xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

aabf42721a2ad015b96fc7311954534f13a041d8

2,395

py

Python

etl/parsers/etw/Microsoft_User_Experience_Virtualization_SQM_Uploader.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_User_Experience_Virtualization_SQM_Uploader.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_User_Experience_Virtualization_SQM_Uploader.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-User Experience Virtualization-SQM Uploader GUID : 57003e21-269b-4bdc-8434-b3bf8d57d2d5 """ 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("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=3, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_3_0(Etw): pattern = Struct( "hresult" / Int32ul ) @declare(guid=guid("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=4, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_4_0(Etw): pattern = Struct( "WString1" / WString ) @declare(guid=guid("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=6, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_6_0(Etw): pattern = Struct( "hr" / Int32ul, "filename" / WString, "http" / Int32sl ) @declare(guid=guid("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=7, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_7_0(Etw): pattern = Struct( "hr" / Int32ul, "filename" / WString, "http" / Int32sl ) @declare(guid=guid("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=8, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_8_0(Etw): pattern = Struct( "error" / Int32ul ) @declare(guid=guid("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=10, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_10_0(Etw): pattern = Struct( "String1" / CString ) @declare(guid=guid("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=12, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_12_0(Etw): pattern = Struct( "uint32" / Int32ul ) @declare(guid=guid("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=13, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_13_0(Etw): pattern = Struct( "WString1" / WString ) @declare(guid=guid("57003e21-269b-4bdc-8434-b3bf8d57d2d5"), event_id=14, version=0) class Microsoft_User_Experience_Virtualization_SQM_Uploader_14_0(Etw): pattern = Struct( "error" / Int32ul )

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null

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null

0

6

aad54e229a30060595d79a80e2c984060081bcd4

37

py

Python

pywaterml/__main__.py

BYU-Hydroinformatics/pywaterml

d4c88a0402dec61d466edb1fa5dbda4544f7a738

[ "BSD-3-Clause" ]

1

2021-11-10T18:28:10.000Z

pywaterml/__main__.py

BYU-Hydroinformatics/pywaterml

d4c88a0402dec61d466edb1fa5dbda4544f7a738

[ "BSD-3-Clause" ]

null

pywaterml/__main__.py

BYU-Hydroinformatics/pywaterml

d4c88a0402dec61d466edb1fa5dbda4544f7a738

[ "BSD-3-Clause" ]

1

2021-03-15T00:18:34.000Z

from pywaterml import cli cli.cli()

9.25

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4.666667

0.666667

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631b20b19ae22e194c18886805f9660aaae9332f

127

py

Python

FinMind/crawler/__init__.py

vishalbelsare/FinMind

9890c631952f3ab91560ada6f49971bff26a5858

[ "Apache-2.0" ]

1,106

2019-10-04T15:16:59.000Z

2022-03-31T03:50:19.000Z

FinMind/crawler/__init__.py

Jerremiah/FinMind

bf57f8e68adc0583495b29135a91e47515cf4cf1

[ "Apache-2.0" ]

119

2019-10-07T09:18:18.000Z

2022-03-12T08:25:58.000Z

FinMind/crawler/__init__.py

Jerremiah/FinMind

bf57f8e68adc0583495b29135a91e47515cf4cf1

[ "Apache-2.0" ]

184

2019-10-06T08:26:53.000Z

2022-03-21T06:25:31.000Z

from FinMind.crawler.commodities import CommoditiesCrawler from FinMind.crawler.government_bonds import GovernmentBondsCrawler

42.333333

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0.905512

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127

8.769231

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0.192982

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0

0.062992

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null

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1

0

1

0

6

2d696cd97b2e70e930df14d83d8e0a89bd580b5a

82

py

Python

site_scons/site_tools/test.py

robobrobro/physics-engine

0000e2155106f3c6c0485af96bc14120dc6d155a

[ "MIT" ]

null

site_scons/site_tools/test.py

robobrobro/physics-engine

0000e2155106f3c6c0485af96bc14120dc6d155a

[ "MIT" ]

8

2019-01-26T03:19:46.000Z

2019-04-16T14:22:53.000Z

site_scons/site_tools/test.py

robobrobro/physics-engine

0000e2155106f3c6c0485af96bc14120dc6d155a

[ "MIT" ]

null

def exists(env): return True def generate(env): env.Replace(MODE='test')

13.666667

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null

0

1

0

1

0

6

2d7a65c14c3f592b101ad9734d2735c93a8be732

81

py

Python

services/modes_microservice/database_service.py

tojoabella/dripi

df8a34116f2c7ed68170364c3d5ab5cbd46d1209

[ "MIT" ]

4

2021-05-15T00:58:46.000Z

2022-02-23T09:25:07.000Z

services/modes_microservice/database_service.py

tojoabella/dripi

df8a34116f2c7ed68170364c3d5ab5cbd46d1209

[ "MIT" ]

null

services/modes_microservice/database_service.py

tojoabella/dripi

df8a34116f2c7ed68170364c3d5ab5cbd46d1209

[ "MIT" ]

null

class DatabaseService: def __init__(self): #TODO: mongo pass

16.2

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16.2

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1

0

1

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1

0

6

2dbf43795adb82653553a589e42ea606522130b0

16,333

py

Python

parsimony/config.py

nguigs/pylearn-parsimony

f712d2828823d6d55a2470ce060bcaeda2d0589a

[ "BSD-3-Clause" ]

41

2015-02-27T13:26:01.000Z

2021-07-13T12:48:14.000Z

parsimony/config.py

nguigs/pylearn-parsimony

f712d2828823d6d55a2470ce060bcaeda2d0589a

[ "BSD-3-Clause" ]

31

2015-01-12T15:02:45.000Z

2022-02-10T07:11:07.000Z

parsimony/config.py

nguigs/pylearn-parsimony

f712d2828823d6d55a2470ce060bcaeda2d0589a

[ "BSD-3-Clause" ]

15

2015-01-12T14:48:39.000Z

2021-07-13T12:48:32.000Z

# -*- coding: utf-8 -*- """Handles configuration settings in pylearn-parsimony. Try to make the sections correspond to packages (sans the parsimony prefix), such that settings for parsimony.algorithms are found in the section "algorithms", and that parsimony.utils.consts is found in the section "utils.consts", etc. Created on Wed Apr 8 21:21:20 2015 Copyright (c) 2013-2017, CEA/DSV/I2BM/Neurospin. All rights reserved. @author: Tommy Löfstedt @email: lofstedt.tommy@gmail.com @license: BSD 3-clause. """ import os.path import inspect import warnings try: import configparser # Python 3 except ImportError: import ConfigParser as configparser # Python 2 __all__ = ["get_option", "get_boolean", "get_float", "get_int", "set_option", "flush"] # TODO: Python 3 destroys the ini file sometimes on delete. Make the config # read the file on every call? Slower, but much safer; especially when # running multiple instances of pylearn-parsimony. #__config__ = None #__ini_file__ = "config.ini" #__flush_dry_run__ = False class __Config(object): __flush_dry_run__ = False def __init__(self, ini_file): self._ini_file = self._ini_file_name(str(ini_file)) self._config = configparser.ConfigParser() if os.path.exists(self._ini_file): try: self._config.read(self._ini_file) except configparser.ParsingError: warnings.warn("Could not parse the config file.", RuntimeWarning) else: warnings.warn("Could not locate the config file.", RuntimeWarning) def __del__(self): # Save updates to configuration file. Cannot call flush here. try: if not self.__flush_dry_run__: if os.path.exists(self._ini_file): with open(self._ini_file, "wb") as fid: self._config.write(fid) else: warnings.warn("Could not locate the config file.", RuntimeWarning) except Exception: # TODO: Anything we can do to resolve this? pass # Couldn't save. Objects used are probably deleted already. def _ini_file_name(self, ini_file): """Extracts the directory of this module. """ fname = inspect.currentframe() # This module. fname = inspect.getfile(fname) # Filename of this module. fname = os.path.abspath(fname) # Absolute path of this module. fname = os.path.dirname(fname) # Directory of this module. if fname[-1] != "/": fname = fname + "/" # Should be there, but just in case ... fname = fname + ini_file # The ini file. return fname def get_option(self, section, option, default=None): """Fetches a configuration option from a section of the ini file. If not found, returns the default value. """ section = str(section) option = str(option) if not self._config.has_section(section): # Subsumed by the below? value = default elif not self._config.has_option(section, option): value = default else: value = self._config.get(section, option) return str(value) def get_boolean(self, section, option, default=False): """Fetches a boolean configuration option from a section of the ini file. If not found, returns the default value False. """ section = str(section) option = str(option) if not self._config.has_section(section): # Subsumed by the below? value = default elif not self._config.has_option(section, option): value = default else: value = self._config.getboolean(section, option) return bool(value) def get_float(self, section, option, default=0.0): """Fetches a floating point configuration option from a section of the ini file. If not found, returns the default value 0.0. """ section = str(section) option = str(option) if not self._config.has_section(section): # Subsumed by the below? value = default elif not self._config.has_option(section, option): value = default else: value = self._config.getfloat(section, option) return float(value) def get_int(self, section, option, default=0): """Fetches an integer configuration option from a section of the ini file. If not found, returns the default value 0. """ section = str(section) option = str(option) if not self._config.has_section(section): # Subsumed by the below? value = default elif not self._config.has_option(section, option): value = default else: value = self._config.getint(section, option) return int(value) def set_option(self, section, option, value, flush_file=False): """Sets a configuration option. """ section = str(section) option = str(option) value = str(value) if not self._config.has_section(section): self._config.add_section(section) self._config.set(section, option, value) if flush_file: self.flush() def flush(self): """Saves the current configuration to disk. """ if os.path.exists(self._ini_file): if not self.__flush_dry_run__: with open(self._ini_file, "wb") as fid: self._config.write(fid) else: warnings.warn("Could not locate the config file.", RuntimeWarning) __config__ = __Config("config.ini") #def __ini_file_name__(): # """Extracts the directory of this module. # """ # fname = inspect.currentframe() # This module. # fname = inspect.getfile(fname) # Filename of this module. # fname = os.path.abspath(fname) # Absolute path of this module. # fname = os.path.dirname(fname) # Directory of this module. # if fname[-1] != "/": # fname = fname + "/" # Should be there, but just in case ... # fname = fname + __ini_file__ # The ini file. # # return fname #def __load_config__(): # """Loads the configuration settings from the ini file. # """ # global __config__ # __config__ = ConfigParser.ConfigParser() # # fname = __ini_file_name__() # if os.path.exists(fname): # try: # __config__.read(fname) # # return True # # except ConfigParser.ParsingError: # warnings.warn("Could not parse the config file.", RuntimeWarning) # else: # warnings.warn("Could not locate the config file.", RuntimeWarning) # # return False def get_option(section, option, default=None): """Fetches a configuration option from a section of the ini file. If not found, returns the default value. Parameters ---------- section : String. The section of the ini file to read from. Try to make the sections correspond to packages (sans the parsimony prefix), such that settings for parsimony.algorithms are found in the section "algorithms", and that parsimony.utils.consts is found in the section "utils.consts", etc. option : String. The option to read from the ini file section. default : Object, but ideally a string. The default value to return if the section or option doesn't exist. Examples -------- >>> import parsimony.config as config >>> >>> config.__config__.__flush_dry_run__ = True # Only for the doctests. >>> config.set_option("test_section", "testing_get", "value") >>> config.get_option("test_section", "testing_get") 'value' """ # if __config__ is None: # if not __load_config__(): # return default # # section = str(section) # option = str(option) # # if not __config__.has_section(section): # Subsumed by the below? # return default # if not __config__.has_option(section, option): # return default # # value = __config__.get_option(section, option) value = __config__.get_option(section, option, default=default) return value def get_boolean(section, option, default=False): """Fetches a boolean configuration option from a section of the ini file. If not found, returns the default value False. Parameters ---------- section : String. The section of the ini file to read from. Try to make the sections correspond to packages (sans the parsimony prefix), such that settings for parsimony.algorithms are found in the section "algorithms", and that parsimony.utils.consts is found in the section "utils.consts", etc. option : String. The boolean option to read from the ini file section. default : Boolean. The default value to return if the section or option does not exist. Default is False. Examples -------- >>> import parsimony.config as config >>> >>> config.__config__.__flush_dry_run__ = True # Only for the doctests. >>> config.set_option("test_section", "testing_get_boolean", "False") >>> config.get_option("test_section", "testing_get_boolean") 'False' >>> config.get_boolean("test_section", "testing_get_boolean") False >>> config.set_option("test_section", "testing_get_boolean", 0) >>> config.get_boolean("test_section", "testing_get_boolean") False >>> config.set_option("test_section", "testing_get_boolean", 1) >>> config.get_boolean("test_section", "testing_get_boolean") True >>> config.set_option("test_section", "testing_get_boolean", "off") >>> config.get_boolean("test_section", "testing_get_boolean") False >>> config.set_option("test_section", "testing_get_boolean", "on") >>> config.get_boolean("test_section", "testing_get_boolean") True >>> config.set_option("test_section", "testing_get_boolean", "no") >>> config.get_boolean("test_section", "testing_get_boolean") False >>> config.set_option("test_section", "testing_get_boolean", "yes") >>> config.get_boolean("test_section", "testing_get_boolean") True >>> config.get_boolean("test_section", "testing_non_existent", True) True """ # if __config__ is None: # if not __load_config__(): # return default # # section = str(section) # option = str(option) # # if not __config__.has_section(section): # Subsumed by the below? # return default # if not __config__.has_option(section, option): # return default # # value = __config__.getboolean(section, option) value = __config__.get_boolean(section, option, default=default) return value def get_float(section, option, default=0.0): """Fetches a floating point configuration option from a section of the ini file. If not found, returns the default value 0.0. Parameters ---------- section : String. The section of the ini file to read from. Try to make the sections correspond to packages (sans the parsimony prefix), such that settings for parsimony.algorithms are found in the section "algorithms", and that parsimony.utils.consts is found in the section "utils.consts", etc. option : String. The floating point option to read from the ini file section. default : Float. The default value to return if the section or option does not exist. Default is 0.0. Examples -------- >>> import parsimony.config as config >>> >>> config.__config__.__flush_dry_run__ = True # Only for the doctests. >>> config.set_option("test_section", "testing_get_float", "3.14159265358") >>> config.get_option("test_section", "testing_get_float") '3.14159265358' >>> config.get_float("test_section", "testing_get_float") 3.14159265358 >>> config.get_float("test_section", "testing_non_existent", 2.71828182845) 2.71828182845 """ # if __config__ is None: # if not __load_config__(): # return default # # section = str(section) # option = str(option) # # if not __config__.has_section(section): # Subsumed by the below? # return default # if not __config__.has_option(section, option): # return default # # value = __config__.getfloat(section, option) value = __config__.get_float(section, option, default=default) return value def get_int(section, option, default=0): """Fetches an integer configuration option from a section of the ini file. If not found, returns the default value 0. Parameters ---------- section : String. The section of the ini file to read from. Try to make the sections correspond to packages (sans the parsimony prefix), such that settings for parsimony.algorithms are found in the section "algorithms", and that parsimony.utils.consts is found in the section "utils.consts", etc. option : String. The integer option to read from the ini file section. default : Integer. The default value to return if the section or option does not exist. Default is 0. Examples -------- >>> import parsimony.config as config >>> >>> config.__config__.__flush_dry_run__ = True # Only for the doctests. >>> config.set_option("test_section", "testing_get_int", "11630") >>> config.get_option("test_section", "testing_get_int") '11630' >>> config.get_int("test_section", "testing_get_int") 11630 >>> config.get_float("test_section", "testing_non_existent", 12407) 12407.0 """ # if __config__ is None: # if not __load_config__(): # return default # # section = str(section) # option = str(option) # # if not __config__.has_section(section): # Subsumed by the below? # return default # if not __config__.has_option(section, option): # return default # # value = __config__.getint(section, option) value = __config__.get_int(section, option, default=default) return value def set_option(section, option, value, flush_file=False): """Sets a configuration option. Parameters ---------- section : String. The section of the ini file to write to. Try to make the sections correspond to packages, such that settings for parsimony.algorithms are found in the section algorithms. option : String. The option to write to the ini file section. value : String. The value to write to the ini file section. flush_file : Boolean. If true, saves the current configuration to disk. Examples -------- >>> import parsimony.config as config >>> >>> config.__config__.__flush_dry_run__ = True # Only for the doctests. >>> config.set_option("test_section", "testing_set", "Theorem VI") >>> config.get_option("test_section", "testing_set") 'Theorem VI' """ # if __config__ is None: # __load_config__() # # section = str(section) # option = str(option) # value = str(value) # # if not __config__.has_section(section): # __config__.add_section(section) # # __config__.set_option(section, option, value) # # if flush_file: # flush() __config__.set_option(section, option, value, flush_file=flush_file) def flush(): """Saves the current configuration to disk. Examples -------- >>> import parsimony.config as config >>> >>> config.__config__.__flush_dry_run__ = True # Only for the doctests. >>> config.set_option("test_section", "testing_flush", "243000000") >>> config.flush() """ # if __config__ is None: # if not __load_config__(): # return # Nothing to save. # # fname = __ini_file_name__() # # if os.path.exists(fname): # if not __flush_dry_run__: # with open(fname, "wb") as fid: # __config__.write(fid) # else: # warnings.warn("Could not locate the config file.", RuntimeWarning) __config__.flush() if __name__ == "__main__": import doctest doctest.testmod()

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2de38955f1b920209949c1223ac002fc2e0043b9

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py

Python

workspace/module/maya-python-2.7/LxMaInterface/maIfMethods/__init__.py

no7hings/Lynxi

43c745198a714c2e5aca86c6d7a014adeeb9abf7

[ "MIT" ]

2

2018-03-06T03:33:55.000Z

2019-03-26T03:25:11.000Z

workspace/module/maya-python-2.7/LxMaInterface/maIfMethods/__init__.py

no7hings/lynxi

43c745198a714c2e5aca86c6d7a014adeeb9abf7

[ "MIT" ]

null

workspace/module/maya-python-2.7/LxMaInterface/maIfMethods/__init__.py

no7hings/lynxi

43c745198a714c2e5aca86c6d7a014adeeb9abf7

[ "MIT" ]

null

# coding:utf-8 from ._maIfMtdTreeview import *

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2de67f932686e333502a2eea3414df28c937a989

34

py

Python

game_engine/__init__.py

dferndz/r2d2

b2cb606a82c4ae4c267c7e6fc9d5e236c762b42e

[ "MIT" ]

1

2020-09-06T06:29:35.000Z

game_engine/__init__.py

dferndz/r2d2

b2cb606a82c4ae4c267c7e6fc9d5e236c762b42e

[ "MIT" ]

null

game_engine/__init__.py

dferndz/r2d2

b2cb606a82c4ae4c267c7e6fc9d5e236c762b42e

[ "MIT" ]

null

from game_engine.game import Game

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6

933f5d04dfa1ce3f6811c0d4423120f1a390b2d1

39

py

Python

terrainbento/derived_models/model_400_basicSa/__init__.py

mcflugen/terrainbento

1b756477b8a8ab6a8f1275b1b30ec84855c840ea

[ "MIT" ]

null

terrainbento/derived_models/model_400_basicSa/__init__.py

mcflugen/terrainbento

1b756477b8a8ab6a8f1275b1b30ec84855c840ea

[ "MIT" ]

null

terrainbento/derived_models/model_400_basicSa/__init__.py

mcflugen/terrainbento

1b756477b8a8ab6a8f1275b1b30ec84855c840ea

[ "MIT" ]

null

from .model_400_basicSa import BasicSa

19.5

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null

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1

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1

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1

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6

9349391bd7b40f1c0f71f4121061bb4ffd595ac6

78

py

Python

tests/test_dp.py

slxiao/partition

53ff137b26816d64bf002baf269dbf97b601a3ca

[ "MIT" ]

5

2019-11-22T08:34:12.000Z

2021-09-21T03:18:31.000Z

tests/test_dp.py

slxiao/partition

53ff137b26816d64bf002baf269dbf97b601a3ca

[ "MIT" ]

3

2019-12-22T10:28:44.000Z

2021-10-09T19:14:31.000Z

tests/test_dp.py

slxiao/partition

53ff137b26816d64bf002baf269dbf97b601a3ca

[ "MIT" ]

1

2020-12-01T15:31:30.000Z

from partition import dp def test_dp(numbers): assert dp.dp(numbers) == 1

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null

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6

934d76435db6527ea3e3414daa3afad02f79c9c0

23

py

Python

exam_gen/exam/__init__.py

rohit507/exam_gen

b6e96955e1762fe8063282917fd69df420142cbb

[ "Apache-2.0" ]

null

exam_gen/exam/__init__.py

rohit507/exam_gen

b6e96955e1762fe8063282917fd69df420142cbb

[ "Apache-2.0" ]

null

exam_gen/exam/__init__.py

rohit507/exam_gen

b6e96955e1762fe8063282917fd69df420142cbb

[ "Apache-2.0" ]

null

from .base import Exam

11.5

22

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null

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1

0

1

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1

0

6

935b55dca4a21fa43df8252ad4c5b46700ba1164

1,319

py

Python

python/tests/generated/api/fieldset/test_copies.py

eno-lang/enolib

4175f7c1e8246493b6758c29bddc80d20eaf15f7

[ "MIT" ]

17

2019-04-15T21:03:37.000Z

2022-01-24T11:03:34.000Z

python/tests/generated/api/fieldset/test_copies.py

eno-lang/enolib

4175f7c1e8246493b6758c29bddc80d20eaf15f7

[ "MIT" ]

20

2019-03-13T23:23:40.000Z

2022-03-29T13:40:57.000Z

python/tests/generated/api/fieldset/test_copies.py

eno-lang/enolib

4175f7c1e8246493b6758c29bddc80d20eaf15f7

[ "MIT" ]

4

2019-04-15T21:18:03.000Z

2019-09-21T16:18:10.000Z

import enolib def test_querying_four_entries_from_a_fieldset_all_of_them_copied_from_another_fieldset_produces_the_expected_result(): input = ("fieldset:\n" "1 = 1\n" "2 = 2\n" "3 = 3\n" "4 = 4\n" "\n" "copy < fieldset") output = [entry.required_string_value() for entry in enolib.parse(input).fieldset('copy').entries()] assert output == ['1', '2', '3', '4'] def test_querying_four_entries_from_a_fieldset_two_of_them_copied_from_another_fieldset_produces_the_expected_result(): input = ("fieldset:\n" "1 = 1\n" "2 = 2\n" "\n" "copy < fieldset\n" "3 = 3\n" "4 = 4") output = [entry.required_string_value() for entry in enolib.parse(input).fieldset('copy').entries()] assert output == ['1', '2', '3', '4'] def test_querying_three_entries_from_a_fieldset_one_owned_one_replaced_one_copied_produces_the_expected_result(): input = ("fieldset:\n" "1 = 1\n" "2 = 0\n" "\n" "copy < fieldset\n" "2 = 2\n" "3 = 3") output = [entry.required_string_value() for entry in enolib.parse(input).fieldset('copy').entries()] assert output == ['1', '2', '3']

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null

0

1

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null

0

6

fad69d287860b7bd12d644265d3cf390818b532d

147

py

Python

backend/modules/camera/admin.py

crowdbotics-apps/my-new-app-31789

c5513ad2df9e73707871e1c10c6768a93690f9a7

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

null

backend/modules/camera/admin.py

crowdbotics-apps/my-new-app-31789

c5513ad2df9e73707871e1c10c6768a93690f9a7

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

null

backend/modules/camera/admin.py

crowdbotics-apps/my-new-app-31789

c5513ad2df9e73707871e1c10c6768a93690f9a7

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

null

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

14.7

38

0.782313

19

147

6.052632

0.684211

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true

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null

0

1

0

null

0

1

0

1

0

6

fafd7fa1401fcb8a54d01929463ecea157ec77a7

43

py

Python

dis_snek/models/__init__.py

Astrea49/dis_snek

c899a6f1caa3c2a45323dbe50ed8ed62676be9d6

[ "MIT" ]

null

dis_snek/models/__init__.py

Astrea49/dis_snek

c899a6f1caa3c2a45323dbe50ed8ed62676be9d6

[ "MIT" ]

null

dis_snek/models/__init__.py

Astrea49/dis_snek

c899a6f1caa3c2a45323dbe50ed8ed62676be9d6

[ "MIT" ]

null

from .discord import * from .snek import *

14.333333

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6

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null

0

1

0

1

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1

0

6

4f07c925586465e06a86d3a27f97f950de54ac82

233

py

Python

server/lib/python/cartodb_services/cartodb_services/refactor/tools/redis_mock.py

digideskio/dataservices-api

246ec135dbeaa3f9a52717fdac50a4ab040ce22b

[ "BSD-3-Clause" ]

22

2016-03-11T17:33:31.000Z

2021-02-22T04:00:43.000Z

server/lib/python/cartodb_services/cartodb_services/refactor/tools/redis_mock.py

digideskio/dataservices-api

246ec135dbeaa3f9a52717fdac50a4ab040ce22b

[ "BSD-3-Clause" ]

338

2016-02-16T16:13:13.000Z

2022-03-30T15:50:17.000Z

server/lib/python/cartodb_services/cartodb_services/refactor/tools/redis_mock.py

CartoDB/dataservices-api

d0f28cc002ef11df9f371d5d1fd2d0901c245f97

[ "BSD-3-Clause" ]

14

2016-09-22T15:29:33.000Z

2021-02-08T03:46:40.000Z

class RedisConnectionMock(object): """ Simple class to mock a dummy behaviour for Redis related functions """ def zscore(self, redis_prefix, day): pass def zincrby(self, redis_prefix, day, amount): pass

25.888889

78

0.67382

29

233

5.344828

0.724138

0.116129

0.193548

0.232258

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0.240343

233

8

79

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0.6

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null

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1

0

null

0

1

0

1

0

6

35703a4d7b8370977c06bb5bc60ba4db0e736a50

74

py

Python

steam/__init__.py

danielsuo/steam

1ff5efdddb3c464dcfedabc9c98e1a54be52850d

[ "MIT" ]

null

steam/__init__.py

danielsuo/steam

1ff5efdddb3c464dcfedabc9c98e1a54be52850d

[ "MIT" ]

null

steam/__init__.py

danielsuo/steam

1ff5efdddb3c464dcfedabc9c98e1a54be52850d

[ "MIT" ]

null

from .constants import * from .helmholtz import * from .property import *

18.5

24

0.756757

9

74

6.222222

0.555556

0.357143

0

0.162162

74

3

25

24.666667

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true

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1

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null

1

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0

null

0

1

0

1

0

1

0

6

ea574f4ffa33cd9df1b11f3d894535ca2c4afab7

112

py

Python

okl4_kernel/okl4_2.1.1-patch.9/tools/magpie-parsers/src/magpieparsers/gnuc/preprocessorinfochannel.py

CyberQueenMara/baseband-research

e1605537e10c37e161fff1a3416b908c9894f204

[ "MIT" ]

77

2018-12-31T22:12:09.000Z

2021-12-31T22:56:13.000Z

okl4_kernel/okl4_2.1.1-patch.9/tools/magpie-parsers/src/magpieparsers/gnuc/preprocessorinfochannel.py

CyberQueenMara/baseband-research

e1605537e10c37e161fff1a3416b908c9894f204

[ "MIT" ]

null

okl4_kernel/okl4_2.1.1-patch.9/tools/magpie-parsers/src/magpieparsers/gnuc/preprocessorinfochannel.py

CyberQueenMara/baseband-research

e1605537e10c37e161fff1a3416b908c9894f204

[ "MIT" ]

24

2019-01-20T15:51:52.000Z

2021-12-25T18:29:13.000Z

# FIXME: Stub class PreprocessorInfoChannel(object): def addLineForTokenNumber(self, line, toknum): pass

14

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0

1

0

1

0

1

0

1

0

6

ea83084b64a6d381e3702e2c27922b3860e439e8

8,576

py

Python

tamcolors/tests/tam_tools_tests/tam_text_box_tests.py

cmcmarrow/tamcolors

65a5f2455bbe35a739b98d14af158c3df7feb786

[ "Apache-2.0" ]

29

2020-07-17T23:46:17.000Z

2022-02-06T05:36:44.000Z

tamcolors/tests/tam_tools_tests/tam_text_box_tests.py

sudo-nikhil/tamcolors

65a5f2455bbe35a739b98d14af158c3df7feb786

[ "Apache-2.0" ]

42

2020-07-25T19:39:52.000Z

2021-02-24T01:19:58.000Z

tamcolors/tests/tam_tools_tests/tam_text_box_tests.py

sudo-nikhil/tamcolors

65a5f2455bbe35a739b98d14af158c3df7feb786

[ "Apache-2.0" ]

8

2020-07-18T23:02:48.000Z

2020-12-30T04:07:35.000Z

# built in libraries import unittest.mock # tamcolors libraries from tamcolors import tam_io from tamcolors import tam_tools from tamcolors.tam_io.tam_colors import * class TAMTextBoxTests(unittest.TestCase): def test_tam_text_box_init(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE) self.assertIsInstance(text_box, tam_tools.tam_text_box.TAMTextBox) def test_tam_text_box_str(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE) self.assertEqual(str(text_box), "hello world!") def test_tam_text_box_str_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("cat world!\n123", 20, 34, "#", YELLOW, PURPLE) self.assertEqual(str(text_box), "cat world!\n123") def test_update(self): text_box = tam_tools.tam_text_box.TAMTextBox("", 20, 15, "#", YELLOW, PURPLE) surface = tam_io.tam_surface.TAMSurface(20, 15, " ", YELLOW, PURPLE) surface2 = tam_io.tam_surface.TAMSurface(20, 15, "@", RED, GREEN) text_box.draw(surface2) for i in range(20): surface.set_spot(i, 0, "#", YELLOW, PURPLE) surface.set_spot(i, 14, "#", YELLOW, PURPLE) for i in range(1, 15): surface.set_spot(0, i, "#", YELLOW, PURPLE) surface.set_spot(19, i, "#", YELLOW, PURPLE) text_box.update() text_box.draw(surface2) self.assertEqual(surface, surface2) def test_draw(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 15, "#", YELLOW, PURPLE) surface = tam_io.tam_surface.TAMSurface(20, 15, " ", YELLOW, PURPLE) surface2 = tam_io.tam_surface.TAMSurface(20, 15, "@", RED, GREEN) text_box.draw(surface2) for i in range(20): surface.set_spot(i, 0, "#", YELLOW, PURPLE) surface.set_spot(i, 14, "#", YELLOW, PURPLE) for i in range(1, 15): surface.set_spot(0, i, "#", YELLOW, PURPLE) surface.set_spot(19, i, "#", YELLOW, PURPLE) for spot, char in enumerate("hello world!"): surface.set_spot(2 + spot, 7, char, YELLOW, PURPLE) text_box.update() text_box.draw(surface2) self.assertEqual(surface, surface2) def test_draw_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 15, "#", YELLOW, PURPLE, clock=1) surface = tam_io.tam_surface.TAMSurface(20, 15, " ", YELLOW, PURPLE) surface2 = tam_io.tam_surface.TAMSurface(20, 15, "@", RED, GREEN) text_box.draw(surface2) self.assertEqual(surface, surface2) for i in range(20): surface.set_spot(i, 0, "#", YELLOW, PURPLE) surface.set_spot(i, 14, "#", YELLOW, PURPLE) for i in range(1, 15): surface.set_spot(0, i, "#", YELLOW, PURPLE) surface.set_spot(19, i, "#", YELLOW, PURPLE) text_box.update() text_box.draw(surface2) self.assertEqual(surface, surface2) for spot, char in enumerate("hello world!"): surface.set_spot(2 + spot, 7, char, YELLOW, PURPLE) text_box.update() text_box.draw(surface2) self.assertEqual(surface, surface2) text_box.update() text_box.draw(surface2) self.assertEqual(surface, surface2) def test_draw_3(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!\ncats\n1\n\nhi", 19, 16, "#", RED, GREEN, center_vertical=False, center_horizontal=True, vertical_space=2, vertical_start=3, char_background="%") surface = tam_io.tam_surface.TAMSurface(19, 16, "%", RED, GREEN) surface2 = tam_io.tam_surface.TAMSurface(19, 16, "@", YELLOW, BLUE) for i in range(19): surface.set_spot(i, 0, "#", RED, GREEN) surface.set_spot(i, 15, "#", RED, GREEN) for i in range(1, 16): surface.set_spot(0, i, "#", RED, GREEN) surface.set_spot(18, i, "#", RED, GREEN) for spot, char in enumerate("hello world!"): surface.set_spot(3 + spot, 3, char, RED, GREEN) for spot, char in enumerate("cats"): surface.set_spot(7 + spot, 5, char, RED, GREEN) surface.set_spot(9, 7, "1", RED, GREEN) for spot, char in enumerate("hi"): surface.set_spot(8 + spot, 11, char, RED, GREEN) text_box.update() text_box.draw(surface2) self.assertEqual(surface, surface2) def test_done(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE) self.assertTrue(text_box.done()) text_box.update() self.assertTrue(text_box.done()) def test_done_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE, clock=1) for _ in range(14): self.assertFalse(text_box.done()) text_box.update() self.assertTrue(text_box.done()) def test_set_colors(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE) self.assertTrue(text_box.done()) text_box.update() self.assertTrue(text_box.done()) text_box.set_colors(BLUE, AQUA) self.assertTrue(text_box.done()) def test_set_colors_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE, clock=1) for _ in range(14): self.assertFalse(text_box.done()) text_box.update() self.assertTrue(text_box.done()) text_box.set_colors(BLUE, AQUA) self.assertTrue(text_box.done()) def test_set_colors_3(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE, clock=1) for _ in range(13): self.assertFalse(text_box.done()) text_box.update() self.assertFalse(text_box.done()) text_box.set_colors(BLUE, AQUA) self.assertFalse(text_box.done()) def test_get_colors(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE) self.assertEqual(text_box.get_colors(), (YELLOW, PURPLE)) def test_get_colors_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", AQUA, RED) self.assertEqual(text_box.get_colors(), (AQUA, RED)) def test_set_char(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "^", YELLOW, PURPLE) text_box.set_char("#") self.assertEqual(text_box.get_char(), "#") def test_set_char_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "@", AQUA, RED) text_box.set_char("$") self.assertEqual(text_box.get_char(), "$") def test_get_char(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE) self.assertEqual(text_box.get_char(), "#") def test_get_char_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "!", AQUA, RED) self.assertEqual(text_box.get_char(), "!") def test_get_text(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE) self.assertEqual(text_box.get_text(), "hello world!") def test_get_text_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("cat world!\n123", 20, 34, "#", YELLOW, PURPLE) self.assertEqual(text_box.get_text(), "cat world!\n123") def test_get_dimensions(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 20, 34, "#", YELLOW, PURPLE) self.assertEqual(text_box.get_dimensions(), (20, 34)) def test_get_dimensions_2(self): text_box = tam_tools.tam_text_box.TAMTextBox("hello world!", 4, 3, "#", YELLOW, PURPLE) self.assertEqual(text_box.get_dimensions(), (4, 3))

40.45283

106

0.585938

1,104

8,576

4.314312

0.076993

0.145497

0.054587

0.072433

0.881377

0.840647

0.82658

0.797817

0.778501

0.748898

0

0.038593

0.2809

8,576

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107

40.64455

0.733744

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0.049561

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0

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1

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false

0

0.02454

0

0.165644

0

null

0

1

0

null

0

6

ea9ccb460241bd5528e300962cfc2d963a9e9eb7

245

py

Python

rbac/ldap/daoex.py

shawnmckinney/py-fortress

ead12bf9b7e37e923c42ccdadd8fd3c5adf027cf

[ "Apache-2.0" ]

16

2018-03-19T02:19:01.000Z

2021-12-30T15:24:40.000Z

rbac/ldap/daoex.py

shawnmckinney/py-fortress

ead12bf9b7e37e923c42ccdadd8fd3c5adf027cf

[ "Apache-2.0" ]

1

2021-12-18T16:46:04.000Z

rbac/ldap/daoex.py

shawnmckinney/py-fortress

ead12bf9b7e37e923c42ccdadd8fd3c5adf027cf

[ "Apache-2.0" ]

2

2018-03-14T21:48:43.000Z

2018-03-19T03:25:40.000Z

''' @copyright: 2022 - Symas Corporation ''' from rbac.util.fortress_error import RbacError class LdapException(RbacError): pass class NotFound(RbacError): pass class NotUnique(RbacError): pass class InvalidCredentials(RbacError): pass

13.611111

46

0.77551

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0.62963

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0.130612

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17

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true

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null

1

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null

0

1

0

1

0

6

57a298a146c80952a9429f88a91bcd6576ae486d

93

py

Python

src/plotshapes/__init__.py

sarang-IITKgp/plot-shapes

33aff54515eabd55afe42bf0091395dc3e6e6829

[ "BSD-3-Clause" ]

null

src/plotshapes/__init__.py

sarang-IITKgp/plot-shapes

33aff54515eabd55afe42bf0091395dc3e6e6829

[ "BSD-3-Clause" ]

null

src/plotshapes/__init__.py

sarang-IITKgp/plot-shapes

33aff54515eabd55afe42bf0091395dc3e6e6829

[ "BSD-3-Clause" ]

null

import plotshapes.conic_sections import plotshapes.quadrilateral import plotshapes.transform

23.25

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93

8.3

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0

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1

0

1

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6

57d8cf08100902923b5e2e85d8e5d3e727d014e8

4,165

py

Python

tests/functional/saltenv/ops/test_func_pin_current_version.py

eitrtechnologies/saltenv

66add964657fe270ed96ddfe50802e27539a6526

[ "Apache-2.0" ]

5

2022-03-25T17:15:04.000Z

2022-03-28T23:24:26.000Z

tests/functional/saltenv/ops/test_func_pin_current_version.py

eitrtechnologies/saltenv

66add964657fe270ed96ddfe50802e27539a6526

[ "Apache-2.0" ]

null

tests/functional/saltenv/ops/test_func_pin_current_version.py

eitrtechnologies/saltenv

66add964657fe270ed96ddfe50802e27539a6526

[ "Apache-2.0" ]

2

2022-03-26T06:33:30.000Z

2022-03-29T19:43:50.000Z

from unittest.mock import patch async def test_func_pin_current_version_no_active_version(mock_hub, hub, tmp_path): """ SCENARIO #1: - There is no active version """ # Link the function to the mock_hub mock_hub.saltenv.ops.pin_current_version = hub.saltenv.ops.pin_current_version # Mock the return of get_current_version to be ("",""), meaning that # there is no active version mock_hub.saltenv.ops.get_current_version.return_value = ("", "") # Check that pin_current_version return False AND # that the override_version_file version IS NOT CHANGED with patch("os.getcwd") as mocked_override_dir: # Set up the mocked_override_file mocked_override_dir.return_value = tmp_path mocked_override_dir.mkdir() mocked_override_file = tmp_path / ".salt-version" existing_override_version = "3004" mocked_override_file.write_text("3004") # Confirm the return is False expected = False actual = await mock_hub.saltenv.ops.pin_current_version() actual == expected # Confirm that the mocked_override_file is unchanged assert mocked_override_file.read_text() == existing_override_version async def test_func_pin_current_version_active_version_matches_override(mock_hub, hub, tmp_path): """ SCENARIO #2: - There is an active version - The active version matches the version in the override file. """ # Link the function to the mock_hub mock_hub.saltenv.ops.pin_current_version = hub.saltenv.ops.pin_current_version # Mock the return of get_current_version to be ("3004", tmp_path/version), where # tmp_path/version is the main version file. The main version file would have 3004 as its value. existing_override_version = "3004" mock_hub.saltenv.ops.get_current_version.return_value = ( existing_override_version, str(tmp_path / "version"), ) # Check that pin_current_version return True AND that the # override_version_file version IS NOT CHANGED with patch("os.getcwd") as mocked_override_dir: # Set up the mocked_override_file mocked_override_dir.return_value = tmp_path mocked_override_dir.mkdir() mocked_override_file = tmp_path / ".salt-version" mocked_override_file.write_text(existing_override_version) # Confirm the return is True expected = True actual = await mock_hub.saltenv.ops.pin_current_version() assert actual == expected # Confirm that the mocked_override_file is unchanged assert mocked_override_file.read_text() == existing_override_version async def test_func_pin_current_version_active_version_does_not_match_override( mock_hub, hub, tmp_path ): """ SCENARIO #3: - There is an active version - The active version does not match the version in the override file. """ # Link the function to the mock_hub mock_hub.saltenv.ops.pin_current_version = hub.saltenv.ops.pin_current_version # Mock the return of get_current_version to be ("3004", tmp_path/version), where # tmp_path/version is the main version file. The main version file would have 3004 as its value. updated_override_version = "3004" mock_hub.saltenv.ops.get_current_version.return_value = ( updated_override_version, str(tmp_path / "version"), ) # Check that pin_current_version return True AND that the # override_version_file version IS CHANGED with patch("os.getcwd") as mocked_override_dir: # Set up the mocked_override_file mocked_override_dir.return_value = tmp_path mocked_override_dir.mkdir() mocked_override_file = tmp_path / ".salt-version" existing_override_version = "3003" mocked_override_file.write_text(existing_override_version) # Confirm the return is True expected = True actual = await mock_hub.saltenv.ops.pin_current_version() assert actual == expected # Confirm that the mocked_override_file is unchanged assert mocked_override_file.read_text() == updated_override_version

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0.718607

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0.090618

0.054371

0.943141

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0.883795

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0.821606

0.789623

0

0.01193

0.215126

4,165

105

101

39.666667

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0

0.020408

0

null

0

1

0

null

0

6

17cd989d97fa54e84249c7d4c7bc186aca87ed41

35

py

Python

nodebox/sound/__init__.py

pepsipepsi/nodebox_opengl_python3

cfb2633df1055a028672b11311603cc2241a1378

[ "BSD-3-Clause" ]

1

2017-03-19T16:56:46.000Z

nodebox/sound/__init__.py

pepsipepsi/nodebox_opengl_python3

cfb2633df1055a028672b11311603cc2241a1378

[ "BSD-3-Clause" ]

null

nodebox/sound/__init__.py

pepsipepsi/nodebox_opengl_python3

cfb2633df1055a028672b11311603cc2241a1378

[ "BSD-3-Clause" ]

null

from nodebox.sound.process import *

35

0.828571

5

35

5.8

1

0

0.085714

35

1

35

0.90625

0

1

0

true

0

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1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

