Datasets:

jon-tow

/

starcoderdata-python-edu

like 8

report_writer/report_writer.py

DoubleBridges/door-order-parser

500

from reportlab.lib.units import inch from reportlab.platypus import SimpleDocTemplate, Spacer from reportlab.rl_config import defaultPageSize from reportlab.lib.units import inch from reportlab.platypus.flowables import Flowable def generate_order(job, path, door_style, doors=[], drawers=[]): PAGE_HEIGHT = defaultPageSize[1] PAGE_WIDTH = defaultPageSize[0] LEFT_MARGIN = 30 LINE_HEIGHT = 18 BACKGROUND_COLOR = (33 / 255, 80 / 255, 156 / 255) CURSOR_HEIGHT = PAGE_HEIGHT - 60 INPUT_HEIGHT = LINE_HEIGHT - (LINE_HEIGHT * 0.1) SPECIES = door_style.species STYLE = door_style.name INSIDE_PROFILE = door_style.inside_profile OUTSIDE_PROFILE = door_style.outside_profile TOTAL_DRS = len(doors) TOTAL_DWRS = len(drawers) def myFirstPage(c, doc): cursor = CURSOR_HEIGHT c.saveState() c.setStrokeColorRGB( BACKGROUND_COLOR[0], BACKGROUND_COLOR[1], BACKGROUND_COLOR[2] ) c.setFillColorRGB(BACKGROUND_COLOR[0], BACKGROUND_COLOR[1], BACKGROUND_COLOR[2]) c.rect( LEFT_MARGIN, PAGE_HEIGHT - 40, PAGE_WIDTH - (LEFT_MARGIN * 2), 24, fill=1 ) c.setFillColorRGB(1, 1, 1) c.setFont("Helvetica-Bold", 16) c.drawCentredString(PAGE_WIDTH / 2.0, PAGE_HEIGHT - 34, "DOOR ORDER FORM") c.setFont("Helvetica", 12) c.setFillColorRGB(0, 0, 0) c.drawString(LEFT_MARGIN, cursor, f"Customer : JS Designs Shop, LLC") c.drawString( (PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, f"Order Date : {job.order_date}", ) cursor -= LINE_HEIGHT c.drawString(LEFT_MARGIN, cursor, f"PO # : {job.name}-{STYLE}-{SPECIES}") c.drawString( (PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, "Delivery Date : ASAP" ) cursor -= LINE_HEIGHT c.setFont("Helvetica-Bold", 12) c.drawString(LEFT_MARGIN, cursor, f"Door Style : {STYLE}") c.setFont("Helvetica", 12) c.drawString( (PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, "Phone : 901-853-7568" ) cursor -= LINE_HEIGHT c.drawString(LEFT_MARGIN, cursor, f"Panel : ") c.acroForm.textfield( x=LEFT_MARGIN + 40, y=cursor - 4, name="Panel", value=" N/A ", height=INPUT_HEIGHT, width=(PAGE_WIDTH / 2) - LEFT_MARGIN - (LEFT_MARGIN / 2) - 60, borderWidth=0, # fillColor=([1, 1, 1]), relative=True, ) c.drawString((PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, "Comments : ") cursor -= LINE_HEIGHT c.drawString(LEFT_MARGIN, cursor, f"Wood Type : {SPECIES}") c.line( (PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, PAGE_WIDTH - LEFT_MARGIN, cursor, ) cursor -= LINE_HEIGHT c.drawString(LEFT_MARGIN, cursor, f"Inside Profile : {INSIDE_PROFILE}") # c.acroForm.textfield( # x=LEFT_MARGIN + 78, # y=cursor - 4, # name="inside_profile", # value=" N/A ", # height=INPUT_HEIGHT, # width=(PAGE_WIDTH / 2) - LEFT_MARGIN - (LEFT_MARGIN / 2) - 98, # borderWidth=0, # # fillColor=([1, 1, 1]), # relative=True, # ) c.line( (PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, PAGE_WIDTH - LEFT_MARGIN, cursor, ) cursor -= LINE_HEIGHT c.drawString(LEFT_MARGIN, cursor, f"Outside Profile : {OUTSIDE_PROFILE}") # c.acroForm.textfield( # x=LEFT_MARGIN + 88, # y=cursor - 4, # name="outside_profile", # value=" N/A ", # height=INPUT_HEIGHT, # width=(PAGE_WIDTH / 2) - LEFT_MARGIN - (LEFT_MARGIN / 2) - 108, # borderWidth=0, # # fillColor=([1, 1, 1]), # relative=True, # ) c.line( (PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, PAGE_WIDTH - LEFT_MARGIN, cursor, ) cursor -= LINE_HEIGHT c.drawString(LEFT_MARGIN, cursor, f"Stile/Rails : ") c.acroForm.textfield( x=LEFT_MARGIN + 62, y=cursor - 4, name="stiles_rails", value=" N/A ", height=INPUT_HEIGHT, width=(PAGE_WIDTH / 2) - LEFT_MARGIN - (LEFT_MARGIN / 2) - 82, borderWidth=0, # fillColor=([1, 1, 1]), relative=True, ) c.setFont("Helvetica-Bold", 12) c.drawString((PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, f"Drawer Fronts : ") c.acroForm.textfield( x=LEFT_MARGIN + 375, y=cursor - 4, name="drawer_fronts", value=" N/A ", height=INPUT_HEIGHT, width=(PAGE_WIDTH / 2) - LEFT_MARGIN - (LEFT_MARGIN / 2) - 92, borderWidth=0, # fillColor=([1, 1, 1]), relative=True, ) c.setFont("Helvetica", 12) cursor -= LINE_HEIGHT c.drawString(LEFT_MARGIN, cursor, f"Boring For Hinges : No") c.drawString( (PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, f"Outside Profile : " ) c.acroForm.textfield( x=LEFT_MARGIN + 370, y=cursor - 4, name="out_profile", value=" N/A ", height=INPUT_HEIGHT, width=(PAGE_WIDTH / 2) - LEFT_MARGIN - (LEFT_MARGIN / 2) - 87, borderWidth=0, # fillColor=([1, 1, 1]), relative=True, ) cursor -= LINE_HEIGHT c.drawString(LEFT_MARGIN, cursor, f"Add Hinges : No") c.drawString( (PAGE_WIDTH / 2) + (LEFT_MARGIN / 2), cursor, f" 5 PC Front: Slab:", ) c.acroForm.textfield( x=LEFT_MARGIN + 350, y=cursor - 4, name="5_pc_front", value=" N/A ", height=INPUT_HEIGHT, width=30, borderWidth=0, # fillColor=([1, 1, 1]), relative=True, ) c.acroForm.textfield( x=LEFT_MARGIN + 430, y=cursor - 4, name="slab_front", value=" N/A ", height=INPUT_HEIGHT, width=30, borderWidth=0, # fillColor=([1, 1, 1]), relative=True, ) cursor -= 12 c.setFont("Times-Italic", 10) c.drawString( LEFT_MARGIN, cursor, f"Boring not available in arched doors, applied mould doors", ) cursor -= 10 c.drawString( LEFT_MARGIN, cursor, f"and raised bead profile mitered doors", ) cursor -= 14 c.setFont("Times-BoldItalic", 12) c.drawString( LEFT_MARGIN, cursor, f'Cullman will not bore any door with 2" stiles' ) cursor -= 20 c.setFont("Helvetica-Bold", 14) c.setFillColorRGB(BACKGROUND_COLOR[0], BACKGROUND_COLOR[1], BACKGROUND_COLOR[2]) c.drawCentredString((PAGE_WIDTH / 4) + 30, cursor, f"Total Doors: {TOTAL_DRS}") c.drawCentredString( ((PAGE_WIDTH / 4) * 3) + 10, cursor, f"Total Drawer Fronts: {TOTAL_DWRS}" ) cursor -= 24 c.setStrokeColorRGB(0, 0, 0) c.setFillColorRGB(BACKGROUND_COLOR[0], BACKGROUND_COLOR[1], BACKGROUND_COLOR[2]) c.rect(LEFT_MARGIN + 38, cursor, 60, 20, fill=1) c.rect(LEFT_MARGIN + 98, cursor, 170, 20, fill=1) c.rect(LEFT_MARGIN + 308, cursor, 60, 20, fill=1) c.rect(LEFT_MARGIN + 368, cursor, 170, 20, fill=1) c.setFont("Helvetica-Bold", 12) c.setFillColorRGB(1, 1, 1) string_center = LEFT_MARGIN + 68 c.drawCentredString(string_center, cursor + 5, "Qty") string_center += 115 c.drawCentredString(string_center, cursor + 5, "Width X Height") string_center += 155 c.drawCentredString(string_center, cursor + 5, "Qty") string_center += 115 c.drawCentredString(string_center, cursor + 5, "Width X Height") c.setFont("Helvetica", 9) c.setFillColorRGB(0, 0, 0) c.drawCentredString( PAGE_WIDTH / 2, 40, f"Page 1 of {job.name}-{STYLE}-{SPECIES}" ) c.drawCentredString( PAGE_WIDTH / 2, 20, 'Reminder : Any doors 46" and over in height will automatically receive a horizontal center rail unless otherwise noted.', ) c.restoreState() def myLaterPages(c, doc): cursor = PAGE_HEIGHT - 54 c.saveState() c.setFont("Helvetica-Bold", 14) c.setFillColorRGB(BACKGROUND_COLOR[0], BACKGROUND_COLOR[1], BACKGROUND_COLOR[2]) c.drawCentredString((PAGE_WIDTH / 4) + 30, cursor, "Doors") c.drawCentredString(((PAGE_WIDTH / 4) * 3) + 10, cursor, "Drawer Fronts") cursor -= 24 c.setStrokeColorRGB(0, 0, 0) c.setFillColorRGB(BACKGROUND_COLOR[0], BACKGROUND_COLOR[1], BACKGROUND_COLOR[2]) c.rect(LEFT_MARGIN + 38, cursor, 60, 20, fill=1) c.rect(LEFT_MARGIN + 98, cursor, 170, 20, fill=1) c.rect(LEFT_MARGIN + 308, cursor, 60, 20, fill=1) c.rect(LEFT_MARGIN + 368, cursor, 170, 20, fill=1) c.setFont("Helvetica-Bold", 12) c.setFillColorRGB(1, 1, 1) string_center = LEFT_MARGIN + 68 c.drawCentredString(string_center, cursor + 5, "Qty") string_center += 115 c.drawCentredString(string_center, cursor + 5, "Width X Height") string_center += 155 c.drawCentredString(string_center, cursor + 5, "Qty") string_center += 115 c.drawCentredString(string_center, cursor + 5, "Width X Height") c.setFont("Helvetica", 9) c.setFillColorRGB(0, 0, 0) c.drawCentredString( PAGE_WIDTH / 2, 40, f"Page {doc.page} of {job.name}-{STYLE}-{SPECIES}" ) c.drawCentredString( PAGE_WIDTH / 2, 20, 'Reminder : Any doors 46" and over in height will automatically receive a horizontal center rail unless otherwise noted.', ) c.restoreState() class OrderEntry(Flowable): """Draws table entry for each item in list of door sizes.""" def __init__( self, xoffset=0, height=20, dr_qty="", dr_size="", dwr_qty="", dwr_size="", index=0, ): Flowable.__init__(self) self.dr_qty = dr_qty self.dr_size = dr_size self.dwr_qty = dwr_qty self.dwr_size = dwr_size self.index = index self.height = height self.idx_box_x = xoffset self.idx_box_width = 40 self.string_center = xoffset + (self.idx_box_width / 2) self.qty_box_x = self.idx_box_width + xoffset self.qty_box_width = 60 self.size_box_x = self.qty_box_width - 10 self.size_box_width = 170 self.second_column_offset = 270 def draw(self): # Door self.canv.setStrokeColorRGB(0, 0, 0) self.canv.setFillColorRGB( BACKGROUND_COLOR[0], BACKGROUND_COLOR[1], BACKGROUND_COLOR[2] ) self.canv.rect(self.idx_box_x, 0, self.idx_box_width, self.height, fill=1) self.canv.setFillColorRGB(1, 1, 1) self.canv.setFont("Helvetica", 12) self.canv.drawCentredString( self.string_center, 0.25 * self.height, str(self.index) ) self.canv.setFillColorRGB(0, 0, 0) self.canv.rect(self.qty_box_x, 0, self.qty_box_width, self.height) self.string_center += (self.idx_box_width / 2) + (self.qty_box_width / 2) self.canv.drawCentredString( self.string_center, 0.25 * self.height, self.dr_qty ) self.canv.rect(self.size_box_x, 0, self.size_box_width, self.height) self.string_center += (self.qty_box_width / 2) + (self.size_box_width / 2) self.canv.drawCentredString( self.string_center, 0.25 * self.height, self.dr_size ) # Drawer if self.dwr_qty != "" and self.dwr_size != "": self.canv.rect( self.second_column_offset + self.qty_box_x, 0, self.qty_box_width, self.height, ) self.string_center += 155 self.canv.drawCentredString( self.string_center, 0.25 * self.height, self.dwr_qty, ) self.canv.rect( self.second_column_offset + self.size_box_x, 0, self.size_box_width, self.height, ) self.string_center += (self.qty_box_width / 2) + ( self.size_box_width / 2 ) self.canv.drawCentredString( self.string_center, 0.25 * self.height, self.dwr_size ) def build_pdf(path, name, door_list, drawer_list): doc = SimpleDocTemplate(f"{path}/{name}-{STYLE}.pdf") Story = [Spacer(1, 3.11 * inch)] num_of_doors = len(door_list) num_of_drawers = len(drawer_list) num_of_entries = max(num_of_doors, num_of_drawers) for i in range(0, num_of_entries): try: door_qty, door_size = door_list[i]["qty"], door_list[i]["size"] except IndexError: door_qty, door_size = "", "" try: drawer_qty, drawer_size = drawer_list[i]["qty"], drawer_list[i]["size"] except IndexError: drawer_qty, drawer_size = "", "" p = OrderEntry( xoffset=-50, dr_qty=door_qty, dr_size=door_size, dwr_qty=drawer_qty, dwr_size=drawer_size, index=i + 1, ) Story.append(p) doc.build(Story, onFirstPage=myFirstPage, onLaterPages=myLaterPages) build_pdf(path, job.name, doors, drawers)

app/retweet_graphs_v2/prep/migrate_daily_bot_probabilities.py

s2t2/tweet-analyzer-py

501

<reponame>s2t2/tweet-analyzer-py from app.bq_service import BigQueryService if __name__ == "__main__": bq_service = BigQueryService() bq_service.migrate_daily_bot_probabilities_table() print("MIGRATION SUCCESSFUL!")

colosseum/mdps/minigrid_doorkey/minigrid_doorkey.py

MichelangeloConserva/Colosseum

502

<gh_stars>0 from copy import deepcopy from dataclasses import asdict, dataclass from enum import IntEnum from colosseum.utils.random_vars import deterministic, get_dist try: from functools import cached_property except: from backports.cached_property import cached_property from typing import Any, Dict, List, Tuple, Type, Union import numpy as np from scipy.stats import beta, rv_continuous from colosseum.mdps import MDP from colosseum.mdps.base_mdp import NextStateSampler from colosseum.mdps.minigrid_rooms.continuous.mdp import MiniGridRoomsContinuous from colosseum.utils.mdps import check_distributions class MiniGridDoorKeyAction(IntEnum): """The action available in the MiniGridDoorKey MDP.""" MoveForward = 0 TurnRight = 1 TurnLeft = 2 PickObject = 3 DropObject = 4 UseObject = 5 class MiniGridDoorKeyDirection(IntEnum): """The possible agent direction in the MiniGridDoorKey MDP.""" UP = 0 RIGHT = 1 DOWN = 2 LEFT = 3 @dataclass(frozen=True) class MiniGridDoorKeyNode: X: int Y: int Dir: MiniGridDoorKeyDirection XKey: int YKey: int IsDoorOpened: bool def __str__(self): return f"X={self.X},Y={self.Y},Dir={MiniGridDoorKeyDirection(self.Dir).name},XKey={self.XKey},YKey={self.YKey},IsDoorOpened{self.IsDoorOpened}" class MiniGridDoorKeyMDP(MDP): @staticmethod def testing_parameters() -> Dict[str, Tuple]: t_params = MDP.testing_parameters() t_params["size"] = (3, 5, 7) t_params["make_reward_stochastic"] = (True, False) t_params["n_starting_states"] = (1, 4) return t_params @staticmethod def get_node_class() -> Type[MiniGridDoorKeyNode]: return MiniGridDoorKeyNode def __init__( self, seed: int, size: int, randomize_actions: bool = True, lazy: float = None, make_reward_stochastic=False, n_starting_states: int = 2, optimal_distribution: Union[Tuple, rv_continuous] = None, other_distribution: Union[Tuple, rv_continuous] = None, **kwargs, ): """ Parameters ---------- seed : int the seed used for sampling rewards and next states. randomize_actions : bool, optional whether the effect of the actions changes for every node. It is particularly important to set this value to true when doing experiments to avoid immediately reaching highly rewarding states in some MDPs by just selecting the same action repeatedly. By default, it is set to true. lazy : float the probability of an action not producing any effect on the MDP. size : int the size of the grid. make_reward_stochastic : bool, optional checks whether the rewards are to be made stochastic. By default, it is set to False. n_starting_states : int, optional the number of states in the starting distribution. By default, it is set to two. optimal_distribution : Union[Tuple, rv_continuous], optional The distribution of the highly rewarding state. It can be either passed as a tuple containing Beta parameters or as a rv_continuous object. other_distribution : Union[Tuple, rv_continuous] The distribution of the non highly rewarding states. It can be either passed as a tuple containing Beta parameters or as a rv_continuous object. """ if type(optimal_distribution) == tuple: optimal_distribution = get_dist( optimal_distribution[0], optimal_distribution[1:] ) if type(other_distribution) == tuple: other_distribution = get_dist(other_distribution[0], other_distribution[1:]) self.n_starting_states = n_starting_states self.size = size self.make_reward_stochastic = make_reward_stochastic dists = [ optimal_distribution, other_distribution, ] if dists.count(None) == 0: self.optimal_distribution = optimal_distribution self.other_distribution = other_distribution else: if make_reward_stochastic: self.other_distribution = beta(1, size ** 2 - 1) self.optimal_distribution = beta(size ** 2 - 1, 1) else: self.optimal_distribution = deterministic(1.0) self.other_distribution = deterministic(0.0) super().__init__( seed=seed, randomize_actions=randomize_actions, lazy=lazy, **kwargs, ) @property def parameters(self) -> Dict[str, Any]: return { **super(MiniGridDoorKeyMDP, self).parameters, **dict( size=self.size, n_starting_states=self.n_starting_states, optimal_distribution=self.optimal_distribution, other_distribution=self.other_distribution, ), } @property def possible_starting_nodes(self) -> List[MiniGridDoorKeyNode]: return self._possible_starting_nodes @cached_property def coordinates_available(self): coords = ( MiniGridRoomsContinuous.get_positions_coords_in_room(self.size, (0, 0)) .ravel() .tolist() ) for i in range(self.size): if self.is_wall_horizontal: coords.remove((i, self.wall_position)) else: coords.remove((self.wall_position, i)) return tuple(coords) @property def num_actions(self): return len(MiniGridDoorKeyAction) def _calculate_next_nodes_prms( self, node: MiniGridDoorKeyNode, action: int ) -> Tuple[Tuple[dict, float], ...]: newnode_prms = deepcopy(asdict(node)) if action == MiniGridDoorKeyAction.TurnRight: newnode_prms["Dir"] = (node.Dir + 1) % 4 if action == MiniGridDoorKeyAction.TurnLeft: newnode_prms["Dir"] = (node.Dir - 1) % 4 if action == MiniGridDoorKeyAction.MoveForward: if node.Dir == MiniGridDoorKeyDirection.UP: next_coord = (node.X, node.Y + 1) if node.Dir == MiniGridDoorKeyDirection.RIGHT: next_coord = node.X + 1, node.Y if node.Dir == MiniGridDoorKeyDirection.DOWN: next_coord = node.X, node.Y - 1 if node.Dir == MiniGridDoorKeyDirection.LEFT: next_coord = node.X - 1, node.Y if next_coord in self.coordinates_available or ( node.IsDoorOpened and next_coord == self.door_position ): newnode_prms["X"], newnode_prms["Y"] = next_coord if action == MiniGridDoorKeyAction.PickObject: if node.X == node.XKey and node.Y == node.YKey: newnode_prms["XKey"] = newnode_prms["YKey"] = -1 if node.XKey == -1 and not node.IsDoorOpened: if action == MiniGridDoorKeyAction.DropObject: newnode_prms["XKey"] = node.X newnode_prms["YKey"] = node.Y if action == MiniGridDoorKeyAction.UseObject: if node.Dir == MiniGridDoorKeyDirection.UP: next_coord = (node.X, node.Y + 1) if node.Dir == MiniGridDoorKeyDirection.RIGHT: next_coord = node.X + 1, node.Y if node.Dir == MiniGridDoorKeyDirection.DOWN: next_coord = node.X, node.Y - 1 if node.Dir == MiniGridDoorKeyDirection.LEFT: next_coord = node.X - 1, node.Y if next_coord == self.door_position: newnode_prms["IsDoorOpened"] = True return ((newnode_prms, 1.0),) def _calculate_reward_distribution( self, node: Any, action: IntEnum, next_node: Any ) -> rv_continuous: return ( self.optimal_distribution if next_node.X == self.goal_position[0] and next_node.Y == self.goal_position[1] else self.other_distribution ) def _check_input_parameters(self): super(MiniGridDoorKeyMDP, self)._check_input_parameters() assert self.size >= 3 check_distributions( [ self.optimal_distribution, self.other_distribution, ], self.make_reward_stochastic, ) def _instantiate_starting_node_sampler(self) -> NextStateSampler: # noinspection PyAttributeOutsideInit self.wall_position = self._rng.randint(self.size - 2) + 1 # noinspection PyAttributeOutsideInit self.is_wall_horizontal = self._rng.rand() > 0.5 if self.is_wall_horizontal: self.door_position = self._rng.randint(self.size), self.wall_position else: self.door_position = self.wall_position, self._rng.randint(self.size) self.is_goal_before = self._rng.rand() > 0.5 coords = MiniGridRoomsContinuous.get_positions_coords_in_room(self.size, (0, 0)) goal_positions = [] starting_positions = [] for i, j in coords.ravel(): if ( i < self.wall_position if self.is_goal_before else i > self.wall_position ): goal_positions.append((j, i) if self.is_wall_horizontal else (i, j)) elif ( i > self.wall_position if self.is_goal_before else i < self.wall_position ): starting_positions.append((j, i) if self.is_wall_horizontal else (i, j)) possible_starting_positions = deepcopy(starting_positions) self._rng.shuffle(goal_positions) self.goal_position = goal_positions[0] self._rng.shuffle(starting_positions) self.start_key_position = starting_positions.pop(0) starting_positions = [ (x, y, dir) for x, y in starting_positions for dir in MiniGridDoorKeyDirection ] assert self.n_starting_states < len(starting_positions) self._possible_starting_nodes = [ MiniGridDoorKeyNode( x, y, dir.value, *self.start_key_position, False, ) for x, y, dir in starting_positions ] return NextStateSampler( next_states=self._possible_starting_nodes[: self.n_starting_states], probs=[1 / self.n_starting_states for _ in range(self.n_starting_states)], seed=self._next_seed(), ) def calc_grid_repr(self, node: Any) -> np.array: grid_size = self.size door_position = self.door_position wall_position = self.wall_position is_wall_horizontal = self.is_wall_horizontal grid = np.zeros((grid_size, grid_size), dtype=str) grid[:, :] = " " grid[self.goal_position[1], self.goal_position[0]] = "G" if self.cur_node.XKey != -1: grid[self.cur_node.YKey, self.cur_node.XKey] = "K" for i in range(grid_size): if not is_wall_horizontal: grid[i, wall_position] = "W_en" else: grid[wall_position, i] = "W_en" grid[door_position[1], door_position[0]] = ( "O" if self.cur_node.IsDoorOpened else "C" ) if self.cur_node.Dir == MiniGridDoorKeyDirection.UP: grid[self.cur_node.Y, self.cur_node.X] = "^" elif self.cur_node.Dir == MiniGridDoorKeyDirection.RIGHT: grid[self.cur_node.Y, self.cur_node.X] = ">" elif self.cur_node.Dir == MiniGridDoorKeyDirection.DOWN: grid[self.cur_node.Y, self.cur_node.X] = "v" elif self.cur_node.Dir == MiniGridDoorKeyDirection.LEFT: grid[self.cur_node.Y, self.cur_node.X] = "<" return grid[::-1, :]

tests/bugs/core_6266_test.py

reevespaul/firebird-qa

503

<filename>tests/bugs/core_6266_test.py #coding:utf-8 # # id: bugs.core_6266 # title: Deleting records from MON$ATTACHMENTS using ORDER BY clause doesn't close the corresponding attachments # decription: # Old title: Don't close attach while deleting record from MON$ATTACHMENTS using ORDER BY clause. # Confirmed bug on 3.0.6.33271. # Checked on 3.0.6.33272 (SS/CS) - works fine. # 22.04.2020. Checked separately on 4.0.0.1931 SS/CS: all OK. FB 4.0 can also be tested since this build. # # tracker_id: CORE-6266 # min_versions: ['3.0.0'] # versions: 3.0 # qmid: None import pytest from firebird.qa import db_factory, isql_act, Action # version: 3.0 # resources: None substitutions_1 = [] init_script_1 = """""" db_1 = db_factory(sql_dialect=3, init=init_script_1) # test_script_1 #--- # import os # import sys # import time # import fdb # # ATT_CNT=5 # ATT_DELAY=1 # # os.environ["ISC_USER"] = user_name # os.environ["ISC_PASSWORD"] = <PASSWORD> # # db_conn.close() # # con_list={} # for i in range(0, ATT_CNT): # if i > 0: # time.sleep( ATT_DELAY ) # # c = fdb.connect(dsn = dsn) # a = c.attachment_id # con_list[ i ] = (a, c) # # print('created attachment ', (a,c) ) # # con_admin = con_list[0][1] # # #print(con_admin.firebird_version) # # # this removes ALL connections --> should NOT be used for reproducing ticket issue: # #con_admin.execute_immediate('delete from mon$attachments where mon$attachment_id != current_connection order by mon$timestamp') # # # this removes ALL connections --> should NOT be used for reproducing ticket issue: # #con_admin.execute_immediate('delete from mon$attachments where mon$system_flag is distinct from 1 and mon$attachment_id != current_connection order by mon$timestamp') # # # This DOES NOT remove all attachments (only 'last' in order of timestamp), but # # DELETE statement must NOT contain phrase 'mon$attachment_id != current_connection': # con_admin.execute_immediate('delete from mon$attachments where mon$system_flag is distinct from 1 order by mon$timestamp') # # con_admin.commit() # # cur_admin = con_admin.cursor() # cur_admin.execute('select mon$attachment_id,mon$user from mon$attachments where mon$system_flag is distinct from 1 and mon$attachment_id != current_connection' ) # i=0 # for r in cur_admin: # print( '### ACHTUNG ### STILL ALIVE ATTACHMENT DETECTED: ', r[0], r[1].strip(), '###' ) # i += 1 # print('Number of attachments that remains alive: ',i) # # cur_admin.close() # # #print('Final cleanup before quit from Python.') # # for k,v in sorted( con_list.items() ): # #print('attempt to close attachment ', v[0] ) # try: # v[1].close() # #print('done.') # except Exception as e: # pass # #print('Got exception:', sys.exc_info()[0]) # #print(e[0]) # # #--- #act_1 = python_act('db_1', test_script_1, substitutions=substitutions_1) expected_stdout_1 = """ Number of attachments that remains alive: 0 """ @pytest.mark.version('>=3.0') @pytest.mark.xfail def test_1(db_1): pytest.fail("Test not IMPLEMENTED")

scripts/senate_crawler.py

tompsh/tompsh.github.io

504

from bs4 import BeautifulSoup import logging import pandas as pd import csv import re import requests from urllib.parse import urljoin logging.basicConfig(format="%(asctime)s %(levelname)s:%(message)s", level=logging.INFO) def get_html(url): return requests.get(url).text class SenateCrawler: def __init__(self): self.base_url = "https://www25.senado.leg.br/" self.search_url = self.base_url + "web/senadores/em-exercicio/-/e/por-nome" self.senate = [] def get_senate(self, url): soup = BeautifulSoup(get_html(self.search_url), "html.parser") trs = soup.find("table").find("tbody").find_all("tr") for tr in trs: cells = tr.find_all("td") senateperson = { "name": cells[0].get_text(), "party": cells[1].get_text(), "email": cells[5].get_text(), } if senateperson["email"]: self.senate.append(senateperson) def run(self): try: self.get_senate(self.search_url) except Exception: logging.exception("global failure") finally: df = pd.DataFrame(self.senate) df.to_csv("senate.csv") logging.info("program exited")

backend-project/small_eod/autocomplete/tests/test_views.py

merito/small_eod

505

from test_plus.test import TestCase from ...administrative_units.factories import AdministrativeUnitFactory from ...cases.factories import CaseFactory from ...channels.factories import ChannelFactory from ...events.factories import EventFactory from ...features.factories import FeatureFactory, FeatureOptionFactory from ...generic.tests.test_views import ReadOnlyViewSetMixin from ...institutions.factories import InstitutionFactory from ...letters.factories import DocumentTypeFactory, ReferenceNumberFactory from ...search.tests.mixins import SearchQueryMixin from ...tags.factories import TagFactory from ...users.factories import UserFactory class AdministrativeUnitAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename = "autocomplete_administrative_unit" factory_class = AdministrativeUnitFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class CaseAutocompleteViewSetTestCase(ReadOnlyViewSetMixin, SearchQueryMixin, TestCase): basename = "autocomplete_case" factory_class = CaseFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class ChannelAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename = "autocomplete_channel" factory_class = ChannelFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class DocumentTypeAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename = "autocomplete_document_type" factory_class = DocumentTypeFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class ReferenceNumberAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename = "autocomplete_reference_number" factory_class = ReferenceNumberFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class EventAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename = "autocomplete_event" factory_class = EventFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class FeatureAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename = "autocomplete_feature" factory_class = FeatureFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class FeatureOptionAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename = "autocomplete_feature_option" factory_class = FeatureOptionFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class InstitutionAutocompleteViewSetTestCase( ReadOnlyViewSetMixin, SearchQueryMixin, TestCase ): basename = "autocomplete_institution" factory_class = InstitutionFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class TagAutocompleteViewSetTestCase(ReadOnlyViewSetMixin, SearchQueryMixin, TestCase): basename = "autocomplete_tag" factory_class = TagFactory def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["name"], self.obj.name) class UserAutocompleteViewSetTestCase(ReadOnlyViewSetMixin, SearchQueryMixin, TestCase): basename = "autocomplete_user" factory_class = UserFactory initial_count = 1 def validate_item(self, item): self.assertEqual(item["id"], self.obj.id) self.assertEqual(item["username"], self.obj.username)

src/lennybot/model/plan.py

raynigon/lenny-bot

506

<filename>src/lennybot/model/plan.py from typing import Any, List from ..actions.iaction import IAction from ..model.state import LennyBotState class LennyBotPlan: def __init__(self, state: LennyBotState, actions: List[IAction]) -> None: self._state = state self._actions = actions @property def applications(self) -> List[str]: result = [] for action in self._actions: result.append(action.application) return list(set(result)) @property def actions(self) -> List[IAction]: return self._actions @property def state(self) -> LennyBotState: return self._state def source_version(self, application: str) -> str: for action in self._actions: if action.application != application: continue return action.source_version return None def target_version(self, application: str) -> str: for action in self._actions: if action.application != application: continue return action.target_version return None

laceworksdk/api/container_registries.py

kiddinn/python-sdk

507

# -*- coding: utf-8 -*- """ Lacework Container Registries API wrapper. """ import logging logger = logging.getLogger(__name__) class ContainerRegistriesAPI(object): """ Lacework Container Registries API. """ def __init__(self, session): """ Initializes the ContainerRegistriesAPI object. :param session: An instance of the HttpSession class :return ContainerRegistriesAPI object. """ super(ContainerRegistriesAPI, self).__init__() self._session = session def create(self, name, type, enabled, data, org=False): """ A method to create a new container registry. :param name: A string representing the container registry name. :param type: A string representing the container registry type. :param enabled: A boolean/integer representing whether the container registry is enabled. (0 or 1) :param data: A JSON object matching the schema for the specified type. :param org: A boolean representing whether the request should be performed at the Organization level :return response json """ logger.info("Creating container registry in Lacework...") # Build the Container Registries request URI api_uri = "/api/v2/ContainerRegistries" data = { "name": name, "type": type, "enabled": int(bool(enabled)), "data": data } response = self._session.post(api_uri, org=org, data=data) return response.json() def get(self, guid=None, type=None, org=False): """ A method to get all container registries. :param guid: A string representing the container registry GUID. :param type: A string representing the container registry type. :param org: A boolean representing whether the request should be performed at the Organization level :return response json """ logger.info("Getting container registry info from Lacework...") # Build the Container Registries request URI if guid: api_uri = f"/api/v2/ContainerRegistries/{guid}" elif type: api_uri = f"/api/v2/ContainerRegistries/{type}" else: api_uri = "/api/v2/ContainerRegistries" response = self._session.get(api_uri, org=org) return response.json() def get_by_type(self, type, org=False): """ A method to get all container registries by type. :param type: A string representing the container registry type. :param org: A boolean representing whether the request should be performed at the Organization level :return response json """ return self.get(type=type, org=org) def get_by_guid(self, guid, org=False): """ A method to get all container registries. :param guid: A string representing the container registry GUID. :param org: A boolean representing whether the request should be performed at the Organization level :return response json """ return self.get(guid=guid, org=org) def search(self, query_data=None, org=False): """ A method to search container registries. :param query_data: A dictionary containing the desired search parameters. (filters, returns) :return response json """ logger.info("Searching container registries from Lacework...") # Build the Container Registries request URI api_uri = "/api/v2/ContainerRegistries/search" response = self._session.post(api_uri, data=query_data, org=org) return response.json() def update(self, guid, name=None, type=None, enabled=None, data=None, org=False): """ A method to update an container registry. :param guid: A string representing the container registry GUID. :param name: A string representing the container registry name. :param type: A string representing the container registry type. :param enabled: A boolean/integer representing whether the container registry is enabled. (0 or 1) :param data: A JSON object matching the schema for the specified type. :param org: A boolean representing whether the request should be performed at the Organization level :return response json """ logger.info("Updating container registry in Lacework...") # Build the Container Registries request URI api_uri = f"/api/v2/ContainerRegistries/{guid}" tmp_data = {} if name: tmp_data["name"] = name if type: tmp_data["type"] = type if enabled is not None: tmp_data["enabled"] = int(bool(enabled)) if data: tmp_data["data"] = data response = self._session.patch(api_uri, org=org, data=tmp_data) return response.json() def delete(self, guid, org=False): """ A method to delete an container registry. :param guid: A string representing the container registry GUID. :param org: A boolean representing whether the request should be performed at the Organization level :return response json """ logger.info("Deleting container registry in Lacework...") # Build the Container Registries request URI api_uri = f"/api/v2/ContainerRegistries/{guid}" response = self._session.delete(api_uri, org=org) if response.status_code == 204: return response else: return response.json()

mllib/nlp/seq2seq.py

pmaxit/dlnotebooks

508

<reponame>pmaxit/dlnotebooks<filename>mllib/nlp/seq2seq.py<gh_stars>0 # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/01_seq2seq.ipynb (unless otherwise specified). __all__ = ['Encoder', 'NewDecoder', 'Seq2Seq'] # Cell from torch import nn from torch import optim import torch import torch.nn.functional as F from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence # Cell class Encoder(nn.Module): def __init__(self, input_size, embedding_size, hidden_size, num_layers=2, p=0.1): super(Encoder, self).__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.dropout = nn.Dropout(p) self.embedding = nn.Embedding(input_size, embedding_size) self.rnn = nn.LSTM(embedding_size, hidden_size, num_layers, dropout=p,batch_first=False) def forward(self, x, x_len): # x shape (seq_length, N) embedding = self.dropout(self.embedding(x)) # embedding shape : (seq_length, N, embedding_size) x_packed = pack_padded_sequence(embedding, x_len.cpu(), batch_first=False, enforce_sorted=False) output_packed, (hidden,cell) = self.rnn(x_packed) # irrelevant because we are interested only in hidden state #output_padded, output_lengths = pad_packed_sequence(output_packed, batch_first=True) # output is irrelevant, context vector is important return hidden,cell # Cell class NewDecoder(nn.Module): def __init__(self, hidden_size, embedding_size, output_size, n_layers=1, dropout_p=0.1): super(NewDecoder, self).__init__() # Define parameters self.hidden_size = hidden_size self.output_size = output_size self.n_layers =n_layers self.dropout_p = dropout_p # Define layers self.embedding = nn.Embedding(output_size, embedding_size) self.dropout=nn.Dropout(dropout_p) self.rnn = nn.LSTM(embedding_size, hidden_size, n_layers, dropout=dropout_p, batch_first=False) self.out = nn.Linear(hidden_size, output_size) def forward(self, word_input, last_hidden, encoder_outputs): # Note that we will only be running forward for a single decoder time step, but will # use all encoder outputs word_input = word_input.unsqueeze(0) # we are not using encoder_outputs here word_embedded = self.embedding(word_input) # 1 X B word_embedded = self.dropout(word_embedded) # 1 X B X emb_length # Combine embedded input word and hidden vector, run through RNN output, hidden = self.rnn(word_embedded, last_hidden) # 1 X B X hidden predictions = self.out(output) # 1, B, out #output = F.log_softmax(predictions) return predictions, hidden # Cell import random import pytorch_lightning as pl import pytorch_lightning.metrics.functional as plfunc from pytorch_lightning.loggers import TensorBoardLogger # Cell class Seq2Seq(pl.LightningModule): """ Encoder decoder pytorch lightning module for training seq2seq model with teacher forcing Module try to learn mapping from one sequence to another """ @staticmethod def add_model_specific_args(parent_parser): parser = ArgumentParser(parents=[parent_parser], add_help=False) parser.add_argument("--emb_dim", type=int, default=32) parser.add_argument('--hidden_dim', type=int, default=64) parser.add_argument('--dropout', type=float, default=0.1) return parser def __init__(self, input_vocab_size, output_vocab_size, padding_index = 0, emb_dim = 8, hidden_dim=32, dropout=0.1, max_length=20, **kwargs): super().__init__() # dynamic, based on tokenizer vocab size defined in datamodule self.input_dim = input_vocab_size self.output_dim = output_vocab_size self.enc_emb_dim = emb_dim self.dec_emb_dim = emb_dim self.enc_hid_dim = hidden_dim self.dec_hid_dim = hidden_dim self.enc_dropout = dropout self.dec_dropout = dropout self.pad_idx = padding_index self.num_layers = 2 self.max_length =10 self.save_hyperparameters() self.max_epochs= kwargs.get('max_epochs',5) self.learning_rate = 0.0005 self._loss = nn.CrossEntropyLoss(ignore_index=self.pad_idx) self.encoder = Encoder( self.input_dim, self.enc_emb_dim, self.enc_hid_dim, self.num_layers, self.enc_dropout ) self.decoder = NewDecoder( self.enc_hid_dim, self.dec_emb_dim, self.output_dim, self.num_layers, self.dec_dropout ) self._init_weights() def _init_weights(self): for name, param in self.named_parameters(): if "weight" in name: nn.init.normal_(param.data, mean=0, std=0.01) else: nn.init.constant_(param.data, 0) def create_mask(self, src): mask = (src != self.pad_idx).permute(1, 0) return mask def forward(self, src_seq, source_len, trg_seq, teacher_force_ratio=0.5): """ teacher_force_ratio is used to help in decoding. In starting, original input token will be sent as input token """ source = src_seq.transpose(0, 1) target_len = self.max_length if trg_seq is not None: target = trg_seq.transpose(0, 1) target_len = target.shape[0] batch_size = source.shape[1] target_vocab_size = self.output_dim outputs = torch.zeros(target_len, batch_size, target_vocab_size).to(self.device) encoder_hidden = self.encoder(source, source_len) # mask = [batch_size, src len] # without sos token at the beginning and eos token at the end #x = target[0,:] decoder_input = torch.ones(batch_size).long().to(self.device) decoder_hidden = encoder_hidden encoder_outputs = None for t in range(target_len): decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs) outputs[t] = decoder_output #(N, english_vocab_size) #best_guess = output.argmax(1) topv, topi = decoder_output.topk(1) decoder_input = topi.squeeze().detach() decoder_input = target[t] if random.random() < teacher_force_ratio and target is not None else decoder_input return outputs def loss(self, logits, target): return self._loss(logits, target) def configure_optimizers(self): optimizer = optim.AdamW(self.parameters(), lr=self.learning_rate) lr_scheduler = { 'scheduler': optim.lr_scheduler.OneCycleLR( optimizer, max_lr = self.learning_rate, steps_per_epoch = 3379, epochs=self.max_epochs, anneal_strategy='linear', final_div_factor=1000, pct_start = 0.01 ), "name": "learning_rate", "interval":"step", "frequency": 1 } return [optimizer],[lr_scheduler] def training_step(self, batch, batch_idx): src_seq, trg_seq, src_lengths = batch['src'],batch['trg'], batch['src_len'] output = self.forward(src_seq, src_lengths,trg_seq) # do not know if this is a problem, loss will be computed with sos token # without sos token at the beginning and eos token at the end output = output.view(-1, self.output_dim) trg_seq = trg_seq.transpose(0, 1) trg = trg_seq.reshape(-1) loss = self.loss(output, trg) self.log('train_loss',loss.item(), on_step = True, on_epoch=True, prog_bar = True, logger=True) return loss def validation_step(self, batch,batch_idx): """ validation is in eval model so we do not have to use placeholder input sensors""" src_seq, trg_seq, src_lengths = batch['src'],batch['trg'], batch['src_len'] outputs = self.forward(src_seq, src_lengths, trg_seq, 0) logits = outputs[1:].view(-1, self.output_dim) trg = trg_seq[1:].reshape(-1) loss = self.loss(logits, trg) pred_seq = outputs[1:].argmax(2) # seq_len*batch_size*vocab_size -> seq_len * batch_size # change layout: sesq_len * batch_size -> batch_size * seq_len pred_seq = pred_seq.T # change layout: seq_len * batch_size -> batch_size * seq_len trg_batch = trg_seq[1:].T # compare list of predicted ids for all sequences in a batch to targets acc = plfunc.accuracy(pred_seq.reshape(-1), trg_batch.reshape(-1)) # need to cast to list of predicted sequences ( as list of token ids ) [ seq_tok1, seqtok2] predicted_ids - pred_seq.tolist() # need to add additional dim to each target reference sequence in order to # conver to format needed by blue_score_func # [seq1=[[reference1],[reference2]], seq2=[reference1]] target_ids = torch.unsqueeze(trg_batch, 1).tolist() bleu_score - plfunc.nlp.bleu_score(predicted_ids, target_ids, n_gram=3).to(self.device) self.log( 'val_loss', loss, on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True) self.log( "val_acc", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True ) self.log( "val_bleu_idx", bleu_score, on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True ) return loss, acc, bleu_score

tests/flows/test_consent.py

mrkday/SATOSA

509

import json import re import responses from werkzeug.test import Client from werkzeug.wrappers import Response from satosa.proxy_server import make_app from satosa.satosa_config import SATOSAConfig class TestConsent: def test_full_flow(self, satosa_config_dict, consent_module_config): api_url = "https://consent.example.com/api" redirect_url = "https://consent.example.com/redirect" consent_module_config["config"]["api_url"] = api_url consent_module_config["config"]["redirect_url"] = redirect_url satosa_config_dict["MICRO_SERVICES"].append(consent_module_config) # application test_client = Client(make_app(SATOSAConfig(satosa_config_dict)), Response) # incoming auth req http_resp = test_client.get("/{}/{}/request".format(satosa_config_dict["BACKEND_MODULES"][0]["name"], satosa_config_dict["FRONTEND_MODULES"][0]["name"])) assert http_resp.status_code == 200 verify_url_re = re.compile(r"{}/verify/\w+".format(api_url)) with responses.RequestsMock() as rsps: # fake no previous consent consent_request_url_re = re.compile(r"{}/creq/\w+".format(api_url)) rsps.add(responses.GET, verify_url_re, status=401) rsps.add(responses.GET, consent_request_url_re, "test_ticket", status=200) # incoming auth resp http_resp = test_client.get("/{}/response".format(satosa_config_dict["BACKEND_MODULES"][0]["name"])) assert http_resp.status_code == 302 assert http_resp.headers["Location"].startswith(redirect_url) with responses.RequestsMock() as rsps: # fake consent rsps.add(responses.GET, verify_url_re, json.dumps({"foo": "bar"}), status=200) # incoming consent response http_resp = test_client.get("/consent/handle_consent") assert http_resp.status_code == 200

qnarre/models/transfo_xl.py

quantapix/qnarre.com

510

<gh_stars>0 # Copyright 2022 Quantapix 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. # ============================================================================= # https://arxiv.org/abs/1901.02860 # https://github.com/kimiyoung/transformer-xl import torch from torch import nn from torch.nn import functional as F from transformers.utils import logging from .. import core as qc from ..core import utils as qu from ..core import forward as qf from ..core import output as qo from ..core.embed import Adaptive, Positional from ..core.ffnet import Positionwise from ..prep.config.transfo_xl import PreTrained log = logging.get_logger(__name__) class Model(PreTrained): def __init__(self, **kw): super().__init__(**kw) cfg = self.get_cfg(kw) self.tok_emb = Adaptive(cfg.cutoffs, div_val=cfg.div_val, **kw) self.pos_emb = Positional(cfg.d_model, **kw) if cfg.untie_r: q_bias = None r_bias = None else: q_bias = nn.Parameter(torch.FloatTensor(cfg.n_heads, cfg.d_head)) r_bias = nn.Parameter(torch.FloatTensor(cfg.n_heads, cfg.d_head)) self.lays = qc.Stack() for _ in range(cfg.n_lays): self.lays.append(Layer(q_bias=q_bias, r_bias=r_bias, **kw)) self.drop = qc.Dropout(cfg.drop, **kw) def init_mems(self, b): cfg = self.cfg if cfg.mem_len > 0: p = next(self.parameters()) kw = dict(dtype=p.dtype, device=p.device) return [torch.zeros(cfg.mem_len, b, cfg.d_model, **kw) for _ in range(cfg.n_lays)] return None def update_mems(self, xs, ys, mlen, qlen): assert len(xs) == len(ys) e = mlen + max(0, qlen) b = max(0, e - self.cfg.mem_len) with torch.no_grad(): return [torch.cat([ys[i], xs[i]], dim=0)[b:e].detach() for i in range(len(xs))] def forward(self, x, mems=None, head_m=None, x_emb=None, **kw): cfg = self.cfg yo = self.get_y_opts(**kw) if x is None: x_emb = x_emb.transpose(0, 1).contiguous() s = x_emb.size()[:-1] else: assert x_emb is None x = x.transpose(0, 1).contiguous() s = x.size() y = self.tok_emb(x) if x_emb is None else x_emb n, b = s if mems is None: mems = self.init_mems(b) mlen = mems[0].size(0) if mems is not None else 0 klen = mlen + n pos = torch.arange(klen - 1, -1, -1.0, device=y.device, dtype=y.dtype) if cfg.clamp_len > 0: pos.clamp_(max=cfg.clamp_len) pos = self.drop(self.pos_emb(pos)) ones = y.new_ones((n, klen), dtype=torch.uint8) if cfg.same_length: d = klen - cfg.mem_len shift = n - d if d > 0 else n dec_m = (torch.triu(ones, 1 + mlen) + torch.tril(ones, -shift))[:, :, None] else: dec_m = torch.triu(ones, diagonal=1 + mlen)[:, :, None] y = self.drop(y) attns = () if yo.attn else None hiddens = () if yo.hidden else None head_m = self.get_head_m2(head_m, cfg.n_lays) for i, lay in enumerate(self.lays): if yo.hidden: hiddens += (y,) m = None if mems is None else mems[i] ys = lay(y, pos, **kw, dec_m=dec_m, head_m=head_m[i], mems=m, yo=yo) y = ys[0] if yo.attn: attns += (ys[1],) y = self.drop(y) mems = None if mems is None else self.update_mems(hiddens, mems, mlen, n) if yo.attn: attns = tuple(x.permute(2, 3, 0, 1).contiguous() for x in attns) if yo.hidden: hiddens += (y,) hiddens = tuple(x.transpose(0, 1).contiguous() for x in hiddens) y = y.transpose(0, 1).contiguous() ys = (y, attns, hiddens, mems) return qo.WithMems(*ys) if yo.kw else ys class ForSeqClassifier(PreTrained): def __init__(self, **kw): super().__init__(**kw) cfg = self.get_cfg(kw) self.model = Model(**kw) self.proj = qc.Linear(cfg.d_embed, cfg.n_labels, bias=False, **kw) forward = qf.forward_seq def post_proj(self, x): cfg = self.cfg b = (x.shape[:2] if x is not None else x_emb.shape[:2])[0] if cfg.PAD is None: n = -1 else: assert b == 1 n = -1 if x is None else torch.ne(x, cfg.PAD).sum(-1) - 1 return x[torch.arange(b, device=self.device), n] class LLMHead(PreTrained): def __init__(self, **kw): super().__init__(**kw) cfg = self.get_cfg(kw) self.model = Model(**kw) assert cfg.sample_softmax <= 0 self.proj = Projector( cfg.s_vocab, cfg.d_embed, cfg.d_model, cfg.cutoffs, div_val=cfg.div_val, **kw ) def tie_weights(self): cfg = self.cfg if cfg.tie_word_embeds: for i in range(len(self.proj.out_layers)): self._tie_or_clone_weights(self.proj.out_layers[i], self.model.tok_emb.lays[i]) if cfg.tie_projs: for i, tie_proj in enumerate(cfg.tie_projs): if tie_proj and cfg.div_val == 1 and cfg.d_model != cfg.d_embed: if cfg.torchscript: self.proj.out_projs[i] = nn.Parameter(self.model.tok_emb.projs[0].clone()) else: self.proj.out_projs[i] = self.model.tok_emb.projs[0] elif tie_proj and cfg.div_val != 1: if cfg.torchscript: self.proj.out_projs[i] = nn.Parameter(self.model.tok_emb.projs[i].clone()) else: self.proj.out_projs[i] = self.model.tok_emb.projs[i] def init_mems(self, bsz): return self.model.init_mems(bsz) def forward(self, x, x_emb=None, labels=None, **kw): yo = self.get_y_opts(**kw) if x is None: assert x_emb is not None b, tgt = x_emb.size(0), x_emb.size(1) else: b, tgt = x.size(0), x.size(1) ys = self.model(x, x_emb=x_emb, **kw, yo=yo) xs = self.proj(ys[0][:, -tgt:], labels) y = xs.view(b, tgt, -1) if labels is None else () loss = xs.view(b, tgt - 1) if labels is not None else None ys = (y,) + ys[1:] + (loss,) return qo.LossMems(*ys) if yo.kw else ys class Projector(qc.Module): def __init__(self, s_vocab, d_embed, d_proj, cutoffs, div_val=1, keep_order=False): super().__init__() self.s_vocab = s_vocab self.d_embed = d_embed self.d_proj = d_proj self.cutoffs = cutoffs + [s_vocab] self.cutoff_ends = [0] + self.cutoffs self.div_val = div_val self.shortlist_size = self.cutoffs[0] self.n_clusters = len(self.cutoffs) - 1 self.head_size = self.shortlist_size + self.n_clusters if self.n_clusters > 0: self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed)) self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters)) self.out_layers = qc.Stack() self.out_projs = nn.ParameterList() if div_val == 1: for i in range(len(self.cutoffs)): if d_proj != d_embed: self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed))) else: self.out_projs.append(None) self.out_layers.append(qc.Linear(d_embed, s_vocab)) else: for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] d_emb_i = d_embed // (div_val**i) self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i))) self.out_layers.append(qc.Linear(d_emb_i, r_idx - l_idx)) self.keep_order = keep_order def _compute_logit(self, x, weight, bias, proj): if proj is None: y = F.linear(x, weight, bias=bias) else: # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1: x = F.linear(x, proj.t().contiguous()) y = F.linear(x, weight, bias=bias) # else: # logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t())) # if bias is not None: # logit = logit + bias return y def forward(self, x, labels=None, keep_order=False): if labels is not None: x = x[..., :-1, :].contiguous() labels = labels[..., 1:].contiguous() x = x.view(-1, x.size(-1)) labels = labels.view(-1) assert x.size(0) == labels.size(0) else: x = x.view(-1, x.size(-1)) if self.n_clusters == 0: y = self._compute_logit( x, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0] ) if labels is not None: y = -F.log_softmax(y, dim=-1).gather(1, labels.unsqueeze(1)).squeeze(1) else: y = F.log_softmax(y, dim=-1) else: ws, bs = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) ws.append(weight_i) bs.append(bias_i) head_weight, head_bias, head_proj = ws[0], bs[0], self.out_projs[0] head_logit = self._compute_logit(x, head_weight, head_bias, head_proj) head_logprob = F.log_softmax(head_logit, dim=1) if labels is None: y = x.new_empty((head_logit.size(0), self.s_vocab)) else: y = torch.zeros_like(labels, dtype=x.dtype, device=x.device) offset = 0 cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1] if labels is not None: mask_i = (labels >= l_idx) & (labels < r_idx) indices_i = mask_i.nonzero().squeeze() if indices_i.numel() == 0: continue target_i = labels.index_select(0, indices_i) - l_idx head_logprob_i = head_logprob.index_select(0, indices_i) hidden_i = x.index_select(0, indices_i) else: hidden_i = x if i == 0: if labels is not None: logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1) else: y[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = ws[i], bs[i], self.out_projs[i] tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i) tail_logprob_i = F.log_softmax(tail_logit_i, dim=1) cluster_prob_idx = self.cutoffs[0] + i - 1 if labels is not None: logprob_i = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather( 1, target_i[:, None] ).squeeze(1) else: logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i y[:, l_idx:r_idx] = logprob_i if labels is not None: if (hasattr(self, "keep_order") and self.keep_order) or keep_order: y.index_copy_(0, indices_i, -logprob_i) else: y[offset : offset + logprob_i.size(0)].copy_(-logprob_i) offset += logprob_i.size(0) return y def log_prob(self, x): if self.n_clusters == 0: y = self._compute_logit( x, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0] ) return F.log_softmax(y, dim=-1) else: ws, bs = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) ws.append(weight_i) bs.append(bias_i) head_weight, head_bias, head_proj = ws[0], bs[0], self.out_projs[0] head_logit = self._compute_logit(x, head_weight, head_bias, head_proj) y = x.new_empty((head_logit.size(0), self.s_vocab)) head_logprob = F.log_softmax(head_logit, dim=1) cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): beg_idx, stop_idx = cutoff_values[i], cutoff_values[i + 1] if i == 0: y[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = ws[i], bs[i], self.out_projs[i] tail_logit_i = self._compute_logit(x, weight_i, bias_i, proj_i) tail_logprob_i = F.log_softmax(tail_logit_i, dim=1) logprob_i = head_logprob[:, -i] + tail_logprob_i y[:, beg_idx, stop_idx] = logprob_i return y class Layer(qc.Module): def __init__(self, **kw): super().__init__() self.attn = Attention(**kw) self.ff = Positionwise(**kw) def forward(self, x, r, dec_m=None, **kw): ys = self.attn(x, r, mask=dec_m, **kw) return (self.ff(ys[0]),) + ys[1:] class Attention(qc.Module): hs = qc.Hypers( {"d_head", "d_model", "drop", "n_heads"}, {"drop_attn": 0.0, "eps": 1e-5, "pre_norm": False}, ) def __init__(self, r_bias=None, q_bias=None, ps={}, hs=[], **kw): super().__init__(ps, [self.hs] + hs, **kw) cfg = self.get_cfg(kw) m, n, h = cfg.d_model, cfg.n_heads, cfg.d_head cfg.scale = 1 / (h**0.5) self.qkv = qc.Linear(m, 3 * n * h, bias=False) self.r_net = qc.Linear(m, n * h, bias=False) if r_bias is None or q_bias is None: self.q_bias = nn.Parameter(torch.FloatTensor(n, h)) self.r_bias = nn.Parameter(torch.FloatTensor(n, h)) else: self.q_bias = q_bias self.r_bias = r_bias self.drop = qc.Dropout(cfg.drop, **kw) self.drop_attn = qc.Dropout(cfg.drop_attn, **kw) self.proj = qc.Linear(n * h, m, bias=False, **kw) self.norm = qc.LayerNorm(m, **kw) def rel_shift(self, x, zero_triu=False): s = (x.size(0), 1) + x.size()[2:] y = torch.zeros(s, device=x.device, dtype=x.dtype) y = torch.cat([y, x], dim=1) s = (x.size(1) + 1, x.size(0)) + x.size()[2:] y = y.view(*s) y = y[1:].view_as(x) if zero_triu: ones = torch.ones((y.size(0), y.size(1))) y = y * torch.tril(ones, y.size(1) - y.size(0))[:, :, None, None] return y def forward(self, x, r, mask=None, mems=None, head_m=None, **kw): cfg = self.cfg yo = self.get_y_opts(**kw) y = x if mems is None else torch.cat([mems, x], 0) y = self.qkv(self.norm(y) if cfg.pre_norm else y) r = self.r_net(r) q, k, v = torch.chunk(a, 3, dim=-1) qlen, klen, rlen = x.size(0), k.size(0), r.size(0) q = q if mems is None else q[-qlen:] b, n, h = x.size(1), cfg.n_heads, cfg.d_head q = q.view(qlen, b, n, h) k = k.view(klen, b, n, h) v = v.view(klen, b, n, h) r = r.view(rlen, n, h) AC = torch.einsum("ibnd,jbnd->ijbn", (q + self.q_bias, k)) BD = self.rel_shift(torch.einsum("ibnd,jnd->ijbn", (q + self.r_bias, r))) a = AC + BD a.mul_(cfg.scale) if mask is not None and torch.sum(mask).item(): mask = mask == 1 i = self.get_minus_inf() if mask.dim() == 2: a = a.float().masked_fill(mask[None, :, :, None], i).type_as(a) elif mask.dim() == 3: a = a.float().masked_fill(mask[:, :, :, None], i).type_as(a) a = self.drop_attn(F.softmax(a, dim=1)) if head_m is not None: a = a * head_m y = torch.einsum("ijbn,jbnd->ibnd", (a, v)) y = y.contiguous().view(y.size(0), y.size(1), n * h) y = x + self.drop(self.proj(y)) ys = (y,) if cfg.pre_norm else (self.norm(y),) if yo.attn: ys += (a,) return ys

