xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

bc648c2976801da1917ecc20a8032cce85084bff

72

py

Python

vnegmas/backend/src/dynetx/readwrite/__init__.py

YueNing/vnegmas

e95adc56ee9aab8d6cd6f28cce04383e199dc2b8

[ "MIT" ]

3

2019-06-29T11:40:29.000Z

2019-09-07T02:15:09.000Z

vnegmas/backend/src/dynetx/readwrite/__init__.py

YueNing/vnegmas

e95adc56ee9aab8d6cd6f28cce04383e199dc2b8

[ "MIT" ]

null

vnegmas/backend/src/dynetx/readwrite/__init__.py

YueNing/vnegmas

e95adc56ee9aab8d6cd6f28cce04383e199dc2b8

[ "MIT" ]

null

from ..readwrite.edgelist import * from ..readwrite.json_graph import *

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6

bcb4cbe870458bcf42356dd082ee6eaea4680eac

43

py

Python

backend/todo_app/serializers/__init__.py

nitinmehra/TodoApp

e1e8938330df6b59b8b064ac1a2dde61744d8392

[ "MIT" ]

null

backend/todo_app/serializers/__init__.py

nitinmehra/TodoApp

e1e8938330df6b59b8b064ac1a2dde61744d8392

[ "MIT" ]

null

backend/todo_app/serializers/__init__.py

nitinmehra/TodoApp

e1e8938330df6b59b8b064ac1a2dde61744d8392

[ "MIT" ]

null

from .todo_serializer import TodoSerializer

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0

1

0

6

bcd94de810d9b5962438f44413d0ccf7a0a6c05f

1,437

py

Python

tests/yaml_cached_view_test.py

connorworley/service_configuration_lib

a25f0fa56119907ccd756817caf0275e8d73df0b

[ "Apache-2.0" ]

6

2017-01-25T05:44:06.000Z

2022-01-03T17:17:06.000Z

tests/yaml_cached_view_test.py

connorworley/service_configuration_lib

a25f0fa56119907ccd756817caf0275e8d73df0b

[ "Apache-2.0" ]

27

2015-11-09T17:54:30.000Z

2022-03-30T19:39:43.000Z

tests/yaml_cached_view_test.py

connorworley/service_configuration_lib

a25f0fa56119907ccd756817caf0275e8d73df0b

[ "Apache-2.0" ]

14

2016-02-10T02:27:00.000Z

2021-03-24T18:01:56.000Z

def test_yaml_configs_file_watcher_creation(yaml_configs_file_watcher): assert len(yaml_configs_file_watcher.configs_view.configs['foo']) == 2 assert yaml_configs_file_watcher.configs_view.configs['foo']['smartstack'] == {'main.fake': 42} assert yaml_configs_file_watcher.configs_view.configs['foo']['authorization'] == \ {'authorization': {'enabled': True}} def test_yaml_configs_file_watcher_delete_not_presented_files(yaml_configs_file_watcher, mock_soa_dir): # Not present in cache, excluded by filter yaml_configs_file_watcher._maybe_remove_path_from_cache(mock_soa_dir.join('foo', 'smartstackX.yaml')) assert len(yaml_configs_file_watcher.configs_view.configs['foo']) == 2 # Not present in cache, not excluded by filter assert len(yaml_configs_file_watcher.configs_view.configs['bar']) == 0 yaml_configs_file_watcher._maybe_remove_path_from_cache(mock_soa_dir.join('bar', 'smartstack.yaml')) assert len(yaml_configs_file_watcher.configs_view.configs['foo']) == 2 assert len(yaml_configs_file_watcher.configs_view.configs['bar']) == 0 def test_yaml_configs_file_watcher_delete(yaml_configs_file_watcher, mock_soa_dir): assert len(yaml_configs_file_watcher.configs_view.configs['foo']) == 2 yaml_configs_file_watcher._maybe_remove_path_from_cache(mock_soa_dir.join('foo', 'smartstack.yaml')) assert len(yaml_configs_file_watcher.configs_view.configs['foo']) == 1

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null

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6

bcdb3577158d523bcb302d0df0cc1c204ef64249

33

py

Python

Week 1: Integers, I-O, simple string operations/08.py

MLunov/Python-programming-basics-HSE

7df8bba105db84d6b932c454fdc39193a648254e

[ "MIT" ]

null

Week 1: Integers, I-O, simple string operations/08.py

MLunov/Python-programming-basics-HSE

7df8bba105db84d6b932c454fdc39193a648254e

[ "MIT" ]

null

Week 1: Integers, I-O, simple string operations/08.py

MLunov/Python-programming-basics-HSE

7df8bba105db84d6b932c454fdc39193a648254e

[ "MIT" ]

null

print((int(input()) // 10) % 10)

16.5

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0.151515

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null

0

1

0

1

0

6

4c1675ca4910a9c71cb9af45310593328acbc528

111

py

Python

multipeak/__init__.py

graphenestandards/raman

045ba19dedb269b133c65ce8b01d8ce8c25335b7

[ "MIT" ]

2

2018-10-01T14:16:42.000Z

2019-03-26T02:27:27.000Z

multipeak/__init__.py

graphenestandards/raman

045ba19dedb269b133c65ce8b01d8ce8c25335b7

[ "MIT" ]

null

multipeak/__init__.py

graphenestandards/raman

045ba19dedb269b133c65ce8b01d8ce8c25335b7

[ "MIT" ]

1

2020-01-04T11:30:13.000Z

from .multipeak import printmd, Dataset, MultiPseudoVoigtModel from .grapheneRaman import GrapheneModelResults

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62

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1

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0

6

4c43212b160294ce98d9839a02e9d07df72d7ad6

3,303

py

Python

acr_module/acr/preprocessing/word_embeddings.py

13520505/bigdataproj

09202c7e13366726415b1111cc93d3083d102cb3

[ "MIT" ]

null

acr_module/acr/preprocessing/word_embeddings.py

13520505/bigdataproj

09202c7e13366726415b1111cc93d3083d102cb3

[ "MIT" ]

9

2020-01-28T23:07:43.000Z

2022-02-10T00:36:23.000Z

acr_module/acr/preprocessing/word_embeddings.py

13520505/bigdataproj

09202c7e13366726415b1111cc93d3083d102cb3

[ "MIT" ]

null

import numpy as np from gensim.models.keyedvectors import KeyedVectors from ..utils import serialize from ..acr_commons import PAD_TOKEN, UNK_TOKEN def load_word_embeddings(path, binary=True): w2v_model = KeyedVectors.load_word2vec_format(path, binary=binary) return w2v_model # for model vietnamese, conflict but keep def load_word_embeddings_vietnamese(path, binary=True): w2v_model = KeyedVectors.load_word2vec_format(path, binary=binary, unicode_errors='ignore') return w2v_model def process_word_embedding_for_corpus_vocab(w2v_model, words_freq, keep_most_frequent_words=100000): print('Tokens vocab. from articles: {}'.format(len(words_freq))) most_freq_words = set(list(map(lambda x: x[0], words_freq.most_common(keep_most_frequent_words)))) print('Most common tokens vocab. from articles: {}'.format(len(most_freq_words))) RESERVED_TOKENS_IN_VOCAB=2 embedding_size = w2v_model.vector_size new_embeddings_list = [] new_vocab = {} last_token_id = RESERVED_TOKENS_IN_VOCAB w2v_vocab = set(w2v_model.wv.index2word) for word in most_freq_words: if word in w2v_vocab: new_vocab[word] = last_token_id last_token_id += 1 new_embeddings_list.append(w2v_model[word]) #Inserting the 2 reserved tokens new_vocab[PAD_TOKEN] = 0 new_vocab[UNK_TOKEN] = 1 np.random.seed(10) unk_vector = np.random.uniform(low=-0.04, high=0.04, size=embedding_size) pad_vector = np.random.uniform(low=-0.04, high=0.04, size=embedding_size) new_embeddings_matrix = np.vstack([unk_vector, pad_vector] + new_embeddings_list) print('Most common tokens with word embeddings: {}'.format(new_embeddings_matrix.shape[0])) return new_vocab, new_embeddings_matrix def process_word_embedding_for_corpus_vocab_second_time(w2v_model, words_freq,word_vocab, keep_most_frequent_words=100000): print('Tokens vocab. from articles: {}'.format(len(words_freq))) most_freq_words = set(list(map(lambda x: x[0], words_freq.most_common(keep_most_frequent_words)))) print('Most common tokens vocab. from articles: {}'.format(len(most_freq_words))) RESERVED_TOKENS_IN_VOCAB= max(word_vocab.values()) embedding_size = w2v_model.vector_size new_embeddings_list = [] new_vocab = {} last_token_id = RESERVED_TOKENS_IN_VOCAB w2v_vocab = set(w2v_model.wv.index2word) for word in most_freq_words: if word in w2v_vocab: new_vocab[word] = last_token_id last_token_id += 1 new_embeddings_list.append(w2v_model[word]) np.random.seed(10) unk_vector = np.random.uniform(low=-0.04, high=0.04, size=embedding_size) pad_vector = np.random.uniform(low=-0.04, high=0.04, size=embedding_size) new_embeddings_matrix = np.vstack([unk_vector, pad_vector] + new_embeddings_list) print('Most common tokens with word embeddings: {}'.format(new_embeddings_matrix.shape[0])) return new_vocab, new_embeddings_matrix def save_word_vocab_embeddings(output_path, word_vocab, word_embeddings_matrix): to_serialize = (word_vocab, word_embeddings_matrix) serialize(output_path, to_serialize)

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null

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6

4c70d474146e85ddf0c2c965050a44e6d6c12bb5

9,216

py

Python

applications/structural_application/custom_problemtype/write_cnd.py

AndreaVoltan/MyKratos7.0

e977752722e8ef1b606f25618c4bf8fd04c434cc

[ "BSD-4-Clause" ]

2

2020-04-30T19:13:08.000Z

2021-04-14T19:40:47.000Z

applications/structural_application/custom_problemtype/write_cnd.py

AndreaVoltan/MyKratos7.0

e977752722e8ef1b606f25618c4bf8fd04c434cc

[ "BSD-4-Clause" ]

1

2020-04-30T19:19:09.000Z

2020-05-02T14:22:36.000Z

applications/structural_application/custom_problemtype/write_cnd.py

AndreaVoltan/MyKratos7.0

e977752722e8ef1b606f25618c4bf8fd04c434cc

[ "BSD-4-Clause" ]

1

2020-06-12T08:51:24.000Z

from __future__ import print_function, absolute_import, division #makes KratosMultiphysics backward compatible with python 2.6 and 2.7 import string; import basicfunctions; # def WriteScalarCond(name, condtype, meshtype, projectname, domaintype): newsection = '' if (string.count(condtype, 'p') >= 1): newsection = newsection + ScalarCond(name, 'point', meshtype, domaintype) if (string.count(condtype, 'l') >= 1): newsection = newsection + ScalarCond(name, 'line', meshtype, domaintype) if (string.count(condtype, 's') >= 1): newsection = newsection + ScalarCond(name, 'surface', meshtype, domaintype) if (string.count(condtype, 'v') >= 1): newsection = newsection + ScalarCond(name, 'volume', meshtype, domaintype) AddBCToCndFile(newsection, projectname, domaintype) def WriteVectorCond(name, condtype, meshtype, projectname, domaintype): newsection = '' if (string.count(condtype, 'p') >= 1): newsection = newsection + VectorCond(name, 'point', meshtype, domaintype) if (string.count(condtype, 'l') >= 1): newsection = newsection + VectorCond(name, 'line', meshtype, domaintype) if (string.count(condtype, 's') >= 1): newsection = newsection + VectorCond(name, 'surface', meshtype, domaintype) if (string.count(condtype, 'v') >= 1): newsection = newsection + VectorCond(name, 'volume', meshtype, domaintype) AddBCToCndFile(newsection, projectname, domaintype) def WriteElemCond(name, elemtype, projectname, domaintype): newsection = '' if (string.count(elemtype, 'p') >= 1): newsection = newsection + ElemCond(name, 'point') if (string.count(elemtype, 'l') >= 1): newsection = newsection + ElemCond(name, 'line') if (string.count(elemtype, 's') >= 1): newsection = newsection + ElemCond(name, 'surface') if (string.count(elemtype, 'v') >= 1): newsection = newsection + ElemCond(name, 'volume') AddElemToCndFile(newsection, projectname, domaintype) # def ScalarCond(name, condtype, meshtype, domaintype): cond = 'CONDITION: ' + condtype + '_' + name + '_('+domaintype+')\n' condt = 'CONDTYPE: over ' + condtype + 's \n' condmeshtype = 'CONDMESHTYPE: over ' + meshtype + '\n' question = 'QUESTION: Value\nVALUE: 0.0\n' end = 'END CONDITION\n' return cond + condt + condmeshtype + question + end def VectorCond(name, condtype, meshtype, domaintype): cond = 'CONDITION: ' + condtype + '_' + name + '_('+domaintype+')\n' cond = cond + 'CONDTYPE: over ' + condtype + 's \n' cond = cond + 'CONDMESHTYPE: over ' + meshtype + '\n' cond = cond + 'QUESTION:' + name + '_X#CB#(0,1)\nVALUE: 0\nDEPENDENCIES: (0,HIDE,Value_X,#CURRENT#)(1,RESTORE,Value_X,#CURRENT#)\n' cond = cond + 'QUESTION: Value_X\nVALUE: 0.0\n' cond = cond + 'QUESTION:' + name + '_Y#CB#(0,1)\nVALUE: 0\nDEPENDENCIES: (0,HIDE,Value_Y,#CURRENT#)(1,RESTORE,Value_Y,#CURRENT#)\n' cond = cond + 'QUESTION: Value_Y\nVALUE: 0.0\n' cond = cond + 'QUESTION:' + name + '_Z#CB#(0,1)\nVALUE: 0\nDEPENDENCIES: (0,HIDE,Value_Z,#CURRENT#)(1,RESTORE,Value_Z,#CURRENT#)\n' cond = cond + 'QUESTION: Value_Z\nVALUE: 0.0\n' cond = cond + 'END CONDITION\n' return cond def ElemCond(name, elemtype): cond = 'CONDITION: ' + elemtype + '_' + name + '\n' condt = 'CONDTYPE: over ' + elemtype + 's \n' condmeshtype = 'CONDMESHTYPE: over body elements\n' question = 'QUESTION: Element_Type#CB#(' + name + ')\nVALUE: ' + name + '\n' end = 'END CONDITION\n' return cond + condt + condmeshtype + question + end # def AddBCToCndFile(newsection, projectname, domaintype): if domaintype == 'Fluid': key = 'BOOK: Fluid_Boundary_Conditions\n' if domaintype == 'Structure': key = 'BOOK: Structure_Boundary_Conditions\n' file = projectname + '.cnd' filecontent = basicfunctions.splitfile(key, basicfunctions.readfile(file)) newcontent = filecontent[0] + key + newsection + filecontent[1] basicfunctions.writefile(file, newcontent) def AddElemToCndFile(newsection, projectname, domaintype): if domaintype == 'Fluid': key = 'BOOK: Fluid_Element_Type\n' if domaintype == 'Structure': key = 'BOOK: Structure_Element_Type\n' file = projectname + '.cnd' filecontent = basicfunctions.splitfile(key, basicfunctions.readfile(file)) newcontent = filecontent[0] + key + newsection + filecontent[1] basicfunctions.writefile(file, newcontent) # ALT ######################################## # def WriteFluidScalarCond(name,condtype,meshtype,projectname): # filecontent = basicfunctions.getkeyword('BOOK: ',basicfunctions.readfile(projectname+'.cnd')) # section = filecontent[0] # restcontent = filecontent[1]+filecontent[2]+filecontent[3]+filecontent[4]+filecontent[5] # if (string.count(condtype,'p')>=1): # section=section+ScalarCond(name,'point',meshtype,'Fluid') # if (string.count(condtype,'l')>=1): # section=section+ScalarCond(name,'line',meshtype,'Fluid') # if (string.count(condtype,'s')>=1): # section=section+ScalarCond(name,'surface',meshtype,'Fluid') # if (string.count(condtype,'v')>=1): # section=section+ScalarCond(name,'volume',meshtype,'Fluid') # filecontent=section+restcontent # basicfunctions.writefile(projectname+'.cnd',filecontent) # def WriteFluidVectorCond(name,condtype,meshtype,projectname): # filecontent = basicfunctions.getkeyword('BOOK: ',basicfunctions.readfile(projectname+'.cnd')) # section = filecontent[0] # restcontent = filecontent[1]+filecontent[2]+filecontent[3]+filecontent[4]+filecontent[5] # if (string.count(condtype,'p')>=1): # section=section+VectorCond(name,'point',meshtype,'Fluid') # if (string.count(condtype,'l')>=1): # section=section+VectorCond(name,'line',meshtype,'Fluid') # if (string.count(condtype,'s')>=1): # section=section+VectorCond(name,'surface',meshtype,'Fluid') # if (string.count(condtype,'v')>=1): # section=section+VectorCond(name,'volume',meshtype,'Fluid') # filecontent=section+restcontent # basicfunctions.writefile(projectname+'.cnd',filecontent) # def WriteStructureScalarCond(name,condtype,meshtype,projectname): # filecontent = basicfunctions.getkeyword('BOOK: ',basicfunctions.readfile(projectname+'.cnd')) # section = filecontent[3] # restcontent1 = filecontent[0]+filecontent[1]+filecontent[2] # restcontent2 = filecontent[4]+filecontent[5] # if (string.count(condtype,'p')>=1): # section=section+ScalarCond(name,'point',meshtype,'Structure') # if (string.count(condtype,'l')>=1): # section=section+ScalarCond(name,'line',meshtype,'Structure') # if (string.count(condtype,'s')>=1): # section=section+ScalarCond(name,'surface',meshtype,'Structure') # if (string.count(condtype,'v')>=1): # section=section+ScalarCond(name,'volume',meshtype,'Structure') # filecontent=restcontent1+section+restcontent2 # basicfunctions.writefile(projectname+'.cnd',filecontent) # def WriteStructureVectorCond(name,condtype,meshtype,projectname): # filecontent = basicfunctions.getkeyword('BOOK: ',basicfunctions.readfile(projectname+'.cnd')) # section = filecontent[3] # restcontent1 = filecontent[0]+filecontent[1]+filecontent[2] # restcontent2 = filecontent[4]+filecontent[5] # if (string.count(condtype,'p')>=1): # section=section+VectorCond(name,'point',meshtype,'Structure') # if (string.count(condtype,'l')>=1): # section=section+VectorCond(name,'line',meshtype,'Structure') # if (string.count(condtype,'s')>=1): # section=section+VectorCond(name,'surface',meshtype,'Structure') # if (string.count(condtype,'v')>=1): # section=section+VectorCond(name,'volume',meshtype,'Structure') # filecontent=restcontent1+section+restcontent2 # basicfunctions.writefile(projectname+'.cnd',filecontent) # def WriteFluidElemCond(name,elemtype,projectname): # filecontent = basicfunctions.getkeyword('BOOK: ',basicfunctions.readfile(projectname+'.cnd')) # section = filecontent[1] # restcontent1 = filecontent[0] # restcontent2 = filecontent[2]+filecontent[3]+filecontent[4]+filecontent[5] # if (string.count(elemtype,'p')>=1): # section=section+ElemCond(name,'point') # if (string.count(elemtype,'l')>=1): # section=section+ElemCond(name,'line') # if (string.count(elemtype,'s')>=1): # section=section+ElemCond(name,'surface') # if (string.count(elemtype,'v')>=1): # section=section+ElemCond(name,'volume') # filecontent=restcontent1+section+restcontent2 # basicfunctions.writefile(projectname+'.cnd',filecontent) # # # def WriteStructureElemCond(name,elemtype,projectname): # filecontent = basicfunctions.getkeyword('BOOK: ',basicfunctions.readfile(projectname+'.cnd')) # section = filecontent[4] # restcontent1 = filecontent[0]+filecontent[1]+filecontent[2]+filecontent[3] # restcontent2 = filecontent[5] # if (string.count(elemtype,'p')>=1): # section=section+ElemCond(name,'point') # if (string.count(elemtype,'l')>=1): # section=section+ElemCond(name,'line') # if (string.count(elemtype,'s')>=1): # section=section+ElemCond(name,'surface') # if (string.count(elemtype,'v')>=1): # section=section+ElemCond(name,'volume') # filecontent=restcontent1+section+restcontent2 # basicfunctions.writefile(projectname+'.cnd',filecontent) #

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d5e4a58c9e17f368c0538057145ccd4c027f0619

115

py

Python

server/views/__init__.py

dpuljic01/financial-dashboard

95ccde046597c281448ba29e903c26685a248e18

[ "MIT" ]

13

2020-09-12T00:15:02.000Z

2022-03-11T12:34:26.000Z

server/views/__init__.py

dpuljic01/masters-thesis

95ccde046597c281448ba29e903c26685a248e18

[ "MIT" ]

7

2020-09-11T21:36:53.000Z

2022-02-27T09:18:00.000Z

server/views/__init__.py

dpuljic01/masters-thesis

95ccde046597c281448ba29e903c26685a248e18

[ "MIT" ]

5

2020-11-08T08:16:40.000Z

2022-02-07T20:39:03.000Z

from server.views import auth, news, portfolio, user, tickers blueprints = (auth, news, portfolio, user, tickers)

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6

910f96ce8dcf7f7adc55b498c61114d65743e306

43,931

py

Python

autolrn/classification/train_calibrate.py

SimonCarozza/autolrn

d0875844a3e9b4fc22510ef320aa498e339b6192

[ "MIT" ]

null

autolrn/classification/train_calibrate.py

SimonCarozza/autolrn

d0875844a3e9b4fc22510ef320aa498e339b6192

[ "MIT" ]

null

autolrn/classification/train_calibrate.py

SimonCarozza/autolrn

d0875844a3e9b4fc22510ef320aa498e339b6192

[ "MIT" ]

null

""" This module allows you to tune and calibrate the best estimator. This module allows you to tune and calibrate the best estimator returned by nested cv evaluation, and to just calibrate the best estimator returned by the non-nested cv evaluation. """ from sklearn.calibration import CalibratedClassifierCV import numpy as np import re from random import randint from scipy.stats import randint as sp_randint import matplotlib.pyplot as plt from . import param_grids_distros as pgd from .. import auto_utils as au from . import eval_utils as eu from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit from sklearn.utils import multiclass as mc from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.pipeline import Pipeline import os import sys stderr = sys.stderr sys.stderr = open(os.devnull, 'w') from keras.wrappers.scikit_learn import KerasClassifier sys.stderr = stderr import warnings warnings.filterwarnings("ignore") def best_keras_clf_estimator( y_type, best_nn_build_fn, nb_epoch, input_dim, labels, batch_size=None): best_model_estim = None best_model_estim = KerasClassifier( build_fn=best_nn_build_fn, nb_epoch=nb_epoch, input_dim=input_dim, verbose=0) if y_type == 'multiclass': if labels is None: raise ValueError("%r is not a valid type for var 'labels'" % labels) elif not isinstance(labels, list): raise TypeError("Multiclass keras models need a list of string labels.") else: output_dim = len(labels) best_model_estim.set_params(output_dim=output_dim) if batch_size is not None and isinstance(batch_size, int): best_model_estim.set_params(batch_size=batch_size) return best_model_estim def confusion_matrix_and_clf_report(y_type, model_name, y_test, y_pred): if y_type == 'binary': tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel() print() print("Test errors for '%s'" % model_name) print("\ttrue negatives: %d" % tn) print("\tfalse positives: %d" % fp) print("\tfalse negatives: %d" % fn) print("\ttrue positives: %d" % tp) else: print() print("Confusion matrix for '%s'.\n" % model_name, confusion_matrix(y_test, y_pred)) print() print("Classification report for '%s'\n" % model_name, classification_report(y_test, y_pred)) def check_model_hasroc(model_name, model_data): has_roc = 0 try: model_data[2] except IndexError: print("ROC_AUC score is not available.") roc_auc = None except Exception as e: print(e) else: roc_auc = model_data[2] has_roc = 1 finally: print( "We have %s data to compare prediction confidence of models." % model_name) if has_roc: print("ROC_AUC included.") return has_roc, roc_auc def logreg_calibration_reference(y_type, scoring, models_params): lr = None lr_params = None if 'LogRClf_2nd' in models_params: lr = models_params['LogRClf_2nd'][0] if y_type == 'binary': lr.set_params(solver='liblinear') else: # y_type == 'multiclass' if scoring == 'neg_log_loss': lr.set_params( solver='lbfgs', penalty='l2', multi_class='multinomial') lr_params = models_params['LogRClf_2nd'][1] else: if y_type == 'binary': lr =\ pgd.full_search_models_and_parameters['LogRClf_2nd'][0].set_params( solver='liblinear') lr_params = pgd.full_search_models_and_parameters['LogRClf_2nd'][1] else: # y_type == 'multiclass' if scoring == 'neg_log_loss': # solver='saga', penalty='l1' lr =\ pgd.full_search_models_and_parameters['LogRClf_2nd'][0].set_params( solver='lbfgs', penalty='l2', multi_class='multinomial') lr_params = pgd.full_search_models_and_parameters['LogRClf_2nd'][1] return lr, lr_params def classic_cv_calibration_process( ref_pred_score, training_estimator, final_estimator, X, y, X_train, y_train, X_test, y_test, y_type, best_model_name, best_model_estim, weights_test, weights_all, scoring, best_score, best_nn_build_fn, nb_epoch, batch_size, tuning_method, models_data, kfold, labels, d_name, serial): temp_estimator = training_estimator # check predicted probabilities for prediction confidence uncalibrated_data = eu.probability_confidence_before_calibration( temp_estimator, X_train, y_train, X_test, y_test, tuning_method, models_data, labels, serial ) input_dim = 0 if best_model_name in ( 'baseline_nn_default_Clf_2nd', 'baseline_nn_smaller_Clf_2nd', 'larger_nn_Clf_2nd', 'deep_nn_Clf_2nd', 'larger_deep_nn_Clf_2nd', 'deeper_nn_Clf_2nd'): del best_model_estim input_dim = int(X_train.shape[1]) else: del temp_estimator unc_pred_score = uncalibrated_data[0] unc_pipeline = uncalibrated_data[1] has_unc_roc, unc_roc_auc = check_model_hasroc("target", uncalibrated_data) predicted = unc_pipeline.predict(X_test) w_unc_acc = unc_pipeline.score(X_test, y_test, sample_weight=weights_test)*100 confusion_matrix_and_clf_report(y_type, best_model_name, y_test, predicted) print() # in case of LogRegression, # you should compare its probability curve against the ideal one # all stuff before plot of calibration curves should go into a function if unc_pred_score < ref_pred_score: print("'%s' is already well calibrated." % best_model_name) print("Let's resume metrics on test data.") if best_model_name not in ( 'baseline_nn_default_Clf_2nd', 'baseline_nn_smaller_Clf_2nd', 'larger_nn_Clf_2nd', 'deep_nn_Clf_2nd', 'larger_deep_nn_Clf_2nd', 'deeper_nn_Clf_2nd'): print('Mean cv score [%s] of best uncalibrated ("%s"): %1.3f' % (scoring.strip('neg_'), best_model_name, best_score)) if has_unc_roc: print('Scoring [%s] of best uncalibrated ("%s") on test data: %1.3f' % (scoring.strip('neg_'), best_model_name, unc_roc_auc)) print('Accuracy of best uncalibrated ("%s") on test data: %.2f%%' % (best_model_name, w_unc_acc)) print() print("=== [task] Refit '%s' on all data." % best_model_name) print() print("X shape: ", X.shape) print("y shape: ", y.shape) print() # best_rscv_pipeline -> final_best_rscv_estimator eu.model_finalizer( final_estimator, X, y, scoring, tuning_method, d_name, serial) else: print("'%s' needs probability calibration." % best_model_name) if best_model_name in ( 'baseline_nn_default_Clf_2nd', 'baseline_nn_smaller_Clf_2nd', 'larger_nn_Clf_2nd', 'deep_nn_Clf_2nd', 'larger_deep_nn_Clf_2nd', 'deeper_nn_Clf_2nd'): print("Calibration of Keras neural networks is not implemented yet") print(""" We assume this is the best calibration we can achieve with a Keras neural network, which approaches LogisticRegression's prediction confidence as the nr of iterations increases. """) else: temp_estimator = training_estimator # In case model needs calibration calib_data = eu.calibrate_probabilities( temp_estimator, X_train, y_train, X_test, y_test, 'sigmoid', tuning_method, models_data, kfold, labels, serial ) calib_pred_score = calib_data[0] calib_pipeline = calib_data[1] has_calib_roc, calib_roc_auc = check_model_hasroc("target calib", calib_data) print() if calib_pred_score >= unc_pred_score: print("Sorry, we could not calibrate '%s' any better." % best_model_name) print("We're rejecting calibrated '%s' and saving the uncalibrated one." % best_model_name) print("Let's resume scores on validation and test data.") print('Mean cross-validated score [%s] of best uncalibrated ("%s"): %1.3f' % (scoring.strip('neg_'), best_model_name, best_score)) if has_unc_roc: print('Scoring [%s] of best uncalibrated ("%s") on test data: %1.3f' % (scoring.strip('neg_'), best_model_name, unc_roc_auc)) print('Accuracy of best uncalibrated ("%s") on test data: %.2f%%' % (best_model_name, w_unc_acc)) print() # final_best_rscv_estimator print("=== [task] Refit '%s' on all data." % best_model_name) print() print("X shape: ", X.shape) print("y shape: ", y.shape) print() # best_rscv_pipeline -> final_best_rscv_estimator eu.model_finalizer( final_estimator, X, y, scoring, tuning_method, d_name, serial) else: print("Achieved better calibration of model '%s'." % best_model_name) print("Let's resume scores on test data.") print('Mean cv score [%s] of best uncalibrated ("%s"): %1.3f' % (scoring.strip('neg_'), best_model_name, best_score)) predicted = calib_pipeline.predict(X_test) print() print("After probability calibration...") confusion_matrix_and_clf_report(y_type, best_model_name, y_test, predicted) print() w_calib_acc = calib_pipeline.score( X_test, y_test, sample_weight=weights_test)*100 if has_calib_roc: print('Scoring [%s] of best calibrated ("%s") on test data: %1.3f' % (scoring.strip('neg_'), best_model_name, calib_roc_auc)) print('Accuracy of best calibrated ("%s") on test data: %.2f%%' % (best_model_name, w_calib_acc)) print() print("=== [task]: Train and calibrate probabilities of " "pre-optimized model '%s' on all data." % best_model_name) print() final_calib_pipeline = CalibratedClassifierCV( final_estimator, method='sigmoid', cv=kfold) final_calib_pipeline.fit(X, y) fin_w_acc = final_calib_pipeline.score(X, y, sample_weight=weights_all)*100 print('Overall accuracy of finalized best CCCV ("%s_rscv"): %.2f%%' % (best_model_name, fin_w_acc)) au.save_model( final_calib_pipeline, best_model_name + '_final_calib_' + tuning_method + '_' + serial + '.pkl', d_name=d_name ) # Uncomment to see pipeline, steps and params # print("Finalized calibrated best model '%s'." % best_model_name) # params = final_calib_pipeline.get_params() # for param_name in sorted(params.keys()): # print("\t%s: %r" % (param_name, params[param_name])) # print() if best_model_name in ( 'baseline_nn_default_Clf_2nd', 'baseline_nn_smaller_Clf_2nd', 'larger_nn_Clf_2nd', 'deep_nn_Clf_2nd', 'larger_deep_nn_Clf_2nd', 'deeper_nn_Clf_2nd'): print('Mean cv score [%s] of best uncalibrated ("%s"): %1.3f' % ( scoring, best_model_name, best_score)) if has_unc_roc: print('Scoring [%s] of best ("%s") on test data: %1.3f' % ( scoring.strip('neg_'), best_model_name, unc_roc_auc)) print('Accuracy of best ("%s") on test data: %.2f%%' % (best_model_name, w_unc_acc)) print() NN_transformer = final_estimator.fit(X, y) X_transformed = NN_transformer.transform(X) input_dim_final = int(X_transformed.shape[1]) print() print("Input dimensions -- training: %d, finalization %d" % (input_dim, input_dim_final)) print() f_name = best_model_name + '_feateng_for_keras_model_' + serial au.save_model(final_estimator, f_name + '.pkl', d_name=d_name) del final_estimator best_model_estim = best_keras_clf_estimator( y_type, best_nn_build_fn, nb_epoch, input_dim_final, labels, batch_size) steps_fin = [] steps_fin.append((best_model_name, best_model_estim)) untrained_NN_pipeline = Pipeline(steps_fin) eu.model_finalizer( untrained_NN_pipeline, X_transformed, y, scoring, tuning_method, d_name, serial ) print() def nested_cv_calibration_process( ref_pred_score, training_estimator, final_estimator, X, y, X_train, y_train, X_test, y_test, y_type, best_model_name, best_model_estim, weights_test, weights_all, scoring, best_score, n_splits, n_iter, best_nn_build_fn, nb_epoch, param_grid, tuning_method, models_data, kfold, labels, d_name, serial, random_state=0): temp_estimator = training_estimator print() # check predicted probabilities for prediction confidence uncalibrated_data = eu.probability_confidence_before_calibration( temp_estimator, X_train, y_train, X_test, y_test, tuning_method, models_data, labels, serial ) # Keras stuff input_dim = 0 if best_model_name == "KerasClf_2nd": input_dim = int(X_train.shape[1]) del best_model_estim else: del temp_estimator unc_pred_score = uncalibrated_data[0] unc_pipeline = uncalibrated_data[1] has_unc_roc, unc_roc_auc = check_model_hasroc("target", uncalibrated_data) predicted = unc_pipeline.predict(X_test) w_unc_acc = unc_pipeline.score(X_test, y_test, sample_weight=weights_test)*100 confusion_matrix_and_clf_report(y_type, best_model_name, y_test, predicted) print() # in case of LogRegression, # you should compare its probability curve against the ideal one if unc_pred_score < ref_pred_score: print("'%s' is already well calibrated." % best_model_name) print("Let's resume metrics on test data.") if best_model_name != "KerasClf_2nd": print('Mean cv score [%s] of best uncalibrated ("%s"): %1.3f' % (scoring.strip('neg_'), best_model_name, best_score)) if has_unc_roc: print('Scoring [%s] of best uncalibrated ("%s") on test data: %1.3f' % (scoring.strip('neg_'), best_model_name, unc_roc_auc)) print('Accuracy of best uncalibrated ("%s") on test data: %.2f%%' % (best_model_name, w_unc_acc)) print() print("=== [task] Refit '%s' on all data." % best_model_name) print() print("X shape: ", X.shape) print("y shape: ", y.shape) print() # best_rscv_pipeline -> final_best_rscv_estimator final_best_pipeline = eu.rscv_tuner( final_estimator, X, y, n_splits, param_grid, n_iter, scoring, refit=True, cv_meth=tuning_method, random_state=random_state) print() print("Best estimator [%s]'s' params after hyp-tuning on all data." % best_model_name) params = final_best_pipeline.get_params() for param_name in sorted(params.keys()): print("\t%s: %r" % (param_name, params[param_name])) print() # input("Press key to continue... \n") w_best_acc = final_best_pipeline.score( X, y, sample_weight=weights_all)*100 au.save_model( final_best_pipeline, best_model_name + '_final_nocalib_' + tuning_method + '_' + serial + '.pkl', d_name=d_name ) print() # Uncomment to see pipeline, steps and params # print("Finalized uncalibrated best model '%s'." # % best_model_name) # for step in final_best_rscv_pipeline.steps: # print(type(step)) # print("step:", step[0]) # params = step[1].get_params() # for param_name in sorted(params.keys()): # print("\t%s: %r" % (param_name, params[param_name])) else: print("'%s' needs probability calibration." % best_model_name) if best_model_name == "KerasClf_2nd": print("Calibration of Keras neural networks is not implemented yet") print(""" We assume this is the best calibration we can achieve with a Keras neural network, which approaches LogisticRegression's prediction confidence as the nr of iterations increases. """) else: temp_pipeline = training_estimator # In case model needs calibration calib_data = eu.calibrate_probabilities( temp_pipeline, X_train, y_train, X_test, y_test, 'sigmoid', tuning_method, models_data, kfold, labels, serial ) calib_rscv_pred_score = calib_data[0] calib_rscv_pipeline = calib_data[1] has_calib_roc, calib_rscv_roc_auc = check_model_hasroc("target", calib_data) if calib_rscv_pred_score >= unc_pred_score: print("Sorry, we could not calibrate '%s' any better." % best_model_name) print("Rejecting calibrated '%s' and saving the uncalibrated one." % best_model_name) print("Let's resume metrics on test data.") print('Mean cv score [%s] of best uncalibrated ("%s"): %1.3f' % ( scoring.strip('neg_'), best_model_name, best_score)) if has_unc_roc: print('Scoring [%s] of best uncalibrated ("%s") on test data: %1.3f' % (scoring.strip('neg_'), best_model_name, unc_roc_auc)) print('Accuracy of best uncalibrated ("%s") on test data: %.2f%%' % (best_model_name, w_unc_acc)) print() # final_best_rscv_estimator final_best_rscv_pipeline = eu.rscv_tuner( final_estimator, X, y, n_splits, param_grid, n_iter, scoring, refit=True, cv_meth=tuning_method, random_state=random_state) w_best_rscv_acc = final_best_rscv_pipeline.score( X, y, sample_weight=weights_all)*100 print('Accuracy of best ("%s") on all data: %.2f%%' % (best_model_name, w_best_rscv_acc)) print() au.save_model( final_best_rscv_pipeline, best_model_name + '_final_nocalib_' + tuning_method + '_' + serial + '.pkl', d_name=d_name) print() # Uncomment to see pipeline, steps and params # print("Finalized uncalibrated best model '%s'." % best_model_name) # for step in final_best_rscv_pipeline.steps: # print(type(step)) # print("step:", step[0]) # params = step[1].get_params() # for param_name in sorted(params.keys()): # print("\t%s: %r" % (param_name, params[param_name])) # print() else: print("Achieved better calibration of model '%s'." % best_model_name) print("Let's resume scors on validation and test data.") print('Mean cv score [%s] of best uncalibrated ("%s"): %1.3f' % (scoring.strip('neg_'), best_model_name, best_score)) w_calib_rscv_acc = calib_rscv_pipeline.score( X_test, y_test, sample_weight=weights_test)*100 if has_calib_roc: print('Scoring [%s] of best uncalibrated ("%s") on test data: %1.3f%%' % (scoring.strip('neg_'), best_model_name, calib_rscv_roc_auc)) print('Accuracy of best calibrated ("%s") on test data: %.2f%%' % (best_model_name, w_calib_rscv_acc)) print() print("=== [task]: Tune '%s'' params with '%s' on all " "data and calibrate probabilities." % (best_model_name, tuning_method)) print() best_rscv_parameters = eu.rscv_tuner( final_estimator, X, y, n_splits, param_grid, n_iter, scoring, refit=False, cv_meth=tuning_method, random_state=random_state ) temp_pipeline.set_params(**best_rscv_parameters) # calib_rscv_pipeline.named_steps[name].set_params(**best_parameters) final_calib_rscv_clf = CalibratedClassifierCV( temp_pipeline, method='sigmoid', cv=kfold) final_calib_rscv_clf.fit(X, y) fin_w_rscv_acc = final_calib_rscv_clf.score( X, y, sample_weight=weights_all)*100 print('Overall accuracy of finalized best CCCV ("%s_%s"): %.2f%%' % (best_model_name, tuning_method, fin_w_rscv_acc)) au.save_model( final_calib_rscv_clf, best_model_name + '_final_calib_' + tuning_method + '_' + serial + '.pkl', d_name=d_name) if best_model_name == "KerasClf_2nd": print('Mean cv score [%s] of best uncalibrated ("%s"): %1.3f' % ( scoring, best_model_name, best_score)) if has_unc_roc: print('Scoring [%s] of best ("%s") on test data: %1.3f' % ( scoring.strip('neg_'), best_model_name, unc_roc_auc)) print('Accuracy of best ("%s") on test data: %.2f%%' % (best_model_name, w_unc_acc)) print() NN_transformer = final_estimator.fit(X, y) X_transformed = NN_transformer.transform(X) input_dim_final = int(X_transformed.shape[1]) print() print("Input dimensions -- training: %d, finalization %d" % (input_dim, input_dim_final)) print() f_name = best_model_name + '_feateng_for_keras_model_' + serial au.save_model(final_estimator, f_name + '.pkl', d_name=d_name) # del final_pipeline if input_dim_final != input_dim: for n in np.arange(0, 3): param_grid[best_model_name + '__units_' + str(n)] = sp_randint( input_dim_final, 5*input_dim_final) best_model_estim = best_keras_clf_estimator( y_type, best_nn_build_fn, nb_epoch, input_dim, labels) # finalize Keras clf steps_fin = [] steps_fin.append((best_model_name, best_model_estim)) untrained_NN_pipeline = Pipeline(steps_fin) # # final_best_rscv_pipeline # final_best_NN_pipeline = eu.rscv_tuner( # untrained_NN_pipeline, X_transformed, y, n_splits, param_grid, n_iter, # scoring, refit=True, random_state=random_state # ) # f_name = best_model_name + '_' + tuning_method + '_' + serial # keras_f_name = au.create_keras_model_filename(f_name, d_name=d_name) # final_best_NN_pipeline.named_steps[best_model_name].model.save( # keras_f_name + '.h5') # this is equivalent to the process above final_best_NN_pipeline = eu.tune_and_evaluate( untrained_NN_pipeline, X_transformed, y, None, None, n_splits, param_grid, n_iter, scoring, [], refit=True, random_state=random_state, serial=serial, d_name=d_name, save=True, cv_meth=tuning_method) w_best_NN_acc = final_best_NN_pipeline.score( X_transformed, y, sample_weight=weights_all)*100 print('Accuracy of best ("%s") on all data: %.2f%%' % ( best_model_name, w_best_NN_acc)) print() # Uncomment to see pipeline, steps and params # print() # print("Best estimator [%s]'s' params after hyp-tuning on all data." # % best_model_name) # for step in final_best_NN_pipeline.steps: # print(type(step)) # print("step:", step[0]) # params = step[1].get_params() # for param_name in sorted(params.keys()): # print("\t%s: %r" % (param_name, params[param_name])) def calibrate_best_model( X, y, X_train, X_test, y_train, y_test, tt_index, preprocessing, scores_of_best_model, all_models_and_parameters, n_splits, nb_epoch, scoring, models_data, d_name, random_state): """ # calibrate best model from cross validation without hyperparameter tuning. --- ... """ # Here start best model's calibration process best_model_name = scores_of_best_model[4][0] best_model_estim = scores_of_best_model[4][1] if best_model_name in ( 'baseline_nn_default_Clf_2nd', 'baseline_nn_smaller_Clf_2nd', 'larger_nn_Clf_2nd', 'deep_nn_Clf_2nd', 'larger_deep_nn_Clf_2nd', 'deeper_nn_Clf_2nd'): best_nn_build_fn = scores_of_best_model[4][2] else: best_nn_build_fn = None best_score = scores_of_best_model[0] best_score_dev = scores_of_best_model[1] best_cv_results = scores_of_best_model[2] # best_brier_score = scores_of_best_model[3] best_exec_time = scores_of_best_model[4] print() print("# Check prediction confidence of '%s' and eventually calibrate it." % best_model_name) print() # you should automatically know which encoding method to use: le or ohe encoding, scaler_tuple, featselector = preprocessing labels = None if 'labels' in all_models_and_parameters: labels = all_models_and_parameters['labels'] print("Checking prediction confidence of multiclass '%s'" % best_model_name) else: print("No list of labels here. It's a binary problem.") # training pipeline steps = [] # here you should also insert imputing and label encoding steps.append((best_model_name, best_model_estim)) training_pipeline = Pipeline(steps) Y_type = mc.type_of_target(y) print("X shape: ", X.shape) print("y shape: ", y.shape) print("X sample:\n", X[:3]) print("Y sample:\n", y[:3]) print() # finalization pipeline -- for all models less Keras ones steps_fin = [] # ... steps_fin.append(scaler_tuple) steps_fin.append(featselector) if best_model_name not in ( 'baseline_nn_default_Clf_2nd', 'baseline_nn_smaller_Clf_2nd', 'larger_nn_Clf_2nd', 'deep_nn_Clf_2nd', 'larger_deep_nn_Clf_2nd', 'deeper_nn_Clf_2nd'): steps_fin.append((best_model_name, best_model_estim)) final_pipeline = Pipeline(steps_fin) # define serial nr. once and for all serial = "%04d" % randint(0, 1000) # Now, this model/pipeline might need calibration print() print("======= Training best estimator [%s], checking predicted " "probabilities and calibrating them" % best_model_name) # Train LogisticRegression for comparison of predicted probas kfold = StratifiedKFold( n_splits=n_splits, shuffle=True, random_state=random_state) w = eu.calculate_sample_weight(y_test) w_all = eu.calculate_sample_weight(y) # LogisticRegression as a calibration reference steps = [] steps.append(('LogRClf_2nd', pgd.starting_point_models_and_params['LogRClf_2nd'])) general_lr_pipeline = Pipeline(steps) temp_pipeline = general_lr_pipeline tuning_method = 'light_opt' print() # check predicted probabilities for prediction confidence uncalibrated_lr_data = eu.probability_confidence_before_calibration( temp_pipeline, X_train, y_train, X_test, y_test, tuning_method, models_data, labels, serial ) del temp_pipeline lr_pred_score = uncalibrated_lr_data[0] lr_pipeline = uncalibrated_lr_data[1] has_roc, lr_roc_auc = check_model_hasroc( "LogRegression reference", uncalibrated_lr_data) print() predicted = lr_pipeline.predict(X_test) confusion_matrix_and_clf_report(Y_type, 'LogRClf_2nd', y_test, predicted) print() print() # Evalute prediction confidence and, in case, calibrate # X = X.astype(np.float32) try: models_data[0] except IndexError: print("No LogRClf_2nd's data here. List 'models_data' is empty.") except Exception as e: print(e) else: print("LogRClf_2nd's data appended to models_data list") print() if best_model_name != "LogRClf_2nd": # eventually calibrating any ther model != LogReg input_dim = 0 batch_size = 0 if best_model_name in ( 'baseline_nn_default_Clf_2nd', 'baseline_nn_smaller_Clf_2nd', 'larger_nn_Clf_2nd', 'deep_nn_Clf_2nd', 'larger_deep_nn_Clf_2nd', 'deeper_nn_Clf_2nd'): # no hyperparam tuning for now tuning_method = 'None' input_dim = int(X_train.shape[1]) batch_size = 32 best_model_estim = best_keras_clf_estimator( Y_type, best_nn_build_fn, nb_epoch, input_dim, labels, batch_size) temp_pipeline = training_pipeline # best model's pipeline # plot a learning curve print() print() print("[task] === Plot a learning curve") y_lim = None if Y_type == "binary": # minimum and maximum yvalues plotted in learning curve plot y_lim = (0.5, 1.01) au.plot_learning_curve( temp_pipeline, X_train, y_train, ylim=y_lim, cv=n_splits, scoring=scoring, n_jobs=-2, serial=serial, tuning=tuning_method, d_name=d_name ) # plt.show() del temp_pipeline # temp_pipeline = training_pipeline print() # tune, etc. print() print("===== Check need for calibration") # here you should be able to automatically assess # whether current model in pipeline # actually needs calibration or not # if no calibration is needed, # you could finalize if you're happy with default hyperparameters # you could also compare # model(default_parameters) vs model(tuned_parameters) ### print("Check '%s''s prediction confidence after CV and calibrate probabilities." % best_model_name) print() # calibration returns models_data ... classic_cv_calibration_process( lr_pred_score, training_pipeline, final_pipeline, X, y, X_train, y_train, X_test, y_test, Y_type, best_model_name, best_model_estim, w, w_all, scoring, best_score, best_nn_build_fn, nb_epoch, batch_size, tuning_method, models_data, kfold, labels, d_name, serial) if Y_type == 'binary': eu.plot_calibration_curves( y_test, best_model_name + '_' + tuning_method, models_data, 1, d_name ) plt.show() else: # best_model_name == 'LogRClf_2nd': print() print() print("[task] === plotting a learning curve") print("Data:", d_name) print() y_lim = None if Y_type == "binary": y_lim = (0.5, 1.01) au.plot_learning_curve( lr_pipeline, X_train, y_train, ylim=y_lim, cv=kfold, scoring=scoring, n_jobs=-2, serial=serial, tuning=tuning_method, d_name=d_name ) # plt.show() del lr_pipeline lr_pipeline = uncalibrated_lr_data[1] print() print("'LogRClf_2nd' is already well calibrated for definition!") print() print("Mean cv score [%s]: %1.3f" % (scoring.strip('neg_'), best_score)) if has_roc: best_lr_roc_auc = lr_roc_auc print("ROC_AUC score on left-out data: %1.3f." % best_lr_roc_auc) print("- The higher, the better.") # refit with RSCV eu.model_finalizer( final_pipeline, X, y, scoring, tuning_method, d_name, serial) # best_lr_pipeline = final_best_lr_pipeline if Y_type == 'binary': eu.plot_calibration_curves( y_test, best_model_name + '_' + tuning_method, models_data, 1, d_name) plt.show() plt.close('all') print() print() def tune_calibrate_best_model( X, y, X_train, X_test, y_train, y_test, tt_index, preprocessing, scores_of_best_model, all_models_and_parameters, n_splits, n_iter, nb_epoch, scoring, models_data, d_name, random_state): """ First line. ---------------------------------------------------------------------------- ... """ # Here start best model's calibration process best_model_name = scores_of_best_model[4][0] best_model_estim = scores_of_best_model[4][1] if best_model_name == "KerasClf_2nd": best_nn_build_fn = scores_of_best_model[4][2] else: best_nn_build_fn = None best_score = scores_of_best_model[0] best_score_dev = scores_of_best_model[1] # best_brier_score = scores_of_best_model[2] best_exec_time = scores_of_best_model[3] # here you should automatically know which encoding method to use: le or ohe encoding, scaler_tuple, featselector = preprocessing labels = None if 'labels' in all_models_and_parameters: labels = all_models_and_parameters['labels'] print("Checking prediction confidence of multiclass '%s'" % best_model_name) else: print("No list of labels here. It's a binary problem.") # training pipeline steps = [] # here you should also insert imputing and label encoding # steps.append(scaler_tuple) # for now # steps.append(featselector) steps.append((best_model_name, best_model_estim)) training_pipeline = Pipeline(steps) Y_type = mc.type_of_target(y) # finalization pipeline -- for all models less Keras ones steps_fin = [] steps_fin.append(scaler_tuple) steps_fin.append(featselector) if best_model_name != "KerasClf_2nd": steps_fin.append((best_model_name, best_model_estim)) final_pipeline = Pipeline(steps_fin) # define serial nr. once and for all serial = "%04d" % randint(0, 1000) # Now, this model/pipeline might need calibration print() print("======= Tuning best estimator [%s], checking predicted " "probabilities and calibrating them" % best_model_name) # Train LogisticRegression for comparison of predicted probas # select param grid associated to resulting best model param_grid = dict() # retrieve n_iter for rscv/bscv from param_grid kfold = StratifiedKFold( n_splits=n_splits, shuffle=True, random_state=random_state) w = eu.calculate_sample_weight(y_test) w_all = eu.calculate_sample_weight(y) # LogisticRegression as a calibration reference lr, lr_params = logreg_calibration_reference( Y_type, scoring, all_models_and_parameters) steps = [] steps.append(('LogRClf_2nd', lr)) general_lr_pipeline = Pipeline(steps) temp_pipeline = general_lr_pipeline print() llr_n_iter = n_iter best_LogRClf_parameters = eu.tune_and_evaluate( temp_pipeline, X_train, y_train, X_test, y_test, n_splits, lr_params, llr_n_iter, scoring, models_data, refit=False, random_state=random_state) temp_pipeline.set_params(**best_LogRClf_parameters) print() # check predicted probabilities for prediction confidence uncalibrated_lr_data = eu.probability_confidence_before_calibration( temp_pipeline, X_train, y_train, X_test, y_test, 'rscv', models_data, labels, serial ) del temp_pipeline print() lr_pred_score = uncalibrated_lr_data[0] lr_pipeline = uncalibrated_lr_data[1] has_roc, lr_roc_auc = check_model_hasroc( "LogRegression reference", uncalibrated_lr_data) predicted = lr_pipeline.predict(X_test) confusion_matrix_and_clf_report(Y_type, 'LogRClf_2nd', y_test, predicted) print() print() try: models_data[0] except IndexError: print("No LogRClf_2nd's data here. List 'models_data' is empty.") except Exception as e: print(e) else: print("LogRClf_2nd's data appended to models_data list") print() # Evalute prediction confidence and, in case, calibrate # X = X.astype(np.float32) tuning_method = None if 'xscv' in all_models_and_parameters: # 'bscv' tuning_method = all_models_and_parameters['xscv'] else: tuning_method = 'rscv' if best_model_name != "LogRClf_2nd": # tuning and eventually calibrating any other model != LogReg if best_model_name == "KerasClf_2nd": input_dim = int(X_train.shape[1]) param_grid = pgd.Keras_param_grid # Keras Clf not included in 'all_models_and_parameters' dict # param_grid[best_model_name + '__units'] = sp_randint(input_dim, 5*input_dim) for n in np.arange(0, 3): param_grid[best_model_name + '__units_' + str(n)] = sp_randint( input_dim, 5*input_dim) else: param_grid = all_models_and_parameters[best_model_name][1] if best_model_name == 'Bagging_SVMClf_2nd': # 'Bagging_SVMClf_2nd' made of 10 estimators del param_grid['Bagging_SVMClf_2nd__n_estimators'] # tune, etc. print() print("===== Randomized Search CV") # here you should be able to automatically assess whether # current model in pipeline actually needs calibration or not # if no calibration is needed, # you could finalize if you're happy with default hyperparameters # you could also compare # model(default_parameters) vs model(tuned_parameters) print() print("Best model's [%s] parameter grid for RSCV:\n" % best_model_name, param_grid) print() temp_pipeline = training_pipeline # best_pipeline # check that the total space of params >= n_iter # best_estimator_2nd best_parameters = eu.tune_and_evaluate( temp_pipeline, X_train, y_train, X_test, y_test, n_splits, param_grid, n_iter, scoring, models_data, refit=False, random_state=random_state, cv_meth=tuning_method) temp_pipeline.set_params(**best_parameters) # plot a learning curve print() print() print("[task] === plotting a learning curve") y_lim = None if Y_type == "binary": y_lim = (0.5, 1.01) au.plot_learning_curve( temp_pipeline, X_train, y_train, ylim=y_lim, cv=kfold, scoring=scoring, n_jobs=-2, serial=serial, tuning=tuning_method, d_name=d_name ) # plt.show() del temp_pipeline ### calib fct print("Check '%s''s prediction confidence after (%s) CV and " "calibrate probabilities." % (best_model_name, tuning_method)) print() nested_cv_calibration_process( lr_pred_score , training_pipeline, final_pipeline, X, y, X_train, y_train, X_test, y_test, Y_type, best_model_name, best_model_estim, w, w_all, scoring, best_score, n_splits, n_iter, best_nn_build_fn, nb_epoch, param_grid, tuning_method, models_data, kfold, labels, d_name, serial, random_state) if Y_type == "binary": eu.plot_calibration_curves( y_test, best_model_name + '_' + tuning_method, models_data, 1, d_name ) plt.show() print() print() else: # best_model_name=='LogRClf_2nd': # plot a learning curve print() print() print("[task] === plotting a learning curve") y_lim = None if Y_type == "binary": y_lim = (0.5, 1.01) au.plot_learning_curve( lr_pipeline, X_train, y_train, ylim=y_lim, cv=kfold, scoring=scoring, n_jobs=-2, serial=serial, tuning=tuning_method, d_name=d_name ) # plt.show() del lr_pipeline lr_pipeline = uncalibrated_lr_data[1] print() print() print("'%s' is already well calibrated for definition!" % best_model_name) print() print('Mean cv score [%s] of best uncalibrated ("%s"): %1.3f' % (scoring.strip('neg_'), best_model_name, best_score)) # best_lr_pipeline = lr_pipeline if lr_roc_auc is not None: print('Scoring [%s] of best uncalibrated ("%s") on test data: %1.3f' % (scoring, best_model_name, lr_roc_auc)) w_lr_acc = lr_pipeline.score(X_test, y_test, sample_weight=w)*100 print('Accuracy of best uncalibrated ("%s") on test data: %.2f%%' % (best_model_name, w_lr_acc)) print() # refit with RSCV; param_grid = pgd.LogR_param_grid final_best_lr_pipeline = eu.rscv_tuner( final_pipeline, X, y, n_splits, all_models_and_parameters['LogRClf_2nd'][1], n_iter, scoring, refit=True, random_state=random_state ) au.save_model( final_best_lr_pipeline, best_model_name + '_final_calib_rscv_' + serial + '.pkl', d_name=d_name) print() print("Performance on all data.") # w_all = calculate_sample_weight(y) # best_lr_pipeline w_lr_acc = final_best_lr_pipeline.score(X, y, sample_weight=w_all)*100 print('Accuracy of best ("%s") on all data: %.2f%%' % (best_model_name, w_lr_acc)) print() # Uncomment to see pipeline, steps and params # print("Finalized '%s'." % best_model_name) # for step in final_best_lr_pipeline.steps: # print("step:", step[0]) # params = step[1].get_params() # for param_name in sorted(params.keys()): # print("\t%s: %r" % (param_name, params[param_name])) # print() if Y_type == 'binary': eu.plot_calibration_curves( y_test, best_model_name + '_rscv', models_data, 1, d_name) plt.show() plt.close('all') print() print()

