGB')
return image
def predict(self, img):
"""
predict vehicle attributes by classifying
:return: vehicle color, direction and type
"""
# image pre-processing
img = self.transforms(img)
img = img.view(1, 3, 224, 224)
# put image data into device
img = img.to(device)
# calculating inference
output = self.net.forward(img)
# get result
# self.get_predict_ce, return pred to host side(cpu)
pred = self.get_predict(output)
color_name = self.color_attrs[pred[0][0]]
direction_name = self.direction_attrs[pred[0][1]]
type_name = self.type_attrs[pred[0][2]]
return color_name, direction_name, type_name
class Car_DC():
def __init__(self,
src_dir,
dst_dir,
car_cfg_path=local_car_cfg_path,
car_det_weights_path=local_car_det_weights_path,
inp_dim=768,
prob_th=0.2,
nms_th=0.4,
num_classes=1):
"""
model initialization
"""
# super parameters
self.inp_dim = inp_dim
self.prob_th = prob_th
self.nms_th = nms_th
self.num_classes = num_classes
self.dst_dir = dst_dir
# clear dst_dir
if os.path.exists(self.dst_dir):
for x in os.listdir(self.dst_dir):
if x.endswith('.jpg'):
os.remove(self.dst_dir + '/' + x)
else:
os.makedirs(self.dst_dir)
# initialize vehicle detection model
self.detector = Darknet(car_cfg_path)
self.detector.load_weights(car_det_weights_path)
# set input dimension of image
self.detector.net_info['height'] = self.inp_dim
self.detector.to(device)
self.detector.eval() # evaluation mode
print('=> car detection model initiated.')
# initiate multilabel classifier
self.classifier = Car_Classifier(num_cls=19,
model_path=local_model_path)
# initiate imgs_path
self.imgs_path = [os.path.join(src_dir, x) for x in os.listdir(
src_dir) if x.endswith('.jpg')]
def cls_draw_bbox(self, output, orig_img):
"""
1. predict vehicle's attributes based on bbox of vehicle
2. draw bbox to orig_img
"""
labels = []
pt_1s = []
pt_2s = []
# 1
for det in output:
# rectangle points
pt_1 = tuple(det[1:3].int()) # the left-up point
pt_2 = tuple(det[3:5].int()) # the right down point
pt_1s.append(pt_1)
pt_2s.append(pt_2)
# turn BGR back to RGB
ROI = Image.fromarray(
orig_img[pt_1[1]: pt_2[1],
pt_1[0]: pt_2[0]][:, :, ::-1])
# ROI.show()
# call classifier to predict
car_color, car_direction, car_type = self.classifier.predict(ROI)
label = str(car_color + ' ' + car_direction + ' ' + car_type)
labels.append(label)
print('=> predicted label: ', label)
# 2
color = (0, 215, 255)
for i, det in enumerate(output):
pt_1 = pt_1s[i]
pt_2 = pt_2s[i]
# draw bounding box
cv2.rectangle(orig_img, pt_1, pt_2, color, thickness=2)
# get str text size
txt_size = cv2.getTextSize(
label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
# pt_2 = pt_1[0] + txt_size[0] + 3, pt_1[1] + txt_size[1] + 5
pt_2 = pt_1[0] + txt_size[0] + 3, pt_1[1] - txt_size[1] - 5
# draw text background rect
cv2.rectangle(orig_img, pt_1, pt_2, color, thickness=-1) # text
# draw text
cv2.putText(orig_img, labels[i], (pt_1[0], pt_1[1]), # pt_1[1] + txt_size[1] + 4
cv2.FONT_HERSHEY_PLAIN, 2, [225, 255, 255], 2)
def process_predict(self,
prediction,
prob_th,
num_cls,
nms_th,
inp_dim,
orig_img_size):
"""
processing detections
"""
scaling_factor = min([inp_dim / float(x)
for x in orig_img_size]) # W, H scaling factor
output = post_process(prediction,
prob_th,
num_cls,
nms=True,
nms_conf=nms_th,
CUDA=True) # post-process such as nms
if type(output) != int:
output[:, [1, 3]] -= (inp_dim - scaling_factor *
orig_img_size[0]) / 2.0 # x, w
output[:, [2, 4]] -= (inp_dim - scaling_factor *
orig_img_size[1]) / 2.0 # y, h
output[:, 1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1, 3]] = torch.clamp(
output[i, [1, 3]], 0.0, orig_img_size[0])
output[i, [2, 4]] = torch.clamp(
output[i, [2, 4]], 0.0, orig_img_size[1])
return output
def detect_classify(self):
"""
detect and classify
"""
for x in self.imgs_path:
# read image data
img = Image.open(x)
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # put image data to device
# vehicle detection
prediction = self.detector.forward(img2det, CUDA=True)
# calculating scaling factor
orig_img_size = list(img.size)
output = self.process_predict(prediction,
self.prob_th,
self.num_classes,
self.nms_th,
self.inp_dim,
orig_img_size)
orig_img = cv2.cvtColor(np.asarray(
img), cv2.COLOR_RGB2BGR) # RGB => BGR
if type(output) != int:
self.cls_draw_bbox(output, orig_img)
dst_path = self.dst_dir + '/' + os.path.split(x)[1]
if not os.path.exists(dst_path):
cv2.imwrite(dst_path, orig_img)
# -----------------------------------------------------------
parser = argparse.ArgumentParser(description='Detect and classify cars.')
parser.add_argument('-src-dir',
type=str,
default='./test_imgs',
help='source directory of images')
parser.add_argument('-dst-dir',
type=str,
default='./test_result',
help='destination directory of images to store results.')
if __name__ == '__main__':
# ---------------------------- Car detect and classify
# DR_model = Car_DC(src_dir='./test_imgs',
# dst_dir='./test_result')
# DR_model.detect_classify()
args = parser.parse_args()
DR_model = Car_DC(src_dir=args.src_dir, dst_dir=args.dst_dir)
DR_model.detect_classify()
|
the-stack_0_919 | """
SimplePose for COCO Keypoint, implemented in TensorFlow.
Original paper: 'Simple Baselines for Human Pose Estimation and Tracking,' https://arxiv.org/abs/1804.06208.
"""
__all__ = ['SimplePose', 'simplepose_resnet18_coco', 'simplepose_resnet50b_coco', 'simplepose_resnet101b_coco',
'simplepose_resnet152b_coco', 'simplepose_resneta50b_coco', 'simplepose_resneta101b_coco',
'simplepose_resneta152b_coco']
import os
import tensorflow as tf
tf.random.set_seed(3)
import tensorflow.keras.layers as nn
from .common import get_activation_layer, BatchNorm, conv1x1, HeatmapMaxDetBlock, is_channels_first
from .resnet import resnet18, resnet50b, resnet101b, resnet152b
from .resneta import resneta50b, resneta101b, resneta152b
class Deconv2d(nn.Layer):
"""
Standard deconvolution layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
kernel_size : int or tuple/list of 2 int
Convolution window size.
strides : int or tuple/list of 2 int, default 1
Strides of the convolution.
padding : int or tuple/list of 2 int, default 0
Padding value for convolution layer.
out_padding : int or tuple/list of 2 int, default 0
Output padding value for deconvolution layer.
dilation : int or tuple/list of 2 int, default 1
Dilation value for convolution layer.
groups : int, default 1
Number of groups.
use_bias : bool, default True
Whether the layer uses a bias vector.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
strides=1,
padding=0,
out_padding=0,
dilation=1,
groups=1,
use_bias=True,
data_format="channels_last",
**kwargs):
super(Deconv2d, self).__init__(**kwargs)
assert (dilation == 1)
assert (groups == 1)
assert (in_channels is not None)
if isinstance(padding, int):
padding = (padding, padding)
self.use_crop = (padding[0] > 0) or (padding[1] > 0)
if self.use_crop:
self.crop = nn.Cropping2D(
cropping=padding,
data_format=data_format,
name="crop")
self.conv = nn.Conv2DTranspose(
filters=out_channels,
kernel_size=kernel_size,
strides=strides,
padding="valid",
output_padding=out_padding,
data_format=data_format,
dilation_rate=dilation,
use_bias=use_bias,
name="conv")
def call(self, x):
x = self.conv(x)
if self.use_crop:
x = self.crop(x)
return x
class DeconvBlock(nn.Layer):
"""
Deconvolution block with batch normalization and activation.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
kernel_size : int or tuple/list of 2 int
Convolution window size.
strides : int or tuple/list of 2 int
Strides of the deconvolution.
padding : int or tuple/list of 2 int
Padding value for deconvolution layer.
out_padding : int or tuple/list of 2 int, default 0
Output padding value for deconvolution layer.
dilation : int or tuple/list of 2 int, default 1
Dilation value for deconvolution layer.
groups : int, default 1
Number of groups.
use_bias : bool, default False
Whether the layer uses a bias vector.
use_bn : bool, default True
Whether to use BatchNorm layer.
bn_eps : float, default 1e-5
Small float added to variance in Batch norm.
activation : function or str or None, default 'relu'
Activation function or name of activation function.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
strides,
padding,
out_padding=0,
dilation=1,
groups=1,
use_bias=False,
use_bn=True,
bn_eps=1e-5,
activation="relu",
data_format="channels_last",
**kwargs):
super(DeconvBlock, self).__init__(**kwargs)
assert (in_channels is not None)
self.activate = (activation is not None)
self.use_bn = use_bn
self.conv = Deconv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
strides=strides,
padding=padding,
out_padding=out_padding,
dilation=dilation,
groups=groups,
use_bias=use_bias,
data_format=data_format,
name="conv")
if self.use_bn:
self.bn = BatchNorm(
epsilon=bn_eps,
data_format=data_format,
name="bn")
if self.activate:
self.activ = get_activation_layer(activation)
def call(self, x, training=None):
x = self.conv(x)
if self.use_bn:
x = self.bn(x, training=training)
if self.activate:
x = self.activ(x)
return x
class SimplePose(tf.keras.Model):
"""
SimplePose model from 'Simple Baselines for Human Pose Estimation and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
backbone : nn.Sequential
Feature extractor.
backbone_out_channels : int
Number of output channels for the backbone.
channels : list of int
Number of output channels for each decoder unit.
return_heatmap : bool, default False
Whether to return only heatmap.
in_channels : int, default 3
Number of input channels.
in_size : tuple of two ints, default (256, 192)
Spatial size of the expected input image.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
"""
def __init__(self,
backbone,
backbone_out_channels,
channels,
return_heatmap=False,
in_channels=3,
in_size=(256, 192),
keypoints=17,
data_format="channels_last",
**kwargs):
super(SimplePose, self).__init__(**kwargs)
assert (in_channels == 3)
self.in_size = in_size
self.keypoints = keypoints
self.return_heatmap = return_heatmap
self.data_format = data_format
self.backbone = backbone
self.backbone._name = "backbone"
self.decoder = tf.keras.Sequential(name="decoder")
in_channels = backbone_out_channels
for i, out_channels in enumerate(channels):
self.decoder.add(DeconvBlock(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=4,
strides=2,
padding=1,
data_format=data_format,
name="unit{}".format(i + 1)))
in_channels = out_channels
self.decoder.add(conv1x1(
in_channels=in_channels,
out_channels=keypoints,
use_bias=True,
data_format=data_format,
name="final_block"))
self.heatmap_max_det = HeatmapMaxDetBlock(
data_format=data_format,
name="heatmap_max_det")
def call(self, x, training=None):
x = self.backbone(x, training=training)
heatmap = self.decoder(x, training=training)
if self.return_heatmap or not tf.executing_eagerly():
return [heatmap]
else:
keypoints = self.heatmap_max_det(heatmap)
return keypoints
def get_simplepose(backbone,
backbone_out_channels,
keypoints,
model_name=None,
data_format="channels_last",
pretrained=False,
root=os.path.join("~", ".tensorflow", "models"),
channels=[256, 256, 256],
**kwargs):
"""
Create SimplePose model with specific parameters.
Parameters:
----------
backbone : nn.Sequential
Feature extractor.
backbone_out_channels : int
Number of output channels for the backbone.
keypoints : int
Number of keypoints.
model_name : str or None, default None
Model name for loading pretrained model.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
net = SimplePose(
backbone=backbone,
backbone_out_channels=backbone_out_channels,
channels=channels,
keypoints=keypoints,
data_format=data_format,
**kwargs)
if pretrained:
if (model_name is None) or (not model_name):
raise ValueError("Parameter `model_name` should be properly initialized for loading pretrained model.")
from .model_store import get_model_file
in_channels = kwargs["in_channels"] if ("in_channels" in kwargs) else 3
input_shape = (1,) + (in_channels,) + net.in_size if net.data_format == "channels_first" else\
(1,) + net.in_size + (in_channels,)
net.build(input_shape=input_shape)
net.load_weights(
filepath=get_model_file(
model_name=model_name,
local_model_store_dir_path=root))
return net
def simplepose_mobilenetv2_coco(mv2_alpha=1.0, keypoints=17, data_format="channels_last", **kwargs):
backbone = tf.keras.applications.MobileNetV2(include_top=False, alpha=mv2_alpha)
return get_simplepose(backbone, backbone_out_channels=512, keypoints=keypoints,
model_name="simplepose_mobilenetv2_coco", data_format=data_format, **kwargs)
def simplepose_mv2_coco(keypoints=17, data_format="channels_last", **kwargs):
from .mv2_cpm import MobileNetV2
backbone = MobileNetV2()
return get_simplepose(backbone, backbone_out_channels=256, keypoints=keypoints,
model_name="simplepose_mv2_coco", data_format=data_format, channels=[256, 256], **kwargs)
def simplepose_resnet18_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet-18 for COCO Keypoint from 'Simple Baselines for Human Pose Estimation and
Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resnet18(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=512, keypoints=keypoints,
model_name="simplepose_resnet18_coco", data_format=data_format, **kwargs)
def simplepose_resnet50b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet-50b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation and
Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resnet50b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resnet50b_coco", data_format=data_format, **kwargs)
def simplepose_resnet101b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet-101b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resnet101b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resnet101b_coco", data_format=data_format, **kwargs)
def simplepose_resnet152b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet-152b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resnet152b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resnet152b_coco", data_format=data_format, **kwargs)
def simplepose_resneta50b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet(A)-50b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resneta50b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resneta50b_coco", data_format=data_format, **kwargs)
def simplepose_resneta101b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet(A)-101b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resneta101b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resneta101b_coco", data_format=data_format, **kwargs)
def simplepose_resneta152b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet(A)-152b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resneta152b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resneta152b_coco", data_format=data_format, **kwargs)
def _test():
import numpy as np
import tensorflow.keras.backend as K
data_format = "channels_last"
# data_format = "channels_first"
in_size = (256, 192)
keypoints = 17
return_heatmap = False
pretrained = False
models = [
simplepose_resnet18_coco,
simplepose_resnet50b_coco,
simplepose_resnet101b_coco,
simplepose_resnet152b_coco,
simplepose_resneta50b_coco,
simplepose_resneta101b_coco,
simplepose_resneta152b_coco,
]
for model in models:
net = model(pretrained=pretrained, in_size=in_size, return_heatmap=return_heatmap, data_format=data_format)
batch = 14
x = tf.random.normal((batch, 3, in_size[0], in_size[1]) if is_channels_first(data_format) else
(batch, in_size[0], in_size[1], 3))
y = net(x)
assert (y.shape[0] == batch)
if return_heatmap:
if is_channels_first(data_format):
assert ((y.shape[1] == keypoints) and (y.shape[2] == x.shape[2] // 4) and
(y.shape[3] == x.shape[3] // 4))
else:
assert ((y.shape[3] == keypoints) and (y.shape[1] == x.shape[1] // 4) and
(y.shape[2] == x.shape[2] // 4))
else:
assert ((y.shape[1] == keypoints) and (y.shape[2] == 3))
weight_count = sum([np.prod(K.get_value(w).shape) for w in net.trainable_weights])
print("m={}, {}".format(model.__name__, weight_count))
assert (model != simplepose_resnet18_coco or weight_count == 15376721)
assert (model != simplepose_resnet50b_coco or weight_count == 33999697)
assert (model != simplepose_resnet101b_coco or weight_count == 52991825)
assert (model != simplepose_resnet152b_coco or weight_count == 68635473)
assert (model != simplepose_resneta50b_coco or weight_count == 34018929)
assert (model != simplepose_resneta101b_coco or weight_count == 53011057)
assert (model != simplepose_resneta152b_coco or weight_count == 68654705)
if __name__ == "__main__":
_test()
|
the-stack_0_920 | """Selector and proactor event loops for Windows."""
import _overlapped
import _winapi
import errno
import math
import msvcrt
import socket
import struct
import time
import weakref
from . import events
from . import base_subprocess
from . import futures
from . import exceptions
from . import proactor_events
from . import selector_events
from . import tasks
from . import windows_utils
from .log import logger
__all__ = (
'SelectorEventLoop', 'ProactorEventLoop', 'IocpProactor',
'DefaultEventLoopPolicy', 'WindowsSelectorEventLoopPolicy',
'WindowsProactorEventLoopPolicy',
)
NULL = 0
INFINITE = 0xffffffff
ERROR_CONNECTION_REFUSED = 1225
ERROR_CONNECTION_ABORTED = 1236
# Initial delay in seconds for connect_pipe() before retrying to connect
CONNECT_PIPE_INIT_DELAY = 0.001
# Maximum delay in seconds for connect_pipe() before retrying to connect
CONNECT_PIPE_MAX_DELAY = 0.100
class _OverlappedFuture(futures.Future):
"""Subclass of Future which represents an overlapped operation.
Cancelling it will immediately cancel the overlapped operation.
"""
def __init__(self, ov, *, loop=None):
super().__init__(loop=loop)
if self._source_traceback:
del self._source_traceback[-1]
self._ov = ov
def _repr_info(self):
info = super()._repr_info()
if self._ov is not None:
state = 'pending' if self._ov.pending else 'completed'
info.insert(1, f'overlapped=<{state}, {self._ov.address:#x}>')
return info
def _cancel_overlapped(self):
if self._ov is None:
return
try:
self._ov.cancel()
except OSError as exc:
context = {
'message': 'Cancelling an overlapped future failed',
'exception': exc,
'future': self,
}
if self._source_traceback:
context['source_traceback'] = self._source_traceback
self._loop.call_exception_handler(context)
self._ov = None
def cancel(self, msg=None):
self._cancel_overlapped()
return super().cancel(msg=msg)
def set_exception(self, exception):
super().set_exception(exception)
self._cancel_overlapped()
def set_result(self, result):
super().set_result(result)
self._ov = None
class _BaseWaitHandleFuture(futures.Future):
"""Subclass of Future which represents a wait handle."""
def __init__(self, ov, handle, wait_handle, *, loop=None):
super().__init__(loop=loop)
if self._source_traceback:
del self._source_traceback[-1]
# Keep a reference to the Overlapped object to keep it alive until the
# wait is unregistered
self._ov = ov
self._handle = handle
self._wait_handle = wait_handle
# Should we call UnregisterWaitEx() if the wait completes
# or is cancelled?
self._registered = True
def _poll(self):
# non-blocking wait: use a timeout of 0 millisecond
return (_winapi.WaitForSingleObject(self._handle, 0) ==
_winapi.WAIT_OBJECT_0)
def _repr_info(self):
info = super()._repr_info()
info.append(f'handle={self._handle:#x}')
if self._handle is not None:
state = 'signaled' if self._poll() else 'waiting'
info.append(state)
if self._wait_handle is not None:
info.append(f'wait_handle={self._wait_handle:#x}')
return info
def _unregister_wait_cb(self, fut):
# The wait was unregistered: it's not safe to destroy the Overlapped
# object
self._ov = None
def _unregister_wait(self):
if not self._registered:
return
self._registered = False
wait_handle = self._wait_handle
self._wait_handle = None
try:
_overlapped.UnregisterWait(wait_handle)
except OSError as exc:
if exc.winerror != _overlapped.ERROR_IO_PENDING:
context = {
'message': 'Failed to unregister the wait handle',
'exception': exc,
'future': self,
}
if self._source_traceback:
context['source_traceback'] = self._source_traceback
self._loop.call_exception_handler(context)
return
# ERROR_IO_PENDING means that the unregister is pending
self._unregister_wait_cb(None)
def cancel(self, msg=None):
self._unregister_wait()
return super().cancel(msg=msg)
def set_exception(self, exception):
self._unregister_wait()
super().set_exception(exception)
def set_result(self, result):
self._unregister_wait()
super().set_result(result)
class _WaitCancelFuture(_BaseWaitHandleFuture):
"""Subclass of Future which represents a wait for the cancellation of a
_WaitHandleFuture using an event.
"""
def __init__(self, ov, event, wait_handle, *, loop=None):
super().__init__(ov, event, wait_handle, loop=loop)
self._done_callback = None
def cancel(self):
raise RuntimeError("_WaitCancelFuture must not be cancelled")
def set_result(self, result):
super().set_result(result)
if self._done_callback is not None:
self._done_callback(self)
def set_exception(self, exception):
super().set_exception(exception)
if self._done_callback is not None:
self._done_callback(self)
class _WaitHandleFuture(_BaseWaitHandleFuture):
def __init__(self, ov, handle, wait_handle, proactor, *, loop=None):
super().__init__(ov, handle, wait_handle, loop=loop)
self._proactor = proactor
self._unregister_proactor = True
self._event = _overlapped.CreateEvent(None, True, False, None)
self._event_fut = None
def _unregister_wait_cb(self, fut):
if self._event is not None:
_winapi.CloseHandle(self._event)
self._event = None
self._event_fut = None
# If the wait was cancelled, the wait may never be signalled, so
# it's required to unregister it. Otherwise, IocpProactor.close() will
# wait forever for an event which will never come.
#
# If the IocpProactor already received the event, it's safe to call
# _unregister() because we kept a reference to the Overlapped object
# which is used as a unique key.
self._proactor._unregister(self._ov)
self._proactor = None
super()._unregister_wait_cb(fut)
def _unregister_wait(self):
if not self._registered:
return
self._registered = False
wait_handle = self._wait_handle
self._wait_handle = None
try:
_overlapped.UnregisterWaitEx(wait_handle, self._event)
except OSError as exc:
if exc.winerror != _overlapped.ERROR_IO_PENDING:
context = {
'message': 'Failed to unregister the wait handle',
'exception': exc,
'future': self,
}
if self._source_traceback:
context['source_traceback'] = self._source_traceback
self._loop.call_exception_handler(context)
return
# ERROR_IO_PENDING is not an error, the wait was unregistered
self._event_fut = self._proactor._wait_cancel(self._event,
self._unregister_wait_cb)
class PipeServer(object):
"""Class representing a pipe server.
This is much like a bound, listening socket.
"""
def __init__(self, address):
self._address = address
self._free_instances = weakref.WeakSet()
# initialize the pipe attribute before calling _server_pipe_handle()
# because this function can raise an exception and the destructor calls
# the close() method
self._pipe = None
self._accept_pipe_future = None
self._pipe = self._server_pipe_handle(True)
def _get_unconnected_pipe(self):
# Create new instance and return previous one. This ensures
# that (until the server is closed) there is always at least
# one pipe handle for address. Therefore if a client attempt
# to connect it will not fail with FileNotFoundError.
tmp, self._pipe = self._pipe, self._server_pipe_handle(False)
return tmp
def _server_pipe_handle(self, first):
# Return a wrapper for a new pipe handle.
if self.closed():
return None
flags = _winapi.PIPE_ACCESS_DUPLEX | _winapi.FILE_FLAG_OVERLAPPED
if first:
flags |= _winapi.FILE_FLAG_FIRST_PIPE_INSTANCE
h = _winapi.CreateNamedPipe(
self._address, flags,
_winapi.PIPE_TYPE_MESSAGE | _winapi.PIPE_READMODE_MESSAGE |
_winapi.PIPE_WAIT,
_winapi.PIPE_UNLIMITED_INSTANCES,
windows_utils.BUFSIZE, windows_utils.BUFSIZE,
_winapi.NMPWAIT_WAIT_FOREVER, _winapi.NULL)
pipe = windows_utils.PipeHandle(h)
self._free_instances.add(pipe)
return pipe
def closed(self):
return (self._address is None)
def close(self):
if self._accept_pipe_future is not None:
self._accept_pipe_future.cancel()
self._accept_pipe_future = None
# Close all instances which have not been connected to by a client.
if self._address is not None:
for pipe in self._free_instances:
pipe.close()
self._pipe = None
self._address = None
self._free_instances.clear()
__del__ = close
class _WindowsSelectorEventLoop(selector_events.BaseSelectorEventLoop):
"""Windows version of selector event loop."""
class ProactorEventLoop(proactor_events.BaseProactorEventLoop):
"""Windows version of proactor event loop using IOCP."""
def __init__(self, proactor=None):
if proactor is None:
proactor = IocpProactor()
super().__init__(proactor)
def run_forever(self):
try:
assert self._self_reading_future is None
self.call_soon(self._loop_self_reading)
super().run_forever()
finally:
if self._self_reading_future is not None:
ov = self._self_reading_future._ov
self._self_reading_future.cancel()
# self_reading_future was just cancelled so if it hasn't been
# finished yet, it never will be (it's possible that it has
# already finished and its callback is waiting in the queue,
# where it could still happen if the event loop is restarted).
# Unregister it otherwise IocpProactor.close will wait for it
# forever
if ov is not None:
self._proactor._unregister(ov)
self._self_reading_future = None
async def create_pipe_connection(self, protocol_factory, address):
f = self._proactor.connect_pipe(address)
pipe = await f
protocol = protocol_factory()
trans = self._make_duplex_pipe_transport(pipe, protocol,
extra={'addr': address})
return trans, protocol
async def start_serving_pipe(self, protocol_factory, address):
server = PipeServer(address)
def loop_accept_pipe(f=None):
pipe = None
try:
if f:
pipe = f.result()
server._free_instances.discard(pipe)
if server.closed():
# A client connected before the server was closed:
# drop the client (close the pipe) and exit
pipe.close()
return
protocol = protocol_factory()
self._make_duplex_pipe_transport(
pipe, protocol, extra={'addr': address})
pipe = server._get_unconnected_pipe()
if pipe is None:
return
f = self._proactor.accept_pipe(pipe)
except OSError as exc:
if pipe and pipe.fileno() != -1:
self.call_exception_handler({
'message': 'Pipe accept failed',
'exception': exc,
'pipe': pipe,
})
pipe.close()
elif self._debug:
logger.warning("Accept pipe failed on pipe %r",
pipe, exc_info=True)
except exceptions.CancelledError:
if pipe:
pipe.close()
else:
server._accept_pipe_future = f
f.add_done_callback(loop_accept_pipe)
self.call_soon(loop_accept_pipe)
return [server]
async def _make_subprocess_transport(self, protocol, args, shell,
stdin, stdout, stderr, bufsize,
extra=None, **kwargs):
waiter = self.create_future()
transp = _WindowsSubprocessTransport(self, protocol, args, shell,
stdin, stdout, stderr, bufsize,
waiter=waiter, extra=extra,
**kwargs)
try:
await waiter
except (SystemExit, KeyboardInterrupt):
raise
except BaseException:
transp.close()
await transp._wait()
raise
return transp
class IocpProactor:
"""Proactor implementation using IOCP."""
def __init__(self, concurrency=0xffffffff):
self._loop = None
self._results = []
self._iocp = _overlapped.CreateIoCompletionPort(
_overlapped.INVALID_HANDLE_VALUE, NULL, 0, concurrency)
self._cache = {}
self._registered = weakref.WeakSet()
self._unregistered = []
self._stopped_serving = weakref.WeakSet()
def _check_closed(self):
if self._iocp is None:
raise RuntimeError('IocpProactor is closed')
def __repr__(self):
info = ['overlapped#=%s' % len(self._cache),
'result#=%s' % len(self._results)]
if self._iocp is None:
info.append('closed')
return '<%s %s>' % (self.__class__.__name__, " ".join(info))
def set_loop(self, loop):
self._loop = loop
def select(self, timeout=None):
if not self._results:
self._poll(timeout)
tmp = self._results
self._results = []
return tmp
def _result(self, value):
fut = self._loop.create_future()
fut.set_result(value)
return fut
def recv(self, conn, nbytes, flags=0):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
try:
if isinstance(conn, socket.socket):
ov.WSARecv(conn.fileno(), nbytes, flags)
else:
ov.ReadFile(conn.fileno(), nbytes)
except BrokenPipeError:
return self._result(b'')
def finish_recv(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_recv)
def recv_into(self, conn, buf, flags=0):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
try:
if isinstance(conn, socket.socket):
ov.WSARecvInto(conn.fileno(), buf, flags)
else:
ov.ReadFileInto(conn.fileno(), buf)
except BrokenPipeError:
return self._result(0)
def finish_recv(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_recv)
def recvfrom(self, conn, nbytes, flags=0):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
try:
ov.WSARecvFrom(conn.fileno(), nbytes, flags)
except BrokenPipeError:
return self._result((b'', None))
def finish_recv(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_recv)
def sendto(self, conn, buf, flags=0, addr=None):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
ov.WSASendTo(conn.fileno(), buf, flags, addr)
def finish_send(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_send)
def send(self, conn, buf, flags=0):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
if isinstance(conn, socket.socket):
ov.WSASend(conn.fileno(), buf, flags)
else:
ov.WriteFile(conn.fileno(), buf)
def finish_send(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_send)
def accept(self, listener):
self._register_with_iocp(listener)
conn = self._get_accept_socket(listener.family)
ov = _overlapped.Overlapped(NULL)
ov.AcceptEx(listener.fileno(), conn.fileno())
def finish_accept(trans, key, ov):
ov.getresult()
# Use SO_UPDATE_ACCEPT_CONTEXT so getsockname() etc work.
buf = struct.pack('@P', listener.fileno())
conn.setsockopt(socket.SOL_SOCKET,
_overlapped.SO_UPDATE_ACCEPT_CONTEXT, buf)
conn.settimeout(listener.gettimeout())
return conn, conn.getpeername()
async def accept_coro(future, conn):
# Coroutine closing the accept socket if the future is cancelled
try:
await future
except exceptions.CancelledError:
conn.close()
raise
future = self._register(ov, listener, finish_accept)
coro = accept_coro(future, conn)
tasks.ensure_future(coro, loop=self._loop)
return future
def connect(self, conn, address):
if conn.type == socket.SOCK_DGRAM:
# WSAConnect will complete immediately for UDP sockets so we don't
# need to register any IOCP operation
_overlapped.WSAConnect(conn.fileno(), address)
fut = self._loop.create_future()
fut.set_result(None)
return fut
self._register_with_iocp(conn)
# The socket needs to be locally bound before we call ConnectEx().
try:
_overlapped.BindLocal(conn.fileno(), conn.family)
except OSError as e:
if e.winerror != errno.WSAEINVAL:
raise
# Probably already locally bound; check using getsockname().
if conn.getsockname()[1] == 0:
raise
ov = _overlapped.Overlapped(NULL)
ov.ConnectEx(conn.fileno(), address)
def finish_connect(trans, key, ov):
ov.getresult()
# Use SO_UPDATE_CONNECT_CONTEXT so getsockname() etc work.
conn.setsockopt(socket.SOL_SOCKET,
_overlapped.SO_UPDATE_CONNECT_CONTEXT, 0)
return conn
return self._register(ov, conn, finish_connect)
def sendfile(self, sock, file, offset, count):
self._register_with_iocp(sock)
ov = _overlapped.Overlapped(NULL)
offset_low = offset & 0xffff_ffff
offset_high = (offset >> 32) & 0xffff_ffff
ov.TransmitFile(sock.fileno(),
msvcrt.get_osfhandle(file.fileno()),
offset_low, offset_high,
count, 0, 0)
def finish_sendfile(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, sock, finish_sendfile)
def accept_pipe(self, pipe):
self._register_with_iocp(pipe)
ov = _overlapped.Overlapped(NULL)
connected = ov.ConnectNamedPipe(pipe.fileno())
if connected:
# ConnectNamePipe() failed with ERROR_PIPE_CONNECTED which means
# that the pipe is connected. There is no need to wait for the
# completion of the connection.
return self._result(pipe)
def finish_accept_pipe(trans, key, ov):
ov.getresult()
return pipe
return self._register(ov, pipe, finish_accept_pipe)
async def connect_pipe(self, address):
delay = CONNECT_PIPE_INIT_DELAY
while True:
# Unfortunately there is no way to do an overlapped connect to
# a pipe. Call CreateFile() in a loop until it doesn't fail with
# ERROR_PIPE_BUSY.
try:
handle = _overlapped.ConnectPipe(address)
break
except OSError as exc:
if exc.winerror != _overlapped.ERROR_PIPE_BUSY:
raise
# ConnectPipe() failed with ERROR_PIPE_BUSY: retry later
delay = min(delay * 2, CONNECT_PIPE_MAX_DELAY)
await tasks.sleep(delay)
return windows_utils.PipeHandle(handle)
def wait_for_handle(self, handle, timeout=None):
"""Wait for a handle.
Return a Future object. The result of the future is True if the wait
completed, or False if the wait did not complete (on timeout).
"""
return self._wait_for_handle(handle, timeout, False)
def _wait_cancel(self, event, done_callback):
fut = self._wait_for_handle(event, None, True)
# add_done_callback() cannot be used because the wait may only complete
# in IocpProactor.close(), while the event loop is not running.
fut._done_callback = done_callback
return fut
def _wait_for_handle(self, handle, timeout, _is_cancel):
self._check_closed()
if timeout is None:
ms = _winapi.INFINITE
else:
# RegisterWaitForSingleObject() has a resolution of 1 millisecond,
# round away from zero to wait *at least* timeout seconds.
ms = math.ceil(timeout * 1e3)
# We only create ov so we can use ov.address as a key for the cache.
ov = _overlapped.Overlapped(NULL)
wait_handle = _overlapped.RegisterWaitWithQueue(
handle, self._iocp, ov.address, ms)
if _is_cancel:
f = _WaitCancelFuture(ov, handle, wait_handle, loop=self._loop)
else:
f = _WaitHandleFuture(ov, handle, wait_handle, self,
loop=self._loop)
if f._source_traceback:
del f._source_traceback[-1]
def finish_wait_for_handle(trans, key, ov):
# Note that this second wait means that we should only use
# this with handles types where a successful wait has no
# effect. So events or processes are all right, but locks
# or semaphores are not. Also note if the handle is
# signalled and then quickly reset, then we may return
# False even though we have not timed out.
return f._poll()
self._cache[ov.address] = (f, ov, 0, finish_wait_for_handle)
return f
def _register_with_iocp(self, obj):
# To get notifications of finished ops on this objects sent to the
# completion port, were must register the handle.
if obj not in self._registered:
self._registered.add(obj)
_overlapped.CreateIoCompletionPort(obj.fileno(), self._iocp, 0, 0)
# XXX We could also use SetFileCompletionNotificationModes()
# to avoid sending notifications to completion port of ops
# that succeed immediately.
def _register(self, ov, obj, callback):
self._check_closed()
# Return a future which will be set with the result of the
# operation when it completes. The future's value is actually
# the value returned by callback().
f = _OverlappedFuture(ov, loop=self._loop)
if f._source_traceback:
del f._source_traceback[-1]
if not ov.pending:
# The operation has completed, so no need to postpone the
# work. We cannot take this short cut if we need the
# NumberOfBytes, CompletionKey values returned by
# PostQueuedCompletionStatus().
try:
value = callback(None, None, ov)
except OSError as e:
f.set_exception(e)
else:
f.set_result(value)
# Even if GetOverlappedResult() was called, we have to wait for the
# notification of the completion in GetQueuedCompletionStatus().
# Register the overlapped operation to keep a reference to the
# OVERLAPPED object, otherwise the memory is freed and Windows may
# read uninitialized memory.
# Register the overlapped operation for later. Note that
# we only store obj to prevent it from being garbage
# collected too early.
self._cache[ov.address] = (f, ov, obj, callback)
return f
def _unregister(self, ov):
"""Unregister an overlapped object.
Call this method when its future has been cancelled. The event can
already be signalled (pending in the proactor event queue). It is also
safe if the event is never signalled (because it was cancelled).
"""
self._check_closed()
self._unregistered.append(ov)
def _get_accept_socket(self, family):
s = socket.socket(family)
s.settimeout(0)
return s
def _poll(self, timeout=None):
if timeout is None:
ms = INFINITE
elif timeout < 0:
raise ValueError("negative timeout")
else:
# GetQueuedCompletionStatus() has a resolution of 1 millisecond,
# round away from zero to wait *at least* timeout seconds.
ms = math.ceil(timeout * 1e3)
if ms >= INFINITE:
raise ValueError("timeout too big")
while True:
status = _overlapped.GetQueuedCompletionStatus(self._iocp, ms)
if status is None:
break
ms = 0
err, transferred, key, address = status
try:
f, ov, obj, callback = self._cache.pop(address)
except KeyError:
if self._loop.get_debug():
self._loop.call_exception_handler({
'message': ('GetQueuedCompletionStatus() returned an '
'unexpected event'),
'status': ('err=%s transferred=%s key=%#x address=%#x'
% (err, transferred, key, address)),
})
# key is either zero, or it is used to return a pipe
# handle which should be closed to avoid a leak.
if key not in (0, _overlapped.INVALID_HANDLE_VALUE):
_winapi.CloseHandle(key)
continue
if obj in self._stopped_serving:
f.cancel()
# Don't call the callback if _register() already read the result or
# if the overlapped has been cancelled
elif not f.done():
try:
value = callback(transferred, key, ov)
except OSError as e:
f.set_exception(e)
self._results.append(f)
else:
f.set_result(value)
self._results.append(f)
# Remove unregistered futures
for ov in self._unregistered:
self._cache.pop(ov.address, None)
self._unregistered.clear()
def _stop_serving(self, obj):
# obj is a socket or pipe handle. It will be closed in
# BaseProactorEventLoop._stop_serving() which will make any
# pending operations fail quickly.
self._stopped_serving.add(obj)
def close(self):
if self._iocp is None:
# already closed
return
# Cancel remaining registered operations.
for address, (fut, ov, obj, callback) in list(self._cache.items()):
if fut.cancelled():
# Nothing to do with cancelled futures
pass
elif isinstance(fut, _WaitCancelFuture):
# _WaitCancelFuture must not be cancelled
pass
else:
try:
fut.cancel()
except OSError as exc:
if self._loop is not None:
context = {
'message': 'Cancelling a future failed',
'exception': exc,
'future': fut,
}
if fut._source_traceback:
context['source_traceback'] = fut._source_traceback
self._loop.call_exception_handler(context)
# Wait until all cancelled overlapped complete: don't exit with running
# overlapped to prevent a crash. Display progress every second if the
# loop is still running.
msg_update = 1.0
start_time = time.monotonic()
next_msg = start_time + msg_update
while self._cache:
if next_msg <= time.monotonic():
logger.debug('%r is running after closing for %.1f seconds',
self, time.monotonic() - start_time)
next_msg = time.monotonic() + msg_update
# handle a few events, or timeout
self._poll(msg_update)
self._results = []
_winapi.CloseHandle(self._iocp)
self._iocp = None
def __del__(self):
self.close()
class _WindowsSubprocessTransport(base_subprocess.BaseSubprocessTransport):
def _start(self, args, shell, stdin, stdout, stderr, bufsize, **kwargs):
self._proc = windows_utils.Popen(
args, shell=shell, stdin=stdin, stdout=stdout, stderr=stderr,
bufsize=bufsize, **kwargs)
def callback(f):
returncode = self._proc.poll()
self._process_exited(returncode)
f = self._loop._proactor.wait_for_handle(int(self._proc._handle))
f.add_done_callback(callback)
SelectorEventLoop = _WindowsSelectorEventLoop
class WindowsSelectorEventLoopPolicy(events.BaseDefaultEventLoopPolicy):
_loop_factory = SelectorEventLoop
class WindowsProactorEventLoopPolicy(events.BaseDefaultEventLoopPolicy):
_loop_factory = ProactorEventLoop
DefaultEventLoopPolicy = WindowsProactorEventLoopPolicy
|
the-stack_0_921 | from django.urls import path, include
from comment.api.views import CommentCreateApiView, CommentListApiView, CommentValidateApiView
app_name = "comment"
urlpatterns = [
path('create/', CommentCreateApiView.as_view(), name='create'),
path('list/', CommentListApiView.as_view(), name='list'),
path('validate/<pk>', CommentValidateApiView.as_view(), name='validate'),
]
|
the-stack_0_922 | def readline(f, newline):
buf = ""
while True:
while newline in buf:
pos = buf.index(newline)
yield buf[:pos]
buf = buf[pos + len(newline):]
chunk = f.read(4096 * 10)
if not chunk:
yield buf
break
buf += chunk
with open("index.txt") as f:
for line in readline(f, "{|}"):
print(line)
|
the-stack_0_924 | """The module defines the abstract interface for resolving container images for tool execution."""
from abc import (
ABCMeta,
abstractmethod,
abstractproperty,
)
from galaxy.util.bunch import Bunch
from galaxy.util.dictifiable import Dictifiable
class ResolutionCache(Bunch):
"""Simple cache for duplicated computation created once per set of requests (likely web request in Galaxy context).
This should not be assumed to be thread safe - resolution using a given cache should all occur
one resolution at a time in a single thread.
"""
mulled_resolution_cache = None
class ContainerResolver(Dictifiable, metaclass=ABCMeta):
"""Description of a technique for resolving container images for tool execution."""
# Keys for dictification.
dict_collection_visible_keys = ["resolver_type", "can_uninstall_dependencies", "builds_on_resolution"]
can_uninstall_dependencies = False
builds_on_resolution = False
read_only = True # not used for containers, but set for when they are used like dependency resolvers
def __init__(self, app_info=None, **kwds):
"""Default initializer for ``ContainerResolver`` subclasses."""
self.app_info = app_info
self.resolver_kwds = kwds
def _get_config_option(self, key, default=None):
"""Look in resolver-specific settings for option and then fallback to
global settings.
"""
if self.app_info and hasattr(self.app_info, key):
return getattr(self.app_info, key)
else:
return default
@abstractmethod
def resolve(self, enabled_container_types, tool_info, resolution_cache=None, **kwds):
"""Find a container matching all supplied requirements for tool.
The supplied argument is a :class:`galaxy.tool_util.deps.containers.ToolInfo` description
of the tool and its requirements.
"""
@abstractproperty
def resolver_type(self):
"""Short label for the type of container resolution."""
def _container_type_enabled(self, container_description, enabled_container_types):
"""Return a boolean indicating if the specified container type is enabled."""
return container_description.type in enabled_container_types
def __str__(self):
return f"{self.__class__.__name__}[]"
|
the-stack_0_925 | import warnings
from itertools import islice
from types import GeneratorType
from typing import (
TYPE_CHECKING,
AbstractSet,
Any,
Callable,
Dict,
Generator,
Iterator,
List,
Optional,
Set,
Tuple,
Type,
TypeVar,
Union,
no_type_check,
)
from .typing import AnyType, display_as_type
from .version import version_info
if TYPE_CHECKING:
from inspect import Signature
from .main import BaseModel, BaseConfig # noqa: F401
from .typing import AbstractSetIntStr, DictIntStrAny, IntStr, MappingIntStrAny, ReprArgs # noqa: F401
from .fields import ModelField # noqa: F401
from .dataclasses import DataclassType # noqa: F401
__all__ = (
'import_string',
'sequence_like',
'validate_field_name',
'lenient_issubclass',
'in_ipython',
'deep_update',
'update_not_none',
'almost_equal_floats',
'get_model',
'to_camel',
'PyObjectStr',
'Representation',
'GetterDict',
'ValueItems',
'version_info', # required here to match behaviour in v1.3
)
def import_string(dotted_path: str) -> Any:
"""
Stolen approximately from django. Import a dotted module path and return the attribute/class designated by the
last name in the path. Raise ImportError if the import fails.
"""
from importlib import import_module
try:
module_path, class_name = dotted_path.strip(' ').rsplit('.', 1)
except ValueError as e:
raise ImportError(f'"{dotted_path}" doesn\'t look like a module path') from e
module = import_module(module_path)
try:
return getattr(module, class_name)
except AttributeError as e:
raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute') from e
def truncate(v: Union[str], *, max_len: int = 80) -> str:
"""
Truncate a value and add a unicode ellipsis (three dots) to the end if it was too long
"""
warnings.warn('`truncate` is no-longer used by pydantic and is deprecated', DeprecationWarning)
if isinstance(v, str) and len(v) > (max_len - 2):
# -3 so quote + string + … + quote has correct length
return (v[: (max_len - 3)] + '…').__repr__()
try:
v = v.__repr__()
except TypeError:
v = v.__class__.__repr__(v) # in case v is a type
if len(v) > max_len:
v = v[: max_len - 1] + '…'
return v
def sequence_like(v: AnyType) -> bool:
return isinstance(v, (list, tuple, set, frozenset, GeneratorType))
def validate_field_name(bases: List[Type['BaseModel']], field_name: str) -> None:
"""
Ensure that the field's name does not shadow an existing attribute of the model.
"""
for base in bases:
if getattr(base, field_name, None):
raise NameError(
f'Field name "{field_name}" shadows a BaseModel attribute; '
f'use a different field name with "alias=\'{field_name}\'".'
)
def lenient_issubclass(cls: Any, class_or_tuple: Union[AnyType, Tuple[AnyType, ...]]) -> bool:
return isinstance(cls, type) and issubclass(cls, class_or_tuple)
def in_ipython() -> bool:
"""
Check whether we're in an ipython environment, including jupyter notebooks.
"""
try:
eval('__IPYTHON__')
except NameError:
return False
else: # pragma: no cover
return True
KeyType = TypeVar('KeyType')
def deep_update(mapping: Dict[KeyType, Any], updating_mapping: Dict[KeyType, Any]) -> Dict[KeyType, Any]:
updated_mapping = mapping.copy()
for k, v in updating_mapping.items():
if k in mapping and isinstance(mapping[k], dict) and isinstance(v, dict):
updated_mapping[k] = deep_update(mapping[k], v)
else:
updated_mapping[k] = v
return updated_mapping
def update_not_none(mapping: Dict[Any, Any], **update: Any) -> None:
mapping.update({k: v for k, v in update.items() if v is not None})
def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:
"""
Return True if two floats are almost equal
"""
return abs(value_1 - value_2) <= delta
def generate_model_signature(
init: Callable[..., None], fields: Dict[str, 'ModelField'], config: Type['BaseConfig']
) -> 'Signature':
"""
Generate signature for model based on its fields
"""
from inspect import Parameter, Signature, signature
present_params = signature(init).parameters.values()
merged_params: Dict[str, Parameter] = {}
var_kw = None
use_var_kw = False
for param in islice(present_params, 1, None): # skip self arg
if param.kind is param.VAR_KEYWORD:
var_kw = param
continue
merged_params[param.name] = param
if var_kw: # if custom init has no var_kw, fields which are not declared in it cannot be passed through
allow_names = config.allow_population_by_field_name
for field_name, field in fields.items():
param_name = field.alias
if field_name in merged_params or param_name in merged_params:
continue
elif not param_name.isidentifier():
if allow_names and field_name.isidentifier():
param_name = field_name
else:
use_var_kw = True
continue
# TODO: replace annotation with actual expected types once #1055 solved
kwargs = {'default': field.default} if not field.required else {}
merged_params[param_name] = Parameter(
param_name, Parameter.KEYWORD_ONLY, annotation=field.outer_type_, **kwargs
)
if config.extra is config.extra.allow:
use_var_kw = True
if var_kw and use_var_kw:
# Make sure the parameter for extra kwargs
# does not have the same name as a field
default_model_signature = [
('__pydantic_self__', Parameter.POSITIONAL_OR_KEYWORD),
('data', Parameter.VAR_KEYWORD),
]
if [(p.name, p.kind) for p in present_params] == default_model_signature:
# if this is the standard model signature, use extra_data as the extra args name
var_kw_name = 'extra_data'
else:
# else start from var_kw
var_kw_name = var_kw.name
# generate a name that's definitely unique
while var_kw_name in fields:
var_kw_name += '_'
merged_params[var_kw_name] = var_kw.replace(name=var_kw_name)
return Signature(parameters=list(merged_params.values()), return_annotation=None)
def get_model(obj: Union[Type['BaseModel'], Type['DataclassType']]) -> Type['BaseModel']:
from .main import BaseModel # noqa: F811
try:
model_cls = obj.__pydantic_model__ # type: ignore
except AttributeError:
model_cls = obj
if not issubclass(model_cls, BaseModel):
raise TypeError('Unsupported type, must be either BaseModel or dataclass')
return model_cls
def to_camel(string: str) -> str:
return ''.join(word.capitalize() for word in string.split('_'))
class PyObjectStr(str):
"""
String class where repr doesn't include quotes. Useful with Representation when you want to return a string
representation of something that valid (or pseudo-valid) python.
"""
def __repr__(self) -> str:
return str(self)
class Representation:
"""
Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
__pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations
of objects.
"""
__slots__: Tuple[str, ...] = tuple()
def __repr_args__(self) -> 'ReprArgs':
"""
Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
Can either return:
* name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
* or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
"""
attrs = ((s, getattr(self, s)) for s in self.__slots__)
return [(a, v) for a, v in attrs if v is not None]
def __repr_name__(self) -> str:
"""
Name of the instance's class, used in __repr__.
"""
return self.__class__.__name__
def __repr_str__(self, join_str: str) -> str:
return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__())
def __pretty__(self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, None, None]:
"""
Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects
"""
yield self.__repr_name__() + '('
yield 1
for name, value in self.__repr_args__():
if name is not None:
yield name + '='
yield fmt(value)
yield ','
yield 0
yield -1
yield ')'
def __str__(self) -> str:
return self.__repr_str__(' ')
def __repr__(self) -> str:
return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
class GetterDict(Representation):
"""
Hack to make object's smell just enough like dicts for validate_model.
We can't inherit from Mapping[str, Any] because it upsets cython so we have to implement all methods ourselves.
"""
__slots__ = ('_obj',)
def __init__(self, obj: Any):
self._obj = obj
def __getitem__(self, key: str) -> Any:
try:
return getattr(self._obj, key)
except AttributeError as e:
raise KeyError(key) from e
def get(self, key: Any, default: Any = None) -> Any:
return getattr(self._obj, key, default)
def extra_keys(self) -> Set[Any]:
"""
We don't want to get any other attributes of obj if the model didn't explicitly ask for them
"""
return set()
def keys(self) -> List[Any]:
"""
Keys of the pseudo dictionary, uses a list not set so order information can be maintained like python
dictionaries.
"""
return list(self)
def values(self) -> List[Any]:
return [self[k] for k in self]
def items(self) -> Iterator[Tuple[str, Any]]:
for k in self:
yield k, self.get(k)
def __iter__(self) -> Iterator[str]:
for name in dir(self._obj):
if not name.startswith('_'):
yield name
def __len__(self) -> int:
return sum(1 for _ in self)
def __contains__(self, item: Any) -> bool:
return item in self.keys()
def __eq__(self, other: Any) -> bool:
return dict(self) == dict(other.items()) # type: ignore
def __repr_args__(self) -> 'ReprArgs':
return [(None, dict(self))] # type: ignore
def __repr_name__(self) -> str:
return f'GetterDict[{display_as_type(self._obj)}]'
class ValueItems(Representation):
"""
Class for more convenient calculation of excluded or included fields on values.
"""
__slots__ = ('_items', '_type')
def __init__(self, value: Any, items: Union['AbstractSetIntStr', 'MappingIntStrAny']) -> None:
if TYPE_CHECKING:
self._items: Union['AbstractSetIntStr', 'MappingIntStrAny']
self._type: Type[Union[set, dict]] # type: ignore
# For further type checks speed-up
if isinstance(items, dict):
self._type = dict
elif isinstance(items, AbstractSet):
self._type = set
else:
raise TypeError(f'Unexpected type of exclude value {items.__class__}')
if isinstance(value, (list, tuple)):
try:
items = self._normalize_indexes(items, len(value))
except TypeError as e:
raise TypeError(
'Excluding fields from a sequence of sub-models or dicts must be performed index-wise: '
'expected integer keys or keyword "__all__"'
) from e
self._items = items
@no_type_check
def is_excluded(self, item: Any) -> bool:
"""
Check if item is fully excluded
(value considered excluded if self._type is set and item contained in self._items
or self._type is dict and self._items.get(item) is ...
:param item: key or index of a value
"""
if self._type is set:
return item in self._items
return self._items.get(item) is ...
@no_type_check
def is_included(self, item: Any) -> bool:
"""
Check if value is contained in self._items
:param item: key or index of value
"""
return item in self._items
@no_type_check
def for_element(self, e: 'IntStr') -> Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']]:
"""
:param e: key or index of element on value
:return: raw values for elemet if self._items is dict and contain needed element
"""
if self._type is dict:
item = self._items.get(e)
return item if item is not ... else None
return None
@no_type_check
def _normalize_indexes(
self, items: Union['AbstractSetIntStr', 'MappingIntStrAny'], v_length: int
) -> Union['AbstractSetIntStr', 'DictIntStrAny']:
"""
:param items: dict or set of indexes which will be normalized
:param v_length: length of sequence indexes of which will be
>>> self._normalize_indexes({0, -2, -1}, 4)
{0, 2, 3}
>>> self._normalize_indexes({'__all__'}, 4)
{0, 1, 2, 3}
"""
if self._type is set:
if '__all__' in items:
if items != {'__all__'}:
raise ValueError('set with keyword "__all__" must not contain other elements')
return {i for i in range(v_length)}
return {v_length + i if i < 0 else i for i in items}
else:
normalized_items = {v_length + i if i < 0 else i: v for i, v in items.items() if i != '__all__'}
all_set = items.get('__all__')
if all_set:
for i in range(v_length):
normalized_items.setdefault(i, set()).update(all_set)
return normalized_items
def __repr_args__(self) -> 'ReprArgs':
return [(None, self._items)]
|
the-stack_0_927 | # _____ ______ _____
# / ____/ /\ | ____ | __ \
# | | / \ | |__ | |__) | Caer - Modern Computer Vision
# | | / /\ \ | __| | _ / Languages: Python, C, C++, Cuda
# | |___ / ____ \ | |____ | | \ \ http://github.com/jasmcaus/caer
# \_____\/_/ \_ \______ |_| \_\
# Licensed under the MIT License <http://opensource.org/licenses/MIT>
# SPDX-License-Identifier: MIT
# Copyright (c) 2020-2021 The Caer Authors <http://github.com/jasmcaus>
from threading import Thread
import time
import math
from queue import Queue
import cv2 as cv
from .constants import FRAME_COUNT, FPS
__all__ = [
'GPUFileStream'
]
class GPUFileStream:
r"""
This is an auxiliary class that enables Video Streaming using the GPU for caer with minimalistic latency, and at the expense of little to no additional computational requirements.
The basic idea behind it is to tracks and save the salient feature array for the given number of frames and then uses these anchor point to cancel out all perturbations relative to it for the incoming frames in the queue. This class relies heavily on **Threaded Queue mode** for error-free & ultra-fast frame handling.
Args:
source (int, str): Source path for the video. If ``source=0``, the default camera device is used. For
multiple external camera devices, use incremented values. For eg: ``source=1`` represents the second camera device on your system.
qsize (int): Default queue size for handling the video streams. Default: 128.
"""
def __init__(self, source, qsize=128):
"""
Source must be a path to a video file
Utilizes your system's GPU to process the stream
"""
if not isinstance(source, str):
raise ValueError(f'Expected either a filepath. Got {type(source)}. Consider using VideoStream which supports both live video as well as pre-existing videos')
# initialize the file video stream along with the boolean
# used to indicate if the thread should be stopped or not
self.stream = cv.VideoCapture(source)
self.kill_stream = False
self.count = 0
# initialize the queue to store frames
self.Q = Queue(maxsize=qsize)
self.width = int(self.stream.get(cv.CAP_PROP_FRAME_WIDTH))
self.height = int(self.stream.get(cv.CAP_PROP_FRAME_HEIGHT))
self.res = (self.width, self.height)
self.fps = math.ceil(self.stream.get(FPS))
self.frames = int(self.stream.get(FRAME_COUNT))
# since we use UMat to store the images to
# we need to initialize them beforehand
self.qframes = [0] * qsize
for ii in range(qsize):
self.qframes[ii] = cv.UMat(self.height, self.width, cv.CV_8UC3)
def begin_stream(self):
# start a thread to read frames from the file video stream
t = Thread(target=self.update, args=())
t.daemon = True
t.start()
return self
def update(self):
# keep looping infinitely
while True:
if self.kill_stream:
return
# otherwise, ensure the queue has room in it
if not self.Q.full():
self.count += 1
target = (self.count-1) % self.Q.maxsize
ret = self.stream.grab()
if not ret:
self.release()
return
self.stream.retrieve(self.qframes[target])
# add the frame to the queue
self.Q.put(target)
def read(self):
while (not self.more() and self.kill_stream):
time.sleep(0.1)
# return next frame in the queue
return self.qframes[self.Q.get()]
def more(self):
# return True if there are still frames in the queue
return self.Q.qsize() > 0
def release(self):
self.kill_stream = True
# wait until stream resources are released
self.thread.join()
# Gets frame count
def count_frames(self):
if not self.kill_stream and not self.live_video:
return self.frames
# if get_opencv_version() == '2':
# return int(self.stream.get(FRAME_COUNT_DEPR))
# else:
# return int(self.stream.get(FRAME_COUNT))
if self.live_video:
print('[WARNING] Frames cannot be computed on live streams')
return -1
# Gets FPS count
def get_fps(self):
if not self.kill_stream:
return self.fps
# Get frame dimensions
def get_res(self):
return self.res |
the-stack_0_928 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('api', '0007_apirequest_extra'),
]
operations = [
migrations.AddField(
model_name='apirequest',
name='api_client',
field=models.CharField(null=True, editable=False, max_length=50),
preserve_default=True,
),
]
|
the-stack_0_929 | #-*- coding:utf-8 -*-
import pytest
import random
import string
import itertools as itt
import collections
import spiceminer as sm
import spiceminer.kernel.lowlevel as lowlevel
### Helpers ###
def rstrings(max_size):
while True:
yield ''.join(random.sample(string.lowercase, random.randint(1, max_size)))
class FakeKernel(object):
def __init__(self, path):
self.path = path
self.loaded = True
def _unload(self):
self.loaded = False
@pytest.fixture(scope='function')
def fake_LOADED(kernelfiles, monkeypatch):
available = random.sample(kernelfiles, random.randint(0, len(kernelfiles)))
substitute = {FakeKernel(path) for path in available}
monkeypatch.setattr(lowlevel, 'LOADED_KERNELS', substitute)
return substitute
@pytest.fixture(scope='function')
def fake_loaders(monkeypatch):
'''Patch lowlevel loader functions to rerturn dummy results.'''
def fake_loader(path):
windows = {'ABC': 'Test'}
return windows
for func in ('_load_sp', '_load_pc', '_load_c', '_load_f'):
monkeypatch.setattr(lowlevel, func, fake_loader)
@pytest.fixture(scope='function')
def fake_furnsh(monkeypatch):
monkeypatch.setattr(lowlevel.spice, 'furnsh', lambda x: None)
monkeypatch.setattr(lowlevel.spice, 'unload', lambda x: None)
### Tests ###
class TestKernelProperties(object):
def test_kp_good(self, kernelfile):
kprops = lowlevel.kernel_properties(kernelfile)
assert kprops.path == kernelfile
assert kprops.arch in lowlevel.ARCH
assert kprops.type in lowlevel.KTYPE
def test_kp_bad(self, nonkernelfile):
with pytest.raises((ValueError, sm.SpiceError)):
kprops = lowlevel.kernel_properties(nonkernelfile)
@pytest.mark.parametrize('ktype', list(lowlevel.KTYPE) + list(
set(itt.islice(rstrings(10), 5)) - lowlevel.KTYPE
))
def test_info_type(ktype):
info = lowlevel._info_type(ktype)
if ktype in lowlevel.KTYPE:
assert info in ('pos', 'rot', 'none')
else:
assert info == None
xValueError = pytest.mark.xfail(raises=ValueError)
@pytest.mark.parametrize('arch', list(lowlevel.ARCH) + [xValueError('?')])
@pytest.mark.parametrize('ktype', list(lowlevel.KTYPE) + [
xValueError(next(rstrings(10)))
])
def test_validate(arch, ktype):
lowlevel._validate('Test', arch, ktype)
@pytest.mark.parametrize('recursive', [True, False])
def test_icollect_kprops(datadir, kernelfiles, recursive):
paths = set(kp.path for kp in lowlevel.icollect_kprops(datadir, recursive, False))
if not recursive:
assert len(paths) < len(kernelfiles)
assert paths - set(kernelfiles) == set()
def test_ifilter_kprops(kernelfiles, fake_LOADED):
kp = collections.namedtuple('KernelPath', 'path')
result = lowlevel.ifilter_kprops(kp(path) for path in kernelfiles)
result_paths = set(kprops.path for kprops in result)
fake_paths = set(k.path for k in fake_LOADED)
assert result_paths.symmetric_difference(fake_paths) == set(kernelfiles)
def test_iunload_kprops(kernelfiles, fake_LOADED):
kp = collections.namedtuple('KernelPath', 'path')
result = lowlevel.iunload_kprops(kp(path) for path in kernelfiles)
result_paths = set(kprops.path for kprops in result)
assert result_paths == set(kernelfiles)
unloaded_paths = {k.path for k in fake_LOADED if not k.loaded}
assert len(unloaded_paths) == len(fake_LOADED)
@pytest.mark.parametrize('types', [
[random.choice(list(lowlevel.KTYPE)) for i in range(10)]
])
def test_split_kprops(types):
kt = collections.namedtuple('KernelType', 'type')
kpmisc, kpbody = lowlevel.split_kprops(kt(t) for t in types)
body_types = {kt.type for kt in kpbody}
misc_types = {kt.type for kt in kpmisc}
assert body_types.union(lowlevel.KTYPE_BODY) == lowlevel.KTYPE_BODY
assert misc_types.intersection(lowlevel.KTYPE_BODY) == set()
assert body_types.union(misc_types) == set(types)
@pytest.mark.usefixtures('fake_loaders', 'fake_furnsh')
def test_load_any(kernelfile):
kp = collections.namedtuple('KernelProperties', ['path', 'type'])
kprops = kp(kernelfile, random.choice(list(lowlevel.KTYPE)))
time_window_map = lowlevel.load_any(kprops)
if kprops.type in lowlevel.KTYPE_BODY:
assert time_window_map == {'ABC': 'Test'}
else:
assert time_window_map == {}
def test_unload_any():
pass
@pytest.mark.parametrize('path', ['.'])
def test_load_dummy(path):
assert lowlevel._load_dummy(path) == {}
@pytest.mark.usefixtures('with_leapseconds')
def test_load_sp(spfile):
time_window_map = lowlevel._load_sp(spfile)
assert time_window_map != {}
@pytest.mark.usefixtures('with_leapseconds', 'with_spacecraftclock')
def test_load_c(cfile):
time_window_map = lowlevel._load_c(cfile)
assert time_window_map != {}
@pytest.mark.usefixtures('with_leapseconds')
def test_load_pc(pcfile):
time_window_map = lowlevel._load_pc(pcfile)
assert time_window_map != {}
@pytest.mark.usefixtures('with_leapseconds')
def test_load_f(ffile):
time_window_map = lowlevel._load_f(ffile)
assert time_window_map != {}
|
the-stack_0_932 | import json
import datetime
from django.utils import timezone
from django.core.exceptions import PermissionDenied
from rest_framework import permissions, generics
from resources.models import Unit, Reservation, Resource, ResourceType
from hmlvaraus.models.hml_reservation import HMLReservation
from hmlvaraus.models.berth import Berth
from django.contrib.gis.geos import GEOSGeometry
from rest_framework import status
from rest_framework.response import Response
from django.utils.dateparse import parse_datetime
import pytz
class ImporterView(generics.CreateAPIView):
base_name = 'importer'
permission_classes = [permissions.IsAuthenticated]
def post(self, request):
request_user = request.user
if not request_user.is_staff:
raise PermissionDenied()
uploaded_file = request.data['file']
data = uploaded_file.read().decode("utf-8")
data_rows = data.split('\n')
# Kohteet
if data_rows[0][0] == '1':
del data_rows[1]
del data_rows[0]
for row in data_rows:
fields = row.split(';')
try:
print('Kohdedataa')
a = fields[5]
except:
continue
location = None
if fields[5] and fields[5] != '':
location = fields[5].split(',')
coordinates = []
for coord in location:
coord = coord.strip()
coord = float(coord)
coordinates = [coord] + coordinates
location = GEOSGeometry(json.dumps({'type': 'Point', 'coordinates': coordinates}))
Unit.objects.get_or_create(name=fields[0], street_address=fields[1], address_zip=fields[2], email=fields[3], phone=fields[4], location=location, description=fields[6])
# Venepaikat
if data_rows[0][0] == '2':
del data_rows[1]
del data_rows[0]
for row in data_rows:
fields = row.split(';')
try:
print('Venepaikkadataa, Kohde:', fields[0])
unit = Unit.objects.get(name=fields[0]);
except:
continue
resource_types = ResourceType.objects.all();
for resource_type in resource_types:
if 'vene' in resource_type.name.lower() or 'boat' in resource_type.name.lower():
type_instance = resource_type
resource = Resource.objects.get_or_create(unit=unit, name=fields[1], description=fields[2], type=type_instance, reservable=True)[0]
is_disabled = False
if fields[3] == 'kyllä':
is_disabled = True
price = 0
if fields[4]:
price = fields[4].replace(',', '.')
price = float(price)
type_mapping = {
'numero': 'number',
'laituri': 'dock',
'poletti': 'ground'
}
length = 0
width = 0
depth = 0
if fields[5] and fields[5] != '':
length = int(fields[5])
if fields[6] and fields[6] != '':
width = int(fields[6])
if fields[7] and fields[7] != '':
depth = int(fields[7])
berth_type = type_mapping.get(fields[8].lower(), None)
Berth.objects.get_or_create(resource=resource, is_disabled=is_disabled, price=price, length_cm=length, width_cm=width, depth_cm=depth, type=berth_type)
# Varaukset
if data_rows[0][0] == '3':
del data_rows[1]
del data_rows[0]
for i, row in enumerate(data_rows):
fields = row.split(';')
try:
print(i, 'Varausdataa, Kohde:', fields[1])
unit = Unit.objects.get(name=fields[1])
resource = Resource.objects.get(unit=unit, name=str(fields[0]), description=str(fields[4]))
except:
continue
resource.reservable = False
berth = Berth.objects.get(resource=resource)
begin = parse_datetime(str(fields[2]) + ' 00:00:00')
begin = pytz.timezone("Europe/Helsinki").localize(begin, is_dst=None)
end = parse_datetime(str(fields[3]) + ' 00:00:00')
end = pytz.timezone("Europe/Helsinki").localize(end, is_dst=None)
state = 'confirmed'
state_updated_at = timezone.now()
is_paid = False
is_paid_at = None
if fields[5] and fields[5].strip() != '':
state_updated_at = datetime.datetime.strptime(fields[5], "%d.%m.%Y %H:%M")
state = 'cancelled'
if fields[6] and fields[6].strip() != '':
is_paid_at = datetime.datetime.strptime(fields[6], "%d.%m.%Y %H:%M")
is_paid = True
reservation = Reservation.objects.create(
resource=resource,
begin=begin,
end=end,
event_description=fields[4] or '',
state=state,
reserver_name=fields[7] or '',
reserver_email_address=fields[8] or '',
reserver_phone_number=fields[9] or '',
reserver_address_street=fields[10] or '',
reserver_address_city=fields[11] or '',
reserver_address_zip=fields[12] or '',
)
HMLReservation.objects.get_or_create(reservation=reservation, berth=berth, state_updated_at=state_updated_at, is_paid_at=is_paid_at, is_paid=is_paid)
resource.save()
return Response(
status=status.HTTP_201_CREATED
)
|
the-stack_0_934 | """Gaussian MLP Policy.
