xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

395981eb97ba0d5630afef67430f063c0cfadca3

65

py

Python

src/setup.py

ThaDeveloper/grind

fa90b65d12e6d9b3d658b132874801ecda08c57f

[ "MIT" ]

1

2019-11-06T22:26:26.000Z

src/setup.py

ThaDeveloper/grind

fa90b65d12e6d9b3d658b132874801ecda08c57f

[ "MIT" ]

5

2021-03-19T02:49:44.000Z

2021-06-10T19:13:00.000Z

src/setup.py

ThaDeveloper/grind

fa90b65d12e6d9b3d658b132874801ecda08c57f

[ "MIT" ]

null

import setuptools setuptools.setup(name='grind', version='1.0')

16.25

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4

395bf15f68b877308dd905ac4aed62d8edba6f44

174

py

Python

LeetCode/Google Interview Questions/Unique Number of Occurrences.py

UtkarshPathrabe/Competitive-Coding

ba322fbb1b88682d56a9b80bdd92a853f1caa84e

[ "MIT" ]

13

2021-09-02T07:30:02.000Z

2022-03-22T19:32:03.000Z

LeetCode/Google Interview Questions/Unique Number of Occurrences.py

UtkarshPathrabe/Competitive-Coding

ba322fbb1b88682d56a9b80bdd92a853f1caa84e

[ "MIT" ]

null

LeetCode/Google Interview Questions/Unique Number of Occurrences.py

UtkarshPathrabe/Competitive-Coding

ba322fbb1b88682d56a9b80bdd92a853f1caa84e

[ "MIT" ]

3

2021-08-24T16:06:22.000Z

2021-09-17T15:39:53.000Z

class Solution: def uniqueOccurrences(self, arr: List[int]) -> bool: countsMap = Counter(arr) return len(countsMap.keys()) == len(set(countsMap.values()))

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39609757874076a7f9fb1412a7aaba1659bb2caf

86

py

Python

workschedule/shift_exception.py

point85/PyShift

46d0e1c23d3c570416633f68d416abd587b8e22f

[ "MIT" ]

null

workschedule/shift_exception.py

point85/PyShift

46d0e1c23d3c570416633f68d416abd587b8e22f

[ "MIT" ]

null

workschedule/shift_exception.py

point85/PyShift

46d0e1c23d3c570416633f68d416abd587b8e22f

[ "MIT" ]

null

## Base class for more specific exceptions class PyShiftException(Exception): pass

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3963f28189e11bd041986953f7c2ffe5e7a4d4a7

288

py

Python

equipment/framework/Mail/AbstractMail.py

didacelgueta/equipment

12cd86bfe4b70bce3e2578e3ec79fc4c0f76c322

[ "MIT" ]

1

2022-03-02T11:32:10.000Z

equipment/framework/Mail/AbstractMail.py

didacelgueta/equipment

12cd86bfe4b70bce3e2578e3ec79fc4c0f76c322

[ "MIT" ]

35

2022-03-02T14:33:49.000Z

2022-03-30T08:14:26.000Z

equipment/framework/Mail/AbstractMail.py

didacelgueta/equipment

12cd86bfe4b70bce3e2578e3ec79fc4c0f76c322

[ "MIT" ]

1

2022-03-24T11:52:01.000Z

import abc from typing import Union from equipment.framework.Mail.Email.Email import Email from equipment.framework.Mail.Email.EmailFactory import EmailFactory class AbstractMail(abc.ABC): def send(self, email: Union[Email, EmailFactory]) -> bool: raise NotImplementedError

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3969eae37682dde13327b382a75d05a161284884

7,164

py

Python

interactiontransformer/utils.py

jlevy44/InteractionTransformer

e92473889cca25a292baa4a54e84490bf7699063

[ "MIT" ]

4

2019-12-16T08:54:42.000Z

2021-11-05T15:29:52.000Z

interactiontransformer/utils.py

jlevy44/InteractionTransformer

e92473889cca25a292baa4a54e84490bf7699063

[ "MIT" ]

5

2020-05-20T00:55:06.000Z

2021-08-28T20:23:09.000Z

interactiontransformer/utils.py

jlevy44/InteractionTransformer

e92473889cca25a292baa4a54e84490bf7699063

[ "MIT" ]

null

from openml import datasets, tasks, runs, flows, config import os, pandas, sklearn, arff, pprint, numpy, seaborn import numpy as np, pandas as pd, pickle import copy from sklearn.model_selection import StratifiedKFold import dask MANUSCRIPT_IDs=np.array([4154, 4329, 4136, 4137, 73, 143, 142, 72, 77, 120, 135, 139, 146, 161, 162, 273, 274, 262, 264, 267, 269, 256, 257, 258, 260, 293, 246, 351, 354, 346, 1161, 1162, 1163, 1164, 1165, 1166, 1141, 1144, 1145, 1146, 890, 1147, 1149, 1150, 1181, 1182, 1178, 1039, 1016, 1236, 1372, 1374, 1375, 1376, 1205, 1212, 1216, 1217, 1219, 1237, 1238, 1241, 1151, 1152, 1153, 1154, 1155, 1157, 1158, 1159, 1160, 1561, 1562, 1563, 1564, 1597, 1085, 446, 1042, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1458, 1412, 1441, 1448, 1449, 1450, 1131, 1132, 1133, 1134, 1135, 1137, 1138, 1139, 1502, 1136, 1369, 41946, 41945, 1371, 1180, 40900, 41949, 41967, 41966, 41964, 1218, 1373, 1211, 1148, 40645, 40646, 40647, 40648, 40649, 40650, 40660, 40588, 40589, 40590, 40665, 40666, 40669, 41228, 41672, 41674, 41675, 41679, 41680, 41682, 41684, 41685, 40514, 40515, 40517, 40518, 40922, 40910, 41144, 41145, 41156, 41157, 41158, 40693, 40702, 40704, 40705, 40706, 40710, 40680, 40681, 40683, 40690, 40713, 40714, 40999, 41026, 41005, 41007, 41538, 41521, 40591, 40592, 40593, 40594, 40595, 40596, 40597, 41496, 4134, 1485, 40994, 40983, 40978, 4534, 132, 1220, 1442, 1443, 1444, 1446, 1547, 1467, 1447, 1451, 1452, 1460, 1463, 1566, 1556, 1495, 1506, 1507, 1496, 1498, 1510, 1511, 1524, 1484, 1488, 1473, 1490, 121, 40701, 126, 41973, 376, 41977, 41978, 41976, 1600, 124, 153, 1471, 1462, 23499, 41896, 41897, 41714, 41715, 41716, 41711, 41712, 41713, 41719, 41720, 41721, 41722, 41723, 41717, 41718, 41709, 41710, 41708, 41814, 41815, 41816, 41803, 41804, 41805, 41806, 41807, 41808, 41800, 41801, 41802, 41817, 41818, 41819, 41811, 41812, 41813, 41798, 41799, 41820, 41821, 41809, 41810, 41879, 41880, 41881, 41893, 41895, 41874, 41875, 41876, 41888, 41889, 41890, 41882, 41883, 41884, 41871, 41872, 41873, 41885, 41886, 41887, 41891, 41892, 41877, 41878, 140, 41759, 41760, 41761, 41767, 41768, 41769, 41754, 41755, 41756, 41748, 41749, 41750, 41751, 41752, 41753, 41762, 41763, 41764, 41770, 41771, 41772, 41765, 41766, 41757, 41758, 41868, 41869, 41870, 41849, 41850, 41851, 41846, 41847, 41848, 41862, 41863, 41864, 41865, 41866, 41867, 41857, 41858, 41859, 41852, 41853, 41854, 41860, 41861, 41855, 41856, 41737, 41738, 41739, 41740, 41741, 41742, 41727, 41707, 41728, 41729, 41730, 41731, 41745, 41746, 41747, 41724, 41725, 41726, 41734, 41735, 41736, 41743, 41744, 41732, 41733, 41786, 41787, 41788, 41789, 41790, 41791, 41792, 41793, 41794, 41795, 41796, 41797, 41775, 41776, 41777, 41778, 41779, 41780, 41781, 41782, 41783, 41773, 41774, 41784, 41785, 41838, 41839, 41840, 41833, 41834, 41835, 41825, 41826, 41827, 41841, 41842, 41843, 41822, 41823, 41824, 41828, 41829, 41830, 41844, 41845, 41836, 41837, 41831, 41832, 128, 41998, 122, 152, 41544, 1480, 1122, 41142, 41161, 41150, 41159, 41143, 41146, 1169, 1140, 1142, 1486, 4135, 40981, 23517, 131, 1167, 1242, 41894, 1130, 1143, 1235, 1461, 1377, 1558, 1464, 164, 59, 137, 459, 450, 444, 448, 41898, 40, 53, 43, 312, 316, 336, 337, 334, 742, 743, 744, 745, 752, 753, 748, 749, 740, 741, 746, 747, 750, 751, 754, 755, 756, 758, 759, 761, 770, 771, 766, 767, 764, 765, 768, 769, 772, 773, 762, 763, 788, 789, 784, 785, 787, 775, 776, 782, 783, 779, 780, 777, 778, 938, 941, 953, 951, 946, 947, 945, 949, 950, 942, 943, 955, 956, 958, 954, 959, 962, 964, 965, 983, 978, 979, 980, 969, 974, 976, 977, 970, 971, 973, 1038, 1045, 1022, 1040, 1046, 1048, 1025, 1026, 1020, 1021, 1075, 1104, 1073, 1107, 1069, 1071, 1068, 927, 928, 919, 920, 916, 917, 918, 923, 924, 922, 925, 926, 921, 915, 914, 910, 911, 907, 905, 906, 913, 909, 912, 908, 896, 903, 904, 894, 901, 902, 882, 884, 895, 886, 900, 934, 935, 936, 937, 931, 932, 933, 929, 991, 987, 996, 997, 988, 994, 995, 1019, 1013, 1014, 1015, 1006, 1009, 1011, 1012, 1004, 1005, 1066, 1067, 1050, 1064, 1065, 1054, 1055, 1063, 1056, 1061, 1062, 1049, 1059, 1060, 1120, 1121, 1370, 251, 310, 311, 350, 357, 801, 793, 794, 790, 797, 799, 791, 792, 796, 795, 800, 774, 806, 803, 807, 808, 805, 479, 476, 467, 472, 461, 463, 464, 465, 717, 718, 719, 720, 682, 683, 713, 714, 715, 716, 721, 722, 734, 735, 723, 724, 732, 733, 736, 737, 728, 729, 730, 731, 726, 727, 725, 812, 818, 819, 816, 817, 811, 814, 821, 815, 804, 813, 833, 834, 824, 825, 832, 829, 830, 820, 826, 827, 828, 823, 851, 837, 838, 849, 850, 843, 847, 848, 845, 846, 841, 835, 836, 862, 863, 867, 868, 864, 855, 857, 860, 859, 853, 865, 866, 879, 873, 874, 869, 870, 877, 878, 875, 876, 871, 887, 888, 889, 891, 892, 893, 885, 880, 881, 31, 3, 1489, 1504, 1494, 50, 1116, 1479, 1453, 1156, 44, 151, 37, 1487, 335, 333, 1455]) def download_openml(ID=0,api_key='',tmp='tmp',dataset_path='datasets'): config.apikey = api_key os.makedirs(tmp,exist_ok=True) os.makedirs(dataset_path,exist_ok=True) config.set_cache_directory(os.path.abspath('tmp')) if not os.path.exists('{}/{}.p'.format(datasets_path,ID)): try: odata = datasets.get_dataset(int(ID)) X, y, categorical, attribute_names = odata.get_data( target=odata.default_target_attribute) y=y.astype(str) if not isinstance(y,np.ndarray): y=y.values X=np.hstack((X,y.reshape(-1,1))) df=pd.DataFrame(X, columns=attribute_names+[odata.default_target_attribute]) pickle.dump(dict(cat=categorical,names=attribute_names+[odata.default_target_attribute],X=df),open('{}/{}.p'.format(dataset_path,ID),'wb')) return 1 except Exception as e: return 0 return 2 def download_multiple_openml(IDs=MANUSCRIPT_IDs,api_key='',tmp='tmp',dataset_path='datasets'): dask.compute(*[dask.delayed(download_openml)(ID,api_key,tmp,dataset_path) for ID in IDs], scheduler='processes', num_workers=10) def get_dataset(dataset='datasets/0.p'): d=pickle.load(open(dataset,'rb')) return d['X'].iloc[:,:-1],d['X'].iloc[:,-1],np.array(d['cat']) def preprocess_data(X,y,cat, return_xy=False): if not X.isnull().sum().sum(): X_nocat,X_cat=X.loc[:,~cat],X.loc[:,cat] if X_cat.shape[1]: ncat=np.array(X_cat.nunique().tolist()) X_cat=X_cat.loc[:,ncat<100] ncat=np.array(X_cat.nunique().tolist()) X_cat=pd.get_dummies(X_cat,columns=X_cat.columns.values[ncat>2]).apply(lambda x: x==x.unique()[0],axis=0) X_cat=X_cat.astype(int) X=pd.concat([X_nocat,X_cat],axis=1).astype(float) y=(y==y.unique()[0]).astype(float) if return_xy: yield X,y else: cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) info_dict=dict(n=X.shape[0],p=X.shape[1],pcat=X_cat.shape[1],cb=max(1.-np.mean(y),np.mean(y))) for train, test in cv.split(X,y): X_train, X_test, y_train, y_test = X.iloc[train, :], X.iloc[test, :], y.iloc[train], y.iloc[test] yield X_train, X_test, y_train, y_test, copy.deepcopy(info_dict)

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396eefa606edee58bbd568f0cb16e09f7c914c23

526

py

Python

diagnosis/models/__init__.py

Naceine/diagnosis

0068fcd597d7607aee1723a551ba47e72047e098

[ "BSD-3-Clause" ]

3

2019-09-29T11:13:38.000Z

2021-12-11T03:23:12.000Z

diagnosis/models/__init__.py

victor-iyiola/diagnosis

0068fcd597d7607aee1723a551ba47e72047e098

[ "BSD-3-Clause" ]

1

2022-02-10T00:36:16.000Z

diagnosis/models/__init__.py

Naceine/diagnosis

0068fcd597d7607aee1723a551ba47e72047e098

[ "BSD-3-Clause" ]

3

2020-06-04T15:50:08.000Z

2021-11-26T14:14:36.000Z

"""`diagnosis.models` - for instantiating models, including data pre-processing and loss functions. @author Victor I. Afolabi Artificial Intelligence Expert & Researcher. Email: javafolabi@gmail.com GitHub: https://github.com/victor-iyiola @project File: __init__.py Package: diagnosis.models Created on 10 July, 2019 @ 02:25 PM. @license BSD-3 Clause license. Copyright (c) 2019. Victor I. Afolabi. All rights reserved. """ from .docproduct import * from . import bert

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3978633c733811885535151c5b199d09d930dc86

3,365

py

Python

test/tests/binaryop.py

jonco3/dynamic

76d10b012a7860595c7d9abbdf542c7d8f2a4d53

[ "MIT" ]

1

2020-11-26T23:37:19.000Z

test/tests/binaryop.py

jonco3/dynamic

76d10b012a7860595c7d9abbdf542c7d8f2a4d53

[ "MIT" ]

null

test/tests/binaryop.py

jonco3/dynamic

76d10b012a7860595c7d9abbdf542c7d8f2a4d53

[ "MIT" ]

null

# output: ok a, b, c, d, e, f = 1000, 1000, 1000, 1000, 1000, 1000 g, h, i, j, k, l = 2, 1000, 1000, 1000, 1000, 1000 a = a + 1 b = b - 2 c = c * 3 d = d / 4 e = e // 5 f = f % 6 g = g ** 7 h = h << 8 i = i >> 9 j = j & 10 k = k ^ 11 l = l | 12 assert a == 1001 assert b == 998 assert c == 3000 assert d == 250 assert e == 200 assert f == 4 assert g == 128 assert h == 256000 assert i == 1 assert j == 8 assert k == 995 assert l == 1004 class Wrapped: def __init__(self, initial): self.value = initial def __add__(self, other): return Wrapped(self.value + other) def __sub__(self, other): return Wrapped(self.value - other) def __mul__(self, other): return Wrapped(self.value * other) def __truediv__(self, other): return Wrapped(self.value / other) def __floordiv__(self, other): return Wrapped(self.value // other) def __mod__(self, other): return Wrapped(self.value % other) def __pow__(self, other): return Wrapped(self.value ** other) def __lshift__(self, other): return Wrapped(self.value << other) def __rshift__(self, other): return Wrapped(self.value >> other) def __or__(self, other): return Wrapped(self.value | other) def __and__(self, other): return Wrapped(self.value & other) def __xor__(self, other): return Wrapped(self.value ^ other) def __radd__(self, other): return other + self.value def __rsub__(self, other): return other - self.value def __rmul__(self, other): return other * self.value def __rtruediv__(self, other): return other / self.value def __rfloordiv__(self, other): return other // self.value def __rmod__(self, other): return other % self.value def __rpow__(self, other): return other ** self.value def __rlshift__(self, other): return other << self.value def __rrshift__(self, other): return other >> self.value def __ror__(self, other): return other | self.value def __rand__(self, other): return other & self.value def __rxor__(self, other): return other ^ self.value a, b, c, d, e, f = Wrapped(1000), Wrapped(1000), Wrapped(1000), Wrapped(1000), Wrapped(1000), Wrapped(1000) g, h, i, j, k, l = Wrapped(2), Wrapped(1000), Wrapped(1000), Wrapped(1000), Wrapped(1000), Wrapped(1000) a = a + 1 b = b - 2 c = c * 3 d = d / 4 e = e // 5 f = f % 6 g = g ** 7 h = h << 8 i = i >> 9 j = j & 10 k = k ^ 11 l = l | 12 assert a.value == 1001 assert b.value == 998 assert c.value == 3000 assert d.value == 250 assert e.value == 200 assert f.value == 4 assert g.value == 128 assert h.value == 256000 assert i.value == 1 assert j.value == 8 assert k.value == 995 assert l.value == 1004 a, b, c, d, e, f = 1000, 1000, 1000, 1000, 1000, 1000 g, h, i, j, k, l = 2, 1000, 1000, 1000, 1000, 1000 a = a + Wrapped(1) b = b - Wrapped(2) c = c * Wrapped(3) d = d / Wrapped(4) e = e // Wrapped(5) f = f % Wrapped(6) g = g ** Wrapped(7) h = h << Wrapped(8) i = i >> Wrapped(9) j = j & Wrapped(10) k = k ^ Wrapped(11) l = l | Wrapped(12) assert a == 1001 assert b == 998 assert c == 3000 assert d == 250 assert e == 200 assert f == 4 assert g == 128 assert h == 256000 assert i == 1 assert j == 8 assert k == 995 assert l == 1004 print('ok')

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397956f488e4d6b404b647125e2db7eee5a5d84e

1,285

py

Python

utils/pattern/pattern_base.py

ElevenPaths/FARO

4d5585a1f08ce74baff3acf92668646dc9919439

[ "MIT" ]

8

2020-04-17T11:35:14.000Z

2022-01-13T05:07:37.000Z

utils/pattern/pattern_base.py

ElevenPaths/FARO

4d5585a1f08ce74baff3acf92668646dc9919439

[ "MIT" ]

1

2020-08-03T15:38:14.000Z

utils/pattern/pattern_base.py

ElevenPaths/FARO

4d5585a1f08ce74baff3acf92668646dc9919439

[ "MIT" ]

1

2020-09-28T02:50:34.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from utils.utils import clean_text class PluginPatternBase: def get_strict_regexp(self): return self.dict_regex_strict def get_lax_regexp(self): return self.dict_regex_lax def get_strict_entities(self): return self._strict_entities def get_consolidated_lax_entities(self): return self._consolidated_entities def get_unconsolidated_lax_entities(self): return self._unconsolidated_entities def get_plugin_path(self): return self.plugin_path @staticmethod def _dict_to_regex_struct(_dict): if not _dict: return [] return [(k, v) for v, k in _dict.items()] @staticmethod def clean_entity(text): return clean_text(text) def strict_regexp(self): pass def lax_regexp(self): pass def validate(self, ent): pass def __init__(self, plugin_path, regex_lax, regex_strict): self._strict_entities = {} self._consolidated_entities = {} self._unconsolidated_entities = {} self.dict_regex_lax = self._dict_to_regex_struct(regex_lax) self.dict_regex_strict = self._dict_to_regex_struct(regex_strict) self.plugin_path = plugin_path

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1

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4

398a169253e6e1ec5b4e70385c86d5d011ee9f49

208

py

Python

ch01_intro/p1.py

mikeckennedy/gk_python_demos

e9a81c5a0775f0ad368843ccec434c5b1d588d71

[ "MIT" ]

null

ch01_intro/p1.py

mikeckennedy/gk_python_demos

e9a81c5a0775f0ad368843ccec434c5b1d588d71

[ "MIT" ]

null

ch01_intro/p1.py

mikeckennedy/gk_python_demos

e9a81c5a0775f0ad368843ccec434c5b1d588d71

[ "MIT" ]

null

print("About to import p2") from p2 import get_user_name print("Done importing p2") print("Downloading data, we'll save it to your profile") name = get_user_name() print("Using /Users/{}/data".format(name))

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3993b323c7167ee26363ee489ca98fb1151fe937

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py

Python

rasa/version.py

yurilq/rasa01

315f0878cf66c39143bc298acd665a05bff8510b

[ "Apache-2.0" ]

1

2020-12-19T10:17:18.000Z

rasa/version.py

yurilq/rasa01

315f0878cf66c39143bc298acd665a05bff8510b

[ "Apache-2.0" ]

null

rasa/version.py

yurilq/rasa01

315f0878cf66c39143bc298acd665a05bff8510b

[ "Apache-2.0" ]

1

2020-12-19T10:10:55.000Z

__version__ = "1.0.0rc9"

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3995ab10ac9e94f32b4f07e6e1ab7695d20eaf1d

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py

Python

homeassistant/components/solax/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

22,481

2020-03-02T13:09:59.000Z

2022-03-31T23:34:28.000Z

homeassistant/components/solax/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

homeassistant/components/solax/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,411

2020-03-02T14:19:20.000Z

2022-03-31T22:46:07.000Z

"""The solax component."""

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39a8586fc20a644391e1e4e582809d826e0ec684

263

py

Python

pivot/tests.py

uw-it-aca/pivot

a3e404b30f4d2ebf37608d7c4595edb667df0222

[ "Apache-2.0" ]

4

2017-11-07T23:49:45.000Z

2019-11-26T10:59:50.000Z

pivot/tests.py

uw-it-aca/pivot

a3e404b30f4d2ebf37608d7c4595edb667df0222

[ "Apache-2.0" ]

204

2017-02-08T00:12:51.000Z

2022-02-02T23:52:46.000Z

pivot/tests.py

uw-it-aca/pivot

a3e404b30f4d2ebf37608d7c4595edb667df0222

[ "Apache-2.0" ]

2

2018-03-20T22:41:27.000Z

2019-07-10T21:40:36.000Z

# Copyright 2021 UW-IT, University of Washington # SPDX-License-Identifier: Apache-2.0 """ Tests for the pivot app. """ from pivot.test.context import ContextProcessorTest from pivot.test.csvapi import CsvDataApiTest from pivot.test.misc import PivotConfigTest

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39c55ce1e035c65cad05929f12d3c687e6db616c

190

py

Python

accounts/admin.py

JAYBLA/mobile_payments

9fcd7b4b3f894e14225fd1ebefc31a9e18c11112

[ "MIT" ]

4

2022-01-03T16:26:42.000Z

2022-01-04T02:55:52.000Z

accounts/admin.py

JAYBLA/mobile_payments

9fcd7b4b3f894e14225fd1ebefc31a9e18c11112

[ "MIT" ]

null

accounts/admin.py

JAYBLA/mobile_payments

9fcd7b4b3f894e14225fd1ebefc31a9e18c11112

[ "MIT" ]

1

2022-01-16T21:41:20.000Z

from django.contrib import admin from django.contrib.auth import get_user_model from .models import Profile User = get_user_model() admin.site.register(User) admin.site.register(Profile)

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39c7048fd0f5c222bf39ed309f3ba33618945132

324

py

Python

Validation/CaloTowers/python/CaloTowersClient_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

Validation/CaloTowers/python/CaloTowersClient_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

Validation/CaloTowers/python/CaloTowersClient_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms from DQMServices.Core.DQMEDHarvester import DQMEDHarvester calotowersClient = DQMEDHarvester("CaloTowersClient", # outputFile = cms.untracked.string('CaloTowersHarvestingME.root'), outputFile = cms.untracked.string(''), DQMDirName = cms.string("/") # root directory )

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f2d0de465a55797339086f1d035cb9f4d7c4a0d2

186

py

Python

scripts/training-data-processing/vector_utilities/utils_readwrite.py

DDS-Lab/disaster-image-processing

5d87e858c8d933bf4d5288061d121129fadb863d

[ "MIT" ]

10

2018-11-07T14:39:15.000Z

2022-03-30T11:27:27.000Z

scripts/training-data-processing/vector_utilities/utils_readwrite.py

DDS-Lab/disaster-image-processing

5d87e858c8d933bf4d5288061d121129fadb863d

[ "MIT" ]

1

2019-09-24T07:22:33.000Z

scripts/training-data-processing/vector_utilities/utils_readwrite.py

DDS-Lab/disaster-image-processing

5d87e858c8d933bf4d5288061d121129fadb863d

[ "MIT" ]

5

2018-08-02T21:25:30.000Z

2020-08-08T18:52:50.000Z

import geopandas as gpd def load_file(file_path): shape = gpd.GeoDataFrame(gpd.read_file(file_path)) return shape def writeFile(shape, new_file): shape.to_file(new_file)

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f2d93bd98441081a60423726cda89abcc0a84869

180

py

Python

scripts/deploy.py

banteg/pink-duck

f770f80fe64589949a2f8319f57f77db5ac825f4

[ "MIT" ]

3

2020-12-08T23:03:47.000Z

2020-12-09T04:11:42.000Z

scripts/deploy.py

banteg/pink-duck

f770f80fe64589949a2f8319f57f77db5ac825f4

[ "MIT" ]

null

scripts/deploy.py

banteg/pink-duck

f770f80fe64589949a2f8319f57f77db5ac825f4

[ "MIT" ]

1

2021-09-06T02:28:24.000Z

import click from brownie import Duck, accounts def main(): user = accounts.load(click.prompt("account", type=click.Choice(accounts.load()))) Duck.deploy({"from": user})

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f2f43f23669b3672ddf80e446c7b0b4ee962e966

365

py

Python

tips_and_tricks/models.py

immanuelnadeak/temenin-isoman

8a497f7defe402820045ab9b748a447fc2e8a5f9

[ "MIT" ]

1

2021-11-04T08:11:18.000Z

tips_and_tricks/models.py

immanuelnadeak/temenin-isoman

8a497f7defe402820045ab9b748a447fc2e8a5f9

[ "MIT" ]

1

2021-11-05T03:52:27.000Z

tips_and_tricks/models.py

immanuelnadeak/temenin-isoman

8a497f7defe402820045ab9b748a447fc2e8a5f9

[ "MIT" ]

4

2021-10-10T12:55:28.000Z

2021-10-29T18:27:42.000Z

from django.db import models # Create your models here. class TipsAndTrick(models.Model): title = models.CharField(max_length=500) source = models.CharField(max_length=500) published_date = models.DateField() brief_description = models.TextField() image_url = models.CharField(max_length=500) article_url = models.CharField(max_length=500)

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f2f7b0ac58a95ec874e2b2da8533825417fb71d4

864

py

Python

tests/test_methods/test_chat.py

jackwardell/SlackTime

c40be4854a26084e1a368a975e220d613c14d8d8

[ "Apache-2.0" ]

2

2020-09-24T00:07:13.000Z

2020-09-27T19:27:06.000Z

tests/test_methods/test_chat.py

jackwardell/SlackTime

c40be4854a26084e1a368a975e220d613c14d8d8

[ "Apache-2.0" ]

null

tests/test_methods/test_chat.py

jackwardell/SlackTime

c40be4854a26084e1a368a975e220d613c14d8d8

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- def test_chat_delete(slack_time): assert slack_time.chat.delete def test_chat_delete_scheduled_message(slack_time): assert slack_time.chat.delete_scheduled_message def test_chat_get_permalink(slack_time): assert slack_time.chat.get_permalink def test_chat_me_message(slack_time): assert slack_time.chat.me_message def test_chat_post_ephemeral(slack_time): assert slack_time.chat.post_ephemeral def test_chat_post_message(slack_time): assert slack_time.chat.post_message def test_chat_schedule_message(slack_time): assert slack_time.chat.schedule_message def test_chat_unfurl(slack_time): assert slack_time.chat.unfurl def test_chat_update(slack_time): assert slack_time.chat.update def test_chat_scheduled_messages_list(slack_time): assert slack_time.chat.scheduled_messages.list

20.571429

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8414af8a79e61004889d24a623e136c739d04808

103

py

Python

scripts/portal/market34.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

9

2021-04-26T11:59:29.000Z

2021-12-20T13:15:27.000Z

scripts/portal/market34.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

null

scripts/portal/market34.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

6

2021-07-14T06:32:05.000Z

2022-02-06T02:32:56.000Z

# Stump Town (866000000) => Free Market sm.setReturnField() sm.setReturnPortal() sm.warp(910000000, 36)

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845a836f69b54690c33dacca21531c5039908fe7

112

py

Python

fastapi/core/models/images.py

ilDug/docker-utils

6580e916a8c2c0d91f2e3da52a9d839507569bb7

[ "MIT" ]

null

fastapi/core/models/images.py

ilDug/docker-utils

6580e916a8c2c0d91f2e3da52a9d839507569bb7

[ "MIT" ]

null

fastapi/core/models/images.py

ilDug/docker-utils

6580e916a8c2c0d91f2e3da52a9d839507569bb7

[ "MIT" ]

null

from pydantic import BaseModel class ImageB64UploadRequestModel(BaseModel): filename: str base64: str

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ffca8e9bbe49907d8307566fdfef601a37da7a91

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py

Python

dlhub_cli/__init__.py

DLHub-Argonne/dlhub_cli

4193683c92b9be546588b8e3ac419c5efecd8ef7

[ "Apache-2.0" ]

6

2018-10-19T03:29:03.000Z

2021-07-09T20:28:37.000Z

dlhub_cli/__init__.py

DLHub-Argonne/dlhub_cli

4193683c92b9be546588b8e3ac419c5efecd8ef7

[ "Apache-2.0" ]

13

2018-11-29T15:23:25.000Z

2019-05-08T20:36:17.000Z

dlhub_cli/__init__.py

DLHub-Argonne/dlhub_cli

4193683c92b9be546588b8e3ac419c5efecd8ef7

[ "Apache-2.0" ]

1

2019-03-06T15:52:47.000Z

from dlhub_cli.main import cli_root __all__ = ['cli_root']

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08134ca88bcdd23eae89c5b9e67c3e8816771d05

29,060

py

Python

vscworkflows/workflows/core.py

zezhong-zhang/vsc-workflows

83fbd534592eac973f4390bead670809cb4e47eb

[ "MIT" ]

null

vscworkflows/workflows/core.py

zezhong-zhang/vsc-workflows

83fbd534592eac973f4390bead670809cb4e47eb

[ "MIT" ]

null

vscworkflows/workflows/core.py

zezhong-zhang/vsc-workflows

83fbd534592eac973f4390bead670809cb4e47eb

[ "MIT" ]

1

2019-11-14T14:55:23.000Z

# coding: utf8 # Copyright (c) Marnik Bercx, University of Antwerp # Distributed under the terms of the MIT License import os import numpy as np from string import ascii_lowercase from fireworks import Workflow from monty.serialization import loadfn from pymatgen.core.surface import Slab, SlabGenerator from vscworkflows.misc import QSlab from vscworkflows.fireworks.core import StaticFW, OptimizeFW, OpticsFW, \ SlabOptimizeFW, SlabDosFW """ Definition of all workflows in the package. """ __author__ = "Marnik Bercx" __copyright__ = "Copyright 2019, Marnik Bercx, University of Antwerp" __version__ = "pre-alpha" __maintainer__ = "Marnik Bercx" __email__ = "marnik.bercx@uantwerpen.be" __date__ = "Jun 2019" MODULE_DIR = os.path.join( os.path.dirname(os.path.abspath(__file__)), "../setup/set_configs" ) def _load_yaml_config(filename): config = loadfn(os.path.join(MODULE_DIR, "%s.yaml" % filename)) return config def _set_up_relative_directory(directory, functional, calculation): # Set up a calculation directory for a specific functional and calculation directory = os.path.join(os.path.abspath(directory), functional[0]) if functional[0] == "pbeu": directory += "_" + "".join(k + str(functional[1]["LDAUU"][k]) for k in functional[1]["LDAUU"].keys()) directory += "_" + calculation return directory def _set_up_functional_params(functional): """ Set up the vasp_input_params based on the functional and some other conventions. Args: functional (tuple): Tuple with the functional details. The first element contains a string that indicates the functional used ("pbe", "hse06", ...), whereas the second element contains a dictionary that allows the user to specify the various functional tags. Returns: dict: dictionary with the standard vasp input parameters. """ vasp_input_params = {"user_incar_settings": {}} # Set up the functional if functional[0] != "pbe": functional_config = _load_yaml_config(functional[0] + "Set") functional_config["INCAR"].update(functional[1]) vasp_input_params["user_incar_settings"].update(functional_config["INCAR"]) return vasp_input_params def get_wf_optimize(structure, directory, functional=("pbe", {}), is_metal=False, in_custodian=False, number_nodes=None, auto_parallelization=False): """ Set up a geometry optimization workflow for a bulk structure. Args: structure (Structure): Input Geometry. directory (str): Directory in which the geometry optimization should be performed. functional (tuple): Tuple with the functional details. The first element contains a string that indicates the functional used ("pbe", "hse06", ...), whereas the second element contains a dictionary that allows the user to specify additional INCAR tags. is_metal (bool): Flag that indicates the material being studied is a metal, which changes the smearing from Gaussian (0.05 eV) to second order Methfessel-Paxton of 0.2 eV. in_custodian (bool): Flag that indicates whether the calculation should be run inside a Custodian. number_nodes (int): Number of nodes that should be used for the calculations. Is required to add the proper `_fworker` to the Firework spec, so it is picked up by a Fireworker running in a job with the specified number of nodes. auto_parallelization (bool): Automatically parallelize the calculation using the VaspParallelizationTask. """ # Add number of nodes to spec, or "none" if number_nodes is not None and number_nodes != 0: spec = {"_fworker": str(number_nodes) + "nodes"} else: spec = {} # --> Set up the geometry optimization vasp_input_params = _set_up_functional_params(functional) spec.update({"_launch_dir": directory}) # For metals, use Methfessel Paxton smearing if is_metal: vasp_input_params["user_incar_settings"].update( {"ISMEAR": 2, "SIGMA": 0.2} ) # Set up the geometry optimization Firework optimize_fw = OptimizeFW(structure=structure, vasp_input_params=vasp_input_params, custodian=in_custodian, spec=spec, auto_parallelization=auto_parallelization) # Set up a clear name for the workflow workflow_name = str(structure.composition.reduced_formula).replace(" ", "") workflow_name += " " + str(functional) # Create the workflow return Workflow(fireworks=[optimize_fw, ], name=workflow_name) def get_wf_energy(structure, directory, functional=("pbe", {}), is_metal=False, in_custodian=False, number_nodes=None): """ Set up an accurate energy workflow for a bulk structure. Starts by optimizing the geometry and then does a static calculation. Args: structure (Structure): Input geometry. directory (str): Directory in which the workflow should be set up. functional (tuple): Tuple with the functional details. The first element contains a string that indicates the functional used ("pbe", "hse06", ...), whereas the second element contains a dictionary that allows the user to specify the various functional INCAR tags. is_metal (bool): Flag that indicates the material being studied is a metal, which changes the smearing of the geometry optimization from Gaussian ( 0.05 eV) to second order Methfessel-Paxton of 0.2 eV. in_custodian (bool): Flag that indicates whether the calculation should be run inside a Custodian. number_nodes (int): Number of nodes that should be used for the calculations. Is required to add the proper `_fworker` to the Firework spec, so it is picked up by a Fireworker running in a job with the specified number of nodes. """ # Add number of nodes to spec, or "none" if number_nodes is not None and number_nodes != 0: spec = {"_fworker": str(number_nodes) + "nodes"} else: spec = {} # --> Set up the geometry optimization vasp_input_params = _set_up_functional_params(functional) spec.update( {"_launch_dir": _set_up_relative_directory(directory, functional, "optimize"), "_pass_job_info": True}) # For metals, use Methfessel Paxton smearing if is_metal: vasp_input_params["user_incar_settings"].update( {"ISMEAR": 2, "SIGMA": 0.2} ) # Set up the geometry optimization Firework optimize_fw = OptimizeFW(structure=structure, vasp_input_params=vasp_input_params, custodian=in_custodian, spec=spec) # -> Set up the static calculation vasp_input_params = _set_up_functional_params(functional) spec.update({"_launch_dir": _set_up_relative_directory(directory, functional, "static")}) # Set up the static Firework static_fw = StaticFW(vasp_input_params=vasp_input_params, parents=optimize_fw, custodian=in_custodian, spec=spec) # Set up a clear name for the workflow workflow_name = str(structure.composition.reduced_formula).replace(" ", "") workflow_name += " " + str(functional) # Create the workflow return Workflow(fireworks=[optimize_fw, static_fw], name=workflow_name) def get_wf_optics(structure, directory, functional=("pbe", {}), k_resolution=None, is_metal=False, user_incar_settings=None, in_custodian=False, number_nodes=None, auto_parallelization=False): """ Set up a workflow to calculate the frequency dependent dielectric matrix. Starts with a geometry optimization. Args: structure (Structure): Input geometry. directory (str): Directory in which the workflow should be set up. functional (tuple): Tuple with the functional details. The first element contains a string that indicates the functional used ("pbe", "hse06", ...), whereas the second element contains a dictionary that allows the user to specify the various functional INCAR tags. k_resolution (float): Resolution of the k-mesh, i.e. distance between two k-points along each reciprocal lattice vector. is_metal (bool): Flag that indicates the material being studied is a metal, which changes the smearing from Gaussian (0.05 eV) to second order Methfessel-Paxton of 0.2 eV; the optics calculation will use a generous gaussian smearing of 0.3 eV instead of the tetrahedron method. user_incar_settings (dict): User INCAR settings. This allows a user to override INCAR settings, e.g., setting a different MAGMOM for various elements or species, or specify parallelization settings ( KPAR, NPAR, ...). Note that the settings specified here will override the INCAR settings for ALL fireworks of the workflow. in_custodian (bool): Flag that indicates whether the calculation should be run inside a Custodian. number_nodes (int): Number of nodes that should be used for the calculations. Is required to add the proper `_fworker` to the Firework spec, so it is picked up by a Fireworker running in a job with the specified number of nodes. auto_parallelization (bool): Automatically parallelize the calculation using the VaspParallelizationTask. """ # Add number of nodes to spec, or "none" if number_nodes is not None and number_nodes != 0: spec = {"_fworker": str(number_nodes) + "nodes"} else: spec = {} # 1. Set up the geometry optimization vasp_input_params = _set_up_functional_params(functional) spec.update( {"_launch_dir": _set_up_relative_directory(directory, functional, "optimize")}) # For metals, use Methfessel Paxton smearing if is_metal: vasp_input_params["user_incar_settings"].update( {"ISMEAR": 2, "SIGMA": 0.2} ) # Override the INCAR settings with the user specifications if user_incar_settings is not None: vasp_input_params["user_incar_settings"].update(user_incar_settings) # Set up the Firework optimize_fw = OptimizeFW(structure=structure, vasp_input_params=vasp_input_params, custodian=in_custodian, spec=spec, auto_parallelization=auto_parallelization) # 2. Set up the optics calculation vasp_input_params = _set_up_functional_params(functional) spec.update({"_launch_dir": _set_up_relative_directory(directory, functional, "optics")}) # For metals, use a good amount of Gaussian smearing if is_metal: vasp_input_params["user_incar_settings"].update( {"ISMEAR": 0, "SIGMA": 0.3} ) k_resolution = k_resolution or 0.05 else: k_resolution = k_resolution or 0.1 vasp_input_params["user_kpoints_settings"] = {"k_resolution": k_resolution} # Override the INCAR settings with the user specifications if user_incar_settings is not None: vasp_input_params["user_incar_settings"].update(user_incar_settings) # Set up the geometry optimization Firework optics_fw = OpticsFW( parents=optimize_fw, vasp_input_params=vasp_input_params, custodian=in_custodian, spec=spec, auto_parallelization=auto_parallelization ) # Set up a clear name for the workflow workflow_name = str(structure.composition.reduced_formula).replace(" ", "") workflow_name += " " + str(functional) # Create the workflow return Workflow(fireworks=[optimize_fw, optics_fw], links_dict={optimize_fw: [optics_fw]}, name=workflow_name) def get_wf_slab_optimize(slab, directory, user_slab_settings, functional=("pbe", {}), is_metal=False, in_custodian=False, number_nodes=None): """ Set up a slab geometry optimization workflow. Args: slab (Qslab): Slab for which to set up the geometry optimization workflow. directory (str): Directory in which the geometry optimization should be performed. user_slab_settings (dict): Allows the user to specify the selective dynamics of the slab geometry optimization. These are passed to the SlabOptimizeSet.fix_slab_bulk() commands as kwargs. functional (tuple): Tuple with the functional details. The first element contains a string that indicates the functional used ("pbe", "hse06", ...), whereas the second element contains a dictionary that allows the user to specify the various functional INCAR tags. is_metal (bool): Flag that indicates the material being studied is a metal, which changes the smearing of the geometry optimization from Gaussian ( 0.05 eV) to second order Methfessel-Paxton of 0.2 eV. in_custodian (bool): Flag that indicates whether the calculation should be run inside a Custodian. number_nodes (int): Number of nodes that should be used for the calculations. Is required to add the proper `_fworker` to the Firework spec, so it is picked up by a Fireworker running in a job with the specified number of nodes. """ # Add number of nodes to spec, or "none" if number_nodes is not None and number_nodes != 0: spec = {"_fworker": str(number_nodes) + "nodes"} else: spec = {} # Set up the geometry optimization Firework vasp_input_params = _set_up_functional_params(functional) spec.update({"_launch_dir": directory}) # For metals, use Methfessel Paxton smearing if is_metal: vasp_input_params["user_incar_settings"].update( {"ISMEAR": 2, "SIGMA": 0.2} ) optimize_fw = SlabOptimizeFW(slab=slab, user_slab_settings=user_slab_settings, vasp_input_params=vasp_input_params, custodian=in_custodian, spec=spec) # Set up a clear name for the workflow workflow_name = str(slab.composition.reduced_formula).replace(" ", "") workflow_name += " " + str(slab.miller_index) workflow_name += " " + str(functional) # Create the workflow return Workflow(fireworks=[optimize_fw, ], name=workflow_name) def get_wf_slab_dos(slab, directory, user_slab_settings=None, functional=("pbe", {}), k_resolution=0.1, calculate_locpot=False, is_metal=False, user_incar_settings=None, in_custodian=False, number_nodes=None, auto_parallelization=False): """ Set up a slab DOS workflow. Starts with a geometry optimization. Args: slab (Qslab): Slab for which to set up the DOS workflow. directory (str): Directory in which the workflow should be set up. user_slab_settings (dict): Allows the user to specify the selective dynamics of the slab geometry optimization. These are passed to the SlabOptimizeSet.fix_slab_bulk() commands as kwargs. functional (tuple): Tuple with the functional details. The first element contains a string that indicates the functional used ("pbe", "hse06", ...), whereas the second element contains a dictionary that allows the user to specify the various functional INCAR tags. k_resolution (float): Resolution of the k-mesh, i.e. distance between two k-points along each reciprocal lattice vector. Note that for a slab calculation we always only consider one point in the c-direction. calculate_locpot (bool): Whether to calculate the the local potential, e.g. to determine the work function. is_metal (bool): Flag that indicates the material being studied is a metal, which changes the smearing of the geometry optimization from Gaussian ( 0.05 eV) to second order Methfessel-Paxton of 0.2 eV. user_incar_settings (dict): User INCAR settings. This allows a user to override INCAR settings, e.g., setting a different MAGMOM for various elements or species, or specify parallelization settings (KPAR, NPAR, ...). Note that the settings specified here will override the INCAR settings for ALL fireworks of the workflow. in_custodian (bool): Flag that indicates whether the calculations should be run inside a Custodian. number_nodes (int): Number of nodes that should be used for the calculations. Is required to add the proper `_fworker` to the Firework spec, so it is picked up by a Fireworker running in a job with the specified number of nodes. auto_parallelization (bool): Automatically parallelize the calculation using the VaspParallelizationTask. """ # Add number of nodes to spec, or "none" if number_nodes is not None and number_nodes != 0: spec = {"_fworker": str(number_nodes) + "nodes"} else: spec = {} # --> Set up the geometry optimization Firework vasp_input_params = _set_up_functional_params(functional) spec.update( {"_launch_dir": _set_up_relative_directory(directory, functional, "optimize")} ) # For metals, use Methfessel Paxton smearing if is_metal: vasp_input_params["user_incar_settings"].update( {"ISMEAR": 2, "SIGMA": 0.2} ) if user_incar_settings is not None: vasp_input_params["user_incar_settings"].update(user_incar_settings) optimize_fw = SlabOptimizeFW(slab=slab, user_slab_settings=user_slab_settings, vasp_input_params=vasp_input_params, custodian=in_custodian, spec=spec, auto_parallelization=auto_parallelization) # --> Set up the DOS Firework vasp_input_params = _set_up_functional_params(functional) # Calculate the local potential if requested (e.g. for the work function) if calculate_locpot: vasp_input_params["user_incar_settings"].update( {"LVTOT": True, "LVHAR": True} ) spec.update( {"_launch_dir": _set_up_relative_directory(directory, functional, "dos")} ) vasp_input_params["user_kpoints_settings"] = {"k_resolution": k_resolution} if user_incar_settings is not None: vasp_input_params["user_incar_settings"].update(user_incar_settings) # Set up the geometry optimization Firework dos_fw = SlabDosFW( vasp_input_params=vasp_input_params, parents=optimize_fw, custodian=in_custodian, spec=spec, auto_parallelization=auto_parallelization ) # Set up a clear name for the workflow workflow_name = str(slab.composition.reduced_formula).replace(" ", "") workflow_name += " " + str(slab.miller_index) workflow_name += " " + str(functional) # Create the workflow return Workflow(fireworks=[optimize_fw, dos_fw], name=workflow_name) def get_wf_quotas(bulk, slab_list, directory, functional=("pbe", {}), k_resolution=0.05, is_metal=False, in_custodian=False, number_nodes=None): """ Generate a full QUOTAS worfklow, i.e. one that: 1. Optimizes the bulk and calculates the optical properties. 2. Optimizes the slabs in 'slab_list' and then calculates the DOS and work function. Args: bulk (Structure): Input bulk geometry. slab_list (list): A list of dictionaries that specify the slabs to be included, as well as the settings to be used for each slab. Here is an overview of the mandatory keys: "slab" - Can be either a QSlab or a list/str that specifies the miller indices of the slab surface. In case only the miller indices are provided, the user must also supply the "min_slab_size" and "min_vacuum_size", which specify the minimum thickness of the slab and vacuum layer in angstrom. "user_slab_settings" (dict) - Settings that will be passed to the slab optimization firework. The most important key of this dict is "free_layers", which specifies the number of surface layers to optimize. directory (str): Directory in which the workflow should be set up. functional (tuple): Tuple with the functional details. The first element contains a string that indicates the functional used ("pbe", "hse06", ...), whereas the second element contains a dictionary that allows the user to specify the various functional INCAR tags. k_resolution (float): Resolution of the k-mesh, i.e. distance between two k-points along each reciprocal lattice vector. Note that for a slab calculation we always only consider one point in the c-direction. is_metal (bool): Flag that indicates the material being studied is a metal, which changes the smearing from Gaussian (0.05 eV) to second order Methfessel-Paxton of 0.2 eV; the optics calculation will use a generous gaussian smearing of 0.3 eV instead of the tetrahedron method. in_custodian (bool): Flag that indicates whether the calculation should be run inside a Custodian. number_nodes (int): Number of nodes that should be used for the calculations. Is required to add the proper `_fworker` to the Firework spec, so it is picked up by a Fireworker running in a job with the specified number of nodes. """ fireworks = list() # Set up the directory for the bulk calculations bulk_dir = os.path.join(directory, "bulk") fireworks.extend(get_wf_optics( directory=bulk_dir, structure=bulk, functional=functional, k_resolution=k_resolution, is_metal=is_metal, in_custodian=in_custodian, number_nodes=number_nodes ).fws) for slab_dict in slab_list: if isinstance(slab_dict["slab"], Slab): slab = slab_dict["slab"] # Set up the directory for the slab calculations slab_dir = "".join([str(c) for c in slab.miller_index]) slab_dir = os.path.join(directory, slab_dir) fireworks.extend(get_wf_slab_dos( slab=slab, directory=slab_dir, functional=functional, k_resolution=k_resolution, user_slab_settings=slab_dict["user_slab_settings"], calculate_locpot=True, is_metal=is_metal, in_custodian=in_custodian, number_nodes=number_nodes ).fws) elif isinstance(slab_dict["slab"], list) \ or isinstance(slab_dict["slab"], str): miller_index = [int(c) for c in slab_dict["slab"]] try: slabgen = SlabGenerator( initial_structure=bulk, miller_index=miller_index, min_slab_size=slab_dict["min_slab_size"], min_vacuum_size=slab_dict["min_vacuum_size"] ) except KeyError: raise ValueError("Either min_slab_size or min_vacuum_size were not" "defined in the slab dictionary for " + slab_dict["slab"] + ".") slab_terminations = slabgen.get_slabs() if len(slab_terminations) == 1: slab = QSlab.from_slab(slab_terminations[0]) # Set up the directory for the slab calculations slab_dir = "".join([str(c) for c in miller_index]) slab_dir = os.path.join(directory, slab_dir) fireworks.extend(get_wf_slab_dos( slab=slab, directory=slab_dir, functional=functional, k_resolution=k_resolution, user_slab_settings=slab_dict["user_slab_settings"], calculate_locpot=True, is_metal=is_metal, in_custodian=in_custodian, number_nodes=number_nodes ).fws) if len(slab_terminations) > 1: print("Multiple slab terminations found. Adding workflow for each " "termination...") for slab, letter in zip(slab_terminations, ascii_lowercase): slab = QSlab.from_slab(slab) # Set up the directory for the slab calculations slab_dir = "".join([str(c) for c in miller_index]) + "_" + letter slab_dir = os.path.join(directory, slab_dir) fireworks.extend(get_wf_slab_dos( slab=slab, directory=slab_dir, functional=functional, k_resolution=k_resolution, user_slab_settings=slab_dict["user_slab_settings"], calculate_locpot=True, is_metal=is_metal, in_custodian=in_custodian, number_nodes=number_nodes ).fws) # Set up a clear name for the workflow workflow_name = str(bulk.composition.reduced_formula).replace(" ", "") workflow_name += " - QUOTAS - " workflow_name += " " + str(functional) return Workflow(fireworks=fireworks, name=workflow_name) def get_wf_parallel(structure, directory, nodes, nbands=None, functional=("pbe", {}), user_kpoints_settings=None, user_incar_settings=None, handlers=None, cores_per_node=28, kpar_range=None, min_npar=1): # Set defaults user_kpoints_settings = user_kpoints_settings or {"reciprocal_density": 300} user_incar_settings = user_incar_settings or {} handlers = handlers or [] n_cores = int(cores_per_node * nodes) kpar_range = kpar_range or [1, n_cores] fw_list = [] suitable_kpars = np.array( [i for i in range(kpar_range[0], kpar_range[1] + 1) if n_cores % i == 0] ) for kpar in suitable_kpars: cores_per_k = n_cores / kpar suitable_npars = np.array( [i for i in range(min_npar, int(cores_per_k) + 1) if cores_per_k % i == 0] ) if nbands is not None: suitable_npars = [npar for npar in suitable_npars if nbands % npar == 0] for npar in suitable_npars: spec = {} # Set up the static calculation spec.update({"_launch_dir": os.path.join( directory, str(nodes) + "nodes", str(kpar) + "kpar", (str(npar) + "npar") )}) spec.update({"_fworker": str(nodes) + "nodes"}) vasp_input_params = _set_up_functional_params(functional) vasp_input_params["user_kpoints_settings"] = user_kpoints_settings vasp_input_params["user_incar_settings"].update(user_incar_settings) vasp_input_params["user_incar_settings"].update( {"KPAR": kpar, "NPAR": npar}) vasp_input_params["force_gamma"] = True # Set up the Firework and add it to the list fw_list.append( StaticFW(structure=structure, vasp_input_params=vasp_input_params, spec=spec, custodian=handlers) ) workflow_name = ("Parallel-Test: " + structure.composition.reduced_formula + " " + str(nodes) + "nodes.") # Create the workflow return Workflow(fireworks=fw_list, name=workflow_name)

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082364d42cde77f34692a39b3f9d58f88475a8f2

210

py

Python

zestipy/__init__.py

akremin/M2FSreduce

42092f18aa1e5d7ad6f6528a395ee93e89165b30

[ "BSD-3-Clause" ]

null

zestipy/__init__.py

akremin/M2FSreduce

42092f18aa1e5d7ad6f6528a395ee93e89165b30

[ "BSD-3-Clause" ]

null

zestipy/__init__.py

akremin/M2FSreduce

42092f18aa1e5d7ad6f6528a395ee93e89165b30

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Nov 17 11:15:36 2015 @author: kremin """ from . import io from . import plotting_tools from . import workflow_funcs from . import z_est from . import data_structures

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084a5ce782bef776e1ab0618cc0ac296ea2c360a

213

py

Python

sessao03/05_57-FuncoesPart4/aula57.py

Ruteski/CursoPythonOM

afe5ad5e50f903dab370be2b76966a81f07e51dd

[ "MIT" ]

null

sessao03/05_57-FuncoesPart4/aula57.py

Ruteski/CursoPythonOM

afe5ad5e50f903dab370be2b76966a81f07e51dd

[ "MIT" ]

null

sessao03/05_57-FuncoesPart4/aula57.py

Ruteski/CursoPythonOM

afe5ad5e50f903dab370be2b76966a81f07e51dd

[ "MIT" ]

null

""" escopo """ variavel = 'valor' def func(): print(variavel) def func2(): global variavel variavel = 'outro valor' print(variavel) def func3(): print(variavel) func() func2() func3()

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null

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4

f24994f3d3e6ec8a1a157412a4393a209bc38f6d

19

py

Python

exercises/en/exc_04_12.py

Lavendulaa/programming-in-python-for-data-science

bc41da8afacf4c180ae0ff9c6dc26a7e6292252f

[ "MIT" ]

1

2020-06-26T20:15:44.000Z

exercises/en/exc_04_12.py

Lavendulaa/programming-in-python-for-data-science

bc41da8afacf4c180ae0ff9c6dc26a7e6292252f

[ "MIT" ]

20

2020-06-15T23:05:20.000Z

2020-09-01T22:07:45.000Z

exercises/en/exc_04_12.py

UBC-MDS/MCL-programming-in-python

22836d9013d3e3d1b1074678ba7dc3ee2e66f398

[ "MIT" ]

