paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
uproot/const.py	guiguem/uproot	0	25113	#!/usr/bin/env python # Copyright (c) 2017, DIANA-HEP # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ROOT constants used in deserialization.""" import numpy # used in unmarshaling kByteCountMask = numpy.int64(0x40000000) kByteCountVMask = numpy.int64(0x4000) kClassMask = numpy.int64(0x80000000) kNewClassTag = numpy.int64(0xFFFFFFFF) kIsOnHeap = numpy.uint32(0x01000000) kIsReferenced = numpy.uint32(1 << 4) kMapOffset = 2 # not used? kNullTag = 0 kNotDeleted = 0x02000000 kZombie = 0x04000000 kBitMask = 0x00ffffff kDisplacementMask = 0xFF000000 ################################################################ core/zip/inc/Compression.h kZLIB = 1 kLZMA = 2 kOldCompressionAlgo = 3 kLZ4 = 4 kUndefinedCompressionAlgorithm = 5 ################################################################ constants for streamers kBase = 0 kChar = 1 kShort = 2 kInt = 3 kLong = 4 kFloat = 5 kCounter = 6 kCharStar = 7 kDouble = 8 kDouble32 = 9 kLegacyChar = 10 kUChar = 11 kUShort = 12 kUInt = 13 kULong = 14 kBits = 15 kLong64 = 16 kULong64 = 17 kBool = 18 kFloat16 = 19 kOffsetL = 20 kOffsetP = 40 kObject = 61 kAny = 62 kObjectp = 63 kObjectP = 64 kTString = 65 kTObject = 66 kTNamed = 67 kAnyp = 68 kAnyP = 69 kAnyPnoVT = 70 kSTLp = 71 kSkip = 100 kSkipL = 120 kSkipP = 140 kConv = 200 kConvL = 220 kConvP = 240 kSTL = 300 kSTLstring = 365 kStreamer = 500 kStreamLoop = 501 ################################################################ constants from core/foundation/inc/ESTLType.h kNotSTL = 0 kSTLvector = 1 kSTLlist = 2 kSTLdeque = 3 kSTLmap = 4 kSTLmultimap = 5 kSTLset = 6 kSTLmultiset = 7 kSTLbitset = 8 kSTLforwardlist = 9 kSTLunorderedset = 10 kSTLunorderedmultiset = 11 kSTLunorderedmap = 12 kSTLunorderedmultimap = 13 kSTLend = 14 kSTLany = 300 ################################################################ IOFeatures kGenerateOffsetMap = 1	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29955, 29892, 22471, 2190, 29909, 29899, 9606, 29925, 13, 29937, 2178, 10462, 21676, 29889, 13, 29937, 29871, 13, 29937, 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checkmate/contrib/plugins/git/commands/update_stats.py	marcinguy/checkmate-ce	80	146862	<reponame>marcinguy/checkmate-ce<gh_stars>10-100 # -- coding: utf-8 -- from __future__ import unicode_literals import logging from collections import defaultdict from checkmate.management.commands.base import BaseCommand from ..models import GitBranch logger = logging.getLogger(__name__) class Command(BaseCommand): def run(self): branches = self.backend.filter(GitBranch,{'project.pk' : self.project.pk}) branches_by_name = dict([(branch.name,branch) for branch in branches]) if 'default_branch' in self.project and self.project.default_branch in branches_by_name: master_branch = branches_by_name[self.project.default_branch] elif 'origin/master' in branches_by_name: master_branch = branches_by_name['origin/master'] else: logger.warning("No default branch defined for project %s" % self.project.pk) return -1 stats = { 'contributors' : self.project.git.repository.get_contributors(), 'branch' : master_branch.name } stats['n_commits'] = sum([contributor['n_commits'] for contributor in stats['contributors']]) self.project.stats = stats with self.backend.transaction(): self.backend.save(self.project)	[ 1, 529, 276, 1112, 420, 29958, 3034, 3277, 8631, 29914, 3198, 25046, 29899, 346, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 5215, 12183, 13, 3166, 16250, 1053, 2322, 8977, 13, 13, 3166, 1423, 25046, 29889, 21895, 29889, 26381, 29889, 3188, 1053, 7399, 6255, 13, 3166, 6317, 9794, 1053, 11786, 29933, 4014, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 1990, 10516, 29898, 5160, 6255, 1125, 13, 13, 1678, 822, 1065, 29898, 1311, 1125, 13, 13, 4706, 14202, 353, 1583, 29889, 27852, 29889, 4572, 29898, 28712, 29933, 4014, 29892, 10998, 4836, 29889, 20571, 29915, 584, 1583, 29889, 4836, 29889, 20571, 1800, 13, 13, 4706, 14202, 29918, 1609, 29918, 978, 353, 9657, 4197, 29898, 17519, 29889, 978, 29892, 17519, 29897, 363, 5443, 297, 14202, 2314, 13, 13, 4706, 565, 525, 4381, 29918, 17519, 29915, 297, 1583, 29889, 4836, 322, 1583, 29889, 4836, 29889, 4381, 29918, 17519, 297, 14202, 29918, 1609, 29918, 978, 29901, 13, 9651, 5835, 29918, 17519, 353, 14202, 29918, 1609, 29918, 978, 29961, 1311, 29889, 4836, 29889, 4381, 29918, 17519, 29962, 13, 4706, 25342, 525, 12574, 29914, 6207, 29915, 297, 14202, 29918, 1609, 29918, 978, 29901, 13, 9651, 5835, 29918, 17519, 353, 14202, 29918, 1609, 29918, 978, 1839, 12574, 29914, 6207, 2033, 13, 4706, 1683, 29901, 13, 9651, 17927, 29889, 27392, 703, 3782, 2322, 5443, 3342, 363, 2060, 1273, 29879, 29908, 1273, 1583, 29889, 4836, 29889, 20571, 29897, 13, 9651, 736, 448, 29896, 13, 13, 4706, 22663, 353, 426, 13, 9651, 525, 21570, 29560, 29915, 584, 1583, 29889, 4836, 29889, 5559, 29889, 19033, 29889, 657, 29918, 21570, 29560, 3285, 13, 9651, 525, 17519, 29915, 584, 5835, 29918, 17519, 29889, 978, 13, 4706, 500, 13, 13, 4706, 22663, 1839, 29876, 29918, 2055, 1169, 2033, 353, 2533, 4197, 21570, 3406, 1839, 29876, 29918, 2055, 1169, 2033, 363, 17737, 3406, 297, 22663, 1839, 21570, 29560, 2033, 2314, 13, 13, 4706, 1583, 29889, 4836, 29889, 16202, 353, 22663, 13, 4706, 411, 1583, 29889, 27852, 29889, 20736, 7295, 13, 9651, 1583, 29889, 27852, 29889, 7620, 29898, 1311, 29889, 4836, 29897, 13, 2 ]
dataset.py	dimitreOliveira/RecsysChallenge_Spotify	3	63064	import pandas as pd def build_columns(): columns = ['pid'] for i in range(1, 501): columns.append('trackuri_%s' % i) return columns def build_output(df_list, id_cloumn, song_column): output = pd.DataFrame(columns=build_columns()) for df in df_list: output = output.append(format_output(df, id_cloumn, song_column)) return output def format_output(df, id_cloumn, song_column): columns = build_columns()[1:501] output = df output['columns'] = columns output = output.pivot(index=id_cloumn, columns='columns', values=song_column) output['pid'] = output.index output = output[build_columns()] return output def output_submission(df, file_name, team_name, contact_information, path='submissions/', challenge_track='main'): file = path + file_name first_row = pd.DataFrame(columns=build_columns()) first_row.loc[0] = build_first_row(team_name, contact_information, challenge_track) output = pd.concat([first_row, df]) output = output[build_columns()] output = output.set_index('pid') output.to_csv(file) def build_first_row(team_name, contact_information, challenge_track='main'): row = ['team_info', challenge_track, team_name, contact_information] for i in range(4, 501): row.append(None) return row	[ 1, 1053, 11701, 408, 10518, 13, 13, 13, 1753, 2048, 29918, 13099, 7295, 13, 1678, 4341, 353, 6024, 5935, 2033, 13, 1678, 363, 474, 297, 3464, 29898, 29896, 29892, 29871, 29945, 29900, 29896, 1125, 13, 4706, 4341, 29889, 4397, 877, 11294, 5338, 29918, 29995, 29879, 29915, 1273, 474, 29897, 13, 13, 1678, 736, 4341, 13, 13, 13, 1753, 2048, 29918, 4905, 29898, 2176, 29918, 1761, 29892, 1178, 29918, 23642, 23521, 29892, 4823, 29918, 4914, 1125, 13, 1678, 1962, 353, 10518, 29889, 17271, 29898, 13099, 29922, 4282, 29918, 13099, 3101, 13, 1678, 363, 4489, 297, 4489, 29918, 1761, 29901, 13, 4706, 1962, 353, 1962, 29889, 4397, 29898, 4830, 29918, 4905, 29898, 2176, 29892, 1178, 29918, 23642, 23521, 29892, 4823, 29918, 4914, 876, 13, 13, 1678, 736, 1962, 13, 13, 13, 1753, 3402, 29918, 4905, 29898, 2176, 29892, 1178, 29918, 23642, 23521, 29892, 4823, 29918, 4914, 1125, 13, 1678, 4341, 353, 2048, 29918, 13099, 580, 29961, 29896, 29901, 29945, 29900, 29896, 29962, 13, 1678, 1962, 353, 4489, 13, 1678, 1962, 1839, 13099, 2033, 353, 4341, 13, 1678, 1962, 353, 1962, 29889, 29886, 11002, 29898, 2248, 29922, 333, 29918, 23642, 23521, 29892, 4341, 2433, 13099, 742, 1819, 29922, 21453, 29918, 4914, 29897, 13, 1678, 1962, 1839, 5935, 2033, 353, 1962, 29889, 2248, 13, 1678, 1962, 353, 1962, 29961, 4282, 29918, 13099, 580, 29962, 13, 13, 1678, 736, 1962, 13, 13, 13, 1753, 1962, 29918, 1491, 6737, 29898, 2176, 29892, 934, 29918, 978, 29892, 3815, 29918, 978, 29892, 6958, 29918, 19678, 29892, 2224, 2433, 1491, 29885, 6847, 29914, 742, 18766, 29918, 11294, 2433, 3396, 29374, 13, 1678, 934, 353, 2224, 718, 934, 29918, 978, 13, 13, 1678, 937, 29918, 798, 353, 10518, 29889, 17271, 29898, 13099, 29922, 4282, 29918, 13099, 3101, 13, 1678, 937, 29918, 798, 29889, 2029, 29961, 29900, 29962, 353, 2048, 29918, 4102, 29918, 798, 29898, 14318, 29918, 978, 29892, 6958, 29918, 19678, 29892, 18766, 29918, 11294, 29897, 13, 13, 1678, 1962, 353, 10518, 29889, 17685, 4197, 4102, 29918, 798, 29892, 4489, 2314, 13, 1678, 1962, 353, 1962, 29961, 4282, 29918, 13099, 580, 29962, 13, 1678, 1962, 353, 1962, 29889, 842, 29918, 2248, 877, 5935, 1495, 13, 1678, 1962, 29889, 517, 29918, 7638, 29898, 1445, 29897, 13, 13, 13, 1753, 2048, 29918, 4102, 29918, 798, 29898, 14318, 29918, 978, 29892, 6958, 29918, 19678, 29892, 18766, 29918, 11294, 2433, 3396, 29374, 13, 1678, 1948, 353, 6024, 14318, 29918, 3888, 742, 18766, 29918, 11294, 29892, 3815, 29918, 978, 29892, 6958, 29918, 19678, 29962, 13, 1678, 363, 474, 297, 3464, 29898, 29946, 29892, 29871, 29945, 29900, 29896, 1125, 13, 4706, 1948, 29889, 4397, 29898, 8516, 29897, 13, 13, 1678, 736, 1948, 13, 2 ]
CodeInterview/[cutz]compression_str.py	cutz-j/AlgorithmStudy	3	151029	<reponame>cutz-j/AlgorithmStudy from collections import defaultdict def solve(s): hash_dict = defaultdict(int) for i in range(len(s)): hash_dict[s[i]] += 1 return	[ 1, 529, 276, 1112, 420, 29958, 29883, 7427, 29899, 29926, 29914, 22461, 4540, 855, 566, 29891, 13, 3166, 16250, 1053, 2322, 8977, 13, 13, 1753, 4505, 29898, 29879, 1125, 13, 1678, 6608, 29918, 8977, 353, 2322, 8977, 29898, 524, 29897, 13, 13, 1678, 363, 474, 297, 3464, 29898, 2435, 29898, 29879, 22164, 13, 4706, 6608, 29918, 8977, 29961, 29879, 29961, 29875, 5262, 4619, 29871, 29896, 13, 13, 13, 1678, 736, 2 ]
pandas_tricks.py	wingwingz/code_snippets	0	107580	<filename>pandas_tricks.py<gh_stars>0 def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=2): """ :param df_1: the left table to join :param df_2: the right table to join :param key1: key column of the left table :param key2: key column of the right table :param threshold: how close the matches should be to return a match, based on Levenshtein distance :param limit: the amount of matches that will get returned, these are sorted high to low :return: dataframe with boths keys and matches """ s = df_2[key2].tolist() m = df_1[key1].apply(lambda x: process.extract(x, s, limit=limit)) df_1['matches'] = m m2 = df_1['matches'].apply(lambda x: ', '.join([i[0] for i in x if i[1] >= threshold])) df_1['matches'] = m2 return df_1	[ 1, 529, 9507, 29958, 15112, 29918, 509, 7358, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 1753, 285, 3365, 1537, 29918, 14634, 29898, 2176, 29918, 29896, 29892, 4489, 29918, 29906, 29892, 1820, 29896, 29892, 1820, 29906, 29892, 16897, 29922, 29929, 29900, 29892, 4046, 29922, 29906, 1125, 13, 1678, 9995, 13, 1678, 584, 3207, 4489, 29918, 29896, 29901, 278, 2175, 1591, 304, 5988, 13, 1678, 584, 3207, 4489, 29918, 29906, 29901, 278, 1492, 1591, 304, 5988, 13, 1678, 584, 3207, 1820, 29896, 29901, 1820, 1897, 310, 278, 2175, 1591, 13, 1678, 584, 3207, 1820, 29906, 29901, 1820, 1897, 310, 278, 1492, 1591, 13, 1678, 584, 3207, 16897, 29901, 920, 3802, 278, 7087, 881, 367, 304, 736, 263, 1993, 29892, 2729, 373, 951, 9852, 29882, 371, 262, 5418, 13, 1678, 584, 3207, 4046, 29901, 278, 5253, 310, 7087, 393, 674, 679, 4133, 29892, 1438, 526, 12705, 1880, 304, 4482, 13, 1678, 584, 2457, 29901, 12205, 411, 1716, 29879, 6611, 322, 7087, 13, 1678, 9995, 13, 1678, 269, 353, 4489, 29918, 29906, 29961, 1989, 29906, 1822, 25027, 391, 580, 13, 13, 1678, 286, 353, 4489, 29918, 29896, 29961, 1989, 29896, 1822, 7302, 29898, 2892, 921, 29901, 1889, 29889, 21111, 29898, 29916, 29892, 269, 29892, 4046, 29922, 13400, 876, 268, 13, 1678, 4489, 29918, 29896, 1839, 20317, 2033, 353, 286, 13, 13, 1678, 286, 29906, 353, 4489, 29918, 29896, 1839, 20317, 13359, 7302, 29898, 2892, 921, 29901, 13420, 15300, 7122, 4197, 29875, 29961, 29900, 29962, 363, 474, 297, 921, 565, 474, 29961, 29896, 29962, 6736, 16897, 12622, 13, 1678, 4489, 29918, 29896, 1839, 20317, 2033, 353, 286, 29906, 13, 13, 1678, 736, 4489, 29918, 29896, 13, 2 ]
distiller/data/jobs.py	18F/FAC-Distiller-	2	108834	""" Use apscheduler to run a daily job to refresh the source tables. """ import random import sys import time import pytz from apscheduler.schedulers.background import BackgroundScheduler from django.conf import settings from django_apscheduler.jobstores import ( DjangoJobStore, register_events, register_job ) from .etls import load_dumps scheduler = BackgroundScheduler() scheduler.add_jobstore(DjangoJobStore(), 'default') @register_job( scheduler, 'cron', hour=0, timezone=pytz.timezone('US/Eastern'), replace_existing=True ) def download_and_update_tables(): for table in load_dumps.FAC_TABLES_NAMES: sys.stdout.write(f'Downloading table "{table}"...\n') sys.stdout.flush() load_dumps.download_table( table, target_dir=settings.LOAD_TABLE_ROOT ) for table in load_dumps.FAC_TABLES_NAMES: sys.stdout.write(f'Loading FAC table "{table}"...\n') sys.stdout.flush() load_dumps.update_table( table, source_dir=settings.LOAD_TABLE_ROOT, ) register_events(scheduler) scheduler.start()	[ 1, 29871, 13, 15945, 29908, 13, 11403, 263, 567, 305, 14952, 304, 1065, 263, 14218, 4982, 304, 11086, 278, 2752, 6131, 29889, 13, 15945, 29908, 13, 13, 5215, 4036, 13, 5215, 10876, 13, 5215, 931, 13, 13, 5215, 282, 3637, 29920, 13, 3166, 263, 567, 305, 14952, 29889, 816, 287, 352, 414, 29889, 7042, 1053, 16585, 4504, 14952, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29918, 481, 816, 14952, 29889, 9057, 303, 2361, 1053, 313, 13, 1678, 15337, 11947, 9044, 29892, 6036, 29918, 13604, 29892, 6036, 29918, 9057, 13, 29897, 13, 13, 3166, 869, 300, 3137, 1053, 2254, 29918, 29881, 17204, 13, 13, 13, 816, 14952, 353, 16585, 4504, 14952, 580, 13, 816, 14952, 29889, 1202, 29918, 9057, 8899, 29898, 29928, 5364, 11947, 9044, 3285, 525, 4381, 1495, 13, 13, 13, 29992, 9573, 29918, 9057, 29898, 13, 1678, 1364, 14952, 29892, 13, 1678, 525, 29883, 1617, 742, 13, 1678, 7234, 29922, 29900, 29892, 13, 1678, 29431, 29922, 2272, 17559, 29889, 2230, 8028, 877, 3308, 29914, 29923, 11530, 5477, 13, 1678, 5191, 29918, 735, 15423, 29922, 5574, 13, 29897, 13, 1753, 5142, 29918, 392, 29918, 5504, 29918, 24051, 7295, 13, 1678, 363, 1591, 297, 2254, 29918, 29881, 17204, 29889, 29943, 2477, 29918, 21009, 29903, 29918, 5813, 29903, 29901, 13, 4706, 10876, 29889, 25393, 29889, 3539, 29898, 29888, 29915, 6767, 13234, 1591, 29850, 2371, 5038, 856, 29905, 29876, 1495, 13, 4706, 10876, 29889, 25393, 29889, 23126, 580, 13, 4706, 2254, 29918, 29881, 17204, 29889, 10382, 29918, 2371, 29898, 13, 9651, 1591, 29892, 13, 9651, 3646, 29918, 3972, 29922, 11027, 29889, 29428, 29918, 21009, 29918, 21289, 13, 4706, 1723, 13, 1678, 363, 1591, 297, 2254, 29918, 29881, 17204, 29889, 29943, 2477, 29918, 21009, 29903, 29918, 5813, 29903, 29901, 13, 4706, 10876, 29889, 25393, 29889, 3539, 29898, 29888, 29915, 23456, 383, 2477, 1591, 29850, 2371, 5038, 856, 29905, 29876, 1495, 13, 4706, 10876, 29889, 25393, 29889, 23126, 580, 13, 4706, 2254, 29918, 29881, 17204, 29889, 5504, 29918, 2371, 29898, 13, 9651, 1591, 29892, 13, 9651, 2752, 29918, 3972, 29922, 11027, 29889, 29428, 29918, 21009, 29918, 21289, 29892, 13, 4706, 1723, 13, 13, 13, 9573, 29918, 13604, 29898, 816, 14952, 29897, 13, 816, 14952, 29889, 2962, 580, 13, 2 ]
teaparty/helpers.py	idooo/teaparty	0	80577	from time import time def cache(function): memo = {} time_to_cache = 60 def wrapper(args): if args in memo and time() < memo[args]['time'] + time_to_cache: return memo[args]['record'] else: rv = function(args) memo[args] = { 'record': rv, 'time': time() } return rv return wrapper	[ 1, 515, 931, 1053, 931, 13, 13, 1753, 7090, 29898, 2220, 1125, 13, 1678, 2626, 29877, 353, 6571, 13, 1678, 931, 29918, 517, 29918, 8173, 353, 29871, 29953, 29900, 13, 13, 1678, 822, 14476, 10456, 5085, 1125, 13, 4706, 565, 6389, 297, 2626, 29877, 322, 931, 580, 529, 2626, 29877, 29961, 5085, 22322, 2230, 2033, 718, 931, 29918, 517, 29918, 8173, 29901, 13, 9651, 736, 2626, 29877, 29961, 5085, 22322, 11651, 2033, 13, 4706, 1683, 29901, 13, 9651, 364, 29894, 353, 740, 10456, 5085, 29897, 13, 9651, 2626, 29877, 29961, 5085, 29962, 353, 426, 13, 18884, 525, 11651, 2396, 364, 29894, 29892, 13, 18884, 525, 2230, 2396, 931, 580, 13, 9651, 500, 13, 9651, 736, 364, 29894, 13, 13, 1678, 736, 14476, 2 ]
tests/test_atise.py	tkg-framework/TKG-framework	0	16403	<reponame>tkg-framework/TKG-framework<filename>tests/test_atise.py<gh_stars>0 import torch import torch.nn as nn from torch.autograd import Variable import torch.functional as F from torch.nn.init import xavier_normal_ import os import sys BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(BASE_DIR) import numpy as np from numpy.random import RandomState from collections import defaultdict import time from tkge.data.dataset import SplitDataset from tkge.data.custom_dataset import ICEWS14AtiseDatasetProcessor from tkge.eval.metrics import Evaluation from tkge.train.sampling import NonNegativeSampler from Dataset import KnowledgeGraph randseed = 9999 np.random.seed(randseed) torch.manual_seed(randseed) class MockAtiseDatasetProcessor(ICEWS14AtiseDatasetProcessor): def __init__(self): self.folder = "/home/gengyuan/workspace/tkge/data/icews14" self.level = "day" self.index = False self.float = True self.train_raw = [] self.valid_raw = [] self.test_raw = [] self.reciprocal_training = True self.ent2id = dict() self.rel2id = dict() with open('/home/gengyuan/workspace/baseline/ATISE/icews14/entity2id.txt', 'r') as f: ent2id = f.readlines() for line in ent2id: line = line.split('\t') self.ent2id[line[0]] = int(line[1]) with open('/home/gengyuan/workspace/baseline/ATISE/icews14/relation2id.txt', 'r') as f: rel2id = f.readlines() for line in rel2id: line = line.split('\t') self.rel2id[line[0]] = int(line[1]) self.ts2id = defaultdict(None) self.train_set = defaultdict(list) self.valid_set = defaultdict(list) self.test_set = defaultdict(list) self.all_triples = [] self.all_quadruples = [] self.load() self.process() self.filter() def index_relations(self, rel: str): if rel.endswith('(RECIPROCAL)'): return self.rel2id[rel[:-12]] + 230 else: return self.rel2id[rel] def index_entities(self, ent: str): if ent == '<NAME>': ent = '<NAME>' return self.ent2id[ent] def process_time(self, origin: str): # TODO (gengyuan) move to init method self.gran = 3 start_sec = time.mktime(time.strptime('2014-01-01', '%Y-%m-%d')) end_sec = time.mktime(time.strptime(origin, '%Y-%m-%d')) day = int((end_sec - start_sec) / (self.gran * 24 * 60 * 60)) return day class MockEvaluation(Evaluation): def __init__(self, dataset, device): self.dataset = dataset self.vocab_size = dataset.num_entities() self.device = device self.filter = "time-aware" self.ordering = "optimistic" self.k = [1, 3, 10] self.filtered_data = defaultdict(None) self.filtered_data['sp_'] = self.dataset.filter(type=self.filter, target='o') self.filtered_data['_po'] = self.dataset.filter(type=self.filter, target='s') class MockSampler(NonNegativeSampler): def __init__(self, dataset, as_matrix): self.filter = False self.as_matrix = as_matrix self.dataset = dataset class ATISE(nn.Module): def __init__(self, n_entity, n_relation, embedding_dim, batch_size, learning_rate, gamma, cmin, cmax, gpu=True): super(ATISE, self).__init__() self.gpu = gpu self.n_entity = n_entity self.n_relation = n_relation self.embedding_dim = embedding_dim self.batch_size = batch_size self.learning_rate = learning_rate self.gamma = gamma self.cmin = cmin self.cmax = cmax # Nets self.emb_E = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.emb_E_var = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.emb_R = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) self.emb_R_var = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) self.emb_TE = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.alpha_E = torch.nn.Embedding(n_entity, 1, padding_idx=0) self.beta_E = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.omega_E = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.emb_TR = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) self.alpha_R = torch.nn.Embedding(n_relation, 1, padding_idx=0) self.beta_R = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) self.omega_R = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) # Initialization r = 6 / np.sqrt(self.embedding_dim) self.emb_E.weight.data.uniform_(-r, r) self.emb_E_var.weight.data.uniform_(self.cmin, self.cmax) self.emb_R.weight.data.uniform_(-r, r) self.emb_R_var.weight.data.uniform_(self.cmin, self.cmax) self.emb_TE.weight.data.uniform_(-r, r) self.alpha_E.weight.data.uniform_(0, 0) self.beta_E.weight.data.uniform_(0, 0) self.omega_E.weight.data.uniform_(-r, r) self.emb_TR.weight.data.uniform_(-r, r) self.alpha_R.weight.data.uniform_(0, 0) self.beta_R.weight.data.uniform_(0, 0) self.omega_R.weight.data.uniform_(-r, r) # Regularization self.normalize_embeddings() if self.gpu: self.cuda() def forward(self, X): # h_i, t_i, r_i, d_i = X[:, 0].astype(np.int64), X[:, 1].astype(np.int64), X[:, 2].astype(np.int64), X[:, # 3].astype( # np.float32) h_i, t_i, r_i, d_i = X[:, 0].long(), X[:, 2].long(), X[:, 1].long(), X[:, 3].float() # if self.gpu: # h_i = Variable(torch.from_numpy(h_i).cuda()) # t_i = Variable(torch.from_numpy(t_i).cuda()) # r_i = Variable(torch.from_numpy(r_i).cuda()) # d_i = Variable(torch.from_numpy(d_i).cuda()) # # else: # h_i = Variable(torch.from_numpy(h_i)) # t_i = Variable(torch.from_numpy(t_i)) # r_i = Variable(torch.from_numpy(r_i)) # d_i = Variable(torch.from_numpy(d_i)) pi = 3.14159265358979323846 h_mean = self.emb_E(h_i).view(-1, self.embedding_dim) + \ d_i.view(-1, 1) * self.alpha_E(h_i).view(-1, 1) * self.emb_TE(h_i).view(-1, self.embedding_dim) \ + self.beta_E(h_i).view(-1, self.embedding_dim) * torch.sin( 2 * pi * self.omega_E(h_i).view(-1, self.embedding_dim) * d_i.view(-1, 1)) t_mean = self.emb_E(t_i).view(-1, self.embedding_dim) + \ d_i.view(-1, 1) * self.alpha_E(t_i).view(-1, 1) * self.emb_TE(t_i).view(-1, self.embedding_dim) \ + self.beta_E(t_i).view(-1, self.embedding_dim) * torch.sin( 2 * pi * self.omega_E(t_i).view(-1, self.embedding_dim) * d_i.view(-1, 1)) r_mean = self.emb_R(r_i).view(-1, self.embedding_dim) + \ d_i.view(-1, 1) * self.alpha_R(r_i).view(-1, 1) * self.emb_TR(r_i).view(-1, self.embedding_dim) \ + self.beta_R(r_i).view(-1, self.embedding_dim) * torch.sin( 2 * pi * self.omega_R(r_i).view(-1, self.embedding_dim) * d_i.view(-1, 1)) h_var = self.emb_E_var(h_i).view(-1, self.embedding_dim) t_var = self.emb_E_var(t_i).view(-1, self.embedding_dim) r_var = self.emb_R_var(r_i).view(-1, self.embedding_dim) out1 = torch.sum((h_var + t_var) / r_var, 1) + torch.sum(((r_mean - h_mean + t_mean) 2) / r_var, 1) - self.embedding_dim out2 = torch.sum(r_var / (h_var + t_var), 1) + torch.sum(((h_mean - t_mean - r_mean) 2) / (h_var + t_var), 1) - self.embedding_dim out = (out1 + out2) / 4 return out def log_rank_loss(self, y_pos, y_neg, temp=0): M = y_pos.size(0) N = y_neg.size(0) y_pos = self.gamma - y_pos y_neg = self.gamma - y_neg C = int(N / M) y_neg = y_neg.view(C, -1).transpose(0, 1) p = F.softmax(temp * y_neg) loss_pos = torch.sum(F.softplus(-1 * y_pos)) loss_neg = torch.sum(p * F.softplus(y_neg)) loss = (loss_pos + loss_neg) / 2 / M if self.gpu: loss = loss.cuda() return loss def rank_loss(self, y_pos, y_neg): M = y_pos.size(0) N = y_neg.size(0) C = int(N / M) y_pos = y_pos.repeat(C) if self.gpu: target = Variable(torch.from_numpy(-np.ones(N, dtype=np.float32))).cuda() else: target = Variable(torch.from_numpy(-np.ones(N, dtype=np.float32))).cpu() loss = nn.MarginRankingLoss(margin=self.gamma) loss = loss(y_pos, y_neg, target) return loss def normalize_embeddings(self): self.emb_E.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_E_var.weight.data.uniform_(self.cmin, self.cmax) self.emb_R.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_R_var.weight.data.uniform_(self.cmin, self.cmax) self.emb_TE.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_TR.weight.data.renorm_(p=2, dim=0, maxnorm=1) def regularization_embeddings(self): lower = torch.tensor(self.cmin).float() upper = torch.tensor(self.cmax).float() if self.gpu: lower = lower.cuda() upper = upper.cuda() self.emb_E_var.weight.data = torch.where(self.emb_E_var.weight.data < self.cmin, lower, self.emb_E_var.weight.data) self.emb_E_var.weight.data = torch.where(self.emb_E_var.weight.data > self.cmax, upper, self.emb_E_var.weight.data) self.emb_R_var.weight.data = torch.where(self.emb_R_var.weight.data < self.cmin, lower, self.emb_R_var.weight.data) self.emb_R_var.weight.data = torch.where(self.emb_R_var.weight.data > self.cmax, upper, self.emb_R_var.weight.data) self.emb_E.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_R.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_TE.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_TR.weight.data.renorm_(p=2, dim=0, maxnorm=1) def rank_left(self, X, facts, kg, timedisc, rev_set=0): rank = [] with torch.no_grad(): if timedisc: for triple, fact in zip(X, facts): X_i = np.ones([self.n_entity, 4]) i_score = torch.zeros(self.n_entity) if self.gpu: i_score = i_score.cuda() for time_index in [triple[3], triple[4]]: for i in range(0, self.n_entity): X_i[i, 0] = i X_i[i, 1] = triple[1] X_i[i, 2] = triple[2] X_i[i, 3] = time_index i_score = i_score + self.forward(X_i).view(-1) if rev_set > 0: X_rev = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_rev[i, 0] = triple[1] X_rev[i, 1] = i X_rev[i, 2] = triple[2] + self.n_relation // 2 X_rev[i, 3] = time_index i_score = i_score + self.forward(X_rev).view(-1) filter_out = kg.to_skip_final['lhs'][(fact[1], fact[2], fact[3], fact[4])] target = i_score[int(triple[0])].clone() i_score[filter_out] = 1e6 rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) else: for triple, fact in zip(X, facts): X_i = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_i[i, 0] = i X_i[i, 1] = triple[1] X_i[i, 2] = triple[2] X_i[i, 3] = triple[3] i_score = self.forward(X_i) if rev_set > 0: X_rev = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_rev[i, 0] = triple[1] X_rev[i, 1] = i X_rev[i, 2] = triple[2] + self.n_relation // 2 X_rev[i, 3] = triple[3] i_score = i_score + self.forward(X_rev).view(-1) if self.gpu: i_score = i_score.cuda() filter_out = kg.to_skip_final['lhs'][(fact[1], fact[2], fact[3], fact[4])] target = i_score[int(triple[0])].clone() i_score[filter_out] = 1e6 rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) print('left') print(rank) return rank def rank_right(self, X, facts, kg, timedisc, rev_set=0): rank = [] with torch.no_grad(): if timedisc: for triple, fact in zip(X, facts): X_i = np.ones([self.n_entity, 4]) i_score = torch.zeros(self.n_entity) if self.gpu: i_score = i_score.cuda() for time_index in [triple[3], triple[4]]: for i in range(0, self.n_entity): X_i[i, 0] = triple[0] X_i[i, 1] = i X_i[i, 2] = triple[2] X_i[i, 3] = time_index i_score = i_score + self.forward(X_i).view(-1) if rev_set > 0: X_rev = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_rev[i, 0] = i X_rev[i, 1] = triple[0] X_rev[i, 2] = triple[2] + self.n_relation // 2 X_rev[i, 3] = time_index i_score = i_score + self.forward(X_rev).view(-1) filter_out = kg.to_skip_final['rhs'][(fact[0], fact[2], fact[3], fact[4])] target = i_score[int(triple[1])].clone() i_score[filter_out] = 1e6 rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) else: for triple, fact in zip(X, facts): X_i = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_i[i, 0] = triple[0] X_i[i, 1] = i X_i[i, 2] = triple[2] X_i[i, 3] = triple[3] i_score = self.forward(X_i) if rev_set > 0: X_rev = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_rev[i, 0] = i X_rev[i, 1] = triple[0] X_rev[i, 2] = triple[2] + self.n_relation // 2 X_rev[i, 3] = triple[3] i_score = i_score + self.forward(X_rev).view(-1) if self.gpu: i_score = i_score.cuda() filter_out = kg.to_skip_final['rhs'][(fact[0], fact[2], fact[3], fact[4])] target = i_score[int(triple[1])].clone() i_score[filter_out] = 1e6 rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) print('right') print(rank) return rank def timepred(self, X): rank = [] with torch.no_grad(): for triple in X: X_i = np.ones([self.n_day, len(triple)]) for i in range(self.n_day): X_i[i, 0] = triple[0] X_i[i, 1] = triple[1] X_i[i, 2] = triple[2] X_i[i, 3:] = self.time_dict[i] i_score = self.forward(X_i) if self.gpu: i_score = i_score.cuda() target = i_score[triple[3]] rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) return rank model_path = "/home/gengyuan/workspace/baseline/ATISE/icews14/ATISE/timediscrete0/dim500/lr0.0000/neg_num10/3day/gamma120/cmin0.0030/params.pkl" model = ATISE(7129, 460, 500, 64, 0, 120, 0.003, 0.3, True) model_state_dict = torch.load(model_path) model.load_state_dict(model_state_dict) def test(): device = 'cuda' if torch.cuda.is_available() else 'cpu' dataset = MockAtiseDatasetProcessor() evaluator = MockEvaluation(dataset, device) sampler = MockSampler(dataset, as_matrix=True) # # print(dataset.ts2id) # print(dataset.index_timestamps(23)) # print(dataset.index_relations('Arrest, detain, or charge with legal action')) # print(dataset.index_entities('Japan')) # print(dataset.index_entities('Police (Japan)')) # print(dataset.filter(type='time-aware', target='s')['None-9-2205-33']) # print(dataset.get("train")['triple'][15442]) # print(dataset.get("train")['timestamp_id'][15442]) # print(dataset.get("train")['timestamp_float'][15442]) # # # assert False valid_loader = torch.utils.data.DataLoader( SplitDataset(dataset.get("test"), ['timestamp_float', 'timestamp_id']), shuffle=False, batch_size=1, num_workers=0 ) with torch.no_grad(): model.eval() metrics = dict() metrics['head'] = defaultdict(float) metrics['tail'] = defaultdict(float) rank_left = [] rank_right = [] scores_head = [] scores_tail = [] filter_left = [] filter_right = [] l = 0 dfs = dataset.filter(type="time-aware", target="s") dfo = dataset.filter(type="time-aware", target="o") for batch in valid_loader: bs = batch.size(0) dim = batch.size(1) l += bs print(l) samples_head, _ = sampler.sample(batch, "head") samples_tail, _ = sampler.sample(batch, "tail") samples_head = samples_head.to(device) samples_tail = samples_tail.to(device) samples_head = samples_head.view(-1, dim) samples_tail = samples_tail.view(-1, dim) batch_scores_head = model.forward(samples_head) batch_scores_tail = model.forward(samples_tail) batch_scores_head = batch_scores_head.view(bs, -1) batch_scores_tail = batch_scores_tail.view(bs, -1) # reciprocal samples_head_reciprocal = samples_head.clone().view(-1, dim) samples_tail_reciprocal = samples_tail.clone().view(-1, dim) samples_head_reciprocal[:, 1] += 230 samples_head_reciprocal[:, [0, 2]] = samples_head_reciprocal.index_select(1, torch.Tensor( [2, 0]).long().to(device)) samples_tail_reciprocal[:, 1] += 230 samples_tail_reciprocal[:, [0, 2]] = samples_tail_reciprocal.index_select(1, torch.Tensor( [2, 0]).long().to(device)) batch_scores_head_reci = model.forward(samples_head_reciprocal) batch_scores_tail_reci = model.forward(samples_tail_reciprocal) batch_scores_head_reci = batch_scores_head_reci.view(bs, -1) batch_scores_tail_reci = batch_scores_tail_reci.view(bs, -1) batch_scores_head += batch_scores_head_reci batch_scores_tail += batch_scores_tail_reci scores_head.append(batch_scores_head) scores_tail.append(batch_scores_tail) batch_metrics = dict() batch_metrics['head'] = evaluator.eval(batch, batch_scores_head, miss='s') batch_metrics['tail'] = evaluator.eval(batch, batch_scores_tail, miss='o') # print filter filter_left.append( dfs[f'None-{int(batch[0, 1])}-{int(batch[0, 2])}-{int(batch[0, -1])}']) filter_right.append( dfo[f'{int(batch[0, 0])}-{int(batch[0, 1])}-None-{int(batch[0, -1])}']) # rank_left.append(batch_metrics['head']['mean_ranking']) # rank_right.append(batch_metrics['tail']['mean_ranking']) # TODO(gengyuan) refactor for pos in ['head', 'tail']: for key in batch_metrics[pos].keys(): metrics[pos][key] += batch_metrics[pos][key] * bs # rank = rank_left + rank_right # torch.save(rank, "/home/gengyuan/workspace/baseline/ATISE/rank_tkge.pt") # rank2 = torch.load("/home/gengyuan/workspace/baseline/ATISE/rank.pt") # # print('assert Equal') # print(rank==rank2) # torch.save(scores_head + scores_tail, "/home/gengyuan/workspace/baseline/ATISE/scores_tkge.pt") torch.save(filter_left, "/home/gengyuan/workspace/baseline/ATISE/filter_left_tkge.pt") torch.save(filter_right, "/home/gengyuan/workspace/baseline/ATISE/filter_right_tkge.pt") for pos in ['head', 'tail']: for key in metrics[pos].keys(): metrics[pos][key] /= l print(f"Metrics(head prediction) in iteration : {metrics['head'].items()}") print(f"Metrics(tail prediction) in iteration : {metrics['tail'].items()}") def test_sc(): def mean_rank(rank): m_r = 0 N = len(rank) for i in rank: m_r = m_r + i / N return m_r def mrr(rank): mrr = 0 N = len(rank) for i in rank: mrr = mrr + 1 / i / N return mrr def hit_N(rank, N): hit = 0 for i in rank: if i <= N: hit = hit + 1 hit = hit / len(rank) return hit kg = KnowledgeGraph(data_dir="/home/gengyuan/workspace/baseline/ATISE/icews14", gran=3, rev_set=1) test_pos = np.array(kg.test_triples) print(test_pos) rank = model.rank_left(test_pos, kg.test_facts, kg, 0, rev_set=1) rank_right = model.rank_right(test_pos, kg.test_facts, kg, 0, rev_set=1) rank = rank + rank_right m_rank = mean_rank(rank) mean_rr = mrr(rank) hit_1 = hit_N(rank, 1) hit_3 = hit_N(rank, 3) hit_5 = hit_N(rank, 5) hit_10 = hit_N(rank, 10) print('validation results:') print('Mean Rank: {:.0f}'.format(m_rank)) print('Mean RR: {:.4f}'.format(mean_rr)) print('Hit@1: {:.4f}'.format(hit_1)) print('Hit@3: {:.4f}'.format(hit_3)) print('Hit@5: {:.4f}'.format(hit_5)) print('Hit@10: {:.4f}'.format(hit_10)) if __name__ == '__main__': test()	[ 1, 529, 276, 1112, 420, 29958, 29873, 9415, 29899, 4468, 29914, 29911, 29968, 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backend/kaos_backend/util/docker.py	aalhour/kaos	98	105164	import boto3 from kaos_backend.constants import DOCKER_REGISTRY, REGION, CLOUD_PROVIDER def get_login_command(): if CLOUD_PROVIDER == 'AWS': # ecr = boto3.client('ecr', region_name=REGION) # # raw_auth_data = ecr.get_authorization_token()['authorizationData'][0]['authorizationToken'] # _, docker_auth_token = b64decode(raw_auth_data).decode('UTF-8').split(":") return f"$(aws ecr get-login --region {REGION} --no-include-email)" elif CLOUD_PROVIDER == "GCP": return f"gcloud auth print-access-token \| docker login -u oauth2accesstoken --password-stdin https://{DOCKER_REGISTRY}" else: return "" def create_docker_repo(repo_name): if CLOUD_PROVIDER == 'AWS': ecr = boto3.client('ecr', region_name=REGION) ecr.create_repository(repositoryName=repo_name) def delete_docker_repo(repo_name): if CLOUD_PROVIDER == 'AWS': ecr = boto3.client('ecr', region_name=REGION) ecr.delete_repository(repositoryName=repo_name, force=True)	[ 1, 1053, 289, 3747, 29941, 13, 13, 3166, 13560, 359, 29918, 27852, 29889, 3075, 1934, 1053, 11662, 7077, 1001, 29918, 18166, 9047, 13207, 29892, 5195, 29954, 2725, 29892, 315, 3927, 15789, 29918, 8618, 13044, 1001, 13, 13, 13, 1753, 679, 29918, 7507, 29918, 6519, 7295, 13, 1678, 565, 315, 3927, 15789, 29918, 8618, 13044, 1001, 1275, 525, 29909, 7811, 2396, 13, 4706, 396, 321, 7283, 353, 289, 3747, 29941, 29889, 4645, 877, 687, 29878, 742, 5120, 29918, 978, 29922, 18166, 2725, 29897, 13, 4706, 396, 13, 4706, 396, 10650, 29918, 5150, 29918, 1272, 353, 321, 7283, 29889, 657, 29918, 8921, 2133, 29918, 6979, 580, 1839, 8921, 2133, 1469, 2033, 29961, 29900, 22322, 8921, 2133, 6066, 2033, 13, 4706, 396, 17117, 10346, 29918, 5150, 29918, 6979, 353, 289, 29953, 29946, 13808, 29898, 1610, 29918, 5150, 29918, 1272, 467, 13808, 877, 10496, 29899, 29947, 2824, 5451, 703, 29901, 1159, 13, 4706, 736, 285, 29908, 12330, 10467, 321, 7283, 679, 29899, 7507, 1192, 12803, 426, 18166, 2725, 29913, 1192, 1217, 29899, 2856, 29899, 5269, 5513, 13, 1678, 25342, 315, 3927, 15789, 29918, 8618, 13044, 1001, 1275, 376, 29954, 6271, 1115, 13, 4706, 736, 285, 29908, 29887, 9274, 4817, 1596, 29899, 5943, 29899, 6979, 891, 10346, 6464, 448, 29884, 288, 5150, 29906, 562, 778, 303, 4476, 1192, 5630, 29899, 4172, 262, 2045, 597, 29912, 3970, 7077, 1001, 29918, 18166, 9047, 13207, 5038, 13, 1678, 1683, 29901, 13, 4706, 736, 5124, 13, 13, 13, 1753, 1653, 29918, 14695, 29918, 20095, 29898, 20095, 29918, 978, 1125, 13, 1678, 565, 315, 3927, 15789, 29918, 8618, 13044, 1001, 1275, 525, 29909, 7811, 2396, 13, 4706, 321, 7283, 353, 289, 3747, 29941, 29889, 4645, 877, 687, 29878, 742, 5120, 29918, 978, 29922, 18166, 2725, 29897, 13, 4706, 321, 7283, 29889, 3258, 29918, 19033, 29898, 19033, 1170, 29922, 20095, 29918, 978, 29897, 13, 13, 13, 1753, 5217, 29918, 14695, 29918, 20095, 29898, 20095, 29918, 978, 1125, 13, 1678, 565, 315, 3927, 15789, 29918, 8618, 13044, 1001, 1275, 525, 29909, 7811, 2396, 13, 4706, 321, 7283, 353, 289, 3747, 29941, 29889, 4645, 877, 687, 29878, 742, 5120, 29918, 978, 29922, 18166, 2725, 29897, 13, 4706, 321, 7283, 29889, 8143, 29918, 19033, 29898, 19033, 1170, 29922, 20095, 29918, 978, 29892, 4889, 29922, 5574, 29897, 13, 2 ]
contrib/workflow/src/main.py	gonicus/clacks	2	141941	<filename>contrib/workflow/src/main.py #!/usr/bin/env python import os.path import MySQLdb from sqlalchemy import * from sqlalchemy.orm import clear_mappers __author__="janw" __date__ ="$21.05.2010 16:55:13$" from SpiffWorkflow.Storage import XmlReader from SpiffWorkflow.Server import * from SpiffWorkflow import Job, Workflow, Task import pickle, pprint data_file = os.path.dirname(__file__) + "/job.pk" workflow_file = os.path.dirname(__file__) + "/../tests/xml/anlage_benutzer.xml" def connectDB(): global engine, db, driver host = "build-lenny-32.intranet.gonicus.de" db_name = "spiff" username = "root" password = "<PASSWORD>" # Connect to MySQL. auth = username + ':' + password dbn = 'mysql://' + auth + '@' + host + '/' + db_name engine = create_engine(dbn) clear_mappers() driver = Driver(engine) def getJob(): global data_file, workflow_file reader = XmlReader() workflow = reader.parse_file(workflow_file).pop() if os.access(data_file, os.R_OK): input = open(data_file, 'rb') job = pickle.load(input) input.close() os.remove(data_file) else: job = Job(workflow) return job def setJob(job): global data_file result = False if not isinstance(job, Job): return result if os.access(os.path.dirname(data_file), os.W_OK): output = open(data_file, 'wb') pickle.dump(job, output, -1) output.close() result = True else: print "Datafile not accessible" return result def main(): #connectDB() job = getJob() job.complete_next() if setJob(job): print "Job has been saved successfully" else: print "Problem saving job" if __name__ == "__main__": main()	[ 1, 529, 9507, 29958, 21570, 29914, 1287, 1731, 29914, 4351, 29914, 3396, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 5215, 2897, 29889, 2084, 13, 5215, 9254, 2585, 13, 3166, 4576, 284, 305, 6764, 462, 418, 1053, 334, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 462, 29871, 1053, 2821, 29918, 655, 22437, 13, 13, 1649, 8921, 1649, 543, 8931, 29893, 29908, 13, 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aiida_wien2k/calculations/x_sgroup.py	rubel75/aiida-wien2k	1	197418	from aiida.common import datastructures from aiida.engine import CalcJob from aiida.orm import SinglefileData, Dict, RemoteData, Code, StructureData import os import ase.io def _cli_options(parameters): """Return command line options for parameters dictionary. :param dict parameters: dictionary with command line parameters """ options = [] for key, value in parameters.items(): # Could validate: is key a known command-line option? if isinstance(value, bool) and value: options.append(f'{key}') elif isinstance(value, str): # Could validate: is value a valid regular expression? options.append(f'{key}') options.append(value) return options def aiida_struct2wien2k(aiida_structure): "prepare structure file for WIEN2k" ase_structure = aiida_structure.get_ase() # AiiDA -> ASE struct ase.io.write("case.struct",ase_structure) # ASE -> WIEN2k, write WIEN2k struct path_to_rm_folder = os.getcwd() # get path to folder path_to_rm_structfile = os.path.join(path_to_rm_folder, 'case.struct') # get path to struct file wien2k_structfile = SinglefileData(file=path_to_rm_structfile) # get proper AiiDA type for a single file (otherwise you cannot return) return wien2k_structfile # orm.SinglefileData type (stored automatically in AiiDA database) class Wien2kXSgroup(CalcJob): """AiiDA calculation plugin for WIEN2k calculation to execute: x sgroup [parameters].""" @classmethod def define(cls, spec): """Define inputs and outputs of the calculation.""" # yapf: disable super(Wien2kXSgroup, cls).define(spec) # inputs/outputs spec.input('code', valid_type=Code, help='WIEN2k x sgroup') spec.input('parameters', valid_type=Dict, required=False, help='Dictionary of input arguments (if any)') spec.input('aiida_structure', valid_type=StructureData, required=False, help='AiiDA structure object') spec.input('structfile_in', valid_type=SinglefileData, required=False, help='Structure file case.struct') spec.input('parent_folder', valid_type=RemoteData, required=False,\ help='parent_folder passed from a previous calulation') spec.inputs['metadata']['options']['resources'].default = { 'num_machines': 1, 'num_mpiprocs_per_machine': 1, } spec.inputs['metadata']['options']['parser_name'].default = 'wien2k-sgroup-parser' spec.output('structfile_out', valid_type=SinglefileData, help='Structure file after x sgroup') spec.exit_code(300, 'ERROR_MISSING_OUTPUT_FILES', message='Calculation did not produce all expected output files.') def prepare_for_submission(self, folder): """ Create input files. :param folder: an `aiida.common.folders.Folder` where the plugin should temporarily place all files needed by the calculation. :return: `aiida.common.datastructures.CalcInfo` instance """ parameters = _cli_options(self.inputs.parameters.get_dict()) # command line args for init_lapw codeinfo = datastructures.CodeInfo() codeinfo.cmdline_params = ['sgroup'] + parameters # x exec [parameters] codeinfo.code_uuid = self.inputs.code.uuid codeinfo.stdout_name = 'x.log' remote_copy_list = [] if('parent_folder' in self.inputs): path_from = os.path.join(self.inputs.parent_folder.get_remote_path(),'case') remote_copy_list = [( self.inputs.parent_folder.computer.uuid, path_from, './' )] else: folder.get_subfolder('case', create=True) # create case subfolder # convert AiiDA structure -> WIEN2k if('aiida_structure' in self.inputs): wien2k_structfile = aiida_struct2wien2k(self.inputs.aiida_structure) wien2k_structfile.store() # Prepare a `CalcInfo` to be returned to the engine calcinfo = datastructures.CalcInfo() calcinfo.codes_info = [codeinfo] calcinfo.local_copy_list = [] if('structfile_in' in self.inputs): # WIEN2k structure is given as input calcinfo.local_copy_list = [ (self.inputs.structfile_in.uuid, self.inputs.structfile_in.filename, 'case/case.struct') ] # copy case.struct to the local folder as new.struct elif('aiida_structure' in self.inputs): # AiiDA structure is given as input calcinfo.local_copy_list = [ (wien2k_structfile.uuid, wien2k_structfile.filename, 'case/case.struct') ] # copy case.struct to the local folder as new.struct calcinfo.remote_copy_list = remote_copy_list calcinfo.retrieve_list = [('case/case.struct_sgroup'), ('case/.error')] return calcinfo	[ 1, 515, 7468, 1458, 29889, 9435, 1053, 6155, 509, 5313, 1973, 13, 3166, 7468, 1458, 29889, 10599, 1053, 3037, 29883, 11947, 13, 3166, 7468, 1458, 29889, 555, 1053, 16740, 1445, 1469, 29892, 360, 919, 29892, 5240, 866, 1469, 29892, 5920, 29892, 3767, 12425, 1469, 13, 5215, 2897, 13, 5215, 263, 344, 29889, 601, 13, 13, 1753, 903, 11303, 29918, 6768, 29898, 16744, 1125, 13, 1678, 9995, 11609, 1899, 1196, 3987, 363, 4128, 8600, 29889, 13, 13, 1678, 584, 3207, 9657, 4128, 29901, 8600, 411, 1899, 1196, 4128, 13, 1678, 9995, 13, 1678, 3987, 353, 5159, 13, 1678, 363, 1820, 29892, 995, 297, 4128, 29889, 7076, 7295, 13, 268, 396, 6527, 12725, 29901, 338, 1820, 263, 2998, 1899, 29899, 1220, 2984, 29973, 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lyapunov_reachability/common/gradient.py	62subinh/deeprl_safety_specification	3	154209	import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def flat_grad(grads): grad_flatten = [] for grad in grads: grad_flatten.append(grad.view(-1)) grad_flatten = torch.cat(grad_flatten) return grad_flatten def flat_hessian(hessians): hessians_flatten = [] for hessian in hessians: hessians_flatten.append(hessian.contiguous().view(-1)) hessians_flatten = torch.cat(hessians_flatten).data return hessians_flatten def kl_divergence(new_actor, old_actor, obs, deterministic=True): if deterministic: mu = new_actor(obs) mu_old = old_actor(obs) mu_old = mu_old.detach() kl = (mu_old - mu).pow(2) / 2.0 return kl.sum(1, keepdim=True) else: mu, std = new_actor(obs) mu_old, std_old = old_actor(obs) mu_old = mu_old.detach() std_old = std_old.detach() # kl divergence between old policy and new policy : D( pi_old \|\| pi_new ) # pi_old -> mu_old, std_old / pi_new -> mu, std # be careful of calculating KL-divergence. It is not symmetric metric. kl = torch.log(std / std_old) + (std_old.pow(2) + (mu_old - mu).pow(2)) / (2.0 * std.pow(2)) - 0.5 return kl.sum(1, keepdim=True) def hessian_vector_product(new_actor, old_actor, obs, p, cg_damping=1e-1): p.detach() kl = kl_divergence(new_actor=new_actor, old_actor=old_actor, obs=obs) kl = kl.mean() kl_grad = torch.autograd.grad(kl, new_actor.parameters(), create_graph=True) kl_grad = flat_grad(kl_grad) kl_grad_p = (kl_grad * p).sum() kl_hessian = torch.autograd.grad(kl_grad_p, new_actor.parameters()) kl_hessian = flat_hessian(kl_hessian) return kl_hessian + p * cg_damping # from openai baseline code # https://github.com/openai/baselines/blob/master/baselines/common/cg.py def conjugate_gradient(actor, target_actor, obs, b, nsteps=10, residual_tol=1e-10): x = torch.zeros(b.size(), device=device) r = b.clone() p = b.clone() rdotr = torch.dot(r, r) for i in range(nsteps): Ap = hessian_vector_product(actor, target_actor, obs, p, cg_damping=1e-1) alpha = rdotr / 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framework/generated/vulkan_generators/vulkan_referenced_resource_consumer_body_generator.py	tomped01/gfxreconstruct	0	27746	<filename>framework/generated/vulkan_generators/vulkan_referenced_resource_consumer_body_generator.py #!/usr/bin/python3 -i # # Copyright (c) 2020 LunarG, Inc. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. import os, re, sys from base_generator import * class VulkanReferencedResourceBodyGeneratorOptions(BaseGeneratorOptions): """Options for generating a C++ class for detecting unreferenced resource handles in a capture file""" def __init__( self, blacklists=None, # Path to JSON file listing apicalls and structs to ignore. platformTypes=None, # Path to JSON file listing platform (WIN32, X11, etc.) defined types. filename=None, directory='.', prefixText='', protectFile=False, protectFeature=True ): BaseGeneratorOptions.__init__( self, blacklists, platformTypes, filename, directory, prefixText, protectFile, protectFeature ) # VulkanReferencedResourceBodyGenerator - subclass of BaseGenerator. # Generates C++ member definitions for the VulkanReferencedResource class responsible for # determining which resource handles are used or unused in a capture file. class VulkanReferencedResourceBodyGenerator(BaseGenerator): """Generate a C++ class for detecting unreferenced resource handles in a capture file""" # All resource and resource associated handle types to be processed. RESOURCE_HANDLE_TYPES = [ 'VkBuffer', 'VkImage', 'VkBufferView', 'VkImageView', 'VkFramebuffer', 'VkDescriptorSet', 'VkCommandBuffer' ] # Handle types that contain resource and child resource handle types. CONTAINER_HANDLE_TYPES = ['VkDescriptorSet'] # Handle types that use resource and child resource handle types. USER_HANDLE_TYPES = ['VkCommandBuffer'] def __init__( self, errFile=sys.stderr, warnFile=sys.stderr, diagFile=sys.stdout ): BaseGenerator.__init__( self, processCmds=True, processStructs=True, featureBreak=False, errFile=errFile, warnFile=warnFile, diagFile=diagFile ) # Map of Vulkan structs containing handles to a list values for handle members or struct members # that contain handles (eg. VkGraphicsPipelineCreateInfo contains a VkPipelineShaderStageCreateInfo # member that contains handles). self.structsWithHandles = dict() self.pNextStructs = dict( ) # Map of Vulkan structure types to sType value for structs that can be part of a pNext chain. self.commandInfo = dict() # Map of Vulkan commands to parameter info self.restrictHandles = True # Determines if the 'isHandle' override limits the handle test to only the values conained by RESOURCE_HANDLE_TYPES. # Method override # yapf: disable def beginFile(self, genOpts): BaseGenerator.beginFile(self, genOpts) write('#include "generated/generated_vulkan_referenced_resource_consumer.h"', file=self.outFile) self.newline() write('#include <cassert>', file=self.outFile) self.newline() write('GFXRECON_BEGIN_NAMESPACE(gfxrecon)', file=self.outFile) write('GFXRECON_BEGIN_NAMESPACE(decode)', file=self.outFile) # yapf: enable # Method override # yapf: disable def endFile(self): for cmd, info in self.commandInfo.items(): returnType = info[0] params = info[2] if params and params[0].baseType == 'VkCommandBuffer': # Check for parameters with resource handle types. handles = self.getParamListHandles(params[1:]) if (handles): # Generate a function to add handles to the command buffer's referenced handle list. cmddef = '\n' # Temporarily remove resource only matching restriction from isHandle() when generating the function signature. self.restrictHandles = False cmddef += self.makeConsumerFuncDecl(returnType, 'VulkanReferencedResourceConsumer::Process_' + cmd, params) + '\n' self.restrictHandles = True cmddef += '{\n' indent = self.INDENT_SIZE * ' ' # Add unreferenced parameter macros. unrefCount = 0 for param in params[1:]: if not param in handles: cmddef += indent + 'GFXRECON_UNREFERENCED_PARAMETER({});\n'.format(param.name) unrefCount += 1 if unrefCount > 0: cmddef += '\n' for index, handle in enumerate(handles): cmddef += self.trackCommandHandle(index, params[0].name, handle, indent=indent) cmddef += '}' write(cmddef, file=self.outFile) self.newline() write('GFXRECON_END_NAMESPACE(decode)', file=self.outFile) write('GFXRECON_END_NAMESPACE(gfxrecon)', file=self.outFile) # Finish processing in superclass BaseGenerator.endFile(self) # yapf: enable # # Method override def genStruct(self, typeinfo, typename, alias): BaseGenerator.genStruct(self, typeinfo, typename, alias) if not alias: self.checkStructMemberHandles(typename, self.structsWithHandles) # Track this struct if it can be present in a pNext chain. parentStructs = typeinfo.elem.get('structextends') if parentStructs: sType = self.makeStructureTypeEnum(typeinfo, typename) if sType: self.pNextStructs[typename] = sType # # Indicates that the current feature has C++ code to generate. def needFeatureGeneration(self): if self.featureCmdParams: return True return False # # Performs C++ code generation for the feature. def generateFeature(self): for cmd in self.getFilteredCmdNames(): self.commandInfo[cmd] = self.featureCmdParams[cmd] # # Override method to check for handle type, only matching resource handle types. def isHandle(self, baseType): if self.restrictHandles: if baseType in self.RESOURCE_HANDLE_TYPES: return True return False else: return BaseGenerator.isHandle(self, baseType) # # Create list of parameters that have handle types or are structs that contain handles. def getParamListHandles(self, values): handles = [] for value in values: if self.isHandle(value.baseType): handles.append(value) elif self.isStruct( value.baseType ) and (value.baseType in self.structsWithHandles): handles.append(value) return handles # # # yapf: disable def trackCommandHandle(self, index, commandParamName, value, valuePrefix='', indent=''): body = '' tail = '' indexName = None countName = None valueName = valuePrefix + value.name isHandle = self.isHandle(value.baseType) if (value.isPointer or value.isArray) and value.name != 'pnext_value': if index > 0: body += '\n' accessOperator = '->' if not valuePrefix: # If there is no prefix, this is the pointer parameter received by the function, which should never be null. body += indent + 'assert({} != nullptr);\n'.format(value.name) body += '\n' else: # If there is a prefix, this is a struct member. We need to determine the type of access operator to use # for the member of a 'decoded' struct type, where handle member types will be HandlePointerDecoder, but # struct member types will be unique_ptr<StructPointerDecoder>. if isHandle: accessOperator = '.' # Add IsNull and HasData checks for the pointer decoder, before accessing its data. # Note that this does not handle the decoded struct member cases for static arrays, which would need to use '.' instead of '->'. body += indent + 'if (!{prefix}{name}{op}IsNull() && ({prefix}{name}{op}HasData()))\n'.format(prefix=valuePrefix, name=value.name, op=accessOperator) body += indent + '{\n' tail = indent + '}\n' + tail indent += ' ' * self.INDENT_SIZE # Get the pointer from the pointer decoder object. valueName = '{}_ptr'.format(value.name) if isHandle: body += indent + 'auto {} = {}{}{}GetPointer();\n'.format(valueName, valuePrefix, value.name, accessOperator) else: body += indent + 'auto {} = {}{}{}GetMetaStructPointer();\n'.format(valueName, valuePrefix, value.name, accessOperator) # Add a for loop for an array of values. if value.isArray: indexName = '{}_index'.format(value.name) countName = '{}_count'.format(value.name) body += indent + 'size_t {} = {}{}{}GetLength();\n'.format(countName, valuePrefix, value.name, accessOperator) body += indent + 'for (size_t {i} = 0; {i} < {}; ++{i})\n'.format(countName, i=indexName) body += indent + '{\n' tail = indent + '}\n' + tail indent += ' ' * self.INDENT_SIZE # Insert commands to add handles to a container, or to process struct members that contain handles. if isHandle: if value.isArray: valueName = '{}[{}]'.format(valueName, indexName) elif value.isPointer: valueName = '({})'.format(valueName) if value.baseType in self.CONTAINER_HANDLE_TYPES: body += indent + 'GetTable().AddContainerToUser({}, {});\n'.format(commandParamName, valueName) elif value.baseType in self.USER_HANDLE_TYPES: body += indent + 'GetTable().AddUserToUser({}, {});\n'.format(commandParamName, valueName) else: body += indent + 'GetTable().AddResourceToUser({}, {});\n'.format(commandParamName, valueName) elif self.isStruct(value.baseType) and (value.baseType in self.structsWithHandles): if value.isArray: accessOperator = '[{}].'.format(indexName) else: accessOperator = '->' for index, entry in enumerate(self.structsWithHandles[value.baseType]): if entry.name == 'pNext': extStructsWithHandles = [extStruct for extStruct in self.registry.validextensionstructs[value.baseType] if extStruct in self.structsWithHandles] if extStructsWithHandles: body += indent + 'const VkBaseInStructure pnext_header = nullptr;\n' body += indent + 'if ({name}->pNext != nullptr)\n'.format(name=valueName) body += indent + '{\n' indent += ' ' * self.INDENT_SIZE body += indent + 'pnext_header = reinterpret_cast<const VkBaseInStructure>({}->pNext->GetPointer());\n'.format(valueName) indent = indent[:-self.INDENT_SIZE] body += indent + '}\n' body += indent + 'while (pnext_header)\n' body += indent + '{\n' indent += ' ' self.INDENT_SIZE body += indent + 'switch (pnext_header->sType)\n' body += indent + '{\n' indent += ' ' * self.INDENT_SIZE body += indent + 'default:\n' indent += ' ' * self.INDENT_SIZE body += indent + 'break;\n' indent = indent[:-self.INDENT_SIZE] for extStruct in extStructsWithHandles: body += indent + 'case {}:\n'.format(self.pNextStructs[extStruct]) body += indent + '{\n' indent += ' ' * self.INDENT_SIZE body += indent + 'auto pnext_value = reinterpret_cast<const Decoded_{}>({}->pNext->GetPointer());\n'.format(extStruct, valueName) body += self.trackCommandHandle(index, commandParamName, ValueInfo('pnext_value', extStruct, 'const {} '.format(extStruct), 1), '', indent=indent) body += indent + 'break;\n' indent = indent[:-self.INDENT_SIZE] body += indent + '}\n' indent = indent[:-self.INDENT_SIZE] body += indent + '}\n' body += indent + 'pnext_header = pnext_header->pNext;\n' indent = indent[:-self.INDENT_SIZE] body += indent + 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sdk/keyvault/azure-keyvault/azure/keyvault/v7_3_preview/models/key_vault_client_enums.py	mccoyp/azure-keyvault-7.3-preview	0	82890	# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from enum import Enum class DeletionRecoveryLevel(str, Enum): purgeable = "Purgeable" #: Denotes a vault state in which deletion is an irreversible operation, without the possibility for recovery. This level corresponds to no protection being available against a Delete operation; the data is irretrievably lost upon accepting a Delete operation at the entity level or higher (vault, resource group, subscription etc.) recoverable_purgeable = "Recoverable+Purgeable" #: Denotes a vault state in which deletion is recoverable, and which also permits immediate and permanent deletion (i.e. purge). This level guarantees the recoverability of the deleted entity during the retention interval (90 days), unless a Purge operation is requested, or the subscription is cancelled. System wil permanently delete it after 90 days, if not recovered recoverable = "Recoverable" #: Denotes a vault state in which deletion is recoverable without the possibility for immediate and permanent deletion (i.e. purge). This level guarantees the recoverability of the deleted entity during the retention interval(90 days) and while the subscription is still available. System wil permanently delete it after 90 days, if not recovered recoverable_protected_subscription = "Recoverable+ProtectedSubscription" #: Denotes a vault and subscription state in which deletion is recoverable within retention interval (90 days), immediate and permanent deletion (i.e. purge) is not permitted, and in which the subscription itself cannot be permanently canceled. System wil permanently delete it after 90 days, if not recovered customized_recoverable_purgeable = "CustomizedRecoverable+Purgeable" #: Denotes a vault state in which deletion is recoverable, and which also permits immediate and permanent deletion (i.e. purge when 7<= SoftDeleteRetentionInDays < 90). This level guarantees the recoverability of the deleted entity during the retention interval, unless a Purge operation is requested, or the subscription is cancelled. customized_recoverable = "CustomizedRecoverable" #: Denotes a vault state in which deletion is recoverable without the possibility for immediate and permanent deletion (i.e. purge when 7<= SoftDeleteRetentionInDays < 90).This level guarantees the recoverability of the deleted entity during the retention interval and while the subscription is still available. customized_recoverable_protected_subscription = "CustomizedRecoverable+ProtectedSubscription" #: Denotes a vault and subscription state in which deletion is recoverable, immediate and permanent deletion (i.e. purge) is not permitted, and in which the subscription itself cannot be permanently canceled when 7<= SoftDeleteRetentionInDays < 90. This level guarantees the recoverability of the deleted entity during the retention interval, and also reflects the fact that the subscription itself cannot be cancelled. class JsonWebKeyType(str, Enum): ec = "EC" ec_hsm = "EC-HSM" rsa = "RSA" rsa_hsm = "RSA-HSM" oct = "oct" oct_hsm = "oct-HSM" class JsonWebKeyCurveName(str, Enum): p_256 = "P-256" p_384 = "P-384" p_521 = "P-521" p_256_k = "P-256K" class JsonWebKeyOperation(str, Enum): encrypt = "encrypt" decrypt = "decrypt" sign = "sign" verify = "verify" wrap_key = "wrapKey" unwrap_key = "unwrapKey" import_enum = "import" export = "export" class KeyEncryptionAlgorithm(str, Enum): ckm_rsa_aes_key_wrap = "CKM_RSA_AES_KEY_WRAP" rsa_aes_key_wrap_256 = "RSA_AES_KEY_WRAP_256" rsa_aes_key_wrap_384 = "RSA_AES_KEY_WRAP_384" class JsonWebKeyEncryptionAlgorithm(str, Enum): rsa_oaep = "RSA-OAEP" rsa_oaep_256 = "RSA-OAEP-256" rsa1_5 = "RSA1_5" a128_gcm = "A128GCM" a192_gcm = "A192GCM" a256_gcm = "A256GCM" a128_kw = "A128KW" a192_kw = "A192KW" a256_kw = "A256KW" a128_cbc = "A128CBC" a192_cbc = "A192CBC" a256_cbc = "A256CBC" a128_cbcpad = "A128CBCPAD" a192_cbcpad = "A192CBCPAD" a256_cbcpad = "A256CBCPAD" class JsonWebKeySignatureAlgorithm(str, Enum): ps256 = "PS256" #: RSASSA-PSS using SHA-256 and MGF1 with SHA-256, as described in https://tools.ietf.org/html/rfc7518 ps384 = "PS384" #: RSASSA-PSS using SHA-384 and MGF1 with SHA-384, as described in https://tools.ietf.org/html/rfc7518 ps512 = "PS512" #: RSASSA-PSS using SHA-512 and MGF1 with SHA-512, as described in https://tools.ietf.org/html/rfc7518 rs256 = "RS256" #: RSASSA-PKCS1-v1_5 using SHA-256, as described in https://tools.ietf.org/html/rfc7518 rs384 = "RS384" #: RSASSA-PKCS1-v1_5 using SHA-384, as described in https://tools.ietf.org/html/rfc7518 rs512 = "RS512" #: RSASSA-PKCS1-v1_5 using SHA-512, as described in https://tools.ietf.org/html/rfc7518 rsnull = "RSNULL" #: Reserved es256 = "ES256" #: ECDSA using P-256 and SHA-256, as described in https://tools.ietf.org/html/rfc7518. es384 = "ES384" #: ECDSA using P-384 and SHA-384, as described in https://tools.ietf.org/html/rfc7518 es512 = "ES512" #: ECDSA using P-521 and SHA-512, as described in https://tools.ietf.org/html/rfc7518 es256_k = "ES256K" #: ECDSA using P-256K and SHA-256, as described in https://tools.ietf.org/html/rfc7518 class KeyUsageType(str, Enum): digital_signature = "digitalSignature" non_repudiation = "nonRepudiation" key_encipherment = "keyEncipherment" data_encipherment = "dataEncipherment" key_agreement = "keyAgreement" key_cert_sign = "keyCertSign" c_rl_sign = "cRLSign" encipher_only = "encipherOnly" decipher_only = "decipherOnly" class ActionType(str, Enum): email_contacts = "EmailContacts" auto_renew = "AutoRenew" class OperationStatus(str, Enum): success = "Success" in_progress = "InProgress" failed = "Failed" class SasTokenType(str, Enum): account = "account" service = "service"	[ 1, 396, 14137, 29922, 9420, 29899, 29947, 13, 29937, 448, 2683, 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boilerplate_app/tasks.py	taher-systango/DjangoUnboxed	68	187728	<gh_stars>10-100 from celery import shared_task @shared_task def add(a, b): return (a+b)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 6432, 708, 1053, 7258, 29918, 7662, 13, 13, 13, 29992, 12366, 29918, 7662, 13, 1753, 788, 29898, 29874, 29892, 289, 1125, 13, 1678, 736, 313, 29874, 29974, 29890, 29897, 13, 2 ]
model/Encoder.py	ECCV7129/ECCV2022_submission_7129	1	173176	from torch import nn import torch from model.Swin import SwinTransformer3D import copy class swin_encoder(nn.Module): def __init__(self , device , drop , checkpoint_encoder): super().__init__() checkpoint = checkpoint_encoder self.device=device self.label= 'demo/label_map_k400.txt' self.model=SwinTransformer3D( embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=(8, 7, 7), patch_size=(2, 4, 4), drop_path_rate=0.1, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=drop, attn_drop_rate=drop, patch_norm=True) checkpoint = torch.load(checkpoint, map_location=device) state_dict2 = copy.deepcopy(checkpoint['state_dict']) state_dict = dict() for key in checkpoint['state_dict']: if 'backbone.' in key: new_key = key.replace('backbone.', '') state_dict[new_key] = state_dict2.pop(key) self.model.load_state_dict(state_dict) self.model = self.model.to(device) self.max_testing_views = None def forward(self, imgs): batches = imgs.shape[0] imgs = imgs.reshape((-1,) + imgs.shape[2:]) feat = self.model.forward(imgs) # perform spatio-temporal pooling avg_pool = nn.AdaptiveAvgPool2d((1, 1)) feat = avg_pool(feat) # squeeze dimensions feat = feat.view(batches, feat.shape[1], feat.shape[2]) feat = feat.permute(0, 2, 1) return feat	[ 1, 515, 4842, 305, 1053, 302, 29876, 13, 5215, 4842, 305, 13, 3166, 1904, 29889, 29903, 5080, 1053, 3925, 262, 13372, 261, 29941, 29928, 13, 5215, 3509, 13, 1990, 2381, 262, 29918, 3977, 6119, 29898, 15755, 29889, 7355, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 1919, 4742, 1919, 5768, 1919, 1423, 3149, 29918, 3977, 6119, 1125, 13, 4706, 2428, 2141, 1649, 2344, 1649, 580, 13, 4706, 1423, 3149, 353, 1423, 3149, 29918, 3977, 6119, 13, 4706, 1583, 29889, 10141, 29922, 10141, 13, 4706, 1583, 29889, 1643, 29922, 525, 17482, 29914, 1643, 29918, 1958, 29918, 29895, 29946, 29900, 29900, 29889, 3945, 29915, 13, 13, 4706, 1583, 29889, 4299, 29922, 29903, 5080, 13372, 261, 29941, 29928, 29898, 13, 9651, 8297, 29918, 6229, 29922, 29896, 29906, 29947, 29892, 13, 9651, 10809, 29879, 11759, 29906, 29892, 29871, 29906, 29892, 29871, 29896, 29947, 29892, 29871, 29906, 1402, 13, 9651, 954, 29918, 2813, 29879, 11759, 29946, 29892, 29871, 29947, 29892, 29871, 29896, 29953, 29892, 29871, 29941, 29906, 1402, 13, 9651, 3474, 29918, 2311, 7607, 29947, 29892, 29871, 29955, 29892, 29871, 29955, 511, 13, 9651, 13261, 29918, 2311, 7607, 29906, 29892, 29871, 29946, 29892, 29871, 29946, 511, 13, 9651, 5768, 29918, 2084, 29918, 10492, 29922, 29900, 29889, 29896, 29892, 13, 9651, 286, 22833, 29918, 3605, 601, 29922, 29946, 1696, 13, 9651, 3855, 27049, 29918, 29890, 3173, 29922, 5574, 29892, 13, 9651, 3855, 29895, 29918, 7052, 29922, 8516, 29892, 13, 9651, 5768, 29918, 10492, 29922, 8865, 29892, 13, 9651, 1098, 29876, 29918, 8865, 29918, 10492, 29922, 8865, 29892, 13, 13, 9651, 13261, 29918, 12324, 29922, 5574, 29897, 13, 13, 4706, 1423, 3149, 353, 4842, 305, 29889, 1359, 29898, 3198, 3149, 29892, 2910, 29918, 5479, 29922, 10141, 29897, 13, 4706, 2106, 29918, 8977, 29906, 353, 3509, 29889, 24535, 8552, 29898, 3198, 3149, 1839, 3859, 29918, 8977, 11287, 13, 4706, 2106, 29918, 8977, 353, 9657, 580, 13, 4706, 363, 1820, 297, 1423, 3149, 1839, 3859, 29918, 8977, 2033, 29901, 13, 9651, 565, 525, 1627, 15933, 6169, 297, 1820, 29901, 13, 18884, 716, 29918, 1989, 353, 1820, 29889, 6506, 877, 1627, 15933, 29889, 742, 27255, 13, 18884, 2106, 29918, 8977, 29961, 1482, 29918, 1989, 29962, 353, 2106, 29918, 8977, 29906, 29889, 7323, 29898, 1989, 29897, 13, 13, 4706, 1583, 29889, 4299, 29889, 1359, 29918, 3859, 29918, 8977, 29898, 3859, 29918, 8977, 29897, 13, 4706, 1583, 29889, 4299, 353, 1583, 29889, 4299, 29889, 517, 29898, 10141, 29897, 13, 4706, 1583, 29889, 3317, 29918, 13424, 29918, 7406, 353, 6213, 13, 13, 13, 13, 1678, 822, 6375, 29898, 1311, 29892, 527, 3174, 1125, 13, 4706, 9853, 267, 353, 527, 3174, 29889, 12181, 29961, 29900, 29962, 13, 4706, 527, 3174, 353, 527, 3174, 29889, 690, 14443, 3552, 29899, 29896, 29892, 29897, 718, 527, 3174, 29889, 12181, 29961, 29906, 29901, 2314, 13, 13, 4706, 1238, 271, 353, 1583, 29889, 4299, 29889, 11333, 29898, 2492, 29879, 29897, 13, 13, 4706, 396, 2189, 805, 20819, 29899, 1356, 1971, 284, 11565, 292, 13, 4706, 1029, 29887, 29918, 10109, 353, 302, 29876, 29889, 29909, 1388, 415, 573, 12810, 29887, 11426, 29906, 29881, 3552, 29896, 29892, 29871, 29896, 876, 13, 4706, 1238, 271, 353, 1029, 29887, 29918, 10109, 29898, 1725, 271, 29897, 13, 4706, 396, 269, 802, 29872, 911, 13391, 13, 4706, 1238, 271, 353, 1238, 271, 29889, 1493, 29898, 16175, 267, 29892, 1238, 271, 29889, 12181, 29961, 29896, 1402, 1238, 271, 29889, 12181, 29961, 29906, 2314, 13, 4706, 1238, 271, 353, 1238, 271, 29889, 17858, 1082, 29898, 29900, 29892, 29871, 29906, 29892, 29871, 29896, 29897, 13, 4706, 736, 1238, 271, 13, 13, 13, 13, 13, 13, 2 ]
module_pie_chart.py	barnett617/python_analysis	2	129135	<gh_stars>1-10 # --coding:utf-8-- # 饼状图 from matplotlib import pyplot as plt import data_source # 数据源 mobile_176xxxx4617 = data_source.mobile_176xxxx4617 mobile_155xxxx9617 = data_source.mobile_155xxxx9617 mobile_173xxxx9636 = data_source.mobile_173xxxx9636 mobile_176xxxx4617_sum = 0 mobile_155xxxx9617_sum = 0 mobile_173xxxx9636_sum = 0 for i in mobile_176xxxx4617: mobile_176xxxx4617_sum += i for i in mobile_155xxxx9617: mobile_155xxxx9617_sum += i for i in mobile_173xxxx9636: mobile_173xxxx9636_sum += i # 调节图形大小，宽，高 plt.figure(figsize=(12, 8)) # 定义饼状图的标签，标签是列表 labels = ['号码176xxxx4617：' + str(round(mobile_176xxxx4617_sum, 3)) + '元', '号码155xxxx9617：' + str(round(mobile_155xxxx9617_sum, 3)) + '元', '号码173xxxx9636：' + str(round(mobile_173xxxx9636_sum, 3)) + '元'] # 每个标签占多大，会自动去算百分比 sizes = [mobile_176xxxx4617_sum, mobile_155xxxx9617_sum, mobile_173xxxx9636_sum] # 将某部分爆炸出来，使用括号，将第一块分割出来，数值的大小是分割出来的与其他两块的间隙 explode = (0.05, 0, 0) patches, l_text, p_text = plt.pie(sizes, explode=explode, labels=labels, labeldistance=1.1, autopct='%3.1f%%', shadow=False, startangle=90, pctdistance=0.6) # labeldistance，文本的位置离远点有多远，1.1指1.1倍半径的位置 # autopct，圆里面的文本格式，%3.1f%%表示小数有三位，整数有一位的浮点数 # shadow，饼是否有阴影 # startangle，起始角度，0，表示从0开始逆时针转，为第一块。一般选择从90度开始比较好看 # pctdistance，百分比的text离圆心的距离 # patches, l_texts, p_texts，为了得到饼图的返回值，p_texts饼图内部文本的，l_texts饼图外label的文本 # 改变文本的大小 # 方法是把每一个text遍历。调用set_size方法设置它的属性 for t in l_text: t.set_size = (30) for t in p_text: t.set_size = (20) # 设置x，y轴刻度一致，这样饼图才能是圆的 plt.axis('equal') plt.legend() def show_plt(): plt.show()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 448, 29930, 29899, 29883, 3689, 29901, 9420, 29899, 29947, 29899, 29930, 29899, 13, 29937, 29871, 236, 168, 191, 31531, 30861, 13, 13, 3166, 22889, 1053, 11451, 5317, 408, 14770, 13, 5215, 848, 29918, 4993, 13, 13, 29937, 29871, 30354, 30763, 31193, 13, 16769, 29918, 29896, 29955, 29953, 14633, 29946, 29953, 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Questions/Chief in Chains/solution.py	leander-dsouza/Abhyudaya_2020	1	59058	<filename>Questions/Chief in Chains/solution.py<gh_stars>1-10 class Solution(object): def reconstructQueue(self, people): people.sort(key=lambda x: (-x[0], x[1])) output = [] for p in people: output.insert(p[1], p) return output m = int(input()) k = Solution() array_input = [] for x in range(m): array_input.append([int(y) for y in input().split()]) gh =k.reconstructQueue(array_input) for s in gh: print(*s)	[ 1, 529, 9507, 29958, 2182, 2297, 29914, 1451, 2575, 297, 678, 2708, 29914, 2929, 918, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1990, 24380, 29898, 3318, 1125, 13, 13, 13, 1678, 822, 337, 11433, 10620, 29898, 1311, 29892, 2305, 1125, 13, 13, 4706, 2305, 29889, 6605, 29898, 1989, 29922, 2892, 921, 29901, 8521, 29916, 29961, 29900, 1402, 921, 29961, 29896, 12622, 13, 13, 4706, 1962, 353, 5159, 13, 4706, 363, 282, 297, 2305, 29901, 13, 9651, 1962, 29889, 7851, 29898, 29886, 29961, 29896, 1402, 282, 29897, 13, 4706, 736, 1962, 13, 13, 13, 13, 13, 29885, 353, 938, 29898, 2080, 3101, 13, 13, 29895, 353, 24380, 580, 13, 13, 2378, 29918, 2080, 353, 5159, 13, 1454, 921, 297, 3464, 29898, 29885, 1125, 13, 1678, 1409, 29918, 2080, 29889, 4397, 4197, 524, 29898, 29891, 29897, 363, 343, 297, 1881, 2141, 5451, 580, 2314, 13, 12443, 353, 29895, 29889, 276, 11433, 10620, 29898, 2378, 29918, 2080, 29897, 13, 13, 1454, 269, 297, 24170, 29901, 13, 1678, 1596, 10456, 29879, 29897, 13, 2 ]
development/testsite/blog/migrations/0002_auto_20190126_1538.py	iggy12345/django_blog	0	150287	<filename>development/testsite/blog/migrations/0002_auto_20190126_1538.py # Generated by Django 2.1.5 on 2019-01-26 21:38 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('utils', '0001_initial'), ('blog', '0001_initial'), ] operations = [ migrations.RemoveField( model_name='blogpost', name='author', ), migrations.RemoveField( model_name='blogpost', name='id', ), migrations.RemoveField( model_name='blogpost', name='is_posted', ), migrations.RemoveField( model_name='blogpost', name='pub_date', ), migrations.RemoveField( model_name='blogpost', name='text', ), migrations.AddField( model_name='blogpost', name='textpost_ptr', field=models.OneToOneField(auto_created=True, default=0, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='utils.TextPost'), preserve_default=False, ), ]	[ 1, 529, 9507, 29958, 25431, 29914, 1688, 2746, 29914, 7312, 29914, 26983, 800, 29914, 29900, 29900, 29900, 29906, 29918, 6921, 29918, 29906, 29900, 29896, 29929, 29900, 29896, 29906, 29953, 29918, 29896, 29945, 29941, 29947, 29889, 2272, 13, 29937, 3251, 630, 491, 15337, 29871, 29906, 29889, 29896, 29889, 29945, 373, 29871, 29906, 29900, 29896, 29929, 29899, 29900, 29896, 29899, 29906, 29953, 29871, 29906, 29896, 29901, 29941, 29947, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 13239, 742, 525, 29900, 29900, 29900, 29896, 29918, 11228, 5477, 13, 4706, 6702, 7312, 742, 525, 29900, 29900, 29900, 29896, 29918, 11228, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 7312, 2490, 742, 13, 9651, 1024, 2433, 8921, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 7312, 2490, 742, 13, 9651, 1024, 2433, 333, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 7312, 2490, 742, 13, 9651, 1024, 2433, 275, 29918, 2490, 287, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 7312, 2490, 742, 13, 9651, 1024, 2433, 5467, 29918, 1256, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 7312, 2490, 742, 13, 9651, 1024, 2433, 726, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 2528, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 7312, 2490, 742, 13, 9651, 1024, 2433, 726, 2490, 29918, 7414, 742, 13, 9651, 1746, 29922, 9794, 29889, 6716, 1762, 6716, 3073, 29898, 6921, 29918, 11600, 29922, 5574, 29892, 2322, 29922, 29900, 29892, 373, 29918, 8143, 29922, 14095, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 29889, 29907, 3289, 5454, 2287, 29892, 3847, 29918, 2324, 29922, 5574, 29892, 7601, 29918, 1989, 29922, 5574, 29892, 28755, 29922, 8824, 29892, 304, 2433, 13239, 29889, 1626, 6747, 5477, 13, 9651, 19905, 29918, 4381, 29922, 8824, 29892, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
unshorten/models.py	bitmazk/django-unshorten	3	1610630	<filename>unshorten/models.py """Models for the ``unshorten`` app.""" from django.conf import settings from django.db import models from django.utils.encoding import python_2_unicode_compatible @python_2_unicode_compatible class APICallHistoryBase(models.Model): """ Stores the amount of API calls per user per day or month. :amount_api_calls: The actual amount of calls on this day. :creation_date: The date these calls are logged. :user: Foreignkey to the user, whose calls are stored. """ amount_api_calls = models.PositiveIntegerField( verbose_name='API call amount', ) creation_date = models.DateField( auto_now_add=True, verbose_name='Creation date', ) user = models.ForeignKey( settings.AUTH_USER_MODEL, verbose_name='unshorten_profile', ) class Meta: abstract = True unique_together = ('creation_date', 'user') def __str__(self): return '{0} ({1})'.format(self.user.email, self.amount_api_calls) class APICallDayHistory(APICallHistoryBase): pass class APICallMonthHistory(APICallHistoryBase): pass @python_2_unicode_compatible class UnshortenURL(models.Model): """ Holds the information about a short to long url relation. :creation_date: The time it was created. :long_url: The long url that resulted out of it. :short_url: The short url that needed lookup. """ creation_date = models.DateTimeField( auto_now_add=True, verbose_name='Creation date', ) long_url = models.CharField( max_length=4000, verbose_name='Long URL', ) short_url = models.CharField( max_length=1024, verbose_name='Short URL', ) def __str__(self): return self.short_url	[ 1, 529, 9507, 29958, 348, 12759, 264, 29914, 9794, 29889, 2272, 13, 15945, 29908, 23785, 363, 278, 4954, 348, 12759, 264, 16159, 623, 1213, 15945, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 2585, 1053, 4733, 13, 3166, 9557, 29889, 13239, 29889, 22331, 1053, 3017, 29918, 29906, 29918, 2523, 356, 29918, 23712, 13, 13, 13, 29992, 4691, 29918, 29906, 29918, 2523, 356, 29918, 23712, 13, 1990, 3450, 5594, 20570, 5160, 29898, 9794, 29889, 3195, 1125, 13, 1678, 9995, 13, 1678, 624, 2361, 278, 5253, 310, 3450, 5717, 639, 1404, 639, 2462, 470, 4098, 29889, 13, 13, 1678, 584, 14506, 29918, 2754, 29918, 29883, 4293, 29901, 450, 3935, 5253, 310, 5717, 373, 445, 2462, 29889, 13, 1678, 584, 1037, 362, 29918, 1256, 29901, 450, 2635, 1438, 5717, 526, 13817, 29889, 13, 1678, 584, 1792, 29901, 19358, 1989, 304, 278, 1404, 29892, 5069, 5717, 526, 6087, 29889, 13, 13, 1678, 9995, 13, 1678, 5253, 29918, 2754, 29918, 29883, 4293, 353, 4733, 29889, 9135, 3321, 7798, 3073, 29898, 13, 4706, 26952, 29918, 978, 2433, 8787, 1246, 5253, 742, 13, 1678, 1723, 13, 13, 1678, 11265, 29918, 1256, 353, 4733, 29889, 2539, 3073, 29898, 13, 4706, 4469, 29918, 3707, 29918, 1202, 29922, 5574, 29892, 13, 4706, 26952, 29918, 978, 2433, 9832, 362, 2635, 742, 13, 1678, 1723, 13, 13, 1678, 1404, 353, 4733, 29889, 27755, 2558, 29898, 13, 4706, 6055, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 29892, 13, 4706, 26952, 29918, 978, 2433, 348, 12759, 264, 29918, 10185, 742, 13, 1678, 1723, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 9846, 353, 5852, 13, 4706, 5412, 29918, 29873, 12966, 353, 6702, 1037, 362, 29918, 1256, 742, 525, 1792, 1495, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 22372, 29900, 29913, 21313, 29896, 1800, 4286, 4830, 29898, 1311, 29889, 1792, 29889, 5269, 29892, 1583, 29889, 14506, 29918, 2754, 29918, 29883, 4293, 29897, 13, 13, 13, 1990, 3450, 5594, 12742, 20570, 29898, 8787, 5594, 20570, 5160, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 3450, 5594, 13953, 20570, 29898, 8787, 5594, 20570, 5160, 1125, 13, 1678, 1209, 13, 13, 13, 29992, 4691, 29918, 29906, 29918, 2523, 356, 29918, 23712, 13, 1990, 853, 12759, 264, 4219, 29898, 9794, 29889, 3195, 1125, 13, 1678, 9995, 13, 1678, 379, 3361, 278, 2472, 1048, 263, 3273, 304, 1472, 3142, 8220, 29889, 13, 13, 1678, 584, 1037, 362, 29918, 1256, 29901, 450, 931, 372, 471, 2825, 29889, 13, 1678, 584, 5426, 29918, 2271, 29901, 450, 1472, 3142, 393, 20601, 714, 310, 372, 29889, 13, 1678, 584, 12759, 29918, 2271, 29901, 450, 3273, 3142, 393, 4312, 16280, 29889, 13, 13, 1678, 9995, 13, 1678, 11265, 29918, 1256, 353, 4733, 29889, 11384, 3073, 29898, 13, 4706, 4469, 29918, 3707, 29918, 1202, 29922, 5574, 29892, 13, 4706, 26952, 29918, 978, 2433, 9832, 362, 2635, 742, 13, 1678, 1723, 13, 13, 1678, 1472, 29918, 2271, 353, 4733, 29889, 27890, 29898, 13, 4706, 4236, 29918, 2848, 29922, 29946, 29900, 29900, 29900, 29892, 13, 4706, 26952, 29918, 978, 2433, 8208, 3988, 742, 13, 1678, 1723, 13, 13, 1678, 3273, 29918, 2271, 353, 4733, 29889, 27890, 29898, 13, 4706, 4236, 29918, 2848, 29922, 29896, 29900, 29906, 29946, 29892, 13, 4706, 26952, 29918, 978, 2433, 21322, 3988, 742, 13, 1678, 1723, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 1583, 29889, 12759, 29918, 2271, 13, 2 ]
proxyclient/tools/reboot.py	EricRabil/m1n1	1,604	54289	<filename>proxyclient/tools/reboot.py<gh_stars>1000+ #!/usr/bin/env python3 # SPDX-License-Identifier: MIT import sys, pathlib sys.path.append(str(pathlib.Path(__file__).resolve().parents[1])) from m1n1.setup import * p.reboot()	[ 1, 529, 9507, 29958, 14701, 4645, 29914, 8504, 29914, 276, 4777, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29900, 29974, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 10937, 29928, 29990, 29899, 29931, 293, 1947, 29899, 12889, 29901, 341, 1806, 13, 5215, 10876, 29892, 2224, 1982, 13, 9675, 29889, 2084, 29889, 4397, 29898, 710, 29898, 2084, 1982, 29889, 2605, 22168, 1445, 1649, 467, 17863, 2141, 862, 1237, 29961, 29896, 12622, 13, 13, 3166, 286, 29896, 29876, 29896, 29889, 14669, 1053, 334, 13, 13, 29886, 29889, 276, 4777, 580, 13, 2 ]
docs/archive/slash/docking-example/scripts/euler2rotmat.py	sjdv1982/seamless	15	126438	import sys, os from read_struc import read_struc from math import sin, cos import numpy as np def euler2rotmat(phi,ssi,rot): cs=cos(ssi) cp=cos(phi) ss=sin(ssi) sp=sin(phi) cscp=cscp cssp=cssp sscp=sscp sssp=sssp crot=cos(rot) srot=sin(rot) r1 = crot * cscp + srot * sp r2 = srot * cscp - crot * sp r3 = sscp r4 = crot * cssp - srot * cp r5 = srot * cssp + crot * cp r6 = sssp r7 = -crot * ss r8 = -srot * ss r9 = cs return ((r1,r2,r3),(r4,r5,r6),(r7,r8,r9)) datfile = sys.argv[1] header, strucs = read_struc(open(datfile)) strucs = list(strucs) pivots = [] for h in header: if not h.startswith("#pivot"): h = h.rstrip() if h.startswith("#centered"): assert h.endswith(" false"), h continue assert not h.startswith("#pivot auto"), h hh = h.split() assert hh[1] == str(len(pivots)+1), h assert len(hh) == 5, h pivot = [float(v) for v in hh[2:5]] pivots.append(np.array(pivot)) results = [] for struc in strucs: result_struc = [] for lnr, l in enumerate(struc[1]): ll = [float(v) for v in l.split()] assert len(ll) == 6 #no ensembles rotmat = euler2rotmat(ll[:3]) rotmat = np.array(rotmat) trans = np.array(ll[3:6]) p = pivots[lnr] pp = (-p rotmat).sum(axis=1) + p trans += pp result = np.eye(4) result[:3,:3] = rotmat result[:3,3] = trans result[3][3] = 1 result_struc.append(result.tolist()) results.append(result_struc) import json print(json.dumps(results, indent=2))	[ 1, 1053, 10876, 29892, 2897, 13, 3166, 1303, 29918, 10582, 29883, 1053, 1303, 29918, 10582, 29883, 13, 3166, 5844, 1053, 4457, 29892, 6776, 13, 5215, 12655, 408, 7442, 13, 13, 1753, 321, 8584, 29906, 5450, 2922, 29898, 2876, 29892, 893, 29875, 29892, 5450, 1125, 13, 1678, 5939, 29922, 3944, 29898, 893, 29875, 29897, 13, 1678, 21447, 29922, 3944, 29898, 2876, 29897, 13, 1678, 17971, 29922, 5223, 29898, 893, 29875, 29897, 13, 1678, 805, 29922, 5223, 29898, 2876, 29897, 13, 1678, 274, 1557, 29886, 29922, 2395, 29930, 6814, 13, 1678, 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pyasyncserver/pipes/__init__.py	agratoth/pyasyncserver	0	179455	from .pipe import Pipe from .http_pipe import HTTPPipe from .nats_pipe import NATSPipe from .directions import PipeDirections from .helpers import PipePool	[ 1, 515, 869, 17760, 1053, 7362, 412, 13, 3166, 869, 1124, 29918, 17760, 1053, 7331, 12197, 412, 13, 3166, 869, 27479, 29918, 17760, 1053, 26038, 5550, 15705, 13, 3166, 869, 20146, 1953, 1053, 7362, 412, 29928, 533, 1953, 13, 13, 3166, 869, 3952, 6774, 1053, 7362, 412, 11426, 2 ]
su4/path_util.py	njuwelkin/su4	0	48855	<filename>su4/path_util.py import os, sys def addModulePath(file): utils_path = os.path.dirname(os.path.abspath(file)) + "/../utils" sys.path.append(utils_path)	[ 1, 529, 9507, 29958, 2146, 29946, 29914, 2084, 29918, 4422, 29889, 2272, 13, 5215, 2897, 29892, 10876, 13, 13, 1753, 788, 7355, 2605, 29898, 1445, 1125, 13, 1678, 3667, 29879, 29918, 2084, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 370, 1028, 493, 29898, 1445, 876, 718, 5591, 6995, 13239, 29908, 13, 1678, 10876, 29889, 2084, 29889, 4397, 29898, 13239, 29918, 2084, 29897, 13, 13, 2 ]
python/tk_houdini_flipbook/create_slate.py	nfa-vfxim/tk-houdini-flipbook	3	1611916	<filename>python/tk_houdini_flipbook/create_slate.py<gh_stars>1-10 # MIT License # Copyright (c) 2020 Netherlands Film Academy # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import os import subprocess import hou import sgtk class CreateSlate(object): def __init__(self, app): # initialize and set paths self.app = app self.nukePath = "%s" % (app.get_setting("nuke_path")) # set slate script path __location__ = os.path.realpath( os.path.join(os.getcwd(), os.path.dirname(__file__)) ) self.slatePath = os.path.join(__location__, "slate.py") def runSlate(self, inputFile, outputFile, settings): # setup environment custom_env = os.environ.copy() if custom_env["PYTHONPATH"] != None: del custom_env["PYTHONPATH"] if custom_env["PYTHONHOME"] != None: del custom_env["PYTHONHOME"] # setup arguments for call context = self.app.context project_name = context.project["name"] user_name = context.user["name"] file_name = hou.hipFile.basename().lower() first_frame = settings["frameRange"] first_frame = first_frame[0] last_frame = settings["frameRange"] last_frame = last_frame[1] task_name = context.step["name"] self.app.logger.debug(task_name) fps = hou.fps() appPath = self.app.disk_location # ensure output path exists self.app.ensure_folder_exists(os.path.dirname(os.path.abspath(outputFile))) # calculate version number template = self.app.get_template("work_file_template") fields = template.get_fields(hou.hipFile.path()) version = fields["version"] resolution = "%d x %d" % ( settings["resolution"][0], settings["resolution"][0], ) # call subprocess of nuke and convert process = subprocess.Popen( [ self.nukePath, "-t", self.slatePath, inputFile, outputFile, project_name, file_name, str(first_frame), str(last_frame), appPath, str(version), resolution, user_name, task_name, str(fps), ], shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=custom_env, ) stdout, stderr = process.communicate() self.app.logger.debug(stdout) if stderr: self.app.logger.error(stderr) if stderr: raise Exception("Could not correctly render file. 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f_highboost.py	shsandro/high-boost-filter	1	151713	<filename>f_highboost.py import scipy.fftpack as fp from scipy import signal import numpy as np import numpy.fft import cv2 import matplotlib.pylab as pylab # carrega a imagem img = cv2.imread(input("Digite o caminho para a imagem de entrada: "))[:, :, 0] # lê o valor de D D = int(input("Digite o valor de D para o kernel: ")) # obtém o filtro gaussiano no domínio espacial gauss_kernel = np.outer(signal.gaussian( img.shape[0], D), signal.gaussian(img.shape[1], D)) # obtém a imagem e o filtro no domínio da frquência freq_img = fp.fft2(img) freq_kernel = fp.fft2(fp.ifftshift(gauss_kernel)) assert(freq_img.shape == gauss_kernel.shape) # aplica convolução (borramento) convolved_img = freq_img * freq_kernel blured_img = fp.ifft2(convolved_img).real blured_img = np.abs(blured_img) blured_img = blured_img * 255 / blured_img.max() blured_img = blured_img.astype(np.uint8) ####################### Mostra todos os passos da convolução na frequência ####################### pylab.figure(figsize=(30, 25)) pylab.gray() pylab.subplot(2, 2, 1), pylab.imshow(img), pylab.title( 'Imagem original', size=10), pylab.axis('off') pylab.subplot(2, 2, 4), pylab.imshow(blured_img) pylab.title('Imagem após convolução', size=10), pylab.axis('off') pylab.subplot(2, 2, 2), pylab.imshow( (20np.log10(0.1 + fp.fftshift(freq_kernel))).astype(int)) pylab.title('Transformada do filtro', size=10), pylab.axis('off') pylab.subplot(2, 2, 3), pylab.imshow( (20np.log10(0.1 + fp.fftshift(convolved_img))).astype(int)) pylab.title('Imagem borrada no domínio da frequência', size=10), pylab.axis('off') pylab.subplots_adjust(wspace=0.2, hspace=1) pylab.show() ################################################################################################## # lê o valor de k k = int(input("Digite o valor de k: ")) # obtem a máscara (arestas) mask = cv2.subtract(img, blured_img) mask = k * mask ####################### Mostra todos os passos da convolução ####################### pylab.subplot(2, 3, 1), pylab.imshow(img), pylab.title( 'Imagem original', size=10), pylab.axis('off') pylab.subplot(2, 3, 3), pylab.imshow(blured_img) pylab.title('Imagem após convolução', size=10), pylab.axis('off') pylab.subplot(2, 3, 5), pylab.imshow(mask) pylab.title('Arestas para aguçamento', size=10), pylab.axis('off') pylab.subplots_adjust(wspace=0.2, hspace=1) pylab.show() ################################################################################################## # obtem imagem final final_image = cv2.add(img, mask) cv2.imwrite(input("Digite o caminho para a imagem de saída: "), final_image) print("Imagem final salva!")	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src/common/status.py	THUKElab/Video2Description	152	191806	from enum import Enum class ModelWeightsStatus(Enum): NO_INFO = 0 SUCCESS = 1 MODEL_NOT_FOUND = 2 WIP = 3	[ 1, 515, 14115, 1053, 1174, 398, 13, 13, 1990, 8125, 4806, 5861, 5709, 29898, 16854, 1125, 13, 1678, 11698, 29918, 11690, 308, 353, 29871, 29900, 13, 1678, 20134, 26925, 308, 353, 29871, 29896, 13, 1678, 16999, 2287, 29931, 29918, 12256, 29918, 5800, 18783, 353, 29871, 29906, 13, 1678, 399, 5690, 632, 353, 29871, 29941, 2 ]
EstruturaDeDecisao/18 terminar.py	TheCarvalho/atividades-wikipython	0	94659	# ex18 - Faça um Programa que peça uma data no formato dd/mm/aaaa e determine se a mesma é uma data válida.	[ 1, 29871, 13, 29937, 429, 29896, 29947, 448, 7748, 4277, 1922, 7835, 29874, 712, 1236, 4277, 3672, 848, 694, 883, 1219, 24488, 29914, 4317, 29914, 27137, 321, 8161, 409, 263, 4883, 655, 904, 3672, 848, 12196, 1458, 29889, 13, 2 ]
tests.py	Airead/pysha3	30	95699	<reponame>Airead/pysha3 from __future__ import print_function import hashlib import hmac import os import sys import unittest import sha3 if sys.version_info[0] == 3: fromhex = bytes.fromhex def tobyte(b): return bytes([b]) def asunicode(s): return s else: def fromhex(s): return s.decode('hex') def tobyte(b): return bytes(b) def asunicode(s): return s.decode('ascii') def read_vectors(hash_name): vector = os.path.join('vectors', hash_name + '.txt') with open(vector) as f: for line in f: line = line.strip() if line.startswith('#') or not line: continue msg, md = line.split(',') yield msg, md class BaseSHA3Tests(unittest.TestCase): new = None name = None digest_size = None block_size = None rate_bits = None capacity_bits = None shake = False vectors = [] # http://wolfgang-ehrhardt.de/hmac-sha3-testvectors.html hmac_vectors = [ ("0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b", "4869205468657265"), ("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "5468697320697320612074657374207573696e672061206c6172676572207" + "468616e20626c6f636b2d73697a65206b657920616e642061206c617267657" + "2207468616e20626c6f636b2d73697a6520646174612e20546865206b65792" + "06e6565647320746f20626520686173686564206265666f7265206265696e6" + "720757365642062792074686520484d414320616c676f726974686d2e") ] hmac_results = [] def assertHashDigest(self, hexmsg, hexdigest): hexdigest = hexdigest.lower() msg = fromhex(hexmsg) digest = fromhex(hexdigest) self.assertEqual(len(digest), self.digest_size) sha3 = self.new(msg) self.assertEqual(sha3.hexdigest(), hexdigest) self.assertEqual(sha3.digest(), digest) sha3 = self.new() sha3.update(msg) self.assertEqual(sha3.hexdigest(), hexdigest) self.assertEqual(sha3.digest(), digest) sha3 = self.new() for b in msg: sha3.update(tobyte(b)) self.assertEqual(sha3.hexdigest(), hexdigest) self.assertEqual(sha3.digest(), digest) def test_basics(self): sha3 = self.new() self.assertEqual(sha3.name, self.name) self.assertEqual(sha3.digest_size, self.digest_size) self.assertEqual(sha3._capacity_bits + sha3._rate_bits, 1600) self.assertEqual(sha3._rate_bits, self.rate_bits) self.assertEqual(sha3._capacity_bits, self.capacity_bits) if self.block_size is not None: self.assertEqual(sha3.block_size, self.block_size) if self.shake: self.assertEqual(len(sha3.digest(4)), 4) self.assertEqual(len(sha3.hexdigest(4)), 8) self.assertEqual(len(sha3.digest(8)), 8) self.assertEqual(len(sha3.hexdigest(8)), 16) self.assertEqual(len(sha3.digest(97)), 97) self.assertEqual(len(sha3.hexdigest(97)), 194) else: self.assertEqual(len(sha3.digest()), self.digest_size) self.assertEqual(len(sha3.hexdigest()), self.digest_size * 2) # object is read-only self.assertRaises(AttributeError, setattr, sha3, "attribute", None) self.assertRaises(AttributeError, setattr, sha3, "digest_size", 3) self.assertRaises(AttributeError, setattr, sha3, "name", "egg") self.new(b"data") self.new(string=b"data") self.assertRaises(TypeError, self.new, None) self.assertRaises(TypeError, sha3.update, None) self.assertRaises(TypeError, self.new, asunicode("text")) self.assertRaises(TypeError, sha3.update, asunicode("text")) sha3type = type(sha3) self.assertEqual(sha3type.__name__, self.name) self.assertEqual(sha3type.__module__, "_pysha3") self.assertIsInstance(sha3type(), sha3type) self.assertIs(sha3type, self.new) self.assertRaises(TypeError, type, sha3type, "subclass", {}) def test_hashlib(self): constructor = getattr(hashlib, self.name) s1 = constructor() self.assertEqual(s1.name, self.name) self.assertEqual(s1.digest_size, self.digest_size) # s2 = hashlib.new(self.name) # self.assertEqual(s2.name, self.name) # self.assertEqual(s2.digest_size, self.digest_size) # self.assertEqual(type(s1), type(s2)) # if sys.version_info < (3, 4): # self.assertEqual(constructor, self.new) def test_vectors(self): for hexmsg, hexdigest in read_vectors(self.name): self.assertHashDigest(hexmsg, hexdigest) def test_vectors_unaligned(self): for hexmsg, hexdigest in self.vectors: hexdigest = hexdigest.lower() msg = fromhex(hexmsg) digest = fromhex(hexdigest) for i in range(1, 15): msg2 = i * b"\x00" + msg unaligned = memoryview(msg2)[i:] self.assertEqual(unaligned, msg) sha3 = self.new(unaligned) self.assertEqual(sha3.hexdigest(), hexdigest) self.assertEqual(sha3.digest(), digest) def test_hmac(self): for (key, msg), expected in zip(self.hmac_vectors, self.hmac_results): key = fromhex(key) msg = fromhex(msg) mac = hmac.new(key, msg, self.new) self.assertEqual(len(mac.digest()), self.digest_size) result = mac.hexdigest() self.assertEqual(result, expected, "%s != %s for %r, %r" % (result, expected, key, msg)) class BaseKeccakTests(BaseSHA3Tests): def test_hashlib(self): self.failIf(hasattr(hashlib, self.name)) class BaseShakeTests(BaseSHA3Tests): shake = True def assertHashDigest(self, hexmsg, hexdigest): hexdigest = hexdigest.lower() msg = fromhex(hexmsg) digest = fromhex(hexdigest) # self.assertEqual(len(digest), self.digest_size) sha3 = self.new(msg) self.assertEqual(sha3.hexdigest(len(digest)), hexdigest) self.assertEqual(sha3.digest(len(digest)), digest) sha3 = self.new() sha3.update(msg) self.assertEqual(sha3.hexdigest(len(digest)), hexdigest) self.assertEqual(sha3.digest(len(digest)), digest) sha3 = self.new() for b in msg: sha3.update(tobyte(b)) self.assertEqual(sha3.hexdigest(len(digest)), hexdigest) self.assertEqual(sha3.digest(len(digest)), digest) class SHA3_224Tests(BaseSHA3Tests): new = sha3.sha3_224 name = "sha3_224" digest_size = 28 block_size = 144 rate_bits = 1152 capacity_bits = 448 hmac_results = [ "3b16546bbc7be2706a031dcafd56373d9884367641d8c59af3c860f7", "c79c9b093424e588a9878bbcb089e018270096e9b4b1a9e8220c866a", ] class SHA3_256Tests(BaseSHA3Tests): new = sha3.sha3_256 name = "sha3_256" digest_size = 32 block_size = 136 rate_bits = 1088 capacity_bits = 512 hmac_results = [ "ba85192310dffa96e2a3a40e69774351140bb7185e1202cdcc917589f95e16bb", "e6a36d9b915f86a093cac7d110e9e04cf1d6100d30475509c2475f571b758b5a", ] class SHA3_384Tests(BaseSHA3Tests): new = sha3.sha3_384 name = "sha3_384" digest_size = 48 block_size = 104 rate_bits = 832 capacity_bits = 768 hmac_results = [ "68d2dcf7fd4ddd0a2240c8a437305f61fb7334cfb5d0226e1bc27dc10a2e72" + "3a20d370b47743130e26ac7e3d532886bd", "cad18a8ff6c4cc3ad487b95f9769e9b61c062aefd6952569e6e6421897054c" + "fc70b5fdc6605c18457112fc6aaad45585", ] class SHA3_512Tests(BaseSHA3Tests): new = sha3.sha3_512 name = "sha3_512" digest_size = 64 block_size = 72 rate_bits = 576 capacity_bits = 1024 hmac_results = [ "eb3fbd4b2eaab8f5c504bd3a41465aacec15770a7cabac531e482f860b5ec7" + "ba47ccb2c6f2afce8f88d22b6dc61380f23a668fd3888bb80537c0a0b86407689e", "dc030ee7887034f32cf402df34622f311f3e6cf04860c6bbd7fa488674782b" + "4659fdbdf3fd877852885cfe6e22185fe7b2ee952043629bc9d5f3298a41d02c66", ] class Shake_128Tests(BaseShakeTests): new = sha3.shake_128 name = "shake_128" digest_size = 0 block_size = 168 rate_bits = 1344 capacity_bits = 256 class Shake_256Tests(BaseShakeTests): new = sha3.shake_256 name = "shake_256" digest_size = 0 block_size = 136 rate_bits = 1088 capacity_bits = 512 class Keccak_224Tests(BaseKeccakTests): new = sha3.keccak_224 name = "keccak_224" digest_size = 28 block_size = 144 rate_bits = 1152 capacity_bits = 448 hmac_results = [ "b73d595a2ba9af815e9f2b4e53e78581ebd34a80b3bbaac4e702c4cc", "92649468be236c3c72c189909c063b13f994be05749dc91310db639e", ] class Keccak_256Tests(BaseKeccakTests): new = sha3.keccak_256 name = "keccak_256" digest_size = 32 block_size = 136 rate_bits = 1088 capacity_bits = 512 hmac_results = [ "9663d10c73ee294054dc9faf95647cb99731d12210ff7075fb3d3395abfb9821", "fdaa10a0299aecff9bb411cf2d7748a4022e4a26be3fb5b11b33d8c2b7ef5484", ] class Keccak_384Tests(BaseKeccakTests): new = sha3.keccak_384 name = "keccak_384" digest_size = 48 block_size = 104 rate_bits = 832 capacity_bits = 768 hmac_results = [ "892dfdf5d51e4679bf320cd16d4c9dc6f749744608e003add7fba894acff87" + "361efa4e5799be06b6461f43b60ae97048", "fe9357e3cfa538eb0373a2ce8f1e26ad6590afdaf266f1300522e8896d27e7" + "3f654d0631c8fa598d4bb82af6b744f4f5", ] class Keccak_512Tests(BaseKeccakTests): new = sha3.keccak_512 name = "keccak_512" digest_size = 64 block_size = 72 rate_bits = 576 capacity_bits = 1024 hmac_results = [ "8852c63be8cfc21541a4ee5e5a9a852fc2f7a9adec2ff3a13718ab4ed81aae" + "a0b87b7eb397323548e261a64e7fc75198f6663a11b22cd957f7c8ec858a1c7755", "6adc502f14e27812402fc81a807b28bf8a53c87bea7a1df6256bf66f5de1a4" + "cb741407ad15ab8abc136846057f881969fbb159c321c904bfb557b77afb7778c8", ] def test_main(): suite = unittest.TestSuite() classes = [ SHA3_224Tests, SHA3_256Tests, SHA3_384Tests, 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synthetic/functions.py	wchen459/MO-PaDGAN-Optimization	3	95912	""" Test functions Author(s): <NAME> (<EMAIL>) """ import numpy as np import tensorflow as tf class Function(object): def __init__(self): pass def evaluate(self, data): x = tf.placeholder(tf.float32, shape=[None, self.dim]) y = self.equation(x) with tf.Session() as sess: values = sess.run(y, feed_dict={x: data}) return values class VLMOP2(Function): ''' Reference: <NAME>., & <NAME>. (1999, February). Multiobjective evolutionary algorithm test suites. In Proceedings of the 1999 ACM symposium on Applied computing (pp. 351-357). ''' def __init__(self): self.dim = 2 self.n_obj = 2 self.name = 'VLMOP2' x1 = np.linspace(-1/self.dim.5, 1/self.dim.5, 100) x2 = np.linspace(-1/self.dim.5, 1/self.dim.5, 100) self.pareto_x = np.vstack((x1, x2)).T self.pareto_y = self.evaluate(self.pareto_x) def equation(self, x): transl = 1 / np.sqrt(2) part1 = (x[:, 0] - transl) 2 + (x[:, 1] - transl) 2 part2 = (x[:, 0] + transl) 2 + (x[:, 1] + transl) 2 y1 = tf.exp(-1 * part1) y2 = tf.exp(-1 * part2) return tf.stack((y1, y2), axis=1) #class OKA1(Function): # ''' # Reference: # <NAME>., <NAME>., <NAME>., & <NAME>. (2004, September). # On test functions for evolutionary multi-objective optimization. # In International Conference on Parallel Problem Solving from Nature (pp. 792-802). # Springer, Berlin, Heidelberg. # ''' # def __init__(self): # self.dim = 2 # self.n_obj = 2 # self.name = 'OKA1' class NKNO1(Function): ''' Normalized KNO1 Reference: J. Knowles. ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. Technical Report TR-COMPSYSBIO-2004-01, University of Manchester, UK, 2004. Available from http://dbk.ch.umist.ac.uk/knowles/pubs.html ''' def __init__(self): self.dim = 2 self.n_obj = 2 self.name = 'NKNO1' x1 = np.linspace(-0.5+4.4116/3-1, 0.5, 100) x2 = 4.4116/3 - x1 - 1 self.pareto_x = np.vstack((x1, x2)).T self.pareto_y = self.evaluate(self.pareto_x) def equation(self, x): x = 3(x+.5) r = 9 - (3tf.sin(5/2(x[:,0]+x[:,1])2) + 3tf.sin(4(x[:,0]+x[:,1])) + 5tf.sin(2(x[:,0]+x[:,1])+2)) phi = np.pi/12(x[:,0]-x[:,1]+3) y1 = r/20tf.cos(phi) y2 = r/20tf.sin(phi) return tf.stack((y1, y2), axis=1) if __name__ == "__main__": import matplotlib.pyplot as plt import matplotlib matplotlib.use('Qt5Agg') N = 1000 xs = np.random.uniform(-.5, .5, size=(N,2)) func_obj = NKNO1() ys = func_obj.evaluate(xs) plt.figure() plt.subplot(121) true_pfx = func_obj.pareto_x plt.plot(true_pfx[:,0], true_pfx[:,1]) plt.subplot(122) plt.scatter(ys[:,0], ys[:,1]) true_pf = func_obj.pareto_y plt.plot(true_pf[:,0], true_pf[:,1]) plt.show()	[ 1, 9995, 29871, 13, 3057, 3168, 13, 13, 13720, 29898, 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580, 2 ]
src/skyciv/classes/model/components/materials/materials.py	skyciv/skyciv-pip	4	114916	<reponame>skyciv/skyciv-pip<gh_stars>1-10 from skyciv.classes.model.components._base_class.model_collection_component import ModelCollectionComponent from skyciv.classes.model.components.materials.default_materials import default_materials from skyciv.classes.model.components.materials.material import Material from skyciv.utils.helpers import next_object_key try: from typing import Literal except ImportError: from typing_extensions import Literal class Materials(ModelCollectionComponent): """Creates an instance of the SkyCiv Materials class. """ def add_custom( self, name: str, density: float, elasticity_modulus: float, poissons_ratio: float, yield_strength: float, ultimate_strength: float, _class: Literal["steel", "aluminium", "masonry", "concrete", "wood", "other"] ) -> int: """Create a custom material with the next available ID. Args: name (str): The name of the material. density (float): The density of the material. elasticity_modulus (float): The Modulus of Elasticity of the material. poissons_ratio (float): The Poisson's Ratio for the material. yield_strength (float): The Yield strength of the material. ultimate_strength (float): The Ultimate strength the material. _class (str): The type of material. {"steel" \| "aluminium" \| "masonry" \| "concrete" \| "wood" \| "other"}. Returns: int: The ID of the created material. """ next_index = next_object_key(self) material = Material( name, density, elasticity_modulus, poissons_ratio, yield_strength, ultimate_strength, _class ) setattr(self, str(next_index), material) return next_index def add(self, material: Literal["Structural Steel", "Aluminium", "Carbon Fibre Reinforced Plastic", "Concrete", "Concrete High Strength", "Oakwood", "Glass"], unit_system: str = "metric") -> int: """Add a default material to the model. Args: material (str): The material name. {"Structural Steel" \| "Aluminium" \| "Carbon Fibre Reinforced Plastic" \| "Concrete" \| "Concrete High Strength" \| "Oakwood" \| "Glass"}. unit_system (str): {'metric' \| 'imperial'} Returns: int: The ID of the created material. """ materials = default_materials[unit_system][material] name = materials["name"] density = materials["density"] elasticity_modulus = materials["elasticity_modulus"] poissons_ratio = materials["poissons_ratio"] yield_strength = materials["yield_strength"] ultimate_strength = materials["ultimate_strength"] _class = materials["class"] index = self.add_custom( name, density, elasticity_modulus, poissons_ratio, yield_strength, ultimate_strength, _class ) return index	[ 1, 529, 276, 1112, 420, 29958, 7912, 29883, 440, 29914, 7912, 29883, 440, 29899, 13096, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 2071, 11078, 440, 29889, 13203, 29889, 4299, 29889, 14036, 3032, 3188, 29918, 1990, 29889, 4299, 29918, 10855, 29918, 9700, 1053, 8125, 7196, 5308, 13, 3166, 2071, 11078, 440, 29889, 13203, 29889, 4299, 29889, 14036, 29889, 15388, 29879, 29889, 4381, 29918, 15388, 29879, 1053, 2322, 29918, 15388, 29879, 13, 3166, 2071, 11078, 440, 29889, 13203, 29889, 4299, 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src/pypytranspy/transformations/fstring_to_format_transformer.py	expobrain/pypytranspy	4	85775	import ast from pypytranspy.transformations.base_transformer import BaseTransformer class FStringToFormatTransformer(BaseTransformer): minimum_version = [3, 6] def pre_JoinedStr(self): # Collect string and args for format() str_value = "" str_args = [] for value in self.cur_node.values: if isinstance(value, ast.Str): str_value += value.s elif isinstance(value, ast.FormattedValue): if value.format_spec is None: str_value += "{}" else: str_value += "{:" + "".join(v.s for v in value.format_spec.values) + "}" str_args.append(value.value) else: raise NotImplementedError(value) # Build Str.format() node str_format = ast.Call( func=ast.Attribute(value=ast.Str(s=str_value), attr="format"), args=str_args, keywords=[], ) self.replace(str_format)	[ 1, 1053, 8717, 13, 13, 3166, 282, 1478, 29891, 3286, 2272, 29889, 9067, 800, 29889, 3188, 29918, 9067, 261, 1053, 7399, 13372, 261, 13, 13, 13, 1990, 383, 1231, 1762, 5809, 13372, 261, 29898, 5160, 13372, 261, 1125, 13, 13, 1678, 9212, 29918, 3259, 353, 518, 29941, 29892, 29871, 29953, 29962, 13, 13, 1678, 822, 758, 29918, 10844, 1312, 5015, 29898, 1311, 1125, 13, 4706, 396, 24930, 1347, 322, 6389, 363, 3402, 580, 13, 4706, 851, 29918, 1767, 353, 5124, 13, 4706, 851, 29918, 5085, 353, 5159, 13, 13, 4706, 363, 995, 297, 1583, 29889, 2764, 29918, 3177, 29889, 5975, 29901, 13, 9651, 565, 338, 8758, 29898, 1767, 29892, 8717, 29889, 5015, 1125, 13, 18884, 851, 29918, 1767, 4619, 995, 29889, 29879, 13, 9651, 25342, 338, 8758, 29898, 1767, 29892, 8717, 29889, 2500, 19667, 1917, 1125, 13, 18884, 565, 995, 29889, 4830, 29918, 6550, 338, 6213, 29901, 13, 462, 1678, 851, 29918, 1767, 4619, 29850, 5038, 13, 18884, 1683, 29901, 13, 462, 1678, 851, 29918, 1767, 4619, 29850, 6160, 718, 376, 1642, 7122, 29898, 29894, 29889, 29879, 363, 325, 297, 995, 29889, 4830, 29918, 6550, 29889, 5975, 29897, 718, 376, 5038, 13, 13, 18884, 851, 29918, 5085, 29889, 4397, 29898, 1767, 29889, 1767, 29897, 13, 9651, 1683, 29901, 13, 18884, 12020, 2216, 1888, 2037, 287, 2392, 29898, 1767, 29897, 13, 13, 4706, 396, 8878, 3767, 29889, 4830, 580, 2943, 13, 4706, 851, 29918, 4830, 353, 8717, 29889, 5594, 29898, 13, 9651, 3653, 29922, 579, 29889, 6708, 29898, 1767, 29922, 579, 29889, 5015, 29898, 29879, 29922, 710, 29918, 1767, 511, 12421, 543, 4830, 4968, 13, 9651, 6389, 29922, 710, 29918, 5085, 29892, 13, 9651, 29361, 11759, 1402, 13, 4706, 1723, 13, 13, 4706, 1583, 29889, 6506, 29898, 710, 29918, 4830, 29897, 13, 2 ]
examples/python/cpu/numpy/example_11.py	kant/ocean-tensor-package	27	1604315	<gh_stars>10-100 import pyOcean_cpu as ocean import numpy as np import pyOceanNumpy a = ocean.tensor([5]) s = ocean.tensor([], ocean.float) s.fill(3); a.fill([[False]]); print(a) a.fill([(1,)]); print(a) a.fill([2.0]); print(a) a.fill(s); print(a) a.fill([s]); print(a) s = np.asarray([4]) print(s) print(type(s)) a.fill(s); print(a) a.fill([(s,)]); print(a)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 5215, 11451, 29949, 11956, 29918, 21970, 408, 23474, 13, 5215, 12655, 408, 7442, 13, 5215, 11451, 29949, 11956, 8009, 2272, 13, 13, 29874, 353, 23474, 29889, 20158, 4197, 29945, 2314, 13, 29879, 353, 23474, 29889, 20158, 4197, 1402, 23474, 29889, 7411, 29897, 13, 29879, 29889, 5589, 29898, 29941, 416, 13, 13, 29874, 29889, 5589, 4197, 29961, 8824, 5262, 416, 259, 1596, 29898, 29874, 29897, 13, 13, 29874, 29889, 5589, 4197, 29898, 29896, 29892, 4638, 416, 418, 1596, 29898, 29874, 29897, 13, 29874, 29889, 5589, 4197, 29906, 29889, 29900, 5691, 539, 1596, 29898, 29874, 29897, 13, 29874, 29889, 5589, 29898, 29879, 416, 965, 1596, 29898, 29874, 29897, 13, 29874, 29889, 5589, 4197, 29879, 5691, 308, 1596, 29898, 29874, 29897, 13, 13, 29879, 353, 7442, 29889, 294, 2378, 4197, 29946, 2314, 13, 2158, 29898, 29879, 29897, 13, 2158, 29898, 1853, 29898, 29879, 876, 13, 29874, 29889, 5589, 29898, 29879, 416, 965, 1596, 29898, 29874, 29897, 13, 29874, 29889, 5589, 4197, 29898, 29879, 29892, 4638, 416, 418, 1596, 29898, 29874, 29897, 13, 13, 2 ]
matplotlib/gallery_python/pyplots/dollar_ticks.py	gottaegbert/penter	13	4270	<reponame>gottaegbert/penter """ ============ Dollar Ticks ============ Use a `~.ticker.FormatStrFormatter` to prepend dollar signs on y axis labels. """ import numpy as np import matplotlib.pyplot as plt import matplotlib.ticker as ticker # Fixing random state for reproducibility np.random.seed(19680801) fig, ax = plt.subplots() ax.plot(100*np.random.rand(20)) formatter = ticker.FormatStrFormatter('$%1.2f') ax.yaxis.set_major_formatter(formatter) for tick in ax.yaxis.get_major_ticks(): tick.label1.set_visible(False) tick.label2.set_visible(True) tick.label2.set_color('green') plt.show() ############################################################################# # # ------------ # # References # """""""""" # # The use of the following functions, methods, classes and modules is shown # in this example: import matplotlib matplotlib.ticker matplotlib.ticker.FormatStrFormatter matplotlib.axis.Axis.set_major_formatter matplotlib.axis.Axis.get_major_ticks matplotlib.axis.Tick	[ 1, 529, 276, 1112, 420, 29958, 7085, 941, 387, 2151, 29914, 29886, 5893, 13, 15945, 29908, 13, 4936, 2751, 13, 29928, 26810, 323, 7358, 13, 4936, 2751, 13, 13, 11403, 263, 421, 30022, 29889, 29873, 6541, 29889, 5809, 5015, 18522, 29952, 304, 8273, 355, 11232, 279, 18906, 373, 343, 9685, 11073, 29889, 13, 15945, 29908, 13, 5215, 12655, 408, 7442, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 22889, 29889, 29873, 6541, 408, 260, 6541, 13, 13, 29937, 24778, 292, 4036, 2106, 363, 9483, 455, 29890, 1793, 13, 9302, 29889, 8172, 29889, 26776, 29898, 29896, 29929, 29953, 29947, 29900, 29947, 29900, 29896, 29897, 13, 13, 1003, 29892, 4853, 353, 14770, 29889, 1491, 26762, 580, 13, 1165, 29889, 5317, 29898, 29896, 29900, 29900, 29930, 9302, 29889, 8172, 29889, 9502, 29898, 29906, 29900, 876, 13, 13, 689, 2620, 353, 260, 6541, 29889, 5809, 5015, 18522, 877, 29938, 29995, 29896, 29889, 29906, 29888, 1495, 13, 1165, 29889, 29891, 8990, 29889, 842, 29918, 21355, 29918, 689, 2620, 29898, 689, 2620, 29897, 13, 13, 1454, 16892, 297, 4853, 29889, 29891, 8990, 29889, 657, 29918, 21355, 29918, 29873, 7358, 7295, 13, 1678, 16892, 29889, 1643, 29896, 29889, 842, 29918, 12872, 29898, 8824, 29897, 13, 1678, 16892, 29889, 1643, 29906, 29889, 842, 29918, 12872, 29898, 5574, 29897, 13, 1678, 16892, 29889, 1643, 29906, 29889, 842, 29918, 2780, 877, 12692, 1495, 13, 13, 572, 29873, 29889, 4294, 580, 13, 13, 13383, 13383, 13383, 13383, 7346, 4136, 29937, 13, 29937, 13, 29937, 448, 1378, 5634, 13, 29937, 13, 29937, 28318, 13, 29937, 9995, 15945, 15945, 15945, 29908, 13, 29937, 13, 29937, 450, 671, 310, 278, 1494, 3168, 29892, 3519, 29892, 4413, 322, 10585, 338, 4318, 13, 29937, 297, 445, 1342, 29901, 13, 13, 5215, 22889, 13, 2922, 17357, 29889, 29873, 6541, 13, 2922, 17357, 29889, 29873, 6541, 29889, 5809, 5015, 18522, 13, 2922, 17357, 29889, 8990, 29889, 16070, 29889, 842, 29918, 21355, 29918, 689, 2620, 13, 2922, 17357, 29889, 8990, 29889, 16070, 29889, 657, 29918, 21355, 29918, 29873, 7358, 13, 2922, 17357, 29889, 8990, 29889, 29911, 860, 13, 2 ]
src/lumigo_tracer/parsing_utils.py	anniyanvr/python_tracer	47	95777	import json import re import urllib.parse from typing import Tuple, Dict, Union, List, Any, Optional from lumigo_tracer.libs import xmltodict import functools import itertools from collections.abc import Iterable from lumigo_tracer.lumigo_utils import Configuration, get_logger def safe_get(d: Union[dict, list], keys: List[Union[str, int]], default: Any = None) -> Any: """ :param d: Should be list or dict, otherwise return default. :param keys: If keys[i] is int, then it should be a list index. If keys[i] is string, then it should be a dict key. :param default: If encountered a problem, return default. :return: d[keys[0]][keys[1]]... """ def get_next_val(prev_result, key): if isinstance(prev_result, dict) and isinstance(key, str): return prev_result.get(key, default) elif isinstance(prev_result, list) and isinstance(key, int): return safe_get_list(prev_result, key, default) else: return default return functools.reduce(get_next_val, keys, d) def safe_get_list(lst: list, index: Union[int, str], default=None): """ This function return the organ in the `index` place from the given list. If this values doesn't exist, return default. """ if isinstance(index, str): try: index = int(index) except ValueError: return default if not isinstance(lst, Iterable): return default return lst[index] if len(lst) > index else default def safe_split_get(string: str, sep: str, index: int, default=None) -> str: """ This function splits the given string using the sep, and returns the organ in the `index` place. If such index doesn't exist, returns default. """ if not isinstance(string, str): return default return safe_get_list(string.split(sep), index, default) def safe_key_from_json(json_str: bytes, key: object, default=None) -> Union[str, list]: """ This function tries to read the given str as json, and returns the value of the desired key. If the key doesn't found or the input string is not a valid json, returns the default. """ try: return json.loads(json_str).get(key, default) except json.JSONDecodeError: return default def safe_key_from_xml(xml_str: bytes, key: str, default=None): """ This function tries to read the given str as XML, and returns the value of the desired key. If the key doesn't found or the input string is not a valid XML, returns the default. We accept keys with hierarchy by `/` (i.e. we accept keys with the format `outer/inner`) If there are some keys with the same name at the same hierarchy, they can be accessed as index in list, e.g: <a><b>val0</b><b>val1</b></a> will be accessed with "a/b/0" or "a/b/1". """ try: result = functools.reduce( lambda prev, sub_key: safe_get_list(prev, sub_key) if isinstance(prev, list) else prev.get(sub_key, {}), key.split("/"), xmltodict.parse(xml_str), ) return result or default except xmltodict.expat.ExpatError: return default def safe_key_from_query(body: bytes, key: str, default=None) -> str: """ This function assumes that the first row in the body is the url arguments. We assume that the structure of the parameters is as follow: * character-escaped using urllib.quote * values separated with '&' * each item is <key>=<value> Note: This function decode the given body, therefore duplicate it's size. Be aware to use only in resources with restricted body length. """ return dict(re.findall(r"([^&]+)=([^&])", urllib.parse.unquote(body.decode()))).get( key, default ) def parse_trace_id(trace_id_str: str) -> Tuple[str, str, str]: """ This function parses the trace_id, and result dictionary the describes the data. We assume the following format: values separated with ';' * each item is <key>=<value> :param trace_id_str: The string that came from the environment variables. """ if not isinstance(trace_id_str, str): return "", "", "" trace_id_parameters = dict(re.findall(r"([^;]+)=([^;])", trace_id_str)) root = trace_id_parameters.get("Root", "") root_end_index = trace_id_str.find(";") suffix = trace_id_str[root_end_index:] if ";" in trace_id_str else trace_id_str return root, safe_split_get(root, "-", 2, default=""), suffix def recursive_json_join(d1: Optional[dict], d2: Optional[dict]): """ This function return the recursive joint dictionary, which means that for every (item, key) in the result dictionary it holds that: if key in d1 and is not dictionary, then the value is d1[key] * if key in d2 and is not dictionary, then the value is d2[key] * otherwise, join d1[key] and d2[key] """ if d1 is None or d2 is None: return d1 or d2 d = {} for key in set(itertools.chain(d1.keys(), d2.keys())): value = d1.get(key, d2.get(key)) if isinstance(value, dict): d[key] = recursive_json_join(d1.get(key), d2.get(key)) # type: ignore else: d[key] = value return d def should_scrub_domain(url: str) -> bool: if url and Configuration.domains_scrubber: for regex in Configuration.domains_scrubber: if regex.match(url): return True return False def str_to_list(val: str) -> Optional[List[str]]: try: if val: return val.split(",") except Exception as e: get_logger().debug("Error while convert str to list", exc_info=e) return None def str_to_tuple(val: str) -> Optional[Tuple]: try: if val: return tuple(val.split(",")) except Exception as e: get_logger().debug("Error while convert str to tuple", exc_info=e) return None def recursive_get_key(d: Union[List, Dict[str, Union[Dict, str]]], key, depth=None, default=None): if depth is None: depth = Configuration.get_key_depth if depth == 0: return default if key in d: return d[key] if isinstance(d, list): for v in d: recursive_result = recursive_get_key(v, key, depth - 1, default) if recursive_result: return recursive_result if isinstance(d, dict): for v in d.values(): if isinstance(v, (list, dict)): recursive_result 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nasws/cnn/search_space/monodepth/params/search-decoder-v1/resnext101_wsl.py	kcyu2014/nas-landmarkreg	8	1600843	<reponame>kcyu2014/nas-landmarkreg<filename>nasws/cnn/search_space/monodepth/params/search-decoder-v1/resnext101_wsl.py from monodepth.models import MidasNet, MidasNetSearch from monodepth.optim.optimizer import MGDA, GDA import torch import torch.optim as optim from monodepth.validation import Validation from monodepth.utils.checkpointer import Checkpointer from monodepth.utils.reporter import Reporter from monodepth.optim.scheduler import ConstLR from monodepth.loss import ( ScaleAndShiftInvariantLoss, ProcrustesLoss, TrimmedProcrustesLoss, ) from monodepth.data import multi_data_loader from monodepth.data.datasets import MegaDepth, ReDWeb, Movies3d from monodepth.data.datasets.transforms import ( RandomCrop, RandomFliplr, Rescale, PrepareForNet, Resize, NormalizeImage, ) from torchvision.transforms import Compose from monodepth.metric import DisparityRmse from collections import OrderedDict from monodepth.nas.api import NonZeroRandomMutator, NonZeroRandomMutatorSync class TrainParams: def __init__(self, config, output, args, do_resume=False, init_from=None): # torch options torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True # device self.device = torch.device("cuda") self.args = args # model self.model = MidasNetSearch(backbone="resnext101_wsl", args=args) if 'spos' in args.supernet_train_method: if args.mutator == 'nonzero': mutator = NonZeroRandomMutator(self.model) elif args.mutator == 'nonzero_sync': mutator = NonZeroRandomMutatorSync(self.model) else: raise NotImplementedError(f"Mutator {args.mutator} is not implemented for SPOS method") else: raise NotImplementedError(f"Method {args.supernet_train_method} is not implemented for MonoDepth") self.mutator = mutator # tasks self.tasks = OrderedDict() self.tasks["ReDWeb"] = { "dataset": ReDWeb( config["datasets"]["ReDWeb"]["train"], config["datasets"]["ReDWeb"]["datapath"], transform=Compose( [ Resize(384, 384, keep_aspect_ratio=True), RandomCrop(384, 384), RandomFliplr(), Rescale(), NormalizeImage( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ), PrepareForNet(), ] ), mode="train", ), "loss": TrimmedProcrustesLoss(), } self.dataloader = multi_data_loader.MultiDataLoader( {k: v["dataset"] for k, v in self.tasks.items()}, batch_size=8, num_workers=4, ) # optimization algo = optim.Adam( [ {"params": self.model.scratch.parameters()}, {"params": self.model.pretrained.parameters(), "lr": args.backbone_learning_rate}, ], lr=args.learning_rate, betas=(0.9, 0.999), weight_decay=args.weight_decay ) if args.backbone_learning_rate == 0.0: for param in self.model.pretrained.parameters(): param.requires_grad = False if args.backbone_weights: # load the pretrained weights weights accordingly state = torch.load(args.backbone_weights) self.model.load_state_dict(state, strict=False) # this should make the training super fast self.epoch_length = 3240 self.num_epochs = args.epochs # wrapping the optimizer self.optimizer = GDA(algorithm=algo, num_tasks=len(self.tasks)) if args.learning_rate_scheduler == 'step': self.scheduler = torch.optim.lr_scheduler.StepLR(algo, args.epochs // 3, gamma=0.1) elif args.learning_rate_scheduler == 'cosine': self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(algo, float(args.epochs), eta_min=args.learning_rate_min) print(f'Using {self.scheduler} with initial learning rate {args.learning_rate}.') # checkpointing self.checkpointer = Checkpointer( output + "/checkpoint.pt", self.model, self.optimizer.algo, self.scheduler, frequency=10, do_resume=do_resume, ) # reporting self.reporter = Reporter( use_console=True, tensorboard_out=output + "/log", img_index=[], logger_out=output + '/log_search.txt' ) # validation validation_tasks = OrderedDict() validation_tasks["ReDWeb"] = { "dataset": ReDWeb( config["datasets"]["ReDWeb"]["validation"], config["datasets"]["ReDWeb"]["datapath"], transform=Compose( 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RecoPPS/Local/python/ctppsDiamondLocalReconstruction_cff.py	ckamtsikis/cmssw	852	39143	<gh_stars>100-1000 import FWCore.ParameterSet.Config as cms # reco hit production from RecoPPS.Local.ctppsDiamondRecHits_cfi import ctppsDiamondRecHits # local track fitting from RecoPPS.Local.ctppsDiamondLocalTracks_cfi import ctppsDiamondLocalTracks ctppsDiamondLocalReconstructionTask = cms.Task( ctppsDiamondRecHits, ctppsDiamondLocalTracks ) ctppsDiamondLocalReconstruction = cms.Sequence(ctppsDiamondLocalReconstructionTask)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29899, 29896, 29900, 29900, 29900, 13, 5215, 383, 29956, 9203, 29889, 9329, 2697, 29889, 3991, 408, 274, 1516, 13, 13, 29937, 337, 1111, 7124, 5802, 13, 3166, 3599, 29877, 29925, 7024, 29889, 7717, 29889, 312, 407, 29879, 29928, 2829, 898, 4789, 29950, 1169, 29918, 6854, 29875, 1053, 274, 29873, 407, 29879, 29928, 2829, 898, 4789, 29950, 1169, 13, 13, 29937, 1887, 5702, 28221, 13, 3166, 3599, 29877, 29925, 7024, 29889, 7717, 29889, 312, 407, 29879, 29928, 2829, 898, 7717, 5323, 4684, 29918, 6854, 29875, 1053, 274, 29873, 407, 29879, 29928, 2829, 898, 7717, 5323, 4684, 13, 13, 312, 407, 29879, 29928, 2829, 898, 7717, 1123, 3075, 4080, 5398, 353, 274, 1516, 29889, 5398, 29898, 13, 1678, 274, 29873, 407, 29879, 29928, 2829, 898, 4789, 29950, 1169, 29892, 13, 1678, 274, 29873, 407, 29879, 29928, 2829, 898, 7717, 5323, 4684, 13, 29897, 13, 312, 407, 29879, 29928, 2829, 898, 7717, 1123, 3075, 4080, 353, 274, 1516, 29889, 20529, 29898, 312, 407, 29879, 29928, 2829, 898, 7717, 1123, 3075, 4080, 5398, 29897, 13, 2 ]