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9110644e34a91156a14239ac1b816c7ffe345127

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py

Python

graph_ensemble/wrappers/__init__.py

YotamFY/GraphEnsemble

a636feb1084a8b45aa1754034744281277474dd1

[ "BSD-3-Clause" ]

2

2017-11-15T14:08:34.000Z

2017-11-18T10:43:07.000Z

graph_ensemble/wrappers/__init__.py

YotamFY/GraphEnsemble

a636feb1084a8b45aa1754034744281277474dd1

[ "BSD-3-Clause" ]

2

2017-11-04T14:33:30.000Z

2017-11-18T10:42:52.000Z

graph_ensemble/wrappers/__init__.py

YotamFY/GraphEnsemble

a636feb1084a8b45aa1754034744281277474dd1

[ "BSD-3-Clause" ]

null

from sklearn_wrapper import *

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1

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6

912e8989a18ad101c36787dc7de396be821ea550

175

py

Python

keras/datasets/__init__.py

nils-werner/keras

78f26df8fb2b8aa5c6262aef44a494a8335a9c6e

[ "MIT" ]

30

2017-04-11T04:17:22.000Z

2020-09-08T08:18:37.000Z

keras/datasets/__init__.py

candleinwindsteve/keras

9eb7ecd3e525c9cff31ebd59a96794f212ca5e1e

[ "MIT" ]

8

2020-09-26T00:55:16.000Z

2022-03-12T00:23:07.000Z

udacity-car/lib/python2.7/site-packages/keras/datasets/__init__.py

808brick/CarND-Capstone

f9e536b4a9d96322d7e971073602c8969dbd9369

[ "MIT" ]

21

2017-03-27T08:06:11.000Z

2020-06-18T09:35:07.000Z

from __future__ import absolute_import from . import mnist from . import imdb from . import reuters from . import cifar10 from . import cifar100 from . import boston_housing

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0

1

0

1

0

6

e68764071b92826b6642ca0a264ae0893e184c3d

93

py

Python

unravel/admin/document_tag_admin.py

cofiem/logomachy

fed77dc4b821f25a60fd9b9474c232107fe98f53

[ "Apache-2.0" ]

null

unravel/admin/document_tag_admin.py

cofiem/logomachy

fed77dc4b821f25a60fd9b9474c232107fe98f53

[ "Apache-2.0" ]

1

2017-10-29T08:16:02.000Z

2017-10-30T14:19:59.000Z

unravel/admin/document_tag_admin.py

cofiem/logomachy

fed77dc4b821f25a60fd9b9474c232107fe98f53

[ "Apache-2.0" ]

null

from unravel.admin.base_admin import BaseAdmin class DocumentTagAdmin(BaseAdmin): pass

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0

6

e6993dd0f9a93fd195df6150af1e21d909ae0c46

44

py

Python

topojson/__init__.py

ataalik/topojson.py

111bfd80fd338ac5d951d8d5ada8ee1471e00a31

[ "BSD-3-Clause" ]

null

topojson/__init__.py

ataalik/topojson.py

111bfd80fd338ac5d951d8d5ada8ee1471e00a31

[ "BSD-3-Clause" ]

null

topojson/__init__.py

ataalik/topojson.py

111bfd80fd338ac5d951d8d5ada8ee1471e00a31

[ "BSD-3-Clause" ]

null

from .conversion import convert as topojson

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6

fc00b4b8eb1f1a7ffa6a78087f0d3c93b2b3fc81

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py

Python

copydog/utils/task.py

coagulant/copydog

81a64738dbcfd6d1149fda914382f08202a3864a

[ "BSD-3-Clause" ]

16

2015-01-04T20:40:08.000Z

2018-01-24T20:19:12.000Z

copydog/utils/task.py

coagulant/copydog

81a64738dbcfd6d1149fda914382f08202a3864a

[ "BSD-3-Clause" ]

3

2015-09-18T10:05:00.000Z

2021-03-25T21:27:26.000Z

copydog/utils/task.py

coagulant/copydog

81a64738dbcfd6d1149fda914382f08202a3864a

[ "BSD-3-Clause" ]

5

2015-04-21T15:07:08.000Z

2017-09-01T17:06:58.000Z

# -*- coding: utf-8 -*- def periodic(scheduler, interval, action, actionargs=()): scheduler.enter(interval, 1, periodic, (scheduler, interval, action, actionargs)) action(*actionargs)

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6

fc2b8abc97922d576ccd8ba092119618fd732632

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py

Python

test/test_convert_pes.py

EmbroiderPy/pyembroidery

3d0db61e7a08bba8e51f4e5873ebfa678c12960b

[ "MIT" ]

null

test/test_convert_pes.py

EmbroiderPy/pyembroidery

3d0db61e7a08bba8e51f4e5873ebfa678c12960b

[ "MIT" ]

null

test/test_convert_pes.py

EmbroiderPy/pyembroidery

3d0db61e7a08bba8e51f4e5873ebfa678c12960b

[ "MIT" ]

null

from __future__ import print_function import unittest from pyembroidery import * from test.pattern_for_tests import * class TestConverts(unittest.TestCase): def position_equals(self, stitches, j, k): self.assertEqual(stitches[j][:1], stitches[k][:1]) def test_convert_pes_to_u01(self): file1 = "convert_u01.pes" file2 = "converted_pes.u01" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_u01(f_pattern, file2) t_pattern = read_u01(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(NEEDLE_SET), 16) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->u01: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_csv(self): file1 = "convert_csv.pes" file2 = "converted_pes.csv" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_csv(f_pattern, file2) t_pattern = read_csv(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->csv: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_exp(self): file1 = "convert_exp.pes" file2 = "converted_pes.exp" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_exp(f_pattern, file2) t_pattern = read_exp(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->exp: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_pes(self): file1 = "convert_pes.pes" file2 = "converted_pes.pes" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_pes(f_pattern, file2) t_pattern = read_pes(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->pes: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_jef(self): file1 = "convert_jef.pes" file2 = "converted_pes.jef" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_jef(f_pattern, file2) t_pattern = read_jef(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->jef: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_pec(self): file1 = "convert_pec.pes" file2 = "converted_pes.pec" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_pec(f_pattern, file2) t_pattern = read_pec(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->pec: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_vp3(self): file1 = "convert_vp3.pes" file2 = "converted_pes.vp3" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_vp3(f_pattern, file2) t_pattern = read_vp3(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->vp3: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_dst(self): file1 = "convert_dst.pes" file2 = "converted_pes.dst" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_dst(f_pattern, file2) t_pattern = read_dst(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->dst: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_gcode(self): file1 = "convert_gcode.pes" file2 = "converted_pes.gcode" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_gcode(f_pattern, file2) t_pattern = read_gcode(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->gcode: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_convert_pes_to_xxx(self): file1 = "convert_xxx.pes" file2 = "converted_pes.xxx" write_pes(get_big_pattern(), file1) f_pattern = read_pes(file1) write_xxx(f_pattern, file2) t_pattern = read_xxx(file2) self.assertIsNotNone(t_pattern) self.assertEqual(t_pattern.count_stitch_commands(COLOR_CHANGE), 15) self.assertEqual(t_pattern.count_stitch_commands(STITCH), 16 * 5) self.position_equals(t_pattern.stitches, 0, -1) print("pes->xxx: ", t_pattern.stitches) self.addCleanup(os.remove, file1) self.addCleanup(os.remove, file2) def test_write_pes_long(self): file1 = "long.pes" write_pes(get_long_jump(), file1) self.addCleanup(os.remove, file1)

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0.072848

0

null

0

1

0

null

0

6

fc3030bbf373749ce797dd5a92ba404e7f2df4f8

1,138

py

Python

src/AOJ/ITP1_6_C.py

nabetama-training/CompetitionProgrammingPractice

0801173df3992c2e78b02b383f2df9ba792cbf2f

[ "BSD-2-Clause" ]

null

src/AOJ/ITP1_6_C.py

nabetama-training/CompetitionProgrammingPractice

0801173df3992c2e78b02b383f2df9ba792cbf2f

[ "BSD-2-Clause" ]

2

2020-07-04T04:19:28.000Z

2020-07-26T06:16:07.000Z

src/AOJ/ITP1_6_C.py

nabetama-training/CompetitionProgrammingPractice

0801173df3992c2e78b02b383f2df9ba792cbf2f

[ "BSD-2-Clause" ]

null

def resolve(): # bills = [ # [ # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ], # [ # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ], # [ # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ], # [ # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # ], # ] bills = [[[0 for _ in range(10)] for _ in range(3)] for _ in range(4)] n = int(input()) for _ in range(n): b, f, r, v = map(int, input().split()) bills[b - 1][f - 1][r - 1] += v count = 0 for bill in bills: count += 1 for floor in bill: [print(" {}".format(r), end='') for r in floor] print('') if count < len(bills): print('####################')

28.45

74

0.282953

186

1,138

1.709677

0.150538

0.748428

1.113208

1.471698

0.377358

0

0.215947

0.471002

1,138

39

75

29.179487

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0.477153

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0.04021

0

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0.071429

false

0

0.071429

0.214286

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1

null

1

0

1

0

1

0

null

0

6

fc398e96a4e8b1a14aa331e080cf17290c4f7ca7

27

py

Python

out_packg/main.py

Firekiss/python_learn

15922af566a08924834ff924982a36a65b724bbf

[ "MIT" ]

null

out_packg/main.py

Firekiss/python_learn

15922af566a08924834ff924982a36a65b724bbf

[ "MIT" ]

null

out_packg/main.py

Firekiss/python_learn

15922af566a08924834ff924982a36a65b724bbf

[ "MIT" ]

null

def main(): print('main')

13.5

15

0.592593

4

27

4

0.75

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0.148148

27

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15

13.5

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0.142857

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true

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null

0

1

0

null

0

1

0

1

0

6

fc77ec8ac86182c524326f90ac9990cd6235a2bf

124

py

Python

bin/src/seq_len.py

pzross/pfsr1-motif

e9bbf370b59680436d9a22d691a522b7d1a59a32

[ "MIT" ]

null

bin/src/seq_len.py

pzross/pfsr1-motif

e9bbf370b59680436d9a22d691a522b7d1a59a32

[ "MIT" ]

null

bin/src/seq_len.py

pzross/pfsr1-motif

e9bbf370b59680436d9a22d691a522b7d1a59a32

[ "MIT" ]

null

def seq_len(sequence): #sequence = sequence.replace("\n", "") sequence = sequence.replace("\r", "") return len(sequence)

24.8

39

0.669355

15

124

5.466667

0.533333

0.585366

0.560976

0

0.120968

124

4

40

31

0.752294

0.298387

0

0.023256

0

1

0.333333

false

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

5d8ccb20542df2a417135ed2c8ec46527e233a4f

207

py

Python

tests/test_problem30.py

nolanwrightdev/blind-75-python

b92ef3449eb0143c760ddd339897a3f0a2972830

[ "MIT" ]

6

2020-02-01T23:29:51.000Z

2022-02-20T20:46:56.000Z

tests/test_problem30.py

nolanwrightdev/blind-75-python

b92ef3449eb0143c760ddd339897a3f0a2972830

[ "MIT" ]

null

tests/test_problem30.py

nolanwrightdev/blind-75-python

b92ef3449eb0143c760ddd339897a3f0a2972830

[ "MIT" ]

null

import unittest from problems.problem30 import solution class Test(unittest.TestCase): def test(self): self.assertEqual(solution([7, 1, 5, 3, 6, 4]), 5) self.assertEqual(solution([7, 6, 4, 3, 1]), 0)

23

51

0.695652

33

207

4.363636

0.575758

0.208333

0.319444

0.333333

0

0.084746

0.144928

207

8

52

25.875

0.728814

0

0.333333

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0.166667

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0

0.333333

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0.666667

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null

1

0

1

0

null

0

1

0

1

0

6

5da76418571acf80c2956f0a0d9098011db947be

43

py

Python

hello world.py

1172100327/hit-1172100327

3d162f46cd765f2581991fecf7796fae8453f0e1

[ "MIT" ]

null

hello world.py

1172100327/hit-1172100327

3d162f46cd765f2581991fecf7796fae8453f0e1

[ "MIT" ]

1

2019-07-07T10:26:33.000Z

hello world.py

1172100327/hit-1172100327

3d162f46cd765f2581991fecf7796fae8453f0e1

[ "MIT" ]

1

2019-07-07T10:09:48.000Z

print("hello world") print("hello python")

14.333333

21

0.72093

6

43

5.166667

0.666667

0.645161

0

0.093023

43

2

22

21.5

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0

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true

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null

1

0

1

0

null

0

1

0

1

0

6

5dd143f87ce4bc538e283a0ba125a465a5f58cbb

70

py

Python

utils/__init__.py

alessandrolamberti/voice_gender_recognition

95cc25a239cb78cc451632474e3307c676f658ee

[ "MIT" ]

1

2022-01-31T13:02:27.000Z

utils/__init__.py

alessandrolamberti/voice_gender_recognition

95cc25a239cb78cc451632474e3307c676f658ee

[ "MIT" ]

null

utils/__init__.py

alessandrolamberti/voice_gender_recognition

95cc25a239cb78cc451632474e3307c676f658ee

[ "MIT" ]

null

from .audio import * from .preprocess import * from .database import *

23.333333

25

0.757143

9

70

5.888889

0.555556

0.377358

0

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70

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23.333333

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0

null

0

1

0

1

0

1

0

6

b9057e092d022a74f9413b63058254a4f9ecc606

96

py

Python

venv/lib/python3.8/site-packages/numpy/lib/tests/test_recfunctions.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/lib/tests/test_recfunctions.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/lib/tests/test_recfunctions.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/a9/08/d1/1ed20267038fe30bf947d889ab58b81a90772e43b1c98262cf4ad9537e

96

0.895833

9

96

9.555556

1

0

0.447917

0

96

1

96

0.447917

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

f8eac8bd36b86ba853fab2bec106dcd5c1bbb2d9

436

py

Python

smtwiki/core/views.py

seahurt/smtwiki

98a5a53fb50ab558a88c6ef2fe907c5d7882a600

[ "MIT" ]

null

smtwiki/core/views.py

seahurt/smtwiki

98a5a53fb50ab558a88c6ef2fe907c5d7882a600

[ "MIT" ]

null

smtwiki/core/views.py

seahurt/smtwiki

98a5a53fb50ab558a88c6ef2fe907c5d7882a600

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. def login(request): pass def logout(request): pass def home(request): pass def profile(request): pass def category_view(request): pass def tag_view(request): pass def archive(request): pass def user_doc_view(request): pass def search(request): pass def logs_view(request): pass def statistics(request): pass

9.276596

35

0.669725

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436

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0.431034

0.421603

0.487805

0.250871

0

0.247706

436

46

36

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0.875

0.052752

0

0.478261

0

1

0.478261

false

0.478261

0.043478

0

0.521739

0

null

1

0

null

0

1

0

1

0

1

0

6

5d0c739576fa227182f3e4456e44795db4abf47e

45

py

Python

pyhac/tracker/__init__.py

zroger49/hac

5905369344c985d5293d572a610c82308306e385

[ "Apache-2.0" ]

28

2021-08-18T09:33:30.000Z

2021-11-08T09:14:35.000Z

pyhac/tracker/__init__.py

zroger49/hac

5905369344c985d5293d572a610c82308306e385

[ "Apache-2.0" ]

3

2021-08-19T14:01:25.000Z

2021-09-06T10:57:46.000Z

pyhac/tracker/__init__.py

zroger49/hac

5905369344c985d5293d572a610c82308306e385

[ "Apache-2.0" ]

4

2021-08-12T02:35:17.000Z

2021-09-21T18:35:16.000Z

from .holistic_tracker import HolisticTracker

45

0.911111

5

45

8

1

0

0.066667

45

1

45

0.952381

0

1

0

true

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5d123c11b362dc8304b5d14baa5ea8f06faa934f

13,044

py

Python

wrappers/python/TestMPINInstall.py

skair39/milagro-crypto-c

819e856f4648d2113891e226da74a5466038f820

[ "Apache-2.0" ]

1

2021-07-13T20:22:34.000Z

wrappers/python/TestMPINInstall.py

skair39/milagro-crypto-c

819e856f4648d2113891e226da74a5466038f820

[ "Apache-2.0" ]

null

wrappers/python/TestMPINInstall.py

skair39/milagro-crypto-c

819e856f4648d2113891e226da74a5466038f820

[ "Apache-2.0" ]

null

#!/usr/bin/env python """ Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import os import unittest import json import hashlib import mpin HASH_TYPE_MPIN = mpin.SHA256 class TestMPIN(unittest.TestCase): """Tests M-Pin crypto code""" def setUp(self): # Form MPin ID endUserData = { "issued": "2013-10-19T06:12:28Z", "userID": "testUser@miracl.com", "mobile": 1, "salt": "e985da112a378c222cfc2f7226097b0c" } self.mpin_id = json.dumps(endUserData) # Hash value of MPIN_ID self.hash_mpin_id = mpin.hash_id(HASH_TYPE_MPIN, self.mpin_id) # Assign a seed value seedHex = "3ade3d4a5c698e8910bf92f25d97ceeb7c25ed838901a5cb5db2cf25434c1fe76c7f79b7af2e5e1e4988e4294dbd9bd9fa3960197fb7aec373609fb890d74b16a4b14b2ae7e23b75f15d36c21791272372863c4f8af39980283ae69a79cf4e48e908f9e0" self.seed = seedHex.decode("hex") self.date = 16238 def test_1(self): """test_1 Good PIN and good token""" PIN1 = 1234 PIN2 = 1234 # random number generator rng = mpin.create_csprng(self.seed) # Generate Client master secret share for MIRACL and Customer rtn, ms1 = mpin.random_generate(rng) self.assertEqual(rtn, 0) rtn, ms2 = mpin.random_generate(rng) self.assertEqual(rtn, 0) # Generate server secret shares rtn, ss1 = mpin.get_server_secret(ms1) self.assertEqual(rtn, 0) rtn, ss2 = mpin.get_server_secret(ms2) self.assertEqual(rtn, 0) # Combine server secret shares rtn, server_secret = mpin.recombine_G2(ss1, ss2) self.assertEqual(rtn, 0) # Generate client secret shares rtn, cs1 = mpin.get_client_secret(ms1, self.hash_mpin_id) self.assertEqual(rtn, 0) rtn, cs2 = mpin.get_client_secret(ms2, self.hash_mpin_id) self.assertEqual(rtn, 0) # Combine client secret shares rtn, client_secret = mpin.recombine_G1(cs1, cs2) self.assertEqual(rtn, 0) # Generate Time Permit shares rtn, tp1 = mpin.get_client_permit( HASH_TYPE_MPIN, self.date, ms1, self.hash_mpin_id) self.assertEqual(rtn, 0) rtn, tp2 = mpin.get_client_permit( HASH_TYPE_MPIN, self.date, ms2, self.hash_mpin_id) self.assertEqual(rtn, 0) # Combine Time Permit shares rtn, time_permit = mpin.recombine_G1(tp1, tp2) self.assertEqual(rtn, 0) # Client extracts PIN from secret to create Token rtn, token = mpin.extract_pin( HASH_TYPE_MPIN, self.mpin_id, PIN1, client_secret) self.assertEqual(rtn, 0) # Client first pass rtn, x, u, ut, sec = mpin.client_1( HASH_TYPE_MPIN, self.date, self.mpin_id, rng, None, PIN2, token, time_permit) self.assertEqual(rtn, 0) # Server calculates H(ID) and H(T|H(ID)) HID, HTID = mpin.server_1(HASH_TYPE_MPIN, self.date, self.mpin_id) # Server generates Random number Y and sends it to Client rtn, y = mpin.random_generate(rng) self.assertEqual(rtn, 0) # Client second pass rtn, v = mpin.client_2(x, y, sec) self.assertEqual(rtn, 0) # Server second pass rtn, E, F = mpin.server_2( self.date, HID, HTID, y, server_secret, u, ut, v) self.assertEqual(rtn, 0) def test_2(self): """test_2 Bad PIN and good token""" PIN1 = 1234 PIN2 = 2000 # random number generator rng = mpin.create_csprng(self.seed) # Generate Client master secret share for MIRACL and Customer rtn, ms1 = mpin.random_generate(rng) self.assertEqual(rtn, 0) rtn, ms2 = mpin.random_generate(rng) self.assertEqual(rtn, 0) # Generate server secret shares rtn, ss1 = mpin.get_server_secret(ms1) self.assertEqual(rtn, 0) rtn, ss2 = mpin.get_server_secret(ms2) self.assertEqual(rtn, 0) # Combine server secret shares rtn, server_secret = mpin.recombine_G2(ss1, ss2) self.assertEqual(rtn, 0) # Generate client secret shares rtn, cs1 = mpin.get_client_secret(ms1, self.hash_mpin_id) self.assertEqual(rtn, 0) rtn, cs2 = mpin.get_client_secret(ms2, self.hash_mpin_id) self.assertEqual(rtn, 0) # Combine client secret shares rtn, client_secret = mpin.recombine_G1(cs1, cs2) self.assertEqual(rtn, 0) # Generate Time Permit shares rtn, tp1 = mpin.get_client_permit( HASH_TYPE_MPIN, self.date, ms1, self.hash_mpin_id) self.assertEqual(rtn, 0) rtn, tp2 = mpin.get_client_permit( HASH_TYPE_MPIN, self.date, ms2, self.hash_mpin_id) self.assertEqual(rtn, 0) # Combine Time Permit shares rtn, time_permit = mpin.recombine_G1(tp1, tp2) self.assertEqual(rtn, 0) # Client extracts PIN from secret to create Token rtn, token = mpin.extract_pin( HASH_TYPE_MPIN, self.mpin_id, PIN1, client_secret) self.assertEqual(rtn, 0) # Client first pass rtn, x, u, ut, sec = mpin.client_1( HASH_TYPE_MPIN, self.date, self.mpin_id, rng, None, PIN2, token, time_permit) self.assertEqual(rtn, 0) # Server calculates H(ID) and H(T|H(ID)) HID, HTID = mpin.server_1(HASH_TYPE_MPIN, self.date, self.mpin_id) # Server generates Random number Y and sends it to Client rtn, y = mpin.random_generate(rng) self.assertEqual(rtn, 0) # Client second pass rtn, v = mpin.client_2(x, y, sec) self.assertEqual(rtn, 0) # Server second pass rtn, E, F = mpin.server_2( self.date, HID, HTID, y, server_secret, u, ut, v) self.assertEqual(rtn, -19) def test_3(self): """test_2 Good PIN and bad token""" PIN1 = 1234 PIN2 = 1234 # random number generator rng = mpin.create_csprng(self.seed) # Generate Client master secret share for MIRACL and Customer rtn, ms1 = mpin.random_generate(rng) self.assertEqual(rtn, 0) rtn, ms2 = mpin.random_generate(rng) self.assertEqual(rtn, 0) # Generate server secret shares rtn, ss1 = mpin.get_server_secret(ms1) self.assertEqual(rtn, 0) rtn, ss2 = mpin.get_server_secret(ms2) self.assertEqual(rtn, 0) # Combine server secret shares rtn, server_secret = mpin.recombine_G2(ss1, ss2) self.assertEqual(rtn, 0) # Generate client secret shares rtn, cs1 = mpin.get_client_secret(ms1, self.hash_mpin_id) self.assertEqual(rtn, 0) rtn, cs2 = mpin.get_client_secret(ms2, self.hash_mpin_id) self.assertEqual(rtn, 0) # Combine client secret shares rtn, client_secret = mpin.recombine_G1(cs1, cs2) self.assertEqual(rtn, 0) # Generate Time Permit shares rtn, tp1 = mpin.get_client_permit( HASH_TYPE_MPIN, self.date, ms1, self.hash_mpin_id) self.assertEqual(rtn, 0) rtn, tp2 = mpin.get_client_permit( HASH_TYPE_MPIN, self.date, ms2, self.hash_mpin_id) self.assertEqual(rtn, 0) # Combine Time Permit shares rtn, time_permit = mpin.recombine_G1(tp1, tp2) self.assertEqual(rtn, 0) # Client extracts PIN from secret to create Token rtn, token = mpin.extract_pin( HASH_TYPE_MPIN, self.mpin_id, PIN1, client_secret) self.assertEqual(rtn, 0) # Client first pass rtn, x, u, ut, sec = mpin.client_1( HASH_TYPE_MPIN, self.date, self.mpin_id, rng, None, PIN2, token, time_permit) self.assertEqual(rtn, 0) # Server calculates H(ID) and H(T|H(ID)) HID, HTID = mpin.server_1(HASH_TYPE_MPIN, self.date, self.mpin_id) # Server generates Random number Y and sends it to Client rtn, y = mpin.random_generate(rng) self.assertEqual(rtn, 0) # Client second pass rtn, v = mpin.client_2(x, y, sec) self.assertEqual(rtn, 0) # Server second pass # v is equal to ut to model a bad token rtn, E, F = mpin.server_2( self.date, HID, HTID, y, server_secret, u, ut, ut) self.assertEqual(rtn, -19) def test_4(self): """test_4 Make sure all client secret are unique""" # random number generator rng = mpin.create_csprng(self.seed) # Generate master secret share rtn, ms1 = mpin.random_generate(rng) self.assertEqual(rtn, 0) s = set() match = 0 for i in range(1, 1000): rand_val = os.urandom(32) hash_mpin_id = mpin.hash_id(HASH_TYPE_MPIN, rand_val) # Generate client secret shares rtn, cs1 = mpin.get_client_secret(ms1, hash_mpin_id) self.assertEqual(rtn, 0) cs1Hex = cs1.encode("hex") if cs1Hex in s: match = 1 self.assertEqual(match, 0) s.add(cs1Hex) def test_5(self): """test_5 Make sure all one time passwords are random i.e. they should collide""" # random number generator rng = mpin.create_csprng(self.seed) s = set() match = 0 for i in range(1, 10000): OTP = mpin.generate_otp(rng) if OTP in s: # print i match = 1 s.add(OTP) self.assertEqual(match, 1) def test_6(self): """test_6 Make sure all random values are random i.e. they should collide""" # random number generator rng = mpin.create_csprng(self.seed) # Generate 4 byte random number s = set() match = 0 for i in range(1, 208900): random = mpin.generate_random(rng, 4) # print i, " ", random.encode("hex") if random in s: match = 1 break s.add(random) self.assertEqual(match, 1) def test_7(self): """test_7 AES-GCM: Successful encryption and decryption""" # Generate 16 byte key key = os.urandom(mpin.PAS) # Generate 12 byte IV iv = os.urandom(mpin.IVL) # Generate a 32 byte random header header = os.urandom(32) # Plaintext input plaintext1 = "A test message" ciphertext, tag1 = mpin.aes_gcm_encrypt(key, iv, header, plaintext1) plaintext2, tag2 = mpin.aes_gcm_decrypt(key, iv, header, ciphertext) self.assertEqual(tag1, tag2) self.assertEqual(plaintext1, plaintext2) def test_8(self): """test_8 AES-GCM: Failed encryption and decryption by changing a ciphertext byte""" # Generate 16 byte key key = os.urandom(mpin.PAS) # Generate 12 byte IV iv = os.urandom(mpin.IVL) # Generate a 32 byte random header header = os.urandom(32) # Plaintext input plaintext1 = "A test message" ciphertext, tag1 = mpin.aes_gcm_encrypt(key, iv, header, plaintext1) new = list(ciphertext) new[0] = "a" ciphertext_bad = ''.join(new) plaintext2, tag2 = mpin.aes_gcm_decrypt( key, iv, header, ciphertext_bad) self.assertNotEqual(tag1, tag2) self.assertNotEqual(plaintext1, plaintext2) def test_9(self): """test_9 AES-GCM: Failed encryption and decryption by changing a header byte""" # Generate 16 byte key key = os.urandom(mpin.PAS) # Generate 12 byte IV iv = os.urandom(mpin.IVL) # Generate a 32 byte random header header = os.urandom(32) # Plaintext input plaintext1 = "A test message" ciphertext, tag1 = mpin.aes_gcm_encrypt(key, iv, header, plaintext1) new = list(header) new[0] = "a" header_bad = ''.join(new) plaintext2, tag2 = mpin.aes_gcm_decrypt( key, iv, header_bad, ciphertext) self.assertNotEqual(tag1, tag2) self.assertEqual(plaintext1, plaintext2) if __name__ == '__main__': # Run tests unittest.main()

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0

null

0

1

0

1

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null

0

6

5d158d2020aa8bbd22f2857f2541159609242ac3

2,290

py

Python

temboo/core/Library/Genability/TariffData/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

7

2016-03-07T02:07:21.000Z

2022-01-21T02:22:41.000Z

temboo/core/Library/Genability/TariffData/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

null

temboo/core/Library/Genability/TariffData/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

8

2016-06-14T06:01:11.000Z

2020-04-22T09:21:44.000Z

from temboo.Library.Genability.TariffData.GetLoadServingEntities import GetLoadServingEntities, GetLoadServingEntitiesInputSet, GetLoadServingEntitiesResultSet, GetLoadServingEntitiesChoreographyExecution from temboo.Library.Genability.TariffData.GetLoadServingEntity import GetLoadServingEntity, GetLoadServingEntityInputSet, GetLoadServingEntityResultSet, GetLoadServingEntityChoreographyExecution from temboo.Library.Genability.TariffData.GetMetricsForZipCode import GetMetricsForZipCode, GetMetricsForZipCodeInputSet, GetMetricsForZipCodeResultSet, GetMetricsForZipCodeChoreographyExecution from temboo.Library.Genability.TariffData.GetPropertyKey import GetPropertyKey, GetPropertyKeyInputSet, GetPropertyKeyResultSet, GetPropertyKeyChoreographyExecution from temboo.Library.Genability.TariffData.GetPropertyKeys import GetPropertyKeys, GetPropertyKeysInputSet, GetPropertyKeysResultSet, GetPropertyKeysChoreographyExecution from temboo.Library.Genability.TariffData.GetSeasonGroups import GetSeasonGroups, GetSeasonGroupsInputSet, GetSeasonGroupsResultSet, GetSeasonGroupsChoreographyExecution from temboo.Library.Genability.TariffData.GetTariff import GetTariff, GetTariffInputSet, GetTariffResultSet, GetTariffChoreographyExecution from temboo.Library.Genability.TariffData.GetTariffs import GetTariffs, GetTariffsInputSet, GetTariffsResultSet, GetTariffsChoreographyExecution from temboo.Library.Genability.TariffData.GetTerritories import GetTerritories, GetTerritoriesInputSet, GetTerritoriesResultSet, GetTerritoriesChoreographyExecution from temboo.Library.Genability.TariffData.GetTerritory import GetTerritory, GetTerritoryInputSet, GetTerritoryResultSet, GetTerritoryChoreographyExecution from temboo.Library.Genability.TariffData.GetTimeOfUseGroup import GetTimeOfUseGroup, GetTimeOfUseGroupInputSet, GetTimeOfUseGroupResultSet, GetTimeOfUseGroupChoreographyExecution from temboo.Library.Genability.TariffData.GetTimeOfUseGroupIntervals import GetTimeOfUseGroupIntervals, GetTimeOfUseGroupIntervalsInputSet, GetTimeOfUseGroupIntervalsResultSet, GetTimeOfUseGroupIntervalsChoreographyExecution from temboo.Library.Genability.TariffData.GetZipCodeDetails import GetZipCodeDetails, GetZipCodeDetailsInputSet, GetZipCodeDetailsResultSet, GetZipCodeDetailsChoreographyExecution

163.571429

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null

0

1

0

1

0

1

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6

5d248b5311805cc669f597a0049416f9ae77266a

38

py

Python

djmo/__init__.py

davidegriffon/djmo

853212471e59a0fa49d22f17cf57caeb552783f8

[ "MIT" ]

5

2015-12-09T08:13:03.000Z

2019-05-12T07:14:25.000Z

djmo/__init__.py

davidegriffon/djmo

853212471e59a0fa49d22f17cf57caeb552783f8

[ "MIT" ]

null

djmo/__init__.py

davidegriffon/djmo

853212471e59a0fa49d22f17cf57caeb552783f8

[ "MIT" ]

null

from .decorators import observe_models

38

0.894737

5

38

6.6

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38

1

38

0.942857

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null

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1

0

null

0

1

0

1

0

1

0

6

5d315c96b6def878c27f286d34a91c832bc294f7

151

py

Python

backend/milestone/admin.py

Tim6FTN/UKS

3cf19f014cdc7845bf0b808b97c4e05dc49b062e

[ "MIT" ]

1

2021-01-10T12:34:59.000Z

backend/milestone/admin.py

Tim6FTN/UKS

3cf19f014cdc7845bf0b808b97c4e05dc49b062e

[ "MIT" ]

37

2021-01-07T22:31:25.000Z

2021-02-20T10:59:46.000Z

backend/milestone/admin.py

Tim6FTN/UKS

3cf19f014cdc7845bf0b808b97c4e05dc49b062e

[ "MIT" ]

null

from django.contrib import admin from milestone.models import Milestone @admin.register(Milestone) class MilestoneAdmin(admin.ModelAdmin): pass

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null

0

1

0

1

0

6

5d5e1f970af6a7da6a0898e0dcd906b99ed1998a

26

py

Python

tests/lif/gt.py

Mieschendahl/assignment-final-stub

19eea657fcc4f8a455c42028f34b918628514cc0

[ "MIT" ]

null

tests/lif/gt.py

Mieschendahl/assignment-final-stub

19eea657fcc4f8a455c42028f34b918628514cc0

[ "MIT" ]

1

2022-03-20T11:08:45.000Z

tests/lif/gt.py

Mieschendahl/assignment-final-stub

19eea657fcc4f8a455c42028f34b918628514cc0

[ "MIT" ]

6

2022-03-13T13:10:25.000Z

2022-03-28T22:18:12.000Z

print(42 if 2 > 1 else 0)

13

25

0.615385

7

26

2.285714

1

0

0.263158

0.269231

26

1

26

0.578947

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null

0

1

0

1

0

6

5d6e70f84903dda3c7d973e8058e45ce37dfba6f

29

py

Python

pam/plot/__init__.py

JosePazNoguera/pam

afb580c57223acd01466938eea8dc3d83097d5dd

[ "MIT" ]

29

2020-04-10T23:24:26.000Z

2021-05-21T12:30:03.000Z

pam/plot/__init__.py

JosePazNoguera/pam

afb580c57223acd01466938eea8dc3d83097d5dd

[ "MIT" ]

63

2020-04-29T19:02:11.000Z

2022-03-29T14:02:04.000Z

pam/plot/__init__.py

JosePazNoguera/pam

afb580c57223acd01466938eea8dc3d83097d5dd

[ "MIT" ]

13

2020-04-16T19:00:18.000Z

2022-03-18T14:42:48.000Z

from pam.plot.plans import *

14.5

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null

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1

0

1

0

1

0

6

538ab429e510bf4bccf77006e4af82d638cd8152

20,123

py

Python

pycle/bicycle-scrapes/epey-scrape/downLink0.py

fusuyfusuy/School-Projects

8e38f19da90f63ac9c9ec91e550fc5aaab3d0234

[ "MIT" ]

null

pycle/bicycle-scrapes/epey-scrape/downLink0.py

fusuyfusuy/School-Projects

8e38f19da90f63ac9c9ec91e550fc5aaab3d0234

[ "MIT" ]

null

pycle/bicycle-scrapes/epey-scrape/downLink0.py

fusuyfusuy/School-Projects

8e38f19da90f63ac9c9ec91e550fc5aaab3d0234

[ "MIT" ]

null

from bs4 import BeautifulSoup import os import wget from urllib.request import Request, urlopen bicycles=[{'name': 'Corelli Snoop 5.1 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-snoop-5-1.html'}, {'name': 'Corelli Snoop 5.2 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-snoop-5-2.html'}, {'name': 'Bianchi Junior Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-junior-24.html'}, {'name': 'Carraro Elite 704 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-elite-704.html'}, {'name': 'Corelli Sprint KR100 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-sprint-kr100.html'}, {'name': 'Kron XC100 29 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc100-29-md.html'}, {'name': 'Carraro Force 720 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-720.html'}, {'name': 'Carraro Race 012 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-cr-race-012.html'}, {'name': 'Carraro Force 650 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-650.html'}, {'name': 'RKS MX35 Bisiklet', 'link': 'https://www.epey.com/bisiklet/rks-mx35.html'}, {'name': 'Corelli Speedo 2.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-speedo-2-0.html'}, {'name': 'Kross Trans 3.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kross-trans-3-0.html'}, {'name': 'Salcano XRS044 Sora Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-xrs044-sora.html'}, {'name': 'Bisan CTS 5300 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-cts-5300-26.html'}, {'name': 'Corelli Mocha Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-mocha.html'}, {'name': 'Carraro Daytona 2624 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-daytona-2624.html'}, {'name': 'Ümit 2955 Kratos 2D Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2955-kratos-2d.html'}, {'name': 'Kron EFT1000 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-eft1000.html'}, {'name': 'Peugeot M11-26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/peugeot-m11-26.html'}, {'name': 'Berg Buzzy Bisiklet', 'link': 'https://www.epey.com/bisiklet/berg-buzzy.html'}, {'name': 'Corelli Snoop 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-snoop-1-0.html'}, {'name': 'Salcano NG700 26 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng700-26-md.html'}, {'name': 'Whistle Miwok 1730 Bisiklet', 'link': 'https://www.epey.com/bisiklet/whistle-miwok-1730.html'}, {'name': 'Cube Attain GTC Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-attain-gtc.html'}, {'name': 'Bianchi Montana D Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-montana-d-26.html'}, {'name': 'RKS RSI-X-Pro Bisiklet', 'link': 'https://www.epey.com/bisiklet/rks-rsi-x-pro.html'}, {'name': 'Carraro E-Pack Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-e-pack.html'}, {'name': 'Gitane Dream Bisiklet', 'link': 'https://www.epey.com/bisiklet/gitane-dream.html'}, {'name': 'Bianchi Aspid Mix 2621 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-aspid-mix-2621.html'}, {'name': 'Corelli Atrox 1.1 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-atrox-1-1.html'}, {'name': 'Bianchi Bella 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-bella-26.html'}, {'name': 'Carraro Big Bang Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-big-bang.html'}, {'name': 'Corelli Chronic 2.2 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-chronic-2-2.html'}, {'name': 'Bisan MTS 4300 24 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-mts-4300-24.html'}, {'name': 'Carraro Force 410 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-410.html'}, {'name': 'Ümit 1248 Racer Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1248-racer.html'}, {'name': 'Ümit 2662 Camaro V Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2662-camaro-v.html'}, {'name': 'Corelli Dolce 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-dolce-1-0.html'}, {'name': 'Corelli Like 2.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-like-2-0.html'}, {'name': 'Corelli Teton 2.3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-teton-2-3.html'}, {'name': 'Bianchi NTH 7 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-nth-7-29.html'}, {'name': 'Salcano Excel 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-excel-26-v.html'}, {'name': 'Corelli Leone 4.1 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-leone-4-1.html'}, {'name': 'Whistle Miwok 1760 Bisiklet', 'link': 'https://www.epey.com/bisiklet/whistle-miwok-1760.html'}, {'name': 'Ghost Square Trekking 2 Bisiklet', 'link': 'https://www.epey.com/bisiklet/ghost-square-trekking-2.html'}, {'name': 'Bianchi Pink Magic 14 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-pink-magic.html'}, {'name': 'Merida BIG.NINE 20 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/merida-big-nine-20-29.html'}, {'name': 'Salcano Badboy 12 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-badboy-12.html'}, {'name': 'Salcano Sarajevo 700 Lady Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-sarajevo-700-lady.html'}, {'name': 'Carraro Big 627 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-big-627-27-5.html'}, {'name': 'Ümit 1645 Ninja Turtles Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1645-ninja-turtles.html'}, {'name': 'Ümit 1616 Hello Kitty Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1616-hello-kitty.html'}, {'name': 'Ümit 2401 Colorado Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2401-colorado.html'}, {'name': 'Salcano Attack 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-attack-20.html'}, {'name': 'Salcano City Fun S 60 V Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-city-fun-s-60-v.html'}, {'name': 'Salcano Lion 27.5 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-lion-27-5.html'}, {'name': 'Carraro Flexi Voyager Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-flexi-voyager.html'}, {'name': 'Corelli Leon 3.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-leon-3-0.html'}, {'name': 'Kron TX100L Lady MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-tx100l-lady-md.html'}, {'name': 'Kron XC100L 24 Lady HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc100l-24-lady-hd.html'}, {'name': 'Kron XC75 26 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc75-26-md.html'}, {'name': 'Kron Ares 4.0 20 V Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-ares-4-0-20-v.html'}, {'name': 'Kron Vortex 3.0 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-vortex-3-0-26.html'}, {'name': 'Bianchi Captain Kidd 16 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-captain-kidd-16.html'}, {'name': 'Peugeot M13-27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/peugeot-m13-27-5.html'}, {'name': 'Peugeot JM246 Bisiklet', 'link': 'https://www.epey.com/bisiklet/peugeot-jm246.html'}, {'name': 'Bisan SPX-3050 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-spx-3050.html'}, {'name': 'Bianchi Discovery 505 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-discovery-505.html'}, {'name': 'Carraro Moggy 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-moggy-20.html'}, {'name': 'Carraro Big 2730 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-big-2730.html'}, {'name': 'Carraro Force 601 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-601.html'}, {'name': 'Berg BMW Street Racer Bisiklet', 'link': 'https://www.epey.com/bisiklet/berg-bmw-street-racer.html'}, {'name': 'Ümit 1449 Little Pony Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1449-little-pony.html'}, {'name': 'Ümit 1675 Spartan Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-1675-spartan.html'}, {'name': 'Ümit 2861 Magnetic HYD Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2861-magnetic-hyd.html'}, {'name': 'Ümit 2475 Spartan Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2475-spartan.html'}, {'name': 'Whistle Tulukai 1465D Bisiklet', 'link': 'https://www.epey.com/bisiklet/whistle-tulukai-1465d.html'}, {'name': 'Kron TX450 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-tx450-hd.html'}, {'name': 'Corelli Agile 2.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-agile-2-0.html'}, {'name': 'Corelli Speedo 4.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-speedo-4-0.html'}, {'name': 'Corelli Snoop 1.3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-snoop-1-3.html'}, {'name': 'Corelli Dusty 1.2 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-dusty-1-2.html'}, {'name': 'Bianchi RCX 729 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-rcx-729.html'}, {'name': 'Scott Contessa JR 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/scott-contessa-jr-20.html'}, {'name': 'Ghost Lanao 1.6 AL Bisiklet', 'link': 'https://www.epey.com/bisiklet/ghost-lanao-1-6-al.html'}, {'name': 'Bianchi Buffalo 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-buffalo-20-md.html'}, {'name': 'Salcano Astro 27.5 Lady V Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-astro-27-5-lady-v.html'}, {'name': 'Salcano NG650 24 Lady HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng650-24-lady-hd.html'}, {'name': 'Salcano NG800 26 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng800-26-hd.html'}, {'name': 'Salcano Efes 24 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-efes-24-md.html'}, {'name': 'Salcano City Explorer 30 V Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-city-explorer-30-v.html'}, {'name': 'Salcano City Sport 20 Lady HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-city-sport-20-lady-hd.html'}, {'name': 'Salcano Excel 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-excel-20.html'}, {'name': 'Lapierre Cyclo Cross CX Karbon Bisiklet', 'link': 'https://www.epey.com/bisiklet/lapierre-cyclo-cross-cx-karbon.html'}, {'name': 'Corelli Pierre 5.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-pierre-5-0.html'}, {'name': 'Corelli Jazz 2.3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-jazz-2-3.html'}, {'name': 'Corelli Cyborg 3.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-cyborg-3-0.html'}, {'name': 'Corelli Edida 2.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-edida-2-0.html'}, {'name': 'Corelli Grace 1.0 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corelli-grace-1-0.html'}, {'name': 'Orbis Reflex 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-reflex-26.html'}, {'name': 'Orbis Jewel 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-jewel-20.html'}, {'name': 'Orbis Kick 16 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-kick-16.html'}, {'name': 'Orbis Jungle Parrot 16 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-jungle-parrot-16.html'}, {'name': 'Orbis Stingray 24 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-stingray-24.html'}, {'name': 'Orbis Sonic 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/orbis-sonic-26.html'}, {'name': 'Tern Link D8 Bisiklet', 'link': 'https://www.epey.com/bisiklet/tern-link-d8.html'}, {'name': 'Bisan XTY - 5600 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-xty-5600-hd.html'}, {'name': 'Kron R1 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-r1.html'}, {'name': 'Kron XC450 29 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc450-29-man-hd.html'}, {'name': 'Kron XC150 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-xc150-20.html'}, {'name': 'Whistle Patwin 1724 Bisiklet', 'link': 'https://www.epey.com/bisiklet/whistle-patwin-1724.html'}, {'name': 'Mosso 20 Marine SR2 Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-20-marine-sr2.html'}, {'name': 'Mosso Legarda 1721 MSM H Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-legarda-1721-msm-h.html'}, {'name': 'Mosso Cavalier 700 Tourney Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-cavalier-700-tourney.html'}, {'name': 'Mosso 680XC SLX Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-680xc-slx.html'}, {'name': 'Mosso 29 Blackedition H Bisiklet', 'link': 'https://www.epey.com/bisiklet/mosso-blackedition-29.html'}, {'name': 'Merida Scultura Disc 400 Bisiklet', 'link': 'https://www.epey.com/bisiklet/merida-scultura-disc400.html'}, {'name': 'Ghost Square Cross 2 Miss Bisiklet', 'link': 'https://www.epey.com/bisiklet/ghost-square-cross-2-miss.html'}, {'name': 'Carraro Grande 704 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-grande-704.html'}, {'name': 'Carraro Barbie 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-barbie-20.html'}, {'name': 'Carraro Sportive 226 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-sportive-226.html'}, {'name': 'Bianchi Angry 24 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-angry-24.html'}, {'name': 'Bianchi Modena Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-modena.html'}, {'name': 'Bianchi TDL 1300 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-tdl-1300.html'}, {'name': 'Sedona 810 Bisiklet', 'link': 'https://www.epey.com/bisiklet/sedona-810.html'}, {'name': 'Salcano NG555 Lady 26 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng555-lady-26-hd.html'}, {'name': 'Salcano NG750 27.5 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng750-27-5-hd.html'}, {'name': 'Bisan XTY 5400 MD Bisiklet', 'link': 'https://www.epey.com/bisiklet/bisan-xty-5400-md.html'}, {'name': 'Ümit 2630 Velocity Man Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2630-velocity-man.html'}, {'name': 'Arbike 2403 Bisiklet', 'link': 'https://www.epey.com/bisiklet/arbike-2403.html'}, {'name': 'Arbike 2009 Bisiklet', 'link': 'https://www.epey.com/bisiklet/arbike-2009.html'}, {'name': 'Cube Attention SL 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-attention-sl-27-5.html'}, {'name': 'Salcano NG650 29 HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng650-hd-29.html'}, {'name': 'Cube Travel Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-travel.html'}, {'name': 'Cube Kathmandu Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-kathmandu.html'}, {'name': 'Cube Aerium HPA Pro Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-aerium-hpa-pro.html'}, {'name': 'Cube Aim SL 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-aim-sl-29.html'}, {'name': 'Cube Reaction GTC 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cube-reaction-gtc-27-5.html'}, {'name': 'Merida BIG.NINE 9000 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/merida-big-nine-9000-29.html'}, {'name': 'Merida BIG.NINE 70 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/merida-big-nine-70-29.html'}, {'name': 'Merida BIG.SEVEN 100 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/merida-big-seven-100-27-5.html'}, {'name': 'Trek X-Caliber 8 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/trek-x-caliber-8-27-5.html'}, {'name': 'Corratec X Vert 0.3 29ER 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corratec-x-vert-0-3-29er-29.html'}, {'name': 'Corratec Inside Link Alu 65X 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corratec-inside-link-alu-65x-27-5.html'}, {'name': 'Corratec 29ER One Gent Cross 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/corratec-29er-one-gent-cross-29.html'}, {'name': 'Cannondale F29 2 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cannondale-f29-2-29.html'}, {'name': 'Cannondale F SI Carbon 3 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cannondale-f-si-carbon-3-29.html'}, {'name': 'Cannondale F SI Carbon 4 29 Bisiklet', 'link': 'https://www.epey.com/bisiklet/cannondale-f-si-carbon-4-29.html'}, {'name': 'Look 695 Light Proteam Shimano Dura Ace Mavic Aksium 28 Bisiklet', 'link': 'https://www.epey.com/bisiklet/look-695-light-proteam-shimano-dura-ace-mavic-aksium-28.html'}, {'name': 'Geotech Belgium 4.0 28 Bisiklet', 'link': 'https://www.epey.com/bisiklet/geotech-belgium-4-0-28.html'}, {'name': 'Kron WSX500 Lady 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/kron-wsx500-lady-26.html'}, {'name': 'Salcano Assos 20 27.5 SLX Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-assos-20-slx-27-5.html'}, {'name': 'Salcano Insomnia 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-insomnia-26.html'}, {'name': 'Salcano City Wind 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-city-wind-20.html'}, {'name': 'Salcano XRS002 105 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-xrs002-105.html'}, {'name': 'Salcano NG750 20 Girl Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-ng750-lady-20.html'}, {'name': 'Salcano Fantom 12 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-fantom-12.html'}, {'name': 'Salcano Didim Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-didim-20.html'}, {'name': 'Salcano Bodrum 26 Lady Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-bodrum-26-lady.html'}, {'name': 'Salcano Istanbul 24 Lady HD Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-istanbul-24-lady-hd.html'}, {'name': 'Scott Genius LTD Karbon Bisiklet', 'link': 'https://www.epey.com/bisiklet/scott-genius-ltd-karbon-26.html'}, {'name': 'Sedona 300 Bisiklet', 'link': 'https://www.epey.com/bisiklet/sedona-300.html'}, {'name': 'Bianchi Touring 730 28 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-touring-730-28.html'}, {'name': 'Bianchi SLR 400 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-slr-400-28.html'}, {'name': 'Bianchi AFX 7127 27.5 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-afx-7127-27-5.html'}, {'name': 'Bianchi Speed 1000 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-speed-1000-26.html'}, {'name': 'Bianchi Hit 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/bianchi-hit-20.html'}, {'name': 'Carraro Sportive 221 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-sportive-221-28.html'}, {'name': 'Carraro Flexi 106 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-flexi-106.html'}, {'name': 'Carraro Crx 970 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-crx-970-26.html'}, {'name': 'Carraro Force 320 Bisiklet', 'link': 'https://www.epey.com/bisiklet/carraro-force-320.html'}, {'name': 'Sedona Totem Bisiklet', 'link': 'https://www.epey.com/bisiklet/sedona-totem.html'}, {'name': 'Ümit 2023 60 Bluepower Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2023-60-bluepower.html'}, {'name': 'Ümit 2637 Octagon 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2637-octagon-26.html'}, {'name': 'Ümit 2057 Albatros Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2057-albatros.html'}, {'name': 'Ümit 2008 Princess Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2008-princess.html'}, {'name': 'Ümit 2616 Hello Kitty 26 Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2616-hello-kitty-26.html'}, {'name': 'Ümit 2454 Shoot 24 Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2454-shoot-24.html'}, {'name': 'Ümit 2449 Monster High 24 Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-2449-monster-high-24.html'}, {'name': 'Ümit Coranna 2039 Panter 20 Bisiklet', 'link': 'https://www.epey.com/bisiklet/umit-coranna-2039-panter-20.html'}, {'name': 'Volta VB3 Bisiklet', 'link': 'https://www.epey.com/bisiklet/volta-vb3.html'}, {'name': 'Salcano Serenity 1 Bisiklet', 'link': 'https://www.epey.com/bisiklet/salcano-serenity-1.html'}] for i in bicycles: url = i['link'] try: req = Request(url, headers={'User-Agent': 'Mozilla/5.0'}) webpage = urlopen(req).read() except: print("err in "+i['link']) else: print("Downloaded "+i['name']+" ", end="\r") fileName = i['name'].replace('/','_') f = open("./listItems/"+fileName+'.html', 'wb') f.write(webpage) f.close

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54f52e70486d5eae11668b4275dbe49b4a320e05

211

py

Python

ramda/F_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

56

2018-08-06T08:44:58.000Z

2022-03-17T09:49:03.000Z

ramda/F_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

28

2019-06-17T11:09:52.000Z

2022-02-18T16:59:21.000Z

ramda/F_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

5

2019-09-18T09:24:38.000Z

2021-07-21T08:40:23.000Z

from ramda.F import F def F_test(): assert not F(1, 2, 3, 4), "Should be False" assert not F(False), "Should be False" assert not F(True), "Should be False" assert not F({}), "Should be False"

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54f70c16e4fa8abebe2028e0531c90f83ebe9a10

131

py

Python

addition_module/DSDG/data/__init__.py

weihaoxie/FaceX-Zoo

db0b087e4f4d28152e172d6c8d3767a8870733b4

[ "Apache-2.0" ]

1

2022-02-07T02:03:37.000Z

addition_module/DSDG/data/__init__.py

weihaoxie/FaceX-Zoo

db0b087e4f4d28152e172d6c8d3767a8870733b4

[ "Apache-2.0" ]

null

addition_module/DSDG/data/__init__.py

weihaoxie/FaceX-Zoo

db0b087e4f4d28152e172d6c8d3767a8870733b4

[ "Apache-2.0" ]

null

from .generation_dataset import GenDataset_s from .recognition_dataset import ImageList, SeparateBatchSampler, SeparateImageList

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076a142acbf736d4da6359a2d314465d2a26dfee

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py

Python

src/genie/libs/parser/iosxr/tests/test_show_segment_routing.py

psolarcz/genieparser

811c197a1dab6a635e6dec145b99194648bf4ff4

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxr/tests/test_show_segment_routing.py

psolarcz/genieparser

811c197a1dab6a635e6dec145b99194648bf4ff4

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxr/tests/test_show_segment_routing.py

psolarcz/genieparser

811c197a1dab6a635e6dec145b99194648bf4ff4

[ "Apache-2.0" ]

