hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c7b9a410ee38d6e6d6db61a866b44bf1f720af4e | 50 | py | Python | __init__.py | pudasainishushant/what_tense | 0765ea5b259bd6da73bde0991eb1bd15df8716e1 | [
"MIT"
] | 1 | 2022-01-06T11:08:47.000Z | 2022-01-06T11:08:47.000Z | __init__.py | pudasainishushant/what_tense | 0765ea5b259bd6da73bde0991eb1bd15df8716e1 | [
"MIT"
] | null | null | null | __init__.py | pudasainishushant/what_tense | 0765ea5b259bd6da73bde0991eb1bd15df8716e1 | [
"MIT"
] | null | null | null | from what_tense.what_tense import TenseCategorizer | 50 | 50 | 0.92 | 7 | 50 | 6.285714 | 0.714286 | 0.409091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.06 | 50 | 1 | 50 | 50 | 0.93617 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
c7f4368bc50b18580f49632ed0fdb01a8ce7a0f3 | 35 | py | Python | translatio/__init__.py | jegork/translatio | f4f4a01a7e5c93038e5bcc653aabe4eb7f750ddc | [
"MIT"
] | null | null | null | translatio/__init__.py | jegork/translatio | f4f4a01a7e5c93038e5bcc653aabe4eb7f750ddc | [
"MIT"
] | null | null | null | translatio/__init__.py | jegork/translatio | f4f4a01a7e5c93038e5bcc653aabe4eb7f750ddc | [
"MIT"
] | null | null | null | from translatio.translatio import * | 35 | 35 | 0.857143 | 4 | 35 | 7.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085714 | 35 | 1 | 35 | 35 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
1bf2a440473b7fdfdbf4d7592029fe17e045d815 | 31,650 | py | Python | Tools/tdd/test/test_createNewModule.py | neubertm/TDD_framework | 31e6df2422221035c86fdc743ca4da09cdfc3b50 | [
"BSD-3-Clause"
] | 1 | 2021-09-23T13:02:10.000Z | 2021-09-23T13:02:10.000Z | Tools/tdd/test/test_createNewModule.py | neubertm/TDD_framework | 31e6df2422221035c86fdc743ca4da09cdfc3b50 | [
"BSD-3-Clause"
] | null | null | null | Tools/tdd/test/test_createNewModule.py | neubertm/TDD_framework | 31e6df2422221035c86fdc743ca4da09cdfc3b50 | [
"BSD-3-Clause"
] | null | null | null | from .context import createNewModule
from .context import TDDConfig
import unittest
from unittest.mock import patch
import sys
import os
from pathlib import Path
class TestCreateNewModule(unittest.TestCase):
@patch('createNewModule.get_input', return_value='yes')
def test_questionYesNo_answerYes(self, get_input):
self.assertEqual(createNewModule.questionYesNo("TEST_TEXT"), True)
@patch('createNewModule.get_input', return_value='y')
def test_questionYesNo_answerY(self, input):
self.assertEqual(createNewModule.questionYesNo("TEST_TEXT"), True)
@patch('createNewModule.get_input', return_value='no')
def test_questionYesNo_answerNo(self, input):
self.assertEqual(createNewModule.questionYesNo("TEST_TEXT"), False)
@patch('createNewModule.get_input', return_value='n')
def test_questionYesNo_answerN(self, input):
self.assertEqual(createNewModule.questionYesNo("TEST_TEXT"), False)
@patch('createNewModule.get_input', return_value='')
def test_questionWithList_UseDefault(self, input):
default = 'HOO'
list = ['FOO', 'HOO', 'POO']
with patch('createNewModule.printout'):
self.assertEqual(createNewModule.questionWithList("TEST_TEXT",list,default), default)
@patch('createNewModule.get_input', return_value='FOO')
def test_questionWithList_UseFOO(self, input):
default = 'HOO'
list = ['FOO', 'HOO', 'POO']
self.assertEqual(createNewModule.questionWithList("TEST_TEXT",list,default), 'FOO')
@patch('createNewModule.get_input', return_value='POO')
def test_questionWithList_UsePOO(self, input):
default = 'HOO'
list = ['FOO', 'HOO', 'POO']
self.assertEqual(createNewModule.questionWithList("TEST_TEXT",list,default), 'POO')
def test_createNewModule_initToDefaultParameters(self):
testDesc = TDDConfig.CTestPkgDescription
newModule = createNewModule.CreateNewModule(testDesc)
self.assertEqual(newModule.str_SRC_FOLDER,"")
self.assertEqual(newModule.str_HEADER_FOLDER,"")
self.assertEqual(newModule.str_SRC_FILE,"")
self.assertEqual(newModule.str_HEADER_FILE,"")
self.assertEqual(newModule.str_FRAMEWORK,"cpputest")
self.assertEqual(newModule.str_TOOLCHAIN,"mingw")
self.assertEqual(newModule.str_LANGUAGE,"c++")
self.assertEqual(newModule.str_COMPONENT_NAME,"")
self.assertEqual(newModule.str_SRC_TYPE,"")
self.assertEqual(newModule.copyFileLst,[])
# @patch('createNewModule.CreateNewModule.setModuleConfiguration')
# @patch('createNewModule.CreateNewModule.createFolder_SUT')
# @patch('createNewModule.CreateNewModule.createFolder_TPKG')
# @patch('createNewModule.CreateNewModule.createAndCopyFiles')
# def test_createNewModule_testTopFunctionForCreateNewModule(self, mock_input1, mock_input2, mock_input3, mock_input4):
# testDesc = TDDConfig.CTestPkgDescription
# newModule = createNewModule.CreateNewModule(testDesc)
# newModule.createNewModule()
# pass
@patch('createNewModule.printout')
@patch('createNewModule.questionWithList', return_value='c')
@patch('createNewModule.CreateNewModule.defineSutFileConfiguration')
@patch('createNewModule.CreateNewModule.defineCoverageCfg', return_value=TDDConfig.CCovCfg())
@patch('createNewModule.CreateNewModule.defineStatAnalysisCfg', return_value=TDDConfig.CStaticAnalysisCfg())
@patch('createNewModule.CreateNewModule.defineToolchainCfg', return_value=TDDConfig.CTestToolchainCfg())
@patch('createNewModule.CreateNewModule.defineCodeStatisticsCfg', return_value=TDDConfig.CCodeStatisticsCfg())
def test_createNewModule_setModuleConfiguration_setC(self, mock_codeStat, mock_toolCH, mock_statA, mock_cover, mock_files, mock_qList, mock_printout):
testDesc = TDDConfig.CTestPkgDescription
newModule = createNewModule.CreateNewModule(testDesc)
newModule.setModuleConfiguration()
self.assertEqual(newModule.str_LANGUAGE,"c")
self.assertTrue(mock_qList.called)
self.assertTrue(mock_files.called)
self.assertTrue(mock_cover.called)
self.assertTrue(mock_statA.called)
self.assertTrue(mock_toolCH.called)
self.assertTrue(mock_codeStat.called)
self.assertEqual(6, len(mock_printout.call_args_list))
pass
@patch('createNewModule.printout')
@patch('createNewModule.questionWithList', return_value='c++')
@patch('createNewModule.CreateNewModule.defineSutFileConfiguration')
@patch('createNewModule.CreateNewModule.defineCoverageCfg', return_value=TDDConfig.CCovCfg())
@patch('createNewModule.CreateNewModule.defineStatAnalysisCfg', return_value=TDDConfig.CStaticAnalysisCfg())
@patch('createNewModule.CreateNewModule.defineToolchainCfg', return_value=TDDConfig.CTestToolchainCfg())
@patch('createNewModule.CreateNewModule.defineCodeStatisticsCfg', return_value=TDDConfig.CCodeStatisticsCfg())
def test_createNewModule_setModuleConfiguration_setCPP(self, mock_codeStat, mock_toolCH, mock_statA, mock_cover, mock_files, mock_qList, mock_printout):
testDesc = TDDConfig.CTestPkgDescription
newModule = createNewModule.CreateNewModule(testDesc)
newModule.setModuleConfiguration()
self.assertEqual(newModule.str_LANGUAGE,"c++")
self.assertTrue(mock_qList.called)
self.assertTrue(mock_files.called)
self.assertTrue(mock_cover.called)
self.assertTrue(mock_statA.called)
self.assertTrue(mock_toolCH.called)
self.assertTrue(mock_codeStat.called)
self.assertEqual(6, len(mock_printout.call_args_list))
pass
@patch('createNewModule.printout')
@patch('createNewModule.CreateNewModule.setModuleConfiguration')
@patch('createNewModule.CreateNewModule.createFolder_SUT')
@patch('createNewModule.CreateNewModule.createFolder_TPKG')
@patch('createNewModule.CreateNewModule.createAndCopyFiles')
def test_createNewModule_testTopFunctionForCreateNewModule(self, mock_fileCopy, mock_createPkgFolder, mock_createSutFolder, mock_setModule, mock_printout):
testDesc = TDDConfig.CTestPkgDescription
newModule = createNewModule.CreateNewModule(testDesc)
newModule.createNewModule()
self.assertTrue(mock_setModule.called)
self.assertTrue(mock_createSutFolder.called)
self.assertTrue(mock_createPkgFolder.called)
self.assertTrue(mock_fileCopy.called)
self.assertTrue(mock_printout.called)
self.assertEqual(5,len(mock_printout.call_args_list))
@patch('createNewModule.CreateNewModule.defineSutFileNames')
@patch('createNewModule.CreateNewModule.defineSutFolders')
def test_defineSutFileConfiguration(self, mock_SFileName, mock_SFlds):
testDesc = TDDConfig.CTestPkgDescription
newModule = createNewModule.CreateNewModule(testDesc)
newModule.defineSutFileConfiguration()
self.assertTrue(mock_SFileName.called)
self.assertTrue(mock_SFlds.called)
pass
@patch('createNewModule.printout')
@patch('createNewModule.questionWithList', side_effect=['cpp','hpp'])
@patch('createNewModule.questionReturnString', return_value='FOOO')
@patch('createNewModule.questionYesNo', return_value=True)
def test_defineSutFileNames_defaultAndFoooName(self, mock_printout, mock_qwl, mock_qrs, mock_qYN):
#mock_qwl.side_effect = ['cpp','hpp']
testDesc = TDDConfig.CTestPkgDescription
newModule = createNewModule.CreateNewModule(testDesc)
newModule.defineSutFileNames()
#Check printout function
#mprintout_args,mprintout_kwargs = mock_printout.call_args
str_fullSrcName = '.'.join([newModule.str_COMPONENT_NAME,"cpp"])
str_fullHeaderName = '.'.join([newModule.str_COMPONENT_NAME,'hpp'])
#self.assertEqual(mprintout_args, ['New SUT object definition:','New SUT file are: \n\t%s\n\t%s' % (str_fullHeaderName, str_fullSrcName)])
self.assertTrue(mock_printout.called)
self.assertTrue(mock_qrs.called)
self.assertTrue(mock_qYN.called)
#other Assertion
self.assertEqual(newModule.str_LANGUAGE, 'c++')
self.assertEqual(newModule.str_COMPONENT_NAME, 'FOOO')
self.assertEqual(newModule.str_SRC_FILE, 'FOOO.cpp')
self.assertEqual(newModule.str_HEADER_FILE, 'FOOO.hpp')
@patch('createNewModule.printout')
@patch('createNewModule.questionWithList', side_effect=['c','h'])
@patch('createNewModule.questionReturnString', return_value='POOO')
@patch('createNewModule.questionYesNo', return_value=True)
def test_defineSutFileNames_cAndPoooName(self, mock_printout, mock_qwl, mock_qrs, mock_qYN):
#mock_qwl.side_effect = ['cpp','hpp']
testDesc = TDDConfig.CTestPkgDescription
newModule = createNewModule.CreateNewModule(testDesc)
newModule.str_LANGUAGE = 'c'
newModule.defineSutFileNames()
#Check printout function
#mprintout_args,mprintout_kwargs = mock_printout.call_args
str_fullSrcName = '.'.join([newModule.str_COMPONENT_NAME,"cpp"])
str_fullHeaderName = '.'.join([newModule.str_COMPONENT_NAME,'hpp'])
#self.assertEqual(mprintout_args, ['New SUT object definition:','New SUT file are: \n\t%s\n\t%s' % (str_fullHeaderName, str_fullSrcName)])
self.assertTrue(mock_printout.called)
self.assertTrue(mock_qrs.called)
self.assertTrue(mock_qYN.called)
#other Assertion
self.assertEqual(newModule.str_LANGUAGE, 'c')
self.assertEqual(newModule.str_COMPONENT_NAME, 'POOO')
self.assertEqual(newModule.str_SRC_FILE, 'POOO.c')
self.assertEqual(newModule.str_HEADER_FILE, 'POOO.h')
@patch('createNewModule.printout')
@patch('createNewModule.questionYesNo', return_value=True)
def test_defineSutFolders_default(self, mock_printout, mock_qyn):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
# this fake calling defineSutNames function
newModule.str_COMPONENT_NAME = 'FOOO'
newModule.str_SRC_FILE = 'FOOO.cpp'
newModule.str_HEADER_FILE = 'FOOO.hpp'
newModule.defineSutFolders()
self.assertTrue(mock_printout.called)
self.assertTrue(mock_qyn.called)
self.assertEqual( str(Path(newModule.pkgDesc.str_srcfldr) / 'src'), newModule.str_SRC_FOLDER )
self.assertEqual( str(Path(newModule.pkgDesc.str_srcfldr) / 'include'), newModule.str_HEADER_FOLDER )
pass
@patch('createNewModule.printout')
@patch('createNewModule.questionYesNo', return_value=False)
@patch('createNewModule.questionReturnString', side_effect=['INCLUDE', 'SOURCE'])
def test_defineSutFolders_UserDefinedSutFolders(self, mock_qrs, mock_qyn, mock_printout):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
# this fake calling defineSutNames function
newModule.str_COMPONENT_NAME = 'FOOO'
newModule.str_SRC_FILE = 'FOOO.cpp'
newModule.str_HEADER_FILE = 'FOOO.hpp'
newModule.defineSutFolders()
self.assertTrue(mock_printout.called)
self.assertTrue(mock_qyn.called)
self.assertTrue(mock_qrs.called)
self.assertEqual( str(Path(newModule.pkgDesc.str_srcfldr) / 'SOURCE'), newModule.str_SRC_FOLDER )
self.assertEqual( str(Path(newModule.pkgDesc.str_srcfldr) / 'INCLUDE'), newModule.str_HEADER_FOLDER )
pass
@patch('createNewModule.questionYesNo', return_value=True)
def test_defineCoverageCfg_default(self,mock_qyn):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.testConfig.co_coverage.isTurnedOn = False
newModule.defineCoverageCfg()
self.assertTrue(mock_qyn.called)
self.assertEqual(newModule.testConfig.co_coverage.isTurnedOn, True)
self.assertEqual(newModule.testConfig.co_coverage.uncoveredLineListLength, 0)
pass
@patch('createNewModule.questionYesNo', return_value=False)
def test_defineCoverageCfg_UserTurnOffCoverage(self,mock_qyn):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.testConfig.co_coverage.isTurnedOn = True
newModule.defineCoverageCfg()
self.assertTrue(mock_qyn.called)
self.assertEqual(newModule.testConfig.co_coverage.isTurnedOn, False)
self.assertEqual(newModule.testConfig.co_coverage.uncoveredLineListLength, 0)
pass
@patch('createNewModule.questionYesNo', side_effect=[ True, True])
@patch('createNewModule.questionWithList', side_effect=['c99t','c++11t'])
def test_defineStatAnalysisCfg_default(self, mock_qyn, mock_qwl):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.testConfig.co_staticAnalysis.isTurnedOn = False
newModule.testConfig.co_staticAnalysis.str_tool = 'unknown tools'
newModule.testConfig.co_staticAnalysis.str_ForcedLang = 'FOO'
newModule.defineStatAnalysisCfg()
self.assertTrue(mock_qyn.called)
self.assertTrue(mock_qwl.called)
self.assertEqual(newModule.testConfig.co_staticAnalysis.isTurnedOn, True)
self.assertEqual(newModule.testConfig.co_staticAnalysis.isLanguageDefinedBySuffix, True)
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_c_version, 'c99t')
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_cpp_version, 'c++11t')
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_tool, 'cppcheck')
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_ForcedLang, newModule.str_LANGUAGE)
pass
@patch('createNewModule.questionYesNo', side_effect=[ True, False])
@patch('createNewModule.questionWithList', side_effect=['c99t','c++11t'])
def test_defineStatAnalysisCfg_languageNotDefineBySuffix(self, mock_qyn, mock_qwl):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.testConfig.co_staticAnalysis.isTurnedOn = False
newModule.testConfig.co_staticAnalysis.str_tool = 'unknown tools'
newModule.testConfig.co_staticAnalysis.str_ForcedLang = 'FOO'
newModule.defineStatAnalysisCfg()
self.assertTrue(mock_qyn.called)
self.assertTrue(mock_qwl.called)
self.assertEqual(newModule.testConfig.co_staticAnalysis.isTurnedOn, True)
self.assertEqual(newModule.testConfig.co_staticAnalysis.isLanguageDefinedBySuffix, False)
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_c_version, 'c99t')
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_cpp_version, 'c++11t')
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_tool, 'cppcheck')
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_ForcedLang, newModule.str_LANGUAGE)
pass
@patch('createNewModule.questionYesNo', side_effect=[ False ])
def test_defineStatAnalysisCfg_switchOff(self, mock_qyn):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.testConfig.co_staticAnalysis.isTurnedOn = True
newModule.defineStatAnalysisCfg()
self.assertTrue(mock_qyn.called)
self.assertEqual(newModule.testConfig.co_staticAnalysis.isTurnedOn, False)
self.assertEqual(newModule.testConfig.co_staticAnalysis.isLanguageDefinedBySuffix, False)
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_c_version, 'c99')
self.assertEqual(newModule.testConfig.co_staticAnalysis.str_cpp_version, 'c++03')
pass
def test_defineToolchainCfg_default(self):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.defineToolchainCfg()
self.assertEqual(newModule.testConfig.co_testToolchain.str_compiler, 'mingw')
self.assertEqual(newModule.testConfig.co_testToolchain.str_testlib, 'cpputest')
@patch('createNewModule.questionYesNo', side_effect=[ True, False, True ])
@patch('createNewModule.questionReturningPositiveInteger', side_effect=[ 10, 20, 30 ])
def test_defineCodeStatisticsCfg_allTurnedOn_10_20_30(self, mock_qyn, mock_qrpi):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.defineCodeStatisticsCfg()
self.assertTrue(mock_qyn.called)
self.assertTrue(mock_qrpi.called)
self.assertEqual(newModule.testConfig.co_codeStatistics.isTurnedOn, True)
self.assertEqual(newModule.testConfig.co_codeStatistics.isUsedTestSpecificOnly, False)
self.assertEqual(newModule.testConfig.co_codeStatistics.isUsedStricter, True)
self.assertEqual(newModule.testConfig.co_codeStatistics.int_mccabeComplex, 10)
self.assertEqual(newModule.testConfig.co_codeStatistics.int_fncLength, 20)
self.assertEqual(newModule.testConfig.co_codeStatistics.int_paramCnt, 30)
@patch('createNewModule.questionYesNo', side_effect=[ True, True])
@patch('createNewModule.questionReturningPositiveInteger', side_effect=[ 10, 20, 30 ])
def test_defineCodeStatisticsCfg_allTurnedOn_10_20_30_notUseStricter(self, mock_qyn, mock_qrpi):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.defineCodeStatisticsCfg()
self.assertTrue(mock_qyn.called)
self.assertTrue(mock_qrpi.called)
self.assertEqual(newModule.testConfig.co_codeStatistics.isTurnedOn, True)
self.assertEqual(newModule.testConfig.co_codeStatistics.isUsedTestSpecificOnly, True)
self.assertEqual(newModule.testConfig.co_codeStatistics.int_mccabeComplex, 10)
self.assertEqual(newModule.testConfig.co_codeStatistics.int_fncLength, 20)
self.assertEqual(newModule.testConfig.co_codeStatistics.int_paramCnt, 30)
@patch('createNewModule.questionYesNo', side_effect=[ False ])
def test_defineCodeStatisticsCfg_SwitchedOff(self, mock_qyn):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.defineCodeStatisticsCfg()
self.assertTrue(mock_qyn.called)
self.assertEqual(newModule.testConfig.co_codeStatistics.isTurnedOn, False)
@patch('createNewModule.createFolder')
def test_createFolder_SUT_OK(self, mock_cf):
testDesc = TDDConfig.CTestPkgDescription()
newModule = createNewModule.CreateNewModule(testDesc)
newModule.str_HEADER_FOLDER = 'TEST_NAME.H'
newModule.str_SRC_FOLDER = 'TEST_NAME.CPP'
newModule.createFolder_SUT()
mock_cf_list = mock_cf.call_args_list
self.assertTrue(len(mock_cf_list), 2)
mock_cf_list[0].assert_called_with('TEST_NAME.H')
mock_cf_list[1].assert_called_with('TEST_NAME.CPP')
@patch('pathlib.Path.__init__', return_value=None)
@patch('pathlib.Path.is_dir', return_value=True)
def test_createFolder_dirExitsDoNothing(self, mock_isDir, mock_PathInit):
createNewModule.createFolder("FOO_FOLDER_NAME")
mock_PathInit.assert_called_with("FOO_FOLDER_NAME")
@patch('pathlib.Path.__init__',return_value=None)
@patch('pathlib.Path.is_dir',side_effect=[False, True])
@patch('pathlib.Path.mkdir')
@patch('createNewModule.assertWithText')
def test_createFolder_dirNotExitSuccesfulCreating(self, mock_awt, mock_mkdir, mock_isDir, mock_PathInit):
createNewModule.createFolder("FOO_FOLDER_NAME")
mock_PathInit.assert_called_with("FOO_FOLDER_NAME")
mock_mkdir.assert_called_with(mode=666,parents=True)
mock_awt.assert_called_with(True, 'Creating folder FOO_FOLDER_NAME failed!')
@patch('pathlib.Path.__init__', return_value=None)
@patch('pathlib.Path.is_dir', return_value=False)
@patch('createNewModule.createFolder')
def test_createFolderTpkg_OK(self, mock_cf, mock_isDir, mock_PathInit):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_COMPONENT_NAME = 'FOOO'
nm.createFolder_TPKG()
mock_PathInit.assert_called_with(nm.pkgDesc.str_testpath)
self.assertTrue(mock_isDir.called)
str_fldrName=str(Path(nm.pkgDesc.str_testpath) / (nm.str_COMPONENT_NAME + nm.pkgDesc.str_testfldr_suffix))
str_fldrNameSrc=str(Path(nm.pkgDesc.str_testpath) / (nm.str_COMPONENT_NAME + nm.pkgDesc.str_testfldr_suffix) / nm.pkgDesc.str_srctestfldr)
mock_cf_list = mock_cf.call_args_list
mock_cf_list[0].assert_called_with(str_fldrName)
self.assertEqual(nm.str_TPKG_FOLDER, str_fldrName)
mock_cf_list[1].assert_called_with(str_fldrNameSrc)
pass
@patch('pathlib.Path.__init__', return_value=None)
@patch('pathlib.Path.is_dir', return_value=True)
@patch('createNewModule.createFolder')
@patch('createNewModule.timeInShortString', return_value='00112233445566778899')
def test_createFolderTpkg_FldrExistsCreateWithTimeStamp(self,mock_timeStr ,mock_cf, mock_isDir, mock_PathInit):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_COMPONENT_NAME = 'FOOO'
nm.createFolder_TPKG()
mock_PathInit.assert_called_with(nm.pkgDesc.str_testpath)
self.assertTrue(mock_isDir.called)
str_fldrName=str(Path(nm.pkgDesc.str_testpath) / (nm.str_COMPONENT_NAME + '00112233445566778899' + nm.pkgDesc.str_testfldr_suffix))
str_fldrNameSrc=str(Path(nm.pkgDesc.str_testpath) / (nm.str_COMPONENT_NAME + '00112233445566778899' + nm.pkgDesc.str_testfldr_suffix) / nm.pkgDesc.str_srctestfldr)
mock_cf_list = mock_cf.call_args_list
mock_cf_list[0].assert_called_with(str_fldrName)
self.assertEqual(nm.str_TPKG_FOLDER, str_fldrName)
mock_cf_list[1].assert_called_with(str_fldrNameSrc)
pass
@patch('createNewModule.CreateNewModule.createHeaderFile')
@patch('createNewModule.CreateNewModule.createSourceFile')
@patch('createNewModule.CreateNewModule.copyAndCreateTestFiles')
@patch('createNewModule.CreateNewModule.createTestInitFile')
@patch('createNewModule.CreateNewModule.createTestCMakefile')
def test_createAndCopyFiles(self,mock_make, mock_ini, mock_test, mock_source, mock_header):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.createAndCopyFiles()
self.assertTrue(mock_header.called)
self.assertTrue(mock_source.called)
self.assertTrue(mock_test.called)
self.assertTrue(mock_ini.called)
self.assertTrue(mock_make.called)
pass
@patch('createNewModule.processFile')
def test_createHeaderFile_cHeader(self, mock_pf):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_LANGUAGE = 'c'
nm.str_HEADER_FOLDER = 'HEADERFOLDER'
nm.str_HEADER_FILE = 'HEADERfile.H'
nm.createHeaderFile()
self.assertTrue(mock_pf.called)
pf_args = mock_pf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'c_file.h')
str_dst = str(Path(nm.str_HEADER_FOLDER) / nm.str_HEADER_FILE)
len_dic = 4
self.assertEqual(str_src, pf_args[0][0])
self.assertEqual(str_dst, pf_args[0][1])
self.assertEqual(len_dic, len(pf_args[0][2]))
@patch('createNewModule.processFile')
def test_createHeaderFile_cppHeader(self, mock_pf):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_LANGUAGE = 'c++'
nm.str_HEADER_FOLDER = 'HEADERFOLDER'
nm.str_HEADER_FILE = 'HEADERfile.HPP'
nm.createHeaderFile()
self.assertTrue(mock_pf.called)
pf_args = mock_pf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'class.hpp')
str_dst = str(Path(nm.str_HEADER_FOLDER) / nm.str_HEADER_FILE)
len_dic = 5
self.assertEqual(str_src, pf_args[0][0])
self.assertEqual(str_dst, pf_args[0][1])
self.assertEqual(len_dic, len(pf_args[0][2]))
@patch('createNewModule.readFileToStr',return_value="ABC")
@patch('createNewModule.writeStringToFile')
@patch('createNewModule.patchString',return_value='ABCD')
def test_processFile(self, mock_ps, mock_wstf, mock_rfts):
str_src = 'SRC_FILE'
str_dst = 'DST_FILE'
dict = {'KEY0': 'VAL0', 'KEY1': 'VAL1'}
createNewModule.processFile(str_src, str_dst, dict)
mock_rfts.assert_called_with(str_src)
mock_ps.assert_called_with('ABC',dict)
mock_wstf.assert_called_with('ABCD',str_dst)
pass
def test_patchString_simple(self):
str_test = "00001111222233334444"
dict = {'0000': '0','1111': '1','2222': '2', '3333': '','4444': ''}
expected = '012'
str_out = createNewModule.patchString(str_test,dict)
self.assertEqual(expected,str_out)
def test_patchString_checkRepeating(self):
str_test = "00001111222233334444"
dict = {'0000': '0','0': 'A','1':'10','2222': '2', '3333': '','4444': ''}
expected = 'A101010102'
str_out = createNewModule.patchString(str_test,dict)
self.assertEqual(expected,str_out)
@patch('createNewModule.processFile')
def test_createSourceFile_cSrc(self, mock_pf):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
self.assertEqual(nm.str_SRC_FOLDER,'')
nm.str_LANGUAGE = 'c'
nm.str_SRC_FOLDER = 'SOURCEFOLDER'
nm.str_SRC_FILE = 'SOURCEfile.C'
nm.createSourceFile()
self.assertTrue(mock_pf.called)
pf_args = mock_pf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'c_file.c')
str_dst = str(Path(nm.str_SRC_FOLDER) / nm.str_SRC_FILE)
len_dic = 5
self.assertEqual(str_src, pf_args[0][0])
self.assertEqual(str_dst, pf_args[0][1])
self.assertEqual(len_dic, len(pf_args[0][2]))
@patch('createNewModule.processFile')
def test_createSourceFile_cppSrc(self, mock_pf):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_LANGUAGE = 'c++'
nm.str_SRC_FOLDER = 'SOURCEFOLDER'
nm.str_SRC_FILE = 'SOURCEfile.CPP'
nm.createSourceFile()
self.assertTrue(mock_pf.called)
pf_args = mock_pf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'class.cpp')
str_dst = str(Path(nm.str_SRC_FOLDER) / nm.str_SRC_FILE)
len_dic = 6
self.assertEqual(str_src, pf_args[0][0])
self.assertEqual(str_dst, pf_args[0][1])
self.assertEqual(len_dic, len(pf_args[0][2]))
@patch('createNewModule.checkIfFolderExists')
@patch('createNewModule.processFile')
def test_createTestInitFile_default(self, mock_pf, mock_chife):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_TPKG_FOLDER = 'TEST_Tpkg'
nm.createTestInitFile();
self.assertTrue(mock_pf.called)
pf_args = mock_pf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'test.ini')
str_dst = str(Path(nm.str_TPKG_FOLDER) / nm.pkgDesc.str_testcfgfilename)
len_dic = 19
self.assertEqual(str_src, pf_args[0][0])
self.assertEqual(str_dst, pf_args[0][1])
self.assertEqual(len_dic, len(pf_args[0][2]))
pathForCreating = Path(nm.str_TPKG_FOLDER)
mock_chife.assert_called_with(pathForCreating)
@patch('createNewModule.checkIfFolderExists')
@patch('createNewModule.copyTxtFile')
@patch('createNewModule.processFile')
def test_copyAndCreateTestFiles_cTest_(self,mock_pf , mock_ctf, mock_chife):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_TPKG_FOLDER = 'TEST_Tpkg'
nm.str_LANGUAGE = 'c'
pathTestFldr = Path(nm.str_TPKG_FOLDER) / nm.pkgDesc.str_srctestfldr
nm.copyAndCreateTestFiles()
# check call of text copy function
ctf_args = mock_ctf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'AllTests.cpp')
str_dst = str(Path(nm.str_TPKG_FOLDER) / nm.pkgDesc.str_srctestfldr / 'AllTests.cpp')
self.assertEqual(str_src, ctf_args[0][0])
self.assertEqual(str_dst, ctf_args[0][1])
# check call of processFile
pf_args = mock_pf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'c_test.cpp')
str_dst = str(Path(nm.str_TPKG_FOLDER) / nm.pkgDesc.str_srctestfldr / 'test.cpp')
self.assertEqual( str_src, pf_args[0][0])
self.assertEqual( str_dst, pf_args[0][1])
self.assertEqual( 6, len(pf_args[0][2]))
#check call of copyAndCreateTestFiles
mock_chife.assert_called_with(pathTestFldr)
@patch('createNewModule.checkIfFolderExists')
@patch('createNewModule.copyTxtFile')
@patch('createNewModule.processFile')
def test_copyAndCreateTestFiles_cppTest_(self,mock_pf , mock_ctf, mock_chife):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_TPKG_FOLDER = 'TEST_Tpkg'
nm.str_LANGUAGE = 'c++'
pathTestFldr = Path(nm.str_TPKG_FOLDER) / nm.pkgDesc.str_srctestfldr
nm.copyAndCreateTestFiles()
# check call of text copy function
ctf_args = mock_ctf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'AllTests.cpp')
str_dst = str(Path(nm.str_TPKG_FOLDER) / nm.pkgDesc.str_srctestfldr / 'AllTests.cpp')
self.assertEqual(str_src, ctf_args[0][0])
self.assertEqual(str_dst, ctf_args[0][1])
# check call of processFile
pf_args = mock_pf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'test.cpp')
str_dst = str(Path(nm.str_TPKG_FOLDER) / nm.pkgDesc.str_srctestfldr / 'test.cpp')
self.assertEqual( str_src, pf_args[0][0])
self.assertEqual( str_dst, pf_args[0][1])
self.assertEqual( 6, len(pf_args[0][2]))
#check call of copyAndCreateTestFiles
mock_chife.assert_called_with(pathTestFldr)
@patch('createNewModule.processFile')
def test_createTestCMakefile(self,mock_pf):
testDesc = TDDConfig.CTestPkgDescription()
nm = createNewModule.CreateNewModule(testDesc)
nm.str_TPKG_FOLDER = 'TEST_Tpkg'
nm.str_COMPONENT_NAME = 'TEST_PACKAGE'
nm.createTestCMakefile()
pf_args = mock_pf.call_args
str_src = str(Path('Tools') / 'defaults' / 'src_templates' / 'CMakeLists.txt')
str_dst = str(Path(nm.str_TPKG_FOLDER) / 'CMakeLists.txt')
self.assertEqual( str_src, pf_args[0][0])
self.assertEqual( str_dst, pf_args[0][1])
self.assertEqual( {'%TESTPACKAGENAME%': 'TEST_PACKAGE'}, pf_args[0][2])
pass
| 46.407625 | 171 | 0.722654 | 3,373 | 31,650 | 6.529795 | 0.083605 | 0.070148 | 0.058842 | 0.052486 | 0.846129 | 0.826334 | 0.791192 | 0.776345 | 0.733485 | 0.710556 | 0 | 0.011418 | 0.169826 | 31,650 | 681 | 172 | 46.475771 | 0.826832 | 0.042464 | 0 | 0.638095 | 0 | 0 | 0.143386 | 0.095326 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.08 | false | 0.028571 | 0.013333 | 0 | 0.095238 | 0.04381 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
400c7a47a820d79dc4c39fd90841f24d726511a5 | 98 | py | Python | src/Checker/__init__.py | darrinl2t/OpenFOAMCaseGenerator | 72c3072814e9447b12c40c3423593c8d9a8c9cb4 | [
"MIT"
] | 3 | 2020-12-23T11:12:12.000Z | 2021-05-19T15:18:15.000Z | src/Checker/__init__.py | darrinl2t/OpenFOAMCaseGenerator | 72c3072814e9447b12c40c3423593c8d9a8c9cb4 | [
"MIT"
] | null | null | null | src/Checker/__init__.py | darrinl2t/OpenFOAMCaseGenerator | 72c3072814e9447b12c40c3423593c8d9a8c9cb4 | [
"MIT"
] | 3 | 2021-01-18T19:31:13.000Z | 2021-06-08T12:28:38.000Z | from .CheckCase import CheckCase
from .CheckCommandLineArguments import CheckCommandLineArguments
| 32.666667 | 64 | 0.897959 | 8 | 98 | 11 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.081633 | 98 | 2 | 65 | 49 | 0.977778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
40f38c3cd2174d07639058a0db7ecaf2243f5c50 | 95 | py | Python | instagram_api/exceptions/settings.py | Yuego/instagram_api | b53f72db36c505a2eb24ebac1ba8267a0cc295bb | [
"MIT"
] | 13 | 2019-08-07T21:24:34.000Z | 2020-12-12T12:23:50.000Z | instagram_api/exceptions/settings.py | Yuego/instagram_api | b53f72db36c505a2eb24ebac1ba8267a0cc295bb | [
"MIT"
] | null | null | null | instagram_api/exceptions/settings.py | Yuego/instagram_api | b53f72db36c505a2eb24ebac1ba8267a0cc295bb | [
"MIT"
] | null | null | null | from .internal import InternalException
class SettingsException(InternalException):
pass
| 15.833333 | 43 | 0.821053 | 8 | 95 | 9.75 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136842 | 95 | 5 | 44 | 19 | 0.95122 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
dc06c0aa2f85efcdadc064e23b89996bab24b239 | 4,841 | py | Python | models.py | belerico/insertion-matcher | 3e117c8ff41ebcd06eca0a861250fd6f8bd0e4a8 | [
"Apache-2.0"
] | 3 | 2020-01-24T22:06:57.000Z | 2020-11-29T00:23:02.000Z | models.py | belerico/aml-project | 3e117c8ff41ebcd06eca0a861250fd6f8bd0e4a8 | [
"Apache-2.0"
] | null | null | null | models.py | belerico/aml-project | 3e117c8ff41ebcd06eca0a861250fd6f8bd0e4a8 | [
"Apache-2.0"
] | null | null | null | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
""" class Model(nn.Module):
def __init__(
self,
TEXT,
hidden_dim,
conv_depth,
kernel_size,
pool_size,
dense_depth,
max_len,
similarity,
trainable,
):
super().__init__()
embedding_matrix = TEXT.vocab.vectors
emb_dim = embedding_matrix.size()[1]
self.similarity = similarity
self.embedding = nn.Embedding.from_pretrained(embedding_matrix)
self.embedding.requires_grad = trainable
self.encoder_left = nn.LSTM(
emb_dim, hidden_dim, num_layers=1, bidirectional=False, batch_first=True
)
self.encoder_right = nn.LSTM(
emb_dim, hidden_dim, num_layers=1, bidirectional=False, batch_first=True
)
self.conv1 = nn.Conv2d(1, conv_depth, kernel_size)
self.batch_norm1 = nn.BatchNorm2d(conv_depth)
output_size = int((((max_len - 2) / 2) ** 2) * conv_depth)
self.max_pool1 = nn.MaxPool2d(pool_size)
self.mlp1 = nn.Linear(output_size, dense_depth)
self.mlp2 = nn.Linear(dense_depth, 16)
self.out = nn.Linear(16, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, seq):
hdn_left, _ = self.encoder_left(self.embedding(seq[0]))
hdn_right, _ = self.encoder_right(self.embedding(seq[1]))
if self.similarity == "dot":
similarity = torch.matmul(hdn_left, torch.transpose(hdn_right, 1, 2))
similarity = torch.unsqueeze(similarity, 1)
else:
num = torch.matmul(hdn_left, torch.transpose(hdn_right, 1, 2))
n1 = torch.norm(hdn_left, dim=-1)
n2 = torch.norm(hdn_right, dim=-1)
den_for_row = (n1 * n2)[:, :, None]
repeat_along_last_axis = num.size()[-1]
similarity = num * torch.repeat_interleave(
den_for_row, repeat_along_last_axis, dim=-1
)
similarity = torch.unsqueeze(similarity, 1)
x = self.conv1(similarity)
x = F.relu(x)
x = self.batch_norm1(x)
x = self.max_pool1(x)
x = torch.flatten(x, start_dim=1)
x = self.mlp1(x)
x = F.relu(x)
x = F.dropout(x, 0.3)
x = self.mlp2(x)
x = F.relu(x)
x = F.dropout(x, 0.3)
x = self.out(x)
x = self.sigmoid(x)
return x """
class Model(nn.Module):
def __init__(
self,
TEXT,
hidden_dim,
conv_depth,
kernel_size,
pool_size,
dense_depth1,
dense_depth2,
max_len,
similarity,
trainable,
):
super().__init__()
embedding_matrix = TEXT.vocab.vectors
emb_dim = embedding_matrix.size()[1]
self.similarity = similarity
self.embedding = nn.Embedding.from_pretrained(embedding_matrix)
self.embedding.requires_grad = trainable
self.encoder_left = nn.LSTM(
emb_dim, hidden_dim, num_layers=1, bidirectional=False, batch_first=True
)
self.encoder_right = nn.LSTM(
emb_dim, hidden_dim, num_layers=1, bidirectional=False, batch_first=True
)
self.conv1 = nn.Conv2d(1, conv_depth, kernel_size)
self.batch_norm1 = nn.BatchNorm2d(conv_depth)
output_size = (math.floor((max_len - kernel_size + 1) / 2) ** 2) * conv_depth
self.max_pool1 = nn.MaxPool2d(pool_size)
self.mlp1 = nn.Linear(output_size, dense_depth1)
self.mlp2 = nn.Linear(dense_depth1, dense_depth2)
self.out = nn.Linear(dense_depth2, 2)
def forward(self, seq):
hdn_left, _ = self.encoder_left(self.embedding(seq[0]))
hdn_right, _ = self.encoder_right(self.embedding(seq[1]))
if self.similarity == "dot":
similarity = torch.matmul(hdn_left, torch.transpose(hdn_right, 1, 2))
similarity = torch.unsqueeze(similarity, 1)
else:
num = torch.matmul(hdn_left, torch.transpose(hdn_right, 1, 2))
n1 = torch.norm(hdn_left, dim=-1)
n2 = torch.norm(hdn_right, dim=-1)
den_for_row = (n1 * n2)[:, :, None]
repeat_along_last_axis = num.size()[-1]
similarity = num * torch.repeat_interleave(
den_for_row, repeat_along_last_axis, dim=-1
)
similarity = torch.unsqueeze(similarity, 1)
x = self.conv1(similarity)
x = F.relu(x)
x = self.batch_norm1(x)
x = self.max_pool1(x)
x = torch.flatten(x, start_dim=1)
x = self.mlp1(x)
x = F.relu(x)
x = F.dropout(x, 0.3)
x = self.mlp2(x)
x = F.relu(x)
x = F.dropout(x, 0.3)
x = self.out(x)
return x
| 33.157534 | 85 | 0.57736 | 628 | 4,841 | 4.227707 | 0.151274 | 0.011299 | 0.00904 | 0.015819 | 0.903202 | 0.887382 | 0.887382 | 0.887382 | 0.887382 | 0.887382 | 0 | 0.028043 | 0.307581 | 4,841 | 145 | 86 | 33.386207 | 0.764021 | 0 | 0 | 0.136364 | 0 | 0 | 0.001218 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.030303 | false | 0 | 0.060606 | 0 | 0.121212 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
dc07d0f4fea1569da0e666e53fe5fa1541d72c75 | 5,625 | py | Python | CODE/IEami/tools.py | pegahani/REUS | 3de7505fcf971333e294c490371d7b429d8fc9d0 | [
"MIT"
] | null | null | null | CODE/IEami/tools.py | pegahani/REUS | 3de7505fcf971333e294c490371d7b429d8fc9d0 | [
"MIT"
] | null | null | null | CODE/IEami/tools.py | pegahani/REUS | 3de7505fcf971333e294c490371d7b429d8fc9d0 | [
"MIT"
] | null | null | null | import os
import re
scenario_based = ['meet_1', 'meet_2', 'meet_3', 'meet_4', 'meet_5', 'meet_6', 'meet_7', 'meet_8', 'meet_10', 'meet_11',
'meet_12', 'meet_13', 'meet_14', 'meet_15', 'meet_16', 'meet_17', 'meet_18', 'meet_19', 'meet_20',
'meet_21', 'meet_22', 'meet_23', 'meet_25', 'meet_26', 'meet_27', 'meet_28', 'meet_29', 'meet_30',
'meet_31', 'meet_32', 'meet_33', 'meet_34', 'meet_35', 'meet_36', 'meet_37', 'meet_38', 'meet_39',
'meet_40', 'meet_41', 'meet_42', 'meet_43', 'meet_44', 'meet_45', 'meet_46', 'meet_47', 'meet_48',
'meet_49', 'meet_50', 'meet_51', 'meet_51b', 'meet_52', 'meet_53', 'meet_54','meet_55', 'meet_56',
'meet_57', 'meet_58', 'meet_59', 'meet_60', 'meet_61', 'meet_62', 'meet_63', 'meet_64', 'meet_65',
'meet_66', 'meet_67', 'meet_68', 'meet_69', 'meet_70', 'meet_71', 'meet_72', 'meet_73', 'meet_74',
'meet_75', 'meet_76', 'meet_77', 'meet_78', 'meet_79', 'meet_80', 'meet_81', 'meet_82', 'meet_83',
'meet_84', 'meet_85', 'meet_86', 'meet_87', 'meet_88', 'meet_89', 'meet_90', 'meet_91', 'meet_92',
'meet_93', 'meet_94', 'meet_95', 'meet_96', 'meet_97', 'meet_98', 'meet_99', 'meet_112', 'meet_113',
'meet_114', 'meet_115', 'meet_116', 'meet_117', 'meet_118', 'meet_119', 'meet_120', 'meet_121',
'meet_122', 'meet_123', 'meet_124', 'meet_125', 'meet_126', 'meet_127', 'meet_128', 'meet_129',
'meet_130', 'meet_131', 'meet_132', 'meet_133', 'meet_134', 'meet_135', 'meet_136', 'meet_137',
'meet_138', 'meet_139', 'meet_140', 'meet_141', 'meet_142', 'meet_143', 'meet_144', 'meet_145',
'meet_146', 'meet_147', 'meet_148', 'meet_149', 'meet_150', 'meet_151']
non_scenario_based = ['meet_156', 'meet_157', 'meet_158', 'meet_159', 'meet_160', 'meet_161', 'meet_162', 'meet_163',
'meet_164', 'meet_165', 'meet_166', 'meet_167', 'meet_168', 'meet_169', 'meet_170', 'meet_171',
'meet_172', 'meet_188', 'meet_173', 'meet_174', 'meet_175', 'meet_176', 'meet_177', 'meet_178',
'meet_179', 'meet_180', 'meet_181', 'meet_182', 'meet_183', 'meet_184', 'meet_185', 'meet_186',
'meet_187']
classification_scenario_based = [['meet_1', 'meet_2', 'meet_3', 'meet_4'], ['meet_5', 'meet_6', 'meet_7', 'meet_8'], ['meet_10', 'meet_11',
'meet_12'], ['meet_13', 'meet_14', 'meet_15', 'meet_16'], ['meet_17', 'meet_18', 'meet_19', 'meet_20'],
['meet_21', 'meet_22', 'meet_23'], ['meet_25', 'meet_26', 'meet_27', 'meet_28'], ['meet_29', 'meet_30',
'meet_31', 'meet_32'], ['meet_33', 'meet_34', 'meet_35', 'meet_36'], ['meet_37', 'meet_38', 'meet_39',
'meet_40'], ['meet_41', 'meet_42', 'meet_43', 'meet_44'], ['meet_45', 'meet_46', 'meet_47', 'meet_48'],
['meet_49', 'meet_50', 'meet_51', 'meet_51b'], ['meet_52', 'meet_53', 'meet_54', 'meet_55'], ['meet_56',
'meet_57', 'meet_58', 'meet_59'], ['meet_60', 'meet_61', 'meet_62', 'meet_63'], ['meet_64', 'meet_65',
'meet_66', 'meet_67'], ['meet_68', 'meet_69', 'meet_70', 'meet_71'], ['meet_72', 'meet_73', 'meet_74',
'meet_75'], ['meet_76', 'meet_77', 'meet_78', 'meet_79'], ['meet_80', 'meet_81', 'meet_82', 'meet_83'],
['meet_84', 'meet_85', 'meet_86', 'meet_87'], ['meet_88', 'meet_89', 'meet_90', 'meet_91'], ['meet_92',
'meet_93', 'meet_94', 'meet_95'], ['meet_96', 'meet_97', 'meet_98', 'meet_99'], ['meet_112', 'meet_113',
'meet_114', 'meet_115'], ['meet_116', 'meet_117', 'meet_118', 'meet_119'], ['meet_120', 'meet_121',
'meet_122', 'meet_123'], ['meet_124', 'meet_125', 'meet_126', 'meet_127'], ['meet_128', 'meet_129',
'meet_130', 'meet_131'], ['meet_132', 'meet_133', 'meet_134', 'meet_135'], ['meet_136', 'meet_137',
'meet_138', 'meet_139'], ['meet_140', 'meet_141', 'meet_142', 'meet_143'], ['meet_144', 'meet_145',
'meet_146', 'meet_147'], ['meet_148', 'meet_149', 'meet_150', 'meet_151']]
tokenize = lambda doc: [d for d in doc.lower().split(" ") if d != '']
def clean_line(data):
data = data.replace('A : ', '')
data = data.replace('B : ', '')
data = data.replace('C : ', '')
data = data.replace('D : ', '')
data = re.sub(r'[^a-zA-Z0-9 ]',r'', data)
return data
def text_to_string(input_file, is_resume_abstract = None, is_resume_extractive = None): # meet_1
data = ''
if is_resume_extractive == None and is_resume_abstract == None:
with open(input_file, 'r') as myfile:
data = myfile.read().replace('\n', '')
data = clean_line(data)
if is_resume_abstract == True:
with open(input_file, 'r') as myfile:
data = myfile.read().replace('\n', ' ')
data = data.replace('abstract : ', '')
data = data.replace('actions : ', '')
data = data.replace('decisions: ', '')
data = data.replace('problems: ', '')
data = clean_line(data)
return data
path = os.getcwd() + '/' #'/IEami/'
adress = path + 'manual_corpus/'
all_scenario_based_documents = [text_to_string(adress + i + '.txt') for i in scenario_based]
all_nonscenario_based_documents = [text_to_string(adress + i + '.txt') for i in non_scenario_based]
| 63.920455 | 139 | 0.546844 | 795 | 5,625 | 3.432704 | 0.29434 | 0.026383 | 0.043972 | 0.013192 | 0.709417 | 0.709417 | 0.709417 | 0.709417 | 0.709417 | 0.709417 | 0 | 0.167171 | 0.236444 | 5,625 | 87 | 140 | 64.655172 | 0.468219 | 0.002667 | 0 | 0.121212 | 0 | 0 | 0.421616 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.030303 | false | 0 | 0.030303 | 0 | 0.090909 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
90c9e6f0fbc593947541bbc2fc6e755d046134f8 | 14,226 | py | Python | tests/components/integration/test_sensor.py | mtarjoianu/core | 44e9146463ac505eb3d1c0651ad126cb25c28a54 | [
"Apache-2.0"
] | 3 | 2019-10-02T04:40:26.000Z | 2020-02-16T13:19:08.000Z | tests/components/integration/test_sensor.py | mtarjoianu/core | 44e9146463ac505eb3d1c0651ad126cb25c28a54 | [
"Apache-2.0"
] | 1,016 | 2019-06-18T21:27:47.000Z | 2020-03-06T11:09:58.000Z | tests/components/integration/test_sensor.py | mtarjoianu/core | 44e9146463ac505eb3d1c0651ad126cb25c28a54 | [
"Apache-2.0"
] | 1 | 2021-12-10T10:33:28.000Z | 2021-12-10T10:33:28.000Z | """The tests for the integration sensor platform."""
from datetime import timedelta
from unittest.mock import patch
from homeassistant.components.sensor import SensorDeviceClass, SensorStateClass
from homeassistant.const import (
ATTR_UNIT_OF_MEASUREMENT,
ENERGY_KILO_WATT_HOUR,
ENERGY_WATT_HOUR,
POWER_KILO_WATT,
POWER_WATT,
STATE_UNKNOWN,
TIME_SECONDS,
)
from homeassistant.core import HomeAssistant, State
from homeassistant.setup import async_setup_component
import homeassistant.util.dt as dt_util
from tests.common import mock_restore_cache
async def test_state(hass) -> None:
"""Test integration sensor state."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.power",
"round": 2,
}
}
now = dt_util.utcnow()
with patch("homeassistant.util.dt.utcnow", return_value=now):
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
hass.states.async_set(entity_id, 1, {ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT})
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
assert state.attributes.get("state_class") is SensorStateClass.TOTAL
assert "device_class" not in state.attributes
future_now = dt_util.utcnow() + timedelta(seconds=3600)
with patch("homeassistant.util.dt.utcnow", return_value=future_now):
hass.states.async_set(
entity_id,
1,
{
"device_class": SensorDeviceClass.POWER,
ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT,
},
force_update=True,
)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
# Testing a power sensor at 1 KiloWatts for 1hour = 1kWh
assert round(float(state.state), config["sensor"]["round"]) == 1.0
assert state.attributes.get("unit_of_measurement") == ENERGY_KILO_WATT_HOUR
assert state.attributes.get("device_class") == SensorDeviceClass.ENERGY
assert state.attributes.get("state_class") is SensorStateClass.TOTAL
async def test_restore_state(hass: HomeAssistant) -> None:
"""Test integration sensor state is restored correctly."""
mock_restore_cache(
hass,
(
State(
"sensor.integration",
"100.0",
{
"device_class": SensorDeviceClass.ENERGY,
"unit_of_measurement": ENERGY_KILO_WATT_HOUR,
},
),
),
)
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.power",
"round": 2,
}
}
assert await async_setup_component(hass, "sensor", config)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state
assert state.state == "100.00"
assert state.attributes.get("unit_of_measurement") == ENERGY_KILO_WATT_HOUR
assert state.attributes.get("device_class") == SensorDeviceClass.ENERGY
async def test_restore_state_failed(hass: HomeAssistant) -> None:
"""Test integration sensor state is restored correctly."""
mock_restore_cache(
hass,
(
State(
"sensor.integration",
"INVALID",
{
"last_reset": "2019-10-06T21:00:00.000000",
},
),
),
)
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.power",
}
}
assert await async_setup_component(hass, "sensor", config)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state
assert state.state == "unknown"
assert state.attributes.get("unit_of_measurement") is None
assert state.attributes.get("state_class") is SensorStateClass.TOTAL
assert "device_class" not in state.attributes
async def test_trapezoidal(hass):
"""Test integration sensor state."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.power",
"round": 2,
}
}
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
hass.states.async_set(entity_id, 0, {})
await hass.async_block_till_done()
# Testing a power sensor with non-monotonic intervals and values
for time, value in [(20, 10), (30, 30), (40, 5), (50, 0)]:
now = dt_util.utcnow() + timedelta(minutes=time)
with patch("homeassistant.util.dt.utcnow", return_value=now):
hass.states.async_set(
entity_id,
value,
{ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT},
force_update=True,
)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
assert round(float(state.state), config["sensor"]["round"]) == 8.33
assert state.attributes.get("unit_of_measurement") == ENERGY_KILO_WATT_HOUR
async def test_left(hass):
"""Test integration sensor state with left reimann method."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"method": "left",
"source": "sensor.power",
"round": 2,
}
}
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
hass.states.async_set(entity_id, 0, {ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT})
await hass.async_block_till_done()
# Testing a power sensor with non-monotonic intervals and values
for time, value in [(20, 10), (30, 30), (40, 5), (50, 0)]:
now = dt_util.utcnow() + timedelta(minutes=time)
with patch("homeassistant.util.dt.utcnow", return_value=now):
hass.states.async_set(
entity_id,
value,
{ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT},
force_update=True,
)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
assert round(float(state.state), config["sensor"]["round"]) == 7.5
assert state.attributes.get("unit_of_measurement") == ENERGY_KILO_WATT_HOUR
async def test_right(hass):
"""Test integration sensor state with left reimann method."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"method": "right",
"source": "sensor.power",
"round": 2,
}
}
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
hass.states.async_set(entity_id, 0, {ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT})
await hass.async_block_till_done()
# Testing a power sensor with non-monotonic intervals and values
for time, value in [(20, 10), (30, 30), (40, 5), (50, 0)]:
now = dt_util.utcnow() + timedelta(minutes=time)
with patch("homeassistant.util.dt.utcnow", return_value=now):
hass.states.async_set(
entity_id,
value,
{ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT},
force_update=True,
)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
assert round(float(state.state), config["sensor"]["round"]) == 9.17
assert state.attributes.get("unit_of_measurement") == ENERGY_KILO_WATT_HOUR
async def test_prefix(hass):
"""Test integration sensor state using a power source."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.power",
"round": 2,
"unit_prefix": "k",
}
}
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
hass.states.async_set(entity_id, 1000, {"unit_of_measurement": POWER_WATT})
await hass.async_block_till_done()
now = dt_util.utcnow() + timedelta(seconds=3600)
with patch("homeassistant.util.dt.utcnow", return_value=now):
hass.states.async_set(
entity_id, 1000, {"unit_of_measurement": POWER_WATT}, force_update=True
)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
# Testing a power sensor at 1000 Watts for 1hour = 1kWh
assert round(float(state.state), config["sensor"]["round"]) == 1.0
assert state.attributes.get("unit_of_measurement") == ENERGY_KILO_WATT_HOUR
async def test_suffix(hass):
"""Test integration sensor state using a network counter source."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.bytes_per_second",
"round": 2,
"unit_prefix": "k",
"unit_time": TIME_SECONDS,
}
}
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
hass.states.async_set(entity_id, 1000, {ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT})
await hass.async_block_till_done()
now = dt_util.utcnow() + timedelta(seconds=10)
with patch("homeassistant.util.dt.utcnow", return_value=now):
hass.states.async_set(
entity_id,
1000,
{ATTR_UNIT_OF_MEASUREMENT: POWER_KILO_WATT},
force_update=True,
)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
# Testing a network speed sensor at 1000 bytes/s over 10s = 10kbytes
assert round(float(state.state)) == 10
async def test_units(hass):
"""Test integration sensor units using a power source."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.power",
}
}
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
# This replicates the current sequence when HA starts up in a real runtime
# by updating the base sensor state before the base sensor's units
# or state have been correctly populated. Those interim updates
# include states of None and Unknown
hass.states.async_set(entity_id, 100, {"unit_of_measurement": None})
await hass.async_block_till_done()
hass.states.async_set(entity_id, 200, {"unit_of_measurement": None})
await hass.async_block_till_done()
hass.states.async_set(entity_id, 300, {"unit_of_measurement": POWER_WATT})
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
# Testing the sensor ignored the source sensor's units until
# they became valid
assert state.attributes.get("unit_of_measurement") == ENERGY_WATT_HOUR
async def test_device_class(hass):
"""Test integration sensor units using a power source."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.power",
}
}
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
# This replicates the current sequence when HA starts up in a real runtime
# by updating the base sensor state before the base sensor's units
# or state have been correctly populated. Those interim updates
# include states of None and Unknown
hass.states.async_set(entity_id, STATE_UNKNOWN, {})
await hass.async_block_till_done()
hass.states.async_set(entity_id, 100, {"device_class": None})
await hass.async_block_till_done()
hass.states.async_set(entity_id, 200, {"device_class": None})
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert "device_class" not in state.attributes
hass.states.async_set(
entity_id, 300, {"device_class": SensorDeviceClass.POWER}, force_update=True
)
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
# Testing the sensor ignored the source sensor's device class until
# it became valid
assert state.attributes.get("device_class") == SensorDeviceClass.ENERGY
async def test_calc_errors(hass):
"""Test integration sensor units using a power source."""
