xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

6949c8d294973b9c15f0cdaf6df462ee0fe3f120

22

py

Python

utils/db_api/__init__.py

zotov-vs/tg_shop

e640e7cfaeac0af1de33a62fb5e6da28d8843651

[ "MIT" ]

1

2021-12-16T10:41:16.000Z

utils/db_api/__init__.py

zotov-vs/tg_shop

e640e7cfaeac0af1de33a62fb5e6da28d8843651

[ "MIT" ]

6

2021-10-11T06:03:48.000Z

2021-10-17T09:42:05.000Z

App(BE)/main/models/__init__.py

osamhack2021/AI_APP_handylib_devlib

62cf67e6df280217e3715e2aa425636cefa7dd6f

[ "MIT" ]

null

from . import database

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15fb43831ed30c20a10a9d980a0fdb47897df9ba

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py

Python

SampleAIs/Sample_Sophie/__init__.py

YSabarad/monopyly

0460f2452c83846b6b9e3b234be411e12a86d69c

[ "MIT" ]

4

2015-11-04T21:18:40.000Z

2020-12-26T21:15:23.000Z

SampleAIs/Sample_Sophie/__init__.py

YSabarad/monopyly

0460f2452c83846b6b9e3b234be411e12a86d69c

[ "MIT" ]

2

2021-08-09T18:19:58.000Z

2021-08-10T14:44:54.000Z

SampleAIs/Sample_Sophie/__init__.py

YSabarad/monopyly

0460f2452c83846b6b9e3b234be411e12a86d69c

[ "MIT" ]

6

2015-08-01T17:54:17.000Z

2022-02-28T00:00:21.000Z

from .sophie import SophieAI

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Python

ex109/teste.py

almmessias/CursoPython

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[ "MIT" ]

null

ex109/teste.py

almmessias/CursoPython

4cec6946f32002cbd5d3b802df11ea1ba74169f5

[ "MIT" ]

null

ex109/teste.py

almmessias/CursoPython

4cec6946f32002cbd5d3b802df11ea1ba74169f5

[ "MIT" ]

null

import moeda n = float(input('Digite o preço: R$')) print (f'O dobro de {moeda.moeda(n)} é {moeda.dobro(n, True)}') print (f'A metade de {moeda.moeda(n)} é {moeda.metade(n, True)}') print (f'O aumento de 10% de {moeda.moeda(n)} é {moeda.aumento(n, 10, True)}') print (f'O desconto de 13% de {moeda.moeda(n)} é {moeda.desconto(n, 13, True)}')

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ba3469f6edcb9686d5729fdce8d6db4a402b74d8

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py

Python

pkgs/ops-pkg/src/genie/libs/ops/msdp/ios/msdp.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/ops-pkg/src/genie/libs/ops/msdp/ios/msdp.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/ops-pkg/src/genie/libs/ops/msdp/ios/msdp.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

# super class from genie.libs.ops.msdp.iosxe.msdp import Msdp as MsdpXE class Msdp(MsdpXE): ''' Msdp Ops Object ''' pass

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ba3faff0153dfc6fc7c4666344478aeca4379d35

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py

Python

gamla/data.py

0xnurl/gamla

f3903ef5138a6fd94b910abf6ee7665e744d8537

[ "MIT" ]

null

gamla/data.py

0xnurl/gamla

f3903ef5138a6fd94b910abf6ee7665e744d8537

[ "MIT" ]

null

gamla/data.py

0xnurl/gamla

f3903ef5138a6fd94b910abf6ee7665e744d8537

[ "MIT" ]

null

import dataclasses import json import dataclasses_json def get_encode_config(): return dataclasses.field( metadata=dataclasses_json.config( encoder=lambda lst: sorted(lst, key=json.dumps, reverse=False) ) )

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baa96ababbc004f7b0ec9bc773951f114fc9b91e

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py

Python

train/__init__.py

SeJV/ComparisonRLapproaches

e1988a97ed5fab10c847350d607e9feafeced61e

[ "MIT" ]

2

2020-12-14T12:59:40.000Z

2020-12-14T14:08:30.000Z

train/__init__.py

SeJV/ComparisonRLapproaches

e1988a97ed5fab10c847350d607e9feafeced61e

[ "MIT" ]

null

train/__init__.py

SeJV/ComparisonRLapproaches

e1988a97ed5fab10c847350d607e9feafeced61e

[ "MIT" ]

null

from train.train_agent import train_agent from train.train_agents import train_agents

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baac3262872d073b7970c4a5798360c41e0f8d75

12,340

py

Python

snooper/db_hadler.py

tehreem09/web-snooper

bd02ef0aa38881321da8dc76b28560a7381b3841

[ "MIT" ]

null

snooper/db_hadler.py

tehreem09/web-snooper

bd02ef0aa38881321da8dc76b28560a7381b3841

[ "MIT" ]

null

snooper/db_hadler.py

tehreem09/web-snooper

bd02ef0aa38881321da8dc76b28560a7381b3841

[ "MIT" ]

null

import json def search_records(): cleaned_data = open('lifetech_cleandata.json') data = json.load(cleaned_data) my_dic={} for record in data: number = record.get("number") cnic = record.get("cnic") my_dic=record my_dic= basic_info_merger(my_dic) result01 = search_taxpayers_record(cnic) result02 = search_redbook_record(cnic, number) result03 = search_terrorists_record(cnic, number) print(f'[+] searching for number > {number}') result = {} if result01 is not None: result = merge_found_records(my_dic, result01) if result02 and result03 is not None: result = merge_found_records(my_dic, result01, result02, result03) elif result02 is not None: result = merge_found_records(my_dic, result01, result02) elif result03 is not None: result = merge_found_records(my_dic, result01, result03) if result02 is not None: result = result02 if result03 is not None: result = merge_found_records(my_dic ,result02, result03) print(result) elif result03 is not None: result = merge_found_records(my_dic, result03) else: result = (my_dic) main_dbt_handler(number, result) # def basic_info_merger(dict): # with open ('basic_number_info.json', 'r') as basic_num_info: # num_info = json.load(basic_num_info) # for data in num_info: # # print(data) # number = str(data.get('number'))[2:-2] # # print(number) # number2 = '+92'+ dict['number'] # # print ("with" + number2) # if ('+92'+dict['number'])==number: # print("number matcheddd") # new_dict = merge_found_records(dict, data) # return new_dict # return dict def basic_info_merger(dict): with open ('basic_number_info.json', 'r') as basic_num_info: num_info = json.load(basic_num_info) for data in num_info: # print(data) number = data.get('number') # print(number) number2 = '+92'+ dict['number'] # print ("with" + number2) if ('+92'+dict['number'])==number: print("number matcheddd") new_dict = merge_found_records(dict, data) return new_dict return dict def merge_found_records(*dicts): return { k: [d[k] for d in dicts if k in d] for k in set(k for d in dicts for k in d) } def search_taxpayers_record(cnic): with open('snooper/sheet7.json', 'r') as tax_payers: tax_payers = json.load(tax_payers) for records in tax_payers['Sheet1']: tax_payers_dictionary = {} if cnic == records['NTN']: # tax_payers_dictionary['CNIC'] = cnic tax_payers_dictionary['BUSINESS_NAME'] = records['BUSINESS_NAME'] tax_payers_dictionary['NAME REGISTERED TO'] = records['NAME'] return tax_payers_dictionary def search_redbook_record(cnic, number): with open('snooper/redbook.json', 'r') as redbook: redbook = json.load(redbook) for data2 in redbook: redbook_dictionary = {} if cnic == (data2['CNIC']): # redbook_dictionary['CNIC'] = cnic redbook_dictionary['F/NAME'] = data2['PARENTAGE'] redbook_dictionary['ADDRESS'] = data2['ADDRESS'] redbook_dictionary['PHONE NUM'] = data2['PHONE NUM'] redbook_dictionary['FIR'] = data2['FIR no.'] return redbook_dictionary def search_terrorists_record(cnic, number): with open('snooper/data.json', 'r') as terrorists: terrorists = json.load(terrorists) for data2 in terrorists: terrorists_dictionary = {} if cnic == (data2['CNIC']): # terrorists_dictionary['CNIC'] = cnic terrorists_dictionary['F/NAME'] = data2['FNAME'] terrorists_dictionary['ADDRESS'] = data2['ADDRESS'] terrorists_dictionary['REWARD'] = data2['REWARD'] terrorists_dictionary['FIR'] = data2['FIR'] terrorists_dictionary['RELIGIOUS/POLITICAL AFFILIATION'] = data2['RELIGIOUS/POLITICAL AFFILIATION'] return terrorists_dictionary def main_dbt_handler(number, record): if record: with open('main_dbt.json', 'a+') as main_dbt: json.dump(record, main_dbt, indent=4) main_dbt.write('\n') main_dbt.close() print(str(record)+'\n') else: print('[-] No criminal record found....\n[-] No business or tax payers record fount....\n') search_records() # import json # def search_records(): # cleaned_data = open('lifetech_cleandata.json') # data = json.load(cleaned_data) # my_dic={} # for record in data: # number = record.get("number") # cnic = record.get("cnic") # my_dic=record # lifetech_dic = {} # lifetech_dic['NAME'] = record['name'] # lifetech_dic['CNIC'] = record['cnic'] # lifetech_dic['PHONE NUM'] = record['number'] # if 'city' in my_dic: # lifetech_dic['CITY'] = record['city'] # if 'address'in my_dic: # lifetech_dic['ADDRESS'] = record['address'] # result01 = search_taxpayers_record(cnic) # result02 = search_redbook_record(cnic, number) # result03 = search_terrorists_record(cnic, number) # print(f'[+] searching for number > {number}') # result = {} # if result01 is not None: # result = merge_found_records(lifetech_dic, result01) # if result02 and result03 is not None: # result = merge_found_records(lifetech_dic, result01, result02, result03) # elif result02 is not None: # result = merge_found_records(lifetech_dic, result01, result02) # elif result03 is not None: # result = merge_found_records(lifetech_dic, result01, result03) # elif result02 is not None: # result = result02 # if result03 is not None: # result = merge_found_records(lifetech_dic ,result02, result03) # print(result) # elif result03 is not None: # result = merge_found_records(lifetech_dic, result03) # else: # result= lifetech_dic # main_dbt_handler(number, result) # def merge_found_records(*dicts): # return { # k: [d[k] for d in dicts if k in d] # for k in set(k for d in dicts for k in d) # } # def search_taxpayers_record(cnic): # with open('snooper/sheet7.json', 'r') as tax_payers: # tax_payers = json.load(tax_payers) # for records in tax_payers['Sheet1']: # tax_payers_dictionary = {} # if cnic == records['NTN']: # # tax_payers_dictionary['CNIC'] = cnic # tax_payers_dictionary['BUSINESS_NAME'] = records['BUSINESS_NAME'] # tax_payers_dictionary['NAME REGISTERED TO'] = records['NAME'] # return tax_payers_dictionary # def search_redbook_record(cnic, number): # with open('snooper/redbook.json', 'r') as redbook: # redbook = json.load(redbook) # for data2 in redbook: # redbook_dictionary = {} # if cnic == (data2['CNIC']): # # redbook_dictionary['CNIC'] = cnic # redbook_dictionary['F/NAME'] = data2['PARENTAGE'] # redbook_dictionary['ADDRESS'] = data2['ADDRESS'] # redbook_dictionary['PHONE NUM'] = data2['PHONE NUM'] # redbook_dictionary['FIR'] = data2['FIR no.'] # return redbook_dictionary # def search_terrorists_record(cnic, number): # with open('snooper/data.json', 'r') as terrorists: # terrorists = json.load(terrorists) # for data2 in terrorists: # terrorists_dictionary = {} # if cnic == (data2['CNIC']): # # terrorists_dictionary['CNIC'] = cnic # terrorists_dictionary['F/NAME'] = data2['FNAME'] # terrorists_dictionary['ADDRESS'] = data2['ADDRESS'] # terrorists_dictionary['REWARD'] = data2['REWARD'] # terrorists_dictionary['FIR'] = data2['FIR'] # terrorists_dictionary['RELIGIOUS/POLITICAL AFFILIATION'] = data2['RELIGIOUS/POLITICAL AFFILIATION'] # return terrorists_dictionary # def main_dbt_handler(number, record): # if record: # with open('main_dbt.json', 'a+') as main_dbt: # json.dump(record, main_dbt, indent=4) # main_dbt.write('\n') # main_dbt.close() # print(str(record)+'\n') # else: # print('[-] No criminal record found....\n[-] No business or tax payers record fount....\n') # search_records() # import json # def search_records(): # cleaned_data = open('lifetech_cleandata.json') # data = json.load(cleaned_data) # for record in data: # number = record.get("number") # cnic = record.get("cnic") # result01 = search_taxpayers_record(cnic) # result02 = search_redbook_record(cnic, number) # result03 = search_terrorists_record(cnic, number) # print(f'[+] searching for number > {number}') # result = {} # if result01 is not None: # result = result01 # if result02 and result03 is not None: # result = merge_found_records(result01, result02, result03) # elif result02 is not None: # result = merge_found_records(result01, result02) # elif result03 is not None: # result = merge_found_records(result01, result03) # elif result02 is not None: # result = result02 # if result03 is not None: # result = merge_found_records(result02, result03) # elif result03 is not None: # result = merge_found_records(result03) # main_dbt_handler(number, result) # def merge_found_records(*dicts): # return { # k: [d[k] for d in dicts if k in d] # for k in set(k for d in dicts for k in d) # } # def search_taxpayers_record(cnic): # with open('sheet7.json', 'r') as tax_payers: # tax_payers = json.load(tax_payers) # for records in tax_payers['Sheet1']: # tax_payers_dictionary = {} # if cnic == records['NTN']: # # tax_payers_dictionary['CNIC'] = cnic # tax_payers_dictionary['BUSINESS_NAME'] = records['BUSINESS_NAME'] # tax_payers_dictionary['NAME REGISTERED TO'] = records['NAME'] # return tax_payers_dictionary # def search_redbook_record(cnic, number): # with open('redbook.json', 'r') as redbook: # redbook = json.load(redbook) # for data2 in redbook: # redbook_dictionary = {} # if cnic == (data2['CNIC']): # # redbook_dictionary['CNIC'] = cnic # redbook_dictionary['F/NAME'] = data2['PARENTAGE'] # redbook_dictionary['ADDRESS'] = data2['ADDRESS'] # redbook_dictionary['PHONE NUM'] = data2['PHONE NUM'] # redbook_dictionary['FIR'] = data2['FIR no.'] # return redbook_dictionary # def search_terrorists_record(cnic, number): # with open('data.json', 'r') as terrorists: # terrorists = json.load(terrorists) # for data2 in terrorists: # terrorists_dictionary = {} # if cnic == (data2['CNIC']): # # terrorists_dictionary['CNIC'] = cnic # terrorists_dictionary['F/NAME'] = data2['FNAME'] # terrorists_dictionary['ADDRESS'] = data2['ADDRESS'] # terrorists_dictionary['REWARD'] = data2['REWARD'] # terrorists_dictionary['FIR'] = data2['FIR'] # return terrorists_dictionary # def main_dbt_handler(number, record): # if record: # with open('main_dbt.json', 'a+') as main_dbt: # json.dump(record, main_dbt, indent=4) # main_dbt.write('\n') # main_dbt.close() # print(str(record)+'\n') # else: # print('[-] No criminal record found....\n[-] No business or tax payers record fount....\n') # search_records()

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py

Python

testsuite/modulegraph-dir/package_with_star_import/__init__.py

xoviat/modulegraph2

766d00bdb40e5b2fe206b53a87b1bce3f9dc9c2a

[ "MIT" ]

9

2020-03-22T14:48:01.000Z

2021-05-30T12:18:12.000Z

testsuite/modulegraph-dir/package_with_star_import/__init__.py

xoviat/modulegraph2

766d00bdb40e5b2fe206b53a87b1bce3f9dc9c2a

[ "MIT" ]

15

2020-01-06T10:02:32.000Z

2021-05-28T12:22:44.000Z

testsuite/modulegraph-dir/package_with_star_import/__init__.py

ronaldoussoren/modulegraph2

b6ab1766b0098651b51083235ff8a18a5639128b

[ "MIT" ]

4

2020-05-10T18:51:41.000Z

2021-04-07T14:03:12.000Z

from no_imports import *

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py

Python

app/db/models/__init__.py

EleutherAGI/eegi-backend

6e013a4928f1cdea4ef495e82fe641f917708cde

[ "MIT" ]

null

app/db/models/__init__.py

EleutherAGI/eegi-backend

6e013a4928f1cdea4ef495e82fe641f917708cde

[ "MIT" ]

2

2021-05-15T15:33:31.000Z

2021-05-28T15:55:21.000Z

app/db/models/__init__.py

EleutherAGI/eegi-backend

6e013a4928f1cdea4ef495e82fe641f917708cde

[ "MIT" ]

2

2021-05-15T15:08:25.000Z

2021-05-16T16:05:55.000Z

from .users import * from .summaries import * from .keys import *

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py

Python

paa191t1/tests/pph/test_pph_median.py

dmmoura/PAA-2021

435005f6494ece76f03807fb524e0d4a3e1d7222

[ "Apache-2.0" ]

null

paa191t1/tests/pph/test_pph_median.py

dmmoura/PAA-2021

435005f6494ece76f03807fb524e0d4a3e1d7222

[ "Apache-2.0" ]

null

paa191t1/tests/pph/test_pph_median.py

dmmoura/PAA-2021

435005f6494ece76f03807fb524e0d4a3e1d7222

[ "Apache-2.0" ]

null

from paa191t1.pph.pph_median import pph_median from paa191t1.tests.pph import TestPPHBase class TestPPHMedian(TestPPHBase): def setUp(self): self.pph = pph_median

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py

Python

venv/lib/python3.8/site-packages/numpy/polynomial/setup.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/polynomial/setup.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/polynomial/setup.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

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null

/home/runner/.cache/pip/pool/75/74/1f/cd550c3fd39c07a88abf9ca8d462c4c05077809e3ca61220a3837e78cd

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Reading Data/lesson-12-vancouver-crime-information/tests/test_late_crimes_2.py

danielgarm/Data-Science-and-Machine-Learning

fa3e85cc42eb2e9f964ab5abb34d1c93e16d1cd9

[ "MIT" ]

null

Reading Data/lesson-12-vancouver-crime-information/tests/test_late_crimes_2.py

danielgarm/Data-Science-and-Machine-Learning

fa3e85cc42eb2e9f964ab5abb34d1c93e16d1cd9

[ "MIT" ]

2

2022-01-11T21:04:51.000Z

2022-01-11T21:05:05.000Z

Reading Data/lesson-12-vancouver-crime-information/tests/test_late_crimes_2.py

danielgarm/Data-Science-and-Machine-Learning

fa3e85cc42eb2e9f964ab5abb34d1c93e16d1cd9

[ "MIT" ]

null

def test_late_crimes_2(): assert late_crimes.loc[7, 'HOUR'] == 20

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tests/executors/multicore_executor_test.py

allenai/tango

80c90caefae4ad1c3f8472718ddada912cd8fcf9

[ "Apache-2.0" ]

52

2021-09-24T17:52:34.000Z

2022-03-29T22:55:02.000Z

tests/executors/multicore_executor_test.py

allenai/tango

80c90caefae4ad1c3f8472718ddada912cd8fcf9

[ "Apache-2.0" ]

90

2021-09-29T04:23:29.000Z

2022-03-31T21:23:02.000Z

tests/executors/multicore_executor_test.py

allenai/tango

80c90caefae4ad1c3f8472718ddada912cd8fcf9

[ "Apache-2.0" ]

8

2021-11-13T01:56:22.000Z

2022-02-27T03:29:42.000Z

import time import pytest from tango.common.logging import initialize_logging from tango.common.testing import TangoTestCase from tango.executors.multicore_executor import MulticoreExecutor from tango.step_graph import StepGraph from tango.workspaces import LocalWorkspace from test_fixtures.package.steps import SleepPrintMaybeFail class TestMulticoreExecutor(TangoTestCase): def setup_method(self): super().setup_method() initialize_logging() def test_simple_execution_in_parallel(self): step_graph = StepGraph( { "step1": SleepPrintMaybeFail(string="hello", seconds=5, fail=False), "step2": SleepPrintMaybeFail(string="hi", seconds=5, fail=False), } ) executor = MulticoreExecutor(workspace=LocalWorkspace(self.TEST_DIR), parallelism=2) start_time = time.time() executor.execute_step_graph(step_graph) end_time = time.time() time_taken = end_time - start_time assert time_taken < 10 # TODO: will this be flaky? assert len(executor.workspace.step_cache) == 2 def test_more_processes_ready_than_parallelism(self): step_graph = StepGraph( { "step1": SleepPrintMaybeFail(string="hello", seconds=5, fail=False), "step2": SleepPrintMaybeFail(string="hi", seconds=5, fail=False), "step3": SleepPrintMaybeFail(string="howdy", seconds=5, fail=False), } ) executor = MulticoreExecutor(workspace=LocalWorkspace(self.TEST_DIR), parallelism=2) start_time = time.time() executor.execute_step_graph(step_graph) end_time = time.time() time_taken = end_time - start_time assert 10 < time_taken < 20 # TODO: will this be flaky? assert len(executor.workspace.step_cache) == 3 @pytest.mark.parametrize("parallelism", [1, 2, 3]) def test_failing_step_no_downstream_task(self, parallelism): step_graph = StepGraph.from_params( { "step1": { "type": "sleep-print-maybe-fail", "string": "string_to_pass_down", "seconds": 0, "fail": False, }, "step2": { "type": "sleep-print-maybe-fail", "string": {"type": "ref", "ref": "step1"}, "seconds": 0, "fail": False, }, "step3": { "type": "sleep-print-maybe-fail", "string": "This is going to fail!", "seconds": 0, "fail": True, }, } ) executor = MulticoreExecutor( workspace=LocalWorkspace(self.TEST_DIR), parallelism=parallelism, include_package=["test_fixtures.package.steps"], ) executor.execute_step_graph(step_graph) assert len(executor.workspace.step_cache) == 2 @pytest.mark.parametrize("parallelism", [1, 2, 3]) def test_failing_step_with_downstream_task(self, parallelism): step_graph = StepGraph.from_params( { "step1": { "type": "sleep-print-maybe-fail", "string": "string_to_pass_down", "seconds": 0, "fail": True, }, "step2": { "type": "sleep-print-maybe-fail", "string": {"type": "ref", "ref": "step1"}, "seconds": 0, "fail": False, }, "step3": { "type": "sleep-print-maybe-fail", "string": "This is going to fail!", "seconds": 0, "fail": False, }, } ) executor = MulticoreExecutor( workspace=LocalWorkspace(self.TEST_DIR), parallelism=parallelism, include_package=["test_fixtures.package.steps"], ) executor.execute_step_graph(step_graph) assert len(executor.workspace.step_cache) == 1 @pytest.mark.parametrize("parallelism", [1, 2, 3]) def test_failing_step_with_further_downstream_task(self, parallelism): step_graph = StepGraph.from_params( { "step1": { "type": "sleep-print-maybe-fail", "string": "string_to_pass_down", "seconds": 0, "fail": True, }, "step2": { "type": "sleep-print-maybe-fail", "string": {"type": "ref", "ref": "step1"}, "seconds": 0, "fail": False, }, "step3": { "type": "sleep-print-maybe-fail", "string": {"type": "ref", "ref": "step2"}, "seconds": 0, "fail": False, }, } ) executor = MulticoreExecutor( workspace=LocalWorkspace(self.TEST_DIR), parallelism=parallelism, include_package=["test_fixtures.package.steps"], ) executor.execute_step_graph(step_graph) assert len(executor.workspace.step_cache) == 0 def test_uncacheable_failing_step_no_downstream_task(self): step_graph = StepGraph.from_params( { "step1": { "type": "sleep-print-maybe-fail", "string": "string_to_pass_down", "seconds": 0, "fail": False, }, "step2": { "type": "sleep-print-maybe-fail", "string": {"type": "ref", "ref": "step1"}, "seconds": 0, "fail": False, }, "step3": { "type": "sleep-print-maybe-fail", "string": "This is going to fail!", "seconds": 0, "fail": True, "cache_results": False, }, } ) executor = MulticoreExecutor( workspace=LocalWorkspace(self.TEST_DIR), parallelism=2, include_package=["test_fixtures.package.steps"], ) executor.execute_step_graph(step_graph) assert len(executor.workspace.step_cache) == 2 def test_uncacheable_failing_step_with_downstream_task(self): step_graph = StepGraph.from_params( { "step1": { "type": "sleep-print-maybe-fail", "string": "string_to_pass_down", "seconds": 0, "fail": True, "cache_results": False, }, "step2": { "type": "sleep-print-maybe-fail", "string": {"type": "ref", "ref": "step1"}, "seconds": 0, "fail": False, }, "step3": { "type": "sleep-print-maybe-fail", "string": "This is going to fail!", "seconds": 0, "fail": False, }, } ) executor = MulticoreExecutor( workspace=LocalWorkspace(self.TEST_DIR), parallelism=2, include_package=["test_fixtures.package.steps"], ) executor.execute_step_graph(step_graph) assert len(executor.workspace.step_cache) == 1 @pytest.mark.parametrize("parallelism", [1, 2, 3]) def test_steps_with_their_own_multiprocessing(self, parallelism): step_graph = StepGraph.from_params( { "step1": {"type": "multiprocessing_step", "num_proc": 2}, "step2": {"type": "multiprocessing_step", "num_proc": 3}, "step3": {"type": "multiprocessing_step", "num_proc": 1}, } ) executor = MulticoreExecutor( workspace=LocalWorkspace(self.TEST_DIR), parallelism=parallelism, include_package=["test_fixtures.package.steps"], ) executor.execute_step_graph(step_graph) assert len(executor.workspace.step_cache) == 3

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torchrl/runners/__init__.py

srikarym/torchrl

fee98e78ac1657a2c9a4063dd8d63ba207a121e2

[ "Apache-2.0" ]

3

2019-02-27T19:00:32.000Z

2020-07-19T03:18:28.000Z

torchrl/runners/__init__.py

srikarym/torchrl

fee98e78ac1657a2c9a4063dd8d63ba207a121e2

[ "Apache-2.0" ]

null

torchrl/runners/__init__.py

srikarym/torchrl

fee98e78ac1657a2c9a4063dd8d63ba207a121e2

[ "Apache-2.0" ]

null

from .base_runner import BaseRunner from .gym_runner import GymRunner

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Python

gather_agent/handlers/__init__.py

burmanm/gather_agent

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[ "Apache-2.0" ]

null

gather_agent/handlers/__init__.py

burmanm/gather_agent

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[ "Apache-2.0" ]

null

gather_agent/handlers/__init__.py

burmanm/gather_agent

37d9eb80cf717d12a132ff1c98a0c80eeeaa5a66

[ "Apache-2.0" ]

null

from rhqmetrics_handler import RHQMetricsHandler

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pyblnet/__init__.py

henfri/pyblnet

0a3a59ea39ab569d4b59be5a918736dc238bcf13

[ "MIT" ]

null

pyblnet/__init__.py

henfri/pyblnet

0a3a59ea39ab569d4b59be5a918736dc238bcf13

[ "MIT" ]

null

pyblnet/__init__.py

henfri/pyblnet

0a3a59ea39ab569d4b59be5a918736dc238bcf13

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from .blnet_web import BLNETWeb, test_blnet from .blnet_conn import BLNETDirect from .blnet import BLNET

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Python

.metadata/.plugins/org.python.pydev.shared_interactive_console/history.py

fullerene12/VOTA

3a5cfc1e210ac7ea274537a8d189b54660416599

[ "MIT" ]

null

.metadata/.plugins/org.python.pydev.shared_interactive_console/history.py

fullerene12/VOTA

3a5cfc1e210ac7ea274537a8d189b54660416599

[ "MIT" ]

null

.metadata/.plugins/org.python.pydev.shared_interactive_console/history.py

fullerene12/VOTA

3a5cfc1e210ac7ea274537a8d189b54660416599

[ "MIT" ]

1

2021-08-01T22:39:18.000Z

import sys; print('%s %s' % (sys.executable or sys.platform, sys.version))

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py

Python

opytimizer/spaces/__init__.py

anukaal/opytimizer

5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9

[ "Apache-2.0" ]

528

2018-10-01T20:00:09.000Z

2022-03-27T11:15:31.000Z

opytimizer/spaces/__init__.py

anukaal/opytimizer

5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9

[ "Apache-2.0" ]

17

2019-10-30T00:47:03.000Z

2022-03-21T11:39:28.000Z

opytimizer/spaces/__init__.py

anukaal/opytimizer

5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9

[ "Apache-2.0" ]

35

2018-10-01T20:03:23.000Z

2022-03-20T03:54:15.000Z

"""Customizable space module that provides different search spaces implementations. """ from opytimizer.spaces.boolean import BooleanSpace from opytimizer.spaces.grid import GridSpace from opytimizer.spaces.hyper_complex import HyperComplexSpace from opytimizer.spaces.search import SearchSpace from opytimizer.spaces.tree import TreeSpace

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Python

tests/location/test_location_utility.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

null

tests/location/test_location_utility.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