identityholder.py	BenNordick/HiLoop	1	46626	<filename>identityholder.py<gh_stars>1-10 class IdentityHolder: """ Wraps an object so that it can be quickly compared by object identity rather than value. Also provides a total order based on object address. This is frequently useful for sets representing cycles: set value equality is O(n) but object identity equality is O(1). Different cycles can have the same nodes in a different order; it is important to distinguish these objects, but useful to represent them as sets for purposes of fast intersection. """ def __init__(self, value, tag=None): """Wrap a value, optionally tagged with extra information.""" self.value = value self.tag = tag def __hash__(self): return id(self.value) def __eq__(self, other): return self.value is other.value def __str__(self): return 'IH`' + str(self.value) + '`' def __repr__(self): return 'IdentityHolder(' + repr(self.value) + ', ' + repr(self.tag) + ')' def order(self, other): """Return a tuple of this holder and the other such that the first item is before the second in a total order.""" return (self, other) if self.isbefore(other) else (other, self) def isbefore(self, other): """Determine whether this holder is before the other in a total order.""" return id(self.value) < id(other.value)	[ 1, 529, 9507, 29958, 22350, 7694, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1990, 27486, 11439, 29901, 13, 1678, 9995, 13, 1678, 399, 336, 567, 385, 1203, 577, 393, 372, 508, 367, 9098, 9401, 491, 1203, 10110, 3265, 1135, 995, 29889, 13, 1678, 3115, 8128, 263, 3001, 1797, 2729, 373, 1203, 3211, 29889, 13, 13, 1678, 910, 338, 13672, 5407, 363, 6166, 15783, 25785, 29901, 731, 995, 17193, 338, 438, 29898, 29876, 29897, 541, 1203, 10110, 17193, 338, 438, 29898, 29896, 467, 13, 1678, 360, 15622, 25785, 508, 505, 278, 1021, 7573, 297, 263, 1422, 1797, 29936, 372, 338, 4100, 304, 20820, 1438, 3618, 29892, 541, 5407, 13, 1678, 304, 2755, 963, 408, 6166, 363, 11976, 310, 5172, 17686, 29889, 13, 1678, 9995, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 995, 29892, 4055, 29922, 8516, 1125, 13, 4706, 9995, 29956, 2390, 263, 995, 29892, 2984, 635, 4055, 3192, 411, 4805, 2472, 1213, 15945, 13, 4706, 1583, 29889, 1767, 353, 995, 13, 4706, 1583, 29889, 4039, 353, 4055, 13, 1678, 822, 4770, 8568, 12035, 1311, 1125, 13, 4706, 736, 1178, 29898, 1311, 29889, 1767, 29897, 13, 1678, 822, 4770, 1837, 12035, 1311, 29892, 916, 1125, 13, 4706, 736, 1583, 29889, 1767, 338, 916, 29889, 1767, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 525, 29902, 29950, 20497, 718, 851, 29898, 1311, 29889, 1767, 29897, 718, 525, 20497, 13, 1678, 822, 4770, 276, 558, 12035, 1311, 1125, 13, 4706, 736, 525, 18415, 11439, 877, 718, 2062, 29898, 1311, 29889, 1767, 29897, 718, 13420, 525, 718, 2062, 29898, 1311, 29889, 4039, 29897, 718, 525, 16029, 13, 1678, 822, 1797, 29898, 1311, 29892, 916, 1125, 13, 4706, 9995, 11609, 263, 18761, 310, 445, 19464, 322, 278, 916, 1316, 393, 278, 937, 2944, 338, 1434, 278, 1473, 297, 263, 3001, 1797, 1213, 15945, 13, 4706, 736, 313, 1311, 29892, 916, 29897, 565, 1583, 29889, 275, 11083, 29898, 1228, 29897, 1683, 313, 1228, 29892, 1583, 29897, 13, 1678, 822, 338, 11083, 29898, 1311, 29892, 916, 1125, 13, 4706, 9995, 6362, 837, 457, 3692, 445, 19464, 338, 1434, 278, 916, 297, 263, 3001, 1797, 1213, 15945, 13, 4706, 736, 1178, 29898, 1311, 29889, 1767, 29897, 529, 1178, 29898, 1228, 29889, 1767, 29897, 13, 308, 2 ]
src/bert.py	emerging-welfare/random_sampling-vs-keyword_filtering	0	83376	<reponame>emerging-welfare/random_sampling-vs-keyword_filtering from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler from torch.autograd import Variable from sklearn.metrics import classification_report, confusion_matrix, f1_score from transformers import * from torch import nn import numpy as np import time import datetime import torch import random import json import os import sys import torch.nn.functional as F import unicodedata import re use_gpu = True seed = 1234 batch_size = 64 # 8 per available GPU max_length = 512 folds = 10 n_epochs = 10 lr = 2e-5 device_ids = [0, 1, 2, 3, 4, 5, 6, 7] #device_ids = [0, 1, 2, 3] if use_gpu and torch.cuda.is_available(): device = torch.device("cuda:0") else: device = torch.device("cpu") tokenizer = None def prepare_set(dataset, max_length=256): """returns input_ids, input_masks, labels for set of data ready in BERT format""" global tokenizer input_ids, labels = [], [] for i in dataset: input_ids.append(i["text"].lower()) labels.append(1 if i["label"] == 1 else 0) inputs = [ tokenizer.encode_plus(i, pad_to_max_length=True, add_special_tokens=True, max_length=max_length) for i in input_ids ] input_ids = [ torch.tensor(x["input_ids"]) for x in inputs ] input_masks = [ torch.tensor(x["attention_mask"]) for x in inputs ] token_type_ids = [ torch.tensor(x["token_type_ids"]) for x in inputs ] input_ids = torch.stack(input_ids) input_masks = torch.stack(input_masks) token_type_ids = torch.stack(token_type_ids) labels = torch.tensor(labels) return (input_ids, input_masks, token_type_ids), labels def predict(self, test): (test_inputs, test_masks, test_type_ids), y_test = prepare_set(test, max_length=max_length) test_data = TensorDataset(test_inputs, test_masks, test_type_ids) test_sampler = SequentialSampler(test_data) test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size) self.eval() with torch.no_grad(): preds = [] for batch in test_dataloader: b_input_ids, b_input_mask, b_token_type_ids = tuple(t.to(device) for t in batch) output = self(b_input_ids, b_input_mask, b_token_type_ids) logits = output[0].detach().cpu().numpy() preds += list(np.argmax(logits, axis=1).flatten()) return preds def get_bert(pretrained_model): global tokenizer tokenizer = BertTokenizer.from_pretrained(pretrained_model) model = BertForSequenceClassification.from_pretrained(pretrained_model, num_labels=2) # if torch.cuda.device_count() > 1 and device.type == "cuda": # model = nn.DataParallel(model, device_ids=device_ids) model.to(device) model.predict = predict.__get__(model) return model def build_bert(train, dev, pretrained_model, n_epochs=15): global tokenizer tokenizer = AutoTokenizer.from_pretrained(pretrained_model) model = AutoModelForSequenceClassification.from_pretrained(pretrained_model) model_path = "temp.pt" print([len(x) for x in (train, dev)]) (train_inputs, train_masks, train_type_ids), y_train = prepare_set(train, max_length=max_length) train_data = TensorDataset(train_inputs, train_masks, train_type_ids, y_train) train_sampler = RandomSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size) # Create the DataLoader for our dev set. (dev_inputs, dev_masks, dev_type_ids), y_dev = prepare_set(dev, max_length=max_length) dev_data = TensorDataset(dev_inputs, dev_masks, dev_type_ids, y_dev) dev_sampler = SequentialSampler(dev_data) dev_dataloader = DataLoader(dev_data, sampler=dev_sampler, batch_size=batch_size) if torch.cuda.device_count() > 1 and device.type == "cuda": model = nn.DataParallel(model, device_ids=device_ids) model.to(device) np.random.seed(seed) torch.manual_seed(seed) if device.type == "cuda": torch.cuda.manual_seed_all(seed) total_steps = len(train_dataloader) * n_epochs optimizer = AdamW(model.parameters(), lr=lr) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps = 0, num_training_steps = total_steps) model.zero_grad() best_score = 0 best_loss = 1e6 train_losses = [] for epoch in range(n_epochs): start_time = time.time() train_loss = 0 model.train() for batch in train_dataloader: b_input_ids, b_input_mask, b_token_type_ids, b_labels = tuple(t.to(device) for t in batch) output = model(b_input_ids, attention_mask=b_input_mask, token_type_ids=b_token_type_ids, labels=b_labels) loss = output[0].sum() loss.backward() optimizer.step() train_loss += loss.item() model.zero_grad() scheduler.step() elapsed = time.time() - start_time model.eval() val_preds = [] with torch.no_grad(): val_loss, batch = 0, 1 for batch in dev_dataloader: b_input_ids, b_input_mask, b_token_type_ids, b_labels = tuple(t.to(device) for t in batch) output = model(b_input_ids, attention_mask=b_input_mask, token_type_ids=b_token_type_ids, labels=b_labels) loss = output[0].sum() val_loss += loss.item() logits = output[1].detach().cpu().numpy() val_preds += list(np.argmax(logits, axis=1).flatten()) model.zero_grad() val_score = f1_score(y_dev.cpu().numpy().tolist(), val_preds, average="macro") #print("Epoch %d Train loss: %.4f. Validation F1-Score: %.4f Validation loss: %.4f. Elapsed time: %.2fs."% (epoch + 1, train_loss, val_score, val_loss, elapsed)) if val_score > best_score: torch.save(model.state_dict(), model_path) #print(classification_report(y_dev.cpu().numpy().tolist(), val_preds, digits=4)) best_score = val_score model.load_state_dict(torch.load(model_path)) model.to(device) model.predict = predict.__get__(model) os.remove(model_path) return model	[ 1, 529, 276, 1112, 420, 29958, 25154, 3460, 29899, 29893, 761, 598, 29914, 8172, 29918, 13445, 10335, 29899, 4270, 29899, 26766, 29918, 4572, 292, 13, 3166, 4842, 305, 29889, 13239, 29889, 1272, 1053, 323, 6073, 16390, 24541, 29892, 3630, 10036, 29892, 16968, 22966, 20069, 29892, 922, 339, 2556, 22966, 20069, 13, 3166, 4842, 305, 29889, 1300, 468, 3665, 1053, 28736, 13, 3166, 2071, 19668, 29889, 2527, 10817, 1053, 12965, 29918, 12276, 29892, 14679, 29918, 5344, 29892, 285, 29896, 29918, 13628, 13, 3166, 4327, 414, 1053, 334, 13, 3166, 4842, 305, 1053, 302, 29876, 13, 5215, 12655, 408, 7442, 13, 5215, 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tests/read_config.py	tyxio/txpy-azurehelper	0	26494	import logging.config import os import yaml __location__ = os.path.realpath( os.path.join(os.getcwd(), os.path.dirname(__file__))) def read_logging_config(): with open(os.path.join(__location__, 'logging.cfg'), 'r') as stream: try: logging_config = yaml.safe_load(stream) logging.config.dictConfig(logging_config) # do not log azure info messages logging.getLogger( 'azure.core.pipeline.policies.http_logging_policy').setLevel(logging.WARNING) return logging except yaml.YAMLError as exc: print(exc) except Exception as ex: print(ex) return None def read_azure_config(): with open(os.path.join(__location__, 'azure.cfg'), 'r') as stream: try: azure_config = yaml.safe_load(stream) return \ azure_config['az_storage_connection_str'],\ azure_config['az_storage_blob_sas_url'],\ azure_config['az_storage_blob_sas_token'] except yaml.YAMLError as exc: print(exc) except Exception as ex: print(ex) return None, None, None	[ 1, 1053, 12183, 29889, 2917, 13, 5215, 2897, 13, 13, 5215, 343, 8807, 13, 13, 1649, 5479, 1649, 353, 2897, 29889, 2084, 29889, 6370, 2084, 29898, 13, 1678, 2897, 29889, 2084, 29889, 7122, 29898, 359, 29889, 657, 29883, 9970, 3285, 2897, 29889, 2084, 29889, 25721, 22168, 1445, 1649, 4961, 13, 13, 13, 1753, 1303, 29918, 21027, 29918, 2917, 7295, 13, 1678, 411, 1722, 29898, 359, 29889, 2084, 29889, 7122, 22168, 5479, 1649, 29892, 525, 21027, 29889, 16859, 5477, 525, 29878, 1495, 408, 4840, 29901, 13, 4706, 1018, 29901, 13, 9651, 12183, 29918, 2917, 353, 343, 8807, 29889, 11177, 29918, 1359, 29898, 5461, 29897, 13, 9651, 12183, 29889, 2917, 29889, 8977, 3991, 29898, 21027, 29918, 2917, 29897, 13, 9651, 396, 437, 451, 1480, 15699, 5235, 7191, 13, 9651, 12183, 29889, 657, 16363, 29898, 13, 18884, 525, 17688, 29889, 3221, 29889, 13096, 5570, 29889, 3733, 293, 583, 29889, 1124, 29918, 21027, 29918, 22197, 2824, 842, 10108, 29898, 21027, 29889, 29956, 25614, 29897, 13, 9651, 736, 12183, 13, 4706, 5174, 343, 8807, 29889, 29979, 5194, 1307, 24616, 408, 5566, 29901, 13, 9651, 1596, 29898, 735, 29883, 29897, 13, 4706, 5174, 8960, 408, 429, 29901, 13, 9651, 1596, 29898, 735, 29897, 13, 4706, 736, 6213, 13, 13, 13, 1753, 1303, 29918, 17688, 29918, 2917, 7295, 13, 1678, 411, 1722, 29898, 359, 29889, 2084, 29889, 7122, 22168, 5479, 1649, 29892, 525, 17688, 29889, 16859, 5477, 525, 29878, 1495, 408, 4840, 29901, 13, 4706, 1018, 29901, 13, 9651, 15699, 29918, 2917, 353, 343, 8807, 29889, 11177, 29918, 1359, 29898, 5461, 29897, 13, 9651, 736, 320, 13, 18884, 15699, 29918, 2917, 1839, 834, 29918, 12925, 29918, 9965, 29918, 710, 7464, 29905, 13, 18884, 15699, 29918, 2917, 1839, 834, 29918, 12925, 29918, 10054, 29918, 29879, 294, 29918, 2271, 7464, 29905, 13, 18884, 15699, 29918, 2917, 1839, 834, 29918, 12925, 29918, 10054, 29918, 29879, 294, 29918, 6979, 2033, 13, 4706, 5174, 343, 8807, 29889, 29979, 5194, 1307, 24616, 408, 5566, 29901, 13, 9651, 1596, 29898, 735, 29883, 29897, 13, 4706, 5174, 8960, 408, 429, 29901, 13, 9651, 1596, 29898, 735, 29897, 13, 4706, 736, 6213, 29892, 6213, 29892, 6213, 13, 2 ]
application.py	meverg/goal_model_generator	1	173126	<filename>application.py<gh_stars>1-10 from flask import Flask, request, render_template import US2SMT import Parser import en_core_web_md nlp = en_core_web_md.load() print('model loaded') app = Flask(__name__) @app.after_request def set_response_headers(response): response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate' response.headers['Pragma'] = 'no-cache' response.headers['Expires'] = '0' return response @app.route('/', methods=['GET']) def index(): return render_template('index.html') @app.route('/solve_us', methods=['POST']) def solve_us(): converter = US2SMT.US2SMT(request.files['us_file'], Parser.Parser(nlp, model_selection=request.form.get('model_selection'), vectorizer_selection=request.form.get('vectorizer_selection')), request.form.get('opt'), request.form.get('opt2'), request.form.get('max')) converter = converter.add_us() converter = converter.weight() smt, dot, dictn = converter.get_smt_input() oms_out = US2SMT.get_oms_out() for line in oms_out.splitlines(): if line.strip(): line = line.replace("(", " ") line = line.lstrip() word = line.split() if word[0] in dictn: if word[1] == "true)": dot.node(word[0], color="limegreen", fillcolor="palegreen1", style='filled') else: dot.node(word[0], color="red3", fillcolor="red", style='filled') dot.graph_attr['rankdir'] = 'LR' goal_model_path = dot.render(directory='./static', cleanup=True, format='png') return render_template('result.html', goal_model_path=goal_model_path) if __name__ == '__main__': app.run(debug=True)	[ 1, 529, 9507, 29958, 6214, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 29784, 1053, 2379, 1278, 29892, 2009, 29892, 4050, 29918, 6886, 13, 5215, 3148, 29906, 29903, 11490, 13, 5215, 1459, 643, 13, 5215, 427, 29918, 3221, 29918, 2676, 29918, 3487, 13, 13, 12938, 29886, 353, 427, 29918, 3221, 29918, 2676, 29918, 3487, 29889, 1359, 580, 13, 2158, 877, 4299, 7500, 1495, 13, 932, 353, 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8382, 29922, 5574, 29897, 13, 2 ]
preql/__main__.py	erezsh/Preql	522	81734	<reponame>erezsh/Preql import json import argparse from pathlib import Path from itertools import chain import time from . import Preql, __version__, Signal from . import settings parser = argparse.ArgumentParser(description='Preql command-line interface (aka REPL)') parser.add_argument('-i', '--interactive', action='store_true', default=False, help="enter interactive mode after running the script") parser.add_argument('-v', '--version', action='store_true', help="print version") parser.add_argument('--install-jupyter', action='store_true', help="installs the Preql plugin for Jupyter notebook") parser.add_argument('--print-sql', action='store_true', help="print the SQL code that's being executed") parser.add_argument('-f', '--file', type=str, help='path to a Preql script to run') parser.add_argument('-m', '--module', type=str, help='name of a Preql module to run') parser.add_argument('--time', action='store_true', help='displays how long the script ran') parser.add_argument('-c', '--config', type=str, help='path to a JSON configuration file for Preql (default: ~/.preql_conf.json)') parser.add_argument('database', type=str, nargs='?', default=None, help="database url (postgres://user:password@host:port/db_name") parser.add_argument('--python-traceback', action='store_true', help="Show the Python traceback when an exception causes the interpreter to quit") def find_dot_preql(): cwd = Path.cwd() for p in chain([cwd], cwd.parents): dot_preql = p / ".preql" if dot_preql.exists(): return dot_preql def update_settings(path): config = json.load(path.open()) if 'debug' in config: settings.debug = config['debug'] if 'color_scheme' in config: settings.color_theme.update(config['color_scheme']) def main(): args = parser.parse_args() if args.version: print(__version__) if args.install_jupyter: from .jup_kernel.install import main as install_jupyter install_jupyter([]) print("Install successful. To start working, run 'jupyter notebook' and create a new Preql notebook.") return from pathlib import Path if args.config: update_settings(Path(args.config)) else: config_path = Path.home() / '.preql_conf.json' if config_path.exists(): update_settings(config_path) kw = {'print_sql': args.print_sql} if args.database: kw['db_uri'] = args.database kw['auto_create'] = True p = Preql(**kw) interactive = args.interactive error_code = 0 start = time.time() try: if args.file: p.load(args.file) elif args.module: p('import ' + args.module) elif args.version or args.install_jupyter: pass else: dot_preql = find_dot_preql() if dot_preql: print("Auto-running", dot_preql) p._run_code(dot_preql.read_text(), dot_preql) interactive = True except Signal as e: p._display.print_exception(e) error_code = -1 if args.python_traceback: raise except KeyboardInterrupt: print("Interrupted (Ctrl+C)") end = time.time() if args.time: print('Script took %.2f seconds to run' % (end -start)) if interactive: p.load_all_tables() p.start_repl() else: return error_code if __name__ == '__main__': main()	[ 1, 529, 276, 1112, 420, 29958, 406, 29920, 845, 29914, 6572, 1519, 13, 5215, 4390, 13, 5215, 1852, 5510, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 4256, 8504, 1053, 9704, 13, 5215, 931, 13, 13, 3166, 869, 1053, 4721, 1519, 29892, 4770, 3259, 1649, 29892, 9954, 284, 13, 3166, 869, 1053, 6055, 13, 13, 16680, 353, 1852, 5510, 29889, 15730, 11726, 29898, 8216, 2433, 6572, 1519, 1899, 29899, 1220, 5067, 313, 8245, 5195, 7390, 29897, 1495, 13, 16680, 29889, 1202, 29918, 23516, 877, 29899, 29875, 742, 525, 489, 1639, 4925, 742, 3158, 2433, 8899, 29918, 3009, 742, 2322, 29922, 8824, 29892, 13, 462, 1678, 1371, 543, 5893, 28923, 4464, 1156, 2734, 278, 2471, 1159, 13, 16680, 29889, 1202, 29918, 23516, 877, 29899, 29894, 742, 525, 489, 3259, 742, 3158, 2433, 8899, 29918, 3009, 742, 1371, 543, 2158, 1873, 1159, 13, 16680, 29889, 1202, 29918, 23516, 877, 489, 6252, 29899, 29926, 786, 25547, 742, 3158, 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utils/visualizer.py	darsh169/Representation-Learning-for-Information-Extraction	53	62686	<reponame>darsh169/Representation-Learning-for-Information-Extraction import json import cv2 def tesseract_ocr(image_path, ocr_path): image = cv2.imread(image_path.as_posix()) with open(ocr_path, 'r') as f: ocr_data = json.load(f) empty_index = [i for i, ele in enumerate(ocr_data['text']) if ele == ""] for key in ocr_data.keys(): ocr_data[key] = [j for i, j in enumerate(ocr_data[key]) if i not in empty_index] for txt, x, y, w, h in zip(ocr_data['text'], ocr_data['left'], ocr_data['top'], ocr_data['width'], ocr_data['height']): x2 = x + w y2 = y + h cv2.rectangle(image, (x, y), (x2, y2), (0, 255, 0), 1) cv2.putText(image, txt, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1, cv2.LINE_AA) return image	[ 1, 529, 276, 1112, 420, 29958, 16702, 845, 29896, 29953, 29929, 29914, 1123, 26081, 29899, 29931, 799, 1076, 29899, 1454, 29899, 20350, 29899, 5647, 13857, 13, 5215, 4390, 13, 5215, 13850, 29906, 13, 13, 1753, 260, 16136, 627, 29918, 8415, 29898, 3027, 29918, 2084, 29892, 288, 7283, 29918, 2084, 1125, 13, 13, 1678, 1967, 353, 13850, 29906, 29889, 326, 949, 29898, 3027, 29918, 2084, 29889, 294, 29918, 1066, 861, 3101, 13, 13, 1678, 411, 1722, 29898, 8415, 29918, 2084, 29892, 525, 29878, 1495, 408, 285, 29901, 13, 4706, 288, 7283, 29918, 1272, 353, 4390, 29889, 1359, 29898, 29888, 29897, 13, 13, 1678, 4069, 29918, 2248, 353, 518, 29875, 363, 474, 29892, 4552, 297, 26985, 29898, 8415, 29918, 1272, 1839, 726, 11287, 565, 4552, 1275, 376, 3108, 13, 1678, 363, 1820, 297, 288, 7283, 29918, 1272, 29889, 8149, 7295, 13, 4706, 288, 7283, 29918, 1272, 29961, 1989, 29962, 353, 518, 29926, 363, 474, 29892, 432, 297, 26985, 29898, 8415, 29918, 1272, 29961, 1989, 2314, 565, 474, 451, 297, 4069, 29918, 2248, 29962, 13, 13, 1678, 363, 13872, 29892, 921, 29892, 343, 29892, 281, 29892, 298, 297, 14319, 29898, 8415, 29918, 1272, 1839, 726, 7464, 288, 7283, 29918, 1272, 1839, 1563, 7464, 288, 7283, 29918, 1272, 1839, 3332, 7464, 288, 7283, 29918, 1272, 1839, 2103, 7464, 288, 7283, 29918, 1272, 1839, 3545, 2033, 1125, 13, 4706, 921, 29906, 353, 921, 718, 281, 13, 4706, 343, 29906, 353, 343, 718, 298, 13, 4706, 13850, 29906, 29889, 1621, 2521, 29898, 3027, 29892, 313, 29916, 29892, 343, 511, 313, 29916, 29906, 29892, 343, 29906, 511, 313, 29900, 29892, 29871, 29906, 29945, 29945, 29892, 29871, 29900, 511, 29871, 29896, 29897, 13, 4706, 13850, 29906, 29889, 649, 1626, 29898, 3027, 29892, 13872, 29892, 313, 29916, 29892, 29891, 511, 13, 462, 1678, 13850, 29906, 29889, 29943, 1164, 29911, 29918, 4448, 7068, 13282, 29918, 5425, 3580, 1307, 29990, 29892, 29871, 29896, 29892, 313, 29900, 29892, 29871, 29900, 29892, 29871, 29906, 29945, 29945, 511, 29871, 29896, 29892, 13850, 29906, 29889, 18521, 29918, 6344, 29897, 13, 13, 1678, 736, 1967, 13, 13, 2 ]
train/ip.py	VCG/gp	0	19753	<reponame>VCG/gp<gh_stars>0 import cPickle as pickle import os; import sys; sys.path.append('..') import gp import gp.nets as nets PATCH_PATH = ('iplb') X_train, y_train, X_test, y_test = gp.Patch.load_rgb(PATCH_PATH) X_train = X_train[:,:-1,:,:] X_test = X_test[:,:-1,:,:] cnn = nets.RGNetPlus() cnn = cnn.fit(X_train, y_train) test_accuracy = cnn.score(X_test, y_test) print test_accuracy # store CNN sys.setrecursionlimit(1000000000) with open(os.path.expanduser('~/Projects/gp/nets/IP_FULL.p'), 'wb') as f: pickle.dump(cnn, f, -1)	[ 1, 529, 276, 1112, 420, 29958, 8257, 29954, 29914, 29887, 29886, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 274, 29925, 860, 280, 408, 5839, 280, 13, 5215, 2897, 29936, 1053, 10876, 29936, 10876, 29889, 2084, 29889, 4397, 877, 636, 1495, 13, 5215, 330, 29886, 13, 5215, 330, 29886, 29889, 1212, 29879, 408, 302, 1691, 13, 13, 29925, 14789, 29918, 10145, 353, 6702, 29875, 572, 29890, 1495, 13, 13, 29990, 29918, 14968, 29892, 343, 29918, 14968, 29892, 1060, 29918, 1688, 29892, 343, 29918, 1688, 353, 330, 29886, 29889, 29925, 905, 29889, 1359, 29918, 23973, 29898, 29925, 14789, 29918, 10145, 29897, 13, 29990, 29918, 14968, 353, 1060, 29918, 14968, 7503, 29892, 13018, 29896, 29892, 29901, 29892, 17531, 13, 29990, 29918, 1688, 353, 1060, 29918, 1688, 7503, 29892, 13018, 29896, 29892, 29901, 29892, 17531, 13, 13, 29883, 15755, 353, 302, 1691, 29889, 29934, 29954, 6779, 29575, 580, 13, 13, 29883, 15755, 353, 274, 15755, 29889, 9202, 29898, 29990, 29918, 14968, 29892, 343, 29918, 14968, 29897, 13, 13, 1688, 29918, 562, 2764, 4135, 353, 274, 15755, 29889, 13628, 29898, 29990, 29918, 1688, 29892, 343, 29918, 1688, 29897, 13, 13, 2158, 1243, 29918, 562, 2764, 4135, 13, 13, 29937, 3787, 29696, 13, 9675, 29889, 842, 3757, 1295, 291, 13400, 29898, 29896, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 29897, 13, 2541, 1722, 29898, 359, 29889, 2084, 29889, 18837, 1792, 877, 20038, 25119, 29914, 29887, 29886, 29914, 1212, 29879, 29914, 5690, 29918, 29943, 3299, 29889, 29886, 5477, 525, 29893, 29890, 1495, 408, 285, 29901, 13, 29871, 5839, 280, 29889, 15070, 29898, 29883, 15755, 29892, 285, 29892, 448, 29896, 29897, 13, 2 ]
src/simple_email/consts.py	deeso/simple-email	0	177708	<gh_stars>0 SMTP_SERVICE_BLOCK = 'smtp-service' SMTP_HOST = 'smtp_host' SMTP_PORT = 'smtp_port' SMTP_USERNAME = 'smtp_username' SMTP_PASSWORD = '<PASSWORD>' SMTP_USE_TLS = 'smtp_use_tls' SMTP_LEVEL = 'smtp_level' SMTP_DEFAULT_VALUES = { SMTP_HOST: '127.0.0.1', SMTP_PORT: '25', SMTP_USERNAME: None, SMTP_PASSWORD: None, SMTP_USE_TLS: True, SMTP_LEVEL: 0, } SMTP_CONFIG = [ SMTP_HOST, SMTP_PORT, SMTP_USERNAME, SMTP_PASSWORD, SMTP_USE_TLS, SMTP_LEVEL, ] FILENAME = 'filename' BUFFERED_CONTENT = 'buffered_content'	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 17061, 3557, 29918, 6304, 19059, 29918, 29933, 21339, 353, 525, 3844, 9392, 29899, 5509, 29915, 13, 17061, 3557, 29918, 20832, 353, 525, 3844, 9392, 29918, 3069, 29915, 13, 17061, 3557, 29918, 15082, 353, 525, 3844, 9392, 29918, 637, 29915, 13, 17061, 3557, 29918, 11889, 5813, 353, 525, 3844, 9392, 29918, 6786, 29915, 13, 17061, 3557, 29918, 25711, 17013, 353, 12801, 25711, 17013, 16299, 13, 17061, 3557, 29918, 17171, 29918, 29911, 8547, 353, 525, 3844, 9392, 29918, 1509, 29918, 29873, 3137, 29915, 13, 17061, 3557, 29918, 1307, 29963, 6670, 353, 525, 3844, 9392, 29918, 5563, 29915, 13, 13, 17061, 3557, 29918, 23397, 29918, 8932, 12996, 353, 426, 13, 1678, 13766, 3557, 29918, 20832, 29901, 525, 29896, 29906, 29955, 29889, 29900, 29889, 29900, 29889, 29896, 742, 13, 1678, 13766, 3557, 29918, 15082, 29901, 525, 29906, 29945, 742, 13, 1678, 13766, 3557, 29918, 11889, 5813, 29901, 6213, 29892, 13, 1678, 13766, 3557, 29918, 25711, 17013, 29901, 6213, 29892, 13, 1678, 13766, 3557, 29918, 17171, 29918, 29911, 8547, 29901, 5852, 29892, 13, 1678, 13766, 3557, 29918, 1307, 29963, 6670, 29901, 29871, 29900, 29892, 13, 29913, 13, 13, 17061, 3557, 29918, 25903, 353, 518, 13, 1678, 13766, 3557, 29918, 20832, 29892, 13, 1678, 13766, 3557, 29918, 15082, 29892, 13, 1678, 13766, 3557, 29918, 11889, 5813, 29892, 13, 1678, 13766, 3557, 29918, 25711, 17013, 29892, 13, 1678, 13766, 3557, 29918, 17171, 29918, 29911, 8547, 29892, 13, 1678, 13766, 3557, 29918, 1307, 29963, 6670, 29892, 13, 29962, 13, 13, 7724, 5813, 353, 525, 9507, 29915, 13, 7838, 28483, 3352, 29918, 22412, 3919, 353, 525, 9040, 287, 29918, 3051, 29915, 13, 2 ]
Python_LEVEL_TWO/Part6_regular_expressions.py	chernyshov-dev/WebDev	4	150961	import re patterns = ["term1", "term2"] text = "This is a string with term1, not the other" split_term = "@" email = "<EMAIL>" # for pattern in patterns: # print("I'm searching for: " + pattern) # # if re.search(pattern, text): # print("MATCH!") # else: # print("NO MATCH!") print(re.split(split_term, email)) # email.split("@") # print(match.start()) print(re.findall("match", "test phrase match in match middle")) def multi_re_find(patterns, phrase): for pat in patterns: print("Searching for pattern {}".format(pat)) print(re.findall(pat, phrase)) print("\n") # test_phrase = "sdsd..sssddd..sdddsddd...dsds...dssssss...sddddd" # test_patterns = ["s[sd]+"] # test_phrase = "This is a string! But it has punctuation. How can we remove it?" # test_patterns = ["[^!.?]+"] # test_patterns = ["[A-Z]+"] test_phrase = "This is a string with numbers 12312 and a symbol #hashtag" # d - digits # D - non-digits # s - whitespace # S - non-whitespace # w - alphanumeric characters # W - non-alphanumeric characters test_patterns = [r"\W+"] multi_re_find(test_patterns, test_phrase)	[ 1, 1053, 337, 13, 13, 11037, 29879, 353, 6796, 8489, 29896, 613, 376, 8489, 29906, 3108, 13, 13, 726, 353, 376, 4013, 338, 263, 1347, 411, 1840, 29896, 29892, 451, 278, 916, 29908, 13, 5451, 29918, 8489, 353, 376, 5507, 13, 5269, 353, 9872, 26862, 6227, 11903, 13, 13, 29937, 363, 4766, 297, 15038, 29901, 13, 29937, 268, 1596, 703, 29902, 29915, 29885, 11975, 363, 29901, 376, 718, 4766, 29897, 13, 29937, 13, 29937, 268, 565, 337, 29889, 4478, 29898, 11037, 29892, 1426, 1125, 13, 29937, 308, 1596, 703, 29924, 14789, 29991, 1159, 13, 29937, 268, 1683, 29901, 13, 29937, 308, 1596, 703, 6632, 341, 14789, 29991, 1159, 13, 13, 2158, 29898, 276, 29889, 5451, 29898, 5451, 29918, 8489, 29892, 4876, 876, 29871, 396, 4876, 29889, 5451, 29475, 1159, 13, 29937, 1596, 29898, 4352, 29889, 2962, 3101, 13, 13, 2158, 29898, 276, 29889, 2886, 497, 703, 4352, 613, 376, 1688, 16549, 1993, 297, 1993, 7256, 5783, 13, 13, 13, 1753, 2473, 29918, 276, 29918, 2886, 29898, 11037, 29879, 29892, 16549, 1125, 13, 1678, 363, 2373, 297, 15038, 29901, 13, 4706, 1596, 703, 7974, 292, 363, 4766, 6571, 1642, 4830, 29898, 5031, 876, 13, 4706, 1596, 29898, 276, 29889, 2886, 497, 29898, 5031, 29892, 16549, 876, 13, 4706, 1596, 14182, 29876, 1159, 13, 13, 13, 29937, 1243, 29918, 24588, 559, 353, 376, 4928, 4928, 636, 893, 29879, 1289, 29881, 636, 29879, 1289, 6289, 1289, 29881, 856, 29881, 4928, 29879, 856, 29881, 893, 893, 893, 856, 29879, 1289, 1289, 29881, 29908, 13, 29937, 1243, 29918, 11037, 29879, 353, 6796, 29879, 29961, 4928, 10062, 3108, 13, 13, 29937, 1243, 29918, 24588, 559, 353, 376, 4013, 338, 263, 1347, 29991, 1205, 372, 756, 6035, 22999, 362, 29889, 1128, 508, 591, 3349, 372, 3026, 13, 29937, 1243, 29918, 11037, 29879, 353, 6796, 22896, 21520, 29973, 10062, 3108, 13, 29937, 1243, 29918, 11037, 29879, 353, 6796, 29961, 29909, 29899, 29999, 10062, 3108, 13, 13, 1688, 29918, 24588, 559, 353, 376, 4013, 338, 263, 1347, 411, 3694, 29871, 29896, 29906, 29941, 29896, 29906, 322, 263, 5829, 396, 5349, 400, 351, 29908, 13, 29937, 270, 448, 13340, 13, 29937, 360, 448, 1661, 29899, 7501, 1169, 13, 29937, 269, 448, 24358, 13, 29937, 317, 448, 1661, 29899, 1332, 3246, 3535, 13, 29937, 281, 448, 394, 16711, 25099, 4890, 13, 29937, 399, 448, 1661, 29899, 284, 16711, 25099, 4890, 13, 1688, 29918, 11037, 29879, 353, 518, 29878, 26732, 29956, 29974, 3108, 13, 13, 9910, 29918, 276, 29918, 2886, 29898, 1688, 29918, 11037, 29879, 29892, 1243, 29918, 24588, 559, 29897, 13, 2 ]
MLplatform/core/migrations/0004_auto_20210410_1339.py	mihai011/Docker-Django-Posgres-Celery-Rabbitmq	0	1615688	# Generated by Django 3.1 on 2021-04-10 13:39 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('core', '0003_auto_20210323_1406'), ] operations = [ migrations.RenameField( model_name='email', old_name='message', new_name='template', ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29941, 29889, 29896, 373, 29871, 29906, 29900, 29906, 29896, 29899, 29900, 29946, 29899, 29896, 29900, 29871, 29896, 29941, 29901, 29941, 29929, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 3221, 742, 525, 29900, 29900, 29900, 29941, 29918, 6921, 29918, 29906, 29900, 29906, 29896, 29900, 29941, 29906, 29941, 29918, 29896, 29946, 29900, 29953, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 29934, 3871, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 5269, 742, 13, 9651, 2030, 29918, 978, 2433, 4906, 742, 13, 9651, 716, 29918, 978, 2433, 6886, 742, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
floodgate/server_state_machine/layer2_decoration_event/tell.py	muzudho/e-gov-vote-kifuwarabe-player-client	0	177865	<reponame>muzudho/e-gov-vote-kifuwarabe-player-client<filename>floodgate/server_state_machine/layer2_decoration_event/tell.py from app import app from floodgate.server_state_machine.layer1_transition_map.tell import TellState as _TransitionState def create(): """ステート生成""" return _DecoratedState() class _DecoratedState(_TransitionState): def __init__(self): super().__init__() def on_reject_c(self, req): pass def on_reject_s(self, req): pass def on_agree(self, req): pass def on_start(self, req): pass	[ 1, 529, 276, 1112, 420, 29958, 2589, 29920, 566, 1251, 29914, 29872, 29899, 13513, 29899, 15814, 29899, 29895, 361, 29884, 4495, 4302, 29899, 9106, 29899, 4645, 29966, 9507, 29958, 29888, 417, 397, 17062, 29914, 2974, 29918, 3859, 29918, 23523, 29914, 13148, 29906, 29918, 19557, 362, 29918, 3696, 29914, 29873, 514, 29889, 2272, 13, 3166, 623, 1053, 623, 13, 3166, 5685, 397, 17062, 29889, 2974, 29918, 3859, 29918, 23523, 29889, 13148, 29896, 29918, 20543, 29918, 1958, 29889, 29873, 514, 1053, 24948, 2792, 408, 903, 4300, 654, 2792, 13, 13, 13, 1753, 1653, 7295, 13, 1678, 9995, 30255, 30572, 30185, 30279, 30486, 30494, 15945, 29908, 13, 1678, 736, 903, 6185, 272, 630, 2792, 580, 13, 13, 13, 1990, 903, 6185, 272, 630, 2792, 7373, 4300, 654, 2792, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 2428, 2141, 1649, 2344, 1649, 580, 13, 13, 1678, 822, 373, 29918, 276, 622, 29918, 29883, 29898, 1311, 29892, 12428, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 373, 29918, 276, 622, 29918, 29879, 29898, 1311, 29892, 12428, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 373, 29918, 351, 929, 29898, 1311, 29892, 12428, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 373, 29918, 2962, 29898, 1311, 29892, 12428, 1125, 13, 4706, 1209, 13, 2 ]
yoti_python_sandbox/doc_scan/task/sandbox_text_extraction_task.py	getyoti/yoti-python-sdk-sandbox	0	96861	import base64 from yoti_python_sdk.utils import YotiSerializable from yoti_python_sandbox.doc_scan.document_filter import ( # noqa: F401 SandboxDocumentFilter, ) from yoti_python_sandbox.doc_scan.task.sandbox_text_extraction_recommendation import ( # noqa: F401 SandboxTextDataExtractionRecommendation, ) class SandboxDocumentIdPhoto(YotiSerializable): def __init__(self, content_type, data): """ :param str content_type: the content type :param bytes data: the image data """ self.__content_type = content_type self.__data = data def to_json(self): return { "content_type": self.__content_type, "data": base64.b64encode(self.__data).decode("utf-8"), } class SandboxDocumentTextDataExtractionTaskResult(YotiSerializable): def __init__( self, document_fields=None, document_id_photo=None, detected_country=None, recommendation=None, ): self.__document_fields = document_fields self.__document_id_photo = document_id_photo self.__detected_country = detected_country self.__recommendation = recommendation @property def document_fields(self): return self.__document_fields @property def document_id_photo(self): return self.__document_id_photo @property def detected_country(self): return self.__detected_country @property def recommendation(self): return self.__recommendation def to_json(self): json = {} if self.document_fields is not None: json["document_fields"] = self.document_fields if self.document_id_photo is not None: json["document_id_photo"] = self.document_id_photo if self.detected_country is not None: json["detected_country"] = self.detected_country if self.recommendation is not None: json["recommendation"] = self.recommendation return json class SandboxDocumentTextDataExtractionTask(YotiSerializable): def __init__(self, result, document_filter): self.__result = result self.__document_filter = document_filter @property def result(self): """ :rtype: SandboxDocumentTextDataExtractionTaskResult """ return self.__result @property def document_filter(self): """ :rtype: SandboxDocumentFilter """ return self.__document_filter def to_json(self): obj = { "result": self.__result, } if self.__document_filter is not None: obj["document_filter"] = self.__document_filter return obj class SandboxDocumentTextDataExtractionTaskBuilder(object): def __init__(self): self.__document_fields = None self.__document_filter = None self.__document_id_photo = None self.__detected_country = None self.__recommendation = None def with_document_field(self, key, value): """ :type key: str :type value: str or dict :rtype: SandboxDocumentTextDataExtractionTaskBuilder """ self.__document_fields = self.__document_fields or {} self.__document_fields[key] = value return self def with_document_fields(self, document_fields): """ :type document_fields: dict :rtype: SandboxDocumentTextDataExtractionTaskBuilder """ self.__document_fields = document_fields return self def with_document_id_photo(self, content_type, data): """ :param str content_type: the content type :param bytes data: the image data """ self.__document_id_photo = SandboxDocumentIdPhoto(content_type, data) return self def with_document_filter(self, document_filter): """ :type document_filter: SandboxDocumentFilter :rtype: SandboxDocumentTextDataExtractionTaskBuilder """ self.__document_filter = document_filter return self def with_detected_country(self, detected_country): """ :param str detected_country: the detected country """ self.__detected_country = detected_country return self def with_recommendation(self, recommendation): """ :type recommendation: SandboxTextDataExtractionRecommendation :rtype: SandboxDocumentTextDataExtractionTaskBuilder """ self.__recommendation = recommendation return self def build(self): result = SandboxDocumentTextDataExtractionTaskResult( self.__document_fields, self.__document_id_photo, self.__detected_country, self.__recommendation, ) return 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tests/utilities/test_tasks.py	elenapalesis/prefect	0	194204	import inspect import pytest from prefect.core import Flow, Task from prefect.engine.flow_runner import FlowRunner from prefect.engine.signals import PAUSE from prefect.engine.state import Paused, Resume from prefect.utilities import tasks class TestTaskDecorator: def test_task_decorator_can_be_used_without_calling(self): @tasks.task def fun(x, y): return x + y def test_task_decorator_generates_new_tasks_upon_subsequent_calls(self): @tasks.task def fun(x, y): return x + y with Flow(name="test"): res1 = fun(1, 2) res2 = fun(1, 2) assert isinstance(res1, Task) assert isinstance(res2, Task) assert res1 is not res2 def test_task_decorator_with_args_must_be_called_in_flow_context(self): @tasks.task def fn(x): return x with pytest.raises(ValueError, match="Could not infer an active Flow context"): fn(1) def test_task_decorator_with_no_args_must_be_called_inside_flow_context(self): @tasks.task def fn(): return 1 with pytest.raises(ValueError): fn() with Flow(name="test"): assert isinstance(fn(), Task) def test_task_decorator_with_default_args_must_be_called_inside_flow_context(self): @tasks.task def fn(x=1): return x with pytest.raises(ValueError): fn() with Flow(name="test"): assert isinstance(fn(), Task) def test_task_decorator_with_required_args_must_be_called_with_args(self): @tasks.task def fn(x): return x with Flow(name="test"): with pytest.raises(TypeError): fn() def test_task_decorator_returns_task_instance(self): @tasks.task def fn(x): return x assert isinstance(fn, Task) def test_task_decorator_validates_run_signature_against_varargs(self): with pytest.raises(ValueError): @tasks.task def fn(args): pass def test_task_decorator_validates_run_signature_against_upstream_tasks_kwarg(self): with pytest.raises(ValueError): @tasks.task def fn(upstream_tasks): pass class TestAsTask: @pytest.mark.parametrize( "obj", [ 1, (3, 4), ["a", "b"], "string", dict(x=42), type(None), lambda args: None, {None: 88}, ], ) def test_as_task_with_basic_python_objs(self, obj): @tasks.task def return_val(x): "Necessary because constant tasks aren't tracked inside the flow" return x with Flow("test") as f: t = tasks.as_task(obj) val = return_val(t) assert isinstance(t, Task) assert t.auto_generated is True res = FlowRunner(f).run(return_tasks=[val]) assert res.is_successful() assert res.result[val].result == obj def test_as_task_doesnt_label_tasks_as_auto_generated(self): t = Task() assert t.auto_generated is False assert tasks.as_task(t).auto_generated is False def test_tag_contextmanager_works_with_task_decorator(): @tasks.task def mytask(): pass @tasks.task(tags=["default"]) def tagged_task(): pass with Flow(name="test"): with tasks.tags("chris"): res = mytask() other = tagged_task() assert res.tags == {"chris"} assert other.tags == {"chris", "default"} def test_copying_then_setting_tags_doesnt_leak_backwards(): with Flow(name="test"): t1 = Task() with tasks.tags("init-tag"): t2 = t1.copy() assert t2.tags == {"init-tag"} assert t1.tags == set() def test_setting_tags_then_calling_copies_tags(): with tasks.tags("init-tag"): t1 = Task() with Flow(name="test"): t2 = t1() assert t2.tags == {"init-tag"} def test_context_manager_for_setting_tags(): """ Test setting Task tags with a context manager, including: - top level - nested - nested with explicit tags """ with tasks.tags("1", "2"): t1 = Task() assert t1.tags == set(["1", "2"]) with tasks.tags("3", "4"): t2 = Task() assert t2.tags == set(["1", "2", "3", "4"]) t3 = Task(tags=["5"]) assert t3.tags == set(["1", "2", "3", "4", "5"]) class TestUnmappedContainer: def test_unmapped_initializes_with_task(self): t1 = Task() unmapped_t1 = tasks.unmapped(t1) assert unmapped_t1.task is t1 def test_unmapped_converts_its_argument_to_task(self): unmapped_t1 = tasks.unmapped(5) assert isinstance(unmapped_t1.task, Task) def test_as_task_unpacks_unmapped_objects(self): t1 = Task() unmapped_t1 = tasks.unmapped(t1) assert tasks.as_task(t1) is t1 assert tasks.as_task(t1).auto_generated is False class TestPauseTask: def test_pause_task_pauses(self): class AddTask(Task): def run(self, x, y): if x == y: tasks.pause_task("test message") return x + y with Flow(name="test") as f: t1 = AddTask()(1, 1) res = FlowRunner(flow=f).run(return_tasks=[t1]) assert isinstance(res.result[t1], Paused) assert res.result[t1].message == "test message" def test_pause_task_doesnt_pause_sometimes(self): class OneTask(Task): def run(self): tasks.pause_task() return 1 class AddTask(Task): def run(self, x, y): if x == y: tasks.pause_task() return x + y with Flow(name="test") as f: t1 = AddTask()(1, 1) t2 = OneTask()(upstream_tasks=[t1]) res = FlowRunner(flow=f).run(task_states={t1: Resume()}, return_tasks=[t1, t2]) assert res.result[t1].is_successful() assert isinstance(res.result[t2], Paused) class TestDefaultFromAttrs: @pytest.fixture def xtask(self): class A(Task): def __init__(self, x=None): self.x = x super().__init__() @tasks.defaults_from_attrs("x") def run(self, x=None): "Lil doc" return x return A @pytest.fixture def multitask(self): class B(Task): def __init__(self, x=None, y=None): self.x = x self.y = y super().__init__() @tasks.defaults_from_attrs("x") def run(self, x=None, y=None): return x, y return B def test_pulls_from_attr_if_not_provided_at_runtime(self, xtask): a = xtask(5) assert a.run() == 5 def test_runtime_takes_precedence(self, xtask): a = xtask(5) assert a.run(x=6) == 6 def test_even_none_at_runtime_takes_precedence(self, xtask): """ This test ensures that `None` isn't some ambiguous special case: keywords provided at runtime _always_ take precedence. """ a = xtask(5) assert a.run(x=None) is None def test_doc_is_unaffected(self, xtask): assert xtask.run.__doc__ == "Lil doc" def test_args_not_listed_are_unaffected(self, multitask): b = multitask(x=1, y=2) assert b.run() == (1, None) def test_works_with_multiple_args(self, multitask): b = multitask(x=1, y=2) assert b.run(y=3, x=55) == (55, 3) def test_works_with_mutiple_attrs(self): class TestTask(Task): def __init__(self, x=None, y=None): self.x = x self.y = y super().__init__() @tasks.defaults_from_attrs("x", "y") def run(self, x=None, y=None): return x, y task = TestTask(x=1, y=2) assert task.run() == (1, 2) assert task.run(x=4) == (4, 2) assert task.run(y=99) == (1, 99) assert task.run(x=None, y=None) == (None, None) def test_raises_if_attr_wasnt_set_at_init(self): """ It would be nice to raise this at creation time, but unfortunately the information just isn't available. 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pyNastran/op2/op2.py	als0052/pyNastran	0	185521	<reponame>als0052/pyNastran #!/usr/bin/env python3 # Filename: op2.py """ Defines the main OP2 class. Defines: - read_op2(op2_filename=None, combine=True, subcases=None, exclude_results=None, include_results=None, log=None, debug=True, debug_file=None, build_dataframe=False, skip_undefined_matrices=True, mode='msc', encoding=None) - OP2(debug=True, log=None, debug_file=None, mode='msc') - build_dataframe() - combine_results(combine=True) - create_objects_from_matrices() - object_attributes(mode='public', keys_to_skip=None, filter_properties=False) - object_methods(mode='public', keys_to_skip=None) - print_subcase_key() - read_op2(op2_filename=None, combine=True, build_dataframe=False, skip_undefined_matrices=False, encoding=None) - set_mode(mode) - transform_displacements_to_global(i_transform, coords, xyz_cid0=None, debug=False) - transform_gpforce_to_global(nids_all, nids_transform, i_transform, coords, xyz_cid0=None) Changes ------- * (11-17-2021) (als0052): Fixed some typing issues; added some comments; tried to make the code more readable and maybe efficient? * (01-29-2021) (als0052): Changes to some minor syntax to use more modern Python features (will probably need to up the minimum Python requirement); Rearranged some code to make it a bit easier to read; Thinned out some comments; added some comments (mostly to do items) To Do Items ----------- .. todo:: How to access the Pandas DataFrame from the OP2 obj? I don't even see Pandas as an import. """ from __future__ import annotations from pyNastran.op2.errors import (SortCodeError, DeviceCodeError, FortranMarkerError, SixtyFourBitError, OverwriteTableError) from pyNastran.op2.op2_interface.hdf5_interface import export_op2_to_hdf5_file from pyNastran.op2.op2_interface.hdf5_interface import \ export_op2_to_hdf5_filename from pyNastran.op2.op2_interface.transforms import ( transform_displacement_to_global, transform_gpforce_to_globali) from pyNastran.op2.op2_interface.hdf5_interface import load_op2_from_hdf5_file from pyNastran.utils import object_attributes, object_methods, ipython_info from pyNastran.op2.op2_interface.hdf5_interface import create_info_group from pyNastran.op2.result_objects.monpnt import MONPNT1, MONPNT3 from pyNastran.op2.op2_interface.op2_scalar import OP2_Scalar from pyNastran.op2.writer.op2_writer import OP2Writer from pyNastran.utils.numpy_utils import integer_types from pyNastran.op2.op2_geom import read_op2_geom from pyNastran.f06.errors import FatalError from pyNastran.utils import check_path from typing import List, Dict, Tuple, Optional, Any, TYPE_CHECKING, Union from pickle import load, dump, dumps from collections import defaultdict import numpy as np import h5py import sys if TYPE_CHECKING: from h5py import File as H5File # import pyNastran # from pyNastran.op2.op2_interface.op2_f06_common import Op2F06Attributes class OP2(OP2_Scalar, OP2Writer): _properties = ['is_real', 'is_complex', 'is_random', '_sort_method', 'is_sort1', 'is_sort2', 'matrix_tables', 'table_name_str'] def __init__(self, debug: Optional[bool] = True, log: Any = None, debug_file: Optional[str] = None, mode: Optional[str] = None ) -> None: """Initializes the OP2 object Parameters ---------- debug : bool/None; default=True used to set the logger if no logger is passed in True: logs debug/info/warning/error messages False: logs info/warning/error messages None: logs warning/error messages log : Log() a logging object to write debug messages to .. seealso:: import logging debug_file : str; default=None (No debug) sets the filename that will be written to mode : str; default=None -> 'msc' {msc, nx} """ self.encoding = None self.mode = mode if mode is not None: self.set_mode(mode) make_geom = False assert make_geom is False, make_geom OP2_Scalar.__init__(self, debug=debug, log=log, debug_file=debug_file) self.ask = False self.post = None self.table_count = defaultdict(int) self._set_mode(mode) self.skip_undefined_matrices = '' def __del__(self) -> None: if hasattr(self, 'h5_file') and self.h5_file is not None: self.h5_file.close() def object_attributes(self, mode: str = 'public', keys_to_skip: Optional[List[str]] = None, filter_properties: bool = False) -> List[str]: """List the names of attributes of a class as strings. Returns public attributes as default. Parameters ---------- mode : str defines what kind of attributes will be listed * 'public' - names that do not begin with underscore * 'private' - names that begin with single underscore * 'both' - private and public * 'all' - all attributes that are defined for the object keys_to_skip : List[str]; default=None -> [] names to not consider to avoid deprecation warnings filter_properties : Returns ------- attribute_names : List[str] sorted list of the names of attributes of a given type or None if the mode is wrong .. todo:: Fill out docstring info on filter_properties parameter .. todo:: kwargs support """ if keys_to_skip is None: keys_to_skip = [] my_keys_to_skip = ['object_methods', 'object_attributes', ] keys_to_skip += my_keys_to_skip kw = {'mode': mode, 'keys_to_skip': keys_to_skip, 'filter_properties': filter_properties} # TODO: kwargs support return object_attributes(self, *kw) def object_methods(self, mode: str = 'public', keys_to_skip: Optional[List[str]] = None) -> List[str]: """List the names of methods of a class as strings. Returns public methods as default. Parameters ---------- obj : instance the object for checking mode : str defines what kind of methods will be listed "public" - names that do not begin with underscore * "private" - names that begin with single underscore * "both" - private and public * "all" - all methods that are defined for the object keys_to_skip : List[str]; default=None -> [] names to not consider to avoid deprecation warnings Returns ------- method : List[str] sorted list of the names of methods of a given type or None if the mode is wrong """ if keys_to_skip is None: keys_to_skip = [] my_keys_to_skip = ['object_methods', 'object_attributes'] keys_to_skip += my_keys_to_skip return object_methods(self, mode=mode, keys_to_skip=keys_to_skip) def __eq__(self, op2_model) -> bool: """Diffs the current op2 model vs. another op2 model. Crashes if they're not equal. FIXME: Probably shouldn't crash if they aren't equal """ try: is_equal = self.assert_op2_equal( op2_model, stop_on_failure=True, debug=False) except (AssertionError, ValueError): # This will never get used if you raise? # is_equal = False raise return is_equal def assert_op2_equal(self, op2_model, skip_results: Optional[List[str]] = None, stop_on_failure: bool = True, debug: bool = False) -> bool: """Diffs the current op2 model vs. another op2 model. Parameters ---------- op2_model : OP2() the model to compare to skip_results : List[str]; default=None -> [] results that shouldn't be compred stop_on_failure : bool; default=True True : Crashes if they're not equal False : Go to the next object debug : bool; default=False give slightly more debugging messages Returns ------- is_equal : bool are the objects equal? Raises ------ AssertionError/ValueError : stop_on_failure=True and and error occurred NotImplementedError : this is a sign of an unsupported object """ if skip_results is None: skip_results = set() else: skip_results = set(skip_results) skip_results.update({'gpdt', 'bgpdt', 'eqexin', 'psds'}) if not self.read_mode == op2_model.read_mode: msg = (f'self.read_mode={self.read_mode} op2_model.read_mode=' f'{op2_model.read_mode} ... assume True') self.log.warning(msg) return True table_types = self.get_table_types() for table_type in table_types: if (table_type in skip_results or table_type.startswith('responses.')): continue adict = self.get_result(table_type) bdict = op2_model.get_result(table_type) if adict is None and bdict is None: continue if len(adict) != len(bdict): # check number of subcases msg = (f'len(self.{table_type}) = {len(adict)} ' f'len(op2_model.{table_type}) = {len(bdict)}') self.log.warning(msg) if stop_on_failure: return False continue for key, avalue in adict.items(): # loop over each DisplacementArray if debug: msg = f'Working on {table_type!r} subcase = {str(key)}' self.log.debug(msg) # get the displacement for model B bvalue = bdict[key] is_equal = self._is_op2_case_equal( table_type, key, avalue, bvalue, stop_on_failure=stop_on_failure, debug=debug) if not is_equal and stop_on_failure: return is_equal return True def _is_op2_case_equal(self, table_type: str, key, a_obj, b_obj, stop_on_failure: bool = True, debug: bool = False ) -> bool: """Helper method for ``assert_op2_equal`` Parameters ---------- table_type : str the type of table (e.g., ``displacements``) key : subcase_id / tuple_obj subcase_id : int the subcase_id tuple_obj : Tuple(???, ???, ...) The fancy tuple thingy that you see in single subcase buckling... subcase_id : int the subcase_id sort_code : int 1 : SORT1 2 : SORT2 title??? : str the case title subtitle??? : str the case subtitle superelement_id : str??? the superelement other terms??? TODO: This docstring needs work. a_obj : Op2Object() a RealDisplacementArray, ComplexDisplacementArray, RealSolidStressArray, etc. for the self model b_obj : Op2Object() a RealDisplacementArray, ComplexDisplacementArray, RealSolidStressArray, etc. for the comparison model stop_on_failure : bool; default=True True : Crashes if they're not equal False : Go to the next object debug : bool; default=False give slightly more debugging messages Returns ------- is_equal : bool are the objects equal? Raises ------ AssertionError/ValueError : stop_on_failure=True and and error occurred NotImplementedError : this is a sign of an unsupported object """ # check the name (e.g., RealDisplacementArray vs. # ComplexDisplacementArray) aname = a_obj.__class__.__name__ bname = b_obj.__class__.__name__ if not aname == bname: self.log.warning(f'type(a)={aname} type(b)={bname}') return False if aname == 'PARAM': # TODO: update this return True if not any(word in aname for word in ['Array', 'Eigenvalues', 'GridPointWeight']): # TODO: Does this ever hit? msg = f'{aname} is not an Array ... assume equal' self.log.warning(msg) raise NotImplementedError(f'{aname} __eq__') # continue # TODO: Use the array methods to check for equality. # TODO: This can crash. is_not_equal = '' # Silence warning undeclared variable before usage try: is_not_equal = a_obj != b_obj except ValueError: if stop_on_failure: raise if is_not_equal: msg = (f'key = {key} table_type = {table_type!r} are not equal; ' f'class_name = {aname!r}') self.log.warning(msg) return False return True def _set_mode(self, mode: str): """Explicitly set the format .. todo:: What other modes are there? What else would this be used for? """ if mode is None: return if mode == 'nasa95': self.set_as_nasa95() def set_mode(self, mode: str) -> None: """Sets the op2 mode Sets the mode as as 'msc', 'nx', 'autodesk', 'nasa95', or 'optistruct' :param mode: :type mode: str :returns: None .. todo:: Fill in info for parameters in docstring """ # Use dict to avoid long if-elif-else blocks to direct flow of traffic traffic_cop = { 'msc': self.set_as_msc(), 'nx': self.set_as_nx(), 'autodesk': self.set_as_autodesk(), 'nasa95': self.set_as_nasa95(), 'optistruct': self.set_as_optistruct(), # 'radioss': self.set_as_optistruct() # FIXME: Work in progress?? } try: traffic_cop[mode.lower()] except KeyError: raise KeyError(f'mode={mode!r} and must be in [msc, nx, autodesk, ' f'nasa95, optistruct]') return None def to_nx(self, msg='') -> None: # TODO: Docstring for this method if self.is_msc: self.log.warning(f'switching to NX{msg}') self.set_as_nx() self.set_table_type() return None def to_msc(self, msg='') -> None: # TODO: Docstring for this method if self.is_nx: self.log.warning(f'switching to MSC{msg}') self.set_as_msc() return None def include_exclude_results(self, exclude_results: Optional[List[str]] = None, include_results: Optional[List[str]] = None ) -> None: """Sets results to include/exclude Parameters ---------- exclude_results / include_results : List[str] / str; default=None a list of result types to exclude/include one of these must be None """ if exclude_results and include_results: msg = (f'exclude_results or include_results must be None\n' f'exclude_results = {exclude_results!r}\ninclude_results = ' f'{include_results!r}\n') raise RuntimeError(msg) if exclude_results: self.remove_results(exclude_results) elif include_results: self.set_results(include_results) def saves(self) -> Union[str, bytes]: """Saves a pickled string""" return dumps(self) def __getstate__(self): """Clears out a few variables in order to pickle the object Copy the object's state from self.__dict__ which contains all our instance attributes. Always use the dict.copy() method to avoid modifying the original state. """ state = self.__dict__.copy() del state['log'] # Remove the unpicklable entries. if hasattr(self, 'results') and hasattr(self._results, 'log'): del state['_results'].log # This block let's us identify the objects that are problematic We # just play with the value of i to delete all objects past some # threshold. Once we find where the code breaks, we dig into the # objects further if 0: # pragma: no cover # FIXME: Why though? What is this even accomplishing? i = 0 for key, value in sorted(state.items()): if isinstance(value, dict) and len(value) == 0: continue if i > 100: del state[key] else: i += 1 return state def save(self, obj_filename: str = 'model.obj', unxref: bool = True ) -> None: """Saves a pickleable object""" if hasattr(self, 'generalized_tables'): del self.generalized_tables if hasattr(self, 'op2_reader'): del self.op2_reader with open(obj_filename, 'wb') as obj_file: dump(self, obj_file) return None def load(self, obj_filename: str = 'model.obj') -> None: """Loads a pickleable object""" with open(obj_filename, 'rb') as obj_file: obj = load(obj_file) keys_to_skip = ['_close_op2', '_count', '_results', '_table_mapper', 'ask', 'binary_debug', 'additional_matrices', 'apply_symmetry', 'debug_file', 'expected_times', 'f', 'generalized_tables', 'is_all_subcases', 'is_debug_file', 'is_geometry', 'is_vectorized', 'isubcase', 'log', 'matrix_tables', 'mode', 'n', 'ntotal', 'num_wide', 'op2_reader', 'table_name', 'use_vector', 'words'] keys = object_attributes(self, mode="all", keys_to_skip=keys_to_skip, filter_properties=True) val = '' # Silence variable usage before declaration? for key in keys: if key.startswith('__') and key.endswith('__'): continue try: val = getattr(obj, key) except NameError: self.log.warning(f'key={key!r} val={val}') continue try: setattr(self, key, val) except AttributeError: print(f'key={key!r} val={val}') raise self.log.debug('done loading!') @property def is_geometry(self) -> bool: # TODO: Why is this a thing? What is it even for? Is this a # future-proofing thing for a WIP feature? So many questions... return False def read_op2(self, op2_filename: Optional[str] = None, combine: bool = True, build_dataframe: Optional[bool] = False, skip_undefined_matrices: bool = False, encoding: Optional[str] = None) -> None: """Starts the OP2 file reading Parameters ---------- op2_filename : str (default=None -> popup) the op2_filename combine : bool; default=True True : objects are isubcase based False : objects are (isubcase, subtitle) based; will be used for superelements regardless of the option build_dataframe : bool (default=None -> True if in iPython, False otherwise) builds a pandas DataFrame for op2 objects None: True if in iPython, False otherwise skip_undefined_matrices : bool; default=False True : prevents matrix reading crashes encoding : str the unicode encoding (default=None; system default) """ if op2_filename: check_path(op2_filename, name='op2_filename') mode = self.mode if build_dataframe is None: build_dataframe = False if ipython_info(): build_dataframe = True if encoding is None: encoding = sys.getdefaultencoding() self.encoding = encoding self.skip_undefined_matrices = skip_undefined_matrices assert self.ask in [True, False], self.ask self.is_vectorized = True self.log.debug(f'combine={combine}') msg = f'{"-"8} reading op2 with read_mode=1 (array sizing) {"-"8}' self.log.debug(msg) self.read_mode = 1 self._close_op2 = False load_as_h5 = False if hasattr(self, 'load_as_h5'): load_as_h5 = self.load_as_h5 try: # get GUI object names, build objects, but don't read data table_names = OP2_Scalar.read_op2(self, op2_filename=op2_filename, load_as_h5=load_as_h5, mode=mode) self.table_names = table_names # TODO: Stuff to figure out objects. # TODO: Stuff to show GUI of table names. # TODO: Clear out objects the user doesn't want. # TODO: Consider a globalized Enum sub-class to do this stuff with # more readable asignments self.read_mode = 2 self._close_op2 = True msg = (f'{"-"8} reading op2 with read_mode=2 (array filling) ' f'{"-"8}') self.log.debug() _create_hdf5_info(self.op2_reader.h5_file, self) OP2_Scalar.read_op2(self, op2_filename=self.op2_filename, mode=mode) except FileNotFoundError: raise except Exception as e: # Added ``Exception as e`` OP2_Scalar.close_op2(self, force=True) raise e self._finalize() if build_dataframe: self.build_dataframe() self.create_objects_from_matrices() self.combine_results(combine=combine) self.log.debug('finished reading op2') str(self.op2_results) return None def create_objects_from_matrices(self) -> None: """Creates objects monitor1, monitor3 creates the following objects: - monitor3 : MONPNT3 object from the MP3F matrix - monitor1 : MONPNT1 object from the PMRF, PERF, PFRF, AGRF, PGRF, AFRF matrices """ if 'MP3F' in self.matrices: self.op2_results.monitor3 = MONPNT3(self._frequencies, self.matrices['MP3F']) # TODO: These are totally wrong...it doesn't go by component; it goes # by inertial, external, flexibility, etc. if 'PERF' in self.matrices: self.op2_results.monitor1 = MONPNT1( self._frequencies, self.matrices, # :) ? :) :)2 ? ? ['PMRF', 'PERF', 'PFRF', 'AGRF', 'PGRF', 'AFRF', ]) return None def _finalize(self) -> None: """internal method .. todo:: Elaborate on this; what does it do? -als0052 """ if hasattr(self, 'subcase'): del self.subcase result_types = self.get_table_types() result_type_list = ['params', 'gpdt', 'bgpdt', 'eqexin', 'psds', 'monitor1', 'monitor3'] for result_type in result_types: if (result_type in result_type_list or result_type.startswith('responses.')): continue result = self.get_result(result_type) try: values = result.values() except AttributeError: self.log.error(f'result_type = {result_type}') raise for obj in values: if hasattr(obj, 'finalize'): obj.finalize() elif hasattr(obj, 'tCode') and not obj.is_sort1: msg = (f'object has not implemented ' f'finalize\n{"".join(obj.get_stats())}') raise RuntimeError(msg) self.del_structs() def build_dataframe(self) -> None: """Converts the OP2 objects into pandas DataFrames .. todo:: fix issues with: - RealDisplacementArray - RealPlateStressArray (???) - RealPlateStrainArray (???) - RealCompositePlateStrainArray (???) """ # TODO: Wouldn't it make more sense for this return a pandas.DataFrame # obj? -als0052 # TODO: sorter = uniques.argsort() # FIXME: C:\Anaconda\lib\site-packages\pandas\core\algorithms.py:198: # DeprecationWarning: unorderable dtypes; returning scalar but # in the future this will be an error no_sort2_classes = ['RealEigenvalues', 'ComplexEigenvalues', 'BucklingEigenvalues'] result_types = self.get_table_types() skip_pandas = ['params', 'gpdt', 'bgpdt', 'eqexin', 'psds', 'monitor1', 'grid_point_weight', 'monitor3'] if len(self.matrices): for key, matrix in sorted(self.matrices.items()): if hasattr(matrix, 'build_dataframe'): matrix.build_dataframe() else: msg = (f'pandas: build_dataframe is not supported for key=' f'{key} type={str(type(matrix))}') self.log.warning(msg) raise NotImplementedError() for result_type in result_types: if (result_type in skip_pandas or result_type.startswith('responses.')): continue result = self.get_result(result_type) for obj in result.values(): class_name = obj.__class__.__name__ obj.object_attributes() obj.object_methods() str(obj) obj.get_stats() if class_name in no_sort2_classes: try: obj.build_dataframe() assert obj.data_frame is not None except MemoryError: raise except Exception as e: self.log.error(obj) msg = f'build_dataframe is broken for {class_name}' self.log.error(msg) raise e continue if obj.is_sort2: msg = ( f'build_dataframe is not supported for {class_name} ' f'- SORT2') self.log.warning(msg) continue # SORT1 try: obj.build_dataframe() except MemoryError: raise except NotImplementedError: self.log.warning(obj) self.log.warning(f'build_dataframe is broken for ' f'{class_name}') raise except Exception as e: self.log.error(obj) self.log.error(f'build_dataframe is broken for ' f'{class_name}') raise e def load_hdf5_filename(self, hdf5_filename: str, combine: bool = True ) -> None: """Loads an h5 file into an OP2 object Parameters ---------- hdf5_filename : str the path to the an hdf5 file combine : bool; default=True runs the combine routine """ check_path(hdf5_filename, 'hdf5_filename') self.op2_filename = hdf5_filename self.log.info(f'hdf5_op2_filename = {hdf5_filename!r}') debug = False with h5py.File(hdf5_filename, 'r') as h5_file: load_op2_from_hdf5_file(self, h5_file, self.log, debug=debug) self.combine_results(combine=combine) return None def load_hdf5_file(self, h5_file: H5File, combine: bool = True) -> None: """Loads an h5 file object into an OP2 object Parameters ---------- h5_file : H5File() an h5py file object combine : bool; default=True runs the combine routine """ debug = False load_op2_from_hdf5_file(self, h5_file, self.log, debug=debug) self.combine_results(combine=combine) return None def export_hdf5_filename(self, hdf5_filename: str) -> None: """Converts the OP2 objects into hdf5 object .. todo:: doesn't support ``BucklingEigenvalues`` """ export_op2_to_hdf5_filename(hdf5_filename, self) return None def export_hdf5_file(self, hdf5_file: H5File, exporter=None) -> None: """Converts the OP2 objects into hdf5 object Parameters ---------- hdf5_file : H5File() an h5py object exporter : HDF5Exporter; default=None unused .. todo:: doesn't support ``BucklingEigenvalues`` """ export_op2_to_hdf5_file(hdf5_file, self) return None def combine_results(self, combine: Union[str, bool] = True) -> None: """Group and format like data by subcase We want the data to be in the same format and grouped by subcase, so we take .. code-block:: python stress = { # isubcase, analysis_code, sort_method, count, superelement_adaptivity_index, pval_step (1, 2, 1, 0, 'SUPERELEMENT 0', '') : result1, (1, 2, 1, 0, 'SUPERELEMENT 10', '') : result2, (1, 2, 1, 0, 'SUPERELEMENT 20', '') : result3, (2, 2, 1, 0, 'SUPERELEMENT 0', '') : result4, code = (isubcase, analysis_code, sort_method, count, ogs, superelement_adaptivity_index, pval_step) } and convert it to: .. code-block:: python stress = { 1 : result1 + result2 + results3, 2 : result4, } """ self.combine = combine result_types = self.get_table_types() results_to_skip = ['bgpdt', 'gpdt', 'eqexin', 'grid_point_weight', 'psds', 'monitor1', 'monitor3'] # set subcase_key for result_type in result_types: if (result_type in results_to_skip or result_type.startswith('responses.')): continue result = self.get_result(result_type) try: case_keys = sorted(result.keys()) except AttributeError: self.log.error(f'result_type = {result_type}') raise for case_key in case_keys: if isinstance(case_key, tuple): isubcasei, analysis_codei, others = case_key if others[4] == 0: value = [analysis_codei] + others[:3] else: value = [analysis_codei] + others if value not in self.subcase_key[isubcasei]: self.subcase_key[isubcasei].append(value) else: break if not combine: subcase_key2 = {} for key, value in self.subcase_key.items(): subcase_key2[key] = value self.subcase_key = subcase_key2 return del result, case_keys isubcases = np.unique(list(self.subcase_key.keys())) unique_isubcases = np.unique(isubcases) self.log.debug('combine_results') for result_type in result_types: if (result_type in results_to_skip or result_type.startswith('responses.')): continue result = self.get_result(result_type) if len(result) == 0: continue for isubcase in unique_isubcases: try: keys = self.subcase_key[isubcase] except TypeError: print('isubcase =', isubcase) print('isubcases =', isubcases) print('self.subcase_key =', self.subcase_key) raise key0 = tuple([isubcase] + list(keys[0])) if len(key0) == 5: # ogs is optional isubcase, analysis_code, others = key0 key1 = [isubcase, analysis_code, 1] + others[0:] key2 = [isubcase, analysis_code, 2] + others[0:] else: isubcase, analysis_code, others = key0 key1 = [isubcase, analysis_code, 1] + others[0:] key2 = [isubcase, analysis_code, 1] + others[0:] if len(keys) == 1: if key0 not in result: continue # One tuple for result; Rename case result[isubcase] = result[key0] del result[key0] elif len(keys) == 2 and key1 in keys and key2 in keys: isubcase, analysis_code, others = key0 if not (key1 in result and key2 in result): if key1 in result: res1 = result[key1] msg = (f"res={res1.__class__.__name__} has a " f"single case; trivial") self.log.info(msg) result[isubcase] = result[key1] del result[key1] elif key2 in result: res2 = result[key2] msg = (f'res={res2.__class__.__name__} has a ' f'single case; trivial') self.log.info(msg) result[isubcase] = result[key2] del result[key2] continue res1 = result[key1] class_name = res1.__class__.__name__ if not hasattr(res1, 'combine'): self.log.info( f'res={class_name} has no method combine') continue self.log.info(f'res={class_name} has combine') res2 = result[key2] del result[key1] del result[key2] res1.combine(res2) result[isubcase] = res1 else: continue setattr(self, result_type, result) subcase_key2 = {} not_results = ['eigenvalues', 'eigenvalues_fluid', 'params', 'gpdt', 'bgpdt', 'eqexin', 'desvars', 'grid_point_weight', 'psds', 'monitor1', 'monitor3'] for result_type in result_types: if (result_type in not_results or result_type.startswith('responses.')): continue result = self.get_result(result_type) try: case_keys = list(result.keys()) except AttributeError: self.log.error(f'result_type = {result_type}') raise try: # FIXME: causes DeprecationWarning case_keys = sorted(case_keys) except TypeError: self.log.error(f'result.keys() = {case_keys}') if len(result) == 0: continue for isubcase in unique_isubcases: if isubcase not in subcase_key2: subcase_key2[isubcase] = [] for isubcase in unique_isubcases: for case_key in case_keys: assert not isinstance(case_key, str), result_type if isinstance(case_key, integer_types): if (isubcase == case_key and case_key not in subcase_key2[isubcase]): subcase_key2[isubcase] = [isubcase] else: try: subcasei = case_key[0] except IndexError: msg = (f'case_key={case_key}; type(case_key)=' f'{type(case_key)}; case_key[0] is not the ' f'subcase id') raise IndexError(msg) if case_key not in subcase_key2[subcasei]: subcase_key2[isubcase].append(case_key) self.subcase_key = subcase_key2 return None def get_key_order(self) -> list[tuple[Any, Any, Any, Any, Union[int, Any], Any, Any]]: """Get the order of the keys Returns ------- keys3 : List[int, int, int, int, int, str] the keys in order """ keys = [] table_types = self.get_table_types() skip_tables = ['gpdt', 'bgpdt', 'eqexin', 'grid_point_weight', 'psds', 'monitor1', 'monitor3'] for table_type in sorted(table_types): if (table_type in skip_tables or table_type.startswith('responses.')): continue result_type_dict = self.get_result(table_type) if len(result_type_dict) == 0: continue for key in result_type_dict: if isinstance(key, str): if (table_type not in ['eigenvalues', 'eigenvalues_fluid', 'params']): self.log.warning(f'table_type = {table_type}') continue if key not in keys: keys.append(key) analysis_codes = set() counts = set() isubcases = set() ogss = set() pval_steps = set() sort_methods = set() superelement_adaptivity_indexs = set() for key in keys: if len(key) == 6: isubcases, analysis_codes, others = key ogss = 0 elif len(key) == 7: isubcases, analysis_codes, others = key else: print(f' {str(key)}') raise RuntimeError(key) isubcases.add(isubcases) analysis_codes.add(analysis_codes) sort_methods.add(sort_methods) counts.add(counts) ogss.add(ogss) superelement_adaptivity_indexs.add(superelement_adaptivity_indexs) pval_steps.add(pval_steps) analysis_code_list = list(analysis_codes) count_list = list(counts) isubcase_list = list(isubcases) ogs_list = list(ogss) pval_step_list = list(pval_steps) sort_method_list = list(sort_methods) superelement_adaptivity_index_list = list( superelement_adaptivity_indexs) analysis_code_list.sort() count_list.sort() isubcase_list.sort() ogs_list.sort() pval_step_list.sort() sort_method_list.sort() superelement_adaptivity_index_list.sort() keys3 = [] # TODO: This needs to go away and be replaced by something else :( for isubcase in isubcase_list: for count in count_list: for analysis_code in analysis_code_list: for sai in superelement_adaptivity_index_list: for pval_step in pval_step_list: for sort_method in sort_method_list: for ogs in ogs_list: key = (isubcase, analysis_code, sort_method, count, ogs, sai, pval_step) if key not in keys3: keys3.append(key) if len(keys3) == 0: msg = f'No results...\n{self.get_op2_stats(short=True)}' self.log.warning(msg) return keys3 def print_subcase_key(self) -> None: # TODO: Docstring for this method self.log.info('---self.subcase_key---') for isubcase, keys in sorted(self.subcase_key.items()): if len(keys) == 1: self.log.info(f'subcase_id = {isubcase} : keys = {keys}') else: self.log.info(f'subcase_id = {isubcase}') for key in keys: self.log.info(f' {str(key)}') return None def transform_displacements_to_global(self, icd_transform: Any, coords: Dict[int, Any], xyz_cid0: Any = None, debug: bool = False) -> None: """Transform displacements into global coords Transforms the ``data`` of displacement-like results into the global coordinate system for those nodes with different output coordinate systems. Takes indices and transformation matrices for nodes with their output in coordinate systems other than the global. Used in combination with ``BDF.get_displacement_index`` Parameters ---------- icd_transform : dict{int cid : int ndarray} Dictionary from coordinate id to index of the nodes in ``BDF.point_ids`` that their output (`CD`) in that coordinate system. coords : dict{int cid :Coord()} Dictionary of coordinate id to the coordinate object Use this if CD is only rectangular Use this if CD is not rectangular xyz_cid0 : (nnodes+nspoints, 3) float ndarray the nodes in the global frame Don't use this if CD is only rectangular Use this if CD is not rectangular debug : bool; default=False developer debug .. warning:: only works if all nodes are included... ``test_pynastrangui isat_tran.dat isat_tran.op2 -f nastran`` .. note:: Nastran has this concept of a basic (cid=0) and global (cid=cd) coordinate system. They occur at the same time. Basic is for positions/properties, while global is for result outputs. pyNastran's OP2 interface uses: - cd=0 for global frames - cd>0 are local frames pyNastran's BDF interface uses: - cp=0 for global frames - cp>0 are local frames """ ato = self.op2_results.ato crm = self.op2_results.crm psd = self.op2_results.psd rms = self.op2_results.rms # ----- I feel like this is more readable even though it is a bit # longer and more verbose # TODO: Should NO results be transformed? # _no_displacements = [no.displacements, no.velocities, # no.accelerations, no.spc_forces, no.mpc_forces] _disp = [ self.displacements, ato.displacements, crm.displacements, psd.displacements, rms.displacements, self.displacements_scaled, self.displacement_scaled_response_spectra_abs, self.displacement_scaled_response_spectra_nrl] _vel = [self.velocities, ato.velocities, crm.velocities, psd.velocities, rms.velocities, self.velocity_scaled_response_spectra_abs] _accel = [ self.accelerations, ato.accelerations, crm.accelerations, psd.accelerations, rms.accelerations, self.acceleration_scaled_response_spectra_abs, self.acceleration_scaled_response_spectra_nrl] _eig = [ self.eigenvectors, self.op2_results.RADCONS.eigenvectors, self.op2_results.RADEFFM.eigenvectors, self.op2_results.RADEATC.eigenvectors, self.op2_results.ROUGV1.eigenvectors] _spc = [self.spc_forces, ato.spc_forces, crm.spc_forces, psd.spc_forces, rms.spc_forces] _mpc = [self.mpc_forces, ato.mpc_forces, crm.mpc_forces, psd.mpc_forces, rms.mpc_forces] _loads = [self.applied_loads, self.load_vectors] disp_like_dicts = _disp + _vel + _accel + _eig + _spc + _mpc + _loads del _disp, _vel, _accel, _eig, _spc, _mpc, _loads # ----- for disp_like_dict in disp_like_dicts: if not disp_like_dict: continue for subcase, result in disp_like_dict.items(): if result.table_name in ['BOUGV1', 'BOPHIG', 'TOUGV1']: continue self.log.debug(f'transforming {result.table_name}') transform_displacement_to_global( subcase, result, icd_transform, coords, xyz_cid0, self.log, debug=debug) return None def transform_gpforce_to_global(self, nids_all, nids_transform, icd_transform, coords, xyz_cid0=None): """Transform GPFORCE into global coords Transforms the ``data`` of GPFORCE results into the global coordinate system for those nodes with different output coordinate systems. Takes indices and transformation matrices for nodes with their output in coordinate systems other than the global. Used in combination with ``BDF.get_displacement_index`` Parameters ---------- nids_all : ??? ??? nids_transform : dict{int cid : int ndarray nds} Dictionary from coordinate id to corresponding node ids. icd_transform : dict{int cid : int ndarray} Dictionary from coordinate id to index of the nodes in ``BDF.point_ids`` that their output (`CD`) in that coordinate system. coords : dict{int cid :Coord()} Dictionary of coordinate id to the coordinate object Use this if CD is only rectangular Use this if CD is not rectangular xyz_cid0 : ??? required for cylindrical/spherical coordinate systems .. todo:: Docstring needs some work. .. todo:: Return type for this method should be specified in signature to be consistent with all other method signatures. """ # TODO: Causes test_op2_solid_shell_bar_01_gpforce_xyz to fail even # though it should be uncommented disp_like_dicts = [self.grid_point_forces] for disp_like_dict in disp_like_dicts: if not disp_like_dict: continue self.log.debug(f'{"-" * 11}') for subcase, result in disp_like_dict.items(): transform_gpforce_to_globali( subcase, result, nids_all, nids_transform, icd_transform, coords, xyz_cid0, self.log) self.log.debug(f'{"-" * 11}') def read_op2(op2_filename: Optional[str] = None, load_geometry: bool = False, combine: bool = True, subcases: Optional[List[int]] = None, exclude_results: Optional[List[str]] = None, include_results: Optional[List[str]] = None, log: Any = None, debug: Optional[bool] = True, build_dataframe: Optional[bool] = False, skip_undefined_matrices: bool = True, mode: Optional[str] = None, encoding: Optional[str] = None) -> OP2: """Creates the OP2 object without calling the OP2 class. Parameters ---------- op2_filename : str (default=None -> popup) the op2_filename load_geometry: bool; default=False False: load results and matrices True: load geometry as well combine : bool; default=True True : objects are isubcase based False : objects are (isubcase, subtitle) based; will be used for superelements regardless of the option subcases : List[int, ...] / int; default=None->all subcases list of [subcase1_ID,subcase2_ID] exclude_results / include_results : List[str] / str; default=None a list of result types to exclude/include one of these must be None build_dataframe : bool; default=False builds a pandas DataFrame for op2 objects None: True if in iPython, False otherwise skip_undefined_matrices : bool; default=False True : prevents matrix reading crashes debug : bool/None; default=True used to set the logger if no logger is passed in True: logs debug/info/warning/error messages False: logs info/warning/error messages None: logs warning/error messages log : Log() a logging object to write debug messages to (.. seealso:: import logging) mode : str; default=None -> 'msc' the version of the Nastran you're using {nx, msc, autodesk, optistruct, nasa95} encoding : str the unicode encoding (default=None; system default) exclude_results: include_results: debug: Returns ------- model : OP2() an OP2 object .. note :: This method will change in order to return an object that does not have so many methods. .. todo:: Creates the OP2 object without all the read methods. .. todo:: Update info for parameters in this function's docstring. """ if op2_filename: check_path(op2_filename, name='op2_filename') if load_geometry: # TODO: kwargs support for methods kw = {'op2_filename': op2_filename, 'combine': combine, 'subcases': subcases, 'exclude_results': exclude_results, 'include_results': include_results, 'validate': True, 'xref': True, 'build_dataframe': build_dataframe, 'skip_undefined_matrices': skip_undefined_matrices, 'mode': mode, 'log': log, 'debug': debug, 'encoding': encoding} model = read_op2_geom(kw) else: model = OP2(log=log, debug=debug, mode=mode) model.set_subcases(subcases) model.include_exclude_results(exclude_results=exclude_results, include_results=include_results) # TODO: kwargs support kw = {'op2_filename': op2_filename, 'build_dataframe': build_dataframe, 'skip_undefined_matrices': skip_undefined_matrices, 'combine': combine, 'encoding': encoding} model.read_op2(kw) # TODO: This will go away when OP2 is refactored. Many methods will be # missing, but it's a start... # Doesn't support F06 writer # obj = Op2F06Attributes() # attr_names = object_attributes(obj, mode="public", keys_to_skip=None) # for attr_name in attr_names: # attr = getattr(model, attr_name) # setattr(obj, attr_name, attr) # obj.get_op2_stats() return model def _create_hdf5_info(h5_file: H5File, op2_model: OP2) -> None: """Exports the h5 info group :param h5_file: :type h5_file: H5File :param op2_model: :type op2_model: OP2 :returns: None .. todo:: Docstring info for parameters. """ load_as_h5 = False if hasattr(op2_model, 'load_as_h5'): load_as_h5 = op2_model.load_as_h5 if not load_as_h5: return create_info_group(h5_file, op2_model) return None	[ 1, 529, 276, 1112, 420, 29958, 1338, 29900, 29900, 29945, 29906, 29914, 2272, 29940, 7614, 273, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 2514, 3871, 29901, 1015, 29906, 29889, 2272, 13, 13, 15945, 29908, 13, 3206, 1475, 278, 1667, 6418, 29906, 770, 29889, 5282, 1475, 29901, 13, 13, 448, 1303, 29918, 459, 29906, 29898, 459, 29906, 29918, 9507, 29922, 8516, 29892, 14405, 29922, 5574, 29892, 1014, 11436, 29922, 8516, 29892, 29871, 13, 9651, 19060, 29918, 9902, 29922, 8516, 29892, 3160, 29918, 9902, 29922, 8516, 29892, 13, 9651, 1480, 29922, 8516, 29892, 4744, 29922, 5574, 29892, 4744, 29918, 1445, 29922, 8516, 29892, 2048, 29918, 1272, 2557, 29922, 8824, 29892, 13, 9651, 14383, 29918, 15955, 29918, 2922, 11669, 29922, 5574, 29892, 4464, 2433, 1516, 29883, 742, 8025, 29922, 8516, 29897, 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spamresponsibly.py	velo223/Velo-s-Spam-Scripts	0	89287	<reponame>velo223/Velo-s-Spam-Scripts<filename>spamresponsibly.py val = int(input(print("Enter a number: "))) option = { "n":"Nae nigga nae \n", "c":"Uohhhhhh :sob::sob::sob: \n", "i":"I am living in your walls, oomfie. \n" } userinput = input("Choose your poison: [N]iggas, [C]unny, [I]solation") with open("outputtings.txt",'w') as f: f.write(option[userinput.lower()] * val) print("Output saved to file. Spam responsibly, fam.")	[ 1, 529, 276, 1112, 420, 29958, 955, 29877, 29906, 29906, 29941, 29914, 29963, 7078, 29899, 29879, 29899, 5592, 314, 29899, 4081, 29879, 29966, 9507, 29958, 1028, 314, 26679, 14981, 29889, 2272, 13, 791, 353, 938, 29898, 2080, 29898, 2158, 703, 10399, 263, 1353, 29901, 376, 4961, 30004, 13, 3385, 353, 3336, 13, 1678, 376, 29876, 4710, 29940, 3660, 302, 335, 3249, 1055, 29872, 320, 29876, 613, 376, 29883, 4710, 29965, 1148, 25446, 25446, 29882, 584, 15975, 1057, 15975, 1057, 15975, 29901, 320, 29876, 15231, 13, 1678, 376, 29875, 4710, 29902, 626, 8471, 297, 596, 14603, 29892, 288, 290, 29888, 347, 29889, 320, 29876, 19451, 13, 1678, 4970, 13, 1792, 2080, 353, 1881, 703, 15954, 852, 596, 27908, 29901, 518, 29940, 29962, 22817, 294, 29892, 518, 29907, 29962, 348, 1460, 29892, 518, 29902, 29962, 2929, 362, 1159, 30004, 13, 2541, 1722, 703, 4905, 29873, 886, 29889, 3945, 613, 29915, 29893, 1495, 408, 285, 29901, 30004, 13, 1678, 285, 29889, 3539, 29898, 3385, 29961, 1792, 2080, 29889, 13609, 580, 29962, 334, 659, 8443, 13, 2158, 703, 6466, 7160, 304, 934, 29889, 1706, 314, 5544, 14981, 29892, 5216, 23157, 30004, 13, 2 ]
utils/__init__.py	jaobernardi/roboscovid-redacted	0	58177	<filename>utils/__init__.py from .parsing import parse_string from .tokens import token from .users import getUsers, getPlaces from .flags import flag_define, value_assign	[ 1, 529, 9507, 29958, 13239, 29914, 1649, 2344, 26914, 2272, 13, 3166, 869, 862, 2976, 1053, 6088, 29918, 1807, 13, 3166, 869, 517, 12360, 1053, 5993, 13, 3166, 869, 7193, 1053, 679, 5959, 29892, 679, 29925, 6048, 13, 3166, 869, 15764, 1053, 7353, 29918, 7922, 29892, 995, 29918, 16645, 2 ]
main.py	b-com/ACUITEE	3	1612198	# main.py – Software ACUITEE # Copyright 2021 b<>com. All rights reserved. # This software is licensed under the Apache License, Version 2.0. # 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 application import app from gevent.pywsgi import WSGIServer # for production deployment, use gevent server if __name__ == '__main__': print('Serving on 5000...') WSGIServer(('0.0.0.0', 5000), app).serve_forever() #print('Serving on https://:5000') #server = WSGIServer(('127.0.0.1', 5000), app, keyfile='server.key', certfile='server.crt') #server.serve_forever() # to start the server asynchronously, call server.start()	[ 1, 396, 1667, 29889, 2272, 785, 18540, 14614, 29965, 9094, 29923, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29896, 289, 25299, 510, 29889, 2178, 10462, 21676, 29889, 13, 29937, 910, 7047, 338, 7794, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 29889, 13, 29937, 887, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19405, 29889, 29871, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19405, 472, 29901, 29871, 13, 29937, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 29871, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 29871, 13, 29937, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 6323, 8707, 29928, 22122, 29903, 8079, 13764, 29979, 476, 22255, 29892, 2845, 4653, 470, 2411, 2957, 29889, 13, 29937, 2823, 278, 19245, 363, 278, 2702, 4086, 14765, 1076, 11239, 322, 29871, 13, 29937, 27028, 1090, 278, 19245, 29889, 13, 13, 3166, 2280, 1053, 623, 13, 3166, 1737, 794, 29889, 2272, 5652, 3146, 1053, 399, 26016, 29902, 6004, 13, 13, 29937, 363, 5802, 18209, 29892, 671, 1737, 794, 1923, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1596, 877, 1748, 1747, 373, 29871, 29945, 29900, 29900, 29900, 856, 1495, 13, 1678, 399, 26016, 29902, 6004, 29898, 877, 29900, 29889, 29900, 29889, 29900, 29889, 29900, 742, 29871, 29945, 29900, 29900, 29900, 511, 623, 467, 16349, 29918, 1079, 369, 580, 13, 13, 1678, 396, 2158, 877, 1748, 1747, 373, 2045, 597, 29901, 29945, 29900, 29900, 29900, 1495, 13, 1678, 396, 2974, 353, 399, 26016, 29902, 6004, 29898, 877, 29896, 29906, 29955, 29889, 29900, 29889, 29900, 29889, 29896, 742, 29871, 29945, 29900, 29900, 29900, 511, 623, 29892, 1820, 1445, 2433, 2974, 29889, 1989, 742, 2284, 1445, 2433, 2974, 29889, 29883, 2273, 1495, 13, 1678, 396, 2974, 29889, 16349, 29918, 1079, 369, 580, 396, 304, 1369, 278, 1923, 408, 9524, 5794, 29892, 1246, 1923, 29889, 2962, 580, 2 ]
app/main/schema/http_status_schema.py	abhishek9sharma/nyctaxiserver	0	114798	<filename>app/main/schema/http_status_schema.py from flask_restplus import Namespace, fields class HTTPStatus: """ Schema for showing messages related to various HTTP Statuses """ ns = Namespace('httpstatus', 'Schema for displaying http status') statusschema = ns.model('httpstatus', { 'message': fields.String(required = True, description = 'description of failure'), 'code': fields.Integer(required= True, description = 'HTTP error code reated to message') })	[ 1, 529, 9507, 29958, 932, 29914, 3396, 29914, 11010, 29914, 1124, 29918, 4882, 29918, 11010, 29889, 2272, 13, 3166, 29784, 29918, 5060, 11242, 1053, 14706, 3535, 29892, 4235, 13, 13, 1990, 7331, 5709, 29901, 13, 1678, 9995, 1102, 2603, 363, 6445, 7191, 4475, 304, 5164, 7331, 16034, 267, 9995, 13, 1678, 17534, 353, 14706, 3535, 877, 1124, 4882, 742, 525, 12763, 363, 16384, 1732, 4660, 1495, 13, 1678, 4660, 11010, 353, 17534, 29889, 4299, 877, 1124, 4882, 742, 426, 13, 462, 462, 462, 259, 525, 4906, 2396, 4235, 29889, 1231, 29898, 12403, 353, 5852, 29892, 6139, 353, 525, 8216, 310, 10672, 5477, 13, 462, 462, 462, 259, 525, 401, 2396, 4235, 29889, 7798, 29898, 12403, 29922, 5852, 29892, 6139, 353, 525, 10493, 1059, 775, 337, 630, 304, 2643, 1495, 13, 462, 462, 462, 965, 13, 462, 462, 462, 5615, 13, 268, 13, 2 ]
python/overlapintegrals/overlapintegrals.py	berquist/overlapintegrals	1	111156	<filename>python/overlapintegrals/overlapintegrals.py from functools import reduce from math import exp, floor, pi from typing import Tuple, Union from scipy.special import binom as binomial Number = Union[int, float] def dist2( xa: Number, ya: Number, za: Number, xb: Number, yb: Number, zb: Number ) -> float: return (xa - xb) * (xa - xb) + (ya - yb) * (ya - yb) + (za - zb) * (za - zb) def product_center_1d(za: float, xa: float, zb: float, xb: float) -> float: return ((za * xa) + (zb * xb)) / (za + zb) def fact2(n): """Compute the double factorial as in https://stackoverflow.com/a/4740229/3249688 """ return reduce(int.__mul__, range(n, 0, -2), 1) def binomial_prefactor(s: int, ia: int, ib: int, xpa: float, xpb: float) -> float: return sum( binomial(ia, s - t) * binomial(ib, t) * (xpa ** (ia - s + t)) * (xpb ** (ib - t)) for t in range(s + 1) if (s - ia) <= t and t <= ib ) def overlap1d(l1: int, l2: int, pax: float, pbx: float, gamma: float) -> float: return sum( binomial_prefactor(2 * i, l1, l2, pax, pbx) * fact2(2 * i - 1) / ((2 * gamma) ** i) for i in range(1 + floor(0.5 * (l1 + l2))) ) def tho66( alpha1: float, alpha2: float, ra: Tuple[float, float, float], rb: Tuple[float, float, float], la: Tuple[float, float, float], lb: Tuple[float, float, float], ) -> float: gamma = alpha1 + alpha2 xa, ya, za = ra xb, yb, zb = rb l1, m1, n1 = la l2, m2, n2 = lb xp = product_center_1d(alpha1, xa, alpha2, xb) yp = product_center_1d(alpha1, ya, alpha2, yb) zp = product_center_1d(alpha1, za, alpha2, zb) pre = ( exp(-alpha1 * alpha2 * dist2(xa, ya, za, xb, yb, zb) / gamma) * (pi / gamma) ** 1.5 ) wx = overlap1d(l1, l2, xp - xa, xp - xb, gamma) wy = overlap1d(m1, m2, yp - ya, yp - yb, gamma) wz = overlap1d(n1, n2, zp - za, zp - zb, gamma) return pre * wx * wy * wz	[ 1, 529, 9507, 29958, 4691, 29914, 957, 6984, 14146, 1338, 29914, 957, 6984, 14146, 1338, 29889, 2272, 13, 3166, 2090, 312, 8789, 1053, 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2018/09/sol/bike.py	lfrommelt/monty	0	103983	""" This module contains the definitions for Bike and its subclasses Bicycle and Motorbike. """ class Bike: """ Class defining a bike that can be ridden and have its gear changed. Attributes: seats: number of seats the bike has gears: number of gears the bike has """ def __init__(self, seats, gears): """ Creates a new Bike object. Args: seats: number of seats the bike has gears: number of gears the bike has """ self.seats = seats self.gears = gears self._curr_gear = 1 # current gear, private self._riding = False # bike is per default not ridden @property def curr_gear(self): """ Purpose of this function is to enable the user to check the gear status, but is only able to change it with a specific method. (was not necessary to implement it this way) """ return self._curr_gear def start_ride(self): """ Starts a bike ride. Returns: True if successful. False if bike is already on a ride. """ # can't ride a bike if already ridden if self._riding: return False self._riding = True return True def end_ride(self): """ Ends a bike ride. Returns: True if successful. False if bike is not currently ridden. """ # can't stop a bike ride if the bike is already standing if not self._riding: return False self._riding = False return True def change_gear(self, new_gear): """ Changes bike gear to a new gear. Args: new_gear: gear to be changed to Returns: True if gear was successfully changed. Raises: ValueError if current gear is same as new gear or new gear is <= 0 or not in range of available gears. """ if self._curr_gear == new_gear or not 0 < new_gear <= self.gears: raise ValueError("Already in this gear or invalid gear number.") self._curr_gear = new_gear return True class Bicycle(Bike): """ Class defining a Bicycle (extending Bike) that can be ridden, have its gear changed and has a bell that can be rung. Attributes: seats: number of seats the bike has gears: number of gears the bike has bell_sound: sound the bell makes when rung """ def __init__(self, seats=1, gears=7, bell_sound="ring ring"): """ Creates a new Bike object. Args: seats: number of seats the bicycle has, defaults to 1 gears: number of gears the bicycle has, defaults to 7 bell_sound: sound the bell makes when rung """ super().__init__(seats, gears) self.bell_sound = bell_sound def ring_bell(self): """ Rings bicycle bell.""" print(self.bell_sound) class Motorbike(Bike): """ Class defining a Motorbike (extending Bike) that can be ridden, have its gear changed and has a tank that can be filled. Attributes: seats: number of seats the bike has gears: number of gears the bike has """ def __init__(self, seats=2, gears=5): """ Creates a new Motorbike object. Args: seats: number of seats the motorbike has, defaults to 2 gears: number of gears the motorbike has, defaults to 5 """ super().__init__(seats, gears) # True means full tank. Private so it can only be changed in # a controlled manner self._tank = True @property def tank(self): """ Purpose of this function is to enable the user to check the tank status, but is only able to fill/empty the tank with specific methods. This was not necessary to implement. """ return self._tank def start_ride(self): """ Starts a motorbike ride. Returns: True if successful. False if motorbike is already on a ride or tank is empty """ # can't ride a motorbike if tank is empty or it is already ridden if not self._tank or not super().start_ride(): return False return True def end_ride(self): """ Ends a motorbike ride and empties tank. Returns: True if successful. False if motorbike is not currently ridden. 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rps/examples/Finite_Time_Barriers/main.py	mohitsrinivasan/Barrier_MotionPlanning	10	193436	import rps.robotarium as robotarium from rps.utilities import * from rps.utilities.barrier_certificates import * from rps.utilities.controllers import * from rps.utilities.transformations import * from reachGoal import reachGoal from matplotlib import patches import numpy as np import time N = 1 initial_conditions = np.transpose(np.array([[-1, 0.2, 0]])) r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions, sim_in_real_time=False) si_to_uni_dyn = create_si_to_uni_dynamics_with_backwards_motion() _, uni_to_si_states = create_si_to_uni_mapping() si_barrier_cert = create_single_integrator_barrier_certificate_with_boundary() hg = -1 ## Visualize goals and obstacles r.axes.add_patch(patches.Circle((1, 0), 0.2, fill=False, zorder=10)) # Goal region r.axes.add_patch(patches.Ellipse((0, 0), 0.4, 1.0, 0, fill=False)) # Obstacle while (hg <= 0): x = r.get_poses() x_si = uni_to_si_states(x) u, hg = reachGoal(x) # Create safe control inputs (i.e., no collisions) u = si_barrier_cert(u, x[0:2]) # Transform single integrator velocity commands to unicycle dxu = si_to_uni_dyn(u, x) # Set the velocities by mapping the single-integrator inputs to unciycle inputs r.set_velocities(np.arange(N), dxu) # Iterate the simulation r.step() r.call_at_scripts_end()	[ 1, 1053, 364, 567, 29889, 307, 7451, 22826, 408, 19964, 22826, 13, 3166, 364, 567, 29889, 4422, 1907, 1053, 334, 13, 3166, 364, 567, 29889, 4422, 1907, 29889, 1646, 4336, 29918, 6327, 928, 1078, 1053, 334, 13, 3166, 364, 567, 29889, 4422, 1907, 29889, 1285, 11897, 1053, 334, 13, 3166, 364, 567, 29889, 4422, 1907, 29889, 9067, 800, 1053, 334, 13, 3166, 6159, 8120, 284, 1053, 6159, 8120, 284, 13, 3166, 22889, 1053, 13261, 267, 13, 5215, 12655, 408, 7442, 13, 5215, 931, 13, 13, 29940, 353, 29871, 29896, 13, 11228, 29918, 1116, 2187, 353, 7442, 29889, 3286, 4220, 29898, 9302, 29889, 2378, 4197, 14352, 29896, 29892, 29871, 29900, 29889, 29906, 29892, 29871, 29900, 5262, 876, 13, 13, 29878, 353, 19964, 22826, 29889, 21860, 327, 22826, 29898, 4537, 29918, 974, 29918, 13716, 1862, 29922, 29940, 29892, 1510, 29918, 4532, 29922, 5574, 29892, 2847, 29918, 1116, 2187, 29922, 11228, 29918, 1116, 2187, 29892, 1027, 29918, 262, 29918, 6370, 29918, 2230, 29922, 8824, 29897, 13, 1039, 29918, 517, 29918, 3909, 29918, 29881, 948, 353, 1653, 29918, 1039, 29918, 517, 29918, 3909, 29918, 29881, 2926, 1199, 29918, 2541, 29918, 1627, 2935, 29918, 29885, 8194, 580, 13, 3383, 443, 29875, 29918, 517, 29918, 1039, 29918, 28631, 353, 1653, 29918, 1039, 29918, 517, 29918, 3909, 29918, 20698, 580, 13, 1039, 29918, 1646, 4336, 29918, 6327, 353, 1653, 29918, 14369, 29918, 14146, 1061, 29918, 1646, 4336, 29918, 6327, 8021, 29918, 2541, 29918, 9917, 653, 580, 13, 13, 29882, 29887, 353, 448, 29896, 13, 13, 2277, 9249, 675, 14433, 322, 14979, 23435, 13, 29878, 29889, 1165, 267, 29889, 1202, 29918, 5041, 29898, 5041, 267, 29889, 23495, 280, 3552, 29896, 29892, 29871, 29900, 511, 29871, 29900, 29889, 29906, 29892, 5445, 29922, 8824, 29892, 503, 2098, 29922, 29896, 29900, 876, 9651, 396, 2921, 284, 5120, 13, 29878, 29889, 1165, 267, 29889, 1202, 29918, 5041, 29898, 5041, 267, 29889, 6489, 5843, 3552, 29900, 29892, 29871, 29900, 511, 29871, 29900, 29889, 29946, 29892, 29871, 29896, 29889, 29900, 29892, 29871, 29900, 29892, 5445, 29922, 8824, 876, 795, 396, 4250, 303, 6436, 13, 13, 8000, 313, 29882, 29887, 5277, 29871, 29900, 1125, 13, 13, 1678, 921, 353, 364, 29889, 657, 29918, 10590, 580, 13, 268, 13, 1678, 921, 29918, 1039, 353, 443, 29875, 29918, 517, 29918, 1039, 29918, 28631, 29898, 29916, 29897, 13, 268, 13, 1678, 318, 29892, 298, 29887, 353, 6159, 8120, 284, 29898, 29916, 29897, 13, 268, 13, 1678, 396, 6204, 9109, 2761, 10970, 313, 29875, 29889, 29872, 1696, 694, 5321, 12112, 29897, 13, 1678, 318, 353, 1354, 29918, 1646, 4336, 29918, 6327, 29898, 29884, 29892, 921, 29961, 29900, 29901, 29906, 2314, 13, 13, 1678, 396, 4103, 689, 2323, 3990, 1061, 12885, 8260, 304, 443, 4245, 2841, 13, 1678, 15414, 29884, 353, 1354, 29918, 517, 29918, 3909, 29918, 29881, 948, 29898, 29884, 29892, 921, 29897, 13, 268, 13, 1678, 396, 3789, 278, 9110, 1907, 491, 10417, 278, 2323, 29899, 14146, 1061, 10970, 304, 443, 455, 13317, 10970, 13, 1678, 364, 29889, 842, 29918, 955, 542, 1907, 29898, 9302, 29889, 279, 927, 29898, 29940, 511, 15414, 29884, 29897, 13, 268, 13, 1678, 396, 20504, 403, 278, 17402, 13, 1678, 364, 29889, 10568, 580, 13, 13, 29878, 29889, 4804, 29918, 271, 29918, 16713, 29918, 355, 580, 2 ]