config = {
"sensor": {
"platform": "integration",
"name": "integration",
"source": "sensor.power",
}
}
assert await async_setup_component(hass, "sensor", config)
entity_id = config["sensor"]["source"]
hass.states.async_set(entity_id, None, {})
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
# With the source sensor in a None state, the Reimann sensor should be
# unknown
assert state is not None
assert state.state == STATE_UNKNOWN
# Moving from an unknown state to a value is a calc error and should
# not change the value of the Reimann sensor.
hass.states.async_set(entity_id, 0, {"device_class": None})
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
assert state.state == STATE_UNKNOWN
# With the source sensor updated successfully, the Reimann sensor
# should have a zero (known) value.
hass.states.async_set(entity_id, 1, {"device_class": None})
await hass.async_block_till_done()
state = hass.states.get("sensor.integration")
assert state is not None
assert round(float(state.state)) == 0
| 32.854503 | 88 | 0.634332 | 1,701 | 14,226 | 5.109347 | 0.105232 | 0.042573 | 0.046945 | 0.052468 | 0.872972 | 0.859395 | 0.856863 | 0.824991 | 0.819583 | 0.791393 | 0 | 0.015163 | 0.25362 | 14,226 | 432 | 89 | 32.930556 | 0.803353 | 0.093491 | 0 | 0.654952 | 0 | 0 | 0.161882 | 0.01998 | 0 | 0 | 0 | 0 | 0.169329 | 1 | 0 | false | 0 | 0.025559 | 0 | 0.025559 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2907188ee0edc6a8ee872a50acd0981000cea2c1 | 1,290 | py | Python | problems/problem_8.py | minuq/project-euler | 00307f01d06aa6dc61e701cf1bd90d2eafcf0478 | [
"MIT"
] | null | null | null | problems/problem_8.py | minuq/project-euler | 00307f01d06aa6dc61e701cf1bd90d2eafcf0478 | [
"MIT"
] | null | null | null | problems/problem_8.py | minuq/project-euler | 00307f01d06aa6dc61e701cf1bd90d2eafcf0478 | [
"MIT"
] | null | null | null | def main():
str = '''73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450'''.replace("\n", "")
result = 0
for i in range(0, len(str)-12):
tmp = 1
for j in range(0, 13):
tmp *= int(str[i+j])
if tmp > result:
result = tmp
print("Problem 8: {0}".format(result))
| 43 | 71 | 0.868217 | 59 | 1,290 | 18.983051 | 0.728814 | 0.0125 | 0.014286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.861775 | 0.091473 | 1,290 | 29 | 72 | 44.482759 | 0.093857 | 0 | 0 | 0 | 0 | 0 | 0.802326 | 0.775194 | 0 | 1 | 0 | 0 | 0 | 1 | 0.034483 | false | 0 | 0 | 0 | 0.034483 | 0.034483 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
293fdde412d2fa0d25bb00ab06a213f5a0a938bf | 137 | py | Python | pyhtmlgui/view/__init__.py | dirk-makerhafen/pyHtmlGui | 41c9f3080a2c815bd664bf6d7213d19745c092be | [
"MIT"
] | 1 | 2022-03-30T19:25:32.000Z | 2022-03-30T19:25:32.000Z | pyhtmlgui/view/__init__.py | dirk-makerhafen/pyHtmlGui | 41c9f3080a2c815bd664bf6d7213d19745c092be | [
"MIT"
] | null | null | null | pyhtmlgui/view/__init__.py | dirk-makerhafen/pyHtmlGui | 41c9f3080a2c815bd664bf6d7213d19745c092be | [
"MIT"
] | null | null | null | from .observableListView import ObservableListView
from .observableDictView import ObservableDictView
from .pyhtmlview import PyHtmlView
| 34.25 | 50 | 0.890511 | 12 | 137 | 10.166667 | 0.416667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.087591 | 137 | 3 | 51 | 45.666667 | 0.976 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
295c595ec90cc8d331f8912314e63fe61ee4f76c | 108 | py | Python | w2020/w8/w8_22/__init__.py | abrance/mine | d4067bf6fb158ebaea3eb7a516ae372dcb8cf419 | [
"MIT"
] | null | null | null | w2020/w8/w8_22/__init__.py | abrance/mine | d4067bf6fb158ebaea3eb7a516ae372dcb8cf419 | [
"MIT"
] | null | null | null | w2020/w8/w8_22/__init__.py | abrance/mine | d4067bf6fb158ebaea3eb7a516ae372dcb8cf419 | [
"MIT"
] | null | null | null | # @Time : 2020/8/22 8:45
# @Author : DaguguJ
# @Email : 1103098607@qq.com
# @File : __init__.py.py
| 21.6 | 30 | 0.583333 | 16 | 108 | 3.6875 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.243902 | 0.240741 | 108 | 4 | 31 | 27 | 0.47561 | 0.916667 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
29800c5f0f35769ff1970011195bfa25b2b131fe | 74 | py | Python | Adversary/__init__.py | jdai8/artificial-adversary | 66763b7fa35c00d11596e3b60bb7b177b03a18db | [
"MIT"
] | 363 | 2018-08-09T16:34:23.000Z | 2022-02-16T21:46:21.000Z | Adversary/__init__.py | afsconan/artificial-adversary | fe577e529becf90b4e8725e795f064c9523c16c3 | [
"MIT"
] | 9 | 2018-08-09T04:05:21.000Z | 2020-09-21T18:22:49.000Z | Adversary/__init__.py | afsconan/artificial-adversary | fe577e529becf90b4e8725e795f064c9523c16c3 | [
"MIT"
] | 54 | 2018-08-09T17:35:07.000Z | 2021-10-03T07:31:02.000Z | from Adversary.adversary import Adversary
from Adversary.attacks import *
| 24.666667 | 41 | 0.851351 | 9 | 74 | 7 | 0.444444 | 0.412698 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 74 | 2 | 42 | 37 | 0.954545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
4637a2fcc729a0ac7bffc55f7cf1462f4f2814c1 | 36 | py | Python | passenger_wsgi.py | ericmuh/recruitment-system | d9964e7c48ac8af74995e28f489135c1d8f940be | [
"MIT"
] | null | null | null | passenger_wsgi.py | ericmuh/recruitment-system | d9964e7c48ac8af74995e28f489135c1d8f940be | [
"MIT"
] | null | null | null | passenger_wsgi.py | ericmuh/recruitment-system | d9964e7c48ac8af74995e28f489135c1d8f940be | [
"MIT"
] | null | null | null | from recruit.wsgi import application | 36 | 36 | 0.888889 | 5 | 36 | 6.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 36 | 1 | 36 | 36 | 0.969697 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
468a82255b3dc21821c3f2d9e292af89eefcee2a | 12,827 | py | Python | ortho_seq_code/tests/test_orthoseqs.py | snafees/ortho_seqs | a9655857335ebdd16b554f2b49827c1db635d2c3 | [
"MIT"
] | 13 | 2020-06-07T22:52:42.000Z | 2021-06-16T13:59:42.000Z | ortho_seq_code/tests/test_orthoseqs.py | snafees/ortho_seqs | a9655857335ebdd16b554f2b49827c1db635d2c3 | [
"MIT"
] | 23 | 2020-06-12T21:39:14.000Z | 2022-01-23T19:57:28.000Z | ortho_seq_code/tests/test_orthoseqs.py | snafees/ortho_seqs | a9655857335ebdd16b554f2b49827c1db635d2c3 | [
"MIT"
] | 2 | 2021-02-26T01:39:50.000Z | 2021-06-22T15:52:44.000Z | import numpy as np
import pandas as pd
import os
from matplotlib import pyplot as plt
from click.testing import CliRunner
from ortho_seq_code.orthogonal_polynomial import orthogonal_polynomial
from ortho_seq_code.cli import cli
from ortho_seq_code.tests import orthoseqs_tst_utils as utils
from ortho_seq_code.utils import get_seq_info
def test_cli(protein_seqs_no_padding, protein_pheno_no_padding):
molecule = "protein"
poly_order = "first"
out_dir = "/tmp"
alphbt_input = None
min_pct = 75
runner = CliRunner()
result = runner.invoke(
cli,
[
protein_seqs_no_padding,
"--pheno_file",
protein_pheno_no_padding,
"--molecule",
molecule,
"--poly_order",
poly_order,
"--out_dir",
out_dir,
"--alphbt_input",
alphbt_input,
"--min_pct",
min_pct,
],
)
assert result.exit_code == 0
def test_cli(protein_seqs_padding, protein_pheno_padding):
molecule = "protein"
poly_order = "first"
out_dir = "/tmp"
alphbt_input = None
min_pct = 75
runner = CliRunner()
result = runner.invoke(
cli,
[
protein_seqs_padding,
"--pheno_file",
protein_pheno_padding,
"--molecule",
molecule,
"--poly_order",
poly_order,
"--out_dir",
out_dir,
"--alphbt_input",
alphbt_input,
"--min_pct",
min_pct,
],
)
assert result.exit_code == 0
def test_cli_precomputed(
protein_seqs_no_padding, protein_pheno_no_padding, protein_data_dir
):
molecule = "protein"
poly_order = "first"
out_dir = protein_data_dir
alphbt_input = None
min_pct = 75
runner = CliRunner()
result = runner.invoke(
cli,
[
protein_seqs_no_padding,
"--pheno_file",
protein_pheno_no_padding,
"--molecule",
molecule,
"--poly_order",
poly_order,
"--out_dir",
out_dir,
"--precomputed",
alphbt_input,
"--alphbt_input",
min_pct,
"--min_pct",
],
)
assert result.exit_code == 0
def assert_equality(expected_path, actual_path):
assert os.path.exists(expected_path)
assert os.path.exists(actual_path)
obtained_arrays = np.load(actual_path)
expected_arrays = np.load(expected_path)
for key, obtained_array in obtained_arrays.items():
expected_array = expected_arrays[key]
np.testing.assert_array_equal(
expected_array, obtained_array, "error at {}".format(key)
)
def test_nucleotide_first_order(
nucleotide_first_order_data_dir, nucleotide_params_first_order
):
with utils.TempDirectory() as location:
nucleotide_params_first_order = nucleotide_params_first_order._replace(
out_dir=location
)
location += "[0]" # Test file paths already exists, but location isn't updated with out_dir, this will update it
orthogonal_polynomial(*nucleotide_params_first_order)
basename = os.path.basename(nucleotide_params_first_order.seqs_filename)
basename_pheno = os.path.basename(nucleotide_params_first_order.pheno_filename)
expected_path = os.path.join(nucleotide_first_order_data_dir, basename + ".npz")
obtained_path = os.path.join(location, basename + ".npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
nucleotide_first_order_data_dir, basename_pheno + "_regressions.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_regressions.npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
nucleotide_first_order_data_dir, basename_pheno + "_covs_with_F.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_covs_with_F.npz")
expected_path = np.load(
os.path.join(nucleotide_first_order_data_dir, basename_pheno + "_Fm.npy")
)
obtained_path = np.load(os.path.join(location, basename_pheno + "_Fm.npy"))
np.testing.assert_array_equal(expected_path, obtained_path)
def test_nucleotide_second_order(
nucleotide_second_order_data_dir, nucleotide_params_second_order
):
with utils.TempDirectory() as location:
nucleotide_params_second_order = nucleotide_params_second_order._replace(
out_dir=location
)
location += "[0]"
orthogonal_polynomial(*nucleotide_params_second_order)
basename = os.path.basename(nucleotide_params_second_order.seqs_filename)
basename_pheno = os.path.basename(nucleotide_params_second_order.pheno_filename)
expected_path = os.path.join(
nucleotide_second_order_data_dir, basename + ".npz"
)
obtained_path = os.path.join(location, basename + ".npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
nucleotide_second_order_data_dir, basename_pheno + "_regressions.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_regressions.npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
nucleotide_second_order_data_dir, basename_pheno + "_covs_with_F.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_covs_with_F.npz")
assert_equality(expected_path, obtained_path)
expected_path = np.load(
os.path.join(nucleotide_second_order_data_dir, basename_pheno + "_Fm.npy")
)
obtained_path = np.load(os.path.join(location, basename_pheno + "_Fm.npy"))
np.testing.assert_array_equal(expected_path, obtained_path)
def test_protein_first_order(protein_data_dir, protein_params_first_order):
with utils.TempDirectory() as location:
protein_params_first_order = protein_params_first_order._replace(
out_dir=location
)
location += "[0]"
orthogonal_polynomial(*protein_params_first_order)
basefile = os.path.abspath(protein_params_first_order.seqs_filename)
assert get_seq_info(basefile, None, None)[:-4] == [18, 6, 6]
basename = os.path.basename(protein_params_first_order.seqs_filename)
basename_pheno = os.path.basename(protein_params_first_order.pheno_filename)
expected_path = os.path.join(protein_data_dir, basename + ".npz")
obtained_path = os.path.join(location, basename + ".npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
protein_data_dir, basename_pheno + "_regressions.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_regressions.npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
protein_data_dir, basename_pheno + "_covs_with_F.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_covs_with_F.npz")
assert_equality(expected_path, obtained_path)
expected_path = np.load(
os.path.join(protein_data_dir, basename_pheno + "_Fm.npy")
)
obtained_path = np.load(os.path.join(location, basename_pheno + "_Fm.npy"))
np.testing.assert_array_equal(expected_path, obtained_path)
def test_protein_padded_first_order(
protein_data_dir, protein_params_first_order_padded
):
with utils.TempDirectory() as location:
protein_params_first_order_padded = protein_params_first_order_padded._replace(
out_dir=location
)
location += "[0]"
orthogonal_polynomial(*protein_params_first_order_padded)
basefile = os.path.abspath(protein_params_first_order_padded.seqs_filename)
assert get_seq_info(basefile, None, None)[:-4] == [21, 6, 10]
basename = os.path.basename(protein_params_first_order_padded.seqs_filename)
basename_pheno = os.path.basename(
protein_params_first_order_padded.pheno_filename
)
expected_path = os.path.join(protein_data_dir, basename + ".npz")
obtained_path = os.path.join(location, basename + ".npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
protein_data_dir, basename_pheno + "_regressions.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_regressions.npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
protein_data_dir, basename_pheno + "_covs_with_F.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_covs_with_F.npz")
assert_equality(expected_path, obtained_path)
expected_path = np.load(
os.path.join(protein_data_dir, basename_pheno + "_Fm.npy")
)
obtained_path = np.load(os.path.join(location, basename_pheno + "_Fm.npy"))
np.testing.assert_array_equal(expected_path, obtained_path)
def test_protein_paddded_custom_aa(protein_data_dir, protein_params_custom_aa):
with utils.TempDirectory() as location:
protein_params_custom_aa = protein_params_custom_aa._replace(out_dir=location)
location += "[0]"
orthogonal_polynomial(*protein_params_custom_aa)
basefile = os.path.abspath(protein_params_custom_aa.seqs_filename)
indices = [0, 1, 2, 5, 6]
assert [get_seq_info(basefile, "ARSY", "protein")[x] for x in indices] == [
6,
6,
10,
["A", "R", "S", "Y", "z", "n"],
["A", "R", "S", "Y", "z", "n"],
]
basename = os.path.basename(protein_params_custom_aa.seqs_filename)
basename_pheno = os.path.basename(protein_params_custom_aa.pheno_filename)
expected_path = os.path.join(protein_data_dir, basename + ".npz")
obtained_path = os.path.join(location, basename + ".npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
protein_data_dir, basename_pheno + "_regressions.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_regressions.npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
protein_data_dir, basename_pheno + "_covs_with_F.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_covs_with_F.npz")
assert_equality(expected_path, obtained_path)
expected_path = np.load(
os.path.join(protein_data_dir, basename_pheno + "_Fm.npy")
)
obtained_path = np.load(os.path.join(location, basename_pheno + "_Fm.npy"))
np.testing.assert_array_equal(expected_path, obtained_path)
def test_protein_padded_custom_aa_2(protein_data_dir, protein_params_custom_aa_2):
with utils.TempDirectory() as location:
protein_params_custom_aa_2 = protein_params_custom_aa_2._replace(
out_dir=location
)
location += "[0]"
orthogonal_polynomial(*protein_params_custom_aa_2)
basefile = os.path.abspath(protein_params_custom_aa_2.seqs_filename)
indices = [0, 1, 2, 5, 6]
assert [get_seq_info(basefile, "AR,SY", "protein")[x] for x in indices] == [
4,
6,
10,
["0", "1", "2", "3"],
["AR", "SY", "CDEFGHIKLMNPQTVW", "n"],
]
basename = os.path.basename(protein_params_custom_aa_2.seqs_filename)
basename_pheno = os.path.basename(protein_params_custom_aa_2.pheno_filename)
expected_path = os.path.join(protein_data_dir, basename + ".npz")
obtained_path = os.path.join(location, basename + ".npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
protein_data_dir, basename_pheno + "_regressions.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_regressions.npz")
assert_equality(expected_path, obtained_path)
expected_path = os.path.join(
protein_data_dir, basename_pheno + "_covs_with_F.npz"
)
obtained_path = os.path.join(location, basename_pheno + "_covs_with_F.npz")
assert_equality(expected_path, obtained_path)
expected_path = np.load(
os.path.join(protein_data_dir, basename_pheno + "_Fm.npy")
)
obtained_path = np.load(os.path.join(location, basename_pheno + "_Fm.npy"))
np.testing.assert_array_equal(expected_path, obtained_path)
| 37.726471 | 121 | 0.658689 | 1,545 | 12,827 | 5.069256 | 0.080906 | 0.050562 | 0.061287 | 0.064351 | 0.883938 | 0.852528 | 0.841548 | 0.815501 | 0.75549 | 0.687436 | 0 | 0.005787 | 0.245576 | 12,827 | 339 | 122 | 37.837758 | 0.803555 | 0.007172 | 0 | 0.611684 | 0 | 0 | 0.067541 | 0 | 0 | 0 | 0 | 0 | 0.116838 | 1 | 0.034364 | false | 0 | 0.030928 | 0 | 0.065292 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
46ab8ff9011c40d4891b568e754bc53bb3936208 | 295 | py | Python | ch03tests/saskatchewan/test_overlap.py | erees1/rsd-engineeringcourse | fcafba9d7aa8657fbce039828a49152fa71ca0e1 | [
"CC-BY-3.0"
] | null | null | null | ch03tests/saskatchewan/test_overlap.py | erees1/rsd-engineeringcourse | fcafba9d7aa8657fbce039828a49152fa71ca0e1 | [
"CC-BY-3.0"
] | null | null | null | ch03tests/saskatchewan/test_overlap.py | erees1/rsd-engineeringcourse | fcafba9d7aa8657fbce039828a49152fa71ca0e1 | [
"CC-BY-3.0"
] | null | null | null | from .overlap import overlap
def test_full_overlap():
assert overlap((1.0, 1.0, 4.0, 4.0), (2.0, 2.0, 3.0, 3.0)) == 1.0
def test_partial_overlap():
assert overlap((1, 1, 4, 4), (2, 2, 3, 4.5)) == 2.0
def test_no_overlap():
assert overlap((1, 1, 4, 4), (4.5, 4.5, 5, 5)) == 0.0
| 21.071429 | 69 | 0.559322 | 63 | 295 | 2.52381 | 0.238095 | 0.132075 | 0.377358 | 0.396226 | 0.301887 | 0.301887 | 0.301887 | 0 | 0 | 0 | 0 | 0.175214 | 0.20678 | 295 | 13 | 70 | 22.692308 | 0.504274 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.428571 | 1 | 0.428571 | true | 0 | 0.142857 | 0 | 0.571429 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
d3bfef8e53013ae8836b16f765e80fa1595c7416 | 146 | py | Python | discord/ext/commands/__init__.py | Hype3808/RPD | 78ba69cf132fd0c07264a9a142866b79e94a60d5 | [
"Apache-2.0"
] | 12 | 2021-11-11T14:10:31.000Z | 2022-03-16T03:08:16.000Z | discord/ext/commands/__init__.py | Hype3808/RPD | 78ba69cf132fd0c07264a9a142866b79e94a60d5 | [
"Apache-2.0"
] | 24 | 2021-12-27T04:03:20.000Z | 2022-01-26T10:24:51.000Z | discord/ext/commands/__init__.py | Hype3808/RPD | 78ba69cf132fd0c07264a9a142866b79e94a60d5 | [
"Apache-2.0"
] | 13 | 2021-11-12T09:06:11.000Z | 2022-03-12T13:42:47.000Z | """
discord.ext.commands
~~~~~~~~~~~~~~~~~~~~
Commands extension for discord.io
"""
from .bot import *
from .context import *
from .core import *
| 16.222222 | 33 | 0.623288 | 17 | 146 | 5.352941 | 0.647059 | 0.21978 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136986 | 146 | 8 | 34 | 18.25 | 0.722222 | 0.513699 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
311653cea9ea6b4bfd4e0d0fac2b62aaf48ecc43 | 2,305 | py | Python | epytope/Data/pssms/smmpmbec/mat/B_08_01_9.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 7 | 2021-02-01T18:11:28.000Z | 2022-01-31T19:14:07.000Z | epytope/Data/pssms/smmpmbec/mat/B_08_01_9.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 22 | 2021-01-02T15:25:23.000Z | 2022-03-14T11:32:53.000Z | epytope/Data/pssms/smmpmbec/mat/B_08_01_9.py | christopher-mohr/epytope | 8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd | [
"BSD-3-Clause"
] | 4 | 2021-05-28T08:50:38.000Z | 2022-03-14T11:45:32.000Z | B_08_01_9 = {0: {'A': 0.296, 'C': -0.117, 'E': 0.03, 'D': 0.215, 'G': 0.34, 'F': -0.416, 'I': 0.101, 'H': 0.041, 'K': 0.213, 'M': -0.643, 'L': -0.257, 'N': -0.159, 'Q': 0.091, 'P': 0.784, 'S': -0.162, 'R': 0.004, 'T': 0.193, 'W': -0.402, 'V': 0.183, 'Y': -0.335}, 1: {'A': -0.435, 'C': 0.252, 'E': 0.404, 'D': 0.584, 'G': 0.094, 'F': -0.069, 'I': -0.396, 'H': 0.522, 'K': 0.071, 'M': -0.636, 'L': -0.62, 'N': -0.053, 'Q': 0.139, 'P': -0.598, 'S': 0.045, 'R': 0.031, 'T': 0.073, 'W': 0.338, 'V': -0.128, 'Y': 0.382}, 2: {'A': 0.095, 'C': 0.192, 'E': 0.268, 'D': 0.404, 'G': 0.059, 'F': -0.041, 'I': 0.051, 'H': -0.201, 'K': -0.674, 'M': -0.608, 'L': -0.205, 'N': 0.034, 'Q': 0.079, 'P': 0.487, 'S': 0.042, 'R': -0.661, 'T': 0.351, 'W': 0.146, 'V': 0.24, 'Y': -0.058}, 3: {'A': -0.002, 'C': 0.066, 'E': 0.225, 'D': 0.376, 'G': 0.244, 'F': -0.036, 'I': -0.069, 'H': -0.004, 'K': -0.325, 'M': -0.265, 'L': -0.052, 'N': 0.001, 'Q': 0.015, 'P': 0.161, 'S': 0.044, 'R': -0.342, 'T': -0.031, 'W': 0.046, 'V': 0.047, 'Y': -0.099}, 4: {'A': 0.378, 'C': 0.173, 'E': 0.365, 'D': 0.283, 'G': 0.241, 'F': -0.022, 'I': 0.065, 'H': -0.68, 'K': -0.723, 'M': -0.163, 'L': 0.11, 'N': 0.071, 'Q': 0.107, 'P': 0.627, 'S': 0.074, 'R': -1.065, 'T': 0.214, 'W': 0.104, 'V': 0.235, 'Y': -0.394}, 5: {'A': 0.058, 'C': 0.032, 'E': 0.429, 'D': 0.264, 'G': 0.192, 'F': -0.539, 'I': -0.056, 'H': -0.332, 'K': -0.103, 'M': 0.215, 'L': -0.006, 'N': 0.24, 'Q': 0.185, 'P': 0.117, 'S': -0.068, 'R': -0.348, 'T': -0.083, 'W': 0.007, 'V': -0.034, 'Y': -0.171}, 6: {'A': -0.05, 'C': -0.111, 'E': 0.363, 'D': 0.367, 'G': 0.341, 'F': 0.184, 'I': 0.281, 'H': -0.176, 'K': -0.061, 'M': -0.117, 'L': -0.201, 'N': -0.112, 'Q': -0.096, 'P': 0.116, 'S': -0.133, 'R': 0.035, 'T': -0.065, 'W': -0.233, 'V': -0.021, 'Y': -0.311}, 7: {'A': -0.481, 'C': -0.155, 'E': 0.111, 'D': 0.117, 'G': 0.193, 'F': 0.191, 'I': -0.148, 'H': 0.011, 'K': 0.277, 'M': 0.148, 'L': 0.235, 'N': 0.178, 'Q': 0.035, 'P': -0.394, 'S': -0.414, 'R': 0.071, 'T': -0.175, 'W': 0.177, 'V': -0.114, 'Y': 0.138}, 8: {'A': -0.454, 'C': 0.044, 'E': -0.041, 'D': 0.415, 'G': 0.15, 'F': -0.625, 'I': -0.682, 'H': 0.437, 'K': 0.516, 'M': -0.726, 'L': -0.98, 'N': 0.413, 'Q': 0.331, 'P': 0.393, 'S': 0.304, 'R': 0.572, 'T': 0.237, 'W': 0.029, 'V': -0.564, 'Y': 0.229}, -1: {'con': 4.94213}} | 2,305 | 2,305 | 0.398265 | 557 | 2,305 | 1.642729 | 0.339318 | 0.019672 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.378364 | 0.161822 | 2,305 | 1 | 2,305 | 2,305 | 0.095238 | 0 | 0 | 0 | 0 | 0 | 0.079358 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
314534b368a958e79d11a75a92c98cd74b375709 | 36,066 | py | Python | gelcoverage/tests.py | genomicsengland/gel-coverage | 61a671a53ac52a0b62c8aea983ced65fd0bed6cc | [
"Apache-2.0"
] | 2 | 2019-07-15T08:13:22.000Z | 2020-09-30T18:47:59.000Z | gelcoverage/tests.py | genomicsengland/gel-coverage | 61a671a53ac52a0b62c8aea983ced65fd0bed6cc | [
"Apache-2.0"
] | null | null | null | gelcoverage/tests.py | genomicsengland/gel-coverage | 61a671a53ac52a0b62c8aea983ced65fd0bed6cc | [
"Apache-2.0"
] | null | null | null | import unittest
import json
import os
import logging
import pybedtools
from gelcoverage.runner import GelCoverageRunner, GelCoverageInputError, BedMaker
from gelcoverage.test.output_verifier import OutputVerifier
import gelcoverage.constants as constants
PANELAPP_HOST = "panelapp.genomicsengland.co.uk/WebServices"
ASSEMBLY = "GRCh37"
SPECIES = "hsapiens"
CELLBASE_VERSION = "latest"
CELLBASE_HOST = os.environ.get('CELLBASE_URL')
FILTER_BASIC_FLAG = "basic"
FILTER_BIOTYPES = "IG_C_gene,IG_D_gene,IG_J_gene,IG_V_gene,IG_V_gene,protein_coding,nonsense_mediated_decay," \
"non_stop_decay,TR_C_gene,TR_D_gene,TR_J_gene,TR_V_gene"
class BedMakerTests(unittest.TestCase):
def setUp(self):
logging.basicConfig(level=logging.DEBUG)
self.config = {
# Sets parameters from CLI
"gene_list": None,
"chr_prefix": False,
"log_level": 10,
"transcript_filtering_flags": "basic",
"transcript_filtering_biotypes": "IG_C_gene,IG_D_gene,IG_J_gene,IG_V_gene,IG_V_gene,protein_coding,"
"nonsense_mediated_decay,non_stop_decay,TR_C_gene,TR_D_gene,TR_J_gene,"
"TR_V_gene",
"cellbase_species": "hsapiens",
"cellbase_version": "latest",
"cellbase_assembly": "grch37",
"cellbase_host": CELLBASE_HOST,
"cellbase_retries": -1,
}
def test1(self):
"""
Generates a bed file for all genes in Cellbase
:return:
"""
runner = BedMaker(config=self.config)
bed = runner.run()
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_bedmaker_1.bed')
observed_genes = self._get_genes_from_bed(bed)
self.assertEqual(len(observed_genes), 20567)
def test2(self):
"""
Generates a bed file for all genes in Cellbase in assembly GRCh38
:return:
"""
self.config['cellbase_assembly'] = "grch38"
runner = BedMaker(config=self.config)
bed = runner.run()
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_bedmaker_1.bed')
observed_genes = self._get_genes_from_bed(bed)
self.assertEqual(len(observed_genes), 20542)
def test3(self):
"""
Generates a bed file for some genes
:return:
"""
self.config['cellbase_assembly'] = "grch38"
expected_genes = [u'SCN2A', u'SPTAN1', u'PLCB1', u'SLC25A22', u'SCN8A', u'STXBP1', u'PNKP']
self.config['gene_list'] = expected_genes
runner = BedMaker(config=self.config)
bed = runner.run()
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_bedmaker_3.bed')
observed_genes = self._get_genes_from_bed(bed)
self.assertEqual(len(observed_genes), len(expected_genes))
self.assertEqual(set(observed_genes), set(expected_genes))
def test4(self):
"""
Generates a bed file for some genes
:return:
"""
expected_genes = [u'SCN2A', u'SPTAN1', u'PLCB1', u'SLC25A22', u'SCN8A', u'STXBP1', u'PNKP']
self.config['gene_list'] = expected_genes
runner = BedMaker(config=self.config)
bed = runner.run()
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_bedmaker_4.bed')
observed_genes = self._get_genes_from_bed(bed)
self.assertEqual(len(observed_genes), len(expected_genes))
self.assertEqual(set(observed_genes), set(expected_genes))
def test5(self):
"""
Generates a bed file for some genes
:return:
"""
expected_genes = [u'SCN2A', u'SPTAN1', u'PLCB1', u'SLC25A22', u'SCN8A', u'STXBP1', u'PNKP']
self.config['gene_list'] = expected_genes
self.config['exon_padding'] = 15
runner = BedMaker(config=self.config)
bed = runner.run()
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_bedmaker_5.bed')
observed_genes = self._get_genes_from_bed(bed)
self.assertEqual(len(observed_genes), len(expected_genes))
self.assertEqual(set(observed_genes), set(expected_genes))
def _get_genes_from_bed(self, bed):
genes = set()
for interval in bed:
# Reads data from BED entry
chromosome, start, end, gene_name, transcript_id, \
exon_number, strand, gc_content = GelCoverageRunner._parse_bed_interval(interval)
genes.add(gene_name)
return list(genes)
class GelCoverageRunnerTests(OutputVerifier):
def setUp(self):
logging.basicConfig(level=logging.INFO)
self.config = {
# Sets parameters from CLI
"bw": "../resources/test/test1.bw",
"configuration_file": "../resources/bigwig_analyser.config",
"panel": "Epileptic encephalopathy",
"panel_version": "1.2",
"coverage_threshold": 30,
"cellbase_species": SPECIES,
"cellbase_version": CELLBASE_VERSION,
"cellbase_assembly": ASSEMBLY,
"cellbase_host": CELLBASE_HOST,
"cellbase_retries": -1,
"panelapp_host": PANELAPP_HOST,
"panelapp_gene_confidence": "HighEvidence",
"panelapp_retries": -1,
"panelapp_assembly": ASSEMBLY,
"transcript_filtering_flags": FILTER_BASIC_FLAG,
"transcript_filtering_biotypes": FILTER_BIOTYPES,
"wg_stats_enabled": False,
"exon_padding": 15,
"wg_regions": None,
"exon_stats_enabled": True,
"coding_region_stats_enabled": True
}
def test1(self):
"""
Test 1: panel from PanelApp
:return:
"""
expected_gene_list = [u'ADSL', u'ALG13', u'ARHGEF9', u'ARX', u'ATP1A3', u'ATRX', u'CDKL5', u'CHD2',
u'CNTNAP2', u'DNM1', u'DOCK7', u'DYRK1A', u'EHMT1', u'FOXG1', u'GABRA1', u'GABRB3',
u'GNAO1', u'GRIN1', u'GRIN2A', u'GRIN2B', u'HCN1', u'IQSEC2', u'KCNA2', u'KCNB1',
u'KCNJ10', u'KCNQ2', u'KCNQ3', u'KCNT1', u'KIAA2022', u'KIF1BP', u'MAPK10', u'MBD5',
u'MECP2', u'MEF2C', u'PCDH19', u'PIGA', u'PLCB1', u'PNKP', u'POLG', u'PRRT2', u'PURA',
u'QARS', u'SCN1A', u'SCN1B', u'SCN2A', u'SCN8A', u'SETD5', u'SIK1', u'SLC12A5',
u'SLC13A5', u'SLC16A2', u'SLC25A22', u'SLC2A1', u'SLC6A1', u'SLC9A6', u'SPTAN1',
u'STX1B', u'STXBP1', u'SYNGAP1', u'TCF4', u'UBE2A', u'UBE3A', u'WDR45', u'WWOX', u'ZEB2']
self.config["panel"] = "Epileptic encephalopathy"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_1.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_1.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
def test1_1(self):
"""
Test 1: panel from PanelApp
:return:
"""
expected_gene_list = [u'MBTPS2']
self.config["panel"] = "568e844522c1fc1c78b67156"
self.config["panel_version"] = "1.0"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_1_1.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_1_1.bed')
# Runs verifications on output JSON
#self.verify_output(output, expected_gene_list)
def test1_2(self):
"""
Test 1: panel from PanelApp
:return:
"""
expected_gene_list = []
self.config["panel"] = "5550b7bebb5a161bf644a3bc"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_1_2.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_1_2.bed')
# Runs verifications on output JSON
#self.verify_output(output, expected_gene_list)
def test2(self):
"""
Test 2: gene list
:return:
"""
self.config["panel"] = None
self.config["panel_version"] = None
self.config["bw"] = "../resources/test/test2.bw"
self.config["gene_list"] = "BRCA1,BRCA2,CFTR,IGHE"
self.config["exon_padding"] = 0
expected_gene_list = map(
lambda x: unicode(x),
self.config["gene_list"].split(",")
)
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_2.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_2.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
def test3(self):
"""
Test 3: panel from PanelApp with exon padding of 15 bp
:return:
"""
expected_gene_list = [u'ADSL', u'ALG13', u'ARHGEF9', u'ARX', u'ATP1A3', u'ATRX', u'CDKL5', u'CHD2',
u'CNTNAP2', u'DNM1', u'DOCK7', u'DYRK1A', u'EHMT1', u'FOXG1', u'GABRA1', u'GABRB3',
u'GNAO1', u'GRIN1', u'GRIN2A', u'GRIN2B', u'HCN1', u'IQSEC2', u'KCNA2', u'KCNB1',
u'KCNJ10', u'KCNQ2', u'KCNQ3', u'KCNT1', u'KIAA2022', u'KIF1BP', u'MAPK10', u'MBD5',
u'MECP2', u'MEF2C', u'PCDH19', u'PIGA', u'PLCB1', u'PNKP', u'POLG', u'PRRT2', u'PURA',
u'QARS', u'SCN1A', u'SCN1B', u'SCN2A', u'SCN8A', u'SETD5', u'SIK1', u'SLC12A5',
u'SLC13A5', u'SLC16A2', u'SLC25A22', u'SLC2A1', u'SLC6A1', u'SLC9A6', u'SPTAN1',
u'STX1B', u'STXBP1', u'SYNGAP1', u'TCF4', u'UBE2A', u'UBE3A', u'WDR45', u'WWOX', u'ZEB2']
self.config["panel"] = "Epileptic encephalopathy"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 15
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_3.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_3.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
def test4(self):
"""
Test 4: gene list with exon padding of 15 bp
:return:
"""
self.config["panel"] = None
self.config["panel_version"] = None
self.config["bw"] = "../resources/test/test2.bw"
self.config["gene_list"] = "BRCA1,BRCA2,CFTR,IGHE"
self.config["exon_padding"] = 15
expected_gene_list = map(
lambda x: unicode(x),
self.config["gene_list"].split(",")
)
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_4.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
# Writes the JSON
with open('../resources/test/sample_output_4.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
def test5(self):
"""
Test 5: tests the union transcript build up
:return:
"""
def create_test_exon(start, end, exon_number):
return {
constants.EXON_START: start,
constants.EXON_END: end,
constants.EXON_PADDED_START: start - self.config["exon_padding"],
constants.EXON_PADDED_END: end + self.config["exon_padding"],
constants.EXON: exon_number
}
# Interval covered in the input file SCN2A: 2: 165,995,882-166,349,242
offset = 165995882
runner = GelCoverageRunner(
config=self.config
)
# Runs union transcript with exon padding 15bp
self.config["exon_padding"] = 15
gene_15bp_padding = {
constants.CHROMOSOME: "chr2",
constants.GENE_NAME: "TEST",
constants.TRANSCRIPTS: [
{
constants.TRANSCRIPT_ID: "1",
constants.EXONS: [
create_test_exon(offset + 10, offset + 15, 1),
create_test_exon(offset + 20, offset + 25, 2),
create_test_exon(offset + 30, offset + 35, 3),
create_test_exon(offset + 40, offset + 45, 4),
create_test_exon(offset + 50, offset + 55, 5),
create_test_exon(offset + 60, offset + 65, 6)
]
},
{
constants.TRANSCRIPT_ID: "2",
constants.EXONS: [
create_test_exon(offset + 10, offset + 15, 1),
create_test_exon(offset + 20, offset + 25, 2),
create_test_exon(offset + 30, offset + 35, 3),
create_test_exon(offset + 40, offset + 45, 4),
create_test_exon(offset + 50, offset + 55, 5),
create_test_exon(offset + 60, offset + 65, 6),
create_test_exon(offset + 70, offset + 75, 7)
]
},
{
constants.TRANSCRIPT_ID: "3",
constants.EXONS: [
create_test_exon(offset + 0, offset + 5, 1),
create_test_exon(offset + 10, offset + 15, 2),
create_test_exon(offset + 20, offset + 25, 3),
create_test_exon(offset + 30, offset + 35, 4),
create_test_exon(offset + 40, offset + 45, 5),
create_test_exon(offset + 50, offset + 55, 6),
create_test_exon(offset + 60, offset + 65, 7)
]
}
]
}
union_transcript = runner._GelCoverageRunner__create_union_transcript(gene_15bp_padding)
self.assertEqual(len(union_transcript[constants.EXONS]), 1)
gene_15bp_padding[constants.UNION_TRANSCRIPT] = union_transcript
self.verify_union_transcript(gene_15bp_padding, True)
# Runs union transcript with exon padding 0bp
self.config["exon_padding"] = 0
gene_0bp_padding = {
constants.CHROMOSOME: "chr2",
constants.GENE_NAME: "TEST",
constants.TRANSCRIPTS: [
{
constants.TRANSCRIPT_ID: "1",
constants.EXONS: [
create_test_exon(offset + 10, offset + 15, 1),
create_test_exon(offset + 20, offset + 25, 2),
create_test_exon(offset + 30, offset + 35, 3),
create_test_exon(offset + 40, offset + 45, 4),
create_test_exon(offset + 50, offset + 55, 5),
create_test_exon(offset + 60, offset + 65, 6)
]
},
{
constants.TRANSCRIPT_ID: "2",
constants.EXONS: [
create_test_exon(offset + 10, offset + 15, 1),
create_test_exon(offset + 20, offset + 25, 2),
create_test_exon(offset + 30, offset + 35, 3),
create_test_exon(offset + 40, offset + 45, 4),
create_test_exon(offset + 50, offset + 55, 5),
create_test_exon(offset + 60, offset + 65, 6),
create_test_exon(offset + 70, offset + 75, 7)
]
},
{
constants.TRANSCRIPT_ID: "3",
constants.EXONS: [
create_test_exon(offset + 0, offset + 5, 1),
create_test_exon(offset + 10, offset + 15, 2),
create_test_exon(offset + 20, offset + 25, 3),
create_test_exon(offset + 30, offset + 35, 4),
create_test_exon(offset + 40, offset + 45, 5),
create_test_exon(offset + 50, offset + 55, 6),
create_test_exon(offset + 60, offset + 65, 7)
]
}
]
}
union_transcript = runner._GelCoverageRunner__create_union_transcript(gene_0bp_padding)
self.assertEqual(len(union_transcript[constants.EXONS]), 8)
gene_0bp_padding[constants.UNION_TRANSCRIPT] = union_transcript
self.verify_union_transcript(gene_0bp_padding, True)
@unittest.skip("long running test")
def test6(self):
"""
Test 6: provided bed of nonN regions and whole genome metrics enabled
:return:
"""
expected_gene_list = [u'ADSL', u'ALG13', u'ARHGEF9', u'ARX', u'ATP1A3', u'ATRX', u'CDKL5', u'CHD2',
u'CNTNAP2', u'DNM1', u'DOCK7', u'DYRK1A', u'EHMT1', u'FOXG1', u'GABRA1', u'GABRB3',
u'GNAO1', u'GRIN1', u'GRIN2A', u'GRIN2B', u'HCN1', u'IQSEC2', u'KCNA2', u'KCNB1',
u'KCNJ10', u'KCNQ2', u'KCNQ3', u'KCNT1', u'KIAA2022', u'KIF1BP', u'MAPK10', u'MBD5',
u'MECP2', u'MEF2C', u'PCDH19', u'PIGA', u'PLCB1', u'PNKP', u'POLG', u'PRRT2', u'PURA',
u'QARS', u'SCN1A', u'SCN1B', u'SCN2A', u'SCN8A', u'SETD5', u'SIK1', u'SLC12A5',
u'SLC13A5', u'SLC16A2', u'SLC25A22', u'SLC2A1', u'SLC6A1', u'SLC9A6', u'SPTAN1',
u'STX1B', u'STXBP1', u'SYNGAP1', u'TCF4', u'UBE2A', u'UBE3A', u'WDR45', u'WWOX', u'ZEB2']
self.config["panel"] = "Epileptic encephalopathy"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
self.config["wg_stats_enabled"] = True
self.config["wg_regions"] = \
"../resources/Homo_sapiens.GRCh37.75.dna.primary_assembly.NonN_Regions.CHR.bed"
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_6.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_6.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
#@unittest.skip("long running test")
def test6_1(self):
"""
Test 6.1: provided bed of nonN regions and whole genome metrics enabled.
Different chromosome notations, should fail.
:return:
"""
expected_gene_list = [u'ADSL', u'ALG13', u'ARHGEF9', u'ARX', u'ATP1A3', u'ATRX', u'CDKL5', u'CHD2',
u'CNTNAP2', u'DNM1', u'DOCK7', u'DYRK1A', u'EHMT1', u'FOXG1', u'GABRA1', u'GABRB3',
u'GNAO1', u'GRIN1', u'GRIN2A', u'GRIN2B', u'HCN1', u'IQSEC2', u'KCNA2', u'KCNB1',
u'KCNJ10', u'KCNQ2', u'KCNQ3', u'KCNT1', u'KIAA2022', u'KIF1BP', u'MAPK10', u'MBD5',
u'MECP2', u'MEF2C', u'PCDH19', u'PIGA', u'PLCB1', u'PNKP', u'POLG', u'PRRT2', u'PURA',
u'QARS', u'SCN1A', u'SCN1B', u'SCN2A', u'SCN8A', u'SETD5', u'SIK1', u'SLC12A5',
u'SLC13A5', u'SLC16A2', u'SLC25A22', u'SLC2A1', u'SLC6A1', u'SLC9A6', u'SPTAN1',
u'STX1B', u'STXBP1', u'SYNGAP1', u'TCF4', u'UBE2A', u'UBE3A', u'WDR45', u'WWOX', u'ZEB2']
self.config["panel"] = "Epileptic encephalopathy"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
self.config["wg_stats_enabled"] = True
self.config["wg_regions"] = \
"../resources/Homo_sapiens.GRCh37.75.dna.primary_assembly.NonN_Regions.CHR.prefix.bed"
try:
runner = GelCoverageRunner(
config=self.config
)
caught_exception = False
except GelCoverageInputError:
caught_exception = True
self.assertTrue(caught_exception, msg="It should have raised an exception as bed and bigwig are using"
"different chromosome notations")
def test6_2(self):
"""
Test 6.2: provided a small bed for the whole genome analysis
:return:
"""
self.config["panel"] = None
self.config["panel_version"] = None
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
self.config["coding_region_stats_enabled"] = False
self.config["wg_stats_enabled"] = True
self.config["wg_regions"] = "../resources/test/test1.bed"
runner = GelCoverageRunner(
config=self.config
)
output, _ = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_6_2.json', 'w') as fp:
json.dump(output, fp)
# Runs verifications on output JSON
self.verify_output(output, None)
@unittest.skip("long running test")
def test7(self):
"""
Test 7: whole genome metrics enabled
:return:
"""
expected_gene_list = [u'ADSL', u'ALG13', u'ARHGEF9', u'ARX', u'ATP1A3', u'ATRX', u'CDKL5', u'CHD2',
u'CNTNAP2', u'DNM1', u'DOCK7', u'DYRK1A', u'EHMT1', u'FOXG1', u'GABRA1', u'GABRB3',
u'GNAO1', u'GRIN1', u'GRIN2A', u'GRIN2B', u'HCN1', u'IQSEC2', u'KCNA2', u'KCNB1',
u'KCNJ10', u'KCNQ2', u'KCNQ3', u'KCNT1', u'KIAA2022', u'KIF1BP', u'MAPK10', u'MBD5',
u'MECP2', u'MEF2C', u'PCDH19', u'PIGA', u'PLCB1', u'PNKP', u'POLG', u'PRRT2', u'PURA',
u'QARS', u'SCN1A', u'SCN1B', u'SCN2A', u'SCN8A', u'SETD5', u'SIK1', u'SLC12A5',
u'SLC13A5', u'SLC16A2', u'SLC25A22', u'SLC2A1', u'SLC6A1', u'SLC9A6', u'SPTAN1',
u'STX1B', u'STXBP1', u'SYNGAP1', u'TCF4', u'UBE2A', u'UBE3A', u'WDR45', u'WWOX', u'ZEB2']
self.config["panel"] = "Epileptic encephalopathy"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
self.config["wg_stats_enabled"] = True
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_7.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_7.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
def test8(self):
"""
Test 8: exon stats disabled
:return:
"""
expected_gene_list = [u'ADSL', u'ALG13', u'ARHGEF9', u'ARX', u'ATP1A3', u'ATRX', u'CDKL5', u'CHD2',
u'CNTNAP2', u'DNM1', u'DOCK7', u'DYRK1A', u'EHMT1', u'FOXG1', u'GABRA1', u'GABRB3',
u'GNAO1', u'GRIN1', u'GRIN2A', u'GRIN2B', u'HCN1', u'IQSEC2', u'KCNA2', u'KCNB1',
u'KCNJ10', u'KCNQ2', u'KCNQ3', u'KCNT1', u'KIAA2022', u'KIF1BP', u'MAPK10', u'MBD5',
u'MECP2', u'MEF2C', u'PCDH19', u'PIGA', u'PLCB1', u'PNKP', u'POLG', u'PRRT2', u'PURA',
u'QARS', u'SCN1A', u'SCN1B', u'SCN2A', u'SCN8A', u'SETD5', u'SIK1', u'SLC12A5',
u'SLC13A5', u'SLC16A2', u'SLC25A22', u'SLC2A1', u'SLC6A1', u'SLC9A6', u'SPTAN1',
u'STX1B', u'STXBP1', u'SYNGAP1', u'TCF4', u'UBE2A', u'UBE3A', u'WDR45', u'WWOX', u'ZEB2']
self.config["panel"] = "Epileptic encephalopathy"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
self.config["exon_stats_enabled"] = False
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_8.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_8.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
def test9(self):
"""
Test 9: gene list containing a gene not covered by the BAM
:return:
"""
self.config["panel"] = None
self.config["panel_version"] = None
self.config["bw"] = "../resources/test/test2.bw"
self.config["gene_list"] = "BRCA1,BRCA2,CFTR,IGHE,PTEN"
self.config["exon_padding"] = 0
expected_gene_list = map(
lambda x: unicode(x),
self.config["gene_list"].split(",")
)
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_9.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_9.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
self.assertEqual(len(output["results"]["uncovered_genes"]), 1,
msg="Uncovered genes should be of length 1")
self.assertEqual(output["results"]["uncovered_genes"][0][constants.GENE_NAME], "PTEN")
def test10(self):
"""
Test 10: gene list containing only a gene not covered by the BAM
:return:
"""
self.config["panel"] = None
self.config["panel_version"] = None
self.config["bw"] = "../resources/test/test2.bw"
self.config["gene_list"] = "PTEN"
self.config["exon_padding"] = 0
expected_gene_list = map(
lambda x: unicode(x),
self.config["gene_list"].split(",")
)
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_10.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_10.bed')
# Runs verifications on output JSON
self.assertEqual(len(output["results"]["uncovered_genes"]), 1,
msg="Uncovered genes should be of length 1")
self.assertEqual(output["results"]["uncovered_genes"][0][constants.GENE_NAME], "PTEN")
@unittest.skip("long running test")
def test11(self):
"""
Test 11: coding region analysis disabled
:return:
"""
expected_gene_list = [u'ADSL', u'ALG13', u'ARHGEF9', u'ARX', u'ATP1A3', u'ATRX', u'CDKL5', u'CHD2',
u'CNTNAP2', u'DNM1', u'DOCK7', u'DYRK1A', u'EHMT1', u'FOXG1', u'GABRA1', u'GABRB3',
u'GNAO1', u'GRIN1', u'GRIN2A', u'GRIN2B', u'HCN1', u'IQSEC2', u'KCNA2', u'KCNB1',
u'KCNJ10', u'KCNQ2', u'KCNQ3', u'KCNT1', u'KIAA2022', u'KIF1BP', u'MAPK10', u'MBD5',
u'MECP2', u'MEF2C', u'PCDH19', u'PIGA', u'PLCB1', u'PNKP', u'POLG', u'PRRT2', u'PURA',
u'QARS', u'SCN1A', u'SCN1B', u'SCN2A', u'SCN8A', u'SETD5', u'SIK1', u'SLC12A5',
u'SLC13A5', u'SLC16A2', u'SLC25A22', u'SLC2A1', u'SLC6A1', u'SLC9A6', u'SPTAN1',
u'STX1B', u'STXBP1', u'SYNGAP1', u'TCF4', u'UBE2A', u'UBE3A', u'WDR45', u'WWOX', u'ZEB2']
self.config["panel"] = "Epileptic encephalopathy"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
self.config["wg_stats_enabled"] = True
self.config["wg_regions"] = \
"../resources/Homo_sapiens.GRCh37.75.dna.primary_assembly.NonN_Regions.CHR.bed"
self.config["coding_region_stats_enabled"] = False
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_11.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(bed, None)
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
def test12(self):
"""
Test 12: coding region and whole genome analysis disabled
:return:
"""
expected_gene_list = [u'ADSL', u'ALG13', u'ARHGEF9', u'ARX', u'ATP1A3', u'ATRX', u'CDKL5', u'CHD2',
u'CNTNAP2', u'DNM1', u'DOCK7', u'DYRK1A', u'EHMT1', u'FOXG1', u'GABRA1', u'GABRB3',
u'GNAO1', u'GRIN1', u'GRIN2A', u'GRIN2B', u'HCN1', u'IQSEC2', u'KCNA2', u'KCNB1',
u'KCNJ10', u'KCNQ2', u'KCNQ3', u'KCNT1', u'KIAA2022', u'KIF1BP', u'MAPK10', u'MBD5',
u'MECP2', u'MEF2C', u'PCDH19', u'PIGA', u'PLCB1', u'PNKP', u'POLG', u'PRRT2', u'PURA',
u'QARS', u'SCN1A', u'SCN1B', u'SCN2A', u'SCN8A', u'SETD5', u'SIK1', u'SLC12A5',
u'SLC13A5', u'SLC16A2', u'SLC25A22', u'SLC2A1', u'SLC6A1', u'SLC9A6', u'SPTAN1',
u'STX1B', u'STXBP1', u'SYNGAP1', u'TCF4', u'UBE2A', u'UBE3A', u'WDR45', u'WWOX', u'ZEB2']
self.config["panel"] = "Epileptic encephalopathy"
self.config["panel_version"] = "1.2"
self.config["bw"] = "../resources/test/test1.bw"
self.config["exon_padding"] = 0
self.config["wg_stats_enabled"] = False
self.config["wg_regions"] = \
"../resources/Homo_sapiens.GRCh37.75.dna.primary_assembly.NonN_Regions.CHR.prefix.bed"
self.config["coding_region_stats_enabled"] = False
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_12.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(bed, None)
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
@unittest.skip("long running test")
def test13(self):
"""
Test 13: panel from PanelApp with exon padding of 15 bp, the biggest panel with 1232 genes
:return:
"""
expected_gene_list = None # too big to set here
self.config["bw"] = "../resources/test/test1.bw"
self.config["panel"] = "Intellectual disability"
self.config["panel_version"] = "1.23"
self.config["exon_padding"] = 15
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_13.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_13.bed')
# Runs verifications on output JSON
self.verify_output(output, expected_gene_list)
def test14(self):
"""
Test panel with no transcript passing filters.