A policy represented by a Gaussian distribution
which is parameterized by a multilayer perceptron (MLP).
"""
# pylint: disable=wrong-import-order
import akro
import numpy as np
import tensorflow as tf
from garage.tf.models import GaussianMLPModel
from garage.tf.policies.policy import StochasticPolicy
class GaussianMLPPolicy(StochasticPolicy):
"""Gaussian MLP Policy.
A policy represented by a Gaussian distribution
which is parameterized by a multilayer perceptron (MLP).
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
name (str): Model name, also the variable scope.
hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for mean. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a tf.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
tf.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
tf.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a tf.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
tf.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
tf.Tensor.
learn_std (bool): Is std trainable.
adaptive_std (bool): Is std a neural network. If False, it will be a
parameter.
std_share_network (bool): Boolean for whether mean and std share
the same network.
init_std (float): Initial value for std.
std_hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for std. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
min_std (float): If not None, the std is at least the value of min_std,
to avoid numerical issues.
max_std (float): If not None, the std is at most the value of max_std,
to avoid numerical issues.
std_hidden_nonlinearity (callable): Nonlinearity for each hidden layer
in the std network. The function should return a tf.Tensor.
std_output_nonlinearity (callable): Nonlinearity for output layer in
the std network. The function should return a
tf.Tensor.
std_parameterization (str): How the std should be parametrized. There
are a few options:
- exp: the logarithm of the std will be stored, and applied a
exponential transformation
- softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
name='GaussianMLPPolicy',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
learn_std=True,
adaptive_std=False,
std_share_network=False,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=tf.nn.tanh,
std_output_nonlinearity=None,
std_parameterization='exp',
layer_normalization=False):
if not isinstance(env_spec.action_space, akro.Box):
raise ValueError('GaussianMLPPolicy only works with '
'akro.Box action space, but not {}'.format(
env_spec.action_space))
super().__init__(name, env_spec)
self._obs_dim = env_spec.observation_space.flat_dim
self._action_dim = env_spec.action_space.flat_dim
self._hidden_sizes = hidden_sizes
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._learn_std = learn_std
self._adaptive_std = adaptive_std
self._std_share_network = std_share_network
self._init_std = init_std
self._min_std = min_std
self._max_std = max_std
self._std_hidden_sizes = std_hidden_sizes
self._std_hidden_nonlinearity = std_hidden_nonlinearity
self._std_output_nonlinearity = std_output_nonlinearity
self._std_parameterization = std_parameterization
self._layer_normalization = layer_normalization
self._f_dist = None
self._dist = None
self.model = GaussianMLPModel(
output_dim=self._action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
adaptive_std=adaptive_std,
std_share_network=std_share_network,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_hidden_sizes=std_hidden_sizes,
std_hidden_nonlinearity=std_hidden_nonlinearity,
std_output_nonlinearity=std_output_nonlinearity,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization,
name='GaussianMLPModel')
self._initialize()
def _initialize(self):
"""Initialize policy."""
with tf.compat.v1.variable_scope(self.name) as vs:
self._variable_scope = vs
state_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, None,
self._obs_dim))
self._dist, mean, log_std = self.model.build(state_input).outputs
self._f_dist = tf.compat.v1.get_default_session().make_callable(
[self._dist.sample(), mean, log_std], feed_list=[state_input])
@property
def input_dim(self):
"""int: Dimension of the policy input."""
return self._obs_dim
def build(self, state_input, name=None):
"""Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.MultivariateNormalDiag: Distribution.
tf.tensor: Mean.
tf.Tensor: Log of standard deviation.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
return self.model.build(state_input, name=name)
@property
def vectorized(self):
"""Vectorized or not.
Returns:
Bool: True if primitive supports vectorized operations.
"""
return True
def get_action(self, observation):
"""Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns an action and a dict, with keys
- mean (numpy.ndarray): Mean of the distribution.
- log_std (numpy.ndarray): Log standard deviation of the
distribution.
"""
sample, mean, log_std = self._f_dist(np.expand_dims([observation], 1))
sample = self.action_space.unflatten(np.squeeze(sample, 1)[0])
mean = self.action_space.unflatten(np.squeeze(mean, 1)[0])
log_std = self.action_space.unflatten(np.squeeze(log_std, 1)[0])
return sample, dict(mean=mean, log_std=log_std)
def get_actions(self, observations):
"""Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns actions and a dict, with keys
- mean (numpy.ndarray): Means of the distribution.
- log_std (numpy.ndarray): Log standard deviations of the
distribution.
"""
samples, means, log_stds = self._f_dist(np.expand_dims(
observations, 1))
samples = self.action_space.unflatten_n(np.squeeze(samples, 1))
means = self.action_space.unflatten_n(np.squeeze(means, 1))
log_stds = self.action_space.unflatten_n(np.squeeze(log_stds, 1))
return samples, dict(mean=means, log_std=log_stds)
@property
def distribution(self):
"""Policy distribution.
Returns:
tfp.Distribution.MultivariateNormalDiag: Policy distribution.
"""
return self._dist
def clone(self, name):
"""Return a clone of the policy.
It only copies the configuration of the primitive,
not the parameters.
Args:
name (str): Name of the newly created policy. It has to be
different from source policy if cloned under the same
computational graph.
Returns:
garage.tf.policies.GaussianMLPPolicy: Newly cloned policy.
"""
return self.__class__(
name=name,
env_spec=self._env_spec,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
learn_std=self._learn_std,
adaptive_std=self._adaptive_std,
std_share_network=self._std_share_network,
init_std=self._init_std,
min_std=self._min_std,
max_std=self._max_std,
std_hidden_sizes=self._std_hidden_sizes,
std_hidden_nonlinearity=self._std_hidden_nonlinearity,
std_output_nonlinearity=self._std_output_nonlinearity,
std_parameterization=self._std_parameterization,
layer_normalization=self._layer_normalization)
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_dist']
del new_dict['_dist']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
super().__setstate__(state)
self._initialize()
|
the-stack_0_937 | from collections import Counter
class Solution:
def removeDuplicateLetters(self, s: str) -> str:
counter = Counter(s)
seen = set()
stack = []
for letter in s:
counter[letter] -= 1
if letter in seen:
continue
while stack and stack[-1] > letter and counter[stack[-1]] > 0:
seen.remove(stack.pop())
stack.append(letter)
seen.add(letter)
return "".join(stack)
|
the-stack_0_939 | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class ConnectionMonitorResult(Model):
"""Information about the connection monitor.
Variables are only populated by the server, and will be ignored when
sending a request.
All required parameters must be populated in order to send to Azure.
:ivar name: Name of the connection monitor.
:vartype name: str
:ivar id: ID of the connection monitor.
:vartype id: str
:param etag: Default value: "A unique read-only string that changes
whenever the resource is updated." .
:type etag: str
:ivar type: Connection monitor type.
:vartype type: str
:param location: Connection monitor location.
:type location: str
:param tags: Connection monitor tags.
:type tags: dict[str, str]
:param source: Required.
:type source:
~azure.mgmt.network.v2017_11_01.models.ConnectionMonitorSource
:param destination: Required.
:type destination:
~azure.mgmt.network.v2017_11_01.models.ConnectionMonitorDestination
:param auto_start: Determines if the connection monitor will start
automatically once created. Default value: True .
:type auto_start: bool
:param monitoring_interval_in_seconds: Monitoring interval in seconds.
Default value: 60 .
:type monitoring_interval_in_seconds: int
:param provisioning_state: The provisioning state of the connection
monitor. Possible values include: 'Succeeded', 'Updating', 'Deleting',
'Failed'
:type provisioning_state: str or
~azure.mgmt.network.v2017_11_01.models.ProvisioningState
:param start_time: The date and time when the connection monitor was
started.
:type start_time: datetime
:param monitoring_status: The monitoring status of the connection monitor.
:type monitoring_status: str
"""
_validation = {
'name': {'readonly': True},
'id': {'readonly': True},
'type': {'readonly': True},
'source': {'required': True},
'destination': {'required': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'source': {'key': 'properties.source', 'type': 'ConnectionMonitorSource'},
'destination': {'key': 'properties.destination', 'type': 'ConnectionMonitorDestination'},
'auto_start': {'key': 'properties.autoStart', 'type': 'bool'},
'monitoring_interval_in_seconds': {'key': 'properties.monitoringIntervalInSeconds', 'type': 'int'},
'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
'start_time': {'key': 'properties.startTime', 'type': 'iso-8601'},
'monitoring_status': {'key': 'properties.monitoringStatus', 'type': 'str'},
}
def __init__(self, **kwargs):
super(ConnectionMonitorResult, self).__init__(**kwargs)
self.name = None
self.id = None
self.etag = kwargs.get('etag', "A unique read-only string that changes whenever the resource is updated.")
self.type = None
self.location = kwargs.get('location', None)
self.tags = kwargs.get('tags', None)
self.source = kwargs.get('source', None)
self.destination = kwargs.get('destination', None)
self.auto_start = kwargs.get('auto_start', True)
self.monitoring_interval_in_seconds = kwargs.get('monitoring_interval_in_seconds', 60)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.start_time = kwargs.get('start_time', None)
self.monitoring_status = kwargs.get('monitoring_status', None)
|
the-stack_0_940 | from django.db.models.signals import pre_save, post_delete
from django.dispatch import receiver
from .serializers import XXTMP_PO_HEADERS, ElasticPO_headersSerializer
@receiver(pre_save, sender=XXTMP_PO_HEADERS, dispatch_uid="update_record")
def update_es_record(sender, instance, **kwargs):
obj = ElasticPO_headersSerializer(instance)
obj.save()
@receiver(post_delete, sender=XXTMP_PO_HEADERS, dispatch_uid="delete_record")
def delete_es_record(sender, instance, *args, **kwargs):
obj = ElasticPO_headersSerializer(instance)
obj.delete(ignore=404)
|
the-stack_0_943 | """Implementation of core Haskell rules"""
load("@bazel_skylib//lib:dicts.bzl", "dicts")
load(
":providers.bzl",
"C2hsLibraryInfo",
"HaddockInfo",
"HaskellInfo",
"HaskellLibraryInfo",
"HaskellToolchainLibraryInfo",
"all_dependencies_package_ids",
)
load(":cc.bzl", "cc_interop_info")
load(
":private/actions/info.bzl",
"compile_info_output_groups",
"library_info_output_groups",
)
load(
":private/actions/link.bzl",
"link_binary",
"link_library_dynamic",
"link_library_static",
)
load(":private/actions/package.bzl", "package")
load(":private/plugins.bzl", "resolve_plugin_tools")
load(":private/actions/runghc.bzl", "build_haskell_runghc")
load(":private/context.bzl", "haskell_context")
load(":private/dependencies.bzl", "gather_dep_info")
load(":private/expansions.bzl", "expand_make_variables", "haskell_library_extra_label_attrs")
load(":private/java.bzl", "java_interop_info")
load(":private/mode.bzl", "is_profiling_enabled")
load(
":private/path_utils.bzl",
"determine_module_names",
"get_dynamic_hs_lib_name",
"get_lib_extension",
"get_static_hs_lib_name",
"infer_main_module",
"ln",
"match_label",
"parse_pattern",
)
load(":private/pkg_id.bzl", "pkg_id")
load(":private/set.bzl", "set")
load(":private/list.bzl", "list")
load(":private/version_macros.bzl", "generate_version_macros")
load(":providers.bzl", "GhcPluginInfo", "HaskellCoverageInfo")
load("@bazel_skylib//lib:paths.bzl", "paths")
load("@bazel_skylib//lib:collections.bzl", "collections")
load("@bazel_skylib//lib:shell.bzl", "shell")
load("@rules_cc//cc:find_cc_toolchain.bzl", "find_cc_toolchain")
load("//haskell/experimental:providers.bzl", "HaskellModuleInfo")
load("//haskell/experimental/private:module.bzl", "build_haskell_modules", "get_module_path_from_target")
# Note [Empty Libraries]
#
# GHC 8.10.x wants to load the shared libraries corresponding to packages needed
# for running TemplateHaskell splices. It wants to do this even when all the
# necessary object files are passed in the command line.
#
# In order to satisfy GHC, and yet avoid passing the linked library as input, we
# create a ficticious package which points to an empty shared library. The
# ficticious and the real package share the same interface files.
#
# Avoiding to pass the real shared library as input is necessary when building
# individual modules with haskell_module, otherwise building the module would
# need to wait until all of the modules of library dependencies have been built.
#
# See Note [Narrowed Dependencies] for an overview of what this feature is
# needed for.
def _prepare_srcs(srcs):
srcs_files = []
import_dir_map = {}
for src in srcs:
# If it has the "files" attribute, it must be a Target
if hasattr(src, "files"):
if C2hsLibraryInfo in src:
srcs_files += src.files.to_list()
for f in src.files.to_list():
import_dir_map[f] = src[C2hsLibraryInfo].import_dir
else:
srcs_files += src.files.to_list()
# otherwise it's just a file
else:
srcs_files.append(src)
return srcs_files, import_dir_map
def haskell_test_impl(ctx):
return _haskell_binary_common_impl(ctx, is_test = True)
def haskell_binary_impl(ctx):
return _haskell_binary_common_impl(ctx, is_test = False)
def _should_inspect_coverage(ctx, hs, is_test):
return hs.coverage_enabled and is_test
def _coverage_enabled_for_target(coverage_source_patterns, label):
for pat in coverage_source_patterns:
if match_label(pat, label):
return True
return False
# Mix files refer to genfile srcs including their root. Therefore, we
# must condition the src filepaths passed in for coverage to match.
def _condition_coverage_src(hs, src):
if not src.path.startswith(hs.genfiles_dir.path):
return src
""" Genfiles have the genfile directory as part of their path,
so declaring a file with the sample path actually makes the new
file double-qualified by the genfile directory.
This is necessary because mix files capture the genfile
path before compilation, and then expect those files to be
qualified by the genfile directory when `hpc report` or
`hpc markup` are used. But, genfiles included as runfiles
are no longer qualified. So, double-qualifying them results in
only one level of qualification as runfiles.
"""
conditioned_src = hs.actions.declare_file(src.path)
hs.actions.run_shell(
inputs = [src],
outputs = [conditioned_src],
arguments = [
src.path,
conditioned_src.path,
],
command = """
mkdir -p $(dirname "$2") && cp "$1" "$2"
""",
)
return conditioned_src
def _resolve_preprocessors(ctx, preprocessors):
if not hasattr(ctx, "resolve_tools"):
# No resolve_tools when ctx is faked (see protobuf.bzl).
return struct(
inputs = depset(),
input_manifests = [],
)
(inputs, input_manifests) = ctx.resolve_tools(tools = preprocessors)
return struct(
inputs = inputs,
input_manifests = input_manifests,
)
def _expand_make_variables(name, ctx, strings):
# All labels in all attributes should be location-expandable.
return expand_make_variables(name, ctx, strings, haskell_library_extra_label_attrs(ctx.attr))
def haskell_module_from_target(m):
""" Produces the module name from a HaskellModuleInfo """
return paths.split_extension(get_module_path_from_target(m))[0].replace("/", ".")
def is_main_as_haskell_module(modules, main_function):
main_module = infer_main_module(main_function).replace(".", "/")
for m in modules:
if haskell_module_from_target(m) == main_module:
return True
return False
def _haskell_binary_common_impl(ctx, is_test):
hs = haskell_context(ctx)
deps = ctx.attr.deps + ctx.attr.narrowed_deps
dep_info = gather_dep_info(ctx.attr.name, ctx.attr.deps)
all_deps_info = gather_dep_info(ctx.attr.name, deps)
modules = ctx.attr.modules
if modules and ctx.files.srcs:
fail("""Only one of "srcs" or "modules" attributes must be specified in {}""".format(ctx.label))
if not modules and ctx.attr.narrowed_deps:
fail("""The attribute "narrowed_deps" can only be used if "modules" is specified in {}""".format(ctx.label))
# Note [Plugin order]
plugin_decl = reversed(ctx.attr.plugins)
non_default_plugin_decl = reversed(ctx.attr.non_default_plugins)
all_plugin_decls = plugin_decl + non_default_plugin_decl
plugin_dep_info = gather_dep_info(
ctx.attr.name,
[dep for plugin in all_plugin_decls for dep in plugin[GhcPluginInfo].deps],
)
package_ids = all_dependencies_package_ids(deps)
# Add any interop info for other languages.
cc = cc_interop_info(
ctx,
override_cc_toolchain = hs.tools_config.maybe_exec_cc_toolchain,
)
java = java_interop_info(deps)
# Make shell tools available.
posix = ctx.toolchains["@rules_sh//sh/posix:toolchain_type"]
# Determine file directories.
interfaces_dir = paths.join("_iface", hs.name)
objects_dir = paths.join("_obj", hs.name)
with_profiling = is_profiling_enabled(hs)
srcs_files, import_dir_map = _prepare_srcs(ctx.attr.srcs)
main_as_haskell_module = is_main_as_haskell_module(modules, ctx.attr.main_function)
module_map = determine_module_names(srcs_files, not main_as_haskell_module, ctx.attr.main_function, ctx.file.main_file)
inspect_coverage = _should_inspect_coverage(ctx, hs, is_test)
dynamic = not ctx.attr.linkstatic
if with_profiling or hs.toolchain.static_runtime:
# NOTE We can't have profiling and dynamic code at the
# same time, see:
# https://ghc.haskell.org/trac/ghc/ticket/15394
# Also, static GHC doesn't support dynamic code
dynamic = False
module_outputs = build_haskell_modules(ctx, hs, cc, posix, "", with_profiling, dynamic, interfaces_dir, objects_dir)
plugins = [resolve_plugin_tools(ctx, plugin[GhcPluginInfo]) for plugin in plugin_decl]
non_default_plugins = [resolve_plugin_tools(ctx, plugin[GhcPluginInfo]) for plugin in non_default_plugin_decl]
preprocessors = _resolve_preprocessors(ctx, ctx.attr.tools)
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
c = hs.toolchain.actions.compile_binary(
hs,
cc,
java,
posix,
dep_info,
plugin_dep_info,
srcs = srcs_files,
module_map = module_map,
import_dir_map = import_dir_map,
extra_srcs = depset(ctx.files.extra_srcs),
user_compile_flags = user_compile_flags,
dynamic = dynamic,
with_profiling = with_profiling,
interfaces_dir = interfaces_dir,
objects_dir = objects_dir,
main_function = ctx.attr.main_function,
version = ctx.attr.version,
inspect_coverage = inspect_coverage,
plugins = plugins,
non_default_plugins = non_default_plugins,
preprocessors = preprocessors,
)
# gather intermediary code coverage instrumentation data
coverage_data = c.coverage_data
for dep in deps:
if HaskellCoverageInfo in dep:
coverage_data += dep[HaskellCoverageInfo].coverage_data
coverage_data = list.dedup_on(_get_mix_filepath, coverage_data)
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
(binary, solibs) = link_binary(
hs,
cc,
posix,
all_deps_info,
ctx.files.extra_srcs,
user_compile_flags,
c.object_files + c.dyn_object_files,
module_outputs.os,
dynamic = dynamic,
with_profiling = with_profiling,
version = ctx.attr.version,
)
hs_info = HaskellInfo(
package_databases = all_deps_info.package_databases,
version_macros = set.empty(),
source_files = c.source_files,
boot_files = c.boot_files,
extra_source_files = c.extra_source_files,
import_dirs = c.import_dirs,
hs_libraries = all_deps_info.hs_libraries,
deps_hs_libraries = all_deps_info.deps_hs_libraries,
interface_dirs = all_deps_info.interface_dirs,
deps_interface_dirs = all_deps_info.deps_interface_dirs,
compile_flags = c.compile_flags,
user_compile_flags = user_compile_flags,
user_repl_flags = _expand_make_variables("repl_ghci_args", ctx, ctx.attr.repl_ghci_args),
)
cc_info = cc_common.merge_cc_infos(
cc_infos = [dep[CcInfo] for dep in deps if CcInfo in dep],
)
target_files = depset([binary])
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
extra_args = _expand_make_variables("runcompile_flags", ctx, ctx.attr.runcompile_flags)
build_haskell_runghc(
hs,
cc,
posix,
runghc_wrapper = ctx.file._ghci_repl_wrapper,
extra_args = extra_args,
user_compile_flags = user_compile_flags,
output = ctx.outputs.runghc,
package_databases = all_deps_info.package_databases,
version = ctx.attr.version,
hs_info = hs_info,
)
executable = binary
extra_runfiles = []
if inspect_coverage:
binary_path = paths.join(ctx.workspace_name, binary.short_path)
hpc_path = paths.join(ctx.workspace_name, hs.toolchain.tools.hpc.short_path)
tix_file_path = hs.label.name + ".tix"
mix_file_paths = [
paths.join(ctx.workspace_name, datum.mix_file.short_path)
for datum in coverage_data
]
mix_file_paths = collections.uniq(mix_file_paths) # remove duplicates
# find which modules to exclude from coverage analysis, by using the specified source patterns
raw_coverage_source_patterns = ctx.attr.experimental_coverage_source_patterns
coverage_source_patterns = [parse_pattern(ctx, pat) for pat in raw_coverage_source_patterns]
modules_to_exclude = [paths.split_extension(datum.mix_file.basename)[0] for datum in coverage_data if not _coverage_enabled_for_target(coverage_source_patterns, datum.target_label)]
modules_to_exclude = collections.uniq(modules_to_exclude) # remove duplicates
expected_covered_expressions_percentage = ctx.attr.expected_covered_expressions_percentage
expected_uncovered_expression_count = ctx.attr.expected_uncovered_expression_count
strict_coverage_analysis = ctx.attr.strict_coverage_analysis
coverage_report_format = ctx.attr.coverage_report_format
if coverage_report_format != "text" and coverage_report_format != "html":
fail("""haskell_test attribute "coverage_report_format" must be one of "text" or "html".""")
wrapper = hs.actions.declare_file("{}_coverage/coverage_wrapper.sh".format(ctx.label.name))
ctx.actions.expand_template(
template = ctx.file._coverage_wrapper_template,
output = wrapper,
substitutions = {
"{binary_path}": shell.quote(binary_path),
"{hpc_path}": shell.quote(hpc_path),
"{tix_file_path}": shell.quote(tix_file_path),
"{expected_covered_expressions_percentage}": shell.quote(str(expected_covered_expressions_percentage)),
"{expected_uncovered_expression_count}": shell.quote(str(expected_uncovered_expression_count)),
"{mix_file_paths}": shell.array_literal(mix_file_paths),
"{modules_to_exclude}": shell.array_literal(modules_to_exclude),
"{strict_coverage_analysis}": str(strict_coverage_analysis),
"{coverage_report_format}": shell.quote(ctx.attr.coverage_report_format),
"{package_path}": shell.quote(ctx.label.package),
},
is_executable = True,
)
executable = wrapper
mix_runfiles = [datum.mix_file for datum in coverage_data]
srcs_runfiles = [_condition_coverage_src(hs, datum.src_file) for datum in coverage_data]
extra_runfiles = [
ctx.file._bash_runfiles,
hs.toolchain.tools.hpc,
binary,
] + mix_runfiles + srcs_runfiles + java.inputs.to_list()
return [
hs_info,
cc_info,
DefaultInfo(
executable = executable,
files = target_files,
runfiles = ctx.runfiles(
files = extra_runfiles + solibs,
collect_data = True,
),
),
OutputGroupInfo(**compile_info_output_groups(
name = ctx.label.name,
workspace_name = ctx.workspace_name,
hs = hs,
cc = cc,
c = c,
posix = posix,
runfiles = ctx.runfiles(collect_data = True).files,
)),
]
def _create_empty_library(hs, cc, posix, my_pkg_id, with_shared, with_profiling, empty_libs_dir):
"""See Note [Empty Libraries]"""
dep_info = gather_dep_info("haskell_module-empty_lib", [])
empty_c = hs.actions.declare_file("empty.c")
hs.actions.write(empty_c, "")
static_library = link_library_static(
hs,
cc,
posix,
dep_info,
depset([empty_c]),
my_pkg_id,
with_profiling = with_profiling,
libdir = empty_libs_dir,
)
libs = [static_library]
if with_shared:
dynamic_library = link_library_dynamic(
hs,
cc,
posix,
dep_info,
depset(),
depset([empty_c]),
my_pkg_id,
[],
empty_libs_dir,
)
libs = [dynamic_library, static_library]
return libs
def haskell_library_impl(ctx):
hs = haskell_context(ctx)
deps = ctx.attr.deps + ctx.attr.exports + ctx.attr.narrowed_deps
dep_info = gather_dep_info(ctx.attr.name, ctx.attr.deps + ctx.attr.exports)
narrowed_deps_info = gather_dep_info(ctx.attr.name, ctx.attr.narrowed_deps)
all_deps_info = gather_dep_info(ctx.attr.name, deps)
all_plugins = ctx.attr.plugins + ctx.attr.non_default_plugins
plugin_dep_info = gather_dep_info(
ctx.attr.name,
[dep for plugin in all_plugins for dep in plugin[GhcPluginInfo].deps],
)
package_ids = all_dependencies_package_ids(deps)
modules = ctx.attr.modules
if modules and ctx.files.srcs:
fail("""Only one of "srcs" or "modules" attributes must be specified in {}""".format(ctx.label))
if not modules and ctx.attr.narrowed_deps:
fail("""The attribute "narrowed_deps" is enabled only if "modules" is specified in {}""".format(ctx.label))
# Add any interop info for other languages.
cc = cc_interop_info(
ctx,
override_cc_toolchain = hs.tools_config.maybe_exec_cc_toolchain,
)
java = java_interop_info(ctx.attr.deps + ctx.attr.narrowed_deps)
# Make shell tools available.
posix = ctx.toolchains["@rules_sh//sh/posix:toolchain_type"]
with_profiling = is_profiling_enabled(hs)
srcs_files, import_dir_map = _prepare_srcs(ctx.attr.srcs)
module_map = determine_module_names(srcs_files)
package_name = getattr(ctx.attr, "package_name", None)
version = getattr(ctx.attr, "version", None)
my_pkg_id = pkg_id.new(ctx.label, package_name, version)
# If we're compiling a package, put the interfaces inside the
# package directory.
interfaces_dir = paths.join(pkg_id.to_string(my_pkg_id), "_iface")
objects_dir = paths.join("_obj", hs.name)
non_empty = srcs_files or modules
with_shared = not ctx.attr.linkstatic
if with_profiling or hs.toolchain.static_runtime:
# NOTE We can't have profiling and dynamic code at the
# same time, see:
# https://ghc.haskell.org/trac/ghc/ticket/15394
# Also, static GHC doesn't support dynamic code
with_shared = False
module_outputs = build_haskell_modules(ctx, hs, cc, posix, pkg_id.to_string(my_pkg_id), with_profiling, with_shared, interfaces_dir, objects_dir)
plugins = [resolve_plugin_tools(ctx, plugin[GhcPluginInfo]) for plugin in ctx.attr.plugins]
non_default_plugins = [resolve_plugin_tools(ctx, plugin[GhcPluginInfo]) for plugin in ctx.attr.non_default_plugins]
preprocessors = _resolve_preprocessors(ctx, ctx.attr.tools)
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
c = hs.toolchain.actions.compile_library(
hs,
cc,
java,
posix,
dep_info,
plugin_dep_info,
srcs = srcs_files,
module_map = module_map,
import_dir_map = import_dir_map,
extra_srcs = depset(ctx.files.extra_srcs),
user_compile_flags = user_compile_flags,
with_shared = with_shared,
with_profiling = with_profiling,
interfaces_dir = interfaces_dir,
objects_dir = objects_dir,
my_pkg_id = my_pkg_id,
plugins = plugins,
non_default_plugins = non_default_plugins,
preprocessors = preprocessors,
)
other_modules = ctx.attr.hidden_modules
exposed_modules_reexports = _exposed_modules_reexports(ctx.attr.reexported_modules)
haskell_module_names = [haskell_module_from_target(m) for m in modules]
exposed_modules = set.from_list(module_map.keys() + exposed_modules_reexports + haskell_module_names)
set.mutable_difference(exposed_modules, set.from_list(other_modules))
exposed_modules = set.to_list(exposed_modules)
if non_empty:
static_library = link_library_static(
hs,
cc,
posix,
all_deps_info,
depset(c.object_files, transitive = [module_outputs.os]),
my_pkg_id,
with_profiling = with_profiling,
)
else:
static_library = None
if with_shared and non_empty:
dynamic_library = link_library_dynamic(
hs,
cc,
posix,
all_deps_info,
depset(ctx.files.extra_srcs),
depset(c.dyn_object_files, transitive = [module_outputs.dyn_os]),
my_pkg_id,
user_compile_flags,
)
else:
dynamic_library = None
conf_file, cache_file = package(
hs,
cc,
posix,
all_deps_info,
with_shared,
exposed_modules,
other_modules,
my_pkg_id,
non_empty,
)
empty_libs_dir = "empty_libs"
conf_file_empty, cache_file_empty = package(
hs,
cc,
posix,
all_deps_info,
with_shared,
exposed_modules,
other_modules,
my_pkg_id,
non_empty,
empty_libs_dir,
)
interface_dirs = depset(
direct = c.interface_files,
transitive = [all_deps_info.interface_dirs, module_outputs.his, module_outputs.dyn_his],
)
version_macros = set.empty()
if version:
package_name = hs.name
if hasattr(ctx.attr, "package_name") and ctx.attr.package_name:
package_name = ctx.attr.package_name
version_macros = set.singleton(
generate_version_macros(ctx, package_name, version),
)
empty_libs = _create_empty_library(hs, cc, posix, my_pkg_id, with_shared, with_profiling, empty_libs_dir)
export_infos = gather_dep_info(ctx.attr.name, ctx.attr.exports)
hs_info = HaskellInfo(
package_databases = depset([cache_file], transitive = [all_deps_info.package_databases]),
empty_lib_package_databases = depset(
direct = [cache_file_empty],
transitive = [
dep_info.package_databases,
narrowed_deps_info.empty_lib_package_databases,
export_infos.empty_lib_package_databases,
],
),
version_macros = version_macros,
source_files = c.source_files,
boot_files = c.boot_files,
extra_source_files = c.extra_source_files,
import_dirs = set.mutable_union(c.import_dirs, export_infos.import_dirs),
hs_libraries = depset(
direct = [lib for lib in [static_library, dynamic_library] if lib],
transitive = [all_deps_info.hs_libraries],
),
deps_hs_libraries = depset(
transitive = [dep_info.hs_libraries, narrowed_deps_info.deps_hs_libraries],
),
empty_hs_libraries = depset(
direct = empty_libs,
transitive = [all_deps_info.empty_hs_libraries, export_infos.empty_hs_libraries],
),
interface_dirs = depset(transitive = [interface_dirs, export_infos.interface_dirs]),
deps_interface_dirs = depset(transitive = [dep_info.interface_dirs, narrowed_deps_info.deps_interface_dirs]),
compile_flags = c.compile_flags,
user_compile_flags = user_compile_flags,
user_repl_flags = _expand_make_variables("repl_ghci_args", ctx, ctx.attr.repl_ghci_args),
per_module_transitive_interfaces = module_outputs.per_module_transitive_interfaces,
per_module_transitive_objects = module_outputs.per_module_transitive_objects,
)
exports = [
reexp[HaskellLibraryInfo]
for reexp in ctx.attr.exports
if HaskellCoverageInfo in reexp
]
lib_info = HaskellLibraryInfo(
package_id = pkg_id.to_string(my_pkg_id),
version = version,
exports = exports,
)
dep_coverage_data = []
for dep in deps:
if HaskellCoverageInfo in dep:
dep_coverage_data += dep[HaskellCoverageInfo].coverage_data
coverage_data = dep_coverage_data + c.coverage_data
coverage_data = list.dedup_on(_get_mix_filepath, coverage_data)
coverage_info = HaskellCoverageInfo(
coverage_data = coverage_data,
)
target_files = depset([file for file in [static_library, dynamic_library] if file])
if hasattr(ctx, "outputs"):
extra_args = _expand_make_variables("runcompile_flags", ctx, ctx.attr.runcompile_flags)
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
build_haskell_runghc(
hs,
cc,
posix,
runghc_wrapper = ctx.file._ghci_repl_wrapper,
extra_args = extra_args,
user_compile_flags = user_compile_flags,
output = ctx.outputs.runghc,
package_databases = all_deps_info.package_databases,
version = ctx.attr.version,
hs_info = hs_info,
lib_info = lib_info,
)
default_info = None
if hasattr(ctx, "runfiles"):
default_info = DefaultInfo(
files = target_files,
runfiles = ctx.runfiles(transitive_files = java.inputs, collect_data = True),
)
else:
default_info = DefaultInfo(
files = target_files,
)
# Create a CcInfo provider so that CC rules can work with
# a haskell library as if it was a regular CC one.
# XXX: protobuf is passing a "patched ctx"
# which includes the real ctx as "real_ctx"
real_ctx = getattr(ctx, "real_ctx", ctx)
cc_toolchain = find_cc_toolchain(real_ctx)
feature_configuration = cc_common.configure_features(
ctx = real_ctx,
cc_toolchain = cc_toolchain,
requested_features = ctx.features,
unsupported_features = ctx.disabled_features,
)
if dynamic_library or static_library:
linker_inputs = [
cc_common.create_linker_input(
owner = ctx.label,
libraries = depset(direct = [
cc_common.create_library_to_link(
actions = ctx.actions,
feature_configuration = feature_configuration,
dynamic_library = dynamic_library,
dynamic_library_symlink_path = dynamic_library.basename if dynamic_library else "",
static_library = static_library,
cc_toolchain = cc_toolchain,
),
]),
),
]
else:
linker_inputs = []
compilation_context = cc_common.create_compilation_context()
linking_context = cc_common.create_linking_context(
linker_inputs = depset(direct = linker_inputs),
)
out_cc_info = cc_common.merge_cc_infos(
cc_infos = [
CcInfo(
compilation_context = compilation_context,
linking_context = linking_context,
),
] + [dep[CcInfo] for dep in deps if CcInfo in dep],
)
return [
hs_info,
out_cc_info,
coverage_info,
default_info,
lib_info,
OutputGroupInfo(**dicts.add(
compile_info_output_groups(
# For haskell_proto_aspect, which doesn't have a ctx.workspace_name,
# just set it to "". It won't matter in practice because those rules don't
# have runfiles and won't be compiled directly anyway.
workspace_name = getattr(ctx, "workspace_name", ""),
hs = hs,
cc = cc,
name = ctx.label.name,
c = c,
posix = posix,
runfiles = default_info.default_runfiles.files if getattr(default_info, "default_runfiles", None) else depset(),
),
library_info_output_groups(
name = ctx.label.name,
hs = hs,
hs_info = hs_info,
lib_info = lib_info,
),
)),
]
# We should not need this provider. It exists purely as a workaround
# for https://github.com/bazelbuild/bazel/issues/8129.
#
# TODO Get rid of this by computing a CcInfo in haskell_import
# instead. Currently blocked on upstream.
HaskellImportHack = provider()
HaskellToolchainLibraries = provider()
def haskell_toolchain_library_impl(ctx):
hs = haskell_context(ctx)
if ctx.attr.package:
package = ctx.attr.package
else:
package = ctx.label.name
libraries = ctx.attr._toolchain_libraries[HaskellToolchainLibraries].libraries
target = libraries.get(package)
if not target:
fail(
"""
{} is not a toolchain library.
Check that it ships with your version of GHC.
The following toolchain libraries are available:
{}
""".format(package, libraries),
)
return [
target.default_info,
target.hs_info,
target.hs_lib_info,
target.cc_info,
target.haddock_info,
HaskellToolchainLibraryInfo(),
OutputGroupInfo(**library_info_output_groups(
hs = hs,
name = ctx.label.name,
hs_info = target.hs_info,
lib_info = target.hs_lib_info,
)),
]
def _toolchain_library_symlink(dynamic_library):
prefix = dynamic_library.owner.workspace_root.replace("_", "_U").replace("/", "_S")
basename = dynamic_library.basename
return paths.join(prefix, basename)
def haskell_toolchain_libraries_impl(ctx):
hs = haskell_context(ctx)
with_profiling = is_profiling_enabled(hs)
with_threaded = "-threaded" in hs.toolchain.ghcopts
cc_toolchain = find_cc_toolchain(ctx)
feature_configuration = cc_common.configure_features(
ctx = ctx,
cc_toolchain = cc_toolchain,
requested_features = ctx.features,
unsupported_features = ctx.disabled_features,
)
libraries = hs.toolchain.libraries
# List of library in left-to-right post-ordering
# Meaning, if package B depends on package A, then A will appear before B.
ordered = depset(transitive = [
target[HaskellImportHack].transitive_depends
for target in hs.toolchain.libraries.values()
])
library_dict = {}
for package in ordered.to_list():
target = libraries[package]
# Construct CcInfo
additional_link_inputs = []
if with_profiling:
# GHC does not provide dynamic profiling mode libraries. The dynamic
# libraries that are available are missing profiling symbols, that
# other profiling mode build results will reference. Therefore, we
# don't import dynamic libraries in profiling mode.
libs = {
get_static_hs_lib_name(hs.toolchain.version, lib): {"static": lib}
for lib in target[HaskellImportHack].static_profiling_libraries.to_list()
}
else:
# Workaround for https://github.com/tweag/rules_haskell/issues/881
# Static and dynamic libraries don't necessarily pair up 1 to 1.
# E.g. the rts package in the Unix GHC bindist contains the
# dynamic libHSrts and the static libCffi and libHSrts.
libs = {}
for lib in target[HaskellImportHack].dynamic_libraries.to_list():
libname = get_dynamic_hs_lib_name(hs.toolchain.version, lib)
if libname == "ffi" and libname in libs:
# Make sure that the file of libffi matching its soname
# ends up in target runfiles. Otherwise, execution will
# fail with "cannot open shared object file" errors.
# On Linux libffi comes in three shapes:
# libffi.so, libffi.so.7, libffi.so.7.1.0
# (version numbers may vary)
# The soname is then libffi.so.7, meaning, at runtime the
# dynamic linker will look for libffi.so.7. So, that file
# should be the LibraryToLink.dynamic_library.
ext_components = get_lib_extension(lib).split(".")
if len(ext_components) == 2 and ext_components[0] == "so":
libs[libname]["dynamic"] = lib
else:
libs[libname] = {"dynamic": lib}
for lib in target[HaskellImportHack].static_libraries.to_list():
name = get_static_hs_lib_name(with_profiling, lib)
entry = libs.get(name, {})
entry["static"] = lib
libs[name] = entry
# Avoid duplicate runtime and ffi libraries. These libraries come
# in threaded and non-threaded flavors. Depending on the
# compilation mode we want to forward only one or the other.
# XXX: Threaded mode should be a per-target property. Use Bazel
# build configurations and transitions to select the threaded or
# non-threaded runtime and ffi on a per-target basis.
if "HSrts_thr" in libs:
if with_threaded:
libs["HSrts"] = libs["HSrts_thr"]
libs.pop("HSrts_thr")
if "Cffi_thr" in libs:
if with_threaded:
libs["ffi"]["static"] = libs["Cffi_thr"]["static"]
libs.pop("Cffi_thr")
linker_inputs = [
cc_common.create_linker_input(
owner = ctx.label,
libraries = depset(direct = [
cc_common.create_library_to_link(
actions = ctx.actions,
feature_configuration = feature_configuration,
dynamic_library = lib.get("dynamic", None),
dynamic_library_symlink_path =
_toolchain_library_symlink(lib["dynamic"]) if lib.get("dynamic") else "",
static_library = lib.get("static", None),
cc_toolchain = cc_toolchain,
)
for lib in libs.values()
]),
user_link_flags = depset(direct = target[HaskellImportHack].linkopts),
),
]
compilation_context = cc_common.create_compilation_context(
headers = target[HaskellImportHack].headers,
includes = target[HaskellImportHack].includes,
)
linking_context = cc_common.create_linking_context(
linker_inputs = depset(direct = linker_inputs),
)
cc_info = CcInfo(
compilation_context = compilation_context,
linking_context = linking_context,
)
library_dict[package] = struct(
default_info = target[DefaultInfo],
hs_info = target[HaskellInfo],
hs_lib_info = target[HaskellLibraryInfo],
cc_info = cc_common.merge_cc_infos(cc_infos = [cc_info] + [
library_dict[dep].cc_info
for dep in target[HaskellImportHack].depends
]),
haddock_info = target[HaddockInfo],
)
return [HaskellToolchainLibraries(libraries = library_dict)]
haskell_toolchain_libraries = rule(
haskell_toolchain_libraries_impl,
attrs = {
"_cc_toolchain": attr.label(
default = Label("@rules_cc//cc:current_cc_toolchain"),
),
},
toolchains = [
"@rules_cc//cc:toolchain_type",
"@rules_haskell//haskell:toolchain",
],
fragments = ["cpp"],
)
"""Generate Haskell toolchain libraries.
This is an internal rule and should not be user facing.
This rule is a work-around for toolchain transitions not being implemented,
yet. See
https://github.com/bazelbuild/proposals/blob/master/designs/2019-02-12-toolchain-transitions.md
This will need to be revisited once that proposal is implemented.
"""
def haskell_import_impl(ctx):
# The `allow_files` attribute of `rule` cannot define patterns of accepted
# file extensions like `.so.*`. Instead, we check for the correct file
# extensions here.
for lib in ctx.files.shared_libraries:
msg = "in shared_libraries attribute of haskell_import rule {}: " + \
"source file '{}' is misplaced here " + \
"(expected .dll, .dylib, .so or .so.*)"
ext = get_lib_extension(lib)
if not (ext in ["dll", "dylib", "so"] or ext.startswith("so.")):
fail(msg.format(str(ctx.label), str(lib.short_path)))
id = ctx.attr.id or ctx.attr.name
target_files = [
file
for file in ctx.files.static_libraries + ctx.files.shared_libraries
]
version_macros = set.empty()
if ctx.attr.version != None:
version_macros = set.singleton(
generate_version_macros(ctx, ctx.label.name, ctx.attr.version),
)
hs_info = HaskellInfo(
# XXX Empty set of conf and cache files only works for global db.
package_databases = depset(),
empty_lib_package_databases = depset(),
version_macros = version_macros,
source_files = depset(),
boot_files = depset(),
extra_source_files = depset(),
import_dirs = set.empty(),
hs_libraries = depset(),
deps_hs_libraries = depset(),
empty_hs_libraries = depset(),
interface_dirs = depset(),
deps_interface_dirs = depset(),
compile_flags = [],
user_compile_flags = [],
user_repl_flags = [],
)
import_info = HaskellImportHack(
# Make sure we're using the same order for dynamic_libraries,
# static_libraries.
dynamic_libraries = depset(ctx.files.shared_libraries),
static_libraries = depset(ctx.files.static_libraries, order = "topological"),
# NOTE: haskell_import is evaluated as a toolchain rule. Even if we
# bazel build with -c dbg, this rule is still executed with
# ctx.var["COMPILATION_MODE"] == "opt". Therefore, we need to carry
# both profiling and non-profiling libraries forward so that a later
# haskell_toolchain_library can select the appropriate artifacts.
static_profiling_libraries = depset(ctx.files.static_profiling_libraries, order = "topological"),
headers = depset(ctx.files.hdrs),
includes = depset(ctx.attr.includes),
linkopts = ctx.attr.linkopts,
depends = [dep.label.name for dep in ctx.attr.deps],
transitive_depends = depset(
direct = [ctx.attr.name],
transitive = [dep[HaskellImportHack].transitive_depends for dep in ctx.attr.deps],
order = "postorder",
),
)
coverage_info = HaskellCoverageInfo(coverage_data = [])
lib_info = HaskellLibraryInfo(
package_id = id,
version = ctx.attr.version,
exports = [],
)
default_info = DefaultInfo(
files = depset(target_files),
)
# This package haddock informations
transitive_html = {id: ctx.file.haddock_html} if ctx.file.haddock_html else {}
transitive_haddocks = {id: ctx.files.haddock_interfaces}
# Add dependencies haddock informations
for dep in ctx.attr.deps:
transitive_html.update(dep[HaddockInfo].transitive_html)
transitive_haddocks.update(dep[HaddockInfo].transitive_haddocks)
haddock_info = HaddockInfo(
package_id = id,
transitive_html = transitive_html,
transitive_haddocks = transitive_haddocks,
)
return [
hs_info,
import_info,
coverage_info,
default_info,
lib_info,
haddock_info,
]
def _exposed_modules_reexports(reexported_modules):
"""Creates a ghc-pkg-compatible list of reexport declarations.
A ghc-pkg registration file declares reexports as part of the
exposed-modules field in the following format:
exposed-modules: A, B, C from pkg-c:C, D from pkg-d:Original.D
Here, the Original.D module from pkg-d is renamed by virtue of a
different name being used before the "from" keyword.
This function creates a ghc-pkg-compatible list of reexport declarations
(as shown above) from a dictionary mapping package targets to "Cabal-style"
reexported-modules declarations. That is, something like:
{
":pkg-c": "C",
":pkg-d": "Original.D as D",
":pkg-e": "E1, Original.E2 as E2",
}
Args:
reexported_modules: a dictionary mapping package targets to "Cabal-style"
reexported-modules declarations.
Returns:
a ghc-pkg-compatible list of reexport declarations.
"""
exposed_reexports = []
for dep, cabal_decls in reexported_modules.items():
for cabal_decl in cabal_decls.split(","):
stripped_cabal_decl = cabal_decl.strip()
cabal_decl_parts = stripped_cabal_decl.split(" as ")
original = cabal_decl_parts[0]
if len(cabal_decl_parts) == 2:
reexported = cabal_decl_parts[1]
else:
reexported = cabal_decl_parts[0]
if HaskellLibraryInfo in dep:
pkg = dep[HaskellLibraryInfo].package_id
exposed_reexport = "{reexported} from {pkg}:{original}".format(
reexported = reexported,
pkg = pkg,
original = original,
)
exposed_reexports.append(exposed_reexport)
return exposed_reexports
def _get_mix_filepath(coverage_datum):
""" Extracts mix file path from a coverage datum.
"""
return coverage_datum.mix_file.short_path
|
the-stack_0_944 | import os
import numpy as np
import json
from itertools import product
class Node():
'''
Class for representing a node in the ImageNet/WordNet hierarchy.
'''
def __init__(self, wnid, parent_wnid=None, name=""):
"""
Args:
wnid (str) : WordNet ID for synset represented by node
parent_wnid (str) : WordNet ID for synset of node's parent
name (str) : word/human-interpretable description of synset
"""
self.wnid = wnid
self.name = name
self.class_num = -1
self.parent_wnid = parent_wnid
self.descendant_count_in = 0
self.descendants_all = set()
def add_child(self, child):
"""
Add child to given node.
Args:
child (Node) : Node object for child
"""
child.parent_wnid = self.wnid
def __str__(self):
return f'Name: ({self.name}), ImageNet Class: ({self.class_num}), Descendants: ({self.descendant_count_in})'
def __repr__(self):
return f'Name: ({self.name}), ImageNet Class: ({self.class_num}), Descendants: ({self.descendant_count_in})'
class ImageNetHierarchy():
'''
Class for representing ImageNet/WordNet hierarchy.
'''
def __init__(self, ds_path, ds_info_path):
"""
Args:
ds_path (str) : Path to ImageNet dataset
ds_info_path (str) : Path to supplementary files for the ImageNet dataset
('wordnet.is_a.txt', 'words.txt' and 'imagenet_class_index.json')
which can be obtained from http://image-net.org/download-API.
"""
self.tree = {}
ret = self.load_imagenet_info(ds_path, ds_info_path)
self.in_wnids, self.wnid_to_name, self.wnid_to_num, self.num_to_name = ret
with open(os.path.join(ds_info_path, 'wordnet.is_a.txt'), 'r') as f:
for line in f.readlines():
parent_wnid, child_wnid = line.strip('\n').split(' ')
parentNode = self.get_node(parent_wnid)
childNode = self.get_node(child_wnid)
parentNode.add_child(childNode)
for wnid in self.in_wnids:
self.tree[wnid].descendant_count_in = 0
self.tree[wnid].class_num = self.wnid_to_num[wnid]
for wnid in self.in_wnids:
node = self.tree[wnid]
while node.parent_wnid is not None:
self.tree[node.parent_wnid].descendant_count_in += 1
self.tree[node.parent_wnid].descendants_all.update(node.descendants_all)
self.tree[node.parent_wnid].descendants_all.add(node.wnid)
node = self.tree[node.parent_wnid]
del_nodes = [wnid for wnid in self.tree \
if (self.tree[wnid].descendant_count_in == 0 and self.tree[wnid].class_num == -1)]
for d in del_nodes:
self.tree.pop(d, None)
assert all([k.descendant_count_in > 0 or k.class_num != -1 for k in self.tree.values()])
self.wnid_sorted = sorted(sorted([(k, v.descendant_count_in, len(v.descendants_all)) \
for k, v in self.tree.items()
],
key=lambda x: x[2],
reverse=True
),
key=lambda x: x[1],
reverse=True
)
@staticmethod
def load_imagenet_info(ds_path, ds_info_path):
"""
Get information about mapping between ImageNet wnids/class numbers/class names.
Args:
ds_path (str) : Path to ImageNet dataset
ds_info_path (str) : Path to supplementary files for the ImageNet dataset
('wordnet.is_a.txt', 'words.txt', 'imagenet_class_index.json')
which can be obtained from http://image-net.org/download-API.
"""
files = os.listdir(os.path.join(ds_path, 'train'))
in_wnids = [f for f in files if f[0]=='n']
f = open(os.path.join(ds_info_path, 'words.txt'))
wnid_to_name = [l.strip() for l in f.readlines()]
wnid_to_name = {l.split('\t')[0]: l.split('\t')[1] \
for l in wnid_to_name}
with open(os.path.join(ds_info_path, 'imagenet_class_index.json'), 'r') as f:
base_map = json.load(f)
wnid_to_num = {v[0]: int(k) for k, v in base_map.items()}
num_to_name = {int(k): v[1] for k, v in base_map.items()}
return in_wnids, wnid_to_name, wnid_to_num, num_to_name
def get_node(self, wnid):
"""
Add node to tree.
Args:
wnid (str) : WordNet ID for synset represented by node
Returns:
A node object representing the specified wnid.
"""
if wnid not in self.tree:
self.tree[wnid] = Node(wnid, name=self.wnid_to_name[wnid])
return self.tree[wnid]
def is_ancestor(self, ancestor_wnid, child_wnid):
"""
Check if a node is an ancestor of another.
Args:
ancestor_wnid (str) : WordNet ID for synset represented by ancestor node
child_wnid (str) : WordNet ID for synset represented by child node
Returns:
A boolean variable indicating whether or not the node is an ancestor
"""
return (child_wnid in self.tree[ancestor_wnid].descendants_all)
def get_descendants(self, node_wnid, in_imagenet=False):
"""
Get all descendants of a given node.
Args:
node_wnid (str) : WordNet ID for synset for node
in_imagenet (bool) : If True, only considers descendants among
ImageNet synsets, else considers all possible
descendants in the WordNet hierarchy
Returns:
A set of wnids corresponding to all the descendants
"""
if in_imagenet:
return set([self.wnid_to_num[ww] for ww in self.tree[node_wnid].descendants_all
if ww in set(self.in_wnids)])
else:
return self.tree[node_wnid].descendants_all
def get_superclasses(self, n_superclasses,
ancestor_wnid=None, superclass_lowest=None,
balanced=True):
"""
Get superclasses by grouping together classes from the ImageNet dataset.
Args:
n_superclasses (int) : Number of superclasses desired
ancestor_wnid (str) : (optional) WordNet ID that can be used to specify
common ancestor for the selected superclasses
superclass_lowest (set of str) : (optional) Set of WordNet IDs of nodes
that shouldn't be further sub-classes
balanced (bool) : If True, all the superclasses will have the same number
of ImageNet subclasses
Returns:
superclass_wnid (list): List of WordNet IDs of superclasses
class_ranges (list of sets): List of ImageNet subclasses per superclass
label_map (dict): Mapping from class number to human-interpretable description
for each superclass
"""
assert superclass_lowest is None or \
not any([self.is_ancestor(s1, s2) for s1, s2 in product(superclass_lowest, superclass_lowest)])
superclass_info = []
for (wnid, ndesc_in, ndesc_all) in self.wnid_sorted:
if len(superclass_info) == n_superclasses:
break
if ancestor_wnid is None or self.is_ancestor(ancestor_wnid, wnid):
keep_wnid = [True] * (len(superclass_info) + 1)
superclass_info.append((wnid, ndesc_in))
for ii, (w, d) in enumerate(superclass_info):
if self.is_ancestor(w, wnid):
if superclass_lowest and w in superclass_lowest:
keep_wnid[-1] = False
else:
keep_wnid[ii] = False
for ii in range(len(superclass_info) - 1, -1, -1):
if not keep_wnid[ii]:
superclass_info.pop(ii)
superclass_wnid = [w for w, _ in superclass_info]
class_ranges, label_map = self.get_subclasses(superclass_wnid,
balanced=balanced)
return superclass_wnid, class_ranges, label_map
def get_subclasses(self, superclass_wnid, balanced=True):
"""
Get ImageNet subclasses for a given set of superclasses from the WordNet
hierarchy.
Args:
superclass_wnid (list): List of WordNet IDs of superclasses
balanced (bool) : If True, all the superclasses will have the same number
of ImageNet subclasses
Returns:
class_ranges (list of sets): List of ImageNet subclasses per superclass
label_map (dict): Mapping from class number to human-interpretable description
for each superclass
"""
ndesc_min = min([self.tree[w].descendant_count_in for w in superclass_wnid])
class_ranges, label_map = [], {}
for ii, w in enumerate(superclass_wnid):
descendants = self.get_descendants(w, in_imagenet=True)
if balanced and len(descendants) > ndesc_min:
descendants = set([dd for ii, dd in enumerate(sorted(list(descendants))) if ii < ndesc_min])
class_ranges.append(descendants)
label_map[ii] = self.tree[w].name
for i in range(len(class_ranges)):
for j in range(i + 1, len(class_ranges)):
assert(len(class_ranges[i].intersection(class_ranges[j])) == 0)
return class_ranges, label_map
def common_superclass_wnid(group_name):
"""
Get WordNet IDs of common superclasses.
Args:
group_name (str): Name of group
Returns:
superclass_wnid (list): List of WordNet IDs of superclasses
"""
common_groups = {
# ancestor_wnid = 'n00004258'
'living_9': ['n02084071', #dog, domestic dog, Canis familiaris
'n01503061', # bird
'n01767661', # arthropod
'n01661091', # reptile, reptilian
'n02469914', # primate
'n02512053', # fish
'n02120997', # feline, felid
'n02401031', # bovid
'n01627424', # amphibian
],
'mixed_10': [
'n02084071', #dog,
'n01503061', #bird
'n02159955', #insect
'n02484322', #monkey
'n02958343', #car
'n02120997', #feline
'n04490091', #truck
'n13134947', #fruit
'n12992868', #fungus
'n02858304', #boat
],
'mixed_13': ['n02084071', #dog,
'n01503061', #bird (52)
'n02159955', #insect (27)
'n03405725', #furniture (21)
'n02512053', #fish (16),
'n02484322', #monkey (13)
'n02958343', #car (10)
'n02120997', #feline (8),
'n04490091', #truck (7)
'n13134947', #fruit (7)
'n12992868', #fungus (7)
'n02858304', #boat (6)
'n03082979', #computer(6)
],
# Dataset from Geirhos et al., 2018: arXiv:1811.12231
'geirhos_16': ['n02686568', #aircraft (3)
'n02131653', #bear (3)
'n02834778', #bicycle (2)
'n01503061', #bird (52)
'n02858304', #boat (6)
'n02876657', #bottle (7)
'n02958343', #car (10)
'n02121808', #cat (5)
'n03001627', #char (4)
'n03046257', #clock (3)
'n02084071', #dog (116)
'n02503517', #elephant (2)
'n03614532', #keyboard (3)
'n03623556', #knife (2)
'n03862676', #oven (2)
'n04490091', #truck (7)
],
'big_12': ['n02084071', #dog (100+)
'n04341686', #structure (55)
'n01503061', #bird (52)
'n03051540', #clothing (48)
'n04576211', #wheeled vehicle
'n01661091', #reptile, reptilian (36)
'n02075296', #carnivore
'n02159955', #insect (27)
'n03800933', #musical instrument (26)
'n07555863', #food (24)
'n03405725', #furniture (21)
'n02469914', #primate (20)
],
'mid_12': ['n02084071', #dog (100+)
'n01503061', #bird (52)
'n04576211', #wheeled vehicle
'n01661091', #reptile, reptilian (36)
'n02075296', #carnivore
'n02159955', #insect (27)
'n03800933', #musical instrument (26)
'n07555863', #food (24)
'n03419014', #garment (24)
'n03405725', #furniture (21)
'n02469914', #primate (20)
'n02512053', #fish (16)
]
}
if group_name in common_groups:
superclass_wnid = common_groups[group_name]
return superclass_wnid
else:
raise ValueError("Custom group does not exist")
|
the-stack_0_948 | from math import *
from prettytable import PrettyTable
def func(x, y):
return x * x + y * y
def main():
mas_x = []; mas_y = []
tmp_x = []; tmp_y = []; tmp_y2 = []
tmp_x3 = []; tmp_y3 = []
matrix = []
beg = 0; end = 10
N = abs(end - beg) - 1
eps = 1e-5
for i in range(beg, end):
tmp_x.append(i)
tmp_y.append(i)
matrix = create_new_matrix(func, tmp_x, tmp_y)
print_matrix(tmp_x, tmp_y, matrix)
n_X = int(input("input n for X: "))
n_Y = int(input("input n for Y: "))
x = float(input("input x: "))
y = float(input("input y: "))
mas_x = create_new_x_y(x, n_X, N, tmp_x)
mas_y = create_new_x_y(y, n_Y, N, tmp_y)
matrix = create_new_matrix(func, mas_x, mas_y)
new_x = []
for i in range(len(mas_x)):
new_x.append(interpolation(y, n_Y, mas_y, matrix[i]))
answer = interpolation(x, n_X, mas_x, new_x)
print("\nF(x, y) = ", answer)
def print_matrix(tmp_x, tmp_y, matrix):
print("|X|Y|", end = " ")
for i in range(0, len(tmp_x)):
print("{:5d}".format(tmp_x[i]), end = " ")
print()
for i in range(0, len(tmp_x)):
print("{:3d}".format(tmp_x[i])," ", end = " ")
for j in range(0, len(tmp_y)):
print( "{:5d}".format(matrix[i][j]), end = " ")
print()
print()
def create_new_matrix(f, tmp_x, tmp_y):
matrix = []
for i in range(0, len(tmp_x)):
matrix.append([])
for j in range(0, len(tmp_y)):
matrix[i].append(f(tmp_x[i], tmp_y[j]))
return matrix
def create_new_x_y(x, n, N, tmp_x):
mas_x = []
if (x <= tmp_x[0]):
for i in range(0, n + 1):
mas_x.append(tmp_x[i])
elif (x >= tmp_x[N]):
for i in range(len(tmp_x) - (n + 1), len(tmp_x)):
mas_x.append(tmp_x[i])
else:
back = 0; up = 0
for i in range(1, N):
if((tmp_x[i - 1] <= x) and (tmp_x[i] > x)):
up = i; back = i - 1
for k in range(0, n + 1):
if (k % 2 == 0):
if (up < len(tmp_x)):
mas_x.append(tmp_x[up])
up += 1
elif (back >= 0):
mas_x.insert(0, tmp_x[back])
back -= 1
else:
if (back >= 0):
mas_x.insert(0, tmp_x[back])
back -= 1
elif(up < len(tmp_x)):
mas_x.append(tmp_x[up])
up += 1
return mas_x
def interpolation(x, n, mas_x, mas_y):
matrix = []
matrix.append([])
for i in range(0, n):
matrix[0].append((mas_y[i] - mas_y[i + 1])/(mas_x[i] - mas_x[i + 1]))
m = n - 1
for i in range(1, n):
matrix.append([])
for j in range(0, m):
matrix[i].append(((matrix[i - 1][j] - matrix[i - 1][j + 1]))/(mas_x[j] - mas_x[j + 2]))
m -= 1
y = mas_y[0]
fact = 1
for i in range(0, n):
fact *= (x - mas_x[i])
y += matrix[i][0] * fact
return y
if __name__ == "__main__":
main();
|
the-stack_0_950 | import configparser
import os
from compute.config import AlgorithmConfig
import numpy as np
from train.utils import TrainConfig
class StatusUpdateTool(object):
@classmethod
def clear_config(cls):
config_file = os.path.join(os.path.dirname(__file__), 'global.ini')
config = configparser.ConfigParser()
config.read(config_file)
config.write(open(config_file, 'w'))
@classmethod
def __write_ini_file(cls, section, key, value):
config_file = os.path.join(os.path.dirname(__file__), 'global.ini')
config = configparser.ConfigParser()
config.read(config_file)
config.set(section, key, value)
config.write(open(config_file, 'w'))
@classmethod
def __read_ini_file(cls, section, key):
config_file = os.path.join(os.path.dirname(__file__), 'global.ini')
config = configparser.ConfigParser()
config.read(config_file)
return config.get(section, key)
@classmethod
def get_num_class(cls):
return TrainConfig.get_out_cls_num()
@classmethod
def get_input_weight(cls):
rs = TrainConfig.get_data_input_size()
return rs[0]
@classmethod
def get_input_height(cls):
rs = TrainConfig.get_data_input_size()
return rs[1]
@classmethod
def get_input_channel(cls):
rs = TrainConfig.get_data_input_size()
return rs[2]
@classmethod
def get_init_params(cls):
g = AlgorithmConfig()
pop_size = int(g.read_ini_file('pop_size'))
max_gen = int(g.read_ini_file('max_gen'))
params = {}
params['pop_size'] = pop_size
params['max_gen'] = max_gen
return params
@classmethod
def begin_evolution(cls):
section = 'evolution_status'
key = 'IS_RUNNING'
cls.__write_ini_file(section, key, "1")
@classmethod
def end_evolution(cls):
section = 'evolution_status'
key = 'IS_RUNNING'
cls.__write_ini_file(section, key, "0")
@classmethod
def is_evolution_running(cls):
rs = cls.__read_ini_file('evolution_status', 'IS_RUNNING')
if rs == '1':
return True
else:
return False
|
the-stack_0_952 | import cv2
import random
import numpy as np
import skimage.transform
from typing import Union, Optional, Sequence, Tuple, Dict
from . import functional as F
from ...core.transforms_interface import DualTransform, to_tuple
__all__ = ["ShiftScaleRotate", "ElasticTransform", "Perspective", "Affine", "PiecewiseAffine"]
class ShiftScaleRotate(DualTransform):
"""Randomly apply affine transforms: translate, scale and rotate the input.
Args:
shift_limit ((float, float) or float): shift factor range for both height and width. If shift_limit
is a single float value, the range will be (-shift_limit, shift_limit). Absolute values for lower and
upper bounds should lie in range [0, 1]. Default: (-0.0625, 0.0625).
scale_limit ((float, float) or float): scaling factor range. If scale_limit is a single float value, the
range will be (-scale_limit, scale_limit). Default: (-0.1, 0.1).
rotate_limit ((int, int) or int): rotation range. If rotate_limit is a single int value, the
range will be (-rotate_limit, rotate_limit). Default: (-45, 45).
interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
Default: cv2.INTER_LINEAR.
border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
Default: cv2.BORDER_REFLECT_101
value (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of int,
list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
shift_limit_x ((float, float) or float): shift factor range for width. If it is set then this value
instead of shift_limit will be used for shifting width. If shift_limit_x is a single float value,
the range will be (-shift_limit_x, shift_limit_x). Absolute values for lower and upper bounds should lie in
the range [0, 1]. Default: None.
shift_limit_y ((float, float) or float): shift factor range for height. If it is set then this value
instead of shift_limit will be used for shifting height. If shift_limit_y is a single float value,
the range will be (-shift_limit_y, shift_limit_y). Absolute values for lower and upper bounds should lie
in the range [0, 1]. Default: None.
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints
Image types:
uint8, float32
"""
def __init__(
self,
shift_limit=0.0625,
scale_limit=0.1,
rotate_limit=45,
interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_REFLECT_101,
value=None,
mask_value=None,
shift_limit_x=None,
shift_limit_y=None,
always_apply=False,
p=0.5,
):
super(ShiftScaleRotate, self).__init__(always_apply, p)
self.shift_limit_x = to_tuple(shift_limit_x if shift_limit_x is not None else shift_limit)
self.shift_limit_y = to_tuple(shift_limit_y if shift_limit_y is not None else shift_limit)
self.scale_limit = to_tuple(scale_limit, bias=1.0)
self.rotate_limit = to_tuple(rotate_limit)
self.interpolation = interpolation
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
def apply(self, img, angle=0, scale=0, dx=0, dy=0, interpolation=cv2.INTER_LINEAR, **params):
return F.shift_scale_rotate(img, angle, scale, dx, dy, interpolation, self.border_mode, self.value)
def apply_to_mask(self, img, angle=0, scale=0, dx=0, dy=0, **params):
return F.shift_scale_rotate(img, angle, scale, dx, dy, cv2.INTER_NEAREST, self.border_mode, self.mask_value)
def apply_to_keypoint(self, keypoint, angle=0, scale=0, dx=0, dy=0, rows=0, cols=0, **params):
return F.keypoint_shift_scale_rotate(keypoint, angle, scale, dx, dy, rows, cols)
def get_params(self):
return {
"angle": random.uniform(self.rotate_limit[0], self.rotate_limit[1]),
"scale": random.uniform(self.scale_limit[0], self.scale_limit[1]),
"dx": random.uniform(self.shift_limit_x[0], self.shift_limit_x[1]),
"dy": random.uniform(self.shift_limit_y[0], self.shift_limit_y[1]),
}
def apply_to_bbox(self, bbox, angle, scale, dx, dy, **params):
return F.bbox_shift_scale_rotate(bbox, angle, scale, dx, dy, **params)
def get_transform_init_args(self):
return {
"shift_limit_x": self.shift_limit_x,
"shift_limit_y": self.shift_limit_y,
"scale_limit": to_tuple(self.scale_limit, bias=-1.0),
"rotate_limit": self.rotate_limit,
"interpolation": self.interpolation,
"border_mode": self.border_mode,
"value": self.value,
"mask_value": self.mask_value,
}
class ElasticTransform(DualTransform):
"""Elastic deformation of images as described in [Simard2003]_ (with modifications).