1

2020-06-25T20:53:13.000Z

____ = ____ # ____

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null

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4

f2a21b477e667dfda44e7f989d0c8fa3fa3aed8d

48,305

py

Python

django_harmonization/ui/models.py

chrisroederucdenver/Kao-Harmonization-Release

1a90db58cd378244a8aba138e27f049376045729

[ "Apache-2.0" ]

null

django_harmonization/ui/models.py

chrisroederucdenver/Kao-Harmonization-Release

1a90db58cd378244a8aba138e27f049376045729

[ "Apache-2.0" ]

null

django_harmonization/ui/models.py

chrisroederucdenver/Kao-Harmonization-Release

1a90db58cd378244a8aba138e27f049376045729

[ "Apache-2.0" ]

null

from django.db import models # for api-token-auth from django.db.models.signals import post_save from django.dispatch import receiver from django.conf import settings # Most tables are NOT managed, TableColumn is #class AttributeDefinition(models.Model): # attribute_definition_id = models.IntegerField(primary_key=True) # attribute_name = models.CharField(max_length=255) # attribute_description = models.TextField(blank=True, null=True) # attribute_type_concept_id = models.IntegerField() # attribute_syntax = models.TextField(blank=True, null=True) # # class Meta: # managed = False # db_table = 'attribute_definition' # #class CareSite(models.Model): # care_site_id = models.IntegerField(primary_key=True) # care_site_name = models.CharField(max_length=255, blank=True, null=True) # place_of_service_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # location = models.ForeignKey('Location', models.DO_NOTHING, blank=True, null=True) # care_site_source_value = models.CharField(max_length=50, blank=True, null=True) # place_of_service_source_value = models.CharField(max_length=50, blank=True, null=True) # # class Meta: # managed = False # db_table = 'care_site' class CategorizationFunctions(models.Model): name = models.CharField(max_length=100) class Meta: managed = False db_table = 'categorization_functions' unique_together = (('name'),) class MappingFunctions(models.Model): name = models.CharField(max_length=100) study_id = models.IntegerField(blank=True, null=True) class Meta: managed = False db_table = 'mapping_function' unique_together = (('name'),) class CategorizationFunctionMetadata(models.Model): extract_study = models.ForeignKey('ExtractStudy', models.DO_NOTHING, primary_key=True) function_name = models.CharField(max_length=100) long_name = models.CharField(max_length=100) rule_id = models.CharField(max_length=20) from_vocabulary_id = models.CharField(max_length=100) from_concept_code = models.CharField(max_length=100) comment = models.CharField(max_length=256, blank=True, null=True) from_table = models.CharField(max_length=20, blank=True, null=True) short_name = models.CharField(max_length=6, blank=True, null=True) class Meta: managed = False db_table = 'categorization_function_metadata' unique_together = (('extract_study', 'function_name', 'long_name', 'rule_id'), ('extract_study', 'function_name', 'long_name', 'rule_id'),) class CategorizationFunctionParameters(models.Model): extract_study = models.ForeignKey('ExtractStudy', models.DO_NOTHING, primary_key=True) function_name = models.CharField(max_length=100) long_name = models.CharField(max_length=100) rule_id = models.CharField(max_length=20) value_limit = models.FloatField(blank=True, null=True) rank = models.IntegerField() from_string = models.CharField(max_length=20, blank=True, null=True) from_concept_id = models.IntegerField(blank=True, null=True) class Meta: managed = False db_table = 'categorization_function_parameters' unique_together = (('extract_study', 'function_name', 'long_name', 'rule_id', 'rank'),) class CategorizationFunctionQualifiers(models.Model): extract_study = models.ForeignKey('ExtractStudy', models.DO_NOTHING, primary_key=True) function_name = models.CharField(max_length=100) long_name = models.CharField(max_length=100) rule_id = models.CharField(max_length=20) vocabulary_id = models.CharField(max_length=100) concept_code = models.CharField(max_length=100) value_vocabulary_id = models.CharField(max_length=100, blank=True, null=True) value_as_string = models.CharField(max_length=100, blank=True, null=True) value_as_number = models.IntegerField(blank=True, null=True) value_as_concept_id = models.CharField(max_length=100, blank=True, null=True) class Meta: managed = False db_table = 'categorization_function_qualifiers' unique_together = (('extract_study', 'function_name', 'long_name', 'rule_id', 'vocabulary_id', 'concept_code'),) class CategorizationFunctionTable(models.Model): extract_study = models.ForeignKey('ExtractStudy', models.DO_NOTHING, primary_key=True) function_name = models.CharField(max_length=100) long_name = models.CharField(max_length=100) from_table = models.ForeignKey('TableColumn', models.DO_NOTHING, db_column='from_table', blank=True, null=True) from_column = models.CharField(max_length=100, blank=True, null=True) from_vocabulary = models.ForeignKey('VocabularyConcept', models.DO_NOTHING, blank=True, null=True) from_concept_code = models.CharField(max_length=100, blank=True, null=True) class Meta: managed = False db_table = 'categorization_function_table' unique_together = (('extract_study', 'function_name', 'long_name'),) #class CdmSource(models.Model): # cdm_source_name = models.CharField(max_length=255) # cdm_source_abbreviation = models.CharField(max_length=25, blank=True, null=True) # cdm_holder = models.CharField(max_length=255, blank=True, null=True) # source_description = models.TextField(blank=True, null=True) # source_documentation_reference = models.CharField(max_length=255, blank=True, null=True) # cdm_etl_reference = models.CharField(max_length=255, blank=True, null=True) # source_release_date = models.DateField(blank=True, null=True) # cdm_release_date = models.DateField(blank=True, null=True) # cdm_version = models.CharField(max_length=10, blank=True, null=True) # vocabulary_version = models.CharField(max_length=20, blank=True, null=True) # # class Meta: # managed = False # db_table = 'cdm_source' #class Cohort(models.Model): # cohort_definition = models.ForeignKey('CohortDefinition', models.DO_NOTHING, primary_key=True) # subject_id = models.IntegerField() # cohort_start_date = models.DateField() # cohort_end_date = models.DateField() # # class Meta: # managed = False # db_table = 'cohort' # unique_together = (('cohort_definition', 'subject_id', 'cohort_start_date', 'cohort_end_date'),) #class CohortAttribute(models.Model): # cohort_definition = models.ForeignKey('CohortDefinition', models.DO_NOTHING, primary_key=True) # cohort_start_date = models.DateField() # cohort_end_date = models.DateField() # subject_id = models.IntegerField() # attribute_definition = models.ForeignKey(AttributeDefinition, models.DO_NOTHING) # value_as_number = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # value_as_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # # class Meta: # managed = False # db_table = 'cohort_attribute' # unique_together = (('cohort_definition', 'subject_id', 'cohort_start_date', 'cohort_end_date', 'attribute_definition'),) #class CohortDefinition(models.Model): # cohort_definition_id = models.IntegerField(primary_key=True) # cohort_definition_name = models.CharField(max_length=255) # cohort_definition_description = models.TextField(blank=True, null=True) # definition_type_concept = models.ForeignKey(Concept, models.DO_NOTHING) # cohort_definition_syntax = models.TextField(blank=True, null=True) # subject_concept_id = models.IntegerField() # cohort_initiation_date = models.DateField(blank=True, null=True) # # class Meta: # managed = False # db_table = 'cohort_definition' class Concept(models.Model): concept_id = models.IntegerField(primary_key=True) concept_name = models.CharField(max_length=255) domain = models.ForeignKey('Domain', models.DO_NOTHING) vocabulary = models.ForeignKey('Vocabulary', models.DO_NOTHING) concept_class = models.ForeignKey('ConceptClass', models.DO_NOTHING) standard_concept = models.CharField(max_length=1, blank=True, null=True) concept_code = models.CharField(max_length=50) valid_start_date = models.DateField() valid_end_date = models.DateField() invalid_reason = models.CharField(max_length=1, blank=True, null=True) class Meta: managed = False db_table = 'concept' class ConceptAncestor(models.Model): ancestor_concept_id = models.IntegerField(primary_key=True) descendant_concept_id = models.IntegerField() min_levels_of_separation = models.IntegerField() max_levels_of_separation = models.IntegerField() class Meta: managed = False db_table = 'concept_ancestor' unique_together = (('ancestor_concept_id', 'descendant_concept_id'),) class ConceptClass(models.Model): concept_class_id = models.CharField(primary_key=True, max_length=20) concept_class_name = models.CharField(max_length=255) concept_class_concept = models.ForeignKey(Concept, models.DO_NOTHING) class Meta: managed = False db_table = 'concept_class' class ConceptRelationship(models.Model): concept_id_1 = models.IntegerField(primary_key=True) concept_id_2 = models.IntegerField() relationship = models.ForeignKey('Relationship', models.DO_NOTHING) valid_start_date = models.DateField() valid_end_date = models.DateField() invalid_reason = models.CharField(max_length=1, blank=True, null=True) class Meta: managed = False db_table = 'concept_relationship' unique_together = (('concept_id_1', 'concept_id_2', 'relationship'),) class ConceptSynonym(models.Model): concept = models.ForeignKey(Concept, models.DO_NOTHING) concept_synonym_name = models.CharField(max_length=1000) language_concept_id = models.IntegerField() class Meta: managed = False db_table = 'concept_synonym' # #class ConditionEra(models.Model): # condition_era_id = models.IntegerField(primary_key=True) # person = models.ForeignKey('Person', models.DO_NOTHING) # condition_concept = models.ForeignKey(Concept, models.DO_NOTHING) # condition_era_start_date = models.DateField() # condition_era_end_date = models.DateField() # condition_occurrence_count = models.IntegerField(blank=True, null=True) # # class Meta: # managed = False # db_table = 'condition_era' #class ConditionOccurrence(models.Model): # condition_occurrence_id = models.IntegerField(primary_key=True) # person = models.ForeignKey('Person', models.DO_NOTHING) # condition_concept = models.ForeignKey(Concept, models.DO_NOTHING) # condition_start_date = models.DateField() # condition_start_datetime = models.DateTimeField() # condition_end_date = models.DateField(blank=True, null=True) # condition_end_datetime = models.DateTimeField(blank=True, null=True) # condition_type_concept = models.ForeignKey(Concept, models.DO_NOTHING) # stop_reason = models.CharField(max_length=20, blank=True, null=True) # provider = models.ForeignKey('Provider', models.DO_NOTHING, blank=True, null=True) # visit_occurrence = models.ForeignKey('VisitOccurrence', models.DO_NOTHING, blank=True, null=True) # condition_source_value = models.CharField(max_length=50, blank=True, null=True) # condition_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # condition_status_source_value = models.CharField(max_length=50, blank=True, null=True) # condition_status_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # # class Meta: # managed = False # db_table = 'condition_occurrence' # #class Cost(models.Model): # cost_id = models.IntegerField(primary_key=True) # cost_event_id = models.IntegerField() # cost_domain_id = models.CharField(max_length=20) # cost_type_concept_id = models.IntegerField() # currency_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # total_charge = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # total_cost = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # total_paid = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # paid_by_payer = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # paid_by_patient = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # paid_patient_copay = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # paid_patient_coinsurance = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # paid_patient_deductible = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # paid_by_primary = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # paid_ingredient_cost = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # paid_dispensing_fee = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # payer_plan_period = models.ForeignKey('PayerPlanPeriod', models.DO_NOTHING, blank=True, null=True) # amount_allowed = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # revenue_code_concept_id = models.IntegerField(blank=True, null=True) # reveue_code_source_value = models.CharField(max_length=50, blank=True, null=True) # drg_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # drg_source_value = models.CharField(max_length=3, blank=True, null=True) # # class Meta: # managed = False # db_table = 'cost' class Death(models.Model): person = models.ForeignKey('Person', models.DO_NOTHING, primary_key=True) death_date = models.DateField() death_datetime = models.DateTimeField(blank=True, null=True) death_type_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'death_type_concept') cause_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'cause_concept') cause_source_value = models.CharField(max_length=50, blank=True, null=True) cause_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'cause_source_concept') class Meta: managed = False db_table = 'death' #class DeviceExposure(models.Model): # device_exposure_id = models.IntegerField(primary_key=True) # person = models.ForeignKey('Person', models.DO_NOTHING) # device_concept = models.ForeignKey(Concept, models.DO_NOTHING) # device_exposure_start_date = models.DateField() # device_exposure_start_datetime = models.DateTimeField() # device_exposure_end_date = models.DateField(blank=True, null=True) # device_exposure_end_datetime = models.DateTimeField(blank=True, null=True) # device_type_concept = models.ForeignKey(Concept, models.DO_NOTHING) # unique_device_id = models.CharField(max_length=50, blank=True, null=True) # quantity = models.IntegerField(blank=True, null=True) # provider = models.ForeignKey('Provider', models.DO_NOTHING, blank=True, null=True) # visit_occurrence = models.ForeignKey('VisitOccurrence', models.DO_NOTHING, blank=True, null=True) # device_source_value = models.CharField(max_length=100, blank=True, null=True) # device_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # # class Meta: # managed = False # db_table = 'device_exposure' class Domain(models.Model): domain_id = models.CharField(primary_key=True, max_length=20) domain_name = models.CharField(max_length=255) domain_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'domain_concept') class Meta: managed = False db_table = 'domain' #class DoseEra(models.Model): # dose_era_id = models.IntegerField(primary_key=True) # person = models.ForeignKey('Person', models.DO_NOTHING) # drug_concept = models.ForeignKey(Concept, models.DO_NOTHING) # unit_concept = models.ForeignKey(Concept, models.DO_NOTHING) # dose_value = models.DecimalField(max_digits=65535, decimal_places=65535) # dose_era_start_date = models.DateField() # dose_era_end_date = models.DateField() # # class Meta: # managed = False # db_table = 'dose_era' # # #class DrugEra(models.Model): # drug_era_id = models.IntegerField(primary_key=True) # person = models.ForeignKey('Person', models.DO_NOTHING) # drug_concept = models.ForeignKey(Concept, models.DO_NOTHING) # drug_era_start_date = models.DateField() # drug_era_end_date = models.DateField() # drug_exposure_count = models.IntegerField(blank=True, null=True) # gap_days = models.IntegerField(blank=True, null=True) # # class Meta: # managed = False # db_table = 'drug_era' # # #class DrugExposure(models.Model): # drug_exposure_id = models.IntegerField(primary_key=True) # person = models.ForeignKey('Person', models.DO_NOTHING) # drug_concept = models.ForeignKey(Concept, models.DO_NOTHING) # drug_exposure_start_date = models.DateField() # drug_exposure_start_datetime = models.DateTimeField() # drug_exposure_end_date = models.DateField() # drug_exposure_end_datetime = models.DateTimeField(blank=True, null=True) # verbatim_end_date = models.DateField(blank=True, null=True) # drug_type_concept = models.ForeignKey(Concept, models.DO_NOTHING) # stop_reason = models.CharField(max_length=20, blank=True, null=True) # refills = models.IntegerField(blank=True, null=True) # quantity = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # days_supply = models.IntegerField(blank=True, null=True) # sig = models.TextField(blank=True, null=True) # route_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # lot_number = models.CharField(max_length=50, blank=True, null=True) # provider = models.ForeignKey('Provider', models.DO_NOTHING, blank=True, null=True) # visit_occurrence = models.ForeignKey('VisitOccurrence', models.DO_NOTHING, blank=True, null=True) # drug_source_value = models.CharField(max_length=50, blank=True, null=True) # drug_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # route_source_value = models.CharField(max_length=50, blank=True, null=True) # dose_unit_source_value = models.CharField(max_length=50, blank=True, null=True) # # class Meta: # managed = False # db_table = 'drug_exposure' # # #class DrugStrength(models.Model): # drug_concept = models.ForeignKey(Concept, models.DO_NOTHING, primary_key=True) # ingredient_concept = models.ForeignKey(Concept, models.DO_NOTHING) # amount_value = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # amount_unit_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # numerator_value = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # numerator_unit_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # denominator_value = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # denominator_unit_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # box_size = models.IntegerField(blank=True, null=True) # valid_start_date = models.DateField() # valid_end_date = models.DateField() # invalid_reason = models.CharField(max_length=1, blank=True, null=True) # # class Meta: # managed = False # db_table = 'drug_strength' # unique_together = (('drug_concept', 'ingredient_concept'),) # class EventsMapping(models.Model): study = models.ForeignKey('Study', models.DO_NOTHING, primary_key=True) from_table = models.ForeignKey('TableColumn', models.DO_NOTHING, db_column='from_table', related_name = 'from_table') from_column = models.CharField(max_length=50) to_table = models.ForeignKey('TableColumn', models.DO_NOTHING, db_column='to_table', related_name = 'to_table') to_column = models.CharField(max_length=50) value_vocabulary_id = models.CharField(max_length=50, blank=True, null=True) value_concept_code = models.CharField(max_length=50, blank=True, null=True) addl_column = models.CharField(max_length=50, blank=True, null=True) addl_value = models.CharField(max_length=50, blank=True, null=True) from_date_column = models.CharField(max_length=50, blank=True, null=True) where_clause = models.CharField(max_length=256) comment = models.CharField(max_length=256, blank=True, null=True) class Meta: managed = False db_table = 'events_mapping' unique_together = (('study', 'from_table', 'from_column', 'to_table', 'to_column', 'where_clause'),) class ExtractStudy(models.Model): extract_study_id = models.IntegerField(primary_key=True) study_id = models.IntegerField(blank=True, null=True) name = models.CharField(max_length=100, blank=True, null=True) comment = models.CharField(max_length=1000, blank=True, null=True) class Meta: managed = False db_table = 'extract_study' #class FactRelationship(models.Model): # domain_concept_id_1 = models.ForeignKey(Concept, models.DO_NOTHING, db_column='domain_concept_id_1') # fact_id_1 = models.IntegerField() # domain_concept_id_2 = models.ForeignKey(Concept, models.DO_NOTHING, db_column='domain_concept_id_2') # fact_id_2 = models.IntegerField() # relationship_concept = models.ForeignKey(Concept, models.DO_NOTHING) # # class Meta: # managed = False # db_table = 'fact_relationship' class Location(models.Model): location_id = models.IntegerField(primary_key=True) address_1 = models.CharField(max_length=50, blank=True, null=True) address_2 = models.CharField(max_length=50, blank=True, null=True) city = models.CharField(max_length=50, blank=True, null=True) state = models.CharField(max_length=2, blank=True, null=True) zip = models.CharField(max_length=9, blank=True, null=True) county = models.CharField(max_length=20, blank=True, null=True) location_source_value = models.CharField(max_length=50, blank=True, null=True) class Meta: managed = False db_table = 'location' class Measurement(models.Model): measurement_id = models.IntegerField(primary_key=True) person = models.ForeignKey('Person', models.DO_NOTHING) measurement_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'measurement_concept') measurement_date = models.DateField() measurement_datetime = models.DateTimeField(blank=True, null=True) measurement_type_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'measurement_type_concept') operator_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'operator_concept') value_as_number = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) value_as_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'measurement_value_as_concept') unit_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'measurement_unit_concept') range_low = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) range_high = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) #provider = models.ForeignKey('Provider', models.DO_NOTHING, blank=True, null=True) provider = models.IntegerField(blank=True, null=True) visit_occurrence = models.ForeignKey('VisitOccurrence', models.DO_NOTHING, blank=True, null=True) measurement_source_value = models.CharField(max_length=50, blank=True, null=True) measurement_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'measurement_source_concept') unit_source_value = models.CharField(max_length=50, blank=True, null=True) value_source_value = models.CharField(max_length=50, blank=True, null=True) class Meta: managed = False db_table = 'measurement' class Note(models.Model): note_id = models.IntegerField(primary_key=True) person = models.ForeignKey('Person', models.DO_NOTHING) note_date = models.DateField() note_datetime = models.DateTimeField(blank=True, null=True) note_type_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'note_type_concept') note_class_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'note_class_concept') note_title = models.CharField(max_length=250, blank=True, null=True) note_text = models.TextField() encoding_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'encoding_concept') language_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'language_concept') #provider = models.ForeignKey('Provider', models.DO_NOTHING, blank=True, null=True) provider = models.IntegerField(blank=True, null=True) visit_occurrence = models.ForeignKey('VisitOccurrence', models.DO_NOTHING, blank=True, null=True) note_source_value = models.CharField(max_length=50, blank=True, null=True) class Meta: managed = False db_table = 'note' #class NoteNlp(models.Model): # note_nlp_id = models.BigIntegerField(primary_key=True) # note = models.ForeignKey(Note, models.DO_NOTHING) # section_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # snippet = models.CharField(max_length=250, blank=True, null=True) # offset = models.CharField(max_length=250, blank=True, null=True) # lexical_variant = models.CharField(max_length=250) # note_nlp_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # note_nlp_source_concept_id = models.IntegerField(blank=True, null=True) # nlp_system = models.CharField(max_length=250, blank=True, null=True) # nlp_date = models.DateField() # nlp_datetime = models.DateTimeField(blank=True, null=True) # term_exists = models.CharField(max_length=1, blank=True, null=True) # term_temporal = models.CharField(max_length=50, blank=True, null=True) # term_modifiers = models.CharField(max_length=2000, blank=True, null=True) # # class Meta: # managed = False # db_table = 'note_nlp' class Observation(models.Model): observation_id = models.IntegerField(primary_key=True) person = models.ForeignKey('Person', models.DO_NOTHING) observation_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'observation_concept') observation_date = models.DateField() observation_datetime = models.DateTimeField(blank=True, null=True) observation_type_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'observation_type_concept') value_as_number = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) value_as_string = models.CharField(max_length=60, blank=True, null=True) value_as_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'observation_value_as_concept') qualifier_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) unit_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'observation_unit_concept') #provider = models.ForeignKey('Provider', models.DO_NOTHING, blank=True, null=True) provider = models.IntegerField(blank=True, null=True) visit_occurrence = models.ForeignKey('VisitOccurrence', models.DO_NOTHING, blank=True, null=True) observation_source_value = models.CharField(max_length=50, blank=True, null=True) observation_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'observation_source_concept') unit_source_value = models.CharField(max_length=50, blank=True, null=True) qualifier_source_value = models.CharField(max_length=50, blank=True, null=True) class Meta: managed = False db_table = 'observation' #class ObservationPeriod(models.Model): # observation_period_id = models.IntegerField(primary_key=True) # person = models.ForeignKey('Person', models.DO_NOTHING) # observation_period_start_date = models.DateField() # observation_period_start_datetime = models.DateTimeField() # observation_period_end_date = models.DateField() # observation_period_end_datetime = models.DateTimeField() # period_type_concept = models.ForeignKey(Concept, models.DO_NOTHING) # # class Meta: # managed = False # db_table = 'observation_period' class OhdsiCalculationArgument(models.Model): vocabulary = models.ForeignKey('VocabularyConcept', models.DO_NOTHING, related_name = 'vocabulary_concept') concept_code = models.CharField(max_length=100) study = models.ForeignKey('Study', models.DO_NOTHING, primary_key=True) function_name = models.CharField(max_length=100) argument_order = models.IntegerField(blank=True, null=True) argument_name = models.CharField(max_length=30, blank=True, null=True) value_field = models.CharField(max_length=20, blank=True, null=True) to_concept_code = models.CharField(max_length=100) to_vocabulary = models.ForeignKey('VocabularyConcept', models.DO_NOTHING, related_name = 'to_vocabulary_concept') from_table = models.CharField(max_length=100, blank=True, null=True) class Meta: managed = False db_table = 'ohdsi_calculation_argument' unique_together = (('study', 'function_name', 'to_concept_code', 'to_vocabulary', 'vocabulary', 'concept_code'),) class OhdsiCalculationFunction(models.Model): study = models.ForeignKey('Study', models.DO_NOTHING, primary_key=True) function_name = models.CharField(max_length=100) to_vocabulary = models.ForeignKey('VocabularyConcept', models.DO_NOTHING) to_concept_code = models.CharField(max_length=100) to_table = models.CharField(max_length=100, blank=True, null=True) to_column = models.CharField(max_length=100, blank=True, null=True) function_order = models.IntegerField(blank=True, null=True) class Meta: managed = False db_table = 'ohdsi_calculation_function' unique_together = (('study', 'function_name', 'to_concept_code', 'to_vocabulary'),) #class PayerPlanPeriod(models.Model): # payer_plan_period_id = models.IntegerField(primary_key=True) # person = models.ForeignKey('Person', models.DO_NOTHING) # payer_plan_period_start_date = models.DateField() # payer_plan_period_end_date = models.DateField() # payer_source_value = models.CharField(max_length=50, blank=True, null=True) # plan_source_value = models.CharField(max_length=50, blank=True, null=True) # family_source_value = models.CharField(max_length=50, blank=True, null=True) # # class Meta: # managed = False # db_table = 'payer_plan_period' class Person(models.Model): person_id = models.IntegerField(primary_key=True) gender_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'gender_concept') year_of_birth = models.IntegerField() month_of_birth = models.IntegerField(blank=True, null=True) day_of_birth = models.IntegerField(blank=True, null=True) birth_datetime = models.DateTimeField(blank=True, null=True) race_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'race_concept') ethnicity_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'ethnicity_concept') location = models.ForeignKey(Location, models.DO_NOTHING, blank=True, null=True) #provider = models.ForeignKey('Provider', models.DO_NOTHING, blank=True, null=True) provider = models.IntegerField(blank=True, null=True) #care_site = models.ForeignKey(CareSite, models.DO_NOTHING, blank=True, null=True) care_site = models.IntegerField(blank=True, null=True) person_source_value = models.CharField(max_length=50, blank=True, null=True) gender_source_value = models.CharField(max_length=50, blank=True, null=True) gender_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'gender_source_concept') race_source_value = models.CharField(max_length=50, blank=True, null=True) race_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'race_source_concept') ethnicity_source_value = models.CharField(max_length=50, blank=True, null=True) ethnicity_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'ethnicity_source_concept') class Meta: managed = False db_table = 'person' class ProcedureOccurrence(models.Model): procedure_occurrence_id = models.IntegerField(primary_key=True) person = models.ForeignKey(Person, models.DO_NOTHING) procedure_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'procedure_concept') procedure_date = models.DateField() procedure_datetime = models.DateTimeField() procedure_type_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'procedure_type_concept') modifier_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'modifier_concept') quantity = models.IntegerField(blank=True, null=True) #provider = models.ForeignKey('Provider', models.DO_NOTHING, blank=True, null=True) provider = models.IntegerField(blank=True, null=True) visit_occurrence = models.ForeignKey('VisitOccurrence', models.DO_NOTHING, blank=True, null=True) procedure_source_value = models.CharField(max_length=50, blank=True, null=True) procedure_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'procedure_source_concept') qualifier_source_value = models.CharField(max_length=50, blank=True, null=True) class Meta: managed = False db_table = 'procedure_occurrence' #class Provider(models.Model): # provider_id = models.IntegerField(primary_key=True) # provider_name = models.CharField(max_length=255, blank=True, null=True) # npi = models.CharField(max_length=20, blank=True, null=True) # dea = models.CharField(max_length=20, blank=True, null=True) # specialty_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # care_site = models.ForeignKey(CareSite, models.DO_NOTHING, blank=True, null=True) # year_of_birth = models.IntegerField(blank=True, null=True) # gender_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # provider_source_value = models.CharField(max_length=50, blank=True, null=True) # specialty_source_value = models.CharField(max_length=50, blank=True, null=True) # specialty_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # gender_source_value = models.CharField(max_length=50, blank=True, null=True) # gender_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # # class Meta: # managed = False # db_table = 'provider' class Relationship(models.Model): relationship_id = models.CharField(primary_key=True, max_length=20) relationship_name = models.CharField(max_length=255) is_hierarchical = models.CharField(max_length=1) defines_ancestry = models.CharField(max_length=1) reverse_relationship = models.ForeignKey('self', models.DO_NOTHING) relationship_concept = models.ForeignKey(Concept, models.DO_NOTHING) class Meta: managed = False db_table = 'relationship' class SourceToConceptMap(models.Model): source_code = models.CharField(max_length=50) source_concept_id = models.IntegerField() source_vocabulary = models.ForeignKey('Vocabulary', models.DO_NOTHING, primary_key=True, related_name = 'source_vocabulary') source_code_description = models.CharField(max_length=255, blank=True, null=True) target_concept = models.ForeignKey(Concept, models.DO_NOTHING) target_vocabulary = models.ForeignKey('Vocabulary', models.DO_NOTHING, related_name = 'target_vocabulary') valid_start_date = models.DateField() valid_end_date = models.DateField() invalid_reason = models.CharField(max_length=1, blank=True, null=True) class Meta: managed = False db_table = 'source_to_concept_map' unique_together = (('source_vocabulary', 'target_concept', 'source_code', 'valid_end_date'),) # #class Specimen(models.Model): # specimen_id = models.IntegerField(primary_key=True) # person = models.ForeignKey(Person, models.DO_NOTHING) # specimen_concept = models.ForeignKey(Concept, models.DO_NOTHING) # specimen_type_concept = models.ForeignKey(Concept, models.DO_NOTHING) # specimen_date = models.DateField() # specimen_datetime = models.DateTimeField(blank=True, null=True) # quantity = models.DecimalField(max_digits=65535, decimal_places=65535, blank=True, null=True) # unit_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # anatomic_site_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # disease_status_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True) # specimen_source_id = models.CharField(max_length=50, blank=True, null=True) # specimen_source_value = models.CharField(max_length=50, blank=True, null=True) # unit_source_value = models.CharField(max_length=50, blank=True, null=True) # anatomic_site_source_value = models.CharField(max_length=50, blank=True, null=True) # disease_status_source_value = models.CharField(max_length=50, blank=True, null=True) # # class Meta: # managed = False # db_table = 'specimen' # class Study(models.Model): study_id = models.IntegerField(primary_key=True) study_name = models.CharField(max_length=100, blank=True, null=True) person_id_range_start = models.IntegerField(blank=True, null=True) person_id_range_end = models.IntegerField(blank=True, null=True) observation_range_start = models.IntegerField(blank=True, null=True) observation_range_end = models.IntegerField(blank=True, null=True) loaded = models.BooleanField() migrated = models.BooleanField() calculated = models.BooleanField() id_field_name = models.CharField(max_length=10, blank=True, null=True) person_id_prefix = models.CharField(max_length=10, blank=True, null=True) person_id_select = models.CharField(max_length=100, blank=True, null=True) person_details_select = models.CharField(max_length=200, blank=True, null=True) study_class = models.CharField(max_length=50, blank=True, null=True) sex_table_name = models.CharField(max_length=100, blank=True, null=True) sex_column_name = models.CharField(max_length=100, blank=True, null=True) sex_function_name = models.CharField(max_length=100, blank=True, null=True) race_table_name = models.CharField(max_length=100, blank=True, null=True) race_column_name = models.CharField(max_length=100, blank=True, null=True) race_function_name = models.CharField(max_length=100, blank=True, null=True) class Meta: managed = False db_table = 'study' class TableColumn(models.Model): table_column_id = models.IntegerField(primary_key=True) study = models.ForeignKey(Study, models.DO_NOTHING, blank=True, null=True) table_name = models.CharField(max_length=50) column_name = models.CharField(max_length=50) class Meta: managed = False db_table = 'table_column' unique_together = (('table_name', 'column_name'),) #class StudyConceptType(models.Model): # study_id = models.IntegerField(primary_key=True) # study_concept_type_id = models.AutoField() # name = models.CharField(max_length=50) # filename = models.CharField(max_length=50) # description = models.CharField(max_length=1000, blank=True, null=True) # considered = models.NullBooleanField() # # class Meta: # managed = False # db_table = 'study_concept_type' # unique_together = (('study_id', 'study_concept_type_id'),) # # #class StudyConceptValue(models.Model): # study = models.ForeignKey(StudyConceptType, models.DO_NOTHING, primary_key=True) # study_concept_type_id = models.IntegerField() # study_concept_value_id = models.AutoField() # name = models.CharField(max_length=250) # description = models.CharField(max_length=1000, blank=True, null=True) # # class Meta: # managed = False # db_table = 'study_concept_value' # unique_together = (('study', 'study_concept_type_id', 'study_concept_value_id'),) class StudyToOhdsiMapping(models.Model): study = models.ForeignKey(Study, models.DO_NOTHING) # WTF, TODO #from_table = models.ForeignKey('TableColumn', models.DO_NOTHING, db_column='from_table', related_name = 'study_from_table') from_table = models.CharField(max_length=100) from_column = models.CharField(max_length=100) function_name = models.CharField(max_length=100, blank=True, null=True) vocabulary_id = models.CharField(max_length=100) concept_code = models.CharField(max_length=100, blank=True, null=True) # should be a single composite key, but django doesn't support that and the demo's due. TODO # https://pypi.python.org/pypi/django-composite-foreignkey #vocabulary_id = models.ForeignKey('Concept', models.DO_NOTHING, blank=True, null=True) #concept_code = models.ForeignKey('Concept', models.DO_NOTHING, blank=True, null=True) # TODO I think, it's OK. We need the FK in the database, but not here?? Can't traverse it here wihtout this, # but the FK in the database ensures we're not making up concepts. #vocabulary = models.ForeignKey('VocabularyConcept', models.DO_NOTHING, blank=True, null=True) #concept_code = models.ForeignKey('VocabularyConcept', models.DO_NOTHHING, blank=True, null=True) #to_table = models.ForeignKey('TableColumn', models.DO_NOTHING, db_column='to_table', blank=True, null=True, related_name = 'ohdsi_to_table') to_table = models.CharField(max_length=100, blank=True, null=True) to_column = models.CharField(max_length=100, blank=True, null=True) addl_value_1 = models.CharField(max_length=20, blank=True, null=True) addl_column_1 = models.CharField(max_length=20, blank=True, null=True) from_where_clause = models.CharField(max_length=100, blank=True, null=True) comment = models.CharField(max_length=250, blank=True, null=True) from_where_column = models.CharField(max_length=100, blank=True, null=True) units = models.CharField(max_length=20, blank=True, null=True) has_date = models.NullBooleanField() class Meta: managed = False db_table = 'study_to_ohdsi_mapping' def __str__(self): return "table:{} coln:{} fun:{} v:{} c:{} table:{} column:{} where:{} where:{} has_date:{}".format( self.from_table, self.from_column, self.function_name, self.vocabulary_id, self.concept_code, self.to_table, self.to_column, self.from_where_column, self.from_where_clause, self.has_date) class VisitOccurrence(models.Model): visit_occurrence_id = models.IntegerField(primary_key=True) person = models.ForeignKey(Person, models.DO_NOTHING) visit_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'visit_concept') visit_start_date = models.DateField() visit_start_datetime = models.DateTimeField(blank=True, null=True) visit_end_date = models.DateField() visit_end_datetime = models.DateTimeField(blank=True, null=True) visit_type_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'visit_type_concept') #provider = models.ForeignKey(Provider, models.DO_NOTHING, blank=True, null=True) provider = models.IntegerField(blank=True, null=True) #care_site = models.ForeignKey(CareSite, models.DO_NOTHING, blank=True, null=True) care_site = models.IntegerField(blank=True, null=True) visit_source_value = models.CharField(max_length=50, blank=True, null=True) visit_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'visit_source_concept') admitting_source_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'admitting_source_concept') admitting_source_value = models.CharField(max_length=50, blank=True, null=True) discharge_to_concept = models.ForeignKey(Concept, models.DO_NOTHING, blank=True, null=True, related_name = 'discharge_to_source_concept') discharge_to_source_value = models.CharField(max_length=50, blank=True, null=True) preceding_visit_occurrence_id = models.IntegerField(blank=True, null=True) class Meta: managed = False db_table = 'visit_occurrence' class Vocabulary(models.Model): vocabulary_id = models.CharField(primary_key=True, max_length=20) vocabulary_name = models.CharField(max_length=255) vocabulary_reference = models.CharField(max_length=255, blank=True, null=True) vocabulary_version = models.CharField(max_length=255, blank=True, null=True) vocabulary_concept = models.ForeignKey(Concept, models.DO_NOTHING, related_name = 'vocabulary_concept') class Meta: managed = False db_table = 'vocabulary' class VocabularyConcept(models.Model): vocabulary_id = models.CharField(max_length=20) concept_code = models.CharField(max_length=50) class Meta: managed = False db_table = 'vocabulary_concept' unique_together = (('vocabulary_id', 'concept_code'),) class StudyMappingArguments(models.Model): study_id = models.IntegerField(blank=True, null=True) from_table = models.CharField(max_length=100, blank=True, null=True) from_column = models.CharField(max_length=100, blank=True, null=True) function_name = models.CharField(max_length=100, blank=True, null=True) from_where_clause = models.CharField(max_length=100, blank=True, null=True) from_where_column = models.CharField(max_length=100, blank=True, null=True) mapped_string = models.CharField(max_length=100, blank=True, null=True) mapped_number = models.IntegerField(blank=True, null=True) mapped_concept_vocabulary_id = models.CharField(max_length=20, blank=True, null=True) mapped_concept_code = models.CharField(max_length=50, blank=True, null=True) transform_factor = models.FloatField(blank=True, null=True) transform_shift = models.FloatField(blank=True, null=True) to_concept_vocabulary_id = models.CharField(max_length=20, blank=True, null=True) to_concept_code = models.CharField(max_length=50, blank=True, null=True) def __str__(self): return "study_id:{} from_table:{} from_column:{} function_name:{} from_where_clause:{} from_where_column:{} mapped_string:{} mapped_number:{} mapped_concept_vocabulary_id:{} mapped_concept_code:{} transform_factor:{} transform_shift:{} to_concept_vocabulary_id:{} to_concept_code:{}".format( self.study_id, self.from_table, self.from_column, self.function_name, self.from_where_clause, self.from_where_column, self.mapped_string, self.mapped_number, self.mapped_concept_vocabulary_id, self.mapped_concept_code, self.transform_factor, self.transform_shift, self.to_concept_vocabulary_id, self.to_concept_code) class Meta: managed = False db_table = 'study_mapping_arguments'

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f2c3001f68fb60667771dffa13aaa385ab3d02ef

87,668

py

Python

pysnmp_mibs/POLICY-BASED-MANAGEMENT-MIB.py

jackjack821/pysnmp-mibs

9835ea0bb2420715caf4ee9aaa07d59bb263acd6

[ "BSD-2-Clause" ]

6

2017-04-21T13:48:08.000Z

2022-01-06T19:42:52.000Z

pysnmp_mibs/POLICY-BASED-MANAGEMENT-MIB.py

jackjack821/pysnmp-mibs

9835ea0bb2420715caf4ee9aaa07d59bb263acd6

[ "BSD-2-Clause" ]

1

2020-05-05T16:42:25.000Z

pysnmp_mibs/POLICY-BASED-MANAGEMENT-MIB.py

jackjack821/pysnmp-mibs

9835ea0bb2420715caf4ee9aaa07d59bb263acd6

[ "BSD-2-Clause" ]