6

17cf54093d3cb7ed30cf7d05f27c647af87fcb1b

74

py

Python

pycrtsh/__init__.py

Te-k/crtshy

cf3c1d6a4ee9ec1e102e2e0cb730eca8625cb2c8

[ "MIT" ]

22

2017-10-31T21:07:48.000Z

2022-03-30T02:15:36.000Z

pycrtsh/__init__.py

Te-k/crtshy

cf3c1d6a4ee9ec1e102e2e0cb730eca8625cb2c8

[ "MIT" ]

11

2019-07-12T17:15:36.000Z

2022-01-07T15:57:55.000Z

pycrtsh/__init__.py

Te-k/crtshy

cf3c1d6a4ee9ec1e102e2e0cb730eca8625cb2c8

[ "MIT" ]

10

2019-07-11T12:33:29.000Z

2021-07-20T08:18:10.000Z

from .api import Crtsh, CrtshInvalidRequestType, CrtshCertificateNotFound

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74

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1

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74

1

74

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0

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null

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1

0

1

0

1

0

6

aa09ece5b1c35b347bccc6ea4782f26b067f9620

131

py

Python

anyway/widgets/all_locations_widgets/__init__.py

shaniwein/anyway

dcd13bf7dc4a120f4d697ab0c08b906f43eea52e

[ "MIT" ]

1

2022-01-19T18:23:03.000Z

anyway/widgets/all_locations_widgets/__init__.py

shaniwein/anyway

dcd13bf7dc4a120f4d697ab0c08b906f43eea52e

[ "MIT" ]

null

anyway/widgets/all_locations_widgets/__init__.py

shaniwein/anyway

dcd13bf7dc4a120f4d697ab0c08b906f43eea52e

[ "MIT" ]

null

from . import ( accident_count_by_severity_widget, most_severe_accidents_widget, most_severe_accidents_table_widget, )

21.833333

39

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16

131

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0.340426

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0

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131

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true

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0

null

1

0

1

0

null

0

1

0

6

a4c1080a48cc641cbb576553dedba1fdfdbcd416

262

py

Python

kanoodlegenius2d/app.py

wkeeling/kanoodlegenius2d

11d18f3809213cc4d80e56cbcab3e418fc39b365

[ "MIT" ]

null

kanoodlegenius2d/app.py

wkeeling/kanoodlegenius2d

11d18f3809213cc4d80e56cbcab3e418fc39b365

[ "MIT" ]

null

kanoodlegenius2d/app.py

wkeeling/kanoodlegenius2d

11d18f3809213cc4d80e56cbcab3e418fc39b365

[ "MIT" ]

null

from kanoodlegenius2d.domain import models from kanoodlegenius2d.ui import fonts, settings from kanoodlegenius2d.ui.masterscreen import MasterScreen def main(): fonts.initialise() settings.initialise() models.initialise() return MasterScreen()

23.818182

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262

7.592593

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0.292683

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0

0.013393

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10

58

26.2

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0

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true

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null

0

1

0

1

0

1

0

6

a4eadc00c01e5d4823704e7c3ad76f76e7a725f3

7,841

py

Python

z2/part2/interactive/jm/random_normal_1/637218507.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

1

2020-04-16T12:13:47.000Z

z2/part2/interactive/jm/random_normal_1/637218507.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:50:15.000Z

2020-05-19T14:58:30.000Z

z2/part2/interactive/jm/random_normal_1/637218507.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:45:13.000Z

2020-06-09T19:18:31.000Z

from part1 import ( gamma_board, gamma_busy_fields, gamma_delete, gamma_free_fields, gamma_golden_move, gamma_golden_possible, gamma_move, gamma_new, ) """ scenario: test_random_actions uuid: 637218507 """ """ random actions, total chaos """ board = gamma_new(6, 7, 5, 10) assert board is not None assert gamma_move(board, 1, 2, 5) == 1 assert gamma_move(board, 1, 1, 0) == 1 board508740979 = gamma_board(board) assert board508740979 is not None assert board508740979 == ("......\n" "..1...\n" "......\n" "......\n" "......\n" "......\n" ".1....\n") del board508740979 board508740979 = None assert gamma_move(board, 3, 4, 2) == 1 assert gamma_move(board, 4, 3, 2) == 1 assert gamma_move(board, 5, 6, 4) == 0 assert gamma_busy_fields(board, 5) == 0 assert gamma_move(board, 1, 2, 3) == 1 assert gamma_move(board, 2, 5, 3) == 1 assert gamma_move(board, 2, 4, 1) == 1 assert gamma_free_fields(board, 2) == 35 assert gamma_move(board, 3, 1, 5) == 1 assert gamma_move(board, 3, 1, 1) == 1 assert gamma_move(board, 4, 4, 3) == 1 board109190569 = gamma_board(board) assert board109190569 is not None assert board109190569 == ("......\n" ".31...\n" "......\n" "..1.42\n" "...43.\n" ".3..2.\n" ".1....\n") del board109190569 board109190569 = None assert gamma_move(board, 5, 0, 2) == 1 assert gamma_busy_fields(board, 5) == 1 assert gamma_move(board, 1, 6, 0) == 0 assert gamma_move(board, 1, 0, 1) == 1 assert gamma_move(board, 2, 5, 3) == 0 assert gamma_move(board, 2, 3, 4) == 1 assert gamma_busy_fields(board, 2) == 3 assert gamma_move(board, 3, 2, 5) == 0 assert gamma_golden_possible(board, 3) == 1 assert gamma_move(board, 4, 2, 4) == 1 assert gamma_move(board, 5, 4, 0) == 1 assert gamma_move(board, 5, 0, 5) == 1 assert gamma_move(board, 2, 2, 2) == 1 assert gamma_move(board, 3, 4, 0) == 0 assert gamma_move(board, 3, 0, 6) == 1 assert gamma_busy_fields(board, 3) == 4 assert gamma_move(board, 4, 0, 5) == 0 assert gamma_move(board, 4, 4, 0) == 0 board837255014 = gamma_board(board) assert board837255014 is not None assert board837255014 == ("3.....\n" "531...\n" "..42..\n" "..1.42\n" "5.243.\n" "13..2.\n" ".1..5.\n") del board837255014 board837255014 = None assert gamma_move(board, 5, 4, 4) == 1 assert gamma_move(board, 5, 5, 5) == 1 assert gamma_golden_possible(board, 1) == 1 assert gamma_golden_move(board, 1, 5, 5) == 1 assert gamma_move(board, 2, 5, 1) == 1 assert gamma_busy_fields(board, 2) == 5 assert gamma_move(board, 3, 6, 2) == 0 assert gamma_move(board, 3, 4, 2) == 0 assert gamma_move(board, 4, 3, 3) == 1 assert gamma_move(board, 5, 6, 3) == 0 assert gamma_move(board, 5, 3, 4) == 0 assert gamma_move(board, 1, 0, 4) == 1 assert gamma_golden_move(board, 1, 0, 4) == 0 assert gamma_move(board, 3, 3, 1) == 1 assert gamma_free_fields(board, 3) == 18 assert gamma_move(board, 4, 6, 4) == 0 assert gamma_move(board, 4, 4, 1) == 0 assert gamma_move(board, 5, 2, 1) == 1 assert gamma_golden_possible(board, 5) == 1 assert gamma_move(board, 1, 5, 4) == 1 assert gamma_move(board, 2, 0, 0) == 1 assert gamma_move(board, 2, 4, 4) == 0 assert gamma_golden_possible(board, 2) == 1 assert gamma_move(board, 3, 6, 3) == 0 assert gamma_move(board, 3, 3, 4) == 0 assert gamma_golden_move(board, 3, 1, 4) == 0 assert gamma_move(board, 4, 2, 1) == 0 assert gamma_move(board, 4, 1, 6) == 1 assert gamma_free_fields(board, 4) == 14 assert gamma_move(board, 5, 5, 4) == 0 assert gamma_free_fields(board, 5) == 14 assert gamma_move(board, 1, 0, 0) == 0 assert gamma_move(board, 2, 0, 1) == 0 assert gamma_move(board, 2, 5, 1) == 0 assert gamma_free_fields(board, 2) == 14 assert gamma_move(board, 3, 0, 4) == 0 assert gamma_move(board, 3, 1, 0) == 0 board815442504 = gamma_board(board) assert board815442504 is not None assert board815442504 == ("34....\n" "531..1\n" "1.4251\n" "..1442\n" "5.243.\n" "135322\n" "21..5.\n") del board815442504 board815442504 = None assert gamma_move(board, 4, 2, 6) == 1 assert gamma_move(board, 5, 0, 3) == 1 assert gamma_move(board, 5, 4, 6) == 1 assert gamma_golden_possible(board, 5) == 1 assert gamma_move(board, 1, 3, 1) == 0 assert gamma_move(board, 1, 3, 5) == 1 assert gamma_golden_possible(board, 1) == 0 assert gamma_move(board, 2, 1, 4) == 1 assert gamma_move(board, 2, 1, 2) == 1 assert gamma_golden_move(board, 2, 4, 2) == 1 assert gamma_move(board, 3, 2, 5) == 0 assert gamma_move(board, 5, 3, 1) == 0 assert gamma_move(board, 5, 1, 6) == 0 assert gamma_move(board, 1, 4, 3) == 0 assert gamma_move(board, 1, 4, 6) == 0 assert gamma_move(board, 2, 3, 1) == 0 assert gamma_move(board, 3, 3, 5) == 0 assert gamma_golden_possible(board, 3) == 1 assert gamma_golden_move(board, 3, 4, 4) == 1 assert gamma_move(board, 4, 2, 5) == 0 assert gamma_golden_possible(board, 4) == 1 assert gamma_move(board, 1, 3, 1) == 0 assert gamma_move(board, 1, 0, 0) == 0 assert gamma_free_fields(board, 1) == 8 assert gamma_golden_move(board, 1, 2, 2) == 0 assert gamma_move(board, 2, 0, 3) == 0 assert gamma_move(board, 2, 1, 5) == 0 assert gamma_move(board, 3, 2, 3) == 0 assert gamma_move(board, 4, 6, 3) == 0 assert gamma_golden_possible(board, 4) == 1 assert gamma_move(board, 5, 1, 2) == 0 assert gamma_move(board, 1, 3, 0) == 1 assert gamma_move(board, 1, 2, 3) == 0 assert gamma_move(board, 2, 0, 5) == 0 assert gamma_move(board, 3, 5, 2) == 1 assert gamma_move(board, 3, 4, 6) == 0 assert gamma_move(board, 4, 4, 1) == 0 assert gamma_golden_move(board, 4, 1, 0) == 1 assert gamma_move(board, 5, 5, 4) == 0 assert gamma_move(board, 5, 5, 6) == 1 assert gamma_move(board, 1, 0, 2) == 0 assert gamma_move(board, 1, 3, 4) == 0 assert gamma_move(board, 2, 6, 3) == 0 assert gamma_move(board, 3, 0, 2) == 0 assert gamma_move(board, 3, 3, 2) == 0 assert gamma_golden_possible(board, 3) == 0 board721229061 = gamma_board(board) assert board721229061 is not None assert board721229061 == ("344.55\n" "5311.1\n" "124231\n" "5.1442\n" "522423\n" "135322\n" "24.15.\n") del board721229061 board721229061 = None assert gamma_move(board, 4, 0, 0) == 0 assert gamma_move(board, 4, 1, 1) == 0 assert gamma_move(board, 5, 0, 5) == 0 assert gamma_move(board, 1, 0, 5) == 0 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_golden_move(board, 2, 5, 3) == 0 assert gamma_move(board, 3, 3, 1) == 0 assert gamma_move(board, 4, 5, 4) == 0 assert gamma_busy_fields(board, 4) == 7 assert gamma_move(board, 5, 3, 6) == 1 assert gamma_move(board, 5, 5, 0) == 1 assert gamma_busy_fields(board, 5) == 9 assert gamma_free_fields(board, 5) == 3 assert gamma_golden_move(board, 5, 6, 0) == 0 assert gamma_move(board, 1, 3, 1) == 0 assert gamma_busy_fields(board, 1) == 8 assert gamma_move(board, 2, 3, 1) == 0 assert gamma_free_fields(board, 2) == 3 assert gamma_move(board, 3, 4, 3) == 0 assert gamma_golden_move(board, 3, 2, 4) == 0 assert gamma_move(board, 4, 0, 2) == 0 assert gamma_move(board, 4, 0, 1) == 0 assert gamma_move(board, 5, 5, 4) == 0 assert gamma_golden_move(board, 5, 1, 0) == 1 assert gamma_move(board, 1, 3, 1) == 0 assert gamma_move(board, 1, 0, 4) == 0 assert gamma_move(board, 2, 4, 4) == 0 assert gamma_move(board, 2, 1, 2) == 0 assert gamma_free_fields(board, 2) == 3 assert gamma_move(board, 3, 3, 1) == 0 assert gamma_move(board, 3, 1, 4) == 0 assert gamma_golden_move(board, 3, 6, 1) == 0 assert gamma_move(board, 4, 0, 2) == 0 assert gamma_move(board, 5, 1, 0) == 0 assert gamma_move(board, 1, 5, 4) == 0 assert gamma_move(board, 1, 3, 2) == 0 assert gamma_move(board, 2, 5, 4) == 0 assert gamma_move(board, 2, 4, 5) == 1 assert gamma_move(board, 3, 0, 2) == 0 assert gamma_move(board, 3, 5, 0) == 0 assert gamma_golden_possible(board, 3) == 0 assert gamma_move(board, 4, 0, 2) == 0 gamma_delete(board)

31.744939

46

0.653105

1,432

7,841

3.424581

0.044693

0.349918

0.354812

0.473083

0.808728

0.804853

0.6823

0.376835

0.267537

0.259788

0

0.140775

0.177401

7,841

246

47

31.873984

0.619535

0

0.209821

0

0.036115

0

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1

0

false

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0

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0

null

1

0

null

0

1

0

6

a4f1b2954bab8eb8dc86f7ef12bbbefe9369af74

4,356

py

Python

tests/test_upower.py

listuser/jc

3ac8d0362b4fb9999fc55a60a9cb20ac80d114f7

[ "MIT" ]

3,215

2019-10-24T15:25:56.000Z

2022-03-31T15:43:01.000Z

tests/test_upower.py

listuser/jc

3ac8d0362b4fb9999fc55a60a9cb20ac80d114f7

[ "MIT" ]

109

2019-11-02T16:22:29.000Z

2022-03-30T17:32:17.000Z

tests/test_upower.py

listuser/jc

3ac8d0362b4fb9999fc55a60a9cb20ac80d114f7

[ "MIT" ]

75

2020-02-07T00:16:32.000Z

2022-03-29T09:29:53.000Z

import os import sys import time import json import unittest import jc.parsers.upower THIS_DIR = os.path.dirname(os.path.abspath(__file__)) # Set the timezone on POSIX systems. Need to manually set for Windows tests if not sys.platform.startswith('win32'): os.environ['TZ'] = 'America/Los_Angeles' time.tzset() class MyTests(unittest.TestCase): def setUp(self): # input with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/ubuntu-18.04/upower-i.out'), 'r', encoding='utf-8') as f: self.ubuntu_18_4_upower_i = f.read() with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/ubuntu-18.04/upower-d.out'), 'r', encoding='utf-8') as f: self.ubuntu_18_4_upower_d = f.read() with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/ubuntu-18.04/upower-d-clocale.out'), 'r', encoding='utf-8') as f: self.ubuntu_18_4_upower_d_clocale = f.read() with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/upower-i-utc.out'), 'r', encoding='utf-8') as f: self.generic_upower_i_utc = f.read() with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/upower-i-non-utc.out'), 'r', encoding='utf-8') as f: self.generic_upower_i_non_utc = f.read() with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/upower-i-c-locale.out'), 'r', encoding='utf-8') as f: self.generic_upower_i_c_locale = f.read() # output with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/ubuntu-18.04/upower-i.json'), 'r', encoding='utf-8') as f: self.ubuntu_18_4_upower_i_json = json.loads(f.read()) with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/ubuntu-18.04/upower-d.json'), 'r', encoding='utf-8') as f: self.ubuntu_18_4_upower_d_json = json.loads(f.read()) with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/ubuntu-18.04/upower-d-clocale.json'), 'r', encoding='utf-8') as f: self.ubuntu_18_4_upower_d_clocale_json = json.loads(f.read()) with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/upower-i-utc.json'), 'r', encoding='utf-8') as f: self.generic_upower_i_utc_json = json.loads(f.read()) with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/upower-i-non-utc.json'), 'r', encoding='utf-8') as f: self.generic_upower_i_non_utc_json = json.loads(f.read()) with open(os.path.join(THIS_DIR, os.pardir, 'tests/fixtures/generic/upower-i-c-locale.json'), 'r', encoding='utf-8') as f: self.generic_upower_i_c_locale_json = json.loads(f.read()) def test_upower_nodata(self): """ Test 'upower' with no data """ self.assertEqual(jc.parsers.upower.parse('', quiet=True), []) def test_upower_i_ubuntu_18_4(self): """ Test 'upower -i' on Ubuntu 18.4 """ self.assertEqual(jc.parsers.upower.parse(self.ubuntu_18_4_upower_i, quiet=True), self.ubuntu_18_4_upower_i_json) def test_upower_d_ubuntu_18_4(self): """ Test 'upower -d' on Ubuntu 18.4 using LANG=en_US.UTF-8 """ self.assertEqual(jc.parsers.upower.parse(self.ubuntu_18_4_upower_d, quiet=True), self.ubuntu_18_4_upower_d_json) def test_upower_d_clocale_ubuntu_18_4(self): """ Test 'upower -d' on Ubuntu 18.4 using LANG=C """ self.assertEqual(jc.parsers.upower.parse(self.ubuntu_18_4_upower_d, quiet=True), self.ubuntu_18_4_upower_d_json) def test_upower_i_utc_generic(self): """ Test 'upower -i' with utc time output """ self.assertEqual(jc.parsers.upower.parse(self.generic_upower_i_utc, quiet=True), self.generic_upower_i_utc_json) def test_upower_i_non_utc_generic(self): """ Test 'upower -i' with non-utc time output """ self.assertEqual(jc.parsers.upower.parse(self.generic_upower_i_non_utc, quiet=True), self.generic_upower_i_non_utc_json) def test_upower_i_c_locale(self): """ Test 'upower -i' with LANG=C time output """ self.assertEqual(jc.parsers.upower.parse(self.generic_upower_i_c_locale, quiet=True), self.generic_upower_i_c_locale_json) if __name__ == '__main__': unittest.main()

42.705882

134

0.664141

696

4,356

3.920977

0.123563

0.082081

0.059362

0.061561

0.843532

0.825577

0.78417

0.713082

0

0.026353

0.189853

4,356

101

135

43.128713

0.746954

0.084252

0

0.038462

0

0.162092

0.134379

0

0.134615

1

0.153846

false

0

0.115385

0

0.288462

0

null

0

1

0

null

0

6

35282cd4dcca91264219958e252431f0a5e67a73

131

py

Python

tests/conftest.py

dseuss/pypllon

f9ae6104555837fe0eb7d7c333ebc2ed585d314a

[ "BSD-3-Clause" ]

null

tests/conftest.py

dseuss/pypllon

f9ae6104555837fe0eb7d7c333ebc2ed585d314a

[ "BSD-3-Clause" ]

null

tests/conftest.py

dseuss/pypllon

f9ae6104555837fe0eb7d7c333ebc2ed585d314a

[ "BSD-3-Clause" ]

null

import numpy as np import pytest as pt @pt.fixture(scope="module") def rgen(): return np.random.RandomState(seed=3476583865)

16.375

49

0.740458

20

131

4.85

0.8

0

0.089286

0.145038

131

7

50

18.714286

0.776786

0

0.045802

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

6

1057320ca1a0eb1c9ddd59c375419b8e9d5482a8

357

py

Python

backend/mmlp/endpoint/result/ResultCount.py

magreiner/MMLP

23113866d8d0062c8c0e54c7fa5a0bbd0fa15f4e

[ "Apache-2.0" ]

null

backend/mmlp/endpoint/result/ResultCount.py

magreiner/MMLP

23113866d8d0062c8c0e54c7fa5a0bbd0fa15f4e

[ "Apache-2.0" ]

null

backend/mmlp/endpoint/result/ResultCount.py

magreiner/MMLP

23113866d8d0062c8c0e54c7fa5a0bbd0fa15f4e

[ "Apache-2.0" ]

null

import json from falcon import Request, Response from mmlp.manager import ResultManager class ResultCount: def __init__(self, result_manager: ResultManager): self._result_manager: ResultManager = result_manager def on_get(self, _: Request, resp: Response): resp.body = json.dumps(dict(count=self._result_manager.result_count()))

27.461538

79

0.753501

44

357

5.818182

0.5

0.203125

0.199219

0.234375

0

0.162465

357

12

80

29.75

0.856187

0

1

0.25

false

0

0.375

0

0.75

0

null

1

0

null

0

1

0

1

0

1

0

6

109e7549ca7ca5e402ec3e03c1d217eb79343001

166

py

Python

ex14.py

wellingtonn96/Revisao_LP2

6cd23606633b07db5a984666b12b2dc2193e799d

[ "Apache-2.0" ]

null

ex14.py

wellingtonn96/Revisao_LP2

6cd23606633b07db5a984666b12b2dc2193e799d

[ "Apache-2.0" ]

null

ex14.py

wellingtonn96/Revisao_LP2

6cd23606633b07db5a984666b12b2dc2193e799d

[ "Apache-2.0" ]

null

from funcoes import exercicio_tupla centenas, dezenas, unidades = exercicio_tupla(945) print('centena: %d, dezena: %d, unidade: %d' % (centenas, dezenas, unidades))

33.2

77

0.746988

21

166

5.809524

0.666667

0.229508

0.377049

0

0.020548

0.120482

166

4

78

41.5

0.815068

0

0.216867

0

1

0

true

0

0.333333

0

0.333333

1

0

null

1

0

1

0

null

0

1

0

1

0

6

529e944ddd640e80ec194142f894ace2e9141428

161

py

Python

views/shop.py

Usamaiqbal789/Flask

b0a3c0be63fb88cfe020e116b37d73261c7bcab1

[ "MIT" ]

null

views/shop.py

Usamaiqbal789/Flask

b0a3c0be63fb88cfe020e116b37d73261c7bcab1

[ "MIT" ]

null

views/shop.py

Usamaiqbal789/Flask

b0a3c0be63fb88cfe020e116b37d73261c7bcab1

[ "MIT" ]

1

2021-10-14T19:14:09.000Z

from flask import Blueprint, render_template shop = Blueprint('shop', __name__) @shop.route('/shop') def shop_page(): return render_template("shop.html")

17.888889

44

0.732919

21

161

5.285714

0.619048

0.252252

0.324324

0

0.130435

161

8

45

20.125

0.792857

0

0.111801

0

1

0.2

false

0

0.2

0.6

0.4

1

0

null

1

0

1

0

null

0

1

0

6

52f8a198bbf1908d77d74d41949bf1ffbffdcae1

38

py

Python

katas/beta/find_the_gcf_of_two_numbers.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/beta/find_the_gcf_of_two_numbers.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/beta/find_the_gcf_of_two_numbers.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

from fractions import gcd as find_GCF

19

37

0.842105

7

38

4.428571

1

0

0.157895

38

1

38

0.96875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

dc1fa3efb29a27feac2eca0949cd7b10e1578962

36

py

Python

otscrape/core/exporter/__init__.py

SSripilaipong/otscrape

73ad2ea3d20841cf5d81b37180a1f21c48e87480

[ "MIT" ]

null

otscrape/core/exporter/__init__.py

SSripilaipong/otscrape

73ad2ea3d20841cf5d81b37180a1f21c48e87480

[ "MIT" ]

null

otscrape/core/exporter/__init__.py

SSripilaipong/otscrape

73ad2ea3d20841cf5d81b37180a1f21c48e87480

[ "MIT" ]

null

from .file.json import JSONExporter

18

35

0.833333

5

36

6

1

0

0.111111

36

1

36

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

dc52f0b09edcc582395d1f1fa0265f2bfa01f3c9

27

py

Python

src/sage/tensor/all.py

UCD4IDS/sage

43474c96d533fd396fe29fe0782d44dc7f5164f7

[ "BSL-1.0" ]

1,742

2015-01-04T07:06:13.000Z

2022-03-30T11:32:52.000Z

src/sage/tensor/all.py

UCD4IDS/sage

43474c96d533fd396fe29fe0782d44dc7f5164f7

[ "BSL-1.0" ]

66

2015-03-19T19:17:24.000Z

2022-03-16T11:59:30.000Z

src/sage/tensor/all.py

UCD4IDS/sage

43474c96d533fd396fe29fe0782d44dc7f5164f7

[ "BSL-1.0" ]

495

2015-01-10T10:23:18.000Z

2022-03-24T22:06:11.000Z

from .modules.all import *

13.5

26

0.740741

4

27

5

1

0

0.148148

27

1

27

0.869565

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

dc5639380e32090db9b3fc5d1dffcf4c9eed3c4a

98

py

Python

tests/archives/extension.py

4nm1tsu/uncompressor

706eb7500ee576fe7e7a6d610dc78bfa837ea8bf

[ "MIT" ]

1

2021-11-08T01:52:58.000Z

tests/archives/extension.py

4nm1tsu/uncompressor

706eb7500ee576fe7e7a6d610dc78bfa837ea8bf

[ "MIT" ]

null

tests/archives/extension.py

4nm1tsu/uncompressor

706eb7500ee576fe7e7a6d610dc78bfa837ea8bf

[ "MIT" ]

null

import mimetypes print(mimetypes.guess_extension(mimetypes.guess_type('./something.tar.xz')[0]))

24.5

79

0.795918

13

98

5.846154

0.769231

0.368421

0

0.010638

0.040816

98

3

80

32.666667

0.797872

0

0.183673

0

1

0

true

0

0.5

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f495a741cc3a9d4155a8adc96792d91041ff25cd

87

py

Python

src/metrics/__init__.py

ryanwongsa/image-inpainting

d20419f3260760f1deb96d2b904dd4de92eeee36

[ "BSD-3-Clause" ]

null

src/metrics/__init__.py

ryanwongsa/image-inpainting

d20419f3260760f1deb96d2b904dd4de92eeee36

[ "BSD-3-Clause" ]

null

src/metrics/__init__.py

ryanwongsa/image-inpainting

d20419f3260760f1deb96d2b904dd4de92eeee36

[ "BSD-3-Clause" ]

null

from metrics.psnr_metric import PSNR_Metric from metrics.loss_metric import Loss_Metric

43.5

43

0.896552

14

87

5.285714

0.428571

0.297297

0

0.08046

87

2

44

43.5

0.925

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f4b18b162035ab5378f719e556b732dd41a4cfdb

4,415

py

Python

export 3d pic /2p.py

aminoj/Interactive-Orbitals-Simulation

20e405d6a23028049c05f4a0fd73e51857ba9270

[ "Apache-2.0" ]

null

export 3d pic /2p.py

aminoj/Interactive-Orbitals-Simulation

20e405d6a23028049c05f4a0fd73e51857ba9270

[ "Apache-2.0" ]

null

export 3d pic /2p.py

aminoj/Interactive-Orbitals-Simulation

20e405d6a23028049c05f4a0fd73e51857ba9270

[ "Apache-2.0" ]

1

2020-04-16T08:02:27.000Z

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_subplot(111, projection='3d') rstride = 15 cstride = 15 MinBound = -3 MaxBound = 3 u = np.linspace(0, 2*np.pi, 100) v = np.linspace(0, np.pi, 100) #------------------------------------------------------------------------------- #Cone x2 = np.outer(np.cos(u), np.sin(v)) y2 = np.outer(np.sin(u), np.sin(v)) z2 = ((1.5*x2)**2+(1.5*y2)**2+0.3)**(0.5) ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cone x2 = np.outer(np.cos(u), np.sin(v)) y2 = np.outer(np.sin(u), np.sin(v)) z2 = -((1.5*x2)**2+(1.5*y2)**2+0.3)**(0.5) ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cone y4 = np.outer(np.sin(u), np.sin(v)) z4 = np.outer(np.cos(u), np.sin(v)) x4 = ((1.5*z4)**2+(1.5*y4)**2+0.3)**(0.5) ax.plot_surface(x4, y4, z4, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cone y4 = np.outer(np.sin(u), np.sin(v)) z4 = np.outer(np.cos(u), np.sin(v)) x4 = -((1.5*z4)**2+(1.5*y4)**2+0.3)**(0.5) ax.plot_surface(x4, y4, z4, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cone z5 = np.outer(np.cos(u), np.sin(v)) x5 = np.outer(np.sin(u), np.sin(v)) y5 = ((1.5*z5)**2+(1.5*x5)**2+0.3)**(0.5) ax.plot_surface(x5, y5, z5, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cone z5 = np.outer(np.cos(u), np.sin(v)) x5 = np.outer(np.sin(u), np.sin(v)) y5 = -((1.5*z5)**2+(1.5*x5)**2+0.3)**(0.5) ax.plot_surface(x5, y5, z5, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cover x6 = np.outer(np.cos(u), np.sin(v)) y6 = np.outer(np.sin(u), np.sin(v)) z6 = (abs(((2*x6)**2)+((2*y6)**2)-15)**(0.5))-1.7 ax.plot_surface(x6, y6, z6, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cover x6 = np.outer(np.cos(u), np.sin(v)) y6 = np.outer(np.sin(u), np.sin(v)) z6 = -(abs(((2*x6)**2)+((2*y6)**2)-15)**(0.5))+1.7 ax.plot_surface(x6, y6, z6, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cover y7 = np.outer(np.sin(u), np.sin(v)) z7 = np.outer(np.cos(u), np.sin(v)) x7 = (abs(((2*x6)**2)+((2*y6)**2)-15)**(0.5))-1.7 ax.plot_surface(x7, y7, z7, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cover z7 = np.outer(np.cos(u), np.sin(v)) y7 = np.outer(np.sin(u), np.sin(v)) x7 = -(abs(((2*z7)**2)+((2*y7)**2)-15)**(0.5))+1.7 ax.plot_surface(x7, y7, z7, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cover x8 = np.outer(np.cos(u), np.sin(v)) z8 = np.outer(np.sin(u), np.sin(v)) y8 = (abs(((2*x8)**2)+((2*z8)**2)-15)**(0.5))-1.7 ax.plot_surface(x8, y8, z8, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) #------------------------------------------------------------------------------- #Cover x8 = np.outer(np.cos(u), np.sin(v)) z8 = np.outer(np.sin(u), np.sin(v)) y8 = -(abs(((2*x8)**2)+((2*z8)**2)-15)**(0.5))+1.7 ax.plot_surface(x8, y8, z8, rstride = rstride, cstride = cstride, color=(0,0.44,1), linewidth=0) plt.show() ax.set_xlim3d(MinBound, MaxBound) ax.set_ylim3d(MinBound, MaxBound) ax.set_zlim3d(MinBound, MaxBound) e = 0 b = 0 for ii in xrange(0,120,1): ax.view_init(elev=e, azim=b*4) if(ii < 10) : plt.savefig("2p/movie00%s.png" %ii) elif(ii < 100): plt.savefig("2p/movie0%s.png" %ii) else : plt.savefig("2p/movie%s.png" %ii) if(ii == 20 or ii == 40 or ii == 60 or ii == 80 or ii == 100): e = e + 12 b = 0 b = b + 1

32.703704

96

0.463194

723

4,415

2.803596

0.136929

0.088801

0.106561

0.082881

0.77257

0.768624

0.727183

0

0.08813

0.110759

4,415

135

97

32.703704

0.428171

0.226954

0

0.493506

0

0.013864

0

1

0

false

0

0.038961

0

0.038961

0

null

0

1

0

1

0

null

0

6

f4e2b8386bdbedfc5c89a0d9305717c57989aa6b

14,564

py

Python

dqn/Model.py

Theling/Delayed_MDP

db1a8808a05917a5915220947cf65268f0524fa9

[ "MIT" ]

null

dqn/Model.py

Theling/Delayed_MDP

db1a8808a05917a5915220947cf65268f0524fa9

[ "MIT" ]

null

dqn/Model.py

Theling/Delayed_MDP

db1a8808a05917a5915220947cf65268f0524fa9

[ "MIT" ]

null

# GRU simple normal nn, this model assumes the two elements in state space is uncorrelated. import tensorflow as tf import numpy as np from collections import deque from DRL_util import Grid DEBUG = False class GRU_Model: def __init__(self, sess, state_dim, action_dim, steps, gru_outshape = 16, pool_maxlen = 10000, *args, **kwargs): self.sess = sess self.state_dim, self.action_dim, self.steps = state_dim, action_dim, steps self.std_c = 1. # coefficient for variance loss self.inputs = {} self.outputs = {} self.other_tensors = {} self.build(gru_outshape) self.tensors = {} self.tensors.update(self.inputs) self.tensors.update(self.outputs) self.tensors.update(self.other_tensors) init = tf.global_variables_initializer() # self.sess.run(init) self.is_trained = False self.data_pool = {"states": deque(maxlen = pool_maxlen), "actions": deque(maxlen = pool_maxlen), "counter": 0} self.pool_maxlen = pool_maxlen def build(self, gru_outshape = 16): actions = tf.keras.Input(shape=(self.steps,self.action_dim), name = "actions_ipt") init_state = tf.keras.Input(shape=(self.state_dim,), name = "state_ipt") self.inputs["actions"] = actions self.inputs["init_state"] = init_state cell_tensor = tf.keras.layers.Dense(gru_outshape, name = "transform_state_ipt", activation = "softmax")(init_state) # mean y = tf.keras.layers.GRU(gru_outshape, name = "mean_gru")(actions, initial_state = cell_tensor) mean = tf.keras.layers.Dense(self.state_dim, name = "mean")(y) # var y = tf.keras.layers.GRU(gru_outshape, name = "var_gru")(actions, initial_state = cell_tensor) var = tf.keras.layers.Dense(self.state_dim, name = "var", activation = "relu")(y) self.outputs["mean"] = mean self.outputs['var'] = var print(self.inputs.values) self.model = tf.keras.Model(inputs = list(self.inputs.values()), outputs = list(self.outputs.values())) print(self.model.summary()) true_y = tf.keras.Input(shape=(self.state_dim, )) target_var = (true_y**2-mean**2) mean_loss = tf.losses.mean_squared_error(labels = true_y, predictions=mean) var_loss = tf.losses.mean_squared_error(labels = target_var, predictions=var) loss = mean_loss+ self.std_c*var_loss updt = tf.train.AdamOptimizer(0.01).minimize(loss) self.other_tensors["target_var"] = target_var self.other_tensors["mean_loss"] = mean_loss self.other_tensors['var_loss'] = var_loss self.other_tensors["loss"] = loss self.other_tensors["true_y"] = true_y self.other_tensors["updt"] = updt def get_tensors(self, keys): return [self.tensors[key] for key in keys] def train(self, iters = 10000): data = self.arrange_data() n_update = 10 n_iter = int(iters/n_update) for _ in range(n_iter): self.do_training(data['train_s'], data['train_as'], data['train_es'], n_update) val_loss = self.validate(data["test_s"], data['test_as'], data['test_es']) print(f"val_loss: {val_loss}") def do_training(self, train_s, train_as, train_es , train_iter): updt, loss, actions, true_y, init_state = self.get_tensors(["updt", 'loss', 'actions', 'true_y', 'init_state']) for _ in range(train_iter): _, l_ = self.sess.run((updt, loss), feed_dict={actions: train_as, true_y : train_es, init_state: train_s}) print(f'Training finished: {train_iter} updates.') self.is_trained = True return l_ def validate(self, train_s, train_as, train_es): updt, loss, actions, true_y, init_state = self.get_tensors(["updt", 'loss', 'actions', 'true_y', 'init_state']) l_ = self.sess.run(loss, feed_dict={actions: train_as, true_y : train_es, init_state: train_s}) return l_ def predict(self, s, as_): if self.is_trained: m, var = self.sess.run((self.tensors["mean"], self.tensors["var"]), feed_dict={self.tensors["actions"]: as_, self.tensors["init_state"]: s}) c = np.sqrt(var) rnds = np.random.normal(size = (len(m), self.state_dim)) #print(m.shape, c.shape, rnds.shape) return m+rnds*c else: raise def run(self, keys, feed_dict): return sess.run([self.tensors[k] for k in keys], feed_dict={self.tensors[k]: feed_dict[k] for k in feed_dict}) def add_data(self, state, action): self.data_pool["states"].append(state) self.data_pool["actions"].append(action) self.data_pool["counter"] = min(self.data_pool["counter"]+1, self.pool_maxlen) def arrange_data(self, val_ratio = 0.2): states = self.data_pool['states'] actions = self.data_pool['actions'] n = self.data_pool['counter'] steps = self.steps states = np.array(states).reshape(n, -1) actions = np.array(actions).reshape(n, -1) split_idx = int(val_ratio*n) train_states = states[0:split_idx] train_actions = actions[0:split_idx] test_states = states[split_idx:] test_actions = actions[split_idx:] train_as = self.convert_ts(train_actions, steps) test_as = self.convert_ts(test_actions, steps) train_s = np.array(train_states[:-steps]) test_s = np.array(test_states[:-steps]) train_es = train_states[steps:, :] test_es = test_states[steps:, :] if DEBUG: print(train_es) return {"train_as": train_as, "test_as": test_as, "train_s": train_s, "test_s": test_s, "train_es": train_es, "test_es": test_es} @staticmethod def convert_ts(raw_data, steps): batchsize = len(raw_data) return np.array([raw_data[i:i+steps, :] for i in range(batchsize-steps)]) class GRU_Model_Discrete: def __init__(self, sess, state_dim, action_dim, steps, state_limits, state_num_bins, gru_outshape = 64, pool_maxlen = 10000, *args, **kwargs): self.sess = sess self.state_dim, self.action_dim, self.steps = state_dim, action_dim, steps self.grid = Grid(state_limits, state_num_bins) self.discrete_output_size = self.grid.num_disc_state self.std_c = 1. # coefficient for variance loss self.inputs = {} self.outputs = {} self.other_tensors = {} self.build(gru_outshape) self.tensors = {} self.tensors.update(self.inputs) self.tensors.update(self.outputs) self.tensors.update(self.other_tensors) init = tf.global_variables_initializer() # self.sess.run(init) self.is_trained = False self.data_pool = {"states": deque(maxlen = pool_maxlen), "actions": deque(maxlen = pool_maxlen), "counter": 0} self.pool_maxlen = pool_maxlen def build(self, gru_outshape = 16): actions = tf.keras.Input(shape=(self.steps,self.action_dim), name = "actions_ipt") init_state = tf.keras.Input(shape=(self.state_dim,), name = "state_ipt") self.inputs["actions"] = actions self.inputs["init_state"] = init_state cell_tensor = tf.keras.layers.Dense(gru_outshape, name = "transform_state_ipt", activation = "softmax")(init_state) # mean y = tf.keras.layers.GRU(gru_outshape, name = "mean_gru")(actions, initial_state = cell_tensor) out_dist = tf.keras.layers.Dense(self.discrete_output_size, activation = "softmax", name = "out_dist")(y) self.outputs["out_dist"] = out_dist print(self.inputs.values) self.model = tf.keras.Model(inputs = list(self.inputs.values()), outputs = list(self.outputs.values())) print(self.model.summary()) true_y = tf.keras.Input(shape=(self.discrete_output_size, )) dist_loss = tf.losses.mean_squared_error(labels = true_y, predictions=out_dist) loss = dist_loss updt = tf.train.AdamOptimizer(0.01).minimize(loss) self.other_tensors["dist_loss"] = dist_loss self.other_tensors["loss"] = loss self.other_tensors["true_y"] = true_y self.other_tensors["updt"] = updt def get_tensors(self, keys): return [self.tensors[key] for key in keys] def train(self, iters = 10000): data = self.arrange_data() n_update = 10 n_iter = int(iters/n_update) for _ in range(n_iter): self.do_training(data['train_s'], data['train_as'], data['train_es'], n_update) val_loss = self.validate(data["test_s"], data['test_as'], data['test_es']) print(f"val_loss: {val_loss}") def do_training(self, train_s, train_as, train_es , train_iter): updt, loss, actions, true_y, init_state = self.get_tensors(["updt", 'loss', 'actions', 'true_y', 'init_state']) for _ in range(train_iter): _, l_ = self.sess.run((updt, loss), feed_dict={actions: train_as, true_y : train_es, init_state: train_s}) print(f'Training finished: {train_iter} updates.') self.is_trained = True return l_ def validate(self, train_s, train_as, train_es): updt, loss, actions, true_y, init_state = self.get_tensors(["updt", 'loss', 'actions', 'true_y', 'init_state']) l_ = self.sess.run(loss, feed_dict={actions: train_as, true_y : train_es, init_state: train_s}) return l_ def predict(self, s, as_): assert len(s) == 1 and len(as_) == 1 if self.is_trained: dist_over_states = self.sess.run(self.tensors["out_dist"], feed_dict={self.tensors["actions"]: as_, self.tensors["init_state"]: s}) return self.grid.get_state_from_dist(dist_over_states[0]) else: raise def run(self, keys, feed_dict): return sess.run([self.tensors[k] for k in keys], feed_dict={self.tensors[k]: feed_dict[k] for k in feed_dict}) def add_data(self, state, action): self.data_pool["states"].append(state) self.data_pool["actions"].append(action) self.data_pool["counter"] = min(self.data_pool["counter"]+1, self.pool_maxlen) def arrange_data(self, val_ratio = 0.2): states = self.data_pool['states'] actions = self.data_pool['actions'] n = self.data_pool['counter'] steps = self.steps states = np.array(states).reshape(n, -1) actions = np.array(actions).reshape(n, -1) split_idx = int(val_ratio*n) train_states = states[0:split_idx] train_actions = actions[0:split_idx] test_states = states[split_idx:] test_actions = actions[split_idx:] train_as = self.convert_ts(train_actions, steps) test_as = self.convert_ts(test_actions, steps) train_s = np.array(train_states[:-steps]) test_s = np.array(test_states[:-steps]) train_es = self.discretize_states(train_states[steps:, :]) test_es = self.discretize_states(test_states[steps:, :]) if DEBUG: print(train_es) print(train_es.shape) return {"train_as": train_as, "test_as": test_as, "train_s": train_s, "test_s": test_s, "train_es": train_es, "test_es": test_es} def discretize_states(self, state_arr): n = len(state_arr) ret = np.zeros(shape = (n, self.discrete_output_size)) indices = [self.grid.search(x) for x in state_arr] ret[np.arange(n), indices] = 1 return ret @staticmethod def convert_ts(raw_data, steps): batchsize = len(raw_data) return np.array([raw_data[i:i+steps, :] for i in range(batchsize-steps)]) if __name__=="__main__": DEBUG = True import gym import os os.environ['KMP_DUPLICATE_LIB_OK']='True' env = gym.make("MountainCar-v0") action_space = [0,1,2] sess = tf.Session() # mo = GRU_Model(sess, state_dim = 2, action_dim = 1, steps = 2) mo = GRU_Model_Discrete(sess, state_dim = 2, action_dim = 1, steps = 2, state_limits = [[-1.2, 0.6], [-0.5, 0.5]], state_num_bins = [20,5]) init = tf.global_variables_initializer() sess.run(init) # populate data states = [] actions = [] s = env.reset() for _ in range(1500): states.append(s) act = np.random.choice(action_space) actions.append([act]) mo.add_data(s, act) s, reward, done, _ = env.step(act) if done: print('done') s = env.reset() # states = np.array(states) # actions = np.array(actions) # train_states = states[0:10000] # train_actions = actions[0:10000] # test_states = states[10000:] # test_actions = actions[10000:] # def convert_ts(raw_data, steps): # batchsize = len(raw_data) # return np.array([raw_data[i:i+steps, :] for i in range(batchsize-steps)]) # train_as = convert_ts(train_actions, steps) # test_as = convert_ts(test_actions, steps) # train_s = np.array(train_states[:-steps]) # test_s = np.array(test_states[:-steps]) # train_es = train_states[steps:, :] # test_es = test_states[steps:, :] mo.train(1000) s = states[0] acts = actions[0:2] # s = np.array(s).reshape(1, -1) # acts = np.array(acts).reshape(1, 2, -1) print(s, acts) [print(mo.predict([s], [acts])) for _ in range(5)] print(states[2]) print(mo.grid.bins_mid)