Based on https://gist.github.com/ernestum/601cdf56d2b424757de5
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
Args:
alpha (float):
sigma (float): Gaussian filter parameter.
alpha_affine (float): The range will be (-alpha_affine, alpha_affine)
interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
Default: cv2.INTER_LINEAR.
border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
Default: cv2.BORDER_REFLECT_101
value (int, float, list of ints, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of ints,
list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
approximate (boolean): Whether to smooth displacement map with fixed kernel size.
Enabling this option gives ~2X speedup on large images.
same_dxdy (boolean): Whether to use same random generated shift for x and y.
Enabling this option gives ~2X speedup.
Targets:
image, mask
Image types:
uint8, float32
"""
def __init__(
self,
alpha=1,
sigma=50,
alpha_affine=50,
interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_REFLECT_101,
value=None,
mask_value=None,
always_apply=False,
approximate=False,
same_dxdy=False,
p=0.5,
):
super(ElasticTransform, self).__init__(always_apply, p)
self.alpha = alpha
self.alpha_affine = alpha_affine
self.sigma = sigma
self.interpolation = interpolation
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
self.approximate = approximate
self.same_dxdy = same_dxdy
def apply(self, img, random_state=None, interpolation=cv2.INTER_LINEAR, **params):
return F.elastic_transform(
img,
self.alpha,
self.sigma,
self.alpha_affine,
interpolation,
self.border_mode,
self.value,
np.random.RandomState(random_state),
self.approximate,
self.same_dxdy,
)
def apply_to_mask(self, img, random_state=None, **params):
return F.elastic_transform(
img,
self.alpha,
self.sigma,
self.alpha_affine,
cv2.INTER_NEAREST,
self.border_mode,
self.mask_value,
np.random.RandomState(random_state),
self.approximate,
self.same_dxdy,
)
def get_params(self):
return {"random_state": random.randint(0, 10000)}
def get_transform_init_args_names(self):
return (
"alpha",
"sigma",
"alpha_affine",
"interpolation",
"border_mode",
"value",
"mask_value",
"approximate",
"same_dxdy",
)
class Perspective(DualTransform):
"""Perform a random four point perspective transform of the input.
Args:
scale (float or (float, float)): standard deviation of the normal distributions. These are used to sample
the random distances of the subimage's corners from the full image's corners.
If scale is a single float value, the range will be (0, scale). Default: (0.05, 0.1).
keep_size (bool): Whether to resize image’s back to their original size after applying the perspective
transform. If set to False, the resulting images may end up having different shapes
and will always be a list, never an array. Default: True
pad_mode (OpenCV flag): OpenCV border mode.
pad_val (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
Default: 0
mask_pad_val (int, float, list of int, list of float): padding value for mask
if border_mode is cv2.BORDER_CONSTANT. Default: 0
fit_output (bool): If True, the image plane size and position will be adjusted to still capture
the whole image after perspective transformation. (Followed by image resizing if keep_size is set to True.)
Otherwise, parts of the transformed image may be outside of the image plane.
This setting should not be set to True when using large scale values as it could lead to very large images.
Default: False
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
def __init__(
self,
scale=(0.05, 0.1),
keep_size=True,
pad_mode=cv2.BORDER_CONSTANT,
pad_val=0,
mask_pad_val=0,
fit_output=False,
interpolation=cv2.INTER_LINEAR,
always_apply=False,
p=0.5,
):
super().__init__(always_apply, p)
self.scale = to_tuple(scale, 0)
self.keep_size = keep_size
self.pad_mode = pad_mode
self.pad_val = pad_val
self.mask_pad_val = mask_pad_val
self.fit_output = fit_output
self.interpolation = interpolation
def apply(self, img, matrix=None, max_height=None, max_width=None, **params):
return F.perspective(
img, matrix, max_width, max_height, self.pad_val, self.pad_mode, self.keep_size, params["interpolation"]
)
def apply_to_bbox(self, bbox, matrix=None, max_height=None, max_width=None, **params):
return F.perspective_bbox(bbox, params["rows"], params["cols"], matrix, max_width, max_height, self.keep_size)
def apply_to_keypoint(self, keypoint, matrix=None, max_height=None, max_width=None, **params):
return F.perspective_keypoint(
keypoint, params["rows"], params["cols"], matrix, max_width, max_height, self.keep_size
)
@property
def targets_as_params(self):
return ["image"]
def get_params_dependent_on_targets(self, params):
h, w = params["image"].shape[:2]
scale = np.random.uniform(*self.scale)
points = np.random.normal(0, scale, [4, 2])
points = np.mod(np.abs(points), 1)
# top left -- no changes needed, just use jitter
# top right
points[1, 0] = 1.0 - points[1, 0] # w = 1.0 - jitter
# bottom right
points[2] = 1.0 - points[2] # w = 1.0 - jitt
# bottom left
points[3, 1] = 1.0 - points[3, 1] # h = 1.0 - jitter
points[:, 0] *= w
points[:, 1] *= h
# Obtain a consistent order of the points and unpack them individually.
# Warning: don't just do (tl, tr, br, bl) = _order_points(...)
# here, because the reordered points is used further below.
points = self._order_points(points)
tl, tr, br, bl = points
# compute the width of the new image, which will be the
# maximum distance between bottom-right and bottom-left
# x-coordiates or the top-right and top-left x-coordinates
min_width = None
max_width = None
while min_width is None or min_width < 2:
width_top = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
width_bottom = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
max_width = int(max(width_top, width_bottom))
min_width = int(min(width_top, width_bottom))
if min_width < 2:
step_size = (2 - min_width) / 2
tl[0] -= step_size
tr[0] += step_size
bl[0] -= step_size
br[0] += step_size
# compute the height of the new image, which will be the maximum distance between the top-right
# and bottom-right y-coordinates or the top-left and bottom-left y-coordinates
min_height = None
max_height = None
while min_height is None or min_height < 2:
height_right = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
height_left = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
max_height = int(max(height_right, height_left))
min_height = int(min(height_right, height_left))
if min_height < 2:
step_size = (2 - min_height) / 2
tl[1] -= step_size
tr[1] -= step_size
bl[1] += step_size
br[1] += step_size
# now that we have the dimensions of the new image, construct
# the set of destination points to obtain a "birds eye view",
# (i.e. top-down view) of the image, again specifying points
# in the top-left, top-right, bottom-right, and bottom-left order
# do not use width-1 or height-1 here, as for e.g. width=3, height=2
# the bottom right coordinate is at (3.0, 2.0) and not (2.0, 1.0)
dst = np.array([[0, 0], [max_width, 0], [max_width, max_height], [0, max_height]], dtype=np.float32)
# compute the perspective transform matrix and then apply it
m = cv2.getPerspectiveTransform(points, dst)
if self.fit_output:
m, max_width, max_height = self._expand_transform(m, (h, w))
return {"matrix": m, "max_height": max_height, "max_width": max_width, "interpolation": self.interpolation}
@classmethod
def _expand_transform(cls, matrix, shape):
height, width = shape
# do not use width-1 or height-1 here, as for e.g. width=3, height=2, max_height
# the bottom right coordinate is at (3.0, 2.0) and not (2.0, 1.0)
rect = np.array([[0, 0], [width, 0], [width, height], [0, height]], dtype=np.float32)
dst = cv2.perspectiveTransform(np.array([rect]), matrix)[0]
# get min x, y over transformed 4 points
# then modify target points by subtracting these minima => shift to (0, 0)
dst -= dst.min(axis=0, keepdims=True)
dst = np.around(dst, decimals=0)
matrix_expanded = cv2.getPerspectiveTransform(rect, dst)
max_width, max_height = dst.max(axis=0)
return matrix_expanded, int(max_width), int(max_height)
@staticmethod
def _order_points(pts: np.ndarray) -> np.ndarray:
pts = np.array(sorted(pts, key=lambda x: x[0]))
left = pts[:2] # points with smallest x coordinate - left points
right = pts[2:] # points with greatest x coordinate - right points
if left[0][1] < left[1][1]:
tl, bl = left
else:
bl, tl = left
if right[0][1] < right[1][1]:
tr, br = right
else:
br, tr = right
return np.array([tl, tr, br, bl], dtype=np.float32)
def get_transform_init_args_names(self):
return ("scale", "keep_size", "pad_mode", "pad_val", "mask_pad_val", "fit_output", "interpolation")
class Affine(DualTransform):
"""Augmentation to apply affine transformations to images.
This is mostly a wrapper around the corresponding classes and functions in OpenCV.
Affine transformations involve:
- Translation ("move" image on the x-/y-axis)
- Rotation
- Scaling ("zoom" in/out)
- Shear (move one side of the image, turning a square into a trapezoid)
All such transformations can create "new" pixels in the image without a defined content, e.g.
if the image is translated to the left, pixels are created on the right.
A method has to be defined to deal with these pixel values.
The parameters `cval` and `mode` of this class deal with this.
Some transformations involve interpolations between several pixels
of the input image to generate output pixel values. The parameters `interpolation` and
`mask_interpolation` deals with the method of interpolation used for this.
Args:
scale (number, tuple of number or dict): Scaling factor to use, where ``1.0`` denotes "no change" and
``0.5`` is zoomed out to ``50`` percent of the original size.
* If a single number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``.
That value will be used identically for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
translate_percent (None, number, tuple of number or dict): Translation as a fraction of the image height/width
(x-translation, y-translation), where ``0`` denotes "no change"
and ``0.5`` denotes "half of the axis size".
* If ``None`` then equivalent to ``0.0`` unless `translate_px` has a value other than ``None``.
* If a single number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``.
That sampled fraction value will be used identically for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
translate_px (None, int, tuple of int or dict): Translation in pixels.
* If ``None`` then equivalent to ``0`` unless `translate_percent` has a value other than ``None``.
* If a single int, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from
the discrete interval ``[a..b]``. That number will be used identically for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
rotate (number or tuple of number): Rotation in degrees (**NOT** radians), i.e. expected value range is
around ``[-360, 360]``. Rotation happens around the *center* of the image,
not the top left corner as in some other frameworks.
* If a number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``
and used as the rotation value.
shear (number, tuple of number or dict): Shear in degrees (**NOT** radians), i.e. expected value range is
around ``[-360, 360]``, with reasonable values being in the range of ``[-45, 45]``.
* If a number, then that value will be used for all images as
the shear on the x-axis (no shear on the y-axis will be done).
* If a tuple ``(a, b)``, then two value will be uniformly sampled per image
from the interval ``[a, b]`` and be used as the x- and y-shear value.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
interpolation (int): OpenCV interpolation flag.
mask_interpolation (int): OpenCV interpolation flag.
cval (number or sequence of number): The constant value to use when filling in newly created pixels.
(E.g. translating by 1px to the right will create a new 1px-wide column of pixels
on the left of the image).
The value is only used when `mode=constant`. The expected value range is ``[0, 255]`` for ``uint8`` images.
cval_mask (number or tuple of number): Same as cval but only for masks.
mode (int): OpenCV border flag.
fit_output (bool): Whether to modify the affine transformation so that the whole output image is always
contained in the image plane (``True``) or accept parts of the image being outside
the image plane (``False``). This can be thought of as first applying the affine transformation
and then applying a second transformation to "zoom in" on the new image so that it fits the image plane,
This is useful to avoid corners of the image being outside of the image plane after applying rotations.
It will however negate translation and scaling.
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
def __init__(
self,
scale: Optional[Union[float, Sequence[float], dict]] = None,
translate_percent: Optional[Union[float, Sequence[float], dict]] = None,
translate_px: Optional[Union[int, Sequence[int], dict]] = None,
rotate: Optional[Union[float, Sequence[float]]] = None,
shear: Optional[Union[float, Sequence[float], dict]] = None,
interpolation: int = cv2.INTER_LINEAR,
mask_interpolation: int = cv2.INTER_NEAREST,
cval: Union[int, float, Sequence[int], Sequence[float]] = 0,
cval_mask: Union[int, float, Sequence[int], Sequence[float]] = 0,
mode: int = cv2.BORDER_CONSTANT,
fit_output: bool = False,
always_apply: bool = False,
p: float = 0.5,
):
super().__init__(always_apply=always_apply, p=p)
params = [scale, translate_percent, translate_px, rotate, shear]
if all([p is None for p in params]):
scale = {"x": (0.9, 1.1), "y": (0.9, 1.1)}
translate_percent = {"x": (-0.1, 0.1), "y": (-0.1, 0.1)}
rotate = (-15, 15)
shear = {"x": (-10, 10), "y": (-10, 10)}
else:
scale = scale if scale is not None else 1.0
rotate = rotate if rotate is not None else 0.0
shear = shear if shear is not None else 0.0
self.interpolation = interpolation
self.mask_interpolation = mask_interpolation
self.cval = cval
self.cval_mask = cval_mask
self.mode = mode
self.scale = self._handle_dict_arg(scale, "scale")
self.translate_percent, self.translate_px = self._handle_translate_arg(translate_px, translate_percent)
self.rotate = to_tuple(rotate, rotate)
self.fit_output = fit_output
self.shear = self._handle_dict_arg(shear, "shear")
def get_transform_init_args_names(self):
return (
"interpolation",
"mask_interpolation",
"cval",
"mode",
"scale",
"translate_percent",
"translate_px",
"rotate",
"fit_output",
"shear",
"cval_mask",
)
@staticmethod
def _handle_dict_arg(val: Union[float, Sequence[float], dict], name: str):
if isinstance(val, dict):
if "x" not in val and "y" not in val:
raise ValueError(
f'Expected {name} dictionary to contain at least key "x" or ' 'key "y". Found neither of them.'
)
x = val.get("x", 1.0)
y = val.get("y", 1.0)
return {"x": to_tuple(x, x), "y": to_tuple(y, y)}
return {"x": to_tuple(val, val), "y": to_tuple(val, val)}
@classmethod
def _handle_translate_arg(
cls,
translate_px: Optional[Union[float, Sequence[float], dict]],
translate_percent: Optional[Union[float, Sequence[float], dict]],
):
if translate_percent is None and translate_px is None:
translate_px = 0
if translate_percent is not None and translate_px is not None:
raise ValueError(
"Expected either translate_percent or translate_px to be " "provided, " "but neither of them was."
)
if translate_percent is not None:
# translate by percent
return cls._handle_dict_arg(translate_percent, "translate_percent"), translate_px
if translate_px is None:
raise ValueError("translate_px is None.")
# translate by pixels
return translate_percent, cls._handle_dict_arg(translate_px, "translate_px")
def apply(
self,
img: np.ndarray,
matrix: skimage.transform.ProjectiveTransform = None,
output_shape: Sequence[int] = None,
**params
) -> np.ndarray:
return F.warp_affine(
img,
matrix,
interpolation=self.interpolation,
cval=self.cval,
mode=self.mode,
output_shape=output_shape,
)
def apply_to_mask(
self,
img: np.ndarray,
matrix: skimage.transform.ProjectiveTransform = None,
output_shape: Sequence[int] = None,
**params
) -> np.ndarray:
return F.warp_affine(
img,
matrix,
interpolation=self.mask_interpolation,
cval=self.cval_mask,
mode=self.mode,
output_shape=output_shape,
)
def apply_to_bbox(
self,
bbox: Sequence[float],
matrix: skimage.transform.ProjectiveTransform = None,
rows: int = 0,
cols: int = 0,
output_shape: Sequence[int] = (),
**params
) -> Sequence[float]:
return F.bbox_affine(bbox, matrix, rows, cols, output_shape)
def apply_to_keypoint(
self,
keypoint: Sequence[float],
matrix: skimage.transform.ProjectiveTransform = None,
scale: dict = None,
**params
) -> Sequence[float]:
return F.keypoint_affine(keypoint, matrix=matrix, scale=scale)
@property
def targets_as_params(self):
return ["image"]
def get_params_dependent_on_targets(self, params: dict) -> dict:
h, w = params["image"].shape[:2]
translate: Dict[str, Union[int, float]]
if self.translate_px is not None:
translate = {key: random.randint(*value) for key, value in self.translate_px.items()}
elif self.translate_percent is not None:
translate = {key: random.uniform(*value) for key, value in self.translate_percent.items()}
translate["x"] = translate["x"] * w
translate["y"] = translate["y"] * h
else:
translate = {"x": 0, "y": 0}
shear = {key: random.uniform(*value) for key, value in self.shear.items()}
scale = {key: random.uniform(*value) for key, value in self.scale.items()}
rotate = random.uniform(*self.rotate)
# for images we use additional shifts of (0.5, 0.5) as otherwise
# we get an ugly black border for 90deg rotations
shift_x = w / 2 - 0.5
shift_y = h / 2 - 0.5
matrix_to_topleft = skimage.transform.SimilarityTransform(translation=[-shift_x, -shift_y])
matrix_shear_y_rot = skimage.transform.AffineTransform(rotation=-np.pi / 2)
matrix_shear_y = skimage.transform.AffineTransform(shear=np.deg2rad(shear["y"]))
matrix_shear_y_rot_inv = skimage.transform.AffineTransform(rotation=np.pi / 2)
matrix_transforms = skimage.transform.AffineTransform(
scale=(scale["x"], scale["y"]),
translation=(translate["x"], translate["y"]),
rotation=np.deg2rad(rotate),
shear=np.deg2rad(shear["x"]),
)
matrix_to_center = skimage.transform.SimilarityTransform(translation=[shift_x, shift_y])
matrix = (
matrix_to_topleft
+ matrix_shear_y_rot
+ matrix_shear_y
+ matrix_shear_y_rot_inv
+ matrix_transforms
+ matrix_to_center
)
if self.fit_output:
matrix, output_shape = self._compute_affine_warp_output_shape(matrix, params["image"].shape)
else:
output_shape = params["image"].shape
return {
"rotate": rotate,
"scale": scale,
"matrix": matrix,
"output_shape": output_shape,
}
@staticmethod
def _compute_affine_warp_output_shape(
matrix: skimage.transform.ProjectiveTransform, input_shape: Sequence[int]
) -> Tuple[skimage.transform.ProjectiveTransform, Sequence[int]]:
height, width = input_shape[:2]
if height == 0 or width == 0:
return matrix, input_shape
# determine shape of output image
corners = np.array([[0, 0], [0, height - 1], [width - 1, height - 1], [width - 1, 0]])
corners = matrix(corners)
minc = corners[:, 0].min()
minr = corners[:, 1].min()
maxc = corners[:, 0].max()
maxr = corners[:, 1].max()
out_height = maxr - minr + 1
out_width = maxc - minc + 1
if len(input_shape) == 3:
output_shape = np.ceil((out_height, out_width, input_shape[2]))
else:
output_shape = np.ceil((out_height, out_width))
output_shape_tuple = tuple([int(v) for v in output_shape.tolist()])
# fit output image in new shape
translation = (-minc, -minr)
matrix_to_fit = skimage.transform.SimilarityTransform(translation=translation)
matrix = matrix + matrix_to_fit
return matrix, output_shape_tuple
class PiecewiseAffine(DualTransform):
"""Apply affine transformations that differ between local neighbourhoods.
This augmentation places a regular grid of points on an image and randomly moves the neighbourhood of these point
around via affine transformations. This leads to local distortions.
This is mostly a wrapper around scikit-image's ``PiecewiseAffine``.
See also ``Affine`` for a similar technique.
Note:
This augmenter is very slow. Try to use ``ElasticTransformation`` instead, which is at least 10x faster.
Note:
For coordinate-based inputs (keypoints, bounding boxes, polygons, ...),
this augmenter still has to perform an image-based augmentation,
which will make it significantly slower and not fully correct for such inputs than other transforms.
Args:
scale (float, tuple of float): Each point on the regular grid is moved around via a normal distribution.
This scale factor is equivalent to the normal distribution's sigma.
Note that the jitter (how far each point is moved in which direction) is multiplied by the height/width of
the image if ``absolute_scale=False`` (default), so this scale can be the same for different sized images.
Recommended values are in the range ``0.01`` to ``0.05`` (weak to strong augmentations).
* If a single ``float``, then that value will always be used as the scale.
* If a tuple ``(a, b)`` of ``float`` s, then a random value will
be uniformly sampled per image from the interval ``[a, b]``.
nb_rows (int, tuple of int): Number of rows of points that the regular grid should have.
Must be at least ``2``. For large images, you might want to pick a higher value than ``4``.
You might have to then adjust scale to lower values.
* If a single ``int``, then that value will always be used as the number of rows.
* If a tuple ``(a, b)``, then a value from the discrete interval
``[a..b]`` will be uniformly sampled per image.
nb_cols (int, tuple of int): Number of columns. Analogous to `nb_rows`.
interpolation (int): The order of interpolation. The order has to be in the range 0-5:
- 0: Nearest-neighbor
- 1: Bi-linear (default)
- 2: Bi-quadratic
- 3: Bi-cubic
- 4: Bi-quartic
- 5: Bi-quintic
mask_interpolation (int): same as interpolation but for mask.
cval (number): The constant value to use when filling in newly created pixels.
cval_mask (number): Same as cval but only for masks.
mode (str): {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
Points outside the boundaries of the input are filled according
to the given mode. Modes match the behaviour of `numpy.pad`.
absolute_scale (bool): Take `scale` as an absolute value rather than a relative value.
keypoints_threshold (float): Used as threshold in conversion from distance maps to keypoints.
The search for keypoints works by searching for the
argmin (non-inverted) or argmax (inverted) in each channel. This
parameters contains the maximum (non-inverted) or minimum (inverted) value to accept in order to view a hit
as a keypoint. Use ``None`` to use no min/max. Default: 0.01
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
def __init__(
self,
scale: Union[float, Sequence[float]] = (0.03, 0.05),
nb_rows: Union[int, Sequence[int]] = 4,
nb_cols: Union[int, Sequence[int]] = 4,
interpolation: int = 1,
mask_interpolation: int = 0,
cval: int = 0,
cval_mask: int = 0,
mode: str = "constant",
absolute_scale: bool = False,
always_apply: bool = False,
keypoints_threshold: float = 0.01,
p: float = 0.5,
):
super(PiecewiseAffine, self).__init__(always_apply, p)
self.scale = to_tuple(scale, scale)
self.nb_rows = to_tuple(nb_rows, nb_rows)
self.nb_cols = to_tuple(nb_cols, nb_cols)
self.interpolation = interpolation
self.mask_interpolation = mask_interpolation
self.cval = cval
self.cval_mask = cval_mask
self.mode = mode
self.absolute_scale = absolute_scale
self.keypoints_threshold = keypoints_threshold
def get_transform_init_args_names(self):
return (
"scale",
"nb_rows",
"nb_cols",
"interpolation",
"mask_interpolation",
"cval",
"cval_mask",
"mode",
"absolute_scale",
"keypoints_threshold",
)
@property
def targets_as_params(self):
return ["image"]
def get_params_dependent_on_targets(self, params) -> dict:
h, w = params["image"].shape[:2]
nb_rows = np.clip(random.randint(*self.nb_rows), 2, None)
nb_cols = np.clip(random.randint(*self.nb_cols), 2, None)
nb_cells = nb_cols * nb_rows
scale = random.uniform(*self.scale)
state = np.random.RandomState(random.randint(0, 1 << 31))
jitter = state.normal(0, scale, (nb_cells, 2))
if not np.any(jitter > 0):
return {"matrix": None}
y = np.linspace(0, h, nb_rows)
x = np.linspace(0, w, nb_cols)
# (H, W) and (H, W) for H=rows, W=cols
xx_src, yy_src = np.meshgrid(x, y)
# (1, HW, 2) => (HW, 2) for H=rows, W=cols
points_src = np.dstack([yy_src.flat, xx_src.flat])[0]
if self.absolute_scale:
jitter[:, 0] = jitter[:, 0] / h if h > 0 else 0.0
jitter[:, 1] = jitter[:, 1] / w if w > 0 else 0.0
jitter[:, 0] = jitter[:, 0] * h
jitter[:, 1] = jitter[:, 1] * w
points_dest = np.copy(points_src)
points_dest[:, 0] = points_dest[:, 0] + jitter[:, 0]
points_dest[:, 1] = points_dest[:, 1] + jitter[:, 1]
# Restrict all destination points to be inside the image plane.
# This is necessary, as otherwise keypoints could be augmented
# outside of the image plane and these would be replaced by
# (-1, -1), which would not conform with the behaviour of the other augmenters.
points_dest[:, 0] = np.clip(points_dest[:, 0], 0, h - 1)
points_dest[:, 1] = np.clip(points_dest[:, 1], 0, w - 1)
matrix = skimage.transform.PiecewiseAffineTransform()
matrix.estimate(points_src[:, ::-1], points_dest[:, ::-1])
return {
"matrix": matrix,
}
def apply(
self, img: np.ndarray, matrix: skimage.transform.PiecewiseAffineTransform = None, **params
) -> np.ndarray:
return F.piecewise_affine(img, matrix, self.interpolation, self.mode, self.cval)
def apply_to_mask(
self, img: np.ndarray, matrix: skimage.transform.PiecewiseAffineTransform = None, **params
) -> np.ndarray:
return F.piecewise_affine(img, matrix, self.mask_interpolation, self.mode, self.cval_mask)
def apply_to_bbox(
self,
bbox: Sequence[float],
rows: int = 0,
cols: int = 0,
matrix: skimage.transform.PiecewiseAffineTransform = None,
**params
) -> Sequence[float]:
return F.bbox_piecewise_affine(bbox, matrix, rows, cols, self.keypoints_threshold)
def apply_to_keypoint(
self,
keypoint: Sequence[float],
rows: int = 0,
cols: int = 0,
matrix: skimage.transform.PiecewiseAffineTransform = None,
**params
):
return F.keypoint_piecewise_affine(keypoint, matrix, rows, cols, self.keypoints_threshold)
|
the-stack_0_953 | from sympy import S, Rational
from sympy.external import import_module
from sympy.stats import Binomial, sample, Die, FiniteRV, DiscreteUniform, Bernoulli, BetaBinomial, Hypergeometric, \
Rademacher
from sympy.testing.pytest import skip, raises
def test_given_sample():
X = Die('X', 6)
scipy = import_module('scipy')
if not scipy:
skip('Scipy is not installed. Abort tests')
assert sample(X, X > 5) == 6
def test_sample_numpy():
distribs_numpy = [
Binomial("B", 5, 0.4),
]
size = 3
numpy = import_module('numpy')
if not numpy:
skip('Numpy is not installed. Abort tests for _sample_numpy.')
else:
for X in distribs_numpy:
samps = sample(X, size=size, library='numpy')
for sam in samps:
assert sam in X.pspace.domain.set
raises(NotImplementedError,
lambda: sample(Die("D"), library='numpy'))
raises(NotImplementedError,
lambda: Die("D").pspace.sample(library='tensorflow'))
def test_sample_scipy():
distribs_scipy = [
FiniteRV('F', {1: S.Half, 2: Rational(1, 4), 3: Rational(1, 4)}),
DiscreteUniform("Y", list(range(5))),
Die("D"),
Bernoulli("Be", 0.3),
Binomial("Bi", 5, 0.4),
BetaBinomial("Bb", 2, 1, 1),
Hypergeometric("H", 1, 1, 1),
Rademacher("R")
]
size = 3
scipy = import_module('scipy')
if not scipy:
skip('Scipy not installed. Abort tests for _sample_scipy.')
else:
for X in distribs_scipy:
samps = sample(X, size=size)
samps2 = sample(X, size=(2, 2))
for sam in samps:
assert sam in X.pspace.domain.set
for i in range(2):
for j in range(2):
assert samps2[i][j] in X.pspace.domain.set
def test_sample_pymc3():
distribs_pymc3 = [
Bernoulli('B', 0.2),
Binomial('N', 5, 0.4)
]
size = 3
pymc3 = import_module('pymc3')
if not pymc3:
skip('PyMC3 is not installed. Abort tests for _sample_pymc3.')
else:
for X in distribs_pymc3:
samps = sample(X, size=size, library='pymc3')
for sam in samps:
assert sam in X.pspace.domain.set
raises(NotImplementedError,
lambda: (sample(Die("D"), library='pymc3')))
def test_sample_seed():
F = FiniteRV('F', {1: S.Half, 2: Rational(1, 4), 3: Rational(1, 4)})
size = 10
libraries = ['scipy', 'numpy', 'pymc3']
for lib in libraries:
try:
imported_lib = import_module(lib)
if imported_lib:
s0 = sample(F, size=size, library=lib, seed=0)
s1 = sample(F, size=size, library=lib, seed=0)
s2 = sample(F, size=size, library=lib, seed=1)
assert all(s0 == s1)
assert not all(s1 == s2)
except NotImplementedError:
continue
|
the-stack_0_956 | """
Writes out submission datetime details (when it was submitted, how long it was in grading
process, etc) to a history.json file which is a list of all grading attempts for a
particular submission (including initial grading of it and all regrades).
"""
import os
import sys
import collections
import json
from datetime import datetime
from submitty_utils import dateutils
import fcntl
import traceback
import zipfile
import stat
import subprocess
import shutil
import codecs
import glob
import docker
from typing import Optional
class Logger:
"""Specialized logger class that accumulates stack traces."""
def __init__(
self, *,
log_dir: str,
stack_trace_dir: str,
capture_traces: bool = False,
# This used to be "UNKNOWN", but "NO JOB" better describes the circumstances.
job_id: str = "NO JOB",
):
self.log_dir = log_dir
self.stack_trace_dir = stack_trace_dir
self.capture_traces = capture_traces
self.accumulated_traces = []
self.job_id = job_id
def _log_filename(self) -> str:
"""Get the name of the file that should be logged into.
Currently, this is in the format YYYYMMDD.txt.
"""
now = dateutils.get_current_time()
return f'{datetime.strftime(now, "%Y%m%d")}.txt'
@property
def log_path(self) -> str:
"""Get the full path to the regular logging file."""
return os.path.join(self.log_dir, self._log_filename())
@property
def stack_trace_path(self) -> str:
"""Get the full path to the stack trace logging file."""
return os.path.join(self.stack_trace_dir, self._log_filename())
def log_message(
self, message: str, *,
is_batch: bool = False,
which_untrusted: str = "",
jobname: str = "",
timelabel: str = "",
elapsed_time: Optional[int] = None,
job_id: Optional[str] = None
):
"""Log a message to this logger's configured log directory."""
now = dateutils.get_current_time()
easy_to_read_date = dateutils.write_submitty_date(now, True)
batch_string = "BATCH" if is_batch else ""
if elapsed_time is None:
elapsed_time = -1
elapsed_time_string = "" if elapsed_time < 0 else '{:9.3f}'.format(elapsed_time)
time_unit = "" if elapsed_time < 0 else "sec"
job_id = job_id or self.job_id
parts = (easy_to_read_date, f"{job_id:>6s}", f"{batch_string:>5s}", f"{which_untrusted:>11s}",
f"{jobname:75s}", f"{timelabel:6s} {elapsed_time_string:>9s} {time_unit:>3s}", message)
write_to_log(self.log_path, ' | '.join((str(x) for x in parts)))
def log_stack_trace(
self, trace: str, *,
is_batch: bool = False,
which_untrusted: str = '',
job_id: Optional[str] = None,
jobname: str = "",
echo_source: Optional[str] = None,
):
"""Log a stack trace to this logger's configured stack trace directory."""
job_id = job_id or self.job_id
# Save the parameters to this trace so we can duplicate these on the
# shipper's end once the job finishes.
#
# TODO: Maybe we want to store time info too? Might need to think a bit
# more in terms of the stack traces log file format.
if self.capture_traces:
self.accumulated_traces.append({
'trace': trace,
'is_batch': is_batch,
'which_untrusted': which_untrusted,
'job_id': job_id,
'jobname': jobname,
})
# Always run this since this could be deleted without us knowing
os.makedirs(self.stack_trace_dir, exist_ok=True)
now = dateutils.get_current_time()
easy_to_read_date = dateutils.write_submitty_date(now, True)
message = f"[{easy_to_read_date}][{job_id:>6s}]\n"
if echo_source is not None:
message += f"== (Echoed from {echo_source})\n"
message += f"== Batch? {is_batch}\n"
message += f"== Which: {which_untrusted}\n"
message += f"== Job: {jobname}\n"
for line in trace.splitlines():
message += f"== {line}\n"
message = message.strip()
write_to_log(self.stack_trace_path, message)
def just_write_grade_history(json_file,assignment_deadline,submission_time,seconds_late,
first_access_time,access_duration,queue_time,batch_regrade,grading_began,
wait_time,grading_finished,grade_time,autograde_total,
revision):
#####################################
# LOAD THE PREVIOUS HISTORY
if os.path.isfile(json_file):
with open(json_file, 'r') as infile:
obj = json.load(infile, object_pairs_hook=collections.OrderedDict)
else:
obj = []
#####################################
# CREATE THE NEWEST INFO BLOB
blob = collections.OrderedDict()
blob["assignment_deadline"] = assignment_deadline
blob["submission_time"] = submission_time
seconds_late = seconds_late
if seconds_late > 0:
minutes_late = int((seconds_late+60-1) / 60)
hours_late = int((seconds_late+60*60-1) / (60*60))
days_late = int((seconds_late+60*60*24-1) / (60*60*24))
blob["days_late_before_extensions"] = days_late
blob["queue_time"] = queue_time
blob["batch_regrade"] = True if batch_regrade == "BATCH" else False
blob["first_access_time"] = first_access_time
blob["access_duration"] = access_duration
blob["grading_began"] = grading_began
blob["wait_time"] = wait_time
blob["grading_finished"] = grading_finished
blob["grade_time"] = grade_time
blob["autograde_result"] = autograde_total
autograde_array = str.split(autograde_total)
if len(autograde_array) > 0 and autograde_array[0] == "Automatic":
blob["autograde_total"] = int(autograde_array[3])
if len(autograde_array) == 6:
blob["autograde_max_possible"] = int(autograde_array[5])
if revision:
blob["revision"] = revision
#####################################
# ADD IT TO THE HISTORY
obj.append(blob)
with open(json_file, 'w') as outfile:
json.dump(obj, outfile, indent=4, separators=(',', ': '))
# ==================================================================================
#
# LOGGING FUNCTIONS
#
# ==================================================================================
def log_container_meta(log_path, event="", name="", container="", time=0):
""" Given a log file, create or append container meta data to a log file. """
now = dateutils.get_current_time()
easy_to_read_date = dateutils.write_submitty_date(now, True)
time_unit = "sec"
parts = (easy_to_read_date, name, container, event, f"{time:.3f}", time_unit)
write_to_log(log_path, ' | '.join(parts))
def write_to_log(log_path, message):
""" Given a log file, create or append message to log file"""
with open(log_path, 'a+') as log_file:
try:
fcntl.flock(log_file, fcntl.LOCK_EX | fcntl.LOCK_NB)
print(message, file=log_file)
fcntl.flock(log_file, fcntl.LOCK_UN)
except:
print("Could not gain a lock on the log file.")
# ==================================================================================
#
# VALIDATION FUNCTIONS
#
# ==================================================================================
def setup_for_validation(config, working_directory, complete_config, is_vcs, testcases, job_id):
""" Prepare a directory for validation by copying in and permissioning the required files. """
tmp_submission = os.path.join(working_directory,"TMP_SUBMISSION")
tmp_work = os.path.join(working_directory,"TMP_WORK")
tmp_results = os.path.join(working_directory,"TMP_RESULTS")
submission_path = os.path.join(tmp_submission, "submission")
checkout_subdirectory = complete_config["autograding"].get("use_checkout_subdirectory","")
tmp_logs = os.path.join(working_directory,"TMP_SUBMISSION","tmp_logs")
tmp_work_test_output = os.path.join(tmp_work, "test_output")
tmp_work_generated_output = os.path.join(tmp_work, "generated_output")
tmp_work_instructor_solution = os.path.join(tmp_work, "instructor_solution")
tmp_autograding = os.path.join(working_directory,"TMP_AUTOGRADING")
os.mkdir(tmp_work_test_output)
os.mkdir(tmp_work_generated_output)
os.mkdir(tmp_work_instructor_solution)
patterns = complete_config['autograding']
# Add all permissions to tmp_work
add_permissions_recursive(tmp_work,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
# Copy required submission/checkout files
pattern_copy("submission_to_validation", patterns['submission_to_validation'], submission_path, tmp_work, tmp_logs)
checkout_subdir_path = os.path.join(tmp_submission, 'checkout', checkout_subdirectory)
if os.path.exists(checkout_subdir_path):
pattern_copy("checkout_to_validation", patterns['submission_to_validation'],checkout_subdir_path,tmp_work,tmp_logs)
for c in testcases:
if c.type == 'Compilation':
pattern_copy("compilation_to_validation", patterns['compilation_to_validation'], c.secure_environment.directory, tmp_work, tmp_logs)
# Copy expected files into the tmp_work_test_output path
test_output_path = os.path.join(tmp_autograding, 'test_output')
copy_contents_into(config, job_id, test_output_path, tmp_work_test_output, tmp_logs)
generated_output_path = os.path.join(tmp_autograding, 'generated_output')
copy_contents_into(config, job_id, generated_output_path, tmp_work_generated_output, tmp_logs)
# Copy in instructor solution code.
instructor_solution = os.path.join(tmp_autograding, 'instructor_solution')
copy_contents_into(config, job_id, instructor_solution, tmp_work_instructor_solution, tmp_logs)
# Copy any instructor custom validation code into the tmp work directory
custom_validation_code_path = os.path.join(tmp_autograding, 'custom_validation_code')
copy_contents_into(config, job_id, custom_validation_code_path, tmp_work, tmp_logs)
# Copy the .submit.notebook to tmp_work for validation
submit_notebook_path = os.path.join(tmp_submission, 'submission', ".submit.notebook")
if os.path.exists(submit_notebook_path):
shutil.copy(
submit_notebook_path,
os.path.join(tmp_work, '.submit.notebook')
)
# Copy the validation script into this directory.
bin_runner = os.path.join(tmp_autograding, "bin","validate.out")
my_runner = os.path.join(tmp_work, "my_validator.out")
shutil.copy(bin_runner, my_runner)
add_permissions_recursive(tmp_work,
stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
add_permissions(my_runner, stat.S_IXUSR | stat.S_IXGRP |stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
# ==================================================================================
#
# ARCHIVAL AND PERMISSIONS FUNCTIONS
#
# ==================================================================================
def add_all_permissions(path):
""" Recursively chmod a directory or file 777. """
if os.path.isdir(path):
add_permissions_recursive(path,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
elif os.path.isfile(path):
add_permissions(path, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
def lock_down_folder_permissions(top_dir):
# Chmod a directory to take away group and other rwx.
os.chmod(top_dir,os.stat(top_dir).st_mode & ~stat.S_IRGRP & ~stat.S_IWGRP & ~stat.S_IXGRP & ~stat.S_IROTH & ~stat.S_IWOTH & ~stat.S_IXOTH)
def cleanup_stale_containers(user_id_of_runner, my_log_function):
# Remove any docker containers left over from past runs.
client = docker.from_env(timeout=60)
try:
# Get all containers (running or not) with user_id_of_runner in their name
# sparse=True gets containers without fully evaluating them. This is important,
# as race conditions with other grading threads can cause this call to fail otherwise.
old_containers = client.containers.list(all=True, filters={"name":user_id_of_runner}, sparse=True)
for old_container in old_containers:
try:
my_log_function(f'Removing stale container {old_container.name}')
old_container.remove(force=True)
except docker.errors.NotFound:
# This is an expected case which does not constitute an error, caused
# by the use of sparse=True
pass
except Exception:
my_log_function("ERROR: Could not remove docker container")
# Get all networks with user_id_of_runner in their name
old_networks = client.networks.list(filters={"name":user_id_of_runner})
for old_network in old_networks:
try:
my_log_function(f'Removing stale network {old_network.name}')
old_network.remove()
except Exception:
my_log_function("ERROR: Could not remove docker network")
finally:
client.close()
def prepare_directory_for_autograding(config, working_directory, user_id_of_runner, autograding_zip_file, submission_zip_file, is_test_environment):
"""
Given a working directory, set up that directory for autograding by creating the required subdirectories
and configuring permissions.
"""
# If an old (stale) version of the working directory exists, we need to remove it.
if os.path.exists(working_directory):
# Make certain we can remove old instances of the working directory.
if not is_test_environment:
untrusted_grant_rwx_access(
config.submitty['submitty_install_dir'], user_id_of_runner, working_directory
)
add_all_permissions(working_directory)
shutil.rmtree(working_directory,ignore_errors=True)
# Create the working directory
os.mkdir(working_directory)
# Important directory variables.
tmp_autograding = os.path.join(working_directory,"TMP_AUTOGRADING")
tmp_submission = os.path.join(working_directory,"TMP_SUBMISSION")
tmp_work = os.path.join(working_directory,"TMP_WORK")
tmp_logs = os.path.join(working_directory,"TMP_SUBMISSION","tmp_logs")
submission_path = os.path.join(tmp_submission, "submission")
tmp_work_test_input = os.path.join(tmp_work, "test_input")
os.mkdir(tmp_work)
os.mkdir(tmp_work_test_input)
# Unzip the autograding and submission folders
unzip_this_file(autograding_zip_file,tmp_autograding)
unzip_this_file(submission_zip_file,tmp_submission)
with open(os.path.join(tmp_autograding, "complete_config.json"), 'r') as infile:
complete_config_obj = json.load(infile)
# Handle the case where a student errantly submits to multiple parts of a one part only gradeable.
if complete_config_obj.get('one_part_only', False) == True:
allow_only_one_part(submission_path, log_path=os.path.join(tmp_logs, "overall.txt"))
with open(os.path.join(tmp_submission,"queue_file.json"), 'r') as infile:
queue_obj = json.load(infile)
job_id = queue_obj["job_id"]
# copy output files
test_input_path = os.path.join(tmp_autograding, 'test_input')
# Copy test input files into tmp_work_test_input.
copy_contents_into(config, job_id, test_input_path, tmp_work_test_input, tmp_logs)
# Lock down permissions on the unzipped folders/test input folder to stop untrusted users from gaining access.
lock_down_folder_permissions(tmp_work_test_input)
lock_down_folder_permissions(tmp_autograding)
lock_down_folder_permissions(tmp_submission)
def archive_autograding_results(
config,
working_directory: os.PathLike,
job_id: str,
which_untrusted: str,
is_batch_job: bool,
complete_config_obj: dict,
gradeable_config_obj: dict,
queue_obj: dict,
is_test_environment: bool
):
""" After grading is finished, archive the results. """
tmp_autograding = os.path.join(working_directory,"TMP_AUTOGRADING")
tmp_submission = os.path.join(working_directory,"TMP_SUBMISSION")
tmp_work = os.path.join(working_directory,"TMP_WORK")
tmp_logs = os.path.join(working_directory,"TMP_SUBMISSION","tmp_logs")
tmp_results = os.path.join(working_directory,"TMP_RESULTS")
submission_path = os.path.join(tmp_submission, "submission")
random_output_path = os.path.join(tmp_work, 'random_output')
if "generate_output" not in queue_obj:
partial_path = os.path.join(queue_obj["gradeable"],queue_obj["who"],str(queue_obj["version"]))
item_name = os.path.join(queue_obj["semester"],queue_obj["course"],"submissions",partial_path)
elif queue_obj["generate_output"]:
item_name = os.path.join(queue_obj["semester"],queue_obj["course"],"generated_output",queue_obj["gradeable"])
results_public_dir = os.path.join(tmp_results,"results_public")
results_details_dir = os.path.join(tmp_results, "details")
patterns = complete_config_obj['autograding']
# Copy work to details
pattern_copy("work_to_details", patterns['work_to_details'], tmp_work, results_details_dir, tmp_logs)
# Copy work to public
if 'work_to_public' in patterns:
pattern_copy("work_to_public", patterns['work_to_public'], tmp_work, results_public_dir, tmp_logs)
if os.path.exists(random_output_path):
pattern_copy("work_to_random_output", [os.path.join(random_output_path, '**', '*.txt'),], tmp_work, tmp_results, tmp_logs)
# timestamp of first access to the gradeable page
first_access_string = ""
# grab the submission time
if "generate_output" in queue_obj and queue_obj["generate_output"]:
submission_string = ""
else:
with open(os.path.join(tmp_submission, 'submission' ,".submit.timestamp"), 'r') as submission_time_file:
submission_string = submission_time_file.read().rstrip()
# grab the first access to the gradeable page (if it exists)
user_assignment_access_filename = os.path.join(tmp_submission, ".user_assignment_access.json")
if os.path.exists(user_assignment_access_filename):
with open(user_assignment_access_filename, 'r') as access_file:
obj = json.load(access_file)
first_access_string = obj[0]["timestamp"]
history_file_tmp = os.path.join(tmp_submission,"history.json")
history_file = os.path.join(tmp_results,"history.json")
if os.path.isfile(history_file_tmp) and not is_test_environment:
shutil.move(history_file_tmp, history_file)
# fix permissions
ta_group_id = os.stat(tmp_results).st_gid
os.chown(history_file, int(config.submitty_users['daemon_uid']),ta_group_id)
add_permissions(history_file, stat.S_IRGRP)
grading_finished = dateutils.get_current_time()
grade_result = ""
if "generate_output" not in queue_obj:
try:
shutil.copy(os.path.join(tmp_work, "grade.txt"), tmp_results)
with open(os.path.join(tmp_work,"grade.txt")) as f:
lines = f.readlines()
for line in lines:
line = line.rstrip('\n')
if line.startswith("Automatic grading total:"):
grade_result = line
except:
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
f.write(f"\n\nERROR: Grading incomplete -- Could not process {os.path.join(tmp_work,'grade.txt')}")
config.logger.log_message(
"ERROR: could not process grade.txt. See stack trace entry for more details.",
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
)
config.logger.log_stack_trace(
traceback.format_exc(),
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
)
gradeable_deadline_string = gradeable_config_obj["date_due"]
submission_datetime = dateutils.read_submitty_date(submission_string)
gradeable_deadline_datetime = dateutils.read_submitty_date(gradeable_deadline_string)
gradeable_deadline_longstring = dateutils.write_submitty_date(gradeable_deadline_datetime)
submission_longstring = dateutils.write_submitty_date(submission_datetime)
seconds_late = int((submission_datetime-gradeable_deadline_datetime).total_seconds())
# compute the access duration in seconds (if it exists)
access_duration = -1
if first_access_string != "":
first_access_datetime = dateutils.read_submitty_date(first_access_string)
access_duration = int((submission_datetime-first_access_datetime).total_seconds())
# note: negative = not late
grading_finished_longstring = dateutils.write_submitty_date(grading_finished)
with open(os.path.join(tmp_submission,".grading_began"), 'r') as f:
grading_began_longstring = f.read()
grading_began = dateutils.read_submitty_date(grading_began_longstring)
gradingtime = (grading_finished - grading_began).total_seconds()
queue_obj["gradingtime"]=gradingtime
queue_obj["grade_result"]=grade_result
queue_obj["which_untrusted"]=which_untrusted
waittime = queue_obj["waittime"]
try:
# Make certain results.json is utf-8 encoded.
results_json_path = os.path.join(tmp_work, 'results.json')
with codecs.open(results_json_path, 'r', encoding='utf-8', errors='ignore') as infile:
results_str = "".join(line.rstrip() for line in infile)
results_obj = json.loads(results_str)
with open(results_json_path, 'w') as outfile:
json.dump(results_obj, outfile, indent=4)
shutil.move(results_json_path, os.path.join(tmp_results, "results.json"))
except:
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
f.write(f"\n\nERROR: Grading incomplete -- Could not open/write {os.path.join(tmp_work,'results.json')}")
config.logger.log_message(
"ERROR: results.json read/write error",
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
)
config.logger.log_stack_trace(
traceback.format_exc(),
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
)
# Rescue custom validator files
custom_validator_output_directory = os.path.join(tmp_results, "custom_validator_output")
pattern_copy("rescue_custom_validator_validation_jsons", [os.path.join(tmp_work, 'validation_results_*.json'),], tmp_work, custom_validator_output_directory, tmp_logs)
pattern_copy("rescue_custom_validator_logs", [os.path.join(tmp_work, 'validation_logfile_*.txt'),], tmp_work, custom_validator_output_directory, tmp_logs)
pattern_copy("rescue_custom_validator_errors", [os.path.join(tmp_work, 'validation_stderr_*.txt'),], tmp_work, custom_validator_output_directory, tmp_logs)
just_write_grade_history(history_file,
gradeable_deadline_longstring,
submission_longstring,
seconds_late,
first_access_string,
access_duration,
queue_obj["queue_time"],
"BATCH" if is_batch_job else "INTERACTIVE",
grading_began_longstring,
int(waittime),
grading_finished_longstring,
int(gradingtime),
grade_result,
queue_obj.get("revision", None))
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
f.write("FINISHED GRADING!\n")
config.logger.log_message(
grade_result,
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
timelabel="grade:",
elapsed_time=gradingtime
)
with open(os.path.join(tmp_results,"queue_file.json"),'w') as outfile:
json.dump(queue_obj,outfile,sort_keys=True,indent=4,separators=(',', ': '))
# save the logs!
shutil.copytree(tmp_logs,os.path.join(tmp_results,"logs"))
# Save the .submit.notebook
# Copy the .submit.notebook to tmp_work for validation
submit_notebook_path = os.path.join(tmp_submission, 'submission', ".submit.notebook")
if os.path.exists(submit_notebook_path):
shutil.copy(
submit_notebook_path,
os.path.join(tmp_results, ".submit.notebook")
)
def allow_only_one_part(path, log_path=os.devnull):
"""
Given a path to a directory, iterate through the directory and detect folders that start with
"part". If there is more than one and they have files, then delete all of the part folders except
for the first one that has files.
An example would be if you had the folder structure:
part1/
test.py
part2/
test.cpp
Then the part2 folder would be deleted, leaving just the part1 folder.
:param path: string filepath to directory to scan for parts in
:param log_path: string filepath to file to write print statements to
"""
if not os.path.isdir(path):
return
with open(log_path, 'a') as log:
clean_directories = []
print('Clean up multiple parts')
log.flush()
for entry in sorted(os.listdir(path)):
full_path = os.path.join(path, entry)
if not os.path.isdir(full_path) or not entry.startswith('part'):
continue
count = len(os.listdir(full_path))
print('{}: {}'.format(entry, count))
if count > 0:
clean_directories.append(full_path)
if len(clean_directories) > 1:
print("Student submitted to multiple parts in violation of instructions.\n"
"Removing files from all but first non empty part.")
for i in range(1, len(clean_directories)):
print("REMOVE: {}".format(clean_directories[i]))
for entry in os.listdir(clean_directories[i]):
print(" -> {}".format(entry))
shutil.rmtree(clean_directories[i])
# go through the testcase folder (e.g. test01/) and remove anything
# that matches the test input (avoid archiving copies of these files!)
def remove_test_input_files(overall_log, test_input_path, testcase_folder):
for path, subdirs, files in os.walk(test_input_path):
for name in files:
relative = path[len(test_input_path)+1:]
my_file = os.path.join(testcase_folder, relative, name)
if os.path.isfile(my_file):
print ("removing (likely) stale test_input file: ", my_file, file=overall_log)
overall_log.flush()
os.remove(my_file)
def add_permissions(item,perms):
if os.getuid() == os.stat(item).st_uid:
os.chmod(item,os.stat(item).st_mode | perms)
# else, can't change permissions on this file/directory!
def add_permissions_recursive(top_dir,root_perms,dir_perms,file_perms):
for root, dirs, files in os.walk(top_dir):
add_permissions(root,root_perms)
for d in dirs:
add_permissions(os.path.join(root, d),dir_perms)
for f in files:
add_permissions(os.path.join(root, f),file_perms)
# copy the files & directories from source to target
# it will create directories as needed
# it's ok if the target directory or subdirectories already exist
# it will overwrite files with the same name if they exist
def copy_contents_into(config, job_id, source, target, tmp_logs):
if not os.path.isdir(target):
config.logger.log_message(
"ERROR: Could not copy contents. The target directory does not exist: " + target,
job_id=job_id
)
raise RuntimeError("ERROR: the target directory does not exist: '", target, "'")
if os.path.isdir(source):
for item in os.listdir(source):
if os.path.isdir(os.path.join(source,item)):
if os.path.isdir(os.path.join(target,item)):
# recurse
copy_contents_into(config, job_id,os.path.join(source,item),os.path.join(target,item),tmp_logs)
elif os.path.isfile(os.path.join(target,item)):
config.logger.log_message(
"ERROR: the target subpath is a file not a directory "
f"'{os.path.join(target,item)}'",
job_id=job_id,
)
raise RuntimeError("ERROR: the target subpath is a file not a directory '", os.path.join(target,item), "'")
else:
# copy entire subtree
shutil.copytree(os.path.join(source,item),os.path.join(target,item))
else:
if os.path.exists(os.path.join(target,item)):
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
print ("\nWARNING: REMOVING DESTINATION FILE" , os.path.join(target,item),
" THEN OVERWRITING: ", os.path.join(source,item), "\n", file=f)
os.remove(os.path.join(target,item))
try:
shutil.copy(os.path.join(source,item),target)
except:
config.logger.log_stack_trace(traceback.format_exc(), job_id=job_id)
return
else:
print(f'{source} is not a directory')
# copy files that match one of the patterns from the source directory
# to the target directory.
def pattern_copy(what, patterns, source, target, tmp_logs):
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
print (what," pattern copy ", patterns, " from ", source, " -> ", target, file=f)
for pattern in patterns:
for my_file in glob.glob(os.path.join(source,pattern),recursive=True):
if (os.path.isfile(my_file)):
# grab the matched name
relpath = os.path.relpath(my_file,source)
# make the necessary directories leading to the file
os.makedirs(os.path.join(target,os.path.dirname(relpath)),exist_ok=True)
# copy the file
shutil.copy(my_file,os.path.join(target,relpath))
print (" COPY ",my_file,
" -> ",os.path.join(target,relpath), file=f)
else:
print ("skip this directory (will recurse into it later)", my_file, file=f)
# give permissions to all created files to the DAEMON_USER
def untrusted_grant_rwx_access(SUBMITTY_INSTALL_DIR, which_untrusted, my_dir):
subprocess.call([os.path.join(SUBMITTY_INSTALL_DIR, "sbin", "untrusted_execute"),
which_untrusted,
"/usr/bin/find",
my_dir,
"-user",
which_untrusted,
"-exec",
"/bin/chmod",
"ugo+rwx",
"{}",
";"])
# Used by packer unpacker
def zip_my_directory(path,zipfilename):
zipf = zipfile.ZipFile(zipfilename,'w',zipfile.ZIP_DEFLATED)
for root,dirs,files in os.walk(path):
for my_file in files:
relpath = root[len(path)+1:]
zipf.write(os.path.join(root,my_file),os.path.join(relpath,my_file))
zipf.close()
# Used by packer unpacker
def unzip_this_file(zipfilename,path):
if not os.path.exists(zipfilename):
raise RuntimeError("ERROR: zip file does not exist '", zipfilename, "'")
zip_ref = zipfile.ZipFile(zipfilename,'r')
zip_ref.extractall(path)
zip_ref.close()
# ==================================================================================
#
# PRE- AND POST-COMMAND FUNCTIONS
#
# ==================================================================================
def pre_command_copy_file(config, source_testcase, source_directory, destination_testcase, destination, job_id, tmp_logs):
""" Handles the cp pre_command. """
source_testcase = os.path.join(str(os.getcwd()), source_testcase)
if not os.path.isdir(source_testcase):
raise RuntimeError("ERROR: The directory {0} does not exist.".format(source_testcase))
if not os.path.isdir(destination_testcase):
raise RuntimeError("ERROR: The directory {0} does not exist.".format(destination_testcase))
source = os.path.join(source_testcase, source_directory)
target = os.path.join(destination_testcase, destination)
# The target without the potential executable.
target_base = '/'.join(target.split('/')[:-1])
# If the source is a directory, we copy the entire thing into the
# target.
if os.path.isdir(source):
# We must copy from directory to directory
copy_contents_into(config, job_id, source, target, tmp_logs)
# Separate ** and * for simplicity.
elif not '**' in source:
# Grab all of the files that match the pattern
files = glob.glob(source, recursive=True)
# The target base must exist in order for a copy to occur
if target_base != '' and not os.path.isdir(target_base):
raise RuntimeError("ERROR: The directory {0} does not exist.".format(target_base))
# Copy every file. This works whether target exists (is a directory) or does not (is a target file)
for file in files:
try:
shutil.copy(file, target)
except Exception as e:
traceback.print_exc()
config.logger.log_message(
f"Pre Command could not perform copy: {file} -> {target}",
job_id=job_id
)
else:
# Everything after the first **.
source_base = source[:source.find('**')]
# The full target must exist (we must be moving to a directory.)
if not os.path.isdir(target):
raise RuntimeError("ERROR: The directory {0} does not exist.".format(target))
# Grab all of the files that match the pattern.
files = glob.glob(source, recursive=True)
# For every file matched
for file_source in files:
file_target = os.path.join(target, file_source.replace(source_base,''))
# Remove the file path.
file_target_dir = '/'.join(file_target.split('/')[:-1])
# If the target directory doesn't exist, create it.
if not os.path.isdir(file_target_dir):
os.makedirs(file_target_dir)
# Copy.
try:
shutil.copy(file_source, file_target)
except Exception as e:
traceback.print_exc()
config.logger.log_message(
f"Pre Command could not perform copy: {file_source} -> {file_target}",
job_id=job_id
)
|
the-stack_0_958 | from __future__ import unicode_literals
import fnmatch
import logging
import os
import re
import shutil
import subprocess
import tempfile
from difflib import SequenceMatcher
from functools import cmp_to_key
from django.core.exceptions import ObjectDoesNotExist
from django.utils import six
from django.utils.encoding import force_text
from django.utils.translation import ugettext as _
from djblets.log import log_timed
from djblets.siteconfig.models import SiteConfiguration
from djblets.util.compat.python.past import cmp
from djblets.util.contextmanagers import controlled_subprocess
from reviewboard.deprecation import RemovedInReviewBoard50Warning
from reviewboard.diffviewer.commit_utils import exclude_ancestor_filediffs
from reviewboard.diffviewer.errors import DiffTooBigError, PatchError
from reviewboard.scmtools.core import PRE_CREATION, HEAD
CHUNK_RANGE_RE = re.compile(
br'^@@ -(?P<orig_start>\d+)(,(?P<orig_len>\d+))? '
br'\+(?P<modified_start>\d+)(,(?P<modified_len>\d+))? @@',
re.M)
NEWLINE_CONVERSION_BYTES_RE = re.compile(br'\r(\r?\n)?')
NEWLINE_CONVERSION_UNICODE_RE = re.compile(r'\r(\r?\n)?')
NEWLINE_BYTES_RE = re.compile(br'(?:\n|\r(?:\r?\n)?)')
NEWLINE_UNICODE_RE = re.compile(r'(?:\n|\r(?:\r?\n)?)')
_PATCH_GARBAGE_INPUT = 'patch: **** Only garbage was found in the patch input.'
def convert_to_unicode(s, encoding_list):
"""Return the passed string as a unicode object.
If conversion to unicode fails, we try the user-specified encoding, which
defaults to ISO 8859-15. This can be overridden by users inside the
repository configuration, which gives users repository-level control over
file encodings.
Ideally, we'd like to have per-file encodings, but this is hard. The best
we can do now is a comma-separated list of things to try.
Returns the encoding type which was used and the decoded unicode object.
Args:
s (bytes or bytearray or unicode):
The string to convert to Unicode.
encoding_list (list of unicode):
The list of encodings to try.
Returns:
tuple:
A tuple with the following information:
1. A compatible encoding (:py:class:`unicode`).
2. The Unicode data (:py:class:`unicode`).
Raises:
TypeError:
The provided value was not a Unicode string, byte string, or
a byte array.
UnicodeDecodeError:
None of the encoding types were valid for the provided string.
"""
if isinstance(s, bytearray):
# Some SCMTool backends return file data as a bytearray instead of
# bytes.
s = bytes(s)
if isinstance(s, six.text_type):
# Nothing to do
return 'utf-8', s
elif isinstance(s, bytes):
try:
# First try strict utf-8
enc = 'utf-8'
return enc, six.text_type(s, enc)
except UnicodeError:
# Now try any candidate encodings
for e in encoding_list:
try:
return e, six.text_type(s, e)
except (UnicodeError, LookupError):
pass
# Finally, try to convert to unicode and replace all unknown
# characters.
try:
enc = 'utf-8'
return enc, six.text_type(s, enc, errors='replace')
except UnicodeError:
raise UnicodeDecodeError(
_("Diff content couldn't be converted to unicode using "
"the following encodings: %s")
% (['utf-8'] + encoding_list))
else:
raise TypeError('Value to convert is unexpected type %s', type(s))
def convert_line_endings(data):
r"""Convert line endings in a file.
Some types of repositories provide files with a single trailing Carriage
Return (``\r``), even if the rest of the file used a CRLF (``\r\n``)
throughout. In these cases, GNU diff will add a ``\ No newline at end of
file`` to the end of the diff, which GNU patch understands and will apply
to files with just a trailing ``\r``.
However, we normalize ``\r`` to ``\n``, which breaks GNU patch in these
cases. This function works around this by removing the last ``\r`` and
then converting standard types of newlines to a ``\n``.
This is not meant for use in providing byte-compatible versions of files,
but rather to help with comparing lines-for-lines in situations where
two versions of a file may come from different platforms with different
newlines.
Args:
data (bytes or unicode):
A string to normalize. This supports either byte strings or
Unicode strings.
Returns:
bytes or unicode:
The data with newlines converted, in the original string type.
Raises:
TypeError:
The ``data`` argument provided is not a byte string or Unicode
string.
"""
# See https://www.reviewboard.org/bugs/386/ and
# https://reviews.reviewboard.org/r/286/ for the rationale behind the
# normalization.
if data:
if isinstance(data, bytes):
cr = b'\r'
lf = b'\n'
newline_re = NEWLINE_CONVERSION_BYTES_RE
elif isinstance(data, six.text_type):
cr = '\r'
lf = '\n'
newline_re = NEWLINE_CONVERSION_UNICODE_RE
else:
raise TypeError(
_('%s is not a valid string type for convert_line_endings.')
% type(data))
if data.endswith(cr):
data = data[:-1]
data = newline_re.sub(lf, data)
return data
def split_line_endings(data):
"""Split a string into lines while preserving all non-CRLF characters.
Unlike :py:meth:`str.splitlines`, this will only split on the following
character sequences: ``\n``, ``\r``, ``\r\n``, and ``\r\r\n``.
This is needed to prevent the sort of issues encountered with
Unicode strings when calling :py:meth:`str.splitlines``, which is that form
feed characters would be split. :program:`patch` and :program:`diff` accept
form feed characters as valid characters in diffs, and doesn't treat them
as newlines, but :py:meth:`str.splitlines` will treat it as a newline
anyway.
Args:
data (bytes or unicode):
The data to split into lines.
Returns:
list of bytes or unicode:
The list of lines.
"""
if isinstance(data, bytes):
lines = NEWLINE_BYTES_RE.split(data)
elif isinstance(data, six.text_type):
lines = NEWLINE_UNICODE_RE.split(data)
else:
raise TypeError('data must be a bytes or unicode string, not %s'
% type(data))
# splitlines() would chop off the last entry, if the string ends with
# a newline. split() doesn't do this. We need to retain that same
# behavior by chopping it off ourselves.
if not lines[-1]:
lines = lines[:-1]
return lines
def patch(diff, orig_file, filename, request=None):
"""Apply a diff to a file.
This delegates out to ``patch`` because noone except Larry Wall knows how
to patch.
Args:
diff (bytes):
The contents of the diff to apply.
orig_file (bytes):
The contents of the original file.
filename (unicode):
The name of the file being patched.
request (django.http.HttpRequest, optional):
The HTTP request, for use in logging.
Returns:
bytes:
The contents of the patched file.
Raises:
reviewboard.diffutils.errors.PatchError:
An error occurred when trying to apply the patch.
"""
log_timer = log_timed('Patching file %s' % filename, request=request)
if not diff.strip():
# Someone uploaded an unchanged file. Return the one we're patching.
return orig_file
# Prepare the temporary directory if none is available
tempdir = tempfile.mkdtemp(prefix='reviewboard.')
try:
orig_file = convert_line_endings(orig_file)
diff = convert_line_endings(diff)
(fd, oldfile) = tempfile.mkstemp(dir=tempdir)
f = os.fdopen(fd, 'w+b')
f.write(orig_file)
f.close()
newfile = '%s-new' % oldfile
process = subprocess.Popen(['patch', '-o', newfile, oldfile],
stdin=subprocess.PIPE, stdout=subprocess.PIPE,
stderr=subprocess.PIPE, cwd=tempdir)
with controlled_subprocess('patch', process) as p:
stdout, stderr = p.communicate(diff)
failure = p.returncode
try:
with open(newfile, 'rb') as f:
new_file = f.read()
except Exception:
new_file = None
if failure:
rejects_file = '%s.rej' % newfile
try:
with open(rejects_file, 'rb') as f:
rejects = f.read()
except Exception:
rejects = None
error_output = force_text(stderr.strip() or stdout.strip())
# Munge the output to show the filename instead of
# randomly-generated tempdir locations.
base_filename = os.path.basename(filename)
error_output = (
error_output
.replace(rejects_file, '%s.rej' % base_filename)
.replace(oldfile, base_filename)
)
raise PatchError(filename=filename,
error_output=error_output,
orig_file=orig_file,
new_file=new_file,
diff=diff,
rejects=rejects)
return new_file
finally:
shutil.rmtree(tempdir)
log_timer.done()
def get_original_file_from_repo(filediff, request=None, encoding_list=None):
"""Return the pre-patched file for the FileDiff from the repository.
The parent diff will be applied if it exists.
Version Added:
4.0
Args:
filediff (reviewboard.diffviewer.models.filediff.FileDiff):
The FileDiff to retrieve the pre-patch file for.
request (django.http.HttpRequest, optional):
The HTTP request from the client.
encoding_list (list of unicode, optional):
A custom list of encodings to try when processing the file. This
will override the encoding list normally retrieved from the
FileDiff and repository.
If there's already a known valid encoding for the file, it will be
used instead.
This is here for compatibility and will be removed in Review Board
5.0.
Returns:
bytes:
The pre-patched file.
Raises:
UnicodeDecodeError:
The source file was not compatible with any of the available
encodings.
reviewboard.diffutils.errors.PatchError:
An error occurred when trying to apply the patch.
reviewboard.scmtools.errors.SCMError:
An error occurred while computing the pre-patch file.