1

2021-07-07T18:07:56.000Z

#!/bin/env python import unittest from unittest.mock import Mock from ats.topology import Device from genie.metaparser.util.exceptions import SchemaEmptyParserError, SchemaMissingKeyError from genie.libs.parser.iosxr.show_segment_routing import ShowPceLsp,\ ShowPceIPV4Peer,\ ShowPceLspDetail,\ ShowPceIPV4PeerDetail,\ ShowPceIPV4PeerPrefix,\ ShowPceIpv4TopologySummary,\ ShowIsisSegmentRoutingPrefixSidMap,\ ShowOspfSegmentRoutingPrefixSidMap,\ ShowSegmentRoutingLocalBlockInconsistencies,\ ShowSegmentRoutingMappingServerPrefixSidMapIPV4,\ ShowSegmentRoutingMappingServerPrefixSidMapIPV4Detail # ============================================================= # Unittest for: # * 'Show Isis Segment Routing Prefix Sid Map' # ============================================================= class test_show_isis_routing_prefix_sid_map(unittest.TestCase): device = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/0/CPU0:router# show isis segment-routing prefix-sid-map active-policy IS-IS 1 active policy Prefix SID Index Range Flags 10.4.1.100/32 100 20 10.4.1.150/32 150 10 Number of mapping entries: 2 RP/0/0/CPU0:router# show isis segment-routing prefix-sid-map backup-policy IS-IS 1 backup policy Prefix SID Index Range Flags 10.4.1.100/32 100 20 10.4.1.150/32 150 10 Number of mapping entries: 2 '''} golden_parsed_output = { 'process_id': { 1: { 'policy': { 'active': { 'sid': { 100: { 'prefix': '10.4.1.100/32', 'range': 20, }, 150: { 'prefix': '10.4.1.150/32', 'range': 10, } }, 'number_of_mapping_entries': 2, }, 'backup': { 'sid': { 100: { 'prefix': '10.4.1.100/32', 'range': 20, }, 150: { 'prefix': '10.4.1.150/32', 'range': 10, } }, 'number_of_mapping_entries': 2, }, } } } } def test_empty_output(self): self.device1 = Mock(**self.empty_output) obj = ShowIsisSegmentRoutingPrefixSidMap(device=self.device1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowIsisSegmentRoutingPrefixSidMap(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ============================================================= # Unittest for: # * 'Show Ospf Segment Routing Prefix Sid Map' # ============================================================= class test_show_ospf_routing_prefix_sid_map(unittest.TestCase): device = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/0/CPU0:router# show ospf segment-routing prefix-sid-map active-policy SRMS active policy for Process ID 1 Prefix SID Index Range Flags 10.4.1.100/32 100 20 10.4.1.150/32 150 10 Number of mapping entries: 2 RP/0/0/CPU0:router# show ospf segment-routing prefix-sid-map backup-policy SRMS backup policy for Process ID 1 Prefix SID Index Range Flags 10.4.1.100/32 100 20 10.4.1.150/32 150 10 Number of mapping entries: 2 '''} golden_parsed_output = { 'process_id': { 1: { 'policy': { 'active': { 'sid': { 100: { 'prefix': '10.4.1.100/32', 'range': 20, }, 150: { 'prefix': '10.4.1.150/32', 'range': 10, } }, 'number_of_mapping_entries': 2, }, 'backup': { 'sid': { 100: { 'prefix': '10.4.1.100/32', 'range': 20, }, 150: { 'prefix': '10.4.1.150/32', 'range': 10, } }, 'number_of_mapping_entries': 2, }, } } } } def test_empty_output(self): self.device1 = Mock(**self.empty_output) obj = ShowOspfSegmentRoutingPrefixSidMap(device=self.device1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowOspfSegmentRoutingPrefixSidMap(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ============================================================= # Unittest for: # * 'Show pce ipv4 peer' # ============================================================= class test_show_pce_ivp4_peer(unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/RSP0/CPU0:router# show pce ipv4 peer PCE's peer database: -------------------- Peer address: 192.168.0.1 State: Up Capabilities: Stateful, Segment-Routing, Update '''} golden_parsed_output = { 'pce_peer_database': { '192.168.0.1': { 'state': 'Up', 'capabilities': { 'stateful': True, 'segment-routing': True, 'update': True } } } } def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowPceIPV4Peer(device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowPceIPV4Peer(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ============================================================= # Unittest for: # * 'show pce ipv4 peer detail' # ============================================================= class test_show_pce_ipv4_peer_detail(unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/RSP0/CPU0:router# show pce ipv4 peer detail PCE's peer database: -------------------- Peer address: 192.168.0.1 State: Up Capabilities: Stateful, Segment-Routing, Update PCEP has been up for: 00:01:50 PCEP session ID: local 0, remote 0 Sending KA every 30 seconds Minimum acceptable KA interval: 20 seconds Peer timeout after 120 seconds Statistics: Keepalive messages: rx 4 tx 4 Request messages: rx 3 tx 0 Reply messages: rx 0 tx 3 Error messages: rx 0 tx 0 Open messages: rx 1 tx 1 Report messages: rx 4 tx 0 Update messages: rx 0 tx 2 Initiate messages: rx 0 tx 0 '''} golden_parsed_output = { 'pce_peer_database': { '192.168.0.1': { 'state': 'Up', 'capabilities': { 'stateful': True, 'segment-routing': True, 'update': True }, 'pcep': { 'uptime': '00:01:50', 'session_id_local': 0, 'session_id_remote': 0 }, 'ka': { 'sending_intervals': 30, 'minimum_acceptable_inteval': 20 }, 'peer_timeout': 120, 'statistics': { 'rx': { 'keepalive_messages': 4, 'request_messages': 3, 'reply_messages': 0, 'error_messages': 0, 'open_messages': 1, 'report_messages': 4, 'update_messages': 0, 'initiate_messages': 0 }, 'tx': { 'keepalive_messages': 4, 'request_messages': 0, 'reply_messages': 3, 'error_messages': 0, 'open_messages': 1, 'report_messages': 0, 'update_messages': 2, 'initiate_messages': 0 } } } } } def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowPceIPV4PeerDetail(device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowPceIPV4PeerDetail(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ============================================================= # Unittest for: # * 'show pce ipv4 prefix' # ============================================================= class test_Show_Pce_IPV4_Peer_prefix(unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/RSP0/CPU0:router# show pce ipv4 prefix PCE's prefix database: ---------------------- Node 1 TE router ID: 192.168.0.4 Host name: rtrD ISIS system ID: 1921.6800.1004 level-1 ASN: 65001 domain ID: 1111 ISIS system ID: 1921.6800.1004 level-2 ASN: 65001 domain ID: 1111 ISIS system ID: 1921.6800.1004 level-2 ASN: 65001 domain ID: 9999 Advertised Prefixes: 192.168.0.4 192.168.0.4 192.168.0.4 192.168.0.6 Node 2 TE router ID: 192.168.0.1 Host name: rtrA ISIS system ID: 1921.6800.1001 level-2 Advertised Prefixes: 192.168.0.1 '''} golden_parsed_output = { 'nodes': { 1: { 'te_router_id': '192.168.0.4', 'host_name': 'rtrD', 'isis_system_id': [ '1921.6800.1004 level-1', '1921.6800.1004 level-2', '1921.6800.1004 level-2'], 'asn': [ 65001, 65001, 65001], 'domain_id': [ 1111, 1111, 9999], 'advertised_prefixes': [ '192.168.0.4', '192.168.0.4', '192.168.0.4', '192.168.0.6']}, 2: { 'te_router_id': '192.168.0.1', 'host_name': 'rtrA', 'isis_system_id': ['1921.6800.1001 level-2'], 'advertised_prefixes': ['192.168.0.1']}}} def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowPceIPV4PeerPrefix(device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowPceIPV4PeerPrefix(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ============================================================= # Unittest for: # * 'show pce ipv4 topology summary' # ============================================================= class test_Show_Pce_Ipv4_Topology_Summary(unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/RSP0/CPU0:router# show pce ipv4 topology summary PCE's topology database summary: -------------------------------- Topology nodes: 4 Prefixes: 4 Prefix SIDs: 4 Links: 12 Adjacency SIDs: 24 '''} golden_parsed_output = { 'pce_topology_database_summary': { 'adjancency_sids': { 'total': 24, }, 'links': { 'total': 12, }, 'prefix_sids': { 'total': 4, }, 'prefixes': 4, 'topology_nodes': 4, }, } expected_output_2 = { 'pce_topology_database_summary': { 'adjancency_sids': { 'epe': 0, 'protected': 0, 'total': 0, 'unprotected': 0, }, 'links': { 'epe': 0, 'total': 0, }, 'prefix_sids': { 'regular': 0, 'strict': 0, 'total': 0, }, 'prefixes': 0, 'private_information': { 'consistent': 'yes', 'lookup_nodes': 0, 'update_stats': { 'links': { 'added': 0, 'deleted': 0, }, 'noded': { 'added': 0, 'deleted': 0, }, 'prefix': { 'added': 0, 'deleted': 0, }, }, }, 'topology_nodes': 0, }, } device_output_2 = {'execute.return_value': ''' PCE's topology database summary: -------------------------------- Topology nodes: 0 Prefixes: 0 Prefix SIDs: Total: 0 Regular: 0 Strict: 0 Links: Total: 0 EPE: 0 Adjacency SIDs: Total: 0 Unprotected: 0 Protected: 0 EPE: 0 Private Information: Lookup Nodes 0 Consistent yes Update Stats (from IGP and/or BGP): Noded added: 0 Noded deleted: 0 Links added: 0 Links deleted: 0 Prefix added: 0 Prefix deleted: 0 '''} def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowPceIpv4TopologySummary(device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowPceIpv4TopologySummary(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) def test_golden_output_2(self): self.maxDiff = None self.dev3 = Mock(**self.device_output_2) obj = ShowPceIpv4TopologySummary(device=self.dev3) parsed = obj.parse() self.assertEqual(parsed, self.expected_output_2) # ============================================================= # Unittest for: # * 'show pce lsp' # ============================================================= class test_show_Pce_Lsp(unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/RSP0/CPU0:router# show pce lsp PCE's tunnel database: ---------------------- PCC 192.168.0.1: Tunnel Name: rtrA_t1 LSPs: LSP[0]: source 192.168.0.1, destination 192.168.0.4, tunnel ID 1, LSP ID 2 State: Admin up, Operation up Setup type: Segment Routing Binding SID: 24013 '''} golden_parsed_output = { 'pcc': { '192.168.0.1': { 'tunnel_name': { 'rtrA_t1': { 'lsps': { 0: { 'source': '192.168.0.1', 'destination': '192.168.0.4', 'tunnel_id': 1, 'lsp_id': 2, 'admin_state': 'up', 'operation_state': 'up', 'setup_type': 'Segment Routing', 'binding_sid': 24013 } } } } } } } def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowPceLsp(device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowPceLsp(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ============================================================= # Unittest for: # * 'show pce lsp detail' # ============================================================= class test_Show_Pce_Lsp_Detail(unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/RSP0/CPU0:router# show pce lsp detail PCE's tunnel database: ---------------------- PCC 192.168.0.1: Tunnel Name: rtrA_t1 LSPs: LSP[0]: source 192.168.0.1, destination 192.168.0.4, tunnel ID 1, LSP ID 2 State: Admin up, Operation up Setup type: Segment Routing Binding SID: 24013 PCEP information: plsp-id 2, flags: D:1 S:0 R:0 A:1 O:1 Reported path: Metric type: TE, Accumulated Metric 42 SID[0]: Adj, Label 24000, Address: local 10.10.10.1 remote 10.10.10.2 SID[1]: Adj, Label 24000, Address: local 10.19.14.2 remote 10.19.14.4 Computed path: Metric type: TE, Accumulated Metric 42 SID[0]: Adj, Label 24000, Address: local 10.10.10.1 remote 10.10.10.2 SID[1]: Adj, Label 24000, Address: local 10.19.14.2 remote 10.19.14.4 Recorded path: None RP/0/RSP0/CPU0:router# show pce lsp detail PCE's tunnel database: ---------------------- PCC 192.168.0.1: Tunnel Name: rtrA_t1 LSPs: LSP[0]: source 192.168.0.1, destination 192.168.0.4, tunnel ID 1, LSP ID 2 State: Admin up, Operation up Setup type: Segment Routing Binding SID: 24013 PCEP information: plsp-id 2, flags: D:1 S:0 R:0 A:1 O:1 Reported path: Metric type: TE, Accumulated Metric 42 SID[0]: Adj, Label 24000, Address: local 10.10.10.1 remote 10.10.10.2 SID[1]: Adj, Label 24000, Address: local 10.19.14.2 remote 10.19.14.4 Computed path: Metric type: TE, Accumulated Metric 42 SID[0]: Adj, Label 24000, Address: local 10.10.10.1 remote 10.10.10.2 SID[1]: Adj, Label 24000, Address: local 10.19.14.2 remote 10.19.14.4 Recorded path: None Event history (latest first): Time Event June 13 2016 13:28:29 Report Symbolic-name: rtrA_t1, LSP-ID: 2, Source: 192.168.0.1 Destination: 192.168.0.4, D:1, R:0, A:1 O:1, Sig.BW: 0, Act.BW: 0 June 13 2016 13:28:28 Report Symbolic-name: rtrA_t1, LSP-ID: 2, Source: 192.168.0.1 Destination: 192.168.0.4, D:1, R:0, A:1 O:1, Sig.BW: 0, Act.BW: 0 June 13 2016 13:28:28 Create Symbolic-name: rtrA_t1, PLSP-ID: 2, Peer: 192.168.0.1 '''} golden_parsed_output = { 'pcc': { '192.168.0.1': { 'tunnel_name': 'rtrA_t1', 'lsps': { 0: { 'source': '192.168.0.1', 'destination': '192.168.0.4', 'tunnel_id': 1, 'lsp_id': 2, 'admin_state': 'up', 'operation_state': 'up', 'setup_type': 'segment routing', 'binding_sid': 24013, 'pcep_information': { 'plsp_id': 2, 'flags': { 'd': 1, 's': 0, 'r': 0, 'a': 1, 'o': 1, } }, 'paths': { 'reported': { 'metric_type': 'TE', 'accumulated_metric': 42, 'sids': { 0: { 'type': 'Adj', 'label': 24000, 'local_address': '10.10.10.1', 'remote_address': '10.10.10.2' }, 1: { 'type': 'Adj', 'label': 24000, 'local_address': '10.19.14.2', 'remote_address': '10.19.14.4' } } }, 'computed': { 'metric_type': 'TE', 'accumulated_metric': 42, 'sids': { 0: { 'type': 'Adj', 'label': 24000, 'local_address': '10.10.10.1', 'remote_address': '10.10.10.2' }, 1: { 'type': 'Adj', 'label': 24000, 'local_address': '10.19.14.2', 'remote_address': '10.19.14.4' } } }, 'recorded': {} } }, 'event_history': { 'June 13 2016 13:28:29': { 'report': { 'symbolic_name': 'rtrA_t1', 'lsp-id': 2, 'source': '192.168.0.1', 'destination': '192.168.0.4', 'flags': { 'd': 1, 'r': 0, 'a': 1, 'o': 1, 'sig_bw': 0, 'act_bw': 0 } } }, 'June 13 2016 13:28:28': { 'report': { 'symbolic_name': 'rtrA_t1', 'lsp-id': 2, 'source': '192.168.0.1', 'destination': '192.168.0.4', 'flags': { 'd': 1, 'r': 0, 'a': 1, 'o': 1, 'sig_bw': 0, 'act_bw': 0 } }, 'create': { 'symbolic_name': 'rtrA_t1', 'plsp-id': 2, 'peer': '192.168.0.1' } } } } } } } def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowPceLspDetail(device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowPceLspDetail(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ============================================================= # Unittest for: # * 'show segment-routing local-block inconsistencies' # ============================================================= class Test_Show_Segment_Routing_Local_Block_Inconsistencies(unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/RSP0/CPU0:router(config)# show segment-routing local-block inconsistencies Tue Aug 15 13:53:30.555 EDT SRLB inconsistencies range: Start/End: 30000/30009 '''} golden_parsed_output = { 'srlb_inconsistencies_range': { 'start': 30000, 'end': 30009, } } def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowSegmentRoutingLocalBlockInconsistencies(device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowSegmentRoutingLocalBlockInconsistencies(device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ==================================================================== # Unittest for: # * 'show segment-routing mapping-server prefix-sid-map ipv4' # ==================================================================== class Test_Show_Segment_Routing_Mapping_Server_Prefix_Sid_Map_IPV4( unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' show segment-routing mapping-server prefix-sid-map ipv4 Prefix SID Index Range Flags 10.186.1.0/24 400 300 10.1.1.1/32 10 200 Number of mapping entries: 2 '''} golden_parsed_output = { 'ipv4': { 'number_of_mapping_entries': 2, 'prefix': { '10.186.1.0/24': { 'sid_index': 400, 'range': 300 }, '10.1.1.1/32': { 'sid_index': 10, 'range': 200 } }, } } def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowSegmentRoutingMappingServerPrefixSidMapIPV4( device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowSegmentRoutingMappingServerPrefixSidMapIPV4( device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) # ==================================================================== # Unittest for: # * 'show segment-routing mapping-server prefix-sid-map ipv4 detail' # ==================================================================== class Test_Show_Segment_Routing_Mapping_Server_Prefix_Sid_Map_IPV_4Detail( unittest.TestCase): dev1 = Device(name='DeviceA') dev2 = Device(name='DeviceB') empty_output = {'execute.return_value': ''} golden_output = {'execute.return_value': ''' RP/0/0/CPU0:router# show segment-routing mapping-server prefix-sid-map ipv4 detail Prefix 10.186.1.0/24 SID Index: 400 Range: 300 Last Prefix: 10.229.44.0/24 Last SID Index: 699 Flags: 10.1.1.1/32 SID Index: 10 Range: 200 '''} golden_parsed_output = { 'ipv4': { 'prefix': { '10.186.1.0/24': { 'sid_index': 400, 'range': 300, 'last_prefix': '10.229.44.0/24', 'last_sid_index': 699 }, '10.1.1.1/32': { 'sid_index': 10, 'range': 200 }, } } } def test_empty_output(self): self.dev1 = Mock(**self.empty_output) obj = ShowSegmentRoutingMappingServerPrefixSidMapIPV4Detail( device=self.dev1) with self.assertRaises(SchemaEmptyParserError): parsed = obj.parse() def test_golden_output(self): self.maxDiff = None self.dev2 = Mock(**self.golden_output) obj = ShowSegmentRoutingMappingServerPrefixSidMapIPV4Detail( device=self.dev2) parsed = obj.parse() self.assertEqual(parsed, self.golden_parsed_output) if __name__ == '__main__': unittest.main()

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0.839255

0.762366

0.741535

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null

0

6

db517619400b36c1337d966e5e515072d896b35f

5,034

py

Python

tests/nn/test_kalman.py

hectorcarrion/sleap

1150c2d0b64543e07d4b2429ea245a5afaa07cee

[ "BSD-3-Clause-Clear" ]

156

2020-05-01T18:43:43.000Z

2022-03-25T10:31:18.000Z

tests/nn/test_kalman.py

oeway/sleap

1eb06f81eb8f0bc1beedd1c3dd10902f8ff9e724

[ "BSD-3-Clause-Clear" ]

299

2020-04-20T16:37:52.000Z

2022-03-31T23:54:48.000Z

tests/nn/test_kalman.py

oeway/sleap

1eb06f81eb8f0bc1beedd1c3dd10902f8ff9e724

[ "BSD-3-Clause-Clear" ]

41

2020-05-14T15:25:21.000Z

2022-03-25T12:44:54.000Z

import numpy as np import sleap.nn.tracker.components import sleap.nn.tracker.kalman as k from sleap.nn.tracker.components import greedy_matching def test_first_choice_matching(): instances = ["instance a", "instance b"] tracks = ["track a", "track b"] # columns are tracks # rows are instances cost_matrix = np.array([[10, 150], [50, 100]]) match_tuples = k.match_tuples_from_match_function( cost_matrix=cost_matrix, row_items=instances, column_items=tracks, match_function=sleap.nn.tracker.components.first_choice_matching, ) assert len(match_tuples) == 2 assert ("instance a", "track a", 10) in match_tuples assert ("instance b", "track a", 50) in match_tuples match_by_track = k.match_dict_from_match_function( cost_matrix=cost_matrix, row_items=instances, column_items=tracks, match_function=sleap.nn.tracker.components.first_choice_matching, ) assert len(match_by_track) == 1 assert match_by_track["track a"] == "instance a" match_by_instance = k.match_dict_from_match_function( cost_matrix=cost_matrix, row_items=instances, column_items=tracks, match_function=sleap.nn.tracker.components.first_choice_matching, key_by_column=False, ) assert len(match_by_instance) == 2 assert match_by_instance["instance a"] == "track a" assert match_by_instance["instance b"] == "track a" # another cost matrix # make sure we get *best* match for each track, regardless of row order cost_matrix = np.array( [ [50, 100], [10, 150], ] ) match_by_track = k.match_dict_from_match_function( cost_matrix=cost_matrix, row_items=instances, column_items=tracks, match_function=sleap.nn.tracker.components.first_choice_matching, ) assert len(match_by_track) == 1 assert match_by_track["track a"] == "instance b" def test_greedy_matching(): instances = ["instance a", "instance b"] tracks = ["track a", "track b"] # columns are tracks # rows are instances cost_matrix = np.array([[10, 200], [75, 150]]) matches = k.matches_from_match_tuples( k.match_tuples_from_match_function( cost_matrix=cost_matrix, row_items=instances, column_items=tracks, match_function=greedy_matching, ) ) assert len(matches) == 2 assert matches[0].track == "track a" assert matches[0].instance == "instance a" assert matches[0].score == 10 assert matches[1].track == "track b" assert matches[1].instance == "instance b" assert matches[1].score == 150 def test_track_instance_matches(): instances = ["instance a", "instance b"] tracks = ["track a", "track b"] # columns are tracks # rows are instances cost_matrix = np.array([[10, 200], [75, 150]]) matches = k.get_track_instance_matches( cost_matrix=cost_matrix, instances=instances, tracks=tracks, are_too_close_function=lambda x, y: True, ) # instance b would prefer track a but gets bumped to track b # since there's no competition for track b, the "too close" check # isn't applied. assert len(matches) == 2 assert matches[0].track == "track a" assert matches[0].instance == "instance a" assert matches[0].score == 10 assert matches[1].track == "track b" assert matches[1].instance == "instance b" assert matches[1].score == 150 # another cost matrix # best match is instance a -> track a # next match is instance b -> track b # but instance b would prefer track a cost_matrix = np.array( [ [10, 100], [50, 150], ] ) matches = k.get_track_instance_matches( cost_matrix=cost_matrix, instances=instances, tracks=tracks, are_too_close_function=lambda x, y: True, ) assert len(matches) == 2 assert matches[0].track == "track a" assert matches[0].instance == "instance a" assert matches[0].score == 10 assert matches[1].track == "track b" assert matches[1].instance == "instance b" assert matches[1].score == 150 # best match is instance b -> track a (cost 10) # next match is instance a -> track b (cost 100) # each instance gets its first choice so "too close" check shouldn't apply cost_matrix = np.array( [ [50, 100], [10, 150], ] ) matches = k.get_track_instance_matches( cost_matrix=cost_matrix, instances=instances, tracks=tracks, are_too_close_function=lambda x, y: True, ) assert len(matches) == 2 assert matches[0].track == "track a" assert matches[0].instance == "instance b" assert matches[0].score == 10 assert matches[1].track == "track b" assert matches[1].instance == "instance a" assert matches[1].score == 100

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0

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1

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null

0

6

dba9e568cac7b83d6d1bb6469479836aa4dd47ee

300

py

Python

eskedit/__init__.py

quinlan-lab/kmertools

93e90919c26e2fc899a905b77748857404389e13

[ "MIT" ]

1

2020-08-25T01:35:38.000Z

eskedit/__init__.py

quinlan-lab/kmertools

93e90919c26e2fc899a905b77748857404389e13

[ "MIT" ]

null

eskedit/__init__.py

quinlan-lab/kmertools

93e90919c26e2fc899a905b77748857404389e13

[ "MIT" ]

1

2021-07-13T23:21:56.000Z

from eskedit.kmethods import * from eskedit.ksplit import * from eskedit.constants import * from eskedit.dpgp_prep import * from eskedit.kclass import * from eskedit.train_model import * from eskedit.chrom_binning import * from eskedit.ktrain import train_kmer_model from eskedit.query import kquery

30

43

0.826667

43

300

5.651163

0.395349

0.407407

0.489712

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0.12

300

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1

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true

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1

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null

0

1

0

1

0

1

0

6

91688482b206717a899a236804cb81d8620dd5c8

26

py

Python

1 Python Basics/8_tuples.py

narayanants/python-mega-course

2ba2980ab21dfbed5f86f00695559f7831b5c566

[ "MIT" ]

null

1 Python Basics/8_tuples.py

narayanants/python-mega-course

2ba2980ab21dfbed5f86f00695559f7831b5c566

[ "MIT" ]

null

1 Python Basics/8_tuples.py

narayanants/python-mega-course

2ba2980ab21dfbed5f86f00695559f7831b5c566

[ "MIT" ]

null

num1 = (1,2,3) print(num1)

13

14

0.615385

6

26

2.666667

0.833333

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0.217391

0.115385

26

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1

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1

0

null

0

1

0

6

918eb8bfdaff2596613fa540d164b040802fa655

25

py

Python

autograder/__init__.py

CC-4/lab7

24a44c5c337dfcfbb0ffd4765e5b5303546a7801

[ "MIT" ]

null

autograder/__init__.py

CC-4/lab7

24a44c5c337dfcfbb0ffd4765e5b5303546a7801

[ "MIT" ]

null

autograder/__init__.py

CC-4/lab7

24a44c5c337dfcfbb0ffd4765e5b5303546a7801

[ "MIT" ]

null

from .termcolor import *

12.5

24

0.76

3

25

6.333333

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0

0.16

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1

25

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null

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1

0

1

0

1

0

6

91d6633cf1da711ae3c3961673cde34cfb498436

30,025

py

Python

tests/components/konnected/test_init.py

tbarbette/core

8e58c3aa7bc8d2c2b09b6bd329daa1c092d52d3c

[ "Apache-2.0" ]

11

2018-02-16T15:35:47.000Z

2020-01-14T15:20:00.000Z

tests/components/konnected/test_init.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

79

2020-07-23T07:13:37.000Z

2022-03-22T06:02:37.000Z

tests/components/konnected/test_init.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

14

2018-08-19T16:28:26.000Z

2021-09-02T18:26:53.000Z

"""Test Konnected setup process.""" from unittest.mock import patch import pytest from homeassistant.components import konnected from homeassistant.components.konnected import config_flow from homeassistant.config import async_process_ha_core_config from homeassistant.const import HTTP_NOT_FOUND from homeassistant.setup import async_setup_component from tests.common import MockConfigEntry @pytest.fixture(name="mock_panel") async def mock_panel_fixture(): """Mock a Konnected Panel bridge.""" with patch("konnected.Client", autospec=True) as konn_client: def mock_constructor(host, port, websession): """Fake the panel constructor.""" konn_client.host = host konn_client.port = port return konn_client konn_client.side_effect = mock_constructor konn_client.ClientError = config_flow.CannotConnect konn_client.get_status.return_value = { "hwVersion": "2.3.0", "swVersion": "2.3.1", "heap": 10000, "uptime": 12222, "ip": "192.168.1.90", "port": 9123, "sensors": [], "actuators": [], "dht_sensors": [], "ds18b20_sensors": [], "mac": "11:22:33:44:55:66", "settings": {}, } yield konn_client async def test_config_schema(hass): """Test that config schema is imported properly.""" config = { konnected.DOMAIN: { konnected.CONF_API_HOST: "http://1.1.1.1:8888", konnected.CONF_ACCESS_TOKEN: "abcdefgh", konnected.CONF_DEVICES: [{konnected.CONF_ID: "aabbccddeeff"}], } } assert konnected.CONFIG_SCHEMA(config) == { "konnected": { "access_token": "abcdefgh", "api_host": "http://1.1.1.1:8888", "devices": [ { "default_options": { "blink": True, "api_host": "http://1.1.1.1:8888", "discovery": True, "io": { "1": "Disabled", "10": "Disabled", "11": "Disabled", "12": "Disabled", "2": "Disabled", "3": "Disabled", "4": "Disabled", "5": "Disabled", "6": "Disabled", "7": "Disabled", "8": "Disabled", "9": "Disabled", "alarm1": "Disabled", "alarm2_out2": "Disabled", "out": "Disabled", "out1": "Disabled", }, }, "id": "aabbccddeeff", } ], } } # check with host info config = { konnected.DOMAIN: { konnected.CONF_ACCESS_TOKEN: "abcdefgh", konnected.CONF_DEVICES: [ {konnected.CONF_ID: "aabbccddeeff", "host": "192.168.1.1", "port": 1234} ], } } assert konnected.CONFIG_SCHEMA(config) == { "konnected": { "access_token": "abcdefgh", "devices": [ { "default_options": { "blink": True, "api_host": "", "discovery": True, "io": { "1": "Disabled", "10": "Disabled", "11": "Disabled", "12": "Disabled", "2": "Disabled", "3": "Disabled", "4": "Disabled", "5": "Disabled", "6": "Disabled", "7": "Disabled", "8": "Disabled", "9": "Disabled", "alarm1": "Disabled", "alarm2_out2": "Disabled", "out": "Disabled", "out1": "Disabled", }, }, "id": "aabbccddeeff", "host": "192.168.1.1", "port": 1234, } ], } } # check pin to zone and multiple output config = { konnected.DOMAIN: { konnected.CONF_ACCESS_TOKEN: "abcdefgh", konnected.CONF_DEVICES: [ { konnected.CONF_ID: "aabbccddeeff", "binary_sensors": [ {"pin": 2, "type": "door"}, {"zone": 1, "type": "door"}, ], "switches": [ { "zone": 3, "name": "Beep Beep", "momentary": 65, "pause": 55, "repeat": 4, }, { "zone": 3, "name": "Warning", "momentary": 100, "pause": 100, "repeat": -1, }, ], } ], } } assert konnected.CONFIG_SCHEMA(config) == { "konnected": { "access_token": "abcdefgh", "devices": [ { "default_options": { "blink": True, "api_host": "", "discovery": True, "io": { "1": "Binary Sensor", "10": "Disabled", "11": "Disabled", "12": "Disabled", "2": "Binary Sensor", "3": "Switchable Output", "4": "Disabled", "5": "Disabled", "6": "Disabled", "7": "Disabled", "8": "Disabled", "9": "Disabled", "alarm1": "Disabled", "alarm2_out2": "Disabled", "out": "Disabled", "out1": "Disabled", }, "binary_sensors": [ {"inverse": False, "type": "door", "zone": "2"}, {"inverse": False, "type": "door", "zone": "1"}, ], "switches": [ { "zone": "3", "activation": "high", "name": "Beep Beep", "momentary": 65, "pause": 55, "repeat": 4, }, { "zone": "3", "activation": "high", "name": "Warning", "momentary": 100, "pause": 100, "repeat": -1, }, ], }, "id": "aabbccddeeff", } ], } } async def test_setup_with_no_config(hass): """Test that we do not discover anything or try to set up a Konnected panel.""" assert await async_setup_component(hass, konnected.DOMAIN, {}) # No flows started assert len(hass.config_entries.flow.async_progress()) == 0 # Nothing saved from configuration.yaml assert hass.data[konnected.DOMAIN][konnected.CONF_ACCESS_TOKEN] is None assert hass.data[konnected.DOMAIN][konnected.CONF_API_HOST] is None assert konnected.YAML_CONFIGS not in hass.data[konnected.DOMAIN] async def test_setup_defined_hosts_known_auth(hass, mock_panel): """Test we don't initiate a config entry if configured panel is known.""" MockConfigEntry( domain="konnected", unique_id="112233445566", data={"host": "0.0.0.0", "id": "112233445566"}, ).add_to_hass(hass) MockConfigEntry( domain="konnected", unique_id="aabbccddeeff", data={"host": "1.2.3.4", "id": "aabbccddeeff"}, ).add_to_hass(hass) assert ( await async_setup_component( hass, konnected.DOMAIN, { konnected.DOMAIN: { konnected.CONF_ACCESS_TOKEN: "abcdefgh", konnected.CONF_DEVICES: [ { config_flow.CONF_ID: "aabbccddeeff", config_flow.CONF_HOST: "0.0.0.0", config_flow.CONF_PORT: 1234, } ], } }, ) is True ) assert hass.data[konnected.DOMAIN][konnected.CONF_ACCESS_TOKEN] == "abcdefgh" assert konnected.YAML_CONFIGS not in hass.data[konnected.DOMAIN] # Flow aborted assert len(hass.config_entries.flow.async_progress()) == 0 async def test_setup_defined_hosts_no_known_auth(hass): """Test we initiate config entry if config panel is not known.""" assert ( await async_setup_component( hass, konnected.DOMAIN, { konnected.DOMAIN: { konnected.CONF_ACCESS_TOKEN: "abcdefgh", konnected.CONF_DEVICES: [{konnected.CONF_ID: "aabbccddeeff"}], } }, ) is True ) # Flow started for discovered bridge assert len(hass.config_entries.flow.async_progress()) == 1 async def test_setup_multiple(hass): """Test we initiate config entry for multiple panels.""" assert ( await async_setup_component( hass, konnected.DOMAIN, { konnected.DOMAIN: { konnected.CONF_ACCESS_TOKEN: "arandomstringvalue", konnected.CONF_API_HOST: "http://192.168.86.32:8123", konnected.CONF_DEVICES: [ { konnected.CONF_ID: "aabbccddeeff", "binary_sensors": [ {"zone": 4, "type": "motion", "name": "Hallway Motion"}, { "zone": 5, "type": "window", "name": "Master Bedroom Window", }, { "zone": 6, "type": "window", "name": "Downstairs Windows", }, ], "switches": [{"zone": "out", "name": "siren"}], }, { konnected.CONF_ID: "445566778899", "binary_sensors": [ {"zone": 1, "type": "motion", "name": "Front"}, {"zone": 2, "type": "window", "name": "Back"}, ], "switches": [ { "zone": "out", "name": "Buzzer", "momentary": 65, "pause": 55, "repeat": 4, } ], }, ], } }, ) is True ) # Flow started for discovered bridge assert len(hass.config_entries.flow.async_progress()) == 2 # Globals saved assert ( hass.data[konnected.DOMAIN][konnected.CONF_ACCESS_TOKEN] == "arandomstringvalue" ) assert ( hass.data[konnected.DOMAIN][konnected.CONF_API_HOST] == "http://192.168.86.32:8123" ) async def test_config_passed_to_config_entry(hass): """Test that configured options for a host are loaded via config entry.""" entry = MockConfigEntry( domain=konnected.DOMAIN, data={config_flow.CONF_ID: "aabbccddeeff", config_flow.CONF_HOST: "0.0.0.0"}, ) entry.add_to_hass(hass) with patch.object(konnected, "AlarmPanel", autospec=True) as mock_int: assert ( await async_setup_component( hass, konnected.DOMAIN, { konnected.DOMAIN: { konnected.CONF_ACCESS_TOKEN: "abcdefgh", konnected.CONF_DEVICES: [{konnected.CONF_ID: "aabbccddeeff"}], } }, ) is True ) assert len(mock_int.mock_calls) == 3 p_hass, p_entry = mock_int.mock_calls[0][1] assert p_hass is hass assert p_entry is entry async def test_unload_entry(hass, mock_panel): """Test being able to unload an entry.""" await async_process_ha_core_config( hass, {"internal_url": "http://example.local:8123"}, ) entry = MockConfigEntry( domain=konnected.DOMAIN, data={konnected.CONF_ID: "aabbccddeeff"} ) entry.add_to_hass(hass) assert await async_setup_component(hass, konnected.DOMAIN, {}) is True assert hass.data[konnected.DOMAIN]["devices"].get("aabbccddeeff") is not None assert await konnected.async_unload_entry(hass, entry) assert hass.data[konnected.DOMAIN]["devices"] == {} async def test_api(hass, aiohttp_client, mock_panel): """Test callback view.""" await async_setup_component(hass, "http", {"http": {}}) device_config = config_flow.CONFIG_ENTRY_SCHEMA( { "host": "1.2.3.4", "port": 1234, "id": "112233445566", "model": "Konnected Pro", "access_token": "abcdefgh", "api_host": "http://192.168.86.32:8123", "default_options": config_flow.OPTIONS_SCHEMA({config_flow.CONF_IO: {}}), } ) device_options = config_flow.OPTIONS_SCHEMA( { "api_host": "http://192.168.86.32:8123", "io": { "1": "Binary Sensor", "2": "Binary Sensor", "3": "Binary Sensor", "4": "Digital Sensor", "5": "Digital Sensor", "6": "Switchable Output", "out": "Switchable Output", }, "binary_sensors": [ {"zone": "1", "type": "door"}, {"zone": "2", "type": "window", "name": "winder", "inverse": True}, {"zone": "3", "type": "door"}, ], "sensors": [ {"zone": "4", "type": "dht"}, {"zone": "5", "type": "ds18b20", "name": "temper"}, ], "switches": [ { "zone": "out", "name": "switcher", "activation": "low", "momentary": 50, "pause": 100, "repeat": 4, }, {"zone": "6"}, ], } ) entry = MockConfigEntry( domain="konnected", title="Konnected Alarm Panel", data=device_config, options=device_options, ) entry.add_to_hass(hass) assert ( await async_setup_component( hass, konnected.DOMAIN, {konnected.DOMAIN: {konnected.CONF_ACCESS_TOKEN: "globaltoken"}}, ) is True ) client = await aiohttp_client(hass.http.app) # Test the get endpoint for switch status polling resp = await client.get("/api/konnected") assert resp.status == HTTP_NOT_FOUND # no device provided resp = await client.get("/api/konnected/223344556677") assert resp.status == HTTP_NOT_FOUND # unknown device provided resp = await client.get("/api/konnected/device/112233445566") assert resp.status == HTTP_NOT_FOUND # no zone provided result = await resp.json() assert result == {"message": "Switch on zone or pin unknown not configured"} resp = await client.get("/api/konnected/device/112233445566?zone=8") assert resp.status == HTTP_NOT_FOUND # invalid zone result = await resp.json() assert result == {"message": "Switch on zone or pin 8 not configured"} resp = await client.get("/api/konnected/device/112233445566?pin=12") assert resp.status == HTTP_NOT_FOUND # invalid pin result = await resp.json() assert result == {"message": "Switch on zone or pin 12 not configured"} resp = await client.get("/api/konnected/device/112233445566?zone=out") assert resp.status == 200 result = await resp.json() assert result == {"state": 1, "zone": "out"} resp = await client.get("/api/konnected/device/112233445566?pin=8") assert resp.status == 200 result = await resp.json() assert result == {"state": 1, "pin": "8"} # Test the post endpoint for sensor updates resp = await client.post("/api/konnected/device", json={"zone": "1", "state": 1}) assert resp.status == HTTP_NOT_FOUND resp = await client.post( "/api/konnected/device/112233445566", json={"zone": "1", "state": 1} ) assert resp.status == 401 result = await resp.json() assert result == {"message": "unauthorized"} resp = await client.post( "/api/konnected/device/223344556677", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "1", "state": 1}, ) assert resp.status == 400 resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "15", "state": 1}, ) assert resp.status == 400 result = await resp.json() assert result == {"message": "unregistered sensor/actuator"} resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "1", "state": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer globaltoken"}, json={"zone": "1", "state": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "4", "temp": 22, "humi": 20}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} # Test the put endpoint for sensor updates resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "1", "state": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} async def test_state_updates_zone(hass, aiohttp_client, mock_panel): """Test callback view.""" await async_process_ha_core_config( hass, {"internal_url": "http://example.local:8123"}, ) device_config = config_flow.CONFIG_ENTRY_SCHEMA( { "host": "1.2.3.4", "port": 1234, "id": "112233445566", "model": "Konnected Pro", "access_token": "abcdefgh", "default_options": config_flow.OPTIONS_SCHEMA({config_flow.CONF_IO: {}}), } ) device_options = config_flow.OPTIONS_SCHEMA( { "io": { "1": "Binary Sensor", "2": "Binary Sensor", "3": "Binary Sensor", "4": "Digital Sensor", "5": "Digital Sensor", "6": "Switchable Output", "out": "Switchable Output", }, "binary_sensors": [ {"zone": "1", "type": "door"}, {"zone": "2", "type": "window", "name": "winder", "inverse": True}, {"zone": "3", "type": "door"}, ], "sensors": [ {"zone": "4", "type": "dht"}, {"zone": "5", "type": "ds18b20", "name": "temper"}, ], "switches": [ { "zone": "out", "name": "switcher", "activation": "low", "momentary": 50, "pause": 100, "repeat": 4, }, {"zone": "6"}, ], } ) entry = MockConfigEntry( domain="konnected", title="Konnected Alarm Panel", data=device_config, options=device_options, ) entry.add_to_hass(hass) # Add empty data field to ensure we process it correctly (possible if entry is ignored) entry = MockConfigEntry(domain="konnected", title="Konnected Alarm Panel", data={}) entry.add_to_hass(hass) assert ( await async_setup_component( hass, konnected.DOMAIN, {konnected.DOMAIN: {konnected.CONF_ACCESS_TOKEN: "1122334455"}}, ) is True ) client = await aiohttp_client(hass.http.app) # Test updating a binary sensor resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "1", "state": 0}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("binary_sensor.konnected_445566_zone_1").state == "off" resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "1", "state": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("binary_sensor.konnected_445566_zone_1").state == "on" # Test updating sht sensor resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "4", "temp": 22, "humi": 20}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("sensor.konnected_445566_sensor_4_humidity").state == "20" assert ( hass.states.get("sensor.konnected_445566_sensor_4_temperature").state == "22.0" ) resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "4", "temp": 25, "humi": 23}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("sensor.konnected_445566_sensor_4_humidity").state == "23" assert ( hass.states.get("sensor.konnected_445566_sensor_4_temperature").state == "25.0" ) # Test updating ds sensor resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "5", "temp": 32, "addr": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("sensor.temper_temperature").state == "32.0" resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"zone": "5", "temp": 42, "addr": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("sensor.temper_temperature").state == "42.0" async def test_state_updates_pin(hass, aiohttp_client, mock_panel): """Test callback view.""" await async_process_ha_core_config( hass, {"internal_url": "http://example.local:8123"}, ) device_config = config_flow.CONFIG_ENTRY_SCHEMA( { "host": "1.2.3.4", "port": 1234, "id": "112233445566", "model": "Konnected", "access_token": "abcdefgh", "default_options": config_flow.OPTIONS_SCHEMA({config_flow.CONF_IO: {}}), } ) device_options = config_flow.OPTIONS_SCHEMA( { "io": { "1": "Binary Sensor", "2": "Binary Sensor", "3": "Binary Sensor", "4": "Digital Sensor", "5": "Digital Sensor", "6": "Switchable Output", "out": "Switchable Output", }, "binary_sensors": [ {"zone": "1", "type": "door"}, {"zone": "2", "type": "window", "name": "winder", "inverse": True}, {"zone": "3", "type": "door"}, ], "sensors": [ {"zone": "4", "type": "dht"}, {"zone": "5", "type": "ds18b20", "name": "temper"}, ], "switches": [ { "zone": "out", "name": "switcher", "activation": "low", "momentary": 50, "pause": 100, "repeat": 4, }, {"zone": "6"}, ], } ) entry = MockConfigEntry( domain="konnected", title="Konnected Alarm Panel", data=device_config, options=device_options, ) entry.add_to_hass(hass) # Add empty data field to ensure we process it correctly (possible if entry is ignored) entry = MockConfigEntry( domain="konnected", title="Konnected Alarm Panel", data={}, ) entry.add_to_hass(hass) assert ( await async_setup_component( hass, konnected.DOMAIN, {konnected.DOMAIN: {konnected.CONF_ACCESS_TOKEN: "1122334455"}}, ) is True ) client = await aiohttp_client(hass.http.app) # Test updating a binary sensor resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"pin": "1", "state": 0}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("binary_sensor.konnected_445566_zone_1").state == "off" resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"pin": "1", "state": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("binary_sensor.konnected_445566_zone_1").state == "on" # Test updating sht sensor resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"pin": "6", "temp": 22, "humi": 20}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("sensor.konnected_445566_sensor_4_humidity").state == "20" assert ( hass.states.get("sensor.konnected_445566_sensor_4_temperature").state == "22.0" ) resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"pin": "6", "temp": 25, "humi": 23}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("sensor.konnected_445566_sensor_4_humidity").state == "23" assert ( hass.states.get("sensor.konnected_445566_sensor_4_temperature").state == "25.0" ) # Test updating ds sensor resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"pin": "7", "temp": 32, "addr": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("sensor.temper_temperature").state == "32.0" resp = await client.post( "/api/konnected/device/112233445566", headers={"Authorization": "Bearer abcdefgh"}, json={"pin": "7", "temp": 42, "addr": 1}, ) assert resp.status == 200 result = await resp.json() assert result == {"message": "ok"} await hass.async_block_till_done() assert hass.states.get("sensor.temper_temperature").state == "42.0"

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37ef59e2c4870fa735dbebd61973d4c001ce1b3c

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py

Python

python/testData/testRunner/env/createConfigurationTest/aid/test/base.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/testRunner/env/createConfigurationTest/aid/test/base.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/testRunner/env/createConfigurationTest/aid/test/base.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

import unittest class TestBase(unittest.TestCase): pass

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py

Python

geoapps/simpegPF/EM/FDEM/FieldsFDEM.py

RichardScottOZ/geoapps

5b3c1d4fd11add45992e8b2497312ac014272b69

[ "MIT" ]

null

geoapps/simpegPF/EM/FDEM/FieldsFDEM.py

RichardScottOZ/geoapps

5b3c1d4fd11add45992e8b2497312ac014272b69

[ "MIT" ]

null

geoapps/simpegPF/EM/FDEM/FieldsFDEM.py

RichardScottOZ/geoapps

5b3c1d4fd11add45992e8b2497312ac014272b69

[ "MIT" ]

