xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

c01a75ce2d0fd048116f0c45f5bcc1944a557708

542

py

Python

isw2-master/src/app/core/estadoCampania.py

marlanbar/academic-projects

bcdc8ca36b6984ab3f83c10b8a3ed45576ecfca1

[ "MIT" ]

null

isw2-master/src/app/core/estadoCampania.py

marlanbar/academic-projects

bcdc8ca36b6984ab3f83c10b8a3ed45576ecfca1

[ "MIT" ]

null

isw2-master/src/app/core/estadoCampania.py

marlanbar/academic-projects

bcdc8ca36b6984ab3f83c10b8a3ed45576ecfca1

[ "MIT" ]

null

class EstadoCampania(): def yaFinalizo(self): raise NotImplemented("'yaFinalizo' no fue implementado") def yaFueEvaluada(self): raise NotImplemented("'yaFueEvaluada' no fue implementado") def puedeEvaluarse(self): raise NotImplemented("'puedeEvaluarse' no fue implementado") def finalizar(self): raise NotImplemented("'finalizar' no fue implementado") def evaluar(self, unaEvaluacion): raise NotImplemented("'evaluar' no fue implementado") def getEvaluacion(self): raise NotImplemented("'getEvaluacion' no fue implementado")

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542

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null

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5

c02bfa39ab4af877076080035e9325baabeca10f

89

py

Python

extensions/.stubs/clrclasses/System/Runtime/DesignerServices/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

1

2020-03-25T03:27:24.000Z

extensions/.stubs/clrclasses/System/Runtime/DesignerServices/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

null

extensions/.stubs/clrclasses/System/Runtime/DesignerServices/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

null

from __clrclasses__.System.Runtime.DesignerServices import WindowsRuntimeDesignerContext

44.5

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1

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null

0

1

0

1

0

5

c0420384fa2ef0c04fc4665e3fbb38fccb5899c8

220

py

Python

fortytwocli/exception.py

dhaiibfiukkiu/42cli

a67da5168fe9e8a8905ed436e575e4c9bba6f608

[ "MIT" ]

4

2020-06-10T08:35:13.000Z

2020-08-14T01:32:36.000Z

fortytwocli/exception.py

4nm1tsu/42cli

a67da5168fe9e8a8905ed436e575e4c9bba6f608

[ "MIT" ]

null

fortytwocli/exception.py

4nm1tsu/42cli

a67da5168fe9e8a8905ed436e575e4c9bba6f608

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf=8 -*- class GitError(Exception): pass class ApiAccessError(Exception): pass class AuthorizeError(Exception): pass class NoConfigFoundError(Exception): pass

11.578947

36

0.686364

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220

6.565217

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0.344371

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0

0.00565

0.195455

220

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0.847458

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null

1

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null

0

1

0

5

fbffed0894436f1e81c761dce6f35b8121ed54dd

53

py

Python

autogl/solver/classifier/hetero/__init__.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

null

autogl/solver/classifier/hetero/__init__.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

null

autogl/solver/classifier/hetero/__init__.py

dedsec-9/AutoGL

487f2b2f798b9b1363ad5dc100fb410b12222e06

[ "MIT" ]

null

from .node_classifier import AutoHeteroNodeClassifier

53

0.924528

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53

9.6

1

0

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53

1

53

0.96

0

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true

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null

0

1

0

null

0

1

0

1

0

1

0

5

2206fa25fb4e590f553090204dedd8a409bc7b24

100

py

Python

autogp/datasets/__init__.py

Alwaysproblem/AutoGP

a1a324246ac0f053e367054e34e956a4af063f65

[ "Apache-2.0" ]

1

2019-01-22T00:41:17.000Z

autogp/datasets/__init__.py

Alwaysproblem/AutoGP

a1a324246ac0f053e367054e34e956a4af063f65

[ "Apache-2.0" ]

null

autogp/datasets/__init__.py

Alwaysproblem/AutoGP

a1a324246ac0f053e367054e34e956a4af063f65

[ "Apache-2.0" ]

null

from __future__ import absolute_import from .dataset import DataSet from .mnist import import_mnist

25

38

0.86

14

100

5.714286

0.428571

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0.12

100

3

39

33.333333

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0

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true

0

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1

0

null

0

1

0

1

0

1

0

5

222186f70d900ccc832cf9b92c028d2a27292c5c

67

py

Python

knx_stack/decode/layer/transport/t_data_group/__init__.py

majamassarini/knx-stack

11a9baac6b7600649b5fbca43c93b200b23676b4

[ "MIT" ]

2

2021-07-28T07:42:28.000Z

2022-01-25T18:56:05.000Z

knx_stack/decode/layer/transport/t_data_group/__init__.py

majamassarini/knx-stack

11a9baac6b7600649b5fbca43c93b200b23676b4

[ "MIT" ]

6

2021-07-25T21:36:01.000Z

2022-02-20T21:11:31.000Z

knx_stack/decode/layer/transport/t_data_group/__init__.py

majamassarini/knx-stack

11a9baac6b7600649b5fbca43c93b200b23676b4

[ "MIT" ]

null

from knx_stack.decode.layer.transport.t_data_group import con, ind

33.5

66

0.850746

12

67

4.5

1

0

0.074627

67

1

67

0.870968

0

1

0

true

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0

null

0

1

0

1

0

1

0

5

222b52be004eda9333e5cda676f139189b5c5a23

232

py

Python

trans_ms/transport_management/doctype/vehicle_documents_type/test_vehicle_documents_type.py

mohsinalimat/transport

3d32bd27f505f64b948f48d0bfc5c7ccaf61c4a2

[ "MIT" ]

null

trans_ms/transport_management/doctype/vehicle_documents_type/test_vehicle_documents_type.py

mohsinalimat/transport

3d32bd27f505f64b948f48d0bfc5c7ccaf61c4a2

[ "MIT" ]

null

trans_ms/transport_management/doctype/vehicle_documents_type/test_vehicle_documents_type.py

mohsinalimat/transport

3d32bd27f505f64b948f48d0bfc5c7ccaf61c4a2

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2017, Aakvatech Limited and Contributors # See license.txt from __future__ import unicode_literals import frappe import unittest class TestVehicleDocumentsType(unittest.TestCase): pass

19.333333

56

0.771552

27

232

6.444444

0.888889

0

0.025253

0.146552

232

11

57

21.090909

0.853535

0.396552

0

1

0

true

0.2

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0

1

0

null

0

null

0

1

0

1

0

5

2241537b5414ee579e31bf13677a065bab92b35f

58

py

Python

PP4E/Examples/PP4E/System/Streams/writer2.py

BeacherHou/Python-_Markdown-

015d79a02d32f49395b80ca10919b3a09b72c4df

[ "MIT" ]

1

2017-05-04T08:23:46.000Z

books/techno/python/programming_python_4_ed_m_lutz/code/chapter_3/09_chaning_programs_with_pipes_2/writer2.py

ordinary-developer/lin_education

13d65b20cdbc3e5467b2383e5c09c73bbcdcb227

[ "MIT" ]

null

books/techno/python/programming_python_4_ed_m_lutz/code/chapter_3/09_chaning_programs_with_pipes_2/writer2.py

ordinary-developer/lin_education

13d65b20cdbc3e5467b2383e5c09c73bbcdcb227

[ "MIT" ]

null

for data in (123, 0, 999, 42): print('%03d' % data)

19.333333

31

0.517241

10

58

3

0.9

0

0.261905

0.275862

58

2

32

29

0.452381

0

0.071429

0

1

0

false

0

0.5

1

0

null

0

1

0

1

0

1

0

null

0

1

0

5

2277e42854d159abc923c688be6837c0b36f8193

7,025

py

Python

terraform_compliance/steps/then/its_value_condition_match_the_search_regex.py

check-spelling/terraform-compliance

83636f0a0bad2e0e73eae70cf153c76f952cd2f3

[ "MIT" ]

null

terraform_compliance/steps/then/its_value_condition_match_the_search_regex.py

check-spelling/terraform-compliance

83636f0a0bad2e0e73eae70cf153c76f952cd2f3

[ "MIT" ]

null

terraform_compliance/steps/then/its_value_condition_match_the_search_regex.py

check-spelling/terraform-compliance

83636f0a0bad2e0e73eae70cf153c76f952cd2f3

[ "MIT" ]

1

2020-07-01T23:31:26.000Z

# -*- coding: utf-8 -*- from terraform_compliance.common.helper import ( EmptyStash, get_resource_name_from_stash ) from terraform_compliance.common.error_handling import Error import re def its_value_condition_match_the_search_regex_regex(_step_obj, condition, search_regex, _stash=EmptyStash, case_insensitive=True): def fail(condition, name=None): text = 'matches' if condition == 'must not' else 'does not match' name = name if (name is not None or name is not False) else _step_obj.context.name pattern = 'Null/None' if regex == '\x00' else regex Error(_step_obj, '{} property in {} {} {} with {} {} regex. ' 'It is set to {}.'.format(_step_obj.context.property_name, name, _step_obj.context.type, text, pattern, regex_flag_error_text, values)) regex = r'{}'.format(search_regex) values = _step_obj.context.stash if _stash is EmptyStash else _stash regex_flags = re.IGNORECASE if case_insensitive else 0 regex_flag_error_text = 'case insensitive' if case_insensitive else 'case sensitive' if isinstance(values, (str, int, bool, float)) or values is None: matches = re.match(regex, str(values), flags=regex_flags) if (condition == 'must' and matches is None) or (condition == "must not" and matches is not None): _stash = get_resource_name_from_stash(_step_obj.context.stash, _stash, _step_obj.context.address) fail(condition, name=_stash.get('address')) elif isinstance(values, list): for value in values: its_value_condition_match_the_search_regex_regex(_step_obj, condition, search_regex, value, case_insensitive=case_insensitive) elif isinstance(values, dict): if not hasattr(_step_obj.context, 'address'): _step_obj.context.address = None _step_obj.context.address = values.get('address', _step_obj.context.address) if 'values' in values: if values['values'] is None and regex == '\x00' and condition == 'must not': values = values['values'] fail(condition, name=_stash.get('address')) else: its_value_condition_match_the_search_regex_regex(_step_obj, condition, search_regex, values.get('values'), case_insensitive=case_insensitive) else: for key, value in values.items(): its_value_condition_match_the_search_regex_regex(_step_obj, condition, search_regex, value, case_insensitive=case_insensitive) def any_of_its_values_condition_match_the_search_regex_regex(_step_obj, condition, search_regex, _stash=EmptyStash, case_insensitive=True): def fail(condition, name=None): text = 'matches' if condition == 'must not' else 'does not match' name = name if ( name is not None or name is not False) else _step_obj.context.name pattern = 'Null/None' if regex == '\x00' else regex Error(_step_obj, '{} property in {} {} {} with {} {} regex. ' 'It is set to {}.'.format(_step_obj.context.property_name, name, _step_obj.context.type, text, pattern, regex_flag_error_text, values)) found = False def search(values): nonlocal found if found: return True if isinstance(values, (str, int, bool, float)) or values is None: matches = re.match(regex, str(values), flags=regex_flags) if (condition == 'must' and matches is not None) or (condition == "must not" and matches is None): found = True return found elif isinstance(values, list): return any(map(search, values)) elif isinstance(values, dict): if not hasattr(_step_obj.context, 'address'): _step_obj.context.address = None _step_obj.context.address = values.get('address', _step_obj.context.address) if 'values' in values: return search(values['values']) else: return any(map(search, values.values())) return False regex = r'{}'.format(search_regex) values = _step_obj.context.stash if _stash is EmptyStash else _stash regex_flags = re.IGNORECASE if case_insensitive else 0 regex_flag_error_text = 'case insensitive' if case_insensitive else 'case sensitive' if not search(values): _stash = get_resource_name_from_stash(_step_obj.context.stash, _stash, _step_obj.context.address) fail(condition, name=_stash.get('address')) def its_singular_value_condition_match_the_search_regex_regex(_step_obj, condition, search_regex, _stash=EmptyStash, case_insensitive=True): resources = _step_obj.context.stash if _stash is EmptyStash else _stash if isinstance(resources, dict): if 'values' in resources: # in case the object is in 'address', 'values', 'type' format resources = resources['values'] else: Error(_step_obj, '{} is multivalued! Please use any/all versions of this step instead.'.format(_step_obj.context.property_name,)) return elif isinstance(resources, list): for resource in resources: if isinstance(resource, dict): resource = resource.get('values', resource) if isinstance(resource, (dict, list)) and len(resource) > 1: Error(_step_obj, '{} is multivalued! Please use any/all versions of this step instead.'.format(_step_obj.context.property_name,)) return its_value_condition_match_the_search_regex_regex(_step_obj, condition, search_regex, _stash, case_insensitive)

48.448276

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4.955801

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false

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null

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1

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null

0

5

97dc8d1bb2c674d7ed77e47b14e9540744584716

108

py

Python

UTILS/memleak_finder.py

binary-husky/hmp2g

1a4f4093cd296f07348f4db4c7503aca6e1fb05c

[ "MIT" ]

2

2022-02-25T12:04:55.000Z

2022-03-15T02:37:59.000Z

UTILS/memleak_finder.py

binary-husky/hmp2g

1a4f4093cd296f07348f4db4c7503aca6e1fb05c

[ "MIT" ]

null

UTILS/memleak_finder.py

binary-husky/hmp2g

1a4f4093cd296f07348f4db4c7503aca6e1fb05c

[ "MIT" ]

null

from pympler import tracker tr = tracker.SummaryTracker() def memdb_print_diff(): tr.print_diff()

15.428571

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108

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5

97e3b0d0020e3e92cd5da09605d821b398a7c255

785

py

Python

YoutubeTags/__init__.py

Sainravi541/YoutubeTags

49963e6e0db7e3b339b4858958f82297febf4e39

[ "MIT" ]

11

2021-09-10T09:40:54.000Z

2021-11-15T07:40:57.000Z

YoutubeTags/__init__.py

Sainravi541/YoutubeTags

49963e6e0db7e3b339b4858958f82297febf4e39

[ "MIT" ]

null

YoutubeTags/__init__.py

Sainravi541/YoutubeTags

49963e6e0db7e3b339b4858958f82297febf4e39

[ "MIT" ]

4

2021-09-19T17:31:13.000Z

2021-10-16T16:30:18.000Z

import html5lib import requests import bs4 from bs4 import BeautifulSoup def videotags(url): try: request = requests.get(url) soup = BeautifulSoup(request.content, 'html5lib') tags = ', '.join([ meta.attrs.get("content") for meta in soup.find_all("meta",{"property": "og:video:tag"}) ]) return tags except: return None def channeltags(url): try: request = requests.get(url) soup = BeautifulSoup(request.content, 'html5lib') tags = ', '.join([ meta.attrs.get("content") for meta in soup.find_all("meta",{"property": "og:video:tag"}) ]) return tags except: return None

28.035714

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5

97e7bd3a133e2cc7375bbf200f04ec0bf54315a2

147

py

Python

doc2json/spp2json/spp/spp_json_to_s2orc_json.py

josephcc/s2orc-doc2json

8a6a21b7a8a3c6ad11cd42bdd0d46ee32a5a990d

[ "Apache-2.0" ]

132

2021-02-15T18:16:12.000Z

2022-03-29T04:47:17.000Z

doc2json/spp2json/spp/spp_json_to_s2orc_json.py

josephcc/s2orc-doc2json

8a6a21b7a8a3c6ad11cd42bdd0d46ee32a5a990d

[ "Apache-2.0" ]

6

2021-02-21T09:52:11.000Z

2022-02-01T17:45:43.000Z

pdf_to_txt/doc2json/spp2json/spp/spp_json_to_s2orc_json.py

Kabongosalomon/task-dataset-metric-nli-extraction

2f7ecd7e1e4a456d2e23d9384f11c453653c4351

[ "MIT" ]

18

2021-02-15T18:18:05.000Z

2022-03-11T19:37:47.000Z

from typing import * from doc2json.s2orc import Paper def convert_spp_json_to_s2orc_json(spp_json: Dict) -> Paper: raise NotImplementedError

21

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5.333333

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147

7

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5

97f0a8767f3e910dd4b242b2c4e782de9d30f367

120

py

Python

grr/core/grr_response_core/lib/rdfvalues/__init__.py

nkrios/grr

399e078ed522bf0555a2666fb086aa7809d54971

[ "Apache-2.0" ]

4,238

2015-01-01T15:34:50.000Z

2022-03-31T08:18:05.000Z

grr/core/grr_response_core/lib/rdfvalues/__init__.py

tomchop/grr

27ba38dc0f5ad4f3e0cdbfb146a0a789e3b0d27b

[ "Apache-2.0" ]

787

2015-01-02T21:34:24.000Z

2022-03-02T13:26:38.000Z

grr/core/grr_response_core/lib/rdfvalues/__init__.py

tomchop/grr

27ba38dc0f5ad4f3e0cdbfb146a0a789e3b0d27b

[ "Apache-2.0" ]

856

2015-01-02T02:50:11.000Z

2022-03-31T11:11:53.000Z

#!/usr/bin/env python """AFF4 RDFValue implementations. This module contains the various RDFValue implementations. """

20

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6.642857

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0.009434

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null

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5

3f38fd20d3f0c0c2f34c6acb1e019373dc99dfe5

136

py

Python

seven/c4.py

xiaolinzi-xl/python_imooc

07bde890e3ab0ddef4467b0c77ef33614339a657

[ "Apache-2.0" ]

null

seven/c4.py

xiaolinzi-xl/python_imooc

07bde890e3ab0ddef4467b0c77ef33614339a657

[ "Apache-2.0" ]

null

seven/c4.py

xiaolinzi-xl/python_imooc

07bde890e3ab0ddef4467b0c77ef33614339a657

[ "Apache-2.0" ]

null

a = [1,2,3,4,5,6,7,8] for i in range(0,len(a),2): # 打印 1,3,5,7 print(a[i],end=' | ') print() b = a[0:len(a):2] # 使用分片更加优雅 print(b)

19.428571

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0.507353

36

136

1.916667

0.555556

0.115942

0.144928

0.173913

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136

7

41

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null

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3f690519304fd00065183904f5405ead1f17b3b2