"""
data = b''
extra_data = filediff.extra_data or {}
# If the file has a parent source filename/revision recorded, we're
# going to need to fetch that, since that'll be (potentially) the
# latest commit in the repository.
#
# This information was added in Review Board 3.0.19. Prior versions
# stored the parent source revision as filediff.source_revision
# (rather than leaving that as identifying information for the actual
# file being shown in the review). It did not store the parent
# filename at all (which impacted diffs that contained a moved/renamed
# file on any type of repository that required a filename for lookup,
# such as Mercurial -- Git was not affected, since it only needs
# blob SHAs).
#
# If we're not working with a parent diff, or this is a FileDiff
# with legacy parent diff information, we just use the FileDiff
# FileDiff filename/revision fields as normal.
source_filename = extra_data.get('parent_source_filename',
filediff.source_file)
source_revision = extra_data.get('parent_source_revision',
filediff.source_revision)
if source_revision != PRE_CREATION:
repository = filediff.get_repository()
data = repository.get_file(
source_filename,
source_revision,
base_commit_id=filediff.diffset.base_commit_id,
request=request)
# Convert to unicode before we do anything to manipulate the string.
encoding_list = get_filediff_encodings(filediff, encoding_list)
encoding, data = convert_to_unicode(data, encoding_list)
# Repository.get_file doesn't know or care about how we need line
# endings to work. So, we'll just transform every time.
#
# This is mostly only a problem if the diff chunks aren't in the
# cache, though if several people are working off the same file,
# we'll be doing extra work to convert those line endings for each
# of those instead of once.
#
# Only other option is to cache the resulting file, but then we're
# duplicating the cached contents.
data = convert_line_endings(data)
# Convert back to bytes using whichever encoding we used to decode.
data = data.encode(encoding)
if not filediff.encoding:
# Now that we know an encoding that works, remember it for next
# time.
filediff.extra_data['encoding'] = encoding
filediff.save(update_fields=('extra_data',))
# If there's a parent diff set, apply it to the buffer.
if (filediff.parent_diff and
not filediff.is_parent_diff_empty(cache_only=True)):
try:
data = patch(diff=filediff.parent_diff,
orig_file=data,
filename=source_filename,
request=request)
except PatchError as e:
# patch(1) cannot process diff files that contain no diff sections.
# We are going to check and see if the parent diff contains no diff
# chunks.
if (e.error_output == _PATCH_GARBAGE_INPUT and
not filediff.is_parent_diff_empty()):
raise
return data
def get_original_file(filediff, request=None, encoding_list=None):
"""Return the pre-patch file of a FileDiff.
Version Changed:
4.0:
The ``encoding_list`` parameter should no longer be provided by
callers. Encoding lists are now calculated automatically. Passing
a custom list will override the calculated one.
Args:
filediff (reviewboard.diffviewer.models.filediff.FileDiff):
The FileDiff to retrieve the pre-patch file for.
request (django.http.HttpRequest, optional):
The HTTP request from the client.
encoding_list (list of unicode, optional):
A custom list of encodings to try when processing the file. This
will override the encoding list normally retrieved from the
FileDiff and repository.
If there's already a known valid encoding for the file, it will be
used instead.
Returns:
bytes:
The pre-patch file.
Raises:
UnicodeDecodeError:
The source file was not compatible with any of the available
encodings.
reviewboard.diffutils.errors.PatchError:
An error occurred when trying to apply the patch.
reviewboard.scmtools.errors.SCMError:
An error occurred while computing the pre-patch file.
"""
if encoding_list:
RemovedInReviewBoard50Warning.warn(
'The encoding_list parameter passed to get_original_file() is '
'deprecated and will be removed in Review Board 5.0.')
data = b''
# If the FileDiff has a parent diff, it must be the case that it has no
# ancestor FileDiffs. We can fall back to the no history case here.
if filediff.parent_diff:
return get_original_file_from_repo(filediff=filediff,
request=request,
encoding_list=encoding_list)
# Otherwise, there may be one or more ancestors that we have to apply.
ancestors = filediff.get_ancestors(minimal=True)
if ancestors:
oldest_ancestor = ancestors[0]
# If the file was created outside this history, fetch it from the
# repository and apply the parent diff if it exists.
if not oldest_ancestor.is_new:
data = get_original_file_from_repo(filediff=oldest_ancestor,
request=request,
encoding_list=encoding_list)
if not oldest_ancestor.is_diff_empty:
data = patch(diff=oldest_ancestor.diff,
orig_file=data,
filename=oldest_ancestor.source_file,
request=request)
for ancestor in ancestors[1:]:
# TODO: Cache these results so that if this ``filediff`` is an
# ancestor of another FileDiff, computing that FileDiff's original
# file will be cheaper. This will also allow an ancestor filediff's
# original file to be computed cheaper.
data = patch(diff=ancestor.diff,
orig_file=data,
filename=ancestor.source_file,
request=request)
elif not filediff.is_new:
data = get_original_file_from_repo(filediff=filediff,
request=request,
encoding_list=encoding_list)
return data
def get_patched_file(source_data, filediff, request=None):
"""Return the patched version of a file.
This will normalize the patch, applying any changes needed for the
repository, and then patch the provided data with the patch contents.
Args:
source_data (bytes):
The file contents to patch.
filediff (reviewboard.diffviewer.models.filediff.FileDiff):
The FileDiff representing the patch.
request (django.http.HttpClient, optional):
The HTTP request from the client.
Returns:
bytes:
The patched file contents.
"""
repository = filediff.get_repository()
diff = repository.normalize_patch(patch=filediff.diff,
filename=filediff.source_file,
revision=filediff.source_revision)
return patch(diff=diff,
orig_file=source_data,
filename=filediff.dest_file,
request=request)
def get_revision_str(revision):
if revision == HEAD:
return "HEAD"
elif revision == PRE_CREATION:
return ""
else:
return _("Revision %s") % revision
def get_filenames_match_patterns(patterns, filenames):
"""Return whether any of the filenames match any of the patterns.
This is used to compare a list of filenames to a list of
:py:mod:`patterns <fnmatch>`. The patterns are case-sensitive.
Args:
patterns (list of unicode):
The list of patterns to match against.
filename (list of unicode):
The list of filenames.
Returns:
bool:
``True`` if any filenames match any patterns. ``False`` if none match.
"""
for pattern in patterns:
for filename in filenames:
if fnmatch.fnmatchcase(filename, pattern):
return True
return False
def get_filediff_encodings(filediff, encoding_list=None):
"""Return a list of encodings to try for a FileDiff's source text.
If the FileDiff already has a known encoding stored, then it will take
priority. The provided encoding list, or the repository's list of
configured encodingfs, will be provided as fallbacks.
Args:
filediff (reviewboard.diffviewer.models.filediff.FileDiff):
The FileDiff to return encodings for.
encoding_list (list of unicode, optional):
An explicit list of encodings to try. If not provided, the
repository's list of encodings will be used instead (which is
generally preferred).
Returns:
list of unicode:
The list of encodings to try for the source file.
"""
filediff_encoding = filediff.encoding
encodings = []
if encoding_list is None:
encoding_list = filediff.get_repository().get_encoding_list()
if filediff_encoding:
encodings.append(filediff_encoding)
encodings += [
encoding
for encoding in encoding_list
if encoding != filediff_encoding
]
else:
encodings += encoding_list
return encodings
def get_matched_interdiff_files(tool, filediffs, interfilediffs):
"""Generate pairs of matched files for display in interdiffs.
This compares a list of filediffs and a list of interfilediffs, attempting
to best match up the files in both for display in the diff viewer.
This will prioritize matches that share a common source filename,
destination filename, and new/deleted state. Failing that, matches that
share a common source filename are paired off.
Any entries in ``interfilediffs` that don't have any match in ``filediffs``
are considered new changes in the interdiff, and any entries in
``filediffs`` that don't have entries in ``interfilediffs`` are considered
reverted changes.
Args:
tool (reviewboard.scmtools.core.SCMTool)
The tool used for all these diffs.
filediffs (list of reviewboard.diffviewer.models.filediff.FileDiff):
The list of filediffs on the left-hand side of the diff range.
interfilediffs (list of reviewboard.diffviewer.models.filediff.
FileDiff):
The list of filediffs on the right-hand side of the diff range.
Yields:
tuple:
A paired off filediff match. This is a tuple containing two entries,
each a :py:class:`~reviewboard.diffviewer.models.filediff.FileDiff` or
``None``.
"""
parser = tool.get_parser(b'')
_normfile = parser.normalize_diff_filename
def _make_detail_key(filediff):
return (_normfile(filediff.source_file),
_normfile(filediff.dest_file),
filediff.is_new,
filediff.deleted)
# In order to support interdiffs properly, we need to display diffs on
# every file in the union of both diffsets. Iterating over one diffset
# or the other doesn't suffice. We also need to be careful to handle
# things like renamed/moved files, particularly when there are multiple
# of them with the same source filename.
#
# This is done in four stages:
#
# 1. Build up maps and a set for keeping track of possible
# interfilediff candidates for future stages.
#
# 2. Look for any files that are common between the two diff revisions
# that have the same source filename, same destination filename, and
# the same new/deleted states.
#
# Unless a diff is hand-crafted, there should never be more than one
# match here.
#
# 3. Look for any files that are common between the two diff revisions
# that have the same source filename and new/deleted state. These will
# ignore the destination filename, helping to match cases where diff 1
# modifies a file and diff 2 modifies + renames/moves it.
#
# 4. Add any remaining files from diff 2 that weren't found in diff 1.
#
# We don't have to worry about things like the order of matched diffs.
# That will be taken care of at the end of the function.
detail_interdiff_map = {}
simple_interdiff_map = {}
remaining_interfilediffs = set()
# Stage 1: Build up the maps/set of interfilediffs.
for interfilediff in interfilediffs:
source_file = _normfile(interfilediff.source_file)
detail_key = _make_detail_key(interfilediff)
# We'll store this interfilediff in three spots: The set of
# all interfilediffs, the detail map (for source + dest +
# is_new file comparisons), and the simple map (for direct
# source_file comparisons). These will be used for the
# different matching stages.
remaining_interfilediffs.add(interfilediff)
detail_interdiff_map[detail_key] = interfilediff
simple_interdiff_map.setdefault(source_file, set()).add(interfilediff)
# Stage 2: Look for common files with the same source/destination
# filenames and new/deleted states.
#
# There will only be one match per filediff, at most. Any filediff or
# interfilediff that we find will be excluded from future stages.
remaining_filediffs = []
for filediff in filediffs:
source_file = _normfile(filediff.source_file)
try:
interfilediff = detail_interdiff_map.pop(
_make_detail_key(filediff))
except KeyError:
remaining_filediffs.append(filediff)
continue
yield filediff, interfilediff
if interfilediff:
remaining_interfilediffs.discard(interfilediff)
try:
simple_interdiff_map.get(source_file, []).remove(interfilediff)
except ValueError:
pass
# Stage 3: Look for common files with the same source/destination
# filenames (when they differ).
#
# Any filediff from diff 1 not already processed in stage 2 will be
# processed here. We'll look for any filediffs from diff 2 that were
# moved/copied from the same source to the same destination. This is one
# half of the detailed file state we checked in stage 2.
new_remaining_filediffs = []
for filediff in remaining_filediffs:
source_file = _normfile(filediff.source_file)
found_interfilediffs = [
temp_interfilediff
for temp_interfilediff in simple_interdiff_map.get(source_file, [])
if (temp_interfilediff.dest_file == filediff.dest_file and
filediff.source_file != filediff.dest_file)
]
if found_interfilediffs:
remaining_interfilediffs.difference_update(found_interfilediffs)
for interfilediff in found_interfilediffs:
simple_interdiff_map[source_file].remove(interfilediff)
yield filediff, interfilediff
else:
new_remaining_filediffs.append(filediff)
remaining_filediffs = new_remaining_filediffs
# Stage 4: Look for common files with the same source filenames and
# new/deleted states.
#
# Any filediff from diff 1 not already processed in stage 3 will be
# processed here. We'll look for any filediffs from diff 2 that match
# the source filename and the new/deleted state. Any that we find will
# be matched up.
new_remaining_filediffs = []
for filediff in remaining_filediffs:
source_file = _normfile(filediff.source_file)
found_interfilediffs = [
temp_interfilediff
for temp_interfilediff in simple_interdiff_map.get(source_file, [])
if (temp_interfilediff.is_new == filediff.is_new and
temp_interfilediff.deleted == filediff.deleted)
]
if found_interfilediffs:
remaining_interfilediffs.difference_update(found_interfilediffs)
for interfilediff in found_interfilediffs:
simple_interdiff_map[source_file].remove(interfilediff)
yield filediff, interfilediff
else:
new_remaining_filediffs.append(filediff)
remaining_filediffs = new_remaining_filediffs
# Stage 5: Look for common files with the same source filenames and
# compatible new/deleted states.
#
# This will help catch files that were marked as new in diff 1 but not in
# diff 2, or deleted in diff 2 but not in diff 1. (The inverse for either
# is NOT matched!). This is important because if a file is introduced in a
# parent diff, the file can end up showing up as new itself (which is a
# separate bug).
#
# Even if that bug did not exist, it's still possible for a file to be new
# in one revision but committed separately (by that user or another), so we
# need these matched.
#
# Any files not found with a matching interdiff will simply be yielded.
# This is the last stage dealing with the filediffs in the first revision.
for filediff in remaining_filediffs:
source_file = _normfile(filediff.source_file)
found_interfilediffs = [
temp_interfilediff
for temp_interfilediff in simple_interdiff_map.get(source_file, [])
if (((filediff.is_new or not temp_interfilediff.is_new) or
(not filediff.is_new and temp_interfilediff.is_new and
filediff.dest_detail == temp_interfilediff.dest_detail)) and
(not filediff.deleted or temp_interfilediff.deleted))
]
if found_interfilediffs:
remaining_interfilediffs.difference_update(found_interfilediffs)
for interfilediff in found_interfilediffs:
# NOTE: If more stages are ever added that deal with
# simple_interdiff_map, then we'll need to remove
# interfilediff from that map here.
yield filediff, interfilediff
else:
yield filediff, None
# Stage 6: Add any remaining files from the interdiff.
#
# We've removed everything that we've already found. What's left are
# interdiff files that are new. They have no file to diff against.
#
# The end result is going to be a view that's the same as when you're
# viewing a standard diff. As such, we can pretend the interdiff is
# the source filediff and not specify an interdiff. Keeps things
# simple, code-wise, since we really have no need to special-case
# this.
for interfilediff in remaining_interfilediffs:
yield None, interfilediff
def get_filediffs_match(filediff1, filediff2):
"""Return whether two FileDiffs effectively match.
This is primarily checking that the patched version of two files are going
to be basically the same.
This will first check that we even have both FileDiffs. Assuming we have
both, this will check the diff for equality. If not equal, we at least
check that both files were deleted (which is equivalent to being equal).
The patched SHAs are then checked. These would be generated as part of the
diff viewing process, so may not be available. We prioritize the SHA256
hashes (introduced in Review Board 4.0), and fall back on SHA1 hashes if
not present.
Args:
filediff1 (reviewboard.diffviewer.models.filediff.FileDiff):
The first FileDiff to compare.
filediff2 (reviewboard.diffviewer.models.filediff.FileDiff):
The second FileDiff to compare.
Returns:
bool:
``True`` if both FileDiffs effectively match. ``False`` if they do
not.
Raises:
ValueError:
``None`` was provided for both ``filediff1`` and ``filediff2``.
"""
if filediff1 is None and filediff2 is None:
raise ValueError('filediff1 and filediff2 cannot both be None')
# For the hash comparisons, there's a chance we won't have any SHA1 (RB
# 2.0+) or SHA256 (RB 4.0+) hashes, so we have to check for them. We want
# to prioritize SHA256 hashes, but if the filediff or interfilediff lacks
# a SHA256 hash, we want to fall back to SHA1.
return (filediff1 is not None and filediff2 is not None and
(filediff1.diff == filediff2.diff or
(filediff1.deleted and filediff2.deleted) or
(filediff1.patched_sha256 is not None and
filediff1.patched_sha256 == filediff2.patched_sha256) or
((filediff1.patched_sha256 is None or
filediff2.patched_sha256 is None) and
filediff1.patched_sha1 is not None and
filediff1.patched_sha1 == filediff2.patched_sha1)))
def get_diff_files(diffset, filediff=None, interdiffset=None,
interfilediff=None, base_filediff=None, request=None,
filename_patterns=None, base_commit=None, tip_commit=None):
"""Return a list of files that will be displayed in a diff.
This will go through the given diffset/interdiffset, or a given filediff
within that diffset, and generate the list of files that will be
displayed. This file list will contain a bunch of metadata on the files,
such as the index, original/modified names, revisions, associated
filediffs/diffsets, and so on.
This can be used along with :py:func:`populate_diff_chunks` to build a full
list containing all diff chunks used for rendering a side-by-side diff.
Args:
diffset (reviewboard.diffviewer.models.diffset.DiffSet):
The diffset containing the files to return.
filediff (reviewboard.diffviewer.models.filediff.FileDiff, optional):
A specific file in the diff to return information for.
interdiffset (reviewboard.diffviewer.models.diffset.DiffSet, optional):
A second diffset used for an interdiff range.
interfilediff (reviewboard.diffviewer.models.filediff.FileDiff,
optional):
A second specific file in ``interdiffset`` used to return
information for. This should be provided if ``filediff`` and
``interdiffset`` are both provided. If it's ``None`` in this
case, then the diff will be shown as reverted for this file.
This may not be provided if ``base_filediff`` is provided.
base_filediff (reviewbaord.diffviewer.models.filediff.FileDiff,
optional):
The base FileDiff to use.
This may only be provided if ``filediff`` is provided and
``interfilediff`` is not.
filename_patterns (list of unicode, optional):
A list of filenames or :py:mod:`patterns <fnmatch>` used to
limit the results. Each of these will be matched against the
original and modified file of diffs and interdiffs.
base_commit (reviewboard.diffviewer.models.diffcommit.DiffCommit,
optional):
An optional base commit. No :py:class:`FileDiffs
<reviewboard.diffviewer.models.filediff.FileDiff>` from commits
before that commit will be included in the results.
This argument only applies to :py:class:`DiffSets
<reviewboard.diffviewer.models.diffset.DiffSet>` with
:py:class:`DiffCommits <reviewboard.diffviewer.models.diffcommit
.DiffCommit>`.
tip_commit (reviewboard.diffviewer.models.diffcommit.DiffSet,
optional):
An optional tip commit. No :py:class:`FileDiffs
<reviewboard.diffviewer.models.filediff.FileDiff>` from commits
after that commit will be included in the results.
This argument only applies to :py:class:`DiffSets
<reviewboard.diffviewer.models.diffset.DiffSet>` with
:py:class:`DiffCommits <reviewboard.diffviewer.models.diffcommit
.DiffCommit>`.
Returns:
list of dict:
A list of dictionaries containing information on the files to show
in the diff, in the order in which they would be shown.
"""
# It is presently not supported to do an interdiff with commit spans. It
# would require base/tip commits for the interdiffset as well.
assert not interdiffset or (base_commit is None and tip_commit is None)
assert base_filediff is None or interfilediff is None
if (diffset.commit_count > 0 and
base_commit and
tip_commit and
base_commit.pk > tip_commit.pk):
# If the base commit is more recent than the tip commit the interval
# **must** be empty.
return []
per_commit_filediffs = None
requested_base_filediff = base_filediff
if filediff:
filediffs = [filediff]
if interdiffset:
log_timer = log_timed("Generating diff file info for "
"interdiffset ids %s-%s, filediff %s" %
(diffset.id, interdiffset.id, filediff.id),
request=request)
else:
log_timer = log_timed("Generating diff file info for "
"diffset id %s, filediff %s" %
(diffset.id, filediff.id),
request=request)
if (diffset.commit_count > 0 and
((base_commit and filediff.commit_id <= base_commit.pk) or
(tip_commit and filediff.commit_id > tip_commit.pk))):
# The requested FileDiff is outside the requested commit range.
return []
else:
if (diffset.commit_count > 0 and
(base_commit is not None or tip_commit is not None)):
# Even if we have base_commit, we need to query for all FileDiffs
# so that we can do ancestor computations.
filediffs = per_commit_filediffs = diffset.per_commit_files
if base_commit:
base_commit_id = base_commit.pk
else:
base_commit_id = 0
if tip_commit:
tip_commit_id = tip_commit.pk
else:
tip_commit_id = None
filediffs = [
f
for f in filediffs
if (f.commit_id > base_commit_id and
(not tip_commit_id or
f.commit_id <= tip_commit_id))
]
filediffs = exclude_ancestor_filediffs(filediffs,
per_commit_filediffs)
else:
filediffs = diffset.cumulative_files
if interdiffset:
log_timer = log_timed("Generating diff file info for "
"interdiffset ids %s-%s" %
(diffset.id, interdiffset.id),
request=request)
else:
log_timer = log_timed("Generating diff file info for "
"diffset id %s" % diffset.id,
request=request)
# Filediffs that were created with leading slashes stripped won't match
# those created with them present, so we need to compare them without in
# order for the filenames to match up properly.
tool = diffset.repository.get_scmtool()
if interdiffset:
if not filediff:
if interdiffset.commit_count > 0:
# Currently, only interdiffing between cumulative diffs is
# supported.
interfilediffs = interdiffset.cumulative_files
else:
interfilediffs = list(interdiffset.files.all())
elif interfilediff:
interfilediffs = [interfilediff]
else:
interfilediffs = []
filediff_parts = []
matched_filediffs = get_matched_interdiff_files(
tool=tool,
filediffs=filediffs,
interfilediffs=interfilediffs)
for temp_filediff, temp_interfilediff in matched_filediffs:
if temp_filediff:
filediff_parts.append((temp_filediff, temp_interfilediff,
True))
elif temp_interfilediff:
filediff_parts.append((temp_interfilediff, None, False))
else:
logging.error(
'get_matched_interdiff_files returned an entry with an '
'empty filediff and interfilediff for diffset=%r, '
'interdiffset=%r, filediffs=%r, interfilediffs=%r',
diffset, interdiffset, filediffs, interfilediffs)
raise ValueError(
'Internal error: get_matched_interdiff_files returned an '
'entry with an empty filediff and interfilediff! Please '
'report this along with information from the server '
'error log.')
else:
# We're not working with interdiffs. We can easily create the
# filediff_parts directly.
filediff_parts = [
(temp_filediff, None, False)
for temp_filediff in filediffs
]
# Now that we have all the bits and pieces we care about for the filediffs,
# we can start building information about each entry on the diff viewer.
files = []
for parts in filediff_parts:
filediff, interfilediff, force_interdiff = parts
newfile = filediff.is_new
if interdiffset:
# First, find out if we want to even process this one.
# If the diffs are identical, or the patched files are identical,
# or if the files were deleted in both cases, then we can be
# absolutely sure that there's nothing interesting to show to
# the user.
if get_filediffs_match(filediff, interfilediff):
continue
source_revision = _('Diff Revision %s') % diffset.revision
else:
source_revision = get_revision_str(filediff.source_revision)
if interfilediff:
dest_revision = _('Diff Revision %s') % interdiffset.revision
else:
if force_interdiff:
dest_revision = (_('Diff Revision %s - File Reverted') %
interdiffset.revision)
elif newfile:
dest_revision = _('New File')
else:
dest_revision = _('New Change')
if interfilediff:
raw_depot_filename = filediff.dest_file
raw_dest_filename = interfilediff.dest_file
else:
raw_depot_filename = filediff.source_file
raw_dest_filename = filediff.dest_file
depot_filename = tool.normalize_path_for_display(raw_depot_filename)
dest_filename = tool.normalize_path_for_display(raw_dest_filename)
if filename_patterns:
if dest_filename == depot_filename:
filenames = [dest_filename]
else:
filenames = [dest_filename, depot_filename]
if not get_filenames_match_patterns(patterns=filename_patterns,
filenames=filenames):
continue
base_filediff = None
if filediff.commit_id:
# If we pre-computed this above (or before) and we have all
# FileDiffs, this will cost no additional queries.
#
# Otherwise this will cost up to
# ``1 + len(diffset.per_commit_files.count())`` queries.
ancestors = filediff.get_ancestors(minimal=False,
filediffs=per_commit_filediffs)
if ancestors:
if requested_base_filediff:
assert len(filediffs) == 1
if requested_base_filediff in ancestors:
base_filediff = requested_base_filediff
else:
raise ValueError(
'Invalid base_filediff (ID %d) for filediff (ID '
'%d)'
% (requested_base_filediff.pk, filediff.pk))
elif base_commit:
base_filediff = filediff.get_base_filediff(
base_commit=base_commit,
ancestors=ancestors)
f = {
'depot_filename': depot_filename,
'dest_filename': dest_filename or depot_filename,
'revision': source_revision,
'dest_revision': dest_revision,
'filediff': filediff,
'interfilediff': interfilediff,
'force_interdiff': force_interdiff,
'binary': filediff.binary,
'deleted': filediff.deleted,
'moved': filediff.moved,
'copied': filediff.copied,
'moved_or_copied': filediff.moved or filediff.copied,
'newfile': newfile,
'is_symlink': filediff.extra_data.get('is_symlink', False),
'index': len(files),
'chunks_loaded': False,
'is_new_file': (
(newfile or
(base_filediff is not None and
base_filediff.is_new)) and
not interfilediff and
not filediff.parent_diff
),
'base_filediff': base_filediff,
}
# When displaying an interdiff, we do not want to display the
# revision of the base filediff. Instead, we will display the diff
# revision as computed above.
if base_filediff and not interdiffset:
f['revision'] = get_revision_str(base_filediff.source_revision)
f['depot_filename'] = tool.normalize_path_for_display(
base_filediff.source_file)
if force_interdiff:
f['force_interdiff_revision'] = interdiffset.revision
files.append(f)
log_timer.done()
if len(files) == 1:
return files
else:
return get_sorted_filediffs(
files,
key=lambda f: f['interfilediff'] or f['filediff'])
def populate_diff_chunks(files, enable_syntax_highlighting=True,
request=None):
"""Populates a list of diff files with chunk data.
This accepts a list of files (generated by get_diff_files) and generates
diff chunk data for each file in the list. The chunk data is stored in
the file state.
"""
from reviewboard.diffviewer.chunk_generator import get_diff_chunk_generator
for diff_file in files:
generator = get_diff_chunk_generator(
request,
diff_file['filediff'],
diff_file['interfilediff'],
diff_file['force_interdiff'],
enable_syntax_highlighting,
base_filediff=diff_file.get('base_filediff'))
chunks = list(generator.get_chunks())
diff_file.update({
'chunks': chunks,
'num_chunks': len(chunks),
'changed_chunk_indexes': [],
'whitespace_only': len(chunks) > 0,
})
for j, chunk in enumerate(chunks):
chunk['index'] = j
if chunk['change'] != 'equal':
diff_file['changed_chunk_indexes'].append(j)
meta = chunk.get('meta', {})
if not meta.get('whitespace_chunk', False):
diff_file['whitespace_only'] = False
diff_file.update({
'num_changes': len(diff_file['changed_chunk_indexes']),
'chunks_loaded': True,
})
def get_file_from_filediff(context, filediff, interfilediff):
"""Return the files that corresponds to the filediff/interfilediff.
This is primarily intended for use with templates. It takes a
RequestContext for looking up the user and for caching file lists,
in order to improve performance and reduce lookup times for files that have
already been fetched.
This function returns either exactly one file or ``None``.
"""
interdiffset = None
key = "_diff_files_%s_%s" % (filediff.diffset.id, filediff.id)
if interfilediff:
key += "_%s" % (interfilediff.id)
interdiffset = interfilediff.diffset
if key in context:
files = context[key]
else:
assert 'user' in context
request = context.get('request', None)
files = get_diff_files(filediff.diffset, filediff, interdiffset,
interfilediff=interfilediff,
request=request)
populate_diff_chunks(files, get_enable_highlighting(context['user']),
request=request)
context[key] = files
if not files:
return None
assert len(files) == 1
return files[0]
def get_last_line_number_in_diff(context, filediff, interfilediff):
"""Determine the last virtual line number in the filediff/interfilediff.
This returns the virtual line number to be used in expandable diff
fragments.
"""
f = get_file_from_filediff(context, filediff, interfilediff)
last_chunk = f['chunks'][-1]
last_line = last_chunk['lines'][-1]
return last_line[0]
def _get_last_header_in_chunks_before_line(chunks, target_line):
"""Find the last header in the list of chunks before the target line."""
def find_last_line_numbers(lines):
"""Return a tuple of the last line numbers in the given list of lines.
The last line numbers are not always contained in the last element of
the ``lines`` list. This is the case when dealing with interdiffs that
have filtered out opcodes.
See :py:func:`get_chunks_in_range` for a description of what is
contained in each element of ``lines``.
"""
last_left = None
last_right = None
for line in reversed(lines):
if not last_right and line[4]:
last_right = line[4]
if not last_left and line[1]:
last_left = line[1]
if last_left and last_right:
break
return last_left, last_right
def find_header(headers, offset, last_line):
"""Return the last header that occurs before a line.
The offset parameter is the difference between the virtual number and
and actual line number in the chunk. This is required because the
header line numbers are original or patched line numbers, not virtual
line numbers.
"""
# In the case of interdiffs, it is possible that there will be headers
# in the chunk that don't belong to it, but were put there due to
# chunks being merged together. We must therefore ensure that the
# header we're looking at is actually in the chunk.
end_line = min(last_line, target_line)
for header in reversed(headers):
virtual_line = header[0] + offset
if virtual_line < end_line:
return {
'line': virtual_line,
'text': header[1]
}
# The most up-to-date header information
header = {
'left': None,
'right': None
}
for chunk in chunks:
lines = chunk['lines']
virtual_first_line = lines[0][0]
if virtual_first_line <= target_line:
if virtual_first_line == target_line:
# The given line number is the first line of a new chunk so
# there can't be any relevant header information here.
break
last_left, last_right = find_last_line_numbers(lines)
if 'left_headers' in chunk['meta'] and lines[0][1]:
offset = virtual_first_line - lines[0][1]
left_header = find_header(chunk['meta']['left_headers'],
offset, last_left + offset)
header['left'] = left_header or header['left']
if 'right_headers' in chunk['meta'] and lines[0][4]:
offset = virtual_first_line - lines[0][4]
right_header = find_header(chunk['meta']['right_headers'],
offset, last_right + offset)
header['right'] = right_header or header['right']
else:
# We've gone past the given line number.
break
return header
def get_last_header_before_line(context, filediff, interfilediff, target_line):
"""Get the last header that occurs before the given line.
This returns a dictionary of ``left`` header and ``right`` header. Each
header is either ``None`` or a dictionary with the following fields:
======== ==============================================================
Field Description
======== ==============================================================
``line`` Virtual line number (union of the original and patched files)
``text`` The header text
======== ==============================================================
"""
f = get_file_from_filediff(context, filediff, interfilediff)
return _get_last_header_in_chunks_before_line(f['chunks'], target_line)
def get_file_chunks_in_range(context, filediff, interfilediff,
first_line, num_lines):
"""Generate the chunks within a range of lines in the specified filediff.
This is primarily intended for use with templates. It takes a
RequestContext for looking up the user and for caching file lists,
in order to improve performance and reduce lookup times for files that have
already been fetched.
See :py:func:`get_chunks_in_range` for information on the returned state
of the chunks.
"""
f = get_file_from_filediff(context, filediff, interfilediff)
if f:
return get_chunks_in_range(f['chunks'], first_line, num_lines)
else:
return []
def get_chunks_in_range(chunks, first_line, num_lines):
"""Generate the chunks within a range of lines of a larger list of chunks.
This takes a list of chunks, computes a subset of those chunks from the
line ranges provided, and generates a new set of those chunks.
Each returned chunk is a dictionary with the following fields:
============= ========================================================
Variable Description
============= ========================================================
``change`` The change type ("equal", "replace", "insert", "delete")
``numlines`` The number of lines in the chunk.
``lines`` The list of lines in the chunk.
``meta`` A dictionary containing metadata on the chunk
============= ========================================================
Each line in the list of lines is an array with the following data:
======== =============================================================
Index Description
======== =============================================================
0 Virtual line number (union of the original and patched files)
1 Real line number in the original file
2 HTML markup of the original file
3 Changed regions of the original line (for "replace" chunks)
4 Real line number in the patched file
5 HTML markup of the patched file
6 Changed regions of the patched line (for "replace" chunks)
7 True if line consists of only whitespace changes
======== =============================================================
"""
for i, chunk in enumerate(chunks):
lines = chunk['lines']
if lines[-1][0] >= first_line >= lines[0][0]:
start_index = first_line - lines[0][0]
if first_line + num_lines <= lines[-1][0]:
last_index = start_index + num_lines
else:
last_index = len(lines)
new_chunk = {
'index': i,
'lines': chunk['lines'][start_index:last_index],
'numlines': last_index - start_index,
'change': chunk['change'],
'meta': chunk.get('meta', {}),
}
yield new_chunk
first_line += new_chunk['numlines']
num_lines -= new_chunk['numlines']
assert num_lines >= 0
if num_lines == 0:
break
def get_enable_highlighting(user):
user_syntax_highlighting = True
if user.is_authenticated():
try:
profile = user.get_profile()
user_syntax_highlighting = profile.syntax_highlighting
except ObjectDoesNotExist:
pass
siteconfig = SiteConfiguration.objects.get_current()
return (siteconfig.get('diffviewer_syntax_highlighting') and
user_syntax_highlighting)
def get_line_changed_regions(oldline, newline):
"""Returns regions of changes between two similar lines."""
if oldline is None or newline is None:
return None, None
# Use the SequenceMatcher directly. It seems to give us better results
# for this. We should investigate steps to move to the new differ.
differ = SequenceMatcher(None, oldline, newline)
# This thresholds our results -- we don't want to show inter-line diffs
# if most of the line has changed, unless those lines are very short.
# FIXME: just a plain, linear threshold is pretty crummy here. Short
# changes in a short line get lost. I haven't yet thought of a fancy
# nonlinear test.
if differ.ratio() < 0.6:
return None, None
oldchanges = []
newchanges = []
back = (0, 0)
for tag, i1, i2, j1, j2 in differ.get_opcodes():
if tag == 'equal':
if (i2 - i1 < 3) or (j2 - j1 < 3):
back = (j2 - j1, i2 - i1)
continue
oldstart, oldend = i1 - back[0], i2
newstart, newend = j1 - back[1], j2
if oldchanges and oldstart <= oldchanges[-1][1] < oldend:
oldchanges[-1] = (oldchanges[-1][0], oldend)
elif not oldline[oldstart:oldend].isspace():
oldchanges.append((oldstart, oldend))
if newchanges and newstart <= newchanges[-1][1] < newend:
newchanges[-1] = (newchanges[-1][0], newend)
elif not newline[newstart:newend].isspace():
newchanges.append((newstart, newend))
back = (0, 0)
return oldchanges, newchanges
def get_sorted_filediffs(filediffs, key=None):
"""Sorts a list of filediffs.
The list of filediffs will be sorted first by their base paths in
ascending order.
Within a base path, they'll be sorted by base name (minus the extension)
in ascending order.
If two files have the same base path and base name, we'll sort by the
extension in descending order. This will make :file:`*.h` sort ahead of
:file:`*.c`/:file:`*.cpp`, for example.
If the list being passed in is actually not a list of FileDiffs, it
must provide a callable ``key`` parameter that will return a FileDiff
for the given entry in the list. This will only be called once per
item.
"""
def cmp_filediffs(filediff1, filediff2):
x = make_key(filediff1)
y = make_key(filediff2)
# Sort based on basepath in ascending order.
if x[0] != y[0]:
a = x[0]
b = y[0]
else:
# Sort based on filename in ascending order, then based on
# the extension in descending order, to make *.h sort ahead of
# *.c/cpp.
x_file, x_ext = os.path.splitext(x[1])
y_file, y_ext = os.path.splitext(y[1])
if x_file == y_file:
a = y_ext
b = x_ext
else:
a = x_file
b = y_file
return cmp(a, b)
def make_key(filediff):
if key:
filediff = key(filediff)
filename = filediff.dest_file
i = filename.rfind('/')
if i == -1:
return '', filename
else:
return filename[:i], filename[i + 1:]
return sorted(filediffs, key=cmp_to_key(cmp_filediffs))
def get_displayed_diff_line_ranges(chunks, first_vlinenum, last_vlinenum):
"""Return the displayed line ranges based on virtual line numbers.
This takes the virtual line numbers (the index in the side-by-side diff
lines) and returns the human-readable line numbers, the chunks they're in,
and mapped virtual line numbers.
A virtual line range may start or end in a chunk not containing displayed
line numbers (such as an "original" range starting/ending in an "insert"
chunk). The resulting displayed line ranges will exclude these chunks.
Args:
chunks (list of dict):
The list of chunks for the diff.
first_vlinenum (int):
The first virtual line number. This uses 1-based indexes.
last_vlinenum (int):
The last virtual line number. This uses 1-based indexes.
Returns:
tuple:
A tuple of displayed line range information, containing 2 items.
Each item will either be a dictionary of information, or ``None``
if there aren't any displayed lines to show.
The dictionary contains the following keys:
``display_range``:
A tuple containing the displayed line range.
``virtual_range``:
A tuple containing the virtual line range that ``display_range``
maps to.
``chunk_range``:
A tuple containing the beginning/ending chunks that
``display_range`` maps to.
Raises:
ValueError:
The range provided was invalid.
"""
if first_vlinenum < 0:
raise ValueError('first_vlinenum must be >= 0')
if last_vlinenum < first_vlinenum:
raise ValueError('last_vlinenum must be >= first_vlinenum')
orig_start_linenum = None
orig_end_linenum = None
orig_start_chunk = None
orig_last_valid_chunk = None
patched_start_linenum = None
patched_end_linenum = None
patched_start_chunk = None
patched_last_valid_chunk = None
for chunk in chunks:
lines = chunk['lines']
if not lines:
logging.warning('get_displayed_diff_line_ranges: Encountered '
'empty chunk %r',
chunk)
continue
first_line = lines[0]
last_line = lines[-1]
chunk_first_vlinenum = first_line[0]
chunk_last_vlinenum = last_line[0]
if first_vlinenum > chunk_last_vlinenum:
# We're too early. There won't be anything of interest here.
continue
if last_vlinenum < chunk_first_vlinenum:
# We're not going to find anything useful at this point, so bail.
break
change = chunk['change']
valid_for_orig = (change != 'insert' and first_line[1])
valid_for_patched = (change != 'delete' and first_line[4])
if valid_for_orig:
orig_last_valid_chunk = chunk
if not orig_start_chunk:
orig_start_chunk = chunk
if valid_for_patched:
patched_last_valid_chunk = chunk
if not patched_start_chunk:
patched_start_chunk = chunk
if chunk_first_vlinenum <= first_vlinenum <= chunk_last_vlinenum:
# This chunk contains the first line that can possibly be used for
# the comment range. We know the start and end virtual line numbers
# in the range, so we can compute the proper offset.
offset = first_vlinenum - chunk_first_vlinenum
if valid_for_orig:
orig_start_linenum = first_line[1] + offset
orig_start_vlinenum = first_line[0] + offset
if valid_for_patched:
patched_start_linenum = first_line[4] + offset
patched_start_vlinenum = first_line[0] + offset
elif first_vlinenum < chunk_first_vlinenum:
# One side of the the comment range may not have started in a valid
# chunk (this would happen if a comment began in an insert or
# delete chunk). If that happened, we may not have been able to set
# the beginning of the range in the condition above. Check for this
# and try setting it now.
if orig_start_linenum is None and valid_for_orig:
orig_start_linenum = first_line[1]
orig_start_vlinenum = first_line[0]
if patched_start_linenum is None and valid_for_patched:
patched_start_linenum = first_line[4]
patched_start_vlinenum = first_line[0]
# Figure out the end ranges, now that we know the valid ending chunks of
# each. We're going to try to get the line within the chunk that represents
# the end, if within the chunk, capping it to the last line in the chunk.
#
# If a particular range did not have a valid chunk anywhere in that range,
# we're going to invalidate the entire range.
if orig_last_valid_chunk:
lines = orig_last_valid_chunk['lines']
first_line = lines[0]
last_line = lines[-1]
offset = last_vlinenum - first_line[0]
orig_end_linenum = min(last_line[1], first_line[1] + offset)
orig_end_vlinenum = min(last_line[0], first_line[0] + offset)
assert orig_end_linenum >= orig_start_linenum
assert orig_end_vlinenum >= orig_start_vlinenum
orig_range_info = {
'display_range': (orig_start_linenum, orig_end_linenum),
'virtual_range': (orig_start_vlinenum, orig_end_vlinenum),
'chunk_range': (orig_start_chunk, orig_last_valid_chunk),
}
else:
orig_range_info = None
if patched_last_valid_chunk:
lines = patched_last_valid_chunk['lines']
first_line = lines[0]
last_line = lines[-1]
offset = last_vlinenum - first_line[0]
patched_end_linenum = min(last_line[4], first_line[4] + offset)
patched_end_vlinenum = min(last_line[0], first_line[0] + offset)
assert patched_end_linenum >= patched_start_linenum
assert patched_end_vlinenum >= patched_start_vlinenum
patched_range_info = {
'display_range': (patched_start_linenum, patched_end_linenum),
'virtual_range': (patched_start_vlinenum, patched_end_vlinenum),
'chunk_range': (patched_start_chunk, patched_last_valid_chunk),
}
else:
patched_range_info = None
return orig_range_info, patched_range_info
def get_diff_data_chunks_info(diff):
"""Return information on each chunk in a diff.
This will scan through a unified diff file, looking for each chunk in the
diff and returning information on their ranges and lines of context. This
can be used to generate statistics on diffs and help map changed regions
in diffs to lines of source files.
Args:
diff (bytes):
The diff data to scan.
Returns:
list of dict:
A list of chunk information dictionaries. Each entry has an ``orig``
and ``modified` dictionary containing the following keys:
``chunk_start`` (``int``):
The starting line number of the chunk shown in the diff, including
any lines of context. This is 0-based.
``chunk_len`` (``int``):
The length of the chunk shown in the diff, including any lines of
context.
``changes_start`` (``int``):
The starting line number of a range of changes shown in a chunk in
the diff.
This is after any lines of context and is 0-based.
``changes_len`` (``int``):
The length of the changes shown in a chunk in the diff, excluding
any lines of context.
``pre_lines_of_context`` (``int``):
The number of lines of context before any changes in a chunk. If
the chunk doesn't have any changes, this will contain all lines of
context otherwise shown around changes in the other region in this
entry.
``post_lines_of_context`` (``int``):
The number of lines of context after any changes in a chunk. If
the chunk doesn't have any changes, this will be 0.
"""
def _finalize_result():
if not cur_result:
return
for result_dict, unchanged_lines in ((cur_result_orig,
orig_unchanged_lines),
(cur_result_modified,
modified_unchanged_lines)):
result_dict['changes_len'] -= unchanged_lines
if result_dict['changes_len'] == 0:
assert result_dict['pre_lines_of_context'] == 0
result_dict['pre_lines_of_context'] = unchanged_lines
else:
result_dict['post_lines_of_context'] = unchanged_lines
process_orig_changes = False
process_modified_changes = False
results = []
cur_result = None
cur_result_orig = None
cur_result_modified = None
orig_unchanged_lines = 0
modified_unchanged_lines = 0
# Look through the chunks of the diff, trying to find the amount
# of context shown at the beginning of each chunk. Though this
# will usually be 3 lines, it may be fewer or more, depending
# on file length and diff generation settings.
for i, line in enumerate(split_line_endings(diff.strip())):
if line.startswith(b'-'):
if process_orig_changes:
# We've found the first change in the original side of the
# chunk. We now know how many lines of context we have here.
#
# We reduce the indexes by 1 because the chunk ranges
# in diffs start at 1, and we want a 0-based index.
cur_result_orig['pre_lines_of_context'] = orig_unchanged_lines
cur_result_orig['changes_start'] += orig_unchanged_lines
cur_result_orig['changes_len'] -= orig_unchanged_lines
process_orig_changes = False
orig_unchanged_lines = 0
elif line.startswith(b'+'):
if process_modified_changes:
# We've found the first change in the modified side of the
# chunk. We now know how many lines of context we have here.
#
# We reduce the indexes by 1 because the chunk ranges
# in diffs start at 1, and we want a 0-based index.
cur_result_modified['pre_lines_of_context'] = \
modified_unchanged_lines
cur_result_modified['changes_start'] += \
modified_unchanged_lines
cur_result_modified['changes_len'] -= modified_unchanged_lines
process_modified_changes = False
modified_unchanged_lines = 0
elif line.startswith(b' '):
# We might be before a group of changes, inside a group of changes,
# or after a group of changes. Either way, we want to track these
# values.
orig_unchanged_lines += 1
modified_unchanged_lines += 1
else:
# This was not a change within a chunk, or we weren't processing,
# so check to see if this is a chunk header instead.
m = CHUNK_RANGE_RE.match(line)
if m:
# It is a chunk header. Start by updating the previous range
# to factor in the lines of trailing context.
_finalize_result()
# Next, reset the state for the next range, and pull the line
# numbers and lengths from the header. We'll also normalize
# the starting locations to be 0-based.
orig_start = int(m.group('orig_start')) - 1
orig_len = int(m.group('orig_len') or '1')
modified_start = int(m.group('modified_start')) - 1
modified_len = int(m.group('modified_len') or '1')
cur_result_orig = {
'pre_lines_of_context': 0,
'post_lines_of_context': 0,
'chunk_start': orig_start,
'chunk_len': orig_len,
'changes_start': orig_start,
'changes_len': orig_len,
}
cur_result_modified = {
'pre_lines_of_context': 0,
'post_lines_of_context': 0,
'chunk_start': modified_start,
'chunk_len': modified_len,
'changes_start': modified_start,
'changes_len': modified_len,
}
cur_result = {
'orig': cur_result_orig,
'modified': cur_result_modified,
}
results.append(cur_result)
process_orig_changes = True
process_modified_changes = True
orig_unchanged_lines = 0
modified_unchanged_lines = 0
else:
logging.warning('Unexpected content on line %d of diff: "%s"',
i, line)
# We need to adjust the last range, if we're still processing
# trailing context.
_finalize_result()
return results
def check_diff_size(diff_file, parent_diff_file=None):
"""Check the size of the given diffs against the maximum allowed size.
If either of the provided diffs are too large, an exception will be raised.
Args:
diff_file (django.core.files.uploadedfile.UploadedFile):
The diff file.
parent_diff_file (django.core.files.uploadedfile.UploadedFile,
optional):
The parent diff file, if any.
Raises:
reviewboard.diffviewer.errors.DiffTooBigError:
The supplied files are too big.
"""
siteconfig = SiteConfiguration.objects.get_current()
max_diff_size = siteconfig.get('diffviewer_max_diff_size')
if max_diff_size > 0:
if diff_file.size > max_diff_size:
raise DiffTooBigError(
_('The supplied diff file is too large.'),
max_diff_size=max_diff_size)
if parent_diff_file and parent_diff_file.size > max_diff_size:
raise DiffTooBigError(
_('The supplied parent diff file is too large.'),
max_diff_size=max_diff_size)
def get_total_line_counts(files_qs):
"""Return the total line counts of all given FileDiffs.
Args:
files_qs (django.db.models.query.QuerySet):
The queryset descripting the :py:class:`FileDiffs
<reviewboard.diffviewer.models.filediff.FileDiff>`.
Returns:
dict:
A dictionary with the following keys:
* ``raw_insert_count``
* ``raw_delete_count``
* ``insert_count``
* ``delete_count``
* ``replace_count``
* ``equal_count``
* ``total_line_count``
Each entry maps to the sum of that line count type for all
:py:class:`FileDiffs
<reviewboard.diffviewer.models.filediff.FileDiff>`.
"""
counts = {
'raw_insert_count': 0,
'raw_delete_count': 0,
'insert_count': 0,
'delete_count': 0,
'replace_count': None,
'equal_count': None,
'total_line_count': None,
}
for filediff in files_qs:
for key, value in six.iteritems(filediff.get_line_counts()):
if value is not None:
if counts[key] is None:
counts[key] = value
else:
counts[key] += value
return counts
|
the-stack_0_959 | #!/usr/bin/env python
#
# Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Creates an AndroidManifest.xml for an APK split.
Given the manifest file for the main APK, generates an AndroidManifest.xml with
the value required for a Split APK (package, versionCode, etc).
"""
import lxml.etree
import optparse
from util import build_utils
MANIFEST_TEMPLATE = """<?xml version="1.0" encoding="utf-8"?>
<manifest
xmlns:android="http://schemas.android.com/apk/res/android"
package="%(package)s"
split="%(split)s">
<uses-sdk android:minSdkVersion="21" />
<application android:hasCode="%(has_code)s">
</application>
</manifest>
"""
def ParseArgs():
"""Parses command line options.
Returns:
An options object as from optparse.OptionsParser.parse_args()
"""
parser = optparse.OptionParser()
build_utils.AddDepfileOption(parser)
parser.add_option('--main-manifest', help='The main manifest of the app')
parser.add_option('--out-manifest', help='The output manifest')
parser.add_option('--split', help='The name of the split')
parser.add_option(
'--has-code',
action='store_true',
default=False,
help='Whether the split will contain a .dex file')
(options, args) = parser.parse_args()
if args:
parser.error('No positional arguments should be given.')
# Check that required options have been provided.
required_options = ('main_manifest', 'out_manifest', 'split')
build_utils.CheckOptions(options, parser, required=required_options)
return options
def Build(main_manifest, split, has_code):
"""Builds a split manifest based on the manifest of the main APK.
Args:
main_manifest: the XML manifest of the main APK as a string
split: the name of the split as a string
has_code: whether this split APK will contain .dex files
Returns:
The XML split manifest as a string
"""
doc = lxml.etree.fromstring(main_manifest)
package = doc.xpath('/manifest/@package')[0]
return MANIFEST_TEMPLATE % {
'package': package,
'split': split.replace('-', '_'),
'has_code': str(has_code).lower()
}
def main():
options = ParseArgs()
main_manifest = file(options.main_manifest).read()
split_manifest = Build(
main_manifest,
options.split,
options.has_code)
with file(options.out_manifest, 'w') as f:
f.write(split_manifest)
if options.depfile:
build_utils.WriteDepfile(
options.depfile,
[main_manifest] + build_utils.GetPythonDependencies())
if __name__ == '__main__':
main()
|
the-stack_0_960 | # -*- coding: utf-8 -*-
# Resource object code
#
# Created: Mon Dec 9 12:39:51 2019
# by: The Resource Compiler for PySide2 (Qt v5.13.0)
#
# WARNING! All changes made in this file will be lost!
from PySide2 import QtCore
qt_resource_data = b"\
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def qInitResources():
QtCore.qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
|
the-stack_0_964 | #!/usr/bin/env python3
#
# Synthesis-based resolution of features/enforcers interactions in CPS
# Copyright 2020 Carnegie Mellon University.
# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING
# INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON
# UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED,
# AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR
# PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF
# THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY
# KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT
# INFRINGEMENT.
# Released under a BSD (SEI)-style license, please see license.txt or contact
# [email protected] for full terms.
# [DISTRIBUTION STATEMENT A] This material has been approved for public
# release and unlimited distribution. Please see Copyright notice for
# non-US Government use and distribution.
# This Software includes and/or makes use of the following Third-Party Software
# subject to its own license:
# 1. JsonCpp
# (https://github.com/open-source-parsers/jsoncpp/blob/master/LICENSE)
# Copyright 2010 Baptiste Lepilleur and The JsonCpp Authors.
# DM20-0762
#
import sys
import os
import numpy as np
import pathlib
import itertools
import random
import json
# Assume ego starts 0,0 always
# Just relative to ego at varying distances
enemy_start_positions = [
'5,5',
'5,0',
'0,5',
'-5,5',
'5,-5',
'-5,-5',
'-5,0',
'0,-5',
'1,1',
'1,0',
'0,1',
'-1,1',
'1,-1',
'-1,-1',
'-1,0',
'0,-1',
'10,10',
'10,0',
'0,10',
'-10,10',
'10,-10',
'-10,-10',
'-10,0',
'0,-10',
]
# Config values that will be changed
config_vals = {
'ENEMY_DRONE_SPEED' : [x for x in np.arange(1.2, 2.1, 0.1)],
'WAYPOINT_SEED' : [x for x in range(0, 999)], # Kinda dumb way to do this space-wise but it's fine
'BOUNDARY_SIZE' : [x for x in np.arange(10, 30, 1)],
# 'SUGGEST_ACTION_RANGE' : [0,1]
}
# FLIGHT_WEIGHT stays constant
weight_vals = [
# Equal weights
{'BOUNDARY_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 1, 'MISSILE_WEIGHT' : 1},
# 1 : 1.5 : 2
{'BOUNDARY_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 1.5,'MISSILE_WEIGHT' : 2},
{'BOUNDARY_WEIGHT' : 1,'MISSILE_WEIGHT' : 1.5,'RUNAWAY_WEIGHT' : 2},
{'RUNAWAY_WEIGHT' : 1,'BOUNDARY_WEIGHT' : 1.5,'MISSILE_WEIGHT' : 2},
{'RUNAWAY_WEIGHT' : 1,'MISSILE_WEIGHT' : 1.5,'BOUNDARY_WEIGHT' : 2},
{'MISSILE_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 1.5,'BOUNDARY_WEIGHT' : 2},
{'MISSILE_WEIGHT' : 1,'BOUNDARY_WEIGHT' : 1.5,'RUNAWAY_WEIGHT' : 2},
# 1 : 2 : 3
{'BOUNDARY_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 2,'MISSILE_WEIGHT' : 3},
{'BOUNDARY_WEIGHT' : 1,'MISSILE_WEIGHT' : 2,'RUNAWAY_WEIGHT' : 3},
{'RUNAWAY_WEIGHT' : 1,'BOUNDARY_WEIGHT' : 2,'MISSILE_WEIGHT' : 3},
{'RUNAWAY_WEIGHT' : 1,'MISSILE_WEIGHT' : 2,'BOUNDARY_WEIGHT' : 3},
{'MISSILE_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 2,'BOUNDARY_WEIGHT' : 3},
{'MISSILE_WEIGHT' : 1,'BOUNDARY_WEIGHT' : 2,'RUNAWAY_WEIGHT' : 3},
]
def make_config_file(base_config_file, outfile, vals):
# Open input and output file
with open(base_config_file, 'r') as base, open(outfile, 'w') as out:
# Convert to list by space delim
for line in base:
line_lst = line.split(' ')
# If this var is one we change, then write what's stored in vars
if(line_lst[0] in vals):
# Handle the case that it's a float differently bc annoying precision
if isinstance(line_lst[0], np.float64):
out.write(line_lst[0] + ' ' + '{:.2f}'.format(vals[line_lst[0]]) + '\n')
else:
out.write(line_lst[0] + ' ' + str(vals[line_lst[0]]) + '\n')
# If this var is not one we change, write it as is
else:
out.write(line)
def default(o):
if isinstance(o, np.int64): return int(o)
raise TypeError
def make_files(config, rootdir, enemy_start_positions, num_configurations):
newdir = ''
# They're all the same at this point -- just get the vals for any coordinator
vals = config["RobustnessCoordinator"]
# Get all the combinations of variable-values we have
combinations = [dict((zip(vals.keys(), t))) for t in itertools.product(*vals.values())]
sample_size = num_configurations
# Get a random sample of the combinations
comb_sample = random.sample(combinations, sample_size)
# Randomly assign weights to each case
for entry in comb_sample:
weights = random.choice(weight_vals)
entry.update(weights)
for coordinator in config:
try:
newdir = rootdir+'/'+coordinator
os.makedirs(newdir, exist_ok=True)
except OSError:
print("Failed to create directory: %s" % newdir)
i=0
# Create a directory and a corresponding config file for each test case
for entry in comb_sample:
# Everything else is random so this is fine
enemy_start_pos_str = enemy_start_positions[i%len(enemy_start_positions)]
i = i+1
# Need to add this manually bc it's not in config file params
controlled_vars = entry.copy();
controlled_vars["enemy_strt_pos"] = enemy_start_pos_str;
dirname = ''
for name in entry:
if isinstance(entry[name], np.float64):
dirname+=name+'{:.2f}'.format(entry[name])+'-'
else:
dirname+=name+str(entry[name])+'-'
# trim the hyphen off the end
dirname = dirname[0:-1]
try:
os.makedirs(rootdir+'/'+coordinator+'/'+dirname, exist_ok=True)
except OSError:
print("Failed to create directory: %s" % rootdir+'/'+coordinator+'/'+dirname)
# Write config file
make_config_file('./drone.cfg', rootdir+coordinator+'/'+dirname+'/'+'drone.cfg', entry)
with open(rootdir+coordinator+'/'+dirname+'/'+'controlled_vars.json', 'w') as controlled_varsfile:
json.dump(controlled_vars, controlled_varsfile, default=default)
# Write positions
with open(rootdir+coordinator+'/'+dirname+'/'+'enemy_start_pos', 'wb') as posfile:
posfile.write(enemy_start_pos_str.encode('utf-8'))
def main():
if(len(sys.argv) != 3):
print("Usage: generate_tests.py <test_dir> <num_configurations>")
exit()
cwd = os.getcwd()
os.makedirs(sys.argv[1], exist_ok=True)
num_configurations = int(sys.argv[2])
rootdir = cwd+'/'+sys.argv[1]+'/'
if((cwd.split('/'))[-1] != 'missionapp'):
print("Must be in missionapp directory to use this script. Given %s\n" % cwd)
print(cwd.split('/'))
exit()
base_config_file = 'drone.cfg'
coordinators = ['PriorityCoordinator', 'RobustnessCoordinator', 'SynthRobustnessCoordinator']
config = { coord : config_vals.copy() for coord in coordinators }
make_files(config, rootdir, enemy_start_positions, num_configurations)
if __name__ == "__main__":
main()
|
the-stack_0_968 | from chainer import cuda
from chainer import function
from chainer import variable
class _DummyFunction(function.Function):
def __init__(self, grads):
self.grads = grads
def forward(self, inputs):
xp = cuda.get_array_module(*inputs)
return xp.array(0),
def backward(self, inputs, outputs):
return self.grads
class Forget(function.Function):
def __init__(self, func):
if not callable(func):
raise TypeError('func must be callable')
self.func = func
def _call_func(self, xs):
outs = self.func(*xs)
if isinstance(outs, tuple):
for i, out in enumerate(outs):
if isinstance(out, variable.Variable):
continue
n = i + 1
suffix = {1: 'st', 2: 'nd', 3: 'rd'}.get(
n if n < 20 else n % 10, 'th')
msg = ('{}{} element of a returned tuple is not Variable, '
'but is {}').format(n, suffix, type(out))
raise RuntimeError(msg)
elif isinstance(outs, variable.Variable):
outs = (outs,)
else:
msg = ('A tuple of Variables or a Variable are expected, but {} '
'is returned.'.format(type(outs)))
raise RuntimeError(msg)
return outs
def forward(self, inputs):
xs = [variable.Variable(x, volatile=True) for x in inputs]
outs = self._call_func(xs)
return tuple(out.data for out in outs)
def backward(self, inputs, grads):
xs = [variable.Variable(x, volatile=False) for x in inputs]
outs = self._call_func(xs)
_DummyFunction(grads)(*outs).backward()
return tuple(x.grad for x in xs)
def forget(func, *xs):
"""Call a function without storing internal results.
On a forward propagation Chainer stores all internal results of
:class:`Function` on a computational graph as they are required on
backward-propagation. These results consume too much memory when the
internal results are too large. This method **forgets** such internal
results on forward propagation, and still supports back-propagation with
recalculation.
In a forward propagation, this method calls a given function with given
variables without creating a computational graph. That means, no internal
results are stored. In a backward propagation this method calls the given
function again to create a computational graph to execute back-propagation.
This method reduces internal memory usage. Instead it requires more
calculation time as it calls the function twice.
.. admonition:: Example
Let ``f`` be a function defined as:
>>> def f(a, b):
... return a + b + a
and, ``x`` and ``y`` be :class:`~chainer.Variable`:
>>> x = chainer.Variable(np.random.uniform(-1, 1, 5).astype('f'))
>>> y = chainer.Variable(np.random.uniform(-1, 1, 5).astype('f'))
When ``z`` is calculated as ``z = f(x, y)``, its internal result
``x + y`` is stored in memory. Instead if you call ``f`` with
:meth:`forget`:
>>> z = F.forget(f, x, y)
internal ``x + y`` is forgotten.
.. note::
The method does not support functions behaving randomly, such as
:meth:`~chainer.functions.dropout` and
:meth:`~chainer.functions.negative_sampling`. It is because first results
of these function differ from the second one.
Args:
func (callable): A function to call. It needs to be called with
:class:`~chainer.Variable` object(s) and to return a
:class:`~chainer.Variable` object or a tuple of
:class:`~chainer.Variable` objects.
xs (~chainer.Variable): Argument variables of the function.
Returns:
~chainer.Variable: A variable ``func`` returns. If it returns a tuple,
the method returns a tuple too.