1

2022-01-17T04:25:49.000Z

tests/location/test_location_utility.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # vim: set noai syntax=python ts=4 sw=4: # # Copyright (c) 2018-2021 Linh Pham # wwdtm is released under the terms of the Apache License 2.0 """Testing for object: :py:class:`wwdtm.location.LocationUtility` """ import json from typing import Any, Dict import pytest from wwdtm.location import LocationUtility @pytest.mark.skip def get_connect_dict() -> Dict[str, Any]: """Read in database connection settings and return values as a dictionary. :return: A dictionary containing database connection settings for use by mysql.connector """ with open("config.json", "r") as config_file: config_dict = json.load(config_file) if "database" in config_dict: return config_dict["database"] @pytest.mark.parametrize("location_id", [95]) def test_location_utility_convert_id_to_slug(location_id: int): """Testing for :py:meth:`wwdtm.location.LocationUtility.convert_id_to_slug` :param location_id: Location ID to test converting into location slug string """ utility = LocationUtility(connect_dict=get_connect_dict()) slug = utility.convert_id_to_slug(location_id) assert slug, f"Location slug for ID {location_id} was not found" @pytest.mark.parametrize("location_id", [-1]) def test_location_utility_convert_invalid_id_to_slug(location_id: int): """Negative testing for :py:meth:`wwdtm.location.LocationUtility.convert_id_to_slug` :param location_id: Location ID to test failing to convert into location slug string """ utility = LocationUtility(connect_dict=get_connect_dict()) slug = utility.convert_id_to_slug(location_id) assert not slug, f"Location slug for ID {location_id} was found" @pytest.mark.parametrize("location_slug", ["the-chicago-theatre-chicago-il"]) def test_location_utility_convert_slug_to_id(location_slug: str): """Testing for :py:meth:`wwdtm.location.LocationUtility.convert_slug_to_id` :param location_slug: Location slug string to test converting into location ID """ utility = LocationUtility(connect_dict=get_connect_dict()) id_ = utility.convert_slug_to_id(location_slug) assert id_, f"Location ID for slug {location_slug} was not found" @pytest.mark.parametrize("location_slug", ["the-chicago-theatre-chicago-li"]) def test_location_utility_convert_invalid_slug_to_id(location_slug: str): """Negative testing for :py:meth:`wwdtm.location.LocationUtility.convert_slug_to_id` :param location_slug: Location slug string to test failing to convert into location ID """ utility = LocationUtility(connect_dict=get_connect_dict()) id_ = utility.convert_slug_to_id(location_slug) assert not id_, f"Location ID for slug {location_slug} was found" @pytest.mark.parametrize("location_id", [95]) def test_location_utility_id_exists(location_id: int): """Testing for :py:meth:`wwdtm.location.LocationUtility.id_exists` :param location_id: Location ID to test if a location exists """ utility = LocationUtility(connect_dict=get_connect_dict()) result = utility.id_exists(location_id) assert result, f"Location ID {location_id} does not exist" @pytest.mark.parametrize("location_id", [-1]) def test_location_utility_id_not_exists(location_id: int): """Negative testing for :py:meth:`wwdtm.location.LocationUtility.id_exists` :param location_id: Location ID to test if a location does not exist """ utility = LocationUtility(connect_dict=get_connect_dict()) result = utility.id_exists(location_id) assert not result, f"Location ID {location_id} exists" @pytest.mark.parametrize("location_slug", ["the-chicago-theatre-chicago-il"]) def test_location_utility_slug_exists(location_slug: str): """Testing for :py:meth:`wwdtm.location.LocationUtility.slug_exists` :param location_slug: Location slug string to test if a location exists """ utility = LocationUtility(connect_dict=get_connect_dict()) result = utility.slug_exists(location_slug) assert result, f"Location slug {location_slug} does not exist" @pytest.mark.parametrize("location_slug", ["the-chicago-theatre-chicago-li"]) def test_location_utility_slug_not_exists(location_slug: str): """Testing for :py:meth:`wwdtm.location.LocationUtility.slug_exists` with venue name :param location_slug: Location slug string to test if a location does not exists """ utility = LocationUtility(connect_dict=get_connect_dict()) result = utility.slug_exists(location_slug) assert not result, f"Location slug {location_slug} exists" @pytest.mark.parametrize("city", ["Chicago"]) def test_location_utility_slugify_location_city(city: str): """Negative testing for :py:meth:`wwdtm.location.LocationUtility.slugify_location` with city name :param city: City to include in the slug string """ with pytest.raises(ValueError): utility = LocationUtility(connect_dict=get_connect_dict()) slug = utility.slugify_location(city=city) assert slug, "Unable to convert into a slug string" assert isinstance(slug, str), "Value returned is not a string" @pytest.mark.parametrize("city, state", [("Chicago", "IL")]) def test_location_utility_slugify_location_city_state(city: str, state: str): """Negative testing for :py:meth:`wwdtm.location.LocationUtility.slugify_location` with city and state names :param city: City to include in the slug string :param state: State to include in the slug string """ with pytest.raises(ValueError): utility = LocationUtility(connect_dict=get_connect_dict()) slug = utility.slugify_location(city=city, state=state) assert slug, "Unable to convert into a slug string" assert isinstance(slug, str), "Value returned is not a string" @pytest.mark.parametrize("location_id, venue, city, state", [(2, "Chase Auditorium", "Chicago", "IL")]) def test_location_utility_slugify_location_full(location_id: int, venue: str, city: str, state: str): """Testing for :py:meth:`wwdtm.location.LocationUtility.slugify_location` with location ID, venue, city and state names :param location_id: Location ID to include in the slug string :param venue: Venue name to include in the slug string :param city: City to include in the slug string :param state: State to include in the slug string """ utility = LocationUtility(connect_dict=get_connect_dict()) slug = utility.slugify_location(location_id=location_id, venue=venue, city=city, state=state) assert slug, "Unable to convert into a slug string" assert isinstance(slug, str), "Value returned is not a string" @pytest.mark.parametrize("location_id, venue", [(2, "Chase Auditorium")]) def test_location_utility_slugify_location_venue(location_id: int, venue: str): """Testing for :py:meth:`wwdtm.location.LocationUtility.slugify_location` with venue name :param location_id: Location ID to include in the slug string :param venue: Venue name to include in the slug string """ utility = LocationUtility(connect_dict=get_connect_dict()) slug = utility.slugify_location(location_id=location_id, venue=venue) assert slug, "Unable to convert into a slug string" assert isinstance(slug, str), "Value returned is not a string" @pytest.mark.parametrize("venue, city, state", [("Chase Auditorium", "Chicago", "IL")]) def test_location_utility_slugify_location_venue_city_state(venue: str, city: str, state: str): """Testing for :py:meth:`wwdtm.location.LocationUtility.slugify_location` :param venue: Venue name to include in the slug string :param city: City to include in the slug string :param state: State to include in the slug string """ utility = LocationUtility(connect_dict=get_connect_dict()) slug = utility.slugify_location(venue=venue, city=city, state=state) assert slug, "Unable to convert into a slug string" assert isinstance(slug, str), "Value returned is not a string" @pytest.mark.parametrize("location_id", [2]) def test_location_utility_slugify_location_id(location_id: int): """Testing for :py:meth:`wwdtm.location.LocationUtility.slugify_location` with venue, city and state names :param location_id: Location ID to include in the slug string """ utility = LocationUtility(connect_dict=get_connect_dict()) slug = utility.slugify_location(location_id=location_id) assert slug, "Unable to convert into a slug string" assert isinstance(slug, str), "Value returned is not a string"

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0.20202

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null

0

1

0

null

0

6

06b29440122743c4d662f5e0b42777454bfb53b1

2,600

py

Python

tfcli/resources/asg.py

leowa/tfcli

21314feabcb56fe802298a98a66eb4e2a9de8cc7

[ "MIT" ]

null

tfcli/resources/asg.py

leowa/tfcli

21314feabcb56fe802298a98a66eb4e2a9de8cc7

[ "MIT" ]

null

tfcli/resources/asg.py

leowa/tfcli

21314feabcb56fe802298a98a66eb4e2a9de8cc7

[ "MIT" ]

null

from .base import BaseResource class Asg(BaseResource): """ autoscaling group resource to generate from current region """ def __init__(self, logger=None): super().__init__(logger) def amend_attributes(self, _type, _name, attributes: dict): if "launch_template" in attributes and attributes["launch_template"]: tpl = attributes["launch_template"][0] if "id" in tpl and "name" in tpl: # remove if from template if name exists del tpl["id"] return attributes @classmethod def ignore_attrbute(cls, key, value): if key in ["id", "owner_id", "arn"]: return True return False @classmethod def included_resource_types(cls): """resource types for this resource and its derived resources """ return [ "aws_autoscaling_group", ] def list_all(self): """list all such kind of resources from AWS :return: list of tupe for a resource (type, name, id) """ asg = self.session.client("autoscaling") items = asg.describe_auto_scaling_groups()["AutoScalingGroups"] for item in items: _name = _id = item["AutoScalingGroupName"] yield "aws_autoscaling_group", _name, _id class LaunchTemplate(BaseResource): """ launch template resource to generate from current region """ def __init__(self, logger=None): super().__init__(logger) def amend_attributes(self, _type, _name, attributes: dict): if "launch_template" in attributes and attributes["launch_template"]: tpl = attributes["launch_template"][0] if "id" in tpl and "name" in tpl: # remove if from template if name exists del tpl["id"] return attributes @classmethod def ignore_attrbute(cls, key, value): if key in ["id", "owner_id", "arn", "default_version", "latest_version"]: return True return False @classmethod def included_resource_types(cls): """resource types for this resource and its derived resources """ return [ "aws_launch_template", ] def list_all(self): """list all such kind of resources from AWS :return: list of tupe for a resource (type, name, id) """ ec2 = self.session.client("ec2") items = ec2.describe_launch_templates()["LaunchTemplates"] for item in items: _name = _id = item["LaunchTemplateId"] yield "aws_launch_template", _name, _id

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2,600

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0.666667

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null

0

1

0

1

0

null

0

6

230db22fc190c68752be940d1363fe5ecdb2a558

169

py

Python

backend/api/models.py

tuguldurio/fullstack-ecommerce

06257e704c657b008587aabb4075750899149b1d

[ "MIT" ]

null

backend/api/models.py

tuguldurio/fullstack-ecommerce

06257e704c657b008587aabb4075750899149b1d

[ "MIT" ]

null

backend/api/models.py

tuguldurio/fullstack-ecommerce

06257e704c657b008587aabb4075750899149b1d

[ "MIT" ]

null

from api.user.models import User from api.cart.models import Cart, CartProduct from api.order.models import Order, OrderProduct from api.product.models import Product

42.25

49

0.822485

26

169

5.346154

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null

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1

0

1

0

1

0

6

23240f288abf89b78f596d8ce66de1c2719d6da7

43

py

Python

app/data/__init__.py

codenio/cvcam

4bfb16ae20375abee9dfdf0383c0df0bb5b31db7

[ "MIT" ]

2

2021-02-12T10:10:41.000Z

2022-02-01T12:29:34.000Z

app/data/__init__.py

codenio/cvcam

4bfb16ae20375abee9dfdf0383c0df0bb5b31db7

[ "MIT" ]

null

app/data/__init__.py

codenio/cvcam

4bfb16ae20375abee9dfdf0383c0df0bb5b31db7

[ "MIT" ]

1

2020-08-08T17:19:05.000Z

from .lite_data_store import LiteDataStore

21.5

42

0.883721

6

43

6

1

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null

0

1

0

1

0

1

0

6

232aa8e2e7ba295ede12f5cba7bf5a933e010de8

31,253

py

Python

pytest_docker_registry_fixtures/fixtures.py

crashvb/pytest-docker-registry-fixtures

aab57393f8478982751da140e259eb4bf81869a7

[ "Apache-2.0" ]

null

pytest_docker_registry_fixtures/fixtures.py

crashvb/pytest-docker-registry-fixtures

aab57393f8478982751da140e259eb4bf81869a7

[ "Apache-2.0" ]

1

2021-02-17T04:23:09.000Z

2021-02-17T04:29:22.000Z

pytest_docker_registry_fixtures/fixtures.py

crashvb/pytest-docker-registry-fixtures

aab57393f8478982751da140e259eb4bf81869a7

[ "Apache-2.0" ]

null

#!/usr/bin/env python # pylint: disable=redefined-outer-name,too-many-arguments,too-many-locals """The actual fixtures, you found them ;).""" import logging import itertools from base64 import b64encode from distutils.util import strtobool from functools import partial from pathlib import Path from ssl import create_default_context, SSLContext from string import Template from time import sleep, time from typing import Dict, Generator, List, NamedTuple import pytest from docker import DockerClient, from_env from lovely.pytest.docker.compose import Services from _pytest.tmpdir import TempPathFactory from .imagename import ImageName from .utils import ( check_url_secure, DOCKER_REGISTRY_SERVICE, DOCKER_REGISTRY_SERVICE_PATTERN, generate_cacerts, generate_htpasswd, generate_keypair, get_docker_compose_user_defined, get_embedded_file, get_user_defined_file, replicate_image, start_service, ) # Caching is needed, as singular-fixtures and list-fixtures will conflict at scale_factor=1 # This appears to only matter when attempting to start the docker secure registry service # for the second time. CACHE = {} LOGGER = logging.getLogger(__name__) class DockerRegistryCerts(NamedTuple): # pylint: disable=missing-class-docstring ca_certificate: Path ca_private_key: Path certificate: Path private_key: Path class DockerRegistryInsecure(NamedTuple): # pylint: disable=missing-class-docstring docker_client: DockerClient docker_compose: Path endpoint: str images: List[ImageName] service_name: str # Note: NamedTuple does not support inheritance :( class DockerRegistrySecure(NamedTuple): # pylint: disable=missing-class-docstring auth_header: Dict[str, str] cacerts: Path certs: DockerRegistryCerts docker_client: DockerClient docker_compose: Path endpoint: str htpasswd: Path images: List[ImageName] password: str service_name: str ssl_context: SSLContext username: str @pytest.fixture(scope="session") def docker_client() -> DockerClient: """Provides an insecure Docker API client.""" return from_env() def _docker_compose_insecure( *, docker_compose_files: List[str], scale_factor: int, tmp_path_factory: TempPathFactory, ) -> Generator[List[Path], None, None]: """ Provides the location of the docker-compose configuration file containing the insecure docker registry service. """ cache_key = _docker_compose_insecure.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue service_name = DOCKER_REGISTRY_SERVICE_PATTERN.format("insecure", i) chain = itertools.chain( get_docker_compose_user_defined(docker_compose_files, service_name), # TODO: lovely-docker-compose uses the file for teardown ... get_embedded_file( tmp_path_factory, delete_after=False, name="docker-compose.yml" ), ) for path in chain: result.append(path) break else: LOGGER.warning("Unable to find docker compose for: %s", service_name) result.append("-unknown-") CACHE[cache_key] = result yield result @pytest.fixture(scope="session") def docker_compose_insecure( docker_compose_files: List[str], tmp_path_factory: TempPathFactory ) -> Generator[Path, None, None]: """ Provides the location of the docker-compose configuration file containing the insecure docker registry service. """ for lst in _docker_compose_insecure( docker_compose_files=docker_compose_files, scale_factor=1, tmp_path_factory=tmp_path_factory, ): yield lst[0] @pytest.fixture(scope="session") def docker_compose_insecure_list( docker_compose_files: List[str], pdrf_scale_factor: int, tmp_path_factory: TempPathFactory, ) -> Generator[List[Path], None, None]: """ Provides the location of the docker-compose configuration file containing the insecure docker registry service. """ yield from _docker_compose_insecure( docker_compose_files=docker_compose_files, scale_factor=pdrf_scale_factor, tmp_path_factory=tmp_path_factory, ) def _docker_compose_secure( *, docker_compose_files: List[str], scale_factor: int, tmp_path_factory: TempPathFactory, ) -> Generator[List[Path], None, None]: """ Provides the location of the templated docker-compose configuration file containing the secure docker registry service. """ cache_key = _docker_compose_secure.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue service_name = DOCKER_REGISTRY_SERVICE_PATTERN.format("secure", i) chain = itertools.chain( get_docker_compose_user_defined(docker_compose_files, service_name), get_embedded_file( tmp_path_factory, delete_after=False, name="docker-compose.yml" ), ) for path in chain: result.append(path) break else: LOGGER.warning("Unable to find docker compose for: %s", service_name) result.append("-unknown-") CACHE[cache_key] = result yield result @pytest.fixture(scope="session") def docker_compose_secure( docker_compose_files: List[str], tmp_path_factory: TempPathFactory ) -> Generator[Path, None, None]: """ Provides the location of the templated docker-compose configuration file containing the secure docker registry service. """ for lst in _docker_compose_secure( docker_compose_files=docker_compose_files, scale_factor=1, tmp_path_factory=tmp_path_factory, ): yield lst[0] @pytest.fixture(scope="session") def docker_compose_secure_list( docker_compose_files: List[str], pdrf_scale_factor: int, tmp_path_factory: TempPathFactory, ) -> Generator[List[Path], None, None]: """ Provides the location of the templated docker-compose configuration file containing the secure docker registry service. """ yield from _docker_compose_secure( docker_compose_files=docker_compose_files, scale_factor=pdrf_scale_factor, tmp_path_factory=tmp_path_factory, ) def _docker_registry_auth_header( *, docker_registry_password_list: List[str], docker_registry_username_list: List[str], scale_factor: int, ) -> List[Dict[str, str]]: """Provides an HTTP basic authentication header containing credentials for the secure docker registry service.""" cache_key = _docker_registry_auth_header.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue auth = b64encode( f"{docker_registry_username_list[i]}:{docker_registry_password_list[i]}".encode( "utf-8" ) ).decode("utf-8") result.append({"Authorization": f"Basic {auth}"}) CACHE[cache_key] = result return result @pytest.fixture(scope="session") def docker_registry_auth_header( docker_registry_password: str, docker_registry_username: str ) -> Dict[str, str]: """Provides an HTTP basic authentication header containing credentials for the secure docker registry service.""" return _docker_registry_auth_header( docker_registry_password_list=[docker_registry_password], docker_registry_username_list=[docker_registry_username], scale_factor=1, )[0] @pytest.fixture(scope="session") def docker_registry_auth_header_list( docker_registry_password_list: List[str], docker_registry_username_list: List[str], pdrf_scale_factor: int, ) -> List[Dict[str, str]]: """Provides an HTTP basic authentication header containing credentials for the secure docker registry service.""" return _docker_registry_auth_header( docker_registry_password_list=docker_registry_password_list, docker_registry_username_list=docker_registry_username_list, scale_factor=pdrf_scale_factor, ) def _docker_registry_cacerts( *, docker_registry_certs_list: List[DockerRegistryCerts], pytestconfig: "_pytest.config.Config", scale_factor: int, tmp_path_factory: TempPathFactory, ) -> Generator[List[Path], None, None]: """ Provides the location of a temporary CA certificate trust store that contains the certificate of the secure docker registry service. """ cache_key = _docker_registry_cacerts.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue chain = itertools.chain( get_user_defined_file(pytestconfig, "cacerts"), generate_cacerts( tmp_path_factory, certificate=docker_registry_certs_list[i].ca_certificate, ), ) for path in chain: result.append(path) break else: LOGGER.warning("Unable to find or generate cacerts!") result.append("-unknown-") CACHE[cache_key] = result yield result @pytest.fixture(scope="session") def docker_registry_cacerts( docker_registry_certs: DockerRegistryCerts, pytestconfig: "_pytest.config.Config", tmp_path_factory: TempPathFactory, ) -> Generator[Path, None, None]: """ Provides the location of a temporary CA certificate trust store that contains the certificate of the secure docker registry service. """ for lst in _docker_registry_cacerts( docker_registry_certs_list=[docker_registry_certs], pytestconfig=pytestconfig, scale_factor=1, tmp_path_factory=tmp_path_factory, ): yield lst[0] @pytest.fixture(scope="session") def docker_registry_cacerts_list( docker_registry_certs_list: List[DockerRegistryCerts], pdrf_scale_factor: int, pytestconfig: "_pytest.config.Config", tmp_path_factory: TempPathFactory, ) -> Generator[List[Path], None, None]: """ Provides the location of a temporary CA certificate trust store that contains the certificate of the secure docker registry service. """ yield from _docker_registry_cacerts( docker_registry_certs_list=docker_registry_certs_list, pytestconfig=pytestconfig, scale_factor=pdrf_scale_factor, tmp_path_factory=tmp_path_factory, ) def _docker_registry_certs( *, scale_factor: int, tmp_path_factory: TempPathFactory ) -> Generator[List[DockerRegistryCerts], None, None]: """Provides the location of temporary certificate and private key files for the secure docker registry service.""" # TODO: Augment to allow for reading certificates from /test ... cache_key = _docker_registry_certs.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue tmp_path = tmp_path_factory.mktemp(__name__) keypair = generate_keypair() docker_registry_cert = DockerRegistryCerts( ca_certificate=tmp_path.joinpath(f"{DOCKER_REGISTRY_SERVICE}-ca-{i}.crt"), ca_private_key=tmp_path.joinpath(f"{DOCKER_REGISTRY_SERVICE}-ca-{i}.key"), certificate=tmp_path.joinpath(f"{DOCKER_REGISTRY_SERVICE}-{i}.crt"), private_key=tmp_path.joinpath(f"{DOCKER_REGISTRY_SERVICE}-{i}.key"), ) docker_registry_cert.ca_certificate.write_bytes(keypair.ca_certificate) docker_registry_cert.ca_private_key.write_bytes(keypair.ca_private_key) docker_registry_cert.certificate.write_bytes(keypair.certificate) docker_registry_cert.private_key.write_bytes(keypair.private_key) result.append(docker_registry_cert) CACHE[cache_key] = result yield result for docker_registry_cert in result: docker_registry_cert.ca_certificate.unlink(missing_ok=True) docker_registry_cert.ca_private_key.unlink(missing_ok=True) docker_registry_cert.certificate.unlink(missing_ok=True) docker_registry_cert.private_key.unlink(missing_ok=True) @pytest.fixture(scope="session") def docker_registry_certs( tmp_path_factory: TempPathFactory, ) -> Generator[DockerRegistryCerts, None, None]: """Provides the location of temporary certificate and private key files for the secure docker registry service.""" for lst in _docker_registry_certs( scale_factor=1, tmp_path_factory=tmp_path_factory ): yield lst[0] @pytest.fixture(scope="session") def docker_registry_certs_list( pdrf_scale_factor: int, tmp_path_factory: TempPathFactory ) -> Generator[List[DockerRegistryCerts], None, None]: """Provides the location of temporary certificate and private key files for the secure docker registry service.""" yield from _docker_registry_certs( scale_factor=pdrf_scale_factor, tmp_path_factory=tmp_path_factory ) def _docker_registry_htpasswd( *, docker_registry_password_list: List[str], docker_registry_username_list: List[str], pytestconfig: "_pytest.config.Config", scale_factor: int, tmp_path_factory: TempPathFactory, ) -> Generator[List[Path], None, None]: """Provides the location of the htpasswd file for the secure registry service.""" cache_key = _docker_registry_htpasswd.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue chain = itertools.chain( get_user_defined_file(pytestconfig, "htpasswd"), generate_htpasswd( tmp_path_factory, username=docker_registry_username_list[i], password=docker_registry_password_list[i], ), ) for path in chain: result.append(path) break else: LOGGER.warning("Unable to find or generate htpasswd!") result.append("-unknown-") CACHE[cache_key] = result yield result @pytest.fixture(scope="session") def docker_registry_htpasswd( docker_registry_password: str, docker_registry_username: str, pytestconfig: "_pytest.config.Config", tmp_path_factory: TempPathFactory, ) -> Generator[Path, None, None]: """Provides the location of the htpasswd file for the secure registry service.""" for lst in _docker_registry_htpasswd( docker_registry_password_list=[docker_registry_password], docker_registry_username_list=[docker_registry_username], pytestconfig=pytestconfig, scale_factor=1, tmp_path_factory=tmp_path_factory, ): yield lst[0] @pytest.fixture(scope="session") def docker_registry_htpasswd_list( docker_registry_password_list: List[str], docker_registry_username_list: List[str], pdrf_scale_factor: int, pytestconfig: "_pytest.config.Config", tmp_path_factory: TempPathFactory, ) -> Generator[List[Path], None, None]: """Provides the location of the htpasswd file for the secure registry service.""" yield from _docker_registry_htpasswd( docker_registry_username_list=docker_registry_username_list, docker_registry_password_list=docker_registry_password_list, pytestconfig=pytestconfig, scale_factor=pdrf_scale_factor, tmp_path_factory=tmp_path_factory, ) def _docker_registry_insecure( *, docker_client: DockerClient, docker_compose_insecure_list: List[Path], docker_services: Services, request, scale_factor: int, tmp_path_factory: TempPathFactory, ) -> Generator[List[DockerRegistryInsecure], None, None]: """Provides the endpoint of a local, mutable, insecure, docker registry.""" cache_key = _docker_registry_insecure.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue service_name = DOCKER_REGISTRY_SERVICE_PATTERN.format("insecure", i) tmp_path = tmp_path_factory.mktemp(__name__) # Create a secure registry service from the docker compose template ... path_docker_compose = tmp_path.joinpath(f"docker-compose-{i}.yml") template = Template(docker_compose_insecure_list[i].read_text("utf-8")) path_docker_compose.write_text( template.substitute( { "CONTAINER_NAME": service_name, # Note: Needed to correctly populate the embedded, consolidated, service template ... "PATH_CERTIFICATE": "/dev/null", "PATH_HTPASSWD": "/dev/null", "PATH_KEY": "/dev/null", } ), "utf-8", ) LOGGER.debug("Starting insecure docker registry service [%d] ...", i) LOGGER.debug(" docker-compose : %s", path_docker_compose) LOGGER.debug(" service name : %s", service_name) endpoint = start_service( docker_services, docker_compose=path_docker_compose, service_name=service_name, ) LOGGER.debug("Insecure docker registry endpoint [%d]: %s", i, endpoint) images = [] if i == 0: LOGGER.debug("Replicating images into %s [%d] ...", service_name, i) images = _replicate_images(docker_client, endpoint, request) result.append( DockerRegistryInsecure( docker_client=docker_client, docker_compose=path_docker_compose, endpoint=endpoint, images=images, service_name=service_name, ) ) CACHE[cache_key] = result yield result @pytest.fixture(scope="session") def docker_registry_insecure( docker_client: DockerClient, docker_compose_insecure: Path, docker_services: Services, request, tmp_path_factory: TempPathFactory, ) -> Generator[DockerRegistryInsecure, None, None]: """Provides the endpoint of a local, mutable, insecure, docker registry.""" for lst in _docker_registry_insecure( docker_client=docker_client, docker_compose_insecure_list=[docker_compose_insecure], docker_services=docker_services, request=request, scale_factor=1, tmp_path_factory=tmp_path_factory, ): yield lst[0] @pytest.fixture(scope="session") def docker_registry_insecure_list( docker_client: DockerClient, docker_compose_insecure_list: List[Path], docker_services: Services, pdrf_scale_factor: int, request, tmp_path_factory: TempPathFactory, ) -> Generator[List[DockerRegistryInsecure], None, None]: """Provides the endpoint of a local, mutable, insecure, docker registry.""" yield from _docker_registry_insecure( docker_client=docker_client, docker_compose_insecure_list=docker_compose_insecure_list, docker_services=docker_services, request=request, scale_factor=pdrf_scale_factor, tmp_path_factory=tmp_path_factory, ) def _docker_registry_password(*, scale_factor: int) -> List[str]: """Provides the password to use for authentication to the secure registry service.""" cache_key = _docker_registry_password.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue result.append(f"pytest.password.{time()}") sleep(0.05) CACHE[cache_key] = result return result @pytest.fixture(scope="session") def docker_registry_password() -> str: """Provides the password to use for authentication to the secure registry service.""" return _docker_registry_password(scale_factor=1)[0] @pytest.fixture(scope="session") def docker_registry_password_list(pdrf_scale_factor: int) -> List[str]: """Provides the password to use for authentication to the secure registry service.""" return _docker_registry_password(scale_factor=pdrf_scale_factor) def _docker_registry_secure( *, docker_client: DockerClient, docker_compose_secure_list: List[Path], docker_registry_auth_header_list: List[Dict[str, str]], docker_registry_cacerts_list: List[Path], docker_registry_certs_list: List[DockerRegistryCerts], docker_registry_htpasswd_list: List[Path], docker_registry_password_list: List[str], docker_registry_ssl_context_list: List[SSLContext], docker_registry_username_list: List[str], docker_services: Services, request, scale_factor: int, tmp_path_factory: TempPathFactory, ) -> Generator[List[DockerRegistrySecure], None, None]: """Provides the endpoint of a local, mutable, secure, docker registry.""" cache_key = _docker_registry_secure.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue service_name = DOCKER_REGISTRY_SERVICE_PATTERN.format("secure", i) tmp_path = tmp_path_factory.mktemp(__name__) # Create a secure registry service from the docker compose template ... path_docker_compose = tmp_path.joinpath(f"docker-compose-{i}.yml") template = Template(docker_compose_secure_list[i].read_text("utf-8")) path_docker_compose.write_text( template.substitute( { "CONTAINER_NAME": service_name, "PATH_CERTIFICATE": docker_registry_certs_list[i].certificate, "PATH_HTPASSWD": docker_registry_htpasswd_list[i], "PATH_KEY": docker_registry_certs_list[i].private_key, } ), "utf-8", ) LOGGER.debug("Starting secure docker registry service [%d] ...", i) LOGGER.debug(" docker-compose : %s", path_docker_compose) LOGGER.debug( " ca certificate : %s", docker_registry_certs_list[i].ca_certificate ) LOGGER.debug(" certificate : %s", docker_registry_certs_list[i].certificate) LOGGER.debug(" htpasswd : %s", docker_registry_htpasswd_list[i]) LOGGER.debug(" private key : %s", docker_registry_certs_list[i].private_key) LOGGER.debug(" password : %s", docker_registry_password_list[i]) LOGGER.debug(" service name : %s", service_name) LOGGER.debug(" username : %s", docker_registry_username_list[i]) check_server = partial( check_url_secure, auth_header=docker_registry_auth_header_list[i], ssl_context=docker_registry_ssl_context_list[i], ) endpoint = start_service( docker_services, check_server=check_server, docker_compose=path_docker_compose, service_name=service_name, ) LOGGER.debug("Secure docker registry endpoint [%d]: %s", i, endpoint) # DUCK PUNCH: Inject the secure docker registry credentials into the docker client ... docker_client.api._auth_configs.add_auth( # pylint: disable=protected-access endpoint, { "password": docker_registry_password_list[i], "username": docker_registry_username_list[i], }, ) images = [] if i == 0: LOGGER.debug("Replicating images into %s [%d] ...", service_name, i) images = _replicate_images(docker_client, endpoint, request) result.append( DockerRegistrySecure( auth_header=docker_registry_auth_header_list[i], cacerts=docker_registry_cacerts_list[i], certs=docker_registry_certs_list[i], docker_client=docker_client, docker_compose=path_docker_compose, endpoint=endpoint, htpasswd=docker_registry_htpasswd_list[i], password=docker_registry_password_list[i], images=images, service_name=service_name, ssl_context=docker_registry_ssl_context_list[i], username=docker_registry_username_list[i], ) ) CACHE[cache_key] = result yield result @pytest.fixture(scope="session") def docker_registry_secure( docker_client: DockerClient, docker_compose_secure: Path, docker_registry_auth_header: Dict[str, str], docker_registry_cacerts: Path, docker_registry_certs: DockerRegistryCerts, docker_registry_htpasswd: Path, docker_registry_password: str, docker_registry_ssl_context: SSLContext, docker_registry_username: str, docker_services: Services, request, tmp_path_factory: TempPathFactory, ) -> Generator[DockerRegistrySecure, None, None]: """Provides the endpoint of a local, mutable, secure, docker registry.""" for lst in _docker_registry_secure( docker_client=docker_client, docker_compose_secure_list=[docker_compose_secure], docker_registry_auth_header_list=[docker_registry_auth_header], docker_registry_cacerts_list=[docker_registry_cacerts], docker_registry_certs_list=[docker_registry_certs], docker_registry_htpasswd_list=[docker_registry_htpasswd], docker_registry_password_list=[docker_registry_password], docker_registry_ssl_context_list=[docker_registry_ssl_context], docker_registry_username_list=[docker_registry_username], docker_services=docker_services, request=request, scale_factor=1, tmp_path_factory=tmp_path_factory, ): yield lst[0] @pytest.fixture(scope="session") def docker_registry_secure_list( docker_client: DockerClient, docker_compose_secure_list: List[Path], docker_registry_auth_header_list: List[Dict[str, str]], docker_registry_cacerts_list: List[Path], docker_registry_certs_list: List[DockerRegistryCerts], docker_registry_htpasswd_list: List[Path], docker_registry_password_list: List[str], docker_registry_ssl_context_list: List[SSLContext], docker_registry_username_list: List[str], docker_services: Services, pdrf_scale_factor: int, request, tmp_path_factory: TempPathFactory, ) -> Generator[List[DockerRegistrySecure], None, None]: """Provides the endpoint of a local, mutable, secure, docker registry.""" yield from _docker_registry_secure( docker_client=docker_client, docker_compose_secure_list=docker_compose_secure_list, docker_registry_auth_header_list=docker_registry_auth_header_list, docker_registry_cacerts_list=docker_registry_cacerts_list, docker_registry_certs_list=docker_registry_certs_list, docker_registry_htpasswd_list=docker_registry_htpasswd_list, docker_registry_password_list=docker_registry_password_list, docker_registry_ssl_context_list=docker_registry_ssl_context_list, docker_registry_username_list=docker_registry_username_list, docker_services=docker_services, request=request, scale_factor=pdrf_scale_factor, tmp_path_factory=tmp_path_factory, ) def _docker_registry_ssl_context( *, docker_registry_cacerts_list: List[Path], scale_factor: int ) -> List[SSLContext]: """ Provides an SSLContext referencing the temporary CA certificate trust store that contains the certificate of the secure docker registry service. """ cache_key = _docker_registry_ssl_context.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue result.append( create_default_context(cafile=str(docker_registry_cacerts_list[i])) ) CACHE[cache_key] = result return result @pytest.fixture(scope="session") def docker_registry_ssl_context(docker_registry_cacerts: Path) -> SSLContext: """ Provides an SSLContext referencing the temporary CA certificate trust store that contains the certificate of the secure docker registry service. """ return _docker_registry_ssl_context( docker_registry_cacerts_list=[docker_registry_cacerts], scale_factor=1 )[0] @pytest.fixture(scope="session") def docker_registry_ssl_context_list( docker_registry_cacerts_list: List[Path], pdrf_scale_factor: int, ) -> List[SSLContext]: """ Provides an SSLContext referencing the temporary CA certificate trust store that contains the certificate of the secure docker registry service. """ return _docker_registry_ssl_context( docker_registry_cacerts_list=docker_registry_cacerts_list, scale_factor=pdrf_scale_factor, ) def _docker_registry_username(*, scale_factor: int) -> List[str]: """Retrieve the name of the user to use for authentication to the secure registry service.""" cache_key = _docker_registry_username.__name__ result = CACHE.get(cache_key, []) for i in range(scale_factor): if i < len(result): continue result.append(f"pytest.username.{time()}") sleep(0.05) CACHE[cache_key] = result return result @pytest.fixture(scope="session") def docker_registry_username() -> str: """Retrieve the name of the user to use for authentication to the secure registry service.""" return _docker_registry_username(scale_factor=1)[0] @pytest.fixture(scope="session") def docker_registry_username_list( pdrf_scale_factor: int, ) -> List[str]: """Retrieve the name of the user to use for authentication to the secure registry service.""" return _docker_registry_username(scale_factor=pdrf_scale_factor) @pytest.fixture(scope="session") def pdrf_scale_factor() -> int: """Provides the number enumerated instances to be instantiated.""" return 1 def _replicate_images( docker_client: DockerClient, endpoint: str, request ) -> List[ImageName]: """ Replicates all marked images to a docker registry service at a given endpoint. Args: docker_client: Docker client with which to replicate the marked images. endpoint: The endpoint of the docker registry service. request: The pytest requests object from which to retrieve the marks. Returns: The list of images that were replicated. """ always_pull = strtobool(str(request.config.getoption("--always-pull", True))) images = request.config.getoption("--push-image", []) # images.extend(request.node.get_closest_marker("push_image", [])) # * Split ',' separated lists # * Remove duplicates - see conftest.py::pytest_collection_modifyitems() images = [image for i in images for image in i.split(",")] images = [ImageName.parse(image) for image in list(set(images))] for image in images: LOGGER.debug("- %s", image) try: replicate_image(docker_client, image, endpoint, always_pull=always_pull) except Exception as exception: # pylint: disable=broad-except LOGGER.warning( "Unable to replicate image '%s': %s", image, exception, exc_info=True ) return images