null

import numpy as np import scipy.sparse as sp import geoapps.simpegPF as spf from .. import Utils from geoapps.simpegPF.EM.Utils import omega from geoapps.simpegPF.Utils import Zero, Identity, sdiag class FieldsFDEM(spf.Problem.Fields): """ Fancy Field Storage for a FDEM survey. Only one field type is stored for each problem, the rest are computed. The fields object acts like an array and is indexed by .. code-block:: python f = problem.fields(m) e = f[srcList,'e'] b = f[srcList,'b'] If accessing all sources for a given field, use the :code:`:` .. code-block:: python f = problem.fields(m) e = f[:,'e'] b = f[:,'b'] The array returned will be size (nE or nF, nSrcs :math:`\\times` nFrequencies) """ knownFields = {} dtype = complex def _GLoc(self, fieldType): """Grid location of the fieldType""" return self.aliasFields[fieldType][1] def _e(self, solution, srcList): """ Total electric field is sum of primary and secondary :param numpy.ndarray solution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: total electric field """ if ( getattr(self, "_ePrimary", None) is None or getattr(self, "_eSecondary", None) is None ): raise NotImplementedError( "Getting e from {!s} is not implemented".format( self.knownFields.keys()[0] ) ) return self._ePrimary(solution, srcList) + self._eSecondary(solution, srcList) def _b(self, solution, srcList): """ Total magnetic flux density is sum of primary and secondary :param numpy.ndarray solution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: total magnetic flux density """ if ( getattr(self, "_bPrimary", None) is None or getattr(self, "_bSecondary", None) is None ): raise NotImplementedError( "Getting b from {!s} is not implemented".format( self.knownFields.keys()[0] ) ) return self._bPrimary(solution, srcList) + self._bSecondary(solution, srcList) def _bSecondary(self, solution, srcList): """ Total magnetic flux density is sum of primary and secondary :param numpy.ndarray solution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: total magnetic flux density """ if getattr(self, "_bSecondary", None) is None: raise NotImplementedError( "Getting b from {} is not implemented".format( self.knownFields.keys()[0] ) ) return self._bSecondary(solution, srcList) def _h(self, solution, srcList): """ Total magnetic field is sum of primary and secondary :param numpy.ndarray solution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: total magnetic field """ if ( getattr(self, "_hPrimary", None) is None or getattr(self, "_hSecondary", None) is None ): raise NotImplementedError( "Getting h from {!s} is not implemented".format( self.knownFields.keys()[0] ) ) return self._hPrimary(solution, srcList) + self._hSecondary(solution, srcList) def _j(self, solution, srcList): """ Total current density is sum of primary and secondary :param numpy.ndarray solution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: total current density """ if ( getattr(self, "_jPrimary", None) is None or getattr(self, "_jSecondary", None) is None ): raise NotImplementedError( "Getting j from {!s} is not implemented".format( self.knownFields.keys()[0] ) ) return self._jPrimary(solution, srcList) + self._jSecondary(solution, srcList) def _eDeriv(self, src, du_dm_v, v, adjoint=False): r""" Total derivative of e with respect to the inversion model. Returns :math:`d\mathbf{e}/d\mathbf{m}` for forward and (:math:`d\mathbf{e}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint :param SimPEG.EM.FDEM.Src.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint) :param numpy.ndarray v: vector to take sensitivity product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: derivative times a vector (or tuple for adjoint) """ if ( getattr(self, "_eDeriv_u", None) is None or getattr(self, "_eDeriv_m", None) is None ): raise NotImplementedError( "Getting eDerivs from {!s} is not implemented".format( self.knownFields.keys()[0] ) ) if adjoint: return (self._eDeriv_u(src, v, adjoint), self._eDeriv_m(src, v, adjoint)) return np.array( self._eDeriv_u(src, du_dm_v, adjoint) + self._eDeriv_m(src, v, adjoint), dtype=complex, ) def _bDeriv(self, src, du_dm_v, v, adjoint=False): r""" Total derivative of b with respect to the inversion model. Returns :math:`d\mathbf{b}/d\mathbf{m}` for forward and (:math:`d\mathbf{b}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint :param SimPEG.EM.FDEM.Src.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint) :param numpy.ndarray v: vector to take sensitivity product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: derivative times a vector (or tuple for adjoint) """ if ( getattr(self, "_bDeriv_u", None) is None or getattr(self, "_bDeriv_m", None) is None ): raise NotImplementedError( "Getting bDerivs from {!s} is not implemented".format( self.knownFields.keys()[0] ) ) if adjoint: return (self._bDeriv_u(src, v, adjoint), self._bDeriv_m(src, v, adjoint)) return np.array( self._bDeriv_u(src, du_dm_v, adjoint) + self._bDeriv_m(src, v, adjoint), dtype=complex, ) def _bSecondaryDeriv(self, src, du_dm_v, v, adjoint=False): r""" Total derivative of b with respect to the inversion model. Returns :math:`d\mathbf{b}/d\mathbf{m}` for forward and (:math:`d\mathbf{b}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: sorce :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint) :param numpy.ndarray v: vector to take sensitivity product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: derivative times a vector (or tuple for adjoint) """ # TODO: modify when primary field is dependent on m return self._bDeriv(src, du_dm_v, v, adjoint=adjoint) def _hDeriv(self, src, du_dm_v, v, adjoint=False): r""" Total derivative of h with respect to the inversion model. Returns :math:`d\mathbf{h}/d\mathbf{m}` for forward and (:math:`d\mathbf{h}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint :param SimPEG.EM.FDEM.Src.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint) :param numpy.ndarray v: vector to take sensitivity product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: derivative times a vector (or tuple for adjoint) """ if ( getattr(self, "_hDeriv_u", None) is None or getattr(self, "_hDeriv_m", None) is None ): raise NotImplementedError( "Getting hDerivs from {!s} is not implemented".format( self.knownFields.keys()[0] ) ) if adjoint: return (self._hDeriv_u(src, v, adjoint), self._hDeriv_m(src, v, adjoint)) return np.array( self._hDeriv_u(src, du_dm_v, adjoint) + self._hDeriv_m(src, v, adjoint), dtype=complex, ) def _jDeriv(self, src, du_dm_v, v, adjoint=False): r""" Total derivative of j with respect to the inversion model. Returns :math:`d\mathbf{j}/d\mathbf{m}` for forward and (:math:`d\mathbf{j}/d\mathbf{u}`, :math:`d\mathb{u}/d\mathbf{m}`) for the adjoint :param SimPEG.EM.FDEM.Src.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: derivative of the solution vector with respect to the model times a vector (is None for adjoint) :param numpy.ndarray v: vector to take sensitivity product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: derivative times a vector (or tuple for adjoint) """ if ( getattr(self, "_jDeriv_u", None) is None or getattr(self, "_jDeriv_m", None) is None ): raise NotImplementedError( "Getting jDerivs from {!s} is not implemented".format( self.knownFields.keys()[0] ) ) if adjoint: return (self._jDeriv_u(src, v, adjoint), self._jDeriv_m(src, v, adjoint)) return np.array( self._jDeriv_u(src, du_dm_v, adjoint) + self._jDeriv_m(src, v, adjoint), dtype=complex, ) class Fields3D_e(FieldsFDEM): """ Fields object for Problem3D_e. :param discretize.BaseMesh.BaseMesh mesh: mesh :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey """ knownFields = {"eSolution": "E"} aliasFields = { "e": ["eSolution", "E", "_e"], "ePrimary": ["eSolution", "E", "_ePrimary"], "eSecondary": ["eSolution", "E", "_eSecondary"], "b": ["eSolution", "F", "_b"], "bPrimary": ["eSolution", "F", "_bPrimary"], "bSecondary": ["eSolution", "F", "_bSecondary"], "j": ["eSolution", "CCV", "_j"], "h": ["eSolution", "CCV", "_h"], } def startup(self): self.prob = self.survey.prob self._edgeCurl = self.survey.prob.mesh.edgeCurl self._aveE2CCV = self.survey.prob.mesh.aveE2CCV self._aveF2CCV = self.survey.prob.mesh.aveF2CCV self._nC = self.survey.prob.mesh.nC self._MeSigma = self.survey.prob.MeSigma self._MeSigmaDeriv = self.survey.prob.MeSigmaDeriv self._MfMui = self.survey.prob.MfMui self._MfMuiDeriv = self.survey.prob.MfMuiDeriv def _GLoc(self, fieldType): if fieldType in ["e", "eSecondary", "ePrimary"]: return "E" elif fieldType in ["b", "bSecondary", "bPrimary"]: return "F" elif (fieldType == "h") or (fieldType == "j"): return "CCV" else: raise Exception("Field type must be e, b, h, j") def _ePrimary(self, eSolution, srcList): """ Primary electric field from source :param numpy.ndarray eSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary electric field as defined by the sources """ ePrimary = np.zeros([self.prob.mesh.nE, len(srcList)], dtype=complex) for i, src in enumerate(srcList): ep = src.ePrimary(self.prob) ePrimary[:, i] = ePrimary[:, i] + ep return ePrimary def _eSecondary(self, eSolution, srcList): """ Secondary electric field is the thing we solved for :param numpy.ndarray eSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary electric field """ return eSolution def _eDeriv_u(self, src, v, adjoint=False): """ Partial derivative of the total electric field with respect to the thing we solved for. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the electric field with respect to the field we solved for with a vector """ return Identity() * v def _eDeriv_m(self, src, v, adjoint=False): """ Partial derivative of the total electric field with respect to the inversion model. Here, we assume that the primary does not depend on the model. Note that this also includes derivative contributions from the sources. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: SimPEG.Utils.Zero :return: product of the electric field derivative with respect to the inversion model with a vector """ return src.ePrimaryDeriv(self.prob, v, adjoint) def _bPrimary(self, eSolution, srcList): """ Primary magnetic flux density from source :param numpy.ndarray eSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary magnetic flux density as defined by the sources """ bPrimary = np.zeros( [self._edgeCurl.shape[0], eSolution.shape[1]], dtype=complex ) for i, src in enumerate(srcList): bp = src.bPrimary(self.prob) bPrimary[:, i] = bPrimary[:, i] + bp return bPrimary def _bSecondary(self, eSolution, srcList): """ Secondary magnetic flux density from eSolution :param numpy.ndarray eSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary magnetic flux density """ C = self._edgeCurl b = C * eSolution for i, src in enumerate(srcList): b[:, i] *= -1.0 / (1j * omega(src.freq)) # freq depends on the source s_m = src.s_m(self.prob) b[:, i] = b[:, i] + 1.0 / (1j * omega(src.freq)) * s_m return b def _bDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the magnetic flux density with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic flux density with respect to the field we solved for with a vector """ C = self._edgeCurl if adjoint: return -1.0 / (1j * omega(src.freq)) * (C.T * du_dm_v) return -1.0 / (1j * omega(src.freq)) * (C * du_dm_v) def _bDeriv_m(self, src, v, adjoint=False): """ Derivative of the magnetic flux density with respect to the inversion model. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the magnetic flux density derivative with respect to the inversion model with a vector """ return self._bDeriv_src(src, v, adjoint=adjoint) def _bDeriv_src(self, src, v, adjoint=False): s_mDeriv = src.s_mDeriv(self.prob, v, adjoint) return 1.0 / (1j * omega(src.freq)) * s_mDeriv + src.bPrimaryDeriv( self.prob, v, adjoint ) def _j(self, eSolution, srcList): """ Current density from eSolution :param numpy.ndarray eSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: current density """ aveE2CCV = self._aveE2CCV # number of components (instead of checking if cyl or not) n = int(aveE2CCV.shape[0] / self._nC) VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) return VI * (aveE2CCV * (self._MeSigma * self._e(eSolution, srcList))) def _jDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the current density with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the current density with respect to the field we solved for with a vector """ # number of components (instead of checking if cyl or not) n = int(self._aveE2CCV.shape[0] / self._nC) VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._eDeriv_u( src, self._MeSigma.T * (self._aveE2CCV.T * (VI.T * du_dm_v)), adjoint=adjoint, ) return VI * ( self._aveE2CCV * (self._MeSigma * (self._eDeriv_u(src, du_dm_v, adjoint=adjoint))) ) def _jDeriv_m(self, src, v, adjoint=False): """ Derivative of the current density with respect to the inversion model. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the current density derivative with respect to the inversion model with a vector """ e = self[src, "e"] n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return ( self._MeSigmaDeriv(e).T * (self._aveE2CCV.T * (VI.T * v)) + self._eDeriv_m(src, self._aveE2CCV.T * (VI.T * v), adjoint=adjoint) ) + src.jPrimaryDeriv(self.prob, v, adjoint) return ( VI * ( self._aveE2CCV * (self._eDeriv_m(src, v, adjoint=adjoint) + self._MeSigmaDeriv(e) * v) ) ) + src.jPrimaryDeriv(self.prob, v, adjoint) def _h(self, eSolution, srcList): """ Magnetic field from eSolution :param numpy.ndarray eSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: magnetic field """ n = int(self._aveF2CCV.shape[0] / self._nC) # Number of Components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) return VI * (self._aveF2CCV * (self._MfMui * self._b(eSolution, srcList))) def _hDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the magnetic field with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic field with respect to the field we solved for with a vector """ n = int(self._aveF2CCV.shape[0] / self._nC) # Number of Components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: v = self._MfMui.T * (self._aveF2CCV.T * (VI.T * du_dm_v)) return self._bDeriv_u(src, v, adjoint=adjoint) return VI * ( self._aveF2CCV * (self._MfMui * self._bDeriv_u(src, du_dm_v, adjoint=adjoint)) ) def _hDeriv_mui(self, src, v, adjoint=False): n = int(self._aveF2CCV.shape[0] / self._nC) # Number of Components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint is True: return self._MfMuiDeriv(self[src, "b"]).T * (self._aveF2CCV.T * (VI.T * v)) return VI * (self._aveF2CCV * (self._MfMuiDeriv(self[src, "b"]) * v)) def _hDeriv_m(self, src, v, adjoint=False): """ Derivative of the magnetic field with respect to the inversion model. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the magnetic field derivative with respect to the inversion model with a vector """ n = int(self._aveF2CCV.shape[0] / self._nC) # Number of Components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._bDeriv_m( src, self._MfMui.T * (self._aveF2CCV.T * (VI.T * v)), adjoint=adjoint ) + self._hDeriv_mui(src, v, adjoint=adjoint) return ( VI * (self._aveF2CCV * (self._MfMui * self._bDeriv_m(src, v, adjoint=adjoint))) ) + self._hDeriv_mui(src, v, adjoint=adjoint) class Fields3D_b(FieldsFDEM): """ Fields object for Problem3D_b. :param discretize.BaseMesh.BaseMesh mesh: mesh :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey """ knownFields = {"bSolution": "F"} aliasFields = { "b": ["bSolution", "F", "_b"], "bPrimary": ["bSolution", "F", "_bPrimary"], "bSecondary": ["bSolution", "F", "_bSecondary"], "e": ["bSolution", "E", "_e"], "ePrimary": ["bSolution", "E", "_ePrimary"], "eSecondary": ["bSolution", "E", "_eSecondary"], "j": ["bSolution", "CCV", "_j"], "h": ["bSolution", "CCV", "_h"], } def startup(self): self.prob = self.survey.prob self._edgeCurl = self.survey.prob.mesh.edgeCurl self._MeSigma = self.survey.prob.MeSigma self._MeSigmaI = self.survey.prob.MeSigmaI self._MfMui = self.survey.prob.MfMui self._MfMuiDeriv = self.survey.prob.MfMuiDeriv self._MeSigmaDeriv = self.survey.prob.MeSigmaDeriv self._MeSigmaIDeriv = self.survey.prob.MeSigmaIDeriv self._Me = self.survey.prob.Me self._aveF2CCV = self.survey.prob.mesh.aveF2CCV self._aveE2CCV = self.survey.prob.mesh.aveE2CCV self._sigma = self.survey.prob.sigma self._mui = self.survey.prob.mui self._nC = self.survey.prob.mesh.nC def _GLoc(self, fieldType): if fieldType in ["e", "eSecondary", "ePrimary"]: return "E" elif fieldType in ["b", "bSecondary", "bPrimary"]: return "F" elif (fieldType == "h") or (fieldType == "j"): return "CCV" else: raise Exception("Field type must be e, b, h, j") def _bPrimary(self, bSolution, srcList): """ Primary magnetic flux density from source :param numpy.ndarray bSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary electric field as defined by the sources """ bPrimary = np.zeros([self.prob.mesh.nF, len(srcList)], dtype=complex) for i, src in enumerate(srcList): bp = src.bPrimary(self.prob) bPrimary[:, i] = bPrimary[:, i] + bp return bPrimary def _bSecondary(self, bSolution, srcList): """ Secondary magnetic flux density is the thing we solved for :param numpy.ndarray bSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary magnetic flux density """ return bSolution def _bDeriv_u(self, src, du_dm_v, adjoint=False): """ Partial derivative of the total magnetic flux density with respect to the thing we solved for. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic flux density with respect to the field we solved for with a vector """ return Identity() * du_dm_v def _bDeriv_m(self, src, v, adjoint=False): """ Partial derivative of the total magnetic flux density with respect to the inversion model. Here, we assume that the primary does not depend on the model. Note that this also includes derivative contributions from the sources. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: SimPEG.Utils.Zero :return: product of the magnetic flux density derivative with respect to the inversion model with a vector """ # assuming primary does not depend on the model return Zero() def _ePrimary(self, bSolution, srcList): """ Primary electric field from source :param numpy.ndarray bSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary electric field as defined by the sources """ ePrimary = np.zeros( [self._edgeCurl.shape[1], bSolution.shape[1]], dtype=complex ) for i, src in enumerate(srcList): ep = src.ePrimary(self.prob) ePrimary[:, i] = ePrimary[:, i] + ep return ePrimary def _eSecondary(self, bSolution, srcList): """ Secondary electric field from bSolution :param numpy.ndarray bSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary electric field """ e = self._edgeCurl.T * (self._MfMui * bSolution) for i, src in enumerate(srcList): s_e = src.s_e(self.prob) e[:, i] = e[:, i] + -s_e return self._MeSigmaI * e def _eDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the electric field with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the electric field with respect to the field we solved for with a vector """ if not adjoint: return self._MeSigmaI * (self._edgeCurl.T * (self._MfMui * du_dm_v)) return self._MfMui.T * (self._edgeCurl * (self._MeSigmaI.T * du_dm_v)) def _eDeriv_m(self, src, v, adjoint=False): """ Derivative of the electric field with respect to the inversion model :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the electric field with respect to the model with a vector """ bSolution = Utils.mkvc(self[src, "bSolution"]) s_e = src.s_e(self.prob) w = -s_e + self._edgeCurl.T * (self._MfMui * bSolution) if adjoint: s_eDeriv = src.s_eDeriv(self.prob, self._MeSigmaI.T * v, adjoint) return ( self._MeSigmaIDeriv(w).T * v + self._MfMuiDeriv(bSolution).T * (self._edgeCurl * (self._MeSigmaI.T * v)) - s_eDeriv + src.ePrimaryDeriv(self.prob, v, adjoint) ) s_eDeriv = src.s_eDeriv(self.prob, v, adjoint) return ( self._MeSigmaIDeriv(w) * v + self._MeSigmaI * (self._edgeCurl.T * (self._MfMuiDeriv(bSolution) * v)) - self._MeSigmaI * s_eDeriv + src.ePrimaryDeriv(self.prob, v, adjoint) ) def _j(self, bSolution, srcList): """ Secondary current density from bSolution :param numpy.ndarray bSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary current density """ n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) j = self._edgeCurl.T * (self._MfMui * bSolution) for i, src in enumerate(srcList): s_e = src.s_e(self.prob) j[:, i] = j[:, i] - s_e return VI * (self._aveE2CCV * j) def _jDeriv_u(self, src, du_dm_v, adjoint=False): """ Partial derivative of the current density with respect to the thing we solved for. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the current density with respect to the field we solved for with a vector """ n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._MfMui.T * ( self._edgeCurl * (self._aveE2CCV.T * (VI.T * du_dm_v)) ) return VI * (self._aveE2CCV * (self._edgeCurl.T * (self._MfMui * du_dm_v))) # forgetting the source term here def _jDeriv_mui(self, src, v, adjoint=False): n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) MfMuiDeriv = self._MfMuiDeriv(self[src, "b"]) if adjoint: return MfMuiDeriv.T * (self._edgeCurl * (self._aveE2CCV.T * (VI.T * v))) return VI * (self._aveE2CCV * (self._edgeCurl.T * (MfMuiDeriv * v))) def _jDeriv_m(self, src, v, adjoint=False): """ Derivative of the current density with respect to the inversion model :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the current density with respect to the model with a vector """ return self._jDeriv_mui(src, v, adjoint) def _h(self, bSolution, srcList): """ Magnetic field from bSolution :param numpy.ndarray bSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: magnetic field """ n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) return VI * (self._aveF2CCV * (self._MfMui * self._b(bSolution, srcList))) def _hDeriv_u(self, src, du_dm_v, adjoint=False): """ Partial derivative of the magnetic field with respect to the thing we solved for. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic field with respect to the field we solved for with a vector """ n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._MfMui.T * (self._aveF2CCV.T * (VI.T * du_dm_v)) return VI * (self._aveF2CCV * (self._MfMui * du_dm_v)) def _hDeriv_mui(self, src, v, adjoint=False): b = self[src, "b"] n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._MfMuiDeriv(b).T * (self._aveF2CCV.T * (VI * v)) return VI * (self._aveF2CCV * (self._MfMuiDeriv(b) * v)) def _hDeriv_m(self, src, v, adjoint=False): """ Derivative of the magnetic field with respect to the inversion model :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic field with respect to the model with a vector """ return src.hPrimaryDeriv(self.prob, v, adjoint) + self._hDeriv_mui( src, v, adjoint ) class Fields3D_j(FieldsFDEM): """ Fields object for Problem3D_j. :param discretize.BaseMesh.BaseMesh mesh: mesh :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey """ knownFields = {"jSolution": "F"} aliasFields = { "j": ["jSolution", "F", "_j"], "jPrimary": ["jSolution", "F", "_jPrimary"], "jSecondary": ["jSolution", "F", "_jSecondary"], "h": ["jSolution", "E", "_h"], "hPrimary": ["jSolution", "E", "_hPrimary"], "hSecondary": ["jSolution", "E", "_hSecondary"], "e": ["jSolution", "CCV", "_e"], "b": ["jSolution", "CCV", "_b"], } def startup(self): self.prob = self.survey.prob self._edgeCurl = self.survey.prob.mesh.edgeCurl self._MeMu = self.survey.prob.MeMu self._MeMuI = self.survey.prob.MeMuI self._MeMuIDeriv = self.survey.prob.MeMuIDeriv self._MfRho = self.survey.prob.MfRho self._MfRhoDeriv = self.survey.prob.MfRhoDeriv self._rho = self.survey.prob.rho self._mu = self.survey.prob.mui self._aveF2CCV = self.survey.prob.mesh.aveF2CCV self._aveE2CCV = self.survey.prob.mesh.aveE2CCV self._nC = self.survey.prob.mesh.nC def _GLoc(self, fieldType): if fieldType in ["h", "hSecondary", "hPrimary"]: return "E" elif fieldType in ["j", "jSecondary", "jPrimary"]: return "F" elif (fieldType == "e") or (fieldType == "b"): return "CCV" else: raise Exception("Field type must be e, b, h, j") def _jPrimary(self, jSolution, srcList): """ Primary current density from source :param numpy.ndarray jSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary current density as defined by the sources """ jPrimary = np.zeros_like(jSolution, dtype=complex) for i, src in enumerate(srcList): jp = src.jPrimary(self.prob) jPrimary[:, i] = jPrimary[:, i] + jp return jPrimary def _jSecondary(self, jSolution, srcList): """ Secondary current density is the thing we solved for :param numpy.ndarray jSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary current density """ return jSolution def _j(self, jSolution, srcList): """ Total current density is sum of primary and secondary :param numpy.ndarray jSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: total current density """ return self._jPrimary(jSolution, srcList) + self._jSecondary(jSolution, srcList) def _jDeriv_u(self, src, du_dm_v, adjoint=False): """ Partial derivative of the total current density with respect to the thing we solved for. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the current density with respect to the field we solved for with a vector """ return Identity() * du_dm_v def _jDeriv_m(self, src, v, adjoint=False): """ Partial derivative of the total current density with respect to the inversion model. Here, we assume that the primary does not depend on the model. Note that this also includes derivative contributions from the sources. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: SimPEG.Utils.Zero :return: product of the current density derivative with respect to the inversion model with a vector """ # assuming primary does not depend on the model return src.jPrimaryDeriv(self.prob, v, adjoint) def _hPrimary(self, jSolution, srcList): """ Primary magnetic field from source :param numpy.ndarray hSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary magnetic field as defined by the sources """ hPrimary = np.zeros( [self._edgeCurl.shape[1], jSolution.shape[1]], dtype=complex ) for i, src in enumerate(srcList): hp = src.hPrimary(self.prob) hPrimary[:, i] = hPrimary[:, i] + hp return hPrimary def _hSecondary(self, jSolution, srcList): """ Secondary magnetic field from bSolution :param numpy.ndarray jSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary magnetic field """ h = self._edgeCurl.T * (self._MfRho * jSolution) for i, src in enumerate(srcList): h[:, i] *= -1.0 / (1j * omega(src.freq)) s_m = src.s_m(self.prob) h[:, i] = h[:, i] + 1.0 / (1j * omega(src.freq)) * (s_m) return self._MeMuI * h def _hDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the magnetic field with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic field with respect to the field we solved for with a vector """ if adjoint: return ( -1.0 / (1j * omega(src.freq)) * self._MfRho.T * (self._edgeCurl * (self._MeMuI.T * du_dm_v)) ) return ( -1.0 / (1j * omega(src.freq)) * self._MeMuI * (self._edgeCurl.T * (self._MfRho * du_dm_v)) ) def _hDeriv_m(self, src, v, adjoint=False): """ Derivative of the magnetic field with respect to the inversion model :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic field with respect to the model with a vector """ jSolution = Utils.mkvc(self[[src], "jSolution"]) MeMuI = self._MeMuI MeMuIDeriv = self._MeMuIDeriv C = self._edgeCurl MfRho = self._MfRho MfRhoDeriv = self._MfRhoDeriv s_m = src.s_m(self.prob) def s_mDeriv(v): return src.s_mDeriv(self.prob, v, adjoint=adjoint) if not adjoint: hDeriv_m = ( 1.0 / (1j * omega(src.freq)) * ( -1.0 * ( MeMuI * (C.T * (MfRhoDeriv(jSolution) * v)) + MeMuIDeriv(C.T * (MfRho * jSolution)) * v ) + MeMuI * s_mDeriv(v) + MeMuIDeriv(s_m) * v ) ) elif adjoint: hDeriv_m = ( 1.0 / (1j * omega(src.freq)) * ( ( -1.0 * ( MfRhoDeriv(jSolution).T * (C * (MeMuI.T * v)) + MeMuIDeriv(C.T * (MfRho * jSolution)).T * v ) ) + s_mDeriv(MeMuI.T * v) + MeMuIDeriv(s_m).T * v ) ) return hDeriv_m + src.hPrimaryDeriv(self.prob, v, adjoint) def _e(self, jSolution, srcList): """ Electric field from jSolution :param numpy.ndarray hSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: electric field """ n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) return VI * (self._aveF2CCV * (self._MfRho * self._j(jSolution, srcList))) def _eDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the electric field with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the electric field with respect to the field we solved for with a vector """ n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._MfRho.T * (self._aveF2CCV.T * (VI.T * du_dm_v)) return VI * (self._aveF2CCV * (self._MfRho * du_dm_v)) def _eDeriv_m(self, src, v, adjoint=False): """ Derivative of the electric field with respect to the inversion model :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the electric field with respect to the model with a vector """ jSolution = Utils.mkvc(self[src, "jSolution"]) n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._MfRhoDeriv(jSolution).T * ( self._aveF2CCV.T * (VI.T * v) ) + src.ePrimaryDeriv(self.prob, v, adjoint) return VI * ( self._aveF2CCV * (self._MfRhoDeriv(jSolution) * v) ) + src.ePrimaryDeriv(self.prob, v, adjoint) def _b(self, jSolution, srcList): """ Secondary magnetic flux density from jSolution :param numpy.ndarray hSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary magnetic flux density """ n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) return VI * (self._aveE2CCV * (self._MeMu * self._h(jSolution, srcList))) def _bDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the magnetic flux density with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic flux density with respect to the field we solved for with a vector """ # if self.prob.mesh._meshType == 'CYL': # self. n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return ( -1.0 / (1j * omega(src.freq)) * self._MfRho.T * (self._edgeCurl * (self._aveE2CCV.T * (VI.T * du_dm_v))) ) return ( -1.0 / (1j * omega(src.freq)) * VI * (self._aveE2CCV * (self._edgeCurl.T * (self._MfRho * du_dm_v))) ) def _bDeriv_m(self, src, v, adjoint=False): """ Derivative of the magnetic flux density with respect to the inversion model :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic flux density with respect to the model with a vector """ jSolution = self[src, "jSolution"] n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) def s_mDeriv(v): return src.s_mDeriv(self.prob, v, adjoint=adjoint) if adjoint: v = self._aveE2CCV.T * (VI.T * v) return 1.0 / (1j * omega(src.freq)) * ( s_mDeriv(v) - self._MfRhoDeriv(jSolution).T * (self._edgeCurl * v) ) + src.bPrimaryDeriv(self.prob, v, adjoint) return 1.0 / (1j * omega(src.freq)) * VI * ( self._aveE2CCV * (s_mDeriv(v) - self._edgeCurl.T * (self._MfRhoDeriv(jSolution) * v)) ) + src.bPrimaryDeriv(self.prob, v, adjoint) class Fields3D_h(FieldsFDEM): """ Fields object for Problem3D_h. :param discretize.BaseMesh.BaseMesh mesh: mesh :param SimPEG.EM.FDEM.SurveyFDEM.Survey survey: survey """ knownFields = {"hSolution": "E"} aliasFields = { "h": ["hSolution", "E", "_h"], "hPrimary": ["hSolution", "E", "_hPrimary"], "hSecondary": ["hSolution", "E", "_hSecondary"], "j": ["hSolution", "F", "_j"], "jPrimary": ["hSolution", "F", "_jPrimary"], "jSecondary": ["hSolution", "F", "_jSecondary"], "e": ["hSolution", "CCV", "_e"], "b": ["hSolution", "CCV", "_b"], } def startup(self): self.prob = self.survey.prob self._edgeCurl = self.survey.prob.mesh.edgeCurl self._MeMu = self.survey.prob.MeMu self._MeMuDeriv = self.survey.prob.MeMuDeriv # self._MeMuI = self.survey.prob.MeMuI self._MfRho = self.survey.prob.MfRho self._MfRhoDeriv = self.survey.prob.MfRhoDeriv self._rho = self.survey.prob.rho self._mu = self.survey.prob.mui self._aveF2CCV = self.survey.prob.mesh.aveF2CCV self._aveE2CCV = self.survey.prob.mesh.aveE2CCV self._nC = self.survey.prob.mesh.nC def _GLoc(self, fieldType): if fieldType in ["h", "hSecondary", "hPrimary"]: return "E" elif fieldType in ["j", "jSecondary", "jPrimary"]: return "F" elif (fieldType == "e") or (fieldType == "b"): return "CCV" else: raise Exception("Field type must be e, b, h, j") def _hPrimary(self, hSolution, srcList): """ Primary magnetic field from source :param numpy.ndarray eSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary magnetic field as defined by the sources """ hPrimary = np.zeros_like(hSolution, dtype=complex) for i, src in enumerate(srcList): hp = src.hPrimary(self.prob) hPrimary[:, i] = hPrimary[:, i] + hp return hPrimary def _hSecondary(self, hSolution, srcList): """ Secondary magnetic field is the thing we solved for :param numpy.ndarray hSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary magnetic field """ return hSolution def _hDeriv_u(self, src, du_dm_v, adjoint=False): """ Partial derivative of the total magnetic field with respect to the thing we solved for. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic field with respect to the field we solved for with a vector """ return Identity() * du_dm_v def _hDeriv_m(self, src, v, adjoint=False): """ Partial derivative of the total magnetic field with respect to the inversion model. Here, we assume that the primary does not depend on the model. Note that this also includes derivative contributions from the sources. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: SimPEG.Utils.Zero :return: product of the magnetic field derivative with respect to the inversion model with a vector """ return src.hPrimaryDeriv(self.prob, v, adjoint) def _jPrimary(self, hSolution, srcList): """ Primary current density from source :param numpy.ndarray hSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: primary current density as defined by the sources """ jPrimary = np.zeros( [self._edgeCurl.shape[0], hSolution.shape[1]], dtype=complex ) for i, src in enumerate(srcList): jp = src.jPrimary(self.prob) jPrimary[:, i] = jPrimary[:, i] + jp return jPrimary def _jSecondary(self, hSolution, srcList): """ Secondary current density from hSolution :param numpy.ndarray hSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: secondary current density """ j = self._edgeCurl * hSolution for i, src in enumerate(srcList): s_e = src.s_e(self.prob) j[:, i] = j[:, i] + -s_e return j def _jDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the current density with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the current density with respect to the field we solved for with a vector """ if not adjoint: return self._edgeCurl * du_dm_v elif adjoint: return self._edgeCurl.T * du_dm_v def _jDeriv_m(self, src, v, adjoint=False): """ Derivative of the current density with respect to the inversion model. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the current density derivative with respect to the inversion model with a vector """ return -src.s_eDeriv(self.prob, v, adjoint) + src.jPrimaryDeriv( self.prob, v, adjoint ) def _e(self, hSolution, srcList): """ Electric field from hSolution :param numpy.ndarray hSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: electric field """ n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) return VI * (self._aveF2CCV * (self._MfRho * self._j(hSolution, srcList))) def _eDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the electric field with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the electric field with respect to the field we solved for with a vector """ n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._edgeCurl.T * ( self._MfRho.T * (self._aveF2CCV.T * (VI.T * du_dm_v)) ) return VI * (self._aveF2CCV * (self._MfRho * self._edgeCurl * du_dm_v)) def _eDeriv_m(self, src, v, adjoint=False): """ Derivative of the electric field with respect to the inversion model. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the electric field derivative with respect to the inversion model with a vector """ hSolution = Utils.mkvc(self[src, "hSolution"]) n = int(self._aveF2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) s_e = src.s_e(self.prob) if adjoint: w = self._aveF2CCV.T * (VI.T * v) return ( self._MfRhoDeriv(self._edgeCurl * hSolution).T * w - self._MfRhoDeriv(s_e).T * w + src.ePrimaryDeriv(self.prob, v, adjoint) ) return VI * ( self._aveF2CCV * ( self._MfRhoDeriv(self._edgeCurl * hSolution) * v - self._MfRhoDeriv(s_e) * v ) ) + src.ePrimaryDeriv(self.prob, v, adjoint) def _b(self, hSolution, srcList): """ Magnetic flux density from hSolution :param numpy.ndarray hSolution: field we solved for :param list srcList: list of sources :rtype: numpy.ndarray :return: magnetic flux density """ h = self._h(hSolution, srcList) n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) return VI * (self._aveE2CCV * (self._MeMu * h)) def _bDeriv_u(self, src, du_dm_v, adjoint=False): """ Derivative of the magnetic flux density with respect to the thing we solved for :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray du_dm_v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the derivative of the magnetic flux density with respect to the field we solved for with a vector """ n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) if adjoint: return self._MeMu.T * (self._aveE2CCV.T * (VI.T * du_dm_v)) return VI * (self._aveE2CCV * (self._MeMu * du_dm_v)) def _bDeriv_mu(self, src, v, adjoint=False): h = self[src, "h"] n = int(self._aveE2CCV.shape[0] / self._nC) # number of components VI = sdiag(np.kron(np.ones(n), 1.0 / self.prob.mesh.vol)) MeMuDeriv = self._MeMuDeriv(h) if adjoint: return MeMuDeriv.T * (self._aveE2CCV.T * (VI.T * v)) return VI * (self._aveE2CCV * (MeMuDeriv * v)) # return VI * (self._aveE2CCV * (self._MeMu * h)) def _bDeriv_m(self, src, v, adjoint=False): """ Derivative of the magnetic flux density with respect to the inversion model. :param SimPEG.EM.FDEM.SrcFDEM.BaseFDEMSrc src: source :param numpy.ndarray v: vector to take product with :param bool adjoint: adjoint? :rtype: numpy.ndarray :return: product of the magnetic flux density derivative with respect to the inversion model with a vector """ return src.bPrimaryDeriv(self.prob, v, adjoint) + self._bDeriv_mu( src, v, adjoint )

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6

5336a59c6050f38d2160cb522400e85aae92a3b7

1,854

py

Python

tests/apis/test_iterjsonlines.py

tavaresrodrigo/kopf

97e1c7a926705a79dabce2931e96a924252b61df

[ "MIT" ]

855

2020-08-19T09:40:38.000Z

2022-03-31T19:13:29.000Z

tests/apis/test_iterjsonlines.py

tavaresrodrigo/kopf

97e1c7a926705a79dabce2931e96a924252b61df

[ "MIT" ]

715

2019-12-23T14:17:35.000Z

2022-03-30T20:54:45.000Z

tests/apis/test_iterjsonlines.py

tavaresrodrigo/kopf

97e1c7a926705a79dabce2931e96a924252b61df

[ "MIT" ]

97

2019-04-25T09:32:54.000Z

2022-03-30T10:15:30.000Z

import asynctest from kopf._cogs.clients.api import iter_jsonlines async def test_empty_content(): async def iter_chunked(n: int): if False: # to make this function a generator yield b'' content = asynctest.Mock(iter_chunked=iter_chunked) lines = [] async for line in iter_jsonlines(content): lines.append(line) assert lines == [] async def test_empty_chunk(): async def iter_chunked(n: int): yield b'' content = asynctest.Mock(iter_chunked=iter_chunked) lines = [] async for line in iter_jsonlines(content): lines.append(line) assert lines == [] async def test_one_chunk_one_line(): async def iter_chunked(n: int): yield b'hello' content = asynctest.Mock(iter_chunked=iter_chunked) lines = [] async for line in iter_jsonlines(content): lines.append(line) assert lines == [b'hello'] async def test_one_chunk_two_lines(): async def iter_chunked(n: int): yield b'hello\nworld' content = asynctest.Mock(iter_chunked=iter_chunked) lines = [] async for line in iter_jsonlines(content): lines.append(line) assert lines == [b'hello', b'world'] async def test_one_chunk_empty_lines(): async def iter_chunked(n: int): yield b'\n\nhello\n\nworld\n\n' content = asynctest.Mock(iter_chunked=iter_chunked) lines = [] async for line in iter_jsonlines(content): lines.append(line) assert lines == [b'hello', b'world'] async def test_few_chunks_split(): async def iter_chunked(n: int): yield b'\n\nhell' yield b'o\n\nwor' yield b'ld\n\n' content = asynctest.Mock(iter_chunked=iter_chunked) lines = [] async for line in iter_jsonlines(content): lines.append(line) assert lines == [b'hello', b'world']

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0

null

0

6

53593a6b103512752bd4b81ea59e314db5646b57

91

py

Python

pypingcli/commands/__init__.py

tameeshB/pyPingCLI

070edab79b24c257a8512b2a1410d12583f96d69

[ "MIT" ]

1

2020-11-09T16:49:51.000Z

pypingcli/commands/__init__.py

tameeshB/pyPingCLI

070edab79b24c257a8512b2a1410d12583f96d69

[ "MIT" ]

7

2018-10-01T01:13:59.000Z

2018-10-03T16:44:34.000Z

pypingcli/commands/__init__.py

tameeshB/pyPingCLI

070edab79b24c257a8512b2a1410d12583f96d69

[ "MIT" ]

6

2018-10-01T10:09:16.000Z

2020-09-16T06:59:16.000Z

from .start import * from .connectIP import * from .setUser import * from .prompts import *

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73

py

Python

lib/ctyEnge/SuggestionsAndRecommendations/__init__.py

JoshOrndorff/snippets

ef06e03de09897014f88d89a84b597aabde7edaa

[ "Unlicense" ]

null

lib/ctyEnge/SuggestionsAndRecommendations/__init__.py

JoshOrndorff/snippets

ef06e03de09897014f88d89a84b597aabde7edaa

[ "Unlicense" ]

null

lib/ctyEnge/SuggestionsAndRecommendations/__init__.py

JoshOrndorff/snippets

ef06e03de09897014f88d89a84b597aabde7edaa

[ "Unlicense" ]

null

from .SuggestionsAndRecommendations import SuggestionsAndRecommendations

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6

728e14d0cb7e3e55c9800e3d196655603249ce46

57,600

py

Python

tests/test_search.py

nazrulworld/fhirpath

3b819870c57a0befcac18916a4d03b64c0e202ca

[ "Apache-2.0" ]

25

2019-05-14T13:35:32.000Z

2022-02-21T23:03:35.000Z

tests/test_search.py

nazrulworld/fhirpath

3b819870c57a0befcac18916a4d03b64c0e202ca

[ "Apache-2.0" ]

29

2020-02-14T08:14:02.000Z

2021-02-23T20:14:42.000Z

tests/test_search.py

nazrulworld/fhirpath

3b819870c57a0befcac18916a4d03b64c0e202ca

[ "Apache-2.0" ]

4

2020-06-30T08:05:54.000Z

2021-08-09T19:10:35.000Z

# _*_ coding: utf-8 _*_ import re from urllib.parse import urlencode from pytest import raises import pytest from fhirpath import Q_ from fhirpath.enums import FHIR_VERSION from fhirpath.enums import OPERATOR from fhirpath.enums import MatchType from fhirpath.enums import SortOrderType from fhirpath.interfaces.fql import IGroupTerm from fhirpath.search import Search from fhirpath.search import AsyncSearch from fhirpath.search import SearchContext from fhirpath.exceptions import ValidationError from fhir.resources.patient import Patient from fhir.resources.observation import Observation from fhir.resources.practitioner import Practitioner from fhir.resources.medicationrequest import MedicationRequest __author__ = "Md Nazrul Islam<email2nazrul@gmail.com>" def test_params_definition(engine): """ """ definition = SearchContext( engine=engine, resource_type="Organization" ).get_parameters_definition(FHIR_VERSION.R4) assert definition[0].name.expression == "Organization.name" def test_parse_query_string(): """ """ params = ( ("status:not", "completed"), ("status", "active"), ("code", "http://acme.org/conditions/codes|ha125"), ("probability", "gt0.8"), ("date", "ge2010-01-01"), ("date", "le2011-12-31"), ("alue-quantity", "5.4|http://unitsofmeasure.org|mg"), ("definition:below", "http:http://acme.com/some-profile"), ("code", "http://loinc.org|1234-5&subject.name=peter"), ("_sort", "status,-date,category"), ("_count", "1"), ("medication.ingredient-code", "abc"), ("_include", "Observation:related-target"), ) result = Search.parse_query_string(urlencode(params)) assert len(result.getall("code")) == 2 assert len(result.getall("date")) == 2 assert len(result.getall("medication.ingredient-code")) == 1 def test_prepare_params(engine): """ """ context = SearchContext(engine, "Task") params = ( ("status:not", "completed"), ("status", "active"), ("code", "http://acme.org/conditions/codes|ha125"), ("probability", "gt0.8"), ("date", "ge2010-01-01"), ("date", "le2011-12-31"), ("code", "http://loinc.org|1234-5&subject.name=peter"), ("_sort", "status,-date,category"), ("_count", "1"), ) fhir_search = Search(context, params=params) # xxx: should be 2 as :not could be normalized? anyway we will figure it out assert len(fhir_search.search_params.getall("status")) == 1 # should be gone from normal search params assert len(fhir_search.search_params.getall("_sort", [])) == 0 assert "_count" not in fhir_search.search_params def test_parameter_normalization(engine): """ """ context = SearchContext(engine, "Task") # TODO we need the [0] because normalize_param returns a list to handle the # case where we search on several resource types path_, value_pack, modifier = context.normalize_param("status:not", ["completed"])[ 0 ] # single valued assert isinstance(value_pack, tuple) # OPERATOR assert value_pack[0] == "eq" # actual value assert value_pack[1] == "completed" field_name, value_pack, modifier = context.normalize_param( "authored-on", ["ge2010-01-01", "le2011-12-31"] )[0] assert modifier is None assert isinstance(value_pack, list) assert len(value_pack) == 2 # OPERATOR assert value_pack[0][0] == "ge" # actual value assert value_pack[0][1] == "2010-01-01" # test with escape comma(,) field_name, value_pack, modifier = context.normalize_param( "code", [ "http://acme.org/conditions/codes|ha125", "http://loinc.org|1\\,234-5&subject.name=peter", ], )[0] assert isinstance(value_pack, list) # OPERATOR assert value_pack[1][0] == "eq" # actual value assert value_pack[1][1] == "http://loinc.org|1\\,234-5&subject.name=peter" # Test IN Operator field_name, value_pack, modifier = context.normalize_param( "_lastUpdated", ["le2019-09-12T13:20:44+0000,2018-09-12T13:20:44+0000"] )[0] assert isinstance(value_pack, tuple) operator_, values = value_pack assert operator_ is None assert len(values) == 2 assert values[0][1] == "2019-09-12T13:20:44+0000" # Test AND+IN Operator field_name, value_pack, modifier = context.normalize_param( "_id", ["567890", "998765555554678,45555555555567"] )[0] assert isinstance(value_pack, list) assert isinstance(value_pack[0], tuple) assert isinstance(value_pack[1][1], list) def test_composite_parameter_normalization(engine): """ """ context = SearchContext(engine, "ChargeItemDefinition") normalize_value = context.normalize_param("context-type-quantity", ["HL7$99"]) assert len(normalize_value) == 2 assert normalize_value[0][0].path.endswith(".code") # value.as(Quantity) | value.as(Range) assert len(normalize_value[1]) == 2 assert normalize_value[1][1][0].path.endswith(".valueRange") is True context = SearchContext(engine, "Observation") normalize_value = context.normalize_param( "code-value-quantity", ["http://loinc.org|11557-6$6.2"] ) assert isinstance(normalize_value[1], tuple) def test_parameter_normalization_with_space_as(engine): """ """ context = SearchContext(engine, "MedicationRequest") path_, value_pack, _ = context.normalize_param( "code", ["http://acme.org/conditions/codes|ha125"] )[0] # single valued assert isinstance(value_pack, tuple) assert path_.path == "MedicationRequest.medicationCodeableConcept" def test_parameter_normalization_empty_value(engine): context = SearchContext(engine, "MedicationRequest") # normalize a param with an empty value: it should be ignored params = context.normalize_param("code", [""]) assert len(params) == 0 def test_parameter_normalization_prefix(engine): """ """ # number context = SearchContext(engine, "MolecularSequence") _, value_pack, _ = context.normalize_param("variant-end", ["gt1"])[0] assert value_pack == ("gt", "1") # quantity context = SearchContext(engine, "Substance") _, value_pack, _ = context.normalize_param("quantity", ["ne1"])[0] assert value_pack == ("ne", "1") # date context = SearchContext(engine, "Patient") _, value_pack, _ = context.normalize_param("death-date", ["lt1980"])[0] assert value_pack == ("lt", "1980") # string _, value_pack, _ = context.normalize_param("name", ["leslie"])[0] assert value_pack == ("eq", "leslie") # token _, value_pack, _ = context.normalize_param("language", ["nepalese"])[0] assert value_pack == ("eq", "nepalese") # reference _, value_pack, _ = context.normalize_param("organization", ["necker"])[0] assert value_pack == ("eq", "necker") def test_create_term(engine): """ """ context = SearchContext(engine, "Task") params = ( ("status:not", "completed"), ("status", "active"), ("code", "http://acme.org/conditions/codes|ha125"), ("probability", "gt0.8"), ("authored-on", "ge2019-07-17T19:32:59.991658"), ("authored-on", "le2013-01-17T19:32:59.991658"), ("code", "http://loinc.org|1\\,234-5&subject.name=peter"), ("_sort", "status,-date,category"), ("_count", "1"), ) fhir_search = Search(context, params=params) path_, value_pack, modifier = context.normalize_param("status:not", ["completed"])[ 0 ] term = fhir_search.create_term(path_, value_pack, modifier) term.finalize(fhir_search.context.engine) assert term.unary_operator == OPERATOR.neg assert term.arithmetic_operator == OPERATOR.and_ assert term.value.value == "completed" path_, value_pack, modifier = context.normalize_param( "authored-on", ["ge2019-07-17T19:32:59.991658", "le2013-01-17T19:32:59.991658"] )[0] term = fhir_search.create_term(path_, value_pack, modifier) term.finalize(fhir_search.context.engine) # Now term should transformed as group of terms # as we see authored-on has multiple value assert IGroupTerm.providedBy(term) is True assert len(term.terms) == 2 assert term.match_operator == MatchType.ANY assert term.arithmetic_operator is None def test_create_codeableconcept_term(engine): """ """ context = SearchContext(engine, "Task") params = ( ("code", "http://acme.org/conditions/codes|ha125"), ("code", "http://terminology.hl7.org/CodeSystem/task-performer-type|"), ("code", "|performer"), ("code:text", "Performer"), ("code:not", "http://loinc.org|1\\,234-5&subject.name=peter"), ) fhir_search = Search(context, params=params) path_, value_pack, modifier = context.normalize_param( "code", [ "http://acme.org/conditions/codes|ha125", "http://terminology.hl7.org/CodeSystem/task-performer-type|", "|performer", ], )[0] terms = fhir_search.create_codeableconcept_term(path_, value_pack, modifier) [t.finalize(fhir_search.context.engine) for t in terms] assert IGroupTerm.providedBy(terms[0]) is True code1_group = terms[0] assert code1_group.terms[0].path.path == "Task.code.coding.system" assert code1_group.terms[1].path.path == "Task.code.coding.code" assert IGroupTerm.providedBy(terms[1]) is True assert IGroupTerm.providedBy(terms[2]) is True code2_group = terms[1] assert code2_group.terms[0].path.path == "Task.code.coding.system" code3_group = terms[2] assert code3_group.terms[0].path.path == "Task.code.coding.code" path_, value_pack, modifier = context.normalize_param("code:text", ["Performer"])[0] term = fhir_search.create_codeableconcept_term(path_, value_pack, modifier) term.finalize(fhir_search.context.engine) assert term.terms[0].path.path == "Task.code.text" def test_create_identifier_term(engine): """ """ context = SearchContext(engine, "Task") params = ( ("identifier", "http://example.com/fhir/identifier/mrn|123456"), ("identifier", "http://terminology.hl7.org/CodeSystem/task-performer-type|"), ("identifier", "|performer"), ("identifier:text", "Performer"), ("identifier:not", "http://example.com/fhir/identifier/mrn|123456"), ) fhir_search = Search(context, params=params) path_, value_pack, modifier = context.normalize_param( "identifier", [ "http://example.com/fhir/identifier/mrn|123456", "http://terminology.hl7.org/CodeSystem/task-performer-type|", "|performer", ], )[0] terms = fhir_search.create_identifier_term(path_, value_pack, modifier) [t.finalize(fhir_search.context.engine) for t in terms] assert IGroupTerm.providedBy(terms[0]) is True identifier_group = terms[0] assert identifier_group.terms[0].path.path == "Task.identifier.system" assert identifier_group.terms[1].path.path == "Task.identifier.value" assert terms[1].terms[0].path.path == "Task.identifier.system" assert terms[2].terms[0].path.path == "Task.identifier.value" path_, value_pack, modifier = context.normalize_param( "identifier:text", ["Performer"] )[0] term = fhir_search.create_identifier_term(path_, value_pack, modifier) term.finalize(fhir_search.context.engine) assert term.terms[0].path.path == "Task.identifier.type.text" path_, value_pack, modifier = context.normalize_param( "identifier:not", ["http://example.com/fhir/identifier/mrn|123456"] )[0] term = fhir_search.create_identifier_term(path_, value_pack, modifier) term.finalize(fhir_search.context.engine) assert term.terms[0].unary_operator == OPERATOR.neg def test_create_quantity_term(engine): """ """ context = SearchContext(engine, "ChargeItem") params = ( ("quantity", "5.4|http://unitsofmeasure.org|mg"), ("quantity", "lt5.1||mg"), ("quantity", "5.40e-3"), ("quantity:not", "ap5.4|http://unitsofmeasure.org|mg"), ) fhir_search = Search(context, params=params) path_, value_pack, modifier = context.normalize_param( "quantity", ["5.4|http://unitsofmeasure.org|mg", "lt5.1||mg", "5.40e-3"] )[0] terms = fhir_search.create_quantity_term(path_, value_pack, modifier) [t.finalize(fhir_search.context.engine) for t in terms] assert IGroupTerm.providedBy(terms[0]) is True quantity_group = terms[0] assert quantity_group.terms[0].path.path == "ChargeItem.quantity.value" assert quantity_group.terms[1].path.path == "ChargeItem.quantity.system" assert quantity_group.terms[2].path.path == "ChargeItem.quantity.code" assert terms[1].terms[1].path.path == "ChargeItem.quantity.unit" assert float(terms[2].value.value) == float("5.40e-3") def test_sa_term(engine): """ """ context = SearchContext(engine, "Organization") params = (("_id:below", "fo"),) fhir_search = Search(context, params=params) path_, value_pack, modifier = context.normalize_param("_id:below", ["fo"])[0] term = fhir_search.create_term(path_, value_pack, modifier) term.finalize(fhir_search.context.engine) assert term.comparison_operator == OPERATOR.sa def test_sort_attachment(engine): """ """ context = SearchContext(engine, "Task") params = (("status", "active"), ("_sort", "status,-modified"), ("_count", "100")) fhir_search = Search(context, params=params) builder = Q_(context.resource_types, context.engine) builder = fhir_search.attach_sort_terms(builder) assert len(builder._sort) == 2 assert builder._sort[1].order == SortOrderType.DESC def test_limit_attachment(engine): """ """ context = SearchContext(engine, "Task") params = (("status", "active"), ("_sort", "status,-modified"), ("_count", "100")) fhir_search = Search(context, params=params) builder = Q_(context.resource_types, context.engine) builder = fhir_search.attach_limit_terms(builder) assert builder._limit.limit == 100 assert builder._limit.offset == 0 params = ( ("status", "active"), ("_sort", "status,-modified"), ("_count", "100"), ("page", "4"), ) fhir_search = Search(context, params=params) builder = Q_(context.resource_types, context.engine) builder = fhir_search.attach_limit_terms(builder) assert builder._limit.offset == 300 def test_build_query_from_search_params(engine): """ """ context = SearchContext(engine, "ChargeItem") params = ( ("subject", "Patient/PAT001"), ("quantity", "5.4|http://unitsofmeasure.org|mg"), ("quantity", "lt5.9||mg"), ("quantity", "5.40e-3"), ("quantity:not", "gt5.4|http://unitsofmeasure.org|mg"), ) fhir_search = Search(context, params=params) builder = Q_(fhir_search.context.resource_types, fhir_search.context.engine) terms_container = list() for param_name in set(fhir_search.search_params): raw_value = list(fhir_search.search_params.getall(param_name, [])) normalized_data = context.normalize_param(param_name, raw_value) fhir_search.add_term(normalized_data, terms_container) builder = builder.where(*terms_container) builder.finalize() query = builder.get_query() assert len(query.get_element()) == 1 # 4 + 1 assert len(query.get_where()) == 5 def test_build_result(engine): """ """ search_context = SearchContext(engine, "Organization") params = ( ("active", "true"), ("_lastUpdated", "2010-05-28T05:35:56+00:00"), ("_profile", "http://hl7.org/fhir/Organization"), ("identifier", "urn:oid:2.16.528.1|91654"), ("type", "http://hl7.org/fhir/organization-type|prov"), ("address-postalcode", "9100 AA"), ("address", "Den Burg"), ("_sort", "_id"), ("_count", "100"), ("page", "4"), ) fhir_search = Search(search_context, params=params) query_result = fhir_search.build() assert query_result.__class__.__name__ == "QueryResult" def test_search_result(es_data, engine): """ """ search_context = SearchContext(engine, "Organization") params = ( ("active", "true"), ("_lastUpdated", "2010-05-28T05:35:56+00:00"), ("_profile", "http://hl7.org/fhir/Organization"), ("identifier", "urn:oid:2.16.528.1|91654"), ("type", "http://hl7.org/fhir/organization-type|prov"), ("address-postalcode", "9100 AA"), ("address", "Den Burg"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_search_result_as_json(es_data, engine): """ """ search_context = SearchContext(engine, "Organization") params = ( ("active", "true"), ("_lastUpdated", "2010-05-28T05:35:56+00:00"), ("_profile", "http://hl7.org/fhir/Organization"), ("identifier", "urn:oid:2.16.528.1|91654"), ("type", "http://hl7.org/fhir/organization-type|prov"), ("address-postalcode", "9100 AA"), ("address", "Den Burg"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search(as_json=True) assert bundle["resourceType"] == "Bundle" assert bundle["total"] == 1 assert isinstance(bundle["entry"][0], dict) def test_search_missing_modifier(es_data, engine): """ """ search_context = SearchContext(engine, "Organization") params = (("active:missing", "false"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert len(bundle.entry) == 1 def test_in_search(es_data, engine): """ """ search_context = SearchContext(engine, "Organization") params = ( ("active", "true"), ("address", "Den Burg,Fake Lane"), ("_profile", "http://hl7.org/fhir/Organization,http://another"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_composite_param_search(es_data, engine): """ """ search_context = SearchContext(engine, "DocumentReference") params = (("relationship", "appends$ref1"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "Observation") params = (("code-value-quantity", "http://loinc.org|718-7$7.2"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_codeableconcept_with_not_modifier(es_data, engine): """ """ # test with single search_context = SearchContext(engine, "ChargeItem") params = (("code:not", "http://snomed.info/sct|01510"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = (("code:not", "http://snomed.info/sct|01510,http://lonic.org|1510-9"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 def test_search_result_with_below_modifier(es_data, engine): """ """ search_context = SearchContext(engine, "Organization") params = (("name:below", "Burge"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 # little bit complex search_context = SearchContext(engine, "Patient") params = (("identifier:below", "|2403"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("given:below", "Eel,Eve"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("gender:below", "ma,naz"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_search_result_with_above_modifier(es_data, engine): """ """ # little bit complex search_context = SearchContext(engine, "Patient") params = (("identifier:above", "|0002"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "Organization") params = (("name:above", "Medical Center"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_search_result_with_contains_modifier(es_data, engine): """ """ # little bit complex search_context = SearchContext(engine, "Patient") params = (("identifier:contains", "|365"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("given:contains", "ect"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "Organization") params = (("name:contains", "Medical"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 @pytest.mark.asyncio async def test_async_search_result_with_contains_modifier(es_data, async_engine): """ """ # little bit complex search_context = SearchContext(async_engine, "Patient") params = (("identifier:contains", "|365"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 params = (("given:contains", "ect"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 search_context = SearchContext(async_engine, "Organization") params = (("name:contains", "Medical"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 def test_search_result_with_exact_modifier(es_data, engine): """ """ search_context = SearchContext(engine, "Patient") params = (("family:exact", "Saint"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("family:exact", "Other"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = (("family:exact", "Sain"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = (("family:exact", "saint"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = (("family:exact", "Sàint"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 def test_search_identifier_modifier(es_data, engine): search_context = SearchContext(engine, "Observation") params = (("subject:identifier", "240365-0002"),) bundle = Search(search_context, params=params)() assert bundle.total == 1 params = (("subject:identifier", "123465789"),) bundle = Search(search_context, params=params)() assert bundle.total == 0 # [param-ref]:identifier=[system]|[value]: the value of [code] matches an # reference.identifier.value, and the value of [system] matches the system # property of the Identifier params = (("subject:identifier", "CPR|240365-0002",),) bundle = Search(search_context, params=params)() assert bundle.total == 1 params = (("subject:identifier", "CPR|123456789",),) bundle = Search(search_context, params=params)() assert bundle.total == 0 # TODO: not working yet, when omitting the system, the search is applied only # on the value (it should also filter by empty system) # [param-ref]:identifier=|[code]: the value of [code] matches a # reference.identifier.value, and the Identifier has no system property params = (("subject:identifier", "|240365-0002"),) bundle = Search(search_context, params=params)() assert bundle.total == 1 params = (("subject:identifier", "|123456789"),) bundle = Search(search_context, params=params)() assert bundle.total == 0 # [param-ref]:identifier=[system]|: any element where the value of [system] matches # the system property of the Identifier params = (("subject:identifier", "CPR|",),) bundle = Search(search_context, params=params)() assert bundle.total == 1 params = (("subject:identifier", "other|"),) bundle = Search(search_context, params=params)() assert bundle.total == 0 def test_issue9_multiple_negative_terms_not_working(es_data, engine): """https://github.com/nazrulworld/fhirpath/issues/9""" search_context = SearchContext(engine, "Task") params = (("status:not", "ready,cancelled"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 @pytest.mark.asyncio async def test_async_issue9_multiple_negative_terms_not_working(es_data, async_engine): """https://github.com/nazrulworld/fhirpath/issues/9""" search_context = SearchContext(async_engine, "Task") params = (("status:not", "ready,cancelled"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 def test_search_negative_address(es_data, engine): """ """ search_context = SearchContext(engine, "Organization") params = (("address:not", "Den Burg"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = (("address-postalcode:not", "9105 PZ"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = ( ("_profile:not", "urn:oid:002.160,urn:oid:002.260,http://hl7.org/fhir/Other",), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 def test_issue8_without_param(es_data, engine): """ """ search_context = SearchContext(engine, "Organization") fhir_search = Search(search_context) bundle = fhir_search() assert bundle.total == 1 def test_search_include(es_data, engine): # typed _include search_context = SearchContext(engine, "Observation") params = (("_include", "Observation:subject:Patient"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Observation) assert isinstance(bundle.entry[1].resource, Patient) # untyped _include search_context = SearchContext(engine, "Observation") params = (("_include", "Observation:subject"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Observation) assert isinstance(bundle.entry[1].resource, Patient) # many types search_context = SearchContext(engine, "Observation") params = ( ("_include", "Observation:subject:Patient"), ("_include", "Observation:subject:Location"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Observation) assert isinstance(bundle.entry[1].resource, Patient) # .where(resolve() is Resource) constraint search_context = SearchContext(engine, "Observation") params = (("_include", "Observation:patient"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 # many references search_context = SearchContext(engine, "Observation") params = ( ("_include", "Observation:subject:Patient"), ("_include", "Observation:performer"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 3 assert isinstance(bundle.entry[0].resource, Observation) assert isinstance(bundle.entry[1].resource, Patient) assert isinstance(bundle.entry[2].resource, Practitioner) # bad syntax search_context = SearchContext(engine, "Observation") params = (("_include", "subject"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "bad _include param 'subject', " "should be Resource:search_param[:target_type]" ), ): fhir_search() # bad searchparam search_context = SearchContext(engine, "Observation") params = (("_include", "Observation:category"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "search parameter Observation.category " "must be of type 'reference', got token" ), ): fhir_search() # unknown searchparam search_context = SearchContext(engine, "Observation") params = (("_include", "Observation:unknown"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "No search definition is available for search " "parameter ``unknown`` on Resource ``Observation``." ), ): fhir_search() # bad target search_context = SearchContext(engine, "Observation") params = (("_include", "Observation:subject:DocumentReference"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "the search param Observation.subject may refer " "to Group, Device, Patient, Location" ", not to DocumentReference" ), ): fhir_search() def test_search_has(es_data, engine): # found search_context = SearchContext(engine, "Patient") params = (("_has:Observation:patient:code", "718-7"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert isinstance(bundle.entry[0].resource, Patient) # not found search_context = SearchContext(engine, "Patient") params = (("_has:Observation:patient:code", "XXX-YYY"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 # bad syntax search_context = SearchContext(engine, "Patient") params = (("_has:Observation:patient", "718-7"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "bad _has param '_has:Observation:patient', " "should be _has:Resource:ref_search_param:value_search_param=value" ), ): fhir_search() # bad searchparam search_context = SearchContext(engine, "Patient") params = (("_has:Observation:category:code", "something"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "search parameter Observation.category must be " "of type 'reference', got token" ), ): fhir_search() # unknown searchparam search_context = SearchContext(engine, "Patient") params = (("_has:Observation:unknown:code", "something"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "No search definition is available for search " "parameter ``unknown`` on Resource ``Observation``." ), ): fhir_search() # bad target search_context = SearchContext(engine, "Patient") params = (("_has:Observation:encounter:identifier", "something"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "invalid reference Observation.encounter (Encounter,EpisodeOfCare) " "in the current search context (Patient)" ), ): fhir_search() def test_search_revinclude(es_data, engine): # untyped search_context = SearchContext(engine, "Patient") params = (("_revinclude", "Observation:subject"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Patient) assert isinstance(bundle.entry[1].resource, Observation) # typed search_context = SearchContext(engine, "Patient") params = (("_revinclude", "Observation:subject:Patient"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Patient) assert isinstance(bundle.entry[1].resource, Observation) # no results search_context = SearchContext(engine, "Location") params = (("_revinclude", "Observation:subject"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 assert len(bundle.entry) == 0 # double _revinclude search_context = SearchContext(engine, "Patient") params = ( ("_revinclude", "Observation:subject"), ("_revinclude", "MedicationRequest:subject"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 3 assert isinstance(bundle.entry[0].resource, Patient) assert isinstance(bundle.entry[1].resource, Observation) assert isinstance(bundle.entry[2].resource, MedicationRequest) # with _has search_context = SearchContext(engine, "Patient") params = ( ("_has:Observation:patient:code", "718-7"), ("_revinclude", "Observation:subject"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Patient) assert isinstance(bundle.entry[1].resource, Observation) # bad syntax search_context = SearchContext(engine, "Patient") params = (("_revinclude", "subject"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "bad _revinclude param 'subject', should be " "Resource:search_param[:target_type]" ), ): fhir_search() # bad syntax search_context = SearchContext(engine, "Patient") params = (("_revinclude", "Observation:subject:too:long"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "bad _revinclude param 'Observation:subject:too:long', " "should be Resource:search_param[:target_type]" ), ): fhir_search() # bad searchparam search_context = SearchContext(engine, "Patient") params = (("_revinclude", "Observation:category"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "search parameter Observation.category must " "be of type 'reference', got token" ), ): fhir_search() # unknown searchparam search_context = SearchContext(engine, "Patient") params = (("_revinclude", "Observation:unknown:code"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "No search definition is available for search " "parameter ``unknown`` on Resource ``Observation``." ), ): fhir_search() # bad target search_context = SearchContext(engine, "Patient") params = (("_revinclude", "Observation:encounter:identifier"),) fhir_search = Search(search_context, params=params) with raises( ValidationError, match=re.escape( "invalid reference Observation.encounter (Encounter,EpisodeOfCare) " "in the current search context (Patient)" ), ): fhir_search() @pytest.mark.asyncio async def test_async_search_revinclude(es_data, async_engine): # untyped search_context = SearchContext(async_engine, "Patient") params = (("_revinclude", "Observation:subject"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Patient) assert isinstance(bundle.entry[1].resource, Observation) # typed search_context = SearchContext(async_engine, "Patient") params = (("_revinclude", "Observation:subject:Patient"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Patient) assert isinstance(bundle.entry[1].resource, Observation) # no results search_context = SearchContext(async_engine, "Location") params = (("_revinclude", "Observation:subject"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 0 assert len(bundle.entry) == 0 # double _revinclude search_context = SearchContext(async_engine, "Patient") params = ( ("_revinclude", "Observation:subject"), ("_revinclude", "MedicationRequest:subject"), ) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 3 assert isinstance(bundle.entry[0].resource, Patient) assert isinstance(bundle.entry[1].resource, Observation) assert isinstance(bundle.entry[2].resource, MedicationRequest) # with _has search_context = SearchContext(async_engine, "Patient") params = ( ("_has:Observation:patient:code", "718-7"), ("_revinclude", "Observation:subject"), ) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 assert len(bundle.entry) == 2 assert isinstance(bundle.entry[0].resource, Patient) assert isinstance(bundle.entry[1].resource, Observation) # bad syntax search_context = SearchContext(async_engine, "Patient") params = (("_revinclude", "subject"),) fhir_search = AsyncSearch(search_context, params=params) with raises( ValidationError, match=re.escape( "bad _revinclude param 'subject', should be " "Resource:search_param[:target_type]" ), ): await fhir_search() # bad syntax search_context = SearchContext(async_engine, "Patient") params = (("_revinclude", "Observation:subject:too:long"),) fhir_search = AsyncSearch(search_context, params=params) with raises( ValidationError, match=re.escape( "bad _revinclude param 'Observation:subject:too:long', " "should be Resource:search_param[:target_type]" ), ): await fhir_search() # bad searchparam search_context = SearchContext(async_engine, "Patient") params = (("_revinclude", "Observation:category"),) fhir_search = AsyncSearch(search_context, params=params) with raises( ValidationError, match=re.escape( "search parameter Observation.category must " "be of type 'reference', got token" ), ): await fhir_search() # unknown searchparam search_context = SearchContext(async_engine, "Patient") params = (("_revinclude", "Observation:unknown:code"),) fhir_search = AsyncSearch(search_context, params=params) with raises( ValidationError, match=re.escape( "No search definition is available for search " "parameter ``unknown`` on Resource ``Observation``." ), ): await fhir_search() # bad target search_context = SearchContext(async_engine, "Patient") params = (("_revinclude", "Observation:encounter:identifier"),) fhir_search = AsyncSearch(search_context, params=params) with raises( ValidationError, match=re.escape( "invalid reference Observation.encounter (Encounter,EpisodeOfCare) " "in the current search context (Patient)" ), ): await fhir_search() def test_search_fhirpath_reference_analyzer(es_data, engine): """ References need to be indexed in a special way in order to be found""" search_context = SearchContext(engine, "Observation") # search Normal params = (("subject", "Patient/19c5245f-89a8-49f8-b244-666b32adb92e"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 # search by ID params = (("subject", "19c5245f-89a8-49f8-b244-666b32adb92e"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 # search by (wrong) ID params = (("subject", "29c5245f-89a8-49f8-b244-666b32adb92e"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 # search by resource_type (should not find anything) params = (("subject", "Patient"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 # test negative: search by last part params = (("subject:not", "19c5245f-89a8-49f8-b244-666b32adb92e"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 # test full URI with wrong last part params = (("subject", "Patient/fake245f-89a8-49f8-b244-666b32adb92e"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 # test full URI with wrong first part params = (("subject", "Device/19c5245f-89a8-49f8-b244-666b32adb92e"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 # search by resource_type as prefix params = (("subject:below", "Patient"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 # search by ID as suffix params = (("subject:above", "19c5245f-89a8-49f8-b244-666b32adb92e"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_searchparam_type_date_period_eq(es_data, engine): search_context = SearchContext(engine, "Encounter") params = (("date", "eq2015-01-17"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 search_context = SearchContext(engine, "CarePlan") params = (("date", "eq2017-06-01"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_searchparam_type_date_period_ne(es_data, engine): search_context = SearchContext(engine, "Encounter") params = (("date", "ne2015-01-17"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "CarePlan") params = (("date", "ne2017-06-01T17:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("date", "ne2017-06-01"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 def test_searchparam_type_date_period_gt(es_data, engine): search_context = SearchContext(engine, "Encounter") params = (("date", "gt2015-01-20"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "CarePlan") params = (("date", "gt2017-06-01T17:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("date", "gt2017-06-02"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 def test_searchparam_type_date_period_lt(es_data, engine): search_context = SearchContext(engine, "Encounter") params = (("date", "lt2015-01-20"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "CarePlan") params = (("date", "lt2017-06-01T15:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 def test_searchparam_type_date_period_ge(es_data, engine): search_context = SearchContext(engine, "Encounter") params = (("date", "ge2015-01-20"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "CarePlan") params = (("date", "ge2017-06-01T17:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("date", "ge2017-06-01"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("date", "ge2017-06-02"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 def test_searchparam_type_date_period_le(es_data, engine): search_context = SearchContext(engine, "Encounter") params = (("date", "le2015-01-20"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "CarePlan") params = (("date", "le2017-06-01T16:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("date", "le2017-06-01T15:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 def test_searchparam_type_date_period_sa(es_data, engine): search_context = SearchContext(engine, "CarePlan") params = (("date", "sa2017-06-01T17:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = (("date", "sa2017-06-01T12:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 @pytest.mark.asyncio async def test_async_searchparam_type_date_period_sa(es_data, async_engine): search_context = SearchContext(async_engine, "CarePlan") params = (("date", "sa2017-06-01T17:00:00"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 0 params = (("date", "sa2017-06-01T12:00:00"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 def test_searchparam_type_date_period_eb(es_data, engine): search_context = SearchContext(engine, "Encounter") params = (("date", "eb2019-01-20"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 search_context = SearchContext(engine, "CarePlan") params = (("date", "eb2017-06-01T18:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = (("date", "eb2019-01-20"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_searchparam_type_date_period_ap(es_data, engine): search_context = SearchContext(engine, "Encounter") params = (("date", "ap2015-01-17T16:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("date", "ap2015-01-16T16:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 params = (("date", "ap2019-01-20"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "CarePlan") params = (("date", "ap2017-06-01T17:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 params = (("date", "ap2017-06-01T19:00:00"),) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 0 @pytest.mark.asyncio async def test_async_searchparam_type_date_period_ap(es_data, async_engine): search_context = SearchContext(async_engine, "Encounter") params = (("date", "ap2015-01-17T16:00:00"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 params = (("date", "ap2015-01-16T16:00:00"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 0 params = (("date", "ap2019-01-20"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 search_context = SearchContext(async_engine, "CarePlan") params = (("date", "ap2017-06-01T17:00:00"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 1 params = (("date", "ap2017-06-01T19:00:00"),) fhir_search = AsyncSearch(search_context, params=params) bundle = await fhir_search() assert bundle.total == 0 def test_searchparam_ignored_pretty_format(es_data, engine): search_context = SearchContext(engine, "Encounter") params = ( ("date", "ap2015-01-17T16:00:00"), ("_pretty", "true"), ("_format", "application/fhir+json"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 search_context = SearchContext(engine, "Patient") params = ( ("_pretty", "true"), ("_format", "application/fhir+json"), ) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1 def test_searchparam_elements(es_data, engine): search_context = SearchContext(engine, "Patient") result = Search(search_context, query_string="_elements=identifier,active,link")() assert result.entry[0].resource.id is not None assert result.entry[0].resource.identifier is not None assert result.entry[0].resource.active is not None assert result.entry[0].resource.link is not None assert result.entry[0].resource.meta is None assert result.entry[0].resource.birthDate is None assert result.entry[0].resource.maritalStatus is None def test_searchparam_unexisting_elements(es_data, engine): """Elements that don't exist should be ignored""" search_context = SearchContext(engine, "Patient") result = Search(search_context, query_string="_elements=active,unexisting")() assert result.entry[0].resource.id is not None assert result.entry[0].resource.identifier is None assert result.entry[0].resource.active is not None assert result.entry[0].resource.link is None assert result.entry[0].resource.meta is None assert result.entry[0].resource.birthDate is None assert result.entry[0].resource.maritalStatus is None def test_searchparam_summary_true(es_data, engine): """Handle _summary=true Return a limited subset of elements from the resource. This subset SHOULD consist solely of all supported elements that are marked as "summary" in the base definition of the resource(s) """ search_context = SearchContext(engine, "Patient") result = Search(search_context, params=(("_summary", "true"),))() assert result.entry[0].resource.text is None assert result.entry[0].resource.id is not None assert result.entry[0].resource.meta is not None assert ( result.entry[0].resource.birthDate is not None ) # birthDate should not be in summary assert ( result.entry[0].resource.maritalStatus is None ) # maritalStatus should not be in summary @pytest.mark.asyncio async def test_async_searchparam_summary_true(es_data, async_engine): """Handle _summary=true Return a limited subset of elements from the resource. This subset SHOULD consist solely of all supported elements that are marked as "summary" in the base definition of the resource(s) """ search_context = SearchContext(async_engine, "Patient") result = await AsyncSearch(search_context, params=(("_summary", "true"),))() assert result.entry[0].resource.text is None assert result.entry[0].resource.id is not None assert result.entry[0].resource.meta is not None assert ( result.entry[0].resource.birthDate is not None ) # birthDate should not be in summary assert ( result.entry[0].resource.maritalStatus is None ) # maritalStatus should not be in summary def test_searchparam_summary_false(es_data, engine): """Handle _summary=false Return all parts of the resource(s) """ search_context = SearchContext(engine, "Patient") result = Search(search_context, params=(("_summary", "false"),))() assert result.entry[0].resource.text is not None assert result.entry[0].resource.id is not None assert result.entry[0].resource.meta is not None assert result.entry[0].resource.link is not None assert result.entry[0].resource.birthDate is not None def test_searchparam_summary_text(es_data, engine): """Handle _summary=text Return only the "text" element, the 'id' element, the 'meta' element, and only top-level mandatory elements """ search_context = SearchContext(engine, "Patient") result = Search(search_context, params=(("_summary", "text"),))() assert result.entry[0].resource.text is not None assert result.entry[0].resource.id is not None assert result.entry[0].resource.meta is not None assert result.entry[0].resource.link is not None # link is a mandatory top element # communication would also be a mandatory top element but is not in the document assert result.entry[0].resource.birthDate is None # birthDate is not mandatory def test_searchparam_summary_data(es_data, engine): """Handle _summary=data Remove the text element """ search_context = SearchContext(engine, "Patient") result = Search(search_context, params=(("_summary", "data"),))() assert result.entry[0].resource.text is None assert result.entry[0].resource.id is not None assert result.entry[0].resource.meta is not None assert result.entry[0].resource.link is not None assert result.entry[0].resource.birthDate is not None @pytest.mark.asyncio async def test_async_searchparam_summary_data(es_data, async_engine): """Handle _summary=data Remove the text element """ search_context = SearchContext(async_engine, "Patient") result = await AsyncSearch(search_context, params=(("_summary", "data"),))() assert result.entry[0].resource.text is None assert result.entry[0].resource.id is not None assert result.entry[0].resource.meta is not None assert result.entry[0].resource.link is not None assert result.entry[0].resource.birthDate is not None def test_searchparam_summary_count(es_data, engine): """Handle _summary=count Search only: just return a count of the matching resources, without returning the actual matches """ search_context = SearchContext(engine, "Patient") result = Search(search_context, params=(("_summary", "count"),))() assert result.entry == [] assert result.total == 1 @pytest.mark.asyncio async def test_async_searchparam_summary_count(es_data, async_engine): """Handle _summary=count Search only: just return a count of the matching resources, without returning the actual matches """ search_context = SearchContext(async_engine, "Patient") result = await AsyncSearch(search_context, params=(("_summary", "count"),))() assert result.entry == [] assert result.total == 1 def test_search_patient_with_name(es_data, engine): """Issue:28""" # little bit complex search_context = SearchContext(engine, "Patient") params = (("name", "Jonson"), ("name", "Saint")) fhir_search = Search(search_context, params=params) bundle = fhir_search() assert bundle.total == 1