"""
return Forget(func)(*xs)
|
the-stack_0_969 |
""" tpm.py
Wrapper classes for swtpm
"""
# pylint: disable=R0902,R0913,R0914,C0302,W0703
#
# swtpm_setup.py
#
# Authors: Stefan Berger <[email protected]>
#
# (c) Copyright IBM Corporation 2020
#
import os
import socket
import struct
import subprocess
import time
# TPM1.2 imports
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import hashes, hmac
from cryptography.hazmat.primitives.asymmetric import padding
from cryptography.hazmat.primitives.asymmetric.rsa import RSAPublicNumbers
from py_swtpm_setup.swtpm_utils import logit, logerr, sha1
CMD_INIT = 0x2
CMD_SHUTDOWN = 0x3
CMD_GET_INFO = 0x12
TPMLIB_INFO_TPMSPECIFICATION = 1
TPMLIB_INFO_TPMATTRIBUTES = 2
#
# swtpm base class for TPM 1.2 and TPM 2.0
#
class Swtpm:
""" Swtpm is the base class for usage of swtpm as TPM 1.2 or TPM 2 """
def __init__(self, swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass, is_tpm2=False):
""" Class constructor
swtpm_exec_l is a list like ["swtpm", "socket"]
"""
self.swtpm_exec_l = swtpm_exec_l
self.state_path = state_path
self.keyopt = keyopt
self.logfile = logfile
self.fds_to_pass = fds_to_pass
self.is_tpm2 = is_tpm2
self.pidfile = None
self.swtpm_proc = None
self.data_client_socket = None
self.data_swtpm_socket = None
self.ctrl_client_socket = None
self.ctrl_swtpm_socket = None
# Probe the socket domain; Linux only has socket.AF_UNIX, Cygwin AF_INET
self.socket_domain = socket.AF_UNIX
try:
s1, s2 = socket.socketpair(self.socket_domain)
s1.close()
s2.close()
except ValueError: # Cygwin gives a ValueError
self.socket_domain = socket.AF_INET
def start(self):
""" The start method starts the TPM 2 """
self.pidfile = os.path.join(self.state_path, ".swtpm_setup.pidfile")
cmdline = self.swtpm_exec_l.copy()
if self.is_tpm2:
cmdline.extend(["--tpm2"])
if self.keyopt:
cmdline.extend(["--key", self.keyopt])
cmdline.extend(["--flags", "not-need-init",
"--tpmstate", "dir=%s" % self.state_path,
"--pid", "file=%s" % self.pidfile])
# cmdline.extend(["--log", "file=/tmp/log,level=20"])
ctr = 0
while ctr < 100:
self.data_client_socket, self.data_swtpm_socket = socket.socketpair(self.socket_domain,
socket.SOCK_STREAM)
os.set_inheritable(self.data_swtpm_socket.fileno(), True)
self.ctrl_client_socket, self.ctrl_swtpm_socket = socket.socketpair(self.socket_domain,
socket.SOCK_STREAM)
os.set_inheritable(self.ctrl_swtpm_socket.fileno(), True)
r_cmdline = cmdline.copy()
r_cmdline.extend(["--server", "type=tcp,fd=%d" % self.data_swtpm_socket.fileno(),
"--ctrl", "type=unixio,clientfd=%d" %
self.ctrl_swtpm_socket.fileno()])
self.remove_pidfile()
# print("starting swtpm: %s\n" % r_cmdline)
try:
pass_fds = [self.data_swtpm_socket.fileno(),
self.ctrl_swtpm_socket.fileno()]
pass_fds.extend(self.fds_to_pass)
self.swtpm_proc = subprocess.Popen(r_cmdline, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, pass_fds=pass_fds)
except Exception as err:
logerr(self.logfile,
"Failed to start swtpm %s: %s\n" % (" ".join(self.swtpm_exec_l), str(err)))
ctr += 1
ctr2 = 0
while True:
# Is it still running?
if self.swtpm_proc.poll():
stderr = self.swtpm_proc.communicate()[0]
print("TPM died? %s\n" % stderr)
self.stop()
break
if os.path.exists(self.pidfile):
print("TPM is listening on Unix socket.")
return 0
ctr2 += 1
time.sleep(0.05)
if ctr2 == 40:
self.stop()
break
return 1
def remove_pidfile(self):
""" Remove the pidfile if it exists """
if self.pidfile:
try:
os.remove(self.pidfile)
except Exception:
pass
def stop(self):
""" Stop the running swtpm instance """
if self.swtpm_proc:
if not self.swtpm_proc.poll():
self.ctrl_shutdown()
try:
self.swtpm_proc.wait(timeout=0.5)
except subprocess.TimeoutExpired:
self.swtpm_proc.kill()
self.swtpm_proc.wait()
self.swtpm_proc = None
self.remove_pidfile()
for sock in [self.data_client_socket, self.data_swtpm_socket,
self.ctrl_client_socket, self.ctrl_swtpm_socket]:
if sock:
sock.close()
self.data_client_socket = None
self.data_swtpm_socket = None
self.ctrl_client_socket = None
self.ctrl_swtpm_socket = None
def destroy(self):
""" Destroy the running swtpm instance """
self.stop()
def transfer(self, req, cmdname, use_ctrl=False):
""" Send a command to swtpm and receive a response """
if use_ctrl:
sock = self.ctrl_client_socket
offset = 0
else:
sock = self.data_client_socket
offset = 6
try:
sock.sendall(req)
rsp = sock.recv(4096)
except Exception as err:
logerr(self.logfile, "transfer error: %s\n" % str(err))
return None, 1
if not use_ctrl:
if len(rsp) < 10:
logerr(self.logfile,
"Response for %s has only %d bytes.\n" % (cmdname, len(rsp)))
return None, 1
returncode = struct.unpack(">I", rsp[offset:offset+4])[0]
if returncode != 0:
logerr(self.logfile, "%s failed: 0x%x\n" % (cmdname, returncode))
return None, 1
return rsp, 0
def ctrl_init(self):
""" Send an Init over the control channel """
req = struct.pack(">I I", CMD_INIT, 0)
_, ret = self.transfer(req, "CMD_INIT", use_ctrl=True)
return ret
def ctrl_shutdown(self):
""" Send an Init over the control channel """
req = struct.pack(">I", CMD_SHUTDOWN)
_, ret = self.transfer(req, "CMD_SHUTDOWN", use_ctrl=True)
return ret
def ctrl_get_tpm_specs_and_attrs(self):
""" Get the TPM specification parameters over the control channel """
req = struct.pack(">I QII", CMD_GET_INFO,
TPMLIB_INFO_TPMSPECIFICATION | TPMLIB_INFO_TPMATTRIBUTES, 0, 0)
rsp, ret = self.transfer(req, "CMD_GET_INFO", use_ctrl=True)
if ret != 0:
return "", 1
length = struct.unpack(">I", rsp[8:12])[0]
# compensate for null-terminated string
length -= 1
data = struct.unpack("%ds" % length, rsp[12:12+length])[0]
return data.decode(), 0
#
# TPM 2 support
#
TPM2_ST_NO_SESSIONS = 0x8001
TPM2_ST_SESSIONS = 0x8002
TPM2_CC_EVICTCONTROL = 0x00000120
TPM2_CC_NV_DEFINESPACE = 0x0000012a
TPM2_CC_PCR_ALLOCATE = 0x0000012b
TPM2_CC_CREATEPRIMARY = 0x00000131
TPM2_CC_NV_WRITE = 0x00000137
TPM2_CC_NV_WRITELOCK = 0x00000138
TPM2_CC_STARTUP = 0x00000144
TPM2_CC_SHUTDOWN = 0x00000145
TPM2_CC_GETCAPABILITY = 0x0000017a
TPM2_SU_CLEAR = 0x0000
TPM2_RH_OWNER = 0x40000001
TPM2_RS_PW = 0x40000009
TPM2_RH_ENDORSEMENT = 0x4000000b
TPM2_RH_PLATFORM = 0x4000000c
TPM2_ALG_RSA = 0x0001
TPM2_ALG_SHA1 = 0x0004
TPM2_ALG_AES = 0x0006
TPM2_ALG_SHA256 = 0x000b
TPM2_ALG_SHA384 = 0x000c
TPM2_ALG_SHA512 = 0x000d
TPM2_ALG_SHA3_256 = 0x0027
TPM2_ALG_SHA3_384 = 0x0028
TPM2_ALG_SHA3_512 = 0x0028
TPM2_ALG_NULL = 0x0010
TPM2_ALG_SM3 = 0x0012
TPM2_ALG_ECC = 0x0023
TPM2_ALG_CFB = 0x0043
TPM2_CAP_PCRS = 0x00000005
TPM2_ECC_NIST_P384 = 0x0004
TPMA_NV_PLATFORMCREATE = 0x40000000
TPMA_NV_AUTHREAD = 0x40000
TPMA_NV_NO_DA = 0x2000000
TPMA_NV_PPWRITE = 0x1
TPMA_NV_PPREAD = 0x10000
TPMA_NV_OWNERREAD = 0x20000
TPMA_NV_WRITEDEFINE = 0x2000
# Use standard EK Cert NVRAM, EK and SRK handles per IWG spec.
# "TCG TPM v2.0 Provisioning Guide"; Version 1.0, Rev 1.0, March 15, 2017
# Table 2
TPM2_NV_INDEX_RSA2048_EKCERT = 0x01c00002
TPM2_NV_INDEX_RSA2048_EKTEMPLATE = 0x01c00004
TPM2_NV_INDEX_RSA3072_HI_EKCERT = 0x01c0001c
TPM2_NV_INDEX_RSA3072_HI_EKTEMPLATE = 0x01c0001d
# For ECC follow "TCG EK Credential Profile For TPM Family 2.0; Level 0"
# Specification Version 2.1; Revision 13; 10 December 2018
TPM2_NV_INDEX_PLATFORMCERT = 0x01c08000
TPM2_NV_INDEX_ECC_SECP384R1_HI_EKCERT = 0x01c00016
TPM2_NV_INDEX_ECC_SECP384R1_HI_EKTEMPLATE = 0x01c00017
TPM2_EK_RSA_HANDLE = 0x81010001
TPM2_EK_RSA3072_HANDLE = 0x8101001c
TPM2_EK_ECC_SECP384R1_HANDLE = 0x81010016
TPM2_SPK_HANDLE = 0x81000001
NONCE_EMPTY = struct.pack('>H', 0)
NONCE_RSA2048 = struct.pack('>H256s', 0x100, ('\0' * 0x100).encode())
NONCE_RSA3072 = struct.pack('>H384s', 0x180, ('\0' * 0x180).encode())
NONCE_ECC_384 = struct.pack('>H48s', 0x30, ('\0' * 0x30).encode())
PCR_BANKS_TO_NAMES = {
TPM2_ALG_SHA1: "sha1",
TPM2_ALG_SHA256: "sha256",
TPM2_ALG_SHA384: "sha384",
TPM2_ALG_SHA512: "sha512",
TPM2_ALG_SM3: "sm3-256",
TPM2_ALG_SHA3_256: "sha3-256",
TPM2_ALG_SHA3_384: "sha3-384",
TPM2_ALG_SHA3_512: "sha3-512",
}
BANK_NAMES_TO_ALGID = {
"sha1": TPM2_ALG_SHA1,
"sha256": TPM2_ALG_SHA256,
"sha384": TPM2_ALG_SHA384,
"sha512": TPM2_ALG_SHA512,
"sm3-256": TPM2_ALG_SM3,
"sha3-256": TPM2_ALG_SHA3_256,
"sha3-384": TPM2_ALG_SHA3_384,
"sha3-512": TPM2_ALG_SHA3_512,
}
class Swtpm2(Swtpm):
""" Class for manufacturing a swtpm TPM 2 """
def __init__(self, swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass):
""" Class constructor
swtpm_exec_l is a list like ["swtpm", "socket"]
"""
super(Swtpm2, self).__init__(swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass,
is_tpm2=True)
def shutdown(self):
""" Shut down the TPM 2 """
fmt = ">HII H"
req = struct.pack(fmt,
TPM2_ST_NO_SESSIONS, struct.calcsize(fmt), TPM2_CC_SHUTDOWN,
TPM2_SU_CLEAR)
_, ret = self.transfer(req, "TPM2_Shutdown")
return ret
def run_swtpm_bios(self):
""" Startup the TPM 2 """
fmt = '>HII H'
req = struct.pack(fmt,
TPM2_ST_NO_SESSIONS, struct.calcsize(fmt), TPM2_CC_STARTUP,
TPM2_SU_CLEAR)
_, ret = self.transfer(req, "TPM2_Startup")
return ret
def get_all_pcr_banks(self):
""" Get all available PCR banks """
fmt = '>HII III'
req = struct.pack(fmt,
TPM2_ST_NO_SESSIONS, struct.calcsize(fmt), TPM2_CC_GETCAPABILITY,
TPM2_CAP_PCRS, 0, 64)
rsp, ret = self.transfer(req, "TPM2_GetCapability")
if ret != 0:
return [], 1
count = struct.unpack('>H', rsp[17:19])[0]
offset = 19
res = []
for _ in range(count):
bank, length = struct.unpack('>HB', rsp[offset:offset+3])
name = PCR_BANKS_TO_NAMES[bank]
if name:
res.append(name)
else:
res.append('%02x' % bank)
offset += 2 + 1 + length
return res, 0
def set_active_pcr_banks(self, pcr_banks, all_pcr_banks):
""" Set the list of active PCR banks to the one the user wants """
pcrselects = "".encode()
count = 0
active = []
# enable the ones the user wants
for pcr_bank in pcr_banks:
if pcr_bank not in all_pcr_banks:
# Skip if not even available
continue
try:
hashalg = BANK_NAMES_TO_ALGID[pcr_bank]
except KeyError:
continue
active.insert(0, pcr_bank)
pcrselects += struct.pack('>H BBBB', hashalg, 3, 0xff, 0xff, 0xff)
#print("activate hashalg = %d\n" % hashalg)
count += 1
if len(active) == 0:
logerr(self.logfile,
"No PCR banks could be allocated. None of the selected algorithms are "
"supported.\n")
return [], 1
# disable the rest
for pcr_bank in all_pcr_banks:
if pcr_bank in pcr_banks:
# Skip if to activate
continue
try:
hashalg = BANK_NAMES_TO_ALGID[pcr_bank]
except KeyError:
continue
#print("deactivate hashalg = %d\n" % hashalg)
pcrselects += struct.pack('>H BBBB', hashalg, 3, 0, 0, 0)
count += 1
authblock = struct.pack('>I HBH', TPM2_RS_PW, 0, 0, 0)
fmt = '>HII I I%ds I %ds' % (len(authblock), len(pcrselects))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_PCR_ALLOCATE,
TPM2_RH_PLATFORM,
len(authblock), authblock,
count,
pcrselects)
_, ret = self.transfer(req, "TPM2_PCR_Allocate")
return active, ret
def evictcontrol(self, curr_handle, perm_handle):
""" Make object at the curr_handler permanent with the perm_handle """
authblock = struct.pack('>IHBH', TPM2_RS_PW, 0, 0, 0)
fmt = '>HII II I%ds I' % len(authblock)
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_EVICTCONTROL,
TPM2_RH_OWNER, curr_handle,
len(authblock), authblock,
perm_handle)
_, ret = self.transfer(req, "TPM2_EvictControl")
return ret
def createprimary_ek_rsa(self, rsa_keysize, allowsigning, decryption):
""" Create an RSA Ek """
if rsa_keysize == 2048:
authpolicy = b'\x83\x71\x97\x67\x44\x84\xb3\xf8\x1a\x90\xcc\x8d' \
b'\x46\xa5\xd7\x24\xfd\x52\xd7\x6e\x06\x52\x0b\x64' \
b'\xf2\xa1\xda\x1b\x33\x14\x69\xaa'
keyflags = 0
symkeylen = 128
havenonce = True
addlen = 0
elif rsa_keysize == 3072:
authpolicy = b'\xB2\x6E\x7D\x28\xD1\x1A\x50\xBC\x53\xD8\x82\xBC' \
b'\xF5\xFD\x3A\x1A\x07\x41\x48\xBB\x35\xD3\xB4\xE4' \
b'\xCB\x1C\x0A\xD9\xBD\xE4\x19\xCA\xCB\x47\xBA\x09' \
b'\x69\x96\x46\x15\x0F\x9F\xC0\x00\xF3\xF8\x0E\x12'
keyflags = 0x40
symkeylen = 256
havenonce = False
addlen = 16
if allowsigning and decryption:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# adminWithPolicy, sign, decrypt
keyflags = keyflags | 0x000600b2
# symmetric: TPM_ALG_NULL
symkeydata = struct.pack(">H", TPM2_ALG_NULL)
off = 72 + addlen
elif allowsigning:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# adminWithPolicy, sign
keyflags = keyflags | 0x000400b2
# symmetric: TPM_ALG_NULL
symkeydata = struct.pack(">H", TPM2_ALG_NULL)
off = 72 + addlen
else:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# adminWithPolicy, restricted, decrypt
keyflags = keyflags | 0x000300b2
# symmetric: TPM_ALG_AES, 128bit or 256bit, TPM_ALG_CFB
symkeydata = struct.pack(">HHH", TPM2_ALG_AES, symkeylen, TPM2_ALG_CFB)
off = 76 + addlen
return self._createprimary_rsa(TPM2_RH_ENDORSEMENT, keyflags, symkeydata, authpolicy,
rsa_keysize, havenonce, off)
def _createprimary_rsa(self, primaryhandle, keyflags, symkeydata, authpolicy,
rsa_keysize, havenonce, off):
""" Create an RSA key with the given parameters """
if rsa_keysize == 2048:
nonce = NONCE_RSA2048
hashalg = TPM2_ALG_SHA256
elif rsa_keysize == 3072:
if not havenonce:
nonce = NONCE_EMPTY
else:
nonce = NONCE_RSA3072
hashalg = TPM2_ALG_SHA384
else:
logerr(self.logfile, "Unsupported keysize %d\n" % rsa_keysize)
return b'', "", 0, 1
authblock = struct.pack('>IHBH', TPM2_RS_PW, 0, 0, 0)
fmt = '>HHI H%ds %ds HH I %ds' % \
(len(authpolicy), len(symkeydata), len(nonce))
public = struct.pack(fmt,
TPM2_ALG_RSA, hashalg, keyflags,
len(authpolicy), authpolicy,
symkeydata,
TPM2_ALG_NULL, rsa_keysize,
0,
nonce)
ek_template = public
fmt = ">HII I I%ds HI H%ds IH" % (len(authblock), len(public))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_CREATEPRIMARY,
primaryhandle,
len(authblock), authblock,
4, 0,
len(public), public,
0, 0)
rsp, ret = self.transfer(req, "TPM2_CreatePrimary")
if ret != 0:
return b'', "", 0, 1
handle = struct.unpack(">I", rsp[10:14])[0]
modlen = struct.unpack(">H", rsp[off:off+2])[0]
if modlen != rsa_keysize >> 3:
logerr(self.logfile, "RSA key: Getting modulus from wrong offset %d\n" % off)
return b'', "", 0, 1
off += 2
ekparam = struct.unpack(">%ds" % modlen, rsp[off:off+modlen])[0].hex()
return ek_template, ekparam, handle, 0
def _createprimary_ecc(self, primaryhandle, keyflags, symkeydata, authpolicy,
curveid, hashalg, nonce, off):
""" Create an ECC key with the given parameters """
authblock = struct.pack('>IHBH', TPM2_RS_PW, 0, 0, 0)
fmt = '>HHI H%ds %ds HH H %ds%ds' % \
(len(authpolicy), len(symkeydata), len(nonce), len(nonce))
public = struct.pack(fmt,
TPM2_ALG_ECC, hashalg, keyflags,
len(authpolicy), authpolicy,
symkeydata,
TPM2_ALG_NULL, curveid,
TPM2_ALG_NULL,
nonce, nonce)
ek_template = public
fmt = '>HII I I%ds HI H%ds IH' % (len(authblock), len(public))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_CREATEPRIMARY,
primaryhandle,
len(authblock), authblock,
4, 0,
len(public), public,
0, 0)
rsp, ret = self.transfer(req, "TPM2_CreatePrimary")
if ret != 0:
return b'', "", 0, 1
handle = struct.unpack('>I', rsp[10:14])[0]
if curveid == TPM2_ECC_NIST_P384:
exp_ksize = 48
cid = "secp384r1"
else:
logerr(self.logfile, "Unknown curveid 0x%x\n" % curveid)
return b'', "", 0, 1
ksize1 = struct.unpack('>H', rsp[off:off+2])[0]
off2 = off + 2 + ksize1
ksize2 = struct.unpack('>H', rsp[off2:off2+2])[0]
if ksize1 != exp_ksize or ksize2 != exp_ksize:
logerr(self.logfile, "ECC: Getting key parameters from wrong offset\n")
return b'', "", 0, 1
off += 2
xparam = struct.unpack(">%ds" % ksize1, rsp[off:off+ksize1])[0]
off2 += 2
yparam = struct.unpack(">%ds" % ksize2, rsp[off2:off2+ksize2])[0]
ekparam = "x=%s,y=%s,id=%s" % (xparam.hex(), yparam.hex(), cid)
return ek_template, ekparam, handle, 0
def createprimary_spk_ecc_nist_p384(self):
""" Create a NIST p384 ECC SPK """
keyflags = 0x00030472
symkeydata = struct.pack('>HHH', TPM2_ALG_AES, 256, TPM2_ALG_CFB)
authpolicy = b''
off = 42
return self._createprimary_ecc(TPM2_RH_OWNER, keyflags, symkeydata, authpolicy,
TPM2_ECC_NIST_P384, TPM2_ALG_SHA384, NONCE_ECC_384, off)
def createprimary_spk_rsa(self, rsa_keysize):
""" Create a primary RSA key with the given keysize """
keyflags = 0x00030472
authpolicy = ''.encode()
if rsa_keysize == 2048:
symkeylen = 128
elif rsa_keysize == 3072:
symkeylen = 256
symkeydata = struct.pack('>HHH', TPM2_ALG_AES, symkeylen, TPM2_ALG_CFB)
off = 44
return self._createprimary_rsa(TPM2_RH_OWNER, keyflags, symkeydata, authpolicy,
rsa_keysize, True, off)
def create_spk(self, isecc, rsa_keysize):
""" Create either an ECC or RSA storage primary key """
if isecc:
_, _, handle, ret = self.createprimary_spk_ecc_nist_p384()
else:
_, _, handle, ret = self.createprimary_spk_rsa(rsa_keysize)
if ret != 0:
return 1
ret = self.evictcontrol(handle, TPM2_SPK_HANDLE)
if ret == 0:
logit(self.logfile,
"Successfully created storage primary key with handle 0x%x.\n" % TPM2_SPK_HANDLE)
return ret
def createprimary_ek_ecc_nist_p384(self, allowsigning, decryption):
""" Create en ECC EK key that may be allowed to sign and/or decrypt """
if allowsigning and decryption:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# userWithAuth, adminWithPolicy, sign, decrypt
keyflags = 0x000600f2
# symmetric: TPM_ALG_NULL
symkeydata = struct.pack(">H", TPM2_ALG_NULL)
off = 86
elif allowsigning:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# userWithAuth, adminWithPolicy, sign
keyflags = 0x000400f2
# symmetric: TPM_ALG_NULL
symkeydata = struct.pack(">H", TPM2_ALG_NULL)
off = 86
else:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# userWithAuth, adminWithPolicy, restricted, decrypt
keyflags = 0x000300f2
# symmetric: TPM_ALG_AES, 256bit, TPM_ALG_CFB
symkeydata = struct.pack(">HHH", TPM2_ALG_AES, 256, TPM2_ALG_CFB)
off = 90
# authPolicy from Ek Credential Profile; Spec v 2.1; rev12; p. 43
authpolicy = b'\xB2\x6E\x7D\x28\xD1\x1A\x50\xBC\x53\xD8\x82\xBC' \
b'\xF5\xFD\x3A\x1A\x07\x41\x48\xBB\x35\xD3\xB4\xE4' \
b'\xCB\x1C\x0A\xD9\xBD\xE4\x19\xCA\xCB\x47\xBA\x09' \
b'\x69\x96\x46\x15\x0F\x9F\xC0\x00\xF3\xF8\x0E\x12'
ek_template, ekparam, handle, ret = \
self._createprimary_ecc(TPM2_RH_ENDORSEMENT, keyflags, symkeydata, authpolicy,
TPM2_ECC_NIST_P384, TPM2_ALG_SHA384, NONCE_EMPTY, off)
if ret != 0:
logerr(self.logfile, "create_spk_ecc failed\n")
return ek_template, ekparam, handle, ret
def create_ek(self, isecc, rsa_keysize, allowsigning, decryption, lock_nvram):
""" Create an ECC or RSA EK """
if isecc:
tpm2_ek_handle = TPM2_EK_ECC_SECP384R1_HANDLE
keytype = "ECC"
nvindex = TPM2_NV_INDEX_ECC_SECP384R1_HI_EKTEMPLATE
else:
if rsa_keysize == 2048:
tpm2_ek_handle = TPM2_EK_RSA_HANDLE
nvindex = TPM2_NV_INDEX_RSA2048_EKTEMPLATE
elif rsa_keysize == 3072:
tpm2_ek_handle = TPM2_EK_RSA3072_HANDLE
nvindex = TPM2_NV_INDEX_RSA3072_HI_EKTEMPLATE
keytype = "RSA %d" % rsa_keysize
if isecc:
ek_template, ekparam, handle, ret = \
self.createprimary_ek_ecc_nist_p384(allowsigning, decryption)
else:
ek_template, ekparam, handle, ret = \
self.createprimary_ek_rsa(rsa_keysize, allowsigning, decryption)
if ret == 0:
ret = self.evictcontrol(handle, tpm2_ek_handle)
if ret != 0:
logerr(self.logfile, "create_ek failed\n")
return "", 1
logit(self.logfile,
"Successfully created %s EK with handle 0x%x.\n" % (keytype, tpm2_ek_handle))
if allowsigning:
nvindexattrs = TPMA_NV_PLATFORMCREATE | \
TPMA_NV_AUTHREAD | \
TPMA_NV_OWNERREAD | \
TPMA_NV_PPREAD | \
TPMA_NV_PPWRITE | \
TPMA_NV_NO_DA | \
TPMA_NV_WRITEDEFINE
ret = self.write_nvram(nvindex, nvindexattrs, ek_template, lock_nvram, "EK template")
if ret == 0:
logit(self.logfile,
"Successfully created NVRAM area 0x%x for %s EK template.\n" %
(nvindex, keytype))
return ekparam, ret
def nv_definespace(self, nvindex, nvindexattrs, size):
""" Define an NVIndex with attributes and given size """
authblock = struct.pack(">IHBH", TPM2_RS_PW, 0, 0, 0)
nvpublic = struct.pack('>IHI H H',
nvindex, TPM2_ALG_SHA256, nvindexattrs,
0,
size)
fmt = ">HII I I%ds H H%ds" % (len(authblock), len(nvpublic))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_NV_DEFINESPACE,
TPM2_RH_PLATFORM,
len(authblock), authblock,
0,
len(nvpublic), nvpublic)
_, ret = self.transfer(req, "TPM2_NV_DefineSpace")
return ret
def nv_write(self, nvindex, data):
""" Write the data into the given NVIndex """
authblock = struct.pack(">IHBH", TPM2_RS_PW, 0, 0, 0)
offset = 0
stepsize = 1024
while offset < len(data):
if offset + stepsize < len(data):
buf = data[offset : offset + stepsize]
else:
buf = data[offset : len(data)]
fmt = ">HII II I%ds H%dsH" % (len(authblock), len(buf))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_NV_WRITE,
TPM2_RH_PLATFORM, nvindex,
len(authblock), authblock,
len(buf), buf, offset)
_, ret = self.transfer(req, "TPM2_NV_Write")
if ret != 0:
return 1
offset += stepsize
return 0
def nv_writelock(self, nvindex):
""" Lock the given index """
authblock = struct.pack(">IHBH", TPM2_RS_PW, 0, 0, 0)
fmt = ">HII II I%ds" % (len(authblock))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_NV_WRITELOCK,
TPM2_RH_PLATFORM, nvindex,
len(authblock), authblock)
_, ret = self.transfer(req, "TPM2_NV_WriteLock")
return ret
def write_nvram(self, nvindex, nvindexattrs, data, lock_nvram, purpose):
""" Define NVRAM space, write data to it and lock it if wanted """
ret = self.nv_definespace(nvindex, nvindexattrs, len(data))
if ret != 0:
logerr(self.logfile, "Could not create NVRAM area 0x%x for %s.\n" % (nvindex, purpose))
return 1
ret = self.nv_write(nvindex, data)
if ret != 0:
logerr(self.logfile,
"Could not write %s into NVRAM area 0x%x.\n" % (purpose, nvindex))
return 1
if lock_nvram:
ret = self.nv_writelock(nvindex)
if ret != 0:
logerr(self.logfile, "Could not lock EK template NVRAM area 0x%x.\n" % nvindex)
return 1
return ret
def write_ek_cert_nvram(self, isecc, rsa_keysize, lock_nvram, ekcert):
""" Write the given ekcert into an NVRAM area appropriate for the key type and size """
if not isecc:
if rsa_keysize == 2048:
nvindex = TPM2_NV_INDEX_RSA2048_EKCERT
elif rsa_keysize == 3072:
nvindex = TPM2_NV_INDEX_RSA3072_HI_EKCERT
keytype = "RSA %d" % rsa_keysize
else:
nvindex = TPM2_NV_INDEX_ECC_SECP384R1_HI_EKCERT
keytype = "ECC"
nvindexattrs = TPMA_NV_PLATFORMCREATE | \
TPMA_NV_AUTHREAD | \
TPMA_NV_OWNERREAD | \
TPMA_NV_PPREAD | \
TPMA_NV_PPWRITE | \
TPMA_NV_NO_DA | \
TPMA_NV_WRITEDEFINE
ret = self.write_nvram(nvindex, nvindexattrs, ekcert, lock_nvram, "EK Certificate")
if ret == 0:
logit(self.logfile,
"Successfully created NVRAM area 0x%x for %s EK certificate.\n" %
(nvindex, keytype))
else:
logerr(self.logfile,
"Could not create NVRAM area 0x%x for %s EK certificate.\n" %
(nvindex, keytype))
return ret
def write_platform_cert_nvram(self, lock_nvram, platformcert):
""" Write the platform certificate into an NVRAM area """
nvindex = TPM2_NV_INDEX_PLATFORMCERT
nvindexattrs = TPMA_NV_PLATFORMCREATE | \
TPMA_NV_AUTHREAD | \
TPMA_NV_OWNERREAD | \
TPMA_NV_PPREAD | \
TPMA_NV_PPWRITE | \
TPMA_NV_NO_DA | \
TPMA_NV_WRITEDEFINE
ret = self.write_nvram(nvindex, nvindexattrs, platformcert, lock_nvram,
"Platform Certificate")
if ret == 0:
logit(self.logfile,
"Successfully created NVRAM area 0x%x for platform certificate.\n" % nvindex)
else:
logerr(self.logfile,
"Could not create NVRAM area 0x%x for platform certificate.\n" % nvindex)
return ret
#
# TPM 1.2 support
#
TPM_TAG_RQU_COMMAND = 0x00c1
TPM_TAG_RQU_AUTH1_COMMAND = 0x00c2
TPM_ORD_OIAP = 0x0000000A
TPM_ORD_OSAP = 0x0000000B
TPM_ORD_TAKE_OWNERSHIP = 0x0000000D
TPM_ORD_OWNER_CLEAR = 0x0000005B
TPM_ORD_PHYSICAL_ENABLE = 0x0000006F
TPM_ORD_PHYSICAL_SET_DEACTIVATED = 0x00000072
TPM_ORD_STARTUP = 0x00000099
TPM_ORD_NV_DEFINE_SPACE = 0x000000CC
TPM_ORD_NV_WRITE_VALUE = 0x000000CD
TSC_ORD_PHYSICAL_PRESENCE = 0x4000000A
TPM_ST_CLEAR = 0x0001
TPM_PHYSICAL_PRESENCE_CMD_ENABLE = 0x0020
TPM_PHYSICAL_PRESENCE_PRESENT = 0x0008
TPM_ALG_RSA = 0x00000001
TPM_KEY_STORAGE = 0x0011
TPM_AUTH_ALWAYS = 0x01
TPM_PID_OWNER = 0x0005
TPM_ES_RSAESOAEP_SHA1_MGF1 = 0x0003
TPM_SS_NONE = 0x0001
TPM_TAG_PCR_INFO_LONG = 0x0006
TPM_TAG_NV_ATTRIBUTES = 0x0017
TPM_TAG_NV_DATA_PUBLIC = 0x0018
TPM_TAG_KEY12 = 0x0028
TPM_LOC_ZERO = 0x01
TPM_LOC_ALL = 0x1f
TPM_NV_INDEX_D_BIT = 0x10000000
TPM_NV_INDEX_EKCERT = 0xF000
TPM_NV_INDEX_PLATFORMCERT = 0xF002
TPM_NV_INDEX_LOCK = 0xFFFFFFFF
TPM_NV_PER_OWNERREAD = 0x00020000
TPM_NV_PER_OWNERWRITE = 0x00000002
TPM_ET_OWNER = 0x02
TPM_ET_NV = 0x0b
TPM_KH_EK = 0x40000006
class Swtpm12(Swtpm):
""" Class for manufacturing a swtpm TPM 1.2 """
def __init__(self, swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass):
""" Class constructor
swtpm_exec_l is a list like ["swtpm", "socket"]
"""
super(Swtpm12, self).__init__(swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass)
def startup(self, startup_type):
""" Run TPM_Startup() """
fmt = ">HII H"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_STARTUP,
startup_type)
_, ret = self.transfer(req, "TPM_Startup")
return ret
def tsc_physicalpresence(self, physicalpresence):
""" Run TSC_PhysicalPresence """
fmt = ">HII H"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TSC_ORD_PHYSICAL_PRESENCE,
physicalpresence)
_, ret = self.transfer(req, "TSC_PhysicalPresence")
return ret
def physical_enable(self):
""" Run TPM_PhysicalEnable """
fmt = ">HII"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_PHYSICAL_ENABLE)
_, ret = self.transfer(req, "TSC_PhysicalEnable")
return ret
def physical_set_deactivated(self, state):
""" Run TPM_PhysicalSetDeactivated """
fmt = ">HI I B"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt),
TPM_ORD_PHYSICAL_SET_DEACTIVATED,
state)
_, ret = self.transfer(req, "TPM_PhysiclaSetDaectivated")
return ret
def run_swtpm_bios(self):
""" Initialize the swtpm """
if self.startup(TPM_ST_CLEAR) or \
self.tsc_physicalpresence(TPM_PHYSICAL_PRESENCE_CMD_ENABLE) or \
self.tsc_physicalpresence(TPM_PHYSICAL_PRESENCE_PRESENT) or \
self.physical_enable() or \
self.physical_set_deactivated(0):
return 1
return 0
def create_endorsement_key_pair(self):
""" Create an endorsement key for the TPM 1.2 """
req = b'\x00\xc1\x00\x00\x00\x36\x00\x00\x00\x78\x38\xf0\x30\x81\x07\x2b' \
b'\x0c\xa9\x10\x98\x08\xc0\x4B\x05\x11\xc9\x50\x23\x52\xc4\x00\x00' \
b'\x00\x01\x00\x03\x00\x02\x00\x00\x00\x0c\x00\x00\x08\x00\x00\x00' \
b'\x00\x02\x00\x00\x00\x00'
rsp, ret = self.transfer(req, "TPM_CreateEndorsementKeyPair")
if ret != 0:
return b'', 1
length = struct.unpack(">I", rsp[34:38])[0]
if length != 256:
logerr(self.logfile, "Offset to EK Public key is wrong.\n")
return b'', 1
pubek = struct.unpack("256s", rsp[38:38+256])[0]
return pubek, 0
def oiap(self):
""" Create an OIAP session """
fmt = ">HII"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_OIAP)
rsp, ret = self.transfer(req, "TPM_OIAP")
if ret != 0:
return b'', 0, 1
authhandle = struct.unpack(">I", rsp[10:14])[0]
nonce_even = struct.unpack("20s", rsp[14:34])[0]
return nonce_even, authhandle, 0
def take_ownership(self, ownerpass_digest, srkpass_digest, pubek):
""" Run TPM_TakeOwernship """
exponent = int('10001', 16)
modulus = int(pubek.hex(), 16)
pubekkey = RSAPublicNumbers(exponent, modulus).public_key(backend=default_backend())
oaep = padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA1()),
algorithm=hashes.SHA1(),
label="TCPA".encode()
)
enc_owner_auth = pubekkey.encrypt(ownerpass_digest, oaep)
enc_srk_auth = pubekkey.encrypt(srkpass_digest, oaep)
nonce_even, auth_handle, ret = self.oiap()
if ret != 0:
return 1
tpm_rsa_key_parms = struct.pack(">III",
2048, # keyLength
2, # numPrimes
0) # exponentSize
tpm_key_parms = struct.pack(">I HH I%ds" % (len(tpm_rsa_key_parms)),
TPM_ALG_RSA, # algorithmId
TPM_ES_RSAESOAEP_SHA1_MGF1, # encScheme
TPM_SS_NONE, # sigScheme
len(tpm_rsa_key_parms), tpm_rsa_key_parms)
tpm_key12 = struct.pack(">HH HIB %ds I I I" %
(len(tpm_key_parms)),
TPM_TAG_KEY12, 0,
TPM_KEY_STORAGE, # keyUsage
0, # keyFlags
TPM_AUTH_ALWAYS, # authDataUsage
tpm_key_parms,
0,
0,
0)
fmt_auth = ">I20sB20s"
fmt = ">HII H I256s I256s %ds" % len(tpm_key12)
nonce_odd = os.urandom(20)
req = struct.pack(fmt,
TPM_TAG_RQU_AUTH1_COMMAND,
struct.calcsize(fmt) + struct.calcsize(fmt_auth),
TPM_ORD_TAKE_OWNERSHIP,
TPM_PID_OWNER,
len(enc_owner_auth), enc_owner_auth,
len(enc_srk_auth), enc_srk_auth,
tpm_key12)
# req needs authhandle, nonceodd & ownerAuth appended
shainput = struct.unpack("%ds" % (len(req) - 6), req[6:len(req)])[0]
in_param_digest = sha1(shainput)
continue_auth_session = 0
in_auth_setup_params = struct.pack(">20s20sB", nonce_even, nonce_odd, continue_auth_session)
macinput = struct.pack(">20s %ds" % len(in_auth_setup_params),
in_param_digest, in_auth_setup_params)
myhmac = hmac.HMAC(ownerpass_digest, hashes.SHA1(), backend=default_backend())
myhmac.update(macinput)
owner_auth = myhmac.finalize()
req += struct.pack(fmt_auth, auth_handle, nonce_odd, continue_auth_session, owner_auth)
_, ret = self.transfer(req, "TPM_TakeOwnership")
return ret
def ownerclear(self, ownerpass_digest):
""" clear TPM ownership """
nonce_even, auth_handle, ret = self.oiap()
if ret != 0:
return 1
nonce_odd = os.urandom(20)
fmt_auth = ">I20sB20s"
fmt = ">H II"
req = struct.pack(fmt,
TPM_TAG_RQU_AUTH1_COMMAND,
struct.calcsize(fmt) + struct.calcsize(fmt_auth), TPM_ORD_OWNER_CLEAR)
shainput = struct.unpack("%ds" % (len(req) - 6), req[6:len(req)])[0]
in_param_digest = sha1(shainput)
continue_auth_session = 0
in_auth_setup_params = struct.pack(">20s20sB", nonce_even, nonce_odd, continue_auth_session)
macinput = struct.pack(">20s %ds" % len(in_auth_setup_params),
in_param_digest, in_auth_setup_params)
myhmac = hmac.HMAC(ownerpass_digest, hashes.SHA1(), backend=default_backend())
myhmac.update(macinput)
owner_auth = myhmac.finalize()
req += struct.pack(fmt_auth, auth_handle, nonce_odd, continue_auth_session, owner_auth)
_, ret = self.transfer(req, "TPM_ClearOwner")
return ret
def nv_define_space(self, nvindex, nvindexattrs, size):
""" Define an nvindex with the given permissions and size """
pcr_info_short = struct.pack(">HBBB B 20s",
3, 0, 0, 0,
TPM_LOC_ALL,
('\x00' * 20).encode())
fmt = ">HI %ds%ds HI BBBI" % (len(pcr_info_short), len(pcr_info_short))
nv_data_public = struct.pack(fmt,
TPM_TAG_NV_DATA_PUBLIC, nvindex,
pcr_info_short, pcr_info_short,
TPM_TAG_NV_ATTRIBUTES, nvindexattrs,
0, 0, 0, size)
fmt = ">HII %ds 20s" % len(nv_data_public)
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_NV_DEFINE_SPACE,
nv_data_public,
('\x00' * 20).encode())
_, ret = self.transfer(req, "TPM_NV_DefineSpace")
return ret
def nv_write_value(self, nvindex, data):
""" Write data to an index """
fmt = ">HII III%ds" % len(data)
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_NV_WRITE_VALUE,
nvindex, 0, len(data), data)
_, ret = self.transfer(req, "TPM_NV_WriteValue")
return ret
def write_ek_cert_nvram(self, data):
""" Write the EK Certificate into NVRAM """
nvindex = TPM_NV_INDEX_EKCERT|TPM_NV_INDEX_D_BIT
ret = self.nv_define_space(nvindex, TPM_NV_PER_OWNERREAD|TPM_NV_PER_OWNERWRITE, len(data))
if ret != 0:
return 1
ret = self.nv_write_value(nvindex, data)
if ret != 0:
return 1
return 0
def write_platform_cert_nvram(self, data):
""" Write the Platform Certificate into NVRAM """
nvindex = TPM_NV_INDEX_PLATFORMCERT|TPM_NV_INDEX_D_BIT
ret = self.nv_define_space(nvindex, TPM_NV_PER_OWNERREAD|TPM_NV_PER_OWNERWRITE, len(data))
if ret != 0:
return 1
return self.nv_write_value(nvindex, data)
def nv_lock(self):
""" Lock the NVRAM """
return self.nv_define_space(TPM_NV_INDEX_LOCK, 0, 0)
|
the-stack_0_973 | # -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# 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.
#
import proto # type: ignore
from google.ads.googleads.v8.resources.types import payments_account
__protobuf__ = proto.module(
package="google.ads.googleads.v8.services",
marshal="google.ads.googleads.v8",
manifest={"ListPaymentsAccountsRequest", "ListPaymentsAccountsResponse",},
)
class ListPaymentsAccountsRequest(proto.Message):
r"""Request message for fetching all accessible payments
accounts.
Attributes:
customer_id (str):
Required. The ID of the customer to apply the
PaymentsAccount list operation to.
"""
customer_id = proto.Field(proto.STRING, number=1,)
class ListPaymentsAccountsResponse(proto.Message):
r"""Response message for
[PaymentsAccountService.ListPaymentsAccounts][google.ads.googleads.v8.services.PaymentsAccountService.ListPaymentsAccounts].
Attributes:
payments_accounts (Sequence[google.ads.googleads.v8.resources.types.PaymentsAccount]):
The list of accessible payments accounts.
"""
payments_accounts = proto.RepeatedField(
proto.MESSAGE, number=1, message=payments_account.PaymentsAccount,
)
__all__ = tuple(sorted(__protobuf__.manifest))
|
the-stack_0_974 | """This is used to patch the QApplication style sheet.
It reads the current stylesheet, appends our modifications and sets the new stylesheet.
"""
from PyQt5 import QtWidgets
def patch_qt_stylesheet(use_dark_theme: bool) -> None:
if not use_dark_theme:
return
app = QtWidgets.QApplication.instance()
style_sheet = app.styleSheet()
style_sheet = style_sheet + '''
/* PayToEdit text was being clipped */
QAbstractScrollArea {
padding: 0px;
}
/* In History tab, labels while edited were being clipped (Windows) */
QAbstractItemView QLineEdit {
padding: 0px;
}
'''
app.setStyleSheet(style_sheet)
|
the-stack_0_978 | import json
import os
import re
import urllib.request
import warnings
from typing import Optional, Union, Tuple, Dict
import ee
import pkg_resources
from ee_extra.STAC.utils import _get_platform_STAC
from ee_extra.utils import _load_JSON
def _get_expression_map(img: ee.Image, platformDict: dict) -> dict:
"""Gets the dictionary required for the map parameter i n ee.Image.expression() method.
Args:
img : Image to get the dictionary from.
platformDict : Dictionary retrieved from the _get_STAC_platform() method.
Returns:
Map dictionary for the ee.Image.expression() method.
"""
def lookupS2(img):
return {
"A": img.select("B1"),
"B": img.select("B2"),
"G": img.select("B3"),
"R": img.select("B4"),
"RE1": img.select("B5"),
"RE2": img.select("B6"),
"RE3": img.select("B7"),
"N": img.select("B8"),
"N2": img.select("B8A"),
"WV": img.select("B9"),
"S1": img.select("B11"),
"S2": img.select("B12"),
"lambdaG": 559.8,
"lambdaR": 664.6,
"lambdaN": 832.8,
}
def lookupL8(img):
return {
"A": img.select("B1"),
"B": img.select("B2"),
"G": img.select("B3"),
"R": img.select("B4"),
"N": img.select("B5"),
"S1": img.select("B6"),
"S2": img.select("B7"),
"T1": img.select("B10"),
"T2": img.select("B11"),
"lambdaG": 560.0,
"lambdaR": 655.0,
"lambdaN": 865.0,
}
def lookupL8C2(img):
return {
"A": img.select("SR_B1"),
"B": img.select("SR_B2"),
"G": img.select("SR_B3"),
"R": img.select("SR_B4"),
"N": img.select("SR_B5"),
"S1": img.select("SR_B6"),
"S2": img.select("SR_B7"),
"T1": img.select("ST_B10"),
"lambdaG": 560.0,
"lambdaR": 655.0,
"lambdaN": 865.0,
}
def lookupL45(img):
return {
"B": img.select("B1"),
"G": img.select("B2"),
"R": img.select("B3"),
"N": img.select("B4"),
"S1": img.select("B5"),
"T1": img.select("B6"),
"S2": img.select("B7"),
"lambdaG": 560.0,
"lambdaR": 660.0,
"lambdaN": 830.0,
}
def lookupL45C2(img):
return {
"B": img.select("SR_B1"),
"G": img.select("SR_B2"),
"R": img.select("SR_B3"),
"N": img.select("SR_B4"),
"S1": img.select("SR_B5"),
"T1": img.select("ST_B6"),
"S2": img.select("SR_B7"),
"lambdaG": 560.0,
"lambdaR": 660.0,
"lambdaN": 830.0,
}
def lookupL7(img):
return {
"B": img.select("B1"),
"G": img.select("B2"),
"R": img.select("B3"),
"N": img.select("B4"),
"S1": img.select("B5"),
"T1": img.select("B6"),
"S2": img.select("B7"),
"lambdaG": 560.0,
"lambdaR": 660.0,
"lambdaN": 835.0,
}
def lookupL7C2(img):
return {
"B": img.select("SR_B1"),
"G": img.select("SR_B2"),
"R": img.select("SR_B3"),
"N": img.select("SR_B4"),
"S1": img.select("SR_B5"),
"T1": img.select("ST_B6"),
"S2": img.select("SR_B7"),
"lambdaG": 560.0,
"lambdaR": 660.0,
"lambdaN": 835.0,
}
def lookupMOD09GQ(img):
return {
"R": img.select("sur_refl_b01"),
"N": img.select("sur_refl_b02"),
"lambdaR": 645.0,
"lambdaN": 858.5,
}
def lookupMOD09GA(img):
return {
"B": img.select("sur_refl_b03"),
"G": img.select("sur_refl_b04"),
"R": img.select("sur_refl_b01"),
"N": img.select("sur_refl_b02"),
"S1": img.select("sur_refl_b06"),
"S2": img.select("sur_refl_b07"),
"lambdaG": 555.0,
"lambdaR": 645.0,
"lambdaN": 858.5,
}
def lookupMCD43A4(img):
return {
"B": img.select("Nadir_Reflectance_Band3"),
"G": img.select("Nadir_Reflectance_Band4"),
"R": img.select("Nadir_Reflectance_Band1"),
"N": img.select("Nadir_Reflectance_Band2"),
"S1": img.select("Nadir_Reflectance_Band6"),
"S2": img.select("Nadir_Reflectance_Band7"),
"lambdaG": 555.0,
"lambdaR": 645.0,
"lambdaN": 858.5,
}
lookupPlatform = {
"COPERNICUS/S2": lookupS2,
"COPERNICUS/S2_SR": lookupS2,
"LANDSAT/LC08/C01/T1_SR": lookupL8,
"LANDSAT/LC08/C01/T2_SR": lookupL8,
"LANDSAT/LC08/C02/T1_L2": lookupL8C2,
"LANDSAT/LC08/C02/T2_L2": lookupL8C2,
"LANDSAT/LC09/C02/T1_L2": lookupL8C2,
"LANDSAT/LC09/C02/T2_L2": lookupL8C2,
"LANDSAT/LE07/C01/T1_SR": lookupL7,
"LANDSAT/LE07/C01/T2_SR": lookupL7,
"LANDSAT/LE07/C02/T1_L2": lookupL7C2,
"LANDSAT/LE07/C02/T2_L2": lookupL7C2,
"LANDSAT/LT05/C01/T1_SR": lookupL45,
"LANDSAT/LT05/C01/T2_SR": lookupL45,
"LANDSAT/LT05/C02/T1_L2": lookupL45C2,
"LANDSAT/LT05/C02/T2_L2": lookupL45C2,
"LANDSAT/LT04/C01/T1_SR": lookupL45,
"LANDSAT/LT04/C01/T2_SR": lookupL45,
"LANDSAT/LT04/C02/T1_L2": lookupL45C2,
"LANDSAT/LT04/C02/T2_L2": lookupL45C2,
"MODIS/006/MOD09GQ": lookupMOD09GQ,
"MODIS/006/MYD09GQ": lookupMOD09GQ,
"MODIS/006/MOD09GA": lookupMOD09GA,
"MODIS/006/MYD09GA": lookupMOD09GA,
"MODIS/006/MOD09Q1": lookupMOD09GQ,
"MODIS/006/MYD09Q1": lookupMOD09GQ,
"MODIS/006/MOD09A1": lookupMOD09GA,
"MODIS/006/MYD09A1": lookupMOD09GA,
"MODIS/006/MCD43A4": lookupMCD43A4,
}
if platformDict["platform"] not in list(lookupPlatform.keys()):
raise Exception(
"Sorry, satellite platform not supported for index computation!"
)
return lookupPlatform[platformDict["platform"]](img)
def _get_indices(online: bool) -> dict:
"""Retrieves the dictionary of indices used for the index() method in ee.Image and ee.ImageCollection classes.
Args:
online : Wheter to retrieve the most recent list of indices directly from the GitHub repository and not from the local copy.
Returns:
Indices.
"""
if online:
with urllib.request.urlopen(
"https://raw.githubusercontent.com/davemlz/awesome-ee-spectral-indices/main/output/spectral-indices-dict.json"
) as url:
indices = json.loads(url.read().decode())
else:
indices = _load_JSON("spectral-indices-dict.json")
return indices["SpectralIndices"]
def _get_kernel_image(
img: ee.Image, lookup: dict, kernel: str, sigma: Union[str, float], a: str, b: str
) -> ee.Image:
"""Creates an ee.Image representing a kernel computed on bands [a] and [b].
Args:
img : Image to compute the kernel on.
lookup : Dictionary retrieved from _get_expression_map().
kernel : Kernel to use.
sigma : Length-scale parameter. Used for kernel = 'RBF'.
a : Key of the first band to use.
b : Key of the second band to use.
Returns:
Kernel image.
"""
if a not in list(lookup.keys()) or b not in list(lookup.keys()):
return None
else:
lookupab = {"a": lookup[a], "b": lookup[b]}
if isinstance(sigma, str):
lookup = {**lookup, **lookupab, "sigma": img.expression(sigma, lookupab)}
else:
lookup = {**lookup, **lookupab, "sigma": sigma}
kernels = {
"linear": "a * b",
"RBF": "exp((-1.0 * (a - b) ** 2.0)/(2.0 * sigma ** 2.0))",
"poly": "((a * b) + c) ** p",
}
return img.expression(kernels[kernel], lookup)
def _remove_none_dict(dictionary: dict) -> dict:
"""Removes elements from a dictionary with None values.
Args:
dictionary : Dictionary to remove None values.
Returns:
Curated dictionary.
"""
newDictionary = dict(dictionary)
for key in dictionary.keys():
if dictionary[key] is None:
del newDictionary[key]
return newDictionary
def _get_kernel_parameters(
img: ee.Image, lookup: dict, kernel: str, sigma: Union[str, float]
) -> dict:
"""Gets the additional kernel parameters to compute kernel indices.
Args:
img : Image to compute the kernel parameters on.
lookup : Dictionary retrieved from _get_expression_map().
kernel : Kernel to use.
sigma : Length-scale parameter. Used for kernel = 'RBF'.
Returns:
Kernel parameters.
"""
kernelParameters = {
"kNN": _get_kernel_image(img, lookup, kernel, sigma, "N", "N"),
"kNR": _get_kernel_image(img, lookup, kernel, sigma, "N", "R"),
"kNB": _get_kernel_image(img, lookup, kernel, sigma, "N", "B"),
"kNL": _get_kernel_image(img, lookup, kernel, sigma, "N", "L"),
"kGG": _get_kernel_image(img, lookup, kernel, sigma, "G", "G"),
"kGR": _get_kernel_image(img, lookup, kernel, sigma, "G", "R"),
"kGB": _get_kernel_image(img, lookup, kernel, sigma, "G", "B"),
"kBB": _get_kernel_image(img, lookup, kernel, sigma, "B", "B"),
"kBR": _get_kernel_image(img, lookup, kernel, sigma, "B", "R"),
"kBL": _get_kernel_image(img, lookup, kernel, sigma, "B", "L"),
"kRR": _get_kernel_image(img, lookup, kernel, sigma, "R", "R"),
"kRB": _get_kernel_image(img, lookup, kernel, sigma, "R", "B"),
"kRL": _get_kernel_image(img, lookup, kernel, sigma, "R", "L"),
"kLL": _get_kernel_image(img, lookup, kernel, sigma, "L", "L"),
}
return kernelParameters
def _get_tc_coefficients(platform: str) -> dict:
"""Gets the platform-specific coefficient dictionary required for tasseled cap
transformation.
Platform matching is strict, meaning that data must be at the processing level
specified by the reference literature that coefficients were sourced from, e.g.
Landsat 8 SR cannot be transformed with Landsat 8 TOA coefficients.
Args:
platform : Platform name retrieved from the STAC.
Returns:
Map dictionary with band names and corresponding coefficients for brightness
greenness, and wetness.
Raises:
Exception : If the platform has no supported coefficients.
"""
SENTINEL2_1C = {
"bands": (
"B1",
"B2",
"B3",
"B4",
"B5",
"B6",
"B7",
"B8",
"B8A",
"B9",
"B10",
"B11",
"B12",
),
"TCB": (
0.2381,
0.2569,
0.2934,
0.3020,
0.3099,
0.3740,
0.4180,
0.3580,
0.3834,
0.0103,
0.0020,
0.0896,
0.0780,
),
"TCG": (
-0.2266,
-0.2818,
-0.3020,
-0.4283,
-0.2959,
0.1602,
0.3127,
0.3138,
0.4261,
0.1454,
-0.0017,
-0.1341,
-0.2538,
),
"TCW": (
0.1825,
0.1763,
0.1615,
0.0486,
0.0170,
0.0223,
0.0219,
-0.0755,
-0.0910,
-0.1369,
0.0003,
-0.7701,
-0.5293,
),
}
# Zhai et al. 2022 also provide coefficients with the blue band, but
# recommend omitting it due to difficulties in atmospheric correction.
LANDSAT8_SR = {
"bands": ("SR_B3", "SR_B4", "SR_B5", "SR_B6", "SR_B7"),
"TCB": (0.4596, 0.5046, 0.5458, 0.4114, 0.2589),
"TCG": (-0.3374, -0.4901, 0.7909, 0.0177, -0.1416),
"TCW": (0.2254, 0.3681, 0.2250, -0.6053, -0.6298)
}
# Zhai et al. 2022 coefficients were included for L8 TOA over the Baig
# et al. 2014 coefficients for consistency with the L8 SR coefficients,
# which were not calculated by Baig et al.
LANDSAT8_TOA = {
"bands": ("B3", "B4", "B5", "B6", "B7"),
"TCB": (0.4321, 0.4971, 0.5695, 0.4192, 0.2569),
"TCG": (-0.3318, -0.4844, 0.7856, -0.0331, -0.1923),
"TCW": (0.2633, 0.3945, 0.1801, -0.6121, -0.6066)
}
# Coefficients for Landsat 8 OLI are usable for Landsat 9 OLI-2, per
# Zhai et al. 2022
LANDSAT9_SR = LANDSAT8_SR
LANDSAT9_TOA = LANDSAT8_TOA
LANDSAT7_TOA = {
"bands": ("B1", "B2", "B3", "B4", "B5", "B7"),
"TCB": (0.3561, 0.3972, 0.3904, 0.6966, 0.2286, 0.1596),
"TCG": (-0.3344, -0.3544, -0.4556, 0.6966, -0.0242, -0.2630),
"TCW": (0.2626, 0.2141, 0.0926, 0.0656, -0.7629, -0.5388),
}
LANDSAT4_DN = {
"bands": ("B1", "B2", "B3", "B4", "B5", "B7"),
"TCB": (0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863),
"TCG": (-0.2848, -0.2435, -0.5435, 0.7243, 0.0840, -0.1800),
"TCW": (0.1509, 0.1973, 0.3279, 0.3406, -0.7112, -0.4572),
}
LANDSAT4_SR = {
"bands": ("SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B7"),
"TCB": (0.2043, 0.4158, 0.5524, 0.5741, 0.3124, 0.2303),
"TCG": (-0.1603, -0.2819, -0.4934, 0.7940, -0.0002, -0.1446),
"TCW": (0.0315, 0.2021, 0.3102, 0.1594, -0.6806, -0.6109),
}
LANDSAT5_DN = {
"bands": ("B1", "B2", "B3", "B4", "B5", "B7"),
"TCB": (0.2909, 0.2493, 0.4806, 0.5568, 0.4438, 0.1706),
"TCG": (-0.2728, -0.2174, -0.5508, 0.7221, 0.0733, -0.1648),
"TCW": (0.1446, 0.1761, 0.3322, 0.3396, -0.6210, -0.4186),
}
MODIS_NBAR = {
"bands": (
"Nadir_Reflectance_Band1",
"Nadir_Reflectance_Band2",
"Nadir_Reflectance_Band3",
"Nadir_Reflectance_Band4",
"Nadir_Reflectance_Band5",
"Nadir_Reflectance_Band6",
"Nadir_Reflectance_Band7",
),
"TCB": (0.4395, 0.5945, 0.2460, 0.3918, 0.3506, 0.2136, 0.2678),
"TCG": (-0.4064, 0.5129, -0.2744, -0.2893, 0.4882, -0.0036, -0.4169),
"TCW": (0.1147, 0.2489, 0.2408, 0.3132, -0.3122, -0.6416, -0.5087),
}
platformCoeffs = {
"COPERNICUS/S2": SENTINEL2_1C,
"MODIS/006/MCD43A4": MODIS_NBAR,
"LANDSAT/LC09/C02/T1_L2": LANDSAT9_SR,
"LANDSAT/LC09/C02/T1_TOA": LANDSAT9_TOA,
"LANDSAT/LC08/C02/T1_L2": LANDSAT8_SR,
"LANDSAT/LC08/C02/T2_L2": LANDSAT8_SR,
"LANDSAT/LC08/C01/T1_TOA": LANDSAT8_TOA,
"LANDSAT/LC08/C01/T1_RT_TOA": LANDSAT8_TOA,
"LANDSAT/LC08/C01/T2_TOA": LANDSAT8_TOA,
"LANDSAT/LE07/C01/T1_TOA": LANDSAT7_TOA,
"LANDSAT/LE07/C01/T1_RT_TOA": LANDSAT7_TOA,
"LANDSAT/LE07/C01/T2_TOA": LANDSAT7_TOA,
"LANDSAT/LT05/C01/T1": LANDSAT5_DN,
"LANDSAT/LT05/C01/T2": LANDSAT5_DN,
"LANDSAT/LT04/C02/T1_L2": LANDSAT4_SR,
"LANDSAT/LT04/C02/T2_L2": LANDSAT4_SR,
"LANDSAT/LT04/C01/T1": LANDSAT4_DN,
"LANDSAT/LT04/C01/T2": LANDSAT4_DN,
}
if platform not in list(platformCoeffs.keys()):
raise Exception(
"Sorry, satellite platform not supported for tasseled cap transformation! Use one of "
+ str(list(platformCoeffs.keys()))
)
return platformCoeffs[platform]
def _match_histogram(
source: ee.Image,
target: ee.Image,
bands: Optional[Dict[str, str]],
geometry: Optional[ee.Geometry],
maxBuckets: int,
) -> ee.Image:
"""Adjust the histogram of an image to match a target image.
Args:
source : Image to adjust.
target : Image to use as the histogram reference.
bands : An optional dictionary of band names to match, with source bands as keys
and target bands as values. If none is provided, bands will be matched by name.
Any bands not included here will be dropped.
geometry : The optional region to match histograms in that overlaps both images.
If none is provided, the geometry of the source image will be used. If the
source image is unbounded and no geometry is provided, histogram matching will
fail.
maxBuckets : The maximum number of buckets to use when building histograms. More
buckets will require more memory and time but will generate more accurate
results. The number of buckets will be rounded to the nearest power of 2.
Returns:
The adjusted image containing the matched source bands.
"""
def histogram_lookup(
source_hist: ee.Array, target_hist: ee.Array
) -> Tuple[ee.List, ee.List]:
"""Build a list of target values with corresponding counts to source values from a source and target histogram.
Args:
source_hist : A histogram for a source image returned by ee.Reducer.autoHistogram
target_hist : A histogram for a target image returned by ee.Reducer.autoHistogram
Returns:
Source histogram values and target histogram values with corresponding counts.
"""
source_vals = source_hist.slice(1, 0, 1).project([0])
source_counts = source_hist.slice(1, 1, 2).project([0])
source_counts = source_counts.divide(source_counts.get([-1]))
target_vals = target_hist.slice(1, 0, 1).project([0])
target_counts = target_hist.slice(1, 1, 2).project([0])
target_counts = target_counts.divide(target_counts.get([-1]))
def lookup_value(n):
"""Find the first target value with at least n counts."""
index = target_counts.gte(n).argmax()
return target_vals.get(index)
target_lookup_vals = source_counts.toList().map(lookup_value)
return (source_vals.toList(), target_lookup_vals)
geometry = ee.Element.geometry(source) if geometry is None else geometry
source_bands = source.bandNames() if bands is None else list(bands.keys())
target_bands = source.bandNames() if bands is None else list(bands.values())
bands = ee.Dictionary.fromLists(source_bands, target_bands)
source = source.select(source_bands)
target = target.select(target_bands)
source_histogram = source.reduceRegion(
reducer=ee.Reducer.autoHistogram(maxBuckets=maxBuckets, cumulative=True),
geometry=geometry,
scale=30,
maxPixels=1e13,
bestEffort=True,
)
target_histogram = target.updateMask(source.mask()).reduceRegion(
reducer=ee.Reducer.autoHistogram(maxBuckets=maxBuckets, cumulative=True),
geometry=geometry,
scale=30,
maxPixels=1e13,
bestEffort=True,
)
def match_bands(source_band: ee.String, target_band: ee.String) -> ee.Image:
"""Match the histogram of one source band to a target band.
Args:
source_band : The name of a band in the source image to adjust.
target_band : The name of a corresponding band in the target image to match to.
Returns:
The source band image histogram-matched to the target band.
"""
x, y = histogram_lookup(
source_histogram.getArray(source_band),
target_histogram.getArray(target_band),
)
matched = source.select([source_band]).interpolate(x, y)
return matched
matched = (
ee.ImageCollection(bands.map(match_bands).values())
.toBands()
.rename(bands.keys())
)
# Preserve the metadata, band types, and band order of the source image.
matched = ee.Image(matched.copyProperties(source, source.propertyNames()))
matched = matched.cast(source.bandTypes(), source.bandNames())
matched = matched.set("ee_extra:HISTOGRAM_TARGET", target)
# If the source image was bounded, clip the matched output to its bounds. If the source
# image doesn't have a `geometry` this will fail, but that seems exceptionally rare.
matched = ee.Algorithms.If(
source.geometry().isUnbounded(),
matched,
matched.clip(source.geometry().bounds()),
)
return ee.Image(matched)
|
the-stack_0_980 | #!/usr/bin/env python3
import argparse
import json
import sys
import traceback
import re
from sonic_py_common import device_info, logger
from swsscommon.swsscommon import SonicV2Connector, ConfigDBConnector, SonicDBConfig
INIT_CFG_FILE = '/etc/sonic/init_cfg.json'
SYSLOG_IDENTIFIER = 'db_migrator'
# Global logger instance
log = logger.Logger(SYSLOG_IDENTIFIER)
class DBMigrator():
def __init__(self, namespace, socket=None):
"""
Version string format:
version_<major>_<minor>_<build>
major: starting from 1, sequentially incrementing in master
branch.
minor: in github branches, minor version stays in 0. This minor
version creates space for private branches derived from
github public branches. These private branches shall use
none-zero values.
build: sequentially increase within a minor version domain.
"""
self.CURRENT_VERSION = 'version_2_0_0'
self.TABLE_NAME = 'VERSIONS'
self.TABLE_KEY = 'DATABASE'
self.TABLE_FIELD = 'VERSION'
db_kwargs = {}
if socket:
db_kwargs['unix_socket_path'] = socket
if namespace is None:
self.configDB = ConfigDBConnector(**db_kwargs)
else:
self.configDB = ConfigDBConnector(use_unix_socket_path=True, namespace=namespace, **db_kwargs)
self.configDB.db_connect('CONFIG_DB')
self.appDB = SonicV2Connector(host='127.0.0.1')
if self.appDB is not None:
self.appDB.connect(self.appDB.APPL_DB)
self.stateDB = SonicV2Connector(host='127.0.0.1')
if self.stateDB is not None:
self.stateDB.connect(self.stateDB.STATE_DB)
version_info = device_info.get_sonic_version_info()
asic_type = version_info.get('asic_type')
self.asic_type = asic_type
if asic_type == "mellanox":
from mellanox_buffer_migrator import MellanoxBufferMigrator
self.mellanox_buffer_migrator = MellanoxBufferMigrator(self.configDB)
def migrate_pfc_wd_table(self):
'''
Migrate all data entries from table PFC_WD_TABLE to PFC_WD
'''
data = self.configDB.get_table('PFC_WD_TABLE')
for key in data:
self.configDB.set_entry('PFC_WD', key, data[key])
self.configDB.delete_table('PFC_WD_TABLE')
def is_ip_prefix_in_key(self, key):
'''
Function to check if IP address is present in the key. If it
is present, then the key would be a tuple or else, it shall be
be string
'''
return (isinstance(key, tuple))
def migrate_interface_table(self):
'''
Migrate all data from existing INTERFACE table with IP Prefix
to have an additional ONE entry without IP Prefix. For. e.g, for an entry
"Vlan1000|192.168.0.1/21": {}", this function shall add an entry without
IP prefix as ""Vlan1000": {}". This is for VRF compatibility.
'''
if_db = []
if_tables = {
'INTERFACE',
'PORTCHANNEL_INTERFACE',
'VLAN_INTERFACE',
'LOOPBACK_INTERFACE'
}
for table in if_tables:
data = self.configDB.get_table(table)
for key in data:
if not self.is_ip_prefix_in_key(key):
if_db.append(key)
continue
for table in if_tables:
data = self.configDB.get_table(table)
for key in data:
if not self.is_ip_prefix_in_key(key) or key[0] in if_db:
continue
log.log_info('Migrating interface table for ' + key[0])
self.configDB.set_entry(table, key[0], data[key])
if_db.append(key[0])
def migrate_intf_table(self):
'''
Migrate all data from existing INTF table in APP DB during warmboot with IP Prefix
to have an additional ONE entry without IP Prefix. For. e.g, for an entry
"Vlan1000:192.168.0.1/21": {}", this function shall add an entry without
IP prefix as ""Vlan1000": {}". This also migrates 'lo' to 'Loopback0' interface
'''
if self.appDB is None:
return
data = self.appDB.keys(self.appDB.APPL_DB, "INTF_TABLE:*")
if data is None:
return
if_db = []
for key in data:
if_name = key.split(":")[1]
if if_name == "lo":
self.appDB.delete(self.appDB.APPL_DB, key)
key = key.replace(if_name, "Loopback0")
log.log_info('Migrating lo entry to ' + key)
self.appDB.set(self.appDB.APPL_DB, key, 'NULL', 'NULL')
if '/' not in key:
if_db.append(key.split(":")[1])
continue
data = self.appDB.keys(self.appDB.APPL_DB, "INTF_TABLE:*")
for key in data:
if_name = key.split(":")[1]
if if_name in if_db:
continue
log.log_info('Migrating intf table for ' + if_name)
table = "INTF_TABLE:" + if_name
self.appDB.set(self.appDB.APPL_DB, table, 'NULL', 'NULL')
if_db.append(if_name)
def migrate_copp_table(self):
'''
Delete the existing COPP table
'''
if self.appDB is None:
return
keys = self.appDB.keys(self.appDB.APPL_DB, "COPP_TABLE:*")
if keys is None:
return
for copp_key in keys:
self.appDB.delete(self.appDB.APPL_DB, copp_key)
def migrate_config_db_buffer_tables_for_dynamic_calculation(self, speed_list, cable_len_list, default_dynamic_th, default_lossless_profiles, abandon_method, append_item_method):
'''
Migrate buffer tables to dynamic calculation mode
parameters
@speed_list - list of speed supported
@cable_len_list - list of cable length supported
@default_dynamic_th - default dynamic th
@default_lossless_profiles - default lossless profiles from the previous image
@abandon_method - a function which is called to abandon the migration and keep the current configuration
if the current one doesn't match the default one
@append_item_method - a function which is called to append an item to the list of pending commit items
any update to buffer configuration will be pended and won't be applied until
all configuration is checked and aligns with the default one
1. Buffer profiles for lossless PGs in BUFFER_PROFILE table will be removed
if their names have the convention of pg_lossless_<speed>_<cable_length>_profile
where the speed and cable_length belongs speed_list and cable_len_list respectively
and the dynamic_th is equal to default_dynamic_th
2. Insert tables required for dynamic buffer calculation
- DEFAULT_LOSSLESS_BUFFER_PARAMETER|AZURE: {'default_dynamic_th': default_dynamic_th}
- LOSSLESS_TRAFFIC_PATTERN|AZURE: {'mtu': '1500', 'small_packet_percentage': '100'}
3. For lossless dynamic PGs, remove the explicit referencing buffer profiles
Before: BUFFER_PG|<port>|3-4: {'profile': 'BUFFER_PROFILE|pg_lossless_<speed>_<cable_length>_profile'}
After: BUFFER_PG|<port>|3-4: {'profile': 'NULL'}
'''
# Migrate BUFFER_PROFILEs, removing dynamically generated profiles
dynamic_profile = self.configDB.get_table('BUFFER_PROFILE')
profile_pattern = 'pg_lossless_([1-9][0-9]*000)_([1-9][0-9]*m)_profile'
for name, info in dynamic_profile.items():
m = re.search(profile_pattern, name)
if not m:
continue
speed = m.group(1)
cable_length = m.group(2)
if speed in speed_list and cable_length in cable_len_list:
log.log_info("current profile {} {}".format(name, info))
log.log_info("default profile {} {}".format(name, default_lossless_profiles.get(name)))
default_profile = default_lossless_profiles.get(name);
if info.get("xon") == default_profile.get("xon") and info.get("size") == default_profile.get("size") and info.get('dynamic_th') == default_dynamic_th:
append_item_method(('BUFFER_PROFILE', name, None))
log.log_info("Lossless profile {} has been removed".format(name))
else:
log.log_notice("Lossless profile {} doesn't match the default configuration, keep using traditional buffer calculation mode")
abandon_method()
return True
# Migrate BUFFER_PGs, removing the explicit designated profiles
buffer_pgs = self.configDB.get_table('BUFFER_PG')
ports = self.configDB.get_table('PORT')
all_cable_lengths = self.configDB.get_table('CABLE_LENGTH')
if not buffer_pgs or not ports or not all_cable_lengths:
log.log_notice("At lease one of tables BUFFER_PG, PORT and CABLE_LENGTH hasn't been defined, skip following migration")
abandon_method()
return True
cable_lengths = all_cable_lengths[list(all_cable_lengths.keys())[0]]
for name, profile in buffer_pgs.items():
# do the db migration
port, pg = name
if pg != '3-4':
continue
try:
profile_name = profile['profile'][1:-1].split('|')[1]
m = re.search(profile_pattern, profile_name)
except Exception:
continue
if not m:
continue
speed = m.group(1)
cable_length = m.group(2)
try:
if speed == ports[port]['speed'] and cable_length == cable_lengths[port]:
append_item_method(('BUFFER_PG', name, {'profile': 'NULL'}))
else:
log.log_notice("Lossless PG profile {} for port {} doesn't match its speed {} or cable length {}, keep using traditional buffer calculation mode".format(
profile_name, port, speed, cable_length))
abandon_method()
return True
except Exception:
continue
# Insert other tables required for dynamic buffer calculation
metadata = self.configDB.get_entry('DEVICE_METADATA', 'localhost')
metadata['buffer_model'] = 'dynamic'
append_item_method(('DEVICE_METADATA', 'localhost', metadata))
append_item_method(('DEFAULT_LOSSLESS_BUFFER_PARAMETER', 'AZURE', {'default_dynamic_th': default_dynamic_th}))
append_item_method(('LOSSLESS_TRAFFIC_PATTERN', 'AZURE', {'mtu': '1500', 'small_packet_percentage': '100'}))
return True
def prepare_dynamic_buffer_for_warm_reboot(self, buffer_pools = None, buffer_profiles = None, buffer_pgs = None):
'''
This is the very first warm reboot of buffermgrd (dynamic) if the system reboot from old image by warm-reboot
In this case steps need to be taken to get buffermgrd prepared (for warm reboot)
During warm reboot, buffer tables should be installed in the first place.