35.964327

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0.696381

3,662

31,253

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null

0

1

0

1

0

null

0

1

0

6

23300efdd697b2575e312f7edd92461f467cdc9c

161

py

Python

src/onegov/gis/forms/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

src/onegov/gis/forms/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

src/onegov/gis/forms/__init__.py

politbuero-kampagnen/onegov-cloud

20148bf321b71f617b64376fe7249b2b9b9c4aa9

[ "MIT" ]

null

from onegov.gis.forms.fields import CoordinatesField from onegov.gis.forms.widgets import CoordinatesWidget __all__ = ['CoordinatesField', 'CoordinatesWidget']

32.2

54

0.832298

17

161

7.647059

0.588235

0.153846

0.2

0.276923

0

0.080745

161

4

55

40.25

0.878378

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0.204969

0

1

0

false

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1

0

null

0

1

0

1

0

6

2354fdf8dad70153d9baf4c5be2ae3e5d8f5ea68

47

py

Python

lotoes/secciones/sorteosLnac/__init__.py

vidddd/lotoes

caf5fe71006e00e590549f921052f110c4bbb75f

[ "MIT" ]

null

lotoes/secciones/sorteosLnac/__init__.py

vidddd/lotoes

caf5fe71006e00e590549f921052f110c4bbb75f

[ "MIT" ]

null

lotoes/secciones/sorteosLnac/__init__.py

vidddd/lotoes

caf5fe71006e00e590549f921052f110c4bbb75f

[ "MIT" ]

null

from .controller_sorteosLnac import sorteosLnac

47

0.914894

5

47

8.4

0.8

0

0.06383

47

1

47

0.954545

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1

0

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1

0

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0

1

0

null

0

1

0

1

0

1

0

6

235b2d901b1bea2fa217606a67dfa81205191041

23

py

Python

sensu_plugins_aws_subnet/__init__.py

supernova106/sensu_plugins_aws_subnet

07edd3b414def15809c331b7269ecdafd3faf762

[ "MIT" ]

12

2021-08-15T04:38:25.000Z

2021-08-16T18:17:25.000Z

sensu_plugins_aws_subnet/__init__.py

supernova106/sensu_plugins_aws_subnet

07edd3b414def15809c331b7269ecdafd3faf762

[ "MIT" ]

1

2020-12-05T18:35:55.000Z

sensu_plugins_aws_subnet/__init__.py

supernova106/sensu_plugins_aws_subnet

07edd3b414def15809c331b7269ecdafd3faf762

[ "MIT" ]

2

2021-08-15T09:29:43.000Z

2021-11-17T05:41:41.000Z

from __main__ import *

11.5

22

0.782609

3

23

4.666667

1

0

0.173913

23

1

23

0.736842

0

1

0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

6

23658b032c06956a00496d7055711bc9d8118a63

26

py

Python

hello_world.py

fordjango/new_profiles_rest_api

b4086ad4211e5e278b2a8bcf3624f48925ea6040

[ "MIT" ]

null

hello_world.py

fordjango/new_profiles_rest_api

b4086ad4211e5e278b2a8bcf3624f48925ea6040

[ "MIT" ]

null

hello_world.py

fordjango/new_profiles_rest_api

b4086ad4211e5e278b2a8bcf3624f48925ea6040

[ "MIT" ]

null

print("hello from santa")

13

25

0.730769

4

26

4.75

1

0

0.115385

26

1

26

0.826087

0

0.615385

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

88cd7b4748dfc9d48b07e74cd1faaed730733d74

55

py

Python

python.py

Ayesha-Anjum-639/assignment

5a57fdfd360467d540cf12fe0f842ddd458371b8

[ "MIT" ]

1

2019-10-12T17:28:12.000Z

python.py

Ayesha-Anjum-639/assignment

5a57fdfd360467d540cf12fe0f842ddd458371b8

[ "MIT" ]

null

python.py

Ayesha-Anjum-639/assignment

5a57fdfd360467d540cf12fe0f842ddd458371b8

[ "MIT" ]

null

print("Hello World") print(5+4) print(5,"+",4,"=",5+4)

13.75

22

0.563636

11

55

2.818182

0.454545

0.193548

0.451613

0

0.117647

0.072727

55

3

23

18.333333

0.490196

0

0.236364

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

88f03654581e59a140ad7f0b316b54846b6a53fc

99

py

Python

openfecli/commands/__init__.py

mikemhenry/openfe

d4c78af62a7ae05b99eb95d173661ac134b7e7b9

[ "MIT" ]

14

2022-01-24T22:01:19.000Z

2022-03-31T04:58:35.000Z

openfecli/commands/__init__.py

mikemhenry/openfe

d4c78af62a7ae05b99eb95d173661ac134b7e7b9

[ "MIT" ]

109

2022-01-24T18:57:05.000Z

2022-03-31T20:13:07.000Z

openfecli/commands/__init__.py

mikemhenry/openfe

d4c78af62a7ae05b99eb95d173661ac134b7e7b9

[ "MIT" ]

4

2022-01-24T18:45:54.000Z

2022-02-21T06:28:24.000Z

# shouldn't apparently need this file, but here we are from . import atommapping from . import echo

33

54

0.777778

16

99

4.8125

0.875

0.25974

0

0.171717

99

3

55

33

0.939024

0.525253

0

1

0

true

0

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0

1

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null

1

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1

0

null

0

1

0

1

0

1

0

6

00366b8cd6a29a13103561a0ee4650cafb902f88

124

py

Python

pangram/pangram.py

oscantillomen/Exercism-Python

1a598769aff0e4dd58294fcd692ca0402061717e

[ "MIT" ]

null

pangram/pangram.py

oscantillomen/Exercism-Python

1a598769aff0e4dd58294fcd692ca0402061717e

[ "MIT" ]

null

pangram/pangram.py

oscantillomen/Exercism-Python

1a598769aff0e4dd58294fcd692ca0402061717e

[ "MIT" ]

null

import string ALPHABET = set(string.ascii_lowercase) def is_pangram(sentence): return ALPHABET <= set(sentence.lower())

24.8

44

0.766129

16

124

5.8125

0.75

0.236559

0

0.120968

124

5

44

24.8

0.853211

0

1

0.25

false

0

0.25

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

cc7588de324a87e070270762efbca68576fe8829

85

py

Python

ndarray/same.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

1

2021-08-02T01:51:35.000Z

ndarray/same.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

1

2021-11-03T08:58:30.000Z

ndarray/same.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

null

mask = 255 print(mask == 255) blue_mask = mask == 255 print(mask) print(blue_mask)

10.625

23

0.682353

14

85

4

0.285714

0.375

0.428571

0.571429

0

0.128571

0.176471

85

8

24

10.625

0.671429

0

1

0

false

0

0.6

1

0

null

1

0

1

0

null

0

1

0

6

aeb56753078f68e7ebf914dfe3362d2ce395b9ab

44

py

Python

holoprot/models/__init__.py

vsomnath/holoprot

9bd6c58491eec701db94ce12f8e15e2143e202b9

[ "MIT" ]

10

2022-01-19T19:01:35.000Z

2022-03-21T13:04:59.000Z

holoprot/models/__init__.py

vsomnath/holoprot

9bd6c58491eec701db94ce12f8e15e2143e202b9

[ "MIT" ]

null

holoprot/models/__init__.py

vsomnath/holoprot

9bd6c58491eec701db94ce12f8e15e2143e202b9

[ "MIT" ]

3

2022-01-11T16:21:32.000Z

2022-03-11T15:33:57.000Z

from holoprot.models.trainer import Trainer

22

43

0.863636

6

44

6.333333

0.833333

0

0.090909

44

1

44

0.95

0

1

0

true

0

1

0

1

0

1

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null

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null

0

1

0

1

0

1

0

6

aef1f46a6f4fb6e4545b68a9cb41e8f97c07f8ea

92

py

Python

custom/plugins/setup_oer_reports_pre.py

M-Spencer-94/configNOW

56828587253202089e77cfdfcf5329f2a7f09b3f

[ "PSF-2.0", "Apache-2.0", "MIT" ]

3

2019-07-09T20:02:48.000Z

2021-11-21T20:00:37.000Z

custom/plugins/setup_oer_reports_pre.py

M-Spencer-94/configNOW

56828587253202089e77cfdfcf5329f2a7f09b3f

[ "PSF-2.0", "Apache-2.0", "MIT" ]

null

custom/plugins/setup_oer_reports_pre.py

M-Spencer-94/configNOW

56828587253202089e77cfdfcf5329f2a7f09b3f

[ "PSF-2.0", "Apache-2.0", "MIT" ]

null

import common.assertions as assertions def run(cfg): assertions.validateAdminPassword(cfg)

23

38

0.836957

11

92

7

0.727273

0

0.086957

92

4

39

23

0.916667

0

0.666667

1

0.333333

false

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4e02feb6bd33bf7b2f8ebc85d438cb20d237fd9e

30

py

Python

blind_blizzards/data/game.py

Starwort/code-jam-5

c11ab7508ca8c68fe64f33118a3a44956c0a8292

[ "MIT" ]

null

blind_blizzards/data/game.py

Starwort/code-jam-5

c11ab7508ca8c68fe64f33118a3a44956c0a8292

[ "MIT" ]

null

blind_blizzards/data/game.py

Starwort/code-jam-5

c11ab7508ca8c68fe64f33118a3a44956c0a8292

[ "MIT" ]

1

2019-06-28T21:59:41.000Z

from .structs import GameNode

15

29

0.833333

4

30

6.25

1

0

0.133333

30

1

30

0.961538

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4e28e3321377547a62600b472fa76b37318df52d

37,697

py

Python

instances/passenger_demand/pas-20210421-2109-int1/68.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int1/68.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int1/68.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 2290 passenger_arriving = ( (0, 5, 9, 3, 0, 0, 3, 7, 5, 2, 1, 0), # 0 (2, 4, 10, 6, 0, 0, 2, 3, 4, 2, 4, 0), # 1 (4, 9, 7, 4, 2, 0, 4, 5, 7, 4, 6, 0), # 2 (9, 9, 6, 4, 1, 0, 9, 8, 2, 5, 3, 0), # 3 (4, 6, 4, 8, 2, 0, 5, 6, 7, 3, 3, 0), # 4 (4, 3, 5, 3, 1, 0, 5, 8, 2, 2, 0, 0), # 5 (2, 2, 4, 5, 2, 0, 1, 1, 7, 1, 1, 0), # 6 (2, 3, 3, 5, 1, 0, 3, 2, 4, 2, 2, 0), # 7 (2, 7, 5, 5, 0, 0, 6, 3, 4, 8, 1, 0), # 8 (1, 9, 7, 4, 2, 0, 8, 7, 4, 8, 2, 0), # 9 (3, 5, 7, 3, 0, 0, 4, 10, 4, 3, 3, 0), # 10 (1, 4, 6, 2, 1, 0, 2, 3, 6, 8, 0, 0), # 11 (5, 2, 1, 2, 0, 0, 4, 9, 6, 2, 1, 0), # 12 (5, 1, 5, 3, 1, 0, 2, 4, 3, 7, 1, 0), # 13 (3, 6, 6, 2, 1, 0, 5, 4, 0, 4, 0, 0), # 14 (4, 2, 7, 2, 1, 0, 7, 10, 7, 4, 2, 0), # 15 (4, 6, 5, 5, 1, 0, 1, 14, 4, 1, 1, 0), # 16 (3, 5, 4, 2, 3, 0, 3, 5, 2, 6, 1, 0), # 17 (4, 4, 8, 2, 2, 0, 3, 5, 6, 3, 0, 0), # 18 (2, 7, 7, 2, 0, 0, 7, 2, 6, 1, 3, 0), # 19 (3, 7, 7, 2, 0, 0, 8, 9, 3, 1, 2, 0), # 20 (2, 8, 6, 2, 1, 0, 5, 5, 4, 3, 0, 0), # 21 (4, 6, 4, 1, 3, 0, 7, 4, 4, 5, 1, 0), # 22 (1, 5, 4, 3, 1, 0, 1, 5, 3, 5, 3, 0), # 23 (2, 9, 4, 1, 0, 0, 6, 6, 4, 7, 2, 0), # 24 (4, 8, 7, 2, 2, 0, 3, 6, 4, 1, 4, 0), # 25 (4, 6, 5, 2, 4, 0, 2, 0, 2, 4, 0, 0), # 26 (3, 4, 6, 4, 2, 0, 5, 10, 2, 3, 3, 0), # 27 (3, 12, 6, 3, 1, 0, 4, 12, 4, 2, 3, 0), # 28 (7, 8, 3, 3, 1, 0, 3, 3, 3, 4, 2, 0), # 29 (1, 12, 5, 0, 4, 0, 1, 4, 4, 5, 0, 0), # 30 (5, 8, 8, 3, 5, 0, 4, 7, 0, 4, 3, 0), # 31 (1, 14, 4, 4, 0, 0, 7, 7, 2, 3, 1, 0), # 32 (3, 7, 4, 2, 1, 0, 2, 5, 3, 2, 2, 0), # 33 (1, 7, 3, 3, 1, 0, 4, 11, 3, 5, 0, 0), # 34 (2, 5, 5, 4, 0, 0, 7, 6, 4, 5, 0, 0), # 35 (4, 7, 7, 3, 2, 0, 5, 7, 5, 1, 0, 0), # 36 (2, 6, 9, 8, 0, 0, 3, 9, 8, 0, 1, 0), # 37 (3, 4, 6, 2, 4, 0, 4, 5, 2, 0, 1, 0), # 38 (2, 6, 6, 1, 1, 0, 5, 7, 3, 8, 1, 0), # 39 (3, 8, 8, 3, 0, 0, 4, 3, 4, 9, 2, 0), # 40 (2, 3, 2, 2, 1, 0, 4, 9, 3, 6, 3, 0), # 41 (1, 8, 10, 0, 0, 0, 5, 12, 4, 4, 4, 0), # 42 (4, 11, 3, 2, 2, 0, 6, 5, 5, 4, 3, 0), # 43 (2, 7, 12, 2, 1, 0, 1, 4, 4, 1, 1, 0), # 44 (0, 9, 5, 1, 4, 0, 10, 4, 4, 6, 0, 0), # 45 (5, 4, 4, 0, 1, 0, 2, 4, 5, 3, 2, 0), # 46 (2, 5, 4, 0, 0, 0, 5, 9, 5, 5, 0, 0), # 47 (1, 10, 3, 4, 1, 0, 3, 3, 4, 4, 1, 0), # 48 (4, 6, 3, 4, 2, 0, 3, 6, 5, 2, 1, 0), # 49 (3, 6, 4, 5, 0, 0, 5, 9, 7, 3, 1, 0), # 50 (3, 6, 7, 2, 1, 0, 4, 5, 1, 3, 8, 0), # 51 (3, 11, 2, 4, 2, 0, 5, 7, 4, 7, 0, 0), # 52 (3, 8, 7, 3, 2, 0, 6, 9, 4, 3, 2, 0), # 53 (2, 7, 9, 1, 3, 0, 7, 6, 5, 2, 2, 0), # 54 (5, 10, 5, 2, 2, 0, 4, 5, 4, 4, 2, 0), # 55 (2, 6, 6, 1, 5, 0, 3, 3, 2, 3, 2, 0), # 56 (3, 3, 2, 3, 0, 0, 5, 6, 4, 8, 0, 0), # 57 (2, 7, 5, 2, 2, 0, 0, 1, 2, 3, 0, 0), # 58 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59 ) station_arriving_intensity = ( (2.649651558384548, 6.796460700757575, 7.9942360218509, 6.336277173913043, 7.143028846153846, 4.75679347826087), # 0 (2.6745220100478, 6.872041598712823, 8.037415537524994, 6.371564387077295, 7.196566506410256, 4.7551721391908215), # 1 (2.699108477221734, 6.946501402918069, 8.07957012282205, 6.406074879227053, 7.248974358974359, 4.753501207729468), # 2 (2.72339008999122, 7.019759765625, 8.120668982969152, 6.4397792119565205, 7.300204326923078, 4.7517809103260875), # 3 (2.747345978441128, 7.091736339085298, 8.160681323193373, 6.472647946859904, 7.350208333333334, 4.750011473429951), # 4 (2.7709552726563262, 7.162350775550646, 8.199576348721793, 6.504651645531401, 7.39893830128205, 4.748193123490338), # 5 (2.794197102721686, 7.231522727272727, 8.237323264781493, 6.535760869565218, 7.446346153846154, 4.746326086956522), # 6 (2.817050598722076, 7.299171846503226, 8.273891276599542, 6.565946180555556, 7.492383814102565, 4.744410590277778), # 7 (2.8394948907423667, 7.365217785493826, 8.309249589403029, 6.595178140096618, 7.537003205128205, 4.7424468599033816), # 8 (2.8615091088674274, 7.429580196496212, 8.343367408419024, 6.623427309782609, 7.580156249999999, 4.740435122282609), # 9 (2.8830723831821286, 7.492178731762065, 8.376213938874606, 6.65066425120773, 7.621794871794872, 4.738375603864734), # 10 (2.9041638437713395, 7.55293304354307, 8.407758385996857, 6.676859525966184, 7.661870993589743, 4.736268531099034), # 11 (2.92476262071993, 7.611762784090908, 8.437969955012854, 6.7019836956521734, 7.700336538461538, 4.734114130434782), # 12 (2.944847844112769, 7.668587605657268, 8.46681785114967, 6.726007321859903, 7.737143429487181, 4.731912628321256), # 13 (2.9643986440347283, 7.723327160493828, 8.494271279634388, 6.748900966183574, 7.772243589743589, 4.729664251207729), # 14 (2.9833941505706756, 7.775901100852272, 8.520299445694086, 6.770635190217391, 7.8055889423076925, 4.7273692255434785), # 15 (3.001813493805482, 7.826229078984287, 8.544871554555842, 6.791180555555555, 7.8371314102564105, 4.725027777777778), # 16 (3.019635803824017, 7.874230747141554, 8.567956811446729, 6.810507623792271, 7.866822916666667, 4.722640134359904), # 17 (3.03684021071115, 7.919825757575757, 8.589524421593831, 6.82858695652174, 7.894615384615387, 4.72020652173913), # 18 (3.053405844551751, 7.962933762538579, 8.609543590224222, 6.845389115338164, 7.9204607371794875, 4.717727166364734), # 19 (3.0693118354306894, 8.003474414281705, 8.62798352256498, 6.860884661835749, 7.944310897435898, 4.71520229468599), # 20 (3.084537313432836, 8.041367365056816, 8.644813423843189, 6.875044157608696, 7.9661177884615375, 4.712632133152174), # 21 (3.099061408643059, 8.076532267115601, 8.660002499285918, 6.887838164251208, 7.985833333333332, 4.710016908212561), # 22 (3.1128632511462295, 8.108888772709737, 8.673519954120252, 6.899237243357488, 8.003409455128205, 4.707356846316426), # 23 (3.125921971027217, 8.138356534090908, 8.685334993573264, 6.909211956521739, 8.018798076923076, 4.704652173913043), # 24 (3.1382166983708903, 8.164855203510802, 8.695416822872037, 6.917732865338165, 8.03195112179487, 4.701903117451691), # 25 (3.1497265632621207, 8.188304433221099, 8.703734647243644, 6.9247705314009655, 8.042820512820512, 4.699109903381642), # 26 (3.160430695785777, 8.208623875473483, 8.710257671915166, 6.930295516304349, 8.051358173076924, 4.696272758152174), # 27 (3.1703082260267292, 8.22573318251964, 8.714955102113683, 6.934278381642512, 8.057516025641025, 4.69339190821256), # 28 (3.1793382840698468, 8.239552006611252, 8.717796143066266, 6.936689689009662, 8.061245993589743, 4.690467580012077), # 29 (3.1875, 8.25, 8.71875, 6.9375, 8.0625, 4.6875), # 30 (3.1951370284526854, 8.258678799715907, 8.718034948671496, 6.937353656045752, 8.062043661347518, 4.683376259786773), # 31 (3.202609175191816, 8.267242897727273, 8.715910024154589, 6.93691748366013, 8.06068439716312, 4.677024758454107), # 32 (3.2099197969948845, 8.275691228693182, 8.712405570652175, 6.936195772058824, 8.058436835106383, 4.66850768365817), # 33 (3.217072250639386, 8.284022727272728, 8.70755193236715, 6.935192810457517, 8.05531560283688, 4.657887223055139), # 34 (3.224069892902813, 8.292236328124998, 8.701379453502415, 6.933912888071895, 8.051335328014185, 4.645225564301183), # 35 (3.23091608056266, 8.300330965909092, 8.69391847826087, 6.932360294117648, 8.046510638297873, 4.630584895052474), # 36 (3.2376141703964194, 8.308305575284091, 8.68519935084541, 6.9305393178104575, 8.040856161347516, 4.614027402965184), # 37 (3.2441675191815853, 8.31615909090909, 8.675252415458937, 6.9284542483660125, 8.034386524822695, 4.595615275695485), # 38 (3.250579483695652, 8.323890447443182, 8.664108016304347, 6.926109375, 8.027116356382978, 4.57541070089955), # 39 (3.2568534207161126, 8.331498579545455, 8.651796497584542, 6.923508986928105, 8.019060283687942, 4.5534758662335495), # 40 (3.26299268702046, 8.338982421874999, 8.638348203502416, 6.920657373366013, 8.010232934397163, 4.529872959353657), # 41 (3.269000639386189, 8.34634090909091, 8.62379347826087, 6.917558823529411, 8.000648936170213, 4.504664167916042), # 42 (3.2748806345907933, 8.353572975852272, 8.608162666062801, 6.914217626633987, 7.990322916666666, 4.477911679576878), # 43 (3.2806360294117645, 8.360677556818182, 8.591486111111111, 6.910638071895424, 7.979269503546099, 4.449677681992337), # 44 (3.286270180626598, 8.367653586647727, 8.573794157608697, 6.906824448529411, 7.967503324468085, 4.420024362818591), # 45 (3.291786445012788, 8.374500000000001, 8.555117149758455, 6.902781045751634, 7.955039007092199, 4.389013909711811), # 46 (3.297188179347826, 8.381215731534091, 8.535485431763284, 6.898512152777777, 7.941891179078015, 4.356708510328169), # 47 (3.3024787404092075, 8.387799715909091, 8.514929347826087, 6.894022058823529, 7.928074468085106, 4.323170352323839), # 48 (3.307661484974424, 8.39425088778409, 8.493479242149759, 6.889315053104576, 7.91360350177305, 4.288461623354989), # 49 (3.312739769820972, 8.40056818181818, 8.471165458937199, 6.884395424836602, 7.898492907801418, 4.252644511077794), # 50 (3.317716951726343, 8.406750532670454, 8.448018342391304, 6.879267463235294, 7.882757313829787, 4.215781203148426), # 51 (3.322596387468031, 8.412796875, 8.424068236714975, 6.87393545751634, 7.86641134751773, 4.177933887223055), # 52 (3.3273814338235295, 8.41870614346591, 8.39934548611111, 6.868403696895425, 7.849469636524823, 4.139164750957854), # 53 (3.332075447570333, 8.424477272727271, 8.373880434782608, 6.8626764705882355, 7.831946808510638, 4.099535982008995), # 54 (3.336681785485933, 8.430109197443182, 8.347703426932366, 6.856758067810458, 7.813857491134752, 4.05910976803265), # 55 (3.341203804347826, 8.435600852272726, 8.320844806763285, 6.8506527777777775, 7.795216312056738, 4.017948296684991), # 56 (3.345644860933504, 8.440951171875001, 8.29333491847826, 6.844364889705882, 7.77603789893617, 3.9761137556221886), # 57 (3.3500083120204605, 8.44615909090909, 8.265204106280192, 6.837898692810458, 7.756336879432624, 3.9336683325004165), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_arriving_acc = ( (0, 5, 9, 3, 0, 0, 3, 7, 5, 2, 1, 0), # 0 (2, 9, 19, 9, 0, 0, 5, 10, 9, 4, 5, 0), # 1 (6, 18, 26, 13, 2, 0, 9, 15, 16, 8, 11, 0), # 2 (15, 27, 32, 17, 3, 0, 18, 23, 18, 13, 14, 0), # 3 (19, 33, 36, 25, 5, 0, 23, 29, 25, 16, 17, 0), # 4 (23, 36, 41, 28, 6, 0, 28, 37, 27, 18, 17, 0), # 5 (25, 38, 45, 33, 8, 0, 29, 38, 34, 19, 18, 0), # 6 (27, 41, 48, 38, 9, 0, 32, 40, 38, 21, 20, 0), # 7 (29, 48, 53, 43, 9, 0, 38, 43, 42, 29, 21, 0), # 8 (30, 57, 60, 47, 11, 0, 46, 50, 46, 37, 23, 0), # 9 (33, 62, 67, 50, 11, 0, 50, 60, 50, 40, 26, 0), # 10 (34, 66, 73, 52, 12, 0, 52, 63, 56, 48, 26, 0), # 11 (39, 68, 74, 54, 12, 0, 56, 72, 62, 50, 27, 0), # 12 (44, 69, 79, 57, 13, 0, 58, 76, 65, 57, 28, 0), # 13 (47, 75, 85, 59, 14, 0, 63, 80, 65, 61, 28, 0), # 14 (51, 77, 92, 61, 15, 0, 70, 90, 72, 65, 30, 0), # 15 (55, 83, 97, 66, 16, 0, 71, 104, 76, 66, 31, 0), # 16 (58, 88, 101, 68, 19, 0, 74, 109, 78, 72, 32, 0), # 17 (62, 92, 109, 70, 21, 0, 77, 114, 84, 75, 32, 0), # 18 (64, 99, 116, 72, 21, 0, 84, 116, 90, 76, 35, 0), # 19 (67, 106, 123, 74, 21, 0, 92, 125, 93, 77, 37, 0), # 20 (69, 114, 129, 76, 22, 0, 97, 130, 97, 80, 37, 0), # 21 (73, 120, 133, 77, 25, 0, 104, 134, 101, 85, 38, 0), # 22 (74, 125, 137, 80, 26, 0, 105, 139, 104, 90, 41, 0), # 23 (76, 134, 141, 81, 26, 0, 111, 145, 108, 97, 43, 0), # 24 (80, 142, 148, 83, 28, 0, 114, 151, 112, 98, 47, 0), # 25 (84, 148, 153, 85, 32, 0, 116, 151, 114, 102, 47, 0), # 26 (87, 152, 159, 89, 34, 0, 121, 161, 116, 105, 50, 0), # 27 (90, 164, 165, 92, 35, 0, 125, 173, 120, 107, 53, 0), # 28 (97, 172, 168, 95, 36, 0, 128, 176, 123, 111, 55, 0), # 29 (98, 184, 173, 95, 40, 0, 129, 180, 127, 116, 55, 0), # 30 (103, 192, 181, 98, 45, 0, 133, 187, 127, 120, 58, 0), # 31 (104, 206, 185, 102, 45, 0, 140, 194, 129, 123, 59, 0), # 32 (107, 213, 189, 104, 46, 0, 142, 199, 132, 125, 61, 0), # 33 (108, 220, 192, 107, 47, 0, 146, 210, 135, 130, 61, 0), # 34 (110, 225, 197, 111, 47, 0, 153, 216, 139, 135, 61, 0), # 35 (114, 232, 204, 114, 49, 0, 158, 223, 144, 136, 61, 0), # 36 (116, 238, 213, 122, 49, 0, 161, 232, 152, 136, 62, 0), # 37 (119, 242, 219, 124, 53, 0, 165, 237, 154, 136, 63, 0), # 38 (121, 248, 225, 125, 54, 0, 170, 244, 157, 144, 64, 0), # 39 (124, 256, 233, 128, 54, 0, 174, 247, 161, 153, 66, 0), # 40 (126, 259, 235, 130, 55, 0, 178, 256, 164, 159, 69, 0), # 41 (127, 267, 245, 130, 55, 0, 183, 268, 168, 163, 73, 0), # 42 (131, 278, 248, 132, 57, 0, 189, 273, 173, 167, 76, 0), # 43 (133, 285, 260, 134, 58, 0, 190, 277, 177, 168, 77, 0), # 44 (133, 294, 265, 135, 62, 0, 200, 281, 181, 174, 77, 0), # 45 (138, 298, 269, 135, 63, 0, 202, 285, 186, 177, 79, 0), # 46 (140, 303, 273, 135, 63, 0, 207, 294, 191, 182, 79, 0), # 47 (141, 313, 276, 139, 64, 0, 210, 297, 195, 186, 80, 0), # 48 (145, 319, 279, 143, 66, 0, 213, 303, 200, 188, 81, 0), # 49 (148, 325, 283, 148, 66, 0, 218, 312, 207, 191, 82, 0), # 50 (151, 331, 290, 150, 67, 0, 222, 317, 208, 194, 90, 0), # 51 (154, 342, 292, 154, 69, 0, 227, 324, 212, 201, 90, 0), # 52 (157, 350, 299, 157, 71, 0, 233, 333, 216, 204, 92, 0), # 53 (159, 357, 308, 158, 74, 0, 240, 339, 221, 206, 94, 0), # 54 (164, 367, 313, 160, 76, 0, 244, 344, 225, 210, 96, 0), # 55 (166, 373, 319, 161, 81, 0, 247, 347, 227, 213, 98, 0), # 56 (169, 376, 321, 164, 81, 0, 252, 353, 231, 221, 98, 0), # 57 (171, 383, 326, 166, 83, 0, 252, 354, 233, 224, 98, 0), # 58 (171, 383, 326, 166, 83, 0, 252, 354, 233, 224, 98, 0), # 59 ) passenger_arriving_rate = ( (2.649651558384548, 5.43716856060606, 4.79654161311054, 2.534510869565217, 1.428605769230769, 0.0, 4.75679347826087, 5.714423076923076, 3.801766304347826, 3.1976944087403596, 1.359292140151515, 0.0), # 0 (2.6745220100478, 5.497633278970258, 4.822449322514997, 2.5486257548309177, 1.439313301282051, 0.0, 4.7551721391908215, 5.757253205128204, 3.8229386322463768, 3.2149662150099974, 1.3744083197425645, 0.0), # 1 (2.699108477221734, 5.557201122334455, 4.8477420736932295, 2.562429951690821, 1.4497948717948717, 0.0, 4.753501207729468, 5.799179487179487, 3.8436449275362317, 3.23182804912882, 1.3893002805836137, 0.0), # 2 (2.72339008999122, 5.6158078125, 4.872401389781491, 2.575911684782608, 1.4600408653846155, 0.0, 4.7517809103260875, 5.840163461538462, 3.863867527173912, 3.2482675931876606, 1.403951953125, 0.0), # 3 (2.747345978441128, 5.673389071268238, 4.896408793916024, 2.589059178743961, 1.4700416666666667, 0.0, 4.750011473429951, 5.880166666666667, 3.883588768115942, 3.2642725292773487, 1.4183472678170594, 0.0), # 4 (2.7709552726563262, 5.729880620440516, 4.919745809233076, 2.6018606582125603, 1.47978766025641, 0.0, 4.748193123490338, 5.91915064102564, 3.9027909873188404, 3.279830539488717, 1.432470155110129, 0.0), # 5 (2.794197102721686, 5.785218181818181, 4.942393958868895, 2.614304347826087, 1.4892692307692306, 0.0, 4.746326086956522, 5.957076923076922, 3.9214565217391306, 3.294929305912597, 1.4463045454545453, 0.0), # 6 (2.817050598722076, 5.83933747720258, 4.964334765959725, 2.626378472222222, 1.498476762820513, 0.0, 4.744410590277778, 5.993907051282052, 3.939567708333333, 3.309556510639817, 1.459834369300645, 0.0), # 7 (2.8394948907423667, 5.89217422839506, 4.985549753641817, 2.638071256038647, 1.5074006410256409, 0.0, 4.7424468599033816, 6.0296025641025635, 3.9571068840579704, 3.3236998357612113, 1.473043557098765, 0.0), # 8 (2.8615091088674274, 5.943664157196969, 5.006020445051414, 2.649370923913043, 1.5160312499999997, 0.0, 4.740435122282609, 6.064124999999999, 3.9740563858695652, 3.3373469633676094, 1.4859160392992423, 0.0), # 9 (2.8830723831821286, 5.993742985409652, 5.025728363324764, 2.660265700483092, 1.5243589743589743, 0.0, 4.738375603864734, 6.097435897435897, 3.990398550724638, 3.3504855755498424, 1.498435746352413, 0.0), # 10 (2.9041638437713395, 6.042346434834456, 5.044655031598114, 2.6707438103864733, 1.5323741987179484, 0.0, 4.736268531099034, 6.129496794871794, 4.0061157155797105, 3.3631033543987425, 1.510586608708614, 0.0), # 11 (2.92476262071993, 6.089410227272726, 5.062781973007712, 2.680793478260869, 1.5400673076923075, 0.0, 4.734114130434782, 6.16026923076923, 4.021190217391304, 3.375187982005141, 1.5223525568181815, 0.0), # 12 (2.944847844112769, 6.134870084525814, 5.080090710689802, 2.690402928743961, 1.547428685897436, 0.0, 4.731912628321256, 6.189714743589744, 4.035604393115942, 3.386727140459868, 1.5337175211314535, 0.0), # 13 (2.9643986440347283, 6.1786617283950624, 5.096562767780632, 2.699560386473429, 1.5544487179487176, 0.0, 4.729664251207729, 6.217794871794871, 4.049340579710144, 3.397708511853755, 1.5446654320987656, 0.0), # 14 (2.9833941505706756, 6.220720880681816, 5.112179667416451, 2.708254076086956, 1.5611177884615384, 0.0, 4.7273692255434785, 6.2444711538461535, 4.062381114130434, 3.408119778277634, 1.555180220170454, 0.0), # 15 (3.001813493805482, 6.26098326318743, 5.126922932733505, 2.716472222222222, 1.5674262820512819, 0.0, 4.725027777777778, 6.2697051282051275, 4.074708333333333, 3.4179486218223363, 1.5652458157968574, 0.0), # 16 (3.019635803824017, 6.299384597713242, 5.140774086868038, 2.724203049516908, 1.5733645833333332, 0.0, 4.722640134359904, 6.293458333333333, 4.0863045742753625, 3.4271827245786914, 1.5748461494283106, 0.0), # 17 (3.03684021071115, 6.3358606060606055, 5.153714652956299, 2.7314347826086958, 1.578923076923077, 0.0, 4.72020652173913, 6.315692307692308, 4.097152173913043, 3.435809768637532, 1.5839651515151514, 0.0), # 18 (3.053405844551751, 6.370347010030863, 5.165726154134533, 2.738155646135265, 1.5840921474358973, 0.0, 4.717727166364734, 6.336368589743589, 4.107233469202898, 3.4438174360896885, 1.5925867525077158, 0.0), # 19 (3.0693118354306894, 6.402779531425363, 5.1767901135389875, 2.7443538647342995, 1.5888621794871793, 0.0, 4.71520229468599, 6.355448717948717, 4.11653079710145, 3.4511934090259917, 1.6006948828563408, 0.0), # 20 (3.084537313432836, 6.433093892045452, 5.186888054305913, 2.750017663043478, 1.5932235576923073, 0.0, 4.712632133152174, 6.372894230769229, 4.125026494565217, 3.4579253695372754, 1.608273473011363, 0.0), # 21 (3.099061408643059, 6.46122581369248, 5.19600149957155, 2.7551352657004826, 1.5971666666666662, 0.0, 4.710016908212561, 6.388666666666665, 4.132702898550725, 3.464000999714367, 1.61530645342312, 0.0), # 22 (3.1128632511462295, 6.487111018167789, 5.204111972472151, 2.759694897342995, 1.6006818910256408, 0.0, 4.707356846316426, 6.402727564102563, 4.139542346014493, 3.4694079816481005, 1.6217777545419472, 0.0), # 23 (3.125921971027217, 6.5106852272727265, 5.211200996143958, 2.763684782608695, 1.6037596153846152, 0.0, 4.704652173913043, 6.415038461538461, 4.1455271739130435, 3.474133997429305, 1.6276713068181816, 0.0), # 24 (3.1382166983708903, 6.531884162808641, 5.217250093723222, 2.7670931461352657, 1.606390224358974, 0.0, 4.701903117451691, 6.425560897435896, 4.150639719202899, 3.4781667291488145, 1.6329710407021603, 0.0), # 25 (3.1497265632621207, 6.550643546576878, 5.222240788346187, 2.7699082125603858, 1.6085641025641022, 0.0, 4.699109903381642, 6.434256410256409, 4.154862318840579, 3.4814938588974575, 1.6376608866442195, 0.0), # 26 (3.160430695785777, 6.566899100378786, 5.226154603149099, 2.772118206521739, 1.6102716346153847, 0.0, 4.696272758152174, 6.441086538461539, 4.158177309782609, 3.484103068766066, 1.6417247750946966, 0.0), # 27 (3.1703082260267292, 6.580586546015712, 5.228973061268209, 2.7737113526570045, 1.6115032051282048, 0.0, 4.69339190821256, 6.446012820512819, 4.160567028985507, 3.4859820408454727, 1.645146636503928, 0.0), # 28 (3.1793382840698468, 6.591641605289001, 5.230677685839759, 2.7746758756038647, 1.6122491987179486, 0.0, 4.690467580012077, 6.448996794871794, 4.162013813405797, 3.487118457226506, 1.6479104013222503, 0.0), # 29 (3.1875, 6.6, 5.23125, 2.775, 1.6124999999999998, 0.0, 4.6875, 6.449999999999999, 4.1625, 3.4875, 1.65, 0.0), # 30 (3.1951370284526854, 6.606943039772726, 5.230820969202898, 2.7749414624183006, 1.6124087322695035, 0.0, 4.683376259786773, 6.449634929078014, 4.162412193627451, 3.4872139794685983, 1.6517357599431814, 0.0), # 31 (3.202609175191816, 6.613794318181818, 5.229546014492753, 2.7747669934640515, 1.6121368794326238, 0.0, 4.677024758454107, 6.448547517730495, 4.162150490196078, 3.4863640096618354, 1.6534485795454545, 0.0), # 32 (3.2099197969948845, 6.620552982954545, 5.227443342391305, 2.774478308823529, 1.6116873670212764, 0.0, 4.66850768365817, 6.446749468085105, 4.161717463235294, 3.4849622282608697, 1.6551382457386363, 0.0), # 33 (3.217072250639386, 6.627218181818182, 5.224531159420289, 2.7740771241830067, 1.6110631205673758, 0.0, 4.657887223055139, 6.444252482269503, 4.16111568627451, 3.4830207729468596, 1.6568045454545455, 0.0), # 34 (3.224069892902813, 6.633789062499998, 5.220827672101449, 2.773565155228758, 1.6102670656028368, 0.0, 4.645225564301183, 6.441068262411347, 4.160347732843137, 3.480551781400966, 1.6584472656249996, 0.0), # 35 (3.23091608056266, 6.6402647727272734, 5.2163510869565215, 2.7729441176470586, 1.6093021276595745, 0.0, 4.630584895052474, 6.437208510638298, 4.159416176470589, 3.477567391304347, 1.6600661931818184, 0.0), # 36 (3.2376141703964194, 6.6466444602272725, 5.211119610507246, 2.7722157271241827, 1.6081712322695032, 0.0, 4.614027402965184, 6.432684929078013, 4.158323590686274, 3.474079740338164, 1.6616611150568181, 0.0), # 37 (3.2441675191815853, 6.652927272727272, 5.205151449275362, 2.7713816993464047, 1.6068773049645388, 0.0, 4.595615275695485, 6.427509219858155, 4.157072549019607, 3.4701009661835744, 1.663231818181818, 0.0), # 38 (3.250579483695652, 6.659112357954545, 5.198464809782608, 2.7704437499999996, 1.6054232712765955, 0.0, 4.57541070089955, 6.421693085106382, 4.155665625, 3.4656432065217384, 1.6647780894886361, 0.0), # 39 (3.2568534207161126, 6.6651988636363635, 5.191077898550724, 2.7694035947712417, 1.6038120567375882, 0.0, 4.5534758662335495, 6.415248226950353, 4.154105392156863, 3.4607185990338163, 1.6662997159090909, 0.0), # 40 (3.26299268702046, 6.671185937499998, 5.1830089221014495, 2.768262949346405, 1.6020465868794325, 0.0, 4.529872959353657, 6.40818634751773, 4.152394424019608, 3.455339281400966, 1.6677964843749995, 0.0), # 41 (3.269000639386189, 6.677072727272728, 5.174276086956522, 2.767023529411764, 1.6001297872340425, 0.0, 4.504664167916042, 6.40051914893617, 4.150535294117646, 3.4495173913043478, 1.669268181818182, 0.0), # 42 (3.2748806345907933, 6.682858380681817, 5.164897599637681, 2.7656870506535944, 1.5980645833333331, 0.0, 4.477911679576878, 6.3922583333333325, 4.148530575980392, 3.4432650664251203, 1.6707145951704543, 0.0), # 43 (3.2806360294117645, 6.688542045454545, 5.154891666666667, 2.7642552287581696, 1.5958539007092198, 0.0, 4.449677681992337, 6.383415602836879, 4.146382843137254, 3.4365944444444443, 1.6721355113636363, 0.0), # 44 (3.286270180626598, 6.694122869318181, 5.144276494565218, 2.7627297794117642, 1.593500664893617, 0.0, 4.420024362818591, 6.374002659574468, 4.144094669117647, 3.4295176630434785, 1.6735307173295453, 0.0), # 45 (3.291786445012788, 6.6996, 5.133070289855073, 2.761112418300653, 1.5910078014184397, 0.0, 4.389013909711811, 6.364031205673759, 4.14166862745098, 3.4220468599033818, 1.6749, 0.0), # 46 (3.297188179347826, 6.704972585227273, 5.12129125905797, 2.759404861111111, 1.588378235815603, 0.0, 4.356708510328169, 6.353512943262412, 4.139107291666666, 3.4141941727053133, 1.6762431463068181, 0.0), # 47 (3.3024787404092075, 6.710239772727273, 5.108957608695651, 2.757608823529411, 1.5856148936170211, 0.0, 4.323170352323839, 6.3424595744680845, 4.136413235294117, 3.4059717391304343, 1.6775599431818182, 0.0), # 48 (3.307661484974424, 6.715400710227271, 5.096087545289855, 2.75572602124183, 1.5827207003546098, 0.0, 4.288461623354989, 6.330882801418439, 4.133589031862745, 3.3973916968599034, 1.6788501775568176, 0.0), # 49 (3.312739769820972, 6.720454545454543, 5.082699275362319, 2.7537581699346405, 1.5796985815602835, 0.0, 4.252644511077794, 6.318794326241134, 4.130637254901961, 3.388466183574879, 1.6801136363636358, 0.0), # 50 (3.317716951726343, 6.725400426136363, 5.068811005434783, 2.7517069852941174, 1.5765514627659571, 0.0, 4.215781203148426, 6.306205851063829, 4.127560477941176, 3.3792073369565214, 1.6813501065340908, 0.0), # 51 (3.322596387468031, 6.730237499999999, 5.054440942028985, 2.7495741830065357, 1.573282269503546, 0.0, 4.177933887223055, 6.293129078014184, 4.124361274509804, 3.3696272946859898, 1.6825593749999999, 0.0), # 52 (3.3273814338235295, 6.7349649147727275, 5.039607291666666, 2.7473614787581697, 1.5698939273049646, 0.0, 4.139164750957854, 6.279575709219858, 4.121042218137255, 3.359738194444444, 1.6837412286931819, 0.0), # 53 (3.332075447570333, 6.739581818181817, 5.024328260869565, 2.745070588235294, 1.5663893617021276, 0.0, 4.099535982008995, 6.2655574468085105, 4.117605882352941, 3.3495521739130427, 1.6848954545454542, 0.0), # 54 (3.336681785485933, 6.744087357954545, 5.008622056159419, 2.7427032271241827, 1.5627714982269503, 0.0, 4.05910976803265, 6.251085992907801, 4.114054840686275, 3.3390813707729463, 1.6860218394886362, 0.0), # 55 (3.341203804347826, 6.74848068181818, 4.9925068840579705, 2.740261111111111, 1.5590432624113475, 0.0, 4.017948296684991, 6.23617304964539, 4.110391666666667, 3.328337922705314, 1.687120170454545, 0.0), # 56 (3.345644860933504, 6.752760937500001, 4.976000951086956, 2.7377459558823527, 1.5552075797872338, 0.0, 3.9761137556221886, 6.220830319148935, 4.106618933823529, 3.317333967391304, 1.6881902343750002, 0.0), # 57 (3.3500083120204605, 6.756927272727271, 4.959122463768115, 2.7351594771241827, 1.5512673758865245, 0.0, 3.9336683325004165, 6.205069503546098, 4.102739215686275, 3.3060816425120767, 1.6892318181818178, 0.0), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_allighting_rate = ( (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 15 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 16 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 17 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 18 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 19 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 20 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 21 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 22 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 23 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 24 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 25 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 26 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 27 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 28 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 29 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 30 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 31 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 32 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 33 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 34 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 35 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 36 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 37 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 38 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 39 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 258194110137029475889902652135037600173 #index for seed sequence child child_seed_index = ( 1, # 0 67, # 1 )