:return:
"""
expected_gene_list = None # too big to set here
self.config["bw"] = "../resources/test/test1.bw"
self.config["panel"] = "5763f2ea8f620350a1996048"
self.config["panel_version"] = "1.0"
self.config["exon_padding"] = 15
runner = GelCoverageRunner(
config=self.config
)
output, bed = runner.run()
# Writes the JSON
with open('../resources/test/sample_output_14.json', 'w') as fp:
json.dump(output, fp)
# Verifies the bed...
self.assertEqual(type(bed), pybedtools.bedtool.BedTool)
# Saves the analysed region as a BED file
bed.saveas('../resources/test/sample_output_14.bed')
# Runs verifications on output JSON
self.expected_gene_list = expected_gene_list
self.assertEqual(type(output), dict)
# Verify that content in parameters is correct
self._verify_dict_field(output, "parameters", dict)
self._verify_parameters(output["parameters"])
| 46.476804 | 119 | 0.554567 | 4,303 | 36,066 | 4.518475 | 0.081803 | 0.064805 | 0.029522 | 0.041146 | 0.871728 | 0.858767 | 0.835931 | 0.816901 | 0.808877 | 0.796636 | 0 | 0.047652 | 0.310486 | 36,066 | 775 | 120 | 46.536774 | 0.734197 | 0.103006 | 0 | 0.660808 | 0 | 0 | 0.246038 | 0.091606 | 0 | 0 | 0 | 0 | 0.063269 | 1 | 0.047452 | false | 0 | 0.01406 | 0.001757 | 0.068541 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3152570f7c8a7a5c0d78d72ce4ebeb472f864874 | 147 | py | Python | Date.py | PALYmyGXG/tensoflow_flask_httpx_id48_project | 45efe14e2dad13eb94a10a0d87b69b08737d02c3 | [
"Apache-2.0"
] | 1 | 2021-07-21T06:46:03.000Z | 2021-07-21T06:46:03.000Z | Date.py | PALYmyGXG/tensoflow_flask_httpx_id48_project | 45efe14e2dad13eb94a10a0d87b69b08737d02c3 | [
"Apache-2.0"
] | null | null | null | Date.py | PALYmyGXG/tensoflow_flask_httpx_id48_project | 45efe14e2dad13eb94a10a0d87b69b08737d02c3 | [
"Apache-2.0"
] | null | null | null | import datetime
import time
def UTC(n, y, r):
return time.mktime(datetime.datetime.strptime('{}-{}-{}'.format(n,y,r), "%Y-%m-%d").timetuple()) | 29.4 | 100 | 0.639456 | 23 | 147 | 4.086957 | 0.652174 | 0.042553 | 0.06383 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102041 | 147 | 5 | 100 | 29.4 | 0.712121 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.5 | 0.25 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
315c1a4e2b6eb634926c6a0fff023e59010e3e1b | 226 | py | Python | jax_meta/utils/metrics.py | tristandeleu/jax-meta-learning | 3e83cc1be77dd99ad7539cbcb47536097e896d3a | [
"MIT"
] | 5 | 2022-03-01T00:47:09.000Z | 2022-03-24T01:48:17.000Z | jax_meta/utils/metrics.py | tristandeleu/jax-meta-learning | 3e83cc1be77dd99ad7539cbcb47536097e896d3a | [
"MIT"
] | null | null | null | jax_meta/utils/metrics.py | tristandeleu/jax-meta-learning | 3e83cc1be77dd99ad7539cbcb47536097e896d3a | [
"MIT"
] | 1 | 2022-03-06T16:03:19.000Z | 2022-03-06T16:03:19.000Z | import jax.numpy as jnp
def accuracy(outputs, targets):
return jnp.mean(jnp.argmax(outputs, axis=-1) == targets)
def binary_accuracy(logits, targets):
return jnp.mean((logits > 0).astype(targets.dtype) == targets)
| 22.6 | 66 | 0.712389 | 32 | 226 | 5 | 0.59375 | 0.1625 | 0.2 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010363 | 0.146018 | 226 | 9 | 67 | 25.111111 | 0.818653 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0.2 | 0.4 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
3166ebe50a4d525d3e9ebde219746d038ffd1d62 | 6,902 | py | Python | wefe/tests/test_metrics.py | Mezosky/wefe | 06b90702f0594206776ef20ecfbf2d8cc0b84a84 | [
"BSD-3-Clause"
] | 1 | 2021-05-21T03:53:51.000Z | 2021-05-21T03:53:51.000Z | wefe/tests/test_metrics.py | Mezosky/wefe | 06b90702f0594206776ef20ecfbf2d8cc0b84a84 | [
"BSD-3-Clause"
] | null | null | null | wefe/tests/test_metrics.py | Mezosky/wefe | 06b90702f0594206776ef20ecfbf2d8cc0b84a84 | [
"BSD-3-Clause"
] | null | null | null | import logging
import pytest
import numpy as np
from ..utils import load_weat_w2v
from ..word_embedding_model import WordEmbeddingModel
from ..datasets.datasets import load_weat
from ..query import Query
from ..metrics import WEAT, RND, RNSB, MAC, ECT
LOGGER = logging.getLogger(__name__)
def test_WEAT():
weat_word_set = load_weat()
model = WordEmbeddingModel(load_weat_w2v(), 'weat_w2v', '')
weat = WEAT()
query = Query([weat_word_set['flowers'], weat_word_set['insects']],
[weat_word_set['pleasant_5'], weat_word_set['unpleasant_5']],
['Flowers', 'Insects'], ['Pleasant', 'Unpleasant'])
results = weat.run_query(query, model)
assert results['query_name'] == 'Flowers and Insects wrt Pleasant and Unpleasant'
assert isinstance(results['result'], (np.float32, np.float64, float))
assert isinstance(results['weat'], (np.float32, np.float64, float))
assert isinstance(results['effect_size'], (np.float32, np.float64, float))
assert results['result'] == results['weat']
assert np.isnan(results['p_value'])
results = weat.run_query(query, model, return_effect_size=True)
assert isinstance(results['result'], (np.float32, np.float64, float))
assert isinstance(results['weat'], (np.float32, np.float64, float))
assert isinstance(results['effect_size'], (np.float32, np.float64, float))
assert results['result'] == results['effect_size']
assert np.isnan(results['p_value'])
results = weat.run_query(query,
model,
calculate_p_value=True,
p_value_iterations=100,
p_value_test_type='left-sided')
assert isinstance(results['result'], (np.float32, np.float64, float))
assert isinstance(results['weat'], (np.float32, np.float64, float))
assert isinstance(results['effect_size'], (np.float32, np.float64, float))
assert isinstance(results['p_value'], (np.float32, np.float64, float))
results = weat.run_query(query,
model,
calculate_p_value=True,
p_value_iterations=100,
p_value_test_type='right-sided')
assert isinstance(results['result'], (np.float32, np.float64, float))
assert isinstance(results['weat'], (np.float32, np.float64, float))
assert isinstance(results['effect_size'], (np.float32, np.float64, float))
assert isinstance(results['p_value'], (np.float32, np.float64, float))
results = weat.run_query(query,
model,
calculate_p_value=True,
p_value_iterations=100,
p_value_test_type='two-sided')
assert isinstance(results['result'], (np.float32, np.float64, float))
assert isinstance(results['weat'], (np.float32, np.float64, float))
assert isinstance(results['effect_size'], (np.float32, np.float64, float))
assert isinstance(results['p_value'], (np.float32, np.float64, float))
def test_RND():
weat_word_set = load_weat()
model = WordEmbeddingModel(load_weat_w2v(), 'weat_w2v', '')
rnd = RND()
query = Query([weat_word_set['flowers'], weat_word_set['insects']],
[weat_word_set['pleasant_5']], ['Flowers', 'Insects'], ['Pleasant'])
results = rnd.run_query(query, model)
assert results['query_name'] == 'Flowers and Insects wrt Pleasant'
assert isinstance(results['result'], (np.float32, np.float64, float))
def test_RNSB(capsys):
weat_word_set = load_weat()
model = WordEmbeddingModel(load_weat_w2v(), 'weat_w2v', '')
rnsb = RNSB()
query = Query([weat_word_set['flowers'], weat_word_set['insects']],
[weat_word_set['pleasant_5'], weat_word_set['unpleasant_5']],
['Flowers', 'Insects'], ['Pleasant', 'Unpleasant'])
results = rnsb.run_query(query, model)
assert results['query_name'] == 'Flowers and Insects wrt Pleasant and Unpleasant'
assert list(results.keys()) == [
'query_name', 'result', 'kl-divergence', 'clf_accuracy',
'negative_sentiment_probabilities', 'negative_sentiment_distribution'
]
assert isinstance(results['result'], (np.float32, np.float64, float, np.float_))
assert isinstance(results['negative_sentiment_probabilities'], dict)
assert isinstance(results['negative_sentiment_distribution'], dict)
query = Query([
weat_word_set['flowers'], weat_word_set['instruments'],
weat_word_set['male_terms'], weat_word_set['female_terms']
], [weat_word_set['pleasant_5'], weat_word_set['unpleasant_5']],
['Flowers', 'Insects', 'Male terms', 'Female terms'],
['Pleasant', 'Unpleasant'])
results = rnsb.run_query(query, model)
assert results[
'query_name'] == 'Flowers, Insects, Male terms and Female terms wrt Pleasant and Unpleasant'
assert isinstance(results['result'], (np.float32, np.float64, float))
# custom classifier, print model eval
results = rnsb.run_query(query, model, print_model_evaluation=True)
print(capsys.readouterr())
captured = capsys.readouterr()
assert 'Classification Report' in captured.out
assert results[
'query_name'] == 'Flowers, Insects, Male terms and Female terms wrt Pleasant and Unpleasant'
assert isinstance(results['result'], (np.float32, np.float64, float))
# lost word threshold test
results = rnsb.run_query(
Query([['bla', 'asd'], weat_word_set['insects']],
[weat_word_set['pleasant_5'], weat_word_set['unpleasant_5']],
['Flowers', 'Insects'], ['Pleasant', 'Unpleasant']), model)
assert np.isnan(np.nan)
def test_MAC():
weat_word_set = load_weat()
model = WordEmbeddingModel(load_weat_w2v(), 'weat_w2v', '')
mac = MAC()
query = Query([weat_word_set['flowers']], [
weat_word_set['pleasant_5'], weat_word_set['pleasant_9'],
weat_word_set['unpleasant_5'], weat_word_set['unpleasant_9']
], ['Flowers'], ['Pleasant 5 ', 'Pleasant 9', 'Unpleasant 5', 'Unpleasant 9'])
results = mac.run_query(query, model)
assert results[
'query_name'] == 'Flowers wrt Pleasant 5 , Pleasant 9, Unpleasant 5 and Unpleasant 9'
assert isinstance(results['result'], (np.float32, np.float64, float))
def test_ECT():
weat_word_set = load_weat()
model = WordEmbeddingModel(load_weat_w2v(), 'weat_w2v', '')
ect = ECT()
query = Query([weat_word_set['flowers'], weat_word_set['insects']],
[weat_word_set['pleasant_5']], ['Flowers', 'Insects'], ['Pleasant'])
results = ect.run_query(query, model)
assert results['query_name'] == 'Flowers and Insects wrt Pleasant'
assert isinstance(results['result'], (np.float32, np.float64, float)) | 41.830303 | 100 | 0.646914 | 818 | 6,902 | 5.235941 | 0.111247 | 0.061639 | 0.084754 | 0.100864 | 0.809246 | 0.774924 | 0.753677 | 0.752043 | 0.747373 | 0.706047 | 0 | 0.025326 | 0.210519 | 6,902 | 165 | 101 | 41.830303 | 0.76069 | 0.008693 | 0 | 0.556452 | 0 | 0 | 0.206871 | 0.018421 | 0 | 0 | 0 | 0 | 0.322581 | 1 | 0.040323 | false | 0 | 0.064516 | 0 | 0.104839 | 0.016129 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
31b07fb36d9ac4dea464bde6d44c3292c470cad5 | 126 | py | Python | tests/test_dictionary.py | JanviRaina/openodia | f33d095afcb2e87108986ecf16a5ee0f59568502 | [
"MIT"
] | null | null | null | tests/test_dictionary.py | JanviRaina/openodia | f33d095afcb2e87108986ecf16a5ee0f59568502 | [
"MIT"
] | null | null | null | tests/test_dictionary.py | JanviRaina/openodia | f33d095afcb2e87108986ecf16a5ee0f59568502 | [
"MIT"
] | null | null | null | from openodia.corpus.dictionary import get_dictionary
def test_get_dictionary():
assert len(get_dictionary()) == 208046
| 21 | 53 | 0.785714 | 16 | 126 | 5.9375 | 0.6875 | 0.410526 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.054545 | 0.126984 | 126 | 5 | 54 | 25.2 | 0.809091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
31cafe6b1112b5876a425f6d6596380236843f21 | 52 | py | Python | venv/Lib/site-packages/cytoolz/_version.py | RafaelHMachado/Cioffis_Automation | 07965ca71c3d4e78f5cee1fce4ba0bbfe2db9811 | [
"MIT"
] | 2 | 2021-04-01T08:46:05.000Z | 2021-04-01T08:46:07.000Z | venv/Lib/site-packages/cytoolz/_version.py | RafaelHMachado/Cioffis_Automation | 07965ca71c3d4e78f5cee1fce4ba0bbfe2db9811 | [
"MIT"
] | 9 | 2021-04-12T13:44:34.000Z | 2021-04-13T16:50:08.000Z | venv/Lib/site-packages/cytoolz/_version.py | RafaelHMachado/Cioffis_Automation | 07965ca71c3d4e78f5cee1fce4ba0bbfe2db9811 | [
"MIT"
] | 1 | 2021-03-31T10:11:02.000Z | 2021-03-31T10:11:02.000Z | __version__ = '0.11.0'
__toolz_version__ = '0.11.0'
| 17.333333 | 28 | 0.692308 | 9 | 52 | 3 | 0.444444 | 0.592593 | 0.740741 | 0.814815 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173913 | 0.115385 | 52 | 2 | 29 | 26 | 0.413043 | 0 | 0 | 0 | 0 | 0 | 0.230769 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
31e29c9baf96f833df25adc3fb546364d7800083 | 522 | py | Python | lib/stdio.py | Polarbear08/AtCoder_Python | 0b2c30ce6dd428afbd8587f0aa88337bccec16ab | [
"MIT"
] | null | null | null | lib/stdio.py | Polarbear08/AtCoder_Python | 0b2c30ce6dd428afbd8587f0aa88337bccec16ab | [
"MIT"
] | null | null | null | lib/stdio.py | Polarbear08/AtCoder_Python | 0b2c30ce6dd428afbd8587f0aa88337bccec16ab | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Standard I/O with sys library
@author: polarbear08
"""
# Input
import sys
stdin = sys.stdin
sys.setrecursionlimit(10**7)
def li(): return map(int, stdin.readline().split())
def li_(): return map(lambda x: int(x)-1, stdin.readline().split())
def lf(): return map(float, stdin.readline().split())
def ls(): return stdin.readline().split()
def ns(): return stdin.readline().rstrip()
def lc(): return list(ns())
def ni(): return int(stdin.readline())
def nf(): return float(stdin.readline()) | 24.857143 | 67 | 0.670498 | 78 | 522 | 4.474359 | 0.461538 | 0.260745 | 0.206304 | 0.240688 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.015317 | 0.124521 | 522 | 21 | 68 | 24.857143 | 0.748359 | 0.153257 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.727273 | false | 0 | 0.090909 | 0.727273 | 0.818182 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
31ec034c3a64f0006d01c25a031ae237d680d076 | 99 | py | Python | bioimageio/spec/model/raw_nodes.py | bioimage-io/spec-bioimage-io | b534001c67b3903dbd47f80a8b3ff027cd249119 | [
"MIT"
] | 6 | 2021-05-10T09:08:12.000Z | 2022-03-27T17:44:08.000Z | bioimageio/spec/model/raw_nodes.py | bioimage-io/spec-bioimage-io | b534001c67b3903dbd47f80a8b3ff027cd249119 | [
"MIT"
] | 227 | 2021-03-16T23:40:13.000Z | 2022-03-17T14:30:53.000Z | bioimageio/spec/model/raw_nodes.py | bioimage-io/spec-bioimage-io | b534001c67b3903dbd47f80a8b3ff027cd249119 | [
"MIT"
] | 5 | 2021-03-26T14:16:49.000Z | 2021-11-28T11:24:40.000Z | # Auto-generated by generate_passthrough_modules.py - do not modify
from .v0_4.raw_nodes import *
| 24.75 | 67 | 0.79798 | 16 | 99 | 4.6875 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.023256 | 0.131313 | 99 | 3 | 68 | 33 | 0.848837 | 0.656566 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9ecd075aa8136e68d55269986eba8a9a7de5d2bb | 38 | py | Python | briefmetrics/lib/service/__init__.py | shazow/briefmetrics | b6ca5ac0980ae00ccc160e6456e36ade8a3e2eeb | [
"MIT"
] | 18 | 2020-02-06T23:31:43.000Z | 2022-02-09T00:32:42.000Z | briefmetrics/lib/service/__init__.py | shazow/briefmetrics | b6ca5ac0980ae00ccc160e6456e36ade8a3e2eeb | [
"MIT"
] | 8 | 2020-02-08T23:00:46.000Z | 2021-07-27T20:52:12.000Z | briefmetrics/lib/service/__init__.py | shazow/briefmetrics | b6ca5ac0980ae00ccc160e6456e36ade8a3e2eeb | [
"MIT"
] | 1 | 2020-07-09T12:59:27.000Z | 2020-07-09T12:59:27.000Z | from .base import registry, OAuth2API
| 19 | 37 | 0.815789 | 5 | 38 | 6.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.030303 | 0.131579 | 38 | 1 | 38 | 38 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7334c9963fc51c85a104c4341cce3ec5267577d1 | 12,988 | py | Python | tests/test_wsdl_soap.py | zapay-pagamentos/python-zeep | 1e4e8e21da75ec114bae00d4626b66e46ec930ee | [
"MIT"
] | 1 | 2021-01-26T06:35:44.000Z | 2021-01-26T06:35:44.000Z | tests/test_wsdl_soap.py | zapay-pagamentos/python-zeep | 1e4e8e21da75ec114bae00d4626b66e46ec930ee | [
"MIT"
] | null | null | null | tests/test_wsdl_soap.py | zapay-pagamentos/python-zeep | 1e4e8e21da75ec114bae00d4626b66e46ec930ee | [
"MIT"
] | 1 | 2021-09-24T20:01:12.000Z | 2021-09-24T20:01:12.000Z | # -*- coding: utf-8 -*-
import platform
import pytest
from lxml import etree
from pretend import stub
from tests.utils import load_xml
from zeep import Client
from zeep.exceptions import Fault, TransportError
from zeep.wsdl import bindings
def test_soap11_no_output():
client = Client('tests/wsdl_files/soap.wsdl')
content = """
<soapenv:Envelope
xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/"
xmlns:stoc="http://example.com/stockquote.xsd">
<soapenv:Body></soapenv:Body>
</soapenv:Envelope>
""".strip()
response = stub(
status_code=200,
headers={},
content=content)
operation = client.service._binding._operations['GetLastTradePriceNoOutput']
res = client.service._binding.process_reply(client, operation, response)
assert res is None
def test_soap11_process_error():
response = load_xml("""
<soapenv:Envelope
xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/"
xmlns:stoc="http://example.com/stockquote.xsd">
<soapenv:Body>
<soapenv:Fault>
<faultcode>fault-code</faultcode>
<faultstring>fault-string</faultstring>
<detail>
<e:myFaultDetails xmlns:e="http://myexample.org/faults">
<e:message>detail-message</e:message>
<e:errorcode>detail-code</e:errorcode>
</e:myFaultDetails>
</detail>
</soapenv:Fault>
</soapenv:Body>
</soapenv:Envelope>
""")
binding = bindings.Soap11Binding(
wsdl=None, name=None, port_name=None, transport=None,
default_style=None)
try:
binding.process_error(response, None)
assert False
except Fault as exc:
assert exc.message == 'fault-string'
assert exc.code == 'fault-code'
assert exc.actor is None
assert exc.subcodes is None
assert 'detail-message' in etree.tostring(exc.detail).decode('utf-8')
def test_soap12_process_error():
response = """
<soapenv:Envelope
xmlns="http://example.com/example1"
xmlns:ex="http://example.com/example2"
xmlns:soapenv="http://www.w3.org/2003/05/soap-envelope">
<soapenv:Body>
<soapenv:Fault>
<soapenv:Code>
<soapenv:Value>fault-code</soapenv:Value>
%s
</soapenv:Code>
<soapenv:Reason>
<soapenv:Text xml:lang="en-US">us-error</soapenv:Text>
<soapenv:Text xml:lang="nl-NL">nl-error</soapenv:Text>
</soapenv:Reason>
<soapenv:Detail>
<e:myFaultDetails
xmlns:e="http://myexample.org/faults" >
<e:message>Invalid credit card details</e:message>
<e:errorcode>999</e:errorcode>
</e:myFaultDetails>
</soapenv:Detail>
</soapenv:Fault>
</soapenv:Body>
</soapenv:Envelope>
"""
subcode = """
<soapenv:Subcode>
<soapenv:Value>%s</soapenv:Value>
%s
</soapenv:Subcode>
"""
binding = bindings.Soap12Binding(
wsdl=None, name=None, port_name=None, transport=None,
default_style=None)
try:
binding.process_error(load_xml(response % ""), None)
assert False
except Fault as exc:
assert exc.message == 'us-error'
assert exc.code == 'fault-code'
assert exc.subcodes == []
try:
binding.process_error(
load_xml(response % subcode % ("fault-subcode1", "")), None)
assert False
except Fault as exc:
assert exc.message == 'us-error'
assert exc.code == 'fault-code'
assert len(exc.subcodes) == 1
assert exc.subcodes[0].namespace == 'http://example.com/example1'
assert exc.subcodes[0].localname == 'fault-subcode1'
try:
binding.process_error(
load_xml(response % subcode % ("fault-subcode1", subcode % ("ex:fault-subcode2", ""))),
None)
assert False
except Fault as exc:
assert exc.message == 'us-error'
assert exc.code == 'fault-code'
assert len(exc.subcodes) == 2
assert exc.subcodes[0].namespace == 'http://example.com/example1'
assert exc.subcodes[0].localname == 'fault-subcode1'
assert exc.subcodes[1].namespace == 'http://example.com/example2'
assert exc.subcodes[1].localname == 'fault-subcode2'
def test_no_content_type():
data = """
<?xml version="1.0"?>
<soapenv:Envelope
xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/"
xmlns:stoc="http://example.com/stockquote.xsd">
<soapenv:Header/>
<soapenv:Body>
<stoc:TradePrice>
<price>120.123</price>
</stoc:TradePrice>
</soapenv:Body>
</soapenv:Envelope>
""".strip()
client = Client('tests/wsdl_files/soap.wsdl')
binding = client.service._binding
response = stub(
status_code=200,
content=data,
encoding='utf-8',
headers={}
)
result = binding.process_reply(
client, binding.get('GetLastTradePrice'), response)
assert result == 120.123
@pytest.mark.skipif(platform.python_implementation() == 'PyPy',
reason="Fails on PyPy")
def test_wrong_content():
data = """
The request is answered something unexpected,
like an html page or a raw internal stack trace
""".strip()
client = Client('tests/wsdl_files/soap.wsdl')
binding = client.service._binding
response = stub(
status_code=200,
content=data,
encoding='utf-8',
headers={}
)
with pytest.raises(TransportError) as exc:
binding.process_reply(
client, binding.get('GetLastTradePrice'), response)
assert 200 == exc.value.status_code
assert data == exc.value.content
@pytest.mark.skipif(platform.python_implementation() == 'PyPy',
reason="Fails on PyPy")
def test_wrong_no_unicode_content():
data = """
The request is answered something unexpected,
and the content charset is beyond unicode òñÇÿ
""".strip()
client = Client('tests/wsdl_files/soap.wsdl')
binding = client.service._binding
response = stub(
status_code=200,
content=data,
encoding='utf-8',
headers={}
)
with pytest.raises(TransportError) as exc:
binding.process_reply(
client, binding.get('GetLastTradePrice'), response)
assert 200 == exc.value.status_code
assert data == exc.value.content
@pytest.mark.skipif(platform.python_implementation() == 'PyPy',
reason="Fails on PyPy")
def test_http_error():
data = """
Unauthorized!
""".strip()
client = Client('tests/wsdl_files/soap.wsdl')
binding = client.service._binding
response = stub(
status_code=401,
content=data,
encoding='utf-8',
headers={}
)
with pytest.raises(TransportError) as exc:
binding.process_reply(
client, binding.get('GetLastTradePrice'), response)
assert 401 == exc.value.status_code
assert data == exc.value.content
def test_mime_multipart():
data = '\r\n'.join(line.strip() for line in """
--MIME_boundary
Content-Type: text/xml; charset=UTF-8
Content-Transfer-Encoding: 8bit
Content-ID: <claim061400a.xml@claiming-it.com>
<?xml version='1.0' ?>
<SOAP-ENV:Envelope
xmlns:SOAP-ENV="http://schemas.xmlsoap.org/soap/envelope/">
<SOAP-ENV:Body>
<claim:insurance_claim_auto id="insurance_claim_document_id"
xmlns:claim="http://schemas.risky-stuff.com/Auto-Claim">
<theSignedForm href="cid:claim061400a.tiff@claiming-it.com"/>
<theCrashPhoto href="cid:claim061400a.jpeg@claiming-it.com"/>
<!-- ... more claim details go here... -->
</claim:insurance_claim_auto>
</SOAP-ENV:Body>
</SOAP-ENV:Envelope>
--MIME_boundary
Content-Type: image/tiff
Content-Transfer-Encoding: base64
Content-ID: <claim061400a.tiff@claiming-it.com>
Li4uQmFzZTY0IGVuY29kZWQgVElGRiBpbWFnZS4uLg==
--MIME_boundary
Content-Type: image/jpeg
Content-Transfer-Encoding: binary
Content-ID: <claim061400a.jpeg@claiming-it.com>
...Raw JPEG image..
--MIME_boundary--
""".splitlines()).encode('utf-8')
client = Client('tests/wsdl_files/claim.wsdl')
binding = client.service._binding
response = stub(
status_code=200,
content=data,
encoding='utf-8',
headers={
'Content-Type': 'multipart/related; type="text/xml"; start="<claim061400a.xml@claiming-it.com>"; boundary="MIME_boundary"'
}
)
result = binding.process_reply(
client, binding.get('GetClaimDetails'), response)
assert result.root is None
assert len(result.attachments) == 2
assert result.attachments[0].content == b'...Base64 encoded TIFF image...'
assert result.attachments[1].content == b'...Raw JPEG image..'
def test_mime_multipart_no_encoding():
data = '\r\n'.join(line.strip() for line in """
--MIME_boundary
Content-Type: text/xml
Content-Transfer-Encoding: 8bit
Content-ID: <claim061400a.xml@claiming-it.com>
<?xml version='1.0' ?>
<SOAP-ENV:Envelope
xmlns:SOAP-ENV="http://schemas.xmlsoap.org/soap/envelope/">
<SOAP-ENV:Body>
<claim:insurance_claim_auto id="insurance_claim_document_id"
xmlns:claim="http://schemas.risky-stuff.com/Auto-Claim">
<theSignedForm href="cid:claim061400a.tiff@claiming-it.com"/>
<theCrashPhoto href="cid:claim061400a.jpeg@claiming-it.com"/>
<!-- ... more claim details go here... -->
</claim:insurance_claim_auto>
</SOAP-ENV:Body>
</SOAP-ENV:Envelope>
--MIME_boundary
Content-Type: image/tiff
Content-Transfer-Encoding: base64
Content-ID: <claim061400a.tiff@claiming-it.com>
Li4uQmFzZTY0IGVuY29kZWQgVElGRiBpbWFnZS4uLg==
--MIME_boundary
Content-Type: text/xml
Content-ID: <claim061400a.jpeg@claiming-it.com>
...Raw JPEG image..
--MIME_boundary--
""".splitlines()).encode('utf-8')
client = Client('tests/wsdl_files/claim.wsdl')
binding = client.service._binding
response = stub(
status_code=200,
content=data,
encoding=None,
headers={
'Content-Type': 'multipart/related; type="text/xml"; start="<claim061400a.xml@claiming-it.com>"; boundary="MIME_boundary"'
}
)
result = binding.process_reply(
client, binding.get('GetClaimDetails'), response)
assert result.root is None
assert len(result.attachments) == 2
assert result.attachments[0].content == b'...Base64 encoded TIFF image...'
assert result.attachments[1].content == b'...Raw JPEG image..'
def test_unexpected_headers():
data = """
<?xml version="1.0"?>
<soapenv:Envelope
xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/"
xmlns:stoc="http://example.com/stockquote.xsd">
<soapenv:Header>
<stoc:IamUnexpected>uhoh</stoc:IamUnexpected>
</soapenv:Header>
<soapenv:Body>
<stoc:TradePrice>
<price>120.123</price>
</stoc:TradePrice>
</soapenv:Body>
</soapenv:Envelope>
""".strip()
client = Client('tests/wsdl_files/soap_header.wsdl')
binding = client.service._binding
response = stub(
status_code=200,
content=data,
encoding='utf-8',
headers={}
)
result = binding.process_reply(
client, binding.get('GetLastTradePrice'), response)
assert result.body.price == 120.123
assert result.header.body is None
assert len(result.header._raw_elements) == 1
def test_response_201():
client = Client('tests/wsdl_files/soap_header.wsdl')
binding = client.service._binding
response = stub(
status_code=201,
content='',
encoding='utf-8',
headers={}
)
result = binding.process_reply(
client, binding.get('GetLastTradePrice'), response)
assert result is None
def test_response_202():
client = Client('tests/wsdl_files/soap_header.wsdl')
binding = client.service._binding
response = stub(
status_code=202,
content='',
encoding='utf-8',
headers={}
)
result = binding.process_reply(
client, binding.get('GetLastTradePrice'), response)
assert result is None
| 30.632075 | 134 | 0.600939 | 1,414 | 12,988 | 5.433522 | 0.148515 | 0.019914 | 0.020305 | 0.027333 | 0.780554 | 0.770793 | 0.770793 | 0.748666 | 0.73565 | 0.730053 | 0 | 0.023927 | 0.266323 | 12,988 | 423 | 135 | 30.704492 | 0.782349 | 0.001617 | 0 | 0.704871 | 0 | 0.005731 | 0.507212 | 0.129348 | 0 | 0 | 0 | 0 | 0.12894 | 1 | 0.034384 | false | 0 | 0.022923 | 0 | 0.057307 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
733b31617b788718e87ad8aece23e000342e0a3c | 8,232 | py | Python | nuage_tempest_plugin/tests/api/cli/test_os_managed_dualstack_cli.py | nuagenetworks/nuage-tempest-plugin | ac1bfb0709c7bbaf04017af3050fb3ed1ad1324a | [
"Apache-1.1"
] | 1 | 2021-01-03T01:47:51.000Z | 2021-01-03T01:47:51.000Z | nuage_tempest_plugin/tests/api/cli/test_os_managed_dualstack_cli.py | nuagenetworks/nuage-tempest-plugin | ac1bfb0709c7bbaf04017af3050fb3ed1ad1324a | [
"Apache-1.1"
] | null | null | null | nuage_tempest_plugin/tests/api/cli/test_os_managed_dualstack_cli.py | nuagenetworks/nuage-tempest-plugin | ac1bfb0709c7bbaf04017af3050fb3ed1ad1324a | [
"Apache-1.1"
] | 1 | 2020-10-16T12:04:39.000Z | 2020-10-16T12:04:39.000Z | # Copyright 2017 - Nokia
# All Rights Reserved.
from netaddr import IPNetwork
from .base_nuage_networks_cli import BaseNuageNetworksCliTestCase
from nuage_tempest_plugin.lib.features import NUAGE_FEATURES
from nuage_tempest_plugin.lib.test import nuage_test
from nuage_tempest_plugin.lib.topology import Topology
from nuage_tempest_plugin.services.nuage_network_client \
import NuageNetworkClientJSON
from tempest.lib.common.utils import data_utils
from tempest.lib import decorators
LOG = Topology.get_logger(__name__)
VALID_MAC_ADDRESS = 'fa:fa:3e:e8:e8:01'
VALID_MAC_ADDRESS_2A = 'fa:fa:3e:e8:e8:2a'
VALID_MAC_ADDRESS_2B = 'fa:fa:3e:e8:e8:2b'
class OSManagedDualStackCliTest(
BaseNuageNetworksCliTestCase):
@classmethod
def skip_checks(cls):
super(OSManagedDualStackCliTest, cls).skip_checks()
if not NUAGE_FEATURES.os_managed_dualstack_subnets:
raise cls.skipException(
'OS Managed Dual Stack is not supported in this release')
@classmethod
def setup_clients(cls):
super(OSManagedDualStackCliTest, cls).setup_clients()
cls.nuage_network_client = NuageNetworkClientJSON(
cls.os_primary.auth_provider,
**cls.os_primary.default_params)
@nuage_test.header()
@decorators.attr(type='smoke')
def test_create_update_delete_dualstack(self):
network_name = data_utils.rand_name('cli_network')
network = self.create_network_with_args(network_name)
self.networks.remove(network)
subnet_name = data_utils.rand_name('cli-subnet')
cidr4 = IPNetwork('1.1.20.0/24')
cidr6 = IPNetwork("2001:5f74:c4a5:b82e::/64")
subnet4 = self.create_subnet_with_args(
network['name'], str(cidr4),
"--name ", subnet_name + "-4")
self.subnets.remove(subnet4)
show_subnet4 = self.show_subnet(subnet4['id'])
self.assertEqual(subnet4['id'],
show_subnet4['id'], "subnet not found")
subnet6 = self.create_subnet_with_args(
network['name'], str(cidr6),
"--name ", subnet_name + "-6",
"--ip-version 6",
"--disable-dhcp ")
self.subnets.remove(subnet6)
show_subnet6 = self.show_subnet(subnet6['id'])
self.assertEqual(subnet6['id'],
show_subnet6['id'], "subnet not found")
# When I delete subnet 4
self.delete_subnet(subnet4['id'])
# Then the subnet 4 is no longer there
self.assertCommandFailed("Unable to find subnet with name or id '{}'"
.format(subnet4['id']),
self.show_subnet,
subnet4['id'])
# When I delete subnet 6
self.delete_subnet(subnet6['id'])
# Then the subnet 6 is no longer there
self.assertCommandFailed("Unable to find subnet with name or id '{}'"
.format(subnet6['id']),
self.show_subnet,
subnet6['id'])
# When I delete the network
self.delete_network(network['id'])
# Then the network is no longer there
self.assertCommandFailed("Unable to find network with name or id '{}'"
.format(network['id']),
self.show_network,
network['id'])
@nuage_test.header()
@decorators.attr(type='smoke')
def test_delete_network_deletes_all_subnets(self):
network_name = data_utils.rand_name('cli_network')
network = self.create_network_with_args(network_name)
self.networks.remove(network)
subnet_name = data_utils.rand_name('cli-subnet')
cidr4 = IPNetwork('1.1.20.0/24')
cidr6 = IPNetwork("2001:5f74:c4a5:b82e::/64")
subnet4 = self.create_subnet_with_args(
network['name'], str(cidr4),
"--name ", subnet_name + "-4")
self.subnets.remove(subnet4)
show_subnet4 = self.show_subnet(subnet4['id'])
self.assertEqual(subnet4['id'],
show_subnet4['id'], "subnet not found")
subnet6 = self.create_subnet_with_args(
network['name'], str(cidr6),
"--name ", subnet_name + "-6",
"--ip-version 6",
"--disable-dhcp ")
self.subnets.remove(subnet6)
show_subnet6 = self.show_subnet(subnet6['id'])
self.assertEqual(subnet6['id'],
show_subnet6['id'], "subnet not found")
# When I delete the network
self.delete_network(network['id'])
# Then the network is no longer there
self.assertCommandFailed("Unable to find network with name or id '{}'"
.format(network['id']),
self.show_network,
network['id'])
# And the subnet 4 is no longer there
self.assertCommandFailed("Unable to find subnet with name or id '{}'"
.format(subnet4['id']),
self.show_subnet,
subnet4['id'])
# And the subnet 6 is no longer there
self.assertCommandFailed("Unable to find subnet with name or id '{}'"
.format(subnet6['id']),
self.show_subnet,
subnet6['id'])
@nuage_test.header()
@decorators.attr(type='smoke')
def test_dualstack_with_reserved_ipv6_address_neg(self):
network_name = data_utils.rand_name('cli_network')
network = self.create_network_with_args(network_name)
self.addCleanup(self._delete_network, network['id'])
self.networks.remove(network)
subnet_name = data_utils.rand_name('cli-subnet')
cidr4 = IPNetwork('1.1.20.0/24')
cidr6 = IPNetwork("2002:5f74:c4a5:b82e::/64")
subnet4 = self.create_subnet_with_args(
network['name'], str(cidr4),
"--name ", subnet_name + "-4")
self.subnets.remove(subnet4)
self.addCleanup(self._delete_subnet, subnet4['id'])
show_subnet4 = self.show_subnet(subnet4['id'])
self.assertEqual(subnet4['id'],
show_subnet4['id'], "subnet not found")
self.assertCommandFailed("IP Address {} is not valid"
" or cannot be in reserved address space."
.format(str(cidr6)),
self.create_subnet_with_args,
network['name'], str(cidr6),
"--name ", subnet_name + "-6",
"--ip-version 6",
"--disable-dhcp ")
@nuage_test.header()
@decorators.attr(type='smoke')
def test_dualstack_with_reserved_ipv6_address_and_ip4v_last_neg(self):
network_name = data_utils.rand_name('cli_network')
network = self.create_network_with_args(network_name)
self.addCleanup(self._delete_network, network['id'])
self.networks.remove(network)
subnet_name = data_utils.rand_name('cli-subnet')
cidr4 = IPNetwork('1.1.20.0/24')
cidr6 = IPNetwork("2002:5f74:c4a5:b82e::/64")
subnet6 = self.create_subnet_with_args(
network['name'], str(cidr6),
"--name ", subnet_name + "-6",
"--ip-version 6",
"--disable-dhcp ")
self.subnets.remove(subnet6)
show_subnet6 = self.show_subnet(subnet6['id'])
self.assertEqual(subnet6['id'],
show_subnet6['id'], "subnet not found")
self.assertCommandFailed("IP Address {} is not valid"
" or cannot be in reserved address space."
.format(str(cidr6)),
self.create_subnet_with_args,
network['name'],
str(cidr4),
"--name ", subnet_name)
| 40.551724 | 78 | 0.565476 | 895 | 8,232 | 4.998883 | 0.148603 | 0.039338 | 0.040232 | 0.050961 | 0.785203 | 0.750559 | 0.750559 | 0.750559 | 0.750559 | 0.740501 | 0 | 0.033052 | 0.323737 | 8,232 | 202 | 79 | 40.752475 | 0.770613 | 0.04361 | 0 | 0.77707 | 0 | 0 | 0.142875 | 0.012214 | 0 | 0 | 0 | 0 | 0.089172 | 1 | 0.038217 | false | 0 | 0.050955 | 0 | 0.095541 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
734d645199e2aa1f08d57b63894557e5a127a67d | 29 | py | Python | satyrus/sat/parser/__init__.py | lucasvg/Satyrus3-FinalProject-EspTopsOTM | 024785752abdc46e3463d8c94df7c3da873c354d | [
"MIT"
] | null | null | null | satyrus/sat/parser/__init__.py | lucasvg/Satyrus3-FinalProject-EspTopsOTM | 024785752abdc46e3463d8c94df7c3da873c354d | [
"MIT"
] | null | null | null | satyrus/sat/parser/__init__.py | lucasvg/Satyrus3-FinalProject-EspTopsOTM | 024785752abdc46e3463d8c94df7c3da873c354d | [
"MIT"
] | null | null | null | from .parser import SatParser | 29 | 29 | 0.862069 | 4 | 29 | 6.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.103448 | 29 | 1 | 29 | 29 | 0.961538 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7354438930149615ec7fc09e8ad09b8a810615bc | 21 | py | Python | saleor/graphql/core/__init__.py | acabezasg/urpi-master | 7c9cd0fbe6d89dad70652482712ca38b21ba6f84 | [
"BSD-3-Clause"
] | 9 | 2021-08-08T22:42:18.000Z | 2021-11-23T06:50:14.000Z | saleor/graphql/core/__init__.py | acabezasg/urpi-master | 7c9cd0fbe6d89dad70652482712ca38b21ba6f84 | [
"BSD-3-Clause"
] | 64 | 2019-02-11T17:02:05.000Z | 2021-06-25T15:16:57.000Z | saleor/graphql/core/__init__.py | acabezasg/urpi-master | 7c9cd0fbe6d89dad70652482712ca38b21ba6f84 | [
"BSD-3-Clause"
] | 2 | 2019-01-08T02:32:42.000Z | 2021-07-05T14:05:55.000Z | from . import fields
| 10.5 | 20 | 0.761905 | 3 | 21 | 5.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.190476 | 21 | 1 | 21 | 21 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
735ae9896fb6a441775a326a7580afd270a283ad | 107 | py | Python | pypy/module/array/test/test_ztranslation.py | nanjekyejoannah/pypy | e80079fe13c29eda7b2a6b4cd4557051f975a2d9 | [
"Apache-2.0",
"OpenSSL"
] | 381 | 2018-08-18T03:37:22.000Z | 2022-02-06T23:57:36.000Z | pypy/module/array/test/test_ztranslation.py | nanjekyejoannah/pypy | e80079fe13c29eda7b2a6b4cd4557051f975a2d9 | [
"Apache-2.0",
"OpenSSL"
] | 16 | 2018-09-22T18:12:47.000Z | 2022-02-22T20:03:59.000Z | pypy/module/array/test/test_ztranslation.py | nanjekyejoannah/pypy | e80079fe13c29eda7b2a6b4cd4557051f975a2d9 | [
"Apache-2.0",
"OpenSSL"
] | 55 | 2015-08-16T02:41:30.000Z | 2022-03-20T20:33:35.000Z |
from pypy.objspace.fake.checkmodule import checkmodule
def test_checkmodule():
checkmodule('array')
| 15.285714 | 54 | 0.775701 | 12 | 107 | 6.833333 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130841 | 107 | 6 | 55 | 17.833333 | 0.88172 | 0 | 0 | 0 | 0 | 0 | 0.047619 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b42568f99316032f0a799faf900b09ad7a192e26 | 40 | py | Python | tests/delete_deployment_test.py | BIX-Digital/mlflow-openshift | 1909191503ec2071d6f1f4e79cd859e7d7ad15e2 | [
"Apache-2.0"
] | 4 | 2021-01-06T07:49:21.000Z | 2021-11-30T13:49:50.000Z | tests/delete_deployment_test.py | pille321/mlflow-openshift | 0450a9ca7ae25dcd462324c41cccc894d40b7be5 | [
"Apache-2.0"
] | 2 | 2021-02-27T10:10:27.000Z | 2022-02-08T08:13:50.000Z | tests/delete_deployment_test.py | pille321/mlflow-openshift | 0450a9ca7ae25dcd462324c41cccc894d40b7be5 | [
"Apache-2.0"
] | 1 | 2021-01-11T06:22:16.000Z | 2021-01-11T06:22:16.000Z | # TODO: check if it is really gone
pass
| 13.333333 | 34 | 0.725 | 8 | 40 | 3.625 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.225 | 40 | 2 | 35 | 20 | 0.935484 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.5 | 0 | 1 | 0 | true | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
b4293a2d34b61fc22bb12b91a733b6a76019b2f3 | 34 | py | Python | results/raw_measurement.py | wirawan0/pyqmc | 8d641ba2b91d1d7a05a90574d0787fb991ee15e2 | [
"Apache-2.0"
] | null | null | null | results/raw_measurement.py | wirawan0/pyqmc | 8d641ba2b91d1d7a05a90574d0787fb991ee15e2 | [
"Apache-2.0"
] | null | null | null | results/raw_measurement.py | wirawan0/pyqmc | 8d641ba2b91d1d7a05a90574d0787fb991ee15e2 | [
"Apache-2.0"
] | null | null | null | # TODO: Move raw_meas_data here.
| 11.333333 | 32 | 0.735294 | 6 | 34 | 3.833333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.176471 | 34 | 2 | 33 | 17 | 0.821429 | 0.882353 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0.5 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
b439ccd90f10920948c6ffe0ef958bc2d3e045ba | 20 | py | Python | pyspk/GL/__init__.py | chromia/pyspk | b13f2b4f3c651376747003761ea88590a6014eda | [
"Zlib"
] | null | null | null | pyspk/GL/__init__.py | chromia/pyspk | b13f2b4f3c651376747003761ea88590a6014eda | [
"Zlib"
] | null | null | null | pyspk/GL/__init__.py | chromia/pyspk | b13f2b4f3c651376747003761ea88590a6014eda | [
"Zlib"
] | null | null | null | from ._GL import *
| 10 | 19 | 0.65 | 3 | 20 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 20 | 1 | 20 | 20 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b46117d6ea526881c8e0598753853803c68cb5c8 | 12,973 | py | Python | open_alchemy/schemas/helpers/iterate.py | rgreinho/OpenAlchemy | 23202bdecb94763d09b6d9e84eb9b29506c811ae | [
"Apache-2.0"
] | null | null | null | open_alchemy/schemas/helpers/iterate.py | rgreinho/OpenAlchemy | 23202bdecb94763d09b6d9e84eb9b29506c811ae | [
"Apache-2.0"
] | 53 | 2020-12-30T15:32:55.000Z | 2022-03-31T10:07:00.000Z | open_alchemy/schemas/helpers/iterate.py | rgreinho/OpenAlchemy | 23202bdecb94763d09b6d9e84eb9b29506c811ae | [
"Apache-2.0"
] | null | null | null | """Functions to expose iterables for schemas."""
import functools
import typing
from ... import exceptions
from ... import helpers
from ... import types
def constructable(
*, schemas: types.Schemas
) -> typing.Iterator[typing.Tuple[str, types.Schema]]:
"""
Create an iterable with all constructable schemas from all schemas.
Iterates over all items in the schemas, checks whether a schema is constructable and
yields those that are.
Args:
schemas: The schemas to iterate over.
Returns:
iterable with all schemas that are constructable.
"""
for name, schema in schemas.items():
try:
if not helpers.schema.constructable(schema=schema, schemas=schemas):
continue
except (exceptions.MalformedSchemaError, exceptions.SchemaNotFoundError):
continue
yield name, schema
def not_constructable(
*, schemas: types.Schemas
) -> typing.Iterator[typing.Tuple[str, types.Schema]]:
"""
Create an iterable with all non-constructable schemas from all schemas.
Iterates over all items in the schemas, checks whether a schema is not constructable
and yields those that are.
Args:
schemas: The schemas to iterate over.
Returns:
iterable with all schemas that are not constructable.
"""
for name, schema in schemas.items():
try:
if helpers.schema.constructable(schema=schema, schemas=schemas):
continue
except (exceptions.MalformedSchemaError, exceptions.SchemaNotFoundError):
continue
yield name, schema
def _calculate_skip_name(
*,
schema: types.Schema,
schemas: types.Schemas,
stay_within_tablename: bool = False,
stay_within_model: bool = False,
) -> typing.Optional[str]:
"""
Calculate the references to skip depending on the inheritance.
Args:
schema: The constructable schems.
schemas: All defined schemas (not just the constructable ones).
stay_within_tablename: Ensures that on properties on the same table are
iterated over. For joined table inheritance, the reference to the parent is
not followed.
stay_within_model: Ensures that each property is only returned once. For both
single and joined table inheritance no reference to the parent is followed.
Returns:
The references to skip or None.
"""
if not stay_within_tablename and not stay_within_model:
return None
inheritance_type = helpers.inheritance.calculate_type(
schema=schema, schemas=schemas
)
if inheritance_type != helpers.inheritance.Type.NONE:
parent_name = helpers.inheritance.retrieve_parent(
schema=schema, schemas=schemas
)
# Check for single
if stay_within_model:
return parent_name
# Check for JOINED
if (
not stay_within_model
and inheritance_type == helpers.inheritance.Type.JOINED_TABLE
and stay_within_tablename
):
return parent_name
return None
def _filter_duplicates(seen_keys: typing.Set[str], args) -> bool:
"""Remove duplicate values."""
key, _ = args
if key in seen_keys:
return False
seen_keys.add(key)
return True
def properties_items(
*,
schema: types.Schema,
schemas: types.Schemas,
stay_within_tablename: bool = False,
stay_within_model: bool = False,
) -> typing.Iterator[typing.Any]:
"""
Create an iterable with all properties of a schema from a constructable schema.
Checks for $ref, if it is there resolves to the underlying schema and recursively
processes that schema.
Checks for allOf, if it is there recursively processes each schema.
Otherwise looks for properties and yields all items if the key exists.
Args:
schema: The constructable schems.
schemas: All defined schemas (not just the constructable ones).
stay_within_tablename: Ensures that only properties on the same table are
iterated over. For joined table inheritance, the reference to the parent is
not followed.
stay_within_model: Ensures that each property is only returned once. For both
single and joined table inheritance no reference to the parent is followed.
Returns:
An iterator with all properties of a schema.
"""
init_filter_duplicates = functools.partial(_filter_duplicates, set())
properties_values_iterator = properties_values(
schema=schema,
schemas=schemas,
stay_within_tablename=stay_within_tablename,
stay_within_model=stay_within_model,
)
for properties_value in properties_values_iterator:
if not isinstance(properties_value, dict):
continue
yield from filter(init_filter_duplicates, properties_value.items())
def properties_values(
*,
schema: types.Schema,
schemas: types.Schemas,
stay_within_tablename: bool = False,
stay_within_model: bool = False,
) -> typing.Iterator[typing.Any]:
"""
Return iterable with all values of the properties key of the constructable schema.
Checks for $ref, if it is there resolves to the underlying schema and recursively
processes that schema.
Checks for allOf, if it is there recursively processes each schema.
Otherwise yields the properties key value.
Args:
schema: The constructable schems.
schemas: All defined schemas (not just the constructable ones).
stay_within_tablename: Ensures that only properties on the same table are
iterated over. For joined table inheritance, the reference to the parent is
not followed.
stay_within_model: Ensures that each properties value is only returned once. For
both single and joined table inheritance no reference to the parent is
followed.
Returns:
An iterator with all properties key values.
"""
skip_name: typing.Optional[str] = None
try:
skip_name = _calculate_skip_name(
schema=schema,
schemas=schemas,
stay_within_tablename=stay_within_tablename,
stay_within_model=stay_within_model,
)
except (
exceptions.MalformedSchemaError,
exceptions.InheritanceError,
exceptions.SchemaNotFoundError,
):
return
yield from _any_key(
schema=schema,
schemas=schemas,
skip_name=skip_name,
key=types.OpenApiProperties.PROPERTIES,
)
def _any_key(
*,
schema: types.Schema,
schemas: types.Schemas,
skip_name: typing.Optional[str],
key: str,
) -> typing.Iterator[typing.Any]:
"""Private interface for properties."""
if not isinstance(schema, dict):
return
# Handle $ref
if schema.get(types.OpenApiProperties.REF) is not None:
try:
_, ref_schema = helpers.ref.resolve(
name="", schema=schema, schemas=schemas, skip_name=skip_name
)
except (exceptions.MalformedSchemaError, exceptions.SchemaNotFoundError):
return
yield from _any_key(
schema=ref_schema, schemas=schemas, skip_name=skip_name, key=key
)
# Handle allOf
all_of = schema.get("allOf")
if all_of is not None:
if not isinstance(all_of, list):
return
all_of_dicts = list(
filter(lambda sub_schema: isinstance(sub_schema, dict), all_of)
)
# Process not $ref first
all_of_no_ref = filter(
lambda sub_schema: not helpers.peek.peek_key(
schema=sub_schema, schemas=schemas, key=types.OpenApiProperties.REF
),
all_of_dicts,
)
for sub_schema in all_of_no_ref:
yield from _any_key(
schema=sub_schema, schemas=schemas, skip_name=skip_name, key=key
)
# Process $ref
all_of_ref = filter(
lambda sub_schema: helpers.peek.peek_key(
schema=sub_schema, schemas=schemas, key=types.OpenApiProperties.REF
),
all_of_dicts,
)
for sub_schema in all_of_ref:
yield from _any_key(
schema=sub_schema, schemas=schemas, skip_name=skip_name, key=key
)
# Handle simple case
value = schema.get(key)
if value is None:
return
yield value
def required_values(
*,
schema: types.Schema,
schemas: types.Schemas,
stay_within_model: bool = False,
) -> typing.Iterator[typing.Any]:
"""
Return iterable with all values of the required key of the constructable schema.
Checks for $ref, if it is there resolves to the underlying schema and recursively
processes that schema.
Checks for allOf, if it is there recursively processes each schema.
Otherwise yields the required key value.
Args:
schema: The constructable schems.
schemas: All defined schemas (not just the constructable ones).
stay_within_model: Ensures that each required value is only returned once. For
both single and joined table inheritance no reference to the parent is
followed.
Returns:
An iterator with all required key values.
"""
skip_name: typing.Optional[str] = None
try:
skip_name = _calculate_skip_name(
schema=schema,
schemas=schemas,
stay_within_tablename=False,
stay_within_model=stay_within_model,
)
except (
exceptions.MalformedSchemaError,
exceptions.InheritanceError,
exceptions.SchemaNotFoundError,
):
return
yield from _any_key(
schema=schema,
schemas=schemas,
skip_name=skip_name,
key=types.OpenApiProperties.REQUIRED,
)
def required_items(
*,
schema: types.Schema,
schemas: types.Schemas,
stay_within_model: bool = False,
) -> typing.Iterator[typing.Any]:
"""
Return iterable with all items of the required key of the constructable schema.
Checks for $ref, if it is there resolves to the underlying schema and recursively
processes that schema.
Checks for allOf, if it is there recursively processes each schema.
Otherwise yields the required key value.
Args:
schema: The constructable schems.
schemas: All defined schemas (not just the constructable ones).
stay_within_model: Ensures that each required value is only returned once. For
both single and joined table inheritance no reference to the parent is
followed.
Returns:
An iterator with all items of the required key.
"""
required_values_iterator = required_values(
schema=schema, schemas=schemas, stay_within_model=stay_within_model
)
for required_value in required_values_iterator:
if not isinstance(required_value, list):
continue
yield from required_value
def backrefs_items(
*,
schema: types.Schema,
schemas: types.Schemas,
) -> typing.Iterator[typing.Any]:
"""
Create an iterable with all backrefs of a schema from a constructable schema.
Checks for $ref, if it is there resolves to the underlying schema and recursively
processes that schema.
Checks for allOf, if it is there recursively processes each schema.
Otherwise looks for x-backrefs and yields all items if the key exists.
Args:
schema: The constructable schems.
schemas: All defined schemas (not just the constructable ones).
Returns:
An iterator with all backrefs of a schema.
"""
init_filter_duplicates = functools.partial(_filter_duplicates, set())
backrefs_values_iterator = backrefs_values(
schema=schema,
schemas=schemas,
)
for backrefs_value in backrefs_values_iterator:
if not isinstance(backrefs_value, dict):
continue
yield from filter(init_filter_duplicates, backrefs_value.items())
def backrefs_values(
*,
schema: types.Schema,
schemas: types.Schemas,
) -> typing.Iterator[typing.Any]:
"""
Return iterable with all values of the backrefs key of the constructable schema.
Checks for $ref, if it is there resolves to the underlying schema and recursively
processes that schema.
Checks for allOf, if it is there recursively processes each schema.
Otherwise yields the backrefs key value.
Args:
schema: The constructable schems.
schemas: All defined schemas (not just the constructable ones).
Returns:
An iterator with all backrefs key values.