35.696078

124

0.583082

1,898

14,564

4.231296

0.10432

0.024654

0.023907

0.018927

0.789441

0.768771

0.764786

0.755572

0.736521

0.720085

0

0.01189

0.289687

14,564

407

125

35.783784

0.764427

0.064749

0

0.703971

0

0.066446

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0.00361

1

0.083032

false

0

0.021661

0.01444

0.166065

0.057762

0

null

0

1

0

1

0

null

0

6

52054d33310c0194f97c41b19da52377dfc2698d

121

py

Python

app/utils/limiter.py

johndatserakis/find-the-state-api

81da6c37eaf635ddfc01cb9964d0d173248721c7

[ "MIT" ]

1

2021-12-23T15:40:53.000Z

app/utils/limiter.py

johndatserakis/find-the-state-api

81da6c37eaf635ddfc01cb9964d0d173248721c7

[ "MIT" ]

null

app/utils/limiter.py

johndatserakis/find-the-state-api

81da6c37eaf635ddfc01cb9964d0d173248721c7

[ "MIT" ]

null

from slowapi import Limiter from slowapi.util import get_remote_address limiter = Limiter(key_func=get_remote_address)

20.166667

46

0.85124

18

121

5.444444

0.555556

0.22449

0.326531

0

0.107438

121

5

47

24.2

0.907407

0

1

0

false

0

0.666667

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1

0

null

0

1

0

1

0

6

522f33837383a367d3e08c1282920fb4cdf516be

11,306

py

Python

draw.py

HJoonKwon/MAVeric

39e93942836c3e3b38a4d56566fb118ce809b72f

[ "MIT" ]

null

draw.py

HJoonKwon/MAVeric

39e93942836c3e3b38a4d56566fb118ce809b72f

[ "MIT" ]

null

draw.py

HJoonKwon/MAVeric

39e93942836c3e3b38a4d56566fb118ce809b72f

[ "MIT" ]

null

import matplotlib as mpl from mpl_toolkits.mplot3d import Axes3D from numpy import * import matplotlib.pyplot as plot def draw_traj(waypoints, trajectory): """ Visualize the trajectories in every dimension by using matplotlib. The code is quite repetitive and might be optimized, but it works... """ mpl.rcParams['legend.fontsize'] = 10 # ============================= # 3D Plot # ============================= ax = plot.subplot2grid((23, 31), (0, 0), colspan=13, rowspan=13, projection='3d') # create Axes3D object, which can plot in 3D for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i+1].time, int((waypoints[i+1].time-waypoints[i].time)*20)) x_path = trajectory[i][0] * t ** 4 + trajectory[i][1] * t ** 3 + trajectory[i][2] * t ** 2 + trajectory[i][3] * t + trajectory[i][4] y_path = trajectory[i][5] * t ** 4 + trajectory[i][6] * t ** 3 + trajectory[i][7] * t ** 2 + trajectory[i][8] * t + trajectory[i][9] z_path = trajectory[i][10] * t ** 4 + trajectory[i][11] * t ** 3 + trajectory[i][12] * t ** 2 + trajectory[i][13] * t + trajectory[i][14] ax.plot(x_path, y_path, z_path, label='[%d] to [%d]' %(i, i+1)) # plot trajectory ax.plot([waypoints[i+1].x], [waypoints[i+1].y], [waypoints[i+1].z],'ro') # plot start if i == 0: ax.plot([waypoints[i].x], [waypoints[i].y], [waypoints[i].z], 'ro') # plot end ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') ax.legend() # ============================= # Position Plots # ============================= # add 2D plot of X over time ax = plot.subplot2grid((23, 31), (13, 0), colspan = 6, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i+1].time, int((waypoints[i+1].time-waypoints[i].time)*20)) x_path = trajectory[i][0] * t ** 4 + trajectory[i][1] * t ** 3 + trajectory[i][2] * t ** 2 + trajectory[i][3] * t + trajectory[i][4] ax.plot(t, x_path) ax.set_ylabel('X') # add 2D plot of Y over time ax = plot.subplot2grid((23, 31), (19, 0), colspan = 6, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i+1].time, int((waypoints[i+1].time-waypoints[i].time)*20)) y_path = trajectory[i][5] * t ** 4 + trajectory[i][6] * t ** 3 + trajectory[i][7] * t ** 2 + trajectory[i][8] * t + trajectory[i][9] ax.plot(t, y_path) ax.set_ylabel('Y') ax.set_xlabel('Time') # add 2D plot of Z over time ax = plot.subplot2grid((23, 31), (13, 7), colspan = 6, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i+1].time, int((waypoints[i+1].time-waypoints[i].time)*20)) z_path = trajectory[i][10] * t ** 4 + trajectory[i][11] * t ** 3 + trajectory[i][12] * t ** 2 + trajectory[i][13] * t + trajectory[i][14] ax.plot(t, z_path) ax.set_ylabel('Z') # add 2D plot of Yaw over time ax = plot.subplot2grid((23, 31), (19, 7), colspan = 6, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i+1].time, int((waypoints[i+1].time-waypoints[i].time)*20)) yaw_path = trajectory[i][15] * t ** 2 + trajectory[i][16] * t + trajectory[i][17] ax.plot(t, yaw_path) ax.set_ylabel('Yaw') ax.set_xlabel('Time') # ============================= # Velocity Plots # ============================= # add 2D plot of X over time ax = plot.subplot2grid((23, 31), (0, 15), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) x_path = 4 * trajectory[i][0] * t ** 3 + 3 * trajectory[i][1] * t ** 2 + 2 * trajectory[i][2] * t + trajectory[i][3] ax.plot(t, x_path) ax.set_ylabel('X') # add 2D plot of Y over time ax = plot.subplot2grid((23, 31), (6, 15), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) y_path = 4 * trajectory[i][5] * t ** 3 + 3 * trajectory[i][6] * t ** 2 + 2* trajectory[i][7] * t + trajectory[i][8] ax.plot(t, y_path) ax.set_ylabel('Y') ax.set_xlabel('Time') # add 2D plot of Z over time ax = plot.subplot2grid((23, 31), (0, 19), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) z_path = 4 * trajectory[i][10] * t ** 3 + 3 * trajectory[i][11] * t ** 2 + 2 * trajectory[i][12] * t + trajectory[i][13] ax.plot(t, z_path) ax.set_ylabel('Z') # add 2D plot of Yaw over time ax = plot.subplot2grid((23, 31), (6, 19), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) yaw_path = 2 * trajectory[i][15] * t + trajectory[i][16] ax.plot(t, yaw_path) ax.set_ylabel('Yaw') ax.set_xlabel('Time') # ============================= # Acceleration Plots # ============================= # add 2D plot of X over time ax = plot.subplot2grid((23, 31), (13, 15), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) x_path = 12 * trajectory[i][0] * t ** 2 + 6 * trajectory[i][1] * t + 2 * trajectory[i][2] ax.plot(t, x_path) ax.set_ylabel('X') # add 2D plot of Y over time ax = plot.subplot2grid((23, 31), (19, 15), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) y_path = 12 * trajectory[i][5] * t ** 2 + 6 * trajectory[i][6] * t + 2 * trajectory[i][7] ax.plot(t, y_path) ax.set_ylabel('Y') ax.set_xlabel('Time') # add 2D plot of Z over time ax = plot.subplot2grid((23, 31), (13, 19), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) z_path = 12 * trajectory[i][10] * t ** 2 + 6 * trajectory[i][11] * t + 2 * trajectory[i][12] ax.plot(t, z_path) ax.set_ylabel('Z') # add 2D plot of Yaw over time ax = plot.subplot2grid((23, 31), (19, 19), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) oneVec = linspace(1,1, int((waypoints[i + 1].time - waypoints[i].time) * 20)) yaw_path = 2 * trajectory[i][15] * oneVec ax.plot(t, yaw_path) ax.set_ylabel('Yaw') ax.set_xlabel('Time') # ============================= # Jerk Plots # ============================= # add 2D plot of X over time ax = plot.subplot2grid((23, 31), (0, 24), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) x_path = 24 * trajectory[i][0] * t + 6 * trajectory[i][1] ax.plot(t, x_path) ax.set_ylabel('X') # add 2D plot of Y over time ax = plot.subplot2grid((23, 31), (6, 24), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) y_path = 24 * trajectory[i][5] * t + 6 * trajectory[i][6] ax.plot(t, y_path) ax.set_ylabel('Y') ax.set_xlabel('Time') # add 2D plot of Z over time ax = plot.subplot2grid((23, 31), (0, 28), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) z_path = 24 * trajectory[i][10] * t + 6 * trajectory[i][11] ax.plot(t, z_path) ax.set_ylabel('Z') ax.set_xlabel('Time') # ============================= # Snap Plots # ============================= # add 2D plot of X over time ax = plot.subplot2grid((23, 31), (13, 24), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) oneVec = linspace(1,1, int((waypoints[i + 1].time - waypoints[i].time) * 20)) x_path = 24 *trajectory[i][0] * oneVec ax.plot(t, x_path) ax.set_ylabel('X') # add 2D plot of Y over time ax = plot.subplot2grid((23, 31), (19, 24), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) oneVec = linspace(1,1, int((waypoints[i + 1].time - waypoints[i].time) * 20)) y_path = 24 * trajectory[i][5] * oneVec ax.plot(t, y_path) ax.set_ylabel('Y') ax.set_xlabel('Time') # add 2D plot of Z over time ax = plot.subplot2grid((23, 31), (13, 28), colspan=3, rowspan=4) for i in range(len(trajectory)): t = linspace(waypoints[i].time, waypoints[i + 1].time, int((waypoints[i + 1].time - waypoints[i].time) * 20)) oneVec = linspace(1,1, int((waypoints[i + 1].time - waypoints[i].time) * 20)) z_path = 24 * trajectory[i][10] * oneVec ax.plot(t, z_path) ax.set_ylabel('Z') ax.set_xlabel('Time') # ============================= # Labels # ============================= ax = plot.subplot2grid((23, 31), (18, 6)) ax.set_frame_on(False) ax.axis('off') ax.text(-0.3,0.7,"Position", fontweight='bold') ax = plot.subplot2grid((23, 31), (5, 18)) ax.set_frame_on(False) ax.axis('off') ax.text(-0.3,0.7,"Velocity", fontweight='bold') ax = plot.subplot2grid((23, 31), (18, 18)) ax.set_frame_on(False) ax.axis('off') ax.text(-0.7,0.7,"Acceleration", fontweight='bold') ax = plot.subplot2grid((23, 31), (5, 27)) ax.set_frame_on(False) ax.axis('off') ax.text(0,0.7,"Jerk", fontweight='bold') ax = plot.subplot2grid((23, 31), (18, 27)) ax.set_frame_on(False) ax.axis('off') ax.text(-0.1,0.7,"Snap", fontweight='bold') #plot.figtext(0, 0, 'Planned Trajectory:\n ' # '(X,Y,Z,Yaw,X_dot,Y_dot,Z_dot)\n ' # 'Start: (%0.2f, %0.2f, %0.2f, %0.2f, %0.2f,%0.2f, %0.2f)\n ' # 'End: (%0.2f, %0.2f, %0.2f, %0.2f, %0.2f, %0.2f, %0.2f) \n' # 'Time for segment: %0.2f' # % (waypoint0.x, waypoint0.y, waypoint0.z, waypoint0.yaw, waypoint0.x_dot, waypoint0.y_dot, # waypoint0.z_dot, # waypoint1.x, waypoint1.y, waypoint1.z, waypoint1.yaw, waypoint1.x_dot, waypoint1.y_dot, # waypoint1.z_dot, # waypoint1.time - waypoint0.time)) # print to screen plot.show()

45.405622

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0.552715

1,745

11,306

3.527221

0.073926

0.146223

0.080422

0.102356

0.808123

0.785378

0.77498

0.750447

0

0.067976

0.229701

11,306

248

146

45.58871

0.638765

0.163276

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0.644172

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0.018217

0

1

0.006135

false

0

0.02454

0

0.030675

0

null

0

1

0

null

0

6

526a538cc535f29c049f7c12811d92feb33a8a42

46

py

Python

mypackage/__init__.py

meinert/pythonprojecttemplate

c368b2d3de2f64afdcf9eb79c9d982d9b037c711

[ "BSD-2-Clause" ]

null

mypackage/__init__.py

meinert/pythonprojecttemplate

c368b2d3de2f64afdcf9eb79c9d982d9b037c711

[ "BSD-2-Clause" ]

null

mypackage/__init__.py

meinert/pythonprojecttemplate

c368b2d3de2f64afdcf9eb79c9d982d9b037c711

[ "BSD-2-Clause" ]

null

from .core import hmm from .mymodule import *

15.333333

23

0.76087

7

46

5

0.714286

0

0.173913

46

2

24

23

0.921053

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5285bbbd49479419e172b9098c929f6b6f6ca4d6

263

py

Python

src/openpersonen/api/views/ouder.py

maykinmedia/open-personen

ddcf083ccd4eb864c5305bcd8bc75c6c64108272

[ "RSA-MD" ]

2

2020-08-26T11:24:43.000Z

2021-07-28T09:46:40.000Z

src/openpersonen/api/views/ouder.py

maykinmedia/open-personen

ddcf083ccd4eb864c5305bcd8bc75c6c64108272

[ "RSA-MD" ]

153

2020-08-26T10:45:35.000Z

2021-12-10T17:33:16.000Z

src/openpersonen/api/views/ouder.py

maykinmedia/open-personen

ddcf083ccd4eb864c5305bcd8bc75c6c64108272

[ "RSA-MD" ]

null

from openpersonen.api.data_classes import Ouder from openpersonen.api.serializers import OuderSerializer from openpersonen.api.views.base import NestedViewSet class OuderViewSet(NestedViewSet): serializer_class = OuderSerializer instance_class = Ouder

26.3

56

0.836502

29

263

7.482759

0.551724

0.221198

0.262673

0

0.117871

263

9

57

29.222222

0.935345

0

1

0

false

0

0.5

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

bfef3fcd14a6ddbd71f0e0ad7e444538abea6494

41

py

Python

contribuicao/arquivo_novo.py

lucasleonardobs/cool-repo

96b2329de8151ba16db4e742e363248fc9a6820c

[ "MIT" ]

null

contribuicao/arquivo_novo.py

lucasleonardobs/cool-repo

96b2329de8151ba16db4e742e363248fc9a6820c

[ "MIT" ]

null

contribuicao/arquivo_novo.py

lucasleonardobs/cool-repo

96b2329de8151ba16db4e742e363248fc9a6820c

[ "MIT" ]

null

print("Essa aqui é minha contribuição!")

20.5

40

0.756098

6

41

5.166667

1

0

0.121951

41

1

41

0.861111

0

0.756098

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

872c374b3a476b8af183a616257568628340ad0c

734

py

Python

src/finitestate/firmware/package_metadata/model.py

FiniteStateInc/clearcode-toolkit

521c3a2ab9d9fa6d7b9059227c6af9d09b031c33

[ "Apache-2.0" ]

null

src/finitestate/firmware/package_metadata/model.py

FiniteStateInc/clearcode-toolkit

521c3a2ab9d9fa6d7b9059227c6af9d09b031c33

[ "Apache-2.0" ]

null

src/finitestate/firmware/package_metadata/model.py

FiniteStateInc/clearcode-toolkit

521c3a2ab9d9fa6d7b9059227c6af9d09b031c33

[ "Apache-2.0" ]

1

2020-12-22T16:51:40.000Z

import attr @attr.s class FSPackageMetadata(): id: str = attr.ib(kw_only=True) name: str = attr.ib(kw_only=True) version: str = attr.ib(kw_only=True) release: str = attr.ib(kw_only=True) file_name: str = attr.ib(kw_only=True) supplier_name: str = attr.ib(kw_only=True) supplier_type: str = attr.ib(kw_only=True) supplier_url: str = attr.ib(kw_only=True) source_information: str = attr.ib(kw_only=True) file_name: str = attr.ib(kw_only=True) download_location: str = attr.ib(kw_only=True) home_page: str = attr.ib(kw_only=True) declared_license: str = attr.ib(kw_only=True) summary_description: str = attr.ib(kw_only=True) detailed_description: str = attr.ib(kw_only=True)

34.952381

53

0.697548

122

734

3.983607

0.237705

0.216049

0.277778

0.339506

0.730453

0.495885

0.269547

0.1893

0

0.173025

734

20

54

36.7

0.800659

0

0.111111

0

1

0

true

0

0.055556

0

0.944444

0

null

1

0

1

0

null

0

1

0

1

0

6

87754db7cd6e79fb73ed0a1b2c5380838fdee166

3,640

py

Python

dbdaora/boolean/_tests/datastore/test_integration_service_boolean_aioredis_datastore_get_one.py

dutradda/sqldataclass

5c87a3818e9d736bbf5e1438edc5929a2f5acd3f

[ "MIT" ]

21

2019-10-14T14:33:33.000Z

2022-02-11T04:43:07.000Z

dbdaora/boolean/_tests/datastore/test_integration_service_boolean_aioredis_datastore_get_one.py

dutradda/sqldataclass

5c87a3818e9d736bbf5e1438edc5929a2f5acd3f

[ "MIT" ]

null

dbdaora/boolean/_tests/datastore/test_integration_service_boolean_aioredis_datastore_get_one.py

dutradda/sqldataclass

5c87a3818e9d736bbf5e1438edc5929a2f5acd3f

[ "MIT" ]

1

2019-09-29T23:51:44.000Z

import asynctest import pytest from aioredis import RedisError @pytest.mark.asyncio async def test_should_get_one( fake_service, serialized_fake_entity, fake_entity ): await fake_service.repository.memory_data_source.set( 'fake:other_fake:fake', serialized_fake_entity, ) entity = await fake_service.get_one('fake', other_id='other_fake') assert entity == fake_entity.id @pytest.mark.asyncio async def test_should_get_one_with_fields( fake_service, serialized_fake_entity, fake_entity ): await fake_service.repository.memory_data_source.set( 'fake:other_fake:fake', serialized_fake_entity, ) fake_entity.number = None fake_entity.boolean = False entity = await fake_service.get_one( 'fake', fields=['id', 'other_id', 'integer', 'inner_entities'], other_id='other_fake', ) assert entity == fake_entity.id @pytest.mark.asyncio async def test_should_get_one_from_cache( fake_service, serialized_fake_entity, fake_entity ): fake_service.repository.memory_data_source.get = asynctest.CoroutineMock() fake_service.cache['fakeother_idother_fake'] = fake_entity.id entity = await fake_service.get_one('fake', other_id='other_fake') assert entity == fake_entity.id assert not fake_service.repository.memory_data_source.get.called @pytest.mark.asyncio async def test_should_get_one_from_fallback_when_not_found_on_memory( fake_service, serialized_fake_entity, fake_entity ): await fake_service.repository.memory_data_source.delete( 'fake:other_fake:fake' ) await fake_service.repository.memory_data_source.delete( 'fake:not-found:other_fake:fake' ) await fake_service.repository.fallback_data_source.put( fake_service.repository.fallback_data_source.make_key( 'fake', 'other_fake:fake' ), {'value': True}, ) entity = await fake_service.get_one('fake', other_id='other_fake') assert entity == fake_entity.id assert ( await fake_service.repository.memory_data_source.get( 'fake:other_fake:fake' ) == b'1' ) @pytest.mark.asyncio async def test_should_get_one_from_fallback_when_not_found_on_memory_with_fields( fake_service, serialized_fake_entity, fake_entity ): await fake_service.repository.memory_data_source.delete( 'fake:other_fake:fake' ) await fake_service.repository.fallback_data_source.put( fake_service.repository.fallback_data_source.make_key( 'fake', 'other_fake:fake' ), {'value': True}, ) fake_entity.number = None fake_entity.boolean = False entity = await fake_service.get_one( 'fake', other_id='other_fake', fields=['id', 'other_id', 'integer', 'inner_entities'], ) assert entity == fake_entity.id assert ( await fake_service.repository.memory_data_source.get( 'fake:other_fake:fake' ) == b'1' ) @pytest.mark.asyncio async def test_should_get_one_from_fallback_after_open_circuit_breaker( fake_service, fake_entity, mocker ): fake_service.repository.memory_data_source.get = asynctest.CoroutineMock( side_effect=RedisError ) key = fake_service.repository.fallback_data_source.make_key( 'fake', 'other_fake', 'fake' ) await fake_service.repository.fallback_data_source.put( key, {'value': True} ) entity = await fake_service.get_one('fake', other_id='other_fake') assert entity == fake_entity.id assert fake_service.logger.warning.call_count == 1

28.4375

81

0.708791

468

3,640

5.136752

0.138889

0.137271

0.106489

0.091514

0.902246

0.868552

0.839434

0.759983

0

0.001024

0.19533

3,640

127

82

28.661417

0.819734

0

0.650485

0

0.107418

0.014286

0

0.097087

1

0

false

0

0.029126

0

0.029126

0

null

0

1

0

null

0

6

5e6d4804b9a1a61a343ba8ce1cca0c1bfe09a8ce

167

py

Python

wideq/__init__.py

stboch/wideq

4acde696e958e00eede87a0e1fe28f490148204f

[ "MIT" ]

null

wideq/__init__.py

stboch/wideq

4acde696e958e00eede87a0e1fe28f490148204f

[ "MIT" ]

null

wideq/__init__.py

stboch/wideq

4acde696e958e00eede87a0e1fe28f490148204f

[ "MIT" ]

null

"""Reverse-engineered client for the LG SmartThinQ API. """ from .core import * # noqa from .client import * # noqa from .ac import * # noqa __version__ = '1.1.1'

20.875

55

0.670659

24

167

4.5

0.625

0.277778

0.259259

0

0.022388

0.197605

167

7

56

23.857143

0.783582

0.407186

0

0.055556

0

1

0

false

0

0.75

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

0d727e672d17895ece977f4d3042f95ae8722662

36

py

Python

rootnum/__init__.py

gXLg/rootnum

a617e30475a12ed6c23f9a1b35f54326824e6d7a

[ "MIT" ]

1

2021-10-04T10:37:31.000Z

rootnum/__init__.py

gXLg/Rootnum

a617e30475a12ed6c23f9a1b35f54326824e6d7a

[ "MIT" ]

null

rootnum/__init__.py

gXLg/Rootnum

a617e30475a12ed6c23f9a1b35f54326824e6d7a

[ "MIT" ]

null

from rootnum.rootnum import Rootnum

18

35

0.861111

5

36

6.2

0.6

0

0.111111

36

1

36

0.96875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0d9d247c1cd1987d88314c1225b8f5691cf72a63

10,530

py

Python

api/queries/end_user_advisories/tests/test_create_end_user_advisories.py

uktrade/lite-ap

4e1a57956bd921992b4a6e2b8fbacbba5720960d

[ "MIT" ]

3

2019-05-15T09:30:39.000Z

2020-04-22T16:14:23.000Z

api/queries/end_user_advisories/tests/test_create_end_user_advisories.py

uktrade/lite-ap

4e1a57956bd921992b4a6e2b8fbacbba5720960d

[ "MIT" ]

85

2019-04-24T10:39:35.000Z

2022-03-21T14:52:12.000Z

api/queries/end_user_advisories/tests/test_create_end_user_advisories.py

uktrade/lite-ap

4e1a57956bd921992b4a6e2b8fbacbba5720960d

[ "MIT" ]

1

2021-01-17T11:12:19.000Z

from django.urls import reverse from parameterized import parameterized from rest_framework import status from api.cases.models import Case from api.parties.enums import PartyType from test_helpers.clients import DataTestClient class EndUserAdvisoryCreateTests(DataTestClient): url = reverse("queries:end_user_advisories:end_user_advisories") def test_create_end_user_advisory_query(self): """ Ensure that a user can create an end user advisory, and that it creates a case when doing so """ data = { "end_user": { "sub_type": "government", "name": "Ada", "website": "https://gov.uk", "address": "123", "signatory_name_euu": "Ada", "country": "GB", "type": PartyType.END_USER, }, "note": "I Am Easy to Find", "reasoning": "Lack of hairpin turns", "nature_of_business": "guns", "contact_name": "Steven", "contact_email": "steven@gov.com", "contact_job_title": "director", "contact_telephone": "0123456789", } response = self.client.post(self.url, data, **self.exporter_headers) response_data = response.json()["end_user_advisory"] self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response_data["note"], data["note"]) self.assertEqual(response_data["reasoning"], data["reasoning"]) self.assertEqual(response_data["contact_email"], data["contact_email"]) self.assertEqual(response_data["contact_telephone"], data["contact_telephone"]) self.assertEqual(response_data["contact_job_title"], data["contact_job_title"]) end_user_data = response_data["end_user"] self.assertEqual(end_user_data["sub_type"]["key"], data["end_user"]["sub_type"]) self.assertEqual(end_user_data["name"], data["end_user"]["name"]) self.assertEqual(end_user_data["website"], data["end_user"]["website"]) self.assertEqual(end_user_data["address"], data["end_user"]["address"]) self.assertEqual(end_user_data["country"]["id"], data["end_user"]["country"]) self.assertEqual(Case.objects.count(), 1) self.assertEqual(Case.objects.get().submitted_by, self.exporter_user) def test_create_copied_end_user_advisory_query(self): """ Ensure that a user can duplicate an end user advisory, it links to the previous query and that it creates a case when doing so """ query = self.create_end_user_advisory("Advisory", "", self.organisation) data = { "end_user": { "sub_type": "government", "name": "Ada", "website": "https://gov.uk", "address": "123", "signatory_name_euu": "Ada", "country": "GB", "type": PartyType.END_USER, }, "note": "I Am Easy to Find", "reasoning": "Lack of hairpin turns", "copy_of": query.id, "nature_of_business": "guns", "contact_name": "Steven", "contact_email": "steven@gov.com", "contact_job_title": "director", "contact_telephone": "0123456789", } response = self.client.post(self.url, data, **self.exporter_headers) response_data = response.json()["end_user_advisory"] self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response_data["note"], data["note"]) self.assertEqual(response_data["reasoning"], data["reasoning"]) self.assertEqual(response_data["copy_of"], str(data["copy_of"])) end_user_data = response_data["end_user"] self.assertEqual(end_user_data["sub_type"]["key"], data["end_user"]["sub_type"]) self.assertEqual(end_user_data["name"], data["end_user"]["name"]) self.assertEqual(end_user_data["website"], data["end_user"]["website"]) self.assertEqual(end_user_data["address"], data["end_user"]["address"]) self.assertEqual(end_user_data["country"]["id"], data["end_user"]["country"]) self.assertEqual(Case.objects.count(), 2) @parameterized.expand( [ ("com", "person", "http://gov.co.uk", "place street", "GB", "", "",), # invalid end user type ("commercial", "", "", "nowhere", "GB", "", ""), # name is empty ("government", "abc", "abc", "nowhere", "GB", "", "",), # invalid web address ("government", "abc", "", "", "GB", "", ""), # empty address ("government", "abc", "", "nowhere", "ALP", "", ""), # invalid country code ("", "", "", "", "", "", ""), # empty dataset ] ) def test_create_end_user_advisory_query_failure( self, end_user_type, name, website, address, country, note, reasoning ): data = { "end_user": { "type": end_user_type, "name": name, "website": website, "address": address, "country": country, }, "note": note, "reasoning": reasoning, } response = self.client.post(self.url, data, **self.exporter_headers) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_end_user_advisory_query_for_organisation_failure(self): """ Fail to create organisation advisory with missing fields """ data = { "end_user": { "sub_type": "commercial", "name": "Ada", "website": "https://gov.uk", "address": "123", "signatory_name_euu": "Ada", "country": "GB", "type": PartyType.END_USER, }, "note": "I Am Easy to Find", "reasoning": "Lack of hairpin turns", "contact_email": "steven@gov.com", "contact_telephone": "0123456789", "nature_of_business": "", "contact_name": "", "contact_job_title": "", } response = self.client.post(self.url, data, **self.exporter_headers) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) errors = response.json()["errors"] self.assertEqual(errors.get("nature_of_business"), ["This field may not be blank"]) self.assertEqual(errors.get("contact_name"), ["This field may not be blank"]) self.assertEqual(errors.get("contact_job_title"), ["This field may not be blank"]) def test_create_end_user_advisory_query_for_government_failure(self): """ Fail to create gov advisory with missing fields """ data = { "end_user": { "sub_type": "commercial", "name": "Ada", "website": "https://gov.uk", "address": "123", "signatory_name_euu": "Ada", "country": "GB", "type": PartyType.END_USER, }, "note": "I Am Easy to Find", "reasoning": "Lack of hairpin turns", "contact_email": "steven@gov.com", "contact_telephone": "0123456789", "contact_name": "", "contact_job_title": "", } response = self.client.post(self.url, data, **self.exporter_headers) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) errors = response.json()["errors"] self.assertEqual(errors.get("contact_name"), ["This field may not be blank"]) self.assertEqual(errors.get("contact_job_title"), ["This field may not be blank"]) def test_create_end_user_advisory_query_for_government(self): """ Successfully creates gov advisory """ data = { "end_user": { "sub_type": "government", "name": "Ada", "website": "https://gov.uk", "address": "123", "signatory_name_euu": "Ada", "country": "GB", "type": PartyType.END_USER, }, "note": "I Am Easy to Find", "reasoning": "Lack of hairpin turns", "contact_email": "steven@gov.com", "contact_telephone": "0123456789", "contact_name": "steven", "contact_job_title": "director", } response = self.client.post(self.url, data, **self.exporter_headers) self.assertEqual(response.status_code, status.HTTP_201_CREATED) def test_create_end_user_advisory_query_for_individual(self): """ Successfully create individual advisory """ data = { "end_user": { "sub_type": "individual", "name": "Ada", "website": "https://gov.uk", "address": "123", "signatory_name_euu": "Ada", "country": "GB", "type": PartyType.END_USER, }, "note": "I Am Easy to Find", "reasoning": "Lack of hairpin turns", "contact_email": "steven@gov.com", "contact_telephone": "0123456789", } response = self.client.post(self.url, data, **self.exporter_headers) response_data = response.json()["end_user_advisory"] self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response_data["note"], data["note"]) self.assertEqual(response_data["reasoning"], data["reasoning"]) self.assertEqual(response_data["contact_email"], data["contact_email"]) self.assertEqual(response_data["contact_telephone"], data["contact_telephone"]) end_user_data = response_data["end_user"] self.assertEqual(end_user_data["sub_type"]["key"], data["end_user"]["sub_type"]) self.assertEqual(end_user_data["name"], data["end_user"]["name"]) self.assertEqual(end_user_data["website"], data["end_user"]["website"]) self.assertEqual(end_user_data["signatory_name_euu"], data["end_user"]["signatory_name_euu"]) self.assertEqual(end_user_data["address"], data["end_user"]["address"]) self.assertEqual(end_user_data["country"]["id"], data["end_user"]["country"]) self.assertEqual(Case.objects.count(), 1)

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0da098f3ce6e533bfaba3eb347a8abb5f40ab89a

152

py

Python

meiduo_mall/meiduo_mall/utils/views.py

1103928458/meiduo_drf

49595755f264b09ea748b4deb8a88bba5eb8557b

[ "MIT" ]

null

meiduo_mall/meiduo_mall/utils/views.py

1103928458/meiduo_drf

49595755f264b09ea748b4deb8a88bba5eb8557b

[ "MIT" ]

null

meiduo_mall/meiduo_mall/utils/views.py

1103928458/meiduo_drf

49595755f264b09ea748b4deb8a88bba5eb8557b

[ "MIT" ]

1

2020-11-10T07:22:42.000Z

from django.contrib.auth import mixins from django.views import View # 判断用户登录----用于继承 class LoginRequiredView(mixins.LoginRequiredMixin,View): pass

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173

py

Python

ciclo1_python/udea/MisionTIC_UdeA_Ciclo1/Material/Semana_7/Semana 7/Mifuncion.py

felipeescallon/mision_tic_2022

20496fc40b18d2e98114d6362928f34fde41aaae

[ "CC0-1.0" ]

7

2021-07-05T21:25:50.000Z

2021-11-09T11:09:41.000Z

ciclo1_python/udea/MisionTIC_UdeA_Ciclo1/Material/Semana_7/Semana 7/Mifuncion.py

felipeescallon/mision_tic_2022

20496fc40b18d2e98114d6362928f34fde41aaae

[ "CC0-1.0" ]

null

ciclo1_python/udea/MisionTIC_UdeA_Ciclo1/Material/Semana_7/Semana 7/Mifuncion.py

felipeescallon/mision_tic_2022

20496fc40b18d2e98114d6362928f34fde41aaae

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- """ @author: Ing. Víctor Fabián Castro Pérez """ def imprimealgo(): print ("Esta es la cadena que se imprime de la funcion MiFuncion\n")

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0df1104fc90b32d9e41bb331b088353d2b4dcb77

48

py

Python

allink_core/core/customisation/dummy_new_app/tests/test_views.py

allink/allink-core

cf2727f26192d8dee89d76feb262bc4760f36f5e

[ "BSD-3-Clause" ]

5

2017-03-13T08:49:45.000Z

2022-03-05T20:05:56.000Z

allink_core/core/customisation/dummy_new_app/tests/test_views.py

allink/allink-core

cf2727f26192d8dee89d76feb262bc4760f36f5e

[ "BSD-3-Clause" ]

28

2019-10-21T08:32:18.000Z

2022-02-10T13:16:38.000Z

allink_core/core/customisation/dummy_new_app/tests/test_views.py

allink/allink-core

cf2727f26192d8dee89d76feb262bc4760f36f5e

[ "BSD-3-Clause" ]

null

# TODO add your tests here or delete this file.

24

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1

0

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6

0dfa8d034fee3127c14bc4cec6c81ca4e9d89283

1,753

py

Python

user/vistas/widgets/chatBox.py

ZerpaTechnology/occoa

a8c0bd2657bc058801a883109c0ec0d608d04ccc

[ "Apache-2.0" ]

null

user/vistas/widgets/chatBox.py

ZerpaTechnology/occoa

a8c0bd2657bc058801a883109c0ec0d608d04ccc

[ "Apache-2.0" ]

null

user/vistas/widgets/chatBox.py

ZerpaTechnology/occoa

a8c0bd2657bc058801a883109c0ec0d608d04ccc

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- print '''<div class="chat '''+str('hidden' if 'chat' in data['hidden'] else '' )+'''"> <style> input,button{ font-size:13px; } .chat{ width:200px; font-size:12px !important; } .chat textarea{ width:100%; } .chat .mensajes{ height:240px; } .conversador1{ text-align:left; } .conversador1 p{ background-color:rgb(200,200,250); border-radius:30px 0px 30px 30px; padding:8px; display:inline-block; margin:0px; } .yo{ text-align:right; } .yo p{ background-color:rgb(150,200,250); border-radius:30px 30px 30px 0px; padding:8px; display:inline-block; margin:0px; } .btn-closeChat{ cursor:pointer; padding:5px; } .chatbox{ min-height:30px; }</style> <div class="bg-ubuntu_blue pad-05"> <span class="titulo">Usuario 1</span> <div class="right"> <img src="'''+str(config.base_url)+'''static/imgs/iconos/005-add.png" class="height-1_5"> <img src="'''+str(config.base_url)+'''static/imgs/iconos/004-settings.png" class="height-1_5"> <span class="btn-closeChat">x</span></div> </div> <div style="overflow-y:scroll" class="bg-white mensajes"> <div> ''' if "conversacion" in data: print ''' ''' for elem in data["conversacion"]: print ''' ''' if elem[0]=="conversador1": print ''' <div class="conversador1"><p>'''+str(elem[1]) +'''</p></div> ''' elif elem[0]=="yo": print ''' <div class="yo"><p >'''+str(elem[1]) +'''</p></div> ''' pass print ''' ''' pass print ''' ''' pass print ''' </div> </div> <textarea class="chatbox"> hola </textarea></div>'''

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218b2b286fc80001c86b7e06dcf171dfa0a36985

68

py

Python

recipes-tag/pyzbar/run_test.py

dmgav/lightsource2-recipes

2014b10a65e173b0b6fdd0707ef81709b0ce1b1f

[ "BSD-3-Clause" ]

4

2016-07-17T23:55:23.000Z

2021-07-18T22:51:40.000Z

recipes-tag/pyzbar/run_test.py

dmgav/lightsource2-recipes

2014b10a65e173b0b6fdd0707ef81709b0ce1b1f

[ "BSD-3-Clause" ]

477

2016-07-05T15:21:30.000Z

2020-03-23T20:02:52.000Z

recipes-tag/pyzbar/run_test.py

dmgav/lightsource2-recipes

2014b10a65e173b0b6fdd0707ef81709b0ce1b1f

[ "BSD-3-Clause" ]

21

2016-07-25T16:18:52.000Z

2021-04-06T01:37:59.000Z

import pyzbar import pyzbar.pyzbar from pyzbar.pyzbar import decode

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10d5d2b61dda1e3cc37e5494b00b5f383db14f81

173

py

Python

simpletransformers/language_generation/__init__.py

taranais/simpletransformers

36b2519cad5d8beed1f1726fa9b1163eb52286f0

[ "Apache-2.0" ]

2

2020-09-14T07:40:14.000Z

2021-04-12T06:14:48.000Z

simpletransformers/language_generation/__init__.py

taranais/simpletransformers

36b2519cad5d8beed1f1726fa9b1163eb52286f0

[ "Apache-2.0" ]

1

2020-05-31T22:54:58.000Z

simpletransformers/language_generation/__init__.py

taranais/simpletransformers

36b2519cad5d8beed1f1726fa9b1163eb52286f0

[ "Apache-2.0" ]

null

from simpletransformers.language_generation.language_generation_model import LanguageGenerationModel from simpletransformers.config.model_args import LanguageGenerationArgs

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8029383880bb17349fa790fa7e645976273db82e

245

py

Python

torchmeta/toy/__init__.py

yusufraji/siren

9416a751f64b9d1e9816f7a05e895531e9506d8a

[ "MIT" ]

1,704

2019-09-16T15:08:18.000Z

2022-03-31T22:36:43.000Z

torchmeta/toy/__init__.py

yusufraji/siren

9416a751f64b9d1e9816f7a05e895531e9506d8a

[ "MIT" ]

135

2019-09-20T15:34:03.000Z

2022-03-13T23:31:17.000Z

torchmeta/toy/__init__.py

yusufraji/siren

9416a751f64b9d1e9816f7a05e895531e9506d8a

[ "MIT" ]

221

2019-09-17T09:01:21.000Z

2022-03-30T03:23:35.000Z

from torchmeta.toy.harmonic import Harmonic from torchmeta.toy.sinusoid import Sinusoid from torchmeta.toy.sinusoid_line import SinusoidAndLine from torchmeta.toy import helpers __all__ = ['Harmonic', 'Sinusoid', 'SinusoidAndLine', 'helpers']

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337f404475a70dd054d2a1d4c710dc6756661761

2,065

py

Python

authors/apps/articles/tests/test_sharing.py

andela/ah-backend-zeus

44e2f554c4a7a10c06bd3c7be42fc91571c09f29

[ "BSD-3-Clause" ]

1

2019-03-22T09:13:35.000Z

authors/apps/articles/tests/test_sharing.py

andela/ah-backend-zeus

44e2f554c4a7a10c06bd3c7be42fc91571c09f29

[ "BSD-3-Clause" ]

13

2018-11-27T16:48:25.000Z

2021-06-10T21:00:19.000Z

authors/apps/articles/tests/test_sharing.py

andela/ah-backend-zeus

44e2f554c4a7a10c06bd3c7be42fc91571c09f29

[ "BSD-3-Clause" ]