src/align/face_align_celeba.py

Dou-Yu-xuan/pykinship

511

import argparse import glob import os import pickle from pathlib import Path import numpy as np from PIL import Image from tqdm import tqdm from src.align.align_trans import get_reference_facial_points, warp_and_crop_face # sys.path.append("../../") from src.align.detector import detect_faces if __name__ == "__main__": parser = argparse.ArgumentParser(description="face alignment") parser.add_argument( "-source_root", "--source_root", help="specify your source dir", default="../../data/fiw-videos/new-processed/", type=str, ) parser.add_argument( "-dest_root", "--dest_root", help="specify your destination dir", default="../../data/fiw-videos/new-processed/", type=str, ) parser.add_argument( "-crop_size", "--crop_size", help="specify size of aligned faces, align and crop with padding", default=112, type=int, ) args = parser.parse_args() source_root = args.source_root # specify your source dir dest_root = args.dest_root # specify your destination dir crop_size = ( args.crop_size ) # specify size of aligned faces, align and crop with padding scale = crop_size / 112.0 reference = get_reference_facial_points(default_square=True) * scale cwd = os.getcwd() # delete '.DS_Store' existed in the source_root os.chdir(source_root) os.system("find . -name '*.DS_Store' -type f -delete") os.chdir(cwd) imfiles = [ f for f in glob.glob(f"{source_root}F????/MID*/faces/msceleb*") if Path(f).is_file() ] # images = {imfile.replace(source_root, ''): Image.open(imfile) for imfile in imfiles} meta = {} # for subfolder in tqdm(os.listdir(source_root)): for imfile in tqdm(imfiles): ref = imfile.replace(source_root, "") print("Processing\t{}".format(imfile)) img = Image.open(imfile) try: # Handle exception bbs, landmarks = detect_faces(img) except Exception: print("{} is discarded due to exception!".format(imfile)) continue ref = imfile.replace(source_root, "") ndetections = len(landmarks) if ( ndetections == 0 ): # If the landmarks cannot be detected, the img will be discarded print("{} is discarded due to non-detected landmarks!".format(imfile)) meta[ref] = [] continue li_meta = [] for i in range(ndetections): im_meta = {} im_meta["face"] = i im_meta["landmarks"] = landmarks[i] im_meta["bb"] = bbs[i] facial5points = [[landmarks[i][j], landmarks[i][j + 5]] for j in range(5)] warped_face = warp_and_crop_face( np.array(img), facial5points, reference, crop_size=(crop_size, crop_size), ) img_warped = Image.fromarray(warped_face) image_name = imfile.replace("images", "cropped").replace( ".jpg", "-{:02d}.jpg".format(i) ) # im_meta['ref'] = "/".join(image_name.split('/')[-5:]) img_warped.save(image_name) li_meta.append(im_meta) meta[ref] = li_meta with open(source_root + "cropped-meta.pkl", "wb") as f: pickle.dump(meta, f)

extract.py

rmalav15/voice-data-extract

512

from srtvoiceext import extract if __name__ == '__main__': ext = extract('video.mkv', 'subtitles.srt', 'outdir')

bacon/readonly_collections.py

aholkner/bacon

513

import collections class ReadOnlyDict(collections.MutableMapping): def __init__(self, store): self.store = store def __getitem__(self, key): return self.store[key] def __setitem__(self, key, value): raise TypeError('Cannot modify ReadOnlyDict') def __delitem__(self, key): raise TypeError('Cannot modify ReadOnlyDict') def __iter__(self): return iter(self.store) def __len__(self): return len(self.store) def __str__(self): return 'ReadOnlyDict(%s)' % self.store def __repr__(self): return 'ReadOnlyDict(%r)' % self.store

rpg_game/gui.py

ricott1/twissh

514

# encoding: utf-8 import urwid import time, os, copy from rpg_game.utils import log, mod, distance from rpg_game.constants import * from urwid import raw_display SIZE = lambda scr=raw_display.Screen(): scr.get_cols_rows() MIN_HEADER_HEIGHT = 3 MAX_MENU_WIDTH = 48 FOOTER_HEIGHT = 4 PALETTE = [ ("line", 'black', 'white', "standout"), ("top","white","black"), ("frame","white","white"), ("player", "light green", "black"), ("other", "light blue", "black"), ("monster", "dark red", "black"), ("fatigued", "dark red", "white", "standout"), ("reversed", "standout", ""), ("common","white","black"), ("common_line","black","white","standout"), ("uncommon","dark cyan","black"), ("uncommon_line","dark cyan","white","standout"), ("rare","yellow","black"), ("rare_line","yellow","white","standout"), ("unique","light magenta","black"), ("unique_line","light magenta","white","standout"), ("set","light green","black"), ("set_line","light green","white","standout"), ("normal","white","black"), ("positive","light green","black"), ("negative","dark red","black"), ("white","white","black"), ("disabled","dark red","black"), ("red","dark red","black"), ("green","light green","black"), ("yellow","yellow","black"), ("brown","brown","black"), ("white_line","black","white", "standout"), ("red_line","dark red","white", "standout"), ("green_line","light green","white", "standout"), ("yellow_line","yellow","white", "standout"), ("cyan","light cyan","black"), ("cyan_line","light cyan","white", "standout"), ("name","white","black"), ] class UiFrame(urwid.Frame): def __init__(self, parent, mind, *args, **kargs): self.parent = parent self.mind = mind urwid.AttrMap(self,"frame") super().__init__(*args, **kargs) @property def player(self): if self.mind.avatar.uuid in self.mind.master.players: return self.mind.master.players[self.mind.avatar.uuid] else: return None @property def connection(self): if self.mind.avatar.uuid in self.mind.connections: return self.mind.connections[self.mind.avatar.uuid] else: return None def handle_input(self, _input): pass def on_update(self): pass def dispatch_event(self, event_type, *args): self.mind.get_GUI_event(event_type, *args) def register_event(self, event_type, callback): self.mind.register_GUI_event(event_type, callback) def disconnect(self): pass def restart(self): pass def focus_next(self): pass def focus_previous(self): pass def update_body(self, title, no_title=False, boxed=False): self.active_body = self.bodies[title] if boxed: if no_title: self.contents["body"] = (urwid.LineBox(self.active_body), None) else: self.contents["body"] = (urwid.LineBox(self.active_body, title=title), None) else: self.contents["body"] = (self.active_body, None) class GUI(UiFrame): def __init__(self, parent, mind): self.bodies = {"Intro" : IntroFrame(self, mind)} self.active_body = self.bodies["Intro"] super().__init__(parent, mind, self.active_body) def on_update(self): self.active_body.on_update() def handle_input(self, _input): # print("HANDLING", _input) self.active_body.handle_input(_input) # def exit(self): # self.disconnect() # self.mind.disconnect()#should use dispatch event def restart(self): self.update_body("Intro", no_title=True) def start_game_frame(self): self.bodies["Game"] = GameFrame(self, self.mind) self.update_body("Game", no_title=True) class IntroFrame(UiFrame): def __init__(self, parent, mind): # urwid.Padding(urwid.BigText(('top', "Hack\'n\'SSH"), urwid.HalfBlock5x4Font())), self.choices = ("Warrior", "Dwarf", "Wizard", "Thief", "Bard") self.descriptions = {"Warrior": "The mighty warrior\n\nStrength +1, Hit points +4\nCharge and parry", "Dwarf": "The short dwarf\n\nStrength +1, Constitution +1, Hit points +6\nDemolish and parry", "Wizard": "The opportune wizard\n\nIntelligence +1\n Fireball, teleport and ice wall", "Thief": "The sneaky thief\n\nDexterity +1, Intelligence +1, Hit points +2\nSneak attack, hide and trap", "Bard": "The noisy bard\n\nCharisma +1, Dexterity +1, Intelligence +1, Hit points +2\nSing and summon"} line = [] for c in self.choices: btn = attr_button(c, self.select_class) line.append(btn) walker = urwid.SimpleFocusListWalker(line) urwid.connect_signal(walker, "modified", self.update_description) self.listbox = SelectableListBox(walker) header = urwid.LineBox(urwid.BoxAdapter(self.listbox, len(self.choices)+1)) super().__init__(parent, mind, urwid.ListBox(urwid.SimpleListWalker([urwid.Text(self.descriptions["Warrior"])])), header=header, focus_part="header") def select_class(self, button): index = min(self.listbox.focus_position, len(self.choices)-1) choice = self.choices[index] self.mind.master.new_player(self.mind.avatar.uuid, choice) self.parent.start_game_frame() def update_description(self): index = min(self.listbox.focus_position, len(self.choices)-1) choice = self.choices[index] self.contents["body"] = (urwid.ListBox(urwid.SimpleListWalker([urwid.Text(self.descriptions[choice])])), None) class GameFrame(UiFrame): def __init__(self, parent, mind): self.mind = mind _header = urwid.LineBox(urwid.BoxAdapter(SelectableListBox(urwid.SimpleFocusListWalker([urwid.Text("")])), self.header_height)) self._menu_view = True self.map = MapFrame(self, mind) self.menu = MenuFrame(self, mind) super().__init__(parent, mind, urwid.Columns([(self.map_width, self.map), (self.menu_width, self.menu)], focus_column=1), header=_header, footer=None, focus_part="body") self.menu_view = True self.update_footer() self.header_widget = self.header.original_widget.box_widget self.footer_content_size = 0 @property def header_height(self): return MIN_HEADER_HEIGHT#max(MIN_HEADER_HEIGHT, self.mind.screen_size[1]//8) @property def menu_width(self): if self.menu_view: return min(MAX_MENU_WIDTH, (3*self.mind.screen_size[0])//7) return 0 @property def map_width(self): if self.menu_view: return self.mind.screen_size[0] - self.menu_width return self.mind.screen_size[0] @property def body_width(self): return self.mind.screen_size[0] @property def body_height(self): return self.mind.screen_size[1] - self.header_height - FOOTER_HEIGHT - 2 @property def menu_view(self): return self._menu_view @menu_view.setter def menu_view(self, value): self._menu_view = value _columns = [(self.map_width, self.map), (self.menu_width, self.menu)] self.contents["body"] = (urwid.Columns(_columns, focus_column=1), None) @property def header_list(self): return sorted([ent for k, ent in self.player.location.entities.items() if distance(self.player.position, ent.position) <= 3 and ent.status], key=lambda ent: distance(self.player.position, ent.position)) def update_footer(self): _size = 0 inv_btns = [] for i, obj in self.player.inventory.content.items(): if obj: _size += 1 if obj.is_equipment and obj.is_equipped: _marker = ["[", (obj.color, f"{obj.marker[0]}"), "]"] elif obj.is_equipment and not obj.is_equipped: _marker = ["]", (obj.color, f"{obj.marker[0]}"), "["] elif obj.is_consumable: _marker = ["(", (obj.color, f"{obj.marker[0]}"), ")"] else: _marker = [f" {obj.marker[0]} "] else: _marker = [f" "] if i < 9: _num = f"\n {i+1} " elif i == 9: _num = "\n 0 " elif i == 10: _num = "\n - " elif i == 11: _num = "\n = " if obj and obj is self.player.inventory.selection: _marker += [("line", _num)] else: _marker += [("top", _num)] btn = urwid.Text(_marker, align="center") inv_btns.append((5, urwid.LineBox(btn))) if self.mind.screen_size != (80, 24): inv_btns.append(urwid.Text("\nSET TERMINAL\nTO 80X24", align="center")) self.contents["footer"] = (SelectableColumns(inv_btns, dividechars=0), None) self.footer_content_size = _size def on_update(self): self.update_header() if self.footer_content_size != len(self.player.inventory.all): self.update_footer() if self.mind.screen_size != (80, 24): self.update_footer() self.map.on_update() if self.menu_view: self.menu.on_update() def handle_input(self, _input): if _input == "tab": self.menu_view = not self.menu_view elif _input == "enter" and self.player.inventory.selection: self.player.use_quick_item(self.player.inventory.selection) self.update_footer() elif _input == "Q" and self.player.inventory.selection: self.player.actions["drop"].use(self.player, obj=self.player.inventory.selection) self.update_footer() elif _input.isnumeric() or _input in ("-", "="): self.select_item(_input) self.update_footer() elif _input == self.mind.key_map["status-menu"] and self.menu_view: self.menu.update_body("Status") elif _input == self.mind.key_map["help-menu"] and self.menu_view: self.menu.update_body("Help") elif _input == self.mind.key_map["equipment-menu"] and self.menu_view: self.menu.update_body("Equipment") elif _input == self.mind.key_map["inventory-menu"] and self.menu_view: self.menu.update_body("Inventory") else: self.map.handle_input(_input) def select_item(self, _input): if _input.isnumeric() and int(_input) > 0: _input = int(_input)-1 elif _input == "0": s_input = 9 elif _input == "-": _input = 10 elif _input == "=": _input = 11 self.player.inventory.selection = self.player.inventory.get(_input) def update_header(self): widgets = [] for p in self.header_list: widgets.append(urwid.AttrMap(urwid.AttrMap(urwid.Text(p.status, wrap="clip"), {self.player.id:"player"}), {p.id:"other" for i, p in self.mind.master.players.items()})) if widgets: self.header_widget.body[:] = widgets class MapFrame(UiFrame): def __init__(self, parent, mind): map_box = urwid.ListBox(urwid.SimpleListWalker([urwid.Text("")])) self.map_box = map_box.body self.layer_view = -1 self.debug_view = False super().__init__(parent, mind, map_box) self.on_update() @property def visible_range(self): header_height = self.parent.header_height + 2 tot_rows = self.mind.screen_size[1] return (tot_rows - header_height - FOOTER_HEIGHT) def on_update(self): if self.layer_view == -1: _map = copy.deepcopy(self.player.location.map) else: _map = self.player.location.layer_from_entities(self.layer_view, self.debug_view) x, y, z = self.player.position w = max(0, y - self.parent.body_width//3) visible_map = [line[w:w+self.parent.body_width] for line in _map] h = max(0, x - self.parent.body_height//2) if h+self.parent.body_height >= len(visible_map): visible_map = visible_map[len(visible_map)-self.parent.body_height:] else: visible_map = visible_map[h:h+self.parent.body_height] map_with_attr = [urwid.AttrMap(urwid.AttrMap(urwid.Text(line, wrap="clip"), {self.player.id:"player"}), {p.id:"other" for i, p in self.mind.master.players.items()}) for line in visible_map] self.map_box[:] = map_with_attr def handle_input(self, _input): if _input == "ctrl f": self.debug_view = not self.debug_view elif _input == "ctrl v": self.layer_view = self.layer_view + 1 if self.layer_view > 2: self.layer_view = -1 elif _input in self.mind.key_map: _action = self.mind.key_map[_input] self.player.handle_input(_action) class MenuFrame(UiFrame): def __init__(self, parent, mind): _frames = ("Inventory", "Status", "Equipment", "Help") self.bodies = {b : globals()[f"{b}Frame"](self, mind) for b in _frames} idx = -1 _title = _frames[idx] self.active_body = self.bodies[_title] super().__init__(parent, mind, urwid.LineBox(self.active_body, title=_title)) def on_update(self): self.active_body.on_update() def selectable(self): return False def update_body(self, _title): self.active_body = self.bodies[_title] self.contents["body"] = (urwid.LineBox(self.active_body, title=_title), None) class InventoryFrame(UiFrame): def __init__(self, parent, mind): columns = urwid.Columns([urwid.Text("")]) box = urwid.ListBox(urwid.SimpleListWalker([columns])) self.box = box.body self.default_header = urwid.Text("0/9-= to select\n\n", align="center") self.default_footer = urwid.Text([("green", f"{'Enter:use/eqp':<14s}"), ("yellow", "Q:drop")], align="center") super().__init__(parent, mind, box, header=self.default_header, footer=self.default_footer) @property def selection_data(self): if not self.player.inventory.selection: return urwid.Text("") i = self.player.inventory.selection _text = [] _text += [i.eq_description, f"\nEncumbrance:{i.encumbrance}\n"] return urwid.Text(_text) def update_header(self): if not self.player.inventory.selection: self.contents["header"] = (self.default_header, None) else: i = self.player.inventory.selection self.contents["header"] = (urwid.Text([(i.color, f"{i.name}\n"), f"{i.description}\n"], align="center"), None) def update_footer(self): if not self.player.inventory.selection: self.contents["footer"] = (self.default_footer, None) else: i = self.player.inventory.selection _text = [] if not i.requisites(self.player): _text += [("red", f"{'Cannot equip':<14s}")] elif not i.is_equipped: _text += [("green", f"{'Enter:equip':<14s}")] elif i.is_equipped: _text += [("green", f"{'Enter:unequip':<14s}")] elif i.is_consumable: _text += [("green", f"{'Enter:use':<14s}")] _text += [("yellow", "Q:drop")] self.contents["footer"] = (urwid.Text(_text, align="center"), None) def update_body(self): side = urwid.Text("║") width = 8 height = 6 _marker_box = ["╔" +"═"*width+"╗\n"] for x in range(height): _marker_box += ["║"] for y in range(width): _marker_box += ["."] _marker_box += ["║\n"] _marker_box += ["╚" +"═"*width+"╝"] if self.player.inventory.selection: i = self.player.inventory.selection X_OFFSET = 2 Y_OFFSET = 4 for m, pos in zip(i.in_inventory_markers, i.in_inventory_marker_positions): x, y = pos _marker_box[(x+X_OFFSET)*(width+2)+y+Y_OFFSET] = (i.color, m) self.box[:] = [urwid.Columns([(width+2, urwid.Text(_marker_box)), self.selection_data], dividechars=1)] def on_update(self): self.update_header() self.update_body() self.update_footer() class StatusFrame(UiFrame): def __init__(self, parent, mind): box = urwid.ListBox(urwid.SimpleListWalker([urwid.Text("")])) self.box = box.body super().__init__(parent, mind, box) def on_update(self): player = self.player x, y, z = player.position _top = f"{player.name:<12s} {player.game_class.name:<10s}\nLev:{player.level:<2d} Exp:{player.exp:<4d} {player.location.name}@({x},{y})\n" _left = [] for s in CHARACTERISTICS: c = getattr(player, s) state = ["normal", "positive", "negative"][-int(c.temp_bonus < 0) + int(c.temp_bonus > 0)] if self.parent.parent.menu_width > 40: _name = c.name[0].upper() + c.name[1:] _left += [f"{_name:<12} ", (state, f"{c.value:>2d}"), f" ({c.mod:<+2d})\n"] elif self.parent.parent.menu_width > 36: _name = c.name[0].upper() + c.name[1:6] _left += [f"{_name:<6} ", (state, f"{c.value:>2d}"), f" ({c.mod:<+2d})\n"] else: _left += [f"{s:<3} ", (state, f"{c.value:>2d}"), f" ({c.mod:<+2d})\n"] _right = [] base = player.STR.mod weapon = player.equipment["main_hand"] if not weapon: min_dmg, max_dmg = (1, 4) else: number, value = weapon.dmg min_dmg, max_dmg = (number * 1, number * value) min_dmg = max(1, base + min_dmg) max_dmg = max(1, base + max_dmg) _right.append(f"Damage {min_dmg:>3d}-{max_dmg:<3d}\n") _right.append(f"Reduction {player.dmg_reduction:<3d}\n") _right.append(f"Encumb ") if player.inventory.encumbrance == EXTRA_ENCUMBRANCE_MULTI*player.encumbrance: _right.append(("red", f"{player.inventory.encumbrance:>2d}")) elif player.inventory.encumbrance > player.encumbrance: _right.append(("yellow", f"{player.inventory.encumbrance:>2d}")) else: _right.append(("white", f"{player.inventory.encumbrance:>2d}")) _right.append(f"/{player.encumbrance:<2d}\n") _right.append(f"Speed {player.movement_speed}\n") _right.append(f"Monsterized {player.MP:<2d}\n") self.box[:] = [urwid.Text(_top), urwid.Columns([urwid.Text(_left), urwid.Text(_right)], dividechars = 1) ] class EquipmentFrame(UiFrame): def __init__(self, parent, mind): box = urwid.ListBox(urwid.SimpleListWalker([urwid.Text("")])) self.box = box.body super().__init__(parent, mind, box) def on_update(self): player = self.player _equipment = [] for t, obj in player.equipment.items(): _name = t.replace("_", " ") _name = _name[0].upper() + _name[1:] if obj: _equipment += [urwid.Text([f"{_name}: ", (obj.color, f"{obj.name}")])] else: _equipment += [urwid.Text([f"{_name}: "])] _bonus = {} for eqp in player.equipment_set: for b in set(list(eqp.bonus.keys()) + list(eqp.set_bonus.keys())): val = player.full_eqp_bonus(eqp, b) if b not in _bonus: _bonus[b] = val else: _bonus[b] += val _top = "" for b, val in _bonus.items(): if b == "dmg_reduction": _top += f"Reduction:{val} " else: _top += f"{b}:{val} " _top += "\n" self.box[:] = [urwid.Text(_top)] + _equipment class HelpFrame(UiFrame): def __init__(self, parent, mind): self.mind = mind map_commands = ["Map commands\n\n", f"←→↑↓:move\n", f"shift+←→↑↓:dash\n", f"a:attack\n", f"q:pickup\n"] class_action_keys = [k for k, act in self.mind.key_map.items() if act.startswith("class_ability")] for i, act in enumerate(self.player.class_actions): k = class_action_keys[i] map_commands.append(f"{k}:{self.player.class_actions[act].description.lower()}\n") menu_commands = ["Menu commands\n\n", f"tab:open/close\n",f"0/9-=:select item\n", f"ctrl+p:respawn\n", f"ctrl+a:inventory\n", f"ctrl+s:status\n", f"ctrl+d:help\n", f"ctrl+e:equipment\n"] columns = urwid.Columns([urwid.Text(map_commands, wrap="clip"), urwid.Text(menu_commands, wrap="clip")], dividechars = 1) super().__init__(parent, mind, urwid.ListBox(urwid.SimpleListWalker([columns]))) class SelectableListBox(urwid.ListBox): def __init__(self, body): super(SelectableListBox, self).__init__(body) def focus_next(self): try: self.focus_position += 1 except IndexError: pass def focus_previous(self): try: self.focus_position -= 1 except IndexError: pass class SelectableColumns(urwid.Columns): def __init__(self, widget_list, focus_column=None, dividechars=0): super().__init__(widget_list, dividechars, focus_column) def focus_next(self): try: self.focus_position += 1 except: pass def focus_previous(self): try: self.focus_position -= 1 except: pass class FrameColumns(urwid.Columns): def __init__(self, parent, widget_list, dividechars=0): self.widget_size = len(widget_list) super(FrameColumns, self).__init__(widget_list, dividechars) self.parent = parent def focus_next(self): try: self.focus_position += 1 if self.focus_position >= self.widget_size: self.focus_position -= self.widget_size new_body = [b for b in self.parent.bodies][self.focus_position] self.parent.update_body(new_body) except: pass def focus_previous(self): try: self.focus_position -= 1 if self.focus_position < 0: self.focus_position += self.widget_size new_body = [b for b in self.parent.bodies][self.focus_position] self.parent.update_body(new_body) except: pass class ButtonLabel(urwid.SelectableIcon): def set_text(self, label): ''' set_text is invoked by Button.set_label ''' self.__super.set_text(label) self._cursor_position = len(label) + 1 class MyButton(urwid.Button): ''' - override __init__ to use our ButtonLabel instead of urwid.SelectableIcon - make button_left and button_right plain strings and variable width - any string, including an empty string, can be set and displayed - otherwise, we leave Button behaviour unchanged ''' button_left = "[" button_right = "]" def __init__(self, label, on_press=None, user_data=None, borders=True, disabled=False): self._label = ButtonLabel("") if borders: cols = urwid.Columns([ ('fixed', len(self.button_left), urwid.Text(self.button_left)), self._label, ('fixed', len(self.button_right), urwid.Text(self.button_right))], dividechars=1) else: cols = urwid.Columns([self._label], dividechars=0) super(urwid.Button, self).__init__(cols) self.disabled = disabled if on_press: urwid.connect_signal(self, 'click', on_press, user_data) self.set_label(label) self.lllavel = label # @property # def disabled(self): # return self._disabled # @disabled.setter # def disabled(self, value): # if self._disabled == value: # return # if self.disabled: # urwid.AttrMap(self, "disabled") # else: # urwid.AttrMap(self, None, "line") def selectable(self): return not self.disabled def attr_button(label, cmd=None, attr_map=None, focus_map = "line", align = "center", user_args = None, borders=True, disabled=False): btn = create_button(label, cmd=cmd, align = align, user_args = user_args, borders=borders, disabled=disabled) return urwid.AttrMap(btn, attr_map, focus_map=focus_map) def create_button(label, cmd=None, align = "center", user_args = None, borders=True, disabled=False): btn = MyButton(label, borders=borders, disabled=disabled) btn._label.align = align if cmd: if user_args: urwid.connect_signal(btn, "click", cmd, user_args = user_args) else: urwid.connect_signal(btn, "click", cmd) return btn

lale/lib/autogen/linear_regression.py

gbdrt/lale

515

from numpy import inf, nan from sklearn.linear_model import LinearRegression as Op from lale.docstrings import set_docstrings from lale.operators import make_operator class LinearRegressionImpl: def __init__(self, **hyperparams): self._hyperparams = hyperparams self._wrapped_model = Op(**self._hyperparams) def fit(self, X, y=None): if y is not None: self._wrapped_model.fit(X, y) else: self._wrapped_model.fit(X) return self def predict(self, X): return self._wrapped_model.predict(X) _hyperparams_schema = { "$schema": "http://json-schema.org/draft-04/schema#", "description": "inherited docstring for LinearRegression Ordinary least squares Linear Regression.", "allOf": [ { "type": "object", "required": ["fit_intercept", "normalize", "copy_X", "n_jobs"], "relevantToOptimizer": ["fit_intercept", "normalize", "copy_X"], "additionalProperties": False, "properties": { "fit_intercept": { "type": "boolean", "default": True, "description": "whether to calculate the intercept for this model", }, "normalize": { "type": "boolean", "default": False, "description": "This parameter is ignored when ``fit_intercept`` is set to False", }, "copy_X": { "type": "boolean", "default": True, "description": "If True, X will be copied; else, it may be overwritten.", }, "n_jobs": { "anyOf": [{"type": "integer"}, {"enum": [None]}], "default": 1, "description": "The number of jobs to use for the computation", }, }, }, { "XXX TODO XXX": "Parameter: n_jobs > only provide speedup for n_targets > 1 and sufficient large problems" }, ], } _input_fit_schema = { "$schema": "http://json-schema.org/draft-04/schema#", "description": "Fit linear model.", "type": "object", "required": ["X", "y"], "properties": { "X": { "anyOf": [ { "type": "array", "items": {"laleType": "Any", "XXX TODO XXX": "item type"}, "XXX TODO XXX": "array-like or sparse matrix, shape (n_samples, n_features)", }, { "type": "array", "items": {"type": "array", "items": {"type": "number"}}, }, ], "description": "Training data", }, "y": { "type": "array", "items": {"type": "array", "items": {"type": "number"}}, "description": "Target values", }, "sample_weight": { "type": "array", "items": {"type": "number"}, "description": "Individual weights for each sample ", }, }, } _input_predict_schema = { "$schema": "http://json-schema.org/draft-04/schema#", "description": "Predict using the linear model", "type": "object", "required": ["X"], "properties": { "X": { "anyOf": [ { "type": "array", "items": {"laleType": "Any", "XXX TODO XXX": "item type"}, "XXX TODO XXX": "array_like or sparse matrix, shape (n_samples, n_features)", }, { "type": "array", "items": {"type": "array", "items": {"type": "number"}}, }, ], "description": "Samples.", } }, } _output_predict_schema = { "$schema": "http://json-schema.org/draft-04/schema#", "description": "Returns predicted values.", "type": "array", "items": {"type": "number"}, } _combined_schemas = { "$schema": "http://json-schema.org/draft-04/schema#", "description": "Combined schema for expected data and hyperparameters.", "documentation_url": "https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.LinearRegression#sklearn-linear_model-linearregression", "import_from": "sklearn.linear_model", "type": "object", "tags": {"pre": [], "op": ["estimator"], "post": []}, "properties": { "hyperparams": _hyperparams_schema, "input_fit": _input_fit_schema, "input_predict": _input_predict_schema, "output_predict": _output_predict_schema, }, } set_docstrings(LinearRegressionImpl, _combined_schemas) LinearRegression = make_operator(LinearRegressionImpl, _combined_schemas)

Models.py

jmj23/Kaggle-Pneumothorax

516

import numpy as np from keras.applications.inception_v3 import InceptionV3 from keras.initializers import RandomNormal from keras.layers import (BatchNormalization, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, Dense, Flatten, GlobalAveragePooling2D, Input, Lambda, MaxPooling2D, Reshape, UpSampling2D, ZeroPadding2D, ZeroPadding3D, add, concatenate) from keras.layers.advanced_activations import ELU, LeakyReLU from keras.models import Model # Parameterized 2D Block Model def BlockModel2D(input_shape, filt_num=16, numBlocks=3): """Creates a Block CED model for segmentation problems Args: input shape: a list or tuple of [rows,cols,channels] of input images filt_num: the number of filters in the first and last layers This number is multipled linearly increased and decreased throughout the model numBlocks: number of processing blocks. The larger the number the deeper the model output_chan: number of output channels. Set if doing multi-class segmentation regression: Whether to have a continuous output with linear activation Returns: An unintialized Keras model Example useage: SegModel = BlockModel2D([256,256,1],filt_num=8) Notes: Using rows/cols that are powers of 2 is recommended. Otherwise, the rows/cols must be divisible by 2^numBlocks for skip connections to match up properly """ use_bn = True # check for input shape compatibility rows, cols = input_shape[0:2] assert rows % 2**numBlocks == 0, "Input rows and number of blocks are incompatible" assert cols % 2**numBlocks == 0, "Input cols and number of blocks are incompatible" # calculate size reduction startsize = np.max(input_shape[0:2]) minsize = (startsize-np.sum(2**np.arange(1, numBlocks+1)))/2**numBlocks assert minsize > 4, "Too small of input for this many blocks. Use fewer blocks or larger input" # input layer lay_input = Input(shape=input_shape, name='input_layer') # contracting blocks x = lay_input skip_list = [] for rr in range(1, numBlocks+1): x1 = Conv2D(filt_num*rr, (1, 1), padding='same', name='Conv1_{}'.format(rr))(x) if use_bn: x1 = BatchNormalization()(x1) x1 = ELU(name='elu_x1_{}'.format(rr))(x1) x3 = Conv2D(filt_num*rr, (3, 3), padding='same', name='Conv3_{}'.format(rr))(x) if use_bn: x3 = BatchNormalization()(x3) x3 = ELU(name='elu_x3_{}'.format(rr))(x3) x51 = Conv2D(filt_num*rr, (3, 3), padding='same', name='Conv51_{}'.format(rr))(x) if use_bn: x51 = BatchNormalization()(x51) x51 = ELU(name='elu_x51_{}'.format(rr))(x51) x52 = Conv2D(filt_num*rr, (3, 3), padding='same', name='Conv52_{}'.format(rr))(x51) if use_bn: x52 = BatchNormalization()(x52) x52 = ELU(name='elu_x52_{}'.format(rr))(x52) x = concatenate([x1, x3, x52], name='merge_{}'.format(rr)) x = Conv2D(filt_num*rr, (1, 1), padding='valid', name='ConvAll_{}'.format(rr))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_all_{}'.format(rr))(x) x = ZeroPadding2D(padding=(1, 1), name='PrePad_{}'.format(rr))(x) x = Conv2D(filt_num*rr, (4, 4), padding='valid', strides=(2, 2), name='DownSample_{}'.format(rr))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_downsample_{}'.format(rr))(x) x = Conv2D(filt_num*rr, (3, 3), padding='same', name='ConvClean_{}'.format(rr))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_clean_{}'.format(rr))(x) skip_list.append(x) # expanding blocks expnums = list(range(1, numBlocks+1)) expnums.reverse() for dd in expnums: if dd < len(skip_list): x = concatenate([skip_list[dd-1], x], name='skip_connect_{}'.format(dd)) x1 = Conv2D(filt_num*dd, (1, 1), padding='same', name='DeConv1_{}'.format(dd))(x) if use_bn: x1 = BatchNormalization()(x1) x1 = ELU(name='elu_Dx1_{}'.format(dd))(x1) x3 = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConv3_{}'.format(dd))(x) if use_bn: x3 = BatchNormalization()(x3) x3 = ELU(name='elu_Dx3_{}'.format(dd))(x3) x51 = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConv51_{}'.format(dd))(x) if use_bn: x51 = BatchNormalization()(x51) x51 = ELU(name='elu_Dx51_{}'.format(dd))(x51) x52 = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConv52_{}'.format(dd))(x51) if use_bn: x52 = BatchNormalization()(x52) x52 = ELU(name='elu_Dx52_{}'.format(dd))(x52) x = concatenate([x1, x3, x52], name='Dmerge_{}'.format(dd)) x = Conv2D(filt_num*dd, (1, 1), padding='valid', name='DeConvAll_{}'.format(dd))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_Dall_{}'.format(dd))(x) x = UpSampling2D(size=(2, 2), name='UpSample_{}'.format(dd))(x) x = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConvClean1_{}'.format(dd))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_Dclean1_{}'.format(dd))(x) x = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConvClean2_{}'.format(dd))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_Dclean2_{}'.format(dd))(x) # classifier lay_out = Conv2D(1, (1, 1), activation='sigmoid', name='output_layer')(x) return Model(lay_input, lay_out) # Parameterized 2D Block Model def BlockModel_Classifier(input_shape, filt_num=16, numBlocks=3): """Creates a Block model for pretraining on classification task Args: input shape: a list or tuple of [rows,cols,channels] of input images filt_num: the number of filters in the first and last layers This number is multipled linearly increased and decreased throughout the model numBlocks: number of processing blocks. The larger the number the deeper the model output_chan: number of output channels. Set if doing multi-class segmentation regression: Whether to have a continuous output with linear activation Returns: An unintialized Keras model Example useage: SegModel = BlockModel2D([256,256,1],filt_num=8) Notes: Using rows/cols that are powers of 2 is recommended. Otherwise, the rows/cols must be divisible by 2^numBlocks for skip connections to match up properly """ use_bn = True # check for input shape compatibility rows, cols = input_shape[0:2] assert rows % 2**numBlocks == 0, "Input rows and number of blocks are incompatible" assert cols % 2**numBlocks == 0, "Input cols and number of blocks are incompatible" # calculate size reduction startsize = np.max(input_shape[0:2]) minsize = (startsize-np.sum(2**np.arange(1, numBlocks+1)))/2**numBlocks assert minsize > 4, "Too small of input for this many blocks. Use fewer blocks or larger input" # input layer lay_input = Input(shape=input_shape, name='input_layer') # contracting blocks x = lay_input skip_list = [] for rr in range(1, numBlocks+1): x1 = Conv2D(filt_num*rr, (1, 1), padding='same', name='Conv1_{}'.format(rr))(x) if use_bn: x1 = BatchNormalization()(x1) x1 = ELU(name='elu_x1_{}'.format(rr))(x1) x3 = Conv2D(filt_num*rr, (3, 3), padding='same', name='Conv3_{}'.format(rr))(x) if use_bn: x3 = BatchNormalization()(x3) x3 = ELU(name='elu_x3_{}'.format(rr))(x3) x51 = Conv2D(filt_num*rr, (3, 3), padding='same', name='Conv51_{}'.format(rr))(x) if use_bn: x51 = BatchNormalization()(x51) x51 = ELU(name='elu_x51_{}'.format(rr))(x51) x52 = Conv2D(filt_num*rr, (3, 3), padding='same', name='Conv52_{}'.format(rr))(x51) if use_bn: x52 = BatchNormalization()(x52) x52 = ELU(name='elu_x52_{}'.format(rr))(x52) x = concatenate([x1, x3, x52], name='merge_{}'.format(rr)) x = Conv2D(filt_num*rr, (1, 1), padding='valid', name='ConvAll_{}'.format(rr))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_all_{}'.format(rr))(x) x = ZeroPadding2D(padding=(1, 1), name='PrePad_{}'.format(rr))(x) x = Conv2D(filt_num*rr, (4, 4), padding='valid', strides=(2, 2), name='DownSample_{}'.format(rr))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_downsample_{}'.format(rr))(x) x = Conv2D(filt_num*rr, (3, 3), padding='same', name='ConvClean_{}'.format(rr))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_skip_{}'.format(rr))(x) # average pooling x = GlobalAveragePooling2D()(x) # classifier lay_out = Dense(1, activation='sigmoid', name='output_layer')(x) return Model(lay_input, lay_out) def ConvertEncoderToCED(model): # Returns a model with frozen encoder layers # and complimentary, unfrozen decoder layers # get input layer # model must be compiled again after using this function lay_input = model.input # get skip connection layer outputs skip_list = [l.output for l in model.layers if 'skip' in l.name] numBlocks = len(skip_list) filt_num = int(skip_list[0].shape[-1]) x = model.layers[-3].output # freeze encoder layers for layer in model.layers: layer.trainable = False use_bn = True # make expanding blocks expnums = list(range(1, numBlocks+1)) expnums.reverse() for dd in expnums: if dd < len(skip_list): x = concatenate([skip_list[dd-1], x], name='skip_connect_{}'.format(dd)) x1 = Conv2D(filt_num*dd, (1, 1), padding='same', name='DeConv1_{}'.format(dd))(x) if use_bn: x1 = BatchNormalization()(x1) x1 = ELU(name='elu_Dx1_{}'.format(dd))(x1) x3 = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConv3_{}'.format(dd))(x) if use_bn: x3 = BatchNormalization()(x3) x3 = ELU(name='elu_Dx3_{}'.format(dd))(x3) x51 = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConv51_{}'.format(dd))(x) if use_bn: x51 = BatchNormalization()(x51) x51 = ELU(name='elu_Dx51_{}'.format(dd))(x51) x52 = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConv52_{}'.format(dd))(x51) if use_bn: x52 = BatchNormalization()(x52) x52 = ELU(name='elu_Dx52_{}'.format(dd))(x52) x = concatenate([x1, x3, x52], name='Dmerge_{}'.format(dd)) x = Conv2D(filt_num*dd, (1, 1), padding='valid', name='DeConvAll_{}'.format(dd))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_Dall_{}'.format(dd))(x) x = UpSampling2D(size=(2, 2), name='UpSample_{}'.format(dd))(x) x = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConvClean1_{}'.format(dd))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_Dclean1_{}'.format(dd))(x) x = Conv2D(filt_num*dd, (3, 3), padding='same', name='DeConvClean2_{}'.format(dd))(x) if use_bn: x = BatchNormalization()(x) x = ELU(name='elu_Dclean2_{}'.format(dd))(x) # classifier lay_out = Conv2D(1, (1, 1), activation='sigmoid', name='output_layer')(x) return Model(lay_input, lay_out) def Inception_model(input_shape=(299, 299, 3)): incep_model = InceptionV3( include_top=False, weights=None, input_shape=input_shape, pooling='avg') input_layer = incep_model.input incep_output = incep_model.output # x = Conv2D(16, (3, 3), activation='relu')(incep_output) # x = Flatten()(x) x = Dense(1, activation='sigmoid')(incep_output) return Model(inputs=input_layer, outputs=x)

initdb.py

dasmerlon/flunky-bot

517

#!/bin/env python """Drop and create a new database with schema.""" from sqlalchemy_utils.functions import database_exists, create_database, drop_database from flunkybot.db import engine, base from flunkybot.models import * # noqa db_url = engine.url if database_exists(db_url): drop_database(db_url) create_database(db_url) base.metadata.drop_all() base.metadata.create_all()

setup.py

awesome-archive/webspider

518

<filename>setup.py #!/usr/bin/env python # -*- coding: utf-8 -*- import os from setuptools import find_packages, setup from app import __version__ # get the dependencies and installs here = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(here, 'requirements.txt')) as f: all_requirements = f.read().split('\n') setup( name='webspider', version=__version__, license='MIT', author='heguozhu', author_email='<EMAIL>', description='lagou.com spider', url='<EMAIL>:GuozhuHe/webspider.git', packages=find_packages(exclude=['tests']), package_data={'webspider': ['README.md']}, zip_safe=False, install_requires=all_requirements, entry_points={ 'console_scripts': [ 'web = app.web_app:main', 'production_web = app.quickly_cmd:run_web_app_by_gunicorn', 'crawl_lagou_data = app.tasks:crawl_lagou_data', 'crawl_jobs_count = app.tasks.jobs_count:crawl_lagou_jobs_count', 'celery_jobs_count_worker = app.quickly_cmd:run_celery_jobs_count_worker', 'celery_lagou_data_worker = app.quickly_cmd:run_celery_lagou_data_worker', 'celery_beat = app.quickly_cmd:run_celery_beat', 'celery_flower = app.quickly_cmd.py:run_celery_flower', ], } )

Doc/conf.py

python-doc-tw/cpython-tw

519

<reponame>python-doc-tw/cpython-tw # # Python documentation build configuration file # # This file is execfile()d with the current directory set to its containing dir. # # The contents of this file are pickled, so don't put values in the namespace # that aren't pickleable (module imports are okay, they're removed automatically). import sys, os, time sys.path.append(os.path.abspath('tools/extensions')) # General configuration # --------------------- extensions = ['sphinx.ext.coverage', 'sphinx.ext.doctest', 'pyspecific', 'c_annotations'] # General substitutions. project = 'Python' copyright = '2001-%s, Python Software Foundation' % time.strftime('%Y') # We look for the Include/patchlevel.h file in the current Python source tree # and replace the values accordingly. import patchlevel version, release = patchlevel.get_version_info() # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: today = '' # Else, today_fmt is used as the format for a strftime call. today_fmt = '%B %d, %Y' # By default, highlight as Python 3. highlight_language = 'python3' # Require Sphinx 1.2 for build. needs_sphinx = '1.2' # Ignore any .rst files in the venv/ directory. exclude_patterns = ['venv/*'] # Options for HTML output # ----------------------- # Use our custom theme. html_theme = 'pydoctheme' html_theme_path = ['tools'] html_theme_options = {'collapsiblesidebar': True} # Short title used e.g. for <title> HTML tags. html_short_title = '%s Documentation' % release # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%b %d, %Y' # Path to find HTML templates. templates_path = ['tools/templates'] # Custom sidebar templates, filenames relative to this file. html_sidebars = { 'index': 'indexsidebar.html', } # Additional templates that should be rendered to pages. html_additional_pages = { 'download': 'download.html', 'index': 'indexcontent.html', } # Output an OpenSearch description file. html_use_opensearch = 'https://docs.python.org/' + version # Additional static files. html_static_path = ['tools/static'] # Output file base name for HTML help builder. htmlhelp_basename = 'python' + release.replace('.', '') # Split the index html_split_index = True # Options for LaTeX output # ------------------------ # The paper size ('letter' or 'a4'). latex_paper_size = 'a4' # The font size ('10pt', '11pt' or '12pt'). latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, document class [howto/manual]). _stdauthor = r'<NAME>\\and the Python development team' latex_documents = [ ('c-api/index', 'c-api.tex', 'The Python/C API', _stdauthor, 'manual'), ('distributing/index', 'distributing.tex', 'Distributing Python Modules', _stdauthor, 'manual'), ('extending/index', 'extending.tex', 'Extending and Embedding Python', _stdauthor, 'manual'), ('installing/index', 'installing.tex', 'Installing Python Modules', _stdauthor, 'manual'), ('library/index', 'library.tex', 'The Python Library Reference', _stdauthor, 'manual'), ('reference/index', 'reference.tex', 'The Python Language Reference', _stdauthor, 'manual'), ('tutorial/index', 'tutorial.tex', 'Python Tutorial', _stdauthor, 'manual'), ('using/index', 'using.tex', 'Python Setup and Usage', _stdauthor, 'manual'), ('faq/index', 'faq.tex', 'Python Frequently Asked Questions', _stdauthor, 'manual'), ('whatsnew/' + version, 'whatsnew.tex', 'What\'s New in Python', '<NAME>', 'howto'), ] # Collect all HOWTOs individually latex_documents.extend(('howto/' + fn[:-4], 'howto-' + fn[:-4] + '.tex', '', _stdauthor, 'howto') for fn in os.listdir('howto') if fn.endswith('.rst') and fn != 'index.rst') # Additional stuff for the LaTeX preamble. latex_preamble = r''' \authoraddress{ \strong{Python Software Foundation}\\ Email: \email{<EMAIL>} } \let\Verbatim=\OriginalVerbatim \let\endVerbatim=\endOriginalVerbatim ''' # Documents to append as an appendix to all manuals. latex_appendices = ['glossary', 'about', 'license', 'copyright'] # Get LaTeX to handle Unicode correctly latex_elements = {'inputenc': r'\usepackage[utf8x]{inputenc}', 'utf8extra': ''} # Options for Epub output # ----------------------- epub_author = 'Python Documentation Authors' epub_publisher = 'Python Software Foundation' # Options for the coverage checker # -------------------------------- # The coverage checker will ignore all modules/functions/classes whose names # match any of the following regexes (using re.match). coverage_ignore_modules = [ r'[T|t][k|K]', r'Tix', r'distutils.*', ] coverage_ignore_functions = [ 'test($|_)', ] coverage_ignore_classes = [ ] # Glob patterns for C source files for C API coverage, relative to this directory. coverage_c_path = [ '../Include/*.h', ] # Regexes to find C items in the source files. coverage_c_regexes = { 'cfunction': (r'^PyAPI_FUNC\(.*\)\s+([^_][\w_]+)'), 'data': (r'^PyAPI_DATA\(.*\)\s+([^_][\w_]+)'), 'macro': (r'^#define ([^_][\w_]+)\(.*\)[\s|\\]'), } # The coverage checker will ignore all C items whose names match these regexes # (using re.match) -- the keys must be the same as in coverage_c_regexes. coverage_ignore_c_items = { # 'cfunction': [...] } # Options for the link checker # ---------------------------- # Ignore certain URLs. linkcheck_ignore = [r'https://bugs.python.org/(issue)?\d+', # Ignore PEPs for now, they all have permanent redirects. r'http://www.python.org/dev/peps/pep-\d+'] # Options for extensions # ---------------------- # Relative filename of the reference count data file. refcount_file = 'data/refcounts.dat' # Translation # ----------- gettext_compact = False locale_dirs = ["locale"]

basic_stats.py/basic_stats.py

RahmB/basic_stats

520

<filename>basic_stats.py/basic_stats.py # Import the matplotlib module here. No other modules should be used. # Import plotting library import matplotlib.pyplot as plt #import.... from os import * # Import Numpy import numpy as np def mean(my_list): # This is the defintion in the head. i = 0 my_sum = 0 for number in my_list: my_sum = my_sum + my_list[i] i+=1 mu = my_sum / i print('mean = ' + str(mu)) return mu def sd(my_list): j = 0 sigma = 0 my_sumsd = 0 mu = mean(my_list) for number in my_list: my_sumsd = my_sumsd + (my_list[j] - mu)**2 j +=1 sigma = (my_sumsd/j)**(.5) print('standard deviation = ' + str(sigma)) return sigma def norm(my_list): k = 0 l = 0 mu = mean(my_list) sigma = sd(my_list) for number in my_list: if abs(my_list[l] - mu) < sigma: k += 1 l += 1 else: l += 1 dist = k / l return dist def is_norm(my_list): dist = norm(my_list) if 0.66 < dist < 0.70: print('Data is normally distributed') return True else: print('Data is not normally distributed') return False def is_skew(my_list): m = 0 skew = 0 sumsk = 0 mu = mean(my_list) sigma = sd(my_list) for numbers in my_list: sumsk = (my_list[m] - mu)**3 + sumsk m +=1 skew = sumsk /(len(my_list)*sigma**3) print('skewness = ' + str(skew)) if skew == 0: print('skewness = 0, therefore sample is normally distributed') else: print('skewness =/= 0, therefore sample is not normally distributed') def graph(my_list): plt.hist(my_list,density=True, facecolor='b') sigma = sd(my_list) #stores standard deviation mu = mean(my_list) #stores mean plt.title('my_list Histogram') plt.xlabel('Number') plt.ylabel('Probability') plt.xlim(mu - 4*sigma, mu + 4*sigma) plt.grid(True) plt.show() def stats(my_list): mu = mean(my_list) std = sd(my_list) dist = norm(my_list) graph(my_list) is_norm(my_list) is_skew(my_list) return (mu, std, dist)

src/catkin_pkg/cli/tag_changelog.py

delftrobotics-forks/catkin_pkg

521

"""This script renames the forthcoming section in changelog files with the upcoming version and the current date""" from __future__ import print_function import argparse import datetime import docutils.core import os import re import sys from catkin_pkg.changelog import CHANGELOG_FILENAME, get_changelog_from_path from catkin_pkg.changelog_generator import FORTHCOMING_LABEL from catkin_pkg.package_version import bump_version from catkin_pkg.packages import find_packages, verify_equal_package_versions def get_forthcoming_label(rst): document = docutils.core.publish_doctree(rst) forthcoming_label = None for child in document.children: title = None if isinstance(child, docutils.nodes.subtitle): title = child elif isinstance(child, docutils.nodes.section): section = child if len(section.children) > 0 and isinstance(section.children[0], docutils.nodes.title): title = section.children[0] if title and len(title.children) > 0 and isinstance(title.children[0], docutils.nodes.Text): title_text = title.children[0].rawsource if FORTHCOMING_LABEL.lower() in title_text.lower(): if forthcoming_label: raise RuntimeError('Found multiple forthcoming sections') forthcoming_label = title_text return forthcoming_label def rename_section(data, old_label, new_label): valid_section_characters = '!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~' def replace_section(match): section_char = match.group(2)[0] return new_label + '\n' + section_char * len(new_label) pattern = '^(' + re.escape(old_label) + ')\n([' + re.escape(valid_section_characters) + ']+)$' data, count = re.subn(pattern, replace_section, data, flags=re.MULTILINE) if count == 0: raise RuntimeError('Could not find section') if count > 1: raise RuntimeError('Found multiple matching sections') return data def main(sysargs=None): parser = argparse.ArgumentParser(description='Tag the forthcoming section in the changelog files with an upcoming version number') parser.add_argument('--bump', choices=('major', 'minor', 'patch'), default='patch', help='Which part of the version number to bump? (default: %(default)s)') args = parser.parse_args(sysargs) base_path = '.' # find packages packages = find_packages(base_path) if not packages: raise RuntimeError('No packages found') print('Found packages: %s' % ', '.join([p.name for p in packages.values()])) # fetch current version and verify that all packages have same version number old_version = verify_equal_package_versions(packages.values()) new_version = bump_version(old_version, args.bump) print('Tag version %s' % new_version) # check for changelog entries changelogs = [] missing_forthcoming = [] already_tagged = [] for pkg_path, package in packages.items(): changelog_path = os.path.join(base_path, pkg_path, CHANGELOG_FILENAME) if not os.path.exists(changelog_path): missing_forthcoming.append(package.name) continue changelog = get_changelog_from_path(changelog_path, package.name) if not changelog: missing_forthcoming.append(package.name) continue # check that forthcoming section exists forthcoming_label = get_forthcoming_label(changelog.rst) if not forthcoming_label: missing_forthcoming.append(package.name) continue # check that new_version section does not exist yet try: changelog.get_content_of_version(new_version) already_tagged.append(package.name) continue except KeyError: pass changelogs.append((package.name, changelog_path, changelog, forthcoming_label)) if missing_forthcoming: print('The following packages do not have a forthcoming section in their changelog file: %s' % ', '.join(sorted(missing_forthcoming)), file=sys.stderr) if already_tagged: print("The following packages do already have a section '%s' in their changelog file: %s" % (new_version, ', '.join(sorted(already_tagged))), file=sys.stderr) # rename forthcoming sections to new_version including current date new_changelog_data = [] new_label = '%s (%s)' % (new_version, datetime.date.today().isoformat()) for (pkg_name, changelog_path, changelog, forthcoming_label) in changelogs: print("Renaming section '%s' to '%s' in package '%s'..." % (forthcoming_label, new_label, pkg_name)) data = rename_section(changelog.rst, forthcoming_label, new_label) new_changelog_data.append((changelog_path, data)) print('Writing updated changelog files...') for (changelog_path, data) in new_changelog_data: with open(changelog_path, 'wb') as f: f.write(data.encode('utf-8'))

tests/optims/distributed_adamw_test.py

AswinRetnakumar/Machina

522

import os import unittest import torch import torch.distributed as dist from torch.multiprocessing import Process import torch.nn as nn from machina.optims import DistributedAdamW def init_processes(rank, world_size, function, backend='tcp'): os.environ['MASTER_ADDR'] = '127.0.0.1' os.environ['MASTER_PORT'] = '29500' dist.init_process_group(backend, rank=rank, world_size=world_size) function(rank, world_size) class TestDistributedAdamW(unittest.TestCase): def test_step(self): def _run(rank, world_size): model = nn.Linear(10, 1) optimizer = DistributedAdamW( model.parameters()) optimizer.zero_grad() loss = model(torch.ones(10).float()) loss.backward() optimizer.step() processes = [] world_size = 4 for rank in range(world_size): p = Process(target=init_processes, args=(rank, world_size, _run)) p.start() processes.append(p) for p in processes: p.join()

rsqueakvm/model/__init__.py

shiplift/RSqueakOnABoat

523

<reponame>shiplift/RSqueakOnABoat<filename>rsqueakvm/model/__init__.py """ Squeak model. W_Object W_SmallInteger W_MutableSmallInteger W_AbstractObjectWithIdentityHash W_AbstractFloat W_Float W_MutableFloat W_Character W_PointersObject W_AbstractObjectWithClassReference W_LargeInteger W_LargeIntegerWord W_LargeIntegerBig W_BytesObject W_WordsObject W_CompiledMethod W_SpurCompiledMethod W_PreSpurCompiledMethod """ from rsqueakvm.model.base import * from rsqueakvm.model.character import * from rsqueakvm.model.compiled_methods import * # from rsqueakvm.model.display import * from rsqueakvm.model.numeric import * from rsqueakvm.model.pointers import * from rsqueakvm.model.variable import *