"""
yield from _any_key(
schema=schema,
schemas=schemas,
skip_name=None,
key=types.ExtensionProperties.BACKREFS,
)
| 30.669031 | 88 | 0.664688 | 1,550 | 12,973 | 5.432903 | 0.090323 | 0.040375 | 0.037406 | 0.040138 | 0.808218 | 0.77188 | 0.765467 | 0.752286 | 0.740648 | 0.709536 | 0 | 0 | 0.272797 | 12,973 | 422 | 89 | 30.741706 | 0.892622 | 0.409697 | 0 | 0.586047 | 0 | 0 | 0.000702 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.051163 | false | 0 | 0.023256 | 0 | 0.130233 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
81eb94c6b40d3dd234afd74324fb77631c1a682a | 26,463 | py | Python | etl/parsers/etw/Microsoft_Windows_SMBDirect.py | IMULMUL/etl-parser | 76b7c046866ce0469cd129ee3f7bb3799b34e271 | [
"Apache-2.0"
] | 104 | 2020-03-04T14:31:31.000Z | 2022-03-28T02:59:36.000Z | etl/parsers/etw/Microsoft_Windows_SMBDirect.py | IMULMUL/etl-parser | 76b7c046866ce0469cd129ee3f7bb3799b34e271 | [
"Apache-2.0"
] | 7 | 2020-04-20T09:18:39.000Z | 2022-03-19T17:06:19.000Z | etl/parsers/etw/Microsoft_Windows_SMBDirect.py | IMULMUL/etl-parser | 76b7c046866ce0469cd129ee3f7bb3799b34e271 | [
"Apache-2.0"
] | 16 | 2020-03-05T18:55:59.000Z | 2022-03-01T10:19:28.000Z | # -*- coding: utf-8 -*-
"""
Microsoft-Windows-SMBDirect
GUID : db66ea65-b7bb-4ca9-8748-334cb5c32400
"""
from construct import Int8sl, Int8ul, Int16ul, Int16sl, Int32sl, Int32ul, Int64sl, Int64ul, Bytes, Double, Float32l, Struct
from etl.utils import WString, CString, SystemTime, Guid
from etl.dtyp import Sid
from etl.parsers.etw.core import Etw, declare, guid
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1, version=1)
class Microsoft_Windows_SMBDirect_1_1(Etw):
pattern = Struct(
"AdapterAlias" / WString,
"RequiredSges" / Int32ul,
"AdapterSupportedSges" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=2, version=1)
class Microsoft_Windows_SMBDirect_2_1(Etw):
pattern = Struct(
"AdapterAlias" / WString,
"RequiredSges" / Int32ul,
"AdapterSupportedSges" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=3, version=1)
class Microsoft_Windows_SMBDirect_3_1(Etw):
pattern = Struct(
"AdapterAlias" / WString,
"RequiredSges" / Int32ul,
"AdapterSupportedSges" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=4, version=1)
class Microsoft_Windows_SMBDirect_4_1(Etw):
pattern = Struct(
"RegistryKeyName" / WString,
"SettingName" / WString,
"MinValidValue" / Int32ul,
"MaxValidValue" / Int32ul,
"DefaultValue" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=5, version=1)
class Microsoft_Windows_SMBDirect_5_1(Etw):
pattern = Struct(
"AdapterAlias" / WString,
"RegistryKeyName" / WString,
"SettingName" / WString,
"Value" / Int32ul,
"ClosestAdapterSupportedValue" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=6, version=1)
class Microsoft_Windows_SMBDirect_6_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"AdapterAlias" / WString,
"TimeoutInMs" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=7, version=1)
class Microsoft_Windows_SMBDirect_7_1(Etw):
pattern = Struct(
"BaseAffinityNode" / Int32ul,
"MaxAffinityNode" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=300, version=1)
class Microsoft_Windows_SMBDirect_300_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=301, version=1)
class Microsoft_Windows_SMBDirect_301_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=302, version=1)
class Microsoft_Windows_SMBDirect_302_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=303, version=1)
class Microsoft_Windows_SMBDirect_303_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=304, version=1)
class Microsoft_Windows_SMBDirect_304_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=305, version=1)
class Microsoft_Windows_SMBDirect_305_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=306, version=1)
class Microsoft_Windows_SMBDirect_306_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"MinVersion" / Int16ul,
"MaxVersion" / Int16ul,
"PeerMinVersion" / Int16ul,
"PeerMaxVersion" / Int16ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=350, version=1)
class Microsoft_Windows_SMBDirect_350_1(Etw):
pattern = Struct(
"ListenSocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=351, version=1)
class Microsoft_Windows_SMBDirect_351_1(Etw):
pattern = Struct(
"ListenSocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=352, version=1)
class Microsoft_Windows_SMBDirect_352_1(Etw):
pattern = Struct(
"ListenSocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=353, version=1)
class Microsoft_Windows_SMBDirect_353_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=354, version=1)
class Microsoft_Windows_SMBDirect_354_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=355, version=1)
class Microsoft_Windows_SMBDirect_355_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=356, version=1)
class Microsoft_Windows_SMBDirect_356_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=357, version=1)
class Microsoft_Windows_SMBDirect_357_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=358, version=1)
class Microsoft_Windows_SMBDirect_358_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=400, version=1)
class Microsoft_Windows_SMBDirect_400_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=401, version=2)
class Microsoft_Windows_SMBDirect_401_2(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"SocketState" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=402, version=2)
class Microsoft_Windows_SMBDirect_402_2(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=403, version=1)
class Microsoft_Windows_SMBDirect_403_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=404, version=1)
class Microsoft_Windows_SMBDirect_404_1(Etw):
pattern = Struct(
"SocketID" / Guid
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=450, version=1)
class Microsoft_Windows_SMBDirect_450_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=451, version=1)
class Microsoft_Windows_SMBDirect_451_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=452, version=1)
class Microsoft_Windows_SMBDirect_452_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=453, version=1)
class Microsoft_Windows_SMBDirect_453_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=454, version=1)
class Microsoft_Windows_SMBDirect_454_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=455, version=1)
class Microsoft_Windows_SMBDirect_455_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=456, version=1)
class Microsoft_Windows_SMBDirect_456_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"PeerSendCreditsGranted" / Int32ul,
"PeerSendCredits" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=457, version=1)
class Microsoft_Windows_SMBDirect_457_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"SendCreditsReceived" / Int32ul,
"SendCreditsAccepted" / Int32ul,
"SendCredits" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=500, version=1)
class Microsoft_Windows_SMBDirect_500_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Type" / Int32ul,
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=501, version=1)
class Microsoft_Windows_SMBDirect_501_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Type" / Int32ul,
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=502, version=1)
class Microsoft_Windows_SMBDirect_502_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=503, version=1)
class Microsoft_Windows_SMBDirect_503_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1000, version=1)
class Microsoft_Windows_SMBDirect_1000_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1001, version=1)
class Microsoft_Windows_SMBDirect_1001_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1002, version=1)
class Microsoft_Windows_SMBDirect_1002_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1003, version=1)
class Microsoft_Windows_SMBDirect_1003_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1004, version=1)
class Microsoft_Windows_SMBDirect_1004_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength)
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1005, version=1)
class Microsoft_Windows_SMBDirect_1005_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Type" / Int32ul,
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1006, version=1)
class Microsoft_Windows_SMBDirect_1006_1(Etw):
pattern = Struct(
"SocketID" / Guid
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1007, version=1)
class Microsoft_Windows_SMBDirect_1007_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1008, version=1)
class Microsoft_Windows_SMBDirect_1008_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"ProtocolVersion" / Int16ul,
"MaxReadWriteSize" / Int32ul,
"MaxReceiveSize" / Int32ul,
"MaxFragmentedReceiveSize" / Int32ul,
"MaxSendSize" / Int32ul,
"MaxFragmentedSendSize" / Int32ul,
"InboundReadDepth" / Int32ul,
"OutboundReadDepth" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1009, version=1)
class Microsoft_Windows_SMBDirect_1009_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"Status" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1010, version=1)
class Microsoft_Windows_SMBDirect_1010_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"Count" / Int32ul,
"IntervalInMicroSeconds" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=1011, version=1)
class Microsoft_Windows_SMBDirect_1011_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"Count" / Int32ul,
"IntervalInMicroSeconds" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=9500, version=1)
class Microsoft_Windows_SMBDirect_9500_1(Etw):
pattern = Struct(
"AdapterAlias" / WString,
"NdkMajorVer" / Int16ul,
"NdkMinorVer" / Int16ul,
"VendorId" / Int32ul,
"DeviceId" / Int32ul,
"MaxRegistrationSize" / Int64ul,
"MaxWindowSize" / Int64ul,
"FrmrPageCount" / Int32ul,
"MaxInitiatorRequestSge" / Int32ul,
"MaxReceiveRequestSge" / Int32ul,
"MaxReadRequestSge" / Int32ul,
"MaxTransferLength" / Int32ul,
"MaxInlineDataSize" / Int32ul,
"MaxInboundReadLimit" / Int32ul,
"MaxOutboundReadLimit" / Int32ul,
"MaxReceiveQueueDepth" / Int32ul,
"MaxInitiatorQueueDepth" / Int32ul,
"MaxSrqDepth" / Int32ul,
"MaxCqDepth" / Int32ul,
"LargeRequestThreshold" / Int32ul,
"MaxCallerData" / Int32ul,
"MaxCalleeData" / Int32ul,
"AdapterFlags" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=10000, version=1)
class Microsoft_Windows_SMBDirect_10000_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"MinVersion" / Int16ul,
"MaxVersion" / Int16ul,
"Reserved" / Int16ul,
"CreditsRequested" / Int16ul,
"PreferredSendSize" / Int32ul,
"MaxReceiveSize" / Int32ul,
"MaxFragmentReassemblyBufferSize" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=10001, version=1)
class Microsoft_Windows_SMBDirect_10001_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"MinVersion" / Int16ul,
"MaxVersion" / Int16ul,
"Reserved" / Int16ul,
"CreditsRequested" / Int16ul,
"PreferredSendSize" / Int32ul,
"MaxReceiveSize" / Int32ul,
"MaxFragmentReassemblyBufferSize" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=10002, version=1)
class Microsoft_Windows_SMBDirect_10002_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"MinVersion" / Int16ul,
"MaxVersion" / Int16ul,
"NegotiatedVersion" / Int16ul,
"Reserved" / Int16ul,
"CreditsRequested" / Int16ul,
"CreditsGranted" / Int16ul,
"Status" / Int32ul,
"MaxReadWriteSize" / Int32ul,
"PreferredSendSize" / Int32ul,
"MaxReceiveSize" / Int32ul,
"MaxFragmentReassemblyBufferSize" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=10003, version=1)
class Microsoft_Windows_SMBDirect_10003_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"MinVersion" / Int16ul,
"MaxVersion" / Int16ul,
"NegotiatedVersion" / Int16ul,
"Reserved" / Int16ul,
"CreditsRequested" / Int16ul,
"CreditsGranted" / Int16ul,
"Status" / Int32ul,
"MaxReadWriteSize" / Int32ul,
"PreferredSendSize" / Int32ul,
"MaxReceiveSize" / Int32ul,
"MaxFragmentReassemblyBufferSize" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=10004, version=1)
class Microsoft_Windows_SMBDirect_10004_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"CreditsRequested" / Int16ul,
"CreditsGranted" / Int16ul,
"Flags" / Int16ul,
"Reserved" / Int16ul,
"RemainingDataLength" / Int32ul,
"DataOffset" / Int32ul,
"DataLength" / Int32ul
)
@declare(guid=guid("db66ea65-b7bb-4ca9-8748-334cb5c32400"), event_id=10005, version=1)
class Microsoft_Windows_SMBDirect_10005_1(Etw):
pattern = Struct(
"SocketID" / Guid,
"LocalAddressLength" / Int32ul,
"LocalAddress" / Bytes(lambda this: this.LocalAddressLength),
"RemoteAddressLength" / Int32ul,
"RemoteAddress" / Bytes(lambda this: this.RemoteAddressLength),
"CreditsRequested" / Int16ul,
"CreditsGranted" / Int16ul,
"Flags" / Int16ul,
"Reserved" / Int16ul,
"RemainingDataLength" / Int32ul,
"DataOffset" / Int32ul,
"DataLength" / Int32ul
)
| 35.809202 | 123 | 0.674224 | 2,471 | 26,463 | 7.101174 | 0.071631 | 0.058927 | 0.080356 | 0.101784 | 0.902376 | 0.902376 | 0.766798 | 0.766798 | 0.766798 | 0.766798 | 0 | 0.102258 | 0.20659 | 26,463 | 738 | 124 | 35.857724 | 0.733473 | 0.003552 | 0 | 0.63355 | 0 | 0 | 0.275379 | 0.09302 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.006515 | 0 | 0.198697 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
81fc4cf5027021cb710ec821bcfcfaab3abab14b | 2,750 | py | Python | test/test_Match.py | gmso/connect-4-cli | da8bcf474d27b7ef801a36fcc1071283516342f5 | [
"MIT"
] | null | null | null | test/test_Match.py | gmso/connect-4-cli | da8bcf474d27b7ef801a36fcc1071283516342f5 | [
"MIT"
] | null | null | null | test/test_Match.py | gmso/connect-4-cli | da8bcf474d27b7ef801a36fcc1071283516342f5 | [
"MIT"
] | null | null | null | import src.constants as const
import src.Match as Match
import utility_MakeBoards as BoardMaker
def test_Match_construction():
match_default = Match.Match()
assert(match_default.column_selected == 0)
assert(match_default.state == const.MatchState.PLAYING)
assert(match_default.player_turn == const.PlayerTurn.RED)
match_other_player = Match.Match(const.PlayerTurn.YELLOW)
assert(match_other_player.player_turn == const.PlayerTurn.YELLOW)
def test_column_changes_match_playing():
match = Match.Match()
match.board = BoardMaker.make_board_playing_with_full_columns()
assert(match.column_selected == 0)
match.column_next()
assert(match.column_selected == 2)
match.column_next()
assert(match.column_selected == 3)
match.column_next()
assert(match.column_selected == 5)
match.column_next()
assert(match.column_selected == 6)
match.column_next()
assert(match.column_selected == 0)
match.column_next()
assert(match.column_selected == 2)
match.column_previous()
assert(match.column_selected == 0)
match.column_previous()
assert(match.column_selected == 6)
match.column_previous()
assert(match.column_selected == 5)
match.column_previous()
assert(match.column_selected == 3)
match.column_previous()
assert(match.column_selected == 2)
match.column_previous()
assert(match.column_selected == 0)
def test_add_checker_win_game_red():
match = Match.Match()
match.board = BoardMaker.make_board_almost_won_both()
assert(match.state == const.MatchState.PLAYING)
assert(match.is_being_played())
assert(match.column_selected == 0)
assert(match.player_turn == const.PlayerTurn.RED)
match.add_checker()
assert(match.state == const.MatchState.RED_WON)
assert not(match.is_being_played())
def test_add_checker_win_game_yellow():
match = Match.Match()
match.board = BoardMaker.make_board_almost_won_both()
assert(match.state == const.MatchState.PLAYING)
assert(match.is_being_played())
assert(match.column_selected == 0)
assert(match.player_turn == const.PlayerTurn.RED)
match.column_previous()
assert(match.column_selected == 6)
match.add_checker()
assert(match.state == const.MatchState.PLAYING)
assert(match.is_being_played())
assert(match.player_turn == const.PlayerTurn.YELLOW)
match.column_previous()
assert(match.column_selected == 5)
match.column_previous()
assert(match.column_selected == 4)
match.add_checker()
assert(match.state == const.MatchState.YELLOW_WON)
assert not(match.is_being_played())
| 28.645833 | 70 | 0.699273 | 339 | 2,750 | 5.40118 | 0.147493 | 0.198252 | 0.167122 | 0.245767 | 0.83834 | 0.803932 | 0.725287 | 0.612234 | 0.535227 | 0.486073 | 0 | 0.008539 | 0.190909 | 2,750 | 95 | 71 | 28.947368 | 0.814382 | 0 | 0 | 0.735294 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.514706 | 1 | 0.058824 | false | 0 | 0.044118 | 0 | 0.102941 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c36223c9d16e55edf62bbcfc6500be733cd638ad | 788 | py | Python | mpl_colors/mixin.py | clbarnes/mpl_colors | 07e22db3620de2b7efc8f9063d6342eaadec96e2 | [
"MIT"
] | null | null | null | mpl_colors/mixin.py | clbarnes/mpl_colors | 07e22db3620de2b7efc8f9063d6342eaadec96e2 | [
"MIT"
] | 3 | 2019-10-16T20:50:56.000Z | 2021-06-25T15:23:56.000Z | mpl_colors/mixin.py | clbarnes/mpl_colors | 07e22db3620de2b7efc8f9063d6342eaadec96e2 | [
"MIT"
] | null | null | null | from typing import NamedTuple, Tuple
import colorsys
from colour import Color
class RgbTuple(NamedTuple):
r: float
g: float
b: float
def color(self) -> Color:
return Color(rgb=self.value)
def hex(self) -> str:
return self.color().hex_l
def hsl(self) -> Tuple[float, float, float]:
return self.color().hsl
def rgb(self) -> Tuple[float, float, float]:
return self.value
def rgba(self, a=1.0) -> Tuple[float, float, float, float]:
return self.value + (a,)
def web(self) -> str:
return self.color().web
def hsv(self) -> Tuple[float, float, float]:
return colorsys.rgb_to_hsv(*self.value)
def yiq(self) -> Tuple[float, float, float]:
return colorsys.rgb_to_yiq(*self.value)
| 22.514286 | 63 | 0.615482 | 110 | 788 | 4.363636 | 0.281818 | 0.229167 | 0.1875 | 0.208333 | 0.485417 | 0.39375 | 0.320833 | 0.179167 | 0.179167 | 0 | 0 | 0.003401 | 0.253807 | 788 | 34 | 64 | 23.176471 | 0.812925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.347826 | false | 0 | 0.130435 | 0.347826 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
5ee00a6c61f261e87df22c59e5cfa51eb5783fdc | 129 | py | Python | boiler/collections/__init__.py | projectshift/shift-boiler | 5f1d236b97fc814ba72897fa8bc76c7518bd1919 | [
"MIT"
] | 19 | 2016-08-06T20:06:21.000Z | 2020-10-22T08:31:49.000Z | boiler/collections/__init__.py | projectshift/shift-boiler | 5f1d236b97fc814ba72897fa8bc76c7518bd1919 | [
"MIT"
] | 104 | 2016-07-31T19:45:00.000Z | 2021-09-15T08:13:36.000Z | boiler/collections/__init__.py | projectshift/shift-boiler | 5f1d236b97fc814ba72897fa8bc76c7518bd1919 | [
"MIT"
] | 1 | 2017-12-30T09:07:38.000Z | 2017-12-30T09:07:38.000Z | from .paginated_collection import PaginatedCollection
from .api_collection import ApiCollection
from .pagination import paginate
| 32.25 | 53 | 0.883721 | 14 | 129 | 8 | 0.642857 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.093023 | 129 | 3 | 54 | 43 | 0.957265 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6f42b84f2082d816e585aa08b1457a48418a6be2 | 39 | py | Python | pytivoclient/__init__.py | jmuhlich/pytivoclient | fd2c49ab9548136ccd2986c2f064604ffaa0f606 | [
"MIT"
] | 2 | 2017-10-28T22:50:16.000Z | 2019-11-22T02:46:49.000Z | pytivoclient/__init__.py | jmuhlich/pytivoclient | fd2c49ab9548136ccd2986c2f064604ffaa0f606 | [
"MIT"
] | null | null | null | pytivoclient/__init__.py | jmuhlich/pytivoclient | fd2c49ab9548136ccd2986c2f064604ffaa0f606 | [
"MIT"
] | null | null | null | from pytivoclient.client import Client
| 19.5 | 38 | 0.871795 | 5 | 39 | 6.8 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102564 | 39 | 1 | 39 | 39 | 0.971429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6f4691cd3db45b2b95f67d035b90db8dccd95509 | 6,139 | py | Python | tests/test_redis.py | yakky/fastapi-plugins | a05eabf99545884b34a2f767e4d4b9a8da5e85f5 | [
"MIT"
] | null | null | null | tests/test_redis.py | yakky/fastapi-plugins | a05eabf99545884b34a2f767e4d4b9a8da5e85f5 | [
"MIT"
] | null | null | null | tests/test_redis.py | yakky/fastapi-plugins | a05eabf99545884b34a2f767e4d4b9a8da5e85f5 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# tests.test_redis
'''
:author: madkote
:contact: madkote(at)bluewin.ch
:copyright: Copyright 2019, madkote
tests.test_redis
-----------
Package
'''
from __future__ import absolute_import
import asyncio
import unittest
import uuid
# import aioredis
import fastapi
import pytest
import fastapi_plugins
from . import VERSION
__all__ = []
__author__ = 'madkote <madkote(at)bluewin.ch>'
__version__ = '.'.join(str(x) for x in VERSION)
__copyright__ = 'Copyright 2019, madkote'
# def redis_must_be_running(cls):
# # TODO: This SHOULD be improved
# try:
# r = redis.StrictRedis('localhost', port=6379)
# r.ping()
# except redis.ConnectionError:
# redis_running = False
# else:
# redis_running = True
# if not redis_running:
# for name, attribute in inspect.getmembers(cls):
# if name.startswith('test_'):
# @wraps(attribute)
# def skip_test(*args, **kwargs):
# pytest.skip("Redis is not running.")
# setattr(cls, name, skip_test)
# cls.setUp = lambda x: None
# cls.tearDown = lambda x: None
# return cls
@pytest.mark.redis
class RedisTest(unittest.TestCase):
def test_connect(self):
async def _test():
app = fastapi.FastAPI()
config = fastapi_plugins.RedisSettings()
await fastapi_plugins.redis_plugin.init_app(app=app, config=config)
await fastapi_plugins.redis_plugin.init()
await fastapi_plugins.redis_plugin.terminate()
event_loop = asyncio.new_event_loop()
asyncio.set_event_loop(event_loop)
coro = asyncio.coroutine(_test)
event_loop.run_until_complete(coro())
event_loop.close()
def test_ping(self):
async def _test():
app = fastapi.FastAPI()
config = fastapi_plugins.RedisSettings()
await fastapi_plugins.redis_plugin.init_app(app=app, config=config)
await fastapi_plugins.redis_plugin.init()
try:
c = await fastapi_plugins.redis_plugin()
r = (await c.ping()).decode()
self.assertTrue(r == 'PONG', 'ping-pong failed == %s' % r)
finally:
await fastapi_plugins.redis_plugin.terminate()
event_loop = asyncio.new_event_loop()
asyncio.set_event_loop(event_loop)
coro = asyncio.coroutine(_test)
event_loop.run_until_complete(coro())
event_loop.close()
def test_get_set(self):
async def _test():
app = fastapi.FastAPI()
config = fastapi_plugins.RedisSettings()
await fastapi_plugins.redis_plugin.init_app(app=app, config=config)
await fastapi_plugins.redis_plugin.init()
try:
c = await fastapi_plugins.redis_plugin()
value = str(uuid.uuid4())
r = await c.set('x', value)
self.assertTrue(r, 'set failed')
r = await c.get('x', encoding='utf-8')
self.assertTrue(r == value, 'get failed')
finally:
await fastapi_plugins.redis_plugin.terminate()
event_loop = asyncio.new_event_loop()
asyncio.set_event_loop(event_loop)
coro = asyncio.coroutine(_test)
event_loop.run_until_complete(coro())
event_loop.close()
@pytest.mark.redis
@pytest.mark.sentinel
class RedisSentinelTest(unittest.TestCase):
def test_connect(self):
async def _test():
app = fastapi.FastAPI()
config = fastapi_plugins.RedisSettings(
redis_type='sentinel',
redis_sentinels='localhost:26379'
)
await fastapi_plugins.redis_plugin.init_app(app=app, config=config)
await fastapi_plugins.redis_plugin.init()
await fastapi_plugins.redis_plugin.terminate()
event_loop = asyncio.new_event_loop()
asyncio.set_event_loop(event_loop)
coro = asyncio.coroutine(_test)
event_loop.run_until_complete(coro())
event_loop.close()
def test_ping(self):
async def _test():
app = fastapi.FastAPI()
config = fastapi_plugins.RedisSettings(
redis_type='sentinel',
redis_sentinels='localhost:26379'
)
await fastapi_plugins.redis_plugin.init_app(app=app, config=config)
await fastapi_plugins.redis_plugin.init()
try:
c = await fastapi_plugins.redis_plugin()
r = (await c.ping()).decode()
self.assertTrue(r == 'PONG', 'ping-pong failed == %s' % r)
finally:
await fastapi_plugins.redis_plugin.terminate()
event_loop = asyncio.new_event_loop()
asyncio.set_event_loop(event_loop)
coro = asyncio.coroutine(_test)
event_loop.run_until_complete(coro())
event_loop.close()
def test_get_set(self):
async def _test():
app = fastapi.FastAPI()
config = fastapi_plugins.RedisSettings(
redis_type='sentinel',
redis_sentinels='localhost:26379'
)
await fastapi_plugins.redis_plugin.init_app(app=app, config=config)
await fastapi_plugins.redis_plugin.init()
try:
c = await fastapi_plugins.redis_plugin()
value = str(uuid.uuid4())
r = await c.set('x', value)
self.assertTrue(r, 'set failed')
r = await c.get('x', encoding='utf-8')
self.assertTrue(r == value, 'get failed')
finally:
await fastapi_plugins.redis_plugin.terminate()
event_loop = asyncio.new_event_loop()
asyncio.set_event_loop(event_loop)
coro = asyncio.coroutine(_test)
event_loop.run_until_complete(coro())
event_loop.close()
if __name__ == "__main__":
# import sys;sys.argv = ['', 'Test.testName']
unittest.main()
| 33.36413 | 79 | 0.59798 | 683 | 6,139 | 5.106881 | 0.175695 | 0.09289 | 0.119839 | 0.151376 | 0.7543 | 0.7543 | 0.7543 | 0.7543 | 0.7543 | 0.7543 | 0 | 0.007413 | 0.296791 | 6,139 | 183 | 80 | 33.546448 | 0.800556 | 0.145463 | 0 | 0.84127 | 0 | 0 | 0.045646 | 0.004411 | 0 | 0 | 0 | 0.005464 | 0.047619 | 1 | 0.047619 | false | 0 | 0.063492 | 0 | 0.126984 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
6f6eb3c78067741077de32cf07465a16c6377263 | 199 | py | Python | shishakai/__init__.py | speg03/shishakai | fe5b9a9efa440cafa87ee7dbde999d5837182ad1 | [
"MIT"
] | null | null | null | shishakai/__init__.py | speg03/shishakai | fe5b9a9efa440cafa87ee7dbde999d5837182ad1 | [
"MIT"
] | 2 | 2020-03-04T14:24:18.000Z | 2020-03-18T16:16:13.000Z | shishakai/__init__.py | speg03/shishakai | fe5b9a9efa440cafa87ee7dbde999d5837182ad1 | [
"MIT"
] | null | null | null | from .moviewalker import MovieWalker # noqa: F401
try:
from importlib.metadata import version
except ImportError:
from importlib_metadata import version
__version__ = version("shishakai")
| 22.111111 | 50 | 0.78392 | 22 | 199 | 6.863636 | 0.545455 | 0.172185 | 0.278146 | 0.357616 | 0.450331 | 0 | 0 | 0 | 0 | 0 | 0 | 0.017964 | 0.160804 | 199 | 8 | 51 | 24.875 | 0.886228 | 0.050251 | 0 | 0 | 0 | 0 | 0.048128 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
4899b81dec3fb993a42d538d29004a48c5f856e1 | 49 | py | Python | pygears_vivado/synth/__init__.py | Anari-AI/pygears-vivado | a9d928d9914b479739ff8fc1e208813292c4b711 | [
"MIT"
] | 1 | 2022-03-19T02:11:12.000Z | 2022-03-19T02:11:12.000Z | pygears_vivado/synth/__init__.py | Anari-AI/pygears-vivado | a9d928d9914b479739ff8fc1e208813292c4b711 | [
"MIT"
] | null | null | null | pygears_vivado/synth/__init__.py | Anari-AI/pygears-vivado | a9d928d9914b479739ff8fc1e208813292c4b711 | [
"MIT"
] | 1 | 2021-06-01T13:21:12.000Z | 2021-06-01T13:21:12.000Z | from ..intf import run
from .entry import synth
| 12.25 | 24 | 0.755102 | 8 | 49 | 4.625 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.183673 | 49 | 3 | 25 | 16.333333 | 0.925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
48ad62df53136d2941630f560aca234d1cd79cb9 | 27 | py | Python | tessellate/viz/__init__.py | FordyceLab/tessellate | a3f0c38b4392027a7503828f48d65ec02eb24698 | [
"MIT"
] | null | null | null | tessellate/viz/__init__.py | FordyceLab/tessellate | a3f0c38b4392027a7503828f48d65ec02eb24698 | [
"MIT"
] | 7 | 2021-03-31T19:41:46.000Z | 2022-01-13T02:39:45.000Z | tessellate/viz/__init__.py | FordyceLab/tessellate | a3f0c38b4392027a7503828f48d65ec02eb24698 | [
"MIT"
] | null | null | null | from .viz import plot_curve | 27 | 27 | 0.851852 | 5 | 27 | 4.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 27 | 1 | 27 | 27 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
48d0b2d84c131f9720cc5cbacdd2aff0a808b4fb | 35 | py | Python | src/echonn/__init__.py | larkwt96/honors-thesis | 7e3a52c285c1fdaf4ae9659497154ba04e522f48 | [
"MIT"
] | null | null | null | src/echonn/__init__.py | larkwt96/honors-thesis | 7e3a52c285c1fdaf4ae9659497154ba04e522f48 | [
"MIT"
] | null | null | null | src/echonn/__init__.py | larkwt96/honors-thesis | 7e3a52c285c1fdaf4ae9659497154ba04e522f48 | [
"MIT"
] | null | null | null | import echonn.ml
import echonn.sys
| 11.666667 | 17 | 0.828571 | 6 | 35 | 4.833333 | 0.666667 | 0.827586 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 35 | 2 | 18 | 17.5 | 0.935484 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
48e9da6f4936a101e1fe9cf8d554cbe22116d481 | 6,849 | py | Python | edx_adapt/data/interface.py | raccoongang/edx-adapt | 9534d5c040aa78d0cfa23b0445de742cf5f2b083 | [
"Apache-2.0"
] | 3 | 2018-02-02T20:33:21.000Z | 2020-11-16T20:23:42.000Z | edx_adapt/data/interface.py | raccoongang/edx-adapt | 9534d5c040aa78d0cfa23b0445de742cf5f2b083 | [
"Apache-2.0"
] | 15 | 2016-10-31T12:37:26.000Z | 2018-08-11T03:27:08.000Z | edx_adapt/data/interface.py | raccoongang/edx-adapt | 9534d5c040aa78d0cfa23b0445de742cf5f2b083 | [
"Apache-2.0"
] | 7 | 2016-10-28T12:04:52.000Z | 2021-07-28T19:33:35.000Z | class StorageInterface(object):
""" Interface for a persistent storage backend. Basic table and key/value storage, with list operations
"""
def __init__(self):
pass
def create_table(self, table_name):
raise NotImplementedError( "Storage module must implement this" )
def get_tables(self):
raise NotImplementedError( "Storage module must implement this" )
def get(self, table_name, key):
raise NotImplementedError( "Storage module must implement this" )
def set(self, table_name, key, val):
raise NotImplementedError( "Storage module must implement this" )
def append(self, table_name, list_key, val):
raise NotImplementedError( "Storage module must implement this" )
def remove(self, table_name, list_key, val):
raise NotImplementedError( "Storage module must implement this" )
class DataInterface(object):
""" This is the interface for the persistent data store
backing an edX-adapt course. It stores information about
courses, such as which skills they teach and which problems
are associated with those skills. It also stores user data,
such as which users are currently progressing through a
course, which users have finished the course, and logs of
the students' interactions with the problems.
"""
def __init__(self, storage_module):
"""@type storage_module: StorageInterface"""
self.store = storage_module
""" Course setup methods """
def post_course(self, course_id):
raise NotImplementedError( "Data module must implement this" )
def post_skill(self, course_id, skill_name):
raise NotImplementedError( "Data module must implement this" )
def post_problem(self, course_id, skill_names, problem_name, tutor_url, pretest, posttest):
raise NotImplementedError( "Data module must implement this" )
def enroll_user(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def post_model_params(self, course_id, prob_list, new):
raise NotImplementedError("Data module must implement this")
def get_model_params(self, course_id):
raise NotImplementedError("Data module must implement this")
""" Retrieve course information """
def get_course_ids(self):
raise NotImplementedError( "Data module must implement this" )
def get_skills(self, course_id):
raise NotImplementedError( "Data module must implement this" )
def get_problems(self, course_id, skill_name, pretest, posttest):
raise NotImplementedError( "Data module must implement this" )
def get_num_pretest(self, course_id, skill_name):
raise NotImplementedError( "Data module must implement this" )
def get_num_posttest(self, course_id, skill_name):
raise NotImplementedError( "Data module must implement this" )
def get_in_progress_users(self, course_id):
raise NotImplementedError( "Data module must implement this" )
def get_finished_users(self, course_id):
raise NotImplementedError( "Data module must implement this" )
""" Add user data """
def post_interaction(self, course_id, problem_name, user_id, correct, attempt, unix_seconds):
raise NotImplementedError( "Data module must implement this" )
def post_load(self, course_id, problem_name, user_id, unix_seconds):
raise NotImplementedError( "Data module must implement this" )
def set_next_problem(self, course_id, user_id, problem_dict):
raise NotImplementedError( "Data module must implement this" )
def advance_problem(self, course_id, user_id):
"""
required: Must set user's next problem to 'None'
"""
raise NotImplementedError( "Data module must implement this" )
""" Retrieve user information """
def get_all_remaining_problems(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_remaining_problems(self, course_id, skill_name, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_all_remaining_posttest_problems(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_remaining_posttest_problems(self, course_id, skill_name, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_all_remaining_pretest_problems(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_remaining_pretest_problems(self, course_id, skill_name, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_whole_trajectory(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_skill_trajectory(self, course_id, skill_name, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_all_interactions(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_interactions(self, course_id, skill_name, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_current_problem(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_next_problem(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_raw_user_data(self, course_id, user_id):
raise NotImplementedError( "Data module must implement this" )
def get_raw_user_skill_data(self, course_id, skill_name, user_id):
raise NotImplementedError( "Data module must implement this" )
""" Methods to group users by experiment, e.g. for AB policy testing """
def post_experiment(self, course_id, experiment_name, start, end):
raise NotImplementedError( "Data module must implement this" )
def get_experiments(self, course_id):
raise NotImplementedError( "Data module must implement this" )
def get_experiment(self, course_id, experiment_name):
raise NotImplementedError( "Data module must implement this" )
def get_subjects(self, course_id, experiment_name):
raise NotImplementedError( "Data module must implement this" )
def delete_experiment(self, course_id, experiment_name):
raise NotImplementedError( "Data module must implement this" )
""" General backing store access: allows other modules
access to persistent storage
"""
def set(self, key, value):
raise NotImplementedError( "Data module must implement this" )
def get(self, key):
raise NotImplementedError( "Data module must implement this" )
class DataException(Exception):
pass
| 39.589595 | 107 | 0.720397 | 844 | 6,849 | 5.650474 | 0.158768 | 0.22143 | 0.175299 | 0.212204 | 0.748165 | 0.737052 | 0.710631 | 0.679178 | 0.641854 | 0.567205 | 0 | 0 | 0.205285 | 6,849 | 172 | 108 | 39.819767 | 0.876171 | 0.08483 | 0 | 0.479167 | 0 | 0 | 0.233683 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.479167 | false | 0.020833 | 0 | 0 | 0.510417 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
d28f518a8023b76d9afe7cc174a5c1a909bc5676 | 502 | py | Python | tensortrade/oms/orders/__init__.py | essamabas/tensortrade | cbbec2235e20be74ec57476009e9741849240bd2 | [
"Apache-2.0"
] | null | null | null | tensortrade/oms/orders/__init__.py | essamabas/tensortrade | cbbec2235e20be74ec57476009e9741849240bd2 | [
"Apache-2.0"
] | null | null | null | tensortrade/oms/orders/__init__.py | essamabas/tensortrade | cbbec2235e20be74ec57476009e9741849240bd2 | [
"Apache-2.0"
] | null | null | null | from tensortrade.oms.orders.trade import Trade, TradeSide, TradeType
from tensortrade.oms.orders.broker import Broker
from tensortrade.oms.orders.order import Order, OrderStatus
from tensortrade.oms.orders.order_listener import OrderListener
from tensortrade.oms.orders.order_spec import OrderSpec
from tensortrade.oms.orders.position import Position
from tensortrade.oms.orders.create import (
market_order,
limit_order,
hidden_limit_order,
risk_managed_order,
proportion_order
)
| 33.466667 | 68 | 0.824701 | 65 | 502 | 6.230769 | 0.353846 | 0.259259 | 0.311111 | 0.414815 | 0.214815 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115538 | 502 | 14 | 69 | 35.857143 | 0.912162 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.538462 | 0 | 0.538462 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9618ab88f8d0514eb34673d3c0082d339b4017c8 | 222 | py | Python | flask_on_fhir/restful_resources/__init__.py | HOT-Ecosystem/flask-on-fhir | a4ed361f24ce75e4fff28f0e08ab01f9e4973596 | [
"MIT"
] | 1 | 2021-02-20T18:51:59.000Z | 2021-02-20T18:51:59.000Z | flask_on_fhir/restful_resources/__init__.py | anofryev/flask-on-fhir | a4ed361f24ce75e4fff28f0e08ab01f9e4973596 | [
"MIT"
] | null | null | null | flask_on_fhir/restful_resources/__init__.py | anofryev/flask-on-fhir | a4ed361f24ce75e4fff28f0e08ab01f9e4973596 | [
"MIT"
] | 1 | 2021-02-20T18:51:53.000Z | 2021-02-20T18:51:53.000Z | from .fhir_resource import FHIRResource
from .capability_statement_resource import CapabilityStatementResource
from .code_system_resource import CodeSystemResource
from .naming_system_resource import NamingSystemResource
| 37 | 70 | 0.905405 | 23 | 222 | 8.434783 | 0.565217 | 0.28866 | 0.206186 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.076577 | 222 | 5 | 71 | 44.4 | 0.946341 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
82c1551b3b3ce2e88dfefd43157c2e1332c6f5c3 | 1,218 | py | Python | build/lib/Element/Find.py | sunnyyukaige/APP_automation_core | b53ad737025a1af44746ea5f1c9a4cbe65d7cfb4 | [
"MIT"
] | null | null | null | build/lib/Element/Find.py | sunnyyukaige/APP_automation_core | b53ad737025a1af44746ea5f1c9a4cbe65d7cfb4 | [
"MIT"
] | null | null | null | build/lib/Element/Find.py | sunnyyukaige/APP_automation_core | b53ad737025a1af44746ea5f1c9a4cbe65d7cfb4 | [
"MIT"
] | null | null | null | from Utilitys.WaitUtils import WaitUtils
class Find:
def __init__(self):
pass
def get_driver(self):
pass
def _appium_context(self):
pass
def _refresh(self):
pass
def find_element(self, by, value):
try:
try:
return self._appium_context().find_element(by, value)
except Exception as handleRetry:
self._refresh()
WaitUtils.wait_for_element_present(self._appium_context(), by, value)
return self._appium_context().find_element(by, value)
except Exception as e:
raise Exception("Cannot find element by [%s]:[%s] under:\n %s \n" % (by, value, self))
def find_elements(self, by, value):
try:
try:
return self._appium_context().find_elements(by, value)
except Exception as handleRetry:
self._refresh()
WaitUtils.wait_for_element_present(self._appium_context(), by, value)
return self._appium_context().find_elements(by, value)
except Exception as e:
raise Exception("Cannot find element by [%s] under: \n %s \n" % (value, self))
| 30.45 | 98 | 0.584565 | 141 | 1,218 | 4.808511 | 0.241135 | 0.09292 | 0.150442 | 0.135693 | 0.747788 | 0.722714 | 0.722714 | 0.722714 | 0.722714 | 0.722714 | 0 | 0 | 0.319376 | 1,218 | 39 | 99 | 31.230769 | 0.817853 | 0 | 0 | 0.666667 | 0 | 0 | 0.073892 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0.133333 | 0.033333 | 0 | 0.4 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
7d5679945f5507ef09ad287a5402eba555772fef | 315 | py | Python | helloworld/config.py | uhussain/pants_testing | a8b2c616461691f8157689fd7d832101904f3cf0 | [
"Apache-2.0"
] | 2 | 2021-04-07T03:51:33.000Z | 2021-05-21T04:13:15.000Z | helloworld/config.py | uhussain/pants_testing | a8b2c616461691f8157689fd7d832101904f3cf0 | [
"Apache-2.0"
] | 1 | 2020-09-15T22:01:19.000Z | 2020-09-15T22:01:19.000Z | helloworld/config.py | uhussain/pants_testing | a8b2c616461691f8157689fd7d832101904f3cf0 | [
"Apache-2.0"
] | 3 | 2020-08-12T01:29:16.000Z | 2020-09-11T18:23:16.000Z | # Copyright 2020 Pants project contributors.
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from helloworld.util.config_loader import load_config_from_json
from helloworld.util.proto.config_pb2 import Config
def load_config() -> Config:
return load_config_from_json(__name__, "config.json")
| 31.5 | 63 | 0.803175 | 45 | 315 | 5.333333 | 0.6 | 0.125 | 0.15 | 0.15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.025271 | 0.120635 | 315 | 9 | 64 | 35 | 0.841155 | 0.330159 | 0 | 0 | 0 | 0 | 0.052885 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.5 | 0.25 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
7d740eb601b78e37598f43307c326e6332303820 | 156 | py | Python | graph/views.py | DanielNery/realtime_graph | cfbb6b081fe817d27fe7f5538164fa69b1f6759c | [
"MIT"
] | 1 | 2021-05-31T15:13:58.000Z | 2021-05-31T15:13:58.000Z | graph/views.py | DanielNery/realtime_graph | cfbb6b081fe817d27fe7f5538164fa69b1f6759c | [
"MIT"
] | null | null | null | graph/views.py | DanielNery/realtime_graph | cfbb6b081fe817d27fe7f5538164fa69b1f6759c | [
"MIT"
] | null | null | null | from django.shortcuts import render
# Create your views here.
def index(request):
return render(request, 'base.html', context={'text': 'hello world'}) | 26 | 72 | 0.724359 | 21 | 156 | 5.380952 | 0.904762 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.141026 | 156 | 6 | 72 | 26 | 0.843284 | 0.147436 | 0 | 0 | 0 | 0 | 0.181818 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
81774f455cc887d2b36a4d471fd43cc6efcd544c | 96 | py | Python | venv/lib/python3.8/site-packages/urllib3/util/ssltransport.py | GiulianaPola/select_repeats | 17a0d053d4f874e42cf654dd142168c2ec8fbd11 | [
"MIT"
] | 1 | 2022-02-22T04:49:18.000Z | 2022-02-22T04:49:18.000Z | venv/lib/python3.8/site-packages/urllib3/util/ssltransport.py | GiulianaPola/select_repeats | 17a0d053d4f874e42cf654dd142168c2ec8fbd11 | [
"MIT"
] | null | null | null | venv/lib/python3.8/site-packages/urllib3/util/ssltransport.py | GiulianaPola/select_repeats | 17a0d053d4f874e42cf654dd142168c2ec8fbd11 | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/dd/b2/c1/a983ebc93f543066f1e624603206f6b23ed510aa79a66443d60f1de952 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4375 | 0 | 96 | 1 | 96 | 96 | 0.458333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8193051ac8a8f5da44da6dc92b64955e836a9aad | 186 | py | Python | src/api/helpers/social_user_info.py | ThaDeveloper/grind | fa90b65d12e6d9b3d658b132874801ecda08c57f | [
"MIT"
] | 1 | 2019-11-06T22:26:26.000Z | 2019-11-06T22:26:26.000Z | src/api/helpers/social_user_info.py | ThaDeveloper/grind | fa90b65d12e6d9b3d658b132874801ecda08c57f | [
"MIT"
] | 5 | 2021-03-19T02:49:44.000Z | 2021-06-10T19:13:00.000Z | src/api/helpers/social_user_info.py | ThaDeveloper/grind | fa90b65d12e6d9b3d658b132874801ecda08c57f | [
"MIT"
] | null | null | null | def get_user_info(user):
return {
'email': user.get('email'),
'first_name': user.get('name').split(' ')[0],
'last_name': user.get('name').split(' ')[1]
}
| 26.571429 | 53 | 0.516129 | 24 | 186 | 3.833333 | 0.5 | 0.228261 | 0.23913 | 0.326087 | 0.434783 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014184 | 0.241935 | 186 | 6 | 54 | 31 | 0.638298 | 0 | 0 | 0 | 0 | 0 | 0.209677 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0 | 0.166667 | 0.333333 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
c491fccb3a5aab8d363555c521f05eb5983681a0 | 79 | py | Python | new.py | jkiran307/devopslab1 | 2f02586e787102fbcfbe12c3331e6b2adc378dc9 | [
"Apache-2.0"
] | null | null | null | new.py | jkiran307/devopslab1 | 2f02586e787102fbcfbe12c3331e6b2adc378dc9 | [
"Apache-2.0"
] | null | null | null | new.py | jkiran307/devopslab1 | 2f02586e787102fbcfbe12c3331e6b2adc378dc9 | [
"Apache-2.0"
] | null | null | null | print "Hello World"
print "Git training"
Print "10-12-2019"
Print "11-12-2019"
| 15.8 | 20 | 0.721519 | 14 | 79 | 4.071429 | 0.642857 | 0.210526 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.231884 | 0.126582 | 79 | 4 | 21 | 19.75 | 0.594203 | 0 | 0 | 0 | 0 | 0 | 0.544304 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.5 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
c4b55284ccb311c8c0c64e9ff49045fe6dd0703e | 899 | py | Python | app/core/grandpy/grandpy.py | SLB974/GrandPyBot-dev | 7a0268d4ffa58c37eed37253c6afb00874dbabe4 | [
"MIT"
] | null | null | null | app/core/grandpy/grandpy.py | SLB974/GrandPyBot-dev | 7a0268d4ffa58c37eed37253c6afb00874dbabe4 | [
"MIT"
] | null | null | null | app/core/grandpy/grandpy.py | SLB974/GrandPyBot-dev | 7a0268d4ffa58c37eed37253c6afb00874dbabe4 | [
"MIT"
] | null | null | null | from random import choice
from .utils import grandpy_intro
from .utils import grandpy_nickname
from .utils import grandpy_catch_phrase
from .utils import grandpy_catch_wiki
from .utils import grandpy_response
from .utils import grandpy_error
from .utils import grandpy_none
from .utils import grandpy_next
class GrandPy:
"""Manage grandpy interactions"""
def empty_response(self):
return choice(grandpy_none)
def catch_phrase(self):
return choice(grandpy_catch_phrase)
def positive_response(self):
return choice(grandpy_response)
def error_response(self):
return choice(grandpy_error)
def anecdote(self):
return choice(grandpy_catch_wiki)
def intro_phrase(self):
return choice(grandpy_intro) + choice(grandpy_nickname) + choice(grandpy_catch_phrase)
def next_query(self):
return choice(grandpy_next)
| 24.297297 | 94 | 0.746385 | 115 | 899 | 5.591304 | 0.217391 | 0.18196 | 0.186625 | 0.273717 | 0.426128 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.191324 | 899 | 36 | 95 | 24.972222 | 0.884457 | 0.030033 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.291667 | false | 0 | 0.375 | 0.291667 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
c4e03d76045c35074e25d3668f5cb1ffe6b9bcea | 6,407 | py | Python | loldib/getratings/models/NA/na_amumu/na_amumu_top.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_amumu/na_amumu_top.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_amumu/na_amumu_top.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | from getratings.models.ratings import Ratings
class NA_Amumu_Top_Aatrox(Ratings):
pass
class NA_Amumu_Top_Ahri(Ratings):
pass
class NA_Amumu_Top_Akali(Ratings):
pass
class NA_Amumu_Top_Alistar(Ratings):
pass
class NA_Amumu_Top_Amumu(Ratings):
pass
class NA_Amumu_Top_Anivia(Ratings):
pass
class NA_Amumu_Top_Annie(Ratings):
pass
class NA_Amumu_Top_Ashe(Ratings):
pass
class NA_Amumu_Top_AurelionSol(Ratings):
pass
class NA_Amumu_Top_Azir(Ratings):
pass
class NA_Amumu_Top_Bard(Ratings):
pass
class NA_Amumu_Top_Blitzcrank(Ratings):
pass
class NA_Amumu_Top_Brand(Ratings):
pass
class NA_Amumu_Top_Braum(Ratings):
pass
class NA_Amumu_Top_Caitlyn(Ratings):
pass
class NA_Amumu_Top_Camille(Ratings):
pass
class NA_Amumu_Top_Cassiopeia(Ratings):
pass
class NA_Amumu_Top_Chogath(Ratings):
pass
class NA_Amumu_Top_Corki(Ratings):
pass
class NA_Amumu_Top_Darius(Ratings):
pass
class NA_Amumu_Top_Diana(Ratings):
pass
class NA_Amumu_Top_Draven(Ratings):
pass
class NA_Amumu_Top_DrMundo(Ratings):
pass
class NA_Amumu_Top_Ekko(Ratings):
pass
class NA_Amumu_Top_Elise(Ratings):
pass
class NA_Amumu_Top_Evelynn(Ratings):
pass
class NA_Amumu_Top_Ezreal(Ratings):
pass
class NA_Amumu_Top_Fiddlesticks(Ratings):
pass
class NA_Amumu_Top_Fiora(Ratings):
pass
class NA_Amumu_Top_Fizz(Ratings):
pass
class NA_Amumu_Top_Galio(Ratings):
pass
class NA_Amumu_Top_Gangplank(Ratings):
pass
class NA_Amumu_Top_Garen(Ratings):
pass
class NA_Amumu_Top_Gnar(Ratings):
pass
class NA_Amumu_Top_Gragas(Ratings):
pass
class NA_Amumu_Top_Graves(Ratings):
pass
class NA_Amumu_Top_Hecarim(Ratings):
pass
class NA_Amumu_Top_Heimerdinger(Ratings):
pass
class NA_Amumu_Top_Illaoi(Ratings):
pass
class NA_Amumu_Top_Irelia(Ratings):
pass
class NA_Amumu_Top_Ivern(Ratings):
pass
class NA_Amumu_Top_Janna(Ratings):
pass
class NA_Amumu_Top_JarvanIV(Ratings):
pass
class NA_Amumu_Top_Jax(Ratings):
pass
class NA_Amumu_Top_Jayce(Ratings):
pass
class NA_Amumu_Top_Jhin(Ratings):
pass
class NA_Amumu_Top_Jinx(Ratings):
pass
class NA_Amumu_Top_Kalista(Ratings):
pass
class NA_Amumu_Top_Karma(Ratings):
pass
class NA_Amumu_Top_Karthus(Ratings):
pass
class NA_Amumu_Top_Kassadin(Ratings):
pass
class NA_Amumu_Top_Katarina(Ratings):
pass
class NA_Amumu_Top_Kayle(Ratings):
pass
class NA_Amumu_Top_Kayn(Ratings):
pass
class NA_Amumu_Top_Kennen(Ratings):
pass
class NA_Amumu_Top_Khazix(Ratings):
pass
class NA_Amumu_Top_Kindred(Ratings):
pass
class NA_Amumu_Top_Kled(Ratings):
pass
class NA_Amumu_Top_KogMaw(Ratings):
pass
class NA_Amumu_Top_Leblanc(Ratings):
pass
class NA_Amumu_Top_LeeSin(Ratings):
pass
class NA_Amumu_Top_Leona(Ratings):
pass
class NA_Amumu_Top_Lissandra(Ratings):
pass
class NA_Amumu_Top_Lucian(Ratings):
pass
class NA_Amumu_Top_Lulu(Ratings):
pass
class NA_Amumu_Top_Lux(Ratings):
pass
class NA_Amumu_Top_Malphite(Ratings):
pass
class NA_Amumu_Top_Malzahar(Ratings):
pass
class NA_Amumu_Top_Maokai(Ratings):
pass
class NA_Amumu_Top_MasterYi(Ratings):
pass
class NA_Amumu_Top_MissFortune(Ratings):
pass
class NA_Amumu_Top_MonkeyKing(Ratings):
pass
class NA_Amumu_Top_Mordekaiser(Ratings):
pass
class NA_Amumu_Top_Morgana(Ratings):
pass
class NA_Amumu_Top_Nami(Ratings):
pass
class NA_Amumu_Top_Nasus(Ratings):
pass
class NA_Amumu_Top_Nautilus(Ratings):
pass
class NA_Amumu_Top_Nidalee(Ratings):
pass
class NA_Amumu_Top_Nocturne(Ratings):
pass
class NA_Amumu_Top_Nunu(Ratings):
pass
class NA_Amumu_Top_Olaf(Ratings):
pass
class NA_Amumu_Top_Orianna(Ratings):
pass
class NA_Amumu_Top_Ornn(Ratings):
pass
class NA_Amumu_Top_Pantheon(Ratings):
pass
class NA_Amumu_Top_Poppy(Ratings):
pass
class NA_Amumu_Top_Quinn(Ratings):
pass
class NA_Amumu_Top_Rakan(Ratings):
pass
class NA_Amumu_Top_Rammus(Ratings):
pass
class NA_Amumu_Top_RekSai(Ratings):
pass
class NA_Amumu_Top_Renekton(Ratings):
pass
class NA_Amumu_Top_Rengar(Ratings):
pass
class NA_Amumu_Top_Riven(Ratings):
pass
class NA_Amumu_Top_Rumble(Ratings):
pass
class NA_Amumu_Top_Ryze(Ratings):
pass
class NA_Amumu_Top_Sejuani(Ratings):
pass
class NA_Amumu_Top_Shaco(Ratings):
pass
class NA_Amumu_Top_Shen(Ratings):
pass
class NA_Amumu_Top_Shyvana(Ratings):
pass
class NA_Amumu_Top_Singed(Ratings):
pass
class NA_Amumu_Top_Sion(Ratings):
pass
class NA_Amumu_Top_Sivir(Ratings):
pass
class NA_Amumu_Top_Skarner(Ratings):
pass
class NA_Amumu_Top_Sona(Ratings):
pass
class NA_Amumu_Top_Soraka(Ratings):
pass
class NA_Amumu_Top_Swain(Ratings):
pass
class NA_Amumu_Top_Syndra(Ratings):
pass
class NA_Amumu_Top_TahmKench(Ratings):
pass
class NA_Amumu_Top_Taliyah(Ratings):
pass
class NA_Amumu_Top_Talon(Ratings):
pass
class NA_Amumu_Top_Taric(Ratings):
pass
class NA_Amumu_Top_Teemo(Ratings):
pass
class NA_Amumu_Top_Thresh(Ratings):
pass
class NA_Amumu_Top_Tristana(Ratings):
pass
class NA_Amumu_Top_Trundle(Ratings):
pass
class NA_Amumu_Top_Tryndamere(Ratings):
pass
class NA_Amumu_Top_TwistedFate(Ratings):
pass
class NA_Amumu_Top_Twitch(Ratings):
pass
class NA_Amumu_Top_Udyr(Ratings):
pass
class NA_Amumu_Top_Urgot(Ratings):
pass
class NA_Amumu_Top_Varus(Ratings):
pass
class NA_Amumu_Top_Vayne(Ratings):
pass
class NA_Amumu_Top_Veigar(Ratings):
pass
class NA_Amumu_Top_Velkoz(Ratings):
pass
class NA_Amumu_Top_Vi(Ratings):
pass
class NA_Amumu_Top_Viktor(Ratings):
pass
class NA_Amumu_Top_Vladimir(Ratings):
pass
class NA_Amumu_Top_Volibear(Ratings):
pass
class NA_Amumu_Top_Warwick(Ratings):
pass
class NA_Amumu_Top_Xayah(Ratings):
pass
class NA_Amumu_Top_Xerath(Ratings):
pass
class NA_Amumu_Top_XinZhao(Ratings):
pass
class NA_Amumu_Top_Yasuo(Ratings):
pass
class NA_Amumu_Top_Yorick(Ratings):
pass
class NA_Amumu_Top_Zac(Ratings):
pass
class NA_Amumu_Top_Zed(Ratings):
pass
class NA_Amumu_Top_Ziggs(Ratings):
pass
class NA_Amumu_Top_Zilean(Ratings):
pass
class NA_Amumu_Top_Zyra(Ratings):
pass
| 15.364508 | 46 | 0.761667 | 972 | 6,407 | 4.59465 | 0.151235 | 0.216301 | 0.370802 | 0.463502 | 0.797582 | 0.797582 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173404 | 6,407 | 416 | 47 | 15.401442 | 0.843278 | 0 | 0 | 0.498195 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.498195 | 0.00361 | 0 | 0.501805 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
481c615bc70727482d099eb3fcf3b556d8202c58 | 214 | py | Python | arithmetic.py | NA5G/software-engineering-ex1 | 0bdfa4a0091109206430e26b6f1acfd64b685685 | [
"MIT"
] | null | null | null | arithmetic.py | NA5G/software-engineering-ex1 | 0bdfa4a0091109206430e26b6f1acfd64b685685 | [
"MIT"
] | 1 | 2015-03-11T11:20:17.000Z | 2015-03-11T11:20:17.000Z | arithmetic.py | NA5G/software-engineering-ex1 | 0bdfa4a0091109206430e26b6f1acfd64b685685 | [
"MIT"
] | null | null | null | def add(a, b):
return a+b
def sub(a, b):
result = a-b
return result
def multi(a, b):
return a*b
def div(a, b):
return a/b
def mod(a, b):
return a%b;
if __name__ == '__main__':
pass
| 11.263158 | 26 | 0.542056 | 41 | 214 | 2.634146 | 0.341463 | 0.185185 | 0.37037 | 0.333333 | 0.453704 | 0.361111 | 0 | 0 | 0 | 0 | 0 | 0 | 0.308411 | 214 | 18 | 27 | 11.888889 | 0.72973 | 0 | 0 | 0 | 0 | 0 | 0.037383 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.384615 | false | 0.076923 | 0 | 0.307692 | 0.769231 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 6 |
484f2a0c7e7ed51797405f8d4289535c34d32ddd | 6,099 | py | Python | support_oppose_deciding/views.py | ranadeepmitra21/WeVoteServer_Ranadeep | 505945209aad5cef21e118b5bfa7f63d0bd462da | [
"MIT"
] | 44 | 2015-11-19T04:52:39.000Z | 2021-03-17T02:08:26.000Z | support_oppose_deciding/views.py | ranadeepmitra21/WeVoteServer_Ranadeep | 505945209aad5cef21e118b5bfa7f63d0bd462da | [
"MIT"
] | 748 | 2015-09-03T04:18:33.000Z | 2022-03-10T14:08:10.000Z | support_oppose_deciding/views.py | ranadeepmitra21/WeVoteServer_Ranadeep | 505945209aad5cef21e118b5bfa7f63d0bd462da | [
"MIT"
] | 145 | 2015-09-19T10:10:44.000Z | 2022-03-04T21:01:12.000Z | # support_oppose_deciding/views.py