9

2018-11-23T11:10:24.000Z

2019-04-04T11:04:33.000Z

from rest_framework.test import APIClient from .base_test import BaseTest from rest_framework import status class TestSharing(BaseTest): def test_api_can_share_an_article_on_facebook(self): created_article = self.client.post( '/api/articles/', data=self.new_article, format='json') slug = self.get_slug(created_article) response = self.client.post( '/api/articles/{}/facebook/'.format(slug), format='json') self.assertEqual(response.status_code, status.HTTP_200_OK) def test_api_can_share_an_article_with_facebook_with_wrong_slug(self): response = self.client.post( '/api/articles/slug/facebook/', ) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_api_can_share_an_article_on_twitter(self): created_article = self.client.post( '/api/articles/', data=self.new_article, format='json') slug = self.get_slug(created_article) response = self.client.post( '/api/articles/{}/twitter/'.format(slug), format='json') self.assertEqual(response.status_code, status.HTTP_200_OK) def test_api_can_share_an_article_with_twitter_with_wrong_slug(self): response = self.client.post( '/api/articles/slug/twitter/', ) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_api_can_share_an_article_with_email(self): created_article = self.client.post( '/api/articles/', data=self.new_article, format='json') slug = self.get_slug(created_article) response = self.client.post( '/api/articles/{}/email/'.format(slug), format='json') self.assertEqual(response.status_code, status.HTTP_200_OK) def test_api_can_share_an_article_with_email_with_wrong_slug(self): response = self.client.post( '/api/articles/slug/email/' ) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)

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null

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1

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null

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338c7fd82c12514e681e73d3d5425a07ff2ddf74

247

py

Python

zipkin/binding/requests/__init__.py

Themimitoof/python-zipkin

f91169d044a49f641930bdfc456f34e497690fe8

[ "Apache-2.0" ]

4

2018-02-28T11:00:36.000Z

2020-01-22T10:52:18.000Z

zipkin/binding/requests/__init__.py

Themimitoof/python-zipkin

f91169d044a49f641930bdfc456f34e497690fe8

[ "Apache-2.0" ]

4

2018-04-21T12:29:46.000Z

2021-06-22T06:48:45.000Z

zipkin/binding/requests/__init__.py

Themimitoof/python-zipkin

f91169d044a49f641930bdfc456f34e497690fe8

[ "Apache-2.0" ]

4

2018-02-28T13:50:10.000Z

2021-07-01T09:47:01.000Z

try: from .impl import bind, request_adapter except ImportError as exc: import logging logging.getLogger(__name__).warn("requests not installed") def bind(): pass def request_adapter(adapter): return adapter

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33b228fde5a419ed1e631234a898b50145f7c788

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py

Python

3.7.0/lldb-3.7.0.src/test/tools/lldb-mi/TestMiFile.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

3

2016-02-10T14:18:40.000Z

2018-02-05T03:15:56.000Z

3.7.0/lldb-3.7.0.src/test/tools/lldb-mi/TestMiFile.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

1

2016-02-10T15:40:03.000Z

3.7.0/lldb-3.7.0.src/test/tools/lldb-mi/TestMiFile.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

null

""" Test lldb-mi -file-xxx commands. """ import lldbmi_testcase from lldbtest import * import unittest2 class MiFileTestCase(lldbmi_testcase.MiTestCaseBase): mydir = TestBase.compute_mydir(__file__) @lldbmi_test @expectedFailureWindows("llvm.org/pr22274: need a pexpect replacement for windows") @skipIfFreeBSD # llvm.org/pr22411: Failure presumably due to known thread races def test_lldbmi_file_exec_and_symbols_file(self): """Test that 'lldb-mi --interpreter' works for -file-exec-and-symbols exe.""" self.spawnLldbMi(args = None) # Test that -file-exec-and-symbols works for filename self.runCmd("-file-exec-and-symbols %s" % self.myexe) self.expect("\^done") # Run self.runCmd("-exec-run") self.expect("\^running") self.expect("\*stopped,reason=\"exited-normally\"") @lldbmi_test @expectedFailureWindows("llvm.org/pr22274: need a pexpect replacement for windows") @skipIfFreeBSD # llvm.org/pr22411: Failure presumably due to known thread races def test_lldbmi_file_exec_and_symbols_absolute_path(self): """Test that 'lldb-mi --interpreter' works for -file-exec-and-symbols fullpath/exe.""" self.spawnLldbMi(args = None) # Test that -file-exec-and-symbols works for absolute path import os path = os.path.join(os.getcwd(), self.myexe) self.runCmd("-file-exec-and-symbols \"%s\"" % path) self.expect("\^done") # Run self.runCmd("-exec-run") self.expect("\^running") self.expect("\*stopped,reason=\"exited-normally\"") @lldbmi_test @expectedFailureWindows("llvm.org/pr22274: need a pexpect replacement for windows") @skipIfFreeBSD # llvm.org/pr22411: Failure presumably due to known thread races def test_lldbmi_file_exec_and_symbols_relative_path(self): """Test that 'lldb-mi --interpreter' works for -file-exec-and-symbols relpath/exe.""" self.spawnLldbMi(args = None) # Test that -file-exec-and-symbols works for relative path path = "./%s" % self.myexe self.runCmd("-file-exec-and-symbols %s" % path) self.expect("\^done") # Run self.runCmd("-exec-run") self.expect("\^running") self.expect("\*stopped,reason=\"exited-normally\"") @lldbmi_test @expectedFailureWindows("llvm.org/pr22274: need a pexpect replacement for windows") @skipIfFreeBSD # llvm.org/pr22411: Failure presumably due to known thread races def test_lldbmi_file_exec_and_symbols_unknown_path(self): """Test that 'lldb-mi --interpreter' works for -file-exec-and-symbols badpath/exe.""" self.spawnLldbMi(args = None) # Test that -file-exec-and-symbols fails on unknown path path = "unknown_dir/%s" % self.myexe self.runCmd("-file-exec-and-symbols %s" % path) self.expect("\^error") if __name__ == '__main__': unittest2.main()

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33b7968facc326b2e573917800145c84f816046a

109

py

Python

pysmap/twitterutil/__init__.py

SMAPPNYU/pysmapp

eb871992f40c53125129535e871525d5623c8c2d

[ "MIT" ]

21

2016-05-22T22:09:54.000Z

2021-08-09T14:46:13.000Z

pysmap/twitterutil/__init__.py

SMAPPNYU/pysmapp

eb871992f40c53125129535e871525d5623c8c2d

[ "MIT" ]

26

2016-05-06T16:34:09.000Z

2020-07-17T19:51:19.000Z

pysmap/twitterutil/__init__.py

SMAPPNYU/pysmapp

eb871992f40c53125129535e871525d5623c8c2d

[ "MIT" ]

6

2016-08-16T10:35:02.000Z

2020-07-14T14:40:58.000Z

''' module ''' from . import smapp_collection, smapp_dataset __all__ = ['smapp_collection', 'smapp_dataset']

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null

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1d0f97d1931d93977d049c7711217dd4ecab685f

43

py

Python

apps/Todo/serializers/__init__.py

Eduardo-RFarias/DjangoReactBackend

b8183ea4b24be5c0aa557ffbc79fc23e0777b8ad

[ "MIT" ]

null

apps/Todo/serializers/__init__.py

Eduardo-RFarias/DjangoReactBackend

b8183ea4b24be5c0aa557ffbc79fc23e0777b8ad

[ "MIT" ]

null

apps/Todo/serializers/__init__.py

Eduardo-RFarias/DjangoReactBackend

b8183ea4b24be5c0aa557ffbc79fc23e0777b8ad

[ "MIT" ]

null

from .TodoSerializer import TodoSerializer

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6

1d83f20852b9dffc42a935bdafe17867ce4c3936

17,434

py

Python

model/Seq2SeqEncoders.py

bigaidream-projects/citylearn-2020-pikapika

8c9389eb4b4e979faf269b8c0ce87b499af97ac1

[ "Apache-2.0" ]

3

2021-12-20T03:40:55.000Z

2022-02-02T04:26:33.000Z

model/Seq2SeqEncoders.py

bigaidream-projects/citylearn-2020-pikapika

8c9389eb4b4e979faf269b8c0ce87b499af97ac1

[ "Apache-2.0" ]

null

model/Seq2SeqEncoders.py

bigaidream-projects/citylearn-2020-pikapika

8c9389eb4b4e979faf269b8c0ce87b499af97ac1

[ "Apache-2.0" ]

4

2022-02-11T20:30:51.000Z

2022-02-27T01:17:34.000Z

import torch from torch import nn from model.BaseModules import TransformerDecoderLayer from model.Encoder import BaseEncoder, AutoEncoder, TemporalConvNet class Seq2SeqLSTM(BaseEncoder): def __init__(self, source_size, target_size, hidden_size, **kwargs): super(Seq2SeqLSTM, self).__init__() self.encoder = nn.LSTM(source_size, hidden_size, **kwargs) self.decoder = nn.LSTM(target_size, hidden_size, **kwargs) def forward(self, src, tgt, **kwargs): enc, enc_hx = self.encoder(src) dec, _ = self.decoder(tgt, hx=enc_hx) return enc, dec class Seq2SeqLSTM_new(BaseEncoder): def __init__(self, source_size, target_size, pred_len, hidden_size, **kwargs): super(Seq2SeqLSTM_new, self).__init__() self.pred_len = pred_len self.encoder = nn.LSTM(source_size, hidden_size, **kwargs) self.decoder = nn.LSTM(target_size, hidden_size, **kwargs) def forward(self, src, **kwargs): """ :param src: (Time, batch*building, State) :param kwargs: :return: """ enc, enc_hx = self.encoder(src) h_x, _ = enc_hx dims = h_x.ndim - 1 dec_in = h_x.repeat(self.pred_len, *([1] * dims)) dec, _ = self.decoder(dec_in, hx=enc_hx) return enc, dec class Seq2SeqAttnEncoder(BaseEncoder): def __init__(self, source_size, target_size, hidden_size, target_fn, auto_encoder_kwargs, attn_kwargs, lstm_kwargs=None, **kwargs): super(Seq2SeqAttnEncoder, self).__init__() if lstm_kwargs is None: lstm_kwargs = {} self.target_fn = target_fn self.auto_encoder = AutoEncoder(source_size, hidden_size, **auto_encoder_kwargs) self.seq2seq = Seq2SeqLSTM(hidden_size, target_size, hidden_size, **lstm_kwargs) self.HistoryTemporalModule = TransformerDecoderLayer(hidden_size, **attn_kwargs) self.ForecastTemporalModule = TransformerDecoderLayer(hidden_size, **attn_kwargs) def forward(self, x): """ :param x: the state sequence :return: hidden state Shape: - x: :math:`(Batch, Building, Time, State)`. - return: :math:`(Batch, Building, Hidden_State*2)`. """ def to_seq_first(tensor): tensor = tensor.unsqueeze(0).transpose(0, -2) return tensor.reshape(tensor.size(0), -1, tensor.size(-1)) def undo_seq_first(tensor, lead_dims): tensor.transpose_(0, -2) return tensor.reshape(*lead_dims, *tensor.shape[-2:]) src, tgt = self.target_fn(x) assert src.shape[:-2] == tgt.shape[:-2] h_s = self.auto_encoder(src.reshape(-1, src.size(-1))).reshape(*src.shape[:-1], -1) h_s, tgt = to_seq_first(h_s), to_seq_first(tgt) _, h_t = self.seq2seq(src=h_s, tgt=tgt) h_cur = h_s[[-1]] h_t = self.ForecastTemporalModule(tgt=h_cur, memory=h_t) h_s = self.HistoryTemporalModule(tgt=h_cur, memory=h_s) out = undo_seq_first(torch.cat((h_s, h_t), dim=-1), src.shape[:-2]).squeeze(-2) # out.transpose_(0, 1) # -> (Building, Batch, State) # out = self.BuildingAttnModule(out) # out.transpose_(0, 1) # -> (Batch, Building, State) return out class Seq2SeqTCNEncoder(BaseEncoder): def __init__(self, source_size, target_size, hidden_size, target_fn, auto_encoder_kwargs, unique_kwargs_history, unique_kwargs_forecast, lstm_kwargs=None, **kwargs): super(Seq2SeqTCNEncoder, self).__init__() if lstm_kwargs is None: lstm_kwargs = {} self.target_fn = target_fn self.auto_encoder = AutoEncoder(source_size, hidden_size, **auto_encoder_kwargs) self.seq2seq = Seq2SeqLSTM(hidden_size, target_size, hidden_size, **lstm_kwargs) self.HistoryTemporalModule = TemporalConvNet(hidden_size, hidden_size, **unique_kwargs_history) self.ForecastTemporalModule = TemporalConvNet(hidden_size, hidden_size, **unique_kwargs_forecast) # self.BuildingAttnModule = TransformerEncoderLayer(hidden_size * 2, **attn_kwargs) def forward(self, x): """ :param x: the state sequence :return: hidden state Shape: - x: :math:`(Batch, Building, Time, State)`. - return: :math:`(Batch, Building, Hidden_State*2)`. """ def to_seq_first(tensor): tensor = tensor.unsqueeze(0).transpose(0, -2) return tensor.reshape(tensor.size(0), -1, tensor.size(-1)) def undo_seq_first(tensor, lead_dims): tensor.transpose_(0, -2) return tensor.reshape(*lead_dims, *tensor.shape[-2:]) def to_TCN_input(tensor): # tensor: (seq, batch*building, s_dim) tensor = tensor.transpose(1, 0) # (batch*building, seq, s_dim) old_shape = tensor.shape return tensor.reshape((-1, 9, *old_shape[-2:])) def reverse_t_dim(tensor): inv_idx = torch.arange(tensor.size(2) - 1, -1, -1).long().to(tensor.device) # or equivalently torch.range(tensor.size(0)-1, 0, -1).long() inv_tensor = tensor.index_select(2, inv_idx) return inv_tensor src, tgt = self.target_fn(x) assert src.shape[:-2] == tgt.shape[:-2] h_s = self.auto_encoder(src.reshape(-1, src.size(-1))).reshape(*src.shape[:-1], -1) h_s, tgt = to_seq_first(h_s), to_seq_first(tgt) _, h_t = self.seq2seq(src=h_s, tgt=tgt) # h_cur = h_s[[-1]] # h_t = self.ForecastTemporalModule(tgt=h_cur, memory=h_t) # h_s = self.HistoryTemporalModule(tgt=h_cur, memory=h_s) # (seq, batch*building, s_dim) # TCN input: (batch, building, seq, s_dim) h_t = self.ForecastTemporalModule(reverse_t_dim(to_TCN_input(h_t))) # (batch, building, 128) h_s = self.HistoryTemporalModule(to_TCN_input(h_s)) # reverse the forecast sequence on t-dim out = torch.cat((h_s, h_t), dim=-1) # (batch, building, 256) # out.transpose_(0, 1) # -> (Building, Batch, State) # out = self.BuildingAttnModule(out) # out.transpose_(0, 1) # -> (Batch, Building, State) return out class Seq2SeqSymTCNEncoder_old(BaseEncoder): def __init__(self, source_size, target_size, hidden_size, target_fn, pred_len, auto_encoder_kwargs, tcn_kwargs, lstm_kwargs=None, **kwargs): super(Seq2SeqSymTCNEncoder_old, self).__init__() if lstm_kwargs is None: lstm_kwargs = {} self.pred_len = pred_len self.target_fn = target_fn self.auto_encoder = AutoEncoder(source_size, hidden_size, **auto_encoder_kwargs) self.seq2seq = Seq2SeqLSTM(hidden_size, target_size, hidden_size, **lstm_kwargs) self.TemporalModule = TemporalConvNet(hidden_size, hidden_size, **tcn_kwargs) # self.BuildingAttnModule = TransformerEncoderLayer(hidden_size * 2, **attn_kwargs) def forward(self, x): """ :param x: the state sequence :return: hidden state Shape: - x: :math:`(Batch, Building, Time, State)`. - return: :math:`(Batch, Building, Hidden_State*2)`. """ def to_seq_first(tensor): tensor = tensor.unsqueeze(0).transpose(0, -2) return tensor.reshape(tensor.size(0), -1, tensor.size(-1)) def undo_seq_first(tensor, lead_dims): tensor.transpose_(0, -2) return tensor.reshape(*lead_dims, *tensor.shape[-2:]) def to_TCN_input(tensor): # tensor: (seq, batch*building, s_dim) tensor = tensor.transpose(1, 0) # (batch*building, seq, s_dim) old_shape = tensor.shape return tensor.reshape((-1, 9, *old_shape[-2:])) src, tgt = self.target_fn(x) assert src.shape[:-2] == tgt.shape[:-2] h_s = self.auto_encoder(src.reshape(-1, src.size(-1))).reshape(*src.shape[:-1], -1) h_s, tgt = to_seq_first(h_s), to_seq_first(tgt) _, h_t = self.seq2seq(src=h_s, tgt=tgt) # h_cur = h_s[[-1]] h = torch.cat((h_s, h_t), dim=0) # (seq, batch*building, s_dim) # TCN input: (batch, building, seq, s_dim) out = self.TemporalModule((to_TCN_input(h))).unbind(-2)[-1] # (batch, building, seq, 128) # out.transpose_(0, 1) # -> (Building, Batch, State) # out = self.BuildingAttnModule(out) # out.transpose_(0, 1) # -> (Batch, Building, State) return out[:, :, -(self.pred_len + 1)] class Seq2SeqMixedTCNEncoder(BaseEncoder): def __init__(self, source_size, target_size, hidden_size, target_fn, auto_encoder_kwargs, unique_kwargs_history, unique_kwargs_forecast, lstm_kwargs=None, **kwargs): super(Seq2SeqMixedTCNEncoder, self).__init__() if lstm_kwargs is None: lstm_kwargs = {} self.target_fn = target_fn self.auto_encoder = AutoEncoder(21, hidden_size, **auto_encoder_kwargs).eval() self.seq2seq = Seq2SeqLSTM(hidden_size, target_size, hidden_size, **lstm_kwargs).eval() self.HistoryTemporalModule = TemporalConvNet(source_size, hidden_size, **unique_kwargs_history) self.ForecastTemporalModule = TemporalConvNet(hidden_size, hidden_size, **unique_kwargs_forecast) # self.BuildingAttnModule = TransformerEncoderLayer(hidden_size * 2, **attn_kwargs) def forward(self, x): """ :param x: the state sequence :return: hidden state Shape: - x: :math:`(Batch, Building, Time, State)`. - return: :math:`(Batch, Building, Hidden_State*2)`. """ def to_seq_first(tensor): tensor = tensor.unsqueeze(0).transpose(0, -2) return tensor.reshape(tensor.size(0), -1, tensor.size(-1)) def undo_seq_first(tensor, lead_dims): tensor.transpose_(0, -2) return tensor.reshape(*lead_dims, *tensor.shape[-2:]) def to_TCN_input(tensor): # tensor: (seq, batch*building, s_dim) tensor = tensor.transpose(1, 0) # (batch*building, seq, s_dim) old_shape = tensor.shape return tensor.reshape((-1, 9, *old_shape[-2:])) def reverse_t_dim(tensor): inv_idx = torch.arange(tensor.size(2) - 1, -1, -1).long().to(tensor.device) # or equivalently torch.range(tensor.size(0)-1, 0, -1).long() inv_tensor = tensor.index_select(2, inv_idx) return inv_tensor src_full, src, tgt = self.target_fn(x) assert src_full.shape[:-2] == src.shape[:-2] == tgt.shape[:-2] h_s = self.auto_encoder(src.reshape(-1, src.size(-1))).reshape(*src.shape[:-1], -1) h_s, tgt = to_seq_first(h_s), to_seq_first(tgt) _, h_t = self.seq2seq(src=h_s, tgt=tgt) # (seq, batch*building, s_dim) # TCN input: (batch, building, seq, s_dim) # reverse the forecast sequence on t-dim h_t = self.ForecastTemporalModule(reverse_t_dim(to_TCN_input(h_t))).unbind(-2)[-1] h_s = self.HistoryTemporalModule(src_full).unbind(-2)[-1] # (batch, building, 128) out = torch.cat((h_s, h_t), dim=-1) # (batch, building, 256) # out.transpose_(0, 1) # -> (Building, Batch, State) # out = self.BuildingAttnModule(out) # out.transpose_(0, 1) # -> (Batch, Building, State) return out class Seq2SeqSymTCNEncoder(BaseEncoder): def __init__(self, source_size, target_size, hidden_size, target_fn, pred_len, auto_encoder_kwargs, tcn_kwargs, lstm_kwargs=None, **kwargs): super(Seq2SeqSymTCNEncoder, self).__init__() if lstm_kwargs is None: lstm_kwargs = {} self.pred_len = pred_len self.target_fn = target_fn self.history_AE = AutoEncoder(source_size, hidden_size, **auto_encoder_kwargs) # src_size = 21 self.auto_encoder = AutoEncoder(source_size - 2, hidden_size, **auto_encoder_kwargs) # src_size = 19 self.seq2seq = Seq2SeqLSTM(hidden_size, target_size, hidden_size, **lstm_kwargs) self.TemporalModule = TemporalConvNet(hidden_size, hidden_size, **tcn_kwargs) # self.BuildingAttnModule = TransformerEncoderLayer(hidden_size * 2, **attn_kwargs) def forward(self, x): """ :param x: the state sequence :return: hidden state Shape: - x: :math:`(Batch, Building, Time, State)`. - return: :math:`(Batch, Building, Hidden_State*2)`. """ def to_seq_first(tensor): tensor = tensor.unsqueeze(0).transpose(0, -2) return tensor.reshape(tensor.size(0), -1, tensor.size(-1)) def undo_seq_first(tensor, lead_dims): tensor.transpose_(0, -2) return tensor.reshape(*lead_dims, *tensor.shape[-2:]) def to_TCN_input(tensor): # tensor: (seq, batch*building, s_dim) tensor = tensor.transpose(1, 0) # (batch*building, seq, s_dim) old_shape = tensor.shape return tensor.reshape((-1, 9, *old_shape[-2:])) src, tgt = self.target_fn(x) src_noSOC = src[:, :, :, :-2] # discard soc states assert src.shape[:-2] == tgt.shape[:-2] # generate hidden states of history seq h_s_out = self.history_AE(src.reshape(-1, src.size(-1))).reshape(*src.shape[:-1], -1) h_s_out = to_seq_first(h_s_out) # generate hidden states of forecast seq h_s = self.auto_encoder(src_noSOC.reshape(-1, src_noSOC.size(-1))).reshape(*src_noSOC.shape[:-1], -1) h_s, tgt = to_seq_first(h_s), to_seq_first(tgt) _, h_t_out = self.seq2seq(src=h_s, tgt=tgt) h = torch.cat((h_s_out, h_t_out), dim=0) # (seq, batch*building, s_dim) # TCN input: (batch, building, seq, s_dim) out = self.TemporalModule((to_TCN_input(h))).unbind(-2)[-1] # (batch, building, seq, 128) # out.transpose_(0, 1) # -> (Building, Batch, State) # out = self.BuildingAttnModule(out) # out.transpose_(0, 1) # -> (Batch, Building, State) return out[:, :, -(self.pred_len + 1)] class Seq2SeqSymTCNEncoder_new(BaseEncoder): def __init__(self, enc_src_size, history_src_size, hidden_size, pred_len, auto_encoder_kwargs, tcn_kwargs, lstm_kwargs=None, **kwargs): super(Seq2SeqSymTCNEncoder_new, self).__init__() if lstm_kwargs is None: lstm_kwargs = {} self.pred_len = pred_len self.history_AE = AutoEncoder(history_src_size, hidden_size, **auto_encoder_kwargs) # src_size = 21 self.auto_encoder = AutoEncoder(enc_src_size, hidden_size, **auto_encoder_kwargs) # src_size = 31 self.seq2seq = Seq2SeqLSTM_new(source_size=hidden_size, target_size=hidden_size, pred_len=pred_len, hidden_size=hidden_size, **lstm_kwargs) self.TemporalModule = TemporalConvNet(hidden_size, hidden_size, **tcn_kwargs) # self.BuildingAttnModule = TransformerEncoderLayer(hidden_size * 2, **attn_kwargs) def forward(self, x): """ :param x: the state sequence :return: hidden state Shape: - x: :math:`(Batch, Building, Time, State)`. - return: :math:`(Batch, Building, Hidden_State)`. """ def extract_history(states): """ :param states: dim=33->21 :return: """ result_list = [ states[:, :, :, 0:10], states[:, :, :, 10:11], states[:, :, :, 14:15], states[:, :, :, 18:19], states[:, :, :, 22:23], states[:, :, :, 26:33], ] return torch.cat(result_list, -1) def to_seq_first(tensor): tensor = tensor.unsqueeze(0).transpose(0, -2) return tensor.reshape(tensor.size(0), -1, tensor.size(-1)) def undo_seq_first(tensor, lead_dims): tensor.transpose_(0, -2) return tensor.reshape(*lead_dims, *tensor.shape[-2:]) def to_TCN_input(tensor): # tensor: (seq, batch*building, s_dim) tensor = tensor.transpose(1, 0) # (batch*building, seq, s_dim) old_shape = tensor.shape return tensor.reshape((-1, 9, *old_shape[-2:])) # x dim = 33 src = extract_history(x) # src dim=21 enc_in = x[:, :, :, :-2] # encoder input: dim = 31 # generate hidden states of history seq h_s_out = self.history_AE(src.reshape(-1, src.size(-1))).reshape(*src.shape[:-1], -1) h_s_out = to_seq_first(h_s_out) # generate hidden states of forecast seq h_s = self.auto_encoder(enc_in.reshape(-1, enc_in.size(-1))).reshape(*enc_in.shape[:-1], -1) h_s = to_seq_first(h_s) _, h_t_out = self.seq2seq(src=h_s) h = torch.cat((h_s_out, h_t_out), dim=0) # (seq, batch*building, s_dim) # TCN input: (batch, building, seq, s_dim) out = self.TemporalModule(to_TCN_input(h)) # (batch, building, seq, 128) # out.transpose_(0, 1) # -> (Building, Batch, State) # out = self.BuildingAttnModule(out) # out.transpose_(0, 1) # -> (Batch, Building, State) return out[:, :, -(self.pred_len + 1)]

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0.829673

0.814796

0.810403

0

0.023155

0.256854

17,434

416

114

41.908654

0.749923

0.217908

0

0.683983

0

0.021645

1

0.155844

false

0

0.017316

0

0.329004

0

null

0

1

0

null

0

6

1d99a7312ae1e219f6c94e7d842340e0e502b902

30

py

Python

test/login.py

schwert0501/manager

1e2f5d1e73cd34c28bb70366ee15e254ccf2d2a7

[ "MIT" ]

null

test/login.py

schwert0501/manager

1e2f5d1e73cd34c28bb70366ee15e254ccf2d2a7

[ "MIT" ]

null

test/login.py

schwert0501/manager

1e2f5d1e73cd34c28bb70366ee15e254ccf2d2a7

[ "MIT" ]

null

a = 10 b = 20 c = 300000

3.333333

10

0.433333

6

30

2.166667

1

0

0.625

0.466667

30

8

11

3.75

0.1875

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

6

d52aace3638073485f08f9cdb66b6bf2bd3f884f

178

py

Python

students/K33401/Nikitin_michael/lab2/lab2/tours/admin.py

mexannik1998/ITMO_ICT_WebDevelopment_2021-2022

0894edd7d49a73abba31f72266fdeb35fc3f6367

[ "MIT" ]

null

students/K33401/Nikitin_michael/lab2/lab2/tours/admin.py

mexannik1998/ITMO_ICT_WebDevelopment_2021-2022

0894edd7d49a73abba31f72266fdeb35fc3f6367

[ "MIT" ]

null

students/K33401/Nikitin_michael/lab2/lab2/tours/admin.py

mexannik1998/ITMO_ICT_WebDevelopment_2021-2022

0894edd7d49a73abba31f72266fdeb35fc3f6367

[ "MIT" ]

null

from django.contrib import admin from .models import * admin.site.register(User) admin.site.register(Tour) admin.site.register(UsersComments) admin.site.register(Booked)

22.25

35

0.780899

24

178

5.791667

0.5

0.258993

0.489209

0

0.11236

178

8

36

22.25

0.879747

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d54b99a826de04b44bc3ff168d6a7f749cd546cb

29

py

Python

simulation/common/flex_lab/__init__.py

LBNL-ETA/LPDM

3384a784b97e49cd7a801b758717a7107a51119f

[ "BSD-3-Clause-LBNL" ]

2

2019-01-05T02:33:38.000Z

2020-04-22T16:57:50.000Z

simulation/common/flex_lab/__init__.py

LBNL-ETA/LPDM

3384a784b97e49cd7a801b758717a7107a51119f

[ "BSD-3-Clause-LBNL" ]

3

2019-04-17T18:13:08.000Z

2021-04-23T22:40:23.000Z

simulation/common/flex_lab/__init__.py

LBNL-ETA/LPDM

3384a784b97e49cd7a801b758717a7107a51119f

[ "BSD-3-Clause-LBNL" ]

1

2019-01-31T08:37:44.000Z

from flex_lab import FlexLab

14.5

28

0.862069

5

29

4.8

1

0

0.137931

29

1

29

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d567a995fca8ac4cfdeec9be20c4e4200c181574

35

py

Python

ads/exercises/string_manipulation/__init__.py

Aminul-Momin/Algorithms_and_Data_Structures

cba73b36b73ad92fb34bc34a0e03503f7a137713

[ "MIT" ]

null

ads/exercises/string_manipulation/__init__.py

Aminul-Momin/Algorithms_and_Data_Structures

cba73b36b73ad92fb34bc34a0e03503f7a137713

[ "MIT" ]

null

ads/exercises/string_manipulation/__init__.py

Aminul-Momin/Algorithms_and_Data_Structures

cba73b36b73ad92fb34bc34a0e03503f7a137713

[ "MIT" ]

null

from .interconvert_str_int import *

35

0.857143

5

35

5.6

1

0

0.085714

35

1

35

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d581e8f8aab5e7037e51e82b9b46ec188c113c46

188

py

Python

sentence_transformers/datasets/__init__.py

azdaly/sentence-transformers

d365d14e6eb3a79b7589c6404020833d5bda7322

[ "Apache-2.0" ]

16

2020-12-22T07:35:20.000Z

2022-02-09T19:49:02.000Z

sentence_transformers/datasets/__init__.py

azdaly/sentence-transformers

d365d14e6eb3a79b7589c6404020833d5bda7322

[ "Apache-2.0" ]

1

2021-12-21T14:33:15.000Z

2021-12-27T20:40:39.000Z

sentence_transformers/datasets/__init__.py

azdaly/sentence-transformers

d365d14e6eb3a79b7589c6404020833d5bda7322

[ "Apache-2.0" ]

3

2020-09-28T09:25:04.000Z

2021-06-23T19:16:53.000Z

from .sampler import * from .ParallelSentencesDataset import ParallelSentencesDataset from .SentenceLabelDataset import SentenceLabelDataset from .SentencesDataset import SentencesDataset

37.6

62

0.888298

15

188

11.133333

0.4

0

0.085106

188

4

63

47

0.97093

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

d5afe3a3ac807cc4d2f0da0f3d889267787923ef

38,413

py

Python

qradar4py/endpoints/system.py

ryukisec/qradar4py

958cdea92709778916f0ff8d84d75b18aaad4a66

[ "MIT" ]

10

2019-11-19T21:13:32.000Z

2021-11-17T19:35:53.000Z

qradar4py/endpoints/system.py

ryukisec/qradar4py

958cdea92709778916f0ff8d84d75b18aaad4a66

[ "MIT" ]

2

2021-05-21T16:15:16.000Z

2021-07-20T12:34:49.000Z

qradar4py/endpoints/system.py

ryukisec/qradar4py

958cdea92709778916f0ff8d84d75b18aaad4a66

[ "MIT" ]