Multi-Task-Learning-PyTorch-master/losses/loss_functions.py

nikola3794/edge-evaluation-PASCAL-MT-tmp

524

<filename>Multi-Task-Learning-PyTorch-master/losses/loss_functions.py # This code is referenced from # https://github.com/facebookresearch/astmt/ # # Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # License: Attribution-NonCommercial 4.0 International import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.modules.module import Module import numpy as np class SoftMaxwithLoss(Module): """ This function returns cross entropy loss for semantic segmentation """ def __init__(self): super(SoftMaxwithLoss, self).__init__() self.softmax = nn.LogSoftmax(dim=1) self.criterion = nn.NLLLoss(ignore_index=255) def forward(self, out, label): assert not label.requires_grad # out shape batch_size x channels x h x w # label shape batch_size x 1 x h x w label = label[:, 0, :, :].long() loss = self.criterion(self.softmax(out), label) return loss class BalancedCrossEntropyLoss(Module): """ Balanced Cross Entropy Loss with optional ignore regions """ def __init__(self, size_average=True, batch_average=True, pos_weight=None): super(BalancedCrossEntropyLoss, self).__init__() self.size_average = size_average self.batch_average = batch_average self.pos_weight = pos_weight def forward(self, output, label, void_pixels=None): assert (output.size() == label.size()) labels = torch.ge(label, 0.5).float() # Weighting of the loss, default is HED-style if self.pos_weight is None: num_labels_pos = torch.sum(labels) num_labels_neg = torch.sum(1.0 - labels) num_total = num_labels_pos + num_labels_neg w = num_labels_neg / num_total else: w = self.pos_weight output_gt_zero = torch.ge(output, 0).float() loss_val = torch.mul(output, (labels - output_gt_zero)) - torch.log( 1 + torch.exp(output - 2 * torch.mul(output, output_gt_zero))) loss_pos_pix = -torch.mul(labels, loss_val) loss_neg_pix = -torch.mul(1.0 - labels, loss_val) if void_pixels is not None and not self.pos_weight: w_void = torch.le(void_pixels, 0.5).float() loss_pos_pix = torch.mul(w_void, loss_pos_pix) loss_neg_pix = torch.mul(w_void, loss_neg_pix) num_total = num_total - torch.ge(void_pixels, 0.5).float().sum() w = num_labels_neg / num_total loss_pos = torch.sum(loss_pos_pix) loss_neg = torch.sum(loss_neg_pix) final_loss = w * loss_pos + (1 - w) * loss_neg if self.size_average: final_loss /= float(np.prod(label.size())) elif self.batch_average: final_loss /= label.size()[0] return final_loss class BinaryCrossEntropyLoss(Module): """ Binary Cross Entropy with ignore regions, not balanced. """ def __init__(self, size_average=True, batch_average=True): super(BinaryCrossEntropyLoss, self).__init__() self.size_average = size_average self.batch_average = batch_average def forward(self, output, label, void_pixels=None): assert (output.size() == label.size()) labels = torch.ge(label, 0.5).float() output_gt_zero = torch.ge(output, 0).float() loss_val = torch.mul(output, (labels - output_gt_zero)) - torch.log( 1 + torch.exp(output - 2 * torch.mul(output, output_gt_zero))) loss_pos_pix = -torch.mul(labels, loss_val) loss_neg_pix = -torch.mul(1.0 - labels, loss_val) if void_pixels is not None: w_void = torch.le(void_pixels, 0.5).float() loss_pos_pix = torch.mul(w_void, loss_pos_pix) loss_neg_pix = torch.mul(w_void, loss_neg_pix) loss_pos = torch.sum(loss_pos_pix) loss_neg = torch.sum(loss_neg_pix) final_loss = loss_pos + loss_neg if self.size_average: final_loss /= float(np.prod(label.size())) elif self.batch_average: final_loss /= label.size()[0] return final_loss class DepthLoss(nn.Module): """ Loss for depth prediction. By default L1 loss is used. """ def __init__(self, loss='l1'): super(DepthLoss, self).__init__() if loss == 'l1': self.loss = nn.L1Loss() else: raise NotImplementedError('Loss {} currently not supported in DepthLoss'.format(loss)) def forward(self, out, label): mask = (label != 255) return self.loss(torch.masked_select(out, mask), torch.masked_select(label, mask)) class Normalize(nn.Module): def __init__(self): super(Normalize, self).__init__() def forward(self, bottom): qn = torch.norm(bottom, p=2, dim=1).unsqueeze(dim=1) + 1e-12 top = bottom.div(qn) return top class NormalsLoss(Module): """ L1 loss with ignore labels normalize: normalization for surface normals """ def __init__(self, size_average=True, normalize=False, norm=1): super(NormalsLoss, self).__init__() self.size_average = size_average if normalize: self.normalize = Normalize() else: self.normalize = None if norm == 1: print('Using L1 loss for surface normals') self.loss_func = F.l1_loss elif norm == 2: print('Using L2 loss for surface normals') self.loss_func = F.mse_loss else: raise NotImplementedError def forward(self, out, label, ignore_label=255): assert not label.requires_grad mask = (label != ignore_label) n_valid = torch.sum(mask).item() if self.normalize is not None: out_norm = self.normalize(out) loss = self.loss_func(torch.masked_select(out_norm, mask), torch.masked_select(label, mask), reduction='sum') else: loss = self.loss_func(torch.masked_select(out, mask), torch.masked_select(label, mask), reduction='sum') if self.size_average: if ignore_label: ret_loss = torch.div(loss, max(n_valid, 1e-6)) return ret_loss else: ret_loss = torch.div(loss, float(np.prod(label.size()))) return ret_loss return loss

trabalho-numerico/tridimensional.py

heissonwillen/tcm

525

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm import numpy as np import os import contorno from constantes import INTERVALOS, PASSOS, TAMANHO_BARRA, DELTA_T, DELTA_X z_temp = contorno.p_3 TAMANHO_BARRA = 2 x = np.linspace(0.0, TAMANHO_BARRA, INTERVALOS+1) y = np.linspace(0.0, DELTA_T, PASSOS+1) z = [] for k in range(PASSOS+1): z_k = np.copy(z_temp) z.append(z_k) for i in range(1, INTERVALOS): z_temp[i] = z_k[i] + (DELTA_T/(DELTA_X**2)) * (z_k[i+1]-2*z_k[i]+z_k[i-1]) z = np.asarray(z) x, y = np.meshgrid(x, y) fig = plt.figure() ax = fig.gca(projection='3d') surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm, antialiased=False) ax.set_xlabel('x') ax.set_ylabel('t') ax.set_zlabel('T(x,t)') fig.colorbar(surf, shrink=0.5, aspect=5) plt.show()

leetcode/0006_ZigZag_Conversion/zigzag_conversion.py

zyeak/leetcode

526

# solution 1: class Solution1: def convert(self, s: str, numRows: int) -> str: if numRows == 1 or numRows >= len(s): return s L = [''] * numRows index, step = 0, 1 for x in s: L[index] += x if index == 0: step = 1 elif index == numRows - 1: step = -1 index += step return ''.join(L) # Solution 2 class Solution: def convert(self, s: str, numRows: int) -> str: # If we have only one row then we can return the string as it is if numRows < 2: return s # We will create an empty string for each row and then fill each element in each row # from row = 0 to row = numRows-1, if we reach bottom (i.e. row = numRows-1) # then we move up. Similarly if we reach top, we change direction and move down # Finally after filling up all the four rows we join them row0 + row1 +.. numRows row = 0 result = [""]*numRows for character in s: if row == 0: move_down = True elif row == numRows-1: move_down = False result[row] += character row = (row+1) if move_down else row-1 return "".join(result) if __name__ == '__main__': # begin s = Solution() print(s.convert("PAYPALISHIRING", 3))

FakeNewsClassifierWithLSTM.py

pratikasarkar/nlp

527

<reponame>pratikasarkar/nlp # -*- coding: utf-8 -*- """ Created on Thu Feb 11 13:42:45 2021 @author: ASUS """ import pandas as pd df = pd.read_csv(r'D:\nlp\fake-news-data\train.csv') df = df.dropna() X = df.drop('label',axis = 1) y = df['label'] import tensorflow as tf from tensorflow.keras.layers import Embedding, Dense, LSTM from tensorflow.keras.models import Sequential from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.preprocessing.text import one_hot # Vocabulary size voc_size = 5000 # One Hot Representation messages = X.copy() messages.reset_index(inplace = True) import nltk import re from nltk.corpus import stopwords # Dataset Preprocessing from nltk.stem import PorterStemmer ps = PorterStemmer() corpus = [] for i in range(len(messages)): print(i) review = re.sub('[^a-zA-Z]',' ',messages['title'][i]) review = review.lower() review = review.split() review = [ps.stem(word) for word in review if word not in stopwords.words('english')] review = " ".join(review) corpus.append(review) onehot_repr = [one_hot(words,voc_size) for words in corpus] sent_len = 20 embedded_doc = pad_sequences(onehot_repr,maxlen = sent_len,padding = 'pre') # Creating the model embedding_vector_features = 40 model = Sequential() model.add(Embedding(voc_size,embedding_vector_features,input_length=sent_len)) model.add(LSTM(100)) model.add(Dense(1,activation='sigmoid')) model.compile(loss='binary_crossentropy',optimizer = 'adam',metrics = ['accuracy']) model.summary() import numpy as np X_final = np.array(embedded_doc) y_final = np.array(y) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X_final,y_final,test_size = 0.33,random_state = 42) model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=64) y_pred = model.predict_classes(X_test) from sklearn.metrics import confusion_matrix, accuracy_score cm = confusion_matrix(y_test,y_pred) acc = accuracy_score(y_test,y_pred)

SVM/SVM_12_Quiz.py

rohit517/Intro-to-machine-learning-Udacity

528

import sys from class_vis import prettyPicture from prep_terrain_data import makeTerrainData import matplotlib.pyplot as plt import copy import numpy as np import pylab as pl features_train, labels_train, features_test, labels_test = makeTerrainData() ########################## SVM ################################# ### we handle the import statement and SVC creation for you here from sklearn.svm import SVC clf = SVC(kernel="linear") #### now your job is to fit the classifier #### using the training features/labels, and to #### make a set of predictions on the test data clf.fit(features_train,labels_train) pred = clf.predict(features_test) #### store your predictions in a list named pred from sklearn.metrics import accuracy_score acc = accuracy_score(pred, labels_test) def submitAccuracy(): return acc

tests/test_auto_scan_logsigmoid.py

yeliang2258/Paddle2ONNX

529

<gh_stars>0 # 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. from auto_scan_test import OPConvertAutoScanTest, BaseNet from hypothesis import reproduce_failure import hypothesis.strategies as st import numpy as np import unittest import paddle class Net(BaseNet): """ simple Net """ def forward(self, inputs): """ forward """ x = paddle.nn.functional.log_sigmoid(inputs) return x class TestLogsigmoidConvert(OPConvertAutoScanTest): """ api: paddle.nn.functional.log_sigmoid OPset version: 7, 9, 15 """ def sample_convert_config(self, draw): input_shape = draw( st.lists( st.integers( min_value=20, max_value=100), min_size=4, max_size=4)) input_spec = [-1] * len(input_shape) dtype = draw(st.sampled_from(["float32", "float64"])) config = { "op_names": ["logsigmoid"], "test_data_shapes": [input_shape], "test_data_types": [[dtype]], "opset_version": [7, 9, 15], "input_spec_shape": [input_spec], } models = Net(config) return (config, models) def test(self): self.run_and_statis(max_examples=30) if __name__ == "__main__": unittest.main()

oasislmf/utils/concurrency.py

bbetov-corelogic/OasisLMF

530

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from builtins import open from builtins import str from future import standard_library standard_library.install_aliases() try: from queue import Queue, Empty except ImportError: from Queue import Queue, Empty import sys import types import billiard from signal import ( signal, SIGINT, ) from threading import ( Event, Thread, ) __all__ = [ 'multiprocess', 'multithread', 'SignalHandler', 'Task' ] class SignalHandler(object): def __init__(self, stopper, threads): self.stopper = stopper self.threads = threads def __call__(self, signum, frame): self.stopper.set() for task in self.threads: task.join() sys.exit(0) class Task(object): def __init__(self, func, args=(), key=None): self._func = func self._args = args self._key = key if key is not None else func.__name__ self._result = None self._is_done = False @property def func(self): """ Task function/method property - getter only. :getter: Gets the task function/method object """ return self._func @property def args(self): """ Task function/method arguments property - getter only. :getter: Gets the task function/method arguments """ return self._args @property def key(self): """ Task function/method key - getter only. :getter: Gets the task function/method key """ return self._key @property def result(self): """ Task function/method result property. :getter: Gets the task function/method result (produced by calling the function on the defined arguments) :setter: Sets the task function/method result """ return self._result @result.setter def result(self, r): self._result = r self._is_done = True @property def is_done(self): """ Task function/method status property - getter only. :getter: Gets the task function/method status """ return self._is_done def multithread(tasks, pool_size=10): """ Executes several tasks concurrently via ``threading`` threads, puts the results into a queue, and generates these back to the caller. """ task_q = Queue() num_tasks = 0 for task in tasks: task_q.put(task) num_tasks += 1 def run(i, task_q, result_q, stopper): while not stopper.is_set(): try: task = task_q.get_nowait() except Empty: break else: task.result = task.func(*task.args) if task.args else task.func() if type(task.result) in (types.GeneratorType, list, tuple, set): for r in task.result: result_q.put((task.key, r,)) else: result_q.put((task.key, task.result,)) task_q.task_done() result_q = Queue() stopper = Event() threads = tuple(Thread(target=run, args=(i, task_q, result_q, stopper,)) for i in range(pool_size)) handler = SignalHandler(stopper, threads) signal(SIGINT, handler) for thread in threads: thread.start() task_q.join() while not result_q.empty(): key, result = result_q.get_nowait() yield key, result def multiprocess(tasks, pool_size=10): """ Executes several tasks concurrently via Python ``multiprocessing`` processes, puts the results into a queue, and generates these back to the caller. """ pool = billiard.Pool(pool_size) result_q = Queue() def build_results(result): if type(result) in (types.GeneratorType, list, tuple, set): for r in result: result_q.put(r) else: result_q.put(result) for task in tasks: run = pool.apply_async(task.func, args=task.args, callback=build_results) run.get() pool.close() pool.join() while not result_q.empty(): result = result_q.get_nowait() yield result

runtime/python/Lib/site-packages/isort/output.py

hwaipy/InteractionFreeNode

531

<reponame>hwaipy/InteractionFreeNode<filename>runtime/python/Lib/site-packages/isort/output.py import copy import itertools from functools import partial from typing import Any, Iterable, List, Optional, Set, Tuple, Type from isort.format import format_simplified from . import parse, sorting, wrap from .comments import add_to_line as with_comments from .identify import STATEMENT_DECLARATIONS from .settings import DEFAULT_CONFIG, Config def sorted_imports( parsed: parse.ParsedContent, config: Config = DEFAULT_CONFIG, extension: str = "py", import_type: str = "import", ) -> str: """Adds the imports back to the file. (at the index of the first import) sorted alphabetically and split between groups """ if parsed.import_index == -1: return _output_as_string(parsed.lines_without_imports, parsed.line_separator) formatted_output: List[str] = parsed.lines_without_imports.copy() remove_imports = [format_simplified(removal) for removal in config.remove_imports] sections: Iterable[str] = itertools.chain(parsed.sections, config.forced_separate) if config.no_sections: parsed.imports["no_sections"] = {"straight": {}, "from": {}} base_sections: Tuple[str, ...] = () for section in sections: if section == "FUTURE": base_sections = ("FUTURE",) continue parsed.imports["no_sections"]["straight"].update( parsed.imports[section].get("straight", {}) ) parsed.imports["no_sections"]["from"].update(parsed.imports[section].get("from", {})) sections = base_sections + ("no_sections",) output: List[str] = [] seen_headings: Set[str] = set() pending_lines_before = False for section in sections: straight_modules = parsed.imports[section]["straight"] if not config.only_sections: straight_modules = sorting.sort( config, straight_modules, key=lambda key: sorting.module_key( key, config, section_name=section, straight_import=True ), reverse=config.reverse_sort, ) from_modules = parsed.imports[section]["from"] if not config.only_sections: from_modules = sorting.sort( config, from_modules, key=lambda key: sorting.module_key(key, config, section_name=section), reverse=config.reverse_sort, ) if config.star_first: star_modules = [] other_modules = [] for module in from_modules: if "*" in parsed.imports[section]["from"][module]: star_modules.append(module) else: other_modules.append(module) from_modules = star_modules + other_modules straight_imports = _with_straight_imports( parsed, config, straight_modules, section, remove_imports, import_type ) from_imports = _with_from_imports( parsed, config, from_modules, section, remove_imports, import_type ) lines_between = [""] * ( config.lines_between_types if from_modules and straight_modules else 0 ) if config.from_first: section_output = from_imports + lines_between + straight_imports else: section_output = straight_imports + lines_between + from_imports if config.force_sort_within_sections: # collapse comments comments_above = [] new_section_output: List[str] = [] for line in section_output: if not line: continue if line.startswith("#"): comments_above.append(line) elif comments_above: new_section_output.append(_LineWithComments(line, comments_above)) comments_above = [] else: new_section_output.append(line) # only_sections options is not imposed if force_sort_within_sections is True new_section_output = sorting.sort( config, new_section_output, key=partial(sorting.section_key, config=config), reverse=config.reverse_sort, ) # uncollapse comments section_output = [] for line in new_section_output: comments = getattr(line, "comments", ()) if comments: section_output.extend(comments) section_output.append(str(line)) section_name = section no_lines_before = section_name in config.no_lines_before if section_output: if section_name in parsed.place_imports: parsed.place_imports[section_name] = section_output continue section_title = config.import_headings.get(section_name.lower(), "") if section_title and section_title not in seen_headings: if config.dedup_headings: seen_headings.add(section_title) section_comment = f"# {section_title}" if section_comment not in parsed.lines_without_imports[0:1]: # pragma: no branch section_output.insert(0, section_comment) if pending_lines_before or not no_lines_before: output += [""] * config.lines_between_sections output += section_output pending_lines_before = False else: pending_lines_before = pending_lines_before or not no_lines_before if config.ensure_newline_before_comments: output = _ensure_newline_before_comment(output) while output and output[-1].strip() == "": output.pop() # pragma: no cover while output and output[0].strip() == "": output.pop(0) if config.formatting_function: output = config.formatting_function( parsed.line_separator.join(output), extension, config ).splitlines() output_at = 0 if parsed.import_index < parsed.original_line_count: output_at = parsed.import_index formatted_output[output_at:0] = output if output: imports_tail = output_at + len(output) while [ character.strip() for character in formatted_output[imports_tail : imports_tail + 1] ] == [""]: formatted_output.pop(imports_tail) if len(formatted_output) > imports_tail: next_construct = "" tail = formatted_output[imports_tail:] for index, line in enumerate(tail): # pragma: no branch should_skip, in_quote, *_ = parse.skip_line( line, in_quote="", index=len(formatted_output), section_comments=config.section_comments, needs_import=False, ) if not should_skip and line.strip(): if ( line.strip().startswith("#") and len(tail) > (index + 1) and tail[index + 1].strip() ): continue next_construct = line break if in_quote: # pragma: no branch next_construct = line break if config.lines_after_imports != -1: formatted_output[imports_tail:0] = [ "" for line in range(config.lines_after_imports) ] elif extension != "pyi" and next_construct.startswith(STATEMENT_DECLARATIONS): formatted_output[imports_tail:0] = ["", ""] else: formatted_output[imports_tail:0] = [""] if parsed.place_imports: new_out_lines = [] for index, line in enumerate(formatted_output): new_out_lines.append(line) if line in parsed.import_placements: new_out_lines.extend(parsed.place_imports[parsed.import_placements[line]]) if ( len(formatted_output) <= (index + 1) or formatted_output[index + 1].strip() != "" ): new_out_lines.append("") formatted_output = new_out_lines return _output_as_string(formatted_output, parsed.line_separator) def _with_from_imports( parsed: parse.ParsedContent, config: Config, from_modules: Iterable[str], section: str, remove_imports: List[str], import_type: str, ) -> List[str]: output: List[str] = [] for module in from_modules: if module in remove_imports: continue import_start = f"from {module} {import_type} " from_imports = list(parsed.imports[section]["from"][module]) if ( not config.no_inline_sort or (config.force_single_line and module not in config.single_line_exclusions) ) and not config.only_sections: from_imports = sorting.sort( config, from_imports, key=lambda key: sorting.module_key( key, config, True, config.force_alphabetical_sort_within_sections, section_name=section, ), reverse=config.reverse_sort, ) if remove_imports: from_imports = [ line for line in from_imports if f"{module}.{line}" not in remove_imports ] sub_modules = [f"{module}.{from_import}" for from_import in from_imports] as_imports = { from_import: [ f"{from_import} as {as_module}" for as_module in parsed.as_map["from"][sub_module] ] for from_import, sub_module in zip(from_imports, sub_modules) if sub_module in parsed.as_map["from"] } if config.combine_as_imports and not ("*" in from_imports and config.combine_star): if not config.no_inline_sort: for as_import in as_imports: if not config.only_sections: as_imports[as_import] = sorting.sort(config, as_imports[as_import]) for from_import in copy.copy(from_imports): if from_import in as_imports: idx = from_imports.index(from_import) if parsed.imports[section]["from"][module][from_import]: from_imports[(idx + 1) : (idx + 1)] = as_imports.pop(from_import) else: from_imports[idx : (idx + 1)] = as_imports.pop(from_import) only_show_as_imports = False comments = parsed.categorized_comments["from"].pop(module, ()) above_comments = parsed.categorized_comments["above"]["from"].pop(module, None) while from_imports: if above_comments: output.extend(above_comments) above_comments = None if "*" in from_imports and config.combine_star: import_statement = wrap.line( with_comments( _with_star_comments(parsed, module, list(comments or ())), f"{import_start}*", removed=config.ignore_comments, comment_prefix=config.comment_prefix, ), parsed.line_separator, config, ) from_imports = [ from_import for from_import in from_imports if from_import in as_imports ] only_show_as_imports = True elif config.force_single_line and module not in config.single_line_exclusions: import_statement = "" while from_imports: from_import = from_imports.pop(0) single_import_line = with_comments( comments, import_start + from_import, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) comment = ( parsed.categorized_comments["nested"].get(module, {}).pop(from_import, None) ) if comment: single_import_line += ( f"{comments and ';' or config.comment_prefix} " f"{comment}" ) if from_import in as_imports: if ( parsed.imports[section]["from"][module][from_import] and not only_show_as_imports ): output.append( wrap.line(single_import_line, parsed.line_separator, config) ) from_comments = parsed.categorized_comments["straight"].get( f"{module}.{from_import}" ) if not config.only_sections: output.extend( with_comments( from_comments, wrap.line( import_start + as_import, parsed.line_separator, config ), removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) for as_import in sorting.sort(config, as_imports[from_import]) ) else: output.extend( with_comments( from_comments, wrap.line( import_start + as_import, parsed.line_separator, config ), removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) for as_import in as_imports[from_import] ) else: output.append(wrap.line(single_import_line, parsed.line_separator, config)) comments = None else: while from_imports and from_imports[0] in as_imports: from_import = from_imports.pop(0) if not config.only_sections: as_imports[from_import] = sorting.sort(config, as_imports[from_import]) from_comments = ( parsed.categorized_comments["straight"].get(f"{module}.{from_import}") or [] ) if ( parsed.imports[section]["from"][module][from_import] and not only_show_as_imports ): specific_comment = ( parsed.categorized_comments["nested"] .get(module, {}) .pop(from_import, None) ) if specific_comment: from_comments.append(specific_comment) output.append( wrap.line( with_comments( from_comments, import_start + from_import, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ), parsed.line_separator, config, ) ) from_comments = [] for as_import in as_imports[from_import]: specific_comment = ( parsed.categorized_comments["nested"] .get(module, {}) .pop(as_import, None) ) if specific_comment: from_comments.append(specific_comment) output.append( wrap.line( with_comments( from_comments, import_start + as_import, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ), parsed.line_separator, config, ) ) from_comments = [] if "*" in from_imports: output.append( with_comments( _with_star_comments(parsed, module, []), f"{import_start}*", removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) ) from_imports.remove("*") for from_import in copy.copy(from_imports): comment = ( parsed.categorized_comments["nested"].get(module, {}).pop(from_import, None) ) if comment: from_imports.remove(from_import) if from_imports: use_comments = [] else: use_comments = comments comments = None single_import_line = with_comments( use_comments, import_start + from_import, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) single_import_line += ( f"{use_comments and ';' or config.comment_prefix} " f"{comment}" ) output.append(wrap.line(single_import_line, parsed.line_separator, config)) from_import_section = [] while from_imports and ( from_imports[0] not in as_imports or ( config.combine_as_imports and parsed.imports[section]["from"][module][from_import] ) ): from_import_section.append(from_imports.pop(0)) if config.combine_as_imports: comments = (comments or []) + list( parsed.categorized_comments["from"].pop(f"{module}.__combined_as__", ()) ) import_statement = with_comments( comments, import_start + (", ").join(from_import_section), removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) if not from_import_section: import_statement = "" do_multiline_reformat = False force_grid_wrap = config.force_grid_wrap if force_grid_wrap and len(from_import_section) >= force_grid_wrap: do_multiline_reformat = True if len(import_statement) > config.line_length and len(from_import_section) > 1: do_multiline_reformat = True # If line too long AND have imports AND we are # NOT using GRID or VERTICAL wrap modes if ( len(import_statement) > config.line_length and len(from_import_section) > 0 and config.multi_line_output not in (wrap.Modes.GRID, wrap.Modes.VERTICAL) # type: ignore ): do_multiline_reformat = True if do_multiline_reformat: import_statement = wrap.import_statement( import_start=import_start, from_imports=from_import_section, comments=comments, line_separator=parsed.line_separator, config=config, ) if config.multi_line_output == wrap.Modes.GRID: # type: ignore other_import_statement = wrap.import_statement( import_start=import_start, from_imports=from_import_section, comments=comments, line_separator=parsed.line_separator, config=config, multi_line_output=wrap.Modes.VERTICAL_GRID, # type: ignore ) if ( max( len(import_line) for import_line in import_statement.split(parsed.line_separator) ) > config.line_length ): import_statement = other_import_statement if not do_multiline_reformat and len(import_statement) > config.line_length: import_statement = wrap.line(import_statement, parsed.line_separator, config) if import_statement: output.append(import_statement) return output def _with_straight_imports( parsed: parse.ParsedContent, config: Config, straight_modules: Iterable[str], section: str, remove_imports: List[str], import_type: str, ) -> List[str]: output: List[str] = [] as_imports = any((module in parsed.as_map["straight"] for module in straight_modules)) # combine_straight_imports only works for bare imports, 'as' imports not included if config.combine_straight_imports and not as_imports: if not straight_modules: return [] above_comments: List[str] = [] inline_comments: List[str] = [] for module in straight_modules: if module in parsed.categorized_comments["above"]["straight"]: above_comments.extend(parsed.categorized_comments["above"]["straight"].pop(module)) if module in parsed.categorized_comments["straight"]: inline_comments.extend(parsed.categorized_comments["straight"][module]) combined_straight_imports = ", ".join(straight_modules) if inline_comments: combined_inline_comments = " ".join(inline_comments) else: combined_inline_comments = "" output.extend(above_comments) if combined_inline_comments: output.append( f"{import_type} {combined_straight_imports} # {combined_inline_comments}" ) else: output.append(f"{import_type} {combined_straight_imports}") return output for module in straight_modules: if module in remove_imports: continue import_definition = [] if module in parsed.as_map["straight"]: if parsed.imports[section]["straight"][module]: import_definition.append((f"{import_type} {module}", module)) import_definition.extend( (f"{import_type} {module} as {as_import}", f"{module} as {as_import}") for as_import in parsed.as_map["straight"][module] ) else: import_definition.append((f"{import_type} {module}", module)) comments_above = parsed.categorized_comments["above"]["straight"].pop(module, None) if comments_above: output.extend(comments_above) output.extend( with_comments( parsed.categorized_comments["straight"].get(imodule), idef, removed=config.ignore_comments, comment_prefix=config.comment_prefix, ) for idef, imodule in import_definition ) return output def _output_as_string(lines: List[str], line_separator: str) -> str: return line_separator.join(_normalize_empty_lines(lines)) def _normalize_empty_lines(lines: List[str]) -> List[str]: while lines and lines[-1].strip() == "": lines.pop(-1) lines.append("") return lines class _LineWithComments(str): comments: List[str] def __new__( cls: Type["_LineWithComments"], value: Any, comments: List[str] ) -> "_LineWithComments": instance = super().__new__(cls, value) instance.comments = comments return instance def _ensure_newline_before_comment(output: List[str]) -> List[str]: new_output: List[str] = [] def is_comment(line: Optional[str]) -> bool: return line.startswith("#") if line else False for line, prev_line in zip(output, [None] + output): # type: ignore if is_comment(line) and prev_line != "" and not is_comment(prev_line): new_output.append("") new_output.append(line) return new_output def _with_star_comments(parsed: parse.ParsedContent, module: str, comments: List[str]) -> List[str]: star_comment = parsed.categorized_comments["nested"].get(module, {}).pop("*", None) if star_comment: return comments + [star_comment] return comments

vixen/project.py

amoeba/vixen

532

import datetime import io import json_tricks import logging import os from os.path import (abspath, basename, dirname, exists, expanduser, join, realpath, relpath, splitext) import re import shutil import sys from traits.api import (Any, Dict, Enum, HasTraits, Instance, List, Long, Str) from whoosh import fields, qparser, query from whoosh.util.times import datetime_to_long, long_to_datetime from .common import get_project_dir from .media import Media, MediaData, get_media_data from .directory import Directory from . import processor logger = logging.getLogger(__name__) if sys.version_info[0] > 2: unicode = str string_types = (str,) import csv else: string_types = (basestring,) import backports.csv as csv INT = fields.NUMERIC(numtype=int) FLOAT = fields.NUMERIC(numtype=float) def get_file_saved_time(path): dt = datetime.datetime.fromtimestamp(os.stat(path).st_ctime) return dt.ctime() def _get_sample(fname): sample = '' with io.open(fname, 'r', newline='', encoding='utf-8') as fp: sample += fp.readline() + fp.readline() return sample def _get_csv_headers(fname): sample = _get_sample(fname) sniffer = csv.Sniffer() has_header = sniffer.has_header(sample) dialect = sniffer.sniff(sample) with io.open(fname, 'r', newline='', encoding='utf-8') as fp: reader = csv.reader(fp, dialect) header = next(reader) return has_header, header, dialect class TagInfo(HasTraits): name = Str type = Enum("string", "text", "int", "float", "bool") default = Any def __repr__(self): return 'TagInfo(%r, %r)' % (self.name, self.type) def _default_default(self): map = {"string": "", "text": "", "int": 0, "float": 0.0, "bool": False} return map[self.type] def open_file(fname_or_file, mode='rb'): if hasattr(fname_or_file, 'read'): return fname_or_file else: return open(fname_or_file, mode) def sanitize_name(name): name = name.lower() name = re.sub(r'\s+', '_', name) return re.sub(r'\W+', '', name) def get_non_existing_filename(fname): if exists(fname): base, ext = splitext(basename(fname)) return join(dirname(fname), base + '_a' + ext) else: return fname COMMON_TAGS = dict( file_name='string', path='string', relpath='string', ctime='string', mtime='string', size='int', type='string' ) def _cleanup_query(q, tag_types): type_map = dict(float=FLOAT.from_bytes, int=INT.from_bytes) for term in q.leaves(): if isinstance(term, query.Term): if isinstance(term.text, (str, unicode, bytes)): fieldtype = tag_types[term.fieldname] if fieldtype in type_map: term.text = type_map[fieldtype](term.text) else: term.text = term.text.lower() elif isinstance(term, query.Phrase): term.words = [x.lower() for x in term.words] def _check_value(value, expr): if isinstance(expr, string_types): return expr in value.lower() else: return expr == value def _check_range(x, term): result = True if term.start is not None: if term.startexcl: result &= x > term.start else: result &= x >= term.start if term.end is not None and result: if term.endexcl: result &= x < term.end else: result &= x <= term.end return result def _check_date_range(x, term): result = True if term.startdate is not None: result &= x >= term.start if term.enddate is not None and result: result &= x <= term.end return result def _search_media(expr, m_key, get_tag): """Given search expression, index to media, and a getter to get the attribute check if the media matches expression. """ if expr.is_leaf(): if isinstance(expr, query.Term): attr = expr.fieldname return _check_value(get_tag(m_key, attr), expr.text) elif isinstance(expr, query.Phrase): attr = expr.fieldname text = " ".join(expr.words) return _check_value(get_tag(m_key, attr), text) elif isinstance(expr, query.DateRange): if expr.fieldname == 'ctime': value = get_tag(m_key, 'ctime_') elif expr.fieldname == 'mtime': value = get_tag(m_key, 'mtime_') return _check_date_range(value, expr) elif isinstance(expr, query.NumericRange): attr = expr.fieldname return _check_range(get_tag(m_key, attr), expr) else: print("Unsupported term: %r" % expr) return False else: if isinstance(expr, query.And): result = True for child in expr.children(): result &= _search_media(child, m_key, get_tag) if not result: break return result elif isinstance(expr, query.Or): result = False for child in expr.children(): result |= _search_media(child, m_key, get_tag) if result: break return result elif isinstance(expr, query.Not): subquery = list(expr.children())[0] return not _search_media(subquery, m_key, get_tag) else: print("Unsupported term: %r" % expr) return False class Project(HasTraits): name = Str description = Str path = Str root = Instance(Directory) tags = List(TagInfo) _media = Dict(Str, Media) extensions = List(Str) processors = List(processor.FactoryBase) number_of_files = Long # Path where the project data is saved. save_file = Str last_save_time = Str _data = Dict _tag_data = Dict _relpath2index = Dict() _query_parser = Instance(qparser.QueryParser) def add_tags(self, tags): tags = list(self.tags) + tags self.update_tags(tags) def update_tags(self, new_tags): old_tags = self.tags new_tag_names = set(tag.name for tag in new_tags) tag_info = dict((tag.name, tag.type) for tag in old_tags) removed = [] added = [] for tag in new_tags: if tag.name not in tag_info: added.append(tag) elif tag_info[tag.name] != tag.type: removed.append(tag) added.append(tag) for tag in old_tags: if tag.name not in new_tag_names: removed.append(tag) for tag in removed: del self._tag_data[tag.name] n_entries = len(self._relpath2index) for tag in added: self._tag_data[tag.name] = [tag.default]*n_entries # The above can be the first time when self._tag_data is accessed, when # creating a new project for example. In this case, # self.__tag_data_default is called, so if self.tags is set then the # removed tags will not exist in _tag_data causing an error. So we only # set self.tags below. self.tags = new_tags # Update the cached media for m in self._media.values(): for tag in removed: del m.tags[tag.name] for tag in added: m.tags[tag.name] = tag.default self._query_parser = self._make_query_parser() def copy(self): """Make a copy of this project. This does not copy the data but only the tags, extensions and the other settings of the project. This will not copy any of the processor states but only their settings. """ name = self.name + ' copy' p = Project(name=name) traits = ['description', 'extensions', 'path', 'processors', 'tags'] p.copy_traits(self, traits, copy='deep') # Clear out the _done information from the processors for proc in p.processors: proc._done.clear() return p # #### CRUD interface to the data #### def update(self, media_data, tags=None): """Create/update the internal data given the media data and tags. Parameters ---------- f: vixen.directory.File instance tags: dict """ relpath = media_data.relpath if not self.has_media(relpath): index = len(self._relpath2index) self._relpath2index[relpath] = index for key in MediaData._fields: self._data[key].append(None) for tag in self.tags: self._tag_data[tag.name].append(tag.default) index = self._relpath2index[relpath] for i, key in enumerate(MediaData._fields): self._data[key][index] = media_data[i] if tags: for key, value in tags.items(): self._tag_data[key][index] = value media = self._media.get(relpath) if media is not None: media.update(media_data, tags) def get(self, relpath): """Given the relative path of some media, return a Media instance. """ if relpath in self._media: return self._media[relpath] else: data = {} index = self._relpath2index[relpath] for key in MediaData._fields: data[key] = self._data[key][index] tags = {} for key in self._tag_data: tags[key] = self._tag_data[key][index] media = Media.from_data(MediaData(**data), tags) media.on_trait_change(self._media_tag_handler, 'tags_items') self._media[relpath] = media return media def remove(self, relpaths): """Given a list of relative path of some media, remove them from the database. """ relpath2index = self._relpath2index indices = [(x, relpath2index[x]) for x in relpaths] for relpath, index in sorted(indices, reverse=True): last = len(relpath2index) - 1 if index == last: self._delete_record(last, relpath) else: self._replace_with_last_record(index, last) self._delete_record(last, relpath) def has_media(self, relpath): """Returns True if the media data is available. """ return relpath in self._relpath2index def keys(self): """Return all the keys for the media relative paths.""" return self._relpath2index.keys() def _get_media_attr(self, index, attr): """Given an index to the media, return its value. """ if attr in self._data: return self._data[attr][index] elif attr in self._tag_data: return self._tag_data[attr][index] # #### End of CRUD interface to the data #### def clean(self): """Scan the project and remove any dead entries. This is useful when you remove or rename files. This does not refresh the directory tree or set the number of files. It simply cleans up the db of files that no longer exist. """ logger.info('Cleaning project: %s', self.name) root_path = self.path to_remove = [] relpath2index = self._relpath2index for rpath in list(relpath2index.keys()): fname = os.path.join(root_path, rpath) if not os.path.exists(fname): to_remove.append(rpath) self.remove(to_remove) def export_csv(self, fname, cols=None): """Export metadata to a csv file. If `cols` are not specified, it writes out all the useful metadata. Parameters ----------- fname: str: a path to the csv file to dump. cols: sequence: a sequence of columns to write. """ logger.info('Exporting CSV: %s', fname) all_keys = ((set(MediaData._fields) | set(self._tag_data.keys())) - set(('ctime_', 'mtime_'))) if cols is None: cols = all_keys cols = list(sorted(cols)) data_cols = set([x for x in cols if x in self._data]) with io.open(fname, 'w', newline='', encoding='utf-8') as of: # Write the header. writer = csv.writer(of) writer.writerow(cols) for i in range(len(self._relpath2index)): line = [] for col in cols: if col in data_cols: elem = self._data[col][i] else: elem = self._tag_data[col][i] line.append(elem) writer.writerow(line) def import_csv(self, fname): """Read tag information from given CSV filename. Returns the success status and the error message if any. Note that this only applies tags for column headers with known tags. Unknown tags are not added. Parameters ---------- fname : str Input filename. """ logger.info('Importing tags from: %s', fname) has_header, header, dialect = _get_csv_headers(fname) if not has_header: return False, "The CSV file does not appear to have a header." if 'path' not in header: msg = "The CSV file does not have a 'path' column." return False, msg tags = {x: header.index(x.name) for x in self.tags if x.name in header} path_idx = header.index('path') TRUE = ('1', 't', 'true', 'y', 'yes') type_map = { 'bool': lambda x: x.lower() in TRUE, 'string': lambda x: x, 'text': lambda x: x, 'int': int, 'float': float } count = 0 total = 0 with io.open(fname, 'r', newline='', encoding='utf-8') as fp: reader = csv.reader(fp, dialect) next(reader) # Skip header for record in reader: total += 1 path = record[path_idx] rpath = relpath(path, self.path) index = self._relpath2index.get(rpath, None) media = self._media.get(rpath) if index is not None: count += 1 for tag, header_index in tags.items(): data = record[header_index] try: value = type_map[tag.type](data) if media is not None: media.tags[tag.name] = value else: self._tag_data[tag.name][index] = value except ValueError: pass msg = "Read tags for %d paths out of %d entries." % (count, total) if count == 0 and total > 0: msg += ("\nPlease check that your path column matches " "the media paths.") return False, msg else: msg += ("\nPlease check the imported tags and make sure you " "save the project.") return True, msg def load(self, fp=None): """Load media info from opened file object. """ if fp is None: if not exists(self.save_file): return fp = open_file(self.save_file, 'rb') else: fp = open_file(fp, 'rb') data = json_tricks.load( fp, preserve_order=False, ignore_comments=False ) fp.close() self.name = data.get('name', '') self.description = data.get('description', '') self.path = data.get('path') self.tags = [TagInfo(name=x[0], type=x[1]) for x in data['tags']] self.processors = [processor.load(x) for x in data.get('processors', [])] version = data.get('version') if version == 1: self._read_version1_media(data['media']) else: self._data = data['media_data'] self._tag_data = data['tag_data'] self._relpath2index = data['relpath2index'] root = Directory() root.__setstate__(data.get('root')) self.extensions = root.extensions self.root = root self.number_of_files = len(self._relpath2index) def save(self): """Save current media info to a file object """ if len(self.save_file) > 0: self.save_as(self.save_file) self._update_last_save_time() else: raise IOError("No valid save file set.") def save_as(self, fp): """Save copy to specified path. """ fp = open_file(fp, 'wb') tags = [(t.name, t.type) for t in self.tags] root = self.root.__getstate__() processors = [processor.dump(x) for x in self.processors] data = dict( version=2, path=self.path, name=self.name, description=self.description, tags=tags, media_data=self._data, tag_data=self._tag_data, relpath2index=self._relpath2index, root=root, processors=processors ) json_tricks.dump(data, fp, compression=True) fp.close() logger.info('Saved project: %s', self.name) def scan(self, refresh=False): """Find all the media recursively inside the root directory. This will not clobber existing records but will add any new ones. """ self._setup_root() def _scan(dir): for f in dir.files: if not self.has_media(f.relpath) or refresh: data = get_media_data(f.path, f.relpath) self.update(data) for d in dir.directories: if refresh: d.refresh() _scan(d) if refresh: self.root.refresh() _scan(self.root) self.number_of_files = len(self._relpath2index) def search(self, q): """A generator which yields the (filename, relpath) for each file satisfying the search query. """ logger.info('Searching for %s', q) try: parsed_q = self._query_parser.parse(q) except Exception: logger.warn("Invalid search expression: %s", q) print("Invalid search expression: %s" % q) return tag_types = self._get_tag_types() _cleanup_query(parsed_q, tag_types) for key, index in self._relpath2index.items(): if _search_media(parsed_q, index, self._get_media_attr): yield basename(key), key def refresh(self): logger.info('Refreshing project: %s', self.name) self.clean() self.scan(refresh=True) # #### Private protocol ################################################ def _setup_root(self): path = abspath(expanduser(self.path)) root = self.root if root is None or realpath(root.path) != realpath(path): self.root = Directory(path=path, extensions=self.extensions) def _tags_default(self): return [TagInfo(name='completed', type='bool')] def _save_file_default(self): if len(self.name) > 0: fname = sanitize_name(self.name) + '.vxn' d = get_project_dir() return get_non_existing_filename(join(d, fname)) else: return '' def _update_last_save_time(self): self.last_save_time = get_file_saved_time(self.save_file) def _last_save_time_default(self): if exists(self.save_file): return get_file_saved_time(self.save_file) else: return '' def _name_changed(self, name): if len(name) > 0: old_save_file = self.save_file old_dir = dirname(old_save_file) new_save_file = join(old_dir, sanitize_name(name) + '.vxn') if new_save_file != old_save_file: self.save_file = new_save_file if exists(old_save_file): shutil.move(old_save_file, self.save_file) def _extensions_changed(self, ext): if self.root is not None: self.root.extensions = ext def _extensions_items_changed(self): if self.root is not None: self.root.extensions = self.extensions def _get_tag_types(self): result = dict(COMMON_TAGS) result.update(dict((t.name, t.type) for t in self.tags)) return result def _make_schema(self): from whoosh.fields import BOOLEAN, DATETIME, TEXT, Schema kw = dict( type=TEXT, file_name=TEXT, path=TEXT, mtime=DATETIME, ctime=DATETIME, size=INT ) type_to_field = dict( string=TEXT, text=TEXT, int=INT, float=FLOAT, bool=BOOLEAN ) for tag in self.tags: kw[tag.name] = type_to_field[tag.type] return Schema(**kw) def _make_query_parser(self): schema = self._make_schema() qp = qparser.QueryParser('path', schema=schema) qp.add_plugin(qparser.GtLtPlugin()) from whoosh.qparser.dateparse import DateParserPlugin qp.add_plugin(DateParserPlugin()) return qp def __query_parser_default(self): return self._make_query_parser() def __data_default(self): data = {} for key in MediaData._fields: data[key] = [] return data def __tag_data_default(self): tags = {} for key in self.tags: tags[key.name] = [] return tags def _media_tag_handler(self, obj, tname, old, new): index = self._relpath2index[obj.relpath] for tag in new.changed: self._tag_data[tag][index] = obj.tags[tag] def _read_version1_media(self, media): data = self.__data_default() tag_data = self.__tag_data_default() relpath2index = {} keymap = dict.fromkeys(MediaData._fields) for k in keymap: keymap[k] = k keymap['_ctime'] = 'ctime_' keymap['_mtime'] = 'mtime_' for index, (key, m) in enumerate(media): relpath2index[key] = index tags = m.pop('tags') for tname, v in tags.items(): tag_data[tname].append(v) for k, v in m.items(): data[keymap[k]].append(v) if 'file_name' not in m: data['file_name'].append(basename(key)) data['mtime_'] = [datetime_to_long(x) for x in data['mtime_']] data['ctime_'] = [datetime_to_long(x) for x in data['ctime_']] self._data = data self._tag_data = tag_data self._relpath2index = relpath2index def _delete_record(self, index, relpath): for key in MediaData._fields: del self._data[key][index] for key in self._tag_data: del self._tag_data[key][index] if relpath in self._media: del self._media[relpath] del self._relpath2index[relpath] def _replace_with_last_record(self, index, last): _data = self._data _tag_data = self._tag_data for key in MediaData._fields: _data[key][index] = _data[key][last] for key in self._tag_data: _tag_data[key][index] = _tag_data[key][last] last_relpath = _data['relpath'][last] self._relpath2index[last_relpath] = index def _save_as_v1(self, fp): """Save copy to specified path. This mainly exists for testing and making sure we still read the old saved files. """ def _rewrite_dir(state): "Rewrite directories in the old format." state['files'] = [x[0] for x in state['files']] state['directories'] = [_rewrite_dir(d) for d in state['directories']] state.pop('relpath') state.pop('name') return state fp = open_file(fp, 'wb') media = [(key, self.get(key).to_dict()) for key in self._relpath2index] tags = [(t.name, t.type) for t in self.tags] root = _rewrite_dir(self.root.__getstate__()) processors = [processor.dump(x) for x in self.processors] for k, m in media: m['_ctime'] = long_to_datetime(m['_ctime']) m['_mtime'] = long_to_datetime(m['_mtime']) data = dict( version=1, path=self.path, name=self.name, description=self.description, tags=tags, media=media, root=root, processors=processors ) json_tricks.dump(data, fp, compression=True) fp.close() logger.info('Saved project: %s', self.name)

prance/util/translator.py

elemental-lf/prance

533

"""This submodule contains a JSON reference translator.""" __author__ = '<NAME>' __copyright__ = 'Copyright © 2021 <NAME>' __license__ = 'MIT' __all__ = () import prance.util.url as _url def _reference_key(ref_url, item_path): """ Return a portion of the dereferenced URL. format - ref-url_obj-path """ return ref_url.path.split('/')[-1] + '_' + '_'.join(item_path[1:]) def _local_ref(path): url = '#/' + '/'.join(path) return {'$ref': url} # Underscored to allow some time for the public API to be stabilized. class _RefTranslator: """ Resolve JSON pointers/references in a spec by translation. References to objects in other files are copied to the /components/schemas object of the root document, while being translated to point to the the new object locations. """ def __init__(self, specs, url): """ Construct a JSON reference translator. The translated specs are in the `specs` member after a call to `translate_references` has been made. If a URL is given, it is used as a base for calculating the absolute URL of relative file references. :param dict specs: The parsed specs in which to translate any references. :param str url: [optional] The URL to base relative references on. """ import copy self.specs = copy.deepcopy(specs) self.__strict = True self.__reference_cache = {} self.__collected_references = {} if url: self.url = _url.absurl(url) url_key = (_url.urlresource(self.url), self.__strict) # If we have a url, we want to add ourselves to the reference cache # - that creates a reference loop, but prevents child resolvers from # creating a new resolver for this url. self.__reference_cache[url_key] = self.specs else: self.url = None def translate_references(self): """ Iterate over the specification document, performing the translation. Traverses over the whole document, adding the referenced object from external files to the /components/schemas object in the root document and translating the references to the new location. """ self.specs = self._translate_partial(self.url, self.specs) # Add collected references to the root document. if self.__collected_references: if 'components' not in self.specs: self.specs['components'] = {} if 'schemas' not in self.specs['components']: self.specs['components'].update({'schemas': {}}) self.specs['components']['schemas'].update(self.__collected_references) def _dereference(self, ref_url, obj_path): """ Dereference the URL and object path. Returns the dereferenced object. :param mixed ref_url: The URL at which the reference is located. :param list obj_path: The object path within the URL resource. :param tuple recursions: A recursion stack for resolving references. :return: A copy of the dereferenced value, with all internal references resolved. """ # In order to start dereferencing anything in the referenced URL, we have # to read and parse it, of course. contents = _url.fetch_url(ref_url, self.__reference_cache, strict=self.__strict) # In this inner parser's specification, we can now look for the referenced # object. value = contents if len(obj_path) != 0: from prance.util.path import path_get try: value = path_get(value, obj_path) except (KeyError, IndexError, TypeError) as ex: raise _url.ResolutionError('Cannot resolve reference "%s": %s' % (ref_url.geturl(), str(ex))) # Deep copy value; we don't want to create recursive structures import copy value = copy.deepcopy(value) # Now resolve partial specs value = self._translate_partial(ref_url, value) # That's it! return value def _translate_partial(self, base_url, partial): changes = dict(tuple(self._translating_iterator(base_url, partial, ()))) paths = sorted(changes.keys(), key = len) from prance.util.path import path_set for path in paths: value = changes[path] if len(path) == 0: partial = value else: path_set(partial, list(path), value, create = True) return partial def _translating_iterator(self, base_url, partial, path): from prance.util.iterators import reference_iterator for _, ref_string, item_path in reference_iterator(partial): ref_url, obj_path = _url.split_url_reference(base_url, ref_string) full_path = path + item_path if ref_url.path == self.url.path: # Reference to the root document. ref_path = obj_path else: # Reference to a non-root document. ref_key = _reference_key(ref_url, obj_path) if ref_key not in self.__collected_references: self.__collected_references[ref_key] = None ref_value = self._dereference(ref_url, obj_path) self.__collected_references[ref_key] = ref_value ref_path = ['components', 'schemas', ref_key] ref_obj = _local_ref(ref_path) yield full_path, ref_obj