112.528358

215

0.727724

5,147

37,697

5.327764

0.216825

0.315075

0.249435

0.472613

0.332251

0.3301

0

0.817979

0.119744

37,697

334

216

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null

1

0

1

0

1

0

null

0

6

4e3a67bc274883baf27d3e4d3e4ad196d7ddbc63

33

py

Python

iturmas/decorators/__init__.py

daniel-ufabc/match-classes

2783cdf1c7363fcc14023a6cacad697b6af0f011

[ "MIT" ]

null

iturmas/decorators/__init__.py

daniel-ufabc/match-classes

2783cdf1c7363fcc14023a6cacad697b6af0f011

[ "MIT" ]

null

iturmas/decorators/__init__.py

daniel-ufabc/match-classes

2783cdf1c7363fcc14023a6cacad697b6af0f011

[ "MIT" ]

null

from .auth import login_required

16.5

32

0.848485

5

33

5.4

1

0

0.121212

33

1

33

0.931034

0

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1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

9da470ea36af0b767f746d020e41a7f0c5dba94a

153

py

Python

python/niveau1/2-Repetitions/6.py

ThomasProg/France-IOI

03ea502e03f686d74ecf31a17273aded7b8e8a1f

[ "MIT" ]

2

2022-02-13T13:35:13.000Z

2022-03-31T21:02:11.000Z

python/niveau1/2-Repetitions/6.py

ThomasProg/France-IOI

03ea502e03f686d74ecf31a17273aded7b8e8a1f

[ "MIT" ]

null

python/niveau1/2-Repetitions/6.py

ThomasProg/France-IOI

03ea502e03f686d74ecf31a17273aded7b8e8a1f

[ "MIT" ]

1

2020-11-15T15:21:24.000Z

for i in range(30): print("a_", end="") print() for i in range(30): print("b_", end="") print() for i in range(30): print("c_", end="")

15.3

23

0.51634

26

153

2.923077

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0.236842

0.434211

0.921053

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0.051724

0.24183

153

10

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0.603448

0

0.625

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0.038961

0

1

0

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1

0

null

0

1

0

null

0

1

0

6

9dabcfa6524e1e4a0e2b51dbe24a327024815ea3

24

py

Python

emailutil/__init__.py

cityofaustin/atd-utils-email

bcf2c55fe770745a2ed6da22e44971ef6ceaae37

[ "CC0-1.0" ]

null

emailutil/__init__.py

cityofaustin/atd-utils-email

bcf2c55fe770745a2ed6da22e44971ef6ceaae37

[ "CC0-1.0" ]

null

emailutil/__init__.py

cityofaustin/atd-utils-email

bcf2c55fe770745a2ed6da22e44971ef6ceaae37

[ "CC0-1.0" ]

null

from .emailutil import *

24

0.791667

3

24

6.333333

1

0

0.125

24

1

24

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9db042c12b1460a61eed0c0cb77f85501b0f72a1

215

py

Python

plugins/dbnd-snowflake/src/dbnd_snowflake/__init__.py

FHoffmannCode/dbnd

82beee1a8c752235bf21b4b0ceace5ab25410e52

[ "Apache-2.0" ]

null

plugins/dbnd-snowflake/src/dbnd_snowflake/__init__.py

FHoffmannCode/dbnd

82beee1a8c752235bf21b4b0ceace5ab25410e52

[ "Apache-2.0" ]

null

plugins/dbnd-snowflake/src/dbnd_snowflake/__init__.py

FHoffmannCode/dbnd

82beee1a8c752235bf21b4b0ceace5ab25410e52

[ "Apache-2.0" ]

null

from dbnd._core.commands.metrics import log_snowflake_table from dbnd_snowflake.snowflake_resources import log_snowflake_resource_usage __all__ = [ "log_snowflake_resource_usage", "log_snowflake_table", ]

23.888889

75

0.827907

27

215

5.962963

0.481481

0.298137

0.223602

0.310559

0

0.111628

215

8

76

26.875

0.842932

0

0.218605

0.130233

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

9dfff168d101cb9f78868b0ee56c24261cd170c9

73

py

Python

01-sample-instance/settings.py

diodonfrost/pulumi-aws-examples

2fa07f3219dc01d00051559eb207c547d3554232

[ "Apache-2.0" ]

null

01-sample-instance/settings.py

diodonfrost/pulumi-aws-examples

2fa07f3219dc01d00051559eb207c547d3554232

[ "Apache-2.0" ]

null

01-sample-instance/settings.py

diodonfrost/pulumi-aws-examples

2fa07f3219dc01d00051559eb207c547d3554232

[ "Apache-2.0" ]

null

# coding: utf8 vpc_cidr = "192.168.0.0/16" http_cidr = "192.168.1.0/24"

14.6

28

0.643836

16

73

2.8125

0.6875

0.311111

0.444444

0

0.333333

0.136986

73

4

29

18.25

0.380952

0.164384

0

0.474576

0

1

0

false

0

1

0

null

1

0

1

0

1

0

1

0

null

0

6

ae1ad8c506c36a888f234786efecf582422e3003

35

py

Python

src/artifice/scraper/supervisor/__init__.py

artifice-project/artifice-scraper

f224a0da22162fd479d6b9f9095ff5cae4723716

[ "MIT" ]

null

src/artifice/scraper/supervisor/__init__.py

artifice-project/artifice-scraper

f224a0da22162fd479d6b9f9095ff5cae4723716

[ "MIT" ]

5

2019-09-18T19:17:14.000Z

2021-03-20T01:46:06.000Z

src/artifice/scraper/supervisor/__init__.py

artifice-project/artifice-scraper

f224a0da22162fd479d6b9f9095ff5cae4723716

[ "MIT" ]

null

from .supervisor import Supervisor

17.5

34

0.857143

4

35

7.5

0.75

0

0.114286

35

1

35

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ae867f0e402cb89db3cccc626cd6f645b33f32f2

40

py

Python

condensate/core/__init__.py

Zwierlein/condensate

34908b7e99785e9a4a9c5c743fe1a8e6f4cbf4ad

[ "MIT" ]

4

2021-07-24T10:57:06.000Z

2021-12-11T01:24:54.000Z

condensate/core/__init__.py

Zwierlein/condensate

34908b7e99785e9a4a9c5c743fe1a8e6f4cbf4ad

[ "MIT" ]

9

2021-07-15T04:13:23.000Z

2021-08-02T21:57:00.000Z

condensate/core/__init__.py

Zwierlein/condensate

34908b7e99785e9a4a9c5c743fe1a8e6f4cbf4ad

[ "MIT" ]

2

2021-07-21T10:39:30.000Z

2021-08-01T03:05:14.000Z

from condensate.core.build import gpcore

40

0.875

6

40

5.833333

1

0

0.075

40

1

40

0.945946

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

88521be531a73b3f205941d7145e1d213b76932c

117

py

Python

tests/test_controllers/test_demo.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

6

2015-01-28T05:59:08.000Z

2018-01-09T07:48:57.000Z

tests/test_controllers/test_demo.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

2

2020-05-09T16:36:43.000Z

2020-05-09T16:52:35.000Z

tests/test_controllers/test_demo.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

1

2016-01-13T07:19:44.000Z

from nose.tools import assert_equal from tests.fixtures import WebTest class TestDemoController(WebTest): pass

16.714286

35

0.811966

15

117

6.266667

0.8

0

0.145299

117

6

36

19.5

0.94

0

0.25

1

0

true

0.25

0.5

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0.75

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null

0

1

0

null

0

1

0

1

0

6

8887cdf2cc8ae9604a5a9ce44664b255c6cabd67

64

py

Python

hanlp/datasets/ner/__init__.py

v-smwang/HanLP

98db7a649110fca4307acbd6a26f2b5bb1159efc

[ "Apache-2.0" ]

27,208

2015-03-27T10:25:45.000Z

2022-03-31T13:26:32.000Z

hanlp/datasets/ner/__init__.py

hushaoyun/HanLP

967b52404c9d0adbc0cff2699690c127ecfca36e

[ "Apache-2.0" ]

1,674

2015-03-30T06:36:44.000Z

2022-03-16T01:52:56.000Z

hanlp/datasets/ner/__init__.py

hushaoyun/HanLP

967b52404c9d0adbc0cff2699690c127ecfca36e

[ "Apache-2.0" ]

7,710

2015-03-27T08:07:57.000Z

2022-03-31T14:57:23.000Z

# -*- coding:utf-8 -*- # Author: hankcs # Date: 2019-12-06 15:32

21.333333

24

0.59375

11

64

3.454545

1

0

0.240741

0.15625

64

3

24

21.333333

0.462963

0.90625

0

null

0

null

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null

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null

true

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0

1

0

1

0

null

0

1

0

6

31f5d73f045c9db55e784a4166f4f9708822341f

5,331

py

Python

great_international/migrations/0023_internationaleuexitformpage_internationaleuexitformsuccesspage.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

6

2018-03-20T11:19:07.000Z

2021-10-05T07:53:11.000Z

great_international/migrations/0023_internationaleuexitformpage_internationaleuexitformsuccesspage.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

802

2018-02-05T14:16:13.000Z

2022-02-10T10:59:21.000Z

great_international/migrations/0023_internationaleuexitformpage_internationaleuexitformsuccesspage.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

6

2019-01-22T13:19:37.000Z

2019-07-01T10:35:26.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.20 on 2019-05-09 12:19 from __future__ import unicode_literals import core.model_fields import core.models import core.validators import core.wagtail_fields from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('wagtailcore', '0040_page_draft_title'), ('great_international', '0022_auto_20190508_1300'), ] operations = [ migrations.CreateModel( name='InternationalEUExitFormPage', fields=[ ('page_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='wagtailcore.Page')), ('service_name', models.CharField(choices=[('FIND_A_SUPPLIER', 'Find a Supplier'), ('EXPORT_READINESS', 'Export Readiness'), ('INVEST', 'Invest'), ('COMPONENTS', 'Components'), ('GREAT_INTERNATIONAL', 'Great International')], db_index=True, max_length=100, null=True)), ('uses_tree_based_routing', models.BooleanField(default=False, help_text="Allow this page's URL to be determined by its slug, and the slugs of its ancestors in the page tree.", verbose_name='tree-based routing enabled')), ('breadcrumbs_label', models.CharField(max_length=50)), ('heading', models.CharField(max_length=255)), ('body_text', core.model_fields.MarkdownField(validators=[core.validators.slug_hyperlinks])), ('submit_button_text', models.CharField(max_length=50)), ('disclaimer', models.TextField(max_length=500)), ('first_name_help_text', core.wagtail_fields.FormHelpTextField(blank=True, max_length=200, null=True, verbose_name='Help text')), ('first_name_label', core.wagtail_fields.FormLabelField(max_length=200, verbose_name='label')), ('last_name_help_text', core.wagtail_fields.FormHelpTextField(blank=True, max_length=200, null=True, verbose_name='Help text')), ('last_name_label', core.wagtail_fields.FormLabelField(max_length=200, verbose_name='label')), ('email_help_text', core.wagtail_fields.FormHelpTextField(blank=True, max_length=200, null=True, verbose_name='Help text')), ('email_label', core.wagtail_fields.FormLabelField(max_length=200, verbose_name='label')), ('organisation_type_help_text', core.wagtail_fields.FormHelpTextField(blank=True, max_length=200, null=True, verbose_name='Help text')), ('organisation_type_label', core.wagtail_fields.FormLabelField(max_length=200, verbose_name='label')), ('company_name_help_text', core.wagtail_fields.FormHelpTextField(blank=True, max_length=200, null=True, verbose_name='Help text')), ('company_name_label', core.wagtail_fields.FormLabelField(max_length=200, verbose_name='label')), ('country_help_text', core.wagtail_fields.FormHelpTextField(blank=True, max_length=200, null=True, verbose_name='Help text')), ('country_label', core.wagtail_fields.FormLabelField(max_length=200, verbose_name='label')), ('city_help_text', core.wagtail_fields.FormHelpTextField(blank=True, max_length=200, null=True, verbose_name='Help text')), ('city_label', core.wagtail_fields.FormLabelField(max_length=200, verbose_name='label')), ('comment_help_text', core.wagtail_fields.FormHelpTextField(blank=True, max_length=200, null=True, verbose_name='Help text')), ('comment_label', core.wagtail_fields.FormLabelField(max_length=200, verbose_name='label')), ], options={ 'abstract': False, }, bases=(core.models.ExclusivePageMixin, 'wagtailcore.page'), ), migrations.CreateModel( name='InternationalEUExitFormSuccessPage', fields=[ ('page_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='wagtailcore.Page')), ('service_name', models.CharField(choices=[('FIND_A_SUPPLIER', 'Find a Supplier'), ('EXPORT_READINESS', 'Export Readiness'), ('INVEST', 'Invest'), ('COMPONENTS', 'Components'), ('GREAT_INTERNATIONAL', 'Great International')], db_index=True, max_length=100, null=True)), ('uses_tree_based_routing', models.BooleanField(default=False, help_text="Allow this page's URL to be determined by its slug, and the slugs of its ancestors in the page tree.", verbose_name='tree-based routing enabled')), ('breadcrumbs_label', models.CharField(max_length=50)), ('heading', models.CharField(max_length=255, verbose_name='Title')), ('body_text', models.CharField(max_length=255, verbose_name='Body text')), ('next_title', models.CharField(max_length=255, verbose_name='Title')), ('next_body_text', models.CharField(max_length=255, verbose_name='Body text')), ], options={ 'abstract': False, }, bases=(core.models.ExclusivePageMixin, 'wagtailcore.page'), ), ]

74.041667

285

0.673982

611

5,331

5.631751

0.214403

0.070619

0.083987

0.055798

0.797152

0.788434

0.771287

0.731764

0

0.026927

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5,331

71

286

75.084507

0.77182

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0.03125

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0

null

0

1

0

1

0

null

0

6

ee0b159a9b052e35cbc0b56e022fa3be6c4dec93

151

py

Python

tests/testing.py

Shlol762/physics

a142e812bac2da8edec36cdd814b49ea765d9cdc

[ "MIT" ]

null

tests/testing.py

Shlol762/physics

a142e812bac2da8edec36cdd814b49ea765d9cdc

[ "MIT" ]

null

tests/testing.py

Shlol762/physics

a142e812bac2da8edec36cdd814b49ea765d9cdc

[ "MIT" ]

null

from physics import * s1, s2 = Speed(9, 3, unit='cm/s', extra_units=['cm/h']), Speed(9, 2, unit='cm/h', extra_units=['cm/h']) print(s2.distance.unit)

30.2

103

0.635762

29

151

3.241379

0.586207

0.095745

0.255319

0.276596

0

0.052239

0.112583

151

5

104

30.2

0.649254

0

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1

0

true

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null

0

1

0

1

0

6

ee62497549e11786eed94ddaf1b321e00e07b0ad

43

py

Python

MultiSourceDataFeeds/Providers/Factal/factal/__init__.py

Esri/ArcGIS-Solutions-for-Business

306b778bb6246f13766ce14245c6ba2aab42ba08

[ "Apache-2.0" ]

1

2021-01-30T04:43:31.000Z

MultiSourceDataFeeds/Providers/Factal/factal/__init__.py

Esri/ArcGIS-Solutions-for-Business

306b778bb6246f13766ce14245c6ba2aab42ba08

[ "Apache-2.0" ]

null

MultiSourceDataFeeds/Providers/Factal/factal/__init__.py

Esri/ArcGIS-Solutions-for-Business

306b778bb6246f13766ce14245c6ba2aab42ba08

[ "Apache-2.0" ]

null

from .factal import * from .schema import *

21.5

21

0.744186

6

43

5.333333

0.666667

0

0.162791

43

2

22

21.5

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c99498c0faf71a46ad1d7a4f4be4a7ad4fc54402

172

py

Python

Coursera/separa_palavras.py

tobiaspontes/ScriptsPython

21ed779e49adca500ce5815dd100f4ec999a2571

[ "MIT" ]

null

Coursera/separa_palavras.py

tobiaspontes/ScriptsPython

21ed779e49adca500ce5815dd100f4ec999a2571

[ "MIT" ]

null

Coursera/separa_palavras.py

tobiaspontes/ScriptsPython

21ed779e49adca500ce5815dd100f4ec999a2571

[ "MIT" ]

null

import re def separa_palavras(frase): '''A funcao recebe uma frase e devolve uma lista das palavras dentro da frase''' print('lista de palavras: ', frase.split())

28.666667

84

0.709302

26

172

4.653846

0.730769

0.214876

0

0.186047

172

5

85

34.4

0.864286

0.430233

0

0.206522

0

1

0.333333

false

0

0.333333

0

0.666667

0.333333

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

c9b8a09501b36968a133bb1816fb52f2dd36b599

42

py

Python

examples/modules/object_tracker/__init__.py

jagin/dvg-utils

a7d19ead75398b09a9f1e146464cf4227f06a476

[ "MIT" ]