6

2020-02-08T20:28:49.000Z

2021-09-14T13:36:46.000Z

# # PySNMP MIB module POLICY-BASED-MANAGEMENT-MIB (http://pysnmp.sf.net) # ASN.1 source http://mibs.snmplabs.com:80/asn1/POLICY-BASED-MANAGEMENT-MIB # Produced by pysmi-0.0.7 at Sun Feb 14 00:23:59 2016 # On host bldfarm platform Linux version 4.1.13-100.fc21.x86_64 by user goose # Using Python version 3.5.0 (default, Jan 5 2016, 17:11:52) # ( OctetString, Integer, ObjectIdentifier, ) = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier") ( NamedValues, ) = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ( ConstraintsUnion, ValueSizeConstraint, SingleValueConstraint, ValueRangeConstraint, ConstraintsIntersection, ) = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "ValueSizeConstraint", "SingleValueConstraint", "ValueRangeConstraint", "ConstraintsIntersection") ( SnmpAdminString, ) = mibBuilder.importSymbols("SNMP-FRAMEWORK-MIB", "SnmpAdminString") ( NotificationGroup, ModuleCompliance, ObjectGroup, ) = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance", "ObjectGroup") ( Integer32, MibScalar, MibTable, MibTableRow, MibTableColumn, Gauge32, IpAddress, ObjectIdentity, ModuleIdentity, mib_2, TimeTicks, Counter64, iso, Unsigned32, Bits, MibIdentifier, Counter32, NotificationType, ) = mibBuilder.importSymbols("SNMPv2-SMI", "Integer32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Gauge32", "IpAddress", "ObjectIdentity", "ModuleIdentity", "mib-2", "TimeTicks", "Counter64", "iso", "Unsigned32", "Bits", "MibIdentifier", "Counter32", "NotificationType") ( DisplayString, RowStatus, TextualConvention, DateAndTime, RowPointer, StorageType, ) = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "RowStatus", "TextualConvention", "DateAndTime", "RowPointer", "StorageType") pmMib = ModuleIdentity((1, 3, 6, 1, 2, 1, 124)).setRevisions(("2005-02-07 00:00",)) if mibBuilder.loadTexts: pmMib.setLastUpdated('200502070000Z') if mibBuilder.loadTexts: pmMib.setOrganization('IETF SNMP Configuration Working Group') if mibBuilder.loadTexts: pmMib.setContactInfo('\n\n\n\n\n Steve Waldbusser\n Phone: +1-650-948-6500\n Fax: +1-650-745-0671\n Email: waldbusser@nextbeacon.com\n\n Jon Saperia (WG Co-chair)\n JDS Consulting, Inc.\n 84 Kettell Plain Road.\n Stow MA 01775\n USA\n Phone: +1-978-461-0249\n Fax: +1-617-249-0874\n Email: saperia@jdscons.com\n\n Thippanna Hongal\n Riverstone Networks, Inc.\n 5200 Great America Parkway\n Santa Clara, CA, 95054\n USA\n\n Phone: +1-408-878-6562\n Fax: +1-408-878-6501\n Email: hongal@riverstonenet.com\n\n David Partain (WG Co-chair)\n Postal: Ericsson AB\n P.O. Box 1248\n SE-581 12 Linkoping\n Sweden\n Tel: +46 13 28 41 44\n E-mail: David.Partain@ericsson.com\n\n Any questions or comments about this document can also be\n directed to the working group at snmpconf@snmp.com.') if mibBuilder.loadTexts: pmMib.setDescription('The MIB module for policy-based configuration of SNMP\n infrastructures.\n\n Copyright (C) The Internet Society (2005). This version of\n this MIB module is part of RFC 4011; see the RFC itself for\n full legal notices.') class PmUTF8String(OctetString, TextualConvention): subtypeSpec = OctetString.subtypeSpec+ValueSizeConstraint(0,65535) pmPolicyTable = MibTable((1, 3, 6, 1, 2, 1, 124, 1), ) if mibBuilder.loadTexts: pmPolicyTable.setDescription('The policy table. A policy is a pairing of a\n policyCondition and a policyAction that is used to apply the\n action to a selected set of elements.') pmPolicyEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 1, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmPolicyAdminGroup"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmPolicyIndex")) if mibBuilder.loadTexts: pmPolicyEntry.setDescription('An entry in the policy table representing one policy.') pmPolicyAdminGroup = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 1), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(0,32))) if mibBuilder.loadTexts: pmPolicyAdminGroup.setDescription('An administratively assigned string that can be used to group\n policies for convenience, for readability, or to simplify\n configuration of access control.\n\n The value of this string does not affect policy processing in\n any way. If grouping is not desired or necessary, this object\n may be set to a zero-length string.') pmPolicyIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 2), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))) if mibBuilder.loadTexts: pmPolicyIndex.setDescription('A unique index for this policy entry, unique among all\n policies regardless of administrative group.') pmPolicyPrecedenceGroup = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 3), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(0,32))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyPrecedenceGroup.setDescription('An administratively assigned string that is used to group\n policies. For each element, only one policy in the same\n precedence group may be active on that element. If multiple\n policies would be active on an element (because their\n conditions return non-zero), the execution environment will\n only allow the policy with the highest value of\n pmPolicyPrecedence to be active.\n\n All values of this object must have been successfully\n transformed by Stringprep RFC 3454. Management stations\n must perform this translation and must only set this object to\n string values that have been transformed.') pmPolicyPrecedence = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 4), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0,65535))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyPrecedence.setDescription("If, while checking to see which policy conditions match an\n element, 2 or more ready policies in the same precedence group\n match the same element, the pmPolicyPrecedence object provides\n the rule to arbitrate which single policy will be active on\n 'this element'. Of policies in the same precedence group, only\n the ready and matching policy with the highest precedence\n value (e.g., 2 is higher than 1) will have its policy action\n periodically executed on 'this element'.\n\n When a policy is active on an element but the condition ceases\n to match the element, its action (if currently running) will\n be allowed to finish and then the condition-matching ready\n policy with the next-highest precedence will immediately\n become active (and have its action run immediately). If the\n condition of a higher-precedence ready policy suddenly begins\n matching an element, the previously-active policy's action (if\n currently running) will be allowed to finish and then the\n higher precedence policy will immediately become active. Its\n action will run immediately, and any lower-precedence matching\n policy will not be active anymore.\n\n In the case where multiple ready policies share the highest\n value, it is an implementation-dependent matter as to which\n single policy action will be chosen.\n\n Note that if it is necessary to take certain actions after a\n policy is no longer active on an element, these actions should\n be included in a lower-precedence policy that is in the same\n precedence group.") pmPolicySchedule = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 5), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicySchedule.setDescription("This policy will be ready if any of the associated schedule\n entries are active.\n\n If the value of this object is 0, this policy is always\n ready.\n\n If the value of this object is non-zero but doesn't\n refer to a schedule group that includes an active schedule,\n then the policy will not be ready, even if this is due to a\n misconfiguration of this object or the pmSchedTable.") pmPolicyElementTypeFilter = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 6), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(0,128))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyElementTypeFilter.setDescription("This object specifies the element types for which this policy\n can be executed.\n\n The format of this object will be a sequence of\n pmElementTypeRegOIDPrefix values, encoded in the following\n BNF form:\n\n elementTypeFilter: oid [ ';' oid ]*\n oid: subid [ '.' subid ]*\n subid: '0' | decimal_constant\n\n For example, to register for the policy to be run on all\n interface elements, the 'ifEntry' element type will be\n registered as '1.3.6.1.2.1.2.2.1'.\n\n If a value is included that does not represent a registered\n pmElementTypeRegOIDPrefix, then that value will be ignored.") pmPolicyConditionScriptIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 7), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))).setMaxAccess("readonly") if mibBuilder.loadTexts: pmPolicyConditionScriptIndex.setDescription("A pointer to the row or rows in the pmPolicyCodeTable that\n contain the condition code for this policy. When a policy\n entry is created, a pmPolicyCodeIndex value unused by this\n policy's adminGroup will be assigned to this object.\n\n A policy condition is one or more PolicyScript statements\n that result(s) in a boolean value that represents whether\n an element is a member of a set of elements upon which an\n action is to be performed. If a policy is ready and the\n condition returns true for an element of a proper element\n type, and if no higher-precedence policy should be active,\n then the policy is active on that element.\n\n Condition evaluation stops immediately when any run-time\n exception is detected, and the policyAction is not executed.\n\n The policyCondition is evaluated for various elements. Any\n element for which the policyCondition returns any nonzero value\n will match the condition and will have the associated\n\n\n\n policyAction executed on that element unless a\n higher-precedence policy in the same precedence group also\n matches 'this element'.\n\n If the condition object is empty (contains no code) or\n otherwise does not return a value, the element will not be\n matched.\n\n When this condition is executed, if SNMP requests are made to\n the local system and secModel/secName/secLevel aren't\n specified, access to objects is under the security\n credentials of the requester who most recently modified the\n associated pmPolicyAdminStatus object. If SNMP requests are\n made in which secModel/secName/secLevel are specified, then\n the specified credentials are retrieved from the local\n configuration datastore only if VACM is configured to\n allow access to the requester who most recently modified the\n associated pmPolicyAdminStatus object. See the Security\n Considerations section for more information.") pmPolicyActionScriptIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 8), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))).setMaxAccess("readonly") if mibBuilder.loadTexts: pmPolicyActionScriptIndex.setDescription("A pointer to the row or rows in the pmPolicyCodeTable that\n contain the action code for this policy. When a policy entry\n is created, a pmPolicyCodeIndex value unused by this policy's\n adminGroup will be assigned to this object.\n\n A PolicyAction is an operation performed on a\n set of elements for which the policy is active.\n\n Action evaluation stops immediately when any run-time\n exception is detected.\n\n When this condition is executed, if SNMP requests are made to\n the local system and secModel/secName/secLevel aren't\n specified, access to objects is under the security\n credentials of the requester who most recently modified the\n associated pmPolicyAdminStatus object. If SNMP requests are\n made in which secModel/secName/secLevel are specified, then\n the specified credentials are retrieved from the local\n configuration datastore only if VACM is configured to\n allow access to the requester who most recently modified the\n associated pmPolicyAdminStatus object. See the Security\n Considerations section for more information.") pmPolicyParameters = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 9), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0,65535))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyParameters.setDescription('From time to time, policy scripts may seek one or more\n parameters (e.g., site-specific constants). These parameters\n may be installed with the script in this object and are\n accessible to the script via the getParameters() function. If\n it is necessary for multiple parameters to be passed to the\n script, the script can choose whatever encoding/delimiting\n mechanism is most appropriate.') pmPolicyConditionMaxLatency = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 10), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0,2147483647))).setUnits('milliseconds').setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyConditionMaxLatency.setDescription('Every element under the control of this agent is\n re-checked periodically to see whether it is under control\n of this policy by re-running the condition for this policy.\n This object lets the manager control the maximum amount of\n time that may pass before an element is re-checked.\n\n In other words, in any given interval of this duration, all\n elements must be re-checked. Note that how the policy agent\n schedules the checking of various elements within this\n interval is an implementation-dependent matter.\n Implementations may wish to re-run a condition more\n quickly if they note a change to the role strings for an\n element.') pmPolicyActionMaxLatency = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 11), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0,2147483647))).setUnits('milliseconds').setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyActionMaxLatency.setDescription("Every element that matches this policy's condition and is\n therefore under control of this policy will have this policy's\n action executed periodically to ensure that the element\n remains in the state dictated by the policy.\n This object lets the manager control the maximum amount of\n\n\n\n time that may pass before an element has the action run on\n it.\n\n In other words, in any given interval of this duration, all\n elements under control of this policy must have the action run\n on them. Note that how the policy agent schedules the policy\n action on various elements within this interval is an\n implementation-dependent matter.") pmPolicyMaxIterations = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 12), Unsigned32()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyMaxIterations.setDescription("If a condition or action script iterates in loops too many\n times in one invocation, the execution environment may\n consider it in an infinite loop or otherwise not acting\n as intended and may be terminated by the execution\n environment. The execution environment will count the\n cumulative number of times all 'for' or 'while' loops iterated\n and will apply a threshold to determine when to terminate the\n script. What threshold the execution environment uses is an\n implementation-dependent manner, but the value of\n this object SHOULD be the basis for choosing the threshold for\n each script. The value of this object represents a\n policy-specific threshold and can be tuned for policies of\n varying workloads. If this value is zero, no\n threshold will be enforced except for any\n implementation-dependent maximum. Regardless of this value,\n the agent is allowed to terminate any script invocation that\n exceeds a local CPU or memory limitation.\n\n Note that the condition and action invocations are tracked\n separately.") pmPolicyDescription = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 13), PmUTF8String()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyDescription.setDescription('A description of this rule and its significance, typically\n provided by a human.') pmPolicyMatches = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 14), Gauge32()).setUnits('elements').setMaxAccess("readonly") if mibBuilder.loadTexts: pmPolicyMatches.setDescription('The number of elements that, in their most recent execution\n of the associated condition, were matched by the condition.') pmPolicyAbnormalTerminations = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 15), Gauge32()).setUnits('elements').setMaxAccess("readonly") if mibBuilder.loadTexts: pmPolicyAbnormalTerminations.setDescription('The number of elements that, in their most recent execution\n of the associated condition or action, have experienced a\n run-time exception and terminated abnormally. Note that if a\n policy was experiencing a run-time exception while processing\n a particular element but runs normally on a subsequent\n invocation, this number can decline.') pmPolicyExecutionErrors = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 16), Counter32()).setUnits('errors').setMaxAccess("readonly") if mibBuilder.loadTexts: pmPolicyExecutionErrors.setDescription("The total number of times that execution of this policy's\n condition or action has been terminated due to run-time\n exceptions.") pmPolicyDebugging = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 17), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("off", 1), ("on", 2),)).clone('off')).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyDebugging.setDescription('The status of debugging for this policy. If this is turned\n on(2), log entries will be created in the pmDebuggingTable\n for each run-time exception that is experienced by this\n policy.') pmPolicyAdminStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 18), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3,))).clone(namedValues=NamedValues(("disabled", 1), ("enabled", 2), ("enabledAutoRemove", 3),))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyAdminStatus.setDescription('The administrative status of this policy.\n\n The policy will be valid only if the associated\n pmPolicyRowStatus is set to active(1) and this object is set\n to enabled(2) or enabledAutoRemove(3).\n\n If this object is set to enabledAutoRemove(3), the next time\n the associated schedule moves from the active state to the\n inactive state, this policy will immediately be deleted,\n including any associated entries in the pmPolicyCodeTable.\n\n The following related objects may not be changed unless this\n object is set to disabled(1):\n pmPolicyPrecedenceGroup, pmPolicyPrecedence,\n pmPolicySchedule, pmPolicyElementTypeFilter,\n pmPolicyConditionScriptIndex, pmPolicyActionScriptIndex,\n pmPolicyParameters, and any pmPolicyCodeTable row\n referenced by this policy.\n In order to change any of these parameters, the policy must\n be moved to the disabled(1) state, changed, and then\n re-enabled.\n\n When this policy moves to either enabled state from the\n disabled state, any cached values of policy condition must be\n erased, and any Policy or PolicyElement scratchpad values for\n this policy should be removed. Policy execution will begin by\n testing the policy condition on all appropriate elements.') pmPolicyStorageType = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 19), StorageType()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyStorageType.setDescription("This object defines whether this policy and any associated\n entries in the pmPolicyCodeTable are kept in volatile storage\n and lost upon reboot or if this row is backed up by\n non-volatile or permanent storage.\n\n\n\n\n If the value of this object is 'permanent', the values for\n the associated pmPolicyAdminStatus object must remain\n writable.") pmPolicyRowStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 1, 1, 20), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyRowStatus.setDescription('The row status of this pmPolicyEntry.\n\n The status may not be set to active if any of the related\n entries in the pmPolicyCode table do not have a status of\n active or if any of the objects in this row are not set to\n valid values. Only the following objects may be modified\n while in the active state:\n pmPolicyParameters\n pmPolicyConditionMaxLatency\n pmPolicyActionMaxLatency\n pmPolicyDebugging\n pmPolicyAdminStatus\n\n If this row is deleted, any associated entries in the\n pmPolicyCodeTable will be deleted as well.') pmPolicyCodeTable = MibTable((1, 3, 6, 1, 2, 1, 124, 2), ) if mibBuilder.loadTexts: pmPolicyCodeTable.setDescription("The pmPolicyCodeTable stores the code for policy conditions and\n actions.\n\n An example of the relationships between the code table and the\n policy table follows:\n\n pmPolicyTable\n AdminGroup Index ConditionScriptIndex ActionScriptIndex\n A '' 1 1 2\n B 'oper' 1 1 2\n C 'oper' 2 3 4\n\n pmPolicyCodeTable\n AdminGroup ScriptIndex Segment Note\n\n\n\n '' 1 1 Filter for policy A\n '' 2 1 Action for policy A\n 'oper' 1 1 Filter for policy B\n 'oper' 2 1 Action 1/2 for policy B\n 'oper' 2 2 Action 2/2 for policy B\n 'oper' 3 1 Filter for policy C\n 'oper' 4 1 Action for policy C\n\n In this example, there are 3 policies: 1 in the '' adminGroup,\n and 2 in the 'oper' adminGroup. Policy A has been assigned\n script indexes 1 and 2 (these script indexes are assigned out of\n a separate pool per adminGroup), with 1 code segment each for\n the filter and the action. Policy B has been assigned script\n indexes 1 and 2 (out of the pool for the 'oper' adminGroup).\n While the filter has 1 segment, the action is longer and is\n loaded into 2 segments. Finally, Policy C has been assigned\n script indexes 3 and 4, with 1 code segment each for the filter\n and the action.") pmPolicyCodeEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 2, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmPolicyAdminGroup"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmPolicyCodeScriptIndex"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmPolicyCodeSegment")) if mibBuilder.loadTexts: pmPolicyCodeEntry.setDescription('An entry in the policy code table representing one code\n segment. Entries that share a common AdminGroup/ScriptIndex\n pair make up a single script. Valid values of ScriptIndex are\n retrieved from pmPolicyConditionScriptIndex and\n pmPolicyActionScriptIndex after a pmPolicyEntry is\n created. Segments of code can then be written to this table\n with the learned ScriptIndex values.\n\n The StorageType of this entry is determined by the value of\n the associated pmPolicyStorageType.\n\n The pmPolicyAdminGroup element of the index represents the\n administrative group of the policy of which this code entry is\n a part.') pmPolicyCodeScriptIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 2, 1, 1), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))) if mibBuilder.loadTexts: pmPolicyCodeScriptIndex.setDescription('A unique index for each policy condition or action. The code\n for each such condition or action may be composed of multiple\n entries in this table if the code cannot fit in one entry.\n Values of pmPolicyCodeScriptIndex may not be used unless\n they have previously been assigned in the\n pmPolicyConditionScriptIndex or pmPolicyActionScriptIndex\n objects.') pmPolicyCodeSegment = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 2, 1, 2), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))) if mibBuilder.loadTexts: pmPolicyCodeSegment.setDescription('A unique index for each segment of a policy condition or\n action.\n\n When a policy condition or action spans multiple entries in\n this table, the code of that policy starts from the\n lowest-numbered segment and continues with increasing segment\n values until it ends with the highest-numbered segment.') pmPolicyCodeText = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 2, 1, 3), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(1,1024))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyCodeText.setDescription('A segment of policy code (condition or action). Lengthy\n Policy conditions or actions may be stored in multiple\n segments in this table that share the same value of\n pmPolicyCodeScriptIndex. When multiple segments are used, it\n is recommended that each segment be as large as is practical.\n\n Entries in this table are associated with policies by values\n of the pmPolicyConditionScriptIndex and\n pmPolicyActionScriptIndex objects. If the status of the\n related policy is active, then this object may not be\n modified.') pmPolicyCodeStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 2, 1, 4), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmPolicyCodeStatus.setDescription('The status of this code entry.\n\n Entries in this table are associated with policies by values\n of the pmPolicyConditionScriptIndex and\n pmPolicyActionScriptIndex objects. If the status of the\n related policy is active, then this object can not be\n modified (i.e., deleted or set to notInService), nor may new\n entries be created.\n\n If the status of this object is active, no objects in this\n row may be modified.') pmElementTypeRegTable = MibTable((1, 3, 6, 1, 2, 1, 124, 3), ) if mibBuilder.loadTexts: pmElementTypeRegTable.setDescription("A registration table for element types managed by this\n system.\n\n The Element Type Registration table allows the manager to\n learn what element types are being managed by the system and\n to register new types, if necessary. An element type is\n registered by providing the OID of an SNMP object (i.e.,\n without the instance). Each SNMP instance that exists under\n that object is a distinct element. The index of the element is\n the index part of the discovered OID. This index will be\n supplied to policy conditions and actions so that this code\n can inspect and configure the element.\n\n For example, this table might contain the following entries.\n The first three are agent-installed, and the 4th was\n downloaded by a management station:\n\n OIDPrefix MaxLatency Description StorageType\n ifEntry 100 mS interfaces - builtin readOnly\n 0.0 100 mS system element - builtin readOnly\n frCircuitEntry 100 mS FR Circuits - builtin readOnly\n hrSWRunEntry 60 sec Running Processes volatile\n\n\n\n\n Note that agents may automatically configure elements in this\n table for frequently used element types (interfaces, circuits,\n etc.). In particular, it may configure elements for whom\n discovery is optimized in one or both of the following ways:\n\n 1. The agent may discover elements by scanning internal data\n structures as opposed to issuing local SNMP requests. It is\n possible to recreate the exact semantics described in this\n table even if local SNMP requests are not issued.\n\n 2. The agent may receive asynchronous notification of new\n elements (for example, 'card inserted') and use that\n information to instantly create elements rather than\n through polling. A similar feature might be available for\n the deletion of elements.\n\n Note that the disposition of agent-installed entries is\n described by the pmPolicyStorageType object.") pmElementTypeRegEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 3, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmElementTypeRegOIDPrefix")) if mibBuilder.loadTexts: pmElementTypeRegEntry.setDescription("A registration of an element type.\n\n Note that some values of this table's index may result in an\n instance name that exceeds a length of 128 sub-identifiers,\n which exceeds the maximum for the SNMP protocol.\n Implementations should take care to avoid such values.") pmElementTypeRegOIDPrefix = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 3, 1, 2), ObjectIdentifier()) if mibBuilder.loadTexts: pmElementTypeRegOIDPrefix.setDescription("This OBJECT IDENTIFIER value identifies a table in which all\n\n\n\n elements of this type will be found. Every row in the\n referenced table will be treated as an element for the\n period of time that it remains in the table. The agent will\n then execute policy conditions and actions as appropriate on\n each of these elements.\n\n This object identifier value is specified down to the 'entry'\n component (e.g., ifEntry) of the identifier.\n\n The index of each discovered row will be passed to each\n invocation of the policy condition and policy action.\n\n The actual mechanism by which instances are discovered is\n implementation dependent. Periodic walks of the table to\n discover the rows in the table is one such mechanism. This\n mechanism has the advantage that it can be performed by an\n agent with no knowledge of the names, syntax, or semantics\n of the MIB objects in the table. This mechanism also serves as\n the reference design. Other implementation-dependent\n mechanisms may be implemented that are more efficient (perhaps\n because they are hard coded) or that don't require polling.\n These mechanisms must discover the same elements as would the\n table-walking reference design.\n\n This object can contain a OBJECT IDENTIFIER, '0.0'.\n '0.0' represents the single instance of the system\n itself and provides an execution context for policies to\n operate on the 'system element' and on MIB objects\n modeled as scalars. For example, '0.0' gives an execution\n context for policy-based selection of the operating system\n code version (likely modeled as a scalar MIB object). The\n element type '0.0' always exists; as a consequence, no actual\n discovery will take place, and the pmElementTypeRegMaxLatency\n object will have no effect for the '0.0' element\n type. However, if the '0.0' element type is not registered in\n the table, policies will not be executed on the '0.0' element.\n\n When a policy is invoked on behalf of a '0.0' entry in this\n table, the element name will be '0.0', and there is no index\n of 'this element' (in other words, it has zero length).\n\n As this object is used in the index for the\n pmElementTypeRegTable, users of this table should be careful\n not to create entries that would result in instance names with\n more than 128 sub-identifiers.") pmElementTypeRegMaxLatency = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 3, 1, 3), Unsigned32()).setUnits('milliseconds').setMaxAccess("readcreate") if mibBuilder.loadTexts: pmElementTypeRegMaxLatency.setDescription('The PM agent is responsible for discovering new elements of\n types that are registered. This object lets the manager\n control the maximum amount of time that may pass between the\n time an element is created and when it is discovered.\n\n In other words, in any given interval of this duration, all\n new elements must be discovered. Note that how the policy\n agent schedules the checking of various elements within this\n interval is an implementation-dependent matter.') pmElementTypeRegDescription = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 3, 1, 4), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(0,64))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmElementTypeRegDescription.setDescription('A descriptive label for this registered type.') pmElementTypeRegStorageType = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 3, 1, 5), StorageType()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmElementTypeRegStorageType.setDescription("This object defines whether this row is kept\n in volatile storage and lost upon reboot or\n backed up by non-volatile or permanent storage.\n\n If the value of this object is 'permanent', no values in the\n associated row have to be writable.") pmElementTypeRegRowStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 3, 1, 6), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmElementTypeRegRowStatus.setDescription('The status of this registration entry.\n\n If the value of this object is active, no objects in this row\n may be modified.') pmRoleTable = MibTable((1, 3, 6, 1, 2, 1, 124, 4), ) if mibBuilder.loadTexts: pmRoleTable.setDescription("The pmRoleTable is a read-create table that organizes role\n strings sorted by element. This table is used to create and\n modify role strings and their associations, as well as to allow\n a management station to learn about the existence of roles and\n their associations.\n\n It is the responsibility of the agent to keep track of any\n re-indexing of the underlying SNMP elements and to continue to\n associate role strings with the element with which they were\n initially configured.\n\n Policy MIB agents that have elements in multiple local SNMP\n contexts have to allow some roles to be assigned to elements\n in particular contexts. This is particularly true when some\n elements have the same names in different contexts and the\n context is required to disambiguate them. In those situations,\n a value for the pmRoleContextName may be provided. When a\n pmRoleContextName value is not provided, the assignment is to\n the element in the default context.\n\n Policy MIB agents that discover elements on other systems and\n execute policies on their behalf need to have access to role\n information for these remote elements. In such situations,\n role assignments for other systems can be stored in this table\n by providing values for the pmRoleContextEngineID parameters.\n\n For example:\n Example:\n element role context ctxEngineID #comment\n ifindex.1 gold local, default context\n ifindex.2 gold local, default context\n repeaterid.1 foo rptr1 local, rptr1 context\n repeaterid.1 bar rptr2 local, rptr2 context\n ifindex.1 gold '' A different system\n ifindex.1 gold '' B different system\n\n The agent must store role string associations in non-volatile\n storage.") pmRoleEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 4, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmRoleElement"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmRoleContextName"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmRoleContextEngineID"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmRoleString")) if mibBuilder.loadTexts: pmRoleEntry.setDescription('A role string entry associates a role string with an\n individual element.\n\n Note that some combinations of index values may result in an\n instance name that exceeds a length of 128 sub-identifiers,\n which exceeds the maximum for the SNMP\n protocol. Implementations should take care to avoid such\n combinations.') pmRoleElement = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 4, 1, 1), RowPointer()) if mibBuilder.loadTexts: pmRoleElement.setDescription("The element with which this role string is associated.\n\n For example, if the element is interface 3, then this object\n will contain the OID for 'ifIndex.3'.\n\n If the agent assigns new indexes in the MIB table to\n represent the same underlying element (re-indexing), the\n agent will modify this value to contain the new index for the\n underlying element.\n\n As this object is used in the index for the pmRoleTable,\n users of this table should be careful not to create entries\n that would result in instance names with more than 128\n sub-identifiers.") pmRoleContextName = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 4, 1, 2), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(0,32))) if mibBuilder.loadTexts: pmRoleContextName.setDescription('If the associated element is not in the default SNMP context\n for the target system, this object is used to identify the\n context. If the element is in the default context, this object\n is equal to the empty string.') pmRoleContextEngineID = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 4, 1, 3), OctetString().subtype(subtypeSpec=ConstraintsUnion(ValueSizeConstraint(0,0),ValueSizeConstraint(5,32),))) if mibBuilder.loadTexts: pmRoleContextEngineID.setDescription('If the associated element is on a remote system, this object\n is used to identify the remote system. This object contains\n the contextEngineID of the system for which this role string\n assignment is valid. If the element is on the local system\n this object will be the empty string.') pmRoleString = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 4, 1, 4), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(0,64))) if mibBuilder.loadTexts: pmRoleString.setDescription('The role string that is associated with an element through\n this table. All role strings must have been successfully\n transformed by Stringprep RFC 3454. Management stations\n must perform this translation and must only set this object\n to string values that have been transformed.\n\n A role string is an administratively specified characteristic\n of a managed element (for example, an interface). It is a\n selector for policy rules, that determines the applicability of\n the rule to a particular managed element.') pmRoleStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 4, 1, 5), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmRoleStatus.setDescription('The status of this role string.\n\n\n\n\n\n If the value of this object is active, no object in this row\n may be modified.') pmCapabilitiesTable = MibTable((1, 3, 6, 1, 2, 1, 124, 5), ) if mibBuilder.loadTexts: pmCapabilitiesTable.setDescription("The pmCapabilitiesTable contains a description of\n the inherent capabilities of the system so that\n management stations can learn of an agent's capabilities and\n differentially install policies based on the capabilities.\n\n Capabilities are expressed at the system level. There can be\n variation in how capabilities are realized from one vendor or\n model to the next. Management systems should consider these\n differences before selecting which policy to install in a\n system.") pmCapabilitiesEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 5, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmCapabilitiesType")) if mibBuilder.loadTexts: pmCapabilitiesEntry.setDescription("A capabilities entry holds an OID indicating support for a\n particular capability. Capabilities may include hardware and\n software functions and the implementation of MIB\n Modules. The semantics of the OID are defined in the\n description of pmCapabilitiesType.\n\n Entries appear in this table if any element in the system has\n a specific capability. A capability should appear in this\n table only once, regardless of the number of elements in the\n system with that capability. An entry is removed from this\n table when the last element in the system that has the\n capability is removed. In some cases, capabilities are\n dynamic and exist only in software. This table should have an\n entry for the capability even if there are no current\n instances. Examples include systems with database or WEB\n services. While the system has the ability to create new\n databases or WEB services, the entry should exist. In these\n cases, the ability to create these services could come from\n other processes that are running in the system, even though\n there are no currently open databases or WEB servers running.\n\n\n\n Capabilities may include the implementation of MIB Modules\n but need not be limited to those that represent MIB Modules\n with one or more configurable objects. It may also be\n valuable to include entries for capabilities that do not\n include configuration objects, as that information, in\n combination with other entries in this table, might be used\n by the management software to determine whether to\n install a policy.\n\n Vendor software may also add entries in this table to express\n capabilities from their private branch.\n\n Note that some values of this table's index may result in an\n instance name that exceeds a length of 128 sub-identifiers,\n which exceeds the maximum for the SNMP\n protocol. Implementations should take care to avoid such\n values.") pmCapabilitiesType = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 5, 1, 1), ObjectIdentifier()).setMaxAccess("readonly") if mibBuilder.loadTexts: pmCapabilitiesType.setDescription("There are three types of OIDs that may be present in the\n pmCapabilitiesType object:\n\n 1) The OID of a MODULE-COMPLIANCE macro that represents the\n highest level of compliance realized by the agent for that\n MIB Module. For example, an agent that implements the OSPF\n MIB Module at the highest level of compliance would have the\n value of '1.3.6.1.2.1.14.15.2' in the pmCapabilitiesType\n object. For software that realizes standard MIB\n Modules that do not have compliance statements, the base OID\n of the MIB Module should be used instead. If the OSPF MIB\n Module had not been created with a compliance statement, then\n the correct value of the pmCapabilitiesType would be\n '1.3.6.1.2.1.14'. In the cases where multiple compliance\n statements in a MIB Module are supported by the agent, and\n where one compliance statement does not by definition include\n the other, each of the compliance OIDs would have entries in\n this table.\n\n\n\n\n MIB Documents can contain more than one MIB Module. In the\n case of OSPF, there is a second MIB Module\n that describes notifications for the OSPF Version 2 Protocol.\n If the agent also realizes these functions, an entry will\n also exist for those capabilities in this table.\n\n 2) Vendors should install OIDs in this table that represent\n vendor-specific capabilities. These capabilities can be\n expressed just as those described above for MIB Modules on\n the standards track. In addition, vendors may install any\n OID they desire from their registered branch. The OIDs may be\n at any level of granularity, from the root of their entire\n branch to an instance of a single OID. There is no\n restriction on the number of registrations they may make,\n though care should be taken to avoid unnecessary entries.\n\n 3) OIDs that represent one capability or a collection of\n capabilities that could be any collection of MIB Objects or\n hardware or software functions may be created in working\n groups and registered in a MIB Module. Other entities (e.g.,\n vendors) may also make registrations. Software will register\n these standard capability OIDs, as well as vendor specific\n OIDs.\n\n If the OID for a known capability is not present in the\n table, then it should be assumed that the capability is not\n implemented.\n\n As this object is used in the index for the\n pmCapabilitiesTable, users of this table should be careful\n not to create entries that would result in instance names\n with more than 128 sub-identifiers.") pmCapabilitiesOverrideTable = MibTable((1, 3, 6, 1, 2, 1, 124, 6), ) if mibBuilder.loadTexts: pmCapabilitiesOverrideTable.setDescription('The pmCapabilitiesOverrideTable allows management stations\n to override pmCapabilitiesTable entries that have been\n registered by the agent. This facility can be used to avoid\n situations in which managers in the network send policies to\n a system that has advertised a capability in the\n pmCapabilitiesTable but that should not be installed on this\n particular system. One example could be newly deployed\n\n\n\n equipment that is still in a trial state in a trial state or\n resources reserved for some other administrative reason.\n This table can also be used to override entries in the\n pmCapabilitiesTable through the use of the\n pmCapabilitiesOverrideState object. Capabilities can also be\n declared available in this table that were not registered in\n the pmCapabilitiesTable. A management application can make\n an entry in this table for any valid OID and declare the\n capability available by setting the\n pmCapabilitiesOverrideState for that row to valid(1).') pmCapabilitiesOverrideEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 6, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmCapabilitiesOverrideType")) if mibBuilder.loadTexts: pmCapabilitiesOverrideEntry.setDescription("An entry in this table indicates whether a particular\n capability is valid or invalid.\n\n Note that some values of this table's index may result in an\n instance name that exceeds a length of 128 sub-identifiers,\n which exceeds the maximum for the SNMP\n protocol. Implementations should take care to avoid such\n values.") pmCapabilitiesOverrideType = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 6, 1, 1), ObjectIdentifier()) if mibBuilder.loadTexts: pmCapabilitiesOverrideType.setDescription('This is the OID of the capability that is declared valid or\n invalid by the pmCapabilitiesOverrideState value for this\n row. Any valid OID, as described in the pmCapabilitiesTable,\n is permitted in the pmCapabilitiesOverrideType object. This\n means that capabilities can be expressed at any level, from a\n specific instance of an object to a table or entire module.\n There are no restrictions on whether these objects are from\n standards track MIB documents or in the private branch of the\n MIB.\n\n\n\n If an entry exists in this table for which there is a\n corresponding entry in the pmCapabilitiesTable, then this entry\n shall have precedence over the entry in the\n pmCapabilitiesTable. All entries in this table must be\n preserved across reboots.\n\n As this object is used in the index for the\n pmCapabilitiesOverrideTable, users of this table should be\n careful not to create entries that would result in instance\n names with more than 128 sub-identifiers.') pmCapabilitiesOverrideState = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 6, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("invalid", 1), ("valid", 2),))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmCapabilitiesOverrideState.setDescription('A pmCapabilitiesOverrideState of invalid indicates that\n management software should not send policies to this system\n for the capability identified in the\n pmCapabilitiesOverrideType for this row of the table. This\n behavior is the same whether the capability represented by\n the pmCapabilitiesOverrideType exists only in this table\n (that is, it was installed by an external management\n application) or exists in this table as well as the\n pmCapabilitiesTable. This would be the case when a manager\n wanted to disable a capability that the native management\n system found and registered in the pmCapabilitiesTable.\n\n An entry in this table that has a pmCapabilitiesOverrideState\n of valid should be treated as though it appeared in the\n pmCapabilitiesTable. If the entry also exists in the\n pmCapabilitiesTable in the pmCapabilitiesType object, and if\n the value of this object is valid, then the system shall\n operate as though this entry did not exist and policy\n installations and executions will continue in a normal\n fashion.') pmCapabilitiesOverrideRowStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 6, 1, 3), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmCapabilitiesOverrideRowStatus.setDescription('The row status of this pmCapabilitiesOverrideEntry.\n\n\n\n If the value of this object is active, no object in this row\n may be modified.') pmSchedLocalTime = MibScalar((1, 3, 6, 1, 2, 1, 124, 7), DateAndTime().subtype(subtypeSpec=ValueSizeConstraint(11,11)).setFixedLength(11)).setMaxAccess("readonly") if mibBuilder.loadTexts: pmSchedLocalTime.setDescription('The local time used by the scheduler. Schedules that\n refer to calendar time will use the local time indicated\n by this object. An implementation MUST return all 11 bytes\n of the DateAndTime textual-convention so that a manager\n may retrieve the offset from GMT time.') pmSchedTable = MibTable((1, 3, 6, 1, 2, 1, 124, 8), ) if mibBuilder.loadTexts: pmSchedTable.setDescription('This table defines schedules for policies.') pmSchedEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 8, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmSchedIndex")) if mibBuilder.loadTexts: pmSchedEntry.setDescription('An entry describing a particular schedule.\n\n Unless noted otherwise, writable objects of this row can be\n modified independently of the current value of pmSchedRowStatus,\n pmSchedAdminStatus and pmSchedOperStatus. In particular, it\n is legal to modify pmSchedWeekDay, pmSchedMonth, and\n pmSchedDay when pmSchedRowStatus is active.') pmSchedIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 1), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))) if mibBuilder.loadTexts: pmSchedIndex.setDescription('The locally unique, administratively assigned index for this\n scheduling entry.') pmSchedGroupIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 2), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedGroupIndex.setDescription('The locally unique, administratively assigned index for the\n schedule group this scheduling entry belongs to.\n\n To assign multiple schedule entries to the same group, the\n pmSchedGroupIndex of each entry in the group will be set to\n the same value. This pmSchedGroupIndex value must be equal to\n the pmSchedIndex of one of the entries in the group. If the\n entry whose pmSchedIndex equals the pmSchedGroupIndex\n for the group is deleted, the agent will assign a new\n pmSchedGroupIndex to all remaining members of the group.\n\n If an entry is not a member of a group, its pmSchedGroupIndex\n must be assigned to the value of its pmSchedIndex.\n\n Policies that are controlled by a group of schedule entries\n are active when any schedule in the group is active.') pmSchedDescr = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 3), PmUTF8String().clone(hexValue="")).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedDescr.setDescription('The human-readable description of the purpose of this\n scheduling entry.') pmSchedTimePeriod = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 4), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(0,31))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedTimePeriod.setDescription("The overall range of calendar dates and times over which this\n schedule is active. It is stored in a slightly extended version\n of the format for a 'period-explicit' defined in RFC 2445.\n This format is expressed as a string representing the\n starting date and time, in which the character 'T' indicates\n the beginning of the time portion, followed by the solidus\n character, '/', followed by a similar string representing an\n end date and time. The start of the period MUST be before the\n end of the period. Date-Time values are expressed as\n substrings of the form 'yyyymmddThhmmss'. For example:\n\n 20000101T080000/20000131T130000\n\n January 1, 2000, 0800 through January 31, 2000, 1PM\n\n The 'Date with UTC time' format defined in RFC 2445 in which\n the Date-Time string ends with the character 'Z' is not\n allowed.\n\n This 'period-explicit' format is also extended to allow two\n special cases in which one of the Date-Time strings is\n replaced with a special string defined in RFC 2445:\n\n 1. If the first Date-Time value is replaced with the string\n 'THISANDPRIOR', then the value indicates that the schedule\n is active at any time prior to the Date-Time that appears\n after the '/'.\n\n 2. If the second Date-Time is replaced with the string\n 'THISANDFUTURE', then the value indicates that the schedule\n is active at any time after the Date-Time that appears\n before the '/'.\n\n\n\n\n Note that although RFC 2445 defines these two strings, they are\n not specified for use in the 'period-explicit' format. The use\n of these strings represents an extension to the\n 'period-explicit' format.") pmSchedMonth = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 5), Bits().clone(namedValues=NamedValues(("january", 0), ("february", 1), ("march", 2), ("april", 3), ("may", 4), ("june", 5), ("july", 6), ("august", 7), ("september", 8), ("october", 9), ("november", 10), ("december", 11),)).clone(namedValues=NamedValues(("january", 0), ("february", 1), ("march", 2), ("april", 3), ("may", 4), ("june", 5), ("july", 6), ("august", 7), ("september", 8), ("october", 9), ("november", 10), ("december", 11),))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedMonth.setDescription('Within the overall time period specified in the\n pmSchedTimePeriod object, the value of this object specifies\n the specific months within that time period when the schedule\n is active. Setting all bits will cause the schedule to act\n independently of the month.') pmSchedDay = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 6), Bits().clone(namedValues=NamedValues(("d1", 0), ("d2", 1), ("d3", 2), ("d4", 3), ("d5", 4), ("d6", 5), ("d7", 6), ("d8", 7), ("d9", 8), ("d10", 9), ("d11", 10), ("d12", 11), ("d13", 12), ("d14", 13), ("d15", 14), ("d16", 15), ("d17", 16), ("d18", 17), ("d19", 18), ("d20", 19), ("d21", 20), ("d22", 21), ("d23", 22), ("d24", 23), ("d25", 24), ("d26", 25), ("d27", 26), ("d28", 27), ("d29", 28), ("d30", 29), ("d31", 30), ("r1", 31), ("r2", 32), ("r3", 33), ("r4", 34), ("r5", 35), ("r6", 36), ("r7", 37), ("r8", 38), ("r9", 39), ("r10", 40), ("r11", 41), ("r12", 42), ("r13", 43), ("r14", 44), ("r15", 45), ("r16", 46), ("r17", 47), ("r18", 48), ("r19", 49), ("r20", 50), ("r21", 51), ("r22", 52), ("r23", 53), ("r24", 54), ("r25", 55), ("r26", 56), ("r27", 57), ("r28", 58), ("r29", 59), ("r30", 60), ("r31", 61),)).clone(namedValues=NamedValues(("d1", 0), ("d2", 1), ("d3", 2), ("d4", 3), ("d5", 4), ("d6", 5), ("d7", 6), ("d8", 7), ("d9", 8), ("d10", 9), ("d11", 10), ("d12", 11), ("d13", 12), ("d14", 13), ("d15", 14), ("d16", 15), ("d17", 16), ("d18", 17), ("d19", 18), ("d20", 19), ("d21", 20), ("d22", 21), ("d23", 22), ("d24", 23), ("d25", 24), ("d26", 25), ("d27", 26), ("d28", 27), ("d29", 28), ("d30", 29), ("d31", 30), ("r1", 31), ("r2", 32), ("r3", 33), ("r4", 34), ("r5", 35), ("r6", 36), ("r7", 37), ("r8", 38), ("r9", 39), ("r10", 40), ("r11", 41), ("r12", 42), ("r13", 43), ("r14", 44), ("r15", 45), ("r16", 46), ("r17", 47), ("r18", 48), ("r19", 49), ("r20", 50), ("r21", 51), ("r22", 52), ("r23", 53), ("r24", 54), ("r25", 55), ("r26", 56), ("r27", 57), ("r28", 58), ("r29", 59), ("r30", 60), ("r31", 61),))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedDay.setDescription("Within the overall time period specified in the\n pmSchedTimePeriod object, the value of this object specifies\n the specific days of the month within that time period when\n the schedule is active.\n\n There are two sets of bits one can use to define the day\n within a month:\n\n Enumerations starting with the letter 'd' indicate a\n day in a month relative to the first day of a month.\n The first day of the month can therefore be specified\n by setting the bit d1(0), and d31(30) means the last\n day of a month with 31 days.\n\n Enumerations starting with the letter 'r' indicate a\n day in a month in reverse order, relative to the last\n day of a month. The last day in the month can therefore\n be specified by setting the bit r1(31), and r31(61) means\n the first day of a month with 31 days.\n\n Setting multiple bits will include several days in the set\n of possible days for this schedule. Setting all bits starting\n with the letter 'd' or all bits starting with the letter 'r'\n will cause the schedule to act independently of the day of the\n month.") pmSchedWeekDay = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 7), Bits().clone(namedValues=NamedValues(("sunday", 0), ("monday", 1), ("tuesday", 2), ("wednesday", 3), ("thursday", 4), ("friday", 5), ("saturday", 6),)).clone(namedValues=NamedValues(("sunday", 0), ("monday", 1), ("tuesday", 2), ("wednesday", 3), ("thursday", 4), ("friday", 5), ("saturday", 6),))).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedWeekDay.setDescription('Within the overall time period specified in the\n pmSchedTimePeriod object, the value of this object specifies\n the specific days of the week within that time period when\n the schedule is active. Setting all bits will cause the\n schedule to act independently of the day of the week.') pmSchedTimeOfDay = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 8), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(0,15)).clone(hexValue="543030303030302F54323335393539")).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedTimeOfDay.setDescription("Within the overall time period specified in the\n pmSchedTimePeriod object, the value of this object specifies\n the range of times in a day when the schedule is active.\n\n This value is stored in a format based on the RFC 2445 format\n for 'time': The character 'T' followed by a 'time' string,\n followed by the solidus character, '/', followed by the\n character 'T', followed by a second time string. The first time\n indicates the beginning of the range, and the second time\n indicates the end. Thus, this value takes the following\n form:\n\n 'Thhmmss/Thhmmss'.\n\n The second substring always identifies a later time than the\n first substring. To allow for ranges that span midnight,\n however, the value of the second string may be smaller than\n the value of the first substring. Thus, 'T080000/T210000'\n identifies the range from 0800 until 2100, whereas\n 'T210000/T080000' identifies the range from 2100 until 0800 of\n the following day.\n\n When a range spans midnight, by definition it includes parts\n of two successive days. When one of these days is also\n selected by either the MonthOfYearMask, DayOfMonthMask, and/or\n DayOfWeekMask, but the other day is not, then the policy is\n active only during the portion of the range that falls on the\n selected day. For example, if the range extends from 2100\n\n\n\n until 0800, and the day of week mask selects Monday and\n Tuesday, then the policy is active during the following three\n intervals:\n\n From midnight Sunday until 0800 Monday\n From 2100 Monday until 0800 Tuesday\n From 2100 Tuesday until 23:59:59 Tuesday\n\n Setting this value to 'T000000/T235959' will cause the\n schedule to act independently of the time of day.") pmSchedLocalOrUtc = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 9), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("localTime", 1), ("utcTime", 2),)).clone('utcTime')).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedLocalOrUtc.setDescription('This object indicates whether the times represented in the\n TimePeriod object and in the various Mask objects represent\n local times or UTC times.') pmSchedStorageType = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 10), StorageType().clone('volatile')).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedStorageType.setDescription("This object defines whether this schedule entry is kept\n in volatile storage and lost upon reboot or\n backed up by non-volatile or permanent storage.\n\n Conceptual rows having the value 'permanent' must allow write\n access to the columnar objects pmSchedDescr, pmSchedWeekDay,\n pmSchedMonth, and pmSchedDay.\n\n If the value of this object is 'permanent', no values in the\n associated row have to be writable.") pmSchedRowStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 8, 1, 11), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: pmSchedRowStatus.setDescription('The status of this schedule entry.\n\n If the value of this object is active, no object in this row\n may be modified.') pmTrackingPETable = MibTable((1, 3, 6, 1, 2, 1, 124, 9), ) if mibBuilder.loadTexts: pmTrackingPETable.setDescription('The pmTrackingPETable describes what elements\n are active (under control of) a policy. This table is indexed\n in order to optimize retrieval of the entire status for a\n given policy.') pmTrackingPEEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 9, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmPolicyIndex"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmTrackingPEElement"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmTrackingPEContextName"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmTrackingPEContextEngineID")) if mibBuilder.loadTexts: pmTrackingPEEntry.setDescription('An entry in the pmTrackingPETable. The pmPolicyIndex in\n the index specifies the policy tracked by this entry.\n\n Note that some combinations of index values may result in an\n instance name that exceeds a length of 128 sub-identifiers,\n which exceeds the maximum for the SNMP\n protocol. Implementations should take care to avoid such\n combinations.') pmTrackingPEElement = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 9, 1, 1), RowPointer()) if mibBuilder.loadTexts: pmTrackingPEElement.setDescription('The element that is acted upon by the associated policy.\n\n As this object is used in the index for the\n pmTrackingPETable, users of this table should be careful not\n to create entries that would result in instance names with\n more than 128 sub-identifiers.') pmTrackingPEContextName = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 9, 1, 2), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(0,32))) if mibBuilder.loadTexts: pmTrackingPEContextName.setDescription('If the associated element is not in the default SNMP context\n for the target system, this object is used to identify the\n context. If the element is in the default context, this object\n is equal to the empty string.') pmTrackingPEContextEngineID = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 9, 1, 3), OctetString().subtype(subtypeSpec=ConstraintsUnion(ValueSizeConstraint(0,0),ValueSizeConstraint(5,32),))) if mibBuilder.loadTexts: pmTrackingPEContextEngineID.setDescription('If the associated element is on a remote system, this object\n is used to identify the remote system. This object contains\n the contextEngineID of the system on which the associated\n element resides. If the element is on the local system,\n this object will be the empty string.') pmTrackingPEInfo = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 9, 1, 4), Bits().clone(namedValues=NamedValues(("actionSkippedDueToPrecedence", 0), ("conditionRunTimeException", 1), ("conditionUserSignal", 2), ("actionRunTimeException", 3), ("actionUserSignal", 4),))).setMaxAccess("readonly") if mibBuilder.loadTexts: pmTrackingPEInfo.setDescription("This object returns information about the previous policy\n script executions.\n\n If the actionSkippedDueToPrecedence(1) bit is set, the last\n execution of the associated policy condition returned non-zero,\n but the action is not active, because it was trumped by a\n matching policy condition in the same precedence group with a\n higher precedence value.\n\n If the conditionRunTimeException(2) bit is set, the last\n execution of the associated policy condition encountered a\n run-time exception and aborted.\n\n If the conditionUserSignal(3) bit is set, the last\n execution of the associated policy condition called the\n signalError() function.\n\n If the actionRunTimeException(4) bit is set, the last\n execution of the associated policy action encountered a\n run-time exception and aborted.\n\n If the actionUserSignal(5) bit is set, the last\n execution of the associated policy action called the\n signalError() function.\n\n Entries will only exist in this table of one or more bits are\n set. In particular, if an entry does not exist for a\n particular policy/element combination, it can be assumed that\n the policy's condition did not match 'this element'.") pmTrackingEPTable = MibTable((1, 3, 6, 1, 2, 1, 124, 10), ) if mibBuilder.loadTexts: pmTrackingEPTable.setDescription('The pmTrackingEPTable describes what policies\n are controlling an element. This table is indexed in\n order to optimize retrieval of the status of all policies\n active for a given element.') pmTrackingEPEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 10, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmTrackingEPElement"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmTrackingEPContextName"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmTrackingEPContextEngineID"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmPolicyIndex")) if mibBuilder.loadTexts: pmTrackingEPEntry.setDescription("An entry in the pmTrackingEPTable. Entries exist for all\n element/policy combinations for which the policy's condition\n matches and only if the schedule for the policy is active.\n\n The pmPolicyIndex in the index specifies the policy\n tracked by this entry.\n\n Note that some combinations of index values may result in an\n instance name that exceeds a length of 128 sub-identifiers,\n which exceeds the maximum for the SNMP protocol.\n Implementations should take care to avoid such combinations.") pmTrackingEPElement = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 10, 1, 1), RowPointer()) if mibBuilder.loadTexts: pmTrackingEPElement.setDescription('The element acted upon by the associated policy.\n\n As this object is used in the index for the\n pmTrackingEPTable, users of this table should be careful\n not to create entries that would result in instance names\n with more than 128 sub-identifiers.') pmTrackingEPContextName = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 10, 1, 2), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(0,32))) if mibBuilder.loadTexts: pmTrackingEPContextName.setDescription('If the associated element is not in the default SNMP context\n\n\n\n for the target system, this object is used to identify the\n context. If the element is in the default context, this object\n is equal to the empty string.') pmTrackingEPContextEngineID = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 10, 1, 3), OctetString().subtype(subtypeSpec=ConstraintsUnion(ValueSizeConstraint(0,0),ValueSizeConstraint(5,32),))) if mibBuilder.loadTexts: pmTrackingEPContextEngineID.setDescription('If the associated element is on a remote system, this object\n is used to identify the remote system. This object contains\n the contextEngineID of the system on which the associated\n element resides. If the element is on the local system,\n this object will be the empty string.') pmTrackingEPStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 10, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("on", 1), ("forceOff", 2),))).setMaxAccess("readwrite") if mibBuilder.loadTexts: pmTrackingEPStatus.setDescription("This entry will only exist if the calendar for the policy is\n active and if the associated policyCondition returned 1 for\n 'this element'.\n\n A policy can be forcibly disabled on a particular element\n by setting this value to forceOff(2). The agent should then\n act as though the policyCondition failed for 'this element'.\n The forceOff(2) state will persist (even across reboots) until\n this value is set to on(1) by a management request. The\n forceOff(2) state may be set even if the entry does not\n previously exist so that future policy invocations can be\n avoided.\n\n Unless forcibly disabled, if this entry exists, its value\n will be on(1).") pmDebuggingTable = MibTable((1, 3, 6, 1, 2, 1, 124, 11), ) if mibBuilder.loadTexts: pmDebuggingTable.setDescription('Policies that have debugging turned on will generate a log\n entry in the policy debugging table for every runtime\n exception that occurs in either the condition or action\n code.\n\n The pmDebuggingTable logs debugging messages when\n policies experience run-time exceptions in either the condition\n or action code and the associated pmPolicyDebugging object\n has been turned on.\n\n The maximum number of debugging entries that will be stored\n and the maximum length of time an entry will be kept are an\n implementation-dependent manner. If entries must\n be discarded to make room for new entries, the oldest entries\n must be discarded first.\n\n If the system restarts, all debugging entries may be deleted.') pmDebuggingEntry = MibTableRow((1, 3, 6, 1, 2, 1, 124, 11, 1), ).setIndexNames((0, "POLICY-BASED-MANAGEMENT-MIB", "pmPolicyIndex"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmDebuggingElement"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmDebuggingContextName"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmDebuggingContextEngineID"), (0, "POLICY-BASED-MANAGEMENT-MIB", "pmDebuggingLogIndex")) if mibBuilder.loadTexts: pmDebuggingEntry.setDescription('An entry in the pmDebuggingTable. The pmPolicyIndex in the\n index specifies the policy that encountered the exception\n that led to this log entry.\n\n Note that some combinations of index values may result in an\n instance name that exceeds a length of 128 sub-identifiers,\n which exceeds the maximum for the SNMP protocol.\n Implementations should take care to avoid such combinations.') pmDebuggingElement = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 11, 1, 1), RowPointer()) if mibBuilder.loadTexts: pmDebuggingElement.setDescription("The element the policy was executing on when it encountered\n the error that led to this log entry.\n\n For example, if the element is interface 3, then this object\n will contain the OID for 'ifIndex.3'.\n\n As this object is used in the index for the\n pmDebuggingTable, users of this table should be careful\n not to create entries that would result in instance names\n with more than 128 sub-identifiers.") pmDebuggingContextName = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 11, 1, 2), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(0,32))) if mibBuilder.loadTexts: pmDebuggingContextName.setDescription('If the associated element is not in the default SNMP context\n for the target system, this object is used to identify the\n context. If the element is in the default context, this object\n is equal to the empty string.') pmDebuggingContextEngineID = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 11, 1, 3), OctetString().subtype(subtypeSpec=ConstraintsUnion(ValueSizeConstraint(0,0),ValueSizeConstraint(5,32),))) if mibBuilder.loadTexts: pmDebuggingContextEngineID.setDescription('If the associated element is on a remote system, this object\n is used to identify the remote system. This object contains\n the contextEngineID of the system on which the associated\n element resides. If the element is on the local system,\n this object will be the empty string.') pmDebuggingLogIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 11, 1, 4), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1,4294967295))) if mibBuilder.loadTexts: pmDebuggingLogIndex.setDescription('A unique index for this log entry among other log entries\n for this policy/element combination.') pmDebuggingMessage = MibTableColumn((1, 3, 6, 1, 2, 1, 124, 11, 1, 5), PmUTF8String().subtype(subtypeSpec=ValueSizeConstraint(0,128))).setMaxAccess("readonly") if mibBuilder.loadTexts: pmDebuggingMessage.setDescription('An error message generated by the policy execution\n environment. It is recommended that this message include the\n time of day when the message was generated, if known.') pmNotifications = MibIdentifier((1, 3, 6, 1, 2, 1, 124, 0)) pmNewRoleNotification = NotificationType((1, 3, 6, 1, 2, 1, 124, 0, 1)).setObjects(*(("POLICY-BASED-MANAGEMENT-MIB", "pmRoleStatus"),)) if mibBuilder.loadTexts: pmNewRoleNotification.setDescription('The pmNewRoleNotification is sent when an agent is configured\n with its first instance of a previously unused role string\n (not every time a new element is given a particular role).\n\n An instance of the pmRoleStatus object is sent containing\n the new roleString in its index. In the event that two or\n more elements are given the same role simultaneously, it is an\n implementation-dependent matter as to which pmRoleTable\n instance will be included in the notification.') pmNewCapabilityNotification = NotificationType((1, 3, 6, 1, 2, 1, 124, 0, 2)).setObjects(*(("POLICY-BASED-MANAGEMENT-MIB", "pmCapabilitiesType"),)) if mibBuilder.loadTexts: pmNewCapabilityNotification.setDescription('The pmNewCapabilityNotification is sent when an agent\n gains a new capability that did not previously exist in any\n element on the system (not every time an element gains a\n particular capability).\n\n An instance of the pmCapabilitiesType object is sent containing\n the identity of the new capability. In the event that two or\n more elements gain the same capability simultaneously, it is an\n implementation-dependent matter as to which pmCapabilitiesType\n instance will be included in the notification.') pmAbnormalTermNotification = NotificationType((1, 3, 6, 1, 2, 1, 124, 0, 3)).setObjects(*(("POLICY-BASED-MANAGEMENT-MIB", "pmTrackingPEInfo"),)) if mibBuilder.loadTexts: pmAbnormalTermNotification.setDescription("The pmAbnormalTermNotification is sent when a policy's\n pmPolicyAbnormalTerminations gauge value changes from zero to\n any value greater than zero and no such notification has been\n sent for that policy in the last 5 minutes.\n\n The notification contains an instance of the pmTrackingPEInfo\n object where the pmPolicyIndex component of the index\n identifies the associated policy and the rest of the index\n identifies an element on which the policy failed.") pmConformance = MibIdentifier((1, 3, 6, 1, 2, 1, 124, 12)) pmCompliances = MibIdentifier((1, 3, 6, 1, 2, 1, 124, 12, 1)) pmGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 124, 12, 2)) pmCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 124, 12, 1, 1)).setObjects(*(("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyManagementGroup"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedGroup"), ("POLICY-BASED-MANAGEMENT-MIB", "pmNotificationGroup"),)) if mibBuilder.loadTexts: pmCompliance.setDescription('Describes the requirements for conformance to\n the Policy-Based Management MIB') pmPolicyManagementGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 124, 12, 2, 1)).setObjects(*(("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyPrecedenceGroup"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyPrecedence"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicySchedule"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyElementTypeFilter"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyConditionScriptIndex"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyActionScriptIndex"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyParameters"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyConditionMaxLatency"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyActionMaxLatency"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyMaxIterations"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyDescription"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyMatches"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyAbnormalTerminations"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyExecutionErrors"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyDebugging"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyStorageType"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyAdminStatus"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyRowStatus"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyCodeText"), ("POLICY-BASED-MANAGEMENT-MIB", "pmPolicyCodeStatus"), ("POLICY-BASED-MANAGEMENT-MIB", "pmElementTypeRegMaxLatency"), ("POLICY-BASED-MANAGEMENT-MIB", "pmElementTypeRegDescription"), ("POLICY-BASED-MANAGEMENT-MIB", "pmElementTypeRegStorageType"), ("POLICY-BASED-MANAGEMENT-MIB", "pmElementTypeRegRowStatus"), ("POLICY-BASED-MANAGEMENT-MIB", "pmRoleStatus"), ("POLICY-BASED-MANAGEMENT-MIB", "pmCapabilitiesType"), ("POLICY-BASED-MANAGEMENT-MIB", "pmCapabilitiesOverrideState"), ("POLICY-BASED-MANAGEMENT-MIB", "pmCapabilitiesOverrideRowStatus"), ("POLICY-BASED-MANAGEMENT-MIB", "pmTrackingPEInfo"), ("POLICY-BASED-MANAGEMENT-MIB", "pmTrackingEPStatus"), ("POLICY-BASED-MANAGEMENT-MIB", "pmDebuggingMessage"),)) if mibBuilder.loadTexts: pmPolicyManagementGroup.setDescription('Objects that allow for the creation and management of\n configuration policies.') pmSchedGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 124, 12, 2, 2)).setObjects(*(("POLICY-BASED-MANAGEMENT-MIB", "pmSchedLocalTime"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedGroupIndex"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedDescr"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedTimePeriod"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedMonth"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedDay"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedWeekDay"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedTimeOfDay"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedLocalOrUtc"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedStorageType"), ("POLICY-BASED-MANAGEMENT-MIB", "pmSchedRowStatus"),)) if mibBuilder.loadTexts: pmSchedGroup.setDescription('Objects that allow for the scheduling of policies.') pmNotificationGroup = NotificationGroup((1, 3, 6, 1, 2, 1, 124, 12, 2, 3)).setObjects(*(("POLICY-BASED-MANAGEMENT-MIB", "pmNewRoleNotification"), ("POLICY-BASED-MANAGEMENT-MIB", "pmNewCapabilityNotification"), ("POLICY-BASED-MANAGEMENT-MIB", "pmAbnormalTermNotification"),)) if mibBuilder.loadTexts: pmNotificationGroup.setDescription('Notifications sent by an Policy MIB agent.') pmBaseFunctionLibrary = MibIdentifier((1, 3, 6, 1, 2, 1, 124, 12, 2, 4)) mibBuilder.exportSymbols("POLICY-BASED-MANAGEMENT-MIB", pmRoleString=pmRoleString, pmSchedIndex=pmSchedIndex, pmCapabilitiesOverrideEntry=pmCapabilitiesOverrideEntry, pmPolicyActionMaxLatency=pmPolicyActionMaxLatency, pmSchedTimeOfDay=pmSchedTimeOfDay, pmTrackingPEContextEngineID=pmTrackingPEContextEngineID, pmPolicyDescription=pmPolicyDescription, PmUTF8String=PmUTF8String, pmDebuggingMessage=pmDebuggingMessage, pmElementTypeRegRowStatus=pmElementTypeRegRowStatus, pmDebuggingContextName=pmDebuggingContextName, pmPolicyCodeSegment=pmPolicyCodeSegment, pmPolicyManagementGroup=pmPolicyManagementGroup, pmPolicyMaxIterations=pmPolicyMaxIterations, pmPolicyExecutionErrors=pmPolicyExecutionErrors, pmCompliance=pmCompliance, PYSNMP_MODULE_ID=pmMib, pmPolicyPrecedenceGroup=pmPolicyPrecedenceGroup, pmPolicyMatches=pmPolicyMatches, pmPolicySchedule=pmPolicySchedule, pmNewRoleNotification=pmNewRoleNotification, pmPolicyDebugging=pmPolicyDebugging, pmAbnormalTermNotification=pmAbnormalTermNotification, pmCapabilitiesOverrideState=pmCapabilitiesOverrideState, pmTrackingEPContextName=pmTrackingEPContextName, pmPolicyAdminStatus=pmPolicyAdminStatus, pmSchedEntry=pmSchedEntry, pmSchedLocalTime=pmSchedLocalTime, pmPolicyConditionScriptIndex=pmPolicyConditionScriptIndex, pmCapabilitiesOverrideRowStatus=pmCapabilitiesOverrideRowStatus, pmPolicyCodeStatus=pmPolicyCodeStatus, pmElementTypeRegTable=pmElementTypeRegTable, pmSchedTimePeriod=pmSchedTimePeriod, pmRoleContextName=pmRoleContextName, pmTrackingEPEntry=pmTrackingEPEntry, pmRoleContextEngineID=pmRoleContextEngineID, pmDebuggingElement=pmDebuggingElement, pmDebuggingContextEngineID=pmDebuggingContextEngineID, pmElementTypeRegMaxLatency=pmElementTypeRegMaxLatency, pmPolicyEntry=pmPolicyEntry, pmPolicyElementTypeFilter=pmPolicyElementTypeFilter, pmMib=pmMib, pmSchedDay=pmSchedDay, pmCapabilitiesTable=pmCapabilitiesTable, pmCompliances=pmCompliances, pmPolicyAdminGroup=pmPolicyAdminGroup, pmCapabilitiesOverrideTable=pmCapabilitiesOverrideTable, pmNotifications=pmNotifications, pmTrackingEPStatus=pmTrackingEPStatus, pmTrackingPEElement=pmTrackingPEElement, pmRoleTable=pmRoleTable, pmConformance=pmConformance, pmDebuggingTable=pmDebuggingTable, pmPolicyPrecedence=pmPolicyPrecedence, pmPolicyCodeEntry=pmPolicyCodeEntry, pmSchedTable=pmSchedTable, pmCapabilitiesType=pmCapabilitiesType, pmSchedGroup=pmSchedGroup, pmPolicyCodeText=pmPolicyCodeText, pmRoleStatus=pmRoleStatus, pmTrackingPETable=pmTrackingPETable, pmRoleElement=pmRoleElement, pmSchedWeekDay=pmSchedWeekDay, pmPolicyCodeTable=pmPolicyCodeTable, pmElementTypeRegEntry=pmElementTypeRegEntry, pmTrackingEPTable=pmTrackingEPTable, pmPolicyIndex=pmPolicyIndex, pmElementTypeRegOIDPrefix=pmElementTypeRegOIDPrefix, pmPolicyActionScriptIndex=pmPolicyActionScriptIndex, pmSchedGroupIndex=pmSchedGroupIndex, pmNewCapabilityNotification=pmNewCapabilityNotification, pmDebuggingEntry=pmDebuggingEntry, pmRoleEntry=pmRoleEntry, pmPolicyCodeScriptIndex=pmPolicyCodeScriptIndex, pmSchedStorageType=pmSchedStorageType, pmTrackingEPContextEngineID=pmTrackingEPContextEngineID, pmPolicyTable=pmPolicyTable, pmTrackingPEContextName=pmTrackingPEContextName, pmPolicyStorageType=pmPolicyStorageType, pmSchedLocalOrUtc=pmSchedLocalOrUtc, pmBaseFunctionLibrary=pmBaseFunctionLibrary, pmNotificationGroup=pmNotificationGroup, pmDebuggingLogIndex=pmDebuggingLogIndex, pmGroups=pmGroups, pmCapabilitiesEntry=pmCapabilitiesEntry, pmPolicyAbnormalTerminations=pmPolicyAbnormalTerminations, pmSchedMonth=pmSchedMonth, pmSchedDescr=pmSchedDescr, pmElementTypeRegDescription=pmElementTypeRegDescription, pmPolicyParameters=pmPolicyParameters, pmElementTypeRegStorageType=pmElementTypeRegStorageType, pmPolicyConditionMaxLatency=pmPolicyConditionMaxLatency, pmTrackingEPElement=pmTrackingEPElement, pmPolicyRowStatus=pmPolicyRowStatus, pmTrackingPEInfo=pmTrackingPEInfo, pmSchedRowStatus=pmSchedRowStatus, pmCapabilitiesOverrideType=pmCapabilitiesOverrideType, pmTrackingPEEntry=pmTrackingPEEntry)

419.464115

4,000

0.71935

12,101

87,668

5.211057

0.111644

0.005772

0.004757

0.00628

0.358791

0.312723

0.27035

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0.232893

0.22111

0

0.03601

0.195111

87,668

208

4,001

421.480769

0.857645

0.003764

0

0.4

0.71691

0.055503

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1

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false

0.025

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0

null

0

1

0

1

0

null

0

4

f2c7dab2890b7f29703291212dfc57406365fba6

112

py

Python

Exercise_4_8.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

Exercise_4_8.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

Exercise_4_8.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

#cubes list = [] for number in range(1,11): list.append(number**3) for number in list: print(number)

11.2

26

0.633929

18

112

3.944444

0.611111

0.253521

0.309859

0

0.045977

0.223214

112

9

27

12.444444

0.770115

0.044643

0

1

0

false

0

0.2

1

0

null

1

0

1

0

null

0

4

4b3f0937b9be14faeaa713b3a310910118e6b68b

145

py

Python

config/conf.py

lllllaurel/bravo

2c25f5af1779e2662533a64ca8b2f4e40d96fd07

[ "Apache-2.0" ]

null

config/conf.py

lllllaurel/bravo

2c25f5af1779e2662533a64ca8b2f4e40d96fd07

[ "Apache-2.0" ]

null

config/conf.py

lllllaurel/bravo

2c25f5af1779e2662533a64ca8b2f4e40d96fd07

[ "Apache-2.0" ]

null

from urllib import parse #数据库配置 class DatabaseConfig(): DBNAME = 'admin' USER = 'bravo' PASSWORD = parse.unquote_plus('Jujiao2020%')

20.714286

48

0.689655

16

145

6.1875

0.9375

0

0.034188

0.193103

145

7

48

20.714286

0.811966

0.034483

0

0.15

0

1

0

false

0.2

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

4

4b49cb90139afc69c594095e7a0ece4f01e09d1b

204

py

Python

step03.py

ml-as-a-service/scrapping-example

a6ddcd0cc03c382b81b89ca953cbfc74c95b62a4

[ "MIT" ]

null

step03.py

ml-as-a-service/scrapping-example

a6ddcd0cc03c382b81b89ca953cbfc74c95b62a4

[ "MIT" ]

null

step03.py

ml-as-a-service/scrapping-example

a6ddcd0cc03c382b81b89ca953cbfc74c95b62a4

[ "MIT" ]

null

from selenium import webdriver from selenium.webdriver.common.keys import Keys import pandas as pd driver = webdriver.Chrome('/usr/bin/chromedriver') driver.get('https://hoopshype.com/salaries/players/')

34

53

0.803922

28

204

5.857143

0.714286

0.146341

0

0.078431

204

6

53

34

0.87234

0

0.292683

0.102439

0

1

0

false

0

0.6

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

4

4b5a662386f9621559f98785b5c92045886293dd

148

py

Python

django_mailbox2/transports/mh.py

sunset-crew/django-mailbox

e9f6079c66e70e705f3c968e2e8c7b00c182ef40

[ "MIT" ]

null

django_mailbox2/transports/mh.py

sunset-crew/django-mailbox

e9f6079c66e70e705f3c968e2e8c7b00c182ef40

[ "MIT" ]

null

django_mailbox2/transports/mh.py

sunset-crew/django-mailbox

e9f6079c66e70e705f3c968e2e8c7b00c182ef40

[ "MIT" ]

null

from mailbox import MH from django_mailbox2.transports.generic import GenericFileMailbox class MHTransport(GenericFileMailbox): _variant = MH

21.142857

65

0.831081

16

148

7.5625

0.75

0

0.007752

0.128378

148

6

66

24.666667

0.930233

0

1

0

false

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

4b76d48b54e922cc8ff511a526cbf261e7dac189

14,467

py

Python

python/GradFuncs.py

ziyuchen7/SpecNet2

ae814e6e1e9a823bd221394a566c461fac6d0a59

[ "MIT" ]

null

python/GradFuncs.py

ziyuchen7/SpecNet2

ae814e6e1e9a823bd221394a566c461fac6d0a59

[ "MIT" ]

null

python/GradFuncs.py

ziyuchen7/SpecNet2

ae814e6e1e9a823bd221394a566c461fac6d0a59

[ "MIT" ]

null

import torch import numpy as np from functools import reduce import time def specnet1_grad_fullu(model, batch_idx, matdata): n = matdata.Size() W = matdata.Wmat(rowidx=batch_idx) D = matdata.Dmat() Y = model.rotate(matdata.Xdata(), D = D) # DY = torch.mul(D[batch_idx], Y[batch_idx,:]) WY = torch.matmul(W, Y) DinvWY = torch.mul(1./D[batch_idx], WY) grad_LA = 2*(Y[batch_idx,:] - DinvWY) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y[batch_idx,:].t(), grad_LA)) apploss.backward() def specnet1_grad_localu(model, batch_idx, matdata): # start = time.time() n_batch = len(batch_idx) W_batch = matdata.Wmat(rowidx=batch_idx, colidx=batch_idx) D_batch = torch.sum(W_batch, 1, keepdim=True) Y_batch = model.rotate(matdata.Xdata(batch_idx), D=D_batch) # DY_batch = torch.mul(D_batch, Y_batch) WY_batch = torch.matmul(W_batch, Y_batch) DinvWY_batch = torch.mul(1./D_batch, WY_batch) grad_LA = 2*(Y_batch - DinvWY_batch) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y_batch.t(), grad_LA)) apploss.backward() # end = time.time() # print(end-start) def specnet1_grad_fake_neighboru(model, batch_idx, matdata): n = matdata.Size() n_batch = len(batch_idx) sp_nbr_idx_batch, sp_val_batch = matdata.spWmat(rowidx=batch_idx) nbr_idx = torch.LongTensor(reduce(np.union1d, sp_nbr_idx_batch)) # nbr_idx = torch.LongTensor(range(n)) D = matdata.Dmat() Z = model(matdata.Xdata()) YDY, DY_nbr, _ = update_YDY(model, nbr_idx, Z[nbr_idx,:], matdata) # YDY = YDY.detach() Y = model.rotate(matdata.Xdata(), D = D, rotate_type = 'ydy', ydy = YDY) # DY_batch = torch.mul(D[batch_idx], Y[batch_idx,:]) WY_batch = torch.zeros(n_batch, Y.shape[1]) for i in range(n_batch): WY_batch[i,:] = torch.sum(torch.mul(sp_val_batch[i], Y[sp_nbr_idx_batch[i].reshape(-1),:]), dim=0) DinvWY_batch = torch.mul(1./D[batch_idx], WY_batch) grad_LA = 2*(Y[batch_idx,:] - DinvWY_batch) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y[batch_idx,:].t(), grad_LA)) apploss.backward() ## SpecNet1 loss_full uses all the data def specnet1_grad_full(model, batch_idx, matdata): W = matdata.Wmat(rowidx=batch_idx) D = matdata.Dmat() Y = model.rotate(matdata.Xdata()) DinvY = torch.mul(1./torch.sqrt(D), Y) WDinvY = torch.matmul(W, DinvY) DinvWDinvY = torch.mul(1./torch.sqrt(D[batch_idx]), WDinvY) grad_LA = 2*(Y[batch_idx,:] - DinvWDinvY) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y[batch_idx,:].t(), grad_LA)) apploss.backward() ## SpecNet1 loss_local only uses mini-batch def specnet1_grad_local(model, batch_idx, matdata): n_batch = len(batch_idx) Y_batch = model.rotate(matdata.Xdata(batch_idx)) W_batch = matdata.Wmat(rowidx=batch_idx, colidx=batch_idx) D_batch = torch.sum(W_batch, 1, keepdim=True) DinvY_batch = torch.mul(1./torch.sqrt(D_batch), Y_batch) WDinvY_batch = torch.matmul(W_batch, DinvY_batch) DinvWDinvY_batch = torch.mul(1./torch.sqrt(D_batch), WDinvY_batch) grad_LA = 2*(Y_batch - DinvWDinvY_batch) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y_batch.t(), grad_LA)) apploss.backward() ## SpecNet1 loss_local only uses mini-batch neighbor def specnet1_grad_neighbor(model, batch_idx, matdata): n = matdata.Size() n_batch = len(batch_idx) sp_nbr_idx_batch, sp_val_batch = matdata.spWmat(rowidx=batch_idx) nbr_idx = torch.LongTensor(reduce(np.union1d, sp_nbr_idx_batch)) batch_idx_nbr = torch.LongTensor([torch.nonzero(nbr_idx==i, \ as_tuple=False) for i in batch_idx]) D_nbr = matdata.Dmat(nbr_idx) with torch.no_grad(): Y_nbr = model(matdata.Xdata(nbr_idx)) update_YDY(model, nbr_idx, Y_nbr, matdata, DisId=True) Y_nbr = model.rotate(matdata.Xdata(nbr_idx), rotate_type = 'ydy') DinvY_nbr = torch.mul(1./torch.sqrt(D_nbr), Y_nbr) WDinvY_batch = torch.zeros(n_batch, DinvY_nbr.shape[1]) for i in range(n_batch): idx = torch.LongTensor([torch.nonzero(nbr_idx==j, \ as_tuple=False) for j in sp_nbr_idx_batch[i]]) WDinvY_batch[i,:] = torch.sum(torch.mul(sp_val_batch[i], DinvY_nbr[idx,:]), dim=0) grad_LA = Y_nbr[batch_idx_nbr,:] - \ torch.mul(1./torch.sqrt(D_nbr[batch_idx_nbr]), WDinvY_batch) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y_nbr[batch_idx_nbr,:].t(), grad_LA)) apploss.backward() def specnet1_grad_fake_neighbor(model, batch_idx, matdata): n_batch = len(batch_idx) sp_nbr_idx_batch, sp_val_batch = matdata.spWmat(rowidx=batch_idx) nbr_idx = torch.LongTensor(reduce(np.union1d, sp_nbr_idx_batch)) D = matdata.Dmat() Y = model(matdata.Xdata()) update_YDY(model, nbr_idx, Y[nbr_idx,:], matdata, DisId=True) Y = model.rotate(matdata.Xdata(), rotate_type = 'ydy') DinvY = torch.mul(1./torch.sqrt(D), Y) WDinvY_batch = torch.zeros(n_batch, DinvY.shape[1]) for i in range(n_batch): WDinvY_batch[i,:] = torch.sum(torch.mul(sp_val_batch[i], DinvY[sp_nbr_idx_batch[i].reshape(-1),:]), dim=0) grad_LA = 2*(Y[batch_idx,:] - \ torch.mul(1./torch.sqrt(D[batch_idx]), WDinvY_batch)) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y[batch_idx,:].t(), grad_LA)) apploss.backward() ## SpecNet2 loss_full uses all the data # def specnet2_grad_full(model, batch_idx, matdata): # n = matdata.Size() # Y = model(matdata.Xdata()) # W = matdata.Wmat(rowidx=batch_idx) # WY = torch.matmul(W, Y) # YDY, DY = update_YDY(model, range(matdata.Size()), Y, matdata) # grad_LA = 4*(- WY/n + torch.matmul(DY[batch_idx,:], YDY)/n**3) # grad_LA = grad_LA.detach() # apploss = torch.trace(torch.matmul(Y[batch_idx,:].t(), grad_LA)) # apploss.backward() def specnet2_grad_full(model, batch_idx, matdata): n = matdata.Size() Y = model(matdata.Xdata()) W = matdata.Wmat(rowidx=batch_idx) D = matdata.Dmat(batch_idx) WY = torch.matmul(W, Y) YDY, DY, DtY = update_YDY(model, range(matdata.Size()), Y, matdata) DDtY = torch.mul(D, DtY) Y_batch = model(matdata.Xdata(batch_idx)) grad_LA = 4*(- WY/n + DDtY/n+ torch.matmul(DY[batch_idx,:], YDY)/n**3) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y_batch.t(), grad_LA)) apploss.backward() def specnet2_grad_symfull(model, batch_idx, matdata): n = matdata.Size() Y = model(matdata.Xdata()) W = matdata.Wmat(rowidx=batch_idx) D = matdata.Dmat() DinvY = torch.mul(1./torch.sqrt(D), Y) WDinvY = torch.matmul(W, DinvY) DinvWDinvY = torch.mul(1./torch.sqrt(D[batch_idx]), WDinvY) YY, _, _ = update_YDY(model, range(matdata.Size()), Y, matdata, DisId=True) v1 = torch.ones(n,1)/np.sqrt(n) DDtY = torch.mul(v1[batch_idx,:], torch.matmul(v1.t(),Y)) grad_LA = 4*(- DinvWDinvY + DDtY+ torch.matmul(Y[batch_idx,:], YY)/n) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y[batch_idx,:].t(), grad_LA)) apploss.backward() ## SpecNet2 loss_local only uses mini-batch def specnet2_grad_local(model, batch_idx, matdata): n_batch = len(batch_idx) Y_batch = model(matdata.Xdata(batch_idx)) W_batch = matdata.Wmat(rowidx=batch_idx, colidx=batch_idx) D_batch = torch.sum(W_batch, 1, keepdim=True) Dt = D_batch.t()/torch.matmul(torch.sqrt(D_batch.t()),torch.sqrt(D_batch)) DDtY = torch.mul(D_batch, torch.matmul(Dt, Y_batch)) WY_batch = torch.matmul(W_batch, Y_batch) DY_batch = torch.mul(D_batch, Y_batch) YDY_batch = torch.matmul(Y_batch.t(), DY_batch) grad_LA = 4*(- WY_batch/n_batch + DDtY/n_batch\ + torch.matmul(DY_batch, YDY_batch)/n_batch**3) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y_batch.t(), grad_LA)) apploss.backward() def specnet2_grad_neighboru(model, batch_idx, matdata): start = time.time() n = matdata.Size() n_batch = len(batch_idx) start4 = time.time() D = matdata.Dmat(batch_idx) end4 = time.time() start5 = time.time() sp_nbr_idx_batch, sp_val_batch = matdata.spWmat(rowidx=batch_idx) nbr_idx = torch.LongTensor(reduce(np.union1d, sp_nbr_idx_batch)) batch_idx_nbr = torch.LongTensor([torch.nonzero(nbr_idx==i, \ as_tuple=False) for i in batch_idx]) end5 = time.time() start2 = time.time() Y_nbr = model(matdata.Xdata(nbr_idx)) Y_batch = model(matdata.Xdata(batch_idx)) end2 = time.time() start1 = time.time() W_batch = matdata.Wmat(rowidx=batch_idx, colidx=nbr_idx) WY_batch = torch.matmul(W_batch, Y_nbr) end1 = time.time() start6 = time.time() YDY, DY_nbr, DtY = update_YDY(model, nbr_idx, Y_nbr, matdata) DDtY = torch.mul(D, DtY) grad_LA = 4*(- WY_batch/n + DDtY/n \ + torch.matmul(DY_nbr[batch_idx_nbr,:], YDY)/n**3) grad_LA = grad_LA.detach() end6 = time.time() apploss = torch.trace(torch.matmul(Y_batch.t(), grad_LA)) start3 = time.time() apploss.backward() end3 = time.time() return end1-start1, end2-start2, end3-start3, end3-start, end4-start4, end5-start5, end6-start6 ## SpecNet2 loss_local only uses mini-batch neighbor def specnet2_grad_neighbor(model, batch_idx, matdata): n = matdata.Size() n_batch = len(batch_idx) D = matdata.Dmat(batch_idx) sp_nbr_idx_batch, sp_val_batch = matdata.spWmat(rowidx=batch_idx) nbr_idx = torch.LongTensor(reduce(np.union1d, sp_nbr_idx_batch)) nbr_idx = nbr_idx.reshape(-1) batch_idx_nbr = torch.LongTensor([torch.nonzero(nbr_idx==i, \ as_tuple=False) for i in batch_idx]) Y_nbr = model(matdata.Xdata(nbr_idx)) Y_batch = model(matdata.Xdata(batch_idx)) W_batch = matdata.Wmat(rowidx=batch_idx, colidx=nbr_idx) WY_batch = torch.matmul(W_batch, Y_nbr) YDY, DY_nbr, DtY = update_YDY(model, nbr_idx, Y_nbr, matdata) DDtY = torch.mul(D, DtY) grad_LA = 4*(- WY_batch/n + DDtY/n \ + torch.matmul(DY_nbr[batch_idx_nbr,:], YDY)/n**3) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y_batch.t(), grad_LA)) apploss.backward() def specnet2_grad_fake_neighbor(model, batch_idx, matdata): n = matdata.Size() n_batch = len(batch_idx) D = matdata.Dmat(batch_idx) Dt = matdata.Dtmat() sp_nbr_idx_batch, sp_val_batch = matdata.spWmat(rowidx=batch_idx) nbr_idx = torch.LongTensor(reduce(np.union1d, sp_nbr_idx_batch)) batch_idx_nbr = torch.LongTensor([torch.nonzero(nbr_idx==i, \ as_tuple=False) for i in batch_idx]) Y = model(matdata.Xdata()) Y_batch = model(matdata.Xdata(batch_idx)) WY_batch = torch.zeros(n_batch, Y.shape[1]) for i in range(n_batch): WY_batch[i,:] = torch.sum(torch.mul(sp_val_batch[i], Y[sp_nbr_idx_batch[i].reshape(-1),:]), dim=0) YDY, DY_nbr, Y_part = update_YDY(model, nbr_idx, Y[nbr_idx,:], matdata) DDtY = torch.mul(D, torch.matmul(Dt, Y_part)) grad_LA = 4*(- WY_batch/n + DDtY/n \ + torch.matmul(DY_nbr[batch_idx_nbr,:], YDY)/n**3) grad_LA = grad_LA.detach() apploss = torch.trace(torch.matmul(Y_batch.t(), grad_LA)) apploss.backward() # def specnet2_grad_fake_neighbor(model, batch_idx, matdata): # n = matdata.Size() # n_batch = len(batch_idx) # sp_nbr_idx_batch, sp_val_batch = matdata.spWmat(rowidx=batch_idx) # nbr_idx = torch.LongTensor(reduce(np.union1d, sp_nbr_idx_batch)) # batch_idx_nbr = torch.LongTensor([torch.nonzero(nbr_idx==i, \ # as_tuple=False) for i in batch_idx]) # Y = model(matdata.Xdata()) # WY_batch = torch.zeros(n_batch, Y.shape[1]) # for i in range(n_batch): # WY_batch[i,:] = torch.sum(torch.mul(sp_val_batch[i], Y[sp_nbr_idx_batch[i].reshape(-1),:]), # dim=0) # YDY, DY_nbr = update_YDY(model, nbr_idx, Y[nbr_idx,:], matdata) # grad_LA = 4*(- WY_batch/n \ # + torch.matmul(DY_nbr[batch_idx_nbr,:], YDY)/n**3) # grad_LA = grad_LA.detach() # apploss = torch.trace(torch.matmul(Y[batch_idx,:].t(), grad_LA)) # apploss.backward() ## Updating YDY approximately def update_YDY(model = None, idx = None, Y_idx = None, matdata = None, DisId=False, GetYDY=False): if update_YDY.Y_old is None: model.eval() with torch.no_grad(): update_YDY.Y_old = model(matdata.Xdata()) update_YDY.Y_old = update_YDY.Y_old.detach() if not DisId: D = matdata.Dmat() Dt = matdata.Dtmat() update_YDY.YDY = torch.matmul( update_YDY.Y_old.t(), torch.mul(D, update_YDY.Y_old)) update_YDY.DtY = torch.matmul(Dt, update_YDY.Y_old) else: update_YDY.YDY = torch.matmul( update_YDY.Y_old.t(), update_YDY.Y_old) if GetYDY: return update_YDY.YDY Y_idx = Y_idx.detach() Y_old_idx = update_YDY.Y_old[idx,:] # Y_idx = Y_idx.detach() if not DisId: D_idx = matdata.Dmat(idx) Dt_idx = matdata.Dtmat(idx) DY_idx = torch.mul(D_idx, Y_idx) DY_old_idx = torch.mul(D_idx, Y_old_idx) DtY_idx = torch.matmul(Dt_idx, Y_idx) DtY_old_idx = torch.matmul(Dt_idx, Y_old_idx) else: DY_idx = Y_idx DY_old_idx = Y_old_idx update_YDY.YDY = update_YDY.YDY \ - torch.matmul(Y_old_idx.t(), DY_old_idx) \ + torch.matmul(Y_idx.t(), DY_idx) update_YDY.DtY = update_YDY.DtY \ - DtY_old_idx + DtY_idx update_YDY.Y_old[idx,:] = Y_idx # update_YDY.YDY = update_YDY.YDY \ # - torch.matmul(Y_old_idx.t(), DY_old_idx) \ # + torch.matmul(Y_idx.t(), DY_idx) # update_YDY.Y_old[idx,:] = Y_idx return update_YDY.YDY, DY_idx, update_YDY.DtY update_YDY.Y_old = None update_YDY.YDY = None update_YDY.DtY = None