src/tests/stream.py

LakshyaaSoni/dropSQL

534

from io import StringIO from unittest import TestCase from dropSQL.parser.streams import * class StreamTestCase(TestCase): def test(self): s = '12' cs = Characters(StringIO(s)) ch = cs.peek().ok() self.assertEqual(ch, '1') ch = cs.peek().ok() self.assertEqual(ch, '1') ch = cs.next().ok() self.assertEqual(ch, '1') ch = cs.next().ok() self.assertEqual(ch, '2') r = cs.next() self.assertFalse(r) self.assertTrue(r.err()) r = cs.next() self.assertFalse(r) cs.back() r = cs.next() self.assertTrue(r) self.assertEqual(r.ok(), '2') cs.back(2) r = cs.next() self.assertTrue(r) self.assertEqual(r.ok(), '1')

tests/test_bugs.py

mmibrah2/OpenQL

535

<reponame>mmibrah2/OpenQL import os import filecmp import unittest import numpy as np from openql import openql as ql from utils import file_compare curdir = os.path.dirname(os.path.realpath(__file__)) output_dir = os.path.join(curdir, 'test_output') class Test_bugs(unittest.TestCase): @classmethod def setUp(self): ql.initialize() ql.set_option('output_dir', output_dir) ql.set_option('use_default_gates', 'yes') ql.set_option('log_level', 'LOG_WARNING') # @unittest.expectedFailure # @unittest.skip def test_typecast(self): sweep_points = [1,2] num_circuits = 1 num_qubits = 2 platf = ql.Platform("starmon", 'cc_light') p = ql.Program('test_bug', platf, num_qubits) p.set_sweep_points(sweep_points) k = ql.Kernel('kernel1', platf, num_qubits) qubit = 1 k.identity(np.int(qubit)) k.identity(np.int32(qubit)) k.identity(np.int64(qubit)) k.identity(np.uint(qubit)) k.identity(np.uint32(qubit)) k.identity(np.uint64(qubit)) # add the kernel to the program p.add_kernel(k) # relates to https://github.com/QE-Lab/OpenQL/issues/171 # various runs of compiles were generating different results or in the best # case strange errors. So multiple (NCOMPILES) runs of compile are executed # to make sure there is no error and output generated in all these runs is same # JvS: more likely, it also had to do with the classical register allocator # depending on stuff like Python's garbage collection to free a register. # The register numbers have to be hardcoded now for that reason. def test_stateful_behavior(self): ql.set_option('optimize', 'no') ql.set_option('scheduler', 'ALAP') platform = ql.Platform("myPlatform", 'cc_light') sweep_points = [1] nqubits = 3 nregs = 3 p = ql.Program("statelessProgram", platform, nqubits, nregs) p.set_sweep_points(sweep_points) k = ql.Kernel("aKernel", platform, nqubits, nregs) k.prepz(0) k.gate('rx180', [0]) k.measure(0) rd = ql.CReg(0) rs1 = ql.CReg(1) rs2 = ql.CReg(2) k.classical(rs1, ql.Operation(3)) k.classical(rs1, ql.Operation(4)) k.classical(rd, ql.Operation(rs1, '+', rs2)) p.add_kernel(k) NCOMPILES=50 QISA_fn = os.path.join(output_dir, p.name+'_last.qasm') for i in range(NCOMPILES): p.compile() self.setUpClass() QISA_fn_i = os.path.join(output_dir, p.name+'_'+str(i)+'_last.qasm') os.rename(QISA_fn,QISA_fn_i) for i in range(NCOMPILES-1): QISA_fn_1 = os.path.join(output_dir, p.name+'_'+str(i)+'_last.qasm') QISA_fn_2 = os.path.join(output_dir, p.name+'_'+str(i+1)+'_last.qasm') self.assertTrue( file_compare(QISA_fn_1, QISA_fn_2)) # Unclear how this test works. # When clear, enable it again. # Now it fails, not clear how to repair, so it is disabled. # def test_empty_infinite_loop(self): # name = 'empty_infinite_loop' # in_fn = 'test_' + name + '.cq' # out_fn = 'test_output/' + name + '_out.cq' # gold_fn = 'golden/' + name + '_out.cq' # ql.initialize() # #ql.set_option('log_level', 'LOG_DEBUG') # ql.compile(in_fn) # self.assertTrue(file_compare(out_fn, gold_fn)) if __name__ == '__main__': unittest.main()

utils/setAddress.py

wedvjin/rs485-moist-sensor

536

#!/usr/bin/python """Looks for sensor on the bus and changes it's address to the one specified on command line""" import argparse import minimalmodbus import serial from time import sleep parser = argparse.ArgumentParser() parser.add_argument('address', metavar='ADDR', type=int, choices=range(1, 248), help='An address to set') args = parser.parse_args() ADDRESS1 = 1 ADDRESS2 = args.address minimalmodbus.CLOSE_PORT_AFTER_EACH_CALL = True minimalmodbus.PARITY=serial.PARITY_NONE minimalmodbus.STOPBITS = 2 minimalmodbus.BAUDRATE=19200 minimalmodbus.CLOSE_PORT_AFTER_EACH_CALL = True def scanModbus(): for i in range(1, 248): try: print('Trying address: ' + str(i)) sensor = minimalmodbus.Instrument('/dev/ttyUSB5', slaveaddress=i) addressRead = sensor.read_register(0, functioncode=3) if(i == addressRead): print('FOUND!') return (True, i) except (IOError): print("nope...") pass return (False, 0) # sensor.debug=True (found, i) = scanModbus() if found: print('Found sensor at address: ' + str(i)) try: sensor = minimalmodbus.Instrument('/dev/ttyUSB5', slaveaddress=i) print("writing new address: " + str(ADDRESS2)) sensor.write_register(0, value=ADDRESS2, functioncode=6) sleep(0.2) sensor = minimalmodbus.Instrument('/dev/ttyUSB5', slaveaddress=ADDRESS2) print("reading address from holding register: ") print(sensor.read_register(0, functioncode=3)) except: print "Could not change the address. Check your connections" else: print('No sensor on the bus found')

python/ray/tune/tests/test_tune_save_restore.py

mgelbart/ray

537

# coding: utf-8 import os import pickle import shutil import tempfile import unittest import ray from ray import tune from ray.rllib import _register_all from ray.tune import Trainable from ray.tune.utils import validate_save_restore class SerialTuneRelativeLocalDirTest(unittest.TestCase): local_mode = True prefix = "Serial" class MockTrainable(Trainable): _name = "MockTrainable" def setup(self, config): self.state = {"hi": 1} def step(self): return {"timesteps_this_iter": 1, "done": True} def save_checkpoint(self, checkpoint_dir): checkpoint_path = os.path.join( checkpoint_dir, "checkpoint-{}".format(self._iteration) ) with open(checkpoint_path, "wb") as f: pickle.dump(self.state, f) return checkpoint_path def load_checkpoint(self, checkpoint_path): with open(checkpoint_path, "rb") as f: extra_data = pickle.load(f) self.state.update(extra_data) def setUp(self): self.absolute_local_dir = None ray.init(num_cpus=1, num_gpus=0, local_mode=self.local_mode) def tearDown(self): if self.absolute_local_dir is not None: shutil.rmtree(self.absolute_local_dir, ignore_errors=True) self.absolute_local_dir = None ray.shutdown() # Without this line, test_tune_server.testAddTrial would fail. _register_all() def _get_trial_dir(self, absoulte_exp_dir): print("looking for", self.MockTrainable._name) print("in", os.listdir(absoulte_exp_dir)) trial_dirname = next( ( child_dir for child_dir in os.listdir(absoulte_exp_dir) if ( os.path.isdir(os.path.join(absoulte_exp_dir, child_dir)) and child_dir.startswith(self.MockTrainable._name) ) ) ) trial_absolute_dir = os.path.join(absoulte_exp_dir, trial_dirname) return trial_dirname, trial_absolute_dir def _train(self, exp_name, local_dir, absolute_local_dir): (trial,) = tune.run( self.MockTrainable, name=exp_name, stop={"training_iteration": 1}, checkpoint_freq=1, local_dir=local_dir, config={"env": "CartPole-v0", "log_level": "DEBUG"}, ).trials exp_dir = os.path.join(absolute_local_dir, exp_name) _, abs_trial_dir = self._get_trial_dir(exp_dir) self.assertIsNone(trial.error_file) self.assertEqual(trial.local_dir, exp_dir) self.assertEqual(trial.logdir, abs_trial_dir) self.assertTrue(os.path.isdir(absolute_local_dir), absolute_local_dir) self.assertTrue(os.path.isdir(exp_dir)) self.assertTrue(os.path.isdir(abs_trial_dir)) self.assertTrue( os.path.isfile( os.path.join(abs_trial_dir, "checkpoint_000001/checkpoint-1") ) ) def _restore(self, exp_name, local_dir, absolute_local_dir): trial_name, abs_trial_dir = self._get_trial_dir( os.path.join(absolute_local_dir, exp_name) ) checkpoint_path = os.path.join( local_dir, exp_name, trial_name, "checkpoint_000001/checkpoint-1" ) # Relative checkpoint path # The file tune would find. The absolute checkpoint path. tune_find_file = os.path.abspath(os.path.expanduser(checkpoint_path)) self.assertTrue( os.path.isfile(tune_find_file), "{} is not exist!".format(tune_find_file) ) (trial,) = tune.run( self.MockTrainable, name=exp_name, stop={"training_iteration": 2}, # train one more iteration. restore=checkpoint_path, # Restore the checkpoint config={"env": "CartPole-v0", "log_level": "DEBUG"}, ).trials self.assertIsNone(trial.error_file) def testDottedRelativePath(self): local_dir = "./test_dotted_relative_local_dir" exp_name = self.prefix + "DottedRelativeLocalDir" absolute_local_dir = os.path.abspath(local_dir) self.absolute_local_dir = absolute_local_dir self.assertFalse(os.path.exists(absolute_local_dir)) self._train(exp_name, local_dir, absolute_local_dir) self._restore(exp_name, local_dir, absolute_local_dir) def testRelativePath(self): local_dir = "test_relative_local_dir" exp_name = self.prefix + "RelativePath" absolute_local_dir = os.path.abspath(local_dir) self.absolute_local_dir = absolute_local_dir self.assertFalse(os.path.exists(absolute_local_dir)) self._train(exp_name, local_dir, absolute_local_dir) self._restore(exp_name, local_dir, absolute_local_dir) def testTildeAbsolutePath(self): local_dir = "~/test_tilde_absolute_local_dir" exp_name = self.prefix + "TildeAbsolutePath" absolute_local_dir = os.path.abspath(os.path.expanduser(local_dir)) self.absolute_local_dir = absolute_local_dir self.assertFalse(os.path.exists(absolute_local_dir)) self._train(exp_name, local_dir, absolute_local_dir) self._restore(exp_name, local_dir, absolute_local_dir) def testTempfile(self): local_dir = tempfile.mkdtemp() exp_name = self.prefix + "Tempfile" self.absolute_local_dir = local_dir self._train(exp_name, local_dir, local_dir) self._restore(exp_name, local_dir, local_dir) def testCheckpointWithNoop(self): """Tests that passing the checkpoint_dir right back works.""" class MockTrainable(Trainable): def setup(self, config): pass def step(self): return {"score": 1} def save_checkpoint(self, checkpoint_dir): with open(os.path.join(checkpoint_dir, "test.txt"), "wb") as f: pickle.dump("test", f) return checkpoint_dir def load_checkpoint(self, checkpoint_dir): with open(os.path.join(checkpoint_dir, "test.txt"), "rb") as f: x = pickle.load(f) assert x == "test" return checkpoint_dir validate_save_restore(MockTrainable) validate_save_restore(MockTrainable, use_object_store=True) if __name__ == "__main__": import pytest import sys sys.exit(pytest.main(["-v", __file__]))

src/manifest.py

silent1mezzo/lightsaber

538

MANIFEST = { "hilt": { "h1": { "offsets": {"blade": 0, "button": {"x": (8, 9), "y": (110, 111)}}, "colours": { "primary": (216, 216, 216), # d8d8d8 "secondary": (141, 141, 141), # 8d8d8d "tertiary": (180, 97, 19), # b46113 }, "length": 24, "materials": "Alloy metal/Salvaged materials", }, "h2": { "offsets": {"blade": 20, "button": {"x": (8, 8), "y": (100, 105)}}, "colours": { "primary": (112, 112, 112), # 707070 "secondary": (0, 0, 0), # 000000 "tertiary": (212, 175, 55), # 000000 }, "length": 24, "materials": "Alloy metal and carbon composite", }, "h3": { "offsets": {"blade": 0, "button": {"x": (10, 10), "y": (100, 118)}}, "colours": { "primary": (157, 157, 157), # 707070 "secondary": (0, 0, 0), # 000000 "tertiary": (180, 97, 19), # b46113 }, "length": 24, "materials": "Alloy metal", }, "h4": { "offsets": {"blade": 7, "button": {"x": (8, 9), "y": (92, 100)}}, "colours": { "primary": (0, 0, 0), # 000000 "secondary": (157, 157, 157), # 9d9d9d "tertiary": (180, 97, 19), # b46113 }, "length": 13, "materials": "Alloy metal", }, "h5": { "offsets": {"blade": 0, "button": {"x": (8, 8), "y": (92, 105)}}, "colours": { "primary": (111, 111, 111), # 6f6f6f "secondary": (0, 0, 0), # 000000 "tertiary": (180, 97, 19), # b46113 }, "length": 24, "materials": "Alloy metal", }, "h6": { "offsets": {"blade": 2, "button": {"x": (8, 9), "y": (112, 113)}}, "colours": { "primary": (120, 120, 120), # 787878 "secondary": (0, 0, 0), # 000000 "tertiary": (180, 97, 19), # b46113 }, "length": 22, "materials": "Alloy metal/Salvaged materials", }, "h7": { "offsets": {"blade": 0, "button": {"x": (8, 9), "y": (105, 113)}}, "colours": { "primary": (192, 192, 192), # c0c0c0 "secondary": (255, 215, 0), # ffd700 "tertiary": (0, 0, 0), # 000000 }, "length": 22, "materials": "Alloy metal and Gold", }, "h8": { "offsets": {"blade": 0, "button": {"x": (8, 9), "y": (100, 111)}}, "colours": { "primary": (216, 216, 216), # d8d8d8 "secondary": (180, 97, 19), # b46113 "tertiary": (0, 0, 0), # 000000 }, "length": 24, "materials": "Alloy metal/Copper", }, }, "blade": { "b1": {"colour": "Red", "crystal": "Adegan crystal", "type": "Sith"}, "b2": {"colour": "Blue", "crystal": "Zophis crystal", "type": "Jedi"}, "b3": {"colour": "Green", "crystal": "Nishalorite stone", "type": "Jedi"}, "b4": {"colour": "Yellow", "crystal": "Kimber stone", "type": "Jedi"}, "b5": {"colour": "White", "crystal": "Dragite gem", "type": "Jedi"}, "b6": {"colour": "Purple", "crystal": "Krayt dragon pearl", "type": "Jedi"}, "b7": {"colour": "Blue/Green", "crystal": "Dantari crystal", "type": "Jedi"}, "b8": { "colour": "Orange", "crystal": ["Ilum crystal", "Ultima Pearl"], "type": "Sith", }, "b9": { "colour": "Black", "crystal": "Obsidian", "type": ["Jedi", "Mandalorian"], }, }, "pommel": { "p1": {"length": 5,}, "p2": {"length": 14,}, "p3": {"length": 3,}, "p4": {"length": 8,}, "p5": {"length": 5,}, "p6": {"length": 5,}, "p7": {"length": 8,}, }, # These are lightsabers for a specific Jedi or Sith. Should use their name instead of "unique_urls": {""}, }

tests/gpuarray/test_basic_ops.py

canyon289/Theano-PyMC

539

import numpy as np import pytest import theano import theano.tensor as tt # Don't import test classes otherwise they get tested as part of the file from tests import unittest_tools as utt from tests.gpuarray.config import mode_with_gpu, mode_without_gpu, test_ctx_name from tests.tensor.test_basic import ( TestAlloc, TestComparison, TestJoinAndSplit, TestReshape, ) from tests.tensor.utils import rand, safe_make_node from theano.gpuarray.basic_ops import ( GpuAlloc, GpuAllocEmpty, GpuContiguous, GpuEye, GpuFromHost, GpuJoin, GpuReshape, GpuSplit, GpuToGpu, GpuTri, HostFromGpu, gpu_contiguous, gpu_join, host_from_gpu, ) from theano.gpuarray.elemwise import GpuDimShuffle, GpuElemwise from theano.gpuarray.subtensor import GpuSubtensor from theano.gpuarray.type import GpuArrayType, get_context, gpuarray_shared_constructor from theano.tensor import TensorType from theano.tensor.basic import alloc pygpu = pytest.importorskip("pygpu") gpuarray = pygpu.gpuarray utt.seed_rng() rng = np.random.RandomState(seed=utt.fetch_seed()) def inplace_func( inputs, outputs, mode=None, allow_input_downcast=False, on_unused_input="raise", name=None, ): if mode is None: mode = mode_with_gpu return theano.function( inputs, outputs, mode=mode, allow_input_downcast=allow_input_downcast, accept_inplace=True, on_unused_input=on_unused_input, name=name, ) def fake_shared(value, name=None, strict=False, allow_downcast=None, **kwargs): from theano.tensor.sharedvar import scalar_constructor, tensor_constructor for c in (gpuarray_shared_constructor, tensor_constructor, scalar_constructor): try: return c( value, name=name, strict=strict, allow_downcast=allow_downcast, **kwargs ) except TypeError: continue def rand_gpuarray(*shape, **kwargs): r = rng.rand(*shape) * 2 - 1 dtype = kwargs.pop("dtype", theano.config.floatX) cls = kwargs.pop("cls", None) if len(kwargs) != 0: raise TypeError("Unexpected argument %s", list(kwargs.keys())[0]) return gpuarray.array(r, dtype=dtype, cls=cls, context=get_context(test_ctx_name)) def makeTester( name, op, gpu_op, cases, checks=None, mode_gpu=mode_with_gpu, mode_nogpu=mode_without_gpu, skip=False, eps=1e-10, ): if checks is None: checks = {} _op = op _gpu_op = gpu_op _cases = cases _skip = skip _checks = checks class Checker(utt.OptimizationTestMixin): op = staticmethod(_op) gpu_op = staticmethod(_gpu_op) cases = _cases skip = _skip checks = _checks def setup_method(self): eval(self.__class__.__module__ + "." + self.__class__.__name__) def test_all(self): if skip: pytest.skip(skip) for testname, inputs in cases.items(): for _ in range(len(inputs)): if type(inputs[_]) is float: inputs[_] = np.asarray(inputs[_], dtype=theano.config.floatX) self.run_case(testname, inputs) def run_case(self, testname, inputs): inputs_ref = [theano.shared(inp) for inp in inputs] inputs_tst = [theano.shared(inp) for inp in inputs] try: node_ref = safe_make_node(self.op, *inputs_ref) node_tst = safe_make_node(self.op, *inputs_tst) except Exception as exc: err_msg = ( "Test %s::%s: Error occurred while making " "a node with inputs %s" ) % (self.gpu_op, testname, inputs) exc.args += (err_msg,) raise try: f_ref = inplace_func([], node_ref.outputs, mode=mode_nogpu) f_tst = inplace_func([], node_tst.outputs, mode=mode_gpu) except Exception as exc: err_msg = ( "Test %s::%s: Error occurred while trying to " "make a Function" ) % (self.gpu_op, testname) exc.args += (err_msg,) raise self.assertFunctionContains1(f_tst, self.gpu_op) ref_e = None try: expecteds = f_ref() except Exception as exc: ref_e = exc try: variables = f_tst() except Exception as exc: if ref_e is None: err_msg = ( "Test %s::%s: exception when calling the " "Function" ) % (self.gpu_op, testname) exc.args += (err_msg,) raise else: # if we raised an exception of the same type we're good. if isinstance(exc, type(ref_e)): return else: err_msg = ( "Test %s::%s: exception raised during test " "call was not the same as the reference " "call (got: %s, expected %s)" % (self.gpu_op, testname, type(exc), type(ref_e)) ) exc.args += (err_msg,) raise for i, (variable, expected) in enumerate(zip(variables, expecteds)): condition = ( variable.dtype != expected.dtype or variable.shape != expected.shape or not TensorType.values_eq_approx(variable, expected) ) assert not condition, ( "Test %s::%s: Output %s gave the wrong " "value. With inputs %s, expected %s " "(dtype %s), got %s (dtype %s)." % ( self.op, testname, i, inputs, expected, expected.dtype, variable, variable.dtype, ) ) for description, check in self.checks.items(): assert check(inputs, variables), ( "Test %s::%s: Failed check: %s " "(inputs were %s, ouputs were %s)" ) % (self.op, testname, description, inputs, variables) Checker.__name__ = name if hasattr(Checker, "__qualname__"): Checker.__qualname__ = name return Checker def test_transfer_cpu_gpu(): a = tt.fmatrix("a") g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g") av = np.asarray(rng.rand(5, 4), dtype="float32") gv = gpuarray.array(av, context=get_context(test_ctx_name)) f = theano.function([a], GpuFromHost(test_ctx_name)(a)) fv = f(av) assert GpuArrayType.values_eq(fv, gv) f = theano.function([g], host_from_gpu(g)) fv = f(gv) assert np.all(fv == av) def test_transfer_gpu_gpu(): g = GpuArrayType( dtype="float32", broadcastable=(False, False), context_name=test_ctx_name )() av = np.asarray(rng.rand(5, 4), dtype="float32") gv = gpuarray.array(av, context=get_context(test_ctx_name)) mode = mode_with_gpu.excluding( "cut_gpua_host_transfers", "local_cut_gpua_host_gpua" ) f = theano.function([g], GpuToGpu(test_ctx_name)(g), mode=mode) topo = f.maker.fgraph.toposort() assert len(topo) == 1 assert isinstance(topo[0].op, GpuToGpu) fv = f(gv) assert GpuArrayType.values_eq(fv, gv) def test_transfer_strided(): # This is just to ensure that it works in theano # libgpuarray has a much more comprehensive suit of tests to # ensure correctness a = tt.fmatrix("a") g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g") av = np.asarray(rng.rand(5, 8), dtype="float32") gv = gpuarray.array(av, context=get_context(test_ctx_name)) av = av[:, ::2] gv = gv[:, ::2] f = theano.function([a], GpuFromHost(test_ctx_name)(a)) fv = f(av) assert GpuArrayType.values_eq(fv, gv) f = theano.function([g], host_from_gpu(g)) fv = f(gv) assert np.all(fv == av) def gpu_alloc_expected(x, *shp): g = gpuarray.empty(shp, dtype=x.dtype, context=get_context(test_ctx_name)) g[:] = x return g TestGpuAlloc = makeTester( name="GpuAllocTester", # The +1 is there to allow the lift to the GPU. op=lambda *args: alloc(*args) + 1, gpu_op=GpuAlloc(test_ctx_name), cases=dict( correct01=(rand(), np.int32(7)), # just gives a DeepCopyOp with possibly wrong results on the CPU # correct01_bcast=(rand(1), np.int32(7)), correct02=(rand(), np.int32(4), np.int32(7)), correct12=(rand(7), np.int32(4), np.int32(7)), correct13=(rand(7), np.int32(2), np.int32(4), np.int32(7)), correct23=(rand(4, 7), np.int32(2), np.int32(4), np.int32(7)), bad_shape12=(rand(7), np.int32(7), np.int32(5)), ), ) class TestGPUAlloc(TestAlloc): dtype = "float32" mode = mode_with_gpu shared = staticmethod(gpuarray_shared_constructor) allocs = [GpuAlloc(test_ctx_name), GpuAlloc(test_ctx_name), tt.Alloc()] def test_alloc_empty(): for dt in ["float32", "int8"]: f = theano.function([], GpuAllocEmpty(dt, context_name=test_ctx_name)(2, 3)) assert len(f.maker.fgraph.apply_nodes) == 1 out = f() assert out.shape == (2, 3) assert out.dtype == dt f = theano.function( [], [ GpuAllocEmpty("uint64", test_ctx_name)(3, 2), GpuAllocEmpty("uint64", test_ctx_name)(3, 2), ], ) out = f() assert out[0].shape == (3, 2) assert out[0].dtype == "uint64" assert out[1].shape == (3, 2) assert out[1].dtype == "uint64" assert ( len( [ node for node in f.maker.fgraph.apply_nodes if isinstance(node.op, GpuAllocEmpty) ] ) == 1 ) def test_shape(): x = GpuArrayType(dtype="float32", broadcastable=[False, False, False])() v = gpuarray.zeros((3, 4, 5), dtype="float32", context=get_context(test_ctx_name)) f = theano.function([x], x.shape) topo = f.maker.fgraph.toposort() assert np.all(f(v) == (3, 4, 5)) if theano.config.mode != "FAST_COMPILE": assert len(topo) == 4 assert isinstance(topo[0].op, tt.opt.Shape_i) assert isinstance(topo[1].op, tt.opt.Shape_i) assert isinstance(topo[2].op, tt.opt.Shape_i) assert isinstance(topo[3].op, tt.opt.MakeVector) mode = mode_with_gpu.excluding("local_shape_to_shape_i") f = theano.function([x], x.shape, mode=mode) topo = f.maker.fgraph.toposort() assert np.all(f(v) == (3, 4, 5)) assert len(topo) == 1 assert isinstance(topo[0].op, tt.Shape) def test_gpu_contiguous(): a = tt.fmatrix("a") i = tt.iscalar("i") a_val = np.asarray(np.random.rand(4, 5), dtype="float32") # The reshape is needed otherwise we make the subtensor on the CPU # to transfer less data. f = theano.function( [a, i], gpu_contiguous(a.reshape((5, 4))[::i]), mode=mode_with_gpu ) topo = f.maker.fgraph.toposort() assert any([isinstance(node.op, GpuSubtensor) for node in topo]) assert any([isinstance(node.op, GpuContiguous) for node in topo]) assert f(a_val, 1).flags.c_contiguous assert f(a_val, 2).flags.c_contiguous assert f(a_val, 2).flags.c_contiguous class TestGPUReshape(TestReshape): def setup_method(self): self.shared = gpuarray_shared_constructor self.op = GpuReshape self.mode = mode_with_gpu self.ignore_topo = ( HostFromGpu, GpuFromHost, theano.compile.DeepCopyOp, GpuDimShuffle, GpuElemwise, tt.opt.Shape_i, tt.opt.MakeVector, ) assert self.op == GpuReshape class TestGPUComparison(TestComparison): def setup_method(self): utt.seed_rng() self.mode = mode_with_gpu self.shared = gpuarray_shared_constructor self.dtypes = ["float64", "float32"] class TestGPUJoinAndSplit(TestJoinAndSplit): def setup_method(self): self.mode = mode_with_gpu.excluding("constant_folding") self.join_op = GpuJoin() self.split_op_class = GpuSplit # Use join instead of MakeVector since there is no MakeVector on GPU self.make_vector_op = GpuJoin() # this is to avoid errors with limited devices self.floatX = "float32" self.hide_error = theano.config.mode not in ["DebugMode", "DEBUG_MODE"] def shared(x, **kwargs): return gpuarray_shared_constructor(x, target=test_ctx_name, **kwargs) self.shared = shared def test_gpusplit_opt(self): # Test that we move the node to the GPU # Also test float16 computation at the same time. rng = np.random.RandomState(seed=utt.fetch_seed()) m = self.shared(rng.rand(4, 6).astype("float16")) o = tt.Split(2)(m, 0, [2, 2]) assert o[0].dtype == "float16" f = theano.function([], o, mode=self.mode) assert any( [ isinstance(node.op, self.split_op_class) for node in f.maker.fgraph.toposort() ] ) o1, o2 = f() assert np.allclose(o1, m.get_value(borrow=True)[:2]) assert np.allclose(o2, m.get_value(borrow=True)[2:]) def test_gpujoin_gpualloc(): a = tt.fmatrix("a") a_val = np.asarray(np.random.rand(4, 5), dtype="float32") b = tt.fmatrix("b") b_val = np.asarray(np.random.rand(3, 5), dtype="float32") f = theano.function( [a, b], tt.join(0, tt.zeros_like(a), tt.ones_like(b)) + 4, mode=mode_without_gpu ) f_gpu = theano.function( [a, b], tt.join(0, tt.zeros_like(a), tt.ones_like(b)), mode=mode_with_gpu ) f_gpu2 = theano.function( [a, b], tt.join(0, tt.zeros_like(a), tt.ones_like(b)) + 4, mode=mode_with_gpu ) assert sum([node.op == tt.alloc for node in f.maker.fgraph.toposort()]) == 2 assert sum([node.op == tt.join_ for node in f.maker.fgraph.toposort()]) == 1 assert ( sum([isinstance(node.op, GpuAlloc) for node in f_gpu.maker.fgraph.toposort()]) == 2 ) assert sum([node.op == gpu_join for node in f_gpu.maker.fgraph.toposort()]) == 1 assert ( sum([isinstance(node.op, GpuAlloc) for node in f_gpu2.maker.fgraph.toposort()]) == 2 ) assert sum([node.op == gpu_join for node in f_gpu2.maker.fgraph.toposort()]) == 1 assert np.allclose(f(a_val, b_val), f_gpu2(a_val, b_val)) def test_gpueye(): def check(dtype, N, M_=None, k=0): # Theano does not accept None as a tensor. # So we must use a real value. M = M_ # Currently DebugMode does not support None as inputs even if this is # allowed. if M is None: M = N N_symb = tt.iscalar() M_symb = tt.iscalar() k_symb = tt.iscalar() out = tt.eye(N_symb, M_symb, k_symb, dtype=dtype) + np.array(1).astype(dtype) f = theano.function([N_symb, M_symb, k_symb], out, mode=mode_with_gpu) result = np.asarray(f(N, M, k)) - np.array(1).astype(dtype) assert np.allclose(result, np.eye(N, M_, k, dtype=dtype)) assert result.dtype == np.dtype(dtype) assert any([isinstance(node.op, GpuEye) for node in f.maker.fgraph.toposort()]) for dtype in ["float32", "int32", "float16"]: check(dtype, 3) # M != N, k = 0 check(dtype, 3, 5) check(dtype, 5, 3) # N == M, k != 0 check(dtype, 3, 3, 1) check(dtype, 3, 3, -1) # N < M, k != 0 check(dtype, 3, 5, 1) check(dtype, 3, 5, -1) # N > M, k != 0 check(dtype, 5, 3, 1) check(dtype, 5, 3, -1) # k > M, -k > N, k > M, k > N check(dtype, 5, 3, 3) check(dtype, 3, 5, 3) check(dtype, 5, 3, -3) check(dtype, 3, 5, -3) check(dtype, 5, 3, 6) check(dtype, 3, 5, -6) def test_hostfromgpu_shape_i(): # Test that the shape is lifted over hostfromgpu m = mode_with_gpu.including( "local_dot_to_dot22", "local_dot22_to_dot22scalar", "specialize" ) a = tt.fmatrix("a") ca = theano.gpuarray.type.GpuArrayType("float32", (False, False))() av = np.asarray(np.random.rand(5, 4), dtype="float32") cv = gpuarray.asarray( np.random.rand(5, 4), dtype="float32", context=get_context(test_ctx_name) ) f = theano.function([a], GpuFromHost(test_ctx_name)(a), mode=m) assert any(isinstance(x.op, GpuFromHost) for x in f.maker.fgraph.toposort()) f = theano.function([a], GpuFromHost(test_ctx_name)(a).shape, mode=m) topo = f.maker.fgraph.toposort() assert isinstance(topo[0].op, tt.opt.Shape_i) assert isinstance(topo[1].op, tt.opt.Shape_i) assert isinstance(topo[2].op, tt.opt.MakeVector) assert tuple(f(av)) == (5, 4) f = theano.function([ca], host_from_gpu(ca), mode=m) assert host_from_gpu in [x.op for x in f.maker.fgraph.toposort()] f = theano.function([ca], host_from_gpu(ca).shape, mode=m) topo = f.maker.fgraph.toposort() assert isinstance(topo[0].op, theano.compile.Shape_i) assert isinstance(topo[1].op, theano.compile.Shape_i) assert isinstance(topo[2].op, tt.opt.MakeVector) assert tuple(f(cv)) == (5, 4) def test_Gpujoin_inplace(): # Test Gpujoin to work inplace. # # This function tests the case when several elements are passed to the # Gpujoin function but all except one of them are empty. In this case # Gpujoin should work inplace and the output should be the view of the # non-empty element. s = tt.lscalar() data = np.array([3, 4, 5], dtype=theano.config.floatX) x = gpuarray_shared_constructor(data, borrow=True) z = tt.zeros((s,)) join = GpuJoin(view=0) c = join(0, x, z) f = theano.function([s], theano.Out(c, borrow=True)) if not isinstance(mode_with_gpu, theano.compile.DebugMode): assert x.get_value(borrow=True, return_internal_type=True) is f(0) assert np.allclose(f(0), [3, 4, 5]) def test_gpu_tril_triu(): def check_l(m, k=0): m_symb = tt.matrix(dtype=m.dtype) k_symb = tt.iscalar() f = theano.function( [m_symb, k_symb], tt.tril(m_symb, k_symb), mode=mode_with_gpu ) result = f(m, k) assert np.allclose(result, np.tril(m, k)) assert result.dtype == np.dtype(dtype) assert any([isinstance(node.op, GpuTri) for node in f.maker.fgraph.toposort()]) def check_u(m, k=0): m_symb = tt.matrix(dtype=m.dtype) k_symb = tt.iscalar() f = theano.function( [m_symb, k_symb], tt.triu(m_symb, k_symb), mode=mode_with_gpu ) result = f(m, k) assert np.allclose(result, np.triu(m, k)) assert result.dtype == np.dtype(dtype) assert any([isinstance(node.op, GpuTri) for node in f.maker.fgraph.toposort()]) utt.seed_rng() test_rng = np.random.RandomState(seed=utt.fetch_seed()) for dtype in ["float64", "float32", "float16"]: # try a big one m = np.asarray(test_rng.rand(5000, 5000) * 2 - 1, dtype=dtype) check_l(m, 0) check_l(m, 1) check_l(m, -1) check_u(m, 0) check_u(m, 1) check_u(m, -1) m = np.asarray(test_rng.rand(10, 10) * 2 - 1, dtype=dtype) check_l(m, 0) check_l(m, 1) check_l(m, -1) check_u(m, 0) check_u(m, 1) check_u(m, -1) m = np.asarray(test_rng.rand(10, 5) * 2 - 1, dtype=dtype) check_l(m, 0) check_l(m, 1) check_l(m, -1) check_u(m, 0) check_u(m, 1) check_u(m, -1) def test_gputri(): def check(dtype, N, M_=None, k=0): # Theano does not accept None as a tensor. # So we must use a real value. M = M_ # Currently DebugMode does not support None as inputs even if this is # allowed. if M is None: M = N N_symb = tt.iscalar() M_symb = tt.iscalar() k_symb = tt.iscalar() out = tt.tri(N_symb, M_symb, k_symb, dtype=dtype) + np.array(1).astype(dtype) f = theano.function([N_symb, M_symb, k_symb], out, mode=mode_with_gpu) result = np.asarray(f(N, M, k)) - np.array(1).astype(dtype) assert np.allclose(result, np.tri(N, M_, k, dtype=dtype)) assert result.dtype == np.dtype(dtype) assert any([isinstance(node.op, GpuTri) for node in f.maker.fgraph.toposort()]) for dtype in ["float64", "float32", "int32", "float16"]: # try a big one check(dtype, 1000, 1000, 0) check(dtype, 1000, 1000, -400) check(dtype, 1000, 1000, 400) check(dtype, 5) # M != N, k = 0 check(dtype, 3, 5) check(dtype, 5, 3) # N == M, k != 0 check(dtype, 3, 3, 1) check(dtype, 3, 3, -1) # N < M, k != 0 check(dtype, 3, 5, 1) check(dtype, 3, 5, -1) # N > M, k != 0 check(dtype, 5, 3, 1) check(dtype, 5, 3, -1) # k > M, -k > N, k > M, k > N check(dtype, 5, 3, 3) check(dtype, 3, 5, 3) check(dtype, 5, 3, -3) check(dtype, 3, 5, -3) check(dtype, 5, 3, 6) check(dtype, 3, 5, -6)

apps/interactor/tests/commander/commands/test_animations.py

Djelibeybi/photons

540

<gh_stars>10-100 # coding: spec from interactor.commander.store import store, load_commands from photons_app.mimic.event import Events from photons_app import helpers as hp from photons_canvas.points.simple_messages import Set64 from unittest import mock import pytest @pytest.fixture() def store_clone(): load_commands() return store.clone() @pytest.fixture() def final_future(): fut = hp.create_future() try: yield fut finally: fut.cancel() @pytest.fixture() async def sender(devices, final_future): async with devices.for_test(final_future) as sender: yield sender @pytest.fixture() async def make_server(store_clone, server_wrapper, FakeTime, MockedCallLater, sender, final_future): with FakeTime() as t: async with MockedCallLater(t) as m: async with server_wrapper(store_clone, sender, final_future) as server: yield server, m @pytest.fixture() def server(make_server): return make_server[0] @pytest.fixture() def m(make_server): return make_server[1] @pytest.fixture(autouse=True) def set_async_timeout(request): request.applymarker(pytest.mark.async_timeout(15)) describe "Animation Commands": async it "can get info and help", server, m: await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, json_output={"animations": {}, "paused": []}, ) got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/help"}, ) assert b"Available animations include" in got assert b"* dice" in got assert b"To see options for a particular animation, run this again" in got got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/help", "args": {"animation_name": "dice"}}, ) assert b"dice animation" in got assert b"This animation has the following options:" in got assert b"colour range options" in got async it "can control an animation", server, m: await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, json_output={"animations": {}, "paused": []}, ) identity = "first" got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/start", "args": {"identity": identity}}, ) assert "animations" in got assert got["animations"] == [identity] assert got["started"] == identity identity2 = "second" got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/start", "args": {"identity": identity2}}, ) assert "animations" in got identities = [identity, identity2] assert got["animations"] == identities assert got["started"] == identity2 info = await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, ) assert info == {"animations": {identity: mock.ANY, identity2: mock.ANY}, "paused": []} # pause await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": identity}}, json_output={"animations": identities, "paused": [identity], "pausing": [identity]}, ) await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": identity2}}, json_output={ "animations": identities, "paused": identities, "pausing": [identity2], }, ) # resume await server.assertCommand( "/v1/lifx/command", {"command": "animation/resume", "args": {"resume": identity2}}, json_output={ "animations": identities, "paused": [identity], "resuming": [identity2], }, ) # pause multiple await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": identities}}, json_output={"animations": identities, "paused": identities, "pausing": identities}, ) # resume await server.assertCommand( "/v1/lifx/command", {"command": "animation/resume", "args": {"resume": identities}}, json_output={ "animations": identities, "paused": [], "resuming": identities, }, ) # pause await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": identity}}, json_output={"animations": identities, "paused": [identity], "pausing": [identity]}, ) # info info = await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, ) assert info["animations"] == {identity: mock.ANY, identity2: mock.ANY} assert info["paused"] == [identity] # stop await server.assertCommand( "/v1/lifx/command", {"command": "animation/stop", "args": {"stop": identity}}, json_output={ "animations": [identity, identity2], "paused": [identity], "stopping": [identity], }, ) await m.add(0.5) # info info = await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, ) assert info["animations"] == {identity2: mock.ANY} assert info["paused"] == [] async it "pausing an animation actually pauses the animation", devices, server, m: tile = devices["tile"] io = tile.io["MEMORY"] store = devices.store(tile) store.clear() first_set_64 = tile.attrs.event_waiter.wait_for_incoming(io, Set64) # start got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/start", "args": {"animations": [["balls", {"every": 3}]]}}, ) identity = got["started"] await first_set_64 now = store.count(Events.INCOMING(tile, io, pkt=Set64)) assert now > 0 await m.add(5) now2 = store.count(Events.INCOMING(tile, io, pkt=Set64)) assert now2 > now identity = got["started"] await m.add(5) assert store.count(Events.INCOMING(tile, io, pkt=Set64)) > now # pause await server.assertCommand( "/v1/lifx/command", {"command": "animation/pause", "args": {"pause": [identity]}}, ) await m.add(5) store.clear() await m.add(5) assert store.count(Events.INCOMING(tile, io, pkt=Set64)) == 0 # resume await server.assertCommand( "/v1/lifx/command", {"command": "animation/resume", "args": {"resume": [identity]}}, ) await m.add(5) assert store.count(Events.INCOMING(tile, io, pkt=Set64)) > 0 # stop await server.assertCommand( "/v1/lifx/command", {"command": "animation/stop", "args": {"stop": [identity]}}, ) store.clear() await m.add(5) store.clear() await m.add(5) assert store.count(Events.INCOMING(tile, io, pkt=Set64)) == 0 # info await server.assertCommand( "/v1/lifx/command", {"command": "animation/info"}, json_output={"animations": {}, "paused": []}, ) async it "can get information", server, m: # start got = await server.assertCommand( "/v1/lifx/command", {"command": "animation/start", "args": {"animations": [["balls", {"every": 0.3}]]}}, ) identity = got["started"] info = await server.assertCommand("/v1/lifx/command", {"command": "animation/info"}) assert info["paused"] == [] assert identity in info["animations"] assert info["animations"][identity]["animations_ran"] == 1 assert info["animations"][identity]["current_animation"] == { "name": "balls", "options": { "ball_colors": "<ManyColor:[((0, 360), (1000.0, 1000.0), (1000.0, 1000.0), (3500.0, 3500.0))]>", "fade_amount": 0.02, "num_balls": 5, "rate": "<Rate 0.9 -> 1>", }, "started": mock.ANY, } assert info["animations"][identity]["options"]["combined"] assert "unlocked" in info["animations"][identity]["options"]["pauser"] assert info["animations"][identity]["options"]["noisy_network"] == 0 specific = await server.assertCommand( "/v1/lifx/command", {"command": "animation/info", "args": {"identity": identity}} ) info["animations"][identity]["current_animation"]["started"] = mock.ANY assert info["animations"][identity] == specific

.ipython/profile_pytube/startup/init.py

showa-yojyo/dotfiles

541

from pytube import YouTube def download_video(watch_url): yt = YouTube(watch_url) (yt.streams .filter(progressive=True, file_extension='mp4') .order_by('resolution') .desc() .first() .download())

ament_tools/setup_arguments.py

richmattes/ament_tools

542

<filename>ament_tools/setup_arguments.py # Copyright 2015 Open Source Robotics Foundation, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ast import distutils.core import os try: import setuptools except ImportError: pass import subprocess import sys from threading import Lock from ament_tools.build_type import get_command_prefix from ament_tools.helper import quote_shell_command setup_lock = None def get_setup_arguments_with_context(build_type, context): """ Capture the arguments of the setup() function in the setup.py file. To provide a custom environment when introspecting the setup() function a separate Python interpreter is being used which can have an extended PYTHONPATH etc. :param build_type: the build type :param context: the context :type context: :py:class:`ament_tools.context.Context` :returns: a dictionary containing the arguments of the setup() function """ prefix = get_command_prefix( '%s__setup' % build_type, context.build_space, context.build_dependencies) ament_tools_path = os.path.dirname(os.path.dirname(__file__)) setuppy = os.path.join(context.source_space, 'setup.py') if os.name == 'nt': ament_tools_path = ament_tools_path.replace(os.sep, os.altsep) setuppy = setuppy.replace(os.sep, os.altsep) code_lines = [ 'import sys', "sys.path.insert(0, '%s')" % ament_tools_path, 'from ament_tools.setup_arguments import get_setup_arguments', "print(repr(get_setup_arguments('%s')))" % setuppy] # invoke get_setup_arguments() in a separate interpreter cmd = prefix + [sys.executable, '-c', ';'.join(code_lines)] cmd = quote_shell_command(cmd) result = subprocess.run( cmd, stdout=subprocess.PIPE, shell=True, check=True) output = result.stdout.decode() return ast.literal_eval(output) def get_setup_arguments(setup_py_path): """ Capture the arguments of the setup() function in the setup.py file. The function is being run within the current Python interpreter. Therefore the processed setup.py file can not have any additional dependencies not available in the current environment. :param setup_py_path: the path to the setup.py file :returns: a dictionary containing the arguments of the setup() function """ global setup_lock if not setup_lock: setup_lock = Lock() assert os.path.basename(setup_py_path) == 'setup.py' # prevent side effects in other threads with setup_lock: # change to the directory containing the setup.py file old_cwd = os.getcwd() os.chdir(os.path.dirname(os.path.abspath(setup_py_path))) try: data = {} mock_setup = create_mock_setup_function(data) # replace setup() function of distutils and setuptools # in order to capture its arguments try: distutils_setup = distutils.core.setup distutils.core.setup = mock_setup try: setuptools_setup = setuptools.setup setuptools.setup = mock_setup except NameError: pass # evaluate the setup.py file with open('setup.py', 'r') as h: exec(h.read()) finally: distutils.core.setup = distutils_setup try: setuptools.setup = setuptools_setup except NameError: pass return data finally: os.chdir(old_cwd) def create_mock_setup_function(data): """ Create a mock function to capture its arguments. It can replace either distutils.core.setup or setuptools.setup. :param data: a dictionary which is updated with the captured arguments :returns: a function to replace disutils.core.setup and setuptools.setup """ def setup(*args, **kwargs): if args: raise RuntimeError( 'setup() function invoked with positional arguments') if 'name' not in kwargs: raise RuntimeError( "setup() function invoked without the keyword argument 'name'") data.update(kwargs) return setup def get_data_files_mapping(data_files): """ Transform the data_files structure into a dictionary. :param data_files: either a list of source files or a list of tuples where the first element is the destination path and the second element is a list of source files :returns: a dictionary mapping the source file to a destination file """ mapping = {} for data_file in data_files: if isinstance(data_file, tuple): assert len(data_file) == 2 dest = data_file[0] assert not os.path.isabs(dest) sources = data_file[1] assert isinstance(sources, list) for source in sources: assert not os.path.isabs(source) mapping[source] = os.path.join(dest, os.path.basename(source)) else: assert not os.path.isabs(data_file) mapping[data_file] = os.path.basename(data_file) return mapping

tests/functional/test_soft_round_inverse.py

tallamjr/NeuralCompression

543

<gh_stars>100-1000 # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import torch from neuralcompression.functional import soft_round, soft_round_inverse def test_soft_round_inverse(): x = torch.linspace(-2.0, 2.0, 50) torch.testing.assert_close( x, soft_round_inverse(x, alpha=1e-13), ) x = torch.tensor([-1.25, -0.75, 0.75, 1.25]) torch.testing.assert_close( x, soft_round_inverse(soft_round(x, alpha=2.0), alpha=2.0), ) for offset in range(-5, 5): x = torch.linspace(offset + 0.001, offset + 0.999, 100) torch.testing.assert_close( torch.ceil(x) - 0.5, soft_round_inverse(x, alpha=5000.0), atol=0.001, rtol=0.002, )

dizoo/pybullet/config/hopper_ppo_default_config.py

konnase/DI-engine

544

from easydict import EasyDict hopper_ppo_default_config = dict( env=dict( env_id='HopperMuJoCoEnv-v0', norm_obs=dict(use_norm=False, ), norm_reward=dict(use_norm=False, ), collector_env_num=8, evaluator_env_num=10, use_act_scale=True, n_evaluator_episode=10, stop_value=3000, ), policy=dict( cuda=True, on_policy=True, recompute_adv=True, model=dict( obs_shape=11, action_shape=3, continuous=True, ), continuous=True, learn=dict( epoch_per_collect=10, batch_size=64, learning_rate=3e-4, value_weight=0.5, entropy_weight=0.0, clip_ratio=0.2, adv_norm=True, value_norm=True, ), collect=dict( n_sample=2048, unroll_len=1, discount_factor=0.99, gae_lambda=0.97, ), eval=dict(evaluator=dict(eval_freq=5000, )), other=dict(replay_buffer=dict( replay_buffer_size=10000, replay_buffer_start_size=0, ), ), ), ) hopper_ppo_default_config = EasyDict(hopper_ppo_default_config) main_config = hopper_ppo_default_config hopper_ppo_create_default_config = dict( env=dict( type='pybullet', import_names=['dizoo.pybullet.envs.pybullet_env'], ), env_manager=dict(type='subprocess'), policy=dict( type='ppo', import_names=['ding.policy.ppo'], ), replay_buffer=dict(type='naive', ), ) hopper_ppo_create_default_config = EasyDict(hopper_ppo_create_default_config) create_config = hopper_ppo_create_default_config

cisco_sdwan_policy/List/Application.py

ljm625/cisco_sdwan_policy_python

545

import json from cisco_sdwan_policy.BaseObject import BaseObject class Application(BaseObject): def __init__(self,name,app_list,is_app_family,id=None,reference=None,**kwargs): self.type = "appList" self.id = id self.name = name self.references = reference self.app_family=is_app_family self._entries = app_list self.url = "template/policy/list/app" super().__init__(**kwargs) self.modified=False def get_entries(self): return self._entries def set_entries(self,entries): self.modified=True self._entries=entries @classmethod def from_json(cls,jsonfile,**kwargs): id = jsonfile["listId"] name = jsonfile["name"] references = jsonfile.get("references") if len(jsonfile["entries"])>0 and jsonfile["entries"][0].get("app"): appFamily=False entries = [i["app"] for i in jsonfile["entries"]] else: if not jsonfile["entries"][0].get("appFamily"): return None else: appFamily=True entries = [i["appFamily"] for i in jsonfile["entries"]] return cls(name,entries,appFamily,id,references,**kwargs) def to_json(self): return { "name":self.name, "description":"Desc Not Required", "type":"app", "entries":[ {"appFamily" if self.app_family else "app":i} for i in self._entries] }

supervisor/docker/dns.py

zeehio/supervisor

546

<filename>supervisor/docker/dns.py """DNS docker object.""" import logging from ..const import ENV_TIME from ..coresys import CoreSysAttributes from .interface import DockerInterface _LOGGER: logging.Logger = logging.getLogger(__name__) DNS_DOCKER_NAME: str = "hassio_dns" class DockerDNS(DockerInterface, CoreSysAttributes): """Docker Supervisor wrapper for Supervisor DNS.""" @property def image(self) -> str: """Return name of Supervisor DNS image.""" return self.sys_plugins.dns.image @property def name(self) -> str: """Return name of Docker container.""" return DNS_DOCKER_NAME def _run(self) -> None: """Run Docker image. Need run inside executor. """ if self._is_running(): return # Cleanup self._stop() # Create & Run container docker_container = self.sys_docker.run( self.image, tag=self.sys_plugins.dns.version.string, init=False, dns=False, ipv4=self.sys_docker.network.dns, name=self.name, hostname=self.name.replace("_", "-"), detach=True, environment={ENV_TIME: self.sys_config.timezone}, volumes={ str(self.sys_config.path_extern_dns): {"bind": "/config", "mode": "rw"} }, ) self._meta = docker_container.attrs _LOGGER.info( "Starting DNS %s with version %s - %s", self.image, self.version, self.sys_docker.network.dns, )

nuitka/codegen/OperatorCodes.py

hclivess/Nuitka

547

<gh_stars>0 # Copyright 2019, <NAME>, mailto:<EMAIL> # # Part of "Nuitka", an optimizing Python compiler that is compatible and # integrates with CPython, but also works on its own. # # 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. # """ Operator code tables These are mostly used to look up the Python C/API from operations or a wrapper used. """ from nuitka.PythonVersions import python_version binary_operator_codes = { # Those commented out in this section have fully specialized variants already. # "Add" : "PyNumber_Add", # "Sub" : "PyNumber_Subtract", # "Div" : "PyNumber_Divide", # "Mult" : "PyNumber_Multiply", # "Mod" : "PyNumber_Remainder", # "Div" : "PyNumber_Divide", # "FloorDiv" : "PyNumber_FloorDivide", # "TrueDiv" : "PyNumber_TrueDivide", # These have their own variants only to make sure the generic code is in-lined # but the CPython code is not in-lined. # "Pow" : "PyNumber_Power", # "IPow" : "PyNumber_InPlacePower", # The others are generic code and would be faster if they had a specialized variant too. "LShift": "PyNumber_Lshift", "RShift": "PyNumber_Rshift", "BitAnd": "PyNumber_And", "BitOr": "PyNumber_Or", "BitXor": "PyNumber_Xor", "IAdd": "PyNumber_InPlaceAdd", "ISub": "PyNumber_InPlaceSubtract", "IMult": "PyNumber_InPlaceMultiply", "IDiv": "PyNumber_InPlaceDivide", "IFloorDiv": "PyNumber_InPlaceFloorDivide", "ITrueDiv": "PyNumber_InPlaceTrueDivide", "IMod": "PyNumber_InPlaceRemainder", "ILShift": "PyNumber_InPlaceLshift", "IRShift": "PyNumber_InPlaceRshift", "IBitAnd": "PyNumber_InPlaceAnd", "IBitOr": "PyNumber_InPlaceOr", "IBitXor": "PyNumber_InPlaceXor", } # Python 3.5 only operator if python_version >= 350: binary_operator_codes["MatMult"] = "PyNumber_MatrixMultiply" binary_operator_codes["IMatMult"] = "PyNumber_InPlaceMatrixMultiply" unary_operator_codes = { "UAdd": ("PyNumber_Positive", 1), "USub": ("PyNumber_Negative", 1), "Invert": ("PyNumber_Invert", 1), "Repr": ("PyObject_Repr", 1), "Not": ("UNARY_NOT", 0), } rich_comparison_codes = { "Lt": "LT", "LtE": "LE", "Eq": "EQ", "NotEq": "NE", "Gt": "GT", "GtE": "GE", } containing_comparison_codes = ("In", "NotIn")