17,449

py

Python

Homework_1/104501527_Hw1.py

woodyhoko/Neural_Networks_coursework

3d12a31047a1eb54a3ae52bb502c5371e5478701

[ "MIT" ]

null

Homework_1/104501527_Hw1.py

woodyhoko/Neural_Networks_coursework

3d12a31047a1eb54a3ae52bb502c5371e5478701

[ "MIT" ]

null

Homework_1/104501527_Hw1.py

woodyhoko/Neural_Networks_coursework

3d12a31047a1eb54a3ae52bb502c5371e5478701

[ "MIT" ]

null

import matplotlib.pyplot as plt from tkinter import * import os import random from mpl_toolkits.mplot3d import Axes3D import numpy as np def read(): filename=e[0].get() f=open(filename,'r') data=f.readlines() random.shuffle(data) data=[list(map(float,line.split())) for line in data] dimension=len(data[0])-1 Label(master,text="Learning rate : ").grid(row=3) Label(master,text="Learning time : ").grid(row=4) Label(master,text="Learn data rate : ").grid(row=5) Label(master,text="Accept correct rate : ").grid(row=6) Label(master,text="initialize").grid(row=7) Label(master,text=("Dimension : "+str(dimension))).grid(row=7,column=1) Label(master,text="Threshold : ").grid(row=8) di=0 i=[Entry]*(dimension+1) for di in range(dimension): Label(master,text=("wieght","of","x"+str(di+1),":")).grid(row=di+9) i[di+1]=Entry(master) i[di+1].grid(row=di+9,column=1) e[2]=Entry(master) e[3]=Entry(master) e[4]=Entry(master) e[5]=Entry(master) i[0]=Entry(master) e[2].grid(row=3,column=1) e[3].grid(row=4,column=1) e[4].grid(row=5,column=1) e[5].grid(row=6,column=1) i[0].grid(row=8,column=1) Button(master,text='Start',command=lambda:realstart(dimension,data,i)).grid(row=50,column=1, sticky=W,pady=4) def realstart(dimension,data,i): areas=sorted(list(map(int,list(set([point[dimension] for point in data]))))) print(areas) cs='brgcmyk' smartlineinfo=[[int]*2 for _ in range(sum(k for k in range(len(areas))))] ax=ay=0 for aa in range(sum(k for k in range(len(areas)))): ay+=1 if ay>=len(areas): ax+=1 ay=ax+1 smartlineinfo[aa][0]=areas[ax] smartlineinfo[aa][1]=areas[ay] savedir=e[1].get() if not os.path.exists(savedir): os.makedirs(savedir) learningrate=float(e[2].get()) ldr=float(e[4].get()) print(data) largest=[float]*(dimension+1) smallest=[float]*(dimension+1) for k in range(dimension+1): largest[k]=smallest[k]=data[0][k] for k in range(len(data)): for w in range(dimension): if largest[w]<data[k][w]: largest[w]=data[k][w] elif smallest[w]>data[k][w]: smallest[w]=data[k][w] smartline=[[float]*(dimension+1) for _ in range(sum(k for k in range(len(areas))))] bestsmartline=[[float]*(dimension+1) for _ in range(sum(k for k in range(len(areas))))] bestrate=0 for aa in range(sum(k for k in range(len(areas)))): thres=float(i[0].get()) smartline[aa][0]=thres for k in range(dimension): smartline[aa][k+1]=float(i[k+1].get()) for aa in range(sum(k for k in range(len(areas)))): print(', '.join(str(x) for x in smartline[aa])) n=int(e[3].get()) acceptrate=float(e[5].get()) raterecord=[float]*(n+1) for aa in range(sum(k for k in range(len(areas)))): for bb in range(dimension+1): if smartline[aa][bb]==0: smartline[aa][bb]+=0.000000001 r=0 currentrate=0 correctnum=0 if r<len(data)-int(len(data)*ldr)!=0: while(r<len(data)-int(len(data)*ldr)): counting=[0]*(sum(k for k in range(len(areas)))+2) besta=0 for aa in range(sum(k for k in range(len(areas)))): if (-smartline[aa][0]+sum(smartline[aa][k+1]*data[(int(len(data)*ldr)+r)][k] for k in range(dimension)))>((smartlineinfo[aa][0]+smartlineinfo[aa][1])/2): counting[smartlineinfo[aa][1]]+=1 else: counting[smartlineinfo[aa][0]]+=1 for aa in range(sum(k for k in range(len(areas)))+2): if counting[aa]>counting[besta]: besta=aa if besta==int(data[(int(len(data)*ldr)+r)][dimension]): correctnum+=1 r+=1 currentrate=correctnum/(len(data)-int(len(data)*ldr)) else: while(r<len(data)): counting=[0]*(sum(k for k in range(len(areas)))+2) besta=0 for aa in range(sum(k for k in range(len(areas)))): if (-smartline[aa][0]+sum(smartline[aa][k+1]*data[r][k] for k in range(dimension)))>((smartlineinfo[aa][0]+smartlineinfo[aa][1])/2): counting[smartlineinfo[aa][1]]+=1 else: counting[smartlineinfo[aa][0]]+=1 for aa in range(sum(k for k in range(len(areas)))+2): if counting[aa]>counting[besta]: besta=aa if besta==int(data[r][dimension]): correctnum+=1 r+=1 currentrate=correctnum/(len(data)) raterecord[0]=currentrate print(currentrate) pic=0 t=0 if(dimension==2): for area, c in zip(areas, cs): fdata=list(filter(lambda point: point[2]==area, data[:int(len(data)*ldr-1):])) xs=[point[0] for point in fdata] ys=[point[1] for point in fdata] plt.scatter(xs, ys, c=c, marker='o') for area, c in zip(areas, cs): fdata=list(filter(lambda point: point[2]==area, data[int(len(data)*ldr):])) xs=[point[0] for point in fdata] ys=[point[1] for point in fdata] plt.scatter(xs, ys, c=c, marker='^') plt.axhline(0,color='g', linestyle='--') plt.axvline(0,color='g', linestyle='--') for aa in range(sum(k for k in range(len(areas)))): plt.plot([smallest[0]-1,largest[0]+1],[(smartline[aa][0]-smartline[aa][1]*(smallest[0]-1))/smartline[aa][2],(smartline[aa][0]-smartline[aa][1]*(largest[1]+1))/smartline[aa][2]],color=cs[aa]) parastr=['{:.3f}'.format(x) for x in smartline[aa]] plt.figtext(0.25,-0.05-0.05*aa,'Parameter'+str(aa)+' = '+'{ '+', '.join(x for x in parastr)+' }') plt.figtext(0.4,-0.05-0.05*(sum(k for k in range(len(areas)))),'correct rate = '+ "%.3f"% currentrate) plt.axis([smallest[0]-1,largest[0]+1,smallest[1]-1,largest[1]+1]) plt.suptitle('Initialized status ', fontsize=12) plt.savefig(savedir+'/'+str(pic)+'.png',bbox_inches='tight', pad_inches=0.3) plt.close('all') elif(dimension==3): fig = plt.figure() aaa = fig.add_subplot(111, projection='3d') for area, c in zip(areas, cs): fdata=list(filter(lambda point: point[3]==area, data[:int(len(data)*ldr-1):])) xs=[point[0] for point in fdata] ys=[point[1] for point in fdata] zs=[point[2] for point in fdata] aaa.scatter(xs, ys, zs, c=c, marker='o') for area, c in zip(areas, cs): fdata=list(filter(lambda point: point[3]==area, data[int(len(data)*ldr):])) xs=[point[0] for point in fdata] ys=[point[1] for point in fdata] zs=[point[2] for point in fdata] aaa.scatter(xs, ys, zs, c=c, marker='^') X=np.arange(smallest[0]-1,largest[0]+1,(-smallest[0]+largest[0])/10) Y=np.arange(smallest[1]-1,largest[1]+1,(-smallest[1]+largest[1])/10) X,Y=np.meshgrid(X,Y) for aa in range(sum(k for k in range(len(areas)))): Z=(((smartlineinfo[aa][0]+smartlineinfo[aa][1])/2)+smartline[aa][0]-smartline[aa][1]*X-smartline[aa][2]*Y)/smartline[aa][3] aaa.plot_wireframe(X,Y,Z,color=cs[aa]) parastr=['{:.3f}'.format(x) for x in smartline[aa]] plt.figtext(0.25,-0.05-0.05*aa,'Parameter'+str(aa+1)+' = '+'{ '+', '.join(x for x in parastr)+' }') aaa.set_zlim3d(smallest[2]-1,largest[2]+1) plt.figtext(0.4,-0.05-0.05*(sum(k for k in range(len(areas)))),'correct rate = '+ "%.3f"% currentrate) plt.suptitle('Iteration '+str(t), fontsize=12) plt.axis([smallest[0]-1,largest[0]+1,smallest[1]-1,largest[1]+1]) plt.savefig(savedir+'/'+str(pic)+'.png',bbox_inches='tight', pad_inches=0.3) plt.close('all') else: for aa in range(sum(k for k in range(len(areas)))): parastr=['{:.3f}'.format(x) for x in smartline[aa]] plt.figtext(0.25,-0.05-0.05*aa,'Parameter'+str(aa+1)+' = '+'{ '+', '.join(x for x in parastr)+' }') plt.suptitle('Iteration '+str(t), fontsize=12) plt.figtext(0.4,-0.05-0.05*(sum(k for k in range(len(areas)))),'correct rate = '+ "%.3f"% currentrate) plt.savefig(savedir+'/'+str(pic)+'.png',bbox_inches='tight', pad_inches=0.3) plt.close('all') while(t<n and currentrate<acceptrate): check=0 for aa in range(sum(k for k in range(len(areas)))): if (-smartline[aa][0]+sum(smartline[aa][k+1]*data[t%int(len(data)*ldr)][k] for k in range(dimension))>=(smartlineinfo[aa][0]+smartlineinfo[aa][1])/2) and (data[t%int(len(data)*ldr)][dimension]<(smartlineinfo[aa][0]+smartlineinfo[aa][1])/2): smartline[aa][0]-=-learningrate for k in range(dimension): smartline[aa][k+1]-=learningrate*data[t%int(len(data)*ldr)][k] check=1 elif (-smartline[aa][0]+sum(smartline[aa][k+1]*data[t%int(len(data)*ldr)][k] for k in range(dimension))<=(smartlineinfo[aa][0]+smartlineinfo[aa][1])/2) and (data[t%int(len(data)*ldr)][dimension]>(smartlineinfo[aa][0]+smartlineinfo[aa][1])/2): smartline[aa][0]+=-learningrate for k in range(dimension): smartline[aa][k+1]+=learningrate*data[t%int(len(data)*ldr)][k] check=1 for aa in range(sum(k for k in range(len(areas)))): for bb in range(dimension+1): if smartline[aa][bb]==0: smartline[aa][bb]+=0.000000001 correctnum=0 r=0 if r<len(data)-int(len(data)*ldr)!=0: while(r<len(data)-int(len(data)*ldr)): counting=[0]*(sum(k for k in range(len(areas)))+2) besta=0 for aa in range(sum(k for k in range(len(areas)))): if (-smartline[aa][0]+sum(smartline[aa][k+1]*data[(int(len(data)*ldr)+r)][k] for k in range(dimension)))>((smartlineinfo[aa][0]+smartlineinfo[aa][1])/2): counting[smartlineinfo[aa][1]]+=1 else: counting[smartlineinfo[aa][0]]+=1 for aa in range(sum(k for k in range(len(areas)))+2): if counting[aa]>counting[besta]: besta=aa if besta==int(data[(int(len(data)*ldr)+r)][dimension]): correctnum+=1 r+=1 currentrate=correctnum/(len(data)-int(len(data)*ldr)) else: while(r<len(data)): counting=[0]*(sum(k for k in range(len(areas)))+2) besta=0 for aa in range(sum(k for k in range(len(areas)))): if (-smartline[aa][0]+sum(smartline[aa][k+1]*data[r][k] for k in range(dimension)))>((smartlineinfo[aa][0]+smartlineinfo[aa][1])/2): counting[smartlineinfo[aa][1]]+=1 else: counting[smartlineinfo[aa][0]]+=1 for aa in range(sum(k for k in range(len(areas)))+2): if counting[aa]>counting[besta]: besta=aa if besta==int(data[r][dimension]): correctnum+=1 r+=1 currentrate=correctnum/(len(data)) t+=1 if check!=0: pic+=1 print(t) for aa in range(sum(k for k in range(len(areas)))): print("neuron"+str(aa+1)+" : "+', '.join(str(x) for x in smartline[aa])) print(currentrate) if(dimension==2): for area, c in zip(areas, cs): fdata=list(filter(lambda point: point[2]==area, data[:int(len(data)*ldr-1):])) xs=[point[0] for point in fdata] ys=[point[1] for point in fdata] plt.scatter(xs, ys, c=c, marker='o') for area, c in zip(areas, cs): fdata=list(filter(lambda point: point[2]==area, data[int(len(data)*ldr):])) xs=[point[0] for point in fdata] ys=[point[1] for point in fdata] plt.scatter(xs, ys, c=c, marker='^') plt.axhline(0,color='g', linestyle='--') plt.axvline(0,color='g', linestyle='--') for aa in range(sum(k for k in range(len(areas)))): plt.plot([smallest[0]-1,largest[0]+1],[(smartline[aa][0]+((smartlineinfo[aa][0]+smartlineinfo[aa][1])/2)-smartline[aa][1]*(smallest[0]-1))/smartline[aa][2],(smartline[aa][0]+((smartlineinfo[aa][0]+smartlineinfo[aa][1])/2)-smartline[aa][1]*(largest[0]+1))/smartline[aa][2]],color=cs[aa]) parastr=['{:.3f}'.format(x) for x in smartline[aa]] plt.figtext(0.25,-0.05-0.05*aa,'Parameter'+str(aa+1)+' = '+'{ '+', '.join(x for x in parastr)+' }') plt.figtext(0.4,-0.05-0.05*(sum(k for k in range(len(areas)))),'correct rate = '+ "%.3f"% currentrate) plt.suptitle('Iteration '+str(t), fontsize=12) plt.axis([smallest[0]-1,largest[0]+1,smallest[1]-1,largest[1]+1]) plt.savefig(savedir+'/'+str(pic)+'.png',bbox_inches='tight', pad_inches=0.3) plt.close('all') elif(dimension==3): fig = plt.figure() aaa = fig.add_subplot(111, projection='3d') for area, c in zip(areas, cs): fdata=list(filter(lambda point: point[3]==area, data[:int(len(data)*ldr-1):])) xs=[point[0] for point in fdata] ys=[point[1] for point in fdata] zs=[point[2] for point in fdata] aaa.scatter(xs, ys, zs, c=c, marker='o') for area, c in zip(areas, cs): fdata=list(filter(lambda point: point[3]==area, data[int(len(data)*ldr):])) xs=[point[0] for point in fdata] ys=[point[1] for point in fdata] zs=[point[2] for point in fdata] aaa.scatter(xs, ys, zs, c=c, marker='^') X=np.arange(smallest[0]-1,largest[0]+1,(-smallest[0]+largest[0])/10) Y=np.arange(smallest[1]-1,largest[1]+1,(-smallest[1]+largest[1])/10) X,Y=np.meshgrid(X,Y) for aa in range(sum(k for k in range(len(areas)))): Z=(((smartlineinfo[aa][0]+smartlineinfo[aa][1])/2)+smartline[aa][0]-smartline[aa][1]*X-smartline[aa][2]*Y)/smartline[aa][3] aaa.plot_wireframe(X,Y,Z,color=cs[aa]) parastr=['{:.3f}'.format(x) for x in smartline[aa]] plt.figtext(0.25,-0.05-0.05*aa,'Parameter'+str(aa+1)+' = '+'{ '+', '.join(x for x in parastr)+' }') aaa.set_zlim3d(smallest[2]-1,largest[2]+1) plt.figtext(0.4,-0.05-0.05*(sum(k for k in range(len(areas)))),'correct rate = '+ "%.3f"% currentrate) plt.suptitle('Iteration '+str(t), fontsize=12) plt.axis([smallest[0]-1,largest[0]+1,smallest[1]-1,largest[1]+1]) plt.savefig(savedir+'/'+str(pic)+'.png',bbox_inches='tight', pad_inches=0.3) plt.close('all') else: for aa in range(sum(k for k in range(len(areas)))): parastr=['{:.3f}'.format(x) for x in smartline[aa]] plt.figtext(0.25,-0.05-0.05*aa,'Parameter'+str(aa+1)+' = '+'{ '+', '.join(x for x in parastr)+' }') plt.suptitle('Iteration '+str(t), fontsize=12) plt.figtext(0.4,-0.05-0.05*(sum(k for k in range(len(areas)))),'correct rate = '+ "%.3f"% currentrate) plt.savefig(savedir+'/'+str(pic)+'.png',bbox_inches='tight', pad_inches=0.3) plt.close('all') raterecord[t]=currentrate if currentrate>bestrate: bestsmartline=smartline bestrate=currentrate plt.suptitle('Learning progress',fontsize=12) for chc in range(n): plt.plot([chc,chc+1],[raterecord[chc],raterecord[chc+1]],color='b') for aa in range(sum(k for k in range(len(areas)))): parastr=['{:.3f}'.format(x) for x in bestsmartline[aa]] plt.figtext(0.25,-0.05-0.05*aa,'Parameter'+str(aa+1)+' = '+'{ '+', '.join(x for x in parastr)+' }') plt.figtext(0.4,-0.05-0.05*(sum(k for k in range(len(areas)))),'best rate = '+ "%.3f"% bestrate) plt.savefig(savedir+'/record.png',bbox_inches='tight', pad_inches=0.3) plt.close('all') master = Tk() master.title("Hw 1") Label(master,text="Data File name : ").grid(row=0) Label(master,text="Save folder name : ").grid(row=1) Button(master,text='Read',command=read).grid(row=2,column=1, sticky=W,pady=4) e=[Entry]*6 e[0]=Entry(master) e[1]=Entry(master) e[0].grid(row=0,column=1) e[1].grid(row=1,column=1) Button(master,text='Quit',command=master.quit,fg="red").grid(row=100,column=1,sticky=W,pady=4) mainloop()

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null

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5

58c45ee225492c080ca905366fe1db057febd136

481

py

Python

pytheos/plot/__init__.py

SHDShim/pytheos

be079624405e92fbec60c5ead253eb5917e55237

[ "Apache-2.0" ]

6

2017-06-23T03:28:51.000Z

2020-12-02T01:06:50.000Z

pytheos/plot/__init__.py

SHDShim/pytheos

be079624405e92fbec60c5ead253eb5917e55237

[ "Apache-2.0" ]

3

2018-03-06T00:07:51.000Z

2018-07-18T17:42:26.000Z

pytheos/plot/__init__.py

SHDShim/pytheos

be079624405e92fbec60c5ead253eb5917e55237

[ "Apache-2.0" ]

6

2017-07-11T19:40:12.000Z

2021-01-12T02:20:39.000Z

""" from .BM3 import * from .conversion import * from .debye import * from .kunc import * from .objs import * from .objs_for_fit import * from .pth import * from .pth_ConstQ import * from .pth_Dorogokupets2007 import * from .pth_Dorogokupets2015 import * from .pth_Speziale2001 import * from .pth_Tange import * from .pvt import * from .vinet import * from .hugoniot import * """ from .static_fit import static_fit_result from .thermal_fit import thermal_data, thermal_fit_result

22.904762

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5.235294

0.323529

0.421348

0.219101

0

0.031785

0.149688

481

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true

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null

0

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1

0

1

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5

58ec809d5d553cae857ea12062fe339fe8420c19

4,549

py

Python

tests/unit/test_four_key_metrics.py

play-code-tools/crawling-gocd

4e2bae3c8bfeb091e49979dac785c96d22b90688

[ "MIT" ]

null

tests/unit/test_four_key_metrics.py

play-code-tools/crawling-gocd

4e2bae3c8bfeb091e49979dac785c96d22b90688

[ "MIT" ]

1

2020-03-18T13:30:05.000Z

tests/unit/test_four_key_metrics.py

play-code-tools/crawling-gocd

4e2bae3c8bfeb091e49979dac785c96d22b90688

[ "MIT" ]

null

import unittest import json import datetime import tests.unit.test_fixture as fixture from crawling_gocd.four_key_metrics import DeploymentFrequency, ChangeFailPercentage, MeanTimeToRestore, ChangeFailPercentage_ignoredContinuousFailed from crawling_gocd.gocd_domain import Pipeline from crawling_gocd.crawler import CrawlingDataMapper from crawling_gocd.calculate_domain import InputsCalcConfig class DeploymentFrequencyTest(unittest.TestCase): def setUp(self): self.pipeline = fixture.generatePipeline() def test_should_calculate_deployment_frequency_correctly(self): handler = DeploymentFrequency() results = handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: DeploymentFrequency, groupName: qa, value: 5 }") class ChangeFailPercentageTest(unittest.TestCase): def setUp(self): self.pipeline = fixture.generatePipeline() self.handler = ChangeFailPercentage() def test_should_calculate_change_fail_percentage_correctly(self): results = self.handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: ChangeFailPercentage, groupName: qa, value: 40.0% }") def test_should_return_NA_when_zero_deployment(self): self.pipeline.calcConfig.endTime = datetime.datetime(2019, 8, 29, 8, 34, tzinfo=datetime.timezone.utc) results = self.handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: ChangeFailPercentage, groupName: qa, value: N/A }") class ChangeFailPercentage_ignoredContinuousFailedTest(unittest.TestCase): def setUp(self): self.pipeline = fixture.generatePipeline() self.handler = ChangeFailPercentage_ignoredContinuousFailed() def test_should_calculate_change_fail_percentage_correctly(self): results = self.handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: ChangeFailPercentage_2, groupName: qa, value: 40.0% }") def test_return_NA_when_zero_deployment(self): self.pipeline.calcConfig.endTime = datetime.datetime(2019, 8, 29, 8, 34, tzinfo=datetime.timezone.utc) results = self.handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: ChangeFailPercentage_2, groupName: qa, value: N/A }") class MeanTimeToRestoreTest(unittest.TestCase): def setUp(self): self.pipeline = fixture.generatePipeline() self.handler = MeanTimeToRestore() def test_should_calculate_mean_time_to_restore_correctly_when_last_history_is_failed(self): results = self.handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: MeanTimeToRestore, groupName: qa, value: 837(mins) }") def test_should_calculate_mean_time_to_restore_correctly_when_last_history_is_successful(self): self.pipeline.calcConfig.endTime = datetime.datetime(2019, 8, 30, 8, 34, tzinfo=datetime.timezone.utc) results = self.handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: MeanTimeToRestore, groupName: qa, value: 69(mins) }") def test_should_calculate_mean_time_to_restore_when_newest_is_failed(self): self.pipeline.histories.pop(-1) self.pipeline.calcConfig.endTime = datetime.datetime(2019, 9, 2, tzinfo=datetime.timezone.utc) results = self.handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: MeanTimeToRestore, groupName: qa, value: 1229(mins) }") def test_should_return_NA_when_zero_depolyment(self): self.pipeline.calcConfig.endTime = datetime.datetime(2019, 8, 29, 8, 34, tzinfo=datetime.timezone.utc) results = self.handler.calculate([self.pipeline], []) self.assertEqual("".join(str(x) for x in results), "{ pipelineName: go_service, metricsName: MeanTimeToRestore, groupName: qa, value: N/A }")

56.160494

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500

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451baa433e183faa4cde044f78d849ee1f6b8680

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py

Python

services/kozipipe/src/errors.py

HerrLeStrate/training-27-10-19

6c95aa03ffc59a0065f93ebb54829c58efadb4e7

[ "WTFPL" ]

null

services/kozipipe/src/errors.py

HerrLeStrate/training-27-10-19

6c95aa03ffc59a0065f93ebb54829c58efadb4e7

[ "WTFPL" ]

2

2021-03-10T06:12:44.000Z

2021-05-11T02:02:43.000Z

services/kozipipe/src/errors.py

HerrLeStrate/training-27-10-19

6c95aa03ffc59a0065f93ebb54829c58efadb4e7

[ "WTFPL" ]