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Python

packages/qsmstoken/qsmstoken/clients/alidayusms/__init__.py

lianxiaopang/camel-store-api

b8021250bf3d8cf7adc566deebdba55225148316

[ "Apache-2.0" ]

12

2020-02-01T01:52:01.000Z

2021-04-28T15:06:43.000Z

packages/qsmstoken/qsmstoken/clients/alidayusms/__init__.py

lianxiaopang/camel-store-api

b8021250bf3d8cf7adc566deebdba55225148316

[ "Apache-2.0" ]

5

2020-02-06T08:07:58.000Z

2020-06-02T13:03:45.000Z

packages/qsmstoken/qsmstoken/clients/alidayusms/__init__.py

lianxiaopang/camel-store-api

b8021250bf3d8cf7adc566deebdba55225148316

[ "Apache-2.0" ]

11

2020-02-03T13:07:46.000Z

2020-11-29T01:44:06.000Z

import os import sys parent_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, parent_dir) from .services import AlidayuSMSClient

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py

Python

lattice_sync/apps.py

PressLabs/zinc

9e1dc852f31f9897e7759962cf0f3e6d42fbe637

[ "Apache-2.0" ]

29

2017-06-29T15:03:49.000Z

2018-01-30T14:07:26.000Z

lattice_sync/apps.py

presslabs/zinc

94146e5203fc93ee0e8bb011a4db0ffcd4b0096e

[ "Apache-2.0" ]

9

2019-01-11T09:07:17.000Z

2022-02-03T12:50:21.000Z

lattice_sync/apps.py

PressLabs/zinc

9e1dc852f31f9897e7759962cf0f3e6d42fbe637

[ "Apache-2.0" ]

1

2020-08-09T18:17:25.000Z

from django.apps import AppConfig class LatticeSyncConfig(AppConfig): name = 'lattice_sync'

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py

Python

Recommender/test.py

wpwbb510582246/PocketFIlm

356d057810fd48a77197fe0f00b1f2adccb02d39

[ "MIT" ]

17

2019-09-11T08:37:26.000Z

2021-08-17T12:08:54.000Z

Recommender/test.py

wpwbb510582246/PocketFIlm

356d057810fd48a77197fe0f00b1f2adccb02d39

[ "MIT" ]

5

2019-10-26T00:28:35.000Z

2021-05-08T09:10:36.000Z

Recommender/test.py

wpwbb510582246/PocketFIlm

356d057810fd48a77197fe0f00b1f2adccb02d39

[ "MIT" ]

8

2020-03-17T08:17:35.000Z

2021-07-30T15:48:36.000Z

from util.CommonUtils import * from util.MongoDbUtils import MongoDbUtils if __name__ == '__main__': print(get_today())

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py

Python

src/words/__init__.py

winkidney/MakeABit

8438b177218fadd6bd8faeedac1b586429e9bc7f

[ "MIT" ]

1

2016-09-08T07:46:39.000Z

src/words/__init__.py

winkidney/MakeABit

8438b177218fadd6bd8faeedac1b586429e9bc7f

[ "MIT" ]

null

src/words/__init__.py

winkidney/MakeABit

8438b177218fadd6bd8faeedac1b586429e9bc7f

[ "MIT" ]

null

import os HERE = os.path.abspath(os.path.dirname(__file__)) TEMPLATES_DIR = os.path.join(HERE, "templates")

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4b1a1cc40d3da2f488c81d227ffdce85b53f6ac8

166

py

Python

chunked_media/apps.py

CerealBoxMedia/chunked_media

324d9b7d76323fa1ca5296d733b82dc5ab5f98c0

[ "BSD-2-Clause" ]

null

chunked_media/apps.py

CerealBoxMedia/chunked_media

324d9b7d76323fa1ca5296d733b82dc5ab5f98c0

[ "BSD-2-Clause" ]

null

chunked_media/apps.py

CerealBoxMedia/chunked_media

324d9b7d76323fa1ca5296d733b82dc5ab5f98c0

[ "BSD-2-Clause" ]

null

from django.apps import AppConfig class ChunkedMediaAppConfig(AppConfig): name = 'chunked_media' label = 'chunked_media' verbose_name = "Chunked media"

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4b260f10cf9f7e288044481e9b95e59f63c5878a

465

py

Python

wwdtm/panelist/__init__.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

null

wwdtm/panelist/__init__.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

1

2022-01-17T04:25:49.000Z

wwdtm/panelist/__init__.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # vim: set noai syntax=python ts=4 sw=4: # # Copyright (c) 2018-2021 Linh Pham # wwdtm is released under the terms of the Apache License 2.0 """Wait Wait Stats: Panelist module""" from wwdtm.panelist.appearances import PanelistAppearances from wwdtm.panelist.panelist import Panelist from wwdtm.panelist.scores import PanelistScores from wwdtm.panelist.statistics import PanelistStatistics from wwdtm.panelist.utility import PanelistUtility

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d9a129abd63f0d9c386cac6d0ce1e4a9b6222916

199

py

Python

ml_web/users/tests/test_models.py

mahidul-islam/ml_web

6e4b0f034aaa6c86b53dc2edb7c5123419e0541d

[ "MIT" ]

1

2021-08-19T17:42:31.000Z

ml_web/users/tests/test_models.py

mahidul-islam/ml_web

6e4b0f034aaa6c86b53dc2edb7c5123419e0541d

[ "MIT" ]

null

ml_web/users/tests/test_models.py

mahidul-islam/ml_web

6e4b0f034aaa6c86b53dc2edb7c5123419e0541d

[ "MIT" ]

null

import pytest from ml_web.users.models import User pytestmark = pytest.mark.django_db def test_user_get_absolute_url(user: User): assert user.get_absolute_url() == f"/users/{user.username}/"

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d9b83d7ce43d5ceb515bbb8e5157b420964a03e2

541

py

Python

cms/templatetags/cms_tags.py

eldarion-client/hedera

a01d44232fadab4e60e8d06971feba9fad21e3f2

[ "MIT" ]

8

2018-10-25T19:30:49.000Z

2021-07-27T18:16:42.000Z

cms/templatetags/cms_tags.py

eldarion-client/hedera

a01d44232fadab4e60e8d06971feba9fad21e3f2

[ "MIT" ]

233

2018-11-01T14:30:25.000Z

2022-03-30T16:38:24.000Z

cms/templatetags/cms_tags.py

eldarion-client/hedera

a01d44232fadab4e60e8d06971feba9fad21e3f2

[ "MIT" ]

3

2019-05-16T14:57:55.000Z

2021-03-16T13:05:01.000Z

from django import template from cms.models import FlatPage, HomePage register = template.Library() @register.simple_tag def get_home_page(): return HomePage.objects.first() @register.simple_tag def get_site_nav(): return FlatPage.objects.live().filter(show_in_menus=True).order_by('title') @register.simple_tag def get_flatpage_nav(): return HomePage.objects.first().get_children().live().order_by('title') @register.simple_tag() def get_flatpage_subnav(page): return page.get_children().live().order_by('title')

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d9c789fed2e7686c7e0513ac9e434c3f6ff56b9b

492

py

Python

KOPy/__init__.py

alexrudy/KOPy

fcce623f1efc2e2891645af484f3dc23b70ce7f8

[ "BSD-3-Clause" ]

null

KOPy/__init__.py

alexrudy/KOPy

fcce623f1efc2e2891645af484f3dc23b70ce7f8

[ "BSD-3-Clause" ]

null

KOPy/__init__.py

alexrudy/KOPy

fcce623f1efc2e2891645af484f3dc23b70ce7f8

[ "BSD-3-Clause" ]

null

# Licensed under a 3-clause BSD style license - see LICENSE.rst """ KOPy - Keck Observing Python """ # Affiliated packages may add whatever they like to this file, but # should keep this content at the top. # ---------------------------------------------------------------------------- from ._astropy_init import * # ---------------------------------------------------------------------------- # For egg_info test builds to pass, put package imports here. if not _ASTROPY_SETUP_: pass

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d9ca34b32611e04843b57807b53f42282d7aa2b4

144

py

Python

posts/widgets.py

Duskhorizon/discoplaytogether

e74a11b0f65d14db6f15d1bb0536411dd546eda6

[ "MIT" ]

null

posts/widgets.py

Duskhorizon/discoplaytogether

e74a11b0f65d14db6f15d1bb0536411dd546eda6

[ "MIT" ]

null

posts/widgets.py

Duskhorizon/discoplaytogether

e74a11b0f65d14db6f15d1bb0536411dd546eda6

[ "MIT" ]

null

from django.forms import DateTimeInput class XDSoftDateTimePickerInput(DateTimeInput): template_name = 'widgets/xdsoft_datetimepicker.html'

36

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144

8.571429

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0

1

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0

4

d9cf9573a2207d7e74cd336a12bbcab09f94e2d9

70

py

Python

create_schema.py

dwahler/spelling-list-generator

acb8c3c0929c8a1caf20db07f8ca896e57827637

[ "MIT" ]

2

2019-03-11T07:36:12.000Z

2019-09-07T03:55:28.000Z

create_schema.py

dwahler/spelling-list-generator

acb8c3c0929c8a1caf20db07f8ca896e57827637

[ "MIT" ]

null

create_schema.py

dwahler/spelling-list-generator

acb8c3c0929c8a1caf20db07f8ca896e57827637

[ "MIT" ]

null

#!/usr/bin/env python from spelling.storage import db db.create_all()

17.5

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null

0

1

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1

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4

d9dac6d49626f764fd967fa8db12e093465915c8

15

py

Python

src/rewoven/__init__.py

radifar/rewoven

048ca4337133fe0fab2caa40c5ec8ddd1c52bcd2

[ "MIT" ]

null

src/rewoven/__init__.py

radifar/rewoven

048ca4337133fe0fab2caa40c5ec8ddd1c52bcd2

[ "MIT" ]

40

2021-11-01T11:22:13.000Z

2022-03-30T11:27:05.000Z

src/rewoven/__init__.py

radifar/rewoven

048ca4337133fe0fab2caa40c5ec8ddd1c52bcd2

[ "MIT" ]

null

"""Rewoven."""

7.5

14

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15

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null

0

1

0

null

0

1

0

4

d9db0e9bf33ddd885dd6ee9a60718e604b127977

3,162

py

Python

databaser/settings.py

yart2211/databaser

e34a738b07fff7be384625635f37143a8cec3e97

[ "MIT" ]

21

2020-04-29T11:10:14.000Z

2022-03-15T11:16:22.000Z

databaser/settings.py

yart2211/databaser

e34a738b07fff7be384625635f37143a8cec3e97

[ "MIT" ]

null

databaser/settings.py

yart2211/databaser

e34a738b07fff7be384625635f37143a8cec3e97

[ "MIT" ]

6

2020-07-16T05:53:14.000Z

2021-09-23T07:15:14.000Z

import logging from core.enums import ( LogLevelEnum, ) from core.helpers import ( get_bool_environ_parameter, get_int_environ_parameter, get_iterable_environ_parameter, get_str_environ_parameter, logger, ) # Logger LOG_LEVEL = get_str_environ_parameter( name='DATABASER_LOG_LEVEL', default=LogLevelEnum.INFO, ) logger.setLevel(getattr(logging, LOG_LEVEL, logging.INFO)) # Src database connection params SRC_DB_HOST = get_str_environ_parameter( name='DATABASER_SRC_DB_HOST', ) SRC_DB_PORT = get_str_environ_parameter( name='DATABASER_SRC_DB_PORT', ) SRC_DB_SCHEMA = get_str_environ_parameter( name='DATABASER_SRC_DB_SCHEMA', default='public', ) SRC_DB_NAME = get_str_environ_parameter( name='DATABASER_SRC_DB_NAME', ) SRC_DB_USER = get_str_environ_parameter( name='DATABASER_SRC_DB_USER', ) SRC_DB_PASSWORD = get_str_environ_parameter( name='DATABASER_SRC_DB_PASSWORD', ) # Dst database connection params DST_DB_HOST = get_str_environ_parameter( name='DATABASER_DST_DB_HOST', ) DST_DB_PORT = get_str_environ_parameter( name='DATABASER_DST_DB_PORT', ) DST_DB_SCHEMA = get_str_environ_parameter( name='DATABASER_DST_DB_SCHEMA', default='public', ) DST_DB_NAME = get_str_environ_parameter( name='DATABASER_DST_DB_NAME', ) DST_DB_USER = get_str_environ_parameter( name='DATABASER_DST_DB_USER', ) DST_DB_PASSWORD = get_str_environ_parameter( name='DATABASER_DST_DB_PASSWORD', ) # Test mode parameters TEST_MODE = get_bool_environ_parameter( name='DATABASER_TEST_MODE', ) if TEST_MODE: logger.warning('TEST MODE ACTIVATED!!!') KEY_TABLE_NAME = get_str_environ_parameter( name='DATABASER_KEY_TABLE_NAME', ) KEY_COLUMN_NAMES = get_iterable_environ_parameter( name='DATABASER_KEY_COLUMN_NAMES', ) KEY_COLUMN_VALUES = get_iterable_environ_parameter( name='DATABASER_KEY_COLUMN_VALUES', type_=int, ) KEY_TABLE_HIERARCHY_COLUMN_NAME = get_str_environ_parameter( name='DATABASER_KEY_TABLE_HIERARCHY_COLUMN_NAME', ) EXCLUDED_TABLES = get_iterable_environ_parameter( name='DATABASER_EXCLUDED_TABLES', ) TABLES_WITH_GENERIC_FOREIGN_KEY = get_iterable_environ_parameter( name='DATABASER_TABLES_WITH_GENERIC_FOREIGN_KEY', ) TABLES_LIMIT_PER_TRANSACTION = get_int_environ_parameter( name='DATABASER_TABLES_LIMIT_PER_TRANSACTION', default=100, ) IS_TRUNCATE_TABLES = get_bool_environ_parameter( name='DATABASER_IS_TRUNCATE_TABLES', ) TABLES_TRUNCATE_INCLUDED = get_iterable_environ_parameter( name='DATABASER_TABLES_TRUNCATE_INCLUDED', ) TABLES_TRUNCATE_EXCLUDED = get_iterable_environ_parameter( name='DATABASER_TABLES_TRUNCATE_EXCLUDED', ) if not any( [ SRC_DB_HOST, SRC_DB_PORT, SRC_DB_NAME, SRC_DB_USER, SRC_DB_PASSWORD, DST_DB_HOST, DST_DB_PORT, DST_DB_NAME, DST_DB_USER, DST_DB_PASSWORD, KEY_TABLE_NAME, KEY_COLUMN_NAMES, KEY_COLUMN_VALUES, ] ): raise ValueError('You must send all params!') VALIDATE_DATA_BEFORE_TRANSFERRING = get_bool_environ_parameter( name='VALIDATE_DATA_BEFORE_TRANSFERRING', )

24.511628

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0.772612

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3,162

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128

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24.703125

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0

null

1

0

null

0

1

0

4

d9dbb4e3cf51668889c1d2dd53f6c2920509e999

73

py

Python

kriegspiel_api_server/kriegspiel/exceptions.py

Kriegspiel/python-api

f25107d5ebc8810af622c66960c153c43a1ffb03

[ "MIT" ]

1

2016-12-30T10:11:11.000Z

kriegspiel_api_server/kriegspiel/exceptions.py

Kriegspiel/python-api

f25107d5ebc8810af622c66960c153c43a1ffb03

[ "MIT" ]

null

kriegspiel_api_server/kriegspiel/exceptions.py

Kriegspiel/python-api

f25107d5ebc8810af622c66960c153c43a1ffb03

[ "MIT" ]

null

# -*- coding: utf-8 -*- class KriegspielException(Exception): pass

12.166667

37

0.643836

7

73

6.714286

1

0

0.016949

0.191781

73

5

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14.6

0.779661

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1

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true

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0

1

0

null

0

1

0

null

0

1

0

4

d9fcbec087b3096414a368ead8a5532c01831c36

96

py

Python

Intelligent-interrogation/IISever/mainFunc/apps.py

Intelligent-interrogation/Intelligent-interrogation

17ecdc85b37a1624bf3115a0539008cb0574d5ce

[ "MIT" ]

7

2017-12-21T03:45:29.000Z

2021-11-06T16:11:50.000Z

Intelligent-interrogation/IISever/mainFunc/apps.py

Intelligent-interrogation/Intelligent-interrogation

17ecdc85b37a1624bf3115a0539008cb0574d5ce

[ "MIT" ]

1

2017-12-13T08:41:51.000Z

Intelligent-interrogation/IISever/mainFunc/apps.py

Intelligent-interrogation/Intelligent-interrogation

17ecdc85b37a1624bf3115a0539008cb0574d5ce

[ "MIT" ]

4

2018-06-19T07:00:38.000Z

2019-09-18T01:15:31.000Z

from django.apps import AppConfig class MainfuncConfig(AppConfig): name = 'mainFunc'

16

34

0.71875

10

96

6.9

0.9

0

0.208333

96

5

35

19.2

0.907895

0

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0

1

0

false

0

0.333333

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1

0

null

0

1

0

1

0

4

8a202dd058b0b797f44343d0a1f94b0f7b2b341c

2,194

py

Python

tests/run_data_tests.py

Cloudlock/gdata-python3

a6481a13590bfa225f91a97b2185cca9aacd1403

[ "Apache-2.0" ]

19

2017-06-09T13:38:03.000Z

2020-12-12T07:45:48.000Z

tests/run_data_tests.py

AlexxIT/gdata-python3

5cc5a83a469d87f804d1fda8760ec76bcb6050c9

[ "Apache-1.1" ]

11

2017-07-22T07:09:54.000Z

2020-12-02T15:08:48.000Z

tests/run_data_tests.py

AlexxIT/gdata-python3

5cc5a83a469d87f804d1fda8760ec76bcb6050c9

[ "Apache-1.1" ]

25

2017-07-03T11:30:39.000Z

2020-10-01T02:21:13.000Z

import os import sys sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '../src')) import atom_test import atom_tests.core_test import atom_tests.http_interface_test import atom_tests.mock_http_test import atom_tests.token_store_test import atom_tests.url_test # Modules whose tests we will run. import gdata_test import gdata_tests.apps.emailsettings.data_test import gdata_tests.apps.multidomain.data_test import gdata_tests.apps_test import gdata_tests.auth_test import gdata_tests.blogger_test import gdata_tests.calendar_resource.data_test import gdata_tests.calendar_test import gdata_tests.client_test import gdata_tests.codesearch_test import gdata_tests.contacts_test import gdata_tests.docs_test import gdata_tests.oauth.data_test import gdata_tests.photos_test import gdata_tests.spreadsheet_test import gdata_tests.webmastertools_test import gdata_tests.youtube_test import module_test_runner def RunAllTests(): test_runner = module_test_runner.ModuleTestRunner() test_runner.modules = [gdata_test, atom_test, atom_tests.url_test, atom_tests.http_interface_test, atom_tests.mock_http_test, atom_tests.core_test, atom_tests.token_store_test, gdata_tests.client_test, gdata_tests.apps_test, gdata_tests.apps.emailsettings.data_test, gdata_tests.apps.multidomain.data_test, gdata_tests.auth_test, gdata_tests.calendar_test, gdata_tests.docs_test, gdata_tests.spreadsheet_test, gdata_tests.photos_test, gdata_tests.codesearch_test, gdata_tests.contacts_test, gdata_tests.youtube_test, gdata_tests.blogger_test, gdata_tests.webmastertools_test, gdata_tests.calendar_resource.data_test, gdata_tests.oauth.data_test] test_runner.RunAllTests() if __name__ == '__main__': RunAllTests()

38.491228

86

0.680948

266

2,194

5.184211

0.195489

0.232052

0.174039

0.232052

0.345178

0.166788

0

0.000619

0.263446

2,194

56

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39.178571

0.852723

0.014585

0

0.006481

0

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0

0.52

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0.54

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null

1

0

1

0

null

0

1

0

1

0

4

8a34fe18f81ca4dd57f7f9d5cb96c87fb91d6fea

205

py

Python

django/contrib/syndication/apps.py

benjaoming/django

6dbe979b4d9396e1b307c7d27388c97c13beb21c

[ "BSD-3-Clause" ]

2

2016-09-27T09:30:19.000Z

2016-10-17T01:47:43.000Z

env/lib/python2.7/site-packages/django/contrib/syndication/apps.py

luiscarlosgph/nas

e5acee61e8bbf12c34785fe971ce7df8dee775d4

[ "MIT" ]

10

2019-12-26T17:31:31.000Z

2022-03-21T22:17:33.000Z

env/lib/python2.7/site-packages/django/contrib/syndication/apps.py

luiscarlosgph/nas

e5acee61e8bbf12c34785fe971ce7df8dee775d4

[ "MIT" ]

1

2020-05-25T08:55:19.000Z

from django.apps import AppConfig from django.utils.translation import ugettext_lazy as _ class SyndicationConfig(AppConfig): name = 'django.contrib.syndication' verbose_name = _("Syndication")

22.777778

55

0.780488

23

205

6.782609

0.695652

0.128205

0

0.141463

205

8

56

25.625

0.886364

0

0.180488

0.126829

0

1

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false

0

0.4

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

8a67d40fcf341e06108d4cbf7ff08865af1229bc

133

py

Python

Shivani/circle.py

63Shivani/Python-BootCamp

2ed0ef95af35d35c0602031670fecfc92d8cea0a

[ "MIT" ]

null

Shivani/circle.py

63Shivani/Python-BootCamp

2ed0ef95af35d35c0602031670fecfc92d8cea0a

[ "MIT" ]

null

Shivani/circle.py

63Shivani/Python-BootCamp

2ed0ef95af35d35c0602031670fecfc92d8cea0a

[ "MIT" ]

null

r=int(input("enter radius\n")) area=3.14*r*r print(area) r=int(input("enter radius\n")) circumference=2*3.14*r print(circumference)

16.625

30

0.721805

26

133

3.692308

0.461538

0.083333

0.1875

0.291667

0.4375

0

0.056452

0.067669

133

7

31

19

0.717742

0

0.333333

0

0.210526

0

1

0

false

0

0.333333

1

0

null

0

1

0

1

0

null

0

4

8a8e8552549687adefcd485b49a51a6a0e84b48e

262

py

Python

core/consts.py

telminov/viber-service

21f671522084ff1afcd320406a7e5bf71a04437f

[ "MIT" ]

null

core/consts.py

telminov/viber-service

21f671522084ff1afcd320406a7e5bf71a04437f

[ "MIT" ]

1

2017-09-21T14:08:57.000Z

core/consts.py

telminov/viber-service

21f671522084ff1afcd320406a7e5bf71a04437f

[ "MIT" ]

null

SEND_TEXT = 'send_text' SEND_IMAGE = 'send_image' SEND_TEXT_AND_BUTTON = 'send_text_and_button' CHECK_STATUS_MESSAGES = 'check_status_messages' API = [SEND_TEXT, SEND_IMAGE, SEND_TEXT_AND_BUTTON, CHECK_STATUS_MESSAGES] API_CHOICES = [(api, api) for api in API]

32.75

74

0.805344

42

262

4.5

0.285714

0.253968

0.190476

0.269841

0.698413

0.566138

0.380952

0

0.103053

262

8

75

32.75

0.804255

0

0.228137

0.079848

0

1

0

false

0

null

1

0

1

0

null

0

4

8a920e80df22a9bd3983e7cd6ef1db5a0e7bf417

59

py

Python

tests/assets/scannersuccess/scannersuccess/in/class.py

SimonBiggs/layer_linter

9eb518b74118e4a2d8079e2f32ecc12612ca9e86

[ "BSD-3-Clause" ]

63

2018-06-21T10:39:54.000Z

2021-06-04T14:28:44.000Z

tests/assets/scannersuccess/scannersuccess/in/class.py

SimonBiggs/layer_linter

9eb518b74118e4a2d8079e2f32ecc12612ca9e86

[ "BSD-3-Clause" ]

86

2018-06-20T13:30:30.000Z

2019-06-04T12:47:28.000Z

tests/assets/scannersuccess/scannersuccess/in/class.py

SimonBiggs/layer_linter

9eb518b74118e4a2d8079e2f32ecc12612ca9e86

[ "BSD-3-Clause" ]

4

2018-08-14T08:49:55.000Z

2019-02-16T09:24:47.000Z

# 'Class' is a reserved keyword so can't be a module name.

29.5

58

0.711864

12

59

3.5

0.916667

0

0.20339

59

1

59

0.893617

0.949153

0

null

0

null

0

null

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true

0

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null

0

1

0

1

0

null

0

1

0

4

8aa85418b10b53fb83bf755181836087da8a451c

18,553

py

Python

VersionAlpha0.0.1/performanceEstimator/indexer/tokenizer.py

GoranBotic/Water_Fern

d4f513315c0cd668b17529cc65271bf455b15adc

[ "MIT" ]

null

VersionAlpha0.0.1/performanceEstimator/indexer/tokenizer.py

GoranBotic/Water_Fern

d4f513315c0cd668b17529cc65271bf455b15adc

[ "MIT" ]

null

VersionAlpha0.0.1/performanceEstimator/indexer/tokenizer.py

GoranBotic/Water_Fern

d4f513315c0cd668b17529cc65271bf455b15adc

[ "MIT" ]

null

# Generated from JavaParser.g4 by ANTLR 4.7.1 from antlr4 import * from indexer.JavaLexer import JavaLexer # This class defines a complete generic visitor for a parse tree produced by JavaParser. #theStuff = dict() from hashlib import md5 class Tokenizer(ParseTreeVisitor): #this provides some default behavior def visit(self, ctx): if str(type(ctx)) == "<class 'antlr4.tree.Tree.TerminalNodeImpl'>": #theStuff[type(ctx.getParent())] = True #by default terminals simply evaluate to their symbol #to override this the vist... method which encounters the terminal should manually its children producing the desired terminal values for the terminal children #if that implementation is done then this line does not need to be changed, the default behavior will still be to use the symbol name return str(JavaLexer.symbolicNames[ctx.getSymbol().type]) theList = ctx.accept(self) theHash = "" if len(theList) > 1: #do the aggregation #right now this simply concatenates all the strings and hashes #this needs to be changed so that similar sub-trees will get similar hashes #have all terminals evaluate to a type #have all non-terminals evaluate to a type #name the thing according to the type frequency count for x in theList: if str(x).strip() != "": theHash = theHash + x + "\n" #theHash = md5(theHash.encode()).hexdigest() else: theHash = theList[0] #store the hash in the DB along with some kind of link to its accociated code #should also search for existing similar code return theHash def visitChildren(self, ctx): ret = [] for x in range(ctx.getChildCount()): # if str(type(ctx.getChild(x))) == "<class 'antlr4.tree.Tree.TerminalNodeImpl'>": # print(ctx.getChild(x).getSymbol()) # else: # print(ctx.getChild(x).getPayload().toStringTree()) ret.append(self.visit(ctx.getChild(x))) return ret # def visitTerminal(self, ctx): # return 1 # Visit a parse tree produced by JavaParser#compilationUnit. def visitCompilationUnit(self, ctx): print("Start parsing") print(ctx.getChildCount()) tmp = self.visitChildren(ctx) ret = "" for x in tmp: ret = ret + x #define a file scope dict called theStuff #in the visit method add something like theStuff[type(ctx.getParent())] = True #then all vist... s with terminal children will be added to the stuff, and the keys of the stuff can be print to produce a list of terminal visit...s which required an implementation # for k in theStuff.keys(): # print(k) ret = ret.split("\n") ret2 = [] for x in ret: if x.strip() != "": ret2.append(x) return ret2 # Visit a parse tree produced by JavaParser#packageDeclaration. def visitPackageDeclaration(self, ctx): print("packages") return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#importDeclaration. def visitImportDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeDeclaration. def visitTypeDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#modifier. def visitModifier(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#classOrInterfaceModifier. def visitClassOrInterfaceModifier(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#variableModifier. def visitVariableModifier(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#classDeclaration. def visitClassDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeParameters. def visitTypeParameters(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeParameter. def visitTypeParameter(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeBound. def visitTypeBound(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#enumDeclaration. def visitEnumDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#enumConstants. def visitEnumConstants(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#enumConstant. def visitEnumConstant(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#enumBodyDeclarations. def visitEnumBodyDeclarations(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#interfaceDeclaration. def visitInterfaceDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#classBody. def visitClassBody(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#interfaceBody. def visitInterfaceBody(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#classBodyDeclaration. def visitClassBodyDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#memberDeclaration. def visitMemberDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#methodDeclaration. def visitMethodDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#methodBody. def visitMethodBody(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeTypeOrVoid. def visitTypeTypeOrVoid(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#genericMethodDeclaration. def visitGenericMethodDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#genericConstructorDeclaration. def visitGenericConstructorDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#constructorDeclaration. def visitConstructorDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#fieldDeclaration. def visitFieldDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#interfaceBodyDeclaration. def visitInterfaceBodyDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#interfaceMemberDeclaration. def visitInterfaceMemberDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#constDeclaration. def visitConstDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#constantDeclarator. def visitConstantDeclarator(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#interfaceMethodDeclaration. def visitInterfaceMethodDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#interfaceMethodModifier. def visitInterfaceMethodModifier(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#genericInterfaceMethodDeclaration. def visitGenericInterfaceMethodDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#variableDeclarators. def visitVariableDeclarators(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#variableDeclarator. def visitVariableDeclarator(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#variableDeclaratorId. def visitVariableDeclaratorId(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#variableInitializer. def visitVariableInitializer(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#arrayInitializer. def visitArrayInitializer(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#classOrInterfaceType. def visitClassOrInterfaceType(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeArgument. def visitTypeArgument(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#qualifiedNameList. def visitQualifiedNameList(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#formalParameters. def visitFormalParameters(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#formalParameterList. def visitFormalParameterList(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#formalParameter. def visitFormalParameter(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#lastFormalParameter. def visitLastFormalParameter(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#qualifiedName. def visitQualifiedName(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#literal. def visitLiteral(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#integerLiteral. def visitIntegerLiteral(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#floatLiteral. def visitFloatLiteral(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#annotation. def visitAnnotation(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#elementValuePairs. def visitElementValuePairs(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#elementValuePair. def visitElementValuePair(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#elementValue. def visitElementValue(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#elementValueArrayInitializer. def visitElementValueArrayInitializer(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#annotationTypeDeclaration. def visitAnnotationTypeDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#annotationTypeBody. def visitAnnotationTypeBody(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#annotationTypeElementDeclaration. def visitAnnotationTypeElementDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#annotationTypeElementRest. def visitAnnotationTypeElementRest(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#annotationMethodOrConstantRest. def visitAnnotationMethodOrConstantRest(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#annotationMethodRest. def visitAnnotationMethodRest(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#annotationConstantRest. def visitAnnotationConstantRest(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#defaultValue. def visitDefaultValue(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#block. def visitBlock(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#blockStatement. def visitBlockStatement(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#localVariableDeclaration. def visitLocalVariableDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#localTypeDeclaration. def visitLocalTypeDeclaration(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#statement. def visitStatement(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#catchClause. def visitCatchClause(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#catchType. def visitCatchType(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#finallyBlock. def visitFinallyBlock(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#resourceSpecification. def visitResourceSpecification(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#resources. def visitResources(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#resource. def visitResource(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#switchBlockStatementGroup. def visitSwitchBlockStatementGroup(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#switchLabel. def visitSwitchLabel(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#forControl. def visitForControl(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#forInit. def visitForInit(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#enhancedForControl. def visitEnhancedForControl(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#parExpression. def visitParExpression(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#expressionList. def visitExpressionList(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#methodCall. def visitMethodCall(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#expression. def visitExpression(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#lambdaExpression. def visitLambdaExpression(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#lambdaParameters. def visitLambdaParameters(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#lambdaBody. def visitLambdaBody(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#primary. def visitPrimary(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#classType. def visitClassType(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#creator. def visitCreator(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#createdName. def visitCreatedName(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#innerCreator. def visitInnerCreator(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#arrayCreatorRest. def visitArrayCreatorRest(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#classCreatorRest. def visitClassCreatorRest(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#explicitGenericInvocation. def visitExplicitGenericInvocation(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeArgumentsOrDiamond. def visitTypeArgumentsOrDiamond(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#nonWildcardTypeArgumentsOrDiamond. def visitNonWildcardTypeArgumentsOrDiamond(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#nonWildcardTypeArguments. def visitNonWildcardTypeArguments(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeList. def visitTypeList(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeType. def visitTypeType(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#primitiveType. def visitPrimitiveType(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#typeArguments. def visitTypeArguments(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#superSuffix. def visitSuperSuffix(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#explicitGenericInvocationSuffix. def visitExplicitGenericInvocationSuffix(self, ctx): return self.visitChildren(ctx) # Visit a parse tree produced by JavaParser#arguments. def visitArguments(self, ctx): return self.visitChildren(ctx)

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1

0

null

0

1

0

1

0

4

8ac3ec5a75dfd6f612253b02a2c195507cfa0eaf

531

py

Python

pyportlib/__init__.py

phil-lo/PortfolioCore

3fbe7460c809a80e48615e934990dcd2d1f5003b

[ "CC0-1.0" ]

2

2021-11-18T21:40:02.000Z

2021-12-13T21:01:18.000Z

pyportlib/__init__.py

phil-lo/PortfolioCore

3fbe7460c809a80e48615e934990dcd2d1f5003b

[ "CC0-1.0" ]

null

pyportlib/__init__.py

phil-lo/PortfolioCore

3fbe7460c809a80e48615e934990dcd2d1f5003b

[ "CC0-1.0" ]

null

from pyportlib.portfolio import Portfolio from pyportlib.position import Position from pyportlib.reporting import plots, html_reports from pyportlib.services.transaction import Transaction from pyportlib.services.cash_change import CashChange from pyportlib.utils.files_utils import set_client_dir from pyportlib.utils import dates_utils, files_utils, time_series, df_utils from pyportlib.utils.indices import Index from pyportlib.metrics import stats from pyportlib.account_sources.questrade_connection import QuestradeConnection

48.272727

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70

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0.284464

0.118162

0

0.082863

531

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null

0

1

0

1

0

1

0

4

8acdc056be73dd5efca538564809ca38737fdb0b

1,794

py

Python

tests/unit/service_test.py

ryanbrainard/fig

9a825c5c35128b0b49eb255966f7a93b4093459a

[ "Apache-2.0" ]

1

2016-04-25T08:43:35.000Z

tests/unit/service_test.py

ryanbrainard/fig

9a825c5c35128b0b49eb255966f7a93b4093459a

[ "Apache-2.0" ]

null

tests/unit/service_test.py

ryanbrainard/fig

9a825c5c35128b0b49eb255966f7a93b4093459a

[ "Apache-2.0" ]

null

from __future__ import unicode_literals from __future__ import absolute_import from .. import unittest from fig import Service from fig.service import ConfigError, split_port class ServiceTest(unittest.TestCase): def test_name_validations(self): self.assertRaises(ConfigError, lambda: Service(name='')) self.assertRaises(ConfigError, lambda: Service(name=' ')) self.assertRaises(ConfigError, lambda: Service(name='/')) self.assertRaises(ConfigError, lambda: Service(name='!')) self.assertRaises(ConfigError, lambda: Service(name='\xe2')) self.assertRaises(ConfigError, lambda: Service(name='_')) self.assertRaises(ConfigError, lambda: Service(name='____')) self.assertRaises(ConfigError, lambda: Service(name='foo_bar')) self.assertRaises(ConfigError, lambda: Service(name='__foo_bar__')) Service('a') Service('foo') def test_project_validation(self): self.assertRaises(ConfigError, lambda: Service(name='foo', project='_')) Service(name='foo', project='bar') def test_config_validation(self): self.assertRaises(ConfigError, lambda: Service(name='foo', port=['8000'])) Service(name='foo', ports=['8000']) def test_split_port(self): internal_port, external_port = split_port("127.0.0.1:1000:2000") self.assertEqual(internal_port, "2000") self.assertEqual(external_port, ("127.0.0.1", "1000")) internal_port, external_port = split_port("127.0.0.1::2000") self.assertEqual(internal_port, "2000") self.assertEqual(external_port, ("127.0.0.1",)) internal_port, external_port = split_port("1000:2000") self.assertEqual(internal_port, "2000") self.assertEqual(external_port, "1000")

39.866667

82

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206

1,794

5.737864

0.18932

0.120981

0.251269

0.307107

0.72335

0.692047

0.688663

0.641286

0.538071

0

0.049391

0.176143

1,794

44

83

40.772727

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0

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0

0.077567

0

0.5

1

0.117647

false

0

0.147059

0

0.294118

0

null

0

1

0

1

0

null

0

1

0

4

8ace10c7b0e37f6633ee727c284be0d9be61c3e2

81

py

Python

docker/python/django/testapp/foo/apps.py

jlvoiseux/apm-integration-testing

53ec49f80bb8dc8175e21e9ac26452fa8c3b7cf0

[ "Apache-2.0" ]

191

2015-02-18T08:52:21.000Z

2022-03-24T18:38:36.000Z

docker/python/django/testapp/foo/apps.py

jlvoiseux/apm-integration-testing

53ec49f80bb8dc8175e21e9ac26452fa8c3b7cf0

[ "Apache-2.0" ]

902

2018-05-25T15:42:22.000Z

2022-03-31T16:12:35.000Z

docker/python/django/testapp/foo/apps.py

jlvoiseux/apm-integration-testing

53ec49f80bb8dc8175e21e9ac26452fa8c3b7cf0

[ "Apache-2.0" ]

68

2015-07-09T09:40:26.000Z

2022-02-07T20:46:35.000Z

from django.apps import AppConfig class FooConfig(AppConfig): name = 'foo'

13.5

33

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81

5

34

16.2

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1

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false

0

0.333333

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1

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null

0

1

0

null

0

1

0

1

0

4

76d976a74595513a5144dc3bbce73fc7ae015b3f

25,531

py

Python

sdk/python/pulumi_azure_native/desktopvirtualization/application.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/desktopvirtualization/application.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/desktopvirtualization/application.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from ._enums import * __all__ = ['ApplicationArgs', 'Application'] @pulumi.input_type class ApplicationArgs: def __init__(__self__, *, application_group_name: pulumi.Input[str], command_line_setting: pulumi.Input[Union[str, 'CommandLineSetting']], resource_group_name: pulumi.Input[str], application_name: Optional[pulumi.Input[str]] = None, application_type: Optional[pulumi.Input[Union[str, 'RemoteApplicationType']]] = None, command_line_arguments: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, file_path: Optional[pulumi.Input[str]] = None, friendly_name: Optional[pulumi.Input[str]] = None, icon_index: Optional[pulumi.Input[int]] = None, icon_path: Optional[pulumi.Input[str]] = None, msix_package_application_id: Optional[pulumi.Input[str]] = None, msix_package_family_name: Optional[pulumi.Input[str]] = None, show_in_portal: Optional[pulumi.Input[bool]] = None): """ The set of arguments for constructing a Application resource. :param pulumi.Input[str] application_group_name: The name of the application group :param pulumi.Input[Union[str, 'CommandLineSetting']] command_line_setting: Specifies whether this published application can be launched with command line arguments provided by the client, command line arguments specified at publish time, or no command line arguments at all. :param pulumi.Input[str] resource_group_name: The name of the resource group. The name is case insensitive. :param pulumi.Input[str] application_name: The name of the application within the specified application group :param pulumi.Input[Union[str, 'RemoteApplicationType']] application_type: Resource Type of Application. :param pulumi.Input[str] command_line_arguments: Command Line Arguments for Application. :param pulumi.Input[str] description: Description of Application. :param pulumi.Input[str] file_path: Specifies a path for the executable file for the application. :param pulumi.Input[str] friendly_name: Friendly name of Application. :param pulumi.Input[int] icon_index: Index of the icon. :param pulumi.Input[str] icon_path: Path to icon. :param pulumi.Input[str] msix_package_application_id: Specifies the package application Id for MSIX applications :param pulumi.Input[str] msix_package_family_name: Specifies the package family name for MSIX applications :param pulumi.Input[bool] show_in_portal: Specifies whether to show the RemoteApp program in the RD Web Access server. """ pulumi.set(__self__, "application_group_name", application_group_name) pulumi.set(__self__, "command_line_setting", command_line_setting) pulumi.set(__self__, "resource_group_name", resource_group_name) if application_name is not None: pulumi.set(__self__, "application_name", application_name) if application_type is not None: pulumi.set(__self__, "application_type", application_type) if command_line_arguments is not None: pulumi.set(__self__, "command_line_arguments", command_line_arguments) if description is not None: pulumi.set(__self__, "description", description) if file_path is not None: pulumi.set(__self__, "file_path", file_path) if friendly_name is not None: pulumi.set(__self__, "friendly_name", friendly_name) if icon_index is not None: pulumi.set(__self__, "icon_index", icon_index) if icon_path is not None: pulumi.set(__self__, "icon_path", icon_path) if msix_package_application_id is not None: pulumi.set(__self__, "msix_package_application_id", msix_package_application_id) if msix_package_family_name is not None: pulumi.set(__self__, "msix_package_family_name", msix_package_family_name) if show_in_portal is not None: pulumi.set(__self__, "show_in_portal", show_in_portal) @property @pulumi.getter(name="applicationGroupName") def application_group_name(self) -> pulumi.Input[str]: """ The name of the application group """ return pulumi.get(self, "application_group_name") @application_group_name.setter def application_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "application_group_name", value) @property @pulumi.getter(name="commandLineSetting") def command_line_setting(self) -> pulumi.Input[Union[str, 'CommandLineSetting']]: """ Specifies whether this published application can be launched with command line arguments provided by the client, command line arguments specified at publish time, or no command line arguments at all. """ return pulumi.get(self, "command_line_setting") @command_line_setting.setter def command_line_setting(self, value: pulumi.Input[Union[str, 'CommandLineSetting']]): pulumi.set(self, "command_line_setting", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ The name of the resource group. The name is case insensitive. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="applicationName") def application_name(self) -> Optional[pulumi.Input[str]]: """ The name of the application within the specified application group """ return pulumi.get(self, "application_name") @application_name.setter def application_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "application_name", value) @property @pulumi.getter(name="applicationType") def application_type(self) -> Optional[pulumi.Input[Union[str, 'RemoteApplicationType']]]: """ Resource Type of Application. """ return pulumi.get(self, "application_type") @application_type.setter def application_type(self, value: Optional[pulumi.Input[Union[str, 'RemoteApplicationType']]]): pulumi.set(self, "application_type", value) @property @pulumi.getter(name="commandLineArguments") def command_line_arguments(self) -> Optional[pulumi.Input[str]]: """ Command Line Arguments for Application. """ return pulumi.get(self, "command_line_arguments") @command_line_arguments.setter def command_line_arguments(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "command_line_arguments", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ Description of Application. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="filePath") def file_path(self) -> Optional[pulumi.Input[str]]: """ Specifies a path for the executable file for the application. """ return pulumi.get(self, "file_path") @file_path.setter def file_path(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "file_path", value) @property @pulumi.getter(name="friendlyName") def friendly_name(self) -> Optional[pulumi.Input[str]]: """ Friendly name of Application. """ return pulumi.get(self, "friendly_name") @friendly_name.setter def friendly_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "friendly_name", value) @property @pulumi.getter(name="iconIndex") def icon_index(self) -> Optional[pulumi.Input[int]]: """ Index of the icon. """ return pulumi.get(self, "icon_index") @icon_index.setter def icon_index(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "icon_index", value) @property @pulumi.getter(name="iconPath") def icon_path(self) -> Optional[pulumi.Input[str]]: """ Path to icon. """ return pulumi.get(self, "icon_path") @icon_path.setter def icon_path(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "icon_path", value) @property @pulumi.getter(name="msixPackageApplicationId") def msix_package_application_id(self) -> Optional[pulumi.Input[str]]: """ Specifies the package application Id for MSIX applications """ return pulumi.get(self, "msix_package_application_id") @msix_package_application_id.setter def msix_package_application_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "msix_package_application_id", value) @property @pulumi.getter(name="msixPackageFamilyName") def msix_package_family_name(self) -> Optional[pulumi.Input[str]]: """ Specifies the package family name for MSIX applications """ return pulumi.get(self, "msix_package_family_name") @msix_package_family_name.setter def msix_package_family_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "msix_package_family_name", value) @property @pulumi.getter(name="showInPortal") def show_in_portal(self) -> Optional[pulumi.Input[bool]]: """ Specifies whether to show the RemoteApp program in the RD Web Access server. """ return pulumi.get(self, "show_in_portal") @show_in_portal.setter def show_in_portal(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "show_in_portal", value) class Application(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, application_group_name: Optional[pulumi.Input[str]] = None, application_name: Optional[pulumi.Input[str]] = None, application_type: Optional[pulumi.Input[Union[str, 'RemoteApplicationType']]] = None, command_line_arguments: Optional[pulumi.Input[str]] = None, command_line_setting: Optional[pulumi.Input[Union[str, 'CommandLineSetting']]] = None, description: Optional[pulumi.Input[str]] = None, file_path: Optional[pulumi.Input[str]] = None, friendly_name: Optional[pulumi.Input[str]] = None, icon_index: Optional[pulumi.Input[int]] = None, icon_path: Optional[pulumi.Input[str]] = None, msix_package_application_id: Optional[pulumi.Input[str]] = None, msix_package_family_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, show_in_portal: Optional[pulumi.Input[bool]] = None, __props__=None): """ Schema for Application properties. API Version: 2021-02-01-preview. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] application_group_name: The name of the application group :param pulumi.Input[str] application_name: The name of the application within the specified application group :param pulumi.Input[Union[str, 'RemoteApplicationType']] application_type: Resource Type of Application. :param pulumi.Input[str] command_line_arguments: Command Line Arguments for Application. :param pulumi.Input[Union[str, 'CommandLineSetting']] command_line_setting: Specifies whether this published application can be launched with command line arguments provided by the client, command line arguments specified at publish time, or no command line arguments at all. :param pulumi.Input[str] description: Description of Application. :param pulumi.Input[str] file_path: Specifies a path for the executable file for the application. :param pulumi.Input[str] friendly_name: Friendly name of Application. :param pulumi.Input[int] icon_index: Index of the icon. :param pulumi.Input[str] icon_path: Path to icon. :param pulumi.Input[str] msix_package_application_id: Specifies the package application Id for MSIX applications :param pulumi.Input[str] msix_package_family_name: Specifies the package family name for MSIX applications :param pulumi.Input[str] resource_group_name: The name of the resource group. The name is case insensitive. :param pulumi.Input[bool] show_in_portal: Specifies whether to show the RemoteApp program in the RD Web Access server. """ ... @overload def __init__(__self__, resource_name: str, args: ApplicationArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Schema for Application properties. API Version: 2021-02-01-preview. :param str resource_name: The name of the resource. :param ApplicationArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(ApplicationArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, application_group_name: Optional[pulumi.Input[str]] = None, application_name: Optional[pulumi.Input[str]] = None, application_type: Optional[pulumi.Input[Union[str, 'RemoteApplicationType']]] = None, command_line_arguments: Optional[pulumi.Input[str]] = None, command_line_setting: Optional[pulumi.Input[Union[str, 'CommandLineSetting']]] = None, description: Optional[pulumi.Input[str]] = None, file_path: Optional[pulumi.Input[str]] = None, friendly_name: Optional[pulumi.Input[str]] = None, icon_index: Optional[pulumi.Input[int]] = None, icon_path: Optional[pulumi.Input[str]] = None, msix_package_application_id: Optional[pulumi.Input[str]] = None, msix_package_family_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, show_in_portal: Optional[pulumi.Input[bool]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = ApplicationArgs.__new__(ApplicationArgs) if application_group_name is None and not opts.urn: raise TypeError("Missing required property 'application_group_name'") __props__.__dict__["application_group_name"] = application_group_name __props__.__dict__["application_name"] = application_name __props__.__dict__["application_type"] = application_type __props__.__dict__["command_line_arguments"] = command_line_arguments if command_line_setting is None and not opts.urn: raise TypeError("Missing required property 'command_line_setting'") __props__.__dict__["command_line_setting"] = command_line_setting __props__.__dict__["description"] = description __props__.__dict__["file_path"] = file_path __props__.__dict__["friendly_name"] = friendly_name __props__.__dict__["icon_index"] = icon_index __props__.__dict__["icon_path"] = icon_path __props__.__dict__["msix_package_application_id"] = msix_package_application_id __props__.__dict__["msix_package_family_name"] = msix_package_family_name if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__.__dict__["resource_group_name"] = resource_group_name __props__.__dict__["show_in_portal"] = show_in_portal __props__.__dict__["icon_content"] = None __props__.__dict__["icon_hash"] = None __props__.__dict__["name"] = None __props__.__dict__["object_id"] = None __props__.__dict__["type"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:desktopvirtualization:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20190123preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20190123preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20190924preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20190924preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20191210preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20191210preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20200921preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20200921preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20201019preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20201019preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20201102preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20201102preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20201110preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20201110preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20210114preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20210114preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20210201preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20210201preview:Application"), pulumi.Alias(type_="azure-native:desktopvirtualization/v20210309preview:Application"), pulumi.Alias(type_="azure-nextgen:desktopvirtualization/v20210309preview:Application")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(Application, __self__).__init__( 'azure-native:desktopvirtualization:Application', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'Application': """ Get an existing Application resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = ApplicationArgs.__new__(ApplicationArgs) __props__.__dict__["application_type"] = None __props__.__dict__["command_line_arguments"] = None __props__.__dict__["command_line_setting"] = None __props__.__dict__["description"] = None __props__.__dict__["file_path"] = None __props__.__dict__["friendly_name"] = None __props__.__dict__["icon_content"] = None __props__.__dict__["icon_hash"] = None __props__.__dict__["icon_index"] = None __props__.__dict__["icon_path"] = None __props__.__dict__["msix_package_application_id"] = None __props__.__dict__["msix_package_family_name"] = None __props__.__dict__["name"] = None __props__.__dict__["object_id"] = None __props__.__dict__["show_in_portal"] = None __props__.__dict__["type"] = None return Application(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="applicationType") def application_type(self) -> pulumi.Output[Optional[str]]: """ Resource Type of Application. """ return pulumi.get(self, "application_type") @property @pulumi.getter(name="commandLineArguments") def command_line_arguments(self) -> pulumi.Output[Optional[str]]: """ Command Line Arguments for Application. """ return pulumi.get(self, "command_line_arguments") @property @pulumi.getter(name="commandLineSetting") def command_line_setting(self) -> pulumi.Output[str]: """ Specifies whether this published application can be launched with command line arguments provided by the client, command line arguments specified at publish time, or no command line arguments at all. """ return pulumi.get(self, "command_line_setting") @property @pulumi.getter def description(self) -> pulumi.Output[Optional[str]]: """ Description of Application. """ return pulumi.get(self, "description") @property @pulumi.getter(name="filePath") def file_path(self) -> pulumi.Output[Optional[str]]: """ Specifies a path for the executable file for the application. """ return pulumi.get(self, "file_path") @property @pulumi.getter(name="friendlyName") def friendly_name(self) -> pulumi.Output[Optional[str]]: """ Friendly name of Application. """ return pulumi.get(self, "friendly_name") @property @pulumi.getter(name="iconContent") def icon_content(self) -> pulumi.Output[str]: """ the icon a 64 bit string as a byte array. """ return pulumi.get(self, "icon_content") @property @pulumi.getter(name="iconHash") def icon_hash(self) -> pulumi.Output[str]: """ Hash of the icon. """ return pulumi.get(self, "icon_hash") @property @pulumi.getter(name="iconIndex") def icon_index(self) -> pulumi.Output[Optional[int]]: """ Index of the icon. """ return pulumi.get(self, "icon_index") @property @pulumi.getter(name="iconPath") def icon_path(self) -> pulumi.Output[Optional[str]]: """ Path to icon. """ return pulumi.get(self, "icon_path") @property @pulumi.getter(name="msixPackageApplicationId") def msix_package_application_id(self) -> pulumi.Output[Optional[str]]: """ Specifies the package application Id for MSIX applications """ return pulumi.get(self, "msix_package_application_id") @property @pulumi.getter(name="msixPackageFamilyName") def msix_package_family_name(self) -> pulumi.Output[Optional[str]]: """ Specifies the package family name for MSIX applications """ return pulumi.get(self, "msix_package_family_name") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the resource """ return pulumi.get(self, "name") @property @pulumi.getter(name="objectId") def object_id(self) -> pulumi.Output[str]: """ ObjectId of Application. (internal use) """ return pulumi.get(self, "object_id") @property @pulumi.getter(name="showInPortal") def show_in_portal(self) -> pulumi.Output[Optional[bool]]: """ Specifies whether to show the RemoteApp program in the RD Web Access server. """ return pulumi.get(self, "show_in_portal") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts" """ return pulumi.get(self, "type")