python_modules/dagster-graphql/dagster_graphql/implementation/external.py

rpatil524/dagster

548

<gh_stars>1-10 import sys from dagster import check from dagster.config.validate import validate_config_from_snap from dagster.core.host_representation import ExternalPipeline, PipelineSelector, RepositorySelector from dagster.core.workspace.context import BaseWorkspaceRequestContext from dagster.utils.error import serializable_error_info_from_exc_info from graphql.execution.base import ResolveInfo from .utils import UserFacingGraphQLError, capture_error def get_full_external_pipeline_or_raise(graphene_info, selector): from ..schema.errors import GraphenePipelineNotFoundError check.inst_param(graphene_info, "graphene_info", ResolveInfo) check.inst_param(selector, "selector", PipelineSelector) if not graphene_info.context.has_external_pipeline(selector): raise UserFacingGraphQLError(GraphenePipelineNotFoundError(selector=selector)) return graphene_info.context.get_full_external_pipeline(selector) def get_external_pipeline_or_raise(graphene_info, selector): from ..schema.pipelines.pipeline_errors import GrapheneInvalidSubsetError from ..schema.pipelines.pipeline import GraphenePipeline check.inst_param(graphene_info, "graphene_info", ResolveInfo) check.inst_param(selector, "selector", PipelineSelector) full_pipeline = get_full_external_pipeline_or_raise(graphene_info, selector) if selector.solid_selection is None: return full_pipeline for solid_name in selector.solid_selection: if not full_pipeline.has_solid_invocation(solid_name): raise UserFacingGraphQLError( GrapheneInvalidSubsetError( message='Solid "{solid_name}" does not exist in "{pipeline_name}"'.format( solid_name=solid_name, pipeline_name=selector.pipeline_name ), pipeline=GraphenePipeline(full_pipeline), ) ) return get_subset_external_pipeline(graphene_info.context, selector) def get_subset_external_pipeline(context, selector): from ..schema.pipelines.pipeline_errors import GrapheneInvalidSubsetError from ..schema.pipelines.pipeline import GraphenePipeline check.inst_param(selector, "selector", PipelineSelector) repository_location = context.get_repository_location(selector.location_name) try: external_pipeline = repository_location.get_external_pipeline(selector) except Exception: error_info = serializable_error_info_from_exc_info(sys.exc_info()) raise UserFacingGraphQLError( GrapheneInvalidSubsetError( message="{message}{cause_message}".format( message=error_info.message, cause_message="\n{}".format(error_info.cause.message) if error_info.cause else "", ), pipeline=GraphenePipeline(context.get_full_external_pipeline(selector)), ) ) return external_pipeline def ensure_valid_config(external_pipeline, mode, run_config): from ..schema.pipelines.config import GrapheneRunConfigValidationInvalid check.inst_param(external_pipeline, "external_pipeline", ExternalPipeline) check.opt_str_param(mode, "mode") # do not type check run_config so that validate_config_from_snap throws validated_config = validate_config_from_snap( config_schema_snapshot=external_pipeline.config_schema_snapshot, config_type_key=external_pipeline.root_config_key_for_mode(mode), config_value=run_config, ) if not validated_config.success: raise UserFacingGraphQLError( GrapheneRunConfigValidationInvalid.for_validation_errors( external_pipeline, validated_config.errors ) ) return validated_config def get_external_execution_plan_or_raise( graphene_info, external_pipeline, mode, run_config, step_keys_to_execute, known_state, ): return graphene_info.context.get_external_execution_plan( external_pipeline=external_pipeline, run_config=run_config, mode=mode, step_keys_to_execute=step_keys_to_execute, known_state=known_state, ) @capture_error def fetch_repositories(graphene_info): from ..schema.external import GrapheneRepository, GrapheneRepositoryConnection check.inst_param(graphene_info, "graphene_info", ResolveInfo) return GrapheneRepositoryConnection( nodes=[ GrapheneRepository(repository=repository, repository_location=location) for location in graphene_info.context.repository_locations for repository in location.get_repositories().values() ] ) @capture_error def fetch_repository(graphene_info, repository_selector): from ..schema.errors import GrapheneRepositoryNotFoundError from ..schema.external import GrapheneRepository check.inst_param(graphene_info, "graphene_info", ResolveInfo) check.inst_param(repository_selector, "repository_selector", RepositorySelector) if graphene_info.context.has_repository_location(repository_selector.location_name): repo_loc = graphene_info.context.get_repository_location(repository_selector.location_name) if repo_loc.has_repository(repository_selector.repository_name): return GrapheneRepository( repository=repo_loc.get_repository(repository_selector.repository_name), repository_location=repo_loc, ) return GrapheneRepositoryNotFoundError( repository_selector.location_name, repository_selector.repository_name ) @capture_error def fetch_workspace(workspace_request_context): from ..schema.external import GrapheneWorkspace, GrapheneWorkspaceLocationEntry check.inst_param( workspace_request_context, "workspace_request_context", BaseWorkspaceRequestContext ) nodes = [ GrapheneWorkspaceLocationEntry(entry) for entry in workspace_request_context.get_workspace_snapshot().values() ] return GrapheneWorkspace(locationEntries=nodes)

tests/test_utils/test_pywriting_utils.py

heylohousing/quickbase-client

549

import os from tempfile import TemporaryDirectory from quickbase_client.utils.pywriting_utils import BasicPyFileWriter from quickbase_client.utils.pywriting_utils import PyPackageWriter class TestBasicFileWriter: def test_outputs_lines(self): w = BasicPyFileWriter() w.add_line('import abc') w.add_line('import os').space() s = w.get_file_as_string() assert s == 'import abc\nimport os\n' def test_indent_dedent(self): w = BasicPyFileWriter() w.add_line('def foo():').indent().add_line('return 5').dedent().space() s = w.get_file_as_string() assert s == 'def foo():\n return 5\n' def test_use_refs(self): w = BasicPyFileWriter() w.add_line('a = "A"') ref = w.make_ref() w.add_line('d = "D"') ref.add_line('b = "B"').add_line('c = "C"') s = w.get_file_as_string() lns = s.split('\n') assert 'a' in lns[0] assert 'b' in lns[1] assert 'c' in lns[2] assert 'd' in lns[3] class TestPyPackageWriter: def test_includes_init(self): with TemporaryDirectory() as d: w = PyPackageWriter(pkg_name='foo', parent_dir=d) assert '__init__' in w.modules assert w.has_module_name('__init__') assert w.pkg_path == os.path.join(d, 'foo') w.write() assert os.path.exists(d) assert os.path.exists(os.path.join(d, 'foo')) assert os.path.exists(os.path.join(d, 'foo', '__init__.py'))

model/resnet.py

DrMMZ/RetinaNet

550

<filename>model/resnet.py """ Residual Networks (ResNet) """ # adapted from # https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py import tensorflow as tf def identity_block( input_tensor, filters, stage, block, train_bn=False ): """ Builds an identity shortcut in a bottleneck building block of a ResNet. Parameters ---------- input_tensor : tf tensor, [batch_size, height, width, channels] An input tensor. filters : list, positive integers The number of filters in 3 conv layers at the main path, where last number is equal to input_tensor's channels. stage : integer A number in [2,5] used for generating layer names. block : string A lowercase letter, used for generating layer names. train_bn : boolean, optional Whether one should normalize the layer input by the mean and variance over the current batch. The default is False, i.e., use the moving average of mean and variance to normalize the layer input. Returns ------- output_tensor : tf tensor, [batch_size, height, width, channels] The output tensor same shape as input_tensor. """ num_filters_1, num_filters_2, num_filters_3 = filters conv_prefix = 'res' + str(stage) + block + '_branch' bn_prefix = 'bn' + str(stage) + block + '_branch' x = tf.keras.layers.Conv2D( num_filters_1, (1,1), name=conv_prefix + '2a')(input_tensor) x = tf.keras.layers.BatchNormalization( name=bn_prefix + '2a')(x, training=train_bn) x = tf.keras.layers.Activation('relu')(x) x = tf.keras.layers.Conv2D( num_filters_2, (3,3), padding='same', name=conv_prefix + '2b')(x) x = tf.keras.layers.BatchNormalization( name=bn_prefix + '2b')(x, training=train_bn) x = tf.keras.layers.Activation('relu')(x) x = tf.keras.layers.Conv2D( num_filters_3, (1,1), name=conv_prefix + '2c')(x) x = tf.keras.layers.BatchNormalization( name=bn_prefix + '2c')(x, training=train_bn) x = tf.keras.layers.Add()([input_tensor, x]) output_tensor = tf.keras.layers.Activation( 'relu', name='res' + str(stage) + block + '_out')(x) return output_tensor def conv_block( input_tensor, filters, stage, block, strides=(2, 2), train_bn=False ): """ Builds a projection shortcut in a bottleneck block of a ResNet. Parameters ---------- input_tensor : tf tensor, [batch_size, height, width, channels] An input tensor. filters : list, positive integers The number of filters in 3 conv layers at the main path. stage : integer A number in [2,5] used for generating layer names. block : string A lowercase letter, used for generating layer names. strides : tuple, integers, optional The conv layer strides. The default is (2, 2). train_bn : boolean, optional Whether one should normalize the layer input by the mean and variance over the current batch. The default is False, i.e., use the moving average of mean and variance to normalize the layer input. Returns ------- output_tensor : tf tensor [batch_size, height//strides, width//strides, num_filters_3] where num_filters_3 is the last number in filters, the output tensor. """ num_filters_1, num_filters_2, num_filters_3 = filters conv_prefix = 'res' + str(stage) + block + '_branch' bn_prefix = 'bn' + str(stage) + block + '_branch' x = tf.keras.layers.Conv2D( num_filters_1, (1,1), strides, name=conv_prefix + '2a')(input_tensor) x = tf.keras.layers.BatchNormalization( name=bn_prefix + '2a')(x, training=train_bn) x = tf.keras.layers.Activation('relu')(x) x = tf.keras.layers.Conv2D( num_filters_2, (3,3), padding='same', name=conv_prefix + '2b')(x) x = tf.keras.layers.BatchNormalization( name=bn_prefix + '2b')(x, training=train_bn) x = tf.keras.layers.Activation('relu')(x) x = tf.keras.layers.Conv2D( num_filters_3, (1,1), name=conv_prefix + '2c')(x) x = tf.keras.layers.BatchNormalization( name=bn_prefix + '2c')(x, training=train_bn) shortcut = tf.keras.layers.Conv2D( num_filters_3, (1,1), strides, name=conv_prefix + '1')(input_tensor) shortcut = tf.keras.layers.BatchNormalization( name=bn_prefix + '1')(shortcut, training=train_bn) x = tf.keras.layers.Add()([shortcut, x]) output_tensor = tf.keras.layers.Activation( 'relu', name='res' + str(stage) + block + '_out')(x) return output_tensor def backbone_resnet(input_image, architecture, stage5=True, train_bn=False): """ Builds a backbone ResNet. Parameters ---------- input_image : tf tensor, [batch_size, height, width, channels] An input tensor. architecture : string The ResNet architecture in {'resnet50', 'resnet101'}. stage5 : boolean, optional Whether create stage5 of network. The default is True. train_bn : boolean, optional Whether one should normalize the layer input by the mean and variance over the current batch. The default is False, i.e., use the moving average of mean and variance to normalize the layer input. Returns ------- outputs : list Feature maps at each stage. """ assert architecture in ['resnet50', 'resnet101'], \ 'Only support ResNet50\101' # stage 1 x = tf.keras.layers.ZeroPadding2D((3,3))(input_image) x = tf.keras.layers.Conv2D(64, (7,7), (2,2), name='conv1')(x) x = tf.keras.layers.BatchNormalization(name='bn_conv1')(x, training=train_bn) x = tf.keras.layers.Activation('relu')(x) C1 = x = tf.keras.layers.MaxPooling2D((3,3), (2,2), padding='same')(x) # stage 2 x = conv_block( x, [64,64,256], stage=2, block='a', strides=(1,1), train_bn=train_bn) x = identity_block(x, [64,64,256], stage=2, block='b', train_bn=train_bn) C2 = x = identity_block( x, [64,64,256], stage=2, block='c', train_bn=train_bn) # stage 3 x = conv_block(x, [128,128,512], stage=3, block='a', train_bn=train_bn) x = identity_block(x, [128,128,512], stage=3, block='b', train_bn=train_bn) x = identity_block(x, [128,128,512], stage=3, block='c', train_bn=train_bn) C3 = x = identity_block( x, [128,128,512], stage=3, block='d', train_bn=train_bn) # stage 4 x = conv_block(x, [256,256,1024], stage=4, block='a', train_bn=train_bn) num_blocks = {'resnet50':5, 'resnet101':22}[architecture] for i in range(num_blocks): x = identity_block( x, [256,256,1024], stage=4, block=chr(98+i), train_bn=train_bn) C4 = x # stage 5 if stage5: x = conv_block(x, [512,512,2048], stage=5, block='a', train_bn=train_bn) x = identity_block( x, [512,512,2048], stage=5, block='b', train_bn=train_bn) C5 = x = identity_block( x, [512,512,2048], stage=5, block='c', train_bn=train_bn) else: C5 = None return [C1, C2, C3, C4, C5]

src/main/python/rds_log_cat/parser/mysql57.py

Scout24/rds-log-cat

551

<reponame>Scout24/rds-log-cat from rds_log_cat.parser.parser import Parser, LineParserException class Mysql57(Parser): def __init__(self): Parser.__init__(self) def compose_timestamp(self, datetime, timezone): if len(datetime) != 27: raise LineParserException('wrong length of datetime - wrong date is: ' + datetime) if not timezone == 'UTC': raise LineParserException('Only able to parse times in UTC. You gave {}'.format(timezone)) return datetime def parse(self, line): """ parses the fields in line to generate json structure """ expected_min_no_fields = 5 if len(line) < expected_min_no_fields: raise LineParserException('line too short') pid = line[1] log_level = line[2].lstrip("[").rstrip("]") timezone = 'UTC' return { '@timestamp': self.compose_timestamp(line[0], timezone), 'log_level': log_level, 'process_id': int(pid), 'message': ' '.join(map(str, line[3:])) }

corpustools/neighdens/neighborhood_density.py

PhonologicalCorpusTools/CorpusTools

552

from functools import partial from corpustools.corpus.classes import Word from corpustools.symbolsim.edit_distance import edit_distance from corpustools.symbolsim.khorsi import khorsi from corpustools.symbolsim.phono_edit_distance import phono_edit_distance from corpustools.symbolsim.phono_align import Aligner from corpustools.multiproc import filter_mp, score_mp def _is_edit_distance_neighbor(w, query, sequence_type, max_distance): w_len = len(getattr(w, sequence_type)) query_len = len(getattr(query, sequence_type)) if w_len > query_len+max_distance: return False if w_len < query_len-max_distance: return False return edit_distance(getattr(w, sequence_type), getattr(query, sequence_type), sequence_type, max_distance) <= max_distance def _is_phono_edit_distance_neighbor(w, query, sequence_type, specifier, max_distance): return phono_edit_distance(getattr(w, sequence_type), getattr(query, sequence_type), sequence_type, specifier) <= max_distance def _is_khorsi_neighbor(w, query, freq_base, sequence_type, max_distance): return khorsi(getattr(w, sequence_type), getattr(query, sequence_type), freq_base, sequence_type, max_distance) >= max_distance def neighborhood_density_all_words(corpus_context, tierdict, tier_type = None, sequence_type = None, algorithm = 'edit_distance', max_distance = 1, output_format = 'spelling', num_cores = -1, settable_attr = None, collapse_homophones = False, stop_check = None, call_back = None): """Calculate the neighborhood density of all words in the corpus and adds them as attributes of the words. Parameters ---------- corpus_context : CorpusContext Context manager for a corpus algorithm : str The algorithm used to determine distance max_distance : float, optional Maximum edit distance from the queried word to consider a word a neighbor. stop_check : callable, optional Optional function to check whether to gracefully terminate early call_back : callable, optional Optional function to supply progress information during the function settable_attr: string Name of attribute that neighbourhood density results will be assigned to """ function = partial(neighborhood_density, corpus_context, tierdict = tierdict, tier_type = tier_type, sequence_type = sequence_type, algorithm = algorithm, max_distance = max_distance, collapse_homophones = collapse_homophones) if call_back is not None: call_back('Calculating neighborhood densities...') call_back(0,len(corpus_context)) cur = 0 results = dict() last_value_removed = None last_key_removed = None if num_cores == -1 or num_cores == 1: for w in corpus_context: if stop_check is not None and stop_check(): return if last_value_removed: tierdict[last_key_removed].append(last_value_removed) w_sequence = getattr(w, corpus_context.sequence_type) last_key_removed = str(w_sequence) for i, item in enumerate(tierdict[last_key_removed]): if str(item) == str(w): last_value_removed = tierdict[last_key_removed].pop(i) break res = neighborhood_density(corpus_context, w, tierdict, tier_type = tier_type, sequence_type = sequence_type, algorithm = algorithm, max_distance = max_distance, collapse_homophones = collapse_homophones) results[str(w)] = [getattr(r, output_format) for r in res[1]] setattr(w.original, settable_attr.name, res[0]) # for w in corpus_context: # if stop_check is not None and stop_check(): # return # cur += 1 # call_back(cur) # res = function(w) # results[str(w)] = [getattr(r, output_format) for r in res[1]] # setattr(w.original, settable_attr.name, res[0]-1) # #the -1 is to account for the fact that words are counted as their own neighbour, and this is incorrect # #subtracting 1 here is easier than fixing the neighbourhood density algorithm else: iterable = ((w,) for w in corpus_context) neighbors = score_mp(iterable, function, num_cores, call_back, stop_check, chunk_size = 1) for n in neighbors: #Have to look up the key, then look up the object due to how #multiprocessing pickles objects setattr(corpus_context.corpus.find(corpus_context.corpus.key(n[0])), #corpus_context.attribute.name, n[1][0]) settable_attr.name, n[1][0]) return results def neighborhood_density(corpus_context, query, tierdict, algorithm = 'edit_distance', max_distance = 1, collapse_homophones = False, force_quadratic = False, file_type = None, tier_type=None, sequence_type = None, stop_check = None, call_back = None): """Calculate the neighborhood density of a particular word in the corpus. Parameters ---------- corpus_context : CorpusContext Context manager for a corpus query : Word The word whose neighborhood density to calculate. algorithm : str The algorithm used to determine distance max_distance : float, optional Maximum edit distance from the queried word to consider a word a neighbor force_quadratic : bool Force use of the less efficient quadratic algorithm even when finding edit distance of 1 neighborhoods stop_check : callable, optional Optional function to check whether to gracefully terminate early call_back : callable, optional Optional function to supply progress information during the function Returns ------- tuple(int, set) Tuple of the number of neighbors and the set of neighbor Words. """ matches = [] query = ensure_query_is_word(query, corpus_context, corpus_context.sequence_type, tier_type) if call_back is not None: call_back('Finding neighbors for {}...'.format(query)) call_back(0,len(corpus_context)) cur = 0 if algorithm == 'edit_distance' and max_distance == 1 and not force_quadratic: return fast_neighborhood_density(corpus_context, query, corpus_context.sequence_type, tier_type, tierdict, file_type=file_type, collapse_homophones=collapse_homophones) if algorithm == 'edit_distance': is_neighbor = partial(_is_edit_distance_neighbor, sequence_type = corpus_context.sequence_type, max_distance = max_distance) elif algorithm == 'phono_edit_distance': is_neighbor = partial(_is_phono_edit_distance_neighbor, specifier = corpus_context.specifier, sequence_type = corpus_context.sequence_type, max_distance = max_distance) elif algorithm == 'khorsi': freq_base = corpus_context.get_frequency_base() is_neighbor = partial(_is_khorsi_neighbor, freq_base = freq_base, sequence_type = corpus_context.sequence_type, max_distance = max_distance) for w in corpus_context: if stop_check is not None and stop_check(): return if call_back is not None: cur += 1 if cur % 10 == 0: call_back(cur) if not is_neighbor(w, query): continue matches.append(w) neighbors = set(matches)-set([query]) return (len(neighbors), neighbors) def fast_neighborhood_density(corpus_context, query, sequence_type, tier_type, tierdict, file_type=None, trans_delimiter='.', collapse_homophones = False): """Generates all neighbors of edit distance <= 1 and searches for them in corpus_context. Will be faster than neighborhood_density when: n > m * (1 + s), where n: number of words in corpus m: length of query s: size of segment inventory """ neighbors = list() query = ensure_query_is_word(query, corpus_context, sequence_type, tier_type, file_type=file_type) for candidate in generate_neighbor_candidates(corpus_context, query, sequence_type): if tier_type.att_type == 'tier': cand_str = trans_delimiter.join(candidate) else: cand_str = ''.join(candidate) if cand_str in tierdict: for w in tierdict[cand_str]: w_sequence = getattr(w, sequence_type) if collapse_homophones and any(getattr(word, sequence_type) == w_sequence for word in neighbors): continue else: neighbors.append(w) return (len(neighbors), neighbors) def generate_neighbor_candidates(corpus_context, query, sequence_type): sequence = getattr(query, sequence_type) yield [str(c) for c in sequence] for i in range(len(sequence)): yield [str(c) for c in sequence[:i]] + [str(c) for c in sequence[i+1:]] # deletion for char in corpus_context.inventory: if str(char) not in ['#', sequence[i]]: yield [str(c) for c in sequence[:i]] + [str(char)] + [str(c) for c in sequence[i:]] # insertion yield [str(c) for c in sequence[:i]] + [str(char)] + [str(c) for c in sequence[i+1:]] # substitution for char in corpus_context.inventory: # final pass to get insertion at len+1 if str(char) not in ['#', sequence[i]]: yield [str(c) for c in sequence[:]] + [str(char)] # insertion def find_mutation_minpairs_all_words(corpus_context, tierdict, tier_type = None, num_cores = -1, collapse_homophones = False, stop_check = None, call_back = None): function = partial(find_mutation_minpairs, corpus_context, tier_type=tier_type, collapse_homophones = collapse_homophones) if call_back is not None: call_back('Calculating neighborhood densities...') call_back(0,len(corpus_context)) cur = 0 results = dict() last_value_removed = None last_key_removed = None if num_cores == -1 or num_cores == 1: for w in corpus_context: if stop_check is not None and stop_check(): return if last_value_removed: tierdict[last_key_removed].append(last_value_removed) w_sequence = getattr(w, corpus_context.sequence_type) last_key_removed = str(w_sequence) for i, item in enumerate(tierdict[last_key_removed]): if str(item) == str(w): last_value_removed = tierdict[last_key_removed].pop(i) break res = find_mutation_minpairs(corpus_context, w, tier_type=tier_type, collapse_homophones = collapse_homophones) results[str(w)] = res[1] setattr(w.original, corpus_context.attribute.name, res[0]) # for w in corpus_context: # if stop_check is not None and stop_check(): # return # cur += 1 # call_back(cur) # res = function(w) # results[str(w)] = res[1]#[str(r) for r in res[1]] # setattr(w.original, corpus_context.attribute.name, res[0]) else: iterable = ((w,) for w in corpus_context) neighbors = score_mp(iterable, function, num_cores, call_back, stop_check, chunk_size= 1) for n in neighbors: #Have to look up the key, then look up the object due to how #multiprocessing pickles objects setattr(corpus_context.corpus.find(corpus_context.corpus.key(n[0])), corpus_context.attribute.name, n[1][0]) return results def find_mutation_minpairs(corpus_context, query, tier_type = None, collapse_homophones = False, stop_check = None, call_back = None): """Find all minimal pairs of the query word based only on segment mutations (not deletions/insertions) Parameters ---------- corpus_context : CorpusContext Context manager for a corpus query : Word The word whose minimal pairs to find stop_check : callable or None Optional function to check whether to gracefully terminate early call_back : callable or None Optional function to supply progress information during the function Returns ------- list The found minimal pairs for the queried word """ matches = [] sequence_type = corpus_context.sequence_type query = ensure_query_is_word(query, corpus_context, corpus_context.sequence_type, tier_type) if call_back is not None: call_back('Finding neighbors...') call_back(0,len(corpus_context)) cur = 0 al = Aligner(features_tf=False, ins_penalty=float('inf'), del_penalty=float('inf'), sub_penalty=1) for w in corpus_context: w_sequence = getattr(w, sequence_type) query_sequence = getattr(query, sequence_type) if stop_check is not None and stop_check(): return if call_back is not None: cur += 1 if cur % 10 == 0: call_back(cur) if (len(w_sequence) > len(query_sequence)+1 or len(w_sequence) < len(query_sequence)-1): continue m = al.make_similarity_matrix(query_sequence, w_sequence) if m[-1][-1]['f'] != 1: continue w_sequence = getattr(w, sequence_type) if collapse_homophones and any(getattr(m, sequence_type) == w_sequence for m in matches): continue else: #matches.append(str(w_sequence)) matches.append(w) matches = [m.spelling for m in matches] neighbors = list(set(matches)-set([str(query_sequence)])) return (len(neighbors), neighbors) def ensure_query_is_word(query, corpus, sequence_type, tier_type, trans_delimiter='.', file_type=None): if isinstance(query, Word): query_word = query else: if tier_type.att_type == 'spelling': if file_type == sequence_type: query_word = Word(**{sequence_type: list(query)}) else: query_word = query.replace(trans_delimiter, '') query_word = Word(**{sequence_type: list(query_word)}) elif tier_type.att_type == 'tier': if file_type == sequence_type: query_with̠td = '.'.join(query) if '.' not in query else query for entry in corpus: corpus_word_with_td = str(getattr(entry, sequence_type)) if query_with̠td == corpus_word_with_td: # if a word in corpus has the same transcription return entry # that word in the corpus is to be referred to. # the following should be run if no word found in corpus with the transcription new_query = parse(query, trans_delimiter) query_word = Word(**{sequence_type: new_query}) else: # if file contains spelling try: query_word = corpus.corpus.find(query) except KeyError: # if the word in the file can't be found in the corpus new_query = parse(query, trans_delimiter) query_word = Word(**{sequence_type: list(new_query)}) return query_word def parse(word, delimiter): return word.split(delimiter) if delimiter in word else list(word)

brokenChains/migrations/0003_auto_20181106_1819.py

bunya017/brokenChains

553

<gh_stars>1-10 # Generated by Django 2.1.1 on 2018-11-06 17:19 from django.conf import settings from django.db import migrations class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('brokenChains', '0002_auto_20181106_1723'), ] operations = [ migrations.AlterUniqueTogether( name='habit', unique_together={('owner', 'name')}, ), ]

ex05-td/ex05-td.py

vijaykumarprabhu/rl-course

554

import gym import numpy as np from itertools import product import matplotlib.pyplot as plt def print_policy(Q, env): """ This is a helper function to print a nice policy from the Q function""" moves = [u'←', u'↓',u'→', u'↑'] if not hasattr(env, 'desc'): env = env.env dims = env.desc.shape policy = np.chararray(dims, unicode=True) policy[:] = ' ' for s in range(len(Q)): idx = np.unravel_index(s, dims) policy[idx] = moves[np.argmax(Q[s])] if env.desc[idx] in ['H', 'G']: policy[idx] = u'·' print('\n'.join([''.join([u'{:2}'.format(item) for item in row]) for row in policy])) def plot_V(Q, env): """ This is a helper function to plot the state values from the Q function""" fig = plt.figure() if not hasattr(env, 'desc'): env = env.env dims = env.desc.shape V = np.zeros(dims) for s in range(len(Q)): idx = np.unravel_index(s, dims) V[idx] = np.max(Q[s]) if env.desc[idx] in ['H', 'G']: V[idx] = 0. plt.imshow(V, origin='upper', extent=[0,dims[0],0,dims[1]], vmin=.0, vmax=.6, cmap=plt.cm.RdYlGn, interpolation='none') for x, y in product(range(dims[0]), range(dims[1])): plt.text(y+0.5, dims[0]-x-0.5, '{:.3f}'.format(V[x,y]), horizontalalignment='center', verticalalignment='center') plt.xticks([]) plt.yticks([]) def plot_Q(Q, env): """ This is a helper function to plot the Q function """ from matplotlib import colors, patches fig = plt.figure() ax = fig.gca() if not hasattr(env, 'desc'): env = env.env dims = env.desc.shape up = np.array([[0, 1], [0.5, 0.5], [1,1]]) down = np.array([[0, 0], [0.5, 0.5], [1,0]]) left = np.array([[0, 0], [0.5, 0.5], [0,1]]) right = np.array([[1, 0], [0.5, 0.5], [1,1]]) tri = [left, down, right, up] pos = [[0.2, 0.5], [0.5, 0.2], [0.8, 0.5], [0.5, 0.8]] cmap = plt.cm.RdYlGn norm = colors.Normalize(vmin=.0,vmax=.6) ax.imshow(np.zeros(dims), origin='upper', extent=[0,dims[0],0,dims[1]], vmin=.0, vmax=.6, cmap=cmap) ax.grid(which='major', color='black', linestyle='-', linewidth=2) for s in range(len(Q)): idx = np.unravel_index(s, dims) x, y = idx if env.desc[idx] in ['H', 'G']: ax.add_patch(patches.Rectangle((y, 3-x), 1, 1, color=cmap(.0))) plt.text(y+0.5, dims[0]-x-0.5, '{:.2f}'.format(.0), horizontalalignment='center', verticalalignment='center') continue for a in range(len(tri)): ax.add_patch(patches.Polygon(tri[a] + np.array([y, 3-x]), color=cmap(Q[s][a]))) plt.text(y+pos[a][0], dims[0]-1-x+pos[a][1], '{:.2f}'.format(Q[s][a]), horizontalalignment='center', verticalalignment='center', fontsize=9, fontweight=('bold' if Q[s][a] == np.max(Q[s]) else 'normal')) plt.xticks([]) plt.yticks([]) def choose_abs_greedy_action(state, Q, epsilon): action = None if np.random.uniform(0, 1) < epsilon: action = np.random.randint(env.action_space.n) else: action = np.argmax(Q[state,:]) return action def max_action_state(state, Q): action = np.argmax(Q[state,:]) return Q[state, action] def sarsa(env, alpha=0.1, gamma=0.9, epsilon=0.1, num_ep=int(1e4)): #Q = np.zeros((env.observation_space.n, env.action_space.n)) Q = np.random.rand(env.observation_space.n, env.action_space.n) # TODO: implement the sarsa algorithm # This is some starting point performing random walks in the environment: for i in range(num_ep): s = env.reset() done = False a = choose_abs_greedy_action(s, Q, epsilon) while not done: s_, r, done, _ = env.step(a) a_ = choose_abs_greedy_action(s_, Q, epsilon) #update Q using sarsa Q[s, a] = Q[s, a] + alpha * (r + (gamma * Q[s_,a_]) - Q[s,a]) s = s_ a = a_ return Q def qlearning(env, alpha=0.1, gamma=0.9, epsilon=0.1, num_ep=int(1e4)): #Q = np.zeros((env.observation_space.n, env.action_space.n)) Q = np.random.rand(env.observation_space.n, env.action_space.n) # TODO: implement the qlearning algorithm for i in range(num_ep): s = env.reset() done = False while not done: a = choose_abs_greedy_action(s, Q, epsilon) s_, r, done, _ = env.step(a) #update Q using Q learning Q[s, a] = Q[s, a] + alpha * (r+ ( gamma * max_action_state(s_, Q)) - Q[s,a] ) s = s_ return Q env=gym.make('FrozenLake-v0') #env=gym.make('FrozenLake-v0', is_slippery=False) #env=gym.make('FrozenLake-v0', map_name="8x8") print("Running sarsa...") Q = sarsa(env) plot_V(Q, env) plot_Q(Q, env) print_policy(Q, env) plt.show() print("Running qlearning") Q = qlearning(env) plot_V(Q, env) plot_Q(Q, env) print_policy(Q, env) plt.show()

app/modules/ai_lab/migrations/0003_ailabcasestudy.py

nickmoreton/nhsx-website

555

# Generated by Django 3.0.4 on 2020-07-14 11:00 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ("core", "0026_auto_20200713_1535"), ("ai_lab", "0002_ailabusecase"), ] operations = [ migrations.CreateModel( name="AiLabCaseStudy", fields=[ ( "articlepage_ptr", models.OneToOneField( auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to="core.ArticlePage", ), ), ( "use_case", models.ForeignKey( on_delete=django.db.models.deletion.PROTECT, to="ai_lab.AiLabUseCase", ), ), ], options={"abstract": False,}, bases=("core.articlepage", models.Model), ), ]

requests/UpdateWorkbookConnectionRequest.py

divinorum-webb/python-tableau-api

556

<filename>requests/UpdateWorkbookConnectionRequest.py<gh_stars>1-10 from .BaseRequest import BaseRequest class UpdateWorkbookConnectionRequest(BaseRequest): """ Update workbook connection request for sending API requests to Tableau Server. :param ts_connection: The Tableau Server connection object. :type ts_connection: class :param server_address: The new server for the connection. :type server_address: string :param port: The new port for the connection. :type port: string :param connection_username: The new username for the connection. :type connection_username: string :param connection_password: The new password for the connection. :type connection_password: string :param embed_password_flag: Boolean; True to embed the password in the connection, False otherwise. :type embed_password_flag: boolean """ def __init__(self, ts_connection, server_address=None, port=None, connection_username=None, connection_password=<PASSWORD>, embed_password_flag=None): super().__init__(ts_connection) self._server_address = server_address self._port = port self._connection_username = connection_username self._connection_password = <PASSWORD> self._embed_password_flag = embed_password_flag self.base_update_workbook_connection_request @property def optional_parameter_keys(self): return [ 'serverAddress', 'serverPort', 'userName', 'password', 'embedPassword' ] @property def optional_parameter_values_exist(self): return [ self._server_address, self._port, self._connection_username, self._connection_password, True if self._embed_password_flag is not None else None ] @property def optional_parameter_values(self): return [ self._server_address, self._port, self._connection_username, self._connection_password, self._embed_password_flag ] @property def base_update_workbook_connection_request(self): self._request_body.update({'connection': {}}) return self._request_body @property def modified_update_workbook_connection_request(self): if any(self.optional_parameter_values_exist): self._request_body['connection'].update( self._get_parameters_dict(self.optional_parameter_keys, self.optional_parameter_values)) return self._request_body @staticmethod def _get_parameters_dict(param_keys, param_values): """Override the inherited _get_parameters_dict() method to allow passing boolean values directly""" params_dict = {} for i, key in enumerate(param_keys): if param_values[i] is not None: params_dict.update({key: param_values[i]}) return params_dict def get_request(self): return self.modified_update_workbook_connection_request

frappe/website/doctype/website_route_meta/test_website_route_meta.py

oryxsolutions/frappe

557

# -*- coding: utf-8 -*- # Copyright (c) 2019, Frappe Technologies and Contributors # License: MIT. See LICENSE import unittest import frappe from frappe.utils import set_request from frappe.website.serve import get_response test_dependencies = ["Blog Post"] class TestWebsiteRouteMeta(unittest.TestCase): def test_meta_tag_generation(self): blogs = frappe.get_all( "Blog Post", fields=["name", "route"], filters={"published": 1, "route": ("!=", "")}, limit=1 ) blog = blogs[0] # create meta tags for this route doc = frappe.new_doc("Website Route Meta") doc.append("meta_tags", {"key": "type", "value": "blog_post"}) doc.append("meta_tags", {"key": "og:title", "value": "My Blog"}) doc.name = blog.route doc.insert() # set request on this route set_request(path=blog.route) response = get_response() self.assertTrue(response.status_code, 200) html = response.get_data().decode() self.assertTrue("""<meta name="type" content="blog_post">""" in html) self.assertTrue("""<meta property="og:title" content="My Blog">""" in html) def tearDown(self): frappe.db.rollback()

test/unittests/test_AgRunoff.py

rajadain/gwlf-e

558

<reponame>rajadain/gwlf-e<filename>test/unittests/test_AgRunoff.py import numpy as np from .VariableUnitTest import VariableUnitTest from gwlfe.MultiUse_Fxns.Runoff import AgRunoff class TestAgRunoff(VariableUnitTest): # @skip("not ready") def test_AgRunoff(self): z = self.z np.testing.assert_array_almost_equal( AgRunoff.AgRunoff_f(z.NYrs, z.DaysMonth, z.Temp, z.InitSnow_0, z.Prec, z.NRur, z.CN, z.AntMoist_0, z.NUrb, z.Grow_0, z.Landuse, z.Area), AgRunoff.AgRunoff(z.NYrs, z.DaysMonth, z.Temp, z.InitSnow_0, z.Prec, z.NRur, z.CN, z.AntMoist_0, z.NUrb, z.Grow_0, z.Landuse, z.Area), decimal=7)

lingvo/tasks/car/car_layers_test.py

Harshs27/lingvo

559

# Lint as: python3 # Copyright 2019 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. # ============================================================================== """Tests for car_layers.""" from lingvo import compat as tf from lingvo.core import py_utils from lingvo.core import test_utils from lingvo.tasks.car import car_layers class CarLayersTest(test_utils.TestCase): def _testNestedOutShape(self, p, input_shape, expected_shape): batch_size, num_points, _ = input_shape g = tf.Graph() with g.as_default(): net = p.Instantiate() input_data = py_utils.NestedMap( points=tf.random.uniform(input_shape[:-1] + (3,)), features=tf.random.uniform(input_shape), padding=tf.zeros((batch_size, num_points), dtype=tf.float32), label=tf.random.uniform((batch_size,), minval=0, maxval=16, dtype=tf.int32)) result = net.FPropDefaultTheta(input_data) with self.session(graph=g): self.evaluate(tf.global_variables_initializer()) np_result = self.evaluate(result) grouped_points_result = np_result.grouped_points self.assertEqual(grouped_points_result.features.shape, expected_shape.grouped_points.features) self.assertEqual(grouped_points_result.points.shape, expected_shape.grouped_points.points) self.assertEqual(grouped_points_result.padding.shape, expected_shape.grouped_points.padding) query_points_result = np_result.query_points self.assertEqual(query_points_result.points.shape, expected_shape.query_points.points) self.assertEqual(query_points_result.padding.shape, expected_shape.query_points.padding) def testSamplingAndGrouping(self): for num_points in [1024, 256]: for input_dims in [3, 6, 9]: for group_size in [32, 64]: p = car_layers.SamplingAndGroupingLayer.Params().Set( name='SampleGroupTest', num_samples=256, ball_radius=0.2, group_size=group_size, sample_neighbors_uniformly=True) grouped_points_shape = py_utils.NestedMap( features=(8, 256, group_size, input_dims), points=(8, 256, group_size, 3), padding=(8, 256, group_size)) query_points_shape = py_utils.NestedMap( points=(8, 256, 3), padding=(8, 256)) expected_shape = py_utils.NestedMap({ 'grouped_points': grouped_points_shape, 'query_points': query_points_shape }) self._testNestedOutShape(p, (8, num_points, input_dims), expected_shape) if __name__ == '__main__': tf.test.main()

starry/_core/ops/lib/include/oblate/tests/test_derivs.py

rodluger/starry

560

<reponame>rodluger/starry<filename>starry/_core/ops/lib/include/oblate/tests/test_derivs.py import oblate import numpy as np import pytest # TODO!

take_snapshot.py

ITCave/sniff-for-changes-in-directory

561

# -*- coding: utf-8 -*- # @Filename : take_snapshot.py # @Date : 2019-07-15-13-44 # @Project: ITC-sniff-for-changes-in-directory # @Author: <NAME> # @Website: http://itcave.eu # @Email: <EMAIL> # @License: MIT # @Copyright (C) 2019 ITGO <NAME> # Generic imports import os import pickle import re import argparse from datetime import datetime def clear_path_string(s): """ Simple function that removes chars that are not allowed in file names :param s: path_string :return: cleaned_path_string """ return (re.sub('[^a-zA-Z]+', '#', s)).lower() def sniff(sniff_path): """ Walks the path and stores information about directory content :param sniff_path: relative or absolute path :return: void """ sniff_path = str(sniff_path).lower() # Variable in which information will be stored dir_store = {} # Recursive loop that walks through all of the subdirectories for subdir, dirs, files in os.walk(sniff_path): if subdir not in dir_store: dir_store[subdir] = {} dir_store[subdir]['subdirs'] = dirs dir_store[subdir]['files'] = files dir_store[subdir]['file_details'] = {} for file in files: f_path = os.path.join(subdir, file) # The information that will be store for each of the files - in this case last file modification date # Important: it's cross-platform relevant! modified_date = os.path.getmtime(f_path) dir_store[subdir]['file_details'][file] = (modified_date,) # Name of a file in which data will be stored dump_name = clear_path_string(sniff_path) + '_' + datetime.now().strftime('%Y%m%d%H%M%S') # Save pickled data with open(dump_name + '.pkl', 'wb') as output: pickle.dump(dir_store, output, pickle.HIGHEST_PROTOCOL) print("Directory Snapshot taken:", dump_name) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Directory Sniffer') parser.add_argument('path', help='Path to the directory that you want to take a snapshot of') args = parser.parse_args() sniff(args.path)

nuitka/nodes/GlobalsLocalsNodes.py

juanfra684/Nuitka

562

# Copyright 2020, <NAME>, mailto:<EMAIL> # # Part of "Nuitka", an optimizing Python compiler that is compatible and # integrates with CPython, but also works on its own. # # 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. # """ Globals/locals/single arg dir nodes These nodes give access to variables, highly problematic, because using them, the code may change or access anything about them, so nothing can be trusted anymore, if we start to not know where their value goes. The "dir()" call without arguments is reformulated to locals or globals calls. """ from .ConstantRefNodes import makeConstantRefNode from .DictionaryNodes import ExpressionKeyValuePair, ExpressionMakeDict from .ExpressionBases import ExpressionBase, ExpressionBuiltinSingleArgBase from .VariableRefNodes import ExpressionTempVariableRef, ExpressionVariableRef class ExpressionBuiltinGlobals(ExpressionBase): kind = "EXPRESSION_BUILTIN_GLOBALS" def __init__(self, source_ref): ExpressionBase.__init__(self, source_ref=source_ref) def finalize(self): del self.parent def computeExpressionRaw(self, trace_collection): return self, None, None def mayHaveSideEffects(self): return False def mayRaiseException(self, exception_type): return False class ExpressionBuiltinLocalsBase(ExpressionBase): # Base classes can be abstract, pylint: disable=abstract-method __slots__ = ("variable_traces", "locals_scope") def __init__(self, locals_scope, source_ref): ExpressionBase.__init__(self, source_ref=source_ref) self.variable_traces = None self.locals_scope = locals_scope def finalize(self): del self.locals_scope del self.variable_traces def mayHaveSideEffects(self): return False def mayRaiseException(self, exception_type): return False def getVariableTraces(self): return self.variable_traces class ExpressionBuiltinLocalsUpdated(ExpressionBuiltinLocalsBase): kind = "EXPRESSION_BUILTIN_LOCALS_UPDATED" def __init__(self, locals_scope, source_ref): ExpressionBuiltinLocalsBase.__init__( self, locals_scope=locals_scope, source_ref=source_ref ) assert locals_scope is not None def getLocalsScope(self): return self.locals_scope def computeExpressionRaw(self, trace_collection): # Just inform the collection that all escaped. self.variable_traces = trace_collection.onLocalsUsage( self.getParentVariableProvider() ) trace_collection.onLocalsDictEscaped(self.locals_scope) return self, None, None class ExpressionBuiltinLocalsRef(ExpressionBuiltinLocalsBase): kind = "EXPRESSION_BUILTIN_LOCALS_REF" def __init__(self, locals_scope, source_ref): ExpressionBuiltinLocalsBase.__init__( self, locals_scope=locals_scope, source_ref=source_ref ) def getLocalsScope(self): return self.locals_scope def computeExpressionRaw(self, trace_collection): if self.locals_scope.isMarkedForPropagation(): result = ExpressionMakeDict( pairs=( ExpressionKeyValuePair( key=makeConstantRefNode( constant=variable_name, source_ref=self.source_ref ), value=ExpressionTempVariableRef( variable=variable, source_ref=self.source_ref ), source_ref=self.source_ref, ) for variable_name, variable in self.locals_scope.getPropagationVariables().items() ), source_ref=self.source_ref, ) new_result = result.computeExpressionRaw(trace_collection) assert new_result[0] is result self.finalize() return result, "new_expression", "Propagated locals dictionary reference." # Just inform the collection that all escaped unless it is abortative. if not self.getParent().isStatementReturn(): trace_collection.onLocalsUsage(self.getParentVariableProvider()) return self, None, None class ExpressionBuiltinLocalsCopy(ExpressionBuiltinLocalsBase): kind = "EXPRESSION_BUILTIN_LOCALS_COPY" def computeExpressionRaw(self, trace_collection): # Just inform the collection that all escaped. self.variable_traces = trace_collection.onLocalsUsage( self.getParentVariableProvider() ) for variable, variable_trace in self.variable_traces: if ( not variable_trace.mustHaveValue() and not variable_trace.mustNotHaveValue() ): return self, None, None # Other locals elsewhere. if variable_trace.getNameUsageCount() > 1: return self, None, None pairs = [] for variable, variable_trace in self.variable_traces: if variable_trace.mustHaveValue(): pairs.append( ExpressionKeyValuePair( key=makeConstantRefNode( constant=variable.getName(), user_provided=True, source_ref=self.source_ref, ), value=ExpressionVariableRef( variable=variable, source_ref=self.source_ref ), source_ref=self.source_ref, ) ) # Locals is sorted of course. def _sorted(pairs): names = self.getParentVariableProvider().getLocalVariableNames() return sorted( pairs, key=lambda pair: names.index(pair.getKey().getCompileTimeConstant()), ) result = ExpressionMakeDict(pairs=_sorted(pairs), source_ref=self.source_ref) return result, "new_expression", "Statically predicted locals dictionary." class ExpressionBuiltinDir1(ExpressionBuiltinSingleArgBase): kind = "EXPRESSION_BUILTIN_DIR1" def computeExpression(self, trace_collection): # TODO: Quite some cases should be possible to predict and this # should be using a slot, with "__dir__" being overloaded or not. # Any code could be run, note that. trace_collection.onControlFlowEscape(self) # Any exception may be raised. trace_collection.onExceptionRaiseExit(BaseException) return self, None, None

tests/chainerx_tests/unit_tests/test_scalar.py

yuhonghong66/chainer

563

<reponame>yuhonghong66/chainer import math import pytest import chainerx def _check_cast_scalar_equals_data(scalar, data): assert bool(scalar) == bool(data) assert int(scalar) == int(data) assert float(scalar) == float(data) all_scalar_values = [ -2, 1, -1.5, 2.3, True, False, float('inf'), float('nan')] @pytest.mark.parametrize('value,dtype', [ (0, chainerx.int64), (-1, chainerx.int64), (0x7fffffffffffffff, chainerx.int64), (-0x8000000000000000, chainerx.int64), (0.0, chainerx.float64), (float('inf'), chainerx.float64), (float('nan'), chainerx.float64), (True, chainerx.bool_), (False, chainerx.bool_), ]) def test_init_without_dtype(value, dtype): scalar = chainerx.Scalar(value) assert scalar.dtype == dtype if math.isnan(value): assert math.isnan(scalar.tolist()) else: assert scalar.tolist() == value assert isinstance(scalar.tolist(), type(value)) @pytest.mark.parametrize('value,cast_dtype,expected_value', [ (0, chainerx.bool_, False), (0, chainerx.int8, 0), (0, chainerx.int16, 0), (0, chainerx.int32, 0), (0, chainerx.int64, 0), (0, chainerx.uint8, 0), (0, chainerx.float32, 0.0), (0, chainerx.float64, 0.0), (0.0, chainerx.bool_, False), (0.0, chainerx.int8, 0), (0.0, chainerx.int16, 0), (0.0, chainerx.int32, 0), (0.0, chainerx.int64, 0), (0.0, chainerx.uint8, 0), (0.0, chainerx.float32, 0.0), (0.0, chainerx.float64, 0.0), (1, chainerx.bool_, True), (1, chainerx.int8, 1), (1, chainerx.int16, 1), (1, chainerx.int32, 1), (1, chainerx.int64, 1), (1, chainerx.uint8, 1), (1, chainerx.float32, 1.0), (1, chainerx.float64, 1.0), (1.0, chainerx.bool_, True), (1.0, chainerx.int8, 1), (1.0, chainerx.int16, 1), (1.0, chainerx.int32, 1), (1.0, chainerx.int64, 1), (1.0, chainerx.uint8, 1), (1.0, chainerx.float32, 1.0), (1.0, chainerx.float64, 1.0), (-1, chainerx.bool_, True), (-1, chainerx.int8, -1), (-1, chainerx.int16, -1), (-1, chainerx.int32, -1), (-1, chainerx.int64, -1), (-1, chainerx.uint8, 0xff), (-1, chainerx.float32, -1.0), (-1, chainerx.float64, -1.0), (0x100, chainerx.bool_, True), (0x100, chainerx.int8, 0), (0x100, chainerx.int16, 0x100), (0x100, chainerx.int32, 0x100), (0x100, chainerx.int64, 0x100), (0x100, chainerx.uint8, 0), (0x10000, chainerx.bool_, True), (0x10000, chainerx.int8, 0), (0x10000, chainerx.int16, 0), (0x10000, chainerx.int32, 0x10000), (0x10000, chainerx.int64, 0x10000), (0x10000, chainerx.uint8, 0), (0x100000000, chainerx.bool_, True), (0x100000000, chainerx.int8, 0), (0x100000000, chainerx.int16, 0), (0x100000000, chainerx.int32, 0), (0x100000000, chainerx.int64, 0x100000000), (0x100000000, chainerx.uint8, 0), (0x7fffffffffffffff, chainerx.bool_, True), (0x7fffffffffffffff, chainerx.int8, -1), (0x7fffffffffffffff, chainerx.int16, -1), (0x7fffffffffffffff, chainerx.int32, -1), (0x7fffffffffffffff, chainerx.int64, 0x7fffffffffffffff), (0x7fffffffffffffff, chainerx.uint8, 255), ]) def test_init_casted(value, cast_dtype, expected_value): scalar = chainerx.Scalar(value, cast_dtype) assert scalar.dtype == cast_dtype if math.isnan(expected_value): assert math.isnan(scalar.tolist()) else: assert scalar.tolist() == expected_value assert isinstance(scalar.tolist(), type(expected_value)) @pytest.mark.parametrize( 'value', [0, 0.0, 1, 1.0, -1, 0x100, 0x10000, 0x100000000, 0x7fffffffffffffff]) @chainerx.testing.parametrize_dtype_specifier('dtype_spec') def test_init_with_dtype(value, dtype_spec): expected_dtype = chainerx.dtype(dtype_spec) scalar = chainerx.Scalar(value, dtype_spec) assert scalar.dtype == expected_dtype assert scalar == chainerx.Scalar(value, expected_dtype) @pytest.mark.parametrize('value1,value2', [ # TODO(niboshi): Support commented-out cases (0, 0), (1, 1), # (1, 1.0), (1.5, 1.5), (-1.5, -1.5), (True, True), (False, False), # (True, 1), # (True, 1.0), # (False, 0), # (False, 0.0), # (float('inf'), float('inf')), ]) def test_equality(value1, value2): scalar1 = chainerx.Scalar(value1) scalar2 = chainerx.Scalar(value2) assert scalar1 == scalar2 assert scalar2 == scalar1 assert scalar1 == value1 assert value1 == scalar1 assert scalar2 == value2 assert value2 == scalar2 assert scalar2 == value1 assert value1 == scalar2 assert scalar1 == value2 assert value2 == scalar1 @pytest.mark.parametrize('value1,value2', [ (0, 1), (-1, 1), (-1.0001, -1.0), (-1.0001, -1), (True, False), (True, 1.1), (1.0001, 1.0002), (float('nan'), float('nan')), ]) def test_inequality(value1, value2): scalar1 = chainerx.Scalar(value1) scalar2 = chainerx.Scalar(value2) assert scalar1 != scalar2 assert scalar2 != scalar1 assert scalar2 != value1 assert value1 != scalar2 assert scalar1 != value2 assert value2 != scalar1 @pytest.mark.parametrize('value', [ -2, 1, -1.5, 2.3, True, False ]) def test_cast(value): scalar = chainerx.Scalar(value) _check_cast_scalar_equals_data(scalar, value) _check_cast_scalar_equals_data(+scalar, +value) if isinstance(value, bool): with pytest.raises(chainerx.DtypeError): -scalar # should not be able to negate bool else: _check_cast_scalar_equals_data(-scalar, -value) @pytest.mark.parametrize('value', all_scalar_values) def test_dtype(value): scalar = chainerx.Scalar(value) if isinstance(value, bool): assert scalar.dtype == chainerx.bool_ elif isinstance(value, int): assert scalar.dtype == chainerx.int64 elif isinstance(value, float): assert scalar.dtype == chainerx.float64 else: assert False @pytest.mark.parametrize('value', all_scalar_values) def test_repr(value): scalar = chainerx.Scalar(value) assert repr(scalar) == repr(value) assert str(scalar) == str(value) def test_init_invalid(): with pytest.raises(TypeError): chainerx.Scalar("1") # string, which is not a numeric