3

2020-02-14T14:10:56.000Z

2020-12-07T07:40:38.000Z

class ServerException(BaseException): pass

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0

1

0

null

0

1

0

5

188d5331cdaa97941f3dbc377d222fc98861bd7d

42

py

Python

hello_test.py

bipul2002star/hello-world-1

2eec63acfdb03f71cbe5b5728725e54a09b12d4e

[ "BSD-2-Clause" ]

null

hello_test.py

bipul2002star/hello-world-1

2eec63acfdb03f71cbe5b5728725e54a09b12d4e

[ "BSD-2-Clause" ]

null

hello_test.py

bipul2002star/hello-world-1

2eec63acfdb03f71cbe5b5728725e54a09b12d4e

[ "BSD-2-Clause" ]

null

#/bin/python #功能 import sys sys.exit()

5.25

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1891311cf51aee0d5ca9587e77f1540d1271fdb6

45

py

Python

focusnfe/core/__init__.py

jdcarvalho/python-focusnfe

c0769e6b4a5bf5123aba311cab7a6a0d4cfc5542

[ "MIT" ]

8

2019-11-19T14:40:39.000Z

2020-03-12T19:03:37.000Z

focusnfe/core/__init__.py

devlarysson/python-focusnfe

ac452d92437e822b04cc73abe1e56d93da5f91c0

[ "MIT" ]

2

2020-03-20T00:01:10.000Z

2021-06-02T00:41:31.000Z

focusnfe/core/__init__.py

devlarysson/python-focusnfe

ac452d92437e822b04cc73abe1e56d93da5f91c0

[ "MIT" ]

2

2020-03-13T13:37:47.000Z

2021-03-02T21:58:40.000Z

from .base import * from .exception import *

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18c19de7bfa3f93ff5c9c24fdea2c954878d21a2

156

py

Python

coptic/ingest/tasks.py

lgessler/cts

c2db155b7b77f7eab8c07b7fa324b2bf05c59ec1

[ "Apache-2.0" ]

1

2021-12-10T08:36:02.000Z

coptic/ingest/tasks.py

lgessler/cts

c2db155b7b77f7eab8c07b7fa324b2bf05c59ec1

[ "Apache-2.0" ]

null

coptic/ingest/tasks.py

lgessler/cts

c2db155b7b77f7eab8c07b7fa324b2bf05c59ec1

[ "Apache-2.0" ]

null

import threading from ingest.ingest import fetch_texts def ingest_asynch( ingest_id ): threading.Thread(target=fetch_texts, args=(ingest_id,)).start()

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1

0

1

0

1

0

5

7a166c4d5d5d12670cec01255a8427a9cc2711bc

2,575

py

Python

qa/qa_process.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

5

2019-03-28T03:52:32.000Z

2021-02-24T07:09:26.000Z

qa/qa_process.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

null

qa/qa_process.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

2

2018-08-07T03:43:09.000Z

2019-12-09T06:41:40.000Z

import re sep = "\t" def set_intersection(s1, s2): return list(set(s1) - (set(s1) - set(s2))) def compare(src, tgt): bi_1 = src + sep + tgt bi_2 = tgt + sep + src fd_1 = re.findall("(.+)(?=.*%s.*\\1)" % sep, bi_1) fd_2 = re.findall("(.+)(?=.*%s.*\\1)" % sep, bi_2) fd = set_intersection(fd_1, fd_2) output_src = src output_tgt = tgt if len(fd) > 0: fd_regex = "(" + "|".join([re.escape(x) for x in fd]) + ")" output_src = re.sub(fd_regex, "<tag>\\1</tag>", output_src) output_tgt = re.sub(fd_regex, "<tag>\\1</tag>", output_tgt) return output_src, output_tgt def compare_get_words(src, tgt): bi_1 = src + sep + tgt bi_2 = tgt + sep + src fd_1 = re.findall("(.+)(?=.*%s.*\\1)" % sep, bi_1) fd_2 = re.findall("(.+)(?=.*%s.*\\1)" % sep, bi_2) fd = set_intersection(fd_1, fd_2) return fd # prefix = '/media/tmxmall/a36811aa-0e87-4ba1-b14f-370134452449' # with open(f"{prefix}/t2t_med/mynmt/data/medicine.sample.txt/medicine.sample.txt.zh", "r", encoding="utf8") as f: # src_lines = [x.strip() for x in f.readlines()] # # with open(f"{prefix}/t2t_med/mynmt/data/medicine.sample.txt/medicine.sample.txt.en", "r", encoding="utf8") as f: # tgt_lines = [x.strip() for x in f.readlines()] # # output_src_lines = [] # output_tgt_lines = [] # for k, (src, tgt) in enumerate(zip(src_lines, tgt_lines)): # output_src, output_tgt = compare(src, tgt) # output_src_lines.append(output_src + "\n") # output_tgt_lines.append(output_tgt + "\n") # if k % 10 == 10 - 1: # print("processing %s" % (k + 1)) # # with open(f"{prefix}/t2t_med/mynmt/data/medicine.sample.txt/medicine.sample.txt.zh.tag", "w", encoding="utf8") as f: # f.writelines(output_src_lines) # # with open(f"{prefix}/t2t_med/mynmt/data/medicine.sample.txt/medicine.sample.txt.en.tag", "w", encoding="utf8") as f: # f.writelines(output_tgt_lines) prefix = '/media/tmxmall/a36811aa-0e87-4ba1-b14f-370134452449' with open(f"{prefix}/t2t_med/mynmt/data/medicine.sample.txt/medicine.sample.txt.zh", "r", encoding="utf8") as f: src_lines = [x.strip() for x in f.readlines()] with open(f"{prefix}/t2t_med/mynmt/data/medicine.sample.txt/medicine.sample.txt.en", "r", encoding="utf8") as f: tgt_lines = [x.strip() for x in f.readlines()] output_words = [] for k, (src, tgt) in enumerate(zip(src_lines, tgt_lines)): words = compare_get_words(src, tgt) output_words += words if k % 10 == 10 - 1: print("processing %s" % (k + 1)) print(sorted(list(set(output_words))))

34.333333

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null

0

1

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null

0

5

e130b4c9de5d6be3972b70ac86b2b0836cf66e0a

182

py

Python

StaticRotor.py

cristian-tamblay/EnigmaMachine

0db12806915ce28f9d345ce2dca3a5924e6e742f

[ "MIT" ]

null

StaticRotor.py

cristian-tamblay/EnigmaMachine

0db12806915ce28f9d345ce2dca3a5924e6e742f

[ "MIT" ]

null

StaticRotor.py

cristian-tamblay/EnigmaMachine

0db12806915ce28f9d345ce2dca3a5924e6e742f

[ "MIT" ]

null

class StaticRotor: def __init__(self): self.permutation = list(range(0, 27)) # Identidad def cipher(self, plainLetter): return self.permutation[plainLetter]

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null

0

1

0

1

0

5

e14b2d305a8bf55212899537d0d19a228ec9c44d

241

py

Python

build_automation/content_management/apps.py

mattjurenka/DLMS

0a69796b1b9940b37ee4ea7bc375a41dd63ec817

[ "MIT" ]

2

2018-08-02T23:38:32.000Z

2019-12-20T10:54:37.000Z

build_automation/content_management/apps.py

mattjurenka/DLMS

0a69796b1b9940b37ee4ea7bc375a41dd63ec817

[ "MIT" ]

28

2018-02-23T21:20:31.000Z

2018-05-02T22:38:31.000Z

build_automation/content_management/apps.py

mattjurenka/DLMS

0a69796b1b9940b37ee4ea7bc375a41dd63ec817

[ "MIT" ]

3

2019-11-16T03:54:48.000Z

2021-09-10T18:53:20.000Z

from django.apps import AppConfig class ContentManagementConfig(AppConfig): name = 'content_management' verbose_name = 'Content Management' def ready(self): import content_management.signals # noqa: F401 pass

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1

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null

0

1

0

1

0

5

e15fee3ad4b826314d122f4024abea7d663cb496

61

py

Python

pyabeles/__init__.py

MiroFurtado/pyabeles

c6de027c3bbd44c213cdf571c091c7b64a250820

[ "Apache-2.0" ]

null

pyabeles/__init__.py

MiroFurtado/pyabeles

c6de027c3bbd44c213cdf571c091c7b64a250820

[ "Apache-2.0" ]

null

pyabeles/__init__.py

MiroFurtado/pyabeles

c6de027c3bbd44c213cdf571c091c7b64a250820

[ "Apache-2.0" ]

null

from .core import Layer, Scanner, Surface, Experiment, Fitter

61

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61

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1

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true

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null

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0

null

0

1

0

1

0

1

0

5

e181786b0bca092779c6bcb3492c40b0806094b4

1,670

py

Python

openprocurement/bridge/rbot/defaults.py

openprocurement/openprocurement.bridge.rbot

32d362287df2f3a3e186b838d6c8fa39a007c452

[ "Apache-2.0" ]

null

openprocurement/bridge/rbot/defaults.py

openprocurement/openprocurement.bridge.rbot

32d362287df2f3a3e186b838d6c8fa39a007c452

[ "Apache-2.0" ]

null

openprocurement/bridge/rbot/defaults.py

openprocurement/openprocurement.bridge.rbot

32d362287df2f3a3e186b838d6c8fa39a007c452

[ "Apache-2.0" ]

1

2021-01-19T14:29:24.000Z

config = { "worker_type": "contracting", "client_inc_step_timeout": 0.1, "client_dec_step_timeout": 0.02, "drop_threshold_client_cookies": 2, "worker_sleep": 5, "retry_default_timeout": 3, "retries_count": 10, "queue_timeout": 3, "bulk_save_limit": 100, "bulk_save_interval": 3, "resources_api_token": "", "resources_api_version": "", "public_resources_api_server": "", "input_resources_api_server": "", "input_public_resources_api_server": "", "input_resource": "tenders", "output_resources_api_server": "", "output_public_resources_api_server": "", "output_resource": "tenders", "handler_rBot": { "resources_api_token": "", "output_resources_api_token": "", "resources_api_version": "", "input_resources_api_token": "", "input_resources_api_server": "", "input_public_resources_api_server": "", "input_resource": "tenders", "output_resources_api_server": "", "output_public_resources_api_server": "", "output_resource": "tenders", 'webreneder_url': 'http://localhost:8080' } } CONFIG_MAPPING = { "input_resources_api_token": "resources_api_token", "output_resources_api_token": "resources_api_token", "resources_api_version": "resources_api_version", "input_resources_api_server": "resources_api_server", "input_public_resources_api_server": "public_resources_api_server", "input_resource": "resource", "output_resource": "resource", "output_resources_api_server": "resources_api_server", "output_public_resources_api_server": "public_resources_api_server" }

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1

0

1

null

0

5

e1a99cefafd7ee90eacf42e733abd408452e8076

34

py

Python

firmware/eppenwolf/protocols/pulse_1.py

0xDBFB7/covidinator

e9c103e5e62bc128169400998df5f5cd13bd8949

[ "MIT" ]

null

firmware/eppenwolf/protocols/pulse_1.py

0xDBFB7/covidinator

e9c103e5e62bc128169400998df5f5cd13bd8949

[ "MIT" ]

null

firmware/eppenwolf/protocols/pulse_1.py

0xDBFB7/covidinator

e9c103e5e62bc128169400998df5f5cd13bd8949

[ "MIT" ]

null

import device_comms import sweep

8.5

19

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5

34

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0.8

0

0.147059

34

3

20

11.333333

0.965517

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true

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null

0

1

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null

0

1

0

1

0

5

e1b0a1339b1f07acaee66dead5639ec23ca8ee22

74

py

Python

shops/__init__.py

tinnuadan/redcogs

7a3cf31e39a7a024e6ee0faf68c9ca7943dfce3e

[ "MIT" ]

1

2020-09-14T06:43:03.000Z

shops/__init__.py

tinnuadan/redcogs

7a3cf31e39a7a024e6ee0faf68c9ca7943dfce3e

[ "MIT" ]

null

shops/__init__.py

tinnuadan/redcogs

7a3cf31e39a7a024e6ee0faf68c9ca7943dfce3e

[ "MIT" ]

null

from .src.cog import ShopsCog def setup(bot): bot.add_cog(ShopsCog())

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4.333333

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false

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0

null

0

1

0

1

0

1

0

5

e1b8024413494418275956522cc5e23a6adef03f

54

py

Python

blogmods/__init__.py

stonescar/multi-user-blog

a402dafde1f7d94031129638aa072ce39223e80e

[ "MIT" ]

null

blogmods/__init__.py

stonescar/multi-user-blog

a402dafde1f7d94031129638aa072ce39223e80e

[ "MIT" ]

null

blogmods/__init__.py

stonescar/multi-user-blog

a402dafde1f7d94031129638aa072ce39223e80e

[ "MIT" ]

null

import models import handlers import utils import seq

10.8

15

0.851852

8

54

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0.625

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0.148148

54

4

16

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1

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true

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1

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0

null

0

1

0

1

0

1

0

5

e1cf3b454d904f76a9080bc8ea7f61f6e340b810

151

py

Python

backend/home/models.py

crowdbotics-dev/testappauto20-dev-23504

39f2855f3e3331c8b0b24440a97d46beb45e4ebf

[ "FTL", "AML", "RSA-MD" ]

null

backend/home/models.py

crowdbotics-dev/testappauto20-dev-23504

39f2855f3e3331c8b0b24440a97d46beb45e4ebf

[ "FTL", "AML", "RSA-MD" ]

null

backend/home/models.py

crowdbotics-dev/testappauto20-dev-23504

39f2855f3e3331c8b0b24440a97d46beb45e4ebf

[ "FTL", "AML", "RSA-MD" ]

null

from django.conf import settings from django.db import models class Home(models.Model): "Generated Model" address = models.BigIntegerField()

18.875

38

0.754967

19

151

6

0.684211

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0

0.165563

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7

39

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1

0

1

0

null

0

1

0

1

0

5

bed71fd8016082e3880af522bcd129efb392c5ab

420

py

Python

pythia/pyre/templates/__init__.py

willic3/pythia

2657b95a0c07fd3c914ab6b5f7ec89a8edba004c

[ "BSD-3-Clause" ]

1

2015-11-30T08:01:39.000Z

pythia/pyre/templates/__init__.py

willic3/pythia

2657b95a0c07fd3c914ab6b5f7ec89a8edba004c

[ "BSD-3-Clause" ]

27

2018-05-24T18:31:25.000Z

2021-10-16T03:57:52.000Z

pythia/pyre/templates/__init__.py

willic3/pythia

2657b95a0c07fd3c914ab6b5f7ec89a8edba004c

[ "BSD-3-Clause" ]

7

2019-07-19T02:30:56.000Z

2021-06-02T22:00:01.000Z

#!/usr/bin/env python # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # California Institute of Technology # (C) 2006 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # def codecTmpl(): from .CodecTmpl import CodecTmpl return CodecTmpl() # end of file

20

80

0.340476

25

420

5.72

0.84

0

0.012308

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420

20

81

21

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0

1

0.333333

true

0

0.333333

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1

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null

0

1

0

null

0

1

0

1

0

5

83174af768e3c75af1a08cd95d27fd309be1c6a0

154

py

Python

flask/app/views.py

Rohitkuru/applicationflask

8137d67222e0cf72a5594ad6c3fd17bee19abe87

[ "MIT" ]

null

flask/app/views.py

Rohitkuru/applicationflask

8137d67222e0cf72a5594ad6c3fd17bee19abe87

[ "MIT" ]

null

flask/app/views.py

Rohitkuru/applicationflask

8137d67222e0cf72a5594ad6c3fd17bee19abe87

[ "MIT" ]

null

#!/app/flask/env/bin/python3 from app import * import os @app.route("/",methods=['POST','GET']) def index(): return render_template("index.html")

14

40

0.662338

22

154

4.590909

0.818182

0

0.007463

0.12987

154

10

41

15.4

0.746269

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0

0.144

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

null

0

1

0

1

0

5

832f776fff38684bb04d25267d808ce9fe7cd58b

123

py

Python

tests/utils.py

codingbeast/webdriver_manager

ec1e1278d18dfdb49e2489b0117018a4dd326031

[ "Apache-2.0" ]

670

2016-12-27T14:33:12.000Z

2022-03-31T05:56:15.000Z

tests/utils.py

codingbeast/webdriver_manager

ec1e1278d18dfdb49e2489b0117018a4dd326031

[ "Apache-2.0" ]

318

2016-12-29T07:11:08.000Z

2022-03-31T22:26:08.000Z

tests/utils.py

codingbeast/webdriver_manager

ec1e1278d18dfdb49e2489b0117018a4dd326031

[ "Apache-2.0" ]

150

2016-12-27T12:50:00.000Z

2022-03-31T05:58:27.000Z

import os project_root = os.path.dirname(os.path.dirname(__file__)) driver_directory = f"{project_root}{os.sep}.drivers"

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null

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1

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5

55d260b061f6368fe3b5750ee850bfb859264b1d

77

py

Python

modules/keras/backend/backend_main.py

jawahar273/scaling-in-python

47a96e17facf4d092ca52eeb4ccd812a2cad45d4

[ "MIT" ]

null

modules/keras/backend/backend_main.py

jawahar273/scaling-in-python

47a96e17facf4d092ca52eeb4ccd812a2cad45d4

[ "MIT" ]

null

modules/keras/backend/backend_main.py

jawahar273/scaling-in-python

47a96e17facf4d092ca52eeb4ccd812a2cad45d4

[ "MIT" ]

null

def first(): print( (10 * 10) ) def second(): print( ( 20 * 20 ) )

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1

0

null

0

1

0

1

0

5

55d7efbbe5b4aa4d19e877e4dc51bfeb91e65240

210

py

Python

ecosante/utils/tests.py

betagouv/recosante-api

4560b2cf2ff4dc19597792fe15a3805f6259201d

[ "MIT" ]

3

2021-09-24T14:07:51.000Z

2021-12-14T13:48:34.000Z

ecosante/utils/tests.py

betagouv/recosante-api

4560b2cf2ff4dc19597792fe15a3805f6259201d

[ "MIT" ]

187

2021-03-25T16:43:49.000Z

2022-03-23T14:40:31.000Z

ecosante/utils/tests.py

betagouv/recosante-api

4560b2cf2ff4dc19597792fe15a3805f6259201d

[ "MIT" ]

null

from ecosante.recommandations.models import Recommandation def published_recommandation(**kw): kw.setdefault('type_', 'indice_atmo') kw.setdefault('status', 'published') return Recommandation(**kw)

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null

0

1

0

1

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5

55fbc81565e9b38105caafcd7718fe7a87bd688b

181

py

Python

deduplipy/blocking/__init__.py

sbrugman/deduplipy

871bc69409a30f097642a6f798f51c39663ca7f7

[ "MIT" ]

null

deduplipy/blocking/__init__.py

sbrugman/deduplipy

871bc69409a30f097642a6f798f51c39663ca7f7

[ "MIT" ]

null

deduplipy/blocking/__init__.py

sbrugman/deduplipy

871bc69409a30f097642a6f798f51c39663ca7f7

[ "MIT" ]

null

from .blocking import Blocking from .blocking_rules import all_rules from .set_cover import greedy_set_cover __all__ = [ "Blocking", "greedy_set_cover", "all_rules", ]

18.1

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0.333333

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9

40

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1

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null

0

1

0

5

55fdad9fb7c7429cae6c299634a63836f90a228e

58

py

Python

WangWangBot/handlers/users/__init__.py

seekdoor/WangWangBot

e0a54da5107e6d005019e5adad3e5c53327b75fc

[ "MIT" ]

57

2021-08-11T16:07:25.000Z

2022-01-29T07:44:42.000Z

WangWangBot/handlers/users/__init__.py

seekdoor/WangWangBot

e0a54da5107e6d005019e5adad3e5c53327b75fc

[ "MIT" ]

11

2021-08-06T15:28:52.000Z

2021-09-26T13:05:17.000Z

WangWangBot/handlers/users/__init__.py

seekdoor/WangWangBot

e0a54da5107e6d005019e5adad3e5c53327b75fc

[ "MIT" ]

9

2021-08-18T08:44:03.000Z

2021-08-24T02:14:08.000Z

from . import help from . import start from . import admin

19.333333

19

0.758621

9

58

4.888889

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0.681818

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0.189655

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null

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1

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1

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5

36077665a795801eac5f3a74c09853d090f15ad2

144

py

Python

deepgp/models/__init__.py

LBJ-Wade/PyDeepGP

f2a1f568a7462633a58ed433520dcf7f0c98515c

[ "BSD-3-Clause" ]

201

2017-02-22T20:13:12.000Z

2022-03-16T13:20:30.000Z

deepgp/models/__init__.py

LBJ-Wade/PyDeepGP

f2a1f568a7462633a58ed433520dcf7f0c98515c

[ "BSD-3-Clause" ]

23

2017-03-27T18:41:56.000Z

2021-06-28T03:05:07.000Z

deepgp/models/__init__.py

LBJ-Wade/PyDeepGP

f2a1f568a7462633a58ed433520dcf7f0c98515c

[ "BSD-3-Clause" ]