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py

Python

src/airfly/_vendor/airflow/providers/qubole/operators/qubole.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

7

2021-09-27T11:38:48.000Z

2022-02-01T06:06:24.000Z

src/airfly/_vendor/airflow/providers/qubole/operators/qubole.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

null

src/airfly/_vendor/airflow/providers/qubole/operators/qubole.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

null

# Auto generated by 'inv collect-airflow' from airfly._vendor.airflow.models.baseoperator import BaseOperator class QuboleOperator(BaseOperator): qubole_conn_id: "str"

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0

1

0

1

0

6

f477d9ff495f260b08c96e89e5bb434fed5d1ec0

116

py

Python

MedTARSQI/src/main/resources/ttk/utilities/printing.py

CDCgov/DCPC

c3fadef1bd6345e01a58afef051491d8ef6a7f93

[ "Apache-2.0" ]

6

2018-11-03T22:43:35.000Z

2022-02-15T17:51:33.000Z

MedTARSQI/src/main/resources/ttk/utilities/printing.py

CDCgov/DCPC

c3fadef1bd6345e01a58afef051491d8ef6a7f93

[ "Apache-2.0" ]

2

2019-04-08T03:42:59.000Z

2019-10-28T13:42:59.000Z

MedTARSQI/src/main/resources/ttk/utilities/printing.py

CDCgov/DCPC

c3fadef1bd6345e01a58afef051491d8ef6a7f93

[ "Apache-2.0" ]

10

2017-04-10T21:40:22.000Z

2022-02-21T16:50:10.000Z

import pprint def pp(stuff): pretty_printer = pprint.PrettyPrinter(indent=3) pretty_printer.pprint(stuff)

16.571429

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0.666667

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null

0

1

0

1

0

6

be4ef979f4d92f8f835e57616524eb8c0cfbffdb

65

py

Python

tests/run.py

buswinka/DetectStereocillia

7205680d9861cb50a447fe730696d2631f8256ba

[ "MIT" ]

null

tests/run.py

buswinka/DetectStereocillia

7205680d9861cb50a447fe730696d2631f8256ba

[ "MIT" ]

null

tests/run.py

buswinka/DetectStereocillia

7205680d9861cb50a447fe730696d2631f8256ba

[ "MIT" ]

1

2022-03-20T03:05:20.000Z

import tests.utils_test tests.utils_test.test_render_keypoints()

21.666667

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true

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1

0

null

0

1

0

1

0

6

be4f5c6a6a3e837d2f81e12045ad6df6b3176166

5,941

py

Python

reader/chartsDBAPIs2_TBD_asyncGather.py

sifarone/gce_k8s_deployment

f596e17b9d0263ae24c61ebba9925af4719b4306

[ "MIT" ]

null

reader/chartsDBAPIs2_TBD_asyncGather.py

sifarone/gce_k8s_deployment

f596e17b9d0263ae24c61ebba9925af4719b4306

[ "MIT" ]

null

reader/chartsDBAPIs2_TBD_asyncGather.py

sifarone/gce_k8s_deployment

f596e17b9d0263ae24c61ebba9925af4719b4306

[ "MIT" ]

1

2021-01-24T17:07:37.000Z

import asyncio import chartsUtils as utils import cashData.cashDbAPIs as cashDbAPIs import fnoData.stkOptDbAPIs as stkOptDbAPIs import fnoData.stkFutDbAPIs as stkFutDbAPIs import fnoData.idxOptDbAPIs as idxOptDbAPIs import fnoData.idxFutDbAPIs as idxFutDbAPIs import indexData.indexDbAPIs as indexDbAPIs async def getData(params, source): #3 if source == 'cash': data = await cashDbAPIs.CashDbAPIs().getCashData(params['symbol'], params['startDate'], params['date']) return data elif source == 'stkOpt': data = await stkOptDbAPIs.StkOptDbAPIs().getStkOptData(params['symbol'], params['stkOptExpiryDate']) return data elif source == 'stkFut': pass elif source == 'idxOpt': pass elif source == 'idxFut': pass elif source == 'index': pass elif source == 'put_stkOptOIvsDeltaOI': # convert dates from string to date types expDate = utils.convertStringToDatetime(params['stkOptExpiryDate']) dd = utils.convertStringToDatetime(params['date']) data = await stkOptDbAPIs.StkOptDbAPIs().getStrikePricePutCallDetailsForADate(params['symbol'], expDate, dd) return data['PE'] elif source == 'call_stkOptOIvsDeltaOI': # convert dates from string to datetime types expDate = utils.convertStringToDatetime(params['stkOptExpiryDate']) dd = utils.convertStringToDatetime(params['date']) data = await stkOptDbAPIs.StkOptDbAPIs().getStrikePricePutCallDetailsForADate(params['symbol'], expDate, dd) return data['CE'] elif source == 'put_idxOptOIvsDeltaOI': # convert dates from string to date types expDate = utils.convertStringToDatetime(params['idxOptExpiryDate']) dd = utils.convertStringToDatetime(params['date']) data = await idxOptDbAPIs.IdxOptDbAPIs().getStrikePricePutCallDetailsForADate(params['symbol'], expDate, dd) print('=== ', data) return data['PE'] elif source == 'call_idxOptOIvsDeltaOI': # convert dates from string to datetime types expDate = utils.convertStringToDatetime(params['idxOptExpiryDate']) dd = utils.convertStringToDatetime(params['date']) data = await idxOptDbAPIs.IdxOptDbAPIs().getStrikePricePutCallDetailsForADate(params['symbol'], expDate, dd) return data['CE'] elif source == 'analytics': pass else: print('Something is not write with charts!!') async def gatherData(params, source): dataSources = {} if source == 'cash': data = await cashDbAPIs.CashDbAPIs().getCashData(params['symbol'], params['startDate'], params['date']) return data elif source == 'stkOpt': data = await stkOptDbAPIs.StkOptDbAPIs().getStkOptData(params['symbol'], params['stkOptExpiryDate']) return data elif source == 'stkFut': pass elif source == 'idxOpt': pass elif source == 'idxFut': pass elif source == 'index': pass elif source == 'put_stkOptOIvsDeltaOI': # convert dates from string to date types expDate = utils.convertStringToDatetime(params['stkOptExpiryDate']) dd = utils.convertStringToDatetime(params['date']) data = await stkOptDbAPIs.StkOptDbAPIs().getStrikePricePutCallDetailsForADate(params['symbol'], expDate, dd) return data['PE'] elif source == 'call_stkOptOIvsDeltaOI': # convert dates from string to datetime types expDate = utils.convertStringToDatetime(params['stkOptExpiryDate']) dd = utils.convertStringToDatetime(params['date']) data = await stkOptDbAPIs.StkOptDbAPIs().getStrikePricePutCallDetailsForADate(params['symbol'], expDate, dd) return data['CE'] elif source == 'put_idxOptOIvsDeltaOI': # convert dates from string to date types expDate = utils.convertStringToDatetime(params['idxOptExpiryDate']) dd = utils.convertStringToDatetime(params['date']) data = await idxOptDbAPIs.IdxOptDbAPIs().getStrikePricePutCallDetailsForADate(params['symbol'], expDate, dd) print('=== ', data) return data['PE'] elif source == 'call_idxOptOIvsDeltaOI': # convert dates from string to datetime types expDate = utils.convertStringToDatetime(params['idxOptExpiryDate']) dd = utils.convertStringToDatetime(params['date']) data = await idxOptDbAPIs.IdxOptDbAPIs().getStrikePricePutCallDetailsForADate(params['symbol'], expDate, dd) return data['CE'] elif source == 'analytics': pass else: print('Something is not write with charts!!') async def getDataFromSources(params, chart): #2 sourceList = chart['sourceList'] aggregatedData = {} for source in sourceList: #data = await getData(params, source) dataSources = await gatherData(params, source) fields = chart[source] for field in fields: print('+++ ', data) aggregatedData.update({field:data[field]}) print('***** ',aggregatedData) return aggregatedData async def getChartsData(request): #1 returnData = {} body = await request.json() if body: params = {} params.update({ 'symbol' : body.get('symbol'), 'startDate' : body.get('startDate'), 'stkOptExpiryDate' : body.get('stkOptExpiryDate'), 'idxOptExpiryDate' : body.get('idxOptExpiryDate'), 'strikePrice' : body.get('strikePrice'), #'optionType' : body.get('optionType'), 'date' : body.get('date') }) charts = body.get('charts') for chart in charts: chartData = await getDataFromSources(params, body.get(chart)) returnData.update({chart : chartData}) else: returnData.update({'ERROR' : ''}) return returnData

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0.162362

0.051653

0.140496

0.045455

0.733471

0

0.000659

0.233967

5,941

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117

42.134752

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0.070022

0

0.658333

0

0.14462

0.03121

0

1

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false

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0.05

0

null

0

1

0

null

0

1

0

6

be7d430ee98a61614b6f0f10d068ce835fa443aa

20

py

Python

PyAPT/__init__.py

burggraaff/PyAPT

08d16bd2600575dd72af9a39532218c2b5ed7634

[ "MIT" ]

29

2015-11-23T17:05:17.000Z

2018-05-09T16:36:41.000Z

PyAPT/__init__.py

burggraaff/PyAPT

08d16bd2600575dd72af9a39532218c2b5ed7634

[ "MIT" ]

16

2015-04-17T23:48:32.000Z

2018-03-27T15:19:10.000Z

PyAPT/__init__.py

burggraaff/PyAPT

08d16bd2600575dd72af9a39532218c2b5ed7634

[ "MIT" ]

19

2015-05-04T20:41:18.000Z

2018-06-12T11:01:04.000Z

from PyAPT import *

10

19

0.75

3

20

5

1

0

0.2

20

1

20

0.9375

0

1

0

true

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1

0

1

0

null

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0

null

0

1

0

1

0

1

0

6

bebdffea9351bc3af91a5692d58a91a014fa6854

41

py

Python

tests/t8.py

jplevyak/pyc

9f4bc49be78ba29427841460945ce63826fcd857

[ "BSD-3-Clause" ]

3

2019-08-21T22:01:35.000Z

2021-07-25T00:21:28.000Z

tests/t8.py

jplevyak/pyc

9f4bc49be78ba29427841460945ce63826fcd857

[ "BSD-3-Clause" ]

null

tests/t8.py

jplevyak/pyc

9f4bc49be78ba29427841460945ce63826fcd857

[ "BSD-3-Clause" ]

null

a = 1 while a < 4: print a a = a + 1

8.2

12

0.439024

10

41

1.8

0.5

0.222222

0

0.130435

0.439024

41

4

13

10.25

0.652174

0

null

0

null

0.25

1

null

1

0

1

0

null

0

1

0

6

fe45efd4882a0523b188ddc2db036914d8d57049

288

py

Python

recbole/data/dataloader/__init__.py

mengruwu/RecBole

251fb3478a35b061a2a411d598e8c716b94133c1

[ "MIT" ]

null

recbole/data/dataloader/__init__.py

mengruwu/RecBole

251fb3478a35b061a2a411d598e8c716b94133c1

[ "MIT" ]

null

recbole/data/dataloader/__init__.py

mengruwu/RecBole

251fb3478a35b061a2a411d598e8c716b94133c1

[ "MIT" ]

null

from recbole.data.dataloader.abstract_dataloader import * from recbole.data.dataloader.general_dataloader import * from recbole.data.dataloader.knowledge_dataloader import * from recbole.data.dataloader.user_dataloader import * from recbole.data.dataloader.customized_dataloader import *

48

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0.285714

0.226337

0.308642

0.514403

0.674897

0

0.069444

288

5

60

57.6

0.906716

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

fe56b708959592002b41797ca9d2ed32f37b916c

141

py

Python

python/macau/__init__.py

edebrouwer/macau

0b22d21ed954209406246e70178523102e98f922

[ "MIT" ]

38

2016-02-27T22:18:42.000Z

2021-11-29T12:17:39.000Z

python/macau/__init__.py

edebrouwer/macau

0b22d21ed954209406246e70178523102e98f922

[ "MIT" ]

11

2016-05-23T14:14:53.000Z

2020-09-16T08:12:40.000Z

python/macau/__init__.py

edebrouwer/macau

0b22d21ed954209406246e70178523102e98f922

[ "MIT" ]

19

2016-04-12T12:13:38.000Z

2021-06-01T15:05:59.000Z

from .version import __version__ from .macau import macau, bpmf, MacauResult from .macau import blockcg, make_train_test, make_train_test_df

35.25

63

0.836879

21

141

5.190476

0.52381

0.165138

0.275229

0

0.113475

141

3

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0

1

0

true

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null

0

1

0

1

0

1

0

6

fe8c16ed3d061d9b74bb54febd630fc1a79a0812

11,337

py

Python

tests/providers/google/firebase/hooks/test_firestore.py

ncolomer/airflow

cb7c67dea9cd9b9c5de10e355b63039446003149

[ "Apache-2.0" ]

1

2021-03-03T14:13:28.000Z

tests/providers/google/firebase/hooks/test_firestore.py

ncolomer/airflow

cb7c67dea9cd9b9c5de10e355b63039446003149

[ "Apache-2.0" ]

null

tests/providers/google/firebase/hooks/test_firestore.py

ncolomer/airflow

cb7c67dea9cd9b9c5de10e355b63039446003149

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """ Tests for Google Cloud Firestore """ import unittest from typing import Optional from unittest import mock from mock import PropertyMock from airflow.exceptions import AirflowException from airflow.providers.google.firebase.hooks.firestore import CloudFirestoreHook from tests.providers.google.cloud.utils.base_gcp_mock import ( GCP_PROJECT_ID_HOOK_UNIT_TEST, mock_base_gcp_hook_default_project_id, mock_base_gcp_hook_no_default_project_id, ) EXPORT_DOCUMENT_BODY = { "outputUriPrefix": "gs://test-bucket/test-namespace/", "collectionIds": ["test-collection"], } TEST_OPERATION = { "name": "operation-name", } TEST_WAITING_OPERATION = {"done": False, "response": "response"} TEST_DONE_OPERATION = {"done": True, "response": "response"} TEST_ERROR_OPERATION = {"done": True, "response": "response", "error": "error"} TEST_PROJECT_ID = "firestore--project-id" class TestCloudFirestoreHookWithPassedProjectId(unittest.TestCase): hook = None # type: Optional[CloudFirestoreHook] def setUp(self): with mock.patch( "airflow.providers.google.common.hooks.base_google.GoogleBaseHook.__init__", new=mock_base_gcp_hook_default_project_id, ): self.hook = CloudFirestoreHook(gcp_conn_id="test") @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook._authorize") @mock.patch("airflow.providers.google.firebase.hooks.firestore.build") @mock.patch("airflow.providers.google.firebase.hooks.firestore.build_from_document") def test_client_creation(self, mock_build_from_document, mock_build, mock_authorize): result = self.hook.get_conn() mock_build.assert_called_once_with('firestore', 'v1', cache_discovery=False) mock_build_from_document.assert_called_once_with( mock_build.return_value._rootDesc, http=mock_authorize.return_value ) self.assertEqual(mock_build_from_document.return_value, result) self.assertEqual(self.hook._conn, result) @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook.get_conn") def test_mmediately_complete(self, get_conn_mock): service_mock = get_conn_mock.return_value mock_export_documents = service_mock.projects.return_value.databases.return_value.exportDocuments mock_operation_get = ( service_mock.projects.return_value.databases.return_value.operations.return_value.get ) (mock_export_documents.return_value.execute.return_value) = TEST_OPERATION (mock_operation_get.return_value.execute.return_value) = TEST_DONE_OPERATION self.hook.export_documents(body=EXPORT_DOCUMENT_BODY, project_id=TEST_PROJECT_ID) mock_export_documents.assert_called_once_with( body=EXPORT_DOCUMENT_BODY, name='projects/firestore--project-id/databases/(default)' ) @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook.get_conn") @mock.patch("airflow.providers.google.firebase.hooks.firestore.time.sleep") def test_waiting_operation(self, _, get_conn_mock): service_mock = get_conn_mock.return_value mock_export_documents = service_mock.projects.return_value.databases.return_value.exportDocuments mock_operation_get = ( service_mock.projects.return_value.databases.return_value.operations.return_value.get ) (mock_export_documents.return_value.execute.return_value) = TEST_OPERATION execute_mock = mock.Mock( **{"side_effect": [TEST_WAITING_OPERATION, TEST_DONE_OPERATION, TEST_DONE_OPERATION]} ) mock_operation_get.return_value.execute = execute_mock self.hook.export_documents(body=EXPORT_DOCUMENT_BODY, project_id=TEST_PROJECT_ID) mock_export_documents.assert_called_once_with( body=EXPORT_DOCUMENT_BODY, name='projects/firestore--project-id/databases/(default)' ) @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook.get_conn") @mock.patch("airflow.providers.google.firebase.hooks.firestore.time.sleep") def test_error_operation(self, _, get_conn_mock): service_mock = get_conn_mock.return_value mock_export_documents = service_mock.projects.return_value.databases.return_value.exportDocuments mock_operation_get = ( service_mock.projects.return_value.databases.return_value.operations.return_value.get ) (mock_export_documents.return_value.execute.return_value) = TEST_OPERATION execute_mock = mock.Mock(**{"side_effect": [TEST_WAITING_OPERATION, TEST_ERROR_OPERATION]}) mock_operation_get.return_value.execute = execute_mock with self.assertRaisesRegex(AirflowException, "error"): self.hook.export_documents(body=EXPORT_DOCUMENT_BODY, project_id=TEST_PROJECT_ID) class TestCloudFirestoreHookWithDefaultProjectIdFromConnection(unittest.TestCase): hook = None # type: Optional[CloudFirestoreHook] def setUp(self): with mock.patch( "airflow.providers.google.common.hooks.base_google.GoogleBaseHook.__init__", new=mock_base_gcp_hook_default_project_id, ): self.hook = CloudFirestoreHook(gcp_conn_id="test") @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook._authorize") @mock.patch("airflow.providers.google.firebase.hooks.firestore.build") @mock.patch("airflow.providers.google.firebase.hooks.firestore.build_from_document") def test_client_creation(self, mock_build_from_document, mock_build, mock_authorize): result = self.hook.get_conn() mock_build.assert_called_once_with('firestore', 'v1', cache_discovery=False) mock_build_from_document.assert_called_once_with( mock_build.return_value._rootDesc, http=mock_authorize.return_value ) self.assertEqual(mock_build_from_document.return_value, result) self.assertEqual(self.hook._conn, result) @mock.patch( 'airflow.providers.google.common.hooks.base_google.GoogleBaseHook.project_id', new_callable=PropertyMock, return_value=GCP_PROJECT_ID_HOOK_UNIT_TEST, ) @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook.get_conn") def test_immediately_complete(self, get_conn_mock, mock_project_id): service_mock = get_conn_mock.return_value mock_export_documents = service_mock.projects.return_value.databases.return_value.exportDocuments mock_operation_get = ( service_mock.projects.return_value.databases.return_value.operations.return_value.get ) (mock_export_documents.return_value.execute.return_value) = TEST_OPERATION mock_operation_get.return_value.execute.return_value = TEST_DONE_OPERATION self.hook.export_documents(body=EXPORT_DOCUMENT_BODY) mock_export_documents.assert_called_once_with( body=EXPORT_DOCUMENT_BODY, name='projects/example-project/databases/(default)' ) @mock.patch( 'airflow.providers.google.common.hooks.base_google.GoogleBaseHook.project_id', new_callable=PropertyMock, return_value=GCP_PROJECT_ID_HOOK_UNIT_TEST, ) @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook.get_conn") @mock.patch("airflow.providers.google.firebase.hooks.firestore.time.sleep") def test_waiting_operation(self, _, get_conn_mock, mock_project_id): service_mock = get_conn_mock.return_value mock_export_documents = service_mock.projects.return_value.databases.return_value.exportDocuments mock_operation_get = ( service_mock.projects.return_value.databases.return_value.operations.return_value.get ) (mock_export_documents.return_value.execute.return_value) = TEST_OPERATION execute_mock = mock.Mock( **{"side_effect": [TEST_WAITING_OPERATION, TEST_DONE_OPERATION, TEST_DONE_OPERATION]} ) mock_operation_get.return_value.execute = execute_mock self.hook.export_documents(body=EXPORT_DOCUMENT_BODY) mock_export_documents.assert_called_once_with( body=EXPORT_DOCUMENT_BODY, name='projects/example-project/databases/(default)' ) @mock.patch( 'airflow.providers.google.common.hooks.base_google.GoogleBaseHook.project_id', new_callable=PropertyMock, return_value=GCP_PROJECT_ID_HOOK_UNIT_TEST, ) @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook.get_conn") @mock.patch("airflow.providers.google.firebase.hooks.firestore.time.sleep") def test_error_operation(self, _, get_conn_mock, mock_project_id): service_mock = get_conn_mock.return_value mock_export_documents = service_mock.projects.return_value.databases.return_value.exportDocuments mock_operation_get = ( service_mock.projects.return_value.databases.return_value.operations.return_value.get ) (mock_export_documents.return_value.execute.return_value) = TEST_OPERATION execute_mock = mock.Mock(**{"side_effect": [TEST_WAITING_OPERATION, TEST_ERROR_OPERATION]}) mock_operation_get.return_value.execute = execute_mock with self.assertRaisesRegex(AirflowException, "error"): self.hook.export_documents(body=EXPORT_DOCUMENT_BODY) class TestCloudFirestoreHookWithoutProjectId(unittest.TestCase): hook = None # type: Optional[CloudFirestoreHook] def setUp(self): with mock.patch( "airflow.providers.google.common.hooks.base_google.GoogleBaseHook.__init__", new=mock_base_gcp_hook_no_default_project_id, ): self.hook = CloudFirestoreHook(gcp_conn_id="test") @mock.patch( 'airflow.providers.google.common.hooks.base_google.GoogleBaseHook.project_id', new_callable=PropertyMock, return_value=None, ) @mock.patch("airflow.providers.google.firebase.hooks.firestore.CloudFirestoreHook.get_conn") def test_create_build(self, mock_get_conn, mock_project_id): with self.assertRaises(AirflowException) as e: self.hook.export_documents(body={}) self.assertEqual( "The project id must be passed either as keyword project_id parameter or as project_id extra in " "Google Cloud connection definition. Both are not set!", str(e.exception), )

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false

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0

null

0

1

0

null

0

6

fea49fc0b9d46b56e695a1332e53a7d8b07db839

13,863

py

Python

pce/src/PCE/tools/schedulers.py

elise-baumgartner/onramp

beb3c807264fcb70d8069ff2e3990b0ce3f59912

[ "BSD-3-Clause" ]

2

2016-09-09T04:19:01.000Z

2019-02-15T20:28:13.000Z

pce/src/PCE/tools/schedulers.py

elise-baumgartner/onramp

beb3c807264fcb70d8069ff2e3990b0ce3f59912

[ "BSD-3-Clause" ]

67

2016-06-02T19:37:56.000Z

2018-02-22T05:23:45.000Z

pce/src/PCE/tools/schedulers.py

elise-baumgartner/onramp

beb3c807264fcb70d8069ff2e3990b0ce3f59912

[ "BSD-3-Clause" ]

9

2015-06-22T22:10:22.000Z

2016-04-26T15:35:45.000Z

"""Encapsulation of functionality provided by various batch schedulers. Exports: SLURMScheduler: Interface to SLURM batch scheduler. Scheduler: Generic instantiator for all implemented schedulers. """ import logging import os from subprocess import CalledProcessError, check_output, STDOUT from PCEHelper import pce_root class _BatchScheduler(object): """Superclass for batch scheduler classes. Subclasses must override the non-magic methods defined here. """ local_python = os.path.join(pce_root, 'src', 'env', 'bin', 'python') @classmethod def is_scheduler_for(cls, type): """Return boolean indicating whether the class provides an interface to the batch scheduler type given. Args: type (str): Batch scheduler type. Returns: True if class provides interface to given batch scheduler, False if not. """ pass def get_batch_script(self, run_name, numtasks=4, num_nodes=1, email=None): """Return the batch script that runs a job as per args formatted for the given batch scheduler. Args: run_name (str): Human-readable label for job run. numtasks (int): Number of tasks to schedule. num_nodes (int): Number of nodes to allocate for job. email (str): Email to send results to upon completion. If None, no email sent. Returns: Batch script implementing given attrs. """ pass def schedule(self, proj_loc): """Schedule a job using the given batch scheduler. Args: proj_loc (str): Folder containing the batch script 'script.sh' for the job to schedule. Returns: Result dict with the following fields: status_code: Status code status_msg: String giving detailed status info. """ pass def check_status(self, scheduler_job_num): """Return job status from scheduler. Args: scheduler_job_num (int): Job number of the job to check state on as given by the scheduler, not as given by OnRamp. Returns: 2-Tuple with 0th item being error code and 1st item being a string giving detailed status info. """ pass def cancel_job(self, scheduler_job_num): """Cancel the given job. Args: scheduler_job_num (int): Job number, as given by the scheduler, of the job to cancel. Returns: 2-Tuple with 0th item being error code and 1st item being a string giving detailed status info. """ pass def __init__(self, type): """Set batch scheduler type and return the instance. Args: type (str): Batch scheduler type. """ self.logger = logging.getLogger('onramp') class SLURMScheduler(_BatchScheduler): @classmethod def is_scheduler_for(cls, type): """Return boolean indicating whether the class provides an interface to the batch scheduler type given. Args: type (str): Batch scheduler type. Returns: True if class provides interface to given batch scheduler, False if not. """ return type == 'SLURM' def get_batch_script(self, run_name, numtasks=4, num_nodes=1, email=None): """Return the batch script that runs a job as per args formatted for the SLURM batch scheduler. Args: run_name (str): Human-readable label for job run. numtasks (int): Number of tasks to schedule. num_nodes (int): Number of nodes to allocate for job. email (str): Email to send results to upon completion. If None, no email sent. Returns: Batch script implementing given attrs. """ contents = '#!/bin/bash\n' contents += '\n' contents += '###################################\n' contents += '# Slurm Submission options\n' contents += '#\n' contents += '#SBATCH --job-name=\"' + run_name + '\"\n' contents += '#SBATCH -o output.txt\n' contents += '#SBATCH -n ' + str(numtasks) + '\n' contents += '#SBATCH -N ' + str(num_nodes) + '\n' if email: self.logger.debug('%s configured for email reporting to %s' % (run_name, email)) contents += '#SBATCH --mail-user=' + email + '\n' contents += '###################################\n' contents += '\n' contents += self.local_python + ' bin/onramp_run.py\n' return contents def schedule(self, proj_loc): """Schedule a job using the SLURM batch scheduler. Args: proj_loc (str): Folder containing the batch script 'script.sh' for the job to schedule. Returns: Result dict with the following fields: status_code: Status code status_msg: String giving detailed status info. """ ret_dir = os.getcwd() os.chdir(proj_loc) try: batch_output = check_output(['sbatch', 'script.sh'], stderr=STDOUT) except CalledProcessError as e: msg = 'Job scheduling call failed' os.chdir(ret_dir) return { 'status_code': e.returncode, 'msg': '%s: %s' % (msg, e.output), 'status_msg': '%s: %s' % (msg, e.output) } os.chdir(ret_dir) output_fields = batch_output.strip().split() if 'Submitted batch job' != ' '.join(output_fields[:-1]): msg = 'Unexpeted output from sbatch' self.logger.error(msg) return { 'status_code': -7, 'status_msg': msg } try: job_num = int(output_fields[3:][0]) except ValueError, IndexError: msg = 'Unexpeted output from sbatch' self.logger.error(msg) return { 'status_code': -7, 'status_msg': msg } return { 'status_code': 0, 'status_msg': 'Job %d scheduled' % job_num, 'job_num': job_num } def check_status(self, scheduler_job_num): """Return job status from scheduler. Args: scheduler_job_num (int): Job number of the job to check state on as given by the scheduler, not as given by OnRamp. Returns: 2-Tuple with 0th item being error code and 1st item being a string giving detailed status info. """ try: job_info = check_output(['scontrol', 'show', 'job', str(scheduler_job_num)]) except CalledProcessError as e: msg = 'Job info call failed' self.logger.error(msg) return (-1, msg) job_state = job_info.split('JobState=')[1].split()[0] if job_state == 'RUNNING': return (0, 'Running') elif job_state == 'COMPLETED': return (0, 'Done') elif job_state == 'PENDING': return (0, 'Queued') elif job_state == 'FAILED': msg = 'Job failed' self.logger.error(msg) return (-1, msg) else: msg = 'Unexpected job state from scheduler' self.logger.error(msg) return (-2, msg) def cancel_job(self, scheduler_job_num): """Cancel the given job. Args: scheduler_job_num (int): Job number, as given by the scheduler, of the job to cancel. Returns: 2-Tuple with 0th item being error code and 1st item being a string giving detailed status info. """ try: result = check_output(['scancel', str(scheduler_job_num)], stderr=STDOUT) except CalledProcessError as e: msg = 'Job cancel call failed' self.logger.error(msg) return (-1, msg) return (0, result) class PBSScheduler(_BatchScheduler): @classmethod def is_scheduler_for(cls, type): """Return boolean indicating whether the class provides an interface to the batch scheduler type given. Args: type (str): Batch scheduler type. Returns: True if class provides interface to given batch scheduler, False if not. """ return type == 'PBS' def get_batch_script(self, run_name, numtasks=4, num_nodes=1, email=None): """Return the batch script that runs a job as per args formatted for the PBS batch scheduler. Args: run_name (str): Human-readable label for job run. numtasks (int): Number of tasks to schedule. num_nodes (int): Number of nodes to allocate for job. email (str): Email to send results to upon completion. If None, no email sent. Returns: Batch script implementing given attrs. """ script = '#!/bin/bash\n' script += '\n' script += '################################################\n' script += '#PBS -l nodes=' + num_nodes + '\n' script += '#PBS -N ' + run_name + '\n' script += '#PBS -V\n' script += '#PBS -j oe\n' script += '#PBS -o output.txt\n' script += '################################################\n' script += '\n' script += 'cd ${PBS_O_WORKDIR}\n' script += self.local_python + ' bin/onramp_run.py\n' return script def schedule(self, proj_loc): """Schedule a job using the PBS batch scheduler. Args: proj_loc (str): Folder containing the batch script 'script.sh' for the job to schedule. Returns: Result dict with the following fields: status_code: Status code status_msg: String giving detailed status info. """ ret_dir = os.getcwd() os.chdir(proj_loc) try: batch_output = check_output(['qsub', 'script.sh'], stderr=STDOUT) except CalledProcessError as e: msg = 'Job scheduling call failed' os.chdir(ret_dir) return { 'returncode': e.returncode, 'msg': '%s: %s' % (msg, e.output) } os.chdir(ret_dir) output_fields = batch_output.strip().split('.') try: job_num = int(output_fields[0]) except ValueError, IndexError: msg = 'Unexpeted output from sbatch' self.logger.error(msg) return { 'status_code': -7, 'status_msg': msg } return { 'status_code': 0, 'status_msg': 'Job %d scheduled' % job_num, 'job_num': job_num } def check_status(self, scheduler_job_num): """Return job status from scheduler. Args: scheduler_job_num (int): Job number of the job to check state on as given by the scheduler, not as given by OnRamp. Returns: 2-Tuple with 0th item being error code and 1st item being a string giving detailed status info. """ try: job_info = check_output(['qstat', '-i', str(scheduler_job_num)], stderr=STDOUT) except CalledProcessError as e: if e.output.startswith('qstat: Unknown Job Id %d' % scheduler_job_num): return (0, 'No info') msg = 'Job info call failed: %s' % e.output self.logger.error(msg) return (-1, msg) last_line = job_info.strip().split('\n')[-1:][0] job_state = last_line.split()[9] if (job_state == 'R' or job_state == 'r' or job_state == 's' or job_state == 'S' or job_state == 't' or job_state == 'T'): return (0, 'Running') elif job_state == 'W' or job_state == 'H': return (0, 'Queued') elif job_state == 'E': msg = 'Job failed' # TODO: Can maybe add error info here by qstat -j job_list option. self.logger.error(msg) return (-1, msg) elif job_state == 'd': msg = 'Job scheduled for deletion' self.logger.error(msg) return (-1, msg) else: msg = 'Unexpected job state from scheduler' self.logger.error(msg) return (-2, msg) def cancel_job(self, scheduler_job_num): """Cancel the given job. Args: scheduler_job_num (int): Job number, as given by the scheduler, of the job to cancel. Returns: 2-Tuple with 0th item being error code and 1st item being a string giving detailed status info. """ try: result = check_output(['qdel', str(scheduler_job_num)], stderr=STDOUT) except CalledProcessError as e: msg = 'Job cancel call failed' self.logger.error(msg) return (-1, msg) return (0, result) def Scheduler(type): """Instantiate the appropriate scheduler class for given type. Args: type (str): Identifier for batch scheduler type. Returns: Instance of a _BatchScheduler for given type. """ for cls in _BatchScheduler.__subclasses__(): if cls.is_scheduler_for(type): return cls(type) raise ValueError

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null

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null

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1

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6

feb1766f170694b224d2531694737fc464d05ff8

234

py

Python

examples/Displayable.py

Ellis0817/Introduction-to-Programming-Using-Python

1882a2a846162d5ff56d4d56c3940b638ef408bd

[ "MIT" ]

null

examples/Displayable.py

Ellis0817/Introduction-to-Programming-Using-Python

1882a2a846162d5ff56d4d56c3940b638ef408bd

[ "MIT" ]

4

2019-11-07T12:32:19.000Z

2020-07-19T14:04:44.000Z

examples/Displayable.py

Ellis0817/Introduction-to-Programming-Using-Python

1882a2a846162d5ff56d4d56c3940b638ef408bd

[ "MIT" ]

5

2019-12-04T15:56:55.000Z

2022-01-14T06:19:18.000Z

class Displayable: def getX(self): # Get x-coordinate of the vertex return 0 def getY(self): # Get y-coordinate of the vertex return 0 def getName(self): # Get display name of the vertex return ""

26

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34

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0.421769

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null

0

1

0

1

0

6

22cb57965d887698eaf8d420378d4e11ac3c4382

10,300

py

Python

solnml/utils/savemodel.py

williamy1996/Autoexpression

b470d9ff67074c8b076abbc1dce359db9a36f921

[ "MIT" ]

null

solnml/utils/savemodel.py

williamy1996/Autoexpression

b470d9ff67074c8b076abbc1dce359db9a36f921

[ "MIT" ]

null

solnml/utils/savemodel.py

williamy1996/Autoexpression

b470d9ff67074c8b076abbc1dce359db9a36f921

[ "MIT" ]

null

import argparse import os import sys import time import pickle import numpy as np import pandas as pd import json import pickle as pkl from sklearn.datasets import load_iris from sklearn.metrics import balanced_accuracy_score from sklearn.model_selection import train_test_split from solnml.utils.data_manager import DataManager from solnml.estimators import Classifier from solnml.utils import saveloadmodel class Ensemble_models: def __init__(self,ensemble_info,mdl_list): self.ensemble_info = ensemble_info self.model_list = mdl_list def predict_proba(self,test_x): print('This is \'predict_proba\'. For regression model, please run \'predict\'.') if(self.ensemble_info['ensemble_method']=='none'): mdl0 = self.model_list[0].replace('\n','') base_model = pickle.load(open(mdl0,'rb')) y_predict = base_model.predict_proba(test_x) return y_predict if(self.ensemble_info['ensemble_method']=='bagging'): y_predict = [] for mdl in self.model_list: mdl = mdl.replace('\n','') base_model = pickle.load(open(mdl,'rb')) y_predict.append(base_model.predict_proba(test_x)) y_predict = np.array(y_predict) return np.average(y_predict,axis=0) if(self.ensemble_info['ensemble_method']=='ensemble_selection'): y_predict = [] weights = np.array(pd.read_json(self.ensemble_info['ensemble_weights']))[:,0] i = 0 for mdl in self.model_list: mdl = mdl.replace('\n','') base_model = pickle.load(open(mdl,'rb')) y_predict.append(base_model.predict_proba(test_x)*weights[i]) i+=1 y_predict = np.array(y_predict) return np.sum(y_predict,axis=0) if(self.ensemble_info['ensemble_method']=='stacking'): meta_learner = pickle.load(open(self.ensemble_info['meta_learner_path'],'rb')) kfold = self.ensemble_info['kfold'] mdl0 = self.model_list[0].replace('\n','') base_model = pickle.load(open(mdl0,'rb')) y_predict = base_model.predict_proba(test_x) n_dim = y_predict.shape[1] sample_dim = y_predict.shape[0] y_predict = [] if(n_dim==2): n_dim = 1 i=0 for mdl in self.model_list: if(i == 0): new_sumpredict = np.zeros([sample_dim,n_dim]) mdl = mdl.replace('\n','') base_model = pickle.load(open(mdl,'rb')) new_predict = base_model.predict_proba(test_x) if(n_dim==1): new_predict = new_predict[:,1:] new_sumpredict = new_sumpredict + new_predict/kfold i+=1 if(i==kfold): i=0 y_predict.append(new_sumpredict) y_predict = np.hstack(y_predict) y_pred = meta_learner.predict_proba(y_predict) return y_pred if(self.ensemble_info['ensemble_method']=='blending'): meta_learner = pickle.load(open(self.ensemble_info['meta_learner_path'],'rb')) mdl0 = self.model_list[0].replace('\n','') base_model = pickle.load(open(mdl0,'rb')) y_predict = base_model.predict_proba(test_x) n_dim = y_predict.shape[1] if(n_dim==2): n_dim = 1 y_predict = [] for mdl in self.model_list: mdl = mdl.replace('\n','') base_model = pickle.load(open(mdl,'rb')) new_predict = base_model.predict_proba(test_x) if(n_dim==1): new_predict = new_predict[:,1:] y_predict.append(new_predict) y_predict = np.hstack(y_predict) y_pred = meta_learner.predict_proba(y_predict) return y_pred def predict(self,test_x): print('This is \'predict\'. For classification model, please run \'predict_proba\'.') if(self.ensemble_info['ensemble_method']=='none'): mdl0 = self.model_list[0].replace('\n','') base_model = pickle.load(open(mdl0,'rb')) y_predict = base_model.predict(test_x) return y_predict if(self.ensemble_info['ensemble_method']=='bagging'): y_predict = [] for mdl in self.model_list: mdl = mdl.replace('\n','') base_model = pickle.load(open(mdl,'rb')) y_predict.append(base_model.predict(test_x)) y_predict = np.array(y_predict) return np.average(y_predict,axis=0) if(self.ensemble_info['ensemble_method']=='ensemble_selection'): y_predict = [] weights = np.array(pd.read_json(self.ensemble_info['ensemble_weights']))[:,0] i = 0 for mdl in self.model_list: mdl = mdl.replace('\n','') base_model = pickle.load(open(mdl,'rb')) y_predict.append(base_model.predict(test_x)*weights[i]) i+=1 y_predict = np.array(y_predict) return np.sum(y_predict,axis=0) if(self.ensemble_info['ensemble_method']=='stacking'): meta_learner = pickle.load(open(self.ensemble_info['meta_learner_path'],'rb')) kfold = self.ensemble_info['kfold'] mdl0 = self.model_list[0].replace('\n','') base_model = pickle.load(open(mdl0,'rb')) y_predict = base_model.predict(test_x) n_dim = y_predict.shape[1] sample_dim = y_predict.shape[0] y_predict = [] if(n_dim==2): n_dim = 1 i=0 for mdl in self.model_list: if(i == 0): new_sumpredict = np.zeros([sample_dim,n_dim]) mdl = mdl.replace('\n','') base_model = pickle.load(open(mdl,'rb')) new_predict = base_model.predict(test_x) if(n_dim==1): new_predict = new_predict[:,1:] new_sumpredict = new_sumpredict + new_predict/kfold i+=1 if(i==kfold): i=0 y_predict.append(new_sumpredict) y_predict = np.hstack(y_predict) y_pred = meta_learner.predict(y_predict) return y_pred if(self.ensemble_info['ensemble_method']=='blending'): meta_learner = pickle.load(open(self.ensemble_info['meta_learner_path'],'rb')) mdl0 = self.model_list[0].replace('\n','') base_model = pickle.load(open(mdl0,'rb')) y_predict = base_model.predict(test_x) n_dim = y_predict.shape[1] if(n_dim==2): n_dim = 1 y_predict = [] for mdl in self.model_list: mdl = mdl.replace('\n','') base_model = pickle.load(open(mdl,'rb')) new_predict = base_model.predict(test_x) if(n_dim==1): new_predict = new_predict[:,1:] y_predict.append(new_predict) y_predict = np.hstack(y_predict) y_pred = meta_learner.predict(y_predict) return y_pred def save_model(mdl,save_dir): mdl_list = '' if not os.path.exists(save_dir): os.makedirs(save_dir) info = mdl.get_ens_model_info() if(info is None): f_ens_info = open(save_dir +'/ens_info','w') ens_dict = {} ens_dict['ensemble_method'] = 'none' f_ens_info.write(json.dumps(ens_dict)) f_ens_info.close() os.system('cp '+ clf.best_algo_path + ' '+save_dir +'/') f_mdl_list = open(save_dir +'/model_list','w') f_mdl_list.write(os.path.basename(clf.best_algo_path)) f_mdl_list.close() return f_ens_info = open(save_dir +'/ens_info','w') ens_dict = {} if(mdl.task_type == 4): ens_dict['task_type'] = 'RGS' else: ens_dict['task_type'] = 'CLF' ens_met = info['ensemble_method'] ens_dict['ensemble_method'] = ens_met if(ens_met=='bagging'): f_ens_info.write(json.dumps(ens_dict)) if(ens_met=='ensemble_selection'): ens_dict['ensemble_weights'] = pd.DataFrame(info['ensemble_weights']).to_json() f_ens_info.write(json.dumps(ens_dict)) if(ens_met=='stacking'): meta_learner_path = save_dir +'/'+os.path.basename(info['meta_learner_path']) os.system('cp '+ info['meta_learner_path'] + ' '+save_dir +'/') ens_dict['meta_learner_path'] = meta_learner_path ens_dict['kfold'] = info['kfold'] f_ens_info.write(json.dumps(ens_dict)) if(ens_met=='blending'): meta_learner_path = save_dir +'/'+os.path.basename(info['meta_learner_path']) os.system('cp '+ info['meta_learner_path'] + ' '+save_dir +'/') ens_dict['meta_learner_path'] = meta_learner_path f_ens_info.write(json.dumps(ens_dict)) f_ens_info.close() if(ens_met=='stacking'): for conf in info['config']: for partpath in conf[-1]: os.system('cp '+ partpath + ' '+save_dir +'/') mdl_list += (os.path.basename(partpath)+'\n') else: for conf in info['config']: os.system('cp '+ conf[-1] + ' '+save_dir +'/') mdl_list += (os.path.basename(conf[-1])+'\n') f_mdl_list = open(save_dir +'/model_list','w') f_mdl_list.write(mdl_list) f_mdl_list.close() def load_model(save_dir): f_ens_info = open(save_dir +'/ens_info','r') ens_info = json.loads(f_ens_info.read()) f_ens_info.close() mdl_list = [] f_mdl_list = open(save_dir +'/model_list','r') for mdl in f_mdl_list: mdl.replace('\n','') mdl_list.append(save_dir +'/'+mdl) f_mdl_list.close() return Ensemble_models(ens_info,mdl_list)

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null

0

6

fe142eb7a790e219b6058125507af4d92e353aee

20,842

py

Python

deep_qa-master/tests/tensors/masked_operations_test.py

RTHMaK/RPGOne

3f3ada7db1762781668bfb2377154fdc00e17212

[ "Apache-2.0" ]