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76f250487d9708ee70a7784ad67d42eb67f7c889

1,266

py

Python

astro_cloud_tests/fits/test_utils.py

RichardScottOZ/astro-cloud

f45d002566e97007f1bdfa7dce9094ec27c3f912

[ "MIT" ]

1

2022-02-02T11:03:54.000Z

astro_cloud_tests/fits/test_utils.py

RichardScottOZ/astro-cloud

f45d002566e97007f1bdfa7dce9094ec27c3f912

[ "MIT" ]

2

2020-10-13T17:31:09.000Z

2021-04-04T22:31:03.000Z

astro_cloud_tests/fits/test_utils.py

RichardScottOZ/astro-cloud

f45d002566e97007f1bdfa7dce9094ec27c3f912

[ "MIT" ]

1

2022-02-02T11:04:37.000Z

from astro_cloud_tests.pytest_utils import fits_files def test__as_np_dtype__uint8(): import numpy as np from astro_cloud.fits.utils import as_np_dtype assert as_np_dtype(8) == np.dtype(np.uint8) def test__as_np_dtype__uint16(): import numpy as np from astro_cloud.fits.utils import as_np_dtype assert as_np_dtype(16) == np.dtype(np.uint16) def test__as_np_dtype__uint32(): import numpy as np from astro_cloud.fits.utils import as_np_dtype assert as_np_dtype(32) == np.dtype(np.uint32) def test__as_np_dtype__float32(): import numpy as np from astro_cloud.fits.utils import as_np_dtype assert as_np_dtype(-32) == np.dtype(np.float32) def test__as_np_dtype__float64(): import numpy as np from astro_cloud.fits.utils import as_np_dtype assert as_np_dtype(-64) == np.dtype(np.float64) def test__find_next_header_offset__primary_header(): from astro_cloud.fits.datatypes import FITSHeader from astro_cloud.fits.utils import find_next_header_offset def test__find_next_header_offset__table(fits_files): from astro_cloud.fits.datatypes import FITSHeader from astro_cloud.fits.utils import find_next_header_offset # import pdb; pdb.set_trace() # import sys; sys.exit(1)

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1

0

4

76fbf00220b15db81ebeef9af48312f51593a2c1

80,376

py

Python

nova/tests/unit/virt/ironic/test_driver.py

badock/nova-tidb

4c4591f2cd887fdc22828e12f0c297c051bbd912

[ "Apache-2.0" ]

null

nova/tests/unit/virt/ironic/test_driver.py

badock/nova-tidb

4c4591f2cd887fdc22828e12f0c297c051bbd912

[ "Apache-2.0" ]

null

nova/tests/unit/virt/ironic/test_driver.py

badock/nova-tidb

4c4591f2cd887fdc22828e12f0c297c051bbd912

[ "Apache-2.0" ]

1

2020-03-01T17:04:57.000Z

# Copyright 2015 Red Hat, Inc. # Copyright 2013 Hewlett-Packard Development Company, L.P. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Tests for the ironic driver.""" from ironicclient import exc as ironic_exception import mock from oslo_config import cfg from oslo_service import loopingcall from oslo_utils import uuidutils import six from testtools.matchers import HasLength from nova.api.metadata import base as instance_metadata from nova.compute import power_state as nova_states from nova.compute import task_states from nova.compute import vm_states from nova import context as nova_context from nova import exception from nova import objects from nova import test from nova.tests.unit import fake_instance from nova.tests.unit import utils from nova.tests.unit.virt.ironic import utils as ironic_utils from nova.virt import configdrive from nova.virt import driver from nova.virt import fake from nova.virt import firewall from nova.virt import hardware from nova.virt.ironic import client_wrapper as cw from nova.virt.ironic import driver as ironic_driver from nova.virt.ironic import ironic_states CONF = cfg.CONF IRONIC_FLAGS = dict( api_version=1, group='ironic', ) FAKE_CLIENT = ironic_utils.FakeClient() class FakeClientWrapper(cw.IronicClientWrapper): def _get_client(self, retry_on_conflict=True): return FAKE_CLIENT class FakeLoopingCall(object): def __init__(self): self.wait = mock.MagicMock() self.start = mock.MagicMock() self.start.return_value = self def _get_properties(): return {'cpus': 2, 'memory_mb': 512, 'local_gb': 10, 'cpu_arch': 'x86_64', 'capabilities': None} def _get_instance_info(): return {'vcpus': 1, 'memory_mb': 1024, 'local_gb': 10} def _get_stats(): return {'cpu_arch': 'x86_64'} FAKE_CLIENT_WRAPPER = FakeClientWrapper() @mock.patch.object(cw, 'IronicClientWrapper', lambda *_: FAKE_CLIENT_WRAPPER) class IronicDriverTestCase(test.NoDBTestCase): @mock.patch.object(cw, 'IronicClientWrapper', lambda *_: FAKE_CLIENT_WRAPPER) def setUp(self): super(IronicDriverTestCase, self).setUp() self.flags(**IRONIC_FLAGS) self.driver = ironic_driver.IronicDriver(None) self.driver.virtapi = fake.FakeVirtAPI() self.ctx = nova_context.get_admin_context() self.instance_uuid = uuidutils.generate_uuid() # mock retries configs to avoid sleeps and make tests run quicker CONF.set_default('api_max_retries', default=1, group='ironic') CONF.set_default('api_retry_interval', default=0, group='ironic') def test_public_api_signatures(self): self.assertPublicAPISignatures(driver.ComputeDriver(None), self.driver) def test_validate_driver_loading(self): self.assertIsInstance(self.driver, ironic_driver.IronicDriver) def test_driver_capabilities(self): self.assertFalse(self.driver.capabilities['has_imagecache'], 'Driver capabilities for \'has_imagecache\'' 'is invalid') self.assertFalse(self.driver.capabilities['supports_recreate'], 'Driver capabilities for \'supports_recreate\'' 'is invalid') self.assertFalse(self.driver.capabilities[ 'supports_migrate_to_same_host'], 'Driver capabilities for ' '\'supports_migrate_to_same_host\' is invalid') self.assertFalse(self.driver.capabilities[ 'supports_attach_interface'], 'Driver capabilities for ' '\'supports_attach_interface\' ' 'is invalid') def test__get_hypervisor_type(self): self.assertEqual('ironic', self.driver._get_hypervisor_type()) def test__get_hypervisor_version(self): self.assertEqual(1, self.driver._get_hypervisor_version()) @mock.patch.object(FAKE_CLIENT.node, 'get_by_instance_uuid') def test__validate_instance_and_node(self, mock_gbiui): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(uuid=node_uuid, instance_uuid=self.instance_uuid) instance = fake_instance.fake_instance_obj(self.ctx, uuid=self.instance_uuid) mock_gbiui.return_value = node result = self.driver._validate_instance_and_node(instance) self.assertEqual(result.uuid, node_uuid) mock_gbiui.assert_called_once_with(instance.uuid, fields=ironic_driver._NODE_FIELDS) @mock.patch.object(FAKE_CLIENT.node, 'get_by_instance_uuid') def test__validate_instance_and_node_failed(self, mock_gbiui): mock_gbiui.side_effect = ironic_exception.NotFound() instance = fake_instance.fake_instance_obj(self.ctx, uuid=self.instance_uuid) self.assertRaises(exception.InstanceNotFound, self.driver._validate_instance_and_node, instance) mock_gbiui.assert_called_once_with(instance.uuid, fields=ironic_driver._NODE_FIELDS) @mock.patch.object(objects.Instance, 'refresh') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def test__wait_for_active_pass(self, fake_validate, fake_refresh): instance = fake_instance.fake_instance_obj(self.ctx, uuid=uuidutils.generate_uuid()) node = ironic_utils.get_test_node( provision_state=ironic_states.DEPLOYING) fake_validate.return_value = node self.driver._wait_for_active(instance) fake_validate.assert_called_once_with(instance) fake_refresh.assert_called_once_with() @mock.patch.object(objects.Instance, 'refresh') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def test__wait_for_active_done(self, fake_validate, fake_refresh): instance = fake_instance.fake_instance_obj(self.ctx, uuid=uuidutils.generate_uuid()) node = ironic_utils.get_test_node( provision_state=ironic_states.ACTIVE) fake_validate.return_value = node self.assertRaises(loopingcall.LoopingCallDone, self.driver._wait_for_active, instance) fake_validate.assert_called_once_with(instance) fake_refresh.assert_called_once_with() @mock.patch.object(objects.Instance, 'refresh') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def test__wait_for_active_fail(self, fake_validate, fake_refresh): instance = fake_instance.fake_instance_obj(self.ctx, uuid=uuidutils.generate_uuid()) node = ironic_utils.get_test_node( provision_state=ironic_states.DEPLOYFAIL) fake_validate.return_value = node self.assertRaises(exception.InstanceDeployFailure, self.driver._wait_for_active, instance) fake_validate.assert_called_once_with(instance) fake_refresh.assert_called_once_with() @mock.patch.object(objects.Instance, 'refresh') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def _wait_for_active_abort(self, instance_params, fake_validate, fake_refresh): instance = fake_instance.fake_instance_obj(self.ctx, uuid=uuidutils.generate_uuid(), **instance_params) self.assertRaises(exception.InstanceDeployFailure, self.driver._wait_for_active, instance) # Assert _validate_instance_and_node wasn't called self.assertFalse(fake_validate.called) fake_refresh.assert_called_once_with() def test__wait_for_active_abort_deleting(self): self._wait_for_active_abort({'task_state': task_states.DELETING}) def test__wait_for_active_abort_deleted(self): self._wait_for_active_abort({'vm_state': vm_states.DELETED}) def test__wait_for_active_abort_error(self): self._wait_for_active_abort({'vm_state': vm_states.ERROR}) @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def test__wait_for_power_state_pass(self, fake_validate): instance = fake_instance.fake_instance_obj(self.ctx, uuid=uuidutils.generate_uuid()) node = ironic_utils.get_test_node( target_power_state=ironic_states.POWER_OFF) fake_validate.return_value = node self.driver._wait_for_power_state(instance, 'fake message') self.assertTrue(fake_validate.called) @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def test__wait_for_power_state_ok(self, fake_validate): instance = fake_instance.fake_instance_obj(self.ctx, uuid=uuidutils.generate_uuid()) node = ironic_utils.get_test_node( target_power_state=ironic_states.NOSTATE) fake_validate.return_value = node self.assertRaises(loopingcall.LoopingCallDone, self.driver._wait_for_power_state, instance, 'fake message') self.assertTrue(fake_validate.called) def _test__node_resource(self, has_inst_info): node_uuid = uuidutils.generate_uuid() props = _get_properties() stats = _get_stats() if has_inst_info: instance_info = _get_instance_info() else: instance_info = {} node = ironic_utils.get_test_node(uuid=node_uuid, instance_uuid=self.instance_uuid, instance_info=instance_info, properties=props) result = self.driver._node_resource(node) wantkeys = ["hypervisor_hostname", "hypervisor_type", "hypervisor_version", "cpu_info", "vcpus", "vcpus_used", "memory_mb", "memory_mb_used", "local_gb", "local_gb_used", "disk_available_least", "supported_instances", "stats", "numa_topology"] wantkeys.sort() gotkeys = result.keys() gotkeys.sort() self.assertEqual(wantkeys, gotkeys) if has_inst_info: props_dict = instance_info expected_cpus = instance_info['vcpus'] else: props_dict = props expected_cpus = props['cpus'] self.assertEqual(0, result['vcpus']) self.assertEqual(expected_cpus, result['vcpus_used']) self.assertEqual(0, result['memory_mb']) self.assertEqual(props_dict['memory_mb'], result['memory_mb_used']) self.assertEqual(0, result['local_gb']) self.assertEqual(props_dict['local_gb'], result['local_gb_used']) self.assertEqual(node_uuid, result['hypervisor_hostname']) self.assertEqual(stats, result['stats']) self.assertIsNone(result['numa_topology']) def test__node_resource(self): self._test__node_resource(True) def test__node_resource_no_instance_info(self): self._test__node_resource(False) def test__node_resource_canonicalizes_arch(self): node_uuid = uuidutils.generate_uuid() props = _get_properties() props['cpu_arch'] = 'i386' node = ironic_utils.get_test_node(uuid=node_uuid, properties=props) result = self.driver._node_resource(node) self.assertEqual('i686', result['supported_instances'][0][0]) self.assertEqual('i386', result['stats']['cpu_arch']) def test__node_resource_unknown_arch(self): node_uuid = uuidutils.generate_uuid() props = _get_properties() del props['cpu_arch'] node = ironic_utils.get_test_node(uuid=node_uuid, properties=props) result = self.driver._node_resource(node) self.assertEqual([], result['supported_instances']) def test__node_resource_exposes_capabilities(self): props = _get_properties() props['capabilities'] = 'test:capability, test2:value2' node = ironic_utils.get_test_node(properties=props) result = self.driver._node_resource(node) stats = result['stats'] self.assertIsNone(stats.get('capabilities')) self.assertEqual('capability', stats.get('test')) self.assertEqual('value2', stats.get('test2')) def test__node_resource_no_capabilities(self): props = _get_properties() props['capabilities'] = None node = ironic_utils.get_test_node(properties=props) result = self.driver._node_resource(node) self.assertIsNone(result['stats'].get('capabilities')) def test__node_resource_malformed_capabilities(self): props = _get_properties() props['capabilities'] = 'test:capability,:no_key,no_val:' node = ironic_utils.get_test_node(properties=props) result = self.driver._node_resource(node) stats = result['stats'] self.assertEqual('capability', stats.get('test')) def test__node_resource_available(self): node_uuid = uuidutils.generate_uuid() props = _get_properties() stats = _get_stats() node = ironic_utils.get_test_node( uuid=node_uuid, instance_uuid=None, power_state=ironic_states.POWER_OFF, properties=props, provision_state=ironic_states.AVAILABLE) result = self.driver._node_resource(node) self.assertEqual(props['cpus'], result['vcpus']) self.assertEqual(0, result['vcpus_used']) self.assertEqual(props['memory_mb'], result['memory_mb']) self.assertEqual(0, result['memory_mb_used']) self.assertEqual(props['local_gb'], result['local_gb']) self.assertEqual(0, result['local_gb_used']) self.assertEqual(node_uuid, result['hypervisor_hostname']) self.assertEqual(stats, result['stats']) @mock.patch.object(ironic_driver.IronicDriver, '_node_resources_unavailable') def test__node_resource_unavailable_node_res(self, mock_res_unavail): mock_res_unavail.return_value = True node_uuid = uuidutils.generate_uuid() props = _get_properties() stats = _get_stats() node = ironic_utils.get_test_node(uuid=node_uuid, instance_uuid=None, properties=props) result = self.driver._node_resource(node) self.assertEqual(0, result['vcpus']) self.assertEqual(0, result['vcpus_used']) self.assertEqual(0, result['memory_mb']) self.assertEqual(0, result['memory_mb_used']) self.assertEqual(0, result['local_gb']) self.assertEqual(0, result['local_gb_used']) self.assertEqual(node_uuid, result['hypervisor_hostname']) self.assertEqual(stats, result['stats']) @mock.patch.object(ironic_driver.IronicDriver, '_node_resources_used') def test__node_resource_used_node_res(self, mock_res_used): mock_res_used.return_value = True node_uuid = uuidutils.generate_uuid() props = _get_properties() stats = _get_stats() instance_info = _get_instance_info() node = ironic_utils.get_test_node( uuid=node_uuid, instance_uuid=uuidutils.generate_uuid(), provision_state=ironic_states.ACTIVE, properties=props, instance_info=instance_info) result = self.driver._node_resource(node) self.assertEqual(0, result['vcpus']) self.assertEqual(instance_info['vcpus'], result['vcpus_used']) self.assertEqual(0, result['memory_mb']) self.assertEqual(instance_info['memory_mb'], result['memory_mb_used']) self.assertEqual(0, result['local_gb']) self.assertEqual(instance_info['local_gb'], result['local_gb_used']) self.assertEqual(node_uuid, result['hypervisor_hostname']) self.assertEqual(stats, result['stats']) @mock.patch.object(ironic_driver.LOG, 'warning') def test__parse_node_properties(self, mock_warning): props = _get_properties() node = ironic_utils.get_test_node( uuid=uuidutils.generate_uuid(), properties=props) # raw_cpu_arch is included because extra_specs filters do not # canonicalized the arch props['raw_cpu_arch'] = props['cpu_arch'] parsed = self.driver._parse_node_properties(node) self.assertEqual(props, parsed) # Assert we didn't log any warning since all properties are # correct self.assertFalse(mock_warning.called) @mock.patch.object(ironic_driver.LOG, 'warning') def test__parse_node_properties_bad_values(self, mock_warning): props = _get_properties() props['cpus'] = 'bad-value' props['memory_mb'] = 'bad-value' props['local_gb'] = 'bad-value' props['cpu_arch'] = 'bad-value' node = ironic_utils.get_test_node( uuid=uuidutils.generate_uuid(), properties=props) # raw_cpu_arch is included because extra_specs filters do not # canonicalized the arch props['raw_cpu_arch'] = props['cpu_arch'] parsed = self.driver._parse_node_properties(node) expected_props = props.copy() expected_props['cpus'] = 0 expected_props['memory_mb'] = 0 expected_props['local_gb'] = 0 expected_props['cpu_arch'] = None self.assertEqual(expected_props, parsed) self.assertEqual(4, mock_warning.call_count) @mock.patch.object(ironic_driver.LOG, 'warning') def test__parse_node_instance_info(self, mock_warning): props = _get_properties() instance_info = _get_instance_info() node = ironic_utils.get_test_node( uuid=uuidutils.generate_uuid(), instance_info=instance_info) parsed = self.driver._parse_node_instance_info(node, props) self.assertEqual(instance_info, parsed) self.assertFalse(mock_warning.called) @mock.patch.object(ironic_driver.LOG, 'warning') def test__parse_node_instance_info_bad_values(self, mock_warning): props = _get_properties() instance_info = _get_instance_info() instance_info['vcpus'] = 'bad-value' instance_info['memory_mb'] = 'bad-value' instance_info['local_gb'] = 'bad-value' node = ironic_utils.get_test_node( uuid=uuidutils.generate_uuid(), instance_info=instance_info) parsed = self.driver._parse_node_instance_info(node, props) expected = { 'vcpus': props['cpus'], 'memory_mb': props['memory_mb'], 'local_gb': props['local_gb'] } self.assertEqual(expected, parsed) self.assertEqual(3, mock_warning.call_count) @mock.patch.object(ironic_driver.LOG, 'warning') def test__parse_node_properties_canonicalize_cpu_arch(self, mock_warning): props = _get_properties() props['cpu_arch'] = 'amd64' node = ironic_utils.get_test_node( uuid=uuidutils.generate_uuid(), properties=props) # raw_cpu_arch is included because extra_specs filters do not # canonicalized the arch props['raw_cpu_arch'] = props['cpu_arch'] parsed = self.driver._parse_node_properties(node) expected_props = props.copy() # Make sure it cpu_arch was canonicalized expected_props['cpu_arch'] = 'x86_64' self.assertEqual(expected_props, parsed) # Assert we didn't log any warning since all properties are # correct self.assertFalse(mock_warning.called) @mock.patch.object(firewall.NoopFirewallDriver, 'prepare_instance_filter', create=True) @mock.patch.object(firewall.NoopFirewallDriver, 'setup_basic_filtering', create=True) @mock.patch.object(firewall.NoopFirewallDriver, 'apply_instance_filter', create=True) def test__start_firewall(self, mock_aif, mock_sbf, mock_pif): fake_inst = 'fake-inst' fake_net_info = utils.get_test_network_info() self.driver._start_firewall(fake_inst, fake_net_info) mock_aif.assert_called_once_with(fake_inst, fake_net_info) mock_sbf.assert_called_once_with(fake_inst, fake_net_info) mock_pif.assert_called_once_with(fake_inst, fake_net_info) @mock.patch.object(firewall.NoopFirewallDriver, 'unfilter_instance', create=True) def test__stop_firewall(self, mock_ui): fake_inst = 'fake-inst' fake_net_info = utils.get_test_network_info() self.driver._stop_firewall(fake_inst, fake_net_info) mock_ui.assert_called_once_with(fake_inst, fake_net_info) @mock.patch.object(cw.IronicClientWrapper, 'call') def test_instance_exists(self, mock_call): instance = fake_instance.fake_instance_obj(self.ctx, uuid=self.instance_uuid) self.assertTrue(self.driver.instance_exists(instance)) mock_call.assert_called_once_with('node.get_by_instance_uuid', self.instance_uuid, fields=ironic_driver._NODE_FIELDS) @mock.patch.object(cw.IronicClientWrapper, 'call') def test_instance_exists_fail(self, mock_call): mock_call.side_effect = ironic_exception.NotFound instance = fake_instance.fake_instance_obj(self.ctx, uuid=self.instance_uuid) self.assertFalse(self.driver.instance_exists(instance)) mock_call.assert_called_once_with('node.get_by_instance_uuid', self.instance_uuid, fields=ironic_driver._NODE_FIELDS) @mock.patch.object(cw.IronicClientWrapper, 'call') @mock.patch.object(objects.Instance, 'get_by_uuid') def test_list_instances(self, mock_inst_by_uuid, mock_call): nodes = [] instances = [] for i in range(2): uuid = uuidutils.generate_uuid() instances.append(fake_instance.fake_instance_obj(self.ctx, id=i, uuid=uuid)) nodes.append(ironic_utils.get_test_node(instance_uuid=uuid)) mock_inst_by_uuid.side_effect = instances mock_call.return_value = nodes response = self.driver.list_instances() mock_call.assert_called_with("node.list", associated=True, limit=0) expected_calls = [mock.call(mock.ANY, instances[0].uuid), mock.call(mock.ANY, instances[1].uuid)] mock_inst_by_uuid.assert_has_calls(expected_calls) self.assertEqual(['instance-00000000', 'instance-00000001'], sorted(response)) @mock.patch.object(cw.IronicClientWrapper, 'call') @mock.patch.object(objects.Instance, 'get_by_uuid') def test_list_instances_fail(self, mock_inst_by_uuid, mock_call): mock_call.side_effect = exception.NovaException response = self.driver.list_instances() mock_call.assert_called_with("node.list", associated=True, limit=0) self.assertFalse(mock_inst_by_uuid.called) self.assertThat(response, HasLength(0)) @mock.patch.object(cw.IronicClientWrapper, 'call') def test_list_instance_uuids(self, mock_call): num_nodes = 2 nodes = [] for n in range(num_nodes): nodes.append(ironic_utils.get_test_node( instance_uuid=uuidutils.generate_uuid())) mock_call.return_value = nodes uuids = self.driver.list_instance_uuids() mock_call.assert_called_with('node.list', associated=True, limit=0) expected = [n.instance_uuid for n in nodes] self.assertEqual(sorted(expected), sorted(uuids)) @mock.patch.object(FAKE_CLIENT.node, 'list') @mock.patch.object(FAKE_CLIENT.node, 'get') def test_node_is_available_empty_cache_empty_list(self, mock_get, mock_list): node = ironic_utils.get_test_node() mock_get.return_value = node mock_list.return_value = [] self.assertTrue(self.driver.node_is_available(node.uuid)) mock_get.assert_called_with(node.uuid, fields=ironic_driver._NODE_FIELDS) mock_list.assert_called_with(detail=True, limit=0) mock_get.side_effect = ironic_exception.NotFound self.assertFalse(self.driver.node_is_available(node.uuid)) @mock.patch.object(FAKE_CLIENT.node, 'list') @mock.patch.object(FAKE_CLIENT.node, 'get') def test_node_is_available_empty_cache(self, mock_get, mock_list): node = ironic_utils.get_test_node() mock_get.return_value = node mock_list.return_value = [node] self.assertTrue(self.driver.node_is_available(node.uuid)) mock_list.assert_called_with(detail=True, limit=0) self.assertEqual(0, mock_get.call_count) @mock.patch.object(FAKE_CLIENT.node, 'list') @mock.patch.object(FAKE_CLIENT.node, 'get') def test_node_is_available_with_cache(self, mock_get, mock_list): node = ironic_utils.get_test_node() mock_get.return_value = node mock_list.return_value = [node] # populate the cache self.driver.get_available_nodes(refresh=True) # prove that zero calls are made after populating cache mock_list.reset_mock() self.assertTrue(self.driver.node_is_available(node.uuid)) self.assertEqual(0, mock_list.call_count) self.assertEqual(0, mock_get.call_count) def test__node_resources_unavailable(self): node_dicts = [ # a node in maintenance /w no instance and power OFF {'uuid': uuidutils.generate_uuid(), 'maintenance': True, 'power_state': ironic_states.POWER_OFF, 'provision_state': ironic_states.AVAILABLE}, # a node in maintenance /w no instance and ERROR power state {'uuid': uuidutils.generate_uuid(), 'maintenance': True, 'power_state': ironic_states.ERROR, 'provision_state': ironic_states.AVAILABLE}, # a node not in maintenance /w no instance and bad power state {'uuid': uuidutils.generate_uuid(), 'power_state': ironic_states.NOSTATE, 'provision_state': ironic_states.AVAILABLE}, # a node not in maintenance or bad power state, bad provision state {'uuid': uuidutils.generate_uuid(), 'power_state': ironic_states.POWER_ON, 'provision_state': ironic_states.MANAGEABLE}, # a node in cleaning {'uuid': uuidutils.generate_uuid(), 'power_state': ironic_states.POWER_ON, 'provision_state': ironic_states.CLEANING}, # a node in cleaning, waiting for a clean step to finish {'uuid': uuidutils.generate_uuid(), 'power_state': ironic_states.POWER_ON, 'provision_state': ironic_states.CLEANWAIT}, # a node in deleting {'uuid': uuidutils.generate_uuid(), 'power_state': ironic_states.POWER_ON, 'provision_state': ironic_states.DELETING}, # a node in deleted {'uuid': uuidutils.generate_uuid(), 'power_state': ironic_states.POWER_ON, 'provision_state': ironic_states.DELETED}, # a node in AVAILABLE with an instance uuid {'uuid': uuidutils.generate_uuid(), 'instance_uuid': uuidutils.generate_uuid(), 'power_state': ironic_states.POWER_OFF, 'provision_state': ironic_states.AVAILABLE} ] for n in node_dicts: node = ironic_utils.get_test_node(**n) self.assertTrue(self.driver._node_resources_unavailable(node)) for ok_state in (ironic_states.AVAILABLE, ironic_states.NOSTATE): # these are both ok and should present as available as they # have no instance_uuid avail_node = ironic_utils.get_test_node( power_state=ironic_states.POWER_OFF, provision_state=ok_state) unavailable = self.driver._node_resources_unavailable(avail_node) self.assertFalse(unavailable) def test__node_resources_used(self): node_dicts = [ # a node in maintenance /w instance and active {'uuid': uuidutils.generate_uuid(), 'instance_uuid': uuidutils.generate_uuid(), 'provision_state': ironic_states.ACTIVE}, ] for n in node_dicts: node = ironic_utils.get_test_node(**n) self.assertTrue(self.driver._node_resources_used(node)) unused_node = ironic_utils.get_test_node( instance_uuid=None, provision_state=ironic_states.AVAILABLE) self.assertFalse(self.driver._node_resources_used(unused_node)) @mock.patch.object(FAKE_CLIENT.node, 'list') def test_get_available_nodes(self, mock_list): node_dicts = [ # a node in maintenance /w no instance and power OFF {'uuid': uuidutils.generate_uuid(), 'maintenance': True, 'power_state': ironic_states.POWER_OFF}, # a node /w instance and power ON {'uuid': uuidutils.generate_uuid(), 'instance_uuid': self.instance_uuid, 'power_state': ironic_states.POWER_ON}, # a node not in maintenance /w no instance and bad power state {'uuid': uuidutils.generate_uuid(), 'power_state': ironic_states.ERROR}, ] nodes = [ironic_utils.get_test_node(**n) for n in node_dicts] mock_list.return_value = nodes available_nodes = self.driver.get_available_nodes() expected_uuids = [n['uuid'] for n in node_dicts] self.assertEqual(sorted(expected_uuids), sorted(available_nodes)) @mock.patch.object(FAKE_CLIENT.node, 'get') @mock.patch.object(FAKE_CLIENT.node, 'list') @mock.patch.object(ironic_driver.IronicDriver, '_node_resource') def test_get_available_resource(self, mock_nr, mock_list, mock_get): node = ironic_utils.get_test_node() node_2 = ironic_utils.get_test_node(uuid=uuidutils.generate_uuid()) fake_resource = 'fake-resource' mock_get.return_value = node # ensure cache gets populated without the node we want mock_list.return_value = [node_2] mock_nr.return_value = fake_resource result = self.driver.get_available_resource(node.uuid) self.assertEqual(fake_resource, result) mock_nr.assert_called_once_with(node) mock_get.assert_called_once_with(node.uuid, fields=ironic_driver._NODE_FIELDS) @mock.patch.object(FAKE_CLIENT.node, 'get') @mock.patch.object(FAKE_CLIENT.node, 'list') @mock.patch.object(ironic_driver.IronicDriver, '_node_resource') def test_get_available_resource_with_cache(self, mock_nr, mock_list, mock_get): node = ironic_utils.get_test_node() fake_resource = 'fake-resource' mock_list.return_value = [node] mock_nr.return_value = fake_resource # populate the cache self.driver.get_available_nodes(refresh=True) mock_list.reset_mock() result = self.driver.get_available_resource(node.uuid) self.assertEqual(fake_resource, result) self.assertEqual(0, mock_list.call_count) self.assertEqual(0, mock_get.call_count) mock_nr.assert_called_once_with(node) @mock.patch.object(FAKE_CLIENT.node, 'get_by_instance_uuid') def test_get_info(self, mock_gbiu): properties = {'memory_mb': 512, 'cpus': 2} power_state = ironic_states.POWER_ON node = ironic_utils.get_test_node(instance_uuid=self.instance_uuid, properties=properties, power_state=power_state) mock_gbiu.return_value = node # ironic_states.POWER_ON should be mapped to # nova_states.RUNNING memory_kib = properties['memory_mb'] * 1024 instance = fake_instance.fake_instance_obj('fake-context', uuid=self.instance_uuid) result = self.driver.get_info(instance) self.assertEqual(hardware.InstanceInfo(state=nova_states.RUNNING, max_mem_kb=memory_kib, mem_kb=memory_kib, num_cpu=properties['cpus']), result) @mock.patch.object(FAKE_CLIENT.node, 'get_by_instance_uuid') def test_get_info_http_not_found(self, mock_gbiu): mock_gbiu.side_effect = ironic_exception.NotFound() instance = fake_instance.fake_instance_obj( self.ctx, uuid=uuidutils.generate_uuid()) result = self.driver.get_info(instance) self.assertEqual(hardware.InstanceInfo(state=nova_states.NOSTATE), result) @mock.patch.object(FAKE_CLIENT, 'node') def test_macs_for_instance(self, mock_node): node = ironic_utils.get_test_node() port = ironic_utils.get_test_port() mock_node.get.return_value = node mock_node.list_ports.return_value = [port] instance = fake_instance.fake_instance_obj(self.ctx, node=node.uuid) result = self.driver.macs_for_instance(instance) self.assertEqual(set([port.address]), result) mock_node.list_ports.assert_called_once_with(node.uuid) @mock.patch.object(FAKE_CLIENT.node, 'get') def test_macs_for_instance_http_not_found(self, mock_get): mock_get.side_effect = ironic_exception.NotFound() instance = fake_instance.fake_instance_obj( self.ctx, node=uuidutils.generate_uuid()) result = self.driver.macs_for_instance(instance) self.assertIsNone(result) @mock.patch.object(objects.Instance, 'save') @mock.patch.object(loopingcall, 'FixedIntervalLoopingCall') @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, '_wait_for_active') @mock.patch.object(ironic_driver.IronicDriver, '_add_driver_fields') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') @mock.patch.object(ironic_driver.IronicDriver, '_start_firewall') def _test_spawn(self, mock_sf, mock_pvifs, mock_adf, mock_wait_active, mock_node, mock_looping, mock_save): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) fake_flavor = objects.Flavor(ephemeral_gb=0) instance.flavor = fake_flavor mock_node.get.return_value = node mock_node.validate.return_value = ironic_utils.get_test_validation() mock_node.get_by_instance_uuid.return_value = node mock_node.set_provision_state.return_value = mock.MagicMock() fake_looping_call = FakeLoopingCall() mock_looping.return_value = fake_looping_call image_meta = ironic_utils.get_test_image_meta() self.driver.spawn(self.ctx, instance, image_meta, [], None) mock_node.get.assert_called_once_with( node_uuid, fields=ironic_driver._NODE_FIELDS) mock_node.validate.assert_called_once_with(node_uuid) mock_adf.assert_called_once_with(node, instance, test.MatchType(objects.ImageMeta), fake_flavor) mock_pvifs.assert_called_once_with(node, instance, None) mock_sf.assert_called_once_with(instance, None) mock_node.set_provision_state.assert_called_once_with(node_uuid, 'active', configdrive=mock.ANY) self.assertIsNone(instance.default_ephemeral_device) self.assertFalse(mock_save.called) mock_looping.assert_called_once_with(mock_wait_active, instance) fake_looping_call.start.assert_called_once_with( interval=CONF.ironic.api_retry_interval) fake_looping_call.wait.assert_called_once_with() @mock.patch.object(ironic_driver.IronicDriver, '_generate_configdrive') @mock.patch.object(configdrive, 'required_by') def test_spawn(self, mock_required_by, mock_configdrive): mock_required_by.return_value = False self._test_spawn() # assert configdrive was not generated self.assertFalse(mock_configdrive.called) @mock.patch.object(ironic_driver.IronicDriver, '_generate_configdrive') @mock.patch.object(configdrive, 'required_by') def test_spawn_with_configdrive(self, mock_required_by, mock_configdrive): mock_required_by.return_value = True self._test_spawn() # assert configdrive was generated mock_configdrive.assert_called_once_with(mock.ANY, mock.ANY, mock.ANY, extra_md={}, files=[]) @mock.patch.object(configdrive, 'required_by') @mock.patch.object(loopingcall, 'FixedIntervalLoopingCall') @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, 'destroy') @mock.patch.object(ironic_driver.IronicDriver, '_wait_for_active') @mock.patch.object(ironic_driver.IronicDriver, '_add_driver_fields') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') @mock.patch.object(ironic_driver.IronicDriver, '_start_firewall') def test_spawn_destroyed_after_failure(self, mock_sf, mock_pvifs, mock_adf, mock_wait_active, mock_destroy, mock_node, mock_looping, mock_required_by): mock_required_by.return_value = False node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) fake_flavor = objects.Flavor(ephemeral_gb=0) instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) instance.flavor = fake_flavor mock_node.get.return_value = node mock_node.validate.return_value = ironic_utils.get_test_validation() mock_node.get_by_instance_uuid.return_value = node mock_node.set_provision_state.return_value = mock.MagicMock() fake_looping_call = FakeLoopingCall() mock_looping.return_value = fake_looping_call deploy_exc = exception.InstanceDeployFailure('foo') fake_looping_call.wait.side_effect = deploy_exc self.assertRaises( exception.InstanceDeployFailure, self.driver.spawn, self.ctx, instance, None, [], None) self.assertEqual(0, mock_destroy.call_count) def _test_add_driver_fields(self, mock_update=None, mock_call=None): node = ironic_utils.get_test_node(driver='fake') instance = fake_instance.fake_instance_obj(self.ctx, node=node.uuid) image_meta = ironic_utils.get_test_image_meta() flavor = ironic_utils.get_test_flavor() self.driver._add_driver_fields(node, instance, image_meta, flavor) expected_patch = [{'path': '/instance_info/image_source', 'op': 'add', 'value': image_meta.id}, {'path': '/instance_info/root_gb', 'op': 'add', 'value': str(instance.root_gb)}, {'path': '/instance_info/swap_mb', 'op': 'add', 'value': str(flavor['swap'])}, {'path': '/instance_info/display_name', 'value': instance.display_name, 'op': 'add'}, {'path': '/instance_info/vcpus', 'op': 'add', 'value': str(instance.vcpus)}, {'path': '/instance_info/memory_mb', 'op': 'add', 'value': str(instance.memory_mb)}, {'path': '/instance_info/local_gb', 'op': 'add', 'value': str(node.properties.get('local_gb', 0))}, {'path': '/instance_uuid', 'op': 'add', 'value': instance.uuid}] if mock_call is not None: # assert call() is invoked with retry_on_conflict False to # avoid bug #1341420 mock_call.assert_called_once_with('node.update', node.uuid, expected_patch, retry_on_conflict=False) if mock_update is not None: mock_update.assert_called_once_with(node.uuid, expected_patch) @mock.patch.object(FAKE_CLIENT.node, 'update') def test__add_driver_fields_mock_update(self, mock_update): self._test_add_driver_fields(mock_update=mock_update) @mock.patch.object(cw.IronicClientWrapper, 'call') def test__add_driver_fields_mock_call(self, mock_call): self._test_add_driver_fields(mock_call=mock_call) @mock.patch.object(FAKE_CLIENT.node, 'update') def test__add_driver_fields_fail(self, mock_update): mock_update.side_effect = ironic_exception.BadRequest() node = ironic_utils.get_test_node(driver='fake') instance = fake_instance.fake_instance_obj(self.ctx, node=node.uuid) image_meta = ironic_utils.get_test_image_meta() flavor = ironic_utils.get_test_flavor() self.assertRaises(exception.InstanceDeployFailure, self.driver._add_driver_fields, node, instance, image_meta, flavor) @mock.patch.object(configdrive, 'required_by') @mock.patch.object(FAKE_CLIENT, 'node') def test_spawn_node_driver_validation_fail(self, mock_node, mock_required_by): mock_required_by.return_value = False node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) flavor = ironic_utils.get_test_flavor() instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) instance.flavor = flavor mock_node.validate.return_value = ironic_utils.get_test_validation( power={'result': False}, deploy={'result': False}) mock_node.get.return_value = node image_meta = ironic_utils.get_test_image_meta() self.assertRaises(exception.ValidationError, self.driver.spawn, self.ctx, instance, image_meta, [], None) mock_node.get.assert_called_once_with( node_uuid, fields=ironic_driver._NODE_FIELDS) mock_node.validate.assert_called_once_with(node_uuid) @mock.patch.object(configdrive, 'required_by') @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, '_start_firewall') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') @mock.patch.object(ironic_driver.IronicDriver, '_cleanup_deploy') def test_spawn_node_prepare_for_deploy_fail(self, mock_cleanup_deploy, mock_pvifs, mock_sf, mock_node, mock_required_by): mock_required_by.return_value = False node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) flavor = ironic_utils.get_test_flavor() instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) instance.flavor = flavor mock_node.get.return_value = node mock_node.validate.return_value = ironic_utils.get_test_validation() image_meta = ironic_utils.get_test_image_meta() class TestException(Exception): pass mock_sf.side_effect = TestException() self.assertRaises(TestException, self.driver.spawn, self.ctx, instance, image_meta, [], None) mock_node.get.assert_called_once_with( node_uuid, fields=ironic_driver._NODE_FIELDS) mock_node.validate.assert_called_once_with(node_uuid) mock_cleanup_deploy.assert_called_with(node, instance, None) @mock.patch.object(configdrive, 'required_by') @mock.patch.object(objects.Instance, 'save') @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, '_generate_configdrive') @mock.patch.object(ironic_driver.IronicDriver, '_start_firewall') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') def test_spawn_node_configdrive_fail(self, mock_pvifs, mock_sf, mock_configdrive, mock_node, mock_save, mock_required_by): mock_required_by.return_value = True node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) flavor = ironic_utils.get_test_flavor() instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) instance.flavor = flavor mock_node.get.return_value = node mock_node.validate.return_value = ironic_utils.get_test_validation() image_meta = ironic_utils.get_test_image_meta() class TestException(Exception): pass mock_configdrive.side_effect = TestException() with mock.patch.object(self.driver, '_cleanup_deploy', autospec=True) as mock_cleanup_deploy: self.assertRaises(TestException, self.driver.spawn, self.ctx, instance, image_meta, [], None) mock_node.get.assert_called_once_with( node_uuid, fields=ironic_driver._NODE_FIELDS) mock_node.validate.assert_called_once_with(node_uuid) mock_cleanup_deploy.assert_called_with(node, instance, None) @mock.patch.object(configdrive, 'required_by') @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, '_start_firewall') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') @mock.patch.object(ironic_driver.IronicDriver, '_cleanup_deploy') def test_spawn_node_trigger_deploy_fail(self, mock_cleanup_deploy, mock_pvifs, mock_sf, mock_node, mock_required_by): mock_required_by.return_value = False node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) flavor = ironic_utils.get_test_flavor() instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) instance.flavor = flavor image_meta = ironic_utils.get_test_image_meta() mock_node.get.return_value = node mock_node.validate.return_value = ironic_utils.get_test_validation() mock_node.set_provision_state.side_effect = exception.NovaException() self.assertRaises(exception.NovaException, self.driver.spawn, self.ctx, instance, image_meta, [], None) mock_node.get.assert_called_once_with( node_uuid, fields=ironic_driver._NODE_FIELDS) mock_node.validate.assert_called_once_with(node_uuid) mock_cleanup_deploy.assert_called_once_with(node, instance, None) @mock.patch.object(configdrive, 'required_by') @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, '_start_firewall') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') @mock.patch.object(ironic_driver.IronicDriver, '_cleanup_deploy') def test_spawn_node_trigger_deploy_fail2(self, mock_cleanup_deploy, mock_pvifs, mock_sf, mock_node, mock_required_by): mock_required_by.return_value = False node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) flavor = ironic_utils.get_test_flavor() instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) instance.flavor = flavor image_meta = ironic_utils.get_test_image_meta() mock_node.get.return_value = node mock_node.validate.return_value = ironic_utils.get_test_validation() mock_node.set_provision_state.side_effect = ironic_exception.BadRequest self.assertRaises(ironic_exception.BadRequest, self.driver.spawn, self.ctx, instance, image_meta, [], None) mock_node.get.assert_called_once_with( node_uuid, fields=ironic_driver._NODE_FIELDS) mock_node.validate.assert_called_once_with(node_uuid) mock_cleanup_deploy.assert_called_once_with(node, instance, None) @mock.patch.object(configdrive, 'required_by') @mock.patch.object(loopingcall, 'FixedIntervalLoopingCall') @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, '_start_firewall') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') @mock.patch.object(ironic_driver.IronicDriver, 'destroy') def test_spawn_node_trigger_deploy_fail3(self, mock_destroy, mock_pvifs, mock_sf, mock_node, mock_looping, mock_required_by): mock_required_by.return_value = False node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) flavor = ironic_utils.get_test_flavor() instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) instance.flavor = flavor image_meta = ironic_utils.get_test_image_meta() mock_node.get.return_value = node mock_node.validate.return_value = ironic_utils.get_test_validation() fake_looping_call = FakeLoopingCall() mock_looping.return_value = fake_looping_call fake_looping_call.wait.side_effect = ironic_exception.BadRequest fake_net_info = utils.get_test_network_info() self.assertRaises(ironic_exception.BadRequest, self.driver.spawn, self.ctx, instance, image_meta, [], None, fake_net_info) self.assertEqual(0, mock_destroy.call_count) @mock.patch.object(configdrive, 'required_by') @mock.patch.object(loopingcall, 'FixedIntervalLoopingCall') @mock.patch.object(objects.Instance, 'save') @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, '_wait_for_active') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') @mock.patch.object(ironic_driver.IronicDriver, '_start_firewall') def test_spawn_sets_default_ephemeral_device(self, mock_sf, mock_pvifs, mock_wait, mock_node, mock_save, mock_looping, mock_required_by): mock_required_by.return_value = False node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) flavor = ironic_utils.get_test_flavor(ephemeral_gb=1) instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) instance.flavor = flavor mock_node.get_by_instance_uuid.return_value = node mock_node.set_provision_state.return_value = mock.MagicMock() image_meta = ironic_utils.get_test_image_meta() self.driver.spawn(self.ctx, instance, image_meta, [], None) self.assertTrue(mock_save.called) self.assertEqual('/dev/sda1', instance.default_ephemeral_device) @mock.patch.object(FAKE_CLIENT, 'node') @mock.patch.object(ironic_driver.IronicDriver, '_cleanup_deploy') def _test_destroy(self, state, mock_cleanup_deploy, mock_node): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' network_info = 'foo' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid, provision_state=state) instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) def fake_set_provision_state(*_): node.provision_state = None mock_node.get_by_instance_uuid.return_value = node mock_node.set_provision_state.side_effect = fake_set_provision_state self.driver.destroy(self.ctx, instance, network_info, None) mock_node.get_by_instance_uuid.assert_called_with( instance.uuid, fields=ironic_driver._NODE_FIELDS) mock_cleanup_deploy.assert_called_with(node, instance, network_info) # For states that makes sense check if set_provision_state has # been called if state in ironic_driver._UNPROVISION_STATES: mock_node.set_provision_state.assert_called_once_with( node_uuid, 'deleted') else: self.assertFalse(mock_node.set_provision_state.called) def test_destroy(self): for state in ironic_states.PROVISION_STATE_LIST: self._test_destroy(state) @mock.patch.object(FAKE_CLIENT.node, 'set_provision_state') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def test_destroy_trigger_undeploy_fail(self, fake_validate, mock_sps): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid, provision_state=ironic_states.ACTIVE) fake_validate.return_value = node instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) mock_sps.side_effect = exception.NovaException() self.assertRaises(exception.NovaException, self.driver.destroy, self.ctx, instance, None, None) @mock.patch.object(FAKE_CLIENT.node, 'set_provision_state') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def _test__unprovision_instance(self, mock_validate_inst, mock_set_pstate, state=None): node = ironic_utils.get_test_node( driver='fake', provision_state=state) instance = fake_instance.fake_instance_obj(self.ctx, node=node.uuid) mock_validate_inst.return_value = node self.driver._unprovision(instance, node) mock_validate_inst.assert_called_once_with(instance) mock_set_pstate.assert_called_once_with(node.uuid, "deleted") def test__unprovision_cleaning(self): self._test__unprovision_instance(state=ironic_states.CLEANING) def test__unprovision_cleanwait(self): self._test__unprovision_instance(state=ironic_states.CLEANWAIT) @mock.patch.object(FAKE_CLIENT.node, 'set_provision_state') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def test__unprovision_fail_max_retries(self, mock_validate_inst, mock_set_pstate): CONF.set_default('api_max_retries', default=2, group='ironic') node = ironic_utils.get_test_node( driver='fake', provision_state=ironic_states.ACTIVE) instance = fake_instance.fake_instance_obj(self.ctx, node=node.uuid) mock_validate_inst.return_value = node self.assertRaises(exception.NovaException, self.driver._unprovision, instance, node) expected_calls = (mock.call(instance), mock.call(instance)) mock_validate_inst.assert_has_calls(expected_calls) mock_set_pstate.assert_called_once_with(node.uuid, "deleted") @mock.patch.object(FAKE_CLIENT.node, 'set_provision_state') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') def test__unprovision_instance_not_found(self, mock_validate_inst, mock_set_pstate): node = ironic_utils.get_test_node( driver='fake', provision_state=ironic_states.DELETING) instance = fake_instance.fake_instance_obj(self.ctx, node=node.uuid) mock_validate_inst.side_effect = exception.InstanceNotFound( instance_id='fake') self.driver._unprovision(instance, node) mock_validate_inst.assert_called_once_with(instance) mock_set_pstate.assert_called_once_with(node.uuid, "deleted") @mock.patch.object(FAKE_CLIENT, 'node') def test_destroy_unassociate_fail(self, mock_node): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid, provision_state=ironic_states.ACTIVE) instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) mock_node.get_by_instance_uuid.return_value = node mock_node.update.side_effect = exception.NovaException() self.assertRaises(exception.NovaException, self.driver.destroy, self.ctx, instance, None, None) mock_node.set_provision_state.assert_called_once_with(node_uuid, 'deleted') mock_node.get_by_instance_uuid.assert_called_with( instance.uuid, fields=ironic_driver._NODE_FIELDS) @mock.patch.object(loopingcall, 'FixedIntervalLoopingCall') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') @mock.patch.object(FAKE_CLIENT.node, 'set_power_state') def test_reboot(self, mock_sp, fake_validate, mock_looping): node = ironic_utils.get_test_node() fake_validate.side_effect = [node, node] fake_looping_call = FakeLoopingCall() mock_looping.return_value = fake_looping_call instance = fake_instance.fake_instance_obj(self.ctx, node=node.uuid) self.driver.reboot(self.ctx, instance, None, None) mock_sp.assert_called_once_with(node.uuid, 'reboot') @mock.patch.object(loopingcall, 'FixedIntervalLoopingCall') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') @mock.patch.object(FAKE_CLIENT.node, 'set_power_state') def test_power_off(self, mock_sp, fake_validate, mock_looping): self._test_power_on_off(mock_sp, fake_validate, mock_looping, method_name='power_off') @mock.patch.object(loopingcall, 'FixedIntervalLoopingCall') @mock.patch.object(ironic_driver.IronicDriver, '_validate_instance_and_node') @mock.patch.object(FAKE_CLIENT.node, 'set_power_state') def test_power_on(self, mock_sp, fake_validate, mock_looping): self._test_power_on_off(mock_sp, fake_validate, mock_looping, method_name='power_on') def _test_power_on_off(self, mock_sp, fake_validate, mock_looping, method_name=None): node = ironic_utils.get_test_node() fake_validate.side_effect = [node, node] fake_looping_call = FakeLoopingCall() mock_looping.return_value = fake_looping_call instance = fake_instance.fake_instance_obj(self.ctx, node=self.instance_uuid) # Call the method under test here if method_name == 'power_on': self.driver.power_on(self.ctx, instance, utils.get_test_network_info()) mock_sp.assert_called_once_with(node.uuid, 'on') elif method_name == 'power_off': self.driver.power_off(instance) mock_sp.assert_called_once_with(node.uuid, 'off') @mock.patch.object(FAKE_CLIENT.node, 'list_ports') @mock.patch.object(FAKE_CLIENT.port, 'update') @mock.patch.object(ironic_driver.IronicDriver, '_unplug_vifs') def test_plug_vifs_with_port(self, mock_uvifs, mock_port_udt, mock_lp): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(uuid=node_uuid) # make the address be consistent with network_info's port = ironic_utils.get_test_port(address=utils.FAKE_VIF_MAC) mock_lp.return_value = [port] instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) network_info = utils.get_test_network_info() port_id = six.text_type(network_info[0]['id']) expected_patch = [{'op': 'add', 'path': '/extra/vif_port_id', 'value': port_id}] self.driver._plug_vifs(node, instance, network_info) # asserts mock_uvifs.assert_called_once_with(node, instance, network_info) mock_lp.assert_called_once_with(node_uuid) mock_port_udt.assert_called_with(port.uuid, expected_patch) @mock.patch.object(FAKE_CLIENT.node, 'get') @mock.patch.object(ironic_driver.IronicDriver, '_plug_vifs') def test_plug_vifs(self, mock__plug_vifs, mock_get): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(uuid=node_uuid) mock_get.return_value = node instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) network_info = utils.get_test_network_info() self.driver.plug_vifs(instance, network_info) mock_get.assert_called_once_with(node_uuid, fields=ironic_driver._NODE_FIELDS) mock__plug_vifs.assert_called_once_with(node, instance, network_info) @mock.patch.object(FAKE_CLIENT.port, 'update') @mock.patch.object(FAKE_CLIENT.node, 'list_ports') @mock.patch.object(ironic_driver.IronicDriver, '_unplug_vifs') def test_plug_vifs_multiple_ports(self, mock_uvifs, mock_lp, mock_port_udt): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(uuid=node_uuid) first_ironic_port_uuid = 'aaaaaaaa-bbbb-1111-dddd-eeeeeeeeeeee' first_port = ironic_utils.get_test_port(uuid=first_ironic_port_uuid, node_uuid=node_uuid, address='11:FF:FF:FF:FF:FF') second_ironic_port_uuid = 'aaaaaaaa-bbbb-2222-dddd-eeeeeeeeeeee' second_port = ironic_utils.get_test_port(uuid=second_ironic_port_uuid, node_uuid=node_uuid, address='22:FF:FF:FF:FF:FF') mock_lp.return_value = [second_port, first_port] instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) first_vif_id = 'aaaaaaaa-vv11-cccc-dddd-eeeeeeeeeeee' second_vif_id = 'aaaaaaaa-vv22-cccc-dddd-eeeeeeeeeeee' first_vif = ironic_utils.get_test_vif( address='22:FF:FF:FF:FF:FF', id=second_vif_id) second_vif = ironic_utils.get_test_vif( address='11:FF:FF:FF:FF:FF', id=first_vif_id) network_info = [first_vif, second_vif] self.driver._plug_vifs(node, instance, network_info) # asserts mock_uvifs.assert_called_once_with(node, instance, network_info) mock_lp.assert_called_once_with(node_uuid) calls = (mock.call(first_ironic_port_uuid, [{'op': 'add', 'path': '/extra/vif_port_id', 'value': first_vif_id}]), mock.call(second_ironic_port_uuid, [{'op': 'add', 'path': '/extra/vif_port_id', 'value': second_vif_id}])) mock_port_udt.assert_has_calls(calls, any_order=True) @mock.patch.object(FAKE_CLIENT.port, 'update') @mock.patch.object(FAKE_CLIENT.node, 'list_ports') @mock.patch.object(ironic_driver.IronicDriver, '_unplug_vifs') def test_plug_vifs_count_mismatch(self, mock_uvifs, mock_lp, mock_port_udt): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(uuid=node_uuid) port = ironic_utils.get_test_port() mock_lp.return_value = [port] instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) # len(network_info) > len(ports) network_info = (utils.get_test_network_info() + utils.get_test_network_info()) self.assertRaises(exception.NovaException, self.driver._plug_vifs, node, instance, network_info) # asserts mock_uvifs.assert_called_once_with(node, instance, network_info) mock_lp.assert_called_once_with(node_uuid) # assert port.update() was not called self.assertFalse(mock_port_udt.called) @mock.patch.object(FAKE_CLIENT.port, 'update') @mock.patch.object(FAKE_CLIENT.node, 'list_ports') @mock.patch.object(ironic_driver.IronicDriver, '_unplug_vifs') def test_plug_vifs_no_network_info(self, mock_uvifs, mock_lp, mock_port_udt): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(uuid=node_uuid) port = ironic_utils.get_test_port() mock_lp.return_value = [port] instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) network_info = [] self.driver._plug_vifs(node, instance, network_info) # asserts mock_uvifs.assert_called_once_with(node, instance, network_info) mock_lp.assert_called_once_with(node_uuid) # assert port.update() was not called self.assertFalse(mock_port_udt.called) @mock.patch.object(FAKE_CLIENT.port, 'update') @mock.patch.object(FAKE_CLIENT, 'node') def test_unplug_vifs(self, mock_node, mock_update): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(uuid=node_uuid) port = ironic_utils.get_test_port(extra={'vif_port_id': 'fake-vif'}) mock_node.get.return_value = node mock_node.list_ports.return_value = [port] instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) expected_patch = [{'op': 'remove', 'path': '/extra/vif_port_id'}] self.driver.unplug_vifs(instance, utils.get_test_network_info()) # asserts mock_node.get.assert_called_once_with( node_uuid, fields=ironic_driver._NODE_FIELDS) mock_node.list_ports.assert_called_once_with(node_uuid, detail=True) mock_update.assert_called_once_with(port.uuid, expected_patch) @mock.patch.object(FAKE_CLIENT.port, 'update') @mock.patch.object(FAKE_CLIENT, 'node') def test_unplug_vifs_port_not_associated(self, mock_node, mock_update): node_uuid = 'aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee' node = ironic_utils.get_test_node(uuid=node_uuid) port = ironic_utils.get_test_port(extra={}) mock_node.get.return_value = node mock_node.list_ports.return_value = [port] instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) self.driver.unplug_vifs(instance, utils.get_test_network_info()) mock_node.get.assert_called_once_with( node_uuid, fields=ironic_driver._NODE_FIELDS) mock_node.list_ports.assert_called_once_with(node_uuid, detail=True) # assert port.update() was not called self.assertFalse(mock_update.called) @mock.patch.object(FAKE_CLIENT.port, 'update') def test_unplug_vifs_no_network_info(self, mock_update): instance = fake_instance.fake_instance_obj(self.ctx) network_info = [] self.driver.unplug_vifs(instance, network_info) # assert port.update() was not called self.assertFalse(mock_update.called) @mock.patch.object(firewall.NoopFirewallDriver, 'unfilter_instance', create=True) def test_unfilter_instance(self, mock_ui): instance = fake_instance.fake_instance_obj(self.ctx) network_info = utils.get_test_network_info() self.driver.unfilter_instance(instance, network_info) mock_ui.assert_called_once_with(instance, network_info) @mock.patch.object(firewall.NoopFirewallDriver, 'setup_basic_filtering', create=True) @mock.patch.object(firewall.NoopFirewallDriver, 'prepare_instance_filter', create=True) def test_ensure_filtering_rules_for_instance(self, mock_pif, mock_sbf): instance = fake_instance.fake_instance_obj(self.ctx) network_info = utils.get_test_network_info() self.driver.ensure_filtering_rules_for_instance(instance, network_info) mock_sbf.assert_called_once_with(instance, network_info) mock_pif.assert_called_once_with(instance, network_info) @mock.patch.object(firewall.NoopFirewallDriver, 'refresh_instance_security_rules', create=True) def test_refresh_instance_security_rules(self, mock_risr): instance = fake_instance.fake_instance_obj(self.ctx) self.driver.refresh_instance_security_rules(instance) mock_risr.assert_called_once_with(instance) @mock.patch.object(firewall.NoopFirewallDriver, 'refresh_instance_security_rules', create=True) def test_refresh_security_group_rules(self, mock_risr): fake_group = 'fake-security-group-members' self.driver.refresh_instance_security_rules(fake_group) mock_risr.assert_called_once_with(fake_group) @mock.patch.object(ironic_driver.IronicDriver, '_wait_for_active') @mock.patch.object(loopingcall, 'FixedIntervalLoopingCall') @mock.patch.object(FAKE_CLIENT.node, 'set_provision_state') @mock.patch.object(ironic_driver.IronicDriver, '_add_driver_fields') @mock.patch.object(FAKE_CLIENT.node, 'get') @mock.patch.object(objects.Instance, 'save') def _test_rebuild(self, mock_save, mock_get, mock_driver_fields, mock_set_pstate, mock_looping, mock_wait_active, preserve=False): node_uuid = uuidutils.generate_uuid() node = ironic_utils.get_test_node(uuid=node_uuid, instance_uuid=self.instance_uuid, instance_type_id=5) mock_get.return_value = node image_meta = ironic_utils.get_test_image_meta() flavor_id = 5 flavor = objects.Flavor(flavor_id=flavor_id, name='baremetal') instance = fake_instance.fake_instance_obj(self.ctx, uuid=self.instance_uuid, node=node_uuid, instance_type_id=flavor_id) instance.flavor = flavor fake_looping_call = FakeLoopingCall() mock_looping.return_value = fake_looping_call self.driver.rebuild( context=self.ctx, instance=instance, image_meta=image_meta, injected_files=None, admin_password=None, bdms=None, detach_block_devices=None, attach_block_devices=None, preserve_ephemeral=preserve) mock_save.assert_called_once_with( expected_task_state=[task_states.REBUILDING]) mock_driver_fields.assert_called_once_with( node, instance, test.MatchType(objects.ImageMeta), flavor, preserve) mock_set_pstate.assert_called_once_with(node_uuid, ironic_states.REBUILD) mock_looping.assert_called_once_with(mock_wait_active, instance) fake_looping_call.start.assert_called_once_with( interval=CONF.ironic.api_retry_interval) fake_looping_call.wait.assert_called_once_with() def test_rebuild_preserve_ephemeral(self): self._test_rebuild(preserve=True) def test_rebuild_no_preserve_ephemeral(self): self._test_rebuild(preserve=False) @mock.patch.object(FAKE_CLIENT.node, 'set_provision_state') @mock.patch.object(ironic_driver.IronicDriver, '_add_driver_fields') @mock.patch.object(FAKE_CLIENT.node, 'get') @mock.patch.object(objects.Instance, 'save') def test_rebuild_failures(self, mock_save, mock_get, mock_driver_fields, mock_set_pstate): node_uuid = uuidutils.generate_uuid() node = ironic_utils.get_test_node(uuid=node_uuid, instance_uuid=self.instance_uuid, instance_type_id=5) mock_get.return_value = node image_meta = ironic_utils.get_test_image_meta() flavor_id = 5 flavor = objects.Flavor(flavor_id=flavor_id, name='baremetal') instance = fake_instance.fake_instance_obj(self.ctx, uuid=self.instance_uuid, node=node_uuid, instance_type_id=flavor_id) instance.flavor = flavor exceptions = [ exception.NovaException(), ironic_exception.BadRequest(), ironic_exception.InternalServerError(), ] for e in exceptions: mock_set_pstate.side_effect = e self.assertRaises(exception.InstanceDeployFailure, self.driver.rebuild, context=self.ctx, instance=instance, image_meta=image_meta, injected_files=None, admin_password=None, bdms=None, detach_block_devices=None, attach_block_devices=None) @mock.patch.object(FAKE_CLIENT.node, 'get') def _test_network_binding_host_id(self, is_neutron, mock_get): node_uuid = uuidutils.generate_uuid() hostname = 'ironic-compute' instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid, host=hostname) if is_neutron: provider = 'neutron' expected = None else: provider = 'none' expected = hostname node = ironic_utils.get_test_node(uuid=node_uuid, instance_uuid=self.instance_uuid, instance_type_id=5, network_provider=provider) mock_get.return_value = node host_id = self.driver.network_binding_host_id(self.ctx, instance) self.assertEqual(expected, host_id) def test_network_binding_host_id_neutron(self): self._test_network_binding_host_id(True) def test_network_binding_host_id_none(self): self._test_network_binding_host_id(False) @mock.patch.object(instance_metadata, 'InstanceMetadata') @mock.patch.object(configdrive, 'ConfigDriveBuilder') class IronicDriverGenerateConfigDriveTestCase(test.NoDBTestCase): @mock.patch.object(cw, 'IronicClientWrapper', lambda *_: FAKE_CLIENT_WRAPPER) def setUp(self): super(IronicDriverGenerateConfigDriveTestCase, self).setUp() self.flags(**IRONIC_FLAGS) self.driver = ironic_driver.IronicDriver(None) self.driver.virtapi = fake.FakeVirtAPI() self.ctx = nova_context.get_admin_context() node_uuid = uuidutils.generate_uuid() self.node = ironic_utils.get_test_node(driver='fake', uuid=node_uuid) self.instance = fake_instance.fake_instance_obj(self.ctx, node=node_uuid) self.network_info = utils.get_test_network_info() def test_generate_configdrive(self, mock_cd_builder, mock_instance_meta): mock_instance_meta.return_value = 'fake-instance' mock_make_drive = mock.MagicMock(make_drive=lambda *_: None) mock_cd_builder.return_value.__enter__.return_value = mock_make_drive self.driver._generate_configdrive(self.instance, self.node, self.network_info) mock_cd_builder.assert_called_once_with(instance_md='fake-instance') mock_instance_meta.assert_called_once_with(self.instance, network_info=self.network_info, extra_md={}, content=None) def test_generate_configdrive_fail(self, mock_cd_builder, mock_instance_meta): mock_cd_builder.side_effect = exception.ConfigDriveMountFailed( operation='foo', error='error') mock_instance_meta.return_value = 'fake-instance' mock_make_drive = mock.MagicMock(make_drive=lambda *_: None) mock_cd_builder.return_value.__enter__.return_value = mock_make_drive self.assertRaises(exception.ConfigDriveMountFailed, self.driver._generate_configdrive, self.instance, self.node, self.network_info) mock_cd_builder.assert_called_once_with(instance_md='fake-instance') mock_instance_meta.assert_called_once_with(self.instance, network_info=self.network_info, extra_md={}, content=None)