However, it isn't able to achieve that when system is warm-rebooted from an old image
without dynamic buffer supported, because the buffer info wasn't in the APPL_DB in the old image.
The solution is to copy that info from CONFIG_DB into APPL_DB in db_migrator.
During warm-reboot, db_migrator adjusts buffer info in CONFIG_DB by removing some fields
according to requirement from dynamic buffer calculation.
The buffer info before that adjustment needs to be copied to APPL_DB.
1. set WARM_RESTART_TABLE|buffermgrd as {restore_count: 0}
2. Copy the following tables from CONFIG_DB into APPL_DB in case of warm reboot
The separator in fields that reference objects in other table needs to be updated from '|' to ':'
- BUFFER_POOL
- BUFFER_PROFILE, separator updated for field 'pool'
- BUFFER_PG, separator updated for field 'profile'
- BUFFER_QUEUE, separator updated for field 'profile
- BUFFER_PORT_INGRESS_PROFILE_LIST, separator updated for field 'profile_list'
- BUFFER_PORT_EGRESS_PROFILE_LIST, separator updated for field 'profile_list'
'''
warmreboot_state = self.stateDB.get(self.stateDB.STATE_DB, 'WARM_RESTART_ENABLE_TABLE|system', 'enable')
mmu_size = self.stateDB.get(self.stateDB.STATE_DB, 'BUFFER_MAX_PARAM_TABLE|global', 'mmu_size')
if warmreboot_state == 'true' and not mmu_size:
log.log_notice("This is the very first run of buffermgrd (dynamic), prepare info required from warm reboot")
else:
return True
buffer_table_list = [
('BUFFER_POOL', buffer_pools, None),
('BUFFER_PROFILE', buffer_profiles, 'pool'),
('BUFFER_PG', buffer_pgs, 'profile'),
('BUFFER_QUEUE', None, 'profile'),
('BUFFER_PORT_INGRESS_PROFILE_LIST', None, 'profile_list'),
('BUFFER_PORT_EGRESS_PROFILE_LIST', None, 'profile_list')
]
for pair in buffer_table_list:
keys_copied = []
keys_ignored = []
table_name, entries, reference_field_name = pair
app_table_name = table_name + "_TABLE"
if not entries:
entries = self.configDB.get_table(table_name)
for key, items in entries.items():
# copy items to appl db
if reference_field_name:
confdb_ref = items.get(reference_field_name)
if not confdb_ref or confdb_ref == "NULL":
keys_ignored.append(key)
continue
items_referenced = confdb_ref.split(',')
appdb_ref = ""
first_item = True
for item in items_referenced:
if first_item:
first_item = False
else:
appdb_ref += ','
subitems = item.split('|')
first_key = True
for subitem in subitems:
if first_key:
appdb_ref += subitem + '_TABLE'
first_key = False
else:
appdb_ref += ':' + subitem
items[reference_field_name] = appdb_ref
keys_copied.append(key)
if type(key) is tuple:
appl_db_key = app_table_name + ':' + ':'.join(key)
else:
appl_db_key = app_table_name + ':' + key
for field, data in items.items():
self.appDB.set(self.appDB.APPL_DB, appl_db_key, field, data)
if keys_copied:
log.log_info("The following items in table {} in CONFIG_DB have been copied to APPL_DB: {}".format(table_name, keys_copied))
if keys_ignored:
log.log_info("The following items in table {} in CONFIG_DB have been ignored: {}".format(table_name, keys_copied))
return True
def version_unknown(self):
"""
version_unknown tracks all SONiC versions that doesn't have a version
string defined in config_DB.
Nothing can be assumped when migrating from this version to the next
version.
Any migration operation needs to test if the DB is in expected format
before migrating date to the next version.
"""
log.log_info('Handling version_unknown')
# NOTE: Uncomment next 3 lines of code when the migration code is in
# place. Note that returning specific string is intentional,
# here we only intended to migrade to DB version 1.0.1.
# If new DB version is added in the future, the incremental
# upgrade will take care of the subsequent migrations.
self.migrate_pfc_wd_table()
self.migrate_interface_table()
self.migrate_intf_table()
self.set_version('version_1_0_2')
return 'version_1_0_2'
def version_1_0_1(self):
"""
Version 1_0_1.
"""
log.log_info('Handling version_1_0_1')
self.migrate_interface_table()
self.migrate_intf_table()
self.set_version('version_1_0_2')
return 'version_1_0_2'
def version_1_0_2(self):
"""
Version 1_0_2.
"""
log.log_info('Handling version_1_0_2')
# Check ASIC type, if Mellanox platform then need DB migration
if self.asic_type == "mellanox":
if self.mellanox_buffer_migrator.mlnx_migrate_buffer_pool_size('version_1_0_2', 'version_1_0_3') \
and self.mellanox_buffer_migrator.mlnx_flush_new_buffer_configuration():
self.set_version('version_1_0_3')
else:
self.set_version('version_1_0_3')
return 'version_1_0_3'
def version_1_0_3(self):
"""
Version 1_0_3.
"""
log.log_info('Handling version_1_0_3')
# Check ASIC type, if Mellanox platform then need DB migration
if self.asic_type == "mellanox":
if self.mellanox_buffer_migrator.mlnx_migrate_buffer_pool_size('version_1_0_3', 'version_1_0_4') \
and self.mellanox_buffer_migrator.mlnx_migrate_buffer_profile('version_1_0_3', 'version_1_0_4') \
and self.mellanox_buffer_migrator.mlnx_flush_new_buffer_configuration():
self.set_version('version_1_0_4')
else:
self.set_version('version_1_0_4')
return 'version_1_0_4'
def version_1_0_4(self):
"""
Current latest version. Nothing to do here.
"""
log.log_info('Handling version_1_0_4')
# Check ASIC type, if Mellanox platform then need DB migration
if self.asic_type == "mellanox":
speed_list = self.mellanox_buffer_migrator.default_speed_list
cable_len_list = self.mellanox_buffer_migrator.default_cable_len_list
buffer_pools = self.configDB.get_table('BUFFER_POOL')
buffer_profiles = self.configDB.get_table('BUFFER_PROFILE')
buffer_pgs = self.configDB.get_table('BUFFER_PG')
default_lossless_profiles = self.mellanox_buffer_migrator.mlnx_get_default_lossless_profile('version_1_0_4')
abandon_method = self.mellanox_buffer_migrator.mlnx_abandon_pending_buffer_configuration
append_method = self.mellanox_buffer_migrator.mlnx_append_item_on_pending_configuration_list
if self.mellanox_buffer_migrator.mlnx_migrate_buffer_pool_size('version_1_0_4', 'version_2_0_0') \
and self.mellanox_buffer_migrator.mlnx_migrate_buffer_profile('version_1_0_4', 'version_2_0_0') \
and self.migrate_config_db_buffer_tables_for_dynamic_calculation(speed_list, cable_len_list, '0', default_lossless_profiles,
abandon_method, append_method) \
and self.mellanox_buffer_migrator.mlnx_flush_new_buffer_configuration() \
and self.prepare_dynamic_buffer_for_warm_reboot(buffer_pools, buffer_profiles, buffer_pgs):
metadata = self.configDB.get_entry('DEVICE_METADATA', 'localhost')
if not metadata.get('buffer_model'):
metadata['buffer_model'] = 'traditional'
self.configDB.set_entry('DEVICE_METADATA', 'localhost', metadata)
log.log_notice('Setting buffer_model to traditional')
else:
log.log_notice('Got buffer_model {}'.format(metadata.get('buffer_model')))
self.set_version('version_2_0_0')
else:
self.prepare_dynamic_buffer_for_warm_reboot()
metadata = self.configDB.get_entry('DEVICE_METADATA', 'localhost')
metadata['buffer_model'] = 'traditional'
self.configDB.set_entry('DEVICE_METADATA', 'localhost', metadata)
log.log_notice('Setting buffer_model to traditional')
self.set_version('version_2_0_0')
return 'version_2_0_0'
def version_2_0_0(self):
"""
Current latest version. Nothing to do here.
"""
log.log_info('Handling version_2_0_0')
return None
def get_version(self):
version = self.configDB.get_entry(self.TABLE_NAME, self.TABLE_KEY)
if version and version[self.TABLE_FIELD]:
return version[self.TABLE_FIELD]
return 'version_unknown'
def set_version(self, version=None):
if not version:
version = self.CURRENT_VERSION
log.log_info('Setting version to ' + version)
entry = { self.TABLE_FIELD : version }
self.configDB.set_entry(self.TABLE_NAME, self.TABLE_KEY, entry)
def common_migration_ops(self):
try:
with open(INIT_CFG_FILE) as f:
init_db = json.load(f)
except Exception as e:
raise Exception(str(e))
for init_cfg_table, table_val in init_db.items():
data = self.configDB.get_table(init_cfg_table)
if data:
# Ignore overriding the values that pre-exist in configDB
continue
log.log_info("Migrating table {} from INIT_CFG to config_db".format(init_cfg_table))
# Update all tables that do not exist in configDB but are present in INIT_CFG
for init_table_key, init_table_val in table_val.items():
self.configDB.set_entry(init_cfg_table, init_table_key, init_table_val)
self.migrate_copp_table()
def migrate(self):
version = self.get_version()
log.log_info('Upgrading from version ' + version)
while version:
next_version = getattr(self, version)()
if next_version == version:
raise Exception('Version migrate from %s stuck in same version' % version)
version = next_version
# Perform common migration ops
self.common_migration_ops()
def main():
try:
parser = argparse.ArgumentParser()
parser.add_argument('-o',
dest='operation',
metavar='operation (migrate, set_version, get_version)',
type = str,
required = False,
choices=['migrate', 'set_version', 'get_version'],
help = 'operation to perform [default: get_version]',
default='get_version')
parser.add_argument('-s',
dest='socket',
metavar='unix socket',
type = str,
required = False,
help = 'the unix socket that the desired database listens on',
default = None )
parser.add_argument('-n',
dest='namespace',
metavar='asic namespace',
type = str,
required = False,
help = 'The asic namespace whose DB instance we need to connect',
default = None )
args = parser.parse_args()
operation = args.operation
socket_path = args.socket
namespace = args.namespace
if args.namespace is not None:
SonicDBConfig.load_sonic_global_db_config(namespace=args.namespace)
if socket_path:
dbmgtr = DBMigrator(namespace, socket=socket_path)
else:
dbmgtr = DBMigrator(namespace)
result = getattr(dbmgtr, operation)()
if result:
print(str(result))
except Exception as e:
log.log_error('Caught exception: ' + str(e))
traceback.print_exc()
print(str(e))
parser.print_help()
sys.exit(1)
if __name__ == "__main__":
main()
|
the-stack_0_981 | # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Downloads and prepares TriviaQA dataset."""
from unittest import mock
from absl import app
from absl import flags
from absl import logging
import apache_beam as beam
import tensorflow_datasets as tfds
from official.projects.triviaqa import dataset # pylint: disable=unused-import
flags.DEFINE_integer('sequence_length', 4096, 'Max number of tokens.')
flags.DEFINE_integer(
'global_sequence_length', None,
'Max number of question tokens plus sentences. If not set, defaults to '
'sequence_length // 16 + 64.')
flags.DEFINE_integer(
'stride', 3072,
'For documents longer than `sequence_length`, where to split them.')
flags.DEFINE_string(
'sentencepiece_model_path', None,
'SentencePiece model to use for tokenization.')
flags.DEFINE_string('data_dir', None, 'Data directory for TFDS.')
flags.DEFINE_string('runner', 'DirectRunner', 'Beam runner to use.')
FLAGS = flags.FLAGS
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
builder = tfds.builder(
'bigbird_trivia_qa/rc_wiki.preprocessed',
data_dir=FLAGS.data_dir,
sentencepiece_model_path=FLAGS.sentencepiece_model_path,
sequence_length=FLAGS.sequence_length,
global_sequence_length=FLAGS.global_sequence_length,
stride=FLAGS.stride)
download_config = tfds.download.DownloadConfig(
beam_options=beam.options.pipeline_options.PipelineOptions(flags=[
f'--runner={FLAGS.runner}',
'--direct_num_workers=8',
'--direct_running_mode=multi_processing',
]))
with mock.patch('tensorflow_datasets.core.download.extractor._normpath',
new=lambda x: x):
builder.download_and_prepare(download_config=download_config)
logging.info(builder.info.splits)
if __name__ == '__main__':
flags.mark_flag_as_required('sentencepiece_model_path')
app.run(main)
|
the-stack_0_982 |
import os
import click
import shutil
import subprocess
import pkg_resources
import sys
import errno
import traceback
from monitor.logs import init_logging, logger
class ValidationExceptionBinaryNotFound(Exception):
pass
class NotRunningRoot(Exception):
pass
@click.group()
def cli():
click.echo("FileWave Monitor v13 configuration.")
delay_30m = 60 * 30
def run_root_command(cmd_array, **kwargs):
try:
os.rename('/etc/foo', '/etc/bar')
except IOError as e:
if (e == errno.EPERM):
return False
proc = subprocess.Popen(cmd_array, stdout=subprocess.PIPE, **kwargs)
return proc.communicate()[0].decode('utf-8')
def run_root_commands(commands):
for c in commands:
run_root_command(c, shell=True)
def running_on_a_fwxserver_host(exist_func=os.path.exists):
'''
Check directories exist to see if we are running on a FileWave server host installation
This should return True if we are, regardless of being Mac/Linux/Docker etc.
'''
dirs_that_must_exist = ["bin", "certs",
"django", "log"]
main_filewave_dir = os.path.join("/usr/local", "filewave")
if not exist_func(main_filewave_dir):
return False
for f in [os.path.join(main_filewave_dir, d) for d in dirs_that_must_exist]:
if not exist_func(f):
return False
return True
@cli.command('integrate', help="Integrates the module assuming you are running this on the FileWave Server")
def install_into_environment():
init_logging()
if running_on_a_fwxserver_host():
if run_root_command(["ls", "-l"]) is False:
logger.info(
"provisioning is requested - but I've detected you are not running as root - aborting")
raise NotRunningRoot(
"provisioning is requested - but I've detected you are not running as root - aborting")
try:
provision_postgres_wal_interval()
provision_apache_mod_status()
provision_prometheus_binary()
provision_mtail_binary()
provision_exporters()
provision_supervisord_conf()
validate_provisioning()
logger.info("Looks like everything is configured, please restart the server now, then validate installation.")
except Exception as e:
logger.error("Error during provisioning, are you using sudo?")
logger.error(e)
traceback.print_exc(file=sys.stdout)
return
else:
logger.info("Didn't detect a FileWave Server host - configuration aborted")
def provision_postgres_wal_interval():
# /usr/local/filewave/fwxserver/DB/pg_data/postgresql.conf
# log_min_duration_statement = 200
#
cmds = [
"sed -i 's/log_min_duration_statement = 10000/log_min_duration_statement = 200/g' /usr/local/filewave/fwxserver/DB/pg_data/postgresql.conf"
]
return run_root_commands(cmds)
def provision_prometheus_binary():
cmds = [
"wget https://github.com/prometheus/prometheus/releases/download/v2.19.2/prometheus-2.19.2.linux-amd64.tar.gz",
"tar xzf prometheus-2.19.2.linux-amd64.tar.gz",
"mkdir -p /usr/local/etc/filewave/prometheus",
"mkdir -p /usr/local/etc/filewave/prometheus/conf.d/rules",
"mkdir -p /usr/local/etc/filewave/prometheus/conf.d/alerts",
"mkdir -p /usr/local/filewave/prometheus/",
"mv prometheus-2.19.2.linux-amd64/prometheus /usr/local/sbin/",
"mv prometheus-2.19.2.linux-amd64/promtool /usr/local/sbin/",
"mv prometheus-2.19.2.linux-amd64/tsdb /usr/local/sbin/",
"mv prometheus-2.19.2.linux-amd64/console_libraries /usr/local/filewave/prometheus/",
"mv prometheus-2.19.2.linux-amd64/consoles /usr/local/filewave/prometheus/",
"mkdir -p /usr/local/etc/filewave/prometheus/conf.d/jobs/http",
"chown -R root:root /usr/local/filewave/prometheus/",
"rm -rf prometheus-2.19.2.linux-amd64"
]
run_root_commands(cmds)
prom_file = "prometheus.yml"
data = pkg_resources.resource_string("monitor.config", prom_file).decode('utf-8')
provisioning_file = os.path.join("/usr/local/etc/filewave/prometheus", prom_file)
with open(provisioning_file, 'w') as f:
f.write(data)
shutil.chown(provisioning_file, user="root", group="root")
shutil.chown("/usr/local/filewave/prometheus", user="root", group="root")
def provision_apache_mod_status():
'''
#LoadModule status_module modules/mod_status.so
# Uncomment following lines to enable mod status = and connect to https://localhost:20443/server-status?refresh=5 to see server status
# Used by the prometheus apache_exporter. Works only on localhost (intentional to reduce security exposure).
<IfModule status_module>
<Location /server-status>
SetHandler server-status
Order Deny,Allow
Deny from all
Allow from 127.0.0.1 ::1
</Location>
ExtendedStatus On
</IfModule>
'''
cmds = [
"sed -i 's/#LoadModule status_module modules\/mod_status\.so/LoadModule status_module modules\/mod_status\.so/g' /usr/local/filewave/apache/conf/httpd.conf"
]
run_root_commands(cmds)
def provision_mtail_binary():
logger.info("downloading mtail...")
# mtail binary: 15th Jul 2020
# https://github.com/google/mtail/releases/download/v3.0.0-rc36/mtail_v3.0.0-rc36_linux_amd64
cmds = [
"mkdir -p /usr/local/etc/filewave/mtail/progs",
"chown -R root:root /usr/local/etc/filewave/mtail",
"wget https://github.com/google/mtail/releases/download/v3.0.0-rc36/mtail_v3.0.0-rc36_linux_amd64",
"cp mtail_v3.0.0-rc36_linux_amd64 /usr/local/sbin/mtail",
"chmod +x /usr/local/sbin/mtail",
"firewall-cmd --zone=public --add-port=21090/tcp --permanent",
"firewall-cmd --reload"
]
run_root_commands(cmds)
# write .mtail programs into /usr/local/etc/filewave/mtail/progs
for mtail_file in pkg_resources.resource_listdir("monitor", "config"):
if mtail_file.endswith(".mtail"):
logger.info(f"writing with: {mtail_file}")
data = pkg_resources.resource_string("monitor.config", mtail_file).decode('utf-8')
provisioning_file = os.path.join("/usr/local/etc/filewave/mtail/progs", mtail_file)
with open(provisioning_file, 'w') as f:
f.write(data)
shutil.chown(provisioning_file, user="root", group="root")
def provision_exporters():
logger.info("downloading postgres exporter...")
# from https://github.com/wrouesnel/postgres_exporter/releases/download/v0.8.0/postgres_exporter_v0.8.0_linux-amd64.tar.gz
cmds = [
"wget https://github.com/wrouesnel/postgres_exporter/releases/download/v0.8.0/postgres_exporter_v0.8.0_linux-amd64.tar.gz",
"tar xzf postgres_exporter_v0.8.0_linux-amd64.tar.gz",
"mv -f postgres_exporter_v0.8.0_linux-amd64/postgres_exporter /usr/local/sbin/ && rm -rf postgres_exporter_v0.8.0_linux-amd64"
]
run_root_commands(cmds)
logger.info("downloading apache exporter...")
cmds = [
"wget https://github.com/Lusitaniae/apache_exporter/releases/download/v0.8.0/apache_exporter-0.8.0.linux-amd64.tar.gz",
"tar xzf apache_exporter-0.8.0.linux-amd64.tar.gz",
"mv -f apache_exporter-0.8.0.linux-amd64/apache_exporter /usr/local/sbin/ && rm -rf apache_exporter-0.8.0.linux-amd64"
]
run_root_commands(cmds)
logger.info("downloading node_exporter")
cmds = [
"wget https://github.com/prometheus/node_exporter/releases/download/v1.0.1/node_exporter-1.0.1.linux-amd64.tar.gz",
"tar xzf node_exporter-1.0.1.linux-amd64.tar.gz",
"mv -f node_exporter-1.0.1.linux-amd64/node_exporter /usr/local/sbin/ && rm -rf node_exporter-1.0.1.linux-amd64"
]
run_root_commands(cmds)
def provision_supervisord_conf():
cmds = [
"sed -i 's/\; port\=\*\:9001/port=127\.0\.0\.1\:9001/g' /usr/local/etc/filewave/supervisor/supervisord-server.conf",
"sed -i 's/\; \[inet_http_server\]/\[inet_http_server\]/g' /usr/local/etc/filewave/supervisor/supervisord-server.conf",
"sed -i 's/\;\[include\]/\[include\]/g' /usr/local/etc/filewave/supervisor/supervisord-server.conf",
"sed -i 's/\;files = relative\/directory\/\*\.ini/files=extras\/\*\.conf/g' /usr/local/etc/filewave/supervisor/supervisord-server.conf",
]
supervisord_dir = os.path.join("/usr/local/etc/filewave/supervisor/", "extras")
if not os.path.exists(supervisord_dir):
os.makedirs(supervisord_dir)
data = pkg_resources.resource_string("monitor.config", "monitor-v13.conf").decode('utf-8')
provisioning_file = os.path.join(supervisord_dir, "monitor-v13.conf")
with open(provisioning_file, "w") as f:
f.write(data)
run_root_commands(cmds)
def validate_provisioning():
binaries = [ "node_exporter", "apache_exporter", "mtail", "postgres_exporter", "prometheus", "promtool", "tsdb" ]
for b in binaries:
f = os.path.join("/usr/local/sbin", b)
if not os.path.exists(f):
raise ValidationExceptionBinaryNotFound(f"failed to find required binary: {f}")
else:
logger.info(f"OK: {f}")
shutil.chown(f, user="root", group="root")
|
the-stack_0_984 | import sys
import csv
import get_info
def main(argv):
skip = int(argv[1])
with open('final_movie_upload_data.csv', mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
for i in range(0, skip):
next(csv_reader)
count = 0
new_data = []
for row in csv_reader:
idx = row["idx"]
title = row["title"]
#if line_count == 0:
# print(f'Column names are {", ".join(row)}')
# line_count += 1
# continue
print(f'\t{idx} {title}')
data = get_info.search(title)
if "title" in data and "description" in data:
data["idx"] = idx
data["title"] = f"{data['title']}({title})"
new_data.append(data)
count += 1
if count == 3 :
break;
print(f'Processed {count} lines.')
append(new_data)
def append(data):
field_names = ['idx','title','description']
with open('movie_info.csv', 'a+', newline='') as write_obj:
dict_writer = csv.DictWriter(write_obj, fieldnames=field_names)
for row in data:
dict_writer.writerow(row)
if __name__ == "__main__":
main(sys.argv)
|
the-stack_0_985 |
from tensorflow.keras.models import model_from_json
import numpy as np
import cv2
import math
import tensorflow as tf
from tensorflow.keras.preprocessing import image
facec = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
from matplotlib import pyplot as plt
import os
import shutil
from skimage.measure import compare_ssim
with open("model.json", "r") as json_file: #Loading the saved model
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("model_weights.h5")
loaded_model._make_predict_function()
label_to_text = {0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4: 'sad'}
def pred(img_path):
label_to_text = {0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4: 'sad'}
img=cv2.imread(img_path) #read Image
gray_fr = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #covert image to grayscale
faces_rects = facec.detectMultiScale(gray_fr, scaleFactor = 1.2, minNeighbors = 5) #opencv's cascade classifier will be used for detecting the face
if len(faces_rects)!=0:
for (x, y, w, h) in faces_rects:
fc = gray_fr[y:y+h, x:x+w] #extracting only the face part
roi = cv2.resize(fc, (48, 48)) #resizing it according to the image that are acceptable by our model
img = image.img_to_array(roi)
img = img/255
img = np.expand_dims(img, axis=0)
return label_to_text[np.argmax(loaded_model.predict(img))],img #model.predict is used for predicting the emotion
else:
return 0,0 #return 0 if the face is not found
def removeout():
shutil.rmtree('output/') #remove output folder
def vidframe(vidname):
if vidname==0:
cap = cv2.VideoCapture(0)
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.mp4',fourcc, 20.0, (640,480))
while(cap.isOpened()):
ret, frame = cap.read()
if ret==True:
out.write(frame)
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
# Release everything if job is finished
cap.release()
out.release()
cv2.destroyAllWindows()
vidname="output.mp4"
if os.path.exists('output'): #if output folder is present then delete it
removeout() #create Output folder for storing frame
os.mkdir('output')
cap = cv2.VideoCapture(vidname) #capture video
frameRate=cap.get(5)
count = 0
while(cap.isOpened()): #store the frames in output folder
frameId = cap.get(1)
ret, frame = cap.read()
if (ret != True):
break
if (frameId % math.floor(frameRate) == 0):
filename ="output/frame%d.jpg" % count;count+=1
cv2.imwrite(filename, frame)
cap.release()
result=[] # used for storing emotion
face=[] #used for storing face images
for filename in os.listdir("output"): #loop through each frame
a,b = pred("output/"+filename) #run pred function to get emotion and face images
result.append(a)
face.append(b)
removeout()
result=[x for x in result if x!=0] #removing null prediction
face=[x for x in face if len(str(x))>1]
return result, face
def ssimscore1(im1,im2):
im1=im1.reshape(48, 48, 1).astype('float32') #reshaping the flattened image array
im2=im2.reshape(48, 48, 1).astype('float32')
(score, diff) = compare_ssim(im1, im2, full=True,multichannel=True) #comparing the image for finding difference using compare_ssim function
return score
|
the-stack_0_987 | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
import pdb
import time
import cv2
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import xml.etree.ElementTree as ET
import torchvision.transforms as transforms
import torchvision.datasets as dset
from scipy.misc import imread
from roi_data_layer.roidb import combined_roidb
from roi_data_layer.roibatchLoader import roibatchLoader
from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from model.rpn.bbox_transform import clip_boxes
from model.nms.nms_wrapper import nms
from model.rpn.bbox_transform import bbox_transform_inv
from model.utils.net_utils import save_net, load_net, vis_detections
from model.utils.blob import im_list_to_blob
from model.faster_rcnn.vgg16 import vgg16
from model.faster_rcnn.resnet import resnet
from model.faster_rcnn.prefood_res50 import PreResNet50
from datasets.food_category import get_categories
from datasets.id2name import id2eng, id2chn
try:
xrange # Python 2
except NameError:
xrange = range # Python 3
beehoonid2name = {'1': 'bee hoon', '2': 'fried noodles', '3': 'kway teow', '4': 'kway teow, yellow noodles mix', '5': 'rice', '51': 'fried rice', '7': 'hokkien mee', '8': 'maggie noodle', '9': 'Glutinous rice', '10': 'beehoon and noodle mix', '110': 'stir fry mee tai mak', '11': 'fried egg', '12': 'scrambled egg', '13': 'cabbage', '131': 'hairy gourd with egg', '14': 'french bean/long bean', '141': 'broccoli', '142': 'celery', '143': 'beansprout', '15': 'deep fried beancurd skin', '16':
'fried beancurd/taukwa', '17': 'taupok', '171': 'braised taupok', '18': 'Acar', '181': 'Stir fried eggplant', '19': 'cucumber', '21': 'luncheon meat', '22': 'hashbrown', '23': 'ngoh hiang', '24': 'begedil', '25': 'spring roll', '31': 'otah', '32': 'fish ball/sotong ball', '33': 'white, yellow fish fillet', '331': 'orange, red fish fillet', '34': 'fish cake', '341': 'ngoh hiang fish cake', '35': 'kuning fish (fried small fish)', '351': 'fried fish steak', '36': 'siew mai',
'41': 'hotdog/taiwan sausage', '42': 'seaweed chicken', '43': 'chicken nugget', '44': 'fried chicken / chicken wings', '441': 'fried chicken chopped up', '45': 'fried chicken cutlet (not ground meat)', '55': 'curry mixed veg', '551': 'curry chicken and potato', '61': 'ikan bilis', '62': 'chilli paste', '63': 'green chilli', '64': 'peanut', '65': 'Sweet Sauce', '66': 'red chilli chopped', '71': 'deep fried fish', '91': 'Butter cereal chicken', '92': 'fried wanton/ dumpling', '93':
'Vegetarian meat', '94': 'Fried onions', '95': 'Crabstick'}
#id2chn = beehoonid2name
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
parser.add_argument('--dataset', dest='dataset',
help='training dataset',
default='pascal_voc', type=str)
parser.add_argument('--cfg', dest='cfg_file',
help='optional config file',
default='cfgs/vgg16.yml', type=str)
parser.add_argument('--net', dest='net',
help='vgg16, res50, res101, res152',
default='res101', type=str)
parser.add_argument('--set', dest='set_cfgs',
help='set config keys', default=None,
nargs=argparse.REMAINDER)
parser.add_argument('--load_dir', dest='load_dir',
help='directory to load models',
default="/srv/share/jyang375/models")
parser.add_argument('--image_dir', dest='image_dir',
help='directory to load images for demo',
default="images")
parser.add_argument('--cuda', dest='cuda',
help='whether use CUDA',
action='store_true')
parser.add_argument('--mGPUs', dest='mGPUs',
help='whether use multiple GPUs',
action='store_true')
parser.add_argument('--cag', dest='class_agnostic',
help='whether perform class_agnostic bbox regression',
action='store_true')
parser.add_argument('--parallel_type', dest='parallel_type',
help='which part of model to parallel, 0: all, 1: model before roi pooling',
default=0, type=int)
parser.add_argument('--checksession', dest='checksession',
help='checksession to load model',
default=1, type=int)
parser.add_argument('--checkepoch', dest='checkepoch',
help='checkepoch to load network',
default=1, type=int)
parser.add_argument('--checkpoint', dest='checkpoint',
help='checkpoint to load network',
default=10021, type=int)
parser.add_argument('--bs', dest='batch_size',
help='batch_size',
default=1, type=int)
parser.add_argument('--vis', dest='vis',
help='visualization mode',
action='store_true')
parser.add_argument('--webcam_num', dest='webcam_num',
help='webcam ID number',
default=-1, type=int)
args = parser.parse_args()
return args
lr = cfg.TRAIN.LEARNING_RATE
momentum = cfg.TRAIN.MOMENTUM
weight_decay = cfg.TRAIN.WEIGHT_DECAY
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_scale_factors.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors)
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg.USE_GPU_NMS = args.cuda
print('Using config:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
# train set
# -- Note: Use validation set and disable the flipped to enable faster loading.
input_dir = args.load_dir + "/" + args.net + "/" + args.dataset
if not os.path.exists(input_dir):
raise Exception(
'There is no input directory for loading network from ' + input_dir)
load_name = os.path.join(input_dir,
'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
#pascal_classes = np.asarray(get_categories("EconomicBeeHoon_train"))
pascal_classes = get_categories('All_train_mt10')
# initilize the network here.
if args.net == 'vgg16':
fasterRCNN = vgg16(pascal_classes, pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'res101':
fasterRCNN = resnet(pascal_classes, 101, pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'res50':
fasterRCNN = resnet(pascal_classes, 50, pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'res152':
fasterRCNN = resnet(pascal_classes, 152, pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'foodres50':
fasterRCNN = PreResNet50(pascal_classes, pretrained=False,
class_agnostic=args.class_agnostic)
else:
print("network is not defined")
pdb.set_trace()
fasterRCNN.create_architecture()
print("load checkpoint %s" % (load_name))
if args.cuda > 0:
checkpoint = torch.load(load_name)
else:
checkpoint = torch.load(
load_name, map_location=(lambda storage, loc: storage))
fasterRCNN.load_state_dict(checkpoint['model'])
if 'pooling_mode' in checkpoint.keys():
cfg.POOLING_MODE = checkpoint['pooling_mode']
print('load model successfully!')
# pdb.set_trace()
print("load checkpoint %s" % (load_name))
# initilize the tensor holder here.
im_data = torch.FloatTensor(1)
im_info = torch.FloatTensor(1)
num_boxes = torch.LongTensor(1)
gt_boxes = torch.FloatTensor(1)
# ship to cuda
if args.cuda > 0:
im_data = im_data.cuda()
im_info = im_info.cuda()
num_boxes = num_boxes.cuda()
gt_boxes = gt_boxes.cuda()
# make variable
im_data = Variable(im_data, volatile=True)
im_info = Variable(im_info, volatile=True)
num_boxes = Variable(num_boxes, volatile=True)
gt_boxes = Variable(gt_boxes, volatile=True)
if args.cuda > 0:
cfg.CUDA = True
if args.cuda > 0:
fasterRCNN.cuda()
fasterRCNN.eval()
start = time.time()
max_per_image = 100
thresh = 0.05
vis = True
webcam_num = args.webcam_num
# Set up webcam or get image directories
if webcam_num >= 0:
cap = cv2.VideoCapture(webcam_num)
num_images = 0
else:
imglist = os.listdir(args.image_dir)
num_images = len(imglist)
print('Loaded Photo: {} images.'.format(num_images))
while (num_images >= 0):
total_tic = time.time()
if webcam_num < 0:
num_images -= 1
# Get image from the webcam
if webcam_num >= 0:
if not cap.isOpened():
raise RuntimeError(
"Webcam could not open. Please check connection.")
ret, frame = cap.read()
im_in = np.array(frame)
# Load the demo image
else:
im_file = os.path.join(args.image_dir, imglist[num_images])
# im = cv2.imread(im_file)
im_in = np.array(imread(im_file))
if len(im_in.shape) == 2:
im_in = im_in[:, :, np.newaxis]
im_in = np.concatenate((im_in, im_in, im_in), axis=2)
# rgb -> bgr
im = im_in[:, :, ::-1]
blobs, im_scales = _get_image_blob(im)
assert len(im_scales) == 1, "Only single-image batch implemented"
im_blob = blobs
im_info_np = np.array(
[[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32)
im_data_pt = torch.from_numpy(im_blob)
im_data_pt = im_data_pt.permute(0, 3, 1, 2)
im_info_pt = torch.from_numpy(im_info_np)
im_data.data.resize_(im_data_pt.size()).copy_(im_data_pt)
im_info.data.resize_(im_info_pt.size()).copy_(im_info_pt)
gt_boxes.data.resize_(1, 1, 5).zero_()
num_boxes.data.resize_(1).zero_()
# pdb.set_trace()
det_tic = time.time()
rois, cls_prob, bbox_pred, \
rpn_loss_cls, rpn_loss_box, \
RCNN_loss_cls, RCNN_loss_bbox, \
rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes)
scores = cls_prob.data
boxes = rois.data[:, :, 1:5]
if cfg.TEST.BBOX_REG:
# Apply bounding-box regression deltas
box_deltas = bbox_pred.data
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
if args.class_agnostic:
if args.cuda > 0:
box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
else:
box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS) \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS)
box_deltas = box_deltas.view(1, -1, 4)
else:
if args.cuda > 0:
box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
else:
box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS) \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS)
box_deltas = box_deltas.view(
1, -1, 4 * len(pascal_classes))
pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
pred_boxes = clip_boxes(pred_boxes, im_info.data, 1)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes, (1, scores.shape[1]))
pred_boxes /= im_scales[0]
scores = scores.squeeze()
pred_boxes = pred_boxes.squeeze()
det_toc = time.time()
detect_time = det_toc - det_tic
misc_tic = time.time()
# get gt
# 1. read xml
if vis:
im2show = np.copy(im)
for j in xrange(1, len(pascal_classes)):
inds = torch.nonzero(scores[:, j] > thresh).view(-1)
# if there is det
if inds.numel() > 0:
cls_scores = scores[:, j][inds]
_, order = torch.sort(cls_scores, 0, True)
if args.class_agnostic:
cls_boxes = pred_boxes[inds, :]
else:
cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4]
cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1)
# cls_dets = torch.cat((cls_boxes, cls_scores), 1)
cls_dets = cls_dets[order]
keep = nms(cls_dets, cfg.TEST.NMS,
force_cpu=not cfg.USE_GPU_NMS)
cls_dets = cls_dets[keep.view(-1).long()]
if vis:
im2show = vis_detections(
im2show, id2eng[pascal_classes[j]], cls_dets.cpu().numpy(), 0.5)
misc_toc = time.time()
nms_time = misc_toc - misc_tic
if webcam_num == -1:
sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s \r'
.format(num_images + 1, len(imglist), detect_time, nms_time))
sys.stdout.flush()
if vis and webcam_num == -1:
# cv2.imshow('test', im2show)
# cv2.waitKey(0)
result_path = os.path.join(
args.image_dir, imglist[num_images][:-4] + "_det.jpg")
cv2.imwrite(result_path, im2show)
else:
#im2showRGB = cv2.cvtColor(im2show, cv2.COLOR_BGR2RGB)
im2showRGB = im2show
cv2.namedWindow("frame", 0)
cv2.resizeWindow("frame", 800, 800)
cv2.imshow("frame", im2showRGB)
total_toc = time.time()
total_time = total_toc - total_tic
frame_rate = 1 / total_time
print('Frame rate:', frame_rate)
if cv2.waitKey(5000) & 0xFF == ord('q'):
break
if webcam_num >= 0:
cap.release()
cv2.destroyAllWindows()
|
the-stack_0_990 | """Test of Ray-tune without RLLib"""
from ray import tune
def objective(step, alpha, beta):
return (0.1 + alpha * step / 100)**(-1) + beta * 0.1
def train(config):
alpha, beta = config["alpha"], config["beta"]
for step in range(10):
score = objective(step, alpha, beta)
tune.report(mean_loss=score)
analysis = tune.run(
train,
config={
"alpha": tune.grid_search([0.001, 0.01]),
"beta": tune.choice([1, 2])
})
print("Best config: ", analysis.get_best_config(metric="mean_loss", mode="min"))
|
the-stack_0_992 | # Copyright 2012 New Dream Network, LLC (DreamHost)
#
# 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.
import contextlib
import mock
import testtools
import webob
from neutron.agent.linux import utils as agent_utils
from neutron.agent.metadata import agent
from neutron.agent import metadata_agent
from neutron.common import constants
from neutron.common import utils
from neutron.tests import base
class FakeConf(object):
admin_user = 'neutron'
admin_password = 'password'
admin_tenant_name = 'tenant'
auth_url = 'http://127.0.0.1'
auth_strategy = 'keystone'
auth_region = 'region'
auth_insecure = False
auth_ca_cert = None
endpoint_type = 'adminURL'
nova_metadata_ip = '9.9.9.9'
nova_metadata_port = 8775
metadata_proxy_shared_secret = 'secret'
nova_metadata_protocol = 'http'
nova_metadata_insecure = True
nova_client_cert = 'nova_cert'
nova_client_priv_key = 'nova_priv_key'
cache_url = ''
class FakeConfCache(FakeConf):
cache_url = 'memory://?default_ttl=5'
class TestMetadataProxyHandlerBase(base.BaseTestCase):
fake_conf = FakeConf
def setUp(self):
super(TestMetadataProxyHandlerBase, self).setUp()
self.log_p = mock.patch.object(agent, 'LOG')
self.log = self.log_p.start()
self.handler = agent.MetadataProxyHandler(self.fake_conf)
self.handler.plugin_rpc = mock.Mock()
self.handler.context = mock.Mock()
class TestMetadataProxyHandlerRpc(TestMetadataProxyHandlerBase):
def test_get_port_filters(self):
router_id = 'test_router_id'
ip = '1.2.3.4'
networks = ('net_id1', 'net_id2')
expected = {'device_id': [router_id],
'device_owner': constants.ROUTER_INTERFACE_OWNERS,
'network_id': networks,
'fixed_ips': {'ip_address': [ip]}}
actual = self.handler._get_port_filters(router_id, ip, networks)
self.assertEqual(expected, actual)
def test_get_router_networks(self):
router_id = 'router-id'
expected = ('network_id1', 'network_id2')
ports = [{'network_id': 'network_id1', 'something': 42},
{'network_id': 'network_id2', 'something_else': 32}]
self.handler.plugin_rpc.get_ports.return_value = ports
networks = self.handler._get_router_networks(router_id)
self.assertEqual(expected, networks)
def test_get_ports_for_remote_address(self):
ip = '1.1.1.1'
networks = ('network_id1', 'network_id2')
expected = [{'port_id': 'port_id1'},
{'port_id': 'port_id2'}]
self.handler.plugin_rpc.get_ports.return_value = expected
ports = self.handler._get_ports_for_remote_address(ip, networks)
self.assertEqual(expected, ports)
def test_get_ports_using_rpc_fallback_to_client(self):
ip = '1.1.1.1'
networks = ('network_id1', 'network_id2')
self.handler.plugin_rpc.get_ports.side_effect = AttributeError
with mock.patch('neutronclient.v2_0.client.Client') as neutron_client:
mock_list_ports = neutron_client.return_value.list_ports
expected_ports = {'ports': ['expected_port']}
mock_list_ports.return_value = expected_ports
ports = self.handler._get_ports_from_server(ip_address=ip,
networks=networks)
self.assertEqual(expected_ports['ports'], ports)
class TestMetadataProxyHandlerCache(TestMetadataProxyHandlerBase):
fake_conf = FakeConfCache
def setUp(self):
super(TestMetadataProxyHandlerCache, self).setUp()
self.qclient_p = mock.patch('neutronclient.v2_0.client.Client')
self.qclient = self.qclient_p.start()
self.handler.use_rpc = False
def test_call(self):
req = mock.Mock()
with mock.patch.object(self.handler,
'_get_instance_and_tenant_id') as get_ids:
get_ids.return_value = ('instance_id', 'tenant_id')
with mock.patch.object(self.handler, '_proxy_request') as proxy:
proxy.return_value = 'value'
retval = self.handler(req)
self.assertEqual(retval, 'value')
def test_call_no_instance_match(self):
req = mock.Mock()
with mock.patch.object(self.handler,
'_get_instance_and_tenant_id') as get_ids:
get_ids.return_value = None, None
retval = self.handler(req)
self.assertIsInstance(retval, webob.exc.HTTPNotFound)
def test_call_internal_server_error(self):
req = mock.Mock()
with mock.patch.object(self.handler,
'_get_instance_and_tenant_id') as get_ids:
get_ids.side_effect = Exception
retval = self.handler(req)
self.assertIsInstance(retval, webob.exc.HTTPInternalServerError)
self.assertEqual(len(self.log.mock_calls), 2)
def test_get_router_networks(self):
router_id = 'router-id'
expected = ('network_id1', 'network_id2')
ports = {'ports': [{'network_id': 'network_id1', 'something': 42},
{'network_id': 'network_id2',
'something_else': 32}],
'not_used': [1, 2, 3]}
mock_list_ports = self.qclient.return_value.list_ports
mock_list_ports.return_value = ports
networks = self.handler._get_router_networks(router_id)
mock_list_ports.assert_called_once_with(
device_id=router_id,
device_owner=constants.ROUTER_INTERFACE_OWNERS)
self.assertEqual(expected, networks)
def _test_get_router_networks_twice_helper(self):
router_id = 'router-id'
ports = {'ports': [{'network_id': 'network_id1', 'something': 42}],
'not_used': [1, 2, 3]}
expected_networks = ('network_id1',)
with mock.patch(
'oslo_utils.timeutils.utcnow_ts', return_value=0):
mock_list_ports = self.qclient.return_value.list_ports
mock_list_ports.return_value = ports
networks = self.handler._get_router_networks(router_id)
mock_list_ports.assert_called_once_with(
device_id=router_id,
device_owner=constants.ROUTER_INTERFACE_OWNERS)
self.assertEqual(expected_networks, networks)
networks = self.handler._get_router_networks(router_id)
def test_get_router_networks_twice(self):
self._test_get_router_networks_twice_helper()
self.assertEqual(
1, self.qclient.return_value.list_ports.call_count)
def _get_ports_for_remote_address_cache_hit_helper(self):
remote_address = 'remote_address'
networks = ('net1', 'net2')
fixed_ips = ["ip_address=%s" % remote_address]
mock_list_ports = self.qclient.return_value.list_ports
mock_list_ports.return_value = {'ports': [{'network_id': 'net1',
'something': 42}]}
self.handler._get_ports_for_remote_address(remote_address, networks)
mock_list_ports.assert_called_once_with(
network_id=networks, fixed_ips=fixed_ips)
self.assertEqual(1, mock_list_ports.call_count)
self.handler._get_ports_for_remote_address(remote_address,
networks)
def test_get_ports_for_remote_address_cache_hit(self):
self._get_ports_for_remote_address_cache_hit_helper()
self.assertEqual(
1, self.qclient.return_value.list_ports.call_count)
def test_get_ports_network_id(self):
network_id = 'network-id'
router_id = 'router-id'
remote_address = 'remote-address'
expected = ['port1']
networks = (network_id,)
with contextlib.nested(
mock.patch.object(self.handler, '_get_ports_for_remote_address'),
mock.patch.object(self.handler, '_get_router_networks')
) as (mock_get_ip_addr, mock_get_router_networks):
mock_get_ip_addr.return_value = expected
ports = self.handler._get_ports(remote_address, network_id,
router_id)
mock_get_ip_addr.assert_called_once_with(remote_address,
networks)
self.assertFalse(mock_get_router_networks.called)
self.assertEqual(expected, ports)
def test_get_ports_router_id(self):
router_id = 'router-id'
remote_address = 'remote-address'
expected = ['port1']
networks = ('network1', 'network2')
with contextlib.nested(
mock.patch.object(self.handler,
'_get_ports_for_remote_address',
return_value=expected),
mock.patch.object(self.handler,
'_get_router_networks',
return_value=networks)
) as (mock_get_ip_addr, mock_get_router_networks):
ports = self.handler._get_ports(remote_address,
router_id=router_id)
mock_get_router_networks.called_once_with(router_id)
mock_get_ip_addr.assert_called_once_with(remote_address, networks)
self.assertEqual(expected, ports)
def test_get_ports_no_id(self):
self.assertRaises(TypeError, self.handler._get_ports, 'remote_address')
def _get_instance_and_tenant_id_helper(self, headers, list_ports_retval,
networks=None, router_id=None):
remote_address = '192.168.1.1'
headers['X-Forwarded-For'] = remote_address
req = mock.Mock(headers=headers)
def mock_list_ports(*args, **kwargs):
return {'ports': list_ports_retval.pop(0)}
self.qclient.return_value.list_ports.side_effect = mock_list_ports
self.qclient.return_value.get_auth_info.return_value = {
'auth_token': None, 'endpoint_url': None}
instance_id, tenant_id = self.handler._get_instance_and_tenant_id(req)
new_qclient_call = mock.call(
username=FakeConf.admin_user,
tenant_name=FakeConf.admin_tenant_name,
region_name=FakeConf.auth_region,
auth_url=FakeConf.auth_url,
password=FakeConf.admin_password,
auth_strategy=FakeConf.auth_strategy,
token=None,
insecure=FakeConf.auth_insecure,
ca_cert=FakeConf.auth_ca_cert,
endpoint_url=None,
endpoint_type=FakeConf.endpoint_type)
expected = []
if router_id:
expected.extend([
new_qclient_call,
mock.call().list_ports(
device_id=router_id,
device_owner=constants.ROUTER_INTERFACE_OWNERS
),
mock.call().get_auth_info()
])
expected.extend([
new_qclient_call,
mock.call().list_ports(
network_id=networks, fixed_ips=['ip_address=192.168.1.1']),
mock.call().get_auth_info()
])
self.qclient.assert_has_calls(expected)
return (instance_id, tenant_id)
def test_get_instance_id_router_id(self):
router_id = 'the_id'
headers = {
'X-Neutron-Router-ID': router_id
}
networks = ('net1', 'net2')
ports = [
[{'network_id': 'net1'}, {'network_id': 'net2'}],
[{'device_id': 'device_id', 'tenant_id': 'tenant_id',
'network_id': 'net1'}]
]
self.assertEqual(
self._get_instance_and_tenant_id_helper(headers, ports,
networks=networks,
router_id=router_id),
('device_id', 'tenant_id')
)
def test_get_instance_id_router_id_no_match(self):
router_id = 'the_id'
headers = {
'X-Neutron-Router-ID': router_id
}
networks = ('net1', 'net2')
ports = [
[{'network_id': 'net1'}, {'network_id': 'net2'}],
[]
]
self.assertEqual(
self._get_instance_and_tenant_id_helper(headers, ports,
networks=networks,
router_id=router_id),
(None, None)
)
def test_get_instance_id_network_id(self):
network_id = 'the_id'
headers = {
'X-Neutron-Network-ID': network_id
}
ports = [
[{'device_id': 'device_id',
'tenant_id': 'tenant_id',
'network_id': 'the_id'}]
]
self.assertEqual(
self._get_instance_and_tenant_id_helper(headers, ports,
networks=('the_id',)),
('device_id', 'tenant_id')
)
def test_get_instance_id_network_id_no_match(self):
network_id = 'the_id'
headers = {
'X-Neutron-Network-ID': network_id
}
ports = [[]]
self.assertEqual(
self._get_instance_and_tenant_id_helper(headers, ports,
networks=('the_id',)),
(None, None)
)
def test_auth_info_cache(self):
router_id = 'the_id'
list_ports = [
[{'network_id': 'net1'}],
[{'device_id': 'did', 'tenant_id': 'tid', 'network_id': 'net1'}]]
def update_get_auth_info(*args, **kwargs):
self.qclient.return_value.get_auth_info.return_value = {
'auth_token': 'token', 'endpoint_url': 'uri'}
return {'ports': list_ports.pop(0)}
self.qclient.return_value.list_ports.side_effect = update_get_auth_info
new_qclient_call = mock.call(
username=FakeConf.admin_user,
tenant_name=FakeConf.admin_tenant_name,
region_name=FakeConf.auth_region,
auth_url=FakeConf.auth_url,
password=FakeConf.admin_password,
auth_strategy=FakeConf.auth_strategy,
token=None,
insecure=FakeConf.auth_insecure,
ca_cert=FakeConf.auth_ca_cert,
endpoint_url=None,
endpoint_type=FakeConf.endpoint_type)
cached_qclient_call = mock.call(
username=FakeConf.admin_user,
tenant_name=FakeConf.admin_tenant_name,
region_name=FakeConf.auth_region,
auth_url=FakeConf.auth_url,
password=FakeConf.admin_password,
auth_strategy=FakeConf.auth_strategy,
token='token',
insecure=FakeConf.auth_insecure,
ca_cert=FakeConf.auth_ca_cert,
endpoint_url='uri',
endpoint_type=FakeConf.endpoint_type)
headers = {'X-Forwarded-For': '192.168.1.10',
'X-Neutron-Router-ID': router_id}
req = mock.Mock(headers=headers)
self.handler._get_instance_and_tenant_id(req)
expected = [
new_qclient_call,
mock.call().list_ports(
device_id=router_id,
device_owner=constants.ROUTER_INTERFACE_OWNERS
),
mock.call().get_auth_info(),
cached_qclient_call,
mock.call().list_ports(network_id=('net1',),
fixed_ips=['ip_address=192.168.1.10']),
mock.call().get_auth_info(),
]
self.qclient.assert_has_calls(expected)
def _proxy_request_test_helper(self, response_code=200, method='GET'):
hdrs = {'X-Forwarded-For': '8.8.8.8'}
body = 'body'
req = mock.Mock(path_info='/the_path', query_string='', headers=hdrs,
method=method, body=body)
resp = mock.MagicMock(status=response_code)
req.response = resp
with mock.patch.object(self.handler, '_sign_instance_id') as sign:
sign.return_value = 'signed'
with mock.patch('httplib2.Http') as mock_http:
resp.__getitem__.return_value = "text/plain"
mock_http.return_value.request.return_value = (resp, 'content')
retval = self.handler._proxy_request('the_id', 'tenant_id',
req)
mock_http.assert_called_once_with(
ca_certs=None, disable_ssl_certificate_validation=True)
mock_http.assert_has_calls([
mock.call().add_certificate(
FakeConf.nova_client_priv_key,
FakeConf.nova_client_cert,
"%s:%s" % (FakeConf.nova_metadata_ip,
FakeConf.nova_metadata_port)
),
mock.call().request(
'http://9.9.9.9:8775/the_path',
method=method,
headers={
'X-Forwarded-For': '8.8.8.8',
'X-Instance-ID-Signature': 'signed',
'X-Instance-ID': 'the_id',
'X-Tenant-ID': 'tenant_id'
},
body=body
)]
)
return retval
def test_proxy_request_post(self):
response = self._proxy_request_test_helper(method='POST')
self.assertEqual(response.content_type, "text/plain")
self.assertEqual(response.body, 'content')
def test_proxy_request_200(self):
response = self._proxy_request_test_helper(200)
self.assertEqual(response.content_type, "text/plain")
self.assertEqual(response.body, 'content')
def test_proxy_request_400(self):
self.assertIsInstance(self._proxy_request_test_helper(400),
webob.exc.HTTPBadRequest)
def test_proxy_request_403(self):
self.assertIsInstance(self._proxy_request_test_helper(403),
webob.exc.HTTPForbidden)
def test_proxy_request_404(self):
self.assertIsInstance(self._proxy_request_test_helper(404),
webob.exc.HTTPNotFound)
def test_proxy_request_409(self):
self.assertIsInstance(self._proxy_request_test_helper(409),
webob.exc.HTTPConflict)
def test_proxy_request_500(self):
self.assertIsInstance(self._proxy_request_test_helper(500),
webob.exc.HTTPInternalServerError)
def test_proxy_request_other_code(self):
with testtools.ExpectedException(Exception):
self._proxy_request_test_helper(302)
def test_sign_instance_id(self):
self.assertEqual(
self.handler._sign_instance_id('foo'),
'773ba44693c7553d6ee20f61ea5d2757a9a4f4a44d2841ae4e95b52e4cd62db4'
)
class TestMetadataProxyHandlerNoCache(TestMetadataProxyHandlerCache):
fake_conf = FakeConf
def test_get_router_networks_twice(self):
self._test_get_router_networks_twice_helper()
self.assertEqual(
2, self.qclient.return_value.list_ports.call_count)
def test_get_ports_for_remote_address_cache_hit(self):
self._get_ports_for_remote_address_cache_hit_helper()
self.assertEqual(
2, self.qclient.return_value.list_ports.call_count)
class TestUnixDomainMetadataProxy(base.BaseTestCase):
def setUp(self):
super(TestUnixDomainMetadataProxy, self).setUp()
self.cfg_p = mock.patch.object(agent, 'cfg')
self.cfg = self.cfg_p.start()
looping_call_p = mock.patch(
'neutron.openstack.common.loopingcall.FixedIntervalLoopingCall')
self.looping_mock = looping_call_p.start()
self.cfg.CONF.metadata_proxy_socket = '/the/path'
self.cfg.CONF.metadata_workers = 0
self.cfg.CONF.metadata_backlog = 128
@mock.patch.object(agent_utils, 'ensure_dir')
def test_init_doesnot_exists(self, ensure_dir):
agent.UnixDomainMetadataProxy(mock.Mock())
ensure_dir.assert_called_once_with('/the')
def test_init_exists(self):
with mock.patch('os.path.isdir') as isdir:
with mock.patch('os.unlink') as unlink:
isdir.return_value = True
agent.UnixDomainMetadataProxy(mock.Mock())
unlink.assert_called_once_with('/the/path')
def test_init_exists_unlink_no_file(self):
with mock.patch('os.path.isdir') as isdir:
with mock.patch('os.unlink') as unlink:
with mock.patch('os.path.exists') as exists:
isdir.return_value = True
exists.return_value = False
unlink.side_effect = OSError
agent.UnixDomainMetadataProxy(mock.Mock())
unlink.assert_called_once_with('/the/path')
def test_init_exists_unlink_fails_file_still_exists(self):
with mock.patch('os.path.isdir') as isdir:
with mock.patch('os.unlink') as unlink:
with mock.patch('os.path.exists') as exists:
isdir.return_value = True
exists.return_value = True
unlink.side_effect = OSError
with testtools.ExpectedException(OSError):
agent.UnixDomainMetadataProxy(mock.Mock())
unlink.assert_called_once_with('/the/path')
@mock.patch.object(agent, 'MetadataProxyHandler')
@mock.patch.object(agent_utils, 'UnixDomainWSGIServer')
@mock.patch.object(agent_utils, 'ensure_dir')
def test_run(self, ensure_dir, server, handler):
p = agent.UnixDomainMetadataProxy(self.cfg.CONF)
p.run()
ensure_dir.assert_called_once_with('/the')
server.assert_has_calls([
mock.call('neutron-metadata-agent'),
mock.call().start(handler.return_value,
'/the/path', workers=0,
backlog=128),
mock.call().wait()]
)
def test_main(self):
with mock.patch.object(agent, 'UnixDomainMetadataProxy') as proxy:
with mock.patch.object(metadata_agent, 'config') as config:
with mock.patch.object(metadata_agent, 'cfg') as cfg:
with mock.patch.object(utils, 'cfg'):
metadata_agent.main()
self.assertTrue(config.setup_logging.called)
proxy.assert_has_calls([
mock.call(cfg.CONF),
mock.call().run()]
)
def test_init_state_reporting(self):
with mock.patch('os.makedirs'):
proxy = agent.UnixDomainMetadataProxy(mock.Mock())
self.looping_mock.assert_called_once_with(proxy._report_state)
self.looping_mock.return_value.start.assert_called_once_with(
interval=mock.ANY)
def test_report_state(self):
with mock.patch('neutron.agent.rpc.PluginReportStateAPI') as state_api:
with mock.patch('os.makedirs'):
proxy = agent.UnixDomainMetadataProxy(mock.Mock())
self.assertTrue(proxy.agent_state['start_flag'])
proxy._report_state()
self.assertNotIn('start_flag', proxy.agent_state)
state_api_inst = state_api.return_value
state_api_inst.report_state.assert_called_once_with(
proxy.context, proxy.agent_state, use_call=True)
|
the-stack_0_995 | import tensorflow as tf
import os
import shutil
from tensorflow.python.saved_model import tag_constants
from tensorflow.python import ops
def get_graph_def_from_file(graph_filepath):
tf.compat.v1.reset_default_graph()
with ops.Graph().as_default():
with tf.compat.v1.gfile.GFile(graph_filepath, 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
return graph_def
def convert_graph_def_to_saved_model(export_dir, graph_filepath, input_name, outputs):
graph_def = get_graph_def_from_file(graph_filepath)
with tf.compat.v1.Session(graph=tf.Graph()) as session:
tf.import_graph_def(graph_def, name='')
tf.compat.v1.saved_model.simple_save(
session,
export_dir,# change input_image to node.name if you know the name
inputs={input_name: session.graph.get_tensor_by_name('{}:0'.format(node.name))
for node in graph_def.node if node.op=='Placeholder'},
outputs={t.rstrip(":0"):session.graph.get_tensor_by_name(t) for t in outputs}
)
print('Optimized graph converted to SavedModel!')