hknlib/election/groups.py	awang193/hknlib	0	1616533	from __future__ import (division, absolute_import, print_function, unicode_literals) from googleapiclient.discovery import build from .settings import SPREADSHEET_ID def add_user_to_group(credentials, user, groupKey): #add USER to a mail list GROUP service = build('admin', 'directory_v1', credentials=credentials) body = { 'email': user + '@hkn.eecs.berkeley.edu', 'role': 'MEMBER', } group = groupKey + '@hkn.eecs.berkeley.edu' response = service.members().hasMember(groupKey=group, memberKey=body.get('email')).execute() if response['isMember']: return _ = service.members().insert(groupKey=group, body=body).execute() print("{}->{}".format(user, groupKey)) return def add_officers_to_committes(credentials, election_data): user_committee = [] if election_data: for i in range(0, len(election_data)): if len(election_data[i]) < 6: continue committee = election_data[i][5][:-1] user_committee.append((election_data[i][3], committee)) #user_committee[users[i]] = committee for user, committee in user_committee: result = add_user_to_group(credentials, user, committee+'-officers') result = add_user_to_group(credentials, user, "current-"+committee) def add_members_to_committes(credentials, election_data): mailing_lists = [] if election_data: for i in range(0, len(election_data)): if len(election_data[i]) < 7: continue row = election_data[i] mailing = row[6][:-1].split('@, ') mailing_lists.append((election_data[i][3], mailing)) #mailing_lists[users[i]] = mailing_list for user, mailing_list in mailing_lists: for committee in mailing_list: add_user_to_group(credentials, user, committee) def add_all_to_committes(credentials, election_data): # 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src/google/appengine/datastore/datastore_query.py	myelin/appengine-python-standard	0	7689	<reponame>myelin/appengine-python-standard<gh_stars>0 #!/usr/bin/env python # # Copyright 2007 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. # """A thin wrapper around datastore query RPC calls. This provides wrappers around the internal only datastore_pb library and is designed to be the lowest-level API to be used by all Python datastore client libraries for executing queries. It provides a layer of protection so the actual RPC syntax can change without affecting client libraries. Any class, function, field or argument starting with an '_' is for INTERNAL use only and should not be used by developers! """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import base64 import collections import functools import pickle import six from google.appengine.api import cmp_compat from google.appengine.api import datastore_errors from google.appengine.api import datastore_types from google.appengine.datastore import datastore_index from google.appengine.datastore import datastore_pb from google.appengine.datastore import datastore_pbs from google.appengine.datastore import datastore_rpc from google.protobuf import message from google.appengine.datastore import entity_bytes_pb2 as entity_pb2 __all__ = ['Batch', 'Batcher', 'CompositeFilter', 'CompositeOrder', 'CorrelationFilter', 'Cursor', 'FetchOptions', 'FilterPredicate', 'Order', 'PropertyFilter', 'PropertyOrder', 'Query', 'QueryOptions', 'ResultsIterator', 'make_filter', 'apply_query', 'inject_results'] if datastore_pbs._CLOUD_DATASTORE_ENABLED: from google.appengine.datastore.datastore_pbs import googledatastore class _BaseComponent(object): """A base class for query components. Currently just implements basic == and != functions. """ def __eq__(self, other): if self.__class__ is not other.__class__: return NotImplemented return self is other or self.__dict__ == other.__dict__ def __ne__(self, other): equal = self.__eq__(other) if equal is NotImplemented: return equal return not equal def make_filter(name, op, values): """Constructs a FilterPredicate from the given name, op and values. Args: name: A non-empty string, the name of the property to filter. op: One of PropertyFilter._OPERATORS.keys(), the operator to use. values: A supported value, the value to compare against. Returns: if values is a list, a CompositeFilter that uses AND to combine all values, otherwise a PropertyFilter for the single value. Raises: datastore_errors.BadPropertyError: if the property name is invalid. datastore_errors.BadValueError: if the property did not validate correctly or the value was an empty list. Other exception types (like OverflowError): if the property value does not meet type-specific criteria. """ datastore_types.ValidateProperty(name, values) properties = datastore_types.ToPropertyPb(name, values) if isinstance(properties, list): filters = [PropertyFilter(op, prop) for prop in properties] return CompositeFilter(CompositeFilter.AND, filters) else: return PropertyFilter(op, properties) def _make_key_value_map(entity, property_names): """Extracts key values from the given entity. Args: entity: The entity_pb2.EntityProto to extract values from. property_names: The names of the properties from which to extract values. Returns: A dict mapping property names to a lists of key values. """ value_map = dict((six.ensure_text(name), []) for name in property_names) for prop in entity.property: prop_name = six.ensure_text(prop.name) if prop_name in value_map: value_map[prop_name].append( datastore_types.PropertyValueToKeyValue(prop.value)) key_prop = six.ensure_text(datastore_types.KEY_SPECIAL_PROPERTY) if key_prop in value_map: value_map[key_prop] = [datastore_types.ReferenceToKeyValue(entity.key)] return value_map class _PropertyComponent(_BaseComponent): """A component that operates on a specific set of properties.""" def _get_prop_names(self): """Returns a set of property names used by the filter.""" raise NotImplementedError class FilterPredicate(_PropertyComponent): """An abstract base class for all query filters. All sub-classes must be immutable as these are often stored without creating a defensive copy. """ def __call__(self, entity): """Applies the filter predicate to the given entity. Args: entity: the datastore_pb.EntityProto to test. Returns: True if the given entity matches the filter, False otherwise. """ return self._apply(_make_key_value_map(entity, self._get_prop_names())) def _apply(self, key_value_map): """Apply the given component to the comparable value map. A filter matches a list of values if at least one value in the list matches the filter, for example: 'prop: [1, 2]' matches both 'prop = 1' and 'prop = 2' but not 'prop = 3' Note: the values are actually represented as tuples whose first item encodes the type; see datastore_types.PropertyValueToKeyValue(). Args: key_value_map: A dict mapping property names to a list of comparable values. Return: A boolean indicating if the given map matches the filter. """ raise NotImplementedError def _prune(self, key_value_map): """Removes values from the given map that do not match the filter. When doing a scan in the datastore, only index values that match the filters are seen. When multiple values that point to the same entity are seen, the entity only appears where the first value is found. This function removes all values that don't match the query so that the first value in the map is the same one the datastore would see first. Args: key_value_map: the comparable value map from which to remove values. Does not need to contain values for all filtered properties. Returns: A value that evaluates to False if every value in a single list was completely removed. This effectively applies the filter but is less efficient than _apply(). """ raise NotImplementedError def _to_pb(self): """Internal only function to generate a pb.""" raise NotImplementedError( 'This filter only supports in memory operations (%r)' % self) def _to_pbs(self): """Internal only function to generate a list of pbs.""" return [self._to_pb()] def _to_pb_v1(self, adapter): """Internal only function to generate a v1 pb. Args: adapter: A datastore_rpc.AbstractAdapter """ raise NotImplementedError( 'This filter only supports in memory operations (%r)' % self) class _SinglePropertyFilter(FilterPredicate): """Base class for a filter that operates on a single property.""" def _get_prop_name(self): """Returns the name of the property being filtered.""" raise NotImplementedError def _apply_to_value(self, value): """Apply the filter to the given value. Args: value: The comparable value to check. Returns: A boolean indicating if the given value matches the filter. """ raise NotImplementedError def _get_prop_names(self): return set([self._get_prop_name()]) def _apply(self, value_map): for other_value in value_map[self._get_prop_name()]: if self._apply_to_value(other_value): return True return False def _prune(self, value_map): if self._get_prop_name() not in value_map: return True values = [value for value in value_map[self._get_prop_name()] if self._apply_to_value(value)] value_map[self._get_prop_name()] = values return bool(values) class PropertyFilter(_SinglePropertyFilter): """An immutable filter predicate that constrains a single property.""" _OPERATORS = { '<': datastore_pb.Query.Filter.LESS_THAN, '<=': datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL, '>': datastore_pb.Query.Filter.GREATER_THAN, '>=': datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL, '=': datastore_pb.Query.Filter.EQUAL, } _OPERATORS_INVERSE = dict((value, key) for key, value in _OPERATORS.items()) _OPERATORS_TO_PYTHON_OPERATOR = { datastore_pb.Query.Filter.LESS_THAN: '<', datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL: '<=', datastore_pb.Query.Filter.GREATER_THAN: '>', datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL: '>=', datastore_pb.Query.Filter.EQUAL: '==', } _INEQUALITY_OPERATORS = frozenset(['<', '<=', '>', '>=']) _INEQUALITY_OPERATORS_ENUM = frozenset([ datastore_pb.Query.Filter.LESS_THAN, datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL, datastore_pb.Query.Filter.GREATER_THAN, datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL, ]) _UPPERBOUND_INEQUALITY_OPERATORS = frozenset(['<', '<=']) def __init__(self, op, value): """Constructor. Args: op: A string representing the operator to use. value: A entity_pb2.Property, the property and value to compare against. Raises: datastore_errors.BadArgumentError if op has an unsupported value or value is not an entity_pb2.Property. """ if op not in self._OPERATORS: raise datastore_errors.BadArgumentError('unknown operator: %r' % (op,)) if not isinstance(value, entity_pb2.Property): raise datastore_errors.BadArgumentError( 'value argument should be entity_pb2.Property (%r)' % (value,)) super(PropertyFilter, self).__init__() self._filter = datastore_pb.Query.Filter() self._filter.op = self._OPERATORS[op] self._filter.property.add().CopyFrom(value) @property def op(self): raw_op = self._filter.op return self._OPERATORS_INVERSE.get(raw_op, str(raw_op)) @property def value(self): return self._filter.property[0] def __repr__(self): prop = self.value name = prop.name value = datastore_types.FromPropertyPb(prop) if six.PY2 and isinstance(value, long): value = int(value) return '%s(%r, <%r, %r>)' % (self.__class__.__name__, six.ensure_str( self.op), six.ensure_str(name), value) def _get_prop_name(self): return self._filter.property[0].name def _apply_to_value(self, value): if not hasattr(self, '_cmp_value'): if self._filter.op == datastore_pb.Query.Filter.EXISTS: return True self._cmp_value = datastore_types.PropertyValueToKeyValue( self._filter.property[0].value) self._condition = ('value %s self._cmp_value' % self._OPERATORS_TO_PYTHON_OPERATOR[self._filter.op]) return eval(self._condition) def _has_inequality(self): """Returns True if the filter predicate contains inequalities filters.""" return self._filter.op in self._INEQUALITY_OPERATORS_ENUM @classmethod def _from_pb(cls, filter_pb): self = cls.__new__(cls) self._filter = filter_pb return self def _to_pb(self): """Returns the internal only pb representation.""" return self._filter def _to_pb_v1(self, adapter): """Returns a googledatastore.Filter representation of the filter. Args: adapter: A datastore_rpc.AbstractAdapter """ filter_pb = googledatastore.Filter() prop_filter_pb = filter_pb.property_filter adapter.get_query_converter()._v3_filter_to_v1_property_filter( self._filter, prop_filter_pb) return filter_pb def __getstate__(self): raise pickle.PicklingError( 'Pickling of datastore_query.PropertyFilter is unsupported.') def __eq__(self, other): if self.__class__ is not other.__class__: if other.__class__ is _PropertyRangeFilter: return [self._filter] == other._to_pbs() return NotImplemented return self._filter == other._filter class _PropertyRangeFilter(_SinglePropertyFilter): """A filter predicate that represents a range of values. Since we allow multi-valued properties there is a large difference between "x > 0 AND x < 1" and "0 < x < 1." An entity with x = [-1, 2] will match the first but not the second. Since the datastore only allows a single inequality filter, multiple in-equality filters are merged into a single range filter in the datastore (unlike equality filters). This class is used by datastore_query.CompositeFilter to implement the same logic. """ _start_key_value = None _end_key_value = None @datastore_rpc._positional(1) def __init__(self, start=None, start_incl=True, end=None, end_incl=True): """Constructs a range filter using start and end properties. Args: start: A entity_pb2.Property to use as a lower bound or None to indicate no lower bound. start_incl: A boolean that indicates if the lower bound is inclusive. end: A entity_pb2.Property to use as an upper bound or None to indicate no upper bound. end_incl: A boolean that indicates if the upper bound is inclusive. """ if start is not None and not isinstance(start, entity_pb2.Property): raise datastore_errors.BadArgumentError( 'start argument should be entity_pb2.Property (%r)' % (start,)) if end is not None and not isinstance(end, entity_pb2.Property): raise datastore_errors.BadArgumentError( 'start argument should be entity_pb2.Property (%r)' % (end,)) if start and end and start.name != end.name: raise datastore_errors.BadArgumentError( 'start and end arguments must be on the same property (%s != %s)' % (start.name, end.name)) if not start and not end: raise datastore_errors.BadArgumentError( 'Unbounded ranges are not supported.') super(_PropertyRangeFilter, self).__init__() self._start = start self._start_incl = start_incl self._end = end self._end_incl = end_incl @classmethod def from_property_filter(cls, prop_filter): op = prop_filter._filter.op if op == datastore_pb.Query.Filter.GREATER_THAN: return cls(start=prop_filter._filter.property[0], start_incl=False) elif op == datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL: return cls(start=prop_filter._filter.property[0]) elif op == datastore_pb.Query.Filter.LESS_THAN: return cls(end=prop_filter._filter.property[0], end_incl=False) elif op == datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL: return cls(end=prop_filter._filter.property[0]) else: raise datastore_errors.BadArgumentError( 'Unsupported operator (%s)' % (op,)) def intersect(self, other): """Returns a filter representing the intersection of self and other.""" if isinstance(other, PropertyFilter): other = self.from_property_filter(other) elif not isinstance(other, _PropertyRangeFilter): raise datastore_errors.BadArgumentError( 'other argument should be a _PropertyRangeFilter (%r)' % (other,)) if other._get_prop_name() != self._get_prop_name(): raise datastore_errors.BadArgumentError( 'other argument must be on the same property (%s != %s)' % (other._get_prop_name(), self._get_prop_name())) start_source = None if other._start: if self._start: result = cmp_compat.cmp( self._get_start_key_value(), other._get_start_key_value()) if result == 0: result = cmp_compat.cmp(other._start_incl, self._start_incl) if result > 0: start_source = self elif result < 0: start_source = other else: start_source = other elif self._start: start_source = self end_source = None if other._end: if self._end: result = cmp_compat.cmp( self._get_end_key_value(), other._get_end_key_value()) if result == 0: result = cmp_compat.cmp(self._end_incl, other._end_incl) if result < 0: end_source = self elif result > 0: end_source = other else: end_source = other elif self._end: end_source = self if start_source: if end_source in (start_source, None): return start_source result = _PropertyRangeFilter(start=start_source._start, start_incl=start_source._start_incl, end=end_source._end, end_incl=end_source._end_incl) result._start_key_value = start_source._start_key_value result._end_key_value = end_source._end_key_value return result else: return end_source or self def _get_start_key_value(self): if self._start_key_value is None: self._start_key_value = datastore_types.PropertyValueToKeyValue( self._start.value) return self._start_key_value def _get_end_key_value(self): if self._end_key_value is None: self._end_key_value = datastore_types.PropertyValueToKeyValue( self._end.value) return self._end_key_value def _apply_to_value(self, value): """Apply the filter to the given value. Args: value: The comparable value to check. Returns: A boolean indicating if the given value matches the filter. """ if self._start: result = cmp_compat.cmp(self._get_start_key_value(), value) if result > 0 or (result == 0 and not self._start_incl): return False if self._end: result = cmp_compat.cmp(self._get_end_key_value(), value) if result < 0 or (result == 0 and not self._end_incl): return False return True def _get_prop_name(self): if self._start: return self._start.name if self._end: return self._end.name assert False def _to_pbs(self): pbs = [] if self._start: if self._start_incl: op = datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL else: op = datastore_pb.Query.Filter.GREATER_THAN pb = datastore_pb.Query.Filter() pb.op = op pb.property.add().CopyFrom(self._start) pbs.append(pb) if self._end: if self._end_incl: op = datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL else: op = datastore_pb.Query.Filter.LESS_THAN pb = datastore_pb.Query.Filter() pb.op = op pb.property.add().CopyFrom(self._end) pbs.append(pb) return pbs def _to_pb_v1(self, adapter): """Returns a googledatastore.Filter representation of the filter. Args: adapter: A datastore_rpc.AbstractAdapter. """ filter_pb = googledatastore.Filter() composite_filter = filter_pb.composite_filter composite_filter.op = googledatastore.CompositeFilter.AND if self._start: if self._start_incl: op = googledatastore.PropertyFilter.GREATER_THAN_OR_EQUAL else: op = googledatastore.PropertyFilter.GREATER_THAN pb = composite_filter.filters.add().property_filter pb.op = op pb.property.name = self._start.name adapter.get_entity_converter().v3_property_to_v1_value( self._start, True, pb.value) if self._end: if self._end_incl: op = googledatastore.PropertyFilter.LESS_THAN_OR_EQUAL else: op = googledatastore.PropertyFilter.LESS_THAN pb = composite_filter.filters.add().property_filter pb.op = op pb.property.name = self._end.name adapter.get_entity_converter().v3_property_to_v1_value( self._end, True, pb.value) return filter_pb def __getstate__(self): raise pickle.PicklingError( 'Pickling of %r is unsupported.' % self) def __eq__(self, other): if self.__class__ is not other.__class__: return NotImplemented return (self._start == other._start and self._end == other._end and (self._start_incl == other._start_incl or self._start is None) and (self._end_incl == other._end_incl or self._end is None)) class _PropertyExistsFilter(FilterPredicate): """A FilterPredicate that matches entities containing specific properties. Only works as an in-memory filter. Used internally to filter out entities that don't have all properties in a given Order. """ def __init__(self, names): super(_PropertyExistsFilter, self).__init__() self._names = frozenset(names) def _apply(self, value_map): for name in self._names: if not value_map.get(name): return False return True def _get_prop_names(self): return self._names def _prune(self, _): raise NotImplementedError def __getstate__(self): raise pickle.PicklingError( 'Pickling of %r is unsupported.' % self) class CorrelationFilter(FilterPredicate): """A filter that isolates correlated values and applies a sub-filter on them. This filter assumes that every property used by the sub-filter should be grouped before being passed to the sub-filter. The default grouping puts each value in its own group. Consider: e = {a: [1, 2], b: [2, 1, 3], c: 4} A correlation filter with a sub-filter that operates on (a, b) will be tested against the following 3 sets of values: {a: 1, b: 2} {a: 2, b: 1} {b: 3} In this case CorrelationFilter('a = 2 AND b = 2') won't match this entity but CorrelationFilter('a = 2 AND b = 1') will. To apply an uncorrelated filter on c, the filter must be applied in parallel to the correlation filter. For example: CompositeFilter(AND, [CorrelationFilter('a = 2 AND b = 1'), 'c = 3']) If 'c = 3' was included in the correlation filter, c would be grouped as well. This would result in the following values: {a: 1, b: 2, c: 3} {a: 2, b: 1} {b: 3} If any set of correlated values match the sub-filter then the entity matches the correlation filter. """ def __init__(self, subfilter): """Constructor. Args: subfilter: A FilterPredicate to apply to the correlated values """ self._subfilter = subfilter @property def subfilter(self): return self._subfilter def __repr__(self): return '%s(%r)' % (self.__class__.__name__, self.subfilter) def _apply(self, value_map): base_map = dict((prop, []) for prop in self._get_prop_names()) value_maps = [] for prop in base_map: grouped = self._group_values(prop, value_map[prop]) while len(value_maps) < len(grouped): value_maps.append(base_map.copy()) for value, m in six.moves.zip(grouped, value_maps): m[prop] = value return self._apply_correlated(value_maps) def _apply_correlated(self, value_maps): """Applies sub-filter to the correlated value maps. The default implementation matches when any value_map in value_maps matches the sub-filter. Args: value_maps: A list of correlated value_maps. Returns: True if any the entity matches the correlation filter. """ for map in value_maps: if self._subfilter._apply(map): return True return False def _group_values(self, prop, values): """A function that groups the given values. Override this function to introduce custom grouping logic. The default implementation assumes each value belongs in its own group. Args: prop: The name of the property who's values are being grouped. values: A list of opaque values. Returns: A list of lists of grouped values. """ return [[value] for value in values] def _get_prop_names(self): return self._subfilter._get_prop_names() class CompositeFilter(FilterPredicate): """An immutable filter predicate that combines other predicates. This class proactively merges sub-filters that are combined using the same operator. For example: CompositeFilter(AND, [f1, f2, CompositeFilter(AND, [f3, f4]), f5, f6]) is equivalent to: CompositeFilter(AND, [f1, f2, f3, f4, f5, f6]) Currently filters can only be combined using an AND operator. """ AND = 'and' _OPERATORS = frozenset([AND]) def __init__(self, op, filters): """Constructor. Args: op: The operator to use to combine the given filters filters: A list of one or more filters to combine Raises: datastore_errors.BadArgumentError if op is not in CompsiteFilter.OPERATORS or filters is not a non-empty list containing only FilterPredicates. """ if not op in self._OPERATORS: raise datastore_errors.BadArgumentError('unknown operator (%s)' % (op,)) if not filters or not isinstance(filters, (list, tuple)): raise datastore_errors.BadArgumentError( 'filters argument should be a non-empty list (%r)' % (filters,)) super(CompositeFilter, self).__init__() self._op = op flattened = [] for f in filters: if isinstance(f, CompositeFilter) and f._op == self._op: flattened.extend(f._filters) elif isinstance(f, FilterPredicate): flattened.append(f) else: raise datastore_errors.BadArgumentError( 'filters argument must be a list of FilterPredicates, found (%r)' % (f,)) if op == self.AND: filters = flattened flattened = [] ineq_map = {} for f in filters: if (isinstance(f, _PropertyRangeFilter) or (isinstance(f, PropertyFilter) and f._has_inequality())): name = f._get_prop_name() index = ineq_map.get(name) if index is not None: range_filter = flattened[index] flattened[index] = range_filter.intersect(f) else: if isinstance(f, PropertyFilter): range_filter = _PropertyRangeFilter.from_property_filter(f) else: range_filter = f ineq_map[name] = len(flattened) flattened.append(range_filter) else: flattened.append(f) self._filters = tuple(flattened) @property def op(self): return self._op @property def filters(self): return self._filters def __repr__(self): op = self.op if op == self.AND: op = 'AND' else: op = str(op) return '%s(%s, %r)' % (self.__class__.__name__, op, list(self.filters)) def _get_prop_names(self): names = set() for f in self._filters: names \|= f._get_prop_names() return names def _apply(self, value_map): if self._op == self.AND: for f in self._filters: if not f._apply(value_map): return False return True raise NotImplementedError def _prune(self, value_map): if self._op == self.AND: matches = collections.defaultdict(set) for f in self._filters: props = f._get_prop_names() local_value_map = dict((k, v) for k, v in value_map.items() if k in props) if not f._prune(local_value_map): return False for (prop, values) in local_value_map.items(): matches[prop].update(values) for prop, value_set in matches.items(): value_map[prop] = sorted(value_set) return True raise NotImplementedError def _to_pbs(self): """Returns the internal only pb representation.""" pbs = [] for f in self._filters: pbs.extend(f._to_pbs()) return pbs def _to_pb_v1(self, adapter): """Returns a googledatastore.Filter. Args: adapter: A datastore_rpc.AbstractAdapter """ if not self._filters: return None if len(self._filters) == 1: return self._filters[0]._to_pb_v1(adapter) pb = googledatastore.Filter() comp_pb = pb.composite_filter if self.op == self.AND: comp_pb.op = googledatastore.CompositeFilter.AND else: raise datastore_errors.BadArgumentError( 'Datastore V4 only supports CompositeFilter with AND operator.') for f in self._filters: comp_pb.filters.add().CopyFrom(f._to_pb_v1(adapter)) return pb def __eq__(self, other): if self.__class__ is other.__class__: return super(CompositeFilter, self).__eq__(other) if len(self._filters) == 1: result = self._filters[0].__eq__(other) if result is NotImplemented and hasattr(other, '__eq__'): return other.__eq__(self._filters[0]) return result return NotImplemented class _IgnoreFilter(_SinglePropertyFilter): """A filter that removes all entities with the given keys.""" def __init__(self, key_value_set): super(_IgnoreFilter, self).__init__() self._keys = key_value_set def _get_prop_name(self): return datastore_types.KEY_SPECIAL_PROPERTY def _apply_to_value(self, value): return value not in self._keys class _DedupingFilter(_IgnoreFilter): """A filter that removes duplicate keys.""" def __init__(self, key_value_set=None): super(_DedupingFilter, self).__init__(key_value_set or set()) def _apply_to_value(self, value): if super(_DedupingFilter, self)._apply_to_value(value): self._keys.add(value) return True return False class Order(_PropertyComponent): """A base class that represents a sort order on a query. All sub-classes must be immutable as these are often stored without creating a defensive copying. This class can be used as either the cmp or key arg in sorted() or list.sort(). To provide a stable ordering a trailing key ascending order is always used. """ @datastore_rpc._positional(1) def reversed(self, group_by=None): """Constructs an order representing the reverse of the current order. This function takes into account the effects of orders on properties not in the group_by clause of a query. For example, consider: SELECT A, First(B) ... GROUP BY A ORDER BY A, B Changing the order of B would effect which value is listed in the 'First(B)' column which would actually change the results instead of just reversing them. Args: group_by: If specified, only orders on properties in group_by will be reversed. Returns: A new order representing the reverse direction. """ raise NotImplementedError def _key(self, lhs_value_map): """Creates a key for the given value map.""" raise NotImplementedError def _cmp(self, lhs_value_map, rhs_value_map): """Compares the given value maps.""" raise NotImplementedError def _to_pb(self): """Internal only function to generate a filter pb.""" raise NotImplementedError def _to_pb_v1(self, adapter): """Internal only function to generate a v1 filter pb. Args: adapter: A datastore_rpc.AbstractAdapter """ raise NotImplementedError def key_for_filter(self, filter_predicate): if filter_predicate: return lambda x: self.key(x, filter_predicate) return self.key def cmp_for_filter(self, filter_predicate): if filter_predicate: return lambda x, y: self.cmp(x, y, filter_predicate) return self.cmp def key(self, entity, filter_predicate=None): """Constructs a "key" value for the given entity based on the current order. This function can be used as the key argument for list.sort() and sorted(). Args: entity: The entity_pb2.EntityProto to convert filter_predicate: A FilterPredicate used to prune values before comparing entities or None. Returns: A key value that identifies the position of the entity when sorted by the current order. """ names = self._get_prop_names() names.add(datastore_types.KEY_SPECIAL_PROPERTY) if filter_predicate is not None: names \|= filter_predicate._get_prop_names() value_map = _make_key_value_map(entity, names) if filter_predicate is not None: filter_predicate._prune(value_map) return (self._key(value_map), value_map[datastore_types.KEY_SPECIAL_PROPERTY]) def cmp(self, lhs, rhs, filter_predicate=None): """Compares the given values taking into account any filters. This function can be used as the cmp argument for list.sort() and sorted(). This function is slightly more efficient that Order.key when comparing two entities, however it is much less efficient when sorting a list of entities. Args: lhs: An entity_pb2.EntityProto rhs: An entity_pb2.EntityProto filter_predicate: A FilterPredicate used to prune values before comparing entities or None. Returns: An integer <, = or > 0 representing the operator that goes in between lhs and rhs that to create a true statement. """ names = self._get_prop_names() if filter_predicate is not None: names \|= filter_predicate._get_prop_names() lhs_value_map = _make_key_value_map(lhs, names) rhs_value_map = _make_key_value_map(rhs, names) if filter_predicate is not None: filter_predicate._prune(lhs_value_map) filter_predicate._prune(rhs_value_map) result = self._cmp(lhs_value_map, rhs_value_map) if result: return result if not lhs.HasField('key') and not rhs.HasField('key'): return 0 lhs_key = (lhs_value_map.get(datastore_types.KEY_SPECIAL_PROPERTY) or datastore_types.ReferenceToKeyValue(lhs.key)) rhs_key = (rhs_value_map.get(datastore_types.KEY_SPECIAL_PROPERTY) or datastore_types.ReferenceToKeyValue(rhs.key)) return cmp_compat.cmp(lhs_key, rhs_key) @cmp_compat.total_ordering_from_cmp class _ReverseOrder(_BaseComponent): """Reverses the comparison for the given object.""" def __init__(self, obj): """Constructor for _ReverseOrder. Args: obj: Any comparable and hashable object. """ super(_ReverseOrder, self).__init__() self._obj = obj def __hash__(self): return hash(self._obj) def __cmp__(self, other): assert self.__class__ == other.__class__, ( 'A datastore_query._ReverseOrder object can only be compared to ' 'an object of the same type.') return -cmp_compat.cmp(self._obj, other._obj) class PropertyOrder(Order): """An immutable class that represents a sort order for a single property.""" ASCENDING = datastore_pb.Query.Order.ASCENDING DESCENDING = datastore_pb.Query.Order.DESCENDING _DIRECTIONS = frozenset([ASCENDING, DESCENDING]) def __init__(self, prop, direction=ASCENDING): """Constructor. Args: prop: the name of the prop by which to sort. direction: the direction in which to sort the given prop. Raises: datastore_errors.BadArgumentError if the prop name or direction is invalid. """ datastore_types.ValidateString(prop, 'prop', datastore_errors.BadArgumentError) if not direction in self._DIRECTIONS: raise datastore_errors.BadArgumentError('unknown direction: %r' % (direction,)) super(PropertyOrder, self).__init__() self.__order = datastore_pb.Query.Order() self.__order.property = six.ensure_binary(prop, 'utf-8') self.__order.direction = direction @property def prop(self): return self.__order.property @property def direction(self): return self.__order.direction def __repr__(self): extra = '' if self.direction == self.DESCENDING: extra = ', DESCENDING' name = repr(six.ensure_str(self.prop))[1:-1] return '%s(<%s>%s)' % (self.__class__.__name__, name, extra) @datastore_rpc._positional(1) def reversed(self, group_by=None): if group_by and self.__order.property not in group_by: return self if self.__order.direction == self.ASCENDING: return PropertyOrder( six.ensure_text(self.__order.property), self.DESCENDING) else: return PropertyOrder( six.ensure_text(self.__order.property), self.ASCENDING) def _get_prop_names(self): return set([self.__order.property]) def _key(self, lhs_value_map): lhs_values = lhs_value_map[self.__order.property] if not lhs_values: raise datastore_errors.BadArgumentError( 'Missing value for property (%s)' % self.__order.property) if self.__order.direction == self.ASCENDING: return min(lhs_values) else: return _ReverseOrder(max(lhs_values)) def _cmp(self, lhs_value_map, rhs_value_map): lhs_values = lhs_value_map[self.__order.property] rhs_values = rhs_value_map[self.__order.property] if not lhs_values and not rhs_values: return 0 if not lhs_values: raise datastore_errors.BadArgumentError( 'LHS missing value for property (%s)' % self.__order.property) if not rhs_values: raise datastore_errors.BadArgumentError( 'RHS missing value for property (%s)' % self.__order.property) if self.__order.direction == self.ASCENDING: return cmp_compat.cmp(min(lhs_values), min(rhs_values)) else: return cmp_compat.cmp(max(rhs_values), max(lhs_values)) @classmethod def _from_pb(cls, order_pb): self = cls.__new__(cls) self.__order = order_pb return self def _to_pb(self): """Returns the internal only pb representation.""" return self.__order def _to_pb_v1(self, adapter): """Returns a googledatastore.PropertyOrder representation of the order. Args: adapter: A datastore_rpc.AbstractAdapter. """ v1_order = googledatastore.PropertyOrder() adapter.get_query_converter().v3_order_to_v1_order(self.__order, v1_order) return v1_order def __getstate__(self): raise pickle.PicklingError( 'Pickling of datastore_query.PropertyOrder is unsupported.') class CompositeOrder(Order): """An immutable class that represents a sequence of Orders. This class proactively flattens sub-orders that are of type CompositeOrder. For example: CompositeOrder([O1, CompositeOrder([02, 03]), O4]) is equivalent to: CompositeOrder([O1, 02, 03, O4]) """ def __init__(self, orders): """Constructor. Args: orders: A list of Orders which are applied in order. """ if not isinstance(orders, (list, tuple)): raise datastore_errors.BadArgumentError( 'orders argument should be list or tuple (%r)' % (orders,)) super(CompositeOrder, self).__init__() flattened = [] for order in orders: if isinstance(order, CompositeOrder): flattened.extend(order._orders) elif isinstance(order, Order): flattened.append(order) else: raise datastore_errors.BadArgumentError( 'orders argument should only contain Order (%r)' % (order,)) self._orders = tuple(flattened) @property def orders(self): return self._orders def __repr__(self): return '%s(%r)' % (self.__class__.__name__, list(self.orders)) @datastore_rpc._positional(1) def reversed(self, group_by=None): return CompositeOrder([order.reversed(group_by=group_by) for order in self._orders]) def _get_prop_names(self): names = set() for order in self._orders: names \|= order._get_prop_names() return names def _key(self, lhs_value_map): result = [] for order in self._orders: result.append(order._key(lhs_value_map)) return tuple(result) def _cmp(self, lhs_value_map, rhs_value_map): for order in self._orders: result = order._cmp(lhs_value_map, rhs_value_map) if result != 0: return result return 0 def size(self): """Returns the number of sub-orders the instance contains.""" return len(self._orders) def _to_pbs(self): """Returns an ordered list of internal only pb representations.""" return [order._to_pb() for order in self._orders] def _to_pb_v1(self, adapter): """Returns an ordered list of googledatastore.PropertyOrder. Args: adapter: A datastore_rpc.AbstractAdapter """ return [order._to_pb_v1(adapter) for order in self._orders] def __eq__(self, other): if self.__class__ is other.__class__: return super(CompositeOrder, self).__eq__(other) if len(self._orders) == 1: result = self._orders[0].__eq__(other) if result is NotImplemented and hasattr(other, '__eq__'): return other.__eq__(self._orders[0]) return result return NotImplemented class FetchOptions(datastore_rpc.Configuration): """An immutable class that contains all options for fetching results. These options apply to any request that pulls results from a query. This class reserves the right to define configuration options of any name except those that start with 'user_'. External subclasses should only define function or variables with names that start with in 'user_'. Options are set by passing keyword arguments to the constructor corresponding to the configuration options defined below and in datastore_rpc.Configuration. This object can be used as the default config for a datastore_rpc.Connection but in that case some options will be ignored, see option documentation below for details. """ @datastore_rpc.ConfigOption def produce_cursors(value): """If a Cursor should be returned with the fetched results. Raises: datastore_errors.BadArgumentError if value is not a bool. """ if not isinstance(value, bool): raise datastore_errors.BadArgumentError( 'produce_cursors argument should be bool (%r)' % (value,)) return value @datastore_rpc.ConfigOption def offset(value): """The number of results to skip before returning the first result. Only applies to the first request it is used with and is ignored if present on datastore_rpc.Connection.config. Raises: datastore_errors.BadArgumentError if value is not a integer or is less than zero. """ datastore_types.ValidateInteger(value, 'offset', datastore_errors.BadArgumentError, zero_ok=True) return value @datastore_rpc.ConfigOption def batch_size(value): """The number of results to attempt to retrieve in a batch. Raises: datastore_errors.BadArgumentError if value is not a integer or is not greater than zero. """ datastore_types.ValidateInteger(value, 'batch_size', datastore_errors.BadArgumentError) return value class QueryOptions(FetchOptions): """An immutable class that contains all options for running a query. This class contains options that control execution process (deadline, batch_size, read_policy, etc) and what part of the query results are returned (keys_only, projection, offset, limit, etc) Options that control the contents of the query results are specified on the datastore_query.Query directly. This class reserves the right to define configuration options of any name except those that start with 'user_'. External subclasses should only define function or variables with names that start with in 'user_'. Options are set by passing keyword arguments to the constructor corresponding to the configuration options defined below and in FetchOptions and datastore_rpc.Configuration. This object can be used as the default config for a datastore_rpc.Connection but in that case some options will be ignored, see below for details. """ ORDER_FIRST = datastore_pb.Query.ORDER_FIRST ANCESTOR_FIRST = datastore_pb.Query.ANCESTOR_FIRST FILTER_FIRST = datastore_pb.Query.FILTER_FIRST _HINTS = frozenset([ORDER_FIRST, ANCESTOR_FIRST, FILTER_FIRST]) @datastore_rpc.ConfigOption def keys_only(value): """If the query should only return keys. Raises: datastore_errors.BadArgumentError if value is not a bool. """ if not isinstance(value, bool): raise datastore_errors.BadArgumentError( 'keys_only argument should be bool (%r)' % (value,)) return value @datastore_rpc.ConfigOption def projection(value): """A list or tuple of property names to project. If None, the entire entity is returned. Specifying a projection: - may change the index requirements for the given query; - will cause a partial entity to be returned; - will cause only entities that contain those properties to be returned; A partial entities only contain the property name and value for properties in the projection (meaning and multiple will not be set). They will also only contain a single value for any multi-valued property. However, if a multi-valued property is specified in the order, an inequality property, or the projected properties, the entity will be returned multiple times. Once for each unique combination of values. However, projection queries are significantly faster than normal queries. Raises: datastore_errors.BadArgumentError if value is empty or not a list or tuple of strings. """ if isinstance(value, list): value = tuple(value) elif not isinstance(value, tuple): raise datastore_errors.BadArgumentError( 'projection argument should be a list or tuple (%r)' % (value,)) if not value: raise datastore_errors.BadArgumentError( 'projection argument cannot be empty') for prop in value: if not isinstance(prop, six.string_types + (six.binary_type,)): raise datastore_errors.BadArgumentError( 'projection argument should contain only strings (%r)' % (prop,)) return value @datastore_rpc.ConfigOption def limit(value): """Limit on the number of results to return. Raises: datastore_errors.BadArgumentError if value is not an integer or is less than zero. """ datastore_types.ValidateInteger(value, 'limit', datastore_errors.BadArgumentError, zero_ok=True) return value @datastore_rpc.ConfigOption def prefetch_size(value): """Number of results to attempt to return on the initial request. Raises: datastore_errors.BadArgumentError if value is not an integer or is not greater than zero. """ datastore_types.ValidateInteger(value, 'prefetch_size', datastore_errors.BadArgumentError, zero_ok=True) return value @datastore_rpc.ConfigOption def start_cursor(value): """Cursor to use a start position. Ignored if present on datastore_rpc.Connection.config. Raises: datastore_errors.BadArgumentError if value is not a Cursor. """ if not isinstance(value, Cursor): raise datastore_errors.BadArgumentError( 'start_cursor argument should be datastore_query.Cursor (%r)' % (value,)) return value @datastore_rpc.ConfigOption def end_cursor(value): """Cursor to use as an end position. Ignored if present on datastore_rpc.Connection.config. Raises: datastore_errors.BadArgumentError if value is not a Cursor. """ if not isinstance(value, Cursor): raise datastore_errors.BadArgumentError( 'end_cursor argument should be datastore_query.Cursor (%r)' % (value,)) return value @datastore_rpc.ConfigOption def hint(value): """Hint on how the datastore should plan the query. Raises: datastore_errors.BadArgumentError if value is not a known hint. """ if value not in QueryOptions._HINTS: raise datastore_errors.BadArgumentError('Unknown query hint (%r)' % (value,)) return value class Cursor(_BaseComponent): """An immutable class that represents a relative position in a query. The position denoted by a Cursor is relative to a result in a query even if the result has been removed from the given query. Usually to position immediately after the last result returned by a batch. A cursor should only be used on a query with an identical signature to the one that produced it or on a query with its sort order reversed. """ @datastore_rpc._positional(1) def __init__(self, urlsafe=None, _cursor_bytes=None): """Constructor. A Cursor constructed with no arguments points the first result of any query. If such a Cursor is used as an end_cursor no results will ever be returned. """ super(Cursor, self).__init__() if urlsafe is not None: if _cursor_bytes is not None: raise datastore_errors.BadArgumentError( 'Can only specify one of urlsafe and _cursor_bytes') _cursor_bytes = self._urlsafe_to_bytes(urlsafe) if _cursor_bytes is not None: self.__cursor_bytes = _cursor_bytes else: self.__cursor_bytes = six.binary_type() def __repr__(self): arg = six.ensure_str(self.to_websafe_string()) if arg: arg = '<%s>' % arg return '%s(%s)' % (self.__class__.__name__, arg) def reversed(self): """DEPRECATED. It is no longer necessary to call reversed() on cursors. A cursor returned by a query may also be used in a query whose sort order has been reversed. This method returns a copy of the original cursor. """ return Cursor(_cursor_bytes=self.__cursor_bytes) def to_bytes(self): """Serialize cursor as a byte string.""" return self.__cursor_bytes @staticmethod def from_bytes(cursor): """Gets a Cursor given its byte string serialized form. The serialized form of a cursor may change in a non-backwards compatible way. In this case cursors must be regenerated from a new Query request. Args: cursor: A serialized cursor as returned by .to_bytes. Returns: A Cursor. Raises: datastore_errors.BadValueError if the cursor argument does not represent a serialized cursor. """ return Cursor(_cursor_bytes=cursor) def urlsafe(self): """Serialize cursor as a websafe string. Returns: A base64-encoded serialized cursor. """ return base64.urlsafe_b64encode(self.to_bytes()) to_websafe_string = urlsafe @staticmethod def from_websafe_string(cursor): """Gets a Cursor given its websafe serialized form. The serialized form of a cursor may change in a non-backwards compatible way. In this case cursors must be regenerated from a new Query request. Args: cursor: A serialized cursor as returned by .to_websafe_string. Returns: A Cursor. Raises: datastore_errors.BadValueError if the cursor argument is not a string type of does not represent a serialized cursor. """ decoded_bytes = Cursor._urlsafe_to_bytes(cursor) return Cursor.from_bytes(decoded_bytes) @staticmethod def _urlsafe_to_bytes(cursor): if not isinstance(cursor, six.string_types + (six.binary_type,)): raise datastore_errors.BadValueError( 'cursor argument should be str or unicode (%r)' % (cursor,)) try: decoded_bytes = base64.urlsafe_b64decode( six.ensure_binary(cursor, 'ascii')) except (ValueError, TypeError) as e: raise datastore_errors.BadValueError( 'Invalid cursor %s. Details: %s' % (cursor, e)) return decoded_bytes def advance(self, offset, query, conn): """Advances a Cursor by the given offset. Args: offset: The amount to advance the current query. query: A Query identical to the one this cursor was created from. conn: The datastore_rpc.Connection to use. Returns: A new cursor that is advanced by offset using the given query. """ datastore_types.ValidateInteger(offset, 'offset', datastore_errors.BadArgumentError) if not isinstance(query, Query): raise datastore_errors.BadArgumentError( 'query argument should be datastore_query.Query (%r)' % (query,)) query_options = QueryOptions( start_cursor=self, offset=offset, limit=0, produce_cursors=True) return query.run(conn, query_options).next_batch( Batcher.AT_LEAST_OFFSET).cursor(0) def __setstate__(self, state): if '_Cursor__compiled_cursor' in state: self.__cursor_bytes = state['_Cursor__compiled_cursor'].SerializeToString() else: self.__dict__ = state class _QueryKeyFilter(_BaseComponent): """A class that implements the key filters available on a Query.""" @datastore_rpc._positional(1) def __init__(self, app=None, namespace=None, kind=None, ancestor=None): """Constructs a _QueryKeyFilter. If app/namespace and ancestor are not defined, the app/namespace set in the environment is used. Args: app: a string representing the required app id or None. namespace: a string representing the required namespace or None. kind: a string representing the required kind or None. ancestor: a entity_pb2.Reference representing the required ancestor or None. Raises: datastore_erros.BadArgumentError if app and ancestor.app() do not match or an unexpected type is passed in for any argument. """ if kind is not None: datastore_types.ValidateString( kind, 'kind', datastore_errors.BadArgumentError) if ancestor is not None: if not isinstance(ancestor, entity_pb2.Reference): raise datastore_errors.BadArgumentError( 'ancestor argument should be entity_pb2.Reference (%r)' % (ancestor,)) ancestor_app = six.ensure_binary(ancestor.app) if app is None: app = ancestor_app elif six.ensure_binary(app) != ancestor_app: raise datastore_errors.BadArgumentError( 'ancestor argument should match app ("%r" != "%r")' % (ancestor.app, app)) ancestor_namespace = six.ensure_binary(ancestor.name_space) if namespace is None: namespace = ancestor_namespace elif six.ensure_binary(namespace) != ancestor_namespace: raise datastore_errors.BadArgumentError( 'ancestor argument should match namespace ("%r" != "%r")' % (six.ensure_binary(namespace), ancestor_namespace)) pb = entity_pb2.Reference() pb.CopyFrom(ancestor) ancestor = pb self.__ancestor = ancestor self.__path = list(ancestor.path.element) else: self.__ancestor = None self.__path = None super(_QueryKeyFilter, self).__init__() self.__app = six.ensure_text(datastore_types.ResolveAppId(app), 'utf-8') self.__namespace = ( six.ensure_text(datastore_types.ResolveNamespace(namespace), 'utf-8')) self.__kind = kind @property def app(self): return self.__app @property def namespace(self): return self.__namespace @property def kind(self): return self.__kind @property def ancestor(self): return self.__ancestor def __call__(self, entity_or_reference): """Apply the filter. Accepts either an entity or a reference to avoid the need to extract keys from entities when we have a list of entities (which is a common case). Args: entity_or_reference: Either an entity_pb2.EntityProto or entity_pb2.Reference. """ if isinstance(entity_or_reference, entity_pb2.Reference): key = entity_or_reference elif isinstance(entity_or_reference, entity_pb2.EntityProto): key = entity_or_reference.key else: raise datastore_errors.BadArgumentError( 'entity_or_reference argument must be an entity_pb2.EntityProto ' + six.ensure_str('or entity_pb2.Reference (%r)' % (entity_or_reference), 'utf-8')) return (six.ensure_text(key.app, 'utf-8') == self.__app and six.ensure_text(key.name_space, 'utf-8') == self.__namespace and (not self.__kind or key.path.element[-1].type == self.__kind) and (not self.__path or key.path.element[0:len(self.__path)] == self.__path)) def _to_pb(self): """Returns an internal pb representation.""" pb = datastore_pb.Query() pb.app = self.__app datastore_types.SetNamespace(pb, self.__namespace) if self.__kind is not None: pb.kind = self.__kind if self.__ancestor: ancestor = pb.ancestor ancestor.CopyFrom(self.__ancestor) return pb def _to_pb_v1(self, adapter): """Returns a v1 internal proto representation of the query key filter. Args: adapter: A datastore_rpc.AbstractAdapter. Returns: A tuple (googledatastore.RunQueryRequest, googledatastore.Filter). The second tuple value is a Filter representing the ancestor portion of the query. If there is no ancestor constraint, this value will be None """ pb = googledatastore.RunQueryRequest() partition_id = pb.partition_id partition_id.project_id = ( adapter.get_entity_converter().app_to_project_id(self.__app)) if self.__namespace: partition_id.namespace_id = self.__namespace if self.__kind is not None: pb.query.kind.add().name = self.__kind ancestor_filter = None if self.__ancestor: ancestor_filter = googledatastore.Filter() ancestor_prop_filter = ancestor_filter.property_filter ancestor_prop_filter.op = ( googledatastore.PropertyFilter.HAS_ANCESTOR) prop_pb = ancestor_prop_filter.property prop_pb.name = datastore_types.KEY_SPECIAL_PROPERTY adapter.get_entity_converter().v3_to_v1_key( self.ancestor, ancestor_prop_filter.value.key_value) return pb, ancestor_filter class _BaseQuery(_BaseComponent): """A base class for query implementations.""" def run(self, conn, query_options=None): """Runs the query using provided datastore_rpc.Connection. Args: conn: The datastore_rpc.Connection to use query_options: Optional query options to use Returns: A Batcher that implicitly fetches query results asynchronously. Raises: datastore_errors.BadArgumentError if any of the arguments are invalid. """ return Batcher(query_options, self.run_async(conn, query_options)) def run_async(self, conn, query_options=None): """Runs the query using the provided datastore_rpc.Connection. Args: conn: the datastore_rpc.Connection on which to run the query. query_options: Optional QueryOptions with which to run the query. Returns: An async object that can be used to grab the first Batch. Additional batches can be retrieved by calling Batch.next_batch/next_batch_async. Raises: datastore_errors.BadArgumentError if any of the arguments are invalid. """ raise NotImplementedError def __getstate__(self): raise pickle.PicklingError( 'Pickling of %r is unsupported.' % self) class Query(_BaseQuery): """An immutable class that represents a query signature. A query signature consists of a source of entities (specified as app, namespace and optionally kind and ancestor) as well as a FilterPredicate, grouping and a desired ordering. """ @datastore_rpc._positional(1) def __init__(self, app=None, namespace=None, kind=None, ancestor=None, filter_predicate=None, group_by=None, order=None, read_time_us=None): """Constructor. Args: app: Optional app to query, derived from the environment if not specified. namespace: Optional namespace to query, derived from the environment if not specified. kind: Optional kind to query. ancestor: Optional ancestor to query, an entity_pb2.Reference. filter_predicate: Optional FilterPredicate by which to restrict the query. group_by: Optional list of properties to group the results by. order: Optional Order in which to return results. read_time_us: Optional timestamp to read the storage from. Internal use only. Raises: datastore_errors.BadArgumentError if any argument is invalid. """ super(Query, self).__init__() if filter_predicate is not None and not isinstance(filter_predicate, FilterPredicate): raise datastore_errors.BadArgumentError( 'filter_predicate should be datastore_query.FilterPredicate (%r)' % (filter_predicate,)) if isinstance(order, CompositeOrder): if order.size() == 0: order = None elif isinstance(order, Order): order = CompositeOrder([order]) elif order is not None: raise datastore_errors.BadArgumentError( 'order should be Order (%r)' % (order,)) if group_by is not None: if isinstance(group_by, list): group_by = tuple(group_by) elif not isinstance(group_by, tuple): raise datastore_errors.BadArgumentError( 'group_by argument should be a list or tuple (%r)' % (group_by,)) if not group_by: raise datastore_errors.BadArgumentError( 'group_by argument cannot be empty') for prop in group_by: if not isinstance(prop, six.string_types + (six.binary_type,)): raise datastore_errors.BadArgumentError( 'group_by argument should contain only strings (%r)' % (prop,)) self._key_filter = _QueryKeyFilter(app=app, namespace=namespace, kind=kind, ancestor=ancestor) self._order = order self._filter_predicate = filter_predicate self._group_by = group_by self._read_time_us = read_time_us @property def app(self): return self._key_filter.app @property def namespace(self): return self._key_filter.namespace @property def kind(self): return self._key_filter.kind @property def ancestor(self): return self._key_filter.ancestor @property def filter_predicate(self): return self._filter_predicate @property def order(self): return self._order @property def group_by(self): return self._group_by @property def read_time_us(self): return self._read_time_us def __repr__(self): args = [] args.append('app=%r' % six.ensure_str(self.app)) ns = self.namespace if ns: args.append('namespace=%r' % six.ensure_str(ns)) kind = self.kind if kind is not None: args.append('kind=%r' % six.ensure_str(kind)) ancestor = self.ancestor if ancestor is not None: websafe = base64.urlsafe_b64encode(ancestor.SerializeToString()) args.append('ancestor=<%s>' % six.ensure_str(websafe)) filter_predicate = self.filter_predicate if filter_predicate is not None: args.append('filter_predicate=%r' % filter_predicate) order = self.order if order is not None: args.append('order=%r' % order) group_by = self.group_by if group_by is not None: args.append('group_by=%r' % (tuple(six.ensure_str(x) for x in group_by),)) read_time_us = self.read_time_us if read_time_us is not None: args.append('read_time_us=%r' % (read_time_us,)) return '%s(%s)' % (self.__class__.__name__, ', '.join(args)) def run_async(self, conn, query_options=None): if not isinstance(conn, datastore_rpc.BaseConnection): raise datastore_errors.BadArgumentError( 'conn should be a datastore_rpc.BaseConnection (%r)' % (conn,)) if not QueryOptions.is_configuration(query_options): query_options = QueryOptions(config=query_options) start_cursor = query_options.start_cursor if not start_cursor and query_options.produce_cursors: start_cursor = Cursor() if conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: req = self._to_pb_v1(conn, query_options) else: req = self._to_pb(conn, query_options) return Batch.create_async(self, query_options, conn, req, start_cursor=start_cursor) @classmethod def _from_pb(cls, query_pb): kind = query_pb.HasField('kind') and query_pb.kind or None ancestor = query_pb.HasField('ancestor') and query_pb.ancestor or None filter_predicate = None if query_pb.filter: filter_predicate = CompositeFilter( CompositeFilter.AND, [PropertyFilter._from_pb(filter_pb) for filter_pb in query_pb.filter]) order = None if query_pb.order: order = CompositeOrder( [PropertyOrder._from_pb(order_pb) for order_pb in query_pb.order]) group_by = None if query_pb.group_by_property_name: group_by = tuple( six.ensure_text(name) for name in query_pb.group_by_property_name) read_time_us = None if query_pb.HasField('read_time_us'): read_time_us = query_pb.read_time_us return Query( app=query_pb.app, namespace=query_pb.name_space, kind=kind, ancestor=ancestor, filter_predicate=filter_predicate, order=order, group_by=group_by, read_time_us=read_time_us) def _to_pb_v1(self, conn, query_options): """Returns a googledatastore.RunQueryRequest.""" v1_req, v1_ancestor_filter = self._key_filter._to_pb_v1(conn.adapter) v1_query = v1_req.query if self.filter_predicate: filter_predicate_pb = self._filter_predicate._to_pb_v1(conn.adapter) if self.filter_predicate and v1_ancestor_filter: comp_filter_pb = v1_query.filter.composite_filter comp_filter_pb.op = googledatastore.CompositeFilter.AND comp_filter_pb.filters.add().CopyFrom(filter_predicate_pb) comp_filter_pb.filters.add().CopyFrom(v1_ancestor_filter) elif self.filter_predicate: v1_query.filter.CopyFrom(filter_predicate_pb) elif v1_ancestor_filter: v1_query.filter.CopyFrom(v1_ancestor_filter) if self._order: for order in self._order._to_pb_v1(conn.adapter): v1_query.order.add().CopyFrom(order) if QueryOptions.keys_only(query_options, conn.config): prop_ref_pb = v1_query.projection.add().property prop_ref_pb.name = datastore_pbs.PROPERTY_NAME_KEY projection = QueryOptions.projection(query_options, conn.config) self._validate_projection_and_group_by(projection, self._group_by) if projection: for prop in projection: prop_ref_pb = v1_query.projection.add().property prop_ref_pb.name = prop if self._group_by: for group_by in self._group_by: v1_query.distinct_on.add().name = group_by limit = QueryOptions.limit(query_options, conn.config) if limit is not None: v1_query.limit.value = limit count = QueryOptions.batch_size(query_options, conn.config) if count is None: count = QueryOptions.prefetch_size(query_options, conn.config) if count is not None: pass if query_options.offset: v1_query.offset = query_options.offset if query_options.start_cursor is not None: v1_query.start_cursor = query_options.start_cursor.to_bytes() if query_options.end_cursor is not None: v1_query.end_cursor = query_options.end_cursor.to_bytes() conn._set_request_read_policy(v1_req, query_options) conn._set_request_transaction(v1_req) return v1_req def _to_pb(self, conn, query_options): """Returns the internal only pb representation.""" pb = self._key_filter._to_pb() if self._filter_predicate: for f in self._filter_predicate._to_pbs(): pb.filter.add().CopyFrom(f) if self._order: for order in self._order._to_pbs(): pb.order.add().CopyFrom(order) if QueryOptions.keys_only(query_options, conn.config): pb.keys_only = True projection = QueryOptions.projection(query_options, conn.config) self._validate_projection_and_group_by(projection, self._group_by) if projection: pb.property_name.extend(projection) if self._group_by: pb.group_by_property_name.extend(self._group_by) if QueryOptions.produce_cursors(query_options, conn.config): pb.compile = True limit = QueryOptions.limit(query_options, conn.config) if limit is not None: pb.limit = limit count = QueryOptions.prefetch_size(query_options, conn.config) if count is None: count = QueryOptions.batch_size(query_options, conn.config) if count is not None: pb.count = count if query_options.offset: pb.offset = query_options.offset if query_options.start_cursor is not None: try: pb.compiled_cursor.ParseFromString( query_options.start_cursor.to_bytes()) except message.DecodeError: raise datastore_errors.BadValueError('invalid cursor') if query_options.end_cursor is not None: try: pb.end_compiled_cursor.ParseFromString( query_options.end_cursor.to_bytes()) except message.DecodeError: raise datastore_errors.BadValueError('invalid cursor') if ((query_options.hint == QueryOptions.ORDER_FIRST and len(pb.order)) or (query_options.hint == QueryOptions.ANCESTOR_FIRST and pb.HasField('ancestor')) or (query_options.hint == QueryOptions.FILTER_FIRST and pb.filter)): pb.hint = query_options.hint if self.read_time_us is not None: pb.read_time_us = self.read_time_us conn._set_request_read_policy(pb, query_options) conn._set_request_transaction(pb) return pb def _validate_projection_and_group_by(self, projection, group_by): """Validates that a query's projection and group by match. Args: projection: A set of string property names in the projection. group_by: A set of string property names in the group by. Raises: datastore_errors.BadRequestError: if the projection and group by sets are not equal. """ if projection: if group_by: extra = set(projection) - set(group_by) if extra: raise datastore_errors.BadRequestError( 'projections includes properties not in the group_by argument: %s' % extra) elif group_by: raise datastore_errors.BadRequestError( 'cannot specify group_by without a projection') def apply_query(query, entities, _key=None): """Performs the given query on a set of in-memory results. This function can perform queries impossible in the datastore (e.g a query with multiple inequality filters on different properties) because all operations are done in memory. For queries that can also be executed on the the datastore, the results produced by this function may not use the same implicit ordering as the datastore. To ensure compatibility, explicit ordering must be used (e.g. 'ORDER BY ineq_prop, ..., __key__'). Order by __key__ should always be used when a consistent result is desired (unless there is a sort order on another globally unique property). Args: query: a datastore_query.Query to apply entities: a list of results, of arbitrary type, on which to apply the query. _key: a function that takes an element of the result array as an argument and must return an entity_pb2.EntityProto. If not specified, the identity function is used (and entities must be a list of entity_pb2.EntityProto). Returns: A subset of entities, filtered and ordered according to the query. """ if not isinstance(query, Query): raise datastore_errors.BadArgumentError( 'query argument must be a datastore_query.Query (%r)' % (query,)) if not isinstance(entities, list): raise datastore_errors.BadArgumentError( 'entities argument must be a list (%r)' % (entities,)) key = _key or (lambda x: x) filtered_results = [r for r in entities if query._key_filter(key(r))] if not query._order: if query._filter_predicate: return [r for r in filtered_results if query._filter_predicate(key(r))] return filtered_results names = query._order._get_prop_names() if query._filter_predicate: names \|= query._filter_predicate._get_prop_names() exists_filter = _PropertyExistsFilter(names) value_maps = [] for result in filtered_results: value_map = _make_key_value_map(key(result), names) if exists_filter._apply(value_map) and ( not query._filter_predicate or query._filter_predicate._prune(value_map)): value_map['__result__'] = result value_maps.append(value_map) value_maps.sort(key=functools.cmp_to_key(query._order._cmp)) return [value_map['__result__'] for value_map in value_maps] class _AugmentedQuery(_BaseQuery): """A query that combines a datastore query with in-memory filters/results.""" @datastore_rpc._positional(2) def __init__(self, query, in_memory_results=None, in_memory_filter=None, max_filtered_count=None): """Constructor for _AugmentedQuery. Do not call directly. Use the utility functions instead (e.g. datastore_query.inject_results) Args: query: A datastore_query.Query object to augment. in_memory_results: a list of pre- sorted and filtered result to add to the stream of datastore results or None . in_memory_filter: a set of in-memory filters to apply to the datastore results or None. max_filtered_count: the maximum number of datastore entities that will be filtered out by in_memory_filter if known. """ if not isinstance(query, Query): raise datastore_errors.BadArgumentError( 'query argument should be datastore_query.Query (%r)' % (query,)) if (in_memory_filter is not None and not isinstance(in_memory_filter, FilterPredicate)): raise datastore_errors.BadArgumentError( 'in_memory_filter argument should be ' + six.ensure_str( 'datastore_query.FilterPredicate (%r)' % (in_memory_filter,), 'utf-8')) if (in_memory_results is not None and not isinstance(in_memory_results, list)): raise datastore_errors.BadArgumentError( 'in_memory_results argument should be a list of' + six.ensure_str('datastore_pv.EntityProto (%r)' % (in_memory_results,), 'utf-8')) datastore_types.ValidateInteger(max_filtered_count, 'max_filtered_count', empty_ok=True, zero_ok=True) self._query = query self._max_filtered_count = max_filtered_count self._in_memory_filter = in_memory_filter self._in_memory_results = in_memory_results @property def app(self): return self._query._key_filter.app @property def namespace(self): return self._query._key_filter.namespace @property def kind(self): return self._query._key_filter.kind @property def ancestor(self): return self._query._key_filter.ancestor @property def filter_predicate(self): return self._query._filter_predicate @property def order(self): return self._query._order @property def group_by(self): return self._query._group_by def run_async(self, conn, query_options=None): if not isinstance(conn, datastore_rpc.BaseConnection): raise datastore_errors.BadArgumentError( 'conn should be a datastore_rpc.BaseConnection (%r)' % (conn,)) if not QueryOptions.is_configuration(query_options): query_options = QueryOptions(config=query_options) if self._query._order: changes = {'keys_only': False} else: changes = {} if self._in_memory_filter or self._in_memory_results: in_memory_offset = query_options.offset in_memory_limit = query_options.limit if in_memory_limit is not None: if self._in_memory_filter is None: changes['limit'] = in_memory_limit elif self._max_filtered_count is not None: changes['limit'] = in_memory_limit + self._max_filtered_count else: changes['limit'] = None if in_memory_offset: changes['offset'] = None if changes.get('limit', None) is not None: changes['limit'] += in_memory_offset else: in_memory_offset = None else: in_memory_offset = None in_memory_limit = None modified_query_options = QueryOptions(config=query_options, **changes) if conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: req = self._query._to_pb_v1(conn, modified_query_options) else: req = self._query._to_pb(conn, modified_query_options) start_cursor = query_options.start_cursor if not start_cursor and query_options.produce_cursors: start_cursor = Cursor() return _AugmentedBatch.create_async(self, modified_query_options, conn, req, in_memory_offset=in_memory_offset, in_memory_limit=in_memory_limit, start_cursor=start_cursor) @datastore_rpc._positional(1) def inject_results(query, updated_entities=None, deleted_keys=None): """Creates a query object that will inject changes into results. Args: query: The datastore_query.Query to augment updated_entities: A list of entity_pb2.EntityProto's that have been updated and should take priority over any values returned by query. deleted_keys: A list of entity_pb2.Reference's for entities that have been deleted and should be removed from query results. Returns: A datastore_query.AugmentedQuery if in memory filtering is required, query otherwise. """ if not isinstance(query, Query): raise datastore_errors.BadArgumentError( 'query argument should be datastore_query.Query (%r)' % (query,)) overridden_keys = set() if deleted_keys is not None: if not isinstance(deleted_keys, list): raise datastore_errors.BadArgumentError( 'deleted_keys argument must be a list (%r)' % (deleted_keys,)) deleted_keys = list(six.moves.filter(query._key_filter, deleted_keys)) for key in deleted_keys: overridden_keys.add(datastore_types.ReferenceToKeyValue(key)) if updated_entities is not None: if not isinstance(updated_entities, list): raise datastore_errors.BadArgumentError( 'updated_entities argument must be a list (%r)' % (updated_entities,)) updated_entities = list( six.moves.filter(query._key_filter, updated_entities)) for entity in updated_entities: overridden_keys.add(datastore_types.ReferenceToKeyValue(entity.key)) updated_entities = apply_query(query, updated_entities) else: updated_entities = [] if not overridden_keys: return query return _AugmentedQuery(query, in_memory_filter=_IgnoreFilter(overridden_keys), in_memory_results=updated_entities, max_filtered_count=len(overridden_keys)) class _BatchShared(object): """Data shared among the batches of a query.""" def __init__(self, query, query_options, conn, augmented_query=None, initial_offset=None): self.__query = query self.__query_options = query_options self.__conn = conn self.__augmented_query = augmented_query self.__was_first_result_processed = False if initial_offset is None: initial_offset = query_options.offset or 0 self.__expected_offset = initial_offset self.__remaining_limit = query_options.limit @property def query(self): return self.__query @property def query_options(self): return self.__query_options @property def conn(self): return self.__conn @property def augmented_query(self): return self.__augmented_query @property def keys_only(self): return self.__keys_only @property def compiled_query(self): return self.__compiled_query @property def expected_offset(self): return self.__expected_offset @property def remaining_limit(self): return self.__remaining_limit @property def index_list(self): """Returns the list of indexes used by the query. Possibly None when the adapter does not implement pb_to_index. """ return self.__index_list def process_batch(self, batch): if self.conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: skipped_results = batch.skipped_results num_results = len(batch.entity_results) else: skipped_results = batch.skipped_results num_results = len(batch.result) self.__expected_offset -= skipped_results if self.__remaining_limit is not None: self.__remaining_limit -= num_results if not self.__was_first_result_processed: self.__was_first_result_processed = True if self.conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: result_type = batch.entity_result_type self.__keys_only = result_type == googledatastore.EntityResult.KEY_ONLY self.__compiled_query = None self.__index_list = None else: self.__keys_only = batch.keys_only if batch.HasField('compiled_query'): self.__compiled_query = batch.compiled_query else: self.__compiled_query = None try: self.__index_list = [ self.__conn.adapter.pb_to_index(index_pb) for index_pb in batch.index ] except NotImplementedError: self.__index_list = None class Batch(object): """A batch of results returned by a query. This class contains a batch of results returned from the datastore and relevant metadata. This metadata includes: query: The query that produced this batch query_options: The QueryOptions used to run the query. This does not contained any options passed to the .next_batch() call that created the current batch. start_cursor, end_cursor: These are the cursors that can be used with a query to re-fetch this batch. They can also be used to find all entities before or after the given batch (by use start_cursor as an end cursor or vice versa). start_cursor can also be advanced to point to a position within the batch using Cursor.advance(). skipped_results: the number of result skipped because of the offset given to the request that generated it. This can be set either on the original Query.run() request or in subsequent .next_batch() calls. more_results: If this is true there are more results that can be retrieved either by .next_batch() or Batcher.next(). This class is also able to fetch the next batch of the query using .next_batch(). As batches of results must be fetched serially, .next_batch() can only be called once. Additional calls to .next_batch() will return None. When there are no more batches .next_batch() will return None as well. Note that batches returned by iterating over Batcher will always return None for .next_batch() as the Bather handles fetching the next batch automatically. A Batch typically represents the result of a single RPC request. The datastore operates on a "best effort" basis so the batch returned by .next_batch() or Query.run_async().get_result() may not have satisfied the requested offset or number of results (specified through FetchOptions.offset and FetchOptions.batch_size respectively). To satisfy these restrictions additional batches may be needed (with FetchOptions that specify the remaining offset or results needed). The Batcher class hides these limitations. """ __skipped_cursor = None __end_cursor = None @classmethod @datastore_rpc._positional(5) def create_async(cls, query, query_options, conn, req, start_cursor): batch_shared = _BatchShared(query, query_options, conn) batch0 = cls(batch_shared, start_cursor=start_cursor) return batch0._make_query_rpc_call(query_options, req) @datastore_rpc._positional(2) def __init__(self, batch_shared, start_cursor=Cursor()): """Constructor. This class is constructed in stages (one when an RPC is sent and another when an rpc is completed) and should not be constructed directly!! Use Query.run_async().get_result() to create a Batch or Query.run() to use a batcher. This constructor does not perform verification. Args: batch_shared: Data shared between batches for a a single query run. start_cursor: Optional cursor pointing before this batch. """ self._batch_shared = batch_shared self.__start_cursor = start_cursor @property def query_options(self): """The QueryOptions used to retrieve the first batch.""" return self._batch_shared.query_options @property def query(self): """The query the current batch came from.""" return self._batch_shared.query @property def results(self): """A list of entities in this batch.""" return self.__results @property def keys_only(self): """Whether the entities in this batch only contain keys.""" return self._batch_shared.keys_only @property def index_list(self): """Returns the list of indexes used to peform this batch's query. Possibly None when the adapter does not implement pb_to_index. """ return self._batch_shared.index_list @property def start_cursor(self): """A cursor that points to the position just before the current batch.""" return self.__start_cursor @property def end_cursor(self): """A cursor that points to the position just after the current batch.""" return self.__end_cursor @property def skipped_results(self): """The number of results skipped because of an offset in the request. An offset is satisfied before any results are returned. The start_cursor points to the position in the query before the skipped results. """ return self._skipped_results @property def more_results(self): """Whether more results can be retrieved from the query.""" return self.__more_results def next_batch(self, fetch_options=None): """Synchronously get the next batch or None if there are no more batches. Args: fetch_options: Optional fetch options to use when fetching the next batch. Merged with both the fetch options on the original call and the connection. Returns: A new Batch of results or None if either the next batch has already been fetched or there are no more results. """ async_ = self.next_batch_async(fetch_options) if async_ is None: return None return async_.get_result() def _compiled_query(self): return self._batch_shared.compiled_query def cursor(self, index): """Gets the cursor that points just after the result at index - 1. The index is relative to first result in .results. Since start_cursor points to the position before the first skipped result, the range of indexes this function supports is limited to [-skipped_results, len(results)]. For example, using start_cursor=batch.cursor(i) and end_cursor=batch.cursor(j) will return the results found in batch.results[i:j]. Note that any result added in the range (i-1, j] will appear in the new query's results. Warning: Any index in the range (-skipped_results, 0) may cause continuation to miss or duplicate results if outside a transaction. Args: index: An int, the index relative to the first result before which the cursor should point. Returns: A Cursor that points to a position just after the result index - 1, which if used as a start_cursor will cause the first result to be batch.result[index]. """ if not isinstance(index, six.integer_types): raise datastore_errors.BadArgumentError( 'index argument should be an integer (%r)' % (index,)) if not -self._skipped_results <= index <= len(self.__results): raise datastore_errors.BadArgumentError( 'index argument must be in the inclusive range [%d, %d]' % (-self._skipped_results, len(self.__results))) if index == -self._skipped_results: return self.__start_cursor elif (index == 0 and self.__skipped_cursor): return self.__skipped_cursor elif index > 0 and self.__result_cursors: return self.__result_cursors[index - 1] elif index == len(self.__results): return self.__end_cursor else: return self.__start_cursor.advance(index + self._skipped_results, self._batch_shared.query, self._batch_shared.conn) def next_batch_async(self, fetch_options=None): """Asynchronously get the next batch or None if there are no more batches. Args: fetch_options: Optional fetch options to use when fetching the next batch. Merged with both the fetch options on the original call and the connection. Returns: An async object that can be used to get the next Batch or None if either the next batch has already been fetched or there are no more results. """ if not self.__datastore_cursor: return None fetch_options, next_batch = self._make_next_batch(fetch_options) if (fetch_options is not None and not FetchOptions.is_configuration(fetch_options)): raise datastore_errors.BadArgumentError('Invalid fetch options.') config = self._batch_shared.query_options.merge(fetch_options) conn = next_batch._batch_shared.conn requested_offset = 0 if fetch_options is not None and fetch_options.offset is not None: requested_offset = fetch_options.offset if conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: if self._batch_shared.expected_offset != requested_offset: raise datastore_errors.BadArgumentError( 'Cannot request the next batch with a different offset than ' ' expected. Expected: %s, Got: %s.' % (self._batch_shared.expected_offset, requested_offset)) limit = self._batch_shared.remaining_limit next_options = QueryOptions(offset=self._batch_shared.expected_offset, limit=limit, start_cursor=self.__datastore_cursor) config = config.merge(next_options) result = next_batch._make_query_rpc_call( config, self._batch_shared.query._to_pb_v1(conn, config)) else: result = next_batch._make_next_rpc_call(config, self._to_pb(fetch_options)) self.__datastore_cursor = None return result def _to_pb(self, fetch_options=None): req = datastore_pb.NextRequest() if FetchOptions.produce_cursors(fetch_options, self._batch_shared.query_options, self._batch_shared.conn.config): req.compile = True count = FetchOptions.batch_size(fetch_options, self._batch_shared.query_options, self._batch_shared.conn.config) if count is not None: req.count = count if fetch_options is not None and fetch_options.offset: req.offset = fetch_options.offset req.cursor.CopyFrom(self.__datastore_cursor) return req def _extend(self, next_batch): """Combines the current batch with the next one. Called by batcher.""" self.__datastore_cursor = next_batch.__datastore_cursor next_batch.__datastore_cursor = None self.__more_results = next_batch.__more_results if not self.__results: self.__skipped_cursor = next_batch.__skipped_cursor self.__results.extend(next_batch.__results) self.__result_cursors.extend(next_batch.__result_cursors) self.__end_cursor = next_batch.__end_cursor self._skipped_results += next_batch._skipped_results def _make_query_rpc_call(self, config, req): """Makes a RunQuery call that will modify the instance. Args: config: The datastore_rpc.Configuration to use for the call. req: The request to send with the call. Returns: A UserRPC object that can be used to fetch the result of the RPC. """ _api_version = self._batch_shared.conn._api_version if _api_version == datastore_rpc._CLOUD_DATASTORE_V1: return self._batch_shared.conn._make_rpc_call( config, 'RunQuery', req, googledatastore.RunQueryResponse(), self.__v1_run_query_response_hook) return self._batch_shared.conn._make_rpc_call(config, 'RunQuery', req, datastore_pb.QueryResult(), self.__query_result_hook) def _make_next_rpc_call(self, config, req): """Makes a Next call that will modify the instance. Args: config: The datastore_rpc.Configuration to use for the call. req: The request to send with the call. Returns: A UserRPC object that can be used to fetch the result of the RPC. """ return self._batch_shared.conn._make_rpc_call(config, 'Next', req, datastore_pb.QueryResult(), self.__query_result_hook) _need_index_header = 'The suggested index for this query is:' def __v1_run_query_response_hook(self, rpc): try: self._batch_shared.conn.check_rpc_success(rpc) except datastore_errors.NeedIndexError: raise batch = rpc.response.batch self._batch_shared.process_batch(batch) if batch.skipped_cursor: self.__skipped_cursor = Cursor(_cursor_bytes=batch.skipped_cursor) self.__result_cursors = [Cursor(_cursor_bytes=result.cursor) for result in batch.entity_results if result.cursor] if batch.end_cursor: self.__end_cursor = Cursor(_cursor_bytes=batch.end_cursor) self._skipped_results = batch.skipped_results if batch.more_results == googledatastore.QueryResultBatch.NOT_FINISHED: self.__more_results = True self.__datastore_cursor = self.__end_cursor or self.__skipped_cursor if self.__datastore_cursor == self.__start_cursor: raise datastore_errors.Timeout( 'The query was not able to make progress.') else: self._end() self.__results = self._process_v1_results(batch.entity_results) return self def __query_result_hook(self, rpc): """Internal method used as get_result_hook for RunQuery/Next operation.""" try: self._batch_shared.conn.check_rpc_success(rpc) except datastore_errors.NeedIndexError as exc: if isinstance(rpc.request, datastore_pb.Query): _, kind, ancestor, props = datastore_index.CompositeIndexForQuery( rpc.request) props = datastore_index.GetRecommendedIndexProperties(props) yaml = datastore_index.IndexYamlForQuery(kind, ancestor, props) xml = datastore_index.IndexXmlForQuery(kind, ancestor, props) raise datastore_errors.NeedIndexError( '\n'.join([str(exc), self._need_index_header, yaml]), original_message=str(exc), header=self._need_index_header, yaml_index=yaml, xml_index=xml) raise query_result = rpc.response self._batch_shared.process_batch(query_result) if query_result.HasField('skipped_results_compiled_cursor'): self.__skipped_cursor = Cursor( _cursor_bytes=query_result.skipped_results_compiled_cursor .SerializeToString()) self.__result_cursors = [ Cursor(_cursor_bytes=result.SerializeToString()) for result in query_result.result_compiled_cursor ] if query_result.HasField('compiled_cursor'): self.__end_cursor = Cursor( _cursor_bytes=query_result.compiled_cursor.SerializeToString()) self._skipped_results = query_result.skipped_results if query_result.more_results: self.__datastore_cursor = query_result.cursor self.__more_results = True else: self._end() self.__results = self._process_results(query_result.result) return self def _end(self): """Changes the internal state so that no more batches can be produced.""" self.__datastore_cursor = None self.__more_results = False def _make_next_batch(self, fetch_options): """Creates the object to store the next batch. Args: fetch_options: The datastore_query.FetchOptions passed in by the user or None. Returns: A tuple containing the fetch options that should be used internally and the object that should be used to contain the next batch. """ return fetch_options, Batch(self._batch_shared, start_cursor=self.__end_cursor) def _process_results(self, results): """Converts the datastore results into results returned to the user. Args: results: A list of entity_pb2.EntityProto's returned by the datastore Returns: A list of results that should be returned to the user. """ converter = self._batch_shared.conn.adapter.pb_to_query_result return [converter(result, self._batch_shared.query_options) for result in results] def _process_v1_results(self, results): """Converts the datastore results into results returned to the user. Args: results: A list of googledatastore.EntityResults. Returns: A list of results that should be returned to the user. """ converter = self._batch_shared.conn.adapter.pb_v1_to_query_result return [converter(result.entity, self._batch_shared.query_options) for result in results] def __getstate__(self): raise pickle.PicklingError( 'Pickling of datastore_query.Batch is unsupported.') class _AugmentedBatch(Batch): """A batch produced by a datastore_query._AugmentedQuery.""" @classmethod @datastore_rpc._positional(5) def create_async(cls, augmented_query, query_options, conn, req, in_memory_offset, in_memory_limit, start_cursor): initial_offset = 0 if in_memory_offset is not None else None batch_shared = _BatchShared(augmented_query._query, query_options, conn, augmented_query, initial_offset=initial_offset) batch0 = cls(batch_shared, in_memory_offset=in_memory_offset, in_memory_limit=in_memory_limit, start_cursor=start_cursor) return batch0._make_query_rpc_call(query_options, req) @datastore_rpc._positional(2) def __init__(self, batch_shared, in_memory_offset=None, in_memory_limit=None, next_index=0, start_cursor=Cursor()): """A Constructor for datastore_query._AugmentedBatch. Constructed by datastore_query._AugmentedQuery. Should not be called directly. """ super(_AugmentedBatch, self).__init__(batch_shared, start_cursor=start_cursor) self.__in_memory_offset = in_memory_offset self.__in_memory_limit = in_memory_limit self.__next_index = next_index @property def query(self): """The query the current batch came from.""" return self._batch_shared.augmented_query def cursor(self, index): raise NotImplementedError def _extend(self, next_batch): super(_AugmentedBatch, self)._extend(next_batch) self.__in_memory_limit = next_batch.__in_memory_limit self.__in_memory_offset = next_batch.__in_memory_offset self.__next_index = next_batch.__next_index def _process_v1_results(self, results): """Process V4 results by converting to V3 and calling _process_results.""" v3_results = [] is_projection = bool(self.query_options.projection) for v1_result in results: v3_entity = entity_pb2.EntityProto() self._batch_shared.conn.adapter.get_entity_converter().v1_to_v3_entity( v1_result.entity, v3_entity, is_projection) v3_results.append(v3_entity) return self._process_results(v3_results) def _process_results(self, results): in_memory_filter = self._batch_shared.augmented_query._in_memory_filter if in_memory_filter: results = list(filter(in_memory_filter, results)) in_memory_results = self._batch_shared.augmented_query._in_memory_results if in_memory_results and self.__next_index < len(in_memory_results): original_query = super(_AugmentedBatch, self).query if original_query._order: if results: next_result = in_memory_results[self.__next_index] next_key = original_query._order.key(next_result) i = 0 while i < len(results): result = results[i] result_key = original_query._order.key(result) while next_key <= result_key: results.insert(i, next_result) i += 1 self.__next_index += 1 if self.__next_index >= len(in_memory_results): break next_result = in_memory_results[self.__next_index] next_key = original_query._order.key(next_result) i += 1 elif results or not super(_AugmentedBatch, self).more_results: results = in_memory_results + results self.__next_index = len(in_memory_results) if self.__in_memory_offset: assert not self._skipped_results offset = min(self.__in_memory_offset, len(results)) if offset: self._skipped_results += offset self.__in_memory_offset -= offset results = results[offset:] if self.__in_memory_limit is not None: results = results[:self.__in_memory_limit] self.__in_memory_limit -= len(results) if self.__in_memory_limit <= 0: self._end() return super(_AugmentedBatch, self)._process_results(results) def _make_next_batch(self, fetch_options): in_memory_offset = FetchOptions.offset(fetch_options) augmented_query = self._batch_shared.augmented_query if in_memory_offset and (augmented_query._in_memory_filter or augmented_query._in_memory_results): fetch_options = FetchOptions(offset=0) else: in_memory_offset = None return (fetch_options, _AugmentedBatch(self._batch_shared, in_memory_offset=in_memory_offset, in_memory_limit=self.__in_memory_limit, start_cursor=self.end_cursor, next_index=self.__next_index)) class Batcher(object): """A class that implements the Iterator interface for Batches. Typically constructed by a call to Query.run(). The class hides the "best effort" nature of the datastore by potentially making multiple requests to the datastore and merging the resulting batches. This is accomplished efficiently by prefetching results and mixing both non-blocking and blocking calls to the datastore as needed. Iterating through batches is almost always more efficient than pulling all results at once as RPC latency is hidden by asynchronously prefetching results. The batches produce by this class cannot be used to fetch the next batch (through Batch.next_batch()) as before the current batch is returned the request for the next batch has already been sent. """ ASYNC_ONLY = None AT_LEAST_OFFSET = 0 AT_LEAST_ONE = object() def __init__(self, query_options, first_async_batch): """Constructor. Although this class can be manually constructed, it is preferable to use Query.run(query_options). Args: query_options: The QueryOptions used to create the first batch. first_async_batch: The first batch produced by Query.run_async(query_options). """ self.__next_batch = first_async_batch self.__initial_offset = QueryOptions.offset(query_options) or 0 self.__skipped_results = 0 def next(self): """Get the next batch. See .next_batch().""" return self.next_batch(self.AT_LEAST_ONE) def __next__(self): return self.next() def next_batch(self, min_batch_size): """Get the next batch. The batch returned by this function cannot be used to fetch the next batch (through Batch.next_batch()). Instead this function will always return None. To retrieve the next batch use .next() or .next_batch(N). This function may return a batch larger than min_to_fetch, but will never return smaller unless there are no more results. Special values can be used for min_batch_size: ASYNC_ONLY - Do not perform any synchrounous fetches from the datastore even if the this produces a batch with no results. AT_LEAST_OFFSET - Only pull enough results to satifiy the offset. AT_LEAST_ONE - Pull batches until at least one result is returned. Args: min_batch_size: The minimum number of results to retrieve or one of (ASYNC_ONLY, AT_LEAST_OFFSET, AT_LEAST_ONE) Returns: The next Batch of results. """ if min_batch_size in (Batcher.ASYNC_ONLY, Batcher.AT_LEAST_OFFSET, Batcher.AT_LEAST_ONE): exact = False else: exact = True datastore_types.ValidateInteger(min_batch_size, 'min_batch_size', datastore_errors.BadArgumentError) if not self.__next_batch: raise StopIteration batch = self.__next_batch.get_result() self.__next_batch = None self.__skipped_results += batch.skipped_results if min_batch_size is not Batcher.ASYNC_ONLY: if min_batch_size is Batcher.AT_LEAST_ONE: min_batch_size = 1 needed_results = min_batch_size - len(batch.results) while (batch.more_results and (self.__skipped_results < self.__initial_offset or needed_results > 0)): if batch.query_options.batch_size: batch_size = max(batch.query_options.batch_size, needed_results) elif exact: batch_size = needed_results else: batch_size = None self.__next_batch = batch.next_batch_async(FetchOptions( offset=max(0, self.__initial_offset - self.__skipped_results), batch_size=batch_size)) next_batch = self.__next_batch.get_result() self.__next_batch = None self.__skipped_results += next_batch.skipped_results needed_results = max(0, needed_results - len(next_batch.results)) batch._extend(next_batch) self.__next_batch = batch.next_batch_async() return batch def __getstate__(self): raise pickle.PicklingError( 'Pickling of datastore_query.Batcher is unsupported.') def __iter__(self): return self class ResultsIterator(six.Iterator): """An iterator over the results from Batches obtained from a Batcher. ResultsIterator implements Python's iterator protocol, so results can be accessed with the for-statement: > it = ResultsIterator(Query(kind='Person').run()) > for person in it: > print 'Hi, %s!' % person['name'] At any time ResultsIterator.cursor() can be used to grab the Cursor that points just after the last result returned by the iterator. """ __current_batch = None __current_pos = 0 __last_cursor = None def __init__(self, batcher): """Constructor. Args: batcher: A datastore_query.Batcher """ if not isinstance(batcher, Batcher): raise datastore_errors.BadArgumentError( 'batcher argument should be datastore_query.Batcher (%r)' % (batcher,)) self.__batcher = batcher def index_list(self): """Returns the list of indexes used to perform the query. Possibly None when the adapter does not implement pb_to_index. """ return self._ensure_current_batch().index_list def cursor(self): """Returns a cursor that points just after the last result returned. If next() throws an exception, this function returns the end_cursor from the last successful batch or throws the same exception if no batch was successful. """ return (self.__last_cursor or self._ensure_current_batch().cursor(self.__current_pos)) def _ensure_current_batch(self): if not self.__current_batch: self.__current_batch = self.__batcher.next_batch(Batcher.AT_LEAST_OFFSET) self.__current_pos = 0 return self.__current_batch def _compiled_query(self): """Returns the compiled query associated with the iterator. Internal only do not use. """ return self._ensure_current_batch()._compiled_query() def __next__(self): """Returns the next query result.""" while (not self.__current_batch or self.__current_pos >= len(self.__current_batch.results)): try: next_batch = self.__batcher.next_batch(Batcher.AT_LEAST_OFFSET) except: if self.__current_batch: self.__last_cursor = self.__current_batch.end_cursor raise self.__current_pos = 0 self.__current_batch = next_batch result = self.__current_batch.results[self.__current_pos] self.__current_pos += 1 return result def __iter__(self): return self def next(self): return self.__next__()	[ 1, 529, 276, 1112, 420, 29958, 1357, 24446, 29914, 932, 10599, 29899, 4691, 29899, 15770, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29900, 29955, 5087, 365, 12182, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 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hw1/0711529_hw1.py	KJ-black/Introduction-to-Operating-Systems	0	77150	<reponame>KJ-black/Introduction-to-Operating-Systems """ Date: 2021/01/02 Describe: OS hw1 Auther: <NAME> ID: 0711529 """ import time # to calculate the running time ## for task1 import hashlib ## for task2 from bs4 import BeautifulSoup import requests ## multithreading import threading import queue class MyThread(threading.Thread): def __init__(self, queue, num, task): threading.Thread.__init__(self) self.queue = queue self.num = num self.task = task def run(self): while self.queue.qsize() > 0: msg = self.queue.get() if self.task==1: proof_of_work(msg) elif self.task==2: get_url_header(msg) def multithreading(task, number, testcase, taskdata): # create the queue to put the unworking task in it my_queue = queue.Queue() # put the data to queue # and the max data size is 100 for i in range(testcase): my_queue.put(taskdata[i]) # create the thread and start threads = [] for i in range(number): threads.append(MyThread(my_queue, i, task)) threads[i].start() # wait for the tthread to finish for i in range(number): threads[i].join() ## multiprocessing from multiprocessing import Pool def multiprocessing(task, number, testcase, taskdata): pool = Pool(number) if task == 1: pool.map(proof_of_work, taskdata[:testcase]) elif task == 2: pool.map(get_url_header, taskdata[:testcase]) pool.close() pool.join() ## coroutine import asyncio async def co_proof_of_work(str, loop): await loop.run_in_executor(None, proof_of_work, str) async def co_get_url_header(url, loop): html = await loop.run_in_executor(None, requests.get, url) soup = BeautifulSoup(html.text, 'html.parser') title = soup.head.title.text print(title) def coroutine(task, testcase, taskdata): loop = asyncio.get_event_loop() if task == 1: tasks = [loop.create_task(co_proof_of_work(t, loop)) for t in taskdata[:testcase]] elif task == 2: tasks = [loop.create_task(co_get_url_header(t, loop)) for t in taskdata[:testcase]] loop.run_until_complete(asyncio.wait(tasks)) ## task # task 1 def proof_of_work(str): a = b = c = d = 33 # initialize while True: for e in range(33, 127): s = hashlib.sha256() str2 = chr(a)+chr(b)+chr(c)+chr(d)+chr(e)+str s.update(str2.encode('utf-8')) h = s.hexdigest() if h[:5]=="00000": print(str2) return str2 d += 1 if d > 126: c += 1 d = 33 if c > 126: b += 1 c = 33 if b > 126: a += 1 b = 33 if a > 126: break # task 2 def get_url_header(url): html = requests.get(url) soup = BeautifulSoup(html.text, 'html.parser') title = soup.head.title.text print(title) return title def debug(task, testcase, taskdata): if task == 1: for i in range(testcase): proof_of_work(taskdata[i]) elif task == 2: for i in range(testcase): get_url_header(taskdata[i]) def main(): task = int(input()) # input the task you want to execute: 1 or 2 m = input() # input the method: 1: multithreading, 2: multiprocessing, 3: coroutine m = m.split() if(len(m)==2): number = int(m[1]) method = int(m[0]) testcase = int(input()) # taskdata file = open("task%d_sample.txt"%task) taskdata = [] for line in file: taskdata.append(line.replace('\n', "")) start = time.perf_counter() if method == 1: # use multithreading multithreading(task, number, testcase, taskdata) elif method == 2: # use multiprocessing multiprocessing(task, number, testcase, taskdata) elif method == 3: # use coroutine coroutine(task, testcase, taskdata) elif method == 4: # for debug debug(task, testcase, taskdata) end = time.perf_counter() print("Total running time: %f"%(end-start)) if __name__=="__main__": main()	[ 1, 529, 276, 1112, 420, 29958, 29968, 29967, 29899, 8517, 29914, 25898, 29899, 517, 29899, 7094, 1218, 29899, 3924, 29879, 13, 15945, 19451, 13, 2539, 29901, 29871, 29906, 29900, 29906, 29896, 29914, 29900, 29896, 29914, 29900, 29906, 30004, 13, 4002, 29581, 29901, 6570, 298, 29893, 29896, 6756, 13, 6147, 2276, 29901, 529, 5813, 3238, 13, 1367, 29901, 29871, 29900, 29955, 29896, 29896, 29945, 29906, 29929, 30004, 13, 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1445, 353, 1722, 703, 7662, 29995, 29881, 29918, 11249, 29889, 3945, 29908, 29995, 7662, 8443, 13, 12, 7662, 1272, 353, 5159, 30004, 13, 12, 1454, 1196, 297, 934, 29901, 30004, 13, 12, 12, 7662, 1272, 29889, 4397, 29898, 1220, 29889, 6506, 28909, 29876, 742, 5124, 876, 30004, 13, 12, 12, 30004, 13, 12, 2962, 353, 931, 29889, 546, 29888, 29918, 11808, 26471, 13, 12, 361, 1158, 1275, 29871, 29896, 29901, 30004, 13, 12, 12, 29937, 671, 1773, 389, 19715, 30004, 13, 12, 12, 4713, 389, 19715, 29898, 7662, 29892, 1353, 29892, 1243, 4878, 29892, 3414, 1272, 8443, 13, 12, 23681, 1158, 1275, 29871, 29906, 29901, 30004, 13, 12, 12, 29937, 671, 6674, 307, 985, 292, 30004, 13, 12, 12, 18056, 307, 985, 292, 29898, 7662, 29892, 1353, 29892, 1243, 4878, 29892, 3414, 1272, 8443, 13, 12, 23681, 1158, 1275, 29871, 29941, 29901, 30004, 13, 12, 12, 29937, 671, 1034, 449, 457, 30004, 13, 12, 12, 2616, 449, 457, 29898, 7662, 29892, 1243, 4878, 29892, 3414, 1272, 8443, 13, 12, 23681, 1158, 1275, 29871, 29946, 29901, 30004, 13, 12, 12, 29937, 363, 4744, 30004, 13, 12, 12, 8382, 29898, 7662, 29892, 1243, 4878, 29892, 3414, 1272, 8443, 13, 12, 355, 353, 931, 29889, 546, 29888, 29918, 11808, 26471, 13, 12, 2158, 703, 11536, 2734, 931, 29901, 1273, 29888, 29908, 29995, 29898, 355, 29899, 2962, 876, 30004, 13, 12, 30004, 13, 361, 4770, 978, 1649, 26359, 1649, 3396, 1649, 1115, 30004, 13, 12, 3396, 580, 2 ]
torch-test/mpich-3.4.3/modules/libfabric/contrib/intel/jenkins/runtests.py	alchemy315/NoPFS	0	22305	import argparse import os import sys sys.path.append(os.environ['CI_SITE_CONFIG']) import ci_site_config import run import common parser = argparse.ArgumentParser() parser.add_argument("--prov", help="core provider", choices=["psm2", "verbs", \ "tcp", "udp", "sockets", "shm"]) parser.add_argument("--util", help="utility provider", choices=["rxd", "rxm"]) parser.add_argument("--ofi_build_mode", help="specify the build configuration", \ choices = ["dbg", "dl"]) args = parser.parse_args() args_core = args.prov args_util = args.util if (args.ofi_build_mode): ofi_build_mode = args.ofi_build_mode else: ofi_build_mode='reg' node = (os.environ['NODE_NAME']).split('-')[0] hosts = [node] # Note: Temporarily disabling all mpich testing # due to mpich options issues which is causing # multiple tests to fail. #mpilist = ['impi', 'mpich', 'ompi'] mpilist = ['impi', 'ompi'] #this script is executed from /tmp #this is done since some mpi tests #look for a valid location before running # the test on the secondary host(client) # but jenkins only creates a valid path on # the primary host (server/test node) os.chdir('/tmp/') if(args_core): for host in ci_site_config.node_map[node]: hosts.append(host) if (args_util == None): run.fi_info_test(args_core, hosts, ofi_build_mode) run.fabtests(args_core, hosts, ofi_build_mode) run.shmemtest(args_core, hosts, ofi_build_mode) for mpi in mpilist: run.intel_mpi_benchmark(args_core, hosts, mpi, ofi_build_mode) run.mpistress_benchmark(args_core, hosts, mpi, ofi_build_mode) run.osu_benchmark(args_core, hosts, mpi, ofi_build_mode) else: run.fi_info_test(args_core, hosts, ofi_build_mode, util=args_util) run.fabtests(args_core, hosts, ofi_build_mode, util=args_util) run.shmemtest(args_core, hosts, ofi_build_mode, util=args_util) for mpi in mpilist: run.intel_mpi_benchmark(args_core, hosts, mpi, ofi_build_mode, \ util=args_util,) run.mpistress_benchmark(args_core, hosts, mpi, ofi_build_mode, \ util=args_util) run.osu_benchmark(args_core, hosts, mpi, ofi_build_mode, \ util=args_util) else: print("Error : Specify a core provider to run tests")	[ 1, 1053, 1852, 5510, 13, 5215, 2897, 13, 5215, 10876, 13, 9675, 29889, 2084, 29889, 4397, 29898, 359, 29889, 21813, 1839, 8426, 29918, 29903, 9094, 29918, 25903, 11287, 13, 5215, 4583, 29918, 2746, 29918, 2917, 13, 5215, 1065, 13, 5215, 3619, 13, 13, 16680, 353, 1852, 5510, 29889, 15730, 11726, 580, 13, 13, 16680, 29889, 1202, 29918, 23516, 703, 489, 16123, 613, 1371, 543, 3221, 13113, 613, 19995, 29922, 3366, 567, 29885, 29906, 613, 376, 369, 5824, 613, 320, 13, 462, 268, 376, 23981, 613, 376, 566, 29886, 613, 376, 578, 9737, 613, 376, 845, 29885, 20068, 13, 16680, 29889, 1202, 29918, 23516, 703, 489, 4422, 613, 1371, 543, 329, 1793, 13113, 613, 19995, 29922, 3366, 17697, 29881, 613, 376, 17697, 29885, 20068, 13, 16680, 29889, 1202, 29918, 23516, 703, 489, 974, 29875, 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pywren_ibm_cloud/libs/cloudpickle/__init__.py	gerardparis/pywren-ibm-cloud	0	52417	import pickle from pywren_ibm_cloud.libs.cloudpickle.cloudpickle import * __version__ = '1.2.2'	[ 1, 1053, 5839, 280, 13, 13, 13, 3166, 282, 5693, 1267, 29918, 29690, 29918, 9274, 29889, 10254, 29889, 9274, 23945, 280, 29889, 9274, 23945, 280, 1053, 334, 13, 13, 13, 1649, 3259, 1649, 353, 525, 29896, 29889, 29906, 29889, 29906, 29915, 13, 2 ]
pyeccodes/defs/grib2/template_3_63_def.py	ecmwf/pyeccodes	7	95750	import pyeccodes.accessors as _ def load(h): h.add(_.Transient('biFourierMakeTemplate', 0)) h.add(_.Label('BiFourier coefficients')) h.add(_.Constant('biFourierCoefficients', 1)) h.add(_.Codetable('spectralType', 1, "3.6.table", _.Get('masterDir'), _.Get('localDir'))) h.alias('spectralDataRepresentationType', 'spectralType') h.add(_.Unsigned('biFourierResolutionParameterN', 4)) h.add(_.Unsigned('biFourierResolutionParameterM', 4)) h.add(_.Codetable('biFourierTruncationType', 1, "3.25.table", _.Get('masterDir'), _.Get('localDir'))) h.add(_.Unsigned('Lx', 8)) h.alias('geography.LxInMetres', 'Lx') h.add(_.Unsigned('Lux', 8)) h.alias('geography.LuxInMetres', 'Lux') h.add(_.Unsigned('Lcx', 8)) h.alias('geography.LcxInMetres', 'Lcx') h.add(_.Unsigned('Ly', 8)) h.alias('geography.LyInMetres', 'Ly') h.add(_.Unsigned('Luy', 8)) h.alias('geography.LuyInMetres', 'Luy') h.add(_.Unsigned('Lcy', 8)) h.alias('geography.LcyInMetres', 'Lcy') h.add(_.Codetable('shapeOfTheEarth', 1, "3.2.table", _.Get('masterDir'), _.Get('localDir'))) h.add(_.Unsigned('scaleFactorOfRadiusOfSphericalEarth', 1)) h.add(_.Unsigned('scaledValueOfRadiusOfSphericalEarth', 4)) h.add(_.Unsigned('scaleFactorOfEarthMajorAxis', 1)) h.alias('scaleFactorOfMajorAxisOfOblateSpheroidEarth', 'scaleFactorOfEarthMajorAxis') h.add(_.Unsigned('scaledValueOfEarthMajorAxis', 4)) h.alias('scaledValueOfMajorAxisOfOblateSpheroidEarth', 'scaledValueOfEarthMajorAxis') h.add(_.Unsigned('scaleFactorOfEarthMinorAxis', 1)) h.alias('scaleFactorOfMinorAxisOfOblateSpheroidEarth', 'scaleFactorOfEarthMinorAxis') h.add(_.Unsigned('scaledValueOfEarthMinorAxis', 4)) h.alias('scaledValueOfMinorAxisOfOblateSpheroidEarth', 'scaledValueOfEarthMinorAxis') h.alias('earthIsOblate', 'one') if (h.get_l('shapeOfTheEarth') == 0): h.add(_.Transient('radius', 6367470)) h.alias('radiusOfTheEarth', 'radius') h.alias('radiusInMetres', 'radius') h.alias('earthIsOblate', 'zero') if (h.get_l('shapeOfTheEarth') == 1): h.add(_.From_scale_factor_scaled_value('radius', _.Get('scaleFactorOfRadiusOfSphericalEarth'), _.Get('scaledValueOfRadiusOfSphericalEarth'))) h.alias('radiusOfTheEarth', 'radius') h.alias('radiusInMetres', 'radius') h.alias('earthIsOblate', 'zero') if (h.get_l('shapeOfTheEarth') == 6): h.add(_.Transient('radius', 6371229)) h.alias('radiusOfTheEarth', 'radius') h.alias('radiusInMetres', 'radius') h.alias('earthIsOblate', 'zero') if (h.get_l('shapeOfTheEarth') == 8): h.add(_.Transient('radius', 6371200)) h.alias('radiusOfTheEarth', 'radius') h.alias('radiusInMetres', 'radius') h.alias('earthIsOblate', 'zero') if (h.get_l('shapeOfTheEarth') == 2): h.add(_.Transient('earthMajorAxis', 6.37816e+06)) h.add(_.Transient('earthMinorAxis', 6.35678e+06)) h.alias('earthMajorAxisInMetres', 'earthMajorAxis') h.alias('earthMinorAxisInMetres', 'earthMinorAxis') if (h.get_l('shapeOfTheEarth') == 3): h.add(_.From_scale_factor_scaled_value('earthMajorAxis', _.Get('scaleFactorOfEarthMajorAxis'), _.Get('scaledValueOfEarthMajorAxis'))) h.add(_.From_scale_factor_scaled_value('earthMinorAxis', _.Get('scaleFactorOfEarthMinorAxis'), _.Get('scaledValueOfEarthMinorAxis'))) h.add(_.Divdouble('earthMajorAxisInMetres', _.Get('earthMajorAxis'), 0.001)) h.add(_.Divdouble('earthMinorAxisInMetres', _.Get('earthMinorAxis'), 0.001)) if (h.get_l('shapeOfTheEarth') == 7): h.add(_.From_scale_factor_scaled_value('earthMajorAxis', _.Get('scaleFactorOfEarthMajorAxis'), _.Get('scaledValueOfEarthMajorAxis'))) h.add(_.From_scale_factor_scaled_value('earthMinorAxis', _.Get('scaleFactorOfEarthMinorAxis'), _.Get('scaledValueOfEarthMinorAxis'))) h.alias('earthMajorAxisInMetres', 'earthMajorAxis') h.alias('earthMinorAxisInMetres', 'earthMinorAxis') if ((h.get_l('shapeOfTheEarth') == 4) or (h.get_l('shapeOfTheEarth') == 5)): h.add(_.Transient('earthMajorAxis', 6.37814e+06)) h.add(_.Transient('earthMinorAxis', 6.35675e+06)) h.alias('earthMajorAxisInMetres', 'earthMajorAxis') h.alias('earthMinorAxisInMetres', 'earthMinorAxis') if (h.get_l('shapeOfTheEarth') == 9): h.add(_.Transient('earthMajorAxis', 6.37756e+06)) h.add(_.Transient('earthMinorAxis', 6.35626e+06)) h.alias('earthMajorAxisInMetres', 'earthMajorAxis') h.alias('earthMinorAxisInMetres', 'earthMinorAxis') h.add(_.Signed('latitudeOfFirstGridPoint', 4)) h.alias('La1', 'latitudeOfFirstGridPoint') h.add(_.Scale('latitudeOfFirstGridPointInDegrees', _.Get('latitudeOfFirstGridPoint'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.latitudeOfFirstGridPointInDegrees', 'latitudeOfFirstGridPointInDegrees') h.alias('La1InDegrees', 'latitudeOfFirstGridPointInDegrees') h.add(_.Unsigned('longitudeOfFirstGridPoint', 4)) h.alias('Lo1', 'longitudeOfFirstGridPoint') h.add(_.Scale('longitudeOfFirstGridPointInDegrees', _.Get('longitudeOfFirstGridPoint'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.longitudeOfFirstGridPointInDegrees', 'longitudeOfFirstGridPointInDegrees') h.alias('Lo1InDegrees', 'longitudeOfFirstGridPointInDegrees') h.add(_.Signed('LaD', 4)) h.alias('latitudeWhereDxAndDyAreSpecified', 'LaD') h.add(_.Scale('LaDInDegrees', _.Get('LaD'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.LaDInDegrees', 'LaDInDegrees') h.add(_.Unsigned('LoV', 4)) h.add(_.Scale('LoVInDegrees', _.Get('LoV'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.LoVInDegrees', 'LoVInDegrees') h.add(_.Codeflag('projectionCentreFlag', 1, "grib2/tables/[tablesVersion]/3.5.table")) h.add(_.Signed('Latin1', 4)) h.alias('FirstLatitude', 'Latin1') h.add(_.Scale('Latin1InDegrees', _.Get('Latin1'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.Latin1InDegrees', 'Latin1InDegrees') h.add(_.Signed('Latin2', 4)) h.alias('SecondLatitude', 'Latin2') h.add(_.Scale('Latin2InDegrees', _.Get('Latin2'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.Latin2InDegrees', 'Latin2InDegrees') h.add(_.Signed('latitudeOfSouthernPole', 4)) h.alias('latitudeOfTheSouthernPoleOfProjection', 'latitudeOfSouthernPole') h.add(_.Scale('latitudeOfSouthernPoleInDegrees', _.Get('latitudeOfSouthernPole'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.latitudeOfSouthernPoleInDegrees', 'latitudeOfSouthernPoleInDegrees') h.add(_.Unsigned('longitudeOfSouthernPole', 4)) h.alias('longitudeOfTheSouthernPoleOfProjection', 'longitudeOfSouthernPole') h.add(_.Scale('longitudeOfSouthernPoleInDegrees', _.Get('longitudeOfSouthernPole'), _.Get('oneConstant'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.longitudeOfSouthernPoleInDegrees', 'longitudeOfSouthernPoleInDegrees')	[ 1, 1053, 282, 4099, 617, 2631, 29889, 5943, 943, 408, 903, 13, 13, 13, 1753, 2254, 29898, 29882, 1125, 13, 13, 1678, 298, 29889, 1202, 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server.py	yenyuuuuu/esys_hw3	0	80803	<reponame>yenyuuuuu/esys_hw3 import socket import json import matplotlib.pyplot as plt import numpy as np HOST = '192.168.50.254' # Standard loopback interface address (localhost) 192.168.50.254 PORT = 3000 # Port to listen on (non-privileged ports are > 1023) # use ggplot style for more sophisticated visuals plt.style.use('ggplot') def main(): size = 100 t_vec = np.linspace(0,1,size+1)[0:-1] x_vec = np.zeros(size) y_vec = np.zeros(size) z_vec = np.zeros(size) line_x=[] line_y=[] line_z=[] with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) print('listen...') s.listen() conn, addr = s.accept() with conn: print('Connected by', addr) while True: data = conn.recv(1024).decode('utf-8') if not data: print("no data!!!") break print(data) j_data=json.loads(data) x_vec = np.append(x_vec[1:],j_data['x']) y_vec = np.append(y_vec[1:],j_data['y']) z_vec = np.append(z_vec[1:],j_data['z']) line_x, line_y, line_z = plotter(t_vec,x_vec,y_vec,z_vec,line_x,line_y,line_z) s.close() return return def plotter(t_vec,y1_data,y2_data,y3_data,line1,line2,line3,identifier='',pause_time=0.5): if (line1==[]) and (line2==[]) and (line3==[]): # this is the call to matplotlib that allows dynamic plotting plt.ion() fig = plt.figure(figsize=(13,6)) ax = fig.add_subplot(111) # create a variable for the line so we can later update it line1, = ax.plot(t_vec,y1_data,'-o',alpha=0.8,color='purple') line2, = ax.plot(t_vec,y2_data,'-o',alpha=0.8,color='red') line3, = ax.plot(t_vec,y3_data,'-o',alpha=0.8,color='blue') #update plot label/title plt.ylim([-1000,1000]) plt.ylabel('Acc') plt.title("Acc (to time t)") plt.show() # after the figure, axis, and line are created, we only need to update the y-data line1.set_ydata(y1_data) line2.set_ydata(y2_data) line3.set_ydata(y3_data) # adjust limits if new data goes beyond bounds # this pauses the data so the figure/axis can catch up - the amount of pause can be altered above plt.pause(pause_time) # return line so we can update it again in the next iteration return line1,line2,line3 if __name__ == '__main__': main()	[ 1, 529, 276, 1112, 420, 29958, 11771, 29891, 29884, 29884, 29884, 29884, 29884, 29914, 267, 952, 29918, 26828, 29941, 13, 5215, 9909, 30004, 13, 5215, 4390, 30004, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 30004, 13, 5215, 12655, 408, 7442, 30004, 13, 30004, 13, 20832, 353, 525, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29945, 29900, 29889, 29906, 29945, 29946, 29915, 29871, 396, 10117, 2425, 1627, 5067, 3211, 313, 7640, 29897, 29871, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29945, 29900, 29889, 29906, 29945, 29946, 30004, 13, 15082, 353, 29871, 29941, 29900, 29900, 29900, 4706, 396, 3371, 304, 11621, 373, 313, 5464, 29899, 22534, 488, 3192, 16169, 526, 1405, 29871, 29896, 29900, 29906, 29941, 8443, 13, 30004, 13, 29937, 671, 25662, 3114, 363, 901, 269, 3021, 4695, 630, 7604, 29879, 30004, 13, 572, 29873, 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examples/apprtc/apprtc.py	nickaknudson/aiortc	1	169141	<filename>examples/apprtc/apprtc.py import argparse import asyncio import json import logging import os import random import time import aiohttp import cv2 import websockets from aiortc import (AudioStreamTrack, RTCPeerConnection, RTCSessionDescription, VideoStreamTrack) from aiortc.contrib.media import frame_from_bgr from aiortc.sdp import candidate_from_sdp ROOT = os.path.dirname(__file__) PHOTO_PATH = os.path.join(ROOT, 'photo.jpg') VIDEO_PTIME = 1 / 30 def description_to_dict(description): return { 'sdp': description.sdp, 'type': description.type } class Signaling: async def connect(self, params): self.websocket = await websockets.connect(params['wss_url'], extra_headers={ 'Origin': 'https://appr.tc' }) async def recv(self): data = await self.websocket.recv() return json.loads(data) async def send(self, data): await self.websocket.send(json.dumps(data)) async def send_message(self, message): print('>', message) await self.send({ 'cmd': 'send', 'msg': json.dumps(message) }) class VideoImageTrack(VideoStreamTrack): def __init__(self): self.counter = 0 self.img = cv2.imread(PHOTO_PATH, cv2.IMREAD_COLOR) self.last = None async def recv(self): # rotate image rows, cols, _ = self.img.shape M = cv2.getRotationMatrix2D((cols / 2, rows / 2), self.counter / 2, 1) rotated = cv2.warpAffine(self.img, M, (cols, rows)) frame = frame_from_bgr(rotated) self.counter += 1 # sleep if self.last: delta = self.last + VIDEO_PTIME - time.time() if delta > 0: await asyncio.sleep(delta) self.last = time.time() return frame async def consume_signaling(signaling, pc, params): async def handle_message(message): print('<', message) if message['type'] == 'bye': return True if message['type'] == 'offer': await pc.setRemoteDescription(RTCSessionDescription(message)) await pc.setLocalDescription(await pc.createAnswer()) await signaling.send_message(description_to_dict(pc.localDescription)) elif message['type'] == 'answer': await pc.setRemoteDescription(RTCSessionDescription(message)) elif message['type'] == 'candidate': candidate = candidate_from_sdp(message['candidate'].split(':', 1)[1]) candidate.sdpMid = message['id'] candidate.sdpMLineIndex = message['label'] pc.addIceCandidate(candidate) return False for data in params['messages']: message = json.loads(data) await handle_message(message) stop = False while not stop: data = await signaling.recv() message = json.loads(data['msg']) stop = await handle_message(message) async def consume_audio(track): """ Drain incoming audio. """ while True: await track.recv() async def consume_video(track): """ Drain incoming video. """ while True: await track.recv() async def join_room(room): consumers = [] # fetch room parameters async with aiohttp.ClientSession() as session: async with session.post('https://appr.tc/join/' + room) as response: # we cannot use response.json() due to: # https://github.com/webrtc/apprtc/issues/562 data = json.loads(await response.text()) assert data['result'] == 'SUCCESS' params = data['params'] # create peer conection pc = RTCPeerConnection() pc.addTrack(AudioStreamTrack()) pc.addTrack(VideoImageTrack()) @pc.on('track') def on_track(track): if track.kind == 'audio': consumers.append(asyncio.ensure_future(consume_audio(track))) elif track.kind == 'video': consumers.append(asyncio.ensure_future(consume_video(track))) # connect to websocket and join signaling = Signaling() await signaling.connect(params) await signaling.send({ 'clientid': params['client_id'], 'cmd': 'register', 'roomid': params['room_id'], }) if params['is_initiator'] == 'true': # send offer await pc.setLocalDescription(await pc.createOffer()) await signaling.send_message(description_to_dict(pc.localDescription)) print('Please point a browser at %s' % params['room_link']) # receive 60s of media try: await asyncio.wait_for(consume_signaling(signaling, pc, params), timeout=60) except asyncio.TimeoutError: pass # shutdown print('Shutting down') await signaling.send_message({'type': 'bye'}) for c in consumers: c.cancel() await pc.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description='AppRTC') parser.add_argument('room', nargs='?') parser.add_argument('--verbose', '-v', action='count') args = parser.parse_args() if not args.room: args.room = ''.join([random.choice('0123456789') for x in range(10)]) if args.verbose: logging.basicConfig(level=logging.DEBUG) asyncio.get_event_loop().run_until_complete(join_room(args.room))	[ 1, 529, 9507, 29958, 19057, 29914, 932, 2273, 29883, 29914, 932, 2273, 29883, 29889, 2272, 13, 5215, 1852, 5510, 13, 5215, 408, 948, 3934, 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good_spot/places/migrations/0040_actualpopulartimes.py	jasmine92122/NightClubBackend	0	119636	# -- coding: utf-8 -- # Generated by Django 1.11.7 on 2018-01-30 13:02 from __future__ import unicode_literals import django.contrib.postgres.fields.jsonb from django.db import migrations, models import django.utils.timezone import model_utils.fields class Migration(migrations.Migration): dependencies = [ ('places', '0039_auto_20180129_0924'), ] operations = [ migrations.CreateModel( name='ActualPopularTimes', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')), ('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')), ('actual_pop_times', django.contrib.postgres.fields.jsonb.JSONField()), ('expire_at', models.DateTimeField()), ], options={ 'abstract': False, }, ), ]	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 3251, 630, 491, 15337, 29871, 29896, 29889, 29896, 29896, 29889, 29955, 373, 29871, 29906, 29900, 29896, 29947, 29899, 29900, 29896, 29899, 29941, 29900, 29871, 29896, 29941, 29901, 29900, 29906, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 5215, 9557, 29889, 21570, 29889, 2490, 7201, 29889, 9621, 29889, 3126, 29890, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 13239, 29889, 2230, 8028, 13, 5215, 1904, 29918, 13239, 29889, 9621, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 29886, 6048, 742, 525, 29900, 29900, 29941, 29929, 29918, 6921, 29918, 29906, 29900, 29896, 29947, 29900, 29896, 29906, 29929, 29918, 29900, 29929, 29906, 29946, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 4391, 3195, 29898, 13, 9651, 1024, 2433, 2865, 950, 12310, 1070, 29164, 742, 13, 9651, 4235, 11759, 13, 18884, 6702, 333, 742, 4733, 29889, 12300, 3073, 29898, 6921, 29918, 11600, 29922, 5574, 29892, 7601, 29918, 1989, 29922, 5574, 29892, 28755, 29922, 8824, 29892, 26952, 29918, 978, 2433, 1367, 1495, 511, 13, 18884, 6702, 11600, 742, 1904, 29918, 13239, 29889, 9621, 29889, 12300, 20399, 3073, 29898, 4381, 29922, 14095, 29889, 13239, 29889, 2230, 8028, 29889, 3707, 29892, 3863, 519, 29922, 8824, 29892, 26952, 29918, 978, 2433, 11600, 1495, 511, 13, 18884, 6702, 1545, 2164, 742, 1904, 29918, 13239, 29889, 9621, 29889, 12300, 8897, 2111, 2164, 3073, 29898, 4381, 29922, 14095, 29889, 13239, 29889, 2230, 8028, 29889, 3707, 29892, 3863, 519, 29922, 8824, 29892, 26952, 29918, 978, 2433, 1545, 2164, 1495, 511, 13, 18884, 6702, 19304, 29918, 7323, 29918, 3706, 742, 9557, 29889, 21570, 29889, 2490, 7201, 29889, 9621, 29889, 3126, 29890, 29889, 7249, 3073, 25739, 13, 18884, 6702, 4548, 533, 29918, 271, 742, 4733, 29889, 11384, 3073, 25739, 13, 9651, 21251, 13, 9651, 3987, 3790, 13, 18884, 525, 16595, 2396, 7700, 29892, 13, 9651, 2981, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