59

2017-03-24T12:45:14.000Z

2022-03-02T05:13:21.000Z

36

59

0.75

23

144

4.695652

0.782609

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0.073171

0.145833

144

4

60

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0.804878

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0

1

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true

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

3614c0a9187487b8da22d43d1bb2b457818f9641

198

py

Python

qr_code/urls.py

HackRoboy/CoinBoy

5e10e763fe2e1e492f733fdf2531c77f13cef3a4

[ "BSD-3-Clause" ]

null

qr_code/urls.py

HackRoboy/CoinBoy

5e10e763fe2e1e492f733fdf2531c77f13cef3a4

[ "BSD-3-Clause" ]

null

qr_code/urls.py

HackRoboy/CoinBoy

5e10e763fe2e1e492f733fdf2531c77f13cef3a4

[ "BSD-3-Clause" ]

null

from django.conf.urls import url from qr_code import views app_name = 'qr_code' urlpatterns = [ url(r'^images/serve_qr_code_image/$', views.serve_qr_code_image, name='serve_qr_code_image') ]

19.8

96

0.762626

33

198

4.212121

0.484848

0.215827

0.23741

0.345324

0

0.126263

198

9

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0.277778

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0

1

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false

0

0.333333

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0.333333

0

null

1

0

1

0

null

0

1

0

5

36482c5301ce8562075cc644a026360bb1d1137f

3,168

py

Python

app/update/result_test.py

limshengli/tinypilot

aeba23e2e108008bea2b7577f16cfef949238648

[ "MIT" ]

1,334

2020-07-14T01:53:02.000Z

2021-06-08T09:48:28.000Z

app/update/result_test.py

limshengli/tinypilot

aeba23e2e108008bea2b7577f16cfef949238648

[ "MIT" ]

320

2020-07-07T20:18:05.000Z

2021-06-07T21:18:42.000Z

app/update/result_test.py

limshengli/tinypilot

aeba23e2e108008bea2b7577f16cfef949238648

[ "MIT" ]

124

2020-07-23T16:39:06.000Z

2021-06-04T10:22:53.000Z

import datetime import io import unittest import update.result class UpdateResultTest(unittest.TestCase): def test_reads_correct_values_for_successful_result(self): self.assertEqual( update.result.Result( error=None, timestamp=datetime.datetime(2021, 2, 10, 8, 57, 35, tzinfo=datetime.timezone.utc), ), update.result.read( io.StringIO(""" { "error": null, "timestamp": "2021-02-10T085735Z" } """))) def test_reads_correct_values_for_failed_result(self): self.assertEqual( update.result.Result( error='dummy update error', timestamp=datetime.datetime(2021, 2, 10, 8, 57, 35, tzinfo=datetime.timezone.utc), ), update.result.read( io.StringIO(""" { "error": "dummy update error", "timestamp": "2021-02-10T085735Z" } """))) def test_reads_default_values_for_empty_dict(self): self.assertEqual( update.result.Result( error=None, timestamp=datetime.datetime.utcfromtimestamp(0), ), update.result.read(io.StringIO('{}'))) def test_writes_success_result_accurately(self): mock_file = io.StringIO() update.result.write( update.result.Result( error=None, timestamp=datetime.datetime(2021, 2, 10, 8, 57, 35, tzinfo=datetime.timezone.utc), ), mock_file) self.assertEqual(('{"error": null, "timestamp": "2021-02-10T085735Z"}'), mock_file.getvalue()) def test_writes_error_result_accurately(self): mock_file = io.StringIO() update.result.write( update.result.Result( error='dummy update error', timestamp=datetime.datetime(2021, 2, 10, 8, 57, 35, tzinfo=datetime.timezone.utc), ), mock_file) self.assertEqual(('{"error": "dummy update error", ' '"timestamp": "2021-02-10T085735Z"}'), mock_file.getvalue())

35.595506

80

0.383207

217

3,168

5.447005

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1

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0

1

0

null

0

5

36b4b0604cf3d976ba7f61fa6dd17c31a2914e99

941

py

Python

venv/Lib/site-packages/tensorflow/keras/__init__.py

caiovini/Image_reader_api

7fae630a17195d3415eb739278ef21a3b58cae76

[ "MIT" ]

null

venv/Lib/site-packages/tensorflow/keras/__init__.py

caiovini/Image_reader_api

7fae630a17195d3415eb739278ef21a3b58cae76

[ "MIT" ]

null

venv/Lib/site-packages/tensorflow/keras/__init__.py

caiovini/Image_reader_api

7fae630a17195d3415eb739278ef21a3b58cae76

[ "MIT" ]

null

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/tools/api/generator/create_python_api.py script. """Implementation of the Keras API meant to be a high-level API for TensorFlow. Detailed documentation and user guides are available at [keras.io](https://keras.io). """ from __future__ import print_function from . import activations from . import applications from . import backend from . import callbacks from . import constraints from . import datasets from . import estimator from . import initializers from . import layers from . import losses from . import metrics from . import models from . import optimizers from . import preprocessing from . import regularizers from . import utils from . import wrappers from tensorflow.python.keras import Input from tensorflow.python.keras import Model from tensorflow.python.keras import Sequential from tensorflow.python.keras import __version__ del print_function

26.885714

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0.802338

129

941

5.75969

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0.228802

0.107672

0.13459

0.166891

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0.140276

941

34

80

27.676471

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true

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0

0.956522

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1

0

null

0

1

0

1

0

1

0

5

36b6486d7762d9a97bfb98a1bdf609c51d90ae75

50

py

Python

evo_gym/envs/atari/__init__.py

SamuelSchmidgall/EvolutionarySelfReplication

1a6f8225378b59423a97b439b56710bbed2754e9

[ "MIT" ]

12

2021-08-19T22:15:26.000Z

2022-03-27T20:31:40.000Z

evo_gym/envs/atari/__init__.py

SamuelSchmidgall/EvolutionarySelfReplication

1a6f8225378b59423a97b439b56710bbed2754e9

[ "MIT" ]

null

evo_gym/envs/atari/__init__.py

SamuelSchmidgall/EvolutionarySelfReplication

1a6f8225378b59423a97b439b56710bbed2754e9

[ "MIT" ]

null

from evo_gym.envs.atari.atari_env import AtariEnv

25

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4.555556

0.888889

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1

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0

null

0

1

0

1

0

1

0

5

36d38aa41e7836253332c4df33b45ab200b7030e

90

py

Python

pyelexon/exceptions.py

atsangarides/pyelexon

fbaa1a8c7e7dcd8b9f75413d7dfb76c148433039

[ "MIT" ]

null

pyelexon/exceptions.py

atsangarides/pyelexon

fbaa1a8c7e7dcd8b9f75413d7dfb76c148433039

[ "MIT" ]

null

pyelexon/exceptions.py

atsangarides/pyelexon

fbaa1a8c7e7dcd8b9f75413d7dfb76c148433039

[ "MIT" ]

null

class InvalidApiKey(Exception): pass class UnsuccessfulRequest(Exception): pass

12.857143

37

0.755556

8

90

8.5

0.625

0.382353

0

0.177778

90

6

38

15

0.918919

0

0.5

0

1

0

true

0.5

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0.5

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1

0

null

1

0

1

0

null

0

1

0

5

36dac338fdf69057e87baf28daca33b9a4ec09cc

28

py

Python

bbpyp/test/test_context.py

BloggerBust/bbpyp

078f940dd38bc3ee7c5adcfb2555c2843a4ca57b

[ "Apache-2.0" ]

null

bbpyp/test/test_context.py

BloggerBust/bbpyp

078f940dd38bc3ee7c5adcfb2555c2843a4ca57b

[ "Apache-2.0" ]

null

bbpyp/test/test_context.py

BloggerBust/bbpyp

078f940dd38bc3ee7c5adcfb2555c2843a4ca57b

[ "Apache-2.0" ]

null

class TestContext: pass

9.333333

18

0.714286

3

28

6.666667

1

0

0.25

28

2

19

14

0.952381

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

36f943429ecbd011b9b02aaba0294be00a318dee

13,632

py

Python

tests/test_outlier_handler.py

kartikra/mlshark

9ce6fb856c04486eab1d617681bdd01b856ad003

[ "BSD-3-Clause" ]

null

tests/test_outlier_handler.py

kartikra/mlshark

9ce6fb856c04486eab1d617681bdd01b856ad003

[ "BSD-3-Clause" ]

null

tests/test_outlier_handler.py

kartikra/mlshark

9ce6fb856c04486eab1d617681bdd01b856ad003

[ "BSD-3-Clause" ]

null

# Authors: Kartik Ramasubramanian <r.kartik@berkeley.edu> # License: BSD 3 clause import pytest import numpy as np import pandas as pd from sklearn.exceptions import NotFittedError from mlshark.feature_builder.outlier_removers import Winsorizer, ArbitraryOutlierCapper, OutlierTrimmer def test_Windsorizer(dataframe_normal_dist, dataframe_na, dataframe_vartypes): # test case 1: mean and std, right tail transformer = Winsorizer(distribution='gaussian', tail='right', fold=1) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] > 0.10727677848029868, 0.10727677848029868, df_transf['var']) # init params assert transformer.distribution == 'gaussian' assert transformer.tail == 'right' assert transformer.fold == 1 # fit params assert transformer.right_tail_caps_ == {'var': 0.10727677848029868} # assert round(transformer.right_tail_caps_['var'], 8) == round(0.10727677848029868, 8) assert transformer.left_tail_caps_ == {} assert transformer.input_shape_ == (100, 1) # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].max() <= 0.10727677848029868 assert dataframe_normal_dist['var'].max() > 0.10727677848029868 # test case 2: mean and std, both tails, different fold value transformer = Winsorizer(distribution='gaussian', tail='both', fold=2) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] > 0.20857275540714884, 0.20857275540714884, df_transf['var']) df_transf['var'] = np.where(df_transf['var'] < -0.19661115230025186, -0.19661115230025186, df_transf['var']) # fit params assert transformer.right_tail_caps_ == {'var': 0.20857275540714884} assert transformer.left_tail_caps_ == {'var': -0.19661115230025186} # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].max() <= 0.20857275540714884 assert X['var'].min() >= -0.19661115230025186 assert dataframe_normal_dist['var'].max() > 0.20857275540714884 assert dataframe_normal_dist['var'].min() < -0.19661115230025186 # test case 3: IQR, both tails, fold 1 transformer = Winsorizer(distribution='skewed', tail='both', fold=1) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] > 0.21180113880445128, 0.21180113880445128, df_transf['var']) df_transf['var'] = np.where(df_transf['var'] < -0.20247907173293223, -0.20247907173293223, df_transf['var']) # fit params assert transformer.right_tail_caps_ == {'var': 0.21180113880445128} assert transformer.left_tail_caps_ == {'var': -0.20247907173293223} # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].max() <= 0.21180113880445128 assert X['var'].min() >= -0.20247907173293223 assert dataframe_normal_dist['var'].max() > 0.21180113880445128 assert dataframe_normal_dist['var'].min() < -0.20247907173293223 # test case 4: IQR, left tail, fold 2 transformer = Winsorizer(distribution='skewed', tail='left', fold=0.8) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] < -0.17486039103044, -0.17486039103044, df_transf['var']) # fit params assert transformer.right_tail_caps_ == {} assert transformer.left_tail_caps_ == {'var': -0.17486039103044} # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].min() >= -0.17486039103044 assert dataframe_normal_dist['var'].min() < -0.17486039103044 # test case 5: quantiles, both tails, fold 10% transformer = Winsorizer(distribution='quantiles', tail='both', fold=0.1) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] > 0.14712481122898166, 0.14712481122898166, df_transf['var']) df_transf['var'] = np.where(df_transf['var'] < -0.12366227743232801, -0.12366227743232801, df_transf['var']) # fit params assert transformer.right_tail_caps_ == {'var': 0.14712481122898166} assert transformer.left_tail_caps_ == {'var': -0.12366227743232801} # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].max() <= 0.14712481122898166 assert X['var'].min() >= -0.12366227743232801 assert dataframe_normal_dist['var'].max() > 0.14712481122898166 assert dataframe_normal_dist['var'].min() < -0.12366227743232801 # test case 6: quantiles, right tail, fold 15% transformer = Winsorizer(distribution='quantiles', tail='right', fold=0.15) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] > 0.11823196128033647, 0.11823196128033647, df_transf['var']) # fit params assert transformer.right_tail_caps_ == {'var': 0.11823196128033647} assert transformer.left_tail_caps_ == {} # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].max() <= 0.11823196128033647 assert dataframe_normal_dist['var'].max() > 0.11823196128033647 # test case 7: dataset contains na and transformer is asked to ignore them transformer = Winsorizer(distribution='gaussian', tail='right', fold=1, variables=['Age', 'Marks'], missing_values='ignore') X = transformer.fit_transform(dataframe_na) df_transf = dataframe_na.copy() df_transf['Age'] = np.where(df_transf['Age'] > 38.79255087111844, 38.79255087111844, df_transf['Age']) df_transf['Marks'] = np.where(df_transf['Marks'] > 0.8970309389976613, 0.8970309389976613, df_transf['Marks']) # fit params assert transformer.right_tail_caps_ == {'Age': 38.79255087111844, 'Marks': 0.8970309389976613} assert transformer.left_tail_caps_ == {} assert transformer.input_shape_ == (8, 6) # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['Age'].max() <= 38.79255087111844 assert dataframe_na['Age'].max() > 38.79255087111844 # test error raises with pytest.raises(ValueError): Winsorizer(distribution='other') with pytest.raises(ValueError): Winsorizer(tail='other') with pytest.raises(ValueError): Winsorizer(missing_values='other') with pytest.raises(ValueError): Winsorizer(fold=-1) with pytest.raises(ValueError): Winsorizer(distribution='quantiles', fold=0.3) # test case 8: when dataset contains na, fit method with pytest.raises(ValueError): transformer = Winsorizer() transformer.fit(dataframe_na) # test case 9: when dataset contains na, transform method with pytest.raises(ValueError): transformer = Winsorizer() transformer.fit(dataframe_vartypes) transformer.transform(dataframe_na[['Name', 'City', 'Age', 'Marks', 'dob']]) with pytest.raises(NotFittedError): transformer = Winsorizer() transformer.transform(dataframe_vartypes) def test_ArbitraryOutlierCapper(dataframe_normal_dist, dataframe_na, dataframe_vartypes): # test case 1: right end capping transformer = ArbitraryOutlierCapper(max_capping_dict={'var': 0.10727677848029868}, min_capping_dict=None) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] > 0.10727677848029868, 0.10727677848029868, df_transf['var']) # init params assert transformer.max_capping_dict == {'var': 0.10727677848029868} assert transformer.min_capping_dict is None assert transformer.variables == ['var'] # fit params assert transformer.right_tail_caps_ == {'var': 0.10727677848029868} assert transformer.left_tail_caps_ == {} assert transformer.input_shape_ == (100, 1) # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].max() <= 0.10727677848029868 assert dataframe_normal_dist['var'].max() > 0.10727677848029868 # test case 2: both tails transformer = ArbitraryOutlierCapper(max_capping_dict={'var': 0.20857275540714884}, min_capping_dict={'var': -0.19661115230025186}) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] > 0.20857275540714884, 0.20857275540714884, df_transf['var']) df_transf['var'] = np.where(df_transf['var'] < -0.19661115230025186, -0.19661115230025186, df_transf['var']) # fit params assert transformer.right_tail_caps_ == {'var': 0.20857275540714884} assert transformer.left_tail_caps_ == {'var': -0.19661115230025186} # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].max() <= 0.20857275540714884 assert X['var'].min() >= -0.19661115230025186 assert dataframe_normal_dist['var'].max() > 0.20857275540714884 assert dataframe_normal_dist['var'].min() < -0.19661115230025186 # test case 3: left tail transformer = ArbitraryOutlierCapper(max_capping_dict=None, min_capping_dict={'var': -0.17486039103044}) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() df_transf['var'] = np.where(df_transf['var'] < -0.17486039103044, -0.17486039103044, df_transf['var']) # init param assert transformer.max_capping_dict is None assert transformer.min_capping_dict == {'var': -0.17486039103044} # fit params assert transformer.right_tail_caps_ == {} assert transformer.left_tail_caps_ == {'var': -0.17486039103044} # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['var'].min() >= -0.17486039103044 assert dataframe_normal_dist['var'].min() < -0.17486039103044 # test case 4: dataset contains na and transformer is asked to ignore them transformer = ArbitraryOutlierCapper(max_capping_dict=None, min_capping_dict={'Age': 20}, missing_values='ignore') X = transformer.fit_transform(dataframe_na) df_transf = dataframe_na.copy() df_transf['Age'] = np.where(df_transf['Age'] < 20, 20, df_transf['Age']) # fit params assert transformer.max_capping_dict is None assert transformer.min_capping_dict == {'Age': 20} assert transformer.input_shape_ == (8, 6) # transform params pd.testing.assert_frame_equal(X, df_transf) assert X['Age'].min() >= 20 assert dataframe_na['Age'].min() < 20 with pytest.raises(ValueError): ArbitraryOutlierCapper(max_capping_dict='other') with pytest.raises(ValueError): ArbitraryOutlierCapper(min_capping_dict='other') with pytest.raises(ValueError): ArbitraryOutlierCapper(min_capping_dict=None, max_capping_dict=None) with pytest.raises(ValueError): ArbitraryOutlierCapper(missing_values='other') df_na = dataframe_normal_dist.copy() df_na.loc[1, 'var'] = np.nan # test case 5: when dataset contains na, fit method with pytest.raises(ValueError): transformer = ArbitraryOutlierCapper(min_capping_dict={'var': -0.17486039103044}) transformer.fit(df_na) # test case 6: when dataset contains na, transform method with pytest.raises(ValueError): transformer = ArbitraryOutlierCapper(min_capping_dict={'var': -0.17486039103044}) transformer.fit(dataframe_normal_dist) transformer.transform(df_na) with pytest.raises(NotFittedError): transformer = ArbitraryOutlierCapper(min_capping_dict={'var': -0.17486039103044}) transformer.transform(dataframe_vartypes) def test_OutlierTrimmer(dataframe_normal_dist, dataframe_na): # test case 1: mean and std, right tail transformer = OutlierTrimmer(distribution='gaussian', tail='right', fold=1) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() outliers = np.where(df_transf['var'] > 0.10727677848029868, True, False) df_transf = df_transf.loc[~outliers] # transform params pd.testing.assert_frame_equal(X, df_transf) assert len(X) == 83 # test case 2: mean and std, both tails, different fold value transformer = OutlierTrimmer(distribution='gaussian', tail='both', fold=2) X = transformer.fit_transform(dataframe_normal_dist) assert len(X) == 96 # test case 3: IQR, left tail, fold 2 transformer = OutlierTrimmer(distribution='skewed', tail='left', fold=0.8) X = transformer.fit_transform(dataframe_normal_dist) df_transf = dataframe_normal_dist.copy() outliers = np.where(df_transf['var'] < -0.17486039103044, True, False) df_transf = df_transf.loc[~outliers] pd.testing.assert_frame_equal(X, df_transf) assert len(X) == 98 # test case 4: dataset contains na, and transformer is asked to ignore transformer = OutlierTrimmer(distribution='gaussian', tail='right', fold=1, variables=['Age'], missing_values='ignore') X = transformer.fit_transform(dataframe_na) df_transf = dataframe_na.copy() outliers = np.where(df_transf['Age'] > 38.79255087111844, True, False) df_transf = df_transf.loc[~outliers] pd.testing.assert_frame_equal(X, df_transf) assert len(X) == 6

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3d0a1f88032d2939321d0fd97811a667565569a9

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py

Python

uvicore/http/middleware/__init__.py

coboyoshi/uvicore

9cfdeeac83000b156fe48f068b4658edaf51c8de

[ "MIT" ]

11

2021-03-22T22:07:49.000Z

2022-03-08T16:18:33.000Z

uvicore/http/middleware/__init__.py

coboyoshi/uvicore

9cfdeeac83000b156fe48f068b4658edaf51c8de

[ "MIT" ]

12

2021-03-04T05:51:24.000Z

2021-09-22T05:16:18.000Z

uvicore/http/middleware/__init__.py

coboyoshi/uvicore

9cfdeeac83000b156fe48f068b4658edaf51c8de

[ "MIT" ]

2

2021-03-25T14:49:56.000Z

2021-11-17T23:20:29.000Z

# Uvicore custom from .authentication import Authentication # Starlette passthrough via class proxy from starlette.middleware.base import BaseHTTPMiddleware as _Base from starlette.middleware.cors import CORSMiddleware as _CORS from starlette.middleware.gzip import GZipMiddleware as _Gzip from starlette.middleware.httpsredirect import HTTPSRedirectMiddleware as _HTTPSRedirect from starlette.middleware.sessions import SessionMiddleware as _Session from starlette.middleware.trustedhost import TrustedHostMiddleware as _TrustedHost from starlette.middleware.wsgi import WSGIMiddleware as _WSGI class Middleware(_Base): pass class CORS(_CORS): pass class Gzip(_Gzip): pass class HTTPSRedirect(_HTTPSRedirect): pass class Session(_Session): pass class TrustedHost(_TrustedHost): pass class WSGI(_WSGI): pass