# Brought to you by We Vote. Be good.
# -*- coding: UTF-8 -*-
from django.http import JsonResponse
from django_user_agents.utils import get_user_agent
from position.models import PositionManager
from voter.models import fetch_voter_id_from_voter_device_link
import wevote_functions.admin
from wevote_functions.functions import get_voter_api_device_id
logger = wevote_functions.admin.get_logger(__name__)
def voter_supporting_candidate_view(request, candidate_id):
logger.debug("voter_supporting_candidate_view {candidate_id}".format(
candidate_id=candidate_id
))
user_agent_string = request.META['HTTP_USER_AGENT']
user_agent_object = get_user_agent(request)
voter_api_device_id = get_voter_api_device_id(request)
voter_id = fetch_voter_id_from_voter_device_link(voter_api_device_id)
position_manager = PositionManager()
results = position_manager.toggle_on_voter_support_for_candidate(voter_id, candidate_id,
user_agent_string, user_agent_object)
if results['success']:
return JsonResponse({0: "success"})
else:
return JsonResponse({0: "failure"})
def voter_stop_supporting_candidate_view(request, candidate_id):
logger.debug("voter_stop_supporting_candidate_view {candidate_id}".format(
candidate_id=candidate_id
))
user_agent_string = request.META['HTTP_USER_AGENT']
user_agent_object = get_user_agent(request)
voter_api_device_id = get_voter_api_device_id(request)
voter_id = fetch_voter_id_from_voter_device_link(voter_api_device_id)
position_manager = PositionManager()
results = position_manager.toggle_off_voter_support_for_candidate(voter_id, candidate_id,
user_agent_string, user_agent_object)
if results['success']:
return JsonResponse({0: "success"})
else:
return JsonResponse({0: "failure"})
def voter_opposing_candidate_view(request, candidate_id):
logger.debug("voter_opposing_candidate_view {candidate_id}".format(
candidate_id=candidate_id
))
user_agent_string = request.META['HTTP_USER_AGENT']
user_agent_object = get_user_agent(request)
voter_api_device_id = get_voter_api_device_id(request)
voter_id = fetch_voter_id_from_voter_device_link(voter_api_device_id)
position_manager = PositionManager()
results = position_manager.toggle_on_voter_oppose_for_candidate(voter_id, candidate_id,
user_agent_string, user_agent_object)
if results['success']:
return JsonResponse({0: "success"})
else:
return JsonResponse({0: "failure"})
def voter_stop_opposing_candidate_view(request, candidate_id):
logger.debug("voter_stop_opposing_candidate_view {candidate_id}".format(
candidate_id=candidate_id
))
user_agent_string = request.META['HTTP_USER_AGENT']
user_agent_object = get_user_agent(request)
voter_api_device_id = get_voter_api_device_id(request)
voter_id = fetch_voter_id_from_voter_device_link(voter_api_device_id)
position_manager = PositionManager()
results = position_manager.toggle_off_voter_oppose_for_candidate(voter_id, candidate_id,
user_agent_string, user_agent_object)
if results['success']:
return JsonResponse({0: "success"})
else:
return JsonResponse({0: "failure"})
def voter_asking_candidate_view(request, candidate_id):
logger.debug("voter_asking_candidate_view {candidate_id}".format(
candidate_id=candidate_id
))
voter_api_device_id = get_voter_api_device_id(request)
voter_id = fetch_voter_id_from_voter_device_link(voter_api_device_id)
logger.debug("voter_asking_candidate_view NOT BUILT YET, voter_id: {voter_id}".format(
voter_id=voter_id
))
return JsonResponse({0: "not working yet - needs to be built"})
def voter_stop_asking_candidate_view(request, candidate_id):
logger.debug("voter_stop_asking_candidate_view {candidate_id}".format(
candidate_id=candidate_id
))
voter_api_device_id = get_voter_api_device_id(request)
voter_id = fetch_voter_id_from_voter_device_link(voter_api_device_id)
logger.debug("voter_stop_asking_candidate_view NOT BUILT YET, voter_id: {voter_id}".format(
voter_id=voter_id
))
return JsonResponse({0: "not working yet - needs to be built"})
def voter_stance_for_candidate_view(request, candidate_id):
logger.debug("voter_stance_for_candidate_view {candidate_id}".format(
candidate_id=candidate_id
))
voter_api_device_id = get_voter_api_device_id(request)
voter_id = fetch_voter_id_from_voter_device_link(voter_api_device_id)
position_manager = PositionManager()
results = position_manager.retrieve_voter_candidate_position(voter_id, candidate_id)
if results['position_found']:
if results['is_support']:
return JsonResponse({0: "support"})
elif results['is_oppose']:
return JsonResponse({0: "oppose"})
elif results['is_no_stance']:
return JsonResponse({0: "no_stance"})
elif results['is_information_only']:
return JsonResponse({0: "information_only"})
elif results['is_still_deciding']:
return JsonResponse({0: "still_deciding"})
return JsonResponse({0: "failure"})
def voter_stance_for_contest_measure_view(request, contest_measure_id):
logger.debug("voter_stance_for_contest_measure_view {contest_measure_id}".format(
contest_measure_id=contest_measure_id
))
voter_api_device_id = get_voter_api_device_id(request)
voter_id = fetch_voter_id_from_voter_device_link(voter_api_device_id)
logger.debug("voter_stance_for_contest_measure_view NOT BUILT YET, voter_id: {voter_id}".format(
voter_id=voter_id
))
return JsonResponse({0: "not working yet - needs to be built"})
| 41.209459 | 107 | 0.72848 | 786 | 6,099 | 5.165394 | 0.103053 | 0.058621 | 0.086207 | 0.098522 | 0.838916 | 0.818473 | 0.801232 | 0.793596 | 0.781034 | 0.680049 | 0 | 0.003647 | 0.190687 | 6,099 | 147 | 108 | 41.489796 | 0.818882 | 0.014757 | 0 | 0.632479 | 0 | 0 | 0.162531 | 0.058784 | 0 | 0 | 0 | 0 | 0 | 1 | 0.068376 | false | 0 | 0.051282 | 0 | 0.264957 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
6f7d3100a2ece92ff40264fbfe98eae8479100ec | 2,735 | py | Python | tests/test_class_oelint_append_protvars.py | Rahix/oelint-adv | b9dc381b181a8bdc7300bb5070f80bf90950efbd | [
"BSD-2-Clause"
] | null | null | null | tests/test_class_oelint_append_protvars.py | Rahix/oelint-adv | b9dc381b181a8bdc7300bb5070f80bf90950efbd | [
"BSD-2-Clause"
] | null | null | null | tests/test_class_oelint_append_protvars.py | Rahix/oelint-adv | b9dc381b181a8bdc7300bb5070f80bf90950efbd | [
"BSD-2-Clause"
] | null | null | null | import os
import sys
import pytest
sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
from base import TestBaseClass
class TestClassOelintAppendProtVars(TestBaseClass):
def __generate_sample_code(self, var, operation):
return '''
{var} {operation} "foo"
'''.format(var=var, operation=operation)
@pytest.mark.parametrize('id', ['oelint.append.protvars'])
@pytest.mark.parametrize('occurance', [1])
@pytest.mark.parametrize('var', [
"PV",
"PR",
"SRCREV",
"LICENSE",
"LIC_FILES_CHKSUM"
])
@pytest.mark.parametrize('operation', ['=', ':='])
def test_bad(self, id, occurance, var, operation):
input = {
'oelint_adv_test.bbappend': self.__generate_sample_code(var, operation)
}
id += '.{}'.format(var)
self.check_for_id(self._create_args(input), id, occurance)
@pytest.mark.parametrize('id', ['oelint.append.protvars'])
@pytest.mark.parametrize('occurance', [0])
@pytest.mark.parametrize('var', [
"PV",
"PR",
"SRCREV",
"LICENSE",
"LIC_FILES_CHKSUM"
])
@pytest.mark.parametrize('operation', ['?=', '??='])
def test_good_weak(self, id, occurance, var, operation):
input = {
'oelint_adv_test.bbappend': self.__generate_sample_code(var, operation)
}
id += '.{}'.format(var)
self.check_for_id(self._create_args(input), id, occurance)
@pytest.mark.parametrize('id', ['oelint.append.protvars'])
@pytest.mark.parametrize('occurance', [0])
@pytest.mark.parametrize('var', [
"PV",
"PR",
"SRCREV",
"LICENSE",
"LIC_FILES_CHKSUM"
])
@pytest.mark.parametrize('operation', ['=', ':='])
def test_good_bb(self, id, occurance, var, operation):
input = {
'oelint_adv_test.bb': self.__generate_sample_code(var, operation)
}
id += '.{}'.format(var)
self.check_for_id(self._create_args(input), id, occurance)
@pytest.mark.parametrize('id', ['oelint.append.protvars'])
@pytest.mark.parametrize('occurance', [0])
@pytest.mark.parametrize('var', [
"PV[vardeps]",
"PV[vardepsexclude]",
"PV[vardepvalue]",
"PV[vardepvalueexclude]",
])
@pytest.mark.parametrize('operation', ['=', ':='])
def test_good_flags(self, id, occurance, var, operation):
input = {
'oelint_adv_test.bbappend': self.__generate_sample_code(var, operation)
}
id += '.{}'.format(var)
self.check_for_id(self._create_args(input), id, occurance) | 32.951807 | 83 | 0.575868 | 280 | 2,735 | 5.407143 | 0.203571 | 0.10568 | 0.221929 | 0.060766 | 0.796565 | 0.796565 | 0.796565 | 0.769485 | 0.769485 | 0.739762 | 0 | 0.002463 | 0.25777 | 2,735 | 83 | 84 | 32.951807 | 0.74335 | 0 | 0 | 0.657534 | 0 | 0 | 0.186404 | 0.06652 | 0 | 0 | 0 | 0 | 0 | 1 | 0.068493 | false | 0 | 0.054795 | 0.013699 | 0.150685 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
6f7f45f2bd281a396394bb142e8e99188372eaf3 | 43 | py | Python | scripts/addons/animation_nodes/algorithms/lsystem/__init__.py | Tilapiatsu/blender-custom_conf | 05592fedf74e4b7075a6228b8448a5cda10f7753 | [
"MIT"
] | 2 | 2020-04-16T22:12:40.000Z | 2022-01-22T17:18:45.000Z | scripts/addons/animation_nodes/algorithms/lsystem/__init__.py | Tilapiatsu/blender-custom_conf | 05592fedf74e4b7075a6228b8448a5cda10f7753 | [
"MIT"
] | null | null | null | scripts/addons/animation_nodes/algorithms/lsystem/__init__.py | Tilapiatsu/blender-custom_conf | 05592fedf74e4b7075a6228b8448a5cda10f7753 | [
"MIT"
] | 2 | 2019-05-16T04:01:09.000Z | 2020-08-25T11:42:26.000Z | from . py_interface import calculateLSystem | 43 | 43 | 0.883721 | 5 | 43 | 7.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.093023 | 43 | 1 | 43 | 43 | 0.948718 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6fd0ca56f869a33e23ef64c2adc965b30a4cc676 | 84 | py | Python | solutions/01_04.py | glemaitre/IBIOM-M2-deep-learning | 001bf7834e57a7357326087d31049fc91ab8967f | [
"MIT"
] | null | null | null | solutions/01_04.py | glemaitre/IBIOM-M2-deep-learning | 001bf7834e57a7357326087d31049fc91ab8967f | [
"MIT"
] | null | null | null | solutions/01_04.py | glemaitre/IBIOM-M2-deep-learning | 001bf7834e57a7357326087d31049fc91ab8967f | [
"MIT"
] | null | null | null | def decision_function(X, coefs, intercept):
return np.dot(X, coefs) + intercept
| 28 | 43 | 0.72619 | 12 | 84 | 5 | 0.75 | 0.2 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.154762 | 84 | 2 | 44 | 42 | 0.84507 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
b513543071ea5887d5aecab750c22cf1d1962eb8 | 15,977 | py | Python | test/test_crystalmodels.py | tianjuchen/neml | 61ca3dcefcfb20c3f4405ca0ca2117a5414ca933 | [
"MIT"
] | null | null | null | test/test_crystalmodels.py | tianjuchen/neml | 61ca3dcefcfb20c3f4405ca0ca2117a5414ca933 | [
"MIT"
] | null | null | null | test/test_crystalmodels.py | tianjuchen/neml | 61ca3dcefcfb20c3f4405ca0ca2117a5414ca933 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
from neml import models, interpolate, elasticity, history
from neml.cp import crystallography, slipharden, sliprules, inelasticity, kinematics, singlecrystal, crystaldamage
from neml.math import rotations, tensors
import unittest
import numpy as np
import numpy.linalg as la
import common
class CommonTangents(object):
def test_no_rots(self):
self.drive(self.model_no_rot)
def drive(self, model):
d_n = np.zeros((6,))
w_n = np.zeros((3,))
s_n = np.zeros((6,))
h_n = model.init_store()
t_n = 0.0
u_n = 0.0
p_n = 0.0
for i in range(self.nsteps):
t_np1 = t_n + self.dt
d_np1 = d_n + self.Ddir * self.dt
w_np1 = w_n + self.Wdir * self.dt
s_np1, h_np1, A_np1, B_np1, u_np1, p_np1 = model.update_ld_inc(
d_np1, d_n, w_np1, w_n, self.T, self.T, t_np1, t_n, s_n, h_n,
u_n, p_n)
A_num = common.differentiate(lambda d: model.update_ld_inc(d, d_n, w_np1, w_n, self.T,
self.T, t_np1, t_n, s_n, h_n, u_n, p_n)[0], d_np1)
# This is somewhat iffy
print(A_np1)
print(A_num)
self.assertTrue(np.allclose(A_np1, A_num, rtol = 1.0e-3))
B_num = common.differentiate(lambda w: model.update_ld_inc(d_np1, d_n, w, w_n, self.T,
self.T, t_np1, t_n, s_n, h_n, u_n, p_n)[0], w_np1)
# Again, why the loose tolerance?
self.assertTrue(np.allclose(B_np1, B_num, rtol = 1.0e-3))
s_n = np.copy(s_np1)
h_n = np.copy(h_np1)
d_n = np.copy(d_np1)
w_n = np.copy(w_np1)
t_n = t_np1
u_n = u_np1
p_n = p_np1
class CommonSolver(object):
def test_jacobian(self):
fn = lambda x: self.model.RJ(x, self.ts)[0]
Jn = common.differentiate(fn, self.x)
R, J = self.model.RJ(self.x, self.ts)
print(J)
print(Jn)
print((J-Jn))
self.assertTrue(np.allclose(J, Jn, rtol = 1.0e-4))
class TestSingleCrystal(unittest.TestCase, CommonTangents, CommonSolver):
def setUp(self):
self.tau0 = 10.0
self.tau_sat = 50.0
self.b = 2.5
self.strengthmodel = slipharden.VoceSlipHardening(self.tau_sat, self.b, self.tau0)
self.g0 = 1.0
self.n = 3.0
self.slipmodel = sliprules.PowerLawSlipRule(self.strengthmodel, self.g0, self.n)
self.imodel = inelasticity.AsaroInelasticity(self.slipmodel)
self.L = crystallography.CubicLattice(1.0)
self.L.add_slip_system([1,1,0],[1,1,1])
self.Q = rotations.CrystalOrientation(35.0,17.0,14.0, angle_type = "degrees")
self.mu = 29000.0
self.E = 120000.0
self.nu = 0.3
self.emodel = elasticity.CubicLinearElasticModel(self.E,
self.nu, self.mu, "moduli")
self.kmodel = kinematics.StandardKinematicModel(self.emodel, self.imodel)
self.model = singlecrystal.SingleCrystalModel(self.kmodel, self.L,
initial_rotation = self.Q)
self.model_no_rot = singlecrystal.SingleCrystalModel(self.kmodel, self.L,
initial_rotation = self.Q, update_rotation = False, verbose = False)
self.T = 300.0
self.stress_n = np.array([120.0,-60.0,170.0,35.0,80.0,-90.0])
self.stress_np1 = np.array([15.0,-40.0,120.0,70.0,-10.0,-50.0])
self.d = np.array([0.1,-0.2,0.25,0.11,-0.05,0.075])
self.w = np.array([0.1,0.2,-0.2])
self.strength_n = 25.0
self.strength_np1 = 30.0
self.S_np1 = tensors.Symmetric(common.usym(self.stress_np1))
self.S_n = tensors.Symmetric(common.usym(self.stress_n))
self.D = tensors.Symmetric(common.usym(self.d))
self.W = tensors.Skew(common.uskew(self.w))
self.strength_n = 25.0
self.strength_np1 = 30.0
self.H_n = history.History()
self.H_n.add_scalar("strength")
self.H_n.set_scalar("strength", self.strength_n)
self.H_np1 = history.History()
self.H_np1.add_scalar("strength")
self.H_np1.set_scalar("strength", self.strength_np1)
self.dt = 2.0
self.fixed = self.kmodel.decouple(self.S_n, self.D, self.W, self.Q,
self.H_n, self.L, self.T, history.History())
self.ts = singlecrystal.SCTrialState(self.D, self.W, self.S_n, self.S_n, self.H_n, self.Q, self.L, self.T, self.dt,
self.fixed)
self.x = np.zeros((self.model.nparams,))
self.x[:6] = self.stress_np1
self.x[6] = self.strength_np1
self.Ddir = np.array([0.01,-0.005,-0.003,0.01,0.02,-0.003]) * 2
self.Wdir = np.array([0.02,-0.03,0.01]) * 2
self.nsteps = 10
def test_strength(self):
h = self.model.init_store()
h[8] = self.strength_np1
self.assertTrue(np.isclose(self.model.strength(h,
self.T), self.strength_np1 + self.tau0))
def test_Fe(self):
Qc = rotations.CrystalOrientation(12.0,35.0,61.0, angle_type = "degrees")
h = self.model.init_store()
h[:4] = Qc.quat
h[4:8] = self.Q.quat
h[8] = self.strength_np1
Fe1 = self.model.Fe(self.stress_np1, h, self.T)
Re = (Qc * self.Q.inverse()).to_matrix()
E = common.usym(np.array(self.emodel.S_tensor(self.T, Qc
).dot(self.S_np1).data)) + np.eye(3)
Fe2 = la.inv(np.dot(E, Re))
self.assertTrue(np.allclose(Fe1,Fe2, rtol = 1e-3))
def test_nhist(self):
self.assertEqual(self.model.nstore, 9)
def test_init_hist(self):
h = self.model.init_store()
self.assertTrue(np.allclose(h[:4], self.Q.quat))
self.assertTrue(np.allclose(h[4:8], self.Q.quat))
self.assertTrue(np.isclose(h[8],0.0))
def test_residual(self):
R, J = self.model.RJ(self.x, self.ts)
Rtrue = np.zeros((self.model.nparams,))
srate = self.kmodel.stress_rate(self.S_np1, self.D, self.W, self.Q, self.H_np1, self.L, self.T, self.fixed)
hrate = np.array(self.kmodel.history_rate(self.S_np1, self.D, self.W, self.Q, self.H_np1, self.L, self.T, self.fixed))
Rtrue[:6] = self.stress_np1 - self.stress_n - srate.data * self.dt
Rtrue[6:] = np.array(self.H_np1) - np.array(self.H_n) - hrate * self.dt
self.assertTrue(np.allclose(Rtrue, R))
def test_set_active(self):
q = rotations.CrystalOrientation(15.0,50.0,60.0, angle_type = "degrees")
h = self.model.init_store()
self.model.set_active_orientation(h, q)
self.assertTrue(np.allclose(q.quat,
self.model.get_active_orientation(h).quat))
def test_set_passive(self):
q = rotations.CrystalOrientation(15.0,50.0,60.0, angle_type = "degrees")
h = self.model.init_store()
self.model.set_passive_orientation(h, q)
self.assertTrue(np.allclose(q.quat,
self.model.get_passive_orientation(h).quat))
self.assertTrue(np.allclose(q.quat,
self.model.get_active_orientation(h).inverse().quat))
class TestComplicatedCrystal(unittest.TestCase, CommonTangents, CommonSolver):
def setUp(self):
self.tau0_0 = 10.0
self.tau_sat_0 = 50.0
self.b_0 = 2.5
self.tau0_1 = 15.0
self.tau_sat_1 = 25.0
self.b_1 = 1.0
self.strengthmodel = slipharden.SumSlipSingleStrengthHardening(
[
slipharden.VoceSlipHardening(self.tau_sat_0, self.b_0, self.tau0_0),
slipharden.VoceSlipHardening(self.tau_sat_1, self.b_1, self.tau0_1)
])
self.g0 = 1.0
self.n = 3.0
self.slipmodel = sliprules.PowerLawSlipRule(self.strengthmodel, self.g0, self.n)
self.imodel = inelasticity.AsaroInelasticity(self.slipmodel)
self.L = crystallography.CubicLattice(1.0)
self.L.add_slip_system([1,1,0],[1,1,1])
self.Q = rotations.CrystalOrientation(35.0,17.0,14.0, angle_type = "degrees")
self.mu = 29000.0
self.E = 120000.0
self.nu = 0.3
self.emodel = elasticity.CubicLinearElasticModel(self.E,
self.nu, self.mu, "moduli")
self.kmodel = kinematics.StandardKinematicModel(self.emodel, self.imodel)
self.model = singlecrystal.SingleCrystalModel(self.kmodel, self.L,
initial_rotation = self.Q)
self.model_no_rot = singlecrystal.SingleCrystalModel(self.kmodel, self.L,
initial_rotation = self.Q, update_rotation = False, verbose = False)
self.T = 300.0
self.stress_n = np.array([120.0,-60.0,170.0,35.0,80.0,-90.0])
self.stress_np1 = np.array([15.0,-40.0,120.0,70.0,-10.0,-50.0])
self.d = np.array([0.1,-0.2,0.25,0.11,-0.05,0.075])
self.w = np.array([0.1,0.2,-0.2])
self.strength_n = 25.0
self.strength_np1 = 30.0
self.S_np1 = tensors.Symmetric(common.usym(self.stress_np1))
self.S_n = tensors.Symmetric(common.usym(self.stress_n))
self.D = tensors.Symmetric(common.usym(self.d))
self.W = tensors.Skew(common.uskew(self.w))
self.strength_n_0 = 25.0
self.strength_np1_0 = 30.0
self.strength_n_1 = 15.0
self.strength_np1_1 = 17.5
self.H_n = history.History()
self.H_n.add_scalar("strength0")
self.H_n.set_scalar("strength0", self.strength_n_0)
self.H_n.add_scalar("strength1")
self.H_n.set_scalar("strength1", self.strength_n_1)
self.H_np1 = history.History()
self.H_np1.add_scalar("strength0")
self.H_np1.set_scalar("strength0", self.strength_np1_0)
self.H_np1.add_scalar("strength1")
self.H_np1.set_scalar("strength1", self.strength_np1_1)
self.dt = 2.0
self.fixed = self.kmodel.decouple(self.S_n, self.D, self.W, self.Q, self.H_n, self.L, self.T,
history.History())
self.ts = singlecrystal.SCTrialState(self.D, self.W, self.S_n, self.S_n, self.H_n, self.Q, self.L, self.T, self.dt,
self.fixed)
self.x = np.zeros((self.model.nparams,))
self.x[:6] = self.stress_np1
self.x[6] = self.strength_np1
self.Ddir = np.array([0.01,-0.005,-0.003,0.01,0.02,-0.003]) * 2
self.Wdir = np.array([0.02,-0.03,0.01]) * 2
self.nsteps = 10
def test_nhist(self):
self.assertEqual(self.model.nstore, 10)
class TestNyeStuffCrystal(unittest.TestCase):
def setUp(self):
self.tau0 = 10.0
self.tau_sat = 50.0
self.b = 2.5
self.k = 1.1
self.strengthmodel = slipharden.VoceSlipHardening(self.tau_sat, self.b,
self.tau0, k = self.k)
self.g0 = 1.0
self.n = 3.0
self.slipmodel = sliprules.PowerLawSlipRule(self.strengthmodel, self.g0, self.n)
self.imodel = inelasticity.AsaroInelasticity(self.slipmodel)
self.L = crystallography.CubicLattice(1.0)
self.L.add_slip_system([1,1,0],[1,1,1])
self.Q = rotations.CrystalOrientation(35.0,17.0,14.0, angle_type = "degrees")
self.mu = 29000.0
self.E = 120000.0
self.nu = 0.3
self.emodel = elasticity.CubicLinearElasticModel(self.E,
self.nu, self.mu, "moduli")
self.kmodel = kinematics.StandardKinematicModel(self.emodel, self.imodel)
self.model = singlecrystal.SingleCrystalModel(self.kmodel, self.L,
initial_rotation = self.Q)
self.model_no_rot = singlecrystal.SingleCrystalModel(self.kmodel, self.L,
initial_rotation = self.Q, update_rotation = False, verbose = False)
self.T = 300.0
self.stress_n = np.array([120.0,-60.0,170.0,35.0,80.0,-90.0])
self.stress_np1 = np.array([15.0,-40.0,120.0,70.0,-10.0,-50.0])
self.d = np.array([0.1,-0.2,0.25,0.11,-0.05,0.075])
self.w = np.array([0.1,0.2,-0.2])
self.strength_n = 25.0
self.strength_np1 = 30.0
self.S_np1 = tensors.Symmetric(common.usym(self.stress_np1))
self.S_n = tensors.Symmetric(common.usym(self.stress_n))
self.D = tensors.Symmetric(common.usym(self.d))
self.W = tensors.Skew(common.uskew(self.w))
self.strength_n = 25.0
self.strength_np1 = 30.0
self.H_n = history.History()
self.H_n.add_scalar("strength")
self.H_n.set_scalar("strength", self.strength_n)
self.H_np1 = history.History()
self.H_np1.add_scalar("strength")
self.H_np1.set_scalar("strength", self.strength_np1)
self.dt = 2.0
self.fixed = self.kmodel.decouple(self.S_n, self.D, self.W, self.Q,
self.H_n, self.L, self.T, history.History())
self.ts = singlecrystal.SCTrialState(self.D, self.W, self.S_n, self.S_n, self.H_n, self.Q, self.L, self.T, self.dt,
self.fixed)
self.x = np.zeros((self.model.nparams,))
self.x[:6] = self.stress_np1
self.x[6] = self.strength_np1
self.Ddir = np.array([0.01,-0.005,-0.003,0.01,0.02,-0.003]) * 2
self.Wdir = np.array([0.02,-0.03,0.01]) * 2
self.nsteps = 10
self.nye = np.array([[0.01,0.02,0.03],[1.2,1.1,1.3],[0.8,0.9,1.0]])
def test_nhist(self):
self.assertEqual(self.model.nstore, 18)
def test_use_nye(self):
self.assertTrue(self.model.use_nye)
def test_set_nye(self):
h = self.model.init_store()
self.assertTrue(np.allclose(h[8:17], np.zeros((3,3)).flatten()))
self.model.update_nye(h, self.nye)
self.assertTrue(np.allclose(h[8:17], self.nye.flatten()))
class TestFakeDamagedCrystal(unittest.TestCase, CommonTangents, CommonSolver):
def setUp(self):
self.tau0_0 = 10.0
self.tau_sat_0 = 50.0
self.b_0 = 2.5
self.tau0_1 = 15.0
self.tau_sat_1 = 25.0
self.b_1 = 1.0
self.strengthmodel = slipharden.SumSlipSingleStrengthHardening(
[
slipharden.VoceSlipHardening(self.tau_sat_0, self.b_0, self.tau0_0),
slipharden.VoceSlipHardening(self.tau_sat_1, self.b_1, self.tau0_1)
])
self.g0 = 1.0
self.n = 3.0
self.slipmodel = sliprules.PowerLawSlipRule(self.strengthmodel, self.g0, self.n)
self.imodel = inelasticity.AsaroInelasticity(self.slipmodel)
self.L = crystallography.CubicLattice(1.0)
self.L.add_slip_system([1,1,0],[1,1,1])
self.Q = rotations.CrystalOrientation(35.0,17.0,14.0, angle_type = "degrees")
self.mu = 29000.0
self.E = 120000.0
self.nu = 0.3
self.emodel = elasticity.CubicLinearElasticModel(self.E,
self.nu, self.mu, "moduli")
self.dmodel = crystaldamage.NilDamageModel()
self.kmodel = kinematics.DamagedStandardKinematicModel(self.emodel,
self.imodel, self.dmodel)
self.model = singlecrystal.SingleCrystalModel(self.kmodel, self.L,
initial_rotation = self.Q)
self.model_no_rot = singlecrystal.SingleCrystalModel(self.kmodel, self.L,
initial_rotation = self.Q, update_rotation = False, verbose = False)
self.T = 300.0
self.stress_n = np.array([120.0,-60.0,170.0,35.0,80.0,-90.0])
self.stress_np1 = np.array([15.0,-40.0,120.0,70.0,-10.0,-50.0])
self.d = np.array([0.1,-0.2,0.25,0.11,-0.05,0.075])
self.w = np.array([0.1,0.2,-0.2])
self.strength_n = 25.0
self.strength_np1 = 30.0
self.S_np1 = tensors.Symmetric(common.usym(self.stress_np1))
self.S_n = tensors.Symmetric(common.usym(self.stress_n))
self.D = tensors.Symmetric(common.usym(self.d))
self.W = tensors.Skew(common.uskew(self.w))
self.strength_n_0 = 25.0
self.strength_np1_0 = 30.0
self.strength_n_1 = 15.0
self.strength_np1_1 = 17.5
self.whatever_n = 0.1
self.whatever_np1 = 0.2
self.H_n = history.History()
self.H_n.add_scalar("strength0")
self.H_n.set_scalar("strength0", self.strength_n_0)
self.H_n.add_scalar("strength1")
self.H_n.set_scalar("strength1", self.strength_n_1)
self.H_n.add_scalar("whatever")
self.H_n.set_scalar("whatever", self.whatever_n)
self.H_np1 = history.History()
self.H_np1.add_scalar("strength0")
self.H_np1.set_scalar("strength0", self.strength_np1_0)
self.H_np1.add_scalar("strength1")
self.H_np1.set_scalar("strength1", self.strength_np1_1)
self.H_np1.add_scalar("whatever")
self.H_np1.set_scalar("whatever", self.whatever_np1)
self.dt = 2.0
self.fixed = self.kmodel.decouple(self.S_n, self.D, self.W, self.Q, self.H_n, self.L, self.T,
history.History())
self.ts = singlecrystal.SCTrialState(self.D, self.W, self.S_n, self.S_n, self.H_n, self.Q, self.L, self.T, self.dt,
self.fixed)
self.x = np.zeros((self.model.nparams,))
self.x[:6] = self.stress_np1
self.x[6] = self.strength_np1
self.Ddir = np.array([0.01,-0.005,-0.003,0.01,0.02,-0.003]) * 2
self.Wdir = np.array([0.02,-0.03,0.01]) * 2
self.nsteps = 10
def test_nhist(self):
self.assertEqual(self.model.nstore, 11)
| 31.450787 | 122 | 0.657633 | 2,691 | 15,977 | 3.765515 | 0.082497 | 0.037501 | 0.015987 | 0.013816 | 0.815948 | 0.78871 | 0.77894 | 0.775881 | 0.775881 | 0.750123 | 0 | 0.072935 | 0.183889 | 15,977 | 507 | 123 | 31.512821 | 0.704195 | 0.004694 | 0 | 0.694678 | 0 | 0 | 0.019688 | 0 | 0 | 0 | 0 | 0 | 0.053221 | 1 | 0.053221 | false | 0.008403 | 0.019608 | 0 | 0.089636 | 0.014006 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
d202af16bad3fa5fda7a9a6da7d541ca53ac02d0 | 119 | py | Python | Easy/Multiplication Tables/main.py | AstrorEnales/CodeEval | eae0fb471d27d3a83d544ff4a4651ed1a2076930 | [
"MIT"
] | null | null | null | Easy/Multiplication Tables/main.py | AstrorEnales/CodeEval | eae0fb471d27d3a83d544ff4a4651ed1a2076930 | [
"MIT"
] | null | null | null | Easy/Multiplication Tables/main.py | AstrorEnales/CodeEval | eae0fb471d27d3a83d544ff4a4651ed1a2076930 | [
"MIT"
] | null | null | null | import sys
for i in range(13)[1::]:
print(''.join(['%4d' % (i * j) for j in range(13)[1::]]).strip())
| 23.8 | 78 | 0.462185 | 20 | 119 | 2.75 | 0.65 | 0.254545 | 0.327273 | 0.363636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08046 | 0.268908 | 119 | 4 | 79 | 29.75 | 0.551724 | 0 | 0 | 0 | 0 | 0 | 0.026087 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0.333333 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
96303caf68a39620d33997c582d62ccd94f9e94a | 38 | py | Python | satellighte/archs/mobilenetv2/blocks/__init__.py | canturan10/satellighte | 09c906fdf8b538296c488d0c2d86c344007f8666 | [
"MIT"
] | null | null | null | satellighte/archs/mobilenetv2/blocks/__init__.py | canturan10/satellighte | 09c906fdf8b538296c488d0c2d86c344007f8666 | [
"MIT"
] | null | null | null | satellighte/archs/mobilenetv2/blocks/__init__.py | canturan10/satellighte | 09c906fdf8b538296c488d0c2d86c344007f8666 | [
"MIT"
] | null | null | null | from .mobilenetv2 import MobileNet_V2
| 19 | 37 | 0.868421 | 5 | 38 | 6.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.058824 | 0.105263 | 38 | 1 | 38 | 38 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
96324d3640ed28bd7f6e071762f81a9f7fec5a98 | 157 | py | Python | runserver.py | Volkova-Natalia/django_rest_auth_email_confirm_reset | 781e63fd97606e48d69acf84fc6bb011e47b10ca | [
"MIT"
] | null | null | null | runserver.py | Volkova-Natalia/django_rest_auth_email_confirm_reset | 781e63fd97606e48d69acf84fc6bb011e47b10ca | [
"MIT"
] | 1 | 2021-02-26T16:58:10.000Z | 2021-03-24T10:12:57.000Z | runserver.py | Volkova-Natalia/django_rest_auth_email_confirm_reset | 781e63fd97606e48d69acf84fc6bb011e47b10ca | [
"MIT"
] | null | null | null | from django.core.management import call_command
from boot_django import boot_django
# call the django setup routine
boot_django()
call_command("runserver")
| 22.428571 | 47 | 0.834395 | 23 | 157 | 5.478261 | 0.521739 | 0.238095 | 0.222222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.10828 | 157 | 6 | 48 | 26.166667 | 0.9 | 0.184713 | 0 | 0 | 0 | 0 | 0.071429 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
9646754becba93f6ba604234d093db837652c38d | 53 | py | Python | teamcat_service/docker_build/target/one_step_build/teamcat/doraemon/toolbox/config.py | zhangyin2088/Teamcat | be9be8d7c1e58c8d2d22ab78d25783d9aee4de71 | [
"Apache-2.0"
] | 6 | 2018-11-26T08:42:52.000Z | 2020-06-01T08:33:48.000Z | teamcat_service/doraemon/doraemon/administrate/config.py | zhangyin2088/Teamcat | be9be8d7c1e58c8d2d22ab78d25783d9aee4de71 | [
"Apache-2.0"
] | null | null | null | teamcat_service/doraemon/doraemon/administrate/config.py | zhangyin2088/Teamcat | be9be8d7c1e58c8d2d22ab78d25783d9aee4de71 | [
"Apache-2.0"
] | 1 | 2019-01-22T06:45:36.000Z | 2019-01-22T06:45:36.000Z | #coding=utf-8
#coding=utf-8
#config file for testjob
| 13.25 | 24 | 0.754717 | 10 | 53 | 4 | 0.7 | 0.45 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.042553 | 0.113208 | 53 | 3 | 25 | 17.666667 | 0.808511 | 0.886792 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
964d5aa7e4a0f8e69718a6ea76fa6d4b13e5f64f | 66 | py | Python | backend/core/utilities/__init__.py | michaelscales88/charles_site | 2cdf85b51aaa5e81da3bbce7d391cc1475ca6561 | [
"MIT"
] | 1 | 2019-06-10T21:15:05.000Z | 2019-06-10T21:15:05.000Z | backend/core/utilities/__init__.py | michaelscales88/charles_site | 2cdf85b51aaa5e81da3bbce7d391cc1475ca6561 | [
"MIT"
] | null | null | null | backend/core/utilities/__init__.py | michaelscales88/charles_site | 2cdf85b51aaa5e81da3bbce7d391cc1475ca6561 | [
"MIT"
] | null | null | null | from .forms import *
from .helpers import *
from .logger import *
| 16.5 | 22 | 0.727273 | 9 | 66 | 5.333333 | 0.555556 | 0.416667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 66 | 3 | 23 | 22 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
964e9b35c9afe7c9392771bfa57c91b519e9885b | 31 | py | Python | spikeforest2/sorters/kilosort/__init__.py | michaeljohnclancy/spikeforest2 | 93bdde2c570aef9426b3d7bceb69f3605c9f005a | [
"Apache-2.0"
] | 26 | 2020-02-03T02:12:20.000Z | 2022-03-25T09:14:32.000Z | spikeforest2/sorters/kilosort/__init__.py | michaeljohnclancy/spikeforest2 | 93bdde2c570aef9426b3d7bceb69f3605c9f005a | [
"Apache-2.0"
] | 27 | 2020-01-10T12:35:55.000Z | 2021-08-01T23:13:52.000Z | spikeforest2/sorters/kilosort/__init__.py | michaeljohnclancy/spikeforest2 | 93bdde2c570aef9426b3d7bceb69f3605c9f005a | [
"Apache-2.0"
] | 11 | 2019-02-15T15:21:47.000Z | 2021-09-23T01:07:24.000Z | from ._kilosort import kilosort | 31 | 31 | 0.870968 | 4 | 31 | 6.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.096774 | 31 | 1 | 31 | 31 | 0.928571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
967d705b45ce28625f42022afc447aa72f2eaee0 | 16,965 | py | Python | Tsetlin/pyTsetlinMachineParallel/pyTsetlinMachineParallel/tm.py | Fiskesuppen/TsetlinMachine-Checkers | 0d8c47e644e002b9cd23adbc282fe942f3722ba4 | [
"MIT"
] | 1 | 2020-01-09T10:46:48.000Z | 2020-01-09T10:46:48.000Z | Tsetlin/pyTsetlinMachineParallel/pyTsetlinMachineParallel/tm.py | Fiskesuppen/TsetlinMachine-Checkers | 0d8c47e644e002b9cd23adbc282fe942f3722ba4 | [
"MIT"
] | null | null | null | Tsetlin/pyTsetlinMachineParallel/pyTsetlinMachineParallel/tm.py | Fiskesuppen/TsetlinMachine-Checkers | 0d8c47e644e002b9cd23adbc282fe942f3722ba4 | [
"MIT"
] | null | null | null | # Copyright (c) 2019 Ole-Christoffer Granmo
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# This code implements the Convolutional Tsetlin Machine from paper arXiv:1905.09688
# https://arxiv.org/abs/1905.09688
import numpy as np
import ctypes as C
import os
this_dir, this_filename = os.path.split(__file__)
_lib = np.ctypeslib.load_library('libTM', os.path.join(this_dir, ".."))
class CMultiClassConvolutionalTsetlinMachine(C.Structure):
None
class CConvolutionalTsetlinMachine(C.Structure):
None
mc_ctm_pointer = C.POINTER(CMultiClassConvolutionalTsetlinMachine)
ctm_pointer = C.POINTER(CConvolutionalTsetlinMachine)
array_1d_uint = np.ctypeslib.ndpointer(
dtype=np.uint32,
ndim=1,
flags='CONTIGUOUS')
array_1d_int = np.ctypeslib.ndpointer(
dtype=np.int32,
ndim=1,
flags='CONTIGUOUS')
# Multiclass Tsetlin Machine
_lib.CreateMultiClassTsetlinMachine.restype = mc_ctm_pointer
_lib.CreateMultiClassTsetlinMachine.argtypes = [C.c_int, C.c_int, C.c_int, C.c_int, C.c_int, C.c_int, C.c_int, C.c_double, C.c_double, C.c_int, C.c_int]
_lib.mc_tm_destroy.restype = None
_lib.mc_tm_destroy.argtypes = [mc_ctm_pointer]
_lib.mc_tm_fit.restype = None
_lib.mc_tm_fit.argtypes = [mc_ctm_pointer, array_1d_uint, array_1d_uint, C.c_int, C.c_int]
_lib.mc_tm_initialize.restype = None
_lib.mc_tm_initialize.argtypes = [mc_ctm_pointer]
_lib.mc_tm_predict.restype = None
_lib.mc_tm_predict.argtypes = [mc_ctm_pointer, array_1d_uint, array_1d_uint, C.c_int]
_lib.mc_tm_ta_state.restype = C.c_int
_lib.mc_tm_ta_state.argtypes = [mc_ctm_pointer, C.c_int, C.c_int, C.c_int]
_lib.mc_tm_ta_action.restype = C.c_int
_lib.mc_tm_ta_action.argtypes = [mc_ctm_pointer, C.c_int, C.c_int, C.c_int]
_lib.mc_tm_set_state.restype = None
_lib.mc_tm_set_state.argtypes = [mc_ctm_pointer, C.c_int, array_1d_uint, array_1d_uint]
_lib.mc_tm_get_state.restype = None
_lib.mc_tm_get_state.argtypes = [mc_ctm_pointer, C.c_int, array_1d_uint, array_1d_uint]
_lib.mc_tm_transform.restype = None
_lib.mc_tm_transform.argtypes = [mc_ctm_pointer, array_1d_uint, array_1d_uint, C.c_int, C.c_int]
_lib.mc_tm_clause_configuration.restype = None
_lib.mc_tm_clause_configuration.argtypes = [mc_ctm_pointer, C.c_int, C.c_int, array_1d_uint]
# Tsetlin Machine
_lib.CreateTsetlinMachine.restype = ctm_pointer
_lib.CreateTsetlinMachine.argtypes = [C.c_int, C.c_int, C.c_int, C.c_int, C.c_int, C.c_int, C.c_double, C.c_double, C.c_int, C.c_int]
_lib.tm_fit_regression.restype = None
_lib.tm_fit_regression.argtypes = [ctm_pointer, array_1d_uint, array_1d_int, C.c_int, C.c_int]
_lib.tm_predict_regression.restype = None
_lib.tm_predict_regression.argtypes = [ctm_pointer, array_1d_uint, array_1d_int, C.c_int]
# Tools
_lib.tm_encode.restype = None
_lib.tm_encode.argtypes = [array_1d_uint, array_1d_uint, C.c_int, C.c_int, C.c_int, C.c_int, C.c_int, C.c_int]
class MultiClassConvolutionalTsetlinMachine2D():
def __init__(self, number_of_clauses, T, s, patch_dim, boost_true_positive_feedback=1, number_of_state_bits=8, append_negated=True, weighted_clauses=False, s_range=False):
self.number_of_clauses = number_of_clauses
self.number_of_clause_chunks = (number_of_clauses-1)/32 + 1
self.number_of_state_bits = number_of_state_bits
self.patch_dim = patch_dim
self.T = int(T)
self.s = s
self.boost_true_positive_feedback = boost_true_positive_feedback
self.mc_ctm = None
self.append_negated = append_negated
self.weighted_clauses = weighted_clauses
if s_range:
self.s_range = s_range
else:
self.s_range = s
def __del__(self):
if self.mc_ctm != None:
_lib.mc_tm_destroy(self.mc_ctm)
def fit(self, X, Y, epochs=100, incremental=False):
number_of_examples = X.shape[0]
if self.mc_ctm == None:
self.number_of_classes = int(np.max(Y) + 1)
self.dim_x = X.shape[1]
self.dim_y = X.shape[2]
if len(X.shape) == 3:
self.dim_z = 1
elif len(X.shape) == 4:
self.dim_z = X.shape[3]
if self.append_negated:
self.number_of_features = int(self.patch_dim[0]*self.patch_dim[1]*self.dim_z + (self.dim_x - self.patch_dim[0]) + (self.dim_y - self.patch_dim[1]))*2
else:
self.number_of_features = int(self.patch_dim[0]*self.patch_dim[1]*self.dim_z + (self.dim_x - self.patch_dim[0]) + (self.dim_y - self.patch_dim[1]))
self.number_of_patches = int((self.dim_x - self.patch_dim[0] + 1)*(self.dim_y - self.patch_dim[1] + 1))
self.number_of_ta_chunks = int((self.number_of_features-1)/32 + 1)
self.mc_ctm = _lib.CreateMultiClassTsetlinMachine(self.number_of_classes, self.number_of_clauses, self.number_of_features, self.number_of_patches, self.number_of_ta_chunks, self.number_of_state_bits, self.T, self.s, self.s_range, self.boost_true_positive_feedback, self.weighted_clauses)
elif incremental == False:
_lib.mc_tm_destroy(self.mc_ctm)
self.mc_ctm = _lib.CreateMultiClassTsetlinMachine(self.number_of_classes, self.number_of_clauses, self.number_of_features, self.number_of_patches, self.number_of_ta_chunks, self.number_of_state_bits, self.T, self.s, self.s_range, self.boost_true_positive_feedback, self.weighted_clauses)
self.encoded_X = np.ascontiguousarray(np.empty(int(number_of_examples * self.number_of_patches * self.number_of_ta_chunks), dtype=np.uint32))
Xm = np.ascontiguousarray(X.flatten()).astype(np.uint32)
Ym = np.ascontiguousarray(Y).astype(np.uint32)
if self.append_negated:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.dim_x, self.dim_y, self.dim_z, self.patch_dim[0], self.patch_dim[1], 1)
else:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.dim_x, self.dim_y, self.dim_z, self.patch_dim[0], self.patch_dim[1], 0)
_lib.mc_tm_fit(self.mc_ctm, self.encoded_X, Ym, number_of_examples, epochs)
return
def predict(self, X):
number_of_examples = X.shape[0]
self.encoded_X = np.ascontiguousarray(np.empty(int(number_of_examples * self.number_of_patches * self.number_of_ta_chunks), dtype=np.uint32))
Xm = np.ascontiguousarray(X.flatten()).astype(np.uint32)
if self.append_negated:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.dim_x, self.dim_y, self.dim_z, self.patch_dim[0], self.patch_dim[1], 1)
else:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.dim_x, self.dim_y, self.dim_z, self.patch_dim[0], self.patch_dim[1], 0)
Y = np.ascontiguousarray(np.zeros(number_of_examples, dtype=np.uint32))
_lib.mc_tm_predict(self.mc_ctm, self.encoded_X, Y, number_of_examples)
return Y
def ta_state(self, mc_tm_class, clause, ta):
return _lib.mc_tm_ta_state(self.mc_ctm, mc_tm_class, clause, ta)
def ta_action(self, mc_tm_class, clause, ta):
return _lib.mc_tm_ta_action(self.mc_ctm, mc_tm_class, clause, ta)
def get_state(self):
state_list = []
for i in range(self.number_of_classes):
ta_states = np.ascontiguousarray(np.empty(self.number_of_clauses * self.number_of_ta_chunks * self.number_of_state_bits, dtype=np.uint32))
clause_weights = np.ascontiguousarray(np.empty(self.number_of_clauses, dtype=np.uint32))
_lib.mc_tm_get_state(self.mc_ctm, i, clause_weights, ta_states)
state_list.append((clause_weights, ta_states))
return state_list
def set_state(self, state_list):
for i in range(self.number_of_classes):
_lib.mc_tm_set_state(self.mc_ctm, i, state_list[i][0], state_list[i][1])
return
def transform(self, X, inverted=True):
number_of_examples = X.shape[0]
self.encoded_X = np.ascontiguousarray(np.empty(int(number_of_examples * self.number_of_patches * self.number_of_ta_chunks), dtype=np.uint32))
Xm = np.ascontiguousarray(X.flatten()).astype(np.uint32)
if self.append_negated:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.dim_x, self.dim_y, self.dim_z, self.patch_dim[0], self.patch_dim[1], 1)
else:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.dim_x, self.dim_y, self.dim_z, self.patch_dim[0], self.patch_dim[1], 0)
X_transformed = np.ascontiguousarray(np.empty(number_of_examples*self.number_of_classes*self.number_of_clauses, dtype=np.uint32))
if (inverted):
_lib.mc_tm_transform(self.mc_ctm, self.encoded_X, X_transformed, 1, number_of_examples)
else:
_lib.mc_tm_transform(self.mc_ctm, self.encoded_X, X_transformed, 0, number_of_examples)
return X_transformed.reshape((number_of_examples, self.number_of_classes*self.number_of_clauses))
class MultiClassTsetlinMachine():
def __init__(self, number_of_clauses, T, s, boost_true_positive_feedback=1, number_of_state_bits=8, append_negated=True, weighted_clauses=False, s_range=False):
self.number_of_clauses = number_of_clauses
self.number_of_clause_chunks = (number_of_clauses-1)/32 + 1
self.number_of_state_bits = number_of_state_bits
self.T = int(T)
self.s = s
self.boost_true_positive_feedback = boost_true_positive_feedback
self.mc_tm = None
self.itm = None
self.append_negated = append_negated
self.weighted_clauses = weighted_clauses
if s_range:
self.s_range = s_range
else:
self.s_range = s
def __del__(self):
if self.mc_tm != None:
_lib.mc_tm_destroy(self.mc_tm)
if self.itm != None:
_lib.itm_destroy(self.itm)
def fit(self, X, Y, epochs=100, incremental=False):
number_of_examples = X.shape[0]
self.number_of_classes = int(np.max(Y) + 1)
if self.mc_tm == None:
if self.append_negated:
self.number_of_features = X.shape[1]*2
else:
self.number_of_features = X.shape[1]
self.number_of_patches = 1
self.number_of_ta_chunks = int((self.number_of_features-1)/32 + 1)
self.mc_tm = _lib.CreateMultiClassTsetlinMachine(self.number_of_classes, self.number_of_clauses, self.number_of_features, 1, self.number_of_ta_chunks, self.number_of_state_bits, self.T, self.s, self.s_range, self.boost_true_positive_feedback, self.weighted_clauses)
elif incremental == False:
_lib.mc_tm_destroy(self.mc_tm)
self.mc_tm = _lib.CreateMultiClassTsetlinMachine(self.number_of_classes, self.number_of_clauses, self.number_of_features, 1, self.number_of_ta_chunks, self.number_of_state_bits, self.T, self.s, self.s_range, self.boost_true_positive_feedback, self.weighted_clauses)
self.encoded_X = np.ascontiguousarray(np.empty(int(number_of_examples * self.number_of_ta_chunks), dtype=np.uint32))
Xm = np.ascontiguousarray(X.flatten()).astype(np.uint32)
Ym = np.ascontiguousarray(Y).astype(np.uint32)
if self.append_negated:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.number_of_features//2, 1, 1, self.number_of_features//2, 1, 1)
else:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.number_of_features, 1, 1, self.number_of_features, 1, 0)
_lib.mc_tm_fit(self.mc_tm, self.encoded_X, Ym, number_of_examples, epochs)
return
def predict(self, X):
number_of_examples = X.shape[0]
self.encoded_X = np.ascontiguousarray(np.empty(int(number_of_examples * self.number_of_patches * self.number_of_ta_chunks), dtype=np.uint32))
Xm = np.ascontiguousarray(X.flatten()).astype(np.uint32)
if self.append_negated:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.number_of_features//2, 1, 1, self.number_of_features//2, 1, 1)
else:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.number_of_features, 1, 1, self.number_of_features, 1, 0)
Y = np.ascontiguousarray(np.zeros(number_of_examples, dtype=np.uint32))
_lib.mc_tm_predict(self.mc_tm, self.encoded_X, Y, number_of_examples)
return Y
def ta_state(self, mc_tm_class, clause, ta):
return _lib.mc_tm_ta_state(self.mc_tm, mc_tm_class, clause, ta)
def ta_action(self, mc_tm_class, clause, ta):
return _lib.mc_tm_ta_action(self.mc_tm, mc_tm_class, clause, ta)
def get_state(self):
state_list = []
for i in range(self.number_of_classes):
ta_states = np.ascontiguousarray(np.empty(self.number_of_clauses * self.number_of_ta_chunks * self.number_of_state_bits, dtype=np.uint32))
clause_weights = np.ascontiguousarray(np.empty(self.number_of_clauses, dtype=np.uint32))
_lib.mc_tm_get_state(self.mc_tm, i, clause_weights, ta_states)
state_list.append((clause_weights, ta_states))
return state_list
def set_state(self, state_list):
for i in range(self.number_of_classes):
_lib.mc_tm_set_state(self.mc_tm, i, state_list[i][0], state_list[i][1])
return
def transform(self, X, inverted=True):
number_of_examples = X.shape[0]
self.encoded_X = np.ascontiguousarray(np.empty(int(number_of_examples * self.number_of_patches * self.number_of_ta_chunks), dtype=np.uint32))
Xm = np.ascontiguousarray(X.flatten()).astype(np.uint32)
if self.append_negated:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.number_of_features//2, 1, 1, self.number_of_features//2, 1, 1)
else:
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.number_of_features, 1, 1, self.number_of_features, 1, 0)
X_transformed = np.ascontiguousarray(np.empty(number_of_examples*self.number_of_classes*self.number_of_clauses, dtype=np.uint32))
_lib.mc_tm_transform(self.mc_tm, self.encoded_X, X_transformed, inverted, number_of_examples)
return X_transformed.reshape((number_of_examples, self.number_of_classes*self.number_of_clauses))
class RegressionTsetlinMachine():
def __init__(self, number_of_clauses, T, s, boost_true_positive_feedback=1, number_of_state_bits=8, weighted_clauses=False, s_range=False):
self.number_of_clauses = number_of_clauses
self.number_of_clause_chunks = (number_of_clauses-1)/32 + 1
self.number_of_state_bits = number_of_state_bits
self.T = int(T)
self.s = s
self.boost_true_positive_feedback = boost_true_positive_feedback
self.rtm = None
self.weighted_clauses = weighted_clauses
if s_range:
self.s_range = s_range
else:
self.s_range = s
def __del__(self):
if self.rtm != None:
_lib.tm_destroy(self.rtm)
def fit(self, X, Y, epochs=100, incremental=False):
number_of_examples = X.shape[0]
self.max_y = np.max(Y)
self.min_y = np.min(Y)
if self.rtm == None:
self.number_of_features = X.shape[1]*2
self.number_of_patches = 1
self.number_of_ta_chunks = int((self.number_of_features-1)/32 + 1)
self.rtm = _lib.CreateTsetlinMachine(self.number_of_clauses, self.number_of_features, 1, self.number_of_ta_chunks, self.number_of_state_bits, self.T, self.s, self.s_range, self.boost_true_positive_feedback, self.weighted_clauses)
elif incremental == False:
_lib.tm_destroy(self.rtm)
self.rtm = _lib.CreateTsetlinMachine(self.number_of_clauses, self.number_of_features, 1, self.number_of_ta_chunks, self.number_of_state_bits, self.T, self.s, self.s_range, self.boost_true_positive_feedback, self.weighted_clauses)
self.encoded_X = np.ascontiguousarray(np.empty(int(number_of_examples * self.number_of_ta_chunks), dtype=np.uint32))
Xm = np.ascontiguousarray(X.flatten()).astype(np.uint32)
Ym = np.ascontiguousarray((Y - self.min_y)/(self.max_y - self.min_y)*self.T).astype(np.int32)
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.number_of_features//2, 1, 1, self.number_of_features//2, 1, 1)
_lib.tm_fit_regression(self.rtm, self.encoded_X, Ym, number_of_examples, epochs)
return
def predict(self, X):
number_of_examples = X.shape[0]
self.encoded_X = np.ascontiguousarray(np.empty(int(number_of_examples * self.number_of_patches * self.number_of_ta_chunks), dtype=np.uint32))
Xm = np.ascontiguousarray(X.flatten()).astype(np.uint32)
_lib.tm_encode(Xm, self.encoded_X, number_of_examples, self.number_of_features//2, 1, 1, self.number_of_features//2, 1, 1)
Y = np.zeros(number_of_examples, dtype=np.int32)
_lib.tm_predict_regression(self.rtm, self.encoded_X, Y, number_of_examples)
return 1.0*(Y)*(self.max_y - self.min_y)/(self.T) + self.min_y
| 43.058376 | 290 | 0.760271 | 2,848 | 16,965 | 4.180829 | 0.080758 | 0.111531 | 0.108844 | 0.050391 | 0.828168 | 0.794323 | 0.783405 | 0.764928 | 0.741749 | 0.732342 | 0 | 0.016957 | 0.127498 | 16,965 | 393 | 291 | 43.167939 | 0.787461 | 0.072325 | 0 | 0.638783 | 0 | 0 | 0.001718 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.08365 | false | 0 | 0.011407 | 0.015209 | 0.174905 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
96b7ce6091ab147829d77fe4acee9dd810bf9477 | 30 | py | Python | file.py | guruprasadv22/shiny-system | ce8a8f461b6a7cfcaa52381fda8486c4a4373890 | [
"Apache-2.0"
] | null | null | null | file.py | guruprasadv22/shiny-system | ce8a8f461b6a7cfcaa52381fda8486c4a4373890 | [
"Apache-2.0"
] | null | null | null | file.py | guruprasadv22/shiny-system | ce8a8f461b6a7cfcaa52381fda8486c4a4373890 | [
"Apache-2.0"
] | null | null | null | print("Anybody reading this?") | 30 | 30 | 0.766667 | 4 | 30 | 5.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.066667 | 30 | 1 | 30 | 30 | 0.821429 | 0 | 0 | 0 | 0 | 0 | 0.677419 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
7393a26b5620d5f6eacd01194bc22434f8f7b965 | 2,114 | py | Python | rzrq.py | ekin1106/ivix | 0999823a7556e7ac2a90de88be75a585ee6f6192 | [
"MIT"
] | null | null | null | rzrq.py | ekin1106/ivix | 0999823a7556e7ac2a90de88be75a585ee6f6192 | [
"MIT"
] | null | null | null | rzrq.py | ekin1106/ivix | 0999823a7556e7ac2a90de88be75a585ee6f6192 | [
"MIT"
] | null | null | null | import requests
import json
import pandas as pd
def getAll():
for pa in range(1, 44):
url = 'http://dcfm.eastmoney.com//EM_MutiSvcExpandInterface/api/js/get?token=70f12f2f4f091e459a279469fe49eca5&st=tdate&sr=1&p={}'.format(pa)+'&ps=50&js=var%20IPNoxhoO={pages:(tp),data:%20(x)}&type=RZRQ_LSTOTAL_NJ&mk_time=1&rt=51328540'
print(url)
session = requests.session()
resp = session.get(url)
resp.encoding = 'utf-8'
jsonResult = resp.text.split('=')[1].replace('pages','"pages"').replace('data', '"data"')
jsonLoad = json.loads(jsonResult)
# print(jsonLoad)
for jl in jsonLoad['data']:
save_data = pd.DataFrame([[
jl['tdate'].split('T')[0], jl['close'], jl['zdf'], jl['rzye'],
jl['rzyezb'], jl['rzmre'], jl['rzche'], jl['rzjmre'],
jl['rqye'], jl['rqyl'], jl['rqmcl'], jl['rqchl'], jl['rqjmcl'],
jl['rzrqye'], jl['rzrqyecz']
]])
print(jl['tdate'].split('T')[0])
save_data.to_csv('rzrq.csv', mode='a', header=False, index=None)
def getToday():
url = 'http://dcfm.eastmoney.com//EM_MutiSvcExpandInterface/api/js/get?token=70f12f2f4f091e459a279469fe49eca5&st=tdate&sr=-1&p=1&ps=50&js=var%20OeDvEAhD={pages:(tp),data:%20(x)}&type=RZRQ_LSTOTAL_NJ&mk_time=1&rt=51331149'
session = requests.session()
resp = session.get(url)
# resp.encoding = 'utf-8'
jsonResult = resp.text.split('=')[1].replace('pages', '"pages"').replace(
'data', '"data"')
jsonLoad = json.loads(jsonResult)
print(jsonLoad['data'][0])
jl = jsonLoad['data'][0]
save_data = pd.DataFrame([[
jl['tdate'].strip('T00:00:00'), jl['close'], jl['zdf'], jl['rzye'],
jl['rzyezb'], jl['rzmre'], jl['rzche'], jl['rzjmre'], jl['rqye'],
jl['rqyl'], jl['rqmcl'], jl['rqchl'], jl['rqjmcl'], jl['rzrqye'],
jl['rzrqyecz']
]])
print(jl['tdate'].split('T')[0])#.strip('T00:00:00'))
save_data.to_csv('rzrq.csv', mode='a', header=False, index=None)
print('SAVE DONE')
#getAll()
getToday() | 43.142857 | 243 | 0.57947 | 282 | 2,114 | 4.294326 | 0.333333 | 0.026424 | 0.029728 | 0.032205 | 0.80512 | 0.799339 | 0.7564 | 0.7564 | 0.7564 | 0.7564 | 0 | 0.058235 | 0.195837 | 2,114 | 49 | 244 | 43.142857 | 0.654118 | 0.033586 | 0 | 0.35 | 0 | 0.075 | 0.340049 | 0.045209 | 0 | 0 | 0 | 0 | 0 | 1 | 0.05 | false | 0 | 0.075 | 0 | 0.125 | 0.125 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
73b32c5457747a267eebcd088ad2d513dccb1520 | 299 | py | Python | configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_18_40LargeMarker.py | THU-DA-6D-Pose-Group/self6dpp | c267cfa55e440e212136a5e9940598720fa21d16 | [
"Apache-2.0"
] | 33 | 2021-12-15T07:11:47.000Z | 2022-03-29T08:58:32.000Z | configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_18_40LargeMarker.py | THU-DA-6D-Pose-Group/self6dpp | c267cfa55e440e212136a5e9940598720fa21d16 | [
"Apache-2.0"
] | 3 | 2021-12-15T11:39:54.000Z | 2022-03-29T07:24:23.000Z | configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_18_40LargeMarker.py | THU-DA-6D-Pose-Group/self6dpp | c267cfa55e440e212136a5e9940598720fa21d16 | [
"Apache-2.0"
] | null | null | null | _base_ = "./resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_01_02MasterChefCan.py"
OUTPUT_DIR = (
"output/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/18_40LargeMarker"
)
DATASETS = dict(TRAIN=("ycbv_040_large_marker_train_pbr",))
| 49.833333 | 115 | 0.869565 | 36 | 299 | 6.527778 | 0.722222 | 0.204255 | 0.238298 | 0.297872 | 0.53617 | 0.53617 | 0.53617 | 0.53617 | 0 | 0 | 0 | 0.102113 | 0.050167 | 299 | 5 | 116 | 59.8 | 0.725352 | 0 | 0 | 0 | 0 | 0 | 0.782609 | 0.782609 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
73dda18d6191d053848a9b3a1352435bed2c0534 | 180 | py | Python | dvc/dependency/gs.py | Abrosimov-a-a/dvc | 93280c937b9160003afb0d2f3fd473c03d6d9673 | [
"Apache-2.0"
] | null | null | null | dvc/dependency/gs.py | Abrosimov-a-a/dvc | 93280c937b9160003afb0d2f3fd473c03d6d9673 | [
"Apache-2.0"
] | null | null | null | dvc/dependency/gs.py | Abrosimov-a-a/dvc | 93280c937b9160003afb0d2f3fd473c03d6d9673 | [
"Apache-2.0"
] | 1 | 2019-12-01T07:43:48.000Z | 2019-12-01T07:43:48.000Z | from __future__ import unicode_literals
from dvc.dependency.base import DependencyBase
from dvc.output.gs import OutputGS
class DependencyGS(DependencyBase, OutputGS):
pass
| 20 | 46 | 0.827778 | 22 | 180 | 6.545455 | 0.681818 | 0.097222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.127778 | 180 | 8 | 47 | 22.5 | 0.917197 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.2 | 0.6 | 0 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
fb525e1b20cca7c4547f19d29bed3e93b0941a38 | 13,296 | py | Python | ENR/models/submodels.py | equivariant-representation/equivariant-representation-learning | b3df1a662e9d0cb5685cabca730403519092fe82 | [
"MIT"
] | null | null | null | ENR/models/submodels.py | equivariant-representation/equivariant-representation-learning | b3df1a662e9d0cb5685cabca730403519092fe82 | [
"MIT"
] | null | null | null | ENR/models/submodels.py | equivariant-representation/equivariant-representation-learning | b3df1a662e9d0cb5685cabca730403519092fe82 | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
from ENR.models.layers import ResBlock2d, ResBlock3d, num_channels_to_num_groups
class ResNet2d(nn.Module):
"""ResNets for 2d inputs.