schedule/views.py	AndrewSaltz/Lab_Schedule	0	25454	from django.shortcuts import render import datetime from datetime import date import calendar from schedule.models import Event, period_choices, cart_choice from django.views.generic import UpdateView, TemplateView, ListView from schedule.forms import ReservationForm from django.http import HttpResponseRedirect, HttpResponse from django import forms import json import calendar from django.http import JsonResponse from django.core.serializers.json import DjangoJSONEncoder from django.views.decorators.csrf import csrf_exempt, ensure_csrf_cookie from django.db import IntegrityError from django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin from django.contrib.auth.models import User class Home(LoginRequiredMixin, ListView): model = Event template_name = "schedule/home.html" login_url = '/login/' redirect_field_name = 'redirect_to' def get_context_data(self, kwargs): context = super(Home, self).get_context_data(kwargs) q = self.kwargs['adjuster'] #Change week adjust = int(q) if adjust != 0: adjust =7 # Add to Monday to move up and down weeks today = date.today() Monday = date.today() - datetime.timedelta(days=date.today().weekday()) Friday = Monday + datetime.timedelta(days=4) Monday = Monday + datetime.timedelta(days=adjust) Friday = Friday + datetime.timedelta(days=adjust) # Alter the days if needed. Really non-pythony context['monday'] = Monday context['q'] = q context['this_week'] = Event.objects.filter(day__gte=Monday,day__lte=Friday) context['the_day'] = calendar.day_name[today.weekday()] periods = [] user = self.request.user context['my_reservation'] = Event.objects.filter(day__gte=Monday,day__lte=Friday,teacher=user.id) for p in period_choices: periods.append(p[1]) context['periods'] = periods context['username'] = self.request.user.username return context @ensure_csrf_cookie def reserve(request): if request.is_ajax(): pk = request.POST['pk'] slot = Event.objects.get(pk=pk) user = request.user if slot.is_reserved == True: if user == slot.teacher: slot.is_reserved = False slot.teacher = None slot.save() result = 1 elif user.is_superuser and user != slot.teacher: # Override as admin slot.is_reserved == True slot.teacher = user slot.save() result = 2 else: result = 3 else: slot.is_reserved = True slot.teacher = user slot.save() result = 2 data = {'result': result} return HttpResponse(json.dumps(data, cls=DjangoJSONEncoder)) class Dashboard(PermissionRequiredMixin,TemplateView): template_name = "schedule/dashboard.html" permission_required = 'is_staff' def get_context_data(self, kwargs): context = super(Dashboard, self).get_context_data(*kwargs) periods = [] for p in period_choices: periods.append(p[1]) today = datetime.date.today() tomorrow = today + datetime.timedelta(days=1) cart_list =[] for cart in cart_choice: cart_list.append(cart[0]) context['periods']=periods # Make this a mixin context['this_day'] = Event.objects.filter(day=today) context['next_day'] = Event.objects.filter(day=tomorrow) context['cart_list'] = cart_list context['today'] = today context['tomorrow'] = tomorrow return context @ensure_csrf_cookie def create_week(request): if request.is_ajax(): to_monday = date.today().weekday() start = date.today() - datetime.timedelta(days=to_monday) #Find Monday day = start # Day will change, start will not end = start + datetime.timedelta(days=4) # One week, edit later for flexibility weekend = set([5, 6]) # Python week starts on Monday as 0 dupe_list = [] total = 0 while day <= end: if day.weekday() not in weekend: for period in period_choices: for cart in cart_choice: open = Event(day=day, period=period[0], cart=cart[0]) try: open.save() total+=1 except IntegrityError: dupe = str(open) dupe_list.append(dupe) pass day += datetime.timedelta(days=1) # Adds one day until the current day is past the end day data = {'start': start, 'end': end, 'dupe_list': dupe_list, 'total': total} return HttpResponse(json.dumps(data, cls=DjangoJSONEncoder)) def redirect_root(request): return HttpResponseRedirect('/week/0/') @ensure_csrf_cookie def create_month(request): if request.is_ajax(): month = date.today().month year = date.today().year # last = calendar.monthrange(year, month) # Last day start = date.today().replace(day=1) # Get the first day of the month end = date.today().replace(day=(calendar.monthrange(year, month)[1])) weekend = set([5, 6]) # Python week starts on Monday as 0 dupe_list = [] total = 0 day = start while day <= end: if day.weekday() not in weekend: for period in period_choices: for cart in cart_choice: open = Event(day=day, period=period[0], cart=cart[0]) try: open.save() total+=1 except IntegrityError: dupe = str(open) dupe_list.append(dupe) pass day += datetime.timedelta(days=1) data = {'start': start, 'end': end, 'dupe_list': dupe_list, 'total': total} return HttpResponse(json.dumps(data, cls=DjangoJSONEncoder)) @ensure_csrf_cookie def create_twelve(request): if request.is_ajax(): to_monday = date.today().weekday() start = date.today() - datetime.timedelta(days=to_monday) # Find Monday fake_end = start + datetime.timedelta(days=84) if fake_end.weekday() != 4: end = fake_end - datetime.timedelta(days=(fake_end.weekday() - 4)) else: end = fake_end day = start total = 0 dupe_list = [] weekend = set([5, 6]) # Python week starts on Monday as 0 while day <= end: if day.weekday() not in weekend: for period in period_choices: for cart in cart_choice: open = Event(day=day, period=period[0], cart=cart[0]) try: open.save() total+=1 except IntegrityError: dupe = str(open) dupe_list.append(dupe) pass day += datetime.timedelta(days=1) data = {'start': start, 'end': end, 'dupe_list': dupe_list, 'total': total} return HttpResponse(json.dumps(data, cls=DjangoJSONEncoder)) @ensure_csrf_cookie def delete_all(request): if request.is_ajax(): Event.objects.all().delete() return HttpResponse()	[ 1, 515, 9557, 29889, 12759, 7582, 29879, 1053, 4050, 13, 5215, 12865, 13, 3166, 12865, 1053, 2635, 13, 5215, 17684, 13, 3166, 20410, 29889, 9794, 1053, 6864, 29892, 3785, 29918, 1859, 1575, 29892, 7774, 29918, 16957, 13, 3166, 9557, 29889, 7406, 29889, 19206, 1053, 10318, 1043, 29892, 25663, 1043, 29892, 22184, 13, 3166, 20410, 29889, 9514, 1053, 2538, 20525, 2500, 13, 3166, 9557, 29889, 1124, 1053, 9056, 5103, 24735, 29892, 9056, 5103, 13, 3166, 9557, 1053, 7190, 13, 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MindSPONGE/mindsponge/md/cybertron/cutoff.py	mindspore-ai/mindscience	3	71618	# ============================================================================ # Copyright 2021 The AIMM team at Shenzhen Bay Laboratory & Peking University # # People: <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This code is a part of Cybertron-Code package. # # The Cybertron-Code is open-source software based on the AI-framework: # MindSpore (https://www.mindspore.cn/) # # 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. # ============================================================================ """cybertron cutoff""" import numpy as np import mindspore as ms from mindspore import nn from mindspore import Tensor from mindspore.ops import operations as P from mindspore.ops import functional as F from .units import units __all__ = [ "CosineCutoff", "MollifierCutoff", "HardCutoff", "SmoothCutoff", "GaussianCutoff", "get_cutoff", ] _CUTOFF_ALIAS = dict() def _cutoff_register(aliases): """Return the alias register.""" def alias_reg(cls): name = cls.__name__ name = name.lower() if name not in _CUTOFF_ALIAS: _CUTOFF_ALIAS[name] = cls for alias in aliases: if alias not in _CUTOFF_ALIAS: _CUTOFF_ALIAS[alias] = cls return cls return alias_reg class Cutoff(nn.Cell): """Cutoff""" def __init__(self, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False ): super().__init__() self.name = 'cutoff' self.hyperparam = hyperparam self.r_min = r_min self.cutoff = r_max self.return_mask = return_mask self.reverse = reverse @_cutoff_register('cosine') class CosineCutoff(Cutoff): r"""Class of Behler cosine cutoff. .. math:: f(r) = \begin{cases} 0.5 \times \left[1 + \cos\left(\frac{\pi r}{r_\text{cutoff}}\right)\right] & r < r_\text{cutoff} \\ 0 & r \geqslant r_\text{cutoff} \\ \end{cases} Args: cutoff (float, optional): cutoff radius. """ def __init__(self, r_max=units.length(1, 'nm'), r_min='default', hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_max=r_max, r_min=r_min, hyperparam=None, return_mask=return_mask, reverse=reverse, ) self.name = 'cosine cutoff' self.pi = Tensor(np.pi, ms.float32) self.cos = P.Cos() self.logical_and = P.LogicalAnd() def construct(self, distances, neighbor_mask=None): """Compute cutoff. Args: distances (mindspore.Tensor): values of interatomic distances. Returns: mindspore.Tensor: values of cutoff function. """ # Compute values of cutoff function cuts = 0.5 (self.cos(distances * self.pi / self.cutoff) + 1.0) if self.reverse: cuts = 1.0 - cuts ones = F.ones_like(cuts) cuts = F.select(distances < cuts, cuts, ones) if neighbor_mask is None: mask = distances >= 0 else: mask = neighbor_mask else: mask = distances < self.cutoff if neighbor_mask is not None: mask = self.logical_and(mask, neighbor_mask) # Remove contributions beyond the cutoff radius cutoffs = cuts * mask if self.return_mask: return cutoffs, mask return cutoffs @_cutoff_register('mollifier') class MollifierCutoff(Cutoff): r"""Class for mollifier cutoff scaled to have a value of 1 at :math:`r=0`. .. math:: f(r) = \begin{cases} \exp\left(1 - \frac{1}{1 - \left(\frac{r}{r_\text{cutoff}}\right)^2}\right) & r < r_\text{cutoff} \\ 0 & r \geqslant r_\text{cutoff} \\ \end{cases} Args: cutoff (float, optional): Cutoff radius. eps (float, optional): offset added to distances for numerical stability. """ def __init__(self, r_max=units.length(1, 'nm'), r_min='default', hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_min=r_min, r_max=r_max, hyperparam=hyperparam, return_mask=return_mask, reverse=reverse, ) self.name = "Mollifier cutoff" if hyperparam == 'default': self.eps = units.length(1.0e-8, 'nm') else: self.eps = hyperparam self.exp = P.Exp() self.logical_and = P.LogicalAnd() def construct(self, distances, neighbor_mask=None): """Compute cutoff. Args: distances (mindspore.Tensor): values of interatomic distances. Returns: mindspore.Tensor: values of cutoff function. """ exponent = 1.0 - 1.0 / (1.0 - F.square(distances / self.cutoff)) cutoffs = self.exp(exponent) if self.reverse: cutoffs = 1. - cutoffs ones = F.ones_like(cutoffs) cutoffs = F.select(distances < self.cutoff, cutoffs, ones) if neighbor_mask is None: mask = (distances + self.eps) >= 0 else: mask = neighbor_mask else: mask = (distances + self.eps) < self.cutoff if neighbor_mask is not None: mask = self.logical_and(mask, neighbor_mask) cutoffs = cutoffs * mask return cutoffs, mask @_cutoff_register('hard') class HardCutoff(Cutoff): r"""Class of hard cutoff. .. math:: f(r) = \begin{cases} 1 & r \leqslant r_\text{cutoff} \\ 0 & r > r_\text{cutoff} \\ \end{cases} Args: cutoff (float): cutoff radius. """ def __init__(self, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_min=r_min, r_max=r_max, hyperparam=None, return_mask=return_mask, reverse=reverse, ) self.name = "Hard cutoff" self.logical_and = P.LogicalAnd() def construct(self, distances, neighbor_mask=None): """Compute cutoff. Args: distances (mindspore.Tensor): values of interatomic distances. Returns: mindspore.Tensor: values of cutoff function. """ if self.reverse: mask = distances >= self.cutoff else: mask = distances < self.cutoff if neighbor_mask is not None: self.logical_and(mask, neighbor_mask) if self.return_mask: return F.cast(mask, distances.dtype), mask return F.cast(mask, distances.dtype) @_cutoff_register('smooth') class SmoothCutoff(Cutoff): r"""Class of smooth cutoff by <NAME>. et al: [ref] <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. Texturing & Modeling: A Procedural Approach; <NAME>: 2003 .. math:: r_min < r < r_max: f(r) = 1.0 - 6 * ( r / r_cutoff ) ^ 5 + 15 * ( r / r_cutoff ) ^ 4 - 10 * ( r / r_cutoff ) ^ 3 r >= r_max: f(r) = 0 r <= r_min: f(r) = 1 reverse: r_min < r < r_max: f(r) = 6 * ( r / r_cutoff ) ^ 5 - 15 * ( r / r_cutoff ) ^ 4 + 10 * ( r / r_cutoff ) ^ 3 r >= r_max: f(r) = 1 r <= r_min: f(r) = 0 Args: d_max (float, optional): the maximum distance (cutoff radius). d_min (float, optional): the minimum distance """ def __init__(self, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_min=r_min, r_max=r_max, hyperparam=None, return_mask=return_mask, reverse=reverse, ) if self.r_min >= self.cutoff: raise ValueError( 'dis_min must be smaller than cutoff at SmmothCutoff') self.dis_range = self.cutoff - self.r_min self.pow = P.Pow() self.logical_and = P.LogicalAnd() def construct(self, distance, neighbor_mask=None): """Compute cutoff. Args: distances (mindspore.Tensor or float): values of interatomic distances. Returns: mindspore.Tensor or float: values of cutoff function. """ dd = distance - self.r_min dd = dd / self.dis_range cuts = - 6. * self.pow(dd, 5) \ + 15. * self.pow(dd, 4) \ - 10. * self.pow(dd, 3) if self.reverse: cutoffs = -cuts mask_upper = distance < self.cutoff mask_lower = distance > self.r_min else: cutoffs = 1 + cuts mask_upper = distance > self.r_min mask_lower = distance < self.cutoff if neighbor_mask is not None: mask_lower = self.logical_and(mask_lower, neighbor_mask) zeros = F.zeros_like(distance) ones = F.ones_like(distance) cutoffs = F.select(mask_upper, cutoffs, ones) cutoffs = F.select(mask_lower, cutoffs, zeros) if self.return_mask: return cutoffs, mask_lower return cutoffs @_cutoff_register('gaussian') class GaussianCutoff(Cutoff): r"""Class of hard cutoff. .. math:: f(r) = \begin{cases} 1 & r \leqslant r_\text{cutoff} \\ 0 & r > r_\text{cutoff} \\ \end{cases} Args: cutoff (float): cutoff radius. """ def __init__(self, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_min=r_min, r_max=r_max, hyperparam=hyperparam, return_mask=return_mask, reverse=reverse, ) if hyperparam == 'default': self.sigma = units.length(1, 'nm') else: self.sigma = hyperparam self.sigma2 = self.sigma * self.sigma self.exp = P.Exp() self.logical_and = P.LogicalAnd() def construct(self, distance, neighbor_mask=None): """construct""" dd = distance - self.cutoff dd2 = dd * dd gauss = self.exp(-0.5 * dd2 / self.sigma2) if self.reverse: cuts = gauss ones = F.ones_like(cuts) cuts = F.select(distance < self.cutoff, cuts, ones) if neighbor_mask is None: mask = distance >= 0 else: mask = neighbor_mask else: cuts = 1. - gauss mask = distance < self.cutoff if neighbor_mask is not None: mask = self.logical_and(mask, neighbor_mask) cuts = cuts * mask if self.return_mask: return cuts, mask return cuts def get_cutoff(obj, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False): """get cutoff""" if obj is None or isinstance(obj, Cutoff): return obj if isinstance(obj, str): if obj not in _CUTOFF_ALIAS.keys(): raise ValueError( "The class corresponding to '{}' was not found.".format(obj)) return _CUTOFF_ALIAS[obj.lower()]( r_min=r_min, r_max=r_max, hyperparam=hyperparam, return_mask=return_mask, reverse=reverse, ) raise TypeError("Unsupported Cutoff type '{}'.".format(type(obj)))	[ 1, 396, 1275, 9166, 9166, 9166, 9166, 4936, 1360, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29896, 450, 319, 29902, 7428, 3815, 472, 1383, 4666, 3169, 6211, 16715, 7606, 669, 349, 1416, 292, 3014, 13, 29937, 13, 29937, 11647, 29901, 529, 5813, 10202, 529, 5813, 10202, 529, 5813, 10202, 529, 5813, 10202, 529, 5813, 10202, 13, 29937, 308, 529, 5813, 10202, 529, 5813, 10202, 529, 5813, 10202, 529, 5813, 10202, 529, 5813, 29958, 13, 29937, 13, 29937, 910, 775, 338, 263, 760, 310, 8045, 495, 509, 265, 29899, 3399, 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variation/validators/single_nucleotide_variation_base.py	GenomicMedLab/varlex	0	180505	"""The module for Single Nucleotide Variation Validation.""" from .validator import Validator import logging logger = logging.getLogger('variation') logger.setLevel(logging.DEBUG) class SingleNucleotideVariationBase(Validator): """The Single Nucleotide Variation Validator Base class.""" def silent_mutation_valid_invalid_results(self, classification_tokens, transcripts, classification, results, gene_tokens, normalize_endpoint, mane_data_found, is_identifier) -> None: """Add validation result objects to a list of results for Silent Mutations. :param list classification_tokens: A list of classification Tokens :param list transcripts: A list of transcript accessions :param Classification classification: A classification for a list of tokens :param list results: Stores validation result objects :param list gene_tokens: List of GeneMatchTokens for a classification :param bool normalize_endpoint: `True` if normalize endpoint is being used. `False` otherwise. :param dict mane_data_found: MANE Transcript information found :param bool is_identifier: `True` if identifier is given for exact location. `False` otherwise. """ valid_alleles = list() for s in classification_tokens: for t in transcripts: errors = list() t = self.get_accession(t, classification) allele = None if s.reference_sequence == 'c': cds_start_end = self.uta.get_cds_start_end(t) if not cds_start_end: cds_start = None errors.append( f"Unable to find CDS start for accession : {t}" ) else: cds_start = cds_start_end[0] else: cds_start = None if not errors: allele = self.to_vrs_allele( t, s.position, s.position, s.reference_sequence, s.alt_type, errors, cds_start=cds_start, alt=s.new_nucleotide ) if not errors: sequence = \ self.seqrepo_access.get_sequence(t, s.position) if sequence is None: errors.append('Sequence index error') if not errors: mane = self.mane_transcript.get_mane_transcript( t, s.position, s.position, s.reference_sequence, gene=gene_tokens[0].token if gene_tokens else None, normalize_endpoint=normalize_endpoint ) self.add_mane_data(mane, mane_data_found, s.reference_sequence, s.alt_type, s, gene_tokens) self.add_validation_result( allele, valid_alleles, results, classification, s, t, gene_tokens, errors ) if is_identifier: break self.add_mane_to_validation_results( mane_data_found, valid_alleles, results, classification, gene_tokens ) def check_ref_nucleotide(self, actual_ref_nuc, expected_ref_nuc, position, t, errors): """Assert that ref_nuc matches s.ref_nucleotide.""" if actual_ref_nuc != expected_ref_nuc: errors.append(f'Needed to find {expected_ref_nuc} at' f' position {position} on {t}' f' but found {actual_ref_nuc}') def concise_description(self, transcript, token) -> str: """Return a HGVS description of the identified variation. :param str transcript: Transcript accession :param Token token: Classification token :return: HGVS expression """ prefix = f'{transcript}:{token.reference_sequence}.{token.position}' if token.new_nucleotide == '=': change = "=" else: change = f"{token.ref_nucleotide}>{token.new_nucleotide}" return prefix + change	[ 1, 9995, 1576, 3883, 363, 16740, 405, 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world_of_warships/war_gaming.py	MaT1g3R/YasenBaka	6	78376	<reponame>MaT1g3R/YasenBaka<gh_stars>1-10 from typing import Optional from wowspy import Region, WowsAsync async def __get_id(coro, logger, key): try: resp = await coro except Exception as e: logger.warn(str(e)) else: data = resp.get('data', None) if not data: return return data[0].get(key, None) async def get_player_id(region: Region, wows_api: WowsAsync, logger, search) -> Optional[int]: """ Get player id by search term. :param region: the region. :param wows_api: the WowsAsync instance. :param logger: the logger. :param search: the search query. :return: the player id. """ coro = wows_api.players(region, search, language='en', limit=1) return await __get_id(coro, logger, 'account_id') async def get_clan_id(region: Region, wows_api: WowsAsync, logger, search) -> Optional[int]: """ Get clan id by search term. :param region: the region. :param wows_api: the WowsAsync instance. :param logger: the logger. :param search: the search query. :return: the clan id. """ coro = wows_api.clans(region, search=search, language='en', limit=1) return await __get_id(coro, logger, 'clan_id')	[ 1, 529, 276, 1112, 420, 29958, 21870, 29911, 29896, 29887, 29941, 29934, 29914, 29979, 294, 264, 29933, 8245, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 19229, 1053, 28379, 13, 13, 3166, 281, 340, 1028, 29891, 1053, 11069, 29892, 399, 1242, 8123, 13, 13, 13, 12674, 822, 4770, 657, 29918, 333, 29898, 2616, 29877, 29892, 17927, 29892, 1820, 1125, 13, 1678, 1018, 29901, 13, 4706, 4613, 353, 7272, 1034, 29877, 13, 1678, 5174, 8960, 408, 321, 29901, 13, 4706, 17927, 29889, 25442, 29898, 710, 29898, 29872, 876, 13, 1678, 1683, 29901, 13, 4706, 848, 353, 4613, 29889, 657, 877, 1272, 742, 6213, 29897, 13, 4706, 565, 451, 848, 29901, 13, 9651, 736, 13, 4706, 736, 848, 29961, 29900, 1822, 657, 29898, 1989, 29892, 6213, 29897, 13, 13, 13, 12674, 822, 679, 29918, 9106, 29918, 333, 29898, 12803, 29901, 11069, 29892, 281, 1242, 29918, 2754, 29901, 399, 1242, 8123, 29892, 13, 462, 4706, 17927, 29892, 2740, 29897, 1599, 28379, 29961, 524, 5387, 13, 1678, 9995, 13, 1678, 3617, 4847, 1178, 491, 2740, 1840, 29889, 13, 1678, 584, 3207, 5120, 29901, 278, 5120, 29889, 13, 1678, 584, 3207, 281, 1242, 29918, 2754, 29901, 278, 399, 1242, 8123, 2777, 29889, 13, 1678, 584, 3207, 17927, 29901, 278, 17927, 29889, 13, 1678, 584, 3207, 2740, 29901, 278, 2740, 2346, 29889, 13, 1678, 584, 2457, 29901, 278, 4847, 1178, 29889, 13, 1678, 9995, 13, 1678, 1034, 29877, 353, 281, 1242, 29918, 2754, 29889, 1456, 414, 29898, 12803, 29892, 2740, 29892, 4086, 2433, 264, 742, 4046, 29922, 29896, 29897, 13, 1678, 736, 7272, 4770, 657, 29918, 333, 29898, 2616, 29877, 29892, 17927, 29892, 525, 10149, 29918, 333, 1495, 13, 13, 13, 12674, 822, 679, 29918, 695, 273, 29918, 333, 29898, 12803, 29901, 11069, 29892, 281, 1242, 29918, 2754, 29901, 399, 1242, 8123, 29892, 13, 462, 418, 17927, 29892, 2740, 29897, 1599, 28379, 29961, 524, 5387, 13, 1678, 9995, 13, 1678, 3617, 1067, 273, 1178, 491, 2740, 1840, 29889, 13, 1678, 584, 3207, 5120, 29901, 278, 5120, 29889, 13, 1678, 584, 3207, 281, 1242, 29918, 2754, 29901, 278, 399, 1242, 8123, 2777, 29889, 13, 1678, 584, 3207, 17927, 29901, 278, 17927, 29889, 13, 1678, 584, 3207, 2740, 29901, 278, 2740, 2346, 29889, 13, 1678, 584, 2457, 29901, 278, 1067, 273, 1178, 29889, 13, 1678, 9995, 13, 1678, 1034, 29877, 353, 281, 1242, 29918, 2754, 29889, 695, 550, 29898, 12803, 29892, 2740, 29922, 4478, 29892, 4086, 2433, 264, 742, 4046, 29922, 29896, 29897, 13, 1678, 736, 7272, 4770, 657, 29918, 333, 29898, 2616, 29877, 29892, 17927, 29892, 525, 695, 273, 29918, 333, 1495, 13, 2 ]
tests/test_pvaapi.py	sasaki77/pvarpc2web	0	178505	<reponame>sasaki77/pvarpc2web from .context import pvarpc2web from .context import config from .context import pvaapi class PvaRpcTimeoutConfig(config.DefaultConfig): PVA_RPC_TIMEOUT = 3 def test_set_timeout(): app = pvarpc2web.create_app(PvaRpcTimeoutConfig) assert pvaapi.timeout == 3	[ 1, 529, 276, 1112, 420, 29958, 29879, 294, 9940, 29955, 29955, 29914, 29886, 1707, 6739, 29906, 2676, 13, 3166, 869, 4703, 1053, 282, 1707, 6739, 29906, 2676, 13, 3166, 869, 4703, 1053, 2295, 13, 3166, 869, 4703, 1053, 282, 1564, 2754, 13, 13, 13, 1990, 349, 1564, 29934, 6739, 10851, 3991, 29898, 2917, 29889, 4592, 3991, 1125, 13, 1678, 349, 20449, 29918, 29934, 9026, 29918, 15307, 12015, 353, 29871, 29941, 13, 13, 13, 1753, 1243, 29918, 842, 29918, 15619, 7295, 13, 1678, 623, 353, 282, 1707, 6739, 29906, 2676, 29889, 3258, 29918, 932, 29898, 29925, 1564, 29934, 6739, 10851, 3991, 29897, 13, 1678, 4974, 282, 1564, 2754, 29889, 15619, 1275, 29871, 29941, 13, 2 ]
kungfucms/apps/account/models.py	kungfusoft/kungfucms	0	75410	<gh_stars>0 from django.core.mail import send_mail from django.utils.translation import ugettext as _ from django.utils import timezone from django.db import models from django.contrib.auth.models import UserManager, PermissionsMixin from django.contrib.auth.base_user import AbstractBaseUser from django.contrib.auth.validators import UnicodeUsernameValidator from kungfucms.apps.system.models import BaseModel class Manager(UserManager): def _create_user(self, username, password, extra_fields): username = self.model.normalize_username(username) user = self.model(username=username, extra_fields) user.set_password(password) user.save(using=self._db) return user def create_user(self, username, password=None, extra_fields): extra_fields.setdefault('is_staff', False) extra_fields.setdefault('is_superuser', False) return self._create_user(username, password, extra_fields) def create_staff(self, username, password, extra_fields): extra_fields.setdefault('is_staff', True) extra_fields.setdefault('is_superuser', False) return self._create_user(username, password, extra_fields) def create_superuser(self, username, password=None, extra_fields): extra_fields.setdefault('is_staff', True) extra_fields.setdefault('is_superuser', True) return self._create_user(username, password, extra_fields) class AbstractUser(AbstractBaseUser, PermissionsMixin): username_validator = UnicodeUsernameValidator() username = models.CharField( _('username'), max_length=128, unique=True, help_text=_('Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.'), validators=[username_validator], error_messages={ 'unique': _("A user with that username already exists."), }, ) is_staff = models.BooleanField( _('staff status'), default=False, help_text=_('Designates whether the user can log into this admin site.'), ) is_active = models.BooleanField( _('active'), default=True, help_text=_( 'Designates whether this user should be treated as active. ' 'Unselect this instead of deleting accounts.' ), ) date_joined = models.DateTimeField(_('date joined'), default=timezone.now) objects = UserManager() USERNAME_FIELD = 'username' class Meta: verbose_name = _('user') verbose_name_plural = _('users') abstract = True def clean(self): super().clean() def get_full_name(self): full_name = '%s %s' % (self.first_name, self.last_name) return full_name.strip() def get_short_name(self): return self.first_name class User(AbstractUser, BaseModel): username = models.CharField(max_length=128, unique=True, verbose_name=_('Username')) USERNAME_FIELD = 'username' objects = Manager() def get_username_field(self): return self.USERNAME_FIELD @classmethod def safe_get(cls, kwargs): try: obj = cls.objects.get(kwargs) except cls.DoesNotExist: obj = None return obj class Meta(AbstractUser.Meta): swappable = 'AUTH_USER_MODEL' verbose_name = _('User') verbose_name_plural = _('Users') class Profile(BaseModel): user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='profiles', related_query_name='profile') name = models.CharField(max_length=128, db_index=True, verbose_name=_('Name')) value = models.CharField(max_length=255, blank=True, default='', verbose_name=_('Value')) class Meta: ordering = ['id'] verbose_name = _('Profile') verbose_name_plural = _('Profiles') class OauthLoginProvider(BaseModel): provider = models.CharField(max_length=128, db_index=True, verbose_name=_('Provider Name')) token = models.CharField(max_length=128, db_index=True, verbose_name=_('Provider Token')) user = models.ForeignKey(User, on_delete=models.CASCADE, related_query_name='oauth_token', related_name='oauth_tokens', verbose_name=_('User')) class Meta: verbose_name = _('Oauth Login Provider') verbose_name_plural = _('Oauth Login Providers') unique_together = ('provider', 'token', 'user') indexes = [models.Index(fields=('user', 'provider'))]	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 9557, 29889, 3221, 29889, 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20249, 8785, 13, 13, 1678, 3148, 1001, 5813, 29918, 3738, 27286, 353, 525, 6786, 29915, 13, 13, 1678, 3618, 353, 15629, 580, 13, 13, 1678, 822, 679, 29918, 6786, 29918, 2671, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 11889, 5813, 29918, 3738, 27286, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 9109, 29918, 657, 29898, 25932, 29892, 3579, 19290, 1125, 13, 4706, 1018, 29901, 13, 9651, 5446, 353, 1067, 29879, 29889, 12650, 29889, 657, 29898, 1068, 19290, 29897, 13, 4706, 5174, 1067, 29879, 29889, 25125, 3664, 1252, 391, 29901, 13, 9651, 5446, 353, 6213, 13, 4706, 736, 5446, 13, 13, 1678, 770, 20553, 29898, 9118, 2659, 29889, 19346, 1125, 13, 4706, 2381, 932, 519, 353, 525, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 29915, 13, 4706, 26952, 29918, 978, 353, 903, 877, 2659, 1495, 13, 4706, 26952, 29918, 978, 29918, 572, 3631, 353, 903, 877, 5959, 1495, 13, 13, 13, 1990, 20802, 29898, 5160, 3195, 1125, 13, 1678, 1404, 353, 4733, 29889, 27755, 2558, 29898, 2659, 29892, 13, 462, 632, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 29892, 13, 462, 632, 4475, 29918, 978, 2433, 771, 5325, 742, 13, 462, 632, 4475, 29918, 1972, 29918, 978, 2433, 10185, 1495, 13, 13, 1678, 1024, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29906, 29947, 29892, 13, 462, 9651, 4833, 29918, 2248, 29922, 5574, 29892, 13, 462, 9651, 26952, 29918, 978, 29922, 29918, 877, 1170, 8785, 13, 13, 1678, 995, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29945, 29892, 13, 462, 632, 9654, 29922, 5574, 29892, 13, 462, 632, 2322, 2433, 742, 13, 462, 632, 26952, 29918, 978, 29922, 29918, 877, 1917, 8785, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 20520, 353, 6024, 333, 2033, 13, 4706, 26952, 29918, 978, 353, 903, 877, 13909, 1495, 13, 4706, 26952, 29918, 978, 29918, 572, 3631, 353, 903, 877, 1184, 5325, 1495, 13, 13, 13, 1990, 438, 5150, 11049, 6980, 29898, 5160, 3195, 1125, 13, 1678, 13113, 353, 4733, 29889, 27890, 29898, 3317, 29918, 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game/views/tests/test_monthly_scoreboard.py	dimadk24/english-fight-api	0	115215	from datetime import timedelta from django.utils import timezone from rest_framework.response import Response from game.models import AppUser, Game url = "/api/monthly_scoreboard" def get_response(api_client) -> Response: return api_client.get(url) def test_when_no_users(api_client): AppUser.objects.all().delete() response = get_response(api_client) assert response.status_code == 200 assert response.json() == [] def test_when_3_users_and_no_games(api_client): users = [ AppUser( username="2", vk_id=2, score=20, first_name="Cat", last_name="Leo", photo_url="2.png", ), AppUser( username="3", vk_id=3, score=1, first_name="Dog", last_name="Cooper", photo_url="3.png", ), AppUser( username="4", vk_id=4, score=12, first_name="Puppet", last_name="Bella", photo_url="4.png", ), ] AppUser.objects.bulk_create(users) response = get_response(api_client) expected = [] assert response.json() == expected def test_when_3_users(api_client): users = [ AppUser( username="2", vk_id=2, score=20, first_name="Cat", last_name="Leo", photo_url="2.png", ), AppUser( username="3", vk_id=3, score=1, first_name="Dog", last_name="Cooper", photo_url="3.png", ), AppUser( username="4", vk_id=4, score=12, first_name="Puppet", last_name="Bella", photo_url="4.png", ), AppUser( username="5", vk_id=5, score=12, first_name="Fox", last_name="Cozy", photo_url="5.png", ), ] AppUser.objects.bulk_create(users) games = [ Game( player=AppUser.objects.get(vk_id=2), points=10, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=2), points=20, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=3), points=40, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=2), points=30, created_at=timezone.now() - timedelta(days=32), ), Game( player=AppUser.objects.get(vk_id=4), points=2, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=5), points=1, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=5), points=1, created_at=timezone.now(), ), ] Game.objects.bulk_create(games) response = get_response(api_client) expected = [ { "first_name": "Dog", "id": AppUser.objects.get(vk_id=3).pk, "last_name": "Cooper", "photo_url": "3.png", "score": 40, }, { "first_name": "Cat", "id": AppUser.objects.get(vk_id=2).pk, "last_name": "Leo", "photo_url": "2.png", "score": 30, }, { "first_name": "Puppet", "id": AppUser.objects.get(vk_id=4).pk, "last_name": "Bella", "photo_url": "4.png", "score": 2, }, { "first_name": "Fox", "id": AppUser.objects.get(vk_id=5).pk, "last_name": "Cozy", "photo_url": "5.png", "score": 2, }, ] assert response.json() == expected	[ 1, 515, 12865, 1053, 5335, 287, 2554, 13, 13, 3166, 9557, 29889, 13239, 1053, 29431, 13, 3166, 1791, 29918, 4468, 29889, 5327, 1053, 13291, 13, 13, 3166, 3748, 29889, 9794, 1053, 2401, 2659, 29892, 8448, 13, 13, 2271, 353, 5591, 2754, 29914, 10874, 368, 29918, 13628, 3377, 29908, 13, 13, 13, 1753, 679, 29918, 5327, 29898, 2754, 29918, 4645, 29897, 1599, 13291, 29901, 13, 1678, 736, 7882, 29918, 4645, 29889, 657, 29898, 2271, 29897, 13, 13, 13, 1753, 1243, 29918, 8256, 29918, 1217, 29918, 7193, 29898, 2754, 29918, 4645, 1125, 13, 1678, 2401, 2659, 29889, 12650, 29889, 497, 2141, 8143, 580, 13, 1678, 2933, 353, 679, 29918, 5327, 29898, 2754, 29918, 4645, 29897, 13, 1678, 4974, 2933, 29889, 4882, 29918, 401, 1275, 29871, 29906, 29900, 29900, 13, 1678, 4974, 2933, 29889, 3126, 580, 1275, 5159, 13, 13, 13, 1753, 1243, 29918, 8256, 29918, 29941, 29918, 7193, 29918, 392, 29918, 1217, 29918, 29887, 1280, 29898, 2754, 29918, 4645, 1125, 13, 1678, 4160, 353, 518, 13, 4706, 2401, 2659, 29898, 13, 9651, 8952, 543, 29906, 613, 13, 9651, 325, 29895, 29918, 333, 29922, 29906, 29892, 13, 9651, 8158, 29922, 29906, 29900, 29892, 13, 9651, 937, 29918, 978, 543, 9694, 613, 13, 9651, 1833, 29918, 978, 543, 3226, 29877, 613, 13, 9651, 15373, 29918, 2271, 543, 29906, 29889, 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29918, 29941, 29918, 7193, 29898, 2754, 29918, 4645, 1125, 13, 1678, 4160, 353, 518, 13, 4706, 2401, 2659, 29898, 13, 9651, 8952, 543, 29906, 613, 13, 9651, 325, 29895, 29918, 333, 29922, 29906, 29892, 13, 9651, 8158, 29922, 29906, 29900, 29892, 13, 9651, 937, 29918, 978, 543, 9694, 613, 13, 9651, 1833, 29918, 978, 543, 3226, 29877, 613, 13, 9651, 15373, 29918, 2271, 543, 29906, 29889, 2732, 613, 13, 4706, 10353, 13, 4706, 2401, 2659, 29898, 13, 9651, 8952, 543, 29941, 613, 13, 9651, 325, 29895, 29918, 333, 29922, 29941, 29892, 13, 9651, 8158, 29922, 29896, 29892, 13, 9651, 937, 29918, 978, 543, 29928, 468, 613, 13, 9651, 1833, 29918, 978, 543, 7967, 3372, 613, 13, 9651, 15373, 29918, 2271, 543, 29941, 29889, 2732, 613, 13, 4706, 10353, 13, 4706, 2401, 2659, 29898, 13, 9651, 8952, 543, 29946, 613, 13, 9651, 325, 29895, 29918, 333, 29922, 29946, 29892, 13, 9651, 8158, 29922, 29896, 29906, 29892, 13, 9651, 937, 29918, 978, 543, 29925, 29884, 7988, 613, 13, 9651, 1833, 29918, 978, 543, 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main.py	NMme/CrusaderAIManager	0	84678	import kivy kivy.require('1.0.6') # replace with your current kivy version ! from kivy.app import App from kivy.uix.label import Label import os import shutil import json import codecs class MyApp(App): def build(self): self.root = "/home/Games/Crusader-HDUP/CrusaderAIManager" self.ai_shorts = {'Caliph': 'ca', 'Frederick': 'fr', 'Pig': 'pg', 'Phillip': 'ph', 'Richard': 'ri', 'Rat': 'rt', 'Saladin': 'sa', 'Sheriff': 'sh', 'Snake': 'sn', 'Sultan': 'su', 'Wolf': 'wf', 'Abbot': 'ab', 'Marshall': 'ma', 'Nizar': 'ni', 'Emir': 'em', 'Wazir': 'wa' } self.characters = ['Caliph', 'Frederick', 'Pig', 'Phillip', 'Richard', 'Rat', 'Saladin', 'Sheriff', 'Snake', 'Sultan', 'Wolf', 'Abbot', 'Marshall', 'Nizar', 'Emir', 'Wazir'] return Label(text='Hello world') def back_up_vanilla(self, sc_path: str): # backup all the vanilla stronghold files # create new folder vanilla_folder = os.path.join(self.root, 'vanilla') os.mkdir(vanilla_folder) os.mkdir(os.path.join(vanilla_folder, '_fx_speech_')) os.mkdir(os.path.join(vanilla_folder, '_binks_')) os.mkdir(os.path.join(vanilla_folder, '_resources_')) # copy all the original files here #shutil.copy2() def create_assets_config_file(): # open speech file with open('char_speech.json', 'r') as f: cs = json.load(f) # open binks file with open('char_binks.json', 'r') as f: bi = json.load(f) vanilla_root_dir = "/data/vanilla/speech" out_file = dict() # iterate over all characters for char in cs.keys(): char_assets = dict() speech_files = dict() bink_files = dict() special_char = False char_assets['SpecialCharacter'] = 0 # check what kind of character we have if cs[char]['num_actions'] == 22: special_char = True char_assets['SpecialCharacter'] = 1 # add all speech actions for item in cs[char]['actions'].items(): if item[1] > 0: if special_char and 'player' in item[0]: file_keys = [cs[char]['prefix'] + '_' + item[0]] else: file_keys = [cs[char]['prefix'] + '_' + item[0] + '_' + str(i).zfill(2) for i in range(1, item[1] + 1)] for f in file_keys: speech_files[f] = os.path.join('vanilla', 'speech', (f + '.wav')) char_assets['Speech'] = speech_files # add special announcements for special characters if special_char: announce_files = {char + '_Attack': os.path.join('vanilla', 'speech', (char + '_Attack.wav')), char + '_Defeat': os.path.join('vanilla', 'speech', (char + '_Defeat.wav'))} char_assets['Announcements'] = announce_files # add all bink files for item in bi[char]['actions'].items(): if item[1] > 0: if bi[char]['numerate_files']: file_keys = [bi[char]['prefix'] + '_' + item[0] + str(i) for i in range(1, item[1] + 1)] else: file_keys = [bi[char]['prefix'] + '_' + item[0]] for f in file_keys: bink_files[f] = os.path.join('vanilla', 'speech', (f + '.bik')) char_assets['Binks'] = bink_files out_file[char] = char_assets return out_file def test_vanilla_replace(replace_char: str = 'Wazir'): # some paths vanilla_dir = "data/mod_base/vanilla" # load robin hood with open('data/robin_hood_mod/robinhood.json', 'r') as f: rh = json.load(f, strict=False) # load vanilla mode with open('data/mod_base/vanilla/vanilla.json', 'r') as f: vanilla = json.load(f, strict=False) characters = ['Caliph', 'Frederick', 'Pig', 'Phillip', 'Richard', 'Rat', 'Saladin', 'Sheriff', 'Snake', 'Sultan', 'Wolf', 'Abbot', 'Marshall', 'Nizar', 'Emir', 'Wazir'] if replace_char not in characters: raise Exception('Invalid standard character') # replace aic # load vanilla aic with open(os.path.join(vanilla_dir, vanilla['AIC']), 'r') as f: aic = json.load(f, strict=False) # get index idx = [i for i in range(len(aic['AICharacters'])) if aic['AICharacters'][i]['Name'] == replace_char][0] # add Custom Name aic['AICharacters'][idx]['CustomName'] = rh['AIC']['CustomName'] aic['AICharacters'][idx]['Personality'] = rh['AIC']['Personality'] # output new AIC with open('data/output/my_aic.json', 'w') as f: json.dump(aic, f) # replace troops # load vanilla troops with codecs.open(os.path.join(vanilla_dir, vanilla['Troops']), 'r', 'utf-8-sig') as f: troops = json.load(f, strict=False) if 'Troops' in rh.keys(): # get index idx = [i for i in range(1, 17) if troops[str(i)]['Name'] == replace_char][0] # add custom troops configuration troops[str(idx)]['Lord'] = rh['Troops']['Lord'] troops[str(idx)]['normal'] = rh['Troops']['normal'] troops[str(idx)]['crusader'] = rh['Troops']['crusader'] troops[str(idx)]['deathmatch'] = rh['Troops']['deathmatch'] # output new Troops file with open('data/output/my_troops.json', 'w') as f: json.dump(troops, f) # set new AVI files with open(os.path.join(vanilla_dir, vanilla['AIV']), 'r') as f: aiv = json.load(f, strict=False) if 'AIV' in rh.keys(): for i, aiv_files in enumerate(aiv[replace_char].keys()): if rh['AIV'][str(i+1)]: aiv[replace_char][aiv_files] = os.path.join(rh['AIV']['base_dir'], rh['AIV'][str(i+1)]) # output new AIV file with open('data/output/my_aiv.json', 'w') as f: json.dump(aiv, f) if __name__ == 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util/mccLog.py	ccchooko/webControlClient	0	11661	<gh_stars>0 #--coding:utf8-- import logging from datetime import datetime class mccLog(object): def __init__(self): logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S', filename= datetime.now().strftime("%Y%m%d%H%M%S") + '.log', filemode='a') def mccWriteLog(self, logContent): logging.info(logContent) def mccError(self, errorContent): logging.error(errorContent)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 29899, 29930, 29899, 29883, 3689, 29901, 9420, 29947, 29899, 29930, 29899, 13, 5215, 12183, 13, 3166, 12865, 1053, 12865, 13, 13, 1990, 286, 617, 3403, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 12183, 29889, 16121, 3991, 29898, 5563, 29922, 21027, 29889, 18525, 29892, 13, 18884, 3402, 2433, 29995, 29898, 294, 312, 603, 29897, 29879, 1273, 29898, 5563, 978, 29897, 29879, 1273, 29898, 4906, 29897, 29879, 742, 13, 18884, 2635, 23479, 2433, 29995, 29979, 19222, 29885, 19222, 29881, 1273, 29950, 16664, 29924, 16664, 29903, 742, 13, 18884, 10422, 29922, 12865, 29889, 3707, 2141, 710, 615, 603, 11702, 29979, 29995, 29885, 29995, 29881, 29995, 29950, 29995, 29924, 29995, 29903, 1159, 718, 15300, 1188, 742, 13, 18884, 934, 8513, 2433, 29874, 1495, 13, 268, 13, 1678, 822, 286, 617, 6113, 3403, 29898, 1311, 29892, 1480, 3916, 1125, 13, 4706, 12183, 29889, 3888, 29898, 1188, 3916, 29897, 13, 13, 1678, 822, 286, 617, 2392, 29898, 1311, 29892, 1059, 3916, 1125, 13, 4706, 12183, 29889, 2704, 29898, 2704, 3916, 29897, 13, 2 ]
reo/migrations/0112_auto_20210713_0037.py	NREL/REopt_API	7	30123	# Generated by Django 3.1.12 on 2021-07-13 00:37 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reo', '0111_auto_20210708_2144'), ] operations = [ migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_from_elec_grid_purchase', new_name='year_one_CO2_emissions_from_elec_grid_purchase', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_from_elec_grid_purchase_bau', new_name='year_one_CO2_emissions_from_elec_grid_purchase_bau', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_from_fuelburn', new_name='year_one_CO2_emissions_from_fuelburn', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_from_fuelburn_bau', new_name='year_one_CO2_emissions_from_fuelburn_bau', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_offset_from_elec_exports', new_name='year_one_CO2_emissions_offset_from_elec_exports', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_offset_from_elec_exports_bau', new_name='year_one_CO2_emissions_offset_from_elec_exports_bau', ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29941, 29889, 29896, 29889, 29896, 29906, 373, 29871, 29906, 29900, 29906, 29896, 29899, 29900, 29955, 29899, 29896, 29941, 29871, 29900, 29900, 29901, 29941, 29955, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 276, 29877, 742, 525, 29900, 29896, 29896, 29896, 29918, 6921, 29918, 29906, 29900, 29906, 29896, 29900, 29955, 29900, 29947, 29918, 29906, 29896, 29946, 29946, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 29934, 3871, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29879, 667, 27224, 742, 13, 9651, 2030, 29918, 978, 2433, 6360, 29918, 650, 29918, 331, 6847, 29918, 3166, 29918, 6146, 29883, 29918, 7720, 29918, 29886, 27574, 742, 13, 9651, 716, 29918, 978, 2433, 6360, 29918, 650, 29918, 3217, 29906, 29918, 331, 6847, 29918, 3166, 29918, 6146, 29883, 29918, 7720, 29918, 29886, 27574, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 29934, 3871, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29879, 667, 27224, 742, 13, 9651, 2030, 29918, 978, 2433, 6360, 29918, 650, 29918, 331, 6847, 29918, 3166, 29918, 6146, 29883, 29918, 7720, 29918, 29886, 27574, 29918, 9738, 742, 13, 9651, 716, 29918, 978, 2433, 6360, 29918, 650, 29918, 3217, 29906, 29918, 331, 6847, 29918, 3166, 29918, 6146, 29883, 29918, 7720, 29918, 29886, 27574, 29918, 9738, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 29934, 3871, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29879, 667, 27224, 742, 13, 9651, 2030, 29918, 978, 2433, 6360, 29918, 650, 29918, 331, 6847, 29918, 3166, 29918, 29888, 2491, 18712, 742, 13, 9651, 716, 29918, 978, 2433, 6360, 29918, 650, 29918, 3217, 29906, 29918, 331, 6847, 29918, 3166, 29918, 29888, 2491, 18712, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 29934, 3871, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29879, 667, 27224, 742, 13, 9651, 2030, 29918, 978, 2433, 6360, 29918, 650, 29918, 331, 6847, 29918, 3166, 29918, 29888, 2491, 18712, 29918, 9738, 742, 13, 9651, 716, 29918, 978, 2433, 6360, 29918, 650, 29918, 3217, 29906, 29918, 331, 6847, 29918, 3166, 29918, 29888, 2491, 18712, 29918, 9738, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 29934, 3871, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29879, 667, 27224, 742, 13, 9651, 2030, 29918, 978, 2433, 6360, 29918, 650, 29918, 331, 6847, 29918, 10289, 29918, 3166, 29918, 6146, 29883, 29918, 26500, 742, 13, 9651, 716, 29918, 978, 2433, 6360, 29918, 650, 29918, 3217, 29906, 29918, 331, 6847, 29918, 10289, 29918, 3166, 29918, 6146, 29883, 29918, 26500, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 29934, 3871, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 29879, 667, 27224, 742, 13, 9651, 2030, 29918, 978, 2433, 6360, 29918, 650, 29918, 331, 6847, 29918, 10289, 29918, 3166, 29918, 6146, 29883, 29918, 26500, 29918, 9738, 742, 13, 9651, 716, 29918, 978, 2433, 6360, 29918, 650, 29918, 3217, 29906, 29918, 331, 6847, 29918, 10289, 29918, 3166, 29918, 6146, 29883, 29918, 26500, 29918, 9738, 742, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
PycharmProjects/PythonExercicios/ex045.py	RodrigoMASRamos/Projects.py	0	38648	# Exercício Python #045 - GAME: <NAME> e Tesoura # # Crie um programa que faça o computador jogar JOKENPÔ com você. # Aprenda a arrumar as cores nas respostas! from random import choice from random import randint # Maneira utilizada na resolução deste exercício from time import sleep print('\033[1;31mATENÇÃO! ESTE É UM JOGO ALTAMENTE PERIGOSO ONDE NÃO HÁ CHANCES DE VITÓRIA PARA VOCÊ!\033[m') Uc = input('\033[0;30mMe diga, \033[1;34mó grande jogador, \033[0;30mvocê escolhe \033[1;35mPEDRA, \033[1;31mPAPEL, ' '\033[0;30mou \033[1;36mTESOURA? ').strip().upper() PC = ['\033[1;35mPEDRA\033[m', '\033[1;31mPAPEL\033[m', '\033[1;36mTESOURA\033[m'] PCc = choice(PC) sleep(0.5) print('JO') sleep(1) print('KEN') sleep(1) print('PO!') if PCc == 'PEDRA' and Uc == 'TESOURA' or PCc == 'TESOURA' and Uc == 'PAPEL' or PCc == 'PAPEL' and Uc == 'PEDRA': print(f'\033[1;31mHAHAHA! Eu venci! \033[0;30mEu escolhi \033[m{PCc} \033[0;30me você \033[m{Uc}\033[0;30m!') elif PCc == Uc: print(f'\033[1;33mEMPATE! Vamos jogar novamente! Eu escolhi \033[m{PCc} \033[0;30me você \033[m{Uc}') else: print(f'\033[0;34mT-T Infelizmente,\033[1;32mvocê venceu... \033[0;30mEu escolhi \033[m{PCc}, \033[0;30me você ' f'escolheu \033[m{Uc}\033[0;30m...\033[m')	[ 1, 396, 1222, 6269, 24394, 5132, 396, 29900, 29946, 29945, 448, 402, 25797, 29901, 529, 5813, 29958, 321, 323, 267, 283, 336, 13, 29937, 13, 29937, 315, 2546, 1922, 16914, 712, 2258, 4277, 288, 2912, 3136, 16812, 279, 435, 8949, 1430, 29925, 30535, 419, 7931, 30037, 29889, 13, 13, 29937, 319, 558, 8395, 263, 3948, 24540, 408, 28337, 8281, 620, 2490, 294, 29991, 13, 13, 3166, 4036, 1053, 7348, 13, 3166, 4036, 1053, 20088, 524, 29871, 396, 341, 1662, 3055, 11824, 1114, 1055, 3770, 29884, 2340, 2731, 29872, 11382, 24394, 13, 3166, 931, 1053, 8709, 13, 13, 2158, 28909, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29896, 29885, 1299, 1430, 30219, 30179, 29949, 29991, 382, 1254, 29923, 3067, 501, 29924, 435, 29949, 17080, 319, 5850, 5194, 3919, 29923, 349, 1001, 6259, 3267, 29949, 6732, 2287, 405, 30179, 29949, 379, 30102, 5868, 2190, 27266, 5012, 478, 1806, 30178, 3960, 29909, 349, 1718, 29909, 478, 20166, 30403, 9903, 29900, 29941, 29941, 29961, 29885, 1495, 13, 29965, 29883, 353, 1881, 28909, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 29885, 6816, 270, 4324, 29892, 320, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29946, 29885, 29980, 6815, 16812, 3136, 29892, 320, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 29324, 542, 30037, 25224, 354, 320, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29945, 29885, 29925, 3352, 4717, 29892, 320, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29896, 29885, 29925, 3301, 6670, 29892, 525, 13, 965, 11297, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 29885, 283, 320, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29953, 29885, 29911, 2890, 22970, 29909, 29973, 525, 467, 17010, 2141, 21064, 580, 13, 13, 9026, 353, 6024, 29905, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29945, 29885, 29925, 3352, 4717, 29905, 29900, 29941, 29941, 29961, 29885, 742, 11297, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29896, 29885, 29925, 3301, 6670, 29905, 29900, 29941, 29941, 29961, 29885, 742, 11297, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29953, 29885, 29911, 2890, 22970, 29909, 29905, 29900, 29941, 29941, 29961, 29885, 2033, 13, 9026, 29883, 353, 7348, 29898, 9026, 29897, 13, 13, 17059, 29898, 29900, 29889, 29945, 29897, 13, 2158, 877, 29967, 29949, 1495, 13, 13, 17059, 29898, 29896, 29897, 13, 2158, 877, 29968, 1430, 1495, 13, 13, 17059, 29898, 29896, 29897, 13, 2158, 877, 13152, 29991, 1495, 13, 13, 361, 9609, 29883, 1275, 525, 29925, 3352, 4717, 29915, 322, 501, 29883, 1275, 525, 29911, 2890, 22970, 29909, 29915, 470, 9609, 29883, 1275, 525, 29911, 2890, 22970, 29909, 29915, 322, 501, 29883, 1275, 525, 29925, 3301, 6670, 29915, 470, 9609, 29883, 1275, 525, 29925, 3301, 6670, 29915, 322, 501, 29883, 1275, 525, 29925, 3352, 4717, 2396, 13, 1678, 1596, 29898, 29888, 12764, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29896, 29885, 15715, 15715, 15715, 29991, 16430, 6003, 455, 29991, 320, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 29885, 29923, 29884, 25224, 2918, 320, 29900, 29941, 29941, 29961, 29885, 29912, 9026, 29883, 29913, 320, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 1004, 7931, 30037, 320, 29900, 29941, 29941, 29961, 29885, 29912, 29965, 29883, 1012, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 29885, 29991, 1495, 13, 23681, 9609, 29883, 1275, 501, 29883, 29901, 13, 1678, 1596, 29898, 29888, 12764, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29941, 29885, 29923, 3580, 3040, 29991, 478, 14054, 16812, 279, 2420, 2503, 29991, 16430, 25224, 2918, 320, 29900, 29941, 29941, 29961, 29885, 29912, 9026, 29883, 29913, 320, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 1004, 7931, 30037, 320, 29900, 29941, 29941, 29961, 29885, 29912, 29965, 29883, 29913, 1495, 13, 2870, 29901, 13, 1678, 1596, 29898, 29888, 12764, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29946, 29885, 29911, 29899, 29911, 9969, 295, 466, 2689, 2053, 29900, 29941, 29941, 29961, 29896, 29936, 29941, 29906, 29324, 542, 30037, 325, 663, 29884, 856, 320, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 29885, 29923, 29884, 25224, 2918, 320, 29900, 29941, 29941, 29961, 29885, 29912, 9026, 29883, 1118, 320, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 1004, 7931, 30037, 525, 13, 3986, 285, 29915, 267, 1054, 354, 29884, 320, 29900, 29941, 29941, 29961, 29885, 29912, 29965, 29883, 1012, 29900, 29941, 29941, 29961, 29900, 29936, 29941, 29900, 29885, 856, 29905, 29900, 29941, 29941, 29961, 29885, 1495, 2 ]
rqalpha/mod/rqalpha_mod_fxdayu_source/data_source/mongo.py	quantModel/Rqalpha-myquant-learning	44	115690	# encoding: utf-8 from datetime import date, datetime, time import motor.motor_asyncio import numpy as np import pandas as pd from dateutil.relativedelta import relativedelta from rqalpha.const import INSTRUMENT_TYPE from rqalpha.model.instrument import Instrument from rqalpha.utils.datetime_func import convert_date_to_int from rqalpha.utils.py2 import lru_cache from rqalpha.mod.rqalpha_mod_fxdayu_source.data_source.common import CacheMixin from rqalpha.mod.rqalpha_mod_fxdayu_source.data_source.common.minite import MiniteBarDataSourceMixin from rqalpha.mod.rqalpha_mod_fxdayu_source.data_source.common.odd import OddFrequencyBaseDataSource from rqalpha.mod.rqalpha_mod_fxdayu_source.utils import Singleton from rqalpha.mod.rqalpha_mod_fxdayu_source.utils.asyncio import get_asyncio_event_loop from rqalpha.mod.rqalpha_mod_fxdayu_source.utils.converter import DataFrameConverter INSTRUMENT_TYPE_MAP = { INSTRUMENT_TYPE.CS: "stock", INSTRUMENT_TYPE.INDX: "stock", } class NoneDataError(BaseException): pass class MongoDataSource(OddFrequencyBaseDataSource, MiniteBarDataSourceMixin): __metaclass__ = Singleton def __init__(self, path, mongo_url): super(MongoDataSource, self).__init__(path) from rqalpha.mod.rqalpha_mod_fxdayu_source.share.mongo_handler import MongoHandler self._handler = MongoHandler(mongo_url) self._client = motor.motor_asyncio.AsyncIOMotorClient(mongo_url) self._db_map = self._get_frequency_db_map() def _get_frequency_db_map(self): map_ = self._handler.client.get_database("meta").get_collection("db_map").find() dct = {item["type"]: item["map"] for item in map_} return dct def _get_db(self, instrument, frequency): try: if isinstance(instrument, Instrument): instrument_type = instrument.enum_type else: instrument_type = instrument type_ = INSTRUMENT_TYPE_MAP[instrument_type] return self._db_map[type_][frequency] except KeyError: message = instrument.order_book_id if isinstance(instrument, Instrument) else instrument raise NoneDataError("MongoDB 中没有品种%s的%s数据" % (message, frequency)) async def _do_get_bars(self, db, collection, filters, projection, fill=np.NaN): dct = {} l = 0 async for doc in self._client[db][collection].find(filters, projection): _l = doc.pop('_l') l += _l for key, values in doc.items(): if isinstance(values, list) and (len(values) == _l): dct.setdefault(key, []).extend(values) for values in dct.values(): if len(values) != l: values.extend([fill] * l) df = pd.DataFrame(dct) if df.size: return df.sort_values("datetime") else: return None def _get_bars_in_days(self, instrument, frequency, params): s_date = params[0]["trade_date"] e_date = params[-1]["trade_date"] s_time = params[0]["start_time"] if "start_time" in params[0] else 0 e_time = params[-1]["end_time"] if "end_time" in params[-1] else 150000 s_dt_int = convert_date_to_int(s_date) + s_time e_dt_int = convert_date_to_int(e_date) + e_time db = self._get_db(instrument=instrument, frequency=frequency) collection = instrument.order_book_id filters = {"_d": {"$gte": datetime.combine(s_date, time=time()), "$lte": datetime.combine(e_date, time=time())}} projection = {"_id": 0, "_d": 0} loop = get_asyncio_event_loop() bars = loop.run_until_complete(self._do_get_bars(db, collection, filters, projection)) if bars is not None and bars.size: bars = DataFrameConverter.df2np(bars) else: bars = DataFrameConverter.empty() s_pos = np.searchsorted(bars["datetime"], s_dt_int) e_pos = np.searchsorted(bars["datetime"], e_dt_int, side="right") return bars[s_pos:e_pos] def raw_history_bars(self, instrument, frequency, start_dt=None, end_dt=None, length=None): # 转换到自建mongodb结构s if frequency.endswith("m"): return MiniteBarDataSourceMixin.raw_history_bars( self, instrument, frequency, start_dt=start_dt, end_dt=end_dt, length=length) else: code = instrument.order_book_id db = self._get_db(instrument, frequency) data = self._handler.read(code, db=db, start=start_dt, end=end_dt, length=length, sort=[("datetime", 1)]). \ reset_index() if data is not None and data.size: return DataFrameConverter.df2np(data) else: return DataFrameConverter.empty() def is_base_frequency(self, instrument, frequency): if isinstance(instrument, Instrument): instrument_type = instrument.enum_type else: instrument_type = instrument type_ = INSTRUMENT_TYPE_MAP[instrument_type] return type_ in self._db_map and frequency in self._db_map[type_] def current_snapshot(self, instrument, frequency, dt): pass def _get_date_range(self, frequency): from pymongo import DESCENDING try: db = self._get_db(INSTRUMENT_TYPE.CS, frequency) except NoneDataError: db = self._get_db(INSTRUMENT_TYPE.CS, "1" + frequency[-1]) key = "_d" if frequency.endswith("m") else "datetime" try: start = self._handler.client.get_database(db).get_collection("600000.XSHG").find() \ .sort(key).limit(1)[0][key] end = self._handler.client.get_database(db).get_collection("600000.XSHG").find() \ .sort(key, direction=DESCENDING).limit(1)[0][key] except IndexError: raise RuntimeError("无法从MongoDb获取数据时间范围") return start.date(), end.date() @lru_cache(maxsize=10) def available_data_range(self, frequency): if frequency.endswith("d") or frequency.endswith("h"): return date(2012, 6, 1), date.today() - relativedelta(days=1) return self._get_date_range(frequency) class MongoCacheDataSource(MongoDataSource, CacheMixin): def __init__(self, path, mongo_url): super(MongoCacheDataSource, self).__init__(path, mongo_url) CacheMixin.__init__(self)	[ 1, 396, 8025, 29901, 23616, 29899, 29947, 13, 3166, 12865, 1053, 2635, 29892, 12865, 29892, 931, 13, 13, 5215, 10992, 29889, 14817, 272, 29918, 294, 948, 3934, 13, 5215, 12655, 408, 7442, 13, 5215, 11701, 408, 10518, 13, 3166, 2635, 4422, 29889, 2674, 1926, 287, 2554, 1053, 14215, 287, 2554, 13, 3166, 364, 29939, 2312, 29889, 3075, 1053, 2672, 10810, 5005, 3919, 29918, 11116, 13, 3166, 364, 29939, 2312, 29889, 4299, 29889, 2611, 15461, 1053, 2799, 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telluric/constants.py	FlorianPignol/telluric	81	47397	"""Useful constants. """ from rasterio.crs import CRS WGS84_SRID = 4326 #: WGS84 CRS. WGS84_CRS = CRS.from_epsg(WGS84_SRID) WEB_MERCATOR_SRID = 3857 #: Web Mercator CRS. WEB_MERCATOR_CRS = CRS.from_epsg(WEB_MERCATOR_SRID) # Best widely used, equal area projection according to # http://icaci.org/documents/ICC_proceedings/ICC2001/icc2001/file/f24014.doc # (found on https://en.wikipedia.org/wiki/Winkel_tripel_projection#Comparison_with_other_projections) #: Eckert IV CRS. EQUAL_AREA_CRS = CRS({'proj': 'eck4'}) DEFAULT_SRID = WGS84_SRID #: Default CRS, set to :py:data:`~telluric.constants.WGS84_CRS`. DEFAULT_CRS = WGS84_CRS def _MERCATOR_RESOLUTION_MAPPING(zoom_level): return (2 * 20037508.342789244) / (256 * pow(2, zoom_level)) MERCATOR_RESOLUTION_MAPPING = dict((i, _MERCATOR_RESOLUTION_MAPPING(i)) for i in range(21)) RASTER_TYPE = 'raster'	[ 1, 9995, 11403, 1319, 17727, 29889, 13, 13, 15945, 29908, 13, 3166, 364, 1901, 601, 29889, 29883, 2288, 1053, 315, 12445, 13, 13, 29956, 10749, 29947, 29946, 29918, 14098, 1367, 353, 29871, 29946, 29941, 29906, 29953, 13, 29937, 29901, 399, 10749, 29947, 29946, 315, 12445, 29889, 13, 29956, 10749, 29947, 29946, 29918, 11341, 29903, 353, 315, 12445, 29889, 3166, 29918, 8961, 29887, 29898, 29956, 10749, 29947, 29946, 29918, 14098, 1367, 29897, 13, 13, 8851, 29933, 29918, 29924, 1001, 23972, 1955, 29918, 14098, 1367, 353, 29871, 29941, 29947, 29945, 29955, 13, 29937, 29901, 2563, 4702, 29883, 1061, 315, 12445, 29889, 13, 8851, 29933, 29918, 29924, 1001, 23972, 1955, 29918, 11341, 29903, 353, 315, 12445, 29889, 3166, 29918, 8961, 29887, 29898, 8851, 29933, 29918, 29924, 1001, 23972, 1955, 29918, 14098, 1367, 29897, 13, 13, 29937, 6407, 17644, 1304, 29892, 5186, 4038, 18246, 5034, 304, 13, 29937, 1732, 597, 983, 455, 29889, 990, 29914, 3225, 29879, 29914, 2965, 29907, 29918, 771, 3947, 886, 29914, 2965, 29907, 29906, 29900, 29900, 29896, 29914, 293, 29883, 29906, 29900, 29900, 29896, 29914, 1445, 29914, 29888, 29906, 29946, 29900, 29896, 29946, 29889, 1514, 13, 29937, 313, 11940, 373, 2045, 597, 264, 29889, 6011, 29889, 990, 29914, 4594, 29914, 29956, 27136, 29918, 3626, 13111, 29918, 771, 6929, 29937, 1523, 20941, 29918, 2541, 29918, 1228, 29918, 771, 24247, 29897, 13, 29937, 29901, 382, 384, 814, 6599, 315, 12445, 29889, 13, 29923, 13356, 1964, 29918, 29909, 1525, 29909, 29918, 11341, 29903, 353, 315, 12445, 3319, 29915, 20865, 2396, 525, 29872, 384, 29946, 29915, 1800, 13, 13, 23397, 29918, 14098, 1367, 353, 399, 10749, 29947, 29946, 29918, 14098, 1367, 13, 29937, 29901, 13109, 315, 12445, 29892, 731, 304, 584, 2272, 29901, 1272, 18078, 30022, 29873, 514, 332, 293, 29889, 3075, 1934, 29889, 29956, 10749, 29947, 29946, 29918, 11341, 29903, 1412, 13, 23397, 29918, 11341, 29903, 353, 399, 10749, 29947, 29946, 29918, 11341, 29903, 13, 13, 13, 1753, 903, 29924, 1001, 23972, 1955, 29918, 15989, 5607, 2692, 2725, 29918, 1529, 18009, 4214, 29898, 2502, 290, 29918, 5563, 1125, 13, 1678, 736, 313, 29906, 334, 29871, 29906, 29900, 29900, 29941, 29955, 29945, 29900, 29947, 29889, 29941, 29946, 29906, 29955, 29947, 29929, 29906, 29946, 29946, 29897, 847, 313, 29906, 29945, 29953, 334, 4764, 29898, 29906, 29892, 19342, 29918, 5563, 876, 13, 13, 13, 29924, 1001, 23972, 1955, 29918, 15989, 5607, 2692, 2725, 29918, 1529, 18009, 4214, 353, 9657, 3552, 29875, 29892, 903, 29924, 1001, 23972, 1955, 29918, 15989, 5607, 2692, 2725, 29918, 1529, 18009, 4214, 29898, 29875, 876, 363, 474, 297, 3464, 29898, 29906, 29896, 876, 13, 13, 4717, 1254, 1001, 29918, 11116, 353, 525, 29878, 1901, 29915, 13, 2 ]
algorithm4.py	guoqing-tang/algorithm	1	73007	# * -- utf-8 -- * # python3 # Author: Tang Time:2018/4/17 '''简述：要求输入某年某月某日提问：求判断输入日期是当年中的第几天？''' year = int(input('请输入年份：')) month = int(input('请输入月份：')) day = int(input('请输入日期：')) if day >31: print ('请正确输入') if month > 12: print('请正确输入') list1 = [31,59,90,130,151,181,212,243,273,304,334] if (year % 4) == 0 or (year % 100) == 0 or (year % 400) == 0 and month >= 2: data = list1[month - 2] + day +1 if month == 1: data = day if month == 2: data = 31 + day print("这一天是这一年第"+str(data)+"天")	[ 1, 396, 334, 1192, 23616, 29899, 29947, 1192, 334, 1678, 396, 3017, 29941, 13, 29937, 13361, 29901, 27378, 259, 5974, 29901, 29906, 29900, 29896, 29947, 29914, 29946, 29914, 29896, 29955, 13, 13, 13, 12008, 234, 177, 131, 235, 194, 179, 30383, 30698, 31376, 31573, 30752, 233, 162, 147, 30470, 233, 162, 147, 30534, 233, 162, 147, 30325, 29871, 31302, 31658, 30383, 31376, 31791, 31683, 31573, 30752, 30325, 31117, 30392, 30948, 30470, 30275, 30210, 30622, 232, 138, 163, 30408, 30882, 12008, 13, 6360, 353, 938, 29898, 2080, 877, 31088, 31573, 30752, 30470, 231, 190, 192, 30383, 8785, 13, 10874, 353, 938, 29898, 2080, 877, 31088, 31573, 30752, 30534, 231, 190, 192, 30383, 8785, 13, 3250, 353, 938, 29898, 2080, 877, 31088, 31573, 30752, 30325, 31117, 30383, 8785, 13, 361, 2462, 1405, 29941, 29896, 29901, 13, 1678, 1596, 6702, 31088, 30724, 31835, 31573, 30752, 1495, 13, 361, 4098, 1405, 29871, 29896, 29906, 29901, 13, 1678, 1596, 877, 31088, 30724, 31835, 31573, 30752, 1495, 13, 1761, 29896, 353, 518, 29941, 29896, 29892, 29945, 29929, 29892, 29929, 29900, 29892, 29896, 29941, 29900, 29892, 29896, 29945, 29896, 29892, 29896, 29947, 29896, 29892, 29906, 29896, 29906, 29892, 29906, 29946, 29941, 29892, 29906, 29955, 29941, 29892, 29941, 29900, 29946, 29892, 29941, 29941, 29946, 29962, 13, 361, 313, 6360, 1273, 29871, 29946, 29897, 1275, 29871, 29900, 470, 313, 6360, 1273, 29871, 29896, 29900, 29900, 29897, 1275, 29871, 29900, 470, 313, 6360, 1273, 29871, 29946, 29900, 29900, 29897, 1275, 29871, 29900, 322, 4098, 6736, 29871, 29906, 29901, 13, 1678, 848, 353, 1051, 29896, 29961, 10874, 448, 29871, 29906, 29962, 718, 2462, 718, 29896, 13, 361, 4098, 1275, 29871, 29896, 29901, 13, 1678, 848, 353, 2462, 13, 361, 4098, 1275, 29871, 29906, 29901, 13, 1678, 848, 353, 29871, 29941, 29896, 718, 2462, 13, 2158, 703, 30810, 30287, 30408, 30392, 30810, 30287, 30470, 30622, 17969, 710, 29898, 1272, 7240, 29908, 30408, 1159, 13, 2 ]
tests/test_decast.py	interesaaat/onnxconverter-common	83	67159	import unittest from onnx import helper from onnx import onnx_pb as onnx_proto from onnxconverter_common.decast import decast class DecastTestCase(unittest.TestCase): def test_decast(self): nodes = [] nodes[0:] = [helper.make_node('Identity', ['input1'], ['identity1'])] nodes[1:] = [helper.make_node('Cast', ['identity1'], ['cast0'], to=1)] nodes[2:] = [helper.make_node('ReduceSum', ['cast0'], ['reduce0'])] nodes[3:] = [helper.make_node('Cast', ['reduce0'], ['cast1'], to=6)] nodes[4:] = [helper.make_node('Identity', ['cast1'], ['output0'])] input0 = helper.make_tensor_value_info('input1', onnx_proto.TensorProto.FLOAT, [1, 1, 2, 3]) output0 = helper.make_tensor_value_info('output0', onnx_proto.TensorProto.FLOAT, [1, 1, 2, 3]) graph = helper.make_graph(nodes, 'test_graph', [input0], [output0]) model = helper.make_model(graph) self.assertIsNotNone(model) converted_model = decast(model, ['ReduceSum']) self.assertTrue(len(converted_model.graph.node) == 3) if __name__ == '__main__': unittest.main()	[ 1, 1053, 443, 27958, 13, 13, 3166, 373, 23818, 1053, 16876, 13, 3166, 373, 23818, 1053, 373, 23818, 29918, 24381, 408, 373, 23818, 29918, 17529, 13, 3166, 373, 23818, 535, 13549, 29918, 9435, 29889, 311, 4384, 1053, 1602, 579, 13, 13, 13, 1990, 3826, 579, 3057, 8259, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 13, 1678, 822, 1243, 29918, 311, 4384, 29898, 1311, 1125, 13, 4706, 7573, 353, 5159, 13, 4706, 7573, 29961, 29900, 17531, 353, 518, 20907, 29889, 5675, 29918, 3177, 877, 18415, 742, 6024, 2080, 29896, 7464, 6024, 22350, 29896, 2033, 4638, 13, 4706, 7573, 29961, 29896, 17531, 353, 518, 20907, 29889, 5675, 29918, 3177, 877, 15738, 742, 6024, 22350, 29896, 7464, 6024, 4384, 29900, 7464, 304, 29922, 29896, 4638, 13, 4706, 7573, 29961, 29906, 17531, 353, 518, 20907, 29889, 5675, 29918, 3177, 877, 29934, 6085, 346, 11139, 742, 6024, 4384, 29900, 7464, 6024, 17469, 29900, 2033, 4638, 13, 4706, 7573, 29961, 29941, 17531, 353, 518, 20907, 29889, 5675, 29918, 3177, 877, 15738, 742, 6024, 17469, 29900, 7464, 6024, 4384, 29896, 7464, 304, 29922, 29953, 4638, 13, 4706, 7573, 29961, 29946, 17531, 353, 518, 20907, 29889, 5675, 29918, 3177, 877, 18415, 742, 6024, 4384, 29896, 7464, 6024, 4905, 29900, 2033, 4638, 13, 13, 4706, 1881, 29900, 353, 16876, 29889, 5675, 29918, 20158, 29918, 1767, 29918, 3888, 877, 2080, 29896, 742, 373, 23818, 29918, 17529, 29889, 29911, 6073, 1184, 517, 29889, 29943, 3927, 1299, 29892, 518, 29896, 29892, 29871, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 2314, 13, 4706, 1962, 29900, 353, 16876, 29889, 5675, 29918, 20158, 29918, 1767, 29918, 3888, 877, 4905, 29900, 742, 373, 23818, 29918, 17529, 29889, 29911, 6073, 1184, 517, 29889, 29943, 3927, 1299, 29892, 518, 29896, 29892, 29871, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 2314, 13, 13, 4706, 3983, 353, 16876, 29889, 5675, 29918, 4262, 29898, 18010, 29892, 525, 1688, 29918, 4262, 742, 518, 2080, 29900, 1402, 518, 4905, 29900, 2314, 13, 4706, 1904, 353, 16876, 29889, 5675, 29918, 4299, 29898, 4262, 29897, 13, 4706, 1583, 29889, 9294, 3624, 3664, 8516, 29898, 4299, 29897, 13, 13, 4706, 11543, 29918, 4299, 353, 1602, 579, 29898, 4299, 29892, 6024, 29934, 6085, 346, 11139, 11287, 13, 4706, 1583, 29889, 9294, 5574, 29898, 2435, 29898, 13441, 287, 29918, 4299, 29889, 4262, 29889, 3177, 29897, 1275, 29871, 29941, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 2 ]
2021/examples-in-class-2021-10-08/three_five_fifteen.py	ati-ozgur/course-python	1	135079	for index in range(1,101): # if index % 15 == 0: # print("fifteen") if index % 3 == 0 and index % 5 == 0 : print("fifteen") elif index % 3 == 0: print("three") elif index % 5 == 0: print("five") else: print(index)	[ 1, 29871, 13, 1454, 2380, 297, 3464, 29898, 29896, 29892, 29896, 29900, 29896, 1125, 13, 29937, 1678, 565, 2380, 1273, 29871, 29896, 29945, 1275, 29871, 29900, 29901, 13, 29937, 4706, 1596, 703, 28491, 9404, 1159, 13, 1678, 565, 2380, 1273, 29871, 29941, 1275, 29871, 29900, 322, 2380, 1273, 29871, 29945, 1275, 29871, 29900, 584, 13, 4706, 1596, 703, 28491, 9404, 1159, 13, 1678, 25342, 2380, 1273, 29871, 29941, 1275, 29871, 29900, 29901, 13, 4706, 1596, 703, 17536, 1159, 13, 1678, 25342, 2380, 1273, 29871, 29945, 1275, 29871, 29900, 29901, 13, 4706, 1596, 703, 20818, 1159, 13, 1678, 1683, 29901, 13, 4706, 1596, 29898, 2248, 29897, 13, 13, 2 ]
certbot_dns_cfproxy/__init__.py	ProfFan/certbot-dns-cfproxy	2	13262	<filename>certbot_dns_cfproxy/__init__.py """ The `~certbot_dns_cfproxy.dns_cfproxy` plugin automates the process of completing a ``dns-01`` challenge (`~acme.challenges.DNS01`) by creating, and subsequently removing, TXT records using the CFProxy API. Examples -------- .. code-block:: bash :caption: To acquire a certificate for ``example.com`` certbot certonly \\ -a certbot-dns-cfproxy:dns-cfproxy \\ -d example.com """	[ 1, 529, 9507, 29958, 6327, 7451, 29918, 29881, 1983, 29918, 6854, 14701, 29914, 1649, 2344, 26914, 2272, 13, 15945, 29908, 13, 1576, 421, 30022, 6327, 7451, 29918, 29881, 1983, 29918, 6854, 14701, 29889, 29881, 1983, 29918, 6854, 14701, 29952, 7079, 3345, 1078, 278, 1889, 310, 13, 5729, 1259, 263, 4954, 29881, 1983, 29899, 29900, 29896, 16159, 18766, 6695, 30022, 562, 1004, 29889, 305, 16047, 267, 29889, 29928, 3059, 29900, 29896, 6348, 491, 4969, 29892, 322, 13, 1491, 27284, 11077, 29892, 323, 12188, 6475, 773, 278, 17861, 14048, 3450, 29889, 13, 13, 1252, 9422, 13, 1378, 13, 13, 636, 775, 29899, 1271, 1057, 10891, 13, 259, 584, 6671, 29901, 1763, 1274, 1548, 263, 12289, 363, 4954, 4773, 29889, 510, 16159, 13, 13, 259, 2284, 7451, 2284, 6194, 2474, 13, 268, 448, 29874, 2284, 7451, 29899, 29881, 1983, 29899, 6854, 14701, 29901, 29881, 1983, 29899, 6854, 14701, 2474, 13, 268, 448, 29881, 1342, 29889, 510, 13, 13, 15945, 29908, 13, 2 ]
recognitionFace/train.py	factzero/pytorch_jaguarface_examples	2	167262	# -- coding: UTF-8 -- import os import argparse import cv2 import time import torch import torch.optim as optim from tqdm import tqdm from config.config import cfg from core.resnet import resnet18 from core.metrics import Arcface from utils.data_gen import get_train_loader parser = argparse.ArgumentParser(description='recognition face Training') parser.add_argument('--training_dataset', default='./data/faces_emore/imgs/', help='Training dataset directory') parser.add_argument('--network', default='resnet18', help='Backbone network mobile0.25 or resnet50') parser.add_argument('--resume_net', default=None, help='resume net for retraining') parser.add_argument('--resume_epoch', default=0, type=int, help='resume iter for retraining') parser.add_argument('--save_folder', default='./weights/', help='Location to save checkpoint models') args = parser.parse_args() def resume_net_param(net, trained_model): print('Loading trained model: {}'.format(trained_model)) state_dict = torch.load(args.resume_net) # create new OrderedDict that does not contain `module.` from collections import OrderedDict new_state_dict = OrderedDict() for k, v in state_dict.items(): head = k[:7] if head == 'module.': name = k[7:] # remove `module.` else: name = k new_state_dict[name] = v net.load_state_dict(new_state_dict) return net def train(): save_folder = args.save_folder if not os.path.exists(args.save_folder): os.mkdir(args.save_folder) print('load dataset ...') with torch.no_grad(): loader, class_num = get_train_loader(cfg, args.training_dataset) print('network ...') device = torch.device("cuda" if cfg['gpu'] else "cpu") if args.network == 'resnet18': net = resnet18(cfg['embedding_size']) if args.resume_net is not None: print('Loading resume network...') net = resume_net_param(net, args.resume_net) net = net.to(device) print('init cirterion ...') criterion = torch.nn.CrossEntropyLoss() metric_fc = Arcface(embedding_size=cfg['embedding_size'], classnum=class_num).to(device) optimizer = optim.SGD([{'params': net.parameters()}, {'params': metric_fc.parameters()}], lr=cfg['initial_lr'], momentum=cfg['momentum'], weight_decay=cfg['weight_decay']) max_epoch = cfg['epoch'] start_epoch = args.resume_epoch net.train() for epoch in range(start_epoch, max_epoch): print('Epoch:{}/{}, batch:{}'.format(epoch, max_epoch, cfg['batch_size'])) for imgs, labels in tqdm(iter(loader)): imgs = imgs.to(device) labels = labels.to(device) # forward features = net(imgs) # backprop output = metric_fc(features, labels) loss = criterion(output, labels) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch % 10 == 0 and epoch > 0): save_w_file = args.network + '_epoch_' + str(epoch) + '_' + str(time.time()) + '.pth' print('saving weights : {}'.format(save_w_file)) torch.save(net.state_dict(), save_folder + save_w_file) if __name__ == "__main__": train() image_path = "./data/87022.jpg" img_raw = 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lib2/tiktok.py	offdhymaz/wa	37	175509	from requests import Session from bs4 import BeautifulSoup import re class tiktok: def __init__(self) -> None: self.request = Session() self.url = "https://ssstik.io" self.html = self.request.get(self.url).text self.key = BeautifulSoup(self.html, "html.parser").find_all("form",attrs={"data-hx-target":"#target"})[0].get("include-vals") self.post = BeautifulSoup(self.html, "html.parser").find_all("form",attrs={"data-hx-target":"#target"})[0].get("data-hx-post") self.tt = re.search("tt\:\'(.?)\'",self.key)[1] self.ts = re.search("ts\:([0-9]{5,15})",self.key)[1] self.header = {"content-type": "application/x-www-form-urlencoded; charset=UTF-8","hx-active-element": "submit","hx-current-url": "https://ssstik.io/","hx-request": "true","hx-target": "target","origin": "https://ssstik.io","sec-fetch-dest": "","sec-fetch-mode": "cors","sec-fetch-site": "same-origin","user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36"} def download(self, url) -> dict: data = {"id": url,"locale": "en","tt": self.tt,"ts": int(self.ts)} post = self.request.post(f"{self.url}{self.post}", headers=self.header, data=data) respon = BeautifulSoup(post.text, "html.parser") hasil = {"video":[f'{self.url}{respon.find_all("a",class_="pure-button pure-button-primary is-center u-bl dl-button download_link without_watermark")[0].get("href")}',f'{self.url}{respon.find_all("a",class_="pure-button pure-button-primary is-center u-bl dl-button download_link without_watermark_direct")[0].get("href")}'],"music":f'{respon.find_all("a",class_="pure-button pure-button-primary is-center u-bl dl-button download_link music")[0].get("href")}'} return hasil class tiktok2: def __init__(self, url) -> None: self.request = Session() self.url =url self.header = {"accept": "/*","accept-language": "en-US,en;q=0.9,id;q=0.8","origin": "https://snaptik.app","referer": "https://snaptik.app/ID","sec-fetch-dest": None,"sec-fetch-mode": "cors","sec-fetch-site": "same-origin","user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36",} def download(self): self.request.post("https://snaptik.app/check_token.php", headers=self.header) self.bs = BeautifulSoup(self.request.post("https://snaptik.app/action-2021.php", headers=self.header, data={"url":self.url}).text, "html.parser") return [self.request,{"title":self.bs('a', attrs={"title":""})[0].text,"date":self.bs("b", attrs={"class":"blur"})[0].text,"url":list(filter(lambda x:x, map(lambda x:x["href"] if "token" in x["href"] else None, self.bs("a", attrs={"class":"abutton is-success is-fullwidth"}))))}, self.header]	[ 1, 515, 7274, 1053, 16441, 13, 3166, 24512, 29946, 1053, 25685, 29903, 1132, 13, 5215, 337, 13, 13, 1990, 260, 638, 17082, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29897, 1599, 6213, 29901, 13, 4706, 1583, 29889, 3827, 353, 16441, 580, 13, 4706, 1583, 29889, 2271, 268, 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nterm.py	KorfLab/qalpha	0	130968	<reponame>KorfLab/qalpha import os import sys for genome in os.listdir(sys.argv[1]): print(f'{sys.argv[1]}/{genome}') ma = [] with open(f'{sys.argv[1]}/{genome}') as fp: for line in fp.readlines(): if not line.startswith('>'): ma.append(line.rstrip()) slen = len(ma[0]) for seq in ma: print(seq[:120]) print()	[ 1, 529, 276, 1112, 420, 29958, 29968, 4877, 28632, 29914, 29939, 2312, 13, 5215, 2897, 13, 5215, 10876, 13, 13, 1454, 2531, 608, 297, 2897, 29889, 1761, 3972, 29898, 9675, 29889, 19218, 29961, 29896, 29962, 1125, 13, 12, 2158, 29898, 29888, 29915, 29912, 9675, 29889, 19218, 29961, 29896, 29962, 6822, 29912, 1885, 608, 29913, 1495, 13, 12, 655, 353, 5159, 13, 12, 2541, 1722, 29898, 29888, 29915, 29912, 9675, 29889, 19218, 29961, 29896, 29962, 6822, 29912, 1885, 608, 29913, 1495, 408, 285, 29886, 29901, 13, 12, 12, 1454, 1196, 297, 285, 29886, 29889, 949, 9012, 7295, 13, 12, 12, 12, 361, 451, 1196, 29889, 27382, 2541, 877, 16299, 1125, 13, 12, 12, 12, 12, 655, 29889, 4397, 29898, 1220, 29889, 29878, 17010, 3101, 13, 12, 29879, 2435, 353, 7431, 29898, 655, 29961, 29900, 2314, 13, 12, 1454, 19359, 297, 611, 29901, 1596, 29898, 11762, 7503, 29896, 29906, 29900, 2314, 13, 12, 2158, 580, 13, 13, 13, 2 ]
src/Hero.py	Astayonix/SailorMoonRPG	0	1601901	<filename>src/Hero.py #!/usr/bin/python class Hero(object): def __init__(self, Name): self.name = Name	[ 1, 529, 9507, 29958, 4351, 29914, 29950, 1489, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 4691, 13, 13, 1990, 22167, 29898, 3318, 1125, 13, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 4408, 1125, 13, 4706, 1583, 29889, 978, 353, 4408, 13, 2 ]
ooi_harvester/utils/parser.py	ooi-data/ooi_harvester	4	37507	import os import datetime import math import traceback from typing import List import requests from loguru import logger from lxml import etree from siphon.catalog import TDSCatalog from dask.utils import memory_repr import numpy as np from dateutil import parser from ooi_harvester.settings import harvest_settings def estimate_size_and_time(raw): m = "" if "requestUUID" in raw: est_size = raw["sizeCalculation"] / 1024 2 size_txt = "MB" if (est_size / 1024) >= 1.0: est_size = est_size / 1024 size_txt = "GB" est_time = raw["timeCalculation"] time_txt = "Seconds" if (est_time / 60) >= 1.0 and (est_time / 60) < 60.0: est_time = math.floor(est_time / 60) time_txt = "Minutes" if est_time == 1: time_txt = "Minute" elif (est_time / 60) >= 60.0: est_time = math.floor(est_time / 60 2) time_txt = "Hours" if est_time == 1: time_txt = "Hour" m = f""" Estimated File size: {est_size:.4} {size_txt} Estimated Time: {est_time} {time_txt} """ elif "message" in raw: m = f""" No estimate calculated. {raw['message']['status']} """ return m def parse_uframe_response(resp): if "allURLs" in resp: return { "request_id": resp["requestUUID"], "thredds_catalog": resp["allURLs"][0], "download_catalog": resp["allURLs"][1], "status_url": resp["allURLs"][1] + "/status.txt", "data_size": resp["sizeCalculation"], "estimated_time": resp["timeCalculation"], "units": { "data_size": "bytes", "estimated_time": "seconds", "request_dt": "UTC", }, "request_dt": datetime.datetime.utcnow().isoformat(), } logger.warning(resp) return None def param_change(name): """ Method to accomodate for param change. https://oceanobservatories.org/renaming-data-stream-parameters/ """ if name == 'pressure_depth': return 'pressure' else: return name def parse_param_dict(param_dict): unit = None if "unit" in param_dict: if isinstance(param_dict["unit"], dict): if "value" in param_dict["unit"]: unit = param_dict["unit"]["value"] product_type = None if "data_product_type" in param_dict: if isinstance(param_dict["data_product_type"], dict): if "value" in param_dict["data_product_type"]: product_type = param_dict["data_product_type"]["value"] return { "pid": param_dict["id"], "reference_designator": param_change(param_dict["name"]), "parameter_name": param_dict["display_name"], "netcdf_name": param_dict["netcdf_name"], "standard_name": param_dict["standard_name"], "description": param_dict["description"], "unit": unit, "data_level": param_dict['data_level'], "data_product_type": product_type, "data_product_identifier": param_dict["data_product_identifier"], "last_updated": datetime.datetime.utcnow().isoformat(), } def parse_global_range_dataframe(global_ranges): """Cleans up the global ranges dataframe""" global_df = global_ranges[global_ranges.columns[:-3]] global_df.columns = [ "reference_designator", "parameter_id_r", "parameter_id_t", "global_range_min", "global_range_max", "data_level", "units", ] return global_df def get_bytes(value, unit): bytes_map = { 'bytes': 1, 'Kbytes': 1024 1, 'Mbytes': 1024 2, 'Gbytes': 1024 ** 3, } return value * bytes_map[unit] def parse_dataset_element(d, namespace): dataset_dict = {} for i in d.getiterator(): clean_tag = i.tag.replace('{' + namespace + '}', '') if clean_tag == 'dataset': dataset_dict = dict(i.attrib) if clean_tag == 'dataSize': dataset_dict = dict( data_size=float(i.text), i.attrib, dataset_dict ) dataset_dict = dict( size_bytes=get_bytes( dataset_dict['data_size'], dataset_dict['units'] ), dataset_dict, ) if clean_tag == 'date': dataset_dict = dict(date_modified=i.text, dataset_dict) return dataset_dict def parse_response_thredds(response): stream_name = response['stream']['table_name'] catalog = TDSCatalog( response['result']['thredds_catalog'].replace('.html', '.xml') ) catalog_dict = { 'stream_name': stream_name, 'catalog_url': catalog.catalog_url, 'base_tds_url': catalog.base_tds_url, 'async_url': response['result']['download_catalog'], } req = requests.get(catalog.catalog_url) catalog_root = etree.fromstring(req.content) namespaces = {} for k, v in catalog_root.nsmap.items(): if k is None: namespaces['cat'] = v else: namespaces[k] = v dataset_elements = catalog_root.xpath( '/cat:catalog/cat:dataset/cat:dataset', namespaces=namespaces ) datasets = [ parse_dataset_element(i, namespaces['cat']) for i in dataset_elements ] catalog_dict['datasets'] = datasets return catalog_dict def filter_and_parse_datasets(cat): import re stream_cat = cat.copy() name = stream_cat['stream_name'] provenance_files = [] filtered_datasets = [] for d in stream_cat['datasets']: m = re.search( r'(deployment(\d{4})_(%s)_(\d{4}\d{2}\d{2}T\d+.\d+)-(\d{4}\d{2}\d{2}T\d+.\d+).nc)' # noqa % (name), str(d['name']), ) prov = re.search( r'(deployment(\d{4})_(%s)_aggregate_provenance.json)' % (name), str(d['name']), ) if m: _, dep_num, _, start, end = m.groups() dataset = dict( deployment=int(dep_num), start_ts=start, end_ts=end, d ) filtered_datasets.append(dataset) elif prov: _, dep_num, _ = prov.groups() provenance = dict(deployment=int(dep_num), d) provenance_files.append(provenance) total_bytes = np.sum([d['size_bytes'] for d in filtered_datasets]) stream_cat['datasets'] = filtered_datasets stream_cat['provenance'] = provenance_files stream_cat['total_data_size'] = memory_repr(total_bytes) stream_cat['total_data_bytes'] = total_bytes return stream_cat def filter_datasets_by_time( datasets: List[dict], start_dt: np.datetime64, end_dt: np.datetime64 ) -> List[dict]: """ Filters datasets collection based on the given start and end datetime. Each dataset dictionary in the collection MUST have `start_ts` and `end_ts`key in it. Parameters ---------- datasets : list The datasets collection to be filtered. start_dt : np.datetime64 The start datetime desired. end_dt : np.datetime64 The end datetime desired. Returns ------- list The filtered datasets collection """ filtered_datasets = [] for d in datasets: start_d = np.datetime64(parser.parse(d['start_ts'])) end_d = np.datetime64(parser.parse(d['end_ts'])) if start_d >= start_dt.astype( start_d.dtype ) and end_d <= end_dt.astype(start_d.dtype): filtered_datasets.append(d) return filtered_datasets def setup_etl(stream, source='ooinet', target_bucket='s3://ooi-data'): name = stream['stream_name'] harvest_location = os.path.expanduser('~/.ooi-harvester') # Setup Local temp folder for netcdf temp_fold = os.path.join(harvest_location, name) if not os.path.exists(os.path.dirname(temp_fold)): os.mkdir(os.path.dirname(temp_fold)) if not os.path.exists(temp_fold): os.mkdir(temp_fold) # Setup S3 Bucket temp_s3_fold = f"s3://temp-ooi-data/{name}.zarr" final_s3_fold = f"{target_bucket}/{name}" if source == 'ooinet': retrieved_dt = stream['result']['request_dt'] else: retrieved_dt = stream['retrieved_dt'] del stream['retrieved_dt'] return dict( temp_fold=temp_fold, temp_bucket=temp_s3_fold, final_bucket=final_s3_fold, retrieved_dt=retrieved_dt, stream, ) def seconds_to_date(num): start_dt = datetime.datetime(1900, 1, 1) return start_dt + datetime.timedelta(seconds=num) def get_storage_options(path): if path.startswith("s3://"): return harvest_settings.storage_options.aws.dict() def get_items(keys, orig_dict): new_dict = {} for k, v in orig_dict.items(): if k in keys: new_dict[k] = v return new_dict def rename_item(old_key, new_key, orig_dict): new_dict = orig_dict.copy() if old_key in new_dict: new_dict.update({new_key: new_dict[old_key]}) del new_dict[old_key] return new_dict def parse_exception(exception): exc_dict = { 'type': type(exception).__name__, 'value': str(exception), 'traceback': "".join( traceback.format_exception( type(exception), exception, exception.__traceback__ ) ), } return exc_dict	[ 1, 1053, 2897, 13, 5215, 12865, 13, 5215, 5844, 13, 5215, 9637, 1627, 13, 3166, 19229, 1053, 2391, 13, 13, 5215, 7274, 13, 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torchqf/model/factor_model.py	simaki/torchqf	7	167082	from typing import TypeVar import torch from torch import Tensor from torch.nn import Module T = TypeVar("T") class FactorModel(Module): """Factor model. Attributes: beta (Tensor): Created after `fit`. Shape: - input: :math:`(, A, T)` :math:`A` is the number of assets. :math:`T` is the number of time steps. - output: :math:`(, A, T)` :math:`A` is the number of assets. :math:`T` is the number of time steps. - factor: :math:`(, F, T)` :math:`F` is the number of assets. :math:`T` is the number of time steps. - beta: :math:`(F, A)` :math:`F` is the number of factor. :math:`A` is the number of assets. Examples: >>> import torchqf >>> from torchqf.stochastic import generate_brownian >>> _ = torch.manual_seed(42) >>> input = generate_brownian((2, 5), 0.1) >>> factor = generate_brownian((2, 5), 0.1) >>> fm = FactorModel().fit(input, factor) >>> fm(input, factor) tensor([[ 0.0032, -0.0023, -0.0046, 0.0138, -0.0099], [-0.0234, 0.0157, -0.0017, -0.0052, 0.0025]]) The method `fit_forward` fits and forwards at once. >>> fm.fit_forward(input, factor) tensor([[ 0.0032, -0.0023, -0.0046, 0.0138, -0.0099], [-0.0234, 0.0157, -0.0017, -0.0052, 0.0025]]) """ def fit(self: T, input: Tensor, factor: Tensor, enable_grad: bool = False) -> T: """Fit the model using the asset returns (`input`) and the factor returns (`factor`). Args: input (Tensor): Returns of assets. factor (Tensor): Returns of factors. enable_grad (bool, default=False): Flag whether to enable grad (True), or disable (False). Returns: FactorModel: Returns `self`. Shape: input : :math:`(, A, T)` :math:`A` is the number of assets. :math:`T` is the number of time steps. factor : :math:`(, F, T)` :math:`F` is the number of assets. :math:`T` is the number of time steps. beta : :math:`(, F, A)` :math:`F` is the number of factors. :math:`A` is the number of assets. """ assert input.size(-1) == factor.size(-1), "numbers of time steps do not match" assert input.ndim == 2, "not supported" assert factor.ndim == 2, "not supported" with torch.set_grad_enabled(mode=enable_grad): tensor_x = factor.t() # shape : (T, F) tensor_y = input.transpose(-2, -1) # shape : (T, A) # Compute beta : (F, T) @ (T, A) = (F, A) self.beta = torch.mm(torch.linalg.pinv(tensor_x), tensor_y) return self def forward(self, input: torch.Tensor, factor: torch.Tensor) -> torch.Tensor: factor_return = torch.mm(self.beta.t(), factor) # shape : (*, A, T) return input - factor_return def fit_forward(self, input: torch.Tensor, factor: torch.Tensor) -> torch.Tensor: return self.fit(input, factor, enable_grad=True)(input, factor)	[ 1, 515, 19229, 1053, 5167, 9037, 13, 13, 5215, 4842, 305, 13, 3166, 4842, 305, 1053, 323, 6073, 13, 3166, 4842, 305, 29889, 15755, 1053, 15591, 13, 13, 29911, 353, 5167, 9037, 703, 29911, 1159, 13, 13, 13, 1990, 383, 7168, 3195, 29898, 7355, 1125, 13, 1678, 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setup.py	Mahima-ai/ALEPython	107	58516	# -- coding: utf-8 -- from setuptools import find_packages, setup with open("README.md") as f: long_description = f.read() with open("requirements.txt", "r") as f: required = f.read().splitlines() setup( name="alepython", description="Python Accumulated Local Effects (ALE) package.", author="<NAME>", author_email="<EMAIL>", license="Apache 2", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/MaximeJumelle/alepython/", install_requires=required, extras_require={"test": ["pytest>=5.4", "pytest-cov>=2.8"]}, setup_requires=["setuptools-scm"], python_requires=">=3.5", use_scm_version=dict(write_to="src/alepython/_version.py"), keywords="alepython", package_dir={"": "src"}, packages=find_packages(where="src"), include_package_data=True, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approched :: Apache 2", "Operating System :: OS Independent", ], )	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 3166, 731, 21245, 8789, 1053, 1284, 29918, 8318, 29892, 6230, 13, 13, 2541, 1722, 703, 16310, 2303, 29889, 3487, 1159, 408, 285, 29901, 13, 1678, 1472, 29918, 8216, 353, 285, 29889, 949, 580, 13, 13, 2541, 1722, 703, 12277, 1860, 29889, 3945, 613, 376, 29878, 1159, 408, 285, 29901, 13, 1678, 3734, 353, 285, 29889, 949, 2141, 5451, 9012, 580, 13, 13, 14669, 29898, 13, 1678, 1024, 543, 744, 4691, 613, 13, 1678, 6139, 543, 11980, 4831, 398, 7964, 9959, 26475, 29879, 313, 29909, 1307, 29897, 3577, 19602, 13, 1678, 4148, 543, 29966, 5813, 28341, 13, 1678, 4148, 29918, 5269, 543, 29966, 26862, 6227, 28341, 13, 1678, 19405, 543, 17396, 1829, 29871, 29906, 613, 13, 1678, 1472, 29918, 8216, 29922, 5426, 29918, 8216, 29892, 13, 1678, 1472, 29918, 8216, 29918, 3051, 29918, 1853, 543, 726, 29914, 3502, 3204, 613, 13, 1678, 3142, 543, 991, 597, 3292, 29889, 510, 29914, 7976, 603, 29967, 398, 1808, 29914, 744, 4691, 29914, 613, 13, 1678, 2601, 29918, 276, 339, 2658, 29922, 12403, 29892, 13, 1678, 429, 10678, 29918, 12277, 3790, 29908, 1688, 1115, 6796, 2272, 1688, 18572, 29945, 29889, 29946, 613, 376, 2272, 1688, 29899, 24542, 18572, 29906, 29889, 29947, 3108, 1118, 13, 1678, 6230, 29918, 276, 339, 2658, 29922, 3366, 842, 21245, 8789, 29899, 1557, 29885, 12436, 13, 1678, 3017, 29918, 276, 339, 2658, 543, 18572, 29941, 29889, 29945, 613, 13, 1678, 671, 29918, 1557, 29885, 29918, 3259, 29922, 8977, 29898, 3539, 29918, 517, 543, 4351, 29914, 744, 4691, 19891, 3259, 29889, 2272, 4968, 13, 1678, 29361, 543, 744, 4691, 613, 13, 1678, 3577, 29918, 3972, 3790, 29908, 1115, 376, 4351, 10758, 13, 1678, 9741, 29922, 2886, 29918, 8318, 29898, 3062, 543, 4351, 4968, 13, 1678, 3160, 29918, 5113, 29918, 1272, 29922, 5574, 29892, 13, 1678, 770, 14903, 11759, 13, 4706, 376, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 613, 13, 4706, 376, 29931, 293, 1947, 4761, 438, 5425, 28268, 26902, 4761, 13380, 29871, 29906, 613, 13, 4706, 376, 7094, 1218, 2184, 4761, 6570, 25266, 613, 13, 1678, 21251, 13, 29897, 13, 2 ]
t3f/batch_ops.py	KhrulkovV/t3f	1	31564	<gh_stars>1-10 import tensorflow as tf from t3f.tensor_train_base import TensorTrainBase from t3f.tensor_train_batch import TensorTrainBatch from t3f import ops def concat_along_batch_dim(tt_list): """Concat all TensorTrainBatch objects along batch dimension. Args: tt_list: a list of TensorTrainBatch objects. Returns: TensorTrainBatch """ ndims = tt_list[0].ndims() if isinstance(tt_list, TensorTrainBase): # Not a list but just one element, nothing to concat. return tt_list for batch_idx in range(len(tt_list)): if not isinstance(tt_list[batch_idx], TensorTrainBatch): raise ValueError('All objects in the list should be TTBatch objects, got ' '%s' % tt_list[batch_idx]) for batch_idx in range(1, len(tt_list)): if tt_list[batch_idx].get_raw_shape() != tt_list[0].get_raw_shape(): raise ValueError('Shapes of all TT-batch objects should coincide, got %s ' 'and %s' % (tt_list[0].get_raw_shape(), tt_list[batch_idx].get_raw_shape())) if tt_list[batch_idx].get_tt_ranks() != tt_list[0].get_tt_ranks(): raise ValueError('TT-ranks of all TT-batch objects should coincide, got ' '%s and %s' % (tt_list[0].get_tt_ranks(), tt_list[batch_idx].get_tt_ranks())) res_cores = [] for core_idx in range(ndims): curr_core = tf.concat([tt.tt_cores[core_idx] for tt in tt_list], axis=0) res_cores.append(curr_core) try: batch_size = sum([tt.batch_size for tt in tt_list]) except TypeError: # The batch sizes are not defined and you can't sum Nones. batch_size = None return TensorTrainBatch(res_cores, tt_list[0].get_raw_shape(), tt_list[0].get_tt_ranks(), batch_size) def multiply_along_batch_dim(batch_tt, weights): """Multiply each TensorTrain in a batch by a number. Args: batch_tt: TensorTrainBatch object, TT-matrices or TT-tensors. weights: 1-D tf.Tensor (or something convertible to it like np.array) of size tt.batch_sie with weights. Returns: TensorTrainBatch """ weights = tf.convert_to_tensor(weights) tt_cores = list(batch_tt.tt_cores) if batch_tt.is_tt_matrix(): weights = weights[:, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis] else: weights = weights[:, tf.newaxis, tf.newaxis, tf.newaxis] tt_cores[0] = weights * tt_cores[0] out_shape = batch_tt.get_raw_shape() out_ranks = batch_tt.get_tt_ranks() out_batch_size = batch_tt.batch_size return TensorTrainBatch(tt_cores, out_shape, out_ranks, out_batch_size) def gram_matrix(tt_vectors, matrix=None): """Computes Gramian matrix of a batch of TT-vecors. If matrix is None, computes res[i, j] = t3f.flat_inner(tt_vectors[i], tt_vectors[j]). If matrix is present, computes res[i, j] = t3f.flat_inner(tt_vectors[i], t3f.matmul(matrix, tt_vectors[j])) or more shortly res[i, j] = tt_vectors[i]^T * matrix * tt_vectors[j] but is more efficient. Args: tt_vectors: TensorTrainBatch. matrix: None, or TensorTrain matrix. Returns: tf.tensor with the Gram matrix. """ return pairwise_flat_inner(tt_vectors, tt_vectors, matrix) def pairwise_flat_inner(tt_1, tt_2, matrix=None): """Computes all scalar products between two batches of TT-objects. If matrix is None, computes res[i, j] = t3f.flat_inner(tt_1[i], tt_2[j]). If matrix is present, computes res[i, j] = t3f.flat_inner(tt_1[i], t3f.matmul(matrix, tt_2[j])) or more shortly res[i, j] = tt_1[i]^T * matrix * tt_2[j] but is more efficient. Args: tt_1: TensorTrainBatch. tt_2: TensorTrainBatch. matrix: None, or TensorTrain matrix. Returns: tf.tensor with the matrix of pairwise scalar products (flat inners). """ ndims = tt_1.ndims() if matrix is None: curr_core_1 = tt_1.tt_cores[0] curr_core_2 = tt_2.tt_cores[0] mode_string = 'ij' if tt_1.is_tt_matrix() else 'i' einsum_str = 'pa{0}b,qc{0}d->pqbd'.format(mode_string) res = tf.einsum(einsum_str, curr_core_1, curr_core_2) for core_idx in range(1, ndims): curr_core_1 = tt_1.tt_cores[core_idx] curr_core_2 = tt_2.tt_cores[core_idx] einsum_str = 'pqac,pa{0}b,qc{0}d->pqbd'.format(mode_string) res = tf.einsum(einsum_str, res, curr_core_1, curr_core_2) else: # res[i, j] = tt_1[i] ^ T * matrix * tt_2[j] if not tt_1.is_tt_matrix() or not tt_2.is_tt_matrix() or not matrix.is_tt_matrix(): raise ValueError('When passing three arguments to pairwise_flat_inner, ' 'the first 2 of them should be TT-vecors and the last ' 'should be a TT-matrix. Got %s, %s, and %s instead.' % (tt_1, tt_2, matrix)) matrix_shape = matrix.get_raw_shape() if not tt_1.get_raw_shape()[0].is_compatible_with(matrix_shape[0]): raise ValueError('The shape of the first argument should be compatible ' 'with the shape of the TT-matrix, that is it should be ' 'possible to do the following matmul: ' 'transpose(tt_1) * matrix. Got the first argument ' '"%s" and matrix "%s"' % (tt_1, matrix)) if not tt_2.get_raw_shape()[0].is_compatible_with(matrix_shape[1]): raise ValueError('The shape of the second argument should be compatible ' 'with the shape of the TT-matrix, that is it should be ' 'possible to do the following matmul: ' 'matrix * tt_2. Got the second argument ' '"%s" and matrix "%s"' % (tt_2, matrix)) vectors_1_shape = tt_1.get_shape() if vectors_1_shape[2] == 1 and vectors_1_shape[1] != 1: # TODO: not very efficient, better to use different order in einsum. tt_1 = ops.transpose(tt_1) vectors_1_shape = tt_1.get_shape() vectors_2_shape = tt_2.get_shape() if vectors_2_shape[2] == 1 and vectors_2_shape[1] != 1: # TODO: not very efficient, better to use different order in einsum. tt_2 = ops.transpose(tt_2) vectors_2_shape = tt_2.get_shape() if vectors_1_shape[1] != 1: # TODO: do something so that in case the shape is undefined on compilation # it still works. raise ValueError('The tt_vectors_1 argument should be vectors (not ' 'matrices) with shape defined on compilation.') if vectors_2_shape[1] != 1: # TODO: do something so that in case the shape is undefined on compilation # it still works. raise ValueError('The tt_vectors_2 argument should be vectors (not ' 'matrices) with shape defined on compilation.') curr_core_1 = tt_1.tt_cores[0] curr_core_2 = tt_2.tt_cores[0] curr_matrix_core = matrix.tt_cores[0] # We enumerate the dummy dimension (that takes 1 value) with `k`. res = tf.einsum('pakib,cijd,qekjf->pqbdf', curr_core_1, curr_matrix_core, curr_core_2) for core_idx in range(1, ndims): curr_core_1 = tt_1.tt_cores[core_idx] curr_core_2 = tt_2.tt_cores[core_idx] curr_matrix_core = matrix.tt_cores[core_idx] res = tf.einsum('pqace,pakib,cijd,qekjf->pqbdf', res, curr_core_1, curr_matrix_core, curr_core_2) # Squeeze to make the result of size batch_size x batch_size instead of # batch_size x batch_size x 1 x 1. return tf.squeeze(res)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 26110, 408, 15886, 13, 13, 3166, 260, 29941, 29888, 29889, 20158, 29918, 14968, 29918, 3188, 1053, 323, 6073, 5323, 262, 5160, 13, 3166, 260, 29941, 29888, 29889, 20158, 29918, 14968, 29918, 16175, 1053, 323, 6073, 5323, 262, 23145, 13, 3166, 260, 29941, 29888, 1053, 288, 567, 13, 13, 13, 1753, 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account/admin.py	RichardLeeH/invoce_sys	0	8968	<gh_stars>0 # -- coding: utf-8 -- from __future__ import unicode_literals from django.contrib import admin from django.contrib.auth.admin import UserAdmin from django.contrib.auth.models import User from rest_framework.authtoken.models import Token from account.models import Profile admin.site.site_header = 'invoce' class TokenAdmin(admin.ModelAdmin): list_display = ('key', 'uid', 'user', 'created') fields = ('user',) ordering = ('-created',) def uid(self, obj): return obj.user.id uid.short_description = u'用户ID' admin.site.unregister(Token) admin.site.register(Token, TokenAdmin) class ProfileInline(admin.StackedInline): model = Profile class UserCustomAdmin(UserAdmin): list_display = ('id', 'username', 'email', 'is_active', 'is_staff') inlines = (ProfileInline, ) ordering = ('-id', ) admin.site.unregister(User) admin.site.register(User, UserCustomAdmin)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 21570, 1053, 4113, 13, 3166, 9557, 29889, 21570, 29889, 5150, 29889, 6406, 1053, 4911, 12754, 13, 3166, 9557, 29889, 21570, 29889, 5150, 29889, 9794, 1053, 4911, 13, 3166, 1791, 29918, 4468, 29889, 1300, 400, 4476, 29889, 9794, 1053, 25159, 13, 13, 3166, 3633, 29889, 9794, 1053, 20802, 13, 13, 6406, 29889, 2746, 29889, 2746, 29918, 6672, 353, 525, 262, 1365, 346, 29915, 13, 13, 13, 1990, 25159, 12754, 29898, 6406, 29889, 3195, 12754, 1125, 13, 1678, 1051, 29918, 4990, 353, 6702, 1989, 742, 525, 5416, 742, 525, 1792, 742, 525, 11600, 1495, 13, 1678, 4235, 353, 6702, 1792, 742, 29897, 13, 1678, 20520, 353, 6702, 29899, 11600, 742, 29897, 13, 13, 1678, 822, 318, 333, 29898, 1311, 29892, 5446, 1125, 13, 4706, 736, 5446, 29889, 1792, 29889, 333, 13, 1678, 318, 333, 29889, 12759, 29918, 8216, 353, 318, 29915, 30406, 31229, 1367, 29915, 13, 13, 6406, 29889, 2746, 29889, 348, 9573, 29898, 6066, 29897, 13, 6406, 29889, 2746, 29889, 9573, 29898, 6066, 29892, 25159, 12754, 29897, 13, 13, 13, 1990, 20802, 797, 1220, 29898, 6406, 29889, 7264, 287, 797, 1220, 1125, 13, 1678, 1904, 353, 20802, 13, 13, 13, 1990, 4911, 7281, 12754, 29898, 2659, 12754, 1125, 13, 1678, 1051, 29918, 4990, 353, 6702, 333, 742, 525, 6786, 742, 525, 5269, 742, 525, 275, 29918, 4925, 742, 525, 275, 29918, 303, 3470, 1495, 13, 1678, 297, 9012, 353, 313, 13909, 797, 1220, 29892, 1723, 13, 1678, 20520, 353, 6702, 29899, 333, 742, 1723, 13, 13, 6406, 29889, 2746, 29889, 348, 9573, 29898, 2659, 29897, 13, 6406, 29889, 2746, 29889, 9573, 29898, 2659, 29892, 4911, 7281, 12754, 29897, 13, 2 ]
1.n_gram.py	Muraru-taketa/100_knocks	0	165976	#N_gram.py #owakatiの分かち書きをする。uni=1,bi=2 tri=3 '''どうやったら分かち書きになるか今日は眠いをbigramに手動ですると今日、日は、は眠、眠い今で始まって日で終わる感じで定義する。''' def n_gram(txt, n): #n=n_garmのn result = [] for i in range(0, len(txt) - n + 1):#例：今日という単語からn個(2)-で+1すると日から始まる result.append(txt[i:i + n]) #iで始まってi+n(nは指定分割個数的なやつ) return result txt = "I am an NLPer" words = txt.split(' ') #文字分け print(n_gram(txt, 2)) #単語 print(n_gram(words, 2))	[ 1, 396, 29940, 29918, 1393, 29889, 2272, 30004, 13, 29937, 340, 557, 2219, 30199, 30748, 30412, 30644, 30854, 30538, 30396, 30427, 30332, 30267, 3909, 29922, 29896, 29892, 5365, 29922, 29906, 3367, 29922, 29941, 30004, 13, 12008, 31250, 30465, 31111, 30665, 30366, 30513, 30748, 30412, 30644, 30854, 30538, 30353, 30371, 30332, 30412, 30004, 13, 31482, 30325, 30449, 234, 159, 163, 30298, 30396, 3752, 2572, 30353, 30880, 31124, 30499, 30427, 30332, 30364, 30004, 13, 31482, 30325, 30330, 30325, 30449, 30330, 30449, 234, 159, 163, 30330, 234, 159, 163, 30298, 30004, 13, 31482, 30499, 31020, 30441, 30665, 30466, 30325, 30499, 234, 184, 133, 31068, 30332, 233, 135, 162, 31115, 30499, 30495, 31131, 30427, 30332, 30267, 12008, 30004, 13, 1753, 302, 29918, 1393, 29898, 3945, 29892, 302, 1125, 30004, 13, 29937, 29876, 29922, 29876, 29918, 29887, 2817, 30199, 29876, 30004, 13, 1678, 1121, 353, 5159, 30004, 13, 1678, 363, 474, 297, 3464, 29898, 29900, 29892, 7431, 29898, 3945, 29897, 448, 302, 718, 29871, 29896, 1125, 29937, 31507, 30383, 31482, 30325, 30364, 30298, 30465, 232, 144, 155, 30968, 30412, 30513, 29876, 232, 131, 142, 29898, 29906, 6817, 30499, 29974, 29896, 30427, 30332, 30364, 30325, 30412, 30513, 31020, 30441, 30332, 30004, 13, 4706, 1121, 29889, 4397, 29898, 3945, 29961, 29875, 29901, 29875, 718, 302, 2314, 30004, 13, 29937, 29875, 30499, 31020, 30441, 30665, 30466, 29875, 29974, 29876, 29898, 29876, 30449, 31084, 30495, 30748, 232, 140, 181, 232, 131, 142, 30354, 30210, 30371, 31111, 30773, 8443, 13, 1678, 736, 1121, 30004, 13, 30004, 13, 3945, 353, 376, 29902, 626, 385, 405, 29931, 5894, 19451, 13, 9303, 353, 13872, 29889, 5451, 877, 525, 8443, 13, 29937, 30333, 30578, 30748, 30807, 30004, 13, 2158, 29898, 29876, 29918, 1393, 29898, 3945, 29892, 29871, 29906, 876, 30004, 13, 29937, 232, 144, 155, 30968, 30004, 13, 2158, 29898, 29876, 29918, 1393, 29898, 9303, 29892, 29871, 29906, 876, 2 ]
examples/chem_equil.py	bjodah/symneqsys	1	126346	#!/usr/bin/env python # -- coding: utf-8 -- from __future__ import division, print_function """ Example of a 4 dimensional root finidng problem (chemical acid/base equilbria) """ from sympy import pprint from symneqsys import SimpleNEQSys, Problem from symneqsys.minpack import MINPACK_Solver class ChemSys(SimpleNEQSys): var_tokens = 'NH3 NH4p Hp OHm H2O' param_tokens = 'Kw Ka ' + ' '.join(['init_'+x for x in var_tokens.split()]) @property def exprs(self): NH3, NH4p, Hp, OHm, H2O, Kw, Ka = [ self[token] for token in ('NH3', 'NH4p', 'Hp', 'OHm', 'H2O', 'Kw', 'Ka')] init = {x: self['init_'+x] for x in self.var_tokens.split()} return [ HpNH3/NH4p-Ka, # NH4+ acid equil HpOHm/H2O-Kw, # Water autoprotolysis equilibrium NH3+NH4p-init['NH3']-init['NH4p'], # preserv. of N atoms 3NH3+4NH4p+Hp+OHm+2H2O-( 3init['NH3']+4init['NH4p'] + init['Hp']+init['OHm']+2init['H2O']), # prsrv H atoms OHm+H2O-init['OHm']-init['H2O'], # preservation of O atoms NH4p+Hp-OHm-(init['NH4p']+init['Hp'] - init['OHm']) # preservation of charge ] def main(Sys, logger=None): """ Solve the example system using Levenberg-Marquardt algorithm from Netlib's MINPACK fortran code. """ sys = Sys() print('Variables:') pprint(sys.v) print('Expressions:') for expr in sys.exprs: pprint(expr) print('Jacobian:') pprint(sys.jac) solver = MINPACK_Solver( save_temp=True, tempdir='./build/chem_equil', logger=logger) solver.abstol = 1e-8 params = {'Kw': 1e-14, 'Ka': 10**-9.26} init = {'NH3': 1e-3, 'NH4p': 0.0, 'Hp': 1e-7, 'OHm': 1e-7, 'H2O': 1.0} params.update({'init_'+key: val for key, val in init.iteritems()}) problem = Problem( sys, params, guess={ k: v if v > 1e-12 else 1e-12 for k, v in init.iteritems()}, solver=solver) success = problem.solve(itermax=100, solver_type='lm') if success: print("Successfully found a root: " + ", ".join([key+'='+str(problem.solution[sys[key]]) for key in init.keys()]) + ", using Levenberg-Marquard (lm)") else: print("Root-finding unsuccessful.") print('Full numerical info:', problem.solver.num_result) if __name__ == '__main__': import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) main(ChemSys, 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bfgame/components/shield.py	ChrisLR/BasicDungeonRL	3	42257	<reponame>ChrisLR/BasicDungeonRL from core.components import Component, listing @listing.register class Shield(Component): NAME = "shield" __slots__ = ["armor_class_melee", "armor_class_missile"] def __init__(self, armor_class_melee, armor_class_missile): super().__init__() self.armor_class_melee = armor_class_melee self.armor_class_missile = armor_class_missile def copy(self): return Shield(self.armor_class_melee, self.armor_class_missile)	[ 1, 529, 276, 1112, 420, 29958, 11626, 275, 29519, 29914, 16616, 29928, 19440, 265, 2241, 13, 3166, 7136, 29889, 14036, 1053, 15924, 29892, 18028, 13, 13, 13, 29992, 1761, 292, 29889, 9573, 13, 1990, 1383, 969, 29898, 5308, 1125, 13, 1678, 27085, 353, 376, 845, 969, 29908, 13, 1678, 4770, 2536, 1862, 1649, 353, 6796, 2817, 272, 29918, 1990, 29918, 1004, 17179, 613, 376, 2817, 272, 29918, 1990, 29918, 9894, 488, 3108, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 5075, 272, 29918, 1990, 29918, 1004, 17179, 29892, 5075, 272, 29918, 1990, 29918, 9894, 488, 1125, 13, 4706, 2428, 2141, 1649, 2344, 1649, 580, 13, 4706, 1583, 29889, 2817, 272, 29918, 1990, 29918, 1004, 17179, 353, 5075, 272, 29918, 1990, 29918, 1004, 17179, 13, 4706, 1583, 29889, 2817, 272, 29918, 1990, 29918, 9894, 488, 353, 5075, 272, 29918, 1990, 29918, 9894, 488, 13, 13, 1678, 822, 3509, 29898, 1311, 1125, 13, 4706, 736, 1383, 969, 29898, 1311, 29889, 2817, 272, 29918, 1990, 29918, 1004, 17179, 29892, 1583, 29889, 2817, 272, 29918, 1990, 29918, 9894, 488, 29897, 13, 2 ]
src/nise/metadata/config.py	kurtp-ni/nimi-python	88	94005	# -- coding: utf-8 -- # This file is generated from NI Switch Executive API metadata version 21.0.0d1 config = { 'api_version': '21.0.0d1', 'c_function_prefix': 'niSE_', 'close_function': 'CloseSession', 'context_manager_name': { }, 'custom_types': [ ], 'driver_name': 'NI Switch Executive', 'driver_registry': 'Switch Executive', 'extra_errors_used': [ 'InvalidRepeatedCapabilityError' ], 'init_function': 'OpenSession', 'library_info': { 'Linux': { '64bit': { 'name': 'nise', 'type': 'cdll' } }, 'Windows': { '32bit': { 'name': 'nise.dll', 'type': 'windll' }, '64bit': { 'name': 'nise.dll', 'type': 'cdll' } } }, 'metadata_version': '2.0', 'module_name': 'nise', 'repeated_capabilities': [ ], 'session_class_description': 'An NI Switch Executive session', 'session_handle_parameter_name': 'vi', 'use_locking': False }	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 910, 934, 338, 5759, 515, 405, 29902, 28176, 28841, 3450, 15562, 1873, 29871, 29906, 29896, 29889, 29900, 29889, 29900, 29881, 29896, 13, 2917, 353, 426, 13, 1678, 525, 2754, 29918, 3259, 2396, 525, 29906, 29896, 29889, 29900, 29889, 29900, 29881, 29896, 742, 13, 1678, 525, 29883, 29918, 2220, 29918, 13506, 2396, 525, 1240, 1660, 29918, 742, 13, 1678, 525, 5358, 29918, 2220, 2396, 525, 11123, 7317, 742, 13, 1678, 525, 4703, 29918, 12847, 29918, 978, 2396, 426, 13, 1678, 2981, 13, 1678, 525, 6341, 29918, 8768, 2396, 518, 13, 1678, 21251, 13, 1678, 525, 9465, 29918, 978, 2396, 525, 12916, 28176, 28841, 742, 13, 1678, 525, 9465, 29918, 1727, 6020, 2396, 525, 24995, 28841, 742, 13, 1678, 525, 17833, 29918, 12523, 29918, 3880, 2396, 518, 13, 4706, 525, 13919, 1123, 412, 630, 12415, 3097, 2392, 29915, 13, 1678, 21251, 13, 1678, 525, 2344, 29918, 2220, 2396, 525, 6585, 7317, 742, 13, 1678, 525, 5258, 29918, 3888, 2396, 426, 13, 4706, 525, 24085, 2396, 426, 13, 9651, 525, 29953, 29946, 2966, 2396, 426, 13, 18884, 525, 978, 2396, 525, 29876, 895, 742, 13, 18884, 525, 1853, 2396, 525, 2252, 645, 29915, 13, 9651, 500, 13, 4706, 2981, 13, 4706, 525, 7685, 2396, 426, 13, 9651, 525, 29941, 29906, 2966, 2396, 426, 13, 18884, 525, 978, 2396, 525, 29876, 895, 29889, 12396, 742, 13, 18884, 525, 1853, 2396, 525, 14800, 645, 29915, 13, 9651, 2981, 13, 9651, 525, 29953, 29946, 2966, 2396, 426, 13, 18884, 525, 978, 2396, 525, 29876, 895, 29889, 12396, 742, 13, 18884, 525, 1853, 2396, 525, 2252, 645, 29915, 13, 9651, 500, 13, 4706, 500, 13, 1678, 2981, 13, 1678, 525, 19635, 29918, 3259, 2396, 525, 29906, 29889, 29900, 742, 13, 1678, 525, 5453, 29918, 978, 2396, 525, 29876, 895, 742, 13, 1678, 525, 276, 412, 630, 29918, 5030, 11614, 2396, 518, 13, 1678, 21251, 13, 1678, 525, 7924, 29918, 1990, 29918, 8216, 2396, 525, 2744, 405, 29902, 28176, 28841, 4867, 742, 13, 1678, 525, 7924, 29918, 8411, 29918, 15501, 29918, 978, 2396, 525, 1403, 742, 13, 1678, 525, 1509, 29918, 908, 292, 2396, 7700, 13, 29913, 13, 2 ]
convert.py	Resist4263/span-aste-1	0	22540	<filename>convert.py<gh_stars>0 from gensim.scripts.glove2word2vec import glove2word2vec (count, dimensions) = glove2word2vec("dataset/glove.42B.300d.txt", "dataset/cropus/42B_w2v.txt")	[ 1, 529, 9507, 29958, 13441, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 26943, 326, 29889, 16713, 29889, 29887, 417, 345, 29906, 1742, 29906, 2003, 1053, 15482, 345, 29906, 1742, 29906, 2003, 13, 13, 29898, 2798, 29892, 13391, 29897, 353, 15482, 345, 29906, 1742, 29906, 2003, 703, 24713, 29914, 29887, 417, 345, 29889, 29946, 29906, 29933, 29889, 29941, 29900, 29900, 29881, 29889, 3945, 613, 376, 24713, 29914, 29883, 1336, 375, 29914, 29946, 29906, 29933, 29918, 29893, 29906, 29894, 29889, 3945, 1159, 2 ]
zhiliao/blog/__init__.py	gladgod/zhiliao	0	135462	""" Provides a blogging app with posts, keywords, categories and comments. Posts can be listed by month, keyword, category or author. """ from __future__ import unicode_literals from zhiliao import __version__	[ 1, 9995, 13, 1184, 29894, 2247, 263, 12618, 3460, 623, 411, 11803, 29892, 29361, 29892, 13997, 322, 6589, 29889, 13, 6747, 29879, 508, 367, 9904, 491, 4098, 29892, 13553, 29892, 7663, 470, 4148, 29889, 13, 15945, 29908, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 503, 29882, 11836, 29877, 1053, 4770, 3259, 1649, 13, 2 ]
wavefront_api_client/models/customer_preferences.py	mdennehy/python-client	0	71969	# coding: utf-8 """ Wavefront REST API <p>The Wavefront REST API enables you to interact with Wavefront servers using standard REST API tools. You can use the REST API to automate commonly executed operations such as automatically tagging sources.</p><p>When you make REST API calls outside the Wavefront REST API documentation you must add the header \"Authorization: Bearer <<API-TOKEN>>\" to your HTTP requests.</p> # noqa: E501 OpenAPI spec version: v2 Contact: <EMAIL> Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from wavefront_api_client.models.user_group import UserGroup # noqa: F401,E501 class CustomerPreferences(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'blacklisted_emails': 'dict(str, int)', 'created_epoch_millis': 'int', 'creator_id': 'str', 'customer_id': 'str', 'default_user_groups': 'list[UserGroup]', 'deleted': 'bool', 'grant_modify_access_to_everyone': 'bool', 'hidden_metric_prefixes': 'dict(str, int)', 'hide_ts_when_querybuilder_shown': 'bool', 'id': 'str', 'invite_permissions': 'list[str]', 'landing_dashboard_slug': 'str', 'show_onboarding': 'bool', 'show_querybuilder_by_default': 'bool', 'updated_epoch_millis': 'int', 'updater_id': 'str' } attribute_map = { 'blacklisted_emails': 'blacklistedEmails', 'created_epoch_millis': 'createdEpochMillis', 'creator_id': 'creatorId', 'customer_id': 'customerId', 'default_user_groups': 'defaultUserGroups', 'deleted': 'deleted', 'grant_modify_access_to_everyone': 'grantModifyAccessToEveryone', 'hidden_metric_prefixes': 'hiddenMetricPrefixes', 'hide_ts_when_querybuilder_shown': 'hideTSWhenQuerybuilderShown', 'id': 'id', 'invite_permissions': 'invitePermissions', 'landing_dashboard_slug': 'landingDashboardSlug', 'show_onboarding': 'showOnboarding', 'show_querybuilder_by_default': 'showQuerybuilderByDefault', 'updated_epoch_millis': 'updatedEpochMillis', 'updater_id': 'updaterId' } def __init__(self, blacklisted_emails=None, created_epoch_millis=None, creator_id=None, customer_id=None, default_user_groups=None, deleted=None, grant_modify_access_to_everyone=None, hidden_metric_prefixes=None, hide_ts_when_querybuilder_shown=None, id=None, invite_permissions=None, landing_dashboard_slug=None, show_onboarding=None, show_querybuilder_by_default=None, updated_epoch_millis=None, updater_id=None): # noqa: E501 """CustomerPreferences - a model defined in Swagger""" # noqa: E501 self._blacklisted_emails = None self._created_epoch_millis = None self._creator_id = None self._customer_id = None self._default_user_groups = None self._deleted = None self._grant_modify_access_to_everyone = None self._hidden_metric_prefixes = None self._hide_ts_when_querybuilder_shown = None self._id = None self._invite_permissions = None self._landing_dashboard_slug = None self._show_onboarding = None self._show_querybuilder_by_default = None self._updated_epoch_millis = None self._updater_id = None self.discriminator = None if blacklisted_emails is not None: self.blacklisted_emails = blacklisted_emails if created_epoch_millis is not None: self.created_epoch_millis = created_epoch_millis if creator_id is not None: self.creator_id = creator_id self.customer_id = customer_id if default_user_groups is not None: self.default_user_groups = default_user_groups if deleted is not None: self.deleted = deleted self.grant_modify_access_to_everyone = grant_modify_access_to_everyone if hidden_metric_prefixes is not None: self.hidden_metric_prefixes = hidden_metric_prefixes self.hide_ts_when_querybuilder_shown = hide_ts_when_querybuilder_shown if id is not None: self.id = id if invite_permissions is not None: self.invite_permissions = invite_permissions if landing_dashboard_slug is not None: self.landing_dashboard_slug = landing_dashboard_slug self.show_onboarding = show_onboarding self.show_querybuilder_by_default = show_querybuilder_by_default if updated_epoch_millis is not None: self.updated_epoch_millis = updated_epoch_millis if updater_id is not None: self.updater_id = updater_id @property def blacklisted_emails(self): """Gets the blacklisted_emails of this CustomerPreferences. # noqa: E501 List of blacklisted emails of the customer # noqa: E501 :return: The blacklisted_emails of this CustomerPreferences. # noqa: E501 :rtype: dict(str, int) """ return self._blacklisted_emails @blacklisted_emails.setter def blacklisted_emails(self, blacklisted_emails): """Sets the blacklisted_emails of this CustomerPreferences. List of blacklisted emails of the customer # noqa: E501 :param blacklisted_emails: The blacklisted_emails of this CustomerPreferences. # noqa: E501 :type: dict(str, int) """ self._blacklisted_emails = blacklisted_emails @property def created_epoch_millis(self): """Gets the created_epoch_millis of this CustomerPreferences. # noqa: E501 :return: The created_epoch_millis of this CustomerPreferences. # noqa: E501 :rtype: int """ return self._created_epoch_millis @created_epoch_millis.setter def created_epoch_millis(self, created_epoch_millis): """Sets the created_epoch_millis of this CustomerPreferences. :param created_epoch_millis: The created_epoch_millis of this CustomerPreferences. # noqa: E501 :type: int """ self._created_epoch_millis = created_epoch_millis @property def creator_id(self): """Gets the creator_id of this CustomerPreferences. # noqa: E501 :return: The creator_id of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._creator_id @creator_id.setter def creator_id(self, creator_id): """Sets the creator_id of this CustomerPreferences. :param creator_id: The creator_id of this CustomerPreferences. # noqa: E501 :type: str """ self._creator_id = creator_id @property def customer_id(self): """Gets the customer_id of this CustomerPreferences. # noqa: E501 The id of the customer preferences are attached to # noqa: E501 :return: The customer_id of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._customer_id @customer_id.setter def customer_id(self, customer_id): """Sets the customer_id of this CustomerPreferences. The id of the customer preferences are attached to # noqa: E501 :param customer_id: The customer_id of this CustomerPreferences. # noqa: E501 :type: str """ if customer_id is None: raise ValueError("Invalid value for `customer_id`, must not be `None`") # noqa: E501 self._customer_id = customer_id @property def default_user_groups(self): """Gets the default_user_groups of this CustomerPreferences. # noqa: E501 List of default user groups of the customer # noqa: E501 :return: The default_user_groups of this CustomerPreferences. # noqa: E501 :rtype: list[UserGroup] """ return self._default_user_groups @default_user_groups.setter def default_user_groups(self, default_user_groups): """Sets the default_user_groups of this CustomerPreferences. List of default user groups of the customer # noqa: E501 :param default_user_groups: The default_user_groups of this CustomerPreferences. # noqa: E501 :type: list[UserGroup] """ self._default_user_groups = default_user_groups @property def deleted(self): """Gets the deleted of this CustomerPreferences. # noqa: E501 :return: The deleted of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._deleted @deleted.setter def deleted(self, deleted): """Sets the deleted of this CustomerPreferences. :param deleted: The deleted of this CustomerPreferences. # noqa: E501 :type: bool """ self._deleted = deleted @property def grant_modify_access_to_everyone(self): """Gets the grant_modify_access_to_everyone of this CustomerPreferences. # noqa: E501 Whether modify access of new entites is granted to Everyone or to the Creator # noqa: E501 :return: The grant_modify_access_to_everyone of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._grant_modify_access_to_everyone @grant_modify_access_to_everyone.setter def grant_modify_access_to_everyone(self, grant_modify_access_to_everyone): """Sets the grant_modify_access_to_everyone of this CustomerPreferences. Whether modify access of new entites is granted to Everyone or to the Creator # noqa: E501 :param grant_modify_access_to_everyone: The grant_modify_access_to_everyone of this CustomerPreferences. # noqa: E501 :type: bool """ if grant_modify_access_to_everyone is None: raise ValueError("Invalid value for `grant_modify_access_to_everyone`, must not be `None`") # noqa: E501 self._grant_modify_access_to_everyone = grant_modify_access_to_everyone @property def hidden_metric_prefixes(self): """Gets the hidden_metric_prefixes of this CustomerPreferences. # noqa: E501 Metric prefixes which should be hidden from user # noqa: E501 :return: The hidden_metric_prefixes of this CustomerPreferences. # noqa: E501 :rtype: dict(str, int) """ return self._hidden_metric_prefixes @hidden_metric_prefixes.setter def hidden_metric_prefixes(self, hidden_metric_prefixes): """Sets the hidden_metric_prefixes of this CustomerPreferences. Metric prefixes which should be hidden from user # noqa: E501 :param hidden_metric_prefixes: The hidden_metric_prefixes of this CustomerPreferences. # noqa: E501 :type: dict(str, int) """ self._hidden_metric_prefixes = hidden_metric_prefixes @property def hide_ts_when_querybuilder_shown(self): """Gets the hide_ts_when_querybuilder_shown of this CustomerPreferences. # noqa: E501 Whether to hide TS source input when Querybuilder is shown # noqa: E501 :return: The hide_ts_when_querybuilder_shown of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._hide_ts_when_querybuilder_shown @hide_ts_when_querybuilder_shown.setter def hide_ts_when_querybuilder_shown(self, hide_ts_when_querybuilder_shown): """Sets the hide_ts_when_querybuilder_shown of this CustomerPreferences. Whether to hide TS source input when Querybuilder is shown # noqa: E501 :param hide_ts_when_querybuilder_shown: The hide_ts_when_querybuilder_shown of this CustomerPreferences. # noqa: E501 :type: bool """ if hide_ts_when_querybuilder_shown is None: raise ValueError("Invalid value for `hide_ts_when_querybuilder_shown`, must not be `None`") # noqa: E501 self._hide_ts_when_querybuilder_shown = hide_ts_when_querybuilder_shown @property def id(self): """Gets the id of this CustomerPreferences. # noqa: E501 :return: The id of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._id @id.setter def id(self, id): """Sets the id of this CustomerPreferences. :param id: The id of this CustomerPreferences. # noqa: E501 :type: str """ self._id = id @property def invite_permissions(self): """Gets the invite_permissions of this CustomerPreferences. # noqa: E501 List of permissions that are assigned to newly invited users # noqa: E501 :return: The invite_permissions of this CustomerPreferences. # noqa: E501 :rtype: list[str] """ return self._invite_permissions @invite_permissions.setter def invite_permissions(self, invite_permissions): """Sets the invite_permissions of this CustomerPreferences. List of permissions that are assigned to newly invited users # noqa: E501 :param invite_permissions: The invite_permissions of this CustomerPreferences. # noqa: E501 :type: list[str] """ self._invite_permissions = invite_permissions @property def landing_dashboard_slug(self): """Gets the landing_dashboard_slug of this CustomerPreferences. # noqa: E501 Dashboard where user will be redirected from landing page # noqa: E501 :return: The landing_dashboard_slug of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._landing_dashboard_slug @landing_dashboard_slug.setter def landing_dashboard_slug(self, landing_dashboard_slug): """Sets the landing_dashboard_slug of this CustomerPreferences. Dashboard where user will be redirected from landing page # noqa: E501 :param landing_dashboard_slug: The landing_dashboard_slug of this CustomerPreferences. # noqa: E501 :type: str """ self._landing_dashboard_slug = landing_dashboard_slug @property def show_onboarding(self): """Gets the show_onboarding of this CustomerPreferences. # noqa: E501 Whether to show onboarding for any new user without an override # noqa: E501 :return: The show_onboarding of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._show_onboarding @show_onboarding.setter def show_onboarding(self, show_onboarding): """Sets the show_onboarding of this CustomerPreferences. Whether to show onboarding for any new user without an override # noqa: E501 :param show_onboarding: The show_onboarding of this CustomerPreferences. # noqa: E501 :type: bool """ if show_onboarding is None: raise ValueError("Invalid value for `show_onboarding`, must not be `None`") # noqa: E501 self._show_onboarding = show_onboarding @property def show_querybuilder_by_default(self): """Gets the show_querybuilder_by_default of this CustomerPreferences. # noqa: E501 Whether the Querybuilder is shown by default # noqa: E501 :return: The show_querybuilder_by_default of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._show_querybuilder_by_default @show_querybuilder_by_default.setter def show_querybuilder_by_default(self, show_querybuilder_by_default): """Sets the show_querybuilder_by_default of this CustomerPreferences. Whether the Querybuilder is shown by default # noqa: E501 :param show_querybuilder_by_default: The show_querybuilder_by_default of this CustomerPreferences. # noqa: E501 :type: bool """ if show_querybuilder_by_default is None: raise ValueError("Invalid value for `show_querybuilder_by_default`, must not be `None`") # noqa: E501 self._show_querybuilder_by_default = show_querybuilder_by_default @property def updated_epoch_millis(self): """Gets the updated_epoch_millis of this CustomerPreferences. # noqa: E501 :return: The updated_epoch_millis of this CustomerPreferences. # noqa: E501 :rtype: int """ return self._updated_epoch_millis @updated_epoch_millis.setter def updated_epoch_millis(self, updated_epoch_millis): """Sets the updated_epoch_millis of this CustomerPreferences. :param updated_epoch_millis: The updated_epoch_millis of this CustomerPreferences. # noqa: E501 :type: int """ self._updated_epoch_millis = updated_epoch_millis @property def updater_id(self): """Gets the updater_id of this CustomerPreferences. # noqa: E501 :return: The updater_id of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._updater_id @updater_id.setter def updater_id(self, updater_id): """Sets the updater_id of this CustomerPreferences. :param updater_id: The updater_id of this CustomerPreferences. # noqa: E501 :type: str """ self._updater_id = updater_id def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(CustomerPreferences, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, CustomerPreferences): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other	[ 1, 396, 14137, 29901, 23616, 29899, 29947, 13, 13, 15945, 29908, 13, 1678, 399, 1351, 8862, 16759, 3450, 13, 13, 1678, 529, 29886, 29958, 1576, 399, 1351, 8862, 16759, 3450, 28936, 366, 304, 16254, 411, 399, 1351, 8862, 12424, 773, 3918, 16759, 3450, 8492, 29889, 887, 508, 671, 278, 16759, 3450, 304, 3345, 403, 15574, 8283, 6931, 1316, 408, 6336, 4055, 3460, 8974, 21106, 29886, 5299, 29886, 29958, 10401, 366, 1207, 16759, 3450, 5717, 5377, 278, 399, 1351, 8862, 16759, 3450, 5106, 366, 1818, 788, 278, 4839, 13218, 25471, 29901, 23606, 261, 669, 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uproot/const.py