7

2020-09-02T08:39:22.000Z

2021-10-13T18:13:04.000Z

examples/modules/object_tracker/__init__.py

jagin/dvg-utils

a7d19ead75398b09a9f1e146464cf4227f06a476

[ "MIT" ]

null

examples/modules/object_tracker/__init__.py

jagin/dvg-utils

a7d19ead75398b09a9f1e146464cf4227f06a476

[ "MIT" ]

null

from .object_tracker import ObjectTracker

21

41

0.880952

5

42

7.2

1

0

0.095238

42

1

42

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c9bd340296dec5cc98f4fa44de42146d4f90d4d2

123

py

Python

python/basic_utils.py

goten-team/Goten

690f1429b62c70caec72f4010ee5b7a9786f0d25

[ "MIT" ]

17

2020-04-28T09:18:28.000Z

2021-12-28T08:38:00.000Z

python/basic_utils.py

goten-team/Goten

690f1429b62c70caec72f4010ee5b7a9786f0d25

[ "MIT" ]

2

2021-09-26T04:10:51.000Z

2022-03-31T05:28:25.000Z

python/basic_utils.py

goten-team/Goten

690f1429b62c70caec72f4010ee5b7a9786f0d25

[ "MIT" ]

2

2021-09-26T05:06:17.000Z

2021-12-14T16:25:06.000Z

import hashlib def str_hash(s): return int(int(hashlib.sha224(s.encode('utf-8')).hexdigest(), 16) % ((1 << 62) - 1))

20.5

88

0.617886

20

123

3.75

0.8

0

0.096154

0.154472

123

5

89

24.6

0.625

0

0.04065

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

c9d7bec33f61ca45367ed74051d9e674ed9eb713

211

py

Python

unit_03/random/passwd1.py

janusnic/21v-pyqt

8ee3828e1c6e6259367d6cedbd63b9057cf52c24

[ "MIT" ]

null

unit_03/random/passwd1.py

janusnic/21v-pyqt

8ee3828e1c6e6259367d6cedbd63b9057cf52c24

[ "MIT" ]

null

unit_03/random/passwd1.py

janusnic/21v-pyqt

8ee3828e1c6e6259367d6cedbd63b9057cf52c24

[ "MIT" ]

2

2019-11-14T15:04:22.000Z

2021-10-31T07:34:46.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- """ генератор случайных чисел """ import random print ''.join([random.choice(list('123456789qwertyuiopasdfghjklzxcvbnmQWERTYUIOPASDFGHJKLZXCVBNM')) for x in range(12)])

26.375

120

0.739336

22

211

7.090909

0.954545

0

0.0625

0.090047

211

8

120

26.375

0.75

0.180095

0

0.438849

0

null

0

0.5

null

0.5

1

0

null

0

1

0

1

null

0

1

0

1

0

1

0

6

a006b38b61a96ab48414b8fa22ea5745e9fed4bd

22

py

Python

Scripts.py

MattOstgard/HLSL_ST3

fbb3dcc7acfeb9c04208dc68b8ff020c76d483b1

[ "MIT" ]

10

2017-11-30T19:43:48.000Z

2022-02-02T11:10:43.000Z

Scripts.py

MattOstgard/HLSL_ST3

fbb3dcc7acfeb9c04208dc68b8ff020c76d483b1

[ "MIT" ]

27

2018-11-06T16:10:57.000Z

2022-02-25T22:55:33.000Z

Scripts.py

MattOstgard/HLSL_ST3

fbb3dcc7acfeb9c04208dc68b8ff020c76d483b1

[ "MIT" ]

2

2018-03-24T04:09:45.000Z

2018-11-06T14:54:10.000Z

from .Scripts import *

22

0.772727

3

22

5.666667

1

0

0.136364

22

1

22

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a008eb9d3812a49e20b4001c7d7b0873ff6642c9

106

py

Python

tests/exog/random/random_exog_32_20.py

jmabry/pyaf

afbc15a851a2445a7824bf255af612dc429265af

[ "BSD-3-Clause" ]

null

tests/exog/random/random_exog_32_20.py

jmabry/pyaf

afbc15a851a2445a7824bf255af612dc429265af

[ "BSD-3-Clause" ]

1

2019-11-30T23:39:38.000Z

2019-12-01T04:34:35.000Z

tests/exog/random/random_exog_32_20.py

jmabry/pyaf

afbc15a851a2445a7824bf255af612dc429265af

[ "BSD-3-Clause" ]

null

import pyaf.tests.exog.test_random_exogenous as testrandexog testrandexog.test_random_exogenous( 32,20);

26.5

60

0.858491

15

106

5.8

0.733333

0.229885

0.436782

0

0.040404

0.066038

106

4

61

26.5

0.838384

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

4e751966b10b05f698edd3d37469d6c2ff784045

31

py

Python

bubble_io/__init__.py

jasontyping/bubble-io-python

487dd253e85814a012df4a5a5a6a08f023517641

[ "MIT" ]

null

bubble_io/__init__.py

jasontyping/bubble-io-python

487dd253e85814a012df4a5a5a6a08f023517641

[ "MIT" ]

null

bubble_io/__init__.py

jasontyping/bubble-io-python

487dd253e85814a012df4a5a5a6a08f023517641

[ "MIT" ]

1

2020-10-25T08:31:59.000Z

from .bubbleio import BubbleIo

15.5

30

0.83871

4

31

6.5

0.75

0

0.129032

31

1

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4e769aee426de55532dd683d9dd832dcae724616

68

py

Python

python/pandas_pbf/core.py

ccharlesgb/pandas-pbf

8c5b1af2c291cfd485b1296a1a5ba34ddc93d995

[ "MIT" ]

null

python/pandas_pbf/core.py

ccharlesgb/pandas-pbf

8c5b1af2c291cfd485b1296a1a5ba34ddc93d995

[ "MIT" ]

null

python/pandas_pbf/core.py

ccharlesgb/pandas-pbf

8c5b1af2c291cfd485b1296a1a5ba34ddc93d995

[ "MIT" ]

null

import pandas as pd def dump(df: pd.DataFrame) -> bytes: pass

11.333333

36

0.661765

11

68

4.090909

0.909091

0

0.235294

68

5

37

13.6

0.865385

0

1

0.333333

false

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4ea5498deec294ffeeebf2d2ad50bbf782de71a8

141

py

Python

esteid/idcard/__init__.py

thorgate/django-esteid

4a4227b20dca7db5441a3514f724f1404575562c

[ "BSD-3-Clause" ]

17

2016-03-30T09:20:19.000Z

2022-01-17T12:04:03.000Z

esteid/idcard/__init__.py

thorgate/django-esteid

4a4227b20dca7db5441a3514f724f1404575562c

[ "BSD-3-Clause" ]

15

2016-02-22T22:49:07.000Z

2021-11-09T12:29:35.000Z

esteid/idcard/__init__.py

thorgate/django-esteid

4a4227b20dca7db5441a3514f724f1404575562c

[ "BSD-3-Clause" ]

2

2016-07-27T10:57:52.000Z

2017-10-05T13:15:59.000Z

__all__ = ["BaseIdCardAuthenticationView", "IdCardSigner"] from .signer import IdCardSigner from .views import BaseIdCardAuthenticationView

28.2

58

0.836879

11

141

10.363636

0.636364

0.280702

0

0.092199

141

4

59

35.25

0.890625

0

0.283688

0.198582

0

1

0

false

0

0.666667

0

0.666667

0

1

0

1

null

1

0

1

0

null

0

1

0

1

0

6

4eb27769bbc6f1af6058f15f8a964479f5a48ebc

484

py

Python

crosshair/libimpl/__init__.py

mristin/CrossHair

66a44a0d10021e0b1e2d847a677274e62ddd1e9d

[ "MIT" ]

null

crosshair/libimpl/__init__.py

mristin/CrossHair

66a44a0d10021e0b1e2d847a677274e62ddd1e9d

[ "MIT" ]

null

crosshair/libimpl/__init__.py

mristin/CrossHair

66a44a0d10021e0b1e2d847a677274e62ddd1e9d

[ "MIT" ]

null

from crosshair.libimpl import builtinslib from crosshair.libimpl import collectionslib from crosshair.libimpl import datetimelib from crosshair.libimpl import mathlib from crosshair.libimpl import randomlib from crosshair.libimpl import relib def make_registrations(): builtinslib.make_registrations() collectionslib.make_registrations() datetimelib.make_registrations() mathlib.make_registrations() randomlib.make_registrations() relib.make_registrations()

30.25

44

0.82438

51

484

7.686275

0.254902

0.303571

0.306122

0.397959

0

0.119835

484

15

45

32.266667

0.920188

0

1

0.076923

true

0

0.461538

0

0.538462

0

null

1

0

null

0

1

0

1

0

1

0

6

4eda24af6ddf82cc5cc2e25951b4fb9c83b51905

159

py

Python

bitmovin/resources/models/encodings/pertitle/auto_representation.py

koraygulcu/bitmovin-python

e8b896e2cb44142c91828533b8fb02f20eb0fbe5

[ "Unlicense" ]

null

bitmovin/resources/models/encodings/pertitle/auto_representation.py

koraygulcu/bitmovin-python

e8b896e2cb44142c91828533b8fb02f20eb0fbe5

[ "Unlicense" ]

null

bitmovin/resources/models/encodings/pertitle/auto_representation.py

koraygulcu/bitmovin-python

e8b896e2cb44142c91828533b8fb02f20eb0fbe5

[ "Unlicense" ]

null

class AutoRepresentation: def __init__(self, adopt_configuration_threshold=None): self.adoptConfigurationThreshold = adopt_configuration_threshold

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14f0031f20c1d451293a9e4ffe1e1cb773cf31df

57

py

Python

flyeye/dynamics/__init__.py

sbernasek/flyeye

95be4c6b52785d5ff3d0c68362308cb0fd1e8ae8

[ "MIT" ]

2

2020-02-22T09:53:17.000Z

2020-02-24T19:02:01.000Z

flyeye/dynamics/__init__.py

sbernasek/flyeye

95be4c6b52785d5ff3d0c68362308cb0fd1e8ae8

[ "MIT" ]

1

2019-11-20T17:11:07.000Z

flyeye/dynamics/__init__.py

sebastianbernasek/flyeye

95be4c6b52785d5ff3d0c68362308cb0fd1e8ae8

[ "MIT" ]

null

from .visualization import plot_mean, plot_mean_interval

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6

14ff6bd96aa976b58904b681f23b026afedef8de

12,852

py

Python

PaddleFSL/examples/image_classification/maml_image_classification.py

tianxin1860/FSL-Mate

74dde9a3e1f789ec92710b9ecdf9c5b060d26fd3

[ "MIT" ]

null

PaddleFSL/examples/image_classification/maml_image_classification.py

tianxin1860/FSL-Mate

74dde9a3e1f789ec92710b9ecdf9c5b060d26fd3

[ "MIT" ]

null

PaddleFSL/examples/image_classification/maml_image_classification.py

tianxin1860/FSL-Mate

74dde9a3e1f789ec92710b9ecdf9c5b060d26fd3

[ "MIT" ]

null

import paddle import paddlefsl from paddlefsl.model_zoo import maml # Set computing device paddle.set_device('gpu:0') # """ --------------------------------------------------------------------------------- # Config: MAML, Omniglot, MLP, 5 Ways, 1 Shot TRAIN_DATASET = paddlefsl.datasets.Omniglot(mode='train', image_size=(28, 28)) VALID_DATASET = paddlefsl.datasets.Omniglot(mode='valid', image_size=(28, 28)) TEST_DATASET = paddlefsl.datasets.Omniglot(mode='test', image_size=(28, 28)) WAYS = 5 SHOTS = 1 MODEL = paddlefsl.backbones.MLP(input_size=(28, 28), output_size=WAYS) META_LR = 0.005 INNER_LR = 0.5 ITERATIONS = 60000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 1 TEST_INNER_ADAPT_STEPS = 3 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 1000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration60000.params' # ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, Omniglot, MLP, 5 Ways, 5 Shots TRAIN_DATASET = paddlefsl.datasets.Omniglot(mode='train', image_size=(28, 28)) VALID_DATASET = paddlefsl.datasets.Omniglot(mode='valid', image_size=(28, 28)) TEST_DATASET = paddlefsl.datasets.Omniglot(mode='test', image_size=(28, 28)) WAYS = 5 SHOTS = 5 MODEL = paddlefsl.backbones.MLP(input_size=(28, 28), output_size=WAYS) META_LR = 0.005 INNER_LR = 0.5 ITERATIONS = 20000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 1 TEST_INNER_ADAPT_STEPS = 3 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 1000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration20000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, Omniglot, Conv, 5 Ways, 1 Shot TRAIN_DATASET = paddlefsl.datasets.Omniglot(mode='train', image_size=(28, 28)) VALID_DATASET = paddlefsl.datasets.Omniglot(mode='valid', image_size=(28, 28)) TEST_DATASET = paddlefsl.datasets.Omniglot(mode='test', image_size=(28, 28)) WAYS = 5 SHOTS = 1 MODEL = paddlefsl.backbones.Conv(input_size=(1, 28, 28), output_size=WAYS, pooling=False) META_LR = 0.005 INNER_LR = 0.5 ITERATIONS = 60000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 1 TEST_INNER_ADAPT_STEPS = 3 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 1000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration60000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, Omniglot, Conv, 5 Ways, 5 Shots TRAIN_DATASET = paddlefsl.datasets.Omniglot(mode='train', image_size=(28, 28)) VALID_DATASET = paddlefsl.datasets.Omniglot(mode='valid', image_size=(28, 28)) TEST_DATASET = paddlefsl.datasets.Omniglot(mode='test', image_size=(28, 28)) WAYS = 5 SHOTS = 5 MODEL = paddlefsl.backbones.Conv(input_size=(1, 28, 28), output_size=WAYS, pooling=False) META_LR = 0.005 INNER_LR = 0.5 ITERATIONS = 20000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 1 TEST_INNER_ADAPT_STEPS = 3 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 1000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration20000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, Mini-ImageNet, Conv, 5 Ways, 1 Shot TRAIN_DATASET = paddlefsl.datasets.MiniImageNet(mode='train') VALID_DATASET = paddlefsl.datasets.MiniImageNet(mode='valid') TEST_DATASET = paddlefsl.datasets.MiniImageNet(mode='test') WAYS = 5 SHOTS = 1 MODEL = paddlefsl.backbones.Conv(input_size=(3, 84, 84), output_size=WAYS, conv_channels=[32, 32, 32, 32]) META_LR = 0.002 INNER_LR = 0.03 ITERATIONS = 60000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 5000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration60000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, Mini-ImageNet, Conv, 5 Ways, 5 Shots TRAIN_DATASET = paddlefsl.datasets.MiniImageNet(mode='train') VALID_DATASET = paddlefsl.datasets.MiniImageNet(mode='valid') TEST_DATASET = paddlefsl.datasets.MiniImageNet(mode='test') WAYS = 5 SHOTS = 5 MODEL = paddlefsl.backbones.Conv(input_size=(3, 84, 84), output_size=WAYS, conv_channels=[32, 32, 32, 32]) META_LR = 0.002 INNER_LR = 0.1 ITERATIONS = 30000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 5000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration30000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, CifarFS, Conv, 5 Ways, 1 Shot TRAIN_DATASET = paddlefsl.datasets.CifarFS(mode='train') VALID_DATASET = paddlefsl.datasets.CifarFS(mode='valid') TEST_DATASET = paddlefsl.datasets.CifarFS(mode='test') WAYS = 5 SHOTS = 1 MODEL = paddlefsl.backbones.Conv(input_size=(3, 32, 32), output_size=WAYS, conv_channels=[32, 32, 32, 32]) META_LR = 0.001 INNER_LR = 0.03 ITERATIONS = 30000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 5000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration30000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, CifarFS, Conv, 5 Ways, 5 Shots TRAIN_DATASET = paddlefsl.datasets.CifarFS(mode='train') VALID_DATASET = paddlefsl.datasets.CifarFS(mode='valid') TEST_DATASET = paddlefsl.datasets.CifarFS(mode='test') WAYS = 5 SHOTS = 5 MODEL = paddlefsl.backbones.Conv(input_size=(3, 32, 32), output_size=WAYS, conv_channels=[32, 32, 32, 32]) META_LR = 0.0015 INNER_LR = 0.15 ITERATIONS = 10000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 5000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration10000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, FC100, Conv, 5 Ways, 1 Shot TRAIN_DATASET = paddlefsl.datasets.FC100(mode='train') VALID_DATASET = paddlefsl.datasets.FC100(mode='valid') TEST_DATASET = paddlefsl.datasets.FC100(mode='test') WAYS = 5 SHOTS = 1 MODEL = paddlefsl.backbones.Conv(input_size=(3, 32, 32), output_size=WAYS) META_LR = 0.002 INNER_LR = 0.05 ITERATIONS = 10000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 2000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration10000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, FC100, Conv, 5 Ways, 5 Shots TRAIN_DATASET = paddlefsl.datasets.FC100(mode='train') VALID_DATASET = paddlefsl.datasets.FC100(mode='valid') TEST_DATASET = paddlefsl.datasets.FC100(mode='test') WAYS = 5 SHOTS = 5 MODEL = paddlefsl.backbones.Conv(input_size=(3, 32, 32), output_size=WAYS) META_LR = 0.003 INNER_LR = 0.08 ITERATIONS = 5000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 1000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration5000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, CubFS, Conv, 5 Ways, 1 Shot TRAIN_DATASET = paddlefsl.datasets.CubFS(mode='train') VALID_DATASET = paddlefsl.datasets.CubFS(mode='valid') TEST_DATASET = paddlefsl.datasets.CubFS(mode='test') WAYS = 5 SHOTS = 1 MODEL = paddlefsl.backbones.Conv(input_size=(3, 84, 84), output_size=WAYS, conv_channels=[32, 32, 32, 32]) META_LR = 0.002 INNER_LR = 0.03 ITERATIONS = 20000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 5000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration20000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, CubFS, Conv, 5 Ways, 5 Shots TRAIN_DATASET = paddlefsl.datasets.CubFS(mode='train') VALID_DATASET = paddlefsl.datasets.CubFS(mode='valid') TEST_DATASET = paddlefsl.datasets.CubFS(mode='test') WAYS = 5 SHOTS = 5 MODEL = paddlefsl.backbones.Conv(input_size=(3, 84, 84), output_size=WAYS, conv_channels=[32, 32, 32, 32]) META_LR = 0.003 INNER_LR = 0.1 ITERATIONS = 10000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 2000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration10000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, Tiered-ImageNet, Conv, 5 Ways, 1 Shot TRAIN_DATASET = paddlefsl.datasets.TieredImageNet(mode='train') VALID_DATASET = paddlefsl.datasets.TieredImageNet(mode='valid') TEST_DATASET = paddlefsl.datasets.TieredImageNet(mode='test') WAYS = 5 SHOTS = 1 MODEL = paddlefsl.backbones.Conv(input_size=(3, 84, 84), output_size=WAYS, conv_channels=[32, 32, 32, 32]) META_LR = 0.002 INNER_LR = 0.03 ITERATIONS = 15000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 5000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration15000.params' ----------------------------------------------------------------------------------""" """ --------------------------------------------------------------------------------- # Config: MAML, Tiered-ImageNet, Conv, 5 Ways, 5 Shots TRAIN_DATASET = paddlefsl.datasets.TieredImageNet(mode='train') VALID_DATASET = paddlefsl.datasets.TieredImageNet(mode='valid') TEST_DATASET = paddlefsl.datasets.TieredImageNet(mode='test') WAYS = 5 SHOTS = 5 MODEL = paddlefsl.backbones.Conv(input_size=(3, 84, 84), output_size=WAYS, conv_channels=[32, 32, 32, 32]) META_LR = 0.002 INNER_LR = 0.01 ITERATIONS = 30000 TEST_EPOCH = 10 META_BATCH_SIZE = 32 TRAIN_INNER_ADAPT_STEPS = 5 TEST_INNER_ADAPT_STEPS = 10 APPROXIMATE = True REPORT_ITER = 10 SAVE_MODEL_ITER = 5000 SAVE_MODEL_ROOT = '~/trained_models' TEST_PARAM_FILE = 'iteration30000.params' ----------------------------------------------------------------------------------""" def main(): train_dir = maml.meta_training(train_dataset=TRAIN_DATASET, valid_dataset=VALID_DATASET, ways=WAYS, shots=SHOTS, model=MODEL, meta_lr=META_LR, inner_lr=INNER_LR, iterations=ITERATIONS, meta_batch_size=META_BATCH_SIZE, inner_adapt_steps=TRAIN_INNER_ADAPT_STEPS, approximate=APPROXIMATE, report_iter=REPORT_ITER, save_model_iter=SAVE_MODEL_ITER, save_model_root=SAVE_MODEL_ROOT) print(train_dir) state_dict = paddle.load(train_dir + '/' + TEST_PARAM_FILE) MODEL.load_dict(state_dict) maml.meta_testing(model=MODEL, test_dataset=TEST_DATASET, test_epoch=TEST_EPOCH, test_batch_size=META_BATCH_SIZE, ways=WAYS, shots=SHOTS, inner_lr=INNER_LR, inner_adapt_steps=TEST_INNER_ADAPT_STEPS, approximate=APPROXIMATE) if __name__ == '__main__': main()

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0.140292

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null

0

1

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null

0

6

092ff28cf017dfa08a6c336b9f9f79e5dc743c1f

25

py

Python

rewx/__init__.py

akrk1986/re-wx

2f50d1c0afe77313548847b279327d7041623721

[ "MIT" ]

103

2021-01-18T22:06:46.000Z

2022-03-24T15:57:25.000Z

rewx/__init__.py

ronny-rentner/re-wx

185c509ef7a590d7abb758be687fb59048019adb

[ "MIT" ]

6

2021-01-26T11:45:40.000Z

2022-01-15T08:18:12.000Z

rewx/__init__.py

ronny-rentner/re-wx

185c509ef7a590d7abb758be687fb59048019adb

[ "MIT" ]

4

2021-01-26T10:13:20.000Z

2022-01-10T09:02:27.000Z

from rewx.core import *

8.333333

23

0.72

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25

4.5

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6

11b673d3e56e187a96e8ce75c9577f8cea8df161

200

py

Python

pymtl3/passes/rtlir/structural/__init__.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

152

2020-06-03T02:34:11.000Z

2022-03-30T04:16:45.000Z

pymtl3/passes/rtlir/structural/__init__.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

139

2019-05-29T00:37:09.000Z

2020-05-17T16:49:26.000Z

pymtl3/passes/rtlir/structural/__init__.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

22

2020-05-18T13:42:05.000Z

2022-03-11T08:37:51.000Z

"""Expose structural RTLIR generation pass. PyMTL user should only interact with the passes exposed here. """ from .StructuralRTLIRGenL4Pass import StructuralRTLIRGenL4Pass as StructuralRTLIRGenPass

33.333333

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null

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1

0

1

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6

ee9a90e09df8676533abaa0b7de5176954a8137e

3,542

py

Python

server/server/apps/course/views.py

tjsga/study-bank

f4cb17bc642d2fd28affde89d2af6a8ecd2286f2

[ "MIT" ]

null

server/server/apps/course/views.py

tjsga/study-bank

f4cb17bc642d2fd28affde89d2af6a8ecd2286f2

[ "MIT" ]

null

server/server/apps/course/views.py

tjsga/study-bank

f4cb17bc642d2fd28affde89d2af6a8ecd2286f2

[ "MIT" ]

null

from django.shortcuts import render, get_object_or_404 from django.core.exceptions import PermissionDenied from django.http import Http404 from .models import Course from ..mod.models import Moderator from ..files.models import File from ..decorators import login # Create your views here. @login def index(request): courses = Course.objects.all() return render(request, 'class/index.html', {'classes': courses}) @login def show(request, course_url): course = get_object_or_404(Course, url=course_url) is_mod = False try: mod = Moderator.objects.get(username=request.session['user']) except Moderator.DoesNotExist: is_mod = False return render(request, 'class/show.html', {'course': course, 'is_mod': is_mod}) if mod.admin: is_mod = True elif course in mod.classes.all(): is_mod = True return render(request, 'class/show.html', {'course': course, 'is_mod': is_mod}) @login def approve(request, course_url, doc_id): course = get_object_or_404(Course, url=course_url) try: mod = Moderator.objects.get(username=request.session['user']) except Moderator.DoesNotExist: raise PermissionDenied if mod.admin or (course in mod.classes.all()): try: doc = course.unapproved_files.get(id=doc_id) except File.DoesNotExist: try: doc = course.files.get(id=doc_id) except File.DoesNotExist: raise Http404("Error: Document Not Related to this Course") raise Http404("Error: Document Already Approved") course.unapproved_files.remove(doc) course.files.add(doc) return render(request, 'class/approve.html', {'doc': doc, 'course': course}) else: raise PermissionDenied @login def remove(request, course_url, doc_id): course = get_object_or_404(Course, url=course_url) try: mod = Moderator.objects.get(username=request.session['user']) except Moderator.DoesNotExist: raise PermissionDenied if mod.admin or (course in mod.classes.all()): try: doc = course.files.get(id=doc_id) except File.DoesNotExist: try: doc = course.unapproved_files.get(id=doc_id) except File.DoesNotExist: raise Http404("Error: Document Not Related to this Course") course.unapproved_files.remove(doc) course.rejected_files.add(doc) return render(request, 'class/remove.html', {'doc': doc, 'course': course}) course.files.remove(doc) course.rejected_files.add(doc) return render(request, 'class/remove.html', {'doc': doc, 'course': course}) else: raise PermissionDenied @login def undelete(request, course_url, doc_id): course = get_object_or_404(Course, url=course_url) try: mod = Moderator.objects.get(username=request.session['user']) except Moderator.DoesNotExist: raise PermissionDenied if mod.admin or (course in mod.classes.all()): try: doc = course.rejected_files.get(id=doc_id) except File.DoesNotExist: raise Http404("Error: Document Not Related to this Course") course.rejected_files.remove(doc) course.files.add(doc) return render(request, 'class/undelete.html', {'doc': doc, 'course': course}) else: raise PermissionDenied

31.625

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3,542

112

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0

null

0

1

0

1

0

null

0

6

eed876b1554e0a4c99de5f131d255d84ecaa3345

78

py

Python

lyrebird/plugins/__init__.py

dodosophia/lyrebird

b3c3d6e0f0f47b8df0cc119a1e5d30763371fa3d

[ "MIT" ]

1

2020-03-18T05:56:53.000Z

lyrebird/plugins/__init__.py

robert0825/lyrebird

18bcbd2030bd4a506d1f519ae0316d8fc667db4f

[ "MIT" ]

null

lyrebird/plugins/__init__.py

robert0825/lyrebird

18bcbd2030bd4a506d1f519ae0316d8fc667db4f

[ "MIT" ]

1

2019-03-11T09:25:36.000Z

from .plugin_loader import manifest from .plugin_manager import PluginManager

26

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0.871795

10

78

6.6

0.7

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78

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null

0

1

0

1

0

1

0

6

eef0f57e2e52d98324d6736af1814a7fec12251f

23

py

Python

Game/History/__init__.py

ritwikd/interom

0b626351fd742f2a99d0a6d11ba8c1a214aab576

[ "MIT" ]

null

Game/History/__init__.py

ritwikd/interom

0b626351fd742f2a99d0a6d11ba8c1a214aab576

[ "MIT" ]

1

2021-03-06T22:08:32.000Z

2021-03-06T22:09:07.000Z

Game/History/__init__.py

ritwikd/interom

0b626351fd742f2a99d0a6d11ba8c1a214aab576

[ "MIT" ]

1

2021-03-03T22:48:07.000Z

from . import Log, Move

23

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4

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4.25

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0.173913

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23

0.894737

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null

0

1

0

1

0

1

0

6

eef62d1ce6768e7a68a4a1159bbd33491dcbc7e8

6,126

py

Python

tests/objects/fiber_manipulation_test.py

jifengting1/fastpliFork

1ef7e2d268e03e21ded9390fc005b9fff2e0a3c1

[ "MIT" ]

null

tests/objects/fiber_manipulation_test.py

jifengting1/fastpliFork

1ef7e2d268e03e21ded9390fc005b9fff2e0a3c1

[ "MIT" ]

null

tests/objects/fiber_manipulation_test.py

jifengting1/fastpliFork

1ef7e2d268e03e21ded9390fc005b9fff2e0a3c1

[ "MIT" ]

null

import unittest import numpy as np import fastpli.objects import fastpli.tools class MainTest(unittest.TestCase): # TODO: implement object.fiber.*manipulations* def setUp(self): self.fiber = np.array([[0, 0, 0, 1], [1, 1, 1, 2]], dtype=float) self.fiber_bundle = [self.fiber.copy()] self.fiber_bundles = [[self.fiber.copy()]] def test_resize(self): fiber = fastpli.objects.fiber.Rescale(self.fiber, 10) self.assertTrue(np.array_equal(fiber, self.fiber * 10)) fb = fastpli.objects.fiber_bundle.Rescale(self.fiber_bundle, 10) for f in fb: self.assertTrue(np.array_equal(f, self.fiber * 10)) fbs = fastpli.objects.fiber_bundles.Rescale(self.fiber_bundles, 10) for fb in fbs: for f in fb: self.assertTrue(np.array_equal(f, self.fiber * 10)) fiber = fastpli.objects.fiber.Rescale(self.fiber, 10, mod='points') self.assertTrue(np.array_equal(fiber[:, :-2], self.fiber[:, :-2] * 10)) self.assertTrue(np.array_equal(fiber[:, -1], self.fiber[:, -1])) fiber = fastpli.objects.fiber.Rescale(self.fiber, 10, mod='radii') self.assertTrue(np.array_equal(fiber[:, :-2], self.fiber[:, :-2])) self.assertTrue(np.array_equal(fiber[:, -1], self.fiber[:, -1] * 10)) def test_rotation(self): fiber = fastpli.objects.fiber.Rotate(self.fiber, fastpli.tools.rotation.x(0)) self.assertTrue(np.array_equal(self.fiber, fiber)) fiber = fastpli.objects.fiber.Rotate( self.fiber, fastpli.tools.rotation.x(np.deg2rad(90))) self.assertTrue( np.allclose(fiber, np.array([[0, 0, 0, 1], [1, -1, 1, 2]]))) fiber = fastpli.objects.fiber.Rotate( self.fiber, fastpli.tools.rotation.x(np.deg2rad(90)), [1, 1, 1]) self.assertTrue( np.allclose(fiber, np.array([[0, 2, 0, 1], [1, 1, 1, 2]]))) for f in self.fiber_bundle: fiber = fastpli.objects.fiber.Rotate( f, fastpli.tools.rotation.x(np.deg2rad(90)), [1, 1, 1]) self.assertTrue( np.allclose(fiber, np.array([[0, 2, 0, 1], [1, 1, 1, 2]]))) for fb in self.fiber_bundles: for f in fb: fiber = fastpli.objects.fiber.Rotate( f, fastpli.tools.rotation.x(np.deg2rad(90)), [1, 1, 1]) self.assertTrue( np.allclose(fiber, np.array([[0, 2, 0, 1], [1, 1, 1, 2]]))) def test_translate(self): fiber = fastpli.objects.fiber.Translate(self.fiber, [1, 1, 1]) self.assertTrue( np.array_equal(fiber[:, :3], self.fiber[:, :3] + np.array([1, 1, 1]))) self.assertTrue(np.array_equal(fiber[:, -1], self.fiber[:, -1])) for f in self.fiber_bundle: fiber = fastpli.objects.fiber.Translate(f, [1, 1, 1]) self.assertTrue( np.array_equal(fiber[:, :3], self.fiber[:, :3] + np.array([1, 1, 1]))) self.assertTrue(np.array_equal(f[:, -1], self.fiber[:, -1])) for fb in self.fiber_bundles: for f in fb: fiber = fastpli.objects.fiber.Translate(f, [1, 1, 1]) self.assertTrue( np.array_equal(fiber[:, :3], self.fiber[:, :3] + np.array([1, 1, 1]))) self.assertTrue(np.array_equal(f[:, -1], self.fiber[:, -1])) def test_cut(self): fiber = np.array([[0, 0, 0, 1], [1, 1, 1, 2]], dtype=float) fibers = fastpli.objects.fiber.Cut(fiber, [[-10] * 3, [10] * 3]) self.assertTrue(len(fibers) == 1) self.assertTrue(np.array_equal(fibers[0], fiber)) fiber = np.array([[0, 0, 0, 1], [10, 10, 10, 2]], dtype=float) fibers = fastpli.objects.fiber.Cut(fiber, [[-5] * 3, [5] * 3]) self.assertTrue(len(fibers) == 1) self.assertTrue(np.array_equal(fibers[0], fiber)) fiber = np.array([[0, 0, 0, 1], [10, 10, 10, 2], [100, 100, 100, 2]], dtype=float) fibers = fastpli.objects.fiber.Cut(fiber, [[-5] * 3, [5] * 3]) self.assertTrue(len(fibers) == 1) self.assertTrue(fibers[0].shape[0] == 2) self.assertTrue(not np.array_equal(fibers[0], fiber)) fiber = np.array([[0, 0, 0, 1], [10, 10, 10, 2], [100, 100, 100, 2], [10, 10, 10, 2], [0, 0, 0, 1]], dtype=float) fibers = fastpli.objects.fiber.Cut(fiber, [[-5] * 3, [5] * 3]) self.assertTrue(len(fibers) == 2) self.assertTrue(fibers[0].shape[0] == 2) self.assertTrue(fibers[1].shape[0] == 2) self.assertTrue(not np.array_equal(fibers[0], fiber)) self.assertTrue(not np.array_equal(fibers[1], fiber)) fiber_bundle = [fiber] cut_fb = fastpli.objects.fiber_bundle.Cut(fiber_bundle, [[-5] * 3, [5] * 3]) fibers = cut_fb self.assertTrue(len(fibers) == 2) self.assertTrue(fibers[0].shape[0] == 2) self.assertTrue(fibers[1].shape[0] == 2) self.assertTrue(not np.array_equal(fibers[0], fiber)) self.assertTrue(not np.array_equal(fibers[1], fiber)) fiber_bundles = [[fiber]] cut_fbs = fastpli.objects.fiber_bundles.Cut(fiber_bundles, [[-5] * 3, [5] * 3]) fibers = cut_fbs[0] self.assertTrue(len(cut_fbs) == 1) self.assertTrue(len(fibers) == 2) self.assertTrue(fibers[0].shape[0] == 2) self.assertTrue(fibers[1].shape[0] == 2) self.assertTrue(not np.array_equal(fibers[0], fiber)) self.assertTrue(not np.array_equal(fibers[1], fiber)) fiber = np.array([[0, 0, 0, 1], [10, 10, 10, 2]], dtype=float) fibers = fastpli.objects.fiber.Cut(fiber, [[5] * 3, [6] * 3]) self.assertTrue(np.array_equal(fibers[0], fiber)) if __name__ == '__main__': unittest.main()