Args:
input_shape (tuple of ints): Shape of the input to the model. Should be
of the form (channels, height, width).
channels (tuple of ints): List of number of channels for each layer.
Length of this tuple corresponds to number of layers in network.
strides (tuple of ints): List of strides for each layer. Length of this
tuple corresponds to number of layers in network. If stride is 1, a
residual layer is applied. If stride is 2 a convolution with stride
2 is applied. If stride is -2 a transpose convolution with stride 2
is applied.
final_conv_channels (int): If not 0, a convolution is added as the final
layer, with the number of output channels specified by this int.
filter_multipliers (tuple of ints): Multipliers for filters in residual
layers.
add_groupnorm (bool): If True, adds GroupNorm layers.
Notes:
The first layer of this model is a standard convolution to increase the
number of filters. A convolution can optionally be added at the final
layer.
"""
def __init__(self, input_shape, channels, strides, final_conv_channels=0,
filter_multipliers=(1, 1), add_groupnorm=True):
super(ResNet2d, self).__init__()
assert len(channels) == len(strides), "Length of channels tuple is {} and length of strides tuple is {} but " \
"they should be equal".format(len(channels), len(strides))
self.input_shape = input_shape
self.channels = channels
self.strides = strides
self.filter_multipliers = filter_multipliers
self.add_groupnorm = add_groupnorm
# Calculate output_shape:
# Every layer with stride 2 divides the height and width by 2.
# Similarly, every layer with stride -2 multiplies the height and width
# by 2
output_channels, output_height, output_width = input_shape
for stride in strides:
if stride == 1:
pass
elif stride == 2:
output_height //= 2
output_width //= 2
elif stride == -2:
output_height *= 2
output_width *= 2
self.output_shape = (channels[-1], output_height, output_width)
# Build layers
# First layer to increase number of channels before applying residual
# layers
forward_layers = [
nn.Conv2d(self.input_shape[0], channels[0], kernel_size=1,
stride=1, padding=0)
]
in_channels = channels[0]
multiplier1x1, multiplier3x3 = filter_multipliers
for out_channels, stride in zip(channels, strides):
if stride == 1:
forward_layers.append(
ResBlock2d(in_channels,
[out_channels * multiplier1x1, out_channels * multiplier3x3],
add_groupnorm=add_groupnorm)
)
if stride == 2:
forward_layers.append(
nn.Conv2d(in_channels, out_channels, kernel_size=4,
stride=2, padding=1)
)
if stride == -2:
forward_layers.append(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4,
stride=2, padding=1)
)
# Add non-linearity
if stride == 2 or stride == -2:
forward_layers.append(nn.GroupNorm(num_channels_to_num_groups(out_channels), out_channels))
forward_layers.append(nn.LeakyReLU(0.2, True))
in_channels = out_channels
if final_conv_channels:
forward_layers.append(
nn.Conv2d(in_channels, final_conv_channels, kernel_size=1,
stride=1, padding=0)
)
self.forward_layers = nn.Sequential(*forward_layers)
def forward(self, inputs):
"""Applies ResNet to image-like features.
Args:
inputs (torch.Tensor): Image-like tensor, with shape (batch_size,
channels, height, width).
"""
return self.forward_layers(inputs)
class ResNet3d(nn.Module):
"""ResNets for 3d inputs.
Args:
input_shape (tuple of ints): Shape of the input to the model. Should be
of the form (channels, depth, height, width).
channels (tuple of ints): List of number of channels for each layer.
Length of this tuple corresponds to number of layers in network.
Note that this corresponds to number of *output* channels for each
convolutional layer.
strides (tuple of ints): List of strides for each layer. Length of this
tuple corresponds to number of layers in network. If stride is 1, a
residual layer is applied. If stride is 2 a convolution with stride
2 is applied. If stride is -2 a transpose convolution with stride 2
is applied.
final_conv_channels (int): If not 0, a convolution is added as the final
layer, with the number of output channels specified by this int.
filter_multipliers (tuple of ints): Multipliers for filters in residual
layers.
add_groupnorm (bool): If True, adds GroupNorm layers.
Notes:
The first layer of this model is a standard convolution to increase the
number of filters. A convolution can optionally be added at the final
layer.
"""
def __init__(self, input_shape, channels, strides, final_conv_channels=0,
filter_multipliers=(1, 1), add_groupnorm=True):
super(ResNet3d, self).__init__()
assert len(channels) == len(strides), "Length of channels tuple is {} and length of strides tuple is {} but they should be equal".format(len(channels), len(strides))
self.input_shape = input_shape
self.channels = channels
self.strides = strides
self.filter_multipliers = filter_multipliers
self.add_groupnorm = add_groupnorm
# Calculate output_shape
output_channels, output_depth, output_height, output_width = input_shape
for stride in strides:
if stride == 1:
pass
elif stride == 2:
output_depth //= 2
output_height //= 2
output_width //= 2
elif stride == -2:
output_depth *= 2
output_height *= 2
output_width *= 2
self.output_shape = (channels[-1], output_depth, output_height, output_width)
# Build layers
# First layer to increase number of channels before applying residual
# layers
forward_layers = [
nn.Conv3d(self.input_shape[0], channels[0], kernel_size=1,
stride=1, padding=0)
]
in_channels = channels[0]
multiplier1x1, multiplier3x3 = filter_multipliers
for out_channels, stride in zip(channels, strides):
if stride == 1:
forward_layers.append(
ResBlock3d(in_channels,
[out_channels * multiplier1x1, out_channels * multiplier3x3],
add_groupnorm=add_groupnorm)
)
if stride == 2:
forward_layers.append(
nn.Conv3d(in_channels, out_channels, kernel_size=4,
stride=2, padding=1)
)
if stride == -2:
forward_layers.append(
nn.ConvTranspose3d(in_channels, out_channels, kernel_size=4,
stride=2, padding=1)
)
# Add non-linearity
if stride == 2 or stride == -2:
forward_layers.append(nn.GroupNorm(num_channels_to_num_groups(out_channels), out_channels))
forward_layers.append(nn.LeakyReLU(0.2, True))
in_channels = out_channels
if final_conv_channels:
forward_layers.append(
nn.Conv3d(in_channels, final_conv_channels, kernel_size=1,
stride=1, padding=0)
)
self.forward_layers = nn.Sequential(*forward_layers)
def forward(self, inputs):
"""Applies ResNet to 3D features.
Args:
inputs (torch.Tensor): Tensor, with shape (batch_size, channels,
depth, height, width).
"""
return self.forward_layers(inputs)
class Projection(nn.Module):
"""Performs a projection from a 3D voxel-like feature map to a 2D image-like
feature map.
Args:
input_shape (tuple of ints): Shape of 3D input, (channels, depth,
height, width).
num_channels (tuple of ints): Number of channels in each layer of the
projection unit.
Notes:
This layer is inspired by the Projection Unit from
https://arxiv.org/abs/1806.06575.
"""
def __init__(self, input_shape, num_channels):
super(Projection, self).__init__()
self.input_shape = input_shape
self.num_channels = num_channels
self.output_shape = (num_channels[-1],) + input_shape[2:]
# Number of input channels for first 2D convolution is
# channels * depth since we flatten the 3D input
in_channels = self.input_shape[0] * self.input_shape[1]
# Initialize forward pass layers
forward_layers = []
num_layers = len(num_channels)
for i in range(num_layers):
out_channels = num_channels[i]
forward_layers.append(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1)
)
# Add non linearites, except for last layer
if i != num_layers - 1:
forward_layers.append(nn.GroupNorm(num_channels_to_num_groups(out_channels), out_channels))
forward_layers.append(nn.LeakyReLU(0.2, True))
in_channels = out_channels
# Set up forward layers as model
self.forward_layers = nn.Sequential(*forward_layers)
def forward(self, inputs):
"""Reshapes inputs from 3D -> 2D and applies 1x1 convolutions.
Args:
inputs (torch.Tensor): Voxel like tensor, with shape (batch_size,
channels, depth, height, width).
"""
batch_size, channels, depth, height, width = inputs.shape
# Reshape 3D -> 2D
reshaped = inputs.view(batch_size, channels * depth, height, width)
# 1x1 conv layers
return self.forward_layers(reshaped)
class InverseProjection(nn.Module):
"""Performs an inverse projection from a 2D feature map to a 3D feature map.
Args:
input_shape (tuple of ints): Shape of 2D input, (channels, height, width).
num_channels (tuple of ints): Number of channels in each layer of the
projection unit.
Note:
The depth will be equal to the height and width of the input map.
Therefore, the final number of channels must be divisible by the height
and width of the input.
"""
def __init__(self, input_shape, num_channels):
super(InverseProjection, self).__init__()
self.input_shape = input_shape
self.num_channels = num_channels
assert num_channels[-1] % input_shape[-1] == 0, "Number of output channels is {} which is not divisible by " \
"width {} of image".format(num_channels[-1], input_shape[-1])
self.output_shape = (num_channels[-1] // input_shape[-1], input_shape[-1]) + input_shape[1:]
# Initialize forward pass layers
in_channels = self.input_shape[0]
forward_layers = []
num_layers = len(num_channels)
for i in range(num_layers):
out_channels = num_channels[i]
forward_layers.append(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1,
padding=0)
)
# Add non linearites, except for last layer
if i != num_layers - 1:
forward_layers.append(nn.GroupNorm(num_channels_to_num_groups(out_channels), out_channels))
forward_layers.append(nn.LeakyReLU(0.2, True))
in_channels = out_channels
# Set up forward layers as model
self.forward_layers = nn.Sequential(*forward_layers)
def forward(self, inputs):
"""Applies convolutions and reshapes outputs from 2D -> 3D.
Args:
inputs (torch.Tensor): Image like tensor, with shape (batch_size,
channels, height, width).
"""
# 1x1 conv layers
features = self.forward_layers(inputs)
# Reshape 3D -> 2D
batch_size = inputs.shape[0]
return features.view(batch_size, *self.output_shape)
| 41.55 | 174 | 0.597247 | 1,591 | 13,296 | 4.82401 | 0.108108 | 0.060977 | 0.04456 | 0.043779 | 0.848208 | 0.831661 | 0.803257 | 0.781889 | 0.752704 | 0.740195 | 0 | 0.019937 | 0.32852 | 13,296 | 319 | 175 | 41.680251 | 0.839718 | 0.340478 | 0 | 0.658683 | 0 | 0 | 0.030394 | 0 | 0 | 0 | 0 | 0 | 0.017964 | 1 | 0.047904 | false | 0.011976 | 0.017964 | 0 | 0.113772 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
fb63ba424c2f7819373bd12187d203951645db69 | 118 | py | Python | designer/objects/dialogue.py | designer-edu/designer | 9c5d004e934300a30fb6d3148f4db313b69057df | [
"MIT"
] | 3 | 2022-01-21T00:08:02.000Z | 2022-03-09T19:00:26.000Z | designer/objects/dialogue.py | designer-edu/designer | 9c5d004e934300a30fb6d3148f4db313b69057df | [
"MIT"
] | 5 | 2022-02-20T15:44:34.000Z | 2022-03-05T15:57:25.000Z | designer/objects/dialogue.py | designer-edu/designer | 9c5d004e934300a30fb6d3148f4db313b69057df | [
"MIT"
] | null | null | null | from designer.objects.designer_object import DesignerObject
def speech(object: DesignerObject, text: str):
pass
| 19.666667 | 59 | 0.79661 | 14 | 118 | 6.642857 | 0.785714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135593 | 118 | 5 | 60 | 23.6 | 0.911765 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
fb9d93148c91c48e3511f562ee1a2fbeff02c478 | 364 | py | Python | pila.py | Gambl3r08/ejercicios-Python | ddf13b40c611f892112ebbe7bc907f5765998ea0 | [
"MIT"
] | null | null | null | pila.py | Gambl3r08/ejercicios-Python | ddf13b40c611f892112ebbe7bc907f5765998ea0 | [
"MIT"
] | null | null | null | pila.py | Gambl3r08/ejercicios-Python | ddf13b40c611f892112ebbe7bc907f5765998ea0 | [
"MIT"
] | null | null | null | class Pila:
def __init__(self):
self.items = []
def estaVacia(self):
return self.items == []
def incluir(self, item):
self.items.append(item)
def extraer(self):
return self.items.pop()
def inspeccionar(self):
return self.items[len(self.items)-1]
def tamano(self):
return len(self.items)
| 19.157895 | 44 | 0.57967 | 45 | 364 | 4.6 | 0.377778 | 0.304348 | 0.202899 | 0.275362 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.003876 | 0.291209 | 364 | 18 | 45 | 20.222222 | 0.79845 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.461538 | false | 0 | 0 | 0.307692 | 0.846154 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
fb9e69f24921a5e44d694a7f8166918afd222ca6 | 8,029 | py | Python | src/pytheas/views/tool_views.py | dcronkite/pytheas | 3cdd6a21bda488e762931cbf5975964d5e574abd | [
"MIT"
] | null | null | null | src/pytheas/views/tool_views.py | dcronkite/pytheas | 3cdd6a21bda488e762931cbf5975964d5e574abd | [
"MIT"
] | null | null | null | src/pytheas/views/tool_views.py | dcronkite/pytheas | 3cdd6a21bda488e762931cbf5975964d5e574abd | [
"MIT"
] | null | null | null | import urllib.parse
import flask
from flask_login import login_required
from pytheas.services import service, tool_service, user_service, connection_service, regex_filter_service
from pytheas.tasks.load_data import load_data
from pytheas.utils.view_modifiers import response
blueprint = flask.Blueprint('tool', __name__, template_folder='../templates')
@blueprint.route('/tool/load', methods=['POST'])
@login_required
def manual_load_data():
load_data()
return {}
@blueprint.route('/tool/sandbox', methods=['GET'])
@login_required
@response(template_file='home/index.html')
def sandbox():
return {
'user': user_service.get_display_name(),
'projects': user_service.get_projects(),
'tools': tool_service.get_all_tools(),
}
@blueprint.route('/tool/build', methods=['GET'])
@login_required
@response(template_file='tool/build.html')
def build():
return {
'user': user_service.get_display_name(),
'projects': user_service.get_projects(),
'tools': tool_service.get_all_tools(),
'connections': connection_service.get_connections(user_service.get_current_username()),
}
@blueprint.route('/tool/connection/check', methods=['POST'])
@login_required
def check_connection():
data = flask.request.get_json()
success, message = connection_service.check_connection(user_service.get_current_username(), **data)
return {
'status': success,
'message': message,
}
@blueprint.route('/tool/connection/create', methods=['POST'])
@login_required
def create_connection():
data = flask.request.get_json()
success, message = connection_service.create_connection(user_service.get_current_username(), **data)
return {
'status': success,
'message': message,
}
@blueprint.route('/tool/explore', methods=['GET'])
@login_required
@response(template_file='home/index.html')
def explore():
return {
'user': user_service.get_display_name(),
'projects': user_service.get_projects(),
'tools': tool_service.get_all_tools(),
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>')
@login_required
@response(template_file='tool/reviewer.html')
def review_connection(connection_name, connection_id):
doc = connection_service.get_next_record(user_service.get_current_username(), connection_id)
regex_filters = regex_filter_service.get_regex_filters(connection_id)
return {
'connection_name': connection_name,
'connection_url': connection_id,
'regex_filters': regex_filters,
'previous': None,
'progress': {},
'document': doc,
'history': [],
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/update/<string:name_url>',
methods=['POST'])
@login_required
def review_connection_update(connection_name, connection_id, name_url):
data = flask.request.get_json()
errors = []
connection_service.update_tbc(connection_id, urllib.parse.unquote_plus(name_url),
user_service.get_current_username(), **data)
return {
'errors': errors,
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/mark/done/<string:name_url>',
methods=['POST'])
@login_required
def review_connection_mark_done(connection_name, connection_id, name_url):
errors = []
connection_service.mark_done(connection_id, urllib.parse.unquote_plus(name_url),
user_service.get_current_username())
return {
'errors': errors,
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/regex/add', methods=['POST'])
@login_required
def review_connection_add_regex(connection_name, connection_id):
errors = []
data = flask.request.get_json()
connection_service.add_regex(connection_id, data['regex'])
return {
'errors': errors
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/regex/remove', methods=['POST'])
@login_required
def review_connection_remove_regex(connection_name, connection_id):
errors = []
data = flask.request.get_json()
connection_service.remove_regex(connection_id, data['regex'])
return {
'errors': errors
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/previous/<string:name_url>', )
@login_required
@response(template_file='tool/reviewer.html')
def review_connection_prev(connection_name, connection_id, name_url):
doc = connection_service.get_previous_record(user_service.get_current_username(), connection_id)
regex_filters = regex_filter_service.get_regex_filters(connection_id)
return {
'connection_name': connection_name,
'connection_url': connection_id,
'regex_filters': regex_filters,
'previous': None,
'progress': {},
'document': doc,
'history': [],
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/next/<string:name_url>')
@login_required
@response(template_file='tool/reviewer.html')
def review_connection_next(connection_name, connection_id, name_url):
doc = connection_service.get_next_record(user_service.get_current_username(), connection_id)
regex_filters = regex_filter_service.get_regex_filters(connection_id)
return {
'connection_name': connection_name,
'connection_url': connection_id,
'regex_filters': regex_filters,
'previous': None,
'progress': {},
'document': doc,
'history': [],
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/reset')
@login_required
@response(template_file='tool/reviewer.html')
def review_connection_reset(connection_name, connection_id):
connection_service.reset_records(user_service.get_current_username(), connection_id)
doc = connection_service.get_next_record(user_service.get_current_username(), connection_id)
regex_filters = regex_filter_service.get_regex_filters(connection_id)
return {
'connection_name': connection_name,
'connection_url': connection_id,
'regex_filters': regex_filters,
'previous': None,
'progress': {},
'document': doc,
'history': [],
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/response/add', methods=['POST'])
@login_required
def review_connection_add_response(connection_name, connection_id):
data = flask.request.get_json()
errors = connection_service.add_response(user_service.get_current_username(), connection_id, data['response'])
return {
'errors': errors
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/filter/add', methods=['POST'])
@login_required
def add_regex_filter(connection_name, connection_id):
data = flask.request.get_json()
errors = regex_filter_service.add_regex_filter(connection_id, data['regex'], data['include'], data['exclude'],
data['ignore'])
return {
'errors': errors
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/filter/delete', methods=['POST'])
@login_required
def delete_regex_filter(connection_name, connection_id):
data = flask.request.get_json()
errors = regex_filter_service.delete_regex_filter(connection_id, data['id'])
return {
'errors': errors
}
@blueprint.route('/tool/review/<string:connection_name>/<string:connection_id>/filter/update', methods=['POST'])
@login_required
def update_regex_filter(connection_name, connection_id):
data = flask.request.get_json()
errors = regex_filter_service.update_regex_filter(connection_id, data['id'], data['regex'], data['include'],
data['exclude'], data['ignore'])
return {
'errors': errors
}
| 34.607759 | 114 | 0.702827 | 918 | 8,029 | 5.819172 | 0.092593 | 0.101086 | 0.089854 | 0.053912 | 0.851554 | 0.816923 | 0.78772 | 0.757769 | 0.745975 | 0.72763 | 0 | 0 | 0.164902 | 8,029 | 231 | 115 | 34.757576 | 0.796719 | 0 | 0 | 0.583333 | 0 | 0.005208 | 0.209491 | 0.118197 | 0 | 0 | 0 | 0 | 0 | 1 | 0.09375 | false | 0 | 0.03125 | 0.015625 | 0.21875 | 0.098958 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
fb9e8083d7b3c659fa3842677ab75b596cce658b | 66 | py | Python | logreporter/report/__init__.py | TakamiChie/LogReporter | c4b3e00dd40d3bfb5296fca3ac4af6acf1770958 | [
"MIT"
] | null | null | null | logreporter/report/__init__.py | TakamiChie/LogReporter | c4b3e00dd40d3bfb5296fca3ac4af6acf1770958 | [
"MIT"
] | 5 | 2020-10-28T12:36:41.000Z | 2021-03-31T09:26:06.000Z | logreporter/report/__init__.py | TakamiChie/LogReporter | c4b3e00dd40d3bfb5296fca3ac4af6acf1770958 | [
"MIT"
] | null | null | null | from logreporter.report.discordwhreporter import DiscordWHReporter | 66 | 66 | 0.924242 | 6 | 66 | 10.166667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.045455 | 66 | 1 | 66 | 66 | 0.968254 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
836d25ddaf9488f38ef0cbb4ded18ebcb814e016 | 103 | py | Python | RandomNumberGenerator/main.py | Gregory-Eales/QA-Reimplementations | bef0b3e67397a73c468e539c426c6629d398433b | [
"MIT"
] | 1 | 2019-05-03T21:48:29.000Z | 2019-05-03T21:48:29.000Z | RandomNumberGenerator/main.py | Gregory-Eales/QA-Reimplementations | bef0b3e67397a73c468e539c426c6629d398433b | [
"MIT"
] | null | null | null | RandomNumberGenerator/main.py | Gregory-Eales/QA-Reimplementations | bef0b3e67397a73c468e539c426c6629d398433b | [
"MIT"
] | null | null | null | import qsharp
from RandomNumberGenerator import GenerateRandomBit
print(GenerateRandomBit.simulate()) | 20.6 | 51 | 0.873786 | 9 | 103 | 10 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.07767 | 103 | 5 | 52 | 20.6 | 0.947368 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0.333333 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
83c11aa6cefea21e02adfeff7dd1105eb47e6da1 | 170 | py | Python | juniper_official/Solutions/Icmp-outlier/micro_milli.py | brahmastra2016/healthbot-rules | 1d24acd298266c39d6adb139ff47d14f8b2d452a | [
"Apache-2.0",
"BSD-3-Clause"
] | 43 | 2018-11-27T00:42:45.000Z | 2022-02-24T01:19:39.000Z | juniper_official/Solutions/Icmp-outlier/micro_milli.py | brahmastra2016/healthbot-rules | 1d24acd298266c39d6adb139ff47d14f8b2d452a | [
"Apache-2.0",
"BSD-3-Clause"
] | 266 | 2018-10-26T10:19:04.000Z | 2022-03-16T04:38:29.000Z | juniper_official/Solutions/Icmp-outlier/micro_milli.py | brahmastra2016/healthbot-rules | 1d24acd298266c39d6adb139ff47d14f8b2d452a | [
"Apache-2.0",
"BSD-3-Clause"
] | 99 | 2018-10-25T09:53:55.000Z | 2021-12-07T09:51:59.000Z | from __future__ import division
'''
This function converts micro seconds to milli seconds
'''
def rtt_micro_milli(micros, **kwargs):
return float(int(micros)/1000.0)
| 24.285714 | 53 | 0.758824 | 24 | 170 | 5.125 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.034014 | 0.135294 | 170 | 6 | 54 | 28.333333 | 0.802721 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
83c9422fd2e1c83ef9c55befc141c0f7e7d1fe8c | 8,492 | py | Python | image_filer/fields.py | emencia/django-image-filer | 488211bed90887891a3afe8266028021b7ea4f48 | [
"MIT"
] | null | null | null | image_filer/fields.py | emencia/django-image-filer | 488211bed90887891a3afe8266028021b7ea4f48 | [
"MIT"
] | null | null | null | image_filer/fields.py | emencia/django-image-filer | 488211bed90887891a3afe8266028021b7ea4f48 | [
"MIT"
] | null | null | null | from django.utils.translation import ugettext as _
from django.utils.text import truncate_words
from django.utils import simplejson
from django.db import models
from django import forms
from django.contrib.admin.widgets import ForeignKeyRawIdWidget
from django.core.urlresolvers import reverse
from django.utils.safestring import mark_safe
from django.conf import settings
from sorl.thumbnail.base import ThumbnailException
from image_filer import context_processors
class ImageFilerImageWidget(ForeignKeyRawIdWidget):
choices = None
input_type = 'hidden'
is_hidden = True
def render(self, name, value, attrs=None):
obj = self.obj_for_value(value)
css_id = attrs.get('id', 'id_image_x')
css_id_thumbnail_img = "%s_thumbnail_img" % css_id
css_id_description_txt = "%s_description_txt" % css_id
if attrs is None:
attrs = {}
related_url = reverse('admin:image_filer-directory_listing-root')
params = self.url_parameters()
if params:
url = '?' + '&'.join(['%s=%s' % (k, v) for k, v in params.items()])
else:
url = ''
if not attrs.has_key('class'):
attrs['class'] = 'vForeignKeyRawIdAdminField' # The JavaScript looks for this hook.
output = []
if obj:
try:
output.append(u'<img id="%s" src="%s" alt="%s" /> ' % (css_id_thumbnail_img, obj.thumbnails['admin_tiny_icon'], obj.label))
except ThumbnailException:
# this means that the image is missing on the filesystem
output.append(u'<img id="%s" src="%s" alt="%s" /> ' % (css_id_thumbnail_img, '', _('image missing!')))
output.append(u' <strong id="%s">%s</strong>' % (css_id_description_txt, obj))
else:
output.append(u'<img id="%s" src="" class="quiet" alt="no image selected">' % css_id_thumbnail_img)
output.append(u' <strong id="%s">%s</strong>' % (css_id_description_txt, ''))
# TODO: "id_" is hard-coded here. This should instead use the correct
# API to determine the ID dynamically.
output.append('<a href="%s%s" class="related-lookup" id="lookup_id_%s" onclick="return showRelatedObjectLookupPopup(this);"> ' % \
(related_url, url, name))
output.append('<img src="%simg/admin/selector-search.gif" width="16" height="16" alt="%s" /></a>' % (settings.ADMIN_MEDIA_PREFIX, _('Lookup')))
output.append('''<a href="" class="deletelink" onclick="return removeImageLink('%s');"> </a>''' % (css_id,))
output.append('</br>')
super_attrs = attrs.copy()
output.append( super(ForeignKeyRawIdWidget, self).render(name, value, super_attrs))
return mark_safe(u''.join(output))
def label_for_value(self, value):
obj = self.obj_for_value(value)
return ' <strong>%s</strong>' % truncate_words(obj, 14)
def obj_for_value(self, value):
try:
key = self.rel.get_related_field().name
obj = self.rel.to._default_manager.get(**{key: value})
except:
obj = None
return obj
class Media:
js = (context_processors.media(None)['IMAGE_FILER_MEDIA_URL']+'js/image_widget_thumbnail.js',
context_processors.media(None)['IMAGE_FILER_MEDIA_URL']+'js/popup_handling.js',)
class ImageFilerImageFormField(forms.ModelChoiceField):
widget = ImageFilerImageWidget
def __init__(self, rel, queryset, to_field_name, *args, **kwargs):
self.rel = rel
self.queryset = queryset
self.to_field_name = to_field_name
self.max_value = None
self.min_value = None
other_widget = kwargs.pop('widget', None)
forms.Field.__init__(self, widget=self.widget(rel), *args, **kwargs)
class ImageFilerModelImageField(models.ForeignKey):
def __init__(self, **kwargs):
from image_filer.models import Image
return super(ImageFilerModelImageField,self).__init__(Image, **kwargs)
def formfield(self, **kwargs):
# This is a fairly standard way to set up some defaults
# while letting the caller override them.
#defaults = {'form_class': ImageFilerImageWidget}
defaults = {
'form_class': ImageFilerImageFormField,
'rel': self.rel,
}
defaults.update(kwargs)
return super(ImageFilerModelImageField, self).formfield(**defaults)
def south_field_triple(self):
"Returns a suitable description of this field for South."
# We'll just introspect ourselves, since we inherit.
from south.modelsinspector import introspector
field_class = "django.db.models.fields.related.ForeignKey"
args, kwargs = introspector(self)
# That's our definition!
return (field_class, args, kwargs)
class ImageFilerFolderWidget(ForeignKeyRawIdWidget):
choices = None
input_type = 'hidden'
is_hidden = True
def render(self, name, value, attrs=None):
obj = self.obj_for_value(value)
css_id = attrs.get('id')
css_id_name = "%s_name" % css_id
if attrs is None:
attrs = {}
related_url = reverse('admin:image_filer-directory_listing-root')
params = self.url_parameters()
params['select_folder'] = 1
if params:
url = '?' + '&'.join(['%s=%s' % (k, v) for k, v in params.items()])
else:
url = ''
if not attrs.has_key('class'):
attrs['class'] = 'vForeignKeyRawIdAdminField' # The JavaScript looks for this hook.
output = []
if obj:
output.append(u'Folder: <span id="%s">%s</span>' % (css_id_name, obj.name))
else:
output.append(u'Folder: <span id="%s">none selected</span>' % css_id_name)
# TODO: "id_" is hard-coded here. This should instead use the correct
# API to determine the ID dynamically.
output.append('<a href="%s%s" class="related-lookup" id="lookup_id_%s" onclick="return showRelatedObjectLookupPopup(this);"> ' % \
(related_url, url, name))
output.append('<img src="%simg/admin/selector-search.gif" width="16" height="16" alt="%s" /></a>' % (settings.ADMIN_MEDIA_PREFIX, _('Lookup')))
output.append('</br>')
super_attrs = attrs.copy()
output.append( super(ForeignKeyRawIdWidget, self).render(name, value, super_attrs))
return mark_safe(u''.join(output))
def label_for_value(self, value):
obj = self.obj_for_value(value)
return ' <strong>%s</strong>' % truncate_words(obj, 14)
def obj_for_value(self, value):
try:
key = self.rel.get_related_field().name
obj = self.rel.to._default_manager.get(**{key: value})
except:
obj = None
return obj
class Media:
js = (context_processors.media(None)['IMAGE_FILER_MEDIA_URL']+'js/popup_handling.js',)
class ImageFilerFolderFormField(forms.ModelChoiceField):
widget = ImageFilerFolderWidget
def __init__(self, rel, queryset, to_field_name, *args, **kwargs):
self.rel = rel
self.queryset = queryset
self.to_field_name = to_field_name
self.max_value = None
self.min_value = None
other_widget = kwargs.pop('widget', None)
forms.Field.__init__(self, widget=self.widget(rel), *args, **kwargs)
class ImageFilerModelFolderField(models.ForeignKey):
def __init__(self, **kwargs):
from image_filer.models import Folder
return super(ImageFilerModelFolderField,self).__init__(Folder, **kwargs)
def formfield(self, **kwargs):
# This is a fairly standard way to set up some defaults
# while letting the caller override them.
#defaults = {'form_class': ImageFilerImageWidget}
defaults = {
'form_class': ImageFilerFolderFormField,
'rel': self.rel,
}
defaults.update(kwargs)
return super(ImageFilerModelFolderField, self).formfield(**defaults)
def south_field_triple(self):
"Returns a suitable description of this field for South."
# We'll just introspect ourselves, since we inherit.
from south.modelsinspector import introspector
field_class = "django.db.models.fields.related.ForeignKey"
args, kwargs = introspector(self)
# That's our definition!
return (field_class, args, kwargs)
| 44.931217 | 151 | 0.639543 | 1,031 | 8,492 | 5.084384 | 0.190107 | 0.015261 | 0.01736 | 0.00992 | 0.757726 | 0.757726 | 0.757726 | 0.743609 | 0.727966 | 0.727966 | 0 | 0.002004 | 0.235987 | 8,492 | 188 | 152 | 45.170213 | 0.805949 | 0.103745 | 0 | 0.702532 | 0 | 0.025316 | 0.187516 | 0.075655 | 0.012658 | 0 | 0 | 0.005319 | 0 | 1 | 0.088608 | false | 0 | 0.094937 | 0 | 0.360759 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
83f9efa8791d32909ae3221b155abbb5a663c8a4 | 19,110 | py | Python | applied_python/applied_python/lib/python2.7/site-packages/pysnmp/hlapi/asyncore/cmdgen.py | mith1979/ansible_automation | 013dfa67c6d91720b787fadb21de574b6e023a26 | [
"Apache-2.0"
] | null | null | null | applied_python/applied_python/lib/python2.7/site-packages/pysnmp/hlapi/asyncore/cmdgen.py | mith1979/ansible_automation | 013dfa67c6d91720b787fadb21de574b6e023a26 | [
"Apache-2.0"
] | null | null | null | applied_python/applied_python/lib/python2.7/site-packages/pysnmp/hlapi/asyncore/cmdgen.py | mith1979/ansible_automation | 013dfa67c6d91720b787fadb21de574b6e023a26 | [
"Apache-2.0"
] | null | null | null | #
# This file is part of pysnmp software.
#
# Copyright (c) 2005-2016, Ilya Etingof <ilya@glas.net>
# License: http://pysnmp.sf.net/license.html
#
from pysnmp.entity.rfc3413 import cmdgen
from pysnmp.smi.rfc1902 import *
from pysnmp.hlapi.auth import *
from pysnmp.hlapi.context import *
from pysnmp.hlapi.lcd import *
from pysnmp.hlapi.varbinds import *
from pysnmp.hlapi.asyncore.transport import *
__all__ = ['getCmd', 'nextCmd', 'setCmd', 'bulkCmd', 'isEndOfMib']
vbProcessor = CommandGeneratorVarBinds()
lcd = CommandGeneratorLcdConfigurator()
isEndOfMib = lambda x: not cmdgen.getNextVarBinds(x)[1]
def getCmd(snmpEngine, authData, transportTarget, contextData,
*varBinds, **options):
"""Performs SNMP GET query.
Based on passed parameters, prepares SNMP GET packet
(:RFC:`1905#section-4.2.1`) and schedules its transmission by
I/O framework at a later point of time.
Parameters
----------
snmpEngine : :py:class:`~pysnmp.hlapi.SnmpEngine`
Class instance representing SNMP engine.
authData : :py:class:`~pysnmp.hlapi.CommunityData` or :py:class:`~pysnmp.hlapi.UsmUserData`
Class instance representing SNMP credentials.
transportTarget : :py:class:`~pysnmp.hlapi.asyncore.UdpTransportTarget` or :py:class:`~pysnmp.hlapi.asyncore.Udp6TransportTarget`
Class instance representing transport type along with SNMP peer
address.
contextData : :py:class:`~pysnmp.hlapi.ContextData`
Class instance representing SNMP ContextEngineId and ContextName
values.
\*varBinds : :py:class:`~pysnmp.smi.rfc1902.ObjectType`
One or more class instances representing MIB variables to place
into SNMP request.
Other Parameters
----------------
\*\*options :
Request options:
* `lookupMib` - load MIB and resolve response MIB variables at
the cost of slightly reduced performance. Default is `True`.
* `cbFun` (callable) - user-supplied callable that is invoked
to pass SNMP response data or error to user at a later point
of time. Default is `None`.
* `cbCtx` (object) - user-supplied object passing additional
parameters to/from `cbFun`. Default is `None`.
Notes
-----
User-supplied `cbFun` callable must have the following call
signature:
* snmpEngine (:py:class:`~pysnmp.hlapi.SnmpEngine`):
Class instance representing SNMP engine.
* sendRequestHandle (int): Unique request identifier. Can be used
for matching multiple ongoing requests with received responses.
* errorIndication (str): True value indicates SNMP engine error.
* errorStatus (str): True value indicates SNMP PDU error.
* errorIndex (int): Non-zero value refers to `varBinds[errorIndex-1]`
* varBinds (tuple): A sequence of
:py:class:`~pysnmp.smi.rfc1902.ObjectType` class instances
representing MIB variables returned in SNMP response in exactly
the same order as `varBinds` in request.
* `cbCtx` : Original user-supplied object.
Returns
-------
sendRequestHandle : int
Unique request identifier. Can be used for matching received
responses with ongoing requests.
Raises
------
PySnmpError
Or its derivative indicating that an error occurred while
performing SNMP operation.
Examples
--------
>>> from pysnmp.hlapi.asyncore import *
>>> def cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBinds, cbCtx):
... print(errorIndication, errorStatus, errorIndex, varBinds)
>>>
>>> snmpEngine = SnmpEngine()
>>> getCmd(snmpEngine,
... CommunityData('public'),
... UdpTransportTarget(('demo.snmplabs.com', 161)),
... ContextData(),
... ObjectType(ObjectIdentity('SNMPv2-MIB', 'sysDescr', 0)),
... cbFun=cbFun)
>>> snmpEngine.transportDispatcher.runDispatcher()
(None, 0, 0, [ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.1.0')), DisplayString('SunOS zeus.snmplabs.com 4.1.3_U1 1 sun4m'))])
>>>
"""
def __cbFun(snmpEngine, sendRequestHandle,
errorIndication, errorStatus, errorIndex,
varBinds, cbCtx):
lookupMib, cbFun, cbCtx = cbCtx
if cbFun:
return cbFun(snmpEngine, sendRequestHandle, errorIndication,
errorStatus, errorIndex,
vbProcessor.unmakeVarBinds(
snmpEngine, varBinds, lookupMib
), cbCtx)
addrName, paramsName = lcd.configure(snmpEngine, authData, transportTarget)
return cmdgen.GetCommandGenerator().sendVarBinds(
snmpEngine, addrName, contextData.contextEngineId,
contextData.contextName,
vbProcessor.makeVarBinds(snmpEngine, varBinds), __cbFun,
(options.get('lookupMib', True),
options.get('cbFun'), options.get('cbCtx'))
)
def setCmd(snmpEngine, authData, transportTarget, contextData,
*varBinds, **options):
"""Performs SNMP SET query.
Based on passed parameters, prepares SNMP SET packet
(:RFC:`1905#section-4.2.5`) and schedules its transmission by
I/O framework at a later point of time.
Parameters
----------
snmpEngine : :py:class:`~pysnmp.hlapi.SnmpEngine`
Class instance representing SNMP engine.
authData : :py:class:`~pysnmp.hlapi.CommunityData` or :py:class:`~pysnmp.hlapi.UsmUserData`
Class instance representing SNMP credentials.
transportTarget : :py:class:`~pysnmp.hlapi.asyncore.UdpTransportTarget` or :py:class:`~pysnmp.hlapi.asyncore.Udp6TransportTarget`
Class instance representing transport type along with SNMP peer
address.
contextData : :py:class:`~pysnmp.hlapi.ContextData`
Class instance representing SNMP ContextEngineId and ContextName
values.
\*varBinds : :py:class:`~pysnmp.smi.rfc1902.ObjectType`
One or more class instances representing MIB variables to place
into SNMP request.
Other Parameters
----------------
\*\*options :
Request options:
* `lookupMib` - load MIB and resolve response MIB variables at
the cost of slightly reduced performance. Default is `True`.
* `cbFun` (callable) - user-supplied callable that is invoked
to pass SNMP response data or error to user at a later point
of time. Default is `None`.
* `cbCtx` (object) - user-supplied object passing additional
parameters to/from `cbFun`. Default is `None`.
Notes
-----
User-supplied `cbFun` callable must have the following call
signature:
* snmpEngine (:py:class:`~pysnmp.hlapi.SnmpEngine`):
Class instance representing SNMP engine.
* sendRequestHandle (int): Unique request identifier. Can be used
for matching multiple ongoing requests with received responses.
* errorIndication (str): True value indicates SNMP engine error.
* errorStatus (str): True value indicates SNMP PDU error.
* errorIndex (int): Non-zero value refers to `varBinds[errorIndex-1]`
* varBinds (tuple): A sequence of
:py:class:`~pysnmp.smi.rfc1902.ObjectType` class instances
representing MIB variables returned in SNMP response in exactly
the same order as `varBinds` in request.
* `cbCtx` : Original user-supplied object.
Returns
-------
sendRequestHandle : int
Unique request identifier. Can be used for matching received
responses with ongoing requests.
Raises
------
PySnmpError
Or its derivative indicating that an error occurred while
performing SNMP operation.
Examples
--------
>>> from pysnmp.hlapi.asyncore import *
>>> def cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBinds, cbCtx):
... print(errorIndication, errorStatus, errorIndex, varBinds)
>>>
>>> snmpEngine = SnmpEngine()
>>> setCmd(snmpEngine,
... CommunityData('public'),
... UdpTransportTarget(('demo.snmplabs.com', 161)),
... ContextData(),
... ObjectType(ObjectIdentity('SNMPv2-MIB', 'sysContact', 0), 'info@snmplabs.com'),
... cbFun=cbFun)
>>> snmpEngine.transportDispatcher.runDispatcher()
(None, 0, 0, [ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.4.0')), DisplayString('info@snmplabs.com'))])
>>>
"""
def __cbFun(snmpEngine, sendRequestHandle,
errorIndication, errorStatus, errorIndex,
varBinds, cbCtx):
lookupMib, cbFun, cbCtx = cbCtx
return cbFun(snmpEngine, sendRequestHandle, errorIndication,
errorStatus, errorIndex,
vbProcessor.unmakeVarBinds(
snmpEngine, varBinds, lookupMib
), cbCtx)
addrName, paramsName = lcd.configure(snmpEngine, authData, transportTarget)
return cmdgen.SetCommandGenerator().sendVarBinds(
snmpEngine, addrName, contextData.contextEngineId,
contextData.contextName, vbProcessor.makeVarBinds(snmpEngine, varBinds),
__cbFun, (options.get('lookupMib', True),
options.get('cbFun'), options.get('cbCtx'))
)
def nextCmd(snmpEngine, authData, transportTarget, contextData,
*varBinds, **options):
"""Performs SNMP GETNEXT query.
Based on passed parameters, prepares SNMP GETNEXT packet
(:RFC:`1905#section-4.2.2`) and schedules its transmission by
I/O framework at a later point of time.
Parameters
----------
snmpEngine : :py:class:`~pysnmp.hlapi.SnmpEngine`
Class instance representing SNMP engine.
authData : :py:class:`~pysnmp.hlapi.CommunityData` or :py:class:`~pysnmp.hlapi.UsmUserData`
Class instance representing SNMP credentials.
transportTarget : :py:class:`~pysnmp.hlapi.asyncore.UdpTransportTarget` or :py:class:`~pysnmp.hlapi.asyncore.Udp6TransportTarget`
Class instance representing transport type along with SNMP peer
address.
contextData : :py:class:`~pysnmp.hlapi.ContextData`
Class instance representing SNMP ContextEngineId and ContextName
values.
\*varBinds : :py:class:`~pysnmp.smi.rfc1902.ObjectType`
One or more class instances representing MIB variables to place
into SNMP request.
Other Parameters
----------------
\*\*options :
Request options:
* `lookupMib` - load MIB and resolve response MIB variables at
the cost of slightly reduced performance. Default is `True`.
* `cbFun` (callable) - user-supplied callable that is invoked
to pass SNMP response data or error to user at a later point
of time. Default is `None`.
* `cbCtx` (object) - user-supplied object passing additional
parameters to/from `cbFun`. Default is `None`.
Notes
-----
User-supplied `cbFun` callable must have the following call
signature:
* snmpEngine (:py:class:`~pysnmp.hlapi.SnmpEngine`):
Class instance representing SNMP engine.
* sendRequestHandle (int): Unique request identifier. Can be used
for matching multiple ongoing requests with received responses.
* errorIndication (str): True value indicates SNMP engine error.
* errorStatus (str): True value indicates SNMP PDU error.
* errorIndex (int): Non-zero value refers to `varBinds[errorIndex-1]`
* varBinds (tuple): A sequence of sequences (e.g. 2-D array) of
:py:class:`~pysnmp.smi.rfc1902.ObjectType` class instances
representing a table of MIB variables returned in SNMP response.
Inner sequences represent table rows and ordered exactly the same
as `varBinds` in request. Response to GETNEXT always contain a
single row.
* `cbCtx` : Original user-supplied object.
Returns
-------
sendRequestHandle : int
Unique request identifier. Can be used for matching received
responses with ongoing requests.
Raises
------
PySnmpError
Or its derivative indicating that an error occurred while
performing SNMP operation.
Examples
--------
>>> from pysnmp.hlapi.asyncore import *
>>> def cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBinds, cbCtx):
... print(errorIndication, errorStatus, errorIndex, varBinds)
>>>
>>> snmpEngine = SnmpEngine()
>>> nextCmd(snmpEngine,
... CommunityData('public'),
... UdpTransportTarget(('demo.snmplabs.com', 161)),
... ContextData(),
... ObjectType(ObjectIdentity('SNMPv2-MIB', 'system')),
... cbFun=cbFun)
>>> snmpEngine.transportDispatcher.runDispatcher()
(None, 0, 0, [ [ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.1.0')), DisplayString('SunOS zeus.snmplabs.com 4.1.3_U1 1 sun4m'))] ])
>>>
"""
def __cbFun(snmpEngine, sendRequestHandle, errorIndication,
errorStatus, errorIndex, varBindTable, cbCtx):
lookupMib, cbFun, cbCtx = cbCtx
return cbFun(snmpEngine, sendRequestHandle, errorIndication,
errorStatus, errorIndex,
[vbProcessor.unmakeVarBinds(snmpEngine, varBindTableRow, lookupMib) for varBindTableRow in varBindTable],
cbCtx)
addrName, paramsName = lcd.configure(snmpEngine, authData, transportTarget)
return cmdgen.NextCommandGenerator().sendVarBinds(
snmpEngine, addrName,
contextData.contextEngineId, contextData.contextName,
vbProcessor.makeVarBinds(snmpEngine, varBinds),
__cbFun, (options.get('lookupMib', True),
options.get('cbFun'), options.get('cbCtx'))
)
def bulkCmd(snmpEngine, authData, transportTarget, contextData,
nonRepeaters, maxRepetitions, *varBinds, **options):
"""Performs SNMP GETBULK query.