guiguem/uproot

0

25113

#!/usr/bin/env python # Copyright (c) 2017, DIANA-HEP # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ROOT constants used in deserialization.""" import numpy # used in unmarshaling kByteCountMask = numpy.int64(0x40000000) kByteCountVMask = numpy.int64(0x4000) kClassMask = numpy.int64(0x80000000) kNewClassTag = numpy.int64(0xFFFFFFFF) kIsOnHeap = numpy.uint32(0x01000000) kIsReferenced = numpy.uint32(1 << 4) kMapOffset = 2 # not used? kNullTag = 0 kNotDeleted = 0x02000000 kZombie = 0x04000000 kBitMask = 0x00ffffff kDisplacementMask = 0xFF000000 ################################################################ core/zip/inc/Compression.h kZLIB = 1 kLZMA = 2 kOldCompressionAlgo = 3 kLZ4 = 4 kUndefinedCompressionAlgorithm = 5 ################################################################ constants for streamers kBase = 0 kChar = 1 kShort = 2 kInt = 3 kLong = 4 kFloat = 5 kCounter = 6 kCharStar = 7 kDouble = 8 kDouble32 = 9 kLegacyChar = 10 kUChar = 11 kUShort = 12 kUInt = 13 kULong = 14 kBits = 15 kLong64 = 16 kULong64 = 17 kBool = 18 kFloat16 = 19 kOffsetL = 20 kOffsetP = 40 kObject = 61 kAny = 62 kObjectp = 63 kObjectP = 64 kTString = 65 kTObject = 66 kTNamed = 67 kAnyp = 68 kAnyP = 69 kAnyPnoVT = 70 kSTLp = 71 kSkip = 100 kSkipL = 120 kSkipP = 140 kConv = 200 kConvL = 220 kConvP = 240 kSTL = 300 kSTLstring = 365 kStreamer = 500 kStreamLoop = 501 ################################################################ constants from core/foundation/inc/ESTLType.h kNotSTL = 0 kSTLvector = 1 kSTLlist = 2 kSTLdeque = 3 kSTLmap = 4 kSTLmultimap = 5 kSTLset = 6 kSTLmultiset = 7 kSTLbitset = 8 kSTLforwardlist = 9 kSTLunorderedset = 10 kSTLunorderedmultiset = 11 kSTLunorderedmap = 12 kSTLunorderedmultimap = 13 kSTLend = 14 kSTLany = 300 ################################################################ IOFeatures kGenerateOffsetMap = 1

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checkmate/contrib/plugins/git/commands/update_stats.py

marcinguy/checkmate-ce

80

146862

<reponame>marcinguy/checkmate-ce<gh_stars>10-100 # -*- coding: utf-8 -*- from __future__ import unicode_literals import logging from collections import defaultdict from checkmate.management.commands.base import BaseCommand from ..models import GitBranch logger = logging.getLogger(__name__) class Command(BaseCommand): def run(self): branches = self.backend.filter(GitBranch,{'project.pk' : self.project.pk}) branches_by_name = dict([(branch.name,branch) for branch in branches]) if 'default_branch' in self.project and self.project.default_branch in branches_by_name: master_branch = branches_by_name[self.project.default_branch] elif 'origin/master' in branches_by_name: master_branch = branches_by_name['origin/master'] else: logger.warning("No default branch defined for project %s" % self.project.pk) return -1 stats = { 'contributors' : self.project.git.repository.get_contributors(), 'branch' : master_branch.name } stats['n_commits'] = sum([contributor['n_commits'] for contributor in stats['contributors']]) self.project.stats = stats with self.backend.transaction(): self.backend.save(self.project)

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dataset.py

dimitreOliveira/RecsysChallenge_Spotify

3

63064

import pandas as pd def build_columns(): columns = ['pid'] for i in range(1, 501): columns.append('trackuri_%s' % i) return columns def build_output(df_list, id_cloumn, song_column): output = pd.DataFrame(columns=build_columns()) for df in df_list: output = output.append(format_output(df, id_cloumn, song_column)) return output def format_output(df, id_cloumn, song_column): columns = build_columns()[1:501] output = df output['columns'] = columns output = output.pivot(index=id_cloumn, columns='columns', values=song_column) output['pid'] = output.index output = output[build_columns()] return output def output_submission(df, file_name, team_name, contact_information, path='submissions/', challenge_track='main'): file = path + file_name first_row = pd.DataFrame(columns=build_columns()) first_row.loc[0] = build_first_row(team_name, contact_information, challenge_track) output = pd.concat([first_row, df]) output = output[build_columns()] output = output.set_index('pid') output.to_csv(file) def build_first_row(team_name, contact_information, challenge_track='main'): row = ['team_info', challenge_track, team_name, contact_information] for i in range(4, 501): row.append(None) return row

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CodeInterview/[cutz]compression_str.py

cutz-j/AlgorithmStudy

3

151029

<reponame>cutz-j/AlgorithmStudy from collections import defaultdict def solve(s): hash_dict = defaultdict(int) for i in range(len(s)): hash_dict[s[i]] += 1 return

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pandas_tricks.py

wingwingz/code_snippets

0

107580

<filename>pandas_tricks.py<gh_stars>0 def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=2): """ :param df_1: the left table to join :param df_2: the right table to join :param key1: key column of the left table :param key2: key column of the right table :param threshold: how close the matches should be to return a match, based on Levenshtein distance :param limit: the amount of matches that will get returned, these are sorted high to low :return: dataframe with boths keys and matches """ s = df_2[key2].tolist() m = df_1[key1].apply(lambda x: process.extract(x, s, limit=limit)) df_1['matches'] = m m2 = df_1['matches'].apply(lambda x: ', '.join([i[0] for i in x if i[1] >= threshold])) df_1['matches'] = m2 return df_1

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distiller/data/jobs.py

18F/FAC-Distiller-

2

108834

""" Use apscheduler to run a daily job to refresh the source tables. """ import random import sys import time import pytz from apscheduler.schedulers.background import BackgroundScheduler from django.conf import settings from django_apscheduler.jobstores import ( DjangoJobStore, register_events, register_job ) from .etls import load_dumps scheduler = BackgroundScheduler() scheduler.add_jobstore(DjangoJobStore(), 'default') @register_job( scheduler, 'cron', hour=0, timezone=pytz.timezone('US/Eastern'), replace_existing=True ) def download_and_update_tables(): for table in load_dumps.FAC_TABLES_NAMES: sys.stdout.write(f'Downloading table "{table}"...\n') sys.stdout.flush() load_dumps.download_table( table, target_dir=settings.LOAD_TABLE_ROOT ) for table in load_dumps.FAC_TABLES_NAMES: sys.stdout.write(f'Loading FAC table "{table}"...\n') sys.stdout.flush() load_dumps.update_table( table, source_dir=settings.LOAD_TABLE_ROOT, ) register_events(scheduler) scheduler.start()

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teaparty/helpers.py

idooo/teaparty

0

80577

from time import time def cache(function): memo = {} time_to_cache = 60 def wrapper(*args): if args in memo and time() < memo[args]['time'] + time_to_cache: return memo[args]['record'] else: rv = function(*args) memo[args] = { 'record': rv, 'time': time() } return rv return wrapper

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tests/test_atise.py

tkg-framework/TKG-framework

0

16403

<reponame>tkg-framework/TKG-framework<filename>tests/test_atise.py<gh_stars>0 import torch import torch.nn as nn from torch.autograd import Variable import torch.functional as F from torch.nn.init import xavier_normal_ import os import sys BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(BASE_DIR) import numpy as np from numpy.random import RandomState from collections import defaultdict import time from tkge.data.dataset import SplitDataset from tkge.data.custom_dataset import ICEWS14AtiseDatasetProcessor from tkge.eval.metrics import Evaluation from tkge.train.sampling import NonNegativeSampler from Dataset import KnowledgeGraph randseed = 9999 np.random.seed(randseed) torch.manual_seed(randseed) class MockAtiseDatasetProcessor(ICEWS14AtiseDatasetProcessor): def __init__(self): self.folder = "/home/gengyuan/workspace/tkge/data/icews14" self.level = "day" self.index = False self.float = True self.train_raw = [] self.valid_raw = [] self.test_raw = [] self.reciprocal_training = True self.ent2id = dict() self.rel2id = dict() with open('/home/gengyuan/workspace/baseline/ATISE/icews14/entity2id.txt', 'r') as f: ent2id = f.readlines() for line in ent2id: line = line.split('\t') self.ent2id[line[0]] = int(line[1]) with open('/home/gengyuan/workspace/baseline/ATISE/icews14/relation2id.txt', 'r') as f: rel2id = f.readlines() for line in rel2id: line = line.split('\t') self.rel2id[line[0]] = int(line[1]) self.ts2id = defaultdict(None) self.train_set = defaultdict(list) self.valid_set = defaultdict(list) self.test_set = defaultdict(list) self.all_triples = [] self.all_quadruples = [] self.load() self.process() self.filter() def index_relations(self, rel: str): if rel.endswith('(RECIPROCAL)'): return self.rel2id[rel[:-12]] + 230 else: return self.rel2id[rel] def index_entities(self, ent: str): if ent == '<NAME>': ent = '<NAME>' return self.ent2id[ent] def process_time(self, origin: str): # TODO (gengyuan) move to init method self.gran = 3 start_sec = time.mktime(time.strptime('2014-01-01', '%Y-%m-%d')) end_sec = time.mktime(time.strptime(origin, '%Y-%m-%d')) day = int((end_sec - start_sec) / (self.gran * 24 * 60 * 60)) return day class MockEvaluation(Evaluation): def __init__(self, dataset, device): self.dataset = dataset self.vocab_size = dataset.num_entities() self.device = device self.filter = "time-aware" self.ordering = "optimistic" self.k = [1, 3, 10] self.filtered_data = defaultdict(None) self.filtered_data['sp_'] = self.dataset.filter(type=self.filter, target='o') self.filtered_data['_po'] = self.dataset.filter(type=self.filter, target='s') class MockSampler(NonNegativeSampler): def __init__(self, dataset, as_matrix): self.filter = False self.as_matrix = as_matrix self.dataset = dataset class ATISE(nn.Module): def __init__(self, n_entity, n_relation, embedding_dim, batch_size, learning_rate, gamma, cmin, cmax, gpu=True): super(ATISE, self).__init__() self.gpu = gpu self.n_entity = n_entity self.n_relation = n_relation self.embedding_dim = embedding_dim self.batch_size = batch_size self.learning_rate = learning_rate self.gamma = gamma self.cmin = cmin self.cmax = cmax # Nets self.emb_E = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.emb_E_var = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.emb_R = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) self.emb_R_var = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) self.emb_TE = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.alpha_E = torch.nn.Embedding(n_entity, 1, padding_idx=0) self.beta_E = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.omega_E = torch.nn.Embedding(n_entity, self.embedding_dim, padding_idx=0) self.emb_TR = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) self.alpha_R = torch.nn.Embedding(n_relation, 1, padding_idx=0) self.beta_R = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) self.omega_R = torch.nn.Embedding(n_relation, self.embedding_dim, padding_idx=0) # Initialization r = 6 / np.sqrt(self.embedding_dim) self.emb_E.weight.data.uniform_(-r, r) self.emb_E_var.weight.data.uniform_(self.cmin, self.cmax) self.emb_R.weight.data.uniform_(-r, r) self.emb_R_var.weight.data.uniform_(self.cmin, self.cmax) self.emb_TE.weight.data.uniform_(-r, r) self.alpha_E.weight.data.uniform_(0, 0) self.beta_E.weight.data.uniform_(0, 0) self.omega_E.weight.data.uniform_(-r, r) self.emb_TR.weight.data.uniform_(-r, r) self.alpha_R.weight.data.uniform_(0, 0) self.beta_R.weight.data.uniform_(0, 0) self.omega_R.weight.data.uniform_(-r, r) # Regularization self.normalize_embeddings() if self.gpu: self.cuda() def forward(self, X): # h_i, t_i, r_i, d_i = X[:, 0].astype(np.int64), X[:, 1].astype(np.int64), X[:, 2].astype(np.int64), X[:, # 3].astype( # np.float32) h_i, t_i, r_i, d_i = X[:, 0].long(), X[:, 2].long(), X[:, 1].long(), X[:, 3].float() # if self.gpu: # h_i = Variable(torch.from_numpy(h_i).cuda()) # t_i = Variable(torch.from_numpy(t_i).cuda()) # r_i = Variable(torch.from_numpy(r_i).cuda()) # d_i = Variable(torch.from_numpy(d_i).cuda()) # # else: # h_i = Variable(torch.from_numpy(h_i)) # t_i = Variable(torch.from_numpy(t_i)) # r_i = Variable(torch.from_numpy(r_i)) # d_i = Variable(torch.from_numpy(d_i)) pi = 3.14159265358979323846 h_mean = self.emb_E(h_i).view(-1, self.embedding_dim) + \ d_i.view(-1, 1) * self.alpha_E(h_i).view(-1, 1) * self.emb_TE(h_i).view(-1, self.embedding_dim) \ + self.beta_E(h_i).view(-1, self.embedding_dim) * torch.sin( 2 * pi * self.omega_E(h_i).view(-1, self.embedding_dim) * d_i.view(-1, 1)) t_mean = self.emb_E(t_i).view(-1, self.embedding_dim) + \ d_i.view(-1, 1) * self.alpha_E(t_i).view(-1, 1) * self.emb_TE(t_i).view(-1, self.embedding_dim) \ + self.beta_E(t_i).view(-1, self.embedding_dim) * torch.sin( 2 * pi * self.omega_E(t_i).view(-1, self.embedding_dim) * d_i.view(-1, 1)) r_mean = self.emb_R(r_i).view(-1, self.embedding_dim) + \ d_i.view(-1, 1) * self.alpha_R(r_i).view(-1, 1) * self.emb_TR(r_i).view(-1, self.embedding_dim) \ + self.beta_R(r_i).view(-1, self.embedding_dim) * torch.sin( 2 * pi * self.omega_R(r_i).view(-1, self.embedding_dim) * d_i.view(-1, 1)) h_var = self.emb_E_var(h_i).view(-1, self.embedding_dim) t_var = self.emb_E_var(t_i).view(-1, self.embedding_dim) r_var = self.emb_R_var(r_i).view(-1, self.embedding_dim) out1 = torch.sum((h_var + t_var) / r_var, 1) + torch.sum(((r_mean - h_mean + t_mean) ** 2) / r_var, 1) - self.embedding_dim out2 = torch.sum(r_var / (h_var + t_var), 1) + torch.sum(((h_mean - t_mean - r_mean) ** 2) / (h_var + t_var), 1) - self.embedding_dim out = (out1 + out2) / 4 return out def log_rank_loss(self, y_pos, y_neg, temp=0): M = y_pos.size(0) N = y_neg.size(0) y_pos = self.gamma - y_pos y_neg = self.gamma - y_neg C = int(N / M) y_neg = y_neg.view(C, -1).transpose(0, 1) p = F.softmax(temp * y_neg) loss_pos = torch.sum(F.softplus(-1 * y_pos)) loss_neg = torch.sum(p * F.softplus(y_neg)) loss = (loss_pos + loss_neg) / 2 / M if self.gpu: loss = loss.cuda() return loss def rank_loss(self, y_pos, y_neg): M = y_pos.size(0) N = y_neg.size(0) C = int(N / M) y_pos = y_pos.repeat(C) if self.gpu: target = Variable(torch.from_numpy(-np.ones(N, dtype=np.float32))).cuda() else: target = Variable(torch.from_numpy(-np.ones(N, dtype=np.float32))).cpu() loss = nn.MarginRankingLoss(margin=self.gamma) loss = loss(y_pos, y_neg, target) return loss def normalize_embeddings(self): self.emb_E.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_E_var.weight.data.uniform_(self.cmin, self.cmax) self.emb_R.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_R_var.weight.data.uniform_(self.cmin, self.cmax) self.emb_TE.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_TR.weight.data.renorm_(p=2, dim=0, maxnorm=1) def regularization_embeddings(self): lower = torch.tensor(self.cmin).float() upper = torch.tensor(self.cmax).float() if self.gpu: lower = lower.cuda() upper = upper.cuda() self.emb_E_var.weight.data = torch.where(self.emb_E_var.weight.data < self.cmin, lower, self.emb_E_var.weight.data) self.emb_E_var.weight.data = torch.where(self.emb_E_var.weight.data > self.cmax, upper, self.emb_E_var.weight.data) self.emb_R_var.weight.data = torch.where(self.emb_R_var.weight.data < self.cmin, lower, self.emb_R_var.weight.data) self.emb_R_var.weight.data = torch.where(self.emb_R_var.weight.data > self.cmax, upper, self.emb_R_var.weight.data) self.emb_E.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_R.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_TE.weight.data.renorm_(p=2, dim=0, maxnorm=1) self.emb_TR.weight.data.renorm_(p=2, dim=0, maxnorm=1) def rank_left(self, X, facts, kg, timedisc, rev_set=0): rank = [] with torch.no_grad(): if timedisc: for triple, fact in zip(X, facts): X_i = np.ones([self.n_entity, 4]) i_score = torch.zeros(self.n_entity) if self.gpu: i_score = i_score.cuda() for time_index in [triple[3], triple[4]]: for i in range(0, self.n_entity): X_i[i, 0] = i X_i[i, 1] = triple[1] X_i[i, 2] = triple[2] X_i[i, 3] = time_index i_score = i_score + self.forward(X_i).view(-1) if rev_set > 0: X_rev = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_rev[i, 0] = triple[1] X_rev[i, 1] = i X_rev[i, 2] = triple[2] + self.n_relation // 2 X_rev[i, 3] = time_index i_score = i_score + self.forward(X_rev).view(-1) filter_out = kg.to_skip_final['lhs'][(fact[1], fact[2], fact[3], fact[4])] target = i_score[int(triple[0])].clone() i_score[filter_out] = 1e6 rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) else: for triple, fact in zip(X, facts): X_i = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_i[i, 0] = i X_i[i, 1] = triple[1] X_i[i, 2] = triple[2] X_i[i, 3] = triple[3] i_score = self.forward(X_i) if rev_set > 0: X_rev = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_rev[i, 0] = triple[1] X_rev[i, 1] = i X_rev[i, 2] = triple[2] + self.n_relation // 2 X_rev[i, 3] = triple[3] i_score = i_score + self.forward(X_rev).view(-1) if self.gpu: i_score = i_score.cuda() filter_out = kg.to_skip_final['lhs'][(fact[1], fact[2], fact[3], fact[4])] target = i_score[int(triple[0])].clone() i_score[filter_out] = 1e6 rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) print('left') print(rank) return rank def rank_right(self, X, facts, kg, timedisc, rev_set=0): rank = [] with torch.no_grad(): if timedisc: for triple, fact in zip(X, facts): X_i = np.ones([self.n_entity, 4]) i_score = torch.zeros(self.n_entity) if self.gpu: i_score = i_score.cuda() for time_index in [triple[3], triple[4]]: for i in range(0, self.n_entity): X_i[i, 0] = triple[0] X_i[i, 1] = i X_i[i, 2] = triple[2] X_i[i, 3] = time_index i_score = i_score + self.forward(X_i).view(-1) if rev_set > 0: X_rev = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_rev[i, 0] = i X_rev[i, 1] = triple[0] X_rev[i, 2] = triple[2] + self.n_relation // 2 X_rev[i, 3] = time_index i_score = i_score + self.forward(X_rev).view(-1) filter_out = kg.to_skip_final['rhs'][(fact[0], fact[2], fact[3], fact[4])] target = i_score[int(triple[1])].clone() i_score[filter_out] = 1e6 rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) else: for triple, fact in zip(X, facts): X_i = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_i[i, 0] = triple[0] X_i[i, 1] = i X_i[i, 2] = triple[2] X_i[i, 3] = triple[3] i_score = self.forward(X_i) if rev_set > 0: X_rev = np.ones([self.n_entity, 4]) for i in range(0, self.n_entity): X_rev[i, 0] = i X_rev[i, 1] = triple[0] X_rev[i, 2] = triple[2] + self.n_relation // 2 X_rev[i, 3] = triple[3] i_score = i_score + self.forward(X_rev).view(-1) if self.gpu: i_score = i_score.cuda() filter_out = kg.to_skip_final['rhs'][(fact[0], fact[2], fact[3], fact[4])] target = i_score[int(triple[1])].clone() i_score[filter_out] = 1e6 rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) print('right') print(rank) return rank def timepred(self, X): rank = [] with torch.no_grad(): for triple in X: X_i = np.ones([self.n_day, len(triple)]) for i in range(self.n_day): X_i[i, 0] = triple[0] X_i[i, 1] = triple[1] X_i[i, 2] = triple[2] X_i[i, 3:] = self.time_dict[i] i_score = self.forward(X_i) if self.gpu: i_score = i_score.cuda() target = i_score[triple[3]] rank_triple = torch.sum((i_score < target).float()).cpu().item() + 1 rank.append(rank_triple) return rank model_path = "/home/gengyuan/workspace/baseline/ATISE/icews14/ATISE/timediscrete0/dim500/lr0.0000/neg_num10/3day/gamma120/cmin0.0030/params.pkl" model = ATISE(7129, 460, 500, 64, 0, 120, 0.003, 0.3, True) model_state_dict = torch.load(model_path) model.load_state_dict(model_state_dict) def test(): device = 'cuda' if torch.cuda.is_available() else 'cpu' dataset = MockAtiseDatasetProcessor() evaluator = MockEvaluation(dataset, device) sampler = MockSampler(dataset, as_matrix=True) # # print(dataset.ts2id) # print(dataset.index_timestamps(23)) # print(dataset.index_relations('Arrest, detain, or charge with legal action')) # print(dataset.index_entities('Japan')) # print(dataset.index_entities('Police (Japan)')) # print(dataset.filter(type='time-aware', target='s')['None-9-2205-33']) # print(dataset.get("train")['triple'][15442]) # print(dataset.get("train")['timestamp_id'][15442]) # print(dataset.get("train")['timestamp_float'][15442]) # # # assert False valid_loader = torch.utils.data.DataLoader( SplitDataset(dataset.get("test"), ['timestamp_float', 'timestamp_id']), shuffle=False, batch_size=1, num_workers=0 ) with torch.no_grad(): model.eval() metrics = dict() metrics['head'] = defaultdict(float) metrics['tail'] = defaultdict(float) rank_left = [] rank_right = [] scores_head = [] scores_tail = [] filter_left = [] filter_right = [] l = 0 dfs = dataset.filter(type="time-aware", target="s") dfo = dataset.filter(type="time-aware", target="o") for batch in valid_loader: bs = batch.size(0) dim = batch.size(1) l += bs print(l) samples_head, _ = sampler.sample(batch, "head") samples_tail, _ = sampler.sample(batch, "tail") samples_head = samples_head.to(device) samples_tail = samples_tail.to(device) samples_head = samples_head.view(-1, dim) samples_tail = samples_tail.view(-1, dim) batch_scores_head = model.forward(samples_head) batch_scores_tail = model.forward(samples_tail) batch_scores_head = batch_scores_head.view(bs, -1) batch_scores_tail = batch_scores_tail.view(bs, -1) # reciprocal samples_head_reciprocal = samples_head.clone().view(-1, dim) samples_tail_reciprocal = samples_tail.clone().view(-1, dim) samples_head_reciprocal[:, 1] += 230 samples_head_reciprocal[:, [0, 2]] = samples_head_reciprocal.index_select(1, torch.Tensor( [2, 0]).long().to(device)) samples_tail_reciprocal[:, 1] += 230 samples_tail_reciprocal[:, [0, 2]] = samples_tail_reciprocal.index_select(1, torch.Tensor( [2, 0]).long().to(device)) batch_scores_head_reci = model.forward(samples_head_reciprocal) batch_scores_tail_reci = model.forward(samples_tail_reciprocal) batch_scores_head_reci = batch_scores_head_reci.view(bs, -1) batch_scores_tail_reci = batch_scores_tail_reci.view(bs, -1) batch_scores_head += batch_scores_head_reci batch_scores_tail += batch_scores_tail_reci scores_head.append(batch_scores_head) scores_tail.append(batch_scores_tail) batch_metrics = dict() batch_metrics['head'] = evaluator.eval(batch, batch_scores_head, miss='s') batch_metrics['tail'] = evaluator.eval(batch, batch_scores_tail, miss='o') # print filter filter_left.append( dfs[f'None-{int(batch[0, 1])}-{int(batch[0, 2])}-{int(batch[0, -1])}']) filter_right.append( dfo[f'{int(batch[0, 0])}-{int(batch[0, 1])}-None-{int(batch[0, -1])}']) # rank_left.append(batch_metrics['head']['mean_ranking']) # rank_right.append(batch_metrics['tail']['mean_ranking']) # TODO(gengyuan) refactor for pos in ['head', 'tail']: for key in batch_metrics[pos].keys(): metrics[pos][key] += batch_metrics[pos][key] * bs # rank = rank_left + rank_right # torch.save(rank, "/home/gengyuan/workspace/baseline/ATISE/rank_tkge.pt") # rank2 = torch.load("/home/gengyuan/workspace/baseline/ATISE/rank.pt") # # print('assert Equal') # print(rank==rank2) # torch.save(scores_head + scores_tail, "/home/gengyuan/workspace/baseline/ATISE/scores_tkge.pt") torch.save(filter_left, "/home/gengyuan/workspace/baseline/ATISE/filter_left_tkge.pt") torch.save(filter_right, "/home/gengyuan/workspace/baseline/ATISE/filter_right_tkge.pt") for pos in ['head', 'tail']: for key in metrics[pos].keys(): metrics[pos][key] /= l print(f"Metrics(head prediction) in iteration : {metrics['head'].items()}") print(f"Metrics(tail prediction) in iteration : {metrics['tail'].items()}") def test_sc(): def mean_rank(rank): m_r = 0 N = len(rank) for i in rank: m_r = m_r + i / N return m_r def mrr(rank): mrr = 0 N = len(rank) for i in rank: mrr = mrr + 1 / i / N return mrr def hit_N(rank, N): hit = 0 for i in rank: if i <= N: hit = hit + 1 hit = hit / len(rank) return hit kg = KnowledgeGraph(data_dir="/home/gengyuan/workspace/baseline/ATISE/icews14", gran=3, rev_set=1) test_pos = np.array(kg.test_triples) print(test_pos) rank = model.rank_left(test_pos, kg.test_facts, kg, 0, rev_set=1) rank_right = model.rank_right(test_pos, kg.test_facts, kg, 0, rev_set=1) rank = rank + rank_right m_rank = mean_rank(rank) mean_rr = mrr(rank) hit_1 = hit_N(rank, 1) hit_3 = hit_N(rank, 3) hit_5 = hit_N(rank, 5) hit_10 = hit_N(rank, 10) print('validation results:') print('Mean Rank: {:.0f}'.format(m_rank)) print('Mean RR: {:.4f}'.format(mean_rr)) print('Hit@1: {:.4f}'.format(hit_1)) print('Hit@3: {:.4f}'.format(hit_3)) print('Hit@5: {:.4f}'.format(hit_5)) print('Hit@10: {:.4f}'.format(hit_10)) if __name__ == '__main__': test()

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backend/kaos_backend/util/docker.py

aalhour/kaos

98

105164

import boto3 from kaos_backend.constants import DOCKER_REGISTRY, REGION, CLOUD_PROVIDER def get_login_command(): if CLOUD_PROVIDER == 'AWS': # ecr = boto3.client('ecr', region_name=REGION) # # raw_auth_data = ecr.get_authorization_token()['authorizationData'][0]['authorizationToken'] # _, docker_auth_token = b64decode(raw_auth_data).decode('UTF-8').split(":") return f"$(aws ecr get-login --region {REGION} --no-include-email)" elif CLOUD_PROVIDER == "GCP": return f"gcloud auth print-access-token | docker login -u oauth2accesstoken --password-stdin https://{DOCKER_REGISTRY}" else: return "" def create_docker_repo(repo_name): if CLOUD_PROVIDER == 'AWS': ecr = boto3.client('ecr', region_name=REGION) ecr.create_repository(repositoryName=repo_name) def delete_docker_repo(repo_name): if CLOUD_PROVIDER == 'AWS': ecr = boto3.client('ecr', region_name=REGION) ecr.delete_repository(repositoryName=repo_name, force=True)

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contrib/workflow/src/main.py

gonicus/clacks

2

141941

<filename>contrib/workflow/src/main.py #!/usr/bin/env python import os.path import MySQLdb from sqlalchemy import * from sqlalchemy.orm import clear_mappers __author__="janw" __date__ ="$21.05.2010 16:55:13$" from SpiffWorkflow.Storage import XmlReader from SpiffWorkflow.Server import * from SpiffWorkflow import Job, Workflow, Task import pickle, pprint data_file = os.path.dirname(__file__) + "/job.pk" workflow_file = os.path.dirname(__file__) + "/../tests/xml/anlage_benutzer.xml" def connectDB(): global engine, db, driver host = "build-lenny-32.intranet.gonicus.de" db_name = "spiff" username = "root" password = "<PASSWORD>" # Connect to MySQL. auth = username + ':' + password dbn = 'mysql://' + auth + '@' + host + '/' + db_name engine = create_engine(dbn) clear_mappers() driver = Driver(engine) def getJob(): global data_file, workflow_file reader = XmlReader() workflow = reader.parse_file(workflow_file).pop() if os.access(data_file, os.R_OK): input = open(data_file, 'rb') job = pickle.load(input) input.close() os.remove(data_file) else: job = Job(workflow) return job def setJob(job): global data_file result = False if not isinstance(job, Job): return result if os.access(os.path.dirname(data_file), os.W_OK): output = open(data_file, 'wb') pickle.dump(job, output, -1) output.close() result = True else: print "Datafile not accessible" return result def main(): #connectDB() job = getJob() job.complete_next() if setJob(job): print "Job has been saved successfully" else: print "Problem saving job" if __name__ == "__main__": main()

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aiida_wien2k/calculations/x_sgroup.py

rubel75/aiida-wien2k

1

197418

from aiida.common import datastructures from aiida.engine import CalcJob from aiida.orm import SinglefileData, Dict, RemoteData, Code, StructureData import os import ase.io def _cli_options(parameters): """Return command line options for parameters dictionary. :param dict parameters: dictionary with command line parameters """ options = [] for key, value in parameters.items(): # Could validate: is key a known command-line option? if isinstance(value, bool) and value: options.append(f'{key}') elif isinstance(value, str): # Could validate: is value a valid regular expression? options.append(f'{key}') options.append(value) return options def aiida_struct2wien2k(aiida_structure): "prepare structure file for WIEN2k" ase_structure = aiida_structure.get_ase() # AiiDA -> ASE struct ase.io.write("case.struct",ase_structure) # ASE -> WIEN2k, write WIEN2k struct path_to_rm_folder = os.getcwd() # get path to folder path_to_rm_structfile = os.path.join(path_to_rm_folder, 'case.struct') # get path to struct file wien2k_structfile = SinglefileData(file=path_to_rm_structfile) # get proper AiiDA type for a single file (otherwise you cannot return) return wien2k_structfile # orm.SinglefileData type (stored automatically in AiiDA database) class Wien2kXSgroup(CalcJob): """AiiDA calculation plugin for WIEN2k calculation to execute: x sgroup [parameters].""" @classmethod def define(cls, spec): """Define inputs and outputs of the calculation.""" # yapf: disable super(Wien2kXSgroup, cls).define(spec) # inputs/outputs spec.input('code', valid_type=Code, help='WIEN2k x sgroup') spec.input('parameters', valid_type=Dict, required=False, help='Dictionary of input arguments (if any)') spec.input('aiida_structure', valid_type=StructureData, required=False, help='AiiDA structure object') spec.input('structfile_in', valid_type=SinglefileData, required=False, help='Structure file case.struct') spec.input('parent_folder', valid_type=RemoteData, required=False,\ help='parent_folder passed from a previous calulation') spec.inputs['metadata']['options']['resources'].default = { 'num_machines': 1, 'num_mpiprocs_per_machine': 1, } spec.inputs['metadata']['options']['parser_name'].default = 'wien2k-sgroup-parser' spec.output('structfile_out', valid_type=SinglefileData, help='Structure file after x sgroup') spec.exit_code(300, 'ERROR_MISSING_OUTPUT_FILES', message='Calculation did not produce all expected output files.') def prepare_for_submission(self, folder): """ Create input files. :param folder: an `aiida.common.folders.Folder` where the plugin should temporarily place all files needed by the calculation. :return: `aiida.common.datastructures.CalcInfo` instance """ parameters = _cli_options(self.inputs.parameters.get_dict()) # command line args for init_lapw codeinfo = datastructures.CodeInfo() codeinfo.cmdline_params = ['sgroup'] + parameters # x exec [parameters] codeinfo.code_uuid = self.inputs.code.uuid codeinfo.stdout_name = 'x.log' remote_copy_list = [] if('parent_folder' in self.inputs): path_from = os.path.join(self.inputs.parent_folder.get_remote_path(),'case') remote_copy_list = [( self.inputs.parent_folder.computer.uuid, path_from, './' )] else: folder.get_subfolder('case', create=True) # create case subfolder # convert AiiDA structure -> WIEN2k if('aiida_structure' in self.inputs): wien2k_structfile = aiida_struct2wien2k(self.inputs.aiida_structure) wien2k_structfile.store() # Prepare a `CalcInfo` to be returned to the engine calcinfo = datastructures.CalcInfo() calcinfo.codes_info = [codeinfo] calcinfo.local_copy_list = [] if('structfile_in' in self.inputs): # WIEN2k structure is given as input calcinfo.local_copy_list = [ (self.inputs.structfile_in.uuid, self.inputs.structfile_in.filename, 'case/case.struct') ] # copy case.struct to the local folder as new.struct elif('aiida_structure' in self.inputs): # AiiDA structure is given as input calcinfo.local_copy_list = [ (wien2k_structfile.uuid, wien2k_structfile.filename, 'case/case.struct') ] # copy case.struct to the local folder as new.struct calcinfo.remote_copy_list = remote_copy_list calcinfo.retrieve_list = [('case/case.struct_sgroup'), ('case/*.error*')] return calcinfo

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lyapunov_reachability/common/gradient.py

62subinh/deeprl_safety_specification

3

154209

import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def flat_grad(grads): grad_flatten = [] for grad in grads: grad_flatten.append(grad.view(-1)) grad_flatten = torch.cat(grad_flatten) return grad_flatten def flat_hessian(hessians): hessians_flatten = [] for hessian in hessians: hessians_flatten.append(hessian.contiguous().view(-1)) hessians_flatten = torch.cat(hessians_flatten).data return hessians_flatten def kl_divergence(new_actor, old_actor, obs, deterministic=True): if deterministic: mu = new_actor(obs) mu_old = old_actor(obs) mu_old = mu_old.detach() kl = (mu_old - mu).pow(2) / 2.0 return kl.sum(1, keepdim=True) else: mu, std = new_actor(obs) mu_old, std_old = old_actor(obs) mu_old = mu_old.detach() std_old = std_old.detach() # kl divergence between old policy and new policy : D( pi_old || pi_new ) # pi_old -> mu_old, std_old / pi_new -> mu, std # be careful of calculating KL-divergence. It is not symmetric metric. kl = torch.log(std / std_old) + (std_old.pow(2) + (mu_old - mu).pow(2)) / (2.0 * std.pow(2)) - 0.5 return kl.sum(1, keepdim=True) def hessian_vector_product(new_actor, old_actor, obs, p, cg_damping=1e-1): p.detach() kl = kl_divergence(new_actor=new_actor, old_actor=old_actor, obs=obs) kl = kl.mean() kl_grad = torch.autograd.grad(kl, new_actor.parameters(), create_graph=True) kl_grad = flat_grad(kl_grad) kl_grad_p = (kl_grad * p).sum() kl_hessian = torch.autograd.grad(kl_grad_p, new_actor.parameters()) kl_hessian = flat_hessian(kl_hessian) return kl_hessian + p * cg_damping # from openai baseline code # https://github.com/openai/baselines/blob/master/baselines/common/cg.py def conjugate_gradient(actor, target_actor, obs, b, nsteps=10, residual_tol=1e-10): x = torch.zeros(b.size(), device=device) r = b.clone() p = b.clone() rdotr = torch.dot(r, r) for i in range(nsteps): Ap = hessian_vector_product(actor, target_actor, obs, p, cg_damping=1e-1) alpha = rdotr / torch.dot(p, Ap) x += alpha * p r -= alpha * Ap new_rdotr = torch.dot(r, r) betta = new_rdotr / rdotr p = r + betta * p rdotr = new_rdotr if rdotr < residual_tol: break return x