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3d3d6d93e9bfa8ad173ccab65e24036ca118e0b0

43

py

Python

gongda/0_Python_notes/2_matplotlib_notes.py

eamarais/eam-group

a9976092a9b7d4cad6035b8965e82cf6ef12f321

[ "MIT" ]

3

2020-04-01T14:31:23.000Z

2020-04-22T08:13:19.000Z

gongda/0_Python_notes/2_matplotlib_notes.py

eamarais/eam-group

a9976092a9b7d4cad6035b8965e82cf6ef12f321

[ "MIT" ]

2

2020-05-28T12:12:57.000Z

2020-06-25T19:34:23.000Z

gongda/0_Python_notes/2_matplotlib_notes.py

eamarais/eam-group

a9976092a9b7d4cad6035b8965e82cf6ef12f321

[ "MIT" ]

2

2020-06-06T19:15:02.000Z

2022-01-05T21:56:58.000Z

#### # Later will summarize matplolib here

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e9e6b4271b0d1d36451045b47f1d3540182618d6

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py

Python

test/test_edit_group.py

daltonik666/python_training

99e243c346aeeeb1698e31be04e1742cce6029d9

[ "Apache-2.0" ]

null

test/test_edit_group.py

daltonik666/python_training

99e243c346aeeeb1698e31be04e1742cce6029d9

[ "Apache-2.0" ]

null

test/test_edit_group.py

daltonik666/python_training

99e243c346aeeeb1698e31be04e1742cce6029d9

[ "Apache-2.0" ]

null

from model.group import Group def test_edit_group(app): app.group.edit(Group(name="groupname1", header="headre1", footer="footer1"))

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0

5

a1126dc2f548a134ac2766df197896ccb22903ec

226

py

Python

pynini/writing.py

daffidilly/pynini

11a8b0756199c17be5864eafaa4256d897933197

[ "Apache-2.0" ]

5

2016-11-21T08:53:59.000Z

2018-01-25T23:21:36.000Z

pynini/writing.py

daffidilly/pynini

11a8b0756199c17be5864eafaa4256d897933197

[ "Apache-2.0" ]

1

2017-02-21T19:07:22.000Z

2018-09-24T16:51:37.000Z

pynini/writing.py

daffidilly/pynini

11a8b0756199c17be5864eafaa4256d897933197

[ "Apache-2.0" ]

null

class TemplateFileWriter(object): """A writer that formats the template to a file.""" def __init__(self): pass def write(self, template, context, out_path): template.stream(context).dump(out_path)

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a116343108a98380741acc938b318eb5265a53e3

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py

Python

dataSc_cli/generator/__init__.py

hatemBT/DataSc_cli

e745e8f338c039856d36bdac3cb7a97cbc49ac1e

[ "MIT" ]

null

dataSc_cli/generator/__init__.py

hatemBT/DataSc_cli

e745e8f338c039856d36bdac3cb7a97cbc49ac1e

[ "MIT" ]

null

dataSc_cli/generator/__init__.py

hatemBT/DataSc_cli

e745e8f338c039856d36bdac3cb7a97cbc49ac1e

[ "MIT" ]

null

""" this package generate md files """

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null

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0

5

a120a40f7b2a4f57e37643fddbe9568b27a6fe91

114

py

Python

Common/WebScrappers/Yahoo/YahooBalanceScrapper.py

enriqueescobar-askida/Kinito.Finance

5308748b64829ac798a858161f9b4a9e5829db44

[ "MIT" ]

2

2020-03-04T11:18:38.000Z

2020-05-10T15:36:42.000Z

Common/WebScrappers/Yahoo/YahooBalanceScrapper.py

enriqueescobar-askida/Kinito.Finance

5308748b64829ac798a858161f9b4a9e5829db44

[ "MIT" ]

6

2020-03-30T16:42:47.000Z

2021-12-13T20:37:21.000Z

Common/WebScrappers/Yahoo/YahooBalanceScrapper.py

enriqueescobar-askida/Kinito.Finance

5308748b64829ac798a858161f9b4a9e5829db44

[ "MIT" ]

1

2020-04-14T11:26:16.000Z

from Common.WebScrappers.YahooScrapper import YahooScrapper class YahooBalanceScrapper(YahooScrapper): pass

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a13a002da51436e7c6684e2e153faaaceae5ccee

978

py

Python

test_unfurl.py

durple/unfurl

d7778a708086a345ab0cf47cfc3c7204fe45449f

[ "MIT" ]

2

2015-11-05T11:42:29.000Z

2016-07-20T13:04:23.000Z

test_unfurl.py

durple/unfurl

d7778a708086a345ab0cf47cfc3c7204fe45449f

[ "MIT" ]

null

test_unfurl.py

durple/unfurl

d7778a708086a345ab0cf47cfc3c7204fe45449f

[ "MIT" ]

2

2016-01-19T23:20:53.000Z

2016-05-06T21:30:24.000Z

# -*- coding: utf-8 -*- from unfurl import expand_url as eu def test_bitly(): assert eu("http://j.mp/Y4seGv") == "http://www.nytimes.com/2013/03/11/world/asia/karzai-accuses-us-and-taliban-of-colluding-in-afghanistan.html?ref=global-home&_r=0" assert eu("http://j.mp/13ND7TO") == "http://www.profnetconnect.com/angela_smith/blog/2013/03/07/angie%E2%80%99s_social_media_angels:_givingtuesday" def test_tco(): assert eu("http://t.co/bxPFQgZ1AV") == "http://www.nytimes.com/2013/03/14/crosswords/bridge/bridge-spring-north-american-championships.html?partner=rss&emc=rss&_r=0" def test_arrows(): assert eu(u"http://➡.ws/kd") == "http://www.theglobeandmail.com/technology/tech-news/crtc-will-rescind-unlimited-use-internet-decision---or-ottawa-will-overturn-it/article565223/" assert eu(u"http://➡.ws/wwwwwwwww") == "http://expandurl.appspot.com/" def test_tinyurl(): assert eu("http://tinyurl.com/l7953rg") == "http://espnfc.com/blog/_/name/espnfcunited/id/9949?cc=5901"

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null

0

1

0

1

0

5

a14400e8a5e312d5140569ffcb4b666968ccbbe7

294

py

Python

flask-api/schema/login.py

PapamichMarios/Intranet

65cd98d08a1a550d70e1afa4859a0b105c049817

[ "MIT" ]

1

2021-12-21T19:13:37.000Z

flask-api/schema/login.py

PapamichMarios/Intranet

65cd98d08a1a550d70e1afa4859a0b105c049817

[ "MIT" ]

null

flask-api/schema/login.py

PapamichMarios/Intranet

65cd98d08a1a550d70e1afa4859a0b105c049817

[ "MIT" ]

null

from marshmallow import Schema, fields, validate class LoginSchema(Schema): username = fields.String(attribute="username", validate=validate.Length(min=3, max=256), required=True) password = fields.String(attribute="password", validate=validate.Length(min=8, max=256), required=True)

42

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false

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1

0

null

0

1

0

1

0

5

a1659a21056ea5c20aeff977978e80b93ede9ceb

1,870

py

Python

src/tests/test_click_set_var_command_line.py

ndejong/pyvboxmanage

6cb49546782ae97f177e7035982b1dc86b8f61db

[ "BSD-2-Clause" ]

1

2020-12-28T02:19:35.000Z

src/tests/test_click_set_var_command_line.py

ndejong/pyvboxmanage

6cb49546782ae97f177e7035982b1dc86b8f61db

[ "BSD-2-Clause" ]

null

src/tests/test_click_set_var_command_line.py

ndejong/pyvboxmanage

6cb49546782ae97f177e7035982b1dc86b8f61db

[ "BSD-2-Clause" ]

null

import os import pytest from click.testing import CliRunner from pyvboxmanage.cli import click # NB: these tests work in isolation, however when they are run together with other runner.invoke() that use `args` # then the result.stdout returns empty - it is not obvious how to resolve this even using a manual `del` on # variable names in other tests does not resolve. # def test_pyvboxmanage_dryrun_test01_set_var_cli(): # runner = CliRunner() # config01_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'test01config.yml') # # result = runner.invoke(click.pyvboxmanage, args='--dry-run --setting source_vmname=FOOBAR "{}"'. # format(config01_filename) ) # assert result.exit_code == 0 # assert 'Successfully executed command line "vboxmanage clonevm FOOBAR --basefolder' in result.stdout # def test_pyvboxmanage_dryrun_test02_set_var_cli(): # runner = CliRunner() # config01_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'test01config.yml') # # result = runner.invoke(click.pyvboxmanage, args='--dry-run --setting source_vmname=FOOBAR "{}"'. # format(config01_filename) ) # assert result.exit_code == 0 # assert 'Successfully executed command line "vboxmanage clonevm FOOBAR --basefolder' in result.stdout # def test_pyvboxmanage_dryrun_test03_set_var_cli(): # # runner = CliRunner() # config_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'test03config.yml') # # result = runner.invoke(click.pyvboxmanage, args='--dry-run --setting unset_variable01=replacement_sourcevm01 "{}"'. # format(config_filename) ) # assert result.exit_code == 0 # assert 'Successfully executed command line "vboxmanage showvminfo replacement_sourcevm01"' in result.stdout

44.52381

121

0.708556

230

1,870

5.569565

0.4

0.042155

0.044496

0.058548

0.653396

0.63466

0

0.018979

0.182888

1,870

41

122

45.609756

0.819372

0.913904

0

1

0

true

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0

1

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null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

a1ae39aa1e19311d8026f36dea4679fb55a8e27d

161

py

Python

Scripts/python/scripts mundo 1/scripts sem cor/Desafio027.py

BrenoNAlmeida/Scripts-Escola

20d886d0401ef7f40a4a46e307eadbf5b1c0a5eb

[ "Apache-2.0" ]

null

Scripts/python/scripts mundo 1/scripts sem cor/Desafio027.py

BrenoNAlmeida/Scripts-Escola

20d886d0401ef7f40a4a46e307eadbf5b1c0a5eb

[ "Apache-2.0" ]

null

Scripts/python/scripts mundo 1/scripts sem cor/Desafio027.py

BrenoNAlmeida/Scripts-Escola

20d886d0401ef7f40a4a46e307eadbf5b1c0a5eb

[ "Apache-2.0" ]

null

nome=str(input('digite seu nome completo =')).split() print('seu primeiro nome é {}'.format(nome[0])) print('e seu ultimo nome é {}'.format(nome[len(nome)-1] ))

40.25

58

0.670807

27

161

4

0.592593

0.092593

0.203704

0.277778

0

0.013889

0.10559

161

3

59

53.666667

0.736111

0

0.434783

0

1

0

false

0

0.666667

1

0

null

0

1

0

1

0

null

0

1

0

5

a1b62117f5ea7b2e1922850c4c63530fcc261ce2

159

py

Python

ex010.py

gabrielwai/exercicios_de_Python

3767775748db7c501a6e0364edf7ba4f079e62f9

[ "MIT" ]

null

ex010.py

gabrielwai/exercicios_de_Python

3767775748db7c501a6e0364edf7ba4f079e62f9

[ "MIT" ]

null

ex010.py

gabrielwai/exercicios_de_Python

3767775748db7c501a6e0364edf7ba4f079e62f9

[ "MIT" ]

null

din = float(input('Digite sua quantidade de dinheiro (em reais):R$')) print('A quantidade de dólares que você pode comprar é: US${:.2f}'.format(din/3.27))

39.75

85

0.691824

27

159

4.074074

0.888889

0.218182

0

0.029412

0.144654

159

3

86

53

0.779412

0

0.677419

0

1

0

false

0

0.5

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0

null

1

0

1

0

1

0

null

0

1

0

5

a1d6e250bf590c86ac7792aacd10dd70e79798f0

2,653

py

Python

yamale/tests/test_command_line.py

kpmadhan/Yamale

36f9b8b1bfd3c5355491e43607dc44ef90473f25

[ "MIT" ]

null

yamale/tests/test_command_line.py

kpmadhan/Yamale

36f9b8b1bfd3c5355491e43607dc44ef90473f25

[ "MIT" ]

null

yamale/tests/test_command_line.py

kpmadhan/Yamale

36f9b8b1bfd3c5355491e43607dc44ef90473f25

[ "MIT" ]

null

import os import pytest from .. import command_line dir_path = os.path.dirname(os.path.realpath(__file__)) parsers = ['pyyaml', 'PyYAML', 'ruamel'] @pytest.mark.parametrize('parser', parsers) def test_bad_yaml(capsys, parser): try: command_line._router( 'yamale/tests/command_line_fixtures/yamls/bad.yaml', 'schema.yaml', 1, parser) except ValueError as e: assert 'Validation failed!' in str(e) captured = capsys.readouterr() assert "map.bad: '12.5' is not a str." in captured.out return assert False @pytest.mark.parametrize('parser', parsers) def test_required_keys_yaml(capsys, parser): try: command_line._router( 'yamale/tests/command_line_fixtures/yamls/required_keys_bad.yaml', 'required_keys_schema.yaml', 1, parser) except ValueError as e: assert 'Validation failed!' in str(e) captured = capsys.readouterr() assert "map.key: Required field missing" in captured.out return assert False @pytest.mark.parametrize('parser', parsers) def test_good_yaml(parser): command_line._router( 'yamale/tests/command_line_fixtures/yamls/good.yaml', 'schema.yaml', 1, parser) @pytest.mark.parametrize('parser', parsers) def test_good_relative_yaml(parser): command_line._router( 'yamale/tests/command_line_fixtures/yamls/good.yaml', '../schema_dir/external.yaml', 1, parser) @pytest.mark.parametrize('parser', parsers) def test_external_glob_schema(parser): command_line._router( 'yamale/tests/command_line_fixtures/yamls/good.yaml', os.path.join(dir_path, 'command_line_fixtures/schema_dir/ex*.yaml'), 1, parser) def test_external_schema(): command_line._router( 'yamale/tests/command_line_fixtures/yamls/good.yaml', os.path.join(dir_path, 'command_line_fixtures/schema_dir/external.yaml'), 1, 'PyYAML') def test_bad_dir(): try: command_line._router( 'yamale/tests/command_line_fixtures/yamls', 'schema.yaml', 4, 'PyYAML') except ValueError as e: assert 'Validation failed!' in str(e) return assert False def test_bad_strict(capsys): try: command_line._router( 'yamale/tests/command_line_fixtures/yamls/required_keys_extra_element.yaml', 'required_keys_schema.yaml', 4, 'PyYAML', strict=True) except ValueError as e: assert 'Validation failed!' in str(e) captured = capsys.readouterr() assert "map.key2: Unexpected element" in captured.out return assert False

29.477778

94

0.672069

334

2,653

5.113772

0.203593

0.122365

0.111241

0.107728

0.796253

0.755269

0.737705

0.7137

0.709016

0

0.005761

0.214851

2,653

89

95

29.808989

0.81421

0

0.608696

0

0.319638

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0

0.15942

1

0.115942

false

0

0.043478

0

0.217391

0

null

0

1

0

null

0

5

b80f987bb8ac6ec8e1b0321d7b81da51e6b9efb4

22

py

Python

avwx/patterns/__init__.py

AirbusDriver/avwx-engine

e3fed8f744a48faca58c3e94ddbf214f9c719d3d

[ "MIT" ]

null

avwx/patterns/__init__.py

AirbusDriver/avwx-engine

e3fed8f744a48faca58c3e94ddbf214f9c719d3d

[ "MIT" ]

3

2019-11-21T17:59:14.000Z

2019-12-04T03:45:05.000Z

avwx/patterns/__init__.py

AirbusDriver/avwx-engine

e3fed8f744a48faca58c3e94ddbf214f9c719d3d

[ "MIT" ]

null

from . import remarks

11

21

0.772727

3

22

5.666667

1

0

0.181818

22

1

22

0.944444

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

6299a0b66f98ba5b888c1638a187be6c4799ed53

168

py

Python

1 Python Basics/4_compound_list_types.py

narayanants/python-mega-course

2ba2980ab21dfbed5f86f00695559f7831b5c566

[ "MIT" ]

null

1 Python Basics/4_compound_list_types.py

narayanants/python-mega-course

2ba2980ab21dfbed5f86f00695559f7831b5c566

[ "MIT" ]

null

1 Python Basics/4_compound_list_types.py

narayanants/python-mega-course

2ba2980ab21dfbed5f86f00695559f7831b5c566

[ "MIT" ]

null

student_grades = list(range(50,100,10)) print(sum(student_grades)) student_grades.append(100) print(student_grades) student_grades.remove(100) print(student_grades)

16.8

39

0.809524

25

168

5.2

0.44

0.6

0.307692

0.4

0

0.082803

0.065476

168

10

40

16.8

0.745223

0

0.333333

0

1

0

false

0

0.5

1

0

null

1

0

null

0

1

0

5

62aa0fc68fd5e906bb2b5e2c63d5729be106e41e

74

py

Python

dags/tasks/__init__.py

jsmithdataanalytics/house_price_tracker

a4795db21c25c014f45ff6742c5bb30ad26ded75

[ "MIT" ]

1

2020-04-23T00:48:52.000Z

dags/tasks/__init__.py

jsmithdataanalytics/house_price_tracker

a4795db21c25c014f45ff6742c5bb30ad26ded75

[ "MIT" ]

null

dags/tasks/__init__.py

jsmithdataanalytics/house_price_tracker

a4795db21c25c014f45ff6742c5bb30ad26ded75

[ "MIT" ]

null

from .get_listings import get_listings from .send_email import send_email

24.666667

38

0.864865

12

74

5

0.5

0.366667

0

0.108108

74

2

39

37

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

62c1c2c0097550597f414e8f7ebf1886bc000f91

92

py

Python

npbench/benchmarks/polybench/gemm/gemm_numpy.py

frahlg/npbench

1bc4d9e2e22f3ca67fa2bc7f40e2e751a9c8dd26

[ "BSD-3-Clause" ]

27

2021-05-10T11:49:13.000Z

2022-03-22T18:07:19.000Z

npbench/benchmarks/polybench/gemm/gemm_numpy.py

frahlg/npbench

1bc4d9e2e22f3ca67fa2bc7f40e2e751a9c8dd26

[ "BSD-3-Clause" ]

3

2021-12-01T13:03:17.000Z

2022-03-17T10:53:00.000Z

npbench/benchmarks/polybench/gemm/gemm_numpy.py

frahlg/npbench

1bc4d9e2e22f3ca67fa2bc7f40e2e751a9c8dd26

[ "BSD-3-Clause" ]

7

2021-06-24T03:40:25.000Z

2022-01-26T09:04:33.000Z

import numpy as np def kernel(alpha, beta, C, A, B): C[:] = alpha * A @ B + beta * C

13.142857

35

0.532609

17

92

2.882353

0.647059

0.204082

0

0.304348

92

6

36

15.333333

0.765625

0

1

0.333333

false

0

0.333333

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0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