app.py

Tiemoue/SnakeGame

564

import sys import pygame from app_window import App_window from button import Button from snake import Snake from food import Food from settings import WIDTH, HEIGHT, FONT, BG_COL, QUIT_BUTTON_COLOUR, PLAY_BUTTON_COLOUR, BLACK, FPS, RED class App: def __init__(self): pygame.init() self.clock = pygame.time.Clock() self.window = pygame.display.set_mode((WIDTH, HEIGHT)) self.gameover = pygame.font.SysFont("Comicsansms", 90, bold=False, italic=True) self.font = pygame.font.SysFont(FONT, 20, bold=1) self.running = True self.state = "intro" self.intro_buttons = [] self.playing_buttons = [] self.gameover_buttons = [] self.active_buttons = self.intro_buttons self.app_window = App_window(self) self.snake = Snake(self) self.food = Food(self) self.make_buttons() def make_buttons(self): # INTRO PLAY AND QUIT BUTTON intro_play_button = Button(self, 50, 300, WIDTH - 100, 50, PLAY_BUTTON_COLOUR, hover_colour=(49, 218, 46), function=self.intro_to_play, text="PLAY") self.intro_buttons.append(intro_play_button) intro_quit_button = Button(self, 50, HEIGHT - 100, WIDTH - 100, 50, QUIT_BUTTON_COLOUR, hover_colour=(219, 53, 43), function=self.intro_quit, text="QUIT") self.intro_buttons.append(intro_quit_button) # PLAYING QUIT BUTTON playing_quit_button = Button(self, (WIDTH // 2) - 50, 20, 100, 33, QUIT_BUTTON_COLOUR, hover_colour=(219, 53, 43), function=self.playing_quit, text="QUIT") self.playing_buttons.append(playing_quit_button) # GAMEOVER BUTTON gameover_play_again_button = Button(self, 50, 300, WIDTH - 100, 50, PLAY_BUTTON_COLOUR, hover_colour=(36, 183, 23), function=self.reset, text="PLAY AGAIN") self.gameover_buttons.append(gameover_play_again_button) gameover_quit_button = Button(self, 50, HEIGHT - 100, WIDTH - 100, 50, QUIT_BUTTON_COLOUR, hover_colour=(216, 53, 43), function=self.intro_quit, text="QUIT") self.gameover_buttons.append(gameover_quit_button) def show_text(self, text, pos): text = self.font.render(text, False, BLACK) self.window.blit(text, (pos[0], pos[1])) def reset(self): # reset the game self.state = "play" self.active_buttons = self.playing_buttons self.snake = Snake(self) FPS[0] = 5 def run(self): while self.running: self.events() self.update() self.draw() self.clock.tick(FPS[0]) pygame.quit() sys.exit() def events(self): if self.state == "intro": self.intro_events() if self.state == "play": self.playing_events() if self.state == "dead": self.gameover_events() def update(self): if self.state == "intro": self.intro_update() if self.state == "play": self.playing_update() if self.state == "dead": self.gameover_update() def draw(self): self.window.fill(BG_COL) if self.state == "intro": self.intro_draw() if self.state == "play": self.playing_draw() if self.state == "dead": self.gameover_draw() pygame.display.update() # INTRO FUNCTIONS def intro_events(self): for event in pygame.event.get(): if event.type == pygame.QUIT: self.running = False if event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE: self.running = False if event.type == pygame.MOUSEBUTTONDOWN: for button in self.active_buttons: if button.hovered: button.click() def intro_update(self): for button in self.active_buttons: button.update() def intro_draw(self): for button in self.active_buttons: button.draw() def intro_to_play(self): self.state = "play" self.active_buttons = self.playing_buttons def intro_quit(self): self.running = False # PlAY FUNCTIONS def playing_events(self): for event in pygame.event.get(): if event.type == pygame.QUIT: self.running = False # checks if a key is pressed down if event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: self.running = False if event.key == pygame.K_LEFT and self.snake.direction != [ 1, 0 ]: self.snake.direction = [-1, 0] if event.key == pygame.K_RIGHT and self.snake.direction != [ -1, 0 ]: self.snake.direction = [1, 0] if event.key == pygame.K_UP and self.snake.direction != [0, 1]: self.snake.direction = [0, -1] if event.key == pygame.K_DOWN and self.snake.direction != [ 0, -1 ]: self.snake.direction = [0, 1] if event.type == pygame.MOUSEBUTTONDOWN: for button in self.active_buttons: if button.hovered: button.click() def playing_update(self): for button in self.active_buttons: button.update() self.app_window.update() def playing_draw(self): self.app_window.draw() for button in self.active_buttons: button.draw() self.show_text("Score: " + str(self.snake.length - 1), [20, 20]) def playing_quit(self): self.running = False # GAMEOVER FUNCTIONS def gameover_events(self): for event in pygame.event.get(): if event.type == pygame.QUIT: self.running = False if event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE: self.running = False if event.type == pygame.MOUSEBUTTONDOWN: for button in self.active_buttons: if button.hovered: button.click() def gameover_update(self): for button in self.active_buttons: button.update() def gameover_draw(self): for button in self.active_buttons: button.draw() self.game_over("GAME OVER", [WIDTH - 440, 30]) def game_over(self, text, pos): text = self.gameover.render(text, False, RED) self.window.blit(text, (pos[0], pos[1]))

pupa/importers/bills.py

datamade/pupa

565

<filename>pupa/importers/bills.py<gh_stars>1-10 from pupa.utils import fix_bill_id from opencivicdata.legislative.models import (Bill, RelatedBill, BillAbstract, BillTitle, BillIdentifier, BillAction, BillActionRelatedEntity, BillSponsorship, BillSource, BillDocument, BillVersion, BillDocumentLink, BillVersionLink) from .base import BaseImporter from ..exceptions import PupaInternalError class BillImporter(BaseImporter): _type = 'bill' model_class = Bill related_models = {'abstracts': (BillAbstract, 'bill_id', {}), 'other_titles': (BillTitle, 'bill_id', {}), 'other_identifiers': (BillIdentifier, 'bill_id', {}), 'actions': (BillAction, 'bill_id', { 'related_entities': (BillActionRelatedEntity, 'action_id', {})}), 'related_bills': (RelatedBill, 'bill_id', {}), 'sponsorships': (BillSponsorship, 'bill_id', {}), 'sources': (BillSource, 'bill_id', {}), 'documents': (BillDocument, 'bill_id', { 'links': (BillDocumentLink, 'document_id', {})}), 'versions': (BillVersion, 'bill_id', { 'links': (BillVersionLink, 'version_id', {})}), } preserve_order = {'actions'} def __init__(self, jurisdiction_id, org_importer, person_importer): super(BillImporter, self).__init__(jurisdiction_id) self.org_importer = org_importer self.person_importer = person_importer def get_object(self, bill): spec = { 'legislative_session_id': bill['legislative_session_id'], 'identifier': bill['identifier'], } if 'from_organization_id' in bill: spec['from_organization_id'] = bill['from_organization_id'] return self.model_class.objects.prefetch_related('actions__related_entities', 'versions__links', 'documents__links', ).get(**spec) def limit_spec(self, spec): spec['legislative_session__jurisdiction_id'] = self.jurisdiction_id return spec def prepare_for_db(self, data): data['identifier'] = fix_bill_id(data['identifier']) data['legislative_session_id'] = self.get_session_id(data.pop('legislative_session')) if data['from_organization']: data['from_organization_id'] = self.org_importer.resolve_json_id( data.pop('from_organization')) for action in data['actions']: action['organization_id'] = self.org_importer.resolve_json_id( action['organization_id']) for entity in action['related_entities']: if 'organization_id' in entity: entity['organization_id'] = self.org_importer.resolve_json_id( entity['organization_id']) elif 'person_id' in entity: entity['person_id'] = self.person_importer.resolve_json_id( entity['person_id']) for sponsor in data['sponsorships']: if 'person_id' in sponsor: sponsor['person_id'] = self.person_importer.resolve_json_id( sponsor['person_id'], allow_no_match=True) if 'organization_id' in sponsor: sponsor['organization_id'] = self.org_importer.resolve_json_id( sponsor['organization_id'], allow_no_match=True) return data def postimport(self): # go through all RelatedBill objs that are attached to a bill in this jurisdiction and # are currently unresolved for rb in RelatedBill.objects.filter( bill__legislative_session__jurisdiction_id=self.jurisdiction_id, related_bill=None): candidates = list(Bill.objects.filter( legislative_session__identifier=rb.legislative_session, legislative_session__jurisdiction_id=self.jurisdiction_id, identifier=rb.identifier) ) if len(candidates) == 1: rb.related_bill = candidates[0] rb.save() elif len(candidates) > 1: # pragma: no cover # if we ever see this, we need to add additional fields on the relation raise PupaInternalError('multiple related_bill candidates found for {}'.format(rb))

utilities/classify_ensemble.py

Hazel1994/Paraphrase-detection-on-Quora-and-MSRP

566

from sklearn.metrics import f1_score,accuracy_score import numpy as np from utilities.tools import load_model import pandas as pd def predict_MSRP_test_data(n_models,nb_words,nlp_f,test_data_1,test_data_2,test_labels): models=[] n_h_features=nlp_f.shape[1] print('loading the models...') for i in range(n_models): models.append(load_model(i+1,nb_words,n_h_features)) preds=[] print('predicting the test data...\n') i=0 for m in models: i+=1 preds_prob=m.predict([test_data_1, test_data_2,nlp_f], batch_size=64, verbose=0) preds.append(preds_prob[:,1]) preds=np.asarray(preds) final_labels=np.zeros(len(test_data_1),dtype=int) #average the predicttion for i in range(len(test_data_1)): final_labels[i]=round(np.mean(preds[:,i])) if i%100==0: print(i ,' out of ',len(test_data_1)) print("test data accuracy: ", accuracy_score(final_labels,test_labels)) print("test data f_measure: ", f1_score(final_labels, test_labels)) submission = pd.DataFrame({"Quality": final_labels}) submission.to_csv("predictions/MSRP.tsv", index=True,index_label='test_id') def predict_Quora_test_data(n_models,nb_words,nlp_f,test_data_1,test_data_2): models=[] n_h_features=nlp_f.shape[1] print('loading the models...') for i in range(n_models): models.append(load_model(i+1,nb_words,n_h_features)) preds=[] print('predicting the test data...\n') i=0 for m in models: i+=1 preds_prob=m.predict([test_data_1, test_data_2,nlp_f], batch_size=125, verbose=0) preds.append(preds_prob[:,1]) preds=np.asarray(preds) final_labels=np.zeros(len(test_data_1),dtype=float) #average the predicttion for i in range(len(test_data_1)): final_labels[i]=np.mean(preds[:,i]) if i%10000==0: print(i ,' out of ',len(test_data_1)) print('making the sumbission file') submission = pd.DataFrame({"is_duplicate": final_labels}) submission.to_csv("predictions/Quora.tsv", index=True,index_label='test_id')

koino/plot/clusters.py

tritas/koino

567

# coding=utf-8 import logging import traceback from os import makedirs from os.path import exists, join from textwrap import fill import matplotlib.patheffects as PathEffects import matplotlib.pyplot as plt import numpy as np import seaborn as sns from koino.plot import big_square, default_alpha from matplotlib import cm from ..utils.base import jaccard def plot_silhouette( X, figure_fp, n_clusters, silhouette_values, cluster_labels, silhouette_avg ): # Create a subplot with 1 row and 2 columns fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(26, 10)) # The 1st subplot is the silhouette plot # The silhouette coefficient can range from -1, 1 but here all # lie within [-0.1, 1] ax1.set_xlim([-0.1, 1]) # The (n_clusters+1)*10 is for inserting blank space between silhouette # plots of individual clusters, to demarcate them clearly. ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10]) y_lower = 10 for k in range(n_clusters): # Aggregate the silhouette scores for samples belonging to # cluster i, and sort them ith_cluster_silhouette_values = np.sort(silhouette_values[cluster_labels == k]) size_cluster_i = ith_cluster_silhouette_values.shape[0] y_upper = y_lower + size_cluster_i color = cm.spectral(float(k) / n_clusters) ax1.fill_betweenx( np.arange(y_lower, y_upper), 0, ith_cluster_silhouette_values, facecolor=color, edgecolor=color, alpha=default_alpha, ) # Label the silhouette plots with their cluster numbers at the # middle ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(k)) # Compute the new y_lower for next plot y_lower = y_upper + 10 # 10 for the 0 samples ax1.set_title("The silhouette plot for the various clusters.") ax1.set_xlabel("The silhouette coefficient values") ax1.set_ylabel("Cluster label") # The vertical line for average silhouette score of all the values ax1.axvline(x=silhouette_avg, color="red", linestyle="--") ax1.set_yticks([]) # Clear the yaxis labels / ticks ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1]) # Construct cluster # 2nd Plot showing the actual clusters formed colors = cm.spectral(cluster_labels.astype(float) / n_clusters) # colors = y ax2.scatter(X[:, 0], X[:, 1], marker=".", s=20, lw=0, alpha=default_alpha, c=colors) ax2.set_title("The visualization of the clustered data.") ax2.set_xlabel("Feature space for the 1st feature") ax2.set_ylabel("Feature space for the 2nd feature") plt.suptitle( ("Silhouette analysis for KMeans " "with n_clusters = %d" % n_clusters), fontsize=14, fontweight="bold", ) plt.savefig(figure_fp) plt.close() plt.clf() def plot_cluster_assignments( X, y, n_clusters, figures_dir, transparent=False, cluster_names=None, title="" ): """Clustering assignments scatter plot Notes ----- Can use mean or median to fix cluster centroid coordinates.""" if cluster_names is None: cluster_names = ["Cluster {}".format(i + 1) for i in range(n_clusters)] # We first reorder the data points according to the centroids labels X = np.vstack([X[y == i] for i in range(n_clusters)]) y = np.hstack([y[y == i] for i in range(n_clusters)]) # Choose a color palette with seaborn. palette = np.array(sns.color_palette("hls", n_clusters)) fig, ax = plt.subplots(figsize=big_square) # for i in range(n_clusters): # mask = y == i # ax.scatter(X[mask, 0], X[mask, 1], lw=0, s=20, c=palette[i], # label=cluster_names[i]) ax.set_title(title) ax.scatter(X[:, 0], X[:, 1], lw=0, s=20, c=palette[y.astype(np.int)]) ax.axis("off") # Add the labels for each cluster. for i in range(n_clusters): # Position of each label. samples = np.atleast_2d(X[y == i, :2]) if not len(samples): logging.warning( "Probably singular cluster {} (shape:{})".format(i + 1, X[y == i].shape) ) continue xtext, ytext = np.median(samples, axis=0) name = fill(cluster_names[i], width=20) assert np.isfinite(xtext) assert np.isfinite(ytext) txt = ax.text(xtext, ytext, name, fontsize=20, wrap=True, ha="left") txt.set_path_effects( [PathEffects.Stroke(linewidth=5, foreground="w"), PathEffects.Normal()] ) # plt.legend() figure_fp = join(figures_dir, "Clustered {}.png".format(title)) fig.tight_layout() try: fig.savefig(figure_fp, transparent=transparent) except ValueError: logging.warning(traceback.format_exc()) finally: plt.close() plt.clf() def overlap_jaccard( indx, y_a, y_b, names_a, names_b, n_a=None, n_b=None, figsize=None, output_dir=None, alabel="socio-demographic", blabel="purchases", transparent=False, ): """Compute and plot contingency tables based on set intersection and jaccard score. # TODO: Normaliser par len(sd_set) ou len(diet_set) ? """ if not (n_a or n_b) or not output_dir: return elif output_dir and not exists(output_dir): makedirs(output_dir) else: assert n_a and n_b assert len(indx) == len(y_a) == len(y_b) assert len(names_a) == n_a assert len(names_b) == n_b a_sets = [set(indx[y_a == i]) for i in range(n_a)] b_sets = [set(indx[y_b == i]) for i in range(n_b)] inter_sets = np.asarray( [[len(set_a & set_t) for set_a in a_sets] for set_t in b_sets], dtype=np.int_ ) fig, ax = plt.subplots(figsize=figsize) plt.title("Overlap between {} and {} clusters".format(alabel, blabel)) sns.heatmap( inter_sets, annot=True, fmt="6.0f", ax=ax, square=True, xticklabels=names_a, yticklabels=names_b, ) plt.tight_layout() inter_path = join(output_dir, "Clusters Intersection.png") plt.savefig(inter_path, transparent=transparent) plt.close() plt.clf() jac_arr = np.asarray( [[jaccard(set_a, set_b) for set_a in a_sets] for set_b in b_sets], dtype=np.float_, ) fig, ax = plt.subplots(figsize=figsize) plt.title("Jaccard scores between {} and {} clusters".format(alabel, blabel)) sns.heatmap( jac_arr, annot=True, fmt=".3f", ax=ax, square=True, xticklabels=names_a, yticklabels=names_b, ) plt.tight_layout() jaccard_path = join(output_dir, "Clusters Jaccard.png") plt.savefig(jaccard_path, transparent=transparent) plt.close() plt.clf()

python/testData/stubs/FullyQualifiedTypingNamedTuple.py

jnthn/intellij-community

568

import typing nt = typing.NamedTuple("name", [("field", str)])

src/plat/index_news_remove.py

jack139/cnnc

569

<reponame>jack139/cnnc #!/usr/bin/env python # -*- coding: utf-8 -*- # import web import time from bson.objectid import ObjectId from config import setting import helper db = setting.db_web # 删除聊天规则 url = ('/plat/index_news_remove') class handler: def GET(self): if not helper.logged(helper.PRIV_USER, 'TALKBOT'): raise web.seeother('/') render = helper.create_render() user_data = web.input(news_id='') if user_data.news_id == '': return render.info('参数错误！') db.index_news.delete_one({'_id':ObjectId(user_data.news_id)}) return render.info('成功删除！', '/plat/index_news')

esppy/windows/score.py

PetreStegaroiu/python-esppy

570

<filename>esppy/windows/score.py<gh_stars>0 #!/usr/bin/env python # encoding: utf-8 # # Copyright SAS Institute # # 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 __future__ import print_function, division, absolute_import, unicode_literals import os import pandas as pd import six from .base import BaseWindow, attribute from .features import SchemaFeature, ModelsFeature, ConnectorsFeature from .utils import get_args, ensure_element class ScoreWindow(BaseWindow, SchemaFeature, ModelsFeature, ConnectorsFeature): ''' Score window Parameters ---------- name : string, optional The name of the window schema : Schema, optional The schema of the window pubsub : bool, optional Publish/subscribe mode for the window. When the project-level value of pubsub is manual, true enables publishing and subscribing for the window and false disables it. description : string, optional Description of the window Attributes ---------- online_models : list-of-OnlineModels List of online model objects offline_models : list-of-OfflineModels List of offline model objects Returns ------- :class:`ScoreWindow` ''' window_type = 'score' def __init__(self, name=None, schema=None, pubsub=None, description=None, copyvars=None): BaseWindow.__init__(self, **get_args(locals())) # Set the online model for subclasses if type(self).__name__ != 'ScoreWindow': self.add_online_model(type(self).__name__) def _create_schema_list(self, variables): ''' Extract schema information from DataFrame Parameters ---------- variables : DataFrame The DataFrame containing schema information Returns ------- list ''' labels = [] labels.append('id*:int64') for name, dtype in zip(variables['Name'], variables['Type']): if dtype == 'Num': labels.append(name + ':double') elif dtype == 'Char': labels.append(name + ':string') return labels def import_schema_from_astore_output(self, output_variables_input): ''' Import a schema from the astore CAS action output format Parameters ---------- output_variables_input : DataFrame or list or string The schema definition ''' if isinstance(output_variables_input, six.string_types): if os.path.isfile(output_variables_input): output_variables_input = pd.read_csv(output_variables_input) else: output_variables_input = pd.read_csv(six.StringIO(output_variables_input)) if isinstance(output_variables_input, pd.DataFrame): self.schema = self._create_schema_list(output_variables_input) elif isinstance(output_variables_input, (tuple, list)): self.schema = list(output_variables_input)

Packs/Pwned/Integrations/PwnedV2/PwnedV2.py

diCagri/content

571

from CommonServerPython import * ''' IMPORTS ''' import re import requests # Disable insecure warnings requests.packages.urllib3.disable_warnings() ''' GLOBALS/PARAMS ''' VENDOR = 'Have I Been Pwned? V2' MAX_RETRY_ALLOWED = demisto.params().get('max_retry_time', -1) API_KEY = demisto.params().get('api_key') USE_SSL = not demisto.params().get('insecure', False) BASE_URL = 'https://haveibeenpwned.com/api/v3' HEADERS = { 'hibp-api-key': API_KEY, 'user-agent': 'DBOT-API', 'Content-Type': 'application/json', 'Accept': 'application/json' } DEFAULT_DBOT_SCORE_EMAIL = 2 if demisto.params().get('default_dbot_score_email') == 'SUSPICIOUS' else 3 DEFAULT_DBOT_SCORE_DOMAIN = 2 if demisto.params().get('default_dbot_score_domain') == 'SUSPICIOUS' else 3 SUFFIXES = { "email": '/breachedaccount/', "domain": '/breaches?domain=', "username": '/breachedaccount/', "paste": '/pasteaccount/', "email_truncate_verified": '?truncateResponse=false&includeUnverified=true', "domain_truncate_verified": '&truncateResponse=false&includeUnverified=true', "username_truncate_verified": '?truncateResponse=false&includeUnverified=true' } RETRIES_END_TIME = datetime.min ''' HELPER FUNCTIONS ''' def http_request(method, url_suffix, params=None, data=None): while True: res = requests.request( method, BASE_URL + url_suffix, verify=USE_SSL, params=params, data=data, headers=HEADERS ) if res.status_code != 429: # Rate limit response code break if datetime.now() > RETRIES_END_TIME: return_error('Max retry time has exceeded.') wait_regex = re.search(r'\d+', res.json()['message']) if wait_regex: wait_amount = wait_regex.group() else: demisto.error('failed extracting wait time will use default (5). Res body: {}'.format(res.text)) wait_amount = 5 if datetime.now() + timedelta(seconds=int(wait_amount)) > RETRIES_END_TIME: return_error('Max retry time has exceeded.') time.sleep(int(wait_amount)) if res.status_code == 404: return None if not res.status_code == 200: if not res.status_code == 401: demisto.error( 'Error in API call to Pwned Integration [%d]. Full text: %s' % (res.status_code, res.text)) return_error('Error in API call to Pwned Integration [%d] - %s' % (res.status_code, res.reason)) return None return res.json() def html_description_to_human_readable(breach_description): """ Converting from html description to hr :param breach_description: Description of breach from API response :return: Description string that altered HTML urls to clickable urls for better readability in war-room """ html_link_pattern = re.compile('<a href="(.+?)"(.+?)>(.+?)</a>') patterns_found = html_link_pattern.findall(breach_description) for link in patterns_found: html_actual_address = link[0] html_readable_name = link[2] link_from_desc = '[' + html_readable_name + ']' + '(' + html_actual_address + ')' breach_description = re.sub(html_link_pattern, link_from_desc, breach_description, count=1) return breach_description def data_to_markdown(query_type, query_arg, api_res, api_paste_res=None): records_found = False md = '### Have I Been Pwned query for ' + query_type.lower() + ': *' + query_arg + '*\n' if api_res: records_found = True for breach in api_res: verified_breach = 'Verified' if breach['IsVerified'] else 'Unverified' md += '#### ' + breach['Title'] + ' (' + breach['Domain'] + '): ' + str(breach['PwnCount']) + \ ' records breached [' + verified_breach + ' breach]\n' md += 'Date: **' + breach['BreachDate'] + '**\n\n' md += html_description_to_human_readable(breach['Description']) + '\n' md += 'Data breached: **' + ','.join(breach['DataClasses']) + '**\n' if api_paste_res: records_found = True pastes_list = [] for paste_breach in api_paste_res: paste_entry = \ { 'Source': paste_breach['Source'], 'Title': paste_breach['Title'], 'ID': paste_breach['Id'], 'Date': '', 'Amount of emails in paste': str(paste_breach['EmailCount']) } if paste_breach['Date']: paste_entry['Date'] = paste_breach['Date'].split('T')[0] pastes_list.append(paste_entry) md += tableToMarkdown('The email address was found in the following "Pastes":', pastes_list, ['ID', 'Title', 'Date', 'Source', 'Amount of emails in paste']) if not records_found: md += 'No records found' return md def create_dbot_score_dictionary(indicator_value, indicator_type, dbot_score): return { 'Indicator': indicator_value, 'Type': indicator_type, 'Vendor': VENDOR, 'Score': dbot_score } def create_context_entry(context_type, context_main_value, comp_sites, comp_pastes, malicious_score): context_dict = dict() # dict if context_type == 'email': context_dict['Address'] = context_main_value else: context_dict['Name'] = context_main_value context_dict['Pwned-V2'] = { 'Compromised': { 'Vendor': VENDOR, 'Reporters': ', '.join(comp_sites + comp_pastes) } } if malicious_score == 3: context_dict['Malicious'] = add_malicious_to_context(context_type) return context_dict def add_malicious_to_context(malicious_type): return { 'Vendor': VENDOR, 'Description': 'The ' + malicious_type + ' has been compromised' } def email_to_entry_context(email, api_email_res, api_paste_res): dbot_score = 0 comp_email = dict() # type: dict comp_sites = sorted([item['Title'] for item in api_email_res]) comp_pastes = sorted(set(item['Source'] for item in api_paste_res)) if len(comp_sites) > 0: dbot_score = DEFAULT_DBOT_SCORE_EMAIL email_context = create_context_entry('email', email, comp_sites, comp_pastes, DEFAULT_DBOT_SCORE_EMAIL) comp_email[outputPaths['email']] = email_context comp_email['DBotScore'] = create_dbot_score_dictionary(email, 'email', dbot_score) return comp_email def domain_to_entry_context(domain, api_res): comp_sites = [item['Title'] for item in api_res] comp_sites = sorted(comp_sites) comp_domain = dict() # type: dict dbot_score = 0 if len(comp_sites) > 0: dbot_score = DEFAULT_DBOT_SCORE_DOMAIN domain_context = create_context_entry('domain', domain, comp_sites, [], DEFAULT_DBOT_SCORE_DOMAIN) comp_domain[outputPaths['domain']] = domain_context comp_domain['DBotScore'] = create_dbot_score_dictionary(domain, 'domain', dbot_score) return comp_domain def set_retry_end_time(): global RETRIES_END_TIME if MAX_RETRY_ALLOWED != -1: RETRIES_END_TIME = datetime.now() + timedelta(seconds=int(MAX_RETRY_ALLOWED)) ''' COMMANDS + REQUESTS FUNCTIONS ''' def test_module(args_dict): """ If the http request was successful the test will return OK :return: 3 arrays of outputs """ http_request('GET', SUFFIXES.get("username", '') + 'test') return ['ok'], [None], [None] def pwned_email_command(args_dict): """ Executing the pwned request for emails list, in order to support list input, the function returns 3 lists of outputs :param args_dict: the demisto argument - in this case the email list is needed :return: 3 arrays of outputs """ email_list = argToList(args_dict.get('email', '')) api_email_res_list, api_paste_res_list = pwned_email(email_list) md_list = [] ec_list = [] for email, api_email_res, api_paste_res in zip(email_list, api_email_res_list, api_paste_res_list): md_list.append(data_to_markdown('Email', email, api_email_res, api_paste_res)) ec_list.append(email_to_entry_context(email, api_email_res or [], api_paste_res or [])) return md_list, ec_list, api_email_res_list def pwned_email(email_list): """ Executing the http requests :param email_list: the email list that needed for the http requests :return: 2 arrays of http requests outputs """ api_email_res_list = [] api_paste_res_list = [] for email in email_list: email_suffix = SUFFIXES.get("email") + email + SUFFIXES.get("email_truncate_verified") paste_suffix = SUFFIXES.get("paste") + email api_email_res_list.append(http_request('GET', url_suffix=email_suffix)) api_paste_res_list.append(http_request('GET', url_suffix=paste_suffix)) return api_email_res_list, api_paste_res_list def pwned_domain_command(args_dict): """ Executing the pwned request for domains list, in order to support list input, the function returns 3 lists of outputs :param args_dict: the demisto argument - in this case the domain list is needed :return: 3 arrays of outputs """ domain_list = argToList(args_dict.get('domain', '')) api_res_list = pwned_domain(domain_list) md_list = [] ec_list = [] for domain, api_res in zip(domain_list, api_res_list): md_list.append(data_to_markdown('Domain', domain, api_res)) ec_list.append(domain_to_entry_context(domain, api_res or [])) return md_list, ec_list, api_res_list def pwned_domain(domain_list): """ Executing the http request :param domain_list: the domains list that needed for the http requests :return: an array of http requests outputs """ api_res_list = [] for domain in domain_list: suffix = SUFFIXES.get("domain") + domain + SUFFIXES.get("domain_truncate_verified") api_res_list.append(http_request('GET', url_suffix=suffix)) return api_res_list def pwned_username_command(args_dict): """ Executing the pwned request for usernames list, in order to support list input, the function returns 3 lists of outputs :param args_dict: the demisto argument - in this case the username list is needed :return: 3 arrays of outputs """ username_list = argToList(args_dict.get('username', '')) api_res_list = pwned_username(username_list) md_list = [] ec_list = [] for username, api_res in zip(username_list, api_res_list): md_list.append(data_to_markdown('Username', username, api_res)) ec_list.append(domain_to_entry_context(username, api_res or [])) return md_list, ec_list, api_res_list def pwned_username(username_list): """ Executing the http request :param username_list: the username list that needed for the http requests :return: an array of http requests outputs """ api_res_list = [] for username in username_list: suffix = SUFFIXES.get("username") + username + SUFFIXES.get("username_truncate_verified") api_res_list.append(http_request('GET', url_suffix=suffix)) return api_res_list command = demisto.command() LOG('Command being called is: {}'.format(command)) try: handle_proxy() set_retry_end_time() commands = { 'test-module': test_module, 'email': pwned_email_command, 'pwned-email': pwned_email_command, 'domain': pwned_domain_command, 'pwned-domain': pwned_domain_command, 'pwned-username': pwned_username_command } if command in commands: md_list, ec_list, api_email_res_list = commands[command](demisto.args()) for md, ec, api_paste_res in zip(md_list, ec_list, api_email_res_list): return_outputs(md, ec, api_paste_res) # Log exceptions except Exception as e: return_error(str(e))

moshmosh/extensions/pipelines.py

Aloxaf/moshmosh

572

<reponame>Aloxaf/moshmosh<gh_stars>100-1000 from moshmosh.extension import Extension from moshmosh.ast_compat import ast class PipelineVisitor(ast.NodeTransformer): """ `a | f -> f(a)`, recursively """ def __init__(self, activation): self.activation = activation def visit_BinOp(self, n: ast.BinOp): if n.lineno in self.activation and isinstance(n.op, ast.BitOr): return ast.Call( self.visit(n.right), [self.visit(n.left)], [], lineno=n.lineno, col_offset=n.col_offset ) return self.generic_visit(n) class Pipeline(Extension): identifier = "pipeline" def __init__(self): self.visitor = PipelineVisitor(self.activation) def rewrite_ast(self, node): return self.visitor.visit(node)

postpatch.py

mr-ma/basic-self-checksumming

573

<reponame>mr-ma/basic-self-checksumming import argparse import os import r2pipe import struct import mmap import base64 from shutil import copyfile import pprint pp = pprint.PrettyPrinter(indent=4) def precompute_hash(r2, offset, size): print('Precomputing hash') h = 0 print("r2 command to get the function body in base64:\np6e {}@{}".format(size, offset)) b64_func = r2.cmd("p6e {}@{}".format(size, offset)) func_bytes = bytearray(base64.b64decode(b64_func)) for b in func_bytes: h = h ^ b print('Precomuted hash:', hex(h)) return h def patch_binary(mm, search_value, patch_value): print("search value:{} patch value:{}".format(search_value, patch_value)) flag = "<I" # little-endian unsigned int search_bytes = struct.pack(flag, search_value) address = mm.find(search_bytes) if address == -1: mm.seek(0) address = mm.find(search_bytes) mm.seek(address, os.SEEK_SET) patch_bytes = struct.pack(flag, patch_value) mm.write(patch_bytes) def get_protected_function_info(r2, function): # find addresses and sizes of all functions r2.cmd("aa") r2.cmd("aac") function_list = r2.cmdj("aflj") # print(function_list) funcs = {} for func in function_list: attr = {'size': func['size'], 'offset': func['offset']} funcs[func['name']] = attr # Basic search for mangled names if function == 'main': # main function is entry0 in the binary function = 'entry0' print("Cannot precompute the expected hash for the main function, why is that?") exit(1) match = 0 mangledName = "" for name, attr in funcs.items(): # sometimes r2 prepends sym. to function names if function in name: mangledName = name match += 1 if match != 1: print("Failed to safely find function in the binary!") pp.pprint(funcs) exit(1) return funcs[mangledName] def main(): parser = argparse.ArgumentParser( description='Postpatch protected C program.') parser.add_argument('-b', action="store", dest="binary", help="program.out protected program binary", required=True) parser.add_argument('-f', action="store", dest="function", help="protected function name", required=True) parser.add_argument('-p', nargs="+", dest="placeholders", help="list of used placeholders in the exact order of function, size, expected hash", required=True) results = parser.parse_args() print("python protect program", results) r2 = r2pipe.open(results.binary) funcInfo = get_protected_function_info(r2, results.function) funcOffset = funcInfo["offset"] funcSize = funcInfo["size"] funcExpectedHash = precompute_hash(r2, funcOffset, funcSize) print("funcOffset:{} funcSize:{} funcExpectedHash:{}".format( funcOffset, funcSize, funcExpectedHash)) binaryFile, _ = os.path.splitext(results.binary) patchedBinary = "{}-patched.out".format(binaryFile) copyfile(results.binary, patchedBinary) with open(patchedBinary, 'r+b') as binary: mm = mmap.mmap(binary.fileno(), 0) patch_binary(mm, int(results.placeholders[0]), int(funcSize)) patch_binary(mm, int(results.placeholders[1]), int(funcExpectedHash)) print("Successfully stored patched binary {}".format(patchedBinary)) status = os.system( "chmod +x {}".format(patchedBinary)) if status != 0: print("Error in setting permission, try:\n sudo chmod +x {}".format(patchedBinary)) exit(1) if __name__ == '__main__': main()

sitewebapp/migrations/0011_auto_20210130_0150.py

deucaleon18/debsoc-nitdgp-website

574

# Generated by Django 2.2.15 on 2021-01-29 20:20 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('sitewebapp', '0010_auditionanswers_auditionquestions_audtionrounds_candidates'), ] operations = [ migrations.CreateModel( name='auditionRounds', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('roundno', models.IntegerField(default=1)), ('candidate', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='inductees', to='sitewebapp.Candidates')), ], ), migrations.AlterField( model_name='auditionquestions', name='round', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='round', to='sitewebapp.auditionRounds'), ), migrations.DeleteModel( name='audtionRounds', ), ]

venv/lib/python3.6/site-packages/ansible_collections/community/azure/plugins/modules/azure_rm_availabilityset_info.py

usegalaxy-no/usegalaxy

575

<reponame>usegalaxy-no/usegalaxy #!/usr/bin/python # -*- coding: utf-8 -*- # Copyright: (c) 2016, <NAME> <<EMAIL>> # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'} DOCUMENTATION = ''' --- module: azure_rm_availabilityset_info short_description: Get Azure Availability Set facts description: - Get facts for a specific availability set or all availability sets. options: name: description: - Limit results to a specific availability set. resource_group: description: - The resource group to search for the desired availability set. tags: description: - List of tags to be matched. extends_documentation_fragment: - azure.azcollection.azure author: - <NAME> (@julienstroheker) deprecated: removed_in: '2.0.0' why: The Ansible collection community.azure is deprecated. Use azure.azcollection instead. alternative: Use M(azure.azcollection.azure_rm_availabilityset_info) instead. ''' EXAMPLES = ''' - name: Get facts for one availability set community.azure.azure_rm_availabilityset_info: name: Testing resource_group: myResourceGroup - name: Get facts for all availability sets in a specific resource group community.azure.azure_rm_availabilityset_info: resource_group: myResourceGroup ''' RETURN = ''' azure_availabilityset: description: List of availability sets dicts. returned: always type: complex contains: location: description: - Location where the resource lives. type: str sample: eastus2 name: description: - Resource name. type: str sample: myAvailabilitySet properties: description: - The properties of the resource. type: dict contains: platformFaultDomainCount: description: - Fault Domain count. type: int sample: 3 platformUpdateDomainCount: description: - Update Domain count. type: int sample: 2 virtualMachines: description: - A list of references to all virtualmachines in the availability set. type: list sample: [] sku: description: - Location where the resource lives. type: str sample: Aligned type: description: - Resource type. type: str sample: "Microsoft.Compute/availabilitySets" tags: description: - Resource tags. type: dict sample: { env: sandbox } ''' from ansible_collections.azure.azcollection.plugins.module_utils.azure_rm_common import AzureRMModuleBase try: from msrestazure.azure_exceptions import CloudError except Exception: # handled in azure_rm_common pass AZURE_OBJECT_CLASS = 'AvailabilitySet' class AzureRMAvailabilitySetInfo(AzureRMModuleBase): """Utility class to get availability set facts""" def __init__(self): self.module_args = dict( name=dict(type='str'), resource_group=dict(type='str'), tags=dict(type='list') ) self.results = dict( changed=False, ansible_info=dict( azure_availabilitysets=[] ) ) self.name = None self.resource_group = None self.tags = None super(AzureRMAvailabilitySetInfo, self).__init__( derived_arg_spec=self.module_args, supports_tags=False, facts_module=True ) def exec_module(self, **kwargs): is_old_facts = self.module._name == 'azure_rm_availabilityset_facts' if is_old_facts: self.module.deprecate("The 'azure_rm_availabilityset_facts' module has been renamed to 'azure_rm_availabilityset_info'", version='3.0.0', collection_name='community.azure') # was 2.13 for key in self.module_args: setattr(self, key, kwargs[key]) if self.name and not self.resource_group: self.fail("Parameter error: resource group required when filtering by name.") if self.name: self.results['ansible_info']['azure_availabilitysets'] = self.get_item() else: self.results['ansible_info']['azure_availabilitysets'] = self.list_items() return self.results def get_item(self): """Get a single availability set""" self.log('Get properties for {0}'.format(self.name)) item = None result = [] try: item = self.compute_client.availability_sets.get(self.resource_group, self.name) except CloudError: pass if item and self.has_tags(item.tags, self.tags): avase = self.serialize_obj(item, AZURE_OBJECT_CLASS) avase['name'] = item.name avase['type'] = item.type avase['sku'] = item.sku.name result = [avase] return result def list_items(self): """Get all availability sets""" self.log('List all availability sets') try: response = self.compute_client.availability_sets.list(self.resource_group) except CloudError as exc: self.fail('Failed to list all items - {0}'.format(str(exc))) results = [] for item in response: if self.has_tags(item.tags, self.tags): avase = self.serialize_obj(item, AZURE_OBJECT_CLASS) avase['name'] = item.name avase['type'] = item.type avase['sku'] = item.sku.name results.append(avase) return results def main(): """Main module execution code path""" AzureRMAvailabilitySetInfo() if __name__ == '__main__': main()

tests/v3_api/common.py

sowmyav27/rancher

576

import inspect import json import os import random import subprocess import time import requests import ast import paramiko import rancher from rancher import ApiError from lib.aws import AmazonWebServices DEFAULT_TIMEOUT = 120 DEFAULT_MULTI_CLUSTER_APP_TIMEOUT = 300 CATTLE_TEST_URL = os.environ.get('CATTLE_TEST_URL', "http://localhost:80") ADMIN_TOKEN = os.environ.get('ADMIN_TOKEN', "None") CATTLE_API_URL = CATTLE_TEST_URL + "/v3" kube_fname = os.path.join(os.path.dirname(os.path.realpath(__file__)), "k8s_kube_config") MACHINE_TIMEOUT = float(os.environ.get('RANCHER_MACHINE_TIMEOUT', "1200")) TEST_IMAGE = "sangeetha/mytestcontainer" CLUSTER_NAME = os.environ.get("RANCHER_CLUSTER_NAME", "") RANCHER_CLEANUP_CLUSTER = \ ast.literal_eval(os.environ.get('RANCHER_CLEANUP_CLUSTER', "True")) env_file = os.path.join( os.path.dirname(os.path.realpath(__file__)), "rancher_env.config") CLUSTER_NAME_2 = "" def random_str(): return 'random-{0}-{1}'.format(random_num(), int(time.time())) def random_num(): return random.randint(0, 1000000) def random_int(start, end): return random.randint(start, end) def random_test_name(name="test"): return name + "-" + str(random_int(10000, 99999)) def get_admin_client(): return rancher.Client(url=CATTLE_API_URL, token=ADMIN_TOKEN, verify=False) def get_client_for_token(token): return rancher.Client(url=CATTLE_API_URL, token=token, verify=False) def get_project_client_for_token(project, token): p_url = project.links['self'] + '/schemas' p_client = rancher.Client(url=p_url, token=token, verify=False) return p_client def get_cluster_client_for_token(cluster, token): c_url = cluster.links['self'] + '/schemas' c_client = rancher.Client(url=c_url, token=token, verify=False) return c_client def up(cluster, token): c_url = cluster.links['self'] + '/schemas' c_client = rancher.Client(url=c_url, token=token, verify=False) return c_client def wait_state(client, obj, state, timeout=DEFAULT_TIMEOUT): wait_for(lambda: client.reload(obj).state == state, timeout) return client.reload(obj) def wait_for_condition(client, resource, check_function, fail_handler=None, timeout=DEFAULT_TIMEOUT): start = time.time() resource = client.reload(resource) while not check_function(resource): if time.time() - start > timeout: exceptionMsg = 'Timeout waiting for ' + resource.baseType + \ ' to satisfy condition: ' + \ inspect.getsource(check_function) if fail_handler: exceptionMsg = exceptionMsg + fail_handler(resource) raise Exception(exceptionMsg) time.sleep(.5) resource = client.reload(resource) return resource def wait_for(callback, timeout=DEFAULT_TIMEOUT, timeout_message=None): start = time.time() ret = callback() while ret is None or ret is False: time.sleep(.5) if time.time() - start > timeout: if timeout_message: raise Exception(timeout_message) else: raise Exception('Timeout waiting for condition') ret = callback() return ret def random_name(): return "test" + "-" + str(random_int(10000, 99999)) def create_project_and_ns(token, cluster, project_name=None, ns_name=None): client = get_client_for_token(token) p = create_project(client, cluster, project_name) c_client = get_cluster_client_for_token(cluster, token) ns = create_ns(c_client, cluster, p, ns_name) return p, ns def create_project(client, cluster, project_name=None): if project_name is None: project_name = random_name() p = client.create_project(name=project_name, clusterId=cluster.id) time.sleep(5) p = wait_until_available(client, p) assert p.state == 'active' return p def create_project_with_pspt(client, cluster, pspt): p = client.create_project(name=random_name(), clusterId=cluster.id) p = wait_until_available(client, p) assert p.state == 'active' return set_pspt_for_project(p, client, pspt) def set_pspt_for_project(project, client, pspt): project.setpodsecuritypolicytemplate(podSecurityPolicyTemplateId=pspt.id) project = wait_until_available(client, project) assert project.state == 'active' return project def create_ns(client, cluster, project, ns_name=None): if ns_name is None: ns_name = random_name() ns = client.create_namespace(name=ns_name, clusterId=cluster.id, projectId=project.id) wait_for_ns_to_become_active(client, ns) ns = client.reload(ns) assert ns.state == 'active' return ns def assign_members_to_cluster(client, user, cluster, role_template_id): crtb = client.create_cluster_role_template_binding( clusterId=cluster.id, roleTemplateId=role_template_id, subjectKind="User", userId=user.id) return crtb def assign_members_to_project(client, user, project, role_template_id): prtb = client.create_project_role_template_binding( projectId=project.id, roleTemplateId=role_template_id, subjectKind="User", userId=user.id) return prtb def change_member_role_in_cluster(client, user, crtb, role_template_id): crtb = client.update( crtb, roleTemplateId=role_template_id, userId=user.id) return crtb def change_member_role_in_project(client, user, prtb, role_template_id): prtb = client.update( prtb, roleTemplateId=role_template_id, userId=user.id) return prtb def create_kubeconfig(cluster): generateKubeConfigOutput = cluster.generateKubeconfig() print(generateKubeConfigOutput.config) file = open(kube_fname, "w") file.write(generateKubeConfigOutput.config) file.close() def validate_psp_error_worklaod(p_client, workload, error_message): workload = wait_for_wl_transitioning(p_client, workload) assert workload.state == "updating" assert workload.transitioning == "error" print(workload.transitioningMessage) assert error_message in workload.transitioningMessage def validate_workload(p_client, workload, type, ns_name, pod_count=1, wait_for_cron_pods=60): workload = wait_for_wl_to_active(p_client, workload) assert workload.state == "active" # For cronjob, wait for the first pod to get created after # scheduled wait time if type == "cronJob": time.sleep(wait_for_cron_pods) pods = p_client.list_pod(workloadId=workload.id).data assert len(pods) == pod_count for pod in pods: wait_for_pod_to_running(p_client, pod) wl_result = execute_kubectl_cmd( "get " + type + " " + workload.name + " -n " + ns_name) if type == "deployment" or type == "statefulSet": assert wl_result["status"]["readyReplicas"] == pod_count if type == "daemonSet": assert wl_result["status"]["currentNumberScheduled"] == pod_count if type == "cronJob": assert len(wl_result["status"]["active"]) >= pod_count return for key, value in workload.workloadLabels.items(): label = key + "=" + value get_pods = "get pods -l" + label + " -n " + ns_name pods_result = execute_kubectl_cmd(get_pods) assert len(pods_result["items"]) == pod_count for pod in pods_result["items"]: assert pod["status"]["phase"] == "Running" return pods_result["items"] def validate_workload_with_sidekicks(p_client, workload, type, ns_name, pod_count=1): workload = wait_for_wl_to_active(p_client, workload) assert workload.state == "active" pods = wait_for_pods_in_workload(p_client, workload, pod_count) assert len(pods) == pod_count for pod in pods: wait_for_pod_to_running(p_client, pod) wl_result = execute_kubectl_cmd( "get " + type + " " + workload.name + " -n " + ns_name) assert wl_result["status"]["readyReplicas"] == pod_count for key, value in workload.workloadLabels.items(): label = key + "=" + value get_pods = "get pods -l" + label + " -n " + ns_name execute_kubectl_cmd(get_pods) pods_result = execute_kubectl_cmd(get_pods) assert len(pods_result["items"]) == pod_count for pod in pods_result["items"]: assert pod["status"]["phase"] == "Running" assert len(pod["status"]["containerStatuses"]) == 2 assert "running" in pod["status"]["containerStatuses"][0]["state"] assert "running" in pod["status"]["containerStatuses"][1]["state"] def validate_workload_paused(p_client, workload, expectedstatus): workloadStatus = p_client.list_workload(uuid=workload.uuid).data[0].paused assert workloadStatus == expectedstatus def validate_pod_images(expectedimage, workload, ns_name): for key, value in workload.workloadLabels.items(): label = key + "=" + value get_pods = "get pods -l" + label + " -n " + ns_name pods = execute_kubectl_cmd(get_pods) for pod in pods["items"]: assert pod["spec"]["containers"][0]["image"] == expectedimage def validate_pods_are_running_by_id(expectedpods, workload, ns_name): for key, value in workload.workloadLabels.items(): label = key + "=" + value get_pods = "get pods -l" + label + " -n " + ns_name pods = execute_kubectl_cmd(get_pods) curpodnames = [] for pod in pods["items"]: curpodnames.append(pod["metadata"]["name"]) for expectedpod in expectedpods["items"]: assert expectedpod["metadata"]["name"] in curpodnames def validate_workload_image(client, workload, expectedImage, ns): workload = client.list_workload(uuid=workload.uuid).data[0] assert workload.containers[0].image == expectedImage validate_pod_images(expectedImage, workload, ns.name) def execute_kubectl_cmd(cmd, json_out=True, stderr=False): command = 'kubectl --kubeconfig {0} {1}'.format( kube_fname, cmd) if json_out: command += ' -o json' if stderr: result = run_command_with_stderr(command) else: result = run_command(command) if json_out: result = json.loads(result) print(result) return result def run_command(command): return subprocess.check_output(command, shell=True, text=True) def run_command_with_stderr(command): try: output = subprocess.check_output(command, shell=True, stderr=subprocess.PIPE) returncode = 0 except subprocess.CalledProcessError as e: output = e.output returncode = e.returncode print(returncode) return (output, returncode) def wait_for_wl_to_active(client, workload, timeout=DEFAULT_TIMEOUT): start = time.time() workloads = client.list_workload(uuid=workload.uuid).data assert len(workloads) == 1 wl = workloads[0] while wl.state != "active": if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) workloads = client.list_workload(uuid=workload.uuid).data assert len(workloads) == 1 wl = workloads[0] return wl def wait_for_ingress_to_active(client, ingress, timeout=DEFAULT_TIMEOUT): start = time.time() ingresses = client.list_ingress(uuid=ingress.uuid).data assert len(ingresses) == 1 wl = ingresses[0] while wl.state != "active": if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) ingresses = client.list_ingress(uuid=ingress.uuid).data assert len(ingresses) == 1 wl = ingresses[0] return wl def wait_for_wl_transitioning(client, workload, timeout=DEFAULT_TIMEOUT, state="error"): start = time.time() workloads = client.list_workload(uuid=workload.uuid).data assert len(workloads) == 1 wl = workloads[0] while wl.transitioning != state: if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) workloads = client.list_workload(uuid=workload.uuid).data assert len(workloads) == 1 wl = workloads[0] return wl def wait_for_pod_to_running(client, pod, timeout=DEFAULT_TIMEOUT): start = time.time() pods = client.list_pod(uuid=pod.uuid).data assert len(pods) == 1 p = pods[0] while p.state != "running": if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) pods = client.list_pod(uuid=pod.uuid).data assert len(pods) == 1 p = pods[0] return p def get_schedulable_nodes(cluster): client = get_admin_client() nodes = client.list_node(clusterId=cluster.id).data schedulable_nodes = [] for node in nodes: if node.worker: schedulable_nodes.append(node) return schedulable_nodes def get_role_nodes(cluster, role): etcd_nodes = [] control_nodes = [] worker_nodes = [] node_list = [] client = get_admin_client() nodes = client.list_node(clusterId=cluster.id).data for node in nodes: if node.etcd: etcd_nodes.append(node) if node.controlPlane: control_nodes.append(node) if node.worker: worker_nodes.append(node) if role == "etcd": node_list = etcd_nodes if role == "control": node_list = control_nodes if role == "worker": node_list = worker_nodes return node_list def validate_ingress(p_client, cluster, workloads, host, path, insecure_redirect=False): time.sleep(10) curl_args = " " if (insecure_redirect): curl_args = " -L --insecure " if len(host) > 0: curl_args += " --header 'Host: " + host + "'" nodes = get_schedulable_nodes(cluster) target_name_list = get_target_names(p_client, workloads) for node in nodes: host_ip = node.externalIpAddress cmd = curl_args + " http://" + host_ip + path validate_http_response(cmd, target_name_list) def validate_ingress_using_endpoint(p_client, ingress, workloads, timeout=300): target_name_list = get_target_names(p_client, workloads) start = time.time() fqdn_available = False url = None while not fqdn_available: if time.time() - start > timeout: raise AssertionError( "Timed out waiting for endpoint to be available") time.sleep(.5) ingress_list = p_client.list_ingress(uuid=ingress.uuid).data assert len(ingress_list) == 1 ingress = ingress_list[0] if hasattr(ingress, 'publicEndpoints'): for public_endpoint in ingress.publicEndpoints: if public_endpoint["hostname"].startswith(ingress.name): fqdn_available = True url = \ public_endpoint["protocol"].lower() + "://" + \ public_endpoint["hostname"] if "path" in public_endpoint.keys(): url += public_endpoint["path"] time.sleep(10) validate_http_response(url, target_name_list) def get_target_names(p_client, workloads): pods = [] for workload in workloads: pod_list = p_client.list_pod(workloadId=workload.id).data pods.extend(pod_list) target_name_list = [] for pod in pods: target_name_list.append(pod.name) print("target name list:" + str(target_name_list)) return target_name_list def get_endpoint_url_for_workload(p_client, workload, timeout=600): fqdn_available = False url = "" start = time.time() while not fqdn_available: if time.time() - start > timeout: raise AssertionError( "Timed out waiting for endpoint to be available") time.sleep(.5) workload_list = p_client.list_workload(uuid=workload.uuid).data assert len(workload_list) == 1 workload = workload_list[0] if hasattr(workload, 'publicEndpoints'): assert len(workload.publicEndpoints) > 0 url = "http://" url = url + workload.publicEndpoints[0]["addresses"][0] + ":" url = url + str(workload.publicEndpoints[0]["port"]) fqdn_available = True return url def wait_until_lb_is_active(url, timeout=300): start = time.time() while check_for_no_access(url): time.sleep(.5) print("No access yet") if time.time() - start > timeout: raise Exception('Timed out waiting for LB to become active') return def check_for_no_access(url): try: requests.get(url) return False except requests.ConnectionError: print("Connection Error - " + url) return True def validate_http_response(cmd, target_name_list, client_pod=None): target_hit_list = target_name_list[:] count = 5 * len(target_name_list) for i in range(1, count): if len(target_hit_list) == 0: break if client_pod is None: curl_cmd = "curl " + cmd result = run_command(curl_cmd) else: wget_cmd = "wget -qO- " + cmd result = kubectl_pod_exec(client_pod, wget_cmd) result = result.decode() result = result.rstrip() print("cmd: \t" + cmd) print("result: \t" + result) assert result in target_name_list if result in target_hit_list: target_hit_list.remove(result) print("After removing all, the rest is: ", target_hit_list) assert len(target_hit_list) == 0 def validate_cluster(client, cluster, intermediate_state="provisioning", check_intermediate_state=True, skipIngresscheck=True, nodes_not_in_active_state=[], k8s_version=""): cluster = validate_cluster_state( client, cluster, check_intermediate_state=check_intermediate_state, intermediate_state=intermediate_state, nodes_not_in_active_state=nodes_not_in_active_state) # Create Daemon set workload and have an Ingress with Workload # rule pointing to this daemonset create_kubeconfig(cluster) if k8s_version != "": check_cluster_version(cluster, k8s_version) if hasattr(cluster, 'rancherKubernetesEngineConfig'): check_cluster_state(len(get_role_nodes(cluster, "etcd"))) project, ns = create_project_and_ns(ADMIN_TOKEN, cluster) p_client = get_project_client_for_token(project, ADMIN_TOKEN) con = [{"name": "test1", "image": TEST_IMAGE}] name = random_test_name("default") workload = p_client.create_workload(name=name, containers=con, namespaceId=ns.id, daemonSetConfig={}) validate_workload(p_client, workload, "daemonSet", ns.name, len(get_schedulable_nodes(cluster))) if not skipIngresscheck: host = "test" + str(random_int(10000, 99999)) + ".com" path = "/name.html" rule = {"host": host, "paths": [{"workloadIds": [workload.id], "targetPort": "80"}]} ingress = p_client.create_ingress(name=name, namespaceId=ns.id, rules=[rule]) wait_for_ingress_to_active(p_client, ingress) validate_ingress(p_client, cluster, [workload], host, path) return cluster def check_cluster_version(cluster, version): cluster_k8s_version = \ cluster.appliedSpec["rancherKubernetesEngineConfig"][ "kubernetesVersion"] assert cluster_k8s_version == version, \ "cluster_k8s_version: " + cluster_k8s_version + \ " Expected: " + version expected_k8s_version = version[:version.find("-")] k8s_version = execute_kubectl_cmd("version") kubectl_k8s_version = k8s_version["serverVersion"]["gitVersion"] assert kubectl_k8s_version == expected_k8s_version, \ "kubectl version: " + kubectl_k8s_version + \ " Expected: " + expected_k8s_version def check_cluster_state(etcd_count): css_resp = execute_kubectl_cmd("get cs") css = css_resp["items"] components = ["scheduler", "controller-manager"] for i in range(0, etcd_count): components.append("etcd-" + str(i)) print("components to check - " + str(components)) for cs in css: component_name = cs["metadata"]["name"] assert component_name in components components.remove(component_name) assert cs["conditions"][0]["status"] == "True" assert cs["conditions"][0]["type"] == "Healthy" assert len(components) == 0 def validate_dns_record(pod, record, expected): # requires pod with `dig` available - TEST_IMAGE host = '{0}.{1}.svc.cluster.local'.format( record["name"], record["namespaceId"]) validate_dns_entry(pod, host, expected) def validate_dns_entry(pod, host, expected): # requires pod with `dig` available - TEST_IMAGE cmd = 'ping -c 1 -W 1 {0}'.format(host) ping_output = kubectl_pod_exec(pod, cmd) ping_validation_pass = False for expected_value in expected: if expected_value in str(ping_output): ping_validation_pass = True break assert ping_validation_pass is True assert " 0% packet loss" in str(ping_output) dig_cmd = 'dig {0} +short'.format(host) dig_output = kubectl_pod_exec(pod, dig_cmd) for expected_value in expected: assert expected_value in str(dig_output) def wait_for_nodes_to_become_active(client, cluster, exception_list=[], retry_count=0): nodes = client.list_node(clusterId=cluster.id).data node_auto_deleted = False for node in nodes: if node.requestedHostname not in exception_list: node = wait_for_node_status(client, node, "active") if node is None: print("Need to re-evalauate new node list") node_auto_deleted = True retry_count += 1 print("Retry Count:" + str(retry_count)) if node_auto_deleted and retry_count < 5: wait_for_nodes_to_become_active(client, cluster, exception_list, retry_count) def wait_for_node_status(client, node, state): uuid = node.uuid start = time.time() nodes = client.list_node(uuid=uuid).data node_count = len(nodes) # Handle the case of nodes getting auto deleted when they are part of # nodepools if node_count == 1: node_status = nodes[0].state else: print("Node does not exist anymore -" + uuid) return None while node_status != state: if time.time() - start > MACHINE_TIMEOUT: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(5) nodes = client.list_node(uuid=uuid).data node_count = len(nodes) if node_count == 1: node_status = nodes[0].state else: print("Node does not exist anymore -" + uuid) return None return node def wait_for_node_to_be_deleted(client, node, timeout=300): uuid = node.uuid start = time.time() nodes = client.list_node(uuid=uuid).data node_count = len(nodes) while node_count != 0: if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) nodes = client.list_node(uuid=uuid).data node_count = len(nodes) def wait_for_cluster_node_count(client, cluster, expected_node_count, timeout=300): start = time.time() nodes = client.list_node(clusterId=cluster.id).data node_count = len(nodes) while node_count != expected_node_count: if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) nodes = client.list_node(clusterId=cluster.id).data node_count = len(nodes) def get_custom_host_registration_cmd(client, cluster, roles, node): allowed_roles = ["etcd", "worker", "controlplane"] cluster_tokens = client.list_cluster_registration_token( clusterId=cluster.id).data if len(cluster_tokens) > 0: cluster_token = cluster_tokens[0] else: cluster_token = create_custom_host_registration_token(client, cluster) cmd = cluster_token.nodeCommand for role in roles: assert role in allowed_roles cmd += " --" + role additional_options = " --address " + node.public_ip_address + \ " --internal-address " + node.private_ip_address cmd += additional_options return cmd def create_custom_host_registration_token(client, cluster): cluster_token = client.create_cluster_registration_token( clusterId=cluster.id) cluster_token = client.wait_success(cluster_token) assert cluster_token.state == 'active' return cluster_token def get_cluster_type(client, cluster): cluster_configs = [ "amazonElasticContainerServiceConfig", "azureKubernetesServiceConfig", "googleKubernetesEngineConfig", "rancherKubernetesEngineConfig" ] if "rancherKubernetesEngineConfig" in cluster: nodes = client.list_node(clusterId=cluster.id).data if len(nodes) > 0: if nodes[0].nodeTemplateId is None: return "Custom" for cluster_config in cluster_configs: if cluster_config in cluster: return cluster_config return "Imported" def delete_cluster(client, cluster): nodes = client.list_node(clusterId=cluster.id).data # Delete Cluster client.delete(cluster) # Delete nodes(in cluster) from AWS for Imported and Custom Cluster if (len(nodes) > 0): cluster_type = get_cluster_type(client, cluster) print(cluster_type) if get_cluster_type(client, cluster) in ["Imported", "Custom"]: nodes = client.list_node(clusterId=cluster.id).data filters = [ {'Name': 'tag:Name', 'Values': ['testcustom*', 'teststess*']}] ip_filter = {} ip_list = [] ip_filter['Name'] = \ 'network-interface.addresses.association.public-ip' ip_filter['Values'] = ip_list filters.append(ip_filter) for node in nodes: ip_list.append(node.externalIpAddress) assert len(ip_filter) > 0 print(ip_filter) aws_nodes = AmazonWebServices().get_nodes(filters) for node in aws_nodes: print(node.public_ip_address) AmazonWebServices().delete_nodes(aws_nodes) def check_connectivity_between_workloads(p_client1, workload1, p_client2, workload2, allow_connectivity=True): wl1_pods = p_client1.list_pod(workloadId=workload1.id).data wl2_pods = p_client2.list_pod(workloadId=workload2.id).data for pod in wl1_pods: for o_pod in wl2_pods: check_connectivity_between_pods(pod, o_pod, allow_connectivity) def check_connectivity_between_workload_pods(p_client, workload): pods = p_client.list_pod(workloadId=workload.id).data for pod in pods: for o_pod in pods: check_connectivity_between_pods(pod, o_pod) def check_connectivity_between_pods(pod1, pod2, allow_connectivity=True): pod_ip = pod2.status.podIp cmd = "ping -c 1 -W 1 " + pod_ip response = kubectl_pod_exec(pod1, cmd) print("Actual ping Response from " + pod1.name + ":" + str(response)) if allow_connectivity: assert pod_ip in str(response) and " 0% packet loss" in str(response) else: assert pod_ip in str(response) and " 100% packet loss" in str(response) def kubectl_pod_exec(pod, cmd): command = "exec " + pod.name + " -n " + pod.namespaceId + " -- " + cmd return execute_kubectl_cmd(command, json_out=False, stderr=True) def exec_shell_command(ip, port, cmd, password): ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) ssh.connect(ip, username="root", password=password, port=port) stdin, stdout, stderr = ssh.exec_command(cmd) response = stdout.readlines() return response def wait_for_ns_to_become_active(client, ns, timeout=DEFAULT_TIMEOUT): start = time.time() time.sleep(2) nss = client.list_namespace(uuid=ns.uuid).data assert len(nss) == 1 ns = nss[0] while ns.state != "active": if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) nss = client.list_namespace(uuid=ns.uuid).data assert len(nss) == 1 ns = nss[0] return ns def wait_for_pod_images(p_client, workload, ns_name, expectedimage, numofpods, timeout=DEFAULT_TIMEOUT): start = time.time() for key, value in workload.workloadLabels.items(): label = key + "=" + value get_pods = "get pods -l" + label + " -n " + ns_name pods = execute_kubectl_cmd(get_pods) for x in range(0, numofpods - 1): pod = pods["items"][x] podimage = pod["spec"]["containers"][0]["image"] while podimage != expectedimage: if time.time() - start > timeout: raise AssertionError( "Timed out waiting for correct pod images") time.sleep(.5) pods = execute_kubectl_cmd(get_pods) pod = pods["items"][x] podimage = pod["spec"]["containers"][0]["image"] def wait_for_pods_in_workload(p_client, workload, pod_count, timeout=DEFAULT_TIMEOUT): start = time.time() pods = p_client.list_pod(workloadId=workload.id).data while len(pods) != pod_count: if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) pods = p_client.list_pod(workloadId=workload.id).data return pods def get_admin_client_and_cluster(): client = get_admin_client() if CLUSTER_NAME == "": clusters = client.list_cluster().data else: clusters = client.list_cluster(name=CLUSTER_NAME).data assert len(clusters) > 0 cluster = clusters[0] return client, cluster def validate_cluster_state(client, cluster, check_intermediate_state=True, intermediate_state="provisioning", nodes_not_in_active_state=[]): if check_intermediate_state: cluster = wait_for_condition( client, cluster, lambda x: x.state == intermediate_state, lambda x: 'State is: ' + x.state, timeout=MACHINE_TIMEOUT) assert cluster.state == intermediate_state cluster = wait_for_condition( client, cluster, lambda x: x.state == "active", lambda x: 'State is: ' + x.state, timeout=MACHINE_TIMEOUT) assert cluster.state == "active" wait_for_nodes_to_become_active(client, cluster, exception_list=nodes_not_in_active_state) return cluster def wait_until_available(client, obj, timeout=DEFAULT_TIMEOUT): start = time.time() sleep = 0.01 while True: time.sleep(sleep) sleep *= 2 if sleep > 2: sleep = 2 try: obj = client.reload(obj) except ApiError as e: if e.error.status != 403: raise e else: return obj delta = time.time() - start if delta > timeout: msg = 'Timeout waiting for [{}:{}] for condition after {}' \ ' seconds'.format(obj.type, obj.id, delta) raise Exception(msg) def delete_node(aws_nodes): for node in aws_nodes: AmazonWebServices().delete_node(node) def cluster_cleanup(client, cluster, aws_nodes=None): if RANCHER_CLEANUP_CLUSTER: client.delete(cluster) if aws_nodes is not None: delete_node(aws_nodes) else: env_details = "env.CATTLE_TEST_URL='" + CATTLE_TEST_URL + "'\n" env_details += "env.ADMIN_TOKEN='" + ADMIN_TOKEN + "'\n" env_details += "env.CLUSTER_NAME='" + cluster.name + "'\n" create_config_file(env_details) def create_config_file(env_details): file = open(env_file, "w") file.write(env_details) file.close() def validate_hostPort(p_client, workload, source_port, cluster): pods = p_client.list_pod(workloadId=workload.id).data nodes = get_schedulable_nodes(cluster) for node in nodes: target_name_list = [] for pod in pods: print(pod.nodeId + " check " + node.id) if pod.nodeId == node.id: target_name_list.append(pod.name) break host_ip = node.externalIpAddress curl_cmd = " http://" + host_ip + ":" + \ str(source_port) + "/name.html" validate_http_response(curl_cmd, target_name_list) def validate_lb(p_client, workload): url = get_endpoint_url_for_workload(p_client, workload) target_name_list = get_target_names(p_client, [workload]) wait_until_lb_is_active(url) validate_http_response(url + "/name.html", target_name_list) def validate_nodePort(p_client, workload, cluster): source_port = workload.publicEndpoints[0]["port"] nodes = get_schedulable_nodes(cluster) pods = p_client.list_pod(workloadId=workload.id).data target_name_list = [] for pod in pods: target_name_list.append(pod.name) print("target name list:" + str(target_name_list)) for node in nodes: host_ip = node.externalIpAddress curl_cmd = " http://" + host_ip + ":" + \ str(source_port) + "/name.html" validate_http_response(curl_cmd, target_name_list) def validate_clusterIp(p_client, workload, cluster_ip, test_pods): pods = p_client.list_pod(workloadId=workload.id).data target_name_list = [] for pod in pods: target_name_list.append(pod["name"]) curl_cmd = "http://" + cluster_ip + "/name.html" for pod in test_pods: validate_http_response(curl_cmd, target_name_list, pod) def wait_for_pv_to_be_available(c_client, pv_object, timeout=DEFAULT_TIMEOUT): start = time.time() time.sleep(2) list = c_client.list_persistent_volume(uuid=pv_object.uuid).data assert len(list) == 1 pv = list[0] while pv.state != "available": if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to available") time.sleep(.5) list = c_client.list_persistent_volume(uuid=pv_object.uuid).data assert len(list) == 1 pv = list[0] return pv def wait_for_pvc_to_be_bound(p_client, pvc_object, timeout=DEFAULT_TIMEOUT): start = time.time() time.sleep(2) list = p_client.list_persistent_volume_claim(uuid=pvc_object.uuid).data assert len(list) == 1 pvc = list[0] while pvc.state != "bound": if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to bound") time.sleep(.5) list = p_client.list_persistent_volume_claim(uuid=pvc_object.uuid).data assert len(list) == 1 pvc = list[0] return pvc def create_wl_with_nfs(p_client, ns_id, pvc_name, wl_name, mount_path, sub_path, is_daemonSet=False): volumes = [{"type": "volume", "name": "vol1", "persistentVolumeClaim": { "readOnly": "false", "type": "persistentVolumeClaimVolumeSource", "persistentVolumeClaimId": pvc_name }}] volumeMounts = [{"readOnly": "False", "type": "volumeMount", "mountPath": mount_path, "subPath": sub_path, "name": "vol1" }] con = [{"name": "test1", "image": TEST_IMAGE, "volumeMounts": volumeMounts }] if is_daemonSet: workload = p_client.create_workload(name=wl_name, containers=con, namespaceId=ns_id, volumes=volumes, daemonSetConfig={}) else: workload = p_client.create_workload(name=wl_name, containers=con, namespaceId=ns_id, volumes=volumes) return workload def write_content_to_file(pod, content, filename): cmd_write = "/bin/bash -c 'echo {1} > {0}'".format(filename, content) output = kubectl_pod_exec(pod, cmd_write) assert output.strip().decode('utf-8') == "" def validate_file_content(pod, content, filename): cmd_get_content = "/bin/bash -c 'cat {0}' ".format(filename) output = kubectl_pod_exec(pod, cmd_get_content) assert output.strip().decode('utf-8') == content def wait_for_mcapp_to_active(client, multiClusterApp, timeout=DEFAULT_MULTI_CLUSTER_APP_TIMEOUT): print("\nuuid:") print(multiClusterApp.uuid) time.sleep(5) mcapps = client.list_multiClusterApp(uuid=multiClusterApp.uuid, name=multiClusterApp.name).data start = time.time() assert len(mcapps) == 1 mapp = mcapps[0] print(mapp.state) while mapp.state != "active": print(mapp.uuid) print(mapp.state) if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) multiclusterapps = client.list_multiClusterApp(uuid=multiClusterApp.uuid, name=multiClusterApp.name).data assert len(multiclusterapps) == 1 mapp = multiclusterapps[0] return mapp def validate_mcapp_cluster(app_id, p_client): mcapp = p_client.list_app(name=app_id).data assert len(mcapp) == 1 app = mcapp[0] return app def wait_for_mcapp_cluster_level_to_active(client, app_id, timeout=DEFAULT_MULTI_CLUSTER_APP_TIMEOUT): mcapps = client.list_app(name=app_id).data start = time.time() assert len(mcapps) == 1 mapp = mcapps[0] while mapp.state != "active": if time.time() - start > timeout: raise AssertionError( "Timed out waiting for state to get to active") time.sleep(.5) apps = client.list_app(name=app_id).data assert len(apps) == 1 mapp = apps[0] return mapp def get_admin_client_and_cluster_mcapp(): clusters = [] client = get_admin_client() if CLUSTER_NAME == "" or CLUSTER_NAME_2 == "": clusters = client.list_cluster().data else: clusters.append(client.list_cluster(name=CLUSTER_NAME).data) clusters.append(client.list_cluster(name=CLUSTER_NAME_2).data) assert len(clusters) == 2 return client, clusters def validate_multi_cluster_app_cluster(app_id1, app_id2, p_client1, p_client2): validate_mcapp_cluster(app_id1, p_client1) if app_id2 != "": validate_mcapp_cluster(app_id2, p_client2) # verify app in cluster is active or not wait_for_mcapp_cluster_level_to_active(p_client1, app_id1) if app_id2 != "": wait_for_mcapp_cluster_level_to_active(p_client2, app_id2)