tf.compat.v1.enable_eager_execution()
# convert this to a TF Serving compatible mode
shutil.rmtree('./saved_model', ignore_errors=True)
convert_graph_def_to_saved_model('./saved_model', './v3-large_224_1.0_float.pb', 'input', ['MobilenetV3/Predictions/Softmax:0'])
|
the-stack_0_997 | # -*- coding: utf-8 -*-
from matplotlib.patches import Patch
from matplotlib.pyplot import axis, legend
from ....Functions.init_fig import init_fig
from ....definitions import config_dict
MAGNET_COLOR = config_dict["PLOT"]["COLOR_DICT"]["MAGNET_COLOR"]
def plot(self, fig=None, display_magnet=True):
"""Plot the Hole in a matplotlib fig
Parameters
----------
self : Hole
A Hole object
fig :
if None, open a new fig and plot, else add to the current
one (Default value = None)
display_magnet : bool
if True, plot the magnet inside the hole, if there is any (Default value = True)
Returns
-------
None
"""
display = fig is None
if display:
color = "k"
else:
color = "w"
surf_hole = self.build_geometry()
patches = list()
for surf in surf_hole:
if "Magnet" in surf.label and display_magnet:
patches.extend(surf.get_patches(color=MAGNET_COLOR))
else:
patches.extend(surf.get_patches(color=color))
# Display the result
(fig, axes, patch_leg, label_leg) = init_fig(fig)
axes.set_xlabel("(m)")
axes.set_ylabel("(m)")
axes.set_title("Hole")
# Add all the hole (and magnet) to fig
for patch in patches:
axes.add_patch(patch)
# Axis Setup
axis("equal")
Lim = self.get_Rbo() * 1.2
axes.set_xlim(-Lim, Lim)
axes.set_ylim(-Lim, Lim)
if display_magnet and "Magnet" in [surf.label for surf in surf_hole]:
patch_leg.append(Patch(color=MAGNET_COLOR))
label_leg.append("Magnet")
legend(patch_leg, label_leg)
fig.show()
|
the-stack_0_998 | from jesse.helpers import get_candle_source, slice_candles, np_shift, same_length
import numpy as np
from numba import njit,jit
import talib
from typing import Union
from jesse.helpers import get_config
from collections import namedtuple
import tulipy as ti
import math
"""
https://www.tradingview.com/script/sxZRzQzQ-Divergence-Indicator-any-oscillator/#chart-view-comment-form
Possibly Accurate, needs more testing
"""
#jesse backtest '2021-01-03' '2021-03-02'
DIVERGENCES = namedtuple('Divergences',['bearCond', 'bullCond', 'hiddenBullCond','hiddenBearCond'])
def divergence(candles: np.ndarray, lbR:int=2, lbL:int=2, rangeUpper:int=200, rangeLower:int=0,source_type: str = "close", sequential: bool = False) -> DIVERGENCES:
candles = slice_candles(candles, sequential)
source1 = get_candle_source(candles, source_type=source_type)
bearCond, bullCond, hiddenBullCond, hiddenBearCond = fast_div(source,source,candles,lbR,lbL,rangeUpper,rangeLower)
if sequential:
return DIVERGENCES(bearCond,bullCond,hiddenBearCond,hiddenBullCond)
else:
return DIVERGENCES(bearCond[-1],bullCond[-1],hiddenBearCond[-1],hiddenBullCond[-1])
def fast_div(source1,source,candles,r,l,rangeUpper,rangeLower):
highmiddlesource = np.full_like(source1,0)
lowmiddlesource = np.full_like(source1,0)
pivothigh = np.full_like(source1,0)
pivotlow = np.full_like(source1,0)
lastpivothighprice = np.full_like(source1,0)
lastpivotlowprice = np.full_like(source1,0)
priceslowest = np.full_like(source1,np.nan)
priceshighest = np.full_like(source1,np.nan)
priceshigh = np.full_like(source1,np.nan)
priceslow = np.full_like(source1,np.nan)
highindices = np.full_like(source1,np.nan)
lowindices = np.full_like(source1,np.nan)
ivar = np.full_like(source1,0)
for i in range(source1.shape[0]):
highmiddlesource[i] = source[i-r]
lowmiddlesource[i] = source[i-l]
if (np.all(highmiddlesource[i] >= source[i-(l+r):i-(r)]) and np.all(highmiddlesource[i] > source[i-(r-1):i+1])):
pivothigh[i] = 1
lastpivothighprice[i] = highmiddlesource[i]
else:
pivothigh[i] = 0
lastpivothighprice[i] = lastpivothighprice[i-1]
if (np.all(lowmiddlesource[i] <= source[i-(l+r):i-(r)]) and np.all(lowmiddlesource[i] < source[i-(r-1):i+1])):
pivotlow[i] = 1
lastpivotlowprice[i] = lowmiddlesource[i]
else:
pivotlow[i] = 0
lastpivotlowprice[i] = lastpivotlowprice[i-1]
if pivothigh[i] == 1:
priceshigh[i] = source[i-r]
priceshighest[i] = candles[:,3][i-r]
highindices[i] = (i-r)
if pivotlow[i] == 1:
priceslow[i] = source[i-l]
priceslowest[i] = candles[:,4][i-l]
lowindices[i] = (i-l)
ivar[i] = i
ivar1 = int(ivar[-1])
priceshigh = priceshigh[~np.isnan(priceshigh)]
priceshigh = np.concatenate((np.full((source.shape[0] - priceshigh.shape[0]), np.nan), priceshigh))
priceshighest = priceshighest[~np.isnan(priceshighest)]
priceshighest = np.concatenate((np.full((source.shape[0] - priceshighest.shape[0]), np.nan), priceshighest))
priceslow = priceslow[~np.isnan(priceslow)]
priceslow = np.concatenate((np.full((source.shape[0] - priceslow.shape[0]), np.nan), priceslow))
priceslowest = priceslowest[~np.isnan(priceslowest)]
priceslowest = np.concatenate((np.full((source.shape[0] - priceslowest.shape[0]), np.nan), priceslowest))
highindices = highindices[~np.isnan(highindices)]
highindices = np.concatenate((np.full((source.shape[0] - highindices.shape[0]), np.nan), highindices))
lowindices = lowindices[~np.isnan(lowindices)]
lowindices = np.concatenate((np.full((source.shape[0] - lowindices.shape[0]), np.nan), lowindices))
oscHL = 1 if source[-(r+1)] > priceslow[-2] and (np.abs(lowindices[-2]-ivar1) >= rangeLower and np.abs(lowindices[-2]-ivar1) <= rangeUpper) else 0
priceLL = 1 if candles[:,4][-(r+1)] < priceslowest[-2] else 0
bullCond = 1 if priceLL == 1 and oscHL == 1 and pivotlow[-1] == 1 else 0
oscLL = 1 if (source[-(r+1)] < priceslow[-2] and np.abs(lowindices[-2]-ivar1) >= rangeLower and np.abs(lowindices[-2]-ivar1) <= rangeUpper) else 0
priceHL = 1 if candles[:,4][-(r+1)] > priceslowest[-2] else 0
hiddenBullCond = 1 if priceHL == 1 and oscLL == 1 and pivotlow[-1] == 1 else 0
oscLH = 1 if source[-(r+1)] < priceshigh[-2] and (np.abs(highindices[-2]-ivar1) >= rangeLower and np.abs(highindices[-2]-ivar1) <= rangeUpper) else 0
priceHH = 1 if candles[:,3][-(r+1)] > priceshighest[-2] else 0
bearCond = 1 if priceHH == 1 and oscLH == 1 and pivothigh[-1] == 1 else 0
oscHH = 1 if source[-(r+1)] > priceshigh[-2] and (np.abs(highindices[-2]-ivar1) >= rangeLower and np.abs(highindices[-2]-ivar1) <= rangeUpper) else 0
priceLH = 1 if candles[:,3][-(r+1)] < priceshighest[-2] else 0
hiddenBearCond = 1 if priceLH == 1 and oscHH == 1 and pivothigh[-1] == 1 else 0
return bearCond, bullCond, hiddenBullCond, hiddenBearCond
|
the-stack_0_999 | from enum import Enum
from itertools import takewhile
from grid import Grid, Point
import grid_utils
class DiscState(Enum):
empty = 0
red = 1
black = 2
class Game(object):
def __init__(self, initial_grid=None):
self.restart(initial_grid)
def restart(self, initial_grid=None):
if initial_grid is None:
self.grid = Grid(6, # Rows
7, # Cols
initial_value=DiscState.empty)
else:
self.grid = initial_grid
self.current_player = DiscState.red
self.winner = None
self.is_end = False
def try_turn(self, color, col_index):
added_point = self.try_move(color, col_index)
if added_point is not None:
winner = self.get_winner(added_point, self.current_player)
if winner:
self.winner = winner
self.is_end = True
return True
else:
if not self.is_board_full():
self.switch_player()
else:
# Tie game
self.is_end = True
return True
return False
def try_move(self, color, col_index):
if self.current_player is not color:
return None
if not self.can_add_disc(col_index):
return None
return self.add_disc(col_index, self.current_player)
def switch_player(self):
if self.current_player is DiscState.red:
self.current_player = DiscState.black
else:
self.current_player = DiscState.red
def is_board_full(self):
for col_index in range(self.grid.width):
if self.can_add_disc(col_index):
return False
return True
def can_add_disc(self, col_index):
if col_index >= self.grid.width:
return False
return self.grid[-1][col_index] is DiscState.empty
def add_disc(self, col_index, color):
for row_index in range(self.grid.height):
if self.grid[row_index][col_index] is DiscState.empty:
self.grid[row_index][col_index] = color
return Point(row_index, col_index)
break
else:
raise ValueError("column %i is full" % col_index)
def get_winner(self, last_move, current_player, row_size=4):
assert self.grid.at(last_move) is not DiscState.empty
if grid_utils.is_in_row_run(self.grid, last_move, row_size) or \
grid_utils.is_in_col_run(self.grid, last_move, row_size) or \
grid_utils.is_in_diag_down_run(self.grid, last_move, row_size) or \
grid_utils.is_in_diag_up_run(self.grid, last_move, row_size):
return current_player
return None
def render_board(self):
str_repr = ["Current board state:\n"]
str_repr += [" %i " % col_index for col_index in range(self.grid.width)] + ["\n"]
for row in reversed(self.grid):
row_repr = []
for disc_value in row:
if disc_value is DiscState.empty:
row_repr.append("| |")
elif disc_value is DiscState.red:
row_repr.append("|O|")
else: # disc_value is black
row_repr.append("|X|")
row_repr.append("\n")
str_repr += row_repr
print("".join(str_repr))
|
the-stack_0_1001 | from typing import Optional
from django.db import models, DatabaseError, transaction
from .message import ChatMediaTypes
from ..users import UserUpdater
from ..base import BaseModel
from .entity_types import EntityTypes
from .entity_types import EntitySourceTypes
from core.globals import logger
from pyrogram import types
from telegram import models as tg_models
class EntityQuerySet(models.QuerySet):
def filter_by_id(self, *, id: str) -> "EntityQuerySet":
return self.filter(id=id)
def get_by_id(self, *, id: str) -> Optional["Entity"]:
try:
return self.get(id=id)
except Entity.DoesNotExist as e:
pass
except DatabaseError as e:
logger.exception(e)
except Exception as e:
logger.exception(e)
return None
def update_or_create_entity(self, *, defaults: dict, **kwargs) -> Optional["Entity"]:
try:
return self.update_or_create(
defaults=defaults,
**kwargs
)[0]
except DatabaseError as e:
logger.exception(e)
except Exception as e:
logger.exception(e)
return None
class EntityManager(models.Manager):
def get_queryset(self) -> EntityQuerySet:
return EntityQuerySet(self.model, using=self._db)
def update_or_create_from_raw(
self,
*,
raw_entity: types.MessageEntity,
db_message: "tg_models.Message",
) -> Optional["Entity"]:
if raw_entity is None or db_message is None:
return None
parsed_entity = self._parse(
raw_entity=raw_entity,
message__has_media=bool(db_message.media_type != ChatMediaTypes.undefined)
)
if parsed_entity:
with transaction.atomic():
db_entity = self.get_queryset().update_or_create_entity(
id=f'{db_message.id}:{raw_entity.offset}',
defaults={
'message': db_message,
**parsed_entity,
},
)
if db_entity:
db_entity.update_or_create_user_from_raw(
model=db_entity,
field_name='user',
raw_user=raw_entity.user
)
return db_entity
return None
@staticmethod
def _parse(*, raw_entity: types.MessageEntity, message__has_media: bool) -> dict:
if raw_entity is None:
return {}
return {
'type': EntityTypes.get_type(raw_entity.type),
'source': EntitySourceTypes.caption if message__has_media else EntitySourceTypes.text,
'offset': raw_entity.offset,
'length': raw_entity.length,
}
class Entity(BaseModel, UserUpdater):
id = models.CharField(max_length=256, primary_key=True) # `message__id:offset`
type = models.CharField(
EntityTypes.choices,
max_length=20,
null=False,
)
source = models.CharField(
EntitySourceTypes.choices,
max_length=20,
null=False,
)
offset = models.IntegerField()
length = models.IntegerField()
# entities, both from `text` and `caption`
message = models.ForeignKey(
'telegram.Message',
on_delete=models.CASCADE,
null=False,
related_name='entities',
)
# For `text_mention` only, the mentioned user.
user = models.ForeignKey(
'telegram.User',
related_name='mentioned_entities',
null=True, blank=True,
on_delete=models.CASCADE,
)
objects = EntityManager()
class Meta:
verbose_name_plural = 'Entities'
ordering = ('message',)
def __str__(self):
return f"{self.type} of type {self.source} in {self.message}"
|
the-stack_0_1002 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('esg_leipzig_homepage_2015', '0005_linktoflatpage'),
]
operations = [
migrations.CreateModel(
name='News',
fields=[
('id', models.AutoField(auto_created=True, verbose_name='ID', primary_key=True, serialize=False)),
('title', models.CharField(verbose_name='Titel', max_length=255, help_text="Beispiel: 'Schrank abzugeben'. Änderungen sind immer in den Sprachfeldern vorzunehmen.")),
('title_de', models.CharField(null=True, verbose_name='Titel', max_length=255, help_text="Beispiel: 'Schrank abzugeben'. Änderungen sind immer in den Sprachfeldern vorzunehmen.")),
('title_en', models.CharField(null=True, verbose_name='Titel', max_length=255, help_text="Beispiel: 'Schrank abzugeben'. Änderungen sind immer in den Sprachfeldern vorzunehmen.")),
('content', models.TextField(blank=True, verbose_name='Inhalt (HTML)', help_text='Es können alle HTML-Tags verwendet werden. Änderungen sind immer in den Sprachfeldern vorzunehmen.')),
('content_de', models.TextField(blank=True, null=True, verbose_name='Inhalt (HTML)', help_text='Es können alle HTML-Tags verwendet werden. Änderungen sind immer in den Sprachfeldern vorzunehmen.')),
('content_en', models.TextField(blank=True, null=True, verbose_name='Inhalt (HTML)', help_text='Es können alle HTML-Tags verwendet werden. Änderungen sind immer in den Sprachfeldern vorzunehmen.')),
('author', models.CharField(verbose_name='Autor', max_length=255, help_text="Beispiel: 'Frank Martin'.")),
('weight', models.IntegerField(verbose_name='Platzierung', help_text='Eine höhere Zahl bedeutet, dass der Eintrag auf der Startseite weiter unten steht.', default=100)),
],
options={
'verbose_name_plural': 'Aktuelle Informationen',
'verbose_name': 'Aktuelle Information',
'ordering': ('weight', 'title'),
},
bases=(models.Model,),
),
]
|
the-stack_0_1003 | from typing import List
from webdnn.backend.code_generator.allocator import MemoryLayout
from webdnn.backend.code_generator.injectors.buffer_injector import BufferInjector
from webdnn.backend.code_generator.injectors.kernel_name_injector import KernelNameInjector
from webdnn.backend.webassembly.generator import WebassemblyDescriptorGenerator
from webdnn.backend.webassembly.kernel import Kernel
from webdnn.graph.axis import Axis
from webdnn.graph.operators.space2depth import Space2Depth
from webdnn.graph.order import OrderNHWC
template = """
void %%FUNC_NAME%%(const int * %%META_BUFFER%%)
{
const float *x = %%LOAD_BUFFER(space2depth_x)%%;
float *y = %%LOAD_BUFFER(space2depth_y)%%;
const int r = %%LOAD_BUFFER(space2depth_r)%%;
const int N = %%LOAD_BUFFER(space2depth_N)%%;
const int C1 = %%LOAD_BUFFER(space2depth_C1)%%;
const int C2 = %%LOAD_BUFFER(space2depth_C2)%%;
const int H1 = %%LOAD_BUFFER(space2depth_H1)%%;
const int H2 = %%LOAD_BUFFER(space2depth_H2)%%;
const int W1 = %%LOAD_BUFFER(space2depth_W1)%%;
const int W2 = %%LOAD_BUFFER(space2depth_W2)%%;
for (int gid = 0; gid < N*H1*W1*C1; gid += 1) {
const int c1 = gid % C1;
const int w1 = gid / C1 % W1;
const int h1 = gid / C1 / W1 % H1;
const int n = gid / C1 / W1 / H1;
const int w2 = w1 / r;
const int h2 = h1 / r;
const int c2 = c1 + (w1 % r) * C1 + (h1 % r) * C1 * r;
y[((n*H2+h2)*W2+w2)*C2+c2] = x[gid];
}
}
"""
@WebassemblyDescriptorGenerator.register_handler(Space2Depth)
def space2depth(op: Space2Depth, memory_layout: MemoryLayout) -> List[Kernel]:
x = op.inputs["x"]
y = op.outputs["y"]
r = op.parameters['r']
assert x.order == OrderNHWC
assert y.order == OrderNHWC
buffer_injector = BufferInjector()
buffer_injector.register({
"space2depth_x": memory_layout[x],
"space2depth_y": memory_layout[y],
'space2depth_r': r,
"space2depth_N": x.shape_dict[Axis.N],
"space2depth_C1": x.shape_dict[Axis.C],
"space2depth_C2": y.shape_dict[Axis.C],
"space2depth_H1": x.shape_dict[Axis.H],
"space2depth_H2": y.shape_dict[Axis.H],
"space2depth_W1": x.shape_dict[Axis.W],
"space2depth_W2": y.shape_dict[Axis.W],
})
name_injector = KernelNameInjector(op)
source = template
source = buffer_injector.inject(source)
source = name_injector.inject(source)
kernel = Kernel(
{name_injector.name: source},
name_injector.name,
buffer_injector.buffer,
buffer_injector.unresolved_value_list
)
return [kernel]
|
the-stack_0_1004 | import configparser
import json
from pathlib import Path
from transformers import AutoTokenizer, Wav2Vec2ForCTC
import sounddevice as sd
import soundfile as sf
import torch
def record_from_mic(config):
"""Record audio from a microphone.
Args:
config (ConfigParser): Config params.
Returns:
audio (ndarray): Recorded audio.
"""
sample_rate = config.getint('config', 'sample_rate')
duration_secs = config.getint('microphone', 'duration_secs')
channels = config.getint('microphone', 'channels')
print("Start recording . . . ")
audio = sd.rec(int(duration_secs*sample_rate), sample_rate, channels)
sd.wait() # Wait until recording is finished
print("Finish recording")
return audio
def wav2vec2_inference(audio, tokenizer, model):
"""Transcript audio with the Wav2Vec2 model.
Args:
audio (ndarray): Audio of interest.
tokenizer (Wav2Vec2Tokenizer): Wav2Vec2 associated tokenizer.
model (Wav2Vec2ForCTC): Wav2Vec2 to perform the transcription.
Returns:
transcriptions (str): Audio transcript.
"""
input_values = tokenizer(audio.ravel(), return_tensors='pt').input_values
logits = model(input_values).logits
# Store predicted id's
predicted_ids = torch.argmax(logits, dim =-1)
# Decode the audio to generate text
transcriptions = tokenizer.decode(predicted_ids[0])
return transcriptions
def main():
config = configparser.ConfigParser()
config.read('config.ini')
# Initialize tokenizer and model from HuggingFace
tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
if config.getboolean('config', 'from_microphone'):
# Record from microphone and transcript
audio = record_from_mic(config)
transcriptions = wav2vec2_inference(audio, tokenizer, model)
print(f"Transcribed audio: {transcriptions}")
if config.getboolean('config', 'save_transcriptions'):
with open('mic_transcription.txt', 'w') as file:
file.write(transcriptions)
print(f"Transcribed audio stored in mic_transcription.txt")
else:
# Transcript files in configuration file
audio_files = json.loads(config.get('config', 'audio_files'))
for audio_file in audio_files:
audio, _ = sf.read(audio_file, dtype='float32')
transcriptions = wav2vec2_inference(audio, tokenizer, model)
print(f"Transcribed audio: {transcriptions}")
if config.getboolean('config', 'save_transcriptions'):
with open(f'{Path(audio_file).stem}.txt', 'w') as file:
file.write(transcriptions)
print(f"Transcribed audio stored in {Path(audio_file).stem}.txt")
if __name__ == '__main__':
main() |
the-stack_0_1005 | import argparse
import pandas as pd
label_map = {
'agree': 'agree',
'disagree': 'refute',
'discuss': 'nostance'
}
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('snopes', help='/path/to/snopes/file')
parser.add_argument('pred', help='/path/to/prediction/file')
parser.add_argument('out', help='/path/to/output/file')
args = parser.parse_args()
snopes = pd.read_csv(args.snopes)
pred = pd.read_csv(args.pred)
pred = pred.rename(index=str, columns={'Stance': 'Predicted Stance', 'Body ID': 'ID', 'Headline': 'Claim'})
# assignment = {
# 'Snippets': lambda x: snopes.loc[snopes['ID'] == x.ID].Snippets,
# 'Gold Stance': lambda x: snopes.loc[snopes['ID'] == x.ID].Stance,
# }
# pred = pred.assign(**assignment)
joined = pred.set_index('ID').join(snopes.set_index('ID'), rsuffix='_right')
joined = joined.rename(index=str, columns={'Stance': 'Gold Stance'})
joined['Predicted Stance'] = joined.apply(lambda row: label_map[row['Predicted Stance']], axis=1)
# pred['Snippets'] = pred.apply(lambda x: snopes.loc[snopes['ID'] == x.ID].Snippets, axis=1)
# pred['Gold Stance'] = pred.apply(lambda x: snopes.loc[snopes['ID'] == x.ID].Stance, axis=1)
joined.to_csv(args.out, columns=['Claim', 'Snippets', 'Gold Stance', 'Predicted Stance'])
|
the-stack_0_1006 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Prints Graph
def print_dict(dictionary):
for k, v in {k: v for k, v in dictionary.items() if v[0] != 'out'}.items(): print(k, ':', *v, sep='\t', end='\n')
# Parsing
def parse(lines, gates):
data = list()
graph = dict()
for i in range(len(lines)):
line = lines[i].replace('\n', '').split('\t')
if len(line) > 3:
if line[2] in gates: data.append(line + lines[i+1].replace('\n', '').split("\t"))
elif (line[0][:1] != '\t'): data.append(line)
out_count = 1
for i in range(len(data)):
if data[i][2] == 'inpt': graph[data[i][0]] = [data[i][2], [], [], ['sa0', 'sa1']]
elif data[i][2] in gates:
for j in data[i][-1*int(data[i][4]):]:
if data[int(j) - 1][1][-3:] == 'fan':
graph[data[int(j) - 1][0]] = ['wire', [data[int(j) - 1][3][:-3]], [data[i][1][:-3]], ['sa0', 'sa1']]
for k in graph[data[int(j) - 1][0]][1]:
if k not in graph.keys(): graph[k] = [data[int(k) - 1][2], data[int(k) - 1][-1*int(data[int(k) - 1][4]):], [j], ['sa0', 'sa1']]
elif j not in graph[k][2]: graph[k][2].append(j)
else: graph[data[int(j) - 1][0]] = [data[int(j) - 1][2], data[int(j) - 1][-1*int(data[int(j) - 1][4]):], [data[i][1][:-3]], ['sa0', 'sa1']]
if data[i][3] == '0':
graph[data[i][0]] = [data[i][2], data[i][-1*int(data[i][4]):], [str(len(data) + out_count)], ['sa0', 'sa1']]
graph[str(len(data) + out_count)] = ['out', [data[i][0]], [], []]
out_count += 1
return graph
|
the-stack_0_1007 | # -*- coding: utf-8 -*-
import os
import os.path
import re
import sys
import string
from django.apps.registry import apps
from django.core.management.base import BaseCommand, CommandError
from python_translate.extractors import base as extractors
from python_translate import operations
from python_translate.translations import MessageCatalogue
from django_translate.utils import bcolors
from django_translate import services
from django_translate import settings
class AnyFormatSpec:
def __format__(self, fmt):
return ''
class Formatter(string.Formatter):
def __init__(self):
self.used = set()
def get_value(self, key, args, kwargs):
self.used.add(key)
return AnyFormatSpec()
class Command(BaseCommand):
help = """Extract translation strings from templates from a given location. It can display them or merge
the new ones into the translation files. When new translation strings are found it can
automatically add a prefix to the translation message.
Example running against app folder
./manage.py tranzdump -l en --path ./ --output-path ./tranz
./manage.py tranzdump -l fr --force --prefix="new_" --app website --exclude ./website/static
"""
def __init__(self, stdout=None, stderr=None, no_color=False):
self.excluded_paths = None
self.locale = None
self.verbosity = None
super(Command, self).__init__(stdout, stderr, no_color)
def add_arguments(self, parser):
parser.add_argument('--locale', '-l', default='en', dest='locale', action='store',
help='Locale to process')
parser.add_argument('--app', '-a', dest='app', action='store',
help='App to scan.')
parser.add_argument('--path', '-p', dest='path', action='store',
help='Path to scan')
parser.add_argument('--output-dir', dest='output_dir', default=None, action='store',
help='Override the default output dir')
parser.add_argument('--exclude-dir', '-x', default=[], dest='excluded_paths', action='append',
help='Paths to exclude. Default is none. Can be used multiple times. '
'Works only with ChainExtractor.')
parser.add_argument('--prefix', dest='prefix', default="__", action='store',
help='Override the default prefix')
parser.add_argument('--format', dest='format', default="yml", action='store',
help='Override the default output format')
parser.add_argument('--dump-messages', dest='dump_messages', action='store_true',
help='Should the messages be dumped in the console')
parser.add_argument('--force', dest='force', action='store_true',
help='Should the update be done')
parser.add_argument('--no-backup', dest='no_backup', action='store_true',
help='Should backup be disabled')
parser.add_argument('--clean', dest='clean', default=False, action='store_true',
help='Should clean not found messages',)
def handle(self, *args, **options):
if options.get('force') != True and options.get('dump_messages') != True:
print((bcolors.WARNING + 'You must choose at least one of --force or --dump-messages' + bcolors.ENDC))
return
if not (bool(options.get('app')) ^ bool(options.get('path'))):
print((bcolors.WARNING + 'You must choose only one of --app or --path' + bcolors.ENDC))
return
if not options.get('output_dir') and (not options.get('app') or not settings.TRANZ_SEARCH_LOCALE_IN_APPS):
print((bcolors.WARNING + 'You must provide an --output-dir when in --path mode, or when TRANZ_SEARCH_LOCALE_IN_APPS ' \
'settings variable is False.' + bcolors.ENDC))
return
self.excluded_paths = [os.path.abspath(path) for path in options['excluded_paths']]
self.excluded_paths += [os.path.abspath(django_translate.__path__[0])]
self.excluded_paths += settings.TRANZ_EXCLUDED_DIRS
# Find directories to scan
if options.get('app'):
for app in list(apps.app_configs.values()):
if app.name == options.get('app'):
current_name = app.name
root_path = app.path
break
else:
raise ValueError("App {0} not found".format(options.get('app')))
else:
root_path = os.path.abspath(options['path'])
current_name = root_path.split("/")[-1]
output_dir = options.get('output_dir') or os.path.join(root_path, 'tranz')
writer = services.writer
print(('Generating "{0}" translation files for "{1}"'.format(options.get('locale'), current_name)))
print("Loading existing messages")
current_catalogue = MessageCatalogue(options['locale'])
loader = services.loader
loader.load_messages(output_dir, current_catalogue)
if len(current_catalogue.messages) == 0:
print(("No messages were loaded, make sure there actually are " \
"translation file in format {{catalog}}.{{locale}}.{{format}} in {0}".format(output_dir)))
return
print("Extracting messages")
extracted_catalogue = MessageCatalogue(options['locale'])
extractor = services.extractor
extractor.set_prefix(options['prefix'])
self.extract_messages(extractor, root_path, extracted_catalogue)
print("Processing catalogues")
operation_class = operations.DiffOperation if options['clean'] else operations.MergeOperation
operation = operation_class(current_catalogue, extracted_catalogue)
if not len(operation.get_domains()):
print("No translations found")
return
if options["dump_messages"]:
for domain in operation.get_domains():
print(("Displaying messages for domain {0}".format(domain)))
new_keys = list(operation.get_new_messages(domain).keys())
all_keys = list(operation.get_messages(domain).keys())
for id in set(all_keys).difference(new_keys):
print(id)
for id in new_keys:
print((bcolors.OKGREEN + id + bcolors.ENDC))
for id in list(operation.get_obsolete_messages(domain).keys()):
print((bcolors.FAIL + id + bcolors.ENDC))
if options["no_backup"]:
writer.disable_backup()
if options["force"]:
print(("Writing files to {0}".format(output_dir)))
writer.write_translations(operation.get_result(), options['format'], {
"path": output_dir,
"default_locale": options['locale']
})
def extract_messages(self, extractor, root_path, extracted_catalogue):
if isinstance(extractor, extractors.ChainExtractor):
subextractors = list(extractor._extractors.values())
else:
subextractors = [extractor]
for subextractor in subextractors:
if not isinstance(subextractor, extractors.BaseExtractor):
subextractor.extract(root_path, extracted_catalogue)
continue
paths = subextractor.extract_files(root_path)
paths = self.filter_exluded_paths(paths)
for path in paths:
try:
subextractor.extract([path], extracted_catalogue)
except Exception as e:
exc_type, exc_value, exc_traceback = sys.exc_info()
msg = 'There was an exception in extractor {0} when processing ' \
'resource "{1}"'.format(type(subextractor).__name__, path)
msg = msg + "\nOriginal message: {0} {1}".format(exc_type.__name__, exc_value)
raise ValueError(msg).with_traceback(exc_traceback)
def filter_exluded_paths(self, paths):
valid = []
for path in paths:
for excluded in self.excluded_paths:
if path.startswith(excluded):
break
else:
valid.append(path)
return valid
|
the-stack_0_1008 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 18 16:55:14 2017
@author: ajaver
"""
import os
import tables
import numpy as np
import warnings
from .getFoodContourNN import get_food_contour_nn
from .getFoodContourMorph import get_food_contour_morph
from tierpsy.helper.misc import TimeCounter, print_flush, get_base_name
def calculate_food_cnt(mask_file, use_nn_food_cnt, model_path, _is_debug=False, solidity_th=0.98):
if use_nn_food_cnt:
if not os.path.exists(model_path):
warnings.warn('The model to obtain the food contour was not found. Nothing to do here...\n If you dont have a valid model. You could try to set `food_method=MORPH` to use a different algorithm.')
return
food_cnt, food_prob,cnt_solidity = get_food_contour_nn(mask_file, model_path, _is_debug=_is_debug)
if cnt_solidity < solidity_th:
food_cnt = np.zeros(0)
else:
food_cnt = get_food_contour_morph(mask_file, _is_debug=_is_debug)
return food_cnt
def getFoodContour(mask_file,
skeletons_file,
use_nn_food_cnt,
model_path,
solidity_th=0.98,
_is_debug = False
):
base_name = get_base_name(mask_file)
progress_timer = TimeCounter('')
print_flush("{} Calculating food contour {}".format(base_name, progress_timer.get_time_str()))
food_cnt = calculate_food_cnt(mask_file,
use_nn_food_cnt = use_nn_food_cnt,
model_path = model_path,
solidity_th= solidity_th,
_is_debug = _is_debug)
#store contour coordinates into the skeletons file and mask_file the contour file
for fname in [skeletons_file, mask_file]:
with tables.File(fname, 'r+') as fid:
if '/food_cnt_coord' in fid:
fid.remove_node('/food_cnt_coord')
#if it is a valid contour save it
if food_cnt is not None and \
food_cnt.size >= 2 and \
food_cnt.ndim == 2 and \
food_cnt.shape[1] == 2:
tab = fid.create_array('/',
'food_cnt_coord',
obj=food_cnt)
tab._v_attrs['use_nn_food_cnt'] = int(use_nn_food_cnt)
|
the-stack_0_1009 | """
Based on https://github.com/asanakoy/kaggle_carvana_segmentation
"""
import torch
import torch.utils.data as data
from torch.autograd import Variable as V
from PIL import Image
import cv2
import numpy as np
import os
import scipy.misc as misc
import Constants
def randomHueSaturationValue(image, hue_shift_limit=(-180, 180),
sat_shift_limit=(-255, 255),
val_shift_limit=(-255, 255), u=0.5):
if np.random.random() < u:
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(image)
hue_shift = np.random.randint(hue_shift_limit[0], hue_shift_limit[1]+1)
hue_shift = np.uint8(hue_shift)
h += hue_shift
sat_shift = np.random.uniform(sat_shift_limit[0], sat_shift_limit[1])
s = cv2.add(s, sat_shift)
val_shift = np.random.uniform(val_shift_limit[0], val_shift_limit[1])
v = cv2.add(v, val_shift)
image = cv2.merge((h, s, v))
#image = cv2.merge((s, v))
image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
return image
def randomShiftScaleRotate(image, mask,
shift_limit=(-0.0, 0.0),
scale_limit=(-0.0, 0.0),
rotate_limit=(-0.0, 0.0),
aspect_limit=(-0.0, 0.0),
borderMode=cv2.BORDER_CONSTANT, u=0.5):
if np.random.random() < u:
height, width, channel = image.shape
angle = np.random.uniform(rotate_limit[0], rotate_limit[1])
scale = np.random.uniform(1 + scale_limit[0], 1 + scale_limit[1])
aspect = np.random.uniform(1 + aspect_limit[0], 1 + aspect_limit[1])
sx = scale * aspect / (aspect ** 0.5)
sy = scale / (aspect ** 0.5)
dx = round(np.random.uniform(shift_limit[0], shift_limit[1]) * width)
dy = round(np.random.uniform(shift_limit[0], shift_limit[1]) * height)
cc = np.math.cos(angle / 180 * np.math.pi) * sx
ss = np.math.sin(angle / 180 * np.math.pi) * sy
rotate_matrix = np.array([[cc, -ss], [ss, cc]])
box0 = np.array([[0, 0], [width, 0], [width, height], [0, height], ])
box1 = box0 - np.array([width / 2, height / 2])
box1 = np.dot(box1, rotate_matrix.T) + np.array([width / 2 + dx, height / 2 + dy])
box0 = box0.astype(np.float32)
box1 = box1.astype(np.float32)
mat = cv2.getPerspectiveTransform(box0, box1)
image = cv2.warpPerspective(image, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=borderMode,
borderValue=(
0, 0,
0,))
mask = cv2.warpPerspective(mask, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=borderMode,
borderValue=(
0, 0,
0,))
return image, mask
def randomHorizontalFlip(image, mask, u=0.5):
if np.random.random() < u:
image = cv2.flip(image, 1)
mask = cv2.flip(mask, 1)
return image, mask
def randomVerticleFlip(image, mask, u=0.5):
if np.random.random() < u:
image = cv2.flip(image, 0)
mask = cv2.flip(mask, 0)
return image, mask
def randomRotate90(image, mask, u=0.5):
if np.random.random() < u:
image=np.rot90(image)
mask=np.rot90(mask)
return image, mask
def argument_Drive_loader(img_path, mask_path):
img = cv2.imread(img_path)
img = cv2.resize(img, Constants.Image_size)
mask = np.array(Image.open(mask_path))
mask = cv2.resize(mask, Constants.Image_size)
mask = np.expand_dims(mask, axis=2)
img = np.array(img, np.float32).transpose(2, 0, 1) / 255.0 * 3.2 - 1.6
mask = np.array(mask, np.float32).transpose(2, 0, 1) / 255.0
mask[mask >= 0.5] = 1
mask[mask <= 0.5] = 0
return img, mask
def argument_CHASEDB_loader(img_path, mask_path):
img = cv2.imread(img_path)
img = cv2.resize(img, Constants.Image_size)
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
mask = cv2.resize(mask, Constants.Image_size)
mask = np.expand_dims(mask, axis=2)
img = np.array(img, np.float32).transpose(2, 0, 1) / 255.0 * 3.2 - 1.6
mask = np.array(mask, np.float32).transpose(2, 0, 1) / 255.0
mask[mask >= 0.5] = 1
mask[mask <= 0.5] = 0
return img, mask
def default_DRIVE_loader(img_path, mask_path):
img = cv2.imread(img_path)
img = cv2.resize(img, Constants.Image_size)
# mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
mask = np.array(Image.open(mask_path))
mask = cv2.resize(mask, Constants.Image_size)
img = randomHueSaturationValue(img,
hue_shift_limit=(-30, 30),
sat_shift_limit=(-5, 5),
val_shift_limit=(-15, 15))
img, mask = randomShiftScaleRotate(img, mask,
shift_limit=(-0.1, 0.1),
scale_limit=(-0.1, 0.1),
aspect_limit=(-0.1, 0.1),
rotate_limit=(-0, 0))
img, mask = randomHorizontalFlip(img, mask)
img, mask = randomVerticleFlip(img, mask)
img, mask = randomRotate90(img, mask)
mask = np.expand_dims(mask, axis=2)
img = np.array(img, np.float32).transpose(2, 0, 1) / 255.0 * 3.2 - 1.6
mask = np.array(mask, np.float32).transpose(2, 0, 1) / 255.0
mask[mask >= 0.5] = 1
mask[mask <= 0.5] = 0
# mask = abs(mask-1)
return img, mask
def default_CHASEDB_loader(img_path, mask_path):
img = cv2.imread(img_path)
img = cv2.resize(img, Constants.Image_size)
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
# mask = np.array(Image.open(mask_path))
mask = cv2.resize(mask, Constants.Image_size)
img = randomHueSaturationValue(img,
hue_shift_limit=(-30, 30),
sat_shift_limit=(-5, 5),
val_shift_limit=(-15, 15))
img, mask = randomShiftScaleRotate(img, mask,
shift_limit=(-0.1, 0.1),
scale_limit=(-0.1, 0.1),
aspect_limit=(-0.1, 0.1),
rotate_limit=(-0, 0))
img, mask = randomHorizontalFlip(img, mask)
img, mask = randomVerticleFlip(img, mask)
img, mask = randomRotate90(img, mask)
mask = np.expand_dims(mask, axis=2)
img = np.array(img, np.float32).transpose(2, 0, 1) / 255.0 * 3.2 - 1.6
mask = np.array(mask, np.float32).transpose(2, 0, 1) / 255.0
mask[mask >= 0.5] = 1
mask[mask <= 0.5] = 0
# mask = abs(mask-1)
return img, mask
def read_DRIVE_datasets(root_path, mode='train'):
images = []
masks = []
if mode=='Hard':
image_root = os.path.join(root_path, 'argtraining/images')
gt_root = os.path.join(root_path, 'argtraining/1st_manual')
else:
image_root = os.path.join(root_path, 'training/images')
gt_root = os.path.join(root_path, 'training/1st_manual')
for image_name in os.listdir(image_root):
image_path = os.path.join(image_root, image_name.split('.')[0] + '.tif')
if int(image_name.split('_')[0])>20:
label_path = os.path.join(gt_root, image_name.split('_')[0] + '_manual1.gif')
else:
label_path = os.path.join(gt_root, image_name.split('_')[0] + '_manual1.tif')
images.append(image_path)
masks.append(label_path)
# print(images, masks)
return images, masks
def read_CHASEDB_datasets(root_path, mode='train'):
images = []
masks = []
if mode == 'Hard':
image_root = os.path.join(root_path, 'argtraining/images')
gt_root = os.path.join(root_path, 'argtraining/1st_manual')
else:
image_root = os.path.join(root_path, 'training/images')
gt_root = os.path.join(root_path, 'training/1st_manual')
for image_name in os.listdir(image_root):
image_path = os.path.join(image_root, image_name.split('.')[0] + '.jpg')
label_path = os.path.join(gt_root, image_name.split('.')[0] + '_1stHO.png')
images.append(image_path)
masks.append(label_path)
# print(images, masks)
return images, masks
class ImageFolder(data.Dataset):
def __init__(self,root_path, datasets='Messidor', mode='train'):
self.root = root_path
self.mode = mode
self.dataset = datasets
assert self.dataset in ['CHASEDB','DRIVE'], \
"the dataset should be in 'CHASEDB', 'DRIVE'."
if self.dataset == 'DRIVE':
self.images, self.labels = read_DRIVE_datasets(self.root, self.mode)
if self.mode == 'Argument':
self.loader = argument_Drive_loader
else:
self.loader = default_DRIVE_loader
else:
self.images, self.labels = read_CHASEDB_datasets(self.root, self.mode)
if self.mode=='Argument':
self.loader=argument_CHASEDB_loader
else:
self.loader = default_CHASEDB_loader
def __getitem__(self, index):
img, mask = self.loader(self.images[index], self.labels[index])
img = torch.Tensor(img)
mask = torch.Tensor(mask)
if self.mode=='Argument':
return img, mask,self.images[index], self.labels[index]
else:
return img, mask
def __len__(self):
assert len(self.images) == len(self.labels), 'The number of images must be equal to labels'
return len(self.images) |
the-stack_0_1011 | class ScoreCalc:
def __init__(self, slices):
self.score = 0
self.slices = slices
self.calculatescore()
def calculatescore(self):
for slice in self.slices:
r1 = slice[0]
c1 = slice[1]
r2 = slice[2]
c2 = slice[3]
self.score += abs(r2-r1+1)*abs(c2-c1+1) |
the-stack_0_1012 | # qubit number=3
# total number=10
import numpy as np
from qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ
import networkx as nx
from qiskit.visualization import plot_histogram
from typing import *
from pprint import pprint
from math import log2
from collections import Counter
from qiskit.test.mock import FakeVigo, FakeYorktown
kernel = 'circuit/bernstein'
def make_circuit(n:int) -> QuantumCircuit:
# circuit begin
input_qubit = QuantumRegister(n,"qc")
prog = QuantumCircuit(input_qubit)
prog.h(input_qubit[0]) # number=1
prog.z(input_qubit[3]) # number=7
prog.h(input_qubit[1]) # number=2
prog.h(input_qubit[2]) # number=3
prog.h(input_qubit[3]) # number=4
for edge in E:
k = edge[0]
l = edge[1]
prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1])
prog.p(gamma, k)
prog.p(gamma, l)
prog.rx(2 * beta, range(len(V)))
prog.swap(input_qubit[3],input_qubit[0]) # number=5
prog.swap(input_qubit[3],input_qubit[0]) # number=6
prog.y(input_qubit[1]) # number=8
prog.y(input_qubit[1]) # number=9
# circuit end
return prog
if __name__ == '__main__':
n = 4
V = np.arange(0, n, 1)
E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]
G = nx.Graph()
G.add_nodes_from(V)
G.add_weighted_edges_from(E)
step_size = 0.1
a_gamma = np.arange(0, np.pi, step_size)
a_beta = np.arange(0, np.pi, step_size)
a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)
F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (
1 + np.cos(4 * a_gamma) ** 2)
result = np.where(F1 == np.amax(F1))
a = list(zip(result[0], result[1]))[0]
gamma = a[0] * step_size
beta = a[1] * step_size
prog = make_circuit(4)
sample_shot =5600
writefile = open("../data/startQiskit98.csv", "w")
# prog.draw('mpl', filename=(kernel + '.png'))
backend = BasicAer.get_backend('qasm_simulator')
circuit1 = transpile(prog, FakeYorktown())
circuit1.measure_all()
prog = circuit1
info = execute(prog,backend=backend, shots=sample_shot).result().get_counts()
print(info, file=writefile)
print("results end", file=writefile)
print(circuit1.depth(), file=writefile)
print(circuit1, file=writefile)
writefile.close()
|
the-stack_0_1013 | # Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
from common.onnx_layer_test_class import OnnxRuntimeLayerTest
class TestLoop(OnnxRuntimeLayerTest):
@staticmethod
def create_const(name, tensor_type, value):
from onnx import helper
from onnx import TensorProto
if tensor_type == TensorProto.INT64:
np_type = np.int64
elif tensor_type == TensorProto.FLOAT:
np_type = np.float
elif tensor_type == TensorProto.BOOL:
np_type = np.bool
else:
return None
return helper.make_node('Constant', inputs=[], outputs=[name],
value=helper.make_tensor(name='const_tensor',
data_type=tensor_type,
dims=value.shape,
vals=value.flatten().astype(np_type)))
@staticmethod
def create_body_graph(input_nodes, output_nodes, input_names, output_names, input_shape,
graph_name):
# input_nodes - list of input nodes with structure {counter, condition, <other inputs>}
# output_nodes - list of output nodes with structure {condition, <back edges>, <external outputs>}.
# In this function I assume that every <other input> have <back edge> and <external output>
# input_shape - shape of all inputs from <other inputs>
from onnx import helper
from onnx import TensorProto
assert len(input_nodes) > 2
assert len(output_nodes) == (len(input_nodes) - 2) * 2 + 1
assert len(input_nodes) == len(input_names)
assert len(output_nodes) == len(output_names)
other_inputs_count = len(input_nodes) - 2
one_value = np.ones(input_shape, dtype=np.float)
one = TestLoop.create_const('one_' + graph_name, TensorProto.FLOAT, one_value)
one_int = TestLoop.create_const('one_int_' + graph_name, TensorProto.INT64, np.ones([1]))
# add one to all inputs except counter and condition
add_one_nodes = []
for i in range(2, len(input_names)):
add_one_nodes.append(
helper.make_node('Add', inputs=[input_names[i], 'one_' + graph_name],
outputs=[output_names[other_inputs_count + i - 1]]))
# add 1 to counter
add_one_to_m_node = helper.make_node(
'Add',
inputs=[input_names[0], 'one_int_' + graph_name],
outputs=['counter_plus_1_' + graph_name]
)
# map inputs to outputs - back edges
identity_nodes = []
for i in range(1, len(input_nodes)):
identity_nodes.append(helper.make_node('Identity',
inputs=[input_names[i]],
outputs=[output_names[i - 1]]))
body_nodes = [one, one_int]
body_nodes.extend(add_one_nodes)
body_nodes.append(add_one_to_m_node)
body_nodes.extend(identity_nodes)
body_graph = helper.make_graph(
body_nodes,
graph_name,
input_nodes,
output_nodes
)
return body_graph
def create_loop(self):
"""
ONNX net
Input->Loop->Output => Only accuracy check
"""
from onnx import helper
from onnx import TensorProto
# Create ONNX model
# Input ---> Loop ---> Identity ---> Result
input_shape = [1, 4, 64, 54]
in_1 = helper.make_tensor_value_info('IN_1', TensorProto.FLOAT, input_shape)
in_1_int = helper.make_tensor_value_info('in_1_int', TensorProto.FLOAT, input_shape)
in_1_int_out = helper.make_tensor_value_info('in_1_int_out', TensorProto.FLOAT, input_shape)
out_1 = helper.make_tensor_value_info('OUT_1', TensorProto.FLOAT, None)
res = helper.make_tensor_value_info('res', TensorProto.FLOAT, None)
m_1 = helper.make_tensor_value_info('m_1', TensorProto.INT64, [1])
cond_int_1 = helper.make_tensor_value_info('cond_int_1', TensorProto.BOOL, [1])
cond_out_1 = helper.make_tensor_value_info('cond_out_1', TensorProto.BOOL, [1])
m_1_value = np.array([10], dtype=np.int64)
cond_value = np.array([True], np.bool)
M_1 = self.create_const('M_1', TensorProto.INT64, m_1_value)
cond = self.create_const('cond', TensorProto.BOOL, cond_value)
body_graph_1 = self.create_body_graph([m_1, cond_int_1, in_1_int],
[cond_out_1, in_1_int_out, out_1],
['m_1', 'cond_int_1', 'in_1_int'],
['cond_out_1', 'in_1_int_out', 'OUT_1'],
input_shape, 'body_graph_1')
node_loop_1 = helper.make_node(
'Loop',
inputs=['M_1', 'cond', 'IN_1'],
outputs=['cond_out_1', 'OUT_1'],
body=body_graph_1
)
res_node = helper.make_node(
'Identity',
inputs=['OUT_1'],
outputs=['res'],
)
graph_def = helper.make_graph(
[M_1, cond, node_loop_1, res_node],
'graph',
[in_1],
[res]
)
onnx_net = helper.make_model(graph_def, producer_name='test_loop_model')
# We do not create reference graph, as it's too complicated to construct it
# So we return None to skip IR comparision
return onnx_net, None
def create_loop_in_loop(self):
"""
ONNX net
Input->Loop(Loop)->Output => Only accuracy check
"""
from onnx import helper
from onnx import TensorProto
# Create ONNX model
input_shape = [1, 4, 64, 54]
in_1 = helper.make_tensor_value_info('IN_1', TensorProto.FLOAT, input_shape)
in_1_int = helper.make_tensor_value_info('in_1_int', TensorProto.FLOAT, input_shape)
in_1_int_out = helper.make_tensor_value_info('in_1_int_out', TensorProto.FLOAT, input_shape)
in_2 = helper.make_tensor_value_info('IN_2', TensorProto.FLOAT, input_shape)
in_2_int = helper.make_tensor_value_info('in_2_int', TensorProto.FLOAT, input_shape)
in_2_int_out = helper.make_tensor_value_info('in_2_int_out', TensorProto.FLOAT, input_shape)
out_1 = helper.make_tensor_value_info('OUT_1', TensorProto.FLOAT, None)
out_2 = helper.make_tensor_value_info('OUT_2', TensorProto.FLOAT, None)
res = helper.make_tensor_value_info('res', TensorProto.FLOAT, None)
m_1 = helper.make_tensor_value_info('m_1', TensorProto.INT64, [1])
m_2 = helper.make_tensor_value_info('m_2', TensorProto.INT64, [1])
cond_int_1 = helper.make_tensor_value_info('cond_int_1', TensorProto.BOOL, [1])
cond_out_1 = helper.make_tensor_value_info('cond_out_1', TensorProto.BOOL, [1])
cond_int_2 = helper.make_tensor_value_info('cond_int_2', TensorProto.BOOL, [1])
cond_out_2 = helper.make_tensor_value_info('cond_out_2', TensorProto.BOOL, [1])
m_1_value = np.array([10], dtype=np.int64)
m_2_value = np.array([5], dtype=np.int64)
cond_value = np.array([True], np.bool)
one_value = np.ones(input_shape, dtype=np.float)
M_1 = self.create_const('M_1', TensorProto.INT64, m_1_value)
M_2 = self.create_const('M_2', TensorProto.INT64, m_2_value)
cond = self.create_const('cond', TensorProto.BOOL, cond_value)
one = self.create_const('one', TensorProto.FLOAT, one_value)
one_int = self.create_const('one_int', TensorProto.INT64, one_value)
# create body of external loop
add_one_node = helper.make_node(
'Add',
inputs=['in_1_int', 'one'],
outputs=['in_1_loop_1']
)
add_one_to_m_node = helper.make_node(
'Add',
inputs=['m_1', 'one_int'],
outputs=['m_1_loop_1']
)
cond_2 = self.create_const('cond_2', TensorProto.BOOL, cond_value)
# create body for internal loop
body_graph_2 = self.create_body_graph([m_2, cond_int_2, in_2_int],
[cond_out_2, in_2_int_out, out_2],
['m_2', 'cond_int_2', 'in_2_int'],
['cond_out_2', 'in_2_int_out', 'OUT_2'], input_shape,
'body_graph_2')
node_loop_2 = helper.make_node(
'Loop',
inputs=['M_2', 'cond_2', 'IN_2'],
outputs=['cond_out_2', 'OUT_2'],
body=body_graph_2
)
# internal loop created
out_1_node = helper.make_node(
'Identity',
inputs=['OUT_2'],
outputs=['OUT_1'],
)
cond_1_node = helper.make_node(
'Identity',
inputs=['cond_int_1'],
outputs=['cond_out_1'],
)
in_1_int_node = helper.make_node(
'Identity',
inputs=['in_1_int'],
outputs=['in_1_int_out'],
)
body_graph_1 = helper.make_graph(
[one, add_one_node, one_int, add_one_to_m_node, M_2, cond_2, node_loop_2, out_1_node,
cond_1_node,
in_1_int_node],
'body_graph_1',
[m_1, cond_int_1, in_1_int],
[cond_out_1, in_1_int_out, out_1],
)
node_loop_1 = helper.make_node(
'Loop',
inputs=['M_1', 'cond', 'IN_1'],
outputs=['cond_out_1', 'OUT_1'],
body=body_graph_1
)
# external loop created
res_node = helper.make_node(
'Identity',
inputs=['OUT_1'],
outputs=['res'],
)
graph_def = helper.make_graph(
[M_1, cond, node_loop_1, res_node],
'graph',
[in_1, in_2],
[res],
)
onnx_net = helper.make_model(graph_def, producer_name='test_loop_in_loop_model')
# We do not create reference graph, as it's too complicated to construct it
# So we return None to skip IR comparision
return onnx_net, None
@pytest.mark.precommit
@pytest.mark.timeout(250)
def test_loop_simple_precommit(self, ie_device, precision, ir_version, temp_dir, api_2):
if ie_device == 'GPU':
pytest.skip('Loop not supported on GPU')
self._test(*self.create_loop(), ie_device, precision, ir_version, temp_dir=temp_dir,
infer_timeout=150, api_2=api_2)
@pytest.mark.precommit
@pytest.mark.timeout(250)
def test_loop_in_loop_simple_precommit(self, ie_device, precision, ir_version, temp_dir, api_2):
if ie_device == 'GPU':
pytest.skip('Loop not supported on GPU')
self._test(*self.create_loop_in_loop(), ie_device, precision, ir_version, temp_dir=temp_dir,
infer_timeout=150, api_2=api_2)
|
the-stack_0_1015 | """
@ProjectName: DXY-2019-nCov-Crawler
@FileName: crawler.py
@Author: Jiabao Lin
@Date: 2020/1/21
"""
from bs4 import BeautifulSoup
from service.db import DB
from service.nameMap import country_type_map, city_name_map, country_name_map, continent_name_map
import re
import json
import time
import logging
import datetime
import requests
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
logger = logging.getLogger(__name__)
headers = {
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.117 Safari/537.36'
}
class Crawler:
def __init__(self):
self.session = requests.session()
self.session.headers.update(headers)
self.db = DB()
self.crawl_timestamp = int()
def run(self):
while True:
self.crawler()
time.sleep(60)
def crawler(self):
while True:
self.crawl_timestamp = int(datetime.datetime.timestamp(datetime.datetime.now()) * 1000)
try:
r = self.session.get(url='https://3g.dxy.cn/newh5/view/pneumonia')
except requests.exceptions.ChunkedEncodingError:
continue
soup = BeautifulSoup(r.content, 'lxml')
overall_information = re.search(r'\{("id".*?)\]\}', str(soup.find('script', attrs={'id': 'getStatisticsService'})))
province_information = re.search(r'\[(.*?)\]', str(soup.find('script', attrs={'id': 'getListByCountryTypeService1'})))
area_information = re.search(r'\[(.*)\]', str(soup.find('script', attrs={'id': 'getAreaStat'})))
abroad_information = re.search(r'\[(.*)\]', str(soup.find('script', attrs={'id': 'getListByCountryTypeService2'})))
news = re.search(r'\[(.*?)\]', str(soup.find('script', attrs={'id': 'getTimelineService'})))
if not overall_information or not province_information or not area_information or not news:
continue
self.overall_parser(overall_information=overall_information)
self.province_parser(province_information=province_information)
self.area_parser(area_information=area_information)
self.abroad_parser(abroad_information=abroad_information)
self.news_parser(news=news)
break
while True:
self.crawl_timestamp = int(datetime.datetime.timestamp(datetime.datetime.now()) * 1000)
try:
r = self.session.get(url='https://file1.dxycdn.com/2020/0127/797/3393185293879908067-115.json')
except requests.exceptions.ChunkedEncodingError:
continue
# Use try-except to ensure the .json() method will not raise exception.
try:
if r.status_code != 200:
continue
elif r.json().get('code') == 'success':
self.rumor_parser(rumors=r.json().get('data'))
break
else:
continue
except json.decoder.JSONDecodeError:
continue
logger.info('Successfully crawled.')
def overall_parser(self, overall_information):
overall_information = json.loads(overall_information.group(0))
overall_information.pop('id')
overall_information.pop('createTime')
overall_information.pop('modifyTime')
overall_information.pop('imgUrl')
overall_information.pop('deleted')
overall_information['countRemark'] = overall_information['countRemark'].replace(' 疑似', '，疑似').replace(' 治愈', '，治愈').replace(' 死亡', '，死亡').replace(' ', '')
if not self.db.find_one(collection='DXYOverall', data=overall_information):
overall_information['updateTime'] = self.crawl_timestamp
self.db.insert(collection='DXYOverall', data=overall_information)
def province_parser(self, province_information):
provinces = json.loads(province_information.group(0))
for province in provinces:
province.pop('id')
province.pop('tags')
province.pop('sort')
province['comment'] = province['comment'].replace(' ', '')
if self.db.find_one(collection='DXYProvince', data=province):
continue
province['provinceEnglishName'] = city_name_map[province['provinceShortName']]['engName']
province['crawlTime'] = self.crawl_timestamp
province['country'] = country_type_map.get(province['countryType'])
self.db.insert(collection='DXYProvince', data=province)
def area_parser(self, area_information):
area_information = json.loads(area_information.group(0))
for area in area_information:
area['comment'] = area['comment'].replace(' ', '')
# Because the cities are given other attributes,
# this part should not be used when checking the identical document.
cities_backup = area.pop('cities')
if self.db.find_one(collection='DXYArea', data=area):
continue
# If this document is not in current database, insert this attribute back to the document.
area['cities'] = cities_backup
area['countryName'] = '中国'
area['countryEnglishName'] = 'China'
area['continentName'] = '亚洲'
area['continentEnglishName'] = 'Asia'
area['provinceEnglishName'] = city_name_map[area['provinceShortName']]['engName']
for city in area['cities']:
if city['cityName'] != '待明确地区':
try:
city['cityEnglishName'] = city_name_map[area['provinceShortName']]['cities'][city['cityName']]
except KeyError:
print(area['provinceShortName'], city['cityName'])
pass
else:
city['cityEnglishName'] = 'Area not defined'
area['updateTime'] = self.crawl_timestamp
self.db.insert(collection='DXYArea', data=area)
def abroad_parser(self, abroad_information):
countries = json.loads(abroad_information.group(0))
for country in countries:
country.pop('id')
country.pop('tags')
country.pop('countryType')
country.pop('provinceId')
country.pop('cityName')
country.pop('sort')
# The original provinceShortName are blank string
country.pop('provinceShortName')
# Rename the key continents to continentName
country['continentName'] = country.pop('continents')
# Ding Xiang Yuan have a large number of duplicates,
# values are all the same, but the modifyTime are different.
# I suppose the modifyTime is modification time for all documents, other than for only this document.
# So this field will be popped out.
country.pop('modifyTime')
# createTime is also different even if the values are same.
# Originally, the createTime represent the first diagnosis of the virus in this area,
# but it seems different for abroad information.
country.pop('createTime')
country['comment'] = country['comment'].replace(' ', '')
if self.db.find_one(collection='DXYArea', data=country):
continue
country['countryName'] = country.get('provinceName')
country['provinceShortName'] = country.get('provinceName')
country['continentEnglishName'] = continent_name_map.get(country['continentName'])
country['countryEnglishName'] = country_name_map.get(country['countryName'])
country['provinceEnglishName'] = country_name_map.get(country['countryName'])
country['updateTime'] = self.crawl_timestamp
self.db.insert(collection='DXYArea', data=country)
def news_parser(self, news):
news = json.loads(news.group(0))
for _news in news:
_news.pop('pubDateStr')
if self.db.find_one(collection='DXYNews', data=_news):
continue
_news['crawlTime'] = self.crawl_timestamp
self.db.insert(collection='DXYNews', data=_news)
def rumor_parser(self, rumors):
for rumor in rumors:
rumor.pop('score')
rumor['body'] = rumor['body'].replace(' ', '')
if self.db.find_one(collection='DXYRumors', data=rumor):
continue
rumor['crawlTime'] = self.crawl_timestamp
self.db.insert(collection='DXYRumors', data=rumor)
if __name__ == '__main__':
crawler = Crawler()
crawler.run()
|
the-stack_0_1016 | """ This is KAMINARIO-FLOCKER-DRIVER Module docstring """
from flocker import node
from kaminario_flocker_driver.k2_blockdevice_api \
import instantiate_driver_instance
from kaminario_flocker_driver.constants import DRIVER_NAME
def api_factory(cluster_id, **kwargs):
"""Entry point for Flocker to load driver instance."""
kwargs['cluster_id'] = cluster_id
return instantiate_driver_instance(
**kwargs)
FLOCKER_BACKEND = node.BackendDescription(
name=DRIVER_NAME,
needs_reactor=False,
needs_cluster_id=True,
api_factory=api_factory,
deployer_type=node.DeployerType.block)
|
the-stack_0_1019 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @File : tests\test_model\test_resnet.py
# @Time : 2022-05-03 12:15:10
# @Author : Bingjie Yan
# @Email : [email protected]
# @License : Apache License 2.0
import torch
from torch import optim
from torch.utils.data import DataLoader
from fedhf.api import opts
from fedhf.model import build_model, build_optimizer
from fedhf.dataset import build_dataset
class TestResnet(object):
args = opts().parse([
'--model', 'resnet_mnist', '--num_classes', '10', '--model_pretrained', '--dataset',
'mnist', '--gpus', '-1', '--task', 'classification', '--resize', '--input_c', '1',
'--image_size', '224'
])
def test_resnet(self):
model = build_model(self.args.model)(self.args)
print(model)
assert model.__class__.__name__ == 'ResNetMNIST'
assert model.net.__class__.__name__ == 'ResNet'
assert model.num_classes == 10
assert model.net.fc.out_features == 10
dataset = build_dataset(self.args.dataset)(self.args)
dataloader = DataLoader(dataset.trainset, batch_size=1, shuffle=False)
model = model.to(self.args.device)
model.train()
for data, target in dataloader:
output = model(data)
assert output.shape == (1, 10)
assert output.dtype == torch.float32
assert output.device == torch.device('cpu')
break
model.save() |
the-stack_0_1020 | import re
from typing import List
import numpy as np
from pandas.util._decorators import Appender, deprecate_kwarg
from pandas.core.dtypes.common import is_extension_array_dtype, is_list_like
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.missing import notna
from pandas.core.arrays import Categorical
import pandas.core.common as com
from pandas.core.frame import DataFrame, _shared_docs
from pandas.core.indexes.api import Index, MultiIndex
from pandas.core.reshape.concat import concat
from pandas.core.tools.numeric import to_numeric
@Appender(
_shared_docs["melt"]
% dict(caller="pd.melt(df, ", versionadded="", other="DataFrame.melt")
)
def melt(
frame: DataFrame,
id_vars=None,
value_vars=None,
var_name=None,
value_name="value",
col_level=None,
) -> DataFrame:
# TODO: what about the existing index?
# If multiindex, gather names of columns on all level for checking presence
# of `id_vars` and `value_vars`
if isinstance(frame.columns, MultiIndex):
cols = [x for c in frame.columns for x in c]
else:
cols = list(frame.columns)
if id_vars is not None:
if not is_list_like(id_vars):
id_vars = [id_vars]
elif isinstance(frame.columns, MultiIndex) and not isinstance(id_vars, list):
raise ValueError(
"id_vars must be a list of tuples when columns are a MultiIndex"
)
else:
# Check that `id_vars` are in frame
id_vars = list(id_vars)
missing = Index(com.flatten(id_vars)).difference(cols)
if not missing.empty:
raise KeyError(
"The following 'id_vars' are not present "
f"in the DataFrame: {list(missing)}"
)
else:
id_vars = []
if value_vars is not None:
if not is_list_like(value_vars):
value_vars = [value_vars]
elif isinstance(frame.columns, MultiIndex) and not isinstance(value_vars, list):
raise ValueError(
"value_vars must be a list of tuples when columns are a MultiIndex"
)
else:
value_vars = list(value_vars)
# Check that `value_vars` are in frame
missing = Index(com.flatten(value_vars)).difference(cols)
if not missing.empty:
raise KeyError(
"The following 'value_vars' are not present in "
f"the DataFrame: {list(missing)}"
)
frame = frame.loc[:, id_vars + value_vars]
else:
frame = frame.copy()
if col_level is not None: # allow list or other?
# frame is a copy
frame.columns = frame.columns.get_level_values(col_level)
if var_name is None:
if isinstance(frame.columns, MultiIndex):
if len(frame.columns.names) == len(set(frame.columns.names)):
var_name = frame.columns.names
else:
var_name = [f"variable_{i}" for i in range(len(frame.columns.names))]
else:
var_name = [
frame.columns.name if frame.columns.name is not None else "variable"
]
if isinstance(var_name, str):
var_name = [var_name]
N, K = frame.shape
K -= len(id_vars)
mdata = {}
for col in id_vars:
id_data = frame.pop(col)
if is_extension_array_dtype(id_data):
id_data = concat([id_data] * K, ignore_index=True)
else:
id_data = np.tile(id_data._values, K)
mdata[col] = id_data
mcolumns = id_vars + var_name + [value_name]
mdata[value_name] = frame._values.ravel("F")
for i, col in enumerate(var_name):
# asanyarray will keep the columns as an Index
mdata[col] = np.asanyarray(frame.columns._get_level_values(i)).repeat(N)
return frame._constructor(mdata, columns=mcolumns)
@deprecate_kwarg(old_arg_name="label", new_arg_name=None)
def lreshape(data: DataFrame, groups, dropna: bool = True, label=None) -> DataFrame:
"""
Reshape long-format data to wide. Generalized inverse of DataFrame.pivot
Parameters
----------
data : DataFrame
groups : dict
{new_name : list_of_columns}
dropna : boolean, default True
Examples
--------
>>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
... 'team': ['Red Sox', 'Yankees'],
... 'year1': [2007, 2007], 'year2': [2008, 2008]})
>>> data
hr1 hr2 team year1 year2
0 514 545 Red Sox 2007 2008
1 573 526 Yankees 2007 2008
>>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
team year hr
0 Red Sox 2007 514
1 Yankees 2007 573
2 Red Sox 2008 545
3 Yankees 2008 526
Returns
-------
reshaped : DataFrame
"""
if isinstance(groups, dict):
keys = list(groups.keys())
values = list(groups.values())
else:
keys, values = zip(*groups)
all_cols = list(set.union(*[set(x) for x in values]))
id_cols = list(data.columns.difference(all_cols))
K = len(values[0])
for seq in values:
if len(seq) != K:
raise ValueError("All column lists must be same length")
mdata = {}
pivot_cols = []
for target, names in zip(keys, values):
to_concat = [data[col]._values for col in names]
mdata[target] = concat_compat(to_concat)
pivot_cols.append(target)
for col in id_cols:
mdata[col] = np.tile(data[col]._values, K)
if dropna:
mask = np.ones(len(mdata[pivot_cols[0]]), dtype=bool)
for c in pivot_cols:
mask &= notna(mdata[c])
if not mask.all():
mdata = {k: v[mask] for k, v in mdata.items()}
return data._constructor(mdata, columns=id_cols + pivot_cols)
def wide_to_long(
df: DataFrame, stubnames, i, j, sep: str = "", suffix: str = r"\d+"
) -> DataFrame:
r"""
Wide panel to long format. Less flexible but more user-friendly than melt.
With stubnames ['A', 'B'], this function expects to find one or more
group of columns with format
A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,...
You specify what you want to call this suffix in the resulting long format
with `j` (for example `j='year'`)
Each row of these wide variables are assumed to be uniquely identified by
`i` (can be a single column name or a list of column names)
All remaining variables in the data frame are left intact.
Parameters
----------
df : DataFrame
The wide-format DataFrame.
stubnames : str or list-like
The stub name(s). The wide format variables are assumed to
start with the stub names.
i : str or list-like
Column(s) to use as id variable(s).
j : str
The name of the sub-observation variable. What you wish to name your
suffix in the long format.
sep : str, default ""
A character indicating the separation of the variable names
in the wide format, to be stripped from the names in the long format.
For example, if your column names are A-suffix1, A-suffix2, you
can strip the hyphen by specifying `sep='-'`.
suffix : str, default '\\d+'
A regular expression capturing the wanted suffixes. '\\d+' captures
numeric suffixes. Suffixes with no numbers could be specified with the
negated character class '\\D+'. You can also further disambiguate
suffixes, for example, if your wide variables are of the form
A-one, B-two,.., and you have an unrelated column A-rating, you can
ignore the last one by specifying `suffix='(!?one|two)'`.
.. versionchanged:: 0.23.0
When all suffixes are numeric, they are cast to int64/float64.
Returns
-------
DataFrame
A DataFrame that contains each stub name as a variable, with new index
(i, j).
Notes
-----
All extra variables are left untouched. This simply uses
`pandas.melt` under the hood, but is hard-coded to "do the right thing"
in a typical case.
Examples
--------
>>> np.random.seed(123)
>>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
... "A1980" : {0 : "d", 1 : "e", 2 : "f"},
... "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
... "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
... "X" : dict(zip(range(3), np.random.randn(3)))
... })
>>> df["id"] = df.index
>>> df
A1970 A1980 B1970 B1980 X id
0 a d 2.5 3.2 -1.085631 0
1 b e 1.2 1.3 0.997345 1
2 c f 0.7 0.1 0.282978 2
>>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
... # doctest: +NORMALIZE_WHITESPACE
X A B
id year
0 1970 -1.085631 a 2.5
1 1970 0.997345 b 1.2
2 1970 0.282978 c 0.7
0 1980 -1.085631 d 3.2
1 1980 0.997345 e 1.3
2 1980 0.282978 f 0.1
With multiple id columns
>>> df = pd.DataFrame({
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
... 'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
... 'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
famid birth ht1 ht2
0 1 1 2.8 3.4
1 1 2 2.9 3.8
2 1 3 2.2 2.9
3 2 1 2.0 3.2
4 2 2 1.8 2.8
5 2 3 1.9 2.4
6 3 1 2.2 3.3
7 3 2 2.3 3.4
8 3 3 2.1 2.9
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
>>> l
... # doctest: +NORMALIZE_WHITESPACE
ht
famid birth age
1 1 1 2.8
2 3.4
2 1 2.9
2 3.8
3 1 2.2
2 2.9
2 1 1 2.0
2 3.2
2 1 1.8
2 2.8
3 1 1.9
2 2.4
3 1 1 2.2
2 3.3
2 1 2.3
2 3.4
3 1 2.1
2 2.9
Going from long back to wide just takes some creative use of `unstack`
>>> w = l.unstack()
>>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
>>> w.reset_index()
famid birth ht1 ht2
0 1 1 2.8 3.4
1 1 2 2.9 3.8
2 1 3 2.2 2.9
3 2 1 2.0 3.2
4 2 2 1.8 2.8
5 2 3 1.9 2.4
6 3 1 2.2 3.3
7 3 2 2.3 3.4
8 3 3 2.1 2.9
Less wieldy column names are also handled
>>> np.random.seed(0)
>>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3),
... 'A(weekly)-2011': np.random.rand(3),
... 'B(weekly)-2010': np.random.rand(3),
... 'B(weekly)-2011': np.random.rand(3),
... 'X' : np.random.randint(3, size=3)})
>>> df['id'] = df.index
>>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
A(weekly)-2010 A(weekly)-2011 B(weekly)-2010 B(weekly)-2011 X id
0 0.548814 0.544883 0.437587 0.383442 0 0
1 0.715189 0.423655 0.891773 0.791725 1 1
2 0.602763 0.645894 0.963663 0.528895 1 2
>>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id',
... j='year', sep='-')
... # doctest: +NORMALIZE_WHITESPACE
X A(weekly) B(weekly)
id year
0 2010 0 0.548814 0.437587
1 2010 1 0.715189 0.891773
2 2010 1 0.602763 0.963663
0 2011 0 0.544883 0.383442
1 2011 1 0.423655 0.791725
2 2011 1 0.645894 0.528895
If we have many columns, we could also use a regex to find our
stubnames and pass that list on to wide_to_long
>>> stubnames = sorted(
... set([match[0] for match in df.columns.str.findall(
... r'[A-B]\(.*\)').values if match != []])
... )
>>> list(stubnames)
['A(weekly)', 'B(weekly)']
All of the above examples have integers as suffixes. It is possible to
have non-integers as suffixes.
>>> df = pd.DataFrame({
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
... 'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
... 'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
famid birth ht_one ht_two
0 1 1 2.8 3.4
1 1 2 2.9 3.8
2 1 3 2.2 2.9
3 2 1 2.0 3.2
4 2 2 1.8 2.8
5 2 3 1.9 2.4
6 3 1 2.2 3.3
7 3 2 2.3 3.4
8 3 3 2.1 2.9
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
... sep='_', suffix='\w+')
>>> l
... # doctest: +NORMALIZE_WHITESPACE
ht
famid birth age
1 1 one 2.8
two 3.4
2 one 2.9
two 3.8
3 one 2.2
two 2.9
2 1 one 2.0
two 3.2
2 one 1.8
two 2.8
3 one 1.9
two 2.4
3 1 one 2.2
two 3.3
2 one 2.3
two 3.4
3 one 2.1
two 2.9
"""
def get_var_names(df, stub: str, sep: str, suffix: str) -> List[str]:
regex = fr"^{re.escape(stub)}{re.escape(sep)}{suffix}$"
pattern = re.compile(regex)
return [col for col in df.columns if pattern.match(col)]
def melt_stub(df, stub: str, i, j, value_vars, sep: str):
newdf = melt(
df,
id_vars=i,
value_vars=value_vars,
value_name=stub.rstrip(sep),
var_name=j,
)
newdf[j] = Categorical(newdf[j])
newdf[j] = newdf[j].str.replace(re.escape(stub + sep), "")
# GH17627 Cast numerics suffixes to int/float
newdf[j] = to_numeric(newdf[j], errors="ignore")
return newdf.set_index(i + [j])
if not is_list_like(stubnames):
stubnames = [stubnames]
else:
stubnames = list(stubnames)
if any(col in stubnames for col in df.columns):
raise ValueError("stubname can't be identical to a column name")
if not is_list_like(i):
i = [i]
else:
i = list(i)
if df[i].duplicated().any():
raise ValueError("the id variables need to uniquely identify each row")
value_vars = [get_var_names(df, stub, sep, suffix) for stub in stubnames]
value_vars_flattened = [e for sublist in value_vars for e in sublist]
id_vars = list(set(df.columns.tolist()).difference(value_vars_flattened))
_melted = [melt_stub(df, s, i, j, v, sep) for s, v in zip(stubnames, value_vars)]
melted = _melted[0].join(_melted[1:], how="outer")
if len(i) == 1:
new = df[id_vars].set_index(i).join(melted)
return new
new = df[id_vars].merge(melted.reset_index(), on=i).set_index(i + [j])
return new
|
the-stack_0_1022 | """
The main purpose of this module is to expose LinkCollector.collect_sources().