47.169014

79

0.647892

9,319

80,376

5.219122

0.05462

0.030717

0.051196

0.038489

0.800831

0.760018

0.726402

0.698378

0.668401

0.646648

0

0.00273

0.261795

80,376

1,703

80

47.196712

0.816985

0.032124

0

0.599006

0

0.07734

0.025411

0

0.156139

1

0.07665

false

0.004258

0.018453

0.002839

0.1022

0

null

0

1

0

1

0

null

0

4

0a0209ad5dbb303b1a095721bb81092d757703f0

399

py

Python

numba_compile.py

saturdays/hyperlearn

42b80c2b83c43d8bb84730efc3a9614e6affc01d

[ "BSD-3-Clause" ]

null

numba_compile.py

saturdays/hyperlearn

42b80c2b83c43d8bb84730efc3a9614e6affc01d

[ "BSD-3-Clause" ]

null

numba_compile.py

saturdays/hyperlearn

42b80c2b83c43d8bb84730efc3a9614e6affc01d

[ "BSD-3-Clause" ]

null

print("******* Now compiling Numba and LLVM code..... *******") print("******* This can be VERY SLOW. Please wait.... *******\n" "Progress: |||||||||||||||", end = "") from hyperlearn.numba.funcs import * print("|||||||||||||||", end = "") from hyperlearn.utils import * print("|||||||||||||||") from hyperlearn.stats import * print("******* Code has been successfully compiled!:) *******")

23.470588

64

0.516291

40

399

5.15

0.675

0.203884

0.165049

0

0.135338

399

16

65

24.9375

0.597101

0

0.550251

0

1

0

true

0

0.333333

0

0.333333

0.555556

0

null

1

0

null

0

1

0

1

0

1

0

4

0a1177d84612d9949f365edb97453814bb6bf39b

5,528

py

Python

go_core/goboard_test.py

DamonDeng/thetago

2dba48460aaf3e3544b6d228deb4cff0d0fd7123

[ "MIT" ]

null

go_core/goboard_test.py

DamonDeng/thetago

2dba48460aaf3e3544b6d228deb4cff0d0fd7123

[ "MIT" ]

null

go_core/goboard_test.py

DamonDeng/thetago

2dba48460aaf3e3544b6d228deb4cff0d0fd7123

[ "MIT" ]

null

from go_core.goboard import GoBoard from gosgf import Sgf_game from go_core.sequence_goboard import SequenceGoBoard from go_core.array_goboard import ArrayGoBoard from data_loader.kgs_zip_reader import KGSZipReader import numpy as np import time def speed_testing(): # reader = KGSZipReader('data') # sgf_generator = reader.get_generator() # time_start=time.time() # for i in range(100): # try: # if i%100 == 0: # print('current game number: ' + str(i)) # (file_name, sgf_name, sgf_content) = sgf_generator.next() # target_board = GoBoard(19) # apply_sgf_content_to(file_name, sgf_name, sgf_content, target_board) # except StopIteration: # print('finished all the files') # break # time_end=time.time() # print ('time for GoBoard: '+ str(time_end-time_start)) reader = KGSZipReader('data') sgf_generator = reader.get_generator() time_start=time.time() for i in range(100): try: if i%100 == 0: print('current game number: ' + str(i)) (file_name, sgf_name, sgf_content) = sgf_generator.next() target_board = ArrayGoBoard(19) apply_sgf_content_to(file_name, sgf_name, sgf_content, target_board) except StopIteration: print('finished all the files') break time_end=time.time(); print ('time for ArrayGoBoard: '+ str(time_end-time_start)) # reader = KGSZipReader('data') # sgf_generator = reader.get_generator() # time_start=time.time() # for i in range(100): # try: # if i%100 == 0: # print('current game number: ' + str(i)) # (file_name, sgf_name, sgf_content) = sgf_generator.next() # target_board = SequenceGoBoard(19) # apply_sgf_content_to(file_name, sgf_name, sgf_content, target_board) # except StopIteration: # print('finished all the files') # break # time_end=time.time(); # print ('time for SequenceGoBoard: '+ str(time_end-time_start)) def apply_sgf_content_to(file_name, sgf_name, sgf_content, go_board): sgf = Sgf_game.from_string(sgf_content) main_sequence_iter = sgf.main_sequence_iter() if sgf.get_handicap() != None and sgf.get_handicap() != 0: # print('handling handicap') for setup in sgf.get_root().get_setup_stones(): for move in setup: go_board.apply_move('b', move) for item in main_sequence_iter: color, move = item.get_move() if not color is None and not move is None: # print('applying move:' + str(color) + ' move:' + str(move)) go_board.apply_move(color, move) def testing(): reader = KGSZipReader('data') sgf_generator = reader.get_generator() for i in range(100000): try: if i%100 == 0: print('current game number: ' + str(i)) (file_name, sgf_name, sgf_content) = sgf_generator.next() apply_sgf_content(file_name, sgf_name, sgf_content) except StopIteration: print('finished all the files') break def apply_sgf_content(file_name, sgf_name, sgf_content): sgf = Sgf_game.from_string(sgf_content) main_sequence_iter = sgf.main_sequence_iter() go_board = GoBoard(19) # sequence_go_board = SequenceGoBoard(19) array_go_board = ArrayGoBoard(19) if sgf.get_handicap() != None and sgf.get_handicap() != 0: # print('handling handicap') for setup in sgf.get_root().get_setup_stones(): for move in setup: go_board.apply_move('b', move) array_go_board.apply_move('b', move) for item in main_sequence_iter: color, move = item.get_move() if not color is None and not move is None: # print('applying move:' + str(color) + ' move:' + str(move)) go_board.apply_move(color, move) array_go_board.apply_move(color, move) go_board_result = go_board.get_array_result() array_go_board_result = array_go_board.get_array_result() if not array_equal(go_board_result, array_go_board_result): print('inconsist!!!!!' + file_name) print('sgf file: ' + sgf_name) print('color:' + str(color)) print('move: ' + str(move)) print(go_board) print(array_go_board) def array_equal(first_array, second_array): i_range = len(first_array) j_range = len(first_array) for i in range(i_range): for j in range(j_range): if not first_array[i][j] == second_array[i][j]: return False return True def move_array_test(): reader = KGSZipReader('data') sgf_generator = reader.get_generator() for i in range(10): try: print('current game number: ' + str(i)) (file_name, sgf_name, sgf_content) = sgf_generator.next() go_board = ArrayGoBoard(19) sgf = Sgf_game.from_string(sgf_content) main_sequence_iter = sgf.main_sequence_iter() if sgf.get_handicap() != None and sgf.get_handicap() != 0: # print('handling handicap') for setup in sgf.get_root().get_setup_stones(): for move in setup: go_board.apply_move('b', move) for item in main_sequence_iter: color, move = item.get_move() if not color is None and not move is None: # print('applying move:' + str(color) + ' move:' + str(move)) go_board.apply_move(color, move) move_array = go_board.get_move_array(8, color) print ('-----------------------------') print (str(move_array[0])) except StopIteration: print('finished all the files') break # testing() # speed_testing() move_array_test()

29.248677

76

0.653401

772

5,528

4.411917

0.11399

0.045214

0.035526

0.048444

0.765414

0.74633

0.721374

0.693188

0.66559

0

0.012155

0.226122

5,528

189

77

29.248677

0.784011

0.243488

0

0.543689

0

0.056239

0.007

0

1

0.058252

false

0

0.067961

0

0.145631

0

null

0

1

0

1

0

null

0

4

0a3a30e7909ba3672e083498c34575db14a1c3bf

97

py

Python

StudentData/apps.py

backstab319/CollegeDataManagement

39864efc3de706f9479f6fff29c1a1a9326c7b73

[ "BSD-3-Clause" ]

null

StudentData/apps.py

backstab319/CollegeDataManagement

39864efc3de706f9479f6fff29c1a1a9326c7b73

[ "BSD-3-Clause" ]

null

StudentData/apps.py

backstab319/CollegeDataManagement

39864efc3de706f9479f6fff29c1a1a9326c7b73

[ "BSD-3-Clause" ]

null

from django.apps import AppConfig class StudentdataConfig(AppConfig): name = 'StudentData'

16.166667

35

0.773196

10

97

7.5

0.9

0

0.154639

97

5

36

19.4

0.914634

0

0.113402

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

0a41d17bc70163b5daa211227b33794caf96d50f

72

py

Python

datefns/__init__.py

ktr/datefns

0b9ac5f36578894705a0cf5bcd8dfd15c00e967b

[ "MIT" ]

null

datefns/__init__.py

ktr/datefns

0b9ac5f36578894705a0cf5bcd8dfd15c00e967b

[ "MIT" ]

null

datefns/__init__.py

ktr/datefns

0b9ac5f36578894705a0cf5bcd8dfd15c00e967b

[ "MIT" ]

null

from .datefns import * from .timefns import * __version__ = '0.0.1a8'

12

23

0.694444

10

72

4.6

0.7

0

0.067797

0.180556

72

5

24

14.4

0.711864

0

0.098592

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

0a42ffe3ebc08032806b7c5e582485e739b8efe2

145

py

Python

python/ex17.py

kazushiyuuki/Lab-ICC

f84ac28b230c9a43d1d323cf0826237c39608429

[ "MIT" ]

null

python/ex17.py

kazushiyuuki/Lab-ICC

f84ac28b230c9a43d1d323cf0826237c39608429

[ "MIT" ]

null

python/ex17.py

kazushiyuuki/Lab-ICC

f84ac28b230c9a43d1d323cf0826237c39608429

[ "MIT" ]

null

a = int(input()) b = int(input()) c = int(input()) max = a if max < b: max = b if max < c: max = c elif max < c: max = c print(max)

11.153846

16

0.489655

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145

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0.321429

0.225352

0.197183

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0.324138

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17

11.153846

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0

0.090909

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0

null

1

0

null

0

4

0a49c12160e1c40311ffe4ef7424ab453f8bd70b

561

py

Python

pyNastran/op2/errors.py

als0052/pyNastran

8493323c30475d1fc3238eed7480bfe9015ce233

[ "BSD-3-Clause" ]

null

pyNastran/op2/errors.py

als0052/pyNastran

8493323c30475d1fc3238eed7480bfe9015ce233

[ "BSD-3-Clause" ]

null

pyNastran/op2/errors.py

als0052/pyNastran

8493323c30475d1fc3238eed7480bfe9015ce233

[ "BSD-3-Clause" ]

null

from pyNastran.f06.errors import FatalError class FortranMarkerError(Exception): pass class EmptyRecordError(SyntaxError): pass class SortCodeError(RuntimeError): pass class DeviceCodeError(SyntaxError): pass class MultipleSolutionNotImplementedError(NotImplementedError): pass class MixedVersionCard(RuntimeError): pass class SixtyFourBitError(NotImplementedError): pass class OverwriteTableError(RuntimeError): pass class DoubleCardError(RuntimeError): pass class EmptyCardError(RuntimeError): pass

13.357143

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0.775401

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561

9.456522

0.478261

0.186207

0.193103

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0.167558

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41

64

13.682927

0.927195

0

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1

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true

0.47619

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0.52381

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null

0

1

0

null

0

1

0

1

0

4

0a61c71d902ed1dcf85b9656ab3824848dca88bd

25

py

Python

tests/__init__.py

trickeydan/pepper2

3aba1c74568cd0a04c9178caba26e9238c90e9ba

[ "MIT" ]

null

tests/__init__.py

trickeydan/pepper2

3aba1c74568cd0a04c9178caba26e9238c90e9ba

[ "MIT" ]

22

2019-12-22T20:11:24.000Z

2020-01-18T19:09:11.000Z

tests/__init__.py

j5api/pepper2

3aba1c74568cd0a04c9178caba26e9238c90e9ba

[ "MIT" ]

null

"""Tests for pepper2."""

12.5

24

0.6

3

25

5

1

0

0.045455

0.12

25

1

25

0.636364

0.72

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1

0

null

0

1

0

null

0

1

0

4

0a66b427bbb59d08092115e691f648c245d168f9

344

py

Python

src/html_generators/_utils.py

quadrant-newmedia/html_generators

c78bb28518efd212a13541c794e8ae426818bb9e

[ "MIT" ]

1

2022-01-19T11:02:00.000Z

src/html_generators/_utils.py

quadrant-newmedia/html_generators

c78bb28518efd212a13541c794e8ae426818bb9e

[ "MIT" ]

null

src/html_generators/_utils.py

quadrant-newmedia/html_generators

c78bb28518efd212a13541c794e8ae426818bb9e

[ "MIT" ]

1

2022-01-19T11:02:08.000Z

''' Various utility methods for building html attributes. ''' def styles(*styles): '''Join multiple "conditional styles" and return a single style attribute''' return '; '.join(filter(None, styles)) def classes(*classes): '''Join multiple "conditional classes" and return a single class attribute''' return ' '.join(filter(None, classes))

34.4

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344

5.813953

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34.4

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true

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1

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null

0

1

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0

4

6a5f4c36218a0cd62343e835f253ab69d9caa7b4

23

py

Python

packages/snet_cli/snet/snet_cli/version.py

Vivek205/snet-cli

e5bf64ab6086af71eafc18b86040fa91ad2c5e9f

[ "MIT" ]

1

2021-02-16T17:03:54.000Z

packages/snet_cli/snet/snet_cli/version.py

Vivek205/snet-cli

e5bf64ab6086af71eafc18b86040fa91ad2c5e9f

[ "MIT" ]

null

packages/snet_cli/snet/snet_cli/version.py

Vivek205/snet-cli

e5bf64ab6086af71eafc18b86040fa91ad2c5e9f

[ "MIT" ]

null

__version__ = "v1.1.6"

11.5

22

0.652174

4

23

2.75

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0.4

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1

0

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1

0

null

0

1

0

1

0

null

0

4

6a7f93bb57b52cfb86a0db8287aed9b39b1c35bb

122

py

Python

packages/dask-task-models-library/src/dask_task_models_library/__init__.py

elisabettai/osparc-simcore

ad7b6e05111b50fe95e49306a992170490a7247f

[ "MIT" ]

null

packages/dask-task-models-library/src/dask_task_models_library/__init__.py

elisabettai/osparc-simcore

ad7b6e05111b50fe95e49306a992170490a7247f

[ "MIT" ]

55

2018-05-15T09:47:00.000Z

2022-03-31T06:56:50.000Z

packages/dask-task-models-library/src/dask_task_models_library/__init__.py

mrnicegyu11/osparc-simcore

b6fa6c245dbfbc18cc74a387111a52de9b05d1f4

[ "MIT" ]

1

2020-04-22T15:06:58.000Z

import pkg_resources __version__: str = pkg_resources.get_distribution( "simcore-dask-task-models-library" ).version

20.333333

50

0.795082

15

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0.8

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122

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51

24.4

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0

1

0

true

0

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0

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0

1

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null

1

0

1

0

null

0

1

0

4

6a87e55dfbcff8cc9517854eb5c942b03924efa5

1,125

py

Python

utils/messages.py

LUNA761/RickBot

8798e08cd1e05c0b3440e27ce0e3d48f4b9b6f12

[ "MIT" ]

1

2022-03-03T21:38:34.000Z

utils/messages.py

LUNA761/RickBot

8798e08cd1e05c0b3440e27ce0e3d48f4b9b6f12

[ "MIT" ]

null

utils/messages.py

LUNA761/RickBot

8798e08cd1e05c0b3440e27ce0e3d48f4b9b6f12

[ "MIT" ]

null

""" Copyright (c) 2022, Zach Lagden All rights reserved. """ import discord from discord.ext import commands from .embed import Embed async def cb_reply(ctx: commands.Context, content: str, mention: bool = False): return await ctx.message.reply(embed=Embed(description=f"`{content}`").raw(), mention_author=mention) async def cb_reply_edit(msg: discord.Message, content: str, mention: bool = False): return await msg.edit(embed=Embed(description=f"`{content}`").raw()) async def cb_send(ctx: commands.Context, content: str, mention: bool = False): return await ctx.send(embed=Embed(description=f"`{content}`").raw(), mention_author=mention) async def e_reply(ctx: commands.Context, content: str, mention: bool = False): return await ctx.message.reply(embed=Embed(description=content).raw(), mention_author=mention) async def reply(ctx: commands.Context, content: str, mention: bool = False): return await ctx.message.reply(content, mention_author=mention) async def send(ctx: commands.Context, content: str, mention: bool = False): return await ctx.send(content, mention_author=mention)

33.088235

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0.152727

0.767273

0.733333

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1,125

33

106

34.090909

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null

0

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0

1

0

null

0

1

0

1

0

4

0a8b1f9750c9b351dae0f0f98acadcb01876caf0

239

py

Python

server/users/serializers/organizations.py

yizhang7210/Syllable

0536763a21db9532fc73cd32d03a7732d73f4ab8

[ "MIT" ]

null

server/users/serializers/organizations.py

yizhang7210/Syllable

0536763a21db9532fc73cd32d03a7732d73f4ab8

[ "MIT" ]

13

2018-09-29T21:34:25.000Z

2018-12-15T18:54:52.000Z

server/users/serializers/organizations.py

yizhang7210/Syllable

0536763a21db9532fc73cd32d03a7732d73f4ab8

[ "MIT" ]

null

from rest_framework import serializers from users.models.organizations import Organization class OrganizationSerializer(serializers.ModelSerializer): class Meta: model = Organization fields = ('id', 'name', 'domain')

26.555556

58

0.74477

23

239

7.695652

0.782609

0

0.175732

239

8

59

29.875

0.898477

0

0.050209

0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

0ad0dfcdad536b5f9b0aab980c6f0d035a99381d

1,235

py

Python

tests/test_plugin_zattoo.py

eleeeeeee/abc

5d81d68c7a47e931f050632ae7cddb3b044971b4

[ "BSD-2-Clause" ]

1

2017-11-26T18:48:29.000Z

tests/test_plugin_zattoo.py

eleeeeeee/abc

5d81d68c7a47e931f050632ae7cddb3b044971b4

[ "BSD-2-Clause" ]

null

tests/test_plugin_zattoo.py

eleeeeeee/abc

5d81d68c7a47e931f050632ae7cddb3b044971b4

[ "BSD-2-Clause" ]

1

2021-06-03T23:08:48.000Z

import unittest from streamlink.plugins.zattoo import Zattoo class TestPluginZattoo(unittest.TestCase): def test_can_handle_url(self): # ewe live self.assertTrue(Zattoo.can_handle_url('http://tvonline.ewe.de/watch/daserste')) self.assertTrue(Zattoo.can_handle_url('http://tvonline.ewe.de/watch/zdf')) # netcologne live self.assertTrue(Zattoo.can_handle_url('https://nettv.netcologne.de/watch/daserste')) self.assertTrue(Zattoo.can_handle_url('https://nettv.netcologne.de/watch/zdf')) # zattoo live self.assertTrue(Zattoo.can_handle_url('https://zattoo.com/watch/daserste')) self.assertTrue(Zattoo.can_handle_url('https://zattoo.com/watch/zdf')) # zattoo vod self.assertTrue(Zattoo.can_handle_url('https://zattoo.com/ondemand/watch/ibR2fpisWFZGvmPBRaKnFnuT-alarm-am-airport')) self.assertTrue(Zattoo.can_handle_url('https://zattoo.com/ondemand/watch/G8S7JxcewY2jEwAgMzvFWK8c-berliner-schnauzen')) # shouldn't match self.assertFalse(Zattoo.can_handle_url('https://ewe.de')) self.assertFalse(Zattoo.can_handle_url('https://netcologne.de')) self.assertFalse(Zattoo.can_handle_url('https://zattoo.com'))

49.4

127

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1,235

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128

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null

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4

0ae199629e255a55d28132986561c869fd109846

238

py

Python

autogl/module/hpo/suggestion/early_stop_algorithm/early_stop_first_trial.py

general502570/AutoGL

cbac7954db8555af576692384b09d305a4ef67c2

[ "MIT" ]

824

2020-11-30T14:38:07.000Z

2022-03-19T10:14:04.000Z

autogl/module/hpo/suggestion/early_stop_algorithm/early_stop_first_trial.py

lucylow/AutoGL

e560ca7ef3b35a8b85d9b89063ce5ce7afaed52f

[ "MIT" ]

38

2020-12-21T12:32:57.000Z

2022-01-31T02:32:05.000Z

autogl/module/hpo/suggestion/early_stop_algorithm/early_stop_first_trial.py

lucylow/AutoGL

e560ca7ef3b35a8b85d9b89063ce5ce7afaed52f

[ "MIT" ]

85

2020-12-21T05:16:09.000Z

2022-03-28T08:44:22.000Z

from suggestion.early_stop_algorithm.abstract_early_stop import ( AbstractEarlyStopAlgorithm, ) class EarlyStopFirstTrialAlgorithm(AbstractEarlyStopAlgorithm): def get_early_stop_trials(self, trials): return [trials[0]]

26.444444

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0.695652

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0.004831

0.130252

238

8

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0.884058

0

1

0.166667

false

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1

0

null

0

1

0

null

0

1

0

4

0aef85f46f929aacb67d9c3b5b200885f0cb7792

190

py

Python

Sets/Sets_Eliminar.py

BrianMarquez3/Python-Course

2622b4ddfd687505becfd246e82a2ed0cb9b76f3

[ "MIT" ]

20

2020-08-19T23:27:01.000Z

2022-02-03T12:02:17.000Z

Sets/Sets_Eliminar.py

BrianMarquez3/Python-Course

2622b4ddfd687505becfd246e82a2ed0cb9b76f3

[ "MIT" ]

1

2021-04-10T18:06:05.000Z

Sets/Sets_Eliminar.py

BrianMarquez3/Python-Course

2622b4ddfd687505becfd246e82a2ed0cb9b76f3

[ "MIT" ]

2

2020-12-03T19:35:36.000Z

2021-11-10T14:58:39.000Z

#Eliminar conjuntos = set() conjuntos = {1,2,3,"brian", 4.6} conjuntos.discard(3) print (conjuntos) ("==================================================================================")

21.111111

86

0.368421

15

190

4.666667

0.733333

0

0.034286

0.078947

190

8

87

23.75

0.365714

0.042105

0

0.480663

0.453039

0

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false

0

0.2

1

0

null

0

1

0

1

0

1

null

0

4

0af374bcb532b4870abb788973b7ef113795908f

242

py

Python

wagtail/wagtailsnippets/apps.py

patphongs/wagtail

32555f7a1c599c139e0f26c22907c9612af2e015

[ "BSD-3-Clause" ]

3

2016-08-17T13:56:36.000Z

2019-04-23T19:59:25.000Z

wagtail/wagtailsnippets/apps.py

patphongs/wagtail

32555f7a1c599c139e0f26c22907c9612af2e015

[ "BSD-3-Clause" ]

11

2016-08-05T15:43:06.000Z

2016-12-16T13:32:23.000Z

wagtail/wagtailsnippets/apps.py

patphongs/wagtail

32555f7a1c599c139e0f26c22907c9612af2e015

[ "BSD-3-Clause" ]

2

2017-08-08T01:39:02.000Z

2018-05-06T06:16:10.000Z

from __future__ import absolute_import, unicode_literals from django.apps import AppConfig class WagtailSnippetsAppConfig(AppConfig): name = 'wagtail.wagtailsnippets' label = 'wagtailsnippets' verbose_name = "Wagtail snippets"

24.2

56

0.789256

24

242

7.666667

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0.119565

0

0.14876

242

9

57

26.888889

0.893204

0

0.223141

0.095041

0

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false

0

0.333333

0

1

0

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null

0

1

0

null

0

1

0

1

0

4

e40b8922234e1569224b37ab6fe5115c827b8061

299

py

Python

src/compas/datastructures/mesh/operations/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

src/compas/datastructures/mesh/operations/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

src/compas/datastructures/mesh/operations/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

from .collapse import * from .insert import * from .split import * from .swap import * from .weld import * from .collapse import __all__ as a from .insert import __all__ as c from .split import __all__ as d from .swap import __all__ as e from .weld import __all__ as f __all__ = a + c + d + e + f

21.357143

34

0.722408

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3.764706

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0

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299

13

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0.813559

0

1

0

false

0

0.909091

0

0.909091

0

null

1

0

null

0

1

0

1

0

4

7c38218c6c3c3b7ff9aca60cf305bf546e5e879a

66,351

py

Python

ex1_w2v/attentionMain.py

minkj1992/Korean_emotion_classification_attention

7e09cd8cd9b18c438d7b9032efb8ee37c505a7fa

[ "MIT" ]

3

2020-04-24T12:54:37.000Z

2021-12-21T02:03:06.000Z

ex1_w2v/attentionMain.py

minkj1992/Korean_emotion_classification_attention

7e09cd8cd9b18c438d7b9032efb8ee37c505a7fa

[ "MIT" ]

null

ex1_w2v/attentionMain.py

minkj1992/Korean_emotion_classification_attention

7e09cd8cd9b18c438d7b9032efb8ee37c505a7fa

[ "MIT" ]