42.839161

79

0.538851

820

6,126

3.956098

0.07439

0.181258

0.088779

0.110049

0.848644

0.792232

0.784217

0.774044

0.741985

0.701911

0

0.062457

0.289096

6,126

142

80

43.140845

0.682434

0.007183

0

0.608696

0

0.003125

0

0.007042

0.365217

1

0.043478

false

0

0.034783

0

0.086957

0

null

0

1

0

null

0

6

e102bdd6852dce95483c7c8cdb3211b3d9ab7231

43

py

Python

run_5395.py

mpi3d/goodix-fp-dump

039940845bd5eeb98cd92d72f267e3be77feb156

[ "MIT" ]

136

2021-05-05T14:16:17.000Z

2022-03-31T09:04:18.000Z

run_5395.py

tsunekotakimoto/goodix-fp-dump

b88ecbababd3766314521fe30ee943c4bd1810df

[ "MIT" ]

14

2021-08-20T09:49:39.000Z

2022-03-20T13:18:05.000Z

run_5395.py

tsunekotakimoto/goodix-fp-dump

b88ecbababd3766314521fe30ee943c4bd1810df

[ "MIT" ]

11

2021-08-02T15:49:11.000Z

2022-02-06T22:06:42.000Z

from driver_53x5 import main main(0x5395)

10.75

28

0.813953

7

43

4.857143

0.857143

0

0.216216

0.139535

43

3

29

14.333333

0.702703

0

0.139535

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

e12ad429759f61a8d7e2d053224398fdfc9dad67

19

py

Python

pkgs/conf-pkg/src/genie/libs/conf/rip/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/conf-pkg/src/genie/libs/conf/rip/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/conf-pkg/src/genie/libs/conf/rip/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

from .rip import *

9.5

18

0.684211

3

19

4.333333

1

0

0.210526

19

1

19

0.866667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

012f17bafc339e27fe0149bdbf1a7b12a681ef93

29

py

Python

demo2022.py

finaleo83/demo01

579782f564ab0f5cc95f6b5e63644c5f930c0019

[ "Unlicense" ]

null

demo2022.py

finaleo83/demo01

579782f564ab0f5cc95f6b5e63644c5f930c0019

[ "Unlicense" ]

null

demo2022.py

finaleo83/demo01

579782f564ab0f5cc95f6b5e63644c5f930c0019

[ "Unlicense" ]

null

print("Hello, World! Again!")

29

0.689655

4

29

5

1

0

0.068966

29

1

29

0.740741

0

0.666667

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

013c77d6a4350f96399efe1ca86c27a469b9fa59

32

py

Python

src/logic_analyzer_bfms/__init__.py

pybfms/pybfms_logic_analyzer

7696e16c53a7248a0660ba1cc8f108cda03c1e08

[ "Apache-2.0" ]

null

src/logic_analyzer_bfms/__init__.py

pybfms/pybfms_logic_analyzer

7696e16c53a7248a0660ba1cc8f108cda03c1e08

[ "Apache-2.0" ]

null

src/logic_analyzer_bfms/__init__.py

pybfms/pybfms_logic_analyzer

7696e16c53a7248a0660ba1cc8f108cda03c1e08

[ "Apache-2.0" ]

1

2020-11-22T08:37:39.000Z

from .la_initiator_bfm import *

16

31

0.8125

5

32

4.8

1

0

0.125

32

2

31

16

0.857143

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

014d029371edfc926a3b46e79008ce4486f7ec74

29

py

Python

pydreamer/models/__init__.py

rogerscristo/pydreamer

e44fdf8b35fe48662ed619100fdd5d9d6858f6db

[ "MIT" ]

75

2021-10-12T13:17:48.000Z

2022-03-04T14:43:30.000Z

pydreamer/models/__init__.py

LvZut/pydreamer

e3a522e13319d3667b526abb5f5747ab68e3c04e

[ "MIT" ]

2

2022-01-17T06:49:50.000Z

2022-02-17T19:45:24.000Z

pydreamer/models/__init__.py

LvZut/pydreamer

e3a522e13319d3667b526abb5f5747ab68e3c04e

[ "MIT" ]

10

2021-11-27T18:20:26.000Z

2022-03-14T09:06:52.000Z

from .dreamer import Dreamer

14.5

28

0.827586

4

29

6

0.75

0

0.137931

29

1

29

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0186c6f9ccb6910901110026b5550d4363a11f93

110

py

Python

tests/collagen/utils/__init__.py

newskylabs/newskylabs-collagen

3e2e331605745e6709f57dce8730ceb9ceaa002c

[ "Apache-2.0" ]

null

tests/collagen/utils/__init__.py

newskylabs/newskylabs-collagen

3e2e331605745e6709f57dce8730ceb9ceaa002c

[ "Apache-2.0" ]

null

tests/collagen/utils/__init__.py

newskylabs/newskylabs-collagen

3e2e331605745e6709f57dce8730ceb9ceaa002c

[ "Apache-2.0" ]

null

from . import test_conversion from . import test_generic from . import test_idxgz from . import test_settings

22

29

0.818182

16

110

5.375

0.4375

0.465116

0.651163

0

0.145455

110

4

30

27.5

0.914894

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

018d64e411b9a079532721baad7937f619846f0d

187

py

Python

tests/test_main.py

ZhuYuJin/cgroup-parser

7132791c496dc87af04d0458ad1f820eac8a8f0f

[ "Apache-2.0" ]

null

tests/test_main.py

ZhuYuJin/cgroup-parser

7132791c496dc87af04d0458ad1f820eac8a8f0f

[ "Apache-2.0" ]

null

tests/test_main.py

ZhuYuJin/cgroup-parser

7132791c496dc87af04d0458ad1f820eac8a8f0f

[ "Apache-2.0" ]

null

import cgroup_parser def test_interface(): cgroup_parser.get_max_procs() cgroup_parser.get_cpu_usage() cgroup_parser.get_memory_limit() cgroup_parser.get_memory_usage()

20.777778

36

0.780749

26

187

5.076923

0.5

0.454545

0.318182

0

0.139037

187

8

37

23.375

0.819876

0

1

0.166667

true

0

0.166667

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

6dba80a9622a3df8b603c41e7552e6d4c8ed3c02

23

py

Python

tests/res/foo.py

lepture/werkzeug

627ac8370bc5aa3a04ba365b4ebcd32b6a859863

[ "BSD-3-Clause" ]

1

2019-04-14T19:58:21.000Z

tests/res/foo.py

lepture/werkzeug

627ac8370bc5aa3a04ba365b4ebcd32b6a859863

[ "BSD-3-Clause" ]

null

tests/res/foo.py

lepture/werkzeug

627ac8370bc5aa3a04ba365b4ebcd32b6a859863

[ "BSD-3-Clause" ]

null

from .bar import value

11.5

22

0.782609

4

23

4.5

1

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6def8fbc025a4ae631780ed754a16d15160b7b0b

6,514

py

Python

knx_stack/client/knxnet_ip_discovery.py

majamassarini/knx-stack

11a9baac6b7600649b5fbca43c93b200b23676b4

[ "MIT" ]

2

2021-07-28T07:42:28.000Z

2022-01-25T18:56:05.000Z

knx_stack/client/knxnet_ip_discovery.py

majamassarini/knx-stack

11a9baac6b7600649b5fbca43c93b200b23676b4

[ "MIT" ]

6

2021-07-25T21:36:01.000Z

2022-02-20T21:11:31.000Z

knx_stack/client/knxnet_ip_discovery.py

majamassarini/knx-stack

11a9baac6b7600649b5fbca43c93b200b23676b4

[ "MIT" ]

null

import struct import socket import asyncio import logging import knx_stack class Request(asyncio.DatagramProtocol): def __init__(self, local_addr: str, local_port: int): """ A KNXnet IP Discovery request service :param local_addr: discovery request instance host ip address :param local_port: discovery request instance binding port Example:: async def send_discovery_request(local_addr: str, local_port: int): sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock.bind(('', knx_stack.knxnet_ip.DISCOVERY_MULTICAST_PORT)) group = socket.inet_aton(knx_stack.knxnet_ip.DISCOVERY_MULTICAST_ADDR) mreq = struct.pack('!4sL', group, socket.INADDR_ANY) sock.setsockopt(socket.IPPROTO_IP, socket.IP_ADD_MEMBERSHIP, mreq) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) sock.setblocking(False) transport, protocol = await loop.create_datagram_endpoint( lambda: Request(local_addr, local_port), sock=sock, ) return transport, protocol """ self._loop = asyncio.get_event_loop() self._transport = None self._local_addr = local_addr self._local_port = local_port self._state = knx_stack.knxnet_ip.State(knx_stack.Medium.knxnet_ip, None, None) self.logger = logging.getLogger(__name__) def connection_made(self, transport): self._transport = transport self.logger.info("Connection made: {}".format(str(self._transport))) msg = knx_stack.encode_msg( self._state, knx_stack.knxnet_ip.core.search.req.Msg( addr=self._local_addr, port=self._local_port ), ) self.logger.info("encode: {}".format(msg)) self._transport.sendto( bytearray.fromhex(str(msg)), ( knx_stack.knxnet_ip.DISCOVERY_MULTICAST_ADDR, knx_stack.knxnet_ip.DISCOVERY_MULTICAST_PORT, ), ) def connection_lost(self, exc): self.logger.error("Connection lost: {}".format(str(exc))) self._transport = None def error_received(self, exc): self.logger.error("Error received: {}".format(str(exc))) def datagram_received(self, data, addr): self.logger.info("read data: {}".format(data.hex())) self.logger.info("read from: {}".format(str(addr))) class Listen(asyncio.DatagramProtocol): """ A KNXnet IP Discovery listener service :param local_addr: discovery listener instance host ip address :param local_port: discovery listener instance binding port Example:: async def listen_discovery_responses(local_addr: str, local_port: int): transport, protocol = await loop.create_datagram_endpoint( lambda: Listen(), local_addr=(local_addr, local_port), ) return transport, protocol if __name__ == '__main__': import sys root = logging.getLogger() root.setLevel(logging.DEBUG) handler = logging.StreamHandler(sys.stdout) root.addHandler(handler) loop = asyncio.get_event_loop() transport1, _ = loop.run_until_complete(loop.create_task(listen_discovery_responses('172.31.10.111', 5544))) transport2, _ = loop.run_until_complete(loop.create_task(send_discovery_request('172.31.10.111', 5544))) try: loop.run_forever() except KeyboardInterrupt: pass print("Closing transport...") transport1.close() transport2.close() loop.close() """ def __init__(self): self._transport = None self._state = knx_stack.knxnet_ip.State(knx_stack.Medium.knxnet_ip, None, None) self.logger = logging.getLogger(__name__) def connection_made(self, transport): self._transport = transport self.logger.info("Connection made: {}".format(str(self._transport))) def connection_lost(self, exc): self.logger.error("Connection lost: {}".format(str(exc))) self._transport = None def error_received(self, exc): self.logger.error("Error received: {}".format(str(exc))) def datagram_received(self, data, addr): self.logger.info("read {}".format(str(data.hex()))) self.logger.info("read {}".format(str(addr))) search_response = knx_stack.decode_msg( self._state, knx_stack.knxnet_ip.Msg.make_from_str(data.hex()) ) self.logger.info("read decoded: {}".format(str(search_response))) async def send_discovery_request(local_addr: str, local_port: int): sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock.bind(("", knx_stack.knxnet_ip.DISCOVERY_MULTICAST_PORT)) group = socket.inet_aton(knx_stack.knxnet_ip.DISCOVERY_MULTICAST_ADDR) mreq = struct.pack("!4sL", group, socket.INADDR_ANY) sock.setsockopt(socket.IPPROTO_IP, socket.IP_ADD_MEMBERSHIP, mreq) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) sock.setblocking(False) transport, protocol = await loop.create_datagram_endpoint( lambda: Request(local_addr, local_port), sock=sock, ) return transport, protocol async def listen_discovery_responses(local_addr: str, local_port: int): transport, protocol = await loop.create_datagram_endpoint( lambda: Listen(), local_addr=(local_addr, local_port), ) return transport, protocol if __name__ == "__main__": import sys root = logging.getLogger() root.setLevel(logging.DEBUG) handler = logging.StreamHandler(sys.stdout) root.addHandler(handler) loop = asyncio.get_event_loop() if len(sys.argv): transport1, _ = loop.run_until_complete( loop.create_task(listen_discovery_responses(sys.argv[0], 5544)) ) transport2, _ = loop.run_until_complete( loop.create_task(send_discovery_request(sys.argv[0], 5544)) ) try: loop.run_forever() except KeyboardInterrupt: pass print("Closing transport...") transport1.close() transport2.close() loop.close()

34.648936

120

0.642462

751

6,514

5.302264

0.174434

0.036163

0.035158

0.040181

0.853591

0.827725

0.798594

0.751381

0.729282

0

0.010647

0.25023

6,514

187

121

34.834225

0.804668

0.315321

0

0.3

0

0.052971

0

1

0.1

false

0.01

0.06

0

0.2

0.01

0

null

0

1

0

null

0

6

0986ca341593898178573e0a204ed21602be920f

99

py

Python

tail/__init__.py

0eu/tail-assignment

a86cdcbee88a6d0bf07b7ab7175a7742a5188a2f

[ "MIT" ]

1

2020-12-01T15:05:21.000Z

tail/__init__.py

0eu/tail-assignment

a86cdcbee88a6d0bf07b7ab7175a7742a5188a2f

[ "MIT" ]

null

tail/__init__.py

0eu/tail-assignment

a86cdcbee88a6d0bf07b7ab7175a7742a5188a2f

[ "MIT" ]

null

from tail.core import read_last_lines, follow_lines __all__ = ["read_last_lines", "follow_lines"]

24.75

51

0.79798

15

99

4.6

0.6

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py

Python

shapes-trainer/training_shapes_module/__init__.py

dakotaJang/shapes

19ba73ad2a9b50b57cafca53560678273aeb7776

[ "MIT" ]

1

2019-02-02T11:46:55.000Z

shapes-trainer/training_shapes_module/__init__.py

dakotaJang/shapes

19ba73ad2a9b50b57cafca53560678273aeb7776

[ "MIT" ]

null

shapes-trainer/training_shapes_module/__init__.py

dakotaJang/shapes

19ba73ad2a9b50b57cafca53560678273aeb7776

[ "MIT" ]

null

from .loader import * from .model import * from .train_and_test import *

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py

Python

pastepdb/__init__.py

pooriaahmadi/pastepdb

166b2e8614ee2ea6c8f2f804af23458defb4674a

[ "MIT" ]

8

2021-03-17T10:48:49.000Z

2021-04-06T08:16:04.000Z

pastepdb/__init__.py

pooriaahmadi/pastepdb

166b2e8614ee2ea6c8f2f804af23458defb4674a

[ "MIT" ]

null

pastepdb/__init__.py

pooriaahmadi/pastepdb

166b2e8614ee2ea6c8f2f804af23458defb4674a

[ "MIT" ]

null

from .pastepdb import pastepdb

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6

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py

Python

nawrapper/__init__.py

xzackli/nawrapper

f67c02b48d04ed35ab05a378b9884fefd9d07d7f

[ "MIT" ]

null

nawrapper/__init__.py

xzackli/nawrapper

f67c02b48d04ed35ab05a378b9884fefd9d07d7f

[ "MIT" ]

9

2019-08-27T11:52:37.000Z

2021-09-21T05:13:25.000Z

nawrapper/__init__.py

xzackli/nawrapper

f67c02b48d04ed35ab05a378b9884fefd9d07d7f

[ "MIT" ]

1

2020-07-07T14:31:43.000Z

"""Package init file. We want the user to get everything right away upon `import nawrapper as nw`. """ from .power import * from .maptools import * from .covtools import * from . import planck

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1157a67a471d97e9b998c20a52b64bbf93cf6c33

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py

Python

multipy/check.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

multipy/check.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

multipy/check.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

"""multipy: Python library for multicomponent mass transfer""" __author__ = "James C. Sutherland, Kamila Zdybal" __copyright__ = "Copyright (c) 2022, James C. Sutherland, Kamila Zdybal" __license__ = "MIT" __version__ = "1.0.0" __maintainer__ = ["Kamila Zdybal"] __email__ = ["kamilazdybal@gmail.com"] __status__ = "Production" import numpy as np import pandas as pd import random import copy import scipy import multipy import warnings gas_constant = 8.31446261815324 ################################################################################ ################################################################################ #### #### Class: Check #### ################################################################################ ################################################################################ class Check: """ Supports performing basic checks of the computed quantities. """ # -------------------------------------------------------------------------- def __init__(self): pass # -------------------------------------------------------------------------- def sum_of_species_fractions(self, species_fractions, tolerance=1e-12, verbose=False): """ Checks if all species mole/mass/volume fractions sum to 1.0 for every observation within a specified tolerance. For mole fractions: .. math:: \\sum_{i=1}^{n} X_i = 1.0 For mass fractions: .. math:: \\sum_{i=1}^{n} Y_i = 1.0 For volume fractions: .. math:: \\sum_{i=1}^{n} V_i = 1.0 where :math:`n` is the number of species. :param species_fractions: scalar ``numpy.ndarray`` specifying **all** species mole/mass/volume fractions in :math:`[-]`. It should be of size ``(n_species, n_observations)`` where ``n_species`` is at least 2. :param tolerance: (optional) ``float`` specifying the tolerance. It should be larger than 0.0 and smaller than 1.0. :param verbose: (optional) ``bool`` for printing verbose information. :return: - **idx** - indices of observations where species mole/mass/volume fractions do not sum to 1.0 within a specified tolerance. """ if not isinstance(species_fractions, np.ndarray): raise ValueError("Parameter `species_fractions` has to be of type `numpy.ndarray`.") try: (n_species, n_observations) = np.shape(species_fractions) except: raise ValueError("Parameter `species_fractions` has to be a matrix.") if n_species < 2: raise ValueError("Species fractions matrix `species_mole_fractions` has to have at least two species.") if n_observations < n_species: warnings.warn("Number of observations in `species_fractions` is smaller than the number of species. Make sure that the `species_fractions` has shape `(n_observations,n_species)`.") if not isinstance(tolerance, float): raise ValueError("Parameter `tolerance` has to be of type `float`.") if tolerance <= 0 or tolerance >= 1: raise ValueError("Parameter `tolerance` has to be larger than 0 and smaller than 1.") if not isinstance(verbose, bool): raise ValueError("Parameter `verbose` has to be of type `bool`.") sums = np.sum(species_fractions, axis=0) sums_boolean = np.zeros_like(sums) for i, observation in enumerate(sums): if (observation < 1+tolerance) and (observation > 1-tolerance): sums_boolean[i] = True else: sums_boolean[i] = False if sums_boolean.all(): if verbose: print('All mole/mass/volume fractions sum to 1.0 within a specified tolerance.') idx = np.array([]) else: if verbose: print('Detected observations where mole/mass/volume fractions do not sum to 1.0 within a specified tolerance.') (idx, ) = np.where(sums_boolean==False) return idx # -------------------------------------------------------------------------- def range_of_species_fractions(self, species_fractions, tolerance=1e-12, verbose=False): """ Checks if all species mole/mass/volume fraction values are bounded between 0 and 1. For mole fractions: .. math:: X_i \\in \\langle 0, 1 \\rangle For mass fractions: .. math:: Y_i \\in \\langle 0, 1 \\rangle For volume fractions: .. math:: V_i \\in \\langle 0, 1 \\rangle :param species_fractions: scalar ``numpy.ndarray`` specifying **all** species mole/mass/volume fractions in :math:`[-]`. It should be of size ``(n_observations,n_species)`` where ``n_species`` is at least 2. :param verbose: (optional) ``bool`` for printing verbose information. :return: - **idx_below_zero** - indices of observations where species mole/mass/volume fractions are less than 0.0 within a specified tolerance. - **idx_above_one** - indices of observations where species mole/mass/volume fractions are larger than 1.0 within a specified tolerance. """ if not isinstance(species_fractions, np.ndarray): raise ValueError("Parameter `species_fractions` has to be of type `numpy.ndarray`.") try: (n_species, n_observations) = np.shape(species_fractions) except: raise ValueError("Parameter `species_fractions` has to be a matrix.") if n_species < 2: raise ValueError("Mole fractions matrix `species_fractions` has to have at least two species.") if n_observations < n_species: warnings.warn("Number of observations in `species_fractions` is smaller than the number of species. Make sure that the `species_fractions` has shape `(n_observations,n_species)`.") if not isinstance(verbose, bool): raise ValueError("Parameter `verbose` has to be of type `bool`.") if not np.greater_equal(species_fractions, 0-tolerance).all(): if verbose: print('Not all mole/mass/volume fractions are larger than 0.0 within a specified tolerance.') (idx_below_zero_i, idx_below_zero_j) = np.where(species_fractions<(0-tolerance)) idx_below_zero = np.hstack((idx_below_zero_i[:,None], idx_below_zero_j[:,None])) else: if verbose: print('All mole/mass/volume fractions are larger than 0.0 within a specified tolerance.') idx_below_zero = np.array([]) if not np.less_equal(species_fractions, 1+tolerance).all(): if verbose: print('Not all mole/mass/volume fractions are smaller than 1.0 within a specified tolerance.') (idx_above_one_i, idx_above_one_j) = np.where(species_fractions>(1+tolerance)) idx_above_one = np.hstack((idx_above_one_i[:,None], idx_above_one_j[:,None])) else: if verbose: print('All mole/mass/volume fractions are smaller than 1.0 within a specified tolerance.') idx_above_one = np.array([]) return (idx_below_zero, idx_above_one) # -------------------------------------------------------------------------- def sum_of_species_gradients(self, species_gradients, tolerance=1e-12, verbose=False): """ Checks if all species mole/mass/volume fraction gradients sum to 0.0 for every observation within a specified tolerance. For mole fractions: .. math:: \\sum_{i=1}^{n} \\nabla X_i = 0.0 For mass fractions: .. math:: \\sum_{i=1}^{n} \\nabla Y_i = 0.0 For volume fractions: .. math:: \\sum_{i=1}^{n} \\nabla V_i = 0.0 where :math:`n` is the number of species. :param species_gradients: scalar ``numpy.ndarray`` specifying **all** species mole/mass/volume fraction gradients in :math:`[-]`. It should be of size ``(n_species, n_observations)`` where ``n_species`` is at least 2. :param tolerance: (optional) ``float`` specifying the tolerance. It should be larger than 0.0 and smaller than 1.0. :param verbose: (optional) ``bool`` for printing verbose information. :return: - **idx** - indices of observations where species mole/mass/volume fraction gradients do not sum to 0.0 within a specified tolerance. """ if not isinstance(species_gradients, np.ndarray): raise ValueError("Parameter `species_gradients` has to be of type `numpy.ndarray`.") try: (n_species, n_observations) = np.shape(species_gradients) except: raise ValueError("Parameter `species_gradients` has to be a matrix.") if n_species < 2: raise ValueError("Species fractions matrix `species_gradients` has to have at least two species.") if n_observations < n_species: warnings.warn("Number of observations in `species_gradients` is smaller than the number of species. Make sure that the `species_fractions` has shape `(n_observations,n_species)`.") if not isinstance(tolerance, float): raise ValueError("Parameter `tolerance` has to be of type `float`.") if tolerance <= 0 or tolerance >= 1: raise ValueError("Parameter `tolerance` has to be larger than 0 and smaller than 1.") if not isinstance(verbose, bool): raise ValueError("Parameter `verbose` has to be of type `bool`.") sums = np.sum(species_gradients, axis=0) sums_boolean = np.zeros_like(sums) for i, observation in enumerate(sums): if (observation < tolerance) and (observation > -tolerance): sums_boolean[i] = True else: sums_boolean[i] = False if sums_boolean.all(): if verbose: print('All mole/mass/volume fraction gradiens sum to 0.0 within a specified tolerance.') idx = np.array([]) else: if verbose: print('Detected observations where mole/mass/volume fraction gradients do not sum to 0.0 within a specified tolerance.') (idx, ) = np.where(sums_boolean==False) return idx # -------------------------------------------------------------------------- def sum_of_species_production_rates(self, species_production_rates, tolerance=1e-12, verbose=False): """ Checks if all species production rates sum to 0.0 for every observation within a specified tolerance: For net molar production rates: .. math:: \\sum_{i=1}^{n} s_i = 0.0 For net mass production rates: .. math:: \\sum_{i=1}^{n} \\omega_i = 0.0 where :math:`n` is the number of species. :param species_production_rates: scalar ``numpy.ndarray`` specifying **all** species production rates, :math:`s_i` in :math:`mole/(m^3s)` or :math:`\\omega_i` in :math:`kg/(m^3s)`. It should be of size ``(n_species,n_observations)`` where ``n_species`` is at least 2. :param tolerance: (optional) ``float`` specifying the tolerance. It should be larger than 0.0 and smaller than 1.0. :param verbose: (optional) ``bool`` for printing verbose information. :return: - **idx** - indices of observations where species source terms do not sum to 0.0 within a specified tolerance. """ if not isinstance(species_production_rates, np.ndarray): raise ValueError("Parameter `species_production_rates` has to be of type `numpy.ndarray`.") try: (n_species, n_observations) = np.shape(species_production_rates) except: raise ValueError("Parameter `species_production_rates` has to be a matrix.") if n_species < 2: raise ValueError("Species source terms matrix `species_production_rates` has to have at least two species.") if n_observations < n_species: warnings.warn("Number of observations in `species_production_rates` is smaller than the number of species. Make sure that the `species_production_rates` has shape `(n_observations,n_species)`.") if not isinstance(tolerance, float): raise ValueError("Parameter `tolerance` has to be of type `float`.") if tolerance <= 0 or tolerance >= 1: raise ValueError("Parameter `tolerance` has to be larger than 0 and smaller than 1.") if not isinstance(verbose, bool): raise ValueError("Parameter `verbose` has to be of type `bool`.") sums = np.sum(species_production_rates, axis=0) sums_boolean = np.zeros_like(sums) for i, observation in enumerate(sums): if (observation < tolerance) and (observation > -tolerance): sums_boolean[i] = True else: sums_boolean[i] = False if sums_boolean.all(): if verbose: print('All species production rates sum to 0.0 within a specified tolerance.') idx = np.array([]) else: if verbose: print('Detected observations where species production rates do not sum to 0.0 within a specified tolerance.') (idx, ) = np.where(sums_boolean==False) return idx # --------------------------------------------------------------------------

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fed9bd2808591485831ae3b90b08dc959af84228

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py

Python

deprecated/origin_stgcn_repo/feeder/__init__.py

fserracant/mmskeleton

44008bdef3dd6354a17c220fac8bcd8cd08ed201

[ "Apache-2.0" ]

2,302

2018-01-23T11:18:30.000Z

2022-03-31T12:24:55.000Z

deprecated/origin_stgcn_repo/feeder/__init__.py

fserracant/mmskeleton

44008bdef3dd6354a17c220fac8bcd8cd08ed201

[ "Apache-2.0" ]

246

2019-08-24T15:36:11.000Z

2022-03-23T06:57:02.000Z

deprecated/origin_stgcn_repo/feeder/__init__.py

fserracant/mmskeleton

44008bdef3dd6354a17c220fac8bcd8cd08ed201

[ "Apache-2.0" ]

651

2018-01-24T00:56:54.000Z

2022-03-25T23:42:53.000Z

from . import tools

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3a1bb607068330f96d4bdb50c12759ee1c1a9528

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py

Python

tests/unit/test_experiments_analytics.py

LastRemote/sagemaker-python-sdk

fddf29d9e4383cd3f939253eef47ee79a464dd37

[ "Apache-2.0" ]

1,690

2017-11-29T20:13:37.000Z

2022-03-31T12:58:11.000Z

tests/unit/test_experiments_analytics.py

LastRemote/sagemaker-python-sdk

fddf29d9e4383cd3f939253eef47ee79a464dd37

[ "Apache-2.0" ]

2,762

2017-12-04T05:18:03.000Z

2022-03-31T23:40:11.000Z

tests/unit/test_experiments_analytics.py

LastRemote/sagemaker-python-sdk

fddf29d9e4383cd3f939253eef47ee79a464dd37

[ "Apache-2.0" ]