1a01abd9369679b7bffaf7f6f397a9e3232838b6

5,198

py

Python

Week 10 - Neural Networks/submission.py

lvolkmann/CS-490-Deep-Learning

48e637e6892d2e87999ed87b994be659cb30f1e2

[ "MIT" ]

null

Week 10 - Neural Networks/submission.py

lvolkmann/CS-490-Deep-Learning

48e637e6892d2e87999ed87b994be659cb30f1e2

[ "MIT" ]

null

Week 10 - Neural Networks/submission.py

lvolkmann/CS-490-Deep-Learning

48e637e6892d2e87999ed87b994be659cb30f1e2

[ "MIT" ]

null

from keras import Sequential from keras.datasets import mnist import numpy as np from keras.layers import Dense from keras.utils import to_categorical from keras.preprocessing.image import img_to_array import matplotlib.pyplot as plt (train_images, train_labels), (test_images, test_labels) = mnist.load_data() # process the data # 1. convert each image of shape 28*28 to 784 dimensional which will be fed to the network as a single feature dimData = np.prod(train_images.shape[1:]) # print(dimData) train_data = train_images.reshape(train_images.shape[0], dimData) test_data = test_images.reshape(test_images.shape[0], dimData) # convert data to float and scale values between 0 and 1 train_data = train_data.astype('float') test_data = test_data.astype('float') # scale data train_data /= 255.0 test_data /= 255.0 # change the labels frominteger to one-hot encoding. to_categorical is doing the same thing as LabelEncoder() train_labels_one_hot = to_categorical(train_labels) test_labels_one_hot = to_categorical(test_labels) # creating network model = Sequential() model.add(Dense(512, activation='relu', input_shape=(dimData,))) model.add(Dense(512, activation='relu')) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) print() history = model.fit(train_data, train_labels_one_hot, batch_size=256, epochs=10, verbose=1, validation_data=(test_data, test_labels_one_hot)) loss, accuracy = model.evaluate(test_data, test_labels_one_hot) print("LOSS: {}".format(loss)) print("ACCURACY: {}".format(accuracy)) # 1 Plot loss and accuracy print("Rendering Loss/Acc Trends...") for key in history.history: plt.plot(history.history[key]) plt.title("{} vs Epoch".format(key)) plt.ylabel(key) plt.xlabel('Epoch') plt.show() # 2 Single test image print("Rendering test image...") test_img_seven = test_images[26] test_data_seven = test_data[[26], :] plt.imshow(test_img_seven, cmap=plt.get_cmap('gray')) plt.title("Model Prediction: {}".format(model.predict_classes(test_data_seven)[0])) plt.show() # 3 Change number of hidden layers and activation print("Training a model with more relu hidden layers...") model = Sequential() model.add(Dense(512, activation='relu', input_shape=(dimData,))) model.add(Dense(512, activation='relu')) model.add(Dense(512, activation='relu')) model.add(Dense(512, activation='relu')) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) print() model.fit(train_data, train_labels_one_hot, batch_size=256, epochs=10, verbose=1, validation_data=(test_data, test_labels_one_hot)) loss2, accuracy2 = model.evaluate(test_data, test_labels_one_hot) print("More relu layer model with respect to original model...") print("NEW LOSS: {} CHANGE: {}".format(loss2, loss2 - loss)) print("NEW ACCURACY: {} CHANGE: {}".format(accuracy2, accuracy2 - accuracy)) print("Training a model with sigmoid activation instead of relu...") model = Sequential() model.add(Dense(512, activation='sigmoid', input_shape=(dimData,))) model.add(Dense(512, activation='sigmoid')) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) print() model.fit(train_data, train_labels_one_hot, batch_size=256, epochs=10, verbose=1, validation_data=(test_data, test_labels_one_hot)) loss2, accuracy2 = model.evaluate(test_data, test_labels_one_hot) print("Sigmoid model with respect to original model...") print("NEW LOSS: {} CHANGE: {}".format(loss2, loss2 - loss)) print("NEW ACCURACY: {} CHANGE: {}".format(accuracy2, accuracy2 - accuracy)) # 4 Without scaling # process the data # 1. convert each image of shape 28*28 to 784 dimensional which will be fed to the network as a single feature dimData = np.prod(train_images.shape[1:]) # print(dimData) train_data = train_images.reshape(train_images.shape[0], dimData) test_data = test_images.reshape(test_images.shape[0], dimData) # convert data to float and scale values between 0 and 1 train_data = train_data.astype('float') test_data = test_data.astype('float') # not scaling the data this time # change the labels from integer to one-hot encoding. to_categorical is doing the same thing as LabelEncoder() train_labels_one_hot = to_categorical(train_labels) test_labels_one_hot = to_categorical(test_labels) print("Training a model with more relu hidden layers...") model = Sequential() model.add(Dense(512, activation='relu', input_shape=(dimData,))) model.add(Dense(512, activation='relu')) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) print() model.fit(train_data, train_labels_one_hot, batch_size=256, epochs=10, verbose=1, validation_data=(test_data, test_labels_one_hot)) loss2, accuracy2 = model.evaluate(test_data, test_labels_one_hot) print("Non-scaled model respect to original model...") print("NEW LOSS: {} CHANGE: {}".format(loss2, loss2 - loss)) print("NEW ACCURACY: {} CHANGE: {}".format(accuracy2, accuracy2 - accuracy))

39.679389

110

0.757599

763

5,198

4.990826

0.182176

0.037815

0.05042

0.042017

0.763393

0.757353

0.753676

0.74291

0.731618

0.720588

0

0.026572

0.109465

5,198

131

111

39.679389

0.796068

0.149673

0

0.651685

0

0.184647

0.021803

0

1

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false

0

0.078652

0

0.078652

0.224719

0

null

0

1

0

null

0

5

a7e7efa3bf5c3da377f2769c1a5691e8c502ef9e

4,935

py

Python

tflite/QuantizationParameters.py

erezinman/tflite-flatbuffer-explorer

0fe1828b80108de3b6b7075de6a66162dfd0d322

[ "MIT" ]

1

2019-10-30T00:52:21.000Z

tflite/QuantizationParameters.py

erezinman/tflite-flatbuffer-explorer

0fe1828b80108de3b6b7075de6a66162dfd0d322

[ "MIT" ]

null

tflite/QuantizationParameters.py

erezinman/tflite-flatbuffer-explorer

0fe1828b80108de3b6b7075de6a66162dfd0d322

[ "MIT" ]

null

# automatically generated by the FlatBuffers compiler, do not modify # namespace: tflite import flatbuffers class QuantizationParameters(object): __slots__ = ['_tab'] @classmethod def GetRootAsQuantizationParameters(cls, buf, offset): n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) x = QuantizationParameters() x.Init(buf, n + offset) return x # QuantizationParameters def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) # QuantizationParameters def Min(self, j): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: a = self._tab.Vector(o) return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) return 0 # QuantizationParameters def MinAsNumpy(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) return 0 # QuantizationParameters def MinLength(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return self._tab.VectorLen(o) return 0 # QuantizationParameters def Max(self, j): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: a = self._tab.Vector(o) return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) return 0 # QuantizationParameters def MaxAsNumpy(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) return 0 # QuantizationParameters def MaxLength(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: return self._tab.VectorLen(o) return 0 # QuantizationParameters def Scale(self, j): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: a = self._tab.Vector(o) return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) return 0 # QuantizationParameters def ScaleAsNumpy(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) return 0 # QuantizationParameters def ScaleLength(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: return self._tab.VectorLen(o) return 0 # QuantizationParameters def ZeroPoint(self, j): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: a = self._tab.Vector(o) return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8)) return 0 # QuantizationParameters def ZeroPointAsNumpy(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o) return 0 # QuantizationParameters def ZeroPointLength(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return self._tab.VectorLen(o) return 0 def QuantizationParametersStart(builder): builder.StartObject(4) def QuantizationParametersAddMin(builder, min): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(min), 0) def QuantizationParametersStartMinVector(builder, numElems): return builder.StartVector(4, numElems, 4) def QuantizationParametersAddMax(builder, max): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(max), 0) def QuantizationParametersStartMaxVector(builder, numElems): return builder.StartVector(4, numElems, 4) def QuantizationParametersAddScale(builder, scale): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(scale), 0) def QuantizationParametersStartScaleVector(builder, numElems): return builder.StartVector(4, numElems, 4) def QuantizationParametersAddZeroPoint(builder, zeroPoint): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(zeroPoint), 0) def QuantizationParametersStartZeroPointVector(builder, numElems): return builder.StartVector(8, numElems, 8) def QuantizationParametersEnd(builder): return builder.EndObject()

41.470588

160

0.703749

555

4,935

6.10991

0.151351

0.059864

0.181657

0.206429

0.654674

0.633441

0.585078

0.572398

0.526393

0

0.019226

0.198987

4,935

118

161

41.822034

0.838604

0.077609

0

0.588235

1

0

0.000882

0

1

0.282353

false

0

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0

null

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1

0

1

0

null

0

1

0

1

0

5

a7ecfc30a353025da83e4a661dca60600376b0c2

187

py

Python

sympy/combinatorics/__init__.py

Narsil/sympy

4d837e074b871af351b42591697fe126411a910f

[ "BSD-3-Clause" ]

1

2020-12-27T18:43:22.000Z

sympy_old/combinatorics/__init__.py

curzel-it/KiPyCalc

909c783d5e6967ea58ca93f875106d8a8e3ca5db

[ "MIT" ]

null

sympy_old/combinatorics/__init__.py

curzel-it/KiPyCalc

909c783d5e6967ea58ca93f875106d8a8e3ca5db

[ "MIT" ]

1

2022-03-21T09:07:27.000Z

from sympy.combinatorics.permutations import Permutation from sympy.combinatorics.prufer import Prufer from sympy.combinatorics.generators import cyclic, alternating, symmetric, dihedral

46.75

83

0.871658

21

187

7.761905

0.571429

0.165644

0.404908

0

0.080214

187

3

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62.333333

0.947674

0

1

0

true

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null

0

1

0

1

0

5

a7f3a8f7033b7547fbf773d593e390e45808f7d0

90

py

Python

app/main/admin.py

alanVergara/trips-manager-api

62d73da4d05c2ad9704911d65da76c493938629d

[ "MIT" ]

null

app/main/admin.py

alanVergara/trips-manager-api

62d73da4d05c2ad9704911d65da76c493938629d

[ "MIT" ]

null

app/main/admin.py

alanVergara/trips-manager-api

62d73da4d05c2ad9704911d65da76c493938629d

[ "MIT" ]

null

from django.contrib import admin from main.models import User admin.site.register(User)

15

32

0.811111

14

90

5.214286

0.714286

0

0.122222

90

5

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1

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true

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1

0

null

0

1

0

1

0

1

0

5

c501e55d8201d36a00f44756bd5629978d43eaa3

106

py

Python

core/admin.py

Esatyilmaz0/MyBlogProject

01997effa0fc51ea61626d90a05344364ff3b8f8

[ "MIT" ]

null

core/admin.py

Esatyilmaz0/MyBlogProject

01997effa0fc51ea61626d90a05344364ff3b8f8

[ "MIT" ]

null

core/admin.py

Esatyilmaz0/MyBlogProject

01997effa0fc51ea61626d90a05344364ff3b8f8

[ "MIT" ]

null

from django.contrib import admin from .sub_models.Category import Category admin.site.register(Category)

21.2

41

0.839623

15

106

5.866667

0.666667

0

0.09434

106

4

42

26.5

0.916667

0

1

0

true

0

0.666667

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null

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1

0

null

0

1

0

1

0

1

0

5

c504726f1d0b4bdd673d62e4a4f023b07abf0533

208

py

Python

src/demos/dynamicprogramming/coinchange8.py

DavidLlorens/algoritmia

40ca0a89ea6de9b633fa5f697f0a28cae70816a2

[ "MIT" ]

6

2018-09-15T15:09:10.000Z

2022-02-27T01:23:11.000Z

src/demos/dynamicprogramming/coinchange8.py

JeromeIllgner/algoritmia

406afe7206f2411557859bf03480c16db7dcce0d

[ "MIT" ]

null

src/demos/dynamicprogramming/coinchange8.py

JeromeIllgner/algoritmia

406afe7206f2411557859bf03480c16db7dcce0d

[ "MIT" ]

5

2018-07-10T20:19:55.000Z

2021-03-31T03:32:22.000Z

#coding: latin1 #< full from algoritmia.problems.generalizedcoinchange.dynamicprogramming8 import IterativeDynamicCoinChanger print(IterativeDynamicCoinChanger([1, 2, 5], [1, 1, 4]).weight(7)) #> full

29.714286

102

0.769231

21

208

7.619048

0.809524

0

0.048649

0.110577

208

7

103

29.714286

0.816216

0.125

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c506b02a9c259fac2cbeac1fbaa5b3252bbe55aa

73

py

Python

tests/test_simple.py

knabben/graphene-telemetry

98e292b4a7d269dac7b83803c1729441642a6e45

[ "MIT" ]

null

tests/test_simple.py

knabben/graphene-telemetry

98e292b4a7d269dac7b83803c1729441642a6e45

[ "MIT" ]

null

tests/test_simple.py

knabben/graphene-telemetry

98e292b4a7d269dac7b83803c1729441642a6e45

[ "MIT" ]

null

class TestSimple(): def test_assertion(self): assert 1 == 1

14.6

29

0.60274

9

73

4.777778

0.888889

0

0.038462

0.287671

73

4

30

18.25

0.788462

0

0.666667

1

0.333333

false

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c51f2414da1ca948c9b45c38d1d5957b8e4fb037

76

py

Python

psipy/visualization/__init__.py

jj-gonzalez-aviles/PsiPy

415436b1f449e7125e9e8ff31385e9fc70af28a7

[ "MIT" ]

4

2021-05-12T07:28:22.000Z

2022-03-23T13:38:14.000Z

psipy/visualization/__init__.py

jj-gonzalez-aviles/PsiPy

415436b1f449e7125e9e8ff31385e9fc70af28a7

[ "MIT" ]

26

2021-07-14T19:26:32.000Z

2022-03-31T13:54:51.000Z

psipy/visualization/__init__.py

jj-gonzalez-aviles/PsiPy

415436b1f449e7125e9e8ff31385e9fc70af28a7

[ "MIT" ]

4

2021-02-11T17:04:00.000Z

2022-03-13T16:31:08.000Z

""" Helper functions for data visualiszation. """ from .matplotlib import *

15.2

41

0.736842

8

76

7

1

0

0.144737

76

4

42

19

0.861538

0.539474

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c528c83209c07f5f77b6e1aa50ee32dd783ff1ef

75

py

Python

mxnext/__init__.py

jie311/RangeDet

5078ce339c6d27a009aed1ca2790911ce4d10bc7

[ "Apache-2.0" ]

125

2021-08-09T02:14:04.000Z

2022-03-30T03:41:56.000Z

mxnext/__init__.py

jie311/RangeDet

5078ce339c6d27a009aed1ca2790911ce4d10bc7

[ "Apache-2.0" ]

15

2021-08-31T06:12:31.000Z

2022-03-17T00:21:35.000Z

mxnext/__init__.py

jie311/RangeDet

5078ce339c6d27a009aed1ca2790911ce4d10bc7

[ "Apache-2.0" ]

8

2021-08-10T03:08:10.000Z

2022-03-09T06:21:11.000Z

from .simple import * from .complicate import * from .initializer import *

18.75

26

0.76

9

75

6.333333

0.555556

0.350877

0

0.16

75

3

27

25

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

c545b6b1b6f7c81294025b786b12910e5c234611

21

py

Python

pox/lib/recoco/__init__.py

brenocg29/TP1RedesInteligentes

3b73b3567089f9eb2e475ec8402113bf8803bb59

[ "Apache-2.0" ]

49

2015-01-07T06:36:30.000Z

2021-03-15T18:49:54.000Z

pox/lib/recoco/__init__.py

brenocg29/TP1RedesInteligentes

3b73b3567089f9eb2e475ec8402113bf8803bb59

[ "Apache-2.0" ]

29

2019-01-17T15:44:48.000Z

2021-06-02T00:19:40.000Z

OFCONTROLLERS/pox/pox/lib/recoco/__init__.py

ViniGarcia/NIEP

5cdf779795b9248e1bbc12195479083475f3edab

[ "MIT" ]

65

2015-02-16T03:19:46.000Z

2021-12-22T15:51:06.000Z

from recoco import *

10.5

20

0.761905

3

21

5.333333

1

0

0.190476

21

1

21

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c54798d2bfba10486252ceff262764a682632919

136

py

Python

code/hello_world.py

StefanoMartella/UnivaqThesisTemplate

3b3ac3ba0ca94a51be6bfd4408ade712209ae90d

[ "MIT" ]

6

2020-07-27T14:36:02.000Z

2022-02-23T13:34:26.000Z

code/hello_world.py

StefanoMartella/UnivaqThesisTemplate

3b3ac3ba0ca94a51be6bfd4408ade712209ae90d

[ "MIT" ]

null

code/hello_world.py

StefanoMartella/UnivaqThesisTemplate

3b3ac3ba0ca94a51be6bfd4408ade712209ae90d

[ "MIT" ]

null

def main_function(): """ This is the main function. """ print('Hello Wolrd!') if __name__ == '__main__': main_function()

15.111111

38

0.610294

16

136

4.5625

0.6875

0.493151

0

0.227941

136

8

39

17

0.695238

0.191176

0

0.196078

0

1

0.25

true

0

0.25

1

0

null

1

0

1

0

null

0

1

0

5

c5673c362f486b623d458617dca12ba3fae50ef3

8,277

py

Python

SGCS/apps/base/views.py

gconelhero/clinica

997da552a033be1fc49ca8b88eef79d7061430ec

[ "MIT" ]

null

SGCS/apps/base/views.py

gconelhero/clinica

997da552a033be1fc49ca8b88eef79d7061430ec

[ "MIT" ]

null

SGCS/apps/base/views.py

gconelhero/clinica

997da552a033be1fc49ca8b88eef79d7061430ec

[ "MIT" ]

null

# -*- coding: utf-8 -*- from decimal import Decimal from django.views.generic import TemplateView from django.shortcuts import render from django.core.exceptions import ObjectDoesNotExist from django.db.models import F, Sum from SGCS.apps.cadastro.models import Cliente, Fornecedor, Produto, Empresa, Transportadora from SGCS.apps.cadastro.models.paciente import Paciente from SGCS.apps.financeiro.models.lancamento import Lancamento from SGCS.apps.vendas.models import OrcamentoVenda, PedidoVenda from SGCS.apps.compras.models import OrcamentoCompra, PedidoCompra from SGCS.apps.financeiro.models import MovimentoCaixa, Entrada, Saida from datetime import datetime class IndexView(TemplateView): template_name = 'base/index.html' def get_context_data(self, **kwargs): context = super(IndexView, self).get_context_data(**kwargs) quantidade_cadastro = {} agenda_hoje = {} alertas = {} data_atual = datetime.now().date() context['data_atual'] = data_atual.strftime('%d/%m/%Y') quantidade_cadastro['clientes'] = Paciente.objects.all().count() quantidade_cadastro['fornecedores'] = Fornecedor.objects.all().count() quantidade_cadastro['produtos'] = Produto.objects.all().count() quantidade_cadastro['empresas'] = Empresa.objects.all().count() quantidade_cadastro[ 'transportadoras'] = Transportadora.objects.all().count() context['quantidade_cadastro'] = quantidade_cadastro agenda_hoje['orcamento_venda_hoje'] = OrcamentoVenda.objects.filter( data_vencimento=data_atual, status='0').count() agenda_hoje['orcamento_compra_hoje'] = OrcamentoCompra.objects.filter( data_vencimento=data_atual, status='0').count() agenda_hoje['pedido_venda_hoje'] = PedidoVenda.objects.filter( data_emissao=data_atual, status='0').count() agenda_hoje['pedido_compra_hoje'] = PedidoCompra.objects.filter( data_entrega=data_atual, status='0').count() agenda_hoje['contas_receber_hoje'] = Entrada.objects.filter( data_vencimento=data_atual, status__in=['1', '2']).count() agenda_hoje['contas_pagar_hoje'] = Saida.objects.filter( data_vencimento=data_atual, status__in=['1', '2']).count() context['agenda_hoje'] = agenda_hoje alertas['produtos_baixo_estoque'] = Produto.objects.filter( estoque_atual__lte=F('estoque_minimo')).count() alertas['orcamentos_venda_vencidos'] = OrcamentoVenda.objects.filter( data_vencimento__lte=data_atual, status='0').count() alertas['pedidos_venda_atrasados'] = PedidoVenda.objects.filter( data_entrega__lte=data_atual, status='0').count() alertas['orcamentos_compra_vencidos'] = OrcamentoCompra.objects.filter( data_vencimento__lte=data_atual, status='0').count() alertas['pedidos_compra_atrasados'] = PedidoCompra.objects.filter( data_entrega__lte=data_atual, status='0').count() alertas['contas_receber_atrasadas'] = Entrada.objects.filter( data_vencimento__lte=data_atual, status__in=['1', '2']).count() alertas['contas_pagar_atrasadas'] = Saida.objects.filter( data_vencimento__lte=data_atual, status__in=['1', '2']).count() context['alertas'] = alertas try: context['movimento_dia'] = MovimentoCaixa.objects.get( data_movimento=data_atual) except (MovimentoCaixa.DoesNotExist, ObjectDoesNotExist): ultimo_mvmt = MovimentoCaixa.objects.filter( data_movimento__lt=data_atual) if ultimo_mvmt: context['saldo'] = ultimo_mvmt.latest( 'data_movimento').saldo_final else: context['saldo'] = '0,00' return context def handler404(request): response = render(request, '404.html', {}) response.status_code = 404 return response def handler500(request): response = render(request, '500.html', {}) response.status_code = 500 return response ''' class IndexView(TemplateView): template_name = 'base/index.html' def get_context_data(self, **kwargs): context = super(IndexView, self).get_context_data(**kwargs) quantidade_cadastro = {} agenda_hoje = {} alertas = {} data_atual = datetime.now().date() context['data_atual'] = data_atual.strftime('%d/%m/%Y') quantidade_cadastro['clientes'] = Paciente.objects.all().count() quantidade_cadastro['fornecedores'] = Fornecedor.objects.all().count() quantidade_cadastro['produtos'] = Produto.objects.all().count() quantidade_cadastro['empresas'] = Empresa.objects.all().count() quantidade_cadastro[ 'transportadoras'] = Transportadora.objects.all().count() context['quantidade_cadastro'] = quantidade_cadastro agenda_hoje['orcamento_venda_hoje'] = OrcamentoVenda.objects.filter( data_vencimento=data_atual, status='0').count() agenda_hoje['orcamento_compra_hoje'] = OrcamentoCompra.objects.filter( data_vencimento=data_atual, status='0').count() agenda_hoje['pedido_venda_hoje'] = PedidoVenda.objects.filter( data_emissao=data_atual, status='0').count() agenda_hoje['pedido_compra_hoje'] = PedidoCompra.objects.filter( data_entrega=data_atual, status='0').count() agenda_hoje['contas_receber_hoje'] = Entrada.objects.filter(data_vencimento=data_atual, status__in=['1', '2']).count() #Novo contas a receber // Pacientes devendo... agenda_hoje['contas_receber_hoje'] = Paciente.objects.filter(limite_de_credito__lt=0).count() #Entrada.objects.filter(data_vencimento=data_atual, status__in=['1', '2']).count() agenda_hoje['contas_pagar_hoje'] = Saida.objects.filter( data_vencimento=data_atual, status__in=['1', '2']).count() context['agenda_hoje'] = agenda_hoje alertas['produtos_baixo_estoque'] = Produto.objects.filter( estoque_atual__lte=F('estoque_minimo')).count() alertas['orcamentos_venda_vencidos'] = OrcamentoVenda.objects.filter( data_vencimento__lte=data_atual, status='0').count() alertas['pedidos_venda_atrasados'] = PedidoVenda.objects.filter( data_entrega__lte=data_atual, status='0').count() alertas['orcamentos_compra_vencidos'] = OrcamentoCompra.objects.filter( data_vencimento__lte=data_atual, status='0').count() alertas['pedidos_compra_atrasados'] = PedidoCompra.objects.filter( data_entrega__lte=data_atual, status='0').count() alertas['contas_receber_atrasadas'] = Entrada.objects.filter( data_vencimento__lte=data_atual, status__in=['1', '2']).count() alertas['contas_pagar_atrasadas'] = Saida.objects.filter( data_vencimento__lte=data_atual, status__in=['1', '2']).count() context['alertas'] = alertas context['movimento_dia'] = float('00.00') try: movimento_dia_saida = float(Saida.objects.filter(data_emissao=data_atual).aggregate(Sum('valor_total'))['valor_total__sum']) except: movimento_dia_saida = float('00.00') try: movimento_dia_entrada = float(Entrada.objects.filter(data_emissao=data_atual).aggregate(Sum('valor_total'))['valor_total__sum']) except: movimento_dia_entrada = float('00.00') try: saldo_inicial = float(Paciente.objects.aggregate(Sum('limite_de_credito'))['limite_de_credito__sum']) except: saldo_inicial = float('00.00') context['salto_inicial'] = saldo_inicial context['moviemnto_dia_saida'] = movimento_dia_saida context['moviemnto_dia_entrada'] = movimento_dia_entrada context['saldo'] = '0,00' return context def handler404(request): response = render(request, '404.html', {}) response.status_code = 404 return response def handler500(request): response = render(request, '500.html', {}) response.status_code = 500 return response '''