3

2020-03-10T10:50:03.000Z

2021-12-21T02:03:07.000Z

import numpy as np import os, re, csv, math, codecs from keras.models import Sequential from keras.layers import Embedding from keras.preprocessing import sequence from keras.preprocessing.text import Tokenizer as t from tqdm import tqdm from keras.layers import merge, Dense, Input, LSTM, Embedding, Dropout, Activation, SpatialDropout1D from keras.layers.core import * from keras.layers.recurrent import LSTM from keras.layers.wrappers import Bidirectional from keras.models import * # to visualize, and to make zero shape matrix from attention_utils import get_activations, get_data_recurrent from Attention import Attention import pandas as pd from konlpy.tag import Okt as Twitter from selfword2vec import tokenization from Anomaly import checkAnomaly_x_y from keras.callbacks import EarlyStopping, ModelCheckpoint from keras.models import load_model from numpy import argmax from keras import backend as K import tensorflow as tf from sklearn.metrics import fbeta_score from keras.layers.normalization import BatchNormalization from keras.optimizers import Adam from keras.regularizers import l2 from keras.losses import binary_crossentropy import json # 1. 모델 저장시키기 # 2. tokenizer와 konlpy morphs 호환 여부 (완성 jupyter note) # 2'. word2vec 2가지 경우 더 추가 to embedding(final_total word2vec, twitter_translated.vec) # 3. 변수들 설정하기 np.random.seed(3) # NUM_WORDS, train으로 input받은 단어의 수 MAX_NB_WORDS = 20000 vocab_size = 0 EMB_DIM = 300 embeddings_index = dict() # columns = ["ID", "Tweet", "anger", "anticipation", "disgust", "fear", "joy", "love", "optimism", "pessimism","sadness","surprise","trust"] columns = ["ID","Tweet","분노","기대","혐오스러운","두려움","기쁨","사랑","낙관론","비관론","슬픔","놀라움","믿음"] ##########################################1. 데이터셋 생성하기 train_array = pd.read_csv("/home/minwookje/coding/ex1_w2v/data/tweet/dump/kor_train.txt",sep="\t", header=None,names=columns).values val_array = pd.read_csv("/home/minwookje/coding/ex1_w2v/data/tweet/dump/kor_dev.txt",sep="\t", header=None,names=columns).values test_array = pd.read_csv("/home/minwookje/coding/ex1_w2v/data/tweet/dump/kor_test_gold.txt",sep="\t", header=None,names=columns).values # 판다 shape print("train_array"+ str(train_array.shape)) print("val_array"+str(val_array.shape)) print("test_array"+str(test_array.shape)) print("Reading data!") # x, y 분할하기 x_train = train_array[1:,1] y_train = train_array[1:,2:] x_val = val_array[1:,1] y_val = val_array[1:,2:] x_test = test_array[1:,1] y_test = test_array[1:,2:] print("checking Anomaly!!") x_train, y_train = checkAnomaly_x_y(x_train,y_train) x_val, y_val = checkAnomaly_x_y(x_val,y_val) x_test, y_test = checkAnomaly_x_y(x_test,y_test) print("ANomaly result!") print("x_train.shape"+ str(x_train.shape)) print("y_train.shape"+ str(y_train.shape)) print("x_val.shape"+ str(x_val.shape)) print("y_val.shape"+ str(y_val.shape)) print("x_test.shape"+ str(x_test.shape)) print("y_test.shape"+ str(y_test.shape)) # print(type(x_train)) np.array로 변형이 필요한가 print("Finished!") # jupyter notebook # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ tmp = [] train_tmp = [] val_tmp = [] test_tmp = [] max_count = 0 set_words = set() print("Tokenizing!") # test의 tmp만 따로 받아주고 나머지는 val train test모두 통합시켜준다. test_tmp, dummy , set_words = tokenization(x_test) val_tmp, dummy , set_words = tokenization(x_val) train_tmp, max_count , set_words = tokenization(x_train) tmp, dummy , set_words = tokenization(np.hstack([x_train,x_val,x_test])) print("Tokenizing finished!") tmp = [] # tmp없애준다 #token shape print("It's by len()") print("train_tmp.len():" + str(len(train_tmp))+","+ str(len(train_tmp[0]))) print("val_tmp shape.len():" + str(len(val_tmp))+","+ str(len(val_tmp[0]))) print("test_tmp shape.len():" + str(len(test_tmp))+","+ str(len(test_tmp[0]))) print("max_count:"+str(max_count)) # 문장길이 100으로 맞춘다. max_count = min(100, max_count) print("Readding Embedding file") # embeddings_index == dict(w2v's word: vector) f = codecs.open('/home/minwookje/coding/ex1_w2v/embedding/1542954106final_total_pos.vec', encoding='utf-8') for line in tqdm(f): values = line.rstrip().rsplit(' ') word = values[0] coefs = np.asarray(values[1:], dtype='float32') embeddings_index[word] = coefs f.close() print('Loaded %s word vectors.' % len(embeddings_index)) # word2index table 생성 {token:index} # 모든 train val test에 사용되는 token별 index table # train 문장 token 갯수 word_index = len(set_words) word_tmp_dict = dict() for i, word in enumerate(set_words): word_tmp_dict[str(word).replace(" ", "")] = i word_tmp_dict['0'] = word_index #dict file 저장 with open('token2index.json','w') as dictionary_file: json.dump(word_tmp_dict,dictionary_file) ## 문장별 토큰화시킨 녀석에 index를 집어 넣어준다. 이때 pad도 동시에 해준다. word_vec = [] word_vec_test = [] word_vec_val = [] for sent in train_tmp: sub = [] for word in sent: if(len(sub)==max_count): break #print(word) #print(type(str(word))) #break # word는 tuple 타입, embeddings_index는 str타입, tuple 타입을 str()화시키면 # 중간에 space가 생성되어 match가 되지 않았다. 이를 해결해주었다. if(str(word).replace(" ", "") in word_tmp_dict): sub.append(word_tmp_dict[str(word).replace(" ", "")]) else: print("sentence index화 실패") count = max_count - len(sub) # padding sub.extend([word_index]*count) word_vec.append(sub) ## 테스트용 복사본 for sent in test_tmp: sub = [] for word in sent: if(len(sub)==max_count): break #print(word) #print(type(str(word))) #break # word는 tuple 타입, embeddings_index는 str타입, tuple 타입을 str()화시키면 # 중간에 space가 생성되어 match가 되지 않았다. 이를 해결해주었다. if(str(word).replace(" ", "") in word_tmp_dict): sub.append(word_tmp_dict[str(word).replace(" ", "")]) else: print("sentence index화 실패") count = max_count - len(sub) # padding sub.extend([word_index]*count) word_vec_test.append(sub) ## 검증용 복사본 for sent in val_tmp: sub = [] for word in sent: if(len(sub)==max_count): break #print(word) #print(type(str(word))) #break # word는 tuple 타입, embeddings_index는 str타입, tuple 타입을 str()화시키면 # 중간에 space가 생성되어 match가 되지 않았다. 이를 해결해주었다. if(str(word).replace(" ", "") in word_tmp_dict): sub.append(word_tmp_dict[str(word).replace(" ", "")]) else: print("sentence index화 실패") count = max_count - len(sub) # padding sub.extend([word_index]*count) word_vec_val.append(sub) print("word_vec shape:" + str(len(word_vec))+","+ str(len(word_vec[0]))) print("word_vec_val shape:" + str(len(word_vec_val))+","+ str(len(word_vec_val[0]))) print("word_vec_test shape:" + str(len(word_vec_test))+","+ str(len(word_vec_test[0]))) # 4번쨰 matrix embedding_matrix {index: vector} # vocab_size = min(MAX_NB_WORDS, word_index) vocab_size = word_index # create a weight matrix for words in training docs embedding_matrix = np.zeros((vocab_size+1, EMB_DIM)) match_count = 0 unmatch_count = 0 for word, i in word_tmp_dict.items(): if word != '0': if (word in embeddings_index): match_count += 1 # embedding_matrix[i] = np.zeros(300) embedding_matrix[i] = embeddings_index[word] # embedding_matrix[i] = np.random.uniform(-0.25,0.25,300) else: unmatch_count += 1 # embedding_matrix[i] = np.zeros(300) # embedding_matrix[i] = np.random.uniform(-0.25,0.25,300) ## used for OOV words embedding_matrix[i] = np.random.uniform(-1.0,1.0,300).astype('float32') print("match:" + str(match_count)) print("unmatch:" + str(unmatch_count)) embedding_matrix.tofile('index2vec.dat') # 여기부터 # 1.앞서 단어당 벡터 테이블(v) // embeddings_index, {w2v_word: vector} # train_word(str(word).replace(" ", "")) == embedding # 2.train 단어별 index (v) // word_tmp_dict {train_word(str(word).replace(" ", "")):index} # 3.sentence padding, sentence to index # 4.index당 vector table (v) //embedding_matrix {index: vector} 이녀석을 embedding weight에 넣어주어야 한다. # 문장 = [index들 나열 ] # 즉 embedding_matrix로 index를 seq에 넣어준묹장들을 train에 넣어줘야한다. # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------ # np.array화 print("word_vec shape:" + str(len(word_vec))+","+ str(len(word_vec[0]))) print("word_vec_val shape:" + str(len(word_vec_val))+","+ str(len(word_vec_val[0]))) print("word_vec_test shape:" + str(len(word_vec_test))+","+ str(len(word_vec_test[0]))) # np.savetxt('word_vec1.txt', word_vec[:500], delimiter=" ", fmt="%s") # np.savetxt('word_vec_val1.txt', word_vec_val[:500], delimiter=" ", fmt="%s") # np.savetxt('word_vec_test1.txt', word_vec_test[:500], delimiter=" ", fmt="%s") word_vec = np.array(word_vec) word_vec_val = np.array(word_vec_val) word_vec_test = np.array(word_vec_test) embedding_matrix = np.matrix(embedding_matrix) y_train = np.array(y_train) y_val = np.array(y_val) y_test = np.array(y_test) print("word_vec shape:" + str(word_vec.shape)) print("word_vec_val shape:" + str(word_vec_val.shape)) print("word_vec_test shape:" + str(word_vec_test.shape)) print("y_train shape:" + str(y_train.shape)) print("y_val shape:" + str(y_val.shape)) print("y_test shape:" + str(y_test.shape)) print("embedding_matrix shape:" + str(embedding_matrix.shape)) # 확인용 np.savetxt('word_vec.txt', word_vec[:500], delimiter=" ", fmt="%s") np.savetxt('word_vec_val.txt', word_vec_val[:500], delimiter=" ", fmt="%s") np.savetxt('word_vec_test.txt', word_vec_test[:500], delimiter=" ", fmt="%s") np.savetxt('embedding_matrix.txt', embedding_matrix[:600], delimiter=" ", fmt="%s") # TODO # 1.padding 23976이 너무 많이 들어간다. 이거 max_count를 정해주어야 한다. 100까지로 줄이자. # 2.그 다음에는 matrix 사이즈를 맞춰주어야 한다. # def fit_multilabel(model, X_train, X_val, y_train, y_val): # y_val = np.array(y_val) # y_train = np.array(y_train) # predictions = np.zeros(y_val.shape) # for i in range(y_val.shape[1]): # model.fit(X_train, y_train[:, i]) # y_p = model.predict(X_val) # predictions[:, i] = y_p # return predictions # from sklearn.metrics import jaccard_similarity_score # def jaccard_distance_loss(y_true, y_pred, smooth=100): # """ # Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|) # = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|)) # The jaccard distance loss is usefull for unbalanced datasets. This has been # shifted so it converges on 0 and is smoothed to avoid exploding or disapearing # gradient. # Ref: https://en.wikipedia.org/wiki/Jaccard_index # @url: https://gist.github.com/wassname/f1452b748efcbeb4cb9b1d059dce6f96 # @author: wassname # """ # print("loss = %s, %s"%(y_true,y_pred)) # y_pred = K.cast_to_floatx(y_pred) # y_true = K.cast_to_floatx(y_true) # intersection = K.sum(K.abs(y_true * y_pred), axis=-1) # sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1) # jac = (intersection + smooth) / (sum_ - intersection + smooth) # print("loss = %s, %s, %s"%(intersection, sum, jac)) # np.zeros_like(y_true, dtype = object) # np.zeros_like(y_pred, dtype = object) # im1 = np.asarray(y_true).astype(np.bool) # im2 = np.asarray(y_pred).astype(np.bool) # intersection = np.logical_and(im1, im2) # union = np.logical_or(im1, im2) # # intersection = K.sum(np.absolute(y_true * y_pred), axis=-1) # # sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1) # # jac = (intersection + smooth) / (sum_ - intersection + smooth) # jac = (intersection.sum()+smooth) / (float(union.sum()+smooth)) # print("loss = %s, %s, %s"%(intersection, sum, jac)) # print("intersection") # print(intersection) # # print(K.eval(intersection)) # print("sum") # print(sum) # # print(K.eval(sum)) # print("jac") # print(jac) # # print(K.eval(jac)) # print("K.eval(1-jac)*smooth") # print((1 - jac) * smooth) # print(type((1 - jac) * smooth)) # print(K.eval(1-jac)*smooth) # returnimport tensorflow as tf (1 - jac) * smooth # def jaccard_distance(y_true, y_pred, smooth=100): # intersection = K.sum(K.abs(y_true * y_pred), axis=-1) # sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1) # jac = (intersection + smooth) / (sum_ - intersection + smooth) # return (1 - jac) * smooth #####################################2. 모델 구성하기 # input_length : 단어의 수 즉 문장의 길이를 나타냅니다 # 임베딩 레이어의 출력 크기는 샘플 수 * output_dim * input_lenth가 됩니다 INPUT_DIM = 300 #wordvec사이즈 max_count = max_count #문장의 max길이 lstm_shape = 250 rate_drop_lstm = 0.3 rate_drop_dense = 0.3 # k_vec = K.variable(word_vec) # k_vec_val = K.variable(word_vec_val) # k_vec_test = K.variable(word_vec_test) # k_y_train = K.variable(y_train) # k_y_val = K.variable(y_val) # k_y_test = K.variable(y_test) # k_embedding_matrix = K.variable(embedding_matrix) k_vec = word_vec k_vec_val = word_vec_val k_vec_test = word_vec_test k_y_train = y_train k_y_val = y_val k_y_test = y_test k_embedding_matrix = embedding_matrix # nan delete index = np.argwhere(np.isnan(k_y_train))[:,0] index2 = np.argwhere(np.isnan(k_y_val))[:,0] index3 = np.argwhere(np.isnan(k_y_test))[:,0] k_y_train = np.delete(k_y_train,index,0) k_vec = np.delete(k_vec,index,0) k_y_val = np.delete(k_y_val,index2,0) k_vec_val = np.delete(k_vec_val,index2,0) k_vec_test = np.delete(k_vec_test,index3,0) k_y_test = np.delete(k_y_test,index3,0) # is nan check print(np.any(np.isnan(k_vec))) print(np.any(np.isnan(k_vec_val))) print(np.any(np.isnan(k_vec_test))) print(np.any(np.isnan(k_y_train))) print(np.any(np.isnan(k_y_val))) print(np.any(np.isnan(k_y_test))) print(np.any(np.isnan(k_embedding_matrix))) def jaccard_distance_loss(y_true, y_pred, smooth=100): """ Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|) = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|)) The jaccard distance loss is usefull for unbalanced datasets. This has been shifted so it converges on 0 and is smoothed to avoid exploding or disapearing gradient. Ref: https://en.wikipedia.org/wiki/Jaccard_index @url: https://gist.github.com/wassname/f1452b748efcbeb4cb9b1d059dce6f96 @author: wassname """ epsilon = tf.convert_to_tensor(1e-7, dtype='float32') intersection = K.sum(K.abs(y_true * y_pred), axis=-1) sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1) jac = (intersection + smooth) / (sum_ - intersection + smooth) return (1 - jac) # matrix accu def jaccard_distance_acc(y_true, y_pred): intersection = K.sum(K.abs(y_true * y_pred), axis=-1) sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1) jac = (intersection) / (sum_ - intersection) return jac # def scaled_binary_cross_entropy(y_true,y_pred): # epsilon = tf.convert_to_tensor(1e-7, dtype='float32') # r =K.binary_crossentropy(y_true,y_pred) # return r/(r.max()+epsilon) def scaled_binary_cross_entropy(y_true,y_pred): epsilon = tf.convert_to_tensor(1e-7, dtype='float32') loss =binary_crossentropy(y_true,y_pred) max_t = K.max(loss) return loss/(max_t+epsilon) def fbeta(y_true, y_pred, threshold_shift=0): beta = 2 # just in case of hipster activation at the final layer y_pred = K.clip(y_pred, 0, 1) # shifting the prediction threshold from .5 if needed y_pred_bin = K.round(y_pred + threshold_shift) tp = K.sum(K.round(y_true * y_pred_bin), axis=1) + K.epsilon() fp = K.sum(K.round(K.clip(y_pred_bin - y_true, 0, 1)), axis=1) fn = K.sum(K.round(K.clip(y_true - y_pred, 0, 1)), axis=1) precision = tp / (tp + fp) recall = tp / (tp + fn) beta_squared = beta ** 2 return K.mean((beta_squared + 1) * (precision * recall) / (beta_squared * precision + recall + K.epsilon())) accu = jaccard_distance_acc # 32하니까 죽어버린다. batch_size = 32 l2_reg = 0.0001 activity_l2 = 0.0001 # Define the model # inp = InputLayer(shape=(max_count,), dtype='float32',sparse=True) # input layer는 # inp = InputLayer(input_shape=(max_count,),sparse=True) # inp = Input(shape=(max_count,)) inp = Input(shape=(max_count,), dtype='float32') emb = Embedding(input_dim=vocab_size+1, output_dim=EMB_DIM, trainable=False, weights=[embedding_matrix], input_length=max_count)(inp) # max_features = vocab_size, maxlen=text_max_words, embed_size=EMB_DIM # emb = Embedding(input_dim=max_features, input_length = maxlen, output_dim=embed_size)(inp) # embedding dropout = 0.1 x = SpatialDropout1D(0.1)(emb) # x = Bidirectional(LSTM(lstm_shape, return_sequences=True, dropout=rate_drop_lstm, recurrent_dropout=rate_drop_lstm, W_regularizer=l2(l2_reg)))(x) # weight, bias, hidden state 수정해보기 , 현재는 kernel만 regularization 시켰다. x = Bidirectional(LSTM(lstm_shape, return_sequences=True, dropout=rate_drop_lstm, recurrent_dropout=rate_drop_lstm,kernel_regularizer=l2(l2_reg)))(x) x, attention = Attention()(x,rate_drop_dense,l2_reg) # BatchNOrmalization 추가 # x = BatchNormalization()(x) # 배치 노멀라이제이션 했을때 결과 # Train on 6474 samples, validate on 867 samples # Epoch 1/50 # 16/6474 [..............................] - ETA: 20:46 - loss: 1.0745 - jaccard 32/6474 [..............................] - ETA: 11:52 - loss: 1.0172 - jaccard 48/6474 [..............................] - ETA: 8:53 - loss: 0.9787 - jaccard_ 64/6474 [..............................] - ETA: 7:24 - loss: 0.9671 - jaccard_ 80/6474 [..............................] - ETA: 6:30 - loss: 0.9672 - jaccard_ 96/6474 [..............................] - ETA: 5:55 - loss: 0.9630 - jaccard_ 112/6474 [..............................] - ETA: 5:29 - loss: 0.9638 - jaccard_ 128/6474 [..............................] - ETA: 5:10 - loss: 0.9557 - jaccard_ 144/6474 [..............................] - ETA: 4:54 - loss: 0.9461 - jaccard_ 160/6474 [..............................] - ETA: 4:42 - loss: 0.9401 - jaccard_ 176/6474 [..............................] - ETA: 4:31 - loss: 0.9331 - jaccard_ 192/6474 [..............................] - ETA: 4:23 - loss: 0.9287 - jaccard_ 208/6474 [..............................] - ETA: 4:15 - loss: 0.9296 - jaccard_ 224/6474 [>.............................] - ETA: 4:09 - loss: 0.9260 - jaccard_ 240/6474 [>.............................] - ETA: 4:03 - loss: 0.9206 - jaccard_ 256/6474 [>.............................] - ETA: 3:59 - loss: 0.9146 - jaccard_ 272/6474 [>.............................] - ETA: 3:54 - loss: 0.9106 - jaccard_ 288/6474 [>.............................] - ETA: 3:50 - loss: 0.9075 - jaccard_ 304/6474 [>.............................] - ETA: 3:47 - loss: 0.9047 - jaccard_ 320/6474 [>.............................] - ETA: 3:44 - loss: 0.9023 - jaccard_ 336/6474 [>.............................] - ETA: 3:41 - loss: 0.8991 - jaccard_ 352/6474 [>.............................] - ETA: 3:38 - loss: 0.8952 - jaccard_ 368/6474 [>.............................] - ETA: 3:36 - loss: 0.8920 - jaccard_ 384/6474 [>.............................] - ETA: 3:34 - loss: 0.8883 - jaccard_ 400/6474 [>.............................] - ETA: 3:32 - loss: 0.8852 - jaccard_ 416/6474 [>.............................] - ETA: 3:29 - loss: 0.8824 - jaccard_ 432/6474 [=>............................] - ETA: 3:28 - loss: 0.8802 - jaccard_ 448/6474 [=>............................] - ETA: 3:27 - loss: 0.8775 - jaccard_ 464/6474 [=>............................] - ETA: 3:25 - loss: 0.8795 - jaccard_ 480/6474 [=>............................] - ETA: 3:23 - loss: 0.8768 - jaccard_ 496/6474 [=>............................] - ETA: 3:22 - loss: 0.8741 - jaccard_ 512/6474 [=>............................] - ETA: 3:20 - loss: 0.8715 - jaccard_ 528/6474 [=>............................] - ETA: 3:18 - loss: 0.8688 - jaccard_ 544/6474 [=>............................] - ETA: 3:17 - loss: 0.8665 - jaccard_ 560/6474 [=>............................] - ETA: 3:15 - loss: 0.8641 - jaccard_ 576/6474 [=>............................] - ETA: 3:14 - loss: 0.8612 - jaccard_ 592/6474 [=>............................] - ETA: 3:13 - loss: 0.8591 - jaccard_ 608/6474 [=>............................] - ETA: 3:11 - loss: 0.8568 - jaccard_ 624/6474 [=>............................] - ETA: 3:10 - loss: 0.8547 - jaccard_ 640/6474 [=>............................] - ETA: 3:09 - loss: 0.8524 - jaccard_ 656/6474 [==>...........................] - ETA: 3:08 - loss: 0.8501 - jaccard_ 672/6474 [==>...........................] - ETA: 3:07 - loss: 0.8476 - jaccard_ 688/6474 [==>...........................] - ETA: 3:05 - loss: 0.8453 - jaccard_ 704/6474 [==>...........................] - ETA: 3:04 - loss: 0.8429 - jaccard_ 720/6474 [==>...........................] - ETA: 3:03 - loss: 0.84 912/6474 [===>..........................] - ETA: 2:53 - loss: 0.8130 - jaccard_distance_acc: 0.1665 - fbeta: 0.2083fbeta: 0.2358 # 6474/6474 [==============================] - 206s 32ms/step - loss: 0.5829 - jaccard_distance_acc: 0.1617 - fbeta: 0.0630 - val_loss: 0.5062 - val_jaccard_distance_acc: 0.1619 - val_fbeta: 0.0180 # Epoch 2/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4948 - jaccard_distance_acc: 0.1611 - fbeta: 0.0348 - val_loss: 0.4914 - val_jaccard_distance_acc: 0.1796 - val_fbeta: 0.0161 # Epoch 3/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4834 - jaccard_distance_acc: 0.1609 - fbeta: 0.0316 - val_loss: 0.4861 - val_jaccard_distance_acc: 0.1738 - val_fbeta: 0.0166 # Epoch 4/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4809 - jaccard_distance_acc: 0.1612 - fbeta: 0.0326 - val_loss: 0.4925 - val_jaccard_distance_acc: 0.1617 - val_fbeta: 0.0176 # Epoch 5/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4811 - jaccard_distance_acc: 0.1614 - fbeta: 0.0344 - val_loss: 0.4842 - val_jaccard_distance_acc: 0.1642 - val_fbeta: 0.0161 # Epoch 6/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4808 - jaccard_distance_acc: 0.1606 - fbeta: 0.0327 - val_loss: 0.4809 - val_jaccard_distance_acc: 0.1715 - val_fbeta: 0.0172 # Epoch 7/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4837 - jaccard_distance_acc: 0.1613 - fbeta: 0.0373 - val_loss: 3.1112 - val_jaccard_distance_acc: 0.2469 - val_fbeta: 0.5642 # Epoch 8/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.5343 - jaccard_distance_acc: 0.1631 - fbeta: 0.0678 - val_loss: 0.5607 - val_jaccard_distance_acc: 0.1851 - val_fbeta: 0.2194 # Epoch 9/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4915 - jaccard_distance_acc: 0.1605 - fbeta: 0.0310 - val_loss: 0.4925 - val_jaccard_distance_acc: 0.1687 - val_fbeta: 0.0161 # Epoch 10/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4810 - jaccard_distance_acc: 0.1611 - fbeta: 0.0325 - val_loss: 0.4936 - val_jaccard_distance_acc: 0.1533 - val_fbeta: 0.0161 # Epoch 11/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4798 - jaccard_distance_acc: 0.1609 - fbeta: 0.0323 - val_loss: 0.4872 - val_jaccard_distance_acc: 0.1572 - val_fbeta: 0.0161 # Epoch 12/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4754 - jaccard_distance_acc: 0.1606 - fbeta: 0.0309 - val_loss: 0.4837 - val_jaccard_distance_acc: 0.1626 - val_fbeta: 0.0161 # Epoch 13/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4821 - jaccard_distance_acc: 0.1606 - fbeta: 0.0315 - val_loss: 0.4865 - val_jaccard_distance_acc: 0.1684 - val_fbeta: 0.0161 # Epoch 14/50 # 6474/6474 [==============================] - 194s 30ms/step - loss: 0.4783 - jaccard_distance_acc: 0.1612 - fbeta: 0.0325 - val_loss: 0.4824 - val_jaccard_distance_acc: 0.1702 - val_fbeta: 0.0161 # Epoch 15/50 # 6474/6474 [==============================] - 186s 29ms/step - loss: 0.4774 - jaccard_distance_acc: 0.1615 - fbeta: 0.0329 - val_loss: 0.4809 - val_jaccard_distance_acc: 0.1674 - val_fbeta: 0.0161 # Epoch 16/50 # 6474/6474 [==============================] - 186s 29ms/step - loss: 0.4771 - jaccard_distance_acc: 0.1609 - fbeta: 0.0309 - val_loss: 0.4808 - val_jaccard_distance_acc: 0.1681 - val_fbeta: 0.0161 # Epoch 17/50 # 6474/6474 [==============================] - 193s 30ms/step - loss: 0.4889 - jaccard_distance_acc: 0.1615 - fbeta: 0.0434 - val_loss: 0.4949 - val_jaccard_distance_acc: 0.1591 - val_fbeta: 0.0161 # Epoch 18/50 # 6474/6474 [==============================] - 186s 29ms/step - loss: 0.5120 - jaccard_distance_acc: 0.1625 - fbeta: 0.0496 - val_loss: 0.5535 - val_jaccard_distance_acc: 0.1604 - val_fbeta: 0.0180 # Epoch 19/50 # 6474/6474 [==============================] - 187s 29ms/step - loss: 0.4868 - jaccard_distance_acc: 0.1611 - fbeta: 0.0316 - val_loss: 0.4873 - val_jaccard_distance_acc: 0.1664 - val_fbeta: 0.0161 # Epoch 20/50 # 6474/6474 [==============================] - 190s 29ms/step - loss: 0.4776 - jaccard_distance_acc: 0.1608 - fbeta: 0.0317 - val_loss: 0.4806 - val_jaccard_distance_acc: 0.1640 - val_fbeta: 0.0161 # Epoch 21/50 # 6474/6474 [==============================] - 201s 31ms/step - loss: 0.4749 - jaccard_distance_acc: 0.1610 - fbeta: 0.0307 - val_loss: 0.4834 - val_jaccard_distance_acc: 0.1651 - val_fbeta: 0.0161 # Epoch 22/50 # 6474/6474 [==============================] - 190s 29ms/step - loss: 0.4760 - jaccard_distance_acc: 0.1608 - fbeta: 0.0307 - val_loss: 0.4805 - val_jaccard_distance_acc: 0.1689 - val_fbeta: 0.0161 # Epoch 23/50 # 144/6474 [..............................] - ETA: 2:58 - loss: 0.4965 - jaccard_distance_acc: 0.1596 - fbeta: 0.0278^Z # Dense(11, activation="sigmoid",activity_regularizer=activity_l2(0.0001)) x = Dense(11, activation="sigmoid")(x) model = Model(inputs=inp, outputs=x) # # 1 # model.compile(loss='binary_crossentropy', # optimizer='adam', # metrics=[accu]) # Epoch 1/10 # 32/6474 [..............................] - ETA: 9:22 - loss: 0.7045 - jaccard_ 64/6474 [..............................] - ETA: 6:18 - loss: 0.6864 - jaccard_ 96/6474 [..............................] - ETA: 5:04 - loss: 0.6675 - jaccard_ 128/6474 [..............................] - ETA: 4:24 - loss: 0.6393 - jaccard_ 160/6474 [..............................] - ETA: 3:59 - loss: 0.6099 - jaccard_ 192/6474 [..............................] - ETA: 3:44 - loss: 0.5952 - jaccard_ 224/6474 [>.............................] - ETA: 3:31 - loss: 0.5786 - jaccard_ 256/6474 [>.............................] - ETA: 3:22 - loss: 0.5659 - jaccard_ 288/6474 [>.............................] - ETA: 3:15 - loss: 0.5540 - jaccard_ 320/6474 [>.............................] - ETA: 3:09 - loss: 0.5526 - jaccard_ 352/6474 [>.............................] - ETA: 3:04 - loss: 0.5446 - jaccard_ 384/6474 [>.............................] - ETA: 3:00 - loss: 0.5385 - jaccard_ 416/6474 [>.............................] - ETA: 2:56 - loss: 0.5341 - jaccard_ 448/6474 [=>............................] - ETA: 2:53 - loss: 0.5309 - jaccard_ 480/6474 [=>............................] - ETA: 2:50 - loss: 0.5289 - jaccard_ 512/6474 [=>............................] - ETA: 2:47 - loss: 0.5280 - jaccard_ 544/6474 [=>............................] - ETA: 2:45 - loss: 0.5263 - jaccard_ 576/6474 [=>............................] - ETA: 2:43 - loss: 0.5224 - jaccard_ 608/6474 [=>............................] - ETA: 2:41 - loss: 0.5201 - jaccard_ 640/6474 [=>............................] - ETA: 2:39 - loss: 0.5217 - jaccard_ 672/6474 [==>...........................] - ETA: 2:38 - loss: 0.5178 - jaccard_ 704/6474 [==>...........................] - ETA: 2:37 - loss: 0.5154 - jaccard_ 736/6474 [==>...........................] - ETA: 2:35 - loss: 0.5115 - jaccard_ 768/6474 [==>...........................] - ETA: 2:34 - loss: 0.5113 - jaccard_ 800/6474 [==>...........................] - ETA: 2:32 - loss: 0.5100 - jaccard_ 832/6474 [==>...........................] - ETA: 2:31 - loss: 0.5091 - jaccard_ 864/6474 [===>..........................] - ETA: 2:29 - loss: 0.5059 - jaccard_ 896/6474 [===>..........................] - ETA: 2:28 - loss: 0.5033 - jaccard_ 928/6474 [===>..........................] - ETA: 2:27 - loss: 0.5028 - jaccard_ 2496/6474 [==========>...................] - ETA: 1:39 - loss: 0.4840 - jaccard_distance_acc: 2592/6474 [===========>..................] - ETA: 1:36 - loss: 0.2624/6474 [===========>..................] - ETA: 1:36 - loss: 0.4848 - jaccard_2656/6474 [===========>..................] - ETA: 1:35 - loss: 0.4852 - jaccard_2688/6474 [===========>..................] - ETA: 1:34 - loss: 0.4850 - jaccard_2720/6474 [===========>..................] - ETA: 1:33 - loss: 0.4851 - jaccard_2752/6474 [===========>..................] - ETA: 1:32 - loss: 0.4851 - jaccard_2784/6474 [===========>..................] - ETA: 1:31 - loss: 0.4850 - jaccard_2816/6474 [============>.................] - ETA: 1:31 - loss: 0.4847 - jaccard_2848/6474 [============>.................] - ETA: 1:30 - loss: 0.4848 - jaccard_2880/6474 [============>.................] - ETA: 1:29 - loss: 0.4846 - jaccard_2912/6474 [============>.................] - ETA: 1:28 - loss: 0.4849 - jaccard_2944/6474 [============>.................] - ETA: 1:27 - loss: 0.4845 - jaccard_2976/6474 [=====6474/6474 [==============================] - 165s 26ms/step - loss: 0.4775 - jaccard_distance_acc: 0.1631 - val_loss: 0.4660 - val_jaccard_distance_acc: 0.1825.1623 # Epoch 2/10 # 1408/6474 [=====>........................] - ETA: 2:01 - loss: 0.4571 - jaccard_1440/6474 [=====>........................] - ETA: 2:01 - loss: 0.4582 - jaccard_1472/6474 [=====>........................] - ETA: 2:00 - loss: 0.4585 - jaccard_1504/6474 [=====>........................] - ETA: 1:59 - loss: 0.4579 - jaccard_1536/6474 [======>.......................] - ETA: 1:59 - loss: 0.4578 - jaccard_1568/6474 [======>.......................] - ETA: 1:58 - loss: 0.4573 - jaccard_1600/6474 [======>.......................] - ETA: 1:57 - loss: 0.4581 - jaccard_1632/6474 [======>.......................] - ETA: 1:56 - loss: 0.4579 - jaccard_1664/6474 [======>.......................] - ETA: 1:56 - loss: 0.4582 - jaccard_1696/6474 [======>.......................] - ETA: 1:55 - loss: 0.4581 - jaccard_1728/6474 [=======>......................] - ETA: 1:54 - loss: 0.4584 - jaccard_1760/6474 [=======>......................] - ETA: 1:53 - loss: 0.4583 - jaccard_1792/6474 [=======>......................] - ETA: 1:53 - loss: 0.4582 - jaccard_1824/6474 [=======>......................] - ETA: 1:52 - loss: 0.4584 - jaccard_1856/6474 [=======>......................] - ETA: 1:51 - loss: 0.4581 - jaccard_1888/6474 [=======>......................] - ETA: 1:50 - loss: 0.4575 - jaccard_1920/6474 [=======>......................] - ETA: 1:50 - loss: 0.4587 - jaccard_1952/6474 [========>.....................] - ETA: 1:49 - loss: 0.4585 - jaccard_1984/6474 [========>.....................] - ETA: 1:48 - loss: 0.4582 - jaccard_2016/6474 [========>.....................] - ETA: 1:47 - loss: 0.4582 - jaccard_2048/6474 [========>.....................] - ETA: 1:46 - loss: 0.4579 - jaccard_2080/6474 [========>.....................] - ETA: 1:46 - loss: 0.4578 - jaccard_2112/6474 [========>.....................] - ETA: 1:45 - loss: 0.4576 - jaccard_2144/6474 [========>.....................] - ETA: 1:44 - loss: 0.4573 - jaccard_2176/6474 [=========>....................] - ETA: 1:43 - loss: 0.4570 - jaccard_2208/6474 [=========>....................] - ETA: 1:43 - loss: 0.4568 - jaccard_2240/6474 [=========>.........4608/6474 [====================>.........] - ETA: 45s - loss: 0.4567 - jaccard_distance_acc: 0.6474/6474 [==============================] - 165s 26ms/step - loss: 0.4560 - jaccard_distance_acc: 0.1827 - val_loss: 0.4383 - val_jaccard_distance_acc: 0.2132 # Epoch 3/10 # 6474/6474 [==============================] - 162s 25ms/step - loss: 0.4378 - jaccard_distance_acc: 0.2064 - val_loss: 0.4245 - val_jaccard_distance_acc: 0.2289 # Epoch 4/10 # 6474/6474 [==============================] - 163s 25ms/step - loss: 0.4251 - jaccard_distance_acc: 0.2249 - val_loss: 0.4217 - val_jaccard_distance_acc: 0.2414 # Epoch 5/10 # 6474/6474 [==============================] - 2968s 458ms/step - loss: 0.4117 - jaccard_distance_acc: 0.2425 - val_loss: 0.4122 - val_jaccard_distance_acc: 0.2415 # Epoch 6/10 # 6474/6474 [==============================] - 168s 26ms/step - loss: 0.4013 - jaccard_distance_acc: 0.2553 - val_loss: 0.4052 - val_jaccard_distance_acc: 0.2642 # Epoch 7/10 # 6474/6474 [==============================] - 169s 26ms/step - loss: 0.3919 - jaccard_distance_acc: 0.2667 - val_loss: 0.4075 - val_jaccard_distance_acc: 0.2673 # Epoch 8/10 # 6474/6474 [==============================] - 163s 25ms/step - loss: 0.3826 - jaccard_distance_acc: 0.2808 - val_loss: 0.4040 - val_jaccard_distance_acc: 0.2709 # Epoch 9/10 # 6474/6474 [==============================] - 163s 25ms/step - loss: 0.3721 - jaccard_distance_acc: 0.2928 - val_loss: 0.4054 - val_jaccard_distance_acc: 0.2911 # Epoch 10/10 # 6474/6474 [==============================] - 163s 25ms/step - loss: 0.3616 - jaccard_distance_acc: 0.3084 - val_loss: 0.4056 - val_jaccard_distance_acc: 0.2884 # 3151/3151 [==============================] - 26s 8ms/step # 2 # model.compile(loss=jaccard_distance_loss, # optimizer='adam', # metrics=[accu]) # Train on 6474 samples, validate on 867 samples # Epoch 1/10 # 6474/6474 [==============================] - 164s 25ms/step - loss: 0.6940 - jaccard_distance_acc: 0.3060 - val_loss: 0.6702 - val_jaccard_distance_acc: 0.3298 # Epoch 2/10 # 6474/6474 [==============================] - 162s 25ms/step - loss: 0.6902 - jaccard_distance_acc: 0.3098 - val_loss: 0.6702 - val_jaccard_distance_acc: 0.3298 # Epoch 3/10 # 6474/6474 [==============================] - 162s 25ms/step - loss: 0.6902 - jaccard_distance_acc: 0.3098 - val_loss: 0.6702 - val_jaccard_distance_acc: 0.3298 # Epoch 4/10 # 2144/6474 [========>.....................] - ETA: 1:43 - loss: 0.6901 - jaccard_distance_acc: 0.3099^Z # [1]+ 정지됨 python3 181120_preprocessing.py # 3 # model.compile(loss='binary_crossentropy', # optimizer='adam', # metrics=['accracy']) # Train on 6474 samples, validate on 867 samples # Epoch 1/10 # 6474/6474 [==============================] - 163s 25ms/step - loss: 0.4774 - acc: 0.7840 - val_loss: 0.4684 - val_acc: 0.7794 # Epoch 2/10 # 6474/6474 [==============================] - 161s 25ms/step - loss: 0.4563 - acc: 0.7935 - val_loss: 0.4335 - val_acc: 0.8041 # Epoch 3/10 # 6474/6474 [==============================] - 162s 25ms/step - loss: 0.4369 - acc: 0.8056 - val_loss: 0.4264 - val_acc: 0.8064 # Epoch 4/10 # 6474/6474 [==============================] - 162s 25ms/step - loss: 0.4242 - acc: 0.8110 - val_loss: 0.4200 - val_acc: 0.8087 # Epoch 5/10 # 6474/6474 [==============================] - 162s 25ms/step - loss: 0.4104 - acc: 0.8174 - val_loss: 0.4119 - val_acc: 0.8171 # Epoch 6/10 # 6474/6474 [==============================] - 162s 25ms/step - loss: 0.4017 - acc: 0.8241 - val_loss: 0.4063 - val_acc: 0.8188 # Epoch 7/10 # 6474/6474 [==============================] - 730s 113ms/step - loss: 0.3942 - acc: 0.8273 - val_loss: 0.4063 - val_acc: 0.8184 # Epoch 8/10 # 6474/6474 [==============================] - 168s 26ms/step - loss: 0.3857 - acc: 0.8318 - val_loss: 0.4036 - val_acc: 0.8199 # Epoch 9/10 # 6474/6474 [==============================] - 164s 25ms/step - loss: 0.3777 - acc: 0.8355 - val_loss: 0.4021 - val_acc: 0.8219 # Epoch 10/10 # 6474/6474 [==============================] - 179s 28ms/step - loss: 0.3676 - acc: 0.8384 - val_loss: 0.4051 - val_acc: 0.8204 # 3151/3151 [==============================] - 27s 9ms/step # score : 0.406278005236acc : 0.823230730148 # 4 # model.compile(loss='binary_crossentropy', # optimizer='rmsprop', # metrics=['acc', accu]) # Epoch 1/50 # 16/6474 [..............................] - ETA: 31:06 - loss: 0.6962 - jaccard_distance_acc: 0.1575 - f 32/6474 [..............................] - ETA: 18:30 - loss: 0.6798 - jaccard_distance_acc: 0.1541 - f 48/6474 [..............................] - ETA: 14:12 - loss: 0.6345 - jaccard_distance_acc: 0.1517 - f 64/6474 [..............................] - ETA: 12:08 - loss: 0.6052 - jaccard_distance_acc: 0.1490 - f 80/6474 [..............................] - ETA: 10:58 - loss: 0.5777 - jaccard_distance_acc: 0.1464 - f 96/6474 [..............................] - ETA: 10:07 - loss: 0.5617 - jaccard_distance_acc: 0.1479 - f 112/6474 [..............................] - ETA: 9:29 - loss: 0.5565 - jaccard_distance_acc: 0.1488 - fb 128/6474 [..............................] - ETA: 9:03 - loss: 0.5461 - jaccard_distance_acc: 0.1512 - fb 144/6474 [..............................] - ETA: 8:39 - loss: 0.5464 - jaccard_distance_acc: 0.1553 - fb 160/6474 [..............................] - ETA: 8:21 - loss: 0.5346 - jaccard_distance_acc: 0.1541 - fb 176/6474 [..............................] - ETA: 8:07 - loss: 0.5358 - jaccard_distance_acc: 0.1555 - fb 192/6474 [..............................] - ETA: 7:53 - loss: 0.5330 - jaccard_distance_acc: 0.1548 - fb 208/6474 [..............................] - ETA: 7:41 - loss: 0.5300 - jaccard_distance_acc: 0.1539 - fb 224/6474 [>.............................] - ETA: 7:31 - loss: 0.5278 - jaccard_distance_acc: 0.1524 - fb 240/6474 [>.............................] - ETA: 7:22 - loss: 0.5257 - jaccard_distance_acc: 0.1515 - fb 256/6474 [>.............................] - ETA: 7:16 - loss: 0.5219 - jaccard_distance_acc: 0.1515 - fb 272/6476474/6474 [==============================] - 377s 58ms/step - loss: 0.4761 - jaccard_distance_acc: 0.1626 - fbeta: 0.0427 - val_loss: 0.4718 - val_jaccard_distance_acc: 0.1743 - val_fbeta: 0.0150 # Epoch 2/50 # 6474/6474 [==============================] - 367s 57ms/step - loss: 0.4506 - jaccard_distance_acc: 0.1899 - fbeta: 0.1640 - val_loss: 0.4286 - val_jaccard_distance_acc: 0.2238 - val_fbeta: 0.2165 # Epoch 3/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.4303 - jaccard_distance_acc: 0.2180 - fbeta: 0.2692 - val_loss: 0.4274 - val_jaccard_distance_acc: 0.2380 - val_fbeta: 0.2963 # Epoch 4/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.4167 - jaccard_distance_acc: 0.2362 - fbeta: 0.3153 - val_loss: 0.4138 - val_jaccard_distance_acc: 0.2498 - val_fbeta: 0.3165 # Epoch 5/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.4004 - jaccard_distance_acc: 0.2586 - fbeta: 0.3697 - val_loss: 0.4156 - val_jaccard_distance_acc: 0.2428 - val_fbeta: 0.2902 # Epoch 6/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3892 - jaccard_distance_acc: 0.2726 - fbeta: 0.3920 - val_loss: 0.4030 - val_jaccard_distance_acc: 0.2778 - val_fbeta: 0.3690 # Epoch 7/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3776 - jaccard_distance_acc: 0.2876 - fbeta: 0.4294 - val_loss: 0.4071 - val_jaccard_distance_acc: 0.2727 - val_fbeta: 0.3754 # Epoch 8/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3661 - jaccard_distance_acc: 0.3027 - fbeta: 0.4526 - val_loss: 0.4072 - val_jaccard_distance_acc: 0.2715 - val_fbeta: 0.3536 # Epoch 9/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3536 - jaccard_distance_acc: 0.3184 - fbeta: 0.4749 - val_loss: 0.4045 - val_jaccard_distance_acc: 0.2980 - val_fbeta: 0.4100 # Epoch 10/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.3456 - jaccard_distance_acc: 0.3320 - fbeta: 0.4995 - val_loss: 0.4084 - val_jaccard_distance_acc: 0.3023 - val_fbeta: 0.4141 # Epoch 11/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.3350 - jaccard_distance_acc: 0.3447 - fbeta: 0.5130 - val_loss: 0.4093 - val_jaccard_distance_acc: 0.2999 - val_fbeta: 0.4063 # Epoch 12/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3227 - jaccard_distance_acc: 0.3613 - fbeta: 0.5413 - val_loss: 0.4135 - val_jaccard_distance_acc: 0.3017 - val_fbeta: 0.4054 # Epoch 13/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.3143 - jaccard_distance_acc: 0.3726 - fbeta: 0.5565 - val_loss: 0.4175 - val_jaccard_distance_acc: 0.3082 - val_fbeta: 0.4300 # Epoch 14/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.3070 - jaccard_distance_acc: 0.3844 - fbeta: 0.5686 - val_loss: 0.4289 - val_jaccard_distance_acc: 0.3154 - val_fbeta: 0.4311 # Epoch 15/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2980 - jaccard_distance_acc: 0.3942 - fbeta: 0.5858 - val_loss: 0.4412 - val_jaccard_distance_acc: 0.3166 - val_fbeta: 0.4379 # Epoch 16/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2850 - jaccard_distance_acc: 0.4098 - fbeta: 0.6062 - val_loss: 0.4378 - val_jaccard_distance_acc: 0.3181 - val_fbeta: 0.4349 # Epoch 17/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2803 - jaccard_distance_acc: 0.4209 - fbeta: 0.6202 - val_loss: 0.4379 - val_jaccard_distance_acc: 0.3192 - val_fbeta: 0.4295 # Epoch 18/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2729 - jaccard_distance_acc: 0.4319 - fbeta: 0.6358 - val_loss: 0.4504 - val_jaccard_distance_acc: 0.3201 - val_fbeta: 0.4383 # Epoch 19/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2633 - jaccard_distance_acc: 0.4444 - fbeta: 0.6461 - val_loss: 0.4479 - val_jaccard_distance_acc: 0.3229 - val_fbeta: 0.4409 # Epoch 20/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.2576 - jaccard_distance_acc: 0.4531 - fbeta: 0.6581 - val_loss: 0.4578 - val_jaccard_distance_acc: 0.3308 - val_fbeta: 0.4555 # Epoch 21/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2511 - jaccard_distance_acc: 0.4639 - fbeta: 0.6706 - val_loss: 0.4673 - val_jaccard_distance_acc: 0.3209 - val_fbeta: 0.4305 # Epoch 22/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2436 - jaccard_distance_acc: 0.4743 - fbeta: 0.6839 - val_loss: 0.4847 - val_jaccard_distance_acc: 0.3328 - val_fbeta: 0.4618 # Epoch 23/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2365 - jaccard_distance_acc: 0.4849 - fbeta: 0.6919 - val_loss: 0.4871 - val_jaccard_distance_acc: 0.3315 - val_fbeta: 0.4600 # Epoch 24/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2320 - jaccard_distance_acc: 0.4910 - fbeta: 0.7011 - val_loss: 0.4853 - val_jaccard_distance_acc: 0.3275 - val_fbeta: 0.4493 # Epoch 25/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2269 - jaccard_distance_acc: 0.4989 - fbeta: 0.7053 - val_loss: 0.4953 - val_jaccard_distance_acc: 0.3331 - val_fbeta: 0.4499 # Epoch 26/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2174 - jaccard_distance_acc: 0.5130 - fbeta: 0.7232 - val_loss: 0.4959 - val_jaccard_distance_acc: 0.3457 - val_fbeta: 0.4814 # Epoch 27/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.2148 - jaccard_distance_acc: 0.5194 - fbeta: 0.7295 - val_loss: 0.5119 - val_jaccard_distance_acc: 0.3323 - val_fbeta: 0.4494 # Epoch 28/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2082 - jaccard_distance_acc: 0.5294 - fbeta: 0.7392 - val_loss: 0.5138 - val_jaccard_distance_acc: 0.3390 - val_fbeta: 0.4622 # Epoch 29/50 # 6474/6474 [==============================] - 361s 56ms/step - loss: 0.2031 - jaccard_distance_acc: 0.5345 - fbeta: 0.7428 - val_loss: 0.5134 - val_jaccard_distance_acc: 0.3390 - val_fbeta: 0.4613 # Epoch 30/50 # 6474/6474 [==============================] - 361s 56ms/step - loss: 0.1986 - jaccard_distance_acc: 0.5456 - fbeta: 0.7544 - val_loss: 0.5245 - val_jaccard_distance_acc: 0.3289 - val_fbeta: 0.4362 # Epoch 31/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.1950 - jaccard_distance_acc: 0.5508 - fbeta: 0.7570 - val_loss: 0.5260 - val_jaccard_distance_acc: 0.3360 - val_fbeta: 0.4513 # Epoch 32/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.1901 - jaccard_distance_acc: 0.5582 - fbeta: 0.7635 - val_loss: 0.5358 - val_jaccard_distance_acc: 0.3401 - val_fbeta: 0.4495 # Epoch 33/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.1874 - jaccard_distance_acc: 0.5636 - fbeta: 0.7708 - val_loss: 0.5402 - val_jaccard_distance_acc: 0.3318 - val_fbeta: 0.4381 # Epoch 34/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.1830 - jaccard_distance_acc: 0.5722 - fbeta: 0.7789 - val_loss: 0.5433 - val_jaccard_distance_acc: 0.3342 - val_fbeta: 0.4462 # Epoch 35/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.1787 - jaccard_distance_acc: 0.5776 - fbeta: 0.7838 - val_loss: 0.5424 - val_jaccard_distance_acc: 0.3500 - val_fbeta: 0.4689 # Epoch 36/50 # 6474/6474 [==============================] - 341s 53ms/step - loss: 0.1774 - jaccard_distance_acc: 0.5819 - fbeta: 0.7876 - val_loss: 0.5405 - val_jaccard_distance_acc: 0.3441 - val_fbeta: 0.4530 # Epoch 37/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1704 - jaccard_distance_acc: 0.5920 - fbeta: 0.7924 - val_loss: 0.5666 - val_jaccard_distance_acc: 0.3288 - val_fbeta: 0.4324 # Epoch 38/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1692 - jaccard_distance_acc: 0.5960 - fbeta: 0.7988 - val_loss: 0.5631 - val_jaccard_distance_acc: 0.3399 - val_fbeta: 0.4492 # Epoch 39/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1657 - jaccard_distance_acc: 0.6000 - fbeta: 0.8028 - val_loss: 0.5748 - val_jaccard_distance_acc: 0.3386 - val_fbeta: 0.4434 # Epoch 40/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1635 - jaccard_distance_acc: 0.6059 - fbeta: 0.8055 - val_loss: 0.5664 - val_jaccard_distance_acc: 0.3453 - val_fbeta: 0.4482 # Epoch 41/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1621 - jaccard_distance_acc: 0.6110 - fbeta: 0.8102 - val_loss: 0.5700 - val_jaccard_distance_acc: 0.3384 - val_fbeta: 0.4450 # Epoch 42/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1555 - jaccard_distance_acc: 0.6179 - fbeta: 0.8133 - val_loss: 0.5807 - val_jaccard_distance_acc: 0.3462 - val_fbeta: 0.4560 # Epoch 43/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1540 - jaccard_distance_acc: 0.6219 - fbeta: 0.8187 - val_loss: 0.5937 - val_jaccard_distance_acc: 0.3437 - val_fbeta: 0.4520 # Epoch 44/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1526 - jaccard_distance_acc: 0.6263 - fbeta: 0.8194 - val_loss: 0.5912 - val_jaccard_distance_acc: 0.3377 - val_fbeta: 0.4440 # Epoch 45/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1491 - jaccard_distance_acc: 0.6319 - fbeta: 0.8246 - val_loss: 0.6023 - val_jaccard_distance_acc: 0.3425 - val_fbeta: 0.4602 # Epoch 46/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1453 - jaccard_distance_acc: 0.6380 - fbeta: 0.8269 - val_loss: 0.5966 - val_jaccard_distance_acc: 0.3441 - val_fbeta: 0.4441 # Epoch 47/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1446 - jaccard_distance_acc: 0.6409 - fbeta: 0.8328 - val_loss: 0.5986 - val_jaccard_distance_acc: 0.3503 - val_fbeta: 0.4610 # Epoch 48/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1432 - jaccard_distance_acc: 0.6418 - fbeta: 0.8332 - val_loss: 0.6044 - val_jaccard_distance_acc: 0.3531 - val_fbeta: 0.4591 # Epoch 49/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1396 - jaccard_distance_acc: 0.6516 - fbeta: 0.8384 - val_loss: 0.6242 - val_jaccard_distance_acc: 0.3518 - val_fbeta: 0.4558 # Epoch 50/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1382 - jaccard_distance_acc: 0.6537 - fbeta: 0.8399 - val_loss: 0.6139 - val_jaccard_distance_acc: 0.3450 - val_fbeta: 0.4513 # 3151/3151 [==============================] - 27s 8ms/step # attention_model = Model(inputs=inp, outputs=attention) # Model to print out the attention data # model.summary() # verbose= ? , validation_split은 validation file로 변환시켜주어야 한다. # 5 # model.compile(loss='binary_crossentropy', # optimizer=Adam(clipnorm=1, lr=0.001), # metrics=[accu, fbeta]) # Epoch 1/50 # 16/6474 [..............................] - ETA: 31:06 - loss: 0.6962 - jaccard_distance_acc: 0.1575 - f 32/6474 [..............................] - ETA: 18:30 - loss: 0.6798 - jaccard_distance_acc: 0.1541 - f 48/6474 [..............................] - ETA: 14:12 - loss: 0.6345 - jaccard_distance_acc: 0.1517 - f 64/6474 [..............................] - ETA: 12:08 - loss: 0.6052 - jaccard_distance_acc: 0.1490 - f 80/6474 [..............................] - ETA: 10:58 - loss: 0.5777 - jaccard_distance_acc: 0.1464 - f 96/6474 [..............................] - ETA: 10:07 - loss: 0.5617 - jaccard_distance_acc: 0.1479 - f 112/6474 [..............................] - ETA: 9:29 - loss: 0.5565 - jaccard_distance_acc: 0.1488 - fb 128/6474 [..............................] - ETA: 9:03 - loss: 0.5461 - jaccard_distance_acc: 0.1512 - fb 144/6474 [..............................] - ETA: 8:39 - loss: 0.5464 - jaccard_distance_acc: 0.1553 - fb 160/6474 [..............................] - ETA: 8:21 - loss: 0.5346 - jaccard_distance_acc: 0.1541 - fb 176/6474 [..............................] - ETA: 8:07 - loss: 0.5358 - jaccard_distance_acc: 0.1555 - fb 192/6474 [..............................] - ETA: 7:53 - loss: 0.5330 - jaccard_distance_acc: 0.1548 - fb 208/6474 [..............................] - ETA: 7:41 - loss: 0.5300 - jaccard_distance_acc: 0.1539 - fb 224/6474 [>.............................] - ETA: 7:31 - loss: 0.5278 - jaccard_distance_acc: 0.1524 - fb 240/6474 [>.............................] - ETA: 7:22 - loss: 0.5257 - jaccard_distance_acc: 0.1515 - fb 256/6474 [>.............................] - ETA: 7:16 - loss: 0.5219 - jaccard_distance_acc: 0.1515 - fb 272/6476474/6474 [==============================] - 377s 58ms/step - loss: 0.4761 - jaccard_distance_acc: 0.1626 - fbeta: 0.0427 - val_loss: 0.4718 - val_jaccard_distance_acc: 0.1743 - val_fbeta: 0.0150 # Epoch 2/50 # 6474/6474 [==============================] - 367s 57ms/step - loss: 0.4506 - jaccard_distance_acc: 0.1899 - fbeta: 0.1640 - val_loss: 0.4286 - val_jaccard_distance_acc: 0.2238 - val_fbeta: 0.2165 # Epoch 3/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.4303 - jaccard_distance_acc: 0.2180 - fbeta: 0.2692 - val_loss: 0.4274 - val_jaccard_distance_acc: 0.2380 - val_fbeta: 0.2963 # Epoch 4/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.4167 - jaccard_distance_acc: 0.2362 - fbeta: 0.3153 - val_loss: 0.4138 - val_jaccard_distance_acc: 0.2498 - val_fbeta: 0.3165 # Epoch 5/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.4004 - jaccard_distance_acc: 0.2586 - fbeta: 0.3697 - val_loss: 0.4156 - val_jaccard_distance_acc: 0.2428 - val_fbeta: 0.2902 # Epoch 6/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3892 - jaccard_distance_acc: 0.2726 - fbeta: 0.3920 - val_loss: 0.4030 - val_jaccard_distance_acc: 0.2778 - val_fbeta: 0.3690 # Epoch 7/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3776 - jaccard_distance_acc: 0.2876 - fbeta: 0.4294 - val_loss: 0.4071 - val_jaccard_distance_acc: 0.2727 - val_fbeta: 0.3754 # Epoch 8/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3661 - jaccard_distance_acc: 0.3027 - fbeta: 0.4526 - val_loss: 0.4072 - val_jaccard_distance_acc: 0.2715 - val_fbeta: 0.3536 # Epoch 9/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3536 - jaccard_distance_acc: 0.3184 - fbeta: 0.4749 - val_loss: 0.4045 - val_jaccard_distance_acc: 0.2980 - val_fbeta: 0.4100 # Epoch 10/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.3456 - jaccard_distance_acc: 0.3320 - fbeta: 0.4995 - val_loss: 0.4084 - val_jaccard_distance_acc: 0.3023 - val_fbeta: 0.4141 # Epoch 11/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.3350 - jaccard_distance_acc: 0.3447 - fbeta: 0.5130 - val_loss: 0.4093 - val_jaccard_distance_acc: 0.2999 - val_fbeta: 0.4063 # Epoch 12/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.3227 - jaccard_distance_acc: 0.3613 - fbeta: 0.5413 - val_loss: 0.4135 - val_jaccard_distance_acc: 0.3017 - val_fbeta: 0.4054 # Epoch 13/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.3143 - jaccard_distance_acc: 0.3726 - fbeta: 0.5565 - val_loss: 0.4175 - val_jaccard_distance_acc: 0.3082 - val_fbeta: 0.4300 # Epoch 14/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.3070 - jaccard_distance_acc: 0.3844 - fbeta: 0.5686 - val_loss: 0.4289 - val_jaccard_distance_acc: 0.3154 - val_fbeta: 0.4311 # Epoch 15/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2980 - jaccard_distance_acc: 0.3942 - fbeta: 0.5858 - val_loss: 0.4412 - val_jaccard_distance_acc: 0.3166 - val_fbeta: 0.4379 # Epoch 16/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2850 - jaccard_distance_acc: 0.4098 - fbeta: 0.6062 - val_loss: 0.4378 - val_jaccard_distance_acc: 0.3181 - val_fbeta: 0.4349 # Epoch 17/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2803 - jaccard_distance_acc: 0.4209 - fbeta: 0.6202 - val_loss: 0.4379 - val_jaccard_distance_acc: 0.3192 - val_fbeta: 0.4295 # Epoch 18/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2729 - jaccard_distance_acc: 0.4319 - fbeta: 0.6358 - val_loss: 0.4504 - val_jaccard_distance_acc: 0.3201 - val_fbeta: 0.4383 # Epoch 19/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2633 - jaccard_distance_acc: 0.4444 - fbeta: 0.6461 - val_loss: 0.4479 - val_jaccard_distance_acc: 0.3229 - val_fbeta: 0.4409 # Epoch 20/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.2576 - jaccard_distance_acc: 0.4531 - fbeta: 0.6581 - val_loss: 0.4578 - val_jaccard_distance_acc: 0.3308 - val_fbeta: 0.4555 # Epoch 21/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2511 - jaccard_distance_acc: 0.4639 - fbeta: 0.6706 - val_loss: 0.4673 - val_jaccard_distance_acc: 0.3209 - val_fbeta: 0.4305 # Epoch 22/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2436 - jaccard_distance_acc: 0.4743 - fbeta: 0.6839 - val_loss: 0.4847 - val_jaccard_distance_acc: 0.3328 - val_fbeta: 0.4618 # Epoch 23/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2365 - jaccard_distance_acc: 0.4849 - fbeta: 0.6919 - val_loss: 0.4871 - val_jaccard_distance_acc: 0.3315 - val_fbeta: 0.4600 # Epoch 24/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2320 - jaccard_distance_acc: 0.4910 - fbeta: 0.7011 - val_loss: 0.4853 - val_jaccard_distance_acc: 0.3275 - val_fbeta: 0.4493 # Epoch 25/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2269 - jaccard_distance_acc: 0.4989 - fbeta: 0.7053 - val_loss: 0.4953 - val_jaccard_distance_acc: 0.3331 - val_fbeta: 0.4499 # Epoch 26/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.2174 - jaccard_distance_acc: 0.5130 - fbeta: 0.7232 - val_loss: 0.4959 - val_jaccard_distance_acc: 0.3457 - val_fbeta: 0.4814 # Epoch 27/50 # 6474/6474 [==============================] - 364s 56ms/step - loss: 0.2148 - jaccard_distance_acc: 0.5194 - fbeta: 0.7295 - val_loss: 0.5119 - val_jaccard_distance_acc: 0.3323 - val_fbeta: 0.4494 # Epoch 28/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.2082 - jaccard_distance_acc: 0.5294 - fbeta: 0.7392 - val_loss: 0.5138 - val_jaccard_distance_acc: 0.3390 - val_fbeta: 0.4622 # Epoch 29/50 # 6474/6474 [==============================] - 361s 56ms/step - loss: 0.2031 - jaccard_distance_acc: 0.5345 - fbeta: 0.7428 - val_loss: 0.5134 - val_jaccard_distance_acc: 0.3390 - val_fbeta: 0.4613 # Epoch 30/50 # 6474/6474 [==============================] - 361s 56ms/step - loss: 0.1986 - jaccard_distance_acc: 0.5456 - fbeta: 0.7544 - val_loss: 0.5245 - val_jaccard_distance_acc: 0.3289 - val_fbeta: 0.4362 # Epoch 31/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.1950 - jaccard_distance_acc: 0.5508 - fbeta: 0.7570 - val_loss: 0.5260 - val_jaccard_distance_acc: 0.3360 - val_fbeta: 0.4513 # Epoch 32/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.1901 - jaccard_distance_acc: 0.5582 - fbeta: 0.7635 - val_loss: 0.5358 - val_jaccard_distance_acc: 0.3401 - val_fbeta: 0.4495 # Epoch 33/50 # 6474/6474 [==============================] - 362s 56ms/step - loss: 0.1874 - jaccard_distance_acc: 0.5636 - fbeta: 0.7708 - val_loss: 0.5402 - val_jaccard_distance_acc: 0.3318 - val_fbeta: 0.4381 # Epoch 34/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.1830 - jaccard_distance_acc: 0.5722 - fbeta: 0.7789 - val_loss: 0.5433 - val_jaccard_distance_acc: 0.3342 - val_fbeta: 0.4462 # Epoch 35/50 # 6474/6474 [==============================] - 363s 56ms/step - loss: 0.1787 - jaccard_distance_acc: 0.5776 - fbeta: 0.7838 - val_loss: 0.5424 - val_jaccard_distance_acc: 0.3500 - val_fbeta: 0.4689 # Epoch 36/50 # 6474/6474 [==============================] - 341s 53ms/step - loss: 0.1774 - jaccard_distance_acc: 0.5819 - fbeta: 0.7876 - val_loss: 0.5405 - val_jaccard_distance_acc: 0.3441 - val_fbeta: 0.4530 # Epoch 37/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1704 - jaccard_distance_acc: 0.5920 - fbeta: 0.7924 - val_loss: 0.5666 - val_jaccard_distance_acc: 0.3288 - val_fbeta: 0.4324 # Epoch 38/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1692 - jaccard_distance_acc: 0.5960 - fbeta: 0.7988 - val_loss: 0.5631 - val_jaccard_distance_acc: 0.3399 - val_fbeta: 0.4492 # Epoch 39/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1657 - jaccard_distance_acc: 0.6000 - fbeta: 0.8028 - val_loss: 0.5748 - val_jaccard_distance_acc: 0.3386 - val_fbeta: 0.4434 # Epoch 40/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1635 - jaccard_distance_acc: 0.6059 - fbeta: 0.8055 - val_loss: 0.5664 - val_jaccard_distance_acc: 0.3453 - val_fbeta: 0.4482 # Epoch 41/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1621 - jaccard_distance_acc: 0.6110 - fbeta: 0.8102 - val_loss: 0.5700 - val_jaccard_distance_acc: 0.3384 - val_fbeta: 0.4450 # Epoch 42/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1555 - jaccard_distance_acc: 0.6179 - fbeta: 0.8133 - val_loss: 0.5807 - val_jaccard_distance_acc: 0.3462 - val_fbeta: 0.4560 # Epoch 43/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1540 - jaccard_distance_acc: 0.6219 - fbeta: 0.8187 - val_loss: 0.5937 - val_jaccard_distance_acc: 0.3437 - val_fbeta: 0.4520 # Epoch 44/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1526 - jaccard_distance_acc: 0.6263 - fbeta: 0.8194 - val_loss: 0.5912 - val_jaccard_distance_acc: 0.3377 - val_fbeta: 0.4440 # Epoch 45/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1491 - jaccard_distance_acc: 0.6319 - fbeta: 0.8246 - val_loss: 0.6023 - val_jaccard_distance_acc: 0.3425 - val_fbeta: 0.4602 # Epoch 46/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1453 - jaccard_distance_acc: 0.6380 - fbeta: 0.8269 - val_loss: 0.5966 - val_jaccard_distance_acc: 0.3441 - val_fbeta: 0.4441 # Epoch 47/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1446 - jaccard_distance_acc: 0.6409 - fbeta: 0.8328 - val_loss: 0.5986 - val_jaccard_distance_acc: 0.3503 - val_fbeta: 0.4610 # Epoch 48/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1432 - jaccard_distance_acc: 0.6418 - fbeta: 0.8332 - val_loss: 0.6044 - val_jaccard_distance_acc: 0.3531 - val_fbeta: 0.4591 # Epoch 49/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1396 - jaccard_distance_acc: 0.6516 - fbeta: 0.8384 - val_loss: 0.6242 - val_jaccard_distance_acc: 0.3518 - val_fbeta: 0.4558 # Epoch 50/50 # 6474/6474 [==============================] - 185s 29ms/step - loss: 0.1382 - jaccard_distance_acc: 0.6537 - fbeta: 0.8399 - val_loss: 0.6139 - val_jaccard_distance_acc: 0.3450 - val_fbeta: 0.4513 # 3151/3151 [==============================] - 27s 8ms/step # 6 # model.compile(loss=scaled_binary_cross_entropy, # optimizer=Adam(clipnorm=1, lr=0.001), # metrics=[accu, fbeta]) # 6' clipnorm=1의 역활이 득이 되는지 아닌지 잘 모르겠다. 평가의 기준이 명확하지 않으니까 # 내가 맞게 평가 하고 학습하는지 잘 모르겠다. # model.compile(loss=scaled_binary_cross_entropy, # optimizer=Adam(lr=0.001), # metrics=[accu, fbeta]) #7 model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=[accu, fbeta]) attention_model = Model(inputs=inp, outputs=attention) # Model to print out the attention data model.summary() # STAMP = './First_AttentionNLP_%.2f_%.2f'%(rate_drop_lstm,rate_drop_dense) # print(STAMP) # early_stopping =EarlyStopping(monitor='val_loss', patience=5) # bst_model_path = STAMP + '.h5' # model_checkpoint = ModelCheckpoint(bst_model_path, save_best_only=True, save_weights_only=True) # # model.fit(X_t, y, validation_data=(x_val,y_val), epochs=3, verbose=1, batch_size=512) # epoch = 50 filepath = "weights-improvement-{epoch:02d}-{loss:.4f}-bigger.hdf5" checkpoint = ModelCheckpoint( filepath, monitor='loss', verbose=0, save_best_only=True, mode='min' ) callbacks_list = [checkpoint] model.fit(k_vec, k_y_train, validation_data=(k_vec_val,k_y_val), epochs=30, verbose=1, batch_size=batch_size,callbacks=callbacks_list) # model.fit(k_vec, k_y_train, validation_data=(k_vec_val,k_y_val), epochs=3, verbose=1, steps_per_epoch=int(6619/batch_size)+1, validation_steps = int(883/batch_size)+1) # 5. 모델 평가하기 test_score = model.evaluate(k_vec_test, k_y_test, batch_size=batch_size) print('') print(str(test_score)) # 6. 모델 저장하기 from datetime import datetime # now = datetime.now() model.save('Attention.h5') def test_return(): return (k_vec_test,k_y_test) # # 7. 모델 아키텍처 보기 # from IPython.display import SVG # from keras.utils.vis_utils import model_to_dot # # %matplotlib inline # # str(now.day)+"/"+str(now.hour)+":"+str(now.minute)+":"+str(now.second)+ # SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg')) # ####################################### # ## make the submission # ######################################## # print('Start making the submission before fine-tuning') # y_test = model.predict(word_vec_test,y_test_size=32, verbose=1) # sample_submission = pd.read_csv("../input/sample_submission.csv") # sample_submission[list_classes] = y_test # sample_submission.to_csv('%.4f_'%(bst_val_score)+STAMP+'.csv', index=False) # # 테스트용 코드 # def get_word_importances(text): # lt = tokenizer.texts_to_sequences([text]) # x = pad_sequences(lt, maxlen=maxlen) # p = model.predict(x) # att = attention_model.predict(x) # return p, [(reverse_token_map.get(word), importance) for word, importance in zip(x[0], att[0]) if word in reverse_token_map] # emb = Embedding(input_dim=vocab_size, output_dim=EMB_DIM, # trainable=False, weights=[embedding_matrix], input_length=text_max_words) # # 임베딩 dropout # # GaussianNoise(mean=0.0, stddev=0.2) # keras.layers.GaussianNoise(stddev=0.2) # keras.layers.GaussianDropout(rate) # # LSTM(310, 250, num_layers=2, batch_first=True, dropout=0.3, bidirectional=True) # # Dropout(p=0.3) # # n_timesteps = text_max_words # # return_sequences = True는 각 lstm의 hidden state를 출력하는 인자이다. # =Bidirectional(LSTM(250, return_sequences=True), input_shape=(text_max_words, EMB_DIM))(emb) # # SelfAttention( # # Sequential( # # Linear(in_features=500, out_features=1, bias=True) # # Tahh() # # Dropout(0.3)f # # Linear(in_features=500, out_features=1, bias=True) # # Tanh() # # Dropout(p=0.3) # # ) # # Softmax() # # ) # # ) # # Linear(in_features=500, out_features=11, bias=True) binary classification # # ) # model.compile() # word_vec = [] # for sent in tmp: # sub = [] # for word in sent: # if(word in embeddings_index.keys()): # sub.append(embeddings_index[word]) # else: # sub.append(np.random.uniform(-0.25,0.25,300)) ## used for OOV words # word_vec.append(sub) # return np.array(word_vec)

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1

0

1

0

null

0

4

7c39a0225a53a4903ba1e5f3e2520ad263e09bd9

55

py

Python

pylibs/hej/__main__.py

srittau/hej

87f2e577205895a988813abc057cd62234c87379

[ "MIT" ]

null

pylibs/hej/__main__.py

srittau/hej

87f2e577205895a988813abc057cd62234c87379

[ "MIT" ]

90

2018-07-31T22:50:13.000Z

2022-03-29T06:29:48.000Z

pylibs/hej/__main__.py

srittau/hej

87f2e577205895a988813abc057cd62234c87379

[ "MIT" ]

null

#!/usr/bin/env python3 from .main import main main()

9.166667

22

0.690909

9

55

4.222222

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0.021739

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null

0

1

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1

0

4

7c50853e04f52f8b7c864cc192d121b7a52f4861

117

py

Python

exceptions/user_exception.py

iseoluwaYN/eden

aa11f14ae41bb752f424abbcb6b8b9037a01c1b0

[ "MIT" ]

null

exceptions/user_exception.py

iseoluwaYN/eden

aa11f14ae41bb752f424abbcb6b8b9037a01c1b0

[ "MIT" ]

1

2021-09-09T10:19:14.000Z

exceptions/user_exception.py

iseoluwaYN/eden

aa11f14ae41bb752f424abbcb6b8b9037a01c1b0

[ "MIT" ]

19

2021-08-19T13:01:18.000Z

2021-08-19T15:08:05.000Z

class UserException(Exception): def __init__(self, message): super(UserException, self).__init__(message)

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1

0

4

7c56bad3cfe0b9d1f85c61ac0034fe4ca5fb750f

18,040

py

Python

x-dump/utilities/miscellaneous_tester.py

eda-ricercatore/python-sandbox

741d23e15f22239cb5df8af6e695cd8e3574be50

[ "MIT" ]

null

x-dump/utilities/miscellaneous_tester.py

eda-ricercatore/python-sandbox

741d23e15f22239cb5df8af6e695cd8e3574be50

[ "MIT" ]

null

x-dump/utilities/miscellaneous_tester.py

eda-ricercatore/python-sandbox

741d23e15f22239cb5df8af6e695cd8e3574be50

[ "MIT" ]

null

#!/usr/local/bin/python3 ###!/Users/zhiyang/anaconda3/bin/python3 """ This Python script is written by Zhiyang Ong to test miscellaneous methods in the miscellaneous class. Synopsis: Perform a subset of the methods in the miscellaneous class. Revision History: July 31, 2018 Version 0.1, initial build. """ __author__ = 'Zhiyang Ong' __version__ = '1.0' __date__ = 'July 31, 2018' # The MIT License (MIT) # Copyright (c) <2018> <Zhiyang Ong> # 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. # Email address: echo "cukj -wb- 23wU4X5M589 TROJANS cqkH wiuz2y 0f Mw Stanford" | awk '{ sub("23wU4X5M589","F.d_c_b. ") sub("Stanford","d0mA1n"); print $5, $2, $8; for (i=1; i<=1; i++) print "6\b"; print $9, $7, $6 }' | sed y/kqcbuHwM62z/gnotrzadqmC/ | tr 'q' ' ' | tr -d [:cntrl:] | tr -d 'ir' | tr y "\n" Che cosa significa? ############################################################### """ Import modules from The Python Standard Library. sys Get access to any command-line arguments. os Use any operating system dependent functionality. os.path For pathname manipulations. subprocess -> call To make system calls. time To measure elapsed time. warnings Raise warnings. re Use regular expressions. calendar For checking if given year is a leap year. """ import sys #import os import os.path #from subprocess import call import subprocess #import time import warnings #import re import calendar ############################################################### # Import Custom Python Modules """ Package and module to print statistics of software testing results. """ from statistic_pkg.test_statistics import statistical_analysis # Package and module to perform file I/O operations. from utilities.file_io import file_io_operations """ Package and module to configure the software application's parameters. """ from utilities.configuration_manager import config_manager """ Package and module to perform methods in the miscellaneous class. """ from utilities.miscellaneous import misc """ Module to test if the generated filename (based on the then-current time stamp) conforms to the specified format. """ from utilities.generate_results_filename_tester import generate_filename_tester ############################################################### ## Module that tests methods that perform miscellaneous tasks. class misc_tester: ## ========================================================= # Method to test the methods that perform file I/O operations # with an invalid file. # @param - Nothing # @return - Nothing. # O(1) method. @staticmethod def test_check_filename_format(): print(" Testing the filename format checker.") prompt = " ... Test: incorrect file extension is '.txt'. {}" statistical_analysis.increment_number_test_cases_used() if misc.check_filename_format("tyuw.iew"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename no file extension has 6 tokens. {}" statistical_analysis.increment_number_test_cases_used() if misc.check_filename_format("HH-MM-SS-uS.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with -ve DD/day. {}" statistical_analysis.increment_number_test_cases_used() if misc.check_filename_format("-5-MM-YY-HH-MM-SS-uS.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with DD/day >29, Feb. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("35-2-2016-00-00-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with DD/day=29, Feb, leap year. {}" statistical_analysis.increment_number_test_cases_used() if misc.check_filename_format("29-2-2016-00-00-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with DD/day=28, Feb, not leap year. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("28-2-2017-00-00-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with DD/day = 34. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("34-6-2017-00-00-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with DD/day=31, 31 day mth. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("31-7-2017-00-00-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with DD/day=30, 30 day mth. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("30-9-2017-00-00-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with MM/month = 0. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("30-0-2017-00-00-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with MM/month = -4. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("30--4-2017-00-00-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with MM/month = 15. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("30-15-2017-00-00-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with MM/month = 9. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("30-9-2017-00-00-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with YY/year = 1582. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("30-11-1582-00-00-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with YY/year = 2083. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("2-2-2083-00-00-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with HH/hour = -3. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("30-5-2015--3-00-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with HH/hour = 25. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("3-5-2017-25-00-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with HH/hour = 17. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("12-1-2013-17-00-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with MM/minute = -8. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("7-4-2012-2--8-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with MM/minute = 73. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-73-00-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with MM/minute = 59. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-59-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with MM/minute = 0. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-0-00-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with SS/second = -4. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-8--4-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with SS/second = 81. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-8-81-00.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with SS/second = 36. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-8-36-00.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with uS/microsecond = -129. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-8-4--129.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with uS/microsecond = 16534785929. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-8-32-16534785929.txt"): print(prompt .format("FAIL!!!")) else: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() prompt = " ... Test: filename with uS/microsecond = 0. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-8-32-0.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: filename with uS/microsecond = 999999. {}" statistical_analysis.increment_number_test_cases_used() """ All fields/tokens need to be numbers, else an exception would be thrown. """ if misc.check_filename_format("25-1-2020-5-8-51-999999.txt"): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) ## ========================================================= # Method to test the miscellaneous method that determines # where to store the results of experimental, simulation, # verification, and testing runs. # This does not correct check if the results file is placed # in the correct subdirectory of the results repository. # #### TO BE COMPLETED # Test if the subdirectory is correct. This is busywork. # @param - Nothing # @return a string representing the location to store the # aforementioned results. # O(1) method. @staticmethod def test_find_desired_location_for_results(): incorrect_format_result = "'filename' needs to have the format: DD-MM-YY-HH-MM-SS-uS.txt." print("== Test: test_find_desired_location_for_results().") test_filename = "25-3-2010-5-8-51-9994073289.dwq" results_location = misc.find_desired_location_for_results(test_filename) prompt = " ... Test: filename is 25-3-2010-5-8-51-9994073289.dwq. {}" statistical_analysis.increment_number_test_cases_used() if misc.find_desired_location_for_results(test_filename) == incorrect_format_result: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) print("== Test: test_find_desired_location_for_results().") test_filename = "25-3-2010-5-8-51-9407.txt" results_location = misc.find_desired_location_for_results(test_filename) prompt = " ... Test: filename 25-3-2010-5-8-51-9407.txt included. {}" statistical_analysis.increment_number_test_cases_used() if misc.check_absolute_path_to_store_results(results_location,test_filename): print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) prompt = " ... Test: 25-3-2010-5-8-51-9407.txt, correct path. {}" statistical_analysis.increment_number_test_cases_used() if misc.get_absolute_path_to_store_results() in results_location: print(prompt .format("OK")) statistical_analysis.increment_number_test_cases_passed() else: print(prompt .format("FAIL!!!")) """ print("results_location:",results_location,"=") print("misc.get_absolute_path_to_store_results():",misc.get_absolute_path_to_store_results(),"=") print(results_location.find(misc.get_absolute_path_to_store_results())) """ f_obj = misc.store_results(results_location) f_obj.write("Storage of experimental, simulation, verification, and testing results work.") file_io_operations.close_file_object(f_obj) ## ========================================================= # Method to test the miscellaneous methods. # @param - Nothing # @return - Nothing. # O(1) method. @staticmethod def test_miscellaneous_methods(): print("") print("") print("== Testing class: misc.") misc_tester.test_check_filename_format() misc_tester.test_find_desired_location_for_results()

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7c5a635ec2fdb6b840f73c0a5f173226e68df761

2,392

py

Python

kubernetes/client/apis/__init__.py

scele/kubernetes-client-python

9e982cbdb5f19dc1a3935a75bdd92288f3b807fb

[ "Apache-2.0" ]

null

kubernetes/client/apis/__init__.py

scele/kubernetes-client-python

9e982cbdb5f19dc1a3935a75bdd92288f3b807fb

[ "Apache-2.0" ]

null

kubernetes/client/apis/__init__.py

scele/kubernetes-client-python

9e982cbdb5f19dc1a3935a75bdd92288f3b807fb

[ "Apache-2.0" ]

null

from __future__ import absolute_import # import apis into api package from .admissionregistration_api import AdmissionregistrationApi from .admissionregistration_v1alpha1_api import AdmissionregistrationV1alpha1Api from .apiextensions_api import ApiextensionsApi from .apiextensions_v1beta1_api import ApiextensionsV1beta1Api from .apiregistration_api import ApiregistrationApi from .apiregistration_v1beta1_api import ApiregistrationV1beta1Api from .apis_api import ApisApi from .apps_api import AppsApi from .apps_v1beta1_api import AppsV1beta1Api from .apps_v1beta2_api import AppsV1beta2Api from .authentication_api import AuthenticationApi from .authentication_v1_api import AuthenticationV1Api from .authentication_v1beta1_api import AuthenticationV1beta1Api from .authorization_api import AuthorizationApi from .authorization_v1_api import AuthorizationV1Api from .authorization_v1beta1_api import AuthorizationV1beta1Api from .autoscaling_api import AutoscalingApi from .autoscaling_v1_api import AutoscalingV1Api from .autoscaling_v2beta1_api import AutoscalingV2beta1Api from .batch_api import BatchApi from .batch_v1_api import BatchV1Api from .batch_v1beta1_api import BatchV1beta1Api from .batch_v2alpha1_api import BatchV2alpha1Api from .certificates_api import CertificatesApi from .certificates_v1beta1_api import CertificatesV1beta1Api from .core_api import CoreApi from .core_v1_api import CoreV1Api from .custom_objects_api import CustomObjectsApi from .extensions_api import ExtensionsApi from .extensions_v1beta1_api import ExtensionsV1beta1Api from .logs_api import LogsApi from .networking_api import NetworkingApi from .networking_v1_api import NetworkingV1Api from .policy_api import PolicyApi from .policy_v1beta1_api import PolicyV1beta1Api from .rbac_authorization_api import RbacAuthorizationApi from .rbac_authorization_v1_api import RbacAuthorizationV1Api from .rbac_authorization_v1alpha1_api import RbacAuthorizationV1alpha1Api from .rbac_authorization_v1beta1_api import RbacAuthorizationV1beta1Api from .scheduling_api import SchedulingApi from .scheduling_v1alpha1_api import SchedulingV1alpha1Api from .settings_api import SettingsApi from .settings_v1alpha1_api import SettingsV1alpha1Api from .storage_api import StorageApi from .storage_v1_api import StorageV1Api from .storage_v1beta1_api import StorageV1beta1Api from .version_api import VersionApi

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py

Python

python-design/code-examples/Section 2/Section 2 Code/5428_02_ CODE_FD_MD_1stdraft_ACC/singleton.py

vermuz/mani-professional-notes

896328e81e376bc113553c81d38ad6c1781b8e0b

[ "CC-BY-3.0" ]

11

2018-06-07T15:54:40.000Z

2021-07-24T19:08:52.000Z

python-design/code-examples/Section 2/Section 2 Code/5428_02_ CODE_FD_MD_1stdraft_ACC/singleton.py

vermuz/mani-professional-notes

896328e81e376bc113553c81d38ad6c1781b8e0b

[ "CC-BY-3.0" ]

null

python-design/code-examples/Section 2/Section 2 Code/5428_02_ CODE_FD_MD_1stdraft_ACC/singleton.py

vermuz/mani-professional-notes

896328e81e376bc113553c81d38ad6c1781b8e0b

[ "CC-BY-3.0" ]

8

2018-02-19T16:12:09.000Z

2021-12-24T09:20:26.000Z

class Singleton: __instance = None def __new__(cls, val=None): if Singleton.__instance is None: Singleton.__instance = object.__new__(cls) Singleton.__instance.val = val return Singleton.__instance

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7c8dc6e2a1b1abf82f396132d37090de977197f4

243

py

Python

xai/explainer/__init__.py

ngpgn/contextual-ai

deb119395ced5242f243b2b31c074507e96646c0

[ "Apache-2.0" ]

83

2020-06-17T04:07:29.000Z

2022-03-12T13:45:24.000Z

xai/explainer/__init__.py

ngpgn/contextual-ai

deb119395ced5242f243b2b31c074507e96646c0

[ "Apache-2.0" ]

15

2020-06-30T09:22:19.000Z

2021-11-11T10:52:40.000Z

xai/explainer/__init__.py

ngpgn/contextual-ai

deb119395ced5242f243b2b31c074507e96646c0

[ "Apache-2.0" ]

11

2020-06-17T17:01:24.000Z

2022-02-27T18:53:03.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # Copyright 2019 SAP SE or an SAP affiliate company. All rights reserved # ============================================================================ from .explainer_factory import ExplainerFactory

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7c9febbc7b0fa16b8d184bf8c1e9bab60512d404

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py

Python

main_console.py

TPei/jawbone_visualizer

dadefbddb47450c3c43d474abaf9ae8d1317e03b

[ "MIT" ]

null

main_console.py

TPei/jawbone_visualizer

dadefbddb47450c3c43d474abaf9ae8d1317e03b

[ "MIT" ]

1

2015-05-11T07:21:21.000Z

main_console.py

TPei/jawbone_visualizer

dadefbddb47450c3c43d474abaf9ae8d1317e03b

[ "MIT" ]

null

__author__ = 'TPei' from data.manager import * #visualize_sleep_per_weekday() #plot_sleep() #plot_step_graph() plot_all(get_all_the_data()) #print(get_all_the_data('awake_time')) #compareDicts(get_all_the_data(), get_all_the_data('awake_time')) #coffee_effect_sleep(get_all_the_data())

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7ca6d61751dd8b252eb5706b6b419e29608de0b6

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py

Python

ehpi_action_recognition/configurator.py

steuwe/ehpi_action_recognition

4318e82e541c9b42bf0af7976815229ed6261c39

[ "MIT" ]

100

2019-04-16T17:18:02.000Z

2022-02-23T08:59:51.000Z

ehpi_action_recognition/configurator.py

steuwe/ehpi_action_recognition

4318e82e541c9b42bf0af7976815229ed6261c39

[ "MIT" ]

15

2019-06-14T13:30:12.000Z

2022-02-17T12:16:07.000Z

ehpi_action_recognition/configurator.py

steuwe/ehpi_action_recognition

4318e82e541c9b42bf0af7976815229ed6261c39

[ "MIT" ]

21

2019-05-08T03:29:12.000Z

2022-03-05T05:51:00.000Z

def setup_application(): # Example to change config stuff, call this method before everything else. # config.cache_config.cache_dir = "abc" a = 1

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4

7cb9993a81a5d7f79507310f093d2f6a43fbee19

1,508

py

Python

main.py

TiffanyXQZ/tf26

52e4779dee0a5f398da62d9ffbd3bdc55b3d463d

[ "MIT" ]

null

main.py

TiffanyXQZ/tf26

52e4779dee0a5f398da62d9ffbd3bdc55b3d463d

[ "MIT" ]

null

main.py

TiffanyXQZ/tf26

52e4779dee0a5f398da62d9ffbd3bdc55b3d463d

[ "MIT" ]

1

2022-01-31T21:59:52.000Z

import tensorflow as tf print(tf.config.list_physical_devices()) # import matplotlib.pyplot as plt # import numpy as np # import tensorflow as tf # # points_n = 200 # clusters_n = 3 # iteration_n = 100 # # points = tf.constant(np.random.uniform(0, 10, (points_n, 2))) # centroids = tf.Variable(tf.slice(tf.random.shuffle(points), [0, 0], [clusters_n, -1])) # # points_expanded = tf.expand_dims(points, 0) # centroids_expanded = tf.expand_dims(centroids, 1) # # distances = tf.reduce_sum(tf.square(tf.subtract(points_expanded, centroids_expanded)), 2) # assignments = tf.argmin(distances, 0) # # means = [] # for c in range(clusters_n): # means.append(tf.reduce_mean( # tf.gather(points, # tf.reshape( # tf.where( # tf.equal(assignments, c) # ), [1, -1]) # ), reduction_indices=[1])) # # new_centroids = tf.concat(means, 0) # # update_centroids = tf.assign(centroids, new_centroids) # init = tf.global_variables_initializer() # # with tf.Session() as sess: # sess.run(init) # for step in range(iteration_n): # [_, centroid_values, points_values, assignment_values] = sess.run( # [update_centroids, centroids, points, assignments]) # # print("centroids", centroid_values) # # plt.scatter(points_values[:, 0], points_values[:, 1], c=assignment_values, s=50, alpha=0.5) # plt.plot(centroid_values[:, 0], centroid_values[:, 1], 'kx', markersize=15) # plt.show()

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927

py

Python

moto/efs/urls.py

symroe/moto

4e106995af6f2820273528fca8a4e9ee288690a5

[ "Apache-2.0" ]

null

moto/efs/urls.py

symroe/moto

4e106995af6f2820273528fca8a4e9ee288690a5

[ "Apache-2.0" ]

1

2022-03-07T07:39:03.000Z

moto/efs/urls.py

symroe/moto

4e106995af6f2820273528fca8a4e9ee288690a5

[ "Apache-2.0" ]

null

from .responses import EFSResponse url_bases = [ r"https?://elasticfilesystem\.(.+)\.amazonaws.com", r"https?://elasticfilesystem\.amazonaws.com", ] response = EFSResponse() url_paths = { "{0}/.*?$": response.dispatch, "/2015-02-01/access-points": response.dispatch, "/2015-02-01/access-points/<access_point_id>": response.dispatch, "/2015-02-01/file-systems": response.dispatch, "/2015-02-01/file-systems/<file_system_id>": response.dispatch, "/2015-02-01/file-systems/<file_system_id>/backup-policy": response.dispatch, "/2015-02-01/file-systems/<file_system_id>/lifecycle-configuration": response.dispatch, "/2015-02-01/mount-targets": response.dispatch, "/2015-02-01/mount-targets/<mount_target_id>": response.dispatch, "/2015-02-01/mount-targets/<mount_target_id>/security-groups": response.dispatch, "/2015-02-01/resource-tags/<resource_id>": response.dispatch, }

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7ccb809f234d6084d3fba2fa3c9122e2a9a0cb1c

57

py

Python

tests/checks/__init__.py

aidanmelen/website_checker

f2324a67cb8e0288bcbee8ecdae9b836bab23f8e

[ "MIT" ]

1

2021-05-18T18:19:45.000Z

tests/checks/__init__.py

aidanmelen/website_checker

f2324a67cb8e0288bcbee8ecdae9b836bab23f8e

[ "MIT" ]

null

tests/checks/__init__.py

aidanmelen/website_checker

f2324a67cb8e0288bcbee8ecdae9b836bab23f8e

[ "MIT" ]

null

"""Test suite for the website_checker.checks package."""