LeetCode/Python3/String/20. Valid Parentheses.py

WatsonWangZh/CodingPractice

577

# Given a string containing just the characters '(', ')', '{', '}', '[' and ']', # determine if the input string is valid. # An input string is valid if: # Open brackets must be closed by the same type of brackets. # Open brackets must be closed in the correct order. # Note that an empty string is also considered valid. # Example 1: # Input: "()" # Output: true # Example 2: # Input: "()[]{}" # Output: true # Example 3: # Input: "(]" # Output: false # Example 4: # Input: "([)]" # Output: false # Example 5: # Input: "{[]}" # Output: true class Solution(object): def isValid(self, s): """ :type s: str :rtype: bool """ dict = {')':'(',']':'[','}':'{'} stack = [] for ch in s: if ch in dict.values(): stack.append(ch) elif ch in dict.keys(): if len(stack) == 0 or (stack.pop() != dict[ch]): return False return len(stack) == 0 def main(): s = Solution() print(s.isValid("()")) print(s.isValid("()[]{}")) print(s.isValid("(]")) print(s.isValid("([)]")) print(s.isValid("{[]}")) if __name__ == "__main__": main()

backend/ibutsu_server/controllers/health_controller.py

rsnyman/ibutsu-server

578

<filename>backend/ibutsu_server/controllers/health_controller.py from flask import current_app from sqlalchemy.exc import InterfaceError from sqlalchemy.exc import OperationalError try: from ibutsu_server.db.model import Result IS_CONNECTED = True except ImportError: IS_CONNECTED = False def get_health(token_info=None, user=None): """Get a health report :rtype: Health """ return {"status": "OK", "message": "Service is running"} def get_database_health(token_info=None, user=None): """Get a health report for the database :rtype: Health """ response = ({"status": "Pending", "message": "Fetching service status"}, 200) # Try to connect to the database, and handle various responses try: if not IS_CONNECTED: response = ({"status": "Error", "message": "Incomplete database configuration"}, 500) else: Result.query.first() response = ({"status": "OK", "message": "Service is running"}, 200) except OperationalError: response = ({"status": "Error", "message": "Unable to connect to the database"}, 500) except InterfaceError: response = ({"status": "Error", "message": "Incorrect connection configuration"}, 500) except Exception as e: response = ({"status": "Error", "message": str(e)}, 500) return response def get_health_info(token_info=None, user=None): """Get the information about this server :rtype: HealthInfo """ return { "frontend": current_app.config.get("FRONTEND_URL", "http://localhost:3000"), "backend": current_app.config.get("BACKEND_URL", "http://localhost:8080"), "api_ui": current_app.config.get("BACKEND_URL", "http://localhost:8080") + "/api/ui/", }

src/python/tsnecuda/TSNE.py

rappdw/tsne-cuda

579

"""Bindings for the Barnes Hut TSNE algorithm with fast nearest neighbors Refs: References [1] <NAME>, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9:2579-2605, 2008. [2] <NAME>, L.J.P. t-Distributed Stochastic Neighbor Embedding http://homepage.tudelft.nl/19j49/t-SNE.html """ import numpy as N import ctypes import os import pkg_resources def ord_string(s): b = bytearray() arr = b.extend(map(ord, s)) return N.array([x for x in b] + [0]).astype(N.uint8) class TSNE(object): def __init__(self, n_components=2, perplexity=50.0, early_exaggeration=2.0, learning_rate=200.0, num_neighbors=1023, force_magnify_iters=250, pre_momentum=0.5, post_momentum=0.8, theta=0.5, epssq=0.0025, n_iter=1000, n_iter_without_progress=1000, min_grad_norm=1e-7, perplexity_epsilon=1e-3, metric='euclidean', init='random', return_style='once', num_snapshots=5, verbose=0, random_seed=None, use_interactive=False, viz_timeout=10000, viz_server="tcp://localhost:5556", dump_points=False, dump_file="dump.txt", dump_interval=1, print_interval=10, device=0, ): """Initialization method for barnes hut T-SNE class. """ # Initialize the variables self.n_components = int(n_components) if self.n_components != 2: raise ValueError('The current barnes-hut implementation does not support projection into dimensions other than 2 for now.') self.perplexity = float(perplexity) self.early_exaggeration = float(early_exaggeration) self.learning_rate = float(learning_rate) self.n_iter = int(n_iter) self.n_iter_without_progress = int(n_iter_without_progress) self.min_grad_norm = float(min_grad_norm) if metric not in ['euclidean']: raise ValueError('Non-Euclidean metrics are not currently supported. Please use metric=\'euclidean\' for now.') else: self.metric = metric if init not in ['random']: raise ValueError('Non-Random initialization is not currently supported. Please use init=\'random\' for now.') else: self.init = init self.verbose = int(verbose) # Initialize non-sklearn variables self.num_neighbors = int(num_neighbors) self.force_magnify_iters = int(force_magnify_iters) self.perplexity_epsilon = float(perplexity_epsilon) self.pre_momentum = float(pre_momentum) self.post_momentum = float(post_momentum) self.theta = float(theta) self.epssq =float(epssq) self.device = int(device) self.print_interval = int(print_interval) # Point dumpoing self.dump_file = str(dump_file) self.dump_points = bool(dump_points) self.dump_interval = int(dump_interval) # Viz self.use_interactive = bool(use_interactive) self.viz_server = str(viz_server) self.viz_timeout = int(viz_timeout) # Return style if return_style not in ['once','snapshots']: raise ValueError('Invalid return style...') elif return_style == 'once': self.return_style = 0 elif return_style == 'snapshots': self.return_style = 1 self.num_snapshots = int(num_snapshots) # Build the hooks for the BH T-SNE library self._path = pkg_resources.resource_filename('tsnecuda','') # Load from current location # self._faiss_lib = N.ctypeslib.load_library('libfaiss', self._path) # Load the ctypes library # self._gpufaiss_lib = N.ctypeslib.load_library('libgpufaiss', self._path) # Load the ctypes library self._lib = N.ctypeslib.load_library('libtsnecuda', self._path) # Load the ctypes library # Hook the BH T-SNE function self._lib.pymodule_bh_tsne.restype = None self._lib.pymodule_bh_tsne.argtypes = [ N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, F_CONTIGUOUS, WRITEABLE'), # result N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, CONTIGUOUS'), # points ctypes.POINTER(N.ctypeslib.c_intp), # dims ctypes.c_float, # Perplexity ctypes.c_float, # Learning Rate ctypes.c_float, # Magnitude Factor ctypes.c_int, # Num Neighbors ctypes.c_int, # Iterations ctypes.c_int, # Iterations no progress ctypes.c_int, # Force Magnify iterations ctypes.c_float, # Perplexity search epsilon ctypes.c_float, # pre-exaggeration momentum ctypes.c_float, # post-exaggeration momentum ctypes.c_float, # Theta ctypes.c_float, # epssq ctypes.c_float, # Minimum gradient norm ctypes.c_int, # Initialization types N.ctypeslib.ndpointer(N.float32, ndim=2, flags='ALIGNED, F_CONTIGUOUS'), # Initialization Data ctypes.c_bool, # Dump points N.ctypeslib.ndpointer(N.uint8, flags='ALIGNED, CONTIGUOUS'), # Dump File ctypes.c_int, # Dump interval ctypes.c_bool, # Use interactive N.ctypeslib.ndpointer(N.uint8, flags='ALIGNED, CONTIGUOUS'), # Viz Server ctypes.c_int, # Viz timeout ctypes.c_int, # Verbosity ctypes.c_int, # Print interval ctypes.c_int, # GPU Device ctypes.c_int, # Return style ctypes.c_int ] # Number of snapshots def fit_transform(self, X, y=None): """Fit X into an embedded space and return that transformed output. Arguments: X {array} -- Input array, shape: (n_points, n_dimensions) Keyword Arguments: y {None} -- Ignored (default: {None}) """ # Setup points/embedding requirements self.points = N.require(X, N.float32, ['CONTIGUOUS', 'ALIGNED']) self.embedding = N.zeros(shape=(X.shape[0],self.n_components)) self.embedding = N.require(self.embedding , N.float32, ['F_CONTIGUOUS', 'ALIGNED', 'WRITEABLE']) # Handle Initialization if y is None: self.initialization_type = 1 self.init_data = N.require(N.zeros((1,1)),N.float32,['CONTIGUOUS','ALIGNED']) else: self.initialization_type = 3 self.init_data = N.require(y, N.float32, ['F_CONTIGUOUS', 'ALIGNED']) # Handle dumping and viz strings self.dump_file_ = N.require(ord_string(self.dump_file), N.uint8, ['CONTIGUOUS', 'ALIGNED']) self.viz_server_ = N.require(ord_string(self.viz_server), N.uint8, ['CONTIGUOUS', 'ALIGNED']) self._lib.pymodule_bh_tsne( self.embedding, # result self.points, # points self.points.ctypes.shape, # dims ctypes.c_float(self.perplexity), # Perplexity ctypes.c_float(self.learning_rate), # Learning Rate ctypes.c_float(self.early_exaggeration), # Magnitude Factor ctypes.c_int(self.num_neighbors), # Num Neighbors ctypes.c_int(self.n_iter), # Iterations ctypes.c_int(self.n_iter_without_progress), # Iterations no progress ctypes.c_int(self.force_magnify_iters), # Force Magnify iterations ctypes.c_float(self.perplexity_epsilon), # Perplexity search epsilon ctypes.c_float(self.pre_momentum), # pre-exaggeration momentum ctypes.c_float(self.post_momentum), # post-exaggeration momentum ctypes.c_float(self.theta), # Theta ctypes.c_float(self.epssq), # epssq ctypes.c_float(self.min_grad_norm), # Minimum gradient norm ctypes.c_int(self.initialization_type), # Initialization types self.init_data, # Initialization Data ctypes.c_bool(self.dump_points), # Dump points self.dump_file_, # Dump File ctypes.c_int(self.dump_interval), # Dump interval ctypes.c_bool(self.use_interactive), # Use interactive self.viz_server_, # Viz Server ctypes.c_int(self.viz_timeout), # Viz timeout ctypes.c_int(self.verbose), # Verbosity ctypes.c_int(self.print_interval), # Print interval ctypes.c_int(self.device), # GPU Device ctypes.c_int(self.return_style), # Return style ctypes.c_int(self.num_snapshots) ) # Number of snapshots return self.embedding

school/admin/__init__.py

leyyin/university-SE

580

# contains any CRUD not related to strictly editing users info and courses info from .views import admin

python/helpers/pydev/pydevd_file_utils.py

kirmerzlikin/intellij-community

581

<reponame>kirmerzlikin/intellij-community r''' This module provides utilities to get the absolute filenames so that we can be sure that: - The case of a file will match the actual file in the filesystem (otherwise breakpoints won't be hit). - Providing means for the user to make path conversions when doing a remote debugging session in one machine and debugging in another. To do that, the PATHS_FROM_ECLIPSE_TO_PYTHON constant must be filled with the appropriate paths. @note: in this context, the server is where your python process is running and the client is where eclipse is running. E.g.: If the server (your python process) has the structure /user/projects/my_project/src/package/module1.py and the client has: c:\my_project\src\package\module1.py the PATHS_FROM_ECLIPSE_TO_PYTHON would have to be: PATHS_FROM_ECLIPSE_TO_PYTHON = [(r'c:\my_project\src', r'/user/projects/my_project/src')] alternatively, this can be set with an environment variable from the command line: set PATHS_FROM_ECLIPSE_TO_PYTHON=[['c:\my_project\src','/user/projects/my_project/src']] @note: DEBUG_CLIENT_SERVER_TRANSLATION can be set to True to debug the result of those translations @note: the case of the paths is important! Note that this can be tricky to get right when one machine uses a case-independent filesystem and the other uses a case-dependent filesystem (if the system being debugged is case-independent, 'normcase()' should be used on the paths defined in PATHS_FROM_ECLIPSE_TO_PYTHON). @note: all the paths with breakpoints must be translated (otherwise they won't be found in the server) @note: to enable remote debugging in the target machine (pydev extensions in the eclipse installation) import pydevd;pydevd.settrace(host, stdoutToServer, stderrToServer, port, suspend) see parameter docs on pydevd.py @note: for doing a remote debugging session, all the pydevd_ files must be on the server accessible through the PYTHONPATH (and the PATHS_FROM_ECLIPSE_TO_PYTHON only needs to be set on the target machine for the paths that'll actually have breakpoints). ''' from _pydevd_bundle.pydevd_constants import IS_PY2, IS_PY3K, DebugInfoHolder, IS_WINDOWS, IS_JYTHON from _pydev_bundle._pydev_filesystem_encoding import getfilesystemencoding import json import os.path import sys import traceback _os_normcase = os.path.normcase basename = os.path.basename exists = os.path.exists join = os.path.join try: rPath = os.path.realpath # @UndefinedVariable except: # jython does not support os.path.realpath # realpath is a no-op on systems without islink support rPath = os.path.abspath # defined as a list of tuples where the 1st element of the tuple is the path in the client machine # and the 2nd element is the path in the server machine. # see module docstring for more details. try: PATHS_FROM_ECLIPSE_TO_PYTHON = json.loads(os.environ.get('PATHS_FROM_ECLIPSE_TO_PYTHON', '[]')) except Exception: sys.stderr.write('Error loading PATHS_FROM_ECLIPSE_TO_PYTHON from environment variable.\n') traceback.print_exc() PATHS_FROM_ECLIPSE_TO_PYTHON = [] else: if not isinstance(PATHS_FROM_ECLIPSE_TO_PYTHON, list): sys.stderr.write('Expected PATHS_FROM_ECLIPSE_TO_PYTHON loaded from environment variable to be a list.\n') PATHS_FROM_ECLIPSE_TO_PYTHON = [] else: # Converting json lists to tuple PATHS_FROM_ECLIPSE_TO_PYTHON = [tuple(x) for x in PATHS_FROM_ECLIPSE_TO_PYTHON] # example: # PATHS_FROM_ECLIPSE_TO_PYTHON = [ # (r'd:\temp\temp_workspace_2\test_python\src\yyy\yyy', # r'd:\temp\temp_workspace_2\test_python\src\hhh\xxx') # ] convert_to_long_pathname = lambda filename:filename convert_to_short_pathname = lambda filename:filename get_path_with_real_case = lambda filename:filename if sys.platform == 'win32': try: import ctypes from ctypes.wintypes import MAX_PATH, LPCWSTR, LPWSTR, DWORD GetLongPathName = ctypes.windll.kernel32.GetLongPathNameW GetLongPathName.argtypes = [LPCWSTR, LPWSTR, DWORD] GetLongPathName.restype = DWORD GetShortPathName = ctypes.windll.kernel32.GetShortPathNameW GetShortPathName.argtypes = [LPCWSTR, LPWSTR, DWORD] GetShortPathName.restype = DWORD def _convert_to_long_pathname(filename): buf = ctypes.create_unicode_buffer(MAX_PATH) if IS_PY2 and isinstance(filename, str): filename = filename.decode(getfilesystemencoding()) rv = GetLongPathName(filename, buf, MAX_PATH) if rv != 0 and rv <= MAX_PATH: filename = buf.value if IS_PY2: filename = filename.encode(getfilesystemencoding()) return filename def _convert_to_short_pathname(filename): buf = ctypes.create_unicode_buffer(MAX_PATH) if IS_PY2 and isinstance(filename, str): filename = filename.decode(getfilesystemencoding()) rv = GetShortPathName(filename, buf, MAX_PATH) if rv != 0 and rv <= MAX_PATH: filename = buf.value if IS_PY2: filename = filename.encode(getfilesystemencoding()) return filename def _get_path_with_real_case(filename): ret = convert_to_long_pathname(convert_to_short_pathname(filename)) # This doesn't handle the drive letter properly (it'll be unchanged). # Make sure the drive letter is always uppercase. if len(ret) > 1 and ret[1] == ':' and ret[0].islower(): return ret[0].upper() + ret[1:] return ret # Check that it actually works _get_path_with_real_case(__file__) except: # Something didn't quite work out, leave no-op conversions in place. if DebugInfoHolder.DEBUG_TRACE_LEVEL > 2: traceback.print_exc() else: convert_to_long_pathname = _convert_to_long_pathname convert_to_short_pathname = _convert_to_short_pathname get_path_with_real_case = _get_path_with_real_case elif IS_JYTHON and IS_WINDOWS: def get_path_with_real_case(filename): from java.io import File f = File(filename) ret = f.getCanonicalPath() if IS_PY2 and not isinstance(ret, str): return ret.encode(getfilesystemencoding()) return ret if IS_WINDOWS: if IS_JYTHON: def normcase(filename): return filename.lower() else: def normcase(filename): # `normcase` doesn't lower case on Python 2 for non-English locale, but Java # side does it, so we should do it manually. if '~' in filename: filename = convert_to_long_pathname(filename) filename = _os_normcase(filename) return filename.lower() else: def normcase(filename): return filename # no-op _ide_os = 'WINDOWS' if IS_WINDOWS else 'UNIX' def set_ide_os(os): ''' We need to set the IDE os because the host where the code is running may be actually different from the client (and the point is that we want the proper paths to translate from the client to the server). :param os: 'UNIX' or 'WINDOWS' ''' global _ide_os prev = _ide_os if os == 'WIN': # Apparently PyCharm uses 'WIN' (https://github.com/fabioz/PyDev.Debugger/issues/116) os = 'WINDOWS' assert os in ('WINDOWS', 'UNIX') if prev != os: _ide_os = os # We need to (re)setup how the client <-> server translation works to provide proper separators. setup_client_server_paths(_last_client_server_paths_set) DEBUG_CLIENT_SERVER_TRANSLATION = os.environ.get('DEBUG_PYDEVD_PATHS_TRANSLATION', 'False').lower() in ('1', 'true') # Caches filled as requested during the debug session. NORM_PATHS_CONTAINER = {} NORM_PATHS_AND_BASE_CONTAINER = {} def _NormFile(filename): abs_path, real_path = _NormPaths(filename) return real_path def _AbsFile(filename): abs_path, real_path = _NormPaths(filename) return abs_path # Returns tuple of absolute path and real path for given filename def _NormPaths(filename): try: return NORM_PATHS_CONTAINER[filename] except KeyError: if filename.__class__ != str: raise AssertionError('Paths passed to _NormPaths must be str. Found: %s (%s)' % (filename, type(filename))) abs_path = _NormPath(filename, os.path.abspath) real_path = _NormPath(filename, rPath) # cache it for fast access later NORM_PATHS_CONTAINER[filename] = abs_path, real_path return abs_path, real_path def _NormPath(filename, normpath): r = normpath(filename) ind = r.find('.zip') if ind == -1: ind = r.find('.egg') if ind != -1: ind += 4 zip_path = r[:ind] inner_path = r[ind:] if inner_path.startswith('!'): # Note (fabioz): although I can replicate this by creating a file ending as # .zip! or .egg!, I don't really know what's the real-world case for this # (still kept as it was added by @jetbrains, but it should probably be reviewed # later on). # Note 2: it goes hand-in-hand with 'exists'. inner_path = inner_path[1:] zip_path = zip_path + '!' if inner_path.startswith('/') or inner_path.startswith('\\'): inner_path = inner_path[1:] if inner_path: r = join(normcase(zip_path), inner_path) return r r = normcase(r) return r _ZIP_SEARCH_CACHE = {} _NOT_FOUND_SENTINEL = object() def exists(file): if os.path.exists(file): return file ind = file.find('.zip') if ind == -1: ind = file.find('.egg') if ind != -1: ind += 4 zip_path = file[:ind] inner_path = file[ind:] if inner_path.startswith("!"): # Note (fabioz): although I can replicate this by creating a file ending as # .zip! or .egg!, I don't really know what's the real-world case for this # (still kept as it was added by @jetbrains, but it should probably be reviewed # later on). # Note 2: it goes hand-in-hand with '_NormPath'. inner_path = inner_path[1:] zip_path = zip_path + '!' zip_file_obj = _ZIP_SEARCH_CACHE.get(zip_path, _NOT_FOUND_SENTINEL) if zip_file_obj is None: return False elif zip_file_obj is _NOT_FOUND_SENTINEL: try: import zipfile zip_file_obj = zipfile.ZipFile(zip_path, 'r') _ZIP_SEARCH_CACHE[zip_path] = zip_file_obj except: _ZIP_SEARCH_CACHE[zip_path] = _NOT_FOUND_SENTINEL return False try: if inner_path.startswith('/') or inner_path.startswith('\\'): inner_path = inner_path[1:] _info = zip_file_obj.getinfo(inner_path.replace('\\', '/')) return join(zip_path, inner_path) except KeyError: return None return None # Now, let's do a quick test to see if we're working with a version of python that has no problems # related to the names generated... try: try: code = rPath.func_code except AttributeError: code = rPath.__code__ if not exists(_NormFile(code.co_filename)): sys.stderr.write('-------------------------------------------------------------------------------\n') sys.stderr.write('pydev debugger: CRITICAL WARNING: This version of python seems to be incorrectly compiled (internal generated filenames are not absolute)\n') sys.stderr.write('pydev debugger: The debugger may still function, but it will work slower and may miss breakpoints.\n') sys.stderr.write('pydev debugger: Related bug: http://bugs.python.org/issue1666807\n') sys.stderr.write('-------------------------------------------------------------------------------\n') sys.stderr.flush() NORM_SEARCH_CACHE = {} initial_norm_paths = _NormPaths def _NormPaths(filename): # Let's redefine _NormPaths to work with paths that may be incorrect try: return NORM_SEARCH_CACHE[filename] except KeyError: abs_path, real_path = initial_norm_paths(filename) if not exists(real_path): # We must actually go on and check if we can find it as if it was a relative path for some of the paths in the pythonpath for path in sys.path: abs_path, real_path = initial_norm_paths(join(path, filename)) if exists(real_path): break else: sys.stderr.write('pydev debugger: Unable to find real location for: %s\n' % (filename,)) abs_path = filename real_path = filename NORM_SEARCH_CACHE[filename] = abs_path, real_path return abs_path, real_path except: # Don't fail if there's something not correct here -- but at least print it to the user so that we can correct that traceback.print_exc() # Note: as these functions may be rebound, users should always import # pydevd_file_utils and then use: # # pydevd_file_utils.norm_file_to_client # pydevd_file_utils.norm_file_to_server # # instead of importing any of those names to a given scope. def _original_file_to_client(filename, cache={}): try: return cache[filename] except KeyError: cache[filename] = get_path_with_real_case(_AbsFile(filename)) return cache[filename] _original_file_to_server = _NormFile norm_file_to_client = _original_file_to_client norm_file_to_server = _original_file_to_server def _fix_path(path, sep): if path.endswith('/') or path.endswith('\\'): path = path[:-1] if sep != '/': path = path.replace('/', sep) return path _last_client_server_paths_set = [] def setup_client_server_paths(paths): '''paths is the same format as PATHS_FROM_ECLIPSE_TO_PYTHON''' global norm_file_to_client global norm_file_to_server global _last_client_server_paths_set _last_client_server_paths_set = paths[:] # Work on the client and server slashes. python_sep = '\\' if IS_WINDOWS else '/' eclipse_sep = '\\' if _ide_os == 'WINDOWS' else '/' norm_filename_to_server_container = {} norm_filename_to_client_container = {} initial_paths = list(paths) paths_from_eclipse_to_python = initial_paths[:] # Apply normcase to the existing paths to follow the os preferences. for i, (path0, path1) in enumerate(paths_from_eclipse_to_python[:]): if IS_PY2: if isinstance(path0, unicode): path0 = path0.encode(sys.getfilesystemencoding()) if isinstance(path1, unicode): path1 = path1.encode(sys.getfilesystemencoding()) path0 = _fix_path(path0, eclipse_sep) path1 = _fix_path(path1, python_sep) initial_paths[i] = (path0, path1) paths_from_eclipse_to_python[i] = (normcase(path0), normcase(path1)) if not paths_from_eclipse_to_python: # no translation step needed (just inline the calls) norm_file_to_client = _original_file_to_client norm_file_to_server = _original_file_to_server return # only setup translation functions if absolutely needed! def _norm_file_to_server(filename, cache=norm_filename_to_server_container): # Eclipse will send the passed filename to be translated to the python process # So, this would be 'NormFileFromEclipseToPython' try: return cache[filename] except KeyError: if eclipse_sep != python_sep: # Make sure that the separators are what we expect from the IDE. filename = filename.replace(python_sep, eclipse_sep) # used to translate a path from the client to the debug server translated = normcase(filename) for eclipse_prefix, server_prefix in paths_from_eclipse_to_python: if translated.startswith(eclipse_prefix): if DEBUG_CLIENT_SERVER_TRANSLATION: sys.stderr.write('pydev debugger: replacing to server: %s\n' % (translated,)) translated = translated.replace(eclipse_prefix, server_prefix) if DEBUG_CLIENT_SERVER_TRANSLATION: sys.stderr.write('pydev debugger: sent to server: %s\n' % (translated,)) break else: if DEBUG_CLIENT_SERVER_TRANSLATION: sys.stderr.write('pydev debugger: to server: unable to find matching prefix for: %s in %s\n' % \ (translated, [x[0] for x in paths_from_eclipse_to_python])) # Note that when going to the server, we do the replace first and only later do the norm file. if eclipse_sep != python_sep: translated = translated.replace(eclipse_sep, python_sep) translated = _NormFile(translated) cache[filename] = translated return translated def _norm_file_to_client(filename, cache=norm_filename_to_client_container): # The result of this method will be passed to eclipse # So, this would be 'NormFileFromPythonToEclipse' try: return cache[filename] except KeyError: # used to translate a path from the debug server to the client translated = _NormFile(filename) # After getting the real path, let's get it with the path with # the real case and then obtain a new normalized copy, just in case # the path is different now. translated_proper_case = get_path_with_real_case(translated) translated = _NormFile(translated_proper_case) if IS_WINDOWS: if translated.lower() != translated_proper_case.lower(): translated_proper_case = translated if DEBUG_CLIENT_SERVER_TRANSLATION: sys.stderr.write( 'pydev debugger: _NormFile changed path (from: %s to %s)\n' % ( translated_proper_case, translated)) for i, (eclipse_prefix, python_prefix) in enumerate(paths_from_eclipse_to_python): if translated.startswith(python_prefix): if DEBUG_CLIENT_SERVER_TRANSLATION: sys.stderr.write('pydev debugger: replacing to client: %s\n' % (translated,)) # Note: use the non-normalized version. eclipse_prefix = initial_paths[i][0] translated = eclipse_prefix + translated_proper_case[len(python_prefix):] if DEBUG_CLIENT_SERVER_TRANSLATION: sys.stderr.write('pydev debugger: sent to client: %s\n' % (translated,)) break else: if DEBUG_CLIENT_SERVER_TRANSLATION: sys.stderr.write('pydev debugger: to client: unable to find matching prefix for: %s in %s\n' % \ (translated, [x[1] for x in paths_from_eclipse_to_python])) translated = translated_proper_case if eclipse_sep != python_sep: translated = translated.replace(python_sep, eclipse_sep) # The resulting path is not in the python process, so, we cannot do a _NormFile here, # only at the beginning of this method. cache[filename] = translated return translated norm_file_to_server = _norm_file_to_server norm_file_to_client = _norm_file_to_client setup_client_server_paths(PATHS_FROM_ECLIPSE_TO_PYTHON) def _is_int(filename): # isdigit() doesn't support negative numbers try: int(filename) return True except: return False def is_real_file(filename): # Check for Jupyter cells return not _is_int(filename) and not filename.startswith("<ipython-input") # For given file f returns tuple of its absolute path, real path and base name def get_abs_path_real_path_and_base_from_file(f): try: return NORM_PATHS_AND_BASE_CONTAINER[f] except: if _NormPaths is None: # Interpreter shutdown return f if f is not None: if f.endswith('.pyc'): f = f[:-1] elif f.endswith('$py.class'): f = f[:-len('$py.class')] + '.py' if not is_real_file(f): abs_path, real_path, base = f, f, f else: abs_path, real_path = _NormPaths(f) base = basename(real_path) ret = abs_path, real_path, base NORM_PATHS_AND_BASE_CONTAINER[f] = ret return ret def get_abs_path_real_path_and_base_from_frame(frame): try: return NORM_PATHS_AND_BASE_CONTAINER[frame.f_code.co_filename] except: # This one is just internal (so, does not need any kind of client-server translation) f = frame.f_code.co_filename if f is not None and f.startswith (('build/bdist.', 'build\\bdist.')): # files from eggs in Python 2.7 have paths like build/bdist.linux-x86_64/egg/<path-inside-egg> f = frame.f_globals['__file__'] if get_abs_path_real_path_and_base_from_file is None: # Interpreter shutdown return f ret = get_abs_path_real_path_and_base_from_file(f) # Also cache based on the frame.f_code.co_filename (if we had it inside build/bdist it can make a difference). NORM_PATHS_AND_BASE_CONTAINER[frame.f_code.co_filename] = ret return ret def get_fullname(mod_name): if IS_PY3K: import pkgutil else: from _pydev_imps import _pydev_pkgutil_old as pkgutil try: loader = pkgutil.get_loader(mod_name) except: return None if loader is not None: for attr in ("get_filename", "_get_filename"): meth = getattr(loader, attr, None) if meth is not None: return meth(mod_name) return None def get_package_dir(mod_name): for path in sys.path: mod_path = join(path, mod_name.replace('.', '/')) if os.path.isdir(mod_path): return mod_path return None

src/networking/SessionsManager.py

OfekHarel/Orion-Connection-Software

582

<gh_stars>1-10 import os import socket from random import randint from src import Constants from src.Constants import Network from src.networking import NetworkPackets, Actions from src.networking.Client import Client from src.utils.DH_Encryption import Encryption from src.utils.Enum import Enum class SessionManager: """ This class is responsible for dealing with any flow of net msgs. """ def __init__(self): address = (Network.SERVER_IP, Network.SERVER_PORT) self.client = Client(str(socket.gethostname()), address) self.val = self.client.connect() if not self.val: Network.IS_ONLINE = False def go_crypto(self): msg = NetworkPackets.split(self.client.receive()) g = int(msg[1]) n = int(msg[2]) g_pow_a_mod_n = int(msg[3]) crypto = Encryption(g, n) crypto.get_full_key(g_pow_a_mod_n) self.client.send(NetworkPackets.assemble(NetworkPackets.NetLogicIncomes.CONNECT.value, str(crypto.get_partial_key()))) self.client.crypto = crypto def gen_id(self) -> str: num = str(randint(1, 9999)) num = num.zfill(4) return num def open_id_file(self): try: open(Constants.Files.ID, 'r+').close() except FileNotFoundError: open(Constants.Files.ID, 'x').close() finally: file = open(Constants.Files.ID, 'r+') return file def sync(self): """ This function contains the full process of the sync phase. """ if Network.IS_ONLINE: self.go_crypto() num = "" file = self.open_id_file() if os.path.getsize(Constants.Files.ID) == 0: # Empty is_valid = False while not is_valid: num = self.gen_id() self.client.send(NetworkPackets.assemble("COMPUTER", "ID_VAL", num)) msg = NetworkPackets.split(self.client.receive()) is_valid = msg[0] == NetworkPackets.NetLogicIncomes.VALID.value file.write(num) else: is_valid = False num = file.read() while not is_valid: self.client.send(NetworkPackets.assemble("COMPUTER", "ID_VAL", num)) msg = NetworkPackets.split(self.client.receive()) is_valid = msg[0] == NetworkPackets.NetLogicIncomes.VALID.value if not is_valid: num = self.gen_id() if num != file.read(): file.close() os.remove(Constants.Files.ID) file = self.open_id_file() file.write(num) file.close() def manage(self, incoming: str): """ This functions deals with the execution of the required operations. :param incoming: Raw net msg. """ if Network.IS_ONLINE: incoming = NetworkPackets.split(incoming)[0] if incoming in Operation.list(): if incoming == Operation.VOL_UP.value: Actions.vol_up() elif incoming == Operation.VOL_DOWN.value: Actions.vol_down() elif incoming == Operation.PAUSE_PLAY_TOGGLE.value: Actions.play_pause() elif incoming == Operation.SKIP.value: Actions.next_song() elif incoming == Operation.PREV.value: Actions.prev_song() elif incoming == Operation.MUTE.value: Actions.mute() elif incoming == Operation.OFF.value: Actions.shut_down() elif incoming == Operation.SLEEP.value: Actions.sleep() elif incoming == Operation.RESTART.value: Actions.restart() elif incoming == Operation.LOCK.value: Actions.lock() elif incoming == Operation.LOG_OUT.value: Actions.log_out() elif incoming == Operation.MAGIC_BTN.value: Actions.run_file() elif incoming == Operation.USAGE.value: self.client.send(NetworkPackets.assemble(arr=Actions.COMPUTER.get_use_as_str_arr())) elif incoming == Operation.DISCONNECT.value: self.client.send(NetworkPackets.assemble(Operation.DISCONNECT.value)) return Operation.DISCONNECT elif incoming in NetworkPackets.NetLogicIncomes.list(): if incoming == NetworkPackets.NetLogicIncomes.PAIRED.value: Constants.Network.IS_PAIRING = True self.client.send(NetworkPackets.assemble(arr=Actions.COMPUTER.get_specs_as_str_arr())) elif incoming == NetworkPackets.NetLogicIncomes.INVALID: pass class Operation(Enum): """ All the operations that can be asked to execute. """ VOL_UP = "VOL_UP" VOL_DOWN = "VOL_DOWN" PAUSE_PLAY_TOGGLE = "PTT" SKIP = "SKIP" PREV = "PREV" MUTE = "MUTE" OFF = "OFF" SLEEP = "SLEEP" RESTART = "RESTRT" LOCK = "LCK" LOG_OUT = "LGOT" DISCONNECT = "DISCON" MAGIC_BTN = "MAGIC" SPECS_INFO = "SPECS" USAGE = "USE"

influxdb_service_sdk/model/container/resource_requirements_pb2.py

easyopsapis/easyops-api-python

583

<filename>influxdb_service_sdk/model/container/resource_requirements_pb2.py # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: resource_requirements.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from influxdb_service_sdk.model.container import resource_list_pb2 as influxdb__service__sdk_dot_model_dot_container_dot_resource__list__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='resource_requirements.proto', package='container', syntax='proto3', serialized_options=_b('ZCgo.easyops.local/contracts/protorepo-models/easyops/model/container'), serialized_pb=_b('\n\x1bresource_requirements.proto\x12\tcontainer\x1a\x38influxdb_service_sdk/model/container/resource_list.proto\"j\n\x14ResourceRequirements\x12\'\n\x06limits\x18\x01 \x01(\x0b\x32\x17.container.ResourceList\x12)\n\x08requests\x18\x02 \x01(\x0b\x32\x17.container.ResourceListBEZCgo.easyops.local/contracts/protorepo-models/easyops/model/containerb\x06proto3') , dependencies=[influxdb__service__sdk_dot_model_dot_container_dot_resource__list__pb2.DESCRIPTOR,]) _RESOURCEREQUIREMENTS = _descriptor.Descriptor( name='ResourceRequirements', full_name='container.ResourceRequirements', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='limits', full_name='container.ResourceRequirements.limits', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='requests', full_name='container.ResourceRequirements.requests', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=100, serialized_end=206, ) _RESOURCEREQUIREMENTS.fields_by_name['limits'].message_type = influxdb__service__sdk_dot_model_dot_container_dot_resource__list__pb2._RESOURCELIST _RESOURCEREQUIREMENTS.fields_by_name['requests'].message_type = influxdb__service__sdk_dot_model_dot_container_dot_resource__list__pb2._RESOURCELIST DESCRIPTOR.message_types_by_name['ResourceRequirements'] = _RESOURCEREQUIREMENTS _sym_db.RegisterFileDescriptor(DESCRIPTOR) ResourceRequirements = _reflection.GeneratedProtocolMessageType('ResourceRequirements', (_message.Message,), { 'DESCRIPTOR' : _RESOURCEREQUIREMENTS, '__module__' : 'resource_requirements_pb2' # @@protoc_insertion_point(class_scope:container.ResourceRequirements) }) _sym_db.RegisterMessage(ResourceRequirements) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