1

2017-04-11T13:03:55.000Z

deep_qa-master/tests/tensors/masked_operations_test.py

RTHMaK/RPGOne

3f3ada7db1762781668bfb2377154fdc00e17212

[ "Apache-2.0" ]

null

deep_qa-master/tests/tensors/masked_operations_test.py

RTHMaK/RPGOne

3f3ada7db1762781668bfb2377154fdc00e17212

[ "Apache-2.0" ]

null

# pylint: disable=no-self-use,invalid-name import numpy from numpy.testing import assert_almost_equal, assert_array_almost_equal import keras.backend as K from deep_qa.tensors.backend import l1_normalize from deep_qa.tensors.masked_operations import masked_batch_dot, masked_softmax from ..common.test_markers import requires_tensorflow class TestMaskedOperations: def test_masked_batch_dot_masks_properly(self): embedding_dim = 3 a_length = 4 b_length = 5 batch_size = 2 tensor_a = numpy.random.rand(batch_size, a_length, embedding_dim) tensor_b = numpy.random.rand(batch_size, b_length, embedding_dim) mask_a = numpy.ones((batch_size, a_length)) mask_a[1, 3] = 0 mask_b = numpy.ones((batch_size, b_length)) mask_b[1, 2] = 0 result = K.eval(masked_batch_dot(K.variable(tensor_a), K.variable(tensor_b), K.variable(mask_a), K.variable(mask_b))) assert numpy.all(result[0, :, :] != numpy.zeros((a_length, b_length))) assert numpy.any(result[1, 0, :] != numpy.zeros((b_length))) assert numpy.any(result[1, 1, :] != numpy.zeros((b_length))) assert numpy.any(result[1, 2, :] != numpy.zeros((b_length))) assert numpy.all(result[1, 3, :] == numpy.zeros((b_length))) assert numpy.any(result[1, :, 0] != numpy.zeros((a_length))) assert numpy.any(result[1, :, 1] != numpy.zeros((a_length))) assert numpy.all(result[1, :, 2] == numpy.zeros((a_length))) assert numpy.any(result[1, :, 3] != numpy.zeros((a_length))) assert numpy.any(result[1, :, 4] != numpy.zeros((a_length))) result = K.eval(masked_batch_dot(K.variable(tensor_a), K.variable(tensor_b), None, None)) assert numpy.all(result[0, :, :] != numpy.zeros((a_length, b_length))) assert numpy.all(result[1, :, :] != numpy.zeros((a_length, b_length))) result = K.eval(masked_batch_dot(K.variable(tensor_a), K.variable(tensor_b), K.variable(mask_a), None)) assert numpy.all(result[0, :, :] != numpy.zeros((a_length, b_length))) assert numpy.any(result[1, 0, :] != numpy.zeros((b_length))) assert numpy.any(result[1, 1, :] != numpy.zeros((b_length))) assert numpy.any(result[1, 2, :] != numpy.zeros((b_length))) assert numpy.all(result[1, 3, :] == numpy.zeros((b_length))) assert numpy.any(result[1, :, 0] != numpy.zeros((a_length))) assert numpy.any(result[1, :, 1] != numpy.zeros((a_length))) assert numpy.any(result[1, :, 2] != numpy.zeros((a_length))) assert numpy.any(result[1, :, 3] != numpy.zeros((a_length))) assert numpy.any(result[1, :, 4] != numpy.zeros((a_length))) result = K.eval(masked_batch_dot(K.variable(tensor_a), K.variable(tensor_b), None, K.variable(mask_b))) assert numpy.all(result[0, :, :] != numpy.zeros((a_length, b_length))) assert numpy.any(result[1, 0, :] != numpy.zeros((b_length))) assert numpy.any(result[1, 1, :] != numpy.zeros((b_length))) assert numpy.any(result[1, 2, :] != numpy.zeros((b_length))) assert numpy.any(result[1, 3, :] != numpy.zeros((b_length))) assert numpy.any(result[1, :, 0] != numpy.zeros((a_length))) assert numpy.any(result[1, :, 1] != numpy.zeros((a_length))) assert numpy.all(result[1, :, 2] == numpy.zeros((a_length))) assert numpy.any(result[1, :, 3] != numpy.zeros((a_length))) assert numpy.any(result[1, :, 4] != numpy.zeros((a_length))) def test_masked_batch_dot_handles_uneven_tensors(self): # We're going to test masked_batch_dot with tensors of shape (batch_size, a_length, # embedding_dim) and (batch_size, embedding_dim). The result should have shape # (batch_size, a_length). embedding_dim = 3 a_length = 5 batch_size = 2 tensor_a = numpy.random.rand(batch_size, a_length, embedding_dim) tensor_b = numpy.random.rand(batch_size, embedding_dim) mask_a = numpy.ones((batch_size, a_length)) mask_a[0, 3] = 0 mask_b = numpy.ones((batch_size,)) mask_b[1] = 0 result = K.eval(masked_batch_dot(K.variable(tensor_a), K.variable(tensor_b), K.variable(mask_a), K.variable(mask_b))) assert result[0, 0] != 0 assert result[0, 1] != 0 assert result[0, 2] != 0 assert result[0, 3] == 0 assert result[0, 4] != 0 assert numpy.all(result[1, :] == numpy.zeros((a_length))) # We should get the same result if we flip the order of the tensors. flipped_result = K.eval(masked_batch_dot(K.variable(tensor_b), K.variable(tensor_a), K.variable(mask_b), K.variable(mask_a))) assert numpy.all(result == flipped_result) @requires_tensorflow def test_masked_batch_dot_handles_uneven_higher_order_tensors(self): # We're going to test masked_batch_dot with tensors of shape (batch_size, common, # a_length, embedding_dim) and (batch_size, common, embedding_dim). The result should have # shape (batch_size, common, a_length). This currently doesn't work with the theano # backend, because of an inconsistency in K.batch_dot for higher-order tensors. The code # will crash if you try to run this in Theano, so we require tensorflow for this test. embedding_dim = 3 common_length = 4 a_length = 5 batch_size = 2 tensor_a = numpy.random.rand(batch_size, common_length, a_length, embedding_dim) tensor_b = numpy.random.rand(batch_size, common_length, embedding_dim) mask_a = numpy.ones((batch_size, common_length, a_length)) mask_a[1, 1, 3] = 0 mask_b = numpy.ones((batch_size, common_length)) mask_b[1, 2] = 0 result = K.eval(masked_batch_dot(K.variable(tensor_a), K.variable(tensor_b), K.variable(mask_a), K.variable(mask_b))) assert numpy.all(result[0, :, :] != numpy.zeros((common_length, a_length))) assert numpy.all(result[1, 0, :] != numpy.zeros((a_length))) assert result[1, 1, 0] != 0 assert result[1, 1, 1] != 0 assert result[1, 1, 2] != 0 assert result[1, 1, 3] == 0 assert result[1, 1, 4] != 0 assert numpy.all(result[1, 2, :] == numpy.zeros((a_length))) assert numpy.all(result[1, 3, :] != numpy.zeros((a_length))) # We should get the same result if we pass the smaller tensor in first. flipped_result = K.eval(masked_batch_dot(K.variable(tensor_b), K.variable(tensor_a), K.variable(mask_b), K.variable(mask_a))) assert numpy.all(result == flipped_result) def test_l1_normalize_no_mask(self): # Testing the general unmasked 1D case. vector_1d = K.variable(numpy.array([[2, 1, 5, 7]])) vector_1d_normalized = K.eval(l1_normalize(vector_1d)) assert_almost_equal(vector_1d_normalized, numpy.array([[0.13333333, 0.06666666, 0.33333333, 0.46666666]])) assert_almost_equal(1.0, numpy.sum(vector_1d_normalized), decimal=6) # Testing the unmasked 1D case with all 0s. vector_1d_zeros = K.variable(numpy.array([[0, 0, 0, 0]])) vector_1d_zeros_normalized = K.eval(l1_normalize(vector_1d_zeros)) assert_array_almost_equal(vector_1d_zeros_normalized, numpy.array([[0.25, 0.25, 0.25, 0.25]])) # Testing the general unmasked batched case when # inputs are not all 0's matrix = K.variable(numpy.array([[2, 1, 5, 7], [2, 2, 2, 2]])) matrix_normalized = K.eval(l1_normalize(matrix)) assert_array_almost_equal(matrix_normalized, numpy.array([[0.13333333, 0.06666666, 0.33333333, 0.46666666], [0.25, 0.25, 0.25, 0.25]])) assert_almost_equal(numpy.array([1.0, 1.0]), numpy.sum(matrix_normalized, axis=1), decimal=6) # Testing the general unmasked batched case when # one row is all 0's matrix = K.variable(numpy.array([[2, 1, 5, 7], [0, 0, 0, 0]])) matrix_normalized = K.eval(l1_normalize(matrix)) assert_array_almost_equal(matrix_normalized, numpy.array([[0.13333333, 0.06666666, 0.33333333, 0.46666666], [0.25, 0.25, 0.25, 0.25]])) assert_almost_equal(numpy.array([1.0, 1.0]), numpy.sum(matrix_normalized, axis=1), decimal=6) def test_l1_normalize_masked(self): # Testing the general masked 1D case. vector_1d = K.variable(numpy.array([[2, 1, 5, 7]])) vector_1d_mask = K.variable(numpy.array([[1, 1, 0, 1]])) vector_1d_normalized = K.eval(l1_normalize(vector_1d, vector_1d_mask)) assert_array_almost_equal(vector_1d_normalized, numpy.array([[0.2, 0.1, 0.0, 0.7]])) assert_almost_equal(1.0, numpy.sum(vector_1d_normalized), decimal=6) vector_1d = K.variable(numpy.array([[1.0, 2.0, 3.0, 4.0]])) vector_1d_mask = K.variable(numpy.array([[1, 1, 0, 1]])) vector_1d_normalized = K.eval(l1_normalize(vector_1d, vector_1d_mask)) assert_array_almost_equal(vector_1d_normalized, numpy.array([[0.14285715, 0.2857143, 0, 0.5714286]])) assert_almost_equal(1.0, numpy.sum(vector_1d_normalized), decimal=6) # Testing the masked 1D case where the mask is # not all zero and the input is all zero. vector_1d_zeros = K.variable(numpy.array([[0, 0, 0, 0]])) vector_1d_zeros_mask = K.variable(numpy.array([[1, 1, 0, 1]])) vector_1d_zeros_normalized = K.eval(l1_normalize(vector_1d_zeros, vector_1d_zeros_mask)) assert_array_almost_equal(vector_1d_zeros_normalized, numpy.array([[0.3333333, 0.3333333, 0.0, 0.3333333]])) vector_1d_zeros = K.variable(numpy.array([[0, 0, 0, 0]])) vector_1d_zeros_mask = K.variable(numpy.array([[0, 0, 0, 0]])) vector_1d_zeros_normalized = K.eval(l1_normalize(vector_1d_zeros, vector_1d_zeros_mask)) assert_array_almost_equal(vector_1d_zeros_normalized, numpy.array([[0.25, 0.25, 0.25, 0.25]])) # Testing the general batched masked case when the input is not # all 0's and the masks are not all 0's. matrix = K.variable(numpy.array([[2, 1, 5, 7], [2, 2, 2, 2]])) matrix_mask = K.variable(numpy.array([[1, 1, 0, 1], [1, 1, 1, 1]])) matrix_normalized = K.eval(l1_normalize(matrix, matrix_mask)) assert_array_almost_equal(matrix_normalized, numpy.array([[0.2, 0.1, 0.0, 0.7], [0.25, 0.25, 0.25, 0.25]])) assert_almost_equal(numpy.array([1.0, 1.0]), numpy.sum(matrix_normalized, axis=1), decimal=6) # Testing the batched masked case when the masks are all 0's # and one of the input rows is all 0's. matrix = K.variable(numpy.array([[2, 1, 5, 7], [0, 0, 0, 0]])) matrix_mask = K.variable(numpy.array([[0, 0, 0, 0], [0, 0, 0, 0]])) matrix_normalized = K.eval(l1_normalize(matrix, matrix_mask)) assert_array_almost_equal(matrix_normalized, numpy.array([[0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25]])) assert_almost_equal(numpy.array([1.0, 1.0]), numpy.sum(matrix_normalized, axis=1), decimal=6) def test_l1_normalize_special_cases(self): # Testing the special masked 1D case where the mask # all zero and the input is all zero as well. vector_1d_zeros = K.variable(numpy.array([[0.0, 0.0, 0.0, 0.0]])) vector_1d_zeros_mask = K.variable(numpy.array([[0, 0, 0, 0]])) vector_1d_zeros_normalized = K.eval(l1_normalize(vector_1d_zeros, vector_1d_zeros_mask)) assert_array_almost_equal(vector_1d_zeros_normalized, numpy.array([[0.25, 0.25, 0.25, 0.25]])) # Testing the special masked 1D case where the mask # all zero and the input is not all zero. vector_1d_zeros = K.variable(numpy.array([[2, 1, 5, 7]])) vector_1d_zeros_mask = K.variable(numpy.array([[0, 0, 0, 0]])) vector_1d_zeros_normalized = K.eval(l1_normalize(vector_1d_zeros, vector_1d_zeros_mask)) assert_array_almost_equal(vector_1d_zeros_normalized, numpy.array([[0.25, 0.25, 0.25, 0.25]])) def test_masked_softmax_no_mask(self): # Testing the general unmasked 1D case. vector_1d = K.variable(numpy.array([[1.0, 2.0, 3.0]])) vector_1d_softmaxed = K.eval(masked_softmax(vector_1d, None)) assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.090031, 0.244728, 0.665241]])) assert_almost_equal(1.0, numpy.sum(vector_1d_softmaxed), decimal=6) vector_1d = K.variable(numpy.array([[1.0, 2.0, 5.0]])) vector_1d_softmaxed = K.eval(masked_softmax(vector_1d, None)) assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.017148, 0.046613, 0.93624]])) # Testing the unmasked 1D case where the input is all 0s. vector_zero = K.variable(numpy.array([[0.0, 0.0, 0.0]])) vector_zero_softmaxed = K.eval(masked_softmax(vector_zero, None)) assert_array_almost_equal(vector_zero_softmaxed, numpy.array([[0.33333334, 0.33333334, 0.33333334]])) # Testing the general unmasked batched case. matrix = K.variable(numpy.array([[1.0, 2.0, 5.0], [1.0, 2.0, 3.0]])) masked_matrix_softmaxed = K.eval(masked_softmax(matrix, None)) assert_array_almost_equal(masked_matrix_softmaxed, numpy.array([[0.01714783, 0.04661262, 0.93623955], [0.09003057, 0.24472847, 0.66524096]])) # Testing the unmasked batched case where one of the inputs are all 0s. matrix = K.variable(numpy.array([[1.0, 2.0, 5.0], [0.0, 0.0, 0.0]])) masked_matrix_softmaxed = K.eval(masked_softmax(matrix, None)) assert_array_almost_equal(masked_matrix_softmaxed, numpy.array([[0.01714783, 0.04661262, 0.93623955], [0.33333334, 0.33333334, 0.33333334]])) def test_masked_softmax_masked(self): # Testing the general masked 1D case. vector_1d = K.variable(numpy.array([[1.0, 2.0, 5.0]])) mask_1d = K.variable(numpy.array([[1.0, 0.0, 1.0]])) vector_1d_softmaxed = K.eval(masked_softmax(vector_1d, mask_1d)) assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.01798621, 0.0, 0.98201382]])) vector_1d = K.variable(numpy.array([[0.0, 2.0, 3.0, 4.0]])) mask_1d = K.variable(numpy.array([[1.0, 0.0, 1.0, 1.0]])) vector_1d_softmaxed = K.eval(masked_softmax(vector_1d, mask_1d)) assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.01321289, 0.0, 0.26538793, 0.72139918]])) # Testing the masked 1D case where the input is all 0s and the mask # is not all 0s. vector_1d = K.variable(numpy.array([[0.0, 0.0, 0.0, 0.0]])) mask_1d = K.variable(numpy.array([[0.0, 0.0, 0.0, 1.0]])) vector_1d_softmaxed = K.eval(masked_softmax(vector_1d, mask_1d)) assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0, 0, 0, 1]])) # Testing the masked 1D case where the input is not all 0s # and the mask is all 0s. vector_1d = K.variable(numpy.array([[0.0, 2.0, 3.0, 4.0]])) mask_1d = K.variable(numpy.array([[0.0, 0.0, 0.0, 0.0]])) vector_1d_softmaxed = K.eval(masked_softmax(vector_1d, mask_1d)) assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.0, 0.0, 0.0, 0.0]])) # Testing the masked 1D case where the input is all 0s and # the mask is all 0s. vector_1d = K.variable(numpy.array([[0.0, 0.0, 0.0, 0.0]])) mask_1d = K.variable(numpy.array([[0.0, 0.0, 0.0, 0.0]])) vector_1d_softmaxed = K.eval(masked_softmax(vector_1d, mask_1d)) assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.0, 0.0, 0.0, 0.0]])) # Testing the general masked batched case. matrix = K.variable(numpy.array([[1.0, 2.0, 5.0], [1.0, 2.0, 3.0]])) mask = K.variable(numpy.array([[1.0, 0.0, 1.0], [1.0, 1.0, 1.0]])) masked_matrix_softmaxed = K.eval(masked_softmax(matrix, mask)) assert_array_almost_equal(masked_matrix_softmaxed, numpy.array([[0.01798621, 0.0, 0.98201382], [0.090031, 0.244728, 0.665241]])) # Testing the masked batch case where one of the inputs is all 0s but # none of the masks are all 0. matrix = K.variable(numpy.array([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]])) mask = K.variable(numpy.array([[1.0, 0.0, 1.0], [1.0, 1.0, 1.0]])) masked_matrix_softmaxed = K.eval(masked_softmax(matrix, mask)) assert_array_almost_equal(masked_matrix_softmaxed, numpy.array([[0.5, 0.0, 0.5], [0.090031, 0.244728, 0.665241]])) # Testing the masked batch case where one of the inputs is all 0s and # one of the masks are all 0. matrix = K.variable(numpy.array([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]])) mask = K.variable(numpy.array([[1.0, 0.0, 1.0], [0.0, 0.0, 0.0]])) masked_matrix_softmaxed = K.eval(masked_softmax(matrix, mask)) assert_array_almost_equal(masked_matrix_softmaxed, numpy.array([[0.5, 0.0, 0.5], [0.0, 0.0, 0.0]])) matrix = K.variable(numpy.array([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]])) mask = K.variable(numpy.array([[0.0, 0.0, 0.0], [1.0, 0.0, 1.0]])) masked_matrix_softmaxed = K.eval(masked_softmax(matrix, mask)) assert_array_almost_equal(masked_matrix_softmaxed, numpy.array([[0.0, 0.0, 0.0], [0.11920292, 0.0, 0.88079708]]))

55.28382

99

0.535985

2,786

20,842

3.813711

0.059225

0.032188

0.035012

0.030494

0.896188

0.874824

0.852706

0.826918

0.814494

0.771953

0

0.09133

0.33754

20,842

376

100

55.430851

0.678207

0.106612

0

0.690722

0

0.28866

1

0.027491

false

0

0.020619

0

0.051546

0

null

0

1

0

null

0

6

a3c14f8b0001a63dd4def44daefd40fa78c9e0b9

155

py

Python

reunition/apps/reunions/admin.py

reunition/reunition

a68d4555092f41f5712c6f7061aa35e816a6283e

[ "MIT" ]

null

reunition/apps/reunions/admin.py

reunition/reunition

a68d4555092f41f5712c6f7061aa35e816a6283e

[ "MIT" ]

null

reunition/apps/reunions/admin.py

reunition/reunition

a68d4555092f41f5712c6f7061aa35e816a6283e

[ "MIT" ]

null

from django.contrib import admin from .models import Reunion class ReunionAdmin(admin.ModelAdmin): pass admin.site.register(Reunion, ReunionAdmin)

15.5

42

0.793548

19

155

6.473684

0.684211

0

0.135484

155

9

43

17.222222

0.91791

0

1

0

true

0.2

0.4

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

6

4316a9baa2536a249fa27dcdfa774b2da21fe791

91

py

Python

trade_app/custom_exceptions/__init__.py

Plazas87/trading_stocks_platform

c34de11152798720cefa552f4b713231508e23a8

[ "MIT" ]

null

trade_app/custom_exceptions/__init__.py

Plazas87/trading_stocks_platform

c34de11152798720cefa552f4b713231508e23a8

[ "MIT" ]

null

trade_app/custom_exceptions/__init__.py

Plazas87/trading_stocks_platform

c34de11152798720cefa552f4b713231508e23a8

[ "MIT" ]

2

2020-10-28T14:07:43.000Z

2021-11-03T22:49:21.000Z

from .exceptions import CreateOrderException, MaxBuyPerTradeException, CloseOrderException

45.5

90

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91

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91

1

91

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true

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null

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1

0

null

0

1

0

1

0

1

0

6

4327d03512b58a005c50d469e35e09bcbdea2993

9,682

py

Python

t3nsor/tensor_train.py

onucharles/tensorized-rnn

69fc031f1efe169ee88327d10bdf5e5bc24f03cf

[ "MIT" ]

16

2020-11-19T16:10:17.000Z

2021-12-02T13:31:30.000Z

t3nsor/tensor_train.py

MohamedAbdelsalam9/TT-Transformer

aeef0f0199207121e3e5621bfeb67b15ffbb3d1d

[ "MIT" ]

2

2021-02-26T08:45:16.000Z

2021-08-11T13:47:43.000Z

t3nsor/tensor_train.py

kritostudent/tensorized-rnn

69fc031f1efe169ee88327d10bdf5e5bc24f03cf

[ "MIT" ]

7

2021-01-21T11:24:31.000Z

2022-03-21T08:59:08.000Z

import torch import numpy as np import torch.nn as nn class TensorTrain(object): def __init__(self, tt_cores, shape=None, tt_ranks=None, convert_to_tensors=True): #tt_cores = list(tt_cores) if convert_to_tensors: for i in range(len(tt_cores)): tt_cores[i] = torch.Tensor(tt_cores[i]) self._tt_cores = tt_cores if len(self._tt_cores[0].shape) == 4: self._is_tt_matrix = True else: self._is_tt_matrix = False if self._is_tt_matrix: self._raw_shape = [[tt_core.shape[1] for tt_core in self._tt_cores], [tt_core.shape[2] for tt_core in self._tt_cores]] self._shape = [int(np.prod(self._raw_shape[0])), int(np.prod(self._raw_shape[1]))] self._ndims = len(self._raw_shape[0]) else: self._raw_shape = [tt_core.shape[1] for tt_core in self._tt_cores] self._shape = [tt_core.shape[1] for tt_core in self._tt_cores] self._ndims = len(self._raw_shape) self._ranks = [tt_core.shape[0] for tt_core in self._tt_cores] + [1, ] self._is_parameter = False self._parameter = None self._dof = np.sum([np.prod(list(tt_core.shape)) for tt_core in self._tt_cores]) self._total = np.prod(self._shape) @property def tt_cores(self): """A list of TT-cores. Returns: A list of 3d or 4d tensors of shape """ return self._tt_cores @property def raw_shape(self): return self._raw_shape @property def is_tt_matrix(self): return self._is_tt_matrix @property def shape(self): return self._shape @property def ranks(self): return self._ranks @property def ndims(self): return self._ndims @property def is_parameter(self): return self._is_parameter @property def parameter(self): if self.is_parameter: return self._parameter else: raise ValueError('Not a parameter, run .to_parameter() first') @property def dof(self): return self._dof @property def total(self): return self._total def to(self, device): new_cores = [] for core in self.tt_cores: new_cores.append(core.to(device)) return TensorTrain(new_cores, convert_to_tensors=False) def detach(self): new_cores = [] for core in self.tt_cores: new_cores.append(core.detach()) return TensorTrain(new_cores, convert_to_tensors=False) def requires_grad_(self, requires_grad=True): new_cores = [] for core in self.tt_cores: new_cores.append(core.requires_grad_(requires_grad)) return TensorTrain(new_cores, convert_to_tensors=False) def to_parameter(self): new_cores = [] for core in self.tt_cores: core = nn.Parameter(core) core.is_tt = True new_cores.append(core) tt_p = TensorTrain(new_cores, convert_to_tensors=False) tt_p._parameter = nn.ParameterList(tt_p.tt_cores) tt_p._is_parameter = True return tt_p def full(self): num_dims = self.ndims ranks = self.ranks shape = self.shape raw_shape = self.raw_shape res = self.tt_cores[0] for i in range(1, num_dims): res = res.view(-1, ranks[i]) curr_core = self.tt_cores[i].view(ranks[i], -1) res = torch.matmul(res, curr_core) if self.is_tt_matrix: intermediate_shape = [] for i in range(num_dims): intermediate_shape.append(raw_shape[0][i]) intermediate_shape.append(raw_shape[1][i]) res = res.view(*intermediate_shape) transpose = [] for i in range(0, 2 * num_dims, 2): transpose.append(i) for i in range(1, 2 * num_dims, 2): transpose.append(i) res = res.permute(*transpose) if self.is_tt_matrix: res = res.contiguous().view(*shape) else: res = res.view(*shape) return res def __str__(self): """A string describing the TensorTrain object, its TT-rank, and shape.""" shape = self.shape tt_ranks = self.ranks device = self.tt_cores[0].device compression_rate = self.total / self.dof if self.is_tt_matrix: raw_shape = self.raw_shape return "A TT-Matrix of size %d x %d, underlying tensor" \ "shape: %s x %s, TT-ranks: %s " \ "\n on device '%s' with compression rate %.2f" % (shape[0], shape[1], raw_shape[0], raw_shape[1], tt_ranks, device, compression_rate) else: return "A Tensor Train of shape %s, TT-ranks: %s" \ "\n on device '%s' with compression rate %.2f" % (shape, tt_ranks, device, compression_rate) class TensorTrainBatch(): def __init__(self, tt_cores, shape=None, tt_ranks=None, convert_to_tensors=True): #tt_cores = list(tt_cores) if convert_to_tensors: for i in range(len(tt_cores)): tt_cores[i] = torch.Tensor(tt_cores[i]) self._tt_cores = tt_cores self._batch_size = self._tt_cores[0].shape[0] if len(self._tt_cores[0].shape) == 5: self._is_tt_matrix = True else: self._is_tt_matrix = False if self._is_tt_matrix: self._raw_shape = [[tt_core.shape[2] for tt_core in self._tt_cores], [tt_core.shape[3] for tt_core in self._tt_cores]] self._shape = [self._batch_size, int( np.prod(self._raw_shape[0])), int(np.prod(self._raw_shape[1]))] self._ndims = len(self._raw_shape[0]) else: self._raw_shape = [tt_core.shape[2] for tt_core in self._tt_cores] self._shape = [self._batch_size, ] + [tt_core.shape[2] for tt_core in self._tt_cores] self._ndims = len(self._raw_shape) self._ranks = [tt_core.shape[1] for tt_core in self._tt_cores] + [1, ] @property def tt_cores(self): """A list of TT-cores. Returns: A list of 4d or 5d tensors. """ return self._tt_cores @property def raw_shape(self): return self._raw_shape @property def is_tt_matrix(self): return self._is_tt_matrix @property def shape(self): return self._shape @property def ranks(self): return self._ranks @property def ndims(self): return self._ndims @property def batch_size(self): return self._batch_size def to(self, device): new_cores = [] for core in self.tt_cores: new_cores.append(core.to(device)) return TensorTrainBatch(new_cores, convert_to_tensors=False) def detach(self): new_cores = [] for core in self.tt_cores: new_cores.append(core.detach()) return TensorTrainBatch(new_cores, convert_to_tensors=False) def requires_grad_(self, requires_grad=True): new_cores = [] for core in self.tt_cores: new_cores.append(core.requires_grad_(requires_grad)) return TensorTrainBatch(new_cores, convert_to_tensors=False) def full(self): num_dims = self.ndims ranks = self.ranks shape = self.shape raw_shape = self.raw_shape res = self.tt_cores[0] batch_size = self.batch_size for i in range(1, num_dims): res = res.view(batch_size, -1, ranks[i]) curr_core = self.tt_cores[i].view(batch_size, ranks[i], -1) res = torch.einsum('oqb,obw->oqw', (res, curr_core)) if self.is_tt_matrix: intermediate_shape = [batch_size] for i in range(num_dims): intermediate_shape.append(raw_shape[0][i]) intermediate_shape.append(raw_shape[1][i]) res = res.view(*intermediate_shape) transpose = [0] for i in range(0, 2 * num_dims, 2): transpose.append(i + 1) for i in range(1, 2 * num_dims, 2): transpose.append(i + 1) res = res.permute(transpose) if self.is_tt_matrix: res = res.contiguous().view(*shape) else: res = res.view(*shape) return res def __str__(self): """A string describing the TensorTrainBatch, its TT-rank and shape.""" shape = self.shape tt_ranks = self.ranks batch_size_str = str(self.batch_size) device = self.tt_cores[0].device if self.is_tt_matrix: raw_shape = self.raw_shape type_str = 'TT-matrices' return "A %s element batch of %s of size %d x %d, underlying tensor " \ "shape: %s x %s, TT-ranks: %s" \ "on device '%s' " % (batch_size_str, type_str, shape[1], shape[2], raw_shape[0], raw_shape[1], tt_ranks, device) else: type_str = 'Tensor Trains' return "A %s element batch of %s of shape %s, TT-ranks: %s \n on device '%s'" % \ (batch_size_str, type_str, shape[1:], tt_ranks, device)

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19,796

py

Python

ivy/functional/ivy/meta.py

Bobbyorr007/ivy

2f4441c7eee681055d6c22e05d922a66e40bc12c

[ "Apache-2.0" ]

1

2022-03-10T23:51:18.000Z

ivy/functional/ivy/meta.py

thecoder12/ivy

84c5fb82ec43c5c7d0154d5110973805e524831c

[ "Apache-2.0" ]

null

ivy/functional/ivy/meta.py

thecoder12/ivy

84c5fb82ec43c5c7d0154d5110973805e524831c

[ "Apache-2.0" ]

null

# global import ivy from ivy.functional.ivy.gradients import gradient_descent_update # Extra # # ------# # Private # def _compute_cost_and_update_grads(cost_fn, order, batch, variables, outer_v, keep_outer_v, average_across_steps_or_final, all_grads, unique_outer, batched, num_tasks): if order == 1: cost, inner_grads = ivy.execute_with_gradients( lambda v: cost_fn(batch, v=variables.set_at_key_chains(v) if unique_outer else v), variables.at_key_chains(outer_v, ignore_none=True) if keep_outer_v else variables.prune_key_chains(outer_v, ignore_none=True), retain_grads=False) if batched: inner_grads = inner_grads * num_tasks if average_across_steps_or_final: all_grads.append(inner_grads) else: cost = cost_fn(batch, v=variables) return cost def _train_task(inner_batch, outer_batch, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, inner_v, keep_innver_v, outer_v, keep_outer_v, batched, num_tasks, stop_gradients): # init total_cost = 0 all_grads = list() # inner and outer unique_inner = inner_v is not None unique_outer = outer_v is not None # iterate through inner loop training steps for i in range(inner_grad_steps): # compute inner gradient for update the inner variables cost, inner_update_grads = ivy.execute_with_gradients( lambda v: inner_cost_fn(inner_batch, v=variables.set_at_key_chains(v) if unique_inner else v), variables.at_key_chains(inner_v, ignore_none=True) if keep_innver_v else variables.prune_key_chains(inner_v, ignore_none=True), retain_grads=order > 1) if batched: inner_update_grads = inner_update_grads * num_tasks # compute the cost to be optimized, and update all_grads if fist order method if outer_cost_fn is None and not unique_inner and not unique_outer: all_grads.append(inner_update_grads) else: cost = _compute_cost_and_update_grads( inner_cost_fn if outer_cost_fn is None else outer_cost_fn, order, outer_batch, variables, outer_v, keep_outer_v, average_across_steps, all_grads, unique_outer, batched, num_tasks) # update cost and update parameters total_cost = total_cost + cost if unique_inner: variables = variables.set_at_key_chains( inner_optimization_step(variables.at_key_chains(inner_v) if keep_innver_v else variables.prune_key_chains(inner_v), inner_update_grads, inner_learning_rate, inplace=False, stop_gradients=stop_gradients)) else: variables = inner_optimization_step(variables, inner_update_grads, inner_learning_rate, inplace=False, stop_gradients=stop_gradients) # once training is finished, compute the final cost, and update all_grads if fist order method final_cost = _compute_cost_and_update_grads( inner_cost_fn if outer_cost_fn is None else outer_cost_fn, order, outer_batch, variables, outer_v, keep_outer_v, True, all_grads, unique_outer, batched, num_tasks) # update variables if stop_gradients: variables = variables.stop_gradients() if not batched: variables = variables.expand_dims(0) # average the cost or gradients across all timesteps if this option is chosen if average_across_steps: total_cost = total_cost + final_cost if order == 1: all_grads = sum(all_grads) / max(len(all_grads), 1) return total_cost / (inner_grad_steps + 1), variables, all_grads # else return only the final values if order == 1: all_grads = all_grads[-1] return final_cost, variables, all_grads def _train_tasks_batched(batch, inner_batch_fn, outer_batch_fn, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, inner_v, keep_innver_v, outer_v, keep_outer_v, return_inner_v, num_tasks, stop_gradients): inner_batch = batch outer_batch = batch if inner_batch_fn is not None: inner_batch = inner_batch_fn(inner_batch) if outer_batch_fn is not None: outer_batch = outer_batch_fn(outer_batch) cost, updated_ivs, grads = _train_task(inner_batch, outer_batch, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, inner_v, keep_innver_v, outer_v, keep_outer_v, True, num_tasks, stop_gradients) grads = grads.reduce_mean(0) if isinstance(grads, ivy.Container) else grads if order == 1: if return_inner_v in ['all', True]: return cost, grads, updated_ivs elif return_inner_v == 'first': return cost, grads, updated_ivs[0:1] return cost, grads if return_inner_v in ['all', True]: return cost, updated_ivs elif return_inner_v == 'first': return cost, updated_ivs[0:1] return cost def _train_tasks_with_for_loop(batch, inner_sub_batch_fn, outer_sub_batch_fn, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, inner_v, keep_innver_v, outer_v, keep_outer_v, return_inner_v, num_tasks, stop_gradients): total_cost = 0 updated_ivs_to_return = list() all_grads = list() if isinstance(inner_v, (list, tuple)) and isinstance(inner_v[0], (list, tuple, dict, type(None))): inner_v_seq = True else: inner_v_seq = False if isinstance(outer_v, (list, tuple)) and isinstance(outer_v[0], (list, tuple, dict, type(None))): outer_v_seq = True else: outer_v_seq = False for i, sub_batch in enumerate(batch.unstack(0, True, num_tasks)): if inner_sub_batch_fn is not None: inner_sub_batch = inner_sub_batch_fn(sub_batch) else: inner_sub_batch = sub_batch if outer_sub_batch_fn is not None: outer_sub_batch = outer_sub_batch_fn(sub_batch) else: outer_sub_batch = sub_batch iv = inner_v[i] if inner_v_seq else inner_v ov = outer_v[i] if outer_v_seq else outer_v cost, updated_iv, grads = _train_task(inner_sub_batch, outer_sub_batch, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, iv, keep_innver_v, ov, keep_outer_v, False, num_tasks, stop_gradients) if (return_inner_v == 'first' and i == 0) or return_inner_v in ['all', True]: updated_ivs_to_return.append(updated_iv) total_cost = total_cost + cost all_grads.append(grads) if order == 1: if return_inner_v: return total_cost / num_tasks, sum(all_grads) / num_tasks, ivy.Container.concat(updated_ivs_to_return, 0) return total_cost / num_tasks, sum(all_grads) / num_tasks if return_inner_v: return total_cost / num_tasks, ivy.Container.concat(updated_ivs_to_return, 0) return total_cost / num_tasks def _train_tasks(batch, inner_batch_fn, outer_batch_fn, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, batched, inner_v, keep_innver_v, outer_v, keep_outer_v, return_inner_v, num_tasks, stop_gradients): if batched: return _train_tasks_batched( batch, inner_batch_fn, outer_batch_fn, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, inner_v, keep_innver_v, outer_v, keep_outer_v, return_inner_v, num_tasks, stop_gradients) return _train_tasks_with_for_loop( batch, inner_batch_fn, outer_batch_fn, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, inner_v, keep_innver_v, outer_v, keep_outer_v, return_inner_v, num_tasks, stop_gradients) # Public # # First Order def fomaml_step(batch, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step=gradient_descent_update, inner_batch_fn=None, outer_batch_fn=None, average_across_steps=False, batched=True, inner_v=None, keep_inner_v=True, outer_v=None, keep_outer_v=True, return_inner_v=False, num_tasks=None, stop_gradients=True): """ Perform step of first order MAML. :param batch: The input batch :type batch: ivy.Container :param inner_cost_fn: callable for the inner loop cost function, receving task-specific sub-batch, inner vars and outer vars :type inner_cost_fn: callable :param outer_cost_fn: callable for the outer loop cost function, receving task-specific sub-batch, inner vars and outer vars. If None, the cost from the inner loop will also be optimized in the outer loop. :type outer_cost_fn: callable, optional :param variables: Variables to be optimized during the meta step :type variables: ivy.Container :param inner_grad_steps: Number of gradient steps to perform during the inner loop. :type inner_grad_steps: int :param inner_learning_rate: The learning rate of the inner loop. :type inner_learning_rate: float :param inner_optimization_step: The function used for the inner loop optimization. Default is ivy.gradient_descent_update. :type inner_optimization_step: callable, optional :param inner_batch_fn: Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None. :type inner_batch_fn: callable, optional :param outer_batch_fn: Function to apply to the task sub-batch, before passing to the outer_cost_fn. Default is None. :type outer_batch_fn: callable, optional :param average_across_steps: Whether to average the inner loop steps for the outer loop update. Default is False. :type average_across_steps: bool, optional :param batched: Whether to batch along the time dimension, and run the meta steps in batch. Default is True. :type batched: bool, optional :param inner_v: Nested variable keys to be optimized during the inner loop, with same keys and boolean values. :type inner_v: dict str or list, optional :param keep_inner_v: If True, the key chains in inner_v will be kept, otherwise they will be removed. Default is True. :type keep_inner_v: bool, optional :param outer_v: Nested variable keys to be optimized during the inner loop, with same keys and boolean values. :type outer_v: dict str or list, optional :param keep_outer_v: If True, the key chains in inner_v will be kept, otherwise they will be removed. Default is True. :type keep_outer_v: bool, optional :param return_inner_v: Either 'first', 'all', or False. 'first' means the variables for the first task inner loop will also be returned. variables for all tasks will be returned with 'all'. Default is False. :type return_inner_v: str, optional :param num_tasks: Number of unique tasks to inner-loop optimize for the meta step. Determined from batch by default. :type num_tasks: int, optional :param stop_gradients: Whether to stop the gradients of the cost. Default is True. :type stop_gradients: bool, optional :return: The cost and the gradients with respect to the outer loop variables. """ if num_tasks is None: num_tasks = batch.shape[0] rets = _train_tasks( batch, inner_batch_fn, outer_batch_fn, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, 1, average_across_steps, batched, inner_v, keep_inner_v, outer_v, keep_outer_v, return_inner_v, num_tasks, stop_gradients) cost = rets[0] if stop_gradients: cost = ivy.stop_gradient(cost, preserve_type=False) grads = rets[1] if return_inner_v: return cost, grads, rets[2] return cost, grads def reptile_step(batch, cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step=gradient_descent_update, batched=True, return_inner_v=False, num_tasks=None, stop_gradients=True): """ Perform step of Reptile. :param batch: The input batch :type batch: ivy.Container :param cost_fn: callable for the cost function, receivng the task-specific sub-batch and variables :type cost_fn: callable :param variables: Variables to be optimized :type variables: ivy.Container :param inner_grad_steps: Number of gradient steps to perform during the inner loop. :type inner_grad_steps: int :param inner_learning_rate: The learning rate of the inner loop. :type inner_learning_rate: float :param inner_optimization_step: The function used for the inner loop optimization. Default is ivy.gradient_descent_update. :type inner_optimization_step: callable, optional :param batched: Whether to batch along the time dimension, and run the meta steps in batch. Default is True. :type batched: bool, optional :param return_inner_v: Either 'first', 'all', or False. 'first' means the variables for the first task inner loop will also be returned. variables for all tasks will be returned with 'all'. Default is False. :type return_inner_v: str, optional :param num_tasks: Number of unique tasks to inner-loop optimize for the meta step. Determined from batch by default. :type num_tasks: int, optional :param stop_gradients: Whether to stop the gradients of the cost. Default is True. :type stop_gradients: bool, optional :return: The cost and the gradients with respect to the outer loop variables. """ if num_tasks is None: num_tasks = batch.shape[0] # noinspection PyTypeChecker rets = _train_tasks( batch, None, None, cost_fn, None, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, 1, True, batched, None, True, None, True, return_inner_v, num_tasks, stop_gradients) cost = rets[0] if stop_gradients: cost = ivy.stop_gradient(cost, preserve_type=False) grads = rets[1] / inner_learning_rate if return_inner_v: return cost, grads, rets[2] return cost, grads # Second Order def maml_step(batch, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step=gradient_descent_update, inner_batch_fn=None, outer_batch_fn=None, average_across_steps=False, batched=True, inner_v=None, keep_inner_v=True, outer_v=None, keep_outer_v=True, return_inner_v=False, num_tasks=None, stop_gradients=True): """ Perform step of vanilla second order MAML. :param batch: The input batch :type batch: ivy.Container :param inner_cost_fn: callable for the inner loop cost function, receing sub-batch, inner vars and outer vars :type inner_cost_fn: callable :param outer_cost_fn: callable for the outer loop cost function, receving task-specific sub-batch, inner vars and outer vars. If None, the cost from the inner loop will also be optimized in the outer loop. :type outer_cost_fn: callable, optional :param variables: Variables to be optimized during the meta step :type variables: ivy.Container :param inner_grad_steps: Number of gradient steps to perform during the inner loop. :type inner_grad_steps: int :param inner_learning_rate: The learning rate of the inner loop. :type inner_learning_rate: float :param inner_optimization_step: The function used for the inner loop optimization. Default is ivy.gradient_descent_update. :type inner_optimization_step: callable, optional :param inner_batch_fn: Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None. :type inner_batch_fn: callable, optional :param outer_batch_fn: Function to apply to the task sub-batch, before passing to the outer_cost_fn. Default is None. :type outer_batch_fn: callable, optional :param average_across_steps: Whether to average the inner loop steps for the outer loop update. Default is False. :type average_across_steps: bool, optional :param batched: Whether to batch along the time dimension, and run the meta steps in batch. Default is True. :type batched: bool, optional :param inner_v: Nested variable keys to be optimized during the inner loop, with same keys and boolean values. :type inner_v: dict str or list, optional :param keep_inner_v: If True, the key chains in inner_v will be kept, otherwise they will be removed. Default is True. :type keep_inner_v: bool, optional :param outer_v: Nested variable keys to be optimized during the inner loop, with same keys and boolean values. :type outer_v: dict str or list, optional :param keep_outer_v: If True, the key chains in inner_v will be kept, otherwise they will be removed. Default is True. :type keep_outer_v: bool, optional :param return_inner_v: Either 'first', 'all', or False. 'first' means the variables for the first task inner loop will also be returned. variables for all tasks will be returned with 'all'. Default is False. :type return_inner_v: str, optional :param num_tasks: Number of unique tasks to inner-loop optimize for the meta step. Determined from batch by default. :type num_tasks: int, optional :param stop_gradients: Whether to stop the gradients of the cost. Default is True. :type stop_gradients: bool, optional :return: The cost and the gradients with respect to the outer loop variables. """ if num_tasks is None: num_tasks = batch.shape[0] unique_outer = outer_v is not None cost, grads, *rets = ivy.execute_with_gradients(lambda v: _train_tasks( batch, inner_batch_fn, outer_batch_fn, inner_cost_fn, outer_cost_fn, variables.set_at_key_chains(v) if unique_outer else v, inner_grad_steps, inner_learning_rate, inner_optimization_step, 2, average_across_steps, batched, inner_v, keep_inner_v, outer_v, keep_outer_v, return_inner_v, num_tasks, False), variables.at_key_chains(outer_v, ignore_none=True) if keep_outer_v else variables.prune_key_chains(outer_v, ignore_none=True)) if stop_gradients: cost = ivy.stop_gradient(cost, preserve_type=False) # noinspection PyRedundantParentheses return (cost, grads.sum(0), *rets)

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0

0.002411

0.245858

19,796

364

121

54.384615

0.860339

0.405587

0

0.406417

0

0.002135

0

1

0.042781

false

0

0.010695

0

0.160428

0

null

0

1

0

null

0

6

4aabac7b053e0355257ed6780bafc40b771be868

79

py

Python

data/typing/numpy.linalg._umath_linalg.py

vfdev-5/python-record-api

006faf0bba9cd4cb55fbacc13d2bbda365f5bf0b

[ "MIT" ]

67

2020-08-17T11:53:26.000Z

2021-11-08T20:16:06.000Z

data/typing/numpy.linalg._umath_linalg.py

vfdev-5/python-record-api

006faf0bba9cd4cb55fbacc13d2bbda365f5bf0b

[ "MIT" ]

36

2020-08-17T11:09:51.000Z

2021-12-15T18:09:47.000Z

data/typing/numpy.linalg._umath_linalg.py

pydata-apis/python-api-record

684cffbbb6dc6e81f9de4e02619c8b0ebc557b2b

[ "MIT" ]

7

2020-08-19T05:06:47.000Z

2020-11-04T05:10:38.000Z

from typing import * # usage.dask: 1 eig: object # usage.dask: 1 inv: object

9.875

20

0.683544

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4.153846

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1

0

6

4ab4aaa472f76a2f49d1a3b7d03db652abf66f2b

96

py

Python

paypalcheckoutsdk/core/util.py

seba-spoon/Checkout-Python-SDK

24a1f7c73fd6e270d21547fb65037e21a5d4f22b

[ "BSD-Source-Code" ]

165

2019-03-06T14:32:47.000Z

2022-03-26T17:49:57.000Z

paypalcheckoutsdk/core/util.py

seba-spoon/Checkout-Python-SDK

24a1f7c73fd6e270d21547fb65037e21a5d4f22b

[ "BSD-Source-Code" ]

24

2019-03-06T00:21:50.000Z

2021-12-22T11:40:05.000Z

paypalcheckoutsdk/core/util.py

seba-spoon/Checkout-Python-SDK

24a1f7c73fd6e270d21547fb65037e21a5d4f22b

[ "BSD-Source-Code" ]

87

2019-02-15T03:59:20.000Z

2022-03-27T13:26:29.000Z

def older_than_27(): import sys return True if sys.version_info[:2] < (2, 7) else False

24

59

0.666667

17

96

3.588235

0.882353

0

0.066667

0.21875

96

4

59

24

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0.333333

true

0

0.333333

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1

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null

0

1

0

1

0

1

0

6

4aba032c66a49f3ee559b91adeba1321b370cfc7

30

py

Python

src/utils/__init__.py

imatge-upc/AI4Agriculture-grape-detection

f8fce3115dfd50e80193f82a4cbba97641ea828c

[ "MIT" ]

3

2021-06-20T22:15:59.000Z

2021-11-18T10:12:47.000Z

src/utils/__init__.py

paumarquez/AI4Agriculture-grape-detection

4adc277320f527f1bbefe4d504e3223928201f69

[ "MIT" ]

4

2021-07-12T09:01:08.000Z

2022-03-12T00:59:15.000Z

src/utils/__init__.py

paumarquez/AI4Agriculture-grape-detection

4adc277320f527f1bbefe4d504e3223928201f69

[ "MIT" ]

1

2021-07-02T09:27:13.000Z

from .transforms import resize

30

0.866667

4

30

6.5

1

0

0.1

30

1

30

0.962963

0

1

0

true

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1

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null

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0

null

0

1

0

1

0

1

0

6

4ac8fe2be4938c25d967a8a8dc23e40bb28a3b42

12,483

py

Python

mapping.py

arthurazs/uff-siscomp

50ea5cc349fef5af5f95599978f258a411024ad6

[ "MIT" ]

null

mapping.py

arthurazs/uff-siscomp

50ea5cc349fef5af5f95599978f258a411024ad6

[ "MIT" ]

null

mapping.py

arthurazs/uff-siscomp

50ea5cc349fef5af5f95599978f258a411024ad6

[ "MIT" ]

null

from random import randint RANDOM = 1 FIFO = 2 LRU = 3 LFU = 4 DIRECT = 1 ASSOCIATIVE = 2 SET_ASSOCIATIVE = 3 class Cache: def __init__(self, mapping, cache_size, policy=RANDOM, frame_size=2): if mapping == DIRECT: print('Mapping => Direct') self._cache = Direct(cache_size) self.alloc = self._cache.alloc elif mapping == ASSOCIATIVE: print('Mapping => Associative') self._cache = Associative(cache_size, policy) self.alloc = self._cache.alloc elif mapping == SET_ASSOCIATIVE: print('Mapping => Set Associative') self._cache = SetAssociative(cache_size, frame_size, policy) self.alloc = self._cache.alloc class Direct: def __init__(self, cache_size): self._cache_size = cache_size self._cache = [None] * self._cache_size self._hit = 0 self._miss = 0 def alloc(self, tag): position = tag % self._cache_size tag_output = f'\nTag:\t\t{tag}' output = f'Old cache:\t{self._cache}\n' result = 'MISS' if tag == self._cache[position]: self._hit += 1 result = 'HIT' else: self._miss += 1 self._cache[position] = tag tag_output += f' ({result})' output += f'New cache:\t{self._cache}\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}') class Associative: def __init__(self, cache_size, policy=RANDOM): self._cache_size = cache_size self._cache = [None] * self._cache_size self._hit = 0 self._miss = 0 self._counter = 0 if policy == RANDOM: print('Policy => Random') self.alloc = self._random_alloc elif policy == FIFO: print('Policy => FIFO') self.alloc = self._fifo_alloc elif policy == LRU: print('Policy => LRU') self.alloc = self._lru_alloc elif policy == LFU: print('Policy => LFU') self._cache = {} self.alloc = self._lfu_alloc def _random_alloc(self, tag): tag_output = f'\nTag:\t\t{tag}' output = '' result = 'MISS' output += f'Old cache:\t{self._cache}\n' if tag in self._cache: self._hit += 1 result = 'HIT' elif self._counter < self._cache_size: # NOT FULL self._cache[self._counter] = tag self._counter += 1 self._miss += 1 else: self._miss += 1 position = randint(0, self._cache_size - 1) self._cache[position] = tag tag_output += f' ({result})' output += f'New cache:\t{self._cache}\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}') def _fifo_alloc(self, tag): tag_output = f'\nTag:\t\t{tag}' output = '' result = 'MISS' output += f'Old cache:\t{self._cache}\n' if tag in self._cache: self._hit += 1 result = 'HIT' elif self._counter < self._cache_size: # NOT FULL self._cache[self._counter] = tag self._counter += 1 self._miss += 1 else: self._miss += 1 self._cache.pop(0) self._cache.append(tag) tag_output += f' ({result})' output += f'New cache:\t{self._cache}\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}') def _lru_alloc(self, tag): tag_output = f'\nTag:\t\t{tag}' output = '' result = 'MISS' output += f'Old cache:\t{self._cache}\n' if tag in self._cache: self._hit += 1 result = 'HIT' self._cache.remove(tag) self._cache.append(tag) else: self._cache.pop(0) self._cache.append(tag) self._miss += 1 tag_output += f' ({result})' output += f'New cache:\t{self._cache}\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}') def _lfu_alloc(self, tag): def find_tag(tag): for frequency, tags in self._cache.items(): if tag in tags: return frequency return None frequency = find_tag(tag) tag_output = f'\nTag:\t\t{tag}' output = '' result = 'MISS' output += 'Old cache:\t{' for key, value in sorted(self._cache.items()): output += f'{key}: {value}, ' output = output[:-2] + '}\n' if frequency is not None: result = 'HIT' self._hit += 1 self._cache[frequency].remove(tag) if len(self._cache[frequency]) == 0: del self._cache[frequency] try: self._cache[frequency + 1].append(tag) except KeyError: self._cache[frequency + 1] = [tag] elif self._counter < self._cache_size: # NOT FULL try: self._cache[0].append(tag) except KeyError: self._cache[0] = [tag] self._counter += 1 self._miss += 1 else: least = min(self._cache) self._cache[least].pop(0) if len(self._cache[least]) == 0: del self._cache[least] try: self._cache[0].append(tag) except KeyError: self._cache[0] = [tag] self._miss += 1 tag_output += f' ({result})' output += 'New cache:\t{' for key, value in sorted(self._cache.items()): output += f'{key}: {value}, ' output = output[:-2] + '}\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}') class SetAssociative: def __init__(self, cache_size, frame_size=2, policy=RANDOM): self._cache_size = cache_size self._frame_size = frame_size self._lines = cache_size // frame_size self._cache = [[None] * frame_size for i in range(self._lines)] self._hit = 0 self._miss = 0 self._counter = {} for index in range(self._lines): self._counter[index] = 0 if policy == RANDOM: print('Policy => Random') self.alloc = self._random_alloc elif policy == FIFO: print('Policy => FIFO') self.alloc = self._fifo_alloc elif policy == LRU: print('Policy => LRU') self.alloc = self._lru_alloc elif policy == LFU: self._cache = [{} for _ in range(self._lines)] print('Policy => LFU') self.alloc = self._lfu_alloc def _random_alloc(self, tag): position = tag % (self._lines) tag_output = f'\nTag:\t\t{tag}' result = 'MISS' output = f'Old cache:\t{self._cache}\n' cache_pos = self._cache[position] if tag in cache_pos: result = 'HIT' self._hit += 1 elif self._counter[position] < self._frame_size: # NOT FULL cache_pos[self._counter[position]] = tag self._counter[position] += 1 self._miss += 1 else: self._miss += 1 random_position = randint(0, self._frame_size - 1) cache_pos[random_position] = tag tag_output += f' ({result})' output += f'New cache:\t{self._cache}\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}') def _fifo_alloc(self, tag): position = tag % (self._lines) tag_output = f'\nTag:\t\t{tag}' result = 'MISS' output = f'Old cache:\t{self._cache}\n' cache_pos = self._cache[position] if tag in cache_pos: result = 'HIT' self._hit += 1 elif self._counter[position] < self._frame_size: # NOT FULL cache_pos[self._counter[position]] = tag self._counter[position] += 1 self._miss += 1 else: self._miss += 1 cache_pos.pop(0) cache_pos.append(tag) tag_output += f' ({result})' output += f'New cache:\t{self._cache}\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}') def _lru_alloc(self, tag): position = tag % (self._lines) tag_output = f'\nTag:\t\t{tag}' result = 'MISS' output = f'Old cache:\t{self._cache}\n' cache_pos = self._cache[position] if tag in cache_pos: result = 'HIT' self._hit += 1 cache_pos.remove(tag) cache_pos.append(tag) else: cache_pos.pop(0) cache_pos.append(tag) self._miss += 1 tag_output += f' ({result})' output += f'New cache:\t{self._cache}\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}') def _lfu_alloc(self, tag): def find_tag_in_set(tag, position, cache): for frequency, tags in cache.items(): if tag in tags: return frequency return None position = tag % (self._lines) tag_output = f'\nTag:\t\t{tag}' result = 'MISS' output = 'Old cache:\t[' for elements in self._cache: output += '{' if elements.items(): for key, value in sorted(elements.items()): output += f'{key}: {value}, ' output = output[:-2] + '}, ' else: output += '}, ' output = output[:-2] + ']\n' cache_pos = self._cache[position] frequency = find_tag_in_set(tag, position, cache_pos) if frequency is not None: result = 'HIT' self._hit += 1 cache_pos[frequency].remove(tag) if len(cache_pos[frequency]) == 0: del cache_pos[frequency] try: cache_pos[frequency + 1].append(tag) except KeyError: cache_pos[frequency + 1] = [tag] elif self._counter[position] < self._frame_size: # NOT FULL try: cache_pos[0].append(tag) except KeyError: cache_pos[0] = [tag] self._counter[position] += 1 self._miss += 1 else: least = min(cache_pos) cache_pos[least].pop(0) if len(cache_pos[least]) == 0: del cache_pos[least] try: cache_pos[0].append(tag) except KeyError: cache_pos[0] = [tag] self._miss += 1 tag_output += f' ({result})' output += 'New cache:\t[' for elements in self._cache: output += '{' if elements.items(): for key, value in sorted(elements.items()): output += f'{key}: {value}, ' output = output[:-2] + '}, ' else: output += '}, ' output = output[:-2] + ']\n' output += f'Hit/Miss:\t{self._hit}/{self._miss}' percentage = self._hit / (self._hit + self._miss) * 100 output += f'\nHit rate:\t{percentage:.2f}%' print(f'{tag_output}\n{output}')

34.771588

73

0.511576

1,499

12,483

4.041361

0.052035

0.109937

0.049026

0.044569

0.834764

0.803896

0.775338

0.742654

0.697425

0.68554

0

0.014213

0.351839

12,483

358

74

34.868715

0.73452

0.004246

0

0.78979

0

0.143041

0.084601

0

1

0.045045

false

0

0.003003

0

0.072072

0.06006

0

null

0

1

0

1

0

1

0

null

0

6

4354800f9af3f297cf4bc70fbd16fc3f6c470f48

10,940

py

Python

lib/rucio/web/rest/flaskapi/v1/requests.py

mkszuba/rucio

32469bb38e7e10f59f28e51669748a8687f33bb7

[ "Apache-2.0" ]

2

2020-02-18T22:34:24.000Z

2022-03-09T16:26:18.000Z

lib/rucio/web/rest/flaskapi/v1/requests.py

mkszuba/rucio

32469bb38e7e10f59f28e51669748a8687f33bb7

[ "Apache-2.0" ]

null

lib/rucio/web/rest/flaskapi/v1/requests.py

mkszuba/rucio

32469bb38e7e10f59f28e51669748a8687f33bb7

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2021-2022 CERN # # 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. # # Authors: # - Benedikt Ziemons <benedikt.ziemons@cern.ch>, 2021 # - Thomas Beermann <thomas.beermann@cern.ch>, 2021 # - Rob Barnsley <rob.barnsley@skao.int>, 2021-2022 import json import flask from flask import Flask, Blueprint, Response from rucio.api import request from rucio.common.exception import RequestNotFound from rucio.common.utils import APIEncoder, render_json from rucio.core.rse import get_rses_with_attribute_value, get_rse_name from rucio.db.sqla.constants import RequestState from rucio.web.rest.flaskapi.v1.common import check_accept_header_wrapper_flask, parse_scope_name, try_stream, \ request_auth_env, response_headers, generate_http_error_flask, ErrorHandlingMethodView class RequestGet(ErrorHandlingMethodView): """ REST API to get requests. """ @check_accept_header_wrapper_flask(['application/json']) def get(self, scope_name, rse): """ List request for given DID to a destination RSE. .. :quickref: RequestGet; list requests :param scope_name: data identifier (scope)/(name). :param rse: destination RSE. :reqheader Content-Type: application/json :status 200: Request found. :status 404: Request not found. :status 406: Not Acceptable. """ try: scope, name = parse_scope_name(scope_name, flask.request.environ.get('vo')) except ValueError as error: return generate_http_error_flask(400, error) try: request_data = request.get_request_by_did( scope=scope, name=name, rse=rse, issuer=flask.request.environ.get('issuer'), vo=flask.request.environ.get('vo'), ) return Response(json.dumps(request_data, cls=APIEncoder), content_type='application/json') except RequestNotFound as error: return generate_http_error_flask(404, error.__class__.__name__, f'No request found for DID {scope}:{name} at RSE {rse}') class RequestHistoryGet(ErrorHandlingMethodView): """ REST API to get historical requests. """ @check_accept_header_wrapper_flask(['application/json']) def get(self, scope_name, rse): """ List request for given DID to a destination RSE. .. :quickref: RequestHistoryGet; list requests :param scope_name: data identifier (scope)/(name). :param rse: destination RSE. :reqheader Content-Type: application/json :status 200: Request found. :status 404: Request not found. """ try: scope, name = parse_scope_name(scope_name, flask.request.environ.get('vo')) except ValueError as error: return generate_http_error_flask(400, error) try: request_data = request.get_request_history_by_did( scope=scope, name=name, rse=rse, issuer=flask.request.environ.get('issuer'), vo=flask.request.environ.get('vo'), ) return Response(json.dumps(request_data, cls=APIEncoder), content_type='application/json') except RequestNotFound as error: return generate_http_error_flask(404, error.__class__.__name__, f'No request found for DID {scope}:{name} at RSE {rse}') class RequestList(ErrorHandlingMethodView): """ REST API to get requests. """ @check_accept_header_wrapper_flask(['application/x-json-stream']) def get(self): """ List requests for a given source and destination RSE or site. .. :quickref: RequestsGet; list requests :reqheader Content-Type: application/x-json-stream :status 200: Request found. :status 404: Request not found. :status 406: Not Acceptable. """ src_rse = flask.request.args.get('src_rse', default=None) dst_rse = flask.request.args.get('dst_rse', default=None) src_site = flask.request.args.get('src_site', default=None) dst_site = flask.request.args.get('dst_site', default=None) request_states = flask.request.args.get('request_states', default=None) if not request_states: return generate_http_error_flask(400, 'MissingParameter', 'Request state is missing') if src_rse and not dst_rse: return generate_http_error_flask(400, 'MissingParameter', 'Destination RSE is missing') elif dst_rse and not src_rse: return generate_http_error_flask(400, 'MissingParameter', 'Source RSE is missing') elif src_site and not dst_site: return generate_http_error_flask(400, 'MissingParameter', 'Destination site is missing') elif dst_site and not src_site: return generate_http_error_flask(400, 'MissingParameter', 'Source site is missing') try: states = [RequestState(state) for state in request_states.split(',')] except ValueError: return generate_http_error_flask(400, 'Invalid', 'Request state value is invalid') src_rses = [] dst_rses = [] if src_site: src_rses = get_rses_with_attribute_value(key='site', value=src_site, lookup_key='site', vo=flask.request.environ.get('vo')) if not src_rses: return generate_http_error_flask(404, 'NotFound', f'Could not resolve site name {src_site} to RSE') src_rses = [get_rse_name(rse['rse_id']) for rse in src_rses] dst_rses = get_rses_with_attribute_value(key='site', value=dst_site, lookup_key='site', vo=flask.request.environ.get('vo')) if not dst_rses: return generate_http_error_flask(404, 'NotFound', f'Could not resolve site name {dst_site} to RSE') dst_rses = [get_rse_name(rse['rse_id']) for rse in dst_rses] else: dst_rses = [dst_rse] src_rses = [src_rse] def generate(issuer, vo): for result in request.list_requests(src_rses, dst_rses, states, issuer=issuer, vo=vo): del result['_sa_instance_state'] yield render_json(**result) + '\n' return try_stream(generate(issuer=flask.request.environ.get('issuer'), vo=flask.request.environ.get('vo'))) class RequestHistoryList(ErrorHandlingMethodView): """ REST API to get requests. """ @check_accept_header_wrapper_flask(['application/x-json-stream']) def get(self): """ List historical requests for a given source and destination RSE or site. .. :quickref: RequestsGet; list requests :reqheader Content-Type: application/x-json-stream :status 200: Request found. :status 404: Request not found. """ src_rse = flask.request.args.get('src_rse', default=None) dst_rse = flask.request.args.get('dst_rse', default=None) src_site = flask.request.args.get('src_site', default=None) dst_site = flask.request.args.get('dst_site', default=None) request_states = flask.request.args.get('request_states', default=None) offset = flask.request.args.get('offset', default=0) limit = flask.request.args.get('limit', default=100) if not request_states: return generate_http_error_flask(400, 'MissingParameter', 'Request state is missing') if src_rse and not dst_rse: return generate_http_error_flask(400, 'MissingParameter', 'Destination RSE is missing') elif dst_rse and not src_rse: return generate_http_error_flask(400, 'MissingParameter', 'Source RSE is missing') elif src_site and not dst_site: return generate_http_error_flask(400, 'MissingParameter', 'Destination site is missing') elif dst_site and not src_site: return generate_http_error_flask(400, 'MissingParameter', 'Source site is missing') try: states = [RequestState(state) for state in request_states.split(',')] except ValueError: return generate_http_error_flask(400, 'Invalid', 'Request state value is invalid') src_rses = [] dst_rses = [] if src_site: src_rses = get_rses_with_attribute_value(key='site', value=src_site, lookup_key='site', vo=flask.request.environ.get('vo')) if not src_rses: return generate_http_error_flask(404, 'NotFound', f'Could not resolve site name {src_site} to RSE') src_rses = [get_rse_name(rse['rse_id']) for rse in src_rses] dst_rses = get_rses_with_attribute_value(key='site', value=dst_site, lookup_key='site', vo=flask.request.environ.get('vo')) if not dst_rses: return generate_http_error_flask(404, 'NotFound', f'Could not resolve site name {dst_site} to RSE') dst_rses = [get_rse_name(rse['rse_id']) for rse in dst_rses] else: dst_rses = [dst_rse] src_rses = [src_rse] def generate(issuer, vo): for result in request.list_requests_history(src_rses, dst_rses, states, issuer=issuer, vo=vo, offset=offset, limit=limit): del result['_sa_instance_state'] yield render_json(**result) + '\n' return try_stream(generate(issuer=flask.request.environ.get('issuer'), vo=flask.request.environ.get('vo'))) def blueprint(): bp = Blueprint('requests', __name__, url_prefix='/requests') request_get_view = RequestGet.as_view('request_get') bp.add_url_rule('/<path:scope_name>/<rse>', view_func=request_get_view, methods=['get', ]) request_history_get_view = RequestHistoryGet.as_view('request_history_get') bp.add_url_rule('/history/<path:scope_name>/<rse>', view_func=request_history_get_view, methods=['get', ]) request_list_view = RequestList.as_view('request_list') bp.add_url_rule('/list', view_func=request_list_view, methods=['get', ]) request_history_list_view = RequestHistoryList.as_view('request_history_list') bp.add_url_rule('/history/list', view_func=request_history_list_view, methods=['get', ]) bp.before_request(request_auth_env) bp.after_request(response_headers) return bp def make_doc(): """ Only used for sphinx documentation """ doc_app = Flask(__name__) doc_app.register_blueprint(blueprint()) return doc_app