6

2020-09-14T13:44:55.000Z

2021-11-17T19:35:55.000Z

from urllib.parse import urljoin from qradar4py.endpoints.api_endpoint import QRadarAPIEndpoint from qradar4py.endpoints.api_endpoint import request_vars from qradar4py.endpoints.api_endpoint import header_vars class System(QRadarAPIEndpoint): """ The QRadar API endpoint group /system and its endpoints. """ __baseurl = 'system/' def __init__(self, url, header, verify): super().__init__(urljoin(url, self.__baseurl), header, verify) @request_vars('fields') def get_about(self, *, fields=None, **kwargs): """ GET /system/about Retrieves the current system information """ function_endpoint = urljoin(self._baseurl, 'about') return self._call('GET', function_endpoint, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_authorization_capabilities(self, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/authorization/capabilities Retrieves a list of capabilities that are currently in the system. UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'authorization/capabilities') return self._call('GET', function_endpoint, headers=headers, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_authorization_password_policies(self, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/authorization/password_policies Retrieves a list of Password Policies that exist on the system """ function_endpoint = urljoin(self._baseurl, 'authorization/password_policies') return self._call('GET', function_endpoint, **kwargs) @header_vars('fields') def post_authorization_password_policies_by_id(self, id, *, policy, fields=None, **kwargs): """ POST /system/authorization/password_policies/{id} See api_mapping.xml """ function_endpoint = urljoin(self._baseurl, 'authorization/password_policies/{id}'.format(id=id)) return self._call('POST', function_endpoint, json=policy, **kwargs) @request_vars('fields') def get_authorization_password_policies_by_id(self, id, *, fields=None, **kwargs): """ GET /system/authorization/password_policies/{id} Retrieves a single Password Policies that exist on the system """ function_endpoint = urljoin(self._baseurl, 'authorization/password_policies/{id}'.format(id=id)) return self._call('GET', function_endpoint, **kwargs) @header_vars('fields') def post_authorization_password_validators(self, *, body, fields=None, **kwargs): """ POST /system/authorization/password_validators Creates a new password validator for the provided password based on the current Password Policy. """ function_endpoint = urljoin(self._baseurl, 'authorization/password_validators') return self._call('POST', function_endpoint, json=body, **kwargs) def post_email_servers(self, *, email_server_details, **kwargs): """ POST /system/email_servers Creates a new email server. """ function_endpoint = urljoin(self._baseurl, 'email_servers') return self._call('POST', function_endpoint, json=email_server_details, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_email_servers(self, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/email_servers Retrieves a list of all email servers. """ function_endpoint = urljoin(self._baseurl, 'email_servers') return self._call('GET', function_endpoint, **kwargs) def post_email_servers_by_email_server_id(self, email_server_id, *, email_server_details, **kwargs): """ POST /system/email_servers/{email_server_id} Updates an existing email server. """ function_endpoint = urljoin(self._baseurl, 'email_servers/{email_server_id}'.format(email_server_id=email_server_id)) return self._call('POST', function_endpoint, json=email_server_details, **kwargs) @request_vars('fields') def get_email_servers_by_email_server_id(self, email_server_id, *, fields=None, **kwargs): """ GET /system/email_servers/{email_server_id} Retrieves an email server based on the supplied email server ID. """ function_endpoint = urljoin(self._baseurl, 'email_servers/{email_server_id}'.format(email_server_id=email_server_id)) return self._call('GET', function_endpoint, **kwargs) def delete_email_servers_by_email_server_id(self, email_server_id, **kwargs): """ DELETE /system/email_servers/{email_server_id} Deletes an email server. """ function_endpoint = urljoin(self._baseurl, 'email_servers/{email_server_id}'.format(email_server_id=email_server_id)) return self._call('DELETE', function_endpoint, response_type='text/plain', **kwargs) def get_eula_acceptances(self, **kwargs): """ GET /system/eula_acceptances Retrieves the list of EULA acceptance statuses that the caller has permission to see. """ function_endpoint = urljoin(self._baseurl, 'eula_acceptances') return self._call('GET', function_endpoint, **kwargs) @header_vars('fields') def post_eula_acceptances_by_id(self, id, *, data, fields=None, **kwargs): """ POST /system/eula_acceptances/{id} Updates an individual EULA acceptance. """ function_endpoint = urljoin(self._baseurl, 'eula_acceptances/{id}'.format(id=id)) return self._call('POST', function_endpoint, json=data, **kwargs) def get_eula_acceptances_by_id(self, id, **kwargs): """ GET /system/eula_acceptances/{id} Retrieves an individual EULA Acceptance by id. """ function_endpoint = urljoin(self._baseurl, 'eula_acceptances/{id}'.format(id=id)) return self._call('GET', function_endpoint, **kwargs) def get_eulas(self, **kwargs): """ GET /system/eulas Retrieves a list of EULAs. """ function_endpoint = urljoin(self._baseurl, 'eulas') return self._call('GET', function_endpoint, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_information_encodings(self, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/information/encodings Retrieves the list of encodings that are supported by the system for event data.. """ function_endpoint = urljoin(self._baseurl, 'information/encodings') return self._call('GET', function_endpoint, **kwargs) @header_vars('Range') @request_vars('sample_type', 'filter', 'fields', 'sort') def get_information_locales(self, *, sample_type=None, filter=None, fields=None, sort=None, Range=None, **kwargs): """ GET /system/information/locales Retrieve Locales. """ function_endpoint = urljoin(self._baseurl, 'information/locales') return self._call('GET', function_endpoint, **kwargs) @request_vars('since', 'limit', 'fields') def get_notifications(self, *, since=None, limit=None, fields=None, **kwargs): """ GET /system/notifications Retrieves notifications UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'notifications') return self._call('GET', function_endpoint, headers=headers, **kwargs) def delete_notifications_by_qid(self, qid, **kwargs): """ DELETE /system/notifications/{qid} dismisses a notification UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'notifications/{qid}'.format(qid=qid)) return self._call('DELETE', function_endpoint, response_type='text/plain', headers=headers, **kwargs) @request_vars('fields') def get_notifications_by_qid(self, qid, *, fields=None, **kwargs): """ GET /system/notifications/{qid} Retrieves notification by QID UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'notifications/{qid}'.format(qid=qid)) return self._call('GET', function_endpoint, headers=headers, **kwargs) def post_proxy_servers(self, *, proxy_server_details, **kwargs): """ POST /system/proxy_servers Create a proxy server """ function_endpoint = urljoin(self._baseurl, 'proxy_servers') return self._call('POST', function_endpoint, json=proxy_server_details, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_proxy_servers(self, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/proxy_servers Read all proxy servers """ function_endpoint = urljoin(self._baseurl, 'proxy_servers') return self._call('GET', function_endpoint, **kwargs) def delete_proxy_servers_by_id(self, id, **kwargs): """ DELETE /system/proxy_servers/{id} Delete a proxy server """ function_endpoint = urljoin(self._baseurl, 'proxy_servers/{id}'.format(id=id)) return self._call('DELETE', function_endpoint, response_type='text/plain', **kwargs) @header_vars('fields') def post_proxy_servers_by_id(self, id, *, proxy_server_details, fields=None, **kwargs): """ POST /system/proxy_servers/{id} Update a proxy server """ function_endpoint = urljoin(self._baseurl, 'proxy_servers/{id}'.format(id=id)) return self._call('POST', function_endpoint, json=proxy_server_details, **kwargs) @request_vars('fields') def get_proxy_servers_by_id(self, id, *, fields=None, **kwargs): """ GET /system/proxy_servers/{id} Read a proxy server """ function_endpoint = urljoin(self._baseurl, 'proxy_servers/{id}'.format(id=id)) return self._call('GET', function_endpoint, **kwargs) @header_vars('fields') def post_server_connection_validator(self, *, request_details, fields=None, **kwargs): """ POST /system/server_connection_validator Creates a server connection validator for the provided hostname and port, based on the provided host ids. UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'server_connection_validator') return self._call('POST', function_endpoint, json=request_details, headers=headers, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_servers(self, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/servers Retrieves a list of all server hosts in the deployment. """ function_endpoint = urljoin(self._baseurl, 'servers') return self._call('GET', function_endpoint, **kwargs) @request_vars('fields') def get_servers_by_server_id(self, server_id, *, fields=None, **kwargs): """ GET /system/servers/{server_id} Retrieves a server host based on the supplied server ID. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}'.format(server_id=server_id)) return self._call('GET', function_endpoint, **kwargs) def post_servers_by_server_id(self, server_id, *, details, **kwargs): """ POST /system/servers/{server_id} Updates an existing server. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}'.format(server_id=server_id)) return self._call('POST', function_endpoint, json=details, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_servers_firewall_rules_by_server_id(self, server_id, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/servers/{server_id}/firewall_rules Retrieves a list of access control firewall rules based on the supplied server ID. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/firewall_rules'.format(server_id=server_id)) return self._call('GET', function_endpoint, **kwargs) def put_servers_firewall_rules_by_server_id(self, server_id, *, rules, **kwargs): """ PUT /system/servers/{server_id}/firewall_rules Sets the access control firewall rules based on the supplied server ID. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/firewall_rules'.format(server_id=server_id)) return self._call('PUT', function_endpoint, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_servers_network_interfaces_bonded_by_server_id(self, server_id, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/servers/{server_id}/network_interfaces/bonded Retrieves a list of the bonded network interfaces based on the supplied server ID. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/network_interfaces/bonded'.format(server_id=server_id)) return self._call('GET', function_endpoint, **kwargs) def post_servers_network_interfaces_bonded_by_server_id(self, server_id, *, details, **kwargs): """ POST /system/servers/{server_id}/network_interfaces/bonded Creates a new bonded network interface. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/network_interfaces/bonded'.format(server_id=server_id)) return self._call('POST', function_endpoint, json=details, **kwargs) def post_servers_server_id_network_interfaces_bonded_by_device_name(self, server_id, device_name, *, details, **kwargs): """ POST /system/servers/{server_id}/network_interfaces/bonded/{device_name} Updates an existing bonded network interface. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/network_interfaces/bonded/{device_name}'.format( server_id=server_id, device_name=device_name)) return self._call('POST', function_endpoint, json=details, **kwargs) def delete_servers_server_id_network_interfaces_bonded_by_device_name(self, server_id, device_name, **kwargs): """ DELETE /system/servers/{server_id}/network_interfaces/bonded/{device_name} Removes a bonded network interface. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/network_interfaces/bonded/{device_name}'.format( server_id=server_id, device_name=device_name)) return self._call('DELETE', function_endpoint, response_type='text/plain', **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_servers_network_interfaces_dag_by_server_id(self, server_id, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/servers/{server_id}/network_interfaces/dag Retrieves a list of DAG network interfaces based on the supplied server ID. UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/network_interfaces/dag'.format(server_id=server_id)) return self._call('GET', function_endpoint, headers=headers, **kwargs) def post_servers_server_id_network_interfaces_dag_by_device_name(self, server_id, device_name, *, details, **kwargs): """ POST /system/servers/{server_id}/network_interfaces/dag/{device_name} Updates an existing DAG network interface. UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/network_interfaces/dag/{device_name}'.format( server_id=server_id, device_name=device_name)) return self._call('POST', function_endpoint, json=details, headers=headers, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_servers_network_interfaces_ethernet_by_server_id(self, server_id, *, Range=None, filter=None, fields=None, **kwargs): """ GET /system/servers/{server_id}/network_interfaces/ethernet Retrieves a list of the ethernet network interfaces based on the supplied server ID. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/network_interfaces/ethernet'.format(server_id=server_id)) return self._call('GET', function_endpoint, **kwargs) def post_servers_server_id_network_interfaces_ethernet_by_device_name(self, server_id, device_name, *, details, **kwargs): """ POST /system/servers/{server_id}/network_interfaces/ethernet/{device_name} Updates an ethernet network interface based on the suppied server_Id and device_name. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/network_interfaces/ethernet/{device_name}'.format( server_id=server_id, device_name=device_name)) return self._call('POST', function_endpoint, json=details, **kwargs) @request_vars('fields') def get_servers_system_time_settings_by_server_id(self, server_id, *, fields=None, **kwargs): """ GET /system/servers/{server_id}/system_time_settings Retrieves the system time and time zone settings of a server host based on the supplied server ID. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/system_time_settings'.format(server_id=server_id)) return self._call('GET', function_endpoint, **kwargs) @header_vars('fields') def post_servers_system_time_settings_by_server_id(self, server_id, *, settings, fields=None, **kwargs): """ POST /system/servers/{server_id}/system_time_settings Sets the system time and time zone settings of to a server host. Services are restarted after the call and service interruptions will occur. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/system_time_settings'.format(server_id=server_id)) return self._call('POST', function_endpoint, json=settings, **kwargs) @request_vars('fields') def get_servers_timezones_by_server_id(self, server_id, *, fields=None, **kwargs): """ GET /system/servers/{server_id}/timezones Retrieves all the available time zones that can be set for a server. """ function_endpoint = urljoin(self._baseurl, 'servers/{server_id}/timezones'.format(server_id=server_id)) return self._call('GET', function_endpoint, **kwargs) @request_vars('fields') def get_summary(self, *, fields=None, **kwargs): """ GET /system/summary Retrieves notifications summary UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'summary') return self._call('GET', function_endpoint, headers=headers, **kwargs) @request_vars('task_id', 'email_addresses') def post_task_management_email_action(self, *, task_id, email_addresses, **kwargs): """ POST /system/task_management/email_action Adds an email action to the TaskStatus. The email will be executed on Completion or Exception of the Task UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/email_action') return self._call('POST', function_endpoint, response_type='text/plain', headers=headers, **kwargs) @request_vars('host_id', 'app_id', 'status_uuid', 'task_class', 'task_type', 'children_ids', 'sub_task_ids', 'task_state', 'task_name_local_info', 'message_local_info', 'progress', 'minimum', 'maximum', 'created_by', 'cancelled_by', 'created_time', 'started_time', 'modified_time', 'completed_time', 'retention', 'result_url', 'result_delete_task', 'is_cancel_requested', 'delete_task_id', 'fields') def post_task_management_internal_tasks_by_id(self, id, *, host_id=None, app_id=None, status_uuid=None, task_class=None, task_type=None, children_ids=None, sub_task_ids=None, task_state=None, task_name_local_info=None, message_local_info=None, progress=None, minimum=None, maximum=None, created_by=None, cancelled_by=None, created_time=None, started_time=None, modified_time=None, completed_time=None, retention=None, result_url=None, result_delete_task=None, is_cancel_requested=None, delete_task_id=None, fields=None, **kwargs): """ POST /system/task_management/internal_tasks/{id} Updates a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/internal_tasks/{id}'.format(id=id)) return self._call('POST', function_endpoint, headers=headers, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_task_management_subtasks(self, *, filter=None, fields=None, Range=None, **kwargs): """ GET /system/task_management/subtasks Gets all TaskSubStatuses UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/subtasks') return self._call('GET', function_endpoint, headers=headers, **kwargs) @request_vars('task_state', 'message_local_info', 'progress', 'minimum', 'maximum', 'created_time', 'started_time', 'modified_time', 'completed_time', 'fields') def post_task_management_subtasks(self, *, task_state, message_local_info, progress=None, minimum=None, maximum=None, created_time=None, started_time=None, modified_time=None, completed_time=None, fields=None, **kwargs): """ POST /system/task_management/subtasks Create a TaskSubStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/subtasks') return self._call('POST', function_endpoint, headers=headers, **kwargs) @request_vars('task_state', 'message_local_info', 'progress', 'minimum', 'maximum', 'created_time', 'started_time', 'modified_time', 'completed_time', 'fields') def post_task_management_subtasks_by_id(self, id, *, task_state=None, message_local_info=None, progress=None, minimum=None, maximum=None, created_time=None, started_time=None, modified_time=None, completed_time=None, fields=None, **kwargs): """ POST /system/task_management/subtasks/{id} Updates a TaskSubStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/subtasks/{id}'.format(id=id)) return self._call('POST', function_endpoint, headers=headers, **kwargs) def delete_task_management_subtasks_by_id(self, id, **kwargs): """ DELETE /system/task_management/subtasks/{id} Deletes a TaskSubStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/subtasks/{id}'.format(id=id)) return self._call('DELETE', function_endpoint, response_type='text/plain', headers=headers, **kwargs) @request_vars('fields') def get_task_management_subtasks_by_id(self, id, *, fields=None, **kwargs): """ GET /system/task_management/subtasks/{id} Gets a TaskSubStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/subtasks/{id}'.format(id=id)) return self._call('GET', function_endpoint, headers=headers, **kwargs) @request_vars('host_id', 'app_id', 'status_uuid', 'children_ids', 'task_type', 'task_state', 'task_name_local_info', 'message_local_info', 'progress', 'minimum', 'maximum', 'created_by', 'cancelled_by', 'created', 'started', 'modified', 'completed', 'retention', 'result_url', 'result_delete_task', 'delete_task_id', 'fields') def post_task_management_task(self, *, app_id, task_type, task_state, task_name_local_info, message_local_info, created_by, host_id=None, status_uuid=None, children_ids=None, progress=None, minimum=None, maximum=None, cancelled_by=None, created=None, started=None, modified=None, completed=None, retention=None, result_url=None, result_delete_task=None, delete_task_id=None, fields=None, **kwargs): """ POST /system/task_management/task Create a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task') return self._call('POST', function_endpoint, headers=headers, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_task_management_task(self, *, filter=None, fields=None, Range=None, **kwargs): """ GET /system/task_management/task Gets all TaskStatuses UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task') return self._call('GET', function_endpoint, headers=headers, **kwargs) @request_vars('id_type', 'fields') def get_task_management_task_by_id(self, id, *, id_type=None, fields=None, **kwargs): """ GET /system/task_management/task/{id} Gets a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task/{id}'.format(id=id)) return self._call('GET', function_endpoint, headers=headers, **kwargs) @request_vars('delete_result') def delete_task_management_task_by_id(self, id, *, delete_result=None, **kwargs): """ DELETE /system/task_management/task/{id} Deletes a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task/{id}'.format(id=id)) return self._call('DELETE', function_endpoint, response_type='text/plain', headers=headers, **kwargs) @request_vars('host_id', 'app_id', 'status_uuid', 'task_type', 'children_ids', 'task_state', 'task_name_local_info', 'message_local_info', 'progress', 'minimum', 'maximum', 'created_by', 'cancelled_by', 'created', 'started', 'modified', 'completed', 'retention', 'result_url', 'result_delete_task', 'is_cancel_requested', 'delete_task_id', 'fields') def post_task_management_task_by_id(self, id, *, host_id=None, app_id=None, status_uuid=None, task_type=None, children_ids=None, task_state=None, task_name_local_info=None, message_local_info=None, progress=None, minimum=None, maximum=None, created_by=None, cancelled_by=None, created=None, started=None, modified=None, completed=None, retention=None, result_url=None, result_delete_task=None, is_cancel_requested=None, delete_task_id=None, fields=None, **kwargs): """ POST /system/task_management/task/{id} Updates a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task/{id}'.format(id=id)) return self._call('POST', function_endpoint, headers=headers, **kwargs) def delete_task_management_task_result_by_id(self, id, **kwargs): """ DELETE /system/task_management/task/{id}/result Gets the result from the TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task/{id}/result'.format(id=id)) return self._call('DELETE', function_endpoint, response_type='text/plain', headers=headers, **kwargs) def get_task_management_task_resume_data_by_id(self, id, **kwargs): """ GET /system/task_management/task/{id}/resume_data Gets the resume from the TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task/{id}/resume_data'.format(id=id)) return self._call('GET', function_endpoint, response_type='text/plain', headers=headers, **kwargs) @request_vars('resume_data') def post_task_management_task_resume_data_by_id(self, id, *, resume_data, **kwargs): """ POST /system/task_management/task/{id}/resume_data Creates the result from the TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task/{id}/resume_data'.format(id=id)) return self._call('POST', function_endpoint, response_type='text/plain', headers=headers, **kwargs) @request_vars('task_id') def post_task_management_task_id_by_uuid(self, uuid, *, task_id, **kwargs): """ POST /system/task_management/task_id/{uuid} No summary provided UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task_id/{uuid}'.format(uuid=uuid)) return self._call('POST', function_endpoint, response_type='text/plain', headers=headers, **kwargs) def get_task_management_task_id_by_uuid(self, uuid, **kwargs): """ GET /system/task_management/task_id/{uuid} No summary provided UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task_id/{uuid}'.format(uuid=uuid)) return self._call('GET', function_endpoint, response_type='text/plain', headers=headers, **kwargs) def delete_task_management_task_id_by_uuid(self, uuid, **kwargs): """ DELETE /system/task_management/task_id/{uuid} No summary provided UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/task_id/{uuid}'.format(uuid=uuid)) return self._call('DELETE', function_endpoint, response_type='text/plain', headers=headers, **kwargs) @request_vars('host_id', 'app_id', 'status_uuid', 'children_ids', 'sub_task_ids', 'task_class', 'task_type', 'task_state', 'task_name_local_info', 'message_local_info', 'progress', 'minimum', 'maximum', 'created_by', 'cancelled_by', 'created_time', 'started_time', 'modified_time', 'completed_time', 'retention', 'result_url', 'result_delete_task', 'delete_task_id', 'fields') def post_task_management_tasks(self, *, app_id, task_class, task_type, task_state, task_name_local_info, message_local_info, created_by, host_id=None, status_uuid=None, children_ids=None, sub_task_ids=None, progress=None, minimum=None, maximum=None, cancelled_by=None, created_time=None, started_time=None, modified_time=None, completed_time=None, retention=None, result_url=None, result_delete_task=None, delete_task_id=None, fields=None, **kwargs): """ POST /system/task_management/tasks Create a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/tasks') return self._call('POST', function_endpoint, headers=headers, **kwargs) @header_vars('Range') @request_vars('filter', 'fields') def get_task_management_tasks(self, *, filter=None, fields=None, Range=None, **kwargs): """ GET /system/task_management/tasks Gets all TaskStatuses UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/tasks') return self._call('GET', function_endpoint, headers=headers, **kwargs) @request_vars('is_cancel_requested', 'fields') def post_task_management_tasks_by_id(self, id, *, is_cancel_requested=None, fields=None, **kwargs): """ POST /system/task_management/tasks/{id} Updates a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/tasks/{id}'.format(id=id)) return self._call('POST', function_endpoint, headers=headers, **kwargs) @request_vars('id_type', 'fields') def get_task_management_tasks_by_id(self, id, *, id_type=None, fields=None, **kwargs): """ GET /system/task_management/tasks/{id} Gets a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/tasks/{id}'.format(id=id)) return self._call('GET', function_endpoint, headers=headers, **kwargs) @request_vars('delete_result') def delete_task_management_tasks_by_id(self, id, *, delete_result=None, **kwargs): """ DELETE /system/task_management/tasks/{id} Deletes a TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/tasks/{id}'.format(id=id)) return self._call('DELETE', function_endpoint, response_type='text/plain', headers=headers, **kwargs) def get_task_management_tasks_result_by_id(self, id, **kwargs): """ GET /system/task_management/tasks/{id}/result Gets the result from the TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/tasks/{id}/result'.format(id=id)) return self._call('GET', function_endpoint, response_type='application/octet-stream', headers=headers, **kwargs) def post_task_management_tasks_result_by_id(self, id, *, result, **kwargs): """ POST /system/task_management/tasks/{id}/result Creates the result from the TaskStatus UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/tasks/{id}/result'.format(id=id)) return self._call('POST', function_endpoint, response_type='text/plain', mime_type={'Content-Type': 'application/octet-stream'}, data=result, headers=headers, **kwargs) def delete_task_management_tasks_result_by_id(self, id, **kwargs): """ DELETE /system/task_management/tasks/{id}/result Deletes a result UNDOCUMENTED """ headers = kwargs.get('headers', {}).update({'Allow-Hidden': True}) function_endpoint = urljoin(self._baseurl, 'task_management/tasks/{id}/result'.format(id=id)) return self._call('DELETE', function_endpoint, response_type='text/plain', headers=headers, **kwargs)

50.477004

148

0.640486

4,311

38,413

5.430063

0.047553

0.094323

0.067794

0.079585

0.896322

0.870135

0.831304

0.790978

0.769405

0.737152

0

0.000102

0.234973

38,413

760

149

50.543421

0.796475

0.165178

0

0.592992

0

0.16109

0.056454

0

1

0.188679

false

0.021563

0.010782

0

0.390836

0

null

0

1

0

null

0

6

639571f1b321312e941cb005a13270910e527b69

2,247

py

Python

frontend/extras/migration/versions/430a70c8aa21_version_1_2_1.py

krisshol/bach-kmno

f40d85b3397bb340e26a671c54d4a753dbbb0d43

[ "Apache-2.0" ]

248

2015-01-08T09:36:44.000Z

2022-01-12T10:29:21.000Z

frontend/extras/migration/versions/430a70c8aa21_version_1_2_1.py

krisshol/bach-kmno

f40d85b3397bb340e26a671c54d4a753dbbb0d43

[ "Apache-2.0" ]

50

2015-01-09T08:31:57.000Z

2022-03-30T10:41:13.000Z

frontend/extras/migration/versions/430a70c8aa21_version_1_2_1.py

krisshol/bach-kmno

f40d85b3397bb340e26a671c54d4a753dbbb0d43

[ "Apache-2.0" ]

74

2015-01-05T09:11:21.000Z

2022-03-29T02:16:54.000Z

"""version 1.2.1 Revision ID: 430a70c8aa21 Revises: 2cc69d5c53eb Create Date: 2015-07-06 16:34:44.422586 """ # revision identifiers, used by Alembic. revision = '430a70c8aa21' down_revision = '2cc69d5c53eb' branch_labels = None depends_on = None from alembic import op import sqlalchemy as sa def upgrade(): op.alter_column('irma_file', 'timestamp_first_scan', nullable=False, type_=sa.Numeric(asdecimal=False), existing_type=sa.Float(precision=2), existing_server_default=False, existing_nullable=False) op.alter_column('irma_file', 'timestamp_last_scan', nullable=False, type_=sa.Numeric(asdecimal=False), existing_type=sa.Float(precision=2), existing_server_default=False, existing_nullable=False) op.alter_column('irma_scanEvents', 'timestamp', nullable=False, type_=sa.Numeric(asdecimal=False), existing_type=sa.Float(precision=2), existing_server_default=False, existing_nullable=False) def downgrade(): op.alter_column('irma_file', 'timestamp_first_scan', nullable=False, type_=sa.Float(precision=2), existing_type=sa.Numeric(asdecimal=False), existing_server_default=False, existing_nullable=False) op.alter_column('irma_file', 'timestamp_last_scan', nullable=False, type_=sa.Float(precision=2), existing_type=sa.Numeric(asdecimal=False), existing_server_default=False, existing_nullable=False) op.alter_column('irma_scanEvents', 'timestamp', nullable=False, type_=sa.Float(precision=2), existing_type=sa.Numeric(asdecimal=False), existing_server_default=False, existing_nullable=False)

34.569231

62

0.543836

210

2,247

5.566667

0.261905

0.133447

0.066724

0.087254

0.788708

0

0.040397

0.372052

2,247

64

63

35.109375

0.788094

0.063195

0

0.84

0

0.088698

0

1

0.04

false

0

0.04

0

0.08

0

null

0

1

0

1

0

null

0

6

63a0d67f1a458d889b69a9cef79f825fb47cc08e

44

py

Python

coffin/contrib/auth/middleware.py

spothero/coffin

9ea6a9173cbfed592c5b4776c489dba8d9280d52

[ "BSD-3-Clause" ]

1

2016-11-19T06:32:20.000Z

coffin/contrib/auth/middleware.py

spothero/coffin

9ea6a9173cbfed592c5b4776c489dba8d9280d52

[ "BSD-3-Clause" ]

null

coffin/contrib/auth/middleware.py

spothero/coffin

9ea6a9173cbfed592c5b4776c489dba8d9280d52

[ "BSD-3-Clause" ]

1

2022-03-08T23:12:00.000Z

from django.contrib.auth.middleware import *

44

0.840909

6

44

6.166667

1

0

0.068182

44

1

44

0.902439

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

894b7547cbf5ac37620d6e1b421a073c16c53c44

20

py

Python

letstest.py

AMARTYA2020/Verify-Service-provider-and-country-code-of-SIM-using-python

d434f7795fefa4509476d1afbce2c6e63ff0c513

[ "MIT" ]

null

letstest.py

AMARTYA2020/Verify-Service-provider-and-country-code-of-SIM-using-python

d434f7795fefa4509476d1afbce2c6e63ff0c513

[ "MIT" ]

null

letstest.py

AMARTYA2020/Verify-Service-provider-and-country-code-of-SIM-using-python

d434f7795fefa4509476d1afbce2c6e63ff0c513

[ "MIT" ]

null

no = "+917481866756"

20

0.7

2

20

7

1

0

0.666667

0.1

20

1

20

0.111111

0

0.619048

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

1

0

null

0

6

89bdcbe5abf1ac15ca682d966cc2aa392c592396

93

py

Python

week3/merge_sort.py

ravichalla/wallbreaker

0d587f12c60df5e4bca47f9183484a69d284d1f5

[ "MIT" ]

null

week3/merge_sort.py

ravichalla/wallbreaker

0d587f12c60df5e4bca47f9183484a69d284d1f5

[ "MIT" ]

null

week3/merge_sort.py

ravichalla/wallbreaker

0d587f12c60df5e4bca47f9183484a69d284d1f5

[ "MIT" ]

null

def mergeSort(lst): pass print(mergeSort([2,3,4,5,15,19,26,27,36,38,44,46,47,48,50] ))

15.5

61

0.634409

21

93

2.809524

0.952381

0

0.317073

0.11828

93

6

61

15.5

0.402439

0

1

0.333333

false

0.333333

0

0.333333

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

982388985bc501fa6da69aaca11839047a391bf1

65

py

Python

NTMY-code/panel/logic/__init__.py

AndreaCossio/PoliTo-Projects

f89c8ce1e04d54e38a1309a01c7e3a9aa67d5a81

[ "MIT" ]

null

NTMY-code/panel/logic/__init__.py

AndreaCossio/PoliTo-Projects

f89c8ce1e04d54e38a1309a01c7e3a9aa67d5a81

[ "MIT" ]

null

NTMY-code/panel/logic/__init__.py

AndreaCossio/PoliTo-Projects

f89c8ce1e04d54e38a1309a01c7e3a9aa67d5a81

[ "MIT" ]

1

2022-02-19T11:26:30.000Z

from . import exceptions from . import graph from . import route

16.25

24

0.769231

9

65

5.555556

0.555556

0.6

0

0.184615

65

3

25

21.666667

0.943396

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

98326c45e7401affd51e2528c345c51f615b4e01

736

py

Python

tests/test_MCPlayerQ.py

maburto00/ndsgo

9cd27adcdf937cdf9863c158e039ad131d6b24eb

[ "MIT" ]

1

2018-02-20T15:51:05.000Z

tests/test_MCPlayerQ.py

maburto00/ndsgo

9cd27adcdf937cdf9863c158e039ad131d6b24eb

[ "MIT" ]

2

2020-02-11T13:11:08.000Z

2020-02-12T16:59:11.000Z

tests/test_MCPlayerQ.py

maburto00/ndsgo

9cd27adcdf937cdf9863c158e039ad131d6b24eb

[ "MIT" ]

null

from unittest import TestCase class TestMCPlayerQ(TestCase): def test_copy(self): pass # def test_new_game(self): # self.fail() # # def test_save_Q(self): # self.fail() # # def test_load_Q(self): # self.fail() # # def test_plot_Q_history(self): # self.fail() # # def test__get_state(self): # self.fail() # # def test_genmove(self): # self.fail() # # def test_update_Q(self): # self.fail() # # def test_automatch(self): # self.fail() # # def test_self_play(self): # self.fail()

21.647059

40

0.447011

74

736

4.189189

0.337838

0.225806

0.348387

0.387097

0.5

0.193548

0

0.436141

736

33

41

22.30303

0.746988

0.506793

0

1

0.25

false

0.25

0

0.75

0

null

1

0

null

0

1

0

1

0

1

0

6

984f8ea6d5d4ca903e86d0ebd154005b9e5d0b28

114

py

Python

src/test/resources/cases/ModulesWithNoFuncs/dunder_and_testable.py

AlexTereshenkov/pybutler

3712e116d9b33fb8ad52b8f00fd41136f1266090

[ "MIT" ]

null

src/test/resources/cases/ModulesWithNoFuncs/dunder_and_testable.py

AlexTereshenkov/pybutler

3712e116d9b33fb8ad52b8f00fd41136f1266090

[ "MIT" ]

null

src/test/resources/cases/ModulesWithNoFuncs/dunder_and_testable.py

AlexTereshenkov/pybutler

3712e116d9b33fb8ad52b8f00fd41136f1266090

[ "MIT" ]

null

def __somefunc__(): return 42 def not_dunder_func(arg1, arg2): """Docstring of function1""" return 42

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985d0fad013a881e0b4d202362ee110159b7a3e1

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py

Python

test/test_authorization.py

periodo/periodo-server

7cd0250cbc6260cdc6f66aa8d95b316d9eaaf9ac

[ "CC0-1.0" ]

9

2015-05-07T07:40:16.000Z

2020-01-13T15:53:01.000Z

test/test_authorization.py

periodo/periodo-server

7cd0250cbc6260cdc6f66aa8d95b316d9eaaf9ac

[ "CC0-1.0" ]

118

2015-01-27T21:14:49.000Z

2022-03-18T07:06:14.000Z

test/test_authorization.py

periodo/periodo-server

7cd0250cbc6260cdc6f66aa8d95b316d9eaaf9ac

[ "CC0-1.0" ]

1

2015-11-09T10:31:16.000Z

import httpx import json import pytest from urllib.parse import urlparse from periodo import app, database def test_unauthorized_user(unauthorized_user): with app.app_context(): row = database.query_db_for_one( "SELECT permissions FROM user WHERE id = ?", (unauthorized_user.id,) ) assert json.loads(row["permissions"]) == [] def test_admin_user(admin_user): with app.app_context(): row = database.query_db_for_one( "SELECT permissions FROM user WHERE id = ?", (admin_user.id,) ) assert json.loads(row["permissions"]) == [ ["action", "submit-patch"], ["action", "create-bag"], ["action", "accept-patch"], ["action", "create-graph"], ] @pytest.mark.client_auth_token("this-token-has-no-permissions") def test_unauthorized_user_submit_patch(unauthorized_user, client): unauthorized_user res = client.patch("/d/") assert res.status_code == httpx.codes.FORBIDDEN assert res.headers["WWW-Authenticate"] == ( 'Bearer realm="PeriodO", error="insufficient_scope", ' + "error_description=" + '"The access token does not provide sufficient privileges", ' + 'error_uri="http://tools.ietf.org/html/rfc6750#section-6.2.3"' ) @pytest.mark.client_auth_token("this-token-has-normal-permissions") def test_authorized_identity_submit_patch(active_user, client, load_json): res = client.patch("/d/", json=load_json("test-patch-replace-values-1.json")) assert res.status_code == httpx.codes.ACCEPTED patch_id = int(res.headers["Location"].split("/")[-2]) with app.app_context(): creator = database.query_db_for_one( "SELECT created_by FROM patch_request WHERE id = ?", (patch_id,) )["created_by"] assert creator == active_user.id @pytest.mark.client_auth_token("this-token-has-normal-permissions") def test_nonadmin_user_merge_patch(active_user, client, load_json): active_user # submit the patch res = client.patch("/d/", json=load_json("test-patch-replace-values-1.json")) # There should be NO link header patch_url = urlparse(res.headers["Location"]).path res = client.get(patch_url) assert "Link" not in res.headers # now try to merge the patch res = client.post(patch_url + "merge") assert res.status_code == httpx.codes.FORBIDDEN assert res.headers["WWW-Authenticate"] == ( 'Bearer realm="PeriodO", error="insufficient_scope", ' + "error_description=" + '"The access token does not provide sufficient privileges", ' + 'error_uri="http://tools.ietf.org/html/rfc6750#section-6.2.3"' ) @pytest.mark.client_auth_token("this-token-has-admin-permissions") def test_admin_user_merge_patch( admin_user, active_user, client, load_json, bearer_auth, ): active_user # submit the patch as normal user res = client.patch( "/d/", auth=bearer_auth("this-token-has-normal-permissions"), json=load_json("test-patch-replace-values-1.json"), ) patch_id = int(res.headers["Location"].split("/")[-2]) # Admin should see a link header patch_url = urlparse(res.headers["Location"]).path res = client.get(patch_url) assert res.headers.get("Link") == f'<{patch_url + "merge"}>;rel="merge"' # now merge the patch res = client.post(patch_url + "merge") assert res.status_code, httpx.codes.NO_CONTENT with app.app_context(): merger = database.query_db_for_one( "SELECT merged_by FROM patch_request WHERE id = ?", (patch_id,) )["merged_by"] assert merger == admin_user.id @pytest.mark.client_auth_token("this-token-has-admin-permissions") def test_noncreator_identity_update_patch( admin_user, active_user, client, load_json, bearer_auth, ): admin_user, active_user # submit the patch as normal user res = client.patch( "/d/", auth=bearer_auth("this-token-has-normal-permissions"), json=load_json("test-patch-replace-values-1.json"), ) # now try to update the patch as a different user (admin) patch_url = urlparse(res.headers["Location"]).path res = client.put( patch_url + "patch.jsonpatch", json=load_json("test-patch-replace-values-2.json"), ) assert res.status_code == httpx.codes.FORBIDDEN assert res.headers["WWW-Authenticate"] == ( 'Bearer realm="PeriodO", error="insufficient_scope", ' + "error_description=" + '"The access token does not provide sufficient privileges", ' + 'error_uri="http://tools.ietf.org/html/rfc6750#section-6.2.3"' ) @pytest.mark.client_auth_token("this-token-has-normal-permissions") def test_creator_identity_update_patch(active_user, client, load_json): active_user # submit the patch res = client.patch("/d/", json=load_json("test-patch-replace-values-1.json")) # update the patch patch_url = urlparse(res.headers["Location"]).path res = client.put( patch_url + "patch.jsonpatch", json=load_json("test-patch-replace-values-2.json"), ) assert res.status_code == httpx.codes.OK @pytest.mark.client_auth_token("this-token-has-normal-permissions") def test_creator_identity_update_merged_patch( admin_user, active_user, client, load_json, bearer_auth, ): admin_user, active_user # submit the patch res = client.patch("/d/", json=load_json("test-patch-replace-values-1.json")) # merge the patch (as admin) patch_url = urlparse(res.headers["Location"]).path res = client.post( patch_url + "merge", auth=bearer_auth("this-token-has-admin-permissions") ) # now try to update the patch (as original creator) res = client.put( patch_url + "patch.jsonpatch", json=load_json("test-patch-replace-values-2.json"), ) assert res.status_code == httpx.codes.FORBIDDEN assert res.headers["WWW-Authenticate"] == ( 'Bearer realm="PeriodO", error="insufficient_scope", ' + "error_description=" + '"The access token does not provide sufficient privileges", ' + 'error_uri="http://tools.ietf.org/html/rfc6750#section-6.2.3"' )

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9885afefa49d0b7e238aeedbd7a21ad99aab13e3

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py

Python

nsedata/__init__.py

Codelif/nsedata

c2e4b2da3bd810c13fe13245d0ba6666cabe2583

[ "MIT" ]

null

nsedata/__init__.py

Codelif/nsedata

c2e4b2da3bd810c13fe13245d0ba6666cabe2583

[ "MIT" ]

1

2021-05-23T15:14:08.000Z

nsedata/__init__.py

Codelif/nsedata

c2e4b2da3bd810c13fe13245d0ba6666cabe2583

[ "MIT" ]

null

from nsedata.nsedata import Nse

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989cdd2c85443bed490122ae67c510db2a173357

33

py

Python

utils/__init__.py

clcert/beacon-verifier

7523756d84c309a01b3606b0602e8d082a47d867

[ "MIT" ]

4

2018-09-04T17:45:52.000Z

2020-10-09T22:18:37.000Z

utils/__init__.py

clcert/beacon-verifier

7523756d84c309a01b3606b0602e8d082a47d867

[ "MIT" ]

7

2018-07-12T18:32:01.000Z

2019-04-24T19:50:10.000Z

utils/__init__.py

clcert/beacon-verifier

7523756d84c309a01b3606b0602e8d082a47d867

[ "MIT" ]

null

from utils.crypto.sloth import *

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7f75e7aed7ded78b7d6dde1274708a4ae73bae84

13,575

py

Python

icem/environments/robotics.py

emiliojorge/iCEM

abf8e08e5993eaad2b61d7f56906808de964330a

[ "MIT" ]

27

2020-11-17T17:59:43.000Z

2022-02-24T16:43:53.000Z

icem/environments/robotics.py

emiliojorge/iCEM

abf8e08e5993eaad2b61d7f56906808de964330a

[ "MIT" ]

4

2021-02-04T04:40:43.000Z

2021-09-10T13:17:06.000Z

icem/environments/robotics.py

emiliojorge/iCEM

abf8e08e5993eaad2b61d7f56906808de964330a

[ "MIT" ]

4

2021-03-17T16:12:14.000Z

2022-01-17T15:08:47.000Z

from gym.envs.robotics.fetch.pick_and_place import FetchPickAndPlaceEnv as FetchPickAndPlaceEnv_v1 from gym.envs.robotics.fetch.reach import FetchReachEnv from gym.envs.robotics.robot_env import RobotEnv from gym.utils import EzPickle from .abstract_environments import * class GymRoboticsGroundTruthSupportEnv(GroundTruthSupportEnv, RobotEnv, ABC): """ adds generic state operations for all Mujoco-based envs """ window_exists = False # noinspection PyPep8Naming def set_GT_state(self, state): self.sim.set_state_from_flattened(state.copy()) self.sim.forward() # noinspection PyPep8Naming def get_GT_state(self): return self.sim.get_state().flatten() # noinspection PyMethodMayBeStatic def prepare_for_recording(self): if not self.window_exists: from mujoco_py import GlfwContext GlfwContext(offscreen=True) self.window_exists = True class FetchPickAndPlace(MaskedGoalSpaceEnvironmentInterface, GymRoboticsGroundTruthSupportEnv, FetchPickAndPlaceEnv_v1): def __init__(self, *, name, sparse, threshold, fixed_object_pos=None, fixed_goal=None, shaped_reward=False, **kwargs): self.fixed_object_pos = fixed_object_pos self.fixed_goal = fixed_goal self.shaped_reward = shaped_reward FetchPickAndPlaceEnv_v1.__init__(self, **kwargs) GymRoboticsGroundTruthSupportEnv.__init__(self, name=name, **kwargs) self.store_init_arguments(locals()) EzPickle.__init__(self, name=name, sparse=sparse, threshold=threshold, **kwargs) # needed to call make the pickling work with the args given assert (isinstance(self.observation_space, spaces.Dict)) orig_obs_len = self.observation_space.spaces['observation'].shape[0] goal_space_size = self.observation_space.spaces['desired_goal'].shape[0] goal_idx = np.arange(orig_obs_len, orig_obs_len + goal_space_size) achieved_goal_idx = [3, 4, 5] self.observation_space = spaces.Box(-np.inf, np.inf, shape=(orig_obs_len + goal_space_size,), dtype='float32') MaskedGoalSpaceEnvironmentInterface.__init__(self, name=name, goal_idx=goal_idx, achieved_goal_idx=achieved_goal_idx, sparse=sparse, threshold=threshold) def _step_callback(self): self.sim.forward() # we need to call forward because part of the model was overwritten and it is not consistent def get_pos_vel_of_joints(self, names): if self.sim.data.qpos is not None and self.sim.model.joint_names: return ( np.array([self.sim.data.get_joint_qpos(name) for name in names]), np.array([self.sim.data.get_joint_qvel(name) for name in names]), ) def set_pos_vel_of_joints(self, names, q_pos, q_vel): if self.sim.data.qpos is not None and self.sim.model.joint_names: for n, p, v in zip(names, q_pos, q_vel): self.sim.data.set_joint_qpos(n, p) self.sim.data.set_joint_qvel(n, v) @staticmethod def flatten_observation(obs): return np.concatenate((obs['observation'], obs['desired_goal'])) def step(self, action): obs, reward, done, info = super().step(action) return self.flatten_observation(obs), reward, done, info def reset(self): # return self.flatten_observation(super().reset()) # Attempt to reset the simulator. Since we randomize initial conditions, it # is possible to get into a state with numerical issues (e.g. due to penetration or # Gimbel lock) or we may not achieve an initial condition (e.g. an object is within the hand). # In this case, we just keep randomizing until we eventually achieve a valid initial # configuration. did_reset_sim = False while not did_reset_sim: did_reset_sim = self._reset_sim() self.goal = self._sample_goal().copy() obs = self._get_obs() return self.flatten_observation(obs) def get_GT_state(self): return np.concatenate((super().get_GT_state(), self.goal)) def set_GT_state(self, state): mj_state = state[:-3] self.goal = state[-3:] super().set_GT_state(mj_state) def set_state_from_observation(self, observation): raise NotImplementedError("FetchPickAndPlace env needs the real GT states to be reset") def _reset_sim(self): self.sim.set_state(self.initial_state) # Randomize start position of object. if self.has_object: if self.fixed_object_pos is not None: object_xpos = self.initial_gripper_xpos[:2] + np.asarray(self.fixed_object_pos) * self.obj_range else: object_xpos = self.initial_gripper_xpos[:2] while np.linalg.norm(object_xpos - self.initial_gripper_xpos[:2]) < 0.1: object_xpos = self.initial_gripper_xpos[:2] + self.np_random.uniform(-self.obj_range, self.obj_range, size=2) object_qpos = self.sim.data.get_joint_qpos('object0:joint') assert object_qpos.shape == (7,) object_qpos[:2] = object_xpos self.sim.data.set_joint_qpos('object0:joint', object_qpos) self.sim.forward() return True def _sample_goal(self): if self.has_object: if self.fixed_goal is not None: goal = self.initial_gripper_xpos[:3] + np.asarray(self.fixed_goal) * self.target_range goal += self.target_offset goal[2] = self.height_offset if self.target_in_the_air: goal[2] += self.fixed_goal[2] * 0.45 else: goal = self.initial_gripper_xpos[:3] + self.np_random.uniform(-self.target_range, self.target_range, size=3) goal += self.target_offset goal[2] = self.height_offset if self.target_in_the_air and self.np_random.uniform() < 0.5: goal[2] += self.np_random.uniform(0, 0.45) else: goal = self.initial_gripper_xpos[:3] + self.np_random.uniform(-0.15, 0.15, size=3) return goal.copy() def cost_fn(self, observation, action, next_obs): dist_box_to_goal = np.linalg.norm(self.goal_from_observation(observation) - self.achieved_goal_from_observation(observation), axis=-1) dist_end_eff_to_box = 0 if self.shaped_reward: dist_end_eff_to_box = np.linalg.norm(observation[:, :3] - observation[:, 3:6], axis=-1) if self.sparse: cost = np.asarray(dist_box_to_goal > self.threshold, dtype=np.float32) + \ np.asarray(dist_end_eff_to_box > self.threshold, dtype=np.float32) * 0.1 else: cost = dist_box_to_goal + dist_end_eff_to_box * 0.1 return cost def is_success(self, observation, action, next_obs): dist = np.linalg.norm(self.goal_from_observation(next_obs) - self.achieved_goal_from_observation(next_obs), axis=-1) is_success = np.asarray(dist <= self.threshold, dtype=np.float32) return is_success class FetchReach(MaskedGoalSpaceEnvironmentInterface, GymRoboticsGroundTruthSupportEnv, FetchReachEnv): def __init__(self, *, name, sparse, threshold, fixed_goal=None, **kwargs): self.fixed_goal = fixed_goal FetchReachEnv.__init__(self, **kwargs) GymRoboticsGroundTruthSupportEnv.__init__(self, name=name, **kwargs) self.store_init_arguments(locals()) EzPickle.__init__(self, name=name, sparse=sparse, threshold=threshold, **kwargs) # needed to call make the pickling work with the args given assert (isinstance(self.observation_space, spaces.Dict)) orig_obs_len = self.observation_space.spaces['observation'].shape[0] self.goal_space_size = self.observation_space.spaces['desired_goal'].shape[0] goal_idx = np.arange(orig_obs_len, orig_obs_len + self.goal_space_size) achieved_goal_idx = [0, 1, 2] self.observation_space = spaces.Box(-np.inf, np.inf, shape=(orig_obs_len + self.goal_space_size,), dtype='float32') MaskedGoalSpaceEnvironmentInterface.__init__(self, name=name, goal_idx=goal_idx, achieved_goal_idx=achieved_goal_idx, sparse=sparse, threshold=threshold) def _step_callback(self): self.sim.forward() # we need to call forward because part of the model was overwritten and it is not consistent def get_pos_vel_of_joints(self, names): if self.sim.data.qpos is not None and self.sim.model.joint_names: return ( np.array([self.sim.data.get_joint_qpos(name) for name in names]), np.array([self.sim.data.get_joint_qvel(name) for name in names]), ) def set_pos_vel_of_joints(self, names, q_pos, q_vel): if self.sim.data.qpos is not None and self.sim.model.joint_names: for n, p, v in zip(names, q_pos, q_vel): self.sim.data.set_joint_qpos(n, p) self.sim.data.set_joint_qvel(n, v) @staticmethod def flatten_observation(obs): return np.concatenate((obs['observation'], obs['desired_goal'])) def step(self, action): obs, reward, done, info = super().step(action) return self.flatten_observation(obs), reward, done, info def reset(self): return self.flatten_observation(super().reset()) def get_GT_state(self): return np.concatenate((super().get_GT_state(), self.goal)) def set_GT_state(self, state): mj_state = state[:-3] self.goal = state[-3:] super().set_GT_state(mj_state) def set_state_from_observation(self, observation): raise NotImplementedError("FetchPickAndPlace env needs the real GT states to be reset") def _reset_sim(self): self.sim.set_state(self.initial_state) # Randomize start position of object. if self.has_object: if self.fixed_object_pos is not None: object_xpos = self.initial_gripper_xpos[:2] + np.asarray(self.fixed_object_pos) * self.obj_range else: object_xpos = self.initial_gripper_xpos[:2] while np.linalg.norm(object_xpos - self.initial_gripper_xpos[:2]) < 0.1: object_xpos = self.initial_gripper_xpos[:2] + self.np_random.uniform(-self.obj_range, self.obj_range, size=2) object_qpos = self.sim.data.get_joint_qpos('object0:joint') assert object_qpos.shape == (7,) object_qpos[:2] = object_xpos self.sim.data.set_joint_qpos('object0:joint', object_qpos) self.sim.forward() return True def _sample_goal(self): if self.has_object: if self.fixed_goal is not None: goal = self.initial_gripper_xpos[:3] + np.asarray(self.fixed_goal) * self.target_range goal += self.target_offset goal[2] = self.height_offset if self.target_in_the_air: goal[2] += self.fixed_goal[2] * 0.45 else: goal = self.initial_gripper_xpos[:3] + self.np_random.uniform(-self.target_range, self.target_range, size=3) goal += self.target_offset goal[2] = self.height_offset if self.target_in_the_air and self.np_random.uniform() < 0.5: goal[2] += self.np_random.uniform(0, 0.45) else: if self.fixed_goal is not None: goal = self.initial_gripper_xpos[:3] + np.asarray(self.fixed_goal) else: goal = self.initial_gripper_xpos[:3] + self.np_random.uniform(-0.15, 0.15, size=3) return goal.copy() def cost_fn(self, observation, action, next_obs): dist_gripper_to_goal = np.linalg.norm(self.goal_from_observation(observation) - self.achieved_goal_from_observation(observation), axis=-1) if self.sparse: cost = np.asarray(dist_gripper_to_goal > self.threshold, dtype=np.float32) else: cost = dist_gripper_to_goal return cost def is_success(self, observation, action, next_obs): dist = np.linalg.norm(self.goal_from_observation(next_obs) - self.achieved_goal_from_observation(next_obs), axis=-1) is_success = np.asarray(dist <= self.threshold, dtype=np.float32) return is_success if __name__ == '__main__': env = FetchPickAndPlace(name='blub', sparse=False, threshold=0.05, fixed_goal=[0.5, -0.3, 0.6], fixed_object_pos=[0.85, 0.85]) while True: env.reset() for _ in range(50): env.render() env.step(env.action_space.sample())

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7f900805da2f659cef185d62b249994806954cca

42

py

Python

Talos_Test/Talos_test/envs/__init__.py

zaceJin/Talos

209439bb254a6884c9f195e247ec5783404a8f6b

[ "MIT" ]

null

Talos_Test/Talos_test/envs/__init__.py

zaceJin/Talos

209439bb254a6884c9f195e247ec5783404a8f6b

[ "MIT" ]

null

Talos_Test/Talos_test/envs/__init__.py

zaceJin/Talos

209439bb254a6884c9f195e247ec5783404a8f6b

[ "MIT" ]

null

from Talos_test.envs.Simple_Env import *

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1

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0

6

f68fb5118d4c711bc52cec7ebbc22a01e3e5f561

220

py

Python

giscube_search/settings.py

aroiginfraplan/giscube-admin

b7f3131b0186f847f3902df97f982cb288b16a49

[ "BSD-3-Clause" ]