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framework/generated/vulkan_generators/vulkan_referenced_resource_consumer_body_generator.py

tomped01/gfxreconstruct

0

27746

<filename>framework/generated/vulkan_generators/vulkan_referenced_resource_consumer_body_generator.py #!/usr/bin/python3 -i # # Copyright (c) 2020 LunarG, Inc. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. import os, re, sys from base_generator import * class VulkanReferencedResourceBodyGeneratorOptions(BaseGeneratorOptions): """Options for generating a C++ class for detecting unreferenced resource handles in a capture file""" def __init__( self, blacklists=None, # Path to JSON file listing apicalls and structs to ignore. platformTypes=None, # Path to JSON file listing platform (WIN32, X11, etc.) defined types. filename=None, directory='.', prefixText='', protectFile=False, protectFeature=True ): BaseGeneratorOptions.__init__( self, blacklists, platformTypes, filename, directory, prefixText, protectFile, protectFeature ) # VulkanReferencedResourceBodyGenerator - subclass of BaseGenerator. # Generates C++ member definitions for the VulkanReferencedResource class responsible for # determining which resource handles are used or unused in a capture file. class VulkanReferencedResourceBodyGenerator(BaseGenerator): """Generate a C++ class for detecting unreferenced resource handles in a capture file""" # All resource and resource associated handle types to be processed. RESOURCE_HANDLE_TYPES = [ 'VkBuffer', 'VkImage', 'VkBufferView', 'VkImageView', 'VkFramebuffer', 'VkDescriptorSet', 'VkCommandBuffer' ] # Handle types that contain resource and child resource handle types. CONTAINER_HANDLE_TYPES = ['VkDescriptorSet'] # Handle types that use resource and child resource handle types. USER_HANDLE_TYPES = ['VkCommandBuffer'] def __init__( self, errFile=sys.stderr, warnFile=sys.stderr, diagFile=sys.stdout ): BaseGenerator.__init__( self, processCmds=True, processStructs=True, featureBreak=False, errFile=errFile, warnFile=warnFile, diagFile=diagFile ) # Map of Vulkan structs containing handles to a list values for handle members or struct members # that contain handles (eg. VkGraphicsPipelineCreateInfo contains a VkPipelineShaderStageCreateInfo # member that contains handles). self.structsWithHandles = dict() self.pNextStructs = dict( ) # Map of Vulkan structure types to sType value for structs that can be part of a pNext chain. self.commandInfo = dict() # Map of Vulkan commands to parameter info self.restrictHandles = True # Determines if the 'isHandle' override limits the handle test to only the values conained by RESOURCE_HANDLE_TYPES. # Method override # yapf: disable def beginFile(self, genOpts): BaseGenerator.beginFile(self, genOpts) write('#include "generated/generated_vulkan_referenced_resource_consumer.h"', file=self.outFile) self.newline() write('#include <cassert>', file=self.outFile) self.newline() write('GFXRECON_BEGIN_NAMESPACE(gfxrecon)', file=self.outFile) write('GFXRECON_BEGIN_NAMESPACE(decode)', file=self.outFile) # yapf: enable # Method override # yapf: disable def endFile(self): for cmd, info in self.commandInfo.items(): returnType = info[0] params = info[2] if params and params[0].baseType == 'VkCommandBuffer': # Check for parameters with resource handle types. handles = self.getParamListHandles(params[1:]) if (handles): # Generate a function to add handles to the command buffer's referenced handle list. cmddef = '\n' # Temporarily remove resource only matching restriction from isHandle() when generating the function signature. self.restrictHandles = False cmddef += self.makeConsumerFuncDecl(returnType, 'VulkanReferencedResourceConsumer::Process_' + cmd, params) + '\n' self.restrictHandles = True cmddef += '{\n' indent = self.INDENT_SIZE * ' ' # Add unreferenced parameter macros. unrefCount = 0 for param in params[1:]: if not param in handles: cmddef += indent + 'GFXRECON_UNREFERENCED_PARAMETER({});\n'.format(param.name) unrefCount += 1 if unrefCount > 0: cmddef += '\n' for index, handle in enumerate(handles): cmddef += self.trackCommandHandle(index, params[0].name, handle, indent=indent) cmddef += '}' write(cmddef, file=self.outFile) self.newline() write('GFXRECON_END_NAMESPACE(decode)', file=self.outFile) write('GFXRECON_END_NAMESPACE(gfxrecon)', file=self.outFile) # Finish processing in superclass BaseGenerator.endFile(self) # yapf: enable # # Method override def genStruct(self, typeinfo, typename, alias): BaseGenerator.genStruct(self, typeinfo, typename, alias) if not alias: self.checkStructMemberHandles(typename, self.structsWithHandles) # Track this struct if it can be present in a pNext chain. parentStructs = typeinfo.elem.get('structextends') if parentStructs: sType = self.makeStructureTypeEnum(typeinfo, typename) if sType: self.pNextStructs[typename] = sType # # Indicates that the current feature has C++ code to generate. def needFeatureGeneration(self): if self.featureCmdParams: return True return False # # Performs C++ code generation for the feature. def generateFeature(self): for cmd in self.getFilteredCmdNames(): self.commandInfo[cmd] = self.featureCmdParams[cmd] # # Override method to check for handle type, only matching resource handle types. def isHandle(self, baseType): if self.restrictHandles: if baseType in self.RESOURCE_HANDLE_TYPES: return True return False else: return BaseGenerator.isHandle(self, baseType) # # Create list of parameters that have handle types or are structs that contain handles. def getParamListHandles(self, values): handles = [] for value in values: if self.isHandle(value.baseType): handles.append(value) elif self.isStruct( value.baseType ) and (value.baseType in self.structsWithHandles): handles.append(value) return handles # # # yapf: disable def trackCommandHandle(self, index, commandParamName, value, valuePrefix='', indent=''): body = '' tail = '' indexName = None countName = None valueName = valuePrefix + value.name isHandle = self.isHandle(value.baseType) if (value.isPointer or value.isArray) and value.name != 'pnext_value': if index > 0: body += '\n' accessOperator = '->' if not valuePrefix: # If there is no prefix, this is the pointer parameter received by the function, which should never be null. body += indent + 'assert({} != nullptr);\n'.format(value.name) body += '\n' else: # If there is a prefix, this is a struct member. We need to determine the type of access operator to use # for the member of a 'decoded' struct type, where handle member types will be HandlePointerDecoder, but # struct member types will be unique_ptr<StructPointerDecoder>. if isHandle: accessOperator = '.' # Add IsNull and HasData checks for the pointer decoder, before accessing its data. # Note that this does not handle the decoded struct member cases for static arrays, which would need to use '.' instead of '->'. body += indent + 'if (!{prefix}{name}{op}IsNull() && ({prefix}{name}{op}HasData()))\n'.format(prefix=valuePrefix, name=value.name, op=accessOperator) body += indent + '{\n' tail = indent + '}\n' + tail indent += ' ' * self.INDENT_SIZE # Get the pointer from the pointer decoder object. valueName = '{}_ptr'.format(value.name) if isHandle: body += indent + 'auto {} = {}{}{}GetPointer();\n'.format(valueName, valuePrefix, value.name, accessOperator) else: body += indent + 'auto {} = {}{}{}GetMetaStructPointer();\n'.format(valueName, valuePrefix, value.name, accessOperator) # Add a for loop for an array of values. if value.isArray: indexName = '{}_index'.format(value.name) countName = '{}_count'.format(value.name) body += indent + 'size_t {} = {}{}{}GetLength();\n'.format(countName, valuePrefix, value.name, accessOperator) body += indent + 'for (size_t {i} = 0; {i} < {}; ++{i})\n'.format(countName, i=indexName) body += indent + '{\n' tail = indent + '}\n' + tail indent += ' ' * self.INDENT_SIZE # Insert commands to add handles to a container, or to process struct members that contain handles. if isHandle: if value.isArray: valueName = '{}[{}]'.format(valueName, indexName) elif value.isPointer: valueName = '(*{})'.format(valueName) if value.baseType in self.CONTAINER_HANDLE_TYPES: body += indent + 'GetTable().AddContainerToUser({}, {});\n'.format(commandParamName, valueName) elif value.baseType in self.USER_HANDLE_TYPES: body += indent + 'GetTable().AddUserToUser({}, {});\n'.format(commandParamName, valueName) else: body += indent + 'GetTable().AddResourceToUser({}, {});\n'.format(commandParamName, valueName) elif self.isStruct(value.baseType) and (value.baseType in self.structsWithHandles): if value.isArray: accessOperator = '[{}].'.format(indexName) else: accessOperator = '->' for index, entry in enumerate(self.structsWithHandles[value.baseType]): if entry.name == 'pNext': extStructsWithHandles = [extStruct for extStruct in self.registry.validextensionstructs[value.baseType] if extStruct in self.structsWithHandles] if extStructsWithHandles: body += indent + 'const VkBaseInStructure* pnext_header = nullptr;\n' body += indent + 'if ({name}->pNext != nullptr)\n'.format(name=valueName) body += indent + '{\n' indent += ' ' * self.INDENT_SIZE body += indent + 'pnext_header = reinterpret_cast<const VkBaseInStructure*>({}->pNext->GetPointer());\n'.format(valueName) indent = indent[:-self.INDENT_SIZE] body += indent + '}\n' body += indent + 'while (pnext_header)\n' body += indent + '{\n' indent += ' ' * self.INDENT_SIZE body += indent + 'switch (pnext_header->sType)\n' body += indent + '{\n' indent += ' ' * self.INDENT_SIZE body += indent + 'default:\n' indent += ' ' * self.INDENT_SIZE body += indent + 'break;\n' indent = indent[:-self.INDENT_SIZE] for extStruct in extStructsWithHandles: body += indent + 'case {}:\n'.format(self.pNextStructs[extStruct]) body += indent + '{\n' indent += ' ' * self.INDENT_SIZE body += indent + 'auto pnext_value = reinterpret_cast<const Decoded_{}*>({}->pNext->GetPointer());\n'.format(extStruct, valueName) body += self.trackCommandHandle(index, commandParamName, ValueInfo('pnext_value', extStruct, 'const {} *'.format(extStruct), 1), '', indent=indent) body += indent + 'break;\n' indent = indent[:-self.INDENT_SIZE] body += indent + '}\n' indent = indent[:-self.INDENT_SIZE] body += indent + '}\n' body += indent + 'pnext_header = pnext_header->pNext;\n' indent = indent[:-self.INDENT_SIZE] body += indent + '}\n' else: body += self.trackCommandHandle(index, commandParamName, entry, valueName + accessOperator, indent) return body + tail # yapf: enable

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sdk/keyvault/azure-keyvault/azure/keyvault/v7_3_preview/models/key_vault_client_enums.py

mccoyp/azure-keyvault-7.3-preview

0

82890

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from enum import Enum class DeletionRecoveryLevel(str, Enum): purgeable = "Purgeable" #: Denotes a vault state in which deletion is an irreversible operation, without the possibility for recovery. This level corresponds to no protection being available against a Delete operation; the data is irretrievably lost upon accepting a Delete operation at the entity level or higher (vault, resource group, subscription etc.) recoverable_purgeable = "Recoverable+Purgeable" #: Denotes a vault state in which deletion is recoverable, and which also permits immediate and permanent deletion (i.e. purge). This level guarantees the recoverability of the deleted entity during the retention interval (90 days), unless a Purge operation is requested, or the subscription is cancelled. System wil permanently delete it after 90 days, if not recovered recoverable = "Recoverable" #: Denotes a vault state in which deletion is recoverable without the possibility for immediate and permanent deletion (i.e. purge). This level guarantees the recoverability of the deleted entity during the retention interval(90 days) and while the subscription is still available. System wil permanently delete it after 90 days, if not recovered recoverable_protected_subscription = "Recoverable+ProtectedSubscription" #: Denotes a vault and subscription state in which deletion is recoverable within retention interval (90 days), immediate and permanent deletion (i.e. purge) is not permitted, and in which the subscription itself cannot be permanently canceled. System wil permanently delete it after 90 days, if not recovered customized_recoverable_purgeable = "CustomizedRecoverable+Purgeable" #: Denotes a vault state in which deletion is recoverable, and which also permits immediate and permanent deletion (i.e. purge when 7<= SoftDeleteRetentionInDays < 90). This level guarantees the recoverability of the deleted entity during the retention interval, unless a Purge operation is requested, or the subscription is cancelled. customized_recoverable = "CustomizedRecoverable" #: Denotes a vault state in which deletion is recoverable without the possibility for immediate and permanent deletion (i.e. purge when 7<= SoftDeleteRetentionInDays < 90).This level guarantees the recoverability of the deleted entity during the retention interval and while the subscription is still available. customized_recoverable_protected_subscription = "CustomizedRecoverable+ProtectedSubscription" #: Denotes a vault and subscription state in which deletion is recoverable, immediate and permanent deletion (i.e. purge) is not permitted, and in which the subscription itself cannot be permanently canceled when 7<= SoftDeleteRetentionInDays < 90. This level guarantees the recoverability of the deleted entity during the retention interval, and also reflects the fact that the subscription itself cannot be cancelled. class JsonWebKeyType(str, Enum): ec = "EC" ec_hsm = "EC-HSM" rsa = "RSA" rsa_hsm = "RSA-HSM" oct = "oct" oct_hsm = "oct-HSM" class JsonWebKeyCurveName(str, Enum): p_256 = "P-256" p_384 = "P-384" p_521 = "P-521" p_256_k = "P-256K" class JsonWebKeyOperation(str, Enum): encrypt = "encrypt" decrypt = "decrypt" sign = "sign" verify = "verify" wrap_key = "wrapKey" unwrap_key = "unwrapKey" import_enum = "import" export = "export" class KeyEncryptionAlgorithm(str, Enum): ckm_rsa_aes_key_wrap = "CKM_RSA_AES_KEY_WRAP" rsa_aes_key_wrap_256 = "RSA_AES_KEY_WRAP_256" rsa_aes_key_wrap_384 = "RSA_AES_KEY_WRAP_384" class JsonWebKeyEncryptionAlgorithm(str, Enum): rsa_oaep = "RSA-OAEP" rsa_oaep_256 = "RSA-OAEP-256" rsa1_5 = "RSA1_5" a128_gcm = "A128GCM" a192_gcm = "A192GCM" a256_gcm = "A256GCM" a128_kw = "A128KW" a192_kw = "A192KW" a256_kw = "A256KW" a128_cbc = "A128CBC" a192_cbc = "A192CBC" a256_cbc = "A256CBC" a128_cbcpad = "A128CBCPAD" a192_cbcpad = "A192CBCPAD" a256_cbcpad = "A256CBCPAD" class JsonWebKeySignatureAlgorithm(str, Enum): ps256 = "PS256" #: RSASSA-PSS using SHA-256 and MGF1 with SHA-256, as described in https://tools.ietf.org/html/rfc7518 ps384 = "PS384" #: RSASSA-PSS using SHA-384 and MGF1 with SHA-384, as described in https://tools.ietf.org/html/rfc7518 ps512 = "PS512" #: RSASSA-PSS using SHA-512 and MGF1 with SHA-512, as described in https://tools.ietf.org/html/rfc7518 rs256 = "RS256" #: RSASSA-PKCS1-v1_5 using SHA-256, as described in https://tools.ietf.org/html/rfc7518 rs384 = "RS384" #: RSASSA-PKCS1-v1_5 using SHA-384, as described in https://tools.ietf.org/html/rfc7518 rs512 = "RS512" #: RSASSA-PKCS1-v1_5 using SHA-512, as described in https://tools.ietf.org/html/rfc7518 rsnull = "RSNULL" #: Reserved es256 = "ES256" #: ECDSA using P-256 and SHA-256, as described in https://tools.ietf.org/html/rfc7518. es384 = "ES384" #: ECDSA using P-384 and SHA-384, as described in https://tools.ietf.org/html/rfc7518 es512 = "ES512" #: ECDSA using P-521 and SHA-512, as described in https://tools.ietf.org/html/rfc7518 es256_k = "ES256K" #: ECDSA using P-256K and SHA-256, as described in https://tools.ietf.org/html/rfc7518 class KeyUsageType(str, Enum): digital_signature = "digitalSignature" non_repudiation = "nonRepudiation" key_encipherment = "keyEncipherment" data_encipherment = "dataEncipherment" key_agreement = "keyAgreement" key_cert_sign = "keyCertSign" c_rl_sign = "cRLSign" encipher_only = "encipherOnly" decipher_only = "decipherOnly" class ActionType(str, Enum): email_contacts = "EmailContacts" auto_renew = "AutoRenew" class OperationStatus(str, Enum): success = "Success" in_progress = "InProgress" failed = "Failed" class SasTokenType(str, Enum): account = "account" service = "service"

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boilerplate_app/tasks.py

taher-systango/DjangoUnboxed

68

187728

<gh_stars>10-100 from celery import shared_task @shared_task def add(a, b): return (a+b)

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model/Encoder.py

ECCV7129/ECCV2022_submission_7129

1

173176

from torch import nn import torch from model.Swin import SwinTransformer3D import copy class swin_encoder(nn.Module): def __init__(self , device , drop , checkpoint_encoder): super().__init__() checkpoint = checkpoint_encoder self.device=device self.label= 'demo/label_map_k400.txt' self.model=SwinTransformer3D( embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=(8, 7, 7), patch_size=(2, 4, 4), drop_path_rate=0.1, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=drop, attn_drop_rate=drop, patch_norm=True) checkpoint = torch.load(checkpoint, map_location=device) state_dict2 = copy.deepcopy(checkpoint['state_dict']) state_dict = dict() for key in checkpoint['state_dict']: if 'backbone.' in key: new_key = key.replace('backbone.', '') state_dict[new_key] = state_dict2.pop(key) self.model.load_state_dict(state_dict) self.model = self.model.to(device) self.max_testing_views = None def forward(self, imgs): batches = imgs.shape[0] imgs = imgs.reshape((-1,) + imgs.shape[2:]) feat = self.model.forward(imgs) # perform spatio-temporal pooling avg_pool = nn.AdaptiveAvgPool2d((1, 1)) feat = avg_pool(feat) # squeeze dimensions feat = feat.view(batches, feat.shape[1], feat.shape[2]) feat = feat.permute(0, 2, 1) return feat

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module_pie_chart.py

barnett617/python_analysis

2

129135

<gh_stars>1-10 # -*-coding:utf-8-*- # 饼状图 from matplotlib import pyplot as plt import data_source # 数据源 mobile_176xxxx4617 = data_source.mobile_176xxxx4617 mobile_155xxxx9617 = data_source.mobile_155xxxx9617 mobile_173xxxx9636 = data_source.mobile_173xxxx9636 mobile_176xxxx4617_sum = 0 mobile_155xxxx9617_sum = 0 mobile_173xxxx9636_sum = 0 for i in mobile_176xxxx4617: mobile_176xxxx4617_sum += i for i in mobile_155xxxx9617: mobile_155xxxx9617_sum += i for i in mobile_173xxxx9636: mobile_173xxxx9636_sum += i # 调节图形大小，宽，高 plt.figure(figsize=(12, 8)) # 定义饼状图的标签，标签是列表 labels = ['号码176xxxx4617：' + str(round(mobile_176xxxx4617_sum, 3)) + '元', '号码155xxxx9617：' + str(round(mobile_155xxxx9617_sum, 3)) + '元', '号码173xxxx9636：' + str(round(mobile_173xxxx9636_sum, 3)) + '元'] # 每个标签占多大，会自动去算百分比 sizes = [mobile_176xxxx4617_sum, mobile_155xxxx9617_sum, mobile_173xxxx9636_sum] # 将某部分爆炸出来，使用括号，将第一块分割出来，数值的大小是分割出来的与其他两块的间隙 explode = (0.05, 0, 0) patches, l_text, p_text = plt.pie(sizes, explode=explode, labels=labels, labeldistance=1.1, autopct='%3.1f%%', shadow=False, startangle=90, pctdistance=0.6) # labeldistance，文本的位置离远点有多远，1.1指1.1倍半径的位置 # autopct，圆里面的文本格式，%3.1f%%表示小数有三位，整数有一位的浮点数 # shadow，饼是否有阴影 # startangle，起始角度，0，表示从0开始逆时针转，为第一块。一般选择从90度开始比较好看 # pctdistance，百分比的text离圆心的距离 # patches, l_texts, p_texts，为了得到饼图的返回值，p_texts饼图内部文本的，l_texts饼图外label的文本 # 改变文本的大小 # 方法是把每一个text遍历。调用set_size方法设置它的属性 for t in l_text: t.set_size = (30) for t in p_text: t.set_size = (20) # 设置x，y轴刻度一致，这样饼图才能是圆的 plt.axis('equal') plt.legend() def show_plt(): plt.show()

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Questions/Chief in Chains/solution.py

leander-dsouza/Abhyudaya_2020

1

59058

<filename>Questions/Chief in Chains/solution.py<gh_stars>1-10 class Solution(object): def reconstructQueue(self, people): people.sort(key=lambda x: (-x[0], x[1])) output = [] for p in people: output.insert(p[1], p) return output m = int(input()) k = Solution() array_input = [] for x in range(m): array_input.append([int(y) for y in input().split()]) gh =k.reconstructQueue(array_input) for s in gh: print(*s)

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development/testsite/blog/migrations/0002_auto_20190126_1538.py

iggy12345/django_blog

0

150287

<filename>development/testsite/blog/migrations/0002_auto_20190126_1538.py # Generated by Django 2.1.5 on 2019-01-26 21:38 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('utils', '0001_initial'), ('blog', '0001_initial'), ] operations = [ migrations.RemoveField( model_name='blogpost', name='author', ), migrations.RemoveField( model_name='blogpost', name='id', ), migrations.RemoveField( model_name='blogpost', name='is_posted', ), migrations.RemoveField( model_name='blogpost', name='pub_date', ), migrations.RemoveField( model_name='blogpost', name='text', ), migrations.AddField( model_name='blogpost', name='textpost_ptr', field=models.OneToOneField(auto_created=True, default=0, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='utils.TextPost'), preserve_default=False, ), ]

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unshorten/models.py

bitmazk/django-unshorten

3

1610630

<filename>unshorten/models.py """Models for the ``unshorten`` app.""" from django.conf import settings from django.db import models from django.utils.encoding import python_2_unicode_compatible @python_2_unicode_compatible class APICallHistoryBase(models.Model): """ Stores the amount of API calls per user per day or month. :amount_api_calls: The actual amount of calls on this day. :creation_date: The date these calls are logged. :user: Foreignkey to the user, whose calls are stored. """ amount_api_calls = models.PositiveIntegerField( verbose_name='API call amount', ) creation_date = models.DateField( auto_now_add=True, verbose_name='Creation date', ) user = models.ForeignKey( settings.AUTH_USER_MODEL, verbose_name='unshorten_profile', ) class Meta: abstract = True unique_together = ('creation_date', 'user') def __str__(self): return '{0} ({1})'.format(self.user.email, self.amount_api_calls) class APICallDayHistory(APICallHistoryBase): pass class APICallMonthHistory(APICallHistoryBase): pass @python_2_unicode_compatible class UnshortenURL(models.Model): """ Holds the information about a short to long url relation. :creation_date: The time it was created. :long_url: The long url that resulted out of it. :short_url: The short url that needed lookup. """ creation_date = models.DateTimeField( auto_now_add=True, verbose_name='Creation date', ) long_url = models.CharField( max_length=4000, verbose_name='Long URL', ) short_url = models.CharField( max_length=1024, verbose_name='Short URL', ) def __str__(self): return self.short_url

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proxyclient/tools/reboot.py

EricRabil/m1n1

1,604

54289

<filename>proxyclient/tools/reboot.py<gh_stars>1000+ #!/usr/bin/env python3 # SPDX-License-Identifier: MIT import sys, pathlib sys.path.append(str(pathlib.Path(__file__).resolve().parents[1])) from m1n1.setup import * p.reboot()

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docs/archive/slash/docking-example/scripts/euler2rotmat.py

sjdv1982/seamless

15

126438

import sys, os from read_struc import read_struc from math import sin, cos import numpy as np def euler2rotmat(phi,ssi,rot): cs=cos(ssi) cp=cos(phi) ss=sin(ssi) sp=sin(phi) cscp=cs*cp cssp=cs*sp sscp=ss*cp sssp=ss*sp crot=cos(rot) srot=sin(rot) r1 = crot * cscp + srot * sp r2 = srot * cscp - crot * sp r3 = sscp r4 = crot * cssp - srot * cp r5 = srot * cssp + crot * cp r6 = sssp r7 = -crot * ss r8 = -srot * ss r9 = cs return ((r1,r2,r3),(r4,r5,r6),(r7,r8,r9)) datfile = sys.argv[1] header, strucs = read_struc(open(datfile)) strucs = list(strucs) pivots = [] for h in header: if not h.startswith("#pivot"): h = h.rstrip() if h.startswith("#centered"): assert h.endswith(" false"), h continue assert not h.startswith("#pivot auto"), h hh = h.split() assert hh[1] == str(len(pivots)+1), h assert len(hh) == 5, h pivot = [float(v) for v in hh[2:5]] pivots.append(np.array(pivot)) results = [] for struc in strucs: result_struc = [] for lnr, l in enumerate(struc[1]): ll = [float(v) for v in l.split()] assert len(ll) == 6 #no ensembles rotmat = euler2rotmat(*ll[:3]) rotmat = np.array(rotmat) trans = np.array(ll[3:6]) p = pivots[lnr] pp = (-p * rotmat).sum(axis=1) + p trans += pp result = np.eye(4) result[:3,:3] = rotmat result[:3,3] = trans result[3][3] = 1 result_struc.append(result.tolist()) results.append(result_struc) import json print(json.dumps(results, indent=2))

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pyasyncserver/pipes/__init__.py

agratoth/pyasyncserver

0

179455

from .pipe import Pipe from .http_pipe import HTTPPipe from .nats_pipe import NATSPipe from .directions import PipeDirections from .helpers import PipePool

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su4/path_util.py

njuwelkin/su4

0

48855

<filename>su4/path_util.py import os, sys def addModulePath(file): utils_path = os.path.dirname(os.path.abspath(file)) + "/../utils" sys.path.append(utils_path)

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python/tk_houdini_flipbook/create_slate.py

nfa-vfxim/tk-houdini-flipbook

3

1611916

<filename>python/tk_houdini_flipbook/create_slate.py<gh_stars>1-10 # MIT License # Copyright (c) 2020 Netherlands Film Academy # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import os import subprocess import hou import sgtk class CreateSlate(object): def __init__(self, app): # initialize and set paths self.app = app self.nukePath = "%s" % (app.get_setting("nuke_path")) # set slate script path __location__ = os.path.realpath( os.path.join(os.getcwd(), os.path.dirname(__file__)) ) self.slatePath = os.path.join(__location__, "slate.py") def runSlate(self, inputFile, outputFile, settings): # setup environment custom_env = os.environ.copy() if custom_env["PYTHONPATH"] != None: del custom_env["PYTHONPATH"] if custom_env["PYTHONHOME"] != None: del custom_env["PYTHONHOME"] # setup arguments for call context = self.app.context project_name = context.project["name"] user_name = context.user["name"] file_name = hou.hipFile.basename().lower() first_frame = settings["frameRange"] first_frame = first_frame[0] last_frame = settings["frameRange"] last_frame = last_frame[1] task_name = context.step["name"] self.app.logger.debug(task_name) fps = hou.fps() appPath = self.app.disk_location # ensure output path exists self.app.ensure_folder_exists(os.path.dirname(os.path.abspath(outputFile))) # calculate version number template = self.app.get_template("work_file_template") fields = template.get_fields(hou.hipFile.path()) version = fields["version"] resolution = "%d x %d" % ( settings["resolution"][0], settings["resolution"][0], ) # call subprocess of nuke and convert process = subprocess.Popen( [ self.nukePath, "-t", self.slatePath, inputFile, outputFile, project_name, file_name, str(first_frame), str(last_frame), appPath, str(version), resolution, user_name, task_name, str(fps), ], shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=custom_env, ) stdout, stderr = process.communicate() self.app.logger.debug(stdout) if stderr: self.app.logger.error(stderr) if stderr: raise Exception("Could not correctly render file. Used Nuke version %s" % (self.nukePath))

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f_highboost.py

shsandro/high-boost-filter

1

151713

<filename>f_highboost.py import scipy.fftpack as fp from scipy import signal import numpy as np import numpy.fft import cv2 import matplotlib.pylab as pylab # carrega a imagem img = cv2.imread(input("Digite o caminho para a imagem de entrada: "))[:, :, 0] # lê o valor de D D = int(input("Digite o valor de D para o kernel: ")) # obtém o filtro gaussiano no domínio espacial gauss_kernel = np.outer(signal.gaussian( img.shape[0], D), signal.gaussian(img.shape[1], D)) # obtém a imagem e o filtro no domínio da frquência freq_img = fp.fft2(img) freq_kernel = fp.fft2(fp.ifftshift(gauss_kernel)) assert(freq_img.shape == gauss_kernel.shape) # aplica convolução (borramento) convolved_img = freq_img * freq_kernel blured_img = fp.ifft2(convolved_img).real blured_img = np.abs(blured_img) blured_img = blured_img * 255 / blured_img.max() blured_img = blured_img.astype(np.uint8) ####################### Mostra todos os passos da convolução na frequência ####################### pylab.figure(figsize=(30, 25)) pylab.gray() pylab.subplot(2, 2, 1), pylab.imshow(img), pylab.title( 'Imagem original', size=10), pylab.axis('off') pylab.subplot(2, 2, 4), pylab.imshow(blured_img) pylab.title('Imagem após convolução', size=10), pylab.axis('off') pylab.subplot(2, 2, 2), pylab.imshow( (20*np.log10(0.1 + fp.fftshift(freq_kernel))).astype(int)) pylab.title('Transformada do filtro', size=10), pylab.axis('off') pylab.subplot(2, 2, 3), pylab.imshow( (20*np.log10(0.1 + fp.fftshift(convolved_img))).astype(int)) pylab.title('Imagem borrada no domínio da frequência', size=10), pylab.axis('off') pylab.subplots_adjust(wspace=0.2, hspace=1) pylab.show() ################################################################################################## # lê o valor de k k = int(input("Digite o valor de k: ")) # obtem a máscara (arestas) mask = cv2.subtract(img, blured_img) mask = k * mask ####################### Mostra todos os passos da convolução ####################### pylab.subplot(2, 3, 1), pylab.imshow(img), pylab.title( 'Imagem original', size=10), pylab.axis('off') pylab.subplot(2, 3, 3), pylab.imshow(blured_img) pylab.title('Imagem após convolução', size=10), pylab.axis('off') pylab.subplot(2, 3, 5), pylab.imshow(mask) pylab.title('Arestas para aguçamento', size=10), pylab.axis('off') pylab.subplots_adjust(wspace=0.2, hspace=1) pylab.show() ################################################################################################## # obtem imagem final final_image = cv2.add(img, mask) cv2.imwrite(input("Digite o caminho para a imagem de saída: "), final_image) print("Imagem final salva!")

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src/common/status.py

THUKElab/Video2Description

152

191806

from enum import Enum class ModelWeightsStatus(Enum): NO_INFO = 0 SUCCESS = 1 MODEL_NOT_FOUND = 2 WIP = 3

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EstruturaDeDecisao/18 terminar.py

TheCarvalho/atividades-wikipython

0

94659

# ex18 - Faça um Programa que peça uma data no formato dd/mm/aaaa e determine se a mesma é uma data válida.

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tests.py

Airead/pysha3

30

95699

<reponame>Airead/pysha3 from __future__ import print_function import hashlib import hmac import os import sys import unittest import sha3 if sys.version_info[0] == 3: fromhex = bytes.fromhex def tobyte(b): return bytes([b]) def asunicode(s): return s else: def fromhex(s): return s.decode('hex') def tobyte(b): return bytes(b) def asunicode(s): return s.decode('ascii') def read_vectors(hash_name): vector = os.path.join('vectors', hash_name + '.txt') with open(vector) as f: for line in f: line = line.strip() if line.startswith('#') or not line: continue msg, md = line.split(',') yield msg, md class BaseSHA3Tests(unittest.TestCase): new = None name = None digest_size = None block_size = None rate_bits = None capacity_bits = None shake = False vectors = [] # http://wolfgang-ehrhardt.de/hmac-sha3-testvectors.html hmac_vectors = [ ("0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b0b", "4869205468657265"), ("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "5468697320697320612074657374207573696e672061206c6172676572207" + "468616e20626c6f636b2d73697a65206b657920616e642061206c617267657" + "2207468616e20626c6f636b2d73697a6520646174612e20546865206b65792" + "06e6565647320746f20626520686173686564206265666f7265206265696e6" + "720757365642062792074686520484d414320616c676f726974686d2e") ] hmac_results = [] def assertHashDigest(self, hexmsg, hexdigest): hexdigest = hexdigest.lower() msg = fromhex(hexmsg) digest = fromhex(hexdigest) self.assertEqual(len(digest), self.digest_size) sha3 = self.new(msg) self.assertEqual(sha3.hexdigest(), hexdigest) self.assertEqual(sha3.digest(), digest) sha3 = self.new() sha3.update(msg) self.assertEqual(sha3.hexdigest(), hexdigest) self.assertEqual(sha3.digest(), digest) sha3 = self.new() for b in msg: sha3.update(tobyte(b)) self.assertEqual(sha3.hexdigest(), hexdigest) self.assertEqual(sha3.digest(), digest) def test_basics(self): sha3 = self.new() self.assertEqual(sha3.name, self.name) self.assertEqual(sha3.digest_size, self.digest_size) self.assertEqual(sha3._capacity_bits + sha3._rate_bits, 1600) self.assertEqual(sha3._rate_bits, self.rate_bits) self.assertEqual(sha3._capacity_bits, self.capacity_bits) if self.block_size is not None: self.assertEqual(sha3.block_size, self.block_size) if self.shake: self.assertEqual(len(sha3.digest(4)), 4) self.assertEqual(len(sha3.hexdigest(4)), 8) self.assertEqual(len(sha3.digest(8)), 8) self.assertEqual(len(sha3.hexdigest(8)), 16) self.assertEqual(len(sha3.digest(97)), 97) self.assertEqual(len(sha3.hexdigest(97)), 194) else: self.assertEqual(len(sha3.digest()), self.digest_size) self.assertEqual(len(sha3.hexdigest()), self.digest_size * 2) # object is read-only self.assertRaises(AttributeError, setattr, sha3, "attribute", None) self.assertRaises(AttributeError, setattr, sha3, "digest_size", 3) self.assertRaises(AttributeError, setattr, sha3, "name", "egg") self.new(b"data") self.new(string=b"data") self.assertRaises(TypeError, self.new, None) self.assertRaises(TypeError, sha3.update, None) self.assertRaises(TypeError, self.new, asunicode("text")) self.assertRaises(TypeError, sha3.update, asunicode("text")) sha3type = type(sha3) self.assertEqual(sha3type.__name__, self.name) self.assertEqual(sha3type.__module__, "_pysha3") self.assertIsInstance(sha3type(), sha3type) self.assertIs(sha3type, self.new) self.assertRaises(TypeError, type, sha3type, "subclass", {}) def test_hashlib(self): constructor = getattr(hashlib, self.name) s1 = constructor() self.assertEqual(s1.name, self.name) self.assertEqual(s1.digest_size, self.digest_size) # s2 = hashlib.new(self.name) # self.assertEqual(s2.name, self.name) # self.assertEqual(s2.digest_size, self.digest_size) # self.assertEqual(type(s1), type(s2)) # if sys.version_info < (3, 4): # self.assertEqual(constructor, self.new) def test_vectors(self): for hexmsg, hexdigest in read_vectors(self.name): self.assertHashDigest(hexmsg, hexdigest) def test_vectors_unaligned(self): for hexmsg, hexdigest in self.vectors: hexdigest = hexdigest.lower() msg = fromhex(hexmsg) digest = fromhex(hexdigest) for i in range(1, 15): msg2 = i * b"\x00" + msg unaligned = memoryview(msg2)[i:] self.assertEqual(unaligned, msg) sha3 = self.new(unaligned) self.assertEqual(sha3.hexdigest(), hexdigest) self.assertEqual(sha3.digest(), digest) def test_hmac(self): for (key, msg), expected in zip(self.hmac_vectors, self.hmac_results): key = fromhex(key) msg = fromhex(msg) mac = hmac.new(key, msg, self.new) self.assertEqual(len(mac.digest()), self.digest_size) result = mac.hexdigest() self.assertEqual(result, expected, "%s != %s for %r, %r" % (result, expected, key, msg)) class BaseKeccakTests(BaseSHA3Tests): def test_hashlib(self): self.failIf(hasattr(hashlib, self.name)) class BaseShakeTests(BaseSHA3Tests): shake = True def assertHashDigest(self, hexmsg, hexdigest): hexdigest = hexdigest.lower() msg = fromhex(hexmsg) digest = fromhex(hexdigest) # self.assertEqual(len(digest), self.digest_size) sha3 = self.new(msg) self.assertEqual(sha3.hexdigest(len(digest)), hexdigest) self.assertEqual(sha3.digest(len(digest)), digest) sha3 = self.new() sha3.update(msg) self.assertEqual(sha3.hexdigest(len(digest)), hexdigest) self.assertEqual(sha3.digest(len(digest)), digest) sha3 = self.new() for b in msg: sha3.update(tobyte(b)) self.assertEqual(sha3.hexdigest(len(digest)), hexdigest) self.assertEqual(sha3.digest(len(digest)), digest) class SHA3_224Tests(BaseSHA3Tests): new = sha3.sha3_224 name = "sha3_224" digest_size = 28 block_size = 144 rate_bits = 1152 capacity_bits = 448 hmac_results = [ "3b16546bbc7be2706a031dcafd56373d9884367641d8c59af3c860f7", "c79c9b093424e588a9878bbcb089e018270096e9b4b1a9e8220c866a", ] class SHA3_256Tests(BaseSHA3Tests): new = sha3.sha3_256 name = "sha3_256" digest_size = 32 block_size = 136 rate_bits = 1088 capacity_bits = 512 hmac_results = [ "ba85192310dffa96e2a3a40e69774351140bb7185e1202cdcc917589f95e16bb", "e6a36d9b915f86a093cac7d110e9e04cf1d6100d30475509c2475f571b758b5a", ] class SHA3_384Tests(BaseSHA3Tests): new = sha3.sha3_384 name = "sha3_384" digest_size = 48 block_size = 104 rate_bits = 832 capacity_bits = 768 hmac_results = [ "68d2dcf7fd4ddd0a2240c8a437305f61fb7334cfb5d0226e1bc27dc10a2e72" + "3a20d370b47743130e26ac7e3d532886bd", "cad18a8ff6c4cc3ad487b95f9769e9b61c062aefd6952569e6e6421897054c" + "fc70b5fdc6605c18457112fc6aaad45585", ] class SHA3_512Tests(BaseSHA3Tests): new = sha3.sha3_512 name = "sha3_512" digest_size = 64 block_size = 72 rate_bits = 576 capacity_bits = 1024 hmac_results = [ "eb3fbd4b2eaab8f5c504bd3a41465aacec15770a7cabac531e482f860b5ec7" + "ba47ccb2c6f2afce8f88d22b6dc61380f23a668fd3888bb80537c0a0b86407689e", "dc030ee7887034f32cf402df34622f311f3e6cf04860c6bbd7fa488674782b" + "4659fdbdf3fd877852885cfe6e22185fe7b2ee952043629bc9d5f3298a41d02c66", ] class Shake_128Tests(BaseShakeTests): new = sha3.shake_128 name = "shake_128" digest_size = 0 block_size = 168 rate_bits = 1344 capacity_bits = 256 class Shake_256Tests(BaseShakeTests): new = sha3.shake_256 name = "shake_256" digest_size = 0 block_size = 136 rate_bits = 1088 capacity_bits = 512 class Keccak_224Tests(BaseKeccakTests): new = sha3.keccak_224 name = "keccak_224" digest_size = 28 block_size = 144 rate_bits = 1152 capacity_bits = 448 hmac_results = [ "b73d595a2ba9af815e9f2b4e53e78581ebd34a80b3bbaac4e702c4cc", "92649468be236c3c72c189909c063b13f994be05749dc91310db639e", ] class Keccak_256Tests(BaseKeccakTests): new = sha3.keccak_256 name = "keccak_256" digest_size = 32 block_size = 136 rate_bits = 1088 capacity_bits = 512 hmac_results = [ "9663d10c73ee294054dc9faf95647cb99731d12210ff7075fb3d3395abfb9821", "fdaa10a0299aecff9bb411cf2d7748a4022e4a26be3fb5b11b33d8c2b7ef5484", ] class Keccak_384Tests(BaseKeccakTests): new = sha3.keccak_384 name = "keccak_384" digest_size = 48 block_size = 104 rate_bits = 832 capacity_bits = 768 hmac_results = [ "892dfdf5d51e4679bf320cd16d4c9dc6f749744608e003add7fba894acff87" + "361efa4e5799be06b6461f43b60ae97048", "fe9357e3cfa538eb0373a2ce8f1e26ad6590afdaf266f1300522e8896d27e7" + "3f654d0631c8fa598d4bb82af6b744f4f5", ] class Keccak_512Tests(BaseKeccakTests): new = sha3.keccak_512 name = "keccak_512" digest_size = 64 block_size = 72 rate_bits = 576 capacity_bits = 1024 hmac_results = [ "8852c63be8cfc21541a4ee5e5a9a852fc2f7a9adec2ff3a13718ab4ed81aae" + "a0b87b7eb397323548e261a64e7fc75198f6663a11b22cd957f7c8ec858a1c7755", "6adc502f14e27812402fc81a807b28bf8a53c87bea7a1df6256bf66f5de1a4" + "cb741407ad15ab8abc136846057f881969fbb159c321c904bfb557b77afb7778c8", ] def test_main(): suite = unittest.TestSuite() classes = [ SHA3_224Tests, SHA3_256Tests, SHA3_384Tests, SHA3_512Tests, Shake_128Tests, Shake_256Tests, Keccak_224Tests, Keccak_256Tests, Keccak_384Tests, Keccak_512Tests, ] for cls in classes: suite.addTests(unittest.makeSuite(cls)) return suite if __name__ == "__main__": result = unittest.TextTestRunner(verbosity=2).run(test_main()) sys.exit(not result.wasSuccessful())

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synthetic/functions.py

wchen459/MO-PaDGAN-Optimization

3

95912

""" Test functions Author(s): <NAME> (<EMAIL>) """ import numpy as np import tensorflow as tf class Function(object): def __init__(self): pass def evaluate(self, data): x = tf.placeholder(tf.float32, shape=[None, self.dim]) y = self.equation(x) with tf.Session() as sess: values = sess.run(y, feed_dict={x: data}) return values class VLMOP2(Function): ''' Reference: <NAME>., & <NAME>. (1999, February). Multiobjective evolutionary algorithm test suites. In Proceedings of the 1999 ACM symposium on Applied computing (pp. 351-357). ''' def __init__(self): self.dim = 2 self.n_obj = 2 self.name = 'VLMOP2' x1 = np.linspace(-1/self.dim**.5, 1/self.dim**.5, 100) x2 = np.linspace(-1/self.dim**.5, 1/self.dim**.5, 100) self.pareto_x = np.vstack((x1, x2)).T self.pareto_y = self.evaluate(self.pareto_x) def equation(self, x): transl = 1 / np.sqrt(2) part1 = (x[:, 0] - transl) ** 2 + (x[:, 1] - transl) ** 2 part2 = (x[:, 0] + transl) ** 2 + (x[:, 1] + transl) ** 2 y1 = tf.exp(-1 * part1) y2 = tf.exp(-1 * part2) return tf.stack((y1, y2), axis=1) #class OKA1(Function): # ''' # Reference: # <NAME>., <NAME>., <NAME>., & <NAME>. (2004, September). # On test functions for evolutionary multi-objective optimization. # In International Conference on Parallel Problem Solving from Nature (pp. 792-802). # Springer, Berlin, Heidelberg. # ''' # def __init__(self): # self.dim = 2 # self.n_obj = 2 # self.name = 'OKA1' class NKNO1(Function): ''' Normalized KNO1 Reference: J. Knowles. ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. Technical Report TR-COMPSYSBIO-2004-01, University of Manchester, UK, 2004. Available from http://dbk.ch.umist.ac.uk/knowles/pubs.html ''' def __init__(self): self.dim = 2 self.n_obj = 2 self.name = 'NKNO1' x1 = np.linspace(-0.5+4.4116/3-1, 0.5, 100) x2 = 4.4116/3 - x1 - 1 self.pareto_x = np.vstack((x1, x2)).T self.pareto_y = self.evaluate(self.pareto_x) def equation(self, x): x = 3*(x+.5) r = 9 - (3*tf.sin(5/2*(x[:,0]+x[:,1])**2) + 3*tf.sin(4*(x[:,0]+x[:,1])) + 5*tf.sin(2*(x[:,0]+x[:,1])+2)) phi = np.pi/12*(x[:,0]-x[:,1]+3) y1 = r/20*tf.cos(phi) y2 = r/20*tf.sin(phi) return tf.stack((y1, y2), axis=1) if __name__ == "__main__": import matplotlib.pyplot as plt import matplotlib matplotlib.use('Qt5Agg') N = 1000 xs = np.random.uniform(-.5, .5, size=(N,2)) func_obj = NKNO1() ys = func_obj.evaluate(xs) plt.figure() plt.subplot(121) true_pfx = func_obj.pareto_x plt.plot(true_pfx[:,0], true_pfx[:,1]) plt.subplot(122) plt.scatter(ys[:,0], ys[:,1]) true_pf = func_obj.pareto_y plt.plot(true_pf[:,0], true_pf[:,1]) plt.show()

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src/skyciv/classes/model/components/materials/materials.py

skyciv/skyciv-pip

4

114916

<reponame>skyciv/skyciv-pip<gh_stars>1-10 from skyciv.classes.model.components._base_class.model_collection_component import ModelCollectionComponent from skyciv.classes.model.components.materials.default_materials import default_materials from skyciv.classes.model.components.materials.material import Material from skyciv.utils.helpers import next_object_key try: from typing import Literal except ImportError: from typing_extensions import Literal class Materials(ModelCollectionComponent): """Creates an instance of the SkyCiv Materials class. """ def add_custom( self, name: str, density: float, elasticity_modulus: float, poissons_ratio: float, yield_strength: float, ultimate_strength: float, _class: Literal["steel", "aluminium", "masonry", "concrete", "wood", "other"] ) -> int: """Create a custom material with the next available ID. Args: name (str): The name of the material. density (float): The density of the material. elasticity_modulus (float): The Modulus of Elasticity of the material. poissons_ratio (float): The Poisson's Ratio for the material. yield_strength (float): The Yield strength of the material. ultimate_strength (float): The Ultimate strength the material. _class (str): The type of material. {"steel" | "aluminium" | "masonry" | "concrete" | "wood" | "other"}. Returns: int: The ID of the created material. """ next_index = next_object_key(self) material = Material( name, density, elasticity_modulus, poissons_ratio, yield_strength, ultimate_strength, _class ) setattr(self, str(next_index), material) return next_index def add(self, material: Literal["Structural Steel", "Aluminium", "Carbon Fibre Reinforced Plastic", "Concrete", "Concrete High Strength", "Oakwood", "Glass"], unit_system: str = "metric") -> int: """Add a default material to the model. Args: material (str): The material name. {"Structural Steel" | "Aluminium" | "Carbon Fibre Reinforced Plastic" | "Concrete" | "Concrete High Strength" | "Oakwood" | "Glass"}. unit_system (str): {'metric' | 'imperial'} Returns: int: The ID of the created material. """ materials = default_materials[unit_system][material] name = materials["name"] density = materials["density"] elasticity_modulus = materials["elasticity_modulus"] poissons_ratio = materials["poissons_ratio"] yield_strength = materials["yield_strength"] ultimate_strength = materials["ultimate_strength"] _class = materials["class"] index = self.add_custom( name, density, elasticity_modulus, poissons_ratio, yield_strength, ultimate_strength, _class ) return index

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src/pypytranspy/transformations/fstring_to_format_transformer.py

expobrain/pypytranspy

4

85775

import ast from pypytranspy.transformations.base_transformer import BaseTransformer class FStringToFormatTransformer(BaseTransformer): minimum_version = [3, 6] def pre_JoinedStr(self): # Collect string and args for format() str_value = "" str_args = [] for value in self.cur_node.values: if isinstance(value, ast.Str): str_value += value.s elif isinstance(value, ast.FormattedValue): if value.format_spec is None: str_value += "{}" else: str_value += "{:" + "".join(v.s for v in value.format_spec.values) + "}" str_args.append(value.value) else: raise NotImplementedError(value) # Build Str.format() node str_format = ast.Call( func=ast.Attribute(value=ast.Str(s=str_value), attr="format"), args=str_args, keywords=[], ) self.replace(str_format)

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examples/python/cpu/numpy/example_11.py

kant/ocean-tensor-package

27

1604315

<gh_stars>10-100 import pyOcean_cpu as ocean import numpy as np import pyOceanNumpy a = ocean.tensor([5]) s = ocean.tensor([], ocean.float) s.fill(3); a.fill([[False]]); print(a) a.fill([(1,)]); print(a) a.fill([2.0]); print(a) a.fill(s); print(a) a.fill([s]); print(a) s = np.asarray([4]) print(s) print(type(s)) a.fill(s); print(a) a.fill([(s,)]); print(a)

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matplotlib/gallery_python/pyplots/dollar_ticks.py

gottaegbert/penter

13

4270

<reponame>gottaegbert/penter """ ============ Dollar Ticks ============ Use a `~.ticker.FormatStrFormatter` to prepend dollar signs on y axis labels. """ import numpy as np import matplotlib.pyplot as plt import matplotlib.ticker as ticker # Fixing random state for reproducibility np.random.seed(19680801) fig, ax = plt.subplots() ax.plot(100*np.random.rand(20)) formatter = ticker.FormatStrFormatter('$%1.2f') ax.yaxis.set_major_formatter(formatter) for tick in ax.yaxis.get_major_ticks(): tick.label1.set_visible(False) tick.label2.set_visible(True) tick.label2.set_color('green') plt.show() ############################################################################# # # ------------ # # References # """""""""" # # The use of the following functions, methods, classes and modules is shown # in this example: import matplotlib matplotlib.ticker matplotlib.ticker.FormatStrFormatter matplotlib.axis.Axis.set_major_formatter matplotlib.axis.Axis.get_major_ticks matplotlib.axis.Tick

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src/lumigo_tracer/parsing_utils.py

anniyanvr/python_tracer

47

95777

import json import re import urllib.parse from typing import Tuple, Dict, Union, List, Any, Optional from lumigo_tracer.libs import xmltodict import functools import itertools from collections.abc import Iterable from lumigo_tracer.lumigo_utils import Configuration, get_logger def safe_get(d: Union[dict, list], keys: List[Union[str, int]], default: Any = None) -> Any: """ :param d: Should be list or dict, otherwise return default. :param keys: If keys[i] is int, then it should be a list index. If keys[i] is string, then it should be a dict key. :param default: If encountered a problem, return default. :return: d[keys[0]][keys[1]]... """ def get_next_val(prev_result, key): if isinstance(prev_result, dict) and isinstance(key, str): return prev_result.get(key, default) elif isinstance(prev_result, list) and isinstance(key, int): return safe_get_list(prev_result, key, default) else: return default return functools.reduce(get_next_val, keys, d) def safe_get_list(lst: list, index: Union[int, str], default=None): """ This function return the organ in the `index` place from the given list. If this values doesn't exist, return default. """ if isinstance(index, str): try: index = int(index) except ValueError: return default if not isinstance(lst, Iterable): return default return lst[index] if len(lst) > index else default def safe_split_get(string: str, sep: str, index: int, default=None) -> str: """ This function splits the given string using the sep, and returns the organ in the `index` place. If such index doesn't exist, returns default. """ if not isinstance(string, str): return default return safe_get_list(string.split(sep), index, default) def safe_key_from_json(json_str: bytes, key: object, default=None) -> Union[str, list]: """ This function tries to read the given str as json, and returns the value of the desired key. If the key doesn't found or the input string is not a valid json, returns the default. """ try: return json.loads(json_str).get(key, default) except json.JSONDecodeError: return default def safe_key_from_xml(xml_str: bytes, key: str, default=None): """ This function tries to read the given str as XML, and returns the value of the desired key. If the key doesn't found or the input string is not a valid XML, returns the default. We accept keys with hierarchy by `/` (i.e. we accept keys with the format `outer/inner`) If there are some keys with the same name at the same hierarchy, they can be accessed as index in list, e.g: <a>val0val1</a> will be accessed with "a/b/0" or "a/b/1". """ try: result = functools.reduce( lambda prev, sub_key: safe_get_list(prev, sub_key) if isinstance(prev, list) else prev.get(sub_key, {}), key.split("/"), xmltodict.parse(xml_str), ) return result or default except xmltodict.expat.ExpatError: return default def safe_key_from_query(body: bytes, key: str, default=None) -> str: """ This function assumes that the first row in the body is the url arguments. We assume that the structure of the parameters is as follow: * character-escaped using urllib.quote * values separated with '&' * each item is <key>=<value> Note: This function decode the given body, therefore duplicate it's size. Be aware to use only in resources with restricted body length. """ return dict(re.findall(r"([^&]+)=([^&]*)", urllib.parse.unquote(body.decode()))).get( key, default ) def parse_trace_id(trace_id_str: str) -> Tuple[str, str, str]: """ This function parses the trace_id, and result dictionary the describes the data. We assume the following format: * values separated with ';' * each item is <key>=<value> :param trace_id_str: The string that came from the environment variables. """ if not isinstance(trace_id_str, str): return "", "", "" trace_id_parameters = dict(re.findall(r"([^;]+)=([^;]*)", trace_id_str)) root = trace_id_parameters.get("Root", "") root_end_index = trace_id_str.find(";") suffix = trace_id_str[root_end_index:] if ";" in trace_id_str else trace_id_str return root, safe_split_get(root, "-", 2, default=""), suffix def recursive_json_join(d1: Optional[dict], d2: Optional[dict]): """ This function return the recursive joint dictionary, which means that for every (item, key) in the result dictionary it holds that: * if key in d1 and is not dictionary, then the value is d1[key] * if key in d2 and is not dictionary, then the value is d2[key] * otherwise, join d1[key] and d2[key] """ if d1 is None or d2 is None: return d1 or d2 d = {} for key in set(itertools.chain(d1.keys(), d2.keys())): value = d1.get(key, d2.get(key)) if isinstance(value, dict): d[key] = recursive_json_join(d1.get(key), d2.get(key)) # type: ignore else: d[key] = value return d def should_scrub_domain(url: str) -> bool: if url and Configuration.domains_scrubber: for regex in Configuration.domains_scrubber: if regex.match(url): return True return False def str_to_list(val: str) -> Optional[List[str]]: try: if val: return val.split(",") except Exception as e: get_logger().debug("Error while convert str to list", exc_info=e) return None def str_to_tuple(val: str) -> Optional[Tuple]: try: if val: return tuple(val.split(",")) except Exception as e: get_logger().debug("Error while convert str to tuple", exc_info=e) return None def recursive_get_key(d: Union[List, Dict[str, Union[Dict, str]]], key, depth=None, default=None): if depth is None: depth = Configuration.get_key_depth if depth == 0: return default if key in d: return d[key] if isinstance(d, list): for v in d: recursive_result = recursive_get_key(v, key, depth - 1, default) if recursive_result: return recursive_result if isinstance(d, dict): for v in d.values(): if isinstance(v, (list, dict)): recursive_result = recursive_get_key(v, key, depth - 1, default) if recursive_result: return recursive_result return default def extract_function_name_from_arn(arn: str) -> str: return safe_split_get(arn, ":", 6)

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nasws/cnn/search_space/monodepth/params/search-decoder-v1/resnext101_wsl.py

kcyu2014/nas-landmarkreg

8

1600843

<reponame>kcyu2014/nas-landmarkreg<filename>nasws/cnn/search_space/monodepth/params/search-decoder-v1/resnext101_wsl.py from monodepth.models import MidasNet, MidasNetSearch from monodepth.optim.optimizer import MGDA, GDA import torch import torch.optim as optim from monodepth.validation import Validation from monodepth.utils.checkpointer import Checkpointer from monodepth.utils.reporter import Reporter from monodepth.optim.scheduler import ConstLR from monodepth.loss import ( ScaleAndShiftInvariantLoss, ProcrustesLoss, TrimmedProcrustesLoss, ) from monodepth.data import multi_data_loader from monodepth.data.datasets import MegaDepth, ReDWeb, Movies3d from monodepth.data.datasets.transforms import ( RandomCrop, RandomFliplr, Rescale, PrepareForNet, Resize, NormalizeImage, ) from torchvision.transforms import Compose from monodepth.metric import DisparityRmse from collections import OrderedDict from monodepth.nas.api import NonZeroRandomMutator, NonZeroRandomMutatorSync class TrainParams: def __init__(self, config, output, args, do_resume=False, init_from=None): # torch options torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True # device self.device = torch.device("cuda") self.args = args # model self.model = MidasNetSearch(backbone="resnext101_wsl", args=args) if 'spos' in args.supernet_train_method: if args.mutator == 'nonzero': mutator = NonZeroRandomMutator(self.model) elif args.mutator == 'nonzero_sync': mutator = NonZeroRandomMutatorSync(self.model) else: raise NotImplementedError(f"Mutator {args.mutator} is not implemented for SPOS method") else: raise NotImplementedError(f"Method {args.supernet_train_method} is not implemented for MonoDepth") self.mutator = mutator # tasks self.tasks = OrderedDict() self.tasks["ReDWeb"] = { "dataset": ReDWeb( config["datasets"]["ReDWeb"]["train"], config["datasets"]["ReDWeb"]["datapath"], transform=Compose( [ Resize(384, 384, keep_aspect_ratio=True), RandomCrop(384, 384), RandomFliplr(), Rescale(), NormalizeImage( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ), PrepareForNet(), ] ), mode="train", ), "loss": TrimmedProcrustesLoss(), } self.dataloader = multi_data_loader.MultiDataLoader( {k: v["dataset"] for k, v in self.tasks.items()}, batch_size=8, num_workers=4, ) # optimization algo = optim.Adam( [ {"params": self.model.scratch.parameters()}, {"params": self.model.pretrained.parameters(), "lr": args.backbone_learning_rate}, ], lr=args.learning_rate, betas=(0.9, 0.999), weight_decay=args.weight_decay ) if args.backbone_learning_rate == 0.0: for param in self.model.pretrained.parameters(): param.requires_grad = False if args.backbone_weights: # load the pretrained weights weights accordingly state = torch.load(args.backbone_weights) self.model.load_state_dict(state, strict=False) # this should make the training super fast self.epoch_length = 3240 self.num_epochs = args.epochs # wrapping the optimizer self.optimizer = GDA(algorithm=algo, num_tasks=len(self.tasks)) if args.learning_rate_scheduler == 'step': self.scheduler = torch.optim.lr_scheduler.StepLR(algo, args.epochs // 3, gamma=0.1) elif args.learning_rate_scheduler == 'cosine': self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(algo, float(args.epochs), eta_min=args.learning_rate_min) print(f'Using {self.scheduler} with initial learning rate {args.learning_rate}.') # checkpointing self.checkpointer = Checkpointer( output + "/checkpoint.pt", self.model, self.optimizer.algo, self.scheduler, frequency=10, do_resume=do_resume, ) # reporting self.reporter = Reporter( use_console=True, tensorboard_out=output + "/log", img_index=[], logger_out=output + '/log_search.txt' ) # validation validation_tasks = OrderedDict() validation_tasks["ReDWeb"] = { "dataset": ReDWeb( config["datasets"]["ReDWeb"]["validation"], config["datasets"]["ReDWeb"]["datapath"], transform=Compose( [ Resize( 384, 384, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=32, resize_method="minimal", ), NormalizeImage( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ), PrepareForNet(), ] ), ), "metric": DisparityRmse(), } self.validation = Validation( self.model, validation_tasks, self.reporter, self.device, frequency=args.save_every_epoch )

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RecoPPS/Local/python/ctppsDiamondLocalReconstruction_cff.py

ckamtsikis/cmssw

852

39143

<gh_stars>100-1000 import FWCore.ParameterSet.Config as cms # reco hit production from RecoPPS.Local.ctppsDiamondRecHits_cfi import ctppsDiamondRecHits # local track fitting from RecoPPS.Local.ctppsDiamondLocalTracks_cfi import ctppsDiamondLocalTracks ctppsDiamondLocalReconstructionTask = cms.Task( ctppsDiamondRecHits, ctppsDiamondLocalTracks ) ctppsDiamondLocalReconstruction = cms.Sequence(ctppsDiamondLocalReconstructionTask)

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identityholder.py

BenNordick/HiLoop

1

46626

<filename>identityholder.py<gh_stars>1-10 class IdentityHolder: """ Wraps an object so that it can be quickly compared by object identity rather than value. Also provides a total order based on object address. This is frequently useful for sets representing cycles: set value equality is O(n) but object identity equality is O(1). Different cycles can have the same nodes in a different order; it is important to distinguish these objects, but useful to represent them as sets for purposes of fast intersection. """ def __init__(self, value, tag=None): """Wrap a value, optionally tagged with extra information.""" self.value = value self.tag = tag def __hash__(self): return id(self.value) def __eq__(self, other): return self.value is other.value def __str__(self): return 'IH`' + str(self.value) + '`' def __repr__(self): return 'IdentityHolder(' + repr(self.value) + ', ' + repr(self.tag) + ')' def order(self, other): """Return a tuple of this holder and the other such that the first item is before the second in a total order.""" return (self, other) if self.isbefore(other) else (other, self) def isbefore(self, other): """Determine whether this holder is before the other in a total order.""" return id(self.value) < id(other.value)

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src/bert.py

emerging-welfare/random_sampling-vs-keyword_filtering

0

83376

<reponame>emerging-welfare/random_sampling-vs-keyword_filtering from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler from torch.autograd import Variable from sklearn.metrics import classification_report, confusion_matrix, f1_score from transformers import * from torch import nn import numpy as np import time import datetime import torch import random import json import os import sys import torch.nn.functional as F import unicodedata import re use_gpu = True seed = 1234 batch_size = 64 # 8 per available GPU max_length = 512 folds = 10 n_epochs = 10 lr = 2e-5 device_ids = [0, 1, 2, 3, 4, 5, 6, 7] #device_ids = [0, 1, 2, 3] if use_gpu and torch.cuda.is_available(): device = torch.device("cuda:0") else: device = torch.device("cpu") tokenizer = None def prepare_set(dataset, max_length=256): """returns input_ids, input_masks, labels for set of data ready in BERT format""" global tokenizer input_ids, labels = [], [] for i in dataset: input_ids.append(i["text"].lower()) labels.append(1 if i["label"] == 1 else 0) inputs = [ tokenizer.encode_plus(i, pad_to_max_length=True, add_special_tokens=True, max_length=max_length) for i in input_ids ] input_ids = [ torch.tensor(x["input_ids"]) for x in inputs ] input_masks = [ torch.tensor(x["attention_mask"]) for x in inputs ] token_type_ids = [ torch.tensor(x["token_type_ids"]) for x in inputs ] input_ids = torch.stack(input_ids) input_masks = torch.stack(input_masks) token_type_ids = torch.stack(token_type_ids) labels = torch.tensor(labels) return (input_ids, input_masks, token_type_ids), labels def predict(self, test): (test_inputs, test_masks, test_type_ids), y_test = prepare_set(test, max_length=max_length) test_data = TensorDataset(test_inputs, test_masks, test_type_ids) test_sampler = SequentialSampler(test_data) test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size) self.eval() with torch.no_grad(): preds = [] for batch in test_dataloader: b_input_ids, b_input_mask, b_token_type_ids = tuple(t.to(device) for t in batch) output = self(b_input_ids, b_input_mask, b_token_type_ids) logits = output[0].detach().cpu().numpy() preds += list(np.argmax(logits, axis=1).flatten()) return preds def get_bert(pretrained_model): global tokenizer tokenizer = BertTokenizer.from_pretrained(pretrained_model) model = BertForSequenceClassification.from_pretrained(pretrained_model, num_labels=2) # if torch.cuda.device_count() > 1 and device.type == "cuda": # model = nn.DataParallel(model, device_ids=device_ids) model.to(device) model.predict = predict.__get__(model) return model def build_bert(train, dev, pretrained_model, n_epochs=15): global tokenizer tokenizer = AutoTokenizer.from_pretrained(pretrained_model) model = AutoModelForSequenceClassification.from_pretrained(pretrained_model) model_path = "temp.pt" print([len(x) for x in (train, dev)]) (train_inputs, train_masks, train_type_ids), y_train = prepare_set(train, max_length=max_length) train_data = TensorDataset(train_inputs, train_masks, train_type_ids, y_train) train_sampler = RandomSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size) # Create the DataLoader for our dev set. (dev_inputs, dev_masks, dev_type_ids), y_dev = prepare_set(dev, max_length=max_length) dev_data = TensorDataset(dev_inputs, dev_masks, dev_type_ids, y_dev) dev_sampler = SequentialSampler(dev_data) dev_dataloader = DataLoader(dev_data, sampler=dev_sampler, batch_size=batch_size) if torch.cuda.device_count() > 1 and device.type == "cuda": model = nn.DataParallel(model, device_ids=device_ids) model.to(device) np.random.seed(seed) torch.manual_seed(seed) if device.type == "cuda": torch.cuda.manual_seed_all(seed) total_steps = len(train_dataloader) * n_epochs optimizer = AdamW(model.parameters(), lr=lr) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps = 0, num_training_steps = total_steps) model.zero_grad() best_score = 0 best_loss = 1e6 train_losses = [] for epoch in range(n_epochs): start_time = time.time() train_loss = 0 model.train() for batch in train_dataloader: b_input_ids, b_input_mask, b_token_type_ids, b_labels = tuple(t.to(device) for t in batch) output = model(b_input_ids, attention_mask=b_input_mask, token_type_ids=b_token_type_ids, labels=b_labels) loss = output[0].sum() loss.backward() optimizer.step() train_loss += loss.item() model.zero_grad() scheduler.step() elapsed = time.time() - start_time model.eval() val_preds = [] with torch.no_grad(): val_loss, batch = 0, 1 for batch in dev_dataloader: b_input_ids, b_input_mask, b_token_type_ids, b_labels = tuple(t.to(device) for t in batch) output = model(b_input_ids, attention_mask=b_input_mask, token_type_ids=b_token_type_ids, labels=b_labels) loss = output[0].sum() val_loss += loss.item() logits = output[1].detach().cpu().numpy() val_preds += list(np.argmax(logits, axis=1).flatten()) model.zero_grad() val_score = f1_score(y_dev.cpu().numpy().tolist(), val_preds, average="macro") #print("Epoch %d Train loss: %.4f. Validation F1-Score: %.4f Validation loss: %.4f. Elapsed time: %.2fs."% (epoch + 1, train_loss, val_score, val_loss, elapsed)) if val_score > best_score: torch.save(model.state_dict(), model_path) #print(classification_report(y_dev.cpu().numpy().tolist(), val_preds, digits=4)) best_score = val_score model.load_state_dict(torch.load(model_path)) model.to(device) model.predict = predict.__get__(model) os.remove(model_path) return model

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tests/read_config.py

tyxio/txpy-azurehelper

0

26494

import logging.config import os import yaml __location__ = os.path.realpath( os.path.join(os.getcwd(), os.path.dirname(__file__))) def read_logging_config(): with open(os.path.join(__location__, 'logging.cfg'), 'r') as stream: try: logging_config = yaml.safe_load(stream) logging.config.dictConfig(logging_config) # do not log azure info messages logging.getLogger( 'azure.core.pipeline.policies.http_logging_policy').setLevel(logging.WARNING) return logging except yaml.YAMLError as exc: print(exc) except Exception as ex: print(ex) return None def read_azure_config(): with open(os.path.join(__location__, 'azure.cfg'), 'r') as stream: try: azure_config = yaml.safe_load(stream) return \ azure_config['az_storage_connection_str'],\ azure_config['az_storage_blob_sas_url'],\ azure_config['az_storage_blob_sas_token'] except yaml.YAMLError as exc: print(exc) except Exception as ex: print(ex) return None, None, None

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application.py

meverg/goal_model_generator

1

173126

<filename>application.py<gh_stars>1-10 from flask import Flask, request, render_template import US2SMT import Parser import en_core_web_md nlp = en_core_web_md.load() print('model loaded') app = Flask(__name__) @app.after_request def set_response_headers(response): response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate' response.headers['Pragma'] = 'no-cache' response.headers['Expires'] = '0' return response @app.route('/', methods=['GET']) def index(): return render_template('index.html') @app.route('/solve_us', methods=['POST']) def solve_us(): converter = US2SMT.US2SMT(request.files['us_file'], Parser.Parser(nlp, model_selection=request.form.get('model_selection'), vectorizer_selection=request.form.get('vectorizer_selection')), request.form.get('opt'), request.form.get('opt2'), request.form.get('max')) converter = converter.add_us() converter = converter.weight() smt, dot, dictn = converter.get_smt_input() oms_out = US2SMT.get_oms_out() for line in oms_out.splitlines(): if line.strip(): line = line.replace("(", " ") line = line.lstrip() word = line.split() if word[0] in dictn: if word[1] == "true)": dot.node(word[0], color="limegreen", fillcolor="palegreen1", style='filled') else: dot.node(word[0], color="red3", fillcolor="red", style='filled') dot.graph_attr['rankdir'] = 'LR' goal_model_path = dot.render(directory='./static', cleanup=True, format='png') return render_template('result.html', goal_model_path=goal_model_path) if __name__ == '__main__': app.run(debug=True)

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preql/__main__.py

erezsh/Preql

522

81734

<reponame>erezsh/Preql import json import argparse from pathlib import Path from itertools import chain import time from . import Preql, __version__, Signal from . import settings parser = argparse.ArgumentParser(description='Preql command-line interface (aka REPL)') parser.add_argument('-i', '--interactive', action='store_true', default=False, help="enter interactive mode after running the script") parser.add_argument('-v', '--version', action='store_true', help="print version") parser.add_argument('--install-jupyter', action='store_true', help="installs the Preql plugin for Jupyter notebook") parser.add_argument('--print-sql', action='store_true', help="print the SQL code that's being executed") parser.add_argument('-f', '--file', type=str, help='path to a Preql script to run') parser.add_argument('-m', '--module', type=str, help='name of a Preql module to run') parser.add_argument('--time', action='store_true', help='displays how long the script ran') parser.add_argument('-c', '--config', type=str, help='path to a JSON configuration file for Preql (default: ~/.preql_conf.json)') parser.add_argument('database', type=str, nargs='?', default=None, help="database url (postgres://user:password@host:port/db_name") parser.add_argument('--python-traceback', action='store_true', help="Show the Python traceback when an exception causes the interpreter to quit") def find_dot_preql(): cwd = Path.cwd() for p in chain([cwd], cwd.parents): dot_preql = p / ".preql" if dot_preql.exists(): return dot_preql def update_settings(path): config = json.load(path.open()) if 'debug' in config: settings.debug = config['debug'] if 'color_scheme' in config: settings.color_theme.update(config['color_scheme']) def main(): args = parser.parse_args() if args.version: print(__version__) if args.install_jupyter: from .jup_kernel.install import main as install_jupyter install_jupyter([]) print("Install successful. To start working, run 'jupyter notebook' and create a new Preql notebook.") return from pathlib import Path if args.config: update_settings(Path(args.config)) else: config_path = Path.home() / '.preql_conf.json' if config_path.exists(): update_settings(config_path) kw = {'print_sql': args.print_sql} if args.database: kw['db_uri'] = args.database kw['auto_create'] = True p = Preql(**kw) interactive = args.interactive error_code = 0 start = time.time() try: if args.file: p.load(args.file) elif args.module: p('import ' + args.module) elif args.version or args.install_jupyter: pass else: dot_preql = find_dot_preql() if dot_preql: print("Auto-running", dot_preql) p._run_code(dot_preql.read_text(), dot_preql) interactive = True except Signal as e: p._display.print_exception(e) error_code = -1 if args.python_traceback: raise except KeyboardInterrupt: print("Interrupted (Ctrl+C)") end = time.time() if args.time: print('Script took %.2f seconds to run' % (end -start)) if interactive: p.load_all_tables() p.start_repl() else: return error_code if __name__ == '__main__': main()

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utils/visualizer.py

darsh169/Representation-Learning-for-Information-Extraction

53

62686

<reponame>darsh169/Representation-Learning-for-Information-Extraction import json import cv2 def tesseract_ocr(image_path, ocr_path): image = cv2.imread(image_path.as_posix()) with open(ocr_path, 'r') as f: ocr_data = json.load(f) empty_index = [i for i, ele in enumerate(ocr_data['text']) if ele == ""] for key in ocr_data.keys(): ocr_data[key] = [j for i, j in enumerate(ocr_data[key]) if i not in empty_index] for txt, x, y, w, h in zip(ocr_data['text'], ocr_data['left'], ocr_data['top'], ocr_data['width'], ocr_data['height']): x2 = x + w y2 = y + h cv2.rectangle(image, (x, y), (x2, y2), (0, 255, 0), 1) cv2.putText(image, txt, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1, cv2.LINE_AA) return image

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train/ip.py

VCG/gp

0

19753

<reponame>VCG/gp<gh_stars>0 import cPickle as pickle import os; import sys; sys.path.append('..') import gp import gp.nets as nets PATCH_PATH = ('iplb') X_train, y_train, X_test, y_test = gp.Patch.load_rgb(PATCH_PATH) X_train = X_train[:,:-1,:,:] X_test = X_test[:,:-1,:,:] cnn = nets.RGNetPlus() cnn = cnn.fit(X_train, y_train) test_accuracy = cnn.score(X_test, y_test) print test_accuracy # store CNN sys.setrecursionlimit(1000000000) with open(os.path.expanduser('~/Projects/gp/nets/IP_FULL.p'), 'wb') as f: pickle.dump(cnn, f, -1)

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src/simple_email/consts.py

deeso/simple-email

0

177708

<gh_stars>0 SMTP_SERVICE_BLOCK = 'smtp-service' SMTP_HOST = 'smtp_host' SMTP_PORT = 'smtp_port' SMTP_USERNAME = 'smtp_username' SMTP_PASSWORD = '<PASSWORD>' SMTP_USE_TLS = 'smtp_use_tls' SMTP_LEVEL = 'smtp_level' SMTP_DEFAULT_VALUES = { SMTP_HOST: '127.0.0.1', SMTP_PORT: '25', SMTP_USERNAME: None, SMTP_PASSWORD: None, SMTP_USE_TLS: True, SMTP_LEVEL: 0, } SMTP_CONFIG = [ SMTP_HOST, SMTP_PORT, SMTP_USERNAME, SMTP_PASSWORD, SMTP_USE_TLS, SMTP_LEVEL, ] FILENAME = 'filename' BUFFERED_CONTENT = 'buffered_content'

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Python_LEVEL_TWO/Part6_regular_expressions.py

chernyshov-dev/WebDev

4

150961

import re patterns = ["term1", "term2"] text = "This is a string with term1, not the other" split_term = "@" email = "<EMAIL>" # for pattern in patterns: # print("I'm searching for: " + pattern) # # if re.search(pattern, text): # print("MATCH!") # else: # print("NO MATCH!") print(re.split(split_term, email)) # email.split("@") # print(match.start()) print(re.findall("match", "test phrase match in match middle")) def multi_re_find(patterns, phrase): for pat in patterns: print("Searching for pattern {}".format(pat)) print(re.findall(pat, phrase)) print("\n") # test_phrase = "sdsd..sssddd..sdddsddd...dsds...dssssss...sddddd" # test_patterns = ["s[sd]+"] # test_phrase = "This is a string! But it has punctuation. How can we remove it?" # test_patterns = ["[^!.?]+"] # test_patterns = ["[A-Z]+"] test_phrase = "This is a string with numbers 12312 and a symbol #hashtag" # d - digits # D - non-digits # s - whitespace # S - non-whitespace # w - alphanumeric characters # W - non-alphanumeric characters test_patterns = [r"\W+"] multi_re_find(test_patterns, test_phrase)

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MLplatform/core/migrations/0004_auto_20210410_1339.py

mihai011/Docker-Django-Posgres-Celery-Rabbitmq

0

1615688

# Generated by Django 3.1 on 2021-04-10 13:39 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('core', '0003_auto_20210323_1406'), ] operations = [ migrations.RenameField( model_name='email', old_name='message', new_name='template', ), ]

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floodgate/server_state_machine/layer2_decoration_event/tell.py

muzudho/e-gov-vote-kifuwarabe-player-client

0

177865

<reponame>muzudho/e-gov-vote-kifuwarabe-player-client<filename>floodgate/server_state_machine/layer2_decoration_event/tell.py from app import app from floodgate.server_state_machine.layer1_transition_map.tell import TellState as _TransitionState def create(): """ステート生成""" return _DecoratedState() class _DecoratedState(_TransitionState): def __init__(self): super().__init__() def on_reject_c(self, req): pass def on_reject_s(self, req): pass def on_agree(self, req): pass def on_start(self, req): pass

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yoti_python_sandbox/doc_scan/task/sandbox_text_extraction_task.py

getyoti/yoti-python-sdk-sandbox

0

96861

import base64 from yoti_python_sdk.utils import YotiSerializable from yoti_python_sandbox.doc_scan.document_filter import ( # noqa: F401 SandboxDocumentFilter, ) from yoti_python_sandbox.doc_scan.task.sandbox_text_extraction_recommendation import ( # noqa: F401 SandboxTextDataExtractionRecommendation, ) class SandboxDocumentIdPhoto(YotiSerializable): def __init__(self, content_type, data): """ :param str content_type: the content type :param bytes data: the image data """ self.__content_type = content_type self.__data = data def to_json(self): return { "content_type": self.__content_type, "data": base64.b64encode(self.__data).decode("utf-8"), } class SandboxDocumentTextDataExtractionTaskResult(YotiSerializable): def __init__( self, document_fields=None, document_id_photo=None, detected_country=None, recommendation=None, ): self.__document_fields = document_fields self.__document_id_photo = document_id_photo self.__detected_country = detected_country self.__recommendation = recommendation @property def document_fields(self): return self.__document_fields @property def document_id_photo(self): return self.__document_id_photo @property def detected_country(self): return self.__detected_country @property def recommendation(self): return self.__recommendation def to_json(self): json = {} if self.document_fields is not None: json["document_fields"] = self.document_fields if self.document_id_photo is not None: json["document_id_photo"] = self.document_id_photo if self.detected_country is not None: json["detected_country"] = self.detected_country if self.recommendation is not None: json["recommendation"] = self.recommendation return json class SandboxDocumentTextDataExtractionTask(YotiSerializable): def __init__(self, result, document_filter): self.__result = result self.__document_filter = document_filter @property def result(self): """ :rtype: SandboxDocumentTextDataExtractionTaskResult """ return self.__result @property def document_filter(self): """ :rtype: SandboxDocumentFilter """ return self.__document_filter def to_json(self): obj = { "result": self.__result, } if self.__document_filter is not None: obj["document_filter"] = self.__document_filter return obj class SandboxDocumentTextDataExtractionTaskBuilder(object): def __init__(self): self.__document_fields = None self.__document_filter = None self.__document_id_photo = None self.__detected_country = None self.__recommendation = None def with_document_field(self, key, value): """ :type key: str :type value: str or dict :rtype: SandboxDocumentTextDataExtractionTaskBuilder """ self.__document_fields = self.__document_fields or {} self.__document_fields[key] = value return self def with_document_fields(self, document_fields): """ :type document_fields: dict :rtype: SandboxDocumentTextDataExtractionTaskBuilder """ self.__document_fields = document_fields return self def with_document_id_photo(self, content_type, data): """ :param str content_type: the content type :param bytes data: the image data """ self.__document_id_photo = SandboxDocumentIdPhoto(content_type, data) return self def with_document_filter(self, document_filter): """ :type document_filter: SandboxDocumentFilter :rtype: SandboxDocumentTextDataExtractionTaskBuilder """ self.__document_filter = document_filter return self def with_detected_country(self, detected_country): """ :param str detected_country: the detected country """ self.__detected_country = detected_country return self def with_recommendation(self, recommendation): """ :type recommendation: SandboxTextDataExtractionRecommendation :rtype: SandboxDocumentTextDataExtractionTaskBuilder """ self.__recommendation = recommendation return self def build(self): result = SandboxDocumentTextDataExtractionTaskResult( self.__document_fields, self.__document_id_photo, self.__detected_country, self.__recommendation, ) return SandboxDocumentTextDataExtractionTask(result, self.__document_filter)

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tests/utilities/test_tasks.py

elenapalesis/prefect

0

194204

import inspect import pytest from prefect.core import Flow, Task from prefect.engine.flow_runner import FlowRunner from prefect.engine.signals import PAUSE from prefect.engine.state import Paused, Resume from prefect.utilities import tasks class TestTaskDecorator: def test_task_decorator_can_be_used_without_calling(self): @tasks.task def fun(x, y): return x + y def test_task_decorator_generates_new_tasks_upon_subsequent_calls(self): @tasks.task def fun(x, y): return x + y with Flow(name="test"): res1 = fun(1, 2) res2 = fun(1, 2) assert isinstance(res1, Task) assert isinstance(res2, Task) assert res1 is not res2 def test_task_decorator_with_args_must_be_called_in_flow_context(self): @tasks.task def fn(x): return x with pytest.raises(ValueError, match="Could not infer an active Flow context"): fn(1) def test_task_decorator_with_no_args_must_be_called_inside_flow_context(self): @tasks.task def fn(): return 1 with pytest.raises(ValueError): fn() with Flow(name="test"): assert isinstance(fn(), Task) def test_task_decorator_with_default_args_must_be_called_inside_flow_context(self): @tasks.task def fn(x=1): return x with pytest.raises(ValueError): fn() with Flow(name="test"): assert isinstance(fn(), Task) def test_task_decorator_with_required_args_must_be_called_with_args(self): @tasks.task def fn(x): return x with Flow(name="test"): with pytest.raises(TypeError): fn() def test_task_decorator_returns_task_instance(self): @tasks.task def fn(x): return x assert isinstance(fn, Task) def test_task_decorator_validates_run_signature_against_varargs(self): with pytest.raises(ValueError): @tasks.task def fn(*args): pass def test_task_decorator_validates_run_signature_against_upstream_tasks_kwarg(self): with pytest.raises(ValueError): @tasks.task def fn(upstream_tasks): pass class TestAsTask: @pytest.mark.parametrize( "obj", [ 1, (3, 4), ["a", "b"], "string", dict(x=42), type(None), lambda *args: None, {None: 88}, ], ) def test_as_task_with_basic_python_objs(self, obj): @tasks.task def return_val(x): "Necessary because constant tasks aren't tracked inside the flow" return x with Flow("test") as f: t = tasks.as_task(obj) val = return_val(t) assert isinstance(t, Task) assert t.auto_generated is True res = FlowRunner(f).run(return_tasks=[val]) assert res.is_successful() assert res.result[val].result == obj def test_as_task_doesnt_label_tasks_as_auto_generated(self): t = Task() assert t.auto_generated is False assert tasks.as_task(t).auto_generated is False def test_tag_contextmanager_works_with_task_decorator(): @tasks.task def mytask(): pass @tasks.task(tags=["default"]) def tagged_task(): pass with Flow(name="test"): with tasks.tags("chris"): res = mytask() other = tagged_task() assert res.tags == {"chris"} assert other.tags == {"chris", "default"} def test_copying_then_setting_tags_doesnt_leak_backwards(): with Flow(name="test"): t1 = Task() with tasks.tags("init-tag"): t2 = t1.copy() assert t2.tags == {"init-tag"} assert t1.tags == set() def test_setting_tags_then_calling_copies_tags(): with tasks.tags("init-tag"): t1 = Task() with Flow(name="test"): t2 = t1() assert t2.tags == {"init-tag"} def test_context_manager_for_setting_tags(): """ Test setting Task tags with a context manager, including: - top level - nested - nested with explicit tags """ with tasks.tags("1", "2"): t1 = Task() assert t1.tags == set(["1", "2"]) with tasks.tags("3", "4"): t2 = Task() assert t2.tags == set(["1", "2", "3", "4"]) t3 = Task(tags=["5"]) assert t3.tags == set(["1", "2", "3", "4", "5"]) class TestUnmappedContainer: def test_unmapped_initializes_with_task(self): t1 = Task() unmapped_t1 = tasks.unmapped(t1) assert unmapped_t1.task is t1 def test_unmapped_converts_its_argument_to_task(self): unmapped_t1 = tasks.unmapped(5) assert isinstance(unmapped_t1.task, Task) def test_as_task_unpacks_unmapped_objects(self): t1 = Task() unmapped_t1 = tasks.unmapped(t1) assert tasks.as_task(t1) is t1 assert tasks.as_task(t1).auto_generated is False class TestPauseTask: def test_pause_task_pauses(self): class AddTask(Task): def run(self, x, y): if x == y: tasks.pause_task("test message") return x + y with Flow(name="test") as f: t1 = AddTask()(1, 1) res = FlowRunner(flow=f).run(return_tasks=[t1]) assert isinstance(res.result[t1], Paused) assert res.result[t1].message == "test message" def test_pause_task_doesnt_pause_sometimes(self): class OneTask(Task): def run(self): tasks.pause_task() return 1 class AddTask(Task): def run(self, x, y): if x == y: tasks.pause_task() return x + y with Flow(name="test") as f: t1 = AddTask()(1, 1) t2 = OneTask()(upstream_tasks=[t1]) res = FlowRunner(flow=f).run(task_states={t1: Resume()}, return_tasks=[t1, t2]) assert res.result[t1].is_successful() assert isinstance(res.result[t2], Paused) class TestDefaultFromAttrs: @pytest.fixture def xtask(self): class A(Task): def __init__(self, x=None): self.x = x super().__init__() @tasks.defaults_from_attrs("x") def run(self, x=None): "Lil doc" return x return A @pytest.fixture def multitask(self): class B(Task): def __init__(self, x=None, y=None): self.x = x self.y = y super().__init__() @tasks.defaults_from_attrs("x") def run(self, x=None, y=None): return x, y return B def test_pulls_from_attr_if_not_provided_at_runtime(self, xtask): a = xtask(5) assert a.run() == 5 def test_runtime_takes_precedence(self, xtask): a = xtask(5) assert a.run(x=6) == 6 def test_even_none_at_runtime_takes_precedence(self, xtask): """ This test ensures that `None` isn't some ambiguous special case: keywords provided at runtime _always_ take precedence. """ a = xtask(5) assert a.run(x=None) is None def test_doc_is_unaffected(self, xtask): assert xtask.run.__doc__ == "Lil doc" def test_args_not_listed_are_unaffected(self, multitask): b = multitask(x=1, y=2) assert b.run() == (1, None) def test_works_with_multiple_args(self, multitask): b = multitask(x=1, y=2) assert b.run(y=3, x=55) == (55, 3) def test_works_with_mutiple_attrs(self): class TestTask(Task): def __init__(self, x=None, y=None): self.x = x self.y = y super().__init__() @tasks.defaults_from_attrs("x", "y") def run(self, x=None, y=None): return x, y task = TestTask(x=1, y=2) assert task.run() == (1, 2) assert task.run(x=4) == (4, 2) assert task.run(y=99) == (1, 99) assert task.run(x=None, y=None) == (None, None) def test_raises_if_attr_wasnt_set_at_init(self): """ It would be nice to raise this at creation time, but unfortunately the information just isn't available. """ class Forgot(Task): @tasks.defaults_from_attrs("x") def run(self, x=None): return x t = Forgot() with pytest.raises(AttributeError, match="no attribute 'x'"): t.run()

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pyNastran/op2/op2.py

als0052/pyNastran

0

185521

<reponame>als0052/pyNastran #!/usr/bin/env python3 # Filename: op2.py """ Defines the main OP2 class. Defines: - read_op2(op2_filename=None, combine=True, subcases=None, exclude_results=None, include_results=None, log=None, debug=True, debug_file=None, build_dataframe=False, skip_undefined_matrices=True, mode='msc', encoding=None) - OP2(debug=True, log=None, debug_file=None, mode='msc') - build_dataframe() - combine_results(combine=True) - create_objects_from_matrices() - object_attributes(mode='public', keys_to_skip=None, filter_properties=False) - object_methods(mode='public', keys_to_skip=None) - print_subcase_key() - read_op2(op2_filename=None, combine=True, build_dataframe=False, skip_undefined_matrices=False, encoding=None) - set_mode(mode) - transform_displacements_to_global(i_transform, coords, xyz_cid0=None, debug=False) - transform_gpforce_to_global(nids_all, nids_transform, i_transform, coords, xyz_cid0=None) Changes ------- * (11-17-2021) (als0052): Fixed some typing issues; added some comments; tried to make the code more readable and maybe efficient? * (01-29-2021) (als0052): Changes to some minor syntax to use more modern Python features (will probably need to up the minimum Python requirement); Rearranged some code to make it a bit easier to read; Thinned out some comments; added some comments (mostly to do items) To Do Items ----------- .. todo:: How to access the Pandas DataFrame from the OP2 obj? I don't even see Pandas as an import. """ from __future__ import annotations from pyNastran.op2.errors import (SortCodeError, DeviceCodeError, FortranMarkerError, SixtyFourBitError, OverwriteTableError) from pyNastran.op2.op2_interface.hdf5_interface import export_op2_to_hdf5_file from pyNastran.op2.op2_interface.hdf5_interface import \ export_op2_to_hdf5_filename from pyNastran.op2.op2_interface.transforms import ( transform_displacement_to_global, transform_gpforce_to_globali) from pyNastran.op2.op2_interface.hdf5_interface import load_op2_from_hdf5_file from pyNastran.utils import object_attributes, object_methods, ipython_info from pyNastran.op2.op2_interface.hdf5_interface import create_info_group from pyNastran.op2.result_objects.monpnt import MONPNT1, MONPNT3 from pyNastran.op2.op2_interface.op2_scalar import OP2_Scalar from pyNastran.op2.writer.op2_writer import OP2Writer from pyNastran.utils.numpy_utils import integer_types from pyNastran.op2.op2_geom import read_op2_geom from pyNastran.f06.errors import FatalError from pyNastran.utils import check_path from typing import List, Dict, Tuple, Optional, Any, TYPE_CHECKING, Union from pickle import load, dump, dumps from collections import defaultdict import numpy as np import h5py import sys if TYPE_CHECKING: from h5py import File as H5File # import pyNastran # from pyNastran.op2.op2_interface.op2_f06_common import Op2F06Attributes class OP2(OP2_Scalar, OP2Writer): _properties = ['is_real', 'is_complex', 'is_random', '_sort_method', 'is_sort1', 'is_sort2', 'matrix_tables', 'table_name_str'] def __init__(self, debug: Optional[bool] = True, log: Any = None, debug_file: Optional[str] = None, mode: Optional[str] = None ) -> None: """Initializes the OP2 object Parameters ---------- debug : bool/None; default=True used to set the logger if no logger is passed in True: logs debug/info/warning/error messages False: logs info/warning/error messages None: logs warning/error messages log : Log() a logging object to write debug messages to .. seealso:: import logging debug_file : str; default=None (No debug) sets the filename that will be written to mode : str; default=None -> 'msc' {msc, nx} """ self.encoding = None self.mode = mode if mode is not None: self.set_mode(mode) make_geom = False assert make_geom is False, make_geom OP2_Scalar.__init__(self, debug=debug, log=log, debug_file=debug_file) self.ask = False self.post = None self.table_count = defaultdict(int) self._set_mode(mode) self.skip_undefined_matrices = '' def __del__(self) -> None: if hasattr(self, 'h5_file') and self.h5_file is not None: self.h5_file.close() def object_attributes(self, mode: str = 'public', keys_to_skip: Optional[List[str]] = None, filter_properties: bool = False) -> List[str]: """List the names of attributes of a class as strings. Returns public attributes as default. Parameters ---------- mode : str defines what kind of attributes will be listed * 'public' - names that do not begin with underscore * 'private' - names that begin with single underscore * 'both' - private and public * 'all' - all attributes that are defined for the object keys_to_skip : List[str]; default=None -> [] names to not consider to avoid deprecation warnings filter_properties : Returns ------- attribute_names : List[str] sorted list of the names of attributes of a given type or None if the mode is wrong .. todo:: Fill out docstring info on filter_properties parameter .. todo:: kwargs support """ if keys_to_skip is None: keys_to_skip = [] my_keys_to_skip = ['object_methods', 'object_attributes', ] keys_to_skip += my_keys_to_skip kw = {'mode': mode, 'keys_to_skip': keys_to_skip, 'filter_properties': filter_properties} # TODO: kwargs support return object_attributes(self, **kw) def object_methods(self, mode: str = 'public', keys_to_skip: Optional[List[str]] = None) -> List[str]: """List the names of methods of a class as strings. Returns public methods as default. Parameters ---------- obj : instance the object for checking mode : str defines what kind of methods will be listed * "public" - names that do not begin with underscore * "private" - names that begin with single underscore * "both" - private and public * "all" - all methods that are defined for the object keys_to_skip : List[str]; default=None -> [] names to not consider to avoid deprecation warnings Returns ------- method : List[str] sorted list of the names of methods of a given type or None if the mode is wrong """ if keys_to_skip is None: keys_to_skip = [] my_keys_to_skip = ['object_methods', 'object_attributes'] keys_to_skip += my_keys_to_skip return object_methods(self, mode=mode, keys_to_skip=keys_to_skip) def __eq__(self, op2_model) -> bool: """Diffs the current op2 model vs. another op2 model. Crashes if they're not equal. FIXME: Probably shouldn't crash if they aren't equal """ try: is_equal = self.assert_op2_equal( op2_model, stop_on_failure=True, debug=False) except (AssertionError, ValueError): # This will never get used if you raise? # is_equal = False raise return is_equal def assert_op2_equal(self, op2_model, skip_results: Optional[List[str]] = None, stop_on_failure: bool = True, debug: bool = False) -> bool: """Diffs the current op2 model vs. another op2 model. Parameters ---------- op2_model : OP2() the model to compare to skip_results : List[str]; default=None -> [] results that shouldn't be compred stop_on_failure : bool; default=True True : Crashes if they're not equal False : Go to the next object debug : bool; default=False give slightly more debugging messages Returns ------- is_equal : bool are the objects equal? Raises ------ AssertionError/ValueError : stop_on_failure=True and and error occurred NotImplementedError : this is a sign of an unsupported object """ if skip_results is None: skip_results = set() else: skip_results = set(skip_results) skip_results.update({'gpdt', 'bgpdt', 'eqexin', 'psds'}) if not self.read_mode == op2_model.read_mode: msg = (f'self.read_mode={self.read_mode} op2_model.read_mode=' f'{op2_model.read_mode} ... assume True') self.log.warning(msg) return True table_types = self.get_table_types() for table_type in table_types: if (table_type in skip_results or table_type.startswith('responses.')): continue adict = self.get_result(table_type) bdict = op2_model.get_result(table_type) if adict is None and bdict is None: continue if len(adict) != len(bdict): # check number of subcases msg = (f'len(self.{table_type}) = {len(adict)} ' f'len(op2_model.{table_type}) = {len(bdict)}') self.log.warning(msg) if stop_on_failure: return False continue for key, avalue in adict.items(): # loop over each DisplacementArray if debug: msg = f'Working on {table_type!r} subcase = {str(key)}' self.log.debug(msg) # get the displacement for model B bvalue = bdict[key] is_equal = self._is_op2_case_equal( table_type, key, avalue, bvalue, stop_on_failure=stop_on_failure, debug=debug) if not is_equal and stop_on_failure: return is_equal return True def _is_op2_case_equal(self, table_type: str, key, a_obj, b_obj, stop_on_failure: bool = True, debug: bool = False ) -> bool: """Helper method for ``assert_op2_equal`` Parameters ---------- table_type : str the type of table (e.g., ``displacements``) key : subcase_id / tuple_obj subcase_id : int the subcase_id tuple_obj : Tuple(???, ???, ...) The fancy tuple thingy that you see in single subcase buckling... subcase_id : int the subcase_id sort_code : int 1 : SORT1 2 : SORT2 title??? : str the case title subtitle??? : str the case subtitle superelement_id : str??? the superelement other terms??? TODO: This docstring needs work. a_obj : Op2Object() a RealDisplacementArray, ComplexDisplacementArray, RealSolidStressArray, etc. for the self model b_obj : Op2Object() a RealDisplacementArray, ComplexDisplacementArray, RealSolidStressArray, etc. for the comparison model stop_on_failure : bool; default=True True : Crashes if they're not equal False : Go to the next object debug : bool; default=False give slightly more debugging messages Returns ------- is_equal : bool are the objects equal? Raises ------ AssertionError/ValueError : stop_on_failure=True and and error occurred NotImplementedError : this is a sign of an unsupported object """ # check the name (e.g., RealDisplacementArray vs. # ComplexDisplacementArray) aname = a_obj.__class__.__name__ bname = b_obj.__class__.__name__ if not aname == bname: self.log.warning(f'type(a)={aname} type(b)={bname}') return False if aname == 'PARAM': # TODO: update this return True if not any(word in aname for word in ['Array', 'Eigenvalues', 'GridPointWeight']): # TODO: Does this ever hit? msg = f'{aname} is not an Array ... assume equal' self.log.warning(msg) raise NotImplementedError(f'{aname} __eq__') # continue # TODO: Use the array methods to check for equality. # TODO: This can crash. is_not_equal = '' # Silence warning undeclared variable before usage try: is_not_equal = a_obj != b_obj except ValueError: if stop_on_failure: raise if is_not_equal: msg = (f'key = {key} table_type = {table_type!r} are not equal; ' f'class_name = {aname!r}') self.log.warning(msg) return False return True def _set_mode(self, mode: str): """Explicitly set the format .. todo:: What other modes are there? What else would this be used for? """ if mode is None: return if mode == 'nasa95': self.set_as_nasa95() def set_mode(self, mode: str) -> None: """Sets the op2 mode Sets the mode as as 'msc', 'nx', 'autodesk', 'nasa95', or 'optistruct' :param mode: :type mode: str :returns: None .. todo:: Fill in info for parameters in docstring """ # Use dict to avoid long if-elif-else blocks to direct flow of traffic traffic_cop = { 'msc': self.set_as_msc(), 'nx': self.set_as_nx(), 'autodesk': self.set_as_autodesk(), 'nasa95': self.set_as_nasa95(), 'optistruct': self.set_as_optistruct(), # 'radioss': self.set_as_optistruct() # FIXME: Work in progress?? } try: traffic_cop[mode.lower()] except KeyError: raise KeyError(f'mode={mode!r} and must be in [msc, nx, autodesk, ' f'nasa95, optistruct]') return None def to_nx(self, msg='') -> None: # TODO: Docstring for this method if self.is_msc: self.log.warning(f'switching to NX{msg}') self.set_as_nx() self.set_table_type() return None def to_msc(self, msg='') -> None: # TODO: Docstring for this method if self.is_nx: self.log.warning(f'switching to MSC{msg}') self.set_as_msc() return None def include_exclude_results(self, exclude_results: Optional[List[str]] = None, include_results: Optional[List[str]] = None ) -> None: """Sets results to include/exclude Parameters ---------- exclude_results / include_results : List[str] / str; default=None a list of result types to exclude/include one of these must be None """ if exclude_results and include_results: msg = (f'exclude_results or include_results must be None\n' f'exclude_results = {exclude_results!r}\ninclude_results = ' f'{include_results!r}\n') raise RuntimeError(msg) if exclude_results: self.remove_results(exclude_results) elif include_results: self.set_results(include_results) def saves(self) -> Union[str, bytes]: """Saves a pickled string""" return dumps(self) def __getstate__(self): """Clears out a few variables in order to pickle the object Copy the object's state from self.__dict__ which contains all our instance attributes. Always use the dict.copy() method to avoid modifying the original state. """ state = self.__dict__.copy() del state['log'] # Remove the unpicklable entries. if hasattr(self, 'results') and hasattr(self._results, 'log'): del state['_results'].log # This block let's us identify the objects that are problematic We # just play with the value of i to delete all objects past some # threshold. Once we find where the code breaks, we dig into the # objects further if 0: # pragma: no cover # FIXME: Why though? What is this even accomplishing? i = 0 for key, value in sorted(state.items()): if isinstance(value, dict) and len(value) == 0: continue if i > 100: del state[key] else: i += 1 return state def save(self, obj_filename: str = 'model.obj', unxref: bool = True ) -> None: """Saves a pickleable object""" if hasattr(self, 'generalized_tables'): del self.generalized_tables if hasattr(self, 'op2_reader'): del self.op2_reader with open(obj_filename, 'wb') as obj_file: dump(self, obj_file) return None def load(self, obj_filename: str = 'model.obj') -> None: """Loads a pickleable object""" with open(obj_filename, 'rb') as obj_file: obj = load(obj_file) keys_to_skip = ['_close_op2', '_count', '_results', '_table_mapper', 'ask', 'binary_debug', 'additional_matrices', 'apply_symmetry', 'debug_file', 'expected_times', 'f', 'generalized_tables', 'is_all_subcases', 'is_debug_file', 'is_geometry', 'is_vectorized', 'isubcase', 'log', 'matrix_tables', 'mode', 'n', 'ntotal', 'num_wide', 'op2_reader', 'table_name', 'use_vector', 'words'] keys = object_attributes(self, mode="all", keys_to_skip=keys_to_skip, filter_properties=True) val = '' # Silence variable usage before declaration? for key in keys: if key.startswith('__') and key.endswith('__'): continue try: val = getattr(obj, key) except NameError: self.log.warning(f'key={key!r} val={val}') continue try: setattr(self, key, val) except AttributeError: print(f'key={key!r} val={val}') raise self.log.debug('done loading!') @property def is_geometry(self) -> bool: # TODO: Why is this a thing? What is it even for? Is this a # future-proofing thing for a WIP feature? So many questions... return False def read_op2(self, op2_filename: Optional[str] = None, combine: bool = True, build_dataframe: Optional[bool] = False, skip_undefined_matrices: bool = False, encoding: Optional[str] = None) -> None: """Starts the OP2 file reading Parameters ---------- op2_filename : str (default=None -> popup) the op2_filename combine : bool; default=True True : objects are isubcase based False : objects are (isubcase, subtitle) based; will be used for superelements regardless of the option build_dataframe : bool (default=None -> True if in iPython, False otherwise) builds a pandas DataFrame for op2 objects None: True if in iPython, False otherwise skip_undefined_matrices : bool; default=False True : prevents matrix reading crashes encoding : str the unicode encoding (default=None; system default) """ if op2_filename: check_path(op2_filename, name='op2_filename') mode = self.mode if build_dataframe is None: build_dataframe = False if ipython_info(): build_dataframe = True if encoding is None: encoding = sys.getdefaultencoding() self.encoding = encoding self.skip_undefined_matrices = skip_undefined_matrices assert self.ask in [True, False], self.ask self.is_vectorized = True self.log.debug(f'combine={combine}') msg = f'{"-"*8} reading op2 with read_mode=1 (array sizing) {"-"*8}' self.log.debug(msg) self.read_mode = 1 self._close_op2 = False load_as_h5 = False if hasattr(self, 'load_as_h5'): load_as_h5 = self.load_as_h5 try: # get GUI object names, build objects, but don't read data table_names = OP2_Scalar.read_op2(self, op2_filename=op2_filename, load_as_h5=load_as_h5, mode=mode) self.table_names = table_names # TODO: Stuff to figure out objects. # TODO: Stuff to show GUI of table names. # TODO: Clear out objects the user doesn't want. # TODO: Consider a globalized Enum sub-class to do this stuff with # more readable asignments self.read_mode = 2 self._close_op2 = True msg = (f'{"-"*8} reading op2 with read_mode=2 (array filling) ' f'{"-"*8}') self.log.debug() _create_hdf5_info(self.op2_reader.h5_file, self) OP2_Scalar.read_op2(self, op2_filename=self.op2_filename, mode=mode) except FileNotFoundError: raise except Exception as e: # Added ``Exception as e`` OP2_Scalar.close_op2(self, force=True) raise e self._finalize() if build_dataframe: self.build_dataframe() self.create_objects_from_matrices() self.combine_results(combine=combine) self.log.debug('finished reading op2') str(self.op2_results) return None def create_objects_from_matrices(self) -> None: """Creates objects monitor1, monitor3 creates the following objects: - monitor3 : MONPNT3 object from the MP3F matrix - monitor1 : MONPNT1 object from the PMRF, PERF, PFRF, AGRF, PGRF, AFRF matrices """ if 'MP3F' in self.matrices: self.op2_results.monitor3 = MONPNT3(self._frequencies, self.matrices['MP3F']) # TODO: These are totally wrong...it doesn't go by component; it goes # by inertial, external, flexibility, etc. if 'PERF' in self.matrices: self.op2_results.monitor1 = MONPNT1( self._frequencies, self.matrices, # :) ? :) :)2 ? ? ['PMRF', 'PERF', 'PFRF', 'AGRF', 'PGRF', 'AFRF', ]) return None def _finalize(self) -> None: """internal method .. todo:: Elaborate on this; what does it do? -als0052 """ if hasattr(self, 'subcase'): del self.subcase result_types = self.get_table_types() result_type_list = ['params', 'gpdt', 'bgpdt', 'eqexin', 'psds', 'monitor1', 'monitor3'] for result_type in result_types: if (result_type in result_type_list or result_type.startswith('responses.')): continue result = self.get_result(result_type) try: values = result.values() except AttributeError: self.log.error(f'result_type = {result_type}') raise for obj in values: if hasattr(obj, 'finalize'): obj.finalize() elif hasattr(obj, 'tCode') and not obj.is_sort1: msg = (f'object has not implemented ' f'finalize\n{"".join(obj.get_stats())}') raise RuntimeError(msg) self.del_structs() def build_dataframe(self) -> None: """Converts the OP2 objects into pandas DataFrames .. todo:: fix issues with: - RealDisplacementArray - RealPlateStressArray (???) - RealPlateStrainArray (???) - RealCompositePlateStrainArray (???) """ # TODO: Wouldn't it make more sense for this return a pandas.DataFrame # obj? -als0052 # TODO: sorter = uniques.argsort() # FIXME: C:\Anaconda\lib\site-packages\pandas\core\algorithms.py:198: # DeprecationWarning: unorderable dtypes; returning scalar but # in the future this will be an error no_sort2_classes = ['RealEigenvalues', 'ComplexEigenvalues', 'BucklingEigenvalues'] result_types = self.get_table_types() skip_pandas = ['params', 'gpdt', 'bgpdt', 'eqexin', 'psds', 'monitor1', 'grid_point_weight', 'monitor3'] if len(self.matrices): for key, matrix in sorted(self.matrices.items()): if hasattr(matrix, 'build_dataframe'): matrix.build_dataframe() else: msg = (f'pandas: build_dataframe is not supported for key=' f'{key} type={str(type(matrix))}') self.log.warning(msg) raise NotImplementedError() for result_type in result_types: if (result_type in skip_pandas or result_type.startswith('responses.')): continue result = self.get_result(result_type) for obj in result.values(): class_name = obj.__class__.__name__ obj.object_attributes() obj.object_methods() str(obj) obj.get_stats() if class_name in no_sort2_classes: try: obj.build_dataframe() assert obj.data_frame is not None except MemoryError: raise except Exception as e: self.log.error(obj) msg = f'build_dataframe is broken for {class_name}' self.log.error(msg) raise e continue if obj.is_sort2: msg = ( f'build_dataframe is not supported for {class_name} ' f'- SORT2') self.log.warning(msg) continue # SORT1 try: obj.build_dataframe() except MemoryError: raise except NotImplementedError: self.log.warning(obj) self.log.warning(f'build_dataframe is broken for ' f'{class_name}') raise except Exception as e: self.log.error(obj) self.log.error(f'build_dataframe is broken for ' f'{class_name}') raise e def load_hdf5_filename(self, hdf5_filename: str, combine: bool = True ) -> None: """Loads an h5 file into an OP2 object Parameters ---------- hdf5_filename : str the path to the an hdf5 file combine : bool; default=True runs the combine routine """ check_path(hdf5_filename, 'hdf5_filename') self.op2_filename = hdf5_filename self.log.info(f'hdf5_op2_filename = {hdf5_filename!r}') debug = False with h5py.File(hdf5_filename, 'r') as h5_file: load_op2_from_hdf5_file(self, h5_file, self.log, debug=debug) self.combine_results(combine=combine) return None def load_hdf5_file(self, h5_file: H5File, combine: bool = True) -> None: """Loads an h5 file object into an OP2 object Parameters ---------- h5_file : H5File() an h5py file object combine : bool; default=True runs the combine routine """ debug = False load_op2_from_hdf5_file(self, h5_file, self.log, debug=debug) self.combine_results(combine=combine) return None def export_hdf5_filename(self, hdf5_filename: str) -> None: """Converts the OP2 objects into hdf5 object .. todo:: doesn't support ``BucklingEigenvalues`` """ export_op2_to_hdf5_filename(hdf5_filename, self) return None def export_hdf5_file(self, hdf5_file: H5File, exporter=None) -> None: """Converts the OP2 objects into hdf5 object Parameters ---------- hdf5_file : H5File() an h5py object exporter : HDF5Exporter; default=None unused .. todo:: doesn't support ``BucklingEigenvalues`` """ export_op2_to_hdf5_file(hdf5_file, self) return None def combine_results(self, combine: Union[str, bool] = True) -> None: """Group and format like data by subcase We want the data to be in the same format and grouped by subcase, so we take .. code-block:: python stress = { # isubcase, analysis_code, sort_method, count, superelement_adaptivity_index, pval_step (1, 2, 1, 0, 'SUPERELEMENT 0', '') : result1, (1, 2, 1, 0, 'SUPERELEMENT 10', '') : result2, (1, 2, 1, 0, 'SUPERELEMENT 20', '') : result3, (2, 2, 1, 0, 'SUPERELEMENT 0', '') : result4, code = (isubcase, analysis_code, sort_method, count, ogs, superelement_adaptivity_index, pval_step) } and convert it to: .. code-block:: python stress = { 1 : result1 + result2 + results3, 2 : result4, } """ self.combine = combine result_types = self.get_table_types() results_to_skip = ['bgpdt', 'gpdt', 'eqexin', 'grid_point_weight', 'psds', 'monitor1', 'monitor3'] # set subcase_key for result_type in result_types: if (result_type in results_to_skip or result_type.startswith('responses.')): continue result = self.get_result(result_type) try: case_keys = sorted(result.keys()) except AttributeError: self.log.error(f'result_type = {result_type}') raise for case_key in case_keys: if isinstance(case_key, tuple): isubcasei, analysis_codei, *others = case_key if others[4] == 0: value = [analysis_codei] + others[:3] else: value = [analysis_codei] + others if value not in self.subcase_key[isubcasei]: self.subcase_key[isubcasei].append(value) else: break if not combine: subcase_key2 = {} for key, value in self.subcase_key.items(): subcase_key2[key] = value self.subcase_key = subcase_key2 return del result, case_keys isubcases = np.unique(list(self.subcase_key.keys())) unique_isubcases = np.unique(isubcases) self.log.debug('combine_results') for result_type in result_types: if (result_type in results_to_skip or result_type.startswith('responses.')): continue result = self.get_result(result_type) if len(result) == 0: continue for isubcase in unique_isubcases: try: keys = self.subcase_key[isubcase] except TypeError: print('isubcase =', isubcase) print('isubcases =', isubcases) print('self.subcase_key =', self.subcase_key) raise key0 = tuple([isubcase] + list(keys[0])) if len(key0) == 5: # ogs is optional isubcase, analysis_code, *others = key0 key1 = [isubcase, analysis_code, 1] + others[0:] key2 = [isubcase, analysis_code, 2] + others[0:] else: isubcase, analysis_code, *others = key0 key1 = [isubcase, analysis_code, 1] + others[0:] key2 = [isubcase, analysis_code, 1] + others[0:] if len(keys) == 1: if key0 not in result: continue # One tuple for result; Rename case result[isubcase] = result[key0] del result[key0] elif len(keys) == 2 and key1 in keys and key2 in keys: isubcase, analysis_code, *others = key0 if not (key1 in result and key2 in result): if key1 in result: res1 = result[key1] msg = (f"res={res1.__class__.__name__} has a " f"single case; trivial") self.log.info(msg) result[isubcase] = result[key1] del result[key1] elif key2 in result: res2 = result[key2] msg = (f'res={res2.__class__.__name__} has a ' f'single case; trivial') self.log.info(msg) result[isubcase] = result[key2] del result[key2] continue res1 = result[key1] class_name = res1.__class__.__name__ if not hasattr(res1, 'combine'): self.log.info( f'res={class_name} has no method combine') continue self.log.info(f'res={class_name} has combine') res2 = result[key2] del result[key1] del result[key2] res1.combine(res2) result[isubcase] = res1 else: continue setattr(self, result_type, result) subcase_key2 = {} not_results = ['eigenvalues', 'eigenvalues_fluid', 'params', 'gpdt', 'bgpdt', 'eqexin', 'desvars', 'grid_point_weight', 'psds', 'monitor1', 'monitor3'] for result_type in result_types: if (result_type in not_results or result_type.startswith('responses.')): continue result = self.get_result(result_type) try: case_keys = list(result.keys()) except AttributeError: self.log.error(f'result_type = {result_type}') raise try: # FIXME: causes DeprecationWarning case_keys = sorted(case_keys) except TypeError: self.log.error(f'result.keys() = {case_keys}') if len(result) == 0: continue for isubcase in unique_isubcases: if isubcase not in subcase_key2: subcase_key2[isubcase] = [] for isubcase in unique_isubcases: for case_key in case_keys: assert not isinstance(case_key, str), result_type if isinstance(case_key, integer_types): if (isubcase == case_key and case_key not in subcase_key2[isubcase]): subcase_key2[isubcase] = [isubcase] else: try: subcasei = case_key[0] except IndexError: msg = (f'case_key={case_key}; type(case_key)=' f'{type(case_key)}; case_key[0] is not the ' f'subcase id') raise IndexError(msg) if case_key not in subcase_key2[subcasei]: subcase_key2[isubcase].append(case_key) self.subcase_key = subcase_key2 return None def get_key_order(self) -> list[tuple[Any, Any, Any, Any, Union[int, Any], Any, Any]]: """Get the order of the keys Returns ------- keys3 : List[int, int, int, int, int, str] the keys in order """ keys = [] table_types = self.get_table_types() skip_tables = ['gpdt', 'bgpdt', 'eqexin', 'grid_point_weight', 'psds', 'monitor1', 'monitor3'] for table_type in sorted(table_types): if (table_type in skip_tables or table_type.startswith('responses.')): continue result_type_dict = self.get_result(table_type) if len(result_type_dict) == 0: continue for key in result_type_dict: if isinstance(key, str): if (table_type not in ['eigenvalues', 'eigenvalues_fluid', 'params']): self.log.warning(f'table_type = {table_type}') continue if key not in keys: keys.append(key) analysis_codes = set() counts = set() isubcases = set() ogss = set() pval_steps = set() sort_methods = set() superelement_adaptivity_indexs = set() for key in keys: if len(key) == 6: isubcases, analysis_codes, *others = key ogss = 0 elif len(key) == 7: isubcases, analysis_codes, *others = key else: print(f' {str(key)}') raise RuntimeError(key) isubcases.add(isubcases) analysis_codes.add(analysis_codes) sort_methods.add(sort_methods) counts.add(counts) ogss.add(ogss) superelement_adaptivity_indexs.add(superelement_adaptivity_indexs) pval_steps.add(pval_steps) analysis_code_list = list(analysis_codes) count_list = list(counts) isubcase_list = list(isubcases) ogs_list = list(ogss) pval_step_list = list(pval_steps) sort_method_list = list(sort_methods) superelement_adaptivity_index_list = list( superelement_adaptivity_indexs) analysis_code_list.sort() count_list.sort() isubcase_list.sort() ogs_list.sort() pval_step_list.sort() sort_method_list.sort() superelement_adaptivity_index_list.sort() keys3 = [] # TODO: This needs to go away and be replaced by something else :( for isubcase in isubcase_list: for count in count_list: for analysis_code in analysis_code_list: for sai in superelement_adaptivity_index_list: for pval_step in pval_step_list: for sort_method in sort_method_list: for ogs in ogs_list: key = (isubcase, analysis_code, sort_method, count, ogs, sai, pval_step) if key not in keys3: keys3.append(key) if len(keys3) == 0: msg = f'No results...\n{self.get_op2_stats(short=True)}' self.log.warning(msg) return keys3 def print_subcase_key(self) -> None: # TODO: Docstring for this method self.log.info('---self.subcase_key---') for isubcase, keys in sorted(self.subcase_key.items()): if len(keys) == 1: self.log.info(f'subcase_id = {isubcase} : keys = {keys}') else: self.log.info(f'subcase_id = {isubcase}') for key in keys: self.log.info(f' {str(key)}') return None def transform_displacements_to_global(self, icd_transform: Any, coords: Dict[int, Any], xyz_cid0: Any = None, debug: bool = False) -> None: """Transform displacements into global coords Transforms the ``data`` of displacement-like results into the global coordinate system for those nodes with different output coordinate systems. Takes indices and transformation matrices for nodes with their output in coordinate systems other than the global. Used in combination with ``BDF.get_displacement_index`` Parameters ---------- icd_transform : dict{int cid : int ndarray} Dictionary from coordinate id to index of the nodes in ``BDF.point_ids`` that their output (`CD`) in that coordinate system. coords : dict{int cid :Coord()} Dictionary of coordinate id to the coordinate object Use this if CD is only rectangular Use this if CD is not rectangular xyz_cid0 : (nnodes+nspoints, 3) float ndarray the nodes in the global frame Don't use this if CD is only rectangular Use this if CD is not rectangular debug : bool; default=False developer debug .. warning:: only works if all nodes are included... ``test_pynastrangui isat_tran.dat isat_tran.op2 -f nastran`` .. note:: Nastran has this concept of a basic (cid=0) and global (cid=cd) coordinate system. They occur at the same time. Basic is for positions/properties, while global is for result outputs. pyNastran's OP2 interface uses: - cd=0 for global frames - cd>0 are local frames pyNastran's BDF interface uses: - cp=0 for global frames - cp>0 are local frames """ ato = self.op2_results.ato crm = self.op2_results.crm psd = self.op2_results.psd rms = self.op2_results.rms # ----- I feel like this is more readable even though it is a bit # longer and more verbose # TODO: Should NO results be transformed? # _no_displacements = [no.displacements, no.velocities, # no.accelerations, no.spc_forces, no.mpc_forces] _disp = [ self.displacements, ato.displacements, crm.displacements, psd.displacements, rms.displacements, self.displacements_scaled, self.displacement_scaled_response_spectra_abs, self.displacement_scaled_response_spectra_nrl] _vel = [self.velocities, ato.velocities, crm.velocities, psd.velocities, rms.velocities, self.velocity_scaled_response_spectra_abs] _accel = [ self.accelerations, ato.accelerations, crm.accelerations, psd.accelerations, rms.accelerations, self.acceleration_scaled_response_spectra_abs, self.acceleration_scaled_response_spectra_nrl] _eig = [ self.eigenvectors, self.op2_results.RADCONS.eigenvectors, self.op2_results.RADEFFM.eigenvectors, self.op2_results.RADEATC.eigenvectors, self.op2_results.ROUGV1.eigenvectors] _spc = [self.spc_forces, ato.spc_forces, crm.spc_forces, psd.spc_forces, rms.spc_forces] _mpc = [self.mpc_forces, ato.mpc_forces, crm.mpc_forces, psd.mpc_forces, rms.mpc_forces] _loads = [self.applied_loads, self.load_vectors] disp_like_dicts = _disp + _vel + _accel + _eig + _spc + _mpc + _loads del _disp, _vel, _accel, _eig, _spc, _mpc, _loads # ----- for disp_like_dict in disp_like_dicts: if not disp_like_dict: continue for subcase, result in disp_like_dict.items(): if result.table_name in ['BOUGV1', 'BOPHIG', 'TOUGV1']: continue self.log.debug(f'transforming {result.table_name}') transform_displacement_to_global( subcase, result, icd_transform, coords, xyz_cid0, self.log, debug=debug) return None def transform_gpforce_to_global(self, nids_all, nids_transform, icd_transform, coords, xyz_cid0=None): """Transform GPFORCE into global coords Transforms the ``data`` of GPFORCE results into the global coordinate system for those nodes with different output coordinate systems. Takes indices and transformation matrices for nodes with their output in coordinate systems other than the global. Used in combination with ``BDF.get_displacement_index`` Parameters ---------- nids_all : ??? ??? nids_transform : dict{int cid : int ndarray nds} Dictionary from coordinate id to corresponding node ids. icd_transform : dict{int cid : int ndarray} Dictionary from coordinate id to index of the nodes in ``BDF.point_ids`` that their output (`CD`) in that coordinate system. coords : dict{int cid :Coord()} Dictionary of coordinate id to the coordinate object Use this if CD is only rectangular Use this if CD is not rectangular xyz_cid0 : ??? required for cylindrical/spherical coordinate systems .. todo:: Docstring needs some work. .. todo:: Return type for this method should be specified in signature to be consistent with all other method signatures. """ # TODO: Causes test_op2_solid_shell_bar_01_gpforce_xyz to fail even # though it should be uncommented disp_like_dicts = [self.grid_point_forces] for disp_like_dict in disp_like_dicts: if not disp_like_dict: continue self.log.debug(f'{"-" * 11}') for subcase, result in disp_like_dict.items(): transform_gpforce_to_globali( subcase, result, nids_all, nids_transform, icd_transform, coords, xyz_cid0, self.log) self.log.debug(f'{"-" * 11}') def read_op2(op2_filename: Optional[str] = None, load_geometry: bool = False, combine: bool = True, subcases: Optional[List[int]] = None, exclude_results: Optional[List[str]] = None, include_results: Optional[List[str]] = None, log: Any = None, debug: Optional[bool] = True, build_dataframe: Optional[bool] = False, skip_undefined_matrices: bool = True, mode: Optional[str] = None, encoding: Optional[str] = None) -> OP2: """Creates the OP2 object without calling the OP2 class. Parameters ---------- op2_filename : str (default=None -> popup) the op2_filename load_geometry: bool; default=False False: load results and matrices True: load geometry as well combine : bool; default=True True : objects are isubcase based False : objects are (isubcase, subtitle) based; will be used for superelements regardless of the option subcases : List[int, ...] / int; default=None->all subcases list of [subcase1_ID,subcase2_ID] exclude_results / include_results : List[str] / str; default=None a list of result types to exclude/include one of these must be None build_dataframe : bool; default=False builds a pandas DataFrame for op2 objects None: True if in iPython, False otherwise skip_undefined_matrices : bool; default=False True : prevents matrix reading crashes debug : bool/None; default=True used to set the logger if no logger is passed in True: logs debug/info/warning/error messages False: logs info/warning/error messages None: logs warning/error messages log : Log() a logging object to write debug messages to (.. seealso:: import logging) mode : str; default=None -> 'msc' the version of the Nastran you're using {nx, msc, autodesk, optistruct, nasa95} encoding : str the unicode encoding (default=None; system default) exclude_results: include_results: debug: Returns ------- model : OP2() an OP2 object .. note :: This method will change in order to return an object that does not have so many methods. .. todo:: Creates the OP2 object without all the read methods. .. todo:: Update info for parameters in this function's docstring. """ if op2_filename: check_path(op2_filename, name='op2_filename') if load_geometry: # TODO: kwargs support for methods kw = {'op2_filename': op2_filename, 'combine': combine, 'subcases': subcases, 'exclude_results': exclude_results, 'include_results': include_results, 'validate': True, 'xref': True, 'build_dataframe': build_dataframe, 'skip_undefined_matrices': skip_undefined_matrices, 'mode': mode, 'log': log, 'debug': debug, 'encoding': encoding} model = read_op2_geom(**kw) else: model = OP2(log=log, debug=debug, mode=mode) model.set_subcases(subcases) model.include_exclude_results(exclude_results=exclude_results, include_results=include_results) # TODO: kwargs support kw = {'op2_filename': op2_filename, 'build_dataframe': build_dataframe, 'skip_undefined_matrices': skip_undefined_matrices, 'combine': combine, 'encoding': encoding} model.read_op2(**kw) # TODO: This will go away when OP2 is refactored. Many methods will be # missing, but it's a start... # Doesn't support F06 writer # obj = Op2F06Attributes() # attr_names = object_attributes(obj, mode="public", keys_to_skip=None) # for attr_name in attr_names: # attr = getattr(model, attr_name) # setattr(obj, attr_name, attr) # obj.get_op2_stats() return model def _create_hdf5_info(h5_file: H5File, op2_model: OP2) -> None: """Exports the h5 info group :param h5_file: :type h5_file: H5File :param op2_model: :type op2_model: OP2 :returns: None .. todo:: Docstring info for parameters. """ load_as_h5 = False if hasattr(op2_model, 'load_as_h5'): load_as_h5 = op2_model.load_as_h5 if not load_as_h5: return create_info_group(h5_file, op2_model) return None

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29898, 29882, 29945, 29918, 1445, 29892, 1015, 29906, 29918, 4299, 29897, 13, 1678, 736, 6213, 13, 2 ]

spamresponsibly.py

velo223/Velo-s-Spam-Scripts

0

89287

<reponame>velo223/Velo-s-Spam-Scripts<filename>spamresponsibly.py val = int(input(print("Enter a number: "))) option = { "n":"Nae nigga nae \n", "c":"Uohhhhhh :sob::sob::sob: \n", "i":"I am living in your walls, oomfie. \n" } userinput = input("Choose your poison: [N]iggas, [C]unny, [I]solation") with open("outputtings.txt",'w') as f: f.write(option[userinput.lower()] * val) print("Output saved to file. Spam responsibly, fam.")