44.983696

184

0.671016

905

8,277

5.845304

0.148066

0.059546

0.089981

0.086767

0.797164

0.771645

0.762571

0

0.014253

0.203214

8,277

184

185

44.983696

0.78787

0.002537

0

0.157895

0

0.118392

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0

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false

0

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0.263158

0

null

0

1

0

null

0

5

3da186ae0d9b6f06f2beee613b97a61d7110a4be

131

py

Python

test/test_project/backends.py

jaap3/django-otp

d7980bf516018319158570cc75353c905375a3ab

[ "BSD-2-Clause" ]

318

2019-08-27T15:57:05.000Z

2022-03-30T08:38:29.000Z

test/test_project/backends.py

jaap3/django-otp

d7980bf516018319158570cc75353c905375a3ab

[ "BSD-2-Clause" ]

77

2019-09-17T11:48:38.000Z

2022-03-13T17:26:56.000Z

test/test_project/backends.py

jaap3/django-otp

d7980bf516018319158570cc75353c905375a3ab

[ "BSD-2-Clause" ]

76

2019-08-30T20:29:40.000Z

2022-03-30T09:14:36.000Z

class DummyBackend: def authenticate(self, request): return None def get_user(self, user_id): return None

18.714286

36

0.648855

16

131

5.1875

0.6875

0.240964

0

0.282443

131

6

37

21.833333

0.882979

0

0.4

0

1

0.4

false

0

0.4

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

3dd5cf84241328b5619847583891af1a62dcecb0

21

py

Python

macmini-liz/wrangle/src/main/python/wrangle/equity/__init__.py

ggear/asystem

c949a1624812eab5b063681f46a88ccc9527266e

[ "Apache-2.0" ]

4

2019-03-26T13:57:54.000Z

2021-11-04T04:55:49.000Z

macmini-liz/wrangle/src/main/python/wrangle/equity/__init__.py

ggear/asystem

c949a1624812eab5b063681f46a88ccc9527266e

[ "Apache-2.0" ]

1

2021-04-03T01:10:11.000Z

macmini-liz/wrangle/src/main/python/wrangle/equity/__init__.py

ggear/asystem

c949a1624812eab5b063681f46a88ccc9527266e

[ "Apache-2.0" ]

2

2019-04-02T19:20:34.000Z

2019-08-13T16:39:52.000Z

from equity import *

10.5

20

0.761905

3

21

5.333333

1

0

0.190476

21

1

21

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

3dfa9b70d1fb34391f28dbabbee1056aca33e3a7

215

py

Python

code/test-dbscan.py

amitthere/clustering-algorithms

a5483032e81a9229b5eea0ff0bd05862c02974fc

[ "Apache-2.0" ]

null

code/test-dbscan.py

amitthere/clustering-algorithms

a5483032e81a9229b5eea0ff0bd05862c02974fc

[ "Apache-2.0" ]

null

code/test-dbscan.py

amitthere/clustering-algorithms

a5483032e81a9229b5eea0ff0bd05862c02974fc

[ "Apache-2.0" ]

1

2018-11-07T05:07:44.000Z

import numpy as np from densitybasedclustering.dbscan import DBSCAN from visualization import Visualization from clustervalidation import ExternalIndex def main(): pass if __name__ == "__main__": main()

16.538462

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9a86f27b7b5922f404237c07c6271e02ee339841

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py

Python

Examples/AppKit/CocoaBindings/FilteringController/FilteringController.py

Khan/pyobjc-framework-Cocoa

f8b015ea2a72d8d78be6084fb12925c4785b8f1f

[ "MIT" ]

132

2015-01-01T10:02:42.000Z

2022-03-09T12:51:01.000Z

mac/pyobjc-framework-Cocoa/Examples/AppKit/CocoaBindings/FilteringController/FilteringController.py

mba811/music-player

7998986b34cfda2244ef622adefb839331b81a81

[ "BSD-2-Clause" ]

6

2015-01-06T08:23:19.000Z

2019-03-14T12:22:06.000Z

mac/pyobjc-framework-Cocoa/Examples/AppKit/CocoaBindings/FilteringController/FilteringController.py

mba811/music-player

7998986b34cfda2244ef622adefb839331b81a81

[ "BSD-2-Clause" ]

27

2015-02-23T11:51:43.000Z

2022-03-07T02:34:18.000Z

# # FilteringController # from PyObjCTools import AppHelper import FilteringControllerDocument import FilteringArrayController AppHelper.runEventLoop()

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py

Python

python/plot_mca_linear_figure.py

eladnoor/small-molecule-regulation

83127f20859093a06ee493128d672ac7428cec83

[ "MIT" ]

3

2018-03-29T12:14:05.000Z

2021-03-22T09:04:22.000Z

python/plot_mca_linear_figure.py

eladnoor/small-molecule-regulation

83127f20859093a06ee493128d672ac7428cec83

[ "MIT" ]

9

2016-05-30T16:43:21.000Z

2017-03-17T13:15:02.000Z

python/plot_mca_linear_figure.py

eladnoor/small-molecule-regulation

83127f20859093a06ee493128d672ac7428cec83

[ "MIT" ]

1

2021-03-22T09:04:26.000Z

#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Wed Feb 22 15:43:18 2017 @author: noore """ import matplotlib.pyplot as plt from matplotlib import rcParams import numpy as np import os from settings import RESULT_DIR rcParams['font.family'] = 'sans-serif' rcParams['mathtext.sf'] = 'serif' rcParams['mathtext.fontset'] = 'cm' fig, axs = plt.subplots(2, 2, figsize=(7, 7)) # first, plot the MM rate law (as a function of s) Vmax = 1 # umol/min Km = 1 # mM s_range = np.linspace(0, 50, 100) # 10 uM - 100 mM v = lambda s: Vmax * s / (Km + s) eps = lambda s: 1 - s / (Km + s) arrowprops = dict(facecolor='black', shrink=0.01, width=1.5, headwidth=4) x_low = 0.1 x_high = 45.0 fig.text(0.5, 0.95, 'Michaelis-Menten kinetics', fontsize=17, ha='center') fig.text(0.5, 0.47, 'non-competitive inhibition', fontsize=17, ha='center') ############################################################################### ax = axs[0, 0] ax.plot(s_range, list(map(v, s_range)), '-') ax.set_xscale('linear') ax.set_yscale('linear') ax.set_xlabel('substrate conc. $s$ [mM]') ax.set_ylabel('rate $v$ [$\mu$mol/min]') ax.annotate(r'$|\epsilon_s^v| \approx 1$', xy=(x_low, v(x_low)), xycoords='data', xytext=(50, 0), textcoords='offset points', va='center', ha='center', fontsize=12, arrowprops=arrowprops) ax.annotate(r'$|\epsilon_s^v| \approx 0$', xy=(x_high, v(x_high)), xycoords='data', xytext=(0, -30), textcoords='offset points', va='center', ha='center', fontsize=12, arrowprops=arrowprops) ax.set_title('rate law') ax.annotate(r'$v = V^+ \, \frac{s}{K_M + s}$', color=(0.2, 0.4, 1.0), xy=(0.5, 0.5), xycoords='axes fraction', fontsize=14, ha='center', va='center') ############################################################################### ax = axs[0, 1] ax.plot(s_range, list(map(eps, s_range)), '-') ax.set_xscale('linear') ax.set_yscale('linear') ax.set_xlabel('substrate conc. $s$ [mM]') ax.set_ylabel('elasticity $\epsilon_s^v$') ax.annotate(r'$|\epsilon_s^v| \approx 1$', xy=(x_low, eps(x_low)), xytext=(50, 0), textcoords='offset points', va='center', ha='center', fontsize=12, arrowprops=arrowprops) ax.annotate(r'$|\epsilon_s^v| \approx 0$', xy=(x_high, eps(x_high)), xytext=(0, 30), textcoords='offset points', va='center', ha='center', fontsize=12, arrowprops=arrowprops) ax.annotate(r'$|\epsilon_s^v| = 1 - \frac{s}{K_M + s}$', color=(0.2, 0.4, 1.0), xy=(0.5, 0.5), xycoords='axes fraction', fontsize=14, ha='center', va='center') ax.set_title('substrate elasticity') ############################################################################### v = lambda s: Vmax * (1 - s / (Km + s)) eps = lambda s: -s / (Km + s) ax = axs[1, 0] ax.plot(s_range, list(map(v, s_range)), '-') ax.set_xscale('linear') ax.set_yscale('linear') ax.set_xlabel('inhibitor conc. $I$ [mM]') ax.set_ylabel('rate $v$ [$\mu$mol/min]') ax.annotate(r'$|\epsilon_I^v| \approx 1$', xy=(x_low, v(x_low)), xytext=(50, 0), textcoords='offset points', va='center', ha='center', fontsize=12, arrowprops=dict(facecolor='black', shrink=0.01, width=1.5, headwidth=4)) ax.annotate(r'$|\epsilon_I^v| \approx 0$', xy=(x_high, v(x_high)), xytext=(0, 30), textcoords='offset points', va='center', ha='center', fontsize=12, arrowprops=dict(facecolor='black', shrink=0.01, width=1.5, headwidth=4)) ax.set_title('rate law') ax.annotate(r'$v = V^+ ( 1 - \frac{I}{K_I + I} ) $', color=(0.2, 0.4, 1.0), xy=(0.5, 0.5), xycoords='axes fraction', fontsize=14, ha='center', va='center') ############################################################################### ax = axs[1, 1] ax.plot(s_range, list(map(eps, s_range)), '-') ax.set_xscale('linear') ax.set_yscale('linear') ax.set_xlabel('inhibitor conc. $I$ [mM]') ax.set_ylabel('elasticity $\epsilon_I^v$') ax.annotate(r'$|\epsilon_I^v| \approx 0$', xy=(x_low, eps(x_low)), xytext=(50, 0), textcoords='offset points', va='center', ha='center', fontsize=12, arrowprops=arrowprops) ax.annotate(r'$|\epsilon_I^v| \approx 1$', xy=(x_high, eps(x_high)), xytext=(0, 30), textcoords='offset points', va='center', ha='center', fontsize=12, arrowprops=arrowprops) ax.annotate(r'$\epsilon_I^v = -\frac{I}{K_I + I}$', color=(0.2, 0.4, 1.0), xy=(0.5, 0.5), xycoords='axes fraction', fontsize=14, ha='center', va='center') ax.set_title('inhibitor elasticity') ############################################################################### fig.tight_layout(pad=4, h_pad=5, w_pad=1) fig.savefig(os.path.join(RESULT_DIR, 'mca_linear.svg')) fig.savefig(os.path.join(RESULT_DIR, 'mca_linear.pdf'))

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null

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null

0

5

b1064b7211e95461a161b5705c752ffc518c1636

167

py

Python

jornalismo_de_dados_V2.0/portal/admin.py

Benedito-Medeiros-Neto-UnB/TacProgWeb

c7d795a69524e428988d4ed796f4a1c2ded035e3

[ "MIT" ]

1

2021-04-12T13:34:00.000Z

jornalismo_de_dados_V2.0/portal/admin.py

Benedito-Medeiros-Neto-UnB/TacProgWeb

c7d795a69524e428988d4ed796f4a1c2ded035e3

[ "MIT" ]

19

2021-05-14T20:56:29.000Z

2022-02-10T11:59:33.000Z

jornalismo_de_dados_V2.0/portal/admin.py

Benedito-Medeiros-Neto-UnB/TacProgWeb

c7d795a69524e428988d4ed796f4a1c2ded035e3

[ "MIT" ]

10

2021-05-13T16:18:53.000Z

2021-11-08T14:30:08.000Z

from django.contrib import admin from .models import Tweet, Article, Reference admin.site.register(Tweet) admin.site.register(Article) admin.site.register(Reference)

23.857143

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5

b10d03a3eac90c4ab766c326eec2ef82e45104ff

249

py

Python

eaa_donations/donations/models/__init__.py

andrewbird2/eaa_donations

40a2cb2431130b330130f101c89bd3f8c503d2e2

[ "MIT" ]

null

eaa_donations/donations/models/__init__.py

andrewbird2/eaa_donations

40a2cb2431130b330130f101c89bd3f8c503d2e2

[ "MIT" ]

13

2020-06-05T19:27:58.000Z

2022-02-26T13:40:54.000Z

eaa_donations/donations/models/__init__.py

andrewbird2/eaa_donations

40a2cb2431130b330130f101c89bd3f8c503d2e2

[ "MIT" ]

null

from .gift import Gift from .partner_charity import PartnerCharity from .pledge import Pledge, PledgeComponent, PaymentMethod from .referral_source import ReferralSource from .transaction import BankTransaction, PaypalTransaction, StripeTransaction

41.5

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b14930583aee1603265a4456bcb213c797cfcc37

94

py

Python

enthought/envisage/i_plugin_manager.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/envisage/i_plugin_manager.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/envisage/i_plugin_manager.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from envisage.i_plugin_manager import *

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5

b162a52a1c42fc792871f7cb51d81fd20da6b0d7

169

py

Python

pages/base.py

FranciscoCarbonell/kivymd-login-example

16aa38742b0467a6e25a89762a2153dfb6ee367e

[ "MIT" ]

7

2020-06-04T12:20:29.000Z

2022-03-20T11:14:00.000Z

pages/base.py

usher-rayko/kivymd-login-example

16aa38742b0467a6e25a89762a2153dfb6ee367e

[ "MIT" ]

1

2020-10-18T18:27:15.000Z

2021-11-26T00:45:35.000Z

pages/base.py

usher-rayko/kivymd-login-example

16aa38742b0467a6e25a89762a2153dfb6ee367e

[ "MIT" ]

6

2020-07-26T04:41:18.000Z

2022-02-13T05:17:32.000Z

from kivy.uix.screenmanager import Screen from kivy.app import App class BaseScreen(Screen): @property def root(self): return App.get_running_app().root

24.142857

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7

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null

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5

490a0f26714b6718bf8f20a7653f8f167d748f4b

100

py

Python

shapes/src/python/pyshapes/primitives/__init__.py

StefanBoca/cppwg

b41ce191be5b8d45607faaa032af8cfb3ead15fd

[ "MIT" ]

null

shapes/src/python/pyshapes/primitives/__init__.py

StefanBoca/cppwg

b41ce191be5b8d45607faaa032af8cfb3ead15fd

[ "MIT" ]

null

shapes/src/python/pyshapes/primitives/__init__.py

StefanBoca/cppwg

b41ce191be5b8d45607faaa032af8cfb3ead15fd

[ "MIT" ]

null

# Bring in everything from the shared module from pyshapes.primitives._pyshapes_primitives import *

33.333333

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0.84

13

100

6.307692

0.769231

0.439024

0

0.12

100

2

55

50

0.931818

0.42

0

1

0

true

0

1

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1

0

1

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null

1

0

1

0

null

0

1

0

1

0

5

49339a24fef8be194a60f757d043ca89d7d970bd

188

py

Python

yape/command_line.py

d-little/yape

f96b4240029238502f05619255689a590bd20316

[ "MIT" ]

12

2017-02-10T13:13:31.000Z

2019-05-10T21:41:55.000Z

yape/command_line.py

d-little/yape

f96b4240029238502f05619255689a590bd20316

[ "MIT" ]

18

2018-04-25T02:40:50.000Z

2021-03-18T06:51:29.000Z

yape/command_line.py

d-little/yape

f96b4240029238502f05619255689a590bd20316

[ "MIT" ]

10

2017-02-20T10:20:50.000Z

2019-07-24T16:51:30.000Z

import yape import cProfile def main(): yape.yape2() def main_profile(): cProfile.run( "import yape; yape.yape2()", "/Users/kazamatzuri/work/yape-testdata/stats" )

14.461538

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1

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1

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5

493cb495264dabbad58b4a94b96c6a2b1a6090dd

57

py

Python

geoplace_pkg/__init__.py

nayonacademy/geoplace

1693b0f71e4341c37205667988c8f755edc1984c

[ "Unlicense" ]

null

geoplace_pkg/__init__.py

nayonacademy/geoplace

1693b0f71e4341c37205667988c8f755edc1984c

[ "Unlicense" ]

null

geoplace_pkg/__init__.py

nayonacademy/geoplace

1693b0f71e4341c37205667988c8f755edc1984c

[ "Unlicense" ]

null

from .functions import average, power from .city import *

28.5

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0.75

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495d9a89091c00bb948ee7a181e761e62bfee3c4

147

py

Python

src/torchprune/torchprune/method/thres_filter/__init__.py

dani3l125/torchprune

f2589ec7514bd531ddaa7da3aed6388bb13712d3

[ "MIT" ]

74

2021-03-05T01:25:00.000Z

2022-03-26T06:15:32.000Z

src/torchprune/torchprune/method/thres_filter/__init__.py

dani3l125/torchprune

f2589ec7514bd531ddaa7da3aed6388bb13712d3

[ "MIT" ]

4

2021-05-25T06:01:22.000Z

2022-01-24T22:38:09.000Z

src/torchprune/torchprune/method/thres_filter/__init__.py

dani3l125/torchprune

f2589ec7514bd531ddaa7da3aed6388bb13712d3

[ "MIT" ]

7

2021-03-24T14:14:32.000Z

2022-02-19T17:27:56.000Z

# flake8: noqa: F403,F401 """The package for classic filter thresholding based on norms.""" from .thres_filter_net import FilterThresNet, SoftNet

29.4

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772549cc6ea67160e0c231c34a9640c84b2ecd32

3,692

py

Python

extensions/.stubs/clrclasses/Autodesk/AutoCAD/Internal/Reactors/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

1

2020-03-25T03:27:24.000Z

extensions/.stubs/clrclasses/Autodesk/AutoCAD/Internal/Reactors/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

null

extensions/.stubs/clrclasses/Autodesk/AutoCAD/Internal/Reactors/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

null

from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import AcEdIPEReactorImpl from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import AcEdOPMObjectFilterReactorImpl from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import AcEdUcsReactorImpl from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import AcEdViewFinalChangeReactorImpl from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import AcSunViewportMonitorReactorImpl from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import AcVsESWDictionaryReactor from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import AcVsESWObjectReactor from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ApplicationDockLayoutChangedEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ApplicationDocumentFrameChangedEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ApplicationEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ApplicationMainWindowMovedEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ApplicationMainWindowSizedEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ApplicationMainWindowVisibleChangedEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import CuiEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import CuiLoadEventArgs from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import DictionaryEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import IPEEventArgs from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import IPEEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import IPEEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import LoadRibbonEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import OPMObjectFilterEventArgs from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import OPMObjectFilterEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import OPMObjectFilterEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import SunViewportMonitorEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import TableSubSelectFilter from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import TableSubSelectFilterEventArgs from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import TableSubSelectFilterEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import UcsEventArgs from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import UcsEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import UcsEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ViewChangeEventArgs from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ViewFinalChangeEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ViewFinalChangeEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ViewResizeEventArgs from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import ViewResizeEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import VisualStyleEventManager from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import VsESWDictionaryEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import VsESWObjectEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import WorkspaceEventArgs from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import WorkspaceRestoreEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import WorkspaceRibbonSaveEventHandler from __clrclasses__.Autodesk.AutoCAD.Internal.Reactors import WorkspaceSettingsSavedEventHandler