961

2017-11-30T16:44:03.000Z

2022-03-30T23:12:09.000Z

from __future__ import absolute_import import mock import pytest import pandas as pd from collections import OrderedDict from sagemaker.analytics import ExperimentAnalytics @pytest.fixture def mock_session(): return mock.Mock() def trial_component(trial_component_name): return { "TrialComponentName": trial_component_name, "DisplayName": "Training", "Source": {"SourceArn": "some-source-arn"}, "Parameters": {"hp1": {"NumberValue": 1.0}, "hp2": {"StringValue": "abc"}}, "Metrics": [ { "MetricName": "metric1", "Max": 5.0, "Min": 3.0, "Avg": 4.0, "StdDev": 1.0, "Last": 2.0, "Count": 2.0, }, { "MetricName": "metric2", "Max": 10.0, "Min": 8.0, "Avg": 9.0, "StdDev": 0.05, "Last": 7.0, "Count": 2.0, }, ], "InputArtifacts": { "inputArtifacts1": {"MediaType": "text/plain", "Value": "s3:/foo/bar1"}, "inputArtifacts2": {"MediaType": "text/plain", "Value": "s3:/foo/bar2"}, }, "OutputArtifacts": { "outputArtifacts1": {"MediaType": "text/csv", "Value": "s3:/sky/far1"}, "outputArtifacts2": {"MediaType": "text/csv", "Value": "s3:/sky/far2"}, }, "Parents": [{"TrialName": "trial1", "ExperimentName": "experiment1"}], } def test_trial_analytics_dataframe_all(mock_session): mock_session.sagemaker_client.search.return_value = { "Results": [ {"TrialComponent": trial_component("trial-1")}, {"TrialComponent": trial_component("trial-2")}, ] } analytics = ExperimentAnalytics(experiment_name="experiment1", sagemaker_session=mock_session) expected_dataframe = pd.DataFrame.from_dict( OrderedDict( [ ("TrialComponentName", ["trial-1", "trial-2"]), ("DisplayName", ["Training", "Training"]), ("SourceArn", ["some-source-arn", "some-source-arn"]), ("hp1", [1.0, 1.0]), ("hp2", ["abc", "abc"]), ("metric1 - Min", [3.0, 3.0]), ("metric1 - Max", [5.0, 5.0]), ("metric1 - Avg", [4.0, 4.0]), ("metric1 - StdDev", [1.0, 1.0]), ("metric1 - Last", [2.0, 2.0]), ("metric1 - Count", [2.0, 2.0]), ("metric2 - Min", [8.0, 8.0]), ("metric2 - Max", [10.0, 10.0]), ("metric2 - Avg", [9.0, 9.0]), ("metric2 - StdDev", [0.05, 0.05]), ("metric2 - Last", [7.0, 7.0]), ("metric2 - Count", [2.0, 2.0]), ("inputArtifacts1 - MediaType", ["text/plain", "text/plain"]), ("inputArtifacts1 - Value", ["s3:/foo/bar1", "s3:/foo/bar1"]), ("inputArtifacts2 - MediaType", ["text/plain", "text/plain"]), ("inputArtifacts2 - Value", ["s3:/foo/bar2", "s3:/foo/bar2"]), ("outputArtifacts1 - MediaType", ["text/csv", "text/csv"]), ("outputArtifacts1 - Value", ["s3:/sky/far1", "s3:/sky/far1"]), ("outputArtifacts2 - MediaType", ["text/csv", "text/csv"]), ("outputArtifacts2 - Value", ["s3:/sky/far2", "s3:/sky/far2"]), ("Trials", [["trial1"], ["trial1"]]), ("Experiments", [["experiment1"], ["experiment1"]]), ] ) ) pd.testing.assert_frame_equal(expected_dataframe, analytics.dataframe()) expected_search_exp = { "Filters": [ {"Name": "Parents.ExperimentName", "Operator": "Equals", "Value": "experiment1"} ] } mock_session.sagemaker_client.search.assert_called_with( Resource="ExperimentTrialComponent", SearchExpression=expected_search_exp ) def test_trial_analytics_dataframe_selected_hyperparams(mock_session): mock_session.sagemaker_client.search.return_value = { "Results": [ {"TrialComponent": trial_component("trial-1")}, {"TrialComponent": trial_component("trial-2")}, ] } analytics = ExperimentAnalytics( experiment_name="experiment1", parameter_names=["hp2"], sagemaker_session=mock_session ) expected_dataframe = pd.DataFrame.from_dict( OrderedDict( [ ("TrialComponentName", ["trial-1", "trial-2"]), ("DisplayName", ["Training", "Training"]), ("SourceArn", ["some-source-arn", "some-source-arn"]), ("hp2", ["abc", "abc"]), ("metric1 - Min", [3.0, 3.0]), ("metric1 - Max", [5.0, 5.0]), ("metric1 - Avg", [4.0, 4.0]), ("metric1 - StdDev", [1.0, 1.0]), ("metric1 - Last", [2.0, 2.0]), ("metric1 - Count", [2.0, 2.0]), ("metric2 - Min", [8.0, 8.0]), ("metric2 - Max", [10.0, 10.0]), ("metric2 - Avg", [9.0, 9.0]), ("metric2 - StdDev", [0.05, 0.05]), ("metric2 - Last", [7.0, 7.0]), ("metric2 - Count", [2.0, 2.0]), ("inputArtifacts1 - MediaType", ["text/plain", "text/plain"]), ("inputArtifacts1 - Value", ["s3:/foo/bar1", "s3:/foo/bar1"]), ("inputArtifacts2 - MediaType", ["text/plain", "text/plain"]), ("inputArtifacts2 - Value", ["s3:/foo/bar2", "s3:/foo/bar2"]), ("outputArtifacts1 - MediaType", ["text/csv", "text/csv"]), ("outputArtifacts1 - Value", ["s3:/sky/far1", "s3:/sky/far1"]), ("outputArtifacts2 - MediaType", ["text/csv", "text/csv"]), ("outputArtifacts2 - Value", ["s3:/sky/far2", "s3:/sky/far2"]), ("Trials", [["trial1"], ["trial1"]]), ("Experiments", [["experiment1"], ["experiment1"]]), ] ) ) pd.testing.assert_frame_equal(expected_dataframe, analytics.dataframe()) expected_search_exp = { "Filters": [ {"Name": "Parents.ExperimentName", "Operator": "Equals", "Value": "experiment1"} ] } mock_session.sagemaker_client.search.assert_called_with( Resource="ExperimentTrialComponent", SearchExpression=expected_search_exp ) def test_trial_analytics_dataframe_selected_metrics(mock_session): mock_session.sagemaker_client.search.return_value = { "Results": [ {"TrialComponent": trial_component("trial-1")}, {"TrialComponent": trial_component("trial-2")}, ] } analytics = ExperimentAnalytics( experiment_name="experiment1", metric_names=["metric1"], sagemaker_session=mock_session ) expected_dataframe = pd.DataFrame.from_dict( OrderedDict( [ ("TrialComponentName", ["trial-1", "trial-2"]), ("DisplayName", ["Training", "Training"]), ("SourceArn", ["some-source-arn", "some-source-arn"]), ("hp1", [1.0, 1.0]), ("hp2", ["abc", "abc"]), ("metric1 - Min", [3.0, 3.0]), ("metric1 - Max", [5.0, 5.0]), ("metric1 - Avg", [4.0, 4.0]), ("metric1 - StdDev", [1.0, 1.0]), ("metric1 - Last", [2.0, 2.0]), ("metric1 - Count", [2.0, 2.0]), ("inputArtifacts1 - MediaType", ["text/plain", "text/plain"]), ("inputArtifacts1 - Value", ["s3:/foo/bar1", "s3:/foo/bar1"]), ("inputArtifacts2 - MediaType", ["text/plain", "text/plain"]), ("inputArtifacts2 - Value", ["s3:/foo/bar2", "s3:/foo/bar2"]), ("outputArtifacts1 - MediaType", ["text/csv", "text/csv"]), ("outputArtifacts1 - Value", ["s3:/sky/far1", "s3:/sky/far1"]), ("outputArtifacts2 - MediaType", ["text/csv", "text/csv"]), ("outputArtifacts2 - Value", ["s3:/sky/far2", "s3:/sky/far2"]), ("Trials", [["trial1"], ["trial1"]]), ("Experiments", [["experiment1"], ["experiment1"]]), ] ) ) pd.testing.assert_frame_equal(expected_dataframe, analytics.dataframe()) expected_search_exp = { "Filters": [ {"Name": "Parents.ExperimentName", "Operator": "Equals", "Value": "experiment1"} ] } mock_session.sagemaker_client.search.assert_called_with( Resource="ExperimentTrialComponent", SearchExpression=expected_search_exp ) def test_trial_analytics_dataframe_search_pagination(mock_session): result_page_1 = { "Results": [{"TrialComponent": trial_component("trial-1")}], "NextToken": "nextToken", } result_page_2 = {"Results": [{"TrialComponent": trial_component("trial-2")}]} mock_session.sagemaker_client.search.side_effect = [result_page_1, result_page_2] analytics = ExperimentAnalytics(experiment_name="experiment1", sagemaker_session=mock_session) expected_dataframe = pd.DataFrame.from_dict( OrderedDict( [ ("TrialComponentName", ["trial-1", "trial-2"]), ("DisplayName", ["Training", "Training"]), ("SourceArn", ["some-source-arn", "some-source-arn"]), ("hp1", [1.0, 1.0]), ("hp2", ["abc", "abc"]), ("metric1 - Min", [3.0, 3.0]), ("metric1 - Max", [5.0, 5.0]), ("metric1 - Avg", [4.0, 4.0]), ("metric1 - StdDev", [1.0, 1.0]), ("metric1 - Last", [2.0, 2.0]), ("metric1 - Count", [2.0, 2.0]), ("metric2 - Min", [8.0, 8.0]), ("metric2 - Max", [10.0, 10.0]), ("metric2 - Avg", [9.0, 9.0]), ("metric2 - StdDev", [0.05, 0.05]), ("metric2 - Last", [7.0, 7.0]), ("metric2 - Count", [2.0, 2.0]), ("inputArtifacts1 - MediaType", ["text/plain", "text/plain"]), ("inputArtifacts1 - Value", ["s3:/foo/bar1", "s3:/foo/bar1"]), ("inputArtifacts2 - MediaType", ["text/plain", "text/plain"]), ("inputArtifacts2 - Value", ["s3:/foo/bar2", "s3:/foo/bar2"]), ("outputArtifacts1 - MediaType", ["text/csv", "text/csv"]), ("outputArtifacts1 - Value", ["s3:/sky/far1", "s3:/sky/far1"]), ("outputArtifacts2 - MediaType", ["text/csv", "text/csv"]), ("outputArtifacts2 - Value", ["s3:/sky/far2", "s3:/sky/far2"]), ("Trials", [["trial1"], ["trial1"]]), ("Experiments", [["experiment1"], ["experiment1"]]), ] ) ) pd.testing.assert_frame_equal(expected_dataframe, analytics.dataframe()) expected_search_exp = { "Filters": [ {"Name": "Parents.ExperimentName", "Operator": "Equals", "Value": "experiment1"} ] } mock_session.sagemaker_client.search.assert_has_calls( [ mock.call(Resource="ExperimentTrialComponent", SearchExpression=expected_search_exp), mock.call( Resource="ExperimentTrialComponent", SearchExpression=expected_search_exp, NextToken="nextToken", ), ] ) def test_trial_analytics_dataframe_filter_trials_search_exp_only(mock_session): mock_session.sagemaker_client.search.return_value = {"Results": []} search_exp = {"Filters": [{"Name": "Tags.someTag", "Operator": "Equals", "Value": "someValue"}]} analytics = ExperimentAnalytics(search_expression=search_exp, sagemaker_session=mock_session) analytics.dataframe() mock_session.sagemaker_client.search.assert_called_with( Resource="ExperimentTrialComponent", SearchExpression=search_exp ) def test_trial_analytics_dataframe_filter_trials_search_exp_with_experiment(mock_session): mock_session.sagemaker_client.search.return_value = {"Results": []} search_exp = {"Filters": [{"Name": "Tags.someTag", "Operator": "Equals", "Value": "someValue"}]} analytics = ExperimentAnalytics( experiment_name="someExperiment", search_expression=search_exp, sagemaker_session=mock_session, ) analytics.dataframe() expected_search_exp = { "Filters": [ {"Name": "Tags.someTag", "Operator": "Equals", "Value": "someValue"}, {"Name": "Parents.ExperimentName", "Operator": "Equals", "Value": "someExperiment"}, ] } mock_session.sagemaker_client.search.assert_called_with( Resource="ExperimentTrialComponent", SearchExpression=expected_search_exp ) def test_trial_analytics_dataframe_throws_error_if_no_filter_specified(mock_session): with pytest.raises(ValueError): ExperimentAnalytics(sagemaker_session=mock_session) def test_trial_analytics_dataframe_filter_trials_search_exp_with_sort(mock_session): mock_session.sagemaker_client.search.return_value = {"Results": []} search_exp = {"Filters": [{"Name": "Tags.someTag", "Operator": "Equals", "Value": "someValue"}]} analytics = ExperimentAnalytics( experiment_name="someExperiment", search_expression=search_exp, sort_by="Tags.someTag", sort_order="Ascending", sagemaker_session=mock_session, ) analytics.dataframe() expected_search_exp = { "Filters": [ {"Name": "Tags.someTag", "Operator": "Equals", "Value": "someValue"}, {"Name": "Parents.ExperimentName", "Operator": "Equals", "Value": "someExperiment"}, ] } mock_session.sagemaker_client.search.assert_called_with( Resource="ExperimentTrialComponent", SearchExpression=expected_search_exp, SortBy="Tags.someTag", SortOrder="Ascending", )

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1

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null

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6

3a48d584ca2b00f4953c04fc6e6edaf62e4524b4

111

py

Python

lab001/load.py

DavidJRichards/fpga_101

9aa3e85211e47c63c29af36960fd767fe88f4d82

[ "BSD-2-Clause" ]

2

2021-08-15T20:19:11.000Z

2021-08-16T07:28:36.000Z

lab001/load.py

DavidJRichards/fpga_101

9aa3e85211e47c63c29af36960fd767fe88f4d82

[ "BSD-2-Clause" ]

null

lab001/load.py

DavidJRichards/fpga_101

9aa3e85211e47c63c29af36960fd767fe88f4d82

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 import os os.system("openocd -f wukong.cfg -c 'init; pld load 0 build/top.bit; exit' ")

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111

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78

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null

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1

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6

3a54d0fda33a47ced2ba7f11cd011f05493c2833

40

py

Python

datasets/__init__.py

ML-Cai/LaneDetector

4e56faf45cf592812284b0bfee149bba4658fac9

[ "MIT" ]

null

datasets/__init__.py

ML-Cai/LaneDetector

4e56faf45cf592812284b0bfee149bba4658fac9

[ "MIT" ]

null

datasets/__init__.py

ML-Cai/LaneDetector

4e56faf45cf592812284b0bfee149bba4658fac9

[ "MIT" ]

null

from .tu_simple_lane import TusimpleLane

40

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6

40

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40

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null

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0

null

0

1

0

1

0

1

0

6

3a5562123f0c3dc18461e7e454e66d71a8d213a8

29

py

Python

dashboard/dashboardmenu/__init__.py

PyFlux/PyFlux

8abae10261e276bf4942aed8d54ef3b5498754ca

[ "Apache-2.0" ]

null

dashboard/dashboardmenu/__init__.py

PyFlux/PyFlux

8abae10261e276bf4942aed8d54ef3b5498754ca

[ "Apache-2.0" ]

10

2020-03-24T17:09:56.000Z

2021-12-13T20:00:15.000Z

dashboard/dashboardmenu/__init__.py

PyFlux/PyFlux-Django-Html

8abae10261e276bf4942aed8d54ef3b5498754ca

[ "Apache-2.0" ]

null

from .dashboard_menu import *

29

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null

0

1

0

1

0

1

0

6

28df1e4de356cb1489acc045615f0942034640d3

61

py

Python

up/tasks/det/data/__init__.py

ModelTC/EOD

164bff80486e9ae6a095a97667b365c46ceabd86

[ "Apache-2.0" ]

196

2021-10-30T05:15:36.000Z

2022-03-30T18:43:40.000Z

eod/tasks/det/data/__init__.py

YZW-explorer/EOD

f10e64de86c0f356ebf5c7e923f4042eec4207b1

[ "Apache-2.0" ]

12

2021-10-30T11:33:28.000Z

2022-03-31T14:22:58.000Z

eod/tasks/det/data/__init__.py

YZW-explorer/EOD

f10e64de86c0f356ebf5c7e923f4042eec4207b1

[ "Apache-2.0" ]

23

2021-11-01T07:26:17.000Z

2022-03-27T05:55:37.000Z

from .datasets import * # noqa from .metrics import * # noqa

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e91e11b03c50d75698f208a10f1b310af5a8ffcc

4,043

py

Python

authors/apps/articles/tests/test_likes_dislikes.py

andela/ah-backend-prime

0708463d4565a4977a5a5dcb839f1dfed52fdc90

[ "BSD-3-Clause" ]

1

2019-09-19T14:30:05.000Z

authors/apps/articles/tests/test_likes_dislikes.py

e-ian/authors-haven-frontend

05829c8088ca49ef2cf0863dc87ec55b44b13534

[ "BSD-3-Clause" ]

22

2019-03-25T16:10:53.000Z

2022-03-11T23:44:21.000Z

authors/apps/articles/tests/test_likes_dislikes.py

e-ian/authors-haven-frontend

05829c8088ca49ef2cf0863dc87ec55b44b13534

[ "BSD-3-Clause" ]

6

2019-03-25T09:39:39.000Z

2021-03-11T23:54:12.000Z

import json from rest_framework import status, response from django.urls import reverse from .base import ArticlesBaseTest from .test_data import VALID_ARTICLE from authors.apps.authentication.tests.test_data import ( VALID_USER_DATA ) from rest_framework.test import APIClient, APITestCase from .base import BaseTest class TestLikeDislikeArticle(ArticlesBaseTest): '''Test likes and dislikes functionality''' def test_like_article(self): '''Test for liking article''' token = self.create_user(VALID_USER_DATA) response = self.client.post( self.create_articles, HTTP_AUTHORIZATION=token, data=VALID_ARTICLE, format='json' ) articles_slug = response.data['article']['slug'] response = self.client.post( f'/api/v1/articles/{articles_slug}/like/', HTTP_AUTHORIZATION=token ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.data['likes'], 1) def test_dislike_article(self): '''Test for disliking article''' token = self.create_user(VALID_USER_DATA) response = self.client.post( self.create_articles, HTTP_AUTHORIZATION=token, data=VALID_ARTICLE, format='json' ) articles_slug = response.data['article']['slug'] response = self.client.post( f'/api/v1/articles/{articles_slug}/dislike/', HTTP_AUTHORIZATION=token ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.data['dislikes'], 1) def test_like_article_twice(self): '''Test for disliking article twice''' token = self.create_user(VALID_USER_DATA) response = self.client.post( self.create_articles, HTTP_AUTHORIZATION=token, data=VALID_ARTICLE, format='json' ) articles_slug = response.data['article']['slug'] self.client.post( f'/api/v1/articles/{articles_slug}/like/', HTTP_AUTHORIZATION=token ) response = self.client.post( f'/api/v1/articles/{articles_slug}/like/', HTTP_AUTHORIZATION=token ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.data['likes'], 0) def test_dislike_article_twice(self): '''Test for disliking article twice''' token = self.create_user(VALID_USER_DATA) response = self.client.post( self.create_articles, HTTP_AUTHORIZATION=token, data=VALID_ARTICLE, format='json' ) articles_slug = response.data['article']['slug'] self.client.post( f'/api/v1/articles/{articles_slug}/dislike/', HTTP_AUTHORIZATION=token ) response = self.client.post( f'/api/v1/articles/{articles_slug}/dislike/', HTTP_AUTHORIZATION=token ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.data['dislikes'], 0) def test_like_disliked_article_twice(self): '''Test for liking a disliked article''' token = self.create_user(VALID_USER_DATA) response = self.client.post( self.create_articles, HTTP_AUTHORIZATION=token, data=VALID_ARTICLE, format='json' ) articles_slug = response.data['article']['slug'] self.client.post( f'/api/v1/articles/{articles_slug}/like/', HTTP_AUTHORIZATION=token ) response = self.client.post( f'/api/v1/articles/{articles_slug}/dislike/', HTTP_AUTHORIZATION=token ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.data['likes'], 0) self.assertEqual(response.data['dislikes'], 1)

33.97479

71

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4,043

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0.133028

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0.090386

0.812243

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0

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0.271581

4,043

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72

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0.816638

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0

0.111111

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0.050505

false

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0.080808

0

0.141414

0

null

0

1

0

null

0

6

3a7e4975152b719956030d04fd87b6aff71f9b39

203

py

Python

app/views/dashboard/leadership/__init__.py

Wern-rm/raton.by

68f862f2bc0551bf2327e9d6352c0cde93f45301

[ "MIT" ]

null

app/views/dashboard/leadership/__init__.py

Wern-rm/raton.by

68f862f2bc0551bf2327e9d6352c0cde93f45301

[ "MIT" ]

null

app/views/dashboard/leadership/__init__.py

Wern-rm/raton.by

68f862f2bc0551bf2327e9d6352c0cde93f45301

[ "MIT" ]

null

from app.views.dashboard.leadership.index import leaderships from app.views.dashboard.leadership.delete import leadership_delete from app.views.dashboard.leadership.activation import leadership_activated

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6.846154

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0.202247

0.353933

0.522472

0

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203

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74

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1

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6

c97f4aad4afc2d34135bd0a531bcabb3725f19f6

10,715

py

Python

tests/unit/states/test_libvirt.py

cvedel/salt

8731f42829ca1f0a38d2434057c485abeff222a7

[ "Apache-2.0", "MIT" ]

null

tests/unit/states/test_libvirt.py

cvedel/salt

8731f42829ca1f0a38d2434057c485abeff222a7

[ "Apache-2.0", "MIT" ]

null

tests/unit/states/test_libvirt.py

cvedel/salt

8731f42829ca1f0a38d2434057c485abeff222a7

[ "Apache-2.0", "MIT" ]

null

# -*- coding: utf-8 -*- ''' :codeauthor: Jayesh Kariya <jayeshk@saltstack.com> ''' # pylint: disable=3rd-party-module-not-gated # Import Python libs from __future__ import absolute_import, print_function, unicode_literals import tempfile import shutil # Import Salt Testing Libs from tests.support.mixins import LoaderModuleMockMixin from tests.support.paths import TMP from tests.support.unit import skipIf, TestCase from tests.support.mock import ( NO_MOCK, NO_MOCK_REASON, MagicMock, mock_open, patch) # Import Salt Libs import salt.states.virt as virt import salt.utils.files class LibvirtMock(MagicMock): # pylint: disable=too-many-ancestors ''' libvirt library mockup ''' class libvirtError(Exception): # pylint: disable=invalid-name ''' libvirt error mockup ''' @skipIf(NO_MOCK, NO_MOCK_REASON) class LibvirtTestCase(TestCase, LoaderModuleMockMixin): ''' Test cases for salt.states.libvirt ''' def setup_loader_modules(self): self.mock_libvirt = LibvirtMock() # pylint: disable=attribute-defined-outside-init self.addCleanup(delattr, self, 'mock_libvirt') loader_globals = { 'libvirt': self.mock_libvirt } return {virt: loader_globals} @classmethod def setUpClass(cls): cls.pki_dir = tempfile.mkdtemp(dir=TMP) @classmethod def tearDownClass(cls): shutil.rmtree(cls.pki_dir) del cls.pki_dir # 'keys' function tests: 1 def test_keys(self): ''' Test to manage libvirt keys. ''' with patch('os.path.isfile', MagicMock(return_value=False)): name = 'sunrise' ret = {'name': name, 'result': True, 'comment': '', 'changes': {}} mock = MagicMock(side_effect=[[], ['libvirt.servercert.pem'], {'libvirt.servercert.pem': 'A'}]) with patch.dict(virt.__salt__, {'pillar.ext': mock}): # pylint: disable=no-member comt = ('All keys are correct') ret.update({'comment': comt}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir), ret) with patch.dict(virt.__opts__, {'test': True}): # pylint: disable=no-member comt = ('Libvirt keys are set to be updated') ret.update({'comment': comt, 'result': None}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir), ret) with patch.dict(virt.__opts__, {'test': False}): # pylint: disable=no-member with patch.object(salt.utils.files, 'fopen', MagicMock(mock_open())): comt = ('Updated libvirt certs and keys') ret.update({'comment': comt, 'result': True, 'changes': {'servercert': 'new'}}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir), ret) def test_keys_with_expiration_days(self): ''' Test to manage libvirt keys. ''' with patch('os.path.isfile', MagicMock(return_value=False)): name = 'sunrise' ret = {'name': name, 'result': True, 'comment': '', 'changes': {}} mock = MagicMock(side_effect=[[], ['libvirt.servercert.pem'], {'libvirt.servercert.pem': 'A'}]) with patch.dict(virt.__salt__, {'pillar.ext': mock}): # pylint: disable=no-member comt = ('All keys are correct') ret.update({'comment': comt}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, expiration_days=700), ret) with patch.dict(virt.__opts__, {'test': True}): # pylint: disable=no-member comt = ('Libvirt keys are set to be updated') ret.update({'comment': comt, 'result': None}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, expiration_days=700), ret) with patch.dict(virt.__opts__, {'test': False}): # pylint: disable=no-member with patch.object(salt.utils.files, 'fopen', MagicMock(mock_open())): comt = ('Updated libvirt certs and keys') ret.update({'comment': comt, 'result': True, 'changes': {'servercert': 'new'}}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, expiration_days=700), ret) def test_keys_with_state(self): ''' Test to manage libvirt keys. ''' with patch('os.path.isfile', MagicMock(return_value=False)): name = 'sunrise' ret = {'name': name, 'result': True, 'comment': '', 'changes': {}} mock = MagicMock(side_effect=[[], ['libvirt.servercert.pem'], {'libvirt.servercert.pem': 'A'}]) with patch.dict(virt.__salt__, {'pillar.ext': mock}): # pylint: disable=no-member comt = ('All keys are correct') ret.update({'comment': comt}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, st='California'), ret) with patch.dict(virt.__opts__, {'test': True}): # pylint: disable=no-member comt = ('Libvirt keys are set to be updated') ret.update({'comment': comt, 'result': None}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, st='California'), ret) with patch.dict(virt.__opts__, {'test': False}): # pylint: disable=no-member with patch.object(salt.utils.files, 'fopen', MagicMock(mock_open())): comt = ('Updated libvirt certs and keys') ret.update({'comment': comt, 'result': True, 'changes': {'servercert': 'new'}}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, st='California'), ret) def test_keys_with_all_options(self): ''' Test to manage libvirt keys. ''' with patch('os.path.isfile', MagicMock(return_value=False)): name = 'sunrise' ret = {'name': name, 'result': True, 'comment': '', 'changes': {}} mock = MagicMock(side_effect=[[], ['libvirt.servercert.pem'], {'libvirt.servercert.pem': 'A'}]) with patch.dict(virt.__salt__, {'pillar.ext': mock}): # pylint: disable=no-member comt = ('All keys are correct') ret.update({'comment': comt}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, country='USA', st='California', locality='Los_Angeles', organization='SaltStack', expiration_days=700), ret) with patch.dict(virt.__opts__, {'test': True}): # pylint: disable=no-member comt = ('Libvirt keys are set to be updated') ret.update({'comment': comt, 'result': None}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, country='USA', st='California', locality='Los_Angeles', organization='SaltStack', expiration_days=700), ret) with patch.dict(virt.__opts__, {'test': False}): # pylint: disable=no-member with patch.object(salt.utils.files, 'fopen', MagicMock(mock_open())): comt = ('Updated libvirt certs and keys') ret.update({'comment': comt, 'result': True, 'changes': {'servercert': 'new'}}) self.assertDictEqual(virt.keys(name, basepath=self.pki_dir, country='USA', st='California', locality='Los_Angeles', organization='SaltStack', expiration_days=700), ret) def test_running(self): ''' running state test cases. ''' ret = {'name': 'myvm', 'changes': {}, 'result': True, 'comment': 'myvm is running'} with patch.dict(virt.__salt__, { # pylint: disable=no-member 'virt.vm_state': MagicMock(return_value='stopped'), 'virt.start': MagicMock(return_value=0) }): ret.update({'changes': {'myvm': 'Domain started'}, 'comment': 'Domain myvm started'}) self.assertDictEqual(virt.running('myvm'), ret) with patch.dict(virt.__salt__, { # pylint: disable=no-member 'virt.vm_state': MagicMock(return_value='stopped'), 'virt.start': MagicMock(side_effect=[self.mock_libvirt.libvirtError('libvirt error msg')]) }): ret.update({'changes': {}, 'result': False, 'comment': 'libvirt error msg'}) self.assertDictEqual(virt.running('myvm'), ret)

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0

null

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0

null

0

6

a36d0ac9736ee7f0f87c898553b9622f6343c622

130

py

Python

katas/kyu_7/product_of_main_diagonal.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_7/product_of_main_diagonal.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/kyu_7/product_of_main_diagonal.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

from operator import mul def main_diagonal_product(matrix): return reduce(mul, (matrix[a][a] for a in xrange(len(matrix))))

21.666667

67

0.730769

21

130

4.428571

0.761905

0

0.146154

130

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0

0.333333

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0

null

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1

0

null

0

1

0

1

0

6

6e8ba5d71602dfafef83788dd25424753fb81302

22

py

Python

rtk/_reports_/__init__.py

rakhimov/rtk

adc35e218ccfdcf3a6e3082f6a1a1d308ed4ff63

[ "BSD-3-Clause" ]

null

rtk/_reports_/__init__.py

rakhimov/rtk

adc35e218ccfdcf3a6e3082f6a1a1d308ed4ff63

[ "BSD-3-Clause" ]

null

rtk/_reports_/__init__.py

rakhimov/rtk

adc35e218ccfdcf3a6e3082f6a1a1d308ed4ff63

[ "BSD-3-Clause" ]

2

2020-04-03T04:14:42.000Z

2021-02-22T05:30:35.000Z

from tabular import *

11

21

0.772727

3

22

5.666667

1

0

0.181818

22

1

22

0.944444

0

1

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null

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1

0

1

0

6

6ed12b3edc7505ed891b2d8f3913b9e4dec71522

152

py

Python

training/config_interface/__init__.py

khoehlein/CNNs-for-Wind-Field-Downscaling

eb8418d4d893fcb2beb929abb241281b7a9b6a95

[ "MIT" ]

5

2021-05-05T06:08:52.000Z

2022-03-24T04:57:52.000Z

training/config_interface/__init__.py

khoehlein/CNNs-for-Wind-Field-Downscaling

eb8418d4d893fcb2beb929abb241281b7a9b6a95

[ "MIT" ]

null

training/config_interface/__init__.py

khoehlein/CNNs-for-Wind-Field-Downscaling

eb8418d4d893fcb2beb929abb241281b7a9b6a95

[ "MIT" ]

2

2021-08-07T05:18:05.000Z

2022-03-31T03:48:37.000Z

from training.config_interface.BaseTrainingProcess import BaseTrainingProcess from training.config_interface.BaseTrainingEpoch import BaseTrainingEpoch

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1

0

6

6ed7f888ecc9bba08e6a0dcd86d63bb68f3e4ae3

12,156

py

Python

KML.py

ncareol/PlanFlight

c38b3e1a99f52655cae9e1b4f4c2ee06e56833eb

[ "BSD-3-Clause" ]