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py

Python

src/vesper/data/base/__init__.py

onecommons/vesper

818c09350b8fe53ea484aaff24deb1002a67f471

[ "Apache-2.0" ]

null

src/vesper/data/base/__init__.py

onecommons/vesper

818c09350b8fe53ea484aaff24deb1002a67f471

[ "Apache-2.0" ]

null

src/vesper/data/base/__init__.py

onecommons/vesper

818c09350b8fe53ea484aaff24deb1002a67f471

[ "Apache-2.0" ]

null

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7,188

py

Python

ejercicios/13_ejercicio.py

jorgemauricio/becarios_utna

5b9082c35520fa162d780e843aae17ef6f003a28

[ "MIT" ]

1

2018-03-06T22:44:35.000Z

ejercicios/13_ejercicio.py

jorgemauricio/becarios_utna

5b9082c35520fa162d780e843aae17ef6f003a28

[ "MIT" ]

null

ejercicios/13_ejercicio.py

jorgemauricio/becarios_utna

5b9082c35520fa162d780e843aae17ef6f003a28

[ "MIT" ]

1

2018-03-06T22:44:39.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ ####################################### # Author: Jorge Mauricio # Email: jorge.ernesto.mauricio@gmail.com # Date: 2018-02-01 # Version: 1.0 ####################################### Objetivo: Dado un texto, contabilizar el número de veces que cada una de las palabras se repiten. texto = "Muy buenas tardes a todas y a todos ustedes. Quiero saludar, con respeto, al Presidente de la Cámara de Diputados y al Presidente de la Cámara de Senadores. A los señores Dirigentes de los partidos políticos en nuestro país. A sus principales fuerzas. De igual forma, saludo con respeto a los señores Gobernadores. A Gobernadores electos. Al Jefe de Gobierno electo del Distrito Federal. Todos ellos con origen en distintas expresiones políticas. Saludo a los señores Coordinadores Parlamentarios de distintas fuerzas políticas que están, hoy, aquí presentes. Con respeto, también, saludo a la representación, en actores políticos distinguidos, de las distintas fuerzas políticas de nuestro país. A todos, les saludo con afecto y reconocimiento por este encuentro, sin duda, inédito, pero que representa un gran paso para impulsar la trasformación de nuestro país. Es el punto de encuentro y de coincidencia que, realmente, aplaudo, celebro, y que sea para el bien de México. México comienza una nueva etapa de su vida democrática. Ha llegado el momento del encuentro y del acuerdo. Ha llegado el momento de dar el siguiente paso en el perfeccionamiento democrático: Transitar del sufragio efectivo al gobierno eficaz. En este propósito, los actores políticos deben, o debemos, caminar juntos. Debemos dialogar para construir consensos. En esta hora decisiva de la vida de la República se requiere que los políticos hagamos de las coincidencias la base para alcanzar los acuerdos esenciales. Se necesita que la pluralidad y la diferencia de visiones, en lugar de ser obstáculo, permitan el ascenso de México, enriquezcan el proyecto de Nación que todos queremos para el Siglo XXI. Como Presidente de la República, estoy plenamente convencido que gobernar en democracia significa estar atento y escuchar a las diversas voces que expresan el sentir de los mexicanos. He señalado, con plena convicción, que seré un Presidente democrático. Esto significa voluntad absoluta para conciliar posiciones. Voluntad para anteponer, invariablemente, el interés superior de la Nación." Resultado: ***** Numero de palabras: 321 Palabra MUY se repite 1 Palabra BUENAS se repite 1 Palabra TARDES se repite 1 Palabra A se repite 10 Palabra TODAS se repite 1 Palabra Y se repite 8 Palabra TODOS se repite 4 Palabra USTEDES se repite 1 Palabra QUIERO se repite 1 Palabra SALUDAR se repite 1 Palabra CON se repite 6 Palabra RESPETO se repite 3 Palabra AL se repite 4 Palabra PRESIDENTE se repite 4 Palabra DE se repite 26 Palabra LA se repite 11 Palabra CÁMARA se repite 2 Palabra DIPUTADOS se repite 1 Palabra SENADORES se repite 1 Palabra LOS se repite 8 Palabra SEÑORES se repite 3 Palabra DIRIGENTES se repite 1 Palabra PARTIDOS se repite 1 Palabra POLÍTICOS se repite 4 Palabra EN se repite 8 Palabra NUESTRO se repite 3 Palabra PAÍS se repite 3 Palabra SUS se repite 1 Palabra PRINCIPALES se repite 1 Palabra FUERZAS se repite 3 Palabra IGUAL se repite 1 Palabra FORMA se repite 1 Palabra SALUDO se repite 4 Palabra GOBERNADORES se repite 2 Palabra ELECTOS se repite 1 Palabra JEFE se repite 1 Palabra GOBIERNO se repite 2 Palabra ELECTO se repite 1 Palabra DEL se repite 4 Palabra DISTRITO se repite 1 Palabra FEDERAL se repite 1 Palabra ELLOS se repite 1 Palabra ORIGEN se repite 1 Palabra DISTINTAS se repite 3 Palabra EXPRESIONES se repite 1 Palabra POLÍTICAS se repite 3 Palabra COORDINADORES se repite 1 Palabra PARLAMENTARIOS se repite 1 Palabra QUE se repite 10 Palabra ESTÁN se repite 1 Palabra HOY se repite 1 Palabra AQUÍ se repite 1 Palabra PRESENTES se repite 1 Palabra TAMBIÉN se repite 1 Palabra REPRESENTACIÓN se repite 1 Palabra ACTORES se repite 2 Palabra DISTINGUIDOS se repite 1 Palabra LAS se repite 3 Palabra LES se repite 1 Palabra AFECTO se repite 1 Palabra RECONOCIMIENTO se repite 1 Palabra POR se repite 1 Palabra ESTE se repite 2 Palabra ENCUENTRO se repite 3 Palabra SIN se repite 1 Palabra DUDA se repite 1 Palabra INÉDITO se repite 1 Palabra PERO se repite 1 Palabra REPRESENTA se repite 1 Palabra UN se repite 2 Palabra GRAN se repite 1 Palabra PASO se repite 2 Palabra PARA se repite 7 Palabra IMPULSAR se repite 1 Palabra TRASFORMACIÓN se repite 1 Palabra ES se repite 1 Palabra EL se repite 11 Palabra PUNTO se repite 1 Palabra COINCIDENCIA se repite 1 Palabra REALMENTE se repite 1 Palabra APLAUDO se repite 1 Palabra CELEBRO se repite 1 Palabra SEA se repite 1 Palabra BIEN se repite 1 Palabra MÉXICO se repite 3 Palabra COMIENZA se repite 1 Palabra UNA se repite 1 Palabra NUEVA se repite 1 Palabra ETAPA se repite 1 Palabra SU se repite 1 Palabra VIDA se repite 2 Palabra DEMOCRÁTICA se repite 1 Palabra HA se repite 2 Palabra LLEGADO se repite 2 Palabra MOMENTO se repite 2 Palabra ACUERDO se repite 1 Palabra DAR se repite 1 Palabra SIGUIENTE se repite 1 Palabra PERFECCIONAMIENTO se repite 1 Palabra DEMOCRÁTICO: se repite 1 Palabra TRANSITAR se repite 1 Palabra SUFRAGIO se repite 1 Palabra EFECTIVO se repite 1 Palabra EFICAZ se repite 1 Palabra PROPÓSITO se repite 1 Palabra DEBEN se repite 1 Palabra O se repite 1 Palabra DEBEMOS se repite 2 Palabra CAMINAR se repite 1 Palabra JUNTOS se repite 1 Palabra DIALOGAR se repite 1 Palabra CONSTRUIR se repite 1 Palabra CONSENSOS se repite 1 Palabra ESTA se repite 1 Palabra HORA se repite 1 Palabra DECISIVA se repite 1 Palabra REPÚBLICA se repite 2 Palabra SE se repite 2 Palabra REQUIERE se repite 1 Palabra HAGAMOS se repite 1 Palabra COINCIDENCIAS se repite 1 Palabra BASE se repite 1 Palabra ALCANZAR se repite 1 Palabra ACUERDOS se repite 1 Palabra ESENCIALES se repite 1 Palabra NECESITA se repite 1 Palabra PLURALIDAD se repite 1 Palabra DIFERENCIA se repite 1 Palabra VISIONES se repite 1 Palabra LUGAR se repite 1 Palabra SER se repite 1 Palabra OBSTÁCULO se repite 1 Palabra PERMITAN se repite 1 Palabra ASCENSO se repite 1 Palabra ENRIQUEZCAN se repite 1 Palabra PROYECTO se repite 1 Palabra NACIÓN se repite 2 Palabra QUEREMOS se repite 1 Palabra SIGLO se repite 1 Palabra XXI se repite 1 Palabra COMO se repite 1 Palabra ESTOY se repite 1 Palabra PLENAMENTE se repite 1 Palabra CONVENCIDO se repite 1 Palabra GOBERNAR se repite 1 Palabra DEMOCRACIA se repite 1 Palabra SIGNIFICA se repite 2 Palabra ESTAR se repite 1 Palabra ATENTO se repite 1 Palabra ESCUCHAR se repite 1 Palabra DIVERSAS se repite 1 Palabra VOCES se repite 1 Palabra EXPRESAN se repite 1 Palabra SENTIR se repite 1 Palabra MEXICANOS se repite 1 Palabra HE se repite 1 Palabra SEÑALADO se repite 1 Palabra PLENA se repite 1 Palabra CONVICCIÓN se repite 1 Palabra SERÉ se repite 1 Palabra DEMOCRÁTICO se repite 1 Palabra ESTO se repite 1 Palabra VOLUNTAD se repite 2 Palabra ABSOLUTA se repite 1 Palabra CONCILIAR se repite 1 Palabra POSICIONES se repite 1 Palabra ANTEPONER se repite 1 Palabra INVARIABLEMENTE se repite 1 Palabra INTERÉS se repite 1 Palabra SUPERIOR se repite 1 """

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6001d0518a371ac1dccd244e1db7dfaa72dd0fe4

214

py

Python

src/spaceone/monitoring/manager/__init__.py

xellos00/plugin-azure-monitor

5409707b2ea068ba35b408e01580c356e6b536c8

[ "Apache-2.0" ]

null

src/spaceone/monitoring/manager/__init__.py

xellos00/plugin-azure-monitor

5409707b2ea068ba35b408e01580c356e6b536c8

[ "Apache-2.0" ]

null

src/spaceone/monitoring/manager/__init__.py

xellos00/plugin-azure-monitor

5409707b2ea068ba35b408e01580c356e6b536c8

[ "Apache-2.0" ]

3

2020-11-23T10:08:28.000Z

2020-12-28T04:41:41.000Z

from spaceone.monitoring.manager.azure_manager import AzureManager from spaceone.monitoring.manager.data_source_manager import DataSourceManager from spaceone.monitoring.manager.metric_manager import MetricManager

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60384b7a56328112edabfe3553a66bb3aa0ad273

168

py

Python

oplab/v3/quant.py

oplab-team/oplab-client-python

a09a67a78c82b983374ae6a43dab68de208706b5

[ "MIT" ]

3

2020-05-18T05:04:54.000Z

2022-02-14T14:09:39.000Z

oplab/v3/quant.py

oplab-team/oplab-client-python

a09a67a78c82b983374ae6a43dab68de208706b5

[ "MIT" ]

1

2020-05-23T19:49:11.000Z

2020-05-23T19:50:04.000Z

oplab/v3/quant.py

oplab-team/oplab-client-python

a09a67a78c82b983374ae6a43dab68de208706b5

[ "MIT" ]

null

class Quant: def __init__(self, client) -> None: self.client = client def url(self): return '%s%s' % (self.client.config['base_url'], 'quant')

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606a02e7537c104b5e2aec16b5931850f0453239

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py

Python

tests/test_file_handling.py

j19sch/pytest-instrument

53e26a2c507456327887e007fd2609e71ec52999

[ "MIT" ]

null

tests/test_file_handling.py

j19sch/pytest-instrument

53e26a2c507456327887e007fd2609e71ec52999

[ "MIT" ]

null

tests/test_file_handling.py

j19sch/pytest-instrument

53e26a2c507456327887e007fd2609e71ec52999

[ "MIT" ]

null

from pathlib import PurePath import re import pytest from tests import helpers @pytest.fixture(scope="function") def tests_filename(testdir): filename = "test_single_test_examples.py" testdir.copy_example(filename) return filename def test_single_json_log_file_is_created_with_json_instrument_option( testdir, tests_filename ): test_to_run = "test_passes" result = testdir.runpytest( "-vs", "--instrument=json", f"{tests_filename}::{test_to_run}" ) result.assert_outcomes(error=0, failed=0, passed=1) log_files = helpers.get_files_from_artifacts_dir_by_extension(testdir, "json") assert len(log_files) == 1 split_log_file_basename = PurePath(log_files[0]).stem.split("_", maxsplit=1) helpers.validate_timestamp(split_log_file_basename[0], "%Y%m%dT%H%M%S") records = helpers.get_json_log_file_from_artifacts_dir_and_return_records(testdir) session_id = records[0]["session_id"] assert split_log_file_basename[1] == session_id[:8] def test_single_plain_log_file_is_created_with_log_instrument_option( testdir, tests_filename ): test_to_run = "test_passes" result = testdir.runpytest( "-vs", "--instrument=log", f"{tests_filename}::{test_to_run}" ) result.assert_outcomes(error=0, failed=0, passed=1) log_files = helpers.get_files_from_artifacts_dir_by_extension(testdir, "log") assert len(log_files) == 1 split_log_file_basename = PurePath(log_files[0]).stem.split("_", maxsplit=1) helpers.validate_timestamp(split_log_file_basename[0], "%Y%m%dT%H%M%S") records = helpers.get_plain_log_file_from_artifacts_dir_and_return_records(testdir) pattern = re.compile(r"^.+ session id: (.+)$") match = pattern.search(records[0]) session_id = match[1] assert split_log_file_basename[1] == session_id[:8] def test_two_log_files_are_created_with_json_and_log_instrument_option( testdir, tests_filename ): test_to_run = "test_passes" result = testdir.runpytest( "-vs", "--instrument=json,log", f"{tests_filename}::{test_to_run}" ) result.assert_outcomes(error=0, failed=0, passed=1) json_log_files = helpers.get_files_from_artifacts_dir_by_extension(testdir, "json") assert len(json_log_files) == 1 plain_log_files = helpers.get_files_from_artifacts_dir_by_extension(testdir, "log") assert len(plain_log_files) == 1 assert PurePath(json_log_files[0]).stem == PurePath(plain_log_files[0]).stem records = helpers.get_json_log_file_from_artifacts_dir_and_return_records(testdir) session_id_json = records[0]["session_id"] records = helpers.get_plain_log_file_from_artifacts_dir_and_return_records(testdir) pattern = re.compile(r"^.+ session id: (.+)$") match = pattern.search(records[0]) session_id_plain = match[1] assert session_id_json == session_id_plain def test_no_file_created_without_instrument_option(testdir, tests_filename): test_to_run = "test_passes" result = testdir.runpytest("-vs", f"{tests_filename}::{test_to_run}") result.assert_outcomes(error=0, failed=0, passed=1) log_files = helpers.get_files_from_artifacts_dir_by_extension(testdir, "json") assert len(log_files) == 0 log_files = helpers.get_files_from_artifacts_dir_by_extension(testdir, "log") assert len(log_files) == 0

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60a8b0b6e6edc75600b92b94704857a8ce70f1a3

34

py

Python

Tox Whitebox/titlecase/__init__.py

adrianopaduam/pytest_studies

05932963539fb43baf64900b08fa12b8178c663b

[ "MIT" ]

null

Tox Whitebox/titlecase/__init__.py

adrianopaduam/pytest_studies

05932963539fb43baf64900b08fa12b8178c663b

[ "MIT" ]

null

Tox Whitebox/titlecase/__init__.py

adrianopaduam/pytest_studies

05932963539fb43baf64900b08fa12b8178c663b

[ "MIT" ]

null

from .titlecase import title_case

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60b85edf927478abb74878a6c6e7914d73182692

147

py

Python

src/testcase/GN_Y201H/input_case/GN_Y201H_Smart_Link.py

maiyajj/AutoTest_script-Appium_Connect

f9c2c42c281a9e2f984acb4a72dda0694b053f22

[ "Apache-2.0" ]

28

2017-11-10T00:19:16.000Z

2022-02-19T16:42:05.000Z

src/testcase/GN_Y201H/input_case/GN_Y201H_Smart_Link.py

maiyajj/AutoTest_script-Appium_Connect

f9c2c42c281a9e2f984acb4a72dda0694b053f22

[ "Apache-2.0" ]

null

src/testcase/GN_Y201H/input_case/GN_Y201H_Smart_Link.py

maiyajj/AutoTest_script-Appium_Connect

f9c2c42c281a9e2f984acb4a72dda0694b053f22

[ "Apache-2.0" ]

23

2017-08-22T06:12:19.000Z

2021-09-18T05:45:41.000Z

# coding=utf-8 try: from src.testcase.GN_Y201H.case.GN_Y201H_SMART_LINK.GN_Y201H_SMART_LINK_001 import * except ImportError as e: print(e)

24.5

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147

5

89

29.4

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null

0

1

0

1

0

6

60c05c63c965aa435cb0eeae775774e83e58951e

25,575

py

Python

rmp/models/processed/chemicals.py

rji-futures-lab/django-rmp-data

6580961dd965025f312127bdd788f2771463f0b5

[ "MIT" ]

null

rmp/models/processed/chemicals.py

rji-futures-lab/django-rmp-data

6580961dd965025f312127bdd788f2771463f0b5

[ "MIT" ]

67

2018-08-29T18:15:56.000Z

2020-06-05T18:57:59.000Z

rmp/models/processed/chemicals.py

rji-futures-lab/django-rmp-data

6580961dd965025f312127bdd788f2771463f0b5

[ "MIT" ]

6

2018-10-18T19:19:38.000Z

2021-09-15T17:21:52.000Z

""" Models for processed RMP data. """ import os from django.conf import settings from django.db import models from django.db.models import ( F, Max, OuterRef, Subquery, Sum, Count, Case, When, Value ) from rmp.fields import ( CopyFromBigIntegerField, CopyFromBooleanField, CopyFromCharField, CopyFromDateField, CopyFromDateTimeField, CopyFromDecimalField, CopyFromForeignKey, CopyFromIntegerField, CopyFromOneToOneField, CopyFromTextField, CopyFromFloatField, ) from rmp.models import raw as raw_models from rmp.models import processed as processed_models from rmp.models.base import BaseRMPModel class FlammablesAltRelease(BaseRMPModel): flammable_id = CopyFromIntegerField( primary_key=True, source_column='flammable_id' ) procchem = CopyFromForeignKey( 'ProcChem', on_delete=models.CASCADE, source_column='process_chemical_id' ) analytical_basis = CopyFromCharField( max_length=255, blank=True, ) scenario = CopyFromCharField( max_length=200, blank=True, ) quantity_released = CopyFromDecimalField( max_digits=5, decimal_places=2, null=True, ) endpoint_used = CopyFromCharField( max_length=30, blank=True, ) lfl_value = CopyFromDecimalField( max_digits=5, decimal_places=1, null=True, ) endpoint_distance = CopyFromDecimalField( source_column="distance2_endpoint", max_digits=5, decimal_places=1, null=True, ) population = CopyFromIntegerField( source_column="residential_population", null=True, verbose_name='Residential population', ) pr_schools = CopyFromBooleanField( verbose_name='Schools' ) pr_residences = CopyFromBooleanField( verbose_name='Residences' ) pr_hospitals = CopyFromBooleanField( verbose_name='Hospitals' ) pr_prisons = CopyFromBooleanField( verbose_name='Prisons/Correctional Facilities' ) pr_public_recreation= CopyFromBooleanField( verbose_name='Recreation Areas' ) pr_comm_ind = CopyFromBooleanField( verbose_name='Major Commercial, office, industrial areas' ) pr_other_type = CopyFromCharField( max_length=200, blank=True, ) er_natl_state_parks = CopyFromBooleanField( verbose_name='National or state parks, forests, or monuments', ) er_wildlife_sactuary = CopyFromBooleanField( verbose_name='Officially designated wildlife sanctuaries, preserves, or refuges', ) er_fed_wilderness = CopyFromBooleanField( verbose_name='Federal wilderness area', ) er_other_type = CopyFromCharField( max_length=200, blank=True, ) pm_dikes = CopyFromBooleanField( verbose_name='Dikes', ) pm_firewalls = CopyFromBooleanField( source_column='pm_fire_walls', verbose_name='Fire wall', ) pm_blastwalls = CopyFromBooleanField( source_column='pm_blast_walls', verbose_name='Blast walls', ) pm_enclosures = CopyFromBooleanField( verbose_name='Enclosures', ) pm_other_type = CopyFromCharField( max_length=200, blank=True, ) am_sprinklers = CopyFromBooleanField( source_column='am_sprinkler_systems', verbose_name='Sprinkler systems', ) am_deluge_systems = CopyFromBooleanField( verbose_name='Deluge systems', ) am_watercurtain = CopyFromBooleanField( source_column='am_water_curtain', verbose_name='Water curtain', ) am_excess_flowvalve = CopyFromBooleanField( source_column='am_excess_flow_valve', verbose_name='Excess flow valve', ) am_other_type = CopyFromCharField( max_length=200, blank=True, ) ptrgraphic = CopyFromCharField( source_column='ptr_graphic', max_length=12, blank=True, ) cbi_flag = CopyFromBooleanField() @classmethod def get_transform_queryset(self): m = raw_models.tblS5FlammablesAltReleases return m.objects.get_default_transform_queryset() @property def public_receptors_within_distance(self): self._public_receptors_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pr_') if self.__dict__[f.name] ] if self.pr_other_type != '': self._public_receptors_within_distance.append( self.pr_other_type ) return self._public_receptors_within_distance @property def public_receptors_not_within_distance(self): self._public_receptors_not_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pr_') if not self.__dict__[f.name] ] return self._public_receptors_not_within_distance @property def environmental_receptors_within_distance(self): self._environmental_receptors_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('er_') if self.__dict__[f.name] ] if self.er_other_type != '': self._environmental_receptors_within_distance.append( self.er_other_type ) return self._environmental_receptors_within_distance @property def environmental_receptors_not_within_distance(self): self._environmental_receptors_not_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('er_') if not self.__dict__[f.name] ] return self._environmental_receptors_not_within_distance @property def passive_mitigation_considered(self): self._passive_mitigation_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pm_') if self.__dict__[f.name] ] if self.pm_other_type != '': self._passive_mitigation_considered.append( self.pm_other_type ) return self._passive_mitigation_considered @property def passive_mitigation_not_considered(self): self._passive_mitigation_not_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pm_') if not self.__dict__[f.name] ] return self._passive_mitigation_not_considered @property def active_mitigation_considered(self): self._active_mitigation_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('am_') if self.__dict__[f.name] ] if self.am_other_type != '': self._active_mitigation_considered.append( self.pm_other_type ) return self._active_mitigation_considered @property def active_mitigation_not_considered(self): self._active_mitigation_not_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('am_') if not self.__dict__[f.name] ] return self._active_mitigation_not_considered class ToxicsAltRelease(BaseRMPModel): id = CopyFromIntegerField( primary_key=True, source_column='toxic_id', ) procchem = CopyFromForeignKey( 'ProcChem', on_delete=models.CASCADE, source_column='ProcessChemicalID', ) percent_weight = CopyFromDecimalField( max_digits=4, decimal_places=1, null=True, ) physical_state = CopyFromForeignKey( "PhysCd", on_delete=models.PROTECT, db_column='physical_state', null=True, ) analytical_basis = CopyFromCharField( max_length=255, blank=True, ) scenario = CopyFromForeignKey( "ScenCd", on_delete=models.PROTECT, db_column='scenario', null=True, ) quantity_released = CopyFromDecimalField( max_digits=5, decimal_places=2, null=True, ) release_duration = CopyFromDecimalField( max_digits=5, decimal_places=2, null=True, ) release_rate = CopyFromBooleanField( null=True, ) wind_speed = CopyFromDecimalField( max_digits=6, decimal_places=2, null=True, ) stability_class = CopyFromCharField( max_length=1, blank=True, ) topography = CopyFromForeignKey( "TopoCd", on_delete=models.PROTECT, db_column='topography', null=True, ) endpoint_distance = CopyFromDecimalField( source_column='distance2_endpoint', max_digits=5, decimal_places=1, null=True, ) residential_population = CopyFromIntegerField( null=True, verbose_name='Residential population', ) pr_schools = CopyFromBooleanField( verbose_name='Schools' ) pr_residences = CopyFromBooleanField( verbose_name='Residences' ) pr_hospitals = CopyFromBooleanField( verbose_name='Hospitals' ) pr_prisons = CopyFromBooleanField( verbose_name='Prisons/Correctional Facilities' ) pr_public_recreation= CopyFromBooleanField( verbose_name='Recreation Areas' ) pr_comm_ind = CopyFromBooleanField( verbose_name='Major Commercial, office, industrial areas' ) pr_other_type = CopyFromCharField( max_length=200, blank=True, ) er_natl_state_parks = CopyFromBooleanField( verbose_name='National or state parks, forests, or monuments', ) er_wildlife_sactuary = CopyFromBooleanField( verbose_name='Officially designated wildlife sanctuaries, preserves, or refuges', ) er_fed_wilderness = CopyFromBooleanField( verbose_name='Federal wilderness area', ) er_other_type = CopyFromCharField( max_length=200, blank=True, ) pm_dikes = CopyFromBooleanField( verbose_name='Dikes', ) pm_enclosures = CopyFromBooleanField( verbose_name='Enclosures', ) pm_berms = CopyFromBooleanField( verbose_name='Berms', ) pm_drains = CopyFromBooleanField( verbose_name='Drains', ) pm_sumps = CopyFromBooleanField( verbose_name='Sumps', ) pm_other_type = CopyFromCharField( max_length=200, blank=True, ) am_sprinkler_systems = CopyFromBooleanField( verbose_name='Sprinkler systems' ) am_deluge_systems = CopyFromBooleanField( verbose_name='Deluge systems' ) am_water_curtain = CopyFromBooleanField( verbose_name='Water curtain' ) am_neutralization = CopyFromBooleanField( verbose_name='Neutralization' ) am_excess_flow_valve = CopyFromBooleanField( verbose_name='Excess flow valve' ) am_flares = CopyFromBooleanField( verbose_name='Flares' ) am_scrubbers = CopyFromBooleanField( verbose_name='Scrubbers' ) am_emergency_shutdown = CopyFromBooleanField( verbose_name='Emergency shutdown' ) am_other_type = CopyFromCharField( max_length=200, blank=True, ) ptr_graphic = CopyFromCharField( max_length=12, blank=True, ) cbi_flag = CopyFromBooleanField() @classmethod def get_transform_queryset(self): m = raw_models.tblS3ToxicsAltReleases return m.objects.get_default_transform_queryset() @property def public_receptors_within_distance(self): self._public_receptors_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pr_') if self.__dict__[f.name] ] if self.pr_other_type != '': self._public_receptors_within_distance.append( self.pr_other_type ) return self._public_receptors_within_distance @property def public_receptors_not_within_distance(self): self._public_receptors_not_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pr_') if not self.__dict__[f.name] ] return self._public_receptors_not_within_distance @property def environmental_receptors_within_distance(self): self._environmental_receptors_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('er_') if self.__dict__[f.name] ] if self.er_other_type != '': self._environmental_receptors_within_distance.append( self.er_other_type ) return self._environmental_receptors_within_distance @property def environmental_receptors_not_within_distance(self): self._environmental_receptors_not_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('er_') if not self.__dict__[f.name] ] return self._environmental_receptors_not_within_distance @property def passive_mitigation_considered(self): self._passive_mitigation_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pm_') if self.__dict__[f.name] ] if self.pm_other_type != '': self._passive_mitigation_considered.append( self.pm_other_type ) return self._passive_mitigation_considered @property def passive_mitigation_not_considered(self): self._passive_mitigation_not_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pm_') if not self.__dict__[f.name] ] return self._passive_mitigation_not_considered @property def active_mitigation_considered(self): self._active_mitigation_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('am_') if self.__dict__[f.name] ] if self.am_other_type != '': self._active_mitigation_considered.append( self.pm_other_type ) return self._active_mitigation_considered @property def active_mitigation_not_considered(self): self._active_mitigation_not_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('am_') if not self.__dict__[f.name] ] return self._active_mitigation_not_considered class ToxicsWorstCase(BaseRMPModel): id = CopyFromIntegerField( primary_key=True, source_column='toxic_id', ) procchem = CopyFromForeignKey( 'ProcChem', on_delete=models.CASCADE, source_column="ProcessChemicalID", ) percent_weight = CopyFromDecimalField( max_digits=4, decimal_places=1, null=True, ) physical_state = CopyFromForeignKey( "PhysCd", on_delete=models.PROTECT, db_column='physical_state', null=True, ) analytical_basis = CopyFromCharField( max_length=255, blank=True, ) scenario = CopyFromForeignKey( "ScenCd", on_delete=models.PROTECT, db_column='scenario', null=True, ) quantity_released = CopyFromDecimalField( max_digits=6, decimal_places=2, null=True, ) release_duration = CopyFromDecimalField( max_digits=7, decimal_places=2, null=True, ) release_rate = CopyFromDecimalField( max_digits=4, decimal_places=1, null=True, ) wind_speed = CopyFromDecimalField( max_digits=4, decimal_places=1, null=True, ) stability_class = CopyFromCharField( max_length=1, blank=True, ) topography = CopyFromForeignKey( "TopoCd", on_delete=models.PROTECT, db_column='topography', null=True, ) distance2_endpoint = CopyFromDecimalField( max_digits=5, decimal_places=1, null=True, ) residential_population = CopyFromIntegerField( null=True, ) pr_schools = CopyFromBooleanField( verbose_name='Schools' ) pr_residences = CopyFromBooleanField( verbose_name='Residences' ) pr_hospitals = CopyFromBooleanField( verbose_name='Hospitals' ) pr_prisons = CopyFromBooleanField( verbose_name='Prisons/Correctional Facilities' ) pr_public_recreation= CopyFromBooleanField( verbose_name='Recreation Areas' ) pr_comm_ind = CopyFromBooleanField( verbose_name='Major Commercial, office, industrial areas' ) pr_other_type = CopyFromCharField( max_length=200, blank=True, ) er_natl_state_parks = CopyFromBooleanField( verbose_name='National or state parks, forests, or monuments', ) er_wildlife_sactuary = CopyFromBooleanField( verbose_name='Officially designated wildlife sanctuaries, preserves, or refuges', ) er_fed_wilderness = CopyFromBooleanField( verbose_name='Federal wilderness area', ) er_other_type = CopyFromCharField( max_length=200, blank=True, ) pm_dikes = CopyFromBooleanField( verbose_name='Dikes', ) pm_enclosures = CopyFromBooleanField( verbose_name='Enclosures', ) pm_berms = CopyFromBooleanField( verbose_name='Berms', ) pm_drains = CopyFromBooleanField( verbose_name='Drains', ) pm_sumps = CopyFromBooleanField( verbose_name='Sumps', ) pm_other_type = CopyFromCharField( max_length=200, blank=True, ) ptr_graphic = CopyFromCharField( max_length=12, blank=True, ) cbi_flag = CopyFromBooleanField() @classmethod def get_transform_queryset(self): m = raw_models.tblS2ToxicsWorstCase return m.objects.get_default_transform_queryset() @property def public_receptors_within_distance(self): self._public_receptors_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pr_') if self.__dict__[f.name] ] if self.pr_other_type != '': self._public_receptors_within_distance.append( self.pr_other_type ) return self._public_receptors_within_distance @property def public_receptors_not_within_distance(self): self._public_receptors_not_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pr_') if not self.__dict__[f.name] ] return self._public_receptors_not_within_distance @property def environmental_receptors_within_distance(self): self._environmental_receptors_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('er_') if self.__dict__[f.name] ] if self.er_other_type != '': self._environmental_receptors_within_distance.append( self.er_other_type ) return self._environmental_receptors_within_distance @property def environmental_receptors_not_within_distance(self): self._environmental_receptors_not_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('er_') if not self.__dict__[f.name] ] return self._environmental_receptors_not_within_distance @property def passive_mitigation_considered(self): self._passive_mitigation_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pm_') if self.__dict__[f.name] ] if self.pm_other_type != '': self._passive_mitigation_considered.append( self.pm_other_type ) return self._passive_mitigation_considered @property def passive_mitigation_not_considered(self): self._passive_mitigation_not_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pm_') if not self.__dict__[f.name] ] return self._passive_mitigation_not_considered class FlammablesWorstCase(BaseRMPModel): id = CopyFromIntegerField( primary_key=True, source_column='flammable_id', ) procchem = CopyFromForeignKey( 'ProcChem', on_delete=models.CASCADE, source_column='ProcessChemicalID', ) analytical_basis = CopyFromCharField( max_length=255, blank=True, ) distance2_endpoint = CopyFromDecimalField( max_digits=5, decimal_places=1, null=True, ) quantity_released = CopyFromIntegerField( null=True, ) residential_population = CopyFromIntegerField( null=True, ) pr_schools = CopyFromBooleanField( verbose_name='Schools' ) pr_residences = CopyFromBooleanField( verbose_name='Residences' ) pr_hospitals = CopyFromBooleanField( verbose_name='Hospitals' ) pr_prisons = CopyFromBooleanField( verbose_name='Prisons/Correctional Facilities' ) pr_public_recreation= CopyFromBooleanField( verbose_name='Recreation Areas' ) pr_comm_ind = CopyFromBooleanField( verbose_name='Major Commercial, office, industrial areas' ) pr_other_type = CopyFromCharField( max_length=200, blank=True, ) er_natl_state_parks = CopyFromBooleanField( verbose_name='National or state parks, forests, or monuments', ) er_wildlife_sactuary = CopyFromBooleanField( verbose_name='Officially designated wildlife sanctuaries, preserves, or refuges', ) er_fed_wilderness = CopyFromBooleanField( verbose_name='Federal wilderness area', ) er_other_type = CopyFromCharField( max_length=200, blank=True, ) pm_blast_walls = CopyFromBooleanField( verbose_name='Blast walls' ) pm_other_type = CopyFromCharField( max_length=200, blank=True, ) ptr_graphic = CopyFromCharField( max_length=12, blank=True, ) cbi_flag = CopyFromBooleanField() @classmethod def get_transform_queryset(self): m = raw_models.tblS4FlammablesWorstCase return m.objects.get_default_transform_queryset() @property def public_receptors_within_distance(self): self._public_receptors_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pr_') if self.__dict__[f.name] ] if self.pr_other_type != '': self._public_receptors_within_distance.append( self.pr_other_type ) return self._public_receptors_within_distance @property def public_receptors_not_within_distance(self): self._public_receptors_not_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pr_') if not self.__dict__[f.name] ] return self._public_receptors_not_within_distance @property def environmental_receptors_within_distance(self): self._environmental_receptors_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('er_') if self.__dict__[f.name] ] if self.er_other_type != '': self._environmental_receptors_within_distance.append( self.er_other_type ) return self._environmental_receptors_within_distance @property def environmental_receptors_not_within_distance(self): self._environmental_receptors_not_within_distance = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('er_') if not self.__dict__[f.name] ] return self._environmental_receptors_not_within_distance @property def passive_mitigation_considered(self): self._passive_mitigation_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pm_') if self.__dict__[f.name] ] if self.pm_other_type != '': self._passive_mitigation_considered.append( self.pm_other_type ) return self._passive_mitigation_considered @property def passive_mitigation_not_considered(self): self._passive_mitigation_not_considered = [ f.verbose_name for f in self._meta.model.get_prefixed_boolean_fields('pm_') if not self.__dict__[f.name] ] return self._passive_mitigation_not_considered

28.012048

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0.647977

2,548

25,575

6.084772

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0.855005

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25,575

912

90

28.042763

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0.040252

false

0.03522

0.010063

0

0.256604

0

null

0

1

0

null

0

6

60df159b4c6dc6625fc0efba0e2665104839d97b

220

py

Python

src/test/pythonFiles/testFiles/standard/tests/test_foreign_nested_tests.py

ChaseKnowlden/vscode-jupyter

9bdaf87f0b6dcd717c508e9023350499a6093f97

[ "MIT" ]

615

2020-11-11T22:55:28.000Z

2022-03-30T21:48:08.000Z

src/test/pythonFiles/testFiles/standard/tests/test_foreign_nested_tests.py

ChaseKnowlden/vscode-jupyter

9bdaf87f0b6dcd717c508e9023350499a6093f97

[ "MIT" ]

8,428

2020-11-11T22:06:43.000Z

2022-03-31T23:42:34.000Z

src/test/pythonFiles/testFiles/standard/tests/test_foreign_nested_tests.py

vasili8m/vscode-python

846eee870e8b7bab38172600836faedb5fb80166

[ "MIT" ]

158

2020-11-12T07:49:02.000Z

2022-03-27T20:50:20.000Z

from .external import ForeignTests class TestNestedForeignTests: class TestInheritingHere(ForeignTests): def test_nested_normal(self): assert True def test_normal(self): assert True

22

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null

0

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1

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6

60f851d7fdf8aef65c1416a21860d448bbcfa769

42

py

Python

harvester/harvester/harvester/__init__.py

HammerMuseum/channel-backend

7c4e4259d6a043daa9f41b9ad5ae0a63fd1c475e

[ "MIT" ]

3

2021-05-05T18:19:27.000Z

2021-05-28T18:03:28.000Z

harvester/harvester/harvester/__init__.py

HammerMuseum/channel-backend

7c4e4259d6a043daa9f41b9ad5ae0a63fd1c475e

[ "MIT" ]

4

2021-05-17T14:13:51.000Z

2021-05-17T14:22:46.000Z

harvester/harvester/harvester/__init__.py

HammerMuseum/channel-backend

7c4e4259d6a043daa9f41b9ad5ae0a63fd1c475e

[ "MIT" ]

null

from .HarvesterBase import HarvesterBase

14

40

0.857143

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41

21

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null

0

1

0

1

0

1

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6

718808394ef31ba7eeab103dde5980ca840178a1

81

py

Python

pyroll/core/profile/__init__.py

pyroll-project/pyroll-core

f59094d58c2f7493ddc6345b3afc4700ca259681

[ "BSD-3-Clause" ]

null

pyroll/core/profile/__init__.py

pyroll-project/pyroll-core

f59094d58c2f7493ddc6345b3afc4700ca259681

[ "BSD-3-Clause" ]

null

pyroll/core/profile/__init__.py

pyroll-project/pyroll-core

f59094d58c2f7493ddc6345b3afc4700ca259681

[ "BSD-3-Clause" ]

null

from .profile import Profile from . import hookspecs from . import base_plugins

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Python

envoy/tests/test_parser.py

remicalixte/integrations-core

b115e18c52820fe1a92495f538fdc14ddf83cfe1

[ "BSD-3-Clause" ]

1

2021-01-28T01:45:37.000Z

envoy/tests/test_parser.py

remicalixte/integrations-core

b115e18c52820fe1a92495f538fdc14ddf83cfe1

[ "BSD-3-Clause" ]

3

2021-01-27T04:56:40.000Z

2021-02-26T06:29:22.000Z

envoy/tests/test_parser.py

remicalixte/integrations-core

b115e18c52820fe1a92495f538fdc14ddf83cfe1

[ "BSD-3-Clause" ]

1

2019-12-23T13:35:17.000Z

import pytest from datadog_checks.envoy.errors import UnknownMetric, UnknownTags from datadog_checks.envoy.metrics import METRIC_PREFIX, METRICS from datadog_checks.envoy.parser import parse_histogram, parse_metric def test_unknown_metric(): with pytest.raises(UnknownMetric): parse_metric('foo.bar') def test_unknown_tag(): with pytest.raises(UnknownTags): parse_metric('stats.major.overflow') def test_runtime(): metric = 'runtime.num_keys' tags = [tag for tags in METRICS[metric]['tags'] for tag in tags] assert parse_metric(metric) == (METRIC_PREFIX + metric, list(tags), METRICS[metric]['method']) def test_cds(): metric = 'cluster_manager.cds.config_reload' tags = [tag for tags in METRICS[metric]['tags'] for tag in tags] assert parse_metric(metric) == (METRIC_PREFIX + metric, list(tags), METRICS[metric]['method']) def test_http_router_filter(): metric = 'http{}.rq_total' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_http_router_filter_vhost(): metric = 'vhost{}.vcluster{}.upstream_rq_time' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_vhost_name' tag1 = 'some_vcluster_name' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_http_rate_limit(): metric = 'cluster{}.ratelimit.ok' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_route_target_cluster' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_ip_tagging(): metric = 'http{}.ip_tagging{}.hit' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_tag_name' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_grpc(): metric = 'cluster{}.grpc{}{}.total' untagged_metric = metric.format('', '', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_route_target_cluster' tag1 = 'some_grpc_service' tag2 = 'some_grpc_method' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1), '.{}'.format(tag2)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1), '{}:{}'.format(tags[2], tag2)], METRICS[untagged_metric]['method'], ) def test_dynamodb_operation(): metric = 'http{}.dynamodb.operation{}.upstream_rq_total' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_operation_name' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_dynamodb_table(): metric = 'http{}.dynamodb.table{}.upstream_rq_total' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_table_name' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_dynamodb_error(): metric = 'http{}.dynamodb.error{}{}' untagged_metric = metric.format('', '', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_table_name' tag2 = 'error_type' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1), '.{}'.format(tag2)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1), '{}:{}'.format(tags[2], tag2)], METRICS[untagged_metric]['method'], ) def test_http_buffer_filter(): metric = 'http{}.buffer.rq_timeout' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_rds(): metric = 'http{}.rds{}.config_reload' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_route_config_name' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_tcp_proxy(): metric = 'tcp{}.downstream_cx_total' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_tls(): metric = 'auth.clientssl{}.update_success' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_network_rate_limit(): metric = 'ratelimit{}.total' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_redis(): metric = 'redis{}.downstream_rq_total' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_redis_splitter(): metric = 'redis{}.splitter.invalid_request' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_redis_command(): metric = 'redis{}.command{}.total' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_command' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_mongo(): metric = 'mongo{}.op_insert' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_mongo_command(): metric = 'mongo{}.cmd{}.total' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_command' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_mongo_collection(): metric = 'mongo{}.collection{}.query.total' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_collection' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_listener(): metric = 'listener{}.ssl.ciphers{}' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = '0.0.0.0_80' tag1 = 'some_ciphers' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_listener_manager(): metric = 'listener_manager.listener_added' tags = [tag for tags in METRICS[metric]['tags'] for tag in tags] assert parse_metric(metric) == (METRIC_PREFIX + metric, list(tags), METRICS[metric]['method']) def test_listener_tls(): metric = 'listener{}.ssl.versions{}' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = '0.0.0.0' tag1 = 'TLSv1.2' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_listener_curves(): metric = 'listener{}.ssl.curves{}' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = '0.0.0.0' tag1 = 'P-256' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_listener_sigalgs(): metric = 'listener{}.ssl.sigalgs{}' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = '0.0.0.0' tag1 = 'rsa_pss_rsae_sha256' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_http(): metric = 'http{}.downstream_cx_total' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_http_user_agent(): metric = 'http{}.user_agent{}.downstream_cx_total' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_stat_prefix' tag1 = 'some_user_agent' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_http_listener(): metric = 'listener{}.http{}.downstream_rq_2xx' untagged_metric = metric.format('', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = '0.0.0.0_80' tag1 = 'some_stat_prefix' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1)], METRICS[untagged_metric]['method'], ) def test_http2(): metric = 'http2.rx_reset' tags = [tag for tags in METRICS[metric]['tags'] for tag in tags] assert parse_metric(metric) == (METRIC_PREFIX + metric, list(tags), METRICS[metric]['method']) def test_cluster_manager(): metric = 'cluster_manager.cluster_added' tags = [tag for tags in METRICS[metric]['tags'] for tag in tags] assert parse_metric(metric) == (METRIC_PREFIX + metric, list(tags), METRICS[metric]['method']) def test_cluster(): metric = 'cluster{}.upstream_cx_total' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_name' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_cluster_health_check(): metric = 'cluster{}.health_check.healthy' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_name' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_cluster_outlier_detection(): metric = 'cluster{}.outlier_detection.ejections_enforced_total' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_name' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_cluster_dynamic_http(): metric = 'cluster{}.upstream_rq_time' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_name' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_cluster_dynamic_http_zones(): metric = 'cluster{}.zone{}{}.upstream_rq_time' untagged_metric = metric.format('', '', '') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_name' tag1 = 'some_table_name' tag2 = 'some_to_zone' tagged_metric = metric.format('.{}'.format(tag0), '.{}'.format(tag1), '.{}'.format(tag2)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0), '{}:{}'.format(tags[1], tag1), '{}:{}'.format(tags[2], tag2)], METRICS[untagged_metric]['method'], ) def test_cluster_load_balancer(): metric = 'cluster{}.lb_healthy_panic' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_name' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_cluster_load_balancer_subsets(): metric = 'cluster{}.lb_subsets_active' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'some_name' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_tag_with_dots(): metric = 'cluster{}.lb_healthy_panic' untagged_metric = metric.format('') tags = [tag for tags in METRICS[untagged_metric]['tags'] for tag in tags] tag0 = 'out.alerting-event-evaluator-test.datadog.svc.cluster.local|iperf' tagged_metric = metric.format('.{}'.format(tag0)) assert parse_metric(tagged_metric) == ( METRIC_PREFIX + untagged_metric, ['{}:{}'.format(tags[0], tag0)], METRICS[untagged_metric]['method'], ) def test_no_match(): metric = 'envoy.http.downstream_rq_time' value = 'No recorded values' assert list(parse_histogram(metric, value)) == [] def test_ignore_nan(): metric = 'envoy.http.downstream_rq_time' value = 'P0(0,0) P25(nan,0)' assert list(parse_histogram(metric, value)) == [('envoy.http.downstream_rq_time.0percentile', 0.0)] def test_correct(): metric = 'envoy.http.downstream_rq_time' value = ( 'P0(0,0) P25(25,0) P50(50,0) P75(75,0) P90(90,1.06) P95(95,1.08) ' 'P99(99,1.096) P99.9(99.9,1.0996) P100(100,1.1)' ) assert list(parse_histogram(metric, value)) == [ ('envoy.http.downstream_rq_time.0percentile', 0.0), ('envoy.http.downstream_rq_time.25percentile', 25.0), ('envoy.http.downstream_rq_time.50percentile', 50.0), ('envoy.http.downstream_rq_time.75percentile', 75.0), ('envoy.http.downstream_rq_time.90percentile', 90.0), ('envoy.http.downstream_rq_time.95percentile', 95.0), ('envoy.http.downstream_rq_time.99percentile', 99.0), ('envoy.http.downstream_rq_time.99_9percentile', 99.9), ('envoy.http.downstream_rq_time.100percentile', 100.0), ] def test_correct_unknown_percentile(): metric = 'envoy.http.downstream_rq_time' value = 'P0(0,0) P25(25,0) P55.5(55.5,0)' assert list(parse_histogram(metric, value)) == [ ('envoy.http.downstream_rq_time.0percentile', 0.0), ('envoy.http.downstream_rq_time.25percentile', 25.0), ('envoy.http.downstream_rq_time.55_5percentile', 55.5), ]

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Python

utz/git/log.py

ryan-williams/jupyter-rc

362d9d758498de317976006ce7dfc65e2bce2c57

[ "MIT" ]

null

utz/git/log.py

ryan-williams/jupyter-rc

362d9d758498de317976006ce7dfc65e2bce2c57

[ "MIT" ]

2

2020-11-18T15:36:35.000Z

2021-06-25T18:21:32.000Z

utz/git/log.py

runsascoded/utz

362d9d758498de317976006ce7dfc65e2bce2c57

[ "MIT" ]

1

2020-06-10T23:14:00.000Z

from ..process import output def msg(ref=None): return output('git','log','-n1','--format=%B',ref).decode().strip()

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py

Python

nbmolviz/methods/__init__.py

jparkhill/notebook-molecular-visualization

2dd61fedcf363d7362b727669b86c5f1c07656fd

[ "Apache-2.0" ]

55

2016-07-21T23:25:59.000Z

2022-02-14T01:04:49.000Z

nbmolviz/methods/__init__.py

jparkhill/notebook-molecular-visualization

2dd61fedcf363d7362b727669b86c5f1c07656fd

[ "Apache-2.0" ]

40

2016-07-26T20:57:04.000Z

2021-09-06T02:31:52.000Z

nbmolviz/methods/__init__.py

Autodesk/notebook-molecular-visualization

2dd61fedcf363d7362b727669b86c5f1c07656fd

[ "Apache-2.0" ]

18

2016-07-25T21:49:02.000Z

2020-10-03T11:17:03.000Z

from .atoms import * from .atomgroups import * from .molecules import * from .method import * from .trajectory import *

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Python

python/graphscope/experimental/nx/tests/algorithms/forward/test_efficiency.py

wenyuanyu/GraphScope

a40ccaf70557e608d8b091eb25ab04477f99ce21

[ "Apache-2.0" ]

2

2020-12-15T08:42:10.000Z

2022-01-14T09:13:16.000Z

python/graphscope/experimental/nx/tests/algorithms/forward/test_efficiency.py

wenyuanyu/GraphScope

a40ccaf70557e608d8b091eb25ab04477f99ce21

[ "Apache-2.0" ]

1

2020-12-22T13:15:40.000Z

python/graphscope/experimental/nx/tests/algorithms/forward/test_efficiency.py

wenyuanyu/GraphScope

a40ccaf70557e608d8b091eb25ab04477f99ce21

[ "Apache-2.0" ]

1

2021-11-23T03:40:43.000Z

import networkx.algorithms.tests.test_efficiency import pytest from graphscope.experimental.nx.utils.compat import import_as_graphscope_nx import_as_graphscope_nx(networkx.algorithms.tests.test_efficiency, decorators=pytest.mark.usefixtures("graphscope_session"))

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e07acd0bbc45233a58c392b320cfd846a1776e04

160

py

Python

AcademicDealerBackend/projectinfo/admin.py

Acciente717/AcademicDealerBackend

8024725f88997fa430fa92e1caa28161ffbb06f6

[ "MIT" ]

5

2019-03-10T06:57:15.000Z

2019-03-17T03:04:40.000Z

AcademicDealerBackend/projectinfo/admin.py

Acciente717/AcademicDealerBackend

8024725f88997fa430fa92e1caa28161ffbb06f6

[ "MIT" ]

11

2019-05-14T15:13:48.000Z

2019-05-31T15:31:33.000Z

AcademicDealerBackend/projectinfo/admin.py

Acciente717/AcademicDealerBackend

8024725f88997fa430fa92e1caa28161ffbb06f6

[ "MIT" ]

null

from django.contrib import admin from .models import Topic, Project, Reply admin.site.register(Topic) admin.site.register(Project) admin.site.register(Reply)

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6

e08a39dea0e90bfce3cd095df91308b4a2a84377

30

py

Python

testmockvpython.py

jonschull/Lyte

e9ba2bb1b07c9398b81a6f591898d2474d1a4609

[ "MIT" ]

1

2018-06-07T17:54:27.000Z

testmockvpython.py

jonschull/Lyte

e9ba2bb1b07c9398b81a6f591898d2474d1a4609

[ "MIT" ]

1

2018-06-28T05:08:57.000Z

testmockvpython.py

jonschull/Lyte

e9ba2bb1b07c9398b81a6f591898d2474d1a4609

[ "MIT" ]

null

import mockvpython print('hi')

15

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e093284d80c715352055e4d617830d17ac985ea3

11,784

py

Python

test/vanilla/low-level/Expected/AcceptanceTests/MediaTypesLowLevel/mediatypeslowlevel/rest/_request_builders_py3.py

cfculhane/autorest.python

8cbca95faee88d933a58bbbd17b76834faa8d387

[ "MIT" ]

null

test/vanilla/low-level/Expected/AcceptanceTests/MediaTypesLowLevel/mediatypeslowlevel/rest/_request_builders_py3.py

cfculhane/autorest.python

8cbca95faee88d933a58bbbd17b76834faa8d387

[ "MIT" ]

null

test/vanilla/low-level/Expected/AcceptanceTests/MediaTypesLowLevel/mediatypeslowlevel/rest/_request_builders_py3.py

cfculhane/autorest.python

8cbca95faee88d933a58bbbd17b76834faa8d387

[ "MIT" ]

1

2022-03-28T08:58:03.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Dict, IO, Optional, TypeVar, Union from azure.core.rest import HttpRequest from msrest import Serializer T = TypeVar("T") JSONType = Any _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def build_analyze_body_request(*, json: JSONType = None, content: Any = None, **kwargs: Any) -> HttpRequest: """Analyze body, that could be different media types. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder into your code flow. :keyword json: Pass in a JSON-serializable object (usually a dictionary). See the template in our example to find the input shape. Input parameter. :paramtype json: JSONType :keyword content: Pass in binary content you want in the body of the request (typically bytes, a byte iterator, or stream input). Input parameter. :paramtype content: any :keyword str content_type: Media type of the body sent to the API. Default value is "application/json". Allowed values are: "application/pdf", "image/jpeg", "image/png", "image/tiff", "application/json." :return: Returns an :class:`~azure.core.rest.HttpRequest` that you will pass to the client's `send_request` method. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this response into your code flow. :rtype: ~azure.core.rest.HttpRequest Example: .. code-block:: python # JSON input template you can fill out and use as your body input. json = b'bytes' # Optional. """ content_type = kwargs.pop("content_type", None) # type: Optional[str] accept = "application/json" # Construct URL url = "/mediatypes/analyze" # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") header_parameters["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="POST", url=url, headers=header_parameters, json=json, content=content, **kwargs) def build_analyze_body_no_accept_header_request( *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: """Analyze body, that could be different media types. Adds to AnalyzeBody by not having an accept type. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder into your code flow. :keyword json: Pass in a JSON-serializable object (usually a dictionary). See the template in our example to find the input shape. Input parameter. :paramtype json: JSONType :keyword content: Pass in binary content you want in the body of the request (typically bytes, a byte iterator, or stream input). Input parameter. :paramtype content: any :keyword str content_type: Media type of the body sent to the API. Default value is "application/json". Allowed values are: "application/pdf", "image/jpeg", "image/png", "image/tiff", "application/json." :return: Returns an :class:`~azure.core.rest.HttpRequest` that you will pass to the client's `send_request` method. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this response into your code flow. :rtype: ~azure.core.rest.HttpRequest Example: .. code-block:: python # JSON input template you can fill out and use as your body input. json = b'bytes' # Optional. """ content_type = kwargs.pop("content_type", None) # type: Optional[str] # Construct URL url = "/mediatypes/analyzeNoAccept" # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") return HttpRequest(method="POST", url=url, headers=header_parameters, json=json, content=content, **kwargs) def build_content_type_with_encoding_request(*, content: Any = None, **kwargs: Any) -> HttpRequest: """Pass in contentType 'text/plain; charset=UTF-8' to pass test. Value for input does not matter. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder into your code flow. :keyword content: Pass in binary content you want in the body of the request (typically bytes, a byte iterator, or stream input). Input parameter. :paramtype content: any :return: Returns an :class:`~azure.core.rest.HttpRequest` that you will pass to the client's `send_request` method. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this response into your code flow. :rtype: ~azure.core.rest.HttpRequest """ content_type = kwargs.pop("content_type", None) # type: Optional[str] accept = "application/json" # Construct URL url = "/mediatypes/contentTypeWithEncoding" # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") header_parameters["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="POST", url=url, headers=header_parameters, content=content, **kwargs) def build_binary_body_with_two_content_types_request( *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: """Binary body with two content types. Pass in of {'hello': 'world'} for the application/json content type, and a byte stream of 'hello, world!' for application/octet-stream. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder into your code flow. :keyword json: Pass in a JSON-serializable object (usually a dictionary). See the template in our example to find the input shape. The payload body. :paramtype json: JSONType :keyword content: Pass in binary content you want in the body of the request (typically bytes, a byte iterator, or stream input). The payload body. :paramtype content: any :return: Returns an :class:`~azure.core.rest.HttpRequest` that you will pass to the client's `send_request` method. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this response into your code flow. :rtype: ~azure.core.rest.HttpRequest Example: .. code-block:: python # JSON input template you can fill out and use as your body input. json = b'bytes' # Optional. """ content_type = kwargs.pop("content_type", None) # type: Optional[str] accept = "text/plain" # Construct URL url = "/mediatypes/binaryBodyTwoContentTypes" # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") header_parameters["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="POST", url=url, headers=header_parameters, json=json, content=content, **kwargs) def build_binary_body_with_three_content_types_request( *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: """Binary body with three content types. Pass in string 'hello, world' with content type 'text/plain', {'hello': world'} with content type 'application/json' and a byte string for 'application/octet-stream'. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder into your code flow. :keyword json: Pass in a JSON-serializable object (usually a dictionary). See the template in our example to find the input shape. The payload body. :paramtype json: JSONType :keyword content: Pass in binary content you want in the body of the request (typically bytes, a byte iterator, or stream input). The payload body. :paramtype content: any :keyword str content_type: Media type of the body sent to the API. Default value is "application/json". Allowed values are: "application/json", "application/octet-stream", "text/plain." :return: Returns an :class:`~azure.core.rest.HttpRequest` that you will pass to the client's `send_request` method. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this response into your code flow. :rtype: ~azure.core.rest.HttpRequest Example: .. code-block:: python # JSON input template you can fill out and use as your body input. json = b'bytes' # Optional. """ content_type = kwargs.pop("content_type", None) # type: Optional[str] accept = "text/plain" # Construct URL url = "/mediatypes/binaryBodyThreeContentTypes" # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") header_parameters["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="POST", url=url, headers=header_parameters, json=json, content=content, **kwargs) def build_put_text_and_json_body_request(*, json: JSONType = None, content: Any = None, **kwargs: Any) -> HttpRequest: """Body that's either text/plain or application/json. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder into your code flow. :keyword json: Pass in a JSON-serializable object (usually a dictionary). See the template in our example to find the input shape. The payload body. :paramtype json: JSONType :keyword content: Pass in binary content you want in the body of the request (typically bytes, a byte iterator, or stream input). The payload body. :paramtype content: any :keyword str content_type: Media type of the body sent to the API. Default value is "application/json". Allowed values are: "text/plain", "application/json." :return: Returns an :class:`~azure.core.rest.HttpRequest` that you will pass to the client's `send_request` method. See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this response into your code flow. :rtype: ~azure.core.rest.HttpRequest Example: .. code-block:: python # JSON input template you can fill out and use as your body input. json = "str" # Optional. """ content_type = kwargs.pop("content_type", None) # type: Optional[str] accept = "text/plain" # Construct URL url = "/mediatypes/textAndJson" # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") header_parameters["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="POST", url=url, headers=header_parameters, json=json, content=content, **kwargs)