5

2018-06-07T12:54:35.000Z

2022-01-14T10:38:38.000Z

giscube_search/settings.py

aroiginfraplan/giscube-admin

b7f3131b0186f847f3902df97f982cb288b16a49

[ "BSD-3-Clause" ]

140

2018-06-18T10:27:28.000Z

2022-03-23T09:53:15.000Z

giscube_search/settings.py

aroiginfraplan/giscube-admin

b7f3131b0186f847f3902df97f982cb288b16a49

[ "BSD-3-Clause" ]

1

2021-04-13T11:20:54.000Z

from django.conf import settings GISCUBE_SEARCH_DEFAULT_DICTIONARY = getattr(settings, 'GISCUBE_SEARCH_DEFAULT_DICTIONARY', 'english') GISCUBE_SEARCH_MAX_RESULTS = getattr(settings, 'GISCUBE_SEARCH_MAX_RESULTS', None)

36.666667

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0.36

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null

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1

0

6

f6eba0f6cca366fdd7f895e1ae24ced5de833d8e

194

py

Python

core/admin.py

jordij/menorkayak

b9b1a80230b111c2bd422de88215102a5f944fe6

[ "MIT" ]

1

2017-04-25T10:17:52.000Z

core/admin.py

jordij/menorkayak

b9b1a80230b111c2bd422de88215102a5f944fe6

[ "MIT" ]

null

core/admin.py

jordij/menorkayak

b9b1a80230b111c2bd422de88215102a5f944fe6

[ "MIT" ]

null

from django.contrib import admin from core.models import Day, Picture, Place, Point admin.site.register(Day) admin.site.register(Picture) admin.site.register(Place) admin.site.register(Point)

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6

f6fc125ad1488503f5a068a6401c1fc3e112f3da

29

py

Python

AC/hunter/__init__.py

imandr/KeRLas

8c347cbfea982f470372fb7cf8943f4d6bda8a8a

[ "BSD-3-Clause" ]

null

AC/hunter/__init__.py

imandr/KeRLas

8c347cbfea982f470372fb7cf8943f4d6bda8a8a

[ "BSD-3-Clause" ]

null

AC/hunter/__init__.py

imandr/KeRLas

8c347cbfea982f470372fb7cf8943f4d6bda8a8a

[ "BSD-3-Clause" ]

null

from .hunter import HunterEnv

29

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1

0

6

63dee7ce8586326e8ee39294742cc5bc14cd9a9b

18,208

py

Python

figs/plot.py

owensgroup/BGHT

0bddb60fe15fe50a23e640da1e87ad3906982a40

[ "Apache-2.0" ]

8

2021-11-15T23:46:37.000Z

2022-03-24T15:54:20.000Z

figs/plot.py

owensgroup/BGHT

0bddb60fe15fe50a23e640da1e87ad3906982a40

[ "Apache-2.0" ]

2

2021-12-09T10:14:44.000Z

2022-01-26T09:12:52.000Z

figs/plot.py

owensgroup/BGHT

0bddb60fe15fe50a23e640da1e87ad3906982a40

[ "Apache-2.0" ]

2

2021-12-01T06:39:54.000Z

2022-03-05T18:47:29.000Z

import pandas as pd import matplotlib.pyplot as plt import numpy as np import sys import argparse import scipy.stats from matplotlib.offsetbox import AnchoredText def remove_failed_experiments(df): df = df.applymap(lambda x: float('nan') if x < 0 else x) return df def print_summary(label, insert_col, find100_col, find50_col, find0_col): mean_insertion_rate = scipy.stats.hmean(insert_col.dropna()) mean_find100_rate = scipy.stats.hmean(find100_col.dropna()) mean_find50_rate = scipy.stats.hmean(find50_col.dropna()) mean_find0_rate = scipy.stats.hmean(find0_col.dropna()) print("{0: <16}".format(label) + ' | ',\ "{0: <15}".format(round(mean_insertion_rate,3)) + '|',\ "{0: <8}".format(round(mean_find100_rate,3)) + ' |',\ "{0: <8}".format(round(mean_find50_rate,3)) + ' |',\ "{0: <8}".format(round(mean_find0_rate,3))) def plot_rates_fixed_lf(results_dir, output_dir, min_find, max_find, min_insert, max_insert, load_factor, probing = 'BCHT'): df = pd.DataFrame() svg_name='' bucket_sizes = [] if probing == 'BCHT': subdir = '/rates_fixed_lf/bcht_rates_lfeq' fmt = '.csv' df = pd.read_csv(results_dir + subdir + str(load_factor) + fmt) svg_name='bcht_rates_lfeq' + str(load_factor) bucket_sizes = [1, 8, 16, 32] elif probing == 'P2BHT': subdir = '/rates_fixed_lf/p2bht_rates_lfeq' fmt = '.csv' df = pd.read_csv(results_dir + subdir + str(load_factor) + fmt) svg_name='p2bht_rates_lfeq' + str(load_factor) bucket_sizes = [16, 32] elif probing == 'IHT': subdir = '/rates_fixed_lf/iht_rates_lfeq' fmt = '.csv' df = pd.read_csv(results_dir + subdir+ str(load_factor) + fmt) svg_name='iht_rates_lfeq' + str(load_factor) bucket_sizes = [16, 32] else: print("Uknown probing scheme") sys.exit() df = remove_failed_experiments(df) df['num_keys'] = df['num_keys'].divide(1.0e6) scale=5 subplots=[] fig = plt.figure(figsize=(4*scale,1*scale)) ax = fig.add_subplot(111) titles = ['100% Positive queries', '50% Positive queries', '0% Positive queries'] subplots.append(fig.add_subplot(141)) for i in range(2, 5): subplots.append(fig.add_subplot(1, 4, i)) subplots[-1].title.set_text(titles[i-2]) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False) markers =['s', 'o', '^', 'D'] print(probing + ': Fixed load factor = 0.' + str(load_factor) + ' summary:') print('Probing scheme | HMean insertion | HMean find 100') print(' | | 100% | 50% | 0%') if probing == 'BCHT' or probing == 'P2BHT': for b, m in zip(bucket_sizes, markers): insert_c = 'insert_' + str(b) find_c = 'find_' + str(b) + '_' l = probing + ', b=' + str(b) subplots[0].plot(df['num_keys'], df[insert_c], marker = m, label=l) subplots[1].plot(df['num_keys'], df[find_c + str(100)], marker = m) subplots[2].plot(df['num_keys'], df[find_c + str(50)], marker = m) subplots[3].plot(df['num_keys'], df[find_c + str(0)], marker = m) print_summary(l, df[insert_c], df[find_c + str(100)], df[find_c + str(50)], df[find_c + str(0)]) elif probing == 'IHT': thresholds = [20, 40, 60, 80] for b in bucket_sizes: for t, m in zip(thresholds, markers): insert_c = 'insert_' + str(b) + '_' + str(t) find_c = 'find_' + str(b) + '_' + str(t) + '_' l = probing + ', b=' + str(b) + ', t=' + str(t) + '%' subplots[0].plot(df['num_keys'], df[insert_c], marker = m, label=l) subplots[1].plot(df['num_keys'], df[find_c + str(100)], marker = m) subplots[2].plot(df['num_keys'], df[find_c + str(50)], marker = m) subplots[3].plot(df['num_keys'], df[find_c + str(0)], marker = m) print_summary(l, df[insert_c], df[find_c + str(100)], df[find_c + str(50)], df[find_c + str(0)]) print('--------------------------------------------------------') ax.set_xlabel('Millions of keys') subplots[0].set_ylabel('Insert Rate (MKey/s) ' + 'load factor = 0.' + str(load_factor)) subplots[1].set_ylabel('Find Rate (MKey/s) ' + 'load factor = 0.' + str(load_factor)) for p in subplots: p.spines["right"].set_visible(False) p.spines["top"].set_visible(False) if min_insert != -1 and max_insert != 1: subplots[0].set_ylim([min_insert, max_insert]) if min_find != -1 and max_find != 1: subplots[1].set_ylim([min_find, max_find]) subplots[2].set_ylim([min_find, max_find]) subplots[3].set_ylim([min_find, max_find]) fig.tight_layout() fig.legend(bbox_to_anchor = (1, 0.9), frameon=False) fig.show() fig.savefig(output_dir + '/' + svg_name + '.svg',bbox_inches='tight') def plot_rates_fixed_keys(results_dir, output_dir, min_find, max_find, min_insert, max_insert, probing = 'BCHT'): df = pd.DataFrame() svg_name='' bucket_sizes = [] if probing == 'BCHT': df = pd.read_csv(results_dir + '/rates_fixed_keys/bcht_rates_fixed_keys.csv') svg_name='bcht_rates_fixed_keys' bucket_sizes = [1, 8, 16, 32] elif probing == 'P2BHT': df = pd.read_csv(results_dir + '/rates_fixed_keys/p2bht_rates_fixed_keys.csv') svg_name='p2bht_rates_fixed_keys' bucket_sizes = [16, 32] elif probing == 'IHT': df = pd.read_csv(results_dir + '/rates_fixed_keys/iht_rates_fixed_keys.csv') svg_name='iht_rates_fixed_keys' bucket_sizes = [16, 32] else: print("Uknown probing scheme") sys.exit() df = remove_failed_experiments(df) scale=5 subplots=[] fig = plt.figure(figsize=(4*scale,1*scale)) ax = fig.add_subplot(111) subplots.append(fig.add_subplot(141)) titles = ['100% Positive queries', '50% Positive queries', '0% Positive queries'] for i in range(2, 5): subplots.append(fig.add_subplot(1, 4, i)) subplots[-1].title.set_text(titles[i-2]) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False) markers =['s', 'o', '^', 'D'] min_x_axis = 0.6 max_x_axis = 1.0 print(probing + ': Fixed number of keys summary:') print('Probing scheme | HMean insertion | HMean find 100') print(' | | 100% | 50% | 0%') if probing == 'BCHT' or probing == 'P2BHT': for b, m in zip(bucket_sizes, markers): insert_c = 'insert_' + str(b) find_c = 'find_' + str(b) + '_' l = probing + ', b=' + str(b) subplots[0].plot(df['load_factor'], df[insert_c], marker = m, label=l) subplots[1].plot(df['load_factor'], df[find_c + str(100)], marker = m) subplots[2].plot(df['load_factor'], df[find_c + str(50)], marker = m) subplots[3].plot(df['load_factor'], df[find_c + str(0)], marker = m) print_summary(l, df[insert_c], df[find_c + str(100)], df[find_c + str(50)], df[find_c + str(0)]) elif probing == 'IHT': thresholds = [20, 40, 60, 80] for b in bucket_sizes: for t, m in zip(thresholds, markers): insert_c = 'insert_' + str(b) + '_' + str(t) find_c = 'find_' + str(b) + '_' + str(t) + '_' l = probing + ', b=' + str(b) + ', t=' + str(t) + '%' subplots[0].plot(df['load_factor'], df[insert_c], marker = m, label=l) subplots[1].plot(df['load_factor'], df[find_c + str(100)], marker = m) subplots[2].plot(df['load_factor'], df[find_c + str(50)], marker = m) subplots[3].plot(df['load_factor'], df[find_c + str(0)], marker = m) print_summary(l, df[insert_c], df[find_c + str(100)], df[find_c + str(50)], df[find_c + str(0)]) print('--------------------------------------------------------') ax.set_xlabel('Load factor') subplots[0].set_ylabel('Insert Rate (MKey/s)') subplots[1].set_ylabel('Find Rate (MKey/s)') for p in subplots: p.spines["right"].set_visible(False) p.spines["top"].set_visible(False) if min_insert != -1 and max_insert != 1: subplots[0].set_ylim([min_insert, max_insert]) if min_find != -1 and max_find != 1: subplots[1].set_ylim([min_find, max_find]) subplots[2].set_ylim([min_find, max_find]) subplots[3].set_ylim([min_find, max_find]) for ax in fig.get_axes(): ax.set_xlim([min_x_axis, max_x_axis]) fig.tight_layout() fig.legend(bbox_to_anchor = (1, 0.9), frameon=False) fig.show() fig.savefig(output_dir + '/' + svg_name + '.svg',bbox_inches='tight') def plot_avg_probes_fixed_keys(results_dir, output_dir, probing = 'BCHT'): df = pd.DataFrame() svg_name='' bucket_sizes = [] if probing == 'BCHT': df = pd.read_csv(results_dir + '/avg_probes/bcht_probes.csv') svg_name='bcht_probes' bucket_sizes = [1, 8, 16, 32] elif probing == 'P2BHT': df = pd.read_csv(results_dir + '/avg_probes/p2bht_probes.csv') svg_name='p2ht_probes' bucket_sizes = [16, 32] elif probing == 'IHT': df = pd.read_csv(results_dir + '/avg_probes/iht_probes.csv') svg_name='iht_probes' bucket_sizes = [16, 32] else: print("Uknown probing scheme") sys.exit() df = remove_failed_experiments(df) scale=5 subplots=[] fig = plt.figure(figsize=(4*scale,1*scale)) ax = fig.add_subplot(111) titles = ['100% Positive queries', '50% Positive queries', '0% Positive queries'] subplots.append(fig.add_subplot(141)) for i in range(2, 5): subplots.append(fig.add_subplot(1, 4, i)) subplots[-1].title.set_text(titles[i-2]) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False) markers =['s', 'o', '^', 'D'] min_x_axis = 0.6 max_x_axis = 1.0 min_y_axis = 1.0 max_y_axis = 4.0 print(probing + ': average probes summary:') print('Probing scheme | HMean insertion | HMean find 100') print(' | | 100% | 50% | 0%') if probing == 'BCHT' or probing == 'P2BHT': for b, m in zip(bucket_sizes, markers): insert_c = 'insert_' + str(b) find_c = 'find_' + str(b) + '_' l = probing + ', b=' + str(b) subplots[0].plot(df['load_factor'], df[insert_c], marker = m, label=l) subplots[1].plot(df['load_factor'], df[find_c + str(100)], marker = m) subplots[2].plot(df['load_factor'], df[find_c + str(50)], marker = m) subplots[3].plot(df['load_factor'], df[find_c + str(0)], marker = m) print_summary(l, df[insert_c], df[find_c + str(100)], df[find_c + str(50)], df[find_c + str(0)]) elif probing == 'IHT': thresholds = [20, 40, 60, 80] for b in bucket_sizes: for t, m in zip(thresholds, markers): insert_c = 'insert_' + str(b) + '_' + str(t) find_c = 'find_' + str(b) + '_' + str(t) + '_' l = probing + ', b=' + str(b) + ', t=' + str(t) + '%' subplots[0].plot(df['load_factor'], df[insert_c], marker = m, label=l) subplots[1].plot(df['load_factor'], df[find_c + str(100)], marker = m) subplots[2].plot(df['load_factor'], df[find_c + str(50)], marker = m) subplots[3].plot(df['load_factor'], df[find_c + str(0)], marker = m) print_summary(l, df[insert_c], df[find_c + str(100)], df[find_c + str(50)], df[find_c + str(0)]) print('--------------------------------------------------------') ax.set_xlabel('Load factor') subplots[0].set_ylabel('Average number of probes per key') for p in subplots: p.spines["right"].set_visible(False) p.spines["top"].set_visible(False) for ax in fig.get_axes(): ax.set_xlim([min_x_axis, max_x_axis]) ax.set_ylim([min_y_axis, max_y_axis]) fig.tight_layout() fig.legend(bbox_to_anchor = (1, 0.9), frameon=False) fig.show() fig.savefig(output_dir + '/' + svg_name + '.svg',bbox_inches='tight') def plot_avg_probes_fixed_keys_best(dfs, xcol, output_dir, svg_name, x_title, y_title): scale=5 subplots=[] fig = plt.figure(figsize=(4*scale,1*scale)) ax = fig.add_subplot(111) titles = ['100% Positive queries', '50% Positive queries', '0% Positive queries'] subplots.append(fig.add_subplot(141)) for i in range(2, 5): subplots.append(fig.add_subplot(1, 4, i)) subplots[-1].title.set_text(titles[i-2]) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False) markers =['s', 'o', '^', 'D'] print('Best ' + svg_name + ' summary:') print('Probing scheme | HMean insertion | HMean find 100') print(' | | 100% | 50% | 0%') best_prefix = ['CHT' , 'BCHT', 'PB2HT', 'IHT'] best_suffix = ['1','16', '32', '32_80'] for df, s, p, m in zip(dfs, best_suffix, best_prefix, markers): insert_c = 'insert_' + s find_c = 'find_' + s + '_' l = s + p subplots[0].plot(df[xcol], df[insert_c], marker = m, label=l) subplots[1].plot(df[xcol], df[find_c + str(100)], marker = m) subplots[2].plot(df[xcol], df[find_c + str(50)], marker = m) subplots[3].plot(df[xcol], df[find_c + str(0)], marker = m) print_summary(l, df[insert_c], df[find_c + str(100)], df[find_c + str(50)], df[find_c + str(0)]) print('--------------------------------------------------------') ax.set_xlabel(x_title) subplots[0].set_ylabel(y_title) for p in subplots: p.spines["right"].set_visible(False) p.spines["top"].set_visible(False) fig.tight_layout() fig.legend(bbox_to_anchor = (1, 0.9), frameon=False) fig.show() fig.savefig(output_dir + '/' + svg_name + '.svg',bbox_inches='tight') def plot_best(results_dir, output_dir): svg_names=['rates_fixed_keys', 'rates_fixed_lf', 'avg_probes'] csv_names=['_rates_fixed_keys', '_rates_lfeq90','_probes'] cols =['load_factor', 'num_keys','load_factor'] titles_x = ['Load factor', 'Number of keys', 'Load Factor'] titles_y = ['Rate (MOperation/s)', 'Rate (MOperation/s)', 'Average number of probes per key'] for s, csv, col, tx, ty in zip(svg_names, csv_names, cols, titles_x, titles_y): dfs = [pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()] dfs[0] = pd.read_csv(results_dir + s + '/' + 'bcht' + csv +'.csv') dfs[1] = pd.read_csv(results_dir + s + '/' + 'bcht' + csv +'.csv') dfs[2] = pd.read_csv(results_dir + '/' + s + '/' + 'p2bht' + csv +'.csv') dfs[3] = pd.read_csv(results_dir + '/' + s + '/' + 'iht' + csv +'.csv') dfs[0] = remove_failed_experiments(dfs[0]) dfs[1] = remove_failed_experiments(dfs[1]) dfs[2] = remove_failed_experiments(dfs[2]) dfs[3] = remove_failed_experiments(dfs[3]) plot_avg_probes_fixed_keys_best(dfs, col, output_dir, s, tx, ty) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('-d', '--dir') parser.add_argument('-od', '--output-dir', default='') parser.add_argument('-mf','--min-find-throughput', default=-1,type=int) parser.add_argument('-xf','--max-find-throughput', default=-1,type=int) parser.add_argument('-mi','--min-insert-throughput', default=-1,type=int) parser.add_argument('-xi','--max-insert-throughput', default=-1,type=int) parser.add_argument('-p','--probing-scheme', default='all') probing_schemes =['BCHT', 'P2BHT', 'IHT'] args = parser.parse_args() print("Reading results from: ", args.dir) load_factors = [80, 90] if args.probing_scheme == 'all': plot_best(args.dir, args.output_dir) for p in probing_schemes: # Plotting rates vs. load factor plot_rates_fixed_keys(args.dir, args.output_dir, args.min_find_throughput, args.max_find_throughput,\ args.min_insert_throughput, args.max_insert_throughput, p) # Plotting rates vs. number of keys for lf in load_factors: plot_rates_fixed_lf(args.dir, args.output_dir, args.min_find_throughput, args.max_find_throughput,\ args.min_insert_throughput, args.max_insert_throughput, lf, p) # Plotting probes count vs. load factor plot_avg_probes_fixed_keys(args.dir, args.output_dir, p) else: # Plotting rates vs. load factor plot_rates_fixed_keys(args.dir, args.output_dir, args.min_find_throughput, args.max_find_throughput,\ args.min_insert_throughput, args.max_insert_throughput, args.probing_scheme) # Plotting rates vs. number of keys for lf in load_factors: plot_rates_fixed_lf(args.dir, args.output_dir, args.min_find_throughput, args.max_find_throughput,\ args.min_insert_throughput, args.max_insert_throughput, lf, args.probing_scheme) # Plotting probes count vs. load factor plot_avg_probes_fixed_keys(args.dir, args.output_dir, args.probing_scheme)

42.44289

124

0.586171

2,601

18,208

3.878893

0.080738

0.024284

0.029141

0.04163

0.822579

0.810388

0.791159

0.767866

0.750223

0.711765

0

0.031468

0.237313

18,208

428

125

42.542056

0.695039

0.011259

0

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false

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0.043103

0.083333

0

null

0

1

0

null

0

6

63ed40bbe3b9d11c423a800c85e3139434d80b7b

11,156

py

Python

network/openpose/CMUnet_loss.py

H-Liu1997/Pytorch_Pose_Estimation_Framework

06616b3459ff639f8486e6ea4f93922597788b2a

[ "MIT" ]

1

2019-09-04T11:52:26.000Z

network/openpose/CMUnet_loss.py

HaiyangLiu1997/Pytorch_Pose_Estimation_Framework

06616b3459ff639f8486e6ea4f93922597788b2a

[ "MIT" ]

null

network/openpose/CMUnet_loss.py

HaiyangLiu1997/Pytorch_Pose_Estimation_Framework

06616b3459ff639f8486e6ea4f93922597788b2a

[ "MIT" ]

null

# ------------------------------------------------------------------------------ # define the CMUnet loss calculation # Written by Haiyang Liu (haiyangliu1997@gmail.com) # ------------------------------------------------------------------------------ import torch import torch.nn as nn def loss_cli(parser,name): print('using',name,'loss success') group = parser.add_argument_group('loss') group.add_argument('--auto_weight', default=False, type=bool) def get_offset_loss(saved_for_loss,target_heat,heat_mask,target_paf,paf_mask,target_offset,args,epoch): loss = {} loss['final'] = 0 batch_size = args.batch_size criterion = My_loss().cuda() criterion_offset = My_loss_offset().cuda() heat_output = saved_for_loss[-1][:,:19,:,:].detach() #print(heat_output.requires_grad,1) heat_output_copy = torch.zeros(heat_output.shape,requires_grad=False) #print(heat_output_copy.requires_grad,2) heat_output_copy.copy_(heat_output) #print(heat_output_copy.requires_grad,3) heat_output_copy = heat_output_copy.cuda() #print(heat_output_copy.requires_grad,4) heat_output_copy_final = torch.zeros([heat_output.shape[0],heat_output.shape[1]*2, heat_output.shape[2],heat_output.shape[3]],requires_grad=False) #print(heat_output_copy_final.requires_grad,5) for i in range(heat_output.shape[1]): heat_output_copy_final[:,2*i,:,:] = heat_output_copy[:,i,:,:] heat_output_copy_final[:,2*i+1,:,:] = heat_output_copy[:,i,:,:] heat_output_copy_final = heat_output_copy_final.cuda() #print(heat_output_copy_final.requires_grad,6) #heat_output_copy_final.requires_grad = False #heat_output_copy.requires_grad = False #heat_output.requires_grad = False #print(heat_output_copy_final.requires_grad,7) #print(heat_output_copy.requires_grad,8) #print(heat_output.requires_grad,9) # for debug # print(target_heat.size()) # print(heat_mask.size()) # print(target_paf.size()) # print(paf_mask.size()) # print(saved_for_loss[0].size()) # print(saved_for_loss[1].size()) for i in range(args.paf_stage): loss['stage_{}'.format(i)] = criterion(saved_for_loss[i] * paf_mask,target_paf * paf_mask,batch_size) loss['final'] += loss['stage_{}'.format(i)] for i in range(args.paf_stage,6): loss_a = criterion(saved_for_loss[i][:,:19,:,:] * heat_mask,target_heat * heat_mask,batch_size) loss_b = criterion_offset(saved_for_loss[i][:,19:,:,:], heat_output_copy_final,target_offset,batch_size) if epoch > 4: loss['stage_{}'.format(i)] = loss_a + loss_b else: loss['stage_{}'.format(i)] = loss_a loss['final'] += loss['stage_{}'.format(i)] '''for i in range(6,7): loss['stage_{}'.format(i)] = criterion_offset(saved_for_loss[-1], heat_output_copy_final,target_offset,batch_size) if epoch > 4: loss['final'] += loss['stage_{}'.format(i)]''' return loss def get_loss(saved_for_loss,target_heat,target_paf,args,wei_con): loss = {} length = len(saved_for_loss) loss['final'] = 0 weights = torch.ones([6,args.paf_num+args.heatmap_num]) #print("weights size",weights.size()) #print("weigcon size",wei_con.size()) if args.auto_weight == True: for i in range(args.paf_num+args.heatmap_num): weights[0][i] = wei_con[0][i] weights = weights.cuda() criterion = nn.MSELoss(size_average=True).cuda() if args.auto_weight == True: for i in range(args.paf_stage): loss['stage_{}'.format(i)] = 0 for j in range(args.paf_num): #print(saved_for_loss[i].size(),target_paf.size()) #print(weights.size()) #loss['stage_{0}_{1}'.format(i,j)] = criterion(saved_for_loss[i][:,j,:,:],target_paf[:,j,:,:]) loss['stage_{0}_{1}'.format(i,j)] = criterion(saved_for_loss[i][:,j,:,:],target_paf[:,j,:,:]) * weights[0][j] loss['stage_{}'.format(i)] += loss['stage_{0}_{1}'.format(i,j)] loss['stage_{}'.format(i)] /= 38 loss['final'] += loss['stage_{}'.format(i)] for i in range(args.paf_stage,length): loss['stage_{}'.format(i)] = 0 for j in range(args.heatmap_num): loss['stage_{0}_{1}'.format(i,j)] = criterion(saved_for_loss[i][:,j,:,:],target_heat[:,j,:,:]) * weights[0][j+args.paf_num] loss['stage_{}'.format(i)] += loss['stage_{0}_{1}'.format(i,j)] loss['stage_{}'.format(i)] /= 19 loss['final'] += loss['stage_{}'.format(i)] else: for i in range(args.paf_stage): loss['stage_{}'.format(i)] = 0 for j in range(args.paf_num): #print(saved_for_loss[i].size(),target_paf.size()) #print(weights.size()) loss['stage_{0}_{1}'.format(i,j)] = criterion(saved_for_loss[i][:,j,:,:],target_paf[:,j,:,:]) loss['stage_{}'.format(i)] += loss['stage_{0}_{1}'.format(i,j)] loss['stage_{}'.format(i)] /= 38 loss['final'] += loss['stage_{}'.format(i)] for i in range(args.paf_stage,length): loss['stage_{}'.format(i)] = 0 for j in range(args.heatmap_num): loss['stage_{0}_{1}'.format(i,j)] = criterion(saved_for_loss[i][:,j,:,:],target_heat[:,j,:,:]) loss['stage_{}'.format(i)] += loss['stage_{0}_{1}'.format(i,j)] loss['stage_{}'.format(i)] /= 19 loss['final'] += loss['stage_{}'.format(i)] return loss class My_loss(nn.Module): def __init__(self): super().__init__() def forward(self, x, y, batch_size): return torch.sum(torch.pow((x - y), 2))/batch_size/2 class My_loss_focus(nn.Module): def __init__(self): super().__init__() def forward(self, x, y, batch_size): return torch.sum(torch.pow((x - y), 4))/batch_size class My_loss_focus2(nn.Module): def __init__(self): super().__init__() def forward(self, x, y, batch_size): return torch.sum(torch.log1p(torch.abs((x - y))))/batch_size/4 class My_loss2(nn.Module): def __init__(self): super().__init__() def forward(self, x, y, batch_size,mask): return torch.sum(torch.pow((x - y), 2) * mask)/batch_size/2 class My_loss_offset(nn.Module): def __init__(self): super().__init__() def forward(self, x, mask, y, batch_size): return torch.sum(torch.abs(torch.pow((x - y), 2) * mask))/batch_size/2 def get_old_loss(saved_for_loss,target_heat,target_paf,args,wei_con): loss = {} loss['final'] = 0 batch_size = args.batch_size criterion = nn.MSELoss(size_average=True) for i in range(6): loss['stage_1_{}'.format(i)] = criterion(saved_for_loss[2*i],target_paf,batch_size) loss['stage_2_{}'.format(i)] = criterion(saved_for_loss[2*i+1],target_heat,batch_size) loss['final'] += loss['stage_1_{}'.format(i)] loss['final'] += loss['stage_2_{}'.format(i)] return loss def get_mask_loss(saved_for_loss,target_heat,heat_mask,target_paf,paf_mask,args,wei_con): loss = {} loss['final'] = 0 batch_size = args.batch_size criterion = My_loss().cuda() # for debug # print(target_heat.size()) # print(heat_mask.size()) # print(target_paf.size()) # print(paf_mask.size()) # print(saved_for_loss[0].size()) # print(saved_for_loss[1].size()) for i in range(6): loss['stage_1_{}'.format(i)] = criterion(saved_for_loss[2*i] * paf_mask,target_paf * paf_mask,batch_size) loss['stage_2_{}'.format(i)] = criterion(saved_for_loss[2*i+1] * heat_mask,target_heat * heat_mask,batch_size) loss['final'] += loss['stage_1_{}'.format(i)] loss['final'] += loss['stage_2_{}'.format(i)] return loss def get_old_loss(saved_for_loss,target_heat,target_paf,args,wei_con): loss = {} loss['final'] = 0 batch_size = args.batch_size criterion = nn.MSELoss(size_average=True) for i in range(6): loss['stage_1_{}'.format(i)] = criterion(saved_for_loss[2*i],target_paf,batch_size) loss['stage_2_{}'.format(i)] = criterion(saved_for_loss[2*i+1],target_heat,batch_size) loss['final'] += loss['stage_1_{}'.format(i)] loss['final'] += loss['stage_2_{}'.format(i)] return loss def get_mask_loss_self(saved_for_loss,target_heat,heat_mask,target_paf,paf_mask_self,args,wei_con): loss = {} loss['final'] = 0 batch_size = args.batch_size criterion = My_loss().cuda() criterion2 = My_loss2().cuda() factors = 1 # for debug # print(target_heat.size()) # print(heat_mask.size()) # print(target_paf.size()) # print(paf_mask.size()) # print(saved_for_loss[0].size()) # print(saved_for_loss[1].size()) for i in range(6): loss['stage_1_{}'.format(i)] = factors * criterion2(saved_for_loss[2*i] * paf_mask_self[:19,:,:],target_paf * paf_mask_self[:19,:,:],batch_size,paf_mask_self[19:,:,:]) loss['stage_2_{}'.format(i)] = criterion(saved_for_loss[2*i+1] * heat_mask,target_heat * heat_mask,batch_size) loss['final'] += loss['stage_1_{}'.format(i)] loss['final'] += loss['stage_2_{}'.format(i)] return loss def get_new_mask_loss(saved_for_loss,target_heat,heat_mask,target_paf,paf_mask,args,wei_con): loss = {} loss['final'] = 0 batch_size = args.batch_size criterion = My_loss().cuda() # for debug # print(target_heat.size()) # print(heat_mask.size()) # print(target_paf.size()) # print(paf_mask.size()) # print(saved_for_loss[0].size()) # print(saved_for_loss[1].size()) for i in range(args.paf_stage): loss['stage_{}'.format(i)] = criterion(saved_for_loss[i] * paf_mask,target_paf * paf_mask,batch_size) loss['final'] += loss['stage_{}'.format(i)] for i in range(args.paf_stage,6): loss['stage_{}'.format(i)] = criterion(saved_for_loss[i] * heat_mask,target_heat * heat_mask,batch_size) loss['final'] += loss['stage_{}'.format(i)] return loss def get_new_focus_loss(saved_for_loss,target_heat,heat_mask,target_paf,paf_mask,args,wei_con): loss = {} loss['final'] = 0 batch_size = args.batch_size criterion = My_loss_focus2().cuda() # for debug # print(target_heat.size()) # print(heat_mask.size()) # print(target_paf.size()) # print(paf_mask.size()) # print(saved_for_loss[0].size()) # print(saved_for_loss[1].size()) for i in range(args.paf_stage): loss['stage_{}'.format(i)] = criterion(saved_for_loss[i] * paf_mask,target_paf * paf_mask,batch_size) loss['final'] += loss['stage_{}'.format(i)] for i in range(args.paf_stage,6): loss['stage_{}'.format(i)] = criterion(saved_for_loss[i] * heat_mask,target_heat * heat_mask,batch_size) loss['final'] += loss['stage_{}'.format(i)] return loss

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6

63f66fd08433472237cfdd758577ddd5133e675f

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py

Python

tests/test_history.py

jcaw/traad

770924d9df89037c9a21f1946096aec35685f73d

[ "MIT" ]

74

2015-01-10T20:02:41.000Z

2021-09-29T15:05:42.000Z

tests/test_history.py

jcaw/traad

770924d9df89037c9a21f1946096aec35685f73d

[ "MIT" ]

37

2015-01-06T08:56:43.000Z

2022-02-18T06:51:32.000Z

tests/test_history.py

jcaw/traad

770924d9df89037c9a21f1946096aec35685f73d

[ "MIT" ]

16

2015-08-02T13:14:58.000Z

2022-02-17T00:14:08.000Z

import common import paths import pytest @pytest.fixture def workspace(activate_package, make_workspace): activate_package(package='basic', into='main') workspace = make_workspace('main') yield workspace def test_undo_undoes_changes(workspace): workspace.perform( workspace.rename( 'basic/foo.py', 8, 'Llama')) with pytest.raises(ValueError): common.compare_workspaces( paths.packages('basic'), paths.active('main', 'basic')) workspace.undo() common.compare_workspaces( paths.packages('basic'), paths.active('main', 'basic')) def test_undo_exceptions(workspace): with pytest.raises(IndexError): workspace.undo() workspace.perform( workspace.rename( 'basic/foo.py', 8, 'Llama')) workspace.undo() with pytest.raises(IndexError): workspace.undo(1) def test_undo_adds_history(workspace): assert len(workspace.root_project.history.undo_list) == 0 workspace.perform( workspace.rename( 'basic/foo.py', 8, 'Llama')) assert len(workspace.root_project.history.undo_list) == 1 def test_redo_redoes_changes(workspace): workspace.perform( workspace.rename( 'basic/foo.py', 8, 'Llama')) with pytest.raises(ValueError): common.compare_workspaces( paths.packages('basic'), paths.active('main', 'basic')) workspace.undo() common.compare_workspaces( paths.packages('basic'), paths.active('main', 'basic')) workspace.redo() with pytest.raises(ValueError): common.compare_workspaces( paths.packages('basic'), paths.active('main', 'basic')) def test_redo_adds_history(workspace): workspace.perform( workspace.rename( 'basic/foo.py', 8, 'Llama')) assert len(workspace.root_project.history.redo_list) == 0 assert len(workspace.root_project.history.undo_list) == 1 workspace.undo() assert len(workspace.root_project.history.redo_list) == 1 assert len(workspace.root_project.history.undo_list) == 0 workspace.redo() assert len(workspace.root_project.history.redo_list) == 0 assert len(workspace.root_project.history.undo_list) == 1 def test_redo_exceptions(workspace): with pytest.raises(IndexError): workspace.redo() workspace.perform( workspace.rename( 'basic/foo.py', 8, 'Llama')) workspace.undo() workspace.redo() with pytest.raises(IndexError): workspace.redo(1) def test_undo_history(workspace): assert len(workspace.undo_history()) == 0 workspace.perform( workspace.rename('basic/foo.py', 8, 'Llama')) assert len(workspace.undo_history()) == 1 def test_undo_info(workspace): workspace.perform( workspace.rename('basic/foo.py', 8, 'Llama')) i = workspace.undo_info(0) for k in ['description', 'time', 'full_change', 'changes']: assert k in i def test_undo_info_exceptions(workspace): with pytest.raises(IndexError): workspace.undo_info(0) workspace.perform( workspace.rename('basic/foo.py', 8, 'Llama')) workspace.undo_info(0) with pytest.raises(IndexError): workspace.undo_info(1) def test_redo_history(workspace): assert len(workspace.redo_history()) == 0 workspace.perform( workspace.rename('basic/foo.py', 8, 'Llama')) workspace.undo() assert len(workspace.redo_history()) == 1 def test_redo_info(workspace): workspace.perform( workspace.rename('basic/foo.py', 8, 'Llama')) workspace.undo() i = workspace.redo_info(0) for k in ['description', 'time', 'full_change', 'changes']: assert k in i def test_redo_info_exceptions(workspace): with pytest.raises(IndexError): workspace.redo_info(0) workspace.perform( workspace.rename('basic/foo.py', 8, 'Llama')) workspace.undo() workspace.redo_info(0)

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12269e563d225dd844e98da3c2a56c49550c9831

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py

Python

minerva/engines/web_server.py

vicotrbb/minerva

628e6c4fda115d2f26d0d3789ae8483053c3960d

[ "MIT" ]

null

minerva/engines/web_server.py

vicotrbb/minerva

628e6c4fda115d2f26d0d3789ae8483053c3960d

[ "MIT" ]

null

minerva/engines/web_server.py

vicotrbb/minerva

628e6c4fda115d2f26d0d3789ae8483053c3960d

[ "MIT" ]

null

class WebServer: def __init__(): pass

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125596d4220d7ab7e24376aff3426f5993f95afb

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py

Python

azure-quantum/azure/quantum/aio/job/__init__.py

Anatoliy-Litvinenko/qdk-python

74b2638a404717424090023ef49afb3045ea920e

[ "MIT" ]

53

2021-01-21T23:38:09.000Z

2022-03-29T16:34:42.000Z

azure-quantum/azure/quantum/aio/job/__init__.py

Anatoliy-Litvinenko/qdk-python

74b2638a404717424090023ef49afb3045ea920e

[ "MIT" ]

152

2021-01-23T07:01:49.000Z

2022-03-31T19:43:21.000Z

azure-quantum/azure/quantum/aio/job/__init__.py

slowy07/qdk-python

e4ce0c433cc986bc1c746e9a58f3f05733c657e2

[ "MIT" ]

47

2021-01-30T20:15:46.000Z

2022-03-25T23:35:28.000Z

from azure.quantum.aio.job.job import Job

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c3eb520f184d01e8efa3848267051abb5ee9217e

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py

Python

__main__.py

jupiterbjy/OpenPortScanner

902a076e0c8538615af9050e2551392a75e89a50

[ "MIT" ]

1

2021-02-12T16:11:26.000Z

__main__.py

jupiterbjy/OpenPortScanner

902a076e0c8538615af9050e2551392a75e89a50

[ "MIT" ]

null

__main__.py

jupiterbjy/OpenPortScanner

902a076e0c8538615af9050e2551392a75e89a50

[ "MIT" ]

null

# Will default to Trio import SharedData

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c3fed29f9b332a9552eb10967c62615b07b25172

73

py

Python

src/stk/calculators/optimization/__init__.py

fiszczyp/stk

56e75c493a472d98ccbf3af14cc9ce7f12cbe3d7

[ "MIT" ]

null

src/stk/calculators/optimization/__init__.py

fiszczyp/stk

56e75c493a472d98ccbf3af14cc9ce7f12cbe3d7

[ "MIT" ]

null

src/stk/calculators/optimization/__init__.py

fiszczyp/stk

56e75c493a472d98ccbf3af14cc9ce7f12cbe3d7

[ "MIT" ]

null

from .optimizers import * from .macromodel import * from .mopac import *

18.25

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1

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6

6139dfdcadb2dea7e819c8edc10dd3ffc01e4fed

91

py

Python

automd/__init__.py

cliftbar/auto-api

bcba991da69551fe964fb5b52ab034fc0c1785c3

[ "MIT" ]

null

automd/__init__.py

cliftbar/auto-api

bcba991da69551fe964fb5b52ab034fc0c1785c3

[ "MIT" ]

25

2020-05-03T01:45:55.000Z

2020-12-17T07:14:56.000Z

automd/__init__.py

cliftbar/auto-api

bcba991da69551fe964fb5b52ab034fc0c1785c3

[ "MIT" ]

null

from automd.decorators import override_webargs_flaskparser override_webargs_flaskparser()

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1

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6

618e1a10038c5ea1fb764f8f744af4c8f26186fb

96

py

Python

venv/lib/python3.8/site-packages/crashtest/solution_providers/solution_provider_repository.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/crashtest/solution_providers/solution_provider_repository.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/crashtest/solution_providers/solution_provider_repository.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/bd/4a/27/2fca0aac2f3217d73f128af063cd6541040a12b4079302c3e945c3c277

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0

6

61a95917e5673acf62f3a933766a3ab403e8995e

321

py

Python

build/lib/tnetwork/DCD/analytics/__init__.py

Yquetzal/tnetwork

43fb2f19aeed57a8a9d9af032ee80f1c9f58516d

[ "BSD-2-Clause" ]

4

2019-02-19T07:49:06.000Z

2020-09-01T16:17:54.000Z

tnetwork/DCD/analytics/__init__.py

Yquetzal/tnetwork

43fb2f19aeed57a8a9d9af032ee80f1c9f58516d

[ "BSD-2-Clause" ]

1

2019-07-13T16:16:28.000Z

2019-07-15T09:34:33.000Z

build/lib/tnetwork/DCD/analytics/__init__.py

Yquetzal/tnetwork

43fb2f19aeed57a8a9d9af032ee80f1c9f58516d

[ "BSD-2-Clause" ]

3

2019-07-13T16:09:20.000Z

2022-02-08T02:23:46.000Z

#from tnetwork.DCD.analytics.dynamic_community import * from tnetwork.DCD.analytics.dynamic_partition import * #analytics_all = ["longitudinal_similarity", "consecutive_sn_similarity", "similarity_at_each_step", "quality_at_each_step", "nb_node_change", "entropy_by_node","SM_N","SM_L","SM_P"] #__all__ = analytics_all

45.857143

182

0.806854

44

321

5.340909

0.590909

0.102128

0.12766

0.204255

0.26383

0

0.065421

321

7

183

45.857143

0.783333

0.803738

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

1

0

6

61acc419e8870356b0407a3ac85c6ad5c9782879

189

py

Python

bip_utils/bip/conf/bip49/__init__.py

MIPPLTeam/bip_utils

c66446e7ac3879d2cf6308c5b8eb7f7705292660

[ "MIT" ]