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utils/__init__.py

jaobernardi/roboscovid-redacted

0

58177

<filename>utils/__init__.py from .parsing import parse_string from .tokens import token from .users import getUsers, getPlaces from .flags import flag_define, value_assign

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main.py

b-com/ACUITEE

3

1612198

# main.py – Software ACUITEE # Copyright 2021 b<>com. All rights reserved. # This software is licensed under the Apache License, Version 2.0. # 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 application import app from gevent.pywsgi import WSGIServer # for production deployment, use gevent server if __name__ == '__main__': print('Serving on 5000...') WSGIServer(('0.0.0.0', 5000), app).serve_forever() #print('Serving on https://:5000') #server = WSGIServer(('127.0.0.1', 5000), app, keyfile='server.key', certfile='server.crt') #server.serve_forever() # to start the server asynchronously, call server.start()

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app/main/schema/http_status_schema.py

abhishek9sharma/nyctaxiserver

0

114798

<filename>app/main/schema/http_status_schema.py from flask_restplus import Namespace, fields class HTTPStatus: """ Schema for showing messages related to various HTTP Statuses """ ns = Namespace('httpstatus', 'Schema for displaying http status') statusschema = ns.model('httpstatus', { 'message': fields.String(required = True, description = 'description of failure'), 'code': fields.Integer(required= True, description = 'HTTP error code reated to message') })

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python/overlapintegrals/overlapintegrals.py

berquist/overlapintegrals

1

111156

<filename>python/overlapintegrals/overlapintegrals.py from functools import reduce from math import exp, floor, pi from typing import Tuple, Union from scipy.special import binom as binomial Number = Union[int, float] def dist2( xa: Number, ya: Number, za: Number, xb: Number, yb: Number, zb: Number ) -> float: return (xa - xb) * (xa - xb) + (ya - yb) * (ya - yb) + (za - zb) * (za - zb) def product_center_1d(za: float, xa: float, zb: float, xb: float) -> float: return ((za * xa) + (zb * xb)) / (za + zb) def fact2(n): """Compute the double factorial as in https://stackoverflow.com/a/4740229/3249688 """ return reduce(int.__mul__, range(n, 0, -2), 1) def binomial_prefactor(s: int, ia: int, ib: int, xpa: float, xpb: float) -> float: return sum( binomial(ia, s - t) * binomial(ib, t) * (xpa ** (ia - s + t)) * (xpb ** (ib - t)) for t in range(s + 1) if (s - ia) <= t and t <= ib ) def overlap1d(l1: int, l2: int, pax: float, pbx: float, gamma: float) -> float: return sum( binomial_prefactor(2 * i, l1, l2, pax, pbx) * fact2(2 * i - 1) / ((2 * gamma) ** i) for i in range(1 + floor(0.5 * (l1 + l2))) ) def tho66( alpha1: float, alpha2: float, ra: Tuple[float, float, float], rb: Tuple[float, float, float], la: Tuple[float, float, float], lb: Tuple[float, float, float], ) -> float: gamma = alpha1 + alpha2 xa, ya, za = ra xb, yb, zb = rb l1, m1, n1 = la l2, m2, n2 = lb xp = product_center_1d(alpha1, xa, alpha2, xb) yp = product_center_1d(alpha1, ya, alpha2, yb) zp = product_center_1d(alpha1, za, alpha2, zb) pre = ( exp(-alpha1 * alpha2 * dist2(xa, ya, za, xb, yb, zb) / gamma) * (pi / gamma) ** 1.5 ) wx = overlap1d(l1, l2, xp - xa, xp - xb, gamma) wy = overlap1d(m1, m2, yp - ya, yp - yb, gamma) wz = overlap1d(n1, n2, zp - za, zp - zb, gamma) return pre * wx * wy * wz

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2018/09/sol/bike.py

lfrommelt/monty

0

103983

""" This module contains the definitions for Bike and its subclasses Bicycle and Motorbike. """ class Bike: """ Class defining a bike that can be ridden and have its gear changed. Attributes: seats: number of seats the bike has gears: number of gears the bike has """ def __init__(self, seats, gears): """ Creates a new Bike object. Args: seats: number of seats the bike has gears: number of gears the bike has """ self.seats = seats self.gears = gears self._curr_gear = 1 # current gear, private self._riding = False # bike is per default not ridden @property def curr_gear(self): """ Purpose of this function is to enable the user to check the gear status, but is only able to change it with a specific method. (was not necessary to implement it this way) """ return self._curr_gear def start_ride(self): """ Starts a bike ride. Returns: True if successful. False if bike is already on a ride. """ # can't ride a bike if already ridden if self._riding: return False self._riding = True return True def end_ride(self): """ Ends a bike ride. Returns: True if successful. False if bike is not currently ridden. """ # can't stop a bike ride if the bike is already standing if not self._riding: return False self._riding = False return True def change_gear(self, new_gear): """ Changes bike gear to a new gear. Args: new_gear: gear to be changed to Returns: True if gear was successfully changed. Raises: ValueError if current gear is same as new gear or new gear is <= 0 or not in range of available gears. """ if self._curr_gear == new_gear or not 0 < new_gear <= self.gears: raise ValueError("Already in this gear or invalid gear number.") self._curr_gear = new_gear return True class Bicycle(Bike): """ Class defining a Bicycle (extending Bike) that can be ridden, have its gear changed and has a bell that can be rung. Attributes: seats: number of seats the bike has gears: number of gears the bike has bell_sound: sound the bell makes when rung """ def __init__(self, seats=1, gears=7, bell_sound="ring ring"): """ Creates a new Bike object. Args: seats: number of seats the bicycle has, defaults to 1 gears: number of gears the bicycle has, defaults to 7 bell_sound: sound the bell makes when rung """ super().__init__(seats, gears) self.bell_sound = bell_sound def ring_bell(self): """ Rings bicycle bell.""" print(self.bell_sound) class Motorbike(Bike): """ Class defining a Motorbike (extending Bike) that can be ridden, have its gear changed and has a tank that can be filled. Attributes: seats: number of seats the bike has gears: number of gears the bike has """ def __init__(self, seats=2, gears=5): """ Creates a new Motorbike object. Args: seats: number of seats the motorbike has, defaults to 2 gears: number of gears the motorbike has, defaults to 5 """ super().__init__(seats, gears) # True means full tank. Private so it can only be changed in # a controlled manner self._tank = True @property def tank(self): """ Purpose of this function is to enable the user to check the tank status, but is only able to fill/empty the tank with specific methods. This was not necessary to implement. """ return self._tank def start_ride(self): """ Starts a motorbike ride. Returns: True if successful. False if motorbike is already on a ride or tank is empty """ # can't ride a motorbike if tank is empty or it is already ridden if not self._tank or not super().start_ride(): return False return True def end_ride(self): """ Ends a motorbike ride and empties tank. Returns: True if successful. False if motorbike is not currently ridden. """ if not super().end_ride(): return False self._tank = False # tank is empty after riding return True # the following method was not necessary to implement, but we want to be # able to ride more than once. def fill_tank(self): """ Fills motorbike tank with fuel. Returns: True if successful. False if tank already full. """ # can't fill tank if already full if self._tank: return False self._tank = True return True

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rps/examples/Finite_Time_Barriers/main.py

mohitsrinivasan/Barrier_MotionPlanning

10

193436

import rps.robotarium as robotarium from rps.utilities import * from rps.utilities.barrier_certificates import * from rps.utilities.controllers import * from rps.utilities.transformations import * from reachGoal import reachGoal from matplotlib import patches import numpy as np import time N = 1 initial_conditions = np.transpose(np.array([[-1, 0.2, 0]])) r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions, sim_in_real_time=False) si_to_uni_dyn = create_si_to_uni_dynamics_with_backwards_motion() _, uni_to_si_states = create_si_to_uni_mapping() si_barrier_cert = create_single_integrator_barrier_certificate_with_boundary() hg = -1 ## Visualize goals and obstacles r.axes.add_patch(patches.Circle((1, 0), 0.2, fill=False, zorder=10)) # Goal region r.axes.add_patch(patches.Ellipse((0, 0), 0.4, 1.0, 0, fill=False)) # Obstacle while (hg <= 0): x = r.get_poses() x_si = uni_to_si_states(x) u, hg = reachGoal(x) # Create safe control inputs (i.e., no collisions) u = si_barrier_cert(u, x[0:2]) # Transform single integrator velocity commands to unicycle dxu = si_to_uni_dyn(u, x) # Set the velocities by mapping the single-integrator inputs to unciycle inputs r.set_velocities(np.arange(N), dxu) # Iterate the simulation r.step() r.call_at_scripts_end()

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hknlib/election/groups.py

awang193/hknlib

0

1616533

from __future__ import (division, absolute_import, print_function, unicode_literals) from googleapiclient.discovery import build from .settings import SPREADSHEET_ID def add_user_to_group(credentials, user, groupKey): #add USER to a mail list GROUP service = build('admin', 'directory_v1', credentials=credentials) body = { 'email': user + '@hkn.eecs.berkeley.edu', 'role': 'MEMBER', } group = groupKey + '@hkn.eecs.berkeley.edu' response = service.members().hasMember(groupKey=group, memberKey=body.get('email')).execute() if response['isMember']: return _ = service.members().insert(groupKey=group, body=body).execute() print("{}->{}".format(user, groupKey)) return def add_officers_to_committes(credentials, election_data): user_committee = [] if election_data: for i in range(0, len(election_data)): if len(election_data[i]) < 6: continue committee = election_data[i][5][:-1] user_committee.append((election_data[i][3], committee)) #user_committee[users[i]] = committee for user, committee in user_committee: result = add_user_to_group(credentials, user, committee+'-officers') result = add_user_to_group(credentials, user, "current-"+committee) def add_members_to_committes(credentials, election_data): mailing_lists = [] if election_data: for i in range(0, len(election_data)): if len(election_data[i]) < 7: continue row = election_data[i] mailing = row[6][:-1].split('@, ') mailing_lists.append((election_data[i][3], mailing)) #mailing_lists[users[i]] = mailing_list for user, mailing_list in mailing_lists: for committee in mailing_list: add_user_to_group(credentials, user, committee) def add_all_to_committes(credentials, election_data): # add all users to their committees and groups add_officers_to_committes(credentials, election_data) add_members_to_committes(credentials, election_data)

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src/google/appengine/datastore/datastore_query.py

myelin/appengine-python-standard

0

7689

<reponame>myelin/appengine-python-standard<gh_stars>0 #!/usr/bin/env python # # Copyright 2007 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. # """A thin wrapper around datastore query RPC calls. This provides wrappers around the internal only datastore_pb library and is designed to be the lowest-level API to be used by all Python datastore client libraries for executing queries. It provides a layer of protection so the actual RPC syntax can change without affecting client libraries. Any class, function, field or argument starting with an '_' is for INTERNAL use only and should not be used by developers! """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import base64 import collections import functools import pickle import six from google.appengine.api import cmp_compat from google.appengine.api import datastore_errors from google.appengine.api import datastore_types from google.appengine.datastore import datastore_index from google.appengine.datastore import datastore_pb from google.appengine.datastore import datastore_pbs from google.appengine.datastore import datastore_rpc from google.protobuf import message from google.appengine.datastore import entity_bytes_pb2 as entity_pb2 __all__ = ['Batch', 'Batcher', 'CompositeFilter', 'CompositeOrder', 'CorrelationFilter', 'Cursor', 'FetchOptions', 'FilterPredicate', 'Order', 'PropertyFilter', 'PropertyOrder', 'Query', 'QueryOptions', 'ResultsIterator', 'make_filter', 'apply_query', 'inject_results'] if datastore_pbs._CLOUD_DATASTORE_ENABLED: from google.appengine.datastore.datastore_pbs import googledatastore class _BaseComponent(object): """A base class for query components. Currently just implements basic == and != functions. """ def __eq__(self, other): if self.__class__ is not other.__class__: return NotImplemented return self is other or self.__dict__ == other.__dict__ def __ne__(self, other): equal = self.__eq__(other) if equal is NotImplemented: return equal return not equal def make_filter(name, op, values): """Constructs a FilterPredicate from the given name, op and values. Args: name: A non-empty string, the name of the property to filter. op: One of PropertyFilter._OPERATORS.keys(), the operator to use. values: A supported value, the value to compare against. Returns: if values is a list, a CompositeFilter that uses AND to combine all values, otherwise a PropertyFilter for the single value. Raises: datastore_errors.BadPropertyError: if the property name is invalid. datastore_errors.BadValueError: if the property did not validate correctly or the value was an empty list. Other exception types (like OverflowError): if the property value does not meet type-specific criteria. """ datastore_types.ValidateProperty(name, values) properties = datastore_types.ToPropertyPb(name, values) if isinstance(properties, list): filters = [PropertyFilter(op, prop) for prop in properties] return CompositeFilter(CompositeFilter.AND, filters) else: return PropertyFilter(op, properties) def _make_key_value_map(entity, property_names): """Extracts key values from the given entity. Args: entity: The entity_pb2.EntityProto to extract values from. property_names: The names of the properties from which to extract values. Returns: A dict mapping property names to a lists of key values. """ value_map = dict((six.ensure_text(name), []) for name in property_names) for prop in entity.property: prop_name = six.ensure_text(prop.name) if prop_name in value_map: value_map[prop_name].append( datastore_types.PropertyValueToKeyValue(prop.value)) key_prop = six.ensure_text(datastore_types.KEY_SPECIAL_PROPERTY) if key_prop in value_map: value_map[key_prop] = [datastore_types.ReferenceToKeyValue(entity.key)] return value_map class _PropertyComponent(_BaseComponent): """A component that operates on a specific set of properties.""" def _get_prop_names(self): """Returns a set of property names used by the filter.""" raise NotImplementedError class FilterPredicate(_PropertyComponent): """An abstract base class for all query filters. All sub-classes must be immutable as these are often stored without creating a defensive copy. """ def __call__(self, entity): """Applies the filter predicate to the given entity. Args: entity: the datastore_pb.EntityProto to test. Returns: True if the given entity matches the filter, False otherwise. """ return self._apply(_make_key_value_map(entity, self._get_prop_names())) def _apply(self, key_value_map): """Apply the given component to the comparable value map. A filter matches a list of values if at least one value in the list matches the filter, for example: 'prop: [1, 2]' matches both 'prop = 1' and 'prop = 2' but not 'prop = 3' Note: the values are actually represented as tuples whose first item encodes the type; see datastore_types.PropertyValueToKeyValue(). Args: key_value_map: A dict mapping property names to a list of comparable values. Return: A boolean indicating if the given map matches the filter. """ raise NotImplementedError def _prune(self, key_value_map): """Removes values from the given map that do not match the filter. When doing a scan in the datastore, only index values that match the filters are seen. When multiple values that point to the same entity are seen, the entity only appears where the first value is found. This function removes all values that don't match the query so that the first value in the map is the same one the datastore would see first. Args: key_value_map: the comparable value map from which to remove values. Does not need to contain values for all filtered properties. Returns: A value that evaluates to False if every value in a single list was completely removed. This effectively applies the filter but is less efficient than _apply(). """ raise NotImplementedError def _to_pb(self): """Internal only function to generate a pb.""" raise NotImplementedError( 'This filter only supports in memory operations (%r)' % self) def _to_pbs(self): """Internal only function to generate a list of pbs.""" return [self._to_pb()] def _to_pb_v1(self, adapter): """Internal only function to generate a v1 pb. Args: adapter: A datastore_rpc.AbstractAdapter """ raise NotImplementedError( 'This filter only supports in memory operations (%r)' % self) class _SinglePropertyFilter(FilterPredicate): """Base class for a filter that operates on a single property.""" def _get_prop_name(self): """Returns the name of the property being filtered.""" raise NotImplementedError def _apply_to_value(self, value): """Apply the filter to the given value. Args: value: The comparable value to check. Returns: A boolean indicating if the given value matches the filter. """ raise NotImplementedError def _get_prop_names(self): return set([self._get_prop_name()]) def _apply(self, value_map): for other_value in value_map[self._get_prop_name()]: if self._apply_to_value(other_value): return True return False def _prune(self, value_map): if self._get_prop_name() not in value_map: return True values = [value for value in value_map[self._get_prop_name()] if self._apply_to_value(value)] value_map[self._get_prop_name()] = values return bool(values) class PropertyFilter(_SinglePropertyFilter): """An immutable filter predicate that constrains a single property.""" _OPERATORS = { '<': datastore_pb.Query.Filter.LESS_THAN, '<=': datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL, '>': datastore_pb.Query.Filter.GREATER_THAN, '>=': datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL, '=': datastore_pb.Query.Filter.EQUAL, } _OPERATORS_INVERSE = dict((value, key) for key, value in _OPERATORS.items()) _OPERATORS_TO_PYTHON_OPERATOR = { datastore_pb.Query.Filter.LESS_THAN: '<', datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL: '<=', datastore_pb.Query.Filter.GREATER_THAN: '>', datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL: '>=', datastore_pb.Query.Filter.EQUAL: '==', } _INEQUALITY_OPERATORS = frozenset(['<', '<=', '>', '>=']) _INEQUALITY_OPERATORS_ENUM = frozenset([ datastore_pb.Query.Filter.LESS_THAN, datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL, datastore_pb.Query.Filter.GREATER_THAN, datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL, ]) _UPPERBOUND_INEQUALITY_OPERATORS = frozenset(['<', '<=']) def __init__(self, op, value): """Constructor. Args: op: A string representing the operator to use. value: A entity_pb2.Property, the property and value to compare against. Raises: datastore_errors.BadArgumentError if op has an unsupported value or value is not an entity_pb2.Property. """ if op not in self._OPERATORS: raise datastore_errors.BadArgumentError('unknown operator: %r' % (op,)) if not isinstance(value, entity_pb2.Property): raise datastore_errors.BadArgumentError( 'value argument should be entity_pb2.Property (%r)' % (value,)) super(PropertyFilter, self).__init__() self._filter = datastore_pb.Query.Filter() self._filter.op = self._OPERATORS[op] self._filter.property.add().CopyFrom(value) @property def op(self): raw_op = self._filter.op return self._OPERATORS_INVERSE.get(raw_op, str(raw_op)) @property def value(self): return self._filter.property[0] def __repr__(self): prop = self.value name = prop.name value = datastore_types.FromPropertyPb(prop) if six.PY2 and isinstance(value, long): value = int(value) return '%s(%r, <%r, %r>)' % (self.__class__.__name__, six.ensure_str( self.op), six.ensure_str(name), value) def _get_prop_name(self): return self._filter.property[0].name def _apply_to_value(self, value): if not hasattr(self, '_cmp_value'): if self._filter.op == datastore_pb.Query.Filter.EXISTS: return True self._cmp_value = datastore_types.PropertyValueToKeyValue( self._filter.property[0].value) self._condition = ('value %s self._cmp_value' % self._OPERATORS_TO_PYTHON_OPERATOR[self._filter.op]) return eval(self._condition) def _has_inequality(self): """Returns True if the filter predicate contains inequalities filters.""" return self._filter.op in self._INEQUALITY_OPERATORS_ENUM @classmethod def _from_pb(cls, filter_pb): self = cls.__new__(cls) self._filter = filter_pb return self def _to_pb(self): """Returns the internal only pb representation.""" return self._filter def _to_pb_v1(self, adapter): """Returns a googledatastore.Filter representation of the filter. Args: adapter: A datastore_rpc.AbstractAdapter """ filter_pb = googledatastore.Filter() prop_filter_pb = filter_pb.property_filter adapter.get_query_converter()._v3_filter_to_v1_property_filter( self._filter, prop_filter_pb) return filter_pb def __getstate__(self): raise pickle.PicklingError( 'Pickling of datastore_query.PropertyFilter is unsupported.') def __eq__(self, other): if self.__class__ is not other.__class__: if other.__class__ is _PropertyRangeFilter: return [self._filter] == other._to_pbs() return NotImplemented return self._filter == other._filter class _PropertyRangeFilter(_SinglePropertyFilter): """A filter predicate that represents a range of values. Since we allow multi-valued properties there is a large difference between "x > 0 AND x < 1" and "0 < x < 1." An entity with x = [-1, 2] will match the first but not the second. Since the datastore only allows a single inequality filter, multiple in-equality filters are merged into a single range filter in the datastore (unlike equality filters). This class is used by datastore_query.CompositeFilter to implement the same logic. """ _start_key_value = None _end_key_value = None @datastore_rpc._positional(1) def __init__(self, start=None, start_incl=True, end=None, end_incl=True): """Constructs a range filter using start and end properties. Args: start: A entity_pb2.Property to use as a lower bound or None to indicate no lower bound. start_incl: A boolean that indicates if the lower bound is inclusive. end: A entity_pb2.Property to use as an upper bound or None to indicate no upper bound. end_incl: A boolean that indicates if the upper bound is inclusive. """ if start is not None and not isinstance(start, entity_pb2.Property): raise datastore_errors.BadArgumentError( 'start argument should be entity_pb2.Property (%r)' % (start,)) if end is not None and not isinstance(end, entity_pb2.Property): raise datastore_errors.BadArgumentError( 'start argument should be entity_pb2.Property (%r)' % (end,)) if start and end and start.name != end.name: raise datastore_errors.BadArgumentError( 'start and end arguments must be on the same property (%s != %s)' % (start.name, end.name)) if not start and not end: raise datastore_errors.BadArgumentError( 'Unbounded ranges are not supported.') super(_PropertyRangeFilter, self).__init__() self._start = start self._start_incl = start_incl self._end = end self._end_incl = end_incl @classmethod def from_property_filter(cls, prop_filter): op = prop_filter._filter.op if op == datastore_pb.Query.Filter.GREATER_THAN: return cls(start=prop_filter._filter.property[0], start_incl=False) elif op == datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL: return cls(start=prop_filter._filter.property[0]) elif op == datastore_pb.Query.Filter.LESS_THAN: return cls(end=prop_filter._filter.property[0], end_incl=False) elif op == datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL: return cls(end=prop_filter._filter.property[0]) else: raise datastore_errors.BadArgumentError( 'Unsupported operator (%s)' % (op,)) def intersect(self, other): """Returns a filter representing the intersection of self and other.""" if isinstance(other, PropertyFilter): other = self.from_property_filter(other) elif not isinstance(other, _PropertyRangeFilter): raise datastore_errors.BadArgumentError( 'other argument should be a _PropertyRangeFilter (%r)' % (other,)) if other._get_prop_name() != self._get_prop_name(): raise datastore_errors.BadArgumentError( 'other argument must be on the same property (%s != %s)' % (other._get_prop_name(), self._get_prop_name())) start_source = None if other._start: if self._start: result = cmp_compat.cmp( self._get_start_key_value(), other._get_start_key_value()) if result == 0: result = cmp_compat.cmp(other._start_incl, self._start_incl) if result > 0: start_source = self elif result < 0: start_source = other else: start_source = other elif self._start: start_source = self end_source = None if other._end: if self._end: result = cmp_compat.cmp( self._get_end_key_value(), other._get_end_key_value()) if result == 0: result = cmp_compat.cmp(self._end_incl, other._end_incl) if result < 0: end_source = self elif result > 0: end_source = other else: end_source = other elif self._end: end_source = self if start_source: if end_source in (start_source, None): return start_source result = _PropertyRangeFilter(start=start_source._start, start_incl=start_source._start_incl, end=end_source._end, end_incl=end_source._end_incl) result._start_key_value = start_source._start_key_value result._end_key_value = end_source._end_key_value return result else: return end_source or self def _get_start_key_value(self): if self._start_key_value is None: self._start_key_value = datastore_types.PropertyValueToKeyValue( self._start.value) return self._start_key_value def _get_end_key_value(self): if self._end_key_value is None: self._end_key_value = datastore_types.PropertyValueToKeyValue( self._end.value) return self._end_key_value def _apply_to_value(self, value): """Apply the filter to the given value. Args: value: The comparable value to check. Returns: A boolean indicating if the given value matches the filter. """ if self._start: result = cmp_compat.cmp(self._get_start_key_value(), value) if result > 0 or (result == 0 and not self._start_incl): return False if self._end: result = cmp_compat.cmp(self._get_end_key_value(), value) if result < 0 or (result == 0 and not self._end_incl): return False return True def _get_prop_name(self): if self._start: return self._start.name if self._end: return self._end.name assert False def _to_pbs(self): pbs = [] if self._start: if self._start_incl: op = datastore_pb.Query.Filter.GREATER_THAN_OR_EQUAL else: op = datastore_pb.Query.Filter.GREATER_THAN pb = datastore_pb.Query.Filter() pb.op = op pb.property.add().CopyFrom(self._start) pbs.append(pb) if self._end: if self._end_incl: op = datastore_pb.Query.Filter.LESS_THAN_OR_EQUAL else: op = datastore_pb.Query.Filter.LESS_THAN pb = datastore_pb.Query.Filter() pb.op = op pb.property.add().CopyFrom(self._end) pbs.append(pb) return pbs def _to_pb_v1(self, adapter): """Returns a googledatastore.Filter representation of the filter. Args: adapter: A datastore_rpc.AbstractAdapter. """ filter_pb = googledatastore.Filter() composite_filter = filter_pb.composite_filter composite_filter.op = googledatastore.CompositeFilter.AND if self._start: if self._start_incl: op = googledatastore.PropertyFilter.GREATER_THAN_OR_EQUAL else: op = googledatastore.PropertyFilter.GREATER_THAN pb = composite_filter.filters.add().property_filter pb.op = op pb.property.name = self._start.name adapter.get_entity_converter().v3_property_to_v1_value( self._start, True, pb.value) if self._end: if self._end_incl: op = googledatastore.PropertyFilter.LESS_THAN_OR_EQUAL else: op = googledatastore.PropertyFilter.LESS_THAN pb = composite_filter.filters.add().property_filter pb.op = op pb.property.name = self._end.name adapter.get_entity_converter().v3_property_to_v1_value( self._end, True, pb.value) return filter_pb def __getstate__(self): raise pickle.PicklingError( 'Pickling of %r is unsupported.' % self) def __eq__(self, other): if self.__class__ is not other.__class__: return NotImplemented return (self._start == other._start and self._end == other._end and (self._start_incl == other._start_incl or self._start is None) and (self._end_incl == other._end_incl or self._end is None)) class _PropertyExistsFilter(FilterPredicate): """A FilterPredicate that matches entities containing specific properties. Only works as an in-memory filter. Used internally to filter out entities that don't have all properties in a given Order. """ def __init__(self, names): super(_PropertyExistsFilter, self).__init__() self._names = frozenset(names) def _apply(self, value_map): for name in self._names: if not value_map.get(name): return False return True def _get_prop_names(self): return self._names def _prune(self, _): raise NotImplementedError def __getstate__(self): raise pickle.PicklingError( 'Pickling of %r is unsupported.' % self) class CorrelationFilter(FilterPredicate): """A filter that isolates correlated values and applies a sub-filter on them. This filter assumes that every property used by the sub-filter should be grouped before being passed to the sub-filter. The default grouping puts each value in its own group. Consider: e = {a: [1, 2], b: [2, 1, 3], c: 4} A correlation filter with a sub-filter that operates on (a, b) will be tested against the following 3 sets of values: {a: 1, b: 2} {a: 2, b: 1} {b: 3} In this case CorrelationFilter('a = 2 AND b = 2') won't match this entity but CorrelationFilter('a = 2 AND b = 1') will. To apply an uncorrelated filter on c, the filter must be applied in parallel to the correlation filter. For example: CompositeFilter(AND, [CorrelationFilter('a = 2 AND b = 1'), 'c = 3']) If 'c = 3' was included in the correlation filter, c would be grouped as well. This would result in the following values: {a: 1, b: 2, c: 3} {a: 2, b: 1} {b: 3} If any set of correlated values match the sub-filter then the entity matches the correlation filter. """ def __init__(self, subfilter): """Constructor. Args: subfilter: A FilterPredicate to apply to the correlated values """ self._subfilter = subfilter @property def subfilter(self): return self._subfilter def __repr__(self): return '%s(%r)' % (self.__class__.__name__, self.subfilter) def _apply(self, value_map): base_map = dict((prop, []) for prop in self._get_prop_names()) value_maps = [] for prop in base_map: grouped = self._group_values(prop, value_map[prop]) while len(value_maps) < len(grouped): value_maps.append(base_map.copy()) for value, m in six.moves.zip(grouped, value_maps): m[prop] = value return self._apply_correlated(value_maps) def _apply_correlated(self, value_maps): """Applies sub-filter to the correlated value maps. The default implementation matches when any value_map in value_maps matches the sub-filter. Args: value_maps: A list of correlated value_maps. Returns: True if any the entity matches the correlation filter. """ for map in value_maps: if self._subfilter._apply(map): return True return False def _group_values(self, prop, values): """A function that groups the given values. Override this function to introduce custom grouping logic. The default implementation assumes each value belongs in its own group. Args: prop: The name of the property who's values are being grouped. values: A list of opaque values. Returns: A list of lists of grouped values. """ return [[value] for value in values] def _get_prop_names(self): return self._subfilter._get_prop_names() class CompositeFilter(FilterPredicate): """An immutable filter predicate that combines other predicates. This class proactively merges sub-filters that are combined using the same operator. For example: CompositeFilter(AND, [f1, f2, CompositeFilter(AND, [f3, f4]), f5, f6]) is equivalent to: CompositeFilter(AND, [f1, f2, f3, f4, f5, f6]) Currently filters can only be combined using an AND operator. """ AND = 'and' _OPERATORS = frozenset([AND]) def __init__(self, op, filters): """Constructor. Args: op: The operator to use to combine the given filters filters: A list of one or more filters to combine Raises: datastore_errors.BadArgumentError if op is not in CompsiteFilter.OPERATORS or filters is not a non-empty list containing only FilterPredicates. """ if not op in self._OPERATORS: raise datastore_errors.BadArgumentError('unknown operator (%s)' % (op,)) if not filters or not isinstance(filters, (list, tuple)): raise datastore_errors.BadArgumentError( 'filters argument should be a non-empty list (%r)' % (filters,)) super(CompositeFilter, self).__init__() self._op = op flattened = [] for f in filters: if isinstance(f, CompositeFilter) and f._op == self._op: flattened.extend(f._filters) elif isinstance(f, FilterPredicate): flattened.append(f) else: raise datastore_errors.BadArgumentError( 'filters argument must be a list of FilterPredicates, found (%r)' % (f,)) if op == self.AND: filters = flattened flattened = [] ineq_map = {} for f in filters: if (isinstance(f, _PropertyRangeFilter) or (isinstance(f, PropertyFilter) and f._has_inequality())): name = f._get_prop_name() index = ineq_map.get(name) if index is not None: range_filter = flattened[index] flattened[index] = range_filter.intersect(f) else: if isinstance(f, PropertyFilter): range_filter = _PropertyRangeFilter.from_property_filter(f) else: range_filter = f ineq_map[name] = len(flattened) flattened.append(range_filter) else: flattened.append(f) self._filters = tuple(flattened) @property def op(self): return self._op @property def filters(self): return self._filters def __repr__(self): op = self.op if op == self.AND: op = 'AND' else: op = str(op) return '%s(%s, %r)' % (self.__class__.__name__, op, list(self.filters)) def _get_prop_names(self): names = set() for f in self._filters: names |= f._get_prop_names() return names def _apply(self, value_map): if self._op == self.AND: for f in self._filters: if not f._apply(value_map): return False return True raise NotImplementedError def _prune(self, value_map): if self._op == self.AND: matches = collections.defaultdict(set) for f in self._filters: props = f._get_prop_names() local_value_map = dict((k, v) for k, v in value_map.items() if k in props) if not f._prune(local_value_map): return False for (prop, values) in local_value_map.items(): matches[prop].update(values) for prop, value_set in matches.items(): value_map[prop] = sorted(value_set) return True raise NotImplementedError def _to_pbs(self): """Returns the internal only pb representation.""" pbs = [] for f in self._filters: pbs.extend(f._to_pbs()) return pbs def _to_pb_v1(self, adapter): """Returns a googledatastore.Filter. Args: adapter: A datastore_rpc.AbstractAdapter """ if not self._filters: return None if len(self._filters) == 1: return self._filters[0]._to_pb_v1(adapter) pb = googledatastore.Filter() comp_pb = pb.composite_filter if self.op == self.AND: comp_pb.op = googledatastore.CompositeFilter.AND else: raise datastore_errors.BadArgumentError( 'Datastore V4 only supports CompositeFilter with AND operator.') for f in self._filters: comp_pb.filters.add().CopyFrom(f._to_pb_v1(adapter)) return pb def __eq__(self, other): if self.__class__ is other.__class__: return super(CompositeFilter, self).__eq__(other) if len(self._filters) == 1: result = self._filters[0].__eq__(other) if result is NotImplemented and hasattr(other, '__eq__'): return other.__eq__(self._filters[0]) return result return NotImplemented class _IgnoreFilter(_SinglePropertyFilter): """A filter that removes all entities with the given keys.""" def __init__(self, key_value_set): super(_IgnoreFilter, self).__init__() self._keys = key_value_set def _get_prop_name(self): return datastore_types.KEY_SPECIAL_PROPERTY def _apply_to_value(self, value): return value not in self._keys class _DedupingFilter(_IgnoreFilter): """A filter that removes duplicate keys.""" def __init__(self, key_value_set=None): super(_DedupingFilter, self).__init__(key_value_set or set()) def _apply_to_value(self, value): if super(_DedupingFilter, self)._apply_to_value(value): self._keys.add(value) return True return False class Order(_PropertyComponent): """A base class that represents a sort order on a query. All sub-classes must be immutable as these are often stored without creating a defensive copying. This class can be used as either the cmp or key arg in sorted() or list.sort(). To provide a stable ordering a trailing key ascending order is always used. """ @datastore_rpc._positional(1) def reversed(self, group_by=None): """Constructs an order representing the reverse of the current order. This function takes into account the effects of orders on properties not in the group_by clause of a query. For example, consider: SELECT A, First(B) ... GROUP BY A ORDER BY A, B Changing the order of B would effect which value is listed in the 'First(B)' column which would actually change the results instead of just reversing them. Args: group_by: If specified, only orders on properties in group_by will be reversed. Returns: A new order representing the reverse direction. """ raise NotImplementedError def _key(self, lhs_value_map): """Creates a key for the given value map.""" raise NotImplementedError def _cmp(self, lhs_value_map, rhs_value_map): """Compares the given value maps.""" raise NotImplementedError def _to_pb(self): """Internal only function to generate a filter pb.""" raise NotImplementedError def _to_pb_v1(self, adapter): """Internal only function to generate a v1 filter pb. Args: adapter: A datastore_rpc.AbstractAdapter """ raise NotImplementedError def key_for_filter(self, filter_predicate): if filter_predicate: return lambda x: self.key(x, filter_predicate) return self.key def cmp_for_filter(self, filter_predicate): if filter_predicate: return lambda x, y: self.cmp(x, y, filter_predicate) return self.cmp def key(self, entity, filter_predicate=None): """Constructs a "key" value for the given entity based on the current order. This function can be used as the key argument for list.sort() and sorted(). Args: entity: The entity_pb2.EntityProto to convert filter_predicate: A FilterPredicate used to prune values before comparing entities or None. Returns: A key value that identifies the position of the entity when sorted by the current order. """ names = self._get_prop_names() names.add(datastore_types.KEY_SPECIAL_PROPERTY) if filter_predicate is not None: names |= filter_predicate._get_prop_names() value_map = _make_key_value_map(entity, names) if filter_predicate is not None: filter_predicate._prune(value_map) return (self._key(value_map), value_map[datastore_types.KEY_SPECIAL_PROPERTY]) def cmp(self, lhs, rhs, filter_predicate=None): """Compares the given values taking into account any filters. This function can be used as the cmp argument for list.sort() and sorted(). This function is slightly more efficient that Order.key when comparing two entities, however it is much less efficient when sorting a list of entities. Args: lhs: An entity_pb2.EntityProto rhs: An entity_pb2.EntityProto filter_predicate: A FilterPredicate used to prune values before comparing entities or None. Returns: An integer <, = or > 0 representing the operator that goes in between lhs and rhs that to create a true statement. """ names = self._get_prop_names() if filter_predicate is not None: names |= filter_predicate._get_prop_names() lhs_value_map = _make_key_value_map(lhs, names) rhs_value_map = _make_key_value_map(rhs, names) if filter_predicate is not None: filter_predicate._prune(lhs_value_map) filter_predicate._prune(rhs_value_map) result = self._cmp(lhs_value_map, rhs_value_map) if result: return result if not lhs.HasField('key') and not rhs.HasField('key'): return 0 lhs_key = (lhs_value_map.get(datastore_types.KEY_SPECIAL_PROPERTY) or datastore_types.ReferenceToKeyValue(lhs.key)) rhs_key = (rhs_value_map.get(datastore_types.KEY_SPECIAL_PROPERTY) or datastore_types.ReferenceToKeyValue(rhs.key)) return cmp_compat.cmp(lhs_key, rhs_key) @cmp_compat.total_ordering_from_cmp class _ReverseOrder(_BaseComponent): """Reverses the comparison for the given object.""" def __init__(self, obj): """Constructor for _ReverseOrder. Args: obj: Any comparable and hashable object. """ super(_ReverseOrder, self).__init__() self._obj = obj def __hash__(self): return hash(self._obj) def __cmp__(self, other): assert self.__class__ == other.__class__, ( 'A datastore_query._ReverseOrder object can only be compared to ' 'an object of the same type.') return -cmp_compat.cmp(self._obj, other._obj) class PropertyOrder(Order): """An immutable class that represents a sort order for a single property.""" ASCENDING = datastore_pb.Query.Order.ASCENDING DESCENDING = datastore_pb.Query.Order.DESCENDING _DIRECTIONS = frozenset([ASCENDING, DESCENDING]) def __init__(self, prop, direction=ASCENDING): """Constructor. Args: prop: the name of the prop by which to sort. direction: the direction in which to sort the given prop. Raises: datastore_errors.BadArgumentError if the prop name or direction is invalid. """ datastore_types.ValidateString(prop, 'prop', datastore_errors.BadArgumentError) if not direction in self._DIRECTIONS: raise datastore_errors.BadArgumentError('unknown direction: %r' % (direction,)) super(PropertyOrder, self).__init__() self.__order = datastore_pb.Query.Order() self.__order.property = six.ensure_binary(prop, 'utf-8') self.__order.direction = direction @property def prop(self): return self.__order.property @property def direction(self): return self.__order.direction def __repr__(self): extra = '' if self.direction == self.DESCENDING: extra = ', DESCENDING' name = repr(six.ensure_str(self.prop))[1:-1] return '%s(<%s>%s)' % (self.__class__.__name__, name, extra) @datastore_rpc._positional(1) def reversed(self, group_by=None): if group_by and self.__order.property not in group_by: return self if self.__order.direction == self.ASCENDING: return PropertyOrder( six.ensure_text(self.__order.property), self.DESCENDING) else: return PropertyOrder( six.ensure_text(self.__order.property), self.ASCENDING) def _get_prop_names(self): return set([self.__order.property]) def _key(self, lhs_value_map): lhs_values = lhs_value_map[self.__order.property] if not lhs_values: raise datastore_errors.BadArgumentError( 'Missing value for property (%s)' % self.__order.property) if self.__order.direction == self.ASCENDING: return min(lhs_values) else: return _ReverseOrder(max(lhs_values)) def _cmp(self, lhs_value_map, rhs_value_map): lhs_values = lhs_value_map[self.__order.property] rhs_values = rhs_value_map[self.__order.property] if not lhs_values and not rhs_values: return 0 if not lhs_values: raise datastore_errors.BadArgumentError( 'LHS missing value for property (%s)' % self.__order.property) if not rhs_values: raise datastore_errors.BadArgumentError( 'RHS missing value for property (%s)' % self.__order.property) if self.__order.direction == self.ASCENDING: return cmp_compat.cmp(min(lhs_values), min(rhs_values)) else: return cmp_compat.cmp(max(rhs_values), max(lhs_values)) @classmethod def _from_pb(cls, order_pb): self = cls.__new__(cls) self.__order = order_pb return self def _to_pb(self): """Returns the internal only pb representation.""" return self.__order def _to_pb_v1(self, adapter): """Returns a googledatastore.PropertyOrder representation of the order. Args: adapter: A datastore_rpc.AbstractAdapter. """ v1_order = googledatastore.PropertyOrder() adapter.get_query_converter().v3_order_to_v1_order(self.__order, v1_order) return v1_order def __getstate__(self): raise pickle.PicklingError( 'Pickling of datastore_query.PropertyOrder is unsupported.') class CompositeOrder(Order): """An immutable class that represents a sequence of Orders. This class proactively flattens sub-orders that are of type CompositeOrder. For example: CompositeOrder([O1, CompositeOrder([02, 03]), O4]) is equivalent to: CompositeOrder([O1, 02, 03, O4]) """ def __init__(self, orders): """Constructor. Args: orders: A list of Orders which are applied in order. """ if not isinstance(orders, (list, tuple)): raise datastore_errors.BadArgumentError( 'orders argument should be list or tuple (%r)' % (orders,)) super(CompositeOrder, self).__init__() flattened = [] for order in orders: if isinstance(order, CompositeOrder): flattened.extend(order._orders) elif isinstance(order, Order): flattened.append(order) else: raise datastore_errors.BadArgumentError( 'orders argument should only contain Order (%r)' % (order,)) self._orders = tuple(flattened) @property def orders(self): return self._orders def __repr__(self): return '%s(%r)' % (self.__class__.__name__, list(self.orders)) @datastore_rpc._positional(1) def reversed(self, group_by=None): return CompositeOrder([order.reversed(group_by=group_by) for order in self._orders]) def _get_prop_names(self): names = set() for order in self._orders: names |= order._get_prop_names() return names def _key(self, lhs_value_map): result = [] for order in self._orders: result.append(order._key(lhs_value_map)) return tuple(result) def _cmp(self, lhs_value_map, rhs_value_map): for order in self._orders: result = order._cmp(lhs_value_map, rhs_value_map) if result != 0: return result return 0 def size(self): """Returns the number of sub-orders the instance contains.""" return len(self._orders) def _to_pbs(self): """Returns an ordered list of internal only pb representations.""" return [order._to_pb() for order in self._orders] def _to_pb_v1(self, adapter): """Returns an ordered list of googledatastore.PropertyOrder. Args: adapter: A datastore_rpc.AbstractAdapter """ return [order._to_pb_v1(adapter) for order in self._orders] def __eq__(self, other): if self.__class__ is other.__class__: return super(CompositeOrder, self).__eq__(other) if len(self._orders) == 1: result = self._orders[0].__eq__(other) if result is NotImplemented and hasattr(other, '__eq__'): return other.__eq__(self._orders[0]) return result return NotImplemented class FetchOptions(datastore_rpc.Configuration): """An immutable class that contains all options for fetching results. These options apply to any request that pulls results from a query. This class reserves the right to define configuration options of any name except those that start with 'user_'. External subclasses should only define function or variables with names that start with in 'user_'. Options are set by passing keyword arguments to the constructor corresponding to the configuration options defined below and in datastore_rpc.Configuration. This object can be used as the default config for a datastore_rpc.Connection but in that case some options will be ignored, see option documentation below for details. """ @datastore_rpc.ConfigOption def produce_cursors(value): """If a Cursor should be returned with the fetched results. Raises: datastore_errors.BadArgumentError if value is not a bool. """ if not isinstance(value, bool): raise datastore_errors.BadArgumentError( 'produce_cursors argument should be bool (%r)' % (value,)) return value @datastore_rpc.ConfigOption def offset(value): """The number of results to skip before returning the first result. Only applies to the first request it is used with and is ignored if present on datastore_rpc.Connection.config. Raises: datastore_errors.BadArgumentError if value is not a integer or is less than zero. """ datastore_types.ValidateInteger(value, 'offset', datastore_errors.BadArgumentError, zero_ok=True) return value @datastore_rpc.ConfigOption def batch_size(value): """The number of results to attempt to retrieve in a batch. Raises: datastore_errors.BadArgumentError if value is not a integer or is not greater than zero. """ datastore_types.ValidateInteger(value, 'batch_size', datastore_errors.BadArgumentError) return value class QueryOptions(FetchOptions): """An immutable class that contains all options for running a query. This class contains options that control execution process (deadline, batch_size, read_policy, etc) and what part of the query results are returned (keys_only, projection, offset, limit, etc) Options that control the contents of the query results are specified on the datastore_query.Query directly. This class reserves the right to define configuration options of any name except those that start with 'user_'. External subclasses should only define function or variables with names that start with in 'user_'. Options are set by passing keyword arguments to the constructor corresponding to the configuration options defined below and in FetchOptions and datastore_rpc.Configuration. This object can be used as the default config for a datastore_rpc.Connection but in that case some options will be ignored, see below for details. """ ORDER_FIRST = datastore_pb.Query.ORDER_FIRST ANCESTOR_FIRST = datastore_pb.Query.ANCESTOR_FIRST FILTER_FIRST = datastore_pb.Query.FILTER_FIRST _HINTS = frozenset([ORDER_FIRST, ANCESTOR_FIRST, FILTER_FIRST]) @datastore_rpc.ConfigOption def keys_only(value): """If the query should only return keys. Raises: datastore_errors.BadArgumentError if value is not a bool. """ if not isinstance(value, bool): raise datastore_errors.BadArgumentError( 'keys_only argument should be bool (%r)' % (value,)) return value @datastore_rpc.ConfigOption def projection(value): """A list or tuple of property names to project. If None, the entire entity is returned. Specifying a projection: - may change the index requirements for the given query; - will cause a partial entity to be returned; - will cause only entities that contain those properties to be returned; A partial entities only contain the property name and value for properties in the projection (meaning and multiple will not be set). They will also only contain a single value for any multi-valued property. However, if a multi-valued property is specified in the order, an inequality property, or the projected properties, the entity will be returned multiple times. Once for each unique combination of values. However, projection queries are significantly faster than normal queries. Raises: datastore_errors.BadArgumentError if value is empty or not a list or tuple of strings. """ if isinstance(value, list): value = tuple(value) elif not isinstance(value, tuple): raise datastore_errors.BadArgumentError( 'projection argument should be a list or tuple (%r)' % (value,)) if not value: raise datastore_errors.BadArgumentError( 'projection argument cannot be empty') for prop in value: if not isinstance(prop, six.string_types + (six.binary_type,)): raise datastore_errors.BadArgumentError( 'projection argument should contain only strings (%r)' % (prop,)) return value @datastore_rpc.ConfigOption def limit(value): """Limit on the number of results to return. Raises: datastore_errors.BadArgumentError if value is not an integer or is less than zero. """ datastore_types.ValidateInteger(value, 'limit', datastore_errors.BadArgumentError, zero_ok=True) return value @datastore_rpc.ConfigOption def prefetch_size(value): """Number of results to attempt to return on the initial request. Raises: datastore_errors.BadArgumentError if value is not an integer or is not greater than zero. """ datastore_types.ValidateInteger(value, 'prefetch_size', datastore_errors.BadArgumentError, zero_ok=True) return value @datastore_rpc.ConfigOption def start_cursor(value): """Cursor to use a start position. Ignored if present on datastore_rpc.Connection.config. Raises: datastore_errors.BadArgumentError if value is not a Cursor. """ if not isinstance(value, Cursor): raise datastore_errors.BadArgumentError( 'start_cursor argument should be datastore_query.Cursor (%r)' % (value,)) return value @datastore_rpc.ConfigOption def end_cursor(value): """Cursor to use as an end position. Ignored if present on datastore_rpc.Connection.config. Raises: datastore_errors.BadArgumentError if value is not a Cursor. """ if not isinstance(value, Cursor): raise datastore_errors.BadArgumentError( 'end_cursor argument should be datastore_query.Cursor (%r)' % (value,)) return value @datastore_rpc.ConfigOption def hint(value): """Hint on how the datastore should plan the query. Raises: datastore_errors.BadArgumentError if value is not a known hint. """ if value not in QueryOptions._HINTS: raise datastore_errors.BadArgumentError('Unknown query hint (%r)' % (value,)) return value class Cursor(_BaseComponent): """An immutable class that represents a relative position in a query. The position denoted by a Cursor is relative to a result in a query even if the result has been removed from the given query. Usually to position immediately after the last result returned by a batch. A cursor should only be used on a query with an identical signature to the one that produced it or on a query with its sort order reversed. """ @datastore_rpc._positional(1) def __init__(self, urlsafe=None, _cursor_bytes=None): """Constructor. A Cursor constructed with no arguments points the first result of any query. If such a Cursor is used as an end_cursor no results will ever be returned. """ super(Cursor, self).__init__() if urlsafe is not None: if _cursor_bytes is not None: raise datastore_errors.BadArgumentError( 'Can only specify one of urlsafe and _cursor_bytes') _cursor_bytes = self._urlsafe_to_bytes(urlsafe) if _cursor_bytes is not None: self.__cursor_bytes = _cursor_bytes else: self.__cursor_bytes = six.binary_type() def __repr__(self): arg = six.ensure_str(self.to_websafe_string()) if arg: arg = '<%s>' % arg return '%s(%s)' % (self.__class__.__name__, arg) def reversed(self): """DEPRECATED. It is no longer necessary to call reversed() on cursors. A cursor returned by a query may also be used in a query whose sort order has been reversed. This method returns a copy of the original cursor. """ return Cursor(_cursor_bytes=self.__cursor_bytes) def to_bytes(self): """Serialize cursor as a byte string.""" return self.__cursor_bytes @staticmethod def from_bytes(cursor): """Gets a Cursor given its byte string serialized form. The serialized form of a cursor may change in a non-backwards compatible way. In this case cursors must be regenerated from a new Query request. Args: cursor: A serialized cursor as returned by .to_bytes. Returns: A Cursor. Raises: datastore_errors.BadValueError if the cursor argument does not represent a serialized cursor. """ return Cursor(_cursor_bytes=cursor) def urlsafe(self): """Serialize cursor as a websafe string. Returns: A base64-encoded serialized cursor. """ return base64.urlsafe_b64encode(self.to_bytes()) to_websafe_string = urlsafe @staticmethod def from_websafe_string(cursor): """Gets a Cursor given its websafe serialized form. The serialized form of a cursor may change in a non-backwards compatible way. In this case cursors must be regenerated from a new Query request. Args: cursor: A serialized cursor as returned by .to_websafe_string. Returns: A Cursor. Raises: datastore_errors.BadValueError if the cursor argument is not a string type of does not represent a serialized cursor. """ decoded_bytes = Cursor._urlsafe_to_bytes(cursor) return Cursor.from_bytes(decoded_bytes) @staticmethod def _urlsafe_to_bytes(cursor): if not isinstance(cursor, six.string_types + (six.binary_type,)): raise datastore_errors.BadValueError( 'cursor argument should be str or unicode (%r)' % (cursor,)) try: decoded_bytes = base64.urlsafe_b64decode( six.ensure_binary(cursor, 'ascii')) except (ValueError, TypeError) as e: raise datastore_errors.BadValueError( 'Invalid cursor %s. Details: %s' % (cursor, e)) return decoded_bytes def advance(self, offset, query, conn): """Advances a Cursor by the given offset. Args: offset: The amount to advance the current query. query: A Query identical to the one this cursor was created from. conn: The datastore_rpc.Connection to use. Returns: A new cursor that is advanced by offset using the given query. """ datastore_types.ValidateInteger(offset, 'offset', datastore_errors.BadArgumentError) if not isinstance(query, Query): raise datastore_errors.BadArgumentError( 'query argument should be datastore_query.Query (%r)' % (query,)) query_options = QueryOptions( start_cursor=self, offset=offset, limit=0, produce_cursors=True) return query.run(conn, query_options).next_batch( Batcher.AT_LEAST_OFFSET).cursor(0) def __setstate__(self, state): if '_Cursor__compiled_cursor' in state: self.__cursor_bytes = state['_Cursor__compiled_cursor'].SerializeToString() else: self.__dict__ = state class _QueryKeyFilter(_BaseComponent): """A class that implements the key filters available on a Query.""" @datastore_rpc._positional(1) def __init__(self, app=None, namespace=None, kind=None, ancestor=None): """Constructs a _QueryKeyFilter. If app/namespace and ancestor are not defined, the app/namespace set in the environment is used. Args: app: a string representing the required app id or None. namespace: a string representing the required namespace or None. kind: a string representing the required kind or None. ancestor: a entity_pb2.Reference representing the required ancestor or None. Raises: datastore_erros.BadArgumentError if app and ancestor.app() do not match or an unexpected type is passed in for any argument. """ if kind is not None: datastore_types.ValidateString( kind, 'kind', datastore_errors.BadArgumentError) if ancestor is not None: if not isinstance(ancestor, entity_pb2.Reference): raise datastore_errors.BadArgumentError( 'ancestor argument should be entity_pb2.Reference (%r)' % (ancestor,)) ancestor_app = six.ensure_binary(ancestor.app) if app is None: app = ancestor_app elif six.ensure_binary(app) != ancestor_app: raise datastore_errors.BadArgumentError( 'ancestor argument should match app ("%r" != "%r")' % (ancestor.app, app)) ancestor_namespace = six.ensure_binary(ancestor.name_space) if namespace is None: namespace = ancestor_namespace elif six.ensure_binary(namespace) != ancestor_namespace: raise datastore_errors.BadArgumentError( 'ancestor argument should match namespace ("%r" != "%r")' % (six.ensure_binary(namespace), ancestor_namespace)) pb = entity_pb2.Reference() pb.CopyFrom(ancestor) ancestor = pb self.__ancestor = ancestor self.__path = list(ancestor.path.element) else: self.__ancestor = None self.__path = None super(_QueryKeyFilter, self).__init__() self.__app = six.ensure_text(datastore_types.ResolveAppId(app), 'utf-8') self.__namespace = ( six.ensure_text(datastore_types.ResolveNamespace(namespace), 'utf-8')) self.__kind = kind @property def app(self): return self.__app @property def namespace(self): return self.__namespace @property def kind(self): return self.__kind @property def ancestor(self): return self.__ancestor def __call__(self, entity_or_reference): """Apply the filter. Accepts either an entity or a reference to avoid the need to extract keys from entities when we have a list of entities (which is a common case). Args: entity_or_reference: Either an entity_pb2.EntityProto or entity_pb2.Reference. """ if isinstance(entity_or_reference, entity_pb2.Reference): key = entity_or_reference elif isinstance(entity_or_reference, entity_pb2.EntityProto): key = entity_or_reference.key else: raise datastore_errors.BadArgumentError( 'entity_or_reference argument must be an entity_pb2.EntityProto ' + six.ensure_str('or entity_pb2.Reference (%r)' % (entity_or_reference), 'utf-8')) return (six.ensure_text(key.app, 'utf-8') == self.__app and six.ensure_text(key.name_space, 'utf-8') == self.__namespace and (not self.__kind or key.path.element[-1].type == self.__kind) and (not self.__path or key.path.element[0:len(self.__path)] == self.__path)) def _to_pb(self): """Returns an internal pb representation.""" pb = datastore_pb.Query() pb.app = self.__app datastore_types.SetNamespace(pb, self.__namespace) if self.__kind is not None: pb.kind = self.__kind if self.__ancestor: ancestor = pb.ancestor ancestor.CopyFrom(self.__ancestor) return pb def _to_pb_v1(self, adapter): """Returns a v1 internal proto representation of the query key filter. Args: adapter: A datastore_rpc.AbstractAdapter. Returns: A tuple (googledatastore.RunQueryRequest, googledatastore.Filter). The second tuple value is a Filter representing the ancestor portion of the query. If there is no ancestor constraint, this value will be None """ pb = googledatastore.RunQueryRequest() partition_id = pb.partition_id partition_id.project_id = ( adapter.get_entity_converter().app_to_project_id(self.__app)) if self.__namespace: partition_id.namespace_id = self.__namespace if self.__kind is not None: pb.query.kind.add().name = self.__kind ancestor_filter = None if self.__ancestor: ancestor_filter = googledatastore.Filter() ancestor_prop_filter = ancestor_filter.property_filter ancestor_prop_filter.op = ( googledatastore.PropertyFilter.HAS_ANCESTOR) prop_pb = ancestor_prop_filter.property prop_pb.name = datastore_types.KEY_SPECIAL_PROPERTY adapter.get_entity_converter().v3_to_v1_key( self.ancestor, ancestor_prop_filter.value.key_value) return pb, ancestor_filter class _BaseQuery(_BaseComponent): """A base class for query implementations.""" def run(self, conn, query_options=None): """Runs the query using provided datastore_rpc.Connection. Args: conn: The datastore_rpc.Connection to use query_options: Optional query options to use Returns: A Batcher that implicitly fetches query results asynchronously. Raises: datastore_errors.BadArgumentError if any of the arguments are invalid. """ return Batcher(query_options, self.run_async(conn, query_options)) def run_async(self, conn, query_options=None): """Runs the query using the provided datastore_rpc.Connection. Args: conn: the datastore_rpc.Connection on which to run the query. query_options: Optional QueryOptions with which to run the query. Returns: An async object that can be used to grab the first Batch. Additional batches can be retrieved by calling Batch.next_batch/next_batch_async. Raises: datastore_errors.BadArgumentError if any of the arguments are invalid. """ raise NotImplementedError def __getstate__(self): raise pickle.PicklingError( 'Pickling of %r is unsupported.' % self) class Query(_BaseQuery): """An immutable class that represents a query signature. A query signature consists of a source of entities (specified as app, namespace and optionally kind and ancestor) as well as a FilterPredicate, grouping and a desired ordering. """ @datastore_rpc._positional(1) def __init__(self, app=None, namespace=None, kind=None, ancestor=None, filter_predicate=None, group_by=None, order=None, read_time_us=None): """Constructor. Args: app: Optional app to query, derived from the environment if not specified. namespace: Optional namespace to query, derived from the environment if not specified. kind: Optional kind to query. ancestor: Optional ancestor to query, an entity_pb2.Reference. filter_predicate: Optional FilterPredicate by which to restrict the query. group_by: Optional list of properties to group the results by. order: Optional Order in which to return results. read_time_us: Optional timestamp to read the storage from. Internal use only. Raises: datastore_errors.BadArgumentError if any argument is invalid. """ super(Query, self).__init__() if filter_predicate is not None and not isinstance(filter_predicate, FilterPredicate): raise datastore_errors.BadArgumentError( 'filter_predicate should be datastore_query.FilterPredicate (%r)' % (filter_predicate,)) if isinstance(order, CompositeOrder): if order.size() == 0: order = None elif isinstance(order, Order): order = CompositeOrder([order]) elif order is not None: raise datastore_errors.BadArgumentError( 'order should be Order (%r)' % (order,)) if group_by is not None: if isinstance(group_by, list): group_by = tuple(group_by) elif not isinstance(group_by, tuple): raise datastore_errors.BadArgumentError( 'group_by argument should be a list or tuple (%r)' % (group_by,)) if not group_by: raise datastore_errors.BadArgumentError( 'group_by argument cannot be empty') for prop in group_by: if not isinstance(prop, six.string_types + (six.binary_type,)): raise datastore_errors.BadArgumentError( 'group_by argument should contain only strings (%r)' % (prop,)) self._key_filter = _QueryKeyFilter(app=app, namespace=namespace, kind=kind, ancestor=ancestor) self._order = order self._filter_predicate = filter_predicate self._group_by = group_by self._read_time_us = read_time_us @property def app(self): return self._key_filter.app @property def namespace(self): return self._key_filter.namespace @property def kind(self): return self._key_filter.kind @property def ancestor(self): return self._key_filter.ancestor @property def filter_predicate(self): return self._filter_predicate @property def order(self): return self._order @property def group_by(self): return self._group_by @property def read_time_us(self): return self._read_time_us def __repr__(self): args = [] args.append('app=%r' % six.ensure_str(self.app)) ns = self.namespace if ns: args.append('namespace=%r' % six.ensure_str(ns)) kind = self.kind if kind is not None: args.append('kind=%r' % six.ensure_str(kind)) ancestor = self.ancestor if ancestor is not None: websafe = base64.urlsafe_b64encode(ancestor.SerializeToString()) args.append('ancestor=<%s>' % six.ensure_str(websafe)) filter_predicate = self.filter_predicate if filter_predicate is not None: args.append('filter_predicate=%r' % filter_predicate) order = self.order if order is not None: args.append('order=%r' % order) group_by = self.group_by if group_by is not None: args.append('group_by=%r' % (tuple(six.ensure_str(x) for x in group_by),)) read_time_us = self.read_time_us if read_time_us is not None: args.append('read_time_us=%r' % (read_time_us,)) return '%s(%s)' % (self.__class__.__name__, ', '.join(args)) def run_async(self, conn, query_options=None): if not isinstance(conn, datastore_rpc.BaseConnection): raise datastore_errors.BadArgumentError( 'conn should be a datastore_rpc.BaseConnection (%r)' % (conn,)) if not QueryOptions.is_configuration(query_options): query_options = QueryOptions(config=query_options) start_cursor = query_options.start_cursor if not start_cursor and query_options.produce_cursors: start_cursor = Cursor() if conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: req = self._to_pb_v1(conn, query_options) else: req = self._to_pb(conn, query_options) return Batch.create_async(self, query_options, conn, req, start_cursor=start_cursor) @classmethod def _from_pb(cls, query_pb): kind = query_pb.HasField('kind') and query_pb.kind or None ancestor = query_pb.HasField('ancestor') and query_pb.ancestor or None filter_predicate = None if query_pb.filter: filter_predicate = CompositeFilter( CompositeFilter.AND, [PropertyFilter._from_pb(filter_pb) for filter_pb in query_pb.filter]) order = None if query_pb.order: order = CompositeOrder( [PropertyOrder._from_pb(order_pb) for order_pb in query_pb.order]) group_by = None if query_pb.group_by_property_name: group_by = tuple( six.ensure_text(name) for name in query_pb.group_by_property_name) read_time_us = None if query_pb.HasField('read_time_us'): read_time_us = query_pb.read_time_us return Query( app=query_pb.app, namespace=query_pb.name_space, kind=kind, ancestor=ancestor, filter_predicate=filter_predicate, order=order, group_by=group_by, read_time_us=read_time_us) def _to_pb_v1(self, conn, query_options): """Returns a googledatastore.RunQueryRequest.""" v1_req, v1_ancestor_filter = self._key_filter._to_pb_v1(conn.adapter) v1_query = v1_req.query if self.filter_predicate: filter_predicate_pb = self._filter_predicate._to_pb_v1(conn.adapter) if self.filter_predicate and v1_ancestor_filter: comp_filter_pb = v1_query.filter.composite_filter comp_filter_pb.op = googledatastore.CompositeFilter.AND comp_filter_pb.filters.add().CopyFrom(filter_predicate_pb) comp_filter_pb.filters.add().CopyFrom(v1_ancestor_filter) elif self.filter_predicate: v1_query.filter.CopyFrom(filter_predicate_pb) elif v1_ancestor_filter: v1_query.filter.CopyFrom(v1_ancestor_filter) if self._order: for order in self._order._to_pb_v1(conn.adapter): v1_query.order.add().CopyFrom(order) if QueryOptions.keys_only(query_options, conn.config): prop_ref_pb = v1_query.projection.add().property prop_ref_pb.name = datastore_pbs.PROPERTY_NAME_KEY projection = QueryOptions.projection(query_options, conn.config) self._validate_projection_and_group_by(projection, self._group_by) if projection: for prop in projection: prop_ref_pb = v1_query.projection.add().property prop_ref_pb.name = prop if self._group_by: for group_by in self._group_by: v1_query.distinct_on.add().name = group_by limit = QueryOptions.limit(query_options, conn.config) if limit is not None: v1_query.limit.value = limit count = QueryOptions.batch_size(query_options, conn.config) if count is None: count = QueryOptions.prefetch_size(query_options, conn.config) if count is not None: pass if query_options.offset: v1_query.offset = query_options.offset if query_options.start_cursor is not None: v1_query.start_cursor = query_options.start_cursor.to_bytes() if query_options.end_cursor is not None: v1_query.end_cursor = query_options.end_cursor.to_bytes() conn._set_request_read_policy(v1_req, query_options) conn._set_request_transaction(v1_req) return v1_req def _to_pb(self, conn, query_options): """Returns the internal only pb representation.""" pb = self._key_filter._to_pb() if self._filter_predicate: for f in self._filter_predicate._to_pbs(): pb.filter.add().CopyFrom(f) if self._order: for order in self._order._to_pbs(): pb.order.add().CopyFrom(order) if QueryOptions.keys_only(query_options, conn.config): pb.keys_only = True projection = QueryOptions.projection(query_options, conn.config) self._validate_projection_and_group_by(projection, self._group_by) if projection: pb.property_name.extend(projection) if self._group_by: pb.group_by_property_name.extend(self._group_by) if QueryOptions.produce_cursors(query_options, conn.config): pb.compile = True limit = QueryOptions.limit(query_options, conn.config) if limit is not None: pb.limit = limit count = QueryOptions.prefetch_size(query_options, conn.config) if count is None: count = QueryOptions.batch_size(query_options, conn.config) if count is not None: pb.count = count if query_options.offset: pb.offset = query_options.offset if query_options.start_cursor is not None: try: pb.compiled_cursor.ParseFromString( query_options.start_cursor.to_bytes()) except message.DecodeError: raise datastore_errors.BadValueError('invalid cursor') if query_options.end_cursor is not None: try: pb.end_compiled_cursor.ParseFromString( query_options.end_cursor.to_bytes()) except message.DecodeError: raise datastore_errors.BadValueError('invalid cursor') if ((query_options.hint == QueryOptions.ORDER_FIRST and len(pb.order)) or (query_options.hint == QueryOptions.ANCESTOR_FIRST and pb.HasField('ancestor')) or (query_options.hint == QueryOptions.FILTER_FIRST and pb.filter)): pb.hint = query_options.hint if self.read_time_us is not None: pb.read_time_us = self.read_time_us conn._set_request_read_policy(pb, query_options) conn._set_request_transaction(pb) return pb def _validate_projection_and_group_by(self, projection, group_by): """Validates that a query's projection and group by match. Args: projection: A set of string property names in the projection. group_by: A set of string property names in the group by. Raises: datastore_errors.BadRequestError: if the projection and group by sets are not equal. """ if projection: if group_by: extra = set(projection) - set(group_by) if extra: raise datastore_errors.BadRequestError( 'projections includes properties not in the group_by argument: %s' % extra) elif group_by: raise datastore_errors.BadRequestError( 'cannot specify group_by without a projection') def apply_query(query, entities, _key=None): """Performs the given query on a set of in-memory results. This function can perform queries impossible in the datastore (e.g a query with multiple inequality filters on different properties) because all operations are done in memory. For queries that can also be executed on the the datastore, the results produced by this function may not use the same implicit ordering as the datastore. To ensure compatibility, explicit ordering must be used (e.g. 'ORDER BY ineq_prop, ..., __key__'). Order by __key__ should always be used when a consistent result is desired (unless there is a sort order on another globally unique property). Args: query: a datastore_query.Query to apply entities: a list of results, of arbitrary type, on which to apply the query. _key: a function that takes an element of the result array as an argument and must return an entity_pb2.EntityProto. If not specified, the identity function is used (and entities must be a list of entity_pb2.EntityProto). Returns: A subset of entities, filtered and ordered according to the query. """ if not isinstance(query, Query): raise datastore_errors.BadArgumentError( 'query argument must be a datastore_query.Query (%r)' % (query,)) if not isinstance(entities, list): raise datastore_errors.BadArgumentError( 'entities argument must be a list (%r)' % (entities,)) key = _key or (lambda x: x) filtered_results = [r for r in entities if query._key_filter(key(r))] if not query._order: if query._filter_predicate: return [r for r in filtered_results if query._filter_predicate(key(r))] return filtered_results names = query._order._get_prop_names() if query._filter_predicate: names |= query._filter_predicate._get_prop_names() exists_filter = _PropertyExistsFilter(names) value_maps = [] for result in filtered_results: value_map = _make_key_value_map(key(result), names) if exists_filter._apply(value_map) and ( not query._filter_predicate or query._filter_predicate._prune(value_map)): value_map['__result__'] = result value_maps.append(value_map) value_maps.sort(key=functools.cmp_to_key(query._order._cmp)) return [value_map['__result__'] for value_map in value_maps] class _AugmentedQuery(_BaseQuery): """A query that combines a datastore query with in-memory filters/results.""" @datastore_rpc._positional(2) def __init__(self, query, in_memory_results=None, in_memory_filter=None, max_filtered_count=None): """Constructor for _AugmentedQuery. Do not call directly. Use the utility functions instead (e.g. datastore_query.inject_results) Args: query: A datastore_query.Query object to augment. in_memory_results: a list of pre- sorted and filtered result to add to the stream of datastore results or None . in_memory_filter: a set of in-memory filters to apply to the datastore results or None. max_filtered_count: the maximum number of datastore entities that will be filtered out by in_memory_filter if known. """ if not isinstance(query, Query): raise datastore_errors.BadArgumentError( 'query argument should be datastore_query.Query (%r)' % (query,)) if (in_memory_filter is not None and not isinstance(in_memory_filter, FilterPredicate)): raise datastore_errors.BadArgumentError( 'in_memory_filter argument should be ' + six.ensure_str( 'datastore_query.FilterPredicate (%r)' % (in_memory_filter,), 'utf-8')) if (in_memory_results is not None and not isinstance(in_memory_results, list)): raise datastore_errors.BadArgumentError( 'in_memory_results argument should be a list of' + six.ensure_str('datastore_pv.EntityProto (%r)' % (in_memory_results,), 'utf-8')) datastore_types.ValidateInteger(max_filtered_count, 'max_filtered_count', empty_ok=True, zero_ok=True) self._query = query self._max_filtered_count = max_filtered_count self._in_memory_filter = in_memory_filter self._in_memory_results = in_memory_results @property def app(self): return self._query._key_filter.app @property def namespace(self): return self._query._key_filter.namespace @property def kind(self): return self._query._key_filter.kind @property def ancestor(self): return self._query._key_filter.ancestor @property def filter_predicate(self): return self._query._filter_predicate @property def order(self): return self._query._order @property def group_by(self): return self._query._group_by def run_async(self, conn, query_options=None): if not isinstance(conn, datastore_rpc.BaseConnection): raise datastore_errors.BadArgumentError( 'conn should be a datastore_rpc.BaseConnection (%r)' % (conn,)) if not QueryOptions.is_configuration(query_options): query_options = QueryOptions(config=query_options) if self._query._order: changes = {'keys_only': False} else: changes = {} if self._in_memory_filter or self._in_memory_results: in_memory_offset = query_options.offset in_memory_limit = query_options.limit if in_memory_limit is not None: if self._in_memory_filter is None: changes['limit'] = in_memory_limit elif self._max_filtered_count is not None: changes['limit'] = in_memory_limit + self._max_filtered_count else: changes['limit'] = None if in_memory_offset: changes['offset'] = None if changes.get('limit', None) is not None: changes['limit'] += in_memory_offset else: in_memory_offset = None else: in_memory_offset = None in_memory_limit = None modified_query_options = QueryOptions(config=query_options, **changes) if conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: req = self._query._to_pb_v1(conn, modified_query_options) else: req = self._query._to_pb(conn, modified_query_options) start_cursor = query_options.start_cursor if not start_cursor and query_options.produce_cursors: start_cursor = Cursor() return _AugmentedBatch.create_async(self, modified_query_options, conn, req, in_memory_offset=in_memory_offset, in_memory_limit=in_memory_limit, start_cursor=start_cursor) @datastore_rpc._positional(1) def inject_results(query, updated_entities=None, deleted_keys=None): """Creates a query object that will inject changes into results. Args: query: The datastore_query.Query to augment updated_entities: A list of entity_pb2.EntityProto's that have been updated and should take priority over any values returned by query. deleted_keys: A list of entity_pb2.Reference's for entities that have been deleted and should be removed from query results. Returns: A datastore_query.AugmentedQuery if in memory filtering is required, query otherwise. """ if not isinstance(query, Query): raise datastore_errors.BadArgumentError( 'query argument should be datastore_query.Query (%r)' % (query,)) overridden_keys = set() if deleted_keys is not None: if not isinstance(deleted_keys, list): raise datastore_errors.BadArgumentError( 'deleted_keys argument must be a list (%r)' % (deleted_keys,)) deleted_keys = list(six.moves.filter(query._key_filter, deleted_keys)) for key in deleted_keys: overridden_keys.add(datastore_types.ReferenceToKeyValue(key)) if updated_entities is not None: if not isinstance(updated_entities, list): raise datastore_errors.BadArgumentError( 'updated_entities argument must be a list (%r)' % (updated_entities,)) updated_entities = list( six.moves.filter(query._key_filter, updated_entities)) for entity in updated_entities: overridden_keys.add(datastore_types.ReferenceToKeyValue(entity.key)) updated_entities = apply_query(query, updated_entities) else: updated_entities = [] if not overridden_keys: return query return _AugmentedQuery(query, in_memory_filter=_IgnoreFilter(overridden_keys), in_memory_results=updated_entities, max_filtered_count=len(overridden_keys)) class _BatchShared(object): """Data shared among the batches of a query.""" def __init__(self, query, query_options, conn, augmented_query=None, initial_offset=None): self.__query = query self.__query_options = query_options self.__conn = conn self.__augmented_query = augmented_query self.__was_first_result_processed = False if initial_offset is None: initial_offset = query_options.offset or 0 self.__expected_offset = initial_offset self.__remaining_limit = query_options.limit @property def query(self): return self.__query @property def query_options(self): return self.__query_options @property def conn(self): return self.__conn @property def augmented_query(self): return self.__augmented_query @property def keys_only(self): return self.__keys_only @property def compiled_query(self): return self.__compiled_query @property def expected_offset(self): return self.__expected_offset @property def remaining_limit(self): return self.__remaining_limit @property def index_list(self): """Returns the list of indexes used by the query. Possibly None when the adapter does not implement pb_to_index. """ return self.__index_list def process_batch(self, batch): if self.conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: skipped_results = batch.skipped_results num_results = len(batch.entity_results) else: skipped_results = batch.skipped_results num_results = len(batch.result) self.__expected_offset -= skipped_results if self.__remaining_limit is not None: self.__remaining_limit -= num_results if not self.__was_first_result_processed: self.__was_first_result_processed = True if self.conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: result_type = batch.entity_result_type self.__keys_only = result_type == googledatastore.EntityResult.KEY_ONLY self.__compiled_query = None self.__index_list = None else: self.__keys_only = batch.keys_only if batch.HasField('compiled_query'): self.__compiled_query = batch.compiled_query else: self.__compiled_query = None try: self.__index_list = [ self.__conn.adapter.pb_to_index(index_pb) for index_pb in batch.index ] except NotImplementedError: self.__index_list = None class Batch(object): """A batch of results returned by a query. This class contains a batch of results returned from the datastore and relevant metadata. This metadata includes: query: The query that produced this batch query_options: The QueryOptions used to run the query. This does not contained any options passed to the .next_batch() call that created the current batch. start_cursor, end_cursor: These are the cursors that can be used with a query to re-fetch this batch. They can also be used to find all entities before or after the given batch (by use start_cursor as an end cursor or vice versa). start_cursor can also be advanced to point to a position within the batch using Cursor.advance(). skipped_results: the number of result skipped because of the offset given to the request that generated it. This can be set either on the original Query.run() request or in subsequent .next_batch() calls. more_results: If this is true there are more results that can be retrieved either by .next_batch() or Batcher.next(). This class is also able to fetch the next batch of the query using .next_batch(). As batches of results must be fetched serially, .next_batch() can only be called once. Additional calls to .next_batch() will return None. When there are no more batches .next_batch() will return None as well. Note that batches returned by iterating over Batcher will always return None for .next_batch() as the Bather handles fetching the next batch automatically. A Batch typically represents the result of a single RPC request. The datastore operates on a "best effort" basis so the batch returned by .next_batch() or Query.run_async().get_result() may not have satisfied the requested offset or number of results (specified through FetchOptions.offset and FetchOptions.batch_size respectively). To satisfy these restrictions additional batches may be needed (with FetchOptions that specify the remaining offset or results needed). The Batcher class hides these limitations. """ __skipped_cursor = None __end_cursor = None @classmethod @datastore_rpc._positional(5) def create_async(cls, query, query_options, conn, req, start_cursor): batch_shared = _BatchShared(query, query_options, conn) batch0 = cls(batch_shared, start_cursor=start_cursor) return batch0._make_query_rpc_call(query_options, req) @datastore_rpc._positional(2) def __init__(self, batch_shared, start_cursor=Cursor()): """Constructor. This class is constructed in stages (one when an RPC is sent and another when an rpc is completed) and should not be constructed directly!! Use Query.run_async().get_result() to create a Batch or Query.run() to use a batcher. This constructor does not perform verification. Args: batch_shared: Data shared between batches for a a single query run. start_cursor: Optional cursor pointing before this batch. """ self._batch_shared = batch_shared self.__start_cursor = start_cursor @property def query_options(self): """The QueryOptions used to retrieve the first batch.""" return self._batch_shared.query_options @property def query(self): """The query the current batch came from.""" return self._batch_shared.query @property def results(self): """A list of entities in this batch.""" return self.__results @property def keys_only(self): """Whether the entities in this batch only contain keys.""" return self._batch_shared.keys_only @property def index_list(self): """Returns the list of indexes used to peform this batch's query. Possibly None when the adapter does not implement pb_to_index. """ return self._batch_shared.index_list @property def start_cursor(self): """A cursor that points to the position just before the current batch.""" return self.__start_cursor @property def end_cursor(self): """A cursor that points to the position just after the current batch.""" return self.__end_cursor @property def skipped_results(self): """The number of results skipped because of an offset in the request. An offset is satisfied before any results are returned. The start_cursor points to the position in the query before the skipped results. """ return self._skipped_results @property def more_results(self): """Whether more results can be retrieved from the query.""" return self.__more_results def next_batch(self, fetch_options=None): """Synchronously get the next batch or None if there are no more batches. Args: fetch_options: Optional fetch options to use when fetching the next batch. Merged with both the fetch options on the original call and the connection. Returns: A new Batch of results or None if either the next batch has already been fetched or there are no more results. """ async_ = self.next_batch_async(fetch_options) if async_ is None: return None return async_.get_result() def _compiled_query(self): return self._batch_shared.compiled_query def cursor(self, index): """Gets the cursor that points just after the result at index - 1. The index is relative to first result in .results. Since start_cursor points to the position before the first skipped result, the range of indexes this function supports is limited to [-skipped_results, len(results)]. For example, using start_cursor=batch.cursor(i) and end_cursor=batch.cursor(j) will return the results found in batch.results[i:j]. Note that any result added in the range (i-1, j] will appear in the new query's results. Warning: Any index in the range (-skipped_results, 0) may cause continuation to miss or duplicate results if outside a transaction. Args: index: An int, the index relative to the first result before which the cursor should point. Returns: A Cursor that points to a position just after the result index - 1, which if used as a start_cursor will cause the first result to be batch.result[index]. """ if not isinstance(index, six.integer_types): raise datastore_errors.BadArgumentError( 'index argument should be an integer (%r)' % (index,)) if not -self._skipped_results <= index <= len(self.__results): raise datastore_errors.BadArgumentError( 'index argument must be in the inclusive range [%d, %d]' % (-self._skipped_results, len(self.__results))) if index == -self._skipped_results: return self.__start_cursor elif (index == 0 and self.__skipped_cursor): return self.__skipped_cursor elif index > 0 and self.__result_cursors: return self.__result_cursors[index - 1] elif index == len(self.__results): return self.__end_cursor else: return self.__start_cursor.advance(index + self._skipped_results, self._batch_shared.query, self._batch_shared.conn) def next_batch_async(self, fetch_options=None): """Asynchronously get the next batch or None if there are no more batches. Args: fetch_options: Optional fetch options to use when fetching the next batch. Merged with both the fetch options on the original call and the connection. Returns: An async object that can be used to get the next Batch or None if either the next batch has already been fetched or there are no more results. """ if not self.__datastore_cursor: return None fetch_options, next_batch = self._make_next_batch(fetch_options) if (fetch_options is not None and not FetchOptions.is_configuration(fetch_options)): raise datastore_errors.BadArgumentError('Invalid fetch options.') config = self._batch_shared.query_options.merge(fetch_options) conn = next_batch._batch_shared.conn requested_offset = 0 if fetch_options is not None and fetch_options.offset is not None: requested_offset = fetch_options.offset if conn._api_version == datastore_rpc._CLOUD_DATASTORE_V1: if self._batch_shared.expected_offset != requested_offset: raise datastore_errors.BadArgumentError( 'Cannot request the next batch with a different offset than ' ' expected. Expected: %s, Got: %s.' % (self._batch_shared.expected_offset, requested_offset)) limit = self._batch_shared.remaining_limit next_options = QueryOptions(offset=self._batch_shared.expected_offset, limit=limit, start_cursor=self.__datastore_cursor) config = config.merge(next_options) result = next_batch._make_query_rpc_call( config, self._batch_shared.query._to_pb_v1(conn, config)) else: result = next_batch._make_next_rpc_call(config, self._to_pb(fetch_options)) self.__datastore_cursor = None return result def _to_pb(self, fetch_options=None): req = datastore_pb.NextRequest() if FetchOptions.produce_cursors(fetch_options, self._batch_shared.query_options, self._batch_shared.conn.config): req.compile = True count = FetchOptions.batch_size(fetch_options, self._batch_shared.query_options, self._batch_shared.conn.config) if count is not None: req.count = count if fetch_options is not None and fetch_options.offset: req.offset = fetch_options.offset req.cursor.CopyFrom(self.__datastore_cursor) return req def _extend(self, next_batch): """Combines the current batch with the next one. Called by batcher.""" self.__datastore_cursor = next_batch.__datastore_cursor next_batch.__datastore_cursor = None self.__more_results = next_batch.__more_results if not self.__results: self.__skipped_cursor = next_batch.__skipped_cursor self.__results.extend(next_batch.__results) self.__result_cursors.extend(next_batch.__result_cursors) self.__end_cursor = next_batch.__end_cursor self._skipped_results += next_batch._skipped_results def _make_query_rpc_call(self, config, req): """Makes a RunQuery call that will modify the instance. Args: config: The datastore_rpc.Configuration to use for the call. req: The request to send with the call. Returns: A UserRPC object that can be used to fetch the result of the RPC. """ _api_version = self._batch_shared.conn._api_version if _api_version == datastore_rpc._CLOUD_DATASTORE_V1: return self._batch_shared.conn._make_rpc_call( config, 'RunQuery', req, googledatastore.RunQueryResponse(), self.__v1_run_query_response_hook) return self._batch_shared.conn._make_rpc_call(config, 'RunQuery', req, datastore_pb.QueryResult(), self.__query_result_hook) def _make_next_rpc_call(self, config, req): """Makes a Next call that will modify the instance. Args: config: The datastore_rpc.Configuration to use for the call. req: The request to send with the call. Returns: A UserRPC object that can be used to fetch the result of the RPC. """ return self._batch_shared.conn._make_rpc_call(config, 'Next', req, datastore_pb.QueryResult(), self.__query_result_hook) _need_index_header = 'The suggested index for this query is:' def __v1_run_query_response_hook(self, rpc): try: self._batch_shared.conn.check_rpc_success(rpc) except datastore_errors.NeedIndexError: raise batch = rpc.response.batch self._batch_shared.process_batch(batch) if batch.skipped_cursor: self.__skipped_cursor = Cursor(_cursor_bytes=batch.skipped_cursor) self.__result_cursors = [Cursor(_cursor_bytes=result.cursor) for result in batch.entity_results if result.cursor] if batch.end_cursor: self.__end_cursor = Cursor(_cursor_bytes=batch.end_cursor) self._skipped_results = batch.skipped_results if batch.more_results == googledatastore.QueryResultBatch.NOT_FINISHED: self.__more_results = True self.__datastore_cursor = self.__end_cursor or self.__skipped_cursor if self.__datastore_cursor == self.__start_cursor: raise datastore_errors.Timeout( 'The query was not able to make progress.') else: self._end() self.__results = self._process_v1_results(batch.entity_results) return self def __query_result_hook(self, rpc): """Internal method used as get_result_hook for RunQuery/Next operation.""" try: self._batch_shared.conn.check_rpc_success(rpc) except datastore_errors.NeedIndexError as exc: if isinstance(rpc.request, datastore_pb.Query): _, kind, ancestor, props = datastore_index.CompositeIndexForQuery( rpc.request) props = datastore_index.GetRecommendedIndexProperties(props) yaml = datastore_index.IndexYamlForQuery(kind, ancestor, props) xml = datastore_index.IndexXmlForQuery(kind, ancestor, props) raise datastore_errors.NeedIndexError( '\n'.join([str(exc), self._need_index_header, yaml]), original_message=str(exc), header=self._need_index_header, yaml_index=yaml, xml_index=xml) raise query_result = rpc.response self._batch_shared.process_batch(query_result) if query_result.HasField('skipped_results_compiled_cursor'): self.__skipped_cursor = Cursor( _cursor_bytes=query_result.skipped_results_compiled_cursor .SerializeToString()) self.__result_cursors = [ Cursor(_cursor_bytes=result.SerializeToString()) for result in query_result.result_compiled_cursor ] if query_result.HasField('compiled_cursor'): self.__end_cursor = Cursor( _cursor_bytes=query_result.compiled_cursor.SerializeToString()) self._skipped_results = query_result.skipped_results if query_result.more_results: self.__datastore_cursor = query_result.cursor self.__more_results = True else: self._end() self.__results = self._process_results(query_result.result) return self def _end(self): """Changes the internal state so that no more batches can be produced.""" self.__datastore_cursor = None self.__more_results = False def _make_next_batch(self, fetch_options): """Creates the object to store the next batch. Args: fetch_options: The datastore_query.FetchOptions passed in by the user or None. Returns: A tuple containing the fetch options that should be used internally and the object that should be used to contain the next batch. """ return fetch_options, Batch(self._batch_shared, start_cursor=self.__end_cursor) def _process_results(self, results): """Converts the datastore results into results returned to the user. Args: results: A list of entity_pb2.EntityProto's returned by the datastore Returns: A list of results that should be returned to the user. """ converter = self._batch_shared.conn.adapter.pb_to_query_result return [converter(result, self._batch_shared.query_options) for result in results] def _process_v1_results(self, results): """Converts the datastore results into results returned to the user. Args: results: A list of googledatastore.EntityResults. Returns: A list of results that should be returned to the user. """ converter = self._batch_shared.conn.adapter.pb_v1_to_query_result return [converter(result.entity, self._batch_shared.query_options) for result in results] def __getstate__(self): raise pickle.PicklingError( 'Pickling of datastore_query.Batch is unsupported.') class _AugmentedBatch(Batch): """A batch produced by a datastore_query._AugmentedQuery.""" @classmethod @datastore_rpc._positional(5) def create_async(cls, augmented_query, query_options, conn, req, in_memory_offset, in_memory_limit, start_cursor): initial_offset = 0 if in_memory_offset is not None else None batch_shared = _BatchShared(augmented_query._query, query_options, conn, augmented_query, initial_offset=initial_offset) batch0 = cls(batch_shared, in_memory_offset=in_memory_offset, in_memory_limit=in_memory_limit, start_cursor=start_cursor) return batch0._make_query_rpc_call(query_options, req) @datastore_rpc._positional(2) def __init__(self, batch_shared, in_memory_offset=None, in_memory_limit=None, next_index=0, start_cursor=Cursor()): """A Constructor for datastore_query._AugmentedBatch. Constructed by datastore_query._AugmentedQuery. Should not be called directly. """ super(_AugmentedBatch, self).__init__(batch_shared, start_cursor=start_cursor) self.__in_memory_offset = in_memory_offset self.__in_memory_limit = in_memory_limit self.__next_index = next_index @property def query(self): """The query the current batch came from.""" return self._batch_shared.augmented_query def cursor(self, index): raise NotImplementedError def _extend(self, next_batch): super(_AugmentedBatch, self)._extend(next_batch) self.__in_memory_limit = next_batch.__in_memory_limit self.__in_memory_offset = next_batch.__in_memory_offset self.__next_index = next_batch.__next_index def _process_v1_results(self, results): """Process V4 results by converting to V3 and calling _process_results.""" v3_results = [] is_projection = bool(self.query_options.projection) for v1_result in results: v3_entity = entity_pb2.EntityProto() self._batch_shared.conn.adapter.get_entity_converter().v1_to_v3_entity( v1_result.entity, v3_entity, is_projection) v3_results.append(v3_entity) return self._process_results(v3_results) def _process_results(self, results): in_memory_filter = self._batch_shared.augmented_query._in_memory_filter if in_memory_filter: results = list(filter(in_memory_filter, results)) in_memory_results = self._batch_shared.augmented_query._in_memory_results if in_memory_results and self.__next_index < len(in_memory_results): original_query = super(_AugmentedBatch, self).query if original_query._order: if results: next_result = in_memory_results[self.__next_index] next_key = original_query._order.key(next_result) i = 0 while i < len(results): result = results[i] result_key = original_query._order.key(result) while next_key <= result_key: results.insert(i, next_result) i += 1 self.__next_index += 1 if self.__next_index >= len(in_memory_results): break next_result = in_memory_results[self.__next_index] next_key = original_query._order.key(next_result) i += 1 elif results or not super(_AugmentedBatch, self).more_results: results = in_memory_results + results self.__next_index = len(in_memory_results) if self.__in_memory_offset: assert not self._skipped_results offset = min(self.__in_memory_offset, len(results)) if offset: self._skipped_results += offset self.__in_memory_offset -= offset results = results[offset:] if self.__in_memory_limit is not None: results = results[:self.__in_memory_limit] self.__in_memory_limit -= len(results) if self.__in_memory_limit <= 0: self._end() return super(_AugmentedBatch, self)._process_results(results) def _make_next_batch(self, fetch_options): in_memory_offset = FetchOptions.offset(fetch_options) augmented_query = self._batch_shared.augmented_query if in_memory_offset and (augmented_query._in_memory_filter or augmented_query._in_memory_results): fetch_options = FetchOptions(offset=0) else: in_memory_offset = None return (fetch_options, _AugmentedBatch(self._batch_shared, in_memory_offset=in_memory_offset, in_memory_limit=self.__in_memory_limit, start_cursor=self.end_cursor, next_index=self.__next_index)) class Batcher(object): """A class that implements the Iterator interface for Batches. Typically constructed by a call to Query.run(). The class hides the "best effort" nature of the datastore by potentially making multiple requests to the datastore and merging the resulting batches. This is accomplished efficiently by prefetching results and mixing both non-blocking and blocking calls to the datastore as needed. Iterating through batches is almost always more efficient than pulling all results at once as RPC latency is hidden by asynchronously prefetching results. The batches produce by this class cannot be used to fetch the next batch (through Batch.next_batch()) as before the current batch is returned the request for the next batch has already been sent. """ ASYNC_ONLY = None AT_LEAST_OFFSET = 0 AT_LEAST_ONE = object() def __init__(self, query_options, first_async_batch): """Constructor. Although this class can be manually constructed, it is preferable to use Query.run(query_options). Args: query_options: The QueryOptions used to create the first batch. first_async_batch: The first batch produced by Query.run_async(query_options). """ self.__next_batch = first_async_batch self.__initial_offset = QueryOptions.offset(query_options) or 0 self.__skipped_results = 0 def next(self): """Get the next batch. See .next_batch().""" return self.next_batch(self.AT_LEAST_ONE) def __next__(self): return self.next() def next_batch(self, min_batch_size): """Get the next batch. The batch returned by this function cannot be used to fetch the next batch (through Batch.next_batch()). Instead this function will always return None. To retrieve the next batch use .next() or .next_batch(N). This function may return a batch larger than min_to_fetch, but will never return smaller unless there are no more results. Special values can be used for min_batch_size: ASYNC_ONLY - Do not perform any synchrounous fetches from the datastore even if the this produces a batch with no results. AT_LEAST_OFFSET - Only pull enough results to satifiy the offset. AT_LEAST_ONE - Pull batches until at least one result is returned. Args: min_batch_size: The minimum number of results to retrieve or one of (ASYNC_ONLY, AT_LEAST_OFFSET, AT_LEAST_ONE) Returns: The next Batch of results. """ if min_batch_size in (Batcher.ASYNC_ONLY, Batcher.AT_LEAST_OFFSET, Batcher.AT_LEAST_ONE): exact = False else: exact = True datastore_types.ValidateInteger(min_batch_size, 'min_batch_size', datastore_errors.BadArgumentError) if not self.__next_batch: raise StopIteration batch = self.__next_batch.get_result() self.__next_batch = None self.__skipped_results += batch.skipped_results if min_batch_size is not Batcher.ASYNC_ONLY: if min_batch_size is Batcher.AT_LEAST_ONE: min_batch_size = 1 needed_results = min_batch_size - len(batch.results) while (batch.more_results and (self.__skipped_results < self.__initial_offset or needed_results > 0)): if batch.query_options.batch_size: batch_size = max(batch.query_options.batch_size, needed_results) elif exact: batch_size = needed_results else: batch_size = None self.__next_batch = batch.next_batch_async(FetchOptions( offset=max(0, self.__initial_offset - self.__skipped_results), batch_size=batch_size)) next_batch = self.__next_batch.get_result() self.__next_batch = None self.__skipped_results += next_batch.skipped_results needed_results = max(0, needed_results - len(next_batch.results)) batch._extend(next_batch) self.__next_batch = batch.next_batch_async() return batch def __getstate__(self): raise pickle.PicklingError( 'Pickling of datastore_query.Batcher is unsupported.') def __iter__(self): return self class ResultsIterator(six.Iterator): """An iterator over the results from Batches obtained from a Batcher. ResultsIterator implements Python's iterator protocol, so results can be accessed with the for-statement: > it = ResultsIterator(Query(kind='Person').run()) > for person in it: > print 'Hi, %s!' % person['name'] At any time ResultsIterator.cursor() can be used to grab the Cursor that points just after the last result returned by the iterator. """ __current_batch = None __current_pos = 0 __last_cursor = None def __init__(self, batcher): """Constructor. Args: batcher: A datastore_query.Batcher """ if not isinstance(batcher, Batcher): raise datastore_errors.BadArgumentError( 'batcher argument should be datastore_query.Batcher (%r)' % (batcher,)) self.__batcher = batcher def index_list(self): """Returns the list of indexes used to perform the query. Possibly None when the adapter does not implement pb_to_index. """ return self._ensure_current_batch().index_list def cursor(self): """Returns a cursor that points just after the last result returned. If next() throws an exception, this function returns the end_cursor from the last successful batch or throws the same exception if no batch was successful. """ return (self.__last_cursor or self._ensure_current_batch().cursor(self.__current_pos)) def _ensure_current_batch(self): if not self.__current_batch: self.__current_batch = self.__batcher.next_batch(Batcher.AT_LEAST_OFFSET) self.__current_pos = 0 return self.__current_batch def _compiled_query(self): """Returns the compiled query associated with the iterator. Internal only do not use. """ return self._ensure_current_batch()._compiled_query() def __next__(self): """Returns the next query result.""" while (not self.__current_batch or self.__current_pos >= len(self.__current_batch.results)): try: next_batch = self.__batcher.next_batch(Batcher.AT_LEAST_OFFSET) except: if self.__current_batch: self.__last_cursor = self.__current_batch.end_cursor raise self.__current_pos = 0 self.__current_batch = next_batch result = self.__current_batch.results[self.__current_pos] self.__current_pos += 1 return result def __iter__(self): return self def next(self): return self.__next__()

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hw1/0711529_hw1.py

KJ-black/Introduction-to-Operating-Systems

0

77150

<reponame>KJ-black/Introduction-to-Operating-Systems """ Date: 2021/01/02 Describe: OS hw1 Auther: <NAME> ID: 0711529 """ import time # to calculate the running time ## for task1 import hashlib ## for task2 from bs4 import BeautifulSoup import requests ## multithreading import threading import queue class MyThread(threading.Thread): def __init__(self, queue, num, task): threading.Thread.__init__(self) self.queue = queue self.num = num self.task = task def run(self): while self.queue.qsize() > 0: msg = self.queue.get() if self.task==1: proof_of_work(msg) elif self.task==2: get_url_header(msg) def multithreading(task, number, testcase, taskdata): # create the queue to put the unworking task in it my_queue = queue.Queue() # put the data to queue # and the max data size is 100 for i in range(testcase): my_queue.put(taskdata[i]) # create the thread and start threads = [] for i in range(number): threads.append(MyThread(my_queue, i, task)) threads[i].start() # wait for the tthread to finish for i in range(number): threads[i].join() ## multiprocessing from multiprocessing import Pool def multiprocessing(task, number, testcase, taskdata): pool = Pool(number) if task == 1: pool.map(proof_of_work, taskdata[:testcase]) elif task == 2: pool.map(get_url_header, taskdata[:testcase]) pool.close() pool.join() ## coroutine import asyncio async def co_proof_of_work(str, loop): await loop.run_in_executor(None, proof_of_work, str) async def co_get_url_header(url, loop): html = await loop.run_in_executor(None, requests.get, url) soup = BeautifulSoup(html.text, 'html.parser') title = soup.head.title.text print(title) def coroutine(task, testcase, taskdata): loop = asyncio.get_event_loop() if task == 1: tasks = [loop.create_task(co_proof_of_work(t, loop)) for t in taskdata[:testcase]] elif task == 2: tasks = [loop.create_task(co_get_url_header(t, loop)) for t in taskdata[:testcase]] loop.run_until_complete(asyncio.wait(tasks)) ## task # task 1 def proof_of_work(str): a = b = c = d = 33 # initialize while True: for e in range(33, 127): s = hashlib.sha256() str2 = chr(a)+chr(b)+chr(c)+chr(d)+chr(e)+str s.update(str2.encode('utf-8')) h = s.hexdigest() if h[:5]=="00000": print(str2) return str2 d += 1 if d > 126: c += 1 d = 33 if c > 126: b += 1 c = 33 if b > 126: a += 1 b = 33 if a > 126: break # task 2 def get_url_header(url): html = requests.get(url) soup = BeautifulSoup(html.text, 'html.parser') title = soup.head.title.text print(title) return title def debug(task, testcase, taskdata): if task == 1: for i in range(testcase): proof_of_work(taskdata[i]) elif task == 2: for i in range(testcase): get_url_header(taskdata[i]) def main(): task = int(input()) # input the task you want to execute: 1 or 2 m = input() # input the method: 1: multithreading, 2: multiprocessing, 3: coroutine m = m.split() if(len(m)==2): number = int(m[1]) method = int(m[0]) testcase = int(input()) # taskdata file = open("task%d_sample.txt"%task) taskdata = [] for line in file: taskdata.append(line.replace('\n', "")) start = time.perf_counter() if method == 1: # use multithreading multithreading(task, number, testcase, taskdata) elif method == 2: # use multiprocessing multiprocessing(task, number, testcase, taskdata) elif method == 3: # use coroutine coroutine(task, testcase, taskdata) elif method == 4: # for debug debug(task, testcase, taskdata) end = time.perf_counter() print("Total running time: %f"%(end-start)) if __name__=="__main__": main()