Based on passed parameters, prepares SNMP GETBULK packet
(:RFC:`1905#section-4.2.3`) and schedules its transmission by
I/O framework at a later point of time.
Parameters
----------
snmpEngine : :py:class:`~pysnmp.hlapi.SnmpEngine`
Class instance representing SNMP engine.
authData : :py:class:`~pysnmp.hlapi.CommunityData` or :py:class:`~pysnmp.hlapi.UsmUserData`
Class instance representing SNMP credentials.
transportTarget : :py:class:`~pysnmp.hlapi.asyncore.UdpTransportTarget` or :py:class:`~pysnmp.hlapi.asyncore.Udp6TransportTarget`
Class instance representing transport type along with SNMP peer
address.
contextData : :py:class:`~pysnmp.hlapi.ContextData`
Class instance representing SNMP ContextEngineId and ContextName
values.
nonRepeaters : int
One MIB variable is requested in response for the first
`nonRepeaters` MIB variables in request.
maxRepetitions : int
`maxRepetitions` MIB variables are requested in response for each
of the remaining MIB variables in the request (e.g. excluding
`nonRepeaters`). Remote SNMP engine may choose lesser value than
requested.
\*varBinds : :py:class:`~pysnmp.smi.rfc1902.ObjectType`
One or more class instances representing MIB variables to place
into SNMP request.
Other Parameters
----------------
\*\*options :
Request options:
* `lookupMib` - load MIB and resolve response MIB variables at
the cost of slightly reduced performance. Default is `True`.
* `cbFun` (callable) - user-supplied callable that is invoked
to pass SNMP response data or error to user at a later point
of time. Default is `None`.
* `cbCtx` (object) - user-supplied object passing additional
parameters to/from `cbFun`. Default is `None`.
Notes
-----
User-supplied `cbFun` callable must have the following call
signature:
* snmpEngine (:py:class:`~pysnmp.hlapi.SnmpEngine`):
Class instance representing SNMP engine.
* sendRequestHandle (int): Unique request identifier. Can be used
for matching multiple ongoing requests with received responses.
* errorIndication (str): True value indicates SNMP engine error.
* errorStatus (str): True value indicates SNMP PDU error.
* errorIndex (int): Non-zero value refers to `varBinds[errorIndex-1]`
* varBinds (tuple): A sequence of sequences (e.g. 2-D array) of
:py:class:`~pysnmp.smi.rfc1902.ObjectType` class instances
representing a table of MIB variables returned in SNMP response.
Inner sequences represent table rows and ordered exactly the same
as `varBinds` in request. Number of rows might be less or equal
to `maxRepetitions` value in request.
* `cbCtx` : Original user-supplied object.
Returns
-------
sendRequestHandle : int
Unique request identifier. Can be used for matching received
responses with ongoing requests.
Raises
------
PySnmpError
Or its derivative indicating that an error occurred while
performing SNMP operation.
Examples
--------
>>> from pysnmp.hlapi.asyncore import *
>>> def cbFun(snmpEngine, sendRequestHandle, errorIndication, errorStatus, errorIndex, varBinds, cbCtx):
... print(errorIndication, errorStatus, errorIndex, varBinds)
>>>
>>> snmpEngine = SnmpEngine()
>>> bulkCmd(snmpEngine,
... CommunityData('public'),
... UdpTransportTarget(('demo.snmplabs.com', 161)),
... ContextData(),
... 0, 2,
... ObjectType(ObjectIdentity('SNMPv2-MIB', 'system')),
... cbFun=cbFun)
>>> snmpEngine.transportDispatcher.runDispatcher()
(None, 0, 0, [ [ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.1.0')), DisplayString('SunOS zeus.snmplabs.com 4.1.3_U1 1 sun4m')), ObjectType(ObjectIdentity(ObjectName('1.3.6.1.2.1.1.2.0')), ObjectIdentifier('1.3.6.1.4.1.424242.1.1')] ])
>>>
"""
def __cbFun(snmpEngine, sendRequestHandle,
errorIndication, errorStatus, errorIndex,
varBindTable, cbCtx):
lookupMib, cbFun, cbCtx = cbCtx
return cbFun(snmpEngine, sendRequestHandle, errorIndication,
errorStatus, errorIndex, [vbProcessor.unmakeVarBinds(snmpEngine, varBindTableRow, lookupMib) for varBindTableRow in varBindTable], cbCtx)
addrName, paramsName = lcd.configure(snmpEngine, authData, transportTarget)
return cmdgen.BulkCommandGenerator().sendVarBinds(
snmpEngine, addrName, contextData.contextEngineId,
contextData.contextName, nonRepeaters, maxRepetitions,
vbProcessor.makeVarBinds(snmpEngine, varBinds), __cbFun,
(options.get('lookupMib', True),
options.get('cbFun'), options.get('cbCtx'))
)
| 41.096774 | 246 | 0.66044 | 1,990 | 19,110 | 6.330653 | 0.131156 | 0.032307 | 0.037149 | 0.040006 | 0.902207 | 0.902207 | 0.895221 | 0.87633 | 0.859422 | 0.853389 | 0 | 0.013134 | 0.23506 | 19,110 | 464 | 247 | 41.185345 | 0.84868 | 0.711617 | 0 | 0.54878 | 0 | 0 | 0.026211 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.097561 | false | 0 | 0.085366 | 0 | 0.280488 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f7ad712f8b525d9e2383eeda1c6e528134b890c6 | 25 | py | Python | core/__init__.py | LorentzB/dl | c2af8498ba868abcd2ddb08eb9e4b4bb79594ba2 | [
"Apache-2.0"
] | 45 | 2018-12-30T14:19:37.000Z | 2021-01-28T08:16:41.000Z | core/__init__.py | LorentzB/dl | c2af8498ba868abcd2ddb08eb9e4b4bb79594ba2 | [
"Apache-2.0"
] | 23 | 2019-01-07T22:32:00.000Z | 2019-10-04T10:23:02.000Z | core/__init__.py | LorentzB/dl | c2af8498ba868abcd2ddb08eb9e4b4bb79594ba2 | [
"Apache-2.0"
] | 36 | 2019-01-11T21:38:02.000Z | 2021-01-28T08:16:53.000Z | from core.config import * | 25 | 25 | 0.8 | 4 | 25 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12 | 25 | 1 | 25 | 25 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f7c9599c090e9ab6b48949bb745d604b363181f1 | 16,242 | py | Python | util/util.py | JinxedQAQ/Generating-Talking-Face-with-Controllable-Eye-Movements-by-Disentangled-Blinking-Feature | c1b68c010ccfb6b1d438dba97a1317ce0ae2aab8 | [
"MIT"
] | null | null | null | util/util.py | JinxedQAQ/Generating-Talking-Face-with-Controllable-Eye-Movements-by-Disentangled-Blinking-Feature | c1b68c010ccfb6b1d438dba97a1317ce0ae2aab8 | [
"MIT"
] | null | null | null | util/util.py | JinxedQAQ/Generating-Talking-Face-with-Controllable-Eye-Movements-by-Disentangled-Blinking-Feature | c1b68c010ccfb6b1d438dba97a1317ce0ae2aab8 | [
"MIT"
] | null | null | null | from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import inspect, re
import torch.nn as nn
import os
from Options_all import BaseOptions
import collections
config = BaseOptions().parse()
def tensor2im(image_tensor, imtype=np.uint8):
image_numpy = image_tensor[0].cpu().float().numpy()
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
PIL_image = image_numpy
return PIL_image.astype(imtype)
def tensor2image(image_tensor, imtype=np.uint8):
image_numpy = image_tensor.cpu().float().numpy()
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
PIL_image = image_numpy
return PIL_image.astype(imtype)
def tensor2mfcc(image_tensor, imtype=np.uint8):
image_numpy = image_tensor[0].cpu().float().numpy()
PIL_image = image_numpy
return PIL_image.astype(imtype)
def diagnose_network(net, name='network'):
mean = 0.0
count = 0
for param in net.parameters():
if param.grad is not None:
mean += torch.mean(torch.abs(param.grad.data))
count += 1
if count > 0:
mean = mean / count
print(name)
print(mean)
def save_image(image_numpy, image_path):
image_pil = Image.fromarray(image_numpy)
image_pil.save(image_path)
def info(object, spacing=10, collapse=1):
"""Print methods and doc strings.
Takes module, class, list, dictionary, or string."""
methodList = [e for e in dir(object) if isinstance(getattr(object, e), collections.Callable)]
processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
print( "\n".join(["%s %s" %
(method.ljust(spacing),
processFunc(str(getattr(object, method).__doc__)))
for method in methodList]) )
def varname(p):
for line in inspect.getframeinfo(inspect.currentframe().f_back)[3]:
m = re.search(r'\bvarname\s*\(\s*([A-Za-z_][A-Za-z0-9_]*)\s*\)', line)
if m:
return m.group(1)
def print_numpy(x, val=True, shp=False):
x = x.astype(np.float64)
if shp:
print('shape,', x.shape)
if val:
x = x.flatten()
print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
def mkdirs(paths):
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def save_checkpoint(state, epoch, filename=config.name + '_checkpoint.pth.tar', step=0):
torch.save(state, os.path.join(config.checkpoints_dir, str(epoch) + "_" + str(step) + "_" + filename))
def copy_state_dict(state_dict, model, strip=None):
tgt_state = model.state_dict()
copied_names = set()
for name, param in state_dict.items():
if strip is not None and name.startswith(strip):
name = name[len(strip):]
if name not in tgt_state:
continue
if isinstance(param, nn.Parameter):
param = param.data
if param.size() != tgt_state[name].size():
print('mismatch:', name, param.size(), tgt_state[name].size())
continue
tgt_state[name].copy_(param)
copied_names.add(name)
missing = set(tgt_state.keys()) - copied_names
if len(missing) > 0:
print("missing keys in state_dict:", missing)
return model
def copy_state_dict_h(state_dict, model,modelname, strip=None):
tgt_state = model.state_dict()
copied_names = set()
for name, param in state_dict.items():
name2 = name.replace('module.','')
if strip is not None and name2.startswith(strip):
name2 = name2[len(strip):]
if name2 not in tgt_state:
continue
if isinstance(param, nn.Parameter):
param = param.data
if param.size() != tgt_state[name2].size():
print('mismatch:', name2, param.size(), tgt_state[name2].size())
continue
tgt_state[name2].copy_(param)
copied_names.add(name2)
missing = set(tgt_state.keys()) - copied_names
if len(missing) > 0:
print('{} of {} is missing in {}'.format(len(missing),len(set(tgt_state.keys())),modelname))
print(modelname," has missing keys in state_dict:", missing)
return model
def copy_state_dict_add_module(state_dict, model, strip=None):
tgt_state = model.state_dict()
copied_names = set()
for name, param in state_dict.items():
name2 = 'module.' + name
if strip is not None and name2.startswith(strip):
name2 = name2[len(strip):]
if name2 not in tgt_state:
continue
if isinstance(param, nn.Parameter):
param = param.data
if param.size() != tgt_state[name2].size():
print('mismatch:', name2, param.size(), tgt_state[name2].size())
continue
tgt_state[name2].copy_(param)
copied_names.add(name2)
missing = set(tgt_state.keys()) - copied_names
if len(missing) > 0:
print(modelname," has missing keys in state_dict:", missing)
return model
def load_checkpoint(resume_path, Model):
resume_path = resume_path
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
total_steps = checkpoint['step']
epoch = checkpoint['epoch']
Model.ID_encoder = copy_state_dict(checkpoint['ID_encoder'], Model.ID_encoder)
Model.Decoder = copy_state_dict(checkpoint['Decoder'], Model.Decoder)
Model.mfcc_encoder = copy_state_dict(checkpoint['mfcc_encoder'], Model.mfcc_encoder)
Model.lip_feature_encoder = copy_state_dict(checkpoint['lip_feature_encoder'], Model.lip_feature_encoder)
Model.netD = copy_state_dict(checkpoint['netD'], Model.netD)
Model.netD_mul = copy_state_dict(checkpoint['netD_mul'], Model.netD_mul)
Model.ID_lip_discriminator = copy_state_dict(checkpoint['ID_lip_discriminator'], Model.ID_lip_discriminator)
Model.model_fusion = copy_state_dict(checkpoint['model_fusion'], Model.model_fusion)
Model.optimizer_D.load_state_dict(checkpoint['optimizer_D'])
#Model.optimizer_G.load_state_dict(checkpoint['optimizer_G'])
print("=> loaded checkpoint '{}' (step {})"
.format(resume_path, checkpoint['step']))
return Model, total_steps, epoch
else:
print("=> no checkpoint found at '{}'".format(resume_path))
def load_checkpoint_without_lip(resume_path, Model):
resume_path = resume_path
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
total_steps = checkpoint['step']
epoch = checkpoint['epoch']
Model.ID_encoder = copy_state_dict(checkpoint['ID_encoder'], Model.ID_encoder)
Model.Decoder = copy_state_dict(checkpoint['Decoder'], Model.Decoder)
Model.mfcc_encoder = copy_state_dict(checkpoint['mfcc_encoder'], Model.mfcc_encoder)
#Model.lip_feature_encoder = copy_state_dict(checkpoint['lip_feature_encoder'], Model.lip_feature_encoder)
Model.netD = copy_state_dict(checkpoint['netD'], Model.netD)
Model.netD_mul = copy_state_dict(checkpoint['netD_mul'], Model.netD_mul)
#Model.ID_lip_discriminator = copy_state_dict(checkpoint['ID_lip_discriminator'], Model.ID_lip_discriminator)
Model.model_fusion = copy_state_dict(checkpoint['model_fusion'], Model.model_fusion)
#Model.optimizer_D.load_state_dict(checkpoint['optimizer_D'])
#Model.optimizer_G.load_state_dict(checkpoint['optimizer_G'])
print("=> loaded checkpoint '{}' (step {})"
.format(resume_path, checkpoint['step']))
return Model, total_steps, epoch
else:
print("=> no checkpoint found at '{}'".format(resume_path))
def load_checkpoint_from101davs(resume_path, Model):
resume_path = resume_path
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
total_steps = checkpoint['step']
epoch = checkpoint['epoch']
Model.ID_encoder = copy_state_dict_h(checkpoint['ID_encoder'], Model.ID_encoder,'ID_encoder')
Model.Decoder = copy_state_dict_h(checkpoint['Decoder'], Model.Decoder,'Decoder')
Model.mfcc_encoder = copy_state_dict_h(checkpoint['mfcc_encoder'], Model.mfcc_encoder,'mfcc_encoder')
Model.lip_feature_encoder = copy_state_dict_h(checkpoint['lip_feature_encoder'], Model.lip_feature_encoder,'lip_feature_encoder')
Model.netD = copy_state_dict_h(checkpoint['netD'], Model.netD,'netD')
Model.netD_mul = copy_state_dict_h(checkpoint['netD_mul'], Model.netD_mul,'netD_mul')
Model.ID_lip_discriminator = copy_state_dict_h(checkpoint['ID_lip_discriminator'], Model.ID_lip_discriminator,'ID_lip_discriminator')
Model.model_fusion = copy_state_dict_h(checkpoint['model_fusion'], Model.model_fusion,'model_fusion')
#Model.optimizer_D.load_state_dict(checkpoint['optimizer_D'])
#Model.optimizer_G.load_state_dict(checkpoint['optimizer_G'])
print("=> loaded checkpoint '{}' (step {})"
.format(resume_path, checkpoint['step']))
return Model, total_steps, epoch
else:
print("=> no checkpoint found at '{}'".format(resume_path))
def load_checkpoint_from101davs_without_lip(resume_path, Model):
resume_path = resume_path
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
total_steps = checkpoint['step']
epoch = checkpoint['epoch']
Model.ID_encoder = copy_state_dict_h(checkpoint['ID_encoder'], Model.ID_encoder,'ID_encoder')
Model.Decoder = copy_state_dict_h(checkpoint['Decoder'], Model.Decoder,'Decoder')
Model.mfcc_encoder = copy_state_dict_h(checkpoint['mfcc_encoder'], Model.mfcc_encoder,'mfcc_encoder')
#Model.lip_feature_encoder = copy_state_dict_h(checkpoint['lip_feature_encoder'], Model.lip_feature_encoder,'lip_feature_encoder')
Model.netD = copy_state_dict_h(checkpoint['netD'], Model.netD,'netD')
Model.netD_mul = copy_state_dict_h(checkpoint['netD_mul'], Model.netD_mul,'netD_mul')
#Model.ID_lip_discriminator = copy_state_dict_h(checkpoint['ID_lip_discriminator'], Model.ID_lip_discriminator,'ID_lip_discriminator')
Model.model_fusion = copy_state_dict_h(checkpoint['model_fusion'], Model.model_fusion,'model_fusion')
#Model.optimizer_D.load_state_dict(checkpoint['optimizer_D'])
#Model.optimizer_G.load_state_dict(checkpoint['optimizer_G'])
print("=> loaded checkpoint '{}' (step {})"
.format(resume_path, checkpoint['step']))
return Model, total_steps, epoch
else:
print("=> no checkpoint found at '{}'".format(resume_path))
def load_checkpoint_from101davs_without_decoder(resume_path, Model):
resume_path = resume_path
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
total_steps = checkpoint['step']
epoch = checkpoint['epoch']
Model.ID_encoder = copy_state_dict_h(checkpoint['ID_encoder'], Model.ID_encoder,'ID_encoder')
#Model.Decoder = copy_state_dict_h(checkpoint['Decoder'], Model.Decoder,'Decoder')
Model.mfcc_encoder = copy_state_dict_h(checkpoint['mfcc_encoder'], Model.mfcc_encoder,'mfcc_encoder')
Model.lip_feature_encoder = copy_state_dict_h(checkpoint['lip_feature_encoder'], Model.lip_feature_encoder,'lip_feature_encoder')
Model.netD = copy_state_dict_h(checkpoint['netD'], Model.netD,'netD')
Model.netD_mul = copy_state_dict_h(checkpoint['netD_mul'], Model.netD_mul,'netD_mul')
Model.ID_lip_discriminator = copy_state_dict_h(checkpoint['ID_lip_discriminator'], Model.ID_lip_discriminator,'ID_lip_discriminator')
Model.model_fusion = copy_state_dict_h(checkpoint['model_fusion'], Model.model_fusion,'model_fusion')
Model.optimizer_D.load_state_dict(checkpoint['optimizer_D'])
#Model.optimizer_G.load_state_dict(checkpoint['optimizer_G'])
print("=> loaded checkpoint '{}' (step {})"
.format(resume_path, checkpoint['step']))
return Model, total_steps, epoch
else:
print("=> no checkpoint found at '{}'".format(resume_path))
def load_separately(opt, Model):
print("=> loading checkpoint '{}'".format(opt.id_pretrain_path))
id_pretrain = torch.load(opt.id_pretrain_path)
Model.ID_encoder = copy_state_dict(id_pretrain['model_fusion'], Model.ID_encoder)
print("=> loading checkpoint '{}'".format(opt.feature_extractor_path))
feature_extractor_check = torch.load(opt.feature_extractor_path)
Model.lip_feature_encoder = copy_state_dict(feature_extractor_check['face_encoder'], Model.lip_feature_encoder)
Model.mfcc_encoder = copy_state_dict(feature_extractor_check['mfcc_encoder'], Model.mfcc_encoder)
Model.model_fusion = copy_state_dict(feature_extractor_check['face_fusion'], Model.model_fusion)
return Model
def load_test_checkpoint(resume_path, Model):
resume_path = resume_path
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
total_steps = checkpoint['step']
epoch = checkpoint['epoch']
Model.ID_encoder = copy_state_dict(checkpoint['ID_encoder'], Model.ID_encoder)
Model.Decoder = copy_state_dict(checkpoint['Decoder'], Model.Decoder)
Model.mfcc_encoder = copy_state_dict(checkpoint['mfcc_encoder'], Model.mfcc_encoder)
Model.lip_feature_encoder = copy_state_dict(checkpoint['lip_feature_encoder'], Model.lip_feature_encoder)
print("=> loaded checkpoint '{}' (step {})"
.format(resume_path, checkpoint['step']))
return Model, total_steps, epoch
else:
print("=> no checkpoint found at '{}'".format(resume_path))
def load_test_checkpoint_nolip(resume_path, Model):
resume_path = resume_path
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
total_steps = checkpoint['step']
epoch = checkpoint['epoch']
Model.ID_encoder = copy_state_dict_add_module(checkpoint['ID_encoder'], Model.ID_encoder)
Model.Decoder = copy_state_dict_add_module(checkpoint['Decoder'], Model.Decoder)
Model.mfcc_encoder = copy_state_dict_add_module(checkpoint['mfcc_encoder'], Model.mfcc_encoder)
#Model.lip_feature_encoder = copy_state_dict(checkpoint['lip_feature_encoder'], Model.lip_feature_encoder)
print("=> loaded checkpoint '{}' (step {})"
.format(resume_path, checkpoint['step']))
return Model, total_steps, epoch
def load_test_checkpoint_combine(resume_path, davs_path, Model):
resume_path = resume_path
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
checkpoint2 = torch.load(davs_path)
total_steps = checkpoint['step']
epoch = checkpoint['epoch']
Model.ID_encoder = copy_state_dict(checkpoint2['ID_encoder'], Model.ID_encoder)
Model.Decoder = copy_state_dict(checkpoint2['Decoder'], Model.Decoder)
#Model.mfcc_encoder = copy_state_dict_add_module(checkpoint['mfcc_encoder'], Model.mfcc_encoder)
Model.mfcc_encoder = copy_state_dict(checkpoint2['mfcc_encoder'], Model.mfcc_encoder)
#Model.lip_feature_encoder = copy_state_dict(checkpoint['lip_feature_encoder'], Model.lip_feature_encoder)
print("=> loaded checkpoint '{}' (step {})"
.format(resume_path, checkpoint['step']))
return Model, total_steps, epoch
else:
print("=> no checkpoint found at '{}'".format(resume_path)) | 47.35277 | 142 | 0.683721 | 2,095 | 16,242 | 5.011456 | 0.092601 | 0.070292 | 0.074293 | 0.053338 | 0.824459 | 0.813411 | 0.798838 | 0.780932 | 0.780932 | 0.776645 | 0 | 0.006298 | 0.188647 | 16,242 | 343 | 143 | 47.35277 | 0.790408 | 0.087551 | 0 | 0.651246 | 0 | 0.007117 | 0.131387 | 0.003109 | 0 | 0 | 0 | 0 | 0 | 1 | 0.081851 | false | 0 | 0.032028 | 0 | 0.170819 | 0.13879 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f7ed6ab1bb0893e0f19fc7df71d259bba0bd3d2f | 82 | py | Python | test/__init__.py | zampanteymedio/gym-satellite-trajectory | 5bd0d951db21d2ccf8ec928707a0f8e10dd6092f | [
"Apache-2.0"
] | 2 | 2020-05-07T12:04:08.000Z | 2021-01-04T14:25:20.000Z | test/__init__.py | zampanteymedio/gym-satellite-trajectory | 5bd0d951db21d2ccf8ec928707a0f8e10dd6092f | [
"Apache-2.0"
] | null | null | null | test/__init__.py | zampanteymedio/gym-satellite-trajectory | 5bd0d951db21d2ccf8ec928707a0f8e10dd6092f | [
"Apache-2.0"
] | 1 | 2021-12-15T08:30:31.000Z | 2021-12-15T08:30:31.000Z | import gym_satellite_trajectory
gym_satellite_trajectory.register_environments()
| 20.5 | 48 | 0.914634 | 9 | 82 | 7.777778 | 0.666667 | 0.342857 | 0.628571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.04878 | 82 | 3 | 49 | 27.333333 | 0.897436 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
f7f969d3f63f785ff953f69cad230024a7cf5eae | 243 | py | Python | PyEvolv/assets/font.py | peerlator/PyEvolv | 7f5644e2ea22257f34547c9b050bc4cdd4f3bdec | [
"MIT"
] | 1 | 2018-08-02T19:42:35.000Z | 2018-08-02T19:42:35.000Z | PyEvolv/assets/font.py | peerlator/PyEvolv | 7f5644e2ea22257f34547c9b050bc4cdd4f3bdec | [
"MIT"
] | 1 | 2018-08-02T19:41:58.000Z | 2018-08-05T17:53:17.000Z | PyEvolv/assets/font.py | peerlator/PyEvolv | 7f5644e2ea22257f34547c9b050bc4cdd4f3bdec | [
"MIT"
] | null | null | null | import pygame
import PyEvolv
import os
pygame.font.init()
path = os.path.join(PyEvolv.__path__[0], 'assets', 'Arial.ttf')
FONT = pygame.font.Font(path, 20)
def get_font(size:int) -> pygame.font.Font:
return pygame.font.Font(path, size)
| 20.25 | 63 | 0.720165 | 39 | 243 | 4.358974 | 0.461538 | 0.235294 | 0.247059 | 0.211765 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014085 | 0.123457 | 243 | 11 | 64 | 22.090909 | 0.784038 | 0 | 0 | 0 | 0 | 0 | 0.061983 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.125 | false | 0 | 0.375 | 0.125 | 0.625 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
79016fa6c6014fafe5eec382b6be4da591309ba9 | 32 | py | Python | edureka.py | jatin06/learning-git | 4e531193a95f6dc8daeb5c4c7a15a2fe786583ae | [
"MIT"
] | null | null | null | edureka.py | jatin06/learning-git | 4e531193a95f6dc8daeb5c4c7a15a2fe786583ae | [
"MIT"
] | null | null | null | edureka.py | jatin06/learning-git | 4e531193a95f6dc8daeb5c4c7a15a2fe786583ae | [
"MIT"
] | null | null | null | print ("welcome to edureka!! ")
| 16 | 31 | 0.65625 | 4 | 32 | 5.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15625 | 32 | 1 | 32 | 32 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0.65625 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
f708b00b0a9edc4940fa8641402e3307b8e92005 | 21,917 | py | Python | motion/components/structural.py | TUM-AAS/motron | 2f8800d1d6e297fc4baab555ceb2d37f55841406 | [
"MIT"
] | null | null | null | motion/components/structural.py | TUM-AAS/motron | 2f8800d1d6e297fc4baab555ceb2d37f55841406 | [
"MIT"
] | null | null | null | motion/components/structural.py | TUM-AAS/motron | 2f8800d1d6e297fc4baab555ceb2d37f55841406 | [
"MIT"
] | null | null | null | from typing import Tuple, Optional, List, Union
import torch
from torch.nn import *
import math
def gmm(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
return torch.einsum('ndo,bnd->bno', w, x)
class GraphLinear(Module):
def __init__(self, in_features: int, out_features: int):
super().__init__()
self.in_features = in_features
self.out_features = out_features
def reset_parameters(self) -> None:
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
#stdv = 1. / math.sqrt(self.weight.size(1))
#self.weight.data.uniform_(-stdv, stdv)
#if self.learn_influence:
# self.G.data.uniform_(-stdv, stdv)
if len(self.weight.shape) == 3:
self.weight.data[1:] = self.weight.data[0]
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def forward(self, input: torch.Tensor, g: Optional[torch.Tensor] = None) -> torch.Tensor:
if g is None and self.learn_influence:
g = torch.nn.functional.normalize(self.G, p=1., dim=1)
#g = torch.softmax(self.G, dim=1)
elif g is None:
g = self.G
w = self.weight[self.node_type_index]
output = self.mm(input, w.transpose(-2, -1))
if self.bias is not None:
bias = self.bias[self.node_type_index]
output += bias
output = g.matmul(output)
return output
class DynamicGraphLinear(GraphLinear):
def __init__(self, num_node_types: int = 1, *args):
super().__init__(*args)
def forward(self, input: torch.Tensor, g: torch.Tensor = None, t: torch.Tensor = None) -> torch.Tensor:
assert g is not None or t is not None, "Either Graph Influence Matrix or Node Type Vector is needed"
if g is None:
g = self.G[t][:, t]
return super().forward(input, g)
class StaticGraphLinear(GraphLinear):
def __init__(self, *args, bias: bool = True, num_nodes: int = None, graph_influence: Union[torch.Tensor, Parameter] = None,
learn_influence: bool = False, node_types: torch.Tensor = None, weights_per_type: bool = False):
"""
:param in_features: Size of each input sample
:param out_features: Size of each output sample
:param num_nodes: Number of nodes.
:param graph_influence: Graph Influence Matrix
:param learn_influence: If set to ``False``, the layer will not learn an the Graph Influence Matrix.
:param node_types: List of Type for each node. All nodes of same type will share weights.
Default: All nodes have unique types.
:param weights_per_type: If set to ``False``, the layer will not learn weights for each node type.
:param bias: If set to ``False``, the layer will not learn an additive bias.
"""
super().__init__(*args)
self.learn_influence = learn_influence
if graph_influence is not None:
assert num_nodes == graph_influence.shape[0] or num_nodes is None, 'Number of Nodes or Graph Influence Matrix has to be given.'
num_nodes = graph_influence.shape[0]
if type(graph_influence) is Parameter:
assert learn_influence, "Graph Influence Matrix is a Parameter, therefore it must be learnable."
self.G = graph_influence
elif learn_influence:
self.G = Parameter(graph_influence)
else:
self.register_buffer('G', graph_influence)
else:
assert num_nodes, 'Number of Nodes or Graph Influence Matrix has to be given.'
eye_influence = torch.eye(num_nodes, num_nodes)
if learn_influence:
self.G = Parameter(eye_influence)
else:
self.register_buffer('G', eye_influence)
if weights_per_type and node_types is None:
node_types = torch.tensor([i for i in range(num_nodes)])
if node_types is not None:
num_node_types = node_types.max() + 1
self.weight = Parameter(torch.Tensor(num_node_types, self.out_features, self.in_features))
self.mm = gmm
self.node_type_index = node_types
else:
self.weight = Parameter(torch.Tensor(self.out_features, self.in_features))
self.mm = torch.matmul
self.node_type_index = None
if bias:
if node_types is not None:
self.bias = Parameter(torch.Tensor(num_node_types, self.out_features))
else:
self.bias = Parameter(torch.Tensor(self.out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
GraphLSTMState = Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]
class BN(Module):
def __init__(self, num_nodes, num_features):
super().__init__()
self.num_nodes = num_nodes
self.num_features = num_features
self.bn = BatchNorm1d(num_nodes * num_features)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.bn(x.view(-1, self.num_nodes * self.num_features)).view(-1, self.num_nodes, self.num_features)
class LinearX(Module):
def __init__(self):
super().__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
return input
class StaticGraphLSTMCell_(Module):
def __init__(self, input_size: int, hidden_size: int, num_nodes: int = None, dropout: float = 0.,
recurrent_dropout: float = 0., graph_influence: Union[torch.Tensor, Parameter] = None,
learn_influence: bool = False, additive_graph_influence: Union[torch.Tensor, Parameter] = None,
learn_additive_graph_influence: bool = False, node_types: torch.Tensor = None,
weights_per_type: bool = False, clockwork: bool = False, bias: bool = True):
"""
:param input_size: The number of expected features in the input `x`
:param hidden_size: The number of features in the hidden state `h`
:param num_nodes:
:param dropout:
:param recurrent_dropout:
:param graph_influence:
:param learn_influence:
:param additive_graph_influence:
:param learn_additive_graph_influence:
:param node_types:
:param weights_per_type:
:param bias:
"""
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.learn_influence = learn_influence
self.learn_additive_graph_influence = learn_additive_graph_influence
if graph_influence is not None:
assert num_nodes == graph_influence.shape[0] or num_nodes is None, 'Number of Nodes or Graph Influence Matrix has to be given.'
num_nodes = graph_influence.shape[0]
if type(graph_influence) is Parameter:
assert learn_influence, "Graph Influence Matrix is a Parameter, therefore it must be learnable."
self.G = graph_influence
elif learn_influence:
self.G = Parameter(graph_influence)
else:
self.register_buffer('G', graph_influence)
else:
assert num_nodes, 'Number of Nodes or Graph Influence Matrix has to be given.'
eye_influence = torch.eye(num_nodes, num_nodes)
if learn_influence:
self.G = Parameter(eye_influence)
else:
self.register_buffer('G', eye_influence)
if additive_graph_influence is not None:
if type(additive_graph_influence) is Parameter:
self.G_add = additive_graph_influence
elif learn_additive_graph_influence:
self.G_add = Parameter(additive_graph_influence)
else:
self.register_buffer('G_add', additive_graph_influence)
else:
if learn_additive_graph_influence:
self.G_add = Parameter(torch.zeros_like(self.G))
else:
self.G_add = 0.
if weights_per_type and node_types is None:
node_types = torch.tensor([i for i in range(num_nodes)])
if node_types is not None:
num_node_types = node_types.max() + 1
self.weight_ih = Parameter(torch.Tensor(num_node_types, 4 * hidden_size, input_size))
self.weight_hh = Parameter(torch.Tensor(num_node_types, 4 * hidden_size, hidden_size))
self.mm = gmm
self.register_buffer('node_type_index', node_types)
else:
self.weight_ih = Parameter(torch.Tensor(4 * hidden_size, input_size))
self.weight_hh = Parameter(torch.Tensor(4 * hidden_size, hidden_size))
self.mm = torch.matmul
self.register_buffer('node_type_index', None)
if bias:
if node_types is not None:
self.bias_ih = Parameter(torch.Tensor(num_node_types, 4 * hidden_size))
self.bias_hh = Parameter(torch.Tensor(num_node_types, 4 * hidden_size))
else:
self.bias_ih = Parameter(torch.Tensor(4 * hidden_size))
self.bias_hh = Parameter(torch.Tensor(4 * hidden_size))
else:
self.bias_ih = None
self.bias_hh = None
self.clockwork = clockwork
if clockwork:
phase = torch.arange(0., hidden_size)
phase = phase - phase.min()
phase = (phase / phase.max()) * 8.
phase += 1.
phase = torch.floor(phase)
self.register_buffer('phase', phase)
else:
phase = torch.ones(hidden_size)
self.register_buffer('phase', phase)
self.dropout = Dropout(dropout)
self.r_dropout = Dropout(recurrent_dropout)
self.num_nodes = num_nodes
self.init_weights()
def init_weights(self):
stdv = 1.0 / math.sqrt(self.hidden_size)
for weight in self.parameters():
if weight is self.G:
continue
if weight is self.G_add:
continue
weight.data.uniform_(-stdv, stdv)
if weight is self.weight_hh or weight is self.weight_ih and len(self.weight_ih.shape) == 3:
weight.data[1:] = weight.data[0]
def forward(self, input: torch.Tensor, state: GraphLSTMState, t: int = 0) -> Tuple[torch.Tensor, GraphLSTMState]:
hx, cx, gx = state
if hx is None:
hx = torch.zeros(input.shape[0], self.num_nodes, self.hidden_size, dtype=input.dtype, device=input.device)
if cx is None:
cx = torch.zeros(input.shape[0], self.num_nodes, self.hidden_size, dtype=input.dtype, device=input.device)
if gx is None and self.learn_influence:
gx = torch.nn.functional.normalize(self.G, p=1., dim=1)
#gx = torch.softmax(self.G, dim=1)
elif gx is None:
gx = self.G
hx = self.r_dropout(hx)
weight_ih = self.weight_ih[self.node_type_index]
weight_hh = self.weight_hh[self.node_type_index]
if self.bias_hh is not None:
bias_hh = self.bias_hh[self.node_type_index]
else:
bias_hh = 0.
c_mask = (torch.remainder(torch.tensor(t + 1., device=input.device), self.phase) < 0.01).type_as(cx)
gates = (self.dropout(self.mm(input, weight_ih.transpose(-2, -1))) +
self.mm(hx, weight_hh.transpose(-2, -1)) + bias_hh)
gates = torch.matmul(gx, gates)
ingate, forgetgate, cellgate, outgate = gates.chunk(4, 2)
ingate = torch.sigmoid(ingate)
forgetgate = torch.sigmoid(forgetgate)
cellgate = torch.tanh(cellgate)
outgate = torch.sigmoid(outgate)
cy = c_mask * ((forgetgate * cx) + (ingate * cellgate)) + (1 - c_mask) * cx
hy = outgate * torch.tanh(cy)
gx = gx + self.G_add
if self.learn_influence or self.learn_additive_graph_influence:
gx = torch.nn.functional.normalize(gx, p=1., dim=1)
#gx = torch.softmax(gx, dim=1)
return hy, (hy, cy, gx)
class StaticGraphLSTM_(Module):
def __init__(self, input_size: int, hidden_size: int, num_layers: int = 1, layer_dropout: float = 0.0, **kwargs):
super().__init__()
self.layers = ModuleList([StaticGraphLSTMCell_(input_size, hidden_size, **kwargs)]
+ [StaticGraphLSTMCell_(hidden_size, hidden_size, **kwargs) for _ in range(num_layers - 1)])
self.dropout = Dropout(layer_dropout)
def forward(self, input: torch.Tensor, states: Optional[List[GraphLSTMState]] = None, t_i: int = 0) -> Tuple[torch.Tensor, List[GraphLSTMState]]:
if states is None:
n: Optional[torch.Tensor] = None
states = [(n, n, n)] * len(self.layers)
output_states: List[GraphLSTMState] = []
output = input
i = 0
for rnn_layer in self.layers:
state = states[i]
inputs = output.unbind(1)
outputs: List[torch.Tensor] = []
for t, input in enumerate(inputs):
out, state = rnn_layer(input, state, t_i+t)
outputs += [out]
output = torch.stack(outputs, dim=1)
output = self.dropout(output)
output_states += [state]
i += 1
return output, output_states
def StaticGraphLSTM(*args, **kwargs):
return torch.jit.script(StaticGraphLSTM_(*args, **kwargs))
GraphGRUState = Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]
class StaticGraphGRUCell_(Module):
def __init__(self, input_size: int, hidden_size: int, num_nodes: int = None, dropout: float = 0.,
recurrent_dropout: float = 0., graph_influence: Union[torch.Tensor, Parameter] = None,
learn_influence: bool = False, additive_graph_influence: Union[torch.Tensor, Parameter] = None,
learn_additive_graph_influence: bool = False, node_types: torch.Tensor = None,
weights_per_type: bool = False, clockwork: bool = False, bias: bool = True):
"""
:param input_size: The number of expected features in the input `x`
:param hidden_size: The number of features in the hidden state `h`
:param num_nodes:
:param dropout:
:param recurrent_dropout:
:param graph_influence:
:param learn_influence:
:param additive_graph_influence:
:param learn_additive_graph_influence:
:param node_types:
:param weights_per_type:
:param bias:
"""
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.learn_influence = learn_influence
self.learn_additive_graph_influence = learn_additive_graph_influence
if graph_influence is not None:
assert num_nodes == graph_influence.shape[0] or num_nodes is None, 'Number of Nodes or Graph Influence Matrix has to be given.'
num_nodes = graph_influence.shape[0]
if type(graph_influence) is Parameter:
assert learn_influence, "Graph Influence Matrix is a Parameter, therefore it must be learnable."
self.G = graph_influence
elif learn_influence:
self.G = Parameter(graph_influence)
else:
self.register_buffer('G', graph_influence)
else:
assert num_nodes, 'Number of Nodes or Graph Influence Matrix has to be given.'
eye_influence = torch.eye(num_nodes, num_nodes)
if learn_influence:
self.G = Parameter(eye_influence)
else:
self.register_buffer('G', eye_influence)
if additive_graph_influence is not None:
if type(additive_graph_influence) is Parameter:
self.G_add = additive_graph_influence
elif learn_additive_graph_influence:
self.G_add = Parameter(additive_graph_influence)
else:
self.register_buffer('G_add', additive_graph_influence)
else:
if learn_additive_graph_influence:
self.G_add = Parameter(torch.zeros_like(self.G))
else:
self.G_add = 0.
if weights_per_type and node_types is None:
node_types = torch.tensor([i for i in range(num_nodes)])
if node_types is not None:
num_node_types = node_types.max() + 1
self.weight_ih = Parameter(torch.Tensor(num_node_types, 3 * hidden_size, input_size))
self.weight_hh = Parameter(torch.Tensor(num_node_types, 3 * hidden_size, hidden_size))
self.mm = gmm
self.register_buffer('node_type_index', node_types)
else:
self.weight_ih = Parameter(torch.Tensor(3 * hidden_size, input_size))
self.weight_hh = Parameter(torch.Tensor(3 * hidden_size, hidden_size))
self.mm = torch.matmul
self.register_buffer('node_type_index', None)
if bias:
if node_types is not None:
self.bias_ih = Parameter(torch.Tensor(num_node_types, 3 * hidden_size))
self.bias_hh = Parameter(torch.Tensor(num_node_types, 3 * hidden_size))
else:
self.bias_ih = Parameter(torch.Tensor(3 * hidden_size))
self.bias_hh = Parameter(torch.Tensor(3 * hidden_size))
else:
self.bias_ih = None
self.bias_hh = None
self.clockwork = clockwork
if clockwork:
phase = torch.arange(0., hidden_size)
phase = phase - phase.min()
phase = (phase / phase.max()) * 8.
phase += 1.
phase = torch.floor(phase)
self.register_buffer('phase', phase)
else:
phase = torch.ones(hidden_size)
self.register_buffer('phase', phase)
self.dropout = Dropout(dropout)
self.r_dropout = Dropout(recurrent_dropout)
self.num_nodes = num_nodes
self.init_weights()
def init_weights(self):
stdv = 1.0 / math.sqrt(self.hidden_size)
for weight in self.parameters():
if weight is self.G:
continue
if weight is self.G_add:
continue
weight.data.uniform_(-stdv, stdv)
#if weight is self.weight_hh or weight is self.weight_ih and len(self.weight_ih.shape) == 3:
# weight.data[1:] = weight.data[0]
def forward(self, input: torch.Tensor, state: GraphGRUState, t: int = 0) -> Tuple[torch.Tensor, GraphGRUState]:
hx, gx = state
if hx is None:
hx = torch.zeros(input.shape[0], self.num_nodes, self.hidden_size, dtype=input.dtype, device=input.device)
if gx is None and self.learn_influence:
gx = torch.nn.functional.normalize(self.G, p=1., dim=1)
#gx = torch.softmax(self.G, dim=1)
elif gx is None:
gx = self.G
hx = self.r_dropout(hx)
weight_ih = self.weight_ih[self.node_type_index]
weight_hh = self.weight_hh[self.node_type_index]
if self.bias_hh is not None:
bias_hh = self.bias_hh[self.node_type_index]
else:
bias_hh = 0.
if self.bias_ih is not None:
bias_ih = self.bias_ih[self.node_type_index]
else:
bias_ih = 0.