149

2020-05-15T08:11:43.000Z

2022-03-29T16:34:42.000Z

bip_utils/bip/conf/bip49/__init__.py

MIPPLTeam/bip_utils

c66446e7ac3879d2cf6308c5b8eb7f7705292660

[ "MIT" ]

41

2020-04-03T15:57:56.000Z

2022-03-31T08:25:11.000Z

bip_utils/bip/conf/bip49/__init__.py

MIPPLTeam/bip_utils

c66446e7ac3879d2cf6308c5b8eb7f7705292660

[ "MIT" ]

55

2020-04-03T17:05:15.000Z

2022-03-24T12:43:42.000Z

from bip_utils.bip.conf.bip49.bip49_coins import Bip49Coins from bip_utils.bip.conf.bip49.bip49_conf import Bip49Conf from bip_utils.bip.conf.bip49.bip49_conf_getter import Bip49ConfGetter

47.25

70

0.873016

31

189

5.096774

0.354839

0.132911

0.227848

0.28481

0.601266

0.417722

0

0.101695

0.063492

189

3

71

63

0.79096

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

61c2f3bdb110860655d587c684cf7f02848a2e8d

82

py

Python

jetway/memberships/messages.py

grow/jetway

e32a6f447922c364d876e694f0303ae75523c7ed

[ "MIT" ]

null

jetway/memberships/messages.py

grow/jetway

e32a6f447922c364d876e694f0303ae75523c7ed

[ "MIT" ]

6

2015-04-10T00:52:05.000Z

2015-04-10T03:11:22.000Z

jetway/memberships/messages.py

grow/jetway

e32a6f447922c364d876e694f0303ae75523c7ed

[ "MIT" ]

null

from protorpc import messages class MembershipMessage(messages.Message): pass

13.666667

42

0.817073

9

82

7.444444

0.888889

0

0.134146

82

5

43

16.4

0.943662

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

f622e3f3f454b1ff32310ac3d3927a7253377603

130

py

Python

kedro_mlflow/io/models/__init__.py

felipeeeantunes/kedro-mlflow

6d7023d7b859e4645053db39b2296a7d1ab67073

[ "Apache-2.0" ]

97

2020-04-18T14:24:57.000Z

2022-03-19T17:42:43.000Z

kedro_mlflow/io/models/__init__.py

felipeeeantunes/kedro-mlflow

6d7023d7b859e4645053db39b2296a7d1ab67073

[ "Apache-2.0" ]

235

2020-04-25T08:15:42.000Z

2022-03-31T22:07:36.000Z

kedro_mlflow/io/models/__init__.py

felipeeeantunes/kedro-mlflow

6d7023d7b859e4645053db39b2296a7d1ab67073

[ "Apache-2.0" ]

14

2020-04-22T14:46:36.000Z

2022-03-10T07:14:45.000Z

from .mlflow_model_logger_dataset import MlflowModelLoggerDataSet from .mlflow_model_saver_dataset import MlflowModelSaverDataSet

43.333333

65

0.923077

14

130

8.142857

0.642857

0.175439

0.263158

0

0.061538

130

2

66

65

0.934426

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

f62a1fd299013647c5482569ef25d6dc0ecb21fd

51

py

Python

test/pkgman_triggers_test.py

KOLANICH/pkgman_triggers.py

7a121da345e55603c3024b8facfc7dad1e8817db

[ "Unlicense" ]

null

test/pkgman_triggers_test.py

KOLANICH/pkgman_triggers.py

7a121da345e55603c3024b8facfc7dad1e8817db

[ "Unlicense" ]

null

test/pkgman_triggers_test.py

KOLANICH/pkgman_triggers.py

7a121da345e55603c3024b8facfc7dad1e8817db

[ "Unlicense" ]

null

def test_trigger(): print("test trigger called")

12.75

29

0.72549

7

51

5.142857

0.714286

0.611111

0

0.137255

51

3

30

17

0.818182

0

0.38

0

1

0.5

true

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

6

f633987ea2d5e16fde0aed4afae267becd24a755

136

py

Python

Chapter04/05_relational_fields/my_library/__init__.py

Sople/Odoo-14-Cookbook-Code

2813676a3f28c942ecb823fdfddd423ea9ca4f97

[ "MIT" ]

null

Chapter04/05_relational_fields/my_library/__init__.py

Sople/Odoo-14-Cookbook-Code

2813676a3f28c942ecb823fdfddd423ea9ca4f97

[ "MIT" ]

null

Chapter04/05_relational_fields/my_library/__init__.py

Sople/Odoo-14-Cookbook-Code

2813676a3f28c942ecb823fdfddd423ea9ca4f97

[ "MIT" ]

null

# -*- coding: utf-8 -*- # 数据关联字段，有三种： # 1.多对一，Many2one # 2.一对多，one2many # 3.多对多，many2many from . import models from . import controllers

19.428571

25

0.683824

20

136

4.65

0.9

0.215054

0

0.060345

0.147059

136

7

25

19.428571

0.741379

0.580882

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

9c8ba27b0e69c79f56c487b279124ac3154f4051

213

py

Python

bclearer_boson_1_1_source/b_code/configurations/resource_constants/resources_namespace_constants.py

boro-alpha/bclearer_boson_1_1

15207d240fd3144b155922dc5c5d14822023026a

[ "MIT" ]

1

2021-07-20T15:48:58.000Z

bclearer_boson_1_1_source/b_code/configurations/resource_constants/resources_namespace_constants.py

boro-alpha/bclearer_boson_1_1

15207d240fd3144b155922dc5c5d14822023026a

[ "MIT" ]

null

bclearer_boson_1_1_source/b_code/configurations/resource_constants/resources_namespace_constants.py

boro-alpha/bclearer_boson_1_1

15207d240fd3144b155922dc5c5d14822023026a

[ "MIT" ]

null

CONTENT_OPERATIONS_RESOURCES_NAMESPACE = \ 'bclearer_boson_1_1_source.resources.content_universes' ADJUSTMENT_OPERATIONS_RESOURCES_NAMESPACE = \ 'bclearer_boson_1_1_source.resources.adjustment_universes'

35.5

62

0.86385

24

213

7

0.416667

0.22619

0.333333

0.428571

0.690476

0

0.020408

0.079812

213

5

63

42.6

0.836735

0

0.511737

0

1

0

false

0

1

0

null

1

0

1

0

1

null

0

6

143707ee29e32d9132daaf92d58a12d1dc0e9560

24

py

Python

ext_resources/__init__.py

Darshan-20310597/RumiGANs

a70d18b7f6f03570f9dae6d3b88f746eb71136d9

[ "MIT" ]

26

2020-10-31T06:00:22.000Z

2022-02-13T19:30:49.000Z

ext_resources/__init__.py

Darshan-20310597/RumiGANs

a70d18b7f6f03570f9dae6d3b88f746eb71136d9

[ "MIT" ]

3

2021-03-01T05:43:03.000Z

2021-07-10T13:08:18.000Z

ext_resources/__init__.py

DarthSid95/RumiGANs

9f7876e89caa0d39bd563947ab9c41f4e3745021

[ "MIT" ]

5

2021-04-12T10:59:20.000Z

2021-06-04T08:52:51.000Z

from .prd_score import *

24

0.791667

4

24

4.5

1

0

0.125

24

1

24

0.857143

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

149ce8fb5fea4e4dd2be51a9c5c12ce1f45d7b76

72,680

py

Python

fastCloningPrimer.py

tommyfuu/BioFoundry

a641d69cca04a2622636d7b1affea1c138bf1a5d

[ "MIT" ]

2

2020-11-08T09:46:41.000Z

2020-11-08T09:48:29.000Z

fastCloningPrimer.py

tommyfuu/BioFoundry

a641d69cca04a2622636d7b1affea1c138bf1a5d

[ "MIT" ]