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e09d4b2ccb6eb24372ef26bf110333f313a43652

173

py

Python

department_app/service/__init__.py

DarishkaAMS/Flask_Projects-EPAM-CRM

ecf8ffc0f6eb8b9b8796d195fea3e3d0ce98d2f9

[ "MIT" ]

2

2021-11-13T18:18:55.000Z

2021-12-05T09:39:20.000Z

department_app/service/__init__.py

DarishkaAMS/Flask_Projects-EPAM-CRM

ecf8ffc0f6eb8b9b8796d195fea3e3d0ce98d2f9

[ "MIT" ]

null

department_app/service/__init__.py

DarishkaAMS/Flask_Projects-EPAM-CRM

ecf8ffc0f6eb8b9b8796d195fea3e3d0ce98d2f9

[ "MIT" ]

null

""" __init__.py file of service module with imported employee_service and department_service submodules """ from . import employee_service from . import department_service

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e0e358b2c98487b271a8d739e5350c0640c6cdbb

4,811

py

Python

beartype_test/a00_unit/a10_pep/test_pep593.py

posita/beartype

e56399686e1f2ffd5128a4030b19314504e32450

[ "MIT" ]

1,056

2020-04-03T10:21:29.000Z

2022-03-28T12:38:16.000Z

beartype_test/a00_unit/a10_pep/test_pep593.py

posita/beartype

e56399686e1f2ffd5128a4030b19314504e32450

[ "MIT" ]

107

2020-04-04T06:00:16.000Z

2022-03-29T18:58:50.000Z

beartype_test/a00_unit/a10_pep/test_pep593.py

posita/beartype

e56399686e1f2ffd5128a4030b19314504e32450

[ "MIT" ]

30

2020-10-06T19:14:25.000Z

2022-03-02T08:02:59.000Z

#!/usr/bin/env python3 # --------------------( LICENSE )-------------------- # Copyright (c) 2014-2021 Beartype authors. # See "LICENSE" for further details. ''' **Beartype** :pep:`593` **unit tests.** This submodule unit tests :pep:`593` support implemented in the :func:`beartype.beartype` decorator. ''' # ....................{ IMPORTS }.................... #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # WARNING: To raise human-readable test errors, avoid importing from # package-specific submodules at module scope. #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # ....................{ TESTS ~ validators }.................... def test_die_unless_hint_pep593() -> None: ''' Test the :beartype._util.hint.pep.proposal.utilpep593.die_unless_hint_pep593` validator. ''' # Defer heavyweight imports. from beartype.roar import BeartypeDecorHintPep593Exception from beartype._util.hint.pep.proposal.utilpep593 import ( die_unless_hint_pep593) from beartype_test.util.mod.pytmodimport import ( import_module_typing_any_attr_or_none_safe) from pytest import raises from typing import Optional # "typing.Annotated" type hint factory imported from either the "typing" or # "typing_extensions" modules if importable *OR* "None" otherwise. Annotated = import_module_typing_any_attr_or_none_safe('Annotated') # If this factory exists, assert this validator avoids raising exceptions # for a type hint subscripting this factory. if Annotated is not None: die_unless_hint_pep593(Annotated[Optional[str], int]) # Assert this validator raises the expected exception for an arbitrary # PEP-compliant type hint *NOT* subscripting this factory. with raises(BeartypeDecorHintPep593Exception): die_unless_hint_pep593(Optional[str]) # ....................{ TESTS ~ getters }.................... def test_get_hint_pep593_metadata() -> None: ''' Test the :beartype._util.hint.pep.proposal.utilpep593.get_hint_pep593_metadata` getter. ''' # Defer heavyweight imports. from beartype.roar import BeartypeDecorHintPep593Exception from beartype._util.hint.pep.proposal.utilpep593 import ( get_hint_pep593_metadata) from beartype_test.util.mod.pytmodimport import ( import_module_typing_any_attr_or_none_safe) from pytest import raises from typing import Optional # "typing.Annotated" type hint factory imported from either the "typing" or # "typing_extensions" modules if importable *OR* "None" otherwise. Annotated = import_module_typing_any_attr_or_none_safe('Annotated') # If this factory exists, assert this getter returns the expected tuple for # an arbitrary PEP 593-compliant type hint. if Annotated is not None: assert get_hint_pep593_metadata(Annotated[ Optional[str], 'Thy', 'caverns', 'echoing', 'to', 'the', "Arve's", 'commotion,' ]) == ( 'Thy', 'caverns', 'echoing', 'to', 'the', "Arve's", 'commotion,') # Assert this getter raises the expected exception for an arbitrary # PEP-compliant type hint *NOT* subscripting this factory. with raises(BeartypeDecorHintPep593Exception): get_hint_pep593_metadata(Optional[str]) def test_get_hint_pep593_metahint() -> None: ''' Test the :beartype._util.hint.pep.proposal.utilpep593.get_hint_pep593_metahint` getter. ''' # Defer heavyweight imports. from beartype.roar import BeartypeDecorHintPep593Exception from beartype._util.hint.pep.proposal.utilpep593 import ( get_hint_pep593_metahint) from beartype_test.util.mod.pytmodimport import ( import_module_typing_any_attr_or_none_safe) from pytest import raises from typing import Optional # "typing.Annotated" type hint factory imported from either the "typing" or # "typing_extensions" modules if importable *OR* "None" otherwise. Annotated = import_module_typing_any_attr_or_none_safe('Annotated') # If this factory exists, assert this getter returns the expected # PEP-compliant type hint for an arbitrary PEP 593-compliant type hint. if Annotated is not None: metahint = Optional[int] assert get_hint_pep593_metahint(Annotated[ metahint, 'A', 'loud', 'lone', 'sound', 'no', 'other', 'sound', 'can', 'tame' ]) is metahint # Assert this getter raises the expected exception for an arbitrary # PEP-compliant type hint *NOT* subscripting this factory. with raises(BeartypeDecorHintPep593Exception): get_hint_pep593_metahint(Optional[str])

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8,388

py

Python

ms_graph/drive_items.py

areed1192/ms-graph-python-client

dad30327f575b3a76cb1b0b7000b2935c16c511a

[ "MIT" ]

39

2020-11-24T20:46:02.000Z

2022-03-29T13:47:09.000Z

ms_graph/drive_items.py

areed1192/ms-graph-python-client

dad30327f575b3a76cb1b0b7000b2935c16c511a

[ "MIT" ]

7

2021-02-11T11:43:27.000Z

2022-01-25T06:04:41.000Z

ms_graph/drive_items.py

areed1192/ms-graph-python-client

dad30327f575b3a76cb1b0b7000b2935c16c511a

[ "MIT" ]

34

2020-10-28T10:47:37.000Z

2022-02-02T09:50:04.000Z

from typing import Dict from ms_graph.session import GraphSession class DriveItems(): """ ## Overview: ---- The driveItem resource represents a file, folder, or other item stored in a drive. All file system objects in OneDrive and SharePoint are returned as driveItem resources. """ def __init__(self, session: object) -> None: """Initializes the `DriveItems` object. ### Parameters ---- session : object An authenticated session for our Microsoft Graph Client. """ # Set the session. self.graph_session: GraphSession = session # Set the endpoint. self.endpoint = 'drive' self.collections_endpoint = 'drives/' def get_drive_item(self, drive_id: str, item_id: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- drive_id : str The Drive ID in which the resource exist. item_id : str The item ID of the object you want to return. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint=self.collections_endpoint + "/{drive_id}/items/{item_id}".format( drive_id=drive_id, item_id=item_id ) ) return content def get_drive_item_by_path(self, drive_id: str, item_path: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- drive_id : str The Drive ID in which the resource exist. item_path : str The path to the Item. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint=self.collections_endpoint + "/{drive_id}/root:/{path}".format( drive_id=drive_id, path=item_path ) ) return content def get_group_drive_item(self, group_id: str, item_id: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- group_id : str The Group ID in which the resource exist. item_id : str The item ID of the object you want to return. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/groups/{group_id}/drive/items/{item_id}".format( group_id=group_id, item_id=item_id ) ) return content def get_group_drive_item_by_path(self, group_id: str, item_path: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- drive_id : str The Drive ID in which the resource exist. item_path : str The path to the Item. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/groups/{group_id}/drive/root:/{item_path}".format( group_id=group_id, item_path=item_path ) ) return content def get_my_drive_item(self, item_id: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- item_id : str The item ID of the object you want to return. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/me/drive/items/{item_id}".format( item_id=item_id ) ) return content def get_my_drive_item_by_path(self, item_path: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- item_path : str The path to the Item. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/me/drive/root:/{item_path}".format( item_path=item_path ) ) return content def get_site_drive_item(self, site_id: str, item_id: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- site_id : str The site ID which to query the item from. item_id : str The item ID of the object you want to return. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/sites/{site_id}/drive/items/{item_id}".format( site_id=site_id, item_id=item_id ) ) return content def get_site_drive_item_by_path(self, site_id: str, item_path: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- site_id : str The site ID which to query the item from. item_path : str The path to the Item. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/sites/{site_id}/drive/root:/{item_path}".format( site_id=site_id, item_path=item_path ) ) return content def get_site_drive_item_from_list(self, site_id: str, list_id: str, item_id: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- site_id : str The site ID which to query the item from. list_id : str The list ID which to query the item from. item_id : str The item ID of the object you want to return. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/sites/{site_id}/lists/{list_id}/items/{item_id}/driveItem".format( site_id=site_id, list_id=list_id, item_id=item_id ) ) return content def get_user_drive_item(self, user_id: str, item_id: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- user_id : str The User ID which to query the item from. item_id : str The item ID of the object you want to return. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/users/{user_id}/drive/items/{item_id}".format( user_id=user_id, item_id=item_id ) ) return content def get_user_drive_item_by_path(self, user_id: str, item_path: str) -> Dict: """Grab's a DriveItem Resource using the Item ID and Drive ID. ### Parameters ---- site_id : str The User ID which to query the item from. item_path : str The path to the Item. ### Returns ---- Dict: A DriveItem resource object. """ content = self.graph_session.make_request( method='get', endpoint="/users/{user_id}/drive/root:/{item_path}".format( user_id=user_id, item_path=item_path ) ) return content

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0.520148

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8,388

4.342324

0.088174

0.067367

0.094601

0.031534

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0.247619

0

null

0

1

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null

0

6

e0eccfe1101a2bc126b23bf30ffd6c0dd1c564be

38

py

Python

tapiriik/services/Smashrun/__init__.py

prohfesor/tapiriik

0c476f8bb6b3d51674f0117b054777405ff2ee0d

[ "Apache-2.0" ]

1,445

2015-01-01T21:43:31.000Z

2022-03-17T13:40:23.000Z

tapiriik/services/Smashrun/__init__.py

prohfesor/tapiriik

0c476f8bb6b3d51674f0117b054777405ff2ee0d

[ "Apache-2.0" ]

441

2015-01-02T03:37:49.000Z

2022-03-31T18:18:03.000Z

tapiriik/services/Smashrun/__init__.py

prohfesor/tapiriik

0c476f8bb6b3d51674f0117b054777405ff2ee0d

[ "Apache-2.0" ]

333

2015-01-06T12:14:15.000Z

2022-03-27T19:58:48.000Z

from .smashrun import SmashrunService

19

37

0.868421

4

38

8.25

1

0

0.105263

38

1

38

0.970588

0

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true

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0

1

0

1

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null

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0

null

0

1

0

1

0

1

0

6

1cc55745fd85dcaa6e80e02a57d5e37bdc3a531d

341

py

Python

py_rete/__init__.py

benthomasson/py_rete

b21e06e891991fe790c95a745cbd076aae9a426d

[ "MIT" ]

18

2020-07-13T16:13:05.000Z

2022-03-18T00:18:51.000Z

py_rete/__init__.py

benthomasson/py_rete

b21e06e891991fe790c95a745cbd076aae9a426d

[ "MIT" ]

null

py_rete/__init__.py

benthomasson/py_rete

b21e06e891991fe790c95a745cbd076aae9a426d

[ "MIT" ]

6

2020-04-16T08:44:53.000Z

2022-02-22T01:07:58.000Z

from py_rete.fact import Fact # noqa F401 from py_rete.common import V # noqa F401 from py_rete.production import Production # noqa F401 from py_rete.network import ReteNetwork # noqa F401 from py_rete.conditions import AND # noqa F401 from py_rete.conditions import Cond # noqa F401 from py_rete.conditions import Filter # noqa F401

42.625

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0.794721

56

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4.714286

0.285714

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0.265152

0.318182

0.590909

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0.164223

341

7

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0.202346

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true

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1

0

null

0

1

0

1

0

1

0

6

e8133419bc46d63c755e090e97bcd07a4552db70

4,792

py

Python

src/resources/classes/enemy.py

41y08h/warah-killer

d1e0270b95b2abfa5d0eeae3cfb0b04b3b68a358

[ "MIT" ]

1

2020-04-09T08:03:29.000Z

resources/classes/enemy.py

TaffoVelikoff/ViolentCityGame

53151a2686282b68d36685927931d31f883a2594

[ "MIT" ]

null

resources/classes/enemy.py

TaffoVelikoff/ViolentCityGame

53151a2686282b68d36685927931d31f883a2594

[ "MIT" ]

null

import os import pygame import random import globals from os import listdir from resources import game from resources import colors from os.path import isfile, join class Enemy(pygame.sprite.Sprite): selectedPos = 'left' speed = 0 spriteWidth = 200 # Constructor def __init__(self): # Call the parent class (Sprite) constructor pygame.sprite.Sprite.__init__(self) # Enemy image self.image = pygame.image.load(os.path.join(globals.data_dir, 'img/enemy/01.png')).convert_alpha() self.rect = self.image.get_rect() # Speed if globals.debug == False: self.speed = 5 + globals.level else: globals.speed = 0 # Create mask self.mask = pygame.mask.from_surface(self.image) ''' The enemy can appear at 4 different places on the screen and start moving in 4 different possitions ''' # Position to place positions = ('left', 'right', 'up', 'down') self.selectedPos = random.choice(positions) # Place sprite on screen (based on the randomly selected position) if self.selectedPos == 'left': self.rect.center = (0, random.randint(self.spriteWidth/2, globals.winHeight - self.spriteWidth/2)) elif self.selectedPos == 'right': self.rect.center = (globals.winWidth, random.randint(self.spriteWidth/2, globals.winHeight - self.spriteWidth/2)) elif self.selectedPos == 'up': self.rect.center = (random.randint(self.spriteWidth/2, globals.winWidth - self.spriteWidth/2), 0) else: self.rect.center = (random.randint(self.spriteWidth/2, globals.winWidth - self.spriteWidth/2), globals.winHeight) def update(self): self.image.set_colorkey((0, 0, 0)) if self.selectedPos == 'left': self.rect.x += self.speed elif self.selectedPos == 'right': self.rect.x -= self.speed elif self.selectedPos == 'up': self.rect.y += self.speed else: self.rect.y -= self.speed class Blood(pygame.sprite.Sprite): frames = [] # Constructor def __init__(self, x = None, y = None): # Counter self.steps = 0 # Call the parent class (Sprite) constructor pygame.sprite.Sprite.__init__(self) # Get all frames path = "data/img/enemy/explode/" frames = [f for f in listdir(path) if isfile(join(path, f))] # Put all frames in a list of Pygame images self.images = [] for frame in frames: self.images.append(pygame.image.load(path + frame).convert_alpha()) self.index = 0 self.image = self.images[self.index] self.rect = self.image.get_rect() # Position mousePos = pygame.mouse.get_pos() self.rect.x = mousePos[0] - 79 self.rect.y = mousePos[1] - 115 def update(self): # ANIMATE AND KILL AFTER ANIMATION if self.index >= len(self.images): self.kill() else: self.image = self.images[self.index] # This will slow down animation if self.steps % 2 == 0: self.index += 1 self.steps += 1 class Beer(pygame.sprite.Sprite): frames = [] selectedPos = 'left' speed = 5 spriteWidth = 128 # Constructor def __init__(self, x = None, y = None): # Counter self.steps = 0 # Call the parent class (Sprite) constructor pygame.sprite.Sprite.__init__(self) # Get all frames path = "data/img/enemy/beer/" frames = [f for f in listdir(path) if isfile(join(path, f))] # Put all frames in a list of Pygame images self.images = [] for frame in frames: self.images.append(pygame.image.load(path + frame).convert_alpha()) self.index = 0 self.image = self.images[self.index] self.rect = self.image.get_rect() # Position to place positions = ('left', 'right', 'up', 'down') self.selectedPos = random.choice(positions) # Place sprite on screen (based on the randomly selected position) if self.selectedPos == 'left': self.rect.center = (0, random.randint(self.spriteWidth/2, globals.winHeight - self.spriteWidth/2)) elif self.selectedPos == 'right': self.rect.center = (globals.winWidth, random.randint(self.spriteWidth/2, globals.winHeight - self.spriteWidth/2)) elif self.selectedPos == 'up': self.rect.center = (random.randint(self.spriteWidth/2, globals.winWidth - self.spriteWidth/2), 0) else: self.rect.center = (random.randint(self.spriteWidth/2, globals.winWidth - self.spriteWidth/2), globals.winHeight) def update(self): # ANIMATE if self.index >= len(self.images): self.index = 0 else: self.image = self.images[self.index] # This will slow down animation if self.steps % 2 == 0: self.index += 1 # Restart steps self.steps += 1 # Transparent self.image.set_colorkey((0, 0, 0)) # Move if self.selectedPos == 'left': self.rect.x += self.speed elif self.selectedPos == 'right': self.rect.x -= self.speed elif self.selectedPos == 'up': self.rect.y += self.speed else: self.rect.y -= self.speed def kill(self): # Put outside of screen self.rect.center = (-300, -300)

27.227273

116

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4,792

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0.178624

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0

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false

0

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0

null

0

1

0

1

0

null

0

6

08ff4b58d0101e49a968fe4799383c92c2d5b460

48

py

Python

solutions/helpers/__init__.py

SebastiaanZ/aoc-2019

e1fe4630b0f375be0b79398e07e23b9c0196efbb

[ "MIT" ]

3

2019-12-02T19:38:14.000Z

2020-01-28T00:06:09.000Z

solutions/helpers/__init__.py

SebastiaanZ/aoc-2019

e1fe4630b0f375be0b79398e07e23b9c0196efbb

[ "MIT" ]

6

2020-03-24T17:58:40.000Z

2022-03-12T00:18:45.000Z

solutions/helpers/__init__.py

SebastiaanZ/aoc-2019

e1fe4630b0f375be0b79398e07e23b9c0196efbb

[ "MIT" ]

null

from .intcode import IntCodeApplication # noqa

24

47

0.8125

5

48

7.8

1

0

0.145833

48

1

48

0.95122

0.083333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

1c3ea98a7585fb03beb4e762dac750a21ae4a86a

119

py

Python

projects/thesis/continuous/custom/continuous/deform_feature/__init__.py

cpark90/rrrcnn

ba66cc391265be76fa3896b66459ff7241b47972

[ "Apache-2.0" ]

null

projects/thesis/continuous/custom/continuous/deform_feature/__init__.py

cpark90/rrrcnn

ba66cc391265be76fa3896b66459ff7241b47972

[ "Apache-2.0" ]

null

projects/thesis/continuous/custom/continuous/deform_feature/__init__.py

cpark90/rrrcnn

ba66cc391265be76fa3896b66459ff7241b47972

[ "Apache-2.0" ]

null

from .deform_feature_map_layer import * from .deform_orienation_layer import * from .deformable_by_grad_layer import *

29.75

39

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17

119

5.470588

0.588235

0.354839

0.322581

0

0.10084

119

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true

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1

0

null

0

1

0

1

0

6

1c3ec6d3f7be58abd038f8964534a0cb398cb936

2,732

py

Python

blog/migrations/0011_auto_20200728_0547.py

tbrlpld/wagtail-gatsby-blog-backend

f68f1d9e2577d5271960f142bf37dcbcdac6767a

[ "MIT" ]

null

blog/migrations/0011_auto_20200728_0547.py

tbrlpld/wagtail-gatsby-blog-backend

f68f1d9e2577d5271960f142bf37dcbcdac6767a

[ "MIT" ]

null

blog/migrations/0011_auto_20200728_0547.py

tbrlpld/wagtail-gatsby-blog-backend

f68f1d9e2577d5271960f142bf37dcbcdac6767a

[ "MIT" ]

null

# Generated by Django 2.2.13 on 2020-07-28 05:47 from django.db import migrations import wagtail.core.blocks import wagtail.core.blocks.static_block import wagtail.core.fields import wagtail.documents.blocks import wagtail.embeds.blocks import wagtail.images.blocks class Migration(migrations.Migration): dependencies = [ ('blog', '0010_auto_20200728_0540'), ] operations = [ migrations.AlterField( model_name='blogpage', name='freeformbody', field=wagtail.core.fields.StreamField([('heading', wagtail.core.blocks.CharBlock(classname='full title')), ('paragraph', wagtail.core.blocks.RichTextBlock()), ('image', wagtail.images.blocks.ImageChooserBlock()), ('text', wagtail.core.blocks.TextBlock()), ('email', wagtail.core.blocks.EmailBlock(help_text='Your email goes here.')), ('integer', wagtail.core.blocks.IntegerBlock(help_text='Just a number.')), ('float', wagtail.core.blocks.FloatBlock(help_text='A floating point number.')), ('decimal', wagtail.core.blocks.DecimalBlock(decimal_places=2, help_text='A decimal number.')), ('regex', wagtail.core.blocks.RegexBlock(error_messages={'invalid': 'You need to have " stuff " in the string.'}, help_text='A string with stuff in the middle.', regex='^.*stuff.*$')), ('url', wagtail.core.blocks.URLBlock()), ('bool', wagtail.core.blocks.BooleanBlock(required=False)), ('date', wagtail.core.blocks.DateBlock()), ('time', wagtail.core.blocks.TimeBlock()), ('datetime', wagtail.core.blocks.DateTimeBlock()), ('rawhtml', wagtail.core.blocks.RawHTMLBlock(help_text='Here you can show off your HTML skills.')), ('blockquote', wagtail.core.blocks.BlockQuoteBlock()), ('choice', wagtail.core.blocks.ChoiceBlock(choices=[('yes', 'Yes'), ('no', 'No'), ('maybe', 'Maybe')])), ('page', wagtail.core.blocks.PageChooserBlock()), ('doc', wagtail.documents.blocks.DocumentChooserBlock()), ('embed', wagtail.embeds.blocks.EmbedBlock()), ('static', wagtail.core.blocks.static_block.StaticBlock(admin_text='Latest Posts (no configuration needed)', help_text='If you include this block, the latest posts will be displayed here.')), ('person', wagtail.core.blocks.StructBlock([('first_name', wagtail.core.blocks.CharBlock()), ('last_name', wagtail.core.blocks.CharBlock()), ('biography', wagtail.core.blocks.TextBlock()), ('pic', wagtail.images.blocks.ImageChooserBlock(required=False))], icon='user')), ('list', wagtail.core.blocks.ListBlock(wagtail.core.blocks.CharBlock(label='List Item'))), ('substream', wagtail.core.blocks.StreamBlock([('image', wagtail.images.blocks.ImageChooserBlock()), ('quote', wagtail.core.blocks.BlockQuoteBlock()), ('author', wagtail.core.blocks.CharBlock(min_length=5))]))], blank=True), ), ]

109.28

2,214

0.725842

331

2,732

5.933535

0.453172

0.173625

0.251018

0.066191

0.100815

0

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0.08858

2,732

24

2,215

113.833333

0.7751

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1

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0.218703

0.008569

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1

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false

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0.388889

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0

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1

0

1

0

null

0

1

0

1

0

6

98be3747a5b4b85bd40719ce7b3809ac7ffea433

510

py

Python

stubs/micropython-v1_13-95-pyboard/gc.py

mattytrentini/micropython-stubs

4d596273823b69e9e5bcf5fa67f249c374ee0bbc

[ "MIT" ]

null

stubs/micropython-v1_13-95-pyboard/gc.py

mattytrentini/micropython-stubs

4d596273823b69e9e5bcf5fa67f249c374ee0bbc

[ "MIT" ]

null

stubs/micropython-v1_13-95-pyboard/gc.py

mattytrentini/micropython-stubs

4d596273823b69e9e5bcf5fa67f249c374ee0bbc

[ "MIT" ]

null

""" Module: 'gc' on pyboard 1.13.0-95 """ # MCU: (sysname='pyboard', nodename='pyboard', release='1.13.0', version='v1.13-95-g0fff2e03f on 2020-10-03', machine='PYBv1.1 with STM32F405RG') # Stubber: 1.3.4 - updated from typing import Any def collect(*args) -> Any: pass def disable(*args) -> Any: pass def enable(*args) -> Any: pass def isenabled(*args) -> Any: pass def mem_alloc(*args) -> Any: pass def mem_free(*args) -> Any: pass def threshold(*args) -> Any: pass

14.571429

145

0.619608

77

510

4.077922

0.532468

0.156051

0.245223

0.267516

0.10828

0

0.091133

0.203922

510

34

146

15

0.682266

0.398039

0

0.466667

0

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true

0.466667

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0

null

0

1

0

null

0

1

0

1

0

6

c726bb342736f4037853205064035977032db551

32,985

py

Python

ta/volatility.py

Ruil/ta

d5593e4123c1ed3338fc01d0fe65a631538bc76e

[ "MIT" ]

null

ta/volatility.py

Ruil/ta

d5593e4123c1ed3338fc01d0fe65a631538bc76e

[ "MIT" ]

null

ta/volatility.py

Ruil/ta

d5593e4123c1ed3338fc01d0fe65a631538bc76e

[ "MIT" ]

null

""" .. module:: volatility :synopsis: Volatility Indicators. .. moduleauthor:: Dario Lopez Padial (Bukosabino) """ import numpy as np import pandas as pd from ta.utils import IndicatorMixin class AverageTrueRange(IndicatorMixin): """Average True Range (ATR) The indicator provide an indication of the degree of price volatility. Strong moves, in either direction, are often accompanied by large ranges, or large True Ranges. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_true_range_atr Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. """ def __init__( self, high: pd.Series, low: pd.Series, close: pd.Series, window: int = 14, fillna: bool = False, ): self._high = high self._low = low self._close = close self._window = min(window, len(self._close)) self._fillna = fillna self._run() def _run(self): close_shift = self._close.shift(1) true_range = self._true_range(self._high, self._low, close_shift) atr = np.zeros(len(self._close)) #print(len(atr), ' window: ', self._window, len(true_range)) atr[self._window - 1] = true_range[0 : self._window].mean() for i in range(self._window, len(atr)): atr[i] = (atr[i - 1] * (self._window - 1) + true_range.iloc[i]) / float( self._window ) self._atr = pd.Series(data=atr, index=true_range.index) def average_true_range(self) -> pd.Series: """Average True Range (ATR) Returns: pandas.Series: New feature generated. """ atr = self._check_fillna(self._atr, value=0) return pd.Series(atr, name="atr") class BollingerBands(IndicatorMixin): """Bollinger Bands https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands Args: close(pandas.Series): dataset 'Close' column. window(int): n period. window_dev(int): n factor standard deviation fillna(bool): if True, fill nan values. """ def __init__( self, close: pd.Series, window: int = 20, window_dev: int = 2, fillna: bool = False, ): self._close = close self._window = window self._window_dev = window_dev self._fillna = fillna self._run() def _run(self): min_periods = 0 if self._fillna else self._window self._mavg = self._close.rolling(self._window, min_periods=min_periods).mean() self._mstd = self._close.rolling(self._window, min_periods=min_periods).std( ddof=0 ) self._hband = self._mavg + self._window_dev * self._mstd self._lband = self._mavg - self._window_dev * self._mstd def bollinger_mavg(self) -> pd.Series: """Bollinger Channel Middle Band Returns: pandas.Series: New feature generated. """ mavg = self._check_fillna(self._mavg, value=-1) return pd.Series(mavg, name="mavg") def bollinger_hband(self) -> pd.Series: """Bollinger Channel High Band Returns: pandas.Series: New feature generated. """ hband = self._check_fillna(self._hband, value=-1) return pd.Series(hband, name="hband") def bollinger_lband(self) -> pd.Series: """Bollinger Channel Low Band Returns: pandas.Series: New feature generated. """ lband = self._check_fillna(self._lband, value=-1) return pd.Series(lband, name="lband") def bollinger_wband(self) -> pd.Series: """Bollinger Channel Band Width From: https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_width Returns: pandas.Series: New feature generated. """ wband = ((self._hband - self._lband) / self._mavg) * 100 wband = self._check_fillna(wband, value=0) return pd.Series(wband, name="bbiwband") def bollinger_pband(self) -> pd.Series: """Bollinger Channel Percentage Band From: https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_perce Returns: pandas.Series: New feature generated. """ pband = (self._close - self._lband) / (self._hband - self._lband) pband = self._check_fillna(pband, value=0) return pd.Series(pband, name="bbipband") def bollinger_hband_indicator(self) -> pd.Series: """Bollinger Channel Indicator Crossing High Band (binary). It returns 1, if close is higher than bollinger_hband. Else, it returns 0. Returns: pandas.Series: New feature generated. """ hband = pd.Series( np.where(self._close > self._hband, 1.0, 0.0), index=self._close.index ) hband = self._check_fillna(hband, value=0) return pd.Series(hband, index=self._close.index, name="bbihband") def bollinger_lband_indicator(self) -> pd.Series: """Bollinger Channel Indicator Crossing Low Band (binary). It returns 1, if close is lower than bollinger_lband. Else, it returns 0. Returns: pandas.Series: New feature generated. """ lband = pd.Series( np.where(self._close < self._lband, 1.0, 0.0), index=self._close.index ) lband = self._check_fillna(lband, value=0) return pd.Series(lband, name="bbilband") class KeltnerChannel(IndicatorMixin): """KeltnerChannel Keltner Channels are a trend following indicator used to identify reversals with channel breakouts and channel direction. Channels can also be used to identify overbought and oversold levels when the trend is flat. https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels """ def __init__( self, high: pd.Series, low: pd.Series, close: pd.Series, window: int = 20, window_atr: int = 10, fillna: bool = False, original_version: bool = True, ): self._high = high self._low = low self._close = close self._window = window self._window_atr = window_atr self._fillna = fillna self._original_version = original_version self._run() def _run(self): min_periods = 1 if self._fillna else self._window if self._original_version: self._tp = ( ((self._high + self._low + self._close) / 3.0) .rolling(self._window, min_periods=min_periods) .mean() ) self._tp_high = ( (((4 * self._high) - (2 * self._low) + self._close) / 3.0) .rolling(self._window, min_periods=0) .mean() ) self._tp_low = ( (((-2 * self._high) + (4 * self._low) + self._close) / 3.0) .rolling(self._window, min_periods=0) .mean() ) else: self._tp = self._close.ewm( span=self._window, min_periods=min_periods, adjust=False ).mean() atr = AverageTrueRange( close=self._close, high=self._high, low=self._low, window=self._window_atr, fillna=self._fillna, ).average_true_range() self._tp_high = self._tp + (2 * atr) self._tp_low = self._tp - (2 * atr) def keltner_channel_mband(self) -> pd.Series: """Keltner Channel Middle Band Returns: pandas.Series: New feature generated. """ tp_middle = self._check_fillna(self._tp, value=-1) return pd.Series(tp_middle, name="mavg") def keltner_channel_hband(self) -> pd.Series: """Keltner Channel High Band Returns: pandas.Series: New feature generated. """ tp_high = self._check_fillna(self._tp_high, value=-1) return pd.Series(tp_high, name="kc_hband") def keltner_channel_lband(self) -> pd.Series: """Keltner Channel Low Band Returns: pandas.Series: New feature generated. """ tp_low = self._check_fillna(self._tp_low, value=-1) return pd.Series(tp_low, name="kc_lband") def keltner_channel_wband(self) -> pd.Series: """Keltner Channel Band Width Returns: pandas.Series: New feature generated. """ wband = ((self._tp_high - self._tp_low) / self._tp) * 100 wband = self._check_fillna(wband, value=0) return pd.Series(wband, name="bbiwband") def keltner_channel_pband(self) -> pd.Series: """Keltner Channel Percentage Band Returns: pandas.Series: New feature generated. """ pband = (self._close - self._tp_low) / (self._tp_high - self._tp_low) pband = self._check_fillna(pband, value=0) return pd.Series(pband, name="bbipband") def keltner_channel_hband_indicator(self) -> pd.Series: """Keltner Channel Indicator Crossing High Band (binary) It returns 1, if close is higher than keltner_channel_hband. Else, it returns 0. Returns: pandas.Series: New feature generated. """ hband = pd.Series( np.where(self._close > self._tp_high, 1.0, 0.0), index=self._close.index ) hband = self._check_fillna(hband, value=0) return pd.Series(hband, name="dcihband") def keltner_channel_lband_indicator(self) -> pd.Series: """Keltner Channel Indicator Crossing Low Band (binary) It returns 1, if close is lower than keltner_channel_lband. Else, it returns 0. Returns: pandas.Series: New feature generated. """ lband = pd.Series( np.where(self._close < self._tp_low, 1.0, 0.0), index=self._close.index ) lband = self._check_fillna(lband, value=0) return pd.Series(lband, name="dcilband") class DonchianChannel(IndicatorMixin): """Donchian Channel https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. """ def __init__( self, high: pd.Series, low: pd.Series, close: pd.Series, window: int = 20, offset: int = 0, fillna: bool = False, ): self._offset = offset self._close = close self._high = high self._low = low self._window = window self._fillna = fillna self._run() def _run(self): self._min_periods = 1 if self._fillna else self._window self._hband = self._high.rolling( self._window, min_periods=self._min_periods ).max() self._lband = self._low.rolling( self._window, min_periods=self._min_periods ).min() def donchian_channel_hband(self) -> pd.Series: """Donchian Channel High Band Returns: pandas.Series: New feature generated. """ hband = self._check_fillna(self._hband, value=-1) if self._offset != 0: hband = hband.shift(self._offset) return pd.Series(hband, name="dchband") def donchian_channel_lband(self) -> pd.Series: """Donchian Channel Low Band Returns: pandas.Series: New feature generated. """ lband = self._check_fillna(self._lband, value=-1) if self._offset != 0: lband = lband.shift(self._offset) return pd.Series(lband, name="dclband") def donchian_channel_mband(self) -> pd.Series: """Donchian Channel Middle Band Returns: pandas.Series: New feature generated. """ mband = ((self._hband - self._lband) / 2.0) + self._lband mband = self._check_fillna(mband, value=-1) if self._offset != 0: mband = mband.shift(self._offset) return pd.Series(mband, name="dcmband") def donchian_channel_wband(self) -> pd.Series: """Donchian Channel Band Width Returns: pandas.Series: New feature generated. """ mavg = self._close.rolling(self._window, min_periods=self._min_periods).mean() wband = ((self._hband - self._lband) / mavg) * 100 wband = self._check_fillna(wband, value=0) if self._offset != 0: wband = wband.shift(self._offset) return pd.Series(wband, name="dcwband") def donchian_channel_pband(self) -> pd.Series: """Donchian Channel Percentage Band Returns: pandas.Series: New feature generated. """ pband = (self._close - self._lband) / (self._hband - self._lband) pband = self._check_fillna(pband, value=0) if self._offset != 0: pband = pband.shift(self._offset) return pd.Series(pband, name="dcpband") class UlcerIndex(IndicatorMixin): """Ulcer Index https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ulcer_index Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. """ def __init__(self, close: pd.Series, window: int = 14, fillna: bool = False): self._close = close self._window = window self._fillna = fillna self._run() def _run(self): _ui_max = self._close.rolling(self._window, min_periods=1).max() _r_i = 100 * (self._close - _ui_max) / _ui_max def ui_function(): def _ui_function(x): return np.sqrt((x ** 2 / self._window).sum()) return _ui_function self._ulcer_idx = _r_i.rolling(self._window).apply(ui_function(), raw=True) def ulcer_index(self) -> pd.Series: """Ulcer Index (UI) Returns: pandas.Series: New feature generated. """ ulcer_idx = self._check_fillna(self._ulcer_idx) return pd.Series(ulcer_idx, name="ui") def average_true_range(high, low, close, window=14, fillna=False): """Average True Range (ATR) The indicator provide an indication of the degree of price volatility. Strong moves, in either direction, are often accompanied by large ranges, or large True Ranges. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_true_range_atr Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = AverageTrueRange( high=high, low=low, close=close, window=window, fillna=fillna ) return indicator.average_true_range() def bollinger_mavg(close, window=20, fillna=False): """Bollinger Bands (BB) N-period simple moving average (MA). https://en.wikipedia.org/wiki/Bollinger_Bands Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = BollingerBands(close=close, window=window, fillna=fillna) return indicator.bollinger_mavg() def bollinger_hband(close, window=20, window_dev=2, fillna=False): """Bollinger Bands (BB) Upper band at K times an N-period standard deviation above the moving average (MA + Kdeviation). https://en.wikipedia.org/wiki/Bollinger_Bands Args: close(pandas.Series): dataset 'Close' column. window(int): n period. window_dev(int): n factor standard deviation fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = BollingerBands( close=close, window=window, window_dev=window_dev, fillna=fillna ) return indicator.bollinger_hband() def bollinger_lband(close, window=20, window_dev=2, fillna=False): """Bollinger Bands (BB) Lower band at K times an N-period standard deviation below the moving average (MA − Kdeviation). https://en.wikipedia.org/wiki/Bollinger_Bands Args: close(pandas.Series): dataset 'Close' column. window(int): n period. window_dev(int): n factor standard deviation fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = BollingerBands( close=close, window=window, window_dev=window_dev, fillna=fillna ) return indicator.bollinger_lband() def bollinger_wband(close, window=20, window_dev=2, fillna=False): """Bollinger Channel Band Width From: https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_width Args: close(pandas.Series): dataset 'Close' column. window(int): n period. window_dev(int): n factor standard deviation fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = BollingerBands( close=close, window=window, window_dev=window_dev, fillna=fillna ) return indicator.bollinger_wband() def bollinger_pband(close, window=20, window_dev=2, fillna=False): """Bollinger Channel Percentage Band From: https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_perce Args: close(pandas.Series): dataset 'Close' column. window(int): n period. window_dev(int): n factor standard deviation fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = BollingerBands( close=close, window=window, window_dev=window_dev, fillna=fillna ) return indicator.bollinger_pband() def bollinger_hband_indicator(close, window=20, window_dev=2, fillna=False): """Bollinger High Band Indicator Returns 1, if close is higher than bollinger high band. Else, return 0. https://en.wikipedia.org/wiki/Bollinger_Bands Args: close(pandas.Series): dataset 'Close' column. window(int): n period. window_dev(int): n factor standard deviation fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = BollingerBands( close=close, window=window, window_dev=window_dev, fillna=fillna ) return indicator.bollinger_hband_indicator() def bollinger_lband_indicator(close, window=20, window_dev=2, fillna=False): """Bollinger Low Band Indicator Returns 1, if close is lower than bollinger low band. Else, return 0. https://en.wikipedia.org/wiki/Bollinger_Bands Args: close(pandas.Series): dataset 'Close' column. window(int): n period. window_dev(int): n factor standard deviation fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = BollingerBands( close=close, window=window, window_dev=window_dev, fillna=fillna ) return indicator.bollinger_lband_indicator() def keltner_channel_mband( high, low, close, window=20, window_atr=10, fillna=False, original_version=True ): """Keltner channel (KC) Showing a simple moving average line (central) of typical price. https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Returns: pandas.Series: New feature generated. """ indicator = KeltnerChannel( high=high, low=low, close=close, window=window, window_atr=window_atr, fillna=fillna, original_version=original_version, ) return indicator.keltner_channel_mband() def keltner_channel_hband( high, low, close, window=20, window_atr=10, fillna=False, original_version=True ): """Keltner channel (KC) Showing a simple moving average line (high) of typical price. https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Returns: pandas.Series: New feature generated. """ indicator = KeltnerChannel( high=high, low=low, close=close, window=window, window_atr=window_atr, fillna=fillna, original_version=original_version, ) return indicator.keltner_channel_hband() def keltner_channel_lband( high, low, close, window=20, window_atr=10, fillna=False, original_version=True ): """Keltner channel (KC) Showing a simple moving average line (low) of typical price. https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Returns: pandas.Series: New feature generated. """ indicator = KeltnerChannel( high=high, low=low, close=close, window=window, window_atr=window_atr, fillna=fillna, original_version=original_version, ) return indicator.keltner_channel_lband() def keltner_channel_wband( high, low, close, window=20, window_atr=10, fillna=False, original_version=True ): """Keltner Channel Band Width https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Returns: pandas.Series: New feature generated. """ indicator = KeltnerChannel( high=high, low=low, close=close, window=window, window_atr=window_atr, fillna=fillna, original_version=original_version, ) return indicator.keltner_channel_wband() def keltner_channel_pband( high, low, close, window=20, window_atr=10, fillna=False, original_version=True ): """Keltner Channel Percentage Band https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Returns: pandas.Series: New feature generated. """ indicator = KeltnerChannel( high=high, low=low, close=close, window=window, window_atr=window_atr, fillna=fillna, original_version=original_version, ) return indicator.keltner_channel_pband() def keltner_channel_hband_indicator( high, low, close, window=20, window_atr=10, fillna=False, original_version=True ): """Keltner Channel High Band Indicator (KC) Returns 1, if close is higher than keltner high band channel. Else, return 0. https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Returns: pandas.Series: New feature generated. """ indicator = KeltnerChannel( high=high, low=low, close=close, window=window, window_atr=window_atr, fillna=fillna, original_version=original_version, ) return indicator.keltner_channel_hband_indicator() def keltner_channel_lband_indicator( high, low, close, window=20, window_atr=10, fillna=False, original_version=True ): """Keltner Channel Low Band Indicator (KC) Returns 1, if close is lower than keltner low band channel. Else, return 0. https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Returns: pandas.Series: New feature generated. """ indicator = KeltnerChannel( high=high, low=low, close=close, window=window, window_atr=window_atr, fillna=fillna, original_version=original_version, ) return indicator.keltner_channel_lband_indicator() def donchian_channel_hband(high, low, close, window=20, offset=0, fillna=False): """Donchian Channel High Band (DC) The upper band marks the highest price of an issue for n periods. https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = DonchianChannel( high=high, low=low, close=close, window=window, offset=offset, fillna=fillna ) return indicator.donchian_channel_hband() def donchian_channel_lband(high, low, close, window=20, offset=0, fillna=False): """Donchian Channel Low Band (DC) The lower band marks the lowest price for n periods. https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = DonchianChannel( high=high, low=low, close=close, window=window, offset=offset, fillna=fillna ) return indicator.donchian_channel_lband() def donchian_channel_mband(high, low, close, window=10, offset=0, fillna=False): """Donchian Channel Middle Band (DC) https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = DonchianChannel( high=high, low=low, close=close, window=window, offset=offset, fillna=fillna ) return indicator.donchian_channel_mband() def donchian_channel_wband(high, low, close, window=10, offset=0, fillna=False): """Donchian Channel Band Width (DC) https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = DonchianChannel( high=high, low=low, close=close, window=window, offset=offset, fillna=fillna ) return indicator.donchian_channel_wband() def donchian_channel_pband(high, low, close, window=10, offset=0, fillna=False): """Donchian Channel Percentage Band (DC) https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = DonchianChannel( high=high, low=low, close=close, window=window, offset=offset, fillna=fillna ) return indicator.donchian_channel_pband() def ulcer_index(close, window=14, fillna=False): """Ulcer Index https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ulcer_index Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ indicator = UlcerIndex(close=close, window=window, fillna=fillna) return indicator.ulcer_index()

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c729c075da998c52671b42fcc0e5b3a16c4e479a

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py

Python

alexi/twitter/__init__.py

harryjjacobs/alexi

1306c26adf339bbf88e9b2c29e038f242e4ca45f

[ "MIT" ]

2

2019-07-20T01:48:20.000Z

2019-11-15T06:50:54.000Z

alexi/twitter/__init__.py

harryjjacobs/alexi

1306c26adf339bbf88e9b2c29e038f242e4ca45f

[ "MIT" ]

5

2020-02-12T08:58:06.000Z

2021-09-22T17:56:42.000Z

alexi/twitter/__init__.py

harryjjacobs/alexi

1306c26adf339bbf88e9b2c29e038f242e4ca45f

[ "MIT" ]

null

from .twitter import Twitter

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c76c04d5eb893cc9436be1837e71d314b18f2c4e

6,272

py

Python

phone_iso3166/nanpa.py

horpto/phone-iso3166

09d6bb8cf69bc0dffb97677924aa3318b579f030

[ "MIT" ]

19

2017-03-28T10:35:22.000Z

2022-03-14T04:39:03.000Z

phone_iso3166/nanpa.py

horpto/phone-iso3166

09d6bb8cf69bc0dffb97677924aa3318b579f030

[ "MIT" ]

17

2016-11-11T11:50:57.000Z

2021-06-22T09:32:17.000Z

phone_iso3166/nanpa.py

horpto/phone-iso3166

09d6bb8cf69bc0dffb97677924aa3318b579f030

[ "MIT" ]

5

2015-09-28T18:25:38.000Z

2021-07-05T11:57:58.000Z

# Generated by get_npna_npa.py # Based on http://nanpa.com/reports/reports_npa.html npa = \ {2: {0: {1: 'US', 2: 'US', 3: 'US', 4: 'CA', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 1: {0: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 2: {0: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'CA', 7: 'US', 8: 'US', 9: 'US'}, 3: {1: 'US', 4: 'US', 6: 'CA', 9: 'US'}, 4: {0: 'US', 2: 'BS', 6: 'BB', 8: 'US', 9: 'CA'}, 5: {0: 'CA', 1: 'US', 2: 'US', 3: 'US', 4: 'US', 6: 'US', 7: 'CA'}, 6: {0: 'US', 2: 'US', 3: 'CA', 4: 'AI', 7: 'US', 8: 'AG', 9: 'US'}, 7: {0: 'US', 2: 'US', 3: 'CA', 4: 'US', 6: 'US', 9: 'US'}, 8: {1: 'US', 3: 'US', 4: 'VG', 9: 'CA'}}, 3: {0: {1: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'CA', 7: 'US', 8: 'US', 9: 'US'}, 1: {0: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 2: {0: 'US', 1: 'US', 3: 'US', 5: 'US', 6: 'US', 7: 'US'}, 3: {0: 'US', 1: 'US', 2: 'US', 4: 'US', 6: 'US', 7: 'US', 9: 'US'}, 4: {0: 'VI', 1: 'US', 3: 'CA', 5: 'KY', 6: 'US', 7: 'US'}, 5: {1: 'US', 2: 'US', 3: 'US', 4: 'CA'}, 6: {0: 'US', 1: 'US', 4: 'US', 5: 'CA', 7: 'CA', 8: 'CA', 9: 'US'}, 8: {0: 'US', 2: 'CA', 5: 'US', 6: 'US', 7: 'CA'}}, 4: {0: {1: 'US', 2: 'US', 3: 'CA', 4: 'US', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 1: {0: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'CA', 7: 'US', 8: 'CA', 9: 'US'}, 2: {3: 'US', 4: 'US', 5: 'US', 8: 'CA'}, 3: {0: 'US', 1: 'CA', 2: 'US', 4: 'US', 5: 'US', 7: 'CA', 8: 'CA'}, 4: {0: 'US', 1: 'BM', 2: 'US', 3: 'US', 5: 'US', 7: 'US', 8: 'US'}, 5: {0: 'CA', 8: 'US'}, 6: {0: 'CA', 3: 'US', 4: 'US', 8: 'CA', 9: 'US'}, 7: {0: 'US', 3: 'GD', 4: 'CA', 5: 'US', 8: 'US', 9: 'US'}, 8: {0: 'US', 4: 'US', 7: 'CA'}}, 5: {0: {1: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'CA', 7: 'US', 8: 'US', 9: 'US'}, 1: {0: 'US', 2: 'US', 3: 'US', 4: 'CA', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'CA'}, 2: {0: 'US', 6: 'US'}, 3: {0: 'US', 1: 'US', 4: 'US', 7: 'CA', 9: 'US'}, 4: {0: 'US', 1: 'US', 8: 'CA'}, 5: {1: 'US', 7: 'US', 9: 'US'}, 6: {1: 'US', 2: 'US', 3: 'US', 4: 'US', 7: 'US', 8: 'CA'}, 7: {0: 'US', 1: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 9: 'CA'}, 8: {0: 'US', 1: 'CA', 2: 'US', 4: 'CA', 5: 'US', 6: 'US', 7: 'CA'}}, 6: {0: {0: 'CA', 1: 'US', 2: 'US', 3: 'US', 4: 'CA', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 1: {0: 'US', 2: 'US', 3: 'CA', 4: 'US', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 2: {0: 'US', 2: 'CA', 3: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 3: {0: 'US', 1: 'US', 6: 'US', 9: 'CA'}, 4: {0: 'US', 1: 'US', 6: 'US', 7: 'CA', 9: 'TC'}, 5: {0: 'US', 1: 'US', 6: 'US', 7: 'US', 8: 'JM', 9: 'US'}, 6: {0: 'US', 1: 'US', 2: 'US', 4: 'MS', 7: 'US', 9: 'US'}, 7: {0: 'MP', 1: 'US', 2: 'CA', 8: 'US', 9: 'US'}, 8: {0: 'US', 1: 'US', 2: 'US', 3: 'CA', 4: 'US', 9: 'US'}}, 7: {0: {1: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'CA', 6: 'US', 7: 'US', 8: 'US', 9: 'CA'}, 1: {0: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 2: {0: 'US', 1: 'SX', 4: 'US', 5: 'US', 6: 'US', 7: 'US'}, 3: {0: 'US', 1: 'US', 2: 'US', 4: 'US', 7: 'US'}, 4: {0: 'US', 2: 'CA', 3: 'US', 7: 'US'}, 5: {3: 'CA', 4: 'US', 7: 'US', 8: 'LC'}, 6: {0: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 7: 'DM', 9: 'US'}, 7: {0: 'US', 1: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 8: 'CA', 9: 'US'}, 8: {0: 'CA', 1: 'US', 2: 'CA', 4: 'VC', 5: 'US', 6: 'US', 7: 'PR'}}, 8: {0: {1: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'US', 7: 'CA', 8: 'US', 9: 'DO'}, 1: {0: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'CA'}, 2: {0: 'US', 5: 'CA', 6: 'US', 8: 'US', 9: 'DO'}, 3: {0: 'US', 1: 'US', 2: 'US', 5: 'US', 8: 'US', 9: 'US'}, 4: {0: 'US', 3: 'US', 5: 'US', 7: 'US', 8: 'US', 9: 'DO'}, 5: {0: 'US', 1: 'CA', 4: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 6: {0: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 7: 'CA', 8: 'TT', 9: 'KN'}, 7: {0: 'US', 1: 'CA', 2: 'US', 3: 'CA', 6: 'JM', 8: 'US', 9: 'CA'}}, 9: {0: {1: 'US', 2: 'CA', 3: 'US', 4: 'US', 5: 'CA', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 1: {0: 'US', 2: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'US'}, 2: {0: 'US', 5: 'US', 8: 'US', 9: 'US'}, 3: {0: 'US', 1: 'US', 4: 'US', 5: 'US', 6: 'US', 7: 'US', 8: 'US', 9: 'PR'}, 4: {0: 'US', 1: 'US', 2: 'CA', 3: 'US', 5: 'US', 7: 'US', 8: 'US', 9: 'US'}, 5: {1: 'US', 2: 'US', 4: 'US', 6: 'US', 9: 'US'}, 7: {0: 'US', 1: 'US', 2: 'US', 3: 'US', 5: 'US', 8: 'US', 9: 'US'}, 8: {0: 'US', 3: 'US', 4: 'US', 5: 'US', 6: 'US', 9: 'US'}}}

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c7a1346eabd5e9b824b636fa8a9f6a56608176f1

4,163

py

Python

getters.py

pierrewinter/EPL_Fantasy_Team_Recommender

ab3e8649f6a4169843c31d9b8ebcc0bcbb9d4552

[ "MIT" ]

1

2019-07-18T18:31:58.000Z

getters.py

pierrewinter/EPL_Fantasy_Team_Recommender

ab3e8649f6a4169843c31d9b8ebcc0bcbb9d4552

[ "MIT" ]

null

getters.py

pierrewinter/EPL_Fantasy_Team_Recommender

ab3e8649f6a4169843c31d9b8ebcc0bcbb9d4552

[ "MIT" ]

null

import requests import json import time def get_data(): """ Retrieve the fpl player data from the hard-coded url """ response = requests.get("https://fantasy.premierleague.com/api/bootstrap-static") if response.status_code != 200: raise Exception("Response was code " + str(response.status_code)) responseStr = response.text data = json.loads(responseStr) return data def get_individual_player_data(player_id): """ Retrieve the player-specific detailed data Args: player_id (int): ID of the player whose data is to be retrieved """ base_url = "https://fantasy.premierleague.com/api/element-summary/" full_url = base_url + str(player_id) response = '' while response == '': try: response = requests.get(full_url) except: time.sleep(5) if response.status_code != 200: raise Exception("Response was code " + str(response.status_code)) data = json.loads(response.text) return data def get_entry_data(entry_id): """ Retrieve the summary/history data for a specific entry/team Args: entry_id (int) : ID of the team whose data is to be retrieved """ base_url = "https://fantasy.premierleague.com/api/entry/" full_url = base_url + str(entry_id) + "/history" response = '' while response == '': try: response = requests.get(full_url) except: time.sleep(5) if response.status_code != 200: raise Exception("Response was code " + str(response.status_code)) data = json.loads(response.text) return data def get_entry_personal_data(entry_id): """ Retrieve the summary/history data for a specific entry/team Args: entry_id (int) : ID of the team whose data is to be retrieved """ base_url = "https://fantasy.premierleague.com/api/entry/" full_url = base_url + str(entry_id) response = '' while response == '': try: response = requests.get(full_url) except: time.sleep(5) if response.status_code != 200: raise Exception("Response was code " + str(response.status_code)) data = json.loads(response.text) return data def get_entry_gws_data(entry_id): """ Retrieve the gw-by-gw data for a specific entry/team Args: entry_id (int) : ID of the team whose data is to be retrieved """ base_url = "https://fantasy.premierleague.com/api/entry/" gw_data = [] for i in range(1, 39): full_url = base_url + str(entry_id) + "/event/" + str(i) response = '' while response == '': try: response = requests.get(full_url) except: time.sleep(5) if response.status_code != 200: raise Exception("Response was code " + str(response.status_code)) data = json.loads(response.text) gw_data += [data] return data def get_entry_transfers_data(entry_id): """ Retrieve the transfer data for a specific entry/team Args: entry_id (int) : ID of the team whose data is to be retrieved """ base_url = "https://fantasy.premierleague.com/api/entry/" full_url = base_url + str(entry_id) + "/transfers" response = '' while response == '': try: response = requests.get(full_url) except: time.sleep(5) if response.status_code != 200: raise Exception("Response was code " + str(response.status_code)) data = json.loads(response.text) return data def get_fixtures_data(): """ Retrieve the fixtures data for the season """ url = "https://fantasy.premierleague.com/api/fixtures/" response = '' while response == '': try: response = requests.get(url) except: time.sleep(5) if response.status_code != 200: raise Exception("Response was code " + str(response.status_code)) data = json.loads(response.text) return data def main(): data = get_data() with open('raw.json', 'w') as outf: json.dump(data, outf) if __name__ == '__main__': main()

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null

0

1

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null

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6

c7b33f222a0009657928a2b64efd10111da75b92

49

py

Python

src/normalizer/__init__.py

Ehsan-Tavan/-boost_converter

7d52be7aa7994c137b1b63aa87eb51291f320cf5

[ "MIT" ]

null

src/normalizer/__init__.py

Ehsan-Tavan/-boost_converter

7d52be7aa7994c137b1b63aa87eb51291f320cf5

[ "MIT" ]

null

src/normalizer/__init__.py

Ehsan-Tavan/-boost_converter

7d52be7aa7994c137b1b63aa87eb51291f320cf5

[ "MIT" ]

null

from .validation import is_normalizer_type_exist

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