tests/test_http_client.py

bhch/async-stripe

584

from __future__ import absolute_import, division, print_function import pytest import json import asyncio import stripe import urllib3 from stripe import six, util from async_stripe.http_client import TornadoAsyncHTTPClient pytestmark = pytest.mark.asyncio VALID_API_METHODS = ("get", "post", "delete") class StripeClientTestCase(object): REQUEST_LIBRARIES = ["AsyncHTTPClient"] @pytest.fixture def request_mocks(self, mocker): request_mocks = {} for lib in self.REQUEST_LIBRARIES: request_mocks[lib] = mocker.patch("async_stripe.http_client.%s" % (lib,)) return request_mocks class TestNewDefaultHttpClient(StripeClientTestCase): @pytest.fixture(autouse=True) def setup_warnings(self, request_mocks): original_filters = stripe.http_client.warnings.filters[:] stripe.http_client.warnings.simplefilter("ignore") yield stripe.http_client.warnings.filters = original_filters def check_default(self, none_libs, expected): for lib in none_libs: setattr(stripe.http_client, lib, None) inst = stripe.http_client.new_default_http_client() assert isinstance(inst, expected) def test_new_default_http_client_tornado(self): self.check_default((), TornadoAsyncHTTPClient) class TestRetrySleepTimeDefaultHttpClient(StripeClientTestCase): from contextlib import contextmanager def assert_sleep_times(self, client, expected): until = len(expected) actual = list( map(lambda i: client._sleep_time_seconds(i + 1), range(until)) ) assert expected == actual @contextmanager def mock_max_delay(self, new_value): original_value = stripe.http_client.HTTPClient.MAX_DELAY stripe.http_client.HTTPClient.MAX_DELAY = new_value try: yield self finally: stripe.http_client.HTTPClient.MAX_DELAY = original_value def test_sleep_time_exponential_back_off(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t with self.mock_max_delay(10): self.assert_sleep_times(client, [0.5, 1.0, 2.0, 4.0, 8.0]) def test_initial_delay_as_minimum(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t * 0.001 initial_delay = stripe.http_client.HTTPClient.INITIAL_DELAY self.assert_sleep_times(client, [initial_delay] * 5) def test_maximum_delay(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t max_delay = stripe.http_client.HTTPClient.MAX_DELAY expected = [0.5, 1.0, max_delay, max_delay, max_delay] self.assert_sleep_times(client, expected) def test_retry_after_header(self): client = stripe.http_client.new_default_http_client() client._add_jitter_time = lambda t: t # Prefer retry-after if it's bigger assert 30 == client._sleep_time_seconds( 2, (None, 409, {"retry-after": "30"}) ) # Prefer default if it's bigger assert 2 == client._sleep_time_seconds( 3, (None, 409, {"retry-after": "1"}) ) # Ignore crazy-big values assert 1 == client._sleep_time_seconds( 2, (None, 409, {"retry-after": "300"}) ) def test_randomness_added(self): client = stripe.http_client.new_default_http_client() random_value = 0.8 client._add_jitter_time = lambda t: t * random_value base_value = stripe.http_client.HTTPClient.INITIAL_DELAY * random_value with self.mock_max_delay(10): expected = [ stripe.http_client.HTTPClient.INITIAL_DELAY, base_value * 2, base_value * 4, base_value * 8, base_value * 16, ] self.assert_sleep_times(client, expected) def test_jitter_has_randomness_but_within_range(self): client = stripe.http_client.new_default_http_client() jittered_ones = set( map(lambda _: client._add_jitter_time(1), list(range(100))) ) assert len(jittered_ones) > 1 assert all(0.5 <= val <= 1 for val in jittered_ones) class TestRetryConditionsDefaultHttpClient(StripeClientTestCase): def test_should_retry_on_codes(self): one_xx = list(range(100, 104)) two_xx = list(range(200, 209)) three_xx = list(range(300, 308)) four_xx = list(range(400, 431)) client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 codes = one_xx + two_xx + three_xx + four_xx codes.remove(409) # These status codes should not be retried by default. for code in codes: assert client._should_retry((None, code, None), None, 0) is False # These status codes should be retried by default. assert client._should_retry((None, 409, None), None, 0) is True assert client._should_retry((None, 500, None), None, 0) is True assert client._should_retry((None, 503, None), None, 0) is True def test_should_retry_on_error(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert client._should_retry(None, api_connection_error, 0) is True api_connection_error.should_retry = False assert client._should_retry(None, api_connection_error, 0) is False def test_should_retry_on_stripe_should_retry_true(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {"stripe-should-retry": "true"} # Ordinarily, we would not retry a 400, but with the header as true, we would. assert client._should_retry((None, 400, {}), None, 0) is False assert client._should_retry((None, 400, headers), None, 0) is True def test_should_retry_on_stripe_should_retry_false(self, mocker): client = stripe.http_client.new_default_http_client() client._max_network_retries = lambda: 1 headers = {"stripe-should-retry": "false"} # Ordinarily, we would retry a 500, but with the header as false, we would not. assert client._should_retry((None, 500, {}), None, 0) is True assert client._should_retry((None, 500, headers), None, 0) is False def test_should_retry_on_num_retries(self, mocker): client = stripe.http_client.new_default_http_client() max_test_retries = 10 client._max_network_retries = lambda: max_test_retries api_connection_error = mocker.Mock() api_connection_error.should_retry = True assert ( client._should_retry( None, api_connection_error, max_test_retries + 1 ) is False ) assert ( client._should_retry((None, 409, None), None, max_test_retries + 1) is False ) class TestHTTPClient(object): @pytest.fixture(autouse=True) def setup_stripe(self): orig_attrs = {"enable_telemetry": stripe.enable_telemetry} stripe.enable_telemetry = False yield stripe.enable_telemetry = orig_attrs["enable_telemetry"] async def test_sends_telemetry_on_second_request(self, mocker): class TestClient(stripe.http_client.HTTPClient): pass stripe.enable_telemetry = True url = "http://fake.url" client = TestClient() response_future = asyncio.Future() response_future.set_result(["", 200, {"Request-Id": "req_123"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ = await client.request_with_retries("get", url, {}, None) assert code == 200 client.request.assert_called_with("get", url, {}, None) response_future = asyncio.Future() response_future.set_result(["", 200, {"Request-Id": "req_234"}]) client.request = mocker.MagicMock( return_value=response_future ) _, code, _ = await client.request_with_retries("get", url, {}, None) assert code == 200 args, _ = client.request.call_args assert "X-Stripe-Client-Telemetry" in args[2] telemetry = json.loads(args[2]["X-Stripe-Client-Telemetry"]) assert telemetry["last_request_metrics"]["request_id"] == "req_123" class ClientTestBase(object): @pytest.fixture def request_mock(self, request_mocks): return request_mocks[self.REQUEST_CLIENT.name] @property def valid_url(self, path="/foo"): return "https://api.stripe.com%s" % (path,) def make_request(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return client.request_with_retries(method, url, headers, post_data) async def make_request_stream(self, method, url, headers, post_data): client = self.REQUEST_CLIENT(verify_ssl_certs=True) return await client.request_stream_with_retries( method, url, headers, post_data ) @pytest.fixture def mock_response(self): def mock_response(mock, body, code): raise NotImplementedError( "You must implement this in your test subclass" ) return mock_response @pytest.fixture def mock_error(self): def mock_error(mock, error): raise NotImplementedError( "You must implement this in your test subclass" ) return mock_error @pytest.fixture def check_call(self): def check_call( mock, method, abs_url, headers, params, is_streaming=False ): raise NotImplementedError( "You must implement this in your test subclass" ) return check_call def test_request(self, request_mock, mock_response, check_call): mock_response(request_mock, '{"foo": "baz"}', 200) for method in VALID_API_METHODS: abs_url = self.valid_url data = "" if method != "post": abs_url = "%s?%s" % (abs_url, data) data = None headers = {"my-header": "header val"} body, code, _ = self.make_request(method, abs_url, headers, data) assert code == 200 assert body == '{"foo": "baz"}' check_call(request_mock, method, abs_url, data, headers) def test_request_stream( self, mocker, request_mock, mock_response, check_call ): for method in VALID_API_METHODS: mock_response(request_mock, "some streamed content", 200) abs_url = self.valid_url data = "" if method != "post": abs_url = "%s?%s" % (abs_url, data) data = None headers = {"my-header": "header val"} print(dir(self)) print("make_request_stream" in dir(self)) stream, code, _ = self.make_request_stream( method, abs_url, headers, data ) assert code == 200 # Here we need to convert and align all content on one type (string) # as some clients return a string stream others a byte stream. body_content = stream.read() if hasattr(body_content, "decode"): body_content = body_content.decode("utf-8") assert body_content == "some streamed content" mocker.resetall() def test_exception(self, request_mock, mock_error): mock_error(request_mock) with pytest.raises(stripe.error.APIConnectionError): self.make_request("get", self.valid_url, {}, None) class TestTornadoAsyncHTTPClient: # :TODO: Write tests for tornado client pass class TestAPIEncode(StripeClientTestCase): def test_encode_dict(self): body = {"foo": {"dob": {"month": 1}, "name": "bat"}} values = [t for t in stripe.api_requestor._api_encode(body)] assert ("foo[dob][month]", 1) in values assert ("foo[name]", "bat") in values def test_encode_array(self): body = {"foo": [{"dob": {"month": 1}, "name": "bat"}]} values = [t for t in stripe.api_requestor._api_encode(body)] assert ("foo[0][dob][month]", 1) in values assert ("foo[0][name]", "bat") in values

http/static/jsonvis.py

cheeseywhiz/cheeseywhiz

585

<filename>http/static/jsonvis.py """\ Provides html file visualization of a json dataset """ import json import subprocess class JsonVis: def _open_list(self): self.instructions.append(('open_list', None)) def _list_item(self, data): self.instructions.append(('list_item', str(data))) def _horiz_rule(self): self.instructions.append(('horiz_rule', None)) def _close_list(self): self.instructions.append(('close_list', None)) def _iterate(self, data: iter): if isinstance(data, dict): for key, value in data.items(): self._iterate(key) self._open_list() self._iterate(value) self._close_list() elif isinstance(data, list): self._open_list() for item in data: self._iterate(item) self._horiz_rule() self._close_list() else: self._list_item(data) def download(self, url: str): """ Store a python dictionary generated from json data at <url> in self.data. Returns self. """ data = subprocess.run( f"curl '{url}'", # Quotes required around url for URL parameters stdout=subprocess.PIPE, shell=True ).stdout self.data = json.loads(data) return self def make_instructions(self): """ Take self.data and return a list of instructions about its html visualization that is parsed by json.html. """ self.instructions = [] self._open_list() self._iterate(self.data) self._close_list() return self.instructions

sim2d_game_analyzer/MainWindow.py

goncamateus/sim2d_game_analyzer

586

import sys from PyQt5 import QtGui from PyQt5.QtCore import QEvent, QPoint, Qt from PyQt5.QtGui import QIcon from PyQt5.QtWidgets import (QApplication, QDialog, QGroupBox, QMainWindow, QTabWidget, QVBoxLayout, QWidget) from sim2d_game_analyzer.fmdb_tab import FMDBTab class MainWindow(QMainWindow): title = "Sim2d Game Analyzer" top = 500 left = 100 width = 70*4 height = 130*4 def __init__(self): QMainWindow.__init__(self) self.setGeometry(self.screen().geometry()) self.setWindowTitle(self.title) self.setWindowIcon(QIcon("sim2d_game_analyzer/figures/icon.png")) vbox = QVBoxLayout() tabWidget = QTabWidget() tabWidget.setFont(QtGui.QFont("Sanserif", 12)) self.fmdb_tab = FMDBTab() tabWidget.addTab(self.fmdb_tab, FMDBTab.NAME) vbox.addWidget(tabWidget) wid = QWidget(self) self.setCentralWidget(wid) wid.setLayout(vbox) if __name__ == "__main__": app = QApplication(sys.argv) mainwindow = MainWindow() sys.exit(app.exec())

cmd/extractor.py

Grammarian/sicle

587

# pip install openpyxl # pip install cuid import os.path import json import datetime from openpyxl import load_workbook import cuid # https://github.com/necaris/cuid.py - create uuid's in the format that graphcool expects SOURCE_XLSX = "./data/CLP_combined.xlsx" EXTRACT_OUTPUT_DIR = "../server/extract" SCHOOL_TITLES = ["ORGANISATION_ID", "ORGANISATION_NAME", "ORG_ELECTORATE", "P_ADDRESS1", "P_SUBURB", "P_STATE", "P_POSTCODE", "S_ADDRESS1", "S_SUBURB", "S_STATE", "S_POSTCODE", "SCHOOL_NAME", "SCH_ELECTORATE", "SCHOOL_ID", "SCHOOL_P_ADDRESS1", "SCHOOL_P_SUBURB", "SCHOOL_P_STATE", "SCHOOL_P_POSTCODE", "SCHOOL_S_ADDRESS1", "SCHOOL_S_SUBURB", "SCHOOL_S_STATE", "SCHOOL_S_POSTCODE", "LOCATION_NAME", "LOC_ELECTORATE", "LOC_S_ADDRESS1", "LOC_S_SUBURB", "LOC_S_STATE", "LOC_S_POSTCODE"] ORGANISATION_FIELDS = {"ORGANISATION_ID": "CLP_ORGANISATION_ID", "ORGANISATION_NAME": "NAME", "ORG_ELECTORATE": "ELECTORATE", "S_ADDRESS1": "ADDRESS", "S_SUBURB": "SUBURB", "S_STATE": "STATE", "S_POSTCODE": "POSTCODE", } SCHOOL_FIELDS = {"SCHOOL_NAME": "NAME", "SCH_ELECTORATE": "ELECTORATE", "SCHOOL_ID": "CLP_SCHOOL_ID", "ORGANISATION_ID": "CLP_ORGANISATION_ID", "SCHOOL_S_ADDRESS1": "ADDRESS", "SCHOOL_S_SUBURB": "SUBURB", "SCHOOL_S_STATE": "STATE", "SCHOOL_S_POSTCODE": "POSTCODE", } LOCATION_FIELDS = {"LOCATION_NAME": "NAME", "LOC_ELECTORATE": "ELECTORATE", "SCHOOL_ID": "CLP_SCHOOL_ID", "LOC_S_ADDRESS1": "ADDRESS", "LOC_S_SUBURB": "SUBURB", "LOC_S_STATE": "STATE", "LOC_S_POSTCODE": "POSTCODE"} TEACHER_TITLES = ["TEACHER_ID", "ORGANISATION_NAME", "SCHOOL_NAME", "TEACHER_NAME", "TITLE", "LNAME", "FNAME", "TEACHER_LANGUAGES", "P_ADDRESS1", "P_ADDRESS2", "P_SUBURB", "P_STATE", "P_POSTCODE", "TELEPHONE", "TEL_EVENING", "EMAIL", "MOBILE", "LEVEL_TAUGHT", "LEVEL_OF_EDUCATION", "FIELD_OF_EDUCATION", "DEGREE_COUNTRY", "DEGREE_YEAR", "ORGANISATION_ID", "SCHOOL_ID"] STUDENT_TITLES = ["SCHOOL_NAME", "SCHOOL_ID", "STUDENT_ID", "STUDENT_SRN", "LOCATION_NAME", "STUDENT_LNAME", "STUDENT_FNAME", "DOB", "TEL", "LOCATION_NAME_1"] TEACHER_FIELDS = {"TEACHER_ID": "CLP_TEACHER_ID", "ORGANISATION_NAME": "ORGANISATION_NAME", "SCHOOL_NAME": "SCHOOL_NAME", "TITLE": "TITLE", "LNAME": "FAMILY_NAME", "FNAME": "GIVEN_NAMES", "TEACHER_LANGUAGES": "LANGUAGES", "P_ADDRESS1": "ADDRESS1", "P_ADDRESS2": "ADDRESS2", "P_SUBURB": "SUBURB", "P_STATE": "STATE", "P_POSTCODE": "POSTCODE", "TELEPHONE": "DAY_PHONE", "TEL_EVENING": "EVENING_PHONE", "EMAIL": "EMAIL", "MOBILE": "MOBILE", "LEVEL_TAUGHT": "LEVEL_TAUGHT", "LEVEL_OF_EDUCATION": "EDUCATION_LEVEL", "FIELD_OF_EDUCATION": "EDUCATION_FIELD", "DEGREE_COUNTRY": "EDUCATION_COUNTRY", "DEGREE_YEAR": "EDUCATION_YEAR", "ORGANISATION_ID": "ORGANISATION_ID", "SCHOOL_ID": "SCHOOL_ID", } STUDENT_FIELDS = {"SCHOOL_NAME": "SCHOOL_NAME", "SCHOOL_ID": "SCHOOL_ID", "STUDENT_ID": "CLP_STUDENT_ID", "STUDENT_SRN": "SRN", "LOCATION_NAME": "LOCATION", "STUDENT_LNAME": "FAMILY_NAME", "STUDENT_FNAME": "GIVEN_NAMES", "DOB": "DATE_OF_BIRTH", "TEL": "PHONE", "LOCATION_NAME_1": "DAY_SCHOOL", } class Sheet: "Data container object to hold the contents of one sheet within an excel spreadsheet" def __init__(self, name, titles=None, rows=None): self.name = name self.titles = titles or [] self.rows = rows or [] def convert_row_to_dict(titles, row): data = {} for (i, cell) in enumerate(row): if cell.Value is not None: data[titles[i]] = str(cell.value) return data def convert_xlsx(xlsx_file): """Convert the given XLSX spreadsheet to iterable of Sheet objects, in which row has been converted into a dictionary""" work_book = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet in work_book: rows = [x for x in sheet.iter_rows()] if rows: titles = [cell.value for cell in rows[0]] dicts = [convert_row_to_dict(titles, row) for row in rows[1:]] yield Sheet(sheet.title, titles, dicts) else: yield Sheet(sheet.title) def to_camel(s): """Convert an underscored title into camel case. 'PARENT_ORGANISATION_ID' => 'parentOrganisationId'""" bits = [(x.lower() if i == 0 else x.title()) for (i, x) in enumerate(s.split("_"))] return "".join(bits) def relative_to_absolute(relative_path): path_to_py = os.path.abspath(os.path.dirname(__file__)) return os.path.join(path_to_py, relative_path) def extract(fields, row_as_dict): data = {} for (k, v) in fields.items(): data[to_camel(v)] = row_as_dict[k] return data def process_sheet(sheet, titles, field_defns): if titles != sheet.titles: print("Sheet doesn't have expected titles:", [(i, x) for (i, x) in enumerate(titles) if x != sheet.titles[i]]) return [] structs = [[extract(defn, x) for x in sheet.rows] for defn in field_defns] return structs def unique(key, dicts): t = {x[key]: x for x in dicts} return t.values() def now_as_iso8601(): return datetime.datetime.now().replace(microsecond=0).isoformat() + "Z" def inject_required(type_name, dicts): "Inject the required fields that graphcool import required" for x in dicts: x["_typeName"] = type_name x["id"] = cuid.cuid() x["createdAt"] = x["updatedAt"] = now_as_iso8601() return list(dicts) def prepare_organisations(organisations): unique_orgs = unique("clpOrganisationId", organisations) fat_orgs = inject_required("ClpOrganisation", unique_orgs) return fat_orgs def prepare_schools(schools): uniques = unique("clpSchoolId", schools) injected = inject_required("ClpSchool", uniques) return injected def prepare_locations(locations): # There are multiple locations, each of which is identitical except that for being related to a different school. # We have to collect all the schools that meet at the same location. uniques = {} for x in locations: # get an existing location with the given name, or add the new location location = uniques.setdefault(x["name"], x) related_schools = location.setdefault("schools", list()) related_schools.append(x.pop("clpSchoolId")) injected = inject_required("ClpLocation", uniques.values()) # FIX THIS - Current extract doesn't include the CLP location id :( Make one up for the time being for x in injected: x["clpLocationId"] = cuid.cuid() return injected def convert_dob_to_datetime(s): "Convert the string from 99/MON/YY to a ISO date" dt = datetime.datetime.strptime(s, "%d/%b/%y") return dt.isoformat() + ".0Z" # GraphCool import insists on microseconds, hence the ".0" def prepare_students(students): uniques = unique("clpStudentId", students) injected = inject_required("ClpStudent", uniques) for x in injected: x["dateOfBirth"] = convert_dob_to_datetime(x["dateOfBirth"]) return injected def prepare_teachers(teachers): # Like locations, the same teacher can have multiple records, # each of which is identitical except that for being related to a different school. # We have to collect all the schools that the same teacher is teaching at. uniques = {} for x in teachers: # get an existing teacher with that id, or add the new teacher record teacher = uniques.setdefault(x["clpTeacherId"], x) related_schools = teacher.setdefault("schools", list()) related_schools.append(x.pop("schoolId")) injected = inject_required("ClpTeacher", uniques.values()) return injected def extract_from_xlsx(file_path): for sheet in convert_xlsx(file_path): if sheet.name == "SCHOOL-ORG": (organisations, schools, locations) = process_sheet( sheet, SCHOOL_TITLES, [ORGANISATION_FIELDS, SCHOOL_FIELDS, LOCATION_FIELDS]) elif sheet.name == "Teacher": (teachers, ) = process_sheet(sheet, TEACHER_TITLES, [TEACHER_FIELDS]) elif sheet.name == "Student": (students, ) = process_sheet(sheet, STUDENT_TITLES, [STUDENT_FIELDS]) else: print("Ignoring sheet:", sheet.name) return (organisations, schools, locations, teachers, students) def copy_without(dicts, *keys_to_remove): "Return iterable that contains copies of the given dictionary with all the given keys removed" copies = [x.copy() for x in dicts] for d in copies: for to_remove in keys_to_remove: d.pop(to_remove, None) return copies def write_nodes(*list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + str(i), "nodes")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, "1.json") with open(path, "w") as f: nodes = { "valueType": "nodes", "values": one_list } f.write(json.dumps(nodes)) def write_relations(list_of_lists): for (i, one_list) in enumerate(list_of_lists): nodes_dir = relative_to_absolute(os.path.join(EXTRACT_OUTPUT_DIR + "-relations" + str(i), "relations")) os.makedirs(nodes_dir, exist_ok=True) path = os.path.join(nodes_dir, "1.json") with open(path, "w") as f: nodes = { "valueType": "relations", "values": list(one_list) } f.write(json.dumps(nodes)) def chunks(n, l): """Yield n successive similar-sized chunks from l.""" chunk_size = 1 + len(l) // n for i in range(0, len(l), chunk_size): yield l[i:i + chunk_size] def prepare(raw_organisations, raw_schools, raw_locations, raw_teachers, raw_students): return ( prepare_organisations(raw_organisations), prepare_schools(raw_schools), prepare_locations(raw_locations), prepare_teachers(raw_teachers), prepare_students(raw_students) ) def make_relation(entity1, id1, field1, entity2, id2, field2): return [ {"_typeName": entity1, "id": id1, "fieldName": field1}, {"_typeName": entity2, "id": id2, "fieldName": field2} ] def generate_relations(organisations, schools, locations, teachers, students): # Build school -> organisation relations org_keys = {x["clpOrganisationId"]: x["id"] for x in organisations} yield [make_relation("ClpOrganisation", org_keys[x["clpOrganisationId"]], "schools", "ClpSchool", x["id"], "organisation") for x in schools] # Build location -> school relations school_keys = {x["clpSchoolId"]: x["id"] for x in schools} yield [make_relation("ClpLocation", location["id"], "schools", "ClpSchool", school_keys[schoolId], "locations") for location in locations for schoolId in location.get("schools", [])] # Build teacher -> school relations yield [make_relation("ClpTeacher", teacher["id"], "schools", "ClpSchool", school_keys[schoolId], "teachers") for teacher in teachers for schoolId in teacher.get("schools", [])] # Build student -> school relations yield [make_relation("ClpStudent", student["id"], "school", "ClpSchool", school_keys[student["schoolId"]], "students") for student in students if student["schoolId"] in school_keys] def main(): xlsx_path = relative_to_absolute(SOURCE_XLSX) raw_collections = extract_from_xlsx(xlsx_path) (organisations, schools, locations, teachers, students) = prepare(*raw_collections) write_nodes( organisations, copy_without(schools, "clpOrganisationId"), copy_without(locations, "schools"), copy_without(teachers, "organisationId", "organisationName", "schools", "schoolName"), *chunks(3, copy_without(students, "schoolId", "schoolName", "location"))) write_relations(generate_relations(organisations, schools, locations, teachers, students)) if __name__ == "__main__": main()

fHDHR/origin/origin_channels.py

crackers8199/fHDHR_USTVGO

588

import os import sys from lxml import html import pathlib import json import m3u8 from seleniumwire import webdriver from selenium.common.exceptions import TimeoutException, NoSuchElementException from selenium.webdriver.firefox.options import Options as FirefoxOptions IFRAME_CSS_SELECTOR = '.iframe-container>iframe' # Disable def blockPrint(): sys.stdout = open(os.devnull, 'w') # Restore def enablePrint(): sys.stdout = sys.__stdout__ class OriginChannels(): def __init__(self, fhdhr, origin): self.fhdhr = fhdhr self.origin = origin self.cache_dir = self.fhdhr.config.dict["filedir"]["epg_cache"]["origin"]["top"] self.m3ucache = pathlib.Path(self.cache_dir).joinpath('m3ucache.json') self.cached_m3u = {} self.load_m3u_cache() def load_m3u_cache(self): if os.path.isfile(self.m3ucache): self.fhdhr.logger.info("Loading Previously Saved Channel m3u.") with open(self.m3ucache, 'r') as m3ufile: self.cached_m3u = json.load(m3ufile) def save_m3u_cache(self): self.fhdhr.logger.info("Saving Channel m3u cache.") with open(self.m3ucache, 'w') as m3ufile: m3ufile.write(json.dumps(self.cached_m3u, indent=4)) def get_channels(self): channel_list = [] chan_names, chan_urls = self.scrape_channels() chan_number_index = 1 for name, url in zip(chan_names, chan_urls): chan_dict = { "name": name.rstrip(), "number": chan_number_index, "callsign": self.format_callsign(url), } channel_list.append(chan_dict) chan_number_index += 1 return channel_list def get_channel_stream(self, chandict, allchandict): caching = True streamlist = [] streamdict = {} if chandict["callsign"] in list(self.cached_m3u): streamurl = self.cached_m3u[chandict["callsign"]] else: streamurl = self.get_ustvgo_stream(chandict) # if self.fhdhr.config.dict["origin"]["force_best"]: streamurl = self.m3u8_beststream(streamurl) streamdict = {"number": chandict["number"], "stream_url": streamurl} streamlist.append(streamdict) return streamlist, caching def m3u8_beststream(self, m3u8_url): bestStream = None videoUrlM3u = m3u8.load(m3u8_url) if not videoUrlM3u.is_variant: return m3u8_url for videoStream in videoUrlM3u.playlists: if not bestStream: bestStream = videoStream elif videoStream.stream_info.bandwidth > bestStream.stream_info.bandwidth: bestStream = videoStream if not bestStream: return bestStream.absolute_uri else: return m3u8_url def scrape_channels(self): channels_url = "https://ustvgo.tv/" chanpage = self.fhdhr.web.session.get(channels_url) tree = html.fromstring(chanpage.content) channel_names_xpath = "/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/text()" channel_urls_xpath = "/html/body/div[1]/div[1]/div/div[2]/div/div/div/article/div[1]/ol/li[*]/strong/a/@href" chan_names = tree.xpath(channel_names_xpath) chan_urls = tree.xpath(channel_urls_xpath) return chan_names, chan_urls def format_callsign(self, url): callsign = (url .split('/')[-2] .replace('-live', '') .replace('-channel', '') .replace('-free', '') .replace('-streaming', '')) return callsign def get_ustvgo_stream(self, chandict): driver = self.get_firefox_driver() blockPrint() driver.get("https://ustvgo.tv/" + chandict["callsign"]) enablePrint() # Get iframe iframe = None try: iframe = driver.find_element_by_css_selector(IFRAME_CSS_SELECTOR) except NoSuchElementException: self.fhdhr.logger.error('Video frame is not found for channel') return None # Detect VPN-required channels try: driver.switch_to.frame(iframe) driver.find_element_by_xpath("//*[text()='This channel requires our VPN to watch!']") need_vpn = True except NoSuchElementException: need_vpn = False finally: driver.switch_to.default_content() if need_vpn: self.fhdhr.logger.warning('Channel needs VPN to be grabbed.') return None # Autoplay iframe.click() try: playlist = driver.wait_for_request('/playlist.m3u8', timeout=10) except TimeoutException: self.fhdhr.logger.error('Channel m3u8 not found.') return None streamurl = str(playlist) driver.close() driver.quit() self.cached_m3u[chandict["callsign"]] = streamurl self.save_m3u_cache() return streamurl def get_firefox_driver(self): ff_options = FirefoxOptions() ff_options.add_argument('--headless') firefox_profile = webdriver.FirefoxProfile() firefox_profile.set_preference('permissions.default.image', 2) firefox_profile.set_preference('dom.ipc.plugins.enabled.libflashplayer.so', 'false') firefox_profile.set_preference('dom.disable_beforeunload', True) firefox_profile.set_preference('browser.tabs.warnOnClose', False) firefox_profile.set_preference('media.volume_scale', '0.0') set_seleniumwire_options = { 'connection_timeout': None, 'verify_ssl': False, 'suppress_connection_errors': True } driver = webdriver.Firefox(seleniumwire_options=set_seleniumwire_options, options=ff_options, firefox_profile=firefox_profile) return driver

version.py

XioNoX/ansible-junos-stdlib-old

589

VERSION = "1.4.0" DATE = "2016-Sept-21"

Episode11-Menu/Pygame/explosion.py

Inksaver/Shmup_With_Pygame_Love2D_Monogame

590

<gh_stars>1-10 import pygame import shared class Explosion(): def __init__(self, images:list, centre:tuple, key:str) -> None: ''' Class variables. key: 'sm', 'lg', 'player ''' self.images = images # list of 8 images self.centre = centre # use for all frames self.key = key # key used later self.image = images[key][0] # set to first image in the sequence self.rect = self.image.get_rect() # define rectangle from image size self.rect.center = self.centre # set centre for all frames self.frame = 0 # no of first frame self.time_passed = 0 # set timer to 0 self.frame_rate = 0.1 # 8 images played at 1 frame per 0.1 secs = 0.8 seconds self.active = True def update(self, dt): self.time_passed += dt if self.time_passed >= self.frame_rate: # 0.1 seconds has passed self.time_passed = 0 # reset timer self.frame += 1 # increase frame number if self.frame >= len(self.images[self.key]): # check if end of list? self.active = False # animation finished else: self.image = self.images[self.key][self.frame] # next frame self.rect = self.image.get_rect() # new rectangle self.rect.center = self.centre # set centre to parameter value return self.active def draw(self): shared.screen.blit(self.image, self.rect) # draw current frame

funcx_endpoint/funcx_endpoint/strategies/base.py

arokem/funcX

591

import sys import threading import logging import time logger = logging.getLogger("interchange.strategy.base") class BaseStrategy(object): """Implements threshold-interval based flow control. The overall goal is to trap the flow of apps from the workflow, measure it and redirect it the appropriate executors for processing. This is based on the following logic: .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : count = get_events_since(start) if count >= THRESHOLD : break callback() This logic ensures that the callbacks are activated with a maximum delay of `interval` for systems with infrequent events as well as systems which would generate large bursts of events. Once a callback is triggered, the callback generally runs a strategy method on the sites available as well asqeuque TODO: When the debug logs are enabled this module emits duplicate messages. This issue needs more debugging. What I've learnt so far is that the duplicate messages are present only when the timer thread is started, so this could be from a duplicate logger being added by the thread. """ def __init__(self, *args, threshold=20, interval=5): """Initialize the flowcontrol object. We start the timer thread here Parameters ---------- - threshold (int) : Tasks after which the callback is triggered - interval (int) : seconds after which timer expires """ self.interchange = None self.threshold = threshold self.interval = interval self.cb_args = args self.callback = self.strategize self._handle = None self._event_count = 0 self._event_buffer = [] self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True def start(self, interchange): """Actually start the strategy Parameters ---------- interchange: funcx.executors.high_throughput.interchange.Interchange Interchange to bind the strategy to """ self.interchange = interchange if hasattr(interchange.config, 'provider'): logger.debug("Strategy bounds-> init:{}, min:{}, max:{}".format( interchange.config.provider.init_blocks, interchange.config.provider.min_blocks, interchange.config.provider.max_blocks)) self._thread.start() def strategize(self, *args, **kwargs): """ Strategize is called everytime the threshold or the interval is hit """ logger.debug("Strategize called with {} {}".format(args, kwargs)) def _wake_up_timer(self, kill_event): """Internal. This is the function that the thread will execute. waits on an event so that the thread can make a quick exit when close() is called Args: - kill_event (threading.Event) : Event to wait on """ while True: prev = self._wake_up_time # Waiting for the event returns True only when the event # is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print("Sleeping a bit more") def notify(self, event_id): """Let the FlowControl system know that there is an event. This method is to be called from the Interchange to notify the flowcontrol """ self._event_buffer.extend([event_id]) self._event_count += 1 if self._event_count >= self.threshold: logger.debug("Eventcount >= threshold") self.make_callback(kind="event") def make_callback(self, kind=None): """Makes the callback and resets the timer. KWargs: - kind (str): Default=None, used to pass information on what triggered the callback """ self._wake_up_time = time.time() + self.interval self.callback(tasks=self._event_buffer, kind=kind) self._event_buffer = [] def close(self): """Merge the threads and terminate.""" self._kill_event.set() self._thread.join() class Timer(object): """This timer is a simplified version of the FlowControl timer. This timer does not employ notify events. This is based on the following logic : .. code-block:: none BEGIN (INTERVAL, THRESHOLD, callback) : start = current_time() while (current_time()-start < INTERVAL) : wait() break callback() """ def __init__(self, callback, *args, interval=5): """Initialize the flowcontrol object We start the timer thread here Args: - dfk (DataFlowKernel) : DFK object to track parsl progress KWargs: - threshold (int) : Tasks after which the callback is triggered - interval (int) : seconds after which timer expires """ self.interval = interval self.cb_args = args self.callback = callback self._wake_up_time = time.time() + 1 self._kill_event = threading.Event() self._thread = threading.Thread(target=self._wake_up_timer, args=(self._kill_event,)) self._thread.daemon = True self._thread.start() def _wake_up_timer(self, kill_event): """Internal. This is the function that the thread will execute. waits on an event so that the thread can make a quick exit when close() is called Args: - kill_event (threading.Event) : Event to wait on """ # Sleep till time to wake up while True: prev = self._wake_up_time # Waiting for the event returns True only when the event # is set, usually by the parent thread time_to_die = kill_event.wait(float(max(prev - time.time(), 0))) if time_to_die: return if prev == self._wake_up_time: self.make_callback(kind='timer') else: print("Sleeping a bit more") def make_callback(self, kind=None): """Makes the callback and resets the timer. """ self._wake_up_time = time.time() + self.interval self.callback(*self.cb_args) def close(self): """Merge the threads and terminate. """ self._kill_event.set() self._thread.join()

hw3 cnn and vis/gradcam.py

mtang1001/ML-Exploration

592

import torch import torchvision import matplotlib import matplotlib.pyplot as plt from PIL import Image from captum.attr import GuidedGradCam, GuidedBackprop from captum.attr import LayerActivation, LayerConductance, LayerGradCam from data_utils import * from image_utils import * from captum_utils import * import numpy as np from visualizers import GradCam plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' X, y, class_names = load_imagenet_val(num=5) # FOR THIS SECTION ONLY, we need to use gradients. We introduce a new model we will use explicitly for GradCAM for this. gc_model = torchvision.models.squeezenet1_1(pretrained=True) gc = GradCam() X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) # Guided Back-Propagation gbp_result = gc.guided_backprop(X_tensor,y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gbp_result.shape[0]): plt.subplot(1, 5, i + 1) img = gbp_result[i] img = rescale(img) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_backprop.png') # GradCam # GradCAM. We have given you which module(=layer) that we need to capture gradients from, which you can see in conv_module variable below gc_model = torchvision.models.squeezenet1_1(pretrained=True) for param in gc_model.parameters(): param.requires_grad = True X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gradcam_val = gradcam_result[i] img = X[i] + (matplotlib.cm.jet(gradcam_val)[:,:,:3]*255) img = img / np.max(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/gradcam.png') # As a final step, we can combine GradCam and Guided Backprop to get Guided GradCam. X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0).requires_grad_(True) y_tensor = torch.LongTensor(y) gradcam_result = gc.grad_cam(X_tensor, y_tensor, gc_model) gbp_result = gc.guided_backprop(X_tensor, y_tensor, gc_model) plt.figure(figsize=(24, 24)) for i in range(gradcam_result.shape[0]): gbp_val = gbp_result[i] gradcam_val = np.expand_dims(gradcam_result[i], axis=2) # Pointwise multiplication and normalization of the gradcam and guided backprop results (2 lines) img = gradcam_val * gbp_val img = np.expand_dims(img.transpose(2, 0, 1), axis=0) img = np.float32(img) img = torch.from_numpy(img) img = deprocess(img) plt.subplot(1, 5, i + 1) plt.imshow(img) plt.title(class_names[y[i]]) plt.axis('off') plt.gcf().tight_layout() plt.savefig('visualization/guided_gradcam.png') # **************************************************************************************** # # Captum model = torchvision.models.squeezenet1_1(pretrained=True) # We don't want to train the model, so tell PyTorch not to compute gradients # with respect to model parameters. for param in model.parameters(): param.requires_grad = False # Convert X and y from numpy arrays to Torch Tensors X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0) y_tensor = torch.LongTensor(y) conv_module = model.features[12] ############################################################################## # TODO: Compute/Visualize GuidedBackprop and Guided GradCAM as well. # # visualize_attr_maps function from captum_utils.py is useful for # # visualizing captum outputs # # Use conv_module as the convolution layer for gradcam # ############################################################################## # Computing Guided GradCam ggc = GuidedGradCam(model, conv_module) attribution_gcc = compute_attributions(ggc, X_tensor, target = y_tensor) # print(X_tensor.shape, y_tensor.shape, attribution_gcc.shape) visualize_attr_maps('visualization/GuidedGradCam.png', X, y, class_names, [attribution_gcc], ['Guided_Grad_Cam']) # Computing Guided BackProp gbp = GuidedBackprop(model) attribution_gbp = compute_attributions(gbp, X_tensor, target = y_tensor) visualize_attr_maps('visualization/GuidedBackpropCam.png', X, y, class_names, [attribution_gbp], ['Guided_Backprop_Cam']) ############################################################################## # END OF YOUR CODE # ############################################################################## # Try out different layers and see observe how the attributions change layer = model.features[3] # Example visualization for using layer visualizations # layer_act = LayerActivation(model, layer) # layer_act_attr = compute_attributions(layer_act, X_tensor) # layer_act_attr_sum = layer_act_attr.mean(axis=1, keepdim=True) ############################################################################## # TODO: Visualize Individual Layer Gradcam and Layer Conductance (similar # # to what we did for the other captum sections, using our helper methods), # # but with some preprocessing calculations. # # # # You can refer to the LayerActivation example above and you should be # # using 'layer' given above for this section # # # # Also note that, you would need to customize your 'attr_preprocess' # # parameter that you send along to 'visualize_attr_maps' as the default # # 'attr_preprocess' is written to only to handle multi channel attributions. # # # # For layer gradcam look at the usage of the parameter relu_attributions # ############################################################################## # Layer gradcam aggregates across all channels from captum.attr import LayerAttribution N, C, H, W = X_tensor.shape LC = LayerConductance(model, layer) LC_attr = compute_attributions(LC, X_tensor, target = y_tensor) LC_attr_sum = LC_attr.mean(axis = 1, keepdim = True) LC_attr_int = LayerAttribution.interpolate(LC_attr_sum, (H,W) ) LC_attr_int = LC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps('visualization/LayerConductance.png', X, y, class_names, [LC_attr_int], ['LayerConductance']) LGC = LayerGradCam(model, layer) LGC_attr = compute_attributions(LGC, X_tensor, target = y_tensor) LGC_attr_sum = LGC_attr.mean(axis = 1, keepdim = True) LGC_attr_int = LayerAttribution.interpolate(LGC_attr_sum, (H,W)) LGC_attr_int = LGC_attr_int.repeat(1, 3, 1, 1) visualize_attr_maps ('visualization/LayerGradCam.png', X, y, class_names, [LGC_attr_int], ['LayerGradCam']) ############################################################################## # END OF YOUR CODE # ##############################################################################

src/genotypes.py

k8lion/admmdarts

593

<gh_stars>0 from collections import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3', 4)], reduce_concat=range(2, 6))

colab_logica.py

smdesai/logica

594

<gh_stars>0 #!/usr/bin/python # # 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. """Library for using Logica in CoLab.""" from .common import color from .common import concertina_lib from .compiler import functors from .compiler import rule_translate from .compiler import universe import IPython from IPython.core.magic import register_cell_magic from IPython.display import display import os import pandas from .parser_py import parse from .common import sqlite3_logica BQ_READY = True # By default. try: from google.cloud import bigquery except: BQ_READY = False print('Could not import google.cloud.bigquery.') try: from google.colab import auth except: BQ_READY = False print('Could not import google.cloud.auth.') try: from google.colab import widgets WIDGETS_IMPORTED = True except: WIDGETS_IMPORTED = False print('Could not import google.colab.widgets.') PROJECT = None # TODO: Should this be renamed to PSQL_ENGINE, PSQL_CONNECTION? DB_ENGINE = None DB_CONNECTION = None USER_AUTHENTICATED = False TABULATED_OUTPUT = True SHOW_FULL_QUERY = True PREAMBLE = None def SetPreamble(preamble): global PREAMBLE PREAMBLE = preamble def SetProject(project): global PROJECT PROJECT = project def SetDbConnection(connection): global DB_CONNECTION DB_CONNECTION = connection def EnsureAuthenticatedUser(): global USER_AUTHENTICATED global PROJECT if USER_AUTHENTICATED: return auth.authenticate_user() if PROJECT is None: print("Please enter project_id to use for BigQuery queries.") PROJECT = input() print("project_id is set to %s" % PROJECT) print("You can change it with logica.colab_logica.SetProject command.") USER_AUTHENTICATED = True def SetTabulatedOutput(tabulated_output): global TABULATED_OUTPUT global SHOW_FULL_QUERY TABULATED_OUTPUT = tabulated_output SHOW_FULL_QUERY = TABULATED_OUTPUT if not WIDGETS_IMPORTED: SetTabulatedOutput(False) def TabBar(*args): """Returns a real TabBar or a mock. Useful for UIs that don't support JS.""" if TABULATED_OUTPUT: return widgets.TabBar(*args) class MockTab: def __init__(self): pass def __enter__(self): pass def __exit__(self, *x): pass class MockTabBar: def __init__(self): pass def output_to(self, x): return MockTab() return MockTabBar() @register_cell_magic def logica(line, cell): Logica(line, cell, run_query=True) def ParseList(line): line = line.strip() if not line: predicates = [] else: predicates = [p.strip() for p in line.split(',')] return predicates def RunSQL(sql, engine, connection=None, is_final=False): if engine == 'bigquery': client = bigquery.Client(project=PROJECT) return client.query(sql).to_dataframe() elif engine == 'psql': if is_final: return pandas.read_sql(sql, connection) else: return connection.execute(sql) elif engine == 'sqlite': statements = parse.SplitRaw(sql, ';') connection.executescript(sql) if is_final: return pandas.read_sql(statements[-1], connection) else: pass return None else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') class SqliteRunner(object): def __init__(self): self.connection = sqlite3_logica.SqliteConnect() # TODO: Sqlite runner should not be accepting an engine. def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) class PostgresRunner(object): def __init__(self): global DB_CONNECTION global DB_ENGINE if DB_CONNECTION: self.engine = DB_ENGINE self.connection = DB_CONNECTION else: (self.engine, self.connection) = PostgresJumpStart() DB_ENGINE = self.engine DB_CONNECTION = self.connection def __call__(self, sql, engine, is_final): return RunSQL(sql, engine, self.connection, is_final) def ShowError(error_text): print(color.Format('[ {error}Error{end} ] ' + error_text)) def Logica(line, cell, run_query): """Running Logica predicates and storing results.""" predicates = ParseList(line) if not predicates: ShowError('No predicates to run.') return try: program = ';\n'.join(s for s in [PREAMBLE, cell] if s) parsed_rules = parse.ParseFile(program)['rule'] except parse.ParsingException as e: e.ShowMessage() return try: program = universe.LogicaProgram(parsed_rules) except functors.FunctorError as e: e.ShowMessage() return engine = program.annotations.Engine() if engine == 'bigquery' and not BQ_READY: ShowError( 'BigQuery client and/or authentification is not installed. \n' 'It is the easiest to run BigQuery requests from Google CoLab:\n' ' https://colab.research.google.com/.\n' 'Note that running Logica on SQLite requires no installation.\n' 'This could be a good fit for working with small data or learning Logica.\n' 'Use {warning}@Engine("sqlite");{end} annotation in your program to use SQLite.') return bar = TabBar(predicates + ['(Log)']) logs_idx = len(predicates) executions = [] sub_bars = [] ip = IPython.get_ipython() for idx, predicate in enumerate(predicates): with bar.output_to(logs_idx): try: sql = program.FormattedPredicateSql(predicate) executions.append(program.execution) ip.push({predicate + '_sql': sql}) except rule_translate.RuleCompileException as e: print('Encountered error when compiling %s.' % predicate) e.ShowMessage() return # Publish output to Colab cell. with bar.output_to(idx): sub_bar = TabBar(['SQL', 'Result']) sub_bars.append(sub_bar) with sub_bar.output_to(0): if SHOW_FULL_QUERY: print( color.Format( 'The following query is stored at {warning}%s{end} ' 'variable.' % ( predicate + '_sql'))) print(sql) else: print('Query is stored at %s variable.' % color.Warn(predicate + '_sql')) with bar.output_to(logs_idx): if engine == 'sqlite': sql_runner = SqliteRunner() elif engine == 'psql': sql_runner = PostgresRunner() elif engine == 'bigquery': EnsureAuthenticatedUser() sql_runner = RunSQL else: raise Exception('Logica only supports BigQuery, PostgreSQL and SQLite ' 'for now.') result_map = concertina_lib.ExecuteLogicaProgram( executions, sql_runner=sql_runner, sql_engine=engine) for idx, predicate in enumerate(predicates): t = result_map[predicate] ip.push({predicate: t}) with bar.output_to(idx): with sub_bars[idx].output_to(1): if run_query: print( color.Format( 'The following table is stored at {warning}%s{end} ' 'variable.' % predicate)) display(t) else: print('The query was not run.') print(' ') # To activate the tabbar. def PostgresJumpStart(): # Install postgresql server. print("Installing and configuring an empty PostgreSQL database.") result = 0 result += os.system('sudo apt-get -y -qq update') result += os.system('sudo apt-get -y -qq install postgresql') result += os.system('sudo service postgresql start') # Ignoring user creation error, as they may already exist. result += 0 * os.system( 'sudo -u postgres psql -c "CREATE USER logica WITH SUPERUSER"') result += os.system( 'sudo -u postgres psql -c "ALTER USER logica PASSWORD \'<PASSWORD>\';"') result += os.system( 'sudo -u postgres psql -U postgres -c \'CREATE DATABASE logica;\'') if result != 0: print("""Installation failed. Please try the following manually: # Install Logica. !pip install logica # Install postgresql server. !sudo apt-get -y -qq update !sudo apt-get -y -qq install postgresql !sudo service postgresql start # Prepare database for Logica. !sudo -u postgres psql -c "CREATE USER logica WITH SUPERUSER" !sudo -u postgres psql -c "ALTER USER logica PASSWORD '<PASSWORD>';" !sudo -u postgres psql -U postgres -c 'CREATE DATABASE logica;' # Connect to the database. from logica import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:[email protected]', pool_recycle=3600); connection = engine.connect(); colab_logica.SetDbConnection(connection)""") return print('Installation succeeded. Connecting...') # Connect to the database. from logica import colab_logica from sqlalchemy import create_engine import pandas engine = create_engine('postgresql+psycopg2://logica:[email protected]', pool_recycle=3600) connection = engine.connect() print('Connected.') return engine, connection

pyccel/ast/basic.py

toddrme2178/pyccel

595

from sympy.core.basic import Basic as sp_Basic class Basic(sp_Basic): """Basic class for Pyccel AST.""" _fst = None def set_fst(self, fst): """Sets the redbaron fst.""" self._fst = fst @property def fst(self): return self._fst

alibi_detect/utils/tests/test_discretize.py

Clusks/alibi-detect

596

from itertools import product import numpy as np import pytest from alibi_detect.utils.discretizer import Discretizer x = np.random.rand(10, 4) n_features = x.shape[1] feature_names = [str(_) for _ in range(n_features)] categorical_features = [[], [1, 3]] percentiles = [list(np.arange(25, 100, 25)), list(np.arange(10, 100, 10))] tests = list(product(categorical_features, percentiles)) n_tests = len(tests) @pytest.fixture def cats_and_percentiles(request): cat, perc = tests[request.param] return cat, perc @pytest.mark.parametrize('cats_and_percentiles', list(range(n_tests)), indirect=True) def test_discretizer(cats_and_percentiles): cat, perc = cats_and_percentiles disc = Discretizer(x, cat, feature_names, perc) to_disc = list(disc.names.keys()) assert len(to_disc) == (x.shape[1] - len(cat)) x_disc = disc.discretize(x) for k, v in disc.names.items(): assert len(v) <= len(perc) + 1 assert callable(disc.lambdas[k]) assert (x_disc[:, k].min() == 0).all() assert (x_disc[:, k].max() == len(perc)).all() for i in range(x.shape[1]): if i not in to_disc: assert (x_disc[:, i] == x[:, i]).all()

tinc/tests/parameter_space_test.py

AlloSphere-Research-Group/tinc-python

597

# -*- coding: utf-8 -*- """ Created on Mon Jun 14 11:49:43 2021 @author: Andres """ import sys,time import unittest from tinc import * class ParameterSpaceTest(unittest.TestCase): def test_parameter(self): p1 = Parameter("param1") p2 = Parameter("param2") ps = ParameterSpace("ps") ps.register_parameters([p1, p2]) def test_process(self): p1 = Parameter("param1") p1.values = [0, 1,2,3,4] p2 = Parameter("param2") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace("ps") ps.register_parameters([p1, p2]) def func(param1, param2): return param1 * param2 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 result = ps.run_process(func) self.assertAlmostEqual(result, p1.value * p2.value) p1.value = 3 p2.value = -0.1 def test_sweep_cache(self): p1 = Parameter("param1") p1.values = [0, 1,2,3,4] p2 = Parameter("param2") p2.values = [-0.3,-0.2, -0.1, 0] ps = ParameterSpace("ps") ps.register_parameters([p1, p2]) ps.enable_cache("ps_test") def func(param1, param2): return param1 * param2 ps.sweep(func) def test_data_directories(self): dim1 = Parameter("dim1") dim1.values = [0.1,0.2,0.3,0.4, 0.5] dim2 = Parameter("dim2") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [0.1,0.2,0.3,0.4, 0.5] dim3 = Parameter("dim3") dim3.set_space_representation_type(parameter_space_representation_types.ID) dim2.values = [0.1,0.2,0.3,0.4, 0.5] ps = ParameterSpace("ps") ps.register_parameters([dim1, dim2, dim3]) ps.set_current_path_template("file_%%dim1%%_%%dim2:INDEX%%") dim1.value=0.2 dim2.value=0.2 self.assertEqual(ps.get_current_relative_path(), 'file_0.2_1') # TODO ML complete tests see C++ tests for parameter space def test_common_id(self): dim1 = Parameter("dim1") dim1.values = [0.1, 0.1, 0.2, 0.2, 0.3, 0.3] dim1.ids = ["0.1_1" ,"0.1_2","0.2_1" ,"0.2_2", "0.3_1" ,"0.3_2"] dim2 = Parameter("dim2") dim2.set_space_representation_type(parameter_space_representation_types.INDEX) dim2.values = [1,1,1,2,2,2] dim2.ids = ["0.1_1", "0.2_1", "0.3_1", "0.1_2", "0.2_2", "0.3_2"] ps = ParameterSpace("ps") ps.register_parameters([dim1, dim2]) dim1.value = 0.1 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.1_1") dim1.value = 0.2 dim2.value = 1 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.2_1") dim1.value = 0.1 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.1_2") dim1.value = 0.2 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.2_2") dim1.value = 0.3 dim2.value = 2 self.assertEqual(ps.get_common_id([dim1, dim2]), "0.3_2") if __name__ == '__main__': unittest.main()

interpretable_ddts/runfiles/gym_runner.py

CORE-Robotics-Lab/Interpretable_DDTS_AISTATS2020

598

# Created by <NAME> on 8/28/19 import gym import numpy as np import torch from interpretable_ddts.agents.ddt_agent import DDTAgent from interpretable_ddts.agents.mlp_agent import MLPAgent from interpretable_ddts.opt_helpers.replay_buffer import discount_reward import torch.multiprocessing as mp import argparse import copy import random def run_episode(q, agent_in, ENV_NAME, seed=0): agent = agent_in.duplicate() if ENV_NAME == 'lunar': env = gym.make('LunarLander-v2') elif ENV_NAME == 'cart': env = gym.make('CartPole-v1') else: raise Exception('No valid environment selected') done = False torch.manual_seed(seed) env.seed(seed) np.random.seed(seed) env.action_space.seed(seed) random.seed(seed) state = env.reset() # Reset environment and record the starting state while not done: action = agent.get_action(state) # Step through environment using chosen action state, reward, done, _ = env.step(action) # env.render() # Save reward agent.save_reward(reward) if done: break reward_sum = np.sum(agent.replay_buffer.rewards_list) rewards_list, advantage_list, deeper_advantage_list = discount_reward(agent.replay_buffer.rewards_list, agent.replay_buffer.value_list, agent.replay_buffer.deeper_value_list) agent.replay_buffer.rewards_list = rewards_list agent.replay_buffer.advantage_list = advantage_list agent.replay_buffer.deeper_advantage_list = deeper_advantage_list to_return = [reward_sum, copy.deepcopy(agent.replay_buffer.__getstate__())] if q is not None: try: q.put(to_return) except RuntimeError as e: print(e) return to_return return to_return def main(episodes, agent, ENV_NAME): running_reward_array = [] for episode in range(episodes): reward = 0 returned_object = run_episode(None, agent_in=agent, ENV_NAME=ENV_NAME) reward += returned_object[0] running_reward_array.append(returned_object[0]) agent.replay_buffer.extend(returned_object[1]) if reward >= 499: agent.save('../models/'+str(episode)+'th') agent.end_episode(reward) running_reward = sum(running_reward_array[-100:]) / float(min(100.0, len(running_reward_array))) if episode % 50 == 0: print(f'Episode {episode} Last Reward: {reward} Average Reward: {running_reward}') if episode % 500 == 0: agent.save('../models/'+str(episode)+'th') return running_reward_array if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-a", "--agent_type", help="architecture of agent to run", type=str, default='ddt') parser.add_argument("-e", "--episodes", help="how many episodes", type=int, default=2000) parser.add_argument("-l", "--num_leaves", help="number of leaves for DDT/DRL ", type=int, default=8) parser.add_argument("-n", "--num_hidden", help="number of hidden layers for MLP ", type=int, default=0) parser.add_argument("-env", "--env_type", help="environment to run on", type=str, default='cart') parser.add_argument("-gpu", help="run on GPU?", action='store_true') args = parser.parse_args() AGENT_TYPE = args.agent_type # 'ddt', 'mlp' NUM_EPS = args.episodes # num episodes Default 1000 ENV_TYPE = args.env_type # 'cart' or 'lunar' Default 'cart' USE_GPU = args.gpu # Applies for 'prolo' only. use gpu? Default false if ENV_TYPE == 'lunar': init_env = gym.make('LunarLander-v2') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n elif ENV_TYPE == 'cart': init_env = gym.make('CartPole-v1') dim_in = init_env.observation_space.shape[0] dim_out = init_env.action_space.n else: raise Exception('No valid environment selected') print(f"Agent {AGENT_TYPE} on {ENV_TYPE} ") # mp.set_start_method('spawn') mp.set_sharing_strategy('file_system') for i in range(5): bot_name = AGENT_TYPE + ENV_TYPE if USE_GPU: bot_name += 'GPU' if AGENT_TYPE == 'ddt': policy_agent = DDTAgent(bot_name=bot_name, input_dim=dim_in, output_dim=dim_out, rule_list=False, num_rules=args.num_leaves) elif AGENT_TYPE == 'mlp': policy_agent = MLPAgent(input_dim=dim_in, bot_name=bot_name, output_dim=dim_out, num_hidden=args.num_hidden) else: raise Exception('No valid network selected') reward_array = main(NUM_EPS, policy_agent, ENV_TYPE)

Week 08/tw10_words_by_prefix.py

andrewn488/OMSBA-5061

599

""" TW10: Words by Prefix Team: <NAME>, <NAME> For: OMSBA 2061, Seattle University Date: 11/3/2020 """ def wordByPrefix(prefix_length, word): my_dict = {} for key in word: for letter in word: prefix_key = letter[:prefix_length] letter = word[:prefix_length] return prefix_key return letter question_2 = ['able', 'ability', 'apple', 'tryst', 'trial', 'tremendous', 'tree'] my_list = [] for elem in question_2: prefix = elem[:2] my_list.append(prefix) print(my_list) def question_3(prefix_length, word): my_list = [] for key in word: prefix = key[:prefix_length] my_list.append(prefix) return my_list def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha(): if letter not in count: count[letter] = 0 count[letter] += 1 return count def wordByPrefix(prefix_length, word): my_list = [] #count = 0 for key in word: prefix = key[:prefix_length] my_list.append(prefix) count = {} for letter in my_list: if letter.isalpha(): if letter not in count: letter[count] = [] count.update(letter) return count