"""
import cgi
import collections
import functools
import itertools
import logging
import os
import re
import urllib.parse
import urllib.request
import xml.etree.ElementTree
from html.parser import HTMLParser
from optparse import Values
from typing import (
TYPE_CHECKING,
Callable,
Dict,
Iterable,
List,
MutableMapping,
NamedTuple,
Optional,
Sequence,
Tuple,
Union,
)
from pipenv.patched.notpip._vendor import html5lib, requests
from pipenv.patched.notpip._vendor.requests import Response
from pipenv.patched.notpip._vendor.requests.exceptions import RetryError, SSLError
from pipenv.patched.notpip._internal.exceptions import NetworkConnectionError
from pipenv.patched.notpip._internal.models.link import Link
from pipenv.patched.notpip._internal.models.search_scope import SearchScope
from pipenv.patched.notpip._internal.network.session import PipSession
from pipenv.patched.notpip._internal.network.utils import raise_for_status
from pipenv.patched.notpip._internal.utils.filetypes import is_archive_file
from pipenv.patched.notpip._internal.utils.misc import pairwise, redact_auth_from_url
from pipenv.patched.notpip._internal.vcs import vcs
from .sources import CandidatesFromPage, LinkSource, build_source
if TYPE_CHECKING:
from typing import Protocol
else:
Protocol = object
logger = logging.getLogger(__name__)
HTMLElement = xml.etree.ElementTree.Element
ResponseHeaders = MutableMapping[str, str]
def _match_vcs_scheme(url: str) -> Optional[str]:
"""Look for VCS schemes in the URL.
Returns the matched VCS scheme, or None if there's no match.
"""
for scheme in vcs.schemes:
if url.lower().startswith(scheme) and url[len(scheme)] in "+:":
return scheme
return None
class _NotHTML(Exception):
def __init__(self, content_type: str, request_desc: str) -> None:
super().__init__(content_type, request_desc)
self.content_type = content_type
self.request_desc = request_desc
def _ensure_html_header(response: Response) -> None:
"""Check the Content-Type header to ensure the response contains HTML.
Raises `_NotHTML` if the content type is not text/html.
"""
content_type = response.headers.get("Content-Type", "")
if not content_type.lower().startswith("text/html"):
raise _NotHTML(content_type, response.request.method)
class _NotHTTP(Exception):
pass
def _ensure_html_response(url: str, session: PipSession) -> None:
"""Send a HEAD request to the URL, and ensure the response contains HTML.
Raises `_NotHTTP` if the URL is not available for a HEAD request, or
`_NotHTML` if the content type is not text/html.
"""
scheme, netloc, path, query, fragment = urllib.parse.urlsplit(url)
if scheme not in {"http", "https"}:
raise _NotHTTP()
resp = session.head(url, allow_redirects=True)
raise_for_status(resp)
_ensure_html_header(resp)
def _get_html_response(url: str, session: PipSession) -> Response:
"""Access an HTML page with GET, and return the response.
This consists of three parts:
1. If the URL looks suspiciously like an archive, send a HEAD first to
check the Content-Type is HTML, to avoid downloading a large file.
Raise `_NotHTTP` if the content type cannot be determined, or
`_NotHTML` if it is not HTML.
2. Actually perform the request. Raise HTTP exceptions on network failures.
3. Check the Content-Type header to make sure we got HTML, and raise
`_NotHTML` otherwise.
"""
if is_archive_file(Link(url).filename):
_ensure_html_response(url, session=session)
logger.debug("Getting page %s", redact_auth_from_url(url))
resp = session.get(
url,
headers={
"Accept": "text/html",
# We don't want to blindly returned cached data for
# /simple/, because authors generally expecting that
# twine upload && pip install will function, but if
# they've done a pip install in the last ~10 minutes
# it won't. Thus by setting this to zero we will not
# blindly use any cached data, however the benefit of
# using max-age=0 instead of no-cache, is that we will
# still support conditional requests, so we will still
# minimize traffic sent in cases where the page hasn't
# changed at all, we will just always incur the round
# trip for the conditional GET now instead of only
# once per 10 minutes.
# For more information, please see pypa/pip#5670.
"Cache-Control": "max-age=0",
},
)
raise_for_status(resp)
# The check for archives above only works if the url ends with
# something that looks like an archive. However that is not a
# requirement of an url. Unless we issue a HEAD request on every
# url we cannot know ahead of time for sure if something is HTML
# or not. However we can check after we've downloaded it.
_ensure_html_header(resp)
return resp
def _get_encoding_from_headers(headers: ResponseHeaders) -> Optional[str]:
"""Determine if we have any encoding information in our headers."""
if headers and "Content-Type" in headers:
content_type, params = cgi.parse_header(headers["Content-Type"])
if "charset" in params:
return params["charset"]
return None
def _determine_base_url(document: HTMLElement, page_url: str) -> str:
"""Determine the HTML document's base URL.
This looks for a ``<base>`` tag in the HTML document. If present, its href
attribute denotes the base URL of anchor tags in the document. If there is
no such tag (or if it does not have a valid href attribute), the HTML
file's URL is used as the base URL.
:param document: An HTML document representation. The current
implementation expects the result of ``html5lib.parse()``.
:param page_url: The URL of the HTML document.
TODO: Remove when `html5lib` is dropped.
"""
for base in document.findall(".//base"):
href = base.get("href")
if href is not None:
return href
return page_url
def _clean_url_path_part(part: str) -> str:
"""
Clean a "part" of a URL path (i.e. after splitting on "@" characters).
"""
# We unquote prior to quoting to make sure nothing is double quoted.
return urllib.parse.quote(urllib.parse.unquote(part))
def _clean_file_url_path(part: str) -> str:
"""
Clean the first part of a URL path that corresponds to a local
filesystem path (i.e. the first part after splitting on "@" characters).
"""
# We unquote prior to quoting to make sure nothing is double quoted.
# Also, on Windows the path part might contain a drive letter which
# should not be quoted. On Linux where drive letters do not
# exist, the colon should be quoted. We rely on urllib.request
# to do the right thing here.
return urllib.request.pathname2url(urllib.request.url2pathname(part))
# percent-encoded: /
_reserved_chars_re = re.compile("(@|%2F)", re.IGNORECASE)
def _clean_url_path(path: str, is_local_path: bool) -> str:
"""
Clean the path portion of a URL.
"""
if is_local_path:
clean_func = _clean_file_url_path
else:
clean_func = _clean_url_path_part
# Split on the reserved characters prior to cleaning so that
# revision strings in VCS URLs are properly preserved.
parts = _reserved_chars_re.split(path)
cleaned_parts = []
for to_clean, reserved in pairwise(itertools.chain(parts, [""])):
cleaned_parts.append(clean_func(to_clean))
# Normalize %xx escapes (e.g. %2f -> %2F)
cleaned_parts.append(reserved.upper())
return "".join(cleaned_parts)
def _clean_link(url: str) -> str:
"""
Make sure a link is fully quoted.
For example, if ' ' occurs in the URL, it will be replaced with "%20",
and without double-quoting other characters.
"""
# Split the URL into parts according to the general structure
# `scheme://netloc/path;parameters?query#fragment`.
result = urllib.parse.urlparse(url)
# If the netloc is empty, then the URL refers to a local filesystem path.
is_local_path = not result.netloc
path = _clean_url_path(result.path, is_local_path=is_local_path)
return urllib.parse.urlunparse(result._replace(path=path))
def _create_link_from_element(
element_attribs: Dict[str, Optional[str]],
page_url: str,
base_url: str,
) -> Optional[Link]:
"""
Convert an anchor element's attributes in a simple repository page to a Link.
"""
href = element_attribs.get("href")
if not href:
return None
url = _clean_link(urllib.parse.urljoin(base_url, href))
pyrequire = element_attribs.get("data-requires-python")
yanked_reason = element_attribs.get("data-yanked")
link = Link(
url,
comes_from=page_url,
requires_python=pyrequire,
yanked_reason=yanked_reason,
)
return link
class CacheablePageContent:
def __init__(self, page: "HTMLPage") -> None:
assert page.cache_link_parsing
self.page = page
def __eq__(self, other: object) -> bool:
return isinstance(other, type(self)) and self.page.url == other.page.url
def __hash__(self) -> int:
return hash(self.page.url)
class ParseLinks(Protocol):
def __call__(
self, page: "HTMLPage", use_deprecated_html5lib: bool
) -> Iterable[Link]:
...
def with_cached_html_pages(fn: ParseLinks) -> ParseLinks:
"""
Given a function that parses an Iterable[Link] from an HTMLPage, cache the
function's result (keyed by CacheablePageContent), unless the HTMLPage
`page` has `page.cache_link_parsing == False`.
"""
@functools.lru_cache(maxsize=None)
def wrapper(
cacheable_page: CacheablePageContent, use_deprecated_html5lib: bool
) -> List[Link]:
return list(fn(cacheable_page.page, use_deprecated_html5lib))
@functools.wraps(fn)
def wrapper_wrapper(page: "HTMLPage", use_deprecated_html5lib: bool) -> List[Link]:
if page.cache_link_parsing:
return wrapper(CacheablePageContent(page), use_deprecated_html5lib)
return list(fn(page, use_deprecated_html5lib))
return wrapper_wrapper
def _parse_links_html5lib(page: "HTMLPage") -> Iterable[Link]:
"""
Parse an HTML document, and yield its anchor elements as Link objects.
TODO: Remove when `html5lib` is dropped.
"""
document = html5lib.parse(
page.content,
transport_encoding=page.encoding,
namespaceHTMLElements=False,
)
url = page.url
base_url = _determine_base_url(document, url)
for anchor in document.findall(".//a"):
link = _create_link_from_element(
anchor.attrib,
page_url=url,
base_url=base_url,
)
if link is None:
continue
yield link
@with_cached_html_pages
def parse_links(page: "HTMLPage", use_deprecated_html5lib: bool) -> Iterable[Link]:
"""
Parse an HTML document, and yield its anchor elements as Link objects.
"""
if use_deprecated_html5lib:
yield from _parse_links_html5lib(page)
return
parser = HTMLLinkParser(page.url)
encoding = page.encoding or "utf-8"
parser.feed(page.content.decode(encoding))
url = page.url
base_url = parser.base_url or url
for anchor in parser.anchors:
link = _create_link_from_element(
anchor,
page_url=url,
base_url=base_url,
)
if link is None:
continue
yield link
class HTMLPage:
"""Represents one page, along with its URL"""
def __init__(
self,
content: bytes,
encoding: Optional[str],
url: str,
cache_link_parsing: bool = True,
) -> None:
"""
:param encoding: the encoding to decode the given content.
:param url: the URL from which the HTML was downloaded.
:param cache_link_parsing: whether links parsed from this page's url
should be cached. PyPI index urls should
have this set to False, for example.
"""
self.content = content
self.encoding = encoding
self.url = url
self.cache_link_parsing = cache_link_parsing
def __str__(self) -> str:
return redact_auth_from_url(self.url)
class HTMLLinkParser(HTMLParser):
"""
HTMLParser that keeps the first base HREF and a list of all anchor
elements' attributes.
"""
def __init__(self, url: str) -> None:
super().__init__(convert_charrefs=True)
self.url: str = url
self.base_url: Optional[str] = None
self.anchors: List[Dict[str, Optional[str]]] = []
def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]) -> None:
if tag == "base" and self.base_url is None:
href = self.get_href(attrs)
if href is not None:
self.base_url = href
elif tag == "a":
self.anchors.append(dict(attrs))
def get_href(self, attrs: List[Tuple[str, Optional[str]]]) -> Optional[str]:
for name, value in attrs:
if name == "href":
return value
return None
def _handle_get_page_fail(
link: Link,
reason: Union[str, Exception],
meth: Optional[Callable[..., None]] = None,
) -> None:
if meth is None:
meth = logger.debug
meth("Could not fetch URL %s: %s - skipping", link, reason)
def _make_html_page(response: Response, cache_link_parsing: bool = True) -> HTMLPage:
encoding = _get_encoding_from_headers(response.headers)
return HTMLPage(
response.content,
encoding=encoding,
url=response.url,
cache_link_parsing=cache_link_parsing,
)
def _get_html_page(
link: Link, session: Optional[PipSession] = None
) -> Optional["HTMLPage"]:
if session is None:
raise TypeError(
"_get_html_page() missing 1 required keyword argument: 'session'"
)
url = link.url.split("#", 1)[0]
# Check for VCS schemes that do not support lookup as web pages.
vcs_scheme = _match_vcs_scheme(url)
if vcs_scheme:
logger.warning(
"Cannot look at %s URL %s because it does not support lookup as web pages.",
vcs_scheme,
link,
)
return None
# Tack index.html onto file:// URLs that point to directories
scheme, _, path, _, _, _ = urllib.parse.urlparse(url)
if scheme == "file" and os.path.isdir(urllib.request.url2pathname(path)):
# add trailing slash if not present so urljoin doesn't trim
# final segment
if not url.endswith("/"):
url += "/"
url = urllib.parse.urljoin(url, "index.html")
logger.debug(" file: URL is directory, getting %s", url)
try:
resp = _get_html_response(url, session=session)
except _NotHTTP:
logger.warning(
"Skipping page %s because it looks like an archive, and cannot "
"be checked by a HTTP HEAD request.",
link,
)
except _NotHTML as exc:
logger.warning(
"Skipping page %s because the %s request got Content-Type: %s."
"The only supported Content-Type is text/html",
link,
exc.request_desc,
exc.content_type,
)
except NetworkConnectionError as exc:
_handle_get_page_fail(link, exc)
except RetryError as exc:
_handle_get_page_fail(link, exc)
except SSLError as exc:
reason = "There was a problem confirming the ssl certificate: "
reason += str(exc)
_handle_get_page_fail(link, reason, meth=logger.info)
except requests.ConnectionError as exc:
_handle_get_page_fail(link, f"connection error: {exc}")
except requests.Timeout:
_handle_get_page_fail(link, "timed out")
else:
return _make_html_page(resp, cache_link_parsing=link.cache_link_parsing)
return None
class CollectedSources(NamedTuple):
find_links: Sequence[Optional[LinkSource]]
index_urls: Sequence[Optional[LinkSource]]
class LinkCollector:
"""
Responsible for collecting Link objects from all configured locations,
making network requests as needed.
The class's main method is its collect_sources() method.
"""
def __init__(
self,
session: PipSession,
search_scope: SearchScope,
index_lookup: Optional[Dict[str, List[str]]] = None,
) -> None:
self.search_scope = search_scope
self.session = session
self.index_lookup = index_lookup if index_lookup else {}
@classmethod
def create(
cls,
session: PipSession,
options: Values,
suppress_no_index: bool = False,
index_lookup: Optional[Dict[str, List[str]]] = None,
) -> "LinkCollector":
"""
:param session: The Session to use to make requests.
:param suppress_no_index: Whether to ignore the --no-index option
when constructing the SearchScope object.
"""
index_urls = [options.index_url] + options.extra_index_urls
if options.no_index and not suppress_no_index:
logger.debug(
"Ignoring indexes: %s",
",".join(redact_auth_from_url(url) for url in index_urls),
)
index_urls = []
# Make sure find_links is a list before passing to create().
find_links = options.find_links or []
search_scope = SearchScope.create(
find_links=find_links, index_urls=index_urls, index_lookup=index_lookup
)
link_collector = LinkCollector(
session=session, search_scope=search_scope, index_lookup=index_lookup
)
return link_collector
@property
def find_links(self) -> List[str]:
return self.search_scope.find_links
def fetch_page(self, location: Link) -> Optional[HTMLPage]:
"""
Fetch an HTML page containing package links.
"""
return _get_html_page(location, session=self.session)
def collect_sources(
self,
project_name: str,
candidates_from_page: CandidatesFromPage,
) -> CollectedSources:
# The OrderedDict calls deduplicate sources by URL.
index_url_sources = collections.OrderedDict(
build_source(
loc,
candidates_from_page=candidates_from_page,
page_validator=self.session.is_secure_origin,
expand_dir=False,
cache_link_parsing=False,
)
for loc in self.search_scope.get_index_urls_locations(project_name)
).values()
find_links_sources = collections.OrderedDict(
build_source(
loc,
candidates_from_page=candidates_from_page,
page_validator=self.session.is_secure_origin,
expand_dir=True,
cache_link_parsing=True,
)
for loc in self.find_links
).values()
if logger.isEnabledFor(logging.DEBUG):
lines = [
f"* {s.link}"
for s in itertools.chain(find_links_sources, index_url_sources)
if s is not None and s.link is not None
]
lines = [
f"{len(lines)} location(s) to search "
f"for versions of {project_name}:"
] + lines
logger.debug("\n".join(lines))
return CollectedSources(
find_links=list(find_links_sources),
index_urls=list(index_url_sources),
)
|
the-stack_0_1023 | # coding: utf-8
# 2019/12/30 @ tongshiwei
import pytest
from CangJie.Features import Stroke, character_glyph, CDict
from CangJie import token2stroke, token2radical, char_features
def test_features():
cdict = CDict.from_file()
char_features("一")
assert len(cdict.get_stroke("一s")) == 1
assert len(cdict.get_radical(["一二", "三"])) == 2
cdict = CDict.from_file(allow_missing=False)
with pytest.raises(KeyError):
assert len(cdict.get_stroke("一s")) == 1
with pytest.raises(TypeError):
print(cdict.get_stroke(123))
def test_stroke():
stroke = Stroke.from_file()
assert len(stroke["一"]) == 1
assert len(stroke["一二"]) == 3
assert len(stroke[["一", "二"]]) == 2
with pytest.raises(TypeError):
print(stroke[123])
assert stroke["s"] == ""
stroke = Stroke.from_file(allow_missing=False)
with pytest.raises(KeyError):
assert stroke["s"] == ""
assert len(token2stroke("一s")) == 1
def test_radical():
token2radical("一")
@pytest.mark.skip(reason="require simsun, which are usually unavailable in most testing platform")
def test_glyph():
character_glyph("一")
|
the-stack_0_1024 | # -*- coding: utf-8 -*-
# Copyright (c) 2013, Vispy Development Team.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
"""
This module contains manual annotations for the gl backends. Together
with the header files, we can generatre the full ES 2.0 API.
Every function-annotations consists of sections that apply to one or
more backends. If no backends are specified in the first section, it
applies to all backends.
"""
import ctypes
## bind / gen / delete stuff
def deleteBuffer(buffer):
# --- desktop angle
n = 1
buffers = (ctypes.c_uint*n)(buffer)
()
# --- pyopengl
GL.glDeleteBuffers(1, [buffer])
def deleteFramebuffer(framebuffer):
# --- desktop angle
n = 1
framebuffers = (ctypes.c_uint*n)(framebuffer)
()
# --- pyopengl
FBO.glDeleteFramebuffers(1, [framebuffer])
def deleteRenderbuffer(renderbuffer):
# --- desktop angle
n = 1
renderbuffers = (ctypes.c_uint*n)(renderbuffer)
()
# --- pyopengl
FBO.glDeleteRenderbuffers(1, [renderbuffer])
def deleteTexture(texture):
# --- desktop angle
n = 1
textures = (ctypes.c_uint*n)(texture)
()
# --- pyopengl
GL.glDeleteTextures([texture])
def createBuffer():
# --- desktop angle
n = 1
buffers = (ctypes.c_uint*n)()
()
return buffers[0]
# --- pyopengl
return GL.glGenBuffers(1)
# --- mock
return 1
def createFramebuffer():
# --- desktop angle
n = 1
framebuffers = (ctypes.c_uint*n)()
()
return framebuffers[0]
# --- pyopengl
return FBO.glGenFramebuffers(1)
# --- mock
return 1
def createRenderbuffer():
# --- desktop angle
n = 1
renderbuffers = (ctypes.c_uint*n)()
()
return renderbuffers[0]
# --- pyopengl
return FBO.glGenRenderbuffers(1)
# --- mock
return 1
def createTexture():
# --- desktop angle
n = 1
textures = (ctypes.c_uint*n)()
()
return textures[0]
# --- pyopengl
return GL.glGenTextures(1)
# --- mock
return 1
## Image stuff
def texImage2D(target, level, internalformat, format, type, pixels):
border = 0
# --- desktop angle
if isinstance(pixels, (tuple, list)):
height, width = pixels
pixels = ctypes.c_void_p(0)
pixels = None
else:
if not pixels.flags['C_CONTIGUOUS']:
pixels = pixels.copy('C')
pixels_ = pixels
pixels = pixels_.ctypes.data
height, width = pixels_.shape[:2]
()
# --- pyopengl
if isinstance(pixels, (tuple, list)):
height, width = pixels
pixels = None
else:
height, width = pixels.shape[:2]
GL.glTexImage2D(target, level, internalformat, width, height, border, format, type, pixels)
def texSubImage2D(target, level, xoffset, yoffset, format, type, pixels):
# --- desktop angle
if not pixels.flags['C_CONTIGUOUS']:
pixels = pixels.copy('C')
pixels_ = pixels
pixels = pixels_.ctypes.data
height, width = pixels_.shape[:2]
()
# --- pyopengl
height, width = pixels.shape[:2]
GL.glTexSubImage2D(target, level, xoffset, yoffset, width, height, format, type, pixels)
def readPixels(x, y, width, height, format, type):
# --- desktop angle mock
# GL_ALPHA, GL_RGB, GL_RGBA
t = {6406:1, 6407:3, 6408:4}[format]
# we kind of only support type GL_UNSIGNED_BYTE
size = int(width*height*t)
# --- desktop angle
pixels = ctypes.create_string_buffer(size)
()
return pixels[:]
# --- mock
return size * b'\x00'
def compressedTexImage2D(target, level, internalformat, width, height, border=0, data=None):
# border = 0 # set in args
# --- desktop angle
if not data.flags['C_CONTIGUOUS']:
data = data.copy('C')
data_ = data
size = data_.size
data = data_.ctypes.data
()
# --- pyopengl
size = data.size
GL.glCompressedTexImage2D(target, level, internalformat, width, height, border, size, data)
def compressedTexSubImage2D(target, level, xoffset, yoffset, width, height, format, data):
# --- desktop angle
if not data.flags['C_CONTIGUOUS']:
data = data.copy('C')
data_ = data
size = data_.size
data = data_.ctypes.data
()
# --- pyopengl
size = data.size
GL.glCompressedTexSubImage2D(target, level, xoffset, yoffset, width, height, format, size, data)
## Buffer data
def bufferData(target, data, usage):
""" Data can be numpy array or the size of data to allocate.
"""
# --- desktop angle
if isinstance(data, int):
size = data
data = ctypes.c_voidp(0)
else:
if not data.flags['C_CONTIGUOUS'] or not data.flags['ALIGNED']:
data = data.copy('C')
data_ = data
size = data_.nbytes
data = data_.ctypes.data
()
# --- pyopengl
if isinstance(data, int):
size = data
data = None
else:
size = data.nbytes
GL.glBufferData(target, size, data, usage)
def bufferSubData(target, offset, data):
# --- desktop angle
if not data.flags['C_CONTIGUOUS']:
data = data.copy('C')
data_ = data
size = data_.nbytes
data = data_.ctypes.data
()
# --- pyopengl
size = data.nbytes
GL.glBufferSubData(target, offset, size, data)
def drawElements(mode, count, type, offset):
# --- desktop angle
if offset is None:
offset = ctypes.c_void_p(0)
elif isinstance(offset, ctypes.c_void_p):
pass
elif isinstance(offset, (int, ctypes.c_int)):
offset = ctypes.c_void_p(int(offset))
else:
if not offset.flags['C_CONTIGUOUS']:
offset = offset.copy('C')
offset_ = offset
offset = offset.ctypes.data
indices = offset
()
# --- pyopengl
if offset is None:
offset = ctypes.c_void_p(0)
elif isinstance(offset, (int, ctypes.c_int)):
offset = ctypes.c_void_p(int(offset))
()
def vertexAttribPointer(indx, size, type, normalized, stride, offset):
# --- desktop angle
if offset is None:
offset = ctypes.c_void_p(0)
elif isinstance(offset, ctypes.c_void_p):
pass
elif isinstance(offset, (int, ctypes.c_int)):
offset = ctypes.c_void_p(int(offset))
else:
if not offset.flags['C_CONTIGUOUS']:
offset = offset.copy('C')
offset_ = offset
offset = offset.ctypes.data
# We need to ensure that the data exists at draw time :(
# PyOpenGL does this too
key = '_vert_attr_'+str(indx)
setattr(glVertexAttribPointer, key, offset_)
ptr = offset
()
# --- pyopengl
if offset is None:
offset = ctypes.c_void_p(0)
elif isinstance(offset, (int, ctypes.c_int)):
offset = ctypes.c_void_p(int(offset))
()
def bindAttribLocation(program, index, name):
# --- desktop angle
name = ctypes.c_char_p(name.encode('utf-8'))
()
# --- pyopengl
name = name.encode('utf-8')
()
## Setters
def shaderSource(shader, source):
# Some implementation do not like getting a list of single chars
if isinstance(source, (tuple, list)):
strings = [s for s in source]
else:
strings = [source]
# --- desktop angle
count = len(strings)
string = (ctypes.c_char_p*count)(*[s.encode('utf-8') for s in strings])
length = (ctypes.c_int*count)(*[len(s) for s in strings])
()
# --- pyopengl
GL.glShaderSource(shader, strings)
## Getters
def _getBooleanv(pname):
# --- desktop angle
params = (ctypes.c_bool*1)()
()
return params[0]
def _getIntegerv(pname):
# --- desktop angle
n = 16
d = -2**31 # smallest 32bit integer
params = (ctypes.c_int*n)(*[d for i in range(n)])
()
params = [p for p in params if p!=d]
if len(params) == 1:
return params[0]
else:
return tuple(params)
def _getFloatv(pname):
# --- desktop angle
n = 16
d = float('Inf')
params = (ctypes.c_float*n)(*[d for i in range(n)])
()
params = [p for p in params if p!=d]
if len(params) == 1:
return params[0]
else:
return tuple(params)
# def _getString(pname):
# # --- desktop angle
# ()
# return res.value
# # --- mock
# return ''
def getParameter(pname):
if pname in [33902, 33901, 32773, 3106, 2931, 2928,
2849, 32824, 10752, 32938]:
# GL_ALIASED_LINE_WIDTH_RANGE GL_ALIASED_POINT_SIZE_RANGE
# GL_BLEND_COLOR GL_COLOR_CLEAR_VALUE GL_DEPTH_CLEAR_VALUE
# GL_DEPTH_RANGE GL_LINE_WIDTH GL_POLYGON_OFFSET_FACTOR
# GL_POLYGON_OFFSET_UNITS GL_SAMPLE_COVERAGE_VALUE
return _glGetFloatv(pname)
elif pname in [7936, 7937, 7938, 35724, 7939]:
# GL_VENDOR, GL_RENDERER, GL_VERSION, GL_SHADING_LANGUAGE_VERSION,
# GL_EXTENSIONS are strings
pass # string handled below
else:
return _glGetIntegerv(pname)
name = pname
# --- desktop angle
()
return res.decode('utf-8') if res else ''
# --- pyopengl
res = GL.glGetString(pname)
return res.decode('utf-8')
def getUniform(program, location):
# --- desktop angle
n = 16
d = float('Inf')
params = (ctypes.c_float*n)(*[d for i in range(n)])
()
params = [p for p in params if p!=d]
if len(params) == 1:
return params[0]
else:
return tuple(params)
# --- pyopengl
n = 16
d = float('Inf')
params = (ctypes.c_float*n)(*[d for i in range(n)])
GL.glGetUniformfv(program, location, params)
params = [p for p in params if p!=d]
if len(params) == 1:
return params[0]
else:
return tuple(params)
def getVertexAttrib(index, pname):
# --- desktop angle
n = 4
d = float('Inf')
params = (ctypes.c_float*n)(*[d for i in range(n)])
()
params = [p for p in params if p!=d]
if len(params) == 1:
return params[0]
else:
return tuple(params)
# --- pyopengl
# From PyOpenGL v3.1.0 the glGetVertexAttribfv(index, pname) does
# work, but it always returns 4 values, with zeros in the empty
# spaces. We have no way to tell whether they are empty or genuine
# zeros. Fortunately, pyopengl also supports the old syntax.
n = 4
d = float('Inf')
params = (ctypes.c_float*n)(*[d for i in range(n)])
GL.glGetVertexAttribfv(index, pname, params)
params = [p for p in params if p!=d]
if len(params) == 1:
return params[0]
else:
return tuple(params)
def getTexParameter(target, pname):
# --- desktop angle
d = float('Inf')
params = (ctypes.c_float*1)(d)
()
return params[0]
def getActiveAttrib(program, index):
# --- desktop angle pyopengl
bufsize = 256
length = (ctypes.c_int*1)()
size = (ctypes.c_int*1)()
type = (ctypes.c_uint*1)()
name = ctypes.create_string_buffer(bufsize)
# --- desktop angle
()
name = name[:length[0]].decode('utf-8')
return name, size[0], type[0]
# --- pyopengl
# pyopengl has a bug, this is a patch
GL.glGetActiveAttrib(program, index, bufsize, length, size, type, name)
name = name[:length[0]].decode('utf-8')
return name, size[0], type[0]
# --- mock
return 'mock_val', 1, 5126
def getVertexAttribOffset(index, pname):
# --- desktop angle
pointer = (ctypes.c_void_p*1)()
()
return pointer[0] or 0
# --- pyopengl
try: # maybe the fixed it
()
except TypeError:
pointer = (ctypes.c_void_p*1)()
GL.glGetVertexAttribPointerv(index, pname, pointer)
return pointer[0] or 0
# --- mock
return 0
def getActiveUniform(program, index):
# --- desktop angle
bufsize = 256
length = (ctypes.c_int*1)()
size = (ctypes.c_int*1)()
type = (ctypes.c_uint*1)()
name = ctypes.create_string_buffer(bufsize)
()
name = name[:length[0]].decode('utf-8')
return name, size[0], type[0]
# --- pyopengl
name, size, type = GL.glGetActiveUniform(program, index)
return name.decode('utf-8'), size, type
def getAttachedShaders(program):
# --- desktop angle
maxcount = 256
count = (ctypes.c_int*1)()
shaders = (ctypes.c_uint*maxcount)()
()
return tuple(shaders[:count[0]])
def getAttribLocation(program, name):
# --- desktop angle
name = ctypes.c_char_p(name.encode('utf-8'))
()
return res
# --- pyopengl
name = name.encode('utf-8')
()
def getUniformLocation(program, name):
# --- desktop angle
name = ctypes.c_char_p(name.encode('utf-8'))
()
return res
# --- pyopengl
name = name.encode('utf-8')
()
def getProgramInfoLog(program):
# --- desktop angle
bufsize = 1024
length = (ctypes.c_int*1)()
infolog = ctypes.create_string_buffer(bufsize)
()
return infolog[:length[0]].decode('utf-8')
# --- pyopengl
res = GL.glGetProgramInfoLog(program)
return res.decode('utf-8')
def getShaderInfoLog(shader):
# --- desktop angle
bufsize = 1024
length = (ctypes.c_int*1)()
infolog = ctypes.create_string_buffer(bufsize)
()
return infolog[:length[0]].decode('utf-8')
# --- pyopengl
res = GL.glGetShaderInfoLog(shader)
return res.decode('utf-8')
def getProgramParameter(program, pname):
# --- desktop angle
params = (ctypes.c_int*1)()
()
return params[0]
def getShaderParameter(shader, pname):
# --- desktop angle
params = (ctypes.c_int*1)()
()
return params[0]
def getShaderPrecisionFormat(shadertype, precisiontype):
# --- desktop angle
range = (ctypes.c_int*1)()
precision = (ctypes.c_int*1)()
()
return range[0], precision[0]
def getShaderSource(shader):
# --- desktop angle
bufsize = 1024*1024
length = (ctypes.c_int*1)()
source = (ctypes.c_char*bufsize)()
()
return source.value[:length[0]].decode('utf-8')
# --- pyopengl
res = GL.glGetShaderSource(shader)
return res.decode('utf-8')
def getBufferParameter(target, pname):
# --- desktop angle
d = -2**31 # smallest 32bit integer
params = (ctypes.c_int*1)(d)
()
return params[0]
def getFramebufferAttachmentParameter(target, attachment, pname):
# --- desktop angle
d = -2**31 # smallest 32bit integer
params = (ctypes.c_int*1)(d)
()
return params[0]
# --- pyopengl
d = -2**31 # smallest 32bit integer
params = (ctypes.c_int*1)(d)
FBO.glGetFramebufferAttachmentParameteriv(target, attachment, pname, params)
return params[0]
def getRenderbufferParameter(target, pname):
# --- desktop angle
d = -2**31 # smallest 32bit integer
params = (ctypes.c_int*1)(d)
()
return params[0]
# --- pyopengl
d = -2**31 # smallest 32bit integer
params = (ctypes.c_int*1)(d)
FBO.glGetRenderbufferParameteriv(target, pname, params)
return params[0]
## ============================================================================
class FunctionAnnotation:
def __init__(self, name, args, output):
self.name = name
self.args = args
self.output = output
self.lines = [] # (line, comment) tuples
def __repr__(self):
return '<FunctionAnnotation for %s>' % self.name
def get_lines(self, call, backend):
""" Get the lines for this function based on the given backend.
The given API call is inserted at the correct location.
"""
backend_selector = backend # first lines are for all backends
lines = []
for line in self.lines:
if line.lstrip().startswith('# ---'):
backend_selector = line
continue
if backend in backend_selector:
if line.strip() == '()':
indent = line.split('(')[0][4:]
line = indent + call
lines.append(line)
return lines
def is_arg_set(self, name):
""" Get whether a given variable name is set.
This allows checking whether a variable that is an input to the C
function is not an input for the Python function, and may be an output.
"""
needle = '%s =' % name
for line, comment in self.lines:
if line.startswith(needle):
return True
else:
return False
def parse_anotations():
""" Parse this annotations file and produce a dictionary of
FunctionAnnotation objects.
"""
functions = {}
function = None
for line in open(__file__, 'rt').readlines():
# Stop?
if '='*40 in line:
break
if line.startswith('def '):
name = line.split(' ')[1].split('(')[0]
args = line.split('(')[1].split(')')[0].split(', ')
args = [arg for arg in args if arg]
out = line.partition('->')[2].strip()
function = FunctionAnnotation(name, args, out)
functions[name] = function
continue
elif not function:
continue
# Add line
line = line.rstrip()
indent = len(line) - len(line.strip())
if line.strip() and indent >=4:
function.lines.append(line)
return functions
if __name__ == '__main__':
print(parse_anotations().keys())
|
the-stack_0_1025 | import json
import logging
import ssl
import requests
import socket
import websocket
import websocket._exceptions
logger = logging.getLogger(__name__)
class MattermostAPI(object):
def __init__(self, url, ssl_verify, token):
self.url = url
self.token = token
self.initial = None
self.default_team_id = None # the first team in API returned value
self.teams_channels_ids = None # struct:{team_id:[channel_id,...],...}
self.ssl_verify = ssl_verify
if not ssl_verify:
requests.packages.urllib3.disable_warnings(
requests.packages.urllib3.exceptions.InsecureRequestWarning)
def _get_headers(self):
return {"Authorization": "Bearer " + self.token}
def channel(self, channel_id):
channel = {'channel': self.get('/channels/{}'.format(channel_id))}
return channel
def create_reaction(self, user_id, post_id, emoji_name):
return self.post(
'/reactions',
{
'user_id': user_id,
'post_id': post_id,
'emoji_name': emoji_name,
})
def create_post(self, user_id, channel_id, message, files=None, pid="", props={}):
# create_at = int(time.time() * 1000)
return self.post(
'/posts',
{
'channel_id': channel_id,
'message': message,
'file_ids': files or [],
'root_id': pid,
'props': props
})
@staticmethod
def create_user_dict(self, v4_dict):
new_dict = {}
new_dict[v4_dict['id']] = v4_dict
return new_dict
def get(self, request):
return json.loads(
requests.get(
self.url + request,
headers=self._get_headers(),
verify=self.ssl_verify
).text)
def get_channel_by_name(self, channel_name, team_id=None):
return self.get('/teams/{}/channels/name/{}'.format(
team_id, channel_name))
def get_channels(self, team_id=None):
if team_id is None:
team_id = self.default_team_id
return self.get('/users/me/teams/{}/channels'.format(team_id))
def get_file_link(self, file_id):
return self.get('/files/{}/link'.format(file_id))
def get_team_by_name(self, team_name):
return self.get('/teams/name/{}'.format(team_name))
def get_team_id(self, channel_id):
for team_id, channels in self.teams_channels_ids.items():
if channel_id in channels:
return team_id
return None
def get_user_info(self, user_id):
return self.get('/users/{}'.format(user_id))
def hooks_create(self, **kwargs):
return self.post(
'/hooks/incoming', kwargs)
def hooks_get(self, webhook_id):
return self.get(
'/hooks/incoming/{}'.format(webhook_id))
def hooks_list(self):
return self.get('/hooks/incoming')
@staticmethod
def in_webhook(url, channel, text, username=None, as_user=None,
parse=None, link_names=None, attachments=None,
unfurl_links=None, unfurl_media=None, icon_url=None,
icon_emoji=None, ssl_verify=True, **kwargs):
return requests.post(
url, data={
'payload': json.dumps({
'channel': channel,
'text': text,
'username': username,
'as_user': as_user,
'parse': parse,
'link_names': link_names,
'attachments': attachments,
'unfurl_links': unfurl_links,
'unfurl_media': unfurl_media,
'icon_url': icon_url,
'icon_emoji': icon_emoji})
}, verify=ssl_verify)
def login(self, team, account, password):
props = {'login_id': account, 'password': password}
response = self._login(props)
if response.status_code in [301, 302, 307]:
# reset self.url to the new URL
self.url = response.headers['Location'].replace(
'/users/login', '')
# re-try login if redirected
response = self._login(props)
if response.status_code == 200:
self.token = response.headers["Token"]
self.load_initial_data()
user = json.loads(response.text)
return user
else:
response.raise_for_status()
def _login(self, props):
return requests.post(
self.url + '/users/login',
data=json.dumps(props),
verify=self.ssl_verify,
allow_redirects=False)
def load_initial_data(self):
self.teams = self.get('/users/me/teams')
if len(self.teams) == 0:
raise AssertionError(
'User account of this bot does not join any team yet.')
self.default_team_id = self.teams[0]['id']
self.teams_channels_ids = {}
for team in self.teams:
self.teams_channels_ids[team['id']] = []
# get all channels belonging to each team
for channel in self.get_channels(team['id']):
self.teams_channels_ids[team['id']].append(channel['id'])
def me(self):
return self.get('/users/me')
def post(self, request, data):
return json.loads(requests.post(
self.url + request,
headers=self._get_headers(),
data=json.dumps(data),
verify=self.ssl_verify
).text)
def update_post(self, message_id, user_id, channel_id,
message, files=None, pid=""):
return self.post(
'/posts/%s' % message_id,
{
'message': message,
})
def user(self, user_id):
return self.get_user_info(user_id)
def upload_file(self, file, channel_id):
files = {
'files': file,
'channel_id': (None, channel_id)
}
return json.loads(requests.post(
self.url + '/files',
headers=self._get_headers(),
files=files,
verify=self.ssl_verify
).text)
class MattermostClient(object):
def __init__(self, url, team, email, password, ssl_verify=True,
token=None, ws_origin=None):
self.users = {}
self.channels = {}
self.mentions = {}
self.api = MattermostAPI(url, ssl_verify, token)
self.user = None
self.websocket = None
self.email = None
self.team = team
self.email = email
self.password = password
self.ws_origin = ws_origin
if token:
self.user = self.api.me()
else:
self.login(team, email, password)
def login(self, team, email, password):
self.email = email
self.user = self.api.login(team, email, password)
return self.user
def react_msg(self, post_id, emoji_name):
return self.api.create_reaction(self.user["id"],
post_id, emoji_name)
def channel_msg(self, channel, message, files=None, pid="", props={}):
c_id = self.channels.get(channel, {}).get("id") or channel
return self.api.create_post(self.user["id"], c_id, "{}".format(message),
files, pid, props=props)
def update_msg(self, message_id, channel, message, pid=""):
c_id = self.channels.get(channel, {}).get("id") or channel
return self.api.update_post(message_id, self.user["id"],
c_id, message, pid=pid)
def connect_websocket(self):
host = self.api.url.replace('http', 'ws').replace('https', 'wss')
url = host + '/websocket'
self._connect_websocket(url, cookie_name='MMAUTHTOKEN')
return self.websocket.getstatus() == 101
def _connect_websocket(self, url, cookie_name):
self.websocket = websocket.create_connection(
url, header=["Cookie: %s=%s" % (cookie_name, self.api.token)],
origin=self.ws_origin,
sslopt={
"cert_reqs": ssl.CERT_REQUIRED if self.api.ssl_verify
else ssl.CERT_NONE})
def messages(self, ignore_own_msg=False, filter_actions=None):
filter_actions = filter_actions or []
if not self.connect_websocket():
return
while True:
try:
data = self.websocket.recv()
except websocket._exceptions.WebSocketException:
if not self.connect_websocket():
raise
continue
if data:
try:
post = json.loads(data)
event_action = post.get('event')
if event_action not in filter_actions:
continue
if event_action == 'posted':
if post.get('data', {}).get('post'):
dp = json.loads(post['data']['post'])
if ignore_own_msg is True and dp.get("user_id"):
if self.user["id"] == dp["user_id"]:
continue
yield post
elif event_action in ['added_to_team', 'leave_team',
'user_added', 'user_removed']:
self.api.load_initial_data() # reload teams & channels
except ValueError:
pass
def ping(self):
try:
self.websocket.ping()
except socket.error:
logger.error('\n'.join([
'socket.error while pinging the mattermost server',
'possible causes: expired cookie or broken socket pipe'
]))
if not self.connect_websocket(): # try to re-connect
logger.info('reconnecting websocket ... failed')
else:
logger.info('reconnecting websocket ... succeeded')
|
the-stack_0_1026 | '''
This module is for DiffChecker class.
'''
import sys
import os
import logging
from importlib import reload
import pickle
import pandas as pd
import numpy as np
sys.path.append('../')
from mlqa import checkers as ch
class DiffChecker():
'''Integrated QA performer on pd.DataFrame with logging functionality.
It only works in numerical columns.
Args:
qa_level (str): quick set for QA level, can be one of ['loose', 'mid', 'strict']
logger (str or logging.Logger): 'print' for print only, every other
str creates a file for logging. using external logging.Logger object
is highly recommended, i.e. logger=<mylogger>.
qa_log_level (int): qa message logging level
log_info (bool): `True` if method calls or arguments also need to be
logged
Notes:
Although `DiffChecker <identifiers.html#identifiers.DiffChecker>`_ is
able to create a `Logger <https://docs.python.org/3/library/logging.html#logging.Logger>`_
object by just passing a file name (i.e. `logger='mylog.log'`), creating
the `Logger <https://docs.python.org/3/library/logging.html#logging.Logger>`_
object externally then passing accordingly (i.e. `logger=<mylogger>`)
is highly recommended.
Example:
Basic usage:
>>> dc = DiffChecker()
>>> dc.fit(pd.DataFrame({'mean_col':[1, 2]*50, 'na_col':[None]*50+[1]*50}))
>>> dc.check(pd.DataFrame({'mean_col':[.99, 2.1]*50, 'na_col':[None]*70+[1]*30}))
True
>>> dc.set_threshold(0.1)
>>> dc.check(pd.DataFrame({'mean_col':[.99, 2.1]*50, 'na_col':[None]*70+[1]*30}))
False
Quick set for `qa_level`:
>>> dc = DiffChecker()
>>> dc.threshold
0.5
>>> dc = DiffChecker(qa_level='mid')
>>> dc.threshold
0.2
>>> dc = DiffChecker(qa_level='strict')
>>> dc.threshold
0.1
Logger can also be initiated:
>>> dc = DiffChecker(logger='mylog.log')
>>> dc.fit(pd.DataFrame({'mean_col':[1, 2]*50, 'na_col':[None]*50+[1]*50}))
>>> dc.set_threshold(0.1)
>>> dc.check(pd.DataFrame({'mean_col':[1, 1.5]*50, 'na_col':[None]*70+[1]*30}))
False
'''
stats = []
threshold = 0.0
threshold_df = pd.DataFrame()
df_fit_stats = pd.DataFrame()
def __init__(
self,
qa_level='loose',
logger=None,
qa_log_level=None,
log_info=False
):
# Class logger reloads logging module in each call not to create
# conflict, this is okay as long as this is the only logger in the
# environment. Having external logger is highly recommended in all
# other cases.
if logger == 'print':
logging.shutdown()
reload(logging)
logging.basicConfig(
format='%(asctime)-15s %(message)s',
level='DEBUG')
self.logger = logging.getLogger('DiffCheckerLogIdToPrint')
elif isinstance(logger, str):
logging.shutdown()
reload(logging)
handler = logging.FileHandler(logger, mode='w+')
handler.setFormatter(logging.Formatter(
fmt='%(levelname)s|%(asctime)s|%(message)s'))
self.logger = logging.getLogger('DiffCheckerLogIdToDump')
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(handler)
else:
# if external logger provided
self.logger = logger
self.log_level = qa_log_level or 30
self.log_info = log_info
qa_levels = {
'loose':{
'stats':['mean', ch.na_rate],
'threshold':.5
},
'mid':{
'stats':['mean', 'std', ch.na_rate],
'threshold':.2
},
'strict':{
'stats':['mean', 'std', 'count', 'min', 'max', ch.na_rate],
'threshold':.1
}
}
if qa_level not in qa_levels.keys():
raise ValueError('`qa_level` not right, choose one of {}'\
.format(qa_levels.keys()))
self.set_stats(qa_levels[qa_level]['stats'])
self.set_threshold(qa_levels[qa_level]['threshold'])
def set_stats(self, funcs):
'''Sets statistic functions list to check by.
Args:
funcs (list): list of functions and/or function names,
e.g. [np.sum, 'mean']
See Also:
`add_stat <#identifiers.DiffChecker.add_stat>`_: just to add one
'''
if not self.df_fit_stats.empty:
raise ValueError('self.stats cannot be altered after `fit()` call')
if not isinstance(funcs, list):
raise TypeError('`funcs` must be a list')
self._method_init_logger(locals())
self.stats = funcs
def add_stat(self, func):
'''Appends a statistic function into the existing list (i.e. `stats <#identifiers.DiffChecker.stats>`_).
Args:
func (func): function name (e.g. np.sum or 'mean')
See Also:
`set_stats <#identifiers.DiffChecker.set_stats>`_: to reset all
'''
if not self.df_fit_stats.empty:
raise ValueError('self.stats cannot be altered after `fit()` call')
if not (isinstance(func, str) or callable(func)):
raise TypeError('`func` must be str or callable')
if func in self.stats:
raise ValueError('`func` is already in `self.stats`')
self._method_init_logger(locals())
self.stats.append(func)
def set_threshold(self, threshold):
'''Sets threshold for statistic-column pairs.
Args:
threshold (float or dict): can be used to set for all or column
statistic pairs.
Example:
>>> dc = DiffChecker()
>>> dc.set_stats(['mean', 'max'])
>>> dc.set_threshold(0.1) # to reset all thresholds
>>> print(dc.threshold)
0.1
>>> dc.fit(pd.DataFrame({'col1':[1, 2, 3, 4], 'col2':[0]*4}))
>>> dc.set_threshold({'col1':0.2, 'col2':0.1}) # to set in column level
>>> print(dc.threshold_df)
col1 col2
mean 0.2 0.1
max 0.2 0.1
>>> dc.set_threshold({'col1':{'mean':0.3}}) # to set in column-stat level
>>> print(dc.threshold_df)
col1 col2
mean 0.3 0.1
max 0.2 0.1
'''
self._method_init_logger(locals())
if isinstance(threshold, dict):
if self.df_fit_stats.empty:
raise ValueError('call `fit()` first for column level threshold')
for col, v1 in threshold.items():
if col not in self.df_fit_stats.columns:
raise ValueError('{} not found in fitted DataFrame'\
.format(col))
if isinstance(v1, dict):
for stat, v2 in v1.items():
if stat not in self.df_fit_stats.index:
raise ValueError(
"'{0}' not set as stat, available stats are {1}"\
.format(stat, self.df_fit_stats.index.tolist()))
th = float(v2)
assert th >= 0
self.threshold_df.loc[stat, col] = th
else:
th = float(v1)
assert th >= 0
self.threshold_df.loc[:, col] = th
else:
th = float(threshold)
assert th >= 0
self.threshold = th
def fit(self, df):
'''Fits given `df`.
Based on given `df` and `stats <#identifiers.DiffChecker.stats>`_ attribute, this method constructs
`df_fit_stats <#identifiers.DiffChecker.df_fit_stats>`_ attribute to store column statistics. This is later to
be used by `check <#identifiers.DiffChecker.check>`_ method. Only works
in numerical columns.
Args:
df (pd.DataFrame): data to be fit
Example:
>>> dc = DiffChecker()
>>> dc.set_stats(['mean', 'max'])
>>> dc.fit(pd.DataFrame({'col1':[1, 2, 3, 4], 'col2':[0]*4}))
>>> print(dc.df_fit_stats)
col1 col2
mean 2.5 0.0
max 4.0 0.0
'''
assert isinstance(self.stats, list) and len(self.stats) >= 1
if not isinstance(df, pd.DataFrame):
raise TypeError('`df` must be a pd.DataFrame')
self._method_init_logger(locals())
self.df_fit_stats = pd.DataFrame()
for col in df.columns:
if pd.api.types.is_numeric_dtype(df[col]):
for stat in self.stats:
if isinstance(stat, str):
stat_name = stat
else:
stat_name = stat.__name__
self.df_fit_stats.loc[stat_name, col] = df[col].agg(stat)
self.threshold_df = self.df_fit_stats.copy()
self.threshold_df.loc[:, :] = np.NaN
def check(self, df_to_check, columns=None, columns_to_exclude=None):
'''Checks given `df_to_check` based on fitted `df` stats.
For each column stat pairs, it checks if stat is in given threshold by
utilizing `qa_array_statistics <checkers.html#checkers.qa_array_statistics>`_.
If any stat qa fails, returns `False`, `True otherwise`.
Args:
df_to_check (pd.DataFrame): data to check
columns (None or list): if given, only these columns will be
considered for qa
columns_to_exclude (None or list): columns to exclude from qa
Returns:
bool: is QA passed or not
Example:
>>> dc = DiffChecker()
>>> dc.set_threshold(0.2)
>>> dc.set_stats(['mean', 'max', np.sum])
>>> dc.fit(pd.DataFrame({'col1':[1, 2, 3, 4], 'col2':[1]*4}))
>>> dc.check(pd.DataFrame({'col1':[1, 2, 3, 4], 'col2':[0]*4}))
False
>>> dc.check(pd.DataFrame({'col1':[1, 2.1, 3.2, 4.2], 'col2':[1.1]*4}))
True
'''
assert isinstance(self.stats, list) and len(self.stats) >= 1
if not isinstance(df_to_check, pd.DataFrame):
raise TypeError('`df_to_check` must be a pd.DataFrame')
if columns is not None and columns_to_exclude is not None:
raise ValueError('only one must be given, '
'`columns` or `columns_to_exclude`')
if columns is not None:
if not isinstance(columns, list):
raise TypeError('`columns` must be a list')
if columns_to_exclude is not None:
if not isinstance(columns_to_exclude, list):
raise TypeError('`columns_to_exclude` must be a list')
self._method_init_logger(locals())
cols_to_check = self.df_fit_stats.columns.tolist()
if columns:
cols_to_check = list(set(cols_to_check) & set(columns))
if columns_to_exclude:
cols_to_check = [c for c in cols_to_check if c not \
in columns_to_exclude]
qa_results = []
for col in cols_to_check:
for stat in self.stats:
if isinstance(stat, str):
stat_name = stat
else:
stat_name = stat.__name__
th = self.threshold_df.loc[stat_name, col]
th = self.threshold if pd.isna(th) else th
val = self.df_fit_stats.loc[stat_name, col]
tol = abs(val)*th
ll, ul = val-tol, val+tol
result = ch.qa_array_statistics(
df_to_check[col],
{stat:[ll, ul]},
logger=self.logger,
log_level=self.log_level,
name=col)
qa_results.append(result)
return all(qa_results)
def to_pickle(self, path='DiffChecker.pkl'):
'''Pickle (serialize) object to a file.
Args:
path (str): file path where the pickled object will be stored
Example:
To save a `*.pkl` file:
>>> dc1 = DiffChecker()
>>> dc1.fit(pd.DataFrame({'col1':[1, 2, 3, 4], 'col2':[0]*4}))
>>> dc1.to_pickle(path='DiffChecker.pkl')
To load the same object later:
>>> import pickle
>>> pkl_file = open('DiffChecker.pkl', 'rb')
>>> dc2 = pickle.load(pkl_file)
>>> pkl_file.close()
>>> os.remove('DiffChecker.pkl')
'''
self._method_init_logger(locals())
self.logger = None
output = open(path, 'wb')
pickle.dump(self, output, -1)
output.close()
def _method_init_logger(self, args, exclude=['self']):
'''Logs method initiation with given arguments.
Args:
args (dict): local arguments, i.e. `locals()`
exclude (list): arguments to exclude, e.g. `self`
'''
if self.logger and self.log_info:
method_name = sys._getframe(1).f_code.co_name
self.logger.info("{} initiated.".format(method_name))
for k, v in args.items():
if k not in exclude:
self.logger.info(method_name+' locals: '+k+'='+str(v)[:100])
if __name__ == "__main__":
import doctest
doctest.testmod()
|
the-stack_0_1027 | from pymoo.algorithms.nsga2 import NSGA2
from pymoo.optimize import minimize
from pymoo.problems.multi.srn import SRN
from pymoo.visualization.scatter import Scatter
problem = SRN()
algorithm = NSGA2(pop_size=100)
res = minimize(problem,
algorithm,
# ('n_gen', 1000),
seed=1,
verbose=True)
plot = Scatter()
plot.add(problem.pareto_set(), plot_type="line", color="black", alpha=0.7)
plot.add(res.X, color="red")
plot.show()
plot = Scatter()
plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
plot.add(res.F, color="red")
plot.show()
|
the-stack_0_1029 | import binascii
import hashlib
import hmac
import json
import time
from datetime import datetime, timedelta
from itertools import chain
import pytz
from django.contrib.auth.models import User
from django.db.models import Prefetch
from django.forms import ChoiceField, Form, IntegerField, ModelForm, Select
from django.utils import timezone
from django.utils.translation import ugettext_lazy as _
from django.utils.translation import ungettext_lazy
from c3nav.control.models import UserPermissions, UserSpaceAccess
from c3nav.mapdata.forms import I18nModelFormMixin
from c3nav.mapdata.models import MapUpdate, Space
from c3nav.mapdata.models.access import (AccessPermission, AccessPermissionToken, AccessPermissionTokenItem,
AccessRestriction, AccessRestrictionGroup)
from c3nav.site.models import Announcement
class UserPermissionsForm(ModelForm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['review_group_reports'].label_from_instance = lambda obj: obj.title
class Meta:
model = UserPermissions
exclude = ('user', 'max_changeset_changes', 'api_secret')
class AccessPermissionForm(Form):
def __init__(self, request=None, author=None, expire_date=None, *args, **kwargs):
super().__init__(*args, **kwargs)
# remember author if this form is saved
self.author = author or request.user
author_permissions = request.user_permissions if request else author.permissions
self.expire_date = expire_date
# determine which access permissions the author can grant
self.author_access_permissions = AccessPermission.get_for_request_with_expire_date(request, can_grant=True)
access_restrictions = AccessRestriction.objects.filter(
pk__in=self.author_access_permissions.keys()
)
self.access_restrictions = {
access_restriction.pk: access_restriction
for access_restriction in access_restrictions
}
access_restrictions_ids = set(self.access_restrictions.keys())
self.access_restriction_choices = {
'all': self.access_restrictions.values(),
**{str(pk): (access_restriction, ) for pk, access_restriction in self.access_restrictions.items()}
}
# get access permission groups
groups = AccessRestrictionGroup.qs_for_request(request).prefetch_related(
Prefetch('accessrestrictions', AccessRestriction.objects.only('pk'))
)
group_contents = {
group.pk: set(r.pk for r in group.accessrestrictions.all())
for group in groups
}
group_contents = {
pk: restrictions for pk, restrictions in group_contents.items()
if not (restrictions - access_restrictions_ids)
}
self.access_restriction_choices.update({
('g%d' % pk): tuple(
self.access_restrictions[restriction] for restriction in restrictions
) for pk, restrictions in group_contents.items()
})
# construct choice field for access permissions
choices = [('', _('choose permissions…')),
('all', ungettext_lazy('everything possible (%d permission)',
'everything possible (%d permissions)',
len(access_restrictions)) % len(access_restrictions))]
choices.append((_('Access Permission Groups'), tuple(
('g%d' % group.pk, group.title)
for group in groups
)))
choices.append((_('Access Permissions'), tuple(
(str(pk), access_restriction.title)
for pk, access_restriction in self.access_restrictions.items()
)))
self.fields['access_restrictions'] = ChoiceField(choices=choices, required=True)
# construct choices for the expire field
expire_choices = [
('', _('never')),
]
for minutes in range(15, 60, 15):
expire_choices.append(
(str(minutes), ungettext_lazy('in %d minute', 'in %d minutes', minutes) % minutes))
for hours in chain(range(1, 6), range(6, 24, 6)):
expire_choices.append(
(str(hours*60), ungettext_lazy('in %d hour', 'in %d hours', hours) % hours)
)
expire_choices.insert(
5, (str(90), _('in 1½ hour'))
)
for days in range(1, 14):
expire_choices.append(
(str(days*24*60), ungettext_lazy('in %d day', 'in %d days', days) % days)
)
self.fields['expires'] = ChoiceField(required=False, initial='60', choices=expire_choices)
# if applicable, add field to grant pass on permissions
if author_permissions.grant_all_access:
choices = [('0', '---')]*6 + [('1', _('can pass on'))] + [('0', '---')]*3
self.fields['can_grant'] = ChoiceField(required=False, initial='60', choices=choices)
def clean_access_restrictions(self):
data = self.cleaned_data['access_restrictions']
return self.access_restriction_choices[data]
def clean_expires(self):
data = self.cleaned_data['expires']
if data == '':
return None
return timezone.now()+timedelta(minutes=int(data))
def save(self, user):
self._save_code(self._create_code(), user)
def get_token(self, unique_key=None):
# create an AccessPermissionToken from this form and return it
restrictions = []
default_expire_date = self.expire_date or self.cleaned_data['expires']
for restriction in self.cleaned_data['access_restrictions']:
expire_date = default_expire_date
author_expire_date = self.author_access_permissions.get(restriction.pk)
# make sure that each permission is not granted for a longer time than the author has it
if author_expire_date is not None:
expire_date = author_expire_date if expire_date is None else min(expire_date, author_expire_date)
restrictions.append(AccessPermissionTokenItem(pk=restriction.pk, expire_date=expire_date,
title=restriction.title))
return AccessPermissionToken(author=self.author,
can_grant=self.cleaned_data.get('can_grant', '0') == '1',
restrictions=tuple(restrictions),
unique_key=unique_key)
def get_signed_data(self, key=None):
if not self.author.permissions.api_secret:
raise ValueError('Author has no api secret.')
data = {
'id': self.data['access_restrictions'],
'time': int(time.time()),
'valid_until': int(self.cleaned_data['expires'].strftime('%s')),
'author': self.author.pk,
}
if key is not None:
data['key'] = key
data = json.dumps(data, separators=(',', ':'))
signature = hmac.new(self.author.permissions.api_secret.encode(),
msg=data.encode(), digestmod=hashlib.sha256).digest()
return '%s:%s' % (data, binascii.b2a_base64(signature).strip().decode())
@classmethod
def load_signed_data(cls, signed_data: str):
if ':' not in signed_data:
raise SignedPermissionDataError('Invalid data.')
raw_data, signature = signed_data.rsplit(':', 1)
try:
data = json.loads(raw_data)
except json.JSONDecodeError:
raise SignedPermissionDataError('Invalid JSON.')
try:
restrictions = data.pop('id')
author_id = data.pop('author')
issue_time = data.pop('time')
valid_until = data.pop('valid_until')
unique_key = data.pop('key', None)
except KeyError as e:
raise SignedPermissionDataError('Missing %s.' % str(e))
for unknown_key in data:
raise SignedPermissionDataError('Unknown value: %s' % unknown_key)
try:
issue_time = int(issue_time)
except ValueError:
raise SignedPermissionDataError('Invalid time.')
try:
valid_until = int(valid_until) if valid_until is not None else None
except ValueError:
raise SignedPermissionDataError('Invalid valid_until.')
else:
valid_until = valid_until and datetime.utcfromtimestamp(valid_until).replace(tzinfo=pytz.utc)
try:
author_id = int(author_id)
except ValueError:
raise SignedPermissionDataError('Invalid author.')
if unique_key is not None and not isinstance(unique_key, str):
raise SignedPermissionDataError('key has to be null or a string.')
if issue_time > time.time()+5:
raise SignedPermissionDataError('time cannot be in the future.')
if issue_time < time.time()-60:
raise SignedPermissionDataError('token has expired.')
if unique_key is not None and not (1 <= len(unique_key) <= 32):
raise SignedPermissionDataError('key has to be 1-32 characters')
try:
author = User.objects.select_related('permissions').get(pk=author_id)
except User.DoesNotExist:
raise SignedPermissionDataError('Author does not exist.')
try:
api_secret = author.permissions.api_secret
except AttributeError:
raise SignedPermissionDataError('Author has no API secret.')
verify_signature = binascii.b2a_base64(hmac.new(api_secret.encode(),
msg=raw_data.encode(), digestmod=hashlib.sha256).digest())
print(verify_signature, signature)
if signature != verify_signature.strip().decode():
raise SignedPermissionDataError('Invalid signature.')
form = cls(author=author, expire_date=valid_until, data={
'access_restrictions': str(restrictions),
})
if not form.is_valid():
raise SignedPermissionDataError(' '.join(form.errors))
return form.get_token(unique_key=unique_key)
class UserSpaceAccessForm(ModelForm):
class Meta:
model = UserSpaceAccess
fields = ('space', 'can_edit')
def __init__(self, *args, request=None, **kwargs):
super().__init__(*args, **kwargs)
self.fields['space'].label_from_instance = lambda obj: obj.title
self.fields['space'].queryset = Space.qs_for_request(request).order_by('slug')
choices = [('0', _('no'))] * 6 + [('1', _('yes'))] + [('0', _('no'))] * 3
self.fields['can_edit'].widget = Select(choices=choices)
class SignedPermissionDataError(Exception):
pass
class AnnouncementForm(I18nModelFormMixin, ModelForm):
class Meta:
model = Announcement
fields = ('text', 'active', 'active_until')
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['active_until'].initial = timezone.now()
class MapUpdateFilterForm(Form):
type = ChoiceField(
choices=(('', _('any type')), ) + MapUpdate.TYPES,
required=False
)
geometries_changed = ChoiceField(
choices=(('', _('any')), ('1', _('geometries changed')), ('0', _('no geometries changed'))),
required=False
)
processed = ChoiceField(
choices=(('', _('any')), ('1', _('processed')), ('0', _('not processed'))),
required=False
)
user_id = IntegerField(min_value=1, required=False)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['user_id'].widget.attrs['placeholder'] = _('user id')
class MapUpdateForm(ModelForm):
class Meta:
model = MapUpdate
fields = ('geometries_changed', )
|
the-stack_0_1031 | """
MIT License
Copyright (c) 2020 Airbyte
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import operator
from functools import partial, reduce
from json.decoder import JSONDecodeError
from typing import Mapping, Tuple
import requests
from base_python import BaseClient
from requests.auth import HTTPBasicAuth
from requests.exceptions import ConnectionError
class Client(BaseClient):
"""
Jira API Reference: https://developer.atlassian.com/cloud/jira/platform/rest/v3/intro/
"""
API_VERSION = 3
DEFAULT_ITEMS_PER_PAGE = 100
PARAMS = {"maxResults": DEFAULT_ITEMS_PER_PAGE, "startAt": 0}
ENTITIES_MAP = {
"projects": {"url": "/project/search", "func": lambda v: v["values"], "params": PARAMS},
"issues": {"url": "/search", "func": lambda v: v["issues"], "params": PARAMS},
"issue_comments": {
"url": "/search",
"func": lambda v: reduce(operator.iadd, [obj["fields"]["comment"]["comments"] for obj in v["issues"]], []),
"params": {**PARAMS, **{"fields": ["comment"]}},
},
"users": {"url": "/users/search", "func": lambda v: v, "params": PARAMS},
"resolutions": {"url": "/resolution", "func": lambda v: v, "params": {}},
}
def __init__(self, api_token, domain, email):
self.auth = HTTPBasicAuth(email, api_token)
self.base_api_url = f"https://{domain}/rest/api/{self.API_VERSION}"
super().__init__()
def lists(self, name, url, params, func, **kwargs):
while True:
response = requests.get(f"{self.base_api_url}{url}", params=params, auth=self.auth)
data = func(response.json())
yield from data
if name == "resolutions" or len(data) < self.DEFAULT_ITEMS_PER_PAGE:
break
params["startAt"] += self.DEFAULT_ITEMS_PER_PAGE
def _enumerate_methods(self) -> Mapping[str, callable]:
return {entity: partial(self.lists, name=entity, **value) for entity, value in self.ENTITIES_MAP.items()}
def health_check(self) -> Tuple[bool, str]:
alive = True
error_msg = None
try:
next(self.lists(name="resolutions", **self.ENTITIES_MAP["resolutions"]))
except ConnectionError as error:
alive, error_msg = False, str(error)
# If the input domain is incorrect or doesn't exist, then the response would be empty, resulting in a JSONDecodeError
except JSONDecodeError:
alive, error_msg = (
False,
"Unable to connect to the Jira API with the provided credentials. Please make sure the input credentials and environment are correct.",
)
return alive, error_msg
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