85.860465

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5

77298be29f5140b68d61b4798a2efcbdd10f94cd

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py

Python

lib/galaxy/tools/util/__init__.py

rikeshi/galaxy

c536a877e4a9b3d12aa0d00fd4d5e705109a0d0a

[ "CC-BY-3.0" ]

1,085

2015-02-18T16:14:38.000Z

2022-03-30T23:52:07.000Z

lib/galaxy/tools/util/__init__.py

rikeshi/galaxy

c536a877e4a9b3d12aa0d00fd4d5e705109a0d0a

[ "CC-BY-3.0" ]

11,253

2015-02-18T17:47:32.000Z

2022-03-31T21:47:03.000Z

lib/galaxy/tools/util/__init__.py

rikeshi/galaxy

c536a877e4a9b3d12aa0d00fd4d5e705109a0d0a

[ "CC-BY-3.0" ]

1,000

2015-02-18T16:18:10.000Z

2022-03-29T08:22:56.000Z

""" Utilities used by various Galaxy tools FIXME: These are used by tool scripts, not the framework, and should not live in this package. """

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1

0

null

0

1

0

1

0

5

91f1544bad4b62627e0c72082155313f02e67835

139

py

Python

code/pay.py

liufuyang/Using_Python_Access_Web_Data

630decbe749fef2ee8aa9d712d262e43ca7d5b1d

[ "MIT" ]

41

2015-02-27T22:13:41.000Z

2021-11-14T15:37:29.000Z

code/pay.py

liufuyang/Using_Python_Access_Web_Data

630decbe749fef2ee8aa9d712d262e43ca7d5b1d

[ "MIT" ]

2

2015-12-15T04:03:15.000Z

2017-01-13T15:29:47.000Z

code/pay.py

liufuyang/Using_Python_Access_Web_Data

630decbe749fef2ee8aa9d712d262e43ca7d5b1d

[ "MIT" ]

45

2015-01-03T17:26:02.000Z

2022-01-09T16:06:04.000Z

inp = raw_input('Enter Hours: ') hours = float(inp) inp = raw_input('Enter Rate: ') rate = float(inp) pay = hours * rate print 'Pay:', pay

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null

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5

621eff62f247351ecdcf2ed5c3afa148f6cf6336

55

py

Python

samanage/api/__init__.py

rodneymandap/samanage-py

9e06b363e2fa43b3e8f92f97aa3975e1f0461a56

[ "MIT" ]

null

samanage/api/__init__.py

rodneymandap/samanage-py

9e06b363e2fa43b3e8f92f97aa3975e1f0461a56

[ "MIT" ]

null

samanage/api/__init__.py

rodneymandap/samanage-py

9e06b363e2fa43b3e8f92f97aa3975e1f0461a56

[ "MIT" ]

1

2022-01-25T21:40:48.000Z

from .incidents import Incident from .sites import Site

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1

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1

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1

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5

6220a8faa6892ebe704d691f84d056983ece10e8

113

py

Python

nullbytetest.py

nckackerman/Tank-d

9c7818c1229dac16250a1dedb9a4f83b2273711b

[ "WTFPL" ]

null

nullbytetest.py

nckackerman/Tank-d

9c7818c1229dac16250a1dedb9a4f83b2273711b

[ "WTFPL" ]

null

nullbytetest.py

nckackerman/Tank-d

9c7818c1229dac16250a1dedb9a4f83b2273711b

[ "WTFPL" ]

null

if '\0' in open('poop.py').read(): print "you have null bytes in your input file" else: print "you don't"

28.25

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113

3.380952

0.857143

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113

4

51

28.25

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null

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1

0

1

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null

0

1

0

1

0

5

6263ff41b468155a17bd475c0471dabe70460fcd

147

py

Python

examples/membership/libs/ezdata/__init__.py

kmcquinn/match

e92a23de28eab13b6b01c5d8fc48086bc1551bd7

[ "MIT" ]

null

examples/membership/libs/ezdata/__init__.py

kmcquinn/match

e92a23de28eab13b6b01c5d8fc48086bc1551bd7

[ "MIT" ]

null

examples/membership/libs/ezdata/__init__.py

kmcquinn/match

e92a23de28eab13b6b01c5d8fc48086bc1551bd7

[ "MIT" ]

null

from .dictdataframe import DictDataFrame from .plotter import Plotter, Group from .simpletable import SimpleTable, AstroTable, AstroHelpers, stats

36.75

69

0.843537

16

147

7.75

0.5625

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0.108844

147

3

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0.946565

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true

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1

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null

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1

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null

0

1

0

1

0

1

0

5

626fafde47b1601cf75628829996b0b29b86d3eb

144

py

Python

basic 0/argv0.py

rdparihar/Python

5287081752e1cf69930ebe3bb94fa31486ae42a7

[ "MIT" ]

null

basic 0/argv0.py

rdparihar/Python

5287081752e1cf69930ebe3bb94fa31486ae42a7

[ "MIT" ]

null

basic 0/argv0.py

rdparihar/Python

5287081752e1cf69930ebe3bb94fa31486ae42a7

[ "MIT" ]

null

import sys if len(sys.argv) == 2: print("hello, {}".format(sys.argv[1])) #print("hello,"+(sys.argv[1])) else: print("hello world")

18

42

0.576389

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144

3.772727

0.545455

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0.02521

0.173611

144

7

43

20.571429

0.672269

0.201389

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0

1

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true

0

0.2

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0.2

0.4

1

0

null

1

0

1

0

null

0

1

0

5

6275204368872c802f88c3e0882fbe8664d42180

66

py

Python

SQS/SQS-NotificationHandler/lambdaFunction.py

ptrehan/Python-AWS-Example

8d6b9e962357a0df54efd9544267ce20043ae632

[ "MIT" ]

null

SQS/SQS-NotificationHandler/lambdaFunction.py

ptrehan/Python-AWS-Example

8d6b9e962357a0df54efd9544267ce20043ae632

[ "MIT" ]

null

SQS/SQS-NotificationHandler/lambdaFunction.py

ptrehan/Python-AWS-Example

8d6b9e962357a0df54efd9544267ce20043ae632

[ "MIT" ]

null

import json def lambda_handler(event, context): print(event)

13.2

35

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9

66

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16.5

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null

0

1

0

null

0

1

0

1

0

1

0

5

65aaa5fab6dbfef421a63ce2c7b794ab1990b026

59

py

Python

helloworld.py

astro-alice/gitworkshop

dc1562c60c712172555057d34675ca3f6a675452

[ "BSD-3-Clause" ]

null

helloworld.py

astro-alice/gitworkshop

dc1562c60c712172555057d34675ca3f6a675452

[ "BSD-3-Clause" ]

null

helloworld.py

astro-alice/gitworkshop

dc1562c60c712172555057d34675ca3f6a675452

[ "BSD-3-Clause" ]

null

print("Hello World") print("Hello Gurl") print("Whats Up")

14.75

20

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9

59

4.555556

0.666667

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59

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19.666667

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true

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null

1

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1

0

null

0

1

0

1

0

5

65c6aa9ee02fe9f86fb5f1967f2bd7a76b72d7c6

2,729

py

Python

l_loc.py

Tavnos/Science_Discord_Bot

94504fde9d91d4a128966c46c1a8266767683e01

[ "Apache-2.0" ]

1

2021-07-09T15:08:05.000Z

l_loc.py

Tavnos/Science_Discord_Bot

94504fde9d91d4a128966c46c1a8266767683e01

[ "Apache-2.0" ]

null

l_loc.py

Tavnos/Science_Discord_Bot

94504fde9d91d4a128966c46c1a8266767683e01

[ "Apache-2.0" ]

1

2020-12-14T20:32:31.000Z

class Loc_D: ls_read = [] def init_ls_list(self): save_txt = open('user_list.txt', 'r') str_read = save_txt.read() save_txt.close() str_read = str_read self.ls_read = str_read.split() def make_user_file(self): save_txt = open('user_list.txt', 'w') txt_nrw = '' save_txt.write(txt_nrw) save_txt.close() def add_user(self, username): username = username.replace('<','') username = username.replace('>','') save_txt = open('user_list.txt', 'r') str_read = save_txt.read() save_txt.close() str_read = str_read + '\n' + username self.ls_read = str_read.split() save_txt = open('user_list.txt', 'w') save_txt.write(str_read) save_txt.close() save_usr_txt = open('{}.txt'.format(username), 'w') usr_txt_nrw = 'personal note:' save_usr_txt.write(usr_txt_nrw) save_usr_txt.close() def display_user_file(self): save_txt = open('user_list.txt', 'r') str_read = save_txt.read() ls_read = str_read.split() u_read = '' for i in ls_read: u_read += "<{}> \n".format(i) return u_read def display_user_file_list(self): save_txt = open('user_list.txt', 'r') str_read = save_txt.read() ls_read = str_read.split() u_read = [] for i in ls_read: u_read += ["{}".format(i)] return u_read def store_note(self, username, d_note): username = username.replace('<','') username = username.replace('>','') if username in self.ls_read: save_usr_txt = open('{}.txt'.format(username), 'r') save_usr_str_read = save_usr_txt.read() save_usr_txt.close() save_usr_str_read = save_usr_str_read + '\n' + d_note save_usr_txt = open('{}.txt'.format(username), 'w') save_usr_txt.write(save_usr_str_read) save_usr_txt.close() def get_note(self, username): username = username.replace('<','') username = username.replace('>','') if username in self.ls_read: save_usr_txt = open('{}.txt'.format(username), 'r') save_usr_str_read = save_usr_txt.read() save_usr_txt.close() return save_usr_str_read def reset_note(self, username): username = username.replace('<','') username = username.replace('>','') if username in self.ls_read: save_usr_txt = open('{}.txt'.format(username), 'w') usr_txt_nrw = 'personal note:'.format(username) save_usr_txt.write(usr_txt_nrw) save_usr_txt.close()

38.43662

65

0.567241

363

2,729

3.92011

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0.105411

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0.832045

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null

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1

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1

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null

0

5

65d3646f382bc9bad6171c53e710fcb76436fd83

23

py

Python

agolutil/__init__.py

cityofaustin/atd-utils-agol

cceee75cd9dcaee1519b7a71a78b1bf1249e09e4

[ "CC0-1.0" ]

1

2019-05-22T02:11:25.000Z

agolutil/__init__.py

cityofaustin/atd-utils-agol

cceee75cd9dcaee1519b7a71a78b1bf1249e09e4

[ "CC0-1.0" ]

null

agolutil/__init__.py

cityofaustin/atd-utils-agol

cceee75cd9dcaee1519b7a71a78b1bf1249e09e4

[ "CC0-1.0" ]

null

from .agolutil import *

23

0.782609

3

23

6

1

0

0.130435

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1

23

0.9

0

1

0

true

0

1

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1

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1

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null

0

1

0

null

0

1

0

1

0

5

02a28f5bd65c7b7cb46a631599f2796520a05150

116

py

Python

src/wagtail_rest_pack/generic_forms/actions/action.py

domibydzovsky/wagtail-rest-pack

821d5d4111a4a7665e50272035e90f836a2c60c2

[ "MIT" ]

null

src/wagtail_rest_pack/generic_forms/actions/action.py

domibydzovsky/wagtail-rest-pack

821d5d4111a4a7665e50272035e90f836a2c60c2

[ "MIT" ]

null

src/wagtail_rest_pack/generic_forms/actions/action.py

domibydzovsky/wagtail-rest-pack

821d5d4111a4a7665e50272035e90f836a2c60c2

[ "MIT" ]

null

from wagtail.core import blocks class FormAction: @staticmethod def block_type() -> tuple: pass

11.6

31

0.655172

13

116

5.769231

1

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0.275862

116

9

32

12.888889

0.892857

0

1

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true

0.2

0

0.6

0

1

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null

0

1

0

null

0

1

0

1

0

5

b828c95798237b235caa00c7f0dc89f7cc8ba99a

125

py

Python

helper_functions.py

justinpaulturner/youtube-kanye-player

f5ec079936113c3d4028f08f33fd6c0701030c1a

[ "CC0-1.0" ]

null

helper_functions.py

justinpaulturner/youtube-kanye-player

f5ec079936113c3d4028f08f33fd6c0701030c1a

[ "CC0-1.0" ]

null

helper_functions.py

justinpaulturner/youtube-kanye-player

f5ec079936113c3d4028f08f33fd6c0701030c1a

[ "CC0-1.0" ]

null

from random import uniform from time import sleep def rand_sleep(minimim=.5,maximum=3): sleep(uniform(minimim,maximum))

20.833333

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125

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125

5

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1

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5

b8404a488a6688cb2ede9ed3a935b8386841f620

8,035

py

Python

tests/test_layers/test_2d/test_layer.py

rahulgupta9202/ColossalAI

993088d45eaa032e39cf5959df2a506f0663bc2e

[ "Apache-2.0" ]

1

2021-11-02T14:00:27.000Z

tests/test_layers/test_2d/test_layer.py

rahulgupta9202/ColossalAI

993088d45eaa032e39cf5959df2a506f0663bc2e

[ "Apache-2.0" ]

null

tests/test_layers/test_2d/test_layer.py

rahulgupta9202/ColossalAI

993088d45eaa032e39cf5959df2a506f0663bc2e

[ "Apache-2.0" ]

null

import torch from torch.nn import Parameter from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.nn import Linear2D, LayerNorm2D, TransformerSelfAttention2D, TransformerMLP2D, TransformerLayer2D from colossalai.utils import get_current_device, print_rank_0 from common import HIDDEN_SIZE, DEPTH, BATCH_SIZE, SEQ_LENGTH, check_equal def check_linear(): device = get_current_device() dtype = torch.float32 INPUT_SIZE = HIDDEN_SIZE OUTPUT_SIZE = 2 * HIDDEN_SIZE j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW) i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) layer = Linear2D(INPUT_SIZE, OUTPUT_SIZE) A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE) A_master = torch.randn(A_shape, dtype=dtype, device=device) torch.distributed.broadcast(A_master, src=0) A = torch.chunk(A_master, DEPTH, dim=0)[i] A = torch.chunk(A, DEPTH, dim=-1)[j] A = A.clone() A.requires_grad = True W_shape = (INPUT_SIZE, OUTPUT_SIZE) W_master = torch.randn(W_shape, dtype=dtype, device=device) torch.distributed.broadcast(W_master, src=0) W = torch.chunk(W_master, DEPTH, dim=0)[i] W = torch.chunk(W, DEPTH, dim=-1)[j] W = W.clone() W.requires_grad = True B_shape = (OUTPUT_SIZE) B_master = torch.randn(B_shape, dtype=dtype, device=device) torch.distributed.broadcast(B_master, src=0) B = torch.chunk(B_master, DEPTH, dim=0)[j] B = B.clone() B.requires_grad = True layer.weight = Parameter(W) layer.bias = Parameter(B) out = layer(A) A_master = A_master.clone() A_master.requires_grad = True W_master = W_master.clone() W_master.requires_grad = True B_master = B_master.clone() B_master.requires_grad = True C_master = torch.matmul(A_master, W_master) + B_master C = torch.chunk(C_master, DEPTH, dim=0)[i] C = torch.chunk(C, DEPTH, dim=-1)[j] check_equal(out, C) print_rank_0('linear forward: pass') grad_shape = C_master.shape grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device()) torch.distributed.broadcast(grad_master, src=0) grad = torch.chunk(grad_master, DEPTH, dim=0)[i] grad = torch.chunk(grad, DEPTH, dim=-1)[j] out.backward(grad) C_master.backward(grad_master) A_grad = A_master.grad A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i] A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[j] check_equal(A_grad, A.grad) W_grad = W_master.grad W_grad = torch.chunk(W_grad, DEPTH, dim=0)[i] W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[j] check_equal(W_grad, layer.weight.grad) B_grad = B_master.grad B_grad = torch.chunk(B_grad, DEPTH, dim=0)[j] if i == 0: check_equal(B_grad, layer.bias.grad) print_rank_0('linear backward: pass') def check_layernorm(): device = get_current_device() dtype = torch.float32 INPUT_SIZE = HIDDEN_SIZE EPS = 1e-12 j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW) i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) layernorm = LayerNorm2D(INPUT_SIZE) A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE) A_master = torch.randn(A_shape, dtype=dtype, device=device) torch.distributed.broadcast(A_master, src=0) A = torch.chunk(A_master, DEPTH, dim=0)[i] A = torch.chunk(A, DEPTH, dim=-1)[j] A = A.clone() A.requires_grad = True out = layernorm(A) A_master = A_master.clone() A_master.requires_grad = True E_master = torch.sum(A_master, dim=-1, keepdim=True) E_master /= INPUT_SIZE V_master = torch.sum(A_master * A_master, dim=-1, keepdim=True) V_master /= INPUT_SIZE V_master = V_master - E_master * E_master V_master = 1.0 / torch.sqrt(V_master + EPS) C_master = (A_master - E_master) * V_master C = torch.chunk(C_master, DEPTH, dim=0)[i] C = torch.chunk(C, DEPTH, dim=-1)[j] check_equal(out, C) print_rank_0('layer norm forward: pass') grad_shape = C_master.shape grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device()) torch.distributed.broadcast(grad_master, src=0) grad = torch.chunk(grad_master, DEPTH, dim=0)[i] grad = torch.chunk(grad, DEPTH, dim=-1)[j] out.backward(grad) C_master.backward(grad_master) A_grad = A_master.grad A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i] A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[j] check_equal(A_grad, A.grad) print_rank_0('layer norm backward: pass') def check_attention(): device = get_current_device() dtype = torch.float32 INPUT_SIZE = HIDDEN_SIZE NUM_ATTENTION_HEADS = 2 j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW) i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) layer = TransformerSelfAttention2D( HIDDEN_SIZE, NUM_ATTENTION_HEADS, attention_dropout_prob=0.5, hidden_dropout_prob=0.5, ) A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE) A_master = torch.randn(A_shape, dtype=dtype, device=device) torch.distributed.broadcast(A_master, src=0) A = torch.chunk(A_master, DEPTH, dim=0)[i] A = torch.chunk(A, DEPTH, dim=-1)[j] A = A.clone() A.requires_grad = True mask_shape = (BATCH_SIZE // DEPTH, NUM_ATTENTION_HEADS // DEPTH, SEQ_LENGTH, SEQ_LENGTH) attention_mask = torch.zeros(mask_shape, dtype=dtype, device=device) out = layer(A, attention_mask) assert out.shape == (BATCH_SIZE // DEPTH, SEQ_LENGTH, INPUT_SIZE // DEPTH) print_rank_0('self attention forward: pass') grad_shape = out.shape grad = torch.randn(grad_shape, dtype=dtype, device=device) out.backward(grad) assert A.grad.shape == A.shape print_rank_0('self attention backward: pass') def check_mlp(): device = get_current_device() dtype = torch.float32 INPUT_SIZE = HIDDEN_SIZE j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW) i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) layer = TransformerMLP2D( HIDDEN_SIZE, dropout_prob=0.5, act_func='gelu', ) A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE) A_master = torch.randn(A_shape, dtype=dtype, device=device) torch.distributed.broadcast(A_master, src=0) A = torch.chunk(A_master, DEPTH, dim=0)[i] A = torch.chunk(A, DEPTH, dim=-1)[j] A = A.clone() A.requires_grad = True out = layer(A) assert out.shape == (BATCH_SIZE // DEPTH, SEQ_LENGTH, INPUT_SIZE // DEPTH) print_rank_0('mlp forward: pass') grad_shape = out.shape grad = torch.randn(grad_shape, dtype=dtype, device=device) out.backward(grad) assert A.grad.shape == A.shape print_rank_0('mlp backward: pass') def check_transformerlayer(): device = get_current_device() dtype = torch.float32 INPUT_SIZE = HIDDEN_SIZE NUM_ATTENTION_HEADS = 2 j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW) i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) layer = TransformerLayer2D( HIDDEN_SIZE, NUM_ATTENTION_HEADS, act_func='gelu', attention_dropout_prob=0.5, hidden_dropout_prob=0.5) A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE) A_master = torch.randn(A_shape, dtype=dtype, device=device) torch.distributed.broadcast(A_master, src=0) A = torch.chunk(A_master, DEPTH, dim=0)[i] A = torch.chunk(A, DEPTH, dim=-1)[j] A = A.clone() A.requires_grad = True mask_shape = (BATCH_SIZE // DEPTH, NUM_ATTENTION_HEADS // DEPTH, SEQ_LENGTH, SEQ_LENGTH) attention_mask = torch.zeros(mask_shape, dtype=dtype, device=device) out = layer(A, attention_mask) assert out.shape == (BATCH_SIZE // DEPTH, SEQ_LENGTH, INPUT_SIZE // DEPTH) print_rank_0('transformerlayer forward: pass') grad_shape = out.shape grad = torch.randn(grad_shape, dtype=dtype, device=device) out.backward(grad) assert A.grad.shape == A.shape print_rank_0('transformerlayer backward: pass')

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