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torch-test/mpich-3.4.3/modules/libfabric/contrib/intel/jenkins/runtests.py

alchemy315/NoPFS

0

22305

import argparse import os import sys sys.path.append(os.environ['CI_SITE_CONFIG']) import ci_site_config import run import common parser = argparse.ArgumentParser() parser.add_argument("--prov", help="core provider", choices=["psm2", "verbs", \ "tcp", "udp", "sockets", "shm"]) parser.add_argument("--util", help="utility provider", choices=["rxd", "rxm"]) parser.add_argument("--ofi_build_mode", help="specify the build configuration", \ choices = ["dbg", "dl"]) args = parser.parse_args() args_core = args.prov args_util = args.util if (args.ofi_build_mode): ofi_build_mode = args.ofi_build_mode else: ofi_build_mode='reg' node = (os.environ['NODE_NAME']).split('-')[0] hosts = [node] # Note: Temporarily disabling all mpich testing # due to mpich options issues which is causing # multiple tests to fail. #mpilist = ['impi', 'mpich', 'ompi'] mpilist = ['impi', 'ompi'] #this script is executed from /tmp #this is done since some mpi tests #look for a valid location before running # the test on the secondary host(client) # but jenkins only creates a valid path on # the primary host (server/test node) os.chdir('/tmp/') if(args_core): for host in ci_site_config.node_map[node]: hosts.append(host) if (args_util == None): run.fi_info_test(args_core, hosts, ofi_build_mode) run.fabtests(args_core, hosts, ofi_build_mode) run.shmemtest(args_core, hosts, ofi_build_mode) for mpi in mpilist: run.intel_mpi_benchmark(args_core, hosts, mpi, ofi_build_mode) run.mpistress_benchmark(args_core, hosts, mpi, ofi_build_mode) run.osu_benchmark(args_core, hosts, mpi, ofi_build_mode) else: run.fi_info_test(args_core, hosts, ofi_build_mode, util=args_util) run.fabtests(args_core, hosts, ofi_build_mode, util=args_util) run.shmemtest(args_core, hosts, ofi_build_mode, util=args_util) for mpi in mpilist: run.intel_mpi_benchmark(args_core, hosts, mpi, ofi_build_mode, \ util=args_util,) run.mpistress_benchmark(args_core, hosts, mpi, ofi_build_mode, \ util=args_util) run.osu_benchmark(args_core, hosts, mpi, ofi_build_mode, \ util=args_util) else: print("Error : Specify a core provider to run tests")

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pywren_ibm_cloud/libs/cloudpickle/__init__.py

gerardparis/pywren-ibm-cloud

0

52417

import pickle from pywren_ibm_cloud.libs.cloudpickle.cloudpickle import * __version__ = '1.2.2'

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pyeccodes/defs/grib2/template_3_63_def.py

ecmwf/pyeccodes

7

95750

import pyeccodes.accessors as _ def load(h): h.add(_.Transient('biFourierMakeTemplate', 0)) h.add(_.Label('BiFourier coefficients')) h.add(_.Constant('biFourierCoefficients', 1)) h.add(_.Codetable('spectralType', 1, "3.6.table", _.Get('masterDir'), _.Get('localDir'))) h.alias('spectralDataRepresentationType', 'spectralType') h.add(_.Unsigned('biFourierResolutionParameterN', 4)) h.add(_.Unsigned('biFourierResolutionParameterM', 4)) h.add(_.Codetable('biFourierTruncationType', 1, "3.25.table", _.Get('masterDir'), _.Get('localDir'))) h.add(_.Unsigned('Lx', 8)) h.alias('geography.LxInMetres', 'Lx') h.add(_.Unsigned('Lux', 8)) h.alias('geography.LuxInMetres', 'Lux') h.add(_.Unsigned('Lcx', 8)) h.alias('geography.LcxInMetres', 'Lcx') h.add(_.Unsigned('Ly', 8)) h.alias('geography.LyInMetres', 'Ly') h.add(_.Unsigned('Luy', 8)) h.alias('geography.LuyInMetres', 'Luy') h.add(_.Unsigned('Lcy', 8)) h.alias('geography.LcyInMetres', 'Lcy') h.add(_.Codetable('shapeOfTheEarth', 1, "3.2.table", _.Get('masterDir'), _.Get('localDir'))) h.add(_.Unsigned('scaleFactorOfRadiusOfSphericalEarth', 1)) h.add(_.Unsigned('scaledValueOfRadiusOfSphericalEarth', 4)) h.add(_.Unsigned('scaleFactorOfEarthMajorAxis', 1)) h.alias('scaleFactorOfMajorAxisOfOblateSpheroidEarth', 'scaleFactorOfEarthMajorAxis') h.add(_.Unsigned('scaledValueOfEarthMajorAxis', 4)) h.alias('scaledValueOfMajorAxisOfOblateSpheroidEarth', 'scaledValueOfEarthMajorAxis') h.add(_.Unsigned('scaleFactorOfEarthMinorAxis', 1)) h.alias('scaleFactorOfMinorAxisOfOblateSpheroidEarth', 'scaleFactorOfEarthMinorAxis') h.add(_.Unsigned('scaledValueOfEarthMinorAxis', 4)) h.alias('scaledValueOfMinorAxisOfOblateSpheroidEarth', 'scaledValueOfEarthMinorAxis') h.alias('earthIsOblate', 'one') if (h.get_l('shapeOfTheEarth') == 0): h.add(_.Transient('radius', 6367470)) h.alias('radiusOfTheEarth', 'radius') h.alias('radiusInMetres', 'radius') h.alias('earthIsOblate', 'zero') if (h.get_l('shapeOfTheEarth') == 1): h.add(_.From_scale_factor_scaled_value('radius', _.Get('scaleFactorOfRadiusOfSphericalEarth'), _.Get('scaledValueOfRadiusOfSphericalEarth'))) h.alias('radiusOfTheEarth', 'radius') h.alias('radiusInMetres', 'radius') h.alias('earthIsOblate', 'zero') if (h.get_l('shapeOfTheEarth') == 6): h.add(_.Transient('radius', 6371229)) h.alias('radiusOfTheEarth', 'radius') h.alias('radiusInMetres', 'radius') h.alias('earthIsOblate', 'zero') if (h.get_l('shapeOfTheEarth') == 8): h.add(_.Transient('radius', 6371200)) h.alias('radiusOfTheEarth', 'radius') h.alias('radiusInMetres', 'radius') h.alias('earthIsOblate', 'zero') if (h.get_l('shapeOfTheEarth') == 2): h.add(_.Transient('earthMajorAxis', 6.37816e+06)) h.add(_.Transient('earthMinorAxis', 6.35678e+06)) h.alias('earthMajorAxisInMetres', 'earthMajorAxis') h.alias('earthMinorAxisInMetres', 'earthMinorAxis') if (h.get_l('shapeOfTheEarth') == 3): h.add(_.From_scale_factor_scaled_value('earthMajorAxis', _.Get('scaleFactorOfEarthMajorAxis'), _.Get('scaledValueOfEarthMajorAxis'))) h.add(_.From_scale_factor_scaled_value('earthMinorAxis', _.Get('scaleFactorOfEarthMinorAxis'), _.Get('scaledValueOfEarthMinorAxis'))) h.add(_.Divdouble('earthMajorAxisInMetres', _.Get('earthMajorAxis'), 0.001)) h.add(_.Divdouble('earthMinorAxisInMetres', _.Get('earthMinorAxis'), 0.001)) if (h.get_l('shapeOfTheEarth') == 7): h.add(_.From_scale_factor_scaled_value('earthMajorAxis', _.Get('scaleFactorOfEarthMajorAxis'), _.Get('scaledValueOfEarthMajorAxis'))) h.add(_.From_scale_factor_scaled_value('earthMinorAxis', _.Get('scaleFactorOfEarthMinorAxis'), _.Get('scaledValueOfEarthMinorAxis'))) h.alias('earthMajorAxisInMetres', 'earthMajorAxis') h.alias('earthMinorAxisInMetres', 'earthMinorAxis') if ((h.get_l('shapeOfTheEarth') == 4) or (h.get_l('shapeOfTheEarth') == 5)): h.add(_.Transient('earthMajorAxis', 6.37814e+06)) h.add(_.Transient('earthMinorAxis', 6.35675e+06)) h.alias('earthMajorAxisInMetres', 'earthMajorAxis') h.alias('earthMinorAxisInMetres', 'earthMinorAxis') if (h.get_l('shapeOfTheEarth') == 9): h.add(_.Transient('earthMajorAxis', 6.37756e+06)) h.add(_.Transient('earthMinorAxis', 6.35626e+06)) h.alias('earthMajorAxisInMetres', 'earthMajorAxis') h.alias('earthMinorAxisInMetres', 'earthMinorAxis') h.add(_.Signed('latitudeOfFirstGridPoint', 4)) h.alias('La1', 'latitudeOfFirstGridPoint') h.add(_.Scale('latitudeOfFirstGridPointInDegrees', _.Get('latitudeOfFirstGridPoint'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.latitudeOfFirstGridPointInDegrees', 'latitudeOfFirstGridPointInDegrees') h.alias('La1InDegrees', 'latitudeOfFirstGridPointInDegrees') h.add(_.Unsigned('longitudeOfFirstGridPoint', 4)) h.alias('Lo1', 'longitudeOfFirstGridPoint') h.add(_.Scale('longitudeOfFirstGridPointInDegrees', _.Get('longitudeOfFirstGridPoint'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.longitudeOfFirstGridPointInDegrees', 'longitudeOfFirstGridPointInDegrees') h.alias('Lo1InDegrees', 'longitudeOfFirstGridPointInDegrees') h.add(_.Signed('LaD', 4)) h.alias('latitudeWhereDxAndDyAreSpecified', 'LaD') h.add(_.Scale('LaDInDegrees', _.Get('LaD'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.LaDInDegrees', 'LaDInDegrees') h.add(_.Unsigned('LoV', 4)) h.add(_.Scale('LoVInDegrees', _.Get('LoV'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.LoVInDegrees', 'LoVInDegrees') h.add(_.Codeflag('projectionCentreFlag', 1, "grib2/tables/[tablesVersion]/3.5.table")) h.add(_.Signed('Latin1', 4)) h.alias('FirstLatitude', 'Latin1') h.add(_.Scale('Latin1InDegrees', _.Get('Latin1'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.Latin1InDegrees', 'Latin1InDegrees') h.add(_.Signed('Latin2', 4)) h.alias('SecondLatitude', 'Latin2') h.add(_.Scale('Latin2InDegrees', _.Get('Latin2'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.Latin2InDegrees', 'Latin2InDegrees') h.add(_.Signed('latitudeOfSouthernPole', 4)) h.alias('latitudeOfTheSouthernPoleOfProjection', 'latitudeOfSouthernPole') h.add(_.Scale('latitudeOfSouthernPoleInDegrees', _.Get('latitudeOfSouthernPole'), _.Get('one'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.latitudeOfSouthernPoleInDegrees', 'latitudeOfSouthernPoleInDegrees') h.add(_.Unsigned('longitudeOfSouthernPole', 4)) h.alias('longitudeOfTheSouthernPoleOfProjection', 'longitudeOfSouthernPole') h.add(_.Scale('longitudeOfSouthernPoleInDegrees', _.Get('longitudeOfSouthernPole'), _.Get('oneConstant'), _.Get('grib2divider'), _.Get('truncateDegrees'))) h.alias('geography.longitudeOfSouthernPoleInDegrees', 'longitudeOfSouthernPoleInDegrees')

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29925, 1772, 797, 29928, 387, 11003, 1495, 13, 2 ]

server.py

yenyuuuuu/esys_hw3

0

80803

<reponame>yenyuuuuu/esys_hw3 import socket import json import matplotlib.pyplot as plt import numpy as np HOST = '192.168.50.254' # Standard loopback interface address (localhost) 192.168.50.254 PORT = 3000 # Port to listen on (non-privileged ports are > 1023) # use ggplot style for more sophisticated visuals plt.style.use('ggplot') def main(): size = 100 t_vec = np.linspace(0,1,size+1)[0:-1] x_vec = np.zeros(size) y_vec = np.zeros(size) z_vec = np.zeros(size) line_x=[] line_y=[] line_z=[] with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) print('listen...') s.listen() conn, addr = s.accept() with conn: print('Connected by', addr) while True: data = conn.recv(1024).decode('utf-8') if not data: print("no data!!!") break print(data) j_data=json.loads(data) x_vec = np.append(x_vec[1:],j_data['x']) y_vec = np.append(y_vec[1:],j_data['y']) z_vec = np.append(z_vec[1:],j_data['z']) line_x, line_y, line_z = plotter(t_vec,x_vec,y_vec,z_vec,line_x,line_y,line_z) s.close() return return def plotter(t_vec,y1_data,y2_data,y3_data,line1,line2,line3,identifier='',pause_time=0.5): if (line1==[]) and (line2==[]) and (line3==[]): # this is the call to matplotlib that allows dynamic plotting plt.ion() fig = plt.figure(figsize=(13,6)) ax = fig.add_subplot(111) # create a variable for the line so we can later update it line1, = ax.plot(t_vec,y1_data,'-o',alpha=0.8,color='purple') line2, = ax.plot(t_vec,y2_data,'-o',alpha=0.8,color='red') line3, = ax.plot(t_vec,y3_data,'-o',alpha=0.8,color='blue') #update plot label/title plt.ylim([-1000,1000]) plt.ylabel('Acc') plt.title("Acc (to time t)") plt.show() # after the figure, axis, and line are created, we only need to update the y-data line1.set_ydata(y1_data) line2.set_ydata(y2_data) line3.set_ydata(y3_data) # adjust limits if new data goes beyond bounds # this pauses the data so the figure/axis can catch up - the amount of pause can be altered above plt.pause(pause_time) # return line so we can update it again in the next iteration return line1,line2,line3 if __name__ == '__main__': main()

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examples/apprtc/apprtc.py

nickaknudson/aiortc

1

169141

<filename>examples/apprtc/apprtc.py import argparse import asyncio import json import logging import os import random import time import aiohttp import cv2 import websockets from aiortc import (AudioStreamTrack, RTCPeerConnection, RTCSessionDescription, VideoStreamTrack) from aiortc.contrib.media import frame_from_bgr from aiortc.sdp import candidate_from_sdp ROOT = os.path.dirname(__file__) PHOTO_PATH = os.path.join(ROOT, 'photo.jpg') VIDEO_PTIME = 1 / 30 def description_to_dict(description): return { 'sdp': description.sdp, 'type': description.type } class Signaling: async def connect(self, params): self.websocket = await websockets.connect(params['wss_url'], extra_headers={ 'Origin': 'https://appr.tc' }) async def recv(self): data = await self.websocket.recv() return json.loads(data) async def send(self, data): await self.websocket.send(json.dumps(data)) async def send_message(self, message): print('>', message) await self.send({ 'cmd': 'send', 'msg': json.dumps(message) }) class VideoImageTrack(VideoStreamTrack): def __init__(self): self.counter = 0 self.img = cv2.imread(PHOTO_PATH, cv2.IMREAD_COLOR) self.last = None async def recv(self): # rotate image rows, cols, _ = self.img.shape M = cv2.getRotationMatrix2D((cols / 2, rows / 2), self.counter / 2, 1) rotated = cv2.warpAffine(self.img, M, (cols, rows)) frame = frame_from_bgr(rotated) self.counter += 1 # sleep if self.last: delta = self.last + VIDEO_PTIME - time.time() if delta > 0: await asyncio.sleep(delta) self.last = time.time() return frame async def consume_signaling(signaling, pc, params): async def handle_message(message): print('<', message) if message['type'] == 'bye': return True if message['type'] == 'offer': await pc.setRemoteDescription(RTCSessionDescription(**message)) await pc.setLocalDescription(await pc.createAnswer()) await signaling.send_message(description_to_dict(pc.localDescription)) elif message['type'] == 'answer': await pc.setRemoteDescription(RTCSessionDescription(**message)) elif message['type'] == 'candidate': candidate = candidate_from_sdp(message['candidate'].split(':', 1)[1]) candidate.sdpMid = message['id'] candidate.sdpMLineIndex = message['label'] pc.addIceCandidate(candidate) return False for data in params['messages']: message = json.loads(data) await handle_message(message) stop = False while not stop: data = await signaling.recv() message = json.loads(data['msg']) stop = await handle_message(message) async def consume_audio(track): """ Drain incoming audio. """ while True: await track.recv() async def consume_video(track): """ Drain incoming video. """ while True: await track.recv() async def join_room(room): consumers = [] # fetch room parameters async with aiohttp.ClientSession() as session: async with session.post('https://appr.tc/join/' + room) as response: # we cannot use response.json() due to: # https://github.com/webrtc/apprtc/issues/562 data = json.loads(await response.text()) assert data['result'] == 'SUCCESS' params = data['params'] # create peer conection pc = RTCPeerConnection() pc.addTrack(AudioStreamTrack()) pc.addTrack(VideoImageTrack()) @pc.on('track') def on_track(track): if track.kind == 'audio': consumers.append(asyncio.ensure_future(consume_audio(track))) elif track.kind == 'video': consumers.append(asyncio.ensure_future(consume_video(track))) # connect to websocket and join signaling = Signaling() await signaling.connect(params) await signaling.send({ 'clientid': params['client_id'], 'cmd': 'register', 'roomid': params['room_id'], }) if params['is_initiator'] == 'true': # send offer await pc.setLocalDescription(await pc.createOffer()) await signaling.send_message(description_to_dict(pc.localDescription)) print('Please point a browser at %s' % params['room_link']) # receive 60s of media try: await asyncio.wait_for(consume_signaling(signaling, pc, params), timeout=60) except asyncio.TimeoutError: pass # shutdown print('Shutting down') await signaling.send_message({'type': 'bye'}) for c in consumers: c.cancel() await pc.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description='AppRTC') parser.add_argument('room', nargs='?') parser.add_argument('--verbose', '-v', action='count') args = parser.parse_args() if not args.room: args.room = ''.join([random.choice('0123456789') for x in range(10)]) if args.verbose: logging.basicConfig(level=logging.DEBUG) asyncio.get_event_loop().run_until_complete(join_room(args.room))

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good_spot/places/migrations/0040_actualpopulartimes.py

jasmine92122/NightClubBackend

0

119636

# -*- coding: utf-8 -*- # Generated by Django 1.11.7 on 2018-01-30 13:02 from __future__ import unicode_literals import django.contrib.postgres.fields.jsonb from django.db import migrations, models import django.utils.timezone import model_utils.fields class Migration(migrations.Migration): dependencies = [ ('places', '0039_auto_20180129_0924'), ] operations = [ migrations.CreateModel( name='ActualPopularTimes', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')), ('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')), ('actual_pop_times', django.contrib.postgres.fields.jsonb.JSONField()), ('expire_at', models.DateTimeField()), ], options={ 'abstract': False, }, ), ]

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schedule/views.py

AndrewSaltz/Lab_Schedule

0

25454

from django.shortcuts import render import datetime from datetime import date import calendar from schedule.models import Event, period_choices, cart_choice from django.views.generic import UpdateView, TemplateView, ListView from schedule.forms import ReservationForm from django.http import HttpResponseRedirect, HttpResponse from django import forms import json import calendar from django.http import JsonResponse from django.core.serializers.json import DjangoJSONEncoder from django.views.decorators.csrf import csrf_exempt, ensure_csrf_cookie from django.db import IntegrityError from django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin from django.contrib.auth.models import User class Home(LoginRequiredMixin, ListView): model = Event template_name = "schedule/home.html" login_url = '/login/' redirect_field_name = 'redirect_to' def get_context_data(self, **kwargs): context = super(Home, self).get_context_data(**kwargs) q = self.kwargs['adjuster'] #Change week adjust = int(q) if adjust != 0: adjust *=7 # Add to Monday to move up and down weeks today = date.today() Monday = date.today() - datetime.timedelta(days=date.today().weekday()) Friday = Monday + datetime.timedelta(days=4) Monday = Monday + datetime.timedelta(days=adjust) Friday = Friday + datetime.timedelta(days=adjust) # Alter the days if needed. Really non-pythony context['monday'] = Monday context['q'] = q context['this_week'] = Event.objects.filter(day__gte=Monday,day__lte=Friday) context['the_day'] = calendar.day_name[today.weekday()] periods = [] user = self.request.user context['my_reservation'] = Event.objects.filter(day__gte=Monday,day__lte=Friday,teacher=user.id) for p in period_choices: periods.append(p[1]) context['periods'] = periods context['username'] = self.request.user.username return context @ensure_csrf_cookie def reserve(request): if request.is_ajax(): pk = request.POST['pk'] slot = Event.objects.get(pk=pk) user = request.user if slot.is_reserved == True: if user == slot.teacher: slot.is_reserved = False slot.teacher = None slot.save() result = 1 elif user.is_superuser and user != slot.teacher: # Override as admin slot.is_reserved == True slot.teacher = user slot.save() result = 2 else: result = 3 else: slot.is_reserved = True slot.teacher = user slot.save() result = 2 data = {'result': result} return HttpResponse(json.dumps(data, cls=DjangoJSONEncoder)) class Dashboard(PermissionRequiredMixin,TemplateView): template_name = "schedule/dashboard.html" permission_required = 'is_staff' def get_context_data(self, **kwargs): context = super(Dashboard, self).get_context_data(**kwargs) periods = [] for p in period_choices: periods.append(p[1]) today = datetime.date.today() tomorrow = today + datetime.timedelta(days=1) cart_list =[] for cart in cart_choice: cart_list.append(cart[0]) context['periods']=periods # Make this a mixin context['this_day'] = Event.objects.filter(day=today) context['next_day'] = Event.objects.filter(day=tomorrow) context['cart_list'] = cart_list context['today'] = today context['tomorrow'] = tomorrow return context @ensure_csrf_cookie def create_week(request): if request.is_ajax(): to_monday = date.today().weekday() start = date.today() - datetime.timedelta(days=to_monday) #Find Monday day = start # Day will change, start will not end = start + datetime.timedelta(days=4) # One week, edit later for flexibility weekend = set([5, 6]) # Python week starts on Monday as 0 dupe_list = [] total = 0 while day <= end: if day.weekday() not in weekend: for period in period_choices: for cart in cart_choice: open = Event(day=day, period=period[0], cart=cart[0]) try: open.save() total+=1 except IntegrityError: dupe = str(open) dupe_list.append(dupe) pass day += datetime.timedelta(days=1) # Adds one day until the current day is past the end day data = {'start': start, 'end': end, 'dupe_list': dupe_list, 'total': total} return HttpResponse(json.dumps(data, cls=DjangoJSONEncoder)) def redirect_root(request): return HttpResponseRedirect('/week/0/') @ensure_csrf_cookie def create_month(request): if request.is_ajax(): month = date.today().month year = date.today().year # last = calendar.monthrange(year, month) # Last day start = date.today().replace(day=1) # Get the first day of the month end = date.today().replace(day=(calendar.monthrange(year, month)[1])) weekend = set([5, 6]) # Python week starts on Monday as 0 dupe_list = [] total = 0 day = start while day <= end: if day.weekday() not in weekend: for period in period_choices: for cart in cart_choice: open = Event(day=day, period=period[0], cart=cart[0]) try: open.save() total+=1 except IntegrityError: dupe = str(open) dupe_list.append(dupe) pass day += datetime.timedelta(days=1) data = {'start': start, 'end': end, 'dupe_list': dupe_list, 'total': total} return HttpResponse(json.dumps(data, cls=DjangoJSONEncoder)) @ensure_csrf_cookie def create_twelve(request): if request.is_ajax(): to_monday = date.today().weekday() start = date.today() - datetime.timedelta(days=to_monday) # Find Monday fake_end = start + datetime.timedelta(days=84) if fake_end.weekday() != 4: end = fake_end - datetime.timedelta(days=(fake_end.weekday() - 4)) else: end = fake_end day = start total = 0 dupe_list = [] weekend = set([5, 6]) # Python week starts on Monday as 0 while day <= end: if day.weekday() not in weekend: for period in period_choices: for cart in cart_choice: open = Event(day=day, period=period[0], cart=cart[0]) try: open.save() total+=1 except IntegrityError: dupe = str(open) dupe_list.append(dupe) pass day += datetime.timedelta(days=1) data = {'start': start, 'end': end, 'dupe_list': dupe_list, 'total': total} return HttpResponse(json.dumps(data, cls=DjangoJSONEncoder)) @ensure_csrf_cookie def delete_all(request): if request.is_ajax(): Event.objects.all().delete() return HttpResponse()

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MindSPONGE/mindsponge/md/cybertron/cutoff.py

mindspore-ai/mindscience

3

71618

# ============================================================================ # Copyright 2021 The AIMM team at Shenzhen Bay Laboratory & Peking University # # People: <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This code is a part of Cybertron-Code package. # # The Cybertron-Code is open-source software based on the AI-framework: # MindSpore (https://www.mindspore.cn/) # # 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. # ============================================================================ """cybertron cutoff""" import numpy as np import mindspore as ms from mindspore import nn from mindspore import Tensor from mindspore.ops import operations as P from mindspore.ops import functional as F from .units import units __all__ = [ "CosineCutoff", "MollifierCutoff", "HardCutoff", "SmoothCutoff", "GaussianCutoff", "get_cutoff", ] _CUTOFF_ALIAS = dict() def _cutoff_register(*aliases): """Return the alias register.""" def alias_reg(cls): name = cls.__name__ name = name.lower() if name not in _CUTOFF_ALIAS: _CUTOFF_ALIAS[name] = cls for alias in aliases: if alias not in _CUTOFF_ALIAS: _CUTOFF_ALIAS[alias] = cls return cls return alias_reg class Cutoff(nn.Cell): """Cutoff""" def __init__(self, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False ): super().__init__() self.name = 'cutoff' self.hyperparam = hyperparam self.r_min = r_min self.cutoff = r_max self.return_mask = return_mask self.reverse = reverse @_cutoff_register('cosine') class CosineCutoff(Cutoff): r"""Class of Behler cosine cutoff. .. math:: f(r) = \begin{cases} 0.5 \times \left[1 + \cos\left(\frac{\pi r}{r_\text{cutoff}}\right)\right] & r < r_\text{cutoff} \\ 0 & r \geqslant r_\text{cutoff} \\ \end{cases} Args: cutoff (float, optional): cutoff radius. """ def __init__(self, r_max=units.length(1, 'nm'), r_min='default', hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_max=r_max, r_min=r_min, hyperparam=None, return_mask=return_mask, reverse=reverse, ) self.name = 'cosine cutoff' self.pi = Tensor(np.pi, ms.float32) self.cos = P.Cos() self.logical_and = P.LogicalAnd() def construct(self, distances, neighbor_mask=None): """Compute cutoff. Args: distances (mindspore.Tensor): values of interatomic distances. Returns: mindspore.Tensor: values of cutoff function. """ # Compute values of cutoff function cuts = 0.5 * (self.cos(distances * self.pi / self.cutoff) + 1.0) if self.reverse: cuts = 1.0 - cuts ones = F.ones_like(cuts) cuts = F.select(distances < cuts, cuts, ones) if neighbor_mask is None: mask = distances >= 0 else: mask = neighbor_mask else: mask = distances < self.cutoff if neighbor_mask is not None: mask = self.logical_and(mask, neighbor_mask) # Remove contributions beyond the cutoff radius cutoffs = cuts * mask if self.return_mask: return cutoffs, mask return cutoffs @_cutoff_register('mollifier') class MollifierCutoff(Cutoff): r"""Class for mollifier cutoff scaled to have a value of 1 at :math:`r=0`. .. math:: f(r) = \begin{cases} \exp\left(1 - \frac{1}{1 - \left(\frac{r}{r_\text{cutoff}}\right)^2}\right) & r < r_\text{cutoff} \\ 0 & r \geqslant r_\text{cutoff} \\ \end{cases} Args: cutoff (float, optional): Cutoff radius. eps (float, optional): offset added to distances for numerical stability. """ def __init__(self, r_max=units.length(1, 'nm'), r_min='default', hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_min=r_min, r_max=r_max, hyperparam=hyperparam, return_mask=return_mask, reverse=reverse, ) self.name = "Mollifier cutoff" if hyperparam == 'default': self.eps = units.length(1.0e-8, 'nm') else: self.eps = hyperparam self.exp = P.Exp() self.logical_and = P.LogicalAnd() def construct(self, distances, neighbor_mask=None): """Compute cutoff. Args: distances (mindspore.Tensor): values of interatomic distances. Returns: mindspore.Tensor: values of cutoff function. """ exponent = 1.0 - 1.0 / (1.0 - F.square(distances / self.cutoff)) cutoffs = self.exp(exponent) if self.reverse: cutoffs = 1. - cutoffs ones = F.ones_like(cutoffs) cutoffs = F.select(distances < self.cutoff, cutoffs, ones) if neighbor_mask is None: mask = (distances + self.eps) >= 0 else: mask = neighbor_mask else: mask = (distances + self.eps) < self.cutoff if neighbor_mask is not None: mask = self.logical_and(mask, neighbor_mask) cutoffs = cutoffs * mask return cutoffs, mask @_cutoff_register('hard') class HardCutoff(Cutoff): r"""Class of hard cutoff. .. math:: f(r) = \begin{cases} 1 & r \leqslant r_\text{cutoff} \\ 0 & r > r_\text{cutoff} \\ \end{cases} Args: cutoff (float): cutoff radius. """ def __init__(self, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_min=r_min, r_max=r_max, hyperparam=None, return_mask=return_mask, reverse=reverse, ) self.name = "Hard cutoff" self.logical_and = P.LogicalAnd() def construct(self, distances, neighbor_mask=None): """Compute cutoff. Args: distances (mindspore.Tensor): values of interatomic distances. Returns: mindspore.Tensor: values of cutoff function. """ if self.reverse: mask = distances >= self.cutoff else: mask = distances < self.cutoff if neighbor_mask is not None: self.logical_and(mask, neighbor_mask) if self.return_mask: return F.cast(mask, distances.dtype), mask return F.cast(mask, distances.dtype) @_cutoff_register('smooth') class SmoothCutoff(Cutoff): r"""Class of smooth cutoff by <NAME>. et al: [ref] <NAME>.; <NAME>.; <NAME>.; <NAME>.; <NAME>. Texturing & Modeling: A Procedural Approach; <NAME>: 2003 .. math:: r_min < r < r_max: f(r) = 1.0 - 6 * ( r / r_cutoff ) ^ 5 + 15 * ( r / r_cutoff ) ^ 4 - 10 * ( r / r_cutoff ) ^ 3 r >= r_max: f(r) = 0 r <= r_min: f(r) = 1 reverse: r_min < r < r_max: f(r) = 6 * ( r / r_cutoff ) ^ 5 - 15 * ( r / r_cutoff ) ^ 4 + 10 * ( r / r_cutoff ) ^ 3 r >= r_max: f(r) = 1 r <= r_min: f(r) = 0 Args: d_max (float, optional): the maximum distance (cutoff radius). d_min (float, optional): the minimum distance """ def __init__(self, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_min=r_min, r_max=r_max, hyperparam=None, return_mask=return_mask, reverse=reverse, ) if self.r_min >= self.cutoff: raise ValueError( 'dis_min must be smaller than cutoff at SmmothCutoff') self.dis_range = self.cutoff - self.r_min self.pow = P.Pow() self.logical_and = P.LogicalAnd() def construct(self, distance, neighbor_mask=None): """Compute cutoff. Args: distances (mindspore.Tensor or float): values of interatomic distances. Returns: mindspore.Tensor or float: values of cutoff function. """ dd = distance - self.r_min dd = dd / self.dis_range cuts = - 6. * self.pow(dd, 5) \ + 15. * self.pow(dd, 4) \ - 10. * self.pow(dd, 3) if self.reverse: cutoffs = -cuts mask_upper = distance < self.cutoff mask_lower = distance > self.r_min else: cutoffs = 1 + cuts mask_upper = distance > self.r_min mask_lower = distance < self.cutoff if neighbor_mask is not None: mask_lower = self.logical_and(mask_lower, neighbor_mask) zeros = F.zeros_like(distance) ones = F.ones_like(distance) cutoffs = F.select(mask_upper, cutoffs, ones) cutoffs = F.select(mask_lower, cutoffs, zeros) if self.return_mask: return cutoffs, mask_lower return cutoffs @_cutoff_register('gaussian') class GaussianCutoff(Cutoff): r"""Class of hard cutoff. .. math:: f(r) = \begin{cases} 1 & r \leqslant r_\text{cutoff} \\ 0 & r > r_\text{cutoff} \\ \end{cases} Args: cutoff (float): cutoff radius. """ def __init__(self, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False ): super().__init__( r_min=r_min, r_max=r_max, hyperparam=hyperparam, return_mask=return_mask, reverse=reverse, ) if hyperparam == 'default': self.sigma = units.length(1, 'nm') else: self.sigma = hyperparam self.sigma2 = self.sigma * self.sigma self.exp = P.Exp() self.logical_and = P.LogicalAnd() def construct(self, distance, neighbor_mask=None): """construct""" dd = distance - self.cutoff dd2 = dd * dd gauss = self.exp(-0.5 * dd2 / self.sigma2) if self.reverse: cuts = gauss ones = F.ones_like(cuts) cuts = F.select(distance < self.cutoff, cuts, ones) if neighbor_mask is None: mask = distance >= 0 else: mask = neighbor_mask else: cuts = 1. - gauss mask = distance < self.cutoff if neighbor_mask is not None: mask = self.logical_and(mask, neighbor_mask) cuts = cuts * mask if self.return_mask: return cuts, mask return cuts def get_cutoff(obj, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False): """get cutoff""" if obj is None or isinstance(obj, Cutoff): return obj if isinstance(obj, str): if obj not in _CUTOFF_ALIAS.keys(): raise ValueError( "The class corresponding to '{}' was not found.".format(obj)) return _CUTOFF_ALIAS[obj.lower()]( r_min=r_min, r_max=r_max, hyperparam=hyperparam, return_mask=return_mask, reverse=reverse, ) raise TypeError("Unsupported Cutoff type '{}'.".format(type(obj)))

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variation/validators/single_nucleotide_variation_base.py

GenomicMedLab/varlex

0

180505

"""The module for Single Nucleotide Variation Validation.""" from .validator import Validator import logging logger = logging.getLogger('variation') logger.setLevel(logging.DEBUG) class SingleNucleotideVariationBase(Validator): """The Single Nucleotide Variation Validator Base class.""" def silent_mutation_valid_invalid_results(self, classification_tokens, transcripts, classification, results, gene_tokens, normalize_endpoint, mane_data_found, is_identifier) -> None: """Add validation result objects to a list of results for Silent Mutations. :param list classification_tokens: A list of classification Tokens :param list transcripts: A list of transcript accessions :param Classification classification: A classification for a list of tokens :param list results: Stores validation result objects :param list gene_tokens: List of GeneMatchTokens for a classification :param bool normalize_endpoint: `True` if normalize endpoint is being used. `False` otherwise. :param dict mane_data_found: MANE Transcript information found :param bool is_identifier: `True` if identifier is given for exact location. `False` otherwise. """ valid_alleles = list() for s in classification_tokens: for t in transcripts: errors = list() t = self.get_accession(t, classification) allele = None if s.reference_sequence == 'c': cds_start_end = self.uta.get_cds_start_end(t) if not cds_start_end: cds_start = None errors.append( f"Unable to find CDS start for accession : {t}" ) else: cds_start = cds_start_end[0] else: cds_start = None if not errors: allele = self.to_vrs_allele( t, s.position, s.position, s.reference_sequence, s.alt_type, errors, cds_start=cds_start, alt=s.new_nucleotide ) if not errors: sequence = \ self.seqrepo_access.get_sequence(t, s.position) if sequence is None: errors.append('Sequence index error') if not errors: mane = self.mane_transcript.get_mane_transcript( t, s.position, s.position, s.reference_sequence, gene=gene_tokens[0].token if gene_tokens else None, normalize_endpoint=normalize_endpoint ) self.add_mane_data(mane, mane_data_found, s.reference_sequence, s.alt_type, s, gene_tokens) self.add_validation_result( allele, valid_alleles, results, classification, s, t, gene_tokens, errors ) if is_identifier: break self.add_mane_to_validation_results( mane_data_found, valid_alleles, results, classification, gene_tokens ) def check_ref_nucleotide(self, actual_ref_nuc, expected_ref_nuc, position, t, errors): """Assert that ref_nuc matches s.ref_nucleotide.""" if actual_ref_nuc != expected_ref_nuc: errors.append(f'Needed to find {expected_ref_nuc} at' f' position {position} on {t}' f' but found {actual_ref_nuc}') def concise_description(self, transcript, token) -> str: """Return a HGVS description of the identified variation. :param str transcript: Transcript accession :param Token token: Classification token :return: HGVS expression """ prefix = f'{transcript}:{token.reference_sequence}.{token.position}' if token.new_nucleotide == '=': change = "=" else: change = f"{token.ref_nucleotide}>{token.new_nucleotide}" return prefix + change

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world_of_warships/war_gaming.py

MaT1g3R/YasenBaka

6

78376

<reponame>MaT1g3R/YasenBaka<gh_stars>1-10 from typing import Optional from wowspy import Region, WowsAsync async def __get_id(coro, logger, key): try: resp = await coro except Exception as e: logger.warn(str(e)) else: data = resp.get('data', None) if not data: return return data[0].get(key, None) async def get_player_id(region: Region, wows_api: WowsAsync, logger, search) -> Optional[int]: """ Get player id by search term. :param region: the region. :param wows_api: the WowsAsync instance. :param logger: the logger. :param search: the search query. :return: the player id. """ coro = wows_api.players(region, search, language='en', limit=1) return await __get_id(coro, logger, 'account_id') async def get_clan_id(region: Region, wows_api: WowsAsync, logger, search) -> Optional[int]: """ Get clan id by search term. :param region: the region. :param wows_api: the WowsAsync instance. :param logger: the logger. :param search: the search query. :return: the clan id. """ coro = wows_api.clans(region, search=search, language='en', limit=1) return await __get_id(coro, logger, 'clan_id')

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tests/test_pvaapi.py

sasaki77/pvarpc2web

0

178505

<reponame>sasaki77/pvarpc2web from .context import pvarpc2web from .context import config from .context import pvaapi class PvaRpcTimeoutConfig(config.DefaultConfig): PVA_RPC_TIMEOUT = 3 def test_set_timeout(): app = pvarpc2web.create_app(PvaRpcTimeoutConfig) assert pvaapi.timeout == 3

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kungfucms/apps/account/models.py

kungfusoft/kungfucms

0

75410

<gh_stars>0 from django.core.mail import send_mail from django.utils.translation import ugettext as _ from django.utils import timezone from django.db import models from django.contrib.auth.models import UserManager, PermissionsMixin from django.contrib.auth.base_user import AbstractBaseUser from django.contrib.auth.validators import UnicodeUsernameValidator from kungfucms.apps.system.models import BaseModel class Manager(UserManager): def _create_user(self, username, password, **extra_fields): username = self.model.normalize_username(username) user = self.model(username=username, **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_user(self, username, password=None, **extra_fields): extra_fields.setdefault('is_staff', False) extra_fields.setdefault('is_superuser', False) return self._create_user(username, password, **extra_fields) def create_staff(self, username, password, **extra_fields): extra_fields.setdefault('is_staff', True) extra_fields.setdefault('is_superuser', False) return self._create_user(username, password, **extra_fields) def create_superuser(self, username, password=None, **extra_fields): extra_fields.setdefault('is_staff', True) extra_fields.setdefault('is_superuser', True) return self._create_user(username, password, **extra_fields) class AbstractUser(AbstractBaseUser, PermissionsMixin): username_validator = UnicodeUsernameValidator() username = models.CharField( _('username'), max_length=128, unique=True, help_text=_('Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.'), validators=[username_validator], error_messages={ 'unique': _("A user with that username already exists."), }, ) is_staff = models.BooleanField( _('staff status'), default=False, help_text=_('Designates whether the user can log into this admin site.'), ) is_active = models.BooleanField( _('active'), default=True, help_text=_( 'Designates whether this user should be treated as active. ' 'Unselect this instead of deleting accounts.' ), ) date_joined = models.DateTimeField(_('date joined'), default=timezone.now) objects = UserManager() USERNAME_FIELD = 'username' class Meta: verbose_name = _('user') verbose_name_plural = _('users') abstract = True def clean(self): super().clean() def get_full_name(self): full_name = '%s %s' % (self.first_name, self.last_name) return full_name.strip() def get_short_name(self): return self.first_name class User(AbstractUser, BaseModel): username = models.CharField(max_length=128, unique=True, verbose_name=_('Username')) USERNAME_FIELD = 'username' objects = Manager() def get_username_field(self): return self.USERNAME_FIELD @classmethod def safe_get(cls, **kwargs): try: obj = cls.objects.get(**kwargs) except cls.DoesNotExist: obj = None return obj class Meta(AbstractUser.Meta): swappable = 'AUTH_USER_MODEL' verbose_name = _('User') verbose_name_plural = _('Users') class Profile(BaseModel): user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='profiles', related_query_name='profile') name = models.CharField(max_length=128, db_index=True, verbose_name=_('Name')) value = models.CharField(max_length=255, blank=True, default='', verbose_name=_('Value')) class Meta: ordering = ['id'] verbose_name = _('Profile') verbose_name_plural = _('Profiles') class OauthLoginProvider(BaseModel): provider = models.CharField(max_length=128, db_index=True, verbose_name=_('Provider Name')) token = models.CharField(max_length=128, db_index=True, verbose_name=_('Provider Token')) user = models.ForeignKey(User, on_delete=models.CASCADE, related_query_name='oauth_token', related_name='oauth_tokens', verbose_name=_('User')) class Meta: verbose_name = _('Oauth Login Provider') verbose_name_plural = _('Oauth Login Providers') unique_together = ('provider', 'token', 'user') indexes = [models.Index(fields=('user', 'provider'))]

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game/views/tests/test_monthly_scoreboard.py

dimadk24/english-fight-api

0

115215

from datetime import timedelta from django.utils import timezone from rest_framework.response import Response from game.models import AppUser, Game url = "/api/monthly_scoreboard" def get_response(api_client) -> Response: return api_client.get(url) def test_when_no_users(api_client): AppUser.objects.all().delete() response = get_response(api_client) assert response.status_code == 200 assert response.json() == [] def test_when_3_users_and_no_games(api_client): users = [ AppUser( username="2", vk_id=2, score=20, first_name="Cat", last_name="Leo", photo_url="2.png", ), AppUser( username="3", vk_id=3, score=1, first_name="Dog", last_name="Cooper", photo_url="3.png", ), AppUser( username="4", vk_id=4, score=12, first_name="Puppet", last_name="Bella", photo_url="4.png", ), ] AppUser.objects.bulk_create(users) response = get_response(api_client) expected = [] assert response.json() == expected def test_when_3_users(api_client): users = [ AppUser( username="2", vk_id=2, score=20, first_name="Cat", last_name="Leo", photo_url="2.png", ), AppUser( username="3", vk_id=3, score=1, first_name="Dog", last_name="Cooper", photo_url="3.png", ), AppUser( username="4", vk_id=4, score=12, first_name="Puppet", last_name="Bella", photo_url="4.png", ), AppUser( username="5", vk_id=5, score=12, first_name="Fox", last_name="Cozy", photo_url="5.png", ), ] AppUser.objects.bulk_create(users) games = [ Game( player=AppUser.objects.get(vk_id=2), points=10, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=2), points=20, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=3), points=40, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=2), points=30, created_at=timezone.now() - timedelta(days=32), ), Game( player=AppUser.objects.get(vk_id=4), points=2, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=5), points=1, created_at=timezone.now(), ), Game( player=AppUser.objects.get(vk_id=5), points=1, created_at=timezone.now(), ), ] Game.objects.bulk_create(games) response = get_response(api_client) expected = [ { "first_name": "Dog", "id": AppUser.objects.get(vk_id=3).pk, "last_name": "Cooper", "photo_url": "3.png", "score": 40, }, { "first_name": "Cat", "id": AppUser.objects.get(vk_id=2).pk, "last_name": "Leo", "photo_url": "2.png", "score": 30, }, { "first_name": "Puppet", "id": AppUser.objects.get(vk_id=4).pk, "last_name": "Bella", "photo_url": "4.png", "score": 2, }, { "first_name": "Fox", "id": AppUser.objects.get(vk_id=5).pk, "last_name": "Cozy", "photo_url": "5.png", "score": 2, }, ] assert response.json() == expected

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main.py

NMme/CrusaderAIManager

0

84678

import kivy kivy.require('1.0.6') # replace with your current kivy version ! from kivy.app import App from kivy.uix.label import Label import os import shutil import json import codecs class MyApp(App): def build(self): self.root = "/home/Games/Crusader-HDUP/CrusaderAIManager" self.ai_shorts = {'Caliph': 'ca', 'Frederick': 'fr', 'Pig': 'pg', 'Phillip': 'ph', 'Richard': 'ri', 'Rat': 'rt', 'Saladin': 'sa', 'Sheriff': 'sh', 'Snake': 'sn', 'Sultan': 'su', 'Wolf': 'wf', 'Abbot': 'ab', 'Marshall': 'ma', 'Nizar': 'ni', 'Emir': 'em', 'Wazir': 'wa' } self.characters = ['Caliph', 'Frederick', 'Pig', 'Phillip', 'Richard', 'Rat', 'Saladin', 'Sheriff', 'Snake', 'Sultan', 'Wolf', 'Abbot', 'Marshall', 'Nizar', 'Emir', 'Wazir'] return Label(text='Hello world') def back_up_vanilla(self, sc_path: str): # backup all the vanilla stronghold files # create new folder vanilla_folder = os.path.join(self.root, 'vanilla') os.mkdir(vanilla_folder) os.mkdir(os.path.join(vanilla_folder, '_fx_speech_')) os.mkdir(os.path.join(vanilla_folder, '_binks_')) os.mkdir(os.path.join(vanilla_folder, '_resources_')) # copy all the original files here #shutil.copy2() def create_assets_config_file(): # open speech file with open('char_speech.json', 'r') as f: cs = json.load(f) # open binks file with open('char_binks.json', 'r') as f: bi = json.load(f) vanilla_root_dir = "/data/vanilla/speech" out_file = dict() # iterate over all characters for char in cs.keys(): char_assets = dict() speech_files = dict() bink_files = dict() special_char = False char_assets['SpecialCharacter'] = 0 # check what kind of character we have if cs[char]['num_actions'] == 22: special_char = True char_assets['SpecialCharacter'] = 1 # add all speech actions for item in cs[char]['actions'].items(): if item[1] > 0: if special_char and 'player' in item[0]: file_keys = [cs[char]['prefix'] + '_' + item[0]] else: file_keys = [cs[char]['prefix'] + '_' + item[0] + '_' + str(i).zfill(2) for i in range(1, item[1] + 1)] for f in file_keys: speech_files[f] = os.path.join('vanilla', 'speech', (f + '.wav')) char_assets['Speech'] = speech_files # add special announcements for special characters if special_char: announce_files = {char + '_Attack': os.path.join('vanilla', 'speech', (char + '_Attack.wav')), char + '_Defeat': os.path.join('vanilla', 'speech', (char + '_Defeat.wav'))} char_assets['Announcements'] = announce_files # add all bink files for item in bi[char]['actions'].items(): if item[1] > 0: if bi[char]['numerate_files']: file_keys = [bi[char]['prefix'] + '_' + item[0] + str(i) for i in range(1, item[1] + 1)] else: file_keys = [bi[char]['prefix'] + '_' + item[0]] for f in file_keys: bink_files[f] = os.path.join('vanilla', 'speech', (f + '.bik')) char_assets['Binks'] = bink_files out_file[char] = char_assets return out_file def test_vanilla_replace(replace_char: str = 'Wazir'): # some paths vanilla_dir = "data/mod_base/vanilla" # load robin hood with open('data/robin_hood_mod/robinhood.json', 'r') as f: rh = json.load(f, strict=False) # load vanilla mode with open('data/mod_base/vanilla/vanilla.json', 'r') as f: vanilla = json.load(f, strict=False) characters = ['Caliph', 'Frederick', 'Pig', 'Phillip', 'Richard', 'Rat', 'Saladin', 'Sheriff', 'Snake', 'Sultan', 'Wolf', 'Abbot', 'Marshall', 'Nizar', 'Emir', 'Wazir'] if replace_char not in characters: raise Exception('Invalid standard character') # replace aic # load vanilla aic with open(os.path.join(vanilla_dir, vanilla['AIC']), 'r') as f: aic = json.load(f, strict=False) # get index idx = [i for i in range(len(aic['AICharacters'])) if aic['AICharacters'][i]['Name'] == replace_char][0] # add Custom Name aic['AICharacters'][idx]['CustomName'] = rh['AIC']['CustomName'] aic['AICharacters'][idx]['Personality'] = rh['AIC']['Personality'] # output new AIC with open('data/output/my_aic.json', 'w') as f: json.dump(aic, f) # replace troops # load vanilla troops with codecs.open(os.path.join(vanilla_dir, vanilla['Troops']), 'r', 'utf-8-sig') as f: troops = json.load(f, strict=False) if 'Troops' in rh.keys(): # get index idx = [i for i in range(1, 17) if troops[str(i)]['Name'] == replace_char][0] # add custom troops configuration troops[str(idx)]['Lord'] = rh['Troops']['Lord'] troops[str(idx)]['normal'] = rh['Troops']['normal'] troops[str(idx)]['crusader'] = rh['Troops']['crusader'] troops[str(idx)]['deathmatch'] = rh['Troops']['deathmatch'] # output new Troops file with open('data/output/my_troops.json', 'w') as f: json.dump(troops, f) # set new AVI files with open(os.path.join(vanilla_dir, vanilla['AIV']), 'r') as f: aiv = json.load(f, strict=False) if 'AIV' in rh.keys(): for i, aiv_files in enumerate(aiv[replace_char].keys()): if rh['AIV'][str(i+1)]: aiv[replace_char][aiv_files] = os.path.join(rh['AIV']['base_dir'], rh['AIV'][str(i+1)]) # output new AIV file with open('data/output/my_aiv.json', 'w') as f: json.dump(aiv, f) if __name__ == '__main__': #MyApp().run() test_vanilla_replace(replace_char='Sultan')

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util/mccLog.py

ccchooko/webControlClient

0

11661

<gh_stars>0 #-*-coding:utf8-*- import logging from datetime import datetime class mccLog(object): def __init__(self): logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S', filename= datetime.now().strftime("%Y%m%d%H%M%S") + '.log', filemode='a') def mccWriteLog(self, logContent): logging.info(logContent) def mccError(self, errorContent): logging.error(errorContent)

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reo/migrations/0112_auto_20210713_0037.py

NREL/REopt_API

7

30123

# Generated by Django 3.1.12 on 2021-07-13 00:37 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reo', '0111_auto_20210708_2144'), ] operations = [ migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_from_elec_grid_purchase', new_name='year_one_CO2_emissions_from_elec_grid_purchase', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_from_elec_grid_purchase_bau', new_name='year_one_CO2_emissions_from_elec_grid_purchase_bau', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_from_fuelburn', new_name='year_one_CO2_emissions_from_fuelburn', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_from_fuelburn_bau', new_name='year_one_CO2_emissions_from_fuelburn_bau', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_offset_from_elec_exports', new_name='year_one_CO2_emissions_offset_from_elec_exports', ), migrations.RenameField( model_name='sitemodel', old_name='year_one_emissions_offset_from_elec_exports_bau', new_name='year_one_CO2_emissions_offset_from_elec_exports_bau', ), ]

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PycharmProjects/PythonExercicios/ex045.py

RodrigoMASRamos/Projects.py

0

38648

# Exercício Python #045 - GAME: <NAME> e Tesoura # # Crie um programa que faça o computador jogar JOKENPÔ com você. # Aprenda a arrumar as cores nas respostas! from random import choice from random import randint # Maneira utilizada na resolução deste exercício from time import sleep print('\033[1;31mATENÇÃO! ESTE É UM JOGO ALTAMENTE PERIGOSO ONDE NÃO HÁ CHANCES DE VITÓRIA PARA VOCÊ!\033[m') Uc = input('\033[0;30mMe diga, \033[1;34mó grande jogador, \033[0;30mvocê escolhe \033[1;35mPEDRA, \033[1;31mPAPEL, ' '\033[0;30mou \033[1;36mTESOURA? ').strip().upper() PC = ['\033[1;35mPEDRA\033[m', '\033[1;31mPAPEL\033[m', '\033[1;36mTESOURA\033[m'] PCc = choice(PC) sleep(0.5) print('JO') sleep(1) print('KEN') sleep(1) print('PO!') if PCc == 'PEDRA' and Uc == 'TESOURA' or PCc == 'TESOURA' and Uc == 'PAPEL' or PCc == 'PAPEL' and Uc == 'PEDRA': print(f'\033[1;31mHAHAHA! Eu venci! \033[0;30mEu escolhi \033[m{PCc} \033[0;30me você \033[m{Uc}\033[0;30m!') elif PCc == Uc: print(f'\033[1;33mEMPATE! Vamos jogar novamente! Eu escolhi \033[m{PCc} \033[0;30me você \033[m{Uc}') else: print(f'\033[0;34mT-T Infelizmente,\033[1;32mvocê venceu... \033[0;30mEu escolhi \033[m{PCc}, \033[0;30me você ' f'escolheu \033[m{Uc}\033[0;30m...\033[m')

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rqalpha/mod/rqalpha_mod_fxdayu_source/data_source/mongo.py

quantModel/Rqalpha-myquant-learning

44

115690

# encoding: utf-8 from datetime import date, datetime, time import motor.motor_asyncio import numpy as np import pandas as pd from dateutil.relativedelta import relativedelta from rqalpha.const import INSTRUMENT_TYPE from rqalpha.model.instrument import Instrument from rqalpha.utils.datetime_func import convert_date_to_int from rqalpha.utils.py2 import lru_cache from rqalpha.mod.rqalpha_mod_fxdayu_source.data_source.common import CacheMixin from rqalpha.mod.rqalpha_mod_fxdayu_source.data_source.common.minite import MiniteBarDataSourceMixin from rqalpha.mod.rqalpha_mod_fxdayu_source.data_source.common.odd import OddFrequencyBaseDataSource from rqalpha.mod.rqalpha_mod_fxdayu_source.utils import Singleton from rqalpha.mod.rqalpha_mod_fxdayu_source.utils.asyncio import get_asyncio_event_loop from rqalpha.mod.rqalpha_mod_fxdayu_source.utils.converter import DataFrameConverter INSTRUMENT_TYPE_MAP = { INSTRUMENT_TYPE.CS: "stock", INSTRUMENT_TYPE.INDX: "stock", } class NoneDataError(BaseException): pass class MongoDataSource(OddFrequencyBaseDataSource, MiniteBarDataSourceMixin): __metaclass__ = Singleton def __init__(self, path, mongo_url): super(MongoDataSource, self).__init__(path) from rqalpha.mod.rqalpha_mod_fxdayu_source.share.mongo_handler import MongoHandler self._handler = MongoHandler(mongo_url) self._client = motor.motor_asyncio.AsyncIOMotorClient(mongo_url) self._db_map = self._get_frequency_db_map() def _get_frequency_db_map(self): map_ = self._handler.client.get_database("meta").get_collection("db_map").find() dct = {item["type"]: item["map"] for item in map_} return dct def _get_db(self, instrument, frequency): try: if isinstance(instrument, Instrument): instrument_type = instrument.enum_type else: instrument_type = instrument type_ = INSTRUMENT_TYPE_MAP[instrument_type] return self._db_map[type_][frequency] except KeyError: message = instrument.order_book_id if isinstance(instrument, Instrument) else instrument raise NoneDataError("MongoDB 中没有品种%s的%s数据" % (message, frequency)) async def _do_get_bars(self, db, collection, filters, projection, fill=np.NaN): dct = {} l = 0 async for doc in self._client[db][collection].find(filters, projection): _l = doc.pop('_l') l += _l for key, values in doc.items(): if isinstance(values, list) and (len(values) == _l): dct.setdefault(key, []).extend(values) for values in dct.values(): if len(values) != l: values.extend([fill] * l) df = pd.DataFrame(dct) if df.size: return df.sort_values("datetime") else: return None def _get_bars_in_days(self, instrument, frequency, params): s_date = params[0]["trade_date"] e_date = params[-1]["trade_date"] s_time = params[0]["start_time"] if "start_time" in params[0] else 0 e_time = params[-1]["end_time"] if "end_time" in params[-1] else 150000 s_dt_int = convert_date_to_int(s_date) + s_time e_dt_int = convert_date_to_int(e_date) + e_time db = self._get_db(instrument=instrument, frequency=frequency) collection = instrument.order_book_id filters = {"_d": {"$gte": datetime.combine(s_date, time=time()), "$lte": datetime.combine(e_date, time=time())}} projection = {"_id": 0, "_d": 0} loop = get_asyncio_event_loop() bars = loop.run_until_complete(self._do_get_bars(db, collection, filters, projection)) if bars is not None and bars.size: bars = DataFrameConverter.df2np(bars) else: bars = DataFrameConverter.empty() s_pos = np.searchsorted(bars["datetime"], s_dt_int) e_pos = np.searchsorted(bars["datetime"], e_dt_int, side="right") return bars[s_pos:e_pos] def raw_history_bars(self, instrument, frequency, start_dt=None, end_dt=None, length=None): # 转换到自建mongodb结构s if frequency.endswith("m"): return MiniteBarDataSourceMixin.raw_history_bars( self, instrument, frequency, start_dt=start_dt, end_dt=end_dt, length=length) else: code = instrument.order_book_id db = self._get_db(instrument, frequency) data = self._handler.read(code, db=db, start=start_dt, end=end_dt, length=length, sort=[("datetime", 1)]). \ reset_index() if data is not None and data.size: return DataFrameConverter.df2np(data) else: return DataFrameConverter.empty() def is_base_frequency(self, instrument, frequency): if isinstance(instrument, Instrument): instrument_type = instrument.enum_type else: instrument_type = instrument type_ = INSTRUMENT_TYPE_MAP[instrument_type] return type_ in self._db_map and frequency in self._db_map[type_] def current_snapshot(self, instrument, frequency, dt): pass def _get_date_range(self, frequency): from pymongo import DESCENDING try: db = self._get_db(INSTRUMENT_TYPE.CS, frequency) except NoneDataError: db = self._get_db(INSTRUMENT_TYPE.CS, "1" + frequency[-1]) key = "_d" if frequency.endswith("m") else "datetime" try: start = self._handler.client.get_database(db).get_collection("600000.XSHG").find() \ .sort(key).limit(1)[0][key] end = self._handler.client.get_database(db).get_collection("600000.XSHG").find() \ .sort(key, direction=DESCENDING).limit(1)[0][key] except IndexError: raise RuntimeError("无法从MongoDb获取数据时间范围") return start.date(), end.date() @lru_cache(maxsize=10) def available_data_range(self, frequency): if frequency.endswith("d") or frequency.endswith("h"): return date(2012, 6, 1), date.today() - relativedelta(days=1) return self._get_date_range(frequency) class MongoCacheDataSource(MongoDataSource, CacheMixin): def __init__(self, path, mongo_url): super(MongoCacheDataSource, self).__init__(path, mongo_url) CacheMixin.__init__(self)

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telluric/constants.py

FlorianPignol/telluric

81

47397

"""Useful constants. """ from rasterio.crs import CRS WGS84_SRID = 4326 #: WGS84 CRS. WGS84_CRS = CRS.from_epsg(WGS84_SRID) WEB_MERCATOR_SRID = 3857 #: Web Mercator CRS. WEB_MERCATOR_CRS = CRS.from_epsg(WEB_MERCATOR_SRID) # Best widely used, equal area projection according to # http://icaci.org/documents/ICC_proceedings/ICC2001/icc2001/file/f24014.doc # (found on https://en.wikipedia.org/wiki/Winkel_tripel_projection#Comparison_with_other_projections) #: Eckert IV CRS. EQUAL_AREA_CRS = CRS({'proj': 'eck4'}) DEFAULT_SRID = WGS84_SRID #: Default CRS, set to :py:data:`~telluric.constants.WGS84_CRS`. DEFAULT_CRS = WGS84_CRS def _MERCATOR_RESOLUTION_MAPPING(zoom_level): return (2 * 20037508.342789244) / (256 * pow(2, zoom_level)) MERCATOR_RESOLUTION_MAPPING = dict((i, _MERCATOR_RESOLUTION_MAPPING(i)) for i in range(21)) RASTER_TYPE = 'raster'

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algorithm4.py

guoqing-tang/algorithm

1

73007

# * -- utf-8 -- * # python3 # Author: Tang Time:2018/4/17 '''简述：要求输入某年某月某日提问：求判断输入日期是当年中的第几天？''' year = int(input('请输入年份：')) month = int(input('请输入月份：')) day = int(input('请输入日期：')) if day >31: print ('请正确输入') if month > 12: print('请正确输入') list1 = [31,59,90,130,151,181,212,243,273,304,334] if (year % 4) == 0 or (year % 100) == 0 or (year % 400) == 0 and month >= 2: data = list1[month - 2] + day +1 if month == 1: data = day if month == 2: data = 31 + day print("这一天是这一年第"+str(data)+"天")

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tests/test_decast.py

interesaaat/onnxconverter-common

83

67159

import unittest from onnx import helper from onnx import onnx_pb as onnx_proto from onnxconverter_common.decast import decast class DecastTestCase(unittest.TestCase): def test_decast(self): nodes = [] nodes[0:] = [helper.make_node('Identity', ['input1'], ['identity1'])] nodes[1:] = [helper.make_node('Cast', ['identity1'], ['cast0'], to=1)] nodes[2:] = [helper.make_node('ReduceSum', ['cast0'], ['reduce0'])] nodes[3:] = [helper.make_node('Cast', ['reduce0'], ['cast1'], to=6)] nodes[4:] = [helper.make_node('Identity', ['cast1'], ['output0'])] input0 = helper.make_tensor_value_info('input1', onnx_proto.TensorProto.FLOAT, [1, 1, 2, 3]) output0 = helper.make_tensor_value_info('output0', onnx_proto.TensorProto.FLOAT, [1, 1, 2, 3]) graph = helper.make_graph(nodes, 'test_graph', [input0], [output0]) model = helper.make_model(graph) self.assertIsNotNone(model) converted_model = decast(model, ['ReduceSum']) self.assertTrue(len(converted_model.graph.node) == 3) if __name__ == '__main__': unittest.main()

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2021/examples-in-class-2021-10-08/three_five_fifteen.py

ati-ozgur/course-python

1

135079

for index in range(1,101): # if index % 15 == 0: # print("fifteen") if index % 3 == 0 and index % 5 == 0 : print("fifteen") elif index % 3 == 0: print("three") elif index % 5 == 0: print("five") else: print(index)

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certbot_dns_cfproxy/__init__.py

ProfFan/certbot-dns-cfproxy

2

13262

<filename>certbot_dns_cfproxy/__init__.py """ The `~certbot_dns_cfproxy.dns_cfproxy` plugin automates the process of completing a ``dns-01`` challenge (`~acme.challenges.DNS01`) by creating, and subsequently removing, TXT records using the CFProxy API. Examples -------- .. code-block:: bash :caption: To acquire a certificate for ``example.com`` certbot certonly \\ -a certbot-dns-cfproxy:dns-cfproxy \\ -d example.com """

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recognitionFace/train.py

factzero/pytorch_jaguarface_examples

2

167262

# -*- coding: UTF-8 -*- import os import argparse import cv2 import time import torch import torch.optim as optim from tqdm import tqdm from config.config import cfg from core.resnet import resnet18 from core.metrics import Arcface from utils.data_gen import get_train_loader parser = argparse.ArgumentParser(description='recognition face Training') parser.add_argument('--training_dataset', default='./data/faces_emore/imgs/', help='Training dataset directory') parser.add_argument('--network', default='resnet18', help='Backbone network mobile0.25 or resnet50') parser.add_argument('--resume_net', default=None, help='resume net for retraining') parser.add_argument('--resume_epoch', default=0, type=int, help='resume iter for retraining') parser.add_argument('--save_folder', default='./weights/', help='Location to save checkpoint models') args = parser.parse_args() def resume_net_param(net, trained_model): print('Loading trained model: {}'.format(trained_model)) state_dict = torch.load(args.resume_net) # create new OrderedDict that does not contain `module.` from collections import OrderedDict new_state_dict = OrderedDict() for k, v in state_dict.items(): head = k[:7] if head == 'module.': name = k[7:] # remove `module.` else: name = k new_state_dict[name] = v net.load_state_dict(new_state_dict) return net def train(): save_folder = args.save_folder if not os.path.exists(args.save_folder): os.mkdir(args.save_folder) print('load dataset ...') with torch.no_grad(): loader, class_num = get_train_loader(cfg, args.training_dataset) print('network ...') device = torch.device("cuda" if cfg['gpu'] else "cpu") if args.network == 'resnet18': net = resnet18(cfg['embedding_size']) if args.resume_net is not None: print('Loading resume network...') net = resume_net_param(net, args.resume_net) net = net.to(device) print('init cirterion ...') criterion = torch.nn.CrossEntropyLoss() metric_fc = Arcface(embedding_size=cfg['embedding_size'], classnum=class_num).to(device) optimizer = optim.SGD([{'params': net.parameters()}, {'params': metric_fc.parameters()}], lr=cfg['initial_lr'], momentum=cfg['momentum'], weight_decay=cfg['weight_decay']) max_epoch = cfg['epoch'] start_epoch = args.resume_epoch net.train() for epoch in range(start_epoch, max_epoch): print('Epoch:{}/{}, batch:{}'.format(epoch, max_epoch, cfg['batch_size'])) for imgs, labels in tqdm(iter(loader)): imgs = imgs.to(device) labels = labels.to(device) # forward features = net(imgs) # backprop output = metric_fc(features, labels) loss = criterion(output, labels) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch % 10 == 0 and epoch > 0): save_w_file = args.network + '_epoch_' + str(epoch) + '_' + str(time.time()) + '.pth' print('saving weights : {}'.format(save_w_file)) torch.save(net.state_dict(), save_folder + save_w_file) if __name__ == "__main__": train() image_path = "./data/87022.jpg" img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR) b, g, r = cv2.split(img_raw) img_1 = cv2.merge([r, g, b]) #cv2.imshow('ori', img_raw) cv2.imshow('img_1', img_1) cv2.waitKey(0)

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lib2/tiktok.py

offdhymaz/wa

37

175509

from requests import Session from bs4 import BeautifulSoup import re class tiktok: def __init__(self) -> None: self.request = Session() self.url = "https://ssstik.io" self.html = self.request.get(self.url).text self.key = BeautifulSoup(self.html, "html.parser").find_all("form",attrs={"data-hx-target":"#target"})[0].get("include-vals") self.post = BeautifulSoup(self.html, "html.parser").find_all("form",attrs={"data-hx-target":"#target"})[0].get("data-hx-post") self.tt = re.search("tt\:\'(.*?)\'",self.key)[1] self.ts = re.search("ts\:([0-9]{5,15})",self.key)[1] self.header = {"content-type": "application/x-www-form-urlencoded; charset=UTF-8","hx-active-element": "submit","hx-current-url": "https://ssstik.io/","hx-request": "true","hx-target": "target","origin": "https://ssstik.io","sec-fetch-dest": "","sec-fetch-mode": "cors","sec-fetch-site": "same-origin","user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36"} def download(self, url) -> dict: data = {"id": url,"locale": "en","tt": self.tt,"ts": int(self.ts)} post = self.request.post(f"{self.url}{self.post}", headers=self.header, data=data) respon = BeautifulSoup(post.text, "html.parser") hasil = {"video":[f'{self.url}{respon.find_all("a",class_="pure-button pure-button-primary is-center u-bl dl-button download_link without_watermark")[0].get("href")}',f'{self.url}{respon.find_all("a",class_="pure-button pure-button-primary is-center u-bl dl-button download_link without_watermark_direct")[0].get("href")}'],"music":f'{respon.find_all("a",class_="pure-button pure-button-primary is-center u-bl dl-button download_link music")[0].get("href")}'} return hasil class tiktok2: def __init__(self, url) -> None: self.request = Session() self.url =url self.header = {"accept": "*/*","accept-language": "en-US,en;q=0.9,id;q=0.8","origin": "https://snaptik.app","referer": "https://snaptik.app/ID","sec-fetch-dest": None,"sec-fetch-mode": "cors","sec-fetch-site": "same-origin","user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36",} def download(self): self.request.post("https://snaptik.app/check_token.php", headers=self.header) self.bs = BeautifulSoup(self.request.post("https://snaptik.app/action-2021.php", headers=self.header, data={"url":self.url}).text, "html.parser") return [self.request,{"title":self.bs('a', attrs={"title":""})[0].text,"date":self.bs("b", attrs={"class":"blur"})[0].text,"url":list(filter(lambda x:x, map(lambda x:x["href"] if "token" in x["href"] else None, self.bs("a", attrs={"class":"abutton is-success is-fullwidth"}))))}, self.header]

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nterm.py

KorfLab/qalpha

0

130968

<reponame>KorfLab/qalpha import os import sys for genome in os.listdir(sys.argv[1]): print(f'{sys.argv[1]}/{genome}') ma = [] with open(f'{sys.argv[1]}/{genome}') as fp: for line in fp.readlines(): if not line.startswith('>'): ma.append(line.rstrip()) slen = len(ma[0]) for seq in ma: print(seq[:120]) print()

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src/Hero.py

Astayonix/SailorMoonRPG

0

1601901

<filename>src/Hero.py #!/usr/bin/python class Hero(object): def __init__(self, Name): self.name = Name

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ooi_harvester/utils/parser.py

ooi-data/ooi_harvester

4

37507

import os import datetime import math import traceback from typing import List import requests from loguru import logger from lxml import etree from siphon.catalog import TDSCatalog from dask.utils import memory_repr import numpy as np from dateutil import parser from ooi_harvester.settings import harvest_settings def estimate_size_and_time(raw): m = "" if "requestUUID" in raw: est_size = raw["sizeCalculation"] / 1024 ** 2 size_txt = "MB" if (est_size / 1024) >= 1.0: est_size = est_size / 1024 size_txt = "GB" est_time = raw["timeCalculation"] time_txt = "Seconds" if (est_time / 60) >= 1.0 and (est_time / 60) < 60.0: est_time = math.floor(est_time / 60) time_txt = "Minutes" if est_time == 1: time_txt = "Minute" elif (est_time / 60) >= 60.0: est_time = math.floor(est_time / 60 ** 2) time_txt = "Hours" if est_time == 1: time_txt = "Hour" m = f""" Estimated File size: {est_size:.4} {size_txt} Estimated Time: {est_time} {time_txt} """ elif "message" in raw: m = f""" No estimate calculated. {raw['message']['status']} """ return m def parse_uframe_response(resp): if "allURLs" in resp: return { "request_id": resp["requestUUID"], "thredds_catalog": resp["allURLs"][0], "download_catalog": resp["allURLs"][1], "status_url": resp["allURLs"][1] + "/status.txt", "data_size": resp["sizeCalculation"], "estimated_time": resp["timeCalculation"], "units": { "data_size": "bytes", "estimated_time": "seconds", "request_dt": "UTC", }, "request_dt": datetime.datetime.utcnow().isoformat(), } logger.warning(resp) return None def param_change(name): """ Method to accomodate for param change. https://oceanobservatories.org/renaming-data-stream-parameters/ """ if name == 'pressure_depth': return 'pressure' else: return name def parse_param_dict(param_dict): unit = None if "unit" in param_dict: if isinstance(param_dict["unit"], dict): if "value" in param_dict["unit"]: unit = param_dict["unit"]["value"] product_type = None if "data_product_type" in param_dict: if isinstance(param_dict["data_product_type"], dict): if "value" in param_dict["data_product_type"]: product_type = param_dict["data_product_type"]["value"] return { "pid": param_dict["id"], "reference_designator": param_change(param_dict["name"]), "parameter_name": param_dict["display_name"], "netcdf_name": param_dict["netcdf_name"], "standard_name": param_dict["standard_name"], "description": param_dict["description"], "unit": unit, "data_level": param_dict['data_level'], "data_product_type": product_type, "data_product_identifier": param_dict["data_product_identifier"], "last_updated": datetime.datetime.utcnow().isoformat(), } def parse_global_range_dataframe(global_ranges): """Cleans up the global ranges dataframe""" global_df = global_ranges[global_ranges.columns[:-3]] global_df.columns = [ "reference_designator", "parameter_id_r", "parameter_id_t", "global_range_min", "global_range_max", "data_level", "units", ] return global_df def get_bytes(value, unit): bytes_map = { 'bytes': 1, 'Kbytes': 1024 ** 1, 'Mbytes': 1024 ** 2, 'Gbytes': 1024 ** 3, } return value * bytes_map[unit] def parse_dataset_element(d, namespace): dataset_dict = {} for i in d.getiterator(): clean_tag = i.tag.replace('{' + namespace + '}', '') if clean_tag == 'dataset': dataset_dict = dict(**i.attrib) if clean_tag == 'dataSize': dataset_dict = dict( data_size=float(i.text), **i.attrib, **dataset_dict ) dataset_dict = dict( size_bytes=get_bytes( dataset_dict['data_size'], dataset_dict['units'] ), **dataset_dict, ) if clean_tag == 'date': dataset_dict = dict(date_modified=i.text, **dataset_dict) return dataset_dict def parse_response_thredds(response): stream_name = response['stream']['table_name'] catalog = TDSCatalog( response['result']['thredds_catalog'].replace('.html', '.xml') ) catalog_dict = { 'stream_name': stream_name, 'catalog_url': catalog.catalog_url, 'base_tds_url': catalog.base_tds_url, 'async_url': response['result']['download_catalog'], } req = requests.get(catalog.catalog_url) catalog_root = etree.fromstring(req.content) namespaces = {} for k, v in catalog_root.nsmap.items(): if k is None: namespaces['cat'] = v else: namespaces[k] = v dataset_elements = catalog_root.xpath( '/cat:catalog/cat:dataset/cat:dataset', namespaces=namespaces ) datasets = [ parse_dataset_element(i, namespaces['cat']) for i in dataset_elements ] catalog_dict['datasets'] = datasets return catalog_dict def filter_and_parse_datasets(cat): import re stream_cat = cat.copy() name = stream_cat['stream_name'] provenance_files = [] filtered_datasets = [] for d in stream_cat['datasets']: m = re.search( r'(deployment(\d{4})_(%s)_(\d{4}\d{2}\d{2}T\d+.\d+)-(\d{4}\d{2}\d{2}T\d+.\d+).nc)' # noqa % (name), str(d['name']), ) prov = re.search( r'(deployment(\d{4})_(%s)_aggregate_provenance.json)' % (name), str(d['name']), ) if m: _, dep_num, _, start, end = m.groups() dataset = dict( deployment=int(dep_num), start_ts=start, end_ts=end, **d ) filtered_datasets.append(dataset) elif prov: _, dep_num, _ = prov.groups() provenance = dict(deployment=int(dep_num), **d) provenance_files.append(provenance) total_bytes = np.sum([d['size_bytes'] for d in filtered_datasets]) stream_cat['datasets'] = filtered_datasets stream_cat['provenance'] = provenance_files stream_cat['total_data_size'] = memory_repr(total_bytes) stream_cat['total_data_bytes'] = total_bytes return stream_cat def filter_datasets_by_time( datasets: List[dict], start_dt: np.datetime64, end_dt: np.datetime64 ) -> List[dict]: """ Filters datasets collection based on the given start and end datetime. Each dataset dictionary in the collection MUST have `start_ts` and `end_ts`key in it. Parameters ---------- datasets : list The datasets collection to be filtered. start_dt : np.datetime64 The start datetime desired. end_dt : np.datetime64 The end datetime desired. Returns ------- list The filtered datasets collection """ filtered_datasets = [] for d in datasets: start_d = np.datetime64(parser.parse(d['start_ts'])) end_d = np.datetime64(parser.parse(d['end_ts'])) if start_d >= start_dt.astype( start_d.dtype ) and end_d <= end_dt.astype(start_d.dtype): filtered_datasets.append(d) return filtered_datasets def setup_etl(stream, source='ooinet', target_bucket='s3://ooi-data'): name = stream['stream_name'] harvest_location = os.path.expanduser('~/.ooi-harvester') # Setup Local temp folder for netcdf temp_fold = os.path.join(harvest_location, name) if not os.path.exists(os.path.dirname(temp_fold)): os.mkdir(os.path.dirname(temp_fold)) if not os.path.exists(temp_fold): os.mkdir(temp_fold) # Setup S3 Bucket temp_s3_fold = f"s3://temp-ooi-data/{name}.zarr" final_s3_fold = f"{target_bucket}/{name}" if source == 'ooinet': retrieved_dt = stream['result']['request_dt'] else: retrieved_dt = stream['retrieved_dt'] del stream['retrieved_dt'] return dict( temp_fold=temp_fold, temp_bucket=temp_s3_fold, final_bucket=final_s3_fold, retrieved_dt=retrieved_dt, **stream, ) def seconds_to_date(num): start_dt = datetime.datetime(1900, 1, 1) return start_dt + datetime.timedelta(seconds=num) def get_storage_options(path): if path.startswith("s3://"): return harvest_settings.storage_options.aws.dict() def get_items(keys, orig_dict): new_dict = {} for k, v in orig_dict.items(): if k in keys: new_dict[k] = v return new_dict def rename_item(old_key, new_key, orig_dict): new_dict = orig_dict.copy() if old_key in new_dict: new_dict.update({new_key: new_dict[old_key]}) del new_dict[old_key] return new_dict def parse_exception(exception): exc_dict = { 'type': type(exception).__name__, 'value': str(exception), 'traceback': "".join( traceback.format_exception( type(exception), exception, exception.__traceback__ ) ), } return exc_dict

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torchqf/model/factor_model.py

simaki/torchqf

7

167082

from typing import TypeVar import torch from torch import Tensor from torch.nn import Module T = TypeVar("T") class FactorModel(Module): """Factor model. Attributes: beta (Tensor): Created after `fit`. Shape: - input: :math:`(*, A, T)` :math:`A` is the number of assets. :math:`T` is the number of time steps. - output: :math:`(*, A, T)` :math:`A` is the number of assets. :math:`T` is the number of time steps. - factor: :math:`(*, F, T)` :math:`F` is the number of assets. :math:`T` is the number of time steps. - beta: :math:`(F, A)` :math:`F` is the number of factor. :math:`A` is the number of assets. Examples: >>> import torchqf >>> from torchqf.stochastic import generate_brownian >>> _ = torch.manual_seed(42) >>> input = generate_brownian((2, 5), 0.1) >>> factor = generate_brownian((2, 5), 0.1) >>> fm = FactorModel().fit(input, factor) >>> fm(input, factor) tensor([[ 0.0032, -0.0023, -0.0046, 0.0138, -0.0099], [-0.0234, 0.0157, -0.0017, -0.0052, 0.0025]]) The method `fit_forward` fits and forwards at once. >>> fm.fit_forward(input, factor) tensor([[ 0.0032, -0.0023, -0.0046, 0.0138, -0.0099], [-0.0234, 0.0157, -0.0017, -0.0052, 0.0025]]) """ def fit(self: T, input: Tensor, factor: Tensor, enable_grad: bool = False) -> T: """Fit the model using the asset returns (`input`) and the factor returns (`factor`). Args: input (Tensor): Returns of assets. factor (Tensor): Returns of factors. enable_grad (bool, default=False): Flag whether to enable grad (True), or disable (False). Returns: FactorModel: Returns `self`. Shape: input : :math:`(*, A, T)` :math:`A` is the number of assets. :math:`T` is the number of time steps. factor : :math:`(*, F, T)` :math:`F` is the number of assets. :math:`T` is the number of time steps. beta : :math:`(*, F, A)` :math:`F` is the number of factors. :math:`A` is the number of assets. """ assert input.size(-1) == factor.size(-1), "numbers of time steps do not match" assert input.ndim == 2, "not supported" assert factor.ndim == 2, "not supported" with torch.set_grad_enabled(mode=enable_grad): tensor_x = factor.t() # shape : (T, F) tensor_y = input.transpose(-2, -1) # shape : (T, A) # Compute beta : (F, T) @ (T, A) = (F, A) self.beta = torch.mm(torch.linalg.pinv(tensor_x), tensor_y) return self def forward(self, input: torch.Tensor, factor: torch.Tensor) -> torch.Tensor: factor_return = torch.mm(self.beta.t(), factor) # shape : (*, A, T) return input - factor_return def fit_forward(self, input: torch.Tensor, factor: torch.Tensor) -> torch.Tensor: return self.fit(input, factor, enable_grad=True)(input, factor)

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setup.py

Mahima-ai/ALEPython

107

58516

# -*- coding: utf-8 -*- from setuptools import find_packages, setup with open("README.md") as f: long_description = f.read() with open("requirements.txt", "r") as f: required = f.read().splitlines() setup( name="alepython", description="Python Accumulated Local Effects (ALE) package.", author="<NAME>", author_email="<EMAIL>", license="Apache 2", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/MaximeJumelle/alepython/", install_requires=required, extras_require={"test": ["pytest>=5.4", "pytest-cov>=2.8"]}, setup_requires=["setuptools-scm"], python_requires=">=3.5", use_scm_version=dict(write_to="src/alepython/_version.py"), keywords="alepython", package_dir={"": "src"}, packages=find_packages(where="src"), include_package_data=True, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approched :: Apache 2", "Operating System :: OS Independent", ], )

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t3f/batch_ops.py

KhrulkovV/t3f

1

31564

<gh_stars>1-10 import tensorflow as tf from t3f.tensor_train_base import TensorTrainBase from t3f.tensor_train_batch import TensorTrainBatch from t3f import ops def concat_along_batch_dim(tt_list): """Concat all TensorTrainBatch objects along batch dimension. Args: tt_list: a list of TensorTrainBatch objects. Returns: TensorTrainBatch """ ndims = tt_list[0].ndims() if isinstance(tt_list, TensorTrainBase): # Not a list but just one element, nothing to concat. return tt_list for batch_idx in range(len(tt_list)): if not isinstance(tt_list[batch_idx], TensorTrainBatch): raise ValueError('All objects in the list should be TTBatch objects, got ' '%s' % tt_list[batch_idx]) for batch_idx in range(1, len(tt_list)): if tt_list[batch_idx].get_raw_shape() != tt_list[0].get_raw_shape(): raise ValueError('Shapes of all TT-batch objects should coincide, got %s ' 'and %s' % (tt_list[0].get_raw_shape(), tt_list[batch_idx].get_raw_shape())) if tt_list[batch_idx].get_tt_ranks() != tt_list[0].get_tt_ranks(): raise ValueError('TT-ranks of all TT-batch objects should coincide, got ' '%s and %s' % (tt_list[0].get_tt_ranks(), tt_list[batch_idx].get_tt_ranks())) res_cores = [] for core_idx in range(ndims): curr_core = tf.concat([tt.tt_cores[core_idx] for tt in tt_list], axis=0) res_cores.append(curr_core) try: batch_size = sum([tt.batch_size for tt in tt_list]) except TypeError: # The batch sizes are not defined and you can't sum Nones. batch_size = None return TensorTrainBatch(res_cores, tt_list[0].get_raw_shape(), tt_list[0].get_tt_ranks(), batch_size) def multiply_along_batch_dim(batch_tt, weights): """Multiply each TensorTrain in a batch by a number. Args: batch_tt: TensorTrainBatch object, TT-matrices or TT-tensors. weights: 1-D tf.Tensor (or something convertible to it like np.array) of size tt.batch_sie with weights. Returns: TensorTrainBatch """ weights = tf.convert_to_tensor(weights) tt_cores = list(batch_tt.tt_cores) if batch_tt.is_tt_matrix(): weights = weights[:, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis] else: weights = weights[:, tf.newaxis, tf.newaxis, tf.newaxis] tt_cores[0] = weights * tt_cores[0] out_shape = batch_tt.get_raw_shape() out_ranks = batch_tt.get_tt_ranks() out_batch_size = batch_tt.batch_size return TensorTrainBatch(tt_cores, out_shape, out_ranks, out_batch_size) def gram_matrix(tt_vectors, matrix=None): """Computes Gramian matrix of a batch of TT-vecors. If matrix is None, computes res[i, j] = t3f.flat_inner(tt_vectors[i], tt_vectors[j]). If matrix is present, computes res[i, j] = t3f.flat_inner(tt_vectors[i], t3f.matmul(matrix, tt_vectors[j])) or more shortly res[i, j] = tt_vectors[i]^T * matrix * tt_vectors[j] but is more efficient. Args: tt_vectors: TensorTrainBatch. matrix: None, or TensorTrain matrix. Returns: tf.tensor with the Gram matrix. """ return pairwise_flat_inner(tt_vectors, tt_vectors, matrix) def pairwise_flat_inner(tt_1, tt_2, matrix=None): """Computes all scalar products between two batches of TT-objects. If matrix is None, computes res[i, j] = t3f.flat_inner(tt_1[i], tt_2[j]). If matrix is present, computes res[i, j] = t3f.flat_inner(tt_1[i], t3f.matmul(matrix, tt_2[j])) or more shortly res[i, j] = tt_1[i]^T * matrix * tt_2[j] but is more efficient. Args: tt_1: TensorTrainBatch. tt_2: TensorTrainBatch. matrix: None, or TensorTrain matrix. Returns: tf.tensor with the matrix of pairwise scalar products (flat inners). """ ndims = tt_1.ndims() if matrix is None: curr_core_1 = tt_1.tt_cores[0] curr_core_2 = tt_2.tt_cores[0] mode_string = 'ij' if tt_1.is_tt_matrix() else 'i' einsum_str = 'pa{0}b,qc{0}d->pqbd'.format(mode_string) res = tf.einsum(einsum_str, curr_core_1, curr_core_2) for core_idx in range(1, ndims): curr_core_1 = tt_1.tt_cores[core_idx] curr_core_2 = tt_2.tt_cores[core_idx] einsum_str = 'pqac,pa{0}b,qc{0}d->pqbd'.format(mode_string) res = tf.einsum(einsum_str, res, curr_core_1, curr_core_2) else: # res[i, j] = tt_1[i] ^ T * matrix * tt_2[j] if not tt_1.is_tt_matrix() or not tt_2.is_tt_matrix() or not matrix.is_tt_matrix(): raise ValueError('When passing three arguments to pairwise_flat_inner, ' 'the first 2 of them should be TT-vecors and the last ' 'should be a TT-matrix. Got %s, %s, and %s instead.' % (tt_1, tt_2, matrix)) matrix_shape = matrix.get_raw_shape() if not tt_1.get_raw_shape()[0].is_compatible_with(matrix_shape[0]): raise ValueError('The shape of the first argument should be compatible ' 'with the shape of the TT-matrix, that is it should be ' 'possible to do the following matmul: ' 'transpose(tt_1) * matrix. Got the first argument ' '"%s" and matrix "%s"' % (tt_1, matrix)) if not tt_2.get_raw_shape()[0].is_compatible_with(matrix_shape[1]): raise ValueError('The shape of the second argument should be compatible ' 'with the shape of the TT-matrix, that is it should be ' 'possible to do the following matmul: ' 'matrix * tt_2. Got the second argument ' '"%s" and matrix "%s"' % (tt_2, matrix)) vectors_1_shape = tt_1.get_shape() if vectors_1_shape[2] == 1 and vectors_1_shape[1] != 1: # TODO: not very efficient, better to use different order in einsum. tt_1 = ops.transpose(tt_1) vectors_1_shape = tt_1.get_shape() vectors_2_shape = tt_2.get_shape() if vectors_2_shape[2] == 1 and vectors_2_shape[1] != 1: # TODO: not very efficient, better to use different order in einsum. tt_2 = ops.transpose(tt_2) vectors_2_shape = tt_2.get_shape() if vectors_1_shape[1] != 1: # TODO: do something so that in case the shape is undefined on compilation # it still works. raise ValueError('The tt_vectors_1 argument should be vectors (not ' 'matrices) with shape defined on compilation.') if vectors_2_shape[1] != 1: # TODO: do something so that in case the shape is undefined on compilation # it still works. raise ValueError('The tt_vectors_2 argument should be vectors (not ' 'matrices) with shape defined on compilation.') curr_core_1 = tt_1.tt_cores[0] curr_core_2 = tt_2.tt_cores[0] curr_matrix_core = matrix.tt_cores[0] # We enumerate the dummy dimension (that takes 1 value) with `k`. res = tf.einsum('pakib,cijd,qekjf->pqbdf', curr_core_1, curr_matrix_core, curr_core_2) for core_idx in range(1, ndims): curr_core_1 = tt_1.tt_cores[core_idx] curr_core_2 = tt_2.tt_cores[core_idx] curr_matrix_core = matrix.tt_cores[core_idx] res = tf.einsum('pqace,pakib,cijd,qekjf->pqbdf', res, curr_core_1, curr_matrix_core, curr_core_2) # Squeeze to make the result of size batch_size x batch_size instead of # batch_size x batch_size x 1 x 1. return tf.squeeze(res)

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account/admin.py

RichardLeeH/invoce_sys

0

8968

<gh_stars>0 # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.contrib import admin from django.contrib.auth.admin import UserAdmin from django.contrib.auth.models import User from rest_framework.authtoken.models import Token from account.models import Profile admin.site.site_header = 'invoce' class TokenAdmin(admin.ModelAdmin): list_display = ('key', 'uid', 'user', 'created') fields = ('user',) ordering = ('-created',) def uid(self, obj): return obj.user.id uid.short_description = u'用户ID' admin.site.unregister(Token) admin.site.register(Token, TokenAdmin) class ProfileInline(admin.StackedInline): model = Profile class UserCustomAdmin(UserAdmin): list_display = ('id', 'username', 'email', 'is_active', 'is_staff') inlines = (ProfileInline, ) ordering = ('-id', ) admin.site.unregister(User) admin.site.register(User, UserCustomAdmin)

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1.n_gram.py

Muraru-taketa/100_knocks

0

165976

#N_gram.py #owakatiの分かち書きをする。uni=1,bi=2 tri=3 '''どうやったら分かち書きになるか今日は眠いをbigramに手動ですると今日、日は、は眠、眠い今で始まって日で終わる感じで定義する。''' def n_gram(txt, n): #n=n_garmのn result = [] for i in range(0, len(txt) - n + 1):#例：今日という単語からn個(2)-で+1すると日から始まる result.append(txt[i:i + n]) #iで始まってi+n(nは指定分割個数的なやつ) return result txt = "I am an NLPer" words = txt.split(' ') #文字分け print(n_gram(txt, 2)) #単語 print(n_gram(words, 2))

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examples/chem_equil.py

bjodah/symneqsys

1

126346

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import division, print_function """ Example of a 4 dimensional root finidng problem (chemical acid/base equilbria) """ from sympy import pprint from symneqsys import SimpleNEQSys, Problem from symneqsys.minpack import MINPACK_Solver class ChemSys(SimpleNEQSys): var_tokens = 'NH3 NH4p Hp OHm H2O' param_tokens = 'Kw Ka ' + ' '.join(['init_'+x for x in var_tokens.split()]) @property def exprs(self): NH3, NH4p, Hp, OHm, H2O, Kw, Ka = [ self[token] for token in ('NH3', 'NH4p', 'Hp', 'OHm', 'H2O', 'Kw', 'Ka')] init = {x: self['init_'+x] for x in self.var_tokens.split()} return [ Hp*NH3/NH4p-Ka, # NH4+ acid equil Hp*OHm/H2O-Kw, # Water autoprotolysis equilibrium NH3+NH4p-init['NH3']-init['NH4p'], # preserv. of N atoms 3*NH3+4*NH4p+Hp+OHm+2*H2O-( 3*init['NH3']+4*init['NH4p'] + init['Hp']+init['OHm']+2*init['H2O']), # prsrv H atoms OHm+H2O-init['OHm']-init['H2O'], # preservation of O atoms NH4p+Hp-OHm-(init['NH4p']+init['Hp'] - init['OHm']) # preservation of charge ] def main(Sys, logger=None): """ Solve the example system using Levenberg-Marquardt algorithm from Netlib's MINPACK fortran code. """ sys = Sys() print('Variables:') pprint(sys.v) print('Expressions:') for expr in sys.exprs: pprint(expr) print('Jacobian:') pprint(sys.jac) solver = MINPACK_Solver( save_temp=True, tempdir='./build/chem_equil', logger=logger) solver.abstol = 1e-8 params = {'Kw': 1e-14, 'Ka': 10**-9.26} init = {'NH3': 1e-3, 'NH4p': 0.0, 'Hp': 1e-7, 'OHm': 1e-7, 'H2O': 1.0} params.update({'init_'+key: val for key, val in init.iteritems()}) problem = Problem( sys, params, guess={ k: v if v > 1e-12 else 1e-12 for k, v in init.iteritems()}, solver=solver) success = problem.solve(itermax=100, solver_type='lm') if success: print("Successfully found a root: " + ", ".join([key+'='+str(problem.solution[sys[key]]) for key in init.keys()]) + ", using Levenberg-Marquard (lm)") else: print("Root-finding unsuccessful.") print('Full numerical info:', problem.solver.num_result) if __name__ == '__main__': import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) main(ChemSys, logger=logger)

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bfgame/components/shield.py

ChrisLR/BasicDungeonRL

3

42257

<reponame>ChrisLR/BasicDungeonRL from core.components import Component, listing @listing.register class Shield(Component): NAME = "shield" __slots__ = ["armor_class_melee", "armor_class_missile"] def __init__(self, armor_class_melee, armor_class_missile): super().__init__() self.armor_class_melee = armor_class_melee self.armor_class_missile = armor_class_missile def copy(self): return Shield(self.armor_class_melee, self.armor_class_missile)

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src/nise/metadata/config.py

kurtp-ni/nimi-python

88

94005

# -*- coding: utf-8 -*- # This file is generated from NI Switch Executive API metadata version 21.0.0d1 config = { 'api_version': '21.0.0d1', 'c_function_prefix': 'niSE_', 'close_function': 'CloseSession', 'context_manager_name': { }, 'custom_types': [ ], 'driver_name': 'NI Switch Executive', 'driver_registry': 'Switch Executive', 'extra_errors_used': [ 'InvalidRepeatedCapabilityError' ], 'init_function': 'OpenSession', 'library_info': { 'Linux': { '64bit': { 'name': 'nise', 'type': 'cdll' } }, 'Windows': { '32bit': { 'name': 'nise.dll', 'type': 'windll' }, '64bit': { 'name': 'nise.dll', 'type': 'cdll' } } }, 'metadata_version': '2.0', 'module_name': 'nise', 'repeated_capabilities': [ ], 'session_class_description': 'An NI Switch Executive session', 'session_handle_parameter_name': 'vi', 'use_locking': False }

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convert.py

Resist4263/span-aste-1

0

22540

<filename>convert.py<gh_stars>0 from gensim.scripts.glove2word2vec import glove2word2vec (count, dimensions) = glove2word2vec("dataset/glove.42B.300d.txt", "dataset/cropus/42B_w2v.txt")

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zhiliao/blog/__init__.py

gladgod/zhiliao

0

135462

""" Provides a blogging app with posts, keywords, categories and comments. Posts can be listed by month, keyword, category or author. """ from __future__ import unicode_literals from zhiliao import __version__

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wavefront_api_client/models/customer_preferences.py

mdennehy/python-client

0

71969

# coding: utf-8 """ Wavefront REST API The Wavefront REST API enables you to interact with Wavefront servers using standard REST API tools. You can use the REST API to automate commonly executed operations such as automatically tagging sources.When you make REST API calls outside the Wavefront REST API documentation you must add the header \"Authorization: Bearer <<API-TOKEN>>\" to your HTTP requests. # noqa: E501 OpenAPI spec version: v2 Contact: <EMAIL> Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from wavefront_api_client.models.user_group import UserGroup # noqa: F401,E501 class CustomerPreferences(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'blacklisted_emails': 'dict(str, int)', 'created_epoch_millis': 'int', 'creator_id': 'str', 'customer_id': 'str', 'default_user_groups': 'list[UserGroup]', 'deleted': 'bool', 'grant_modify_access_to_everyone': 'bool', 'hidden_metric_prefixes': 'dict(str, int)', 'hide_ts_when_querybuilder_shown': 'bool', 'id': 'str', 'invite_permissions': 'list[str]', 'landing_dashboard_slug': 'str', 'show_onboarding': 'bool', 'show_querybuilder_by_default': 'bool', 'updated_epoch_millis': 'int', 'updater_id': 'str' } attribute_map = { 'blacklisted_emails': 'blacklistedEmails', 'created_epoch_millis': 'createdEpochMillis', 'creator_id': 'creatorId', 'customer_id': 'customerId', 'default_user_groups': 'defaultUserGroups', 'deleted': 'deleted', 'grant_modify_access_to_everyone': 'grantModifyAccessToEveryone', 'hidden_metric_prefixes': 'hiddenMetricPrefixes', 'hide_ts_when_querybuilder_shown': 'hideTSWhenQuerybuilderShown', 'id': 'id', 'invite_permissions': 'invitePermissions', 'landing_dashboard_slug': 'landingDashboardSlug', 'show_onboarding': 'showOnboarding', 'show_querybuilder_by_default': 'showQuerybuilderByDefault', 'updated_epoch_millis': 'updatedEpochMillis', 'updater_id': 'updaterId' } def __init__(self, blacklisted_emails=None, created_epoch_millis=None, creator_id=None, customer_id=None, default_user_groups=None, deleted=None, grant_modify_access_to_everyone=None, hidden_metric_prefixes=None, hide_ts_when_querybuilder_shown=None, id=None, invite_permissions=None, landing_dashboard_slug=None, show_onboarding=None, show_querybuilder_by_default=None, updated_epoch_millis=None, updater_id=None): # noqa: E501 """CustomerPreferences - a model defined in Swagger""" # noqa: E501 self._blacklisted_emails = None self._created_epoch_millis = None self._creator_id = None self._customer_id = None self._default_user_groups = None self._deleted = None self._grant_modify_access_to_everyone = None self._hidden_metric_prefixes = None self._hide_ts_when_querybuilder_shown = None self._id = None self._invite_permissions = None self._landing_dashboard_slug = None self._show_onboarding = None self._show_querybuilder_by_default = None self._updated_epoch_millis = None self._updater_id = None self.discriminator = None if blacklisted_emails is not None: self.blacklisted_emails = blacklisted_emails if created_epoch_millis is not None: self.created_epoch_millis = created_epoch_millis if creator_id is not None: self.creator_id = creator_id self.customer_id = customer_id if default_user_groups is not None: self.default_user_groups = default_user_groups if deleted is not None: self.deleted = deleted self.grant_modify_access_to_everyone = grant_modify_access_to_everyone if hidden_metric_prefixes is not None: self.hidden_metric_prefixes = hidden_metric_prefixes self.hide_ts_when_querybuilder_shown = hide_ts_when_querybuilder_shown if id is not None: self.id = id if invite_permissions is not None: self.invite_permissions = invite_permissions if landing_dashboard_slug is not None: self.landing_dashboard_slug = landing_dashboard_slug self.show_onboarding = show_onboarding self.show_querybuilder_by_default = show_querybuilder_by_default if updated_epoch_millis is not None: self.updated_epoch_millis = updated_epoch_millis if updater_id is not None: self.updater_id = updater_id @property def blacklisted_emails(self): """Gets the blacklisted_emails of this CustomerPreferences. # noqa: E501 List of blacklisted emails of the customer # noqa: E501 :return: The blacklisted_emails of this CustomerPreferences. # noqa: E501 :rtype: dict(str, int) """ return self._blacklisted_emails @blacklisted_emails.setter def blacklisted_emails(self, blacklisted_emails): """Sets the blacklisted_emails of this CustomerPreferences. List of blacklisted emails of the customer # noqa: E501 :param blacklisted_emails: The blacklisted_emails of this CustomerPreferences. # noqa: E501 :type: dict(str, int) """ self._blacklisted_emails = blacklisted_emails @property def created_epoch_millis(self): """Gets the created_epoch_millis of this CustomerPreferences. # noqa: E501 :return: The created_epoch_millis of this CustomerPreferences. # noqa: E501 :rtype: int """ return self._created_epoch_millis @created_epoch_millis.setter def created_epoch_millis(self, created_epoch_millis): """Sets the created_epoch_millis of this CustomerPreferences. :param created_epoch_millis: The created_epoch_millis of this CustomerPreferences. # noqa: E501 :type: int """ self._created_epoch_millis = created_epoch_millis @property def creator_id(self): """Gets the creator_id of this CustomerPreferences. # noqa: E501 :return: The creator_id of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._creator_id @creator_id.setter def creator_id(self, creator_id): """Sets the creator_id of this CustomerPreferences. :param creator_id: The creator_id of this CustomerPreferences. # noqa: E501 :type: str """ self._creator_id = creator_id @property def customer_id(self): """Gets the customer_id of this CustomerPreferences. # noqa: E501 The id of the customer preferences are attached to # noqa: E501 :return: The customer_id of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._customer_id @customer_id.setter def customer_id(self, customer_id): """Sets the customer_id of this CustomerPreferences. The id of the customer preferences are attached to # noqa: E501 :param customer_id: The customer_id of this CustomerPreferences. # noqa: E501 :type: str """ if customer_id is None: raise ValueError("Invalid value for `customer_id`, must not be `None`") # noqa: E501 self._customer_id = customer_id @property def default_user_groups(self): """Gets the default_user_groups of this CustomerPreferences. # noqa: E501 List of default user groups of the customer # noqa: E501 :return: The default_user_groups of this CustomerPreferences. # noqa: E501 :rtype: list[UserGroup] """ return self._default_user_groups @default_user_groups.setter def default_user_groups(self, default_user_groups): """Sets the default_user_groups of this CustomerPreferences. List of default user groups of the customer # noqa: E501 :param default_user_groups: The default_user_groups of this CustomerPreferences. # noqa: E501 :type: list[UserGroup] """ self._default_user_groups = default_user_groups @property def deleted(self): """Gets the deleted of this CustomerPreferences. # noqa: E501 :return: The deleted of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._deleted @deleted.setter def deleted(self, deleted): """Sets the deleted of this CustomerPreferences. :param deleted: The deleted of this CustomerPreferences. # noqa: E501 :type: bool """ self._deleted = deleted @property def grant_modify_access_to_everyone(self): """Gets the grant_modify_access_to_everyone of this CustomerPreferences. # noqa: E501 Whether modify access of new entites is granted to Everyone or to the Creator # noqa: E501 :return: The grant_modify_access_to_everyone of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._grant_modify_access_to_everyone @grant_modify_access_to_everyone.setter def grant_modify_access_to_everyone(self, grant_modify_access_to_everyone): """Sets the grant_modify_access_to_everyone of this CustomerPreferences. Whether modify access of new entites is granted to Everyone or to the Creator # noqa: E501 :param grant_modify_access_to_everyone: The grant_modify_access_to_everyone of this CustomerPreferences. # noqa: E501 :type: bool """ if grant_modify_access_to_everyone is None: raise ValueError("Invalid value for `grant_modify_access_to_everyone`, must not be `None`") # noqa: E501 self._grant_modify_access_to_everyone = grant_modify_access_to_everyone @property def hidden_metric_prefixes(self): """Gets the hidden_metric_prefixes of this CustomerPreferences. # noqa: E501 Metric prefixes which should be hidden from user # noqa: E501 :return: The hidden_metric_prefixes of this CustomerPreferences. # noqa: E501 :rtype: dict(str, int) """ return self._hidden_metric_prefixes @hidden_metric_prefixes.setter def hidden_metric_prefixes(self, hidden_metric_prefixes): """Sets the hidden_metric_prefixes of this CustomerPreferences. Metric prefixes which should be hidden from user # noqa: E501 :param hidden_metric_prefixes: The hidden_metric_prefixes of this CustomerPreferences. # noqa: E501 :type: dict(str, int) """ self._hidden_metric_prefixes = hidden_metric_prefixes @property def hide_ts_when_querybuilder_shown(self): """Gets the hide_ts_when_querybuilder_shown of this CustomerPreferences. # noqa: E501 Whether to hide TS source input when Querybuilder is shown # noqa: E501 :return: The hide_ts_when_querybuilder_shown of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._hide_ts_when_querybuilder_shown @hide_ts_when_querybuilder_shown.setter def hide_ts_when_querybuilder_shown(self, hide_ts_when_querybuilder_shown): """Sets the hide_ts_when_querybuilder_shown of this CustomerPreferences. Whether to hide TS source input when Querybuilder is shown # noqa: E501 :param hide_ts_when_querybuilder_shown: The hide_ts_when_querybuilder_shown of this CustomerPreferences. # noqa: E501 :type: bool """ if hide_ts_when_querybuilder_shown is None: raise ValueError("Invalid value for `hide_ts_when_querybuilder_shown`, must not be `None`") # noqa: E501 self._hide_ts_when_querybuilder_shown = hide_ts_when_querybuilder_shown @property def id(self): """Gets the id of this CustomerPreferences. # noqa: E501 :return: The id of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._id @id.setter def id(self, id): """Sets the id of this CustomerPreferences. :param id: The id of this CustomerPreferences. # noqa: E501 :type: str """ self._id = id @property def invite_permissions(self): """Gets the invite_permissions of this CustomerPreferences. # noqa: E501 List of permissions that are assigned to newly invited users # noqa: E501 :return: The invite_permissions of this CustomerPreferences. # noqa: E501 :rtype: list[str] """ return self._invite_permissions @invite_permissions.setter def invite_permissions(self, invite_permissions): """Sets the invite_permissions of this CustomerPreferences. List of permissions that are assigned to newly invited users # noqa: E501 :param invite_permissions: The invite_permissions of this CustomerPreferences. # noqa: E501 :type: list[str] """ self._invite_permissions = invite_permissions @property def landing_dashboard_slug(self): """Gets the landing_dashboard_slug of this CustomerPreferences. # noqa: E501 Dashboard where user will be redirected from landing page # noqa: E501 :return: The landing_dashboard_slug of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._landing_dashboard_slug @landing_dashboard_slug.setter def landing_dashboard_slug(self, landing_dashboard_slug): """Sets the landing_dashboard_slug of this CustomerPreferences. Dashboard where user will be redirected from landing page # noqa: E501 :param landing_dashboard_slug: The landing_dashboard_slug of this CustomerPreferences. # noqa: E501 :type: str """ self._landing_dashboard_slug = landing_dashboard_slug @property def show_onboarding(self): """Gets the show_onboarding of this CustomerPreferences. # noqa: E501 Whether to show onboarding for any new user without an override # noqa: E501 :return: The show_onboarding of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._show_onboarding @show_onboarding.setter def show_onboarding(self, show_onboarding): """Sets the show_onboarding of this CustomerPreferences. Whether to show onboarding for any new user without an override # noqa: E501 :param show_onboarding: The show_onboarding of this CustomerPreferences. # noqa: E501 :type: bool """ if show_onboarding is None: raise ValueError("Invalid value for `show_onboarding`, must not be `None`") # noqa: E501 self._show_onboarding = show_onboarding @property def show_querybuilder_by_default(self): """Gets the show_querybuilder_by_default of this CustomerPreferences. # noqa: E501 Whether the Querybuilder is shown by default # noqa: E501 :return: The show_querybuilder_by_default of this CustomerPreferences. # noqa: E501 :rtype: bool """ return self._show_querybuilder_by_default @show_querybuilder_by_default.setter def show_querybuilder_by_default(self, show_querybuilder_by_default): """Sets the show_querybuilder_by_default of this CustomerPreferences. Whether the Querybuilder is shown by default # noqa: E501 :param show_querybuilder_by_default: The show_querybuilder_by_default of this CustomerPreferences. # noqa: E501 :type: bool """ if show_querybuilder_by_default is None: raise ValueError("Invalid value for `show_querybuilder_by_default`, must not be `None`") # noqa: E501 self._show_querybuilder_by_default = show_querybuilder_by_default @property def updated_epoch_millis(self): """Gets the updated_epoch_millis of this CustomerPreferences. # noqa: E501 :return: The updated_epoch_millis of this CustomerPreferences. # noqa: E501 :rtype: int """ return self._updated_epoch_millis @updated_epoch_millis.setter def updated_epoch_millis(self, updated_epoch_millis): """Sets the updated_epoch_millis of this CustomerPreferences. :param updated_epoch_millis: The updated_epoch_millis of this CustomerPreferences. # noqa: E501 :type: int """ self._updated_epoch_millis = updated_epoch_millis @property def updater_id(self): """Gets the updater_id of this CustomerPreferences. # noqa: E501 :return: The updater_id of this CustomerPreferences. # noqa: E501 :rtype: str """ return self._updater_id @updater_id.setter def updater_id(self, updater_id): """Sets the updater_id of this CustomerPreferences. :param updater_id: The updater_id of this CustomerPreferences. # noqa: E501 :type: str """ self._updater_id = updater_id def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(CustomerPreferences, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, CustomerPreferences): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

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