c_mask = (torch.remainder(torch.tensor(t + 1., device=input.device), self.phase) < 0.01).type_as(hx)
x_results = self.dropout(self.mm(input, weight_ih.transpose(-2, -1))) + bias_ih
h_results = self.mm(hx, weight_hh.transpose(-2, -1)) + bias_hh
x_results = torch.matmul(gx, x_results)
h_results = torch.matmul(gx, h_results)
i_r, i_z, i_n = x_results.chunk(3, 2)
h_r, h_z, h_n = h_results.chunk(3, 2)
r = torch.sigmoid(i_r + h_r)
z = torch.sigmoid(i_z + h_z)
n = torch.tanh(i_n + r * h_n)
hy = n - torch.mul(n, z) + torch.mul(z, hx)
hy = c_mask * hy + (1 - c_mask) * hx
gx = gx + self.G_add
if self.learn_influence or self.learn_additive_graph_influence:
gx = torch.nn.functional.normalize(gx, p=1., dim=1)
#gx = torch.softmax(gx, dim=1)
return hy, (hy, gx)
class StaticGraphGRU_(Module):
def __init__(self, input_size: int, hidden_size: int, num_layers: int = 1, layer_dropout: float = 0.0, **kwargs):
super().__init__()
self.layers = ModuleList([StaticGraphGRUCell_(input_size, hidden_size, **kwargs)]
+ [StaticGraphGRUCell_(hidden_size, hidden_size, **kwargs) for _ in range(num_layers - 1)])
self.dropout = Dropout(layer_dropout)
def forward(self, input: torch.Tensor, states: Optional[List[GraphGRUState]] = None, t_i: int = 0) -> Tuple[torch.Tensor, List[GraphGRUState]]:
if states is None:
n: Optional[torch.Tensor] = None
states = [(n, n)] * len(self.layers)
output_states: List[GraphGRUState] = []
output = input
i = 0
for rnn_layer in self.layers:
state = states[i]
inputs = output.unbind(1)
outputs: List[torch.Tensor] = []
for t, input in enumerate(inputs):
out, state = rnn_layer(input, state, t_i+t)
outputs += [out]
output = torch.stack(outputs, dim=1)
output = self.dropout(output)
output_states += [state]
i += 1
return output, output_states
def StaticGraphGRU(*args, **kwargs):
return torch.jit.script(StaticGraphGRU_(*args, **kwargs))
| 42.067179 | 149 | 0.60793 | 2,823 | 21,917 | 4.505136 | 0.069075 | 0.073754 | 0.048435 | 0.029722 | 0.821591 | 0.791634 | 0.763799 | 0.747602 | 0.730775 | 0.704356 | 0 | 0.008069 | 0.293197 | 21,917 | 520 | 150 | 42.148077 | 0.812924 | 0.080166 | 0 | 0.671795 | 0 | 0 | 0.036748 | 0 | 0 | 0 | 0 | 0 | 0.025641 | 1 | 0.058974 | false | 0 | 0.010256 | 0.012821 | 0.120513 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f70d94efdaf5515a4cd7eccef6c077140b0a5904 | 24 | py | Python | sitgan/data/__init__.py | clintonjwang/sitgan | 05210ec13073bcca9b4dbff798fb626d963082dc | [
"MIT"
] | null | null | null | sitgan/data/__init__.py | clintonjwang/sitgan | 05210ec13073bcca9b4dbff798fb626d963082dc | [
"MIT"
] | null | null | null | sitgan/data/__init__.py | clintonjwang/sitgan | 05210ec13073bcca9b4dbff798fb626d963082dc | [
"MIT"
] | null | null | null | from . import transforms | 24 | 24 | 0.833333 | 3 | 24 | 6.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 24 | 1 | 24 | 24 | 0.952381 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f71454f89dd5ee3d75234b5625c7636f0d8d8344 | 99 | py | Python | vecino/__init__.py | sniperkit/snk.fork.vecino | a140171795e68fb7c9e26a72a585bd6aeb4e35a9 | [
"Apache-2.0"
] | null | null | null | vecino/__init__.py | sniperkit/snk.fork.vecino | a140171795e68fb7c9e26a72a585bd6aeb4e35a9 | [
"Apache-2.0"
] | null | null | null | vecino/__init__.py | sniperkit/snk.fork.vecino | a140171795e68fb7c9e26a72a585bd6aeb4e35a9 | [
"Apache-2.0"
] | null | null | null | from vecino.similar_repositories import SimilarRepositories
from vecino.__main__ import initialize
| 33 | 59 | 0.89899 | 11 | 99 | 7.636364 | 0.727273 | 0.238095 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.080808 | 99 | 2 | 60 | 49.5 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f76ec68c45d675b6309efe38899c12a7eeb80587 | 22,566 | py | Python | tunnistamo/attribute-maps/basic.py | seitztimo/tunnistamo | 666601489d38ab168f9a2e2020f7441c233282a9 | [
"MIT"
] | 8 | 2018-11-13T06:05:07.000Z | 2021-09-18T22:01:52.000Z | tunnistamo/attribute-maps/basic.py | seitztimo/tunnistamo | 666601489d38ab168f9a2e2020f7441c233282a9 | [
"MIT"
] | 185 | 2017-06-08T12:48:47.000Z | 2022-03-22T08:26:36.000Z | tunnistamo/attribute-maps/basic.py | seitztimo/tunnistamo | 666601489d38ab168f9a2e2020f7441c233282a9 | [
"MIT"
] | 19 | 2017-06-08T13:02:37.000Z | 2021-02-15T13:10:35.000Z |
MAP = {
"identifier": "urn:oasis:names:tc:SAML:2.0:attrname-format:basic",
"fro": {
'urn:mace:dir:attribute-def:aRecord': 'aRecord',
'urn:mace:dir:attribute-def:aliasedEntryName': 'aliasedEntryName',
'urn:mace:dir:attribute-def:aliasedObjectName': 'aliasedObjectName',
'urn:mace:dir:attribute-def:associatedDomain': 'associatedDomain',
'urn:mace:dir:attribute-def:associatedName': 'associatedName',
'urn:mace:dir:attribute-def:audio': 'audio',
'urn:mace:dir:attribute-def:authorityRevocationList': 'authorityRevocationList',
'urn:mace:dir:attribute-def:buildingName': 'buildingName',
'urn:mace:dir:attribute-def:businessCategory': 'businessCategory',
'urn:mace:dir:attribute-def:c': 'c',
'urn:mace:dir:attribute-def:cACertificate': 'cACertificate',
'urn:mace:dir:attribute-def:cNAMERecord': 'cNAMERecord',
'urn:mace:dir:attribute-def:carLicense': 'carLicense',
'urn:mace:dir:attribute-def:certificateRevocationList': 'certificateRevocationList',
'urn:mace:dir:attribute-def:cn': 'cn',
'urn:mace:dir:attribute-def:co': 'co',
'urn:mace:dir:attribute-def:commonName': 'commonName',
'urn:mace:dir:attribute-def:countryName': 'countryName',
'urn:mace:dir:attribute-def:crossCertificatePair': 'crossCertificatePair',
'urn:mace:dir:attribute-def:dITRedirect': 'dITRedirect',
'urn:mace:dir:attribute-def:dSAQuality': 'dSAQuality',
'urn:mace:dir:attribute-def:dc': 'dc',
'urn:mace:dir:attribute-def:deltaRevocationList': 'deltaRevocationList',
'urn:mace:dir:attribute-def:departmentNumber': 'departmentNumber',
'urn:mace:dir:attribute-def:description': 'description',
'urn:mace:dir:attribute-def:destinationIndicator': 'destinationIndicator',
'urn:mace:dir:attribute-def:displayName': 'displayName',
'urn:mace:dir:attribute-def:distinguishedName': 'distinguishedName',
'urn:mace:dir:attribute-def:dmdName': 'dmdName',
'urn:mace:dir:attribute-def:dnQualifier': 'dnQualifier',
'urn:mace:dir:attribute-def:documentAuthor': 'documentAuthor',
'urn:mace:dir:attribute-def:documentIdentifier': 'documentIdentifier',
'urn:mace:dir:attribute-def:documentLocation': 'documentLocation',
'urn:mace:dir:attribute-def:documentPublisher': 'documentPublisher',
'urn:mace:dir:attribute-def:documentTitle': 'documentTitle',
'urn:mace:dir:attribute-def:documentVersion': 'documentVersion',
'urn:mace:dir:attribute-def:domainComponent': 'domainComponent',
'urn:mace:dir:attribute-def:drink': 'drink',
'urn:mace:dir:attribute-def:eduOrgHomePageURI': 'eduOrgHomePageURI',
'urn:mace:dir:attribute-def:eduOrgIdentityAuthNPolicyURI': 'eduOrgIdentityAuthNPolicyURI',
'urn:mace:dir:attribute-def:eduOrgLegalName': 'eduOrgLegalName',
'urn:mace:dir:attribute-def:eduOrgSuperiorURI': 'eduOrgSuperiorURI',
'urn:mace:dir:attribute-def:eduOrgWhitePagesURI': 'eduOrgWhitePagesURI',
'urn:mace:dir:attribute-def:eduPersonAffiliation': 'eduPersonAffiliation',
'urn:mace:dir:attribute-def:eduPersonEntitlement': 'eduPersonEntitlement',
'urn:mace:dir:attribute-def:eduPersonNickname': 'eduPersonNickname',
'urn:mace:dir:attribute-def:eduPersonOrgDN': 'eduPersonOrgDN',
'urn:mace:dir:attribute-def:eduPersonOrgUnitDN': 'eduPersonOrgUnitDN',
'urn:mace:dir:attribute-def:eduPersonPrimaryAffiliation': 'eduPersonPrimaryAffiliation',
'urn:mace:dir:attribute-def:eduPersonPrimaryOrgUnitDN': 'eduPersonPrimaryOrgUnitDN',
'urn:mace:dir:attribute-def:eduPersonPrincipalName': 'eduPersonPrincipalName',
'urn:mace:dir:attribute-def:eduPersonScopedAffiliation': 'eduPersonScopedAffiliation',
'urn:mace:dir:attribute-def:eduPersonTargetedID': 'eduPersonTargetedID',
'urn:mace:dir:attribute-def:email': 'email',
'urn:mace:dir:attribute-def:emailAddress': 'emailAddress',
'urn:mace:dir:attribute-def:employeeNumber': 'employeeNumber',
'urn:mace:dir:attribute-def:employeeType': 'employeeType',
'urn:mace:dir:attribute-def:enhancedSearchGuide': 'enhancedSearchGuide',
'urn:mace:dir:attribute-def:facsimileTelephoneNumber': 'facsimileTelephoneNumber',
'urn:mace:dir:attribute-def:favouriteDrink': 'favouriteDrink',
'urn:mace:dir:attribute-def:fax': 'fax',
'urn:mace:dir:attribute-def:federationFeideSchemaVersion': 'federationFeideSchemaVersion',
'urn:mace:dir:attribute-def:friendlyCountryName': 'friendlyCountryName',
'urn:mace:dir:attribute-def:generationQualifier': 'generationQualifier',
'urn:mace:dir:attribute-def:givenName': 'givenName',
'urn:mace:dir:attribute-def:gn': 'gn',
'urn:mace:dir:attribute-def:homePhone': 'homePhone',
'urn:mace:dir:attribute-def:homePostalAddress': 'homePostalAddress',
'urn:mace:dir:attribute-def:homeTelephoneNumber': 'homeTelephoneNumber',
'urn:mace:dir:attribute-def:host': 'host',
'urn:mace:dir:attribute-def:houseIdentifier': 'houseIdentifier',
'urn:mace:dir:attribute-def:info': 'info',
'urn:mace:dir:attribute-def:initials': 'initials',
'urn:mace:dir:attribute-def:internationaliSDNNumber': 'internationaliSDNNumber',
'urn:mace:dir:attribute-def:janetMailbox': 'janetMailbox',
'urn:mace:dir:attribute-def:jpegPhoto': 'jpegPhoto',
'urn:mace:dir:attribute-def:knowledgeInformation': 'knowledgeInformation',
'urn:mace:dir:attribute-def:l': 'l',
'urn:mace:dir:attribute-def:labeledURI': 'labeledURI',
'urn:mace:dir:attribute-def:localityName': 'localityName',
'urn:mace:dir:attribute-def:mDRecord': 'mDRecord',
'urn:mace:dir:attribute-def:mXRecord': 'mXRecord',
'urn:mace:dir:attribute-def:mail': 'mail',
'urn:mace:dir:attribute-def:mailPreferenceOption': 'mailPreferenceOption',
'urn:mace:dir:attribute-def:manager': 'manager',
'urn:mace:dir:attribute-def:member': 'member',
'urn:mace:dir:attribute-def:mobile': 'mobile',
'urn:mace:dir:attribute-def:mobileTelephoneNumber': 'mobileTelephoneNumber',
'urn:mace:dir:attribute-def:nSRecord': 'nSRecord',
'urn:mace:dir:attribute-def:name': 'name',
'urn:mace:dir:attribute-def:norEduOrgAcronym': 'norEduOrgAcronym',
'urn:mace:dir:attribute-def:norEduOrgNIN': 'norEduOrgNIN',
'urn:mace:dir:attribute-def:norEduOrgSchemaVersion': 'norEduOrgSchemaVersion',
'urn:mace:dir:attribute-def:norEduOrgUniqueIdentifier': 'norEduOrgUniqueIdentifier',
'urn:mace:dir:attribute-def:norEduOrgUniqueNumber': 'norEduOrgUniqueNumber',
'urn:mace:dir:attribute-def:norEduOrgUnitUniqueIdentifier': 'norEduOrgUnitUniqueIdentifier',
'urn:mace:dir:attribute-def:norEduOrgUnitUniqueNumber': 'norEduOrgUnitUniqueNumber',
'urn:mace:dir:attribute-def:norEduPersonBirthDate': 'norEduPersonBirthDate',
'urn:mace:dir:attribute-def:norEduPersonLIN': 'norEduPersonLIN',
'urn:mace:dir:attribute-def:norEduPersonNIN': 'norEduPersonNIN',
'urn:mace:dir:attribute-def:o': 'o',
'urn:mace:dir:attribute-def:objectClass': 'objectClass',
'urn:mace:dir:attribute-def:organizationName': 'organizationName',
'urn:mace:dir:attribute-def:organizationalStatus': 'organizationalStatus',
'urn:mace:dir:attribute-def:organizationalUnitName': 'organizationalUnitName',
'urn:mace:dir:attribute-def:otherMailbox': 'otherMailbox',
'urn:mace:dir:attribute-def:ou': 'ou',
'urn:mace:dir:attribute-def:owner': 'owner',
'urn:mace:dir:attribute-def:pager': 'pager',
'urn:mace:dir:attribute-def:pagerTelephoneNumber': 'pagerTelephoneNumber',
'urn:mace:dir:attribute-def:personalSignature': 'personalSignature',
'urn:mace:dir:attribute-def:personalTitle': 'personalTitle',
'urn:mace:dir:attribute-def:photo': 'photo',
'urn:mace:dir:attribute-def:physicalDeliveryOfficeName': 'physicalDeliveryOfficeName',
'urn:mace:dir:attribute-def:pkcs9email': 'pkcs9email',
'urn:mace:dir:attribute-def:postOfficeBox': 'postOfficeBox',
'urn:mace:dir:attribute-def:postalAddress': 'postalAddress',
'urn:mace:dir:attribute-def:postalCode': 'postalCode',
'urn:mace:dir:attribute-def:preferredDeliveryMethod': 'preferredDeliveryMethod',
'urn:mace:dir:attribute-def:preferredLanguage': 'preferredLanguage',
'urn:mace:dir:attribute-def:presentationAddress': 'presentationAddress',
'urn:mace:dir:attribute-def:protocolInformation': 'protocolInformation',
'urn:mace:dir:attribute-def:pseudonym': 'pseudonym',
'urn:mace:dir:attribute-def:registeredAddress': 'registeredAddress',
'urn:mace:dir:attribute-def:rfc822Mailbox': 'rfc822Mailbox',
'urn:mace:dir:attribute-def:roleOccupant': 'roleOccupant',
'urn:mace:dir:attribute-def:roomNumber': 'roomNumber',
'urn:mace:dir:attribute-def:sOARecord': 'sOARecord',
'urn:mace:dir:attribute-def:searchGuide': 'searchGuide',
'urn:mace:dir:attribute-def:secretary': 'secretary',
'urn:mace:dir:attribute-def:seeAlso': 'seeAlso',
'urn:mace:dir:attribute-def:serialNumber': 'serialNumber',
'urn:mace:dir:attribute-def:singleLevelQuality': 'singleLevelQuality',
'urn:mace:dir:attribute-def:sn': 'sn',
'urn:mace:dir:attribute-def:st': 'st',
'urn:mace:dir:attribute-def:stateOrProvinceName': 'stateOrProvinceName',
'urn:mace:dir:attribute-def:street': 'street',
'urn:mace:dir:attribute-def:streetAddress': 'streetAddress',
'urn:mace:dir:attribute-def:subtreeMaximumQuality': 'subtreeMaximumQuality',
'urn:mace:dir:attribute-def:subtreeMinimumQuality': 'subtreeMinimumQuality',
'urn:mace:dir:attribute-def:supportedAlgorithms': 'supportedAlgorithms',
'urn:mace:dir:attribute-def:supportedApplicationContext': 'supportedApplicationContext',
'urn:mace:dir:attribute-def:surname': 'surname',
'urn:mace:dir:attribute-def:telephoneNumber': 'telephoneNumber',
'urn:mace:dir:attribute-def:teletexTerminalIdentifier': 'teletexTerminalIdentifier',
'urn:mace:dir:attribute-def:telexNumber': 'telexNumber',
'urn:mace:dir:attribute-def:textEncodedORAddress': 'textEncodedORAddress',
'urn:mace:dir:attribute-def:title': 'title',
'urn:mace:dir:attribute-def:uid': 'uid',
'urn:mace:dir:attribute-def:uniqueIdentifier': 'uniqueIdentifier',
'urn:mace:dir:attribute-def:uniqueMember': 'uniqueMember',
'urn:mace:dir:attribute-def:userCertificate': 'userCertificate',
'urn:mace:dir:attribute-def:userClass': 'userClass',
'urn:mace:dir:attribute-def:userPKCS12': 'userPKCS12',
'urn:mace:dir:attribute-def:userPassword': 'userPassword',
'urn:mace:dir:attribute-def:userSMIMECertificate': 'userSMIMECertificate',
'urn:mace:dir:attribute-def:userid': 'userid',
'urn:mace:dir:attribute-def:x121Address': 'x121Address',
'urn:mace:dir:attribute-def:x500UniqueIdentifier': 'x500UniqueIdentifier',
},
"to": {
'aRecord': 'urn:mace:dir:attribute-def:aRecord',
'aliasedEntryName': 'urn:mace:dir:attribute-def:aliasedEntryName',
'aliasedObjectName': 'urn:mace:dir:attribute-def:aliasedObjectName',
'associatedDomain': 'urn:mace:dir:attribute-def:associatedDomain',
'associatedName': 'urn:mace:dir:attribute-def:associatedName',
'audio': 'urn:mace:dir:attribute-def:audio',
'authorityRevocationList': 'urn:mace:dir:attribute-def:authorityRevocationList',
'buildingName': 'urn:mace:dir:attribute-def:buildingName',
'businessCategory': 'urn:mace:dir:attribute-def:businessCategory',
'c': 'urn:mace:dir:attribute-def:c',
'cACertificate': 'urn:mace:dir:attribute-def:cACertificate',
'cNAMERecord': 'urn:mace:dir:attribute-def:cNAMERecord',
'carLicense': 'urn:mace:dir:attribute-def:carLicense',
'certificateRevocationList': 'urn:mace:dir:attribute-def:certificateRevocationList',
'cn': 'urn:mace:dir:attribute-def:cn',
'co': 'urn:mace:dir:attribute-def:co',
'commonName': 'urn:mace:dir:attribute-def:commonName',
'countryName': 'urn:mace:dir:attribute-def:countryName',
'crossCertificatePair': 'urn:mace:dir:attribute-def:crossCertificatePair',
'dITRedirect': 'urn:mace:dir:attribute-def:dITRedirect',
'dSAQuality': 'urn:mace:dir:attribute-def:dSAQuality',
'dc': 'urn:mace:dir:attribute-def:dc',
'deltaRevocationList': 'urn:mace:dir:attribute-def:deltaRevocationList',
'departmentNumber': 'urn:mace:dir:attribute-def:departmentNumber',
'description': 'urn:mace:dir:attribute-def:description',
'destinationIndicator': 'urn:mace:dir:attribute-def:destinationIndicator',
'displayName': 'urn:mace:dir:attribute-def:displayName',
'distinguishedName': 'urn:mace:dir:attribute-def:distinguishedName',
'dmdName': 'urn:mace:dir:attribute-def:dmdName',
'dnQualifier': 'urn:mace:dir:attribute-def:dnQualifier',
'documentAuthor': 'urn:mace:dir:attribute-def:documentAuthor',
'documentIdentifier': 'urn:mace:dir:attribute-def:documentIdentifier',
'documentLocation': 'urn:mace:dir:attribute-def:documentLocation',
'documentPublisher': 'urn:mace:dir:attribute-def:documentPublisher',
'documentTitle': 'urn:mace:dir:attribute-def:documentTitle',
'documentVersion': 'urn:mace:dir:attribute-def:documentVersion',
'domainComponent': 'urn:mace:dir:attribute-def:domainComponent',
'drink': 'urn:mace:dir:attribute-def:drink',
'eduOrgHomePageURI': 'urn:mace:dir:attribute-def:eduOrgHomePageURI',
'eduOrgIdentityAuthNPolicyURI': 'urn:mace:dir:attribute-def:eduOrgIdentityAuthNPolicyURI',
'eduOrgLegalName': 'urn:mace:dir:attribute-def:eduOrgLegalName',
'eduOrgSuperiorURI': 'urn:mace:dir:attribute-def:eduOrgSuperiorURI',
'eduOrgWhitePagesURI': 'urn:mace:dir:attribute-def:eduOrgWhitePagesURI',
'eduPersonAffiliation': 'urn:mace:dir:attribute-def:eduPersonAffiliation',
'eduPersonEntitlement': 'urn:mace:dir:attribute-def:eduPersonEntitlement',
'eduPersonNickname': 'urn:mace:dir:attribute-def:eduPersonNickname',
'eduPersonOrgDN': 'urn:mace:dir:attribute-def:eduPersonOrgDN',
'eduPersonOrgUnitDN': 'urn:mace:dir:attribute-def:eduPersonOrgUnitDN',
'eduPersonPrimaryAffiliation': 'urn:mace:dir:attribute-def:eduPersonPrimaryAffiliation',
'eduPersonPrimaryOrgUnitDN': 'urn:mace:dir:attribute-def:eduPersonPrimaryOrgUnitDN',
'eduPersonPrincipalName': 'urn:mace:dir:attribute-def:eduPersonPrincipalName',
'eduPersonScopedAffiliation': 'urn:mace:dir:attribute-def:eduPersonScopedAffiliation',
'eduPersonTargetedID': 'urn:mace:dir:attribute-def:eduPersonTargetedID',
'email': 'urn:mace:dir:attribute-def:email',
'emailAddress': 'urn:mace:dir:attribute-def:emailAddress',
'employeeNumber': 'urn:mace:dir:attribute-def:employeeNumber',
'employeeType': 'urn:mace:dir:attribute-def:employeeType',
'enhancedSearchGuide': 'urn:mace:dir:attribute-def:enhancedSearchGuide',
'facsimileTelephoneNumber': 'urn:mace:dir:attribute-def:facsimileTelephoneNumber',
'favouriteDrink': 'urn:mace:dir:attribute-def:favouriteDrink',
'fax': 'urn:mace:dir:attribute-def:fax',
'federationFeideSchemaVersion': 'urn:mace:dir:attribute-def:federationFeideSchemaVersion',
'friendlyCountryName': 'urn:mace:dir:attribute-def:friendlyCountryName',
'generationQualifier': 'urn:mace:dir:attribute-def:generationQualifier',
'givenName': 'urn:mace:dir:attribute-def:givenName',
'gn': 'urn:mace:dir:attribute-def:gn',
'homePhone': 'urn:mace:dir:attribute-def:homePhone',
'homePostalAddress': 'urn:mace:dir:attribute-def:homePostalAddress',
'homeTelephoneNumber': 'urn:mace:dir:attribute-def:homeTelephoneNumber',
'host': 'urn:mace:dir:attribute-def:host',
'houseIdentifier': 'urn:mace:dir:attribute-def:houseIdentifier',
'info': 'urn:mace:dir:attribute-def:info',
'initials': 'urn:mace:dir:attribute-def:initials',
'internationaliSDNNumber': 'urn:mace:dir:attribute-def:internationaliSDNNumber',
'janetMailbox': 'urn:mace:dir:attribute-def:janetMailbox',
'jpegPhoto': 'urn:mace:dir:attribute-def:jpegPhoto',
'knowledgeInformation': 'urn:mace:dir:attribute-def:knowledgeInformation',
'l': 'urn:mace:dir:attribute-def:l',
'labeledURI': 'urn:mace:dir:attribute-def:labeledURI',
'localityName': 'urn:mace:dir:attribute-def:localityName',
'mDRecord': 'urn:mace:dir:attribute-def:mDRecord',
'mXRecord': 'urn:mace:dir:attribute-def:mXRecord',
'mail': 'urn:mace:dir:attribute-def:mail',
'mailPreferenceOption': 'urn:mace:dir:attribute-def:mailPreferenceOption',
'manager': 'urn:mace:dir:attribute-def:manager',
'member': 'urn:mace:dir:attribute-def:member',
'mobile': 'urn:mace:dir:attribute-def:mobile',
'mobileTelephoneNumber': 'urn:mace:dir:attribute-def:mobileTelephoneNumber',
'nSRecord': 'urn:mace:dir:attribute-def:nSRecord',
'name': 'urn:mace:dir:attribute-def:name',
'norEduOrgAcronym': 'urn:mace:dir:attribute-def:norEduOrgAcronym',
'norEduOrgNIN': 'urn:mace:dir:attribute-def:norEduOrgNIN',
'norEduOrgSchemaVersion': 'urn:mace:dir:attribute-def:norEduOrgSchemaVersion',
'norEduOrgUniqueIdentifier': 'urn:mace:dir:attribute-def:norEduOrgUniqueIdentifier',
'norEduOrgUniqueNumber': 'urn:mace:dir:attribute-def:norEduOrgUniqueNumber',
'norEduOrgUnitUniqueIdentifier': 'urn:mace:dir:attribute-def:norEduOrgUnitUniqueIdentifier',
'norEduOrgUnitUniqueNumber': 'urn:mace:dir:attribute-def:norEduOrgUnitUniqueNumber',
'norEduPersonBirthDate': 'urn:mace:dir:attribute-def:norEduPersonBirthDate',
'norEduPersonLIN': 'urn:mace:dir:attribute-def:norEduPersonLIN',
'norEduPersonNIN': 'urn:mace:dir:attribute-def:norEduPersonNIN',
'o': 'urn:mace:dir:attribute-def:o',
'objectClass': 'urn:mace:dir:attribute-def:objectClass',
'organizationName': 'urn:mace:dir:attribute-def:organizationName',
'organizationalStatus': 'urn:mace:dir:attribute-def:organizationalStatus',
'organizationalUnitName': 'urn:mace:dir:attribute-def:organizationalUnitName',
'otherMailbox': 'urn:mace:dir:attribute-def:otherMailbox',
'ou': 'urn:mace:dir:attribute-def:ou',
'owner': 'urn:mace:dir:attribute-def:owner',
'pager': 'urn:mace:dir:attribute-def:pager',
'pagerTelephoneNumber': 'urn:mace:dir:attribute-def:pagerTelephoneNumber',
'personalSignature': 'urn:mace:dir:attribute-def:personalSignature',
'personalTitle': 'urn:mace:dir:attribute-def:personalTitle',
'photo': 'urn:mace:dir:attribute-def:photo',
'physicalDeliveryOfficeName': 'urn:mace:dir:attribute-def:physicalDeliveryOfficeName',
'pkcs9email': 'urn:mace:dir:attribute-def:pkcs9email',
'postOfficeBox': 'urn:mace:dir:attribute-def:postOfficeBox',
'postalAddress': 'urn:mace:dir:attribute-def:postalAddress',
'postalCode': 'urn:mace:dir:attribute-def:postalCode',
'preferredDeliveryMethod': 'urn:mace:dir:attribute-def:preferredDeliveryMethod',
'preferredLanguage': 'urn:mace:dir:attribute-def:preferredLanguage',
'presentationAddress': 'urn:mace:dir:attribute-def:presentationAddress',
'protocolInformation': 'urn:mace:dir:attribute-def:protocolInformation',
'pseudonym': 'urn:mace:dir:attribute-def:pseudonym',
'registeredAddress': 'urn:mace:dir:attribute-def:registeredAddress',
'rfc822Mailbox': 'urn:mace:dir:attribute-def:rfc822Mailbox',
'roleOccupant': 'urn:mace:dir:attribute-def:roleOccupant',
'roomNumber': 'urn:mace:dir:attribute-def:roomNumber',
'sOARecord': 'urn:mace:dir:attribute-def:sOARecord',
'searchGuide': 'urn:mace:dir:attribute-def:searchGuide',
'secretary': 'urn:mace:dir:attribute-def:secretary',
'seeAlso': 'urn:mace:dir:attribute-def:seeAlso',
'serialNumber': 'urn:mace:dir:attribute-def:serialNumber',
'singleLevelQuality': 'urn:mace:dir:attribute-def:singleLevelQuality',
'sn': 'urn:mace:dir:attribute-def:sn',
'st': 'urn:mace:dir:attribute-def:st',
'stateOrProvinceName': 'urn:mace:dir:attribute-def:stateOrProvinceName',
'street': 'urn:mace:dir:attribute-def:street',
'streetAddress': 'urn:mace:dir:attribute-def:streetAddress',
'subtreeMaximumQuality': 'urn:mace:dir:attribute-def:subtreeMaximumQuality',
'subtreeMinimumQuality': 'urn:mace:dir:attribute-def:subtreeMinimumQuality',
'supportedAlgorithms': 'urn:mace:dir:attribute-def:supportedAlgorithms',
'supportedApplicationContext': 'urn:mace:dir:attribute-def:supportedApplicationContext',
'surname': 'urn:mace:dir:attribute-def:surname',
'telephoneNumber': 'urn:mace:dir:attribute-def:telephoneNumber',
'teletexTerminalIdentifier': 'urn:mace:dir:attribute-def:teletexTerminalIdentifier',
'telexNumber': 'urn:mace:dir:attribute-def:telexNumber',
'textEncodedORAddress': 'urn:mace:dir:attribute-def:textEncodedORAddress',
'title': 'urn:mace:dir:attribute-def:title',
'uid': 'urn:mace:dir:attribute-def:uid',
'uniqueIdentifier': 'urn:mace:dir:attribute-def:uniqueIdentifier',
'uniqueMember': 'urn:mace:dir:attribute-def:uniqueMember',
'userCertificate': 'urn:mace:dir:attribute-def:userCertificate',
'userClass': 'urn:mace:dir:attribute-def:userClass',
'userPKCS12': 'urn:mace:dir:attribute-def:userPKCS12',
'userPassword': 'urn:mace:dir:attribute-def:userPassword',
'userSMIMECertificate': 'urn:mace:dir:attribute-def:userSMIMECertificate',
'userid': 'urn:mace:dir:attribute-def:userid',
'x121Address': 'urn:mace:dir:attribute-def:x121Address',
'x500UniqueIdentifier': 'urn:mace:dir:attribute-def:x500UniqueIdentifier',
}
}
| 69.009174 | 100 | 0.701143 | 2,240 | 22,566 | 7.063393 | 0.079018 | 0.14069 | 0.200986 | 0.381873 | 0.993806 | 0.993806 | 0 | 0 | 0 | 0 | 0 | 0.002584 | 0.14256 | 22,566 | 326 | 101 | 69.220859 | 0.815133 | 0 | 0 | 0 | 0 | 0 | 0.771903 | 0.628363 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.006154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f76f73db71618f83a909dd91b3b6d7600d69cb56 | 103 | py | Python | client/src/state/end/__init__.py | juan-nunez/Space_combat | e732ef932e89c404827ac0c96aebb4f0446cefdf | [
"MIT"
] | null | null | null | client/src/state/end/__init__.py | juan-nunez/Space_combat | e732ef932e89c404827ac0c96aebb4f0446cefdf | [
"MIT"
] | null | null | null | client/src/state/end/__init__.py | juan-nunez/Space_combat | e732ef932e89c404827ac0c96aebb4f0446cefdf | [
"MIT"
] | null | null | null | from end_view import EndView
from end_controller import EndController
from end_state import EndState | 34.333333 | 41 | 0.864078 | 15 | 103 | 5.733333 | 0.6 | 0.244186 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135922 | 103 | 3 | 42 | 34.333333 | 0.966292 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f79680a0c71d535b98147a243b6f5b9eba0e8f0a | 105 | py | Python | app/api/__init__.py | saury2013/Memento | dbb2031a5aff3064f40bcb5afe631de8724a547e | [
"MIT"
] | null | null | null | app/api/__init__.py | saury2013/Memento | dbb2031a5aff3064f40bcb5afe631de8724a547e | [
"MIT"
] | null | null | null | app/api/__init__.py | saury2013/Memento | dbb2031a5aff3064f40bcb5afe631de8724a547e | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from flask import Blueprint
api = Blueprint("api",__name__)
import app.api.view | 21 | 31 | 0.695238 | 15 | 105 | 4.6 | 0.733333 | 0.347826 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.011111 | 0.142857 | 105 | 5 | 32 | 21 | 0.755556 | 0.2 | 0 | 0 | 0 | 0 | 0.036145 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
e3bb9c8068ae4cb97c00893207e0796e5d319a4b | 4,778 | py | Python | decryption.py | saivarunr/zemoso-training | 56a6f52336d03457d42c76e704264de3edd7bbd5 | [
"Apache-2.0"
] | null | null | null | decryption.py | saivarunr/zemoso-training | 56a6f52336d03457d42c76e704264de3edd7bbd5 | [
"Apache-2.0"
] | null | null | null | decryption.py | saivarunr/zemoso-training | 56a6f52336d03457d42c76e704264de3edd7bbd5 | [
"Apache-2.0"
] | null | null | null | from itertools import product
input_data=[79,59,12,2,79,35,8,28,20,2,3,68,8,9,68,45,0,12,9,67,68,4,7,5,23,27,1,21,79,85,78,79,85,71,38,10,71,27,12,2,79,6,2,8,13,9,1,13,9,8,68,19,7,1,71,56,11,21,11,68,6,3,22,2,14,0,30,79,1,31,6,23,19,10,0,73,79,44,2,79,19,6,28,68,16,6,16,15,79,35,8,11,72,71,14,10,3,79,12,2,79,19,6,28,68,32,0,0,73,79,86,71,39,1,71,24,5,20,79,13,9,79,16,15,10,68,5,10,3,14,1,10,14,1,3,71,24,13,19,7,68,32,0,0,73,79,87,71,39,1,71,12,22,2,14,16,2,11,68,2,25,1,21,22,16,15,6,10,0,79,16,15,10,22,2,79,13,20,65,68,41,0,16,15,6,10,0,79,1,31,6,23,19,28,68,19,7,5,19,79,12,2,79,0,14,11,10,64,27,68,10,14,15,2,65,68,83,79,40,14,9,1,71,6,16,20,10,8,1,79,19,6,28,68,14,1,68,15,6,9,75,79,5,9,11,68,19,7,13,20,79,8,14,9,1,71,8,13,17,10,23,71,3,13,0,7,16,71,27,11,71,10,18,2,29,29,8,1,1,73,79,81,71,59,12,2,79,8,14,8,12,19,79,23,15,6,10,2,28,68,19,7,22,8,26,3,15,79,16,15,10,68,3,14,22,12,1,1,20,28,72,71,14,10,3,79,16,15,10,68,3,14,22,12,1,1,20,28,68,4,14,10,71,1,1,17,10,22,71,10,28,19,6,10,0,26,13,20,7,68,14,27,74,71,89,68,32,0,0,71,28,1,9,27,68,45,0,12,9,79,16,15,10,68,37,14,20,19,6,23,19,79,83,71,27,11,71,27,1,11,3,68,2,25,1,21,22,11,9,10,68,6,13,11,18,27,68,19,7,1,71,3,13,0,7,16,71,28,11,71,27,12,6,27,68,2,25,1,21,22,11,9,10,68,10,6,3,15,27,68,5,10,8,14,10,18,2,79,6,2,12,5,18,28,1,71,0,2,71,7,13,20,79,16,2,28,16,14,2,11,9,22,74,71,87,68,45,0,12,9,79,12,14,2,23,2,3,2,71,24,5,20,79,10,8,27,68,19,7,1,71,3,13,0,7,16,92,79,12,2,79,19,6,28,68,8,1,8,30,79,5,71,24,13,19,1,1,20,28,68,19,0,68,19,7,1,71,3,13,0,7,16,73,79,93,71,59,12,2,79,11,9,10,68,16,7,11,71,6,23,71,27,12,2,79,16,21,26,1,71,3,13,0,7,16,75,79,19,15,0,68,0,6,18,2,28,68,11,6,3,15,27,68,19,0,68,2,25,1,21,22,11,9,10,72,71,24,5,20,79,3,8,6,10,0,79,16,8,79,7,8,2,1,71,6,10,19,0,68,19,7,1,71,24,11,21,3,0,73,79,85,87,79,38,18,27,68,6,3,16,15,0,17,0,7,68,19,7,1,71,24,11,21,3,0,71,24,5,20,79,9,6,11,1,71,27,12,21,0,17,0,7,68,15,6,9,75,79,16,15,10,68,16,0,22,11,11,68,3,6,0,9,72,16,71,29,1,4,0,3,9,6,30,2,79,12,14,2,68,16,7,1,9,79,12,2,79,7,6,2,1,73,79,85,86,79,33,17,10,10,71,6,10,71,7,13,20,79,11,16,1,68,11,14,10,3,79,5,9,11,68,6,2,11,9,8,68,15,6,23,71,0,19,9,79,20,2,0,20,11,10,72,71,7,1,71,24,5,20,79,10,8,27,68,6,12,7,2,31,16,2,11,74,71,94,86,71,45,17,19,79,16,8,79,5,11,3,68,16,7,11,71,13,1,11,6,1,17,10,0,71,7,13,10,79,5,9,11,68,6,12,7,2,31,16,2,11,68,15,6,9,75,79,12,2,79,3,6,25,1,71,27,12,2,79,22,14,8,12,19,79,16,8,79,6,2,12,11,10,10,68,4,7,13,11,11,22,2,1,68,8,9,68,32,0,0,73,79,85,84,79,48,15,10,29,71,14,22,2,79,22,2,13,11,21,1,69,71,59,12,14,28,68,14,28,68,9,0,16,71,14,68,23,7,29,20,6,7,6,3,68,5,6,22,19,7,68,21,10,23,18,3,16,14,1,3,71,9,22,8,2,68,15,26,9,6,1,68,23,14,23,20,6,11,9,79,11,21,79,20,11,14,10,75,79,16,15,6,23,71,29,1,5,6,22,19,7,68,4,0,9,2,28,68,1,29,11,10,79,35,8,11,74,86,91,68,52,0,68,19,7,1,71,56,11,21,11,68,5,10,7,6,2,1,71,7,17,10,14,10,71,14,10,3,79,8,14,25,1,3,79,12,2,29,1,71,0,10,71,10,5,21,27,12,71,14,9,8,1,3,71,26,23,73,79,44,2,79,19,6,28,68,1,26,8,11,79,11,1,79,17,9,9,5,14,3,13,9,8,68,11,0,18,2,79,5,9,11,68,1,14,13,19,7,2,18,3,10,2,28,23,73,79,37,9,11,68,16,10,68,15,14,18,2,79,23,2,10,10,71,7,13,20,79,3,11,0,22,30,67,68,19,7,1,71,8,8,8,29,29,71,0,2,71,27,12,2,79,11,9,3,29,71,60,11,9,79,11,1,79,16,15,10,68,33,14,16,15,10,22,73
];
alphabets=list('abcdefghijklmnopqrstuvwxyz') #Given in problem statement key is of length and contains lower case alphabets
alpha_ascii=[ord(i) for i in alphabets] #Conversion of alphabets to their respective ascii values
probable_password=list(product(alpha_ascii,repeat=3)) #All possible combinations of alphabets with length 3 for decryption
j=len(input_data)-1
i=0
c=[] #this will contain the password after the iteration ends
t=-1
key_thing=ord(" ") #Most common thing in any sentence would be a space, now the key logic is to compare every XORed/decrypted value with ascii value of space, the one which is valid will contain most spaces
for password in probable_password:
i=0
count=0
while i<j:
count+=1 if password[0]^input_data[i]==key_thing else 0
count+=1 if password[1]^input_data[i+1]==key_thing else 0
count+=1 if password[2]^input_data[i+2]==key_thing else 0
i+=3
count+=1 if password[0]^input_data[i]==key_thing else 0
if count>t:
#If count of spaces in decryption is greater than previous count of spaces then update both present state and password
t=count
c=password
s=""
i=0
count=0
while i<j:
t1=c[0]^input_data[i]
t2=c[1]^input_data[i+1]
t3=c[2]^input_data[i+2]
s+=(chr(t1)+chr(t2)+chr(t3))
count+=(t1+t2+t3)
i+=3
s+=chr(c[0]^input_data[i])
print "The decrypted text is:"
print s
print "-------------------------------\n\n\n"
print "The sum of ascii values of decrypted/original text is:"
print count
| 113.761905 | 3,214 | 0.648179 | 1,477 | 4,778 | 2.083954 | 0.109682 | 0.019493 | 0.019493 | 0.015595 | 0.344704 | 0.240416 | 0.165692 | 0.146524 | 0.122807 | 0.089994 | 0 | 0.454788 | 0.060276 | 4,778 | 41 | 3,215 | 116.536585 | 0.230735 | 0.116785 | 0 | 0.282051 | 0 | 0 | 0.033223 | 0.01495 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0.179487 | 0.025641 | null | null | 0.128205 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
e3c172556f06dc93e0a3f1567026c8baeebb5771 | 390 | py | Python | odoo-13.0/addons/website_sale/tests/__init__.py | VaibhavBhujade/Blockchain-ERP-interoperability | b5190a037fb6615386f7cbad024d51b0abd4ba03 | [
"MIT"
] | null | null | null | odoo-13.0/addons/website_sale/tests/__init__.py | VaibhavBhujade/Blockchain-ERP-interoperability | b5190a037fb6615386f7cbad024d51b0abd4ba03 | [
"MIT"
] | null | null | null | odoo-13.0/addons/website_sale/tests/__init__.py | VaibhavBhujade/Blockchain-ERP-interoperability | b5190a037fb6615386f7cbad024d51b0abd4ba03 | [
"MIT"
] | null | null | null | from . import test_customize
from . import test_sale_process
from . import test_sitemap
from . import test_website_sale_cart_recovery
from . import test_website_sale_mail
from . import test_website_sale_pricelist
from . import test_website_sale_product_attribute_value_config
from . import test_website_sale_image
from . import test_website_sequence
from . import test_website_sale_visitor
| 35.454545 | 62 | 0.871795 | 58 | 390 | 5.37931 | 0.327586 | 0.320513 | 0.448718 | 0.471154 | 0.480769 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102564 | 390 | 10 | 63 | 39 | 0.891429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e3d7d80ddd4ee85d9074585b279a68b17e3c546d | 7,168 | py | Python | cloudify_kubernetes/tests/test_k8s_operations.py | ahmadiesa-abu/cloudify-kubernetes-plugin | 3f5363e82f12bb97dafee178c972491898f54680 | [
"Apache-2.0"
] | null | null | null | cloudify_kubernetes/tests/test_k8s_operations.py | ahmadiesa-abu/cloudify-kubernetes-plugin | 3f5363e82f12bb97dafee178c972491898f54680 | [
"Apache-2.0"
] | null | null | null | cloudify_kubernetes/tests/test_k8s_operations.py | ahmadiesa-abu/cloudify-kubernetes-plugin | 3f5363e82f12bb97dafee178c972491898f54680 | [
"Apache-2.0"
] | null | null | null | # Copyright (c) 2017-2019 Cloudify Platform Ltd. All rights reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from mock import MagicMock
from kubernetes.client.rest import ApiException
from cloudify_kubernetes.k8s.operations import (KubernetesCreateOperation,
KubernetesReadOperation,
KubernetesUpdateOperation,
KubernetesDeleteOperation)
from cloudify_kubernetes.k8s.exceptions import KuberentesApiOperationError
class TestKubernetesCreateOperation(unittest.TestCase):
def test_init(self):
instance = KubernetesCreateOperation("api_method", ['a', 'b'])
self.assertEqual(instance.api_method, 'api_method')
self.assertEqual(instance.api_method_arguments_names, ['a', 'b'])
def test_prepare_arguments(self):
instance = KubernetesCreateOperation("api_method", ['a', 'b'])
self.assertEqual(
instance._prepare_arguments({'a': 'c', 'b': 'd'}),
{'a': 'c', 'b': 'd'}
)
self.assertEqual(
instance._prepare_arguments({'a': 'd', 'b': 'e', 'c': 'f'}),
{'a': 'd', 'b': 'e'}
)
with self.assertRaises(KuberentesApiOperationError) as error:
instance._prepare_arguments({'a': 'd', 'c': 'f'})
self.assertEqual(
str(error.exception),
"Invalid input data for execution of Kubernetes API method: "
"input argument b is not defined but is mandatory"
)
def test_execute(self):
call_mock = MagicMock(return_value="!")
instance = KubernetesCreateOperation(call_mock, ['a', 'b'])
self.assertEqual(
instance.execute({'a': 'd', 'b': 'e', 'c': 'f'}),
"!"
)
call_mock.assert_called_with(a='d', b='e')
def test_execute_ApiException(self):
call_mock = MagicMock(side_effect=ApiException("!"))
instance = KubernetesCreateOperation(call_mock, ['a', 'b'])
with self.assertRaises(KuberentesApiOperationError) as error:
instance.execute({'a': 'd', 'b': 'e', 'c': 'f'})
self.assertEqual(
str(error.exception),
"Operation execution failed. Exception during Kubernetes API "
"call: (!)\nReason: None\n"
)
call_mock.assert_called_with(a='d', b='e')
class TestKubernetesReadOperation(unittest.TestCase):
def test_init(self):
instance = KubernetesReadOperation("api_method", ['a', 'b'])
self.assertEqual(instance.api_method, 'api_method')
self.assertEqual(instance.api_method_arguments_names, ['a', 'b'])
def test_prepare_arguments(self):
instance = KubernetesReadOperation("api_method", ['a', 'b'])
self.assertEqual(
instance._prepare_arguments({'a': 'c', 'b': 'd'}),
{'a': 'c', 'b': 'd'}
)
self.assertEqual(
instance._prepare_arguments({
'a': 'd', 'b': 'e', 'exact': 'f', 'export': 'g', 'h': 'i'
}),
{'a': 'd', 'b': 'e', 'exact': 'f', 'export': 'g'}
)
with self.assertRaises(KuberentesApiOperationError) as error:
instance._prepare_arguments({'a': 'd', 'c': 'f'})
self.assertEqual(
str(error.exception),
"Invalid input data for execution of Kubernetes API method: "
"input argument b is not defined but is mandatory"
)
class TestKubernetesUpdateOperation(unittest.TestCase):
def test_init(self):
instance = KubernetesUpdateOperation("api_method", ['a', 'b'])
self.assertEqual(instance.api_method, 'api_method')
self.assertEqual(instance.api_method_arguments_names, ['a', 'b'])
def test_prepare_arguments(self):
instance = KubernetesUpdateOperation("api_method", ['a', 'b'])
self.assertEqual(
instance._prepare_arguments({'a': 'c', 'b': 'd'}),
{'a': 'c', 'b': 'd'}
)
self.assertEqual(
instance._prepare_arguments({'a': 'd', 'b': 'e', 'c': 'f'}),
{'a': 'd', 'b': 'e'}
)
with self.assertRaises(KuberentesApiOperationError) as error:
instance._prepare_arguments({'a': 'd', 'c': 'f'})
self.assertEqual(
str(error.exception),
"Invalid input data for execution of Kubernetes API method: "
"input argument b is not defined but is mandatory"
)
def test_execute(self):
call_mock = MagicMock(return_value="!")
instance = KubernetesUpdateOperation(call_mock, ['a', 'b'])
self.assertEqual(
instance.execute({'a': 'd', 'b': 'e', 'c': 'f'}),
"!"
)
call_mock.assert_called_with(a='d', b='e')
def test_execute_ApiException(self):
call_mock = MagicMock(side_effect=ApiException("!"))
instance = KubernetesUpdateOperation(call_mock, ['a', 'b'])
with self.assertRaises(KuberentesApiOperationError) as error:
instance.execute({'a': 'd', 'b': 'e', 'c': 'f'})
self.assertEqual(
str(error.exception),
"Operation execution failed. Exception during Kubernetes API "
"call: (!)\nReason: None\n"
)
call_mock.assert_called_with(a='d', b='e')
class TestKubernetesDeleteOperation(unittest.TestCase):
def test_init(self):
instance = KubernetesDeleteOperation("api_method", ['a', 'b'])
self.assertEqual(instance.api_method, 'api_method')
self.assertEqual(instance.api_method_arguments_names, ['a', 'b'])
def test_prepare_arguments(self):
instance = KubernetesDeleteOperation("api_method", ['a', 'b'])
self.assertEqual(
instance._prepare_arguments({'a': 'c', 'b': 'd'}),
{'a': 'c', 'b': 'd'}
)
self.assertEqual(
instance._prepare_arguments({
'a': 'd', 'b': 'e', 'exact': 'f',
'grace_period_seconds': 'g', 'propagation_policy': 'i'
}), {
'a': 'd', 'b': 'e', 'grace_period_seconds': 'g',
'propagation_policy': 'i'
}
)
with self.assertRaises(KuberentesApiOperationError) as error:
instance._prepare_arguments({'a': 'd', 'c': 'f'})
self.assertEqual(
str(error.exception),
"Invalid input data for execution of Kubernetes API method: "
"input argument b is not defined but is mandatory"
)
if __name__ == '__main__':
unittest.main()
| 36.948454 | 74 | 0.5851 | 752 | 7,168 | 5.430851 | 0.194149 | 0.052889 | 0.101371 | 0.015671 | 0.778893 | 0.777669 | 0.753183 | 0.708619 | 0.702742 | 0.702742 | 0 | 0.002685 | 0.2726 | 7,168 | 193 | 75 | 37.139896 | 0.780591 | 0.08245 | 0 | 0.727273 | 0 | 0 | 0.151455 | 0 | 0 | 0 | 0 | 0 | 0.237762 | 1 | 0.083916 | false | 0 | 0.034965 | 0 | 0.146853 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5405c8787d6e6b818fc3731d729e6bd0c32ec734 | 5,593 | py | Python | tests/sqlite/schema/test_table_diff.py | resmo/orm | 4e10708409e9c95a793b0a8b585095c679a56053 | [
"MIT"
] | null | null | null | tests/sqlite/schema/test_table_diff.py | resmo/orm | 4e10708409e9c95a793b0a8b585095c679a56053 | [
"MIT"
] | null | null | null | tests/sqlite/schema/test_table_diff.py | resmo/orm | 4e10708409e9c95a793b0a8b585095c679a56053 | [
"MIT"
] | null | null | null | import unittest
from src.masoniteorm.schema import Column, Table
from src.masoniteorm.schema.platforms.SQLitePlatform import SQLitePlatform
from src.masoniteorm.schema.TableDiff import TableDiff
class TestTableDiff(unittest.TestCase):
def setUp(self):
self.platform = SQLitePlatform()
def test_rename_table(self):
table = Table("users")
table.add_column("name", "string")
diff = TableDiff("users")
diff.from_table = table
diff.new_name = "clients"
sql = ["ALTER TABLE users RENAME TO clients"]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
def test_drop_index(self):
table = Table("users")
table.add_index("name", "name_index", "unique")
diff = TableDiff("users")
diff.from_table = table
diff.remove_index("name")
sql = ["DROP INDEX name"]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
def test_drop_index_and_rename_table(self):
table = Table("users")
table.add_index("name", "name_unique", "unique")
diff = TableDiff("users")
diff.from_table = table
diff.new_name = "clients"
diff.remove_index("name_unique")
sql = ["DROP INDEX name_unique", "ALTER TABLE users RENAME TO clients"]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
def test_alter_add_column(self):
table = Table("users")
diff = TableDiff("users")
diff.from_table = table
diff.add_column("name", "string")
diff.add_column("email", "string")
sql = [
"ALTER TABLE users ADD COLUMN name VARCHAR NOT NULL",
"ALTER TABLE users ADD COLUMN email VARCHAR NOT NULL",
]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
def test_alter_rename(self):
table = Table("users")
table.add_column("post", "integer")
diff = TableDiff("users")
diff.from_table = table
diff.rename_column("post", "comment", "integer")
sql = [
"CREATE TEMPORARY TABLE __temp__users AS SELECT post FROM users",
"DROP TABLE users",
'CREATE TABLE "users" (comment INTEGER NOT NULL)',
'INSERT INTO "users" (comment) SELECT post FROM __temp__users',
"DROP TABLE __temp__users",
]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
def test_alter_advanced_rename_columns(self):
table = Table("users")
table.add_column("post", "integer")
table.add_column("user", "integer")
table.add_column("email", "integer")
diff = TableDiff("users")
diff.from_table = table
diff.rename_column("post", "comment", "integer")
sql = [
"CREATE TEMPORARY TABLE __temp__users AS SELECT post, user, email FROM users",
"DROP TABLE users",
'CREATE TABLE "users" (comment INTEGER NOT NULL, user INTEGER NOT NULL, email INTEGER NOT NULL)',
'INSERT INTO "users" (comment, user, email) SELECT post, user, email FROM __temp__users',
"DROP TABLE __temp__users",
]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
def test_alter_rename_column_and_rename_table(self):
table = Table("users")
table.add_column("post", "integer")
diff = TableDiff("users")
diff.from_table = table
diff.new_name = "clients"
diff.rename_column("post", "comment", "integer")
sql = [
"CREATE TEMPORARY TABLE __temp__users AS SELECT post FROM users",
"DROP TABLE users",
'CREATE TABLE "users" (comment INTEGER NOT NULL)',
'INSERT INTO "users" (comment) SELECT post FROM __temp__users',
"DROP TABLE __temp__users",
"ALTER TABLE users RENAME TO clients",
]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
def test_alter_rename_column_and_rename_table_and_drop_index(self):
table = Table("users")
table.add_column("post", "integer")
table.add_index("name", "name_unique", "unique")
diff = TableDiff("users")
diff.from_table = table
diff.new_name = "clients"
diff.rename_column("post", "comment", "integer")
diff.remove_index("name")
sql = [
"DROP INDEX name",
"CREATE TEMPORARY TABLE __temp__users AS SELECT post FROM users",
"DROP TABLE users",
'CREATE TABLE "users" (comment INTEGER NOT NULL)',
'INSERT INTO "users" (comment) SELECT post FROM __temp__users',
"DROP TABLE __temp__users",
"ALTER TABLE users RENAME TO clients",
]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
def test_alter_can_drop_column(self):
table = Table("users")
table.add_column("post", "integer")
table.add_column("name", "string")
table.add_column("email", "string")
diff = TableDiff("users")
diff.from_table = table
diff.drop_column("post")
sql = [
"CREATE TEMPORARY TABLE __temp__users AS SELECT name, email FROM users",
"DROP TABLE users",
'CREATE TABLE "users" (name VARCHAR NOT NULL, email VARCHAR NOT NULL)',
'INSERT INTO "users" (name, email) SELECT name, email FROM __temp__users',
"DROP TABLE __temp__users",
]
self.assertEqual(sql, self.platform.compile_alter_sql(diff))
| 34.103659 | 109 | 0.617736 | 654 | 5,593 | 5.044343 | 0.084098 | 0.075781 | 0.042437 | 0.051834 | 0.835708 | 0.788421 | 0.788421 | 0.777508 | 0.730524 | 0.6505 | 0 | 0 | 0.272305 | 5,593 | 163 | 110 | 34.312883 | 0.810565 | 0 | 0 | 0.66129 | 0 | 0.008065 | 0.333989 | 0 | 0 | 0 | 0 | 0 | 0.072581 | 1 | 0.080645 | false | 0 | 0.032258 | 0 | 0.120968 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5827b4a9631cbb8e1d7c0f1295ec9c7e2cbcebfc | 17 | py | Python | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/anonymization/data/variables/in_2_reassignment.py | JetBrains-Research/ast-transformations | 0ab408af3275b520cc87a473f418c4b4dfcb0284 | [
"MIT"
] | 8 | 2021-01-19T21:15:54.000Z | 2022-02-23T19:16:25.000Z | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/anonymization/data/variables/in_2_reassignment.py | JetBrains-Research/ast-transformations | 0ab408af3275b520cc87a473f418c4b4dfcb0284 | [
"MIT"
] | 4 | 2020-11-17T14:28:25.000Z | 2022-02-24T07:54:28.000Z | ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/transformations/anonymization/data/variables/in_2_reassignment.py | nbirillo/ast-transformations | 717706765a2da29087a0de768fc851698886dd65 | [
"MIT"
] | 1 | 2022-02-23T19:16:30.000Z | 2022-02-23T19:16:30.000Z | x = 1
x = 2
x = 3 | 5.666667 | 5 | 0.352941 | 6 | 17 | 1 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 0.470588 | 17 | 3 | 6 | 5.666667 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
583809c77aa1767c941aa3d03abe177d99b07a90 | 40 | py | Python | openmedicalio/__init__.py | openmedicalio/openmedicalio | 1806428b7badb0276e07523af5289e122b5bd893 | [
"MIT"
] | 1 | 2019-09-29T15:51:09.000Z | 2019-09-29T15:51:09.000Z | openmedicalio/__init__.py | openmedicalio/openmedicalio | 1806428b7badb0276e07523af5289e122b5bd893 | [
"MIT"
] | null | null | null | openmedicalio/__init__.py | openmedicalio/openmedicalio | 1806428b7badb0276e07523af5289e122b5bd893 | [
"MIT"
] | null | null | null | from openmedicalio.client import Client
| 20 | 39 | 0.875 | 5 | 40 | 7 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 40 | 1 | 40 | 40 | 0.972222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
585646d454787e7bb120d9ea6359f88eafc3c72f | 618,655 | py | Python | Petya.py | LinarAbdrazakov/HungryGeese | 053a492f47e105a84ed413b67841aa12c967f03e | [
"MIT"
] | null | null | null | Petya.py | LinarAbdrazakov/HungryGeese | 053a492f47e105a84ed413b67841aa12c967f03e | [
"MIT"
] | null | null | null | Petya.py | LinarAbdrazakov/HungryGeese | 053a492f47e105a84ed413b67841aa12c967f03e | [
"MIT"
] | null | null | null | import pickle
import bz2
import base64
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import time
from copy import deepcopy
from kaggle_environments.envs.hungry_geese.hungry_geese import Action, translate
from kaggle_environments.helpers import histogram
# The model’s parameters from https://www.kaggle.com/yuricat/smart-geese-trained-by-reinforcement-learning
PARAM = 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'
class MCTS():
def __init__(self, game, nn_agent, eps=1e-8, cpuct=1.0):
self.game = game
self.nn_agent = nn_agent
self.eps = eps
self.cpuct = cpuct
self.Qsa = {} # stores Q values for s,a (as defined in the paper)
self.Nsa = {} # stores #times edge s,a was visited
self.Ns = {} # stores #times board s was visited
self.Ps = {} # stores initial policy (returned by neural net)
self.Vs = {} # stores game.getValidMoves for board s
self.last_obs = None
def getActionProb(self, obs, timelimit=2.0):
start_time = time.time()
while time.time() - start_time < timelimit:
self.search(obs, self.last_obs)
s = self.game.stringRepresentation(obs)
i = obs.index
counts = [
self.Nsa[(s, i, a)] if (s, i, a) in self.Nsa else 0
for a in range(self.game.getActionSize())
]
prob = counts / np.sum(counts)
self.last_obs = obs
return prob
def search(self, obs, last_obs):
s = self.game.stringRepresentation(obs)
if s not in self.Ns:
values = [-10] * 4
for i in range(4):
if len(obs.geese[i]) == 0:
continue
# leaf node
self.Ps[(s, i)], values[i] = self.nn_agent.predict(obs, last_obs, i)
valids = self.game.getValidMoves(obs, last_obs, i)
self.Ps[(s, i)] = self.Ps[(s, i)] * valids # masking invalid moves
sum_Ps_s = np.sum(self.Ps[(s, i)])
if sum_Ps_s > 0:
self.Ps[(s, i)] /= sum_Ps_s # renormalize
self.Vs[(s, i)] = valids
self.Ns[s] = 0
return values
best_acts = [None] * 4
for i in range(4):
if len(obs.geese[i]) == 0:
continue
valids = self.Vs[(s, i)]
cur_best = -float('inf')
best_act = self.game.actions[-1]
# pick the action with the highest upper confidence bound
for a in range(self.game.getActionSize()):
if valids[a]:
if (s, i, a) in self.Qsa:
u = self.Qsa[(s, i, a)] + self.cpuct * self.Ps[(s, i)][a] * math.sqrt(
self.Ns[s]) / (1 + self.Nsa[(s, i, a)])
else:
u = self.cpuct * self.Ps[(s, i)][a] * math.sqrt(
self.Ns[s] + self.eps) # Q = 0 ?
if u > cur_best:
cur_best = u
best_act = self.game.actions[a]
best_acts[i] = best_act
next_obs = self.game.getNextState(obs, last_obs, best_acts)
values = self.search(next_obs, obs)
for i in range(4):
if len(obs.geese[i]) == 0:
continue
a = self.game.actions.index(best_acts[i])
v = values[i]
if (s, i, a) in self.Qsa:
self.Qsa[(s, i, a)] = (self.Nsa[(s, i, a)] * self.Qsa[
(s, i, a)] + v) / (self.Nsa[(s, i, a)] + 1)
self.Nsa[(s, i, a)] += 1
else:
self.Qsa[(s, i, a)] = v
self.Nsa[(s, i, a)] = 1
self.Ns[s] += 1
return values
class HungryGeese(object):
def __init__(self,
rows=7,
columns=11,
actions=[Action.NORTH, Action.SOUTH, Action.WEST, Action.EAST],
hunger_rate=40):
self.rows = rows
self.columns = columns
self.actions = actions
self.hunger_rate = hunger_rate
def getActionSize(self):
return len(self.actions)
def getNextState(self, obs, last_obs, directions):
next_obs = deepcopy(obs)
next_obs.step += 1
geese = next_obs.geese
food = next_obs.food
for i in range(4):
goose = geese[i]
if len(goose) == 0:
continue
head = translate(goose[0], directions[i], self.columns, self.rows)
# Check action direction
if last_obs is not None and head == last_obs.geese[i][0]:
geese[i] = []
continue
# Consume food or drop a tail piece.
if head in food:
food.remove(head)
else:
goose.pop()
# Add New Head to the Goose.
goose.insert(0, head)
# If hunger strikes remove from the tail.
if next_obs.step % self.hunger_rate == 0:
if len(goose) > 0:
goose.pop()
goose_positions = histogram(
position
for goose in geese
for position in goose
)
# Check for collisions.
for i in range(4):
if len(geese[i]) > 0:
head = geese[i][0]
if goose_positions[head] > 1:
geese[i] = []
return next_obs
def getValidMoves(self, obs, last_obs, index):
geese = obs.geese
pos = geese[index][0]
obstacles = {position for goose in geese for position in goose[:-1]}
if last_obs is not None: obstacles.add(last_obs.geese[index][0])
valid_moves = [
translate(pos, action, self.columns, self.rows) not in obstacles
for action in self.actions
]
return valid_moves
def stringRepresentation(self, obs):
return str(obs.geese + obs.food)
# Neural Network for Hungry Geese
class TorusConv2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, bn):
super().__init__()
self.edge_size = (kernel_size[0] // 2, kernel_size[1] // 2)
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size=kernel_size)
self.bn = nn.BatchNorm2d(output_dim) if bn else None
def forward(self, x):
h = torch.cat([x[:,:,:,-self.edge_size[1]:], x, x[:,:,:,:self.edge_size[1]]], dim=3)
h = torch.cat([h[:,:,-self.edge_size[0]:], h, h[:,:,:self.edge_size[0]]], dim=2)
h = self.conv(h)
h = self.bn(h) if self.bn is not None else h
return h
class GeeseNet(nn.Module):
def __init__(self):
super().__init__()
layers, filters = 12, 32
self.conv0 = TorusConv2d(17, filters, (3, 3), True)
self.blocks = nn.ModuleList([TorusConv2d(filters, filters, (3, 3), True) for _ in range(layers)])
self.head_p = nn.Linear(filters, 4, bias=False)
self.head_v = nn.Linear(filters * 2, 1, bias=False)
def forward_helper(self, x):
h = F.relu_(self.conv0(x))
for block in self.blocks:
h = F.relu_(h + block(h))
h_head = (h * x[:,:1]).view(h.size(0), h.size(1), -1).sum(-1)
h_avg = h.view(h.size(0), h.size(1), -1).mean(-1)
p = torch.softmax(self.head_p(h_head), 1)
v = torch.tanh(self.head_v(torch.cat([h_head, h_avg], 1)))
return p, v
def forward(self, x):
vertical_idx = torch.LongTensor([i for i in range(x.size(2)-1, -1, -1)])
horisontal_idx = torch.LongTensor([i for i in range(x.size(3)-1, -1, -1)])
x_original = x
x_vertical_flipped = x.index_select(2, vertical_idx)
x_horizontal_flipped = x.index_select(3, horisontal_idx)
x_vert_horiz_flipped = x_vertical_flipped.index_select(3, horisontal_idx)
x_expanded = torch.cat((x_original, x_vertical_flipped, x_horizontal_flipped, x_vert_horiz_flipped))
p_expanded, v_expanded = self.forward_helper(x_expanded)
batch_size = int(x.shape[0])
p_original, v_original = p_expanded[:batch_size], v_expanded[:batch_size]
p_vertical_flipped, v_vertical_flipped = p_expanded[batch_size:2*batch_size], v_expanded[batch_size:2*batch_size]
p_horizontal_flipped, v_horizontal_flipped = p_expanded[2*batch_size:3*batch_size], v_expanded[2*batch_size:3*batch_size]
p_vert_horiz_flipped, v_vert_horiz_flipped = p_expanded[3*batch_size:4*batch_size], v_expanded[3*batch_size:4*batch_size]
# and so on
order_original = [0, 1, 2, 3] #[Action.NORTH, Action.SOUTH, Action.WEST, Action.EAST]
order_vertical_flipped = [1, 0, 2, 3]
order_horizontal_flipped = [0, 1, 3, 2]
order_vert_horiz_flipped = [1, 0, 3, 2]
v = (v_original + v_vertical_flipped + v_horizontal_flipped + v_vert_horiz_flipped)
p = (p_original +
p_vertical_flipped[:,order_vertical_flipped] +
p_horizontal_flipped[:,order_horizontal_flipped] +
p_vert_horiz_flipped[:,order_vert_horiz_flipped]) / 4
return p, v
class NNAgent():
def __init__(self, state_dict):
self.model = GeeseNet()
self.model.load_state_dict(state_dict)
self.model.eval()
def predict(self, obs, last_obs, index):
x = self._make_input(obs, last_obs, index)
with torch.no_grad():
xt = torch.from_numpy(x).unsqueeze(0)
p, v = self.model(xt)
return p.squeeze(0).detach().numpy(), v.item()
# Input for Neural Network
def _make_input(self, obs, last_obs, index):
b = np.zeros((17, 7 * 11), dtype=np.float32)
for p, pos_list in enumerate(obs.geese):
# head position
for pos in pos_list[:1]:
b[0 + (p - index) % 4, pos] = 1
# tip position
for pos in pos_list[-1:]:
b[4 + (p - index) % 4, pos] = 1
# whole position
for pos in pos_list:
b[8 + (p - index) % 4, pos] = 1
# previous head position
if last_obs is not None:
for p, pos_list in enumerate(last_obs.geese):
for pos in pos_list[:1]:
b[12 + (p - index) % 4, pos] = 1
# food
for pos in obs.food:
b[16, pos] = 1
return b.reshape(-1, 7, 11)
game = HungryGeese()
state_dict = pickle.loads(bz2.decompress(base64.b64decode(PARAM)))
agent = NNAgent(state_dict)
mcts = MCTS(game, agent)
def alphageese_agent(obs, config):
action = game.actions[np.argmax(
mcts.getActionProb(obs, timelimit=config.actTimeout))]
return action.name
| 2,015.162866 | 607,595 | 0.961119 | 19,918 | 618,655 | 29.840596 | 0.905563 | 0.000074 | 0.000076 | 0.00013 | 0.002663 | 0.002145 | 0.00147 | 0.000917 | 0.000634 | 0.000496 | 0 | 0.154846 | 0.006181 | 618,655 | 306 | 607,596 | 2,021.748366 | 0.811867 | 0.001216 | 0 | 0.145923 | 0 | 0.004292 | 0.983349 | 0.983345 | 0 | 1 | 0 | 0 | 0 | 1 | 0.072961 | false | 0 | 0.051502 | 0.008584 | 0.201717 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
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