1

2021-06-16T01:10:35.000Z

KML.py

NCAR/PlanFlight

c38b3e1a99f52655cae9e1b4f4c2ee06e56833eb

[ "BSD-3-Clause" ]

null

KML.py

NCAR/PlanFlight

c38b3e1a99f52655cae9e1b4f4c2ee06e56833eb

[ "BSD-3-Clause" ]

null

# file KML.py # "Produces a kml file from the track as defined in ModuleConstructor.Track." # Strategy here is to produce two .kml files, one that references # google.com and one that references acserver.raf.ucar.edu, the latter # for use on the aircraft to avoid remote connections to google.com # in flight. The latter is named PlanAC.kml, the former Plan.kml. # # This is awkward code that writes many things repeatedly where I'm sure # there is an efficient way to do this. Someday should clean this up -- # but it works, so leave it for now. It was copied from a Google-Earth- # constructed representation of the track, so I'm just taking all the # kml that was in that file and duplicating it without understanding what # I'm doing... import Specs WaypointNumber = 0 KMLFileName = 'Plan.kml' lonx = Specs.TakeoffLocation()[0] latx = Specs.TakeoffLocation()[1] galtx = Specs.TakeoffLocation()[2] # header info for .kml file def KMLHeader(KMLFileName): "Opens the file and writes the required header." # XXXX fix this global WaypointNumber # changed here so needs to be global KMLACFileName = KMLFileName.replace ('Plan', 'PlanAC') print 'kml file name: ', KMLFileName, ', new name is: ', KMLACFileName KMLFile = open(KMLFileName,'w') KMLACFile = open(KMLACFileName,'w') KMLFile.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n") KMLFile.write("<kml xmlns=\"http://earth.google.com/kml/2.2\">\n") KMLFile.write("<Document>\n") # might need to replace .kml with .kmz here? KMLFile.write("\t <name>"+KMLFileName+"</name>\n") KMLFile.write("\t<StyleMap id=\"msn_triangle_copy1\">\n") KMLFile.write("\t\t<Pair>\n") KMLFile.write("\t\t\t<key>normal</key>\n") KMLFile.write("\t\t\t<styleUrl>#sn_triangle_copy1"\ + "</styleUrl>\n") KMLFile.write("\t\t</Pair>\n") KMLFile.write("\t\t<Pair>\n") KMLFile.write("\t\t\t<key>highlight</key>\n") KMLFile.write("\t\t\t<styleUrl>#sh_triangle_copy1"\ +"</styleUrl>\n") KMLFile.write("\t\t</Pair>\n") KMLFile.write("\t</StyleMap>\n") KMLFile.write("\t <Style id=\"sh_triangle_copy1\">\n") KMLFile.write("\t\t <IconStyle>\n") KMLFile.write("\t\t\t <color>ff0000ff</color>\n") KMLFile.write("\t\t\t <scale>0.8</scale>\n") KMLFile.write("\t\t\t <Icon>\n") # KMLFile.write("\t\t\t\t <href>http://acserver.raf.ucar.edu/flight_data/display/triangle.png</href>\n") KMLFile.write("\t\t\t\t <href>http://maps.google.com/mapfiles/kml/shapes/placemark_square.png</href>\n") KMLFile.write("\t\t\t </Icon>\n") KMLFile.write("\t\t </IconStyle>\n") KMLFile.write("\t\t <LabelStyle>\n") KMLFile.write("\t\t\t <color>ff0000ff</color>\n") KMLFile.write("\t\t </LabelStyle>\n") KMLFile.write("\t\t <LineStyle>\n") KMLFile.write("\t\t\t <color>ff00aaff</color>\n") KMLFile.write("\t\t\t <width>2</width>\n") KMLFile.write("\t\t </LineStyle>\n") KMLFile.write("\t\t <ListStyle>\n") KMLFile.write("\t\t </ListStyle>\n") KMLFile.write("\t </Style>\n") KMLFile.write("\t <Style id=\"sn_triangle_copy1\">\n") KMLFile.write("\t\t <IconStyle>\n") KMLFile.write("\t\t\t <color>ff0000ff</color>\n") KMLFile.write("\t\t\t <scale>0.8</scale>\n") KMLFile.write("\t\t\t <Icon>\n") # KMLFile.write("\t\t\t\t <href>http://acserver.raf.ucar.edu/flight_data/display/triangle.png</href>\n") KMLFile.write("\t\t\t\t <href>http://maps.google.com/mapfiles/kml/shapes/placemark_square.png</href>\n") KMLFile.write("\t\t\t </Icon>\n") KMLFile.write("\t\t </IconStyle>\n") KMLFile.write("\t\t <LabelStyle>\n") KMLFile.write("\t\t\t <color>ff0000ff</color>\n") KMLFile.write("\t\t </LabelStyle>\n") KMLFile.write("\t\t <LineStyle>\n") KMLFile.write("\t\t\t <color>ff00aaff</color>\n") KMLFile.write("\t\t\t <width>2</width>\n") KMLFile.write("\t\t </LineStyle>\n") KMLFile.write("\t\t <ListStyle>\n") KMLFile.write("\t\t </ListStyle>\n") KMLFile.write("\t </Style>\n") KMLACFile.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n") KMLACFile.write("<kml xmlns=\"http://earth.google.com/kml/2.2\">\n") KMLACFile.write("<Document>\n") # might need to replace .kml with .kmz here? KMLACFile.write("\t <name>"+KMLACFileName+"</name>\n") KMLACFile.write("\t<StyleMap id=\"msn_triangle_copy1\">\n") KMLACFile.write("\t\t<Pair>\n") KMLACFile.write("\t\t\t<key>normal</key>\n") KMLACFile.write("\t\t\t<styleUrl>#sn_triangle_copy1"\ + "</styleUrl>\n") KMLACFile.write("\t\t</Pair>\n") KMLACFile.write("\t\t<Pair>\n") KMLACFile.write("\t\t\t<key>highlight</key>\n") KMLACFile.write("\t\t\t<styleUrl>#sh_triangle_copy1"\ +"</styleUrl>\n") KMLACFile.write("\t\t</Pair>\n") KMLACFile.write("\t</StyleMap>\n") KMLACFile.write("\t <Style id=\"sh_triangle_copy1\">\n") KMLACFile.write("\t\t <IconStyle>\n") KMLACFile.write("\t\t\t <color>ff0000ff</color>\n") KMLACFile.write("\t\t\t <scale>0.8</scale>\n") KMLACFile.write("\t\t\t <Icon>\n") # KMLACFile.write("\t\t\t\t <href>http://acserver.raf.ucar.edu/flight_data/display/triangle.png</href>\n") KMLACFile.write("\t\t\t\t <href>http://acserver.raf.ucar.edu/flight_data/display/placemark_square.png</href>\n") KMLACFile.write("\t\t\t </Icon>\n") KMLACFile.write("\t\t </IconStyle>\n") KMLACFile.write("\t\t <LabelStyle>\n") KMLACFile.write("\t\t\t <color>ff0000ff</color>\n") KMLACFile.write("\t\t </LabelStyle>\n") KMLACFile.write("\t\t <LineStyle>\n") KMLACFile.write("\t\t\t <color>ff00aaff</color>\n") KMLACFile.write("\t\t\t <width>2</width>\n") KMLACFile.write("\t\t </LineStyle>\n") KMLACFile.write("\t\t <ListStyle>\n") KMLACFile.write("\t\t </ListStyle>\n") KMLACFile.write("\t </Style>\n") KMLACFile.write("\t <Style id=\"sn_triangle_copy1\">\n") KMLACFile.write("\t\t <IconStyle>\n") KMLACFile.write("\t\t\t <color>ff0000ff</color>\n") KMLACFile.write("\t\t\t <scale>0.8</scale>\n") KMLACFile.write("\t\t\t <Icon>\n") KMLACFile.write("\t\t\t\t <href>http://acserver.raf.ucar.edu/flight_data/display/placemark_square.png</href>\n") # KMLACFile.write("\t\t\t\t <href>http://maps.google.com/mapfiles/kml/shapes/triangle.png</href>\n") KMLACFile.write("\t\t\t </Icon>\n") KMLACFile.write("\t\t </IconStyle>\n") KMLACFile.write("\t\t <LabelStyle>\n") KMLACFile.write("\t\t\t <color>ff0000ff</color>\n") KMLACFile.write("\t\t </LabelStyle>\n") KMLACFile.write("\t\t <LineStyle>\n") KMLACFile.write("\t\t\t <color>ff00aaff</color>\n") KMLACFile.write("\t\t\t <width>2</width>\n") KMLACFile.write("\t\t </LineStyle>\n") KMLACFile.write("\t\t <ListStyle>\n") KMLACFile.write("\t\t </ListStyle>\n") KMLACFile.write("\t </Style>\n") WaypointNumber = 0 return(KMLFile, KMLACFile) def KMLclose(KMLFile, KMLACFile): "Adds trailer to the .kml file and then closes it." KMLFile.write("</Document>\n") KMLFile.write("</kml>\n") KMLFile.close() KMLACFile.write("</Document>\n") KMLACFile.write("</kml>\n") KMLACFile.close() def PlotPoints (KMLFile, KMLACFile, points): "Plot the set of points on the .kml file" KMLFile.write("\t <Placemark>\n") KMLFile.write("\t\t <styleUrl>#msn_triangle_copy1</styleUrl>\n") KMLFile.write("\t\t <LineString>\n") KMLFile.write("\t\t\t <tessellate>1</tessellate>\n") KMLFile.write("\t\t\t <coordinates>\n") for x in points: KMLFile.write ("\t\t\t\t " + format (x[0], 'f') + ','\ + format (x[1], 'f') + ','\ + format (x[2], 'f') + ' \n') KMLFile.write("\t\t\t </coordinates>\n") KMLFile.write("\t\t\t <altitudeMode>absolute</altitudeMode>\n") KMLFile.write("\t\t </LineString>\n") KMLFile.write("\t </Placemark>\n") KMLACFile.write("\t <Placemark>\n") KMLACFile.write("\t\t <styleUrl>#sh_triangle_copy1</styleUrl>\n") KMLACFile.write("\t\t <LineString>\n") KMLACFile.write("\t\t\t <tessellate>1</tessellate>\n") KMLACFile.write("\t\t\t <coordinates>\n") for x in points: KMLACFile.write ("\t\t\t\t " + format (x[0], 'f') + ','\ + format (x[1], 'f') + ','\ + format (x[2], 'f') + ' \n') KMLACFile.write("\t\t\t </coordinates>\n") KMLACFile.write("\t\t </LineString>\n") KMLACFile.write("\t </Placemark>\n") def PlotWaypoint (KMLFile, KMLACFile, wp, label='', symbol = 'triangle'): "Adds waypoint symbol to the .kml file for plotting on Google Earth etc." # Copy from a Google-Earth-generated example # (I don't understand all this; it's just copied verbatim here. # It's likely this could be made more compact.) global WaypointNumber, lonx, latx, galtx # These are global because they are saved in order to # draw lines from the last point to this one. longitude = wp[0] latitude = wp[1] altitude = wp[2] WaypointNumber += 1 if (label == ''): label="WP"+format(WaypointNumber,'d') KMLFile.write("\t <Placemark>\n") KMLFile.write("\t\t <name>"+label+"</name>\n") KMLFile.write("\t\t <description>WayPoint "\ +format(round(altitude/(100))*100.,'.0f')+' ft'+"</description>\n") KMLFile.write("\t\t <styleUrl>#msn_triangle_copy1"\ +"</styleUrl>\n") KMLFile.write("\t\t <Point>\n") KMLFile.write("\t\t\t <coordinates>"+format(longitude,'f')\ +','+format(latitude,'f')+','+format(altitude,'f')+"</coordinates>\n") KMLFile.write("\t\t\t <altitudeMode>absolute</altitudeMode>\n") KMLFile.write("\t\t </Point>\n") KMLFile.write("\t </Placemark>\n") KMLFile.write("\t <Placemark>\n") KMLFile.write("\t\t <name>"+"Path"+format(WaypointNumber,'d')+"</name>\n") KMLFile.write("\t\t <styleUrl>#msn_triangle_copy1</styleUrl>\n") KMLFile.write("\t\t <LineString>\n") KMLFile.write("\t\t\t <tessellate>1</tessellate>\n") KMLFile.write("\t\t\t <coordinates>\n") KMLFile.write("\t\t\t\t "+format(lonx,'f')+','+format(latx,'f')+','\ +format(galtx,'f')+' '+format(longitude,'f')+','\ +format(latitude,'f')+','+format(altitude,'f')+'\n') # print 'Waypoint'+format(WaypointNumber,'d')+' '+format(longitude, '.2f')\ # +','+format(latitude, '.2f')+',' + format(round(altitude/100.)*100., '.0f') KMLFile.write("\t\t\t </coordinates>\n") KMLFile.write("\t\t\t <altitudeMode>absolute</altitudeMode>\n") KMLFile.write("\t\t </LineString>\n") KMLFile.write("\t </Placemark>\n") KMLACFile.write("\t <Placemark>\n") KMLACFile.write("\t\t <name>"+label+"</name>\n") KMLACFile.write("\t\t <description>WayPoint "\ +format(round(altitude/(100))*100.,'.0f')+' ft'+"</description>\n") KMLACFile.write("\t\t <styleUrl>#msn_triangle_copy1"\ +"</styleUrl>\n") KMLACFile.write("\t\t <Point>\n") KMLACFile.write("\t\t\t <coordinates>"+format(longitude,'f')\ +','+format(latitude,'f')+','+format(altitude,'f')+"</coordinates>\n") KMLACFile.write("\t\t </Point>\n") KMLACFile.write("\t </Placemark>\n") KMLACFile.write("\t <Placemark>\n") KMLACFile.write("\t\t <name>"+"Path"+format(WaypointNumber,'d')+"</name>\n") KMLACFile.write("\t\t <styleUrl>#msn_triangle_copy1</styleUrl>\n") KMLACFile.write("\t\t <LineString>\n") KMLACFile.write("\t\t\t <tessellate>1</tessellate>\n") KMLACFile.write("\t\t\t <coordinates>\n") KMLACFile.write("\t\t\t\t "+format(lonx,'f')+','+format(latx,'f')+','\ +format(galtx,'f')+' '+format(longitude,'f')+','\ +format(latitude,'f')+','+format(altitude,'f')+'\n') # print 'Waypoint'+format(WaypointNumber,'d')+' '+format(longitude, '.2f')\ # +','+format(latitude, '.2f')+',' + format(round(altitude/100.)*100., '.0f') KMLACFile.write("\t\t\t </coordinates>\n") KMLACFile.write("\t\t </LineString>\n") KMLACFile.write("\t </Placemark>\n") lonx = longitude latx = latitude galtx = altitude return()

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6edc5a13e61a1bdcdf25bd7cc6d12ff98125bfdd

39

py

Python

Python/Tests/TestData/SendToInteractiveWorkspace/PrintInterpreter.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

404

2019-05-07T02:21:57.000Z

2022-03-31T17:03:04.000Z

Python/Tests/TestData/SendToInteractiveWorkspace/PrintInterpreter.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

1,672

2019-05-06T21:09:38.000Z

2022-03-31T23:16:04.000Z

Python/Tests/TestData/SendToInteractiveWorkspace/PrintInterpreter.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

186

2019-05-13T03:17:37.000Z

2022-03-31T16:24:05.000Z

import sys print(sys.version_info[:2])

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6

6e0cf115db4bb95a08b1d4ece55fa11c8d6418e1

222

py

Python

src/mot/motion_models/__init__.py

neer201/Multi-Object-Tracking-for-Automotive-Systems-in-python

886cd9e87283982381713dbf2e4ef695030f81de

[ "Apache-2.0" ]

6

2021-11-21T10:47:01.000Z

2022-03-17T01:14:53.000Z

src/mot/motion_models/__init__.py

neer201/Multi-Object-Tracking-for-Automotive-Systems-in-python

886cd9e87283982381713dbf2e4ef695030f81de

[ "Apache-2.0" ]

3

2021-04-12T12:37:41.000Z

2021-04-30T14:29:53.000Z

src/mot/motion_models/__init__.py

neer201/Multi-Object-Tracking-for-Automotive-Systems-in-python

886cd9e87283982381713dbf2e4ef695030f81de

[ "Apache-2.0" ]

null

# flake8: noqa from mot.motion_models.base_motion_model import MotionModel from mot.motion_models.CT_motion_model import CoordinateTurnMotionModel from mot.motion_models.CV_motion_model import ConstantVelocityMotionModel

37

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0

6

28461474953cc9c257de317f17581d4ef1a01795

18,209

py

Python

DQN/network.py

Xin-Ye-1/HIEM

6764f579eef6ec92dd85a005af27419f630df7da

[ "Apache-2.0" ]

2

2021-04-12T02:41:00.000Z

2021-05-15T02:18:15.000Z

DQN/network.py

Xin-Ye-1/HIEM

6764f579eef6ec92dd85a005af27419f630df7da

[ "Apache-2.0" ]

null

DQN/network.py

Xin-Ye-1/HIEM

6764f579eef6ec92dd85a005af27419f630df7da

[ "Apache-2.0" ]

null

#! /usr/bin/env python import tensorflow as tf import tensorflow.contrib.slim as slim seed = 0 def fc2d(inputs, num_outputs, activation_fn, scope): with tf.variable_scope(scope, reuse=tf.AUTO_REUSE) as s: n0, n1, n2 = inputs.get_shape().as_list() weights = tf.get_variable(name='weights', shape=[n2, num_outputs], initializer=tf.contrib.layers.xavier_initializer(seed=seed), trainable=True) wx = tf.einsum('ijk,kl->ijl', inputs, weights) biases = tf.get_variable(name='biases', shape=[num_outputs], initializer=tf.zeros_initializer(), trainable=True) wx_b = wx + biases result = wx_b if activation_fn is None else activation_fn(wx_b, name=s.name) return result def conv3d(scope_name, input, filter_size): with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE) as scope: conv_filter = tf.get_variable(name='weights', shape=filter_size, initializer=tf.contrib.layers.xavier_initializer(seed=seed), trainable=True) conv = tf.nn.conv3d(input=input, filter=conv_filter, strides=[1, 1, 1, 1, 1], padding='VALID') biases = tf.get_variable(name='biases', shape=[filter_size[-1]], initializer=tf.zeros_initializer(), trainable=True) bias = tf.nn.bias_add(conv, biases) result = tf.nn.relu(bias, name=scope.name) return result class Highlevel_Network(): def __init__(self, window_size, num_labels, # action_size, history_steps, scope ): with tf.variable_scope('highlevel'): with tf.variable_scope(scope): self.visions = tf.placeholder(shape=[None, history_steps * window_size * window_size, num_labels], dtype=tf.float32) self.depths = tf.placeholder(shape=[None, history_steps * window_size * window_size, 1], dtype=tf.float32) self.targets = tf.placeholder(shape=[None, num_labels], dtype=tf.float32) related_visions = fc2d(inputs=self.visions, num_outputs=1, activation_fn=None, scope='vision_preprocess') related_visions = slim.flatten(related_visions) depths = slim.flatten(self.depths) hidden_visions = slim.fully_connected(inputs=related_visions, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='vision_hidden') hidden_depths = slim.fully_connected(inputs=depths, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='depth_hidden') hidden_targets = slim.fully_connected(inputs=self.targets, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='target_hidden') vision_depth_feature = tf.concat([hidden_visions, hidden_depths, hidden_targets], -1) embed_feature = slim.fully_connected(inputs=vision_depth_feature, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='embed') qvalue = slim.fully_connected(inputs=embed_feature, num_outputs=num_labels, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='qvalue') self.qvalue = qvalue terminations = slim.fully_connected(inputs=embed_feature, num_outputs=num_labels, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='termination') self.terminations = tf.sigmoid(terminations) # highlevel training if not scope.startswith('global'): self.chosen_objects = tf.placeholder(shape=[None], dtype=tf.int32) self.target_qvalue = tf.placeholder(shape=[None], dtype=tf.float32) self.highlevel_lr = tf.placeholder(dtype=tf.float32) object_onehot = tf.one_hot(self.chosen_objects, num_labels, dtype=tf.float32) qvalue_for_chosen_object = tf.reduce_sum(self.qvalue*object_onehot, axis=1) td_error = tf.square(self.target_qvalue - qvalue_for_chosen_object) self.qvalue_loss = 0.5*tf.reduce_mean(td_error) highlevel_trainer = tf.train.RMSPropOptimizer(learning_rate=self.highlevel_lr) highlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'highlevel/%s' % scope) gradients = tf.gradients(self.qvalue_loss, highlevel_params) norm_gradients, _ = tf.clip_by_global_norm(gradients, 40.0) global_highlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'highlevel/global/main') self.highlevel_update = highlevel_trainer.apply_gradients(zip(norm_gradients, global_highlevel_params)) class Lowlevel_Network(): def __init__(self, window_size, num_labels, action_size, history_steps, scope='global' ): with tf.variable_scope('lowlevel'): with tf.variable_scope(scope): self.visions = tf.placeholder(shape=[None, history_steps * window_size * window_size, num_labels], dtype=tf.float32) self.depths = tf.placeholder(shape=[None, history_steps * window_size * window_size, 1], dtype=tf.float32) self.subtargets = tf.placeholder(shape=[None, num_labels], dtype=tf.float32) subtargets_expanded = tf.tile(tf.expand_dims(self.subtargets, 1), [1, history_steps * window_size * window_size, 1]) masked_visions = tf.reduce_sum(self.visions * subtargets_expanded, axis=-1) masked_visions = slim.flatten(masked_visions) depths = slim.flatten(self.depths) hidden_visions = slim.fully_connected(inputs=masked_visions, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='vision_hidden') hidden_depths = slim.fully_connected(inputs=depths, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='depth_hidden') vision_depth_feature = tf.concat([hidden_visions, hidden_depths], 1) embed_feature = slim.fully_connected(inputs=vision_depth_feature, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='embed') # value estimation hidden_value = slim.fully_connected(inputs=embed_feature, num_outputs=20, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='value_hidden') self.qvalues = slim.fully_connected(inputs=hidden_value, num_outputs=action_size, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='qvalue') # Lowlevel training if not scope.startswith('global'): self.chosen_actions = tf.placeholder(shape=[None], dtype=tf.int32) self.target_qvalues = tf.placeholder(shape=[None], dtype=tf.float32) self.lowlevel_lr = tf.placeholder(dtype=tf.float32) actions_onehot = tf.one_hot(self.chosen_actions, action_size, dtype=tf.float32) qvalues_for_chosen_actions = tf.reduce_sum(self.qvalues * actions_onehot, axis=-1) self.qvalue_loss = 0.5 * tf.reduce_mean(tf.square(self.target_qvalues - qvalues_for_chosen_actions)) local_lowlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'lowlevel/%s'%scope) gradients = tf.gradients(self.qvalue_loss, local_lowlevel_params) norm_gradients, _ = tf.clip_by_global_norm(gradients, 40.0) lowlevel_trainer = tf.train.RMSPropOptimizer(learning_rate=self.lowlevel_lr) global_lowlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'lowlevel/global/in/main') self.lowlevel_update = lowlevel_trainer.apply_gradients(zip(norm_gradients, global_lowlevel_params)) class Lowlevel_Network_ex(): def __init__(self, window_size, num_labels, action_size, history_steps, scope ): with tf.variable_scope('lowlevel'): with tf.variable_scope(scope): self.visions = tf.placeholder(shape=[None, history_steps * window_size * window_size, num_labels], dtype=tf.float32) self.depths = tf.placeholder(shape=[None, history_steps * window_size * window_size, 1], dtype=tf.float32) self.targets = tf.placeholder(shape=[None, num_labels], dtype=tf.float32) related_visions = fc2d(inputs=self.visions, num_outputs=1, activation_fn=None, scope='vision_preprocess') related_visions = slim.flatten(related_visions) depths = slim.flatten(self.depths) hidden_visions = slim.fully_connected(inputs=related_visions, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer( seed=seed), biases_initializer=tf.zeros_initializer(), scope='vision_hidden') hidden_depths = slim.fully_connected(inputs=depths, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer( seed=seed), biases_initializer=tf.zeros_initializer(), scope='depth_hidden') hidden_targets = slim.fully_connected(inputs=self.targets, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer( seed=seed), biases_initializer=tf.zeros_initializer(), scope='target_hidden') vision_depth_feature = tf.concat([hidden_visions, hidden_depths, hidden_targets], -1) embed_feature = slim.fully_connected(inputs=vision_depth_feature, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer( seed=seed), biases_initializer=tf.zeros_initializer(), scope='embed') action_qvalues = slim.fully_connected(inputs=embed_feature, num_outputs=action_size, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer( seed=seed), biases_initializer=tf.zeros_initializer(), scope='action_qvalue') self.action_qvalues = action_qvalues # highlevel training if not scope.startswith('global'): self.chosen_actions = tf.placeholder(shape=[None], dtype=tf.int32) self.target_action_qvalues = tf.placeholder(shape=[None], dtype=tf.float32) self.highlevel_lr = tf.placeholder(dtype=tf.float32) action_onehot = tf.one_hot(self.chosen_actions, action_size, dtype=tf.float32) qvalue_for_chosen_action = tf.reduce_sum(self.action_qvalues * action_onehot, axis=1) td_error = tf.square(self.target_action_qvalues - qvalue_for_chosen_action) self.action_qvalue_loss = 0.5 * tf.reduce_mean(td_error) highlevel_trainer = tf.train.RMSPropOptimizer(learning_rate=self.highlevel_lr) highlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'lowlevel/%s' % scope) gradients = tf.gradients(self.action_qvalue_loss, highlevel_params) norm_gradients, _ = tf.clip_by_global_norm(gradients, 40.0) global_highlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'lowlevel/global/ex/main') self.highlevel_update = highlevel_trainer.apply_gradients( zip(norm_gradients, global_highlevel_params))

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tests/strategies/__init__.py

lycantropos/rsrc_web

6702840befa4fa70114ce10543144410b453aa30

[ "MIT" ]

null

tests/strategies/__init__.py

lycantropos/rsrc_web

6702840befa4fa70114ce10543144410b453aa30

[ "MIT" ]

4

2019-06-18T18:36:50.000Z

2019-07-10T13:14:48.000Z

tests/strategies/__init__.py

lycantropos/rsrc_web

6702840befa4fa70114ce10543144410b453aa30

[ "MIT" ]

null

from .literals import booleans from .models import (readable_web_streams, web_streams, writeable_web_streams) from .paths import web_url_strings

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Python

Lib/site-packages/git/index/__init__.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

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2021-05-31T07:18:08.000Z

2022-03-19T09:20:11.000Z

Lib/site-packages/git/index/__init__.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

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2017-05-10T08:10:23.000Z

2020-03-23T10:23:37.000Z

Lib/site-packages/git/index/__init__.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

38

2017-04-26T14:13:37.000Z

2021-06-24T11:36:38.000Z

"""Initialize the index package""" # flake8: noqa from __future__ import absolute_import from .base import * from .typ import *

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251e7d6fbbff67cb94790461d92eb77f3f88ed53

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py

Python

comet/handler/__init__.py

shinybrar/Comet

4229092fca74c130a7d4ecd4dbd22ae85f7e6308

[ "BSD-2-Clause" ]

15

2015-11-29T18:53:58.000Z

2022-03-09T15:47:30.000Z

comet/handler/__init__.py

shinybrar/Comet

4229092fca74c130a7d4ecd4dbd22ae85f7e6308

[ "BSD-2-Clause" ]

29

2016-01-21T18:10:45.000Z

2021-10-01T16:41:12.000Z

comet/handler/__init__.py

shinybrar/Comet

4229092fca74c130a7d4ecd4dbd22ae85f7e6308

[ "BSD-2-Clause" ]

11

2016-01-22T14:05:51.000Z

2022-03-09T17:49:56.000Z

# Comet VOEvent Broker. # Event handlers. from comet.handler.relay import * from comet.handler.spawn import *

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py

Python

test/test_tensor_reorganization.py

entn-at/BrnoLM

9f8c62523382098809c1c0967f62a67d151eafe0

[ "MIT" ]

17

2020-02-04T16:42:40.000Z

2021-11-11T14:37:32.000Z

test/test_tensor_reorganization.py

entn-at/BrnoLM

9f8c62523382098809c1c0967f62a67d151eafe0

[ "MIT" ]

null

test/test_tensor_reorganization.py

entn-at/BrnoLM

9f8c62523382098809c1c0967f62a67d151eafe0

[ "MIT" ]

4

2020-02-04T12:59:04.000Z

2021-05-30T14:10:54.000Z

from brnolm.runtime.tensor_reorganization import TensorReorganizer import torch from torch.autograd import Variable from .common import TestCase class Dummy_lstm(): def __init__(self, nb_hidden): self._nb_hidden = nb_hidden def init_hidden(self, batch_size): return ( torch.FloatTensor([[[0.0] * self._nb_hidden] * batch_size] * 2), torch.FloatTensor([[[0.0] * self._nb_hidden] * batch_size] * 2) ) class TensorReorganizerTests(TestCase): def setUp(self): self.lm = Dummy_lstm(nb_hidden=2) self.reorganizer = TensorReorganizer(self.lm.init_hidden) def test_passing(self): last_h = ( torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]*2), torch.FloatTensor([[[1, 1], [2, 2], [3, 3]]]*2), ) mask = torch.LongTensor([0, 1, 2]) bsz = 3 new_h = self.reorganizer(last_h, mask, bsz) self.assertEqual(new_h, last_h) def test_shrinks(self): last_h = ( torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]*2), torch.FloatTensor([[[1, 1], [2, 2], [3, 3]]]*2), ) mask = torch.LongTensor([0, 2]) bsz = 2 new_h = self.reorganizer(last_h, mask, bsz) expected = ( torch.FloatTensor([[[0.1, 0.1], [0.3, 0.3]]]*2), torch.FloatTensor([[[1, 1], [3, 3]]]*2), ) self.assertEqual(new_h, expected) def test_requires_bsz_greater_than_mask(self): last_h = ( torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]*2), torch.FloatTensor([[[1, 1], [2, 2], [3, 3]]]*2), ) mask = torch.LongTensor([0, 1, 2]) bsz = 2 self.assertRaises(ValueError, self.reorganizer, last_h, mask, bsz) def test_on_empty_mask_zeros(self): last_h = ( torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]*2), torch.FloatTensor([[[1, 1], [2, 2], [3, 3]]]*2), ) mask = torch.LongTensor([]) bsz = 2 new_h = self.reorganizer(last_h, mask, bsz) expected = self.lm.init_hidden(bsz) self.assertEqual(new_h, expected) def test_completion_by_zeros(self): last_h = ( torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]*2), torch.FloatTensor([[[1, 1], [2, 2], [3, 3]]]*2), ) mask = torch.LongTensor([1]) bsz = 2 new_h = self.reorganizer(last_h, mask, bsz) expected = ( torch.FloatTensor([[[0.2, 0.2], [0.0, 0.0]]]*2), torch.FloatTensor([[[2.0, 2.0], [0.0, 0.0]]]*2), ) self.assertEqual(new_h, expected) def test_bug_regression_single_addition(self): last_h = ( torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]*2), torch.FloatTensor([[[1, 1], [2, 2], [3, 3]]]*2), ) mask = torch.LongTensor([1, 2]) bsz = 3 new_h = self.reorganizer(last_h, mask, bsz) expected = ( torch.FloatTensor([[[0.2, 0.2], [0.3, 0.3], [0.0, 0.0]]]*2), torch.FloatTensor([[[2.0, 2.0], [3.0, 3.0], [0.0, 0.0]]]*2), ) self.assertEqual(new_h, expected) class Dummy_srn(): def __init__(self, nb_hidden): self._nb_hidden = nb_hidden self._nb_layers = 1 def init_hidden(self, batch_size): return torch.FloatTensor(self._nb_layers, batch_size, self._nb_hidden).zero_() class TensorReorganizerTests_SRN(TestCase): def setUp(self): lm = Dummy_srn(nb_hidden=2) self.reorganizer = TensorReorganizer(lm.init_hidden) def test_passing(self): last_h = torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]) mask = torch.LongTensor([0, 1, 2]) bsz = 3 new_h = self.reorganizer(last_h, mask, bsz) self.assertEqual(new_h, last_h) def test_passing_variables(self): last_h = Variable(torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]])) mask = Variable(torch.LongTensor([0, 1, 2])) bsz = 3 new_h = self.reorganizer(last_h, mask, bsz) self.assertEqual(new_h, last_h) def test_shrinks(self): last_h = torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]) mask = torch.LongTensor([0, 2]) bsz = 2 new_h = self.reorganizer(last_h, mask, bsz) expected = torch.FloatTensor([[[0.1, 0.1], [0.3, 0.3]]]) self.assertEqual(new_h, expected) def test_requires_bsz_greater_than_mask(self): last_h = torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]) mask = torch.LongTensor([0, 1, 2]) bsz = 2 self.assertRaises(ValueError, self.reorganizer, last_h, mask, bsz) def test_on_empty_mask_zeros(self): last_h = torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]) mask = torch.LongTensor([]) bsz = 2 new_h = self.reorganizer(last_h, mask, bsz) expected = torch.FloatTensor([[[0.0, 0.0], [0.0, 0.0]]]) self.assertEqual(new_h, expected) def test_completion_by_zeros(self): last_h = torch.FloatTensor([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]]) mask = torch.LongTensor([1]) bsz = 2 new_h = self.reorganizer(last_h, mask, bsz) expected = torch.FloatTensor([[[0.2, 0.2], [0.0, 0.0]]]) self.assertEqual(new_h, expected)

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