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7ce5fb2da678eac7006b6e95ceba3b54b072463f

42,915

py

Python

tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py

tianyapiaozi/tensorflow

fb3ce0467766a8e91f1da0ad7ada7c24fde7a73a

[ "Apache-2.0" ]

522

2016-06-08T02:15:50.000Z

2022-03-02T05:30:36.000Z

tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py

shrikunjsarda/tensorflow

7e8927e7af0c51ac20a63bd4eab6ff83df1a39ae

[ "Apache-2.0" ]

133

2017-04-26T16:49:49.000Z

2019-10-15T11:39:26.000Z

tensorflow/contrib/legacy_seq2seq/python/kernel_tests/seq2seq_test.py

shrikunjsarda/tensorflow

7e8927e7af0c51ac20a63bd4eab6ff83df1a39ae

[ "Apache-2.0" ]

108

2016-06-16T15:34:05.000Z

2022-03-12T13:23:11.000Z

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for functional style sequence-to-sequence models.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import math import random import numpy as np from tensorflow.contrib.legacy_seq2seq.python.ops import seq2seq as seq2seq_lib from tensorflow.contrib.rnn.python.ops import core_rnn_cell from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import init_ops from tensorflow.python.ops import nn_impl from tensorflow.python.ops import rnn from tensorflow.python.ops import rnn_cell from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import adam class Seq2SeqTest(test.TestCase): def testRNNDecoder(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 _, enc_state = rnn.static_rnn( rnn_cell.GRUCell(2), inp, dtype=dtypes.float32) dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 cell = core_rnn_cell.OutputProjectionWrapper(rnn_cell.GRUCell(2), 4) dec, mem = seq2seq_lib.rnn_decoder(dec_inp, enc_state, cell) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testBasicRNNSeq2Seq(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 cell = core_rnn_cell.OutputProjectionWrapper(rnn_cell.GRUCell(2), 4) dec, mem = seq2seq_lib.basic_rnn_seq2seq(inp, dec_inp, cell) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testTiedRNNSeq2Seq(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 cell = core_rnn_cell.OutputProjectionWrapper(rnn_cell.GRUCell(2), 4) dec, mem = seq2seq_lib.tied_rnn_seq2seq(inp, dec_inp, cell) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual(1, len(res)) self.assertEqual((2, 2), res[0].shape) def testEmbeddingRNNDecoder(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 cell_fn = lambda: rnn_cell.BasicLSTMCell(2) cell = cell_fn() _, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32) dec_inp = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(3) ] # Use a new cell instance since the attention decoder uses a # different variable scope. dec, mem = seq2seq_lib.embedding_rnn_decoder( dec_inp, enc_state, cell_fn(), num_symbols=4, embedding_size=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 2), res[0].shape) res = sess.run([mem]) self.assertEqual(1, len(res)) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) def testEmbeddingRNNSeq2Seq(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): enc_inp = [ constant_op.constant( 1, dtypes.int32, shape=[2]) for i in range(2) ] dec_inp = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(3) ] cell_fn = lambda: rnn_cell.BasicLSTMCell(2) cell = cell_fn() dec, mem = seq2seq_lib.embedding_rnn_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) # Test with state_is_tuple=False. with variable_scope.variable_scope("no_tuple"): cell_nt = rnn_cell.BasicLSTMCell(2, state_is_tuple=False) dec, mem = seq2seq_lib.embedding_rnn_seq2seq( enc_inp, dec_inp, cell_nt, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 4), res[0].shape) # Test externally provided output projection. w = variable_scope.get_variable("proj_w", [2, 5]) b = variable_scope.get_variable("proj_b", [5]) with variable_scope.variable_scope("proj_seq2seq"): dec, _ = seq2seq_lib.embedding_rnn_seq2seq( enc_inp, dec_inp, cell_fn(), num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, output_projection=(w, b)) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 2), res[0].shape) # Test that previous-feeding model ignores inputs after the first. dec_inp2 = [ constant_op.constant( 0, dtypes.int32, shape=[2]) for _ in range(3) ] with variable_scope.variable_scope("other"): d3, _ = seq2seq_lib.embedding_rnn_seq2seq( enc_inp, dec_inp2, cell_fn(), num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=constant_op.constant(True)) with variable_scope.variable_scope("other_2"): d1, _ = seq2seq_lib.embedding_rnn_seq2seq( enc_inp, dec_inp, cell_fn(), num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=True) with variable_scope.variable_scope("other_3"): d2, _ = seq2seq_lib.embedding_rnn_seq2seq( enc_inp, dec_inp2, cell_fn(), num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=True) sess.run([variables.global_variables_initializer()]) res1 = sess.run(d1) res2 = sess.run(d2) res3 = sess.run(d3) self.assertAllClose(res1, res2) self.assertAllClose(res1, res3) def testEmbeddingTiedRNNSeq2Seq(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): enc_inp = [ constant_op.constant( 1, dtypes.int32, shape=[2]) for i in range(2) ] dec_inp = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(3) ] cell = functools.partial(rnn_cell.BasicLSTMCell, 2, state_is_tuple=True) dec, mem = seq2seq_lib.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell(), num_symbols=5, embedding_size=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) # Test when num_decoder_symbols is provided, the size of decoder output # is num_decoder_symbols. with variable_scope.variable_scope("decoder_symbols_seq2seq"): dec, mem = seq2seq_lib.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell(), num_symbols=5, num_decoder_symbols=3, embedding_size=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 3), res[0].shape) # Test externally provided output projection. w = variable_scope.get_variable("proj_w", [2, 5]) b = variable_scope.get_variable("proj_b", [5]) with variable_scope.variable_scope("proj_seq2seq"): dec, _ = seq2seq_lib.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell(), num_symbols=5, embedding_size=2, output_projection=(w, b)) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 2), res[0].shape) # Test that previous-feeding model ignores inputs after the first. dec_inp2 = [constant_op.constant(0, dtypes.int32, shape=[2])] * 3 with variable_scope.variable_scope("other"): d3, _ = seq2seq_lib.embedding_tied_rnn_seq2seq( enc_inp, dec_inp2, cell(), num_symbols=5, embedding_size=2, feed_previous=constant_op.constant(True)) with variable_scope.variable_scope("other_2"): d1, _ = seq2seq_lib.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell(), num_symbols=5, embedding_size=2, feed_previous=True) with variable_scope.variable_scope("other_3"): d2, _ = seq2seq_lib.embedding_tied_rnn_seq2seq( enc_inp, dec_inp2, cell(), num_symbols=5, embedding_size=2, feed_previous=True) sess.run([variables.global_variables_initializer()]) res1 = sess.run(d1) res2 = sess.run(d2) res3 = sess.run(d3) self.assertAllClose(res1, res2) self.assertAllClose(res1, res3) def testAttentionDecoder1(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): cell_fn = lambda: rnn_cell.GRUCell(2) cell = cell_fn() inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32) attn_states = array_ops.concat([ array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs ], 1) dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 # Create a new cell instance for the decoder, since it uses a # different variable scope dec, mem = seq2seq_lib.attention_decoder( dec_inp, enc_state, attn_states, cell_fn(), output_size=4) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testAttentionDecoder2(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): cell_fn = lambda: rnn_cell.GRUCell(2) cell = cell_fn() inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32) attn_states = array_ops.concat([ array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs ], 1) dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 # Use a new cell instance since the attention decoder uses a # different variable scope. dec, mem = seq2seq_lib.attention_decoder( dec_inp, enc_state, attn_states, cell_fn(), output_size=4, num_heads=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testDynamicAttentionDecoder1(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): cell_fn = lambda: rnn_cell.GRUCell(2) cell = cell_fn() inp = constant_op.constant(0.5, shape=[2, 2, 2]) enc_outputs, enc_state = rnn.dynamic_rnn( cell, inp, dtype=dtypes.float32) attn_states = enc_outputs dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 # Use a new cell instance since the attention decoder uses a # different variable scope. dec, mem = seq2seq_lib.attention_decoder( dec_inp, enc_state, attn_states, cell_fn(), output_size=4) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testDynamicAttentionDecoder2(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): cell_fn = lambda: rnn_cell.GRUCell(2) cell = cell_fn() inp = constant_op.constant(0.5, shape=[2, 2, 2]) enc_outputs, enc_state = rnn.dynamic_rnn( cell, inp, dtype=dtypes.float32) attn_states = enc_outputs dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 # Use a new cell instance since the attention decoder uses a # different variable scope. dec, mem = seq2seq_lib.attention_decoder( dec_inp, enc_state, attn_states, cell_fn(), output_size=4, num_heads=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testAttentionDecoderStateIsTuple(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): single_cell = lambda: rnn_cell.BasicLSTMCell( # pylint: disable=g-long-lambda 2, state_is_tuple=True) cell_fn = lambda: rnn_cell.MultiRNNCell( # pylint: disable=g-long-lambda cells=[single_cell() for _ in range(2)], state_is_tuple=True) cell = cell_fn() inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32) attn_states = array_ops.concat([ array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs ], 1) dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 # Use a new cell instance since the attention decoder uses a # different variable scope. dec, mem = seq2seq_lib.attention_decoder( dec_inp, enc_state, attn_states, cell_fn(), output_size=4) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual(2, len(res[0])) self.assertEqual((2, 2), res[0][0].c.shape) self.assertEqual((2, 2), res[0][0].h.shape) self.assertEqual((2, 2), res[0][1].c.shape) self.assertEqual((2, 2), res[0][1].h.shape) def testDynamicAttentionDecoderStateIsTuple(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): cell_fn = lambda: rnn_cell.MultiRNNCell( # pylint: disable=g-long-lambda cells=[rnn_cell.BasicLSTMCell(2) for _ in range(2)]) cell = cell_fn() inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32) attn_states = array_ops.concat([ array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs ], 1) dec_inp = [constant_op.constant(0.4, shape=[2, 2])] * 3 # Use a new cell instance since the attention decoder uses a # different variable scope. dec, mem = seq2seq_lib.attention_decoder( dec_inp, enc_state, attn_states, cell_fn(), output_size=4) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual(2, len(res[0])) self.assertEqual((2, 2), res[0][0].c.shape) self.assertEqual((2, 2), res[0][0].h.shape) self.assertEqual((2, 2), res[0][1].c.shape) self.assertEqual((2, 2), res[0][1].h.shape) def testEmbeddingAttentionDecoder(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): inp = [constant_op.constant(0.5, shape=[2, 2])] * 2 cell_fn = lambda: rnn_cell.GRUCell(2) cell = cell_fn() enc_outputs, enc_state = rnn.static_rnn(cell, inp, dtype=dtypes.float32) attn_states = array_ops.concat([ array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs ], 1) dec_inp = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(3) ] # Use a new cell instance since the attention decoder uses a # different variable scope. dec, mem = seq2seq_lib.embedding_attention_decoder( dec_inp, enc_state, attn_states, cell_fn(), num_symbols=4, embedding_size=2, output_size=3) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 3), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testEmbeddingAttentionSeq2Seq(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): enc_inp = [ constant_op.constant( 1, dtypes.int32, shape=[2]) for i in range(2) ] dec_inp = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(3) ] cell_fn = lambda: rnn_cell.BasicLSTMCell(2) cell = cell_fn() dec, mem = seq2seq_lib.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) # Test with state_is_tuple=False. with variable_scope.variable_scope("no_tuple"): cell_fn = functools.partial( rnn_cell.BasicLSTMCell, 2, state_is_tuple=False) cell_nt = cell_fn() dec, mem = seq2seq_lib.embedding_attention_seq2seq( enc_inp, dec_inp, cell_nt, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 4), res[0].shape) # Test externally provided output projection. w = variable_scope.get_variable("proj_w", [2, 5]) b = variable_scope.get_variable("proj_b", [5]) with variable_scope.variable_scope("proj_seq2seq"): dec, _ = seq2seq_lib.embedding_attention_seq2seq( enc_inp, dec_inp, cell_fn(), num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, output_projection=(w, b)) sess.run([variables.global_variables_initializer()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 2), res[0].shape) # TODO(ebrevdo, lukaszkaiser): Re-enable once RNNCells allow reuse # within a variable scope that already has a weights tensor. # # # Test that previous-feeding model ignores inputs after the first. # dec_inp2 = [ # constant_op.constant( # 0, dtypes.int32, shape=[2]) for _ in range(3) # ] # with variable_scope.variable_scope("other"): # d3, _ = seq2seq_lib.embedding_attention_seq2seq( # enc_inp, # dec_inp2, # cell_fn(), # num_encoder_symbols=2, # num_decoder_symbols=5, # embedding_size=2, # feed_previous=constant_op.constant(True)) # sess.run([variables.global_variables_initializer()]) # variable_scope.get_variable_scope().reuse_variables() # cell = cell_fn() # d1, _ = seq2seq_lib.embedding_attention_seq2seq( # enc_inp, # dec_inp, # cell, # num_encoder_symbols=2, # num_decoder_symbols=5, # embedding_size=2, # feed_previous=True) # d2, _ = seq2seq_lib.embedding_attention_seq2seq( # enc_inp, # dec_inp2, # cell, # num_encoder_symbols=2, # num_decoder_symbols=5, # embedding_size=2, # feed_previous=True) # res1 = sess.run(d1) # res2 = sess.run(d2) # res3 = sess.run(d3) # self.assertAllClose(res1, res2) # self.assertAllClose(res1, res3) def testOne2ManyRNNSeq2Seq(self): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): enc_inp = [ constant_op.constant( 1, dtypes.int32, shape=[2]) for i in range(2) ] dec_inp_dict = {} dec_inp_dict["0"] = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(3) ] dec_inp_dict["1"] = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(4) ] dec_symbols_dict = {"0": 5, "1": 6} def EncCellFn(): return rnn_cell.BasicLSTMCell(2, state_is_tuple=True) def DecCellsFn(): return dict((k, rnn_cell.BasicLSTMCell(2, state_is_tuple=True)) for k in dec_symbols_dict) outputs_dict, state_dict = (seq2seq_lib.one2many_rnn_seq2seq( enc_inp, dec_inp_dict, EncCellFn(), DecCellsFn(), 2, dec_symbols_dict, embedding_size=2)) sess.run([variables.global_variables_initializer()]) res = sess.run(outputs_dict["0"]) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run(outputs_dict["1"]) self.assertEqual(4, len(res)) self.assertEqual((2, 6), res[0].shape) res = sess.run([state_dict["0"]]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) res = sess.run([state_dict["1"]]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) # Test that previous-feeding model ignores inputs after the first, i.e. # dec_inp_dict2 has different inputs from dec_inp_dict after the first # time-step. dec_inp_dict2 = {} dec_inp_dict2["0"] = [ constant_op.constant( 0, dtypes.int32, shape=[2]) for _ in range(3) ] dec_inp_dict2["1"] = [ constant_op.constant( 0, dtypes.int32, shape=[2]) for _ in range(4) ] with variable_scope.variable_scope("other"): outputs_dict3, _ = seq2seq_lib.one2many_rnn_seq2seq( enc_inp, dec_inp_dict2, EncCellFn(), DecCellsFn(), 2, dec_symbols_dict, embedding_size=2, feed_previous=constant_op.constant(True)) with variable_scope.variable_scope("other_2"): outputs_dict1, _ = seq2seq_lib.one2many_rnn_seq2seq( enc_inp, dec_inp_dict, EncCellFn(), DecCellsFn(), 2, dec_symbols_dict, embedding_size=2, feed_previous=True) with variable_scope.variable_scope("other_3"): outputs_dict2, _ = seq2seq_lib.one2many_rnn_seq2seq( enc_inp, dec_inp_dict2, EncCellFn(), DecCellsFn(), 2, dec_symbols_dict, embedding_size=2, feed_previous=True) sess.run([variables.global_variables_initializer()]) res1 = sess.run(outputs_dict1["0"]) res2 = sess.run(outputs_dict2["0"]) res3 = sess.run(outputs_dict3["0"]) self.assertAllClose(res1, res2) self.assertAllClose(res1, res3) def testSequenceLoss(self): with self.test_session() as sess: logits = [constant_op.constant(i + 0.5, shape=[2, 5]) for i in range(3)] targets = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(3) ] weights = [constant_op.constant(1.0, shape=[2]) for i in range(3)] average_loss_per_example = seq2seq_lib.sequence_loss( logits, targets, weights, average_across_timesteps=True, average_across_batch=True) res = sess.run(average_loss_per_example) self.assertAllClose(1.60944, res) average_loss_per_sequence = seq2seq_lib.sequence_loss( logits, targets, weights, average_across_timesteps=False, average_across_batch=True) res = sess.run(average_loss_per_sequence) self.assertAllClose(4.828314, res) total_loss = seq2seq_lib.sequence_loss( logits, targets, weights, average_across_timesteps=False, average_across_batch=False) res = sess.run(total_loss) self.assertAllClose(9.656628, res) def testSequenceLossByExample(self): with self.test_session() as sess: output_classes = 5 logits = [ constant_op.constant( i + 0.5, shape=[2, output_classes]) for i in range(3) ] targets = [ constant_op.constant( i, dtypes.int32, shape=[2]) for i in range(3) ] weights = [constant_op.constant(1.0, shape=[2]) for i in range(3)] average_loss_per_example = (seq2seq_lib.sequence_loss_by_example( logits, targets, weights, average_across_timesteps=True)) res = sess.run(average_loss_per_example) self.assertAllClose(np.asarray([1.609438, 1.609438]), res) loss_per_sequence = seq2seq_lib.sequence_loss_by_example( logits, targets, weights, average_across_timesteps=False) res = sess.run(loss_per_sequence) self.assertAllClose(np.asarray([4.828314, 4.828314]), res) # TODO(ebrevdo, lukaszkaiser): Re-enable once RNNCells allow reuse # within a variable scope that already has a weights tensor. # # def testModelWithBucketsScopeAndLoss(self): # """Test variable scope reuse is not reset after model_with_buckets.""" # classes = 10 # buckets = [(4, 4), (8, 8)] # with self.test_session(): # # Here comes a sample Seq2Seq model using GRU cells. # def SampleGRUSeq2Seq(enc_inp, dec_inp, weights, per_example_loss): # """Example sequence-to-sequence model that uses GRU cells.""" # def GRUSeq2Seq(enc_inp, dec_inp): # cell = rnn_cell.MultiRNNCell( # [rnn_cell.GRUCell(24) for _ in range(2)]) # return seq2seq_lib.embedding_attention_seq2seq( # enc_inp, # dec_inp, # cell, # num_encoder_symbols=classes, # num_decoder_symbols=classes, # embedding_size=24) # targets = [dec_inp[i + 1] for i in range(len(dec_inp) - 1)] + [0] # return seq2seq_lib.model_with_buckets( # enc_inp, # dec_inp, # targets, # weights, # buckets, # GRUSeq2Seq, # per_example_loss=per_example_loss) # # Now we construct the copy model. # inp = [ # array_ops.placeholder( # dtypes.int32, shape=[None]) for _ in range(8) # ] # out = [ # array_ops.placeholder( # dtypes.int32, shape=[None]) for _ in range(8) # ] # weights = [ # array_ops.ones_like( # inp[0], dtype=dtypes.float32) for _ in range(8) # ] # with variable_scope.variable_scope("root"): # _, losses1 = SampleGRUSeq2Seq( # inp, out, weights, per_example_loss=False) # # Now check that we did not accidentally set reuse. # self.assertEqual(False, variable_scope.get_variable_scope().reuse) # with variable_scope.variable_scope("new"): # _, losses2 = SampleGRUSeq2Seq # inp, out, weights, per_example_loss=True) # # First loss is scalar, the second one is a 1-dimensional tensor. # self.assertEqual([], losses1[0].get_shape().as_list()) # self.assertEqual([None], losses2[0].get_shape().as_list()) def testModelWithBuckets(self): """Larger tests that does full sequence-to-sequence model training.""" # We learn to copy 10 symbols in 2 buckets: length 4 and length 8. classes = 10 buckets = [(4, 4), (8, 8)] perplexities = [[], []] # Results for each bucket. random_seed.set_random_seed(111) random.seed(111) np.random.seed(111) with self.test_session() as sess: # We use sampled softmax so we keep output projection separate. w = variable_scope.get_variable("proj_w", [24, classes]) w_t = array_ops.transpose(w) b = variable_scope.get_variable("proj_b", [classes]) # Here comes a sample Seq2Seq model using GRU cells. def SampleGRUSeq2Seq(enc_inp, dec_inp, weights): """Example sequence-to-sequence model that uses GRU cells.""" def GRUSeq2Seq(enc_inp, dec_inp): cell = rnn_cell.MultiRNNCell( [rnn_cell.GRUCell(24) for _ in range(2)], state_is_tuple=True) return seq2seq_lib.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=classes, num_decoder_symbols=classes, embedding_size=24, output_projection=(w, b)) targets = [dec_inp[i + 1] for i in range(len(dec_inp) - 1)] + [0] def SampledLoss(labels, logits): labels = array_ops.reshape(labels, [-1, 1]) return nn_impl.sampled_softmax_loss( weights=w_t, biases=b, labels=labels, inputs=logits, num_sampled=8, num_classes=classes) return seq2seq_lib.model_with_buckets( enc_inp, dec_inp, targets, weights, buckets, GRUSeq2Seq, softmax_loss_function=SampledLoss) # Now we construct the copy model. batch_size = 8 inp = [ array_ops.placeholder( dtypes.int32, shape=[None]) for _ in range(8) ] out = [ array_ops.placeholder( dtypes.int32, shape=[None]) for _ in range(8) ] weights = [ array_ops.ones_like( inp[0], dtype=dtypes.float32) for _ in range(8) ] with variable_scope.variable_scope("root"): _, losses = SampleGRUSeq2Seq(inp, out, weights) updates = [] params = variables.global_variables() optimizer = adam.AdamOptimizer(0.03, epsilon=1e-5) for i in range(len(buckets)): full_grads = gradients_impl.gradients(losses[i], params) grads, _ = clip_ops.clip_by_global_norm(full_grads, 30.0) update = optimizer.apply_gradients(zip(grads, params)) updates.append(update) sess.run([variables.global_variables_initializer()]) steps = 6 for _ in range(steps): bucket = random.choice(np.arange(len(buckets))) length = buckets[bucket][0] i = [ np.array( [np.random.randint(9) + 1 for _ in range(batch_size)], dtype=np.int32) for _ in range(length) ] # 0 is our "GO" symbol here. o = [np.array([0] * batch_size, dtype=np.int32)] + i feed = {} for i1, i2, o1, o2 in zip(inp[:length], i[:length], out[:length], o[:length]): feed[i1.name] = i2 feed[o1.name] = o2 if length < 8: # For the 4-bucket, we need the 5th as target. feed[out[length].name] = o[length] res = sess.run([updates[bucket], losses[bucket]], feed) perplexities[bucket].append(math.exp(float(res[1]))) for bucket in range(len(buckets)): if len(perplexities[bucket]) > 1: # Assert that perplexity went down. self.assertLess(perplexities[bucket][-1], # 20% margin of error. 1.2 * perplexities[bucket][0]) def testModelWithBooleanFeedPrevious(self): """Test the model behavior when feed_previous is True. For example, the following two cases have the same effect: - Train `embedding_rnn_seq2seq` with `feed_previous=True`, which contains a `embedding_rnn_decoder` with `feed_previous=True` and `update_embedding_for_previous=True`. The decoder is fed with "<Go>" and outputs "A, B, C". - Train `embedding_rnn_seq2seq` with `feed_previous=False`. The decoder is fed with "<Go>, A, B". """ num_encoder_symbols = 3 num_decoder_symbols = 5 batch_size = 2 num_enc_timesteps = 2 num_dec_timesteps = 3 def TestModel(seq2seq): with self.test_session(graph=ops.Graph()) as sess: random_seed.set_random_seed(111) random.seed(111) np.random.seed(111) enc_inp = [ constant_op.constant( i + 1, dtypes.int32, shape=[batch_size]) for i in range(num_enc_timesteps) ] dec_inp_fp_true = [ constant_op.constant( i, dtypes.int32, shape=[batch_size]) for i in range(num_dec_timesteps) ] dec_inp_holder_fp_false = [ array_ops.placeholder( dtypes.int32, shape=[batch_size]) for _ in range(num_dec_timesteps) ] targets = [ constant_op.constant( i + 1, dtypes.int32, shape=[batch_size]) for i in range(num_dec_timesteps) ] weights = [ constant_op.constant( 1.0, shape=[batch_size]) for i in range(num_dec_timesteps) ] def ForwardBackward(enc_inp, dec_inp, feed_previous): scope_name = "fp_{}".format(feed_previous) with variable_scope.variable_scope(scope_name): dec_op, _ = seq2seq(enc_inp, dec_inp, feed_previous=feed_previous) net_variables = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope_name) optimizer = adam.AdamOptimizer(0.03, epsilon=1e-5) update_op = optimizer.minimize( seq2seq_lib.sequence_loss(dec_op, targets, weights), var_list=net_variables) return dec_op, update_op, net_variables dec_op_fp_true, update_fp_true, variables_fp_true = ForwardBackward( enc_inp, dec_inp_fp_true, feed_previous=True) _, update_fp_false, variables_fp_false = ForwardBackward( enc_inp, dec_inp_holder_fp_false, feed_previous=False) sess.run(variables.global_variables_initializer()) # We only check consistencies between the variables existing in both # the models with True and False feed_previous. Variables created by # the loop_function in the model with True feed_previous are ignored. v_false_name_dict = { v.name.split("/", 1)[-1]: v for v in variables_fp_false } matched_variables = [(v, v_false_name_dict[v.name.split("/", 1)[-1]]) for v in variables_fp_true] for v_true, v_false in matched_variables: sess.run(state_ops.assign(v_false, v_true)) # Take the symbols generated by the decoder with feed_previous=True as # the true input symbols for the decoder with feed_previous=False. dec_fp_true = sess.run(dec_op_fp_true) output_symbols_fp_true = np.argmax(dec_fp_true, axis=2) dec_inp_fp_false = np.vstack((dec_inp_fp_true[0].eval(), output_symbols_fp_true[:-1])) sess.run(update_fp_true) sess.run(update_fp_false, { holder: inp for holder, inp in zip(dec_inp_holder_fp_false, dec_inp_fp_false) }) for v_true, v_false in matched_variables: self.assertAllClose(v_true.eval(), v_false.eval()) def EmbeddingRNNSeq2SeqF(enc_inp, dec_inp, feed_previous): cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=True) return seq2seq_lib.embedding_rnn_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingRNNSeq2SeqNoTupleF(enc_inp, dec_inp, feed_previous): cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=False) return seq2seq_lib.embedding_rnn_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingTiedRNNSeq2Seq(enc_inp, dec_inp, feed_previous): cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=True) return seq2seq_lib.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingTiedRNNSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous): cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=False) return seq2seq_lib.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingAttentionSeq2Seq(enc_inp, dec_inp, feed_previous): cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=True) return seq2seq_lib.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingAttentionSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous): cell = rnn_cell.BasicLSTMCell(2, state_is_tuple=False) return seq2seq_lib.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) for model in (EmbeddingRNNSeq2SeqF, EmbeddingRNNSeq2SeqNoTupleF, EmbeddingTiedRNNSeq2Seq, EmbeddingTiedRNNSeq2SeqNoTuple, EmbeddingAttentionSeq2Seq, EmbeddingAttentionSeq2SeqNoTuple): TestModel(model) if __name__ == "__main__": test.main()

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7ce819ce9237fcd0822279c4b4ad2383ea8a5407

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py

Python

ossdbtoolsservice/query/data_storage/__init__.py

DaeunYim/pgtoolsservice

b7e548718d797883027b2caee2d4722810b33c0f

[ "MIT" ]

33

2019-05-27T13:04:35.000Z

2022-03-17T13:33:05.000Z

ossdbtoolsservice/query/data_storage/__init__.py

DaeunYim/pgtoolsservice

b7e548718d797883027b2caee2d4722810b33c0f

[ "MIT" ]

31

2019-06-10T01:55:47.000Z

2022-03-09T07:27:49.000Z

ossdbtoolsservice/query/data_storage/__init__.py

DaeunYim/pgtoolsservice

b7e548718d797883027b2caee2d4722810b33c0f

[ "MIT" ]

25

2019-05-13T18:39:24.000Z

2021-11-16T03:07:33.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- from ossdbtoolsservice.query.data_storage.storage_data_reader import StorageDataReader from ossdbtoolsservice.query.data_storage.service_buffer_file_stream_writer import ServiceBufferFileStreamWriter from ossdbtoolsservice.query.data_storage.service_buffer_file_stream_reader import ServiceBufferFileStreamReader from ossdbtoolsservice.query.data_storage.file_stream_factory import FileStreamFactory from ossdbtoolsservice.query.data_storage.save_as_csv_writer import SaveAsCsvWriter from ossdbtoolsservice.query.data_storage.save_as_csv_file_stream_factory import SaveAsCsvFileStreamFactory from ossdbtoolsservice.query.data_storage.save_as_json_writer import SaveAsJsonWriter from ossdbtoolsservice.query.data_storage.save_as_json_file_stream_factory import SaveAsJsonFileStreamFactory from ossdbtoolsservice.query.data_storage.save_as_excel_writer import SaveAsExcelWriter from ossdbtoolsservice.query.data_storage.save_as_excel_writer_factory import SaveAsExcelFileStreamFactory __all__ = [ 'FileStreamFactory', 'SaveAsCsvWriter', 'SaveAsJsonWriter', 'SaveAsExcelWriter', 'SaveAsExcelFileStreamFactory', 'SaveAsJsonFileStreamFactory', 'SaveAsCsvFileStreamFactory', 'ServiceBufferFileStreamWriter', 'ServiceBufferFileStreamReader', 'StorageDataReader' ]

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null

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1

0

4

7cee0a9ba7f29bf6898fd8366b5ada8790465a30

104

py

Python

Python Basics/While Loop/Lab/Task03.py

DonikaChervenkova/SoftUni

bff579c037ec48f39ed193b34bc3502a32e90732

[ "MIT" ]

1

2022-03-16T10:23:04.000Z

Python Basics/While Loop/Lab/Task03.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

Python Basics/While Loop/Lab/Task03.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

1

2021-12-04T12:30:57.000Z

x = int(input()) numbers = 0 while numbers < x: y = int(input()) numbers += y print(numbers)

10.4

20

0.567308

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3.933333

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0.271186

0.508475

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104

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null

1

0

1

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null

0

4

6b17667c63ee699f67543db9de9de99bc805f59c

122

py

Python

gui/apps.py

narsi84/digilib

eec9632e7b7d3cb64a9832e313f47719621219c6

[ "MIT" ]

null

gui/apps.py

narsi84/digilib

eec9632e7b7d3cb64a9832e313f47719621219c6

[ "MIT" ]

null

gui/apps.py

narsi84/digilib

eec9632e7b7d3cb64a9832e313f47719621219c6

[ "MIT" ]

null

from __future__ import unicode_literals from django.apps import AppConfig class GuiConfig(AppConfig): name = 'gui'

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null

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1

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1

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4

6b3abb8f30388b08a82b7a065add3a309e439bc7

106

py

Python

assignments/essential/jinpackage/count.py

JinTang96/cv

530a3082bf20a9d624d09a1ace1bce6703e524ac

[ "MIT" ]

null

assignments/essential/jinpackage/count.py

JinTang96/cv

530a3082bf20a9d624d09a1ace1bce6703e524ac

[ "MIT" ]

null

assignments/essential/jinpackage/count.py

JinTang96/cv

530a3082bf20a9d624d09a1ace1bce6703e524ac

[ "MIT" ]

null

# 2. Write a function that count 1 to n in the module. def count(n): print([i for i in range(1,n+1)])

26.5

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0.641509

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106

2.833333

0.708333

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106

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35.333333

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null

0

1

0

null

0

1

0

1

0

4

6b45128f58ded7b150dcb763a0e88c5175ac0e32

89

py

Python

checkin/apps.py

mahoyen/web

1d190a86e3277315804bfcc0b8f9abd4f9c1d780

[ "MIT" ]

1

2020-07-16T02:41:35.000Z

checkin/apps.py

borsezf2/dscatc-webapp

68867cb528cdd1698b251156d26049b384357c7d

[ "MIT" ]

22

2021-02-26T20:32:50.000Z

2021-10-21T06:21:20.000Z

checkin/apps.py

borsezf2/dscatc-webapp

68867cb528cdd1698b251156d26049b384357c7d

[ "MIT" ]

9

2019-09-25T08:22:52.000Z

2019-10-02T14:11:33.000Z

from django.apps import AppConfig class CheckinConfig(AppConfig): name = 'checkin'

14.833333

33

0.752809

10

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89

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1

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1

0

4

86099710b6f8db2187636f8e6f63fb2cb177b87d

5,475

py

Python

project-euler/13.py

hydroo/coding-and-math-exercises

c0c9b8ae48e043b0809e4c592444f3e4bc3222d8

[ "CC0-1.0" ]

2

2015-05-29T21:03:26.000Z

2019-11-11T02:10:53.000Z

project-euler/13.py

hydroo/coding-and-math-exercises

c0c9b8ae48e043b0809e4c592444f3e4bc3222d8

[ "CC0-1.0" ]

null

project-euler/13.py

hydroo/coding-and-math-exercises

c0c9b8ae48e043b0809e4c592444f3e4bc3222d8

[ "CC0-1.0" ]

null

#! /usr/bin/python nums = (37107287533902102798797998220837590246510135740250 ,46376937677490009712648124896970078050417018260538 ,74324986199524741059474233309513058123726617309629 ,91942213363574161572522430563301811072406154908250 ,23067588207539346171171980310421047513778063246676 ,89261670696623633820136378418383684178734361726757 ,28112879812849979408065481931592621691275889832738 ,44274228917432520321923589422876796487670272189318 ,47451445736001306439091167216856844588711603153276 ,70386486105843025439939619828917593665686757934951 ,62176457141856560629502157223196586755079324193331 ,64906352462741904929101432445813822663347944758178 ,92575867718337217661963751590579239728245598838407 ,58203565325359399008402633568948830189458628227828 ,80181199384826282014278194139940567587151170094390 ,35398664372827112653829987240784473053190104293586 ,86515506006295864861532075273371959191420517255829 ,71693888707715466499115593487603532921714970056938 ,54370070576826684624621495650076471787294438377604 ,53282654108756828443191190634694037855217779295145 ,36123272525000296071075082563815656710885258350721 ,45876576172410976447339110607218265236877223636045 ,17423706905851860660448207621209813287860733969412 ,81142660418086830619328460811191061556940512689692 ,51934325451728388641918047049293215058642563049483 ,62467221648435076201727918039944693004732956340691 ,15732444386908125794514089057706229429197107928209 ,55037687525678773091862540744969844508330393682126 ,18336384825330154686196124348767681297534375946515 ,80386287592878490201521685554828717201219257766954 ,78182833757993103614740356856449095527097864797581 ,16726320100436897842553539920931837441497806860984 ,48403098129077791799088218795327364475675590848030 ,87086987551392711854517078544161852424320693150332 ,59959406895756536782107074926966537676326235447210 ,69793950679652694742597709739166693763042633987085 ,41052684708299085211399427365734116182760315001271 ,65378607361501080857009149939512557028198746004375 ,35829035317434717326932123578154982629742552737307 ,94953759765105305946966067683156574377167401875275 ,88902802571733229619176668713819931811048770190271 ,25267680276078003013678680992525463401061632866526 ,36270218540497705585629946580636237993140746255962 ,24074486908231174977792365466257246923322810917141 ,91430288197103288597806669760892938638285025333403 ,34413065578016127815921815005561868836468420090470 ,23053081172816430487623791969842487255036638784583 ,11487696932154902810424020138335124462181441773470 ,63783299490636259666498587618221225225512486764533 ,67720186971698544312419572409913959008952310058822 ,95548255300263520781532296796249481641953868218774 ,76085327132285723110424803456124867697064507995236 ,37774242535411291684276865538926205024910326572967 ,23701913275725675285653248258265463092207058596522 ,29798860272258331913126375147341994889534765745501 ,18495701454879288984856827726077713721403798879715 ,38298203783031473527721580348144513491373226651381 ,34829543829199918180278916522431027392251122869539 ,40957953066405232632538044100059654939159879593635 ,29746152185502371307642255121183693803580388584903 ,41698116222072977186158236678424689157993532961922 ,62467957194401269043877107275048102390895523597457 ,23189706772547915061505504953922979530901129967519 ,86188088225875314529584099251203829009407770775672 ,11306739708304724483816533873502340845647058077308 ,82959174767140363198008187129011875491310547126581 ,97623331044818386269515456334926366572897563400500 ,42846280183517070527831839425882145521227251250327 ,55121603546981200581762165212827652751691296897789 ,32238195734329339946437501907836945765883352399886 ,75506164965184775180738168837861091527357929701337 ,62177842752192623401942399639168044983993173312731 ,32924185707147349566916674687634660915035914677504 ,99518671430235219628894890102423325116913619626622 ,73267460800591547471830798392868535206946944540724 ,76841822524674417161514036427982273348055556214818 ,97142617910342598647204516893989422179826088076852 ,87783646182799346313767754307809363333018982642090 ,10848802521674670883215120185883543223812876952786 ,71329612474782464538636993009049310363619763878039 ,62184073572399794223406235393808339651327408011116 ,66627891981488087797941876876144230030984490851411 ,60661826293682836764744779239180335110989069790714 ,85786944089552990653640447425576083659976645795096 ,66024396409905389607120198219976047599490197230297 ,64913982680032973156037120041377903785566085089252 ,16730939319872750275468906903707539413042652315011 ,94809377245048795150954100921645863754710598436791 ,78639167021187492431995700641917969777599028300699 ,15368713711936614952811305876380278410754449733078 ,40789923115535562561142322423255033685442488917353 ,44889911501440648020369068063960672322193204149535 ,41503128880339536053299340368006977710650566631954 ,81234880673210146739058568557934581403627822703280 ,82616570773948327592232845941706525094512325230608 ,22918802058777319719839450180888072429661980811197 ,77158542502016545090413245809786882778948721859617 ,72107838435069186155435662884062257473692284509516 ,20849603980134001723930671666823555245252804609722 ,53503534226472524250874054075591789781264330331690 ) res = 0 for a in nums : res += a print res

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null

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4

861aefe5d6b193c96144e3a1d5c3d6871cc34e1f

3,565

py

Python

multiworld/envs/pygame/__init__.py

szk9876/multiworld

c90149d2913280298d5c54236ac40b793b0c2632

[ "MIT" ]

null

multiworld/envs/pygame/__init__.py

szk9876/multiworld

c90149d2913280298d5c54236ac40b793b0c2632

[ "MIT" ]

null

multiworld/envs/pygame/__init__.py

szk9876/multiworld

c90149d2913280298d5c54236ac40b793b0c2632

[ "MIT" ]

null

from gym.envs.registration import register import logging LOGGER = logging.getLogger(__name__) _REGISTERED = False def register_custom_envs(): global _REGISTERED if _REGISTERED: return _REGISTERED = True LOGGER.info("Registering multiworld pygame gym environments") register( id='Point2DLargeEnv-offscreen-v0', entry_point='multiworld.envs.pygame.point2d:Point2DEnv', tags={ 'git-commit-hash': '166f0f3', 'author': 'Vitchyr' }, kwargs={ 'images_are_rgb': True, 'target_radius': 1, 'ball_radius': 1, 'render_onscreen': False, }, ) register( id='Point2DLargeEnv-onscreen-v0', entry_point='multiworld.envs.pygame.point2d:Point2DEnv', tags={ 'git-commit-hash': '166f0f3', 'author': 'Vitchyr' }, kwargs={ 'images_are_rgb': True, 'target_radius': 1, 'ball_radius': 1, 'render_onscreen': True, }, ) register( id='Point2DWalls-corner-v0', entry_point='multiworld.envs.pygame.point2d:Point2DWallEnv', tags={ 'author': 'Kyle' }, kwargs={ 'wall_shape': 'maze', 'initial_position': (-8, -8), 'images_are_rgb': True, 'target_radius': 0, 'ball_radius': 0.25, 'render_onscreen': False, 'fixed_goal': False, 'randomize_position_on_reset': False, 'render_size': 84, 'boundary_dist': 10, 'action_limit': 1.0, 'show_goal': False } ) register( id='Point2D-center-v0', entry_point='multiworld.envs.pygame.point2d:Point2DEnv', tags={ 'author': 'Kyle' }, kwargs={ 'initial_position': (0, 0), 'images_are_rgb': True, 'target_radius': 0, 'ball_radius': 0.25, 'render_onscreen': False, 'fixed_goal': False, 'randomize_position_on_reset': False, 'render_size': 84, 'boundary_dist': 100, 'action_limit': 1.0, 'show_goal': False } ) register( id='Point2D-center-v2', entry_point='multiworld.envs.pygame.point2d:Point2DEnv', tags={ 'author': 'Saurabh' }, kwargs={ 'initial_position': (0, 0), 'images_are_rgb': True, 'target_radius': 0.5, 'ball_radius': 0.25, 'render_onscreen': False, 'fixed_goal': (3.5, 3.5), 'randomize_position_on_reset': False, 'render_size': 84, 'boundary_dist': 4, 'action_limit': 1.0, 'show_goal': False } ) register( id='Point2DWalls-corner-v2', entry_point='multiworld.envs.pygame.point2d:Point2DWallEnv', tags={ 'author': 'Saurabh' }, kwargs={ 'wall_shape': '--', 'initial_position': (0, -4), 'images_are_rgb': True, 'target_radius': 0.5, 'ball_radius': 0.25, 'render_onscreen': False, 'fixed_goal': (0, 3.5), 'randomize_position_on_reset': False, 'render_size': 84, 'boundary_dist': 4, 'action_limit': 1.0, 'show_goal': False } ) register_custom_envs()

27.007576

68

0.501543

333

3,565

5.111111

0.24024

0.032902

0.070505

0.084606

0.740306

0.73678

0.59577

0

0.041871

0.370266

3,565

131

69

27.21374

0.716258

0

0.636364

0

0.360449

0.129313

0

1

0.008264

false

0

0.016529

0

0.033058

0

null

0

1

0

null

0

4

861ffd1f8c52997d9ec4325c68e4d23aec753915

343

py

Python

spearmint/sampling/__init__.py

fernandezdaniel/Spearmint

3c9e0a4be6108c3d652606bd957f0c9ae1bfaf84

[ "RSA-MD" ]

1,590

2015-01-02T19:11:29.000Z

2022-03-31T13:36:16.000Z

spearmint/sampling/__init__.py

fernandezdaniel/Spearmint

3c9e0a4be6108c3d652606bd957f0c9ae1bfaf84

[ "RSA-MD" ]

99

2015-02-20T06:45:49.000Z

2021-12-06T13:28:44.000Z

spearmint/sampling/__init__.py

fernandezdaniel/Spearmint

3c9e0a4be6108c3d652606bd957f0c9ae1bfaf84

[ "RSA-MD" ]

366

2015-01-17T20:29:49.000Z

2022-02-21T16:22:31.000Z

from abstract_sampler import AbstractSampler from slice_sampler import SliceSampler from whitened_prior_slice_sampler import WhitenedPriorSliceSampler from elliptical_slice_sampler import EllipticalSliceSampler __all__ = ["AbstractSampler", "SliceSampler", "WhitenedPriorSliceSampler", "EllipticalSliceSampler"]

57.166667

100

0.804665

28

343

9.464286

0.464286

0.196226

0.203774

0

0.154519

343

6

100

57.166667

0.913793

0

0.215116

0.136628

0

1

0

false

0

0.8

0

0.8

0

1

0

null

0

1

0

1

0

null

0

1

0

4

8658813448afef752d768bb87e84d83a145883b7

79

py

Python

encrypt/reverse_encrypt.py

edgells/python-commons

38c0aa0ec10304a4147ea231c92c9e34da462052

[ "MIT" ]

null

encrypt/reverse_encrypt.py

edgells/python-commons

38c0aa0ec10304a4147ea231c92c9e34da462052

[ "MIT" ]

null

encrypt/reverse_encrypt.py

edgells/python-commons

38c0aa0ec10304a4147ea231c92c9e34da462052

[ "MIT" ]

null

target_str = "hello python world" # reverse encrypt print(target_str[-1::-1])

15.8

33

0.721519

12

79

4.583333

0.75

0.327273

0

0.028986

0.126582

79

4

34

19.75

0.768116

0.189873

0

0.290323

0

1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

4

869724dbb9d849f8284d2b7696d35a28fb3d893d

2,811

py

Python

z2/part3/updated_part2_batch/jm/parser_errors_2/193497097.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

1

2020-04-16T12:13:47.000Z

z2/part3/updated_part2_batch/jm/parser_errors_2/193497097.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:50:15.000Z

2020-05-19T14:58:30.000Z

z2/part3/updated_part2_batch/jm/parser_errors_2/193497097.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:45:13.000Z

2020-06-09T19:18:31.000Z

from part1 import ( gamma_board, gamma_busy_fields, gamma_delete, gamma_free_fields, gamma_golden_move, gamma_golden_possible, gamma_move, gamma_new, ) """ scenario: test_random_actions uuid: 193497097 """ """ random actions, total chaos """ board = gamma_new(3, 4, 3, 5) assert board is not None assert gamma_move(board, 1, 0, 3) == 1 assert gamma_golden_possible(board, 1) == 0 assert gamma_move(board, 2, 2, 2) == 1 assert gamma_move(board, 2, 2, 0) == 1 assert gamma_free_fields(board, 2) == 9 assert gamma_golden_possible(board, 2) == 1 assert gamma_move(board, 3, 2, 1) == 1 assert gamma_move(board, 1, 3, 1) == 0 assert gamma_move(board, 2, 0, 1) == 1 assert gamma_move(board, 2, 1, 2) == 1 assert gamma_move(board, 3, 2, 0) == 0 assert gamma_move(board, 3, 0, 0) == 1 assert gamma_move(board, 1, 1, 1) == 1 assert gamma_move(board, 1, 1, 0) == 1 assert gamma_busy_fields(board, 1) == 3 assert gamma_golden_move(board, 1, 1, 2) == 1 assert gamma_move(board, 2, 1, 0) == 0 assert gamma_move(board, 3, 0, 3) == 0 assert gamma_move(board, 1, 2, 0) == 0 board142535448 = gamma_board(board) assert board142535448 is not None assert board142535448 == ("1..\n" ".12\n" "213\n" "312\n") del board142535448 board142535448 = None assert gamma_move(board, 2, 2, 2) == 0 assert gamma_move(board, 2, 1, 1) == 0 assert gamma_golden_possible(board, 2) == 1 board838097404 = gamma_board(board) assert board838097404 is not None assert board838097404 == ("1..\n" ".12\n" "213\n" "312\n") del board838097404 board838097404 = None assert gamma_move(board, 3, 0, 3) == 0 assert gamma_move(board, 3, 2, 0) == 0 assert gamma_move(board, 1, 0, 3) == 0 assert gamma_move(board, 2, 0, 0) == 0 assert gamma_free_fields(board, 2) == 3 assert gamma_move(board, 3, 2, 0) == 0 assert gamma_move(board, 3, 2, 0) == 0 assert gamma_golden_possible(board, 3) == 1 assert gamma_move(board, 1, 1, 3) == 1 assert gamma_move(board, 2, 2, 0) == 0 assert gamma_golden_possible(board, 2) == 1 assert gamma_move(board, 3, 2, 0) == 0 assert gamma_move(board, 3, 1, 2) == 0 board284563281 = gamma_board(board) assert board284563281 is not None assert board284563281 == ("11.\n" ".12\n" "213\n" "312\n") del board284563281 board284563281 = None assert gamma_move(board, 1, 0, 1) == 0 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_golden_possible(board, 2) == 1 assert gamma_move(board, 3, 2, 0) == 0 assert gamma_golden_possible(board, 3) == 1 assert gamma_golden_move(board, 3, 3, 1) == 0 assert gamma_move(board, 1, 3, 2) == 0 assert gamma_free_fields(board, 2) == 2 assert gamma_move(board, 3, 1, 0) == 0 board886503188 = gamma_board(board) assert board886503188 is not None assert board886503188 == ("11.\n" ".12\n" "213\n" "312\n") del board886503188 board886503188 = None gamma_delete(board)

28.683673

58

0.695126

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0.086957

0.257858

0.247736

0.330314

0.626532

0.582312

0.540757

0.336708

0.291955

0.256793

0

0.163352

0.159374

2,811

97

59

28.979381

0.630978

0

0.181818

0

0.029379

0

0.688312

1

0

false

0

0.012987

0

0.012987

0

null

1

0

null

0

1

0

4

86cf5ff1f1bec5e1c0d650d428c95182f9c4dc59

295

py

Python

Participants/Whos_Rem/main/__init__.py

python-discord/game-jam-2020

cdcb1c2c9246825518a29b05cc39a679b36d666e

[ "MIT" ]

15

2020-04-17T12:02:14.000Z

2022-03-16T03:01:34.000Z

Participants/Whos_Rem/main/__init__.py

python-discord/game-jam-2020

cdcb1c2c9246825518a29b05cc39a679b36d666e

[ "MIT" ]

9

2020-04-25T01:57:16.000Z

2020-04-29T11:42:34.000Z

Participants/Whos_Rem/main/__init__.py

python-discord/game-jam-2020

cdcb1c2c9246825518a29b05cc39a679b36d666e

[ "MIT" ]

55

2020-04-17T12:01:11.000Z

2021-12-28T10:14:02.000Z

from .settings import Settings from .main_menu import MainMenu from .song_select import SongSelection from .perspective_objects import Shape, ShapeManager from .display.input_tools import Button, Slider from .display.utility import ListFunctions, ColourBlend from .play_screen import GameScreen

36.875

55

0.854237

38

295

6.5

0.631579

0.089069

0

0.105085

295

7

56

42.142857

0.935606

0

1

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true

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null

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