1

2021-04-18T23:46:16.000Z

fastCloningPrimer.py

tommyfuu/BioFoundry

a641d69cca04a2622636d7b1affea1c138bf1a5d

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Author : Tom Fu Date : 2020 Nov 7 FileName : fastCloningPrimer.py (for the BioFoundry Project at the HMC BioMakerspace) Description : Find primer pairs for fast cloning """ import primer3 from Bio import SeqIO import pandas as pd import sys import copy import math from Bio.Seq import Seq from selenium import webdriver from selenium.webdriver.common.keys import Keys import os import time ################# ### TESTCASES ### ################# # TODO: when I run: # ============================================================================== # fastCloningPrimers(royaTestPlasmidSeq, royaTestInsertPlasmidSeq, # royaTestVectorSeq, royaTestInsertSeq, maxTempDiff=MAX_TEMP_DIFF, # destinationAddress='fastCloningPrimerInfo.csv', # benchlingAddress='benchlingfastCloningPrimerInfo.csv', # benchling=True, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE) # ============================================================================== # benchlingfastCloningPrimerInfo.csv is empty. However, when I run the provided # test cases, the correct primers are outputted. # from # https://benchling.com/roygoli/f/lib_mvm3FehI-biofoundry/seq_s57xycXu-copy-of-biofoundry-copy-of-pdms123/edit royaTestPlasmidSeq = "GCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTTTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGGGTAATGACTCTCTAGCTTGAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCCTGAGTAGGACAAATCCGCCCTCTAGCAGCCCGGGCTGCggccgcTATTTCTCCTTTCGCGCAGTACGTGGTTCGCGGCTTAATCCTGCTGGCAGCGGTGATCTTCGACCGTTACAAGCAAAAAGCGAAACGCACTGTCTGATGCTTTTTTCTGCAACAATTTAGCGTTTTTTCCCACCATAGCCAACCGCCATAACGGTTGGCTGTTCTTCGTTGCAAATGGCGACCCCCGTCACACTGTCTATACTTACATGTCTGTAAAGCGCGTTCTGCGCAACACAATAAGAAAAGAGAAGGAGGAGAACCGGgtgACAGAACCGTTAACCGAAACCCCTGAACTATCCGCGAAATATGCCTGGTTTTTTGATCTTGATGGAACGCTGGCGGAAATCAAACCGCATCCCGATCAGGTCGTCGTGCCTGACAATATTCTGCAAGGACTACAGCTACTGGCAACCGCAAGTGATGGTGCATTGGCATTGATATCAGGGCGCTCAATGGTGGAGCTTGACGCACTGGCAAAACCTTATCGCTTCCCGTtCTAGATTTAAGAAGGAGATATACATATGAGTAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCAGTGGAGAGGGTGAAGGTGATGCTACATACGGAAAGCTTACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCATGGCCAACACTTGTCACTACTTTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCATATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGAACTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAAGGTATTGATTTTAAAGAAGATGGAAACATTCTCGGACACAAACTCGAGTACAACTATAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACATTGAAGATGGATCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGCCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAATAATGAATTCCAGCTGAGCGCCGGTCGCTACCATTACCAGTTGGTCTGGTGTCAAAAATAATAATAACCGGGCAGGCCATGTCTGCCCGTATTTCGCGTAAGGAAATCCATTATGTACTATTTAATTCTTGAAGACGAAAGGGCCTCGTGATACGCCTATTTTTATAGGTTAATGTCATGATAATAATGGTTTCTTAGACGTCAGGTGGCGATATCGGGCTAGCCGGCCCGACGCACTTTGCGCCGAATAAATACCTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACGTAAGAGGTTCCAACTTTCACCATAATGAAATAAGATCACTACCGGGCGTATTTTTTGAGTTATCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTCCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTCTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGTGCTACGCCTGAATAAGTGATAATAAGCGGATGAATGGCAGAAATGACGGATATCGTCCATTCCGACAGCATCGCCAGTCACTATGGCGTGCTGCTAGCGCTTTTAGCCGCTTTAGCGGCCTTTCCCCCTACCCGAAGGGTGGGGGCGCGTGTGCAGCCCCGCAGGGCCTGTCTCGGTCGATCATTCAGCCCGGCTCATCCTTCTGGCGTGGCGGCAGACCGAACAAGGCGCGGTCGTGGTCGCGTTCAAGGTACGCATCCATTGCCGCCATGAGCCGATCCTCCGGCCACTCGCTGCTGTTCACCTTGGCCAAAATCATGGCCCCCACCAGCACCTTGCGCCTTGTTTCGTTCTTGCGCTCTTGCTGCTGTTCCCTTGCCCGCACCCGCTGAATTTCGGCATTGATTCGCGCTCGTTGTTCTTCGAGCTTGGCCAGCCGATCCGCCGCCTTGTTGCTCCCCTTAACCATCTTGACACCCCATTGTTAATGTGCTGTCTCGTAGGCTATCATGGAGGCACAGCGGCGGCAATCCCGACCCTACTTTGTAGGGGAGGGCGCACTTACCGGTTTCTCTTCGAGAAACTGGCCTAACGGCCACCCTTCGGGCGGTGCGCTCTCCGAGGGCCATTGCATGGAGCCGAAAAGCAAAAGCAACAGCGAGGCAGCATGGCGATTTATCACCTTACGGCGAAAACCGGCAGCAGGTCGGGCGGCCAATCGGCCAGGGCCAAGGCCGACTACATCCAGCGCGAAGGCAAGTATGCCCGCGACATGGATGAAGTCTTGCACGCCGAATCCGGGCACATGCCGGAGTTCGTCGAGCGGCCCGCCGACTACTGGGATGCTGCCGACCTGTATGAACGCGCCAATGGGCGGCTGTTCAAGGAGGTCGAATTTGCCCTGCCGGTCGAGCTGACCCTCGACCAGCAGAAGGCGCTGGCGTCCGAGTTCGCCCAGCACCTGACCGGTGCCGAGCGCCTGCCGTATACGCTGGCCATCCATGCCGGTGGCGGCGAGAACCCGCACTGCCACCTGATGATCTCCGAGCGGATCAATGACGGCATCGAGCGGCCCGCCGCTCAGTGGTTCAAGCGGTACAACGGCAAGACCCCGGAGAAGGGCGGGGCACAGAAGACCGAAGCGCTCAAGCCCAAGGCATGGCTTGAGCAGACCCGCGAGGCATGGGCCGACCATGCCAACCGGGCATTAGAGCGGGCTGGCCACGACGCCCGCATTGACCACAGAACACTTGAGGCGCAGGGCATCGAGCGCCTGCCCGGTGTTCACCTGGGGCCGAACGTGGTGGAGATGGAAGGCCGGGGCATCCGCACCGACCGGGCAGACGTGGCCCTGAACATCGACACCGCCAACGCCCAGATCATCGACTTACAGGAATACCGGGAGGCAATAGACCATGAACGCAATCGACAGAGTGAAGAAATCCAGAGGCATCAACGAGTTAGCGGAGCAGATCGAACCGCTGGCCCAGAGCATGGCGACACTGGCCGACGAAGCCCGGCAGGTCATGAGCCAGACCCAGCAGGCCAGCGAGGCGCAGGCGGCGGAGTGGCTGAAAGCCCAGCGCCAGACAGGGGCGGCATGGGTGGAGCTGGCCAAAGAGTTGCGGGAGGTAGCCGCCGAGGTGAGCAGCGCCGCGCAGAGCGCCCGGAGCGCGTCGCGGGGGTGGCACTGGAAGCTATGGCTAACCGTGATGCTGGCTTCCATGATGCCTACGGTGGTGCTGCTGATCGCATCGTTGCTCTTGCTCGACCTGACGCCACTGACAACCGAGGACGGCTCGATCTGGCTGCGCTTGGTGGCCCGATGAAGAACGACAGGACTTTGCAGGCCATAGGCCGACAGCTCAAGGCCATGGGCTGTGAGCGCTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTCAGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTGCAGGCATCGTGGTGTCACGCTCGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCATAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGTGTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAACACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTCATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCGTGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAAAAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGTTATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAAAGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTCTTCAAGAATTCGAGCTCGGTACCGGATCCGTCGACCTGCAGCCAAGCTTAATTAGCTGAGCTTGGACTCCTGTTGATAGATCCAGTAATGACCTCAGAACTCCATCTGGATTTGTTCAGAACGCTCGGTTGCCGCCGGGCGTTTTTTATTGGTGAGAATCCAAGCTAGCTTGGCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTTCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGT" # from # https://benchling.com/roygoli/f/lib_mvm3FehI-biofoundry/seq_cttcEI6n-copy-of-biofoundry-copy-of-e-coli-iram-annotated/edit royaTestInsertPlasmidSeq = "GCTAAAGTTGGATACTTAAGAAATGCTTCATAATTCAGTAAGGCATTAGCATAATGGAAATAAAAGTGCAGAGACTATCTCTATGGATGATTAATACTGTCTTTTTATTGTCACCCATAAATAATCACCAGACTAATACTATCAACTTGATATTTGAAATGTGATCACTTGACTTTTGATACGTTATTTTATAACGGTTAACATATTTATAAAAACAACGGCCGTGCCACACGTCCGTTTCAATACTTAACGCACATGTATTTTGGTTTAGTCATCATCCGGTTATATGTATTTTAGCCAGGAACAGGTTAAATCATTCCTATATAACTCAAAAATTGAAACCTTATTCTCATGTCATGCTTATATTCATTATTATCGTTATATAAAAAGGCAACCATAATGTTTAGCAAATTGGCACAAAGTAGCATAAAGGCTATGTTTTAATTACAGGATGTTCAGTCATTTGAATGTATAACATTATAGCTAAACAAATCTAAAACGAAGTCAATAATTTATTGCTTTCACAAAATCTCATTTTGTTTAACATCCATTGAGATTCCTTGCTTTAAATTTTATTTTATATAAGCCATCATTTTAATTAATTTATTTTTTTGAGGGGGGGGTAATATACTCATATGCAAAATCAAGAAATAAACATCCTAATGAACCATATTAAATACCGTGGGATAAGACATAACAAatgAAGTGGATAGTAATTGACACGGTAATTCAACCTACATGTGGTATATCTTTTTCAGCCATATGGGGTAATATGAAAATGATCATCTGGTATCAATCTACTATATTTCTCCCTCCTGGCAGTATATTTACACCGGTTAAGTCTGGTATTATCCTTAAGGATAAAGAATATCCTATTACTATTTATCACATCGCACCATTCAACAAGGATTTATGGAGTTTACTCAAAAGCAGTCAAGAGTGTCCTCCAGGAGAAAGCAAAATAACAAATAAATGTTTACATAATAGTTGCATTATAAAAATATGCCCATATGGGCTCAAGtaa" # from # https://benchling.com/roygoli/f/lib_mvm3FehI-biofoundry/seq_s57xycXu-copy-of-biofoundry-copy-of-pdms123/edit royaTestVectorSeq = "CTAGATTTAAGAAGGAGATATACATATGAGTAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCAGTGGAGAGGGTGAAGGTGATGCTACATACGGAAAGCTTACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCATGGCCAACACTTGTCACTACTTTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCATATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGAACTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAAGGTATTGATTTTAAAGAAGATGGAAACATTCTCGGACACAAACTCGAGTACAACTATAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACATTGAAGATGGATCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGCCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAATAATGAATTCCAGCTGAGCGCCGGTCGCTACCATTACCAGTTGGTCTGGTGTCAAAAATAATAATAACCGGGCAGGCCATGTCTGCCCGTATTTCGCGTAAGGAAATCCATTATGTACTATTTAATTCTTGAAGACGAAAGGGCCTCGTGATACGCCTATTTTTATAGGTTAATGTCATGATAATAATGGTTTCTTAGACGTCAGGTGGCGATATCGGGCTAGCCGGCCCGACGCACTTTGCGCCGAATAAATACCTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACGTAAGAGGTTCCAACTTTCACCATAATGAAATAAGATCACTACCGGGCGTATTTTTTGAGTTATCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTCCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTCTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGTGCTACGCCTGAATAAGTGATAATAAGCGGATGAATGGCAGAAATGACGGATATCGTCCATTCCGACAGCATCGCCAGTCACTATGGCGTGCTGCTAGCGCTTTTAGCCGCTTTAGCGGCCTTTCCCCCTACCCGAAGGGTGGGGGCGCGTGTGCAGCCCCGCAGGGCCTGTCTCGGTCGATCATTCAGCCCGGCTCATCCTTCTGGCGTGGCGGCAGACCGAACAAGGCGCGGTCGTGGTCGCGTTCAAGGTACGCATCCATTGCCGCCATGAGCCGATCCTCCGGCCACTCGCTGCTGTTCACCTTGGCCAAAATCATGGCCCCCACCAGCACCTTGCGCCTTGTTTCGTTCTTGCGCTCTTGCTGCTGTTCCCTTGCCCGCACCCGCTGAATTTCGGCATTGATTCGCGCTCGTTGTTCTTCGAGCTTGGCCAGCCGATCCGCCGCCTTGTTGCTCCCCTTAACCATCTTGACACCCCATTGTTAATGTGCTGTCTCGTAGGCTATCATGGAGGCACAGCGGCGGCAATCCCGACCCTACTTTGTAGGGGAGGGCGCACTTACCGGTTTCTCTTCGAGAAACTGGCCTAACGGCCACCCTTCGGGCGGTGCGCTCTCCGAGGGCCATTGCATGGAGCCGAAAAGCAAAAGCAACAGCGAGGCAGCATGGCGATTTATCACCTTACGGCGAAAACCGGCAGCAGGTCGGGCGGCCAATCGGCCAGGGCCAAGGCCGACTACATCCAGCGCGAAGGCAAGTATGCCCGCGACATGGATGAAGTCTTGCACGCCGAATCCGGGCACATGCCGGAGTTCGTCGAGCGGCCCGCCGACTACTGGGATGCTGCCGACCTGTATGAACGCGCCAATGGGCGGCTGTTCAAGGAGGTCGAATTTGCCCTGCCGGTCGAGCTGACCCTCGACCAGCAGAAGGCGCTGGCGTCCGAGTTCGCCCAGCACCTGACCGGTGCCGAGCGCCTGCCGTATACGCTGGCCATCCATGCCGGTGGCGGCGAGAACCCGCACTGCCACCTGATGATCTCCGAGCGGATCAATGACGGCATCGAGCGGCCCGCCGCTCAGTGGTTCAAGCGGTACAACGGCAAGACCCCGGAGAAGGGCGGGGCACAGAAGACCGAAGCGCTCAAGCCCAAGGCATGGCTTGAGCAGACCCGCGAGGCATGGGCCGACCATGCCAACCGGGCATTAGAGCGGGCTGGCCACGACGCCCGCATTGACCACAGAACACTTGAGGCGCAGGGCATCGAGCGCCTGCCCGGTGTTCACCTGGGGCCGAACGTGGTGGAGATGGAAGGCCGGGGCATCCGCACCGACCGGGCAGACGTGGCCCTGAACATCGACACCGCCAACGCCCAGATCATCGACTTACAGGAATACCGGGAGGCAATAGACCATGAACGCAATCGACAGAGTGAAGAAATCCAGAGGCATCAACGAGTTAGCGGAGCAGATCGAACCGCTGGCCCAGAGCATGGCGACACTGGCCGACGAAGCCCGGCAGGTCATGAGCCAGACCCAGCAGGCCAGCGAGGCGCAGGCGGCGGAGTGGCTGAAAGCCCAGCGCCAGACAGGGGCGGCATGGGTGGAGCTGGCCAAAGAGTTGCGGGAGGTAGCCGCCGAGGTGAGCAGCGCCGCGCAGAGCGCCCGGAGCGCGTCGCGGGGGTGGCACTGGAAGCTATGGCTAACCGTGATGCTGGCTTCCATGATGCCTACGGTGGTGCTGCTGATCGCATCGTTGCTCTTGCTCGACCTGACGCCACTGACAACCGAGGACGGCTCGATCTGGCTGCGCTTGGTGGCCCGATGAAGAACGACAGGACTTTGCAGGCCATAGGCCGACAGCTCAAGGCCATGGGCTGTGAGCGCTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTCAGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTGCAGGCATCGTGGTGTCACGCTCGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCATAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGTGTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAACACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTCATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCGTGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAAAAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGTTATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAAAGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTCTTCAAGAATTCGAGCTCGGTACCGGATCCGTCGACCTGCAGCCAAGCTTAATTAGCTGAGCTTGGACTCCTGTTGATAGATCCAGTAATGACCTCAGAACTCCATCTGGATTTGTTCAGAACGCTCGGTTGCCGCCGGGCGTTTTTTATTGGTGAGAATCCAAGCTAGCTTGGCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTTCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTTTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGGGTAATGACTCTCTAGCTTGAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCCTGAGTAGGACAAATCCGCCCTCTAGCAGCCCGGGCTGC" # from # https://benchling.com/roygoli/f/lib_mvm3FehI-biofoundry/seq_cttcEI6n-copy-of-biofoundry-copy-of-e-coli-iram-annotated/edit royaTestInsertSeq = "GCTAAAGTTGGATACTTAAGAAATGCTTCATAATTCAGTAAGGCATTAGCATAATGGAAATAAAAGTGCAGAGACTATCTCTATGGATGATTAATACTGTCTTTTTATTGTCACCCATAAATAATCACCAGACTAATACTATCAACTTGATATTTGAAATGTGATCACTTGACTTTTGATACGTTATTTTATAACGGTTAACATATTTATAAAAACAACGGCCGTGCCACACGTCCGTTTCAATACTTAACGCACATGTATTTTGGTTTAGTCATCATCCGGTTATATGTATTTTAGCCAGGAACAGGTTAAATCATTCCTATATAACTCAAAAATTGAAACCTTATTCTCATGTCATGCTTATATTCATTATTATCGTTATATAAAAAGGCAACCATAATGTTTAGCAAATTGGCACAAAGTAGCATAAAGGCTATGTTTTAATTACAGGATGTTCAGTCATTTGAATGTATAACATTATAGCTAAACAAATCTAAAACGAAGTCAATAATTTATTGCTTTCACAAAATCTCATTTTGTTTAACATCCATTGAGATTCCTTGCTTTAAATTTTATTTTATATAAGCCATCATTTTAATTAATTTATTTTTTTGAGGGGGGGGTAATATACTCATATGCAAAATCAAGAAATAAACATCCTAATGAACCATATTAAATACCGTGGGATAAGACATAACAA" royaTestInsertSeq1 = 'TCACTTGACTTTTGATACGTTATTTTATAACGGTTAACATATTTATAAAAACAACGGCCGTGCCACACGTCCGTTTCAATACTTAACGCACATGTATTTTGGTTTAGTCATCATCCGGTTATATGTATTTTAGCCAGGAACAGGTTAAATCATTCCTATATAACTCAAAAATTGAAACCTTATTCTCATGTCATGCTTATATTCATTATTATCGTTATATAAAAAGGCAACCATAATGTTTAGCAAATTGGCACAAAGTAGCATAAAGGCTATGTTTTAATTACAGGATGTTCAGTCATTTGAATGTATAACATTATAGCTAAACAAATCTAAAACGAAGTCAATAATTTATTGCTTTCACAAAATCTCATTTTGTTTAACATCCATTGAGATTCCTTGCTTTAAATTTTATTTTATATAAGCCATCATTTTAATTAATTTATTTTTTTGAGGGGGGGGTAATATACTCATATGCAAAATCAAGAAATAAACATCCTAATGAACCATATTAAATACCGTGGGATAAGACATAACAA' richardTestPrimerForward = "CCCGTTCTAGATTTAAGAAGGAGA" richardTestPrimerReverse = "GTCATTACCCCAGGCGTTTA" primer3pySeq = 'GCTTGCATGCCTGCAGGTCGACTCTAGAGGATCCCCCTACATTTTAGCATCAGTGAGTACAGCATGCTTACTGGAAGAGAGGGTCATGCAACAGATTAGGAGGTAAGTTTGCAAAGGCAGGCTAAGGAGGAGACGCACTGAATGCCATGGTAAGAACTCTGGACATAAAAATATTGGAAGTTGTTGAGCAAGTNAAAAAAATGTTTGGAAGTGTTACTTTAGCAATGGCAAGAATGATAGTATGGAATAGATTGGCAGAATGAAGGCAAAATGATTAGACATATTGCATTAAGGTAAAAAATGATAACTGAAGAATTATGTGCCACACTTATTAATAAGAAAGAATATGTGAACCTTGCAGATGTTTCCCTCTAGTAG' vectorPlasmid1AddressGB = 'biofoundry-copy-of-pdms123.gb' vectorPlasmid1AddressFA = 'biofoundry-copy-of-pdms123.fasta' insertPlasmid1AddressGB = 'biofoundry-copy-of-e-coli-iram-annotated.gb' insertPlasmid1AddressFA = 'biofoundry-copy-of-e-coli-iram-annotated.fasta' vectorPlasmidSeq1 = 'TTCGAGCTCGGTACCGGATCCGTCGACCTGCAGCCAAGCTTAATTAGCTGAGCTTGGACTCCTGTTGATAGATCCAGTAATGACCTCAGAACTCCATCTGGATTTGTTCAGAACGCTCGGTTGCCGCCGGGCGTTTTTTATTGGTGAGAATCCAAGCTAGCTTGGCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTTCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTTTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGGGTAATGACTCTCTAGCTTGAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCCTGAGTAGGACAAATCCGCCCTCTAGCAGCCCGGGCTGCggccgcTATTTCTCCTTTCGCGCAGTACGTGGTTCGCGGCTTAATCCTGCTGGCAGCGGTGATCTTCGACCGTTACAAGCAAAAAGCGAAACGCACTGTCTGATGCTTTTTTCTGCAACAATTTAGCGTTTTTTCCCACCATAGCCAACCGCCATAACGGTTGGCTGTTCTTCGTTGCAAATGGCGACCCCCGTCACACTGTCTATACTTACATGTCTGTAAAGCGCGTTCTGCGCAACACAATAAGAAAAGAGAAGGAGGAGAACCGGgtgACAGAACCGTTAACCGAAACCCCTGAACTATCCGCGAAATATGCCTGGTTTTTTGATCTTGATGGAACGCTGGCGGAAATCAAACCGCATCCCGATCAGGTCGTCGTGCCTGACAATATTCTGCAAGGACTACAGCTACTGGCAACCGCAAGTGATGGTGCATTGGCATTGATATCAGGGCGCTCAATGGTGGAGCTTGACGCACTGGCAAAACCTTATCGCTTCCCGTtCTAGATTTAAGAAGGAGATATACATATGAGTAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCAGTGGAGAGGGTGAAGGTGATGCTACATACGGAAAGCTTACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCATGGCCAACACTTGTCACTACTTTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCATATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGAACTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAAGGTATTGATTTTAAAGAAGATGGAAACATTCTCGGACACAAACTCGAGTACAACTATAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACATTGAAGATGGATCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGCCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAATAATGAATTCCAGCTGAGCGCCGGTCGCTACCATTACCAGTTGGTCTGGTGTCAAAAATAATAATAACCGGGCAGGCCATGTCTGCCCGTATTTCGCGTAAGGAAATCCATTATGTACTATTTAATTCTTGAAGACGAAAGGGCCTCGTGATACGCCTATTTTTATAGGTTAATGTCATGATAATAATGGTTTCTTAGACGTCAGGTGGCGATATCGGGCTAGCCGGCCCGACGCACTTTGCGCCGAATAAATACCTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACGTAAGAGGTTCCAACTTTCACCATAATGAAATAAGATCACTACCGGGCGTATTTTTTGAGTTATCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTCCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTCTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGTGCTACGCCTGAATAAGTGATAATAAGCGGATGAATGGCAGAAATGACGGATATCGTCCATTCCGACAGCATCGCCAGTCACTATGGCGTGCTGCTAGCGCTTTTAGCCGCTTTAGCGGCCTTTCCCCCTACCCGAAGGGTGGGGGCGCGTGTGCAGCCCCGCAGGGCCTGTCTCGGTCGATCATTCAGCCCGGCTCATCCTTCTGGCGTGGCGGCAGACCGAACAAGGCGCGGTCGTGGTCGCGTTCAAGGTACGCATCCATTGCCGCCATGAGCCGATCCTCCGGCCACTCGCTGCTGTTCACCTTGGCCAAAATCATGGCCCCCACCAGCACCTTGCGCCTTGTTTCGTTCTTGCGCTCTTGCTGCTGTTCCCTTGCCCGCACCCGCTGAATTTCGGCATTGATTCGCGCTCGTTGTTCTTCGAGCTTGGCCAGCCGATCCGCCGCCTTGTTGCTCCCCTTAACCATCTTGACACCCCATTGTTAATGTGCTGTCTCGTAGGCTATCATGGAGGCACAGCGGCGGCAATCCCGACCCTACTTTGTAGGGGAGGGCGCACTTACCGGTTTCTCTTCGAGAAACTGGCCTAACGGCCACCCTTCGGGCGGTGCGCTCTCCGAGGGCCATTGCATGGAGCCGAAAAGCAAAAGCAACAGCGAGGCAGCATGGCGATTTATCACCTTACGGCGAAAACCGGCAGCAGGTCGGGCGGCCAATCGGCCAGGGCCAAGGCCGACTACATCCAGCGCGAAGGCAAGTATGCCCGCGACATGGATGAAGTCTTGCACGCCGAATCCGGGCACATGCCGGAGTTCGTCGAGCGGCCCGCCGACTACTGGGATGCTGCCGACCTGTATGAACGCGCCAATGGGCGGCTGTTCAAGGAGGTCGAATTTGCCCTGCCGGTCGAGCTGACCCTCGACCAGCAGAAGGCGCTGGCGTCCGAGTTCGCCCAGCACCTGACCGGTGCCGAGCGCCTGCCGTATACGCTGGCCATCCATGCCGGTGGCGGCGAGAACCCGCACTGCCACCTGATGATCTCCGAGCGGATCAATGACGGCATCGAGCGGCCCGCCGCTCAGTGGTTCAAGCGGTACAACGGCAAGACCCCGGAGAAGGGCGGGGCACAGAAGACCGAAGCGCTCAAGCCCAAGGCATGGCTTGAGCAGACCCGCGAGGCATGGGCCGACCATGCCAACCGGGCATTAGAGCGGGCTGGCCACGACGCCCGCATTGACCACAGAACACTTGAGGCGCAGGGCATCGAGCGCCTGCCCGGTGTTCACCTGGGGCCGAACGTGGTGGAGATGGAAGGCCGGGGCATCCGCACCGACCGGGCAGACGTGGCCCTGAACATCGACACCGCCAACGCCCAGATCATCGACTTACAGGAATACCGGGAGGCAATAGACCATGAACGCAATCGACAGAGTGAAGAAATCCAGAGGCATCAACGAGTTAGCGGAGCAGATCGAACCGCTGGCCCAGAGCATGGCGACACTGGCCGACGAAGCCCGGCAGGTCATGAGCCAGACCCAGCAGGCCAGCGAGGCGCAGGCGGCGGAGTGGCTGAAAGCCCAGCGCCAGACAGGGGCGGCATGGGTGGAGCTGGCCAAAGAGTTGCGGGAGGTAGCCGCCGAGGTGAGCAGCGCCGCGCAGAGCGCCCGGAGCGCGTCGCGGGGGTGGCACTGGAAGCTATGGCTAACCGTGATGCTGGCTTCCATGATGCCTACGGTGGTGCTGCTGATCGCATCGTTGCTCTTGCTCGACCTGACGCCACTGACAACCGAGGACGGCTCGATCTGGCTGCGCTTGGTGGCCCGATGAAGAACGACAGGACTTTGCAGGCCATAGGCCGACAGCTCAAGGCCATGGGCTGTGAGCGCTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTCAGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTGCAGGCATCGTGGTGTCACGCTCGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCATAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGTGTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAACACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTCATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCGTGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAAAAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGTTATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAAAGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTCTTCAAGAA' insertPlasmidSeq1 = 'GCTAAAGTTGGATACTTAAGAAATGCTTCATAATTCAGTAAGGCATTAGCATAATGGAAATAAAAGTGCAGAGACTATCTCTATGGATGATTAATACTGTCTTTTTATTGTCACCCATAAATAATCACCAGACTAATACTATCAACTTGATATTTGAAATGTGATCACTTGACTTTTGATACGTTATTTTATAACGGTTAACATATTTATAAAAACAACGGCCGTGCCACACGTCCGTTTCAATACTTAACGCACATGTATTTTGGTTTAGTCATCATCCGGTTATATGTATTTTAGCCAGGAACAGGTTAAATCATTCCTATATAACTCAAAAATTGAAACCTTATTCTCATGTCATGCTTATATTCATTATTATCGTTATATAAAAAGGCAACCATAATGTTTAGCAAATTGGCACAAAGTAGCATAAAGGCTATGTTTTAATTACAGGATGTTCAGTCATTTGAATGTATAACATTATAGCTAAACAAATCTAAAACGAAGTCAATAATTTATTGCTTTCACAAAATCTCATTTTGTTTAACATCCATTGAGATTCCTTGCTTTAAATTTTATTTTATATAAGCCATCATTTTAATTAATTTATTTTTTTGAGGGGGGGGTAATATACTCATATGCAAAATCAAGAAATAAACATCCTAATGAACCATATTAAATACCGTGGGATAAGACATAACAAatgAAGTGGATAGTAATTGACACGGTAATTCAACCTACATGTGGTATATCTTTTTCAGCCATATGGGGTAATATGAAAATGATCATCTGGTATCAATCTACTATATTTCTCCCTCCTGGCAGTATATTTACACCGGTTAAGTCTGGTATTATCCTTAAGGATAAAGAATATCCTATTACTATTTATCACATCGCACCATTCAACAAGGATTTATGGAGTTTACTCAAAAGCAGTCAAGAGTGTCCTCCAGGAGAAAGCAAAATAACAAATAAATGTTTACATAATAGTTGCATTATAAAAATATGCCCATATGGGCTCAAGtaa' vectorSeq1 = 'CTAGATTTAAGAAGGAGATATACATATGAGTAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCAGTGGAGAGGGTGAAGGTGATGCTACATACGGAAAGCTTACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCATGGCCAACACTTGTCACTACTTTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCATATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGAACTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAAGGTATTGATTTTAAAGAAGATGGAAACATTCTCGGACACAAACTCGAGTACAACTATAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACATTGAAGATGGATCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGCCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAATAATGAATTCCAGCTGAGCGCCGGTCGCTACCATTACCAGTTGGTCTGGTGTCAAAAATAATAATAACCGGGCAGGCCATGTCTGCCCGTATTTCGCGTAAGGAAATCCATTATGTACTATTTAATTCTTGAAGACGAAAGGGCCTCGTGATACGCCTATTTTTATAGGTTAATGTCATGATAATAATGGTTTCTTAGACGTCAGGTGGCGATATCGGGCTAGCCGGCCCGACGCACTTTGCGCCGAATAAATACCTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACGTAAGAGGTTCCAACTTTCACCATAATGAAATAAGATCACTACCGGGCGTATTTTTTGAGTTATCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTCCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTCTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGTGCTACGCCTGAATAAGTGATAATAAGCGGATGAATGGCAGAAATGACGGATATCGTCCATTCCGACAGCATCGCCAGTCACTATGGCGTGCTGCTAGCGCTTTTAGCCGCTTTAGCGGCCTTTCCCCCTACCCGAAGGGTGGGGGCGCGTGTGCAGCCCCGCAGGGCCTGTCTCGGTCGATCATTCAGCCCGGCTCATCCTTCTGGCGTGGCGGCAGACCGAACAAGGCGCGGTCGTGGTCGCGTTCAAGGTACGCATCCATTGCCGCCATGAGCCGATCCTCCGGCCACTCGCTGCTGTTCACCTTGGCCAAAATCATGGCCCCCACCAGCACCTTGCGCCTTGTTTCGTTCTTGCGCTCTTGCTGCTGTTCCCTTGCCCGCACCCGCTGAATTTCGGCATTGATTCGCGCTCGTTGTTCTTCGAGCTTGGCCAGCCGATCCGCCGCCTTGTTGCTCCCCTTAACCATCTTGACACCCCATTGTTAATGTGCTGTCTCGTAGGCTATCATGGAGGCACAGCGGCGGCAATCCCGACCCTACTTTGTAGGGGAGGGCGCACTTACCGGTTTCTCTTCGAGAAACTGGCCTAACGGCCACCCTTCGGGCGGTGCGCTCTCCGAGGGCCATTGCATGGAGCCGAAAAGCAAAAGCAACAGCGAGGCAGCATGGCGATTTATCACCTTACGGCGAAAACCGGCAGCAGGTCGGGCGGCCAATCGGCCAGGGCCAAGGCCGACTACATCCAGCGCGAAGGCAAGTATGCCCGCGACATGGATGAAGTCTTGCACGCCGAATCCGGGCACATGCCGGAGTTCGTCGAGCGGCCCGCCGACTACTGGGATGCTGCCGACCTGTATGAACGCGCCAATGGGCGGCTGTTCAAGGAGGTCGAATTTGCCCTGCCGGTCGAGCTGACCCTCGACCAGCAGAAGGCGCTGGCGTCCGAGTTCGCCCAGCACCTGACCGGTGCCGAGCGCCTGCCGTATACGCTGGCCATCCATGCCGGTGGCGGCGAGAACCCGCACTGCCACCTGATGATCTCCGAGCGGATCAATGACGGCATCGAGCGGCCCGCCGCTCAGTGGTTCAAGCGGTACAACGGCAAGACCCCGGAGAAGGGCGGGGCACAGAAGACCGAAGCGCTCAAGCCCAAGGCATGGCTTGAGCAGACCCGCGAGGCATGGGCCGACCATGCCAACCGGGCATTAGAGCGGGCTGGCCACGACGCCCGCATTGACCACAGAACACTTGAGGCGCAGGGCATCGAGCGCCTGCCCGGTGTTCACCTGGGGCCGAACGTGGTGGAGATGGAAGGCCGGGGCATCCGCACCGACCGGGCAGACGTGGCCCTGAACATCGACACCGCCAACGCCCAGATCATCGACTTACAGGAATACCGGGAGGCAATAGACCATGAACGCAATCGACAGAGTGAAGAAATCCAGAGGCATCAACGAGTTAGCGGAGCAGATCGAACCGCTGGCCCAGAGCATGGCGACACTGGCCGACGAAGCCCGGCAGGTCATGAGCCAGACCCAGCAGGCCAGCGAGGCGCAGGCGGCGGAGTGGCTGAAAGCCCAGCGCCAGACAGGGGCGGCATGGGTGGAGCTGGCCAAAGAGTTGCGGGAGGTAGCCGCCGAGGTGAGCAGCGCCGCGCAGAGCGCCCGGAGCGCGTCGCGGGGGTGGCACTGGAAGCTATGGCTAACCGTGATGCTGGCTTCCATGATGCCTACGGTGGTGCTGCTGATCGCATCGTTGCTCTTGCTCGACCTGACGCCACTGACAACCGAGGACGGCTCGATCTGGCTGCGCTTGGTGGCCCGATGAAGAACGACAGGACTTTGCAGGCCATAGGCCGACAGCTCAAGGCCATGGGCTGTGAGCGCTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTCAGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTGCAGGCATCGTGGTGTCACGCTCGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCATAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGTGTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAACACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTCATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCGTGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAAAAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGTTATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAAAGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTCTTCAAGAATTCGAGCTCGGTACCGGATCCGTCGACCTGCAGCCAAGCTTAATTAGCTGAGCTTGGACTCCTGTTGATAGATCCAGTAATGACCTCAGAACTCCATCTGGATTTGTTCAGAACGCTCGGTTGCCGCCGGGCGTTTTTTATTGGTGAGAATCCAAGCTAGCTTGGCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTTCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTTTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGGGTAATGACTCTCTAGCTTGAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCCTGAGTAGGACAAATCCGCCCTCTAGCAGCCCGGGCTGC' insertSeq1 = 'TCACTTGACTTTTGATACGTTATTTTATAACGGTTAACATATTTATAAAAACAACGGCCGTGCCACACGTCCGTTTCAATACTTAACGCACATGTATTTTGGTTTAGTCATCATCCGGTTATATGTATTTTAGCCAGGAACAGGTTAAATCATTCCTATATAACTCAAAAATTGAAACCTTATTCTCATGTCATGCTTATATTCATTATTATCGTTATATAAAAAGGCAACCATAATGTTTAGCAAATTGGCACAAAGTAGCATAAAGGCTATGTTTTAATTACAGGATGTTCAGTCATTTGAATGTATAACATTATAGCTAAACAAATCTAAAACGAAGTCAATAATTTATTGCTTTCACAAAATCTCATTTTGTTTAACATCCATTGAGATTCCTTGCTTTAAATTTTATTTTATATAAGCCATCATTTTAATTAATTTATTTTTTTGAGGGGGGGGTAATATACTCATATGCAAAATCAAGAAATAAACATCCTAATGAACCATATTAAATACCGTGGGATAAGACATAACAA' vectorSeq1X = 'CTAGATTTAAGAAGGAGATATACATATGAGTAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCAGTG' insertSeq1X = 'TCACTTGACTTTTGATACGTTATTTTATAACGGTTAACATATTTATAAAAACAACGGCCGTGCCACACGTCCGTTTCAATACTTAACGCACATGTATTTTGGTTTAGTCATCATCCGGTTATATGTATTTTAGCCAGGAACAGGTTAAATCATTCCTATATAACTCAAAAATTGAAACCTTATTCTCATGTCATGCTTATATTCATTATTATCGTTATATAAAAAGGCAACCATAATGTTTAGCAAATTGGCACAAAGTAGCATAAAGG' testOutput1 = 'ggccgcTATTTCTCCTTTCGCGCAGTACGTGGTTCGCGGCTTAATCCTGCTGGCAGCGGTGATCTTCGACCGTTACAAGCAAAAAGCGAAACGCACTGTCTGATGCTTTTTTCTGCAACAATTTAGCGTTTTTTCCCACCATAGCCAACCGCCATAACGGTTGGCTGTTCTTCGTTGCAAATGGCGACCCCCGTCACACTGTCTATACTTACATGTCTGTAAAGCGCGTTCTGCGCAACACAATAAGAAAACTAGATTTAAGAAGGAGATATACATATGAGTAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCAGTGGAGAGGGTGAAGGTGATGCTACATACGGAAAGCTTACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCATGGCCAACACTTGTCACTACTTTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCATATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGAACTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAAGGTATTGATTTTAAAGAAGATGGAAACATTCTCGGACACAAACTCGAGTACAACTATAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACATTGAAGATGGATCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGCCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAATAATGAATTCCAGCTGAGCGCCGGTCGCTACCATTACCAGTTGGTCTGGTGTCAAAAATAATAATAACCGGGCAGGCCATGTCTGCCCGTATTTCGCGTAAGGAAATCCATTATGTACTATTTAATTCTTGAAGACGAAAGGGCCTCGTGATACGCCTATTTTTATAGGTTAATGTCATGATAATAATGGTTTCTTAGACGTCAGGTGGCGATATCGGGCTAGCCGGCCCGACGCACTTTGCGCCGAATAAATACCTGTGACGGAAGATCACTTCGCAGAATAAATAAATCCTGGTGTCCCTGTTGATACCGGGAAGCCCTGGGCCAACTTTTGGCGAAAATGAGACGTTGATCGGCACGTAAGAGGTTCCAACTTTCACCATAATGAAATAAGATCACTACCGGGCGTATTTTTTGAGTTATCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTCCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTCTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGTGCTACGCCTGAATAAGTGATAATAAGCGGATGAATGGCAGAAATGACGGATATCGTCCATTCCGACAGCATCGCCAGTCACTATGGCGTGCTGCTAGCGCTTTTAGCCGCTTTAGCGGCCTTTCCCCCTACCCGAAGGGTGGGGGCGCGTGTGCAGCCCCGCAGGGCCTGTCTCGGTCGATCATTCAGCCCGGCTCATCCTTCTGGCGTGGCGGCAGACCGAACAAGGCGCGGTCGTGGTCGCGTTCAAGGTACGCATCCATTGCCGCCATGAGCCGATCCTCCGGCCACTCGCTGCTGTTCACCTTGGCCAAAATCATGGCCCCCACCAGCACCTTGCGCCTTGTTTCGTTCTTGCGCTCTTGCTGCTGTTCCCTTGCCCGCACCCGCTGAATTTCGGCATTGATTCGCGCTCGTTGTTCTTCGAGCTTGGCCAGCCGATCCGCCGCCTTGTTGCTCCCCTTAACCATCTTGACACCCCATTGTTAATGTGCTGTCTCGTAGGCTATCATGGAGGCACAGCGGCGGCAATCCCGACCCTACTTTGTAGGGGAGGGCGCACTTACCGGTTTCTCTTCGAGAAACTGGCCTAACGGCCACCCTTCGGGCGGTGCGCTCTCCGAGGGCCATTGCATGGAGCCGAAAAGCAAAAGCAACAGCGAGGCAGCATGGCGATTTATCACCTTACGGCGAAAACCGGCAGCAGGTCGGGCGGCCAATCGGCCAGGGCCAAGGCCGACTACATCCAGCGCGAAGGCAAGTATGCCCGCGACATGGATGAAGTCTTGCACGCCGAATCCGGGCACATGCCGGAGTTCGTCGAGCGGCCCGCCGACTACTGGGATGCTGCCGACCTGTATGAACGCGCCAATGGGCGGCTGTTCAAGGAGGTCGAATTTGCCCTGCCGGTCGAGCTGACCCTCGACCAGCAGAAGGCGCTGGCGTCCGAGTTCGCCCAGCACCTGACCGGTGCCGAGCGCCTGCCGTATACGCTGGCCATCCATGCCGGTGGCGGCGAGAACCCGCACTGCCACCTGATGATCTCCGAGCGGATCAATGACGGCATCGAGCGGCCCGCCGCTCAGTGGTTCAAGCGGTACAACGGCAAGACCCCGGAGAAGGGCGGGGCACAGAAGACCGAAGCGCTCAAGCCCAAGGCATGGCTTGAGCAGACCCGCGAGGCATGGGCCGACCATGCCAACCGGGCATTAGAGCGGGCTGGCCACGACGCCCGCATTGACCACAGAACACTTGAGGCGCAGGGCATCGAGCGCCTGCCCGGTGTTCACCTGGGGCCGAACGTGGTGGAGATGGAAGGCCGGGGCATCCGCACCGACCGGGCAGACGTGGCCCTGAACATCGACACCGCCAACGCCCAGATCATCGACTTACAGGAATACCGGGAGGCAATAGACCATGAACGCAATCGACAGAGTGAAGAAATCCAGAGGCATCAACGAGTTAGCGGAGCAGATCGAACCGCTGGCCCAGAGCATGGCGACACTGGCCGACGAAGCCCGGCAGGTCATGAGCCAGACCCAGCAGGCCAGCGAGGCGCAGGCGGCGGAGTGGCTGAAAGCCCAGCGCCAGACAGGGGCGGCATGGGTGGAGCTGGCCAAAGAGTTGCGGGAGGTAGCCGCCGAGGTGAGCAGCGCCGCGCAGAGCGCCCGGAGCGCGTCGCGGGGGTGGCACTGGAAGCTATGGCTAACCGTGATGCTGGCTTCCATGATGCCTACGGTGGTGCTGCTGATCGCATCGTTGCTCTTGCTCGACCTGACGCCACTGACAACCGAGGACGGCTCGATCTGGCTGCGCTTGGTGGCCCGATGAAGAACGACAGGACTTTGCAGGCCATAGGCCGACAGCTCAAGGCCATGGGCTGTGAGCGCTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTCAGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTGCAGGCATCGTGGTGTCACGCTCGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCATAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGTGTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAACACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTCATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCGTGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAAAAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGTTATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAAAGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTCTTCAAGAATTCGAGCTCGGTACCGGATCCGTCGACCTGCAGCCAAGCTTAATTAGCTGAGCTTGGACTCCTGTTGATAGATCCAGTAATGACCTCAGAACTCCATCTGGATTTGTTCAGAACGCTCGGTTGCCGCCGGGCGTTTTTTATTGGTGAGAATCCAAGCTAGCTTGGCGAGATTTTCAGGAGCTAAGGAAGCTAAAATGGAGAAAAAAATCACTGGATATACCACCGTTGATATATCCCAATGGCATCGTAAAGAACATTTTGAGGCATTTCAGTCAGTTGCTCAATGTACCTATAACCAGACCGTTCAGCTGGATATTACGGCCTTTTTAAAGACCGTAAAGAAAAATAAGCACAAGTTTTATCCGGCCTTTATTCACATTCTTGCCCGCCTGATGAATGCTCATCCGGAATTTCGTATGGCAATGAAAGACGGTGAGCTGGTGATATGGGATAGTGTTCACCCTTGTTACACCGTTTTCCATGAGCAAACTGAAACGTTTTCATCGCTCTGGAGTGAATACCACGACGATTTCCGGCAGTTTCTACACATATATTCGCAAGATGTGGCGTGTTACGGTGAAAACCTGGCCTATTTCCCTAAAGGGTTTATTGAGAATATGTTTTTCGTCTCAGCCAATCCCTGGGTGAGTTTCACCAGTTTTGATTTAAACGTGGCCAATATGGACAACTTCTTCGCCCCCGTTTTCACCATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCAGGTTCATCATGCCGTTTGTGATGGCTTCCATGTCGGCAGAATGCTTAATGAATTACAACAGTACTGCGATGAGTGGCAGGGCGGGGCGTAATTTTTTTAAGGCAGTTATTGGTGCCCTTAAACGCCTGGGGTAATGACTCTCTAGCTTGAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCCTGAGTAGGACAAATCCGCCCTCTAGCAGCCCGGGCTGCGAGAAGGAGGAGAACCGGgtgACAGAACCGTTAACCGAAACCCCTGAACTATCCGCGAAATATGCCTGGTTTTTTGATCTTGATGGAACGCTGGCGGAAATCAAACCGCATCCCGATCAGGTCGTCGTGCCTGACAATATTCTGCAAGGACTACAGCTACTGGCAACCGCAAGTGATGGTGCATTGGCATTGATATCAGGGCGCTCAATGGTGGAGCTTGACGCACTGGCAAAACCTTATCGCTTCCCGTt' ################### ### PARAMETERS ### ################### # (copied from primer3 with subtle changes according to our first primer designed) SEQUENCE_ID = 'MH1000' PRIMER_OPT_TM = 59.0 PRIMER_MIN_TM = 50.0 PRIMER_MAX_TM = 70.0 PRIMER_PRODUCT_SIZE_RANGE = [[100, 300], [150, 250], [301, 400], [ 401, 500], [501, 600], [601, 700], [701, 850], [851, 1000]] MAX_TEMP_DIFF = 7.0 PRIMER_MIN_SIZE = 18 # dictionary of delta H and delta S values for pairs of sequences, enthalpyEntropyValuesSequencePairs = { 'AA':(-7.9, -22.2), 'AA':(-7.9,-22.2), 'AT':( -7.2, -20.4), 'TA':(-7.2, -21.3), 'CA':(-8.5, -22.7), 'GT':(-8.4, -22.4), 'CT':(-7.8, -21.0), 'GA':(-8.2, -22.2), 'CG':(-10.6, -27.2), 'GC':(-9.8, -24.4), 'GG':(-8.0, -19.9), 'TT':(-7.9, -22.2), 'CC':(-8.0, -19.9), 'CA':(-8.5, -22.7), 'TG':(-8.5, -22.7), 'AC':(-8.4, -22.4), 'AG':(-7.8, -21.0), 'TC':(-8.2, -22.2), } ################### ### WEBSCRAPING ### ################### # CHANGE def primerDictToNEBPrimerSeq(primerDict): """turn a primer dict from primer3 in fastCloningPrimer to the NEB readdable format""" NEBPrimerString = "" for primerPairName, primerPairInfo in primerDict.items(): currentLPrimerName = str(primerPairName) + "Left" currentLPrimerSeq = primerPairInfo[0][2] currentRPrimerName = str(primerPairName) + "Right" currentRPrimerSeq = primerPairInfo[1][2] NEBPrimerString += currentLPrimerName + "; " + currentLPrimerSeq + \ "; " + currentRPrimerName + "; " + currentRPrimerSeq + "\n" return NEBPrimerString def NEBWebscraper(primersSeq, phusionprimerOptTm): """Use NEB to check the melting temperature and annealing temperature of all primers""" # open the tm calculator headlessly options = webdriver.chrome.options.Options() # options.headless = True cwd = os.getcwd() + '/chromedriver' driver = webdriver.Chrome(options=options, executable_path=cwd) driver.get("https://tmcalculator.neb.com/#!/batch") time.sleep(1) # set the enzyme to phusion driver.find_element_by_xpath( "/html/body/div[3]/div[2]/div/div/div/div[2]/div[1]/form/div/div[1]/div/select[1]").send_keys("P\n") time.sleep(1) # set the primer input driver.find_element_by_id("batchinput").send_keys( primersSeq) # set the primer concentration driver.find_element_by_id("ct").clear() driver.find_element_by_id("ct").send_keys(100) # blur the focus to produce outputs driver.execute_script("document.getElementById('batchinput').blur()") # fetch the result table rows = driver.find_elements_by_css_selector( "table.batchresultstablex>tbody>tr") table = [[col.get_attribute("innerHTML").splitlines( ) for col in row.find_elements_by_css_selector("td")] for row in rows] # close the chrome driver # turn into a dictionary for easier manipulation NEBprimerDict = {} farthestTempDist = 0 for primerIndex in range(len(table)): if primerIndex % 2 == 0: # left primer Lprimer = table[primerIndex] currentLPrimerName = Lprimer[0][0] currentLPrimerSeq = Lprimer[1][0][1:] currentLPrimerTm = Lprimer[2][0] currentLPrimerTa = float(Lprimer[3][0]) # right primers Rprimer = table[primerIndex+1] currentRPrimerName = Rprimer[0][0] currentRPrimerSeq = Rprimer[1][0][1:] currentRPrimerTm = Rprimer[2][0] currentRPrimerTa = float(Rprimer[3][0]) # primer pair name primerPairName = currentLPrimerName[:-4] phusionPrimerLowerBound = float(phusionprimerOptTm)-5 phusionPrimerUpperBound = float(phusionprimerOptTm)+5 if (currentLPrimerTa >= phusionPrimerLowerBound) and (currentLPrimerTa <= phusionPrimerUpperBound): if (currentRPrimerTa >= phusionPrimerLowerBound) and (currentRPrimerTa <= phusionPrimerUpperBound): currentfarthestTempDist = max(abs( currentLPrimerTa-phusionprimerOptTm), abs(currentRPrimerTa-phusionprimerOptTm)) NEBprimerDict.update( {primerPairName: [['left', currentLPrimerTa, currentLPrimerSeq], ['right', currentRPrimerTa, currentRPrimerSeq]]}) if farthestTempDist < currentfarthestTempDist: farthestTempDist = currentfarthestTempDist time.sleep(5) driver.close() return NEBprimerDict, farthestTempDist ########################### ### SEQUENCE PROCESSING ### ########################### def fileParsing(vectorPlasmidAddress, insertPlasmidAddress): """Take in two addresses, one for vector plasmid and one for insert plasmid, turn into biopython seq objects""" if vectorPlasmidAddress[-5:] == 'fasta': vectorPlasmidSeq = SeqIO.read(vectorPlasmidAddress, "fasta").seq insertPlasmidSeq = SeqIO.read(insertPlasmidAddress, "fasta").seq return vectorPlasmidSeq, insertPlasmidSeq elif (vectorPlasmidAddress[-3:] == '.gb') or (vectorPlasmidAddress[-3:] == 'gbk'): vectorPlasmidSeq = SeqIO.read(vectorPlasmidAddress, "genbank").seq insertPlasmidSeq = SeqIO.read(insertPlasmidAddress, "genbank").seq return vectorPlasmidSeq, insertPlasmidSeq else: sys.exit('Unsupported file format.') return def pseudoCircularizePlasmid(plasmidSeq, goalSeq): """Reorder (pseudo-circularize) a plasmid sequence so that it is essentially still the same plasmid but contains the complete goalSeq. Note that there are two scenarios: (1) plasmidSeq = vectorPlasmidSeq; goalSeq = insertPlasmidSeq (2) plasmidSeq = vectorSeq; goalSeq = insertSeq We assume that the non-vector section will be longer than 2*17=34 bases. The first output will be a pseudo-circularized DNA sequence which is essentially the same as the input plasmidSeq, but will be prepared to be put into primer3. We also output the starting and ending indexes of the goalSeq in the pseudo-circularized DNA sequence. """ # 1. get two segments of goalSeq separated by lineared plasmid seq finalPart1 = '' finalPart2 = '' for index in range(len(goalSeq)): currentPart1 = goalSeq[0:index] currentPart2 = goalSeq[index:] if (currentPart1 in plasmidSeq) and (currentPart2 in plasmidSeq): finalPart1 = currentPart1 finalPart2 = currentPart2 break # 2. get the indexes of the two parts in the plasmid seq part1StartInPlasmid = plasmidSeq.find(finalPart1) part1EndInPlasmid = part1StartInPlasmid + len(finalPart1) part2StartInPlasmid = plasmidSeq.find(finalPart2) part2EndInPlasmid = part2StartInPlasmid + len(finalPart2) # 3. generate pseudo-circularized plasmid # 3.1 part 1 is at the end of the plasmid sequence if part1EndInPlasmid == len(plasmidSeq): nonVectorSegment = plasmidSeq[part2EndInPlasmid:part1StartInPlasmid] arbitraryMiddleIndex = len(nonVectorSegment)//2 output = nonVectorSegment[arbitraryMiddleIndex:] + finalPart1 + \ finalPart2 + nonVectorSegment[:arbitraryMiddleIndex] # 3.2 part 2 is at the end of the plasmid sequence elif part2EndInPlasmid == len(plasmidSeq): nonVectorSegment = plasmidSeq[part1EndInPlasmid:part2StartInPlasmid] arbitraryMiddleIndex = len(nonVectorSegment)//2 output = nonVectorSegment[:arbitraryMiddleIndex] + finalPart2 + \ finalPart1 + nonVectorSegment[arbitraryMiddleIndex:] # 3.3 the plasmid sequence already contains the complete goalSeq else: output = plasmidSeq # figure out the starting and ending indexes of goalSeq in the output sequence outputStart = output.find(goalSeq) outputEnd = outputStart + len(goalSeq) return output, outputStart, outputEnd def primer3ShortCut(seq, goalStart, goalEnd, primerOptTm=PRIMER_OPT_TM, primerMinTm=PRIMER_MIN_TM, primerMaxTm=PRIMER_MAX_TM, primerMinSize=PRIMER_MIN_SIZE): """Take in three outputs of pseudoCircularizePlasmid, call primer3 to create primers, with parameters if needed""" goalLen = goalEnd - goalStart usedLen = goalLen if 100 < goalLen: usedLen = 100 LsequenceMap = { 'SEQUENCE_ID': SEQUENCE_ID, 'SEQUENCE_TEMPLATE': seq, 'SEQUENCE_TARGET': [goalStart, usedLen] } LparamMap = { 'PRIMER_OPT_TM': primerOptTm, 'PRIMER_MIN_TM': primerMinTm, 'PRIMER_MAX_TM': primerMaxTm, 'PRIMER_MIN_SIZE': primerMinSize, } RsequenceMap = { 'SEQUENCE_ID': SEQUENCE_ID, 'SEQUENCE_TEMPLATE': seq, 'SEQUENCE_TARGET': [goalEnd-usedLen, usedLen] } RparamMap = { 'PRIMER_OPT_TM': primerOptTm, 'PRIMER_MIN_TM': primerMinTm, 'PRIMER_MAX_TM': primerMaxTm, 'PRIMER_MIN_SIZE': primerMinSize, } return primer3.bindings.designPrimers(LsequenceMap, LparamMap), primer3.bindings.designPrimers(RsequenceMap, RparamMap) def plasmidPrimerDesign(plasmidSeq, goalSeq, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE): """Uses the primer3-py api to find the primer info for isolating the current goalSeq from the plasmidSeq""" preppedPlasmidSeq, goalSeqStart, goalSeqEnd = pseudoCircularizePlasmid( plasmidSeq, goalSeq) leftPrimerInfo, rightPrimerInfo = primer3ShortCut( preppedPlasmidSeq, goalSeqStart, goalSeqEnd, primerOptTm, primerMinSize) return leftPrimerInfo, rightPrimerInfo def cleanPrimerInfo(leftPrimerInfo, rightPrimerInfo): """read primerInfo, the output of the previous function, and turn it into a more readable and analyzable data structure""" primerPairDict = {} leftPrimerL = [] rightPrimerL = [] for key in leftPrimerInfo: if key[-8:] == 'SEQUENCE' and key[:11] == 'PRIMER_LEFT': currentSequence = leftPrimerInfo[key] primerNum = key[12] if int(primerNum) <= 2: primerTM = leftPrimerInfo[key[:13]+'_TM'] leftPrimerL.append( ['leftPrimer'+str(primerNum), primerTM, currentSequence]) for key in rightPrimerInfo: if key[-8:] == 'SEQUENCE' and key[:12] == 'PRIMER_RIGHT': currentSequence = rightPrimerInfo[key] primerNum = key[13] if int(primerNum) <= 2: primerTM = rightPrimerInfo[key[:14]+'_TM'] rightPrimerL.append( ['rightPrimer'+str(primerNum), primerTM, currentSequence]) # update resultant dict primerPairNum = 0 for leftPrimer in leftPrimerL: for rightPrimer in rightPrimerL: primerPairNum += 1 primerPairKey = "primerPair" + str(primerPairNum) leftPrimerCopy = copy.deepcopy(leftPrimer) leftPrimerCopy[0] = 'leftPrimer' + str(primerPairNum) rightPrimerCopy = copy.deepcopy(rightPrimer) rightPrimerCopy[0] = 'rightPrimer' + str(primerPairNum) primerPairDict.update( {primerPairKey: [leftPrimerCopy, rightPrimerCopy]}) return primerPairDict def primer3Only(plasmidSeq, goalSeq, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE): """A quick wrapper for non-fastCloning specific primer design""" leftPrimerInfo, rightPrimerInfo = plasmidPrimerDesign( plasmidSeq, goalSeq, primerOptTm, primerMinSize) print(' _________\n / \\\n | /\\ /\\ |\n | - |\n | \\___/ |\n \\_________/') print('PROCESSING') print('author: Tom Fu, Richard Chang; HMC BioMakerspace') return cleanPrimerInfo(leftPrimerInfo, rightPrimerInfo) def tempDiffRestrict(primerInfo, maxTempDiff=MAX_TEMP_DIFF): """Checks the differnce in annealing temperatures between two primers. Difference should not be greater than 5 degrees.""" for key in primerInfo.copy(): if abs(primerInfo[key][0][1] - primerInfo[key][1][1]) > maxTempDiff: del primerInfo[key] return primerInfo def TaqvectorPrimerDesign(vectorPlasmidSeq, vectorSeq, maxTempDiff=MAX_TEMP_DIFF, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE): """Find the primers isolating vectorSeq from vectorPlasmidSeq; meanwhile getting two overhang sequences that need to be attached to the insert primer pairs""" cleanedPrimerInfo = primer3Only( vectorPlasmidSeq, vectorSeq, primerOptTm, primerMinSize) rightTempPrimerInfo = tempDiffRestrict(cleanedPrimerInfo, maxTempDiff) for key, val in rightTempPrimerInfo.copy().items(): currentLeftPrimer = val[0][2] currentRightPrimer = val[1][2] if (len(currentLeftPrimer) >= 18) and (len(currentRightPrimer) >= 18): leftOverHang = currentLeftPrimer[:16] rightOverHang = currentRightPrimer[:16] val[0].append(leftOverHang) val[1].append(rightOverHang) else: sys.exit( "The following primer pair is not long enough for FastCloning, thus removed", str(val)) return rightTempPrimerInfo def TaqinsertPrimerDesign(rightTempVectorPrimerInfoWOverhang, insertPlasmidSeq, insertSeq, maxTempDiff=MAX_TEMP_DIFF, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE): """Find the primers isolating insertSeq from insertPlasmidSeq; meanwhile attaching the two overhang sequences to the insert primer pairs""" cleanedInsertPrimerInfo = primer3Only( insertPlasmidSeq, insertSeq, primerOptTm, primerMinSize) rightTempInsertPrimerInfo = tempDiffRestrict( cleanedInsertPrimerInfo, maxTempDiff) outputDict = {} outputL = [] primer4Num = 1 for vkey, currentVPrimerPair in rightTempVectorPrimerInfoWOverhang.items(): for ikey, currentIPrimerPair in rightTempInsertPrimerInfo.items(): # vector primers vcurrentLSeq = currentVPrimerPair[0][2] vcurrentLTemp = currentVPrimerPair[0][1] vcurrentLOverhang = currentVPrimerPair[0][3] vcurrentRSeq = currentVPrimerPair[1][2] vcurrentRTemp = currentVPrimerPair[1][1] vcurrentROverhang = currentVPrimerPair[1][3] # insert primers icurrentLSeq = currentIPrimerPair[0][2] icurrentLTemp = currentIPrimerPair[0][1] icurrentRSeq = currentIPrimerPair[1][2] icurrentRTemp = currentIPrimerPair[1][1] # attach the left overhang to right iprimers and vice versa newiCurrentLSeq = vcurrentROverhang.lower() + icurrentLSeq newiCurrentRSeq = vcurrentLOverhang.lower() + icurrentRSeq # save current info outputDict.update( {('vectorLeftPrimer' + str(primer4Num)): [vcurrentLTemp, vcurrentLSeq]}) outputDict.update( {('vectorRightPrimer' + str(primer4Num)): [vcurrentRTemp, vcurrentRSeq]}) outputDict.update( {('insertLeftPrimer' + str(primer4Num)): [icurrentLTemp, newiCurrentLSeq]}) outputDict.update( {('insertRightPrimer' + str(primer4Num)): [icurrentRTemp, newiCurrentRSeq]}) outputL.append( [('vectorLeftPrimer' + str(primer4Num)), vcurrentLTemp, vcurrentLSeq]) outputL.append( [('vectorRightPrimer' + str(primer4Num)), vcurrentRTemp, vcurrentRSeq]) outputL.append( [('insertLeftPrimer' + str(primer4Num)), icurrentLTemp, newiCurrentLSeq]) outputL.append( [('insertRightPrimer' + str(primer4Num)), icurrentRTemp, newiCurrentRSeq]) primer4Num += 1 return outputDict, outputL def primerTemp(primerSeq, primerConcentration = 500e-9, saltConcentration = 50e-3, magnesiumConcentration = 0): """Calculates the annealing temperature of a primer using the NEB calculator formula """ temp = 0 seq = Seq(primerSeq) dH = 0 dS = 0 symmetryFactor = 0 initial_Thermodynamic_Penalty = [0.2, -5.7] symmetry_Thermodynamic_Penalty = [0, -1.4] termial_AT_Thermodynamic_Penalty = [2.2, 6.9] gasConstant = 1.9872 dH += initial_Thermodynamic_Penalty[0] dS += initial_Thermodynamic_Penalty[1] if primerSeq == seq.reverse_complement(): dH += symmetry_Thermodynamic_Penalty[0] dS += symmetry_Thermodynamic_Penalty[1] symmetryFactor = 1 else: symmetryFactor = 4 if primerSeq[len(primerSeq)-1] == 'A' or primerSeq[len(primerSeq)-1] == 'T': dH += termial_AT_Thermodynamic_Penalty[0] dS += termial_AT_Thermodynamic_Penalty[1] saltEffect = saltConcentration + (magnesiumConcentration * 140) dS += (0.368 * (len(primerSeq)-1) * math.log10(saltEffect)) for i in range(len(primerSeq)-1): dH += enthalpyEntropyValuesSequencePairs[primerSeq[i:i+2]][0] dS += enthalpyEntropyValuesSequencePairs[primerSeq[i:i+2]][1] temp = dH*1000/(dS + gasConstant*math.log(primerConcentration/symmetryFactor)) - 273.15 return temp def vectorPrimerDesign(vectorPlasmidSeq, vectorSeq, maxTempDiff=MAX_TEMP_DIFF, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE): """Find the primers isolating vectorSeq from vectorPlasmidSeq; meanwhile getting two overhang sequences that need to be attached to the insert primer pairs""" currentLen = 0 rightTempPrimerInfo = {} bestFarthestTempDist = float("inf") # for value in range(-5, -3): for value in range(-5, 5): print("VECTOR") print(value) cleanedPrimerInfo = primer3Only( vectorPlasmidSeq, vectorSeq, primerOptTm+value, primerMinSize) temprightTempPrimerInfo = tempDiffRestrict( cleanedPrimerInfo, maxTempDiff) # check phusion for temperature primerSeqNEB = primerDictToNEBPrimerSeq( temprightTempPrimerInfo) temprightTempPrimerInfo, currentfarthestTempDist = NEBWebscraper( primerSeqNEB, primerOptTm) if temprightTempPrimerInfo != {}: if bestFarthestTempDist > currentfarthestTempDist or len(temprightTempPrimerInfo) > currentLen: bestFarthestTempDist = currentfarthestTempDist print(bestFarthestTempDist) rightTempPrimerInfo = temprightTempPrimerInfo currentLen = len(rightTempPrimerInfo) print("UPDATE") print(rightTempPrimerInfo) # go on and find overhang # rightTempPrimerInfoNoOverhang = copy.deepcopy(rightTempPrimerInfo) rightTempPrimerInfoOverhang = rightTempPrimerInfo.copy() for key, val in rightTempPrimerInfo.items(): currentLeftPrimer = val[0][2] currentRightPrimer = val[1][2] if (len(currentLeftPrimer) >= 18) and (len(currentRightPrimer) >= 18): leftOverHang = currentLeftPrimer[:16] rightOverHang = currentRightPrimer[:16] val[0].append(leftOverHang) val[1].append(rightOverHang) else: sys.exit( "The following primer pair is not long enough for FastCloning, thus removed", str(val)) print(rightTempPrimerInfoOverhang) return rightTempPrimerInfoOverhang # elif enzyme == "phusion": # return rightTempPrimerInfo def insertPrimerDesign(rightTempVectorPrimerInfoWOverhang, insertPlasmidSeq, insertSeq, maxTempDiff=MAX_TEMP_DIFF, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE): """Find the primers isolating insertSeq from insertPlasmidSeq; meanwhile attaching the two overhang sequences to the insert primer pairs""" currentLen = 0 rightTempInsertPrimerInfo = {} bestFarthestTempDist = float("inf") for value in range(-5, 5): # for value in range(-5, 6): print("INSERT") print(value) cleanedPrimerInfo = primer3Only( insertPlasmidSeq, insertSeq, primerOptTm+value, primerMinSize) temprightTempPrimerInfo = tempDiffRestrict( cleanedPrimerInfo, maxTempDiff) # check phusion for temperature primerSeqNEB = primerDictToNEBPrimerSeq( temprightTempPrimerInfo) temprightTempPrimerInfo, currentfarthestTempDist = NEBWebscraper( primerSeqNEB, primerOptTm) if temprightTempPrimerInfo != {}: if bestFarthestTempDist > currentfarthestTempDist or len(temprightTempPrimerInfo) > currentLen: bestFarthestTempDist = currentfarthestTempDist print(bestFarthestTempDist) rightTempInsertPrimerInfo = temprightTempPrimerInfo currentLen = len(rightTempInsertPrimerInfo) print("UPDATE") print(rightTempInsertPrimerInfo) # go on outputDict = {} outputL = [] primer4Num = 1 for vkey, currentVPrimerPair in rightTempVectorPrimerInfoWOverhang.items(): for ikey, currentIPrimerPair in rightTempInsertPrimerInfo.items(): # vector primers vcurrentLSeq = currentVPrimerPair[0][2] vcurrentLTemp = currentVPrimerPair[0][1] vcurrentLOverhang = currentVPrimerPair[0][3] vcurrentRSeq = currentVPrimerPair[1][2] vcurrentRTemp = currentVPrimerPair[1][1] vcurrentROverhang = currentVPrimerPair[1][3] # insert primers icurrentLSeq = currentIPrimerPair[0][2] icurrentLTemp = currentIPrimerPair[0][1] icurrentRSeq = currentIPrimerPair[1][2] icurrentRTemp = currentIPrimerPair[1][1] # attach the left overhang to right iprimers and vice versa newiCurrentLSeq = vcurrentROverhang.lower() + icurrentLSeq newiCurrentRSeq = vcurrentLOverhang.lower() + icurrentRSeq # save current info outputDict.update( {('vectorLeftPrimer' + str(primer4Num)): [vcurrentLTemp, vcurrentLSeq]}) outputDict.update( {('vectorRightPrimer' + str(primer4Num)): [vcurrentRTemp, vcurrentRSeq]}) outputDict.update( {('insertLeftPrimer' + str(primer4Num)): [icurrentLTemp, newiCurrentLSeq]}) outputDict.update( {('insertRightPrimer' + str(primer4Num)): [icurrentRTemp, newiCurrentRSeq]}) outputL.append( [('vectorLeftPrimer' + str(primer4Num)), vcurrentLTemp, vcurrentLSeq]) outputL.append( [('vectorRightPrimer' + str(primer4Num)), vcurrentRTemp, vcurrentRSeq]) outputL.append( [('insertLeftPrimer' + str(primer4Num)), icurrentLTemp, newiCurrentLSeq]) outputL.append( [('insertRightPrimer' + str(primer4Num)), icurrentRTemp, newiCurrentRSeq]) primer4Num += 1 return outputDict, outputL # WRAPPER FUNCTIONS def plasmidPrimers(plasmidSeq, goalSeq, benchling=True, destinationAddress='plasmidPrimerInfo.csv', benchlingAddress='benchlingPlasmidPrimerInfo.csv', primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE, enzyme="Taq", maxTempDiff=MAX_TEMP_DIFF): # Use NEB to check temp if enzyme == "Taq": primersDict = primer3Only( plasmidSeq, goalSeq, primerOptTm, primerMinSize) tempString = "meltingTemp (in degree C)" elif enzyme == "phusion": tempString = 'annealingTemp (in degree C)' currentLen = 0 primersDict = {} bestFarthestTempDist = float("inf") for value in range(-5, 5): cleanedPrimerInfo = primer3Only( plasmidSeq, goalSeq, primerOptTm+value, primerMinSize) temprightTempPrimerInfo = tempDiffRestrict( cleanedPrimerInfo, maxTempDiff) # check phusion for temperature primerSeqNEB = primerDictToNEBPrimerSeq( temprightTempPrimerInfo) print(primerSeqNEB) temprightTempPrimerInfo, currentfarthestTempDist = NEBWebscraper( primerSeqNEB, primerOptTm) print(currentfarthestTempDist) if temprightTempPrimerInfo != {}: if bestFarthestTempDist > currentfarthestTempDist or len(temprightTempPrimerInfo) > currentLen: bestFarthestTempDist = currentfarthestTempDist print(bestFarthestTempDist) currentLen = len(temprightTempPrimerInfo) primersDict = temprightTempPrimerInfo print(temprightTempPrimerInfo) print("FinalPrimersDict") print(primersDict) # go on outputL = [] primerPairNum = 1 for key, currentPrimerPair in primersDict.items(): currentLeftPrimerSeq = currentPrimerPair[0][2] currentLeftPrimerTemp = currentPrimerPair[0][1] currentRightPrimerSeq = currentPrimerPair[1][2] currentRightPrimerTemp = currentPrimerPair[1][1] outputL.append([('leftPrimer' + str(primerPairNum)), currentLeftPrimerTemp, currentLeftPrimerSeq]) outputL.append([('rightPrimer' + str(primerPairNum)), currentRightPrimerTemp, currentRightPrimerSeq]) primerPairNum += 1 currentDF = pd.DataFrame( outputL, columns=['primerInfo', tempString, 'sequence']) currentDF.to_csv(destinationAddress) print("Check out the following file for your primers:") print(destinationAddress) if benchling == True: benchlingL = [[currentPrimer[0], currentPrimer[2]] for currentPrimer in outputL] benchlingDF = pd.DataFrame( benchlingL) benchlingDF.to_csv(benchlingAddress, index=False) print("Your benchling-ready csv file is:") print('benchling'+destinationAddress) return def plasmidPrimersFile(plasmidSeqFile, goalSeq, benchling=True, destinationAddress='plasmidPrimerInfo.csv', benchlingAddress='benchlingPlasmidPrimerInfo.csv', primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE, enzyme="Taq"): if plasmidSeqFile[-5:] == 'fasta': plasmidSeq = str(SeqIO.read(plasmidSeqFile, "fasta").seq) elif (plasmidSeqFile[-3:] == '.gb') or (plasmidSeqFile[-3:] == 'gbk'): plasmidSeq = str(SeqIO.read(plasmidSeqFile, "genbank").seq) else: sys.exit('Unsupported file format.') return plasmidPrimers(plasmidSeq, goalSeq, benchling, destinationAddress, benchlingAddress, primerOptTm, primerMinSize, enzyme) def fastCloningPrimers(vectorPlasmidSeq, insertPlasmidSeq, vectorSeq, insertSeq, maxTempDiff=MAX_TEMP_DIFF, destinationAddress='fastCloningPrimerInfo.csv', benchlingAddress='benchlingfastCloningPrimerInfo.csv', benchling=True, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE, enzyme="phusion"): """Wrapper function that generates 2 primer pairs for the given circular raw vector and insert sequences Args: vectorPlasmidSeq ([str]): vector plasmid insertPlasmidSeq ([str]): insert plasmid vectorSeq ([str]): vector sequence insertSeq ([str]): insert sequence """ if enzyme == "phusion": rightTempVectorPrimerInfoWOverhang = vectorPrimerDesign( vectorPlasmidSeq, vectorSeq, maxTempDiff, primerOptTm, primerMinSize) outputDict, outputL = insertPrimerDesign( rightTempVectorPrimerInfoWOverhang, insertPlasmidSeq, insertSeq, maxTempDiff, primerOptTm, primerMinSize) currentDF = pd.DataFrame( outputL, columns=['primerInfo', 'annealingTemp (in degree C)', 'sequence']) currentDF.to_csv(destinationAddress) print("Check out the following file for your primers:") print(destinationAddress) if benchling == True: benchlingL = [[currentPrimer[0], currentPrimer[2]] for currentPrimer in outputL] benchlingDF = pd.DataFrame( benchlingL) benchlingDF.to_csv(benchlingAddress, index=False) print("Your benchling-ready csv file is:") print(benchlingAddress) elif enzyme == "Taq": rightTempVectorPrimerInfoWOverhang = TaqvectorPrimerDesign( vectorPlasmidSeq, vectorSeq, maxTempDiff, primerOptTm, primerMinSize) outputDict, outputL = TaqinsertPrimerDesign( rightTempVectorPrimerInfoWOverhang, insertPlasmidSeq, insertSeq, maxTempDiff, primerOptTm, primerMinSize) currentDF = pd.DataFrame( outputL, columns=['primerInfo', 'annealingTemp (in degree C)', 'sequence']) currentDF.to_csv(destinationAddress) print("Check out the following file for your primers:") print(destinationAddress) if benchling == True: benchlingL = [[currentPrimer[0], currentPrimer[2]] for currentPrimer in outputL] benchlingDF = pd.DataFrame( benchlingL) benchlingDF.to_csv(benchlingAddress, index=False) print("Your benchling-ready csv file is:") print(benchlingAddress) return def fastCloningPrimersFile(vectorPlasmidAddress, insertPlasmidAddress, vectorSeq, insertSeq, maxTempDiff=MAX_TEMP_DIFF, destinationAddress='fastCloningPrimerInfo.csv', benchlingAddress='benchlingfastCloningPrimerInfo.csv', benchling=True, primerOptTm=PRIMER_OPT_TM, primerMinSize=PRIMER_MIN_SIZE, enzyme="phusion"): """Wrapper function that generates 2 primer pairs for the given circular raw vector and insert sequences given fasta/gb files Args: vectorPlasmidAddress ([str]): address for vector plasmid insertPlasmidAddress ([str]): address for insert plasmid vectorSeq ([str]): vector sequence insertSeq ([str]): insert sequence """ vectorPlasmidSeq, insertPlasmidSeq = fileParsing( vectorPlasmidAddress, insertPlasmidAddress) vectorPlasmidSeq = str(vectorPlasmidSeq) insertPlasmidSeq = str(insertPlasmidSeq) return fastCloningPrimers(vectorPlasmidSeq, insertPlasmidSeq, vectorSeq, insertSeq, maxTempDiff, destinationAddress, benchlingAddress, benchling, primerOptTm, primerMinSize, enzyme)

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